international journal of computers, communications & control vol. ii (2007), no. 1, pp. 37-38 international conference on virtual learning – icvl 2006 the current issue of the journal contains seven extended papers published in the "proceedings of the 1st international conference on virtual learning, october 27 29, 2006, bucharest, romania, (icvl 2006)" (m. vlada, g. albeanu & d.m. popovici eds.). the first edition of the international conference on virtual learning icvl 2006 was organized by university of bucharest, faculty of mathematics and computer science in association with european intuition project (the intuition network of excellence in europe http://www.intuition-eunetwork.net/) and in conjunction with the fourth national conference on virtual learning cniv 2006 to celebrate one hundred years from the birth of the great romanian mathematician and "computer pioneer award" of ieee computer society (1996) grigore moisil (1906 1973). the icvl was structured to provide a vision of european e-learning and e-training policies, to take notice of the situation existing today in the international community and to work towards developing a forward looking approach in virtual learning from the viewpoint of modelling methods and methodological aspects (m&m), information technologies (tech) and software solutions (soft). the conference has established a large area of topics to cover the following subjects, but not limited to: innovative teaching and learning technologies, web-based methods and tools in traditional, online education and training, collaborative e-learning, e-pedagogy, design and development of online courseware, information and knowledge processing, knowledge representation and ontologism, cognitive modelling and intelligent systems, algorithms and programming for modelling, advanced distributed learning technologies, web, virtual reality/ar and mixed technologies, mobile e-learning, communication technology applications, computer graphics and computational geometry, intelligent virtual environments, new software environments for education and training, streaming multimedia applications in learning, scientific web-based laboratories and virtual labs, soft computing in virtual reality and artificial intelligence, avatars and intelligent agents. initially 72 abstracts were received and 55 of them were selected. finally only 34 papers were accepted for presentation at the icvl and publication in proceedings of the icvl bucharest university press (isbn 978-973-737-218-5). participants coming from europe, japan, australia and canada have discussed various aspects concerning the future developments in the virtual learning field during the conference. four invited papers talking about trends in professional learning, time series modelling, analysis and forecasting in e-learning environments, ael the e-learning universal platform and, the teaching through projects methodology have been presented as plenary lectures. ten papers proposed different software solutions, while twenty papers were dedicated to modelling methods and methodological aspects. some icvl papers are considered for publishing in the current issue of the international journal of computers, communications and control. let us present an introduction of the selected papers. the paper of a. andreatos is dedicated to define and classify the virtual communities and their importance for informal learning, and to examine their social impact and resulting trends in technology management. a bibliographical review, and some case studies illustrate the aforementioned tasks. a. anohina, in her paper, considers the intelligent tutoring systems (including architecture topics based on two layers approach) powered by adaptive support for learners in order to solve practical problems. copyright c© 2006-2007 by ccc publications 38 grigore albeanu the minimax algorithm is considered as a practical illustration. the paper of n. doukas and a. andreatos presents a computer-aided assessment system (e-xaminer; a web-based interface system) based on parametrically designed questions that uses meta-language concepts to automatically generate tests. in their paper, i. kitagaki and his colleagues, present an algorithm for groupware modelling for a collaborative learning environment using mobile terminals. they show not only the grouping algorithm but also some considerations about discussion in a classroom. m. lambiris presents the concepts and a technique used to design a methodology for providing individualised computer-generated feedback to students. such an approach can also be used to provide detailed and high accuracy information to the instructor about the performance of the whole group. the paper of g. moise describes a software system for online learning using intelligent agents (an execution agent and a supervisor agent) and conceptual maps. experimental results are also considered. m. oprea presents in her paper a multi-agent system design procedure to be applied for universities course timetable scheduling that is a difficult administrative task. a preliminary evaluation of the proposed multi-agent system is presented in order to show the benefits obtained when a university uses such an approach. considering the successful story of the icvl 2006 event, the scientific community shows great interest to the second edition (icvl 2007: 26-28th of october) that will take place at ovidius university of constanta, romania. grigore albeanu guest editor icvl technical program chair unesco chair in information technologies university of oradea, university street, no. 1 410087, oradea, bihor, romania int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 613-615 writing as a form of freedom and happiness celebrating the 60th birthday of gheorghe păun g. ciobanu gabriel ciobanu romanian academy, institute of computer science and a.i.cuza university of iasi, romania e-mail: gabriel@info.uaic.ro gheorghe păun (born on december 6, 1950) graduated the faculty of mathematics of the bucharest university in 1974 and got his phd at the same faculty in 1977. he has won many scholarships, in germany, finland, the netherlands, spain, etc. presently he is a senior researcher at the institute of mathematics of the romanian academy, bucharest, and a ramon y cajal research professor at sevilla university, spain. since 1997 he is a corresponding member of the romanian academy, and since 2006 a member of academia europaea. his main research fields are formal language theory (regulated rewriting, contextual grammars, grammar systems), automata theory, combinatorics on words, computational linguistics, dna computing, membrane computing (this last area was initiated by him in 1998). he has (co)authored and (co)edited more than fifty books in these areas, and he has (co)authored more than 400 research papers. in the last two decades he has visited many universities from europe, usa, canada, japan, also participating to many international conferences, several times as an invited speaker. he is a member of the editorial board of numerous computer science journals and professional associations. figure 1: a copy cropped from [1] essentially writing is form of thinking on paper, and a way of learning. according to winston churchill, writing a book is an adventure. "to begin with, it is a toy and an amusement; then it becomes a mistress, and then it becomes a master, and then a tyrant. the last phase is that just as you are about to be reconciled to your servitude, you kill the monster, and fling him out to the public." on the other hand, writing could be a form of freedom by escaping the madness of a period, and reducing the anxiety. in many situations the authors write to save themselves, to survive as individuals. gheorghe păun is an example of a person affirming his own existence by writing. he is a prolific writer with a huge number of papers: tens of scientific books, hundreds of articles, several novels, poems, and books on games. a list of his scientific publications is posted at http://www.imar.ro/~gpaun/papers.php [2], while his books are listed at http://www.imar. ro/~gpaun/books.php [1] his way of distributing information is not by speaking, but by writing. gheorghe păun did not like very much to teach in universities. he preferred a form of "teaching by researching", combining ideas with nice metaphors and distributing his knowledge in articles and books. in this way he wrote several papers having a high impact in the scientific community. his seminal paper "computing with membranes" published in journal of computers and system sciences in 2000 and his fundamental book on computation theory "membrane computing" (springer, 2003) has over 1,000 citations [6] (and his author was recognized as an "isi highly cited researcher" [5]). he has defined new branches, new theories. the field of membrane computing was initiated by gheorghe păun as a branch of natural computing [3]; p systems are copyright c⃝ 2006-2010 by ccc publications 614 g. ciobanu inspired by the hierarchical membrane structure of eukaryotic cells [4]. an impressive handbook of membrane computing was published recently (2010) by oxford university press. after 1990 he becomes a traveling scientist, visiting several countries and receiving many research fellowships and awards. fruitful scientific collaboration at magdeburg university (germany), and at university of turku (finland). the trio gheorghe păun, grzegorz rozenberg and arto salomaa is well-known for several successful books. the last years were spent in spain, first in tarragona and now in sevilla. several collaborations were possible during his trips, and there are over 100 co-authors from many countries. his scientific reputation is related to the large number of invited talks provided at many international conferences and universities. he is a member of the editorial boards for several international journals, corresponding member of the romanian academy (from 1997), and member of academia europaea (from 2006). it is not possible to understand the personality of gheorghe păun without mentioning his activity as writer of novels and poems; he is a member of the romanian writers association for a long time. another aspect of his life is related to the intellectual seduction of games; he was the promoter of go in romania, writing many books about go and other "mathematical" games. personally, i am impressed by the speed of his mind (it is enough to say few words about some new results, and he is able to complete quickly the whole approach), his wide-ranging curiosity and intelligence, rich imagination and humor, talent and passion. he is highly motivated by challenging projects, and work hard to conclude them successfully. there are very few scientists having such an interesting profile, and i am very happy to learn a lot from him. celebrating his 60th birthday, we wish him a good health, long life, and new interesting achievements! figure 2: g. păun-an isi highly cited researcher (a copy cropped from [5]) writing as a form of freedom and happiness celebrating the 60th birthday of gheorghe păun 615 bibliography [1] gheorghe păun, one more universality result for p systems with objects on membranes, international journal of computers, communications & control, 1(1): 25-32, 2006 (free access at http://www.journal.univagora.ro/download/pdf/21.pdf) [2] http://www.imar.ro/ gpaun/ [3] http://esi-topics.com/ [4] http://ppage.psystems.eu/ [5] http://hcr3.isiknowledge.com/ [6] http://interaction.lille.inria.fr/ roussel/projects/scholarindex/index.cgi international journal of computers, communications & control vol. ii (2007), no. 1, pp. 103-105 management information systems: managing the digital firm 9th edition, authors: keneth c. laudon and jane p. laudon (book review) florin g. filip management information systems: managing the digital firm 9th edition authors: keneth c. laudon and jane p. laudon pearson prentice hall, pearson education, inc., upper saddle river, new jersey 97458. isbn 0-13-153841-1; xxxii+641+22+14+2+21 pags. this is the 9th edition of a successful textbook. the authors are two well-known and productive writers. k.c. laudon, a professor of information systems at stern school of business of the new york university, took his ba in economics from stanford and his ph.d. from columbia university. he is the author of twelve books and over forty articles about social, organizational and management impacts of information systems, privacy, ethics, and multimedia technology. jane p. laudon, a management consultant in information systems area, took her m.a. from harvard university and her ph.d. from columbia university and authored seven books. her main scientific interests are systems analysis, data management and software evaluation. the background, scientific interests and expertise of the authors and their previous works had an obvious impact on the manner this book was conceived, written and accompanied by auxiliary materials (the cd rom and companion web site). the authors start from the premise that, nowadays, "information systems knowledge is essential for creating successful, competitive firms, managing global corporations, adding business value and providing useful products and service to customers" (p. xix). moreover, they state that "in many industries survival and even existence without the extensive use of it is inconceivable" (p.31). an important development the authors remark is the emergence of the digital firm, "where nearly all core business processes and relationship with customers, suppliers and employees are digitally enabled" (p.31). in the book the management information systems (mis) is defined at large as "the study of [computer based] information systems in business and management" (p.44). besides, the authors adopt a broader view of information systems (is) "which encompasses an understanding of the management and organizational dimensions as well as technical dimensions of the systems as information systems literacy" (p.20). consequently, this book can be viewed as an effort made by the laudons with a view to contributing to building up and consolidating such an information system literacy for current and future managers, which are to be confronted with several "major challenges concerning: a) "information system investments", b) "strategic business"„ c) "globalization", d) "information infrastructure", and e) "ethics and security" (p.28). the authors have noticed a "user designer communication gap". in table 15.3 (p.552) they give several examples of that gap. while the user is concerned with problem solving related questions such as: "will the system deliver the information i need for my work?, "how quickly can i access the data?", ..., "how will the system fit into my daily business schedule?", the designer is preoccupied to find optimal answers to technology-oriented questions such as: "how many lines of program code will it take to perform this function?", ...,"what database management system should we use? in order to help the future managers to successfully face the major challenges mentioned above and to solve the possible communication gap, the authors adopt a sociotechnical view and style of presentation. they combine technical aspects (drawn from computer science, management science, and operations copyright c© 2006-2007 by ccc publications 104 florin g. filip research) with behavioral elements (drawn from economics, sociology, psychology). throughout the book, the presentation method chosen contains, besides the introductimg mis concepts, facts about realworld experiences (cca 200 examples are given), new technologies, and various exercises. in accordance with the sociotechnical perspective adopted, the book is organized in four parts as it follows. part one, which is made up of five chapters, addresses the organizational and managerial foundations of information systems. it introduces real-world systems and highlights their relationship with the organizations and managements. the concept of the digital firm is explained together with the fashionable concepts of e-business and e-commerce. various types of information systems are reviewed (and they will be presented in detail in the remaining chapters). the four major business functions (sales and marketing, production and manufacturing, human resources and finance and accounting) are introduced with a view to being resorted to in an ending section of all remaining chapters. a special chapter is dedicated to ethical and social issues in the digital firm. part two, which is made up of five chapters, addresses the various facets of the information technology infrastructure (iti). iti is viewed as a set of technology resources (hardware and software) and as a set of services (computing platform service, telecommunication service, data management service, application software service, it management service, standards service, education and research and development service), composed both of human and technical capabilities (p.186). a particular emphasis is laid on describing the leading edge wireless technologies, and security and control aspects. part three, which is made up of three chapters, describes several types of information systems. one by one, the main concepts of the enterprise applications (including enterprise [resource planning] erp, customer relationship managing crm, and supply chain management scm), knowledge management systems, and [group, and executive] decision support systems are presented. part four, which is made up of three chapters, presents the process of building, implementing and managing the systems in organizations. several important topics such as development approaches (based on lifecycle or prototyping), managing the necessary changes in the organizations, and various methods for evaluating the business value of the project are reviewed. the last chapter addresses the specific aspects of managing international information systems. in comparison with previous editions, the present edition contains much up-to-date information about leading edge technologies. the chapters which address "wireless revolution", enterprise applications, knowledge management systems are new. other chapters were re-written or/and completed with new topics. throughout the book, all chapters are organized in the same manner. each chapter opens with the statement of five learning objectives. then, five (in few cases, four) subchapters of text follow to provide the mis concepts and real-world examples which are related to the learning objectives. other common features to all chapters are: a) the opening case which describes a real world example and the coresponding diagram to analyze the case in terms of management, organization and technology model; b) the concluding section on management opportunities, challenges and solutions related to the theme of the chapter; c) the "make it your business" section to place the concepts described in the chapter in relation with the major business functions; d) the "chapter summary" organized in accordance with chapter objectives, e) the list of key terms, f) the review questions, g) the application software exercise to develop solutions to real-world business problems, h) the running case project (on a simulated firm entitled dirt bikes). the book also contains an impressive list of references which is organized on chapters, a glossary, three appendices of "hands-on" type (for analyzing a case study, designing a data base, and sql), an index of terms and an index of organizations. the text about information systems is supplemented by two it based learning aids: a) the companion web site (to guide the interactive study, to facilitate internet connections, and to provide additional case studies), and b) the interactive multimedia cd-rom (to be used either as an interactive study guide or as an alternative to the text). management information systems: managing the digital firm 9th edition, authors: keneth c. laudon and jane p. laudon (book review) 105 to conclude this review, i think this book, together with the cd-rom and companion web site, is an excellent dynamic and active learning environment. i recommend it to be used as a textbook for the undergraduate students for information systems courses in business administration departments. it can also be utilized by the students in computer science as a complementary text, which can help them build a broader view on information systems. florin-gheorghe filip romanian academy 125, calea victoriei 010071 bucharest-1, romania e-mail: ffilip@acad.ro received: december 31, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 946-952 contributions to the study of semantic interoperability in multi-agent environments an ontology based approach i.f. toma iulian-florin toma university of pitesti romania, 110040 pitesti, 1 targu din vale e-mail: tif@tif.ro abstract: this paper details the results of our work in the field of multi-agent ontology-based environment simulation. we analyze the impact of introducing some techniques for the alignment / translation / mapping of agent ontologies, which allows for collaborative understanding of distributed ontologies. in the end, we analyze the difficulties / gaps that are to be filled for an actual deployment of the technology / concept in a real life environment. keywords: ontology, semantic interoperability, agents. 1 introduction the ability to communicate depends on understanding the syntax and the semantics of a language. we used an ontology model to facilitate semantic interoperability in a simulated multi-agent environment. current studies relieve the fact that using ontologies associated with the agents allows for building semantic-aware distributed multi-agent systems. an ontology is a formal specification for shared understanding of a domain of interest and offers the possibility of building a formal and machine manipulable model of a domain of interest. ontologies describe entities and relations between them, classes of objects and their attributes, and are complemented by logical rules that constrain the meaning assigned to the terms. these constraints are represented by inference rules that can be used by agents to perform the reasoning on which the autonomy and proactiveness of the agents are based. thus semantic interoperability facilitates the increase of the autonomy of agents. the reasoning process of agents in multi-agent ontology-based environments is mainly focused on the alignment of their own ontologies with ontologies exposed by other agents. having considered the multi-agent environment as heterogeneous, we analyse the introduction of guidelines for ontology development and evolution which should facilitate ontology reuse that may underpin a usage model for ontologies. 2 multi-agent environment the multi-agent environment we propose is part of an e-commerce scenario where a user’s agent agu is activated to find, request and gather offers for a specific product from a series of suppliers’ agents ags1, ..., agsn. in our implementation, the suppliers’ agents (ags agents) are published in the fipa compliant directory facilitator (df) of the jade platform. fipa is an ieee computer society standards organization that promotes agent-based technology and the interoperability of its standards with other technologies. jade is a software framework to develop agent applications in compliance with the fipa specifications for interoperable intelligent multi-agent systems. we propose the implementation of a mediator agent (agm) to make the interactions with the df and to handle the negotiation process between agents. the ags agents (suppliers’ agents) use their own ontologies. suppliers can adhere to centralized/standardized/publicly available ontologies, can extend such ontologies based on their needs, copyright c⃝ 2006-2010 by ccc publications contributions to the study of semantic interoperability in multi-agent environments an ontology based approach 947 but can also build their own ontologies from scratch. facing this heterogeneity of ontologies used by the ags agents, the agu agent has to achieve semantic interoperability to negotiate with each ags agent. fipa recommends using an external ontology agent (ago) for handling the interoperability problems in heterogeneous multi-agent environments [9]. this agent handles the tasks of matching ontologies and translating the values of some attributes (translate notions based on their specified language, convert currencies, convert units of measure). figure 1 illustrates the architecture of the proposed system: ������ ��� �� ������ ������ ����� ����� ���� ��� � ��� ���� ���� figure 1: ontology-based multi-agent environment 3 ontologies the semantical description of an entity offers a better understanding of the entity and puts the entity in a context. the idea is not necessarily to achieve the level of understanding and association to other entities that a human subject would have for that entity, but to improve the understanding of a software regarding that entity only to satisfy specific goals. the main heterogeneity problem in our simulated environment is that the agents will typically use different ontologies. ontology-based interoperation provides a solution in environments with heterogeneous semantics. ontologies can capture both the structure and semantics of information environments [7]. an ontology-based search agent like agu can handle both simple keyword-based queries as well as complex queries on structured data. figure 2 contains some sample ontologies exposed by 2 book suppliers’ agents [1]. 948 i.f. toma figure 2: sample ontologies [1] the ontologies can be designed in any ontology editor, like the protégé software [6] (figure 3). a large variety of formal languages exists for describing the ontologies (e.g. owl [2] [8]). figure 3: ontology editing in protégé 2000 4 implementation the test simulation can be implemented using the jade environment. the jade environment has direct support for ontologies only if they are represented as java classes. these classes can be written by hand of generated using the beangenerator plug-in of the protégé software. although the java classes can be generated automatically, this approach has the disadvantage that it is very rigid when conceptual changes are necessary. under this approach, modifications of the ontology in protégé have to be followed by the regeneration of the java classes using the contributions to the study of semantic interoperability in multi-agent environments an ontology based approach 949 beangenerator tool, the re-integration in the main project and the recompiling. any modification in an ontology represented using java classes may also require modifications in the java code of the agent. a solution to simplify this process is to access the jena framework for writing ontology-based applications. this framework allows keeping the ontologies in the owl format generated by protégé, eliminating any further need to transform the ontology in another format. the parser of this framework has direct access to the owl files and provides access to the classes of the ontology using java language. using this framework, the agent can access the instances of the classes, retrieve the value of attributes, determine subclasses etc. 5 negotiation the negotiation is implemented using the fipa contract net interaction protocol [10]. the workflow for this protocol is represented in figure 4. figure 4: the workflow of thhe fipa contract net interaction protocol the semantic analysis of the ontologies takes place between the call for proposals (represented as "cfp") and the receival of the refusals/proposals from the suppliers’ agents. the semantic analysis is performed by the dedicated agent ago . the suppliers’ agents ags1...agsn (noted: ags) receive the cfp. if the concept/item is understood by ags, it starts to evaluate de conditions/constraints contained in the cfp. if the conditions match, ags returns a proposal to the agu agent, otherwise it returns a reject-proposal message. the messages may be sent using predicates defined in a separate common / generalized / standard centralized ontology (ontreqstd) specific to the process of requesting offers of products . agents agu and ags could adhere to this ontology and ags could then respond with a more descriptive message 950 i.f. toma like "availableonlywithpreorder", "stockempty" rather than responding with a simple yes/no message. the types of ontologies proposed for our model are represented in figure 5. ����� ������ ���� ����� ���� ���� �������� � figure 5: the role of the ago (ontology agent) ontology matching analysis starts if the ags does not "understand" the concepts/attributes inside the cfp. in this case, the ags agents call the ago agent. the ago agent requests contact details to ags to contact the agu agent. then ago queries the agu agent about its exposed ontology. ago starts a matching process to obtain an alignment between the agu request (cfp), agu ontology (ontu) and ags ontology. if the matching succeeds (with a high confidence mark), ago reformulates the cfp to the ags and the ags may start the simple quantitative evaluation and send an answer to agu accordingly to the result. ago may also play the role of converting units of measure, currencies, translating notions between languages etc.) and use the values to reformulate the cfp. 6 ontology mapping the same concept/product may be described using different attributes, structures and relations, conducting to distinct ontologies. even so, taking into consideration that the concept is part of a specific domain, there exists a set of common characteristics that should be mandatory when representing a specific product, like "width", "height", "material", etc. thus lexical measures can be used to compare attributes and relations between concepts. some of the lexical measures that can be applied to achieve ontology-matching are n-gram similarity, hamming distance, levenshtein distance, jaro measure, jaro-winkler measure and the token-based distances like cosine similarity, term frequency-inverse document frequency (tfidf) described in [1]. contributions to the study of semantic interoperability in multi-agent environments an ontology based approach 951 the ago agent can also make use of the language-based methods by calling external resources like lexicons and thesauri. wordnet [3] is such an electronic lexical database for english, based on the notion of synsets or sets of synonyms. a synset denotes a concept or a sense of a group of terms. wordnet also provides an hypernym (superconcept/subconcept) structure as well as other relations such as meronym (part of relations). it also provides textual descriptions of the concepts (gloss) containing definitions and examples. there are lots of techniques available for analyzing similarities using wordnet [4] [5]. in addition to comparing their names or identifiers, the structure of entities that can be found in ontologies can be compared. this comparison can be subdivided into a comparison of the internal structure of an entity, i.e., besides its name and annotations, its properties or, in the case of owl ontologies, the properties which take their values in a datatype, or the comparison of the entity with other entities to which it is related. using these well-known techniques, the ago agent can run a detailed analysis on the matching of the ontu and onts ontologies. if an alignment is achieved, the ago agent can reformulate the cfp (call for proposals) to match the onts ontologies of the ags agents. 7 conclusions and the future of semantic interoperability in this paper, we proposed a feasible implementation of a multi-agent environment which makes use of ontologies and ontology mapping to achieve semantic interoperability. the ontologybased multi-agent environments and semantic-enabled communications are, in the present (20092010), under theoretical study only. there are numerous attempts to bring this technology to public use e.g. building public libraries of ontologies, but this field still resides only in the boundaries of the scientifical community. to bring it to public use, tools should emerge, that would facilitate the semantic annotation and semantic description of entities, by any internet user, in various environments like web services, web pages, distributed software, reusable components etc. these tools should be easy to use by any internet user. they should incorporate automatic validation based on the recommendations and restrictions of the ontology engineering discipline. only then, when the advantages of these technologies will overcome the difficulties of defining and maintaining the ontologies, will semantic-enabled communications be a part of common software and human activities. bibliography [1] j. euzenat, p. shvaiko, ontology matching, springer, 2007. [2] owl 2 web ontology language, w3c recommendation, http://www.w3.org/tr/owl2overview/, october 27, 2009. [3] g.a. miller, wordnet: a lexical database for english, communication of acm, 38(11):39-41, 1995. [4] pedersen, patwardhan, and michelizzi, wordnet::similarity measuring the relatedness of concepts, proceedings of the nineteenth national conference on artificial intelligence (aaai-04), pp. 1024-1025, july 25-29, 2004, san jose, ca (intelligent systems demonstration) [5] a. budanitsky, g. hirst, semantic distance in wordnet: an experimental, applicationoriented evaluation of five measures, proceedings of the workshop on wordnet and other 952 i.f. toma lexical resources, second meeting of the north american chapter of the association for computational linguistics, pittsburgh, usa, 2001. [6] j. gennari, m.a. musen, r.w. fergerson, w.e. grosso, m. crubezy, h. eriksson, n.f. noy, s.w. tu, the evolution of protege: an environment for knowledge-based systems development, technical report, smi report number: smi-2002-0943, 2002. [7] m.p. singh, m.n. huhns, service-oriented computing: semantics, processes, agents, wiley, 2005 [8] a. gómez-pérez, m. fernández-lópez, o. corcho , ontological engineering : with examples from the areas of knowledge management, e-commerce and the semantic web , springer, 2004 [9] ontology service specification, fipa-oss, http://www.fipa.org/specs/fipa00086 [10] fipa contract net interaction protocol specification, fipa tc communication, sc00029h, http://www.fipa.org/specs/fipa00029/sc00029h.html. international journal of computers, communications & control vol. iii (2008), no. 1, pp. 51-59 mobile message passing using a scatternet framework brendan j. donegan, daniel c. doolan, sabin tabirca abstract: the mobile message passing interface is a library which implements mpi functionality on bluetooth enabled mobile phones. it provides many of the functions available in mpi, including point-to-point and global communication. the main restriction of the library is that it was designed to work over bluetooth piconets. piconet based networks provide for a maximum of eight devices connected together simultaneously. this limits the libraries usefulness for parallel computing. a solution to solve this problem is presented that provides the same functionality as the original mobile mpi library, but implemented over a bluetooth scatternet. a scatternet may be defined as a number of piconets interconnected by common node(s). an outline of the scatternet design is explained and its major components discussed. keywords: bluetooth, scatternet, message passing, network formation 1 introduction mobile technology is one of the fastest growing fields of technology, with over one billion mobile phones shipped during 2006 [8]. the power of mobile devices is also growing quickly, in october 2005 arm announced the arm cortex a8 [1][2], having a clock speed of 1ghz. this increase in both availability and performance makes mobile devices a prime candidate for parallel computing systems. having a multitude of mobile devices available one can now take advantage of the situation by performing complex computational tasks across several devices. the original mmpi library was restricted in terms of its world size by the upper bound of the piconet network standard, this limits the maximum number of nodes in an mmpi world to eight. the evolution of the mmpi library to allow for scatternet formation requires significantly more work behind the scenes, both to setup the network infrastructure and to allow for complete inter-device communication. 1.1 need for mobile parallel computing the mmpi library was created to allow for parallel computing across mobile devices [6]. mobile devices have very limited resources and processing capabilities. the nokia 6630 and 6680 [14] mobile phones have 220mhz processors and just a few megabytes of memory. along with such limited capabilities they also have a finite amount of battery power. running processor intensive applications on such devices drains battery power at a higher rate than standard phone usage. therefore if one device doesn’t have sufficient battery power it may not be capable of solving a complex problem. such a problem would also take too long to process from the users perspective. splitting the processing among several devices not only speeds up the processing, but also distributes the battery drain across all devices, allowing more computationally intensive tasks to be performed. 1.2 review of scatternet formation algorithms the bluetooth specification [3], describes the concept of a scatternet. a scatternet (figure 1) is defined as two or more piconets joined together through the mechanism of a common node (bridging node). significant research has been conducted on scatternets, much of it focusing on how they can be optimized. miklos [10] described some of the aspects of scatternet formation, including the performance bottleneck caused by the bridging node switching between the frequency hopping patterns of its masters. copyright © 2006-2008 by ccc publications 52 brendan j. donegan, daniel c. doolan, sabin tabirca figure 1: scatternet topology the dynamp project [13] is a mobile parallel computing architecture that uses bluetooth scatternets as the underlying infrastructure. the application area for this body of work is in mobile robotics, specifically that of the cybot toy. the main reason for the parallel computing architecture is real-time image processing of data acquired from the robots sensor system. to avoid the upgrading of the processing capabilities of the robots, the path of distributing the processing requirements across a group of robots was investigated. the target architecture is the tini from dallas semiconductor that uses java as its native environment. the ipaq pda is also used, running a linux based os and the keffe jvm. unlike native message passing libraries such as the c and fortran based mpi that use a static number of nodes, a far more dynamic approach is required within the mobile environment. the head node is therefore responsible for discovering all the active nodes within the network. this is achieved by broadcasting a process request packet throughout the network. an example of a simple node topology is given in the paper that shows a linear like architecture, thereby communication between distant nodes would certainly require one or more intermediary nodes for forward on the message. bluetrees [15] generates a tree-like scatternet. the network formation algorithm is initiated by a single node that forms the root of the tree. the root node begins by acquiring slave devices that are within it vicinity. these in turn page their own neighbours. in order to limit the number of slave devices connected to another node at the level above some branch reorganization may be required. jayanna & zaruba [9] use an approach where by all nodes maintain a dynamically generated list of its neighbours based on the number of hops required to reach the node in question. the strategy ensures that if neighbour information is included for one and two-hop piconets, then two adjacent piconets can share only one bridging node. therefore all nodes of one piconet share a single path to all nodes of a neighbouring piconet. 1.3 mobile message passing interface the mobile message passing interface (mmpi) [6] provides many of the functions that can be found in a standard mpi [11] [12] implementation. the main difference between mmpi and standard mpi implementations is that it is designed for mobile devices that communicate via bluetooth [4] [5]. the library has one drawback that makes it less than an ideal candidate as a platform to support parallel computation on mobile devices. it was designed to operate only on a bluetooth piconet of up to eight mobile message passing using a scatternet framework 53 devices. in a parallel computing system in general one can achieve faster computation by throwing more processors at the problem. 2 enhanced mmpi library structure the problem of creating scatternets and performing communication within them after they have been created is a complex one. the original mmpi library could not simply be adapted, as the differences between communication in a piconet and communication in a scatternet are too great. a new solution was therefore designed from the ground up, whilst retaining all of the original functionality. the enhanced library comprises of a number of distinct components that provides an abstraction from lower level layers. 2.1 components of the library the enhanced mmpi library is made up of a number of components. the most important parts of the architecture are shown in figure 2 figure 2: library components the commscenter class forms the heart of the library. its role is to receive raw data from the network and translate it into mmpi messages. the mmpinode class provides the interface between the commscenter and the mmpi class and also performs the initial device discovery. finally, the mmpi class is the interface to the library as a whole, and is the only class whose methods are exposed to the developer using mmpi. relevant data is fed up through the hierarchy, starting at the commscenter and continuing up to the mmpi class. this simplifies matters greatly as information that is relevant at a particular level of the hierarchy is available only at that level. 2.2 messages on receipt of a message by the commscentre it must inspect the header to identify the type of the message it has received and accordingly perform the appropriate action. the message types are categorised as follows: • bridge the node should take up the role of bridging node. 54 brendan j. donegan, daniel c. doolan, sabin tabirca • master the node should take up the role of master. • slave the node should be a slave exclusively. • confirm the network formation is complete. • data the message contains a data payload. the first four messages are used only during the formation of the scatternet and the data message is used only after the scatternet is formed. messages will only be processed by the node for which they are addressed, otherwise they will be forwarded by the message routing system. 2.3 scatternet formation formation of the scatternet is initiated at a chosen node (the ‘root node’) by first performing an inquiry for devices that provide the mmpi service. the root node then determines how many piconets are required to support this number of nodes. in the case of thirteen mmpi capable devices are discovered then two piconets will be created. the root node selects a device to be the bridge node and sends it a list of addresses of the other nodes in the network (algorithm 1). data: list of all mmpi capable devices initialize list of devices to send to bridge node; foreach device in the list do if bridge node then create slave connection; add bridge node to routing table; else if prior to bridge node then create slave connection; add slave node to routing table; send slave message; else if after bridge node then add device to list of devices to send to bridge node; add node to routing table; add number of devices to bridge message; add list of devices to the bridge message; send bridge message; algorithm 1: establishing initial connections at the root node the bridge node then chooses one of these to be the master of the other piconet and sends it the list minus the device chosen (algorithm 2). the second master makes connections to each device on the list, completing the scatternet formation. it then sends a confirmation message which propagates through the network via the message routing system. 2.4 message routing in order for a message to reach a recipient with which the sender has no direct connection, the message must be routed through the network. the library uses a simple message routing table (table 1) where each node keeps a table of all the nodes except itself. each entry in the routing table contains an index that is used to navigate between nodes. mobile message passing using a scatternet framework 55 data: list of devices sent by root node initialize list of devices to send to additional master; foreach device in the list do if second master then create master connection; add second master to routing table; else add node to routing table; add number of devices to master message; add list of devices to the master message; send master message; algorithm 2: establishing connections at the bridge node node node 2 node 0 node 5 node 6 0 0 0 0 1 0 0 0 0 2 1 0 0 3 0 2 0 0 4 0 3 0 0 5 0 4 0 6 0 4 1 7 0 4 1 1 8 0 4 1 2 9 0 4 1 3 table 1: routing tables for nodes 2, 0, 5 & 6 of a ten node scatternet in order to send a message from node 2 to node 8 (figure 3), the following steps are taken. node 2 looks up node 8 in its routing table (table 1). since node 2 has only one link, the index will be 0, and the message will be sent through that link to node 0. node 0 then looks up its routing table, finding the index 4, and sends the message through that link to node 5. node 5 looks up the routing table again and sends the message through link 1 to node 6. node 6 then looks up node 8, finding the index 2 and sends the message to it through that link. in summary, for a slave node of piconet one (on the left) (figure 3) to communicate to a slave node of piconet two (on the right) all communications traffic is first routed through the master node for piconet one and forwarded on to the bridge between the two networks. this is then picked up the the master node of piconet two and again forwarded on to the correct end point. for a node such as the master node of piconet one to communicate with the master node of piconet two, data need only be forwarded directly through the bridging node. 3 using the library using the library to write message passing programs is nearly identical to the original library. the operations that were supported in the original version of the library are still supported. 3.1 creating an mmpi node to create an mmpi node, the developer first calls the initialize(...) method of the mmpi class. a handle to the midlet which is using the mmpi library needs to be passed, as does a boolean value 56 brendan j. donegan, daniel c. doolan, sabin tabirca figure 3: structure of a ten node scatternet topology indicating whether the node is the root node. the developer needs to determine how this value will be set themselves, and it should be ensured that all non-root nodes are set up before the root node. this is because calling initialize(...) activates the bluetooth device and registers the mmpi service, allowing the node to be discovered. the root nodes reaction to the initialize(...) call will be to create the network. the non-root nodes reaction will be to wait until this event has occurred and completed. when network formation is complete, the program can continue execution. 3.2 message passing operations after calling initialize the communications world has been created thus giving the developer the ability to work with both point to point and global communication methods (listing 1). unlike many methods of the original mmpi library (and the mpi libraries), several of the new communications methods return the actual data received as an object. this is quite different to the other libraries that would pass a reference into the method for population with the received data. int broadcast(object buffer, int count, int datatype, int root, int tag) int scatter(object sendbuf, int sendcnt, int sendtype, int rcvcnt, int rcvtype, int root) int send(object buffer, int count, int datatype,int dest, int tag) object receive(int count, int datatype, int source, int tag) object gather(object sendbuf, int sendcnt, int sendtype, int recvcnt, int recvtype, int root ←↩ ) object reduce(object sendbuf, int count, int datatype, int op, int root) listing 1: mmpi communication method prototypes 4 evaluation to fully test the library one needs a significant number of mobile devices (far in excess of eight). such resources were unavailable at the time of implementation, but the system was developed and tested using the j2me emulators of the sun wireless toolkit. the system performed very well on the emulator, and several applications were developed for test purposes, including such classical applications as the mandelbrot set. using the mandelbrot set as an example it takes on average 139,360ms to generate an image of 2002 pixels at 500 iterations on a single device using the wtk emulator. this is significantly slower that a real world phone such as the nokia 6630, capable of generating the same image in 52,344ms. when mobile message passing using a scatternet framework 57 multiple instances of the emulator are running and carrying out a complex processing task they do not appear to achieve total parallelism as one would expect. many of the emulated devices however do give processing times as expected on the order of 13 to 14 seconds, when the application is executed with a set of ten phones. 4.1 other applications although we have developed the framework mainly to alleviate the node restriction on the existing version of mmpi, it is not limited to this use only. mmpi itself has been used as a communications library for bluetooth gaming [7] and this framework could be used to increase the number of players that may participate in the game. 5 conclusion this paper has outlined a library for creating scatternet based applications, which is capable of parallel computing on adhoc bluetooth networks of more than eight devices, using the scatternet framework. the structure and operation of the library has been outlined. it was found that there is a performance overhead associated with message routing and parsing. the framework can be used for a myriad of applications, such as multiplayer gaming or chat applications, quite easily. the most important aspect of the framework is that it can be deployed to any mobile device with midp 2.0 and bluetooth functionality, therefore it is capable of running on a significant number of todays mobile devices. 6 acknowledgment development of the mmpi library was funded under the “irish research council for science, engineering and technology” funded by the “national development plan”. bibliography [1] arm. arm cortex-a8, 2005. http://www.arm.com/products/cpus/arm_ cortex-a8.html. [2] arm. arm introduces industry’s fastest processor for low-power mobile and consumer applications, oct 2005. http://www.arm.com/news/10548.html. [3] bluetooth-sig. bluetooth specification version 1.1. [4] bluetooth.com. the official bluetooth website. http://www.bluetooth.com/. [5] bluetooth.org. the official bluetooth membership site. http://www.bluetooth.org/. [6] d. c. doolan, s. tabirca, and l. t. yang., mobile parallel computing, in 5th international symposium on parallel and distributed computing (ispdc06), pp 161–167, timisoara, romania, july 2006. [7] k. duggan, d. c. doolan, s. tabirca, and l. t. yang. single to multiplayer bluetooth gaming framework. in 6th international symposium on parallel and distributed computing (ispdc07), hagenberg, austria, july 2007. [8] itfacts. 1.019 billion mobile phones shipped in 2006. http://www.itfacts.biz/index. php?id=p8049. 58 brendan j. donegan, daniel c. doolan, sabin tabirca [9] d. jayanna, g. zaruba, a dynamic and distributed scatternet formation protocol for real-life bluetooth scatternets in proceedings of the 38th annual hawaii international conference on system sciences (hicss05), 2005. [10] g. miklos, a. racz, z. turanyi, a. valko, and p. johansson. performance aspects of bluetooth scatternet formation. in mobile and ad hoc networking and computing, 2000. mobihoc. 2000 first annual workshop on, pages 147–148, 11 aug. 2000. [11] mpi. the message passing interface (mpi) standard. http://www-unix.mcs.anl.gov/ mpi/. [12] mpich. mpich free implementation of mpi. http://www-unix.mcs.anl.gov/mpi/ mpich/. [13] r. shepherd, j. story, s. mansoor, parallel computation in mobile systems using bluetooth scatternets and java in proceedings of the international conference on parallel and distributed computing and networks, 2004. [14] symbian freak. nokia 6680 is loosing the battle to 6630. http://www.symbian-freak. com/news/0305/6680.htm. [15] g. zaruba, s. basagni, i. chlamtac, bluetrees scatternet formation to enable bluetooth based adhoc networks in ieee international conference communications (icc2001), pp 273–277, 2001. brendan j. donegan, daniel c. doolan, sabin tabirca university college cork department of computer science college road, cork, ireland e-mail: d.doolan@cs.ucc.ie, tabirca@cs.ucc.ie received: october 18, 2007 mobile message passing using a scatternet framework 59 brendan j. donegan, was a postgraduate student in the computer science department of university college cork between 2005-2006, studying mobile networking and computing. he is now working as graduate software engineer for symbian software limited in london. this work is the result of the msc project titled “mobile parallel computing” he undertook in the mobile multimedia group. daniel c. doolan, is currently in the final stages of completing a phd in the area of mobile computing. he holds a bsc in computer applications, an msc in multimedia technology, and over half a dozen other degrees at certificate and diploma level in the areas of business and computing. he has authored approximately 40 publications, including 6 book chapters, covering topics such as mobile computing, computer graphics and parallel processing. sabin tabirca is a lecturer in department of computer science of national university of ireland, cork. his main research interest is on mobile and parallel computing for scientific problems. he has published more than 100 articles in the areas of mobile multimedia, parallel computation, number theory and combinatorial optimization. international journal of computers, communications & control vol. i (2006), no. 3, pp. 25-32 pathogen variability. a genomic signal approach paul dan cristea abstract: the conversion of genomic symbolic sequences into digital signals has been applied for the analysis pathogen variability. results are given on the variability of human immunodeficiency virus, type 1, subtype f, isolated in romania, and of the type a avian influenza virus h5n1, for which sequences have been downloaded from genbank [1]. nucleotide sequence analysis is corroborated with techniques based on the genomic signal approach to detect pathogen resistance to antiretroviral treatment. in the case of protease (pr) inhibitors, it is found that the treatment induces single nucleotide polimorphisms (snps) in specific sites. for moderate resistance, the changes affect the pr enzyme only at the level of the protein, whereas for multiple drug resistance, the rna gene secondary structure also changes. keywords: genomic signals, pathogen variability, hiv, influenza, orthomyxoviridae, drug resistance 1 introduction as shown in a series of previous papers [2-4], the conversion of nucleotide and amino acid sequences into digital signals offers the possibility to apply signal processing methods for the analysis of genomic data. the genomic signal conversion used in our work is a one-to-one mapping of symbolic genomic sequences into complex signals, as described in [2]. the idea is to conserve all the information in the initial symbolic sequence, while bringing in foreground some features significant for the subsequent processing and analysis. this direct method has proven its potential in revealing large scale features of dna sequences, maintained at the scale of whole genomes or chromosomes, including both coding and non-coding regions. one of the most conspicuous results is that the unwrapped phase of dna complex genomic signals varies almost linearly along all investigated chromosomes, for both prokaryotes and eukaryotes. the slope is specific for various taxa and chromosomes. this regularity of the genomic signals reveals a corresponding large scale regularity in the distribution of pairs of successive nucleotides, which is similar to chargaff’s first order rules for the frequencies of occurrence of the nucleotides [5]. we applied the same genomic signal approach for studying the variability of several pathogens, including the human immunodeficiency virus, type 1 (hiv-1), subtype f, isolated from romanian patients at the national institute of infectious diseases “prof. dr. matei bals”, bucharest [3], and the avian influenza virus type a, based on genomic sequences downloaded from genbank [1].we have used mainly the phase analysis of the complex genomic signals attached to the nucleotide sequences describing viral genes, as well as the analysis of the corresponding secondary rna structure and of the phylogenetic neighbor-joining trees for some of these genes. the focus of the study is primarily on the enzyme changes involved in generating pathogen resistance to multiple drug treatment. a novel methodology for describing sets of related genomic signals, based on a common reference and on individual differences has been developed. variability signals with respect to average, median and maximum flat references, and digital derivatives of genomic signals are applied to this purpose. applying this method, it has been found that the mutations in the genes of the analyzed viruses occur only in some specific, well defined locations, while the largest part of their genome remains unchanged. the mutations conferring drug resistance are a subset of all mutations occurring in the studied viruses. on the other hand, for the case of hiv protease, it has been shown that the changes in response to the antiretroviral drug treatment occur not only at the level of the final enzyme product, preventing the action copyright c© 2006 by ccc publications selected from icccc 2006 (invited paper) 26 paul dan cristea of the drug on the active protease catalytic site, but also at the level of protease gene rna secondary structure. these type of changes have been found only for multiple drug resistant viruses. 2 symbolic sequence conversion for convenience we repeat here the mapping used in our work for the representation of the nucleotides [2] a = 1 + j, c = −1− j, g = −1 + j,t = 1− j (1) apart of the mapping of the four nucleotides (a, c, g,t), the complete genomic signal representation of nucleotide sequences also comprises the mapping of all the other iupac symbols for nucleotide classes: s = {c, g} strongly bonded, w = {a,t} weakly bonded, r = {a, g} purines, y = {c,t} pyrimidines, m = {a, c} amine, k = {g,t} ketone, b = {c, g,t} = ¬a, d = {a, g,t} = ¬c, h = {a, c,t} = ¬g, v = {a, c, g} = ¬t, and n = {a, c, g,t} [2]. these symbols occur in the nucleotide sequences generated by genotyping because of the multiplicities determined either by the variability within the virus population or by noise. but this is not the case of the consensus sequences downloaded from genbank [1], which are curated to contain only the (a, c, g,t) nucleotide symbols. the mapping in equation (1) has the advantage of conserving all the information in the initial symbolic sequence, as it uses a bijective mapping, while being as little biased as possible. 3 representation by reference and variation to study the variability of the genomic signals in a given set, for example, the signals for multiple resistant viruses, it is convenient to use a description comprising two types of components: (1) the reference a certain signal considered to best describe the common variation of all components in the considered cluster; (2) the difference of each signal in the cluster with respect to the common reference. in such an approach, it is important to introduce in the common reference as much as possible of the variation shared by all the signals, and keep for the individual differences of each signal only the variations belonging actually to the that signal, without external variation. the reference can be chosen as one of the following possibilities: • average (mean) of the signals, or another linear combination of the signals; • median the signal in the central position, or the average of the pair of signals placed centrally; • maximum flat signal a modified median that keeps better local variations on the signals where they occur avoiding spurious transfers on other signals. when the reference equals the average, the dispersion of the cluster of signals is minimum, i.e., the sum of the squares of the individual differences between each signal and the reference is minimized. but the average, as any other linear combination, has the important disadvantage that a localized variation of only one of the signals is transmitted to the reference, so that all the other signals will have an apparent variation of opposite sign in that point. the median reference performs better, being a nonlinear function of the signals in the cluster, so that it decouples the common reference from the local variations of each of the individual signals. the median reference minimizes the sum of the absolute values of the differences between each signal and the reference. a variation localized on only one of the signals is no longer transmitted to the reference, so that it does not affect the variation with respect to the reference of the other signals. the exception occurs when the signal on which the localized variation occurs is just the median. the maximum flat (maxflat) reference is equal to the median wherever the median has no variations which are not shared by other signals. elsewhere, the maxflat reference assumes the minimal variation pathogen variability. a genomic signal approach 27 that corresponds to its trend, if possible remaining constant. consequently, the variation signals show better the changes that occur in each individual signal, with less "crosstalk". the digital derivatives of the variation signals show only the actual changes, caused by the variability in each of the signals and, for genomic signals, correspond directly to the snps. 4 hiv-1 subtype f variability a phase analysis has been performed on a segment of about 1302 base pairs, approximately aligning with the standard sequence of hiv-1 (nc001802) in genbank [1] over the interval 1799..2430 bp. this segment, which is currently used for the standard identification and assessment of hiv-1 strains, comprises the protease (pr) gene and almost two thirds of the reverse transcriptase (rt) gene. the pr and rt segments are contiguous and have been analyzed both together, as one entity, and independently, as two distinct encoding regions. the pr gene has the length 297 bp and is located in the first interval (1..297 bp) of the sequenced dna segment, respectively along the 1799..2095 bp region of the nc001802 sequence. the rt encoding segment that has been analyzed has a length of 1005 bp and is located in the second interval (298..1302 bp) of the analyzed dna segment, respectively along the 2096..3100 bp region of the nc001802 sequence. the entire rt gene has 1680 bp located in the interval 2096..3775 of the sequence. figures 1 and 2 show the cumulated and unwrapped phase of genomic signals for the protease (pr) genes from nine instances of hiv type 1, f clade [1, 6]. three cases come from treatment naïve patients (s sensitive), three from patients that developed resistance to one of the drugs (r), and three with multiple resistance to ther antiretroviral treatment (m). the cumulated phase is proportional to the unbalance in the number of nucleotides (statistics of first order) along the nucleic acid strand given by: 3(ng − nc) + (na − nt ), up to a π/4 factor, whereas the unwrapped phase is proportional to the difference between the number of direct and inverse nucleotide transitions (statistics of second order) along the nucleic acid strand (n+ − n−), with a π/2 factor [2]. figures 3 and 4 give the same informtion for the segment comprising 1005 bp of reverse transcriptase (rt) genes, out of the total of 1680 bp in this gene, for the same isolates in figs. 1 and 2. as expected, the cumulated phase varies less than the unwrapped phase for these instances, as all mutations are of the snp type and affect more the nucleotide pair distribution than the nucleotide distribution itself. even for the unwrapped phase, the variation of the signal along the strand is quite similar for most of the sequences, but the local changes cumulate along the strands. because of the mutations are local, the general shape of the phase signals are similar. it is also to be noticed that all the genomic material in these sequences is encoding and uses the same reading frame. the vertical strips in these figures mark the positions of the mutations (snps) that induce resistance to protease inhibitors (indinavir, ritonavir, saquinavir, nelfinavir, and amprenavir) [1]. the mutations that lead to multiple drug resistance are concentrated in several sites. in most of the remaining genome, the viruses have the same longitudinal structure. the sequences display mutations in several other locations. the effect of the mutations can easier be seen on the unwrapped phase, which is more sensitive to snps. the successive mutations of the snp type do not induce the divergence that could be expected, so that the signals do not actually diverge from one another. on the contrary, the signals tend to cluster, as the variations tend to compensate each other, so that the overall span of the signals does not increase directly with the number of mutations and the number of signals. this is another proof of the fact that, from the structural point of view, a genomic sequence satisfies more restrictions than a "plain text", which must just correspond to a certain semantics and to certain grammar rules, and resembles more to a “poem”, which additionally obeys rules of symmetry, giving its “rhythm” and “rhyme”. the recurrence of such patterned structures is reflected in simple mathematical rules satisfied by the corresponding genomic signals. 28 paul dan cristea the representation can be improved by using the reference-difference description, choosing the maximum flat (maxflat) reference, as shown in fig. 5 for the unwrapped phase in fig. 4. in this case, the largest possible part of the common behavior of the signals is introduced in the reference signal, whereas each individual variation signal maintains only the changes occurring in that particular signal, or to the class it belongs to. the reference signal is no longer necessarily equal in each interval with one of the signals, even when the number of signals is odd. the digital derivatives of the difference signals, shown in fig. 6 show only the actual changes caused by the variability in each of the signals. in the case of hiv, these changes correspond directly to the snps. for multiple resistant strains, the pulses correspond to the sites known from literature to confer resistance to various drugs. figure 1: cumulated phase expressed by 3(ng − nc) + (na − nt ) [2] for the protease (pr) gene of nine isolates of hiv-1, subtype f, showing sensitivity (s), resistance (r) and multiple resistance (m) to drugs. figure 2: unwrapped phase expressed by n+ − n− [2] for the protease gene of the isolates of hiv-1 in fig. 1. hiv-1 makes many of its proteins in one long chain, and protease (pr) has the essential role of cutting this ’polyprotein’ into the proper pieces, with the proper timing. consequently, pr has been chosen as an important target for the current drug anti-hiv therapy. pr is a small enzyme, comprising two identical peptide chains, each of 99 amino acids long, which are encoded by the same gene of 297 nucleotides. the two chains form a tunnel that holds the polyprotein, which is cut at an active site located in the center of the tunnel. drugs bind to pr, blocking its action. studying the estimated secondary structure pathogen variability. a genomic signal approach 29 figure 3: cumulated phase of rt genomic signals for the isolates shown in figs. 1 and 2. figure 4: unwrapped phase for the rt gene in the isolates shown in figs. 1 and 2. of the pr rna for the nine virions previously analyzed, it can be shown [3] that the structures are quite similar for drug sensitive and drug simple resistant viruses. this result is consistent with the generally accepted model stating that the genomic changes of hiv, which induce resistance to drugs, operates at the level of the protein (the final protease enzime), preventing the blocking of its catalytic site. on the other hand, it is found the remarkable fact that, for drug multiple resistant strains, there is a significant change in the rna secondary structure. large loops and bulges are replaced with similar, but smaller, less vulnerable, closed-loop structures. these results indicate that there is a certain action of the drug at the level of the protease rna, effect that becomes evident when mutations conferring multiple drug resistance occur. 30 paul dan cristea figure 5: the unwrapped phase in fig. 4 shown with respect to the maxflat reference. figure 6: digital derivatives of the variation signals in fig. 5. pathogen variability. a genomic signal approach 31 5 variability of hemagglutinin gene of influenza h5n1 virus the influenza virus envelope embeds two specific antigenic glycoproteins that project out of the virion surface, the hemagglutinin (ha) and the neuraminidase (na). many different combinations of ha and na proteins are possible, but only the h1n1 (spanish endemic), h1n2 (asian epidemic), and h3n2 (hong kong epidemic) subtypes have circulated worldwide among humans. ha protein selectively binds to the sialic acid of the host cell surface receptors, thus recognizing the cells that the virus can invade [4, 6]. figure 7 gives the cumulated phase of the ha gene for h5n1 viruses isolated from two humans (af046080, af046097) and one chicken (af046088), in hong kong, in 1997 [6, 7]. the genes for viruses isolated close in time are similar, even when crossing the inter-species barrier, whereas a large variation can be seen for genes isolated at larger time intervals. only several snps are found in fig. 8 which gives the difference cumulated phases with respect to the maxflat reference. the same result has been obtained for all the genes in the eight segments of the h5n1 virus [4, 6]. figure 7: cumulated phase of the ha gene, h5n1 virus (accessions af046080, 88, 97 [1, 6]). figure 8: differences of ha gene cumulated phases in fig.7 with respect to the maxflat reference. 6 further work further work will be focused on: 32 paul dan cristea • the dynamics of influenza type a viruses that have crossed till now the species barrier from birds to humans, and which hold the potential to become highly contagious and highly lethal in humans, including the h5n1 subtype, • extending the study from the nucleotide to the amino acid level, which could be more significant from the phenotypic point of view, • using genomic signals for helping clustering viruses in classes. acknowledgments the sequences of hiv presented in this paper have been genotyped by dr. dan otelea from the national institute of infectious diseases “prof. dr. matei bals”, bucharest, romania. results referring to the study of hiv variability have been previously jointly published [3]. references [1] national center for biotechnology information, national institutes of health, national library of medicine, national center for biotechnology information, genbank, http://www.ncbi.nlm.nih.gov/genoms [2] p. d. cristea, "representation and analysis of dna sequences", in genomic signal processing and statistics, editors e.g. dougherty, i. shmulevici, jie chen, z. j. wang, book series on signal processing and communications, hidawi, 2005, pp.15-65. [3] p. d. cristea, d. otelea, rodica tuduce, "study of hiv variability based on genomic signal analysis of protease and reverse transcriptase genes", embc’05, sept. 2005, shanghai, china. [4] p. d. cristea, "genomic signal analysis of pathogen variability", spie, bo24, paper 5699-52, san jose, jan, 2005, 12 pg. [5] e. chargaff, "structure and function of nucleic acids as cell constituents", fed. proc., 10, pp. 654659, 1951. [6] d.l. suarez,.et.al., comparisons of highly virulent h5n1 influenza a viruses isolated from humans and chickens from hong kong, j. virology, vol. 72 (8), pp. 6678-6688, 1998. (af046080-99). [7] e. ghedin, n. sengamalay, m. shumway et. al., "large-scale sequencing of human influenza reveals the dynamic nature of viral genome evolution", nature, vol. 437, oct. 2005, pp.1162-1166. [8] m.s. hirsch et al., "antiretroviral drug resistance testing in adult hiv-1 infection", in proc. recommendations of an international aids society usa panel, jama, vol. 283, no. 18, may 10, 2000, pp.2417-2426. paul dan cristea university politehnica of bucharest biomedical engineering center address: spl. independentei 313, sect. 6 060042 bucharest, romania e-mail: pcristea@dsp.pub.ro int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 2, pp. 216-223 synthetic genes for artificial ants. diversity in ant colony optimization algorithms s.c. negulescu, i. dzitac, a.e. lascu sorin c. negulescu "lucian blaga" university of sibiu, "hermann oberth" faculty of engineering 10, victoriei bd, 550024 sibiu, romania email: sorin.negulescu@ulbsibiu.ro ioan dzitac "aurel vlaicu" university of arad, faculty of exact sciences, department of mathematics-informatics, str. elena dragoi, nr. 2, complex universitar m, arad, romania and r&d agora ltd. oradea [cercetare dezvoltare agora] 8, piata tineretului, 410526 oradea, romania e-mail ioan.dzitac@uav.ro alina e. lascu "lucian blaga" university of sibiu, "hermann oberth" faculty of engineering 10, victoriei bd, 550024 sibiu, romania e-mail alina.lascu@ulbsibiu.ro abstract: inspired from the fact that the real world ants from within a colony are not clones (although they may look alike, they are different from one another), in this paper, the authors are presenting an adapted ant colony optimisation (aco) algorithm that incorporates methods and ideas from genetic algorithms (ga). following the first (introductory) section of the paper is presented the history and the state of the art, beginning with the stigmergy and genetic concepts and ending with the latest aco algorithm variants as multiagent systems (mas). the rationale and the approach sections are aiming at presenting the problems with current stigmergy-based algorithms and at proposing a (possible yet to be fully verified) solution to some of the problems ("synthetic genes" for artificial ants). a model used for validating the proposed solution is presented in the next section together with some preliminary simulation results. some of the conclusions regarding the main subject of the paper (synthetic genes: agents within the mas with different behaviours) that are closing the paper are: a) the convergence speed of the aco algorithms can be improved using this approach; b) these "synthetic genes" can be easily implemented (as local variables or properties of the agents); c) the mas is self-adapting to the specific problem that needs to be optimized. keywords: ant colony optimization, genetic algorithms, multiagent systems, stigmergy. 1 introduction there are about ten thousand species of ants living on earth, grouped in colonies, all of them having or one or (at most) two queens and sterile female workers (all the other ants). as the research conducted by biologists showed [5], there is no central control or no management whatsoever inside a colony, but the behaviour of the colony changes (as it gets older and larger). it was also determined that there are four types of ants [5]: harvester ants, foraging ants (out on a foraging trail searching for food or bringing food back), patrolling (out early in the morning before the foragers are active, somehow choosing the directions that the forager ants will go, and just by coming back to the nest they indicate to the foragers that is safe to go out), maintenance (work inside the nest with moist soil for building the walls), midden work (put some kind of territorial chemical in the garbage they create). another interesting fact is that 50% of the ants are doing nothing, acting as a reserve for exceptionally situations. one class of multi-agent systems (mas) are copying some of the characteristics of the foraging ants within the colonies (depending on how much these characteristics are helpful in solving a particular type of problem) such as [10]: copyright c⃝ 2006-2010 by ccc publications synthetic genes for artificial ants. diversity in ant colony optimization algorithms 217 • being a distributed society of autonomous individuals/agents; • having fully distributed control among the agents; • having localized communications among the individual; • taking stochastic decisions; • having system-level behaviours that are transcending the behavioural repertoire of the single (minimalist) agent; • following simple interaction rules. as a result, the overall very important features of these systems are: robustness, adaptability and scalability. the rest of this paper is arranged as follows: the history section presents the stigmergy and genetic concepts and variants of aco algorithms as mas. the rationale and the approach sections are aiming at presenting the problems with current stigmergy-based algorithms and at proposing a (possible yet to be fully verified) solution to some of the problems ("synthetic genes" for artificial ants). a model used for validating the proposed solution is presented in the next section together with some preliminary simulation results. some of the conclusions regarding the main subject of the paper presented in the last section are: a) the convergence speed of the aco algorithms can be improved using this approach; b) these "synthetic genes" can be easily implemented (as local variables or properties of the agents); c) the mas is self-adapting to the specific problem that needs to be optimized. 2 history and related work swarm intelligence systems (inspired from biological systems as ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling) are in fact simple agents that are interrelated, being able to communicate one with another and to interact with their environment. they follow simple rules and there is no centralized control [9]. in swarm intelligence systems the focus is not on modelling, inspired from biologic (sub-symbolic) paradigms, but merely on simulating biologic behaviour [8]. the ant colony optimization algorithm is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs [17]. this technique was initially proposed by marco dorigo in 1992 in his phd thesis [6] [7] and it was aiming at searching an optimal path in a graph (i.e. the travelling salesperson problem tsp) based on the behaviour of ants seeking a path between their colony and a source of food [4]. the algorithm was further developed and, as a result, some common extensions appeared such as the elitist ant system (eas) where the ant that found a better solution could deposit an extra amount of pheromone; the max-min ant system (mmas)[16] where the pheromone intensity was bounded to certain minimum and maximum values (tmax, tmin), and where only the ant that found a better solution could deposit pheromone. the mmas was the first algorithm that was constantly checking its convergence to solution; all the edges in the graph were initialized to tmax and reinitialized to tmax when the algorithm was near stagnation [17]. another version of the aco is the rank-based ant system (asrank) where all solutions are ranked according to their fitness. the amount of pheromone deposited is then weighted for each solution, such that the solutions with better fitness deposit more pheromone than the solutions with worse fitness [17]. besides these common variants of the algorithm [12], the authors proposed some different approaches such as: • human-driven stigmergic control [3] aimed at exploring the relationship between stigmergy and synergy, based on the threshold principle, specifically, focusing on affordability and keeping a definite engineering perspective, the purpose was to save computer resources in applying stigmergic control to industrial problems by exploring the relationship between the number of digital ants and problem complexity. the long-range target was to follow the analogy to superconductivity: moving the threshold in order to improve performance and/or save computing resources. the research proved that the threshold exists and it depends on problem type and complexity; the same solution quality can be obtained with fewer ants. • user-driven heuristics [1] [12] the approach emphasised the significance of the environment in the agent system and the attention that must be paid to the dynamics that emerges from the indirect interactions of the agents. finding out the form and parameters that influence the system behaviour is non-trivial for humans and the research investigated the methods that would enable user-driven solutions in dealing with system tuning. the proposed methods were based on two essential functional requirements: a) at the macro level, the need to monitor and represent in an intuitive way the system behaviour and b) at the micro level, the need to control and track the system state space. the approach proved to be workable on usual configurations and effective in dealing with combinatorial explosion. 218 s.c. negulescu, i. dzitac, a.e. lascu • (sub-)symbolic inferences in multi-agent systems [2] the research aimed at showing that new logics, already useful in modern software engineering, become necessary mainly for mas, despite obvious adversities. the features asked for by the paradigm of computing as intelligent interaction, based on "nuances of nuancedreasoning", that should be reflected by agent logics were outlined. by injecting symbolic reasoning in systems based on sub-symbolic "emergent synthesis" the authors presented the way in which how quantifiable synergy can be reached even in advanced challenging domains, such as stigmergic coordination. the original idea has diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behaviour of ants such as solving combinatorial optimization, classification and image processing problems [15]. the authors successfully applied the aco algorithms to solve difficult problems such as: vehicle route allocation (with multiple constraints) [13] [14] and optimal capacitor banks placement in power distribution networks [15] where the criterion of the mathematical optimization model was a nonlinear function based on costs and the model imposed equality constraints described by the network operating equations and inequality constraints required to maintain within admissible limits the parameters characterizing the system state. 3 rationale and approach the motive for proposing synthetic genes for artificial ants and promoting the diversity in ant colony optimization algorithms is that the convergence speed of current variations of aco algorithms is strongly dependent of the problem type and size. the real world ants from within a colony are not clones as current aco algorithms are modelling them; although they may look alike, they are different from one another. the vast majority of aco algorithms have general variables that are determining the overall system behaviour such as: • pheromone evaporation speed when updating the pheromones intensities in the graph (ρ ); • pheromone quantity deposited on graph (q); • pheromone influence is on edge selection (α ); • distance influence on edge selection (β ); • the multiplication value for the pheromone intensity of the elitist ant (ε ) only for eas. when a certain problem must be solved, the values of the above presented parameters are set and after a few runs they are adjusted in order to improve the overall convergence speed or solution quality. when dealing with dynamic optimisation problems the above mentioned approach accentuates once more its weak points, as it is almost impossible to determine good parameter values. a possible manner to combat these limitations is to adopt some ideas from the ga field. although hybrid approaches between mas (such as aco or eas) and ga are not new, the technique proposed herein is innovative because: • it does not absolutely require to implement all the ga concepts (like selection, crossover and mutation); • the fitness function may or may not exist (depending on problem type); • the generations from ga can be associated to the iteration number from mas; • the global parameters belonging to the algorithm are converted to local parameters belonging to ants. as depicted in figure 1b, each virtual ant within the colony has its own governing parameters in the same manner that real ants exhibit different behaviour. more details about the way in which such a system should work are presented in the next section. 4 the model since the model proposed herein imposes that each virtual ant within the colony must have its own parameters that are determining its behaviour, it is important for the user to have the ability to specify some general limits in which these parameters may vary. in this regard, some other general parameters must be defined as follows: • minimum and maximum values for the exploitation of the good old found paths αmin, αmax; • minimum and maximum values for the exploration of the other paths within the graph βmin, βmax; • minimum and maximum values for the multiplication value for the pheromone intensity of the (elitist) ants that found a better solution εmax, εmax; synthetic genes for artificial ants. diversity in ant colony optimization algorithms 219 (a) (b) figure 1: exemplification of a) global parameters versus b) local parameters for the ant system depending on the problem to be solved some other global parameters can be defined to enforce the limits for the local parameters (e.g. for the vehicle route allocation problem [13] a local history parameter was used and hmin, hmax as global parameters). after finding a better solution, different actions can be taken by the algorithm to auto-tune the ant local parameters: • the parameters will be adjusted for some, all or none of the ants; • parameters can be adjusted using different types of equations e.g. form simple average, weighted average (depending on solution quality), or other linear functions to polynomial or exponential functions. the initialization of the ant parameters can be easily done in a random manner as shown in figure 2, but some other initialization methods can be applied as well (e.g. gaussian centred to the average value: min+(max-min)/2 ). although the research on the proposed model is in progress the results so far are promising. the algorithm is self-tuning to the problem type and size. some other methods must be checked such as starting with a big number of ants and reducing it to a minimum in time, with a twofold advantage: • having a large genetic pool at the beginning will assure that the algorithm can adapt to very diverse problems; • reducing the number of ants will increase the computation speed; this number can be drastically reduced as presented in [3]. 220 s.c. negulescu, i. dzitac, a.e. lascu synthetic genes for artificial ants. diversity in ant colony optimization algorithms 221 figure 2: the proposed pseudocode for the "synthetic genes" enhanced eas. 222 s.c. negulescu, i. dzitac, a.e. lascu 5 conclusions and future work since this paper presents an ongoing research, the conclusions will be presented tanking in consideration two time ranges: a) short (current relevant results) and b) medium-long (directions of future work). a1. the proposed aco algorithms have the ability to self-adapt to different optimisation problem types and sizes. a2. the convergence speed of the aco algorithms can be improved using this approach. a3. the "synthetic genes" can be easily implemented (as local variables or properties of the agents); only some other global variables that will impose the limits of variation must be defined. a4. different types of initialisation of the genes values can be used (e.g. random). ideas for this particular subject can be used from the ga field. a5. the genes values can be automatically adjusted for some, all or none of the ants. moreover, this can be achieved using different types of equations e.g. form simple average, weighted average (depending on solution quality), or other linear functions to polynomial or exponential functions. b1. some other methods of initialization/algorithm evolution must be tested. as presented in the previous section, the algorithm can start with a big number of ants and reduce it to a minimum in time. b2. other concepts form ga can be implemented taking into account both the advantages (solution quality) and the disadvantages (computing time) like selection, crossover or mutation. b3. finding a solution to the stagnation problem that is intrinsic both to aco and ga approaches. another possible technique would be to reinitialize the local parameters while keeping the pheromones intensities on the graph intact. bibliography [1] bǎrbat b.e, moiceanu a., pleşca s., negulescu s.c., affordability and paradigms in agent-based systems, computer sc. j. of moldova, 15, 2(44):178-201, 2007. [2] bǎrbat b.e., negulescu s.c., from algorithms to (sub-)symbolic inferences in multi-agent systems. international journal of computers, communications and control, 1(3):5-12, 2006. [3] bǎrbat b.e., negulescu s.c., zamfirescu c.b., human-driven stigmergic control. moving the threshold. in n. simonov (ed.), proc. of the 17th imacs world congress (scientific computation, applied mathematics and simulation), paris, pp.86-92, 2005. [4] bonabeau e., dorigo m., theraulaz g., swarm intelligence: from natural to artificial systems, new york: oxford university press, 1999. [5] deborah gordon (2008), dig ants, retrived 11.2009, from ted. ideas worth spreading. web site: http://www.ted.com/talks/lang/eng/deborah_gordon_digs _ants.html. [6] dorigo m., maniezzo v., colorni a., the ant system: optimisation by a colony of cooperating agents, ieee transactions on systems, man, and cybernetics, part b, 26 (1):29-42, 1996. [7] dorigo, m., optimization, learning and natural algorithms, phd thesis. italy: politecnico di milano, 1992. [8] dzitac i., bǎrbat b.e., artificial intelligence + distributed systems = agents, international journal of computers, communications and control, 4(1):17-26, 2009. [9] dzitac i., moisil i., advanced ai techniques for web mining, proceedings of the 10th wseas international conference on mathematical methods, computational techniques and intelligent systems, corfu, pp.343-346, 2008. [10] gambardella l.m., di caro g. (2005), the ant colony optimization (aco) metaheuristic: a swarm intelligence framework for complex optimization tasks. retrived 2008, from university of bologna: first summer school on aspects of complexity. web site: http://www.cs.unibo.it/ fioretti/ac/ac2005/ docs/slides_dicaro.pdf. [11] negulescu s.c., bǎrbat b.e., enhancing the effectiveness of simple multi-agent systems through stigmergic coordination, in icsc-naiso (ed.), fourth international icsc symposium on engineering of intelligent systems (eis 2004), canada: icsc-naiso academic press, pp.149-156, 2004. synthetic genes for artificial ants. diversity in ant colony optimization algorithms 223 [12] negulescu s.c., zamfirescu c.b., bǎrbat b.e., user-driven heuristics for nondeterministic problems. studies in informatics and control (special issue dedicated to the 2nd romanian-hungarian joint symp. on applied computational intelligence), 15(3):289-296, 2006. [13] negulescu, s.c., kifor, c.v., oprean, c., ant colony solving multiple constraints problem: vehicle route allocation. international journal of computers, communications and control (ijccc), 3(4):366-373, 2008. [14] negulescu, s.c., oprean, c., kifor, c.v., carabulea, i., elitist ant system for route allocation problem. in mastorakis, n.e. et al (ed.), proceedings of the 8th conference on applied informatics and communications, greece: world scientific and engineering academy and society (wseas), pp.62-67, 2008. [15] secui, d.c., dzitac, s., bendea, g.v., dzitac, i., an aco algorithm for optimal capacitor banks placement in power distribution networks. studies in informatics and control, 18(4):305-314, 2009. [16] stützle t., hoos h.h., max-min ant system, future generation computer systems, 16 (8):889-914, 2000. [17] wikipedia, the free encyclopedia (2009), ant colony optimization. retrived 11.2009, from wikipedia. web site: http://en.wikipedia.org/wiki/ ant_colony_optimization. sorin c. negulescu has a m.sc. degree in quality mangement (2005) a m.sc. degree in computer science (2003), "lucian blaga" university, sibiu and a ph. d in industrial engineering. his research interests include multi-agent systems with applications in manufacturing systems using stigmergic coordination, factory automation and the holonic paradigm. ioan dzitac (b. february 14, 1953) received his m.sc. in mathematics (1977) and phd in information sciences (2002) from "babes-bolyai" university of cluj-napoca. now he is full professor of informatics at mathematics-informatics department, faculty of exact sciences, "aurel vlaicu" university of arad, romania and director of research & development agora ltd. of oradea, romania. his current research interests include different aspects of artificial intelligence and distributed systems. he has (co-)authored 18 books and more than 70 papers, more than 50 de conferences participation, member in international program committee of more than 40 conferences and workshops. alina e. lascu has a m.sc. degree in international relations and european studies (2008), is currently in the last year of her master studies in the domain of computer science and in the first year of the doctoral studies in the domain of computers and ict. as depicted from her educational background her research interests are very much trans-disciplinary: language technology, intelligent agents, agent orientation, computer-aided semiosis, cultural and interdisciplinary studies. international journal of computers, communications & control vol. i (2006), no. 1, pp. 9-15 visualising infinity on a mobile device daniel c. doolan, sabin tabirca abstract: this paper discusses how a java 2 micro edition (j2me) application may be developed to visualise a wide variety of differing fractal types on a mobile phone. a limited number of j2me applications are available that are capable of generating the mandelbrot set. at present there are no j2me applications capable of generating a multitude of fractal image types. keywords: j2me, mobile phone, fractals 1 introduction it has been shown that mobile devices are capable of generating high quality images of infinite detail [2]. the generated images have generally been limited to the mandelbrot set. since the late 19th century fractals have been a favourite topic for mathematicians. since the dawn of modern day microprocessor based computing the study of fractals has taken a radical leap, as it is within the computing domain that all the nuances of fractal type images can be visualised. benoit mandelbrot made a huge contribution in the late 1970’s with the discovery of the mandelbrot set (an index for all the possible julia sets). the dawn of the 21st century has seen a radical changed in what we consider a computer to be. it has seen the widespread uptake of mobile phones throughout the world. devices that started life in the latter years of the 20th century as a mobile communications medium have evolved and mutated into mobile computing devices of considerable processing power. no longer are mobile phones used for just telecommunications but for just about any type of application that a standard desktop machine is capable of. the 2d and 3d visualisation of fractal images is one interesting topic that is coming into the realm of reality within the mobile computing domain. current high end phones have processing speeds in the region of 100 to 200mhz [7] [1], typically running arm9 type processors. the next evolution in processing power will see such devices fitted with the arm11 processor cores with speeds as high as 500mhz. mobile devices clearly have a huge processing potential especially if the combined processing power of the many millions of phones around the world were put to task on a singular problem. examples of such computation are already in existence for example “distributed fractal generation across a piconet” [3] demonstrates that the combined processing power of several mobile devices may be used to distribute the processing load between several devices that are connected together over a bluetooth network. 1.1 mobile phone market penetration the uptake of mobile devices around the world is staggering. in september 2004 the market penetration stood at 89% in ireland [10], by march 2005 it stood at 94% [11]. this is a huge increase when in 2001 penetration stood at only 67% [8]. ireland achieved 100% penetration in september 2005, showing an increase of over 11% in a twelve month period. this allows ireland to join spain, finland the netherlands and austria in having a 100% penetration rage. luxembourg is currently on top with a rate of 156% [12]. it is expected that western europe will exceed 100% usage by 2007 [15]. the year 2015 should see half the world’s population (four billion people) using mobile phones as a communications medium [9]. 1.2 primary aims the primary purpose of this paper is the development of an application capable of running on a single mobile device that has the ability to generate a variety of two dimensional fractal images. one of the chief aims is that the application should be easy to use. this would allow it to be used as a teaching tool. to achieve this each section of the application has a easy to use graphical user interface (gui) to allow the user to specify the parameters for the image generation process. once the user is happy with the image parameters they can then press a button to begin the image generation process. the resultant output will be a fractal image based on the input parameters. copyright c© 2006 by ccc publications 10 daniel c. doolan, sabin tabirca 2 mandelbrot & julia set generation the discovery of the julia set in 1918 by gaston julia described in his paper “mémoire sur l’itération des fonctions rationnelles” proved to be a most important work at the time. it became almost forgotten until benoit mandelbrot brought it back to the forefront with his discovery of the mandelbrot set. this ensued a new field of research that became known as fractal geometry. both the julia and mandelbrot set images can be generated by the repeated iteration of a simple function (see figure 2). the mandelbrot set is an index into the julia set, in other words every possible julia set can be represented within the mandelbrot set (see figure 1). figure 1: index of the julia set (the mandelbrot set) jc = { z0 ∈c| lim n→∞ zn 6= ∞ } where : z0 = c, zn+1 = f (zn), n ≥ 0 m = { c ∈c| lim n→∞ zn 6= ∞ } where : z0 = 0, zn+1 = f (zn), n ≥ 0 figure 2: julia & mandelbrot set definitions fractal images are usually obtained when the generating function f (z) is non linear. the mandelbrot set is obtained by iterating the function f (z) = z2 + c. when the generating function has the form of f (z) = zu + cv many other mandel-like sets may be produced. algorithms 1 and 2 show how both the julia and mandelbrot sets can be generated. algorithm 1 the julia set algorithm for each (x,y) in [xmin, xmax]×[ymin, ymax] construct z0 = x + j ×y; find the orbit of z0 [first niter elements] if(all the orbit points are under the threshold) draw (x,y) algorithm 2 the mandelbrot set algorithm for each (x,y) in [xmin, xmax]×[ymin, ymax] c = x + i×y; find the orbit of z0 while under the threshold r if(all the orbit points are not under the threshold) draw (x,y) 2.1 implementation a simple to use graphical user interface (gui) is provided within the application to allow the user to enter various parameters detailing the type of image to be generated (see figure 3). the parameters dealing with the fractal image itself include: the image size, number of iterations, radius, cpower, zpower, formula type and image inversion. the other options are for the rate of fractal zoom, and the accuracy of the crosshair (seen in the image output screen (canvas)). figure 3: mandelbrot settings gui and output image figure 4: julia set image & results output screen a thread is used for the generation of the fractal image to allow for user interaction with the image as it is being generated. the outputted image is redrawn at regular intervals so the user can see the progress of the image generation process. the fractal image itself is generated as an array of integers (listing 1). this array is then passed visualising infinity on a mobile device 11 to the creatergbimage(. . .) method of the image class to generate the image object that is ultimately displayed within the onscreen canvas. the canvas has a crosshair present to allow the user to navigate around the image and select an area to zoom on. the crosshair is controlled by the directional keys of the mobile device. the user may also view the corresponding julia set (figure 4) for any point that the crosshair is currently indicating, by selecting the “view julia set” option from the mandelbrot set canvas menu. for(int i=0;i<sizex;i++)for(int j=0;j<sizey;j++){ complex c = new complex((xmin+i*stepx),(ymin+j*stepy)); complex z = new complex(); for (k=0;k<nr_iter;k++){ z=f(z,c); if(z.getabs()>r){ r = c[k%l][0]; g = c[k%l][1]; b = c[k%l][2]; color = b + (g<<8) + (r<<16) + alpha; pixels[(j*sizex) + i] = color; break; } } } listing 1: code listing of mandelbrot set function the application allows for a variety of mandel-like images to be generated of the form f (z) = zu + cv, f (z) = zu − cv and f (z) = zu + cv + z. the cpower and zpower parameters of the gui dictate the values of u and v. the inverted representation of each form may also be generated by selecting the appropriate option from the gui, producing images of the form f (z) = zu + inv(cv), f (z) = zu −inv(cv) and f (z) = zu + inv(cv) + z. 2.2 processing results the application was tested using a number of image sizes as well as a varying the number of iterations (see table 1). the 6680 was unable to generate an image of 600 pixels square. testing with a nokia 3220 it was unable to generate an image of 2002 pixels. however it was capable of generating images of 1002 and 1502 pixels, but with considerable processing times, 56,503ms and 298,365ms for 1002 at 50 and 500 iterations respectively. device iter 100×100 200×200 300×300 400×400 500×500 600×600 nokia 6630 50 3,000ms 9,812ms 19,484ms 33,812ms 53,359ms 76,859ms nokia 6680 50 3,141ms 12,516ms 21,859ms 36,234ms 60,859ms n/a nokia 6630 500 13,281ms 52,344ms 111,484ms 196,125ms 301,344ms 429,047ms nokia 6680 500 12,359ms 48,000ms 103,219ms 187,797ms 290,062ms n/a table 1: image generation times for the mandelbrot set (xmin,ymin -2.0, xmax,ymax 2.0). 3 prime number fractal generation mathematicians have been studying prime numbers for thousands of years, with it origins going all the way back to ancient greece and the period of euclid. prime numbers have remained a mystery throughout the intervening centuries. their is currently ongoing work to find a prime number with ten million digits or more [4]. the largest prime found to date was discovered 15th december 2005 by dr. curtis cooper and dr. steven boone, the 43rd mersenne prime 230,402,457 − 1 being 9, 152, 052 digits in length. the second largest was discovered by dr. martin nowak 18th february 2005, the 42nd mersenne prime, 225,964,951 −1 containing 7,816,230 digits. the next largest was discovered 15th may 2004 by josh findley (41st mersenne prime, 224,036,583 −1 containing 7,235,733 digits. these discoveries were part of the great internet mersenne prime search (gimps) project. it is likely that within the next year or so a ten million digit prime number will be found. figure 5: pnf examples with 5 & 20 million primes figure 6: prime number fractal user interface the visualisation of prime numbers is again of interest to many in the mathematical community. probably the most well know visualisation was discovered by stanislaw ulam (the ulam spiral) in 1963 while doodling during 12 daniel c. doolan, sabin tabirca a scientific meeting [14]. an alternate visualisation is the prime number fractal (figure 5) where the resultant image has a central area of brightness and typically resembles a gaseous nebula or some other cosmic object. it is generally recognised that adrian leatherland from monash university in australia constructed the first prime number fractal [6]. the application has a very simple user interface with only a few components (figure 6). the options are: the sieve size, image size, and initial coordinates for x and y. 3.1 theoretical background the most important and evident feature of a prime number fractal image is the central area of brightness. this results in the pixels within the vicinity of the central area are visited more often than pixels around the periphery of the image. the movements in the up, down, left, right directions occur randomly but each direction produces approximately the same number of moves (see table 3). hence the trajectory of moves is random, generally staying around the central area of brightness. theorem 1. the number of up, down, back, forward movements are asymptotically equal. proof. dirichlet’s theorem assures if a and b are relatively prime then there are an infinity of primes in the set a·k + b, k > 0. this means that the random walk has an infinity of up, down, left, right moves. if πa,b(x) denotes the number of primes of the form a ·k + b less than x then we know from a very recent result of weisstein [16] that limx→∞ πa,b(x) li(x) = 1 ϕ(a) where li(x) is the logarithmic integral function and ϕ(a) the euler totient function. the particular case a = 5 gives limx→∞ π5,k (x) li(x) = 1 ϕ(5) = 1 4 ,∀k ∈ 1, 2, 3, 4, which means that π5,1 ≈ π5,2 ≈ π5,3 ≈ π5,4(x) ≈ li(x)4 clearly the two dimensional prime number fractal algorithm has asymptotically the same number of up, down, left and right moves. even on desktop systems the process of generating primes can take a significant amount of time for example 21,157ms on a amd athlon xp2600 system (table 4). the generation of the primes examines all the numbers between 0 and n the input size. as the prime numbers become larger their distribution becomes far sparser (table 2). their should be at least one prime between n and 2(n). # primes sieve size 1 million 15,485,865 2 million 32,452,850 5 million 86,028,130 10 million 179,424,680 table 2: sieve size #primes 1 million 5 million 10 million 20 million left 249,934 1,249,832 2,499,755 4,999,690 right 205,015 1,250,079 2,500,284 5,000,241 up 250,110 1,250,195 2,500,209 5,000,270 down 249,940 1,249,893 2,499,751 4,999,798 table 3: distribution of moves for a two dimensional prime number fractal #primes 1 million 5 million 10 million athlon xp2600 1,781ms 9,752ms 21,157ms amd 500mhz system 4,084ms 23,946ms 51,552ms table 4: processing times to generate primes on desktop systems 3.2 the fractal algorithm the generation of the fractal image requires the iteration through a loop (algorithm 3) for all numbers from 1 to n. the first requirement of the algorithm is to determine if the number i is prime or not. the sieve of eratosthenes is an time efficient method for determining primality of a sequence of numbers. the time complexity is o(n · log log n), however the space requirement is o(n). the algorithm iterates through the sequence from 2 to n crossing off all numbers > 2 that are divisible by 2. it then moves on to the smallest remaining number, and removes all of its multiples. the process of moving on and removing multiples continues until all numbers up to √ n have been crossed off. in java one may use an array of boolean values to indicate if the number at index i of the array is prime. a boolean in j2se requires one byte for storage, however the bitset class may be used. this reduces the storage requirements to o( n8 ) bytes. as mobile devices have such a limited memory visualising infinity on a mobile device 13 this approach cannot be used, hence the need for an alternate method. determination of the primality is achieved by calling the method isprime(i) (algorithm 4). it returns a boolean value indicating if the number is prime or not. if the number is prime then the process of plotting the prime takes place. firstly the direction is calculated by carrying out modular division by 5 yielding p mod 5 ∈ {1, 2, 3, 4} this mapping can be clearly seen in table 5. next depending of the direction the current pixel location is updated to reflect the direction indicated by the prime. lastly the colour of the pixel at the new cell location is incremented. algorithm 3 2d prime number fractal algorithm for i = 0 to n -1 do begin if(isprime(i)) then dir = p[i] mod 5; if dir = 1 then x–; if dir = 2 then x++ if dir = 3 then y– if dir = 4 then y++ pixels[x,y] ++; end if; end for; algorithm 4 isprime( ) algorithm boolean isprime(long p) begin long d; if(p=1) return false; if(p=2 || p=3) return true; if(p % 2=0 || p % 3=0) return false; for(d=3;d <= sqrt(p); d=d+2) if(p % d=0) return false; return true; end 1. left: p mod 5 = 1 ⇒ (x, y) goes in (x−inc, y) 2. right: p mod 5 = 2 ⇒ (x, y) goes in (x + inc, y) 3. up: p mod 5 = 3 ⇒ (x, y) goes in (x, y−inc) 4. down: p mod 5 = 4 ⇒ (x, y) goes in (x, y + inc) table 5: mapping of direction values to pixel movement 3.3 processing results as is the case with desktop systems the generation of the primes is the most computationally expensive operation. the results (table 6) show that a significant amount of time is required to generate the prime numbers. this limits the computation of the image to just a few hundred thousand primes so the image may be generated and displayed to the user in a reasonable amount of time. #primes 20, 000 40, 000 60, 000 80, 000 100, 000 nokia 6630 44,797ms 129,235ms 240,313ms 373,437ms 524,703ms nokia 6680 44,984ms 129,000ms 240,078ms 372,875ms 524,046ms table 6: processing times to generate pnf on mobile devices 4 plasma fractal generation plasma fractals are often referred to as “fractal clouds” and the resultant image typically has a cloud like appearance (figure 7). the generation of this type of fractal uses a recursive algorithm known as random midpoint displacement. applying the exact same algorithm in the 3d universe to height values will result in the generation of fractal terrain. an example of this method being used for the generation of terrain in the film industry is star trek ii “the wrath of kahn” where random midpoint displacement was used to generate the terrain of a moon, the scene being called the “geneses sequence”. the procedure for generating a “fractal cloud” (algorithm 5) begins by assigning a colour to each of the four corners of a blank image. executing the “divide(. . .)” algorithm will firstly find the colour for the central point of the image by calculating the average value of the four corners. the colour value at the central point is then randomly displaced. the image area is then divided into four smaller sections by recursively calling the “divide(. . .)” again for each of the four quadrants. this division process will continue until the image cannot be 14 daniel c. doolan, sabin tabirca figure 7: plasma fractal examples (grain 1.2 , 2.2, 5.8, 9.6) further broken down. by this time the sub quadrants have reached the pixel level. several example applets that use this procedure may be found on the internet [13] [5]. algorithm 5 plasma fractal algorithm divide(x,y,w,h,tlc,trc,brc,blc) float nw = w /2, nh = h /2; if(w > 1 || h > 1) int displace = displace(nw,nh); color top = avgcolors() + displace; color right = avgcolors() + displace; color bottom = avgcolors() + displace; color left = avgcolors() + displace; color centre = avgcolors() + displace; divide(x,y,nw,nh,tlc,top,centre,left); divide(x+nw,y,w,h,top,trc,right,centre); divide(x+nw,y+nh,w,h,centre,right,brc,bottom); divide(x,y+nh,w,h,left,centre,bottom,blc); else drawpixel(x,y) 4.1 processing results the results show that mobile devices are capable of generating respectably sized plasma fractal images in a reasonable amount of time (table 7). image size 100×100 200×200 300×300 400×400 500×500 600×600 nokia 6630 672ms 2,219ms 6,672ms 6,328ms 6,156ms 23,453ms nokia 6680 671ms 2,328ms 7,468ms 7,344ms 6,828ms 25,329ms table 7: processing times to generate plasma fractals 5 further work the primary focus of this paper was on the generation of two dimensional fractal on mobile devices. the obvious progression from this application is to expand the generating functions into the third dimension. in the case of the plasma fractal this will yield a randomly generated terrain if the height values of a plane are randomly displaced instead of the colour values of a two dimensional image. 6 conclusion an integrated tool has been developed to generate a variety of two dimensional fractals. the fractals in question being the mandelbrot set, julia set, prime number fractal and the plasma fractal. it has been shown that the mandelbrot and plasma fractal image can be generated in reasonable time. the generation of the pnf image however does involve significant computation resources, especially when the number of primes is greater than 100 thousand. visualising infinity on a mobile device 15 references [1] a. baker, “mini review enhancements in nokia 6680,” http://www.i-symbian.com/forum/ images/articles/43/mini_review-nokia_6680_enhancements.pdf, 2005. 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[16] e. weisstein, “arbitrarily long progressions of primes,” http://mathworld.wolfram.com/news/ 2004-04-12/primeprogressions/, 2004. daniel c. doolan, sabin tabirca university college cork department of computer science cork, ireland e-mail: {d.doolan, tabirca }@cs.ucc.ie international journal of computers communications & control issn 1841-9836, 9(4):430-438, august, 2014. distributed compressed sensing algorithm for hierarchical wsns n. jiang, h. you, f. jiang, l. liu, y. he nan jiang* college of information engineering, east china jiaotong university nanchang, 330013, jiangxi, p.r.china state key laboratory breeding base of nuclear resources and environment, east china institute of technology, nanchang, 330013, jiangxi, p.r. china *corresponding author: jiangnan1018@gmail.com hui you, lingfeng liu college of information engineering, east china jiaotong university nanchang, 330013, jiangxi, p.r.china youhui106@gmail.com, liulingfeng1983@gmail.com feng jiang school of computer science and technology, harbin institute of technology harbin, 15001, heilongjiang, p.r.china fjiang@hit.edu.cn yueshun he college of information engineering, east china institute of technology nanchang, 330013, jiangxi, p.r.china hys8418@163.com abstract: in the traditional theory of cs, each sensor node in wireless sensor networks (wsns) sends the information to sink node directly, and only considers the correlation between internal nodes information when recover, this would lead to the loss of the node information and also cause too much energy consumption. in this paper, combined with deec protocol, based on simultaneous orthogonal matching pursuit (somp) algorithm, we propose a novel distributed compressed sensing algorithm for hierarchical wireless sensor networks (dcsh algorithm). this algorithm use the spatial correlation between the nodes and joint sparse model jsm-2, to compress and recover the sensor node information according to somp algorithm. simulation results show that dcsh algorithm can not only obtain accurate reconstruction values of the node information and reduce the energy consumption greatly, but also can prolong the network lifetime. keywords: distributed compressed sensing; deec algorithm; jms-2 model; cluster architecture; wireless sensor networks (wsns). 1 introduction in the past few years, the capability of receiving and transmitting data in wireless sensor networks has been constantly enhanced, simultaneously, the amount of data, which need to be handled, is also growing very quickly. traditional nyquist sampling theory, needs the sampling rate of signal not less than 2 times the bandwidth of the signal; undoubtedly, this is higher requirement for signal processing capability and hardware equipments. therefore, how to deal with bigger and faster data and find a new method is attracted more and more attentions. in 2004, donoho and candes et al proposed compressed sensing (cs) theory, it is a novel theory which make full use of sparse and compressibility of the signal [1] [2]. this theory shows that when the signal is sparse or compressible, it can realize the refactoring value of signal exactly copyright © 2006-2014 by ccc publications distributed compressed sensing algorithm for hierarchical wsns 431 through collecting the projection values of small amount of signal. different from traditional data processing method, sampling and compression of signal can conduct at the same time with a low rate, to the method of data processing, sampling process also completed the compression, so the amount of sampling data is greatly reduced, the nyquist sampling is evenly spaced sampling, and compressed sampling is random sampling. generally, compressed sensing theory study how to use the intra-signal on compression. considering distributed deployment and limited capability of sensor nodes in wireless sensor networks, it is necessary to use inter-signal with distributed compressed sensing (dcs), d.baron gave the basic concept and theory of distributed compressed sensing [3], and then proved the upper and lower bounds of the number of measurements required for decoding [4]. in [5], the author presented another joint sparse model for the application scenarios such as the mimo communication and speech signal, and designed the corresponding joint decoding algorithm. however, data fusion technology for wireless sensor networks based on the dcs theory is still at the starting stage. therefore, it is necessary to study how dcs theory do the joint reconstruction through the observation vectors of each node in cooperation way, through define joint sparse of each node based on spatial correlation data. our contributions are showed as follows: • based on jsm-2 model, signals are related to each other, each signal has different coefficient value, while they are made up with the base vector of the same sparse coefficient set. • by using somp algorithm, the decoding process can be simplified and corresponding measured values can also be reduced. in decoder side, the reconstruction efficiency will be improved. • cluster architecture is widely used in wsns, such as classic leach, heed, deec and so on. we try to bring cluster into cs theory. on one hand, it will save the transmission energy; on the other hand, sink nodes will receive more complete information in the case of limited sensor node processing capacity. 2 simultaneous orthogonal matching pursuit(somp) algorithm 2.1 distributed source coding [11] according to the characteristics of the distributed sensor networks, a number of distributed coding algorithms are developed, these algorithms all involved the cooperation of sensor nodes, including predictive coding, distributed klt and distributed wavelet transform, and also the three dimensional wavelet algorithm which uses inter-correlation between signal and in signal at the same time. x y x̂ ŷ figure 1: distributed source coding. figure 1 is distributed source coding of two different source information. processing of signal y is completely independent, the transmission information is h(y ), and processing of the 432 n. jiang, h. you, f. jiang, l. liu, y. he signal x need to use correlation with the signals y . at the receiving side, the decoder h(y ) can first restore the original signal y ; as the edge information y , use the correlation between the information x and y that contained in h(y ), and send the joint information h(x|y ), complete the joint decoding of the source information x, here with the sum of the amount of the transmission information x and y must be less than the joint entropy h(x|y ), so it can ensure the compression effect of source information. in one word, for distributed source coding theory, the key issue is how to use the original related characteristics of the signals x and y , and then to do the coding and joint decoding independently. 2.2 somp algorithm and jsm-2 model jsm-2 [3]: the signals in this model have different coefficient value, while they are all made up with the base vector of the same sparse coefficient set. this model can be applied some important applications, such as mimo telecommunication and audio signal array. the signals which are accorded with jsm-2 model may be sparsity in the fourier domain, for example, degenerative frequency component which is caused by different propagation paths. in jsm-2 model, the expression of the original signal shown by the following formula: xj = ψ · θj, j ∈ 1, 2, ..., j (1) among them, the coefficient of each vector θj, corresponding to a collection of indicators ij ⊂ 1, 2, ..., n , and ij only has k elements, namely, that is ∥ij∥l0 = k, the sparsity of each original signal xj is k. paper [7] presented the greedy algorithm to recover the related signal, which is called simultaneous orthogonal matching pursuit (somp) algorithm. the algorithm is very similar to the omp algorithm, but there are some small changes. somp algorithm is based on the concept of distributed source coding. the basic idea of somp algorithm is: assuming that the sensor nodes in wsns are all consistent with the jsm-2 model, namely each part of the original signal includes only part of the information and do the sparse representation on the same sparse basis, but the sparsity of each of the original signal sparse are not the same. suppose that there are b distributed sampling signals y1, y2, ..., yb,, change the second step of the omp algorithm to find the index, using the index to solve the following simple optimization problem: maxω∈ω b∑ 1 |⟨rk,t−1, φj| (2) in which rk,t−1 is the residual value of the first k distributed sample signal, the rest of the steps are the same with omp algorithm. when b = 1, this process can be summarized as the standard omp algorithm. in conclusion, the somp algorithm is divided into the following steps: 1) initialization: residual r0 = y, the number of iterations t = 1, the index set ∧ 0 = ϕ; 2) find the index λt, solve a simple optimization problem; 3) update the indexes set λt = λt−1 ∪ λt, and update the selected column space θt = [θt−1, φjt]. by convention, θt is an empty matrix; 4) by solving a least squares problem, get a new estimation of the signal; 5) calculate the new approximate data and the new at = θtxt , rt = y − at; 6) t = t + 1, if t < m, go back to step 2), otherwise, to step 7); distributed compressed sensing algorithm for hierarchical wsns 433 7) if ∥θt∥∞ > γ, choose h1, otherwise choose h0. in which γ is nonzero threshold, h0 : θs = 0, h1 : θs ̸= 0, θs is sparse coefficient. 3 deec clustering algorithm deec [9] is a classical clustering algorithm, which can save the energy effectively and prolong the network lifetime. it can be able to construct optimized cluster structure for and data aggregation through the exchange of the message between the local nodes, and also balance network energy consumption effectively, then be better adapted to periodically collect data of sensor network applications. simulation results show that deec can prolong the network lifetime of leach of about 45% under the smaller latency of network. aggregator head relay figure 2: the structure of deec. clusters in the deec protocol contain four types of the nodes: the sensor, the head, the aggregator and the sender. nodes in a network choose itself as the cluster head nodes with a certain probability value to form a cluster head node collection hheadi, become a cluster head node which is responsible for the set of clusters. the distance between each of the sensor nodes which are in the cluster i and the head nodes to meet the following conditions: min1≤k≤|h|dist(vij, headk) (3) but the head is not responsible for data aggregation, fusion and sending information to the base station immediately, it plays a role of a "call" in the first place. in the process of the set of clusters, we determine the aggregator and the sender through the calculation. the former is used to gather the information which is sent by clusters and then make the data processing and fusion, the latter accept the information transmitted by the former and then send them to the base station. of course, set clusters, gather data and send information to the base station, those three types of task may be borne by one or two nodes. the process of the head selection of deec is similar to leach: each node generate a random number, if it less than the threshold, the node will be the cluster head. but the determination of the threshold here is different from leach, deec neither consider the effect of the current round nor consider whether the node within several rounds of cluster heads, for deec, cluster head set h only have the effect of dividing the network into several clusters. the threshold of deec is the probability of each node to become cluster head phead, phead = kopt/n, in which kopt is the optimal value of number of clusters in the network; n is the number of nodes in the network during initialization. the derivation of kopt has a related discussion in the literature [8], here only assume that it is a system parameter during the initialization time of the network. when a node is selected as the cluster head, it broadcasts messages inv ite to 434 n. jiang, h. you, f. jiang, l. liu, y. he all other nodes, the nodes which receive the news choose the nearest cluster heads to join and send a message to its cluster head join(id, position). in this way, the entire network is divided into several clusters. 4 dcsh algorithm in above algorithm, the ability of sensor nodes in wsns is limited to only receive the information sent by a small amount of sensor nodes, and each sensor node has only its local information, therefore the joint decoding side cannot recover the full information, this will lead to the loss of the target source information when recover. in this paper, based on our previous works [12] [13] [14], we propose an improvement scheme of dcs reconstruction algorithm (dcsh algorithm) which is based on somp. its basic idea is: by using deec algorithm to divide sensor nodes into clusters, then select the cluster head, and the information of the nodes all gathered to the cluster head, then the information on the cluster head is the information within the entire cluster nodes. the sensor nodes send the information to the cluster head after data fusion and then use somp algorithm to recover the information. on one hand, the dcsh algorithm reduces the number of the nodes which transfer the information directly to the joint decoding end, avoids the loss of transmit information; on the other hand, by using the advantage of the deec protocol, our algorithm can achieve the goal of saving energy. system model for dcsh algorithm is shown in fig.3: x x nm 1 c m c 1 y 2 y 1 n y 1n m y n y 1 x 2 x 1 n x n m x 1n m x n x sink n m y figure 3: system model for dcsh algorithm. suppose that there are n sensor nodes randomly distribute in the wireless sensor networks, meanwhile the sink node is in the center of the network area. there is a target source in the network, information on the target source will be sent to all nodes in the network, any sensor nodes can communicate with the sink node directly, and the signals all meet the joint sparse model jsm-2. first, divide n nodes into m clusters, then elect the cluster heads c1, c2, ..., cm of m clusters respectively, and the number of the nodes in each cluster is n1, n2, ..., nm , transmit the cluster information to each cluster head and then use dcs algorithm to encode information on the cluster head, getting the measured value y1, y2, ..., ym which is transmitted to the sink node for the joint decoding. at last, get the reconstruction value x̂1, x̂2, ..., x̂n , thereby, recover the target source information accurately. distributed compressed sensing algorithm for hierarchical wsns 435 dcsh algorithm can be shown as follows: 1) based on deec protocol, divide the sensor nodes into cluster, and choose the cluster head. the threshold phead, phead = kopt/n of deec is the probability value of each node to become cluster head, in which kopt is the optimal value of number of clusters in the network; n is the number of nodes in the initialized network. 2) determine the aggregator: when the head know location information of all nodes, aggregator can be determined through simple algorithm find_aggregator [10]. 3) determine the sender: using the algorithm find_sender [10]. 4) transmit the information of the sensor nodes to the cluster head, using their original measurement matrix to do the independently coding on the cluster head. 5) use somp algorithms to joint reconstruction of the nodes information, thus recover the full information of the network. 5 the results of simulation and analysis this paper uses matlab as a tool for simulation, 100 sensor nodes are randomly distributed in deployment area , the center of the area is the cluster head, each of the node information conform to jsm-2 models. first of all, we verify the performance of the somp algorithm. get the reconstruction value x̂1 of the sensor node information x1 by somp algorithm and omp algorithm. the reconstruction error of is 5.1672 × 10−15 and 8.1596 × 10−15 . figure 4 and figure 5 show us the comparison between the two kinds of algorithms, and it apparently show that somp algorithm has the excellent effect of reconstruction. after compared with omp algorithms, we will find somp algorithm has good performance and also the accuracy is higher. 0 100 200 300 400 500 −1.5 −1 −0.5 0 0.5 1 1.5 recover original signal recovery signal figure 4: comparison of somp algorithm. suppose that the leach protocol and deec protocol have the same number of the original data nodes, figure 6 studies the performance under the conditions of same numbers of cluster nodes. in this figure, the two curves represent the protocol as leach and deec. its specific results are shown in figure 6, deec protocol has m less dead nodes when the algorithm run same time. known that the initial energy of the nodes is 50j, the energy of the nodes decrease with the number of the election round, figure 7 shows the relationship between the rounds and active 436 n. jiang, h. you, f. jiang, l. liu, y. he 0 50 100 150 200 250 300 −1.5 −1 −0.5 0 0.5 1 1.5 2 recovery original figure 5: comparison of omp algorithm. 0 1000 2000 3000 4000 5000 0 2 4 6 8 10 12 x 10 4 x(time) y( d a ta ) comparation of data transaction between leach and deec leach deec figure 6: the comparison of the lifecycle. nodes of deec algorithm. we can conclude that, in the same case, networks lifetime of deec is longer than traditional leach protocol, to a great extent, improve the network cycle. for the further study of these two reconstruction algorithms, once again, we compare the average absolute error performance of the somp and omp algorithm. figure 8 demonstrates the relationship between the absolute error performances of the two algorithms, it can be seen that, in the case of the same number of measured values, the absolute error performance of somp algorithm is lower than the absolute error performance of omp algorithm. to sum up, the comparison and analysis of the performance of somp algorithm and omp algorithm in figure 8 give a conclusion that in the same conditions, somp algorithm can ensure the accuracy rating of the reconstruction while reducing the number of measured values. in this way we can save the energy of the network and improve the efficiency of the signal reconstruction. 6 conclusions this paper proposes a distributed compressed sensing algorithm for hierarchical wireless sensor networks (dcsh algorithm). this algorithm utilize deec algorithm to get the accurate information by recovering the information of sensor nodes in the network. the sensor nodes distributed compressed sensing algorithm for hierarchical wsns 437 0 200 400 600 800 1000 1200 1400 1600 1800 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 election rounds in the net/r t h e r e m a in in g e n e rg y o f n o d e s/ j leach deec figure 7: the comparison of the nodes and the wheel. 40 60 80 100 120 140 160 180 200 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 number of the measured value a ve ra g e a b so lu te e rr o r omp algorithm somp algorithm figure 8: the change of two kinds of algorithm. choose the cluster head by deec protocol. we take the residual energy of nodes into consideration while in the process of choosing the cluster head, prevent some nodes energy exhausted prematurely, this can not only extend the network lifetime effectively, but also reduce the number of the nodes which transmit data directly to the gathering node. then based on the spatial correlation of the cluster nodes and joint sparse model (jsm-2), by using distributed compressed sensing reconstruction algorithm(somp), recover the original node information from a small amount of information accurately. the simulation results show that this algorithm has better performance, which can improve the network lifetime, and can be better adapted to periodically collect data of the wsns application. acknowledgments this work is supported by national natural science foundation of china under grant no. 61063037 and no. 51364001, open program of state key laboratory breeding base of nuclear resources and environment under grant no.nre1102, key projects in the science and technology pillar program of jiangxi province of china under grant no. 20111bbg70031-2 and 20133bbe50033, educational commission of jiangxi province of china under grant no. 438 n. jiang, h. you, f. jiang, l. liu, y. he gjj13335 and gjj13354, and foundation for young scientists of jiangxi province of china under grant no. 20133bcb23016. bibliography [1] donoho d l.(2006); compressed sensing, ieee transactions on information theory, 2006, issn 0018-9448, 52(4): 1289-1306. [2] candés e.(2006); compressive sampling, in: proceedings of international congress of mathematicians, isbn 978-3-03719-022-7, 1433-1452. [3] d.baron et al (2005); distributed compressive sensing, technical report ,pre-print. [4] d.baron et al (2005); an information theoretic approach to distributed compressed sensing, in: conference on communication, control, and computing , isbn: 9781604234916. [5] m.f.duarte et al (2005); distributed compressed sensing of jointly sparse signals, in: proceeding of the 39th asilomar conference on signals, systems and computation , issn 10586393, 1537-1541. [6] j tropp; a. gilbert; m strauss(2006); algorithms for simultaneous sparse approximation, part i: greedy pursuit, journal of signal processing, issn 0165-1684, 86: 572-588. [7] w dai; o milenkovic(2009); subspace pursuit for compressive sensing signal reconstruction. ieee transactions on information theory, issn 0018-9448, 55(5): 2230-2249. [8] l.zhao; l.q.lian(2005); distributed and energy efficient self-organization for on-off wireless sensor networks, international journal of wireless information networks, issn 10689605, 12(1) : 211-215. [9] l.qing; q.zhu; m.wang(2006); design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks, elsevier, computer communications,issn 0140-3664, 29(12): 2230-2237. [10] y.x.liu et al(2010); regularized adaptive matching pursuit algorithm for signal reconstruction based on compressive sensing, journal of electronics and information, issn 10095896, 32(11): 2713-2717. [11] d. slepain; j. k. wolf(1973); noiseless coding of correlated information sources. ieee transaction on information theory, issn 0018-9448, 19(9): 471-480. [12] nan jiang(2014). wdem: weighted dynamics and evolution models for energyconstrained wireless sensor networks, physica a: statistical mechanics and its applications, issn 0378-4371, 404: 323-331. [13] nan jiang; sixin jin; yan guo; yueshun he(2013); localization of wireless sensor network based on genetic algorithm, international journal of computers communications & control, issn 1841-9844, 8(6): 825-837. [14] nan jiang; rigui zhou; qiulin ding(2009); dynamics of wireless sensor networks, international journal of distributed sensor networks, issn 1550-1329, 5(6): 693-707. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 116-124 e-learning multimedia applications: towards an engineering of content creation maria dolores afonso suarez, cayetano guerra artal, francisco mario tejera hernandez abstract: in the same manner that e-learning applications are becoming increasingly important at the university, there are still some critical questions that should be solved with the objective of making use of the potential offered by current web technologies. the creation of contents that are able of capturing the attention of interest of the students and their disposal in an appropriate way constitute the main purpose of this work. the teaching content engineering expounded shows the different stages that should form part of the process. a development team, composed of different professional profiles, will work together with the lecturers of the subject to which the contents are been created, i.e. multimedia videos and interactive applications. this process should be developed according to a methodology that assure the use of appropriate resources, all that tasks -suitable of beingshould be modularized and factorized. this paper presents the acquired experience in the development and use of multimedia contents for e-learning applications, created for some of the subjects of the degree in computer science engineering. the deliveries of these contents make use of internet and video streaming techniques. the result of the work shows the students satisfaction, including their comments. keywords: teaching content engineering, didactic objectives, active learning, passive learning. 1 introduction during recent years university education has experienced an important progress with respect to the resources used for lecturing. techniques have changed from traditional blackboard methods to the use of projector transparencies (slices) and, later on, the use of video projector and powerpoint to present the content of the lessons. nowadays, the use of these technologies has become more common. as far as content availability for the student is concerned, it has passed from traditional notes taken in classrooms, books and photocopies to digital format of books or notes taken in classrooms, which are available in the subject’s web page. in fact, the universities use to place courses on the web focusing on information delivery rather than learning. the proposal of european higher education area provides the framework to take an important step forward in techniques used in lectures and in the methodology used to create them. for this reason, new methodological approaches are introduced in order to value an active learning [1] as opposed to a traditional passive learning. to value the effort that students should make in order to assimilate knowledge and not only consider the number of hours of attendance to classes. the use of e-learning techniques makes this easier; its wide range of application allows increasing education quality and delivery of information. 2 e-learning in ehea the european higher education area proposes the setting up of a convergence process of educational material in europe. new methodological approaches are introduced in order to value an active learning as opposed to a traditional passive learning. to value the effort that students should make in order to assimilate knowledge and not only consider the number of hours of attendance to classes. all this is made easier by using an e-learning [2] approach, whose extensive work field, in all range of subjects, copyright © 2006-2008 by ccc publications e-learning multimedia applications: towards an engineering of content creation 117 allows the quality of education and its availability to increase [3]. the creation of multimedia contents for e-learning could be developed together with other european universities, and its use could be shared within the same knowledge areas. this allows a new common line of work to be opened. it will be possible to promote european cooperation to guarantee the quality of higher education using comparable methodologies and criteria. 3 teaching content engineering we understand for that concept the whole methodological corpus that allows inserting development elearning technologies in the production of didactic solutions with appropriate invests in production time, resources and person/hour. in this manner, the creation of contents will be developed in a systematic way, planning the process and using adequate techniques. the integration of these techniques and the extent of the teaching material development project require a multidisciplinary team. this multidisciplinary team composed of a group of different professional profiles will carry out different tasks using a wide variety of technologies. these tasks will come together in order to create a production line to maximize results. the improvement of productivity and quality in teaching content creation will constitute the main objective of this engineering [4]. the proposed engineering of teaching content is carried out by means of a methodology, which divides the multimedia production process into phases and assigns tasks to each member of this professional development team. all these different phases include research on suitability of the content for e-learning, a previous analysis to select multimedia techniques to apply, the development of the contents based on the project design and, finally, the maintenance. with respect to the multidisciplinary team, it will be composed of a project manager, programmers, designers, and multimedia experts, as well as the lecturers of each subject. 4 methodology the obtaining of an optimum workflow [5] and the use of resources by the different production tasks is the main purpose of the proposed methodology. for that, we define different production phases. 4.1 suitability of the content for e-learning to create multimedia material for subjects using e-learning techniques means a considerable resource investment. therefore, some factors, which help to make a decision about its profitability, should be taken into consideration. in this first phase a study on the suitability is made in accordance with the factors below: the content validity. the period of time in which teaching contents do not need to be updated. the number of students who attend classroom training. this factor takes into consideration the number of resources used in their learning. the modularity and reusability of independently operable units, which are part of the total structure for creating more contents. 4.2 contents analysis the creation of multimedia contents is carried out through the division of teaching contents in didactic objectives. in this phase the modularization of content takes place. the extraction of the lecturers’ knowledge will be necessary for the selection and proposal of different multimedia didactic elements, 118 maria dolores afonso suarez, cayetano guerra artal, francisco mario tejera hernandez which will constitute part of these multimedia didactic units. it is recommendable to follow a methodology belonging to knowledge engineering, where the knowledge is produced according to the lecturer’s subject specific knowledge, and contains the knowledge and analytical skills of one or more human experts. to achieve this aim, the tasks below are introduced: meetings with the lecturers. in these meetings lecturers expound the teaching objectives of the subject, explain the lesson content and the current way to portray the contents to the students. the selection of lessons for multimedia format. the project manager, together with the lecturers, carries out the selection of lessons for which multimedia teaching content will be created proposal and techniques selection [6]. according to the techniques selected, and the modules repository, the project manager makes a proposal to the lecturers. 4.3 development starting from the analysis of the previous phase, a formal design is carried out, this formal design will identify the activities and work planning that will be done under the supervision of the project manager [7]. therefore, this phase is structured as shown below: formal design includes all the multimedia didactic elements to be used. in accordance with this design, both human and material resources are managed. it is a process of problem solving and planning for reaching the objectives of the project. development of interactive applications will follow a development methodology belonging to software engineering. creation of videos, for which the work will be divided into: script writing, recordings, postproduction and codification [8]. on-line disposition of multimedia contents, becoming, this way, accessible for the students. in the process the creativity of the development team is considered fundamental. 4.4 maintenance once the project is finished, it is difficult to assure that it will work properly unless it is tested. in order to realize a high quality solution, testing throughout each phase of the process is proposed. the project team should be involved in the maintenance phase. they are expected to work on their known issues and prepare for a new release of the created material. in order to detect defects and deficiencies in the multimedia material, some tasks are introduced: interviews with lecturers and students, to know first hand how they feel about this new content to use, in various aspects: accessibility, manage, design. questionnaires, for recover statistics results about all the aspects to be evaluated. comparison of academic results, where an evaluation report will be made, and academic results will be compared with those of other groups of students that have made use of traditional learning methodologies. all these tasks will help to obtain a constructive feedback and to enhance and optimize this multimedia material and its different aspects evaluated. a thorough study of time and staff required for project development will take us to a suitable planning and this to an appropriate economic investment. therefore, cost and resources affect directly in the amortization of the investment made for the creation of contents. e-learning multimedia applications: towards an engineering of content creation 119 5 resources among the means that will be used to carry out this technology we find both human and material resources. the competences of the multidisciplinary team are defined by different profiles: lecturers, whose main tasks include the structure of the subject program, script writing, and the proposal and collective agreement with the project manager on the multimedia didactic units. project manager, who advises lecturers, coordinates efforts of the development team and assigns tasks according to the planning made. designers, which will carry out tasks of graphic design, user interfaces development, and 2d 3d elements creation. analysts/programmers, to analyze and develop software application and programs. multimedia technicians, in charge of recording, editing, and postproduction. regarding to material resources, these are comprised of the multimedia recording studio and the laboratory room for development of software applications and editing of contents. the workstations of the editing room in the laboratory are fully equipped with the necessary computer material for creating and editing audiovisual content. figure 1. figure 1: on the left we can observe the record studio used for the recording of lessons. on the right workstation of the editing room. 6 other methodologies there are different strategies for the creation of teaching content for e-learning. these approaches often use a similar set of stages or phases for content development. among them we find the "waterfall philosophy" characterized by the use of a number of phases strictly ordered in such a way that each phase begins once the previous one is finished; and the "evolutionary philosophy" or "based in prototypes", characterized by considering since the beginning that, although the project start out with a set of requirements, will arise changes in these requirements as the project is developed. however, the methodological proposal made in this work for an e-learning content engineering focuses on lecturing and this means a set of differences, related below: scope. traditional methodologies are directed, generally, towards content development for corporate e-learning. the proposed methodology is focused on content development for lecturing. 120 maria dolores afonso suarez, cayetano guerra artal, francisco mario tejera hernandez contents modularity. in each knowledge area to create independently operable units intended for reuse in the same or a different one is proposed. this means the use of a knowledge area thorough analysis in order to make these operable units relocation possible when creating new teaching programs. extend of involvement. lecturers should be part of the work team that will create multimedia contents for e-learning, should be involved not only in didactic advises tasks, according to the teaching objectives of the subject, but also should take part in the conception of the whole set of tools proposed by the project manager. resources. the wide variety and quantity of multimedia resources used are essential in the proposed methodology for e-learning content creation. multimedia technology and the development of interactive applications provide suitable capabilities to develop quality contents at the university level. 7 lecturing experiences for the evaluation of e-learning techniques in lecture content, some subjects have been selected. specifically, the subjects correspond to bachelor and graduate degrees on "computer science engineering", multimedia and automaton theory and formal languages ii. specially, the subject of multimedia has the particular feature of covering two fundamental aspects in the teaching of computer science knowledge. on the one hand, we find purely theoretical and mathematical content which supports audio compression, image and video technologies. on the other, we find content that makes reference to the use of multimedia tools and web programming languages. in particular, the subject of multimedia has a teaching timetable of 30 theoretical hours (twice a week), using traditional classrooms, and other 30 hours of practical lessons in the laboratory. the prepared material embraces half the timetable in the classroom. the content generated for automaton theory and formal languages ii corresponds to the practical exercises of the subject. as lecturers can now count on this new multimedia teaching material, the methodology, which has been followed till now in multimedia, has changed substantially. firstly, the students still have the same number of learning hours; however, they receive some lessons by means of video in a dedicated server. this means that they receive, weekly, one hour in the classroom and another one through video streaming. this new material allows traditional lessons to be divided into two groups. in this way the lesson in the classroom is repeated and the student attends the most convenient classes. therefore, not only do they not miss the opportunity to ask the lecturer doubts, but also the number of students is reduced, allowing a more personalized treatment. with respect to automaton theory and formal languages ii, the prepared material only provides support to the personal training of the student. the results of this experience have been very positive. students have valuated unanimously this new teaching model as completely recommendable. even, the students have taken part in the improvement of the material contributing with their own ideas, needs, and wishes, like including random questions in order to implement interactivity [9]. from the valuation questionnaire issues below are recovered: multimedia videos are very appreciated. no comments were made about accessibility or reproduction difficulties or even misunderstanding. although students think it is necessary: the inclusion of subject’s content in a pdf format file and an index to facilitate access directly to each unit of the lesson. and just only one student pointed out that the teacher should propose more practical material in the classroom. the valuation questionnaire results are represented in the table 1. as we can observe, percentage for the last one question is quite good. figure 2. with respect to the academic efficiency revealed in the assessments, we should say that the percentage of success in the exams remains constant if we compare with the results of past years where lessons were taught in a traditional way. however, a particular fact, which is known by all lecturers when the exams’ e-learning multimedia applications: towards an engineering of content creation 121 table 1: results from bachelor and graduate degrees on "computer science engineering" figure 2: graphical representation for general assessment. results from bachelor and graduate degrees on "computer science engineering" 122 maria dolores afonso suarez, cayetano guerra artal, francisco mario tejera hernandez dates come close, should be commented. in the previous weeks to exam dates the students come more frequently, and in many cases only on those dates, to the tutorials. it is remarkable that during this teaching experience there have been practically no consultations in tutorials in all the academic year, not even on dates coming up to the exams. 8 future research the acquired experience in the development of multimedia contents for e-learning applications shows that this emergent task should be tackled through the use of a systematic, disciplined, quantifiable approach to the development, manage and maintenance of this teaching content material. in the near future, we will test the process of development of a new project from the university of las palmas de gran canaria. this project involves the creation of multimedia material for subjects of three different areas: the technical area, the health care science area, and the human, society and legal area. the election of the subjects has been made according to the factors expounded in the phase of suitability of the subject. it has been preferential for the technical department to choose subjects of the first courses of various degrees with similar contents, what implies a considerable number of lessons in common. so that, there will be subjects with the 85 or 90 per cent of their content delivered with multimedia material, which is more profitable than having only one or two subjects with the 100 per cent of the content delivered with this new material. the subject is named mathematics in some degrees and calculus in other ones. something similar has happened in the other two knowledge areas: in the health care science area anatomy has been chosen, this is a subject of the first course of "medical degree" with a huge number of students. for the human, society and legal area has been chosen constitutional law, which corresponds to two different degrees: "law" and "economics and business sciences", degrees with a large number of students in the first courses too. in this way, we will delve into each phase of this new engineering focusing on improving the efficiency and cost-effectiveness of the development of multimedia contents for e-learning in these subjects selected. 9 conclusions new information technologies and e-learning will be indispensable tools in lecturing in the near future. the increasing bandwidth available for internet connection and multimedia capacities found in current computers allow them to be used as a completely valid way for teaching. however, the lack of multimedia content production is a very important cause that limits an e-learning widespread. the creation of large-scale teaching content for lecturing needs a production methodology to assure the optimization of resources and, therefore, a reduction in costs. this work intends to contribute with the methodological lines applied in the production of multimedia teaching content for lecturing. new teaching methodologies require new teaching aims; the role of lecturers in the new educational process should be restated in the whole educational process. lecturers should reduce the time dedicated to teaching lessons using traditional blackboard methods. this activity could be mainly, replaced by e-learning techniques. lecturers could dedicate time to activities that offer a better quality in teaching, transmitting motivation about the subject being studied and directing the students in their studies [10]. the future of teaching in the european society should see an improvement in the quality of education, its availability, and a lower cost for students as well as for educative organizations. 10 acknowledgment we wish to thank the programm committee of icvl 2007 that recommended the publication of an extended version of this paper. e-learning multimedia applications: towards an engineering of content creation 123 bibliography [1] a. m. barry, creating and sustaining an effective learning environment!. aishe conference 2006. [2] http://ec.europa.eu/education/programmes/elearning/index_en.html [3] http://ec.europa.eu/education/policies/educ/higher/higher_en.html [4] b. shackelford, project managing e-learning. astd. eeuu, 2002. [5] r. pedrosa and s. petitz, production of interactive contents for training in e-learning environmentsprocedures and methodologies. iadis international conference e-learning 2007. [6] w. horton, k.horton, e-learning tools and technologies, (1st edition), wiley. eeuu, 2003. [7] l. dublin, j. cross, implementing e-learning. astd. eeuu, 2002. [8] j. casteleira and p. leão, production and distribution of contents in digital video of quality for the distance learning. iadis international conference e-learning 2007. [9] r. rheeder, r. diseko and g. lautenbach, the design of interactivity for a web-based learning environment at a higher education institution. iadis international conference e-learning 2007. [10] k. fraser, a blended learning approach to teaching "introduction to multimedia" the e bit!. aishe conference 2006. maria dolores afonso suarez, cayetano guerra artal francisco mario hernandez tejera instituto universitario de sistemas inteligentes y aplicaciones numericas en ingenieria iusiani universidad de las palmas de gran canaria departamento de informatica y sistemas edif. de informatica y matematicas. campus universitario de tafira. 35017. las palmas. spain. e-mail: mafonso@dis.ulpgc.es, cguerra@iusiani.ulpgc.es mhernandez@iusiani.ulpgc.es received: november 30, 2007 124 maria dolores afonso suarez, cayetano guerra artal, francisco mario tejera hernandez maria dolores afonso suarez, graduated at the university of las palmas de gran canaria in computer science. she has worked in private enterprises developing software and as associated professor at the computer science and systems department of the university of las palmas de gran canaria. her main research fields are multimedia and e-learning. cayetano guerra artal, graduated at the university of las palmas de gran canaria in computer science. he has been teaching multimedia for seven years at the computer science and systems department of the university of las palmas de gran canaria. he received his ph.d. in computer vision. his main research fields are computer vision, computer graphics, multimedia and e-learning. francisco mario hernandez tejera graduated in electrical engineering and received his ph.d. in computer science at the university of las palmas of gran canaria. he is assistant professor of computer science and engineering at the computer science and systems department of the university of las palmas de gran canaria. is member of the institute of electrical and electronic engineers ieee , the international association of pattern recognition (iapr), the asociacion española de reconocimiento de formas y analisis de imgenes (aerfai) and the asociacion española para la inteligencia artificial (aepia). his current research interests are in autonomous systems, knowledge-based systems, active vision, visual learning, scene analysis, mobile robotics and interactive robotic systems, that is robots that interact with people. as well as multimedia and e-learning. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 783-791 an algorithm for customer order fulfillment in a make-to-stock manufacturing system d. lečić-cvetković, n. atanasov, s. babarogić danica lečić-cvetković, nikola atanasov, sladan babarogić university of belgrade, faculty of organizational sciences jove ilića 154, 11000 belgrade, serbia e-mail: {danica,nikola.atanasov,sladjan}@fon.rs abstract: in the competitive environment, many manufacturers are increasingly focusing on designing the systems that help them to manage variable demand and supply situations. dynamic allocation of demands is very important in case of customer order allocations. order promising and allocation can be based on the simple sequence that enables a manufacturing company to receive orders unless there are some other priority orders. manufacturing company can also manage allocations of supply to key customers and channels, thereby ensuring that they can meet contractual agreements and service levels in the priority that yields better profit. this paper will focus on a maketo-stock order fulfillment system facing random demand with random orders from different classes of customers. available-to-promise (atp) calculating from master production schedule (mps) exhibits availability of finished goods that can be used to support customer order allocation. this order allocation system is adapted in mts (make-to-stock) production model and all orders are treated according to maximization of customer service policy. it allows incoming purchase orders as well as existing inventory on hand to be selected and allocated to customer sale orders and back orders. the system then automatically allocates the available stock to the selected sales orders. we developed an integrated system for allocation of inventory in anticipation of customer service of high priority customers and for order promising in real-time. our research exhibits three distinct features: (1) we explicitly classified customers in groups based on target customer service level; (2) we defined higher level of customer selection directly defined according to company strategy to develop small and medium customers; (3) we considered backorders that manufacturing company has to fulfill in order to maximize overall customer service for certain customers. keywords: manufacturing system, order allocation, customer service. 1 introduction production companies are facing the dilemma on whether to increase or decrease the capacities of production lines and/or the capacities of production plants. everyday, production managers are facing the dilemma on whether to buy a new machine, to increase the number of shifts within the production process, or to employ new workers, in spite of the fact that such decisions also affect the middle-term and long-term production planning, and should not be made hastily. sales forecasting and identification of an increase in demand represent the starting point for production capacity planning, and also for making a decision on capacity expansion. until the available capacities are increased, the production company has a goal of satisfying the market copyright c⃝ 2006-2010 by ccc publications 784 d. lečić-cvetković, n. atanasov, s. babarogić demand by means of available production capacities. the allocation of available finished products to customer orders requires an efficient system of allocation aimed at improving the overall operating efficiency. operating efficiency is directly related to quantities of goods produced and the profit from sales. in addition to profit-oriented decision on selection of orders to be fulfilled, one needs to consider the customer service, since the overall operating of a production company depends on customers. customers, who account for a large share in the sales results, require special attention, and the fulfillment of every order. there is also a group of customers who constantly increase their orders, and thus expect adequate and better service. small but numerous customers, thus shape the overall sales of a production company. some of them represent the future potential for the increase in sales and also for the increase in incomes of the production company. the abovementioned facts highlight the importance of making a decision while selecting the orders to be fulfilled, and the necessity of having an algorithm that will efficiently and effectively perform the allocation of limited quantities of available finished products. this paper is structured as follows: the second chapter presents relevant approaches of other authors in solving the observed problem. the third chapter defines the problem of allocation of limited resources in production companies. the problem of allocation refers to the distribution of limited amount of production to customer orders, with the goal of maximizing the percentage of fulfilled orders. aiming to define efficient and effective allocation, the fourth chapter describes the procedure of the development of algorithms for fulfilling the orders in the make-to-stock production system. in their conclusion, authors cited the main advantages of the proposed algorithm for allocation of limited production, as well as the possible directions of development and upgrading the algorithm aimed at improving the performances of the production company. 2 related work providing the ordered quantities of products represents the key function in planning operations within the entire supply chain. the system of providing stocks is recognized as a key challenge in production companies. the basic goal of the process of providing stocks atp/ctp (atp – available-to-promise, and ctp – capable-to-promise) is providing a reliable response to customer demands, taking into consideration the wide range of information and limitations that exist in the distribution channel network. key measures of performances within production companies, according to [6] are recognized in measuring the level of customer service, and customer satisfaction. traditional approaches to order provision, adjusted to mts production system, were described in [1] while considering the available stocks of finished products for order fulfillment in line with the principle first come – first served (fcfs), without awarding priority to either customers or orders. the priority of received orders also represents a significant factor in providing stocks for order fulfillment. optimization of orders is carried out based on the maximization of profit for the entire operating, through the system of production planning represented in [9]. priority is given to orders in the following way: (1) orders are given the priority if they are already in the forecast of sales, i.e. (2) orders are given high-priority if the delivery of such orders should bring higher profit to operating of a production company. the general application of atp/ctp decision system was implemented with an aim to improve the profit and system performances, in line with [6]. by considering the advanced planning systems (aps), it is possible to identify various approaches in defining the priority of customers, based on which it will be possible to fulfill the incoming orders. the essential idea of the approach described in [9] is the segmentation of customers with the goal of increasing the overall income of the production company through order acceptance, and delivery of orders with the greatest profit. atp model allows for such system, since it provides quantities in advance in accordance with certain customer segments, by an algorithm for customer order fulfillment in a make-to-stock manufacturing system785 satisfying only the orders of priority customers. atp system is based on mixed-integer programming, with the goal of optimizing the utilization of limited production capacities, in order to provide timely information regarding the fulfillment of a customers order. in addition, the abovementioned problem is according to [2] classified amongst the dynamic models of order management with limited capacities based on the profitability analyses. if a production company has limited production capacities, it is clear that the company will decide to reject some of the incoming orders, which will directly affect the profitability of operating. the decision on rejecting orders is based on the comparison of orders, since company rejects those who yield lower profit. atp system that is based on providing quotes for important customers in a defined timeframe, in order to provide timely information on the delivery date, is described in [9]. atp defined in a way that orders yielding higher profit for the producer must be fulfilled with priority, in spite of the fact that there are less profitable orders still ’on hold’. meyr focused atp on mts system through the assumption that the supply of finished products is fixed with available stocks and the ongoing production, which will be available in a short period of time. the model of allocation of limited quantities of finished products through customer segmentation, developed by kilger and meyr, is based on profit assessment that is generated by fulfillment of orders in accordance with their priority. new orders could be fulfilled by the allocation of quantities granted to the group of customers they belong to, i.e. if there is no ’free stock’ in that group from the quantities granted to lower-priority groups. such method prevents orders of low-priority customers to be fulfilled prior to orders of high-priority customers, who help in generating higher profit. the atp allocation system is based on defining the priority classes with the goal of maximizing the overall profit of the production company. on the grounds of various researches, according to [9], it was determined that the fcfs system for customers orders bring the best results with limited production capacities, if case the production is realized based on the forecast accuracy. if there are classes defined in accordance with the priority of customers, the quantity awarded to certain class within a cycle, if not allocated, stays booked for the same class in the following cycle. the same author in [10] presents that the provisional allocation of stocks for certain customer classes within a defined period of time could give a significant contribution if the customers demand within the class could be anticipated with guaranteed accuracy. 3 order fulfillment problem definition production companies operating on markets with irregular demand often face the problem of insufficient stock of finished products. until it makes a decision on the expansion of production capacities, a production company has the goal of fulfilling as much orders as possible, only with available production capacities and quantities of finished products. the allocation of finished products to customer orders is realized through the process of allocation within the mts (maketo-stock) production system. the problem of allocation of available products to incoming orders is listed among problems of the allocation of limited resources. in case the incoming customer orders within one cycle do not exceed the available stocks of finished products, the allocation is complete and all orders are fulfilled. in the opposite case, when the sum of overall orders is greater than the quantity of available stock, there is a need to define the way of product allocation, i.e. rules must be introduced to help the allocation of products to incoming customer orders. with the goal of maximizing the effect of allocation of available products, the need for the development of allocation algorithm has been perceived, with which to enable the fulfillment of orders in line with their priority. the fulfillment of orders of high-priority customers contributes to the maintenance of preferred service for those customers who rank high because of their share in incomes and the profit of the production company. the order fulfillment algorithm refers to 786 d. lečić-cvetković, n. atanasov, s. babarogić products of fmcg (fast moving consumer goods) industry, and with minor modifications, it is possible to apply it to the problem of allocation of products in other industries too. 4 algorithm the problem of allocation of limited quantities of finished products to customer orders could be solved by creating an algorithm that systematizes the allocation process. the proposed allocation algorithm tends to maximize the customers service that is expressed by the number of fulfilled orders and the percentage of order fulfillment, all this through the processing of orders in accordance with previously defined groups and partitions of customers. the criterion of dividing customers into groups is based on the consideration of revenue per customer within the company, profit per customer, development potential, service rate per customer, strategic partnership with the customers, etc. with the goal of providing strategic potential for the development of a customer network, customers are classified into partitions that are provided with guaranteed quantity of products for certain groups of customers, which would otherwise be marginalized. the proposed algorithm has polynomial complexity. table 1: variable descriptions variables description ptl total available production in l-iteration (l = 1, ..., r) otl total order in l-iteration (l = 1, ..., r) pk partition (k = 1, ..., p) gj group (j = 1, ..., m) ci customer (i = 1, ..., n) algorithm is applicable only when otl > ptl. in the beginning of the year, or at the beginning of iteration: 1. form the list of customers with parameters (i = 1, ..., n) the list comprises basic data on customers and their business indicators for the previous year, for the last three years, and last five years. 2. define the number of groups (j = 1, ..., m) every identified group has its priority. smaller ordinal number of a group brings higher priority. the recommendation is to form three groups, in spite of the fact that the algorithm functions even if only two groups are present. 3. clustering customers into groups at the beginning of the year, a company disposes of the list of customers, and it revises business results from the previous period for every respective customer, which is actually the base for classification into groups (figure 1a). every customer within the same group is equal with other members of the group when it comes to products’ allocation. clustering (classification) into groups could be done based on results of applying methods such as abc method of prioritizing. ci ∈ gj, for i ∈ {1, ..., n} and j ∈ {1, ..., m} information on which customer belongs to what group are kept in the matrix customer group (mcg), as shown on figure 1b. if a new customer emerges at the very beginning of new iteration, he is been added to the system and attached to some of the existing groups. cn+1 ∈ gj, for j ∈ {1, ..., m} an algorithm for customer order fulfillment in a make-to-stock manufacturing system787 4. define the number of partitions with protective percentage quotes (k = 1, ..., p) partitions are being introduced with an aim to protect small customers with an opportunity to growth in the future. granting a quote to the partition with groups containing small customers will provide a certain quantity of products for small customers who have great potential for development and also for the increase in quantities to be ordered in the future. the protective quote, i.e. the percentage portion of the overall production in l-iteration is defined for every partition, and it will be available to the observed partition. 0 ≤ kpk ≤ 1, for k ∈ {1, ..., p}, p∑ k=1 kpk = 1 in addition, the number of partitions is smaller or equal to the number of groups. the concept of partition will lose sense (purpose) if only one partition has been defined. 5. clustering groups into partitions every defined partition must have at least one group, even though such case counts as if every group has been awarded the protective quote (figure 1a). gj ∈ pk, for j ∈ {1, ..., m} and k ∈ {1, ..., p} information on which group belongs to what partition are kept in the matrix group partition (mgp), as shown on figure 1c. figure 1a: customers, groups, partitions figure 1b: matrix customer group figure 1c: matrix group partition if we mark the customer i demand with oci, then we could say that the total order for products in l-iteration is: otl = n∑ i=1 oci or otl = m∑ j=1 n∑ i=1 oci · mcgij, for l ∈ {1, ..., r} variable apk is then introduced with the goal to keep the amount of products allocated to k-partition in l-iteration: apk = ptl · kpk, ptl = p∑ k=1 apk, k ∈ {1, ..., p} and l ∈ {1, ..., r} where ptl is the total production in l-iteration. table 2: variable descriptions 788 d. lečić-cvetković, n. atanasov, s. babarogić figure 2: algorithm represented as uml 2.0 activity diagram an algorithm for customer order fulfillment in a make-to-stock manufacturing system789 variables description oci order of customer i in l-iteration aci allocated quantity of customer i in l-iteration ncj = n∑ i=1 mcgij number of customers in group j ogj = n∑ i=1 mcgij · oci total order of group j ngk = m∑ j=1 mgpjk number of groups in partition k opk = m∑ j=1 n∑ i=1 oci · mcgij · mgpjk total order of all customers in partition k bl buffer in l-iteration mci memory remembered quantity of unfulfilled part of the order of customer i in iteration l gmtk order number of group in partition k where token is set the diagram on figure 2 shows that the allocation algorithm in every iteration starts from checking whether apk > opk. checking goes from the last partition to the first one, and if this condition has been met, then all allocated surplus is transferred to a joint buffer, whereas the allocated amount of finished products transferred to k-partition now equals the overall demand in given partition (first set bl = bl + (apk − opk), and then assign apk = opk).this situation might happen in partitions that gather customers with small orders in case that higher quote than necessary is given in the beginning. this is a sign that one has to reaccess the analysis of partition of ’small’ customers, and award smaller and more reasonable protective quote (fine tuning). within the algorithm, all amounts transferred to buffer are added to the allocated amount of finished products of the first partition (ap1 = ap1 + bl). one of the most important concepts of the proposed algorithm is group memory token (gmt). it is introduced in order to designate a group of customers whose order has not been fulfilled during an observed iteration. there could be only one group memory token (pointer) in each partition. in the end of one allocation iteration, token stays in a certain group with unfulfilled demand, which gives a priority to that group in the following iteration, so that the customers would get the unfulfilled order in the following iteration as a priority. in order to remember the unfulfilled order of group of customers with gmt, the variable mc (memory of customer) is introduced. we need to stress that mc obtains the value for every customer from the groups 790 d. lečić-cvetković, n. atanasov, s. babarogić that kept token in the observed iteration, and it is used only during the allocation process in the following iteration. formula for calculating the unfulfilled part of an order is: mci = oci ogj · (ogj − apk) · mcgij where mci is remembered quantity of unfulfilled part of an order of i-customer in l-iteration that is being transferred to the following iteration. in case gmt in several iterations stays in the same group of the first partition of important customers, then the necessity of expanding capacities becomes clear. 5 conclusion the goal of this paper was to create an algorithm for solving the problem of allocation of limited stocks to incoming orders. order fulfillment is carried out by letting the allocation algorithm grant stocks in line with the priority of customers, with the goal of providing high level of customer service. the algorithm proposed in this paper provides following: (1) classification in groups will provide the order of allocation with primarily focus to satisfy customers that are important for the company, in accordance with previously defined criteria, (2) application of partitions will help certain groups of lower priority within protected partitions to be involved in allocation so that the low-ranked customers would be at least partially satisfied, which keeps all the customers in the system and sends a useful signal to company’s management saying that there is still unsatisfied demand, and they need to enlarge the production capacities, (3) introduction of gmt allows all customers within a group to be delivered the backorders from the previous cycle, with the extended delivery lead time, with which they improve the overall customer service. the main differences between the allocation algorithm presented in this paper, and the models described in the related work are: (1) this allocation algorithm directly affects the long-term operating results by accomplishing the customer service as the primary goal, unlike other models that focus only on short-term profit generation, and (2) there is a tendency of bounding customers with different priority for a long-term period, i.e. the tendency of keeping the customers within the system. in addition, there are also differences in basic goals of models: the primary goal of advanced algorithm is the maximization of customer service, unlike other systems that are primarily orientated towards profit maximization. further research will be focused on testing and improving algorithm based on real data obtained in other companies and also from different industries. in addition, there are possibilities for further improvement of the allocation algorithm by creating a report system that would support management in making timely decisions on the change in operating strategy. bibliography [1] cederborg o., rudberg m.: customer segmentation and capable-to-promise in a capacity constrained manufacturing environment, 16th international annual euroma conference, göteborg, sweden, june 14-17, 2009. [2] chan f.t., chung s.h.: a modified multi-criterion genetic algorithm for order fulfillment in manufacturing network, proceedings of the 9th asia pacific industrial engineering & management system conference, apiems, indonesia, 2008. an algorithm for customer order fulfillment in a make-to-stock manufacturing system791 [3] chen j.h., lin j., wu y.s.: order promising rolling planning with atp/ctp reallocation mechanism, iems, apiems, vol. 7, no.1, 2008. [4] kaschel h., bernal l.m.s.: importance of flexibility in manufacturing systems, international journal of computers, communications & control, vol. i, no. 2, 2006. [5] lawrence g.: introducing aps: getting production in lock step with customer demand, automotive manufacturing&production, vol. 110, issue 5, 1998. [6] lin j., chen j.h.: enhance order promising with atp allocation planning considering material and capacity constraints, journal of the chinese institute of industrial engineers, vol.22, no.4, 2005. [7] lupse v., dzitac i., dzitac s., manolescu a., manolescu m.-j., crm kernel-based integrated information system for a sme: an object-oriented design, international journal of computers communications and control, issn 1841-9836, suppl. s, 3(s): 375-380, 2008. [8] makatsoris h.c., chang y.s., richards h.d.: design of a distributed order promising system and environment for a globally dispersed supply chain, int. j. computer integrated manufacturing, vol. 17, no. 8, 2004. [9] meyr h.: customer segmentation, allocation planning and order promising in make-to-stock production, or spectrum, vol 31, no 1, 2009. [10] rudberg w., wikner j.: mass customization in terms of the customer order decoupling point, production planning & control, vol. 15, no. 4, 2004. [11] wikner j., rudberg m.: introducing a customer order decoupling zone in logistics decisionmaking, international journal of logistics: research and applications, vol 8, no. 3, 2005. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 125-134 design of a mimo pid robust controller using moments based approach abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou abstract: in this paper we present a new technique for robust mimo controllers synthesis and reduction based on a reference model and moments approach intended to control a mimo thermal system. the reference model allows to specify the performances requirements for the closed loop and improve the controller robustness while the moments tool (frequency and time ones) is used to reduce the controller structure using a non linear optimization. the implementation on the real system associates this methodology of mimo pid controllers synthesis with broïda’s identification technique in order to carry out a auto-tuning procedure [2][11]. keywords: pid control, reference model, moments, optimization, robustness, broïda’s identification technique. 1 introduction the method of moments was introduced in a previous contribution [1] to show how we can treat single-input single-output (siso) control systems. we saw that the moments represent a good tool to obtain a reduced robust controller in order to approximate the closed loop behaviour to a reference model one. the reference model gathers all performances requirements like time response and overshoot. an extension of this method to the multivariable (multi-inputs multi-outputs) systems is presented in this paper; in this case the fundamental idea is to choose a diagonal reference model to make inputs outputs pairing [10] where the diagonal reference transfer functions are chosen using the same method for the siso case. in this paper we present our control methodology for mimo systems with an application to a thermal system. the control of the mimo thermal system is realized with the help of a pid controller using moments based approach and reference model. the aim of this technique is to synthesize a reduced robust controller (pid for example) for the implementation. the identification was realized thanks to broïda’s method which is an elementary technique very used in the industry in order to develop a auto-tuning procedure requiring a minimum intervention of the user. the controller achieving the performances for the worst case model is called ideal controller [10] which will be reduced using the moments based approach to have a pid structure for implementation [4]. the reduced controller must preserve the same performances as the ideal one; this rises from frequency and time moments which represent a good tool for synthesis and analysis [7]. concretely, the reduction procedure is based on a non linear optimization and its initialization is given by the least squares algorithm [8]. the paper is organized as follows: in section 2 we give a complete description of the application; in section 3 we develop our synthesis methodology; in section 4 and 5 we describe the moments theory and the reduction method and we finish this communication by presenting the different results obtained and a conclusion. 2 description the general diagram of the temperature control device is illustrated by fig. 1. copyright © 2006-2008 by ccc publications 126 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou amplification u1 +vcc t1 u2 θ1 θ2 stage amplifying stage amplifying y2y1 t2 figure 1: the general diagram of the system it is composed of a radiator on which we placed two power transistors t1 and t2 controlled by the tensions u1 and u2 which vary between 0 and 10 volts. near these power transistors, which represent the heat source, two temperature sensors (pt 100) are laid out. after adaptation and amplification, we obtain the tensions y1 and y2 (which vary between 0 and 10 volts) proportional to the temperatures θ1 and θ2. the ratio tension-temperature is of 0.02volts/c. 2.1 modelling the aim is to define the model connecting the tensions delivered by the temperature sensors y1 and y2 to the control tensions u1 and u2 applied to the transistors. the conduction of the heat in the radiator, towards the temperature sensors produced by the power transistors is governed by the following diffusion equation: µ ∂ 2θ ∂ x2 = ∂ θ ∂ t (1) where µ is the coefficient of thermal diffusion process. consequently the resulting physical model is of distributed parameters and we preferred to present a black box model adapted to the synthesis of a control law, this model is given from identification. let:   y1 (s) y2 (s)   =   g11 (s) g12 (s) g21 (s) g22 (s)     u1 (s) u2 (s)   (2) where g (s) is the transfer matrix of the system. notice that the system is symmetrical because of the provision of the heat sources and the position of the sensors. indeed, we must obtain: { g11 (s) = g22 (s) g12 (s) = g21 (s) (3) in addition, since the sensors θ1 and θ2 are very close to the sources t1 and t2, the transfers g11 and g22 can be reasonably modelled by a first order system ( k11 1 + τ11s ). on the other hand θ2 is less sensitive to the effect of t1 than that of t2 (so k21 < k11), with a settling time definitely higher. we can thus approach this transfer by an aperiodic nth order system with (n >> 1) or more simply by a first order system with a time delay ( k12e−α12s 1 + τ12s ). design of a mimo pid robust controller using moments based approach 127 2.2 identification several sophisticated identification techniques (minimization of a quadratic cost by least squares or non linear optimization) (ljung [5] and walter [12]) can be used to estimate the parameters of g (s). taking into account the fact that the aim is to synthesize pid controllers (in a auto-tuning objective), we preferred to choose a basic identification method requiring only step tests. our choice thus is broïda’s method which delivers an approached model of the form ( ke−α s 1 + τ s ), so well adapted to the selected modelling [2][11]. let us recall that the coefficient {k, α et τ} are obtained with the help of the following formulas:    ki j = y∞ αi j = 2.8t1 −1.8t2 τi j = 5.5 (t2 −t1) (4) where y∞ is the final value of the system step response, t1 (respectively t2) is the time where the output attains 28% (respectively 40%) of its final value. we made 5 tests which provided: test1 test2 test3 test4 test5 k11 0.1221 0.1221 0.1172 0.1172 0.1318 τ11 577 414 449 451 457 k12 0.0634 0.0636 0.0586 0.0586 0.0684 τ12 910.5 862.95 684.2 683.1 863 α12 7 26.2 59.6 58.9 6 k21 0.083 0.0781 0.0684 0.0684 0.083 τ21 715 654.5 808.5 808.5 715 α21 113 99.8 73.4 73.4 113 k22 0.0977 0.0977 0.0977 0.0928 0.1074 τ22 450 445 447 447 600 the settling times of g11 (s) and g22 (s) are close and equal 33minutes. 2.3 the nominal model the nominal model was obtained by carrying out the average of the 5 tests, thus: gnom (s) =   0.122 470s+1 0.0625e−31.5s 801s+1 0.0762e−94.5s 740s+1 0.0987 478s+1   (5) from the values of the preceding table, we note that the theoretical symmetry is not checked in practice, well that g11 (s) and g22 (s) are close (g12 (s) and g21 (s) respectively), these differences are the consequences of the noises level disturbing the measurements and the non perfect reproducibility of the assembly. 2.4 taking into account of uncertainties identification uncertainties will deteriorate the performances of the control device if they are not taken into account during the synthesis. the table recapitulating the 5 tests shows that two uncertainties types are present: 128 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou • uncertainties on the d.c gains (of type low frequencies). • uncertainties on time-constants and time delays, which causes modelling errors (of type high frequencies). since the transfer matrix g (s) is theoretically symmetrical, we decided to make the synthesis by basing us on a symmetrical worst case model gp (s). using gp (s), we took into account all the uncertainties by raising: • low frequencies uncertainties by taking kmax. • high frequencies uncertainties by introducing a time delay e−δ s (see [1]). remark 1. the worst case model has been chosen to take into account all the identification uncertainties: • low frequencies uncertainties, by choosing the max of the d.c gains. • high frequencies, by introducing time delays in all input-output transfers. thus, we obtain the following worst case model: gp (s) =   0.132e−5s 500s+1 0.085e−125s 800s+1 0.085e−125s 800s+1 0.132e−5s 500s+1   (6) 3 synthesis the control configuration is illustrated by fig. 2 g(s) r e u po y pi c(s) figure 2: unitary feedback configuration c (s) is the controller to implement, g (s) is the plant, r represent the reference inputs, y the outputs to be controlled, u the control inputs, pi and po are the input and output disturbances and e is the tracking error. the synthesis of c (s) is based on the internal model control [6][10], so: c (s) = q (s) (i −g (s) q (s))−1 (7) where q (s) is any stable transfer matrix [3]. design of a mimo pid robust controller using moments based approach 129 3.1 the reference model q (s) is calculated using gp (s) in order to approach the closed loop tyr (s) to a reference model tre f (s), so: tyr (s) = gp (s) q (s) = tre f (s) (8) the reference model gathers all the objectives of the synthesis in terms of stability and performances (i.e inputs-outputs decoupling, settling time... etc). in our case, tre f (s) is given by: tre f (s) =   t2(s)e−δ s d(s) 0 0 t2(s)e −δ s d(s)   (9) where t2 (s) represent the dominant poles to fixe the closed loop dynamic, d (s) represent the auxiliary poles, which reduce the control input energy, confer robustness to the controller by making a sufficient roll-off of tyr (s) and ensure its causality, finally e−δ s represents the singularity of gp (s) which is integrated in tre f (s) to have q (s) stable and realizable. so q (s) is given by: q (s) = gp (s) −1 tre f (s) (10) since the singularities of gp (s) (i.e time delays and rhp zeros) are integrated in the reference model tre f (s), so the stability of q (s) is guaranteed [3]. t2 (s) and d (s) are given by: t2 (s) = ω 2n s2+2ξ ωns+ω 2n d (s) = (1 + η s)n (11) where ωn and ξ , fixe the settling time and the overshoot of the outputs. we can recapitulate our reference model by saying that it imposes a inputs-outputs decoupling and the same dynamics for the two outputs of g (s). 3.2 robustness and the reference model it is well-known that the relative modelling errors ∆r (s) modify the dynamics of the system with a possibility of instability because: sypo (s) = ( i −g (s) q (s) (i + ∆ (s) q (s))−1 ) = snom (s) (i + ∆r (s) tre f (s)) −1 (12) so robustness of stability and performances is guaranteed if: ‖ ∆r (s) tre f (s) ‖∞≤ δ −1sr (13) where δsr, represents stability margin; notice that from (13), we can act on the auxiliary poles of tre f (s) to ensure the robustness [3]. 130 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou 3.3 the ideal controller from (10), we deduce the ideal controller c (s): c (s) = q (s) (i −gp (s) q (s))−1 (14) it is clear that the implementation of c (s) is very hard to do because of time delays; for that we will reduce its structure to get an implementable one which must preserve the same performances of those ensured by the ideal controller. the moments tool, particularity of our approach is used for reduction. 4 the moments let us consider a linear siso system, characterized by its transfer function g (s) analytic in the rhp plan (.i.e re (s) > 0) and let g (t) be its impulse response: g (s) = ∫ ∞ 0 g (t) e−st dt (15) the transfer function is given by the following state space (not necessary minimal) realization: g (s) s = [ a b c d ] = c (si −a)−1 b + d (16) where a ∈ rn×n, b ∈ rn×1,c ∈ r1×n and d ∈ r1×1. 4.1 computing the moments using state space realization using the following equality: (si −a) ( −a−1 −sa−2 −s2a−3 −···− ) = i ⇒ (si −a)−1 = − ∞ ∑ n=0 ( sna−(n+1) ) (17) frequency moments realizing a variable change µ = jω − jω0, equation (15) becomes: g (µ) = ∞ ∑ n=0 (−1)n (µ)n an,ω0 (g) (18) and (16): g (µ) = c (µ i −(− jω0i + a))−1 b + d (19) so, we get: a0,ω0 (g) = −c (− jω0i + a) −1 b + d (20) an,ω0 (g) = (−1) n+1 c (− jω0i + a)−(n+1) b, (n = 1···∞) (21) remark 2. the time moments are giving by remplacing ω0 = 0. both time and frequency moments can be calculated easily using the previous algorithm. thanks to the computation of the moments using state space realization, we can compute moments for mimo systems (multi inputs multi outputs). design of a mimo pid robust controller using moments based approach 131 5 the controller reduction for more details of our model reduction procedure, please refer to [1]. it is interesting to have a reduced structure of the implemented controller (pid for example) [6]. let: θ =   θ 11 (s) ··· θ 1m (s) ... . . . ... θ m1 (s) ··· θ mm (s)   (22) be the matrix representing the controller’s parameters to be calculated. θ i j represents the parameters vector of numerator and denominator of cr,i j (s); i.e the reduced controller between the jth input and the ith output. let us define our cost function j as the 2 norm of the errors between the different moments of the ideal controller and those of the reduced one. j = n ∑ n=0 ||εn||22 = n ∑ n=0 ||an,ω0 (c)−an,ω0 (cr)||22 (23) where an,ω0 (cr) represents n th order moments matrix, which is function of the parameters θ : an,ω0 (cr) = fn (θ ) (24) let: j = n ∑ n=0 ||an,ω0 (c)− fn (θ )||22 (25) the objective is to determine the estimated parameters θ̂ minimizing j around ω0. this frequency ω0 is chosen in order to preserve stability of the system (i.e critical pulsation), (see the generalized nyquist criterion [10]). 5.1 linear optimization the first step consists on imposing the common denominator dr (s) of the reduced controller (for example we can take the auxiliary poles of the reference model dr (s) = d (s)). so only the zeros have to be determined; the function fn (θ ) is linear; thus the minimization of j is obtained by least squares, let: cr (s) = cls (s) (26) be the reduced controller which will be used to initialize the non linear programming algorithm. remark 3. by imposing the poles of the reduced controller, for example a pole with an integral action we define so a pid structure. for example we can take dr (s) = s (1 + η s). 5.2 non linear optimization the fact of imposing the poles of the reduced controller, this will limit its performances and consequently those of the closed loop. so, it is preferable to optimize all the structure (i.e. poles and zeros); the function fn (θ ) is non linear; the estimation of θ is obtained by non linear programming [7]. we use marquardt’s algorithm which is a good combination between rapidity and convergence [8]. the parameters are updated with the help of the following algorithm: θ̂i+1 = θ̂i −{[j ′′ + λii]−1.j ′}θ̂ =θ̂i (27) 132 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou where: ( ∂ j ∂ θ ) = j ′ : gradient vector (28) ( ∂ 2j ∂ θ 2 ) = j ′′ : hessian matrix (29) λi : coefficient to adjust (30) the initialization is given by the least squares solution: θ̂0 = θ̂ls (31) 6 results and comments the controllers synthesis and reduction technique was applied to the thermal system with 2 inputs and 2 outputs. let us recall that this thermal system is characterized by an important open loop settling time (approximately 33 minutes); moreover the elementary modelling used in a disturbed context involves important uncertainties. the objective of the temperature control, in a auto-tuning context, will be double: • reduce considerably the closed loop settling time by using a controller of a simplified structure: pid controller. • guarantee robustness in spite of the simplicity of the controller, the level of uncertainty and the reduction of the settling time. let us specify that the implementation of the pid controllers was ensured thanks to xpc target software of matlab. we used for that a sample time te = 1s. our objective is to accelerate the system reasonably, in a ratio of 6, thus passing from 33mn in open loop to 5mn in closed loop all while maintaining the relative overshoot around 5%; for that, we fixed: ωn = 0.02rd/s ξ = 0.7 the auxiliary poles ensuring the robustness condition (13) are: d (s) = (1 + 3s)3 the pid controller obtained using the synthesis and the reduction procedures described obviously is: cr (s) = [ c11 (s) c12 (s) c21 (s) c22 (s) ] (32) with: c11 (s) = c22 (s) = −3.162s 2+52.81s+0.14 s(1+34.87s) c12 (s) = c21 (s) = −2019e −7s2+4403e−6s−7193e−5 s(1+15.88s) (33) the implementation provides the results illustrated by fig. (3) and fig. (4). design of a mimo pid robust controller using moments based approach 133 0 1000 2000 3000 4000 5000 6000 2.5 2.55 2.6 2.65 time(sec) o u tp u t( v o lt) 0 1000 2000 3000 4000 5000 6000 2.5 2.55 2.6 2.65 time(sec) o u tp u t( v o lt) tref y1 tref y2 figure 3: step responses 0 1000 2000 3000 4000 5000 6000 3 4 5 6 7 8 9 10 time(sec) co ntr ol int pu t(v olt ) u1 u2 figure 4: the control inputs remark 4. we can check that the system’s responses correspond perfectly to those of the reference model. we can also check the perfect decoupling of the system outputs, y2 being insensitive to the reference input applied to y1 and reciprocally. it is checked finally that the control input obtained is completely reasonable, that it is with respect to its initial magnitude or of its insensitivity to the output noise (of considerable level). 7 conclusion we presented in this communication a synthesis and reduction technique of robust controllers to the multivariable control of a thermal system. this methodology is based on a reference model integrating explicitly the desired performances. the experimental results show that the closed loop system verifies well the performances described by the reference model. the moments approach, characteristic of this methodology, allows the reduction of the ideal controller to lead to a pid structure, while guaranteeing the dynamic performances and especially the robustness as testify the experimental results. let us recall finally that this synthesis methodology and reduction was associated with broïda’s identification technique to carry out the auto tuning of multivariable pid controllers. 134 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou bibliography [1] a.bentayeb, n.maamri and j-c.trigeassou "the moments in control: a tool for analysis, reduction and design", international journal of computers, communication and control, vol ii (2007). [2] a.anon "special edition on pid tuning methods". computing and control engineering journal 10(2), (1999). [3] j.c.doyle, b.a.francis and a.tannenbaum "feedback control theory". macmillan, new york (1991). [4] c.e.garcia and m.morari "internal model control: an unifying review and some new results". ind.eng.chem.proc.dec.dev, vol(91.2) (1982). [5] ljung "system identification. theory for the user". prentice hall (1987). [6] m.morari and s.zafiriou "robust process control". prentice hall (1989). [7] n.maamri, a.bentayeb and j-c.trigeassou "design and iterative optimization of reduced robust controllers with equality-constraints" rocond-milan (2003). [8] d.w.marquardt "an algorithm for least-squares estimation of non linear parameters" journal of soc. indust. appl. math v(11-2) (1963). [9] q.g.wang "decoupling control", lecture notes in control and information sciences, springer verlag (2003). [10] s.skogestad and i.postlethwaite "multivariable feedback control", jw (1996). [11] r.toscano "a simple robust pi/pid controller design via numerical optimization approach", journal of process control (15), (2005). [12] e.walter and l.pronzato "identification of parametric models from experimental data". communications and control engineering series. springer (1997). a. bentayeb, n. maamri and j-c. trigeassou university of poitiers laboratoire d’automatique et d’informatique industrielle 40 avenue du recteur pineau 86022 poitiers e-mail: abdelmadjid.bentayeb@gmail.com received: january 16, 2007 abdelmadjid bentayeb was born in 1977, he obtained the phd thesis in automatic control at the university of poitiers in 2006. his current research interests include robust control and model reduction. nezha maamri is lecturer at the unisversity of poitiers, hers current research interests include identification and robust control. jean-claude trigeassou is professor at the university of poitiers, his current research interests include identification and control. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 450-458 data consistency in emergency management d. ergu, g. kou, y. peng, f. li, y. shi daji ergu college of electrical and information engineering southwest university for nationalities chengdu, china, 610041 e-mail: ergudaji@163.com gang kou, yi peng , feixiong li school of management and economics university of electronic science and technology of china chengdu, china, 610054 e-mail: kougang@yahoo.com; pengyicd@gmail.com; lifx@uestc.edu.cn yong shi 1. research center on fictitious economy and data sciences chinese academy of sciences, beijing 100190, china, and 2. college of information science & technology university of nebraska at omaha, omaha, ne 68182, usa e-mail: yshi@gucas.ac.cn abstract: timely response is extremely important in emergency management. however, cardinal inconsistent data may exist in a judgment matrix because of the limited expertise, preference conflict as well as the complexity nature of the decision problems. the existing inconsistent data processing models for positive reciprocal matrix either are complicated or dependent on the priority weights, which will delay the decision making process in emergency. in this paper, a geometric mean induced bias matrix (gmibm), which is only based on the original matrix a, is proposed to quickly identify the most inconsistent data in the judgment matrix. the correctness and effectiveness of the proposed model are proved mathematically and illustrated by two numerical examples. the results show that the proposed model not only preserves most of the original information in matrix a, but also is faster than existing methods. keywords: cardinal inconsistency, positive reciprocal matrix, geometric mean induced bias matrix (gmibm), inconsistency identification 1 introduction emergency management is an interdisciplinary field, and is in essence a complex multi-objective optimization problem [1]. multi-criteria decision making (mcdm) methods have therefore been extensively employed to study emergency management, for instance, the ordered weighted averaging (owa) [2], technique for order preference by similarity to ideal solution (topsis) [3], preference ranking organization method for enrichment evaluations (promethee) [4], analytic hierarchy process (ahp) ( [5], [6],and [7]), analytic network process (anp) ( [8], [9]), decision making trial and evaluation laboratory (dematel) [10], fusion approach of mcdm (famcdm) methods [11] etc. among these mcdm methods, ahp and anp are two of the most popular methods for studying emergency management, and usually used to assess the emergency management performance, select the best emergency response alternatives or emergency recovery alternatives and allocate reasonable relief resources etc. copyright c⃝ 2006-2012 by ccc publications data consistency in emergency management 451 in the ahp/anp, the tangible and intangible attributes or criteria are always measured by numerical data through pairwise comparisons, and displayed in a judgment matrix whose numerical data are positive and reciprocal. the data of judgment matrix are usually provided by experts and collected through questionnaire survey [12]. therefore, the data may be inconsistent because of the limited expertise, preference conflict as well as the complexity nature of the decision problems etc ( [13], [14]). for instance, assume there are three emergency response alternatives, a, b, and c, the ith expert thinks that alternative a is preferred to b 2 times, and b is preferred to c 4 times, but alternative a is preferred to c 3 times in stead of 8 times. this is called cardinal inconsistency, and for an inconsistent judgment, the inconsistent data should be identified and adjusted before it is used to make a valid decision. therefore, the inconsistent data processing issue in a judgment matrix has become a hot topic since the introduction of the ahp/anp. currently, there are many methods for identifying and adjusting the inconsistent data, for example, auto-adaptive algorithms in ( [14], [16]), absolute differences methods in ( [17], [18]), perturbation matrix method in [19] etc. however, the existing methods are either too complicated to delay the speed of inconsistent data analysis or are difficult to preserve most of the original information in the judgment matrix, or are extremely dependent on the priority weights. therefore, it is necessary to propose a cardinal inconsistent data analysis model to effectively and simply identify the inconsistent data in order to make a valid decision, which is independent to the priority weights while preserving most of the original information in a judgment matrix. in an attempt to establish such models, the absolute differences of geometric mean matrix in [20], the induced bias matrix model (ibmm) in [21], were proposed to identify the possible inconsistent data in a judgment matrix. in this paper, a geometric mean induced bias matrix (gmibm), which is only based on the original matrix a, is established to identify the most inconsistent data in a judgment matrix. through observing and adjusting the largest bias data in the induced bias matrix, the consistency ratio of the judgment matrix can be quickly improved to make a fast decision for emergence management. besides, a general estimating formula of the mined cardinal inconsistent data of gmibm is provided. the remaining parts of this paper are organized as follows. the next section briefly describes the cardinal inconsistency in a judgment matrix. the theorems, corollaries and identifying processes of gmibm are further proposed and presented in section 3. two numerical examples introduced in [21] are used to test the proposed model in section 4. section 5 concludes the paper. 2 cardinal inconsistency let the judgment matrix be a = [ai j]n×n, where ai j>0 and ai j =1/ a ji for all i, j,and k, if ai j = aikak j holds for all i, jandk, then matrix a is said to be perfectly cardinal consistency, otherwise, it is called cardinal inconsistency. in practice, it is unrealistic to obtain a perfectly cardinal consistency of matrix a, therefore ahp allows a certain level of cardinal inconsistency of the judgment matrix. to measure the consistency of a judgment matrix and determine a certain acceptable level of inconsistency, saaty proposed a consistency index (ci), denoted as: ci = λmax − n n − 1 (1) where λmax is the maximum eigenvalue of matrix a, and n is the order of matrix a. to define a unique consistency test index that does not rely on the order of judgment matrices, the consistency index (ci) was extended and the consistency ratio (cr) method was further proposed by saaty [17], cr = ci ri (2) where ci is the consistency index shown in equation (1) while ri is the average random index based on matrix size, as shown in table 1. 452 d. ergu, g. kou, y. peng, f. li, y. shi table 1 the average random index n 1 2 3 4 5 6 7 8 9 10 ri 0 0 0.52 0.89 1.11 1.25 1.35 1.4 1.45 1.49 if the cr of a judgment matrix is less than or equal to 0.1 (cr≤0.1), indicating the inconsistency is relatively small, the judgment matrix is said to be of acceptable inconsistency. if cr is greater than 0.1 (cr>0.1), the judgment matrix is of unacceptable cardinal inconsistency, and the decision makers are asked to revise their judgments. to effectively identify the cardinal inconsistent data and improve the consistency ratio of a judgment matrix, a geometric mean induced bias matrix (gmibm) by hadarmad product is proposed in the following sections. 3 geometric mean induced bias matrix (gmibm) 3.1 theorem of gmibm to identify the inconsistent data, a geometric mean induced bias matrix (for short gmibm, hereinafter), which is only based on the original judgment matrix and independent to the priority weights, is established to amplify the most inconsistent data in this paper. then, the most inconsistent data can be identified by observing the largest data in the induced bias matrix. the related theorems and corollaries are presented in this section. theorem 1. the geometric mean induced bias matrix (gmibm) c should be a u matrix if judgment matrix a is perfectly consistent, that is, c = ā ◦ at = ( ci j ) = ( n √√ n∏ k=1 aikak j · a ji) = u i f aikak j = ai j (3) where ā = ( āi j ) n×n = ( n √ n∏ k=1 aikak j ) n×n represents an n-by-n geometric mean matrix composed of all geometric mean of aikak j for all i, j and k, u =  1 · · · 1 ... ... ... 1 · · · 1 , n denotes the order of a, at represents the transpose of matrix a. symbol ′ ◦ ′ denotes hadamard product (e.g. c = a ◦ b means ci j = ai jbi j for all i and j ). proof: if the judgment matrix satisfies the perfect consistency condition, namely, aikak j = ai j holds for all i, j and k. since ai j =1/a ji, we have ci j = n √√ n∏ k=1 aikak j · a ji = n √√ n∏ k=1 ai j · a ji = n √ ai jn · a ji = ai ja ji = 1 (4) therefore, all entries in matrix c are ones if the matrix is perfectly consistent, and matrix c is a u matrix, whose entries are ones. � to simply compute the gmibm and easily understand the theorem of gmibm, the theorem 1 is transformed to following theorem. data consistency in emergency management 453 theorem 2. the geometric mean induced bias matrix (gmibm) c should be a u matrix if judgment matrix a is perfectly consistent, that is, c = l × r ◦ at = ( ci j ) = ( n √√ n∏ k=1 aik · n √√ n∏ k=1 ak j · a ji) = u i f aikak j = ai j (5) where l = ( n √ n∏ k=1 aik ) n×1 represents an n-by-one column matrix composed of geometric mean of rows in matrix a, while r = ( n √ n∏ k=1 ak j ) 1×n denotes an one-by-n row matrix composed of geometric mean of columns in matrix a. corollary 3. the geometric mean induced bias matrix (gmibm) c should be as close as possible to a u matrix if judgment matrix a is approximately consistent. corollary 4. there must be some inconsistent data in the geometric mean induced bias matrix (gmibm) c deviating far away from one if the judgment matrix is inconsistent. based on corollary 4, we can identify the most inconsistent data in matrix a by observing the largest data deviating from 1 in the geometric mean induced bias matrix (gmibm) c. details of inconsistency identification processes are presented below. 3.2 inconsistency identification and adjustment processes of gmibm to propose the inconsistency identification and adjustment processes of gmibm based on above theorems and corollaries, the aforementioned n-by-n judgment matrix a = [ai j]n×n is used in the following. the processes of inconsistent data analysis and adjustment of gmibm include two major steps, inconsistency identification and inconsistency adjustment: step i: inconsistency identification step 1: compute a column matrix l and a row matrix r, which are composed of geometric means of rows and columns respectively. l = a =  a11 · · · a1i · · · a1 j · · · a1n ... ... ... ai1 · · · aii · · · ai j · · · ain ... ... a j1 · · · a ji a j j ... a jn an1 · · · ani · · · an j · · · ann  n √ n∏ k=1 a1k n √ n∏ k=1 aik n √ n∏ k=1 a jk n √ n∏ k=1 ank r = n √ n∏ k=1 ak1, n √ n∏ k=1 aki, n √ n∏ k=1 ak j, n √ n∏ k=1 akn . where  l = ( n √ n∏ k=1 a1k, · · · n √ n∏ k=1 aik, · · · n √ n∏ k=1 a jk, · · · n √ n∏ k=1 ank )t r = ( n √ n∏ k=1 ak1, · · · n √ n∏ k=1 aki, · · · n √ n∏ k=1 ak j, · · · n √ n∏ k=1 akn ) (6) 454 d. ergu, g. kou, y. peng, f. li, y. shi step 2: compute geometric mean matrix by formula ā = l × r (7) we can obtain the geometric mean matrix ā. the computing processes are shown below: ā = l × r =  n √ n∏ k=1 a1k ... n √ n∏ k=1 aik ... n √ n∏ k=1 a jk ... n √ n∏ k=1 ank  n×1 × ( n √ n∏ k=1 ak1, · · · n √ n∏ k=1 aki, · · · n √ n∏ k=1 ak j, · · · n √ n∏ k=1 ank ) 1×n =  n √ n∏ k=1 a1kak1 · · · n √ n∏ k=1 a1kaki · · · n √ n∏ k=1 a1kak j · · · n √ n∏ k=1 a1kakn ... · · · ... ... n √ n∏ k=1 aikak1 ... n √ n∏ k=1 aikaki · · · n √ n∏ k=1 aikak j · · · n √ n∏ k=1 aikakn ... ... ... ... n √ n∏ k=1 a jkak1 · · · n √ n∏ k=1 a jkaki · · · n √ n∏ k=1 a jkak j · · · n √ n∏ k=1 a jkakn ... ... ... ... n √ n∏ k=1 ankak1 · · · n √ n∏ k=1 ankaki · · · n √ n∏ k=1 ankak j · · · n √ n∏ k=1 ankakn  n×n . =  n √√ n∏ k=1 aikak j  n×n where l is an n-by-one column matrix, which is composed of all geometric means of rows while r is a one-by-n row matrix r composed of all geometric means of columns, as shown in formula (6) and the two edges of matrix a in step 1. the geometric mean matrix ā can be easily computed by multiplying l to r. step 3: compute geometric mean induced bias matrix (gmibm) c by formula, c = ā ◦ at = ( ci j ) = ( āi j · a ji ) = ( n √√ n∏ k=1 aikak j · a ji) (8) step 4: identify the data with the largest value, denoted as cmaxi j , deviating from 1 in matrix c, then the corresponding ai j is regarded as the most inconsistent data in matrix a. if there are other data, say cmn, cpq, whose values are also deviating far away from 1, then their corresponding data in matrix a, amn, apq, can also be considered as the possible inconsistent elements. once the inconsistent data are data consistency in emergency management 455 identified, the following two steps are proposed to adjust the inconsistent data. step ii: inconsistency adjustment: step 1: estimate the value of identified inconsistent data by formula ãi j =n−2 √√√ n∏ k=1,,i, j aikak j =n−2 √√ āni j a2i j = āi j ( āi j ai j ) 2 n−2 (9) where ãi j denotes the estimated value of the most inconsistent data ai j while āi j is the geometric mean value located at the ith row and the jth column of geometric mean matrix ā. step 2: test the consistency of the revised matrix a by replacing the inconsistent data with the estimated values. to summarize, the processes of dealing with inconsistency by gmibm include two major steps, that is, inconsistency identification and inconsistency adjustment. the first two steps in step i are used to show the specific procedure of computing geometric mean matrix ā. for simplicity, one can directly use the latter two steps as the sub-steps of step i to identify the most inconsistent data. to verify the effectiveness and accuracy of gmibm, in the following, two numerical examples introduced in [20] are used to illustrate the proposed model. 4 illustrative examples to test the effectiveness and correctness of the proposed gmibm, and illustrate the processes of the proposed specific inconsistency identification and adjustment by numerical examples, two numerical examples in [21] are used in this paper. example 1 the example 1 used in [21], which was firstly introduced in [20], is a 4 × 4 inconsistent pair-wise comparison matrix a with cr=0.173>0.1. a =  1 1/9 3 1/5 9 1 5 2 1/3 1/5 1 1/2 5 1/2 2 1  apply the gmibm to this matrix: step i: inconsistency identification step 1: compute the column matrix l and the row matrix r by formula (6), l = ( 0.5081 3.0801 0.4273 1.4953 )t ,r = ( 1.968 0.3247 2.3403 0.6687 ) step 2: compute geometric mean matrix by formula (7), ā = l × r =  1 0.165 1.1892 0.3398 6.0615 1 7.2084 2.0598 0.8409 0.1387 1 0.2857 2.9428 0.4855 3.4996 1  456 d. ergu, g. kou, y. peng, f. li, y. shi step 3: compute geometric mean induced bias matrix (gmibm) c by formula (8) , c = ā◦at =  1 1.4848 0.3964 1.6990 0.6735 1 1.4417 1.0299 2.5227 0.6936 1 0.5715 0.5886 0.9710 1.7498 1  step 4: identify the largest value cmaxi j in matrix c. here c max i j = c max 31 = 2.5227, deviating from 1 in matrix c, then the corresponding element a31 in matrix a is regarded as the most inconsistent element,indicating that it is smaller than its average values. step ii: inconsistency adjustment step 1: estimate the possible proper value of a31 using the estimating formula (9) ã31 = 4−2 √√ ā431 a231 = √ 0.84094 (1/3)2 = 2.1213 ≈ 2 step 2: test the consistency of the revised matrix a by replacing the inconsistent elements a31 and a13 with the estimated values 2 and 1/2. the revised matrix passed with cr=0.0028<0.1. the identified inconsistent data and its estimated value are the same as the ones in [19] and [20], but the proposed method is faster to find the inconsistent element and estimate the values. example 2 the second example in [20] is a 4 × 4 inconsistent pair-wise comparison matrix a with cr=1.0242>0.1. a =  1 2 4 18 1 2 1 2 4 1 4 1 2 1 2 8 14 1 2 1  apply the gmibm to this matrix: step i: inconsistency identification step 1: compute the column matrix r and the row matrix l by formula (6), l = ( 1 1.4142 0.7071 1 )t , r = ( 1 0.7071 1.4142 1 ) step 2: compute geometric mean matrix by formula (7), ā = l × r =  1 0.7071 1.4142 1 1.4142 1 2 1.4142 0.7071 0.5 1 0.7071 1 0.7071 1.4142 1  data consistency in emergency management 457 step 3: compute geometric mean induced bias matrix (gmibm) c by formula (8), c = ā◦at =  1 0.3536 0.3536 8 2.2828 1 1 0.3536 2.2828 1 1 0.3536 0.125 2.8284 2.8284 1  step 4: identify the largest value cmaxi j in matrix c. here c max i j = c max 14 = 8, deviating from 1 in matrix c, then the corresponding element a14 in matrix a is regarded as the most inconsistent element, indicating that it is smaller than its average values. step ii: inconsistency adjustment step 1: estimate the possible proper value of a14 using the estimating formula (9), ã14 = ā14 ( ā14 a14 ) 2 4−2 = 1 · ( 1 1/8 ) = 8 step 2: test the consistency of the revised matrix a by replacing the inconsistent elements a14 and a41with the estimated values 8 and 1/8. the revised matrix passed with cr=0<0.1. the identified inconsistent data and the estimated value are the same as the ones in [20], but the proposed method is faster to find the inconsistent data and estimate the values. 5 conclusions in this paper, we proposed a geometric mean induced bias matrix (gmibm), which is only based on the original matrix and independent to the way of deriving the priority weights, to identify the cardinal inconsistent data in the judgment matrix. the inconsistent data identification process includes two major steps, namely, inconsistency identification and inconsistency adjustment. the inconsistent data can be easily and quickly identified by observing the data with the largest value(s) deviating from 1 in the induced bias matrix c. besides, the identified data can be estimated by the estimated formula. two examples are used to illustrate the proposed model. the results show that the proposed model is easier and faster to identify and adjust the inconsistent data than existing models. acknowledgments this research has been partially supported by grants from academic degree programs construction at southwest university for nationalities (#2012xwd-s1201), and grants from the national natural science foundation of china (#70901015 and #70921061), the fundamental research funds for the central universities and program for new century excellent talents in university (ncet-10-0293). no additional external funding received for this study. the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. bibliography [1] tufekci, s., wallace, w.a, the emerging area of emergency management and engineering,ieee transactions on engineering management, 1998, 45(2):103-105, 1998. 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[21] ergu, d., kou, g., peng, y., shi, y., a simple method to improve the consistency ratio of the pairwise comparison matrix in anp, european journal of operational research, 213(1):246–259, 2011. international journal of computers, communications & control vol. i (2006), no. 2, pp. 41-51 a new technique for texture classification using markov random fields mauricio gomez, renato a. salinas abstract: this paper proposes, applies and evaluates a new technique for texture classification in digital images. the work describes, as far as possible in a quantitative way, the concept of texture in digital images. furthermore, we developed an innovative model that allows classifying and characterizing texture in digital images, to be used as a useful tool in noninvasive inspection of visual surfaces. the proposed methodology extracts the statistical order from an image of texture. the extraction of the high statistical order has been made using as a tool markov random fields. the backpropagation neural net is used for designing a classification module that will serve to test the performance of the configuration histograms, which are based on the statistical order. furthermore, the research suggests the evaluation of the proposed technique from a qualitative perspective. keywords: texture, backpropagation, configuration histograms, classification, markov random fields 1 introduction digital image processing is a current subject of research that has being developed for a long time. of all the invested efforts, multitude of applications have aroused in quite diverse fields such as industrial, medical, spatial, etc. where very different tasks are made, such as: codification, edge detection, color processing, etc. we are therefore, in front of a subject in constant growth and expansion, that it is giving rise to a high number of projects and publications. one of the characteristics that are almost always present in an image is the texture, which generally can be associated to different elements from the scene. in order to be able to apply the concept of texture to the digital analysis of images a quantitative characterization of the texture is required. nevertheless, it does not exist a precise definition nor a mathematical expression for the quantitative description of this property. for the analysis of the texture, there exist three widely used formulations in image processing, they are: statistical [1], structural and spectral [2]. the statistical formulation indicates if an image is smooth, rustic, granulated, etc. [1]. the structural technique, on the other hand, indicates the primitive features that exist in the image, such as regularity of parallel lines; and the spectral technique is based on the energy properties and it is used mainly to detect global regularity in an image, indicating small peaks of high energy in its spectrum [2]. these techniques can used separately or jointly to detect different textures. the detection of textures has wide spectra of applications, mainly in the following areas: • medicine: in several types of images (x-rays, ultrasound, magnetic resonance, etc.); the properties of the texture are important for the diagnosis. cancer frequently is characterized by analysis of texture in different types of medical images; also techniques of texture have been applied satisfactorily for the detection of abnormalities in mammographic images [3]. • remote sensors: numerous formulations for recognition of textures in remote sensors have been proposed. their applications include: land classification, cloud classification, recognitions of seismic patterns, etc. copyright c© 2006 by ccc publications 42 mauricio gomez, renato a. salinas • industrial inspection: in industrial processes, the detection of texture in defective manufactured products or natural materials is of crucial importance. the manual inspection frequently is tedious and laborious, for that reason, the automation is very useful [4]. however, none of these classic descriptions leads to a quantitative measurement universally accepted of texture. due to this difficulty, research in this field continues being interesting until a method be obtained that is satisfactory for most of the applications. this research is an attempt to describe in a quantitative way, the concept of texture in digitized images. furthermore, it develops an innovative model that allows to classify and to characterize textures in digitized images, and it can be used as a useful tool in noninvasive visual inspection. 2 models of the texture in the image processing literature, texture is often defined as the spatial interaction between the values of pixels. that is to say, the texture analysis attempts capturing its visual characteristics in an analytical way, in order to be able to model mathematically those spatial interactions. modeling successfully the visual characteristics, we will be able to associate and to discriminate different textures analytically. this type of analysis will require of a number and type of textures previously known. this model only needs to capture sufficient textural characteristics on a training set to classify the texture, however, the procedure is best adapted for textures that belong to the training set. of these models of understanding of texture, there are four of them that stand out in the literature of digital image processing. statistical models of texture: it is based on a set of features to represent the characteristics of the texture of an image. those features are contrast, correlation, entropy, etc. they are usually derived from “measurement of the gray level of the image”, “it differentiates from the values of gray” or “cooccurrence matrix” [1]. the characteristics are selected in heuristic form; nevertheless, an image similar to the analyzed one cannot be recreated using some measurement of the set of features. structural models of the texture: some textures can be seen as two-dimensional patterns, composed of a set of primitives or sub-patterns, which are organized according to a certain rule of positioning. examples of such textures are: brick walls and mosaics; the primitives used are areas of constant gray level, lines, curves and polygons. the correct identification of those primitives is quite difficult. however, if the textures primitives are identified completely, then it is possible to recreate the texture from the primitives. a work using a structural model is indicated in [4]. models of signal processing for the texture: psychological researchers have given evidence that the human brain analyzes the frequency of the images. the texture is specially fit for this type of analysis due to its properties. most of the techniques try to process certain characteristics from filtered images, which are used in tasks of classification and segmentation. among the methods we can mention: filters in the spatial domain, fourier analysis, wavelets and gabor filters. works using this type of formulation are given in [5]. stochastics models of the texture: a texture is assumed to be the realization of a stochastic process, which is governed by some parameters. the analysis is executed, defining a model and considering the parameters. this way, the stochastic processes can be reproduced from the model and associated to the parameters. the estimation of the parameters can serve to classify and to segment textures. this type of model offers a good possibility to recreate realistic examples of natural textures. stochastic formulation of the texture is used in [6]. a new technique for texture classification using markov random fields 43 3 our model of texture as seen previously, there are several problems associated with modeling texture. a key factor in our model of texture analysis is that it be able to distinguish between different textures. conventional techniques have been based on prior knowledge of the number and types of textures that are desired to analyze. in our model we shall use the same philosophy of analysis. therefore, we need to construct a model that can take a texture and capture the totality of the characteristics that distinguish that group of textures. but, in which way we can capture the real characteristics that distinguish a group of textures? and, which will be our approach in the direction of constructing such characteristics? we believe that an understanding of the statistical order applied to images of digitized textures will allow us to capture the real characteristics that distinguish natural textures. we know that the statistical understanding of the texture was proposed by julesz as early as 1962. the statistical order in the understanding of the texture, applied to digital processing of images has been used in [1], where second order statistics are used to define 14 types of textural features from the cooccurrence matrix. in the texture models that use the auto-models and multilevel logistic models also use second order statistics; nevertheless, they have not provided a base of understanding applicable to natural textures, although in [7] modest results were reached using a gaussian model of markov random field that use precisely second order statistics. models that have managed to recreate natural textures using several multiresolution filters do not use third or higher statistical orders, and even more, they become indefinite (ill-defined) if the election of the filters is globally optimal for all the analyzed textures. julesz in 1962 had already indicated the existence of textural information in the high statistical order. in such a way, starting from the knowledge already generated and using the approach of the human visual perception proposed by julesz, is that we have raised our hypothesis applied to textures in digitized images, which establishes that to model textures, models of different statistical orders are required and for it we have chosen the markov random fields as the basis of our model of texture due to its flexibility in defining the statistical dimension and the order. 4 markov random fields the theory of the markov random fields (mrf) provides suitable tools for us to model context dependent entities such as pixels or other characteristics of spatial correlation [8], [9]. markov random fields are defined with respect to a system of neighborhoods. the mathematical interpretation of the model is defined with respect to the corresponding set of cliques. in this research, a systematic method for the total extraction of the set of local cliques has been used from any system of neighborhood in which the markov random field can be defined. the fundamental property of a mrf is that given a point j on lattice, the probability of that point, given the values of all the other points of the lattice, is the same as the probability of that point j, given only the values of the neighboring points to j [8, 9]. in other words, the mrfs are characterized by a conditional probability function defined with respect to a system of neighborhoods. we denote a set of sites on a lattice by s and the system of vicinities on s as g = {gs, s ∈ s}, where g is the set of neighbors for s so that gs gs ⊂ s, s /∈ gs. given the variable xs in site s with valuexs the function of conditional probability of a markov random field with its respective system of vicinity g is defined by the theorem of hammersley and clifford [9] as: p(xs = xs|xr = xr, r ∈ gs) = 1 zs exp{−∑ c vc(x)} (1) where zs is a constant and v c is a potential function defined on clique c. the sum is over all cliques in the local set of cliques cs. 44 mauricio gomez, renato a. salinas 1 2 0 3 4 1 2 3 4 0 5 6 7 8 1 2 3 4 5 6 0 7 8 9 10 11 12 figure 1: three different neighborhoods. level 10 ? ? ? ? ? ? ? ? ? level 21 2 3 4 5 6 7 8 ? ? ? ? ? ? ? r r j r r level 32 4 3 4 5 5 6 7 7 8 7 8 ? ? ? ? level 44 5 7 8 figure 2: tree diagram for the 3x3 neighborhood. the theorem of hammersley and clifford, indicates that for a system of neighborhoods is necessary that s ∈ gr ⇐⇒ r ∈ gs. three different neighborhoods are shown in figure 1. given a system of neighborhoods g, a clique is a set c ⊆ s so that s, r ∈ c, s 6= r, then s ∈ gr . that is, all the pairs of different sites in a clique are neighbors. in such a way that the local set of cliques for site s is defined as: cs = {c ⊆ s : s ∈ c} (2) the method used for the extraction of the local set of cliques from a system of neighborhoods of a mrf, is based on a structure of a tree diagram [10]. the root of the tree represents only a site, whereas the branches in the first level represent all the pairs of connections between the site and each one of the components of the neighborhoods. the other branches of the highest levels represent connections of high order that form more complex cliques . each level in the tree diagram will be represented by a node, which is indicated by a number within the neighborhood previously defined. for example, the tree diagram for the neighborhood of the figure 1.b is shown in figure 2. 5 characterization of texture using order statistics in order to characterize the texture of a digitized image, we will use a vector called of directional homogeneity [10], which considers the relation between the pixel corresponding to a site s and all the remaining pixels of neighborhood gs. the relation to be established between the site and the other pixels of the neighborhood will depend on the type of clique and the statistical order used. figure 1 shows different types from neighborhood with site s. let us suppose that x is a random field of the class described in [8, 9], with a space of discrete range z = {zk, k = 1, ..., m}. let us also suppose that we have an instance x of this one random field that a new technique for texture classification using markov random fields 45 v1 u2 v2 u1 s u3 v4 u4 v3 figure 3: site s and its neighborhood. can be used for the estimation of parameters of the distribution. figure 3 shows a site (i, j) and its system of neighborhood with the notation employed. furthermore, we define as homogeneity function the function i such that: i(z1, ..., zk) = {−1, if z1 = z2 = ... = zk; 1, otherwise (3) this homogeneity function, for the case of cliques of first order will be defined as: i(s) = {−1 if s = zm; 0, otherwise (4) using the terms defined until now, we can express the potential functions of a gibbs distribution, v (s, e, θ ) = φ (s, e)t θ (5) where is the vector of parameters and the vector that we will call of directional homogeneity. for example, for the neighborhood of figure 3 the component of the vector , using the tree of cliques (see figure 2) is defined as: φ3 =    (i(s, u2, v2) + i(s, u4, u3) + i(s, u1, v4)), (i(s, u4, v3) + i(s, u2, u3) + i(s, u1, v1)), (i(s, u2, v1) + i(s, u1, u4) + i(s, u3, v3)), (i(s, u1, u2) + i(s, u4, v4) + i(s, u3, v2))    (6) the set of components φk of vector φ represents the different statistical order extracted from the image, where k is the statistical order with k ∈ {1, 2, 3, 4} for a neighborhood of second order like the one shown in figure 3. 6 direct measurement of the statistical order this section presents our proposal to characterize digitized images of texture that makes use of the properties of vector φ in each point of the digitized image. taking the expressions 3, 4and 5 for a second order neighborhood (figure 3) and applying them to the calculation of the components of vector φ , we see that the values that can take components are the following: φ1 ∈ {0, 1} (7) φ2 ∈ {−2, 0, 2} (8) φ3 ∈ {−3,−1, 1, 3} (9) φ4 ∈ {−1, 1} (10) of the possible values that can take different components φk , we establish equivalence between the value and its respective binary, ternary or tetranary value, as corresponds. table 1 illustrates some examples of equivalences. 46 mauricio gomez, renato a. salinas table 1: equivalence between the value and ternary number. value ternary number -2 0 0 1 2 2 figure 4: brodatz textures used in our experiments. top row: d22, d103, d105 and d79; bottom row: d76, d37, d5 and d28. now, our interest is only in the values that take the components of vector φ , that is why we will work with a decimal number, instead of considering its vector array. this way, given a digitized image of texture and applying the different statistical orders through the different cliques used, we will obtain histograms of frequency versus directionality, which will be different and particular for each of the analyzed textures. 7 classification of textures using histograms of configurations the process of texture classification will be based on the histograms of configurations proposed in the previous section. in the present work we have used a set of 8 classes of textures that have been obtained from the album of textures of brodatz [11]. each image has a dimension of 640x640 with 256 gray levels (8 bits/pixel). the images of textures used are shown in figure 4. for each of these images, 32 sub-images of 64x64 pixels have been extracted. for each sub-image, the histogram of configurations has been computed. in figures 6, 7 and 8, can be seen some of those histograms. the form in which the 32 sub-images have been extracted is explained in a scheme (figure 5) that represents the 32 sub-images taken from one of the images shown in figure 4. the white squares represent the 32 sub-images and the dark background represents the greater image of the texture (figure 5). images with an asterisk have been taken to construct a characteristic vector, which will be used to train the a new technique for texture classification using markov random fields 47 figure 5: layout employed to select the training and validation subimages. each cell represents a subimage, and every cell containing an asterisk is used for training. figure 6: texture d22 and its corresponding configuration histogram. classification module. this procedure has been performed with the eight images of textures shown in figure 4. the sub-images without asterisk in figure 5 are used to test the performance of the texture classification module. the architecture that has been developed for classification of the texture consists of four basic blocks (see figure 9): a) extraction of characteristic vector. the purpose of this block is to construct a vector made of the frequencies of the histogram of configurations used; b) classification: the objective of this block is to classify the different characteristic vectors, that is to say, the information contained in the digitized input image (texture). for this block it has been proposed using a backpropagation (bp) neural network; c) decision: this block makes the final decision from data generated by the classification block; d) data base: it stores parameters and models to control adaptively the operation of the various blocks. once determined the configuration histograms for different statistical orders for the 32 sub images and for each texture category, each configuration histogram is evaluated with respect to performance. 48 mauricio gomez, renato a. salinas figure 7: texture d103 and its corresponding configuration histogram. figure 8: texture d105 and its corresponding configuration histogram. a new technique for texture classification using markov random fields 49 extraction of characteristic vector classification decision 6 data base figure 9: block diagram of the proposed system. table 2: proposed binary codes for the seven output nodes. texture 1st 2nd 3rd 4th 5th 6th 7th d22 0 0 0 0 0 0 0 d5 0 0 0 0 0 0 1 d79 0 0 0 0 0 1 0 d76 0 0 0 0 1 0 0 d28 0 0 0 1 0 0 0 d37 0 0 1 0 0 0 0 d105 0 1 0 0 0 0 0 d103 1 0 0 0 0 0 0 the classification module, is built using a bp neural net that is trained with 16 sub images and validated with the remaining 16 unused sub images. a bp neural net is trained with a predefined set of input-output pairs. each input pattern (vector) is applied to the input layer, and it propagates through the successive layers until it generates a given output. this current output is compared against the target output to obtain the so called error vector. these errors are back propagated, beginning at the output layer and then propagated to the hidden layers until the input layer is reached. based upon an error minimization procedure, the connection weights of each neuron are modified, in such a way as to reduce the final error the next time the same input pattern be presented to the network. for this research, a bp neural net with 3 layers has been used. the number of nodes of the input layer is the same as the number of components of the characteristic vector. for the hidden layer, from 12 to 16 nodes have been used, and finally, for the output layer, 7 neurons with proper codification have been employed. table 2 shows the proposed code for the seven output nodes. the bp neural net has been programmed and trained using matlab r©. in particular, two algorithms have been evaluated for weight modification: gradient descent (traingd) and levenberg-marquardt (trainlm). 50 mauricio gomez, renato a. salinas table 3: results using a 3x3 neighborhood and second order histograms. texture d22 d103 d105 d79 d76 d37 d5 d28 total d22 16 d103 14 d105 2 11 2 1 d79 3 12 1 d76 2 13 2 2 d37 3 16 d5 1 11 3 d28 1 2 10 % 100 87.5 68.8 75 81.3 100 68.8 62.5 80.47 table 4: results using a 5x5 neighborhood and third order histograms. texture d22 d103 d105 d79 d76 d37 d5 d28 total d22 16 1 d103 14 d105 1 13 1 d79 12 d76 2 13 1 d37 3 16 d5 1 2 13 1 d28 1 3 14 % 100 87.5 81.3 75 81.3 100 81.3 87.5 86.72 8 experimental results using histograms of configurations table 3 shows the classification results obtained using a 3x3 neighborhood and histograms of configurations of second order statistics. in this case the classification module was built with 16 hidden layer neurons, and trained for 500 epochs. later on, the performance was evaluated with 5x5 neighborhoods and third order histograms. table 4 shows the results of this experiment. from the above results, we can confirm that the histograms of configurations are sufficiently representative of the textures analyzed using different statistical orders. furthermore, we achieve a classification rate of 86.72% while employing third statistical order. 9 summary and conclusions a new texture classification method has been developed, based on the extraction of the statistical order from a digitized texture image. this later operation has been performed using the set of local cliques from a neighborhood. the use of histograms of configurations in the process of texture classification has been quite successful, achieving a classification rate of around 87%. the decision of employing a bp neural network was also adequate for the classification. best results a new technique for texture classification using markov random fields 51 were reached when training the neural net with the levenberg-marquardt method. the proposed classification architecture requires of just a few features, thus achieving a reduction in training time and smaller computational efforts during the classification stage. the work developed so far constitutes the initial stage of a series of applications and works related to texture and non-destructive visual inspection of materials. references [1] haralick r., shanmugam k. and dinstein i., textural features for image classification, ieee trans. on systems, man, and cybernetics, vol. smc-3, no. 6, pp. 610-621, 1973. [2] hsin h., texture segmentation using modulated wavelet transform, ieee trans. on image processing, vol 8. no. 7, 2000. [3] hernández l., torrealba v. and reigosa a., clasificación automática del carcinoma de la mama, mediante un sistema de reconocimiento basado en redes neuronales, memorias ii congreso latinoamericano de ingeniería biomédica, la habana, cuba, 2001. [4] mery d., da silva r., calôva l. and rebello j., detección de fallas en piezas fundidas usando metodología de reconocimiento de patrones, 3rd panamerican conference for nondestructive testing, rio de janeiro, brazil, 2003. [5] bader d., jájá j. and chellapa r., scalable data parallel algorithms for texture synthesis using gibbs ramdon fields, ieee transactions on image processing, vol. 4 no. 10 , pp. 1456-1460, 1995. [6] pun c. and lee m., rotation invariant texture classification using a two stage wavelet packet features approach, iee proc. vis. image signal process, vol. no148, no6, pp 422-428, 2001. [7] manjunath b., simchony t. and chellappa r., stochastic and deterministic networks for texture segmentation, ieee transactions on acoustics speech and signal processing, vol. no6, pp. 10391047, 1990. [8] li s., modeling image analysis problems using markov random fields, handbook of statistics, vol. no 20, 2000. [9] li s., markov random fields models in computer vision, proceedings of ieee computer society conference on computer vision and pattern recognition, pp 866-869, 1994. [10] gómez m., modelación y clasificación de texturas utilizando campos aleatorios de markov, master thesis, electrical engineering, universidad de santiago de chile, santiago, chile, 2004. [11] brodatz p., textures. new york: dover, 1966. renato salinas, mauricio gomez universidad de santiago de chile electrical engineering department ave. ecuador 3519, santiago, chile e-mail: rsalinas@lauca.usach.cl, mauriciogomez@esfera.cl int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 101-112 an optimal task scheduling algorithm in wireless sensor networks l. dai, y. chang, z. shen liang dai, yilin chang, zhong shen state key laboratory of integrated service networks xidian university xi’an 710071, china e-mail: ldai1981@gmail.com, ylchang@xidian.edu.cn, zhshen@mail.xidian.edu.cn abstract: sensing tasks should be allocated and processed among sensor nodes in minimum times so that users can draw useful conclusions through analyzing sensed data. furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. to minimize the execution time (makespan) of a given task, an optimal task scheduling algorithm (otsa-wsn) in a clustered wireless sensor network is proposed based on divisible load theory. the algorithm consists of two phases: intra-cluster task scheduling and inter-cluster task scheduling. intra-cluster task scheduling deals with allocating different fractions of sensing tasks among sensor nodes in each cluster; inter-cluster task scheduling involves the assignment of sensing tasks among all clusters in multiple rounds to improve overlap of communication with computation. otsa-wsn builds from eliminating transmission collisions and idle gaps between two successive data transmissions. by removing performance degradation caused by communication interference and idle, the reduced finish time and improved network resource utilization can be achieved. with the proposed algorithm, the optimal number of rounds and the most reasonable load allocation ratio on each node could be derived. finally, simulation results are presented to demonstrate the impacts of different network parameters such as the number of clusters, computation/communication latency, and measurement/communication speed, on the number of rounds, makespan and energy consumption. keywords: wireless sensor networks; divisible load theory; multi-round task scheduling. 1 introduction & motivation owing to the wireless sensor network node with limited energy, the task should be completed within the shortest possible amount of time. divisible load theory [1] provides an effective solution to wireless sensor networks for task scheduling [2-5]. different from other heuristic solutions of task scheduling problem in wireless sensor networks [6, 7], this scheme can get not only the optimal solution, but also the analytic solution, thus ensuring the consistency of the results of scheduling. divisible load theory has been intensively studied in the past decades. it is mainly concerned with obtaining an optimal partitioning and scheduling strategy for a given task such that it can be processed in the shortest amount of time. divisible load scheduling algorithm can be divided into single-round scheduling algorithms [2-5] and multi-round scheduling algorithms [8, 9]. single-round scheduling algorithm is relatively simple, but its computation and communication overlap are rather poor, and the extra overhead is relatively large. single-round copyright c⃝ 2006-2011 by ccc publications 102 l. dai, y. chang, z. shen scheduling algorithms were applied to wireless sensor networks in [2-5]. although the authors derived closed-form solutions to obtain the optimal finish time, the network topology discussed in those papers is single-level tree structure. while in wireless sensor networks, as compared with the single-level tree structure, clustered structure (multi-level tree structure) has a great of advantages [10]. multi-round scheduling algorithm has the characteristics of better computation and communication overlap, thus properly reducing the scheduling overhead. however, it is more difficult to analyze, so fewer results of multi-round scheduling algorithm are available and the existing multi-round scheduling algorithms are designed based on grid computing environments. therefore, we present a multi-round task scheduling algorithm (otsa-wsn) in clustered wireless sensor networks. the goal of this algorithm is to minimize the overall execution time (hereafter called makespan) and fully utilize network resources, by finding an optimal strategy of splitting the original tasks received by sink into a number of sub-tasks as well as distributing these sub-tasks to the clusters in the right order. 2 optimal scheduling algorithm wireless sensor networks construct clusters several times in its life cycle. each cluster will have a set-up phase and a steady-state phase[10]. we discuss our multi-round task scheduling algorithm in a steady-phase phase. the original tasks received by sink are divided into two stages: inter-cluster task scheduling and intra-cluster task scheduling. first, inter-cluster task scheduling partitions the entire tasks into each cluster, and then the sub-tasks in a cluster is assigned to each intra-cluster sensor node by intra-cluster task scheduling. to improve overlap of communication with computation, inter-cluster task scheduling assigned sensing tasks among all clusters in multiple rounds. according to divisible load theory, to remove performance degradation caused by communications interference, sink sends each round’s tasks to cluster heads sequentially. after each cluster finishing its tasks and fusing the data, the cluster heads also send this round’s results to sink sequentially. that in every moment only allows sink node sends sub-tasks to a cluster head, or a cluster head return fusion data to the sink. 2.1 intra-cluster task scheduling in order to ensure that the tasks are processed orderly, sink allocates the tasks to each cluster according to the task-processing rate of each cluster, which guarantees that the task execution time of all clusters in each round remains the same. definition: the task-processing rate of a cluster is the average rate the cluster takes to complete the intra-cluster tasks, that is the number of tasks dealt (measurement and reporting data) per unit of time. assuming there are k nodes in a cluster, according to divisible load theory, the cluster’s task-processing rate is as follows: s = (1 + k∑ i=2 i∏ j=2 hj)/(1/s1 + 1/b1) (1) where hi = (1/si−1)/(1/si + 1/bi), i = 2, · · ·, k, where si is node i ’s measuring rate, in this case, the number of tasks completed per unit of time, bi states node i ’s transmitting rate to cluster head, in this case, the number of tasks transmitted per unit of time. an optimal task scheduling algorithm in wireless sensor networks 103 measurement data reporting n1 n2 1 1/ sα 2 2/ sα 2 2/bα 3 3/bα 1 1/bα 3 3/ sα / k k bα/ k k sα t n3 nk figure 1: timing diagram for in-cluster task-processing proof: αi is defined as the fraction of sensing task assigned to node ni by the cluster head. it is assumed that every node will be assigned non-zero task, i.e., 0 < αi < 1, and the task for all nodes in this cluster sums to 1. by definition we can see: k∑ i=1 αi = 1 (2) so, the time for node ni measuring its tasks and reporting results to cluster head are αi/si and αi/bi, respectively. one cluster head and a set of sensor nodes constitute a cluster where each node is able to communicate with the cluster head directly. sensor nodes measure data from surroundings related to the given task, and then report these data to the cluster head. to complete certain amount of sensor readings in minimum finishing time, the sensing task should be allocated to each sensor node and scheduled to avoid transmission conflicts and idle time on the cluster head. fig.1 illustrates the timing diagram for a set of sensor nodes, indexed from n1 to nk, in one cluster. from fig.1, it can be observed that there is no time gap between every two successive nodes because the divisible workload can be transferred in the cluster. all sensor nodes start to measure data at the same time. once the previous node finishes transmitting data, the other one completes its measuring task and starts to report its data. as a result, the proposed timing diagram minimizes the finish time by scheduling the measuring time and reporting time of each senor node. moreover, since the intra-cluster scheduling tries to avoid the transmission conflicts at the cluster head, energy spent on retransmission are conserved. the working time of a sensor node can be divided into two parts: measuring time and reporting time. in fig.1, one can set up the following corresponding recursive load distribution equations: αi−1/si−1 = αi/si + αi/bi, i = 2, 3, ...k (3) rewriting the above set of equations as: αi = hiαi−1 (4) where hi = (1/si−1)/(1/si + 1/bi), i = 2, 3, ...k using fig.1, eq.(2) and eq.(4), the largest workload for the sensor node can be solved as: α1 = 1/(1 + k∑ i=2 i∏ j=2 hj) (5) 104 l. dai, y. chang, z. shen similarly, the workloads of other sensor nodes given by eq.(4) can be obtained: αi = i∏ j=2 (hj)/(1 + k∑ i=2 i∏ j=2 hj) (6) fig.1 indicates that when the node with the largest measuring data finishes transmission, the local cluster completes its assigned sensing task. then, the finish time of measuring and reporting data for the cluster is: t = (1/s1 + 1/b1)/(1 + k∑ i=2 i∏ j=2 hj) (7) we can get the task-processing rate of the cluster: s = (1 + k∑ i=2 i∏ j=2 hj)/(1/s1 + 1/b1) (8) it’s not difficult to see that in the homogeneous network environment, every cluster have the same parameters, and their task-processing rate is: s = (1−hk)/(1/s + 1/b)(1−h) (9) where h = (1/s)/(1/s + 1/b) theoretical analysis and simulation in this paper are based on leach protocol[10] family (inter-cluster one-hop topology). for the multi-hop networks, namely, the multi-layer tree structure, we can do the calculations on parent node in the tree using eq.(3) eq.(8). according to divisible load theory, the conflict-free scheduling strategy for each parent node is calculated in order to save energy and prolong network lifetime. in wireless sensor networks, cluster head is responsible for data exchange for sink and incluster nodes. in order to reduce energy consumption caused by transmitting redundant data, lower latency and prolong the survival period, cluster head needs fuse the data [11]. a new estimation method for data fusion information utilization constant is introduced [5] in this paper. information utilization constant is based on a technique of information accuracy estimation. through estimating accuracy of information, cluster head can know the approximate percentage of data fusion. 2.2 inter-cluster task scheduling the following notations will be used throughout this paper: • wtotal: total amount of workload that resides at sink; • wji :number of tasks assigned to cluster i in round j. • si: rate of cluster i’s task-processing, that is, the tasks processed in a unit time. • bi: downlink communication speed sink to cluster head i; b′i: uplink transmission rate cluster head i to sink, that is, the number of tasks transmitted per unit time. • tj: processing time of round j. • wj: size of the total load dispatched during round j. an optimal task scheduling algorithm in wireless sensor networks 105 task allocation reporting the fuzed data measuring and reporting data round j+1round j communication/co mputing latency wj,1/b1 wj+1,1/b1’ wj,i/bn wj+1,n/bn α2 φiwj,1/b1’ φiwj,n/bn’ β3 β3’ cn c3 c2 wj,1/s1 wj+1,1/s1 wj,2/s2 wj+1,2/s2 wj,n/sn wj,3/s3 wj+1,n/sn wj+1,3/s3 figure 2: process of multi-round scheduling algorithm • φi: information utility constant of cluster head i. thus, the entire load for cluster i in round j can be sensed (measured, transmitted) is wji/si. in round j, the time for sink sending tasks to cluster i and for cluster head i sending the fused data are wji/bi and φiwji/b′i, respectively. in practical wireless sensor network environment, communication and computing latency caused by pre-initialization are inevitable. suppose affine cost parameters are as follows: • αi: computing latency of cluster i, that is, the time taken for initialization. • βi(resp. βi′): communication latency incurred by sink to initiate a data transferring to cluster head i. (resp. start-up time for communication from the cluster head i to sink). fig.2 describes the procedure of sink dispatching the tasks to each cluster, each cluster measuring and reporting data, as well as cluster heads reporting the fused data to sink. in this paper, we assume that there are total n clusters in a stable stage, where ci, i = 1, · · ·, n represents each cluster. as the computational cost of each cluster remains the same, so there are: αi + wji/si = tj, j = 1, · · ·, m −1 (10) where tj is only related to the number of rounds j, and m is the optimal scheduling round. the sum of tasks allocated to every cluster in round j is equal to the tasks in round j: wj = n∑ i=1 wji (11) from the eq.(10) and eq.(11) we can compute: wji = aiwj + bi (12) where ai = si/ n∑ k=1 sk, bi = (si/ n∑ k=1 sk) n∑ k=1 (skαk)−siαi. 106 l. dai, y. chang, z. shen as shown in fig.2, in order to fully utilize the bandwidth and avoid cluster waiting among different rounds, sink must send the tasks allocated in round j + 1 to all the cluster heads and receive the fused data from all the cluster heads in the round j, before cluster n finished the tasks in round j. when the time for intra-cluster nodes processing the tasks in round j is exactly equal to the sum of the time for sink sending sub-tasks to all cluster heads in round j +1 and receiving the fused data from all the cluster heads in round j, then, the best bandwidth utilization is achieved, that is: n∑ i=1 [(wj+1,i/bi) + βi + (φiwj,i/b ′ i) + βi ′] = tj (13) utilizing eq.(10), eq.(12) and eq.(13), we have: wj+1 = wj ∗ 1−φi n∑ i=1 (si/b ′ i) n∑ i=1 (si/bi) + n∑ i=1 (siαi) n∑ i=1 si n∑ i=1 (ai/bi) − n∑ i=1 (βi + bi/bi + βi ′ + φibi/b ′ i) n∑ i=1 (ai/bi) (14) simplify the eq.(14) as follows: wj = θ j(w0 −η) + η (15) where θ = [1−φi n∑ i=1 (si/b ′ i)]/ n∑ i=1 (si/bi), η = n∑ i=1 (siαi) [ n∑ i=1 (si/bi)+φi n∑ i=1 (si/b′i)]−1 − n∑ i=1 si n∑ i=1 (βi+bi/bi+βi ′+φibi/b ′ i) [ n∑ i=1 (si/bi)+φi n∑ i=1 (si/b′i)]−1 also the total load is equal to the sum of the tasks allocated in all rounds: m−1∑ j=0 wj = wtotal (16) the following constraint relations can be obtained: g(m, w0) = (w0 −η)(1−θm)/(1−θ) + mη −wtotal = 0 (17) the problem of minimizing the total task finish time in scheduling algorithm[8] is described below: ex(m, w0) = m−1∑ j=0 tj + 1 2 n∑ i=1 [(w0,i/bi) + βi + (φiwm−1,i/b ′ i) + βi ′] (18) the above minimization problem can be solved through the lagrange multiplication. w0 = (1−θ)/(1−θm)(wtotal −mη) + η (19) after solving w0 and m, the sizes of all the chunks wj,i can be obtained using eq.(12) and eq.(15). an optimal task scheduling algorithm in wireless sensor networks 107 3 wireless energy use in this section, the energy model of the otsa-wsn algorithm is presented in detail and the equations of energy consumption of individual sensor nodes are derived. the model is based on first-order radio model [10]. there are three kinds of energy consumption in the wireless sensor network: measurement, data fusion, and communication. because nodes in the sensor network cooperate with each other via data transmission. energy consumption of communications exist in sensor nodes, cluster heads and sink. it is not necessary for cluster heads and sink to perform any sensing task. thus, there is no energy cost for cluster heads due to the measurement of these nodes, while the additional energy cost of cluster heads attributes to data fusion. the energy to sense, fuse, and transmit a unit sensory data are denoted by es, ep, and etx, respectively. sensor nodes also consume the energy of erx to receive one unit of data. the distance between the sender and the receiver is d. the energy use for each kind of nodes is outlined as follows: energy use for individual sensor nodes j in cluster i: ei,j = αi,j(es + etxd 2), i = 1, · · ·, k , j = 1, · · ·, ni (20) energy use for individual cluster head: ei = αi(erx + ep + φietxd 2), i = 1, · · ·, k (21) energy use for sink: esink = k∑ i=1 αiφietx (22) 4 performance evaluation in the above sections, we have obtained the optimal number of rounds m for a given sensing task, and energy use for individual sensor nodes. in this section, we investigate the effects of three network parameters, such as the number of clusters, computation/communication latency, and measurement/communication speed, on the number of rounds, makespan and energy consumption in the homogeneous network environment. in the simulation, the following energy parameters are adopted: transmitting a unit of sensor reading over a unit distance takes etx=200nj, receiving one unit of sensor reading consumes erx=150nj, measuring one unit of sensor reading needs es=100nj, fusing one unit of observation consumes ep=20nj and the distance between the sender and the receiver is d=100m. there are 20 sensor nodes in each cluster. the simulation results are shown in fig.3 to fig.6. firstly, fig.3 plots the m values computed by otsa-wsn versus computation/communication latency when they vary among 0 and 1.0. assume the communication speeds of the uplink and downlink between sink and cluster head are identical, namely, β=β′. as can be seen from fig.3, m decreases with either the communication or computation latency increasing, owing to the reason that fewer rounds may result in less overhead. because when the communication latency and computation latency increase, the tasks allocated for each round will increase to meet the requirement, which makes full use of bandwidth. therefore, the number of rounds will be reduced. next, the makesapn against the number of clusters are plotted in fig.4. assume that transmission rate of all links between nodes is the same, that is, b=b′ = b. in fig.4(a), the value of s 108 l. dai, y. chang, z. shen figure 3: impact of communications and computing latency on the number of rounds is chosen from 4 to 10, while b is fixed to 1.0. this figure shows that measurement speed almost does not affect the makespan because sensing takes a small fraction of the entire execution time. fig.4(b) shows that when the communication speed of sensor nodes increases, the makespan of a given task is reduced. it can be found that the four lines in fig.4(b) converge when the number of clusters becomes large. then, the third simulation is about the energy consumption of intra-cluster sensor nodes. sink and cluster heads are not taken into account because generally, sink has no energy constraint and the chosen cluster heads have the possibly enough energy. the network is configured with 20 clusters. without loss of generality, the intra-cluster sensor nodes in the first cluster are chosen to study the energy consumption, as shown in fig.5. fig.5 presents the energy consumption of all the nodes in the first cluster as given by eq.(20), where the intra-cluster nodes are indexed from 1 to 20. in each case, the energy consumption of sensor nodes monotonically decreases due to the reduced workload. fig.5(a) shows the higher the in-cluster node’s measuring speed, the more evenly the tasks allocated to each nodes, hence the smaller the energy consumption of the nodes. fig.5(b) presents the larger communication speed between nodes, the smaller the energy consumption of the in-cluster nodes. finally, to simulate the otsa-wsn algorithm in the heterogeneous network environments, the measuring speed of intra-cluster nodes is set as the random numbers vary from 4 to 10, and the communication speed of links from 0.4 to 1.0. through 8 experiments, the performance of scheduling algorithms in the heterogeneous network environment is analyzed. fig.6(a) shows the impact of random measuring speed and communication speed on the makespan with the increasing number of clusters in the heterogeneous network environment. fig.6(b) presents the impact of random measuring speed and communication speed on energy-consuming in the order of tasks allocation. compared with fig.4 and fig.5, it can be seen that, the impact of measuring speed on the makespan and energy consumption is less than communication speed. the makespan decreases with the increasing number of clusters, and the energy consumption are reduced in the order of tasks allocation. an optimal task scheduling algorithm in wireless sensor networks 109 (a) (b) figure 4: impact of measuring speed and bandwidth on the makespan 110 l. dai, y. chang, z. shen (a) (b) figure 5: the impact of measuring speed and bandwidth on the energy consumption in in-cluster nodes an optimal task scheduling algorithm in wireless sensor networks 111 (a) (b) figure 6: impact of random measuring speed and bandwidth on the makespan and energy consumption 112 l. dai, y. chang, z. shen 5 conclusions as the wireless sensor network node with limited energy, so the tasks should be completed as quickly as possible, and the network resources should be fully utilized. in this paper, we present a multi-round task scheduling algorithm (otsa-wsn) in clustered wireless sensor networks. the goal of this algorithm 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[11] x. tang, j. xu, optimizing lifetime for continuous data aggregation with precision guarantees in wireless sensor networks. ieee/acm transactions on networking, vol.16, no.4, pp. 904 917,2008. ijcccv4n1draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 1, pp. 65-72 bayesian network classifier for medical data analysis beáta reiz, lehel csató beáta reiz biological research center, central labs, bioinformatics group 62 temesvári krt., hu-6701, szeged, hungary e-mail: beareiz@brc.hu lehel csató babeş bolyai university, faculty of mathematics and computer science 1 kogălniceanu str. ro-400084 cluj-napoca, romania e-mail: csatol@cs.ubbcluj.ro abstract: bayesian networks encode causal relations between variables using probability and graph theory. they can be used both for prediction of an outcome and interpretation of predictions based on the encoded causal relations. in this paper we analyse a tree-like bayesian network learning algorithm optimised for classification of data and we give solutions to the interpretation and analysis of predictions. the classification of logical – i.e. binary – data arises specifically in the field of medical diagnosis, where we have to predict the survival chance based on different types of medical observations or we must select the most relevant cause corresponding again to a given patient record. surgery survival prediction was examined with the algorithm. bypass surgery survival chance must be computed for a given patient, having a data-set of  medical examinations for  patients. keywords: bayesian networks, classification, medical data analysis, causal discovery. 1 introduction in this paper we analyse tree-like bayesian network (bn) implementation for medical data classification. we consider a general case for data attributes, where the observations can be both continuous and discrete, and general to almost all medical data missing observations also can occur. we aim to establish causal relationships between variables representing medical examinations. whilst interested in a good classification performance, we also want to interpret and analyse the predictions in terms of the encoded causal relations. the database we used consists of  medical examinations of  people containing both discrete and continuous observations. the task is thus to predict the surgery survival chance based on the available data – the medical examinations [1, 2], and analysis of impact of a specific examination on patient survival. partial observability characterises the database, the number of missing values is . our aim is to predict target variables value – survival – for a particular patient and to obtain the “most relevant” variables affecting the output of the classifier. of equal interest is to analyse the decisions in terms of encoded relationships between data attributes. this analysis is usually done to provide support for physicians. we encode the dependencies between the class variable and the observations using a tree with root node the class variable. the other attributes are inside the tree with coresponding conditional probability tables “learned” from the data-set. finding the most appropriate structure is an extremely difficult task. we reduce the complexity of constructing the tree of immediate causal relationships between copyright © 2006-2009 by ccc publications 66 beáta reiz, lehel csató class variable and observations [3]. a tree-like bayesian network structure was inferred from the data [4], where the root of the tree is the class variable and remaining nodes are attributes. direct causal relations between attributes and class variable were revealed in the first phase of the algorithm, constructing a naive bayesian network. attribute-attribute correlations were searched based on chow-liu’s algorithm in the second phase of the algorithm. in practical situations we also have to face the problem – general to almost all medical data – of missing observations for some patients, meaning incomplete data items; this issue can also be considered in a principled way with a bayesian network. the paper is organised as follows: next we present the bayesian networks, then a stochastic algorithm to extract a plausible network sructures from the data, and we also analise experimental results of applying the algorithm to real data-sets. 2 bayesian networks bayesian networks (bns) [5] are triplets (v,e, p), where (v,e) is a directed acyclic graph (dag) with nodes v , edges e, and a set of probability distributions p, called parameters, whose elements are assigned to the nodes of the graph. the nodes represent domain variables and edges mark direct causal relations between these variables. the network encodes a joint probability distribution function representative to the domain: p(x) = n∏ i= p(xi|par(xi)) where n is the number of domain variables, xi is a node from the bn and par(xi) is the set of xi’s parents. the aciclicity of the graph ensures the product to be finite. we employed a tree-like representation for the topology of bn in order to increase efficiency in class variable estimation and interpretation. in section 3. we describe this algorithm, where we construct a tree in such a way that the root of the tree will be the class variable and the remaining nodes are attributes. direct causal relations encoded by the bn are interpreted as the maximum of mutual respective conditional mutual information [6, 7, 8] between nodes. now we present the necessary information theoretical concepts [9] for our algorithm. we will use the following notations: x and y are random variables defined on probability spaces ωx respective ωy with corresponding distribution functions p(x) respective p(y). we use their joint and conditional probability functions, denoted with p(x,y) and p(x|y) respectively. information theory offers us numerical characterisation of uncertainty in domain variables. uncertainty is measured using the information entropy of the respective variable. information entropy can be understood as the average minimal message length that should be sent on a channel to encode the message and is defined as follows: h(x) = − ∑ x∈ωx p(x)log p(x) mutual information is the quantity of information two random variables contain about each other, defined as: i(x,y ) = ∑ x∈ωx ∑ y∈ωy p(x,y) log p(x,y) p(x)p(y) (1) in next section we present a two-phase tree-like bayesian network structure learning algorithm. the algorithm consists of an extension of naive bayesian structure learning algorithm with inner structure learning for finding causal relations between attributes. bayesian network classifier for medical data analysis 67 3 network topology learning bayesian network classification consists of emphasising the node corresponding to the class variable during inference. as an optimisation of the learning process and inference we construct the network topology such a way to optimise the efficiency of prediction and data attribute impact estimation on target variable. we set out the class variable and in the first phase we’re searching for direct dependencies between attributes and class variable, this way constructing a naive bayesian network. naive bayes classifiers [10, 11] are widely used in classification problems. they are called naive because of the independence assumption of the attributes. although this is strong assumption when facing real data-sets, the naive bayes classification is a powerful tool for its simplicity and often gives convenient results. during the naive bayesian network learning process direct dependencies between class variable and attributes has to be find. dependency relations are interpreted as class variable specifiers, so an edge from x to class variable y means that variable x has information about class variable y . mutual information between class variable and attributes, conditioning on attributes already placed between direct dependencies of class variable, gives the amount of new information the respective attribute has regarding the class variable [12]. considering the problem this solutions means that we choose some medical examinations which we place in the network and exclude the rest of the attributes. this is a strong restriction considering that some examinations are replaced by others in different hospitals. the second phase of the algorithm consists of applying chow-liu algorithm [13] to learn the inner structure of network and reveal attributeattribute correlations. the naive bayesian network is formed of class variable y respective variables x directly linked to the class variable. our next task is to place the excluded attributes in the bayesian network. we use mutual information maximisation to discover the causal relations between attributes from the network and excluded attributes. class variable could be ignored. mutual information maximisation is enough in this case, because dependency now has the meaning of replaceability. we are searching for the excluded attributes that carry almost the same information about class variable as the attributes already placed in the network. before presenting the algorithm, we introduce the notations used in the following: xxx set of attributes not yet placed in the net zzz set of attributes in the net x one attribute from xxx zi an element from zzz y the class variable i(x,y |zzz) conditional mutual information of i(x,y ) mutual information of x and y x and y given zzz our algorithm introduces a threshold parameter – denoted with α – which is the minimum “information” required when putting a new attribute in the network during the naive bayesian structure learning. this parameter controls the number of direct connections between class variable and attributes. the algorithm is presented in algorithm. 1. the threshold parameter α assures the selection of relevant attributes respect to the class variable, controlling the number of direct causal relations of class variable and attributes. the result is a tree-like bayesian network as in figure 2, where the root of the tree is the class variable, and the other nodes are attribute variables. the orientation of edges is from parent to the child, this way minimising the modification of network parameters during a learning step. the algorithm above is deterministic in sense that it generates the same network for the same data all the time. we introduce importance sampling [14] in order to avoid the determinism of the algorithm in case of selection from equal information quantities. the distribution used for sampling is based on mutual information. it has the maximum where the mutual information is maximal. we used two functions during the tests. the first function – denoted f – is the conversion of the 68 beáta reiz, lehel csató algorithm 1 tree-like bayesian network structure learning. 1: place the class variable y in the network 2: zzz = ∅ 3: {naive bayesian structure learning} 4: while i(x, y|zzz) ≥ α1 do 5: x̂ = argmax xxx i(x, y|zzz) 6: place x̂ in the network 7: xxx = xxx − {x̂} 8: zzz = zzz ∪ {x̂} 9: end while 10: {inner structure learning} 11: while xxx 6= ∅ do 12: [ x̂, ẑ ] = argmax xi,zj i(xi, zj) 13: place edge between x̂ and ẑ 14: xxx = xxx − {x̂} 15: zzz = zzz ∪ {x̂} 16: end while figure 1: tree-like bayesian network structure learning. x1 x2 x3 x4 x5 x6 x7 x8 x9 y figure 2: structure of a bn mutual information to a distribution function: f1(x) = i(x,y ) ∑ x ′∈xxx i(x ′,y ) (2) figure 3(b). illustrates what edges are inferred when using importance sampling with function f from artificial data. the generator network for the data is presented on figure 3(a). on the horizontal plane is the adjacency matrix of the graph and the non-zero columns represent the edges. figure 3(a). points the edges of the generator network, and figure 3(b). shows the frequency of learned edges during  tests. the second function – denoted f – uses the exponentiation of the mutual information. it has a β parameter which can be understood as a temperature parameter and it controls the constructed distribution function. the higher this parameter is the higher is the probability of selecting the maximum mutual information. on lower values of β the probabilities of selecting an attribute becomes closer to the uniform distribution. f(x) = exp(β ·f1(x))∑ x ′∈xxx exp(β ·f1(x)) (3) figure 3. shows the histogram of learned edges using the presented approaches and also the generator network topology of data on figure 3(a). in each graph on the horizontal plane is the adjacency matrix of the network topology, and the vertical columns represent the histogram edges. we consider the first bayesian network classifier for medical data analysis 69 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 0 1 child parent ed ge (a) generator network topology 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 0 50 100 150 200 250 child parent ed ge f re qu en cy (b) learned topologies with function f 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 0 50 100 150 200 childparent ed ge fr eq ue nc y (c) learned topologies with function f, β =  1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 0 100 200 300 childparent ed ge fr eq ue nc y (d) learned topologies with function f, β =  figure 3: generator network and histogram of bn edges attribute from the network as the class variable, so it is the root of the constructed tree. this means, there’s no arc from any attribute to the class variable, hence it’s column is  at each point. figure 3(a). represents the generator network topology for the data, hence each edge appears once. the other graphs – on figures 3(b), 3(c), and 3(d) – represent the frequency of edges in the inferred network topologies. although there is a randomness introduced with importance sampling in our first approach, the generated structure is relatively stable through the iterations when learning with function f. the direct causal relations between the class variable and attributes are almost the same during the  tests simulations, differences can be observed only in the causal relations between the attributes, more precisely the differences are on the third level of the tree. there in approximately  cases – out of  – a single edge is placed differently compared to the generator network. a bit more unstable structure (figure 3(c)) can be observed when learning with function f with parameter β = . this is due to the fact, that the separation between lower and higher values of mutual information is more sensitive for lower values of β . figure 3(d). represents the learned structures for β = . one can see that in this case the structure is fully stable, which assures the former statement. in next sections we will analyse the convergence of the learning process and the usage of the constructed network. 4 results in this section we will present the learned topologies in case of real data. to fully settle the bypass problem, we have to perform the binarisation of data. this is due to the very low number of data samples in bypass database considering conditional probability distribution function estimation. we made  test of the algorithm on the fully specified attributes from the database. figure 4. presents the results of these testings. one can observe a high order of uncertainty when learning with function f with β = . this is reduced by the increase of β to . function f highlights almost the same dependency relations as function f with β = , but there is an annoying level of uncertainty in these relations. further analysis of the algorithm meant to check stability of it on real data-set. for this reason leave-one-out method on attributes was used: we eliminated one attribute from the data, and tested the algorithm on the remaining ones. the results are showed on figure 5., where figure 5(a)., figure 5(c). 70 beáta reiz, lehel csató 1 2 3 4 5 6 7 8 9 10 1112 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 child parent ed ge fre qu en cy (a) histogram of edges with function f 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 0 20 40 60 child parent ed ge fre qu en cy (b) histogram of edges with function f, β =  1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 150 childparent ed ge fre qu en cy (c) histogram of edges with function f, β =  figure 4: learned topologies of fully specified attributes from the database and figure 5(e). depicts the histogram of learned edges, and on figure 5(b)., figure 5(d). and figure 5(f). the most frequent learned edges are drawn. figure 5(a). and figure 5(b). depicts the histogram of learned edges respective the most frequent edges learned with function f, β =  for all fully specified attributes from the bypass database. one can see, that there are two crucial attributes in the database, namely the thirrd and seventh, which are central players in attribute dependence relations. next sub-figures – figure 5(c). and figure 5(d). elimination of 3rd. attribute, respective figure 5(e). and figure 5(e). elimination of 3rd. attribute – depicts the learned bns when eliminating these attributes. one can see that most of the dependence relations are stable, although there is a reorganisation between dependencies, when eliminating a crucial attribute. the most observable instability in dependence relations is that the edge  →  becomes an edge  →  when eliminating either the third attribute or the seventh attribute. but when analysing the dependency of  → , respective  → , one can see that their is not so much significant difference between the frequency of the two edges when learning on all attributes. 5 conclusions in this paper we presented a tree-like bayesian network classifier algorithm developed for medical decision making problems and a stochastic algorithm to find the most appropriate structure of the network. we tried two functions for eliminating determinism from the algorithm, with the two functions f and f, defined with eq. 2, respective eq. 3. learned topologies with the presented algorithm and functions were presented both for artificial and real data. in this section we will present results considering the inference, and compare them with logistic regression and svm. table 1. shows the results of efficiency of the presented algorithms compared to logistic regression. comparing the first and second approach we described above the results are surprising. although the high order of uncertainty in some cases, the results of efficiency are similar for all cases. one can observe that the tree-like bayesian networks constructed with the presented algorithm perform better than logistic regression, but support vector machines with linear kernel obtain higher prediction accuracy than bn-s. it has to be mentioned, that although better accuracy on svm, they don’t allow interpretation of predictions, while bns do, and interpretation in case of tree-like bayesian networks, bayesian network classifier for medical data analysis 71 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 150 childparent ed ge fr eq ue nc y (a) histogram of edges all attributes 1 2 3 4 5 6 7 8 9 10 11 12 (b) most frequent edges all attributes 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 0 50 100 150 200 child parent ed ge fre qu en cy (c) histogram of edges loo 3. attribute 1 2 4 5 6 7 8 9 10 11 12 (d) most frequent edges loo 3. attribute 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 0 50 100 150 child parent ed ge fr eq ue nc y (e) histogram of edges loo 7. attribute 1 2 3 4 5 6 8 9 10 11 12 (f) most frequent edges loo 7. attribute figure 5: leave-one-out results on attributes method accuracy bayesian network f .% bayesian network f, β =  .% bayesian network f, β =  .% bayesian network f, β =  .% logistic regression .% svm with linear kernel .% table 1: efficiency of presented algorithms constructed as above, can be done efficiently. as for a summaryy of results it has to be mentioned that the learned structure by the algorithm is generally stable; the interpretation of the results is possible and partial observability is not a problem in case of prediction and interpretation. acknowledgements we acknowledge the program committee of the international conference on computers, communication and control 2008 for the recommendation and also thank for the problem description and the medical database to béla vizvári from department of operations research, eötvös loránd university, budapest. this work was partially supported by the romanian ministry of education and research through grant 11-039/2007. 72 beáta reiz, lehel csató bibliography [1] zs. csizmadia, p.l.hammer, b. vizvári. generation of artificial attributes for data analysis. rutcor research report rrr 42-2004, rutgers center for operations research, rutgers university, 2004. [2] zs. csizmadia, b. vizvári. methods for the analysis of large real-valued medical databases by logical analysis of data. rutcor research report rrr 42-2004, rutgers center for operations research, rutgers university, 2004. [3] judea pearl. causality: modeling, reasoning, and inference. cambridge university press, cambridge, 2000. [4] beáta reiz, lehel csató. tree-like bayesian network classifiers for surgery survival chance prediction. in proceedings of international conference on computers, communications and control, vol. iii, pp. 470-474, 2008. [5] kevin p. murphy. learning bayes net structure from sparse data sets. technical report, comp. sci. div., uc berkeley, 2001. [6] jie cheng, david a. bell, and weiru liu. an algorithm for bayesian belief network construction from data, 1997. [7] jie cheng, david a. bell, and weiru liu. learning belief networks from data: an information theory based approach. in cikm, pages 325–331, 1997. [8] mieczyslaw a. klopotek. mining bayesian network structure for large sets of variables. in ismis, pages 114–122, 2002. [9] thomas m. cover and joy a. thomas. elements of information theory. wiley-interscience, new york, ny, usa, 1991. [10] nir friedman, dan geiger, and moises goldszmidt. bayesian network classifiers. machine learning, 29(2-3):131–163, 1997. [11] david heckerman and christopher meek. models and selection criteria for regression and classification. technical report msr-tr-97-08, microsoft research, 1997. [12] f. fleuret. fast binary feature selection with conditional mutual information. journal of machine learning research, 5:1531–1555, november 2004. [13] c. chow and c. liu. approximating discrete probability distributions with dependence trees. information theory, ieee transactions on, 14(3):462–467, 1968. [14] christian p. robert and george casella. monte carlo statistical methods (springer texts in statistics). springer-verlag new york, inc., secaucus, nj, usa, 2005. lehel csató obtained his bsc and msc degrees at the babeş–bolyai university, cluj–napoca, and his phd degree from the neural computing research group at the university of aston, the united kingdom. he was interested in the applications of machine learning techniques, specifically to apply non-parametric methods in bayesian inference. his thesis investigated methods to approximate solutions of bayesian regression using stochastic gaussian processes, centring on sparse solutions that approximate the gaussian processes. he is teaching at the babes-bolyai university and he is interested in applications of bayesian techniques in modern data processing and probabilistic methods in robotics. he is heading the “data mining research group” at the same university. web–page: http://www.cs.ubbcluj.ro/∼csatol beáta reiz obtained her bsc and msc degrees at the babeş–bolyai university, cluj–napoca. currently she is a phd student in hungary, university of szeged and she is working in the bioinformatics group of the biological research center, from szeged under the supervision of sándor pongor and jános csirik. international journal of computers, communications & control vol. i (2006), no. 4, pp. 9-19 new discrete tanaka sugeno kang fuzzy systems characterization and stability domain mohamed benrejeb, dhaou soudani, anis sakly, pierre borne abstract: in this paper, an analytical approach to characterize discrete tanaka sugeno kang (tsk) fuzzy systems is presented. this characterization concerns the choice of the adequate conjunctive operator between input variables of discrete tsk fuzzy models, t-norm, and its impact on stability domain estimation. this new approach is based on stability conditions issued from vector norms corresponding to a vector-lyapunov function. in particular, second order discrete tsk models are considered and this work concludes that zadeh’s t-norm, logic product min, gives the largest estimation of stability domain. keywords: discrete nonlinear systems, discrete tsk fuzzy model, t-norm, stability domain, vector norm, arrow form matrix. 1 introduction fuzzy control of systems presents a major interest in several applications including industrial ones. however, closed loop system properties are not easily understood and the design of the fuzzy system is generally based on intuitive approaches. different fuzzy control strategies exist. in particular, tsk fuzzy approach permits the description and the control of a system by defining different models related via a rule base. the stability of tsk systems has been one of the central issues and is subject of many works either in the continuous case or in the discrete one. to do this, different approaches are considered mainly based on lyapunov functions [5, 10, 12]. in particular, the linear matrix inequality (lmi) formulation is used [11] and, according to the considered system, permits the stability problem resolution. in our previous work [1], the used approach is led through the convergence of a regular vector norm [3]. the vector norm approach, based on the comparison and overevaluating principle, has a major advantage that it deals with a very large class of systems, since no restrictive assumption is made on the matrices of state equations. so, in [1], the approach estimates the stability domain of continuous tsk fuzzy systems and its dependence on the choice of the conjunctive operator between inputs. in this way, many authors have presented and analyzed several of these operators defined by t-norms and so said t-operators [4, 6, 7]. in particular, in [6], six t-norms are used to represent this connective operator in the inverse pendulum control. in another work [9], are used the same t-norms to study their impact on fuzzy control performances of a second order process by defining a performance criterion based on error measurement of the closed loop system. however, then exist other operators to represent this connective such as mean operators developed in [14]. except [1], all these studies don’t treat the influence of the choice of the conjunctive operator between inputs on the stability conditions obtained and often only prod or min operators are used without theoretical argumentation and just for simplification reasons. therefore, in this way the study will be considered and constitutes a generalization of [1] in the discrete case seeing the soft implementation nature of this control strategy. by exploiting conditions or hypothesis obtained from stability analysis of discrete tsk fuzzy systems based on vector norms approach, the influence of the choice of t-norm is studied. similarly to [1], only second order tsk fuzzy models are considered for simplification reason. in the next section, is presented the structure of discrete tsk fuzzy models. in section 3, several definitions dealing with t-norm notion as well as its properties are given. in section 4, sufficient stability conditions of these tsk systems based on vector norms approach are presented. then, the study of the copyright c© 2006 by ccc publications 10 mohamed benrejeb, dhaou soudani, anis sakly, pierre borne impact of the choice of t-norm on the proposed stability conditions will be considered in section 5. in particular, second order tsk fuzzy models are considered. a dc motor is studied in section 6 as an application example validating this study. finally, some concluding remarks are pointed out. 2 tsk discrete models description an r-order tsk discrete fuzzy model of an n-order nonlinear system to be controlled is given by a rule base where the ith rule is in the form: ri : if x1 is g i 1 and x2 is g i 2 and···and xn is gin t hen { x(k + 1) = aix(k) + biu(k) y(k) = cix(k) i = 1, 2, ..., r (1) where gij, j=1,2,...,n, is the i th fuzzy set of the state vector x j. the state vector x ∈ ℜn, the control input u ∈ ℜ and the matrices ai, bi and ci are of appropriate dimensions. according to the parallel distributed compensation (pdc) concept [13], the rule base r′i of the fuzzy controller stabilizing the former system is in the form: r′i : if x1 is g i 1 and x2 is g i 2 and···and xn is gin t hen u(k) = −kix(k) i = 1, 2, ..., r (2) with ki = [k1i ,k 2 i ,...,k n i ]. by substituting u in the equation (1) above, it comes: x(k + 1) = r ∑ i=1 r ∑ j=1 hih j(ai −bik j)x(k) (3) with: hi = wi/ r ∑ i=1 wi (4) wi = t (g i 1(x1), g i 2(x2), ..., g i n(xn)) (5) and t a t-norm. when linear models of the system to be controlled are considered in the controllable form given by: ai =   0 1 ··· 0 ... . . . . . . ... 0 ··· 0 1 −a1i ··· −an−1i −ani   and bi = b =   0 ... 0 1   (6) relation (3) becomes: x(k + 1) = r ∑ i=1 r ∑ j=1 hih j(ai −bik j)x(k) = r ∑ i=1 r ∑ j=1 hih j(ai −bk j)x(k) = r ∑ i=1 r ∑ j=1 hih jaix(k)− r ∑ i=1 r ∑ j=1 hih jbk jx(k) = r ∑ i=1 hi r ∑ j=1 h jaix(k)− r ∑ j=1 h j r ∑ i=1 hibk jx(k) = r ∑ i=1 hiaix(k)− r ∑ j=1 h jbk jx(k) new discrete tanaka sugeno kang fuzzy systems characterization and stability domain 11 then finally: x(k + 1) = r ∑ i=1 hi(ai −bki)x(k) (7) 3 t-norms an important task to be performed in the design of tsk fuzzy systems is the choice of the conjunctive operator materializing the connective and between input variables in the rule base and so corresponding to the intersection operation between fuzzy subsets relatively to different inputs. often, this operator is defined by a t-norm t , whose definition and proprieties are presented as follows. definition 1. a triangular norm (t-norm) is a function t :[0,1]×[0,1] → [0,1] verifying for every u, v, w and t of [0,1] : i) t (u,v) = t (v,u) (commutativity) ii) t (u,t (v,w)) = t (t (u,v),w) (associativity) iii) t (u,v) ≤ t (w,t) si u ≤ w et v ≤ t (monotonicity) iv) t (u, 1) = u (one identity) (8) min operator is the largest of all possible t-norms, t (u,v) ≤ min (u,v). the intersection operation between fuzzy subsets is defined by a t-norm t such as: c = a∩b, then ∀ x ∈ x µc(x) = t (µa(x),µb(x)) (9) the commonly encountered t-norms are given in table 1. table 1: main t-norms t-norm name min(u, v) zadeh (logical operators) uv bandler (probabilistic operators) max(u + v−1, 0) lukasiewicz (bounded operators)    u if v = 1 v if u = 1 0 otherwise weber (drastic operators) definition 2. a t-norm t is said archimedean if and only if t (u, v) is continuous and t (u, u) < u for each u of ]0,1[. moreover, t is said strict archimedean if and only if t (u, v) < t (w,t) as soon as u < w and v < t. any function t :[0,1] × [0,1] → [0,1] is an archimedean t-norm if and only if it exists a continuous decreasing function f : [0,1]→[0,+∞[ such that f (1)=0 satisfying : t (u,v) = f −1∗( f (u) + f (v)) (10) where f −1∗ is the pseudo-inverse of f such as f −1∗ = ( f −1(w) if w ∈ [0, f (0)[ 0 if w ∈ [ f (0), + ∞[ . 12 mohamed benrejeb, dhaou soudani, anis sakly, pierre borne besides, t is strict if and only if f (0)= +∞. f is said the additive generator of the t-norm t . the additive generators associated to main parameterized archimedean t-norms are presented in table 2 [14]. table 2: main parametrized t-norms and their additive generators t-norm generator f (u) parameter name uv γ + (1−γ)(u + v−uv) 1 γ log [ γ + (1−γ )u u ] strict archimedean γ > 0 hamacher 1 [( 1 u )p + ( 1 v )p −1 ] 1 p 1 (1 + u) 1 p strict archimedean p > 0 schweizer & sklar 1 1 + [ ( 1u −1)λ + ( 1v −1)λ ] 1 λ 1 1 + u 1 λ strict archimedean λ > 0 dombi 1−min [1, ((1−u)ω + (1−v)ω ) 1ω ] (1−u)ω archimedean no strict ω > 0 yager uv max (u,v,α ) no archimedean α ∈ [0,1] dubois & prade 4 new stability conditions in [2], a change of base of (6) under the arrow form give: x(k + 1) = r ∑ i=1 himix(k) (11) where mi is a matrix in the arrow form and p is the corresponding passage matrix: mi = p −1(ai −bki)p (12) new discrete tanaka sugeno kang fuzzy systems characterization and stability domain 13 mi =   α1 0 ··· 0 β1 0 . . . . . . ... ... ... . . . . . . 0 ... 0 ··· 0 αn−1 βn−1 γ 1i ··· ··· γ n−1i γ ni   and p =   1 1 ··· 1 0 α1 α2 ··· αn−1 0 α 21 α 2 2 ··· α 2n−1 ... ... ... ··· ... 0 α n−11 α n−1 2 ··· α n−1n−1 1   (13) with: β j = n−1 ∏ q = 1 q 6= j (α j −αq)−1 ∀ j = 1,2,...,n−1    γ ji = −pi(α j) ∀ j = 1,2,...,n−1 pi(λ ) = λ n + n−1 ∑ q=0 (aq+1i + k q+1 i )λ q γ ni = −(ani + kni )− n−1 ∑ j=1 α j (14) the application of the classical borne-gentina criterion [3] leads to the following theorem. theorem 3. the discrete system described by (7) is asymptotically stable if there exist 0 < α j < 1, α j 6= αk, ∀ j 6= k, such as ∀x ∈ s: i) 1− ∣∣α j ∣∣ > 0 ∀ j = 1, 2, ..., n−1 ii) 1− ∣∣∣∣ r ∑ i=1 hiγ ni ∣∣∣∣− n−1 ∑ j=1 ∣∣∣∣ r ∑ i=1 hiγ j i β j ∣∣∣∣(1− ∣∣α j ∣∣ )−1 > 0 (15) if s = ℜn, the stability is global. furthermore, if there exist α j, j = 1, 2, ..., n−1, such as : i) 0 < α j < 1 j = 1, 2, ..., n−1 ii) r ∑ i=1 hiγ ni > 0 iii) r ∑ i=1 hiγ j i β j > 0 j = 1, 2, ..., n−1 (16) then the previous theorem can be simplified to the following corollary. corollary 4. the discrete system described by (7) is asymptotically stable if there exist 0 < α j < 1, α j 6= αk, ∀ j 6= k, such as ∀x ∈ s: bph < 0 (17) where matrices b ∈ ℜn×n, p∈ ℜn×r and h∈ ℜr×1 are such as : b =   β1 0 ··· 0 0 . . . . . . ... ... ··· βn−1 0 0 ··· 0 −1   , p =   p1 (α1) ··· pr(α1) ... ··· ... p1 (αn−1) ··· pr(αn−1) p1 (1) ··· pr(1)   and h =   h1 ... hr   (18) if s = ℜn, the stability is global. according to the choice of the coefficients α j, different conditions on the parameters hi can be obtained. 14 mohamed benrejeb, dhaou soudani, anis sakly, pierre borne 5 t-norm influence on the proposed stability conditions let us consider the following classical fuzzy partition of a second order tsk model given by figure 1, with: µi = g1i (xi) i = 1, 2, . . ., n (19) and: g1i (xi) = min (1, max (0, li −xi 2li )) and g2i (xi) = 1−g1i (xi) (20) µ figure 1: fuzzy partition of state variables for r=2 then, the whole fuzzy state space considered is the open subset ω =]−l1, l1[×]−l2, l2[×. . .×]−ln, ln[. so, the notion of global stability, whenever used in the literature on fuzzy control, refers to local stability within a such domain [8]. for second order tsk model, i.e. r=2, then h2 = 1−h1 and: h1 = w1 w1 + w2 = t (µ1,µ2, ..., µn) t (µ1,µ2,...,µn) + t (1− µ1,1− µ2,...,1− µn) (21) where t is a t-norm. propositions [1] 1. let µ1, µ2, . . ., µn and µmax ∈ [0, 1] such as µ1 ≤ µmax, µ2 ≤ µmax, ..., and µn ≤ µmax. then, we obtain: h1(µ1,µ2,...,µn) ≤ h1(µmax,µmax,...,µmax) (22) this proposition allows the determination of the domain s by its characteristic point m in figure 2. x 1 s x 2 x 1max 0 1 1 0.5 0.5 max x 2max l 1 l 2 m ω µ 1 2 µµ figure 2: correspondence between the state space variables domain and the membership values domain new discrete tanaka sugeno kang fuzzy systems characterization and stability domain 15 2. let us note ϕn(µ) = t (µ1, µ2, ..., µn) when µ 1=µ 2 =. . . =µ n=µ . ϕ n is an increasing function on [0, 1] with ϕn(0) = 0 and ϕn(1) = 1. if the following conditions are satisfied : i) ϕn is continuous ii) ∀ µ > 0 , ϕn(µ ) 6= 0 iii) ∀ µ1, µ2 ∈ ]0,1] with µ2 > µ1 , ϕn(µ2)µ2 ≥ ϕn(µ1) µ1 (23) then we have: { h1(µ ,µ ,...,µ ) ≥ µ if µ ≥ 0.5 h1(µ ,µ ,...,µ ) ≤ µ if µ ≤ 0.5 (24) this proposition means that for a given µ , the smallest coefficient h1 is obtained with the t-norm of zadeh, the logic product min; and so for the condition (22) we obtain the largest rectangular form domain s with this t-norm by taking µ max = c. 3. conditions (23) of the proposition 2 are satisfied for each strict archimedean t-norm such as ϕn(µ ) is derivable on ]0,1]. the proposed approach is illustrated by considering the following parameterized t-norms: • the hamacher one: t (µ1,µ2) = µ1 µ2γ+(1−γ)(µ1+µ2−µ1 µ2) for γ>0, • the yager one: t (µ1,µ2) = 1−min (1,((1− µ1)ω + (1− µ2)ω ) 1 ω ) for ω>0, • and the dubois one: t (µ1,µ2) = µ1 µ2max (µ1,µ2,α) for α ∈ [0,1]. 6 application example as an example, let consider in figure 3 the case of a dc motor with two linear models g1(p) and g2(p) with δ 1=0.5 and δ 2=1. )( 1 )( i i pp pg δ+ = u x i=1,2)(0 pb figure 3: dc motor discrete models b0(p) = 1−e −te p p is a zero order holder and te=0.2s is the sampling time. the discrete models of the dc motor are given by the following z-transmittances: gi(z) = ni(z) (z−1)(z−ψi) i = 1, 2 with ψi = e−δite and ni(z) = δite+e −δi te−1 δ 2i z + 1−e −δi te (1+δite) δ 2i . the two discrete dc linear models are considered in the following controllable form: a1 = [ 0 1 −0.905 1.905 ] , a2 = [ 0 1 −0.819 1.819 ] and b = [ 0 1 ] we suppose that for particular constraints the choice of ki is imposed such that the pole placement is different for the two models by taking: 16 mohamed benrejeb, dhaou soudani, anis sakly, pierre borne k1 = [ −0.7 1.2 ] and k2 = [ −1.1 1.2 ] according to [3], the minimal overvaluing matrix relatively to the regular vector norm p: p(x) = [|x1|,|x2|]t is such as: m(.) = [ |α| 1 |γ1| |γ2| ] with γ1 = (0.486−0.086α )h1 + α 2 −0.619α −0.281 γ2 = 0.086h1 + 0.619−α then, stability conditions deduced from the corollary are: i) 0 < α < 1 ii) 0.086h1 + 0.619−α > 0 iii) (0.486−0.086α)h1 + α 2 −0.619α −0.281 < 0 iv) 0.4h1 + 0.1 > 0 when i) is satisfied, relations ii) and iv) are too. then condition iii) leads to the following inequality h1 < − α 2 −0.619α −0.281 0.486−0.086α = c when 0.5 < c < 1 , let s a neighborhood of the equilibrium point 0, included in ω, ω =]−1, 1[×]−1, 1[, that verifies a such condition. s is an overvaluing domain of the fuzzy system and an estimation of a symmetrical domain s with respect to 0 imposes: 1−c < h1 < c. now, consider the study of the impact of the t-norm t on the width of the neighborhood s of the equilibrium point 0, and then the determination of the largest stability domain d included in s verifying the previous condition. for α = 0.345, we obtain the maximal value of c, c=0.82. thus, the overvaluing matrix m(·) is constant: m(·) = m = [ α 1 0 α ] whitch is triangular and not irreductible. however, for c = 0.80, this irreductibility is skirted and for α = 0.345, its comes the following overvaluing matrix m = [ 0.345 1 0.01 0.343 ] whitch is irreductible, whose principal eigenvalue is λm = 0.446, the corresponding vector is: um = [9.88 1] t and the largest estimated stability domain d is such as d = { x ∈ s/pt (x)um = 9.88|x1|+|x2| ≤ xmax } where s is the square form domain and xmax is its width. s depends on the condition h1 < c with c = 0.8 and so on the t-norm t . new discrete tanaka sugeno kang fuzzy systems characterization and stability domain 17 table 3: µmax and xmax corresponding to different t-norms t-norm µmax xmax zadeh 0.80 0.60 bandler 0.67 0.34 hamacher (γ = 0) 0.75 0.50 hamacher (γ = ∞) 0.58 0.16 yager (ω = 2) 0.62 0.24 dubois (α = 0.5) 0.70 0.40 for n = 2, table 3 gives µmax, corresponding to the membership values domain, and xmax, corresponding to the square form domain s, with respect to different t-norms. the results obtained in table 3 shows that for n = 2, the greatest value of µmax and so the largest domain s is obtained specifically for the logic product min. figure 4 presents an estimation of the largest stability domain d respectively for zadeh and bandler t-norms. x 1 x 2 1−1 1 -1 s 0.6−0.6 0.6 −0.6 0.06−0.06 d ω x 1 x 2 1−1 1 -1 0.34 s 0.34−0.34 −0.34 0.03−0.03 d ω a) zadeh’s t-norm (min) b) bandler’s t-norm (prod) figure 4: stability domains obtained for two different t-norms 7 conclusion in this paper, we are interested in the stability study of discrete tsk fuzzy systems and the correspondent domain estimation relatively to the used conjunctive operator, materialized by a t-norm. the stability approach is based on vector norms. when second order tsk models are considered, a mathematical study permits to conclude that zadeh’s t-norm, min operator, yields to the largest stability domain. a dc motor with two discrete models is considered as an illustrative example and the estimated stability domains for different t-norms confirm the former result. however, it suits to remark that this study can be generalized for r-order tsk models, in the two 18 mohamed benrejeb, dhaou soudani, anis sakly, pierre borne cases continuous and discrete, with r greater than 2 and then we must distinguish between the two cases, r odd or even. for two cases, the stability study can be leaded in different symmetrical rectangular zones surrounding the equilibrium point, from the smallest to the largest. references [1] benrejeb m., sakly a., ben othman k. and borne p.: choice of conjunctive operator of tsk fuzzy systems and stability domain study. to appear in matcom (mathematics and computers and simulation journal). [2] benrejeb m., borne p. and laurent f. : sur une application de la représentation en flèche à l’analyse des processus. rairo automatique, 16 (2). (1982) 133-146. [3] borne p., richard j. p. and radhy n. e. : stabilité, stabilisation, régulation : approche par les normes vectorielles, systèmes non linéaires, tome 2 : stabilité-stabilisation. coordonné par fossard a. j. et normand-cyrot d., responsable du tome : borne p., 45-90. editions masson, paris (1993). [4] butkiewicz b.s. : control error of fuzzy system with different reasoning and defuzzification methods. 7th international fuzzy systems association world congress (ifsa) , iii. praga, czech (1997). [5] cao s. g., rees n. w. and feng g. : quadratic stability analysis and design of continuous-time fuzzy control systems. int. j. syst. sci., 27(2). (1996) 193-203. [6] cardenas e., castillo j.c., cordon o., herrera f. and peregrin a. : applicability of t-norms in fuzzy control. busefal, 61. (1995) 28-37. [7] gupta m.m. and qi j. : design of fuzzy logic controllers based on generalized t-operators. fuzzy sets and systems, 40. (1991) 473-489. [8] palm r., driankov d. and hellendoorn h. : model based fuzzy control. springer-verlag, berlin heidelberg (1997). [9] sakly a., ben othman k. and benrejeb m. : sur le choix de l’opérateur de conjonction en commande floue basé sur la robustesse de critères de performances. 2ème conférence internationale jtea, tome 1, sousse nord (2002). [10] sugeno m. : on stability of fuzzy systems expressed by fuzzy rules with singleton consequences. ieee transactions on fuzzy systems, 7(2). (1999) 201-223. [11] tanaka k., ikeda t. and wang h.o. : fuzzy regulators and fuzzy observers : relaxed stability conditions and lmi-based designs. ieee transactions on fuzzy systems, 6(2). (1998) 1-16. [12] tanaka k. and sugeno m. : stability analysis and design of fuzzy control systems. fuzzy sets and systems, 45. (1992) 135-156. [13] tanaka k. and sano m. : a robust stabilization problem of fuzzy control systems and its application to backing up control of a truck-trailer. ieee transactions on fuzzy systems, ii. (1994) 119-134. [14] yager r. r. and filev d. p. : essentials of fuzzy modeling and control. john wiley & sons, inc., usa (1994). new discrete tanaka sugeno kang fuzzy systems characterization and stability domain 19 mohamed benrejeb1, dhaou soudani1, anis sakly1, pierre borne2 1ecole nationale d’ingénieurs de tunis unité de recherche lara-automatique bp 37, le belvédère, 1002 tunis, tunisie 2ecole centrale de lille, cité scientifique laboratoire d’automatique, génie informatique et signal bp 48, 59651 villeneuve d’ascq cedex, france e-mail: mohamed.benrejeb@enit.rnu.tn, dhaou.soudani@enit.rnu.tn, sakly_anis@yahoo.fr, pierre.borne@ec-lille.fr received: november 10, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 701-709 stable factorization of strictly hurwitz polynomials ö. eğecioğlu, b. s. yarman ömer eğecioğlu department of computer science university of california, santa barbara ca 93106, usa e-mail: omer@cs.ucsb.edu b. siddik yarman department of electric and electronics engineering college of engineering, istanbul university 34320 avcilar, istanbul, turkey e-mail: sbyarman@gmail.com abstract: we propose a stable factorization procedure to generate a strictly hurwitz polynomial from a given strictly positive even polynomial. this problem typically arises in applications involving real frequency techniques. the proposed method does not require any root finding algorithm. rather, the factorization process is directly carried out to find the solution of a set of quadratic equations in multiple variables employing newton’s method. the selection of the starting point for the iterations is not arbitrary, and involves interrelations among the coefficients of the set of solution polynomials differing only in the signs of their roots. it is hoped that this factorization technique will provide a motivation to perform the factorization of two-variable positive function to generate scattering hurwitz polynomials in two variables for which root finding methods are not applicable. keywords: routh-hurwitz stability, hurwitz polynomial, stable factorization, newton’s method. 1 introduction in many microwave communication system design, modeling and simulation problems, description of lossless two ports in one or two kinds of elements is essential [5]. in the design of microwave matching networks, amplifiers or in modeling passive one port devices such as antennas, lossless two ports are either described in terms of driving point immitance or reflectance functions [6,7]. the methods known as real frequency techniques (rft) are excellent tools for design and modeling [5, 8]. once the independent descriptive parameters are selected, numerical implementations of real frequency techniques demands the construction of strictly hurwitz polynomials. for example, in the simplified real frequency technique (srft), the numerator polynomial h(p) = h0 + h1p + · · · + hnpn of the driving point input reflectance s11(p) = h(p) g(p) completely specifies the scattering parameters of the lumped element reciprocal lossless two port as follows: s12 = s21 = f(p) g(p) and s22 = f(p) f(−p) h(−p) g(p) (1) provided that the monic-polynomial f(p) which is constructed on the transmission zeros of the system under consideration, is pre-selected. in this representation, the denominator polynomial g(p) = g0 + g1p + · · · + gnpn is generated as a strictly hurwitz polynomial from the equation g(p2) = g(p)g(−p) = h(p)h(−p) + f(p)f(−p) = g0 + g1p 2 + · · · + gnp2n (2) copyright c⃝ 2006-2010 by ccc publications 702 ö. eğecioğlu, b. s. yarman which is obtained by means of the lossless condition. once f(p) is selected, (2) is specified in terms of the real coefficients {h0, h1, . . . , hn} of h(p). for many practical problems, it may be sufficient to choose f(p) as f(p) = pk, k ≤ n. in this case, (2) results in a set of quadratic equations such that g0 = g 2 0 = h 2 0 g1 = −g 2 1 + 2g0g2 = −h 2 1 + 2h0h2 ... gi = (−1) ig2i + 2(g2ig0 + i∑ j=2 (−1)jgj−1g2j−i+1) = (−1) ih2i + 2(h2ih0 + i∑ j=2 (−1)jhj−1h2j−i+1) ... (3) gk = g(i=k) + (−1) k ... gn = (−1) ng2n = (−1) nh2n it should be mentioned that the general form of f(p)f(−p) may be described as f(p2) = f(p)f(−p) = f0 + f1p 2 + · · · + fnp2n. (4) then it is straightforward to revise (4.3) with the help of (4.4). at this point it is the crucial issue to generate g(p) as a strictly hurwitz polynomial either employing (2) or (4.3). if one employs (2), it is sufficient to find the roots of g(p2) and then, construct g(p) on the left halfplane roots of g(p2), yielding g(p) = g0 +g1p+· · ·+gnpn. this has been the common practice of the srft. however, if the problem under consideration demands the construction of lossless two-ports with two kinds of elements, then there is no way to carry out the computation by means of root finding techniques. in this case, one has to rewrite (2) in two variables as g(p, λ) = g(p, λ)g(−p, −λ) and revise (4.3) accordingly. eventually one needs to solve (4.3) to generate g(p, λ) as a “two variable scattering hurwitz polynomial" [1,2]. in this representation the complex variable p = σ+ jω is associated with first kind of elements and the complex variable λ = σ+jω is associated with the second kind of elements of the lossless two-port. actually, this way of posing the problem may be understood as the factorization of the two variable polynomial g(p, λ) as g(p, λ)g(−p, −λ), which in turn yields the scattering hurwitz polynomial g(p, λ). based on the knowledge of the authors, there is no explicit solution for the factorization of two variable polynomials in the current literature. however, for the single variable case, root finding techniques provide excellent results as described within srft. therefore, in this paper, to provide an insight to the general factorization problem, an attempt will be made to come up with a numerical procedure to solve (4.3) which is specified in single variable, with the hope that the numerical procedure presented in this paper may be extended to cover the two variable factorization case. 2 mathematical problem statement let g(z2) = g0 + g1z2 + g2z4 + · · · + gnz2n be a real polynomial with g0 > 0. consider a factorization of g of the form g(z2) = g(z)g(−z) (5) stable factorization of strictly hurwitz polynomials 703 for a real polynomial g(z) = g0 + g1z + g2z2 + · · · + gnzn as required in (4.3). call (5) a stable factorization of g, if the polynomial g is stable: that is, the real parts of the zeros of g are strictly negative. we also refer to a stable polynomial as strictly hurwitz. from physical considerations that give rise to the problem, g0, g1, . . . , gn are such that g admits a stable factorization. our aim is to determine the coefficients of g(z) as a function of g0, g1, . . . , gn. 2.1 on root finding this one dimensional problem is theoretically solvable quite easily by root finding: since g is a real polynomial, it can be factored as g(z2) = c(z2 − α1)(z 2 − α2) · · · (z2 − αn) with c > 0 and the αi complex. for i = 1, 2, . . . , n, let βi = ± √ αi, where the sign is picked so that βi has a negative real part. then g(z) = √ c(z − β1)(z − β2) · · · (z − βn), and the gi can be computed from this product. however, we wish to avoid this approach as the real motivation behind the treatment of the one variable case is the factorization problem in two variables to generate scattering hurwitz polynomials, for which root finding techniques do not apply. 2.2 basic elements of routh-hurwitz stability the conditions for a real polynomial g(z) = g0 + g1z + g2z 2 + · · · + gnzn (6) with g0 > 0 to be strictly hurwitz are given in terms of the positivity of the hurwitz determinants ∆i = det   g1 g3 g5 . . . g2i−1 g0 g2 g4 . . . g2i−2 0 g1 g3 . . . g2i−3 0 g0 g2 . . . g2i−4 . . . . . . . . . . . . . gi   . the indices in each row increase by two and the indices in each column decrease by one. the term gj is taken to be zero if j < 0 or j > n. note that ∆1 = g1. theorem 1. (routh-hurwitz stability) a necessary and sufficient condition that the polynomial (6) is strictly hurwitz is that ∆1, ∆2, . . . , ∆n be all positive [3]. since ∆n = gn∆n−1, the condition that ∆n−1 and ∆n be positive is equivalent to the requirement that ∆n−1 and gn be positive. furthermore, a necessary condition for (6) to be strictly hurwitz is that all coefficients g0 through gn be positive. 3 the main quadratic system comparing coefficients in (5), we derive a quadratic system of n+1 equations in the variables g0, g1, . . . , gn: gk = ∑ i+j=2k (−1)igigj, (k = 0, 1, . . . n) (7) 704 ö. eğecioğlu, b. s. yarman this is the system we are aiming to solve in the factorization problem. the additional constraint is that the polynomial (6) is stable. when n = 5, g0 = g 2 0 g1 = −g 2 1 + 2g0g2 g2 = g 2 2 + 2g0g4 − 2g1g3 g3 = −g 2 3 − 2g1g5 + 2g2g4 (8) g4 = g 2 4 − 2g3g5 g5 = −g 2 5 so in this case the stable factorization problem is to find a solution (g0, g1, g2, g3, g4, g5) of the quadratic system (8) in which each gi > 0, and in addition the constraints ∆2 = det [ g1 g3 g0 g2 ] > 0, ∆3 = det   g1 g3 0g0 g2 g4 0 g1 g3   > 0, ∆4 = det   g1 g3 g5 0 g0 g2 g4 0 0 g1 g3 g5 0 g0 g2 g4   > 0 are satisfied. in the general case g0, g1, . . . , gn with g0 > 0 are given as input. the output the solution required is real g0, g1, . . . , gn, with g0 > 0 such that g0, g1, . . . , gn is a solution of the associated quadratic system (7) of n + 1 equations and g(z) = g0 + g1z + · · · + gnzn is strictly hurwitz. we assume that the gk are given so that the system has a solution of the required type. 3.1 newton’s method we consider the vector valued function f : irn+1 → irn+1 which has as its set of real zeros the solutions to the quadratic system (7). for n = 5, f : ir6 → ir6 is f = (f0, f1, . . . , f5)t with f0 = x 2 0 − g0 f1 = −x 2 1 + 2x0x2 − g1 f2 = x 2 2 + 2x0x4 − 2x1x3 − g2 f3 = −x 2 3 − 2x1x5 + 2x2x4 − g3 f4 = x 2 4 − 2x3x5 − g4 f5 = −x 2 5 − g5 we compute the jacobian matrix as jf = 2   x0 0 0 0 0 0 x2 −x1 x0 0 0 0 x4 −x3 x2 −x1 x0 0 0 −x5 x4 −x3 x2 −x1 0 0 0 −x5 x4 −x3 0 0 0 0 0 −x5   . we calculate by elementary operations det(jf) = 2 6(−x0x5) det   −x1 x0 0 0 −x3 x2 −x1 x0 −x5 x4 −x3 x2 0 0 −x5 x4   = 26(−x0x5) det   x1 x3 x5 0 x0 x2 x4 0 0 x1 x3 x5 0 x0 x2 x4   stable factorization of strictly hurwitz polynomials 705 and det(jf) = 26(−x0)∆5. for general n we have a similar identity relating the jacobian of f and ∆n as det(jf) = 2 n+1(−1)nx0∆n. (9) thus the jacobian jf does not vanish at (g0, g1, . . . , gn) at if (g0, g1, . . . , gn) corresponds to a stable g(x). in other words, starting from an initial point that is close enough to the stable solution, the jacobian of f does not vanish. starting with an initial vector x0 = (x0, x1, . . . , xn)t we compute the iterates by newton’s method as xn+1 = xn − j −1 f (xn)f(xn) until successive iterates are within a given tolerance. the invertibility of jf at the point xn is guaranteed for xn close to a stable solution (g0, g1, . . . , gn). however a real solution g(z) of the quadratic system found by newton’s method is not necessarily strictly hurwitz. the polynomial we want is obtained from g(z) by flipping the sign of some of its roots and making each one have negative real part, even though we do not have access to the roots themselves. example 1. suppose g(x2) = g0 + g1x2 + g2x4 + g3x6 + g4x8 with g0 = 9.244, g1 = 72.286, g2 = 217.183, g3 = 296.638, g4 = 155.673. g(x2) = g(x)g(−x) where g(x) = g0 + g1x + g2x 2 + g3x 3 + g4x 4 with g0 = 3.040, g1 = 2.289, g2 = 12.749, g3 = 4.637, g4 = 12.476 is strictly hurwitz. starting with the initial random vector of coefficients (1.933, 2.008, 0.181, 0.870, 2.582), and tolerance 0.01, newton’s method converges to the polynomial 3.040 + 0.004x + 11, 887x2 − 0.003x3 + 12.477x4 of the quadratic system in 14 iterations. this polynomial is not stable. its roots are −0.0928 ± 0.6956j and 0.0929 ± 0.6972j. 3.2 an auxiliary problem the necessity of being able to “flip" the sign of certain roots of a given real polynomial as indicated above results in the following auxiliary problem: given a real polynomial g(x) = g0 + g1x + · · · + gnxn of degree n with g0 > 0, construct the real polynomial h(x) = h0 + h1x + · · · + hnxn with h0 = g0, such that the roots of h are ± roots of g and h is strictly hurwitz. if we could generate the polynomials h whose roots differ from the roots of g only in their sign, then we could test each polynomial generated by the routh-hurwitz criteria to see if it is stable. but there cannot be an analytic way involving radicals to do this: consider a generic fifth degree polynomial g(x) = g0 + g1x + · · · + g5x5 with g0 > 0. let r be a real root of g, and let h(x) = h0 + h1x + · · · + h5x5 be the polynomial with h0 = g0, which has identical roots as g(x), except for its fifth root it has −r instead of r. since g4 and h4 are the negative of the sums of the roots of g(x) and h(x) respectively, we have r = 1 2 (h4 − g4). we can also calculate g(x)/(x − r) by synthetic division and compute the roots of this quartic by radicals. thus if there were a way of computing the coefficients of h(x) from those of g(x) by means of radicals, then this would allow us to express the roots of a general fifth degree polynomial by radicals. 706 ö. eğecioğlu, b. s. yarman 4 algorithmic approaches to finding g(x) there are two essentially distinct approaches to find the strictly hurwitz polynomial g(x) given the input data g0, g1, . . . , gn with g0 > 0. both have a random component. a1 : generate an initial vector x0 = (x0, x1, . . . , xn)t and run newton’s method starting with x0. let the converged polynomial be h(x). if h(x) passes the routh-hurwitz criteria, then it is the strictly hurwitz polynomial desired and we are done. if not, generate another x0 and continue. a2 : generate an initial vector x0 = (x0, x1, . . . , xn)t and run newton’s method starting with x0. let the converged polynomial be h(x). if h(x) passes the routh-hurwitz criteria, then it is the strictly hurwitz polynomial desired and we are done. if not, use the coefficients of h(x) to generate another x0 and continue. a1 is simple to implement. on the other hand the number of executions of the newton method is fewer for a2, which essentially goes from a computed h(x) to another polynomial whose roots are negatives of some of the roots of h(x). we shall indicate a number of methods for a2. example 2. in [8], the data given for a monopole antenna is modeled using the linear interpolation technique proposed for positive real functions. we used this problem for the experimental evaluation of a1 and a2. for this model g(x2) = g0+g1x2+g2x4+g3x6+g4x8 with g0 = 9.244, g1 = 72.286, g2 = 217.183, g3 = 296.638, g4 = 155.673. employing a1, the strictly hurwitz polynomial g(x) = g0 + g1x + g2x 2 + g3x 3 + g4x 4 with g0 = 3.040, g1 = 2.289, g2 = 12.749, g3 = 4.637, g4 = 12.476 was found. the initial vectors x0 = (x0, x1, x2, x3, x4) t were generated by picking xi independently and uniformly in the range 0 < xi < √ g0. the average number of different starting points required for the newton method for convergence to g(x) with a tolerance of 0.001 is about 8 with a standard deviation of 5. next we consider two properties of the family of polynomials which are solutions to (4.3) and differ only in the signs of their roots. 4.1 selection of starting points for a2, we use the following idea: suppose we have two real solutions g(x) and h(x) to the quadratic system of equations (7). then g(x2) = g(x)g(−x) = h(x)h(−x) where g(x) and h(x) have the same roots up to signs. define f(x) = h(x)/g(x). since h(x)/g(x) = g(−x)/h(−x), f(x) satisfies the functional equation f(x)f(−x) = 1 . (10) put f(x) = c0 + c1x + c2x2 + · · · with c0 = 1. >from (10), we have 1 = c20 0 = −c21 + 2c2 0 = c22 + 2c0c4 − 2c1c3 0 = −c23 + 2c0c6 − 2c1c5 + 2c2c4 0 = c24 + 2c0c8 − 2c1c7 + 2c2c6 − 2c3c5 ... = ... stable factorization of strictly hurwitz polynomials 707 the general form of the k-th equation for k ≥ 1 is 0 = ∑ i+j=2k (−1)icicj. in this infinite system, each of c2, c4, c6, . . . can be expressed in terms of the coefficients c1, c3, c5, . . .. in fact, we can represent c2k as a polynomial in c1, c3, . . . c2k−1. >from the second equation, c2 = 1 2 c21. using this with the third equation we get c4 = − 1 2 c22 + c1c3 = − 1 8 c41 + c1c3 and c6 = 1 2 c23 + c1c5 − 1 2 c31c3 + 1 16 c61 . in the general case we can write c2k = 1 2 (−1)k+1c2k + k−1∑ i=1 (−1)i+1cic2k−i . (11) in (11), we repeatedly substitute the expressions obtained for the earlier coefficients with even indices, we arrive at the expression of c2k in terms of c1, c3, c5, . . .. therefore f(x) = 1 + c1x + 1 2 c21x 2 + c3x 3 + (c1c3 − 1 8 c41)x 4 + c5x 5 + (1 2 c23 + c1c5 − 1 2 c31c3 + 1 16 c61)x 6 + c7x 7 + · · · thus h(x) = g(x) ( 1 + c1x + 1 2 c21x 2 + c3x 3 + (c1c3 − 1 8 c41)x 4 + c5x 5 + · · · ) for some real numbers c1, c3, c5, . . .. we can use the form of the coefficients of f(x) to pick a new starting point if the solution g(x) we obtain from the newton’s method fails to be stable. for algorithm a2, we generate a new initial point h(x) for newton’s method from the current computed solution g(x) = g0 + g1x + · · · + gnxn with g0 > 0 by setting hk = ∑k i=0 gick−i for k = 0, 1, . . . , n with c0 = 1 and 0 = ∑k i=0 gick−i for k > n. we then express the even indexed ci in terms of the odd index ones. after this stage, the hk’s involve only c1, c3, . . . , cn−1 (or up to cn if n is odd.) we pick random values for these ci’s satisfying these constraints. example 3. for the data in example 2, we considered the experimental evaluation of a2. the initial vector x0 = (x0, x1, x2, x3, x4)t was generated by picking xi independently and uniformly in the range 0 < xi < √ g0. following that the algorithm jumps to the next initial vector using the ideas presented above. the average number of iterations to converge to the strictly hurwitz polynomial within a tolerance of 0.001 was 2, with a standard deviation of 1. 4.2 a linear algebraic property given a real polynomial g(x) = g0 + g1x + · · · + gnxn of degree n, we briefly consider the problem of constructing a new polynomial h(x) = h0 + h1x + · · · + hnxn whose roots depend on the roots of g, without actually finding the roots themselves. without loss of generality, gn = 1. suppose the roots of g are β1, . . . , βn and the required roots of h are p(β1), . . . , p(βn) for some polynomial p. consider the companion matrix of g defined by c =   0 1 0 0 0 0 1 0 ... . . . ... 0 1 −g0 −g1 · · · −gn−1   708 ö. eğecioğlu, b. s. yarman the characteristic polynomial of c is det(xi − c) = g(x). then h can be expressed in terms of only the coefficients of g as det(xi − p(c)) = h(x) without calculating the zeros β1, . . . , βn. for example for g(x) = g0 + g1x + x2 with zeros β1, β2, c = [ 0 1 −g0 −g1 ] and the characteristic polynomial of c2 − 3c has zeros β21 − 3β1, β 2 2 − 3β2. we compute c2 − 3c = [ 0 −2 3g0 + g 2 0 3g1 + g 2 1 ] and therefore h(x) = det( [ x 2 −3g0 − g 2 0 x − 3g1 − g 2 1 ] ) = 2g0(3 + g0) − g1(3 + g1)x + x 2 . the reason for this is that c is similar to an upper triangular matrix with β1, . . . , βn on the diagonal [4], bcb−1 =   β1 0 β2 ∗ ... . . . 0 . . . 0 βn   , so that bp(c)b−1 =   p(β1) 0 p(β2) ∗ ... . . . 0 . . . 0 p(βn)   . functions other than polynomials can be used for p (e.g. mixtures of exponential and certain rational functions). however for this approach to work, each βi must be transformed by the same function p. we only want to change the sign of one of the βi at a time. let i(i) be the matrix that is obtained from the identity matrix by changing the ith 1 to a −1. we would like to construct the matrix si(c) such that bsi(c)b −1 = i(i)bcb−1. (12) then the characteristic polynomial of si(c) has zeros β1, . . . , βi−1, −βi, βi+1, . . . , βn. >from (12), si(c) = bi(i)b−1c. if b1, . . . , bn are the column vectors of b and c1, . . . , cn are the row vectors of b−1, then si(c) and c are related by si(c) = (i − 2bici)c . (13) we do not need the matrix b exactly (this may involve finding eigenvalues, which are not permitted in this approach). the characteristic polynomial of the perturbed matrix in (13) will be used as a starting point for the next iteration of newton’s method, so bi and ci and the outer product bici can be approximate. 5 conclusions and further work we have proposed a stable factorization procedure generate strictly hurwitz polynomial from a given strictly positive even polynomial. the factorization process is carried out directly to find the solution of a set of quadratic equations in many variables employing newton’s method. it is hoped that the method presented in this paper generalizes to two-variable polynomials. this would make possible the generation of scattering hurwitz polynomials, which are the twodimensional analogues of strict hurwitz polynomials. stable factorization of strictly hurwitz polynomials 709 bibliography [1] aksen a., “design of lossless two-ports with mixed, lumped and distributed elements for broadband matching," phd. dissert., lehrstuhl für nachrichtentechnik, ruhr universitaet bochum, 1994. [2] fettweis a., “on the scattering matrix and the transfer scattering matrix of multi dimensional lossless two-ports," int. j. of communication, vol. 36, pp. 374-381. [3] henrici p., applied and computational complex analysis, vol. ii, wiley, new york, 1977. [4] lang s., linear algebra, addison-wesley, reading, ma, 1966, p. 183. [5] yarman b. s., “broadband networks," wiley encyclopedia of electrical and electronics engineering, vol. ii, pp. 589-604, 1999. [6] yarman b. s. and aksen a., “a reflectance-based computer aided modeling tool for high speed/high frequency communication systems," proc. ieee-iscas 2001; 4, pp. 270-273. [7] yarman b. s., aksen a. and kilinc a., “immitance data modeling via linear interpolation techniques," proc. ieee-iscas 2002; 3, pp. 527-530. [8] yarman b. s., kilinc a. and aksen a., “immitance data modeling via linear interpolation techniques: a classical circuit theory approach," int. j. of circuit theory and applications, 2004; 32, pp. 537-563. [9] yarman b. s. and carlin h. j., “a simplified real frequency technique applied to broadband multi-stage amplifiers," ieee trans. mtt; 30, pp. 2216-2222, 1982. international journal of computers, communications & control vol. ii (2007), no. 4, pp. 355-366 robust predictive control using a gobf model for miso systems ali douik, jalel ghabi, hassani messaoud abstract: in this paper we develop a new method for robust predictive control for miso systems represented on the generalized orthonormal basis functions. unknown but bounded error approaches are used to update the uncertainty domain of the resultant model coefficients. this method uses a worst case strategy solved by a min-max optimization problem taking into account the constraints relative to parameter uncertainties and to measurement signals. keywords: predictive control, robust, generalized orthonormal basis functions, miso, ubbe. 1 introduction there has been interest in the use of orthogonal basis functions for the purposes of robust model predictive control (rmpc) [1, 2, 3, 4]. the most common model structure employing these bases is the well known fir one. however, the number of terms in the series expansion is high, and this may lead to poor accuracy in the estimated uncertainty domain parameter as well as the control strategy. another approach is to use laguerre or kautz models that are more suitable to represent systems having near or oscillating dynamics [5, 6]. moreover, using the popular armax model structure [7] involves a small number of parameters but the criterion to be minimized is not convex which may complicate the optimization problem. this paper is a contribution overlapping these methods by developing a new rmpc algorithm for a miso system represented on the generalized orthonormal basis functions (gobf) [8, 9]. however, the main features of using gobf model in rmpc methods is that the common fir, laguerre and kautz model structures are special cases of this complete construction [10, 11, 12], it is not sensitive to sampling interval choice, it doesn’t requires a prior knowledge of the system delay and it operates on a small number of parameters. furthermore, the criterion is convex on the uncertainty domain of the gobf model coefficients. the uncertainty domain is determined with unknown but bounded error approaches (ubbe) that updates polytopes, orthotopes, parallelotopes, ellipsoids or limited complexity polytopes [13, 14, 15]. the optimal poles of these basis functions are estimated using a new technique of poles estimation [16, 17]. the paper is organized as follows: in section 2 we present the state space model for the miso system represented on the gobf. the predictor output is expressed in section 3. in section 4, robust predictive control method is detailed and the main results are developed. simulation examples are in section 5 and finally, some conclusions are given in section 6. 2 state-space model this paper considers a miso system having m input sequences {u1(k), u2(k),··· , um(k)} and an output sequence {y(k)} that are related according to: y(k) = m ∑ j=1 g j(q −1)u j(k) + e(k) (1) where q−1 is the backward shift (q−1u j(k) = u j(k − 1) ). { g j(q−1) } describe the unknown system dynamics (assumed stable) and e(k) is the model uncertainty. copyright © 2006-2007 by ccc publications 356 ali douik, jalel ghabi, hassani messaoud the discrete time state-space model for a miso system represented on the gobf is defined by: { x(k + 1) = ax(k) + bu(k) ŷ(k) = θ t x(k) (2) with: u(k) ∈ ℜm and ŷ(k) are the input signal vector and the model output respectively. x(k) is an n dimensional state vector of elements { x jn(k) } j=1,2,···,m n=0,1,···,n j defined by: x jn(k) = z −1 { b jn(z, ξ j) } u j(k) (3) where z−1 is the inverse transform of z. n j and ξ j are the truncating order and the poles vector respectively for the j-network of the gobf. n = m ∑ j=1 (n j + 1) is the number of the gobf model parameter for the miso system and { b j n(z, ξ j) } j=1,2,···,m n=0,1,···,n j is the gobf expression given by: b jn(z) = √ 1− ∣∣∣ξ jn ∣∣∣ 2 z−ξ jn n−1 ∏ k=0 ( 1− ξ̄ jk z z−ξ jk ) (4) where ξ jk and its conjugate ξ̄ j k are the poles for the k-filter of the gobf. θ ∈ ℜn is the parameter vector. a and b are (n × n) and (n × m) dimensional matrices respectively defined by: a = diag (a j) j=1,2,···,m , b = diag (b j) j=1,2,···,m (5) where the (1 + n j)×(1 + n j) dimensional matrix a j and the (1 + n j) dimensional vector b j are given by: a j(a, b) =    ξ ja−1 if a = b, fj(a, b) if a â b, 0 if a ≺ b. (6) fj(a, b) = (−1)a+b+1α ja−1(1−ξ j b−1ξ̄ j b−1) a−1 ∏ `=b+1 α j`−1ξ̄ j `−1 (7) b j(b) = (−1)b+1α jb−1 b−1 ∏̀ =1 α j`−1ξ̄ j `−1 (b = 1,··· , n j + 1) (8) and we assume: α j` = √ 1− ∣∣∣ξ j` ∣∣∣ 2 √ 1− ∣∣∣ξ j`−1 ∣∣∣ 2 , α j0 = √ 1− ∣∣∣ξ j0 ∣∣∣ 2 (9) 3 step-ahead predictor equation system (2) can be written in incremental form as: δ x(k + 1) = aδ x(k) + bδ u(k) (10) ŷ(k) = ŷ(k −1) + θ t δ x(k) (11) robust predictive control using a gobf model for miso systems 357 where: δ u(k) = u(k)−u(k −1), δ x(k) = x(k)−x(k −1) (12) when the error on the gobf model is unknown but bounded, the fourier coefficients are defined by uncertainty intervals. equation (11) can be then rewritten as: ŷ(k) = ŷ(k −1) + θ t (ε)δ x(k) (13) where ε ∈ ω is the vector of parameter uncertainties and ω the parameter uncertainty domain. from (13), the p-step ahead predictor can be written as: ŷ(k + p/k) = ŷ(k + p−1/k) + θ t (ε)δ x(k + p); p ≥ 1 (14) using (10) and by successive substitutions we can write: δ x(k + p) = apδ x(k) + p ∑ q=1 ap−qbδ u(k + p−1) (15) thus, by successive substitution of (15) into (14) we finally have: ŷ(k + p/k) = ŷ(k) + θ t (ε) [kp −in ] δ x(k) + θ t (ε) p ∑ q=1 kp−qbδ u(k + q−1) (16) where in is the identity matrix and kp is an (n ×n) dimensional matrix defined by: kp =    p ∑ q=0 aq for p ≥ 0 0 for p ≺ 0 (17) the p-step ahead predictor can be written as a sum of two components: the free part and the forced part: ŷ(k + p/k) = ŷl (k + p/k) + ŷ f (k + p/k) (18) with: ŷl (k + p/k) = ŷ(k) + θ t (ε) [kp −in ] δ x(k) (19) ŷ f (k + p/k) = θ t (ε) p ∑ q=1 kp−qbδ u(k + q−1) (20) we note by h1, h2 and hu (hu ≺ h2) the output prediction horizons and the control horizon successively. we assume that h1 = 1. on the prediction horizon [k + 1, k + h2], (18) can be written in matrix form as: ŷ (k, ε) = ŷf (k, ε) + ŷl (k, ε) (21) where ŷ (k, ε) is the predictor vector of dimension h2 defined by: ŷ (k, ε) =   ŷ(k + 1/k, ε) ... ŷ(k + h2/k, ε)   (22) the vectors ŷl (k) and ŷf (k) can be computed using (19) and (20) respectively for (p = 1, 2,··· , h2). thus, we can write: ŷf (k, ε) = g(ε)δu (k) (23) 358 ali douik, jalel ghabi, hassani messaoud with: δu (k) is the control increment vector of dimension (mhu) defined by: δu (k) =   δ u(k) δ u(k + 1) ... δ u(k + hu −1)   (24) where δ u(k + p) represent the control increment vector defined by: δ u(k + p) = u(k + p)−u(k + p−1) ∀ p ∈ [0, hu −1] (25) u(k + p) = p ∑ q=0 δ u(k + p−q) + u(k −1) (26) g(ε) is an h2 ×(mhu) dimensional matrix that represents the impulse response coefficients and defined by: g(ε) =   g1(ε) 0 ··· 0 g2(ε) g1(ε) ··· ... ... ... . . . ... ghu (ε) ··· ··· g1(ε) ... ... . . . ... gh2 (ε) ··· ··· gh2−hu+1(ε)   (27) with gtp (ε) is a vector of dimension m given by: gp(ε) = θ t (ε)kp−1b = p ∑ q=1 θ t (ε)aq−1b (p = 1, 2,··· , h2) (28) 4 robust predictive control algorithm 4.1 constraints the constraints are resulting from uncertainties on the gobf model coefficients and bounds on control signals and control increments over the control horizon hu. umin ≤ u(k + p) ≤ umax ∀ p ∈ [0, hu −1] (29) δ umin ≤ δ u(k + p) ≤ δ umax ∀ p ∈ [0, hu −1] (30) where: umax =   u1 max ... um max   , umin =   u1 min ... um min   (31) δ umax =   δ u1 max ... δ um max   , δ umin =   δ u1 min ... δ um min   (32) using (26), (29) and (30) we define the set δ ψ of constraints on control signals as follows: δ ψ = {δu /γδu ≤ v} (33) robust predictive control using a gobf model for miso systems 359 with γ is an (4mhu)×(mhu) dimensional matrix and v a vector of dimension (4mhu). γ =   imhu −imhu ∆ −∆   , v =   δumax −δumin umax −ϕ −umin + ϕ   (34) where imhu is the (mhu) dimensional identity matrix. the matrix ∆ of dimension (mhu)×(mhu) and the vector ϕ of dimension (mhu) are given by: ∆ =   1 0 ··· 0 1 1 . . . ... ... . . . . . . 0 1 ··· 1 1   , ϕ(k −1) =   u(k −1) ... u(k −1)   (35) umax, umin, δumax and δumin are (mhu) dimensional vectors defined as: umax =   umax ... umax   , umin =   umin ... umin   (36) δumax =   δ umax ... δ umax   , δumin =   δ umin ... δ umin   (37) 4.2 optimization criterion the robust predictive control algorithm using an uncertainty model, is based on a worst case strategy that consists to resolve a min-max optimization problem given by: min δu∈δ ψ max ε∈ω j(δu, ε) (38) the quadratic criterion to be minimized is defined by: j(δu, ε) = h2 ∑ p=1 (ŷ(k + p)−r(k + p))2 + m ∑ j=1 { hu−1 ∑ p=0 λ pj δ u 2 j (k + p) } (39) with: δ u(k + p) = 0 for p ≥ hu (40) where λ pj â 0 ( j = 1, 2,··· , m) is a weighting factor generally considered constant and equals to λ j. r(k + p) represent the reference signal defined on the prediction horizon [k + 1, k + h2]. the quadratic criterion j(δu, ε) can be written in matrix form as: j(δu, ε) = ∥∥ŷ (k, ε)−r(k) ∥∥2 + ∥∥∥λ1/2δu (k) ∥∥∥ 2 (41) >from (41), we can write: j(δu, ε) = ( ŷ (k, ε)−r(k) )t ( ŷ (k, ε)−r(k) ) + δu t (k)λδu (k) (42) 360 ali douik, jalel ghabi, hassani messaoud where r(k) is an h2 dimensional reference vector defined by: r(k) =   r(k + 1) ... r(k + h2)   (43) λ is an (mhu ×mhu) dimensional weighting diagonal matrix defined by: λ = diag(λ0, λ1,··· , λhu−1) λp = diag(λ1, λ2,··· , λm); p = 0,··· , hu −1 (44) using (21), the matrix form (42) can be rewritten as: j(δu, ε) = δu t φ (ε)δu + 2ρ t (ε)δu + β (ε) (45) where φ is an (mhu ×mhu) dimensional positive definite matrix: φ (ε) = gt (ε)g(ε) + λ (46) ρ is a vector of dimension (mhu): ρ(ε) = gt (ε) [ ŷl (k, ε)−r(k) ] (47) β is a scalar defined as follows: β (ε) = [ ŷl (k, ε)−r(k) ]t [ ŷl (k, ε)−r(k) ] (48) since the criterion is convex over the parameter uncertainty set, the maximization problem over this set can be reduced to the maximization over its vertices. when the parameter set is an ellipsoid, it is approximated by the orthotope containing it. therefore the optimization problem (38) becomes: min δu∈δ ψ max ε∈s j(δu, ε) (49) where s is the set of vertices of the orthotope. the number of constraints is given by: l = 2n + 4mhu (50) where 2n is the number of the vertices of the domain s for the miso system. the rmpc algorithm using a gobf model for a miso system can be summarized as follow: – compute the matrices a and b from (5), – determine the set of vertices, – select the parameters h2 and hu, – select the weighting matrix coefficients, – compute the matrices kp (p = 1,··· , h2) from (17), – compute the coefficients gp (p = 1,··· , h2) from (28), – compute the references. computation at each sampling period: – compute the free component ŷl (k) using (19), – compute the quadratic criterion using (45), – determine the control increment vector using (49). robust predictive control using a gobf model for miso systems 361 5 simulation examples in this section we will illustrate the utility of the robust predictive control method by presenting some simulation examples. to begin with, suppose we have a miso system with m = 2 input sequences and a number of h = 300 point data record generated by the following model: y(k) = 0.102z−1 −0.751z−2 1−0.745z−1 u1(k) + −(0.152z−1 + 0.255z−2) (1 + 0.7047z−1)(1−0.3547z−1) u2(k) + e(k) (51) where u1(k), u2(k), y(k) and e(k) are the inputs, the output and the model error respectively. the model error is assumed to be bounded such |e(k)| ≤ 4.51 and the input signals are uniformly distributed sequences. in this simulation we approximate this model by the gobf model where the truncating order and the optimal poles are: nopt = 4; ξopt = (0.7450 0 0.3547 −0.7047). the process output and the gobf model output are illustrated in figure 1. 0 100 200 300 −20 −10 0 10 20 25 process output gobf model output figure 1: process output and gobf model output the center and uncertainty intervals (ui) of the ellipsoid are given in table 1. the tuning parameters used in this simulation are: h2 = 8, hu = 2, λ1 = 1 and λ2 = 1. table 1: ellipsoid performances ellipsoidal center -0.6326 -0.9135 -0.2266 -0.1260 uncertainty intervals 0.3797 0.9320 0.7085 1.9076 to validate the control method we plot in figure 2 the gobf model output and the reference signal. the control signals and the control increment signals are illustrated in figure 3 and 4 successively. the picks of the control signals as well as the control increment signals are due to the changed reference signal from -40 to +40 at the iterations 100 and 200. therefore, we notice the rapid convergence of the model output to the reference signal. this is predictable since we optimize a tracking criterion. other simulation examples with different gobf models and reference signals have been studied and yielded the same results. 362 ali douik, jalel ghabi, hassani messaoud 0 100 200 300 −50 −40 0 40 50 gobf model output reference signal figure 2: reference signal and gobf model output 0 100 200 300 −40 −30 −20 −10 0 10 20 30 40 control signal u1 0 100 200 300 −15 −10 −5 0 5 10 control signal u2 figure 3: control signals 0 100 200 300 −20 −15 −10 −5 0 5 10 15 20 25 control increment signal 1 0 100 200 300 −25 −20 −15 −10 −5 0 5 10 15 20 25 control increment signal 2 figure 4: control increment signals robust predictive control using a gobf model for miso systems 363 on the other hand, the influence of the error bounds on the gobf model output in the case of an ellipsoid domain is studied by considering 3 different snr (signal to noise ratio). the table 2 gives the centers and the uncertainly intervals where the figure 5 illustrates the model outputs and the reference signal fixed arbitrary. this figure shows the similar convergence of the model outputs to the reference signal. thus, we conclude that for different error bounds, we obtain the same gobf model output. the control method has been tested with different reference signals and error bounds that yielded the same results. finally we study the influence of different uncertainty domains such an ellipsoid, an orthotope and a polytope. the table 3 regroups the centers and the uncertainty intervals of these domains. the model outputs correspondent are shown in figure 6. by examining this figure we notice that the model outputs converge simultaneously to the reference signal. so, we conclude that the type of the parameter domain has no influence on this control method. other experiences with different reference signals and domain parameter have been realized and yielded the same results. table 2: ellipsoid performances for different error bounds snr=5 center -0.5698 -1.0975 -0.2557 -0.0844 ui 0.7915 1.9507 1.4634 3.9237 snr=10 center -0.6071 -0.9886 -0.2377 -0.1086 ui 0.5517 1.3550 1.0246 2.7549 snr=20 center -0.6326 -0.9135 -0.2266 -0.1260 ui 0.3797 0.9320 0.7085 1.9076 table 3: domain performances (snr=20) ellipsoid center -0.6326 -0.9135 -0.2266 -0.1260 ui 0.3797 0.9320 0.7085 1.9076 orthotope center -0.6950 -0.7551 0.1082 -0.1356 ui 0.6403 1.6896 1.7069 4.2133 polytope center -0.6924 -0.7556 -0.1968 -0.1754 ui 0.0236 0.0307 0.0472 0.1095 0 100 200 300 −60 −50 0 50 60 gobf model output (snr=20) gobf model output (snr=10) gobf model output (snr=5) reference signal figure 5: model outputs for 3 different snr of an ellipsoid domain 364 ali douik, jalel ghabi, hassani messaoud 0 150 300 −40 −30 0 30 40 gobf model output (ellipsoid) gobf model output (orthotope) gobf model output (polytope) reference signal figure 6: model outputs for different uncertainty domains 6 conclusion this paper has presented a new robust predictive control method based on the gobf model for a miso system. a min-max problem is solved taking into account the uncertainties on the model coefficients and the constraints on the control signals. the uncertainty parameter domain can be an ellipsoid, an orthotope or a polytope and the performance criterion is optimized with respect to constraints relative to parameter uncertainties and measurement constraints. the implication of these results in the context of system controls is that the gobf can be used to deliver state space models suitable to synthesize a robust predictive control without affecting the computational complexity and the performance of the method. finally, it should also be noted that this control method provides best results and may be synthesized for a mimo system represented on the gobf. bibliography [1] g. oliveira, g. favier, g. dumont, and w. amara, predictive controller based on laguerre filters modeling, in proc. 13th ifac world congress, san francisco, usa, vol. g, pp. 375-380, 1996. [2] a. mbarek, h. messaoud and g. favier, robust predictive control using kautz model, in proc. 10th ieee international conf. electronics, circuits and systems, pp. 184-187, december 2003. [3] l.p. wang, discrete time model predictive control design using laguerre functions, journal of process control, 14:131-142, 2004. [4] e.m. el adel, m. ouladsine, and l. radouane, predictive steering control using laguerre series representation, in proc. 2003 american control conf., vol.1, pp. 439-445, june 2003. [5] b. wahlberg, system identification using laguerre models, ieee trans. on automatic control, 36(5): 551-562, 1991. [6] b. wahlberg, system identification using kautz models, ieee trans. on automatic control, 39(6):1276-1282, 1994. [7] a. gutierrez and e. camacho, robust adaptive control for processes with bounded uncertainties, in proc. 3rd ecc, roma, vol.2, pp. 1295-1300, 1995. robust predictive control using a gobf model for miso systems 365 [8] j. ghabi, a. douik and h. messaoud, a new modelling approach of mimo linear systems using the generalized orthonormal basis functions, in proc. of the 6th ispra’07, greece, pp. 192-196, february 2007. [9] j. ghabi, a. douik and h. messaoud, new methods of modelling and parameter estimation for mimo linear systems using generalized orthonormal basis functions, journal on systems and control, vol. 2, pp. 133-140, 2007. [10] b. ninness and f. gustafsson, a unifying construction of orthonormal bases for system identification, ieee trans. on automatic control, 42 (4): 515-521, 1997. [11] p. s. c. heuberger, p. m. j. van den hof, and o. h. bosgra, a generalized orthonormal basis for linear dynamical systems, ieee trans. on automatic control, 40(3):451-465, 1995. [12] paul van den hof, brett ninness, system identification with generalized orthonormal basis functions, in modelling and identification with rational orthogonal basis functions, springer verlag, chap.4, pp. 61-102, 2005. [13] g. favier and l. arruda, review and comparison of ellipsoidal bounding algorithms, in m. et al., editor, bounding approaches to system identification, plenum press, new york, chap.4, pp. 43-68, 1996. [14] h. messaoud and g. favier, recursive determination of parameter uncertainty intervals for linear models with unknown but bounded errors, in proc. 10th ifac symposium on system identification, copenhagen, denmark, pp. 365-370, 1994. [15] s. maraoui and h. messaoud, design and comparative study of limited complexity bounding error identification algorithms, ifac, symposium on system structure and control, prague, cheque republique, pp. 29-31, 2001. [16] j. ghabi, a. douik and h. messaoud, a new estimation method of the poles for the generalized orthonormal bases filters, in proc. of the 6th ispra’07, greece, pp. 197-202, february 2007. [17] j. ghabi, a. douik and h. messaoud, a new technique of poles estimation for generalized orthonormal basis functions, journal on systems and control, vol.2, pp. 125-132, 2007. ali douik, jalel ghabi and hassani messaoud ecole nationale d’ingénieurs de monastir (enim) département de génie electrique laboratoire atsi rue ibn el jazzar, 5019 monastir tunisie e-mail: ali.douik@enim.rnu.tn, jalel.ghabi@yahoo.fr, hassani.messaoud@enim.rnu.tn received: june 30, 2007 366 ali douik, jalel ghabi, hassani messaoud ali douik was born in tunis, tunisia. he received the master degree in 1990 and the ph.d. degree in 1996 of science electrical genius from the “ecole normale supérieur de l’enseignement technique de tunis”. he is currently “maitre assistant”in the “ecole nationale d’ingénieurs de monastir”and director of the “département de génie electrique”. his research is related to signal processing and automatic control. jalel ghabi was born in kairouan, tunisia. he received the diploma of graduate engineer in electrical genius from the “ecole nationale d’ingénieurs de monastir”, in 1997. he obtained her master of automatic and industrial data processing from “ecole nationale d’ingénieurs de sfax”, in 2003. he is currently preparing the phd. degree in control automatic in the laboratory atsi “automatique, traitement de signal et imagerie”. his research is related to the robust predictive control of mimo systems using generalized orthonormal basis functions. hassani messaoud was born in mahdia, tunisia. he received the master degree from the “ecole normale supérieur de l’enseignement technique de tunis”, in 1985 and the ph.d. degree in automatic control from the “ecole centrale de lille, france”, in 1993. in 2001, he received the ability degree from the “ecole nationale d’ingénieurs de tunis”. he is presently professor in the “ecole nationale d’ingénieurs de monastir”and director of the laboratory atsi. he has been the supervisor of several phd thesis and is author or co-author of several journal articles. his research is related to automatic control and signal processing. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 2, pp. 148-157 applying rbf neural nets for position control of an inter/scara robot fernando passold pontifical catholic university of valparaíso college of electrical engineering avenida brasil 2147, valparaíso, chile e-mail: fernando.passold@ ucv.cl abstract: this paper describes experimental results applying artificial neural networks to perform the position control of a real scara manipulator robot. the general control strategy consists of a neural controller that operates in parallel with a conventional controller based on the feedback error learning architecture. the main advantage of this architecture is that it does not require any modification of the previous conventional controller algorithm. mlp and rbf neural networks trained on-line have been used, without requiring any previous knowledge about the system to be controlled. these approach has performed very successfully, with better results obtained with the rbf networks when compared to pid and sliding mode positional controllers. keywords: manipulator robots, position-force control, neural networks. 1 introduction this paper describes and discusses practical results obtained with the use of computational intelligence techniques, specifically artificial neural networks, applied to the position control of a real manipulator robot. the neural controller proposed in this work was applied in a real scara robot installed at the industrial automation laboratory of the federal university of santa catarina, brazil. the work is the result of a cooperation between the automation and mechanical engineering departments. the robot was manufactured by the institute of robotics (ifr) of the eth (swiss federal institute of technology, http://www.ifr.mavt.ethz.ch/ ). differently from most industrial manipulator robots, this one has an open architecture, which allows the implementation of any type of control law. the main purpose was to evaluate new algorithms for position/force control, since the robot is also equipped with a force sensor. fig. 1 shows the inter/scara robot. manipulator robots are a type of non-linear and time variant systems. conventional controllers, such as pd and pid, are used among other advanced and robust controllers that require some knowledge about the dynamic model of the system under control. in the case of manipulator robots, it is difficult to obtain some parameters, as the inertia matrix and mass centers of any joint, with sufficient accuracy. therefore, either adaptive controller are required to overcome these inaccurate parameters or control laws based on lyapunov functions are developed to guarantee some kind of stability [1]. both approaches require some knowledge about the system. this work explores a computational intelligence technique, namely artificial neural networks, to deal with this situation. in particular, control algorithms based on neural networks and fuzzy logic techniques are considered intelligent control approaches that do not require any previous knowledge about the system to be controlled. the main goal of this work is to explore an effective computational intelligence technique to control this complex system using a structure as simple as possible, with preference to one that requires less change in the previous conventional controller that has installed in the system, do not overload the main processor and is robustness against disturbance and load effect variations. copyright © 2006-2009 by ccc publications applying rbf neural nets for position control of an inter/scara robot 149 q3–, z z ↓↓ q3+, z z ↑↑ q0 z0 q1 qq22yy00 xx00 xx yy xx yy θθ–– zzmáxmáx= 44.55 [cm]= 44.55 [cm] zzminmin= 18.45 [cm]= 18.45 [cm] l1 l2 rrminmin=25=25 [cm][cm] rrmáxmáx=50=50 [cm][cm] figure 1: inter/scara robot and its coordinate system. 2 neural controller types artificial neural networks (nns) have been applied to several cases of control systems, showing special adequacy when we have to deal with complexity, non-linearity or uncertainty [13]. the neural approach is interesting, notably in the cases where: a) mathematical models of the process are poor or do not even exist and linear models are inadequate to represent the system with sufficient accuracy; b) the system works satisfactorily with an existing controller but its performance deteriorates substantially when high performances (e.g. speeds) are required, so non-linear controllers become necessary. nns have proved their ability to approximate arbitrary nonlinear maps and the availability of methods for adjusting their parameters on basis of input and output data makes them particularly attractive in identifiers and controllers [2]. narendra also comments that it seems to be valuable to keep linear and non-linear components working in parallel, where the neural networks represent the non-linear components [2]. he also mentions the brief learning time periods and the increase of accuracy that result from this kind of integration. in the control systems area, a few neural models have been proved to be more suitable than others, mainly: 1) multilayer perceptron networks (mlp), and; 2) radial base function networks (rbf). among several ways to apply nns in a control scheme, we can cite: (i) inverse identification (requires undesirable off-line training), (ii) reference model structure, (iii) internal model control, (iv) predictive control (uses two nns, one of them trained off-line to identify the system), (v) optimal control (also requires two nets: the first one is used to quantify the state space of the system and the next acts as a 150 fernando passold classifier). katič and vukobratovič [3] and morris and khemaissia [4] discuss two learning architectures that seem to be the most appropriate and promising: a) feedback-error learning architecture; and b) adaptive learning architecture. the feedback error learning approach is characterized by the nn inserted in the feedback path to capture the nonlinear characteristics of the system. the ann weights are tuned on-line with no off-line learning phase and, when compared with the adaptive technique, we do not require any knowledge of the robot dynamics, linearity in the unknown system parameters or the tedious computation of a regression matrix. thus, this approach is model-free and represents an improvement over adaptive techniques [5]. 2.1 multilayer perceptron net this network consists of a set of sensory input units (source nodes) that constitute the input layer, one or more hidden layers and an output layer. the input signal propagates through the network in a forward direction, on a layer-by-layer basis [6]. multilayer perceptrons could be trained in a supervised manner using the back-propagation algorithm. back-propagation is based on the error-correction learning rule and uses a least-mean-square error algorithm to adjust its connection weights. the error back-propagation learning consists of two stages: first, a forward phase, when the input vector is applied to the sensory nodes of the network, and its effect propagates through the network layer by layer. finally, an output set is produced as the current response of the network. during the forward phase the weights of the network are kept unchanged. in the second phase, the backward phase, an error signal is propagated backward through the network against the direction of synaptic connections and the weights are adjusted to make the current response of the network move closer to the desired response based on a steepest descent algorithm, or back propagation weight update rule, also called generalized delta-rule [6]. 2.2 radial base functions net the basic structure of the rbf network consists of three layers. different from the mlp networks, the layers here have different tasks. the first layer is passive and only connects the model to the real world. the second layer is a unique hidden layer. it performs a non-linear transformation from the input vector space to the internal vector space. the last layer is the output layer and transforms internal vector space into output vector space in a linear manner. there are several algorithms available to train the network [6, 7, 8]. these two types of neural nets can be universal function approximators [6, 8]. 3 controller proposed the controller proposed here uses a conventional pd or pid that performs in parallel with an artificial neural network trained on-line. this kind of architecture for neural controllers is known as feedback error learning because the net uses the output signal generated by the conventional controller as its own error signal that is back propagated for learning purposes ([2, 5]). fig. 2 shows the architecture applied in this case. both types of nns tested deal with the same input data vector: xnn = [ q q̇ q̈d ] , where q is the vector of current joint positions; q̇ is the vector of current joint speeds (obtained through numerical differentiation); q̈d corresponds to the desired joint accelerations (like in other manipulators, there is no accelerometer available for each joint, so the desired acceleration computed by the path generator was used). these inputs were bounded into its maximum and minimum possible operational values for this robot and then scaled between the neural input range -0.9 up to +0.9 only for the mlp nets. note that, different from mlp nets, the rbf nets do not need a scale procedure sice that they could deal directly with rough data, but it was necessary to organize the input data into three different classes: 1) joint positions, 2) joint speeds and 3) joint accelerations – see fig. 3. the idea behind organizing the data applying rbf neural nets for position control of an inter/scara robot 151 path generator conventional controller robot robot q q�,dq�� qq �,dddqqq��� ,, fbτ nnτneuralnetwork finalτ + + + figure 2: feedback-error-learning neural controller used. in different classes comes from previous skills with fuzzy inference systems. rbf networks could be compared with fuzzy inference systems [9]. each class of input data could be understood as linguistic operations of fuzzy systems. each class of data is mapped using m gaussian functions and could be compared to the m membership functions that will be used in a fuzzy system. and finally, the rules and the way they are evaluated in a fuzzy system were performed by the output layer of a rbf network. each synaptic connection of the output layer could be compared to the fuzzy if-then-rules. the overall outputs are derived from the weighted sum done by the output layer [10]. hence, m gaussian functions were created to categorize each vector of each class of input data, as can be seen in fig. 3. it could be argumented that the massive amount of data required for the input layer (3 classes × 4 d.o.f. × m gaussian functions) is a drawback of this approach, but this solution was related to the final application in mind in this case. motions in the plane xy were done by the first two joints of the robot. height (z) and final orientation (θ) is performed by the last two joints of this robot but there is a mechanical coupling between them (a ball-screw-spline system), i.e., changes only in the final orientation of the robot result in a small change in the final height reached by the robot. that, represents an extra challenge to develop an effective controller to this kind of robot. the centers xi of the m desired gaussian functions are fixed, based on the range (minimal and maximal values) of the input vector. that, allows defining the maximum euclidian distance, dmax, between each gaussian centers as: dmax = (xmax − xmin) / (m − 1) and then fixing the standard deviation (or spread) of each gaussian function to be used according to: σ = dmax√ 2m (1) the traditional back-propagation algorithm expanded with momentum term was used to adjust in real-time the weights of the mlp and rbf networks [6]. the addition of the momentum term to the delta rule traditionally used to update the weights of the net (based on the method of the descending gradient of the error signal), speeds up this algorithm, it eliminates decurrent oscillations of the calculation of the gradient and prevents the net to get paralyzed into a point of minimum local (and not global) in its surface errors [6]. both networks end with 4 neurons, each one to evaluate the torque needed to command each joint motor of the robot. the final torque sent to each joint is defined as: τfinal = τfb + τnn (2) where τfb is related to the torque evaluated by the conventional feedback controller that performs in parallel with one of these networks. pd and pid controllers working in the joint space have been used. the equation for the pid used is given by: τfb = b̂(q) [ kpq̃ + ki ∫ q̃ dt + kd ˙̃q ] (3) 152 fernando passold ϕ0 ϕi ϕn-1 ϕ0 ϕi ϕn-1 ϕ0 ϕi ϕn-1 ϕ0 ϕi ϕn-1 ϕ0 ϕi ϕn-1 ϕ0 ϕi ϕn-1 ϕ0 ϕi ϕn-1 ϕ0 ϕi ϕn-1 green functions hidden layer output layer input layer 1q� 1dq�� σ00q� 0dq�� σ1 2q� 2dq�� σ2 3q� 3dq�� σ3 0q 1q 2q 3q 11−m ϕ 0nn τ 1nn τ 2nn τ 3nn τ 11−m ϕ 11−m ϕ 11−m ϕ ijw 0ϕ iϕ 0ϕ iϕ 0ϕ iϕ 0ϕ iϕ figure 3: structure of the rbf net implemented. where b̂(q) refers to the inertia matrix of the robot (estimated); q̃ = qd − q represents the error between the desired and the actual joint position; ˙̃q refers to the velocity error; kp is the vector of proportional gains for each joint; ki refers to the integral vector gains and kd is the derivative gain vector for each joint. to get a pd action over the system, the ki vector was not used (equal to zero). 4 experimental results the proposed controllers were implemented to the inter/scara robot (fig 1). the first two links of the inter/scara robot acts like an xy planar robot, and each one has 0.5 meters of length and its mechanical transmissions use harmonic drives (hd) to reduce motor-joints frictions at a minimal level. the last two joints use a ball-screw-spline mechanical scheme that allows movement in z direction and the definition of the final orientation of these robot (angle θ). this robot could be manipulated in the z direction between 18.45 (cm) and 44.55 (cm). tab. 1 shows the denavit-hartenberg parameters among others of this robot. the inter/scara uses the xo/2 real-time object oriented operational system (www.xo2.org) developed by the institute of informatics of the swiss federal institute of technology (eth). it includes a high level object oriented programming language called oberon which is a kind of successor of modula 2 and pascal (see http://www.oberon.ethz.ch. the robot runs a xo/2 version over a powerpc 200 mhz applying rbf neural nets for position control of an inter/scara robot 153 table 1: denavit-hartenberg parameters of inter/scara robot. joint αi ai di qi mli τmaxi 0 0 250 665 q0 ∼= 6.3 333.0 1 180o 250 0 q1 ∼= 19.5 157.0 2 0 0 q2 0 * 877.0 3 0 0 0 q3 * 16.7 (degrees) (mm) (mm) (kg) (nm) note: ml1 includes the last 2 joints(*). equipped with 16 mbytes of memory, which communicates with its i/o devices using an industrial vme bus (67 mhz). the user interface is done through a tcp/ip connection with a pc running the oberon system 3 for windows win32 2.3 (http://www.oberon.ethz.ch/) over the windows 2000 pro. the user develops the whole control system of the robot (including text command interface with the user, initialization and security functions) and through a cross compiler the execution programm is downloaded to the cpu of this robot (up to 4mbytes of code). the algorithms for the proposed neural controllers were implemented as a real-time task running within a sampling rate of 1 millisecond. in each millisecond is evaluated the action of conventional controller, also it is evaluated the forward phase of the neural net and still the backward phase when the trajectory error, q̃ > 0.0001(rad|m). the controller was tested over the trajectory shown by fig. 4. table 2 shows the joint positions, speeds and accelerations developed for each joint. all the four joints were moved simultaneously. −15 −10 −5 0 5 10 15 20 25 30 25 30 35 40 45 50 55 x [cm] y [ cm ] vista superior [xyphi] a b step=0.246[s] (a) xyθ view. −5 0 5 10 15 20 20 25 30 35 40 a b vista lateral [xz] x [cm] z [ cm ] step=0.246[s] (b) xz view figure 4: trajectory used for the tests. the pd/pid controller were digitally implemented using a 1(ms) sampling rate, and also an anti wind-up scheme was introduced with it to limit the integrative values up to 10000. table 3 shows the parameters used for the tested pd/pid controllers. their gains was settled initially based on zieglernichols parameters and then better adjusted using trial-and-error method. fig. 5 shows the output torques developed by different controllers for the joint 3 (the last and faster). note the different performances developed by the pd+mlp1c (mlp with one single hidden layer), pd+mlp2c (mlp with two hidden layers), pd+rbf(5) (rbf with 5 gaussian functions) and pid+rbf controllers tested. 154 fernando passold table 2: parameters of the trajectory used for the tests. parameter joint 0 joint 1 joint 2 joint 3 qa 2.45 -1.85 -0.25 -1.57 qb 1.25 0.75 -0.46 0.00 [rad] [rad] [m] [rad] q̇máx -0.71 1.19 -0.22 0.85 [rad/s] [rad/s] [m/s] [rad/s] q̈máx 0.85 1.00 0.47 -0.93 [rad/s2] [rad/s2] [m/s2] [rad/s2] table 3: pd/pid gains used. joint 0 joint 1 joint 2 joint 3 kp 4900 12100 90000 14400 kd 140 220 600 240 ki 478 1200 9200 1410 fig. 6 shows the trajectory error during all the time. during the initial one-third of the robot configuration change period the nns are in their learning time and the conventional controller still predominates in the joint control. but even before the end of this period of time, it could be seen that the nns output torque takes predominance over the final torque evaluated (fig. 6). it could be seen that the nn learns the dynamic behavior of the system and then does the dynamic compensation that results in higher performance compared to a conventional controller. the best results for the mlp nets were achieved with learning rate η = 0.035 and momentum term α = 0.5. related to the rbf nets, the best learning parameters founded were: η = 0.005 and α = 0.5. note that a pid performing with a rbf net, allows the better performance (fig. 5(d) and 5(e)) followed by the pd+rbf, pd+mlp and finally, the single pd. it was noted that the use of two hidden layers for the mlp nn does not imply in a better performance and moreover adds a small residual memory effect in presence of a disturbance on the system (this behavior has been observed in tests where an elastic string was placed in the middle of a linear trajectory). experimental results have also demonstrated that the addition of more than 5 gaussian functions in the rbf nn controller, slightly increase the performance, but at the expenses of a significant higher computational cost. 5 conclusions this paper has presented a practical and successful application of a neural controller performing in parallel with a conventional controller in the position and trajectory following control of a real robot. the use of a conventional controller performing in parallel with the nns is advantageous to maintain the robustness of the system when the nn become saturated (due to high learning rates) and it is important to force the readjustment of the synaptic weights of the nn used when the robot changes its configuration. as soon as the nn captures the dynamic behavior of the system, the final torque is given quite totally by the nn and a higher performance could be achieved. both the mlp and rbf ann perform very well, applying rbf neural nets for position control of an inter/scara robot 155 2 3 4 5 6 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 time (s) (n m ) torques joint 3 (a) pd controller output torques. 2 3 4 5 6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 time (s) (n m ) torques joint 3 fb final nn τfinal τnn τ fb (b) pd + mlp1c controller outputs torques. 3 4 5 6 7 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 time (s) (n m ) torques joint 3 fb final nn τfinal τ nn τ fb (c) pd + mlp2c controller outputs torques. 3 4 5 6 7 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 time (s) (n m ) torques joint 3 fb final nn τ final τnn τfb (d) d) pd + rbf(5) controller outputs torques. 2 3 4 5 6 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 time (s) (n m ) torques joint 3 fb final nn τ final τnn τ fb (e) pid + rbf(5) controller outputs torques. figure 5: controllers output torques. 156 fernando passold 300 400 500 600 700 800 900 1000 -5 0 5 10 15 x 10 -3 sampling time (k) (r a d ) joint 3 error position pd pd+mlp2c pd+rbf pid+rbf pd pd+mlp2c pd+rbf pid+rbf figure 6: joint 3 some tracking error. but rbf does it better and faster than a mlp, mainly if it works in parallel with a pid controller. an additional and unexpected advantage could be achieved with the pid+rbf controller: robot motion with lowest noise levels (quite silent). on the other hand, there is also a drawback: nn requires more processing power to work in parallel with the conventional controller – see table 4 and fig 7 that summarize the results achieved by the different controllers. table 4: power computer resources required. processing time controller min max pd 104 104 pd + mlp2c 194 385 pd + rbf(5) 333 579 pd + rbf(7) 425 679 pd + rbf(9) 104 809 note: values expressed in microseconds (µs). even if computer resources are short, a simple pd+mlp with one hidden layer allows better results than a pd controller. otherwise, if higher processing power is available, a pid+rbf achieves the best results. since the neural controllers proposed here have performed very successfully, future directions of this works intend to establish an integrated position/force control over a hybrid control architecture to deal with robot applications that imply some contact with the environment. applying rbf neural nets for position control of an inter/scara robot 157 vs c pi d pd pd +m lp 1c pd +m lp 2c pd +r bf (5) pd +r bf (7 ) 0 500 1000 1500 2000 pd +m lp 2c pi d+ m lp 2 c 0 500 1000 1500 2000 vs c pd pi d pd +m lp 1c pi d+ m lp 2c pd +r bf (5 ) 0 500 1000 1500 2000 flops l o g (m s e ) less (1e-7) larger (1e 0) test w/disturbance trajectory “b” trajectory “a” better relation cost × benefits� � larger errors larger cost � figure 7: relationship within computational cost × benefit of the controllers tested here. bibliography [1] sciavicco, l. and siciliano, b., modeling and control of robot manipulators, mcgraw-hill, 1996. [2] narendra, k.s., neural networks for real-time control. in 36th ieee conference on decision and control cdc’97, 1026–1031, san diego, california, usa, 1997. [3] katič, d. and vukobratovič, m., connectionist based robot control: an overview. in 13th ifac, volume 1b-05 6, 169–174. san francisco, usa, 1996. [4] morris, a.s. and khemaissia, s., artificial neural network based intelligent robot dynamic control. in a.m.s. zalzala and a.s. morris (eds.), neural networks for robotic control – theory and applications, chapter 2, 26–63, ellis horwood, great britain. 1996. [5] kim, y.h. and lewis, f.l., neural network output feedback control of robot manipulators. ieee transaction on robotics and automation, 15(2), 301–309, 1999. [6] haykin, s., neural networks a comprehensive foundation, prentice hall, new jersey, usa, 2nd edition, 1999. [7] gabrijel, i. and dobnikar, a., adaptative rbf neural network. in soco’97 conference, 164–170. nimes, france. url http://cherry.fer.uni-lj.si:80/∼gabriel/soco97/soco97.zip, 1997. [8] girosi, f. and poggio, t., networks and the best approximation property. in m.m. gupta and d.h. rao (eds.), neuro-control systems, theory and applications, 257–264. ieee pres, piscataway, new jersey, usa, 1993. [9] fritzke, b., incremental neuro-fuzzy systems. in applications of soft computing, spie international symposium on optical science, engineering and instrumentation, san diego, 1997. [10] kiguchi, k. and fukuda, t., intelligent position/force controller for industrial robot manipulators – application of fuzzy neural networks, ieee transactions on industrial electronics, 44(6), 753–761, 1997. http://horos.rdsor.ro/ijcccv3n4draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 4, pp. 374-383 computation results of finding all efficient points in multiobjective combinatorial optimization milan stanojević, mirko vujošević, bogdana stanojević abstract: the number of efficient points in criteria space of multiple objective combinatorial optimization problems is considered in this paper. it is concluded that under certain assumptions, that number grows polynomially although the number of pareto optimal solutions grows exponentially with the problem size. in order to perform experiments, an original algorithm for obtaining all efficient points was formulated and implemented for three classical multiobjective combinatorial optimization problems. experimental results with the shortest path problem, the steiner tree problem on graphs and the traveling salesman problem show that the number of efficient points is much lower than a polynomial upper bound. keywords: multiple objective optimization, combinatorial optimization, complexity of computation. 1 introduction for all combinatorial problems cardinality of the feasible solution set grows exponentially with the problem size. for one group of combinatorial problems (e.g. the shortest path problem, the shortest spanning tree problem, assignment problem etc.) algorithms that can find a solution of a single-criterion problem in polynomial time are known. this problem class is denoted by p. for other combinatorial problems (e.g. traveling salesman problem, steiner tree problem, knapsack problem etc.) so called non deterministic polynomial algorithms exist. these problems belong to the class denoted by np. for this problem’s class it is not proved whether polynomial algorithms exist or not. there is a third class of problems (e.g. finding all spanning trees for a given graph) for which it is known that they can be solved only by exponential algorithms. in such problems result usually consists of exponential amount of data, so the exponential time is needed just to represent them. let’s denote this class by e. in all known literature which concerns multiobjective combinatorial optimization (moco), mostly the set of pareto optimal solutions is being observed, and it has been stated that its size can grow exponentially with the problem size. moreover, with sufficient number of uncorrelated criteria it is possible to achieve that every feasible solution is pareto optimal [1]. this implies that there is no efficient method of determining all pareto optimal solutions for problems of bigger dimensions, because such a procedure would not be even np hard, but strictly exponential, i.e. it would belong to class e. such results are confirmed for many known problems, such as: the shortest spanning tree, the shortest path problem, traveling salesman problem, assignment problem and knapsack problem [2, 3, 4, 5, 7]. in section 2 some multiobjective optimization models are introduced. section 3 presents the implemented algorithm which is applied in order to find all efficient points of multiobjective combinatorial optimization problems. experimental results described in section 4 show that the number of efficient points is even much lower than a polynomial upper bound. in section 5 some concluding remarks are formulated. 2 multiobjective optimization models the general model of multiobjective optimization problem can be briefly formulated as follows. min x∈x f (x) (1) copyright © 2006-2008 by ccc publications computation results of finding all efficient points in multiobjective combinatorial optimization 375 where x is the decision variable, x is the feasible solution set, and f = ( f1, . . . , f p) is the p-dimensional vector of objective functions. the solution x∗ ∈ x of problem (1) is pareto optimal if there is no other solution x ∈ x such that fk(x) ≤ fk(x∗) ∀k = 1, . . . , p, where at least one of these inequalities is a strict inequality. if x∗ is pareto optimal solution of problem (1), the point y∗ = f (x∗) is called the efficient point in the criteria space. the set of all pareto optimal solutions is called the pareto set and it is denoted by xpar. the set of all efficient points in the criteria space is called the efficient points set and it is denoted by ye f f . a solution obtained by minimizing the objective function fk(x) over x, is called the marginal solution for the k-th criterion of problem (1). one marginal solution which is pareto optimal is called pareto optimal marginal solution. if x is a pareto optimal marginal solution then f (x) is called efficient marginal point. remark 1. for each criterion k = 1, . . . , p at least one pareto optimal marginal solution exists. it can be obtained by lexicographic optimization putting k-th criterion to have the first priority. all notions regarding problem (1) can be applied to the next models which are defined as its particular cases. the model of multiobjective combinatorial optimization problem can be formulated as follows. min x∈χ f (x) (2) where x is the decision variable, χ is the feasible solution set of the model which is a subset of the power set of a finite set e (i.e. χ ⊂℘(e), e = {e1, ..., em}), and f = ( f1, ..., f p) is the p−dimensional vector of objective functions. moreover, x ∈ χ can be represented by a binary m-dimensional variable (x1, x2, ..., xm) ∈ {0,1}m where xk = 1 if and only if the corresponding element ek ∈ e belongs to x and xk = 0 if and only if the corresponding element ek ∈ e does not belong to x. the experiments related to the number of efficient points of multiobjective optimization problems were done on three types of multiple objective network problems: multiobjective shortest path problem (spp), multiobjective steiner tree problem on graphs (stp) and multiobjective traveling salesman problem (tsp). for all the three problems, an undirected graph g = (v, e) with |v | = v vertices, |e| = m edges and wk : e → z+, k = 1,2, ..., p weight functions on the edges is given. in addition, for spp starting vertex s ∈ v and target vertex t ∈ v and for stp set of terminal vertices t ⊂ v are given. for each of the problems, each feasible solution represents a specific graph structure. for spp it is a path from s to t and the feasible set χ is a set of all such paths. for stp, χ represents set of all steiner trees of graph g and terminal nodes t . and for tsp χ is a set of all hamiltonian cycles. for any of the problems, a feasible solution x ∈ χ is a set of edges that belong to a feasible graph structure. alternatively, a feasible solution is a vector (x1, x2, . . . , xm) ∈ {0,1}m that satisfies a set of constraints specific to each problem. the form of goal functions in each of above mentioned problems can be twofold, depending on the type of k-th criterion: • when a criterion represents length or weight, the corresponding goal function is linear as follows fk(x) = m ∑ j=1 c k j x j (3) and it is minimized. • when a criterion type represents capacity, the corresponding goal function is fk(x) = min 1≤ j≤m c k j x j (4) 376 milan stanojević, mirko vujošević, bogdana stanojević and it is maximized. problems of minimizing a function of type (3) are called minisum, while problems of maximizing a function of type (4) are called maximin or bottleneck problems. coefficients ckj, j = 1, . . . , m, k = 1, . . . , p in general case are real numbers that represent length, height, weight or capacity of elements of set e. in our experiments it is presumed that coefficients ckj, j = 1, . . . , m, k = 1, . . . , p have integer values. practically that does not mean a loss of generality because in real life problems coefficients are rational numbers which can be transformed to integers. in one multiobjective combinatorial optimization problem both kinds of goal function can exist. if functions of type (4) exist, they can be transformed to fk(x) = max 1≤ j≤m ( −ckj x j ) , (5) that have to be minimized, in order to match the general formulation (2). 3 applied algorithm in order to find all efficient points for mentioned problems an original algorithm was formulated and implemented. the algorithm is based on ε-constraints method which is usualy used in apriori approach (when the decision maker’s preferences about criteria are known before optimization). our method is developed for aposteriori approach (when optimization is done with purpose to present information about nondominated solutions to decision maker). f 2 1 f1 ε a d c b figure 1: sketch of applying algorithm for finding all efficient points of a combinatorial optimization problem with two objective functions min{( f1 (x) , f2 (x))|x ∈ x} the following algorithm was applied. algorithm 2. input: parameters of the problem. 1. for both criteria find efficient marginal points f k = ( f k1 , f k 2 ) , k = 1,2. 2. identify index s such as ∣ ∣ f 1s − f 2s ∣ ∣ = min { ∣ ∣ f 11 − f 21 ∣ ∣ , ∣ ∣ f 12 − f 22 ∣ ∣ } . use fs as a search criterion and another one, denoted by fo, as an optimization criterion. computation results of finding all efficient points in multiobjective combinatorial optimization 377 3. initialize ye f f = {( f 11 , f 1 2 ) , ( f 21 , f 1 2 )} and εs = f o s −1. 4. solve problem min{ fo (x)|x ∈ x , fs (x) ≤ εs} and denote by x∗ its solution. 5. ye f f = ye f f ∪{( f1 (x∗) , f2 (x∗))}. 6. put εs = fs (x ∗) −1. if εs > f ss then go to step 4, otherwise stop. output: set ye f f . an example of a part of criteria space and efficient points obtained by algorithm 2 is graphically represented in figure 1. points a and b represent eficient marginal points ( f 11 , f 1 2 ) , ( f 21 , f 2 2 ) of the problem. they were obtained by independent optimization of criteria f1 and f2 respectively and they are efficient points of the multiobjective problem. in this example f1 is the search criterion and f2 is the optimization criterion. points c and d are also efficient points of the problem and they were obtained in two succesive iterations. efficient point c is obtained in the first iteration. in the next iteration point d will be obtained by optimizing second objective function over the initial feasible set reduced by the additional constraint f1 (x) ≤ ε1. remark 3. algorithm 2 can be extended for problems with more than two criteria by putting one criterion to be optimization criterion and all others to be search criteria. efficiency of such procedure can be questionable because all search criteria have to be recursively checked. this implies time and resource consumption problems. also, efficient marginal points can be difficult to be determined in this case. 4 experiments problem spp, in its single objective version, belongs to the class p and its multiobjective version represents one of the most studied problems of moco. problem stp in its single objective version with a minisum criterion belongs to the class np, but if objective function is of type maximin, it belongs to the class p [9]. the single objective tsp, with any type of objective function (minisum or maximin) is an np hard problem. 4.1 description of the developed computer programs for each of the problems, a specific computer program was developed by authors. each program determines all efficient points (and one solution for each of them) using algorithm 2 adapted to the corresponding problem. the programs for spp and stp support instances with both kinds of objective functions minisum and maximin. program for tsp supports only instances with minisum criteria type. for all the experiments random instances with certain characteristics were generated. all instances had two non correlated criteria. each result of experiments is obtained as an average of 10 randomly generated instances with the same characteristics. for spp problem instances had specific structure in order to provide paths to have at least √ m edges. graph density varied between 36% and 60% depending on the number of vertices. instances with smaller number of vertices had higher density. instances for examining stp problem were generated so each vertex has a certain probability to be connected to any of other vertices. both, too dense or too spare graphs would not be proper for this kind of problem. average number of a vertex degree was 7 for all dimensions, e.g. average density was 35% for graphs with 20 vertices and 14% for graphs with 50 vertices. the number of terminals was 5 for all instances. 378 milan stanojević, mirko vujošević, bogdana stanojević tsp instances were generated in a similar way as stp, but density was higher, 50% for all graph dimensions. 4.2 example in order to present the procedure of performing experiments a short example of finding all efficient points of a small instance of stp problem is given below. the instance has the following characteristic: unoriented graph with 10 vertices and 20 edges (consequently, graph density is 44% and average number of vertex degree is 4), 5 terminal nodes, two criteria of type minisum. edges weights were generated randomly from interval [10,99]. criteria were not correlated. the upper triangular matrix of edges weights for both criteria is given below.                 0 98/95 45/17 92/12 41/45 0 67/58 12/89 32/47 0 65/98 22/61 71/67 19/32 0 33/49 83/32 0 18/70 11/93 51/47 10/53 0 44/51 0 12/29 0 0 15/25 0                 results obtained by developed program using algoritm 2 are presented in table 1. table 1: output results coordinates of efficient points no. first criterion values second criterion values remarks 1 130 286 supported point 2 174 276 3 176 236 supported point 4 199 234 5 232 231 6 236 200 supported point 7 275 186 supported point seven efficient points were obtained and their coordinates in criteria space are given in columns 2 and 3 of table 1. also, for each efficient point it is determined wether they are supported or non supported points (information given in column 4 of table 1). 4.3 types of the performed experiments tree groups of experiments were performed. the first group was inspired by a supposition that the upper bound for the number of efficient points depends on the length of the intervals from which edges lengths can get integer values. three such intervals were defined: i1=[10, 99], i2=[100, 999] and i3=[1000, 9999] (as sets of two, three and four digits numbers) which contain sets of integer values of cardinality 90, 900 and 9000, respectively. instances were generated so the lengths of edges would get random values from a certain interval, independently for each criterion. all combinations of the intervals for the first (c1) and second (c2) criterion were computation results of finding all efficient points in multiobjective combinatorial optimization 379 checked. all the experiments from this group were performed on graphs with 20 vertices. both criteria were of minisum type. for all problems, spp, stp and tsp, two values were observed: upper bound (ub) and actual numbers of efficient points (eff). the upper bound was calculated by the formula u b = min { f 2 1 − f 11 , f 12 − f 22 } + 1 (6) where ( f k1 , f k 2 ) , k = 1,2 are efficient marginal points of the k-th criterion. both ub and eff are calculated as the average of results obtained from 10 randomly generated instances. in paper [6] a different upper bound was also considered. although that upper bound has a polynomial growth with the problems size, it is more rough than ub. on the other side for its calculation were used only parameters of instances. upper bound ub presented in this paper is far more precise. but, in order to obtain it, p2 optimizations per instance are necessary to be performed in order to obtain efficient marginal points. the analysis and comparisons of upper bound and the actual number of efficient points were performed in order to get an idea about order of magnitude and relations between the values of the ub and eff. results of this group of experiments are given in table 2. table 2: dependence of the upper bound and efficient points number on the range of edge lengths for spp, stp and tsp prob: spp stp tsp c1 c2 ub eff ub eff ub eff i1 i1 136 9.6 133 7.5 483 40.5 i1 i2 147 6.6 172 8.9 546 44.1 i1 i3 166 7.6 159 7.9 498 39.3 i2 i2 1027 6.5 905 8.7 4951 44.0 i2 i3 1870 9.8 1804 9.3 5489 45.1 i3 i3 13967 8.5 13461 8.0 47606 39.1 average 8.1 8.4 42.0 the second group of experiments was performed in order to check the dependency of the number of efficient points on the graph size. because of the exponential complexity of the algorithms for finding all efficient points for all tree problem types when objective functions are of minisum type, the experiments were performed for instances with up to 50 vertices. performing experiments it was concluded that the number of efficient points for stp with both criteria types minisum and maximin, significantly more depends on the number of terminal vertices than on total number of vertices in graph. because of that, instances of problems spp and tsp with minisum criteria types were compared separately. experiments results for this group are represented in table 3. in order to demonstrate “independence” of efficient points number on graphs size for stp, some experimental results are given for maximin criteria types. for this problem, algorithm is polynomial, so instances with much bigger number of vertices were able to be solved. these results are given in table 4. the third group of experiments considered the types of criteria. three combinations were observed: when both criteria were of type minisum (s/s), when the first criterion was of type maximin and second was of type minisum (m/s) and when both criteria were of type maximin (m/m). this time the tsp problem was excluded from the experiments because the available software did not support solving tsp with maximin criterion. instances with both, 20 and 50 vertices were observed. 380 milan stanojević, mirko vujošević, bogdana stanojević table 3: dependence of the efficient points number on the graph size for spp and tsp problem spp tsp v ub eff ub eff 10 622 3.5 771 4.7 20 1096 8.3 4809 43.9 30 2158 15.0 8365 105.5 40 2333 19.1 14116 223.5 50 2371 16.8 18521 373.9 table 4: depedence of the efficient points number on the graph size for maximin stp intervals: i1 i2 i3 average for size v ub eff ub eff ub eff eff 50 35 5,1 224 3,9 3928 7,1 5,4 100 33 5,7 235 3,6 4269 7,2 5,5 200 24 4,0 267 5,5 3233 5,0 4,8 500 26 4,3 181 3,1 2357 3,7 3,7 1000 13 2,3 153 4,1 1858 3,9 3,4 2000 14 3,6 161 4,9 1830 4,1 4,2 average for interval 4,2 4,2 5,2 4,5 the results of the third group of experiments are represented in table 5. table 5: dependence of the efficient points number on the type of criteria for spp and stp criteria type: c=s/s c=m/s c=m/m problem v ub eff ub eff ub eff spp 20 2886 8.1 938 5.4 309 2.6 50 5689 20.0 1141 12.8 739 5.4 stp 20 2772 8.4 908 7.8 667 5.5 50 2815 8.9 818 9.6 674 5.8 5 conclusion we can make the following conclusions which are based on results presented in tables 2, 3, 5. although the problems spp, stp and tsp have very different nature, their number of efficient points show very similar characteristics. the most interesting are the values in columns eff. first of all, they are surprisingly small, and second, the influence of the observed parameters to it is very low or even insignificant. observing table 2, it is obvious and expected that upper bound ub grows with the size of the intervals from which edges take their values. very unexpected is that the actual number of efficient points does computation results of finding all efficient points in multiobjective combinatorial optimization 381 not show any dependence on the size of the intervals for all three problems. it is also obvious that tsp has about five times bigger number of efficient points than spp and stp which have similar number. explanation is that for instances we used, tsp solutions contain more edges than solutions of spp and stp. consequences are that the distance between the two efficient marginal points is bigger, because of that ub is also bigger and it is expected that bigger number of efficient points can be between them. observing the results from table 3 a little deviation can be noticed for spp between graphs with 40 and 50 vertices. namely, the number of efficient points on this stage starts to decrease. however, we concluded that the deviation is accidental, especially because in the next group of experiments, on different instances with the same characteristics (spp, 50 vertices, s/s) is obtained value 20.0 (shown in table 5) which matches the value expected in table 3. here the number of efficient points is bigger for tsp and moreover, it grows faster than for stp. this is in accordance to the previous explanation because the number of edges in solution for tsp grows linearly with number of vertices and for spp is approximately √ m. finally, the results from table 5 show that the number of efficient points is smaller for maximin than for minisum type of criteria, i.e. if more criteria are of maximin type, smaller will be the number of efficient points. a slightly deviation of that rule is in the last row where for m/s combination of criteria types is a bigger number of efficient points than for s/s. still we consider this as an accidental deviation. it was mentioned before and presented in table 4 that the number of efficient points for stp does not depend much on the number of vertices and it is also obvious from the last two rows of table 5. in all the considerations in this paper it was assumed that criteria are not correlated. on the other hand, the number of efficient points decreases with the increase of correlation between criteria. since it is known that between many criteria used in practice correlation exists (the length, time and price of path, price and reliability etc.), we can expect even less number of efficient points when it comes to practical problems. bibliography [1] m. ehrgott, multicriteria optimization, springer-verlag, 2000. [2] m. ehrgott, x. gandibleux, a survey and annotated bibliography of multiobjective combinatorial optimization, or spektrum, vol. 22, pp. 425-46 2000. [3] v.a. emelichev, v.a. perepelitsa, on cardinality of the set of alternatives in discrete manycriterion problems, discrete math. appl. vol. 2, pp. 461-471, 1992. [4] h.w. hamacher, g. ruhe, on spanning tree problems with multiple objectives, ann. oper. res., vol. 52, pp. 209-230, 1994. [5] i.v. sergienko, v.a. perepelitsa, finding the set of alternatives in discrete multicriterion problems, cybernetics vol. 23, pp. 673-683, 1987. [6] m. stanojević, m. vujos̆ević, b. stanojević, number of efficient points in some multiobjective combinatorial optimization problems, international journal of computers, communications & control, vol.iii (supl. issue), pp. 497-502, 2008. [7] m. visée, j. teghem, m. pirlot, e.l. ulungu, two-phases method and branch and bound procedures to solve the bi-objective knapsack problem, j. glob. optim. , vol. 12, pp. 139-155, 1998. [8] m. vujošević, m. stanojević, multiobjective traveling salesman problem and a fuzzy set approach to solving it. in: d. ivanchev, m.d. todorov (eds), applications of mathematics in engeneering and economics, heron press, sofia, pp. 111-118, 2002. 382 milan stanojević, mirko vujošević, bogdana stanojević [9] m. vujošević, m. stanojević, a bicriterion steiner tree problem on graph”, yugosl. j. oper. res., vol. 13, pp. 25-33, 2003. milan stanojević university of belgrade faculty of organizational sciences 154 jove ilića, 11000 belgrade, serbia e-mail: milans@fon.bg.ac.yu mirko vujošević university of belgrade faculty of organizational sciences 154 jove ilića, 11000 belgrade, serbia e-mail: mirkov@fon.bg.ac.yu bogdana stanojević transilvania university of braşov department of computer science 50 iuliu maniu, braşov, romania e-mail: bpop@unitbv.ro computation results of finding all efficient points in multiobjective combinatorial optimization 383 milanstanojević was born in belgrade, serbia in 1965. he graduated at university of belgrade, faculty of organizational sciences in 1990. he obtained doctoral degree at the same faculty in 2005. since 1993 he works at faculty of organizational sciences, in the begining as a teaching assistant and now as an assistant professor of operational research. he has published more than 40 papers in national and international journals, and conference procceedings in the field of operational research. his research interest includes multiobjective optimization, combinatorial optimization and software for operational research. mirko vujošević was born in podgorica, yugoslavia in 1951. he graduated in electrical engineering at university of belgrade where he also finished his postgraduate studies and earned his doctorate. from 1976 to 1995 he worked at mihailo pupin institute, belgrade, and now he hold the chair of operational research and statistics at the faculty of organizational sciences, university of belgrade. he has published more than 150 professional papers on different topics of operational research, reliability, maintenance, inventory control and applied mathematics. he is author and co-author of two monographs, (one of them published by elsevier), five textbooks, and several chapters in monographies. he is member of editorial boards of several scientific and professional journals and associated editor of yujor yugoslav journal of operational research. he is member of programme committees of several national and interanational conferences, as well as several professional societies: dopis yugoslav operational research society (he was its president for eight years), prim serbian society for applied and industrial mathematics, ieee institute of electric and electronic engineers, ors operational research society (u.k.), isir international society for inventory research and informs institute for operations research and management science. he is member of academy of engineering sciences of serbia. bogdana stanojević was born in oradea, romania in 1972. she graduated mathematics and computer science specialization at “transilvania” university of braşov, in 1995 and she obtained her doctoral degree in mathematics in 2003 from the romanian academy. currently she is an associate professor at computer science department of transilvania university of braşov. her research interests include different aspects of fuzzy optimization, multiple objective optimization and mathematical fundamentals of computers. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 4, pp. 326-334 a framework for enhancing competitive intelligence capabilities using decision support system based on web mining techniques i. anica-popa, g. cucui ionuţ anica-popa academy of economic studies of bucharest, romania e-mail: ionut.anica@ase.ro gabriel cucui valahia university of targoviste, romania abstract: nowadays competitive intelligence (ci) represents one of the most important pieces in strategic management of organizations in order to sustain and enhance competitive advantage over competitors. there are some studies that claim that a successful strategic management is influenced by the accuracy of external environment’s evaluation and, in the same time, in order to have correct and complete business strategies it is necessary to be sustained by competitive advantage. but till at the beginning of ’80 the things were totally different. this paper will present the evolution and the objectives of ci, the results of using ci in organizations and how can be improved the ci process using tools and techniques provided by business intelligence (bi). the study will propose a framework of a decision support system based on web mining techniques in order to enhance capabilities of organization’s competitive intelligence. keywords: competitive intelligence, information system, decision making process, web mining 1 introduction several studies ([13], [27], [30], [28], [32]) consider that starting with the second half of the 20th century a series of researches were focused on the enhancing competitiveness of the organization based on concept of intelligence as a process. [3], [37] state that the accuracy of external environment’s evaluation has a big influence over a successful strategic management and correct and complete business strategies must be sustained by competitive advantage. in the context of using methods and tools provided by information technology and communication (itc), the increasing significance of using information and knowledge in order to obtain a competitive advantage and the enhancing ability of organization in order to maximize the efficiency of using internal and external linkages through new data and information flows [32] represent an activity which must be closely supervised in every organization. there are studies claiming that competitive intelligence (ci), as a research area, attracted growing attention over the last two decades and especially in the last years ([7], [5]). some of the main factors which had a great influence over this evolution are: (1) spectacular development of the information technology and communication (itc) domain, (2) methods and tools provided by itc, (3) exponentially use of the internet, many ci researchers considering that internet represents the one of the major information data sources used by ci ([36], [41], [5]). the research objectives of this study are outlined below: • presenting a short overview of competitive intelligence (ci) evolution and possible ways of developing in future; " identifying the objectives of using competitive intelligence (ci) in organizations; • presenting a short overview of some specific tools and techniques provided by business intelligence (bi) which can be used in competitive intelligence; • identifying the results of using competitive intelligence (ci) in organizations; copyright c© 2006-2009 by ccc publications a framework for enhancing competitive intelligence capabilities using decision support system based on web mining techniques 327 • proposing a framework for a decision support system using web mining techniques. an extensive review of literature will be conducted in order to accomplish the goals of this study. 2 definition and evolution of competitive intelligence the literature related to intelligence suggests that are three directions for this area [5]: • the first is represented by the military domain where can be identified one of the earliest studies regarding intelligence, which is the art of war by sun tzu (translated by griffith in 1963); • the second is represented by the national security as a policy issue [4]; • the third is represented by the economic domain, where the intelligence determines an increasing competitiveness of organizations [12]. [21] consider that information is factual because consist in numbers, indicators, data about competitors, customers, suppliers regarding past actions of them, but intelligence represents a collection of cleaned, filtered and analyzed information that support managers in decision making process. the modern approach of competitive intelligence (ci) was appeared after the second world war and started to become important in the ’80 [15], the indicators that are sustaining this being: • the number of conferences having the topic competitive intelligence (ci) which is increasing constantly; • the increasing number of books, articles, studies, papers and other types of publication about competitive intelligence (ci); • the increasing number of academic courses and professional programs dedicated to competitive intelligence (ci); • the developing and increasing role of society of competitive intelligence professionals (scip) which is a global organization with declared mission to "be the global organization of choice for professionals engaged in competitive intelligence and related disciplines" and to "be the premier advocate for the skilled use of intelligence to enhance business decision-making and organizational performance" (www.scip.org) and having the declared vision: "better decisions through competitive intelligence". starting with the 1980 an important number of researchers tried to define the concept of competitive intelligence (ci). ci it is represented by information which describe how competitive is an organization and, in the same time, it is being able to predict moves of the business environment actors (competitors, customers, suppliers, government etc) [16]. ci represents an iterative and systematic process for gathering and analyzing data and information about activities of competitors, business environment and business trends in order to fulfill the goals of organization [21]. ci represents a process that predicts behavior and moves of the actors (competitor, customer, suppliers, government etc) which interact with organization or influence either the business environment or the behavior of organization [6]. the results provided by ci are used in order to identify potential business opportunities and to minimize the possibility of appearance for unpredictable situations, those facts define the goal of the ci: to predict the future situations (what is going to happen) rather than debate past situations (what did happen). 328 i. anica-popa, g. cucui ci is a process that aims to monitor the external business environment of organization in order to identify relevant information for decision making process [9]. competitive intelligence represents a process that consists of two levels [25]: • accessing legal and ethical data sources (publicly or semipublicly) in order to gather data for building new data sources regarding competitors, competition, environmental conditions, past, present or future trends etc. • analyzing those new data sources in order to transform data into usable and valuable information and knowledge that will support the decision making process. summarizing all those points of view we consider that competitive intelligence represents a continuous process of gathering data, information and knowledge about actors (competitors, customers, suppliers, government etc) which interact with organization in the business environment in order to support decision making process for enhancing competitiveness of organization. the ci process consists in the following steps: monitoring business environment (external data, information and knowledge), gathering, analyzing, filtering and disseminating intelligence that will support decision making process in order to increase competitiveness and improve position of organization. figure 1: competitive intelligence (ci) process [33] considers that evolution of ci could be divided in following stages: (1) "competitive intelligence gathering" occurred through the 60s and 70s; (2) "industry and competitor analysis" occurred through 1980 and 1987; (3) "competitive intelligence for strategic decision making" occurred through 1987 and 2000; (4) "competitive intelligence as a core capability" which represent the present stage in ci evolution. in the competitive intelligence gathering stage of ci the key defining event was the book "competitive strategy" published by michael porter in 1980. personnel involved in ci activities were located mostly in library or marketing department and the primary skill was capability to find information [33]. despite the fact that organization was collected large amount of data, over gathered data rarely were applied some static analyses. another important thing was represented by very weak connection between ci and decision making process. development of skills in information acquisition was the key issue of this stage. in the industry and competitor analysis stage of ci the key defining event was the founding of the society of competitive intelligence professionals (scip). personnel involved in ci activities were located mostly in planning or marketing department, over gathered data were applied quantitative analyses, and between ci and decision making process was a weak connection [33]. building a business case for ci, spy image and analytical skill development were the keys issues of this stage. a framework for enhancing competitive intelligence capabilities using decision support system based on web mining techniques 329 in the competitive intelligence for strategic decision making stage of ci the key defining event was the establishment of the competitive intelligence review. personnel involved in ci activities were located mostly in planning, marketing or ci unit, over gathered data were applied quantitative and qualitative analyses, and between ci and decision making process was a strong connection. demonstrating bottom-line input, role of information technology, ci technology, international ci, demands vs. supplydriven ci, counter-intelligence were the keys issues of this stage [33]. in the competitive intelligence as a core capability stage of ci the key defining event is represented by ci courses taught in universities and in business school across the world. personnel involved in ci activities are located mostly in ci units, planning or marketing, over gathered data are be applied qualitative emphasis analyses and information and knowledge provided by ci represent a raw material for decision making. managing the parallel process, intelligence infrastructures for multinationals, ci as learning, network analysis are the keys issues of this stage. 3 the objectives and results of using ci in organizations from the beginning is important to emphasize that is not necessary that every organization to develop and implement an organized ci activity. in many cases formal information gathered from external sources (internet, media, government etc) and informal data and information collected, analyzed and filtered by managers represent a very flexible and efficient informal ci. but in the case of some organization, an informal ci is not enough in order to support tactic and strategic decision making process, fact that conduct to develop and implement an organized ci system. the ci can be used in order to increase the capabilities of the organizational memory (om) of the enterprise because the om contains organizational memory information systems which are based on knowledge management techniques [39]. in the last years, enterprises are constantly working to improve and to enhance their operations [40]. the reasons which are staying at the base of decision to implement a ci in organization are various [21]: • the globalization process which determine an increasing level of competition among organizations because the number of competitor is increasing and, in the same time, the quantity and quality of goods and services provided by them growing continuously. • the new products, services, methods and tools provided by information technology and communication domain. • the rapidly changing of the business environment where new business opportunities appear and disappear very fast and the period of time allocated for decision making processes is decreasing constantly. • political changes which affect and influence the business environment as well as the evolution of organizations. a review of the literature related to objectives of using ci in organization reveals quite a wide and ambitious arrangement of objectives for ci [31]. [1] consider that objectives of using an organized information system of ci are: (1) identifying and analyzing new business opportunities or the market trends; (2) developing or updating software using latest technologies, methods and tools for software developing; (3) maximizing revenues and minimizing expenses; (4) identifying, understanding and analyzing strategies, already implemented or in the phase of implementation. [31] consider that objectives of using an organized information system of ci are: (1) help organization in order to gain a competitive edge; (2) reveal opportunities and threats by surveying weak signals; 330 i. anica-popa, g. cucui (3) process and combine data, information and knowledge in order to produce new knowledge about competitors, customers, suppliers etc.; (4) provide useful information for managers in decision making process and reduce the period of time used by decision making process. in our opinion, the objectives of using an organized information system of ci in organizations are: • enhancing organization’s competitiveness. • predicting, with a high level of trust, business environment’s evolutions, competitors’ actions, customers’ requirements, even influences generated by political changes. • providing a better and better support for strategic decision making process. there are studies that identify some benefit derived from using organized information system of ci [16]: (1) increasing analytical skill for managers and the ability to anticipate moves of the other actors from organization’s business environment; (2) sharing ideas and knowledge inside organization in order to develop new ideas or knowledge or to integrate the existing into organization. some authors identified new benefits from using organized information system of ci [21]: (1) discovering new potential competitors or customers and supporting starting of new businesses (2) identifying and analyzing new technologies, products and processes that influence organization’s activities and behavior; (3) identifying and analyzing political or legislative standards or regulations that influence organization’s activities and behavior; (4) identifying and analyzing situations, from competitors, customers, suppliers or other, that evolved into successes or into failures. 4 decision support system and web mining in the last years, due to the economic evolution, the amount of data and information which must be used in order to adopt better decision has increase extraordinary; therefore the decisional process from organizations has a spectacular evolution [42]. companies gathering data from a various sources and deposit them in data warehouses which represent a collection of subject-oriented, integrated, timevariant and non-volatile data used in order to support the process of decision-making [20]. each person involved in the process of decision-making can and must be supported in performing his activities by specialized it tools [14]. [8] consider that decision support systems (dss) represents a specific class of information system that supports business and organizational decision-making activities. [19] state that business intelligence represents the ability of companies of analyzing and studying the behavior and actions from previous periods of time in order to understand actual position of organization and to predict or try to change what is going to happen in future period of time. [35] consider that business intelligence is composed from following components: (1) extraction, transformation and loading etl; (2) data mining; (3) online analytical processing olap; (4) enterprise reporting. in last decade, the internet growing was spectacular and the amount of information which reside on the internet is huge and nowadays the world wide web became represents one of the major source of data and information for all activities [11]. in order to extract relevant information from the web it is necessary to examine and to analyze the content, structure and usage of web resources using techniques of data mining [26]. because the scope of data mining is the identify patterns which are hidden in large databases [29], in the last decade, the web mining technologies developed in this period determine an increasing of volume of valuable material which can easily browsed and identified over the internet [18]. web data mining represents a group of three concepts (figure 2): web content mining, web structure mining and web usage mining and it is very important to make a clear distinction between these concepts ([10], [24], [22]). [34] consider that web content mining represents "the process of searching for contextually relevant sources of web data whose embedded information can be extracted and used to generate actionable knowledge". [24] consider that "web structure mining tries to discover the model underlying a framework for enhancing competitive intelligence capabilities using decision support system based on web mining techniques 331 the web hyperlink structure". [34] consider that the web usage mining represents "the process of searching for user behavior patterns by mining the data stored in referrer logs, server access logs, and other web user behavior data repositories". figure 2: structure of web data mining there are studies ([23]; [38]) that consider that web content mining and web structure mining can be used to gather data about business competitors in order to enhance business decision-making and organizational performance 5 proposed framework for a decision support system using web mining capabilities because a modern company’s information system must also contain data and information acquired from the web which are extremely dynamically. in the same time, during the decision making process, the information system must be updated with the latest data, information and knowledge. in this section will be presented a framework for decision support system using web mining techniques based on architecture consist of three tiers (figure 3): decision tier, logic tier and data tier. the decision tier, which is the top-most level of the model, receives the request from the user and invokes the logic tier for resolving the request after translating the request. after the receiving of results from logic layer, the decision layer transposes these into a format that help user to understand and use them. the logic tier, which is the middle level of the model, has the following functions: • gather data, information and knowledge from company’s information system, invoking data mining algorithms, performing calculation, making evaluations and finally transformation of the obtained data into useful information [2]; • gather data, information and knowledge from the web using specific web mining techniques and provide them to the data tier. data tier store and retrieve information from all databases contained by company’s information system and transmit then to the logic tier for processing. 6 conclusions understanding business environment dynamics and predicting its evolution is a challenging and difficult task for every information system of ci, but represents, in the same time, one of the most expected needs and requirements from organizations. an information system of ci used in an efficient way will determine an increasing level of efficiency of adopted and applied decisions. information system of ci 332 i. anica-popa, g. cucui figure 3: framework for dss based on web mining techniques is recommended to be developed, implemented and used in order to create a competitive advantage over competitors and became more and more evident that ci process is necessary in every organization which intends to increase the existing level of intelligence. bibliography [1] s. alampalli, role of ci in opportunity assessment online version. 2002, available from: http://www.scip.org/publications/cimarticledetail.cfm?itemnumber=985 (downloaded: 21 september 2006). 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[43] *** http://www.scip.org international journal of computers, communications & control vol. ii (2007), no. 3, pp. 269-278 efficient variable length block switching mechanism jaidhar c.d, a.v. reddy abstract: most popular and widely used packet switch architecture is the crossbar. its attractive characteristics are simplicity, non-blocking and support for simultaneous multiple packet transmission across the switch. the special version of crossbar switch is combined input crossbar queue (cicq) switch. it overcomes the limitations of un-buffered crossbar by employing buffers at each crosspoint in addition to buffering at each input port. adoption of crosspoint buffer (cb) simplifies the scheduling complexity and adapts the distributed nature of scheduling. as a result, matching operation is not needed. moreover, it supports variable length packets transmission without segmentation. native switching of variable length packet transmission results in unfairness. to overcome this unfairness, fixed length block transfer mechanism has been proposed. it has the following drawbacks: (a) fragmented packets are reassembled at the crosspoint buffer (cb). hence, minimum buffer requirement at each crosspoint is twice the maximum size of the block. when number of ports are more, existence of such a switch is infeasible, due to the restricted memory available in switch core. (b) reassembly circuit at each crosspoint adds the cost of the switch. (c) packet is eligible to transfer from cb to output only when the entire packet arrives at the cb, which increases the latency of the fragmented packet in the switch. to overcome these drawbacks, this paper presents variable length block transfer mechanism. it does not require internal speedup, segmentation and reassembly circuits. using simulation it is shown that proposed mechanism is superior to fixed length block transfer mechanism in terms of delay and throughput. keywords: crossbar switch, un-buffered crossbar switch, buffered crossbar switch, combined input crossbar queue switch. 1 introduction packet switching technology has become the predominant technology for high speed data networks and has begun to be used for applications like voice communication which have traditionally relied on circuit switching. recently, many large-scale fast routers have used input queued (iq) switches. in an input queued switch, variable length packets arriving at the inputs are segmented into fixed size packets known as cells for transmission over the switch and reassembled into packets at the output before being transmitted. cell transmission time is fixed and is called as cell time or time slot. when a packet size is not an integral multiple of cell size, padding bytes are needed for the last fragment and is called as segmentation overhead. smaller cell size generates more number of cells per packet and leads to a large switch header overhead. in high-speed switches/routers, segmentation results in heavy load. moreover, if an optical switching technology is introduced, it is even more difficult to segment optical domain packets. in high speed optical networks, it is more reasonable that incoming ip packets pass through a switch fabric based on packet by packet switching scheme. variable length packet switching considers the entire ip packet as a single switching unit, doing away with padding bytes and reassembly buffers. for high speed switches/routers, non-existence of reassembly buffers and circuits is an attractive feature. in ip networks, the speed at which a scheduler can switch cells is not really significant, as even if only one cell is remaining for switching in an input queue, it is impossible to reassemble a complete ip packet in the reassembly buffer. hence, it is reasonable to compare the packet switching with cell switching, from the point of view of quality of service (qos). copyright © 2006-2007 by ccc publications 270 jaidhar c.d, a.v. reddy in general, latency problem in packet switching scheme is worse than in cell switching scheme because of a decrease in the statistical multiplexing effect. however, from the packet latency view point, packet switching scheme performs better than cell switching scheme. in addition, packet latency is much better than that of cell latency for qos requirement, because each packet represents complete information. variable length packets dominate network traffic (e.g., ip packets in ethernet frames). thus, a study of high-speed switches that support variable length packet switching is needed. a high performance variable length packet switching mechanism is proposed, for efficiently switching variable length ip packets. performance of the proposed mechanism is evaluated in terms of packet latency and throughput. overall, performance of our mechanism is better than earlier one. 2 previous work buffers in an input queued (iq) switch can be a single queue or multiple queues. simplest one is single first in first out (fifo) queue, in which cells are served according to their arrival order i.e., first come first served (fcfs) basis. due to the head of line (hol) blocking problem, fcfs service discipline limits the maximum throughput to 58.6% [1]. to overcome hol blocking problem several techniques have been proposed. one of the techniques is virtual output queue (voq) [2] in which each input maintains separate queue for each output. voq approach overcomes the hol blocking problem. however, it creates an input contention. output contention occurs when more than one input wishes to send a packet to the same output at the same time. to resolve both input and output contentions, schedulers are used, whose function is to find one to one conflict free match between inputs and outputs in every time slot. recently many scheduling algorithms have been proposed for input queued switch [3– 8]. to cope with the overheads and the scheduler inefficiencies, internal speedup is used as an alternative solution. the switch speedup s is defined as the ratio of the switch bandwidth to the bandwidth of the line rates. internal speedup is a good solution but, it incurs significant cost. if the speedup is n for n ×n switch, arriving cells at the inputs get immediately transferred to their corresponding outputs. hence, buffers are used only at the output side and this kind of architecture is named as output queued (oq) switch. an oq switch can provide qos guarantees to individual data flow or groups of data flow [9]. however, oq switch is inherently less scalable when number of ports is more or link rate is higher. this makes an iq switch an attractive candidate when line rate is higher or number of ports is more. but, iq switch fails to provide guaranteed qos. to satisfy both the requirements of high switching capacity and guaranteed qos simultaneously, buffered crossbar switch was proposed as an alternative. buffered crossbar (bc) switch overcomes the limitation of un-buffered crossbar switch by employing buffers at each crosspoint. adoption of crosspoint buffer (cb) drastically improves the overall performance of the switch. the first bc switch was implemented as a large multi cabinet unit [10]. pure buffered crossbar (pb) switch employs buffers only at each crosspoint and nowhere else. incoming cells at the input, enter directly into the switch core to reside in their corresponding cb. pb switch consists of a fifo cb, preceded by an address filter (af) was proposed [11]. yet another pb switch with a restricted cb of size 16 kb was introduced [12]. there is a possibility for packet loss due to the restricted cb size. to prevent packet loss completely, larger cb is needed, but cb size is inversely proportional to switch size. when port size increases, it forces larger memory requirement to switch core. the requirement of larger cb could be minimized by employing buffers at the input side, in addition to cbs. such an architecture is known as combined input crossbar queue (cicq) switch. it separates input and output contentions. schedulers at each input and output port work independently and in parallel. cicq switch to use fifo input buffer was proposed [13, 14]. using simulation it is shown that the throughput of the fifo input buffered cicq switch is limited to 91% due to the hol blocking problem [13]. to overcome the hol blocking problem, voq’s have been used in most of the proposed efficient variable length block switching mechanism 271 cicq architectures. in the rest of the paper, virtual output queued cicq switch is referred as cicq switch. the cicq switch having voq was first proposed in [15]. to schedule the cells, oldest cell first (ocf) selection strategy is employed at all the contention points. cicq switch in which cb size is one cell, has been proposed [16] in which the longest queue first (lqf) scheduling is used at the input side and round robin (rr) scheduling is used at the output side. yet another cicq switch architecture, where cb is restricted to a single cell, has been proposed [17] in which rr scheduling style used at all the contention points. all these scheduling algorithms were just simple mapping of earlier algorithms, proposed for un-buffered crossbar switch into the new cicq switch architecture. 3 combined input crossbar queue switch (cicq) architecture our n ×n cicq switch model is shown in fig. 1 and has a structure as described below. figure 1: combined input crossbar queue switch with round robin scheduling at all the contention points. input queue: there are n voq’s at each input, one for each output. packets arriving at the input i destined for output j are stored in voqi, j. internal fabric consists of n2 crosspoint buffers. the cbi, j stores the packets coming from input i destined to output j, where i, j = 1, 2,. . . , n and its size is set to 2250 bytes. scheduler: each input and output port has its own scheduler and each of them work independently and in parallel. at all the contention points round robin (rr) scheduling is used. input scheduler selects voq from among the active voqs. a voqi, j is said to be active for being scheduled in the input scheduling process, if it is not empty and the corresponding cbi, j has enough space to accommodate the incoming block. i.e., the value of its credit counter is greater than or equal to the size of its head block. the output scheduler is responsible for: (a) selecting the next eligible flow (cb) in its column; (b) initializing the transmission of packets to the specific switch output and sending a credit back to the appropriate input credit scheduler. a flow is eligible when the corresponding cb is not empty. if there is more than one eligible flow, the output scheduler has to select one of them in a rr fashion. flow control: a credit based flow control mechanism is used in order to provide lossless transmission between input port and cb [18]. each input i maintain n credit counters, one for each voq. initially, value of these counters is set to the cb size. whenever an input scheduler forwards a block from a voq, it decrements its respective credit counter by the size of the forwarded block. when a block departs from cbi, j, its corresponding output scheduler sends a credit back to the respective voqi, j. 272 jaidhar c.d, a.v. reddy 3.1 variable length packet switching significant advantage of the cicq switch is their capacity to directly switch variable length packets without segmenting it. all the input and output schedulers operate independently and in parallel. hence, there is no global “time-frame” that constrains the system to transmit fixed size packets. this does not hold good for unbuffered crossbar switch. native switching of variable length packets eliminates the internal speedup and reassembly circuit at all the outputs. however, native switching of variable length packets results in unfairness. consider two voq’s at a port i, where voqi,1 is saturated with large packets and voqi,2 is saturated with small packets. in this scenario, rr polling alternatively selects voqi,1 and voqi,2 regardless of packet size and voqi,1 achieves high transfer rate than voqi,2. fig. 2 shows unfairness caused by rr selection strategy for a 2 × 2 switch. to overcome this unfairness, fixed length block transfer mechanism has figure 2: native switching of variable length packets. been proposed [19]. in this mechanism, voq is eligible to transfer predefined block bytes of data (i.e., 1500 b) to its cb, when it gets the chance. each block consists of a set of entire packets and/or packet segments and packet reassembly is performed at the cb. hence, minimum cb requirement at each crosspoint is twice the maximum size of the block. due to restricted memory available to switch core, it does not work when port size is larger. packet is eligible to transfer from cb to output port only when entire packet has arrived at the cb. as a result, latency of the fragmented packet is increased. consider the worst case scenario where packet p1 of size 40 b and p2 of size 1500 b alternatively arrive at the input port. each block contains a fragmented packet. fragmented packet is delayed at the cb till the arrival of the complete packet, even when there is no output contention. moreover, number of reassembly circuits is square of the switch size, which adds to the cost of the switch. to overcome these problems variable length block transfer mechanism is proposed in this paper. variable length block transfer mechanism transfers up to a block of bytes, of a data packet from a selected voq to cb. block size may vary from block to block and its maximum size is restricted to 2250 bytes. block may contain set of entire packets or a single complete packet. packets that share a common destination, are packed inside the block continuously one after other. when entire packet cannot be accommodated in a single block, it is packed into a new block instead of fragmenting it. unlike cell switching, our mechanism does not use padding bytes to fill the block. hence, speedup is eliminated. fig. 3 shows variable length block transfer mechanism for the input port 1. the block 1 at the voq1,1 consists of set of entire packets p1,1 and p1,2. sum of the size of p1,1p1,2 and p1,3 is greater than 2250 bytes. hence, packet p1,3 is packed into new block, block 2 without fragmenting it. under heavy load, block size may be maximized. as a result, header overhead is reduced and crossbar operates very close to maximum efficiency. table 1 compares the proposed mechanism and fixed length block transfer mechanism. efficient variable length block switching mechanism 273 figure 3: variable length block transfer mechanism. table 1: comparison of the proposed mechanism and fixed length block transfer mechanism. characteristics variable length block transfer mechanism fixed length block transfer mechanism cross-point buffer size 2250 bytes 3000 bytes segmentation and reassembling circuit no yes scheduler round robin round robin packet segmentation no yes block size variable size fixed size 4 simulation experiment three simulation experiments were designed in order to compare the performance of the proposed mechanism with earlier ones. for all experiments, a 32 × 32 switch, port speed of 10 gbps, no internal speedup and single priority is assumed. round trip time (rtt) between line cards and switch fabric has been set to 40 b times (corresponding to 32 ns at 10 gbps line rate) which is the sum of the following delays (a) input arbitration (b) the transmission of a packet from an input port to the switch crossbar (c) the output arbitration and (d) the transmission of the flow control information back from the crossbar to the input port. delay is measured as the time interval between the first byte of the packet arriving at the input port and its first byte departing from the output port. the reported delay is averaged over all packets. experiment #1: poisson arrivals of variable length packets are assumed and each of the 32 input ports chooses an output port with a uniform distribution over the 32 output ports (λi, j = ρ/n for all i and j). every input port has identical offered load ranging from 80 to 98%. experiment #2: each input i hosts two active flows, flow i → i and i → (i + 1) mod n. the former flow consumes two thirds (2/3) of the incoming load and the latter consumes the remaining one third (1/3). poisson arrival of variable length packets is assumed and the offered load ranges from 50 to 98% and 80 to 98% for small and large size packets respectively. experiment #3: both variable length and fixed length block transfer mechanisms are modeled under non-uniform traffic such as unbalanced traffic as defined in [17]. it uses a probability w, as the 274 jaidhar c.d, a.v. reddy fraction of the input load directed to a single predetermined output, while the rest of the input load is directed to all outputs with uniform distribution. let us consider input port s, output port d, and the offered load for each input port ρs,d . the traffic load from input port s to output port d, ρs,d , is given by ρs,d =    ρ ( w + 1−wn ) , if s = d ρ ( 1−w n ) , otherwise when w = 0, the offered traffic is uniform. on the other hand, when w = 1 the traffic is completely directional from input i to output j, i.e., i = j. poisson arrivals of variable length packets are assumed and the throughput is measured as a fraction of the maximum possible one (320 gbps in our simulation). 5 experimental results fig. 4(a) shows the results for experiment #1 under bimodal packet size distribution in which packet size of 40 b and 1500 b alternatively arrived at the input ports. average delay of the proposed mechanism is lower than fixed length block transfer mechanism. in a fixed length block transfer mechanism, each block contains a fragmented packet. when the block arrives at the cb, the fragmented packets’ reassembly is delayed until the next packet arrives at the cb. segmentation and reassembly delay increases the latency of the packet. these types of delay are non-existent in our mechanism. hence, our mechanism exhibits shorter delay than the earlier mechanism. block of data bytes are eligible for transfer from cb to output, when cb gets the chance to transfer. if there is no output contention in a column, block of data bytes can immediately be transferred without waiting for the next packet. fig. 4(b) shows the results of experiment #1 in which packet size is uniform. our mechanism shows lower average delay than fixed length block transfer mechanism due to the non existence of segmentation and reassembly delay. figs. 5(a) and 5(b) show the results for the experiment #2 for small and large packets respectively. mean delay of block transfer mechanism is higher for larger packets than output queued switch. however, it shows smaller average delay for small packets. presence of two-stage buffering in a cicq switch introduces priority, based on packet lengths. the transfer time required for packets from a voq to a cp is proportional to the size of the packet. thus, a smaller packet requires less transfer time from a voq to a cp. suppose the transfer of a small packet from port 1 to cb1,1 and the transfer of a large packet from port 2 to cb2,1 begin at the same time. the small packet arrives at a cb1,1 before the large packet does at a cb2,1. thus, the small packet will be transmitted before the large packet, to an output link if the remaining cbi,1 for all ports i are empty. the effect is demonstrated by the smaller mean delay than that of the oq switch, for small packets, at a high offered load. the block transfer mechanism further adds the packet size-based priority within each port. in the block transfer mechanism, a multiple of small packets of a single block, are eligible for transfer from a single voq in the rr polling at an input port, giving higher priority to smaller packets over larger packets within the input port. this explains the lower small packet mean delay of the block transfer mechanism than the output queued switch for all offered loads considered. fig. 6 shows the results for the experiment #3. it is observed that our mechanism shows higher throughput than earlier ones. under heavy load, block size is maximized and as a result header overhead is reduced. 6 conclusions and future work in this paper, variable length block transfer mechanism is proposed to overcome the limitations of fixed length block transfer mechanism. arriving packets at the inputs are not segmented as a result padding bytes and internal speedup is not required. in addition it eliminates reassembly circuit and efficient variable length block switching mechanism 275 1 10 100 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 m ea n sw it ch de la y (n an os ec on ds ) offered load (%) fixed length block transfer mechanism 3 3 3 3 3 3 3 3 variable length block transfer mechanism + + + + + + + + (a) bimodel packet size 1 10 100 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 m ea n sw it ch de la y (n an os ec on ds ) offered load (%) fixed length block transfer mechanism 3 3 3 3 3 3 3 3 variable length block transfer mechanism + + + + + + + + output queued switching 2 2 2 2 2 2 2 2 (b) uniform packet figure 4: delay performance of the proposed mechanism, fixed length block transfer under uniform traffic. 276 jaidhar c.d, a.v. reddy 0.1 1 10 100 0.5 0.6 0.7 0.8 0.9 1 m ea n sw it ch de la y (m ic ro se co nd s) offered load (%) output queued switch 3 3 3 3 3 3 3 3 3 3 3 3 3 3 fixed length block transfer mechanism + + + + + + + + + + + + + + variable length block transfer mechanism 2 2 2 2 2 2 2 2 2 2 2 2 2 2 (a) small packets 1 10 100 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 m ea n sw it ch de la y (m ic ro se co nd s) offered load (%) output queued switch 3 3 3 3 3 3 3 3 fixed length block transfer mechanism + + + + + + + + variable length block transfer mechanism 2 2 2 2 2 2 2 2 (b) larger packets figure 5: results for diagonal experiment. efficient variable length block switching mechanism 277 0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1 0 0.2 0.4 0.6 0.8 1 t hr ou gh pu t unbalanced probability factor w variable length block transfer mechanism 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 fixed length block transfer mechanism + + + + + + + + + + + + + + + + + + figure 6: throughput experiment under unbalanced traffic with load 100%. buffers which reduces the cost of the switch. block size is maximized under heavy load resulting in reduction of header overhead, scheduler rate and power consumption. mean switch delay of the proposed mechanism is lower than earlier because of the not existence of the segmentation and reassembly. memory requirement at the each crosspoint is 25% lesser than the earlier one and it is feasible to implement. through simulation, proposed mechanism is compared with earlier one and found to be superior in terms of switch throughput and packet mean delay. our mechanism may produces the unfairness in terms of service rate when different voq’s have different arrival rate which we are trying to rectify in our further work. unfairness problem can be overcome by maintaining a counter called service counter for each voq and cb (vsci, j and cbsci, j). initial value of these counters is set to zero and the value may vary at the time of scheduling. during the time of scheduling, input port scheduler examines the content of the service counter vsci, j. if the value of vsci, j is greater than or equal to threshold value, voqi, j is not eligible in the current round and its value is updated as vcsi, j = vcsi, j—threshold value. otherwise voqi, j is eligible in the current round and its service counter value is updated as vcsi, j =vcsi, j+ voqi, j [block size] − minimum block size. bibliography [1] m. j. karol, m. g. hluchyj and s. p. morgan, input versus output queuing on a space-division packet switch, ieee transactions on communications, vol. 35, pp. 1347–1356, 1987. 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[18] h. t. kung and r. morris, credit-based flow control for atm networks, ieee network magazine, vol. 9, pp. 40–48, 1995. [19] k. yoshigoe and k.j. christensen, an evaluation to crossbar switches with virtual output queuing and buffered cross points, ieee network, vol. 17, pp. 48–56, 2003. jaidhar c.d. department of computer science and engineering national institute of technology tiruchirappalli – 620 015, india e-mail: csk0305@nitt.edu a.v reddy department of computer applications national institute of technology tiruchirappalli – 620 015, india e-mail: reddy@nitt.edu received: march 30, 2007 revised: may 19, 2007 international journal of computers, communications & control vol. ii (2007), no. 2, pp. 174-184 ant systems & local search optimization for flexible job shop scheduling production noureddine liouane, ihsen saad, slim hammadi, pierre borne abstract: the problem of efficiently scheduling production jobs on several machines is an important consideration when attempting to make effective use of a multimachines system such as a flexible job shop scheduling production system (fjsp). in most of its practical formulations, the fjsp is known to be np-hard [8][9], so exact solution methods are unfeasible for most problem instances and heuristic approaches must therefore be employed to find good solutions with reasonable search time. in this paper, two closely related approaches to the resolution of the flexible job shop scheduling production system are described. these approaches combine the ant system optimisation meta-heuristic (as) with local search methods, including tabu search. the efficiency of the developed method is compared with others. keywords: flexible production, ant colony, tabu search, job shop scheduling, makespan, optimisation. 1 introduction modern hybrid heuristics are by their nature non-exhaustive, and so there is often scope for different approaches to better previous solution methods according to the execution speed or the quality of feasible solutions. traditional approaches to resolve the fjsp are as varied as the different formulations of the problem, but include fast, simple heuristics [2][12], tabu search [15], evolutionary approaches [5] and modern hybrid meta-heuristics that consolidate the advantages of various different approaches [1][13]. the ant colony optimisation (aco) was described by dorigo in his phd thesis [6] and was inspired by the ability and the organisation of real ant colony using external chemical pheromone trails acting as a means of communication. ant system algorithms have since been widely employed on the np-hard combinatorial optimisation problems including problems related to continuous design spaces research [4], and job shop scheduling [16]. however, they have not previously been applied to the fjsp described in what follows. local search methods encompass many optimisation approaches and have been shown that the efficiency of their use with an ant system approach [7]. the approach described in this paper for the fjsp shows the quality of solutions found, using benchmark problems. the performances of the proposed approach are evaluated and compared with the results obtained from other methods. in this paper, an application of the ant system algorithms combined by the tabu search heuristic is proposed for solving the fjsp. thus, the fjsp is described and formulated in section 2. then, in section 3, the suggested approach by aco with the tabu search is described. an illustrative example is given in section 4. the last section will be devoted to the presentation of some results and some conclusions relating to this research work. 2 problem formulation the fjsp may be formulated as follows. consider a set of n independent jobs, noted ℑ = {j1, j2, ..., jn, 1 ≤ j ≤ j}, which are carried out by k machines mk, m = {m1, m2, ..., mk, 1 ≤ k ≤ k}. each job j j consists of a sequence of n j operations oi, j, copyright © 2006-2007 by ccc publications ant systems & local search optimization for flexible job shop scheduling production 175 i = 1, 2, ...n j. each routing has to be performed to achieve a job. the execution of each operation i of a job j j requires one ressource selected from a set of available machines. the assignment of the operation oi, j to the machine mk ⊆ m entails the occupation of the latter one during a processing time, noted pi, j,k. the problem is thus to both determine an assignment scheme and a sequence of the operations on all machines that minimize some criteria. • a set of j independent jobs. • each job is characterized by the earliest starting time r j and the latest finishing time d j. • denote by pti, j and ri, j respectively the processing time and the ready date of the operation oi, j. the pi, j,k represent the processing time pti, j with the machine mk. • a started operation can not be interrupted. • each machine can not perform more than one operation at the same time. • the objective is to find an operation ordering set satisfying a cost function under problem constraints. in this paper, the considered objective is to minimize the makespan cmax. 3 aco and tabu search for fjsp scheduling in this stage, the application of the combined ant systems with tabu search techniques in the resolution of fjsp problem are described. 3.1 construction graph and constraints generally, the fjsp can be represented by a bipartite graph with two categories of nodes: oi, j and mk. a task is mapped to a oi, j node; a machine is mapped to a mk. there is an edge between the oi, j node and the mk node if and only if the corresponding task can be assigned to the corresponding machine while respecting the availability of the machine and the precedence constraints among the operations of different jobs. the cost of assignment is directly related to the processing time of the task upon the machine. to model the process in a more straightforward manner, we use the construction graph that is derived from the utilization matrix. below is a sample construction graph. table 1: construction graph of 4 machines and 7 tasks. m1 m2 m3 m4 o1,1 10 7 6 13 o2,1 4 5 8 12 o3,1 9 5 6 12 o1,2 15 12 8 6 o2,2 9 5 7 13 o1,3 7 16 5 11 o2,3 9 16 8 11 with this construction graph, we can transform the fjsp into a traveling ant problem. specifically, given the representative table of n rows and m columns, and each of its cells is associated with pi, j,k, representing this one distance among oi, j and mk. an ant seeks to travel across the table in such a way 176 noureddine liouane, ihsen saad, slim hammadi, pierre borne that all of the following constraints will be satisfied: one and only one cell is visited for each of the rows. in the rest of this paper, "tour" and "solution" are used interchangeably; a pair of (operation, machine) means: operation is assigned to machine, table 2. table 2: solution of construction graph table 1 m1 m2 m3 m4 o1,1 6 o2,1 4 o3,1 5 o1,2 6 o2,2 5 o1,3 5 o2,3 8 3.2 ant systems scheduling the ant system approach was inspired by the behaviour of the real ants. the ants depose the chemical pheromone when they move in their environment, they are also able to detect and to follow pheromone trails. in our case, the pheromone trail describes how the ant systems build the solution of the fjsp problem. the probability of choosing a branch at a certain time depends on the total amount of pheromone on the branch, which in turn is proportional to the number of ants that used the branch until that time. the probability p fi jk that an ant will assign an operation oi, j of job j j to an available machine mk. each of the ants builds a solution using a combination of the information provided by the pheromone trail τi jk and by the heuristic function defined by ηi jk = pi, j,k. formally, the probability of picking that an ant f th will assign an operation oi, j of job j j to the machine mk is given in equation 1. p fi jk =    (τi jk)α∗(ηi jk)−β ∑ l∈d (τi jk)α∗(ηi jk)−β i f j ∈ d 0 i f j /∈ d (1) in this equation, d denotes the set of available non-executed operations set and where α and β are parameters that control the relative importance of trail versus visibility. therefore the transition probability is a trade-off between visibility and trail intensity at the given time. 3.3 updating the pheromone trail to allow the ants to share information about good solutions, the updating of the pheromone trail must be established. after each iteration of the ant systems algorithm, equation 2 describes in detail the pheromone update used when all ants have completed an own scheduling solution denote lants, that represent the length of ant tour. in order to guide the ant systems towards good solutions, a mechanism is required to assess the quality of the best solution. the obvious choice would be to use the best makespan lmin = cmax of all solutions given by a set of ant. ∆τ fi jk = { lmin lants i f i, j, k ∈ lants 0 otherwise (2) ant systems & local search optimization for flexible job shop scheduling production 177 after all of the ants have completed their tours, the trail levels on all of the arcs need to be updated. the evaporation factor ρ ensures that pheromone is not accumulated infinitely and denotes the proportion of śoldš pheromone that is carried over to the next iteration of the algorithm. then for each edge the pheromone deposited by each ant that used this edge are added up, resulting in the following pheromonelevel-update equation: τi jk = ρ.τi jk + nba ∑ f =1 ∆τ fi jk (3) where nba defines the number of ants to use in the colony. 3.4 tabu search optimisation a simple tabu search was also implemented for this optimisation fjsp problem. the proposed approach is to allow the ants to build their solutions as described in section 3.2 and then the resulting solutions are taken to a local optimum by the local search mechanism. each of these ant solutions is then used in the pheromone update stage. the local search is performed on every ant solution, every iteration, so it needs to be fairly fast. in the case of the fjsp problem, the method is to pick the machine responsible to the cmax and check if any operations oi, j could be swapped between other machines which would result in a lower makespan. following their concept, the local search considers one problem machine at a time and attempts to swap one operation from the problem machine with any other (non-problem) machine in the solution (non-problem operations). then the ants are used to generate promising scheduling production solutions and the tabu search algorithm is used to try to improve these solutions. the tabu search is performed on each problem machine and continues until there is no further improvement in the makspean value of the solution. an example of this algorithm is shown in section 4. 3.5 the set up parameter values the set up parameter values used in the ant system scheduling algorithms are often very important in getting good results, however the appropriate values are very often entirely problem dependent [7], and cannot always be derived from features of the problem itself: • α determines the degree to which pheromone trail is used as the ants build their solution. the lower the value, the less ‘attention’ the ants pay to the pheromone trail, but the higher values implicate the ants then perform too little exploration, after testing values in the range 0.1-0.75 this algorithm works well with relatively high values (around 0.5-0.75). • β determines the extent to which heuristic information is used by the ants. again, values between 0.1-0.75 were tested, and a value around 0.5 appeared to offer the best trade-off between following the heuristic and allowing the ants to explore the research space. • τ is the value to which the pheromone trail values are initialized. initially the value of the parameter should be moderately high to encourage initial exploration, while the pheromone evaporation procedure will gradually stabilise the pheromone trail. • ρ is the pheromone evaporation parameter and is always set to be in the range [0 < ρ < x]. it defines how quickly the ants ‘forget’ past solutions. a higher value makes for a more aggressive search; it tests a value of around 0.5-0.75 to find good solutions. 178 noureddine liouane, ihsen saad, slim hammadi, pierre borne • nba defines the number of ants to use in the colony, a low value speeds the algorithm up because less search is done, a high value slows the search down, as more ants run before each pheromone update is performed. a value of 10 appeared to be a good compromise between execution speed and the quality of the solution achieved. it is interesting to note that for each value of parameters the ant systems scheduling meta-heuristics yields a good solution. moreover, its convergence speed depends essentially on the number of used ants nba. 3.6 building a solution steps the main steps in the strategy of the fjsp system by ant systems and tabu search algorithm are given below. • initialize parameters nba, α , β , τ0, ρ . • create an initial solution and an empty tabu list of a given size. in order to generate feasible and diverse solutions, initial ants are represented by solutions issued from heuristic rules [12] (spt, dl, fifo, etc) and a random method. heuristics are used to approximate an optima solution as near as possible. • repeat the following steps until the termination criteria are met: – find new solution by ant systems procedure scheduling given in section 3.2. – evaluate the quality of the new solution. – if a new solution is improved then the current best solution becomes new solution – else if no new solution was improved then apply the tabu search optimisation given in section 3.4. – add solution to the tabu list, if the tabu list is full then delete the oldest entry in the list. – apply the updating pheromone trail procedure given in section 3.3. • end repeat 4 illustration example let us consider a flexible job shop scheduling problem, this example is to execute three jobs j j (j=1,2,3) and six machines mk (k = 1, . . ., 6) described in table 1. applying the ant systems meta-heuristic, the simulation propose four different scheduling with cmax = 19 ut (unit of time), shown in table 2 to 7. the solution given in the table 7 has a makespan equal to 19 ut. the machine m5 is the cause of this value of makespan. to solve this problem, the tabu search optimisation is applied for this solution. indeed, this method finds the operation o2,2 for job j2 on m2 that can be swapped with other machines which will reduce makespan to 18 ut. and this method finds that the operation o1,3 for the job j1 executed by m2 and can be swapped with m5 who will execute the operation o2,2 for the job j2. finally, the obtained solution by the tabu search is better than before, table 8. ant systems & local search optimization for flexible job shop scheduling production 179 table 3: example benchmark 3 jobs 6 machines : processing time and ordering operation. m1 m2 m3 m4 m5 m6 j1 o1,1 10 7 6 13 5 1 o2,1 4 5 8 12 7 11 o3,1 9 5 6 12 6 17 o4,1 7 8 4 10 15 3 j2 o1,2 15 12 8 6 10 9 o2,2 9 5 7 13 4 7 o3,2 14 13 14 20 8 17 j3 o1,3 7 16 5 11 17 9 o2,3 9 16 8 11 6 3 o3,3 6 14 8 18 21 14 table 4: solution 1 for benchmark 3 jobs 6 machines. nba = 10; α = 0.5; β = 0.5; τ0 = 0.01; ρ = 0.5 s1 o1 o2 o3 o4 j1 m6: [0,1] m1: [1,5] m2: [11,16] m6: [16,19] j2 m4: [0,6] m2: [6, 11] m5: [11,19] *** j3 m3: [0,5] m3: [5,13] m1: [13; 19] *** table 5: solution 2 for benchmark 3 jobs 6 machines. nba = 10; α = 0.75; β = 0.25; τ0 = 0.01; ρ = 0.5 s2 o1 o2 o3 o4 j1 m6: [0,1] m1: [1,5] m2: [5,10] m6: [10,13] j2 m4: [0,6] m5: [6,10] m5: [10,18] *** j3 m3: [0,5] m3: [5,13] m1: [13,19] *** table 6: solution 3 for benchmark 3 jobs 6 machines. nba = 10; α = 0.25; β = 0.75; τ0 = 0.01; ρ = 0.5 s3 o1 o2 o3 o4 j1 m6: [0,1] m1: [1,5] m5: [5,11] m6: [11,14] j2 m4: [0,6] m2: [6,11] m5: [11; 19] *** j3 m3: [0,5] m6: [5,8] m1: [8,14] *** table 7: solution 4 for benchmark 3 jobs 6 machines. nba = 10; α = 0.3; β = 0.7; τ0 = 0.01; ρ = 0.5 s4 o1 o2 o3 o4 j1 m6: [0,1] m1: [1,5] m5: [5,11] m6: [10,13] j2 m4: [0,6] m2: [6,11] m5: [11,19] *** j3 m3: [0,5] m6: [5,8] m1: [8,14] *** 180 noureddine liouane, ihsen saad, slim hammadi, pierre borne table 8: tabu search optimisation solution. o1 o2 o3 o4 j1 m6; 0; 1 m1; 1; 5 m2; 5; 10 m6; 10; 13 j2 m4; 0; 6 m5; 6; 10 m5; 11; 18 *** j3 m3; 0; 5 m6; 5; 8 m1; 8; 14 *** table 9: results of problem sets solution. results problem sets from [11] problem set kacem et al genace ant systems and tabu search optimisation fjsp 4-5 16 11 11 fjsp 10-7 15 12 11 fjsp 10-10 7 7 7 fjsp 15-10 23 12 12 results problem sets from [3] fjsp 10-6 32 29 28 fjsp 10-15 86 68 68 table 10: result example : fjsp 10-10 from [11] nba = 10; α = 0.1; β = 0.9; τ0 = 0.1; ρ = 0.25 * o1 o2 o3 j1 m7 :[0,2] m3 :[2,3] m4 :[3,4] j2 m1 :[1,3] m10 :[3,4] m10 :[4,6] j3 m10 :[0,1] m8 :[1,2] m8 :[2,4] j4 m9 :[0,1] m3 :[3,6] m4 :[6,7] j5 m9 :[1,3] m9 :[3,4] m4 :[4,5] j6 m6 :[1,3] m9 :[4,6] m9 :[6,7] j7 m1 :[0,1] m3 :[1,2] m4 :[2,3] j8 m5 :[0,2] m2 :[2,5] m2 :[5,7] j9 m3 :[0,1] m7 :[2,3] m6 :[3,4] j10 m6 :[0,1] m4 :[1,2] m7 :[3,5] ant systems & local search optimization for flexible job shop scheduling production 181 table 11: result example : fjsp 10-6 from [3] nba = 10; α = 0.25; β = 0.75; τ0 = 0.2; ρ = 0.5 * o1 o2 o3 o4 o5 o6 j1 m6 :[0,1] m6 :[4,9] m1 :[9,10] m2 :[15,21] m6:[23,26] m2 :[27,28] j2 m4 :[1,3] m6 :[9,12] m5 :[13,15] m3 :[16,19] m3 :[19,21] m1 :[21,24] j3 m1 :[2,3] m1 :[5,7] m2 :[14,15] m5 :[15,17] m4 :[21,22] m5 :[24,27] j4 m5 :[0,2] m6 :[3,4] m2 :[8,14] m1 :[14,15] m4 :[15,20] m4 :[20,21] j5 m1 :[0,2] m2 :[2,5] m4 :[5,11] m1 :[11,13] m4 :[13,15] m3 :[24,27] j6 m5 :[2,5] m3 :[8,12] m6 :[12,14] m1 :[15,17] m2 :[21,27] m6 :[27,28] j7 m4 :[0,1] m5 :[5,7] m1 :[7,9] m5 :[10,13] m6 :[20,23] m4 :[24,27] j8 m3 :[1,4] m2 :[5,8] m1 :[10,12] m5 :[17,20] m5 :[20,24] m1 :[24,27] j9 m3 :[4,8] m3 :[12,16] m6 :[16,17] m1 :[17,21] m3 :[21,24] m4 :[27,28] j10 m6 :[1,3] m1 :[3,5] m5 :[7,10] m4 :[11,12] m6 :[17,20] m4 :[22,24] 5 validation and comparison all ant systems and tabu search optimisation results presented are for 1000 iterations with 10 the number of ants, and each run was performed 10 times. the algorithms have been coded in vb language and tested using a p4 pentium processor 2.4 ghz and windows xp system. to illustrate the effectiveness and performance of the algorithm proposed in this paper, six representative benchmark fjsp instances (represented by problem n × m) based on practical data have been selected to compute. these benchmark instances are all taken from of brandimarte [3] and from kacem [11] as well as those from genace [14]. the different results obtained by proposed approach is presented and compared with the other methods in table 9. concerning the fjsp instances, the different results show that the solutions obtained are generally acceptable and satisfactory. the values of the different objective functions show the efficiency of the suggested approach, table 9. moreover, the proposed method enables us to obtain good results in a polynomial computation time. in fact, the efficiency of this approach can be explained by the quality of the ant system algorithms combined by the tabu search heuristic to the optimization of solutions. 6 conclusion in this paper, a new approach based on the combination of the ant system with tabu search algorithm for solving flexible job-shop scheduling problems, is presented. the results for the reformulated problems show that the ant systems with local search meta-heuristic can find optimal solutions for different problems that can be adapted to deal with the fjsp problem. the performances of the new approach are evaluated and compared with the results obtained from other methods. the obtained results show the effectiveness of the proposed method. ant system algorithms and the tabu search techniques described are very effective and they alone can outperform all the alternative techniques. references [1] a. c. f. alvim, f. glover, c. c. ribiero and d. j. aloise. a hybrid improvement heuristic for the bin packing problem, 2002. available from: http://citeseer.nj.nec.com/557429.html. [2] t. d. braun, h. j. siegel, n. beck, l. l. bölöni, m. maheswaran, a. i. reuther, j. p. robertson, m. d. theys, b. yao, d. hensgen and r. f. freund. a comparison of eleven static heuristics for 182 noureddine liouane, ihsen saad, slim hammadi, pierre borne mapping a class of independent tasks onto heterogeneous distributed computing systems. journal of parallel and distributed computing, 61(6):810-837, 2001. [3] brandimarte p., routing and scheduling in a flexible job-shop by tabu search, annals of operations research, vol. 2, pp. 158-183, 1993. [4] bilchev, g., parmee, i.c. : the ant colony metaphor for searching continuous design spaces. lecture notes in computer science, 993,pp. 25-39, 1995. [5] a. l. corcoran and r. l. wainwright. a parallel island model genetic algorithm for the multiprocessor scheduling problem. in selected areas in cryptography, pp. 483-487, 1994. [6] m. dorigo. optimization, learning and natural algorithms. phd thesis, dei, polytecnico di milano, milan, 1992. [7] m. dorigo and t. stützle. the ant colony optimization metaheuristic: algorithms, applications, and advances. in glover, f. and kochenberger, g., editors, handbook of metaheuristics, vol. 57 of international series in operations research and management science, pp. 251-285. kluwer academic publishers, 2002. [8] m. garey, d. johnson, r. sethi. the complexity of flow shop and job-shop schedules. mathematics of operations research, vol. 1(2), pp. 117-129, 1976. [9] m. garey and d. johnson. computers and intractability: a guide to the theory of np-completeness. freeman and company, san francisco, 1979. [10] i. kacem, s. hammadi and p. borne. approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. ieee transactions on systems, man and cybernetics, vol. 32(1), pp. 1-13, 2002. [11] i. kacem, s. hammadi and p. borne. pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. mathematics and computer in simulation, vol. 60, pp. 245-276, 2002. [12] n. liouane, s. hammadi and p. borne. robust methodology for scheduling production in uncertain environment. imacs multi-conference on computational engineering in systems applications, cesa’98, hammamet, 1998. [13] k. mesghouni. application des algorithmes évolutionnistes dans les problèmes d’optimisation en ordonnancement de production. thèse de doctorat, université de lille1, lille, 1998. [14] j. c. tay and n. b. ho. genace: an efficient cultural algorithm for solving the flexible jobshop problem. proceedings of the ieee congress of evolutionary computation, pp. 1759-1766, 2004. [15] a. thiesen,. design and evaluation of tabu search algorithms for multiprocessor scheduling. journal of heuristics, vol. 4, pp.141-160, 1998. [16] s. van der zwaan and c. marques. ant colony optimisation for job shop scheduling. in proceedings of the third workshop on genetic algorithms and artificial life, gaal 99,1999. noureddine liouane1, ihsen saad2,3, slim hammadi2, pierre borne2 1atsi : ecole nationale d’ingénieurs de monastir, rue ibn el jazzar, 5019 monastir, tunisie ant systems & local search optimization for flexible job shop scheduling production 183 2ecole centrale de lille, cité scientifique laboratoire d’automatique, genie informatique et signal bp 48, 59651 villeneuve d’ascq cedex, france 3ecole nationale d’ingénieurs de tunis unité de recherche lara-automatique bp 37, le belvédère, 1002 tunis, tunisie e-mail: noureddine.liouane@enim.rnu.tn, ihsen.saad@enit.rnu.tn, slim.hammadi@ec-lille.fr, pierre.borne@ec-lille.fr noureddine liouane was born in kairouan, tunisia, in 1963. he received the master degree of science electrical genius in 1988 from the “ecole normale supérieur de l’enseignement technique de tunis”. he received the ph.d. degree from ecole centrale de lille, france, in 1998. he is currently “maitre assistant” in the “ecole nationale d’ingénieurs de monastir” and director of the “institut supérieure des sciences appliquées et de la technologie de kairouan”. his research is related to the evolutionary optimization methods for discrete events systems, computer science and operational research. ihsen saad was born in monastir, tunisia, in 1977. he received the engineer diploma degree in electrical automatic control engineering from the “ecole nationale d’ingénieur de gabès”, tunisia, in 2002. he obtained the master of automatic and signal treatment in 2004 at the “ecole nationale d’ingénieur de tunis”. he is currently preparing the ph.d. degree in automatic and computer science within the framework of lagis-ec-lille and lara-enit cooperation. his research is related to the evolutionary optimization methods for discrete events systems, computer science and operational research. slim hammadi is a full professor of production planning and control at the ecole centrale de lille (french “grande ecole”). born in gafsa (tunisia) in 1962, he has obtained by 1988 the master degree in computer science from the university of lille (france). pr hammadi obtained a p.h.d degree in job-shop scheduling and control in 1991 at ecole centrale de lille. he is a senior member of ieee/smc and has served as a referee for numerous journals including the ieee transactions on smc. pr. s. hammadi was co-organizer of a symposium (ims) of the imacs/ieee smc multiconference cesa’98 held in hammamet (tunisia) in april 1998. he has organized several invited sessions in different smc conferences where he was session chairman. he was chairman of the international congress on “logistic and transport” lt’2004, mhosi’2005 and lt’2006. his teaching and research interests focus on the areas of production control, production planning, computer science, discrete and dynamic programming and computer integrated manufacturing. 184 noureddine liouane, ihsen saad, slim hammadi, pierre borne pierre borne was born in corbeil, france in 1944, he received the master degree of physics in 1967, the masters of electronics, of mechanics and of applied mathematics in 1968. the same year he obtained the diploma of "ingénieur idn" (french “grande ecole”). he obtained the phd in automatic control of the university of lille in 1970 and the dsc of the same university in 1976. he became doctor honoris causa of the moscow institute of electronics and mathematics (russia) in 1999 and of the university of waterloo (canada) in 2006. he is author or coauthor of about 200 journal articles and book chapters, and of 30 plenary lectures and of more than 250 communications in international conferences. he has been the supervisor of 64 phd thesis and is author of 20 books. he has been president of the ieee/smc society in 2000 and 2001. he is presently professor “de classe exceptionnelle” at the ecole centrale de lille and director of the french pluriformations national group of research in automatic control. international journal of computers, communications & control vol. i (2006), no. 4, pp. 101-109 computation on the optimal control of networked control systems with multiple switching modes over high speed local area networks gang zheng, wenli zeng, fanjiang xu abstract: the optimal control problem for the networked control system with multiple switching modes over high speed local area networks is addressed, where an initial state is a parametric vector. because in the general case, the time delay is much less than the sampling period and the possibility of the packets collision is much lower, it can be assumed that the influence of the time delay and the packets loss on the optimal controller design can be ignored. on the basis of the assumption, the networked control systems with multiple switching modes are modeled as a hybrid system. moreover, based on the bellman type inequality for the hybrid systems, a dynamic program to solve the optimal control with a parameter vector is proposed, in every step of the technique, the feasible region is divided into evenly distributed grid points, and then, the optimal control law is transformed into maximizing the lower bound of the cost to go function in grid points. finally, an experiment setup of the networked control system with multiple switching modes is constructed and a simulation example is given to illustrate the optimal control computation results. keywords: networked control systems, hybrid systems, optimal control, dynamic program, multiparametric program 1 introduction networked control systems (ncs) are feedback control systems wherein the control loops are closed through a real-time network[1]. the defining feature of an ncs is that information (reference input, plant output, control input, etc.) is exchanged using a network among control system components (sensors, controller, actuators, etc.). the use of a communication network offers advantages in terms of reliability, enhanced resource utilization, reduced wiring, and reconfigurability. networked control systems typically involve switching between several different modes, depending on the range of operation. in order to improve the performance of the system and save the operation cost, it is important to design an optimal control law for the systems with different switching modes. at present the methodologies of designing the optimal controllers for such networked control systems are few. [2] assumes that the controller-plant communication in ncs is periodic, the optimal controller design problem is formulated as one for sampled-data feedback systems with periodic discrete-time components, and give a necessary and sufficient condition for existence of discrete time periodic controller in terms of lmis, and derive a controller construction algorithm. [3]discusses the optimal h∞ control problem for networked systems with limited communication constraint, which is formulated as a periodic control problem, and proposes a heuristic search approach in conjunction with the convex optimization of controller to solve the optimization problem. the switching process in [2] and [3] is time-controlled. however, in practice, the switching may be completely determined by the system state, for such networked control systems, the whole state space is divided into cells which each correspond to a particular control mode, so that each cell has its own continuous dynamics associated to it. this type of the system is state-controlled and can be regarded as a hybrid system. hybrid systems have attracted considerable attention in recent years [4]. also, in practice, the initial state of networked control systems may be not determined, and for those with multiple switching modes, the unpredicted initial states may make the optimization process more complex. this paper model the class of networked control systems with multiple switching modes as a hybrid system, based on the model, a general cost function is defined and the optimal control problem copyright c© 2006 by ccc publications 102 gang zheng, wenli zeng, fanjiang xu for a general cost function is addressed, where a initial state is a parametric vector and is not predicted. furthermore, a solution to the optimal feedback control law is proposed, which combine the dynamic program and multiparametric program. finally, an example is given to illustrate the optimal computation results. 2 problem formulation 2.1 modeling networked control systems with multiple switching modes consider a class of networked control systems shown in figure 1. the plant, the sensors, the controller and the actuators are spatially distributed and connected together through a control network. in general, there are multiple control modes in the controller of the class of systems, and every mode corresponds to the range of the continuous state of the physical plant. we assume that the control network is a high speed local area network, e.g. ethernet, and the transmitting data policy is scheduled releasing policy, the time delay is much less than the sampling period in practice and the possibility of message collision is much lower, therefore, in this paper, it is reasonable to ignore the influence of the time delay and packets loss on the optimal controller design in the networked control systems with multiple switching modes. figure 1: the networked control system with multiple control modes on the basis of the assumption, the class of networked control systems with multiple modes can be modeled as a hybrid system. hybrid systems contain continuous dynamics and discrete events [4]. the controller in networked control systems can calculate the state of the plant and determine the control modes, and the switching between different modes corresponds to the occurrence of the discrete event in hybrid systems. we assume that clock-driven sensors sample the plant outputs periodically at sampling instants, then the networked control system can be modeled as a hybrid system as following: { x(k + 1) = fq(k)(x(k), u(k)) q(k + 1) = v(x(k), q(k)) (1) where continuous state variable x ∈ x , rn, continuous input variableu ∈ u , rm, k denotes k-th sampling period, k = 0, 1, 2, ... , q ∈ q, q is a finite countable set, whose elements correspond to the modes in the networked control system, fq : x × u → x denotes the continuous dynamics in discrete mode q, v : x × q → q denotes the discrete dynamics, which is a transition process between operation modes. without the loss of the generality, we think the transition between two modes occurs in the sampling time instant. computation on the optimal control of networked control systems with multiple switching modes over high speed local area networks 103 the hybrid system model (1) of the networked control system with multiple modes can also be denoted a hybrid automaton[5]. in addition, if the continuous dynamics can be denoted by the linear time invariant system, the model can transformed into the piecewise affine system[6], where different modes correspond to different ranges of state variable x. 2.2 optimal control formulation based on the model (1), the optimal control formulation in finite time horizon is discussed as follows. firstly, define the cost function of the networked control system j(un , x(0)) = fq(n)(xn ) + ∑n−1k=0 [ fq(k)(xk, uk) + g(xk, q(k), q(k + 1)) ] (2) where n is the time horizon, starting from the state x0 = x (0), xk denotes the continuous state vector at time k and q(k) denotes the discrete state vector corresponding operation mode at time k, the column vector un ,[ut0 , ... , utn−1]t ∈ rm, fq(n)(xn ) > 0 is a cost at end time, fq(k)(xk, uk) > 0 is a cost for continuous evolution in an operation mode and g(xk, q(k), q(k + 1)) ≥ 0 is a cost for switching from one mode to another mode, if there is no switching, i.e. q(k) = q(k + 1), g(xk, q(k), q(k + 1)) = 0. note that, we don’t consider the problem of infinitely many jumps in transition time instants. also, it should be noted that q(0) is not written in the left of cost function (2), because the operation modes correspond to the range of the state variable, if the initial condition x(0) is given, and the discrete mode q(0) is fixed, too. the optimal control problem of the networked control system is j∗(x(0)) ∆ = min {un} j(un , x(0)) (3) s.t.    xk+1 = fq(k)(xk, uk) q(k + 1) = v(xk, q(k)) xn ∈ x f x0 = x(0) (4) where x f is the terminal region, in addition, x j denotes the set of states x j at time j for which (3) is feasible. in general, the optimal control problem (3)-(4) may not have a minimum for some feasible x(0). this is caused by discontinuity of the hybrid system in the input space. we will consider problem (3)-(4) assuming that a minimizer u∗n (x(0)) exists for all feasible x(0). note that in this paper we will distinguish between the current state x(k) of the system (1) at time k and the variable xk in the optimization problem (3)-(4), that is the predicted state of the system (1) at time k obtained by starting from the state x0 = x(0) and applying the input sequence u0, ..., uk−1to system (1) . analogously, u(k) is the input applied to system (1) at time k while uk is the k-th optimization variable of the optimal control problem (3)-(4). in the optimal control problem (3)-(4), the initial state x(0) is a vector of parameters, the goal in this paper is to solve (3)-(4) for all values of x(0) of interest, and to make this dependence explicit. therefore, the optimal control problem of the networked control system with multiple modes is a multiparameter program problem. 3 solution to the optimal control of the networked control systems for the networked control systems with multiple modes, the optimal control problem can be viewed as a parametric programming one. the parametric program is one of the important optimization techniques in mathematical program, where the optimality is affected by the uncertainty of the parameters 104 gang zheng, wenli zeng, fanjiang xu in the cost function or the constrained conditions. because the parameters are either unknown or that will be decided later, parametric programming need to subdivide the space of parameters into characteristic regions, which depict the feasibility and corresponding performance as a function of the uncertain parameters, and hence provide the decision maker with a complete map of various outcomes [6]. in the networked control system, different operation modes correspond to different range of the state variable, the switching sequence between different modes is related with the initial condition of the system, which is not fixed. if the switching sequence is fixed, the optimal control problem is equivalent to a finite time optimal control problem for a hybrid system, which can be solved by multiparametric program, in this case, the optimal solution is a optimal control candidate for the networked control system. if all of the switching sequences in the networked control system are given, the solution to the optimal control problem (5) can be obtained by comparing the values of cost function corresponding to the optimal control candidate in different switching sequences. however, with the increase of the control modes, the computing process gets more complex and the computing efficiency gets much lower because we must enumerate all the possible switching sequences of the networked control system, which is a little same as the famous traveling salesman problem (tsp) in the optimization theory. in order to decrease the computation complexity and improve the computation efficiency, the dynamic program technique is proposed to solve the optimal control problem of the networked control system with multiple control modes. the dynamic programming solution of the optimal control problem is denoted as follows: j∗n (xn) ∆ = min {un} {[ fq(n)(x(n), u(n)) + g(x(n), q(n), q(n + 1)) ] + j∗n+1(x(n + 1)) } (5) s.t. x(n + 1) ∈ xn+1, n = n −1, ..., 0 (6) with terminal conditions: xn = x f (7) j∗n (x(n)) = fq(n)(xn ) (8) where x j is the set of all initial states for which dynamic program problem (5)-(8) is feasible,x j = {x ∈ rn|∃u, fq( j)(x, u) ∈ x j+1}. the first step of dynamic program is as follows: j∗n−1(x(n −1)) , min{un−1} {[fq(n−1)(x(n −1), u(n −1)) + g(x(n −1), q(n −1), q(n))] + j∗n (x(n))} (9) s.t.    fq(n−1)(x(n −1), un−1) ∈ x f j∗n (x(n)) = fq(n)(xn ) = fq(n)( fq(n−1)(x(n −1), un−1)) (10) it is clear that the optimal control problem (9)-(10) is a multiprarametric program problem, where x(n −1) is a parametric vector. in terms of terminal region x f and the system equation (1), the feasible region xn−1 at time n − 1 is obtained. [7] discuss the discretized computation on the optimal control of hybrid systems, where the initial state and the terminal state are determined and the key point is to search the maximum lower bound of the value function. the parameter vector increases the complexity of computing the optimal control feedback law. based on the algorithm in [7], the value function vq(x) is introduced, when x = x0, q = q0, vq0 (x0) is a lower bound on the cost for optimally bring to the system from determined initial state (x0, q0) to the terminal state (xt f , qt f ). it should be noted that without the loss of the generality, we discuss the case where continuous state space is two-dimensional system. computation on the optimal control of networked control systems with multiple switching modes over high speed local area networks 105 let e1 = [1 0]t , e2 = [0 1]t , ∀x ∈ xn−1, let x jk n−1 = xn−1 + jhe1 + khe2, x jk n−1 ∈ xn−1, and j, k ∈z, y jk n−1 , {x : x = x jk n−1 + θ1he1 + θ2he2,−1 ≤ θ1 ≤ 1,−1 ≤ θ2 ≤ 1}, ( f jk q(n−1) ) i = min x∈y jkn−1,u∈u { fq(n−1)(x, u)ei } , f jk q(n−1) = min x∈y jkn−1,u∈u { fq(n−1)(x, u) } , i = 1, 2 v jk q(n−1) = vq(n−1)(x jk n−1), ∆iv jk q(n−1) = (vq(n−1)(x jk n−1 + hei)−vq(n−1)(x jk n−1))/h, ∆−iv jk q(n−1) = (vq(n−1)(x jk n−1)−vq(n−1)(x jk n−1 −hei))/h, i = 1, 2. introduce new vector variables, λ jk q(n−1) ∈r2 for ( j, k) at time n −1, construct the following inequalities:    0 ≤ ( λ jk q(n−1) ) 1 + ( λ jk q(n−1) ) 2 + f jk q(n−1) ( λ jk q(n−1) ) |i| ≤ ( f jk q(n−1) ) |i| ∆iv jk q(n−1), i = −2,−1, 1, 2 ( λ jk q(n−1) ) |i| ≤ ( f jk q(n−1) ) |i| ∆iv jk q(n−1) 0 ≤ v jk q(n) −v jk q(n−1) + g(x jk n−1, q(n −1), q(n)), x jk n−1 ∈ sq(n−1),q(n) 0 ≥ v xn q(n), xn ∈ x f (11) if there exists v jk q(n−1) such that inequalities (11) hold, which are bellman type inequalities. it can be proved that that v jk q(n−1) is the lower bound of the cost to go function jn−1(xn−1) , and the proof is given in [7]. however, any function that meets the constraints is a lower bound on the cost to go function, thus to yield the useful bounds, it is necessary to find an maximum of all grid points in the feasible region xn−1, j∗(x(n −1)) ∆= max x jk n−1∈xn−1, j,k∈z { v jk q(n−1)(x jk n−1) } (12) assuming that v j0k0 q(n−1) = max x jk n−1∈xn−1, j,k∈z { v jk q(n−1)(x jk n−1) } (13) 106 gang zheng, wenli zeng, fanjiang xu when j = j0, k = k0, for x = x j0k0 + θ1he1 + θ2he2 ∈ y j0k0 n−1, define the interpolating function: vq(n−1)(x) = (1−θ1)(1−θ2)v j0k0q(n−1) + θ1(1−θ2)v ( j0+1)k0 q(n−1) +(1−θ1)θ2v j0(k0+1)q(n−1) + θ1θ2v ( j0+1)(k0+1) q(n−1) (14) from (14), the optimal feedback control law can be calculated as un−1 = arg min u∈u { ∂vq(n−1) ∂ x fq(n−1)(x, u) + fq(n−1)(x, u) } (15) so far, the optimal control law at the first step is finished. from the second step n = n − 2 to the last one n = 0, the cost to go function is defined on the feasible region xn+1, we will solve the problem (5), which is still a multiparametric program problem, the computing process is analogous as the first step, which is presented as follows: (i) divide the feasible region xn into evenly distributed grid points, and introduce x jk n , y jk n , ( f jk q(n) ) i , ( f jk q(n) ) i , f jk q(n), v jk q(n), ∆iv jk q(n), ∆−iv jk q(n), i = 1, 2; (ii) solve the linear program problem: j∗(x(n)) , max x jk n ∈xn, j,k∈z { v jk q(n)(x jk n ) } s.t.    0 ≤ ( λ jk q(n) ) 1 + ( λ jk q(n) ) 2 + f jk q(n) ( λ jk q(n) ) |i| ≤ ( f jk q(n) ) |i| ∆iv jk q(n), i = −2,−1, 1, 2 ( λ jk q(n) ) |i| ≤ ( f jk q(n) ) |i| ∆iv jk q(n), i = −2,−1, 1, 2 0 ≤ v jk q(n+1) −v jk q(n) + g(x jk n , q(n), q(n + 1)), x jk n ∈ sq(n),q(n+1) (16) (iii) after solving the maximum value of v jk q(n), define the interpolating function vq(n)(x), then calculating the optimal feedback input un = arg min u∈u { ∂vq(n) ∂ x fq(n)(x, u) + fq(n)(x, u) } (17) it should be noted that the dynamic program methodology to compute the optimal control feed back input in this paper is different from [6]. in [6], the cost function is quadratic and the optimal controller design is for the piecewise affine (pwa) system, if the hybrid system can not be modeled as a pwa system, and the methodology in computing the optimal controller needs to be further discussed. also, in [7], the initial state and the terminal state are determined, thus there are no parameters in the optimal control problems. in this paper, the initial state is a vector parametric vector, the solution to optimal control must make an dynamic enumeration to search the maximum lower bound in every sample period, the problem is different from [7]. 4 simulation example we use the local area network in the laboratory to construct a networked control system, where the network contains ethernet and the communication between nodes is done using tcp/ip sockets. the experiment setup is shown in figure 2. computation on the optimal control of networked control systems with multiple switching modes over high speed local area networks 107 figure 2: the networked control system experiment setup in our experiment setup, one computer (computer 1) works as a plant, and the other computer (computer 2) works as a controller, these two computers and the other computers are connected over the local area net in the lab. computer 1 simulate the dynamics of the plant, which can obtain the control signal from the net, make the output calculation, and send the computation results to the controller computer. computer 2 obtains the plant output, and calculates the control signal, then send the control signals to the plant computer. note that the networked control systems with multiple switching modes can be carried out in the experiment setup. based on the hybrid system model of the networked control system, the different dynamics of the plant can be simulated in the plant computer, and the controller computer calculates the control results and sends them to the plant computer and control the dynamics of the plant in different modes. because tcp/ip is used in the ethernet in the lab, the end to end data transmission is reliable, and the packets loss between the plant computer and the controller computer can not be considered. but the network-induced delay needs to be considered when the plant computer and the controller computer exchange data across the network. we test the delays between two computers in the network, it can be found that the time delay generally maintains a fixed value, in our experiment setup, the fixed value is much less than the sampling period. therefore, it is reasonable that the networked-induced delay can not be considered in this paper, and the model and the solution to the optimal control can be validated in this simulation. the networked control system simulated in the experiment setup is given as follows. x(k + 1) = aqi x(k) + bu(k), i = 1, 2 (18) where aq1 = [ √ 2 2 − √ 2 2√ 2 2 √ 2 2 ] , aq2 = [ √ 2 2 √ 2 2 − √ 2 2 √ 2 2 ] , b = [ 0 1 ] , sq1,q2 = {x : [ 1 0 ] x(k) < 0}, sq1,q2 = {x : [ 1 0 ] x(k) ≥ 0}, x(k) ∈ [−10, 10]×[−10, 10], u(k) ∈ [−1, 1], (19) 108 gang zheng, wenli zeng, fanjiang xu define the cost function j(un , x(0)) ∆ = xtn pxn + ∑n−1k=0 xtk qxk + utk ruk (20) where p = q = [ 700 0 0 700 ] , r = 1, terminal region x f = [−0.01, 0.01]×[−0.01, 0.01], n = 3. figure 3: the optimal trajectory of the state variable x figure 4: the optimal control input when x = [−1 1]t the optimal control problem is minimizing the cost function and solving the optimal control feedback input. in terms of the steps presented in section 3 and some toolbox related with multiparametric programming [8] and optimal control [9], the solving program is designed, and then the optimal control can be obtained. when x0 = [−1 1]t , the optimal trajectory of the state variable x is shown in fig. 3, and the control input is shown in fig. 4. 5 conclusion in networked control systems with multiple switching modes, it is necessary to calculate the optimal control feedback input, which can improve the system performance and save the cost. this paper models computation on the optimal control of networked control systems with multiple switching modes over high speed local area networks 109 the class of networked control systems as a hybrid system and addresses a optimal control problem, where initial state is a parametric vector. consider switching modes and unknown initial states, the dynamic program techniques are proposed to solve the optimal control problem, where every step calculation need enumerate the grid points in feasible region to find a maximum lower bound of the cost function and solve the optimal feedback input. finally, an example is given to illustrate the techniques. it should be noted that, the switching modes in networked control systems make the calculation of the optimal control input more complex, dynamic program can reduce the calculation complexity than the heuristic enumeration. in the future research, it is an important task to improve the computation efficiency and reduce the complexity. in addition, time delays are common in networked control systems, it is an interesting area to work into the optimal control problem with delay. references [1] w. zhang, m. s. branicky , s. m. phillips . stability of networked control systems. ieee control systems magazine, vol. 21, pp. 84-99, 2001. [2] h. fujika, k. ito. performance optimization for a class of networked control systems with communication constraints. proceedings of the american control conference, pp. 248-253, 2003. [3] l. lu, l. xie, m. fu. optimal control of networked systems with limited communication: a combined heuristic and convex optimization approach. proc. of 42th ieee int. conf. on cdc, pp. 11941199 2003. [4] a. van der schaft, h. schumacher,an introduction to hybrid dynamical systems,berlin: springerverlag, 2000. [5] r. alur,c. courcoubetis, t. a. henzinger. hybrid automata: an algorithmic approach to the specification and verification of hybrid systems. in: grossman r l, nerode a, ravn a p, et al, eds. hybrid systems, lecture notes in computer science,berlin: springer-verlag, vol. 736, pp. 209-229, 1993. [6] f. borrelli, constrained optimal control of linear and hybrid systems, berlin: springer-verlag, 2003. [7] s. hedlund, a. rantzer. optimal control of hybrid systems. proc. 38th ieee int. conf. on cdc,pp. 3972-3977, 1999. [8] m. kvasnica, p. grieder, m. baotic, et al, multi-parametric toolbox. eth, swiss federal institute of technology, 2004. [9] s. hedlund, a. rantzer, cdp tool: a matlab tool for optimal control of hybrid systems. department of automatic control, lund institute of technology, 1999. authors: gang zheng, wenli zeng, fanjiang xu laboratory of integrated information systems technology institute of software, chinese academy of sciences no.4 south fourth street, zhongguancun, beijing, china,100080 e-mail: gangzhengcn@yahoo.com.cn received: november 6, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 314-324 image segmentation using euler graphs t.n. janakiraman, p.v.s.s.r. chandra mouli t.n. janakiraman department of mathematics national institute of technology, trichy, india. e-mail: janaki@nitt.edu p.v.s.s.r. chandra mouli school of computing science and engineering v.i.t. university, vellore, india. e-mail: mouli.chand@gmail.com abstract: this paper presents a new algorithm for image segmentation problem using the concepts of euler graphs in graph theory. by treating image as an undirected weighted non-planar finite graph (g), image segmentation is handled as graph partitioning problem. the proposed method locates region boundaries or clusters and runs in polynomial time. subjective comparison and objective evaluation shows the efficacy of the proposed approach in different image domains. keywords: image segmentation, graph theory, euler graphs, cycles. 1 introduction image segmentation can be treated as a graph partitioning problem which is solved by making use of cuts in a weighted graph based on certain criterion. the proposed method deals the image segmentation problem in a diverse manner. an excellent review for image segmentation is available in [8], [9], [15]. earlier approaches to image segmentation are categorized into three groups: (1) cluster the low level feature, such as histogram thresholding by [14], k–means / k–centroid by [12], [27] and mixture of gaussians (mog) by [2], (2) edge linking such as dynamic programming by [26], relaxation approach by [16] and saliency network by [25] and (3) region operations, such as region splitting and merging by [24], [23], region growing methods by [3] and by [13], by [17], by [11] and region competition by [20]. applications of segmentation are abundant. it is heavily used in medical imaging. for example, segmentation of internal brain nuclei in mri images as discussed in [38]. this work is aimed to bring robust image segmentation using graph theoretic concepts like euler graphs and cycles. the proposed method finds the cycles of a given graph so that the image regions are formed by connecting all relevant pixels together. the relevancy of pixels is determined based on two parameters namely, edge weight similarity and node label similarity, which are described in the subsequent sections. the algorithm may end up at a particular stage when there is no possibility of refinement due to constraints imposed on cycle formation. such paths are tried for further refinement. if the refinement is not possible then those paths are treated as open paths and may be treated as cuts. all the procedures of the proposed method run in polynomial time. the rest of the paper is organized as follows. in section 2, a brief review on graph based segmentation is discussed. the basic definitions related to euler graphs and some of its properties are presented in section 3. in section 4, the proposed algorithm and the experimental results are presented. section 5 concludes the work. 2 graph approaches recently graph based image segmentation has attracted growing interest. graph theory and its concepts has been dominating in image processing research. the concepts of graph theory like maximum copyright c© 2006-2010 by ccc publications image segmentation using euler graphs 315 flow, maximum clique, shortest path, minimum spanning tree etc have been used for image processing problems. [21] discussed the various types of graph algorithms in computer vision. a special issue on graph based image processing is published in [32]. early graph-based methods include by [4], [18] and more recent formulations in terms of graph cuts by [22], [31] and spectral methods by [30]. the notion of a connectivity graph was introduced by [19] to allow for image processing on a foveal sensor. this notion is introduced specifically to model the sampling of the macaque retina in [5]. the work of zahn (1971) presents a segmentation method based on the minimum spanning tree (mst) of the graph. the segmentation criterion in zahn’s method is to break mst edges with large weights. the algorithm proposed by urquhart (1982) normalizes the weight of an edge using the smallest weight incident on the vertices touching that edge. work by wu and leahy (1993) introduced such a cut criterion, but it was biased toward finding small components. this bias was addressed with the normalized cut criterion developed by shi and malik (2000), which takes into account self-similarity of regions. these cutbased approaches to segmentation capture non-local properties of the image, in contrast with the early graph-based methods. weiss (1999) has shown how the eigenvector-based approximations developed by shi and malik relate to more standard spectral partitioning methods on graphs. however, all such methods are too slow for many practical applications. an alternative to the graph cut approach is to look for cycles in a graph embedded in the image plane. [10] described the quality of each cycle is normalized in a way that is closely related to the normalized cuts approach. [7] described an efficient graph-based segmentation in which they defined a predicate for measuring the evidence for a boundary between two regions. using that predicate, an algorithm is developed which makes greed decisions to produce segmentations that satisfy global properties. the literature in the most recent times reveal many improvements over these existing methods but for comparison and evaluation, the methods by shi and malik, pedro f. felzenzwalb etc are treated as benchmark works. a method to build a hierarchy of partitions of an image is introduced by [29] in which they build a hierarchy of partitions of an image by comparing in a pairwise manner the difference along the boundary of two components relative to the differences of components’ internal differences. they stated the drawback of this method as the maximum and minimum criterion introduced are very sensitive to noise, although in practice it has a small impact. a mst pyramid based segmentation is carried out by [28] using dual graph contraction. for evaluating the segmentation results of the proposed methods with other existing methods, precision, recall and f-measure have been implemented since berkeley images [34] for segmentation have been evaluated using these three measures. the methods considered for comparison are [35], [36] and [37]. 3 background leonhard euler discussed [6] graphs for the first time while solving the famous seven bridges of k .. onigsberg problem. the following are some of the terms and their definitions used in this work. these definitions are taken as they are defined in [1]. 3.1 basic definitions let g(v,e) be the given graph with v and e representing the vertex set and edge set respectively. definition 1. a trail that traverses every edge of g is called an euler trail. it is named as euler trail because euler was the first to investigate the existence of such trails in graphs. definition 2. an euler tour is a tour which covers all the edges of g. definition 3. a graph is an euler graph or eulerian if it contains an euler tour. euler proved the following theorem and corollary through which a graph has euler tour can be determined. the following characterizations are taken as they are defined and proved in bondy and murty (1982). 316 t.n. janakiraman, p.v.s.s.r. chandra mouli theorem 4. a non-empty connected graph is eulerian if and only if it has no vertices of odd degree. corollary: a connected graph has an euler trail if and only if it has at most two vertices of odd degree. 3.2 extraction / development of euler graphs from non-euler graphs if a graph does not have an euler circuit, it still might be interested in knowing how it could be traveled with as few retraced edges as possible (starting and ending at the same vertex). eulerian can be obtained in two ways. (i) by adding one spurious multiple edge which joins two adjacent odd degree vertices and (ii) by deleting the edges joining two adjacent odd degree vertices. 4 proposed method the euler graph and its properties are exposed in this work for solving image segmentation problem. the basic idea is that euler graph is decomposed into edge disjoint cycles. the steps of the proposed method are given below: ¨ ¥ step-1: representation of image as a grid graph step-2: conversion of grid graph into eulerian step-3: segmentation procedure step-4: refinement of segments ­§ these stages are discussed in detail in the following sub-sections. 4.1 representation of image as a grid graph the image to be segmented is represented as a graph g(v, e). to do so, each pixel is treated as a vertex of the graph. edges are defined based on 8–connectivity of the neighborhood vertices. an edge (vi, v j) ∈ e corresponds to a pair of neighboring vertices. the graph g, thus obtained is an undirected weighted non-planar graph. clearly, an image of size n × n contains n vertices, (n − )n vertical edges, n(n − ) horizontal edges and (n − ) diagonal edges. thus, in total there are (n − )n + n(n − ) + (n − )= n − n +  edges. let m = n − n + . the graph thus formed is visualized as a grid and hence called as grid graph. a sample grid graph of size  ×  is shown in figure 1. the weights are assigned to the edges by using the absolute intensity difference between the adjacent pixels. figure 1: grid graph of an image image segmentation using euler graphs 317 4.2 conversion of grid graph into eulerian the grid graph thus obtained is a connected non-eulerian because some of the vertices have odd degree. the procedure for the conversion to eulerian guarantees the formation of cycles covering all edges since all the vertices are of even degree. border vertices are the vertices on the first row, last row, first column and last column. for this reason, the grid graph can be converted to eulerian so that all vertices have even degree. this can be achieved in two ways. in the first case i.e., by adding one extra multiple edge for each of disjoint pair of adjacent odd degree vertices. the same weight is allocated to both duplicated and original edge to avoid ambiguity. the process is repeated until no such pair exists. in figure 2, (a) and (d) show two grid graphs of size  × and  ×  respectively. figure 2(b) and (c) represent the two possible euler graphs of (a), (e) and (f) represent the euler graphs of (d). figure 2: grid graph and its corresponding euler graphs in the second case, instead of adding duplicate edges to the pair of adjacent vertices of odd degree, alternate edges are removed at the boundary to maintain even degree. it is found that there is no loss of information from images by removing such edges because all the edges removed are due to border vertices. in practice, there is not much information available at the border vertices and experimentally it is found that there is no variation in the segments formed in either way. 4.3 segmentation procedure once the given image is represented as eulerian, the segmentation procedure is carried out over the eulerian. the algorithms for image segmentation and segments_formed are given below. ¨ ¥ 1. color all the edges as white. 2. call segments_formed procedure. 3. call regions_refinement procedure. ­§ ¨ ¥ 1. select arbitrarily a white colored edge. 2. the selected edge is included in the temporary_growing_vector if it satisfies the threshold. 3. if the temporary_growing_vector forms a cycle then the closed path is stored in cycles_formed vector. 3a. the cycle formed is treated as a region. in the region formed, the edges in the closed path represent the boundary edges of the region. the edges present inside the region are internal edges of that region. the corresponding vertices are called boundary vertices and internal vertices respectively. 3b. the boundary edges of the region are colored black and internal edges are colored gray. 318 t.n. janakiraman, p.v.s.s.r. chandra mouli 4. if the temporary_growing_vector has no cycle then choose the next minimum weighted white colored adjacent edge satisfying the threshold and goto step-2. 4a. if there is no edge available or which satisfies the threshold then backtrack its parent and search for next white colored adjacent edge satisfying the threshold. 4b. if no parent exits then the temporary_growing_vector is stored in open_paths vector. color all the edges in open_paths as black. 4c. else 4d. remove the last included edge in the temporary_growing_vector. 4e. choose the next white colored adjacent edge and goto step-2. 5. if all the vertices are not covered or all edges are not colored gray or black then goto step-1. 6. if all the vertices are covered in the region either as boundary or as internal vertices then it induces the initial segmentation for the given threshold. 7. if all the edges are colored either gray or black representing the internal or boundary edges then the segmentation is subjected for refinement. ­§ the algorithm uses a color structure which labels the edges as given below: • initially all edges are in white color • a visited edges is in gray color • an edge in black color indicates that it is a part of the boundary of a region. the black colored edges are marked permanently so that they are not considered for refinement. only white and gray colored edges are subjected for refinement. the criteria that is imposed on every edge to form a segment is defined in equation (1). the equation (1) refers to the difference of the maximum and minimum vertex labels in the cycle formed. in this case, it is used as the difference of the maximum and minimum vertex labels in the temporary_growing_vector. t = maxv − minv  (1) the algorithm starts by randomly choosing a white colored edge. at the first execution, the edge chosen is included directly in the temporary_growing_vector. since a cycle cannot be formed with one edge, line 4 is executed where the algorithm tries to choose a white colored edge adjacent to the previously chosen edge. the edge is selected based on the threshold criteria. if no minimum weighted, white colored adjacent edge is available then, the algorithm backtracks to its parent and searches for another minimum weighted white colored adjacent edge. if it finds, then the last included edge in the temporary_growing_vector is removed since the algorithm could not traverse from that edge and adds the newly selected edge into temporary_growing_vector. line 3 of the algorithm checks for any cycle in temporary_growing_vector. to check this, bfs algorithm is used. each cycle is treated as one region. if cycle is formed, then the closed path is stored in cycles_formed vector. the edges of the closed path are colored black indicating that they are boundary vertices. these edges are not chosen for forming any other cycles. the edges present inside the region are colored gray. these edges may be used for forming cycles once the white colored edges are exhausted. this will help in avoiding self overlapping region formation that means that the traversal starts from an image segmentation using euler graphs 319 internal edge and traverses to outside the region is termed as self overlapping. self overlapping is avoided at the initial stage in order to get maximum number of non–overlapping regions but it is carried out in region refinement stage, if necessary. during the execution of the algorithm, if it chooses a white colored edge outside any region and on its traversal, overlaps the existing region, then it is allowed because the internal edges of one region act like boundary edges of another region. another possibility during the traversal is that the temporary_growing_vector may not grow further because no further edge satisfies the criteria at any level (neither at the current edge nor at any of its parent edges), then the temporary_growing_vector stops traversing. by nature, eulerian guarantees cycle formation but due to the threshold criteria, it may not form cycles all the cases. in such case, the temporary_growing_vector contains an open path and such paths are stored separately in open_paths vector. in this way, the algorithm tries to traverse until it covers all vertices. this completes the first stage where, it induces an initial segmentation of image. refinement of regions formed at this stage, all the edges are labeled to either gray or black. refinement of black colored edges is not possible because they represent the boundaries of the regions already formed. the gray colored edges are subjected for refinement. the same procedure is used to form regions by choosing any randomly selected gray colored edge and for further traversals. in this way, the algorithm tries to refine the segmentation for regions formation. too much of refinement leads to over segmentation and no refinement leads to under segmentation. a moderate level of refinement is necessary. this is controlled by threshold selection. 5 experimental results the proposed method is tested on standard berkeley image database. two trivial synthetic images have been created and tested the algorithm on them. the results of the two synthetic images and the corresponding results are shown in figure 3. the results presented in figure 3 are the induced segmentations obtained before refinement process. figure 3: segmentation results-i of synthetic images in figure 3, (a) and (c) are the two synthetic images created and the corresponding segmentations are shown in (b) and (d). these two synthetic images are created in such a way to study the behavior of the algorithm in open_paths case. as mentioned in the algorithm, the temporary_growing_vector stops traversing when there is no suitable edge satisfying the criteria. in such case the path is not closed and hence it is stored in open_paths vector. in figure 3b, the segmentation result shows two open_paths 320 t.n. janakiraman, p.v.s.s.r. chandra mouli (cross lines). the two different ends of the two open_paths are adjacent to one region formed. thus, segmentation output gives a visualization that there are two closed regions labeled 1 and 2; and three open regions labeled 3, 4 and 5. in figure 3d, the segmentation output shows two open_paths for which no end is adjacent to any other region. the four open regions formed by the two open_paths are labeled 1,2,3 and 4 in figure 3d. after applying the refinement procedure, the segments obtained are shown figure 4: segmentation results-ii of synthetic images in figure 4. in figure 4b, the segmentation output shows five regions labeled. similarly in figure 4d, there are 4 closed regions. the refinement process, in these cases, tried to get closed regions and in that process lead to over segmentation. this may be true in real images also. hence, the refinement procedure is executed depending on the user’s choice. the results of some real images taken from berkeley image database are shown in figure 5. in figure 5, the first and third columns represent the original image and second and fourth columns represent the segmentation result obtained. figure 5: segmentation results of some sample images in berkeley image database the proposed algorithm is executed on the 100 images in the database. the results of 50 images have been tabulated in table 1. from the table, the following observations have been made. it is observed that those images having uniform background or average intensity range obtained best results; images having overlapping of objects or having complex structures, the statistical results are almost equivalent to the other existing methods chosen for comparison and for those images having high overlapping of objects or very dark images which cannot be visualized perfectly with the human eye, the proposed method could not segment the images and the statistical results revals that the f-measure for such images for the existing methods is better compared to the proposed method. the graphical representation of the results is shown in figure 6 respectively. image segmentation using euler graphs 321 figure 6: comparison of segmentation evaluation results table 1: comparison of segmentation evaluation results with other existing methods image name bel gpb xren eg image name bel gpb xren eg 119082 0.7 0.74 0.8 0.75 89072 0.68 0.71 0.71 0.69 42049 0.92 0.91 0.85 0.92 126007 0.72 0.78 0.76 0.75 167062 0.67 0.76 0.75 0.75 296007 0.66 0.69 0.69 0.65 24077 0.74 0.76 0.76 0.76 175032 0.49 0.62 0.63 0.6 38092 0.78 0.78 0.73 0.74 103070 0.68 0.68 0.62 0.65 101085 0.74 0.83 0.78 0.76 285079 0.71 0.72 0.71 0.69 41033 0.62 0.68 0.68 0.66 167083 0.61 0.75 0.75 0.7 291600 0.57 0.61 0.59 0.6 271035 0.73 0.73 0.71 0.71 130026 0.52 0.51 0.47 0.52 12084 0.48 0.52 0.49 0.5 241004 0.85 0.81 0.81 0.85 69015 0.79 0.82 0.79 0.75 147091 0.71 0.77 0.75 0.75 58060 0.5 0.55 0.58 0.49 189080 0.78 0.8 0.77 0.79 163085 0.49 0.5 0.6 0.49 14037 0.65 0.7 0.65 0.71 220075 0.62 0.64 0.62 0.59 62096 0.79 0.79 0.78 0.78 45096 0.76 0.79 0.78 0.73 227092 0.75 0.88 0.85 0.88 16077 0.57 0.61 0.54 0.58 253027 0.63 0.65 0.69 0.68 219090 0.71 0.74 0.74 0.72 229036 0.67 0.76 0.72 0.75 300091 0.57 0.79 0.76 0.65 3096 0.9 0.89 0.88 0.85 156065 0.66 0.67 0.64 0.63 170057 0.66 0.68 0.7 0.66 76053 0.61 0.61 0.62 0.59 157055 0.73 0.76 0.79 0.74 304034 0.47 0.49 0.47 0.41 295087 0.71 0.78 0.78 0.75 86016 0.39 0.52 0.42 0.48 78004 0.79 0.8 0.8 0.77 8023 0.41 0.5 0.42 0.4 43074 0.66 0.67 0.78 0.65 108082 0.43 0.46 0.47 0.43 86000 0.62 0.7 0.7 0.67 69040 0.5 0.55 0.57 0.52 322 t.n. janakiraman, p.v.s.s.r. chandra mouli 6 conclusion in this paper, a novel algorithm for segmenting an image into different regions using euler graphs has been proposed. the algorithm starts by randomly choosing an edge and tries to form closed regions. in cases, open paths are formed. the color look up table is used for edges to trace their transition. a white color indicates unvisited edge, a gray color indicates visited and may go for refinement and black color indicates visited and marked permanently for no refinement since it is already a part of a region boundary. the procedures discussed run in polynomial time. the mst and cycles method performs better compared to euler graph method in terms of precision, recall and f measures. bibliography [1] j.a. bondy and u.s.r. murty. graph theory with applications, fifth printing. elsevier science publishing co., inc., 52, vanderbilt avenue, new york, 1982. 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[31] z.wu and r.leahy. an optimal graph theoretic approach to data clustering: theory and its application to image segmentation. ieee transactions on pattern analysis and machine intelligence, 15(11):1101–1113, 1993. 324 t.n. janakiraman, p.v.s.s.r. chandra mouli [32] dickinson, s., pelillo, m. and zabih, r. introduction to the special section on graph algorithms in computer vision ieee transactions on pattern analysis and machine intelligence, 23(10):1049– 1052, 2001. [33] d. martin and c. fowlkes and d. tal and j. malik a database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics proc. 8th int’l conf. computer vision, volume 2, pages 416–423, 2001. [34] http://www.eecs.berkeley.edu/research/projects/cs/vision/bsds/ [35] piotr dollar, zhuowen tu, and serge belongie supervised learning of edges and object boundaries proc. ieee computer vision and pattern recognition, cvpr, june, 2006. [36] michael maire, pablo arbelaez, charless fowlkes and jitendra malik using contours to detect and localize junctions in natural images proc. ieee computer vision and pattern recognition, cvpr, 2008. [37] xiaofeng ren multi-scale improves boundary detection in natural images proc. eccv conference, 2008. [38] marius george linguraru, miguel á ngel gonz á lez ballester, nicholas ayache deformable atlases for the segmentation of internal brain nuclei in magnetic resonance imaging international journal of computers, communications & control, volume 2, no. 1, pages 26–36, 2007. t. n. janakiraman (born on february 14, 1953) received his ph.d. in graph theory and its applications from madras university, india in 1991. he did his p.d.f. from the same university in graph theory and its applications in 1994. he is currently associated with department of mathematics, national institute of technology, trichy, india. he has two sponsored research projects to his credit and published around 40 papers in refereed international journals. his main research interests are in graph theory and its applications in digital image processing, wireless ad hoc networks and digital video processing. p.v.s.s.r. chandra mouli (born on may 21, 1976) received his ph.d. from national institue of trichy in 2010. he is currently working as associate professor in school of computing science and engineering, vit university, vellore. his research interests include image segmentation, pattern classification and wireless ad–hoc networks. he has published 10 refereed research papers in various international journals and conferences. he was co–investigator to research project sponsored by defence research development organization (drdo), new delhi and also worked as a research fellow in another research project sponsored by drdo, new delhi, india. he is a life member of iste, and also member of csi. he is reviewer for many international journals. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 852-861 agent technology in monitoring systems b. pătruţ, c. tomozei bogdan pătruţ, cosmin tomozei “vasile alecsandri” university of bacău, romania, 600115, calea mărăşeşti, 157 e-mail: bogdan@edusoft.ro, cosmin.tomozei@ub.ro abstract: the aim of this paper is to make a brief presentation of the results obtained by the authors regarding agent technology application in distributed monitoring systems development. consequently, we would like to present magelan and conttest as monitoring systems based on intelligent agents technology. formal aspects regarding intelligent agents will be mentioned. quantitative issues concerning efficiency, maintenance and reengineering are also to be taken into account. keywords: intelligent agents, distributed systems; monitoring systems; hyper-encyclopedia, reengineering. 1 intelligent agents in distributed computing software applications are nowadays in position and necessity to solve some problematical situations by their own, in order to save time and reduce cost. intelligent agent integration in distributed computing proved to be an important way of achieving this objective in the last years. furthermore, the union distributed computing with intelligent agents theoretical perspective offers a good practical basis to distributed artificial intelligence [2]. intelligent agents have to be reliable, robust in order to offer accuracy in the results, in dissimilar, open or unpredictable environments [7], [8]. software agents are situated in particular environments and capable of autonomous actions, in order to fulfill their objectives. the concept of autonomy and the fact that intelligent agents are enriched with it presume that human action is minimized. in [8] it is affirmed that the agent is an entity which can perceive the environment by sensors and acts in order to realize its objectives by effectors. on the other hand, distribution of hardware, software and data offers the possibility for the agents to be replicated on diverse nodes on the computer network. 2 developing students’ metacognitive competences metacognitive skills development is an important formative intellectual object in education of the students, as reaching this level involves a route through effective education, appropriate to each one in particular [7]. metacognitive skills suppose that students are aware of their own cognitive activity, i.e. learning activity, and self-adjustment mechanisms consisting in cognitive controls (rules, procedures, strategies). next we will introduce the notions of environment, agent, s-agent, necessary to develop some intelligent systems as those described in chapter 5 of this article. the following theoretical concepts are different from the fipa standard [9] ones or from the concepts defined by authors like wooldridge in [8], and are necessary for the implementation of the systems magelan and conttest. the following statements are to introduce theoretical concepts regarding intelligent agents and distributed technology. copyright c⃝ 2006-2010 by ccc publications agent technology in monitoring systems 853 definition 1. we use the name of environment for a set of elements e = {e0, e1, e2, e3, ..., en} among which there is a relation of partial order marked with "<". we use the notation e 6 f for the fact that e < f or e = f, respectively f > e if e < f. the environment can be, at a certain point, in a certain state e, which we will express by st(e) = e. at first, the environment leaves an initial state e0, for which e0, e1, ∀i ∈ {1, 2, ...n} . the state en is called final state for which it is considered that ei, en, ∀i ∈ {1, 2, ...n}. definition 2. we use the name of agent for a triple of the type (3.1) a = (s, s0, r) where s is a finite set of states, s0 in s is called the agent’s initial state, and r is a set of evolution rules. if agent a is in state s, then we express this by st(a) = s. among the states of s there is a special state marked with λ. at first st(a) = s0, and when st(a) = λ we say that the agent is inactive. for the rest, the agent is active. the rules in r are of the type (1) or (2): r1 = (a, s, e) → (a, t, f) (2.1) r2 = (b, s, e) → (b, t, f) (2.2) rule (1) states that if st(a) = s, st(e) = e, then st(a) becomes t and st(e) becomes f, if e 6 f. the second rule (2) states that agent a ceases its implementation (st(a) becomes λ), transferring the control to agent b, for which st(b) becomes t, and the environment remains in the same state. if st(e) = e and there are two agents a and b with st(a) = a ̸= λ and st(b) = b ̸= λ and (a, s, e) → (c, z, f) and (b, t, e) → (d, z′, f′), then we will consider st(e) = max(f, f′) if f and f′ are comparable, one of them respectively, if f and f′ are not comparable, and st(a) = 0 and st(c) = z if f < f′, respectively st(b) = 0 and st(d) = z′, if f < f′. this can be generalized for more active agents. definition 3. we use the name of s-agent for an n-uple of the type (3) s = (c, a1, a2, ..., an) (2.3) where c is an agent called coordinating agent, and a1, a2, ..., an are agents corresponding to the definition above that are called effectors or atomic agents. the coordinating agent will interact directly with the user and the architecture and its functionality depends on the concrete implementation (the examples will be offered in the following chapters). because inside an s-agent, the agents transfer the control form one to another, an s-agent behaves like a communicative multi-agent system. definition 4. let there be s = (c, a1, a2, ..., an) an s-agent. if s1, s2, ..., sn are the states of the atomic agents that make up s (without the coordinator c), we then say that (s1, s2, ..., sn) is the state of s (at a given moment). definition 5. let there be s = (c, a1, a2, ..., an) an s-agent in the environment e, that cannot be modified by the user. if the initial state of s is of the type (0, λ, λ, ..., λ) we say that s is a normal s-agent (s01 marks the initial state of the agent a1). directly, we obtain the following lemma: lemma 1. in a normal s-agent, {s1, s2, ..., sn} = {s, λ, λ, ..., λ} is enacted, with s ̸= λ, at any point of time t. 854 b. pătruţ, c. tomozei proof. the s-agent being normal, it follows that its initial state (at the moment t0) is (0, λ, λ, ..., λ), therefore {s01, s02, ..., s0n} = {s01, λ, λ, ..., λ}, and s01 ̸= λ. let us suppose that the state in the moment ti is {s, λ, λ, ..., λ}, with s ̸= λ. this means that an active ai agent exists and that it is unique, with and st(ai) = s ̸= λ and st(aj) = λ, ∀j ̸= i. if there is a relation of evolution of the type (ai, s, e) → (ajt, f), then by applying this relation of evolution, we will obtain in the moment ti+1 : st(ai) = λ and st(aj) = λ, with t ̸= λ. therefore the state of the s-agent will become (no matter the situation) (λ, λ, ..., λ, t, λ, ...λ), with t ̸= λ, on the position j, therefore. if there is no relation of evolution with (ai, s, e) on the left side, then the s-agent will remain in the state {s, λ, λ, ..., λ}, with s ̸= λ. the fact that the agent will remain in that certain state will be called blockage, a notion that we will eventually formally define. definition 6. we use the name of multi-agent monitoring system (mams) of the environment e, based on s-agents (or on groups) for a triple of the type (4) mams = (sa, l, e) (2.4) where sa is a set of s-agents, having the same structure, e is the environment within which these exist and are implemented, and l are communication or linkage rules of the type ci → cj, where ci and cj are coordinating agents of some s-agents from sa. the communication relations among the s-agents form an oriented graph depending on the architecture and the concrete implementation of the multi-agent system, as we will see in the following chapters. our interest lies in some evolution rules of the environment within an s-agent and the structure and graphic representation of an s-agent. a mams containing only normal s-agents is called a normal mams. definition 7. the purpose of an s-agent is to take the environment e to a state as close as possible to its final en state. if the relation (5) can be obtained, we then say that the aim of the s-agent can be carried out. st(e) = e ∧ ¬∃f ∈ e, f ̸= en : e < f (2.5) by extension, we can say that the aim of mams is reached if all the targets of the component s-agents are achieved. definition 8. two rules of evolution of the type r1 = a → b and r2 = b → c, where a, b, and c are triples of the type of those in (1) or (2), they are called adjacent. definition 9. let there be r1, r2, ..., rk a line of adjacent rules of evolution, two by two. we will use the notation (6) and we will call this relation as the derivation relation (see (6)). r1 ⇒ rk (2.6) within an s-agent, the following results are obvious: proposition 1. if st(e)=e, there is an agent a with st(a)=s ̸= λ, (a, s, e) ⇒ (a’, s’, e’) and there is no f ̸= en, so that e′ < f, then the aim of the s-agent can be reached (with the environment in state e′). (from the very beginning, we have noted the final state of the environment with en). most of the times, set e is not fully ordered. if, however, a fully ordered relation "<" is found, then the following sentence is enacted. agent technology in monitoring systems 855 proposition 2. if the relation "<" is fully ordered, then the purpose of the s-agent can be reached with the environment in the final state (en) if there is a derivation of the type (a, sa0 , e0) ⇒ (b, t, en). proposition 2 affirms that the purpose of the s-agent can be reached if there is a derivation which leads the environment to the state en, starting from the environment’s initial state e0 and the initial state of any agent (a), without the user’s intervention (in the case of a fully ordered relation "<"). if |{a; st(a) ̸= λ}| = 1, for any e = st(e) (therefore a single agent is active at a given moment, as in lemma 1), then the mams operates sequentially. this is what happens most of the times. we consider that the environment e modifies its current state in two cases: • as a result of applying an evolution rule, by one of the system’s agents; • as a result of the direct intervention of a human user. we can also consider, in some exceptional cases, that the state a certain agent is in can be modified by the evolution rules as by the user as well. therefore, we cannot hold control over what is going to happen, or how the state of the environment is going to evolve within a certain interval of time. if, ideally, the human user cannot randomly modify neither the environment e nor the current state of the agents, then the system is entirely deterministic. 3 blockages and infinite cycles our interest does not lie simply in building absolutely sequential systems or deterministic systems, but in specifying in the best possible way the evolution rules so that blockages cannot occur. definition 10. if relation (7) is certified, then we say that the s-agent is under blockage. ∃a ∈ s : st(a) = s, st(e) = e, e ̸= en ∧ ¬∃(a, s, e′) → (b, s′, f), f, e′ ̸= en (3.1) therefore, we say that an s-agent is under blockage when an atomic agent of the system gets into a state s, and the environment is in a non-final state e, and there is no evolution relation that can allow for the agent’s passing out from the state s, although the environment is suddenly modified by the user, into another non-final state e′. in (7) f and e′ are certain non-final states of the environment (e′ and f may also be even e), and s′ a certain state of a certain agent b from the s-agent, being even possible for b to be a. in other words, there is no evolution rule, with s on the left side, which can lead to another state of a or within another agent b, no matter the evolution of the environment e. definition 11. in an s-agent a derivation of the type (a, s, e) ⇒ (a, s, e) is called infinite cycle. this occurs when, if the user does not intervene through modifying the environment, the execution of the s-agent’s agents cycles infinitely, without the environment reaching the final state. in this case, the user may intervene to take the mams out of the cycle. blockages and infinite cycles can be identified easier if we represent the s-agents and the mms. graphically, a multi-agent monitoring system (mams) can be represented in the shape of a graph oriented thus (figure 1): • optionally, more s-agents are represented in the shape of some polygons or other geometrical figures, containing more s-agents, the internal structure being represented only for one of them; • optionally, the links among the s-agents will have the shape of some curves; graphically only one s-agent will be represented, because all s-agents are considered to have the same internal structure; 856 b. pătruţ, c. tomozei • the generic s-agent will be represented through a geometrical figure where all atomic agents are represented; • the atomic agents are represented through some rectangles labeled with their names; inside those squares there will be circles representing the different states of the respective agent; • each state will represent a point in an oriented graph, where the edges are the evolution relations, labeled with a pair of clues for the states of the environment: the starting state and the state that is finally reached. figure 1: a simple s-agent containing a blockage in a1 and an infinite cycle in a2 figure 2: a normal s-agent and its reduced s-agent this is an ideal example of mams functioning, illustrated by the bold arcs in figure 2. obviously, the other arcs except the bold ones are useless in this graph, because they will never be passed through. if, however, the user interferes, for example the moment the mams is in state 2 from the atomic agent a1, modifying the state of the environment from e2 to e3, then the evolution (a1, 2, e3) → (a2, 1, e4) will occur, expressed in figure 3 through the dotted arc. we can obviously "endow" an s-agent with many evolution rules. inside a co-operating working environment, different users will introduce different rules of evolution. the question is, if under the circumstances of some normal agents, some of those rules will ever be applicable, will ever come into action. it is as if we had a program with functions or pieces of codes which are not resorted to by anywhere, cannot be touched, or an expert system with production rules whose part of the premises will never be fulfilled. thus, we will show that normal agents can be reduced to other normal agents, on the basis of an algorithm, so that certain evolution rules could become useless and could be eliminated from the system, the later behavior being not affected. on the contrary, the systems become more simple. by an s-agent’ behavior we mean the applicability of the rules of its component agents. therefore, if a certain evolution rule could ever be applied, it will be a part of the s-agent’s agent technology in monitoring systems 857 behavior, and if not, then it will not be a part of this behavior. definition 12. let there be s1 and s2 two normal s-agents, functioning simultaneously within the same environment e. if the behavior of s1 is identical with the one of s2 at all moments, meaning that the same evolution rule is applied both in s1 and s2, we say that the two agents are equivalents. equivalence also refers to the situations of blockages or infinite cycles. 4 reducing the normal s-agents we will further consider a normal s-agent. we will show that the following theorem is enacted. theorem 1. a normal s-agent s can be reduced to a normal s-agent t that is equivalent with s, by eliminating some evolution relations, according to the following algorithm. reduction algorithm(s-agent s, s-agent t); {local variables: a, s, e, ai, blockage, obj_realized, m, t = ∅; step 1: for each ai from s ; if st(ai) ̸= λ then (a, s, e) ← (ai, st(ai), st(e)); end for ; step 2: m = ∅; cycle = false; blockage = false; obj_realized = false; while (not cycle) and (not obj_realized) and (not blockage); {m = m ∪ (a, s, e); if in s exists edge (a, s)→ (b, t) labeled with (e, f) then {; add t in vertex s in agent a; vertex t in agent b ; add edge (a, s) → (b, t) between s and t; label edge (a, s) → (b, t) with (e, f); if f = en then obj_realized = true else; if ¬ ∋ f’ > f and f’ ̸= en then obj_realized = true; else if (b, t, f) ∈ m then cycle=true }; else blockage=true } }. proof. according to lemma 1, the s-agent s has always a state made up of n-1 inactive states (λ) and a single active state belonging to one single agent. this means that the ist part in an algorithm will determine a unique agent a with a singular active state s, and e will be the current state of the environment. the cycle "while" from the second part ends because inside it all possible cases are brought into discussion on the "if-then-else" branches. building the s-agent t is done using the instructions in the framed part. we have used the graph representation of s and t. the instructions inside the dotted frame realize the inclusion in t of the agents a and b (unless they are already there), of their states s and t (unless they are already there) and of the relation of evolution (a, s, e) → (b, t, f). the algorithm will extract out of the graph of s a chain t, until the final state has been reached, the "largest" final state, a blockage will occur or an infinite cycle will be entered into. t will thus be a normal s-agent, because if, at a given moment, according to lemma 1, {s1, s2, ..., sn} = {t1, t2, ..., tn} and s1, s2, ..., sn = {s, λ, λ, ..., λ} is enacted, it also results that {t1, t2, ..., tn} = {s, λ, λ, ..., λ}. observation 1. the previous result is equivalent, in the theory of formal and automatic languages, with the elimination of inaccessible and unusable states from a finite deterministic automate [1]. corollary 1. an s-agent s gets blocked if and only if its reduced agent t gets blocked. 858 b. pătruţ, c. tomozei corollary 3.2. an s-agent s has an infinite cycle if and only if its reduced agent t has an infinite cycle. according to the algorithm in theorem 1, the s-agent t is equivalent to the s-agent s, therefore the implementations of s and t are identical. if s gets blocked, then t gets blocked and reciprocally. if t has an infinite cycle, then s has the same cycle too (though it may have some others as well). the algorithm in theorem 1 can be completed with a sequence of pseudo code, in order to display the configuration in which both s and t get blocked or enter an infinite cycle. 5 practical implementations one first example of mams is that where the environment is given by an intelligent hyperencyclopedia. we described in [4] the concept of intelligent hyper-encyclopedia, which, intuitively means an encyclopedia distributive developed by several users and which uses artificial intelligence tools. for the time being, we only mention the fact that the environment e is made up of the content of the hyper-encyclopedia. by the content of hyper-encyclopedia, we understand the totality of its entries. each entry is defined by a word, a definition of this and the references to the other related entries. the states of the environment are states of the content of this hyperencyclopedia at a certain moment, that is the totality of the entries introduced by the respective moment. we certainly have an initial state e0 when the hyper-encyclopedia contains nothing, but also a final state en, when the hyper-encyclopedia is considered by the users to be definite, complete. the order relation "<" between the states of the hyper-encyclopedia is a relation of partial order, because we can say that one state is smaller than the other only if the contents of the encyclopedia at a certain moment is included in the contents of the encyclopedia at another (later) moment. however, we cannot compare two states in which the hyper-encyclopedia has completely different contents, or has only one common part. the aim of the mams is to develop the hyper-encyclopedia as much as possible. the s-agents will be local multi-agent systems. each user who contributes to the enlargement of the hyper-encyclopedia has his/her own s-agent. in [3], [4] we presented a real architecture of such an s-agent. within each s-agent there is a series of atomic agents bearing different tasks. we presented the agents responsible for monitoring the microsoft word editing activity of the content of the hyper-encyclopedia, while we have dealt with the agents that monitor the web search by using the internet explorer browser. the encyclopedia behaves intelligently in these directions: it raises certain questions in order to generate new information, based on the existing ones; it answers certain questions from the users in order to provide them with the information necessary to generate new knowledge; it adapts itself to the users’ language and to their way of working (consults and modifies the databases or interacting with the others; it provides an intelligent mediation in the communication among users, so that these may be able to learn from each other, and the encyclopedia from them. the intelligent hyper-encyclopedia having so many "human" traits, even in its limited alternative will have a network structure where each knot will detain a local data basis, a human user and a system of intelligent agents, thus: an agent which observes the user’s behavior while consulting the encyclopedia and adapts itself to it; an agent which observes the user’s behavior while updating the local data basis (modifying, deleting, adding data or generating rules) and acts accordingly; an agent which alone modifies the local data basis in order to adapt it to the user from that knot (in order to ease his/her work). all the above mentioned agents communicate among themselves and cooperate to learn from each other and to modify their own local data bases, but also the way in which they interact with the users from the knots. communication among agents and human users will often be achieved by natural language, through certain agent technology in monitoring systems 859 conversational agents. they are also capable of "metamorphosis’ in order to adapt themselves to the users, or, in case this cannot be done, agents can move from one knot to another. the magelan [9] operates to solve tasks characteristic of artificial trainers which assist (monitor, help and manage) several students (users) in developing an intelligent hyper-encyclopedia. one rule of evolution of the type (a, s, e) → (a, t, f) can be thus interpreted: if the hyperencyclopedia is in state e, and the agent a notices that the user searches the web for the term s, then the hyper-encyclopedia passes to the state f (its content is improved by creating some new links between the articles or by some other means) and the user is suggested to search for the term t. obviously, the user may or may not follow the system’s piece of advice. a second example is given by an instructive system, conttest [5]. the environment is represented by the knowledge of the learner. in this case, first of all we assume that the student knows nothing, therefore the environment lies in the state e0. when the student has achieved what we had had in mind the environment is in its final state. the aim of the mams is to make the student know everything, or at least as much as possible. if we assume that the s-agents are multi-agent systems, each of them responsible for one lesson or one learning unit, then each s-agent will contain atomic agents which will have specific pedagogical tasks. for example, an agent will be responsible for teaching some content, another will be responsible for offering examples, another one for generating tests. the states of an s-agent are stages in teaching, illustration, testing and so on. let us consider the agents as being didactic actions. for example, agent a can mark the teaching, and agent b, the testing. one rule of evolution of the type (a, s, e) → (a, t, f) will communicate to us something like this: if we have taught (a) the notion s, and the student’s level is e, then we are to teach the notion t next and the student will reach level f. one rule of evolution of the type (a, s, e) → (b, t, f) will communicate to us that if we have taught the notion s and the student’s level is e, we can then pass on to testing (agent b) the student through exercise t and we expect the student to reach the (superior) level f. 6 reengineering mms systems multi-agent monitoring systems are being submitted to the process of reengineering in order to achieve new objectives. software reengineering proves to be [6] valuable in large-scale application development. by applying this procedure, we will keep a valuable software system, such as magelan hyper-encyclopedia in working condition for a longer period of time. the cost is significantly reduced and the new tasks are efficiently accomplished. if we take into consideration the application’s functionalities, the following formula (8) will describe the elements regarding the connection of the modules that remain unchanged. elements which are to be modified or deleted will be identified as well, for the entire application to become a powerfully connected system. ir = n∪ i=1 functi0 + n∪ i=1 functi1 − n∪ i=1 delfuncti (6.1) ir represents the indicator of reengineering, functi0 is the functionality in the initial moment, functi1 is the functionality in the present moment added due to reengineering, consequently delfuncti is the functionality eliminated by reengineering, and n represents the number of functionalities. conttest (figure 3) significantly evolved due to reengineering, the agents being enriched with new teaching and learning methods and procedures. therefore, evolution in objectives brought about evolution in the functionalities of agents. pedagogical tasks of agents have been 860 b. pătruţ, c. tomozei considerably improved. consequently, higher educational objectives and elevated results in the evaluation process have been obtained. figure 3: validation of answers we may state that each software project has been enhanced with a higher level of quality, evolving correspondingly with the evolution in demands, by integrating reengineering. 7 conclusions our aim was to summarize the results obtained in development and maintenance of distributed applications and multi-agent monitoring systems. practical examples, such as magelan and conttest have been given, in order to implement and to test the theoretical approaches. multi-agent monitoring systems bring quality and efficiency in almost every area of activity, including scientific research, education, healthcare or defense. by implementing the solid theoretical concepts of artificial intelligence and reuniting them to the infinite resources of distributed systems and the internet, we get the possibility to increase the quality of the results and reduce the duration of reaching the objectives. reengineering maintains the multi-agent systems and expands their utility in time and functionality. consequently, software which proved to be precious in the activity continues to be efficient in service for a longer period of time. bibliography [1] atanasiu, a., 2007, formal languages and automata, infodata publishing house, clujnapoca, romania [2] dzitac, i., bărbat, b.e., artificial intelligence + distributed systems = agents, int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844, 4(1):17-26, 2009 [3] pătruţ, b., pandele, i., 2008, how to compute the references emergencies in a hyperencyclopedya, in "recent advances in systems engineering and applied mathematics. selected papers from the wseas conferences in istanbul, may 27-30, 2008", isbn 978-9606766-91-6, issn 1790-2769, istanbul, turkey, pp.72-75 [4] pătruţ, b., socaciu, t., 2008, constructing a hyper-enciclopedya: how to transfer the emergencies between the nodes, in proceedings of the fourth international bulgarian-greek conference (computer science’ 2008), 18-19 september 2008, kavala, greece, pp. 468-473 agent technology in monitoring systems 861 [5] pătruţ, b., vârlan s. e., socaciu, t., 2008, conttest a multiagent system for accounting education, in proceedings of the third international multi-conference on computing in the global information technology, iccgi 2008, july 27 august 1, 2008, athens, greece [6] tomozei, c., 2009, security engineering and reengineering on windows 2008 server based distributed systems, journal of information technology & communication security, secitc 2009, bucharest, pp. 63-73 [7] vlassis, n., 2007, a concise introduction to multiagent systems and distributed artificial intelligence. synthesis lectures on artificial intelligence and machine learning, department of production engineering and management, technical university of crete, morgan and claypool publishers [8] weiss, g. (ed.), 1999, multiagent systems a modern approach to distributed artificial intelligence, mit press, isbn 978-0-262-23203-6 [9] fipa standards specifications: http://www.fipa.org/specifications/index.html ijcccv4n3draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 3, pp. 291-300 p systems computing the period of irreducible markov chains mónica cardona-roca, m. ángels colomer-cugat, agustín riscos-núñez, miquel rius-font mónica cardona-roca, m. ángels colomer-cugat dpt. of mathematics, university of lleida av. alcalde rovira roure, 191. 25198 lleida, spain e-mail: {mcardona, colomer}@matematica.udl.es agustín riscos-núñez research group on natural computing dpt. of computer science and artificial intelligence university of sevilla avda. reina mercedes s/n, 41012 sevilla, spain e-mail: ariscosn@us.es miquel rius-font department of applied mathematics iv universitat politécnica de catalunya edifici c3, despatx 016, av. del canal olímpic, s/n, 08860 castelldefels, spain e-mail: mrius@ma4.upc.edu received: april 5, 2009 accepted: may 30, 2009 abstract: it is well known that any irreducible and aperiodic markov chain has exactly one stationary distribution, and for any arbitrary initial distribution, the sequence of distributions at time n converges to the stationary distribution, that is, the markov chain is approaching equilibrium as n → ∞. in this paper, a characterization of the aperiodicity in existential terms of some state is given. at the same time, a p system with external output is associated with any irreducible markov chain. the designed system provides the aperiodicity of that markov chain and spends a polynomial amount of resources with respect to the size of the input. a comparative analysis with respect to another known solution is described. keywords: markov chain, p sytems, membrane computing 1 introduction a discrete-time markov chain is a stochastic process such that the past time is irrelevant to predict the future, given knowledge of the present time. that is, given the present time, the future does not depend on the past time: the result of each event depends only on the result of the previous event. in order to study the evolution in time of a markov chain as well as the existence of the stationary distribution, it is suitable to classify its states. this classification depends on the path structure of the chain. one of the central issues in markov theory is the study of the asymptotic behavior of markov chains. it is well known that for any irreducible and aperiodic markov chain: (a) there exists at least one stationary distribution (that is, a probability distribution on the state space which is an invariant for the transition matrix associated with the chain), and (b) for any initial distribution, µ () and for any stationary distribution π for the markov chain, the sequence (µ (n))n∈nnn converges to π in total variation as n → ∞ (that is, the markov chain is approaching equilibrium as n → ∞). copyright c© 2006-2009 by ccc publications 292 mónica cardona-roca, m. ángels colomer-cugat, agustín riscos-núñez, miquel rius-font in paper [2], a classification of states of a finite and homogeneous markov chain is provided by using p systems. moreover, the period was calculated for recurrent classes. the design of the p systems was inspired in properties used in classic algorithms that deal with the problem of the classification. especially, this solution allows us to decide whether an irreducible markov chain is aperiodic or not. the main goal of this paper is to design a p system associated with an irreducible markov chain which provides an answer to the aperiodicity of the chain. if the answer is negative, then the system provides the period of the chain. the solution presented is based on a characterization of the aperiodicity in existential terms of some state and a natural number, and it is semi–uniform, in the sense that for each markov chain, a p system associated with it is constructed. besides, the solution spends a polynomial amount of resources in the sense of the computational complexity theory in membrane computing. the solution presented in the paper improves the solution obtained in [2], because less computational resources are used. the paper is organized as follows. in the following section, we recall some basic notions and results that we use in the paper. in section 3, a p system associated with an irreducible markov chain is constructed in order to study the periodicity of that class. in section 4, the solution presented is compared with another solution given in [2]. finally some conclusions are presented. 2 preliminaries a discrete markov chain is a sequence {xt | t ∈ n} of random variables whose values are called states, that verifies the following property: p(xt+ = j/x = i, x = i, . . . , xt = it ) = p(xt+ = j/xt = it ). without loss of generality, we can suppose that the state space is the set of nonnegative integers. the value of variable xt is interpreted as the state of the process at instant t. in this paper we work with markov chains having a finite state space s = {s, . . . , sk}. a discrete markov chain is characterized by the transition probability pi j(t) = p(xt = s j/xt− = si), ∀t ≥ , where pi j(t) provides the transition from state si to state s j at time t − . the matrix of transition probabilities p(t) = (pi j(t))≤i, j≤k , is a stochastic matrix, that is, is nonnegative for all t and the sum of each row is equal to 1, ∑k j= pi j(t) = . we say that the chain is time homogeneous or stationary if pi j(t) = pi j for each t and it verifies the kolmogorov-chapman equation: p () i j = pi j, p () i j = k∑ l= pil pl j, . . . , p (n) i j = k∑ l= pil p (n−) l j , where p (n) i j is the transition probability of state si to state s j at time n. we denote the initial distribution by means of the vector µ () = (µ () , . . . , µ () k ) = (p(x = s), p(x = s), . . . , p(x = sk)), and the distribution of the markov chain at time n is µ (n) = (µ (n) , . . . , µ (n) k ) = (p(xn = s), p(xn = s), . . . , p(xn = sk)). p systems computing the period of irreducible markov chains 293 then, µ (n) = µ () · p(n), where p = (pi j) is the transition matrix of the homogeneous markov chain. next, we introduce some concepts and results related to the states of a homogeneous markov chain. we say that a state s j communicates with another state si (and we denote it by si → s j), if there exists a natural number n >  such that p (n) i j >  (that is, if the chain has a positive probability of ever reaching s j when we start from si. we say that the states si and s j intercommunicate (and we denote it by si ↔ s j) if si → s j and s j → si. in the finite state space s = {s, . . . , sk} of a markov chain, the relation ↔ is an equivalence relation and we can consider the corresponding quotient set {s, . . . , sk}/ ↔ whose elements are the classes of equivalence by ↔. a markov chain with state space s = {s, . . . , sk} is said to be irreducible if there exists only equivalence class with respect to ↔; that is, if for all si, s j ∈ e we have si ↔ s j. otherwise, the chain is said to be reducible. we say that a state si is recurrent or essential if for each natural number m and for each state s j verifying p (m) i j >  there exists a natural number n such that p (n) ji > . otherwise, the state is said to be transient. a recurrent class is the equivalence class determined by a recurrent state. it is easy to prove that from a recurrent state, only recurrent states belonging to the same class are reachable. a recurrence time of si is a natural number n >  such that p (n) ii > . the period of a state si is defined as d(i) = g.c.d. {n ≥  | p (n) ii > }, that is, it is the greatest common divisor of the recurrence times associated with it. all states belonging to the same class have the same period. then, we can define the period of a class of a given markov chain in a natural manner: it is the period of any state of the class (see [3] and [4] for more details). definition 1. a markov chain is said to be aperiodic if all its states are aperiodic; that is, their periods are equal to 1. otherwise, the chain is said to be periodic. next, we provide a method to compute the period of a recurrent class and a characterization of the periodicity of a class. theorem 2. let a = {s, . . . , sr} be a recurrent class. the period of a is d = g.c.d. {n | p (n) ii > ;  ≤ i, n ≤ r}. proof. as all states have the same period d, we have d = d() = d() = . . . = d(r) = g.c.d. {n ≥  | p (n) ii > ;  ≤ i ≤ r}. let d ′ = g.c.d.{n| p (n) ii > ;  ≤ i, n ≤ r}. let us see that d = d ′. for that, let n > r be a time of recurrence associated with a state si ∈ a, that is, p (n) ii > . there exists a state si such that p (n) ii ≥ p (n′) ii · p (n) ii · p (n′′) ii > , where n = n′ + n + n ′′. thus, n and n ′ + n′′ are also times of recurrence. if n > r or n ′ + n′′ > r, then we repeat the process until we obtain a decomposition p (n) ii ≥ p (n′) ii · p (n) ii · p (n) ii . . . p (nt ) it it · p (n′′) it i > , with  ≤ i, . . . , it ≤ r, n = n ′ + n + . . . + nt + n ′′ verifying n′ + n′′ ≤ r and n, . . . , nt ≤ r. finally, let us notice that substituting p (n) ii , with n > r, by a suitable sequence of p (m) ii , with m ≤ r, the g.c.d. is the same. lemma 3. let a = {a,··· , ar} be a set of natural numbers. let us suppose g.c.d. {a,··· , ar} = . let us denote by a+ the set of all positive linear combinations λa + ··· + λrar, with λi ∈ z +,  ≤ i ≤ r. then, there exists a natural number n such that n ∈ a+ for all n ≥ n. 294 mónica cardona-roca, m. ángels colomer-cugat, agustín riscos-núñez, miquel rius-font proof. see, e.g., the appendix of [1] next, we characterize the aperiodicity of a recurrent class of a finite markov chain through the existence of a state s j reachable from each state si. theorem 4. let {xt | t ∈ n} be a markov chain with state space s = {s, . . . , sk} and transition matrix p = (pi j). (1) if {xt | t ∈ n} is aperiodic, then there exists a natural number n such that p (n) ii > , for all i ( ≤ i ≤ k) and all n ≥ n. (2) if {xt | t ∈ n} is irreducible and aperiodic, then there exists a natural number m such that p (n) i j > , for all i, j ( ≤ i, j ≤ k) and all n ≥ m. proof. see, e.g., chapter 4 from [3] theorem 5. let a = {s, . . . , sr} be a recurrent class of a finite markov chain. the following are equivalent: (1) class a is aperiodic. (2) there exists a state s j ∈ a and a natural number m ∈ n such that p (m) i j >  for all state si ∈ a. proof. let us suppose that class a is aperiodic. then all states in a have the same period d = . from theorem 4 there exists a natural number n such that p (n) ii > , for all i ( ≤ i ≤ r) and all n ≥ n. given j ( ≤ j ≤ r), we define ni( j) = min{n | p (n) i j > }, for each si ∈ a, n( j) = max{n( j), . . . , nr( j)}, and m = n + n( j). let us see that p (m) i j > , for each i ( ≤ i ≤ r). we have p (m) i j ≥ p (ni( j)) i j p (m−ni( j)) j j >  because of p (ni( j)) i j >  by definition of ni( j), and p (m−ni( j)) j j >  by theorem 4. conversely, let us suppose that there exists m ≥  and a state s j ∈ a such that ∀ si ∈ a we have p (m) i j > . in particular, p (m) j j >  so m is a recurrence time. on the one hand, if d is the period of the class, then m is a multiple of d. on the other hand, if si ∈ a is a state such that p ji > , then  < p (m) i j p ji ≤ p (m+) ii , so m +  is a multiple of d. hence, d = . 3 a p system associated with an irreducible markov chain the goal of this paper is to study the aperiodicity of an irreducible markov chain with state space s = {s, . . . , sk}, k ≥ , by using p systems. in the affirmative case, the answer of the system is y es, on the contrary, the system sends an object encoding the period of the class to the environment. 3.1 the design of the p system let pk = (pi j)≤i, j≤k be a boolean matrix associated with a class with a finite and homogeneous markov chain of order k such that pi j =  if the transition from si to s j is possible, and pi j =  otherwise; that is, pk is the adjacency matrix of the directed graph associated with the recurrent class. the solution presented in this paper is a semi–uniform one in the following sense: we give a family πππ = {π (pk) | k ∈ nnn}, associating with pk a p system with external output, such that (a) there exists a deterministic turing machine working in polynomial time which constructs the system π (pk) from pk; and (b) the output of the p system π (pk) provides the classification of the recurrent class of the markov chain as well as the period of the states. we associate with the matrix pk the p system of degree 4 with external output, p systems computing the period of irreducible markov chains 295 π (pk) = (γ (pk), µ(pk), m, m, m, m, r) defined as follows: • working alphabet: γ (pk) = {si j, ti j, τi j |  ≤ i, j ≤ k} ∪ {si jr |  ≤ i, j, r ≤ k} ∪ {tr |  ≤ r ≤ k} ∪ {βl |  ≤ l ≤ k − } ∪ {bi, pi |  ≤ i ≤ k} ∪ {ci, di |  ≤ i ≤ α} ∪ {yes,y es, σ } where α = k + ⌈ k  ⌉. • membrane structure: µ(pk) = [ [ [ [ ] ] ]]. • initial multisets: m = {t pi j i j |  ≤ i, j ≤ k} ∪ {β}; m = {sii |  ≤ i ≤ k} m = {bi |  ≤ i ≤ k} ∪ {d}; m = /0 • the set r of evolution rules consists of the following rules: r = [ti j → τi jt k i j],  ≤ i, j ≤ k r = [βi → βi+],  ≤ i ≤ k −  r = [βk−] → c k  r = [crsi jτ p j j . . . τ p jk jk ] → [s p j i . . . s p jk ik c γ j r+]s p j ir+ . . . s p jk ikr+t p ji r+,  ≤ i, j ≤ k,  ≤ r ≤ α − , γ j = ∑k l= p jl r = [σ ] → σ r = [s jr . . . sk jr] → [σ ] yes,  ≤ j ≤ k,  ≤ r ≤ α r = [trbr → pr],  ≤ r ≤ k r = [pi pi+l → pi pl ],  ≤ i ≤ k,  ≤ l ≤ k − i r = [p  i → pi],  ≤ i ≤ k r = [di → di+],  ≤ i ≤ α −  r = [dα pr] → pr[ ],  ≤ r ≤ k r = [dα p] → yes[ ] r = [yes] → y es[ ] r = [pr] → pr[ ],  ≤ r ≤ k 3.2 an overview of computations initially, membrane 1 contains objects ti j that codify the elements pi j of the boolean matrix associated with the transition matrix of the markov chain, together with the counter β. this counter allows us to dissolve membrane 1 at a certain instant. membrane 2 contains initially objects sii that codify the states si of the chain. membrane 3 contains objects bi that will be used in order to avoid that repeated recurrence times smaller than or equal to k appear. the counter d in membrane 2 will be used to trigger the answer at the suitable instant. the design of the p system π (pk) implements a process that is structured by stages. the first one consists of k steps which allow the production of sufficiently many new copies τi j of objects ti j. this is done by applying rules of type r and r in membrane 1 at k −  first steps and applying at step k rule r that dissolves membrane 1. 296 mónica cardona-roca, m. ángels colomer-cugat, agustín riscos-núñez, miquel rius-font at the second stage, all paths between states with length at most k, as well as recurrence times smaller than or equal to k, are generated. this stage starts at step k +  and it spends at most k steps. first, rules of type r are applied producing objects si jr in membrane 3 that codify the existence of a path with length r from state si to state s j, as well as the objects tr codifying the existence of a recurrence time equal to r. simultaneously, it is checked if there exists a state s j and a natural number m such that p (m) i j > , for all states si. in that case, an object σ is produced in membrane 2 and the system expels an object y es to the environment. the third stage is only applied if an object y es has not been expelled to the environment. at this stage, the period of the class is computed and it takes k + ⌈ k  ⌉ steps. by applying rules of type r, objects pr encoding recurrence times smaller than or equal to k, are obtained. such recurrence times are different from each other. by applying rules of types r and r, the greatest common divisor of these times is computed. if the period of the class is equal to , then the system sends an object y es to the environment, otherwise, the system expels an object pn that encodes the period of the class to the environment. 4 results and discussions in [2] a p system was constructed which allows us to classify the states of a markov chain. thus, that p system can be adapted to characterize the aperiodicity of such a chain. specifically, if pk = (pi j)≤i, j≤k is the boolean matrix associated with the states of a recurrent class of a finite and homogeneous markov chain of order k, then we define the system π ′(pk) = (γ ′(pk), µ ′(pk), m ′ , m ′ , m ′ , m ′ , r ′, ρ ′), as follows: • working alphabet: γ ′(pk) = {a, ri, ti j |  ≤ i, j ≤ k} ∪ {cr |  ≤ r ≤ k + } ∪ {ti jur |  ≤ i, j, u ≤ k,  ≤ r ≤ k} ∪ {βi |  ≤ i ≤ γ + } ∪ {si jr |  ≤ i, j ≤ k,  ≤ r ≤ k} ∪ {di |  ≤ i ≤ (k − )} where γ = k +  + ⌈lgk⌉ + (k−)(k+)  . • membrane structure: µ ′(pk) = [ [ [ [ ] ] ] ]. • initial multisets: m′ = /0; m ′  = {β}; m ′  = {c}; m ′  = {sii t pi j (k−) i j |  ≤ i, j ≤ k}. • the set r of evolution rules consists of the following rules: – rules in the skin membrane labeled by 1: r = {dp → (rp, out) |  < p ≤ k} r = {d → (a, out)} – rules in the membrane labeled by 2: r = {βi → βi+ |  ≤ i ≤ γ} ∪ {βγ+ → λ }. r = {d  j → d j |  ≤ j ≤ k} r = {d jd j+l → d jdl |  ≤ j ≤ k,  ≤ j + l ≤ k} p systems computing the period of irreducible markov chains 297 – rules in the membrane labeled by 3: r = {ti jur → (ti j su j(r+), in) | pi j = , u 6= j,  ≤ i, j, u ≤ k,  ≤ r < (k − )} r = {ti ju(k−) → (ti j, in) | pi j = , u 6= j,  ≤ i, j, u ≤ k} r = {ti j jr → (ti j, in) dr+ | pi j = ,  ≤ i, j ≤ k,  ≤ r < (k − )} r = {ti j j(k−) → (ti j, in) | pi j = ,  ≤ i, j ≤ k} r = {cr → cr+ |  ≤ r ≤ (k − ) + } ∪ {c(k−)+ → λ } – rules in the membrane labeled by 4: r = {suirt pi i . . .t pik ik → (t pi iur . . .t pik ikur, out) |  ≤ u, i ≤ k,  ≤ r ≤ (k − )}. • there is only a priority relation in the membrane labeled by 2: {r > r}. in order to study the efficiency of the p system π (pk) constructed in this work, we will compare the results with those obtained by the p system π ′(pk) described above. for that purpose, a comparative analysis of the computational resources required in both p systems is given first. secondly, an analysis of the times of execution obtained on designed simulators for both p systems with some case studies is presented. 4.1 computational resources required the resources required initially to construct the systems π (pk) and π ′(pk), and the number of steps taken by the systems, are the following: π (pk) π ′(pk) size of the alphabet θ (k) θ (k) initial number of membranes 4 4 sum of the sizes of initial multisets θ (k) θ (k) number of rules θ (k) θ (k) maximal length of a rule θ (k) θ (k) number of priority relations 0 θ (k) number of steps θ (k) θ (k) in the previous table, let us notice that the amount of resources requested by π (pk) is smaller than the ones requested by π ′(pk). indeed, the size of the alphabet and the number of rules pass from power 3 to power 4, and the system π (pk) has no priority relation. the number of steps is of the same asymptotic order. 4.2 case studies we have realized a simulator for each system π (pk) and π ′(pk). these simulators have been written in c++ language and they have been executed on a pentium 4 computer with 512 mb ram and 3.20 ghz. in both simulators objects ti j have been represented by means of arrays of dimension 2; objects si j have been represented by vectors of dimension 2 and recurrent times have been represented by onedimensional vectors. the simulator of the system π (pk) generates the trajectories with a length at most k + ⌈k/⌉ in a sequential way, keeping the times of recurrence smaller than or equal to k. if assertion () in theorem 5 is fulfilled, the simulator halts displaying the time of execution and the aperiodicity of the markov chain. otherwise the simulator computes the g.c.d. of the recurrence times obtained where all of them are different. 298 mónica cardona-roca, m. ángels colomer-cugat, agustín riscos-núñez, miquel rius-font similarly, a simulator for the system π ′(pk) has been implemented. the main difference with respect to the previously mentioned one is that it can keep more than a copy of the times of recurrence. all trajectories of the markov chain with a length smaller than or equal to (k − ) and their recurrence time are computed. then the g.c.d. of these times is obtained. when the markov chain is aperiodic, the p system π (pk) can finish before all trajectories with a length k + ⌈k/⌉ are computed. in case it is necessary to calculate the period, bearing in mind that all recurrence times are different, system π (pk) is faster than π ′(pk) in computing the g.c.d. of these times. when the markov chain is periodic the length of the trajectories computed by π (pk) are longer than those computed by π ′(pk). nonetheless, in order to compute the period, recurrence times used in π (pk) are all different. the simulators designed have been executed on eight recurrent markov chains with 100 states. four of these markov chains are periodic and the others are aperiodic. table 1 shows the values equal to 1 of the adjacency matrix of the graph associated with the recurrent markov chains. the execution times are described in table 2. example 1 pi,i+ =   ≤ i ≤  p, =  2 pi,i+ =   ≤ i ≤  pi, =   ≤ i ≤  3 p j+i, j+i+ =   ≤ i ≤   ≤ j ≤  p j, j− =   ≤ j ≤  p j+, j+ =   ≤ j ≤  p, =  4 p j+i, j+i+ =   ≤ i ≤   ≤ j ≤  p j, j− =   ≤ j ≤  p j+, j+ =   ≤ j ≤  p, =  p, =  5 p j+i, j+i+ =   ≤ i ≤   ≤ j ≤  p j, j− =   ≤ j ≤  p j+, j+ =   ≤ j ≤  p, =  p, =  6 p j+i, j+i+ =   ≤ i ≤   ≤ j ≤  p j, j− =   ≤ j ≤  p j+, j+ =   ≤ j ≤  p, =  7 pi,i+ =   ≤ i ≤  pi+,i =   ≤ i ≤  p+i,+i =   ≤ i ≤  8 pi,i+ =   ≤ i ≤  pi+,i =   ≤ i ≤  p+i,+i =   ≤ i ≤  p, =  table 1. adjacency values of the examples 5 conclusions markov chains have applications in different fields such as physics, economics, biology, statistics, social sciences, etc. in these applications it is important to know whether the markov chain associated with the process is convergent or not. when the markov chain is aperiodic, the transition matrix converges and the process becomes stable. in other cases, the process does not reach an equilibrium. in this work, a characterization of the aperiodicity of a markov chain has been given in terms of the p systems computing the period of irreducible markov chains 299 example period π ′(pk) π (pk) 1 100 0 0 2 1 146 0 3 10 0 0 4 1 122 35 5 1 1 2 6 5 11 20 7 2 381 169 8 1 1101 104 table 2. observed run times existence of a state reachable from any other state. based on this property, a computational p system has been constructed that allows us to know whether the markov chain is aperiodic and calculate its period if not. in [2], every finite and homogeneous markov chain has associated a p system that provides a classification of its recurrent classes. that p system can be adapted to study the aperiodicity of a markov chain and then its period can be calculated. the solution presented in this work improves the solution derived from the p system described in [2]. for that purpose, simulators have been constructed for these p systems and the respective times of execution on eight examples have been analyzed. for the computational study of the aperiodicity of a markov chain it would be interesting to design new p systems that incorporate additional features such as electrical charges, active membranes, etc. and that improve quantitatively the amount of computational resources used. acknowledgement the authors wish to thank mario j. pérez-jiménez for his advices, suggestions and constant help. among the numerous virtues of this excellent professor, we would like to highlight his enviable capacity of work and his great human quality. as it is usually said, behind a great man there is a great woman and so we cannot end theses line without expressing our gratitude to queta for her immense patience with all of us. the third author acknowledges the support of the project tin2006–13425 of the ministerio de educación y ciencia of spain, cofinanced by feder funds, and the support of the project of excellence with investigador de reconocida valía of the junta de andalucía, grant p08-tic-04200. bibliography [1] p. brémaud: markov chains: gibbs fields, monte carlo simulation, and queues. springer, new york, 1998. [2] m. cardona, m.a. colomer, m.j. pérez–jiménez, a. zaragoza: classifying states of a finite markov chains with membrane computing. lecture notes in computer science. springer,vol 4361, pp. 266278, 2006. [3] o. häggstöm: finite markov chains and algorithmic applications. london mathematical society, cambridge university press, cambridge, 2003. [4] r. nelson: probability, stochastic processes, and queing theory. springer, new york, 1995. 300 mónica cardona-roca, m. ángels colomer-cugat, agustín riscos-núñez, miquel rius-font mónica cardona-roca received his degree in mathematics in 2000, from the barcelona university. he is associate professor at the department of mathematics of the university of lleida where he has been teaching for eight years. here main research areas are natural computing and membrane computing. she has co-authored two books in mathematics. she has co-authored 5 papers on international journals in natural computing. she has co-authored 3 papers in statistics published in proceedings of spanish conferences. m. ángels colomer-cugat received his degree in engineer agronomist from the upc (politecnic universitiy of catalonia) and doctor degree in mathematics in 1996 from the upc. currently, she is titular professor of statistics and operative investigation where she has been teaching for more than twenty five years. in the current moment she is the head of the emergent research group on models of membrane computation applied to ecosystems. she has published seven books on statistics and quality control. she has published twelve scientifics papers in international journals. her main research area are models of computation with membranes applied to the ecology and biology processes. hers first researches were centred on the stocastics models, concretely markov chain applied to natural processes where she has some works. agustín riscos-núñez got his master degree in mathematics in 2000, and then his phd degree on 2004. currently he is an associate professor at the department of computer science and artificial intelligence in the university of sevilla (spain). he is a member of the research group on natural computing in the same university, and his main research interests within the membrane computing area are complexity theory, models for biological processes, and computer simulation. he has co-authored about 30 papers on international journals in the last years, and he has also participated in 15 conferences and workshops. miquel rius-font received his bachelor degree in mathematics form the universitat autónoma de barcelona and his doctor degree in mathematics from university of barcelona. he is currently teaching mathematics at catalonian techology university and his research area is graph theory and natural computing. his interest involves isoperimetric problems as well as graph labeling problems. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 3, pp. 295-303 tissue p systems with cell division gheorghe păun, mario j. pérez-jiménez, agustín riscos-núñez abstract: in tissue p systems several cells (elementary membranes) communicate through symport/antiport rules, thus carrying out a computation. we add to such systems the basic feature of (cell–like) p systems with active membranes – the possibility to divide cells. as expected (as it is the case for p systems with active membranes), in this way we get the possibility to solve computationally hard problems in polynomial time; we illustrate this possibility with sat problem. keywords: tissue-like p systems, cell division rule, sat problem, np-complete problem 1 introduction in membrane computing, there are two main classes of p systems: with the membranes arranged hierarchically, inspired from the structure of the cell, and with the membranes placed in the nodes of a graph, all of them at the same level, inspired from the cell inter-communication in tissues. a particularly interesting sub-class of the first class are the systems with active membranes, where the membrane division can be used in order to solve hard problems, e.g., np-complete problems, in polynomial or even linear time, by a space-time trade-off. in the tissue p systems, the communication among cells is performed by means of symport/antiport rules, well-known in biology. details can be found in [8], [10], as well as in the comprehensive page from the web address http://ppage.psystems.eu). in this paper we combine the two definitions, and consider tissue p systems (with the communication done through symport/antiport rules) with cell division rules of the same form as in p systems with active membranes, but without using polarizations. the rules are used in the non-deterministic maximally parallel way, with the restriction that if a division rule is used for dividing a cell, then this cell does not participate in any other rule, for division or communication (the intuition is that when dividing, the interaction of the cell with other cells or with the environment is blocked); the cells obtained by division have the same labels as the mother cell, hence the rules to be used for evolving them or their objects are inherited (the label precisely identifies the available rules). this natural extension of tissue p systems provides the possibility of solving sat problem in polynomial time (with respect to the number of variables and of clauses), in a confluent way: at precise times, one of the objects yes or no is sent to the environment, giving the answer to the question whether the input propositional formula is satisfiable. the construction is uniform: in a polynomial time, a family of recognizing tissue p systems with cell division is constructed, which, receiving as inputs encodings of instances of sat, tells us whether or not these instances are satisfiable. 2 tissue p systems with cell division we assume the reader to be familiar with basic elements of membrane computing and we directly define the class of p systems which is investigated in this paper. a tissue p system with cell division of degree m ≥ 1 is a construct π = (o, e, w1, . . . , wm, r, io), where: copyright © 2006-2008 by ccc publications 296 gheorghe păun, mario j. pérez-jiménez, agustín riscos-núñez 1. m ≥ 1 (the initial degree of the system; the system contains m cells, labeled with 1, 2, . . . , m; we will use 0 to refer to the environment); 2. o is the alphabet of objects; 3. w1, . . . , wm are strings over o, describing the multisets of objects placed in the m cells of the system at the beginning of the computation; 4. e ⊆ o is the set of objects present in the environment in arbitrarily many copies each; 5. r is a finite set of evolution rules, of the following forms: (a) (i, x/y, j), for i, j ∈ {0, 1, 2, . . . , m}, i 6= j, and x, y ∈ o∗; communication rules; 1, 2, . . . , m identify the cells of the system, 0 is the environment; when applying a rule (i, x/y, j), the objects of the multiset represented by x are sent from region i to region j and simultaneously the objects of the multiset y are sent from region j to region i; (b) [ a ] i → [ b ] i[ c ] i, where i ∈ {1, 2, . . . , m} and a, b, c ∈ o; division rules; under the influence of object a, the cell with label i is divided in two cells with the same label; in the first copy the object a is replaced by b, in the second copy the object a is replaced by c; all other objects are replicated and copies of them are placed in the two new cells. 6. io ∈ {1, 2, . . . , m} is the output cell. therefore, we use antiport rules for communication (for a rule (i, x/y, j) we say that the maximum of the lengths of x and y is the weight of the rule), and division rules as in p systems with active membranes. the rules of a system as above are used in the non-deterministic maximally parallel manner as customary in membrane computing. in each step, all cells which can evolve must evolve in a maximally parallel way (in each step we apply a multiset of rules which is maximal, no further rule can be added), with the following important mentioning: if a cell is divided, then the division rule is the only one which is applied for that cell in that step, its objects do not evolve by means of communication rules. this is like saying that a cell which divides first cuts all its communication channels with the other cells and with the environment; the dotter cells will participate to the interaction with other cells or with the environment only in the next step – providing that they are not divided once again. their label precisely identify the rules which can be applied to them. the computation starts from the initial configuration and proceeds as defined above; only halting computations give a result, and the result is the number of objects present in the halting configuration in cell io; the set of numbers computed in this way by the various halting computations in π is denoted by n(π). in the present paper we are not interested in the computing power of systems as above – already systems without membrane division are known to be turing complete (see [8], [6], etc.), but in their computing efficiency. that is why we introduce a variant of tissue p systems with membrane division, namely recognizing systems with input following the definitions of complexity classes in terms of membrane computing (see [9]). such a system has the form π = (o, σ, e, w1, . . . , wm, r, iin), where: • (o, e, w1, . . . , wm, r, 0) is a tissue p system with cell division of initial degree m ≥ 1 (as defined in the previous section, but with the environment, indicated by taking io = 0, used for reading the output of a computation), and w1, . . . , wm are strings over o−σ. • the working alphabet o has two distinguished objects yes and no, present in at least one copy in some initial multisets w1, . . . , wm, but not present in e. • σ is an (input) alphabet strictly contained in o. • iin ∈ {1, . . . , m} is the input cell. tissue p systems with cell division 297 • all computations halt. • if c is a computation of π, then either the object yes or the object no (but not both) must have been released into the environment, and only in the last step of the computation. the computations of the system π with input w ∈ σ∗ start from a configuration of the form (w1, w2, . . . , wiin w, . . . , wm; e), that is, after adding the multiset w to the contents of the input cell iin. we say that the multiset w is recognized by π if and only if the object yes is sent to the environment, in the last step of the corresponding computation. we say that c is an accepting computation (respectively, rejecting computation) if the object yes (respectively, no) appears in the environment associated with the corresponding halting configuration of c. definition 1. we say that a decision problem x = (ix , θx ) is solvable in polynomial time by a family π = {π(n) | n ∈ n} of recognizer tissue-like p systems with cell division if the following holds: • the family π is polynomially uniform by turing machines, that is, there exists a deterministic turing machine working in polynomial time which constructs the system π(n) from n ∈ n. • there exists a pair (cod, s) of polynomial-time computable functions over ix (called a polynomial encoding of ix in π) such that: − for each instance u ∈ ix , s(u) is a natural number and cod(u) is an input multiset of the system π(s(u)); − the family π is polynomially bounded with regard to (x , cod, s), that is, there exists a polynomial function p, such that for each u ∈ ix every computation of π(s(u)) with input cod(u) is halting and, moreover, it performs at most p(|u|) steps; − the family π is sound with regard to (x , cod, s), that is, for each u ∈ ix , if there exists an accepting computation of π(s(u)) with input cod(u), then θx (u) = 1; − the family π is complete with regard to (x , cod, s), that is, for each u ∈ ix , if θx (u) = 1, then every computation of π(s(u)) with input cod(u) is an accepting one. we denote by pmct d the set of all decision problems which can be solved by means of recognizer tissue-like p systems with cell division in polynomial time. this class is closed under polynomial–time reduction and under complement. we close this section with an important remark about the previous way of solving decision problems. specifically, we have said nothing about the way the computations proceed; in particular, they can be non-deterministic, as standard in membrane computing. it is important however that the systems always stop and always they send out an object which is the correct answer to the input problem. from the soundness and completeness conditions above we deduce that every p system π(n) is confluent, in the following sense: every computation of a system with the same input multiset must always give the same answer. 3 solving sat in polynomial time as expected, the possibility to divide cells means the possibility to create an exponential space in a linear time, and this space can be used in order to obtain fast solutions to computationally hard problems. theorem 1. tissue p systems with active membranes can solve sat in polynomial time. (otherwise stated, sat ∈ pmct d.) 298 gheorghe păun, mario j. pérez-jiménez, agustín riscos-núñez proof. let us consider a propositional formula γ = c1 ∧···∧cm, consisting of m clauses c j = y j,1 ∨···∨ y j,k j , 1 ≤ j ≤ m, where y j,i ∈ {xl ,¬xl | 1 ≤ l ≤ n}, 1 ≤ i ≤ k j (there are used n variables). without loss of generality, we may assume that no clause contains two occurrences of some xi or two occurrences of some ¬xi (the formula is not redundant at the level of clauses), or both xi and ¬xi (otherwise such a clause is trivially satisfiable, hence can be removed). we codify γ , which is an instance of sat with size parameters n and m, by the multiset cod(γ) = {si, j | y j,r = xi, 1 ≤ i ≤ n, 1 ≤ j ≤ m, 1 ≤ r ≤ k j} ∪ {s′i, j | y j,r = ¬xi, 1 ≤ i ≤ n, 1 ≤ j ≤ m, 1 ≤ r ≤ k j}. (we replace each variable xi from each clause c j with si, j and each negated variable ¬xi from each clause c j with s′i, j, then we remove all parentheses and connectives. in this way we pass from γ to cod(γ) in a number of steps which is linear with respect to n·m.) the instance γ will be processed by the tissue p system π(s(γ)) with input cod(γ), where s(γ) = 〈n, m〉 = (n+m)·(n+m+1)2 + n. we construct the recognizing tissue p system (of degree 2) with input π(〈n, m〉) = (o, σ, e, w1, w2, r, 2), with the following components: o = σ∪{ai,ti, fi | 1 ≤ i ≤ n}∪{ri | 1 ≤ i ≤ m} ∪ {ti, fi | 1 ≤ i ≤ n}∪{ti, j, fi, j | 1 ≤ i ≤ n, 1 ≤ j ≤ m + 1} ∪ {bi | 1 ≤ i ≤ 3n + m + 1}∪{ci | 1 ≤ i ≤ n + 1} ∪ {di | 1 ≤ i ≤ 3n + nm + m + 2}∪{ei | 1 ≤ i ≤ 3n + nm + m + 4} ∪ { f , g,yes,no}, σ = {si, j, s′i j | 1 ≤ i ≤ n, 1 ≤ j ≤ m}, e = o−{yes,no}, w1 = yes no b1 c1 d1 e1, w2 = f g a1 a2 . . . an, and the following rules. 1. division rules: [ ai ] 2 → [ ti ] 2[ fi ] 2, for all i = 1, 2, . . . , n. (membrane 2 is repeatedly divided, each time expanding one object ai, corresponding to a variable xi, into ti and fi, corresponding to the values true and false which this variable may assume. in this way, in n steps, we get 2n cells with label 2, each one containing one of the 2n truth-assignments possible for the n variables. the objects f , g are duplicated, hence a copy of each of them will appear in each cell.) 2. communication rules: (1, bi/b2i+1, 0), for all i = 1, 2, . . . , n + 1, (1, ci/c2i+1, 0), for all i = 1, 2, . . . , n + 1, (1, di/d2i+1, 0), for all i = 1, 2, . . . , n + 1, (1, ei/ei+1, 0), for all i = 1, 2, . . . , 3n + nm + m + 3. (in parallel with the operation of dividing cell 2, the counters bi, ci, di, ei from cell 1 grow their subscripts. in each step, the number of copies of objects of the first three types is doubled, hence tissue p systems with cell division 299 after n steps we get 2n copies of bn+1, cn+1, and dn+1. objects bi will check which clauses are satisfied by a given truth-assignment, objects ci are used in order to multiply the number of copies of ti, fi as we will see immediately, di are used to check whether there is at least one truth-assignment which satisfies all clauses, and ei will be used in order to produce the object no, if this will be the case, in the end of the computation.) (1, bn+1cn+1/ f , 2), (1, dn+1/g, 2). (in step n + 1, the counters bn+1, cn+1, dn+1 are brought in cells with label 2, in exchange of f and g. because we have 2n copies of each object of these types and 2n cells 2, each one containing exactly one copy of f and one of g, due to the maximality of the parallelism of using the rules, each cell 2 gets precisely one copy of each of bn+1, cn+1, dn+1. note that cells 2 cannot divide any more, because the objects ai were exhausted.) (2, cn+1ti/cn+1ti,1, 0), (2, cn+1fi/cn+1fi,1, 0), for each i = 1, 2, . . . , n, (2, ti, j/titi, j+1, 0), (2, fi, j/ fifi, j+1, 0), for each i = 1, 2, . . . , n and j = 1, 2, . . . , m. (in the presence of cn+1, the objects ti, fi introduce the objects ti,1 and fi,1, respectively, which initiates the possibility of introducing m copies of each ti and fi in each cell 2. the idea is that because we have m clauses, in order to check their values for a given truth-assignment of variables, it is possible to need one value for each variable for each clause. note that this phase needs 2n steps for introducing the double-subscripted objects ti,1, fi,1 – for each one we need one step, because we have only one copy of cn+1 available – then further m steps are necessary for each ti,1, fi,1 to grow its second subscript; all these steps are done in parallel, but for the last introduced ti,1, fi,1 we have to continue m steps after the 2n necessary for priming. in total, we perform 2n + m steps.) (2, bi/bi+1, 0), (2, di/di+1, 0), for all i = n + 1, . . . , (n + 1) + (2n + m)−1. (in parallel with the previous operations, the counters bi and di increase their subscripts, until reaching the value 3n + m + 1. this is done in all cells 2 at the same time. simultaneously, ei increases its subscript in cell 1.) (2, b3n+m+1tisi, j/b3n+m+1r j, 0), (2, b3n+m+1 fis′i, j/b3n+m+1r j, 0), for all 1 ≤ i ≤ n and 1 ≤ j ≤ m, (2, di/di+1, 0), for all i = 3n + m + 1, . . . , (3n + m + 1) + nm−1. (in the presence of b3n+m+1 – and not before – we check the values assumed by clauses for the truth-assignments from each cell 2. we have only one copy of b3n+m+1 in each cell, hence we need at most nm steps for this: each clause contains at most n literals, and we have m clauses. in parallel, d increases the subscript, until reaching the value 3n + nm + m + 1.) (2, d3n+nm+m+iri/d3n+nm+m+i+1, 0), for all i = 1, 2, . . . , m. (in each cell with label 2 we check whether or not all clauses are satisfied by the corresponding truth-assignment. for each clause which is satisfied, we increase by one the subscript of d, hence the subscript reaches the value 3n + nm + 2m + 1 if and only if all clauses are satisfied.) (2, d3n+nm+2m+1/ f yes, 1). (if one of the truth-assignments from a cell 2 has satisfied all clauses, then we reach d3n+nm+2m+1, which is sent to cell 1 in exchange of the objects yes and f .) 300 gheorghe păun, mario j. pérez-jiménez, agustín riscos-núñez (2,yes/λ , 0). (in the next step, the object yes leaves the system, signaling the fact that the formula is satisfiable. in cell 1, the counter e will increase one more step its subscript, but after that it will remain unchanged – it can leave cell 1 only in the presence of f , but this object was already moved to cell 2.) (1, e3n+nm+2m+2 f no/λ , 2), (2,no/λ , 0). (if the counter e reaches the subscript 3n + nm + 2m + 2 and the object f is still in cell 1, then the object no can be moved to a cell 2, randomly chosen, and from here it exits the system, signaling that the formula is not satisfiable.) in order to show that the family π = {π(〈n, m〉) | n, m ∈ n} is polynomially uniform by deterministic turing machines we first note that the sets of rules associated with the system π(〈n, m〉) are recursive. hence, it is enough to note that the amount of necessary resources for defining each system is quadratic in max{n, m}, and this is indeed the case, since those resources are the following: 1. size of the alphabet: 6nm + 17n + 4m + 12 ∈ θ(nm). 2. initial number of cells: 2 ∈ θ(1). 3. initial number of objects: n + 8 ∈ θ(n). 4. number of rules: 4nm + 10n + 3m + 16 ∈ θ(nm). 5. upper bound for the length of the rules: 3 ∈ θ(1) from the previous explanations, one can see that, starting with the multiset cod(γ) added to cell 2, which is the input cell, the system correctly answers the question whether or not γ is satisfiable. the duration of the computation is polynomial in terms of n and m: the answer yes is sent out in step 3n + nm + 2m + 2, while the answer no is sent out in step 3n + nm + 2m + 4. this concludes the proof. 2 the antiport rules from the previous construction are of weight at most 3, but the weight can be reduced to two, at the expense of some slowdown of the system. for instance, instead of the rule (1, e3n+nm+2m+2 f no/λ , 2) we can consider the rules (1, e3n+nm+2m+2 f /h, 0), (1, h no/λ , 2), where h is a new object. we can proceed in the same way with the rules (2, b3n+m+1tisi, j/b3n+m+1r j, 0), (2, b3n+m+1 fis′i, j/b3n+m+1r j, 0), for 1 ≤ i ≤ n and 1 ≤ j ≤ m, but in this way instead of at most nm steps for finding the satisfied clauses we will need at most 2nm steps. the details are left to the reader. taking into account that sat is an np–complete problem and the class pmct d is closed under polynomial–time reduction and under complement, we have: corollary 2. np ∪ co-np ⊆ pmct d 4 final remarks we have proven that by adding the membrane division feature to tissue p systems (with the communication done by antiport rules of a small weight) we can solve np-complete problems in polynomial time. we exemplify this possibility with sat problem. it remains as a research topic to consider the same extension for other types of systems, for instance, for cell p systems with symport/antiport rules, or for neural p systems (with states associated with cells and multiset rewriting rules for processing the objects. the difficulty in the case of cell p tissue p systems with cell division 301 systems with symport/antiport comes from the fact that only the skin membrane can communicate with the environment; on the other hand, the skin membrane cannot be divided, hence we need exponentially many objects for communication with inner membranes, and such objects should be brought in from the environment. in turn, neural p systems with the maximal use of rules and replicated communication are already known to be able to solve np-complete problems in polynomial time; the challenge now is not to use replication). in spite of these difficulties, we expect results similar to the above one also in these cases. another problem which remains open is to consider tissue p systems with the communication using only symport rules. a previous version of the present paper was circulated in the volume of the second brainstorming week on membrane computing, held in sevilla, in february 2004, and in the meantime several papers have considered tissue-like p systems with cell division as a framework for devising polynomial solutions to np-complete problems. for instance, [2] deals with the subset sum problem, [3] deals with the partition problem, [5] deals with the vertex cover problem, and [4] considers the 3–coloring problem. what is not yet investigated is the possibility to also solve pspace problems, as it is the case, for instance, for cell-like p systems with division of non-elementary membranes (see [11]) or with membrane creation (see [7]). this last possibility for producing working space, cell creation rules, has been only recently considered for tissue p systems [1]. let us recall that this kind of rules does not perform replication of objects, as it happens with cell-division rules, and it is an open question whether tissue p systems with communication and membrane creation rules can solve efficiently computationally hard problems. acknowledgements. the support of this research through the project tin2006-13425 of the ministerio de educación y ciencia of spain, cofinanced by feder funds, and the support of the project of excellence tic-581 of the junta de andalucía, is gratefully acknowledged. bibliography [1] d. díaz–pernil: sistemas celulares de tejidos: formalización y eficiencia computacional, phd thesis, university of sevilla, 2008. [2] d. díaz–pernil, m.a. gutiérrez–naranjo, m.j. pérez–jiménez, a. riscos–núñez: solving subset sum in linear time by using tissue p systems with cell division. in j. mira, j.r. alvarez, eds. bioinspired modeling of cognitive tasks, second international work–conference on the interplay between natural and artificial computation, iwinac 2007, la manga del mar menor, spain, june 2007,part i, lncs 4527, springer, 2007, 170–179. [3] d. díaz–pernil, m.a. gutiérrez–naranjo, m.j. pérez–jiménez, a. riscos–núñez: solving the partition problem by using tissue-like p systems with cell division. in d. díaz, c. graciani, m.a. gutiérrez, gh. păun, i. pérez–hurtado, a. riscos, eds., proceedings of the sixth brainstorming week on membrane computing, report rgnc 01/08, fénix editora, 2008, 123–134. [4] d. díaz–pernil, m.a. gutiérrez–naranjo, m.j. pérez–jiménez, a. riscos–núñez: a linear-time tissue p system based solution for the 3-coloring problem, electronic notes in theoretical computer science, 171 (2007), 81–93. [5] d. díaz–pernil, m.j. pérez–jiménez, a. riscos–núñez, a. romero–jiménez: computational efficiency of cellular division in tissue-like membrane systems, submitted, 2008. 302 gheorghe păun, mario j. pérez-jiménez, agustín riscos-núñez [6] p. frisco, h.j. hoogeboom: simulating counter automata by p systems with symport/antiport. in gh. păun, g. rozenberg, a. salomaa, c. zandron, eds., membrane computing. international workshop wmc 2002, curtea de argeş, romania, revised papers, lncs 2597, springer, 2003, 288–301. [7] m.a. gutiérrez–naranjo, m.j. pérez–jiménez, f.j. romero–campero: a linear time solution for qsat with membrane creation. in r. freund, gh. păun, g. rozenberg, a. salomaa, eds., membrane computing, 6th international workshop, wmc 2005, vienna, austria, july 18-21, 2005, revised selected and invited papers, lncs 3850, springer, 2006, 241–252. [8] gh. păun: computing with membranes: an introduction, springer, berlin, 2002. [9] m.j. pérez–jiménez: an approach to computational complexity in membrane computing. in g. mauri, gh. păun, m.j. pérez-jiménez, g. rozenberg, a. salomaa, eds., membrane computing, 5th international workshop, wmc5, revised selected and invited papers, lncs 3365, springer, 2005, 85–109. [10] m. pérez–jiménez, a. romero–jiménez, f. sancho–caparrini, teoría de la complejidad en modelos de computatión celular con membranas, editorial kronos, sevilla, 2002. [11] p. sosik, a. rodríguez-patón: membrane computing and complexity theory: a characterization of pspace, international journal of foundations of computer science, 73, 1 (2007), 137–152. gheorghe păun1,2, mario j. pérez-jiménez2, agustín riscos-núñez2 1institute of mathematics of the romanian academy po box 1-764, 014700 bucureşti, romania 2 research group on natural computing department of computer science and artificial intelligence technical higher school of computer science engineering university of sevilla avda. reina mercedes s/n, 41012 sevilla, spain e-mail: {gpaun, marper, ariscosn}@us.es mario j., pérez–jiménez received his degree in mathematics from the barcelona university and doctor degree in mathematics from the sevilla university. in the past, he was associated professor at the university of barcelona. currently, he is titular professor of computer science and artificial intelligence at the university of sevilla, where is the head of the research group on natural computing. his main research fields are computational complexity theory, unconventional models of computation, natural computing, membrane computing, bioinformatics, and computational modelling for systems biology. he has published twelve books in computer science and mathematics, and over 100 scientific papers in international journals (collaborating with many researchers worldwide). he is the main researcher in various european, spanish and andalusia research grants. he has been and independent expert to the evaluation of nest (new and emergent science and technology) proposals under the sixth framework programme of the european community, and from may 2006 he is an european science foundation peer reviewer. tissue p systems with cell division 303 gheorghe păun, graduated the faculty of mathematics, university of bucharest, in 1974 and received his ph.d. in mathematics (with specialization in computer science) from the same university in 1977. he held a research position at the university of bucharest, and from 1990 he is at the institute of mathematics of the romanian academy, where he is currently a senior researcher. he visited numerous universities in europe, asia, and north america, with frequent and/or longer stays in turku (finland), leiden (the netherlands), magdeburg (germany, including an alexander von humboldt fellowship, in 1992-93), tarragona, madrid, and sevilla (spain, including a ramon y cajal scholarship, in 2001–2006), london-ontario (canada), rome, milano, pisa (italy), tokyo (japan), warsaw (poland), vienna (austria), budapest (hungary), etc. his main research areas are formal language theory and its applications, computational linguistics, dna computing, and membrane computing; this last research area was initiated by him, in 1998, and the respective models are now called p systems, see http://ppage.psystems.eu). he has published a large number of research papers (collaborating with many researchers worldwide), has lectured at over 100 universities, and gave numerous invited talks at recognized international conferences. he has published eleven monographs in mathematics and computer science (some of them translated in japanese, chinese, russian), has (co)edited over seventy collective volumes and special issues of journals, and also published many popular science books, books on recreational mathematics (games), and fiction books. he is a member of the editorial board of more than a dozen international journals and was/is involved in the program/steering/organizing committees for many recognized conferences and workshops. in 1997 he was elected a member of the romanian academy and from 2006 he is a member of academia europaea. he also got other honors, in romania or abroad (professional and literary prizes, honorary citizenship titles, doctor honoris causa of the silesian university in opava, czech republic, etc.). int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 4, pp. 427-438 fuzzy controller based qos routing algorithm with a multiclass scheme for manet b. sun, c. gui, q. zhang, h. chen baolin sun, chao gui, qifei zhang school of computing hubei university of economics wuhan 430205, china e-mail: blsun@163.com, guichao@126.com, cheffly@gmail.com hua chen college of science wuhan university of science and engineering wuhan 430073, china e-mail: qiuchen_1022@163.com abstract: a mobile ad hoc network (manet) consists of a set of mobile hosts that can communicate with each other without the assistance of base stations. due to the dynamic nature of the network topology and restricted resources, quality of service (qos) and multicast routing in manet are challenging tasks which attract the interests of many people. in this paper, we present a fuzzy controller based qos routing algorithm with a multiclass scheme (fqra) in mobile ad hoc networks. the performance of this scheduler is studied using ns2 (network simulator version 2) and evaluated in terms of quantitative measures such as packet delivery ratio, path success ratio and average end-to-end delay. simulations show that the approach is efficient, promising and applicable in ad hoc networks. keywords: mobile ad hoc networks, fuzzy controllers, routing algorithm, qos 1 introduction mobile ad hoc networks (manet) consist of mobile nodes that autonomously establish connectivity via multihop wireless communications. there is no use of a static network infrastructure such as base station or any centralized administration in manet. in ad hoc network, if two nodes are not within radio range, all message communication between them must pass through one or more intermediate nodes. all the nodes are free to move around randomly, thus changing the network topology dynamically [1-10]. these type of networks have many advantages, such as self reconfiguration and adaptability to highly variable mobile characteristics like the transmission conditions, propagation channel distribution characteristics and power level. they are useful in many situations such as military applications, conferences, lectures, emergency search, and rescue operations. however, such benefits come with new challenges which mainly resides in the unpredictability of the network topology due to mobility of nodes and the limited available bandwidth due to the wireless channel. these characteristics demand a new way of designing and operating these type of networks. for such networks, an effective routing protocol is critical for adapting to node mobility as well as possible channel error to provide a feasible path for data transmission [1-10]. aodv is an on-demand distance vector routing protocol [2]. the protocol is well known for the use in ad hoc networks. the use of multicasting with the network has many benefits. multicasting reduces the communication cost for applications that sending the same data to many recipients [3, 5-8]. instead of sending via multiple unicast, multicast reduces the channel bandwidth, sender and router processing and delivery delay. in addition, multicast gives robust communication whereby the receiver address is unknown or modifiable without the knowledge of the source within the wireless environment. copyright c© 2006-2009 by ccc publications 428 b. sun, c. gui, q. zhang, h. chen quality of service (qos) support for multimedia applications is closely related to resource allocation, the objective of which is to decide how to reserve resources such that qos requirements of all the applications can be satisfied [3, 6-11, 16, 21]. the goals of qos routing are twofold: finding a suitable route through the network between the source and the destination that will have the necessary resources available to meet the qos constraints; and achieving global efficiency in the utilization of resources. however, it is a significant technical challenge to provide reliable high-speed end-to-end communications in these networks, due to their dynamic topology, distributed management, and multi-hop connections. the provision of qos requirements is of utmost importance for the development of future networks. for supporting qos-aware applications, qos based routing algorithms such as ticket base routing (tbr) [3] and core extraction dynamic source routing (cedar) [4] are proposed. in cedar, each host compares degree with its neighbors. the host with the largest degrees among its neighbors is selected as the core node. the core node is responsible for recording all information for its members. when a source host requests to establish a qos routing path to some destination host, its core node must construct a qos routing path. the advantage of core-based management is that it reduces the number of control packets during path construction. chang proposes a two-level management approach for efficiently constructing and maintaining a qos routing path in ad hoc wireless networks, significantly reducing the quantity of control packets [5]. in the first phase, the mobile hosts are partitioned into a number of complete graphs, each represented by a supernode managed by an agent. in the second phase, some agents of a larger degree than neighboring agents are selected as core nodes. lorenz and orda demonstrate in the literature [16] that this uncertainty places additional constraints on qos provisioning. xiao et al proposed a dynamic backup routes routing protocol (dbr2p), a backup node mechanism for quick reconnection during link failures [20]. dbr2p is an on-demand routing protocol and it can set up many routes to reach a destination node in a given period. these algorithms determine a path that satisfies the required qos. fuzzy logic exploits the pervasive imprecision, uncertainty and partial truth of the real world using simple linguistic statements and thereby achieves tractability, robustness, and low solution cost [11, 1319, 23]. fuzzy logic based decision algorithm influences caching decisions of multiple paths uncovered during route discovery and avoids low quality paths [11, 14-19]. differentiated resource allocation considering message type and network queue status is evaluated using fuzzy logic scheme [11]. fernandez, hu, kazemian, zhang et al propose the use of fuzzy logic controllers for the dynamic reconfiguration of edge and core routers [13-15, 23]. this reconfiguration allows for adjusting the network provisioning according to the incoming traffic and the qos level achieved. hu and peter, utilize a self tuning fuzzy controller to apply an end-to-end rate-based feedback flow control algorithm for the available bit rate (abr) service in atm [14]. sheng, et al propose an adaptive routing algorithm in which the link cost was dynamically assigned using a fuzzy system [18]. their results show the traffic in the networks is rerouted to have less congestion or spare capacity. sheu and chen propose a fuzzy bandwidth allocation controller (fbac) to support services including restricted time-bounded service such as voice and video in wireless networks [19]. their results show the traffic in the network is rerouted to have less congestion or spare capacity. in those studies, the parameters and rules were further calibrated to obtain a more efficient evaluation. because the ad hoc network traffics have self-similarity, a common statistical method has been widely used to verify self-similarity of time-series, so that the characteristics of the ad hoc wireless networks can be grasped. in this paper, we present a fuzzy controller based qos routing algorithm with a multiclass scheme (fqra) in mobile ad hoc networks. fqra proposes to deal with route table management for keeping the active routes’ lifetime. fqra applies a fuzzy logic system to dynamically evaluate the route expiry time. the fuzzy logic is chosen because there are uncertainties associated with node mobility and the estimation of link crash; moreover, there is a mathematical model capable of estimating the node mobility. in addition, fqra is able to take some controlling factors into consideration. therefore, fqra is a multiclass scheme fuzzy evaluation for qos routing protocol. the performance of the fqra is studied using ns2 [24] and evaluated in terms of quantitative measures such as improved path success ratio, fuzzy controller based qos routing algorithm with a multiclass scheme for manet 429 reduced average end-to-end delay and increased packet delivery ratio. the rest of the paper is organized as follows. in section 2, we introduces the ad hoc network model and route issues. in section 3, we presents the fuzzy qos controller. in section 4, we evaluate the performance of the algorithm and present the simulation results. finally, section 5 concludes the summary of the work and future challenges. 2 network model and routing issues a network is usually represented as a weighted digraph g = (n, e), where n denotes the set of nodes and e denotes the set of communication links connecting the nodes. |n| and |e| denote the number of nodes and links in the network respectively [3-11, 16]. in g(n, e), considering a qos constrained multicast routing problem from a source node to multi-destination nodes, namely given a non-empty set m={s, u, u, . . . , um}, m ⊆ n, s is source node, u ={u, u, . . . , um} is a set of destination nodes. multicast tree t = (nt , et ), wherent ⊆ n, et ⊆ e, if c(t ) is the cost of t , pt (s, u) is the path from source node s to destination u ∈ u in t , dt (s, u) and bt (s, u) are the delay and usable bandwidth of pt (s, u). definition 1. the cost of multicast tree t is: c(t ) = ∑ e∈et c(e), e ∈ et . definition 2. the bandwidth, and delay route of multicast tree t is the value of link bandwidth, and delay in the path from source node s to each destination node u ∈ u . i.e. bt (s, u)= min(b(e), e ∈ et ). dt (s, u)= max( ∑ e∈pt (s, u) d(e), u ∈ u ). definition 3. assume the minimum bandwidth constraint of multicast tree is b and the maximum delay constraint is d, given a multicast demand r; then, the problem of bandwidth and delay constrained multicast routing is to find a multicast tree t , satisfying: (1) bandwidth constraint: bt (s, u) ≥ b, u ∈ u . (2) delay constraint: dt (s, u) ≤ d, u ∈ u . suppose s(r) is the set, and s(r) satisfies the conditions above; then, the multicast tree t which we find is: c(t ) = min (c(ts), ts ∈ s(r)) 3 fuzzy qos controller 3.1 fuzzy logic controller the fuzzy logic was introduced by zadeh as a generalization of the boolean logic [22]. the difference between these logics is that fuzzy set theory provides a form to represent uncertainties; that is, it accepts conditions partially true or partially false. fuzzy logic is a good logic to treat random uncertainty, i.e., when the prediction of a sequence of events is not possible. fuzzy logic control system is rule-based system in which a set of so-called fuzzy rules represents a control decision mechanism to adjust the effects of certain causes that come from the system. the aim of the fuzzy control system is normally to substitute for or replace a skilled human operator with a fuzzy rule-based system. specifically, based on the current state of a system, an inference engine equipped 430 b. sun, c. gui, q. zhang, h. chen with a fuzzy rule base determines an on-line decision to adjust the system behavior in order to guarantee that it is optimal in some certain senses. there are generally two kinds of fuzzy logic controllers. one is the feedback controller, which is not suitable for the high performance communication networks. another one, which is used in this paper, is shown in figure 1. the output of the fuzzy logic controller in figure 1 is used to tune the controlled system’s parameters based on the state of the system. this control mechanism is different from the conventional feedback control and considered as an adaptive control [11, 14, 19, 23]. figure 1: the fuzzy logic controller the specific features of the fuzzy controller depend on the model under control and performance measurement. however, in principle, in the fuzzy controller we explore the implicit and explicit relationships within the system and subsequently develop the optimal fuzzy control rules as well as a knowledge base. in the route discovery process, new routes are created. the creation of new routes makes use of forward packet (table 1) and backward packet (table 2). a forward packet is broadcasted by the sender and will be relayed by the neighbors of the sender. when the forward packet reaches the destination node, it extracts the information collected by the forward packet and destroys it and subsequently creates a backward packet which follows the track of the forward packet, but in the reverse direction. table 1: forward packet. source address destination address sequence number number of hops qos metric intermediate node table 2: backward packet. source address destination address number of hops qos metric intermediate node the packets flow through router is presented in figure 2. the aim was to decrease tcp and udp traffic settling, rise and fall times; decrease overshoots and undershoots; and to stabilize throughput in the router’s output connections to let the model adapt for load and link capacity variations. fuzzy controller based qos routing algorithm with a multiclass scheme for manet 431 figure 2: schematic presentation of packets flow through router 3.2 scheduler controller the packet scheduler used in our architecture is wrr (weighted round robin). in this scheduler, queues are served according to a configurable weight that can be changed during network operation. this allows having control of the bandwidth assigned to each service class. the packet delay and discard rate for each queue (class) can be controlled by changing this weight. an example of membership function of schedule controller is showed in figure 3. the fuzzy scheduler proposed here calculates the priority index of each packet. here we consider all the inputs which decide the priority associated with the packet, unlike the previous scheduling schemes. other membership functions are: packet delay in the expedited forwarding queue and discard rate due to queue overflow in the besteffort class. the output membership functions are also defined as trapezoid functions by the same previous reasons. we make use of the center of gravity defuzzification method, since it gives better results. the output membership function gives the weights assigned to each class in the wrr scheduler. figure 3: scheduler membership functions 3.3 fuzzy rule base (fuzzy routing) fuzzy systems reason with multi-valued fuzzy sets instead of crisp sets. the fuzzy logic controller (flc) (figure 1) has two inputs: residual bandwidth and traffic class and one output: fuzzy routing decision [12, 22]. mamdani fuzzy-rule based systems constitute of a linguistic description in both the antecedent parts and the consequent parts. each rule (table 3) is a description of a condition-action statement that may be clearly interpreted by the users. rule base is an if-then rule group with fuzzy sets that represents the desired behavior of a fuzzy system. it can be defined in agreement with the administrative policy. if x is ai and . . . and xn is ain then y is ci, i = 1, 2, . . . , l where l is the number of fuzzy rules, x j ∈ u j, j=1, 2, . . . , n, are the input variables, y is the output variable, ai j are the fuzzy sets of the input linguistic variable x j and ci is called the set of the output linguistic variable y. ai j and ci are characterized by both membership functions. inputs are of the form: x is a′, x is a ′ , . . . , xn is a ′ n where a ′ , a ′ , . . . , a ′ n are fuzzy subsets of u, u, . . . un, which are the universe of discourse of inputs. 432 b. sun, c. gui, q. zhang, h. chen table 3: fuzzy rule base qos classes and application type. qos class application type 1 video conference 2 sdtv-quality voice 3 cd-quality audio 4 high-quality voice 4 simulation 4.1 random graph generation in generating random graphs, we have adopted the method used in kazemian, where vertices are placed randomly in a rectangular coordinate grid by generating uniformly distributed values for their x and y coordinates [15]. the remaining edges of the graph are chosen by examining each possible edge (u,v) and generating a random number 0 ≤ r <1. if r is less than a probability function p(u, v) based on the edge distance between u and v, then the edge is included in the graph. the distance for each edge is the euclidean distance denoted as d(u, v) between the nodes that form the end-points of the edge. we use the probability p(u, v) = β exp[− d(u,v)α l ]. where d(u, v) is geometric distance from node u to node v and l is maximum distance between two nodes. the parameters α and β are in the range (0, 1) and can be used to obtain certain desirable characteristics in the topology; parameter α can be used to control short edge and long edge of the random graph, and parameter β can be used to control the value of average degree of the random graph. 4.2 simulation model to conduct the simulation studies, we used randomly generated networks on which the algorithms were executed. this ensures that the simulation results are independent of the characteristics of any particular network topology. using randomly generated network topologies also provides the necessary flexibility to tune various network parameters such as average degree, number of nodes, and number of edges, and to study the effect of these parameters on the performance of the algorithms. the platform used was the ns2 (network simulator version 2) [24]. ns2 is a discrete event simulator targeted at networking research. ns2 provides substantial support for simulation of tcp, routing, and multicast protocols over wired and wireless networks. fuzzy routing decision was implemented using the fuzzy logic toolbox in matlab [25]. the simulator ran with various input configuration settings and the statistics collected were analyzed in comparison with other well-known on demand routing protocol aodv [2]. our simulation modeled a network of mobile nodes placed randomly within 1000 × 1000 meter area. each node had a radio propagation range of 250 meters and channel capacity of 10 mbps. two-ray propagation model was used. the ieee 802.11 distributed coordination function was used as the medium access control protocol. a random waypoint mobility model was used: each node randomly selected a position and moves toward that location with a speed ranging from just above 0 m/s to 10 m/s. when the node reached that position, it became stationary for a programmable pause time; then it selected another position and repeated the process. the simulation was repeated with different seed values. a traffic fuzzy controller based qos routing algorithm with a multiclass scheme for manet 433 generator was developed to simulate cbr (constant bit rate) sources. the size of the data payload was 512 bytes. data sessions with randomly selected sources and destinations were simulated. each source transmitted data packets at a minimum rate of 4 packets/sec. and maximum rate of 10 packets/sec. traffic classes were randomly assigned and simulation was carried out with different bandwidth requirements. there were no network partitions throughout the simulation. each simulation was executed for 600 seconds of simulation time. multiple runs with different seed values were conducted for each scenario and collected data was averaged over those runs. table 4 lists the simulation parameters which are used as default values unless otherwise specified. table 4: simulation parameters number of nodes 100 terrain range 1000m × 1000m transmission range 250 m average node degree 3-5 node’s mobility speed 0-10 m/s mobility model random way point propagation model free space channel bandwidth 5 mbps links delay 20-200 ms traffic type cbr data payload 512 bytes/packet node pause time 0-10 seconds 4.3 performance measures the following measures are used in computing the scheduler performance. these measures were derived from one suggested by the manet working group for routing protocol evaluation. packet delivery ratio: packet delivery ratio is the ratio of the number of data packets actually delivered to the destinations to the number of data packets supposed to be received. this estimate gives us an idea about how successful the protocol is in delivering packets to the application layer. a high value of packet delivery ratio indicates that most of the packets are being delivered to the higher layers and is a good indicator of the algorithm performance. path success ratio: the ratio of the number of connection request discover to the destinations to the number of routed connection requests. the successful routing request is defined as the path computed and established by the algorithm satisfies the delay and bandwidth constraints. this number presents the effectiveness of the algorithm. average end-to-end delay: this indicates the end-to-end delay experienced by packets from source to destination. the average end-to-end packet delay is computed as the ratio of total end-to-end delays to the aggregate number of packets successfully delivered to the destination nodes during a simulation run. a higher value of end-to-end delay means that the network is congested and hence the routing algorithm does not perform well. 434 b. sun, c. gui, q. zhang, h. chen 4.4 simulation results in this performance evaluation the following performance measures were evaluated: packet delivery ratio, percentile of path success ratio and edge-to-edge delay. for each evaluation, we used cbr. all simulations started with initial scheduler configuration with 60% of the bandwidth for each class. to eliminate simulation results with an empty network, we started collecting results 30 seconds after the beginning of the simulation. after optimization procedure was executed, we could verify the result comparing packet delivery ratio, path success ratio, and average end-to-end delay. figure 4 shows the performance analysis of the packet delivery ratio vs. network size for the fqra, aodv, and non-qos (not quality of service metric constraints) in ad hoc network. using non-qos constraints algorithm as a base line for comparison, the result shows that both fqra and aodv are much better than the non-qos constraints algorithm, fqra is considerably better than aodv. this is because that aodv needs to rediscover the route to retransmit data packets that are lost due to the node’s mobility or unreal route paths during the communication. the advantage of fqra is resulted from choosing the right routing path or updating the unreal route paths just in time by the virtue of the suitable route lifetime estimation. figure 4: packet delivery ratio vs. networks size figure 5 shows the comparison of the three algorithms with respect to the average end-to-end delay vs. number of nodes. it shows that the end-to-end delay increases usually with the increasing node. from the figure 5, we can see that when the nodes increase, fqra algorithm average end-to-end delay is lower than that of aodv and non-qos algorithm. this is because the fuzzy scheduler controller has stability routes and gives more precedence to the packets. both the aodv and non-qos algorithm need more time and more control overhead than the fqra does to recover unreal paths (broken paths) and to discover new paths. as the nodes increase, more packets are received and thus end-to-end delay increases. the end-to-end delay is important as many real time applications require a small latency to deliver usable data within stipulated period of time. figure 6 depicts a comparison path success rate to find the path through fqra, aodv and non-qos in ad hoc network. with the relaxation of bandwidth constraints, the path success rate becomes higher for non-qos. the success rate is still higher than that of non-qos, which means more suitable for the routing choosing under timely data transmission application and dynamic network structure. the average and-to-end delay performance as shown in the figure 7, proves that the end-to-end delay improves when scheduler is included. as the mobility varies from 0-10 m/s, the fuzzy controllers scheduler provides an end-to-end delay reduced by around 0.01 seconds to 0.05 seconds. it shows that the end-to-end delay increases usually with the increasing speed. it also shows that fqra is much better than the other two. both the non-qos and the aodv need more time and more control overhead than the fqra does to recover unreal paths and to discover new paths. fuzzy controller based qos routing algorithm with a multiclass scheme for manet 435 figure 5: average end-to-end delay vs. networks size figure 6: path success ratio vs. bandwidth figure 7: average end-to-end delay vs. node’s mobility speed 436 b. sun, c. gui, q. zhang, h. chen 5 conclusion and future work our qos routing algorithm has produced significant improvements in throughput, average end-toend delay and path success ratio. the fuzzy controllers scheduler algorithm attaches a qos class to each packet in the queue of the node. unlike the normal sorting procedure for scheduling packet, the crisp qos class is calculated by the fuzzy scheduler based on the above inputs which are derived from the network. the membership functions and rule bases of the fuzzy scheduler are carefully designed. the use of fuzzy logic improves the handling of inaccuracy and uncertainties of the ingress traffic into the domain. in this paper, we present a fuzzy controller based qos routing algorithm with a multiclass scheme in mobile ad hoc networks. the performance of this scheduler is studied using ns2 and evaluated in terms of quantitative measures such as path success ratio, average end-to-end delay and throughput. simulation shows that the approach is efficient, promising and applicable in ad hoc networks. future work includes comparison with "crisp" versions of the fuzzy algorithm to isolate the contributions of fuzzy logic, as well as applications of fuzzy control to power consumption and directional antennas in manets. we also intend to compare fqra with other qos routing algorithm. acknowledgement the authors thank the editors and the anonymous reviewers for their valuable comments that helped to improve the paper. this work is supported by china postdoctoral science foundation of china (no. 20070410955), the young and middle-aged elitists’ scientific and technological innovation team project of the institutions of higher education in hubei province (no. t200902), and key scientific research project of hubei education department (no. d20081904, q20091903, b20091904). bibliography [1] m. a. rajan, m. g. chandra, l. c. reddy and p. hiremath, concepts of graph theory relevant to ad-hoc networks. international journal of computers, communications & control, vol. 3, no. suppl, pp. 465-469, 2008. 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[25] fuzzy logic tool box (for use with matlab) users’ guide version 2, the mathworks inc., http://www.mathtools.net/matlab/fuzzy_logic/. 438 b. sun, c. gui, q. zhang, h. chen baolin sun (born in 1963), graduated in mathematics (the huazhong normal university, wuhan, china) and received ph.d. in computer science (wuhan university of technology, wuhan, china). he is currently a professor, and one of editorial board guest members of world scitech r&d, one of the paper reviewers of chinese of journal electronics, journal of electronics & information technology, high technology letters, and kgcm, iccit program committee members, and also an international standard draft organizing members of iso/iec jtc1/sc6. his research interests include qos multicast routing, distributed computing, network optimization and ad hoc networks. professor sun has published over 100 research papers and is the author of four books. he was awarded the province special prize by the hubei province government in 2007. chao gui (born in 1964), graduated in physics (the wuahn university, wuhan, china) and received m.s. degree in electrical engineering (wuhan university of technology, wuhan, china). he is currently a associate professor in computer science. his research interests include wireless communication, performance analysis and analytical modeling. he has published over 30 research papers. qifei zhang (born in 1977), graduated in computer science (the jianghan university, wuhan, china) and received ph.d. in information and communication engineering (huazhong university of science and technology, wuhan, china). he is currently a lecturer in computer science. his research interests include wireless local area networks, mesh networks, network protocol and congestion control. he has published over 30 research papers. hua chen (born in 1977), graduated in graduated in mathematics (the huazhong normal university, wuhan, china) and received m.s. degree in mathematics (huazhong university of science and technology, wuhan, china). she is currently a lecturer in mathematics. her research interests include wireless communication, performance analysis, network model and algorithm. she has published over 20 research papers. international journal of computers, communications & control vol. i (2006), no. 1, pp. 73-79 grigore c. moisil: a life becoming a myth solomon marcus “all what is correct thinking is either mathematics or feasible to be transposed in a mathematical model.” grigore c. moisil (1906 1973) grigore c. mosil, romanian mathematician, professor at the university of bucharest faculty of mathematics and computer science, member of the romanian academy, member of the academy of sciences in bologna and of the international institute of philosophy, computer pioneer award of ieee computer society (ieee 1996). grigore c. moisil belongs to the fifth generation of romanian mathematicians. the first generation includes the founders spiru haret and david emmanuel, both with doctorates at sorbonne (paris). they were born at the middle of the xixth century and have the merit to initiate the high level mathematical research in romania. (we leave aside transylvania, with janos bolyai, who already in the first half of the xixth century discovered non-euclidean geometry). the second generation includes the first romanian mathematicians with a long career of scientific research: gh. ţiţeica, d. pompeiu (both with doctorates at sorbonne), al. myller and vera myller (both with doctorates at gottingen, germany). they had already an important impact on moisil’s formation as a mathematician. the third generation includes victor valcovici, traian lalescu and simion stoilow (considered as the most important romanian mathematician until the sixties of the xxth century), born in the eighties of the xixth century. lalescu died very young (in 1929), but valcovici and stoilow became great professors not only before, but also after the second world war. we have then a fourth generation of mathematicians born in the last decade of the xixth century: octav onicescu (the initiator of the romanian school of probability theory), petre sergescu (the organizer of the first two congresses of romanian mathematicians, before the second world war), dan barbilian (the same as the poet ion barbu), alexandru froda and gheorghe vranceanu (the greatest romanian geometer after ţiţeica). directly or indirectly, all these mathematicians had an impact on moisil’s personality. born in tulcea (dobrogea), with a long genealogic tree of high intellectuals in the north-western part(bistriţa-năsăud) of romania, with school training partly in bucharest, partly in iasi (moldova), moisil’s childhood and adolescence are well known from his own diary published in the recent years by moisil’s widow viorica moisil, who took great care of the whole scientific and human heritage of her husband. the main teachers of the child moisil were his parents; his father was an important historian, while his mother was just an educator for elementary school. the child grigore enjoyed to look around, to give free expression to his curiosity and wonder, to ask questions, to read books of a large diversity, from science to literature and from practical jobs to philosophy. he felt always the need to react to what he was seeing, listening and reading. his diary is an excellent mirror of this fact. his main pleasure was always of an intellectual nature, he was not attracted to play with other children or to practice various sport games. his mother taught him to count and to make calculations and only in a second step to read and to write. the most important part of the learning process took place at his home, with his parents and some time with his brothers and his sister, all of them becoming copyright c© 2006 by ccc publications 74 solomon marcus intellectuals. moisil’s diary should be known by the children of the new generations. the attitude of the child moisil towards learning remained his attitude towards life. he enjoyed to interact with people, to read, to write and to learn. he did all these things with pleasure and with humor, he was able to contaminate people with his capacity to discover something new where most people see nothing new; to invent questions where most people believe that everything was already answered. his attraction for mathematics did not diminish his interest for the other school disciplines. his curiosity was total and remained total during his whole life. but his passion for mathematics and his way to understand mathematics lead him to see the world through the glasses of mathematics; so mathematics was not for him only a profession, a job, it was a way to look at the life and at the universe. when moisil had to become a university student, choosing math was equivalent for most people with choosing engineering, so his parents pressed him to choose polytechnical school, where he became a student in 1924; but one year before he became also a student of the faculty of science, mathematical section. he did not like engineering, so he never finished polytechnical school, but the irony of life decided that moisil had to return to engineering much later, when he discovered that logic and engineering share some very important features. on the other hand, the university student moisil attended many classes of philosophy, of history and of art. his teachers were ţiţeica, lalescu, davidoglu and others, but the university teacher who impressed him the most and became his spiritual model was dimitrie pompeiu. his phd thesis (1929) was about the analytic mechanics of continuous systems. as a characteristic feature of his works published in the twenties and the thirties of the past century, we observe that most of them were at the interference of differential equations (with partial derivatives), differential geometry, function theory and mechanics. the main reason of this situation was that moisil was devoted to the topics most considered by his predecessors and his colleagues (pompeiu, with his areolar derivative, vrânceanu, with his neolonomic varieties, nicolae teodorescu with his papers on finsler spaces etc.), but he also paid attention to the work of some great western mathematicians of his time: eli cartan, w. blaschke, and mainly vito volterra and jacques hadamard. having the opportunity to work with volterra (1931-1932) in rome, moisil became a pioneer of the functional methods in differential geometry and mechanics (let us recall that volterra is one of the initiators of functional analysis). but before this, moisil was in paris, working with great french mathematicians such as hadamard (1930-1931); he spent a second period in paris after returninf from rome. so, in this atmosphere moisil obtained a generalization of volterra’s conjugate functions and he also generalized hadamard’s total geodesic varieties. from the same period let us recall the joint paper by moisil and teodorescu on holomorphic functions in the space (1931), the first joint work in romanian mathematics. joint works remained a rare phenomenon until the middle of the past century. at nevember 1, 1932, moisil is named a provisional associate professor of algebra at the university of iasi. at that moment, moisil was already the author of an important number of scientific papers, but no of them was in the field of algebra. his first algebraic paper (in the field of non-associative algebra) was published only in 1934. so, this fact came as a surprise. but for a good observer, nothing surprising was in this fact. behind the diversity of topics, moisil was, in most of his mathematical papers, mainly an algebraist, his genuine thinking was almost always of an algebraic nature. in everything he did, he projected an algebraic spirit. within the framework of his traditional preoccupations of analysis, geometry and mechanics (monogeneity, function theory, geodesics in some riemann spaces, partial differential equations etc.), he works with algebraic tools such as functional groups, parametric groups, monogenic quaternions, hypercomplex numbers, ideals of polynomials, areolar polynomials, polynomials associated to some bilinear differential expressions with constant coefficients, interpretation of the fundamental group of a differential variety etc. to the above fact, we have to add an event having an important role in moisil’s life: the publication, in the first part of the fourth decade of the past century, of the book of van der waerden, "moderne algebra". it was an important sign of the move of algebra from the quantitative to the qualitative, from the algorithmic to the structural phase of its evolution. it was a turning moment in moisil’s mathematical life, an event having a huge impact on moisil’s further evolution. in this order of ideas, we should observe that moisil is the first to introduce bourbaki’s ideas in romania, at a moment when bourbaki’s mathematical structuralism, strongly influenced by the german school of structural algebra, was only a project. in his course of general analysis (analiza genarala) published towards the end of the thirties of the past century, moisil gives an account of henri cartan’s theory of filters, while uniform spaces were also presented there. but moisil was not the first to teach in iaşi modern structural algebra; he was preceded in this respect by vera myller. on the other hand, emmanuel and lalescu had also, at some moments, the opportunity to teach some ideas on groups and fields. however, it seems that moisil was the first to give to this structural style the whole amplitude. only barbilian will go further in this respect, but this will happen grigore c. moisil: a life becoming a myth 75 in the second half of the fourties of the past century. moisil became full associate professor of algebra at january 1, 1935, but at november 1, 1936 he got a position of professor of differential and integral calculus and then full professor of calculus in november 1939, all of them at iaşi university. moisil spent ten years at iaşi university (1931-1941). it was a period in which he alternated his old interests in continuous mathematics, with applications to mechanics and physics, with his new interests in discrete mathematics, mainly in algebra and logic. moisil was very impressed by an article due to the polish logician jan lukasiewicz, on the logic with three values (then the interest moved to several values) and by the analogy proposed by lukasiewicz between the non-classical logics, on the one hand, and the non-euclidean geometries, on the other hand. as the initiator of the logic of three values, lukasiewicz was considered as the lobatchevski of logic. it was one more reason for moisil to be attracted by logic: his philosophical interests lead him to pay great attention to the philosophical consequences of quantum mechanics, where aristotle logic is no longer valid. indeed, the principle of excluded middle is here replced by a principle of included middle. in the thirties of the past century, important authors, such as john von neumann, paid attention to the logic of quantum mechanics. it was also the principle of universal determinism which was under question; certainty is replaced sometimes by probability. moisil and some of his colleagues (onicescu, procopiu, barbilian etc.) organized some debates on this topic and reading his writings in this respect one can understand how this interest motivated him to orient his attention towards mathematical logic, a field where he published his first paper in 1936, in a volume devoted to the 75th birthday of vitto volterra. he will never leave this field, but concomitantly he will remain also stable in his old interests. only in the last decade of his life he will be devoted exclusively to discrete mathematics. but, being first of all an algebraist, moisil will project in his studies in logic the same algebraic spirit. his main project was to build for lukasiewicz’s logic of several values an algebraic framework in a way similar to the way george boole has proposed in the xixth century an algebraic model for aristotle’s logic (based on the principles of identity, non-contradiction and excluded middle). moisil called this framework "lukasiewicz algebras", but ultimately these algebras received the more appropriate name "lukasiewicz-moisil algebras"; this name became the title of a monograph published at north holland publishing house by a team lead by professor sergiu rudeanu. his move from iaşi to bucharest university, at the end of the year 1941. was a dramatic one. not only moisil, but also vrânceanu, barbilian and miron nicolescu were competing for the same position of professor. among them, moisil was the youngest and with the smallest chance to win. the winner was vrânceanu. then, moisil had the happy idea to convince the ministry of education, ion petrovici, to create three different chairs and so all of them became professors; it was the great chance for the next generation (that of the author of these lines) to benefit of such great professors. after the second world war, moisil had many interesting initiatives. one of them was related to an idea proposed by shannon in his phd thesis and independently by some russian engineers, to associate electric circuits with binary logic, because each of them works with two values: yes or no in logic, while the circuit is open or closed. moisil succeeded to develop this idea in many variants, stimulating a whole team of researchers to articulate engineering, classical and non-classical logics and various types of algebraic structures with some ideas from number theory. another fruitful idea was to associate some matrices to some systems of linear partial differential equations. but perhaps more important was the way moisil understood, in the early fifties, that the emergence of the new paradigm of information, communication and computation could change to a large extent the social, cultural and scientific life of the next decades. in 1949, he initiates a whole school in the field of the algebraic theory of automatic mechanisms as a professor of the bucharest university, he was the first to teach there mathematical logic. articulating logic and automata, moisil was well prepared to organize the romanian development in the emergent field of computer science. he monitorized the building of he first romanian computer, by victor toma, at the institute of atomic physics, in 1957, and, on the other hand, he organized courses in the field of computation at the faculty of mathematics, university of bucharest. he also directed the first promotion of students in mathematics to work with the team of victor toma, at the institute of atomic physics; they were trained to learn programming at the new computers cifa (calculatorul institutului de fizica atomica). the first romanian team of mathematicians included dragos vaida, i..moldovanu, gh. zamfirescu, g. klarsfeld. so, we can say that 1957 is the date of birth of romanian computer science, under the guidance of professor moisil and by the collaboration between engineers and mathematicians. in 1962, moisil initiates a new section "computing machines" at the faculty of mathematics and physics of the univ of bucharest and, associated with this section, the computing center of the univ of bucharest (ccub); as a matter of fact, ccub was under the guidance of the chair of algebra, whose chief was moisil. in 1963, 76 solomon marcus ccub is endowed with the computer cifa 3, the third version of the first romanian electronic computer, and with an analogic computer of meda type. moisil is very active in preparing the corresponding mathematical background: learning of algol 60, organization of a seminar of algebraic theory of automatic mechanisms (started in 1954), organization of a course of logic applied to electric circuits and of a seminar of mathematical logic (started in 1966); numerical analysis and combinatorics are also stimulated. in a further step, mathematical biology and mathematical linguistics as well as perspectives of computation in various fields of the humanities: history, archeology, musical composition etc. in 1968, ccub is endowed with a computer ibm 360/30 of the third generation and the learning of fortran and cobol is introduced. ccub became a place where people from all cultural horizons came to learn from professor moisil how could they take profit in using in their own field the mathematical and computational thinking. lawyers and musicians (among them, aurel stroe), engineers and economists, linguists and philosophers, biologists and medical doctors, painters and writers were visiting ccub and the main reason of this fact was the presence there of professor moisil, who had the gift to leave the mathematical jargon and to address non-mathematicians in the simplest possible language; and in this simple language he was able to explain the mathematical and computational way of thinking. the capacity of moisil to seduce and to fascinate the auditory became very fast an element of attraction for mass media. newspapers, radio and television began to invite him and moisil became a star whose fame was in competition with that of the most popular singers and actors. his unique voice, his way to transform the speech in a song, his spontaneity, his humor, his permanent state of joy made moisil so popular, that even today, 33 years after his death in may 1973, his statements are still in the attention of the public. "new ideas appear first as paradoxes, then they become common truth and ultimately they die as prejudices"; "is logic a practical science ? yes, because you learn from it how to take decisions" ; "you lose a lot of time when you believe that you know what in fact you don’t know". many of his jokes have a mathematical structure. here is a joke illustrating the recursive thinking: "every man has right to a glass of wine; but when you drink a glass of wine, you become another man" (the corollary: every man has right to infinitely many glasses of wine). a joke illustrating self-reference: a child asked him: "professor moisil, do you like dreams ?" – "yes, i had once a dream in which i was sleeping during a session and when i waked up i was really in a session". jokes having the same pattern, being so able to be produced algorithmically: "the water is bad, even in the shoes"; "everything can be proved; even the truth"; "you can fall in love with any women; even with your wife"; "every joke can make you to be in the best mood; even the above ones". all these jokes have as a common denominator the confusion between normality and exceptionality: the water in the shoes, the proof of a truth; to be in love with your wife. as soon as you understood the pattern, you can produce infinitely many jokes of similar type. at 11 february 1971, moisil sends a letter to the rector of the bucharest university, proposing a whole program of organization of education in the field of computers and their mathematics. in january 1973, he sends another letter to the dean of the faculty of mathematics, where he explains that this faculty has a great responsibility concerning the formation of the teachers of computer science with a solid mathematical background. moisil explains that the main job in this respect is to assure the computational literacy of the coming generations, because in the emergent period of the information and computation all professions will need in some way familiarity with computing and programming. very few people were aware at that moment of this truth which today is obvious. in this respect, we can consider moisil as a kind of spiru haret of the second half of the past century: haret was an important fighter against illiteracy, while moisil was an important fighter against computational illiteracy. due to his multiple interests, moisil succeeded to form a lot of disciples in various directions: in mechanics of solids (nicolae cristescu, p.p.teodorescu, m. predeleanu, george dinca), in logic (sergiu rudeanu, george georgescu, afrodita iorgulescu), in computer science (dragoş vaida, constantin popovici, paul constantinescu), in logic of electric circuits (l. livovschi), in algebra, in analysis, in differential geometry. if the child moisil revealed a total curiosity, the same totality characterizes the creative work of the adult moisil. within mathematics, he interrelates all its domains; beyond mathematics, he is looking for the way mathematics may have an impact on natural and social sciences; beyond science, he is questioning the relation between math and philosophy, between math and art; beyond culture, he is interested in the impact of math in the everyday life. scholars are of two types: the ant type, looking for what happens in a specific area of knowledge and trying to deepen more and more the respective segment of investigation; but there is also the bee type, going from flower to flower and changing frequently the area of investigation. obviously, moisil was of the second type. but, looking with more attention at his behavior, we realize that he was sometimes of a mixed type, because he liked to go back to flowers already visited. i remember his renewed interest in the sixties in the problem of mechanics he discussed long time ago in his phd thesis. after the second world war, his growing interest in discrete mathematics was concomitant with the continuation of his work in continuous mathematics. his general grigore c. moisil: a life becoming a myth 77 strategy was to trust the unity of mathematics and the potential solidarity between its different parts, including the case when these parts seem to be completely away each other. for this reason, he used to oblige his phd students to pass examinations on some chapters of math which apparently were very far from the object of direct interest of the respective student. this is the reason why most of his papers combine different branches of math. another interesting feature of moisil’s works is their strong link with the works of his colleagues and of his professors. a typical example are his papers of geometry. arrived in iaşi at the end of the year 1931, when he was only 26, but with his thesis published by gauthier-villars (paris, 1929), moisil found an adequate atmosphere in the seminar lead by al. myller, predominantly concerning differential geometry. at that moment, he was considered, in view of his already published works, the founder of the theory of infinite dimensional riemann spaces. to this, he added the study of infinite lie groups and of mechanical systems with infinitely many degrees of freedom. but the geometric methods used by moisil were for him a tool to investigate the mechanics of systems of material points with infinitely many degrees of freedom. for him, the respective geometric model consists of infinite dimensional subvarieties in an adequate hilbert space. in other situations, he is oriented towards the geometrization of systems of equations with partial derivatives. so, analysis, geometry, mechanics form an organic mathematical entity. see, for more, in this respect, the article by acad. radu miron in "academica" (forthcoming, 2006), from which we have borrowed some elements. in order to illustrate the style of work done by moisil in the field of math and humanities, we will indicate some ideas he developed in the field of what he called "the mechanical grammar of romanian". he proposed some new classifications of romanian nouns and verbs. two ideas deserve to be mentioned. the first one concerns the possibility to use, in the declension of nouns and conjugation of verbs, of what he calls the method of variable letters, by means of which he copes, in a very elegant way, with the phenomenon called in linguistics "morphological alternances". the variable letters are like the functions defined by means of two or several analytic expressions, each of them for a specific part of the domain of definition of the function. another idea proposed by moisil concerns the conjugation of verbs, where a classification is made according to the behavior of what is called "the more than the perfect" (mai mult ca perfectul). another paper concerns a comparative analysis between the linguistic conjunction "and" and the mathematical conjunction "and", in the case of romanian language, but things remain valid, to a large extent, for other languages too. for instance, it is shown the contrast between the possibility to iterate indefinitely the use of "and" in logic, and the impossibility of doing the same in linguistics. to his own works in this respect, moisil added his capcity to stimulate and to guide the first steps in the development of mathematical and of computational linguistics in romania. he guided the first algorithm of automatic translation (english-romanian) by erica nistor, the similar work by minerva bocsa in timisoara, the work done by the team lead by p. schveiger in cluj-napoca and the work done by eliza roman in the field of automatic abstracts and automatic documentation. the author of this lines remains indebted to moisil for his major help jointly with the linguist alexandru rosetti, concerning the first steps of mathematical and computational linguistics in romania. moisil and rosetti made possible the organization of the first university courses with this profile; they founded "cahiers de linguistique theorique et appliquee", a journal of an interdisciplinary nature, devoted to the interferences among linguistics, mathematics, computer science and poetics. the remarkable fact, in these articles, is the capacity of moisil to develop the mathematical way of thinking in absence of the usual mathematical jargon, consisting of formulas, equations, calculations etc. he never leaves in these texts the natural language. another aspect of moisil’s personality can be seen in his philosophical writings. mathematics and philosophy were for him two faces of the same coin, each of them requiring the other. already during his childhood and adolescence, his interrogative nature and his readings prepared the way towards his philosophical personality. he reads poincare and selects in his diary statements such as : "science deserves to be studied for the glory of human spirit" and "for the enormous pleasure offered by the knowledge of truth", more than "for its practical utility". somewhere he notes: "life is a work of art. it is a pleasure to think to what happened sometimes in the past and will never happen again !". in another place he notes: "pleasure is more attractive when you are looking for it than when you feel it". a lot of remarks related to his readings in the field of history, of literature, of natural sciences, of religion etc. all of them, when he was 7, 8, 9, 10, 11, 12, 13, 14. here is a comment about the cause of wars: "if we cancel the fights having no rational motivation, only a few of wars remain in the memory of the history". a word of wisdom: "you don’t have to exagerate in love, because you risk to end by exagerating in hate". the articles in the field of philosophy deserve a special attention. the first one, chronologically, was published in 1937 and concerns the successive steps in the development of the mathematical knowledge. moisil was at that time under the strong influence of the ideas of vito volterra, who pays attention to the way mathematical knowledge is born from the pre-mathematical knowledge. in a first step, qualitative descriptions are converted 78 solomon marcus in quantitative ones, by means of measurements and counting. then the mathematics of quantity are developed, until the moment when qualitative aspects are again in attention. the development of physics and of mathematical analysis are followed concomitantly. the mechanical stage, the energy stage and the einsteinian stage of the generalized relativity theory are analyzed. then he directs his attention towards the development of qualitative mathematics and the main example is here the notion of a group, observed on the particular cases of the group of rotations and the group of permutations. we are lead in this way to what moisil will call structural mathematics and the way is prepared to connect it to the general emergence of structuralism (in psychology, in linguistics, in anthropology and before them in chemistry, with the idea of isomerism). moisil will be among the first to observe the big change brought by the new fields of topology, functional analysis, combinatorics, graph theory, mathematical logic, abstract algebra. in the same way, moisil stresses the importance of the theory of complex numbers and of their structural aspects. moisil analyzes the way classical infinitesimal calculus lead to general topology. the idea of a differential is both quantitative and structural (having the structure of a polynomial). logistics is a clear example of reduction of quantity to structure and this is the task of russell’s and whitehead’s "principia mathematica". starting from the contrast observed by other authors between the quantitative aspect of mathematics and the qualitative aspect of the acts of thinking, moisil observes that the acts of thinking have a structural rather than a qualitative aspect and so the mathematician’s job is to investigate the algebraic structure of the acts of thinking. so, concludes moisil, "ce n’est pas a l’ancienne logique de la qualite qu’on devra s’adresser, mais a la nouvelle algebre de la structure. ce n’est pas en effet trop tot si on essaie de construire une theorie coherente de la vie spirituelle". in his "la logique formelle et son probleme actuel" (1939), moisil investigates the principles of classical logic (identity, contradiction, excluded middle) and their modifications in brouwer-heyting’s intuitionistic logic, in kolmogorov’s intuitionistic logic, in lukasiewicz’s ternary logic; he also discusses the very nature of axiomatic deductive systems, with special attention to hilbert, russell and poincare. this investigation is continued in "sur l’autonomie des mathematiques" (1941), where he is using the term "panmathematisme" understood as the process of approaching mathematically the natural sciences and those of the human spirit (sciences de l’esprit)". in this order of ideas, he characterizes the mathematical activity as being irreducible, i.e., independent of empirical investigations and of any previous rational development. this is for him the autonomy of mathematics. moisil meets, in this respect, the way old greeks (pythagoras, platon), then kant and goethe, conceived mathematics (for kant, mathematics is what is called in german geisteswissenschaft). the utilitarian function of mathematics is in most cases a consequence of its cognitive function, but the temporal distance between the cognitive moment and the utilitarian one is usually imprevisible. we stress this fact, because in his writings after 1950, in view of the ideological constraints, he will no longer state explicitly the autonomy of mathematics, but he will defend strongly the need to develop pure mathematics, mathematics for its own sake, as a condition to reach applied mathematics. moisil considers that the human spirit can reach the center of a deductive discipline; mathematics can bridge the self and the non-self. he is consistent with the idea emergent much later, according to which the subject-object distinction will no longer be considered as sharp as in the classical science; consequently, the distinction between natural and human sciences is under question. as a matter of fact, mathematics is for moisil by excellence a human discipline. the last philosophical article he published before the communist regime was his "closure lecture at the university of iaşi" (16 january 1942): "the perspectives of axiomatic philosophy". we learn from this lecture that romanian scientists paid a great attention to the philosophical problems of their science, mainly those related to non-euclidean geometries and to relativity and to quantum mechanics. philosophers like ion petrovici, physicists like st. procopiu, mathematicians like victor valcovici, simion stoilow, octav onicescu, dan barbilian and gr. c. moisil were involved in hot discussions on these topics. ultimately, moisil stresses the human side of mathematics. "the axiomatic freedom of mathematics does not fit with something similar in the real life" observes moisil and this premonitory statement is followed by other similar statements: "human creative work is by excellence one of expression. the deep human desire is to be understood by other peoples. this expression which succeeds to be communicated is just what we call culture". will this communication remain possible ? such thoughts expressed in the hot year 1942, when the war knew dramatic changes and the future became increasingly unsure are just the thoughts of moisil at the moment when his stage in iasi ends and a new university life will begin in bucharest. at some moment, moisil makes reference to "goedel paradox". clearly, he has in view the famous 1931 goedel incompleteness theorem, but it is clear that this theorem had to wait until it will be understood in its deep meaning and huge consequences for the whole mathematics. this was not only moisil’s shortcoming, but a shortcoming of the quasi totality of the mathematical community. during the summer of the year 1942, stoilow and barbilian had a daily correspondence about foundations of mathematics, but the reference to goedel 1931 theorem did not exist grigore c. moisil: a life becoming a myth 79 in their dialogue. sometimes, moisil is very near to some ideas emergent much later. for instance, in "determinism si inlantuire" (1940), he refers to georges bouligand in connection with the fact that small changes in the initial data may have a big impact on the further development of a phenomenon. "throwing a small stone may have some influence on the movement of the sun", observes moisil. does not anticipate it the modern "butterfly effect"? in another place, he refers to the involvement of continuous nowhere differentiable functions in the study of brownian motion. so, chaotic systems and fractals are suggested to a contemporary reader. let us recall that several decades later brownian motion will be recognized as a strange fractal (whose hausdorff dimension is an integer). in one respect only, moisil failed. he did not succeed to organize his life in order to make it more efficient. moreover, he did not try this. ho died at 67, when his head was still full of ideas and projects. he did not know how to alternate work and rest, how to pay attention to his health. i remember my last meeting with him.it was in an evening of the spring of the year 1973. we left together the university and we were walking in the direction of his home, armeneasca street, 14. when we arrived at his house, he told me: "you know that sudan is the real author of the first recursive function that is not primitive recursive ?" (all treatises of mathematical logic claim that the author of such an example is g. ackermann). very interesting, i said. where did you find this ? then, moisil said: "it is too late now, i will tell you this at a next occasion". this "next occasion" never arrived. moisil left next days for canada, where he died on 21 may 1973. today is may 18. in three days we can say: moisil died just 33 years ago. but my curiosity to find out what is behind the mysterious message left to me by moisil obliged me to accomplish the respective research. together with two young students, cristian calude and ionel tevy, we took piece by piece all the published papers of gabriel sudan. no of them had in its title or in its introduction something suggesting the presence of such an example. it is perhaps a rule of the nature that more interesting is something, more hidden it is and more effort we need to discover it. this was the reality. the respective example was hidden in the last part of an article which was explicitly concerned with a problem having nothing to do with a recursive function which is not primitive recursive. clearly, sudan was not aware of the fact, like moliere’s hero, jourdain, who remained a symbol of such situations. i had the happy opportunity to edit a part of the scientific work of moisil, in three volumes published by the publishing house of the romanian academy. other writings of moisil were published also after his death, some of them under the care of viorica moisil. i edited his "lectii depre logica rationamentului nuantat" and his articles in the newspaper "contemporanul", under the label "stiinta si umanism". the articles published in "viata economica" were edited in a small book "indoieli si certitudini". now, we have in front of us the duty to publish his papers of "mechanical grammar of romanian" and his philosophical papers. moisil’s heritage belongs to the romanian culture and the new generations deserve to know this unusual personality. solomon marcus romanian academy e-mail: solomon.marcus@imar.ro editor’s note about the author: solomon marcus member of the romanian academy born: march 1, 1925, bacău, romania. elementary and high school in bacau. diploma of merit in mathematics, university of bucharest. asistant professor(1950), lecturer, associate professor, professor (1966) at the faculty of mathematics and computer science, university of bucharest. phd in mathematics 1956, doctor in science 1967, corresponding member of the romanian academy 1993, full member of the romanian academy 2001. research and teaching in mathematical analysis, theoretical computer science, linguistics, semiotics, poetics, history and philosophy of science, fields where he published about 50 books in romanian, english, french, german, italian, spanish,russian, greek, hungarian, czech, serbocroatian and about 400 research articles in specialized journals in almost all european countries, in usa, canada, south america, japan, india, new zealand etc. more than 1000 authors quoted his works. he is recognized as one of the initiators of mathematical linguistics and of mathematical poetics. hundreds invited lectures at various international scientific meetings. member of the editorial board of several tens of international scientific journals. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 953-960 using qsps in developing and realization of a production line in automotive industry n. tudor, v.c. kifor, c. oprean nicolae tudor continental automotive systems test department e-mail: nicolae.tudor@continental-corporation.com vasile claudiu kifor “lucian blaga” university of sibiu, director of the research department constantin oprean “lucian blaga” university of sibiu rector abstract: using the qsps (quality system for the production software) for industrial projects and not only therefore, has led to accurate running of the production line from beginning of the sop (start of production). this paper presents the application way of the qsps at one of the strongest european automotive company. by using of this system several significant costs savings and quality improvement can be observed. the content of this paper will show step by step how to use qsps for the integration of a production line in the traceability system from a big company in automotive industry. the production line involved contains 56 production equipments, which have to be passed trough by the product before being packet and deliver to the customer. the control of the line is done by this traceability system, so the impact of this system with the quality of the product is very high. the structure of this system contains 7 steps. all of these steps are followed and executed in each system (test, pilot and production environment). keywords: quality improvement, control, savings, efficiency, capability. 1 introduction the traceability software for the production lines becomes more and more important and is a very significant process while running complex production lines with a high difficulty degree. the entire customer claims issues, statistics, the control of the process, cpk (process capability index) studies, fpy (first pass yield) and ppm (defective parts per million) reports are done very simple, based on a strong traceability system. therefore a lot of techniques for implementing the traceability software were tested and checked, some of them successful and some less successful depending on the kind of the project. the paper below describes the practical usage of the qsps system in real situation for the implementation of the traceability software of a complex line which contains 56 stations. the qsps system, should improve the quality of work, cost and time saving at one side, but on the other side the system should be very simple and easy to be used in order to simplify the work activities and not to make it more complicated. copyright c⃝ 2006-2010 by ccc publications 954 n. tudor, v.c. kifor, c. oprean also the system should cover and prevent all the risk which can appear in all 3 qsps phases (test, pilot and production) because of non conformity of improper software. figure 1: qsps framework the next chapters describe step by step the implementation strategy of the traceability software, based on qsps, of the already specified production line. all of the steps are described in the test (model) system, which have to be implemented and build up much closed (or even identical) to the characteristics of the production system. those steps should be followed in all of the 3 environments systems of the qsps (test, pilot and production). 2 step i: time and cost evaluation this phase of the project should help to understand the deepness of the project, the difficulty level, and cost of the project, to make very clear and transparent the resources needed (time resources and headcounts) and to define a due date very closed to the reality. therefore the project should be split out in work packages, which can be better followed up, controlled and evaluated. the table 1 shows how the project was divided in work packages and subprojects. subproject work packages difficulty level process definition 2 process description 2 process review 2 technical requirements specification 3 target specification 3 software implementation concept 3 programming 3 documentation of the software 2 installation and configuration of the 56 modules 2 testing the 56 modules 2 advising and user support 2 project evaluation(the time for evaluation itself) 2 internal software release compliance to technical requirements 3 compliance to the software iso norms 2 calibration and adjustments(bug solving) 3 capability measurements 2 usage work instruction 2 user manual 2 ergonomics and design 2 customer software release efficiency analyze 3 defects analyze 3 corrective actions 2 analyze after corrective actions 2 customer release reports 2 validation and verification of the implemented software package lifetime test 3 test procedure for modification issues 2 continued on next page using qsps in developing and realization of a production line in automotive industry955 table 1 – continued from previous page subproject work packages difficulty level approval procedure 2 final releases 2 functional follow up of the software during production software audit 2 statistical rate of the occurred errors 2 customer claims problem recording 1 analyze and solving procedure 2 analyze report 1 table 1 project segmentation with this information the time evaluation can be done more accurate using the pert algorithm. the results of the time evaluation can be observed in figure 2. resource, time and costs evaluation project name version date employees rep. vo vn vp va total deviation process definition 1,0 30 30 32 30,3 30,3 0,1 2 2821 process description 1,0 16 20 20 19,3 19,3 0,4 2 1798 process review 1,0 8 10 10 9,7 9,7 0,1 2 899 technical requirements specification 1,0 80 90 90 88,3 88,3 2,8 3 14486,66667 target specification 1,0 40 50 50 48,3 48,3 2,8 3 7926,666667 software implementation concept 1,0 80 85 85 84,2 84,2 0,7 3 13803,33333 programming 1,0 160 160 170 161,7 161,7 2,8 3 26513,33333 documentation of the software 1,0 40 50 50 48,3 48,3 2,8 2 4495 installation and configuration of the 56 modules 1,0 40 50 50 48,3 48,3 2,8 2 4495 testing the 56 modules 1,0 40 50 50 48,3 48,3 2,8 2 4495 advising and user support 1,0 8 10 10 9,7 9,7 0,1 2 899 project evaluation(the time for evaluation itself) 1,0 4 5 5 4,8 4,8 0,0 2 449,5 internal software release 1,0 0,0 0,0 0,0 0 compliance to technical requirements 1,0 40 50 50 48,3 48,3 2,8 3 7926,666667 compliance to the software iso norms 1,0 8 9 9 8,8 8,8 0,0 2 821,5 calibration and adjustments(bug solving) 1,0 16 18 18 17,7 17,7 0,1 3 2897,333333 capability measurements 1,0 8 10 10 9,7 9,7 0,1 2 899 usage work instruction 1,0 8 8 8 8,0 8,0 0,0 2 744 user manual 1,0 8 8 8 8,0 8,0 0,0 2 744 ergonomics and design 1,0 8 8 8 8,0 8,0 0,0 2 744 customer software release 1,0 0,0 0,0 0,0 0 efficiency analyze 1,0 4 4 4 4,0 4,0 0,0 3 656 defects analyze 1,0 4 4 4 4,0 4,0 0,0 3 656 corrective actions 1,0 4 4 4 4,0 4,0 0,0 2 372 analyze after corrective actions 1,0 4 4 4 4,0 4,0 0,0 2 372 customer release reports 1,0 2 3 3 2,8 2,8 0,0 2 263,5 validation and verification of the implemented software package 1,0 0,0 0,0 0,0 0 lifetime test 1,0 8 9 9 8,8 8,8 0,0 3 1448,666667 test procedure for modification issues 1,0 4 5 6 5,0 5,0 0,1 2 465 approval procedure 1,0 8 8 8 8,0 8,0 0,0 2 744 final releases 1,0 8 8 8 8,0 8,0 0,0 2 744 functional follow up of the software during production 1,0 0,0 0,0 0,0 0 software audit 1,0 4 5 6 5,0 5,0 0,1 2 465 statistical rate of the occurred errors 1,0 4 6 6 5,7 5,7 0,1 2 527 customer claims 1,0 0,0 0,0 0,0 0 problem recording 1,0 0,5 0,5 0,5 0,5 0,5 0,0 1 30 analyze and solving procedure 1,0 3 3 3 3,0 3,0 0,0 2 279 analyze report 1,0 0,5 0,5 0,5 0,5 0,5 0,0 1 30 total 626 705 717 693,8 693,8 21,2 93549 work package (wp) costs (euro) time[h] dificulty level traceability project of production line 24.11.2009 number figure 2: project time and cost evaluation 3 step ii: internal software release this step is the first evaluation of the software package from quality point of view. at the same time it is the first control tool used to signalize if the software was done in the right way, accordingly to the requirements, to the quality norms and not at least to assure the production of qualitative products. therefore each of the steps below has to be verified. 3.1 compliance to customer specification this check has to be done based on the target specification. each step from this chapter must be checked. for help a check table can be used and the characteristics from the target specification should be proved. for the line in our example following characteristics were verified: 956 n. tudor, v.c. kifor, c. oprean • program flow; • special cases in real life (like emergency stop, current interruption); • fail /pass /scrap situations; • handshake protocol between the industrial equipment and traceability software; • communication syntax; • repair scenarios; • limitation of the nr. of repair process; • check in/check out process; • process parameter; • process results, cpk measurements; • statistics of the process; • statistic of the failure; • product logistic on the line (process flow); • line flow control; • back flushing to the main database, in order to administrate material stocks; • label scanning; • label syntax; • packing. check if the product pass all the processes in the line before being packed; • quality alert; • alert notification. 3.2 check the implemented software package for software techniques and standards for this point it was used the iso/iec 9899, for programming language c. based on technical corrigenda 1:2001 the source code was reviewed. also the next criteria were inspected at the very early beginning: • is the right software? • the target is reached? • interface to the users is clear and unique while using it? • at least one diagnostic message? • the programming editor was used correctly? (tabulator, empty spaces etc.) • requirements resulted from coded character set. • requirements resulted from binding techniques. • comments in the source code. • documentation. • usage documentation. 3.3 software calibration and adjustments (bugs solving) after the two chapters above were effectuated and all of the situations where verified, so all the bugs could be resolved, bugs which can occurred if the programmer doesn‘t consider a state from the program logic. 3.4 capability measurements (using pareto distribution, cpk, iso 15504) this measurement was done even in the test system. production units can be created virtual for a good simulation. knowing the handshake protocol between the traceability system and the using qsps in developing and realization of a production line in automotive industry957 industrial equipment, a simulation can be done very closed to the reality situation. with these results the real running of the production can be compared. the upper, respectively the lower limits are in this case the cycle time of the process admitted by the customer while processing a production unit. cpk was done as described in table 2, respectively the pareto distribution for the analyses of the software package in case of rejected software, table 3 respectively figure 3. unit id measured time lower limit 31 upper limit 36 1 33,5 30 36 2 33,2 30 36 3 34,1 30 36 4 33,9 30 36 5 33 30 36 6 34,3 30 36 7 33,2 30 36 8 32,9 30 36 9 33,8 30 36 10 33,6 30 36 11 33,7 30 36 12 33,6 30 36 13 32,9 30 36 14 33,1 30 36 15 33,8 30 36 16 34,3 30 36 17 33,9 30 36 18 33,6 30 36 19 33,2 30 36 20 33,3 30 36 21 32,7 30 36 22 33,5 30 36 23 33,8 30 36 24 34 30 36 25 33,6 30 36 26 33 30 36 27 33,8 30 36 28 32,9 30 36 29 33,4 30 36 30 33,9 30 36 average 33,51666667 deviation 0,435560149 cp 1,913245127 cpk 1,90049016 table 2: cpk calculation 3.5 software ergonomic and designs therefore we based on the iso 9241. this iso norm is a standard of ergonomics of human system interaction. following rules were proved: • suitability for the task (the software interface should be suitable for the user‘s task and skill level); • self-descriptiveness (the interface should make it clear what the user should do next); • controllability (the user should be able to control the pace and sequence of the interaction); • conformity with user expectations (it should be consistent); • error tolerance (the software should be forgiving); • suitability for individualization (the software should be able to be customized to suit the user) and • suitability for learning (the software should support learning). 4 step iii: customer software release (run@rate) for this step we run the line with 100 production units (in the test system we simulate the running of the line with virtual units) and observe the behavior of the software, of the users 958 n. tudor, v.c. kifor, c. oprean software return reasons problem count percent of total cumulative percent not compatible 21 30,67 30,67 does not perform as expected 18 18 50,33 missing hardware resources 15 16 54 not suitable for self learning 11 14 65,4 missing user manual 10 7 71 bad user interface 8 5,33 78 too complicated 7 4 88,9 bad cycle time 4 3 98,3 bad backup of recorded data 3 2 100 total 97 100 100 table 3: pareto analyze software return reasons 0 10 20 30 40 50 60 70 80 90 100 n ot c om pa tib le d oe s no t p er fo rm a s ex pe ct ed m is si ng h ar dw ar e re so ur ce s n ot s ui ta bl e fo r se lf le ar ni ng m is si ng u se r m an ua l b ad us er in te rf ac e t oo co m pl ic at ed b ad cy cl e tim e b ad b ac ku p of re co rd ed d at a percent of total cumulative percent figure 3: pareto distribution (operators) while using it in different situation and intentionally we cause extreme situation (like emergency stop of the equipment, current interruption... etc.) for a better software rating. a statistic is made for each equipment, inclusively the packing station. this statistic contains the number of the parts being processed on each station during one hour. also a pareto was realized similar to the figure 3. the following issues were also effectuated: • efficiency analyze (how many parts per hour per equipment); • defects (bugs) analyze; • causes and corrective actions; • reevaluation procedure after corrective actions; • creating reports (like 8d report). 5 step iv: validation and verification of the implemented software package after release and after the customer agreed the implemented software, the final release is made by the project manager. that means that the software is officially registered as a production software. at this level the project manager analyze the robustness of the software package by making some research about the following properties: • lifetime (how long the software will run without any modifications or maintenance), iso 12207 using qsps in developing and realization of a production line in automotive industry959 – acquisition analyze (how was the software required); – supply analyze (how was the software provided); – how it was develop; – how is the operating mode; – how much maintenance is needed. • modifications handling (how will be the modification make and tested); • documents like ppap (product part approval process) and psw (part submission warranty) are made and signed from the project manager. 6 step v: functional follow up of the software during the production after the software was accepted and installed in the production line, periodical (every 2 weeks) software audits were made to assure that the software is running in conformity with the requirements and the production criteria are fulfilled. in this software audit the main criteria (see chap. 3.1) are checked again. statistics are made for the cycle time, and errors occurred, in order to make the difference between software errors and process error. the main aim of this step is to find improvements for the software in order to make it more robust and efficient. 7 step vi: customer claims customer claim escalation plan is also created like in the figure 4 in order to obtain the best reaction time in case of software error which could appear, and disturb the normal running of the production. this will drive to very small solving and reaction times for eventually downtimes on the line because of software problems. 8 conclusions using the qsps system, the downtime of the line (containing the 56 production equipments) because of production software is decreased to almost 0%. the target of 0% is only possible when the technical requirements from the customer are done accurate and to 100% complete. even with incomplete requirements, using the qsps system the possibility to reach the target of 0% is possible. therefore the implementation and running of the software should have a much longer period of time running in the pilot environment, to assure a safe lunch in the production. in most all of the cases this is not possible because the production should always start even if some problems are unsolved, because of time pressure. qsps should eliminate that inconveniences, provoked by time pressure or other factors, due to a much easier structure, better and faster to be used, compared to other systems met till know in other studies. the advantage of using the qsps is that during the 3 environments (test, pilot and production), using the rules and iso norms for each of them, the almost or all troubles which occurred are filtered before running the real production. that means that after the sop (start of production) the interruption of the production line because of the software is almost inexistent. 960 n. tudor, v.c. kifor, c. oprean figure 3: escalation plan figure 4: escalation plan bibliography [1] kai-yuan cai, bey-bey yin, software execution processes as an evolving complex network, information science, april 2008. [2] georg kühner, torsten bluhm, employing industrial standards in software engineering for w7x, fusion engineering and design, 2009. [3] j.-w. li, modeling a quality assurance information system for product development projects with the uml approach, vol. 20, nr.4 of international journal of computer integrated manufacturing, june 2007. [4] n. tudor, c. kifor and c. oprean, quality system for production software qsps. [5] oprean, c., kifor c. v., suciu, o., managementul integrat al calităţii, sibiu, editura universităţii lucian blaga din sibiu, 2005, isbn 973-739-034-2. [6] n. tudor, d. dumitrascu, the benefits of project structuring in sub-projects and work packages, http://imtuoradea.ro/auo.fmte/files-2008/mie_files/tudor%20nicolae%202.pdf. [7] n. tudor, d. dumitrascu, advance estimate expenses for project execution time, http://imtuoradea.ro/auo.fmte/files-2008/mie_files/tudor%20nicolae%201.pdf. international journal of computers, communications & control vol. ii (2007), no. 2, pp. 111-120 variable selection and grouping in a paper machine application timo ahola, esko juuso, kauko leiviskä abstract: this paper describes the possibilities of variable selection in large-scale industrial systems. it introduces knowledge-based, data-based and model-based methods for this purpose. as an example, case-based reasoning application for the evaluation of the web break sensitivity in a paper machine is introduced. the application uses linguistic equations approach and basic fuzzy logic. the indicator combines the information of on-line measurements with expert knowledge and provides a continuous indication of the break sensitivity. the web break sensitivity defines the current operating situation at the paper mill and gives new information to the operators. together with information of the most important variables this prediction gives operators enough time to react to the changing operating situation. keywords: variable selection, grouping, paper machine, web breaks 1 introduction data-driven modelling requires always variable selection or grouping. in small systems, expert knowledge gives a clear basis for the variable selection since possible interactions and causal effects are known fairly well. in these cases, few modelling alternatives can be compared interactively. variable selection becomes important when the number of variables increases, especially when normal process data is used. as a model should include a reasonable number of variables, a modular approach based on variable grouping provides a better process insight, which makes the model assessment easier. in practical cases, variable selection is necessary either because it is computationally infeasible to use all available variables, or because of estimation problems when limited data samples with a large number of variables are present. variable grouping means finding feasible groups and combinations of variables for modelling. it is closely connected to data clustering since the interactions can depend on the operating area. in large-scale systems, the number of possible variable combinations becomes easily very large, e.g. the case models of the web break indicator included originally 24 variables, which mean 2024 alternative three variable combinations. the newest version has 72 variables leading to 59,640 three variable groups, 1,028,790 four variable groups and 13,991,544 five variable groups. most of these alternatives are useless, and therefore, methods for selecting reasonable variables for modelling are crucial. there is a lot of recent literature on variable selection and both model and data-based techniques are in use. spectroscopic data, multi-sensor systems, multivariate analysis and modelling of large-scale systems seem to require efficient methods for variable selection. four different methods for variable selection ű genetic algorithm, iterative pls, uninformative variable elimination by pls and interactive variable selection for pls in partial least square (pls) regression are studied and compared to a calibration made with manually selected wavelengths in [1]. the application is nir analysis of pharmaceutical tablets. it has been found that multiresolution analysis (haar wavelets) pre-processing before variable selection leads to simpler models with lower errors than single-wavelength selection in nir data [2]. wavelength selection for process monitoring has also been done using genetic algorithms (ga) coupled with a curve resolution method (opa) [3]. variable selection is also an important topic in using multiway methods in modelling nir spectra from a pharmaceutical batch process [4]. nir analysis of sugar cane juice has utilized partial least squares (pls) pruning for variable selection [5]. uv-vis and nir spectrometry of oils takes advantage of the successive projections algorithm (spa) in large-scale variable selection [6]. copyright © 2006-2007 by ccc publications 112 timo ahola, esko juuso, kauko leiviskä quantitative structureűactivity relationship (qsar) studies require also sophisticated methods for variable selection. there is a report on applying multi-objective genetic programming (gp) to the hept data and constructing the nonlinear qsar model using counter-propagation (cp) neural network with the selected variables [7]. particle swarm optimizer (pso) applies for the same purpose and the comparison with gp is in [8]. norinder [9] reports also on the use of support vector machine (svm) in qsar. statistical parametric mapping (spm), relying on the general linear model and classical hypothesis testing, is a benchmark tool for assessing human brain activity using data from fmri experiments [10]. prediction-based variable selection has reported to give 82 % success rate in quantitative structureűproperty relationship models (qspr) based on in vivo bloodűbrain permeation data [11]. in multi-sensor systems, variable selection problem originates from two reasons: the high dimensionality in the data used is due to a high number of sensors or many features extracted, or both. fuzzy artmap classifier analyses the results from a 12-element gas sensor array [12, 13]. fast wavelet transform is useful in feature selection before calibration in stripping voltammetry [14]. principal component analysis (pca) is a well-known method for variable selection. testing of loadings and their estimated standard uncertainties are used to calculate significance on each variable for each component [15]. variable selection can also mean identifying a k-subset of a set of original variables that is optimal for a given criterion that adequately approximates the whole data set [16]. the application of principal component regression to the trajectories of the process variables (block-wise pcr) has given straightforward results without requiring a deep knowledge of the process [17]. in this case, variable selection methods and technical information of the process has allowed the process variables most correlated with the final quality be revealed. genetic algorithms (gas) have been proposed recently for many applications including variable selection for multivariate calibration, molecular modelling, regression analysis, model identification, curve fitting, and classification. gašs are also incorporated with fisher discriminant analysis (fda) for key variable identification for trouble-shooting problems of the tennessee eastman process [18]. ga and simulated annealing have also been combined for reduction in the number of variables in neural network models [19]. two other approaches for the selection of variables in neural networks are in [20] and [21]. this paper is organised as follows: section 2 concerns with knowledge-based variable selection and grouping, section 3 with variable grouping with data analysis and section 4 with model-based variable selection. the case-based reasoning system for evaluating paper machine web breaks is shortly revisited in section 5. 2 knowledge-based variable selection knowledge can be used in decreasing the number of variables. for example, if we have a case with 10 process variables and group them in all possible groups with three, four and five variables, we end up to 582 groups. if we can, based on the process knowledge, include variable 1 in all groups with three variables, variable 10 in all groups with four variables, and variables 5 and 6 in all groups with five variables, we have 176 groups to analyse. this means that using process knowledge has decreased the number of alternatives by 70 percent. some variable combinations should be avoided, e.g. calculated variables should not be used together with the variables used in calculating them. also a group containing a controlled variable and its setpoint is not usually appropriate. these problems are avoided by defining the inappropriate groups as non-groups, i.e. as variables groups, which should not be a part of any acceptable variable group. variable selection and grouping in a paper machine application 113 3 variable grouping with data analysis correlation is a statistical technique which can show whether and how strongly pairs of variables are related. binary correlations and their combinations are used in pruning the set of acceptable groups defined by the domain expertise. for forecasting models, input variables should have a high correlation with the output variables, but a low one with each other. for case detection, causality is not always as clear: there is nor necessarily any definite output variable i.e. also groups where several variables have a high correlation between each other are acceptable. this sets new requirements for the model assessment. in practical cases, the results from correlation analysis are improved with appropriate filtering and using correct time delays between the variables. calculation of moving averages, medians and value ranges includes already a time delay, which depends on the calculation window and the applied methodology. nonlinear scaling is the essential feature in using linguistic equations method [22]. it improves the correlation analysis of curvilinear relationships, since the correlation analysis is a linear method. finding patterns in data with high dimension is difficult. however, in data sets with many variables, groups of variables often move together as they are measuring the same phenomena. a host of clustering approaches helps in digging out these interactions. as shown in introduction, principal component analysis (pca) is a conventional method to decrease the dimensionality in data without losing the information stored in the correlated variables. it searches for new fewer linear combinations of the original variables that explain the most of the variance of the original data. these linear combinations can be viewed as a linear transformation to the hyperplane defined by the principal components or a rotation and a stretch that transform original data to a new bias. principal components are calculated by defining the eigenvectors of the covariance matrix or utilizing the singular value decomposition. usually, only few first principal components (2 or 3) are used ű they are enough to explain most of the variance in the data set. there are also extensions in the basic methods that apply for analyzing time trajectories. 4 model-based variable selection isokangas and ruusunen [23] describe the automated procedure for finding interactions between variables from large datasets. this occurs systematically by constructing simple dynamic model candidates with complete input combinations for data segments of the varying and sliding window size. the final analysis goes on according to the structure properties of the best candidate models. model candidate construction, validation and testing proceed in the following way: the half of all available data is used in training and validation so that model candidates are constructed systematically from the beginning of data with selected data window size. after a data window has been used for training, the window of the same size is taken for validation. the procedure uses a partly overlapping data window. for example, if the data window is 400 minutes, first models are constructed using training data from 1 400 minutes and data from 401 800 for validation of a model candidate under evaluation. next, all model candidates are constructed using training data from 201 600 and validation data from range 601 1000 minutes. to define the right size of training data, different window sizes are systematically tested at this stage. models are evaluated with the correlation coefficient and rms-error measure using validation data. best models are further tested with independent testing data, which is another half of available data. 114 timo ahola, esko juuso, kauko leiviskä 5 paper mill example paper web breaks commonly account for 2-7 percent of the total production loss, depending on the paper machine type and its operation. this could mean 1.5 million euros lost annually at a single paper machine. according to statistics only 10-15 percent of web breaks have a distinct reason. the most of the indistinct breaks are due to dynamical changes in the chemical process conditions. the main area of interest in the indicator development is the paper making process before the actual paper machine. this includes also the short circulation and the wet end of the paper machine. in this area, the paper making process is typically non-linear with many, long delays that change in time and with process conditions, there are process recycles at several levels, there are closed control loops, there exist factors that can not be measured and there are interactions between physical and chemical factors. also several different paper grades are produced with different production conditions and operating parameters. this section shows how to combine on-line measurements and expert knowledge in paper machine modelling in developing the sensitivity indicator for paper web breaks [24]. the indicator would give the process operators a continuous indication of the web break sensitivity in an easily understandable way. being able to indicate the break risk would give a possibility to react on changes of the break sensitivity in time and therefore avoid breaks. 5.1 experimental data the actual measurements from a paper machine were used. the main interest was in paper machine variables and the variables just before the paper machine. the final selection of variables used expert knowledge and altogether 73 variables (72 variables + information on the break occurrence) were studied. these variables were supposed to influence on paper web breaks. the measurements were collected from the mill automation system during normal operation and no special test runs were made. the measurements were used as such to retain their information content, and, on the other hand, to keep the application as simple as possible. only a simple filtering was added to the indicator software to make rapid changes slower and to cut the outliers from the data. the measurement data was divided into periods of 24 hours. further the data sets were classified into five categories, depending on how many breaks there were in one day: no breaks (0), a few breaks (1−2), normal (3−4), many breaks (5−6) and a lot of breaks (> 6). 5.2 reasons for web breaks different statistical methods were used, but reliable correlations between single variables and web breaks did not exist. therefore, the only way to proceed was the classification and modelling of break situations to find out differences between operating situations leading to breaks. case-based reasoning was used for the identification of different operating situations instead of trying to predict a single break occurrence. identified operating situations contain information about how many breaks there will be in the near future and this information is given to the process operators as the web break sensitivity. the identification is performed using linguistic equation approach and fuzzy logic [24]. 5.3 correlation analysis before modelling, correlation analysis was used in order to find out binary interactions between different process variables. the basic tool used for these analyses was microsoft excel spreadsheet. the correlation exceeding 0.6 was considered worth mentioning. according to this analysis, correlation variable selection and grouping in a paper machine application 115 varies quite a lot with time. the variation in correlation rates is due to the usage of normal on-line measurements, which include the effects of different control operations. the most important result of this analysis was that interactions vary in different operating situations, and the number of breaks also varies with time, and this was the basis for different case models. due to different interactions, also different variables became important in different operating situations. 5.4 model-based variable grouping the web break sensitivity indicator was developed as a case-based reasoning type application with linguistic equations approach and fuzzy logic [24]. the case base contains case models with different number of breaks. a new case is presented to the system as a collection of on-line measurements. the indicator compares the new case to the examples in the case base and uses the information of the best fitting case to calculate the predicted break sensitivity. as output the system gives numerical value for the predicted amount of breaks [24, 25]. figure 1 shows the principal structure of the case base, and figures 2 and 3 the different stages of case-based reasoning. figure 1: structure of the case base [25]. figure 2: the structure of retrieve and reuse stages [25]. the case base of this application contains modelled example cases classified according to the number of related breaks. models consist of equations that are stored as simple numerical matrices, which are indexed with break class information and number of examples in class. equations itself describe the interactions between 3-5 variables. the variables in equations are found using a partly knowledge-based, partly model-based grouping technique. for complex systems, a set of alternative variable groups are generated and models created with these groups. process knowledge can be used in defining these groups. another approach is to generate all 116 timo ahola, esko juuso, kauko leiviskä possible groups containing three, four or five variables and modelling them. groups can also contain different number of variables. correlation analysis has also use in grouping. it should be noted that for prediction the input variables should have a high correlation with the output variable, but a low one with each other. for state detection, causality is not clear, and the group where all variables correlate with each other are acceptable. here, however, the limitations given in section 2 must be taken into account. groups with three, four or five variables can be generated automatically with fuzzequ toolbox [22]. the generation of the alternatives is based on groups with three variables: all groups with four variables have one variable in common, and all groups with five variables have two variables in common. the subsets of the variables and the common variables in the groups with four or five variables can be based on process knowledge. figure 3: the structure of revise and retain stages [25]. 5.5 some feedback ű importance of parameters after modelling, the importance of variables was analysed based on the occurrence of variables in case models. this was also considered as a useful tool to reveal less important variables not included in the models. for the operating personnel, the list of importance might give some new information about which variables are responsible for different operating problems. there is also some difference in the collection of important variables between cases with different number of breaks. the user interface presents continuously the six variables that best describe the current operating situation. these are simply the variables of the two best fitting equations of the best fitting case. in addition to these, also the most important variable of the best fitting case model is presented with a trend value of 8 hours. the variables are presented with their current measurement values marked with colours as normal, low or high and very low or very high. this information gives the process operators useful information of the current process conditions together with the reason for the current break sensitivity level. the follow-up of the variable importance can lead to the need to update the whole system. the system updating is a straightforward task, all though time consuming when the whole case base is changed. a single model with 73 variables takes only few minutes to build. the same time is required for validation and tuning and it makes altogether 15 to 20 minutes. the updating of the system with 40 cases could take the working hours of two days. however, the automation of these tasks makes this time shorter. variable selection and grouping in a paper machine application 117 6 summary and conclusions this paper has considered possibilities to variable selection in large-scale industrial systems. it introduced knowledge-based, data-based and model-based methods for this purpose. as an example, case-based reasoning application for the evaluation of the web break sensitivity in a paper machine was introduced. the application was build with linguistic equations approach and basic fuzzy logic. the case base of the system contains models of example cases with different number of breaks. a new case is presented to the system as a collection of on-line measurements. the indicator compares the new case to the examples in the case base and uses the information of the best fitting case to evaluate the break sensitivity. the latest version of the indicator operates with a case base of 40 example models. although the size of this case base is rather small, the results have been considerably good compared to the real break sensitivity. the indicator combines the information of on-line measurements with expert knowledge and provides a continuous indication of the break sensitivity. the web break sensitivity defines the current operating situation at the paper mill and gives new information to the operators. the web break sensitivity is presented as a continuous signal with information of the actual web breaks as a trend of 8 hours. the trend shows how the situation has developed and the current value gives the prediction for next 24 hours if the situation stays as it is now. together with information of the most important variables this prediction gives operators enough time to react to the changing operating situation. the variable selection and grouping utilize knowledge-based and model-based approaches. automatic group and model generation makes also the interactive variable selection possible. references [1] c. abrahamsson, j. johansson, a. sparén, and f. lindgren, comparison of different variable selection methods conducted on nir transmission measurements on intact tablets, chemometrics and intelligent laboratory systems, vol. 69, pp. 3–12, 2003. 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[17] m. zarzo and a ferrer, batch process diagnosis: pls with variable selection versus block-wise pcr, chemometrics and intelligent laboratory systems, vol. 73, pp. 15–27, 2004. [18] l. h. chiang and r.j. pell, genetic algorithms combined with discriminant analysis for key variable identification, journal of process control, vol. 14, pp. 143–155, 2004. [19] a. alexandridis, p. patrinos, h. sarimveis, and g. tsekouras, a two-stage evolutionary algorithm for variable selection in the development of rbf neural network models, chemometrics and intelligent laboratory systems, vol. 75, pp. 149–162, 2005. [20] f. dieterle, s. busche, and g. gauglitz, growing neural networks for a multivariate calibration and variable selection of time-resolved measurements, analytica chimica acta, vol. 490, pp. 71–83, 2003. [21] i. drezga and s. rahman, input variable selection for ann-based short-term load forecasting, power systems, ieee transactions on, vol. 13, pp. 1238–1244, 1998. [22] e. k. juuso, integration of intelligent systems in development of smart adaptive systems, international journal of approximate reasoning, vol. 35, pp. 307–337, 2004. variable selection and grouping in a paper machine application 119 [23] a. isokangas and m. ruusunen, systematic approach for data survey, in proceedings of the international conference on informatics in control, automation and robotics. september 14 17, 2005, barcelona, spain, pp. 60–65, 2005. [24] t. ahola, intelligent estimation of web break sensitivity in paper machines. doctoral dissertation. university of oulu, department of process and environmental engineering. acta universitatis ouluensis, technica c 232, 92 p., oulu, 2005. [25] t. ahola and k. leiviskä, case-based reasoning in web break sensitivity evaluation in a paper machine, journal of advanced computational intelligence and intelligence informatics, vol. 9, pp. 555–561, 2005. timo ahola, esko juuso, kauko leiviskä university of oulu, control engineering laboratory p.o. box 4300, fi-90014 university of oulu, finland e-mail: esko.juuso@oulu.fi received: march 21, 2007 timo ahola, born in hämeenkyrö, finland on december 18, 1965, recieved his m.sc. (eng.) in process engineering in 1992 and lic. tech. in control engineering in 2001 from the university of oulu. he recieved d. tech. in control engineering from the university of oulu in 2006 with the thesis on intelligent estimation of web break sensitivity in paper machines. he worked as a research scientist in the control engineering laboratory at the university of oulu 1993-2006. since 2007 he has been belonging to the outokumpu stainless oy, tornio research centre, finland. presently he is a research engineer in process development and he is working with predictive maintenance issues. esko juuso, born in ylitornio, finland on december 12, 1951, received m.sc. (eng.) in technical physics in 1979 from university of oulu. he has worked as research engineer in outokumpu metallurgical research centre and computer analyst in outokumpu electronics. since 1986, he has been belonging to university of oulu, oulu, finland. presently he is senior assistant in control engineering. he has been active in finnish simulation forum (finsim), scandinavian simulation society (sims) and eurosim, currently he is chairman of finsim. he has been member of steering committee and co-chairman of technical committee on production industry in eunite network of excellence, 2000-2004. his main research fields are intelligent systems and simulation in industrial applications, including control and fault diagnosis. in 1991 he introduced the linguistic equation (le) methodology, and he has authored more than 200 publications on his research field. 120 timo ahola, esko juuso, kauko leiviskä kauko leiviskä, born in pyhäntä, finland, 1950, received m.sc.(eng.) in process engineering in 1975 and lic. tech. in control engineering in 1976 from the university of oulu. he received the d. tech. in control engineering from the university of oulu in 1982 with the thesis on short term production scheduling of the pulp mill. since 1975, he has been belonging to university of oulu, oulu, finland. he has been professor of control engineering and head of control engineering laboratory in the same university since 1988. he has been active in ifac since 1988, currently he is member of ifac tc on large scale complex systems, tc on cognition and control and tc on mining, mineral and metal processing. he has been member of steering committee and chairman of technical committee on primary and process industries in erudit network of excellence, 1997-2000, and the scientific director of eunite, the european network of excellence, 2000-2004. he is participating in eu/ca project nisis (nature-inspired smart information systems). a list of more than 200 publications of which he is (co)author is available. recently his work concentrates on modelling and control of industrial processes, intelligent control methods, production scheduling and millwide control. he has also been consulting industry on control engineering and millwide control applications. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 625-633 generic multimodal ontologies for human-agent interaction a. braşoveanu, a. manolescu, m.n. spînu adrian braşoveanu lucian blaga univeristy of sibiu, romania e-mail: adrian.brasoveanu@gmail.com adriana manolescu agora university, oradea and r&d agora ltd. cercetare dezvoltare agora oradea, romania e-mail: adrianamanolescu@gmail.com marian nicu spînu aurel vlaicu university of arad, faculty of exact sciences department of mathematics-informatics romania, 310330 arad, 2 elena drăgoi abstract: watching the evolution of the semantic web (sw) from its inception to these days we can easily observe that the main task the developers face while building it is to encode the human knowledge into ontologies and the human reasoning into dedicated reasoning engines. now, the sw needs to have efficient mechanisms to access information by both humans and artificial agents. the most important tools in this context are ontologies. the last years have been dedicated to solving the infrastructure problems related to ontologies: ontology management, ontology matching, ontology adoption, but as time goes by and these problems are better understood the research interests in this area will surely shift towards the way in which agents will use them to communicate between them and with humans. despite the fact that interface agents could be bilingual, it would be more efficient, safe and swift that they should use the same language to communicate with humans and with their peers. since anthropocentric systems entail nowadays multimodal interfaces, it seems suitable to build multimodal ontologies. generic ontologies are needed when dealing with uncertainty. multimodal ontologies should be designed taking into account our way of thinking (mind maps, visual thinking, feedback, logic, emotions, etc.) and also the processes in which they would be involved (multimodal fusion and integration, error reduction, natural language processing, multimodal fission, etc.). by doing this it would be easier for us (and also fun) to use ontologies, but in the same time the communication with agents (and also agent to agent talk) would be enhanced. this is just one of our conclusions related to why building generic multimodal ontologies is very important for future semantic web applications. keywords: multimodal ontology, ontology matching, interface agents, semantic web, human-agent interaction 1 introduction the knowledge society (ks) is a society where information is the primary resource which can be consumed by both humans and machines. if we want to build such a society in a proper way we need different kinds of infrastructure: hardware, software, organizational, etc. sw and copyright c⃝ 2006-2010 by ccc publications 626 a. braşoveanu, a. manolescu, m.n. spînu agents represent only a small part of the large infrastructure needed in order to build the true ks. sw ( [1], [2], [3], and [4]) is one of those disruptive technologies which tend to be talked about years before their coming of age. one of the visions presented in [1] was that of agents replacing humans for simple everyday tasks like buying tickets for a concert or making appointments to the doctor. the main reason why this vision hasn’t yet come to life is one that is now well understood and also explained in the article’s revision [2]: encoding the human knowledge into ontologies and the human reasoning into dedicated reasoning engines is not an easy task. this process requires trans-disciplinary knowledge, dedicated tools and repositories, and advanced techniques from mathematics, logics and software. it is in fact an extremely difficult procedure which relies entirely on the cooperation between hundreds or thousands of organizations and different standards. since the standardization processes take a long time even in these days and the time of adoption for new technologies is sometimes around 2-3 years at least, we should not be surprised that it will take a while until the sw reaches the critical mass. ontologies represent the key to a successful communication between human and agents if they are done right. we are only beginning to understand the implications of using the ontologies for the great tasks we assigned for them, but some problems like ontology management (versioning, change, tools and standards), ontology matching (finding correspondences between different ontologies) and the adoption of ontologies on large scale by developers and users proved to be quite challenging. ontology dynamics is definitely a field on which we should keep an eye on. according to [30] there is still no clear winner in the process of ontology matching (in other words: a standard or a methodology with clear rules to match almost everything automatically or semi-automatically since sometimes humans will need to check the results). therefore we should not be surprised, when reading a journal or conference proceeding, that most of the articles refer to these tasks rather than to the desired using of ontologies which is to give agents a way of understanding our world and reason about it. it is the way things should be: in order to build a functional system we always need to have its parts figured out. we should however not lose sight of the system we need to build and this is one of the purposes of this paper: to look at the current state of the art in several fields of study and see if we are heading in the right direction. in this context we will especially examine some problems related to the multimodal communication between human and agent and try to see how they are solved by using ontologies. 2 rationale and approach: why complicate things and use generic multimodal ontologies? first we need to clear one question: what is an ontology? some answers to this (and also some examples of how to use ontologies) can be found in [12], [15], [16], [17], [22], [23] and [31]. the classic definition proposed by gruber tells us that an ontology is "explicit specification of a conceptualization" [12]. this definition is examined and extended by many papers, most recently by guarino, oberle and staab in [16] which also focuses on the importance of "shared explicit specifications" because without commiting to ontologies every agent would understand something else (they also take the opportunity to revise the semiotic triangle). ontologies are us, mika’s thesis [23] is a simple yet powerful statement. it tells us that since we are the ones who design the ontologies they will only express what we want them to express and will sometimes be useless without the context in which they have been created. the main problem when designing ontologies is to carefully choose the concepts within a domain and the relationships between them in such a way as the ontology to be well founded because "any ontology will always be less complete and less formal than it would be desirable in generic multimodal ontologies for human-agent interaction 627 theory" [16]. in the light of this statement it should become quite clear why we sometimes need to use generic ontologies: there is simply no other way to address the problem of uncertainty when developing ontologies than genericity. figure 1: one of the most popular programs for ontology matching: coma++, developed at the university of leipzig. in this screenshot we can see how we can establish some correspondences between two ontologies representing a computer science department nowadays there are probably thousands of ontologies in use, but if the sw will ever look like berners-lee’s visions then ontologies will be common place for every designer, developer or user. usually an ontology only addresses the problems from a narrow field of knowledge (domain ontology) so it is not uncommon that applications may use many ontologies for different purposes. in some of these cases it is useful to also use upper level ontologies which are general ontologies that represent concepts that are the same across all domains. a unique upper level ontology which should encompass all the human knowledge is not feasible and will never be built because of practical reasons (each society has its concepts, every field of knowledge has a certain language to protect itself, etc.), but upper level ontologies are used for mediation mainly in the idea that universal agreement between different ontologies will be/is possible. in other cases in order to use different ontologies the applications will use ontology matching schemes like those discussed in [10]. since ontologies are the building blocks of sw, any application from this area must use them, even if that means adding layers of complexity because of the matching process, apis, uncertainty. for everybody working in the it industry these days it should be clear that the medium in which we work is becoming more and more like ohdue (open heterogenous dynamic uncertain environment) [8] and ontologies are part of this medium. these issues are addressed in articles and books like [10], [19], [30] (ontology matching), [26] (automatic generation of ontology apis), [8] (ohdue, agents). because the field of ontology engineering is becoming more popular we should not be surprised that we will also hear a lot about the ontology driven software engineering. ontology driven information systems (odis) [36] is just one of the recent examples which fell into this category. given all these complications that appear when designing and working with ontologies it is interesting to ask a new question: why would we want to complicate our life even more by using multimodal ontologies? it is not enough that the ontology management or ontology matching problems still pose so many challenges? are these new breed of ontologies even feasible? certainly from a user’s perspective multiple modalities to enter input into a system (touch, voice, mouse, pen, etc.) can only mean increased usability (do we need to remember how touch screens became the norm in the mobile phones industry after iphone was launched?), while from 628 a. braşoveanu, a. manolescu, m.n. spînu figure 2: the multimodal communication dream: to use all the five senses (smell, sight, touch, taste, sound) during the process of communication. a developer’s perspective this means that software gets even more complicated than it is now. this is the right moment for such a development since for the multiple streams of data that come with multimodal communication we need distributed systems. since multi-core processors are now luckily the norm in desktop computing we should have no problem (at least not hardware) dealing with the huge flux of data. in the past 40 years scientists have developed different mechanisms for getting audio, video and touch input, but the integration of all five senses in the communication between man and machine remains a dream. it is enough however to use one sense in different ways (for example for seeing we have images, text, video) to be able to speak about multimodal communication. in this respect different research groups (most notably [29]) started to develop also multimodal ontologies, but most of them took the approach of developing different ontologies for text, images, video or voice and then use ontology alignment to match them (multimodal integration through ontology matching [29]). a multimodal ontology gets us all the benefits of having such different ontologies. like all things in life, multimodal ontologies do not come without bad parts (even harder to design, mantain and match), but they are definitely closer to our way of thinking. is this a sufficient reason to try it? it might not be, but it is not the only one. the usage of multimodal ontologies will allow us to give a more natural, even realistic, feeling during communication between agents and humans, enhanced usability, the possibility to model mechanisms that are closer to the way we understand the world (diagrams, mind maps, feedback, brainstorming, slides, visual thinking, and others). it should be clear that it’s not just art for art’s sake, but rather art for a better life in the future. 3 generic multimodal ontologies for human-agent interaction the process of multimodal ontology modeling is still open to exploratory research because ontologies are not everywhere. without ontologies for all possible fields, and tools to match these ontologies it is debatable whether we will achieve an efficient semantic web, but rather the illusion of a semantic web maintained by few successful applications in certain areas (like social generic multimodal ontologies for human-agent interaction 629 networking, language translation or medicine). since multimodal communication is difficult to process it is clear that in the first phase of any research regarding this subject, the communication between agents and humans will not be efficient. the question we need to ask ourselves in this situation is: if it is not efficient why should we bother at all to try something like this? the answer is simple and is typical for exploratory research: it takes time to find the best way to integrate multiple streams of data in an efficient manner and it also takes time to develop efficient ontology matching processes for such tasks. the role of exploratory research is to discover niches. the task of creating efficient mechanisms is one best suited for incremental research. since this area of research is relatively new there is enough room for exploratory research and for breakthroughs. generic ontologies are rarely used by developers. most of the articles present different ontologies and clearly state that they do not use generic ontologies because the problem’s domain was well understood. generic ontologies are best suited for modelling as we can see from [17], and [13]. it is easier to say you have an ontology with few concepts and not define all of them when doing modelling. the task of defining all the concepts and relationships between them is one that remains to the ontology engineer or to the developer. when dealing with models that are related to multimodal communication it makes sense to use generic multimodal ontologies. it also makes sense to use a generic ontology whenever dealing with uncertainty as suggested by [8] [28]. the agents of tomorrow will be built taking into account recent findings like the requirementsdriven self-reconfiguration [6], multi-party, multi-issue, multi-strategy negotiation [35], natural language [18], and controlled natural language [32]. if we are to follow berners-lee vision from [1] we absolutely need to integrate such findings into our work. in fact according to [18] ontologies are the "common ground for virtual humans". their architecture suggests using multimodal communication, but this is not clearly stated in the article since the ontology is not multimodal. if we look at [6] and [35] we can envision agents that dynamically change their strategies according to the environment and the context of conversations. this requires designing flexible ontologies, another reason to make them generic. the agents must use ontologies if they are to understand something from this world. they also need to share them and commit to them if we want them to be able to talk between them. the multimodal ontology helps in some of the phases of multimodal communication: fusion and integration (getting the input from different channels), natural language processing, disambiguation, error reduction and fission (preparing the output). when designing a multimodal ontology one must also take into account the problems related to designing multimodal systems as described in [25], and also the medium in which these agents will evolve because an agent that needs to evolve in the urban computing environment [34] will have different needs than an agent that just surfs the web. the focus of research is usually on multimodal fusion, but a recent survey [9] shows that the interest in multimedia fission is increasing. designing a multimodal ontology thus requires taking into account all these findings because the agent must be able to give us a response not only to understand our requirements. probably one of the big challenges ahead is to annotate the multimodal content in real-time. this is particularly hard to do for video content, but not impossible, as [27] suggests. m3o (multimedia metadata ontology) allows us to annotate the multimedia content from a page to retrieve it easier. if such ontologies will be improved then the road to the visions from [1] will be shorter. 4 related work the current state of the art in multimodal hci is presented in [7] and [20]. one of the conclusions from [7] leaves further space for improvements: "most researchers process each channel (visual, audio) independently, and multimodal fusion is still in its infancy". the same can be 630 a. braşoveanu, a. manolescu, m.n. spînu rendered as true for the multimodal ontologies too. since [7] is more recent we will use it as a basis for further investigation in this field. since there are only few interesting articles related to multimodal ontologies every year, we have selected a few of them to be used as basis for future research. when searching for definitions related to ontologies and trends in the field of ontology development /matching some of the best research groups in the world are the ones from trento (loa and university of trento), and koblenz-landau. many of the articles cited in this paper come from some of the members of the trento group: [6], [10], [16], [17], [30]. these are related to definitions of ontology, ontology matching, and modelling with ontologies. we have also used articles from the koblenz-landau group: [6], [26], [27] related to definitions, automatic generations of ontology apis and m3o. one interesting idea is that of multimodal context-aware interaction presented by cearreta and his team in [5]. if we have to model emotions there might be no other solution than to use multimodal ontologies combined with special reasoners. another article related to our subject is [29]. their approach of using different ontologies for text and images and then use ontology matching can definitely be improved on the long term. they clearly state that for the moment multimodal ontology do not offer fast communication, but that in time speed might be improved. also [24], [32] and [33] study the relationships between natural language processing (nlp) and sw. the work of these research groups must be studied. one of them [32] is from southampton, one of the workplaces of timothy berners-lee. when it comes to generic ontologies and tools for working with ontologies, one of the best research groups that needs to be followed is stanford’s [11], [28]. their work on biomedicine ontologies and protégé is fundamental. 5 conclusions and future work the sw tools are now an important part of the it industry, the main clients coming from the fields of biomedicine, aeronautics, automotive, government and local administrations, and media. this sudden interest might be related to the success of social media [14], [21] and means that developers are starting to tap into the potential promises of the field. even so there is a lot of work to be done regarding multimodal ontologies. the reason is one that was mentioned several times during this paper: the task of designing such ontologies is still difficult. as we do not have yet universal methods for ontology matching we do not have a clear methodology of designing multimodal ontologies (regardless of the fact that they are generic or not). the main advantages of using generic multimodal ontologies should be better understood now: they offer us a modality to design the process of communication with agents as close to our way of thinking as possible and also play a very important role in several phases of the multimodal communication (multimodal fusion and integration, disambiguation, nlp, error reduction, multimodal fission, etc.). the main disadvantage will probably be efficiency for the next years, but given the exploratory nature of the research this is normal. the future work of our group will consider implementing new mechanisms for linking the generic multimodal ontologies and affective interfaces with recent research in semantic web and hci in a 3 years interval (during the phd studies of the first author). the objectives are to be fulfilled involving european teams of researchers interested in this kind of projects. acknowledgements this work was partially supported by the 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[36] m. uschold. ontology-driven information systems: past, present and future. in proceedings of the 5thinternational conference on formal ontology in information systems (fois2008), saarbrücken, germany, (oct 31st nov 3rd), 2008. international journal of computers, communications & control vol. ii (2007), no. 1, pp. 39-47 virtual communities and their importance for informal learning antonios andreatos abstract: this paper deals with the concept of informal learning in virtual communities on the internet. initially we discuss the need for continuing education and its relation with informal learning. virtual communities are next defined and then compared to real communities. case studies are employed, focused on some specific kinds of virtual communities. we examine how they operate, how their members interact, what values they share and what kind of knowledge they gather. the learning process within virtual communities is then examined. we look at the kind of information and knowledge available in some particular virtual communities, and comment on its organisation. next, the learning process of virtual communities is compared to that of open universities. finally, we claim that the participation in virtual communities is not only a form of continuing education but also a contribution towards the multiliteracies needed for working as well as living in the 21st century. keywords: virtual communities, informal learning, multiliteracies. 1 introduction 1.1 defining informal learning learning is a natural, spontaneous and lifelong process of human nature. education, on the other hand, is a formal, structured, organised process with specific goals. the terms ‘learning’ and ‘education’ are often confused, because education is based on the learning process [1]. learning may be formal, non-formal or informal [2]. 1. formal learning (what we usually call education) is offered by elementary schools, high schools, colleges and universities; it is based on the teacher-student model. 2. non-formal learning is still organised learning but outside the formal learning system; it is offered by official organisations such as governmental services, youth organisations, training services, scientific unions, enterprises, voluntary and non-profit organisations, etc. 3. informal learning on the other hand is not organised nor organized but casual; even travelling or watching tv may lead to informal learning [1]. it is what we learn from everyday life [2]. 1.2 continuing education and informal learning social changes and the evolution of human knowledge in the digital era are so fast, that make further education imperative for many professionals. like regular education, continuing education may be formal, non-formal or informal. in a recent research among the engineers members of the technical chamber of greece, it was found out that [3]: • the great majority (92,5 %) believes that continuing professional education is necessary for finding a (good) job. a percentage of 50,6 % believes that this education should take place every 3 years, while another 22 % places this time to every 5 years. • a majority of 56,4 % believes that the most important needs for continuing professional education are related to computers and new technologies. copyright c© 2006-2007 by ccc publications selected paper from icvl 2006 40 antonios andreatos • a percentage of 49 % has attended (or attends currently) a professional continuing education program, while most of those who have not attended such a program (60,1 %) declare as the most important reason the lack of time due to work overload. from the above we conclude that professional continuing education program is necessary for many professionals. according to the institute for research on learning, located in menlo park (2000), at least 80 % of the professional knowledge, skills and practices needed for many jobs is informal [4]. since a lot of professionals lack the time needed for attending a non-formal professional continuing education program, they have to learn new things and acquire new skills informally. since informal education is so important, it is worth to be further examined. the most important characteristics of informal learning are [1], [4]: • it does not take place in special educational establishments standing out from normal life and professional practice; • it has no curriculum and is not professionally organized; it rather stems accidentally, sporadically, in association with certain occasions, from the changing practical requirements; • it is not planned pedagogically, nor systematically organised in subjects; • it is not qualification-oriented, nor officially recognised; • it is not formally organised and financed by institutions; • it is rather practical than theoretical; • it is rather unconscious, incidental problem-related and therefore, well-focused; • it is not instructed by a teacher or a course designer but rather self-directed; • it is closely related to professional practice; • it is a tool for living and survival. the ability of informal continuous self-education and training is a vital skill for today’s professionals. knowledge is outdated fast, so a professional has to continually update his/her experiences and knowledge profile, if they want to be competitive. in this paper we are interested in studying informal learning, in relation to virtual communities (vcs). it is a process which lies between the non-formal education, defined above, and casual learning. the actual point varies from person to person [5]. it is not casual learning, because it aims at a goal; and the goal has to do with the common interest of the vc members. a user participates in a vc which deals with his/her interest(s), either for professional reasons or as a hobby. 2 defining virtual communities 2.1 the era of new media the internet has dramatically changed the way people get informed, interact and communicate in the 21st century. distribution of information and knowledge is nowadays carried out more and more via the internet [5]. it is characteristic that new terms such as blogs, bots, wikies and podcasting [6], [7] were unknown some years ago, and are still not registered in most (paper) dictionaries. here lies the internet advantage: it is the only medium that instantaneously follows today’s social evolutions. not only that, but it is actually driving the evolution. in 1988 there were about 30.000 blogs available; today, there are more virtual communities and their importance for informal learning 41 than 35 million [8] and it is estimated that every second a new blog is created (www.technorati.com). on the other hand, philip meyer, author of the book “vanishing newspaper: saving journalism in the information age”, estimates that, with current trends, the last newspaper reader will recycle that last newspaper in april 2040 [8]! some types of new media, along with representative examples, are given below [9]: • bbs: the well, genie • blog: livejournal, xanga, myspace, facebook, blogspot, blogger, myciab • webcomic: userfriendly, penny arcade, sluggy freelance • habitat: lucasfilm’s habitat, vzones • im: icq, yahoo! messenger, msn messenger, aim • irc/efnet • mmorpg: everquest, ultima online, world of warcraft, silk road online • moo: lambdamoo • mud/mush: tinymud • p2p: napster, kazaa, gnutella, morpheus • usenet • wiki: wikipedia, wikiwikiweb, pbwiki, wetpaint • www: ebay, geocities, slashdot. 2.2 towards a definition the birth of virtual communities is placed at the early years of the arpanet, back to the seventies; the world wide web was not invented yet. today they are well-established forums, i.e. virtual places for communicating and exchanging information. however, the term virtual community appeared in 1993 and it is attributed to the homonymous book by howard rheingold [10]. the book discusses a variety of information and communication technology (ict) -based communication and social groups. the technologies included usenet, internet relay chat (irc), chat rooms, electronic mailing lists and gaming communities such as multi-user dungeon (mud) and its clones (e.g., mush and moo). rheingold pointed out that belonging to such a group has some potential benefits for the personal psychological health, as well as for the society in general [9]. according to rheingold, virtual communities are formed “when people carry on public discussions long enough, with sufficient human feeling, to form webs of personal relationships” [10]. the explosive diffusion of the internet in certain countries was also accompanied by the proliferation of virtual communities. the nature of those communities and communications is rather diverse [9]. today, virtual communities or online communities are used by a variety of social groups interacting via the internet. different virtual communities, like real communities, have different levels of interaction and participation among their members. an important characteristic of a community is the interaction among its members. thus, an email distribution list with hundreds of recipients with zero or low interaction among members, may not be called a virtual community. similarly, placing comments or tags to a blog or message board may not constitute a community. the highest degree of interaction is achieved in video gaming communities, where users compete online against other users. like traditional social 42 antonios andreatos groups or clubs, virtual communities often divide themselves into cliques or even separate to form new communities. also, membership turnover rate varies greatly from vc to vc [9]. each community shares its own interests, values, jargon [6], titles, leaders, ways of communicating and exchanging information and knowledge. 2.3 comparison of vcs to real communities there is of course no substitute to interpersonal communication, but it may be limited by distance. on the other hand, in vcs the distance factor is not applicable. the ability to interact with likeminded individuals instantaneously from anywhere on the globe has considerable benefits. perhaps the greatest advantage is that the common interests are guaranteed in vcs, whereas this is not the case in real companies based on proximity. the use of multimedia technologies greatly facilitates long-distance communication today. evolution of technology will eventually bring multimedia (image, video etc) dimensions in digital communication, a fact which will enrich it further. of course, the participation in a vc presupposes some familiarisation with ict and the relevant equipment (pc, internet connection etc). in real-life friendships, age is often a critical factor. usually, one’s friends are around the same age. generation gap constitutes a strong unwritten law in many societies. yet, in virtual communities there is no age barrier. this is very important in many countries -including greecewhere the majority of internet users are young people and higher age groups are minorities [11]. since the personal characteristics of live communication are absent in vcs, user personalities are denoted by other symbols like nickname, personal information (such as email, website, blog, irc number, skype usernames etc), image/ personal mark/ signature, equipment related to the community interests (e.g. car, pc, cameras etc), user’s achievements related to the community interests etc. vcs should be seen as supplementary to the real communities and not as alternatives or substitutes. 3 case studies the examination of some case studies will further clarify the above discussion. 1. scientific union of adult education (of greece, www.adulteduc.gr). the common interest here is professional. the union organises conferences, seminars and meetings all around greece; it also issues an online bimonthly bulletin for briefing and member communication. this also contains information on newly-edited books and scientific journals and the corresponding links on adult learning, information on instructor certification etc. a similar example is ‘the hellenic network of open and distance education’ (www.opennet.gr). these communities have a professional character, are a bit more formal (e.g., no nicknames), have a hierarchy (president and members). they have a continuing education as well as a selfeducation character. 2. hellenic linux club (www.hellug.gr). this club is a greek official and non-profit association of people working with or using or positively predisposed to linux. its aim is the union of such individuals, the communication among them in order to tighten their privities, as well as, the further proliferation of this operating system. means for achieving the above goals are: meetings, problem solving support, translation of documents and articles in greek; improvement of greek language support in linux; development of free software; presence in meetings, conferences and exhibitions; collaboration with peer clubs with common goals; diffusion of know-how; follow-up and intervention whenever the interests of linux are threatened. a similar site is: www.linux.gr. it contains news, documentation, articles, download material, links, guidelines for various linux distributions, guidelines for beginners, indexing and an electronic magazine. linux blogs (for instance: http://linuxhelp.blogspot.com, virtual communities and their importance for informal learning 43 http://www.computerworld.com/blogs/software/os/linux, http://linux-blogger.com, and http://www.suseblog.com) are also available. 3. www.overclockers.com: perhaps what is more admired here is the extra mhz a user can get out of his new pc, or, the exotic water cooler system one has constructed. vcs such as the second and the third one listed above, may be characterised as hobbyist or amateur communities rather than professional. such vcs are more free, more non-formal, more casual. they share different values than the formal ones. nicknames are used instead of real names. a couple of examples (with pseudonyms) are: “john smith aka shroomer in the forums”, “my name is valentino jones, a.k.a cr@zyvj on the net, and friends simply call me vj”. also, former education titles are not so important; the most important virtues are: expertise, participation and voluntarism to help other users. 4 learning in virtual communities 4.1 organisation of knowledge in vcs in a formal distance learning environment the educational material is well organised: (i) the courses are structured in a prerequisite order, from the fundamental to the most complicated. (ii) the educational material is composed of learning objects (los) [2]. many los form a course and many courses form a curriculum. among the various courses there is no (or minimal) overlap. (iii) the educational material is usually managed by a learning management system (lms) [2], [13]. let us assume that the information / knowledge resources of a vc are the contents of its node (such as website or a blog). in this case, the material is rather chaotically organised, with high overlaps, no particular structure, no particular management. homepages link to several sub-pages and other related nodes. the various similar vc nodes (e.g., linux communities) are loosely connected. the ability to find specific information requires sometimes specific skills of searching and data mining. however, there is still a hidden hierarchy: the first level is the knowledge present in the node, which may be downloaded; the second level is the knowledge and experience of the community members, which is not seen. 4.2 looking for information in vcs the most common ways for getting access to specific information from vcs are: 1. download articles from their nodes 2. participate in fora and pose questions 3. read faqs and search for keywords 4. use the site search engine (if available) 5. contact sage membres (‘gurus’, ‘masters’ etc.) directly. however, since the material is not organised, a user may have to search for several minutes in order to find what he/she is looking for. 4.3 comparison to open university practices there exist strong similarities but also differences between the ways learning is achieved on line in virtual communities and open universities (ou) using the web. as an example of an open university we shall consider the hellenic open university (hou) [www.eap.gr]. hou students interact with their 44 antonios andreatos instructor as well as with each other over the internet frequently, in order to ask questions and get answers about the educational material and particularly the assignments they have to carry out. mostly the interaction is done by emails and forums. the students are all provided with the same books and are supposed to follow a specific syllabus. the students meet regularly five times throughout the course of a year; attendance is not required. in the end of the academic year they also take an exam (live) which is mandatory and counts for 70 % of the final grade. all these practices do not occur in vcs, where learning is informal. but there is a strong similarity in that the students study and learn on their own. this practice is fundamental for the institution and operation of all ous worldwide [3]. similarities between ous and vcs are listed in table 1 below. table 2 lists some differences. students / members study on their own they depend a lot on the educational material they help each other gain specific knowledge or skills they may be assessed by knowledge or skills (titles or grades or expertise) they may be anywhere in the world they are adults and therefore, self-motivated they are moderated by an instructor or a list moderator or owner of the site table 1: similarities between ous and vcs open universities virtual communities students are directed by the instructors members of vcs are self-directed students are provided with the same education material members study different material and practice a lot students are supposed to follow a specific syllabus there is no syllabus students have seen each other at least once real life interaction may never take place focus primarily on knowledge focus primarily on expertise provide a title do not provide a title knowledge is more theoretical knowledge is more practical and empirical table 2: differences between ous and vcs 4.4 professionals and continuing education today the internet is used by millions of people as an interminable pool of knowledge, as a huge online encyclopaedia. a user seeking particular information on a subject, may find a lot of it, not only in online encyclopaedias or dictionaries but also in specialised vc nodes. for this reason, the ability of informal self-education and training is a vital skill for today’s professionals. based on personal experience, we believe that information and knowledge gathered in some specific vc-related nodes, is superior to that available in traditional, even academic, sources such as books, electronic or conventional, in terms of practicality and in-depth and up-to-date coverage. since vcs update continually the (practical) skills of their members, we can claim that they offer some kind of informal education [15]. the user groups of these professional sites may be regarded as loose professional communities with no or limited interaction amongst users. many profit and non-profit organisations offer (often for free) seminars via the web (also knows as ‘webinars’) to their customers or community members. the main purpose of most such webinars is to demonstrate the use of the companies’ products (such as software tools, integrated circuits, e-learning platforms, etc.). let’s look at some examples. virtual communities and their importance for informal learning 45 the mathworks inc. offers free, live and interactive monthly webinars concerning the use of matlab toolboxes. saba, an e-learning systems company, also offers live online webinars (www.saba.com). national semiconductor (www.national.com) offers online seminars for design engineers. microsoft maintains a large ‘knowledge base’ with articles for computer and network professionals. teacher unions and communities do not lag behind. let us examine two case studies from the greek national internet domain: • ‘eeep’, the greek primary teachers association for the valorisation of ict in education, a nonprofit open community. they issue a journal, organize conferences and maintain a vivacious site (eeep.gr). users can read news and download their electronic magazine (sometimes called a ‘webzine’). • the aforementioned scientific union of adult education (of greece) [www.adulteduc.gr] is another example. they organise conferences, seminars and meetings all around greece; it also issues an online bimonthly bulletin for briefing and member communication. 4.5 vcs and ‘multiliteracies’ in a pioneer as well as important article published in 1996, the ‘new london group’ argues that today’s world is characterised by an increasing cultural and linguistic diversity and a variety of new communication ways and channels, due to the evolution of ict. according to the authors, traditional language-based pedagogical approaches do not provide adequate skills for working and living in general in today’s multi-cultural societies, and that, a new approach to literacy pedagogy, which they have called ‘multiliteracies’, is needed instead. multiliteracies are based on the assumption that the multiple linguistic and cultural differences in our society is essential to the working and private lives of students. the use of multiliteracies approaches to pedagogy will enable students to achieve the following two goals: a) create access to the evolving language of work and community; and b) foster the critical engagement necessary for them to design their social futures and succeed through satisfying employment [16], [17]. 5 discussion and conclusion in this paper we have examined virtual communities (vcs); more specifically, we have dealt with three types of vcs: video game vcs, professional vcs and amateur vcs. we have identified some differences among them, as well as, some similarities and differences between vcs and real-life communities. next we have examined informal learning in vcs and we have compared the organisation of knowledge in vcs to that of distance learning courses. learning gained by the participation in vcs was briefly compared to the methods followed by open universities. furthermore, it was claimed that new ‘digital’ skills are needed by 21st-century citizens. from the discussion above we may conclude that for a professional, participation in professional vcs may be akin to continuing education, whereas for a non-professional, it may merely serve as entertainment. of course, professionals may also benefit from non-professional vcs. in any case, however, free-will participation in vcs is very important, because it fosters the necessary ‘digital behaviour’ and cultivates ‘digital communication’ skills. based on personal experience, we believe that information and knowledge gathered in some communityrelated nodes concerning practical subjects, is superior to that available in traditional, even academic, sources such as books, electronic or conventional. nowadays, where a multiliteracy education is needed for living and working in the digital era, digital communication skills are necessary. ‘digital behaviour’ and ‘digital communication’ rules and ethics are being developed; therefore, all contemporary people should be ‘digitally literate’, in order to be able to survive in a changing and competitive environment. real communication skills are not enough; ‘digital 46 antonios andreatos communication’ skills are also needed. the ability to use the internet and the new media is vital for surviving in the 21st century. vcs will continue to play an important role in 21st-century society, due to social evolution, the globalisation of economy and knowledge, competition and new media technologies. 6 acknowledgement author wishes to thank mr. m. vidalis for reviewing the manuscript. references [1] a. rogers, teaching adults, open university press. 1996. [2] http://en.wikipedia.org/wiki/learning, retrieved on dec. 11, 2006. [3] continuous further education is necessary changes in syllabus are imperative, news bulletin of the technical chamber of greece, no. 2423, jan. 15, pp. 6-8. 2007. [4] http://en.wikipedia.org/wiki/informal_learning, retrieved on dec. 11, 2006. [5] a. vardamaskou and p. antoniou, informal learning: evaluation of an internet-based physical activity educational program for adults, proceedings of the 3rd international conference on open and distance learning, patra, greece, vol. a, pp. 405-417 (in greek), 2005. [6] s. ververidis, the glossary of new media, kathimerini (newspaper) special edition: new media: the alternative choice, 28, pp. 88-89 (in greek). 2006. [7] d. doulgeridis, electronic diaries in common view, tachydromos magazine, no. 266, pp. 44-49 (in greek). (2005) [8] c. angelopoulos, blogs change the landscape of communication, special edition of kathimerini (newspaper): new media: the alternative choice, 28, pp. 78-79 (in greek), 2006. [9] http://en.wikipedia.org/wiki/virtual_communities, retrieved on may 15, 2006. [10] h. rheingold, the virtual community: homesteading on the electronic frontier, harper perennial, san francisco, 1993; also available on line at : www.rheingold.com/vc/book, retrieved on may 29, 2006. [11] vprc national research for new technologies and information society, available online at: http://www.vprc.gr/2/1232/21_gr.html, in greek, 2005, retrieved on sept. 29, 2006 [12] f. pantano-rokou, educational design for e-learning: models, meaning and impact on learning, open education, 1, pp. 45-68 (in greek), 2005. [13] g. dimauro et al., an lms to support e-learning activities in the university environment, wseas transactions on advances in engineering education, vol. 3(5), pp. 367-374. 2006. [14] d. vergidis, a. lionarakis, a. lykourgiotis, b. makrakis and ch.matralis, open and distance learning, vol. 1, institution and operation, hellenic open university, patra (in greek; title of book translated by paper author), 1998. virtual communities and their importance for informal learning 47 [15] a. margetousaki and p. michaelides, communities of practice as a place of learning and development, proceedings of the 3rd pan-hellenic conference on the didactics of information science, corinth (in greek), oct. 2005. [16] the new london group, a pedagogy of multiliteracies: designing social futures, harvard educational review, vol.66, no.1, pp. 60-92, 1996. [17] j. salpeter, 21st century skills: will our students be prepared?, available online at: www.techlearning.com/story/showarticle.jhtml?articleid=15202090, 2003, retrieved on march 22, 2006. antonios andreatos dept. of aeronautical sciences div. of computer engineering and informatics hellenic air force academy dekeleia, attica, tga-1010 greece e-mail: andreatos@hafa.gr received: november 17, 2006 editor’s note about the author: antonios andreatos is a professor at the computer engineering division of the hellenic air force academy. he was born in 1960 in athens, greece. he received the diploma in electrical engineering from the university of patras in 1983, the m.s. degree from the university of massachusetts (amherst) in 1985 and the ph.d. from the national technical university of athens (ntua) in 1989. he was a research scholar at the european joint research centre of ispra, italy. he has published various papers in journals and conferences; he has also authored a book on the design of microcomputer systems in 2001. he has also taught at the hellenic open university. his main technical interests lie in the areas of microprocessors, computer architecture, computer networks, e-learning and adult education. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 1, pp. 106-124 gaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system j. p. wachs juan p. wachs purdue university department: school of industrial engineering address: 315 n. grant st., w. lafayette, in 47907-2023 usa e-mail: jpwachs@purdue.edu abstract: this paper describes the design of intelligent, collaborative operating rooms based on highly intuitive, natural and multimodal interaction. intelligent operating rooms minimize surgeon’s focus shifts by minimizing both the focus spatial offset (distance moved by surgeon’s head or gaze to the new target) and the movement spatial offset (distance surgeon covers physically). these spatio-temporal measures have an impact on the surgeon’s performance in the operating room. i describe how machine vision techniques are used to extract spatio-temporal measures and to interact with the system, and how computer graphics techniques can be used to display visual medical information effectively and rapidly. design considerations are discussed and examples showing the feasibility of the different approaches are presented. keywords: posture recognition, behavior analysis, intelligent rooms. 1 introduction intelligent systems can assist in improving safety and performance during surgery in many ways. intelligent operating rooms assist the surgeon in timeand safety-critical situations. multimodal cues from humans and the environment can be used to extract behavior, which in turn can be used to derive contextual and event information. some of these behaviors include, for example, whether there are distractions, whether the subject is busy with certain tasks or how frequently the doctor switches his focus of attention. 1.1 previous research hansen [1] studied focus shift in the operation room and he used the terms focus spatial offset and movement spatial offset. focus spatial offset is the change in the doctor’s gaze as a result of focusing in a new spot. conversely, movement spatial offset results from the doctor’s change in position. it is possible to perform basic behavior analysis of body language in order to determine which type of offset of attention occurs in any point in time. 1.2 hypothesis with behavioral information the following questions could be answered: 1. how can surgeon’s behavioral cues be leveraged to improve the operating room (or) layout dynamically? 2. how can innovative channels of interactions in this enhanced layout minimize unnecessary focus shifts? copyright c© 2006-2010 by ccc publications gaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system 107 3. how can the doctor and the operating room collaborate to display time sensitive visual information effectively? our hypothesis is that a reconfigurable smart operating room can organize the displayed information in such a way that unnecessary focus shifts are minimized. this has an impact on the surgery performance time. moreover, with the adoption of natural modalities of interaction, unnecessary instructions to the nurses will be eliminated. this also has an impact on the task completion time. 1.3 previous works previous research has demonstrated the negative effects of attention shift on the performance of cognitive and motor tasks [2], [3]. a study conducted by godell et al., [4] looked at virtual reality laparoscopic surgical simulation tasks designed to replicate the levels of cognitive and motor demands in surgical procedures, and found that there was a 30-40% increase in task completion time in the distracted vs. undistracted condition. recent advances have been proposed to counteract unnecessary distracting behavior through the integration of doctor behavior analysis and context awareness into the operating room [5], [6]. the analysis of body language is critical in determining when the surgeon is operating or analyzing medical imagery or just chatting with the staff. providing the intelligent operating room with the ability to understand the doctor’s behavior and the context of the situation allows the projection of patient imagery in the area that allows the least shift of focus and the most optimal interaction space. thus, focus shift is reduced and task performance is improved. for example, when a surgeon interacts with a particular display to obtain information which must be cross checked with magnetic resonance imaging (mri) images obtained previously, in the current operating room’s layout, she will need to move to the main control (see fig 1). this causes focus and spatial shifting and hence distraction and unintended delay. an intelligent operating room can use the surgeon’s body language to determine that she is currently interacting with a control and can then determine the best orientation and position to project the patient mri imagery. a doctor’s assistance system mitigates shift of focus and distractions because it senses the center of the surgeon’s attention through context and uses this knowledge to display relevant patient imagery at this attention point. the system also provides a sterile interface for interaction with the visual information in the spot where the surgeon is already standing, thereby avoiding spatial shift. in this paper, torso and head orientation can be used to find the focus of attention and the context of the event, and how gestures and voice can be combined to interact with medical imagery is discussed. this concept is depicted in figure 1. also, by extracting the dynamics of head pose (eye gaze) and hand gestures, it may be possible to identify those attention shifts which are associated with premeditated or task-oriented attention shifts. for example, a hand gesture oriented to the display combined with gaze directed towards it may indicate that the attention shift was goal-oriented. if the surgeon is bored or distracted he would likely not point to the display. intelligent environments could use this semantic information to assist doctors and improve the safety and comfort of their interactions and surroundings. examples from two different real-time working systems to support collaborative work: real-time pose, posture and head orientation recognition, and a sterile hand gesture recognition system for displaying medical imagery. the remainder of the paper is organized as follows. section two covers previous work, and a description of the methodology proposed for displaying imagery and interacting with the intelligent room are presented in section three. a concluding discussion appears in section four. 108 j. p. wachs 2 related work a large amount of research has recently been conducted on intelligent rooms based on the detection of human behaviors and supporting collaborative work with the room. many of these examples use machine vision techniques to extract important clues, such as head-gaze orientation and posture patterns. they also use computer graphics techniques to display the visual information with high detail and accuracy using special displays or projectors. to interact with the room, voice, body and hand gestures are the prominent channels of natural communication.below, an overview of related research pertaining to the health-care scenario is presented. 2.1 intelligent rooms traditional intelligent rooms are multi-modal environments where audible and visible clues are captured and analyzed exclusively from people’s actions. these systems typically run in real-time, and utilize embedded sensors small enough as to be unnoticeable by people. intelligent rooms in collaborative environments, however, allow human-machine interaction. recent research has supported the incorporation of sensors that look inside a living or working space (the room) to observe inhabitant behavior and potentially infer his intent. one of the pioneering works is the digitaldesk [7], [8] where a computer display is projected onto the desk, and video cameras pointed down at the desk feed an image-processing system that can sense what the user is doing. this application allows users to construct a mixed paper and electronic drawing device. not surprisingly, some of the ideas first presented in the digitaldesk system can now be found in more sophisticated applications. one example is the multi-touch screen interaction on the current microsoft surface [9] device. current approaches used in intelligent rooms combine robotics and vision technology with speech understanding algorithms and distributed agent-based architectures to provide information services in real-time for users engaged in everyday activities [10]. real-time three-dimensional people-tracking by a network of video cameras using a rectilinear video array tracker and an omnidirectional video array tracker (o-vat) in order to develop a real-time system for tracking the head and face of a person in an intelligent room is described in [11]. in the globeall system, [12], a modular four-component prototype for a vision-based intelligent room is developed. the prototype consists of a video input from an electronic pan-tilt-zoom camera array, which performs tracking and interpretation. an intelligent room for welfare applications called the "percept-room" was developed in [13]. the system is capable of detecting human faces and classifying hand postures in japanese sign language in color images. in the human interaction loop (chil) smart room project [14] perceptual and situation-modeling components are used to provide context-aware services to the users of the smart room. anthropocentric interfaces based on intuitive and natural aspects is undergoing which is expected to improve the usability of current systems based on multimodal interaction [15]. the intelligent room reverses the traditional model of teaching the user to interact with computers. instead the intelligent room supports natural interaction, enabling the user to feel as if the computer weren’t there. the smart room research is particularly encouraging: it enabled people to use home appliances and perform every-day tasks that would be more awkward to do in other ways. our goal is to bring these achievements into the operating room in the context of a collaborative support system. the steps involved in this transition are described in the next sections. 2.2 smart displays and projectors smart projectors allow such applications as augmented reality and immersive displays for three dimensional video-conferencing, real-time annotation and simulation and complex and high volume imagery display. increasing computing power, smaller projectors and fast graphic cards make projectorgaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system 109 based interaction an attractive feature for intelligent rooms. for example, a patient’s basic information and condition could be displayed in his field of view, or an mri image projected over a patient’s head could help a surgeon determine more accurately a brain tumor’s location. projection in side walls is the method adopted in this work. wide screen projection using multiple projectors is a challenging problem since it requires undistorting the image, projector calibration and a setting a position and orientation for the projectors, shadow elimination, and because the image surface is of unknown geometry. if the surface to be projected over is uneven the problem becomes even more complex. [16] presents an image projection method that allows arbitrary observer-projector-screen geometries, relying on a robust structured light approach which can be easily adapted to multi-projector configurations. an interactive projector automatic calibration process for multi-projector-camera environments is demonstrated in [17]. this method does not require calibration rigs nor does it assume fixed display positions; however it requires the cameras to be to be pre-calibrated. this is done through detecting self-identifying tags projected in freely moving targets. overlapping tiled projected images are obtained from multiple steerable projectors in [18]. the projectors operate simultaneously and misalignments on the projected image are corrected through a preliminary process. in addition the authors present a virtual synchronization method based on the dynamic memory architecture for the projectors. a high resolution video display system using multiple projectors to build a surround video display system is presented in [19]. bhasker et al. [20] suggested a registration technique allowing severely distorted projectors to be used which opens up the possibility of mounting inexpensive lenses on projectors. this technique can handle imperfect and uncalibrated devices in planar multi-projector displays. more specifically, it can correct for geometrically imperfect projectors and photometrically uncalibrated cameras. approaches to solve occlusion and the resulting blinding light are reported in [21] related to front projection. an algorithm based on a distributed calibration framework for multi-projector displays where the projectors cooperatively re-estimate the poses of all projectors during actual display use is discussed in [22]. 2.3 hand gesture recognition in healthcare natural hand poses and gestures are used to control, teach, treat and manipulate systems in diverse areas of the healthcare environment. gestures can be used to control the distribution of resources in a hospital, to interact with medical instrumentation, visualization displays, to help handicapped users as an alternative interface to computers and as part of rehabilitation therapy. when the hands are attached to sensors to provide haptic (tactile and force) feedback, a surgeon’s gestures can be used to perform long distance surgeries with the help of telerobots. additional systems use voice, gaze and gestures together, profiting from the combined advantages of these modalities to convey richer and redundant information. some gesture concepts have been exploited for improving medical procedures and systems. the "come as you are" requirement is addressed in face mouse [23], where a surgeon can control the motion of the laparoscope by simply making the appropriate face gesture, without hand or foot switches or voice input. current research to incorporate hand gestures into doctor-computer interfaces has appeared in graetzel et al.[24]. they developed a computer vision system that enables surgeons to perform standard mouse functions (pointer movement and button presses) with hand gestures while addressing the "intuitiveness" requirement. a hand gesture tracking device for browsing mri images in the operating room (or), called "gestix" was developed in [25] and it was validated in a real brain biopsy (see figure 1). "gestix" addressed both the "come as you are" and "intuitiveness" requirements by providing a natural effective interface. the "comfort" requirement is fulfilled in "wearit@work" [26], a rfid reader is used to identify the patient and to interact with the hospital information system (his) using gestures to fill out exams and prescriptions. this project ensures sterility. however, since this is an encumbered interface, the "come as you are" requirement is violated. from the patient side, the most prominent requirements in a hand gesture interface system are "user 110 j. p. wachs figure 1: a surgeon using gestix to browse medical images adaptability and feedback" and "come as you are" because impaired users may be limited in the classes of gestures that they can learn and the devices that they can wear. in this context, wheelchairs as mobility aids have been enhanced with robotic/intelligent vehicles able to recognize the user’s commands indicated by hand gestures [27]. the gesture pendant [28] is a wearable gesture recognition system that can be used to control home devices and provides additional functionality as a medical diagnostic tool. the "user adaptability and feedback" requirement is addressed in staying alive[29], which is a virtual reality imagery and relaxation tool which allows cancer patients to navigate through a virtual scene. a haptic glove attached to the hand was used to rehabilitate post-stroke patients in the chronic phase in [30]. these reviewed systems indicate that hand gesture interfaces in medical domains still represent a novel area of research and that few systems are currently in use in healthcare environments. nevertheless, current works highlight the potential of gestures as a natural modality for assisting in the advancement of medical research and surgery, and indicate the need for additional research and evaluation procedures so that such systems can be widely adopted. 3 methods in this section, the philosophy behind the design of our system is presented, and how to develop an "intelligent" operating room based on off-the shelf hardware (a servo controlled projector, four pan-tiltzoom cameras, four microphones, and a controlled connected to a dedicated computer) is described. the system consists of four sub-systems: (a) steerable projectors, (b) focus of attention determination, (c) hand gesture interaction and (d) speech interaction. first the surgeon’s posture, pose and orientation are tracked and detected. this information is sent to the steerable projector, which controls a servo-mirror where the projector ray is deflected. the head orientation is used to determine the closest wall in front of the surgeon where the medical imaging can be reflected. the projection system is activated only when the surgeon is standing straight and staring at a wall, and also evokes a command by saying "computerproject-now". once the image is projected, the surgeon can interact with the images using hand gestures gaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system 111 and voice commands. the system is turned off when the surgeon performs a specific gesture command. these functionalities are described in the schema presented in figure 2. figure 2: architecture for the intelligent operating room in the following sub-sections the subsystems are described in greater detail. most emphasis is given to focus of attention and hand gesture interaction because those are the most difficult challenges of this environment as showed in previous research. steerable projectors and speech recognition are the focus of future work. 3.1 steerable projectors for non-uniform projection surfaces a steerable projector allows us to move the projected image in real time such that the projected image is in front of the surgeon, perpendicular to his torso. the implementation of the steerable projector proposed here is similar to [31] where a standard digital projector is combined with a mirror attached to a pan-tilt unit. this approach is more compact and much cheaper than standard steerable projectors. this model includes the internal parameters of the projector, the projector pose, the pose of the pantilt mirror system, and the internal details of the mirror system. by changing the pan and tilt angles programmatically, the mirror rotates about the pan and tilt axes respectively and the projected image changes in turn. the transformation between the projector pose and the mirror pose can be calculated through a calibration process. since the pan-tilt angles will have an impact on the registration process between the projected image and the camera model of the 3d view, the use of a fixed set of projector poses is suggested, one for each wall, to reflect the surgeon’s possible orientations: upper abdominal procedures are usually performed with the surgeon standing in the midline; central and pelvic operations are generally performed with the surgeon on the lateral side of the patient [32]. therefore, a set of four reflected projector poses is calculated, one for each mirror pose. each pair of θ and ϕ values results in a different mirror pose and hence a different set of projected corner points on the surface. the rigid transform from the reflected projector position for each of those mirror poses is 112 j. p. wachs figure 3: concept image from the intelligent operating room calculated using the procedure detailed in [33]. this requires the use of a camera to capture the projected image on the display surface, and assumes that the display surface is uneven. since in our case the display surface could potentially be any wall around the surgeon, a pan/tilt/zoom camera dedicated to the projector is allocated, in addition to those used to capture the surgeon postures. let the display surface be represented in a 3d mesh. i try to find the transform that relates a given 2d pixel in the projector image plane (z), to a 3d pixel in the display surface (k), given that the place of the camera is so that the same point (v) appears in the camera image as z’. see figure 4. figure 4: . camera and projector 3d view geometry representation each point x = (x, y, w)t in the uneven display surface is a result of a ray originated in the center of the projector op traversing the projector plane in point z = (ûp, v̂p), which in turn appears on the image captured by the pan/tilt camera as point z ′ = (c, v̂c). the goal is to find the static parameter f for every point relating the projector plane and the display surface. knowing the internal parameters of the projector, and the internal parameters of the camera and the homogeneity matrix, [33] show that each gaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system 113 sample point k in the 3d mesh follows can be found using (1):   x y z   = p−   −p̂ + f   ûp p̂p      (1) where p is the rotation 3x3 matrix, p̂ is the translation 3x1 vector, and f is the parametric scalar value. it is possible to show that the parameter f can be estimated using traditional correspondence approaches, or using predictive methods such as particle filtering or kalman filters. in [33] a bank of lalman-filters were used to find the correspondences between the projector pixel z and the projected point k, one filter for each point z. to reduce the complexity of the process, in every frame only one point z is selected for matching. once a point is selected using a certain method (pseudo-randomly, distance based, feature detector), a patch around the neighboring pixels is selected. this patch is cross-correlated with the predicted location on the camera’s image, and close to the epipolar line. once the new coordinate is found on the camera’s image, the parameters for the 3d mesh are updated. this process corrects the projected image over the uneven surface so that curves and discontinuities are not perceived by the viewer, however errors (maximum 6.78mm and mean 2.41mm) similar to [33] are expected. sensitivity studies based on these errors are the focus of future work. 3.2 focus of attention determination in this section, it is shown that by extracting the surgeon’s head orientation (which will be used as a proxy for eye gaze) and torso posture and orientation (which are indicators of the action being performed), it may be possible to identify focus spatial offset and movement spatial offset which are associated with attention shifts. in the first case, gaze orientation helps us to know where to project the medical imagery (which wall to use as the display surface). in the second case, torso position (standing straight or bent) indicates the surgeon’s possible intention: interaction with the patient, interaction with the medical imaging display or other activity. our method to extract head and torso position, posture and orientation is to look at each body configuration as a different class: for example, the following notation describes the current state of a surgeon: s1 = standing, torso 90 degrees, facing 0 degrees. thus, in this section, a multi-class classifier based on parts-based models is described to find each of these configurations. the approach is briefly described for object detection using single and multi-class detectors, in the context of us marine detection, as a case study only, but it is clear that the same approach can be applied to any person. first, it is described the feature extraction process from patches (or parts) and then the basic and shared classifiers are discussed. 3.2.1 dictionary creation initially, a dictionary is created from square sub-region patches extracted from a set of images per class, similar to [34]-these are also called "features". each image is convolved with a filter from a bank of filters, grayscale normalized and re-scaled to a standard scale of 128x48 for the standing and 64x48 for marines kneeling. then patches are selected in x, y locations found using an interest point detector. in those locations patches are extracted from all the filtered images. each patch is associated with the place from where it was extracted, relative to the center of the object. this location information is stored in two vectors containing the x, y offset distances respectively lx, ly, after applying a blurred delta function to them. hence, each entry i in the dictionary has the form vi={filter, patch, lx, ly, image no.}. if 8 images per class are used to create the dictionary, 20 patches are extracted per image, a bank of 4 filters is used, and by classifying into 8 classes, a total of 640 entries is obtained. the procedure is shown in figure 5. 3.2.2 the feature vectors the training set is created from a sample set of images excluding those used for the dictionary creation. for each of the eight (classes) objects i found all the images that include that type of object. in each image, feature vectors are obtained using the following method: 114 j. p. wachs figure 5: . . dictionary entries: patches selected randomly (on the left image) are convolved with a bank of filters. the position of the patches is represented by the location matrix (right). since the red patch is at almost the same horizontal position and at the top, relative to the center, the position matrix has a bright spot 1. scale all the images in the training set so the object of interest is bounded by a rectangle of size 128x48 and 64x48 (region of interest, roi) for standing and kneeling respectively, and the images are not larger than 200x200. 2. for each image j normalized in scale, each entry i of the dictionary is applied to it: this means that this image is convolved with the filter in entry i, and convolved again with a gaussian to smooth the response. next, it is cross-correlated with the patch in entry i, yielding a strong response where this patch appears in the filtered image. finally, the 1d filters lx and ly are applied to the cross-correlated image, effectively "voting" for the object center. this is summarized in (2): vi(x, y) = [(i ∗ fi)⊗pi]∗ltx ly (2) where * is the convolution operator, ⊗ is the normalized cross correlation operator, vi(x, y) is the feature vector entry i, f is a filter, p is a patch, and lx and ly are the x,y location vectors with respect to the center of the image respectively. each training feature vector is coupled with a class label (1 to 8) and -1 for negative samples. for a sample set of 25 images per class, 4000 negative and 200 positive samples are obtained, with 640 features, see figure 6. 3.2.3 sharing the features effectively in this section i briefly describe the joint boosting algorithm used for multi-class multi-view object detection. for a more detailed discussion, refer to [35]. a boosting algorithm is an additive model where weak learners are sequentially added to form a strong classifier. for the multiclass case, the strong learner is defined as: h(v, c) = m∑ m= hm(v, c) (3) where v is the input feature vector, m is the number of boosting iterations, c is a specific class and gaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system 115 figure 6: . positive and negative vector set creation using the dictionary entries and sampling the center out of the silhouette points. each sampled point, is a vector, where an entry j in the vector represents the number of votes assigned by patch pi h(v,c)=log p(zc=1|v)/p(zc = -1|v) is the logistic function where z is the membership label (ą1). when the expectation is replaced by an average over the training data, the cost function can be written as: jwse = c∑ c= n∑ i= wci (z c i − hm(vi, c))  (4) where n is the number of training vectors,wci are the weights for sample i and for class c,z c i is the membership label for sample i for class c (±1). the weak shared learner, also called, regression "stump" is defined for the multiclass in (5): hm(v, c) =    as if v f i > θ andc ∈ s(n) bs if v f i ≤ θ andc ∈ s(n) kcs if c 6∈ s(n) (5) where v f is the component f th from the vector v, θ is a threshold, δ is the indicator function, as and bs are regression parameters. s(n) is a subset of the classes labels. each round of boosting consists of selecting the shared "stump" and the shared feature f that minimizes (3), from the subset of classes s(n), in the following stated procedure: pick a subset of classes s(n). search all the components f of the feature vector v, for each component, search over all the discrete values of θ and for each couple {f, θ }, find the optimal regression parameters as and bs using (6-8). finally, select {f,θ , as, bs} that minimizes (4). as( f , θ ) = ∑ c∈s(n) ∑ i w c i z c i δ (v f i > θ )∑ c∈s(n) ∑ i w c i δ (v f i > θ ) (6) bs( f , θ ) = ∑ c∈s(n) ∑ i w c i z c i δ (v f i ≤ θ )∑ c∈s(n) ∑ i w c i δ (v f i ≤ θ ) (7) kc = ∑ i w c i z c i∑ i w c i (8) 116 j. p. wachs therefore a shared weak learner is associated with a set of 6 parameters {f, θ , as,bs, kc, sn} of the subset of classes selected. it is more efficient to keep a pointer to the entry in the dictionary from which f was obtained rather than keeping the whole feature vector (figure 7 displays all the entries in the dictionary). this will also provides us with the patch, filter and location vectors entries in the dictionary which will be used for the detection stage. this new weak learner is added to the previous accumulated learner, for each training example: h(vi, c)= h(vi, c)+hm(vi, c) where hm is computed for the optimal subset of classes. the optimal subset of classes is the one that minimize the misclassification error by selecting a feature shared by those classes. finally, the chain of weak learners is stored in the accumulated learner. figure 7: dictionary entries selected by the multiclass adaboost 3.2.4 detection to detect an object of class c in a test image i need to compute the score for every pixel in the image, provided by the strong classifier h(v,c) evaluated in all the pixels. if the score exceeds some threshold the object is detected. in order to calculate h(v,c) i use the following procedure. i find all the shared weak learners that shares class c, and for each sharing weak learner: 1. obtain the 4-tuple {f, θ , as, bs} from the weak learner. since f is associated with an entry in the dictionary, i retrieve the corresponding filter, patch and vectors lx, ly from the dictionary, and apply them to the test image using (1). 2. calculate hm(v)= aδ (v f > θ )+b where vf is the image obtained in the previous step. finally i add up all the weak learners. each weak learner votes for the center of the object sought, and it is expressed by a grayscale image obtained in step 2. the accumulated image will have bright pixels where the weak learners "agreed" about the center of the object in the "voting space". a maximum in the accumulated image indicates the probability to find the object in that location. each strong detector of a different class outputs an accumulated image. thus, it is possible that more than one strong detector will vote for the same (or very close) pixel coordinates. this situation is not rare since some postures are very similar. to solve this conflict, peaks that are closer than a given radius are clustered together, and the resulting class of the detection is the one from the class with the highest maximum. 3.2.4.1 torso and head orientation the maximum voting schema, from the strong detector results, is one class from the four possible gaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system 117 classes. each class is associated with a torso orientation. class 1 to 4 corresponds to 0, 90, 180, 270 degrees of rotation in the azimuth axes of the torso, accordingly. once the class is selected, a bounding box with the size equal to the average size of the dictionary annotated boxes for that class, and which center corresponds to the peak of the maximum votes. a color bounding box with an arrow in the direction of the torso is plotted on the image tested to represent the direction where the torso is rotated towards. for head orientation, which is our proxy for gaze direction (this assumption holds when the object visualized is far enough from the observer, a different multiclass detector was trained around the head of the subject. this additional multi-class classifier was trained to detect the head in four orientations. the torso detection is performed first and determines a limited search region for the head. experiments yielded the top 1/7th of the body detection area with added margins above the top to be a sufficient head search region. should multiple body postures have been detected, a heuristic increases the search region, taking nearby body detections into account. figure 8: . examples of head and torso recognition procedure in different scenarios 3.3 hand gesture interaction a hand gesture interaction system used in the or for doctors/surgeons should follow the following specifications [36]: (1) real time feedback and operation; (2) low fatigue; (3) high intuitiveness; (4) robust to unintended action; (5) robust recognition; (6) easy to use and to learn; (7) unencumbered (no wired attached). following this considerations, our approach is described next. 3.3.1 approach four pan/title/zoom network cameras placed in the vertices of the operating room’s ceiling captures a sequence of images of the hand. the hand is tracked by a tracking module which segments the hand from the background using color and motion information. to clean the main object, morphological image processing operations are used. the location of the hand is represented by the 2d coordinates of its centroid which is sampled continuously resulting in a trajectory. these trajectories are mapped into a set of commands. for example, a flick gesture is the rapid movement of the hand from a neutral position to a specific direction, and return to the original position. ’flick’ gestures are used to navigate through the projected image on the walls of the or. the doctors/surgeons intended actions/commands are recognized by extracting features from the spatio-temporal data of the gestures. using the corresponding commands to which the gestures are associated, doctors can bring up x-rays images, select a patient record from the database or annotate a region on the image. a two layer architecture is used. the lower level provides tracking and recognition functions, while the higher level manages the user interface. as opposed to field conditions, may raise challenges related to limited lighting conditions and unfixed environments. we plan to address this problem using simulation under operating room conditions to obtain a better assessment of system’s performance. 3.3.2 hand segmentation and tracking a 2d histogram is generated in real-time during ’calibration’ from the doctor’s hand. the calibration process is initiated when the user places his hand slowly into a boundary without touching the screen. this, in turn, is used to build a hand color distribution model. a pixel at location (x, y) is converted to the 118 j. p. wachs probability that the pixel is classified as a hand (or gloved hand), in any frame using the 2d histogram lookup table created earlier (figure 9). figure 9: . user hand skin color calibration in order to avoid false motion clues originated by non-hand motion in the calibration phase, a background maintenance procedure was developed. first, an initial image of the background is created. changes are detected background differencing. when this difference is coherently significant, i assume that the present object is a hand. the background stored image is updated using a running smoothed average (9). bk(i, j) = λ × f (i, j) + ( − λ )×bk−(i, j) (9) where, bk is the updated stored background image at frame k, bk− is the stored background image at frame k-1, λ is the smoothing coefficient (regulates update speed), f(i, j) is the current background image at frame k. to track the hand, camshift is used [37]. it uses a probability distribution image comprised of pixels representing hand colors. this hand image is created from a 2d hue-saturation skin color histogram [38]. a histogram is used as a look-up-table to convert the acquired camera images of the hand into corresponding hand pixels, a process known as back projection. in the original camshift algorithm the probability of a pixel belonging to the hand is determined by the grayscale value of the pixel only. in lieu of using color probability alone, i modify it with motion information according to (2) to represent a hand pixel probability. the relative weights between color and motion are shifted according to the amount of motion in the scene resulting in an adaptive fusion system. using the centroid and size of the hand pixel an iterative procedure based on a generalization of the mean shift algorithm [39]. is used to update the tracking window at each frame. thresholding to black and white followed by morphological operations is used to obtain a single component for further processing to classify the gestures. 3.3.3 operation the gesture interface can be used to browse medical databases and manipulate the projected imagery such as x-rays and mris. the finite state machine in figure 10 illustrates the operational modes with the gesture interface. after the calibration procedure dynamic gestures are used to browse images and hand poses are used to switch between modes of operation. the central area in the middle of the frame is called the "neutral area", see figure 11. when a doctor decides to perform a specific operation on a medical image, he/she places the hand in the ’neutral area’ momentarily, which will be followed by the zoom posture or rotation pose gesture. gaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system 119 figure 10: state machine for the gesture-based medical browser figure 11: four quadrants mapped to cursor movements 120 j. p. wachs navigation gestures are designed to browse through a medical data browser projected on the wall. the immersion sense is created by representing 3d objects, where each image is a side of a cube and arranged in numerous levels. the cube can be rotated cw and ccw, and moved up and down in the vertical direction to exhibit various levels on the screen. hence, any image on the screen can be accessed directly by four navigation commands. a ’flick’ gesture is performed when a doctor/surgeon moves the hand out from a ’neutral area’ toward any of four directions, and then back to the neutral area. interaction is designed in such a way that the gestures commands are carried out only when the doctor’s body posture is standing still, instead of bending towards the patient (which may indicate that the surgeon is operating). zoom is used to change the size of an image. when the zoom-mode is triggered, the size of image changes according to the proximity of the palm to the camera. to go back to the normal mode, the hand is moved out from the neutral area to any of the 4 directions. the "zoom mode" is activated, when the hand is in the neutral area, rotates suddenly ccw by 90ş. rotation is achieved through the rotation of a sterilized straight instrument hold on the doctor’s hand. in rotation mode, the angle to which the medical image is rotated is determined by the angle made by the instrument and the horizontal axis, in increments of 90ş. the orientation’s instrument can be found using the probabilistic hough transform (pht). only lines on the nearby proximity of the hand are considered (2.5 times around the interaction window). to avoid the recognition of unintentional gestures, when the doctor wants to stop temporarily the operation of the hand gesture recognition system, he can either gaze down towards the patient, or user moves the hand to the lowest part of the screen, keeping it within the screen boundaries. to return to the "normal mode" a wake up gesture is used whereby the user waves the hand over the small rectangle in the upper left corner of the screen. this is useful when the doctor wishes to discuss details on the projected image without being "tracked" by the system. 3.4 speech interaction one of the main goals regarding the intelligent operating room is twofold: (a) identify the user (id tagging) automatically and (b) to recognize spoken utterances. the first goal, on one hand grants the user (either a nurse or the main surgeon) access to digital patient records and medical imaging tools according to the privileges that have been assigned to him, and on the second hand allows the room to keep track of the subject when ambiguity occurs due to light changing, occlusions and merging of several subjects. if i want to allow only the surgeon to manipulate and annotate the patient’s medical images during surgery, the operation will be activated only when spoken utterances are recognized by the room as belonging to the surgeon’s voice. to achieve this goal, the users will be required to say the word "login" [38] and compared to prototype feature vectors using some distance measure, such as maximum likelihood. for every participant in the or, a profile is created and matched to the existing profiles in order to assess the level of permission that needs to be granted. the second goal involves the recognition of words and sentences using off-the-shelf voice recognition software, called "dragondictate", which can explicitly build continuous speech and context-free recognition grammars [39]. to transmit the speech to the recognition module, multiple microphones are used. since i want to keep the sterility requirements, the microphones are not attached to the doctors. instead, the microphones are set-up in key locations on the or’s ceiling. voice commands are used to evoke functions that are very difficult to map to hand gestures since there is no natural association between them. for example, to retrieve medical images of the patient being operated, the surgeon can say the patient’s name. to discriminate between environment noise, which can mistakenly being recognized as a command, the user has to start the command by saying "system" shortly followed by the command to be carried out. this approach was suggested in [39] in the context of smart rooms, where the vision recognition and audio recognition modules are independent, and hence it fits the requirements for the operation room. environment noise can still be too high and gaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system 121 interfere with the interaction. we plan to test these scenarios in further work. 4 conclusions and future works this work presents one possible application for smart rooms: the intelligent, collaborative operating room. by monitoring the surgeon’s activities while performing surgery, the system can collaborate with her by displaying relevant medical imaging information in a convenient location in the or. the or depends on the surgeon’s body language as the essential key to understanding his focus of attention. one goal of the suggested approach is to use computer vision techniques to detect body postures and gaze to determine focus of attention. smart projectors combined with computer graphics techniques are used to project medical imaging in front of the surgeon, based on the knowledge provided by the posture recognizer. person-detection with simultaneous human body posture recognition is achieved using parts-based models and a multiclass boosting approach: each body part is matched with the image captured by the camera, and each part votes for the center of the person. focus of attention is assessed by simultaneous detection of the surgeon’s posture and orientation. detection and classification are possible since features of different human postures have shared subspaces as opposed to the non-person class. one challenge here is the focus on postures that cannot be easily distinguished by their aspect ratio or silhouette, but rather require a bottom-up approach. parts-based detection does not require explicit models, nor the labeling of individual body parts. the detection and posture classifications are performed in a single pass over the image, and the strength of the recognition is proportional to the ensemble of votes from parts patches. a vision-based system is used for interaction with the medical images,. it recognizes the surgeon’s gestures in real-time which are used to browse, zoom and rotate the images projected on the wall in front of him. the system is user independent since it is calibrated using a multi-modal two step procedure: first the user’s id is recognized using a voice identification system, then cameras are used to model the gamut of colors of the surgeon’s hand. camshift is used to track the hand, which allows dynamic hand gesture navigation control. the decision to use hand gestures as the main modality of interaction is based on previous work done by the author [25], where it was shown that hand gesture interaction in the operating room is the preferred modality of interaction by the doctors in the or, due to their proficiency at using the hand as their main tool of work. hand gestures offer the following benefits: (i) ease of use: surgeons are already quite proficient in their use of hands as a primary work tool, (ii) rapid reaction: hand gesture commands are intuitive and fast, (iii) unencumbered: does not require the surgeon to be wired to any device, and (iv) sterility: non contact interaction. issues related to image processing performance algorithm under unfixed environments require further analysis and more robust vision algorithms. for example, in [25] we used only one frontal camera, and in the current research i implement four. it is not likely that the surgeon will be in front of any of these cameras. to correct for this, the image need to be re-projected using the homography matrix found in the calibration process. still some areas may remain occluded. in future work these challenges will be addressed and a simulation framework is going to be adopted in order to quantitatively validate the hypothesis suggested in this work. surgeons must command a high level of cognitive and motor skills in order to complete their tasks successfully. previous research has shown that during the performance of a specific task, most gaze changes are related to the task-oriented visual search. distractions may have an adverse effect on task performance since they compete with the mental resources allocated for that task. i specifically address the problem of the distraction to the surgeon, (or shift of focus) required for closer 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[39] m. coen, design principles for intelligent environments, in proc. of aaai, madison, wisconsin, 1998. juan wachs is an assistant professor at purdue university in the school of industrial engineering. he completed a postdoctoral training at the naval postgraduate school’s moves institute in the area of computer vision. his research interests are machine and computer vision, robotics, tele-operations, human factors, assistive technologies and health support systems. juan wachs is a member of ieee and the operation research society of israel (orsis). he has published in journals including ieee trans. systems, man, and cybernetics, journal of american medical informatics, journal of image and graphics, and the international journal of semantic computing. he has been a visiting professor at the buenos aires university, argentina in the area of image processing. he has taught digital electronics, multimedia and web development at ort and intel colleges, israel. he received his b.ed.tech in electrical education from the ort academic college in jerusalem, his m.sc and ph.d in industrial engineering and management, information systems and intelligent systems tracks, respectively, from the ben-gurion university of the negev. international journal of computers, communications & control vol. i (2006), no. 1, pp. 17-24 novel features for off-line signature verification banshider majhi, y santhosh reddy, d prasanna babu abstract: in this paper a novel feature extraction scheme has been suggested for offline signature verification. the proposed method used geometric center for feature extraction. euclidean distance model was used for classification. this classifier is well suitable for features extracted and fast in computation. method proposed in this paper leads to better results than existing offline signature verification methods. threshold selection is based on statistical parameters like average and standard deviation (σ ). keywords: feature extraction, geometric center, euclidean distance model, standard deviation and off-line signature verification. 1 introduction signature verification is an important research area in the field of person authentication. we can generally distinguish between two different categories of verification systems: online, for which the signature signal is captured during the writing process, thus making the dynamic information available, and offline for which the signature is captured once the writing processing is over and, thus, only a static image is available[8]. the objective of the signature verification system is to discriminate between two classes: the original and the forgery, which are related to intra and interpersonal variability. the variation among signatures of same person is called inrea personal variation. the vatiation between originals and forgeries is called inter personal variation[7]. in this paper we concentrated on offline verification system. upto now many signature verification methods proposed based on different strategies but no verification system classified near forgeries which were classified by this method. and the main advantage of this algorithm is efficiency and computational complexity. for general purpose applications like smart cards we want quick and efficient verification system[2]. this method is based on the geometric center and signature strokes distribution. section 2 discusses the feature extraction from signature. this is a recursive method which applying on signature recursively. a lot of work has been done in the field of automatic off-line signature verification. while a large portion of work is focused on random forgery detection, more efforts are still needed to address the problem of skilled forgery detection[6]. our method will be the first verification system which seperates some skilled forgeries from originals. this paper organized in the following sections: section 1.1 provides the different types of forgeries. section 2 introduces new feature extraction method. section 3 discusses classification based on euclidean distance model. section 4 discussed about threshold selection. section 5 shows training, testing and results and section 6 gives conclusion and furthure working directions. 1.1 types of forgeries there are three different types of forgeries to take into account. the first, known as random forgery which writtn by the person who don’t know the shape of original signature. the second, called simple forgery, is represented by a signature sample which written by the person who know the shape of original signature without much practice. the last type is skilled forgery, represented by a suitable imitation of the genuine signature model[3]. each type of forgery requires different types of verification approach[4]. hybrid systems have also been developed[9] fig. 1 shows the different types of forgeries and how much they are varies from original signature[5]. figure 1: (a) random forgery (b) simple forgery (c) skilled forgery (d)original signature by using this method we can easily eliminate random and simple forgeries. some of the skilled forgeries also eliminated. copyright c© 2006 by ccc publications 18 banshider majhi, y santhosh reddy, d prasanna babu figure 2: (a) before adjustment of signature (b) after adjustment of signature 2 feature extraction the geometric features proposed by this paper are based on two sets of points in two-dimentional plane. each set having six feature points which represent the stroke distribution of signature pixels in image. these twelve feature points are calculated by geometric center[1]. vertical splitting and horizontal splitting are two main steps to retrieve these feature points. vertical splitting is discussed in section 2.2 and horizontal splitting is discussed in section 2.3. before finding feature points we have to do some adjustments to the signature image. that is moving signature strokes to the center of the image which discussed in section 2.1. 2.1 moving signature to the center of image in this step signatures are moving to the center of image. because of this we can reduce intra-personal variations. here first we have to find out the geometric center of the image and move the signature pixels such that the geometric center should reside at center of image. fig. 2 shows the signature images before moving and after moving. 2.2 feature points based on vertical splitting six feature points are retrieving based on vertical splitting. here feature points are nothing but geometric centers. the procedure for finding feature points by vertical splitting is mentioned in algorithm. algorithm this is the procedure for generating feature points based on verical splitting. input: static signature image after moving the signature to center of image output: v1, v2, v3, v4, v5, v6 (feature points) (a)split image with vertical line at the center of image then we will get left and right parts of image. (b)find geometric centers v1 and v2 for left and right parts correspondingly. (c)split left part horizontal line at v1 and find out geometric centers v3 and v4 for top and bottom parts of left part currespondingly. (d)split right part horizontal line at v2 and find out geometric centers v5 and v6 for top and bottom parts of left part currespondingly. fig. 3 shows the feature points retrieved from signature image and o is the center of image. these features we have to calculate for every signatrure image in both training and testing. 2.3 feature points based on horizontal splitting six feature points are retrieving based on horizontal splitting. here feature points are nothing but geometric centers. the procedure for finding feature points by horizontal splitting is mentioned in algorithm. novel features for off-line signature verification 19 figure 3: feature points based on vertical splitting figure 4: feature points based on horizontal splitting algorithm this is the procedure for generating feature points based on horizontal splitting. input: static signature image after moving the signature to center of image output: h1, h2, h3, h4, h5, h6 (feature points) (a)split image with horizontal line at the center of image then we will get top and bottom parts of image. (b)find geometric centers h1 and h2 for top and bottom parts correspondingly. (c)split top part with vertical line at h1 and find out geometric centers h3 and h4 for left and right parts of top part currespondingly. (d)split bottom part with vertical line at h2 and find out geometric centers v5 and h6 for left and right parts of left part currespondingly. fog. 4 shows the feature points retrieved from signature image and o is the center of image. these features we have to calculate for every signatrure image in both training and testing. now total twelve feature points (v1, ..., v6 and h1, ..., h6) are calculated by vertical and horizontal splittings. in section 4 we will see how each feature point can classify. 3 classification in this paper features are based on geometric properties. so we used euclidean distance model for classification. this is the simple distance between a pair of vectors of size n. here vectors are nothing but feature points, so the size of vector is 2. how to calculate distance using eucliden distance model is descibed in section 3.1. in threshold calculation these distances are useful. 3.1 euclidean distance model let a(a1, a2, ..., an) and b(b1, b2, ..., bn) are two vectors of size n. we can calculate distance(d) by using equarion 1. distance(d) = √ n ∑ i=1 (ai −bi)2 (1) 20 banshider majhi, y santhosh reddy, d prasanna babu in our application, vectors are points on plane. so d is the simple distance between two points. 4 threshold individual thresholds for vertical splitting and horizontal splitting. here we proposed one method for threshold selection which used in section 5.1. fig. 5 shows the variations in single curresponding feature points of training signatures. let n is the number of training signatures and x1, x2, ..., xn are curresponding single feature points of training signatures(taking one curresponding feature point from each signature). xmedian is the median of n features from n signatures. let d1, ..., dn are distances defined here, d1 = distance(xmedian, x1) d2 = distance(xmedian, x2) ... dn = distance(xmedian, xn) (2) two main parameters we used in threshold calculation are davg and σ . equations 3 and 4 shows the calculation d avg + σ x i x 2 x n−1 x 1 x 3 d avg x n σ figure 5: davg (average distance) and σ (standard deviation) derivation from distances of these two parameters. davg = average(d1, d2, ..., dn) (3) σ = sd(d1, d2, ..., dn) (4) like this total six different feature points are there for both vertical and horizontal splitting based on average distance (davg) and standard deviation (σ ). equation 5 shows the main formula for threshold. threshold(t) = √√√√ 6∑ i=1 (davg,i + σi)2 (5) novel features for off-line signature verification 21 5 experiments & results for experiment we took 30 original signatures from each person and selected 9 for training. these original signatures are taken in different days. forgeries taken by three persons and 10 from each. total 21 originals and 30 forgeries for each person signature are going to be tested. there are two thresholds (one based on vertical splitting and another based on horizontal splitting) for each person signature. 5.1 training let n signatures are taking for training from each person. there are 12 feature points from each original signature, 6 are taken by vertical splitting (section2.2) and 6 are taken by horizontal splitting (section2.3). individual thresholds and patterns are calculating for vertical splitting and horizontal splitting. pattern points based on vertical splitting are shown below. vpattern,1 = median(v1,1, v2,1, ..., vn,1) vpattern,2 = median(v1,2, v2,2, ..., vn,2) vpattern,3 = median(v1,3, v2,3, ..., vn,3) vpattern,4 = median(v1,4, v2,4, ..., vn,4) (6) vpattern,5 = median(v1,5, v2,5, ..., vn,5) vpattern,6 = median(v1,6, v2,6, ..., vn,6) where vi,1, vi,2, ..., vi,6 are vertical splitting features of i th training signature sample. threshold based on vertical splitting is shown below. vthreshold = √√√√ 6∑ i=1 (vdavg,i + σv,i)2 (7) in equation 9 vdavg,i is same as average distance and σv,i is same as standard deviation shown in section 4. pattern points based on horizontal splitting are shown below. hpattern,1 = median(h1,1, h2,1, ..., hn,1) hpattern,2 = median(h1,2, h2,2, ..., hn,2) hpattern,3 = median(h1,3, h2,3, ..., hn,3) hpattern,4 = median(h1,4, h2,4, ..., hn,4) (8) hpattern,5 = median(h1,5, h2,5, ..., hn,5) hpattern,6 = median(h1,6, h2,6, ..., hn,6) where hi,1, hi,2, ..., hi,6 are horizontal splitting features of i th training signature sample. threshold based on horizontal splitting is shown below. hthreshold = √√√√ 6∑ i=1 (hdavg,i + σh,i)2 (9) we will store pattern points and thresholds of both horizontal splitting and vertical splitting. these values are useful in testing. 5.2 testing when new signature comes for testing we have to calculate features of vertical splitting and horizontal splitting. feature points based vertical splitting are vnew,1, vnew,2, vnew,3, vnew,4, vnew,5, vnew,6. distances between new signature 22 banshider majhi, y santhosh reddy, d prasanna babu features and pattern feature points based on vertical splitting are shown below. vdnew,1 = distance(vpattern,1, vnew,1) vdnew,2 = distance(vpattern,2, vnew,2) vdnew,3 = distance(vpattern,3, vnew,3) vdnew,4 = distance(vpattern,4, vnew,4) (10) vdnew,5 = distance(vpattern,5, vnew,5) vdnew,6 = distance(vpattern,6, vnew,6) for classification of new signature we have to calculate vdistance and compare this with vthreshold . if vdistance is less than or equal to vthreshold then new signature is acceptable by vertical splitting. vdistance = √√√√ 6∑ i=1 vd2new,i (11) feature points based vertical splitting are hnew,1, hnew,2, hnew,3, hnew,4, hnew,5, hnew,6. distances between new signature features and pattern feature points based on vertical splitting are shown below. hdnew,1 = distance(hpattern,1, hnew,1) hdnew,2 = distance(hpattern,2, hnew,2) hdnew,3 = distance(hpattern,3, hnew,3) hdnew,4 = distance(hpattern,4, hnew,4) (12) hdnew,5 = distance(hpattern,5, hnew,5) hdnew,6 = distance(hpattern,6, hnew,6) for classification of new signature we have to calculate hdistance and compare this with hthreshold . if hdistance is less than or equal to hthreshold then new signature is acceptable by horizontal splitting. hdistance = √√√√ 6∑ i=1 hd2new,i (13) new signature features have to satisfy both vertical splitting and horizontal splitting thresholds. 5.3 results false acceptance rate (far) and false rejection rate (frr) are the two parameters using for measuring performance of any signature verification method. far is calculated by equation 14 and frr is calculated by equation 15. far = number o f f orgeries accepted number o f f orgeries tested ×100 (14) f rr = number o f originals re jected number o f originals tested ×100 (15) table 1 shows the false acceptance rate of our method for different types of forgeries. table 2 shows the false rejection rate for original sigature. table 1: false acceptance rate (far) forgery type far(%) random forgeries 2.08 simple forgeries 9.75 skilled forgeries 16.36 in general there are different thresholds for different types of forgery detections. but here 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[9] a. zimmer and l.l. ling, "a hybrid on/off line handwritten signature verification system", seventh international conference on document analysis and recognition, vol.1, pp.424-428, aug.2003. banshider majhi, y santhosh reddy, d prasanna babu department of csea nit rourkela india 769008 e-mail: ysantosh@rediffmail.com int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 792-798 the development of students’ metacognitive competences. a case study d. mara daniel mara “lucian blaga” university of sibiu e-mail: danielmara11@yahoo.com abstract: in the information society metacognitive competencies are essential. based on some activities from the enrichment instrumental program elaborated by professor reuven feuerstein we have designed a program for developing the students capacities of selfcontrol, selfknowing and intelectual learning strategies. the case study presents the formation of students’ metacognitive competences at the "lucian blaga" university of sibiu, "hermann oberth" faculty of enginereeing, department of computers sciences. a web based application has been developed in order to enable students to self-evaluate their metacognitive competencies and to acquire self-regulatory abilities. keywords: metacognitive competences, instrumental enrichment program, computer science, higher education, relational competences, motivation for didactic career, web based application. 1 introduction metacognitive skills are a must for students preparing to have a career in the information/knowledge society. for those that want to embrace a didactic career that is essential. therefore the department for teaching staff training [8] has started a special training program consisting of two modules. the education plans for the first module consists of the following courses: psychology of education, pedagogy 1 (foundations of pedagogy, curriculum theory and methodology), pedagogy 2 (instruction theory and methodology, evaluation theory and methodology), specialty didactics, teaching practice, optional courses, final evaluation-didactic portfolio [9]. in the second module the following courses are included: curriculum area didactics, class management, counseling and vocational guidance, computer-assisted instruction, psychology of education, optional i (1 of 4: intercultural education, educational politics, contemporary pedagogical doctrines, management of school organization), optional ii (1 of 4: psycho-pedagogy of adults, foundations of special psycho-pedagogy, sociology of education, research methodology in the sciences of education), final evaluation-project, teaching probation (42 hours for those who did not teach during the period between the attendance of the first module and the enrollment at the second module). 2 developing students’ metacognitive competences metacognitive skills development is an important formative intellectual object in education of the students, as reaching this level involves a route through effective education, appropriate to each one in particular [6]. metacognitive skills suppose that students are aware of their own cognitive activity, i.e. learning activity, and self-adjustment mechanisms consisting in cognitive controls (rules, procedures, strategies). copyright c⃝ 2006-2010 by ccc publications the development of students’ metacognitive competences. a case study 793 2.1 the background the development of metacognitive skills goes in the same direction with the strategies used in developing cognition. the main steps in the formation, in the affirmation of conscience gripping meta-cognition are: • affirmation of trust and intuition (ah schoenfeld’s model) in solving the problems, or tasks, based on knowledge, on previous experiences; in this step the trainer looks to identify in the student a sense of referral tasks, intuition, a way of understanding and finding solutions taking into account all possibilities; • personal reflection on the knowledge involved; the student must become aware of the solutions found, the instruments used, her/his capacity of analysis and comparison, the way to analyse the difficulties of other methods previously used; • self-awareness or awareness of effective solutions addresses the solving style, based on selfobservation, analysis of results and of the ways to solve, progress and cognitive acting. the development stages show that metacognitive skills are associated with knowledge from management, and construction and that these are the conditions in which the knowledge appears. cognition managerial approach reveals the fact that metacognitive includes: awareness of how to understand the problem and how to solve it, planning the processes and finding the pathways necessary, monitoring the application solutions, the resources used, constraints, necessary instruments, decisions and analysis of results [4]. f.p. büchel considers that training of metacognitive competences is more efficient if working in groups, in a climate of cooperation and confrontation, because there is the possibility of mutual evaluation. in self-training, the individual student is more concerned about solving the problem itself, bout the acquisition of knowledge and s/he is less concerned by the understanding of how knowledge is acquired, how solutions were found or decisions taken. researchers have built a hierarchical model of the criteria-assessment questions in the classroom climate in function of different elements: diversity of awareness, respect for others’ style, commitment, encouragement, student-teacher relationship, student-group-class relationship, learning with pleasure, and sense of humour, comfortable participation and freedom of expression. studies that explore the effects of attitudes and emotions on learning indicate that stress and constant fear, at any age, can circumvent the brain’s normal circuits. a person’s physical and emotional well-being is closely linked to the ability to think and to learn effectively [1]. 2.2 the instrumental enrichment program the instrumental enrichment program is composed of a set of exercises divided into 14 tools that are used as means for developing mental capacities. the exercises do not concern the acquisition of specific knowledge, but the acquisition of mental skills, of ways to use concepts in different situations [2]. each instrument is focused on specific cognitive functions and provides means for developing cognitive capacities necessary for solving tasks that require a high level of abstraction. the instrumental enrichment program components are: • organization points; • spatial orientation i. • comparisons; • analytical perception; • pictures; • spatial orientation ii, • classification, • temporal relations, 794 d. mara • instructions, • family relations, • numerical progression; • syllogism; • transferable relations; • outlines. the exercises have images and temporal relations that are organized differently and provide a gradual increase in difficulty. in this way the student is encourage to a progressive acquisition of skills necessary to solve the problems or tasks, thus strengthening motivation, the feeling of competence and autonomy in organizing intrinsic work. subjects become aware of the importance and need for discussion about the work done and to make transfers on the basis of principles/rules/patterns formulated during activity. the development of principles/rules/patterns and the implementation of transfers are very important elements. all the details of the page of an exercise must be caught and analyzed and a synthesizing valid principle must be identified and expressed in a concise sentence. a principle is important because it can highlight a complex problem, newly learned information, or a necessary element to solve the exercise. the transfer is created as a link between the principles/patterns/rules resulting from the reflection necessary for understanding and addressing new events [3]. during an activity, two or more instruments are used in order to avoid monotony of using for a long period the same type of exercises, or the feeling of failure resulted from difficulties in solving an exercise. students are lead to use different instruments and to learn to choose the right ones. an activity is made up of elements called "pages". a page contains a story, illustrated by images. each instrument begins with a picture page (cover or homepage), which is used for placing the instrument, creating a horizon for motivation and development through the following pages. any learning (instructional) form may be tackled from the point of view of the general systems theory, distance learning forms included. a system is defined by a set of elements that interact and work together in order to achieve an objective [7, 8]. cover pages have certain features that remain unchanged from instrument to instrument to highlight the continuity of work, but each instrument is different from others. the mediator/trainer oriented subjects to consider the symbol on the cover to deduce the exercises that will solve the issues and that they will discuss. 2.3 organize an activity of the instrumental enrichment program an activity of the instrumental enrichment program is organized respecting some rules and some key moments: the introduction, individual work, discussion and conclusions [5]: in the following i will briefly present the key moments and the rules to follow. introduction. by going through this phase the mediator wants to awake the group interest in the work that will be developed and to define the problems they will have to solve. the introduction begins with revision, i.e. data from previous lessons. the mediator shall ensure that requirements and concepts were well understood, and that the vocabulary necessary to solve the task is assimilated. students will learn to analyze the page autonomously. the trainer guides students in observing and identifying objectives [10]. individual work. in this stage students will be asked to solve an individual task, after which they will be involved in a discussion aimed at highlighting possible strategies for solving the exercises from the page. students must understand that it is important not to finish quickly the exercises of a page. is important to understand how to solve a task and how they form and develop certain abilities. an activity based on reflection, even if not fully effective, may be more useful, more fruitful than the one done in a hurry, because it is based on a more deep analyze the processes that formed it. the development of students’ metacognitive competences. a case study 795 discussion. when most students have completed the individual task the trainer may start the discussion stage. being particularly interested in mental processes that led to finding the solution, it is appropriate to insist on correct answers and to explain the wrong ones, to understand the mental processes through which solutions were found [12]. at first it is recommended that the mediator identifies the link between work and other applied situations, then the students will gradually create these connections between the instruments and the surrounding reality. each transfer is built on a solid and appropriate explanation of the type of connection between the examples and the proposed developments. conclusion. at the end of each lesson there should be a revision of the whole activity. even if it is short it should highlight the steps taken to achieve the objective, the new words acquired, targets and strategies set out above for achieving the aim of the lesson. it is possible to encourage valorisation activities to determine individually or in small groups the utility obtained by applying different tools. 3 the case study development activities of the metacognitive skills students were conducted by applying the tools instrumental enrichment program developed by reuven feuerstein a group of 75 students from the faculty of engineering "hermann oberth", section computer sciences, of the university "lucian blaga" principles: one event can’t be observed by itself , it has to be seen in the whole context, before and after. we have to make a difference between opened eyes dreams and reality, between what is possible and impossible. we have to be aware about our goals, about their importance and about the risks they implied. the 75 students have bee enrolled in a trening program aimed to develop their metacognitive skills. one group (40) has worked only in the classroom and one group (35) has used also the web based application designed to support them in developing metacognitive comptences. the web based application is preparing the student for the training program, the students becoming familiar with the kind of exercises used in the fuerstein program. at the end of the training program the overall scores of the students that have used also the web based application was significant higher than the score of the group that worked only in the classroom. gender has not a significant effect on students’ perception of their metacognitive skills. a rather uncomfortable conclusion is that more than 75% of the students (no gender significant differences) have difficulties in expressing in words their thoughts and experience. the group that has a pre-training with the cogitino web based application was significant more rapid in solving different tasks, but has the same difficulties in expressing in words their thoughts and experience as the control group. 4 the web application in order to help students to better understand their thoughts and experiences a support software cogitino has been designed and implemented. cogitino is a web based application (fig.1) that offers a set of resources concerning meta-cognitive skills and acts as an adviser for the student that is enrolled in the metacognitive skills development training program. cogitino is a multi-agent system (fig.2 ) that through its profiler agent determines the student’s level of meta-cognitive competences and then recommend different training paths (fig.3). 796 d. mara the system is rating students’ metacognitive competences based on the answers to several questionnaires and problems’ results. before applying the instrumental enrichment program students are asked to solve several problems similar to those that they will have to solve during the class. principle of e-learning applications have been observed [1, 7] figure 1: the home page of the cogitino application figure 3: cogitino training resources the development of students’ metacognitive competences. a case study 797 figure 2: cogitino general structure 5 conclusions metacognitive skills are mandatory for today students. they must be aware and must know their mental processes and they must be able to self-monitor, regulate, and direct their actions to their global aim. metacognitive training becomes an important and basic tool also in business and management efficiency, skill and competences. the result of the research carried out leads to evidence of at least three essential aspects in the development of students’ meta-cognition competences. first, students balance their attention in preparation, implementation and evaluation of the educational and training process itself, but their qualitative analysis is poor. they have a reduce vocabulary and therefore a difficulty in explaining their experience and performance. secondly, being enrolled in a technical program they feel, at least at the beginning of the training program, that they do not need to express themselves in words. and last but not least, the lack of general culture is an obstacle in understanding some of the tasks and problems they have been asked to solve. meta-cognition components are usually observed only in the final stage of evaluation. another conclusion is that the web based application has been appreciated by students as very helpful. considering this aspect and that in educational practice meta-cognition principles can be developed and applied efficient by students following a training program that included theoretical aspects and practical-application, my future work will consist in enriching (with the help of colleagues from the computer science department of our university) cogitino with two new modules : one module that will automatically generate explanations after a task has been solved, showing to the students how s/he has proceed, and another one that will be a "vocabulary training" for the student. acknowledgment this work was supported by cncsis-uefiscsu, project number 882/19/01/2009 pnii idei, code 471/2008. 798 d. mara bibliography [1] chen, z., learning about learners: system learning in virtual learning environment. international journal of computers, communications & control, vol. iii (2008), no. 1, pp. 33-40. [2] feuerstein r., rand j., rynders j.e., non accettarmi come sono, sansoni, milano, italy, (1995). [3] feuerstein, r., rand, y., hoffman, m. b., miller, r., instrumental enrichment: an intervention program for cognitive modifiability, baltimore university park press, baltimore, usa, (1980). [4] joiţa, e., educaţia cognitivă. fundamente. metodologie, editura polirom, iaşi, romania, (2002). [5] kopciowski camerini, j., l’apprendimento mediato. orientamenti teorici ed esperienze pratiche del metodo feuerstein, la scuola, brescia, italy, (2002). [6] miclea, m., psihologie cognitiv?. modele teoretico-experimentale, editura polirom, iaşi, românia, (1999). [7] moise, g., a formal description of the systemic theory based e-learning. international journal of computers, communications & control, vol. iii (2008), no. 1, pp. 90-102. [8] moisil, i., a model of the student behavior in a virtual educational environment, international journal of computers, communications & control, vol. iii (2008), suppl. issue: proceedings of icccc 2008, pp. 108-115 [9] neculau, a., (coord.), (1996), psihologie socială. aspecte contemporane, editura polirom, iaşi. [10] neculau, a., (coord.), (1997), universitatea: valorile şi actorii săi, în "câmpul universitar şi actorii săi", a. neculau (coord.), editura polirom, iaşi. [11] vanini, p., potenziare la mente? una scommessa possibile: l’apprendimento mediato secondo il metodo feuerstein, vannini editrice, gussago (brescia), italy, (2003). [12] vanini, p., il metodo feuerstein: una strada per lo sviluppo del pensiero, irrsae emilia romagna, bologna, italy, (2001). int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 2, pp. 158-166 task resource allocation in grid using swift scheduler k. somasundaram, s. radhakrishnan arulmigul kalasalingam college of engineering department of computer science and engineering krishnankoil-626190, tamilnadu, india e-mail: soms72@yahoo.com abstract: in nature, grid computing is the combination of parallel and distributed computing where running computationally intensive applications like sequence alignment, weather forecasting, etc are needed a proficient scheduler to solve the problems awfully fast. most of the grid tasks are scheduled based on the first come first served (fcfs) or fcfs with advanced reservation, shortest job first (sjf) and etc. but these traditional algorithms seize more computational time due to soar waiting time of jobs in job queue. in grid scheduling algorithm, the resources selection is npcomplete. to triumph over the above problem, we proposed a new dynamic scheduling algorithm which is the combination of heuristic search algorithm and traditional sjf algorithm called swift scheduler. the proposed algorithm takes care of job’s memory and cpu requirements along with the priority of jobs and resources. our experimental results shows that our scheduler reduces the average waiting time in the job queue and reduces the over all computational time. keywords: grid computing, swift scheduler, dynamic scheduling algorithm, first come first serve, shortest job first. 1 introduction: a computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities. according to the function, grid is classified into three types: computing grid, data grid, and service grid. computing grid is used to connect varied computing resource on the network to construct a virtual high performance computer, which could offer high performance computer [1]. the traditional computing grid systems involve many technologies such as certification, task scheduling, communication protocols, fault tolerance and so on. the task of grid resource broker and scheduler is to dynamically identify and characterize the available resources and to select and allocate the most appropriate resources for a given job [2]. the resources are typically heterogeneous locally administered and accessible under different local policies. advance reservation [3] is currently being added to portable batch system (pbs). in a grid scheduler, the mapping of grid resources and an independent job in optimized manner is so hard where we couldn’t predict optimized mapping. so the combination of uninformed search and informed search will provide the good optimal solution for mapping a resources and jobs, to provide minimal turn around time with minimal cost and minimize the average waiting time of the jobs in the queue. a heuristic algorithm is an algorithm that ignores whether the solution to the problem can be proven to be correct, but which usually produces a good solution. heuristics are typically used when there is no known way to find an optimal solution, or when it is desirable to give up finding the optimal solution for an improvement in run time. copyright © 2006-2009 by ccc publications task resource allocation in grid using swift scheduler 159 the primary objective of this research is to investigate effective resource allocation techniques based on computational economy through simulation. we like to simulate millions of resources and thousands of users with varied requirements and study scalability of systems, algorithms, efficiency of resource allocation policies and satisfaction of users. in our simulation we would like to model applications in the areas of biotechnology, astrophysics, network design, and high-energy physics in order to study usefulness of our resource allocation techniques. the results of our work will have significant impact on the way resource allocation is performed for solving problems on grid computing systems. the organization of this paper is as follows. in section 2, the related works are discussed. in section 3, we introduce our scheduling algorithm model. in section 4 we present and discuss the experimental results. we conclude this study in section 5. 2 related work: job scheduling in parallel system has been extensively researched in the past. typically this research has focused on allocating a single resource type (e.g., cpu usage) to jobs in the ready queue. the use of many of these scheduling algorithms has been limited due to restriction in application designs, runtime system, or the job management system itself. therefore simple allocation scheme such as first come first serve (fcfs) or fcfs with first fit back fill (fcfs/ff) are used in practice [4]. current job scheduling practices typically support variable resource allocation to a job, and run to completion scheduling. scheduling policies are also heavily based on first-come-first-serve (fcfs) methods [5]. a fcfs scheduling algorithm allocates resources to jobs in the order that they arrive. the fcfs algorithm schedules the next job in ready queue as soon as sufficient system resources become available to meet all of the job requirements. the advantage is that this provides level of determinism on the waiting time of each job[6]. the disadvantage of fcfs shows up when the jobs at the head of the ready queue cannot be scheduled immediately due to insufficient system resources, but jobs further down the queue would be able to execute given the currently available system resources. these latter jobs are essentially blocked from executing while the system resource remains idle. fidanova [7] compared the simulated annealing approach with the ant algorithm for scheduling jobs in grid. david beasley, marek mika and grzegorz waligora [8] formulated the scheduling problem as a linear programming problem and proposed local search meta-heuristic to schedule workflow jobs on a grid. fair share scheduling [12] is compared with simple fare task order scheduling, adjusted fair task order scheduling and max-min fair share scheduling algorithm are developed and tested with existing scheduling algorithms. rafael a. moreno [9] addresses the issues that the resource broker has to tackle like resource discovery and selection, job scheduling, job monitoring and migration etc. resource management system [10, 11] was discussed and models of grid rms availability by considering both the failures of resource management (rm) servers and the length limitation of request queues. the resource management systems (rms) can divide service tasks into execution blocks (eb), and send these blocks to different resources. to provide a desired level of service reliability, the rms assigns the same eb to several independent resources for parallel (redundant) execution. 3 swift scheduler (ss) model: let n be the number of jobs in job queue ’jq’ which is indicated as, jq = {j1, j2, j3, ...., jn} (1) jobs are allotted to m number of resources in resource queue’rq’which is indicated as, 160 k. somasundaram, s. radhakrishnan rq = {r1, r2, r3, ...., rm} (2) let f(ji, rj) be the overall job completion time for the ith job in jth resources can be calculated as, n∑ i=0 m∑ j=0 f(ji, rj) = n∑ i=0 m∑ j=0 g(ji, rj) + n∑ i=0 m∑ j=0 h(ji, rj) (3) let g(ji, rj) be the expected job completion time of the ith job in jth resources which can be calculated as, n∑ i=0 m∑ j=0 g(ji, rj) = n∑ i=0 m∑ j=0 (jli/rcj) (4) jli be the job length of ith jobs and rcj be the capacity of the jth resources. let h(ji, rj) be the heuristic function of the ith job in jth resources which can be calculated as n∑ i=0 m∑ j=0 h(ji, rj) = n∑ i=0 m∑ j=0 (jli/rcj) + communicationoverhead (5) 3.1 working principle of swift scheduler (ss): figure 1: architecture of ss figure 1 shows working principles and architecture of our proposed algorithm ss as follows: incoming jobs from different users are collected and stored in job list and available resources are stored in resource list. jobs are randomly arrived to job queue as well as resources are selected based on the availability. the swift scheduler in gridsim [13] maps the jobs from job queue and resources from resource queue where the resources are selected using the heuristics function. the function will select the optimized resource for the particular job to complete it with minimum time. task resource allocation in grid using swift scheduler 161 3.2 pseudo code for swift scheduler (ss): 4 performance analysis: in this section, we analyze the performance of swift scheduler with existing simple fair task order scheduling (sfto) against large set of independent jobs with varying size and large number of heterogeneous resources. assume, the arrival rates of jobs are based on the poisson distribution. the following fig.(2) and fig.(3) shows the job allocation methods used in sfto and ss respectively and the following table (1) and table (2) shows the arrival order of the particular jobs and at what time, the jobs will start its execution in the particular resource and its service time of the jobs in the particular resource where the selection of resources are based on the algorithms. for example, in sfto, the jobs j0 and j3 are allotted in resource r1. the residing time (tr) of jobs j0 is the combination of jobs j0 waiting time (tw) in queue and service time (ts) of j0 (i.e tr = tw + ts = 2820.03ms + 61.35ms = 2881.38ms). similarly, the residing time of job j3 is 1490 ms. the job j2 is allotted in resource r2 where the residing time of j2 is 1257.19 ms and jobs j4 and j1 are chosen 162 k. somasundaram, s. radhakrishnan jobid resource name start time in ms residing time in ms 4 r3 1851.12 1886.86 1 r3 3337.99 3391.73 2 r2 1214.79 1257.19 3 r1 1330.66 1490.66 0 r1 2820.03 2881.38 table 1: job, resource, start and residing time for sfto jobid resource name start time in ms residing time in ms 2 r1 1210.02 1316.02 0 r1 2805.39 2980.39 3 r2 1093.04 1178.27 1 r2 2688.17 2772.17 4 r3 1453.51 1489.25 table 2: job, resource, start and residing time for ss resources r3 where the residing time of j4 is 1886.86 ms and j1 is 3391.73 ms. the average waiting time of all jobs in grid system is 2181.56ms. figure 2: job and resource allocation for sfto in ss, resource selection and jobs allocation are based on heuristic searching algorithm on sjf, which reduces the average waiting time of the jobs in queue. so, overall turn around time is reduced and resource utilization is increased. for example, in ss, the jobs j2 and j0 are allotted to resource r1 where its residing time is 1316.02 ms and 2980.39 ms respectively. similarly, jobs j3, j1 and j4 are chosen by resources r2, r2 and r3 respectively where residing time of jobs j3,j1 and j4 are 1178.29 ms, 2772.17 ms and 1489.25 ms respectively. the average waiting time of all jobs in grid system is 1947.22 ms which is less than sfto. the statistical data presented here is acquired by averaging the scheduling performance over different runs. the following figs and tables shows the cost based, total processing time and resource utilization based comparison test results of fcfs, sjf, sfto and ss against varying number of jobs and resources. our proposed algorithm swift scheduler (ss) is compared with fcfs, sjf and simple fare task order (sfto) scheduler. we have tested ss in gridsim by varying number of resources, no. of jobs task resource allocation in grid using swift scheduler 163 figure 3: : job and resource allocation for ss against total processing time, cost, and resource utilization. we can vary the number of resources like 5, 10, 15, 20,... etc. for experimental purposes, two sample simulation results are shown in figure 4, 5, 6, 7, 8 & 9. by analyzing the obtained results from the simulator, swift scheduler completed all jobs with minimum time and cost by utilizing maximum amount of resources compares with other scheduler like fcfs, sjf and sfto. total processing time analysis figure 4: : no of jobs vs total processing time figure 5: : no of jobs vs total processing time 164 k. somasundaram, s. radhakrishnan cost analysis figure 6: : no of jobs vs total cost figure 7: : no of jobs vs total cost resource utilization figure 8: : no. of jobs vs resource utilization figure 9: : no. of jobs vs resource utilization task resource allocation in grid using swift scheduler 165 5 conclusion and future work: in this paper, we have presented the design and analyze the new scheduling algorithm swift scheduler. our proposed swift scheduler completed a task by using highly utilized low cost resources with minimum computational time. our scheduling algorithm uses the heuristic function to select the best resources to achieve a higher throughput while maintaining the desired success rate of the job completion. this algorithm is performing better for real time job parameters and suitable for different job sizes in real environment. however, in all conditions, the proposed algorithms outperform the traditional ones. the ss policy is more effective than the fcfs, sjf and sfto in the extent of computational complexity with lower cost but higher resources utilization. in future, we can hybrid the swift scheduler with any evolutionary scheduling algorithm like genetic algorithm, particle swarm optimization technique to achieve a high throughput and high resource utilization. bibliography [1] ranganathan,k, and i.foster, "decoupling computation and data scheduling in data intensive applications", 11th international symposium on high perfromance distributed computing, edinburgh, scotland, condor project, condor-g, 2002. 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[7] stefka fidanova, "simulated annealing for grid scheduling problem", ieee international symposium on international symposium on modern computing, 2006. jva apos;06 3-6 oct. 2006 page(s):41 45 [8] marek mika, grzegorz waligora and jan weglarz, a meta-heuristic approach to scheduling workflow jobs on a grid, grid resource management: state of the art and future trends, isbn : 1-40207575-8 , kluwer academic publishers, norwell, ma, usa; pp : 295 318, 2004. [9] moreno, r., alonso-conde a.b, job scheduling and resource management techniques in dynamic grid environments in et al., f.f.r., ed.: across grids 2003, volume 2970 of lecture notes in computer science, springer, pp : 25 32, 2004. [10] yuan-shun dai, min xie and kim-leng poh "availability modeling and cost optimization for the grid resource management system" ieee transactions on systems, man and cybernetics, part a volume 38, issue 1, jan. 2008 page(s):170 179. 166 k. somasundaram, s. radhakrishnan [11] yuan-shun dai and gregory levitin "optimal resource allocation for maximizing performance and reliability in tree-structured grid services" ieee transactions on reliability volume 56, issue 3, sept. 2007 page(s):444 453. [12] doulamis, n.d.; doulamis, a.d.; varvarigos, e.a.; varvarigou, t.a "fair scheduling algorithms in grids" ieee transactions on parallel and distributed systems, volume 18, issue 11, nov. 2007 page(s):1630 1648 [13] anthony sulistio, uros cibej, srikumar venugopal, borut robic and rajkumar buyya "a toolkit for modelling and simulating data grids: an extension to gridsim", concurrency and computation: practice and experience (ccpe), online issn: 1532-0634, printed issn: 1532-0626, 20(13): 1591-1609, wiley press, new york, usa, sep. 2008. international journal of computers, communications & control vol. i (2006), no. 2, pp. 53-60 importance of flexibility in manufacturing systems héctor kaschel c., luis manuel sánchez y bernal abstract: flexibility refers to the ability of a manufacturing system to respond cost effectively and rapidly to changing production needs and requirements. this capability is becoming increasingly important for the design and operation of manufacturing systems, as these systems do operate in highly variable and unpredictable environments. it is important to system designers and managers to know of different levels of flexibility and / or determine the amount of flexibility required to achieve a certain level of performance. this paper shows several representations schemes for product flexibility and discusses the usefulness and limitations of each. keywords: flexibility, fuzzy logic, disjunctive graph, constraints temporal network, complexity in manufacturing, non lineal mathematic programming 1 introduction present market demands require that manufacturing systems develop their activities under a dynamic and uncertain production environment (calinescu et al., 2003). to understand the problems affecting flexible manufacturing systems (fms), it is necessary to incorporate the concept of flexibility in the scheduling (deshmukh et al. 2002). flexibility constitutes a strategic topic in decision making to give quick and efficient answers to the demands of the national and international markets (pelaez, and ruiz, 2004). in order to define the flexibility in fms, the scientific literature reports a wide variety of concepts. for example, flexibility for sethi and sethi (sethi and sethi, 1992) represents the capacity of fms to modify manufacturing resources to produce different products efficiently maintaining an acceptable quality. for gupta (gupta, 2004), it is necessary to make the difference between flexibility inherent to the fms and flexibility required during the production cycle. the first one is defined as the capacity of resources and human operators to implement decisions of the factory company. while required flexibility is defined by the changes in the environment that surrounds the fms and for the implementation of new manufacturing strategies. starting from these differences, it is possible to determine the flexibility type and measure of the fms quantitatively. for (benjaafar and ramakrishman, 1996), it is important to differentiate the types of flexibility in the fms. they define the flexibility of the fms as product flexibility and process flexibility. the first one refers to the variety of factory options for a certain product. while process flexibility is defined as a characteristic of an industrial process to operate under diverse dynamic conditions of operation. both general flexibilities can be grouped hierarchically as shown in the figure 1. the classification proposed by (benjaafar and ramakrishman, 1996) shows flexibility as a consequence of the physical and logical characteristics that the manufacturing system presents to quantify the operative flexibility of job i, a function is defined starting from the time of dependent configuration of the sequence between operations: φ(pi) = −∑nij=1 ∑ ki j l=1 ( 1 si jl ∑ ki j l=1 1 si jl ) log ( 1 si jl ∑ ki j l=1 1 si jl ) ni (1) where: copyright c© 2006 by ccc publications 54 héctor kaschel c., luis manuel sánchez y bernal figure 1: classification of the fms flexibility • si jl represents the time of configuration of job i during the commutation of the operation j to the operation l. • ni represents the total number of operations associated to job i. in the present paper different models were analyzed as described in several studies dealing with product flexibility in fms. 2 product flexibility in all fms, each job or jobs group is associated to a certain scheduling, consisting in an orderly sequence of operations to be executed in a group of machines. an example of scheduling of the fms is illustrated in figure 2(a). if we incorporate conditions of flexibility to the scheduling (operational flexibility), an operation can be carried out in other machines. these, in general, can be different or they can have different operational levels (figure 2 (b)). the classification of the operations can also be varied (sequence flexibility), where some operations should be carried out in sequence while others don’t have this restriction. an example of this type of flexibility is shown in figure 2(c), where operations 2 and 3 can be carried out in any order after executing operation 1. finally, in a highly flexible fms, a job can have a group of schedulings (processing flexibility). this means that the group of operations required to manufacture a job can be variable and depending on the operation conditions of the fms. for example, in figure 2(d), operations 1 and 2 can be replaced by the operation 5 while operation 3 can be divided in operations 6 and 7. 3 analysis models for product flexibility the product flexibility constitutes an important aspect in the functionality of the fms, where the variability in the scheduling and the operability of the machines allow several alternatives to process the jobs. in what follows some models of analysis of the product flexibility are presented. importance of flexibility in manufacturing systems 55 figure 2: types of product flexibility 3.1 disjunctive graph it is a model of directed graph, whose nodes have a certain weight assigned. mathematically it is expressed as 3 tupla (benjaafar and ramakrishman, 1996): g = (n, a, e) (2) where: • n: represents the group of nodes that represent the operations. in this model are defined two particular nodes, called initial and final. the positive weight assigned to each node represents the processing time of the corresponding operation. the initial node is connected with the first operation of each job, and in the same manner the last operation of each job is connected with the final node. • a: represents the group of conjunctive directed arcs that indicate the precedence restrictions among the operations of each job. • e: represents the group of disjunctive directed arcs that indicate the restrictions of machines operative capacity. to illustrate the application of the disjunctive graph, an example of scheduling of 4 different jobs is presented (j1, j2, j3, j4) in three types of machines (m1, m2, m3). the distribution of processing times and assignment of machines is shown in table 1, starting from this information we proceed to generate the disjunctive graph of figure 3, that shows the precedence restrictions between the operations (conjunctive arcs) and the flexibility in the machines operative sequence (disjunctive arcs). 56 héctor kaschel c., luis manuel sánchez y bernal processing time machines assignment m1 m2 m3 1 2 3 j1 5 8 2 m1 m2 m3 j2 3 9 7 m3 m1 m2 j3 1 10 7 m1 m3 m2 j4 7 4 11 m2 m3 m1 table 1: machines assignment and processing times figure 3: disjunctive graph generated by table 1 3.2 temporal constraints network it is a model of directed graph that allows the representation of temporary constraints assigned to the precedence relationships between operations that are part of a scheduling of a fms. the nodes of the graph represent the operations and the arcs have associated temporary intervals to denote the temporary constraints that exist between two nodes. all the times are calculated starting from of the scheduling initial state. the development of the network is based mathematically from disjunctive metric temporal algebra (alfonso, 2001). this methodology is used to solve scheduling problems characterized from a group of temporary constraints, consequently, the techniques developed in areas like temporary reasoning and constraints satisfaction can be applied. in general, two techniques are used: closure and constraint satisfaction problem (csp) (barber, 2000). closure is a deductive process by means of which new constraints are inferred, starting from existing constraints. it also allows to detect possible inconsistencies (value of the variables that don’t lead to any solution) that can be eliminated. the main advantage of the closure techniques consists in the reduction of the search space. therefore, the closure is used fundamentally as a previous step to a process of search of solutions. techniques of constraints satisfaction problem constitutes a process of search of solutions by means of the successive assignment of values to the variables that are part of the constraints of the problem. in a process of constraints satisfaction problem, it is important to use different heuristics to make the search process more efficient (beck and fox, 2000). to illustrate the use of the temporary constraints network, a distribution of temporary constraints is presented among four operations of a fms (see table 2). starting from the information in table 2, we proceed to establish the group of operations (o), the importance of flexibility in manufacturing systems 57 o1 o2 o3 o4 o0 initial state (10, 20) (10, 20) (20, 50) (60, 70) o1 (30,40), (60,60) (10,30), (40,40) (40, 60) o2 (0, 30) o3 (10, 20) (20,30), (40,50) table 2: precedence temporary constraints between operations group of precedence relationships between operations (p) and the group of temporal relationships between operations (r). finally, starting from the definitions of the groups we proceed to generate the corresponding graph of temporal constraints (see figure 4): g = (o, p) (3) o = (o0, o1, o2, o3, o4) (4) p =    (00, o1), (00, o2), (00, o3), (00, o4) (01, o2), (01, o3), (01, o4), (02, o4) (03, o2), (03, o4)    (5) r =    (o0(10, 20)o1), (o0(10, 20)o2), o0(20, 50)o3), (o0(60, 70)o4, (o1(30, 40)(60, 60)o2), (o1(10, 30)(40, 40)o3), (o1(40, 60)o4), (o2(0, 30)o4), (o3(10, 20)o2) (o3(20, 30)(40, 50)o4)    (6) figure 4: temporary constraints network generated by table 2 3.3 model based on mathematical programming chandra and tombak (chandra and tombak, 1993) propose a operations sequence flexibility model, based on the theory of lineal programming. the model incorporates the concepts of machines reliability and machines processing capacity. machine reliability is defined as the probability of an operation to be executed by a machine in a certain interval of time. machine processing capacity, represents the total number of jobs that a machine can execute in a interval of time. 58 héctor kaschel c., luis manuel sánchez y bernal the fms base for the development of the model consists of a finite group of machines that can present independent fault events. the model is formulated considering the jobs flow on a fixed scheduling and it is expressed mathematically as: rf = max ∑ i ∑ h cihxih (7) subject to the following constraints: ∑ i ∑ h|bihk=1 tik pk xih ≤ tk,∀k (8) ∑ h xih > di,∀i (9) xih ≥ 0,∀i, h (10) where, • tik represents the processing time of job i in machine k; • tk represents the total processing time of machine k; • pk represents the probability that machine k is operative in a certain time; • bihk represents the possibility that job i is processed in machine k along route h; • ch represents the factor of cost of job i when processed in route h. this factor depends on the reliability machines located along route h; • di represents the minimum demand of job i and, • xih represents the flow of job i on route h. the model represents the processing capacity of each machine considering its reliability, where the factor tikpk represents the time required to process job i in machine k. the model considered n + m constraints and m(1 + ∑n−2k=1 k!c n−2 k ) variables in case of full machines connections. 3.4 model based on fuzzy logic in (tsourveloudis and phillis, 1998) and (fortemps, 2000), analyze the flexibility starting from a methodology based on the knowledge that the human expert has on the structural and dynamic behavior of the fms. for (tsourveloudis and phillis, 1998) the flexibility is represented by means of a group of diffuse linguistic rules of the type: if 〈antecedent fuzzy〉 then 〈consequent fuzzy〉 (11) this proposal is based on the fuzzy behavior that the fms presents, in cases of concurrence and synchronization of operations or fault machines events. for (tsourveloudis and phillis, 1998), a fms presents seven different types of flexibility: 1. materials manipulation system flexibility: it measures the capacity of the system to transfer several jobs types efficiently between two neighboring points. 2. product flexibility: it measures the capacity of the system to produce mixed jobs. importance of flexibility in manufacturing systems 59 3. operative flexibility: it measures the capacity to modify the sequence of operations to produce a job. 4. process flexibility: it measures the capacity of the system to produce different jobs without modifying the fms structure. 5. volume flexibility: it measures the operative capacity of the fms for different levels of productivity. 6. scalability flexibility: it measures the cost or time required to enlarge a fms. 7. labor flexibility: it measures the operator’s capacity to develop different operative tasks of fms. each one of these types of flexibility is expressed by means of a fuzzy rule that represents the expert’s knowledge about this flexibility. the fuzzy rule is expressed mathematically as: if f1 is a1 and . . . and fn is an then fmf is mf (12) where: ai = {low, about_low, average, about_high, high} represents the group of linguistic values for the flexibility parameter. for example, the following fuzzy rule expresses the operative flexibility of the fms: if c0 is tc0 and sb is tsb then fr is tfr (13) for this rule, c0 represents the number of operations common to machines group and sb represents the fms capacity to route a jobs group under conditions of machines faults. 4 conclusions the models described in this paper define a set of basic principles that should satisfy all parameters associated to the fms flexibility, such as: • they should be specified and derived according to the methodology proposed for the analysis of the flexibility. • they should incorporate structural and operational aspects of the fms. • they should incorporate and accumulate the human expert’s knowledge. of the models, we can conclude that the problem of the flexibility is analyzed starting from one of the three big current problems affecting the fms [calinescu et al 2003]: • making decisions problem, analyzed by the methods based on mathematical programming and fuzzy logic. • structural problem, analyzed by the method of the disjunctive graph and • behavior problem, analyzed by the method of the temporary constraints network. 60 héctor kaschel c., luis manuel sánchez y bernal references [1] alfonso, m., an integration model of temporary constraints based on methodology closure and csp: application to scheduling problems, doctoral thesis presented at university of alicante, spain, 2001. [2] barber, f., "reasoning on interval and point-based disjunctive metric constraints in temporal contexts". journal of artificial intelligence research, pp:35-86, 2000. [3] beck, j. and fox m., "constraint directed techniques for scheduling alternative activities". journal of artificial intelligence, pp: 211-250, 2000. [4] benjaafar, s. and ramakrishman, r., "modeling, measurement and evaluation of sequencing flexibility in manufacturing systems", international journal of production research, vol. 34, pp:11951220, 1996 [5] chandra, p. and tombak, m., models for the evaluation of routing and machine flexibility, technical report of decision craft analytics, www.decisioncraft.com, 1993. [6] deshmukh, a., talavage, j. and barash, m., "complexity in manufacturing systems", iie transaction on manufacturing systems, vol 30, pp: 645 655, 2002. [7] fortemps, p., "introducing flexibility in scheduling: the preference approach", advances in scheduling and sequencing under fuzziness, ed. springer verlag, pp:61-79, 2000. [8] gupta, a., "approach to characterize manufacturing flexibility", second world conference on production & operations management society, cancun méxico, pp:100-120, 2004. [9] calinescu, a., sivadasan, s., schirn, j. and huaccho, l., complexity in manufacturing: an information theoretic approach, technical report of manufacturing system research group, department of engineering science, university of oxford, england, 2003. [10] pelaez, j. and ruiz, j., measuring operational flexibility, technical report, university of murcia, faculty of economy and enterprise, spain, 2004. [11] sethi, a. and sethi, s., "flexibility in manufacturing: a survey", international journal of operations and production management, pp:35-45, 1992. [12] tsourveloudis, n. and phillis, y., "fuzzy assessment of machine flexibility", ieee transaction on engineering management, vol. 45, pp:78-87, 1998. héctor kaschel c. and luis manuel sánchez y bernal depto. de ingeniería eléctrica and depto. de matemática y ciencia de la computación universidad de santiago de chile avda. ecuador ] 3519 estación central. santiago chile e-mail: hkaschel@lauca.usach.cl o msanchez@lauca.usach.cl int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 125-134 design of a mimo pid robust controller using moments based approach abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou abstract: in this paper we present a new technique for robust mimo controllers synthesis and reduction based on a reference model and moments approach intended to control a mimo thermal system. the reference model allows to specify the performances requirements for the closed loop and improve the controller robustness while the moments tool (frequency and time ones) is used to reduce the controller structure using a non linear optimization. the implementation on the real system associates this methodology of mimo pid controllers synthesis with broïda’s identification technique in order to carry out a auto-tuning procedure [2][11]. keywords: pid control, reference model, moments, optimization, robustness, broïda’s identification technique. 1 introduction the method of moments was introduced in a previous contribution [1] to show how we can treat single-input single-output (siso) control systems. we saw that the moments represent a good tool to obtain a reduced robust controller in order to approximate the closed loop behaviour to a reference model one. the reference model gathers all performances requirements like time response and overshoot. an extension of this method to the multivariable (multi-inputs multi-outputs) systems is presented in this paper; in this case the fundamental idea is to choose a diagonal reference model to make inputs outputs pairing [10] where the diagonal reference transfer functions are chosen using the same method for the siso case. in this paper we present our control methodology for mimo systems with an application to a thermal system. the control of the mimo thermal system is realized with the help of a pid controller using moments based approach and reference model. the aim of this technique is to synthesize a reduced robust controller (pid for example) for the implementation. the identification was realized thanks to broïda’s method which is an elementary technique very used in the industry in order to develop a auto-tuning procedure requiring a minimum intervention of the user. the controller achieving the performances for the worst case model is called ideal controller [10] which will be reduced using the moments based approach to have a pid structure for implementation [4]. the reduced controller must preserve the same performances as the ideal one; this rises from frequency and time moments which represent a good tool for synthesis and analysis [7]. concretely, the reduction procedure is based on a non linear optimization and its initialization is given by the least squares algorithm [8]. the paper is organized as follows: in section 2 we give a complete description of the application; in section 3 we develop our synthesis methodology; in section 4 and 5 we describe the moments theory and the reduction method and we finish this communication by presenting the different results obtained and a conclusion. 2 description the general diagram of the temperature control device is illustrated by fig. 1. copyright © 2006-2008 by ccc publications 126 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou amplification u1 +vcc t1 u2 θ1 θ2 stage amplifying stage amplifying y2y1 t2 figure 1: the general diagram of the system it is composed of a radiator on which we placed two power transistors t1 and t2 controlled by the tensions u1 and u2 which vary between 0 and 10 volts. near these power transistors, which represent the heat source, two temperature sensors (pt 100) are laid out. after adaptation and amplification, we obtain the tensions y1 and y2 (which vary between 0 and 10 volts) proportional to the temperatures θ1 and θ2. the ratio tension-temperature is of 0.02volts/c. 2.1 modelling the aim is to define the model connecting the tensions delivered by the temperature sensors y1 and y2 to the control tensions u1 and u2 applied to the transistors. the conduction of the heat in the radiator, towards the temperature sensors produced by the power transistors is governed by the following diffusion equation: µ ∂ 2θ ∂ x2 = ∂ θ ∂ t (1) where µ is the coefficient of thermal diffusion process. consequently the resulting physical model is of distributed parameters and we preferred to present a black box model adapted to the synthesis of a control law, this model is given from identification. let:   y1 (s) y2 (s)   =   g11 (s) g12 (s) g21 (s) g22 (s)     u1 (s) u2 (s)   (2) where g (s) is the transfer matrix of the system. notice that the system is symmetrical because of the provision of the heat sources and the position of the sensors. indeed, we must obtain: { g11 (s) = g22 (s) g12 (s) = g21 (s) (3) in addition, since the sensors θ1 and θ2 are very close to the sources t1 and t2, the transfers g11 and g22 can be reasonably modelled by a first order system ( k11 1 + τ11s ). on the other hand θ2 is less sensitive to the effect of t1 than that of t2 (so k21 < k11), with a settling time definitely higher. we can thus approach this transfer by an aperiodic nth order system with (n >> 1) or more simply by a first order system with a time delay ( k12e−α12s 1 + τ12s ). design of a mimo pid robust controller using moments based approach 127 2.2 identification several sophisticated identification techniques (minimization of a quadratic cost by least squares or non linear optimization) (ljung [5] and walter [12]) can be used to estimate the parameters of g (s). taking into account the fact that the aim is to synthesize pid controllers (in a auto-tuning objective), we preferred to choose a basic identification method requiring only step tests. our choice thus is broïda’s method which delivers an approached model of the form ( ke−α s 1 + τ s ), so well adapted to the selected modelling [2][11]. let us recall that the coefficient {k, α et τ} are obtained with the help of the following formulas:    ki j = y∞ αi j = 2.8t1 −1.8t2 τi j = 5.5 (t2 −t1) (4) where y∞ is the final value of the system step response, t1 (respectively t2) is the time where the output attains 28% (respectively 40%) of its final value. we made 5 tests which provided: test1 test2 test3 test4 test5 k11 0.1221 0.1221 0.1172 0.1172 0.1318 τ11 577 414 449 451 457 k12 0.0634 0.0636 0.0586 0.0586 0.0684 τ12 910.5 862.95 684.2 683.1 863 α12 7 26.2 59.6 58.9 6 k21 0.083 0.0781 0.0684 0.0684 0.083 τ21 715 654.5 808.5 808.5 715 α21 113 99.8 73.4 73.4 113 k22 0.0977 0.0977 0.0977 0.0928 0.1074 τ22 450 445 447 447 600 the settling times of g11 (s) and g22 (s) are close and equal 33minutes. 2.3 the nominal model the nominal model was obtained by carrying out the average of the 5 tests, thus: gnom (s) =   0.122 470s+1 0.0625e−31.5s 801s+1 0.0762e−94.5s 740s+1 0.0987 478s+1   (5) from the values of the preceding table, we note that the theoretical symmetry is not checked in practice, well that g11 (s) and g22 (s) are close (g12 (s) and g21 (s) respectively), these differences are the consequences of the noises level disturbing the measurements and the non perfect reproducibility of the assembly. 2.4 taking into account of uncertainties identification uncertainties will deteriorate the performances of the control device if they are not taken into account during the synthesis. the table recapitulating the 5 tests shows that two uncertainties types are present: 128 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou • uncertainties on the d.c gains (of type low frequencies). • uncertainties on time-constants and time delays, which causes modelling errors (of type high frequencies). since the transfer matrix g (s) is theoretically symmetrical, we decided to make the synthesis by basing us on a symmetrical worst case model gp (s). using gp (s), we took into account all the uncertainties by raising: • low frequencies uncertainties by taking kmax. • high frequencies uncertainties by introducing a time delay e−δ s (see [1]). remark 1. the worst case model has been chosen to take into account all the identification uncertainties: • low frequencies uncertainties, by choosing the max of the d.c gains. • high frequencies, by introducing time delays in all input-output transfers. thus, we obtain the following worst case model: gp (s) =   0.132e−5s 500s+1 0.085e−125s 800s+1 0.085e−125s 800s+1 0.132e−5s 500s+1   (6) 3 synthesis the control configuration is illustrated by fig. 2 g(s) r e u po y pi c(s) figure 2: unitary feedback configuration c (s) is the controller to implement, g (s) is the plant, r represent the reference inputs, y the outputs to be controlled, u the control inputs, pi and po are the input and output disturbances and e is the tracking error. the synthesis of c (s) is based on the internal model control [6][10], so: c (s) = q (s) (i −g (s) q (s))−1 (7) where q (s) is any stable transfer matrix [3]. design of a mimo pid robust controller using moments based approach 129 3.1 the reference model q (s) is calculated using gp (s) in order to approach the closed loop tyr (s) to a reference model tre f (s), so: tyr (s) = gp (s) q (s) = tre f (s) (8) the reference model gathers all the objectives of the synthesis in terms of stability and performances (i.e inputs-outputs decoupling, settling time... etc). in our case, tre f (s) is given by: tre f (s) =   t2(s)e−δ s d(s) 0 0 t2(s)e −δ s d(s)   (9) where t2 (s) represent the dominant poles to fixe the closed loop dynamic, d (s) represent the auxiliary poles, which reduce the control input energy, confer robustness to the controller by making a sufficient roll-off of tyr (s) and ensure its causality, finally e−δ s represents the singularity of gp (s) which is integrated in tre f (s) to have q (s) stable and realizable. so q (s) is given by: q (s) = gp (s) −1 tre f (s) (10) since the singularities of gp (s) (i.e time delays and rhp zeros) are integrated in the reference model tre f (s), so the stability of q (s) is guaranteed [3]. t2 (s) and d (s) are given by: t2 (s) = ω 2n s2+2ξ ωns+ω 2n d (s) = (1 + η s)n (11) where ωn and ξ , fixe the settling time and the overshoot of the outputs. we can recapitulate our reference model by saying that it imposes a inputs-outputs decoupling and the same dynamics for the two outputs of g (s). 3.2 robustness and the reference model it is well-known that the relative modelling errors ∆r (s) modify the dynamics of the system with a possibility of instability because: sypo (s) = ( i −g (s) q (s) (i + ∆ (s) q (s))−1 ) = snom (s) (i + ∆r (s) tre f (s)) −1 (12) so robustness of stability and performances is guaranteed if: ‖ ∆r (s) tre f (s) ‖∞≤ δ −1sr (13) where δsr, represents stability margin; notice that from (13), we can act on the auxiliary poles of tre f (s) to ensure the robustness [3]. 130 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou 3.3 the ideal controller from (10), we deduce the ideal controller c (s): c (s) = q (s) (i −gp (s) q (s))−1 (14) it is clear that the implementation of c (s) is very hard to do because of time delays; for that we will reduce its structure to get an implementable one which must preserve the same performances of those ensured by the ideal controller. the moments tool, particularity of our approach is used for reduction. 4 the moments let us consider a linear siso system, characterized by its transfer function g (s) analytic in the rhp plan (.i.e re (s) > 0) and let g (t) be its impulse response: g (s) = ∫ ∞ 0 g (t) e−st dt (15) the transfer function is given by the following state space (not necessary minimal) realization: g (s) s = [ a b c d ] = c (si −a)−1 b + d (16) where a ∈ rn×n, b ∈ rn×1,c ∈ r1×n and d ∈ r1×1. 4.1 computing the moments using state space realization using the following equality: (si −a) ( −a−1 −sa−2 −s2a−3 −···− ) = i ⇒ (si −a)−1 = − ∞ ∑ n=0 ( sna−(n+1) ) (17) frequency moments realizing a variable change µ = jω − jω0, equation (15) becomes: g (µ) = ∞ ∑ n=0 (−1)n (µ)n an,ω0 (g) (18) and (16): g (µ) = c (µ i −(− jω0i + a))−1 b + d (19) so, we get: a0,ω0 (g) = −c (− jω0i + a) −1 b + d (20) an,ω0 (g) = (−1) n+1 c (− jω0i + a)−(n+1) b, (n = 1···∞) (21) remark 2. the time moments are giving by remplacing ω0 = 0. both time and frequency moments can be calculated easily using the previous algorithm. thanks to the computation of the moments using state space realization, we can compute moments for mimo systems (multi inputs multi outputs). design of a mimo pid robust controller using moments based approach 131 5 the controller reduction for more details of our model reduction procedure, please refer to [1]. it is interesting to have a reduced structure of the implemented controller (pid for example) [6]. let: θ =   θ 11 (s) ··· θ 1m (s) ... . . . ... θ m1 (s) ··· θ mm (s)   (22) be the matrix representing the controller’s parameters to be calculated. θ i j represents the parameters vector of numerator and denominator of cr,i j (s); i.e the reduced controller between the jth input and the ith output. let us define our cost function j as the 2 norm of the errors between the different moments of the ideal controller and those of the reduced one. j = n ∑ n=0 ||εn||22 = n ∑ n=0 ||an,ω0 (c)−an,ω0 (cr)||22 (23) where an,ω0 (cr) represents n th order moments matrix, which is function of the parameters θ : an,ω0 (cr) = fn (θ ) (24) let: j = n ∑ n=0 ||an,ω0 (c)− fn (θ )||22 (25) the objective is to determine the estimated parameters θ̂ minimizing j around ω0. this frequency ω0 is chosen in order to preserve stability of the system (i.e critical pulsation), (see the generalized nyquist criterion [10]). 5.1 linear optimization the first step consists on imposing the common denominator dr (s) of the reduced controller (for example we can take the auxiliary poles of the reference model dr (s) = d (s)). so only the zeros have to be determined; the function fn (θ ) is linear; thus the minimization of j is obtained by least squares, let: cr (s) = cls (s) (26) be the reduced controller which will be used to initialize the non linear programming algorithm. remark 3. by imposing the poles of the reduced controller, for example a pole with an integral action we define so a pid structure. for example we can take dr (s) = s (1 + η s). 5.2 non linear optimization the fact of imposing the poles of the reduced controller, this will limit its performances and consequently those of the closed loop. so, it is preferable to optimize all the structure (i.e. poles and zeros); the function fn (θ ) is non linear; the estimation of θ is obtained by non linear programming [7]. we use marquardt’s algorithm which is a good combination between rapidity and convergence [8]. the parameters are updated with the help of the following algorithm: θ̂i+1 = θ̂i −{[j ′′ + λii]−1.j ′}θ̂ =θ̂i (27) 132 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou where: ( ∂ j ∂ θ ) = j ′ : gradient vector (28) ( ∂ 2j ∂ θ 2 ) = j ′′ : hessian matrix (29) λi : coefficient to adjust (30) the initialization is given by the least squares solution: θ̂0 = θ̂ls (31) 6 results and comments the controllers synthesis and reduction technique was applied to the thermal system with 2 inputs and 2 outputs. let us recall that this thermal system is characterized by an important open loop settling time (approximately 33 minutes); moreover the elementary modelling used in a disturbed context involves important uncertainties. the objective of the temperature control, in a auto-tuning context, will be double: • reduce considerably the closed loop settling time by using a controller of a simplified structure: pid controller. • guarantee robustness in spite of the simplicity of the controller, the level of uncertainty and the reduction of the settling time. let us specify that the implementation of the pid controllers was ensured thanks to xpc target software of matlab. we used for that a sample time te = 1s. our objective is to accelerate the system reasonably, in a ratio of 6, thus passing from 33mn in open loop to 5mn in closed loop all while maintaining the relative overshoot around 5%; for that, we fixed: ωn = 0.02rd/s ξ = 0.7 the auxiliary poles ensuring the robustness condition (13) are: d (s) = (1 + 3s)3 the pid controller obtained using the synthesis and the reduction procedures described obviously is: cr (s) = [ c11 (s) c12 (s) c21 (s) c22 (s) ] (32) with: c11 (s) = c22 (s) = −3.162s 2+52.81s+0.14 s(1+34.87s) c12 (s) = c21 (s) = −2019e −7s2+4403e−6s−7193e−5 s(1+15.88s) (33) the implementation provides the results illustrated by fig. (3) and fig. (4). design of a mimo pid robust controller using moments based approach 133 0 1000 2000 3000 4000 5000 6000 2.5 2.55 2.6 2.65 time(sec) o u tp u t( v o lt) 0 1000 2000 3000 4000 5000 6000 2.5 2.55 2.6 2.65 time(sec) o u tp u t( v o lt) tref y1 tref y2 figure 3: step responses 0 1000 2000 3000 4000 5000 6000 3 4 5 6 7 8 9 10 time(sec) co ntr ol int pu t(v olt ) u1 u2 figure 4: the control inputs remark 4. we can check that the system’s responses correspond perfectly to those of the reference model. we can also check the perfect decoupling of the system outputs, y2 being insensitive to the reference input applied to y1 and reciprocally. it is checked finally that the control input obtained is completely reasonable, that it is with respect to its initial magnitude or of its insensitivity to the output noise (of considerable level). 7 conclusion we presented in this communication a synthesis and reduction technique of robust controllers to the multivariable control of a thermal system. this methodology is based on a reference model integrating explicitly the desired performances. the experimental results show that the closed loop system verifies well the performances described by the reference model. the moments approach, characteristic of this methodology, allows the reduction of the ideal controller to lead to a pid structure, while guaranteeing the dynamic performances and especially the robustness as testify the experimental results. let us recall finally that this synthesis methodology and reduction was associated with broïda’s identification technique to carry out the auto tuning of multivariable pid controllers. 134 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou bibliography [1] a.bentayeb, n.maamri and j-c.trigeassou "the moments in control: a tool for analysis, reduction and design", international journal of computers, communication and control, vol ii (2007). [2] a.anon "special edition on pid tuning methods". computing and control engineering journal 10(2), (1999). [3] j.c.doyle, b.a.francis and a.tannenbaum "feedback control theory". macmillan, new york (1991). [4] c.e.garcia and m.morari "internal model control: an unifying review and some new results". ind.eng.chem.proc.dec.dev, vol(91.2) (1982). [5] ljung "system identification. theory for the user". prentice hall (1987). [6] m.morari and s.zafiriou "robust process control". prentice hall (1989). [7] n.maamri, a.bentayeb and j-c.trigeassou "design and iterative optimization of reduced robust controllers with equality-constraints" rocond-milan (2003). [8] d.w.marquardt "an algorithm for least-squares estimation of non linear parameters" journal of soc. indust. appl. math v(11-2) (1963). [9] q.g.wang "decoupling control", lecture notes in control and information sciences, springer verlag (2003). [10] s.skogestad and i.postlethwaite "multivariable feedback control", jw (1996). [11] r.toscano "a simple robust pi/pid controller design via numerical optimization approach", journal of process control (15), (2005). [12] e.walter and l.pronzato "identification of parametric models from experimental data". communications and control engineering series. springer (1997). a. bentayeb, n. maamri and j-c. trigeassou university of poitiers laboratoire d’automatique et d’informatique industrielle 40 avenue du recteur pineau 86022 poitiers e-mail: abdelmadjid.bentayeb@gmail.com received: january 16, 2007 abdelmadjid bentayeb was born in 1977, he obtained the phd thesis in automatic control at the university of poitiers in 2006. his current research interests include robust control and model reduction. nezha maamri is lecturer at the unisversity of poitiers, hers current research interests include identification and robust control. jean-claude trigeassou is professor at the university of poitiers, his current research interests include identification and control. int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 759-766 transmission control for future internet including error-prone wireless region i. ryoo, s. kim intae ryoo, seokhoon kim dept. of computer engineering & ru-iptv research center kyunghee univ., 1, seocheon-dong, giheung-gu, yongin-si, gyeonggi-do, korea e-mail: itryoo@khu.ac.kr, kimsh@khu.ac.kr abstract: this paper introduces a transmission control scheme which is aimed at enhancing overall transmission capability of internetworks including error-prone wireless regions. the proposed scheme can accurately adapt to wireless communication environments by integrating new approaches of bandwidth estimation, loss detection, and error recovery techniques while differentiating data losses due to physical bit error characteristics of error-prone wireless regions from those due to network congestion. from the simulations, it has been verified that the proposed scheme shows better throughput performances than the existing major transmission control schemes such as new reno, vegas, and westwood+, while achieving satisfied levels of fairness and friendliness with them. keywords: transmission control, error-prone wireless region, bandwidth estimation, loss detection, error recovery 1 introduction wireless and mobile communication environments have already been pervasively deployed, and never-ending communication demand in error-prone wireless regions of upcoming future internet makes it difficult to maintain the same level of performances of legacy transmission controls. this is mainly due to the inability of the existing transmission control schemes to differentiate data losses resulting from inevitable bit errors in error-prone wireless regions [1] from those resulting from network congestion. as bad channel conditions and aperiodic disconnections are typically transient phenomena, legacy tcp congestion control responses may be inappropriate and undesirable for the networks with error-prone wireless regions. in order to achieve a higher level of transmission efficiency, we have proposed a new transmission control method that is well suited for error-prone wireless regions as well as wired regions. many solutions have been proposed to improve tcp performances in wireless networks and can be categorized into end to end (e2e), split connection, and link layer solutions [2][3]. the proposed transmission control protocol for error-prone wireless regions (tcp ewr) falls into the e2e category as it measures packet arrival rates and acts according to current network status. it, however, differs from the general e2e solutions in the point that it is aiming to improve its performances in case of random or sporadic data losses by performing a time-stamp based bandwidth estimation at the receiving tcp rather than at the sending tcp, calculating the expected and actual bandwidths, and reacting accordingly to the random wireless packet loss events. it measures bandwidth utilization by using packet arrival information at the receiver rather than by using acknowledgement (ack ) information at the sender like tcp new jersey [4]. as a result, sending feedback information to the receiver is not necessary in our scheme. moreover, in order to make the tcp ewr operate in a stable status, we have introduced two thresholds, α and β, which corresponds to having too little and too much data enroute to the destination, respectively. in addition, the tcp ewr performs an enhanced error recovery in copyright c⃝ 2006-2012 by ccc publications 760 i. ryoo, s. kim case that the network suffers from timeout expiration due to wireless packet losses. that is, if there is any sign of data loss either by retransmission timeout or by three duplicate ack s, the tcp ewr sets slow start threshold (ssthresh) value to optimal congestion window (ocwnd). with these features, the overall performance of the tcp ewr can be increased for the networks including error-prone wireless regions. in sect. 2, we discuss the bandwidth estimation, loss detection, enhanced error recovery, and congestion control procedures of the tcp ewr. we also give a pitch of our scheme through performance tests in sect. 3, and finally bring this paper to a conclusion in sect. 4. 2 transmision control for future internet including error-prone wireless regions the proposed transmission control scheme is composed of bandwidth estimation at the receiver, loss detection, enhanced error recovery, and congestion control. the first step of bandwidth estimation is to calculate a sample bandwidth (bwsample(n)) when the nth packet arrives at the receiver at time tn: bwsample(n) = dn/(tn − tn−1) (1) where dn is the amount of data currently received by the receiver and tn−1 is the previous (n −1)th packet arrival time. that is, differently from the existing schemes such as westwood+ and new jersey [5], our scheme uses packet arrival information at the receiver instead of using returning ack information at the sender. note that westwood+ and new jersey are considered in this work as they are representative transmission control schemes that can distinguish wireless packet losses from congestion packet losses and react accordingly. by incorporating the actual arrival rate information in bandwidth estimation and, at the same time, by including congestionrelated packet loss probability into the packet arrival rate calculation [6], the proposed scheme can probe the available bandwidth more accurately. the next step is to estimate the available bandwidth with a time varying coefficient, exponentially weighted moving average filter, which yields to following equation. bwestimated(n) = bwestimated(n−1)× δn + bwsample(n)× (1− δn) (2) where δn is a constant filter gain. in our simulation, we set δn to 0.7 for n≥2 and zero for n=1 for simplicity. thus, the optimal congestion window size ocwndn is computed as following: ocwndn = rttmin ×bwestimated(n)/seg_size (3) where rtt min is minimum measured round-trip time (rtt ) and seg_size is the length of the tcp segment. note that we do not consider the available buffer space in the receiver in this work, and ocwndn is used as a receiver-advertised congestion window size. simulation results in sect. 3 show that this yields good bandwidth estimates for random packet loss scenarios with error-prone wireless region. in order to proactively detect the incipient stage of congestion and to efficiently figure out the reason for packet losses, the proposed scheme calculates maximum expected throughput and measures actual throughput as followings similar to vega and veno approaches [7]: expected throughput = windowsize/rttmin (4) actual throughput = windowsize/rtt (5) transmission control for future internet including error-prone wireless region 761 where rtt is the smoothed round-trip time measured. the difference d between these two throughputs indicates the amount of data that is currently residing on the corresponding path and/or going away due to network congestion or error-prone wireless regions. also, by using the backlog variable x = d ∗ rtt min and two thresholds, α and β, which correspond to having relatively a few and too many data on the route respectively, we can grasp how to manage the sender’s congestion window (cwnd) size based on the current network situation. in our simulations, the values of α=1 and β=9 are used, but they can be set to better adapt to future internet environment, which is for further study. with these indicators for the current network status, the tcp ewr performs an enhanced error recovery (eer) mechanism. if there is any sign of data loss either by retransmission timeout (rto) at the sender or by three duplicate acks (dupacks) from the receiver, the proposed scheme sets the ssthresh value to the ocwndn. note that, if the connection is restored after physical bit errors, the window size should grow quickly to make up for the previous data loss event. in the proposed scheme, the sender immediately adjusts its congestion window size to the ocwndn. it is because the receiver has continuously monitored the available bandwidth of the corresponding path as given in eq. (1) and (2). there is no need to employ any kinds of slow start, additive increase, or fast recovery algorithms whenever the connection is recovered. with regard to congestion window updates when a packet loss is detected, there are three different cases depending on the values of backlog variable x, and two thresholds α and β. when x<α, the proposed scheme updates the congestion window size cwnd to ocwndn or follows an additive-increase paradigm (linear increase state) by comparing the current cwnd with a new ssthresh value. the reason why the proposed scheme updates the cwnd like this is that the network condition is not too bad as the backlog x<α implies, although there exits an incident that packets have been lost during transmission. when α≤x≤β, it updates the congestion window size to ocwndn or keeps the previous cwnd value unchanged by comparing the current cwnd with a new ssthresh value. the backlog α≤x≤β implies that there is a high probability that packets will be lost due to either network congestion or wireless bit errors. so, it is desirable to retain the previous cwnd (wait-and-see state) rather than following an additive-increase/adaptive-decrease paradigm. when x>β, it decreases the congestion window size adaptively. the backlog x>β means that the network falls into a congestion state. by adopting this eer approach, we can use the available bandwidth efficiently. the pseudo code of the proposed tcp ewr transmission control algorithm is given below: a) when packets successfully arrive at the receiver: bwsample(n), bwestimated(n), and ocwndn are computed; b) on ack reception at the sender: cwnd = ocwndn; c) when 3 dupacks are received or rto expires: if (x<α) ssthresh = ocwndn; if (cwnd > ssthresh) cwnd = ocwndn; else /* linear increase state */ cwnd=cwnd+2; else if (α ≤ x ≤ β) ssthresh=ocwndn; if (cwnd > ssthresh) cwnd = ocwndn; else /* wait-and-see state */ cwnd is retained; 762 i. ryoo, s. kim end if else /* congestion state */ ssthresh = ocwndn; cwnd = 1; note that the cwnd is increased by two in the linear increase state because it is updated after one rto rather than after each rtt . 3 simulation results the proposed transmission control scheme has been simulated by using ns-2 and its performances are compared with several representative schemes such as new reno, vegas, westwood+, and new jersey. performance metrics used in simulations include throughput, fairness, and friendliness. although the tcp ewr is proposed for adapting to future internet including error-prone wireless regions, it must also operate well for any topology with lossy or congested links. figure 1 shows a single bottleneck scenario for comparing performance metrics of the tcp ewr with those of well-known schemes under the condition that there exist various link error rates, and background traffics. figure 1: single bottleneck scenario figure 2 shows throughput comparison results. the tcp ewr shows almost similar performances when link error rate is low. however, as the link error rate increases, especially from the point of 8 % link error rate, the tcp ewr outperforms other schemes. in addition, fairness index [8] has been compared by considering 10 same tcp flows that share a 10 mbps bottleneck link. different tcp schemes have been simulated individually and the corresponding results are summarized in table 1. a perfect fairness of bandwidth allocation leads to the fair index of 1. it has been verified that, except 0 % link error rate, the tcp ewr’s fairness index achieves a satisfactory margin compared to other schemes. the reason why the tcp ewr shows an inferior result under the very low link error condition is that it does not aggressively use the bandwidth but maintain the optimal congestion window size ocwndn. figure 3 shows a mixed wired and error-prone wireless scenario where there exist multiple transmission control for future internet including error-prone wireless region 763 figure 2: throughput comparison for single bottleneck scenario table 1: fairness comparison for single bottleneck scenario error rate(%) new reno westwood+ ewr 0 0.76 0.80 0.64 0.1 0.31 0.93 0.98 1 0.25 0.74 0.76 10 0.57 0.23 0.62 tcp connections. in this scenario, bidirectional ftp background traffics flow between n1 and n3 and between n2 and n4. the queue sizes of wired links and the wireless link are set to 100 and 10, respectively. figure 3: mixed wired/error-prone wireless scenario with these simulation conditions, we have compared the tcp ewr with new reno, vegas, and westwood+. new reno is considered as it is the leading internet congestion control scheme. vegas is considered as it also proposes, as westwood+, a new mechanism to throttle the congestion window based on measuring the network congestion status. westwood+ is considered as it remarkably improves utilization of wireless links that are affected by losses not due to congestion. figure 4 shows the corresponding throughput comparison results. new reno, westwood+, and ewr show almost similar throughput results when the link error rate is less than 1 %. when 764 i. ryoo, s. kim the link error rate is greater than 1% and less than 10 %, the ewr outperforms westwood+. vegas shows poor performance throughout the whole link error range. from the results shown in figure 2 and figure 4, we can conclude that the ewr shows better throughput performances than the other schemes for wireless scenario as well as for wired scenario. the reason why the ewr shows again similar result as new reno and westwood+ is that the link with more than 10 % error rate makes all the above control schemes fall into congestion state and cannot be logically managed any more. figure 4: throughtput comparison for mixed wired/error-prone wireless scenario for the same topology shown in figure 3, fairness indices of new reno, westwood+, and the ewr are compared. there are seven tcp flows of the same version that share a bottleneck link of which the bandwidth is 5 mbps. the results are shown in table 2. from these results, we can verify that the ewr also achieves a satisfactory level of fairness index as new reno and westwood+ do. table 2: fairness comparison for mixed wired/error-prone wireless scenario error rate(%) new reno westwood+ ewr 0 0.95 0.94 0.96 0.1 0.94 0.94 0.89 1 0.95 1.00 0.95 10 0.96 0.95 0.96 table 3: friendliness comparison of the ewr with new reno (throughputs in kbps) number of number of mean throughput mean throughput connections(new reno) connections(ewr) (new reno) (ewr) 3 7 69.24 59.97 5 5 77.91 77.68 7 3 71.55 71.03 transmission control for future internet including error-prone wireless region 765 figure 5: a network topology for verifying friendliness table 4: friendliness comparison of the ewr with westwood+ (throughputs in kbps) number of number of mean throughput mean throughput connections(new reno) connections(ewr) (new reno) (ewr) 3 7 73.71 81.95 5 5 78.03 97.03 7 3 63.25 70.62 finally, the friendliness of the proposed scheme with new reno and westwood+ has been tested by using a simple network topology shown in figure 5. in simulations, there are 10 pairs of connections where m is the number of the hosts that communicate based on the proposed scheme and n is the number of hosts that use new reno or westwood+. wireless link error rates have been set to vary between 0.1 and 10, and the corresponding results are summarized in table 3 and table 4. from the results, it has been shown that the proposed tcp ewr has good controllable friendliness compared with other two major tcp control schemes in error-prone wireless networking environment. 766 i. ryoo, s. kim 4 conclusion this paper introduces a new transmission control scheme for future internetworking environment with error-prone wireless regions. the proposed scheme is designed to adjust its congestion window optimally based on the current network situation while estimating the available bandwidth at the receiver. as a result, accurate window update and transmission control can be possible to obtain high throughput while achieving satisfactory levels of fairness and friendliness, which are important indices for the proposed tcp ewr to be a feasible scheme. acknowledgement this research was supported by kyung hee university, korea (20090724). bibliography [1] sumit rangwala, apoorva jindal, ki-young jang, and konstantinos psounis, understanding congestion control in multi-hop wireless mesh networks, proceedings of the 14th acm international conference on mobile computing and networking, san franscisco, california, usa, pp. 291-302, 2008. [2] dhiman barman, ibrahim matta, eitan altman, and rachid el azouzi, tcp optimization through fec, arq and transmission power tradeoffs, lecture notes in computer science, vol. 2957, pp. 87-98, 2004. [3] rajashree paul and ljiljana trajković, selective-tcp for wired/ wireless networks, in proceedings spects 2006, calgary, al, canada, pp. 339-346, 2006. [4] kai xu, ye tian, and nirwan ansari, tcp-jersey for wireless ip communications ,ieee journal on selected areas in communications, vol. 22, no. 4, pp.747-756, 2004. [5] luigi a. grieco and saverio mascolo, performance evaluation and comparison of westwood+, new reno and vegas tcp congestion control , acm sigcomm computer communications review, vol. 34, no. 2, pp. 25-38, 2004. [6] jitendra padhye, victor firoiu, don towsley, and jim kurose, modeling tcp throughput: a simple model and its empirical validation , acm sigcomm computer communication review, vol. 28, issue 4, pp. 303-314, 1998. [7] cheng peng fu and soung c. liew, tcp veno: tcp enhancement for transmission over wireless access networks , ieee journal on selected areas in communications, vol. 21, no. 2, pp. 216-228, 2003. [8] rajendra. k. jain, dah-ming w. chiu, and william r. hawe, a quantitative measure of fairness and discrimination for resource allocation in shared computer system, dec-tr301, eastern research lab., http://www.cs.wustl.edu/ jain/papers/ftp/fairness.pdf international journal of computers, communications & control vol. ii (2007), no. 4, pp. 367-374 an efficient numerical integration algorithm for cellular neural network based hole-filler template design v. murugesh, krishnan batri abstract: this paper presents, a design method for the template of the hole-filler used to improve the performance of the character recognition using numerical integration algorithms. this is done by analyzing the features of the hole-filler template and the dynamic process of cnn and by using popular numerical algorithms to obtain a set of inequalities satisfying its output characteristics as well as the parameter range of the hole-filler template. some simulation results and comparisons are also presented. keywords: cellular neural networks; euler algorithm; rk-gill algorithm; rkbutcher algorithm; ordinary differential equations, hole-filler. 1 introduction cellular neural networks (cnns) are analog, time-continuous, nonlinear dynamical systems and formally belong to the class of recurrent neural networks. since their introduction in 1988 (by chua and yang [1, 2], it has been the subject of intense research. initial applications include image processing, signal processing, pattern recognition and solving partial differential equations, etc. runge-kutta (rk) methods have become very popular, both as a computational technique as well as a subject of research, which are discussed by butcher [3, 4]. this method was derived by runge around the year 1894 and extended by kutta a few years later. they developed algorithms to solve differential equations efficiently and these are the equivalent of approximating the exact solutions by matching ‘n’ terms of the taylor series expansion. butcher [3] derived the best rk pair along with an error estimate and by all statistical measures it appeared as the rk-butcher algorithm. this rk-butcher algorithm is nominally considered as sixth order, since it requires six function evaluations, but in actual practice the “working order” is close to five (fifth order). bader [4, 5] introduced the rk-butcher algorithm for finding the truncation error estimates and intrinsic accuracies and the early detection of stiffness in coupled differential equations that arise in theoretical chemistry problems. recently devarajan et al [7] used the rk-butcher algorithm for finding the numerical solution of an industrial robot arm control problem. oliveria [8] introduced the popular rk-gill algorithm for the evaluation of ’effectiveness factor’ of immobilized enzymes. in this paper, we describe the dynamic behavior of cnn in section 2, hole-filler template design ideas in section 3, numerical integration algorithms and its description is shown in section 4, and simulation results in section 5. 2 dynamic analysis of cnn the dynamic equation of cell c(i, j) in an m x n cellular neural network is given by chua and yang [1, 2]. c dxi j(t) dt = − 1 rx xi j(t) + ∑ c(k,l)∈nr (i, j) a(i, j; k, l)ykl (t) + ∑ c(k,l)∈nr (i, j) b(i, j; k, l)ukl + i (1) yi j(t) = [∣∣xi j(t) + 1 ∣∣− ∣∣xi j(t) ∣∣−1 ] , 1 ≤ i ≤ m, 1 ≤ j ≤ n (2) copyright © 2006-2007 by ccc publications 368 v. murugesh, krishnan batri uij xij · · · yij c r i yij n i uij n i figure 1: cnn–cell wherexi j, yi j and ui j are the state voltage, output voltage and input voltage respectively and they are functions of time t. rx is a linear resistance, c is a linear capacitor, and a(i, j; k, l) and b(i, j; k, l) are the transconductances of the output and input voltages of c(k, l) with respect to c(i, j) called the cloning templates of cnn. nr(i, j) denotes the rthneighbor of c(i, j) and i is an independent current source. from equation (2) one can see that the output voltage is nonlinear. we can rewrite the cell equation (1) as follows: c dxi j(t) dt = − f [xi j(t)] + g(t) (3) where f [xi j(t)] = 1 rx xi j(t) (4) g(t) = ∑ c(k,l)∈nr (i, j) c(k,l)6=c(i, j) a(i, j; k, l)ykl (t) + ∑ c(k,l) b(i, j; k, l)ukl + i (5) 3 hole-filler template design the hole-filler is a cellular neural network discussed by yin et al [9], which fills up all the holes and remains unaltered outside the holes in a bipolar image. let rx = 1, c = 1 and let +1 stand for the black pixel and -1 for the white one. we shall discuss the images having holes enclosed by the black pixels, when the bipolar image is input with u = {ui j} into cnn. the initial state values are set as xi j(0) = 1. from the equation (2) the output values are yi j(0) = 1, 1 ≤ i ≤ m, 1 ≤ j ≤ n. suppose that the template a and b and the independent current source i are given as a =   0 a 0 a b a 0 a 0   , a > 0, b > 0, b =   0 0 0 0 4 0 0 0 0   , i = −1 (6) where the template parameters a and b are to be determined. in order to make the outer edge cells become the inner ones, normally auxiliary cells are added along the outer boundary of the image, and their state values are set to zeros by circuit realization, resulting in the zero output values. the state equation (1) can be rewritten as dxi j(t) dt = −xi j(t) + ∑ c(k,l)∈nr (i, j) a(i, j; k, l)yi j(t) + 4ui j(t)−i (7) for the cell c(i, j), we call the cells c(i + 1, j), c(i − 1, j), c(i, j + 1) and c(i, j − 1) to be the nondiagonal cells. here, several cases are to be considered. an efficient numerical integration algorithm for cellular neural network based hole-filler template design 369 case 1: the input value ui j = +1 for cell c(i, j), signaling the black pixel. because the initial state value of the cell c(i, j) has been set to 1, xi j(0) = 1, and from equation (2) its initial output value is also yi j(0) = 1 . according to the hole-filler demands, its eventual output should be yi j(∞) = 1. to obtain this result we set dxi j(t) dt ≥ 0 (8) substituting this input ui j = 1 and equation (6) into equation (7), we obtain dxi j(t) dt = −xi j(t) + a [ y(i−1) j(t) + y(i+1) j(t) + yi( j−1)(t) + yi( j+1)(t) ] + byi j(t) + 3 (9) combining equations (8) and (9) and considering the minimum value of xi j(t) = 1 this case yields a [ y(i−1) j(t) + y(i+1) j(t) + yi( j−1)(t) + yi( j+1)(t) ] + byi j(t) + 2 ≥ 0 (10) to facilitate our discussion, two sub cases are distinguished. sub case 1: the cell c(i, j) is inside the holes. since xi j(0) = 1 , from equation (2) its initial output value yi j(0) = 1. considering equations (8) and (2), yi j(t) ≥ 1. according to the hole-filler demands, its initial output of non-diagonal black pixels should not be changed inside the holes. the weights of a and b are equal to +4 and +1, respectively. since a(i, j; k, l) > 1rx the parameter b is found to be b > 1, or 4a + b + 2 ≥ 0, b > 1 (11a) sub case 2: the cell c(i, j) is outside the holes. to satisfy equation (10), we need to check only the minimum value on the left-hand side of equation (10). this is true when there are four non-diagonal white pixels around the cell c(i, j), where the weight of a in equation (10) is -4. since yi j(t) ≥ 1, the weight of b is equal to 1. combining this with b > 1 gives −4a + b + 2 ≥ 0, b > 1 (11b) case 2: the input value of cell c(i, j) is ui j = 1, signaling the white pixel. substituting this input value in equation (7) gives dxi j(t) dt = −xi j(t) + a [ y(i−1) j(t) + y(i+1) j(t) + yi( j−1)(t) + yi( j+1)(t) ] + byi j(t)−5 (12) sub case 1: the cell c(i, j) is inside the holes. since xi j(0) = 1, from equation (2) its initial output value is yi j(0) = 1. according to the hole-filler demands, the holes should be filled by the black pixels, whereas its initial black pixels remain unaltered: dxi j(t) dt ≥ 0 (13) combining equations (12) and (13) and considering xi j(t) ≥ 1 yields aby(i−1) j(t) + y(i+1) j(t) + yi( j−1)(t) + yi( j+1)(t)c+ byi j(t)−6 ≥ 0 (14) where we use the minimum value of xi j(t) in equation (12). since the cell is inside the holes, its initial output of non-diagonal black pixels remain unchanged. the weight of a and b are equal to +4 and +1, respectively. combining this with b > 1 gives 4a + b−6 ≥ 0, b > 1 (15) 370 v. murugesh, krishnan batri sub case 2: the cell c(i, j) is outside the holes. since xi j(0) = 1, from equation (2) its initial output value is yi j(0) = 1. according to the hole-filler demands, the final output of this cell should be white, and in this case yi j(∞) ≤ −1. dxi j(t) dt < 0 (16) combining equations (12) and (16) and considering xi j(t) ≤ 1. we get aby(i−1) j(t) + y(i+1) j(t) + yi( j−1)(t) + yi( j+1)(t)c+ byi j(t)−6 < 0 (17) where we use the maximum value of xi j(t) in equation (12). initially yi j(0) = 1. how can the output of cell c(i, j) be changed to -1?. where does this change begin?. first we consider the situation where the change begins from the inside of the bipolar image. if the maximum value on the left-hand side in equation (17) is less than zero, equation (17) holds. inside the image and outside the holes, the maximum weights of a and b are +4 and +1, respectively. this case was described by equation (15). in fact, the change of the output of the cell c(i, j) is like a wave propagating from the edges to the inside of the image and it is verified from the simulated result. therefore, we should first consider the edge cell c(i, j), i = 1 or m, j = 1 or n. for this the maximum weight of a in equation (17) is +3, which is also the maximum weight of a outside the holes. the maximum weight of b is +1, occurring at the initial time: 3a + b−6 < 0, b > 1 (18) combining cases 1 and 2, we obtain 3a + b−6 < 0, 4a + b−6 ≥ 0, (19) −4a + b + 2 ≥ 0. 4 numerical integration algorithms the cnn dynamics on a digital architecture requires discretization in time and suitable numerical integration algorithms. three of the most widely used numerical integration algorithms are used in raster cnn simulation described here. they are the euler’s algorithm, rk-gill algorithm discussed by oliveria [8] and the rk-butcher algorithm discussed by badder [5, 6] and murugesh and murugesan [10, 11, 12]. 4.1 euler algorithm euler’s method is the simplest of all algorithms for solving odes. it is explicit formula which uses the taylor-series expansion to calculate the approximation. xi j((n + 1)τ) = xi j(π τ) + τ f ′(x(π τ)) (20) 4.2 rk-gill algorithm the rk-gill algorithm discussed by oliveria [8] is an explicit method requiring the computation of four derivatives per time step. the increase of the state variable xi j is stored in the constant ki j1 . this an efficient numerical integration algorithm for cellular neural network based hole-filler template design 371 result is used in the next iteration for evaluating ki j2 . the same must be done for k i j 3 and k i j 4 . ki j1 = f ′ (xi j (τ π)) ki j2 = f ′ ( xi j (τ π) + 1 2 ki j1 ) (21) ki j3 = f ′ ( xi j (τ π) + ( 1√ 2 − 1 2 ) ki j1 + ( 1− 1√ 2 ) ki j2 ) ki j4 = f ′ ( xi j (τ π)− 1√ 2 ki j2 + ( 1 + 1√ 2 ) ki j3 ) the final integration is a weighted sum of the four calculated derivatives: xi j((n + 1)τ) = xi j + 1 6 [ ki j1 + ( 2− √ 2 ) ki j2 + ( 2 + √ 2 ) ki j3 + k i j 4 ] (22) 4.3 rk-butcher algorithm the rk-butcher algorithm discussed by badder [5, 6] and murugesh and murugesan [10, 11, 12], is an explicit method. it starts with a simple euler step. the increase of the state variable xi j is stored in the constant ki j1 . this result is used in the next iteration for evaluating k i j 2 . the same must be done for ki j3 , k i j 4 , k i j 5 and k i j 6 . ki j1 = τ f ′ (xi j (π τ)) ki j2 = τ f ′ ( xi j (π τ) + 1 4 ki j1 ) ki j3 = τ f ′ ( xi j (π τ) + 1 8 ki j1 + 1 8 ki j2 ) ki j4 = τ f ′ ( xi j (π τ)− 1 2 ki j2 + k i j 3 ) (23) ki j5 = τ f ′ ( xi j (π τ) + 3 16 ki j1 + 9 16 ki j4 ) ki j6 = ∆t f ( xi j (π τ)− 3 7 ki j1 + 2 7 ki j2 + 12 7 ki j3 − 12 7 ki j4 + 8 7 ki j5 ) the final integration is a weighted sum of the five calculated derivatives: xi j ((n + 1) τ) = 1 90 ( 7ki j1 + 32k i j 3 + 12k i j 4 + 32k i j 5 + 7k i j 6 ) (24) 5 simulated results this hole-filler template has been simulated using pentium iv machine with 3.0 ghz. speed using different numerical integration algorithms. the settling time t and integration time ts is obtained with various step sizes is to be displayed below in the table-1. the settling time ts describes the time from start of integration until the last cell leaves the interval [-1.0, 1.0] which is based on certain limit (e.g., ∣∣ dx dt < 0.01 ∣∣). the simulation shows the desired output for every cell. we use +1 and -1 to indicate the black and white pixels, respectively. 372 v. murugesh, krishnan batri (a) (b) figure 2: image before and after hole filling step size euler algorithm rk-gill algorithm rk-butcher algorithm settling time(t) integration time(ts) settling time(t) integration time(ts) settling time(t) integration time(ts) 0.5 6.5 2.5 6.8 2.4 5.8 2.4 0.6 15.5 12.7 16.4 13.7 11.4 12.5 0.7 32.5 28.3 32.0 27.4 30.0 27.2 0.8 35.0 30.7 34.6 30.0 32.4 29.6 0.9 36.8 32.6 36.6 32.0 34.2 31.6 1.0 37.9 33.6 37.6 33.0 36.0 32.8 1.5 44.8 36.8 45.7 36.9 41.1 36.0 2.0 47.4 43.2 48.2 43.6 46.2 42.8 2.5 50.6 45.6 52.6 44.5 48.3 44.7 3.0 53.5 49.3 54.8 50.2 52.3 49.2 table 1: simulated results of hole-filler template design example the templates a, b and i are given as follows: a =   0 1.0 0 1.0 3.0 1.0 0 1.0 0.0   , b =   0 0 0 0 4 0 0 0 0   , i = −1.0 using the simulation program developed in c++, the input image is shown in figure-2(a) and the output image in figure-2(b). the obtained result is represented in table-1. from the table-1, we find that rk-butcher algorithm yields less settling time and integration time compared to euler and rk-gill algorithms. 6 conclusion it is shown that the cellular neural network based hole-filler template could be designed from its dynamic behavior using different numerical algorithms, and also the template for other cellular neural network can similarly be designed. the hole is filled and the outside image remains the same. the templates of the cellular neural network are not unique and this is important in its implementation. an efficient numerical integration algorithm for cellular neural network based hole-filler template design 373 bibliography [1] l. o. chua, l. yang, cellular neural networks: theory, ieee transactions on circuits and systems, vol. 35, pp. 1257 1272, 1988. [2] l. o. chua, l. yang, cellular neural networks: applications, ieee transactions on circuits and systems, vol. 35, pp. 1273 1290, 1988. [3] j. c. butcher, the numerical analysis of ordinary differential equations: runge-kutta and general linear methods, chichester: john wiley, 1987. [4] j. c. butcher, numerical methods for ordinary differential equations, chichester: john wiley, 2003. [5] m. bader, a comparative study of new truncation error estimates and intrinsic accuracies of some higher order runge-kutta algorithms, computers & chemistry, vol. 11 pp. 121-124, 1987. [6] m. bader, a new technique for the early detection of stiffness in coupled differential equations and application to standard runge-kutta algorithms, theoretical chemistry accounts, vol. 99, pp. 215-219, 1988. [7] g. devarajan, v. murugesh, k. murugesan, numerical solution of second-order robot arm control problem using runge-kutta butcher algorithm, international journal of computer mathematics, vol. 83 pp. 345-356, 2006. [8] s. c. oliveira, evaluation of effectiveness factor of immobilized enzymes using runge-kutta-gill method: how to solve mathematical undetermination at particle center point?, bio process engineering, vol. 20, pp. 85-187, 1999. [9] c. l. yin, j. l. wan, h. lin, w. k. chen, the cloning template design of a cellular neural network, journal of the franklin institute, vol. 336, pp. 903-909, 1999. [10] v. murugesh, k. murugesan, comparison of numerical integration algorithms in raster cnn simulation, lecture notes in computer science, vol. 3285 pp. 115-122, 2004. [11] v. murugesh, k. murugesan, simulation of cellular neural networks using the rk-butcher algorithm, international journal of management and systems, vol. 21, pp. 65-78, 2005. [12] v. murugesh, k. murugesan, simulation of time-multiplexing cellular neural networks with numerical integration algorithms, lecture notes in computer science, vol. 3991, pp. 115-122, 2005. v. murugesh department of information and communication engineering hannam university 133 ojung-dong daeduk-gu, daejeon 306-791, republic of korea e-mail: murugesh72@gmail.com k. batri department of computer science and engineering muthyammal engineering college rasipuram 637 408, india e-mail: krishnan.batri@gmail.com received: july 25, 2007 374 v. murugesh, krishnan batri dr. v. murugesh obtained his bachelor of science in computer science and master of computer applications degree from bharathiar university, coimbatore, india during 1992 and 1995 respectively. completed his phd in computer science from bharathidasan university, tiruchirappalli, india during 2006. he has held various positions at national institute of technology, tiruchirappalli, india and sona college of technology, salem, india. currently, he is working as assistant professor in the department of information and communication engineering at hannam university, daejeon, republic of korea. his fields of interest are in neural network based image processing and scientific computing. he has published more than 30 technical papers in international, national journals and conferences. krishnan batri received the m.e. from madurai kamarj university in 2003. he is a research scholar with the department of computer science and engineering in the national institute of technology tiruchirapalli, tamil nadu, india. currently he is working as a assistant professor with the department of computer science and engineering at muthayammal engineering college, rasipuram, tamilnadu, india. his research interests include information retrieval, data fusion and genetic algorithms. int j comput commun, issn 1841-9836 9(1):71-78, february, 2014. energy efficient key management scheme for wireless sensor networks n. suganthi, v. sumathy n.suganthi* dept of information technology kumaraguru college of technology, coimbatore-49 *corresponding author suganthiduraisamy@yahoo.co.in v.sumathy ece department government college of technology, coimbatore-13 sumi_gct2001@yahoo.co.in abstract: designing an efficient key establishment scheme is of great importance to the data security in wireless sensor networks. the traditional cryptographic techniques are impractical in wireless sensor networks because of associated high energy and computational overheads. this algorithm supports the establishment of three types of keys for each sensor node, an individual key shared with the base station, a pair wise key shared with neighbor sensor node, and a group key that is shared by all the nodes in the network. the algorithm used for establishing and updating these keys are energy efficient and minimizes the involvement of the base station. polynomial function is used in the study to calculate the keys during initialization, membership change and key compromise. periodically the key will be updated. to overcome the problem of energy insufficiency and memory storage and to provide adequate security, the energy efficient scheme is proposed. it works well in undefined deployment environment. unauthorized nodes should not be allowed to establish communication with network nodes. this scheme when compared with other existing schemes has a very low overhead in computation, communication and storage. keywords: key management, sensor nodes, polynomial function 1 introduction these tiny sensor nodes, which consist of sensing, data processing and communicating components, leverage the idea of sensor networks based on the collaborative effort of a large number of nodes. sensor nodes are deployed in hostile environments or over large geographical area. the nodes could either have a fixed location or could be randomly deployed to monitor the environment. the nodes then sense environmental changes and report them to other nodes over flexible network architecture. they have thus found application domains in battlefield communication, homeland security, pollution sensing and traffic monitoring. the limited factors of using sensor nodes are that they have limited battery power and less memory capacity. to control information access in a sensor environment only authorized node must know the key to disseminate the information that is unknown to the compromised nodes. the communication keys may be pair wise [7],chan,du or group wise [1], these keys to be updated to maintain security and resilience to attacks. some of the proposed work was based on static schemes [7]liu and some are on dynamic schemes [1]eltoweissy though many protocols have been designed for the purpose of security in sensor environment, unfortunately, node compromising is rarely or not enough investigated and most of these protocols have a weak resilience to attack [13]. in this paper, we propose a key management scheme for wsns in which the pair wise keys and the group wise key are set up through the broadcast information during the network initialization copyright © 2006-2014 by ccc publications 72 n. suganthi, v. sumathy phase and no further message exchange is needed afterwards. consequently, the communication overhead is very low. therefore, the compromise of some sensor nodes will not affect any other non-compromised pair wise keys. for the establishment of keys for new nodes, we propose a composite mechanism based on an algorithm in which resource consumption can also be kept very low and also the transmission of information. here only the polynomial identifier needs to be communicated to the nodes for establishing group key and pair wise key. the rest of this paper is organized as follows. in section 2, some related work and their drawbacks are discussed. in section 3, energy-efficient key management scheme, in sections 4 and 5, the security and performance of energy efficient key management scheme are analyzed. finally, section 6 deals with the conclusion. 2 related works many pair wise key distribution schemes [5] [7] [8] [9] have been developed for peer to peer wireless sensor networks and heterogeneous network [6] [12] [14]. in one of the hierarchical schemes [1], the base node calculates the group key using partial keys in bottom up fashion. the partial key of the child node is generated using random number and which is passed to its parent to calculate its partial key which further goes in bottom up fashion finally to calculate the group key. the partial keys are calculated by using a function. the function is expressed as f(k1,k2) = α k1+k2mod p (1) p is the prime number, k1, k2 are the partial keys the decision for choosing a number of partial keys is based on the key size for the security requirements and the corresponding energy consumption. to guarantee that all the nodes in a group received the information, they send the reply (rep) message. if the cluster head does not get the (rep) from all the node, it re-broadcasts. when a new node joins the group, the group key is recalculated and again the cluster head broadcasts the newly created group key to all the nodes in the group. the same is repeated when a node leaves the group. this makes the old node, which is deleted, not to know the new key that is created. also communication takes places between two cluster heads. due to poor memory capacity and low power of sensor nodes it will be difficult to store all the partial keys and the communication becomes costly as it needs to broadcast the group key once it is created and changed. in our energy efficient key management protocol scheme the group key need not be broadcasted each time. a tree based key management protocol [2] in which each sensor node is pre-deployed with three keys. one of the keys is used for initial communication i.e for key exchange and tree spanning. after the tree is spanned, this key is deleted from the memory of the sensor node. then for further communication, the remaining two keys are used. one of these keys is symmetric and is used to encrypt (or decrypt) information sent from the child to the parent. the third key is also symmetric and is used to encrypt (or decrypt) information sent from the parent to the child. the two keys are used to make the task of cryptanalysis attacker difficult. the disadvantage of this scheme is that an attacker gaining access (physical) to the sensor node can obtain the information. xing zhang et al [11] proposed an energy efficient distributed deterministic key management protocol (eddk). though this scheme provides higher security than the above two schemes namely hierarchical and tree-based protocol, it requires large memory to store data. sencun zhu et al [10] proposed a leap: efficent security mechanisms for large scale distributed sensor networks and du et al [11]proposed a scheme using depolyment knowledge. energy efficient key management scheme for wireless sensor networks 73 3 proposed scheme: energy-efficient key management scheme to fix the flaws present in the existing key management schemes, we propose an energy efficient key management scheme for wsns. this scheme mainly focuses on the establishment and maintenance of the pair wise keys as well as the group keys. unlike hierarchical and treebased key management schemes, this scheme does not require additional memory for storing the keys after deployment. in this scheme, no key is broadcasted. each and every node generates the group key and pair wise key using one of the polynomial functions. the polynomial function is identified with the help of its id. to enhance message security in the network initialization phase, each sensor node makes use of its own individual key. the base station computes the individual key of all the nodes using the unique keys and the ids that it has stored. this method also enhances security in data transmission with periodic key update. to avoid the replay attack, sequence number is used. 3.1 overall system description the diagram (figure 1) shows the overall system description. key will be established after node initialization. if any node joins in the system or a node is compromised, key update will be performed. after detecting the compromised node the keys will be removed from the node memory. the overall system is designed to reduce the computation overhead and it requires less communication between nodes. figure 1: system model 3.2 spanning of tree before deployment, every sensor node is pre-distributed with a network wide shared pseudorandom function (rf) and an initial key. it is assumed that each node is tamper proof, and it will not be affected by capture attack. the pseudorandom function and the initial key is used by every node in the network to compute its own individual key. this individual key is used for initial communication with the base station. for example, node a s individual key can be computed as per equation (2). ka = rf(ki,ida). (2) ki initial key, ida individual node identifier 74 n. suganthi, v. sumathy ka individual key shared with base station, rf -shared pseudorandom function the hello message which is used to span the tree is also encrypted using this individual key. the hello message contains the id of the sender and the hello keyword. the base station broadcasts hello message. the nodes that reply to hello message become the children of the base station. here acknowledgment of the child node is essential to accept the node as the child. these nodes then broadcast hello message to other nodes. the nodes that reply to hello message become the children of these nodes. the spanning of the tree is stopped when the nodes do not get a reply to the hello message. 3.3 key establishment phase base station will transmit function identifier and random number, encrypted with individual key to individual nodes as shown in equation (3). eka(pfid,rn) (3) pfid function identifier , rn random number pair wise key after sensor nodes are placed in the sensor field, each sensor node communicates with its neighbor via the pair wise key. this key is used to make sure that the message to the intended neighbor node is not known by other neighbor nodes. nodes a and b are used to show the calculation of the pair wise key using equation (4). let us consider that node a wants to communicate with node b. kab = pfid(rn,ida) (4) kab pairwise key, rn random number, ida identifier of node a the polynomial function takes the random number and the id of the node which initiates the communication as input to calculate the pair wise key. the id of the polynomial function is used to identify one of the functions from the set of polynomial functions. the base node communicates the random number and the id of the polynomial function to all the nodes. after calculating the pair wise key, it will transmit the message encrypted with this key to the node b along with its id in plain. then node b will calculate the pair wise key as it has all the information needed for calculation. using the key it will decrypt the message transmitted by node a. here each node, thus, calculates the pair wise key by knowing initiator id. as no transmission of id or key information takes place, communication overhead is avoided here. group key a group key is a key shared by all the nodes in the network, and it is needed when the base station is distributing a secure message, (e.g. a query on some event of interest or a confidential instruction) to all the sensor nodes in the network. in a conventional method the parent encrypts m with its cluster key and then broadcasts the message. each neighbor receiving the message decrypts it to get the message and re-encrypts with its own cluster key, and then transmits the m. this process is repeated until all the nodes receive the message. however, this method has a drawback. in this method, every node has to encrypt and decrypt the message, thus consuming a large amount of energy on computation. so encryption using group key is the most desirable one from the performance point of view. the simple way to store the group key for a node is to preload every node with it. an important problem that arises immediately is the secured energy efficient key management scheme for wireless sensor networks 75 updation of this key when a compromised node is detected. in our proposed scheme, to enable base station-individual nodes communication, group key is used. the group key generator that is present in all the nodes is used for generating the group key using the equation(5). the random number that is transmitted to each node is also involved in key calculation. the group key is calculated as follows. kg = pfid(rn,gk) (5) kg group key , pfid polynomial function , rn random number, gk group key generator the timer is set for the reestablishment of the key. when the timer reaches the threshold value that is assigned, the re-keying is done. here re-keying is done by changing the coefficient of the polynomial function. the previously calculated keys are deleted periodically. therefore, even if an adversary could compromise some legitimate nodes, it still could not compute the pair wise keys and the group key. note that each sensor node only needs to broadcast one communication message during the key establishment phase with no further message exchange required for key calculation. thus, the communication overhead can be very low. during the data transfer phase the sequence number is used to indicate the message transfer between the nodes. once the sequence number reaches the threshold value that is already set, the sequence number is reset to 1 again and it can prevent the replay attacks. this sequence number helps to know the number of the messages sent and received. it is also used for receiving the acknowledgement. 3.4 key update phase pair wise key and group key should be updated to avoid cryptanalysis and to prevent attacks from adversaries after one or more sensor nodes are compromised. and also after the threshold time, nodes need to update the keys using the same formula as mentioned in equation (4) and (5). the coefficient of the polynomial function is changed. it is done by adding the constant with the previous values and the modulus is taken to be used as new coefficients. thus it makes the key update easily and avoids communication overhead. 4 system analysis for system analysis, we have implemented the key management algorithm in matlab. compared with the eddk, tree based protocol and hierarchical scheme. 4.1 computation costs computation costs are measured in terms of number of encryptions required to change the keys in the event of node compromise and node addition. when a node is added, only the new random number and new id of the polynomial function will be transmitted by base station. individual nodes will receive it and compute the group key and pair wise key using the formulas. calculation using the polynomial function will consume less energy. but in other schemes lot of encryption and decryption is involved to get the key update. it consumes lot of battery energy. the comparison is made between the existing schemes and the proposed scheme in terms of time. this graph (figure 2) shows the computation time differences between various schemes and proposed scheme depending upon the number of nodes during initial key calculation. 4.2 memory requirement for key storage let x represent the number of neighboring nodes around a sensor and n be the number of polynomial functions. each sensor node has x storage units for the pair wise keys, nt + n 76 n. suganthi, v. sumathy figure 2: time required to calculate initial key storage units for the t-degree polynomial functions, two storage units for random number and pseudo random function and single storage unit to store the group key. in terms of memory requirement to store keys for each scheme, the proposed scheme needs less memory, hence it provides scalability. even when the number of nodes increases, the memory required to store the keys remains the same. on the contrary the tree based protocol requires more memory as the number of nodes increases. the following graph (figure 3) shows the comparison of memory requirement for all schemes. the proposed scheme need less memory even when number of nodes increases. figure 3: memory requirement for key storage 4.3 communication overhead for key exchange communication cost is measured in terms of number of messages needed to be exchanged in order to update the existing keys as a result of events like; addition of new node, node compromise, and key refreshing at regular intervals. the communication overhead for the existing scheme is more when compared to the proposed scheme. this is because they need to exchange the keys to enable communication. the proposed scheme requires transmitting the ids of the polynomial function and random numbers only. so overhead for key exchange is minimal. as shown in figure 4, the number of nodes increases, the communication overhead increases for tree based protocol; whereas eddk and proposed scheme require less communication overhead. energy efficient key management scheme for wireless sensor networks 77 figure 4: communication overhead for key exchange 5 security analysis adversaries with a single compromised node and adversaries with n compromised nodes are chosen selectively. in all cases we study their impact on the desired network properties assuming that the adversary acts maliciously at different layers of the communication protocols. insiders are adversaries that can compromise nodes or otherwise have a valid identity in a network with appropriate key material. insiders therefore have the same capabilities as outsiders plus the ability to participate in the network protocols and deviate from the normal behavior of the protocols. stronger security considerations have to be taken into account for insiders. a minimum level of fault tolerance has to be designed into the network inside attackers. but in our proposed algorithm every node is loaded with the set of polynomials, and every time one function will be used to calculate the key. adversary nodes cannot generate this polynomial functions. and also it doesn’t know which function will be used to calculate the key at that time. the random number will be communicated to the individual nodes by the base station after encrypting with secret key shared by the base station and the individual node. 6 conclusion the key exchange problem for sensor networks has been introduced and it is believed that information can be secured by not exchanging the keys directly. a mechanism that makes use of pre-deployed functions has been proposed to fulfill our idea. by using this mechanism, the impacts of many attacks in wireless sensor networks can be limited. this scheme incorporates mechanism that allows for the scalability of memory in the sensor nodes. by comparing the proposed scheme with the existing scheme, it becomes clear that memory required to store the key information is less. similarly the communication overhead to exchange the keys is also low. because of the increase in the complexity of the algorithm, the immunity of the sensor networks towards various attacks has been greatly increased. thus the proposed naming mechanism shields the network from various attacks. bibliography [1] biswajit panja; sanjay madria; bharat bhargava; energy-efficient group key management protocols for hierarchical sensor networks,int. j. of distributed sensor networks taylor francis group, 201-223, doi:10.1080/15501320701205225, 2007. 78 n. suganthi, v. sumathy [2] messai,l.; aliouat,m.; seba,h.; tree based protocol for key management in wireless sensor networks, eurasip j.on wireless communications and networking, article id 910695, doi:10.1155/2010/910695, 2010. 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[7] eschenauer,l.; gligor,v.d.(2002); a key-management scheme for distributed sensor networks, proc. of the 9th acm conference on computer and communications security, washington, dc, usa, 41-47, 2002. [8] chan,h.; perrig,a.; song, d.; randomkey predistribution schemes for sensor networks, proc. of ieee symposium on security and privacy, 197-213, 2003. [9] liu,d.; ning, p.; establishing pairwise keys in distributed sensor networks,proc. of the 10th acm conference on computer and communications security (ccs 03), washington, dc, usa, 52-61, 2003. [10] sencun zhu; sanjeev setia; sushil jajodia (2003); leap: efficient security mechanisms for large scale distributed sensor networks, proc. of the 10th acm conference on computer and communications security, pp.62-72. [11] du,w.; deng,j.; han,y.s.; chen,s.; varshney,p.; a key management scheme for wireless sensor networks using deployment knowledge, proc. ieee infocom04, 586-597, 2004. [12] kausar,f.; hussain,s.; yang,l.t.; masood,a.; scalable and efficient key management for heterogeneous sensor networks, j. of supercomputing, 45(1):44-65, 2008. [13] xiao,y.; rayi,v.k.; sun,b.; du,x.; hu,f.; galloway,m.; a survey of key management schemes in wireless sensor networks,j. of computers communications, 30(11-12):2314-2341, 2007. [14] du,x.; xiao, y.; guizani, m.; chen,h.h.; an effective key management scheme for heterogeneous sensor networks, j. of ad hoc networks, 5(1):24-34, 2007. international journal of computers, communications & control vol. i (2006), no. 3, pp. 33-39 descriptive timed membrane petri nets for modelling of parallel computing emilian guţuleac abstract: in order to capture the compartmentation and behaviour of membrane systems for modelling of parallel computing, we introduce the descriptive dynamic rewriting descriptive membrane timed petri nets (dm-nets) that can at in run-time modify their own structure by rewriting some of their descriptive expression components. furthermore, this descriptive approach facilitates the understanding of complex models and their component-based construction as well as the application of modern computer engineering concepts. keywords: descriptive petri nets, membrane systems, modelling, parallel computing. 1 introduction recent technological achievements require advances beyond the existing computational models in order to be used effectively. pragmatic aspects of current and future computer systems will be modelled so that realistic estimates of efficiency can be given for algorithms in these new settings. petri nets (pn) are very popular formalism for the analysis and representation of parallel and distributed computing in concurrent systems that has draw much attention to modelling and verification of this type of systems [1]. p systems, also referred to as membrane systems, are a class of parallel and distributed computing models [6]. the interest of relating p systems with the pn model of computation lead to several important results on simulation and decidability issues. some efforts have been made to simulate p systems with petri nets [2, 5, 7] to verifying the many useful behavioral properties such as reachability, boundedness, liveness, terminating, etc. in this paper we propose a new approach to express the components of continuous-time p systems [6] throughout components of escriptive petri nets (pn) using descriptive expressions (de) [3] for modelling of parallel computing. the de are used for analytical representation and compositional construction of pn models. to model specific rules of p-systems within the framework of the descriptive rewriting timed pn (rtn) [4] we introduce a new extensions ű the descriptive membrane rtn, called dm-nets, that can modify dynamically their own structures by rewriting rules some of their components. 2 labeled extended petri nets in this section, we define a variant of pn called labeled extended pn. let l be a set of labels l = lp ]lt . each place pi labeled l(pi) ∈ p a local state and transition t j has action labeled as l(t j) ∈ lt . a labeled extended pn is structure as a γ =< p, t, pre, post, test, inh, g, pri, kp, l >, where: p is the finite set of places and t is a finite set of transitions that p ∩ t = /0. in the graphical representation, the place is drawn as a circle and the transition is drawn as a black bar; the pre, test and inh : p×t ×n|p| → n+ respectively is a forward flow, test and inhibition functions and is a backward flow function in the multi-sets of p, where defined the set of arcs a and describes the marking-dependent cardinality of arcs connecting transitions and places. the set a is partitioned into tree subsets: ad , ah, and at . the subset ad contains the directed arcs which can be seen as ad : ((p×t )∪(t ×p))×n|p| → n+ and are drawn as single arrows. the inhibitory arcs ah : (p × t ) × n|p| → n+are drawn with a small circle at the end. the test arcs at : (p × t )×n|p| → n+are directed from a place to a transition, and are drawn as copyright c© 2006 by ccc publications selected paper from icccc 2006 34 emilian guţuleac dotted single arrows. it does not consume the content of the source place. the arc of net is drawn if the cardinality is not identically zero and this is labeled next to the arc and by a default value being 1; g : e × n|p| → {true, f alse} is the guard function transitions. for t ∈ t a guard function g(t, m) that will be evaluated in each marking, and if it evaluates to true, the transition t may be enabled, otherwise t is disabled (the default value is true); pri : t → n+ defines the priority functions for the firing of each transition that maps transitions onto natural numbers representing their priority level. the enabling of a transition with higher priority disables all the lower priority transitions; kp : p → n+ is the capacity of places, and by default being infinite value; the l : t ∪ p → l, is a labeling function that assigns a label to a transition and places. in this way that maps transition name into action names that l(t j) = l(tk) = α but t j 6= tk and l(pi) = l(pn) = β but pi 6= pn. a marked labeled extended pn net is a pair n =< γ, m0 >, where γ is a labeled pn structure and m0 is the initial marking of the net. m : p → n+ is the current marking of net which is described by a symbolic vector-column m = (mi pi), mi ≥ 0,∀pi ∈ p, where the (mi pi) is the number mi of tokens in place pi. the m is the state of net that assigns to each place tokens, represented by black dots. the details concerning on enabling and firing rules, and evolution for of n =< γ, m0 > can be found in [3] as they require a great deal of space. 3 descriptive expressions of petri nets due to the space restrictions we will only give a brief overview to this topic and refer the reader to [3, 4] and the references therein. in following for abuse of notation, labels and name of transitions/places are the same. we use the concept of a basic descriptive element (bde) for a basic pn (bpn) introduced in [2] as following: bde = |α jt j m0i pi[w +i ,w −i ]| αk tk . the translation of this bpn is shown in figure 1a, where respectively is input transition (action type α j ) and tk = p•i is the output transition (action type αk ) of place pi ∈ p with initial marking m0i , and the flow type relation functions w +i = pre(t j, pi) and w −i = post(t j, pi), respectively which return the multiplicity of input and output arcs of the place pi ∈ p. the derivative elements of bde are for p•i = /0,w −i = 0 is | α j t j m 0 i [wi] with final place pi of t j and • pi = /0,w +i = 0 is m 0 i pi[wi]|αktk with entry place pi of tk. if the initial marking m0i of place is a zero tokens we can omit m0i in bde. by default, if the type of action α is not mentioned this to match the name of a transition t. from a bde we can build more complex de of pn components by using composition operations. also by default,if w +i = w − i = 1, we present bde and it derivatives as following:| α j t j m 0 i pi|αktk , |α jt j m0 pi or m0i pi| αk tk . a descriptive expression (de) of a labeled pn is either bde or a composition of de a n: de ::= bde|de ∗de|◦de, where ∗ represents any binary composition operation and ◦ any unary operation. descriptive compositional operations. in the following by default the labels of n are encoded in the name of the transitions and places. the composition operations are reflected at the level of the de components of n models by fusion of places, fusion of transitions with same type and same name (label) or sharing of as subnets. place-sequential operation. this binary operation, denoted by the “ | “ sequential operator, determines the logic of a interaction between two local states pi (pre-condition) and pk (post-condition) by t j action that are in precedence and succeeding (causality-consequence) relation relative of this action. sequential operator is the basic mechanism to build de of n models. this operation is an associative, reflexive and transitive property, but is not commutative operation. the means the fact de1 = m0i pi[wi]| α j t j m 0 k pk[wk] 6= m0k pk[wk]| α j t j m 0 i pi[wi] that the specified conditions (local state) associated with place-symbol pi are fulfilled always happens before then the occurrence of the conditions associated with place-symbol pk by means of the action t j. also, the pn modelling of the iteration operation is obtained by the fusion of head (entry) place with the tail (final) place that are the same name (closing operation) in de which describes this net. the self-loop of n2 net described by an: descriptive timed membrane petri nets for modelling of parallel computing 35 de2 = m0i pi[wi]| α j t j pi[wi] = m 0 i p̃i[wi]| α j t j , it is the test operator "p̃", i.e. represent the test arc. the translation of de2 in n2 is shows in figure 2b. inhibition operation. this unary operation is represented by inhibitory operator "" (place-symbol with overbar) and it de3 = m0i p̄i[wi]| α j t j describe the inhibitor arc with a weight wi = inh(pi,t j). synchronization operation. this binary operation is represented by the " •" or " ∧”join operator describe the rendez-vous synchronization (by transition tt j ) of a two or more conditions represented respectively by symbol-place pi ∈• t j, i = 1, n, i.e. it indicate that all preceding conditions of occurrence actions must have been completed. this operation is a commutative, associative and reflexive. split operation . this binary operation represented by the " ♦" split operator and it describe the causal relations between activity t jand its post-conditions: after completion of the preceding action of t j concomitantly several other post-condition can take occurs in parallel ("message sending"). property of split operation is a commutative, associative and reflexive. competing parallelism operation. this compositional binary operation is represented by the " ∨" competing parallelism operator, and it can be applied over two na with dea = a and nb with deb = b or internally into resulting nr with der = r, between the places of a single nr which the symbol-places with the same name are fused, respectively. we can represent the resulting der = a ∨ b as a set of ordered pairs of places with the same name to be fused, with the first element belonging to a the second to b. the fused places will inherit the arcs of the place in a and b . also, this compositional binary operation is a commutative, associative and reflexive property. precedence relations between the operations. we introduce the following precedence relation between the compositional operations in the de: a) the evaluation of operations in de are applied left-toright; b) an unary operation binds stronger than a binary one; c) the "• "operation is superior to"|" and " ♦", in turn, its are superior the "∨ " operation. further details on definitions, enabling and firing rules, and evolution for of n can be found in [3] as they require a great deal of space. 4 dynamic rewriting petri nets in this section we introduce the model of descriptive dynamic net rewriting pn system. let x ρy is a binary relation. the domain of is the dom(ρ) = ρy and the codomain of ρ is the cod(ρ) = x ρ . let a =< pre, post, test, inh > is a set of arcs belong to netγ . a descriptive dynamic rewriting pn system is a structure rn =< γ, r, φ , gtr, gr, m >, where: =< p, t, pre, post, test, inh, g, pri, kp, l >; r = r1, ..., rk is a finite set of rewriting rules about the runtime structural modification of net that p ∩ t ∩ r = /0. in the graphical representation, the rewriting rule is drawn as a two embedded empty rectangle. we let e = t ∪ r denote the set of events of the net; φ : e → t, r is a function indicate for every rewriting rule the type of event can occur; gtr : r×n|p| → {true, f alse} and gr : r × n|p| → {true, f alse} is the transition rule guard function associated with r ∈ r and the rewriting rule guard function defined for each rule of r ∈ r , respectively. for ∀r ∈ r, the gtr ∈ gtr and gr ∈ grwill be evaluated in each marking and if its are evaluates to true, the rewriting rule r may be enabled, otherwise it is disabled. default value of gtr ∈ gtr is true and for gr ∈ gr is false. let rn =< rγ, m > and rγ =< γ, r, φ , gtr, gr > described with the descriptive expression derγ and dern , respectively. a dynamic rewriting structure modifying rule r ∈ r of rn is a map r : del ¤ dew , where whose codomain of the rewriting operator ¤ is a fixed descriptive expression del of a subnet rnl of current net rn, where rnl ⊆ rn,with pl ⊆ p, el ⊆ eand set of arcs al ⊆ a and whose domain of the ¤ is a descriptive expression dew of a new rnw subnet with pw ⊆ p, ew ⊆ e and set of arcs aw . the ¤ rewriting operator represent binary operation which produce a structure change in the dern and the net rn by replacing (rewriting) of the fixed current del of subnet rnl (del and rnl are dissolved) by the new dew of subnet rnw now belong to the new modified resulting dern′ of net rn ′ = (rn\rnl)∪rnw with p ′ = (p\pl) ∪ pw and e ′ = (e\el) ∪ ew , where a ′ = (p\pl) ∪ aw the meaning of \ (and ∪) is 36 emilian guţuleac operation to removing (adding) rnl from (rnw to) net rn. in this new net rn ′ , obtained by execution (fires) of enabled rewriting rule r ∈ r , the places and events with the same attributes which belong rn′ are fused, respectively. by default the rewriting rules r : del ¤ /0 andr : /0 ¤ dew describe the rewriting rule which fooling holds rn ′ = (rn\rnl) and rn ′ = (rn ∪ rnw ), respectively. a state of a net rn is a pair (rγ, m), where rγ is the configuration of net together with a current marking m. also, the pair (rγ0, m0) with p0 ⊆ p, e0 ⊆ e and marking m0 is called the initial state of the net. enabling and firing of events. the enabling of events depends on the marking of all places. we say that a transition t j of event e j is enabled in current marking m if the following enabling condition ec(t j, m) is verified: ec(t j, m) = (∧∀pi∈•t j (mi ≥ pre(pi,t j))∧(∧∀pk∈◦t j (mk < inh(pi,t j)))∧(∧∀pl∈∗t j (ml ≥ test(pl ,t j)))∧ (∧∀pn∈t•j ((kpn −mi) ≥ post(pn,t j))))∧g(t j, m)). similarly, the rewriting rule r j ∈ r is enabled in current marking m if the following enabling condition ectr(r j, m)is verified: ectr(r j, m) = (∧∀pi∈•r j (mi ≥ pre(pi, r j))∧(∧∀pk∈◦r j (mk < inh(pi, r j)))∧(∧∀pl∈∗r j (ml ≥ test(pl , r j)))∧ (∧∀pn∈r•j ((kpn −mi) ≥ post(pn, r j))))∧g(r j, m)). let the t (m) and r(m) is respectively the set of enabled transitions and rewriting rule in current marking m. let the e(m) = t (m) ] r(m), is the set of enabled events in a current marking m. the event e j ∈ e(m) fire if no other event ek ∈ e(m) with higher priority has enabled. hence, for e j event i f ((φ j = t j)∨(φ j = r j)∧(gtr(r j, m) = f alse)) then (the firing of transition t j ∈ t (m) or rewriting rule r j ∈ r(m) change only the current marking:(rγ, m) e j→ (rγ, m′) ⇔ (rγ = rγ′ and m[e j > m ′ )). also, for e j event i f ((φ j = r j)∧(gr(r j, m) = true) then (the event e j occur to firing of rewriting rule r j and it occurrence change configuration and marking of current net:(rγ, m) r j→ (rγ′, m′), m[r j > m ′ ). the accessible state graph of a netrn =< γ, m > is the labeled directed graph whose nodes are the states and whose arcs which is labeled with events of rn are of two kinds: a) firing of a enabled event e j ∈ e(m): arcs from state (rγ, m) to state(rγ, m ′ ) labeled with evente j then this event can fire in the net configuration rγ at marking m and leads to new marking m ′ : (rγ, m) e j→ (rγ′, m′) ⇔ (rγ = rγ′ and m[e j > m ′ in rγ); b) change configuration: arcs from state (rγ, m) to state( rγ ′ , m ′ ) labeled with rewriting rule r j :(rγl, ml)¤ (rγw , mw ) which represent the change configuration of current rn net: (rγ, m) e j→ (rγ′, m′) and m[r j > m ′ . figure 1: translation of (a) derγ1in rn1 and (b) derγ2 in rn2 let we consider the rn1 given by the following descriptive expression:derγ1 = p1|r1 p2 ∨ de ′ rγ1, de ′ rγ1 = (p2· p5)|t1 p3|t2 p4|t3 (p1♦p5), m0 = (5p1, 1p5), gr(r1, m) = (m1 = 3)&(m5 = 0) and r1 : derγ1 ¤ derγ2. also, for r j is required to identify if rnl belong the rγ. upon firing, the enabled events or rewriting rule modify the current marking and/or and modify the structure and current marking of net rn1 in rn2 given by: derγ2 = p1|t1 p2 ∨ de ′ rγ2, de ′ rγ2 = (p2 · p6)|t2 p3(|t3 p4|t4 p5 ∨|t5 p5|r2 (p1♦p6)), m = (1p1, 3p2, 1p3), gr(r2, m) = (m1 = 4)&(m5 = 1), r2 = r −1 1 : derγ2 ¤ derγ1. figure 1 show the translation of derγ1 in rn1 and derγ2 in rn2, respectively. descriptive timed membrane petri nets for modelling of parallel computing 37 5 dynamic rewriting timed petri nets systems are described in timed pn (tpn) as interactions of components that can performed a set of activities associated with events. an event e = (α, θ ), where α ∈ e is the type of the activity (action name), and θ is the firing delay. a descriptive dynamic rewriting tpn as a rt n =< rn, θ >, where: rn =< γ, r, φ , gtr, gr, m >, γ =< p, t, pre, post, test, inh, g, pri, k p, l > (see definition 2 and 3) with set of events e which can be partitioned into a set e0 of immediate events and a set eτ of timed events e = e0 ] eτ . the immediate event is drawn as a thin bar and timed event is drawn as a black rectangle for transition or a two embedded empty rectangle for rewriting rules, and pri(e0) > pri(eτ ) ; θ : e ×n|p| → r+ is the weight function that maps events onto real numbers r+ (delays or weight speeds). its can be marking dependent. the delays θ (ek, m) = dk(m)defining the events firing parameters governing its duration for each timed events of eτ . if several timed events are enabled concurrently e j ∈ e(m) for e j ∈ • pi = ∀e j ∈ e : pre(pi, e j) > 0 , either in competition or independently, we assume that a race race competition condition exists between them. the evolution of the model will determine whether the other timed events have been aborted or simply interrupted by the resulting state change. the θ (e j, m) = w j(m) is weight speeds of immediate events e j∈e0 . if several enabled immediate events are scheduled to fire at the same time in vanishing marking m with the weight speeds, and the probability to enabled immediate event e j can fire is: qi(m) = w(e j, m)/ ∑el∈(e(m)&• pi) w(e j, m) ,where e(m) is the set of enabled events in m. an immediate events e j ∈ t0 has a zero firing time. 6 p systems and descriptive timed membrane petri nets here we give a brief review of p systems and its encoding with dm-nets. the main components of p systems are membrane structures consisting of membranes hierarchically embedded in the outermost skin membrane. a full guide for p systems can be referred to [3]. in general, a basic evolution-communication p system with active membranes (of degree n ≥ 0) is π = (o, h, µ, ω, (ρ, π)), where: o is the alphabets of objects; h is a finite set of labels for membranes;µ is a membrane structure consisting of n membranes labeled with elements h in h;ω is the configuration, that is a mapping from membranes of π(nodes in µ ) to multisets of objects ωk ∈ ω, k = 1, ,|ω|, from o;ρ and π is respectively the set off developmental rules ρh and πhits priorities , h = 0, 1, , n − 1. thus the can be of two forms of rules: a) the object rules (or), i.e., evolving and communication rules concerning the objects; b) the membranes rules (mr), i.e., the rules about the structural modification of membranes. here we define dm-nets for encoding of p systems mentioned above into descriptive dynamic rewriting tpn as a rt n. the basis for dm-nets is a membrane rt n that is de net structure comprise: places; transitions; weighed directed arcs from places to transitions and vice-versa; a capacity for each place; weighed inhibitory and test arcs; priority and guard function of transitions. the dm − nets of degree n ≥ 0 is a construct dm = ∨n−1h=0[hdeh]h , where deh is the descriptive expression of rt nh that represent the configuration of membrane [h ]h in a p system π. consider the p system π.the encoding of π into rt nπ is decomposed into two separate steps. first, for every membrane [h ]h we associate: to each object ωi ∈ ω one place ph,i = [hm0i pi]h labeled as ωi with the initial marking m0i , and to each rule ρh, j ∈ ρ one event eh, j = [he j]h labeled as ρh, j that acts on the this membrane. second, for every membrane [h ]h we define the deh of rt nh that it correspond to the initial configuration of the p system π as [hdeh]h. let u, v , and u′, v′ , is a multiset of objects. the evolving object rule ρh′ , j : [h[h′ u → v]h′ ]h with multiset of objects u, v , which will be kept in membrane [h]h is encoded as [h[h′ pu|t j pv]h′ ]h . the antiport rule ρh′ , j : [hu[h′ v]h′ ]h → [hv ′ [h′ u ′ ]h′ ]h , that realize a synchronized wich object c the exchange of objects, is encoded as [h[h′ (pu · pv · p̃c)|t j (pu′♦pv′ )]h′ ]h. also, the symport rule ρh′ ,k : [hu[h′ ]h′ ]h → [h[h′ u ′ ]h′ ]h that 38 emilian guţuleac move objects from inside to outside a membrane, or vice-versa is encoded as [h[h′ (pu · p̃c)|tk pu′ ]h′ ]h. because a configuration mean both a membrane structure and the associated multisets, we need rules for processing membranes and multisets of objects as: mr = change, dissolve,create, divide, merge, separate, move. the above membrane rewriting rules (realized by the rewriting events in de) are defined as follows: changerewriting rule [h[h′ (deh′ , mh′ )]h′ ]h ¤ [h[h′ (de ′ h ′ , m ′ h ′ )]h′ ]h that in runtime the current structure and the multisets of objects to membrane h, encoded by descriptive expression deh′ and marking mh′ is changed in a new structure de ′ h′ with new marking m ′ h′ ; dissolve rewriting rule [h(deh, mh)[h′ (deh′ , mh′ )]h′ ]h ¤[h(deh, m ′ h)]h that the objects and sub-membranes of membrane h ′ now belong to its parent membrane h , the skin membrane cannot be dissolved; create rewriting rule [h(deh, mh)]h ¤ [h(de ′ h, m ′ h)[h′ (de ′′ h ′ , m ′′ h ′ )]h′ ]h with mh = m ′ h + m ′′ h′ that the new membrane h ′ is created and m ′′ h ′ are added into membrane h ′ , the rest remain in the parent membrane h; divide rewriting rule [h(deh, mh)]h ¤[h[h′ (deh, mh)]h′ [h′′ (deh, mh)]h′′ ]hthat the objects and submembranes are reproduced and added into membrane h ′ and membrane h ′′ , respectively; merge rewriting rule that the objects of membrane h ′ and h ′′ are added to a new membrane h is: [h[h′ (de ′ h ′ , mh′ )]h′ [h′′ (de ′′ h ′′ , m ′′ h ′′ )]h′′ ]h ¤ [h(de ′ h ′ ∨de′′ h ′′ , mh′ + m ′′ h ′′ )]h; separate rewriting rule is the counterpart of merge is done by a rewriting rule of the form ¤[h(de ′ h ′ ∨ de ′′ h ′′ , mh′ + m ′′ h ′′ )]h ¤ [h[h′ (de ′ h ′ , mh′ )]h′ [h′′ (de ′′ h ′′ , m ′′ h ′′ )]h′′ ]h with the meaning that the content of membrane h is split into two membranes, with labels h ′ and h ′′ . moverewriting rule where a membrane h ′′ can be moved out or moved into a membrane h ′ as a whole is: [h[h′ (deh′ , mh′ )[h′′ (de ′′ h ′′ , m ′′ h ′′ )]h′′ ]h′ ]h ¤ [h[h′ (deh′ , mh′ )]h′ [h′′ (de ′′ h ′′ , m ′′ h ′′ )]h′′ ]h or [h[h′ (deh′ , mh′ )]h′ [h′′ (de ′′ h ′′ , m ′′ h ′′ )]h′′ ]h ¤ [h[h′ (deh′ , mh′ )[h′′ (de ′′ h ′′ , m ′′ h ′′ )]h′′ ]h′ ]h. thus, using the dm − nets facilitates a compact and flexible specification to visual simulate of p systems with dynamic rewriting tpn nets that permit the verification of the its many useful behavioral properties such as reachability, boundedness, liveness, terminating, etc., and the performance evaluation of parallel computing models. 7 summary and conclusions in this paper we have proposed an approach to the performance modeling of the behaviour of psystems through a class of petri nets, called descriptive membrane timed pn (dm-nets). based upon the introduction of a set of descriptive composition operation and rewriting rules attached with transitions for the creation of dynamic rewriting tpn, the membrane structure can be successfully encoded as a membrane descriptive rewriting timed petri nets models which permit the description the behavioral state based process run-time structure change of p systems. we are currently developing a software visual simulator with a friendly interface for verifying and performance evaluation of descriptive rewriting tpn models and dm-nets. references [1] m. ajmone-marsan, g. balbo, g. conte, s. donatelli, and g. francheschinis, “modeling with generalized stochastic petri nets,” ser. in parallel computing ,new york: wiley, 1995. [2] s. dal zilio, e. formenti, “on the dynamics of pb system: a petri net view,” in proceedings wmc 2003, lecture notes in computer science 2933 , springer-verlag, pp. 153-167, 2004. descriptive timed membrane petri nets for modelling of parallel computing 39 [3] e. gutuleac, “descriptive compositional construction of gspn models for performance evaluation of computer systems,” in proceedings of the 8-th international symposium on automatic control and computer science, saccs2004, 22-23 october,iasi, romania, cd, 2004. [4] e. gutuleac, “descriptive dynamic rewriting gspn-based performance modeling of computer systems,” proceedings of the 15th international conference on control systems and computer science, cscs15, 25-27 may 2005,bucuresti, romania, pp. 656-661, 2005. [5] j. kleijn, m. koutny, g. rozenberg, “towards a petri net semantics for membrane systems,” in proceedings of the wmc6 2005, july 18-21, wien, austria, pp. 439-459, 2005. [6] gh. paun, “membrane computing. an introduction,” natural computing series. ed. g. rozenberg, th. back, a.e. eibenj.n. kok, h.p. spaink, leiden center for natural computing, springer-verlag, berlin, p. 420, 2002. [7] z. qi, j. you, and h. mao, “p systems and petri nets,” proceedings wmc 2003, lecture notes in computer science, vol. 2933, springer-verlag, berlin, pp. 387-403, 2003. emilian guţuleac, technical university of moldova, computer science department, address: 168, bd. stefan cel mare, md-2004, chişinău, republic of moldova e-mail: egutuleac@mail.utm.md int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 862-870 a novel qos framework based on admission control and self-adaptive bandwidth reconfiguration a. peculea, b. iancu, v. dadarlat, i. ignat adrian peculea, bogdan iancu, vasile dadarlat, iosif ignat technical university of cluj-napoca romania, 400020 cluj-napoca, 15 constantin daicoviciu e-mail: {adrian.peculea,bogdan.iancu,vasile.dadarlat,iosif.ignat}@cs.utcluj.ro abstract: this paper proposes a novel end-to-end qos framework, called self-adaptive bandwidth reconfiguration qos framework (sar). sar provides end-to-end qos guarantees on a per-flow basis through admission control and end-to-end bandwidth reservation. in order to adapt to short and long time traffic load changing, sar performs dynamic bandwidth reconfiguration. due to a new organization of the network physical lines, sar allows for a better utilization of the links’ capacity and a smaller number of rejected flows, increasing the network’s availability. keywords: end-to-end qos, admission control, bandwidth reconfiguration. 1 introduction computer networks transport simultaneously several flows, fact that makes necessary a multiplexing mechanism. transport procedures affect the traffic flows, reason for which the traffic has to be characterized and quality of service (qos) requirements need to be established. traffic types and their qos requirements impose the implementation of qos methods and architectures. this paper presents the design and implementation of a new end-to-end qos framework with self-adaptive bandwidth reconfiguration. integrated services (intserv) [1] provide end-to-end quality of service (qos) guarantees for individual flows by maintaining the state and by reserving bandwidth for each flow at routers on the path between source and destination. the additional loading introduced by the perflow bandwidth reservation processing and by the per-flow state maintaining at each router is significant and is increasing along with the network. for this reason, integrated services presents scalability problems. differentiated services (diffserv) [1] group the flows in traffic classes at the edge of the network. interior routers forward each packet function of the per-hop behavior associated to the traffic class of the packet. because of the flow aggregation and the lack of admission control, differentiated services do not provide end-to-end qos guarantees to individual flows. on-demand qos path (odp) [2] provides end-to-end qos guarantees to individual flows introducing an additional load much lower than in the case of integrated services and maintaining a similar scalability to the one of the differentiated services. odp exercises per-flow admission control and end-to-end bandwidth reservation at the edge of the network. inside the network odp differentiates the traffic classes as in the differentiated services. the main disadvantage of odp is that the bandwidth adjustment is only inside the traffic class and does not allow for bandwidth redistribution between classes. the free bandwidth of the provisioned links that are not used or present a low utilization can not be made available for other provisioned links, the free bandwidth remaining unused. another disadvantage of this framework is the fact that it does not include a module for determination of the bandwidth necessary for each input flow. in order to eliminate the disadvantages above mentioned, we elaborated, implemented and proposed a framework for end-to-end quality of service guaranteeing through admission control copyright c⃝ 2006-2010 by ccc publications a novel qos framework based on admission control and self-adaptive bandwidth reconfiguration 863 and self-adaptive bandwidth reconfiguration, which allows for bandwidth redistribution between classes. in this approach, the physical line is divided into two main sections, a part being the guaranteed link (gl) necessary for guaranteeing a minimum bandwidth (where is the case) for traffic classes (tcs), and a common part named common link (cl), which can be used by any tc. having two separated sections, the framework guarantees a minimum bandwidth for any trunk and offers a common bandwidth which can be used by every trunk, irrespective to their tc. this allows for better bandwidth utilization and for the decrease of the rejected flows number. this paper is organized in the following manner. section ii presents related work, section iii describes the architecture and the functioning of the proposed framework, section iv and section v present the admission control method, and respectively the self adaptive reconfiguration technique of the proposed framework and, finally, section vi presents the experimental results and the concluding remarks. 2 related work integrated services (intserv) framework uses resource reservation protocol (rsvp) to reserve bandwidth for each flow at every router along the path of the flows. using per-flow based hop-by-hop signaling, consisting of path and resv messages, integrated services provides endto-end guarantees. these guarantees come with the overhead of processing per-flow bandwidth reservation and maintaining per-flow state at each router along the flow’s path. because this overhead is significant and is increasing along with the network size, intserv presents scalability problems. differentiated services (diffserv) framework classifies packets into traffic classes at the boundary of the network. during the classification process each packet is marked according to its traffic class. the routers inside the network recognize the traffic class of the packets and, using a scheduling mechanism, forward each packet function of the per-hop behavior associated to the traffic class of the packet. in the case of this framework, the service is provided on a per-class basis instead of a per-flow basis as in intserv framework. this approach removes the overhead specific to intserv framework reason for which diffserv framework is much more scalable. however, diffserv framework does not exercise admission control at the edge of the network, so the network can be overloaded, reason for which this framework does not provide end-to-end guarantees. on-demand qos path (odp) provides end-to-end qos guarantees to individual flows with less overhead than in the case of intserv, maintaining a similar scalability to the one of the diffserv. two types or routers are defined in this framework: edge and core. odp exercises per-flow admission control and end-to-end bandwidth reservation at the edge of the network. inside the network odp differentiates the traffic classes as in the diffserv. odp organizes link bandwidth hierarchically. each physical link is statically divided into several provisioned links (pls), each pl being dedicated to a traffic class. each pl is divided into several trunks, each trunk being dedicated to an edge router. an edge router keeps track of available bandwidth of its trunks and performs admission control locally without hop-by-hop signaling through network. the main disadvantage of odp is that the bandwidth adjustment is only inside the traffic class and does not allow for bandwidth redistribution between classes. the free bandwidth of the provisioned links that are not used or present a low utilization can not be made available for other provisioned links, the free bandwidth remaining unused. another disadvantage of this framework is the fact that it does not include a module for determination of the necessary bandwidth for each input flow. 864 a. peculea, b. iancu, v. dadarlat, i. ignat 3 the architecture of the framework the proposed framework serves the user networks and defines two types of routers, edge and core, and entities for common bandwidth control. figure 1: bandwidth organization in the proposed framework edge routers (ers), connected to served networks, determine the necessary bandwidth for each input flow, take admission or rejection decision for each input flow, dynamically reconfigure the bandwidth assigned to trunks, map flows to corresponding tcs and transmit the packets belonging to the admitted flows in the network. core routers (crs), connected to edge or core routers, recognize tcs and provide class based service differentiation. entities for common bandwidth control monitor and update common bandwidths utilization and accept or reject the requests for additional bandwidth for trunks received from ers. the bandwidth is hierarchically organized. each physical line is divided in two sections as it is presented in figure 1. a first section guarantees the minimum bandwidth, which can be also 0, for each class and each trunk. the second section, cl, offers a common bandwidth which can be used by every trunk function of their bandwidth requirements, irrespective to their belonging tc or er. so, trunks can acquire additional bandwidth without being conditioned by the available bandwidth of the belonging class. first section is statically divided in several guaranteed class links (gcls). each gcl is reserved to a tc existing a one to one mapping between the tcs supported by the physical line and gcls. each gcl is divided in several trunks, each trunk being dedicated to an er. a trunk belonging to a gcl supports the flows belonging to the tc that corresponds to the considered gcl, originating from the er to which the trunk is dedicated, irrespective to their destination. an er keeps track of available bandwidth of its assigned trunks and performs admission control locally, without hop-by-hop signaling through network. a virtual ip path (vip) is a path from a source er to a destination er for a tc, being a concatenation of trunks belonging to the source er over a source-destination path. the bandwidth assigned to trunks has a minimum guaranteed value which can be also 0 and, by using cl, is dynamically adjusted function of the network traffic modifications. function of the entities for common bandwidth control there are three possible approaches: central control (cc), router-aided (ra) and edge-to-edge (ee). the architecture of the framework is presented in figure 2 and it is composed of two ena novel qos framework based on admission control and self-adaptive bandwidth reconfiguration 865 tities: edge router and entity for common bandwidth control. the edge router determines the necessary bandwidth for each input flow, takes the admission or rejection decision for each input flow, reserves the necessary bandwidth for each admitted flow, dynamically reconfigures the bandwidth assigned to trunks and classifies the packets belonging to the admitted flows. the entity for common bandwidth control monitors and updates common bandwidths utilization and accepts or rejects the additional bandwidth requests for trunks, received from edge routers. the communication between the two entities is realized through a predefined message set. figure 2: the architecture of the proposed framework the edge router is composed of two planes: local resources monitoring plane and flow management and local resources control plane. the local resources monitoring plane is composed of the following tables: classification and reservation table which realizes a correspondence between flow types, the elements that identify them, corresponding traffic class, their necessary bandwidth and the maximum necessary bandwidth for any flow from the respective traffic class, flow table which stores the admitted flows and the time of the last packet from each flow, routing to vip correspondence table which allows for vips determination, vip table which stores the vips and trunk table which stores the reserved bandwidth, bandwidth being used and minimum reserved bandwidth for every trunk belonging to the er. the flow management and local resources control plane takes the packets from the traffic policy module and delivers them to the routing process being composed of the following blocks: packet reception time storage which reads the receiving time of each packet, flow identification which determines and identifies the packets membership to admitted flows, flow table update which updates the reception time of the last packet from each flow from flow table, admission control and additional resources acquiring which admits the flows for which there are enough resources and rejects the flows when there is not enough bandwidth for them, acquires additional bandwidth for trunks, reserves the necessary bandwidth for the admitted flows and inserts the admitted flows in the flow table and packet classification which identifies the packets function of classification and reservation table criteria and marks them according to the identification criteria. the second task of this plane is to determine finished admitted flows and release the acquired resources used for these flows. the following blocks realize this task: clock generates the time period when acquired resources are released and acquired resources release which determines finished admitted flows and releases reserved acquired resources for these flows. the entity for common bandwidth control is composed of two planes: common resources monitoring plane and common resources control plane. the common resources monitoring plane 866 a. peculea, b. iancu, v. dadarlat, i. ignat contains common bandwidth table which stores the reservation and utilization for common bandwidths of cls. the common resources control plane contains the common resources control block which updates the common bandwidth table and decides if additional bandwidth requests for trunks received from ers can be accepted or not. 4 admission control admission control is performed at the arrival of the first packet from a new flow, by the source er. admission control and additional resources acquiring module stores the packet into the not admitted flows memory and determines if there are other packets belonging to this flow stored in the memory. if there are no more such packets, it determines from classification and reservation table the necessary bandwidth for the flow and tc, determines from routing to vip correspondence table the flows corresponding vip and extracts from vip table the trunks that belong to the determined vip. then, for trunks which have enough available bandwidth, reserves the flows necessary bandwidth by updating the bdw being used field. for a trunk, the condition to have enough available bandwidth is: reserved_bdw ≥ bdw_being_used + necessary_bdw (4.1) where reserverd_bdw and bdw_being_used are the amounts of reserved and utilized bandwidth for the trunk and necessary_bdw is the flows necessary bandwidth. the update of the bdw_being_used field is done in the following manner: bdw_being_used = bdw_being_used + necessary_bdw (4.2) if the vip has enough bandwidth to support the input flow, the admission control accepts the flow. if there are trunks which do not have enough available bandwidth, admission control and additional resources acquiring module tries to increase the reserved bandwidth of those trunks sending in this sense a request to the entities for common bandwidth control. if the request is admitted, the reserved bandwidth of the trunks is increased by updating reserved_bdw field, so that these trunks too will have enough available bandwidth to support the input flow. for these trunks, admission control and additional resources acquiring module reserves the flows necessary bandwidth by updating the bdw_being_used field. in this case too, the admission control accepts the flow. after a flow acceptance, the flow is inserted into the flow table and the packets belonging to this flow, stored into the not admitted flow memory, will be transmitted to the flow table update module for the rest of the processing and transmission. if the request is rejected, the flow is rejected, the reservations made on the trunks which had enough available bandwidth are canceled by updating the bdw_being_used field and the packets belonging to the flow, stored into the not admitted flow memory, are discarded. a flow is considered finished after an inactivity period that exceeds a predefined value. each er, using the acquired resources release module, periodically inspects its own flow table in order to identify the finished flows and, as a consequence of finished flow identification, releases the bandwidths correspondingly. if there are finished flows, the reserved bandwidth and tc for these flows are determined from the classification and reservation table and the flows are discarded from the flow table. then, the acquired resources release module, determines from routing to vip correspondence table the corresponding vips and extracts from the vip table the trunks belonging to the determined vips. after this, releases the reserved bandwidth for the flows by updating the bandwidth being used field from the trunk table for each trunk belonging to the vips. the update of the bdw_being_used field is done in the following manner: bdw_being_used = bdw_being_used − necessary_bdw (4.3) a novel qos framework based on admission control and self-adaptive bandwidth reconfiguration 867 also, it extracts from the classification and reservation table the maximum amount of bandwidth for the corresponding tcs and verifies if the trunks utilization is under the predetermined lower threshold. for a trunk, the condition to have the utilization under a predetermined lower threshold is: reserved_bdw > bdw_being_used + n ∗ tc_maximum_necessary_bdw (4.4) where tc_maximum_necessary_bdw is the maximum amount of bandwidth for the corresponding tc and n is a predefined parameter having a value larger than or equal to 1. also, it extracts from the trunk table the minimum reserved bandwidth for the trunks and verifies if the trunks have additional bandwidth acquired from cls. for a trunk, the condition to have additional bandwidth acquired from cl is: reserved_bdw > trunk_minimum_reserved_bdw (4.5) where trunk_minimum_reserved_bdw is the minimum reserved bandwidth for the trunk if there are trunks whose bandwidth being used is under the predetermined lower threshold and the trunks have additional bandwidth acquired from cls, the acquired resources release module, in the limit of the acquired bandwidth, computes de bandwidth that will be released from the reserved bandwidth of the trunks. reduction of the reserved bandwidths is accompanied by appropriated resources release for the common bandwidths. 5 self-adaptive bandwidth reconfiguration the proposed framework dynamically adjusts the bandwidth assigned to the trunks, in order to adapt to changes in network traffic. a source edge router has the option to request additional bandwidth for its trunks or it can release bandwidth not used by the trunks, depending on bandwidth usage of his trunks. bandwidth adjustment is done using the cl’s bandwidth. this adjustment allows all trunks, regardless of the class of traffic or the edge router where they belong, to share the bandwidth provided by lc. the trunk reconfiguration process of the proposed framework involves three main actions: (1) the control of the common bandwidth table, (2) the release of bandwidth not used by the trunks, and (3) acquisition of additional bandwidth for trunks. a common bandwidth table stores the common bandwidth utilization of the network cls. as shown in figure 3, an entry in this table contains: cls identifier, the reserved amount of shared bandwidth and the amount of shared bandwidth for the cl. figure 3: common bandwidth table depending on the share bandwidth entities, three approaches are being proposed: central control (cc), router-aided (ra) and edge-to-edge (ee). in the central control approach the common bandwidth table is managed by a network management server (nms) and the common bandwidth table stores the bandwidth utilization of all cls in the network. in the router-aided approach, each core router manages a common bandwidth table, and each of these tables stores the bandwidth utilization of the lcs belonging to all physical links directly connected to that core router. in the edge-to-edge approach each edge router manages a common bandwidth table, which will store the bandwidth utilization of all the cls in the network. 868 a. peculea, b. iancu, v. dadarlat, i. ignat figure 4: reserved bandwidth update algorithm each edge router periodically examines its own flow table and determines which flows are finished. if there are any finished flows, the flow table and the trunk table will be updated. a next step for the edge router is to examine the trunk table and to obtain the bandwidth utilization of its own trunks. if the bandwidth utilization of any trunk is under a predetermined lower threshold and those trunks have additional bandwidth acquired from the common link, the source edge router computes the amount of bandwidth to be released from the reserved bandwidth, adjusts the released bandwidth of the trunks, in the limit of the additional acquired bandwidth, updates its own trunk table and sends a control message to the entities for common bandwidth control, in order to release the used shared bandwidth. adjusting a trunk’s bandwidth is done only in the limit of the additional acquired bandwidth. the algorithm that describes the reserved bandwidth update process for the trunks is presented in figure 4. the trunk reconfiguration process is always initiated by a source edge router using a threshold and computed values driven mechanism. 6 experimental results and conclusions for the development and testing of the proposed qos framework (sar framework) and also for the developing of new ones, an experimental methodology was used, rather than simulation techniques, thus an integrated solution a development tool, was created [3]. also a benchmarking system for qos parameters [4] was developed in order to allow the testing of the proposed sar qos framework. the benchmarking system generates traffic for the defined testbed and measures the following parameters: delay, ip delay variation (ipdv) or jitter and bandwidth, both on tcp and udp. the benchmarking allows a user to define and store complex traffic patterns that can be recharged for making further measurements, to test various qos techniques based on the same traffic characteristics. for simulations purposes, the self-adaptive bandwidth reconfiguration qos framework (sar) described in the previous section and the odp framework were tested, in a comparative manner, using the development tool and the benchmarking system. the final testbed is a network of programmable routers, and consisted of three edge routers and three served networks. the tests were intended as performance comparison between odp and sar frameworks. traffic classes and traffic patterns were defined similarly in both frameworks tested. four classes of traffic were considered. two test traffic patterns were defined. in the first traffic pattern considered flows are injected from classes 2 and 3 and in the case of the second traffic pattern flows belonging to class 2 are injected. for both traffic patterns a balanced distribution of traffic from and to the served networks is ensured. after testing and analyzing the results (figure 5) it was found that the number of flows a novel qos framework based on admission control and self-adaptive bandwidth reconfiguration 869 figure 5: test results admitted flows admitted for sar framework is higher than in the case of odp framework, on both tested traffic patterns, which demonstrates a more efficient use of network resources. also, the equal number of control messages transmitted by the two frameworks shows that sar is a scalable framework. finally, tests confirmed that admission control has eliminated network congestion. this paper presents a new end-to-end qos framework, called self-adaptive bandwidth reconfiguration qos framework (sar). the proposed dynamic allocation method guaranties a minimum bandwidth available for each traffic class and trunk, and provides a common bandwidth section which can be used by every trunk, function of their bandwidth requirements, irrespective to their belonging tc or er. thus, trunks can acquire additional bandwidth without being conditioned by the available bandwidth of the belonging class. the new framework, sar, uses the proposed bandwidth organization, allowing the increase of the traffic volume it handles, guaranteeing end-to-end quality of service through network resources monitoring, admission control and resource reservation for new flows. the end-to-end qos framework with self-adaptive bandwidth reconfiguration overcomes the disadvantages of odp by providing minimum service guaranties and bandwidth redistribution between classes. acknowledgments this work was supported by the pnii-idei 328/2007 qaf quality of service aware frameworks for networks and middleware research project within the framework national research, development and innovation programme initiated by the national university research council romania (cncsis uefiscsu). 870 a. peculea, b. iancu, v. dadarlat, i. ignat bibliography [1] z. wang, internet qos: architectures and mechanisms for quality of service, morgan kaufmann, san francisco, 2001. [2] m. yang, y. huang, j. kim, m. lee, t. suda, m. daisuke, an end-to-end qos framework with on-demand bandwidth reconfiguration, twenty-third annual joint conference of the ieee computer and communications societies (infocom), hong-kong, vol. 3, pp. 2072 2083, 2004. [3] a. peculea, v. dadarlat, i. ignat, b.iancu, l. cobarzan, on developing a qos framework with self-adaptive bandwidth reconfiguration, pollack periodica an international journal for engineering and information sciences, vol.4, no.1, pp. 121-129, 2009. [4] a. peculea, b. iancu, v. dadarlat, i. ignat, e. cebuc, z. baruch, benchmarking system for qos parameters, proceedings of the ieee 3rd international conference on intelligent computer communication and processing 2007 (iccp 2007), cluj-napoca, romania, p.255p.258, 2007. international journal of computers communications & control issn 1841-9836, 12(3), 323-329, june 2017. learning speed enhancement of iterative learning control with advanced output data based on parameter estimation g.-m. jeong, s.-h. ji gu-min jeong school of electrical engineering kookmin university, korea gm1004@kookmin.ac.kr sang-hoon ji* robot r&bd group kitech, korea *corresponding author: robot91@kitech.re.kr abstract: learning speed enhancement is one of the most important issues in learning control. if we can improve both learning speed and tracking performance, it will be helpful to the applicability of learning control. considering these facts, in this paper, we propose a learning speed enhancement scheme for iterative learning control with advanced output data (adilc) based on parameter estimation. we consider linear discrete-time non-minimum phase (nmp) systems, whose model is unknown, except for the relative degree and the number of nmp zeros. in each iteration, estimates of the impulse response are obtained from input-output relationship. then, learning gain matrix is calculated from the estimates, and by using new learning gain matrix, learning speed can be enhanced. simulation results show that the learning speed has been enhanced by applying the proposed method. keywords: iterative learning control, speed enhancement, parameter estimation, learning gain estimation. 1 introduction by using iterative learning control (ilc), the tracking performance can be enhanced when the the same task is performed iteratively [7]– [9]. among various ilc schemes, iterative learning control with advanced output data (adilc) [4] [5] has been proposed for the learning control in discrete time non-minimum phase (nmp) systems. adilc stabilizes inverse mapping by using output-to-input mapping directly with time-advanced output data. its learning structure is simple since it consists of an input update law that depends on the relative degree and number of nmp zeros. on the other hand, due to the complexity of computation, learning speed enhancement is one of the most important issues in iterative learning control (ilc). considering this, various approaches for direct learning control (dlc) [9] have been proposed. in [5], an adilc scheme based on the estimation of the impulse response is proposed for linear discrete-time nmp systems, whose model is unknown, except for the relative degree and the number of nmp zeros. instead of using an approximate model of the system, the first part of impulse response is estimated and used for the adilc. however, considering the computational cost of this method, we need a novel scheme to enhance learning speed. in this paper, we propose a new speed enhancement scheme for discrete time nmp systems, extending the results in [5]. by using the estimates of the learning matrix, an estimate of the desired input is derived and the learning speed can be significantly enhanced. further, an illustrative example is provided to demonstrate the applicability of the proposed method. copyright © 2006-2017 by ccc publications 324 g.-m. jeong, s.-h. ji 2 adilc for discrete-time nmp systems in this section, some preliminary results of the adilc in [4] are briefly summarized. let us consider a linear time invariant(lti) system described by x(i + 1) = ax(i) + bu(i) y(i) = cx(i) (1) where, u ∈ r1, x = [x1, · · · ,xn]t ∈ rn, and y ∈ r1 are the input, the state, and the output of the system, respectively. a, b and c are matrices of appropriate dimensions. let xd(i), yd(i) and ud(i) represent the state, the output and the input corresponding to the desired trajectory respectively. further, let the desired output yd(i), i ∈ [σ,n + σ − 1] be given and u[i,j] := [u(i), · · · ,u(j)]t , y[i,j] := [y(i), · · · ,y(j)]t . the transfer function of the system is represented by g(z) = β1z n−1+···+βn zn+α1zn−1+···+αn . here, it is assumed that the number of nmp zeros, d0, and the relative degree, σ, are known a priori (i.e., β1 = · · · = βσ−1 = 0). in the adilc, the following input-output mapping is used to stabilize the inverse mapping. y[σ+d0,n+σ+d0−1] = hx(0) + ju[0,n−1], (2) h = [ (hd0+1) t , · · · , (hn+d0 ) t ]t , j =   jd0+1 jd0 · · · 0 jd0+2 jd0+1 · · · 0 ... ... ... ... jn+d0 jn+d0−1 · · · jd0+1   , where hl = caσ+l−1, jl = caσ+l−2b. the time interval for the output of interest is [σ + d0,n + σ + d0 − 1] in (2), whereas it is [σ,n + σ− 1] for minimum phase systems (i.e., d0 = 0). for an adilc, we set the input horizon to [0,n + d0 − 1] with u[n,n+d0−1] = 0 and the output horizon to [0,n +σ +d0−1]. the desired trajectory, yd, is given in [σ,n +σ−1]. we set yd [n+σ,n+σ+d0−1] to some appropriate constants. further, at every iteration, we set x k(0) = xd(0) and uk(i) = ud(i) = 0,n ≤ i ≤ n − 1 + d0. to analyze the stability of the inverse mapping, we need the following assumptions: • (a1) the system is stable, controllable and observable. • (a2) the matrix a is invertible. • (a3) βn 6= 0 in g(z). • (a4) the matrix j is nonsingular. with these assumptions, lemma 1 shows that the inverse mapping (2) is stable using the time advancing of the output data, even though it is an nmp system. lemma 2.1. (stable inversion using time advancing) the inverse mapping from yd [σ+d0,n+σ+d0−1] to u d [0,n−1] is stable. the input update law is derived from lemma 1 as follows: uk+1 [0,n−1] = u k [0,n−1] + s kek[σ+d0,n+σ+d0−1], (3) learning speed enhancement of iterative learning control with advanced output data based on parameter estimation 325 where ek [l,m] = yd [l,m] − yk [l,m] and sk ∈ rn×n is the learning gain matrix. the next lemma shows that the input uk [0,n−1] converges to u d [0,n−1] as k → ∞ using the input update law (3). it should be noted that this inverse mapping is stable. lemma 2.2. the uncertain system (1) satisfies (a1)–(a4). if the condition ‖i − skj‖≤ ρ < 1 (4) holds, the input uk [0,n−1] converges to u d [0,n−1] as k →∞. 3 adilc with the estimation of the impulse response in this section, the impulse response estimation scheme in [5] is slightly modified for the learning speed enhancement detailed in the next section. after estimating the first p impulse responses, we select the first l ≤ p responses to obtain j̄, the estimate of j in (2). with j̄, which consists of the estimations of the first l impulse responses (j1,· · · ,jl), the adilc scheme can be applied to unknown nmp systems. since we set x(0) = 0 for the learning scheme, from (2), we can obtain y[σ+d0,n+σ+d0−1] = ju[0,n−1]. (5) by exchanging the location of j and u[0,n−1], (5) can be changed into y[σ+d0,n+σ+d0−1] = umaxj[1,n+d0+1]. (6) here, umax =   u(d0) · · · u(0) · · · 0... ... ... ... ... u(d0 + n − 1) · · · u(n − 1) · · · u(0)   , j[1,n+d0+1] = [j1, · · · ,jn+d0+1] t . (7) to estimate the first p impulse responses, we make an approximation for j[1,p]. as i becomes larger, the impulse response ji approaches 0. by selecting a sufficient large p and discarding the impulse responses from p + 1, the approximation is made as y[σ+d0,n+σ+d0−1] ≈ upj[1,p]. (8) here, up =  u(d0) u(d0 − 1) · · · 0 u(d0 + 1) u(d0) · · · 0 ... ... ... ... u(d0 + n − 1) u(d0 + n − 2) · · · u(d0 + n − l− 1)   , j[1,p] = [j1, · · · ,jp] t . (9) using the least square method, we can obtain j̄[1,p] consisting of the estimates of j[1,p], as j̄[1,p] = (u t p · up) −1utp y[σ+d0,n+σ+d0−1]. (10) 326 g.-m. jeong, s.-h. ji after estimating the impulse responses, we select the first l ≤ p impulse responses and obtain j̄ which is the estimates of j in (2). in [5], a learning control scheme was presented based on impulse response estimation. at step 0, u1 [0,n−1] can be determined by setting s 0 as an appropriate matrix, e.g., αi and u0 [0,n−1] = 0. then, we can estimate j̄k [1,n] similarly to (10) and learning control can be performed using (3) with sk = α(j̄k)−1 for some α, 0 < α < 1. 4 learning speed enhancement using the estimation of the impulse response in this section, a new learning speed enhancement algorithm is presented using (10) and the learning scheme for unknown nmp systems. since the estimates of impulse responses can be used to estimate the desired input, the learning speed can be enhanced. at k = 0, since u0 [0,n−1] = 0, y 0 [σ+d0,n+σ+d0−1] will be zero. thus, u 1 [0,n−1] = s 0yd [σ+d0,n+σ+d0−1]. here, we set s0 to be an appropriate matrix, e.g., αi. for k ≥ 1, we estimate the impulse response j̄k [1,p] using (10) from uk [0,n−1] and y k [σ+d0,n+σ+d0−1], derive j̄ k from the estimates of first l impulse responses. likewise, for a sufficient k, e.g., k = 1, we can obtain the estimate of the input ūd [0,n−1] as follows: ūd[0,n−1] = (j̄ k)−1y[σ+d0,n+σ+d0−1]. (11) if the estimation is successfully made and l is sufficiently enough, ūd [0,n−1] will be considerably close to ud [0,n−1]. for k + 1, e.g., k = 2, we can set uk [0,n−1] = ū d [0,n−1] and s = α(j̄ k)−1. if s satisfies the convergence condition, we can obtain the desired input with the proposed method. throughout this approach, we can enhance the learning speed. we can summarize the learning rule as follows: the proposed learning algorithm • step 0: when k = 0. set s0 to be an appropriate matrix and obtain u1 [0,n−1]. • step 1: for the first iteration, obtain y1 [σ+d0,n+σ+d0−1]. if ‖ek [σ+d0,n+σ+d0−1]‖≤ �, then stop. else, derive j̄k using (10). calculate the estimated value of the desired input ūd [0,n−1] from (11) and set u 2 [0,n−1] = ūd [0,n−1] . • step k: for the k-th iteration, if ‖ek [σ+d0,n+σ+d0−1]‖≤ �, then stop. set s = α(j̄k)−1. update the input using (3), increment k, and repeat step k until termination. learning speed enhancement of iterative learning control with advanced output data based on parameter estimation 327 theorem 1. the nmp system (1) satisfies (a1)-(a4), the relative degree and the number of nmp zeros are known, and the system dynamics may not be known completely. let us assume that we update the input based on the proposed learning algorithm. if the condition (4) holds for all k ≥ 1, the input uk [0,n−1] converges to u d [0,n−1] as k →∞. proof: this can be easily shown using lemma 2.1, and theorem 1 from [5]. 2 (a) outputs using the proposed method and y5 in [5] (b) inputs using the proposed method and u5 in [5] figure 1: outputs and inputs for different values of k 5 simulation results let us consider an example of nmp system for a positioning table in [10] as follows: g(z) = 0.0082z4 + 0.031z (z + 0.29)(z − 0.2)(z − 0.46)(z2 − 1.7z + 0.73) 328 g.-m. jeong, s.-h. ji this system has one nmp zero (z = −3.7805) and satisfies (a1)–(a4). the desired trajectory is given as yd(i) = { 0, i = 0, 1, 83, 84, 85, 86 −0.2 cos(0.05π(i− 2)), 2 ≤ i ≤ 82. (12) here, we set n = 85, s0 = 0.1i and u(85) = u(86) = 0. the input update law is given as uk+1 [0,84] = uk [0,84] + skek [2,86] . from (10), we set p = 85. the impulse response is estimated using j̄k[1,85] = ((u k)t · uk)−1(uk)t yk[2,86]. (13) in addition, j̄1 is obtained using l = 30. then, we set u2 = ūd and enhance the learning speed. in this case, the convergence condition is satisfied as ‖i − skjk‖ < 0.568 when sk = 0.5(j̄k)−1. fig. 1(a) and 1(b) show the outputs and inputs for different values of k, respectively. the root mean square (rms) error for the output error is 0.0012 for k = 2, and is smaller than the rms error of 0.0036 for k = 10 reported in [5]. from this example, we can see that the learning speed is significantly enhanced. 6 conclusion in this paper, we have proposed a new learning speed enhancement algorithm of adilc for discrete-time nmp systems. first, we have presented an estimation algorithm of impulse responses based on the input-output mapping of adilc. next, learning speed enhancement algorithm has been derived from new learning gain, which is calculated with the estimates of impulse response. simulation results for the nmp system have demonstrated the learning speed enhancement of the proposed method. robust algorithms over disturbances for learning control can be considered with the proposed method. it remains as a future work. acknowledgment this work was supported by the national research foundation of korea(nrf) grant funded by the korean government(msip)(nrf-2016r1a5a1012966), and also supported by basic science research program through the national research foundation of korea(nrf) funded by the ministry of education(nrf-2015r1d1a1a01060917) bibliography [1] arimoto s., kawamura s., miyazaki f. (1984); bettering operation of robots by learning, journal of robotic systems, 1(2), 123–140, 1984. [2] bien z., xu j.-x. (1998); iterative learning control analysis, design, integration and applications, kluwer academic publishers, 1998. [3] jang t.-j., ahn h.-s., choi c.-h. (1994); iterative learning control for discrete-time nonlinear systems, international journal of systems science, 25(7): 1179-1189.. [4] jeong g.-m., choi c.-h. (2002); iterative learning control for linear discrete time nonminimum phase systems, automatica, 38(2), 287–291, 2002. learning speed enhancement of iterative learning control with advanced output data based on parameter estimation 329 [5] jeong g.-m., ji s.-h. (2013); iterative learning control with advanced output data using an estimation of the impulse response, ieice transactions on fundamentals, e96-a (6), 1488-1491, 2013.. [6] ngo t., wang y., mai t.l., ge j., nguyen m.h., wei s. n. 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(1999); design of discrete-time repetitive control system for pole placement and application,ieee/asme transactions on mechatronics, 4(2), 110118, 1999. ijcccv4n1draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 1, pp. 73-81 modeling of errors realized by a human learner in virtual environment for training thanh-hai trinh, cédric buche, ronan querrec, jacques tisseau université européenne de bretagne, ecole nationale d’ingénieurs de brest, laboratoire informatique des systèmes complexes, centre européen de réalite virtuelle technopôle brest-iroise, 29238 brest cedex 3, france e-mail: {trinh,buche,querrec,tisseau}@enib.fr abstract: this study focuses on the notion of erroneous actions realized by human learners in virtual environments for training. our principal objective is to develop an intelligent tutoring system (its) suggesting pedagogical assistances to the human teacher. for that, the its must obviously detect and classify erroneous actions produced by learners during the realization of procedural and collaborative work. further, in order to better support human teacher and facilitate his comprehension, it is necessary to show the teacher why learner made an error. addressing this issue, we firstly modeling the cognitive reliability and error analysis method (cream). then, we integrate the retrospective analysis mechanism of cream into our existing its, thus enable the system to indicate the path of probable cause-effect explaining reasons why errors have occurred. keywords: intelligent tutoring system, erroneous actions, retrospective analysis 1 introduction in order to simulate procedural and collaborative work, we developed the model mascaret (multi-agent system for collaborative adaptive and realistic environment for training) where human learners and agents collaborate to realize a task [1]. learners are gathered in team consisting of several predefined roles, every role contains a number of actions to be realized by learners with specific resources. during realization of the tasks, it is essential to take into account that human learners may make erroneous actions in comparing to their predefined correct procedure. in [2], we have proposed a model of intelligent tutoring system (its) whose principal objective is to suggest pedagogical assistances to the teacher adapted to the simulation context and to the learner’s behaviours (including erroneous actions). however, this works exclusively concerns errors detection and tagging. once erroneous actions are detected in our existing its, it were be classified in different types (cf. figure 1(a)) whose explications are based on a knowledge base on classical errors. in order to better support the teacher and facilitate his comprehension, it lacks a model that could explain reasons why the learner made an error. our approach bases on the cognitive reliability and error analysis method (cream) in human team error error team proceduralerror proceduralerror actionerror usageerror (a) errors types in its [2] erroneous action t im in g duration sequence object forcedirection distance speed t o o e a rly , to o la te , o m is s io n t o o lo n g , to o s h o rt r e s e rv a l, re p e titio n , c o m m is s io n , in s tru s io n w ro n g a c tio n , w ro n g o b je c t t o o m u c h , to o little w ro n g d ire c tio n t o o fa r, to o s h o rt t o o fa s t, to o s lo w (b) dimensions of error modes [3] figure 1: errors types and errors’s phenotypes reliability analysis field [3]. this approach proposed a classification scheme which makes a distinction between observations of errors (phenotypes, cf. figure 1(b)) and its causes (genotypes) classified in copyright © 2006-2009 by ccc publications 74 thanh-hai trinh, cédric buche, ronan querrec, jacques tisseau three categories: m(an), t(echnology) and o(rganization). for example, since the learner made a mistake about the order of actions, the erroneous action observed is in phenotype sequence and that can be futher explained by some genotypes such as inattention (man related genotype), communication failure (technology related genotype), etc. the causal links between phenotype-genotype are represented using a number of consequent-antecedent links. finally, the scheme could be associated with both a method of retrospective analysis (the search for causes) and a performance prediction method. however, in our goal of erroneous actions detection and then searching for the causes, we interested in human learner’s performance analyses, in other words, in retrospective analyses. implementation of cream was object in the work of el-kechaï [4][5] which firstly proposed a task model named metisse in order to recognize learner’s plans in virtual environments for training (vet), then this model could be used to detect for erroneous actions according to classification of hollnagel. nevertheless, implementation of metisse was not complete, and integration of cream into a really its was not performed. in this paper, we will firstly propose an approach to model cream (section 2). next, in section 3, we will present the integration of retrospective analysis mechanism of cream into our existing its as well as our evaluation. 2 implementation of cream 2.1 classification scheme representation there are several graphic tools that permit to keep track of analyses processes such as cream navigator developed by serwy and rantanen [7]. however, this navigator is completely closed in the sense that it does not maintain an explicit representation of possible errors modes and probable causes. for that, [4] proposed using a rules base for represent consequent-antecedent links, hence the search for the causes was executed by backward inferences. limitation of this method obviously lies on the performance of inference mechanism, other problem maybe occurs in adding, removing another potential errors that will demand a considerable modification on the rules base. for our development, as suggested in [3], we intent to separate the analysis method (cf. section 2.3 and 2.4) and the representation of errors modes using a group of four data files in format xml detailed below: questionnaire.xml : proposing to represent a list of questions from which we could evaluate the common performance conditions (see section 2.2 in following). phenotype.xml : proposing to maintain the phenotypes and its antecedents (cf. figure 2). <phenotypes> <phenotype name="time/during" description="..."> <generalantecedents> <item>inadequate plan</item> <item>inattention</item> </generalantecedents> <specificantecedents> <item>earlier omission</item> </specificantecedents> </phenotype> ... </phenotypes> figure 2: representation of phenotypes genotype.xml : containing all possible causes classified in three groups (m,t,o), each group is then detailed into several categories. the important point is that this data file also represents relations between each consequent and its antecedents (cf. figure 3). repartition.xml : proposing to determine repartition of specific antecedents (cf. figure 4) in three factors (m,t,o) which serves to initialize the mass of each specific antecedent as a probable cause (cf. section 2.4). finally, in considering that cream is naturally a flexible method and adaptable to different analysis contexts, this strategy of classification scheme representation permits customize the scheme without any modification on analysis method. modeling of errors realized by a human learner in virtual environment for training 75 <genotypes> <group name="man"> <category name="planning"> <generalconsequent name="inadequate plan" description="..."> <generalantecedents> <item>distraction</item> <item>excessive demand</item> </generalantecedents> <specificantecedents> <item>error in goal</item> <item>inadequate training</item> </specificantecedents> </generalconsequent> ... </category> ... </group> ... </genotypes> figure 3: representation of genotypes <repartition> <item name="earlier omission" group="man" description="..."/> <item name="message misunderstood" group=technology description="..."/> ... </repartition> figure 4: repartition of specific antecedents in three factors (m,t,o) 2.2 define the common performance conditions (cpc’s) in cream, hollnagel highlighted that the context strongly influence human actions. it is therefore essential to take into account the description of virtual environment in which the human learner is immersed. the objective is to determine how each factor (m,t,o) influences the training context. here, we are inspired from the proposition presented in [5] using a predefined questionnaire which will be answered by the teacher before training session (cf. figure 5). next, each factor will be assigned one <questionnaire> <question name="the visual quality of the interface is it bad?" group="technology" answer="yes"/> <question name="does the learner have concentration trouble?" group="man" answer="no"/> ... </questionnaire> figure 5: define the cpc’s by questionnaire [5] coefficient calculated using formula below: coe f ficientgroup i = number o f yes answers associated to group i total number o f yes answers (1) where group i is respectively in (man, technology, organization). these values permit define the most probable factor leading to erroneous actions. 2.3 modelling of consequent-antecedent relations one advantage of cream lies on its recursive analysis approach, rather than strictly sequential in compare with other traditional analysis methods. so that, it also conducts to a non-hierarchical data structure to connect the direct as well as indirect links: (i) between a phenotype and its antecedent; and (ii) between a consequent and its antecedents. figure 6 shows our model to represent the connection between consequent-antecedent. here, we are going to construct a causal graph where we use the term node to point to either a consequent or an antecedent. each node is described by its name; the group of errors modes that it is associated and its category in group; the description in text helps better explain the error’s semantics in particular context. the boolean attribute terminal permit to identify if that is a terminal-cause or not. the most important is that, each node contains two lists: one includes its antecedents, other points to its consequents, in others words, they represent edges in/out one node in the causal graph. at last, each node must also 76 thanh-hai trinh, cédric buche, ronan querrec, jacques tisseau +a dda ntec ede nt() +a ddc ons eq uen t() +c alcu latem a ss () -_na m e : string -_grou p : string -_ca te go ry : s trin g -_de scription : s trin g -_m as s : dou ble -_term ina l : boo l -_list_a ntec ede nt -_list_c ons equ en t node + getq u estio nna ire () + getph eno type s () + getsp ecifica ntec ede nts () + getg e nera lc ons eq uen ts () + getg e nera la ntec ede nts () util 1 0 ..1 0..1 0..* 1 1 « use s» q ue stion naire .xm l ph eno ty pe .xm l g en otyp e .x m l r e partitio n.xm l +ge tantec ede ntfro m phe noty pe () +ge tg eno type from a ntec ede nt() +finds pec ific ante ced entr epa rtition () +cre ateg rap hf ro m ph eno ty pe () +findl istte rm in al() +so rtl istte rm in al() -_ grap h : s tring genotypeanalyzer figure 6: uml diagram for modeling consequent-antecedent links include a value of mass which represent the certitude of choosing this node as a probable cause. the two methods addantecedent() and addconsequent() serve for maintaining the two lists of antecedents and consequents of one node. note that once a node calls the method addantecedent() serving for adding a "parent" node like one of its antecedents, this node will also add itself to the consequents list of the "parent" node (using the method addconsequent() of the parent node), the value of the attribute terminal then will be set to false. 2.4 search for the causes the retrospective analysis is executed by a genotypeanalyzer containing graph attribute which is initialized by pointing to the phenotype input (root node), then the analyzer calls accurate methods to find the root causes (the nodes with the attribute terminal having value false). this mechanism is presented below (cf. algorithm 1). algorithm 1 retrospective analysis require: phenotype of erroneous action 1: initialization: construct the "root" node pointing to phenotype input 2: {step 1: finding antecedents of phenotype input} 3: read from file phenotype.xml, find all general antecedents of phenotype input 4: for each antecedent do 5: add it into antecedents list of "root" node 6: end for 7: {step 2: construction the causal graph} 8: for each unvisited node in the graph do 9: find its antecedents from file genotype.xml 10: add them to antecedents list 11: end for 12: return step 2. this recursive search terminates when the node selected is a specific antecedent node or a general consequent node without antecedents. with this algorithm, we finally attain a causal network where each node is associated with its antecedents and consequents. the "leaves" are terminal nodes (or "root" causes) whose antecedents list is empty. in order to calculate the certitude of choosing each node as a probable cause, we inherit the proposition presented in [5] using dempster-shafer’s evidence theory: mass(a) = coe f ficient (g(a)) ∗ ∑ ∀c∈cons(a) ( mass(c) ∑ ∀i∈{m,t,o} (coe f ficient(i) ∗nic) ) (2) where: modeling of errors realized by a human learner in virtual environment for training 77 • mass(a) : mass of antecedent a • g(a) : group of a • cons(a) : consequents list of a • coe f ficient(i) : coefficient of group i calculated in formula 1 • nic : number of antecedents of c classified in group i figure 7: cream explorer finally, the figure 7 illustrates our tool cream explorer which was developped in this phase permitting to maintain the errors scheme, answer the questionnaire for define the cpc’s and execute the retrospective analysis. 3 integration of retrospective analysis into our existing its 3.1 learner’s plans recognition in order to detect the erroneous actions realized by a human learner, it is indispensable to know: (i) the learner’s activities in the past; (ii) his current action (in the meaning that the action has just been done); (iii) the actions that the human learner intents to do in according to a predefined correct procedure. our existing its as proposed in [2] bases on the model mascaret [1] where we used an multi-agent system to simulate collaboration between human learners and agents during their realization of tasks. learners are gathered in team consisting of several predefined roles, every role contains a number of tasks associated eventually with accurate resources, every leaner also owns an epistemic memory containing all actions realized in the past, etc. finally, we could retrieve from mascaret following informations relating to learner’s plan in vet: 78 thanh-hai trinh, cédric buche, ronan querrec, jacques tisseau • action(s) before: learner’s action(s) in the past (note that, in mascaret, every action is eventually associated with its accurate resource(s)) • current action: action has just been done by learner • action(s) correct (according to role): action(s) must be done by learner in his role(s) • action(s) correct (according to plan): action(s) may be done by learners in the context. here, it is essential to make distinction betweens action(s) correct according to role and action(s) correct according to plan. in the first case, because the learner could play several roles, it represents all correct actions that the system expects from the learners. the second one concerns the cases where there are more than one learner in vet to realize together a mission. therefore, in this case, it is possible that a leaner performs a correct action according to the plan but it is not correct in compare to his role. • next correct action(s) in the role: next action(s) must be done by learner in his role(s) • full correct plan: description of all accurate actions (associated with resources) in predetermined procedure that the learner must respect. in next section, we present our mechanism for mapping erroneous actions detected by our existing its with hollnagel’s classification scheme of errors modes. 3.2 classification of erroneous actions according to the scheme of cream erroneous actions in phenotype "sequence" according to hollnagel, performing an action at the wrong place in a sequence or procedure is a common erroneous action, and it is more realistic in our context of simulation of procedural and collaborative work. the "sequence" problem consists of several specific effects: omission (an action was not carried out); jump forward/ jump backwards (actions in a sequence were skipped/carried out again); repetition (the previous action is repeated); reversal (the order of two neighbouring action is reversed); wrong action (an extraneous or irrelevant action is carried out). we present in following our mechanism to detect erroneous actions in phenotype "sequence": algorithm 2 detection of erroneous actions in phenotype sequence 1: if current action exists in actions correct according to role then 2: this is a correct action (phenotype sequence does not occur) 3: else 4: if current action does not exist in actions correct according to plan then 5: specific effect = "wrong action" 6: else 7: if current action exist in last action before then 8: specific effect = "repetition" 9: end if 10: compare the relative order of current action to the order of next correct action(s) in the role using the full correct plan 11: if id current action < id correct action in role then 12: specific effect = "jump backwards and/or omission" 13: else 14: specific effect = "jump forward and/or omission" 15: end if 16: if id current action = id correct action in role + 1 then 17: specific effect = "reversal" 18: end if 19: end if 20: end if erroneous actions in phenotype "wrong object" in [3], the author clarified that "action at wrong object" is one of the more frequent error modes, such as pressing the wrong button, looking at the wrong indicator, etc. in our context, during realisation of modeling of errors realized by a human learner in virtual environment for training 79 collaborative work, it is possible that learner performs a correct action but on a wrong object. therefore, the detection of erroneous actions in phenotype "wrong object" must be implemented independently with the detection of phenotype "sequence". this phenotype is detailed into following specific effects: neighbour/similar object (an object that is proximity/similar to the object that should have been used); unrelated object (an object that was used by mistake). in order to detect erroneous actions in phenotype "wrong object", we use the same principle presented in the case of phenotype "sequence" by using following informations retrieved from model mascaret: • current resource: resource associated with current action • resource(s) correct (according to role): resource(s) must be used by learner in his role(s) • resource(s) correct (according to plan): list of resource(s) associated with all action(s) in action(s) correct according to plan. our algorithm is detailed in following: algorithm 3 detection of erroneous actions in phenotype wrong object 1: if current resource exists in resource(s) correct according to role then 2: this is a correct resource (phenotype wrong object does not occur) 3: else 4: if current resource does not exist in resources correct according to plan then 5: specific effect = "unrelated object" 6: else 7: specific effect = "neighbour and/or similar object" 8: end if 9: end if erroneous actions in phenotype "time/during" the phenotype time/during is divided in several specific effects: too early/ too late (an action started too early/too late); omission (an action that was not done at all); too long/too short (an action that continued/was stopped beyond the point when it should have been). hollnagel noted that the error modes of timing and duration refer to a single action, rather than to the temporal relation between two or more actions. in our context, the realization of tasks in model mascaret is sequential, therefore, an action is considered to be too early when it was realized before several actions in plan; also, action(s) are considered to be omitted when they were not carried out. finally, in order to detect erroneous actions in phenotype time/durring, we propose that: • action having specific effect jump forward also has specific effect too early • action described by specific effect omission (in error mode sequence) will be considered as an action having specific effect omission (in error mode time/during) 3.3 experiment & results in order to evaluate our integration of retrospective analysis into its, we take place in gaspar application [6] whose objective aims at simulate aviation activities by virtual reality. the learners are immersed in virtual environment simulating the aircraft carrier in order to realize together the tasks. during the realization of these collaborative works, our its follows the learners and then apply the algorithms depicted above for detecting learner’s erroneous actions. next, for interpreting the causes of errors, we use the classification scheme of error modes proposed in [5] which were particularly adapted to vet. table 1 and table 2 respectively illustrate results of retrospective analysis for the phenotype sequence and wrong object. we change coefficients of three factors (m,t,o) for evaluating how cpc’s influence the analysis result. for each phase in analysis process, we select and display the most probable cause by ordering mass values. 80 thanh-hai trinh, cédric buche, ronan querrec, jacques tisseau coefficient (m,t,o) causal links (0.333 0.333 0.333) 1, design failure (0.125) → inadequate scenario (0.125) → sequence 2, adverse ambient condition (0.125) → inattention (0.125) → sequence 3, long time since learning (0.042) → memory failure (0.125) → sequence (1 0 0) 1, other priority (0.2) → memory failure (0.2) → sequence 2, error in mental model (0.067) → faulty diagnosis (0.2) →sequence 3, erroneous analogy (0.067) → faulty diagnosis (0.2) → sequence (0 1 0) 1, equipment failure (0.1) → access problems (0.5) → sequence 2, distance (0.1) → access problems (0.5) → sequence 3, localisation problem (0.1) → access problems (0.5) → sequence (0 0 1) 1, noise (1) → communication failure (1) → sequence table 1: causal links of phenotype sequence coefficient (m,t,o) causal links (0.333 0.333 0.333) 1, access problems (0.125) → wrong object 2, design failure (0.125) → inadequate scenario (0.125) → wrong object 3, adverse ambient condition (0.042) → inattention (0.125) → wrong object (1 0 0) 1, fatigue (0.1) → performance variability (0.2) → wrong object 2, virtual reality sickness (0.1) → performance variability (0.2) → wrong object 3, anticipation (0.05) → wrong identification (0.2) → wrong object (0 1 0) 1, access problems (0.5) → wrong object (0 0 1) 1, noise (1) → communication failure (1) → wrong object table 2: causal links of phenotype wrong object 4 conclusion & future work in this paper, we proposed an approach to modelling the cognitive reliability and error analysis method (cream). we separated the representation of classification scheme of erroneous actions and the analysis method; therefore, our description of errors modes is adaptable to different training context without any modification on analysis method. we started by defining the common performance conditions, then the direct and indirect relations between consequent-antecedent are modelled using a non-hierarchical data structure. finally, the most probable cause-effect links could be found using dempster-shafer’s theory presented in [5]. in order to integrate the retrospective analysis described above into our existing its, we based on the model mascaret to retrieve information concerning learner’s plans and then detect erroneous actions. finally, we presented our proposition to mapping erroneous actions with hollnage’s classification. the experimental results in gaspar project are also presented. so that, in addition to the detection and tagging of erroneous actions, the its could furthermore indicate the path of probable cause-effect modeling of errors realized by a human learner in virtual environment for training 81 explaining reasons that the errors occur. in the future work, we will concentrate our attention on evaluation of mascaret so that this model could permit to describe more complex tasks in taking into account other factors such as force, distance, speed, direction, etc. hence, other different types of errors modes could be detected and then explained using the retrospective analysis. acknowledgement this article is an extended version of our paper [8] published in proceedings of the rd international conference on virtual learning (icvl’08). the authors would like to thank the scientific committee of icvl’08 (chaired by dr. grigore albeanu) that recommended the publishing of our extended work in ijccc. bibliography [1] r. querrec, c. buche , e. maffre , and p. chevaillier, multiagents systems for virtual environment for training: application to fire-fighting, international journal of computers and applications (ijca), pp. 25-34, june 2004. [2] c. buche and r. querrec, intelligent tutoring system for mascaret, simon richir and bernard taravel, editors, 7th virtual reality international conference (vric’05), pp. 105-108, april 2005, laval, france. [3] e. hollnagel, cognitive reliability and error analysis method, oxford: elsevier science ltd, 1998. [4] n. el-kechaï and c. després, a plan recognition process, based on a task model, for detecting learner’s erroneous actions, intelligent tutoring systems its 2006, pp. 329-338, june 2006, jhongli, taăżwan. [5] n. el-kechaï and c. després, proposing the underlying causes that lead to the trainee’s erroneous actions to the trainer, ec-tel : european conference on technology enhanced learning, pp. 41-55, september 2007, crète, grèce. [6] n. marion, c. septseault, a. boudinot and r. querrec, gaspar : aviation management on an aircraft carrier using virtual reality, cyberworlds, 2007. [7] r.d. serwy and e.m. rantanen, cream navigator, http://www.ews.uiuc.edu/ serwy/cream/v0.6beta/, version 0.6, september, 2007. [8] t.h. trinh, c. buche and j. tisseau, modeling of errors realized by a human learner in virtual environment for training, rd international conference on virtual learning icvl 2008, pp. 71-80, octobre 31, constanta, romania. thanh-hai trinh, received his msc in artificial intelligent & multimedia at the francophone institute for computer science. he is currently a phd student in cerv (virtual reality european centre held at brest, france). his current research interests are in the applications of multi-agent systems and artificial intelligent in virtual environements for training. cédric buche is a professor assistant in computer science and works at the cerv. he works on the use of the behavior modeling agent applied to virtual environment for human learning. he is the leader of the its project in mascaret. ronan querrec is professor assistant in computer science and works at the cerv. his reasearch work is about virtual environment for training. in this theme, he works on the mascaret project, a virtual environment meta-model. jacques tisseau is professor in computer science at the engineer school of brest (enib) where he leads the computer science for complex systems laboratory (lisyc). his research focus on autonomous virtual entities, interaction with these entities and epistemology of virtual reality. international journal of computers, communications & control vol. ii (2007), no. 2, pp. 185-194 fault detection for large scale systems using dynamic principal components analysis with adaptation jesús mina, cristina verde abstract: the dynamic principal component analysis is an adequate tool for the monitoring of large scale systems based on the model of multivariate historical data under the assumption of stationarity, however, false alarms occur for non-stationary new observations during the monitoring phase. in order to reduce the false alarms rate, this paper extends the dpca based monitoring for non-stationary data of linear dynamic systems, including an on-line means estimator to standardize new observations according to the estimated means. the effectiveness of the proposed methodology is evaluated for fault detection in a interconnected tanks system. keywords: fault detection, statistical analysis, dynamic principal component analysis, time series analysis, non-stationary signals. 1 introduction the on-line process monitoring for fault detection and isolation, fdi, is an important task to ensure plant safety and product quality. one of the most consolidated fdi techniques of the last twenty years is the analytical approach, which is based on explicit modeling, this is, models obtained from primary physical principles, some of the analytical approaches are very well revised e.g. [1]. in the case of large scale processes whose analytical models are not available or are difficult to obtain the fdi techniques based on data-driven can help to overcome the problem of modeling, these techniques are based on implicit modeling through multivariate statistical methods, some of these methods are resumed in [2]. principal component analysis (pca) is a multivariate statistical method which models the linear correlation structure of a multivariate process from nominal historical data. pca transforms a set of multivariate observations to a lower dimension orthogonal space, retaining the most variability of the original data [3]. because of the simplification and the orthogonal property obtained with pca, this has been used with success for fault diagnosis issues as in [4] and [5]. it is important to note that like other multivariate statistical methods, pca works under three assumptions: the data follows a multivariate normal distribution; there exist no auto-correlation among observations; and the variables are stationary, this is, the variables should keep constant mean and standard deviation over time [6], [7]. in the case of data with non normal distribution it is possible to carry out an appropriate transformation like square root or logarithm [8], in order to improve the distribution of data. in the case of dynamic systems the auto-correlation in variables is taking into account incorporating time lags of the time series during the modeling stage, this extension is called dynamic principal component analysis, dpca [9]. since pca and dpca assume stationarity during modeling process, high rate of false alarms are generated in the diagnosis stage if the test data are non-stationary. the non stationary problem has been tackled with adaptive versions of pca like in [10] and [11]. although these algorithms adapt means, covariance, and the pca model, however, they can not be used for fdi tasks since the adaptation is based in the variations of actual multivariate observations without distinguish the real causes of changes in the variables. copyright © 2006-2007 by ccc publications supported by dgapa-unam-in11403-2 and the eoli project of the european community inco program contract number ica4-ct-2002-10012 186 jesús mina, cristina verde a non-stationary condition has many possible causes e.g. due to components aging, to faults, even to normal changes in the operating point of the plant, this problem motivated the development of a fault detection algorithm which is robust to changes in the operating point but sensitive to faults. the proposal is based in the fact that the correlation structure between system signals is invariant under nominal conditions, i.e. the relations between variables is the same despite the nominal changes in the mean of the signals, this is a result of the affinity property of nominal signals in linear systems [12]; therefore this work proposes: in the modeling stage, obtain a nominal dpca model from nominal historical data and identify a set of nominal inputs-output relations; in the diagnosis stage, keep the nominal dpca model but estimate actual means of input variables through exponentially weighted moving average (ewma) and estimate means of the output variables from input means through the identified nominal inputs-output relations in order to carry out an appropriate standardization with respect to the estimated means. in the following sections the recursive means estimation process is summarized; next, the proposed extension of dpca based fault detection for changes in the operating point will be described. finally the methodology will be evaluated for faults detection in a three interconnected tanks system. 2 backgrounds 2.1 identification of the inputs-output relations it is proposed the recursive mean estimation of the input signals using ewma and the outputs mean estimation from inputs estimated means and through inputs-output nominal relations. here is proposed to identify the inputs-output relations with moving average models, ma(), for each one of the output variables. lets consider the case of a mimo linear system with r inputs and s outputs y = au (1) u = un + η y = yn + ν (2) where η and ν are stationary white noise vectors added to the inputs and to the outputs, with zero mean and variances σ 2η and σ 2 ν , respectively. each one of the output variables can be expressed as a linear combination of the inputs with corresponding time lag orders q1i, q2i,··· , qri yni (t) = q1i ∑ k=0 ak1i u1 (t −k) +···+ qri ∑ k=0 akriur (t −k) (3) for i = 1,··· , s. the ak1i,··· , akri parameters are obtained through correlation analysis [13]. the identified input-output relations can be expressed in compact form as follows ŷni (t) = âi −→u i (t) (4) where âi = [ â01i ... âq1i 1i ... â0ri ... âqri ri ] fault detection for large scale systems using dynamic principal components analysis with adaptation 187 and −→u i (t) = [ u1 (t) ··· u1 (t −q1i) ··· ur (t) ··· ur (t −qri) ]t the orders q ji of the model (4) is selected through a validation procedure taking the minimal value of the sum of square error as a function of q ji. finally, from (4) for all of the outputs, the relations in matrix notation are given by ŷn (t) = â −→u (t) (5) where ŷn (t) = [ ŷn1 (t) ··· ŷns (t) ]t (s×1) −→u (t) = [ u1 (t) ··· u1 (t −l) ··· ur (t) ··· ur (t −l) ]t (o×1) and â ∈ ℜs×o is made up by the coefficients âk ji = { âk ji , k ≤ q ji f or j = 1, ..., r 0 , q ji < k ≤ l i = 1, ..., s l = max{q11, ..., qr1, ..., q1s, ..., qrs} and o = r (l + 1). 2.2 means recursive estimation once the identification of ma models (5), for the system (1), are obtained, the recursive means estimation is carried out in the following way. the inputs mean recursive estimation can be computed as follows µ̂−→u (t) = β µ̂−→u (t−1) + (1−β )−→u (t) (6) with (0 < β ≤ 1) the forgetting factor of ewma. by the other side, the outputs means at time instant t are estimated from the input means given in (6) and through (5) according with µ̂y(t) = µ̂yn(t) = âµ̂−→u (t) (7) 3 dpca based fault detection with mean parameter estimation the dpca statistical tool is used to obtain an implicit model of a dynamic system from nominal historical data, and use this implicit model to carry out fault detection tasks. the proposed algorithm is illustrated in fig. 1. the idea is not only to obtain a dpca based statistical model but also identify nominal inputs-output relations. so, estimating actual means of input variables through ewma and estimating means of the output variables from input means using the nominal inputs-output relations, an appropriate standardization can be carried out. 188 jesús mina, cristina verde figure 1: proposed fault detection algorithm. (a) modeling stage carried out off-line, (b) detection stage carried out on-line 3.1 dpca based statistical modeling let matrix x be a set of historical data made up of nt observations of r input variables and s output variables, taken from a dynamic system working under nominal conditions and around an operating point x = [ u1 ··· ur y1 ··· ys ] (nt×p) (8) each column in x represents an auto-correlated time series. in dpca the serial correlation is included constructing the named trajectory matrix applying w time lags on each time series, this is −→ x = [ −→ u 1 ··· −→ u r −→ y 1 ··· −→ y s ] (n×m) (9) where e.g. −→ u 1 = [u1 (t) u1 (t −1) ... u1 (t −w)]; n = nt −w and m = p (w + 1). to avoid that some particular variables dominate the modeling process, it is convenient to carry out a data standardization in matrix −→ x in relation to its means and standard deviations. thus, the means of−→ x are given by µ̂−→x = [ 1 n −→ x t 1 ]t = [ µ̂−→u µ̂−→y ] (1×m) (10) where 1 = [1, 1, ..., 1]t ∈ ℜn. by the other side, the covariance matrix of −→x is given by s = 1 n−1 (−→ x −1µ̂−→x )t (−→ x −1µ̂−→x ) from which the standard deviations can be obtained σ̂−→x = √ diag (s) = [ σ̂−→u σ̂−→y ] (1×m) (11) thus, the data standardization is computed in the following way x̃ (i, j) = −→ x (i, j)− µ̂−→x ( j) σ̂−→x ( j) for i = 1, . . . , n and j = 1, . . . , m. fault detection for large scale systems using dynamic principal components analysis with adaptation 189 the uncorrelated principal components z of dimension n × l are obtained through the following transformation z = x̃vt (12) where the orthonormal transformation matrix vt ∈ ℜm×l is composed of an appropriate selection of l eigenvectors, called loading vectors, associated to the correlation matrix r = 1n−1 x̃ t x̃. the data matrix x̃ can be expressed as x̃ = ̂̃x + e = zvtt + e where ̂̃x is the information captured by the l-principal components and e is the information in the neglected m − l-components. so, for the detection purpose it is possible to use the hotelling statistic, t 2zi from z and/or the squared prediction error (spe) from e. in this paper the hotelling statistic is used for fault detection because our interest is not the evaluation of these two statistical parameters but the illustration of reduction of false alarms. for each l-variate observation in z the hotelling univariate statistic t 2zi is given by t 2zi = zis −1 z z t i (13) where sz is the covariance matrix of z. finally, a threshold of normal condition from the probability density function of the set of parameters t 2zi is calculated. [6] propose, among others, for a beta distribution of the data set t 2zi the threshold ucl as ucl = (n−1)2 ( l n−l−1 ) f ( α 2 ; l, n−l −1 ) n ( 1 + ( l n−l−1 ) f ( α 2 ; l, n−l −1 )) (14) where n and l are the dimensions of z and α is a level of significance. in a dpca based modeling conventional approach the implicit model consist of the means and standard deviations vectors (10), (11); the loading vectors in vt ; the variance in the principal components given by sz; and the nominal threshold ucl. however, according with the proposal it is just considered σ̂−→x ,vt , sz and ucl, since µ̂−→x will be estimated recursively. additionally to the statistical modeling it is carried out the identification of the inputs-output relations (5). 3.2 fault detection dpca detects a deviation of vector of actual observation −→x a from the nominal reference in terms of its mean and its standard deviation. however, it is important to note that the modeling process is based in the data set −→ x which was obtained in a particular operating point of the system, so any change in the nominal values of the signals is interpreted by dpca as a fault, even when the process is healthy, this misinterpretation is because of the time variant behavior of components in (10). for linear systems, a change in the operating point means a new assignment in the input variables with consequent variations in the output variables, this is, changes in the mean values of input and output variables but no changes in their correlation structure. however, faults in the system produce changes in the mean values and in the correlation structure between variables. thus, here it is proposed during the detection stage, the on line estimation of the statistical set (10) using nominal linear inputs-output relations in order to adapt the standardization procedure. 190 jesús mina, cristina verde so, according to the proposed extension to dpca based fault detection algorithm, the procedure to evaluate and classify an actual observation −→x a ∈ ℜ1×m is summarized as follows. be the actual observation vector, with input and output variables, expressed in w time lags −→x a = [ −→u a1 ··· −→u ar −→y a1 ··· −→y as ] (15) 1. estimate through (6) the means of the actual input data, µ̂−→u a , and through (7) the nominal means of the output variables, µ̂−→y ; next construct the vector µ̂−→x a = [ µ̂−→u a µ̂−→y ] (1×m) (16) 2. standardize the m terms in (15) using the means estimated given in (16) and the historical standard deviations (11), this is x̃a ( j) = −→x a ( j)− µ̂−→x a ( j) σ̂−→x ( j) for j = 1, . . . , m. 3. transform the x̃a vector to the principal components subspace za through vt za = x̃avt 4. map za in the behaviour symptom t 2za through t 2za = zas −1 z z t a 5. if the resulting value deviates from the normal condition threshold ucl then a fault is present in the system. the key of the proposed methodology is in the continuous estimation of nominal means (16) using the nominal linear inputs-output relations (5) in order to carry out an appropriate standardization. in the following section the proposed fault detection algorithm is applied to detect faults in a three interconnected tanks system considering simple relations for the means estimation. 4 three tanks system the tanks system is composed of three cylindrical tanks, interconnected at the bottom by pipes and with valves v1 in the link between tanks 2 and 3, and v2 in the link between tank 2 and the outside, which aperture can be manipulated in order to emulate faults (e.g. pipe blockage), see fig. 2. the tank dimensions are: ht = 0.63m, at = 0.01539m2. the system is feed by two inputs q1 to the tank 1 and q2 to the tank 2 which are measured just as the output variables h1, h2 and h3 which correspond to tanks levels. the mathematical model is the following at dh1 dt = q1 + q31 −q10 at dh3 dt = q23 −q31 at dh2 dt = q2 −q23 −q20 ; q10 = k1 √ h1 q31 = k31ρ (h3 −h1) q23 = k23ρ (h2 −h3) q20 = k2 √ h2 (17) where ρ (x) , sgn (x) √ |x|. fault detection for large scale systems using dynamic principal components analysis with adaptation 191 q1 q2 at h1 h3 h2 q10 q31 q23 q20 v1 v2 figure 2: three tanks system for the experiments the system was simulated under the following operation point: q01 = 4.75e − 5m3/s, ( σ 2q1 = 1.07e−10 ) ; q02 = 7.35e − 5m3/s, ( σ 2q2 = 1.05e−10 ) ; h01 = 0.147m, ( σ 2h1 = 1.96e−4 ) ; h02 = 0.276m, ( σ 2h2 = 4.81e−4 ) ; h03 = 0.195m, ( σ 2h3 = 2.65e−4 ) ; k1 = 1.816e−4, k31 = 1.005e−4, k02 = 9.804e−5 and k023 = 7.804e−5. taking a set of 400 nominal observations measured every 10s it was obtained a dpca based principal components space of dimensions 301×68, so, for an α = 0.01 the resulting threshold is ucl = 95.886. by the other side, the inputs-output relations identified were h1 = f (q1, q2, q1), h2 = f (q1, q2, q2) and h3 = f (q1, q2, q3) with time lags of order q1 = 61, q2 = 61 and q3 = 60, respectively. 4.1 detection results using a forgetting factor of β = 0.95 for the recursive inputs means estimation (6) the fault detection algorithm is evaluated considering the following cases: 1. fault condition, blockage in the pipe which links tanks 2 and 3, the fault occurrence is at 8000s. 2. normal operation of the system during 15000s, with changes in the means of u1 of +20% in 3000s < t < 6000s; −20% in 9000s < t < 12000s and in u2 of +20% in 4500s < t < 7500s; −20% in 10500s < t < 13500s. 3. change in the mean of u1 of +20% from 4000s and fault condition, blockage in the pipe between tank 2 and 3, at 8000s. the first test is to compare the performance of the dpca based conventional fault detection and the fault detection based in the proposed algorithm, under fault conditions. the monitoring results are given in fig. 3 which shows that both algorithms are able to detect the fault. the second test evaluate the performance of both algorithms before changes in the operation point, the monitoring results are given in fig. 4, where it is cleared observed that the traditional dpca-based fault detection (mon1) interprets the normal changes in the operation point as faults, however, the proposed algorithm (mon2) is robust before these changes, which reduces the false alarm rate. finally, the third test shows the capability of the proposed fault detection algorithm to distinguish between normal variations in the operating point and the presence of faults, see fig. 5. 5 conclusions here, a modification to the dpca algorithm for fault detection has been proposed, in which an appropriate standardization with respect to on-line estimated statistical parameters is carried out if simple 192 jesús mina, cristina verde 0 5000 10000 15000 0 50 100 150 200 250 300 350 400 450 t(s) mon1 mon2 ucl figure 3: fault condition: ucl threshold of normal condition; mon1 dpca-based monitoring; mon2 dpca with adaptation 0 5000 10000 15000 0 200 400 600 800 1000 1200 1400 1600 1800 t(s) mon1 mon2 ucl figure 4: normal condition: ucl threshold of normal condition; mon1 dpca-based monitoring; mon2 dpca with adaptation 0 5000 10000 15000 0 100 200 300 400 500 600 t(s) mon1 mon2 ucl figure 5: normal and fault condition: ucl threshold of normal condition; mon1 dpca-based monitoring; mon2 dpca with adaptation fault detection for large scale systems using dynamic principal components analysis with adaptation 193 healthy relations between variables can be obtained. this idea allows to deal with non-stationary signals and to reduce significatively the rate of false alarms. it was shown through a series of tests the effectiveness of the proposed fault detection algorithm to distinguish between normal changes in signals and the variations due to the presence of faults. references [1] r. j. patton, p. m. frank, r. n. clark, issues of fault diagnosis for dynamic systems, springerverlag, 1989, london. [2] l. h. chiang, e. l. russell, r. d. braatz, fault detection and diagnosis in industrial systems, advanced textbooks in control and signal processing, springer-verlag, 2001, london. [3] j. e. jackson, a users guide to principal components, john wiley, 1991, new york. [4] j. v. kresta, j. f. macgregor, t. e. marlin, multivariate statistical monitoring of process operating performance, the canadian journal of chemical engineering, vol. 69, pp. 35-47, february, 1991. [5] a. raich, a. çinar, statistical process monitoring and disturbance diagnosis in multivariable continuous processes, aiche journal, vol. 42, no. 4, pp. 995-1009, april, 1996. [6] n. d. tracy, j. c. young, r. l. mason, multivariate control charts for individual observations, journal of quality technology, vol. 24, no. 2, pp. 88-95, april, 1992. [7] a. norvilas, a. negiz, j. decicco, a. çinar, intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring, journal of process control, vol. 10, no. 4, pp. 341-350, august, 2000. [8] d. c. montgomery, introduction to statistical quality control, john wiley, 2001, new york. [9] w. ku, r. h. storer, ch. georgakis, disturbance detection and isolation by dynamic principal component analysis, chemometrics and intelligent laboratory systems, vol. 30, no. 1, pp. 179196, november, 1995. [10] n. b. gallagher, b. m. wise, s. w. butler, d. d. white, g. g. barna, development and benchmarking of multivariate statistical process control tools for a semiconductor etch process: improving robustness through model updating, adchem’97, banff, canada, pp. 78-83, 9-11 june, 1997. [11] w. li, h. h. yue, s. valle-cervantes, s. j. qin, recursive pca for adaptive process monitoring, journal of process control, vol. 10, no. 5, pp. 471-486, october, 2000. [12] t. kailath, a. h. sayed, b. hassibi, linear estimation, prentice hall, 2000, new jersey. [13] g. e. p. box, g. m. jenkins, g. c. reinsei, time series analysis: forecasting and control, prentice hall, 1994, new jersey. jesús mina, cristina verde instituto de ingeniería-unam automatización coyoacán, df, 04510, méxico, fax: (52)-55-56233600 ext 8052 e-mail: jminaa@iingen.unam.mx, verde@servidor.unam.mx received: january 15, 2007 194 jesús mina, cristina verde jesús mina received the bs degree in electric eng. from tuxtla gutiérrez technological institute, méxico in 1999; the ms degree in electronic eng. from the research and technological development national center, méxico in 2002; and currently is a student in the phd program in electrical eng. of the national university from méxico. he was professor from 2002 to 2003 in the zacatepec technological institute. he has carried out research in non-linear control for power active filters and currently is interested in fault diagnosis based in multivariate statistical analysis. jesús mina is a member of the international society of automation. cristina verde received the bs degree in electronic and communication eng. from national polytechnic, méxico in 1973, the ms degree in electrical eng. from the national polytechnic, in méxico 1974 and the phd degree in electrical eng. from the duisburg university in germany in 1983. in 1984, she joined the national university from méxico (unam) and became the head of the automatic control department in 1988. she has been the coordinator of the postgraduate school in computer science and engineering from the national university. she has used the control theory to improve distribution, regulation and quality of water in méxico and her main topics of research interests include automatic fault detection and diagnosis for dynamic systems and integrity of industrial process. she got the prize sor juana ines de la cruz given by the national university to the outstanding women in the engineering field in 2005. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 135-148 feedback gain design method for the full-order flux observer in sensorless control of induction motor abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi abstract: this paper deals with a feedback gain design method for the full-order flux observer with adaptive speed loop, which enables the minimizing the unstable operation region of this observer to a line in the torque-speed plane. the stability in regenerating mode is studied using necessary condition of stability based on determinant of matrix and a linearized model. simulations results where the proposed observer is compared with an exiting solution (where the unstable region is not totally removed) are presented to validate the proposed observer design. keywords: induction motor, full-order flux observer, sensorless control, stability analysis, adaptive speed estimator, regenerating mode 1 introduction the speed-sensorless control of induction motor drives have developed significantly during the last number of years. speed adaptive full observers introduced by [8], [15] are promising flux estimators for inductions motors drives. the speed adaptive observer consists of a state variable observer augmented with a speed adaptation loop. the observer gain and the speed adaptive law determine the properties of the observer. the speed adaptation law is based on the component of the current estimation error with the estimated rotor flux. the adaptation law was originally derived using the lyapunov stability theory [8]. however, the stability of the adaptation law is not guaranteed and stability problem exist in the regenerating mode. the derivation in [8] neglects a term including the actual rotor flux (which is not measurable). the positive-realness condition is not satisfied as shown [5]. some limits of operation were quickly highlighted [9], [13]. in particular, a well known instability region was described in regenerating mode. thus, the drive stability can’t be guaranteed when this type of observer is associated with a field oriented control. there was many work in order to reduce this region of instability which is due to inadequate observer design [1, 2, 5, 14]. in this paper, we describe the design of an adaptation law that minimizes the instability region of an adaptive speed estimator. the paper is organized as follows. the induction motor model and the speed adaptive flux observer are first defined in section 2 and 3 respectively. we introduce the observer gain design in section 4 leading to a reduced instability region limited to a line. finally, simulations results are presented and discussed in section 5, where the proposed observer is compared with an exiting solution [5, 13]. 2 induction motor model the induction motor is described by the following state equations in the synchronous rotating reference frame with complex notations: d dt x = a (ω, ωs) x + bus (1) is = cx (2) where x = [ ψ r is ] t a =   −( 1 tr + jωsl ) lm tr lm b ( 1 tr − jω) −(a + jωs)   , b =   0 1 σ ls   , c = [ 0(2×2) i ] , i = [ 1 0 0 1 ] (3) copyright © 2006-2008 by ccc publications 136 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi and the mechanical equation is: d dt ω = p2 lm jlr ℑ(isψ ∗ r )− p tl j (4) where ∗ means a conjugate, j a complex number and ℑ an imaginary part. ψ r : rotor flux; is : stator current; us : stator voltage; ωs : stator angular frequency; ω : motor angular speed; ωsl = ωs − ω : slip angular frequency; rs, rr : stator and rotor resistance; ls, lr : stator and rotor self-inductance; lm : mutual inductance; tl : load torque; j : rotor inertia ; p : number of pole pairs; tr = lr/rr : rotor time constant; a = (l2r rs + l 2 mrr)/(σ lsl 2 r ); b = σ lslr; σ = 1−(l2m)/(lslr) : leakage coefficient. 3 adaptive observer the conventional full-order observer, which estimates the stator current and the rotor flux together [10, 11], is written as the following state equation. d dt x̂ = â (ω̂, ω̂s) x̂ + bus + g(is − îs) (5) îs = cx̂ (6) where ̂ means the estimated values and g = [ g1 g2 ]t is the observer gain matrix. we assume that all machine parameters are perfectly known except the motor speed. using the assumption of constant angular rotor speed ω̇ = 0 (i.e. the speed variations are slow with respect to electrical mode) [8], [5], the speed adaptive law is [8]: d dt ω̂ = λ lm b (eid ψ̂rq −eiqψ̂rd ) (7) where λ is a positive constant and will be tuned in (7) to improve observer dynamics. in practice, proportional-integral action is used in order to improve the dynamic behavior of the estimator. d dt ω̂ = kp d dt (eid ψ̂rq −eiqψ̂rd ) + ki(eid ψ̂rq −eiqψ̂rd ) (8) where eid = isd − îsd , eiq = isq − îsq, (isd , isq) are (d,q) components of stator current, (ψrd , ψrq) are (d,q) components of rotor flux. the speed adaptive observer scheme with the speed adaptation mechanism is presented in fig. 1. 4 observer gain design 4.1 linearized model the nonlinear and complicated dynamics of the speed adaptive observer can be studied via smallsignal linearization. it is useful to proceed with a local analysis based in the principle of stability in the first approximation [12, 7]. we will choose the particular form g1 = g1i2×2 where i2×2 is the identity matrix and g2 = 02×2. the complete adaptive observer may be written as equation (10). note that according the assumption ω̇ = 0, the motor model (1) may be written as (9)    d dt ψ r = −( 1 tr + jωsl )ψ r + lm tr is d dt is = lm b ( 1 tr − jω)ψ r −(a + jωs)is + 1 σ ls us d dt ω = 0 (9) feedback gain design method for the full-order flux observer in sensorless control of induction motor 137 induction motor b â speed adaptive law eq. (8) + + + ∫ ω̂ g c i s x̂ î s − + u s 1 figure 1: speed adaptive observer    d dt ψ̂ r = −( 1 tr + jω̂sl )ψ̂ r + lm tr îs + g1δ ei d dt îs = lm b ( 1 tr − jω̂)ψ̂ r −(a + jω̂s)̂is + 1 σ ls us d dt ω̂ = kp d dt (eid ψ̂rq −eiqψ̂rd ) + ki (eid ψ̂rq −eiqψ̂rd ) (10) we investigate the stability of the observer by linearizing the two systems (10) and (9) around an equilibrium operating point.defining the new state vectors x = xo + δ x with xo = [ ψ ro iso ωo ] t , δ x = [ δ ψ r δ is δ ω ]t and x̂ = x̂o + δ x̂ with x̂o = [ ψ̂ ro îso ω̂o ] t , δ x̂ = [ δ ψ̂ r δ îs δ ω̂ ] t . the reference frame is synchronized with the estimated rotor flux (ψ̂rqo = 0), then its two components are ψ̂rd = ψ̂o + δ ψ̂rd and ψ̂rq = δ ψ̂rq. in these two systems, the stator frequencies are regarded as identical : ωs = ω̂s [5]. preserving only dynamic parts, the two systems (9), (10) become after linearization,:    d dt δ ψ r = −( 1 tr + jωslo )δ ψ r + lm tr δ is − jψoδ ωsl d dt δ is = lm b ( 1 tr − jωo)δ ψ r −(a + jωso)δ is + 1 σ ls δ us − j lm b ψoδ ω − jisoδ ωs d dt δ ω = 0, (11)    d dt δ ψ̂ r = −( 1 tr + jω̂slo )δ ψ̂ r + lm tr δ îs − jψ̂oδ ω̂sl + g1δ ei d dt δ îs = lm b ( 1 tr − jω̂o)δ ψ̂ r −(a + jω̂so)δ îs + 1 σ ls δ us − j lm b ψ̂oδ ω̂ − jîsoδ ω̂s d dt δ ω̂ = −kp(− lm b ωoψ̂oδ ψ̂ rd + lm btr ψ̂oδ ψ̂ rq −ωsoψ̂oδ îsd −aψ̂oδ îsq − lm b ψ̂oδ ω̂s) −ki(−eidoδ ψ̂ rq + eiqoδ ψ̂ rd + ψ̂ oδ eiq). (12) 138 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi defining δ e = [ δ eψ δ ei δ eω ]t , the system describing the estimation error is as follows:    d dt δ eψ = −( 1 tr + jωslo )δ eψ + ( lm tr −g1)δ ei − jeψo δ ωsl + jeωo δ ψ̂ r + jψ̂oδ eω d dt δ ei = lm b ( 1 tr − jωo)δ eψ −(a + jωso)δ ei − j lm b eψo δ ω − j lm b eωo δ ψ̂ r − jeioδ ωs − j lm b ψ̂ o δ eω d dt δ eω = kp(− lm b ωoψ̂oδ eψ d + lm btr ψ̂oδ eψ q −ωsoψ̂oδ eid −aψ̂oδ eiq − lm b ψ̂oδ eω ) +ki(−eidoδ ψ̂ rq + eiqoδ ψ̂ rd + ψ̂ oδ eiq). (13) separating each state in d and q components, we obtain the corresponding state matrix â1: â1 =   − 1 tr ωslo lm tr −g1 0 0 −ωslo − 1 tr 0 lm tr −g1 ψ̂o lm btr lm b ωo −a ωso 0 −lm b ωo lm btr −ωso −a − lm b ψ̂o −lm b kpωoψ̂o lm btr kpψ̂o −kpωsoψ̂o (ki −akp)ψ̂o − lm b kpψ̂o   (14) note the dependency of the dynamic matrix â1 by the operating condition. in order to obtain analytic conditions about the local stability using the necessary condition for stability based on the determinant of (14) [4], it is possible to obtain a relevant result as reported in the next section. 4.2 stability criterion we use the following property: det(â1) = 5 ∏ i=1 λi (15) where λi are the eigenvalues of matrix â1. the determinant of matrix â1 is: det(â1) = −lmψ̂ 2o kiωso((ωso −ωo)abtr + l2mωo −lmωog1tr + ωsob)/(b2tr) (16) the condition det(â) = 0 leads to: ωso = 0, (17a) ωso = ωo g1lm + rslr (rrls + rslr) . (17b) these conditions of stability may be expressed in the torque/speed plane. let us consider the mechanical equation: d dt ω = p2 lm jlr ℑ(isψ ∗ r )− p tl j . (18) under rfoc conditions and steady state (ψ̂rqo = ψrqo = 0), we obtain: 0 = p lm lr ψ̂oisqo −tlo (19) feedback gain design method for the full-order flux observer in sensorless control of induction motor 139 then isqo = lr plmψ̂o tlo. (20) from system (1), in the same conditions, we find : ωslo = lm trψ̂o isqo. (21) finally using ωso = ωslo + ωo, equations (17a) and (17b) become tlo = − pψ̂ 2o rr ωo (22a) tlo = − pψ̂ 2o rr (1− g1lm rrls )ωo (1 + ts tr ) (22b) with ts = ls/rs. above relations describe respectively two lines, defining two well known instability regions in regenerating mode. an sufficient condition for instability is then: det(â1) > 0. (23) the condition (23) defines a set whose the instability region is a subset. in order to complete the study of local stability, we plot for each eigenvalue, the locus in the torque/plane where conditions (ℜ(λi) > 0, i = 1 . . . 5) are verified. in one hand, if we chose a zero observer gain, as in [9], g1 = 0 (24) we obtain the instability region limited by lines d1 and d2, (fig. 1) where ℜλi > 0, i = 1...5, are the positive real part of the eigenvalues λi of the state matrix â1. the eigenvalues correspond respectively to the states variables δ eψrd , δ eψrq , δ eid , δ eiq and δ eω . tlo = − pψ̂ 2o rr ωo (1 + tr ts ) (25) in other hand, in order to reduce (not totally remove) the unstable region, a real valued observer gain was considered in [13] which corresponds to the region limited by lines d1 and d3, (fig. 3). the value of the parameter g1 selected is: g1 = −0.25rs (26) it is be noted that the curves corresponding to zero observer gain are similar, except that the unstable region is larger. tlo = − pψ̂ 2o rr (1 + 0.25rslm rrls )ωo (1 + tr ts ) (27) the principle of the instability reduction proposed here consists in the calculation of the feedback gain so that the unstable region will be limited to the inobservabilité line (d1). we can note that, whatever the structure of the matrix g, (d1) is always defined by ωso = 0. from equation (16), we can write the 140 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi −200 −150 −100 −50 0 50 100 150 200 −50 −40 −30 −20 −10 0 10 20 30 40 50 ω o (rad/s) t l o (n m ) region (ω o ,t lo ), zero observer gain ℜλ 2 of δψ rq ℜ 0λ 3 of δi sd reλ 4 of δi sq ℜλ 5 of δω tlo − tlo d1, wso=0 d2 figure 2: torque/speed plane,g1 = 0, ℜ(λi) > 0, i = 1...5 −200 −150 −100 −50 0 50 100 150 200 −50 −40 −30 −20 −10 0 10 20 30 40 50 ω o (rad/s) t l o (n m ) region (ω o , t lo ), observer gain g 1 = − 0.25 r s ℜλ 2 of δψ rq ℜ 0λ 3 of δi sd ℜλ 4 of δi sq ℜλ 5 of δω t lo − t lo d 1 , (ω wso =0) d 2 d 3 figure 3: torque/speed plane, g1 = −0.25rs, ℜ(λi) > 0, i = 1...5 feedback gain design method for the full-order flux observer in sensorless control of induction motor 141 condition ωso = 0 in equation (28) (ωso −ωo)abtr + l2mωo −lmωog1tr + ωsob = 0 (28) which can be achieved by choosing the following observer gains g1 = − lrrs lm (29) the straight line (d1) correspond to zero synchronous speed ωs = 0. it is known in the literature as the inobservability line (normally referred as dc-excitation) [6, 3] and seems to be a generic problem for sensorless control of induction motors. 5 simulations results in order to validate the proposed design, the regenerating mode low speed operation of the speed adaptive observer was investigate by means of simulations. a rotor flux oriented control (rfoc) is simulated using matlab/simulink software. the block diagram of the control system is shown in fig.4. the flux reference is fixed to the nominal value ψ re fo where ref denotes the reference value. the proposed usd -+ ω ref ω̂ + i ref sq -+ ψ ref o ψ̂r + i ref sd cω(s) cψ(s) ci(s) ci(s) isd isq usq motor adaptive observer eq. (5) usd usq isd isq ψ̂r ω̂ i sd i sq ψrd ψrq ω model eq. (1) 1 figure 4: block diagram of sensorless rfoc induction motor simulator. observer is compared with an exiting solution [13]. in order to validate the proposed design, we studied a conventional test used by industrial drive designers: very low and progressive load torque increase under constant speed; fig. 5 depicts results in regenerating mode obtained using the observer gain g1 = −0.25rs, [13], [5]. the speed reference was set to (−25rad/s) (dashed line) and a rated-load torque ramp was applied at t=0. after applying the load progressively, the drive should operate in the regenerating mode. however, the actual angular speed et actual flux of the motor collapse and the system becomes unstable. fig. 6 present results obtained using the proposed observer design. the system behaves stably. on fig. 7, the observer gain g1 = −0.25rs was used. real speed diverges. first subplot shows reference (dashed line) and actual angular speed. second subplot shows rated-load torque ramp. third subplot present actual flux components (ψrα , ψrβ ) in stator reference frame. fouth subplot, shows control voltages. in the fifth and six subplot respectively, we present current and current norm. we note that when the load torque increases, the control voltage, the current and the current norm increase too. on fig.8, the proposed observer design was used. the system becames stable. real rotor angular speed converges well towards the reference value in response to the same rated-load torque. note the behavior of the actual flux at (t ≈ 3.75 s) when the real angular rotor speed crosses the line (d1 = d3). the system becomes unobservable at this time. 142 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi −50 −45 −40 −35 −30 −25 −20 −15 −10 −5 0 0 5 10 15 20 25 t l o ( n m ) region (ω o ,t lo ) d 1 d 3 −50 −45 −40 −35 −30 −25 −20 −15 −10 −5 0 0 5 10 15 20 25 ω o (rad/s) t l o ( n m ) figure 5: a rated-load torque ramp is applied with the observer gain g1 = −0.25rs. first subplot shows region (ωo, tlo) with the two lines d1 and d3. second subplot shows the actual angular speed. feedback gain design method for the full-order flux observer in sensorless control of induction motor 143 −50 −45 −40 −35 −30 −25 −20 −15 −10 −5 0 0 5 10 15 20 25 t l o ( n m ) region (ω o ,t lo ) d 1 , (ω so =0) d 3 −50 −45 −40 −35 −30 −25 −20 −15 −10 −5 0 0 5 10 15 20 25 ω o (rad/s) t l o ( n m ) figure 6: a proposed observer design was used. first subplot shows region (ωo, tlo) with the line d1 = d3. second subplot shows the actual angular speed. 144 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi 0 2 4 6 −40 −30 −20 −10 0 ω o (r a d /s ) 0 2 4 6 0 5 10 15 20 25 t l o ( n m ) 0 2 4 6 −3 −2 −1 0 1 2 time(s) ψ rα , ψ rβ ( w b ) 0 2 4 6 −400 −200 0 200 u sα , u sβ ( v ) 0 2 4 6 −30 −20 −10 0 10 20 i s α , i s β (a ) 0 2 4 6 0 10 20 30 c u rr e n t n o rm ( a ) time(s) figure 7: instability phenomenon with observer gain g1 = −0.25rs. feedback gain design method for the full-order flux observer in sensorless control of induction motor 145 0 2 4 6 −40 −30 −20 −10 0 ω o (r a d /s ) 0 2 4 6 0 5 10 15 20 25 t l o ( n m ) 0 2 4 6 −3 −2 −1 0 1 2 time(s) ψ rα , ψ rβ ( w b ) 0 2 4 6 −400 −200 0 200 u sα , u sβ ( v ) 0 2 4 6 −30 −20 −10 0 10 20 i s α , i s β (a ) 0 2 4 6 0 10 20 30 c u rr e n t n o rm ( a ) time(s) figure 8: the instability was removed by the proposed observer design g1 = −lrrs/lm. 146 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi 6 appendix 6.1 induction motor parameters voltage rating : 380 v , current rating: 2.2 a, number of phases: 3, rated power: 1.1 kw , frequency: 50 hz, rated speed: 1430 r pm/min, p = 2, ls = 0.472 h, lr = 0.4721 h, lm = 0.4475 h, rs = 9.65 ω, rr = 4.3 ω. 7 conclusions the feedback gain design method proposed in this paper reduces the instability region of adaptive observer to a inobservability line (d1) (ωso = 0). the observer using the proposed gain does not have the unstable region, which was shown by means of speed/torque plane and a linearized model. the stability of the regenerating-mode operation was also confirmed by simulations. bibliography [1] a. bouhenna, c. chaigne, n. bensiali, e. etien and g. champenois, design of speed adaptation law in sensorless control of induction motor in regenerating mode, simulat. modell. pract. theory, elsevier, doi:10.1016, simpat.2007.04.005, vol. 15, no.7, pp. 847-863, 2007. 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[15] g. yang and t. chin, adaptive-speed identification scheme for a vector-controlled speed sensorless inverter-induction motor drive, ieee trans. ind. appl. electron., vol. 29, pp. 820-825, 1993. a. bouhenna1, a. mansouri1, m. chenafa1, and a. belaidi1 1 e.n.s.e.t. d’oran, laboratoire d’automatique et d’analyses des systèmes, (l.a.a.s) département de génie électrique, b.p 1523, el m’naouer, oran, algérie e-mail: bouhenna @ enset-oran.dz, (abouhenna @ yahoo.fr) received: october 10, 2007. 148 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi abderrahmane bouhenna was born in 1955. he receive the dipl. eng. degree in electronic engineering, the m. s degree and the doctorat in automatic from u.s.t.o, oran, algeria in 1980, 1987 and 2007 respectively. he is currently a professeur searcher in the laboratory of automatic and analysis systems at enset of oran (algeria). he work on the subject of sensorless control and observers of induction motors and obtained some results in this domain for the stabilisation of the observers and the control of the mas in regenerating mode at low speed. abdellah mansouri was born in oran in algeria, in 1953. he received his bs degree in electronic engineering from usto (algeria) in 1979, the ms degree in engineering control from usto (algeria) in 1991, and the phd degree in engineering control from usto (algeria) in 2004. he is currently professor of automatic control at enset of oran (algeria). his research interests are non linear control and observers applied in induction motor and manipulator robot. mohammed chenafa was born in oran in algeria, in 1954. he received his bs degree in electronic engineering from usto (algeria) in 1979, the ms degree in signal processing and robotic from usto (algeria) in 1998, and the phd degree in engineering control from usto (algeria) in 2005. he is currently professor of automatic control at enset of oran (algeria). his research interests are non linear control and observers applied in induction motor and manipulator robot. abdelkader belaidi, professor in 1981 he obtained a ph.d in radiation physics at the university of east anglia, norwich england. his fields of interest are collision damage in materials and neural and fuzzy logic. now, he is a professor of physics and applied computing at the higher school of education oran algeria. he is also head of automatic and system analysis laboratory (laas). international journal of computers, communications & control vol. iii (2008), no. 1, pp. 60-68 evaluation of the recorded state mechanism for protecting agent integrity against malicious hosts kamalrulnizam abu bakar, b. s. doherty abstract: as agent technology is expected to become a possible base platform for an electronic services framework, especially in the area of electronic commerce, reliable security protection is a crucial aspect, since some transactions in this area might involve confidential information, such as credit card number, bank account information or some form of digital cash, that has value and might therefore be attacked. in addition, without proper and reliable security protection, the wide spread use of agent technology in real world applications could be impeded. in this paper, evaluation of the recorded state mechanism (rsm) previously proposed by the authors is presented. the evaluation examines the rsm security protection and implementation overhead, in order to analyse the rsm security strength and implementation feasibility in real world application. keywords: agent security, malicious host, recorded state mechanism. 1 introduction problem in agent technology arise when agents are used in an open and unsecured environment. for example, a customised agent application is sent out to visit several airline servers (in an open and unsecured environment) to find a suitable flight. in this example, the agent application is allowed to completely migrate to (the agent originating host transfer the agent’s code, data and state to the remote server) and execute in (the remote server executed the receiving agent application) the remote server environment, to take advantage of exploiting resource near the data source and thus reducing network traffic [20]. this opens a greater opportunity for the agent application to be abused by the executing host, because the agent application is fully under control of the executing host [10, 19]. an example of attack by the executing host (the malicious host) is to tamper with the agent’s data or state, so that the agent will forget all the previous visits and offers held by the agent, and thus force the agent (application) to accept an offer from the malicious host even though the malicious host’s offer is not the best offer [10, 17, 19]. this kind of attack is known as a manipulation attack [11, 10]. in this attack, the owner of the agent may not know the attack has happened. this is because the malicious host may make subtle changes in the agent’s code, data and state, which are difficult to detect, thus enabling the malicious host to achieve its objective. in addition, the agent (application) that returns from the malicious host does not show any different behaviour from an untampered agent, which makes the attack difficult to detect and prevent. the problem of manipulation attack has been addressed by the authors using the recorded state mechanism (rsm) [1, 2]. the rsm uses the state of an agent, which is recorded during the agent execution process inside an execution host environment to detect the malicious host manipulation attack. in this paper the evaluation of the recorded state mechanism is presented. the evaluation analyse the rsm’s security and overhead its feasibility in real world applications. the paper is organized as follows: section 2 presents the evaluation of the recorded state mechanism, which includes the analysis on the security and implementation overhead of the rsm. section 3 presents a discussion and the conclusion is presented in section 4. copyright © 2006-2008 by ccc publications evaluation of the recorded state mechanism for protecting agent integrity against malicious hosts 61 2 the evaluation of the recorded state mechanism the recorded state mechanism is an integrity protection mechanism that is able to detect manipulation attacks from a malicious host. the mechanism consists of three different types of container, the recordedreadonly, recordedexecuteonly and recordedcollectonly that are used to record the agent state information. this recorded agent state information, which consists of the data of the agent (located in its variables) and the execution information (such as the program counter, the call stack and a few more items) is used for detecting any modification attacks from the malicious host in order to protect the integrity of the agent during agent execution inside the malicious host environment. the evaluation of the recorded state mechanism is presented by examines the rsm security protection and implementation overhead that will be discussed in the next section. 2.1 the security analysis of the recorded state mechanism to assess the strength of the recorded state mechanism, its ability to handle well-known attacks is discussed in table 1. summary of evaluation of recorded state mechanism the recorded state mechanism is able to detect most of the malicious host attacks that try to tamper with the agent’s data and state integrity. this mechanism when combined with distributed migration pattern, can prevent collaboration attacks by two or more hosts and extraction of information by the malicious host. however attacks such as an execution host lying about input data cannot be detected or prevented by this mechanism, because the attack does not alter any state information, and so leaves no trace. 2.2 the overhead of implementing the recorded state mechanism the experiments to measure the overhead of implementing the recorded state mechanism are conducted using six 400 mhz sun ultra sparc 5 workstations with 128 mb of main memory. each of the workstations is running the solaris 8 operating system and is connected to the others using 100 mbit/s utp1 cable. all of the workstations involved in this experiment were situated in the same room. in this configuration, one workstation will be chosen among the six workstations to be the home host for the agent, and only this host has the permission to manage and dispatch the agent. the rest of the workstations are assumed to be the remote host, having only the capability to receive and dispatch the agent back to its home host. to evaluate the security overhead for implementing the recorded state mechanism in an agentbased application, times are measured starting from sending of the agents to the remote hosts and ending by receiving the agents back from the remote hosts. the times, are measured using the “system.currenttimemillis()” method in the java language. this method produces a specific instant in time with millisecond precision [14]. the experiments are done using four different remote host starting with one remote host, two remote hosts, three remote hosts and five remote hosts on three different types of agent: plain agent2, agent with cryptographic security mechanism (crypto) and agent with these security mechanisms and the recorded state mechanism (crypto+rsm). there are four different experiments used in examining the overhead for implementing the recorded state mechanism: one input and one cycle, one hundred inputs and one cycle, one input and one thousand cycles, and one hundred inputs and one thousand cycles. the input is 1unshielded twisted pair category 5e 2agents without security mechanisms 62 kamalrulnizam abu bakar, b. s. doherty a t t a c k s s o l u t i o n s t h e m a l i c i o u s h o s t c o u l d m a k e s u b t l e c h a n g e s o n r e a d o n l y d a t a i n s i d e t h e r e c o r d e d r e a d o n l y c o n t a i n e r t o e n a b l e i t t o a c h i e v e i t s o b j e c t i v e , i n s u c h a w a y t h a t t h e o w n e r ' s d i g i t a l s i g n a t u r e t h a t w a s s i g n e d i n t h e r e c o r d e d r e a d o n l y c o n t a i n e r s t i l l r e m a i n s v a l i d . t h i s a t t a c k c a n b e r u l e d o u t , b e c a u s e t h e d i g i t a l s i g n a t u r e ( u s i n g s h a 1 ) , w h i c h i s u s e d i n t h e r e c o r d e d s t a t e m e c h a n i s m i s s e c u r e d a g a i n s t b r u t e f o r c e c o l l i s i o n a n d i n v e r s i o n a t t a c k s , w h e r e b y u s i n g t h e s h a 1 a s t h e d i g i t a l s i g n a t u r e f u n c t i o n c o u l d m a k e t h e a t t a c k e r c o m p u t a t i o n a l l y i n f e a s i b l e t o f i n d a m e s s a g e w h i c h c o r r e s p o n d s t o a g i v e n m e s s a g e d i g e s t , o r t o f i n d t w o d i f f e r e n t m e s s a g e s w h i c h p r o d u c e t h e s a m e m e s s a g e d i g e s t . t h e m a l i c i o u s h o s t c o u l d a l s o m a k e s u b t l e c h a n g e s o n b o t h t h e r e a d o n l y d a t a a n d t h e d i g i t a l s i g n a t u r e f o r t h e r e c o r d e d r e a d o n l y c o n t a i n e r , i n o r d e r t o m a k e b o t h o f t h e m a p p e a r t o b e v a l i d . t h i s p o s s i b l e a t t a c k c a n a l s o b e r u l e d o u t b e c a u s e i n o r d e r t o c r e a t e a n e w d i g i t a l s i g n a t u r e t h a t w i l l b e v a l i d f o r o t h e r h o s t , t h e m a l i c i o u s h o s t n e e d s t o h a v e a p r i v a t e k e y o f t h e a g e n t o w n e r . h o w e v e r , o n l y t h e k e y o w n e r h a s t h i s k e y a n d n o o t h e r e n t i t y c a n p r o d u c e t h i s k e y f r o m a m o d i f i e d h a s h v a l u e . t h e m a l i c i o u s h o s t c o u l d a l s o t a m p e r w i t h t h e a g e n t s t a t e r e c o r d e d i n t h e r e c o r d e d e x e c u t e o n l y a n d r e c o r d e d c o l l e c t o n l y c o n t a i n e r b y m o d i f y i n g t h e a g e n t s t a t e b e f o r e t h e s t a t e i s r e c o r d e d i n t o b o t h c o n t a i n e r s . t h i s d u e t o t h e f a c t t h a t t h e r e c o r d e d p r o c e s s o f t h e r e c o r d e d s t a t e m e c h a n i s m i s u n d e r t h e m a l i c i o u s h o s t ' s c o n t r o l a n d t h e r e f o r e , t h e m a l i c i o u s h o s t c a n d o a n y t h i n g t o i t . t h i s a t t a c k c a n a l s o b e r u l e d o u t b e c a u s e t h e m a l i c i o u s h o s t h a s t o u s e i t s o w n p r i v a t e k e y t h a t c o n t a i n s i t s i d e n t i t y i n o r d e r t o c o m p u t e a n d r e c o m p u t e t h e d i g i t a l s i g n a t u r e o n t h e t a m p e r e d s t a t e , t h u s r e v e a l i n g i t s e l f d u r i n g t h e v e r i f i c a t i o n p r o c e s s . t h e m a l i c i o u s h o s t c o u l d a l s o a t t a c k t h e a g e n t b y l a u n c h i n g c o l l a b o r a t i o n a t t a c k s i n c o o p e r a t i o n w i t h t w o o r m o r e c o n s e c u t i v e h o s t s i n o r d e r t o d e n y t h e c h e c k i n g p r o c e s s f o r d e t e c t i n g a n y m a l i c i o u s h o s t a t t a c k f r o m t h e p r e v i o u s v i s i t o r t o r e m o v e a n y a g e n t s t a t e t h a t r e c o r d s t h e c h a n g e s m a d e b y t h e p r e v i o u s h o s t o n t h e a g e n t d u r i n g i t s e x e c u t i o n s e s s i o n . i n a d d i t i o n , t h e m a l i c i o u s h o s t c o u l d a l s o u s e t h e c o l l a b o r a t i o n a t t a c k t o e x t r a c t i n f o r m a t i o n f r o m t h e a g e n t t o w i n s o m e c o m p e t i t i o n o v e r o t h e r h o s t s . t h e a t t a c k c a n b e r u l e d o u t s i n c e t h e u s e d o f m a s t e r s l a v e a g e n t a r c h i t e c t u r e i n i m p l e m e n t i n g t h e r e c o r d e d s t a t e m e c h a n i s m o n l y a l l o w s d i f f e r e n t a g e n t s t o b e s e n t a n d s e r v e d b y d i f f e r e n t r e m o t e h o s t s . a n a g e n t v i s i t s o n l y o n e h o s t , t h u s p r e c l u d i n g t h e c o l l a b o r a t i n g a t t a c k . i n a d d i t i o n , t h e i n f o r m a t i o n e x t r a c t e d f r o m a s i n g l e a g e n t d o e s n o t g i v e e n o u g h i n f o r m a t i o n t o t h e m a l i c i o u s h o s t t o a l l o w i t t o w i n a n y c o m p e t i t i o n o v e r o t h e r h o s t s b e c a u s e t h e e x t r a c t e d i n f o r m a t i o n i s n o t s u f f i c i e n t b y i t s e l f . t h e m a l i c i o u s h o s t c o u l d a l s o l i e a b o u t t h e i n p u t d a t a , w h i c h i s r e c o r d e d i n t h e r e c o r d e d e x e c u t e o n l y a n d t h e r e c o r d e d c o l l e c t o n l y c o n t a i n e r s i n o r d e r t o d e c e i v e t h e o w n e r o f t h e a g e n t . t h i s a t t a c k i s u n a b l e t o b e r u l e d o u t b y t h e r e c o r d e d s t a t e m e c h a n i s m b e c a u s e t h e i n p u t d a t a t h a t w a s s u p p l i e d b y t h e m a l i c i o u s h o s t i s a s s u m e d t o b e a c o r r e c t d a t a b y t h e a g e n t , s i n c e o n l y t h e m a l i c i o u s h o s t k n o w s w h e t h e r t h e i n p u t d a t a i s c o r r e c t o r i n c o r r e c t . h o w e v e r , t h e o w n e r o f t h e a g e n t k n o w s t h e i d e n t i t y o f t h e h o s t , w h i c h s u p p l i e s t h e i n p u t d a t a t o t h e a g e n t b e c a u s e a l l t h e d a t a a n d s t a t e a r e d i g i t a l l y s i g n e d b y t h e e x e c u t i o n h o s t b e f o r e t h e d a t a a n d s t a t e l e a v e d t h e e x e c u t i o n h o s t . t h u s , t h e o w n e r o f t h e a g e n t k n o w s w h i c h e x e c u t i o n h o s t i s r e s p o n s i b l e f o r s u p p l y i n g t h e f a l s e i n p u t d a t a . t h e m a l i c i o u s h o s t c o u l d a l t e r t h e r a n d o m s e q u e n c e 3 l e v e l o b f u s c a t i o n a l g o r i t h m t o e x e c u t e i n m a n y d i f f e r e n t w a y s ( i n c o r r e c t e x e c u t i o n a t t a c k ) t h i s a t t a c k c a n b e r u l e d o u t s i n c e t h e r e c o r d e d s t a t e m e c h a n i s m w i l l c h e c k t h e r e s u l t s g a t h e r e d b y t h e r e t u r n i n g s l a v e a g e n t b y e x e c u t i n g t h e s a m e e x e c u t i o n p r o c e s s t h a t i s a s s u m e d , h a s b e e n d o n e b y t h e s l a v e a g e n t i n s i d e t h e r e m o t e h o s t e x e c u t i o n e n v i r o n m e n t . table 1: possible attacks and solutions of the recorded state mechanism represents a character. this character is use as a data that need to be protected by the agent. the cycle, on the other hand represents a loop that is used to simulate an agent tasks. note that all of the experiments on the recorded state mechanism are using master-slave agent architecture and operates on the distributed migration pattern [1, 2]. the experiment is performed for 20 runs and the result for each run is gathered in milliseconds. from evaluation of the recorded state mechanism for protecting agent integrity against malicious hosts 63 the author’s observation, all the 20 runs in this experiment give very similar results and for this reason, 20 runs of the experiment are considered sufficient. the average result of all the 20 runs is taken and converted into seconds. the result is then rounded up and presented in two decimal places as given and illustrated in tables 2 to 5 and figure 1 to 4 respectively. number of mean standard error standard deviation remote hosts plain cyp cyp+rsm plain cyp cyp+rsm plain cyp cyp+rsm 1 1.54 29.15 28.91 0.003 2.18 1.23 0.01 9.73 5.52 2 2.49 30.89 31.11 0.003 2 1.38 0.02 8.93 6.19 3 3.37 31 32 0.004 0.93 0.98 0.02 4.15 4.38 5 5.35 36.36 36.03 0.011 1.5 1.87 0.05 6.7 8.37 plain = without cryptographic mechanism cyp (crypto) = cryptographic mechanism rsm = recorded state mechanism table 2: summary statistics of the recorded state mechanism overhead (1 input and 1 cycle experiment) s e c u r i t y o v e r h e a d f o r i m p l e m e n t i n g t h e r e c o r d e d s t a t e m e c h a n i s m 0 1 0 2 0 3 0 4 0 1 2 3 5 n u m b e r o f r e m o t e h o s t s ti m e i n se co nd s p l a i n c y p ( c r y p t o ) c y p + r s m figure 1: security overhead of the recorded state mechanism (1 input and 1 cycle experiment) based on the observation on the results gained through the experiments done, it can be seen that the standard error and the standard deviation of the security overhead are similar regarding the number of remote host but different between agents. agents with security mechanism give larger standard error since the agents have to execute many tasks such as generate the cryptography key, generate digital signature, verify digital signature and execute encryption and decryption. from the results given in table 2 and illustrated in figure 1, it can be seen that the mean of the security overhead is almost the same for agents with security mechanism and agents with security mechanism plus rsm, where the security overhead for the agent with security mechanism plus rsm is just 7.82 % higher than the overhead for the agent with security mechanism. however, both agent’s security overheads are higher by up to 1792.86 % than the overhead for the plain agent. from table 3 and figure 2, the security overhead for the plain agent is almost the same as with one input given in table 2 and figure 1, but the security overhead for the agents with security mechanism is increased by up to 60.52 % along the security overhead with one input. results in table 4, table 5, figure 3 and figure 4 show that the security overhead for all the agents 64 kamalrulnizam abu bakar, b. s. doherty number of mean standard error standard deviation remote hosts plain cyp cyp+rsm plain cyp cyp+rsm plain cyp cyp+rsm 1 1.55 45.43 45.32 0.003 1.21 1.19 0.01 5.42 5.34 2 2.53 45.99 48.21 0.005 1.35 1.83 0.02 6.03 8.18 3 3.37 49.76 49.42 0.002 1.13 1.26 0.01 5.03 5.65 5 5.43 53.09 53.14 0.005 1.09 1.76 0.02 4.86 7.85 table 3: summary statistics of the recorded state mechanism overhead (100 input and 1 cycle experiment) s e c u r i t y o v e r h e a d f o r i m p l e m e n t i n g t h e r e c o r d e d s t a t e m e c h a n i s m 0 1 0 2 0 3 0 4 0 5 0 6 0 1 2 3 5 n u m b e r o f r e m o t e h o s t s ti m e i n se co nd s p l a i n c y p ( c r y p t o ) c y p + r s m figure 2: security overhead of the recorded state mechanism (100 input and 1 cycle experiment) is similar to the security overhead of the agents with the same number of input but different number of cycle given in table 2, table 3, figure 1 and figure 2 respectively. therefore, it is worth noting that number of cycles does not affect the security overhead of the agents. number of mean standard error standard deviation remote hosts plain cyp cyp+rsm plain cyp cyp+rsm plain cyp cyp+rsm 1 2.41 27.65 28.94 0.008 0.77 1.55 0.04 3.45 6.94 2 3.53 30.91 31.31 0.008 1.38 1.68 0.04 6.18 7.53 3 5.17 30.94 33.36 0.005 1 1.04 0.02 4.46 4.65 5 7.34 38.46 37.57 0.01 1.29 1.69 0.05 5.76 7.55 table 4: summary statistics of the recorded state mechanism overhead (1 input and 10000 cycle experiment) summary of experimental results it can be seen from the results shown in tables 2 to 5 and illustrated in figures 1 to 4 that the implementation of the recorded state mechanism does increase the overhead by only up to an acceptable 7.82 % when compared to the agent with security mechanism but 2830.96 % when compared to the plain agent. however, the low overhead of the plain agent is not important since the plain agent does not have any security protection. evaluation of the recorded state mechanism for protecting agent integrity against malicious hosts 65 s e c u r i t y o v e r h e a d f o r i m p l e m e n t i n g t h e r e c o r d e d s t a t e m e c h a n i s m 0 1 0 2 0 3 0 4 0 5 0 1 2 3 5 n u m b e r o f r e m o t e h o s t s ti m e i n se co nd s p l a i n c y p ( c r y p t o ) c y p + r s m figure 3: security overhead of the recorded state mechanism (1 input and 10000 cycle experiment) number of mean standard error standard deviation remote hosts plain cyp cyp+rsm plain cyp cyp+rsm plain cyp cyp+rsm 1 2.39 45.93 47.26 0.005 1.06 1.11 0.02 4.76 4.97 2 3.56 48.63 48.85 0.005 0.73 1.42 0.02 3.28 6.36 3 5.17 50.86 51.54 0.008 1.52 1.57 0.03 6.82 7.01 5 7.4 54.36 54.48 0.015 1.8 1.94 0.07 8.05 8.66 table 5: summary statistics of the recorded state mechanism overhead (100 input and 10000 cycle experiment) s e c u r i t y o v e r h e a d f o r i m p l e m e n t i n g t h e r e c o r d e d s t a t e m e c h a n i s m 0 1 0 2 0 3 0 4 0 5 0 6 0 1 2 3 5 n u m b e r o f r e m o t e h o s t s ti m e i n se co nd s p l a i n c y p ( c r y p t o ) c y p + r s m figure 4: security overhead of the recorded state mechanism (100 input and 10000 cycle experiment) 3 discussion integrity protection is one of the main requirements for protecting agents against a malicious host attacks. the requirement was successfully fulfilled3 by the recorded state mechanism, which is able to detect most of the malicious host attacks that try to tamper with the agent’s data and state integrity. the analysis on security strength and implementation feasibility of the recorded state mechanism in real world applications has been conducted. the security strength of the recorded state mechanism has 3detect or prevent some attacks and made others more difficult 66 kamalrulnizam abu bakar, b. s. doherty been analysed by evaluating the mechanism against well-known attack scenarios, and from the results, it can be seen that the mechanism is capable to prevent or detect some of the attacks and made other attacks more difficult. the implementation feasibility is measured by examining the overhead imposed by the mechanism in protecting agents integrity against malicious host attacks. the result shows that the rsm imposed an acceptable overhead. 4 conclusion this paper presented the evaluation of the recorded state mechanism for protecting the integrity of the agents against the malicious host attacks. the evaluation produced significant results on the strength of the recorded state mechanism, where it is able to prevent or detect some attacks and made other attacks more difficult with an acceptable overhead. in conclusion, the mechanism offered significant advances in protection of agents against malicious host attacks and is therefore suitable for use in real world applications. bibliography [1] abu bakar, k. and doherty, b. s. a new model for protecting mobile agents against malicious host. proceedings of the iadis international conference www/internet. iadis press, portugal (2002) 780-784 [2] abu bakar, k. and doherty, b. s. protecting mobile agents against a malicious host attacks using recorded state mechanism. proceedings of the 2003 international conference on informatics, cybernetics and systems. i-shou university(2003) 396 – 401 [3] chess, d.m. security issues in mobile code systems. g. vigna(ed.): mobile agents and security, vol. 1419. springer verlag (1998) 1 – 14 [4] chess, d.m. and harrison, c.g. and kershenbaum, a. mobile agents: are they a good idea?. ibm research report. ibm research division (1995). http://www.research.ibm.com/iagents/publications.html [5] corradi, a. and cremonini, m. and montanari, r. and stefanelli, c. mobile agents integrity for electronic commerce application. information system. elsevier science (1999) 519 – 533 [6] diaz, j. and gutierrez, d. and lovelle, j. an implementation of a secure java2-based mobile agent system. proceedings of the second international conference on the practical application of java. practical application company (2000) 125 – 142 [7] farmer, w.m. and guttman, j.d. and swarup, v.: security for mobile agents: issues and requirements. proceedings of the 19th national information system security conference. baltimore (1996) 591-597 [8] ford, w. and baum, m. secure electronic commerce, ed. 2nd. prentice hall (2001) [9] guan, x. and yang, y. and you, j. pom a mobile agent security model against malicious hosts. proceedings of is & n’99 spring verlag (1999) 155 – 167 [10] hohl, f. a framework to protect mobile agents by using reference states. in: proceedings of the 20th international conference on distributed computing systems (icdcs 2000). ieee computer society (2000) 410 – 417 evaluation of the recorded state mechanism for protecting agent integrity against malicious hosts 67 [11] hohl, f.: time limited blackbox security: protecting mobile agents from malicious hosts. in: g. vigna (ed.). mobile agent and security. lecture notes in computer science, vol. 1419. springerverlag, berlin(1998) 92–113 [12] hohl, f. a model of attacks of malicious hosts against mobile agents. in 4th ecoop workshop on mobile object systems (mos’98): secure internet mobile computations. (1998) http://mole.informatik.uni-stuttgart.de/papers.html [13] hohl, f. an approach to solve the problem of malicious hosts. institute of parallel and distributed high-performance systems (ipvr), university of stuttgart, germany (1997) [14] sun microsystems, inc. java 2 platform std. ed. v1.3.1 http://java.sun.com/j2se/ 1.3/docs/api/index.html (2004) [15] kun, y. and xin, g. and dayou, l. security in mobile agent system: problems and approaches. operating system review, vol. 34, no. 1. acm (2000) 21 – 28 [16] reisner, j. and donkor, e. protecting software agents from malicious hosts using quantum computing. proceedings of spie the international society for optical engineering. iee (2000) 50 – 57 [17] sander, t. and tschudin, c.: protecting mobile agent against malicious hosts. in: g. vigna (ed.). mobile agent and security. lecture notes in computer science, vol. 1419. springer-verlag, berlin(1998) 44-60 [18] schneier, s. applied cryptography, ed. 2nd. wiley & son (1996) [19] vigna, g. cryptographic traces for mobile agents. in: g. vigna (ed.). mobile agent and security. lecture note in computer science, vol. 1419. springer verlag (1998) 137 – 153 [20] wang, t., guan, s. and chan, t.: integrity protection for code-on-demand mobile agents in e-commerce. the journal of systems and software. elsevier (2002) 211-221 kamalrulnizam abu bakar faculty of computer science and information system universiti teknologi malaysia 81310 utm skudai johor d. t. malaysia e-mail: kamarul@fsksm.utm.my b. s. doherty school of engineering and applied science aston university aston triangle, birmingham b4 7et united kingdom e-mail: b.s.doherty@aston.ac.uk received: july 22, 2006 68 kamalrulnizam abu bakar, b. s. doherty kamalrulnizam abu bakar is a lecturer at universiti teknologi malaysia, malaysia. he received the diploma and degree of computer science in 1994 and 1996 respectively from universiti teknologi malaysia, malaysia. he then received masters in computer communication and networks degree from leeds metropolitan university, united kingdom in 1998 and phd in network security from aston university, united kingdom in 2004. his current research interests include computer and network security, distributed systems and parallel processing, grid computing, wireless and cellular network. bernard s. doherty (born october 2nd, 1945) obtained the degrees of bachelor of engineering (electrical), bachelor of arts and master of engineering science from the university of melbourne in 1967, 1971 and 1981 respectively. he has held positions with the state electricicity commission of victoria, lm ericsson pty ltd, swinburne college of technology (all in melbourne) and, since 1980, at aston university (birmingham, uk), where is presently lecturer in computer science. his main fields of teaching and research are distributed and networked applications and information security. in addition to supervising a number of doctoral students, he has developed computer-based administration and teaching software, written a number of papers and presented papers at international conferences. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 113-124 eecda: energy efficient clustering and data aggregation protocol for heterogeneous wireless sensor networks d. kumar, t.c. aseri, r.b. patel dilip kumar centre for development of advanced computing, mohali, india e-mail: dilip.k78@gmail.com trilok c. aseri pec university of technology, chandigarh, india e-mail: a_trilok_chand@yahoo.com r.b. patel m.m. university, mullana, india e-mail: patel_r_b@yahoo.com abstract: in recent years, energy efficiency and data gathering is a major concern in many applications of wireless sensor networks (wsns). one of the important issues in wsns is how to save the energy consumption for prolonging the network lifetime. for this purpose, many novel innovative techniques are required to improve the energy efficiency and lifetime of the network. in this paper, we propose a novel energy efficient clustering and data aggregation (eecda) protocol for the heterogeneous wsns which combines the ideas of energy efficient cluster based routing and data aggregation to achieve a better performance in terms of lifetime and stability. eecda protocol includes a novel cluster head election technique and a path would be selected with maximum sum of energy residues for data transmission instead of the path with minimum energy consumption. simulation results show that eecda balances the energy consumption and prolongs the network lifetime by a factor of 51%, 35% and 10% when compared with low-energy adaptive clustering hierarchy (leach), energy efficient hierarchical clustering algorithm (eehca) and effective data gathering algorithm (edga), respectively. keywords: clustering; data aggregation; lifetime; heterogeneous wireless sensor networks. 1 introduction for past few years, wireless sensor networks (wsns) attracted lots of researchers because of its potential wide applications and many research challenges. early study on wsns mainly focused on technologies based on the homogeneous wsn in which all nodes have same system resources. however, heterogeneous wsn is becoming more and more popular because the benefits of using heterogeneous wsns with different capabilities in order to meet the demands of various applications have been presented in recent literature [1], [2]. one of the crucial challenges in the organization of the wsns is energy efficiency and stability because battery capacities of sensor nodes are limited and replacing them are impractical. since, sensor nodes use a large amount of energy for data transmission and aggregation. therefore, new energy efficient routing protocols are required to save energy consumption. in this paper, we propose a novel energy-efficient clustering and data aggregation (eecda) protocol for heterogeneous wsn. in this approach, a new cluster head (ch) election and data communication mechanism is presented to extend the lifetime and stability of the network. after the copyright c⃝ 2006-2011 by ccc publications 114 d. kumar, t.c. aseri, r.b. patel chs election, a path with maximum sum of residual energy would be selected for data communication instead of the path with minimum energy consumption. therefore, each ch first aggregates the received data and then transmits the aggregated data to the base station (bs). the main contributions of eecda protocol is to provide longest stability (when the first node is dead) and improves the network lifetime in comparison to low-energy adaptive clustering hierarchy (leach), energy-efficient hierarchical clustering algorithm (eehca) and effective data gathering algorithm (edga). the rest of this paper is organized as follows. section 2 presents related works. section 3 describes the eecda protocol. section 4 explores on simulation results, and finally paper is concluded in section 5. 2 related work many recent research works in the area of cluster-based wsns have extensively focussed on energy efficiency, lifetime, stability and scalability. in the past few years, numerous clustering algorithms have been proposed for a wide range of applications [3], [4], [5]. data aggregation and hierarchical mechanism are commonly used in many critical applications of wsns. it reduces the data redundancy and communication load [6]. leach [7] is the first clustering protocol based on single-hop communication model. in leach, during the setup phase, each node generates a random number between 0 and 1. if this random number is smaller than the threshold value, t(s) , which is given by equation (1), then the node becomes a ch for the current round. during each round, new chs are elected and as a result balanced load energy is distributed among the chs and other nodes of the network. t(s) =   popt 1−popt×(r mod 1popt ) if s ϵ g 0 otherwise   (1) where popt is the desired percentage of chs, r is the count of current round, g is the set of sensor nodes that have not been chs in the last 1 popt rounds. in this paper, we refer round, 1 popt , as epoch of the heterogeneous wsn. power-efficient gathering in sensor information systems (pegasis) [8] is a chain-based power efficient protocol based on leach. it assumes that each node must know the location of all other nodes. it starts with the farthest node and the chain is constructed by using a greedy algorithm. the chain leader aggregates data and forwards it to the bs. in order to balance the overhead involved in communication between the chain leader and the bs, each node in the chain takes turn to be the leader. in [9], the authors described a heuristic approach to solve the data-gathering problem with aggregation in sensor networks. in this scheme, the data is collected in an efficient manner from all the sensor nodes and transmitted to the bs to maximize the lifetime of the network. in [10], the authors have studied the impact of heterogeneity of sensor nodes in terms of their energy and proposed a heterogeneous-aware protocol to prolong the time interval before the death of the first node. in [11] a cost-based comparative study between homogeneous and heterogeneous clustered wsns is proposed to estimate the optimal distribution among different types of sensors, but this result is hard to use if the heterogeneity is due to the operation of the network. in [12], authors have developed energy efficient clustering protocol in wsn which is more suitable for periodical data gathering applications. a survey on many ad hoc and mobile ad hoc network clustering schemes are presented in [13]. in this article authors observed that new clustering schemes are required to handle the topology maintenance and managing node eecda: energy efficient clustering and data aggregation protocol for heterogeneous wireless sensor networks 115 movement in the network. in [14], the authors have proposed a new data gathering approach for single-hop transmission wherein both the data gathering and the aggregation are performed by the same sensor in a cluster but the report to the bs may be done by a different sensor. in [15], authors have investigated the problem of cluster formation for data fusion by focusing on two aspects: (i) how does one can estimate the number of clusters needed to utilize efficiently data correlation of sensors for a sensor network, and (ii), how does one can pick the chs to cover the whole network more efficiently. in [16], the authors have analyzed the strengths and weaknesses of many existing clustering algorithms and observed many solutions of appropriate aggregation metrics those have been recently proposed in the literature. energy-efficient protocol with static clustering (eepsc) which partitions the network into static clusters and utilizes chs to distribute the energy load among high power sensor nodes and extends the network lifetime [17]. a distributed energy saving clustering algorithm called bpec has been proposed in [18]. in this algorithm, chs are selected by two probabilities. first is based on the ratio between average residual energy of neighbor nodes and its residual energy and second is the node’s degree. by using this algorithm, the entire network broadcasting complexity is o(n), the entire network computing complexity is o(1). the results show that when the network has a higher communication coverage density, analytical and experimental results are very close. energy-efficient hierarchical clustering algorithm (eehca) [19] has adopted a new method for ch election, which can avoid the frequent election of chs. a new concept of backup chs is introduced which improves the performance over leach and hybrid energy-efficient distributed clustering (heed), in terms of network lifetime. an energy efficient hierarchical data gathering protocol, called edga adopts weighted election probabilities of each heterogeneous sensor node to become a ch which better handle heterogeneous energy circumstances [20]. the results demonstrate that edga significantly outperforms leach and heed in terms of network lifetime. the authors in [21] have discussed a new ch election problem based on a set of coverage-aware cost metrics which favor nodes deployed in densely populated network areas. the coverage-aware election of ch nodes, active sensor nodes and routers in clustered wsn increases the lifetime as compared with traditional energy based election methods. in [22], the authors have presented an important corona model to maximize the network lifetime by using maximal transmission range of sensors into different levels. the sensor nodes belong to the same corona have the same transmission range, whereas different coronas have different transmission ranges. in [23] authors have presented a short survey on the main techniques used for energy conservation in wsns. the main focus is primarily on duty cycle scheme which represents the most suitable technique for energy saving. in [24], the authors reviewed many existing definitions of network lifetime and discussed about the merits and demerits of these definitions. 3 eecda protocol the main goal of eecda protocol is to maintain efficiently the energy consumption of sensor nodes by involving them in a single-hop communication within a cluster. the data aggregation and fusion technique is used to reduce the number of transmitted messages to the bs to save the energy and prevent the congestion. to make the protocol implementation, we have adopted a few reasonable assumptions as follows: (i) n sensor nodes are uniformly dispersed within a square field; (ii) all sensor nodes and the bs are stationary after deployment; (iii) the wsn consists of heterogeneous nodes in terms of node energy; (iv) chs perform data aggregation; (v) the bs is not energy limited in comparison with the energy of other nodes in the network. we use the same radio model defined in [7]. the amount of energy required to transmit a l bit packet over a distance, d, is given by equation (2). 116 d. kumar, t.c. aseri, r.b. patel etx(l, d) =   l×eelec + l× ϵfs ×d2 if d <= d0 l×eelec + l× ϵmp ×d4 if d >= d0   (2) eelec is the energy being dissipated to run the transmitter or receiver circuitry. the parameters ϵmp and ϵfs is the amount of energy dissipates per bit in the radio frequency amplifier according to the distance d0, which is given by equation (3). d0 = √ ϵfs ϵmp (3) the amount of energy required to receive a packet is given by equation (4). erx(l) = l×eelec (4) 3.1 impacts of heterogeneity on network performance by placing few heterogeneous nodes in the network can bring three main benefits: (i) extending network lifetime: the average energy consumption for forwarding a packet from the heterogeneous nodes to a bs will be much less than the energy consumed in homogeneous sensor networks, (ii) improving reliability of data communication: the heterogeneous sensor network can get much higher end-to-end delivery rate than the homogeneous sensor network and (iii) decreasing latency of data transmission: the heterogeneous nodes can decrease the forwarding latency by using fewer hops to the bs. 3.2 optimal number of clusters the optimal probability of a node to become a ch is very important in wsns. this clustering is optimal in the sense that energy consumption is well distributed among all the sensor nodes and the total energy consumption should be minimum. such optimal clustering highly depends on the energy model. for eecda, we have used similar energy model as discussed in [7]. let us assume an area a = m ×m square meters over which n nodes are uniformly distributed. for simplicity, assume the bs is located in the center of the field, and the distance of any node to the bs or its ch is d0. therefore, the energy dissipated by the ch node during a round is given by the equation (5). ech = ( n k )×l× (eelec + eda) + l× ϵfs ×d2bs (5) where k is the number of clusters, eda is the data aggregation and dbs is the average distance between a ch and the bs which is given by equation (6). d2bs = ∫ √ (x2 + y2)× 1 a = 0.765× m 2 (6) the energy dissipated by a non-ch node is given by equation (7). ench = l× (eelec + ϵfs ×d2ch) (7) where dch is the average distance between a non-ch node and its associated ch, which is given by equation (8) [10]. d2ch = ∫ ∫ (x2 + y2)×ρ(x, y)dxdy = m2 2πk (8) eecda: energy efficient clustering and data aggregation protocol for heterogeneous wireless sensor networks 117 where ρ(x, y) is the node distribution and m is the area of monitoring field. the total energy dissipated in a cluster per round is given by equation (9). et = ech + ench (9) by substituting equation (5) and equation (7) in equation (9), we obtain energy dissipating during a round which is given by equation (10). et = l× (2×n×eelec + n×eda + ϵfs × (k ×d2bs + n× m2 2πk ) (10) by setting the derivative et with respect to k to zero, we derive the optimal number of clusters which is given by equation (11). kopt = √ n 2π × √ ϵfs ϵmp × m d2bs (11) using equation (6) and equation (11), the optimal probability of a node to become a ch, popt, can be computed by equation (12) popt = 1 0.765 × √ 2 nπ × √ ϵfs ϵmp (12) if the clusters are not constructed in an optimal way, the total energy dissipated per round is increased exponentially either when the number of clusters is greater or less than the optimal value. 3.3 ch election phase eecda considers three types of nodes (i.e., normal, advanced and super) which have deployed in a harsh wireless environment where battery replacement is impossible. nodes with higher battery energy are advanced and super nodes and the remaining nodes are normal nodes. the main aim of eecda is to increase the energy efficiency, lifetime and stability of the network in the presence of heterogeneous nodes. let m be the fraction of advanced nodes among the normal nodes and (mo) be the proportion of super nodes among the advanced nodes. let us assume the initial energy of each normal node is e0 . the initial energy of each advanced and super node is e0 ×(1 +α) and e0 ×(1 +β), where both α and β means the advanced and super node have α and β times more energy than the normal node. intuitively, advanced and super nodes have to become chs more often than the normal nodes, which is equivalent to a fairness constraint on energy consumption. the new heterogeneous setting has no affect on the spatial density of the network so the priori setting of, popt, does not change but the total energy of the network will be changed. the total initial energy of the new heterogeneous network setting is given by equation (13). n×e0×{(1−m)+m×((1−mo)×(1+α)+mo×(1+β)}) = n×e0×(1+m×(α−mo×(α−β))) (13) the first improvement to the existing protocols is to increase the epoch of the sensor network in proportion to the energy increment. in order to optimize the stable region of the system, the new epoch must become equal to ( 1 popt )× (1 + m× (α−mo × (α−β))) because the system has m× (α −mo × (α −β)) times more energy due to heterogeneous nodes. 118 d. kumar, t.c. aseri, r.b. patel if we set the same threshold value for super, advanced and normal nodes with the difference that each normal node ϵg becomes a ch once every ( 1 popt ) × (1 + m × (α − mo × (α − β))) rounds per epoch, and each advanced and super node ϵg becomes a ch (1 + α) and (1 + β) times every ( 1 popt )× (1 + m× (α−mo × (α−β))) rounds per epoch, then there is no guarantee that the number of chs per round per epoch will be popt ×n. this problem can be overcome by modifying the threshold equation (1). in eecda, we assign a weight to the optimal probability popt. this weight must be equal to the initial energy of each node divided by the initial energy of the normal node. let us define pn, pa, and ps are the weighted election probabilities for normal, advanced and super nodes. virtually there are n × (1 + m × (α − mo × (α − β))) nodes with energy equal to the initial energy of a normal node. in order to maintain the minimum energy consumption in each round within an epoch, the average number of chs per round per epoch must be constant and equal to popt × n. in the heterogeneous scenario, the average number of chs per round per epoch is equal to (1 + m×(α−mo ×(α−β)))×n×pn because each virual node has the initial energy of a normal node. therefore, the weighed probabilities for normal, advanced and super nodes are respectively given by equations (14-16). pn = popt (1 + m× (α −mo × (α −β)) (14) pa = popt (1 + m× (α −mo × (α −β)) × (1 + α) (15) ps = popt (1 + m× (α −mo × (α −β)) × (1 + β) (16) by substituting equation (14) in equation (1) and a new threshold is derived for normal nodes which is given by equation (17). t(sn) =   pn 1−pn×(r mod 1pn ) if sn ϵ g ′ 0 otherwise   (17) where r is the current round, g′ is the set of normal nodes that have not become chs within the last, 1 pn , rounds of the epoch and t(sn) is the threshold applied to a population of n × (1 − m) normal nodes. this guarantees that each normal node will become a ch exactly once every ( 1 popt )×(1 + m×(α−mo ×(α−β))) rounds per epoch, and that the average number of chs of normal nodes per round per epoch is equal to (n × (1 − m) × pn). similarly, new thresholds for advanced and super nodes can be derived by substituting equation (15) and (16) into equation (1), which are given by equation (18) and equation (19). t(sa) =   pa 1−pa×(r mod 1pa ) if sa ϵ g ′′ 0 otherwise   (18) t(ss) =   ps 1−ps×(r mod 1ps ) if ss ϵ g ′′′ 0 otherwise   (19) eecda: energy efficient clustering and data aggregation protocol for heterogeneous wireless sensor networks 119 route selection phase once all chs are elected in a specific round by using weighted election probability, each ch first estimates its energy residue e(chr)s and broadcast this information with its ch role to the neighboring nodes. the value of e(chr)s can be calculated by equation (20). e(chr)s = (e(chrem)s − (e(bs)s) s ϵ gc (20) where gc is the set of elected chs per round. (e(chrem)s indicates the remaining energy of chs in current round and (e(bs)s) indicates the communication energy dissipated from chs to the bs. then, each ch records the value of (e(chr)s) in advertisement message and broadcasts advertisement message to the rest of the nodes in the wsn. during the ch election phase, each non-ch node receives all advertisement messages, and extracts all of energy residue data of chs from advertisement messages. moreover, each non-ch node also calculates energy residues (e(nchr)i) to every ch respectively which is given by equation (21). e(nchr)i = (e(nchrem)i − (e(ch)is) i ϵ gn (21) where gn is the set of non-ch nodes. (e(nchrem)i) indicates the residual energy of non-ch node i in the current round and (e(ch)is) indicates the communication energy from non-ch node i to ch node s. finally, each non-ch node would associate one of the existing ch according to maximum energy residue which is given by equation (22). therfore, a path with a maximum sum of energy residues would be selected for data transmission in spite of that path with minimum energy consumption. max{e(chr)s + e(nchr)i} s ϵ gc, i ϵ gn (22) data communication phase in data communication phase, each non-ch node transmits its data to the associated ch. each ch will receive all sensed data from its associated non-ch nodes and sends it to the bs. 4 simulation results and discussion to evaluate and compare the performance of eecda with eehca, edga and leach in the heterogeneous wsn, we have conducted simulations for two scenarios: first, a network with 100 nodes deployed over an area of size 100 × 100 square meter, and second, a network with 200 nodes deployed over an area of size 200 ×200 square meter as shown in figure 1 and figure 2, we denote a normal node with (o), an advanced node with (+), a super node with (*) and the bs with (x). the simulation parameters are summarized in table 1. the performance metrics used for these protocols are: (i) network lifetime: this is the time interval from the start of the operation until the first and last node dies; (ii) stability period: this is the time interval from the start of the operation until the death of the first alive node; (iii) instability period: this is the time interval from the death of the first alive node until the death of the last alive node and (iv) number of alive nodes per round: this is the instantaneous measure reflects that the total number of alive nodes per round that have not yet expended all of their energy. figure 3 and figure 4 show that both leach and eehca fails to take the full advantage of heterogeneity in both the scenarios where the first and last node dies earlier as compared to edga and eecda. therefore, eecda prolongs the network lifetime by 51% , 35% and 10% 120 d. kumar, t.c. aseri, r.b. patel figure 1: random deployment of 100 nodes over an area 100×100 m2. figure 2: random deployment of 200 nodes over an area 200×200 m2. table 1: simulation parameters parameter scenario i and ii network area 100 ×100m2 , 200 ×200m2 bs location (50, 50), (100, 350) n 100, 200 eda 5nj/bit/report packet size 50bytes ϵmp 0.0013pj/bit/m 4 ϵfs 10pj/bit/m 2 eelec 50nj/bit when compared with leach, eehca and edga protocols, respectively. figures 3, 4, 5 and 6 present that the unstable region for eecda is shorter than that of leach, eehca and edga because the normal nodes die in both the scenarios very fast in case of leach, eehca and edga that result in the sensing field it become sparse very fast. on the other hand, advanced and super nodes die in a very slow fashion, because they are not selected as chs very often after the death of the normal nodes, which again affects the election process of chs and makes the network unstable. it is quite evident that the stable region of eecda is extended as compared with leach, eehca and edga for both the scenarios. figure 5 and figure 6 indicate that the number of alive nodes are more per round in case of eecda as compared with edga, eehca and leach because a path with a maximum sum of energy residual would be selected for data transmission in spite of that path with minimum energy in case of eecda. figures 7, 8, 9, 10, 11 and 12 illustrate the performance of residual energy of normal, advanced and super nodes under the heterogeneous settings of eecda, edga, eehca and leach. initially, eecda has the same initial energy as edga, leach and eehca, but gradually it decreases in edga, eehca and leach over rounds. so, edga, eehca and leach have less residual energy left after certain rounds for both the scenarios. therefore, more the residual energy more efficient is the system. 5 conclusion most existing research considers homogeneous sensor networks. however, a homogeneous sensor network suffers from poor performance and scalability. in this paper, we have developed eecda: energy efficient clustering and data aggregation protocol for heterogeneous wireless sensor networks 121 figure 3: network lifetime as a function of first and last dead nodes over an area 100×100 m2. figure 4: network lifetime as a function of first and last dead nodes over an area 200×200 m2. figure 5: stability as a function of number of alive nodes per round over an area 100×100 m2. figure 6: stability as a function of number of alive nodes per round over an area 200×200 m2. figure 7: residual energy of normal nodes per round over an area 100×100 m2. figure 8: residual energy of normal nodes per round over an area 200×200 m2. figure 9: residual energy of advanced nodes per round overan area 100×100 m2. figure 10: residual energy of advanced nodes per round over an area 200×200 m2. 122 d. kumar, t.c. aseri, r.b. patel figure 11: residual energy of super nodes per round overan area 100×100 m2. figure 12: residual energy of super nodes per round over an area 200×200 m2. a novel energy efficient clustering and data aggregation (eecda) protocol to improve the network performance by using some heterogeneous nodes in the network. a novel cluster head election technique and a path with maximum sum of energy residual for data transmission can maintain the balance of energy consumption in the network. simulation results show that eecda has better network lifetime, stability and energy efficiency when compared with edga, eehca and leach protocols. the future work includes more levels of hierarchy with some mobility in the network. bibliography [1] r kumar, v. tsiatsis, m.b. srivastava, computation hierarchy for in-network processing, proceedings of 2nd acm international workshop on wireless networks and applications, san diego, ca, 68-77, 2003. 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[24] i. dietrich, f. dressler, on the lifetime of wireless sensor networks, acm transactions on sensor networks , 5(1): 5:1-5:39, 2009. http://horos.rdsor.ro/ijcccv3n4draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 4, pp. 384-394 redistributing fragments into a distributed database leon ţâmbulea, manuela horvat-petrescu abstract: a distributed system database performance is strongly related to the fragment allocation in the nodes of the network. an heuristic algorithm for redistributing the fragments is proposed. the algorithm uses the statistical information relative to the requests send to a distributed database. this algorithm minimizes the size of the data transferred for solving a request. assuming that a distribution of the fragments in the nodes of a network is known, the algorithm generates a plan to transfer data fragments, plan that will be used to evaluate a request. keywords: distributed database, fragment allocation, allocation algorithm, transfer cost, heuristic algorithm, redistribution algorithm 1 introduction let’s consider a distributed database c, formed by n nodes (sites) si, 0 <= i <m. each node contains a local database and has the capability to evaluate requests. the distributed database c contains a set of n fragments f j, 0 <= j <n. each f j fragment has a specific dimension dim(f j) the dimension can be measured in bytes, pages, so on. different fragments noted with l j can be stored in a si node. let’s assume that a user is sending a request q (a select, an update, a query). for solving this request, are necessary a set of fragments r(q) and for each fragment an access right (read/write depending of the request) is required. the great majority of requests received by a distributed database are requests used for data retrieving. the fragments that should be transferred between the nodes of the network are required when evaluating a request. an optimal allocation of the fragments in the nodes of the network is necessary for the request evaluation time to be minimum. in [1, 3, 4, 7, 9, 10, 11] are mentioned other solutions for solving the allocation problem. because of the complexity of this problem (np-complete problem) more heuristic algorithms were proposed, algorithms with a lower complexity that provide only an approximate solution. in [8] is described a model that propose a dynamic redistribution of the fragments using the statistical information gathered in a specific period of time. queries and information about the performed fragment transfer can be obtained for a specific period of time in a distributed database. using this data obtained over a longer period of time, a redistribution of the fragments can be performed for minimizing the size of the data transfer. this article is organized as follows: in section 2 is described the model for evaluating a query, and also the useful information that can be obtained when evaluating this query. the third section describes the problem of redistributing the fragments in the nodes of a distributed database. for solving this problem is proposed an heuristic algorithm that minimizes the size of the data transferred between the network nodes. in section 4 is proposed an algorithm for generating a transfer plan of the fragments when evaluating a query. this transfer plan is obtained using the query evaluation plan and the fragments distribution in the nodes of the network. in the final section of the paper is presented a conclusion section. 2 query evaluation an execution plan can be determined for each request q. in centralized databases, lots of studies regarding how to find an execution plan with a minimum cost were performed, and some results were implemented in commercial database management systems. copyright © 2006-2008 by ccc publications redistributing fragments into a distributed database 385 using the execution plan, a request q can be split in a number of sub requests { qi, i ∈ iq}, and each sub request qi corresponds to an operator in the relational algebra. an operator requires one fragment (if it’s an unary operator) or two fragments (if is a binary operator). these arguments (fragments) can be stored in a node in the distributed database or can be the result of evaluating other operators. in a distributed system, a request q can be evaluated as follows [5, 6]: • centralized, in a specific node s from the network. all the fragments r(q) necessary for solving the request q should be transferred in this node s. the node s in which the evaluation is performed can be determined as the total fragments r(q) transfer cost to be minimum. • distributed: each sub request qi is evaluated in a separate node s j in the network and in the s j node should be found only the r(qi) fragments. the r(qi) fragments can be fragments stored in the s j node, can be the result of a previous evaluation in this node, or can be transferred from other node of the network. in order to evaluate the cost of a request q, there has to be evaluated the cost of the operators and the cost of the fragments transfer to the nodes where these operators are evaluated ([5, 6]). in this section as in the next one, we will analyze the fragment redistribution problem in the nodes of a distributed database in order to obtain a minimum cost for data fragments transfer when evaluating requests. let’s note with ci, j the cost of the transferring a data unit from the node si to the node s j. for a fragment f (that is stored in the node or is a fragment resulted from evaluating previous requests) transferred from node si to the node s j the total cost is ci j * dim(f). because the ci j cost is relative but the difference between them (in absolute terms) is relatively small, we’ll suppose in this paper that they are constants; ci, j =1, 0 <= i, j <m. this assumption simplifies the solution for the proposed problem. a request q can be split into elementary queries (operations) that execute over different fragments stored in the node n. if such an elementary request (a join operator for example) uses fragments that are not stored in the same node, than all the fragments should be transferred to the same node. in the following example, the distributed database is composed by two relations: a and b, and has horizontal fragmentation, such as: a = a1 ∪ a2 ; b = b1 ∪ b2 ; the request/query q for this database is: q = a * σ c(b) where c is a condition and "*" represents a join operator. if all the fragments are stored in the same node than the request q can be evaluated in the node without requiring other data transfers. if all the fragments (a1, a2, b1 and b2 ) are stored in different nodes (noted with s0, s1, s2 and s3) then evaluation of the request q implies a data transfer cost. we’ll consider that the requests q is required in a node s different from the previous mentioned nodes (s0, s1, s2 and s3) where the data fragments are stored. two evaluation strategies can be considered: 1. the evaluation is performed in the node s, so each required fragment is transferred from the node where is stored into the s node. the size of the whole transfer is: dim(a1) + dim(a2) + dim(b1) + dim(b2). 2. the evaluation is performed in a distributed manner: for this case the request q can be transformed as follows: 386 leon ţâmbulea, manuela horvat-petrescu q = a * σ c (b) = (a1 ∪a2 )* σ c (b1 ∪ b2) = = [a1 * σ c (b1 ∪ b2)] ∪[a2 * σ c (b1 ∪ b2] = = [a1 * ( σ c (b1) ∪σ c (b2))] ∪[a2 * (σ c (b1) ∪σ c (b2))] the request q can be evaluated using the following graph, where we consider that the result of evaluating σ c (bi) will have a size/dimension di smaller that dim(bi), i=1,2. let’s note with b’i = σ c (bi), i=1,2 the results of the selection from the s2 and s3 nodes and d3 = dim(b’1 ), d4 = dim(b’2). in the nodes of the following graph (the nodes of the graph represent the nodes of the network) are presented the operators (unary and binary) that are evaluated and on the links are shown the data transfers required in the evaluation process. the total cost of the transfer will be 2 (d1 + d2 + d3 ) + d4. in a distributed database the fragments can be replicated a fragment can be stored in more than one site/node. for the distributed database presented above, we’ll exemplify for two fragment distribution plans the corresponding execution plan. 3. node s0 s1 s2 s3 fragments a1 , a2 b1 , b2 a1 , b1 a2 , b2 4. node s0 s1 s2 s3 fragments a1 , b1 a1 , b2 a2 , b1 a2 , b2 redistributing fragments into a distributed database 387 3 redistribution of the fragments let’s assume the following situation: in a distributed database for a specific period of time t is required to obtain the answers to a set of requests q. the problem raised is the redistribution of the fragments as the size of the data transfers between the nodes for the same set of requests q (if they are repeated) to be decreased. the size of the transfer will be zero if the database in completed distributed (so each fragment is stored in each node). we’ll assume that the size of each node in the network is limited by a maximum value noted with dmax, and the number of the replica r j for a fragment f j is between two limits: 1 <= r j <= rmax j , 0 <= j <n. the problem of optimal redistribution of the fragments is quite difficult as the optimization performed by the query optimizer must be taken into account and also must be taken into account the size of the data transfer between the nodes. due to the complexity of this problem, more heuristic algorithms were proposed (for example [1, 3, 4, 7, 9, 10, 11]). we describe a new and much simpler algorithm which offers an approximate solution for the proposed problem. the result of generating an execution plan for a query q is a list of operators that should be evaluated: op1(x) for an unary operator or op2(x, y) in case of binary operators, where x and y are fragments stored in the database or are the result of evaluating previous operators. if is an unary operator, then the evaluation can be performed in the node where the operand is stored and no data transfer is required. in case of a binary operator op2(x, y), the evaluation can be performed in the node where the x fragment is stored or is determined, or where the y fragment is stored or is determined. the node is chosen in order to minimize the data transfer size. from the information obtained when evaluating the set of requests q, in a fragment redistribution is used only the information related to the data transfer: "the fragment x (or the fragment obtained from an evaluation in a node where the fragment x is stored) is transferred in a node where is stored the fragment y (or the fragment obtained from an evaluation in a node where the fragment y is stored)". the data transfers occur only when evaluating binary operators. for example, for the evaluation strategy b from the previous section the following transfers occur: 388 leon ţâmbulea, manuela horvat-petrescu binary operator data fragment observations r’1 = a1 * b’1 b1, a1, d1 the meaning of the first line is: d1 is transferred from a node where b1 is stored to a node where a1 is stored r’2 = a1 * b’2 b2, a1, d1 r"1 = a2 * b’1 b1, a2, d1 r"2 = a2 * b’2 b2, a2, d2 r’ = r’1 ∪ r"2 a1, a1, d3 does not require transfer r" = r"1 ∪ r"2 a2, a2, d4 does not require transfer r = r’ ∪ r" a1, a2, d3 the final result where the fragment a2 with size d3 + d4 is stored must be transferred to the node s. for a set of requests q that were evaluated in a period of time, a journal with two types of information can be created: 1. a set of tuples: t1 = { ( fi, f j, di ), i ∈ i }, where a tuple t = (fi, f j, di) has the meaning that from a node that stores the fi fragment, some data with size di is transferred in a node where the fragment f j is stored. 2. a set of tuples: t2 = { ( fq, sq, dq ), q ∈ q}, where a tuple t = (fq, sq, dq) corresponds to a query q ∈ q and has the following meaning: from the node where the fragment fq is stored (and where the query evaluation was finalized) some data with size dq are transferred in the node sq where the result for the query q is required. the algorithm has two phases: first phase: the distribution of the fragments in the virtual nodes is performed such as the total size of the data transfer to be minimum. second phase: the m virtual nodes created in the first phase are associated with the real nodes in such a manner as the size of the data transfer involved in answering the queries to be minimum. a pair of fragments fi and f j can be found in more than one tuple from t1 set, and can correspond to a set of binary operators required by the queries from the set q. if these two fragments are stored in the same node, than there is no data transfer for neither one of the binary operators. in the proposed algorithm, a matrix v = (vi, j) , 0 <= i, j <n will be determined. the vi, j represents the size/dimension of the data fragments that don’t have to be transferred when evaluating the queries from q in the case when the fragments fi and f j are stored in the same node. the v matrix can be easily determined using the following algorithm: 1. vi, j = 0, 0 <= i, j <n; 2. for each t = (fi, f j, di) ∈ t1: vi, j = vi, j + di; 3. for each 0 <= i <n, 0 <= j <i: w = vi, j + v j,i ; vi, j = w, v j,i = w the v matrix is symmetrical and the values have the mentioned significance. the values: vi, j , 0 <= j <= i <n represent the inferior block of the matrix v. these values will be sorted in a descending order and will be used (in this order) to generate a new data fragments distribution in the distributed database. the proposed algorithm is given in the next paragraph and contains four phases: redistributing fragments into a distributed database 389 1. for each node si , 0 <= i <m the following initializations will be performed: (a) si = 0; ( the size/dimension used by the node si) (b) li =φ; ( the set of the fragments stored in the node si) (c) for each fragment f j, 0 <= j <n: r j = 0; (the number of replicas of the fragment f j) 2. the values from the v matrix are retrieved in a descending order. a tuple t = (fi, f j, vi, j) corresponding to a value vi, j from the v matrix represents a gain obtained for the total transfer size/dimension if the fragments fi and f j are stored in the same node. for this tuple t will be determined the node that offers the best advantage in the case that the mentioned fragments will be added there. for finding this node, the algorithm will compute the gain ck obtained if these fragments are attached to the node sk, and will keep track of an indicator sitk. the value from sitk is referring to the state of the fragments {fi, f j } relative to the set lk , for 0 <=k <m. (lk is the set of the nodes existing in sk). (a) if fi ∈ lk and f j ∈ lk, (the fragments fi, f j are already added to the node sk, then: ck = 0; sitk = 1; (b) if fi ∈ lk and f j /∈ lk, then: if r j <rmax j and sk + dim(f j) <= dmax, then sitk = 2, ck = 0; for fp ∈ lk : ck = ck + v j p; note: the fragment f j can be added to the node sk and the gain obtained will be ck; else sitk = 1, ck = 0; (c) if fi /∈ lk and f j ∈ lk, then: if ri <rmaxi and sk + dim(fi) <= dmax, then sitk = 3, ck = 0; for fp ∈ lk : ck = ck + vi p; else sitk = 1, ck = 0; (d) if fi /∈ lk and f j /∈ lk, then: if ri <rmaxi and r j <rmax j, and sk + dim(fi) + dim(f j) <= dmax, then sitk = 4, ck = vi j; for fp ∈ lk : ck = ck + vi p+ v j p; else sitk = 1, ck = 0; a node sk with ck maxim will be determined. depending on the value sitk the following data are modified: lk, sk, ri, r j. 3. if a fragment fi was not used in the binary operators obtained from q in the period of time t used to analyze and to evaluate the requests from set q, then the result in the precedent step will be ri = 0, and that means that the fragment fi was not stored in the nodes of the network. in this case, a node from the network with enough space to store the fragment fi without removing other fragments will be selected. (a) sit = 0; (b) for each 0 <= k <n: if sk + dim (fi) <= dmax, then fi is added to the node sk, (sk, lk will change; ri = 1); 390 leon ţâmbulea, manuela horvat-petrescu sit = 1; endif if ind=0 after running the previous algorithm it means that the fragment fi could not be added to a node then other fragment gk ∈ lk will be searched for. the fragment gk is chosen as the node sk will have the smaller lost when removing it: (c) for 0 <= k <m: ck =∞; for each fp ∈ lk: cc = ∑ vi, j; f j ∈ lk, j 6= p if cc <ck then ck = cc, gk = fp (d) is determined the sk node having a minimum value for ck. in the sk node the gk fragment is replaced by the fi fragment. the value from lk, ri, and r position corresponding to gk will be updated. 4. the ri set containing the nodes where the fragment fi is stored is computed for each fragment fi. (ri is the set of the fi replicas) at the end of this algorithm were determined the value of lk for each node sk, 0 <= k <n. the virtual nodes can be allocated to the real nodes using the information obtained from the set of requests q. the virtual nodes can be allocated to the real nodes according to the node where evaluation is required. this allocation will be done using a second set of tuples: t2 = {(fq, sq, dq), q ∈ q} . for a request q, if the fragment fq is stored in the node sq (sq is the node where the answer to the request q is required) then the data with the size dq are not transferred. we’ll assume that the virtual nodes where the fragments are allocated are s k, 0 <= k <m, and the real nodes from the distributed database are sr k, 0 <= k <m. with ci, j is noted the total gain obtained when transferring the answers of the requests from q if the virtual node si is stored in the real node sr j. (ci, j is the size of the data from all the answers for all the requests that are transferred from si to sr j.) the ci, j values can be computed as follows: ci, j = 0; 0 <= i , j <m; for each t = (fi, sr j, d) ∈ t2, for each sk ∈ ri (the nodes containing fi replicas): ck, j = ck, j + d is build a graph with the nodes sk, srk, 0 <= k <m, and the value ci, j will be associated to the line from si to sr j. for this graph is determined the maximum cupling with the maximum value [2]. using this coupling, a virtual node si will correspond to a real node sr j where the li replicas are stored. using this redistribution the ri set could be recomputed. 4 fragments transfer plan when evaluating requests a request q is split by the execution plan in as set of operators {op0, ... opn−1}. the result of the request q is the result of evaluating the last operator, as the other operators generate intermediate results. evaluation plans can be created for each operator, these plans detailing all the data transfers required in the evaluation process and the size of the transfers. if more than one evaluation plan (that evaluates in the same manner) can be created for an operator, than the plan that requires less data (in terms of size) redistributing fragments into a distributed database 391 for transfer will be used. coming next is an evaluation plan for a request showing the data transfers. the plan can be noted with: p = ( evaluationlist; dim), where "evaluationlist" is a succession of actual evaluation of the operators: evaluationlist : (nodi, op1, nod f ) [, (nodi’, op2, nod f ’)]... a tuple (nodi, op1, nod f ) has the following meaning: the operator "op1" is evaluated in the "nod f " after a data transfer from "nodi". if nodi = nod f then there is no data transfer when the operator is evaluated (the unary operators always encounter this situation). the fragments used from the nodes nodi and nod f are effectively stored in the nodes or are the result of previous evaluations performed in these nodes. "dim" is the total size/dimension of the data transferred in order to evaluate the plan from the evaluationlist. this size can represent: a) the number of the transfers involved in evaluation b) an average size/dimension of the transferred data. the average size can be found by using values v’i, j computed in the same time with the values vi, j from section 3. (v’i, j would be the average size of the data transferred between the node si and s j when the request set q is evaluated.) in the following algorithm will be used for simplicity the first meaning of the "dim" (explained in the paragraph a) from above). we will note with p(op) the set of evaluation plans for an operator op. if p = (t0, t1, .... tn−1; d) ∈ p(op) where ti = (s1 i, opi, s 2 i), then the following notations can be used: dim(p) = d; plan(p) = t 0, t 1, .... t n− 1 = the transfers list required by the plan p result(p) = s2 n − 1 = the node where can be found the result of the plan p evaluation an algorithm for generating the plan p(op) is presented in the following paragraph (the data transfers are taken into consideration). 1. if "op" is an unary operator, so it has the form op(x) then: (a) if x is a fragment stored in the distributed database then p(op) = {((s, op, s); o)|s ∈ r(x )}, where r(x) represents the set of the nodes where the fragment x is stored. (b) if x is a fragment obtain from a previous evaluation of an operator op’ then: p(op) = {(plan(p), (result(p), op, result(p)); dim(p))|p ∈ p(op)} 2. if "op" is a binary operator, so it has the form op(x,y) then: (a) if x and y are fragments stored in the distributed database then: p(op) = { ( (s1, op, s2) ; 1) where s1 ∈ r(x) r(y) , s2 ∈ r(y)} ∪ { ( (s1, op, s2) ; 1) where s1 ∈ r(y) r(x) , s2 ∈ r(x)} ∪ { ( (s1, op, s1) ; 0) where s1 ∈ r(x) ∩ r(y)}. the evaluation of the operator "op" can be made in a node where the fragment x is stored and fragment y is transferred, or in a node where the fragment y is stored and fragment x is 392 leon ţâmbulea, manuela horvat-petrescu transferred, or in a node where both fragments are stored. (b) if x is a fragment stored in the distributed database and y is a fragment obtained from a previous evaluation of a op’ operator, then: p(op) = p1(op) ∪ p2(op) ∪ p3(op) where: p1(op) = {(plan (p), (result(p), op, s); dim(p) + 1) where p ∈ p(op’), s ∈ r(x) { result(p) }} p2(op) = {(plan (p), (s, op, result(p)); dim(p) + 1)} where p ∈ p(op’), s ∈ r(x) { result(p) }} p3(op) = {(plan(p), ( result(p), op, result(p)); dim(p)) where p ∈ p(op’), if result(p) in r(x)} the evaluation of the operator "op’" can be made in: a node where the fragment x is stored by transferring the result from a node where op’ can be evaluated in a node where op’ is evaluated by transferring the x fragment a node there op’ can be evaluated and where the fragment x is stored (c) if x is a fragment obtained from a previous evaluation of a op’ operator and y is a fragment stored in the distributed database, the plan is build as in b2. (d) if x and y are the results of evaluation the operators op1 and op2 then: p(op) = p1(op) ∪ p2(op) ∪ p3(op) where: p1(op) = {(plan (p1), plan (p2), (result(p1), op, result(p2)); dim(p1) + dim(p2) + 1) where p1 ∈ p(op1), p2 ∈ p(op2), result(p1) 6= result(p2)} p2(op) = ∪ {(plan(p1), plan(p2), (result(p2), op, result(p1)); dim(p1) + dim(p2) +1) where p1 ∈ p(op1), p2 ∈ p(op2), result(p1) 6= result(p2)} p3(op) = ∪ {(plan(p1), plan(p2), (result(p1), op, result(p1)); dim(p1) + dim(p2)) where p1 ∈ p(op1), p2 ∈ p(op2), result(p1) = result(p2)}. after the plans were generated in the cases a2, b2, b3, b4 there is the chance to obtain for an operator two different plans p1 6= p2 but with the same final evaluation node f inal(p1) = f inal(p2). in this case is chosen the plan with smaller dim. using this transformation an operator will have maximum one plan for a final node. example: considering the following distribution of the fragments: node s1 s2 s3 fragments a b a, c assuming the request: q = (a * σ c (b)) ∪ c. the operator’s plans are: redistributing fragments into a distributed database 393 p(σ c ) = {(( s2, σ c, s2); 0 )} p( * ) = {(( s2, σ c, s2), (s2, *, s1); 1), ((s2, σ c, ,s2), (s2, *, s3); 1), ((s2, σ c, s2), (s1, *, s3); 1), ((s2, σ c, s2), (s3, *, s2); 1)} p( ∪) = {((s2, σ c, s2), (s2, *, s1), (s1, ∪, s3 ); 2), (( s2, σ c, s2), ( s2, *, s3); ( s3, ∪, s3 ); 1), ........ } in the previous example can be noticed that exists a request evaluation plan with only one transferred fragment. 5 conclusion in section 3 is proposed an algorithm for redistribution of the fragments of a distributed database. the algorithm uses the request execution plan and some information that can be obtained when evaluating the relational determinant operators from the execution plan. this algorithm minimizes the size of the data transferred between the nodes of the network when the requests are evaluated. for evaluating a request several fragments are needed, the fragments are stored in the nodes of a distributed database. an algorithm that determines an execution plan which minimizes the number of the fragments transferred between the nodes is proposed in section s4. the results can be extended as follows: -in the data transfer take into consideration the cost of the transfer instead of the size of the transferred data. -in redistribution algorithm use a maximum size/dimension for each node instead a unique maximum size for all nodes. -in the algorithm that finds the transfer plan between the nodes (algorithm used to evaluate a request) use a transfer cost instead of the number of the tranferred fragments. bibliography [1] c.h. cheng, w.k. lee, k.f. wong, "a genetic algorithm-based clustering approach for database partitioning", ieee transactions on systems man and cybernetics part c-applications and reviews, 32: 215-230, 2002. [2] r. diestel, "graph theory", springer-verlag, heidelberg 2000, electronic edition. [3] j. graham, "efficient allocation in distributed object oriented databases", proceedings of the isca 16th international conference on parallel and distributed computing systems , reno nevada, august 2003. [4] y. huang, j. chen, "fragment allocation in distributed database design", fragment allocation in distributed database design, journal of information science and engineering, 17, 491-506 (2001). [5] i. lungu, a. g. fodor, "optimizing queries in distributed systems," revista informatica economica nr. 1 (37), 67-72, 2006. [6] m. t. õzsu and p. valduriez, "principles of distributed database systems", 2nd ed., prentice-hall international editions, 1999. 394 leon ţâmbulea, manuela horvat-petrescu [7] a. sleit, w. almobaideen, s. al-areqi, a. yahya, "a dynamic object fragmentation and replication algorithm in distributed database systems", american journal of applied sciences 4 (8), 613-618, 2007. [8] l. tâmbulea, m. horvat, "dynamic distribution model in distributed database", int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844, vol. iii (2008), suppl. issue: proceedings of icccc 2008, pp. 512-515. [9] t. ulus, m. uysal, "heuristic approach to dynamic data allocation in distributed database systems", pakistan journal of information and technology 2 (3) , 231-239, 2003. [10] s. upadhyaya, s. lata, "task allocation in distributed computing vs distributed database systems: a comparative study", ijcsns international journal of computer science and network security , vol.8 no.3, march 2008. [11] o. wolfson, s. jajodia, "an algorithm for dynamic data distribution", proceedings of the 2nd workshop on the management of replicated data (wmrd-ii) , monterey, ca, nov. 1992. leon ţâmbulea babeş bolyai university, cluj-napoca faculty of mathematics and computer science department of computer science m.kogãlniceanu no.1 e-mail: leon@cs.ubbcluj.ro manuela horvat babeş bolyai university, cluj-napoca faculty of mathematics and computer science department of computer science m.kogãlniceanu no.1 e-mail: manuela.petrescu@gmail.ro manuelahorvat-petrescu is a phd.c. in computer science department at faculty of mathematics and computer science of babes bolyai university of cluj-napoca.she received the batchelor degree in computer science, master degree in component based programming and is working now as a software engineer in montran corporation. ijcccv4n3draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 3, pp. 224-233 variants of p colonies with very simple cell structure lucie ciencialová, erzsébet csuhaj-varjú, alica kelemenová, györgy vaszil lucie ciencialová, alica kelemenová institute of computer science, faculty of philosophy and science, silesian university in opava bezručovo nám. 13, 74601 opava, czech republic e-mail: {lucie.ciencialova, alica.kelemenova}@fpf.slu.cz erzsébet csuhaj-varjú, györgy vaszil computer and automation research institute of the hungarian academy of sciences kende utca 13-17, 1111 budapest, hungary e-mail: {csuhaj, vaszil}@sztaki.hu erzsébet csuhaj-varjú department of algorithms and their applications, faculty of informatics, eötvös loránd university pázmány péter sétány 1/c, 1117 budapest, hungary alica kelemenová department of computer science, catholic university ružomberok nám. a. hlinku 56, 03401 ružomberok, slovakia received: april 5, 2009 accepted: may 30, 2009 abstract: we study two very simple variants of p colonies: systems with only one object inside the cells, and systems with insertion-deletion programs, so called p colonies with senders and consumers. we show that both of these extremely simple types of systems are able to compute any recursively enumerable set of vectors of non-negative integers. keywords: p systems, colonies, p colonies, register machines 1 introduction p colonies form a class of abstract computing devices modeling a community of simple agents acting and evolving in a shared environment. they were introduced in [5] as very simple membrane systems, similar in simplicity and architecture to so called colonies of formal grammars. (see [7] for more information on membrane systems and [2, 4] for details on grammar systems theory.) a p colony consists of a collection of cells, each having a number of objects inside and an associated set of rules through which it can process these objects. communication between the cells is only possible indirectly through the environment which is common to all of them. the capabilities of the computing agents are very restricted, and the number of objects present inside a cell during the functioning of the system is previously fixed: it is usually one, two or three. the rules are also of a very simple form. as we will see, they allow the transformation of objects inside the cells and the transportation of objects between the cells and the environment. the rules are grouped into programs. a program contains exactly as many rules, as the number of objects allowed to be present inside the cell. the rules of the programs are applied to the objects inside the associated cells in parallel, and this also affects the objects which are in the environment. the p colony executes a computation by synchronously applying the programs to the objects inside the cells and outside in the environment until a halting configuration is reached. the result of the computation is obtained as the vector of copies of certain “final” objects present in the environment after the system halts. copyright c© 2006-2009 by ccc publications research supported in part by the czech science foundation, “gačr”, project no. 201/09/p075 (l. ciencialová), by the hungarian scientific research fund, “otka”, project no. k75952 (e. csuhaj-varjú, gy. vaszil), and by the scientific grant agency of slovakia, “vega”, project variants of p colonies with very simple cell structure 225 in the following, after providing the formal definitions, we first give a short overview of results on the computational completeness of the different p colony variants. then we present new results about two types of systems: first about the simplest possible p colonies, those which only have one object inside every cell, and then about a new type called p colonies with senders and consumers, which have special rules for insertion-deletion. we show that both kinds of these very simple devices are able to compute any recursively enumerable set of vectors of non-negative integers. 2 preliminaries let v be an alphabet, let v ∗ be the set of all words over v , and let ε denote the empty word. we denote the number of occurrences of a symbol a ∈ v in w by |w|a. the set of non-negative integers is denoted by n. a multiset over an arbitrary (not necessarily finite) set v is a mapping m : v → n which assigns to each object a ∈ v its multiplicity m(a) in m. the support of m is the set supp(m) = {a | m(a) ≥ }. if v is a finite set, then m is called a finite multiset. a multiset m is empty if its support is empty, supp(m) = /0. we will represent a finite multiset m over v by a string w over the alphabet v with |w|a = m(a), a ∈ v , and ε will represent the empty multiset. we will also need the notion of a register machine which consists of a finite number of registers each of which can hold an arbitrarily large non-negative integer (we say that the register is empty if it holds zero), and a set of labeled instructions which specify how the numbers stored in the registers can be changed. formally, a register machine is a construct m = (m, h, l, lh, r), where m is the number of registers, h is the set of instruction labels, l is the start label, lh is the halting label, and r is the set of instructions. each label from h labels only one instruction from r. there are several types of instructions which can be used. for li, l j, lk ∈ h and r ∈ {, . . . , m} we have • li : (add(r), l j, lk) nondeterministic add: add one to register r and then go to one of the instructions with labels l j or lk, non-deterministically chosen. • li : (su b(r), l j, lk) subtract: if register r is non-empty, then subtract one from it and go to the instruction with label l j, if the value of register r is zero, go to instruction lk. • lh : halt halt: stop the machine. a register machine m computes a set n(m) of numbers in the following way: it starts with empty registers by executing the instruction with label l and proceeds by applying instructions as indicated by the labels (and made possible by the contents of the registers). if the halt instruction is reached, then the number stored at that time in register 1 is said to be computed by m. because of the non-determinism in choosing the continuation of the computation in the case of add instructions, n(m) can be an infinite set. it is known (see, e.g., [6]) that in this way we can compute all sets of numbers which are turing computable. if a set of output registers i, . . . , ir,  ≤ r ≤ m, i j ∈ {, . . . , m} is specified, then m computes a set of vectors of non-negative integers as follows. if the halt instruction is reached, then (v, . . . , vr), where vk is the number stored in register ik,  ≤ k ≤ r, is the vector of numbers computed by m, i.e., the result of that computation. now we recall the definition of a p colony from [5]. a p colony is a construct π = (v, e, f,c, . . . ,cn), n ≥ , where v is an alphabet (its elements are called objects). there are two kinds of distinguished objects: e ∈ v (the environmental object), and the objects in f ⊆ v (the set of final objects). the cells of the colony are denoted by c, . . . ,cn. each cell is a pair ci = (oi, pi), where oi is a multiset over {e} 226 lucie ciencialová, erzsébet csuhaj-varjú, alica kelemenová, györgy vaszil having the same cardinality called capacity (here we only consider |oi| ∈ {, }) for all i,  ≤ i ≤ n (the initial state of the cell), and pi is a finite set of programs. each program consists of rules of the following forms: • a → b (internal point mutation), specifying that an object a ∈ v inside the cell is changed to b ∈ v . • c −→ d (one object exchange with the environment), specifying that if c ∈ v is contained inside the cell and d ∈ v is present in the environment, then c is sent out of the cell while d is brought inside. • c −→ d/c −→ d ′ (checking rule for one object exchange with the environment), specifying that if c ∈ v is inside the cell then it is exchanged with d ∈ v from the environment, or if there is no d outside but d ′ ∈ v is present, then c is exchanged with d ′. • c −→ d/c → d ′ (checking rule for one object exchange with the environment or internal point mutation), specifying that if the exchange of c ∈ v inside and d ∈ v outside is not possible, then c is changed to d ′ ∈ v . the programs contain one rule for each element of oi, thus, the number of rules of a program coincides with the cardinality of oi,  ≤ i ≤ n. in addition, p colonies with capacity of two may have programs of the form • 〈a, in; bc → d〉 with a, b, c, d ∈ v (deletion programs), specifying that if bc is present inside the cell and a is present in the environment, then the objects inside are changed to d and a is brought in (a is “deleted” from the environment). • 〈a, out; b → cd〉 with a, b, c, d ∈ v (insertion programs), specifying that if ab is inside the cell, then a is sent out (a is “inserted” into the environment) and b is changed to cd. the programs of the cells are used in the non-deterministic maximally parallel manner: in each time unit, each cell which is able to use one of its programs should use one. the use of a program means the application of the rule(s) of the program to the object(s) in the cell. this way, transitions among the configurations of the colony are obtained. a sequence of transitions is a computation which is halting if it reaches a configuration where no cell can use any program. the result of a halting computation is obtained from the number of copies of objects from f present in the environment in the halting configuration. because of the non-determinism in choosing the programs, several computations can be obtained from a given initial configuration, hence with a p colony π we can associate a set of vectors of non-negative integers computed by all possible halting computations of π . initially, the environment contains arbitrarily many copies of the environmental object e, and the cells also contain one or two copies of e inside, depending on the capacity of the p colony. for a p colony π = (v, e, f,c, . . . ,cn) as above, a configuration can be formally written as an (n +)tuple (w, . . . , wn; we ), where wi ∈ v ∗ represents the multiset of objects from cell ci,  ≤ i ≤ n, and we ∈ (v − {e}) ∗ represents the multiset of objects from the environment different from the environmental object e. the initial configuration is (ei, . . . , ei; ε) where i ∈ {, } is the capacity of the cells. a transition from a configuration to another is denoted as (w, . . . , wn; we ) ⇒ (w ′ , . . . , w ′ n; w ′ e ) where w′e and each w ′ i is obtained from wi,  ≤ i ≤ n, and we by executing one of the programs of pi. variants of p colonies with very simple cell structure 227 the set of vectors in nm, m = |f|, f = {o, . . . , om}, computed by a p colony π is defined as n(π ) = {(|ve |o , . . . , |ve |om ) | (e i, . . . , ei; ε) ⇒∗ (v, . . . , vn, ve )} where (ei, . . . , ei, ε), i ∈ {, }, is the initial configuration, (v, . . . , vn, ve ) is a halting configuration, and ⇒∗ denotes the reflexive and transitive closure of ⇒. let us denote by pcol(i, j, k, check) and pcol(i, j, k, no-check) the classes of sets of vectors generated by p colonies with at most j ≥  cells of capacity i ∈ {, }, having at most k ≥  programs associated to a cell which contain or do not contain checking rules, respectively. if a numerical parameter is unbounded, we denote it by a ∗. p colonies can simulate register machines with a rather limited number of programs per cell. in [3], it was shown that pcol(,∗, , check) = pcol(,∗, , check) = nre where nre denotes the class of recursively enumerable sets of integer vectors. even one cell is enough, if it may have an arbitrarily large number of programs, that is, pcol(, ,∗, check) = nre. similar results were also obtained without the use of checking rules. in this case we have pcol(,∗, , no-check) = pcol(,∗, , no-check) = nre. 3 p colonies with one object in [1] it was shown that if checking rules are allowed to be used, then all recursively enumerable sets of vectors can even be generated by p colonies with capacity one, that is, pcol(, ,∗, check) = nre. in the following we show that p colonies with six components generate all vectors even if checking rules are not used. theorem 1. pcol(, ,∗, no-check) = nre. proof. we construct a p colony simulating the computations of a register machine. let us consider an mregister machine m = (m, h, l, lh, p) and represent the content of the register i by the number of copies of a specific object ai in the environment. we construct the p colony π = (v, e, f,c, . . . ,c) with: v = {e, li, l ′ i , l ′′ i , l̄i, ki, li, l ′ i , l ′′ i , l ′′′ i , ei, fi, $i | for each li ∈ h} ∪ {ai, ai, j |  ≤ i ≤ m,  ≤ j ≤ |h|} ∪ {d, d ′, t }, f = {ai | register i is an output register}, and ci = (e, pi), for  ≤ i ≤ . because initially there are only copies of e in the environment and inside the cells, we have to initialize the simulation of the computation of m by generating the initial the label l, and an arbitrary number of l ′i , l ′′ i for all li ∈ h. these symbols are generated by c and c with the following programs: p ⊃ {〈e → l ′ r〉,〈l ′ r −→ e〉,〈e → l ′′ r 〉,〈l ′′ r −→ e〉 | lr ∈ h} ∪ {〈e −→ d′〉,〈d′ → l〉,〈l −→ d〉}, p ⊃ {〈e → d ′〉,〈d′ → d′〉,〈d′ −→ l ′〉,〈l ′  → d〉,〈d −→ l ′′  〉}. 228 lucie ciencialová, erzsébet csuhaj-varjú, alica kelemenová, györgy vaszil with these programs, from the configuration (e, e, e, e, e, e; ε), we obtain (d, l ′′ , e, e, e, e; lw) where the environment contains the label of the initial instruction, l, and w, a multiset of primed and double primed instruction labels. to simulate the instruction li : (add(r), l j, lk), cells c and c cooperate to add one copy of object ar and object l j or lk to the environment. p p i : 〈d −→ ar,i〉 i : 〈kk → lk〉 i : 〈e −→ li〉 i : 〈l ′i → kk〉 i : 〈ar,i → ar〉 i : 〈l j −→ d〉 i : 〈li → ar,i〉 i : 〈k j −→ e〉 i : 〈ar −→ k j〉 i : 〈lk −→ d〉 i : 〈ar,i −→ l ′i 〉 i : 〈kk −→ e〉 i : 〈ar −→ kk〉 i : 〈ar,i → t〉 i : 〈t → t〉 i : 〈k j → l j〉 i : 〈l ′i → k j〉 it is not difficult to follow how the interplay of these two cells produce the configuration (d, l ′′ , e, e, e, e; l jarw ′) or (d, l ′′ , e, e, e, e; lkarw ′) from a configuration (d, l ′′ , e, e, e, e; liw) where w, w ′ are multisets of l ′i , l ′′ i for li ∈ h and ar,  ≤ r ≤ m. if there is no l ′ i present in the environment when the program i of cell c should be used, then the programs i and i do not allow the halting of the computation. for each subtract instruction l f : (su b(r), lg, ln) there are the following programs in p, p, p and in p: p p p p f : 〈d ↔ l f 〉 f : 〈e −→ l f 〉 f : 〈l f → t〉 f : 〈e ↔ l′f 〉 f : 〈e ↔ l ′′ f 〉 f : 〈l f → e f 〉 f : 〈l f → l f 〉 f : 〈l′f → t〉 f : 〈l ′ f → l ′ f 〉 f : 〈l ′′ f → l ′ f 〉 f : 〈e f → ff 〉 f : 〈l f ↔ l ′f 〉 f : 〈t → t〉 f : 〈l ′ f ↔ ar〉 f : 〈l ′ f −→ $ f 〉 f : 〈ff → $ f 〉 f : 〈l ′f → l ′ f 〉 f : 〈l ′ f ↔ $ f 〉 f : 〈$ f → lg〉 f : 〈$ f ↔ d〉 f : 〈l′f −→ l ′′ f 〉 f : 〈$ f → l̄n〉 f : 〈lg ↔ e〉 f : 〈l ′′ f → l ′′′ f 〉 f : 〈ar → e〉 f : 〈l ′ f −→ l̄n〉 f : 〈l ′′′ f → l ′′ f 〉 f : 〈l̄n ↔ e〉 f : 〈l̄n → ln〉 f : 〈l ′′ f −→ e〉 f : 〈ln −→ e〉 in the following table we show how a subtract instruction can be simulated by the programs above. since c and c cannot apply any of their rules in any step of the following simulation, we omit them from the table. the multiset of objects in the environment is denoted by [. . .], and for now we assume that it always contains a sufficient amount of l ′i , l ′′ i objects for any li ∈ h. first we consider the case when there is at least one object ar in the environment, that is, if the variants of p colonies with very simple cell structure 229 simulation starts in a configuration (d, l ′′ , e, e, e, e; l f ar[. . .]). configuration of π programs to be applied c c c c env p p p p . d e e e l f ar[. . .] − f − − . d l f e e ar[. . .] − f − − . d l f e e ar[. . .] − f − − . d l ′f e e l f ar[. . .] f f − − . l f l ′ f e e dar[. . .] f f − − . e f l ′′ f e e l ′ f dar[. . .] f f f − . ff l ′′′ f l ′ f e dar[. . .] f f f − . $ f l ′′ f l ′ f e dar[. . .] f f f − . d e ar e $ f l ′′ f [. . .] − − f f . d e e l′′f $ f [. . .] − − − f . d e e l ′f $ f [. . .] − − − f . d e e $ f [. . .] − − − f . d e e lg [. . .] − − − f . d e e e lg[. . .] − g − − in 13 steps, from a configuration (d, l ′′ , e, e, e, e; l f ar[. . .]) we obtain (d, l ′′  , e, e, e, e; lg[. . .]) where lg is the label of the instruction which should follow the successful decrease of the value of the nonempty register r, and the environment contains a multiset of objects l ′i , l ′′ i for li ∈ h. now we consider the case when register r, which is the register to be decremented, stores zero, that is, if the simulation starts in a configuration (d, l ′′ , e, e, e, e; l f [. . .]) where the environment does not contain any object ar. configuration of π programs to be applied c c c c env p p p p . d e e e l f [. . .] − f − − . d l f e e [. . .] − f − − . d l f e e [. . .] − f − − . d l ′f e e l f [. . .] f f − − . l f l ′ f e e d[. . .] f f − − . e f l ′′ f e e l ′ f d[. . .] f f f − . ff l ′′′ f l ′ f e d[. . .] f f f − . $ f l ′′ f l ′ f e d[. . .] f f − − . d e l ′f e $ f l ′′ f [. . .] − − f f . d e $ f l ′′ f [. . .] − − f f . d e l̄n l ′ f [. . .] − − f − . d e e l ′f l̄n[. . .] − − − f . d e e l̄n [. . .] − − − f . d e e ln [. . .] − − − f . d e e e ln[. . .] − n − − similarly to the previous case, in 14 steps we obtain a configuration (d, l ′′ , e, e, e, e; ln[. . .]) where ln is the label of the instruction which should follow l f if register r is empty, that is, if the decrease of its value is not possible. consider now what happens if there is an insufficient amount of objects l ′i , l ′′ i for li ∈ h is present in the environment. notice that such symbols are needed in step 3 and 5 by cell c. if there is no more 230 lucie ciencialová, erzsébet csuhaj-varjú, alica kelemenová, györgy vaszil available (not enough of them were produced in the initial phase by c and c), then the programs f, f, and f do not allow the halting of the computation. from these considerations we can see that after the initialization phase, all instructions of the register machine m can be simulated by the p colony. if the label of the halt instruction, lh is produced, the computation halts since there is no program for processing the object lh. the reader can immediately see that π computes the same set of vectors as m. 4 p colonies with senders and consumers now we continue with the investigation of two object p colonies with insertion-deletion programs. it is not too difficult to see that if we allow a cell to contain both types of programs, then we can simulate the other types of programs in two steps, thus, it is more interesting to consider p colonies having cells which contain either insertion or deletion programs, but not both types at the same time. we call these systems p colonies with senders and consumers. a sender is a cell with only insertion programs, a consumer is a cell with only deletion programs. let us denote by pcol(i, j, s-c) the class of sets of numbers generated by p colonies with senders and consumers having at most i ≥  cells with at most j ≥  program each. example 2. (a) a p colony with one sender cell can generate the parikh set of a regular language l ⊆ t ∗. let g = (n, t, p, s) be a regular grammar such that l(g) = l. for generating the parikh vectors of the words in l, we use, for each s → ab of p, the programs 〈e, out; e → es〉, 〈e, out; s → ab〉 and then 〈x, out; a → ab〉, x ∈ t for every a → ab in p. finally, for every rule of the form a → a we need 〈x, out; a → af〉, x ∈ t, 〈a, out; f → f f〉, where f /∈ t ∪ n. (b) a p colony with one consumer cell can “consume” the parikh set of a regular language l. to see this, let m = (q, t, δ , q, f) be a deterministic finite automaton such that l(m) = l. we need the program 〈e, in; ee → q〉, and to every transition δ (qi, a) = q j in m, we introduce 〈a, in; xqi → q j〉, x ∈ t ∪{e}. if q j ∈ f in δ (qi, a) = q j we have to add the programs 〈a, in; xqi → f〉, x ∈ t, where f /∈ q ∪ t . now we show that three cells, one sender and two consumers are sufficient to generate all recursively enumerable sets of integer vectors. theorem 3. pcol(,∗, s-c) = nre. proof. consider an m-register machine m = (m, h, l, lh, p), m ≥ . we simulate m by representing the content of the register i by the number of copies of a specific object ai in the environment. we construct the p colony π = (v, e, f,c,c,c) with: v = {e, l, l ′, l ′′, l ′′′, liv, lv, l̄, ¯̄l | l ∈ h} ∪ {ai |  ≤ i ≤ m} ∪ {k, t, t, t, t, t}, f = {ai | register i is an output register}, and ci = (ee, pi) for  ≤ i ≤ . the p colony π starts its computation in the initial configuration (ee, ee, ee; ε). we initialize the computation by generating the initial label l with a program from p, 〈e, out; e → ll〉 ∈ p obtaining (ll, ee, ee; ε). the simulation of an instruction with label li starts from a configuration (lili, ee, ee; w) where w ∈ v ∗, the multiset of objects in the environment, represents the counter contents of m. to simulate an add instruction, we use the programs of p and p. for each li, l j, lk ∈ h with li being variants of p colonies with very simple cell structure 231 the label of an instruction li : (add(r), l j, lk), we have the following programs: p p i : 〈li, out; li → arl j〉 i : 〈li, in; ee → t〉 i : 〈li, out; li → arlk〉 i : 〈e, in; lit → e〉 i : 〈ar, out; l j → l jl j〉 i : 〈li, in; ¯̄lit → t〉 i : 〈ar, out; lk → lklk〉 using these programs, we obtain a sequence of configurations (lili, ee, ee; w) ⇒ (arl, ee, ee; liw) ⇒ (ll, ee, lit; arw) where l is the label of the next instruction, that is, we either have (l jl j, ee, lit; arw), or the configuration (lklk, ee, lit; arw). the contents of cell c, lit, will change in the next step to ee independently of the several ways of the continuation of the computation, as we shall see later. the program labeled with i is used if the instruction simulated before li was a sub instruction (see below). in this case, the configuration in which the simulation of li starts is (lili, ee, l̄it; ¯̄liw) and we need the steps (lili, ee, l̄it; ¯̄liw) ⇒ (arl, ee, ¯̄lit; liw) ⇒ (ll, ee, lit; arw) and program i to obtain the same configuration as before. now we show how to simulate a sub instruction. for each l j, lk, ll ∈ h with l j being the label of an instruction l j : (su b(r), lk, ll ), and for all labels ls ∈ h, we have the following programs. p p p j : 〈l j, out; l j → l ′j l ′ j〉 j : 〈l j, in; ee → e〉 j : 〈l ′ j, in; ee → t〉 j : 〈l ′ j, out; l ′ j → l ′′ j l ′′ j 〉 j : 〈ar, in; el j → e〉 j : 〈e, in; l ′ j t → t〉 j : 〈l ′′ j , out; l ′′ j → l ′′′ j l iv j 〉 j : 〈l ′′ j , in; el j → e〉 j : 〈l ′′ j , in; et → t〉 j : 〈l ′′′ j , out; l iv j → l̄kl̄k〉 j : 〈l ′′′ j , in; are → e〉 j,s : 〈l̄s, in; l ′′ j t → t〉 j : 〈l iv j , out; l ′′′ j → l̄l l̄l〉 j : 〈e, in; l ′′′ j e → e〉 j,s : 〈l̄s, in; et → t〉 j : 〈l̄k, out; l̄k → ¯̄lk ¯̄lk〉 j : 〈l iv j , in; are → k〉 j,s : 〈 ¯̄ls, in; l̄st → t〉 j : 〈 ¯̄lk, out; ¯̄lk → lklk〉 j : 〈e, in; livj k → k〉 j,s : 〈e, in; ¯̄lst → e〉 j : 〈l̄l , out; l̄l → ¯̄ll ¯̄ll〉 j : 〈e, in; ek → k〉 j : 〈 ¯̄ll , out; ¯̄lk → ll ll〉 j : 〈l ′′′j , in; l ′′ j e → k〉 j : 〈e, in; l ′′′ j k → k〉 j : 〈l iv j , in; l ′′ j e → e〉 j : 〈e, in; l iv j e → e〉 in the following tables we show how the programs above simulate the execution of the instruction l j : (su b(r), lk, ll ). to save space, we use the sign “/” to separate the different possible multisets which might appear in the same row of the table. first we consider the case when register r is not empty, that is, when there is at least one object ar present in the environment. we see that starting with a configuration where c contains the objects l jl j and the environment contains ar, in six steps we obtain a configuration where the object ar is removed from the environment, and c either contains the label of the next instruction lk, or because of the presence of program j, in p, the computation will never be able to halt. 232 lucie ciencialová, erzsébet csuhaj-varjú, alica kelemenová, györgy vaszil configuration of π programs to be applied c c c env p p p . l jl j ee ? arw ′ j − ? . l ′j l ′ j ee ? l jarw ′′ j j ? . l ′′j l ′′ j l je ee l ′ j arw j j j . l ′′′j l iv j are l ′ j t l ′′ j w j/ j − j . l̄kl̄k/l̄l l̄l are et (l ′′′ j /l iv j )l ′′ j w j/ j j/ j j . ¯̄lk ¯̄lk/ ¯̄ll ¯̄ll l ′′′ j e/l iv j k l ′′ j t (l̄k/l̄l )w j/ j j/ j j,k/ j,l . lklk/ll ll ee/ek (l̄k/l̄l )t ( ¯̄lk/ ¯̄ll )w k/l −/ j j,k/ j,l . l ′ k l ′ k /l ′ l l ′ l ee/ek ( ¯̄lk/ ¯̄ll )t (lk/ll )w k/l k/ j j,k/ j,l . l ′′ k l ′′ k /l ′′ l l ′′ l (lk/ll )e/ek ee (l ′ k /l ′ l )w k/l k/ j j now we show the simulation of the l j : (su b(r), lk, ll ) instruction when there is no object ar is present in the environment, that is, when register r is empty. in this case, similarly to the previous one, we either get the objects lklk in the cell c, or the computation will not be able to halt. configuration of π rules to be applied c c c env p p p . l jl j ee ? w j − ? . l ′j l ′ j ee ? l jw j j ? . l ′′j l ′′ j l je ee l ′ j w j − j . l ′′′j l iv j l je l ′ j t l ′′ j w j/ j j j . l̄kl̄k/l̄l l̄l l ′′ j e et (l ′′′ j /l iv j )w j/ j j/ j − . ¯̄lk ¯̄lk/ ¯̄ll ¯̄ll l ′′′ j k/l iv j e et (l̄k/l̄l )w j/ j j/ j j,k/ j,l . lklk/ll ll ek/ee (l̄k/l̄l )t ( ¯̄lk/ ¯̄ll )w k/l j/− j,k/ j,l . l ′ k l ′ k /l ′ l l ′ l ek/ee ( ¯̄lk/ ¯̄ll )t (lk/ll )w k/l j/k j,k/ j,l . l ′′ k l ′′ k /l ′′ l l ′′ l ek/(lk/ll )e ee (l ′ k /l ′ l )w k/l j/k j the rules to be applied and the objects contained by the cell c in row 1. and row 2. of the tables above depend on the instruction li which was simulated before l j. if li is an add instruction, then we have lit in the first row, and applying the program i from p we get ee in the second row, where no program is applied until the next step. also, w = w′ = w′′ in this case. if li is a sub instruction, then (as we can also see from row 7. and row 8.) the contents of the cell c is l̄ jt and ¯̄l jt in the first two rows where the programs i, j and i, j are applied. in this case w ′′ = ¯̄l jw, and w′ = w. as we have seen above, the p colony successfully simulates each instruction of m and since there is no program to process lh, the label of the halt instruction, it also halts when the computation of m is finished. it is also easy to see that m and π compute the same set of vectors of non-negative integers. 5 conclusion we have examined extremely simplified variants of p colonies: p colonies of capacity one with no checking rules, and p colonies with capacity two, but only with senders and consumers. we have shown that even these very simple variants are able to simulate arbitrary register machines, that is, to compute all turing computable sets of vectors. variants of p colonies with very simple cell structure 233 bibliography [1] l. cienciala, l. ciencialová, a. kelemenová. on the number of agents in p colonies. in: membrane computing. 8th international workshop, wmc 2007. thessaloniki, greece, june 25-28, 2007. revised selected and invited papers. edited by g. eleftherakis, p. kefalas, gh. păun, g. rozenberg, a. salomaa. volume 4860 of lecture notes in computer science, springer-verlag, berlin-heidelberg, 2007, 193-208. [2] e. csuhaj-varjú, j. dassow, j. kelemen, gh. păun. grammar systems – a grammatical approach to distribution and cooperation. gordon and breach, london, 1994. [3] e. csuhaj-varjú, j. kelemen, a. kelemenová, gh. păun, gy. vaszil. computing with cells in environment: p colonies. journal of multi-valued logic and soft computing 12:201-215, 2006. [4] j. kelemen, a. kelemenová. a grammar-theoretic treatment of multi-agent systems. cybernetics and systems 23:621-633, 1992. [5] j. kelemen, a. kelemenová, gh. păun. preview of p colonies: a biochemically inspired computing model. in: workshop and tutorial proceedings. ninth international conference on the simulation and synthesis of living systems (alife ix). edited by m. bedau et al. boston mass., 2004, 82-86. [6] m. minsky. computation – finite and infinite machines. prentice hall, englewood cliffs, nj, 1967. [7] gh. păun. membrane computing – an introduction. springer-verlag, berlin, 2002. lucie ciencialová has received her phd. in 2008 and she works as an assistant professor at the institute of computer science, silesian university in opava. her interests include theoretical informatics and natural computing. erzsébet csuhaj-varjú d.sc, dr. habil., is head of the theoretical computer science research group at the computer and automaton research institute of the hungarian academy of sciences. she has also been affiliated with the department of algorithms and applications of the eötvös loránd university, budapest, hungary, as science advisor. her main research interests are formal languages, distributed systems, and nature-motivated computing. in these areas she has more than 150 papers, a monograph, and eleven edited volumes published in international publication forums. alica kelemenová is an associated professor at the institute of computer science, silesian university in opava, czech republic and at the department of computer science catholic university in ružomberok, slovakia. her main research interest is theoretical computer science, especially formal language theory and biologically motivated generative devices like l systems and p systems. györgy vaszil, phd. is a senior research fellow at the theoretical computer science research group of the computer and automation research institute of the hungarian academy of sciences. his research interests are formal language and automata theory, nature motivated computational models. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 710-718 bounded rationality through the filter of the lisbon objectives r. fabian, m.j. manolescu, l. galea, g. bologa ralf fabian “lucian blaga” univeristy of sibiu romania, 550012 sibiu, 5-7 dr. ion ratiu st. e-mail: ralf.fabian@ulbsibiu.ro misu-jan manolescu, loredana galea, gabriela bologa agora university, oradea. cercetare dezvoltare agora e-mail: rectorat@univagora.ro, loredana.galea@univagora.ro, gabi_fiat@yahoo.com abstract: information and communication technologies (ict) have created best conditions for grows of knowledge societies. an emerging global information society serves to building global knowledge societies as source for further development. conventional paradigms of sciences starts to be more blemish and prone to redefinition of there foundations, understood as scientific knowledge. the perspective of knowledge and ideals of rationality are both heavily influenced by a new contemporary scientific thinking, through tools, inherent of autonomy and uncertainty. a new understanding of the world in terms of open dynamic heterogeneous uncertain systems is needed. among the conclusions: classical rational reasoning is mainly aiming at effectiveness, not at uncertain knowledge processing, because of its temporality (mainly its ineffectiveness in dealing with future events); a bounded-rationality approach enables both, better economic models and better modelling, being based on trends in economic modelling as well as on agent-oriented software engineering. keywords: knowledge society paradigms, bounded rationality, "just in time" paradigm, uncertain knowledge processing. 1 introduction the approach proposed in this paper, trials to a better reaction to uncertain and rapidly changing environments as result of ict implications in the "grows of economy". as far target the paper has three specific aims: (a) showing that both bounded rationality and agents are, at the same time, unavoidable restrictions, and valuable means when developing applications. (b) underlining the inadequacy of conventional formal methods with algorithm-based computational techniques applied in ai, in general but firstly for treating uncertainty in applications. (c) outlining, on this groundwork, an agent-oriented approach to combine synergistically bounded rationality and agents in modelling. in a society that strives to be progressively technically, technology is becoming a tool for social interaction bridging the strands between online and offline activities, respectively, digital and social behaviour. studies demonstrate clearly that the information and communication technologies (ict) experienced in people’s everyday life sets a milestone for an active participation in the knowledge society (ks) development [9]. the "lisbon strategy", developed by the european council for the time period 2000-2010, aims to make the eu "the most dynamic and competitive knowledge-based economy in the world capable of sustainable economic growth with more and better jobs and greater social cohesion, copyright c⃝ 2006-2010 by ccc publications bounded rationality through the filter of the lisbon objectives 711 and respect for the environment by 2010" [20] [9] by considering an economic, a social and an environmental pillar. information society policies address core objectives of the lisbon strategy: drive productivity growth, create an open and competitive digital economy, and stimulate innovation to tackle changes of globalization and demographic change. ict affects economic performance by driving innovation through investment in ict; improving business processes and reduce companies administration costs; increasing efficiency in public administration; enhance productivity grow and create new markets. for the period beyond 2010 the eu develops a new strategy to make a recovery from financial crises and speed up towards a greener, more sustainable and innovative economy. in a formal "consultation on the future "eu 2020" strategy" [9] [20] the eu commission considers that the key drivers of eu 2020 should be focused on the following thematic: creating value by basing growth on knowledge; empowering people in inclusive societies; and creating a competitive, connected and greener economy. conventional paradigms of sciences starts to be more blemish and prone to redefinition of there foundations understood as scientific knowledge. accepted aspects are questioned and relocate the place of man in the world. the perspective of knowledge and ideals of rationality are both heavily influenced by a new contemporary scientific thinking, through tools, inherent of autonomy and uncertainty. innovative theoretical solutions, such as the approach of complexity, change old fashion models of rationality. [15] [16] the new rationality prioritizes the view of complexity as an essential characteristic of the surrounding reality (social and non-social). the classical ideal of rationality is centred on the supremacy of reason, which is to be overcome by the approach of complexity. a new understanding of the world in terms of dynamic systems is needed, where interaction between the elements of the system and the environment rules. for the society, education systems are institutional spaces for generation and transmission of complex knowledge. a premise for a global knowledge based society is an adequate amount of highly qualified scientists. the universities have been aware in the last decades to expend access to higher education. beside the need fore more graduates (this objective has reached a significant progress since 2000, increasing the number of mathematics, science and technology graduates, by more then 25%. [9]), an even more important issue is to assure that after graduating, lifelong learning takes place in work and leisure time. the insertions of new ict and artificial intelligence in the human society, together with the promotion through the lisbon objectives, have reached, in the last century, every area of human activities, from education and economy to political systems. if we endeavour to undertake any kind of professional research and development (r&d), we have to analyze the applicability and changes brought us through them. premises for the research described here, as well as for other research surveyed in this paper: a) in the last decade most non-trivial it applications are meant for, and hosted by, open, dynamic, and uncertain environments. b) ever more services have to be provided in line with the "just in time" (jit) paradigm; c) developing applications for jit services implies both bounded rationality (fact of life) and artificial intelligence (powerful it instrument) as cardinal designspace dimensions. [3] [22] [8] the basic idea is that the dynamics of every facet of our society (first of all, economy) are so intense that no field of study could afford a slower development pace. present-day it environments, except for some irrelevant applications, move fast from limited, homogeneous, changing slowly, deterministic (even if partly approximated or even unknown) towards (ohdue). that means: "open and heterogeneous (the resources involved are unalike and their availability is not warranted), dynamic (the pace of exogenous and endogenous changes 712 r. fabian, m.j. manolescu, l. galea, g. bologa is high) and uncertain (both information and its processing rules are revisable, fuzzy, uncertain and intrinsically non-deterministic" [22] [8] as most environmental and almost all human stimuli generators). this paper is organized as follows: after summarizing the history of the undertaking and revising basic concepts, (section 2), its rationale and approach are outlined in (section 3) together with considerations highlighting the disadvantages of conventional it, because of inadequate interaction in dynamic environments. decision making in from the perspective of bounded rationality is suggested. conclusions and future work (section 6) close the paper. 2 history and basic concepts conventional it undertakings relaying on formal methods of computation used for modelling economic systems [10], showed the limitations of the conceptual bases. the rather rigid character and incapability to adapt over time to a rapid changing environmental context, has been addressed afterwards by introducing "total fuzzy grammars" [12]. trying to manage non deterministic intelligent systems, from economic modelling [12] [10] to natural language processing for chatbots [11], with intrinsic deterministic tools, proved the be inadequate of this kind endower. tools unable to deal with a certain degree of uncertainty are not worth to be considered in any kind of modelling real life scenarios. the approach from [22] addresses bounded-rationality and enables better economic modelling, being based on trends in economic modelling as well as on agent-oriented software engineering. uncertainty was analyzed with regard to approximation and undecidability [22] [3] and emphasis that they are two incommensurable kinds of uncertainty: approximation is deterministic and atemporal and undecidability is nondeterministic and has basic temporal dimension. thus, approximation aims at optimization and efficiency and doesn’t fit as instrument for uncertainty. on the other hand, undecidability has to deal with future events in any decision making process and can not be treated exclusively through approximation or other conventional prediction methods (probabilistic, stochastic, etc.) research directions on non-algorithmic approaches begun in 2007 and have the role to support the approach in this paper. since the conclusions from [21] [4] are general, they can be easily adapted to real world applications by further investigations and development. information and knowledge. the essence of communication in ks is the message to be transmitted. to clarify the meaning used in this paper, of three overlapping and often misleadingly used concepts, regarding message transmission, we consider answering the following questions: what is (will be) passing through modern electronic pipelines? data, information or knowledge? data alone caries no meaning and as such represents the lowest level of abstraction. it is used as carrier for collections of numbers, characters, images, bits and bytes etc. data represents "qualitative or quantitative attributes of a variable or set of variables" (http://en.wikipedia.org/wiki/data). for data to become information they have to be interpreted in such way that it gets associated with a meaning. information bears a diversity of meanings from everyday usage to technical settings and is depending on the context of use. hence, we can see it as second level of abstract while the highest one will be knowledge. according to oxford english dictionary, knowledge is defined as: a) expertise, and skills acquired by a person through experience or education; the theoretical or practical understanding of a subject, (2) what is known in a particular field or in total; facts and information; c) awareness or familiarity gained by experience of a fact or situation. though, the defining of knowledge is subject for several ongoing philosophical debates. bounded rationality through the filter of the lisbon objectives 713 putting al three terms together we can say that information is the result of processing data such that it enlarges the knowledge of a person. therefore, trough the pipelines are passing a mix of all three, decisive being the nature of sender and receiver. information and knowledge society. information society refers to a society where information and ict are explicitly ruling. a common vision on the information society, "where everyone can create, access, utilize and share information and knowledge, enabling individuals, communities and peoples to achieve their full potential in promoting their sustainable development and improving their quality of life...", was settled by the world summit on the information society (wsis, declaration of principals, geneva phase 2003). the idea of information society is based on technological breakthroughs while the knowledge society encompasses much broader social, ethical and political dimensions. historically, the information society concept was overtaken rapidly by the idea of the knowledge society. the main issues of settling for the idea of knowledge society were that the information society was to restricted for policy and knowledge is more usable than information. "knowledge is today recognized as the object of huge economic, political and cultural stakes, to the point of justifiably qualifying the societies currently emerging." [25] a ks is "a society that creates shares and uses knowledge for the prosperity and well-being of its people". new dynamics have emerge since the "third industrial revolution" (that of new technologies) and constantly evolved from education, science and technique to cultural aspects. knowledge is central to these changes. the need for an accepted diversity means that knowledge societies will have to be societies of shared knowledge. ict, especially by the emergence of the internet to a public network, carries new opportunities for a universal access to knowledge. consequently, it is unacceptable that ict should lead to a fatalistic technological determinism. the changes in sciences and implicitly in everyday life produced a revolution in the conception of man by the means of how to regard and produce knowledge and sciences. the long term impact of ict to knowledge production is described by manuel castells as "application of such knowledge to knowledge generation and information processing/communication devices, in a cumulative feedback loop between innovation and the uses of innovation." [18] 3 rationale and approach the acknowledgement of the systemic nature of the world implies a vision of the complex reality together with there diverse interrelationships. it is necessary to adopt methods which allow working from a holistic perspective in order to address adequately complex problems. now, more then ever, in the knowledge society, any activity is affected in one way ore another by ict tools. scientists are able to create state of the art technologies designed for collaboration and transdisciplinarity. the paper proposes an approach for achieving and promoting the lisbon objectives through the ict perspective by narrowing the gape between the technologic perspective and the anthropocentric one. in this endower we consider three paradigms as main pillars for sustainable development in new societies: • open heterogeneous dynamic uncertain environment (ohdue) as natural real world environment; • bounded rationality (br) as kind of rationality in decision making; • just in time paradigm (jit) as limit of perception. we concentrate now on ict paradigms impulsed by the lisbon objectives to absorb the speed of change in/for the ks, to sustain the pillars considered above. since the required change of mentality is profound and urgent, the approach must avoid "solutions in search of a problem". hence, total pragmatism to begin with: a) to validate the approach at least in ovo a 714 r. fabian, m.j. manolescu, l. galea, g. bologa relevant, cardinal, and for the most part ill-defined real-world problem is focused on: decision making in economic modelling. b) the solution must reduce complexity: b1) cognitive; that means obvious functionality, no sophisticated concepts or instruments (agents, temporal logics, explicit uncertain information processing, computability theory and computational complexity theory, bayesian methods, certainty factors, etc.); b2) structural; that means simple mechanisms, immediate applicability in current designs; conventional software engineering. [8] it paradigms shift from "client-server" to "computing as interaction". however, since the prevalence of older paradigms are still active and the issue is central to this paper, a historical perspective is helpful: for over 40 years, determinism and bivalent logic were the pillars of computer science; likewise, algorithms were the backbone of computer programs, complying with their etymon: pro-gramma = what is written in advance. when early real-time applications (firstly, operating systems) required less autistic programs, algorithms tried to adapt accepting "unsolicited input", to fit the incipient non-determinism due to user free will. bivalence not only survived, but also, strongly backed by hardware, grew in importance. in the early 70’s, the role of bivalent logic transcended the borders of narrow data processing, penetrating "computer-aided x", where x stays for almost any intellectual activity (including decision-making). [22] [5] uncertainty as epistemic concept. the relationship between uncertainty and non-determinism is intricate but irrelevant in decision-making context, because uncertainty can appear in deterministic problems too (playing chess is a manifest example: certainty about the best move is given up to speed better said, to inexorable time restrictions). thus, research can avoid nondeterminism and focus only on uncertainty, its species and degrees. due to its vital role in any kind of decision-making, a subsection in [7] is dedicated to this topic. uncertain knowledge processing. since "in the knowledge-based process planning system, the attribute values are usually miscellaneous, heterogeneous and uncertain" and in "manufacturing process planning, experts often make decisions based on different decision thresholds under uncertainty [...], a novel approach to integrating fuzzy clustering is proposed" in [28] able to "discover association rules more effectively and practically in process planning with such thresholds". due to the characteristics of this century, when speed and efficiency are vital in all fields of interest, decision making plays an important role in almost every domain of human activity. in such tasks, alternatives are considered and there outcomes are measured until a certain threshold is reached, at which time the solution is found. human do not use explicit probabilistic/stochastic thinking when they make time constrained decisions [22] [3] [8]. undecidability. definitions on the web: a) "property of knowledge representation language that the truth of some of the true statements within that language cannot be established by any algorithm" (www.dbmi.columbia. edu/homepages/wandong/kr/krglossary.html); b) "undecidable has more than one meaning in mathematical logic: b1) a decision problem is called (recursively) undecidable if no algorithm can decide it, such as for turing’s halting problem. b2) "undecidable" is sometimes used as a synonym of "independent", where a formula in mathematical logic is independent of a logical theory if neither that formula nor its negation can be proved within the theory." (en.wikipedia.org/wiki/undecidability). obviously such definitions are too "full of mathematics" to be thoroughly comprehended in common decision-making. on the contrary, the definition "within a system, a statement is said to be undecidable when it cannot be shown to be either true or false." (ddi.cs.uni-potsdam.de/lehre/turinglectures/math notions.htm is relevant for this paper. indeed, even when the fact that accurate numeric data are hard to get is accepted, the emphasis is on approximated, predicted, evaluated by rule of thumb, or even on intrinsically fuzzy data, rather than on missing ones (lacking sensor or market information, delayed previous decisions, server crash etc.). however, decisions are difficult because a relevant event not happened yet, not because a result is imprecise. approximation. a web definition of approximation states "inexact representation of somebounded rationality through the filter of the lisbon objectives 715 thing that is still close enough to be useful. although approximation is most often applied to numbers, it is also frequently applied to such things as mathematical functions, shapes, and physical laws." (en.wikipedia.org/wiki/approximation). these definition is relevant as regards: a) both the commonsensical and the scientific (mainly, mathematical) meanings of approximation; b) its role as degree of uncertainty (as "measure" of: imprecision, difference between a reported value and a real one, possible error or range of error, etc.); c) its major function as optimization tool. indeed, approximation, seen as a "don’t care"-like uncertainty, can speed up remarkably data processing in key it subdomains (e.g., image processing, form and motion recognition, surface design, web classification, networks). [3] [22] but on the other hand: approximation is inherently unsuitable for "don’t know" like uncertainty, not even in deterministic contexts. [8] bounded rationality. the very principle of bounded rationality had been developed a halfcentury ago by the chicago school and endorsed brilliantly by simon [24]. rubinstein describes "models in which procedural aspects of decision making are explicitly included" in [23]. kahneman received a nobel prize for seeing bounded rationality as a means to improve economic modelling [16] linking it to psychological processes or to communication faults that could explain (or not) ill-applied statistical thinking in decision-making. gigerenzer proposed alternatives for decision making, based on simple heuristics [15] that "lead to better decisions than the theoretically optimal procedure" (en.wikipedia.org/wiki/bounded rationality); for instance, priority heuristics [18]). a more recent advance by edward tsang presents the rubinstein approach in a computational point of view, referred as cider (computational intelligence determines effective rationality) theory. this way of interpreting br enables to reason about economic systems when the full rationality assumption is relaxed. [26] the problem is that none of the aspects mentioned above are taken into account sufficiently, neither in economic modelling, nor in decision-making. moreover, ignoring the fact that bounded rationality is "a form of behaviour associated with uncertainty where individuals do not examine every possible option open to them" (www.pestmanagement.co.uk/lib/glossary/glossary_b.shtml), the mathematical tools still recommended for modelling economic processes (taking place in ohdue) are ill-applied when they try to deal with undecidability and complexity. bounded rationality is defined as concept that decision makers (irrespective of their level of intelligence) have to work under three unavoidable constraints: (a) only limited, often unreliable, information is available regarding possible alternatives and their consequences; (b) human mind has only limited capacity to evaluate and process the information that is available, and (c) only a limited amount of time is available to make a decision. therefore even individuals who intend to make rational choices are bound to make satisfying (rather than maximizing or optimizing) choices in complex situations. consequently, considering bounded rationality in modelling, would have two convergent, major beneficial effects: reducing attempts in the counterproductive direction (i.e., treating undecidability) and promoting efforts in the valuable course (i.e., offering "just in time" answers). thus, the paradox of approximation complexity in modelling would fade away. inaccurate complexity management. due the growing information complexity encountered in every research fields, machine learning techniques gain increasing popularity. software tools (like weka and rapidminer) offer state of the art feature selection techniques and a large number of similarity functions allowing clustering and instance based learning techniques, respectively to deal with height dimensional domains. when complexity is overwhelming (requiring unaffordable resources), instead of simplifying the model, subsymbolic paradigms mainly artificial neural networks (ann) or evolutionary algorithms (ea) are used, ignoring their general weaknesses (no explanations, weak convergence, poor reliability because of inadequate exploitation/exploration ratio) or specific ones: need for training example sets (for ann) or endogamic limitation (for ea). for instance, what responsible human will make critical (e.g., macroeconomic) decisions 716 r. fabian, m.j. manolescu, l. galea, g. bologa without any justification? modern artificial intelligence. in an historical early phase of intelligent system development, when expert systems based on the newell-simon hypothesis ("a physical symbol system has the necessary and sufficient means for general intelligent action") where "on vogue", they start to disappoint in all nuances of the world. a prompt reaction upon the limits of the symbolic paradigm came timely by replacing "gofa" (good old-fashioned ai) with "bic" (biologically inspired computing). bringing all paradigms together, "modern artificial interlace" means now agents [17] [29] [1]. by a formal standard [14] the agent is now acknowledged as process. the user impact of agent technology through icts [6] [5] breaks up from reversing the three questions: "what for?" the aim; "what?" the architecture; "how?" the structure. redressing the balance from an end user viewpoint to a user centered one, the three questions become a different interpretation: "what for?" to get easy and fast help; "what?" new applications domains become affordable; "how?" by imposing user needs and not ict ones. "technological determinism" for "technology pushed" users is not distinctive for agents, it is just a matter of impact measurement. 4 conclusions and future work in the last century we witnessed an explosive growth in the number and diversity of networked devices and portals as main communication channels bringing a height degree of mobility, heterogeneity, and interactions among devices connected to global networks. knowledge is the engine for sustainable growth. in a fast-changing world, what makes the difference is education and research, innovation and creativity. building on its strengths in technology and knowledge, europe should tap fully the potential of the digital economy. economic models cannot anymore avoid the consequences of bounded rationality. it has to accept undecidability as concept and to resort to approximation as means. to be relevant, the endower should always address real world problems and not artificially models born and living in abstracted laboratory simulated conditions. software agents will be seamlessly integrated in our everyday life and therefore they have to constantly sense and react to the environment. great opportunities for agent oriented software architecture are opened by the dynamic and heterogeneous environmental character. negative impacts of icts are partially rooted in the uncertainty coming from the inability to assimilate the complexity, diversity, magnitude and space of the ict world. positive impacts increase the possibility of communication by significantly widening the filed of existing applications and facilitates the emergence of new ones. in this context, the negative or positive impact is wrongly attributed to the technology itself, and rather to there applications. the gape between the user expectations and the technological offer is deepened due insufficient innovation and lake in use of new agent oriented potential. the future research will focus on agent oriented software engineering as powerful just in time solution in an environment striving toward a knowledge society, where traditional rationality has to be replaced (ore at least heavily supported) by bounded rational approaches, in order to be able dealing with the increasing complexity of real life scenarios. bibliography [1] gentlink iii. agent based computing. agentlink roadmap: overview and consultation report. university of southampton, sept. 2005. http://www.agentlink.org/roadmap/al3rm.pdf bounded rationality through the filter of the lisbon objectives 717 [2] alain ambrosi, valérie peugeot and daniel pimienta, world matters: multicultural perspectives on information societies, sally burch the information society / the knowledge society, c & f éditions, 2005, isbn 2-915825-03-3, http://vecam.org/article517.html [3] bă rbat boldur e., fabian ralf, brumar cristina, muntean roxana, bounded rationality and approximation in modern artificial intelligence, proceedings of the international workshop new approaches, algorithm and advanced computational techniques in approximation theory and its applications, sibiu, romania, september, 2007, pg. 64 67, lucian blaga university press, isbn 978 973 739 949 2 [4] bărbat, b.e. from e-learning to e-nursing, applying non-algorithmic paths. medinf 2007 workshop: e-learning aspects in medicine and nursing. 29th international conference of the romanian medical informatics society, sibiu, 2007. 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[29] zambonelli, f., a. omicini. challenges and research directions in agent-oriented software engineering. autonomous agents and multi-agent systems, 9, 253-283, kluwer academic publishers, 2004. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 4, pp. 348-348 an lmi technique for the global stabilization of nonlinear polynomial systems m. moez belhaouane, r. mtar, h. belkhiria ayadi, n. benhadj braiek mohamed moez belhaouane, riadh mtar, hela belkhiria ayadi, naceur benhadj braiek laboratoire d’etude et commande automatique de processus lecap ecole polytechnique de tunisie (ept), bp.743, 2078 la marsa, tunis, tunisie. e-mail: {moez.belhaouane, riadh.mtar, hela.ayadi, naceur.benhadj}@ept.rnu.tn abstract: this paper deals with the global asymptotic stabilization of nonlinear polynomial systems within the framework of linear matrix inequalities (lmis). by employing the well-known lyapunov stability direct method and the kronecker product properties, we develop a technique of designing a state feedback control law which stabilizes quadratically the studied systems. our main goal is to derive sufficient lmi stabilization conditions which resolution yields a stabilizing control law of polynomial systems. keywords: nonlinear polynomial systems, lyapunov method, global stabilization, kronecker product, lmi approach. 1 introduction the control theoretician role may be viewed as one of developing methods that allows the control engineer to make which seems relatively natural and physically motivated [1, 2]. generally the main and first object in the control theory is to ensure the stability and the convergence of the considered system. in this context, the problem of stabilization of nonlinear systems has received a great deal of attention and several methods have been proposed in the literature [3, 4, 5, 6]. however, the proposed approaches remain restrictive to particular classes of nonlinear models, and there is no general method for the analysis or synthesis of general nonlinear systems. that is the reason of continuing research on study and control of nonlinear systems. the polynomial systems constitute an important class of nonlinear systems which has the advantage to describe the dynamical behavior of a large set of processes as electrical machines and robot manipulators and has also the ability to approach any analytical nonlinear system, since any analytical nonlinear function can be approximated by a polynomial expansion. let’us note that a lot of works have considered the modeling, analysis and control of the polynomial systems [7, 8, 9, 10, 11, 12, 13, 14, 15, 16]. the main key of these developments is the description of the polynomial system by using the kronecker power of the state vector [17]. in the other hand, linear matrix inequalities (lmis) have emerged as a powerful formulation and design technique for a variety of control problems [18, 19, 20]. since solving lmi’s is a convex optimization problem, such formulation offer a numerically tractable means of attacked problems that lack an analytical solution. besides, efficient interior-point algorithms are now available to solve the generic lmi problems. they are applied to several important process control applications including control structure selection, robust controller analysis and design, and optimal design of experiments [21, 22, 23]. consequently, reducing a control design problem to an lmi can be considered as a practical solution [24]. the contribution of the present paper consists on the use of the lyapunov method with a quadratic candidate function, to derive a practice sufficient condition ensuring the global asymptotic stabilization of the original equilibrium of a polynomial system. this condition is then reformulated in the form of an copyright c© 2006-2009 by ccc publications 336 m. moez belhaouane, r. mtar, h. belkhiria ayadi, n. benhadj braiek lmi feasibility problem which can be solved using the numerical software as matlab. this paper is organized as follows: in section 2 the description of the studied systems and necessary mathematical notations are introduced. then, in the next section, the problem of stabilizing control law synthesis of polynomial systems is investigated. the section 4 proposes an lmi formulation of the obtained stabilization condition. an illustrative example is reported in section 5 to implement the developed approach. 2 studied polynomial systems and mathematical notations 2.1 studied polynomial systems the studied nonlinear polynomial systems are described by the following state equation: ẋ = f (x ) + gu, (1) where f (x ) is a vectorial polynomial function of x . f (x ) = r∑ i= fix [i] = r∑ i= f̃ix̃ [i], (2) with x = [x, . . . , xn]t ∈rn, { x [] =  x [i] = x [i−] ⊗x = x ⊗x [i−] f or i ≥ , (3) ⊗ is the symbol of the kronecker product [17]. x̃ [i]i=,...,r ∈ rni where ni = ( n + i −  i ) , is the nun-redundant kronecker power of the state vector x defined as x̃ [] = x [] = x , ∀ i ≥ , x̃ [i] = [xi, xi− x, ..., xi− xn, ..., xi− xn, ..., xi− x, ..., xin]t , (4) i.e., the components of x̃ [i] are the same that those of x [i] with omission of the repeated terms. fi,i=,...,r ∈rn×n i (resp. f̃i ∈rn×ni ) are constant matrices. the polynomial order r is considered odd: r = s − , with s ∈n∗. u ∈rm is the input vector and g is a constant (n×m) matrix. 2.2 notations in this section, we introduce some useful notations and needed rules and functions. let the matrices and vectors of the following dimensions a(p×q), b(r ×s), c(q× f ), x (n×) ∈rn, y (m×) ∈rm. (i) we consider the following notations: in : (n×n) identity matrix; n×m : (n×m) zero matrix; : zero matrix of convenient dimension; at : transpose of matrix a; a > (a ≥ ) : symmetric positive definite (semi-definite) matrix; eqk : q dimensional unit vector which has 1 in the k th element and zero elsewhere. an lmi technique for the global stabilization of nonlinear polynomial systems 337 (ii) the relation between the redundant and the nun-redundant kronecker power of the state vector x can be stated as follows { ∀i ∈n ∃ ! ti ∈ rn i×ni , x [i] = tix̃ [i], (5) a procedure of the determination of the matrix ti is given in [25]. (iii) the permutation matrix denoted un×m is defined as un×m = n∑ i= m∑ k= ( eni ·(emk )t ) ⊗ ( emk ·(eni )t ) . (6) this matrix is square (nm × nm) and has precisely a single 1 in each row and in each column. among the main properties of this matrix presented in [17], [11], we recall the following useful ones (b⊗a) = ur×p(a⊗b)uq×s, (7) (x ⊗y ) = un×m(y ⊗x ), (8) ∀i ≤ k x [k] = uni×nk−i x [k]. (9) (iv) an important vector valued function of matrix denoted vec(.) was defined in [17] as follows a = [ c c ... cq ] ∈rp×q, where ∀i ∈ {, ..., q} , ci ∈rp are the columns of a vec(a) = [ ct c t  ... c t q ]t ∈rpq. we recall the following useful rules [17] of this function vec(bac) = (ct ⊗b)vec(a), (10) vec(at ) = up×qvec(a). (11) (v) a special function mat(n,m) (.) can be defined as follows if v is a vector of dimension p = n.m then m = mat(n,m)(v ) is the (n × m) matrix verifying v = vec(m). (vi) for a polynomial vectorial function a (x ) = r∑ i= aix [i], (12) where x ∈rn and ai are (n×ni) constant matrices, we define the (υ ×υ) matrix m(a) as m(a) =   m(a) m(a)  . . .   m(a) . . . . . . ... ... . . . . . . . . .  ... . . . ms−,s−(as−) ms−,s(as−)  . . . . . .  ms,s(as−)   , (13) 338 m. moez belhaouane, r. mtar, h. belkhiria ayadi, n. benhadj braiek with υ = n + n + ... + ns. • for j = , ..., s m j, j(a j−) =   mat(n j−,n j ) ( at j− ) mat(n j−,n j ) ( at j− ) ... mat(n j−,n j ) ( ant j− )   , (14) • for j = , ..., s −  m j, j+(a j) =   mat(n j−,n j ) ( at j ) mat(n j−,n j ) ( at j ) ... mat(n j−,n j ) ( ant j )   , (15) where aik is the i th row of the matrix ak ak = [ atk a t k ... a nt k ]t . (16) (vii) we introduce the matrix r defined by r = τ +[] ·u·h ·τ, (17) where τ = diag ( ti,i=,...,s ) , (18) with τ + is the moore-penrose pseudo-inverse of τ and τ +[]  = τ +  ⊗τ + . τ = diag ( tj, j=,...,s ) , (19) u = diag ( uni,i=,...,s×η ) , (20) h =   iη  η×η iη η×(η+η) iη ... . . . ηs×(η+η+...+ηs−) iηs   , (21) for j = , ....., s : η j = n j · ( s∑ i= ni ) . we note γ the matrix defined by γ = (iη  + uη×η ) ( r+t rt − iη  ) , (22) an lmi technique for the global stabilization of nonlinear polynomial systems 339 with η = s∑ j= n j = s∑ j= ( n + j −  j ) and r+ is the moore-penrose pseudo-inverse of r. β = rank(γ ) (23) and ci, i=,...,β are β linearly independent columns of γ . (iix) for a (n×l) matrix φ , we define ds(φ ) the (υ ×υ ) matrix defined as ds(φ ) =   φ  φ ⊗in . . .  φ ⊗ins−   . (24) in the case where the matrix φ is square (l = n), the matrix ds(φ ) is also square (υ × υ), with υ is defined in (vi). as well, if φ is square and is symmetric positive definite, then so is ds(φ ). 3 the proposed global stabilization condition of controlled polynomial systems we consider the polynomial nonlinear systems defined by the equation (1). our purpose is to determine a polynomial feedback control law u = k(x ) = r∑ i= kix [i], (25) with ki,i=,...,r are constant gains matrices which stabilizes asymptotically and globally the equilibrium (x = ) of the considered system. applying this control law to the open-loop system (1), one obtains the closed-loop system ẋ = a(x ) = ( f + gk)(x ), = r∑ i= aix [i], (26) where ai = fi + gki. (27) using a quadratic lyapunov function v (x ) and computing the derivative v̇ (x ), lead to the sufficient condition of the global asymptotic stabilization of the polynomial system, given by the following theorem 1. theorem 1. the nonlinear polynomial system defined by the equation (1) is globally stabilized by the control law (25), if there exist • an (n×n)-symmetric positive definite matrix p; • arbitrary parameters µi,i=,...,β ∈r; • gain matrices ki,i=,...,r; 340 m. moez belhaouane, r. mtar, h. belkhiria ayadi, n. benhadj braiek such that the (η ×η) symmetric matrix q defined by q = τ t [ds(p)m( f ) + m( f ) t ds(p)]τ + τ t [ds(p)gm(k) + (ds(p)gm(k)) t ]τ + β∑ i= µimat(η,η)(ci), (28) be negative definite. where β and ci,i=,...,β are defined in (23). proof. consider the quadratic lyapunov function v (x ) = x t px , (29) differentiating v (x ) along the trajectory of the system (26), we obtains v̇ (x ) = r∑ k= (x t pakx [k] + x [k] t atk px ), =  r∑ k= x t pakx [k]. (30) using the rule of the vec-function (10), the relation (30) can be written as v̇ (x ) =  r∑ k= v tk x [k+], (31) where vk = vec(pak). (32) we can write r=s−∑ k= v tk x [k+] = s−∑ j= v t jx [ j+] + s∑ j= v t j−x [ j], (33) using the mat-function defined in section 2, one has v̇ (x ) = [ s−∑ j= x [ j] t mat(n j ,n j+)(v t  j)x [ j+] + s∑ j= x [ j] t mat(n j ,n j )(v t  j−)x [ j]]. (34) applying the following lemma [11] lemma 2. consider a (n×nk) matrix a (k ∈n) and a (n×n) matrix p. let i and j two integers verifying i + j = k +  and i ≥ . then mat(ni, n j )(vec(pa)) = uni−×n(p⊗ini− ).m, with m =   mat(ni−,n j ) ( at ) mat(ni−,n j ) ( at ) ... mat(ni−,n j ) ( ant )   , where ai denotes the ith row of the matrix a a = [ at at ... ant ]t an lmi technique for the global stabilization of nonlinear polynomial systems 341 leads to the following relations mat(n j ,n j+)(v t  j) = un j−×n(p⊗in j− )m j, j+(a j), (35) mat(n j ,n j )(v t  j−) = un j−×n(p⊗in j− )m j, j(a j−), (36) where m j, j+(a j) and m j, j(a j−) are defined respectively in (15) and (14) and un j−×n is mentioned in (20). using the results (35) and (36), the equality (34) can be expressed as v̇ (x ) = [ s−∑ j= x [ j] t un j−×n(p⊗in j− )m j, j+(a j)x [ j+] + s∑ j= x [ j] t un j−×n(p⊗in j− )m j, j(a j−)x [ j]], (37) by means of the relation (9), one obtains v̇ (x ) = [ s−∑ j= x [ j] t (p⊗in j− )m j, j+(a j)x [ j+] + s∑ j= x [ j] t (p⊗in j− )m j, j(a j−)x [ j]]. (38) consequently, we obtain v̇ (x ) = xt ds(p)m(a)x, = xt (ds(p)m(a) + m(a)t ds(p))x, (39) with x = [ x t x [] t ··· x [s]t ]t (40) ds(p) and m(a) are defined respectively in (24) and (13). using the nun-redundant kronecker product power form, the vector x can be written as x = τx̃, (41) where x̃ = [ x̃ t x̃ [] t ··· x̃ [s]t ]t ∈rη , η = s∑ j= n j and τ is defined in (18). then v̇ (x ) can be written in the following form v̇ (x ) = x̃t τ t (ds(p)m(a) + m(a) t ds(p))τx̃, (42) a sufficient condition of the global asymptotic stability of the equilibrium (x = ) is that the quadratic form v̇ (x ) is negative definite. this condition can be ensured if there exists a symmetric negative definite q ∈rη×η such that x̃t τ t (ds(p)m(a) + m(a) t ds(p))τx̃ = x̃t qx̃, (43) using the vec-function, the equality (43) can be expressed as vect ( q − τ t (ds(p)m(a) + m(a) t ds(p))τ ) x̃[] = . (44) but, it can be easily checked that x̃[] can be written as x̃[] = rx̃, (45) 342 m. moez belhaouane, r. mtar, h. belkhiria ayadi, n. benhadj braiek where x̃ = [ x̃[]t . . . x̃[s+]t x̃[s+]t . . . x̃[s]t ]t , (46) and r is the matrix defined in (17). the proof of the relation (45) is given in [11]. therefore the equality (44) yields the following equation rt vec(s) = , (47) with: s = q − τ t ( ds(p)m(a) + m(a)t ds(p) ) τ. the η -vector vec(s) solution of (47) can be expressed as vec(s) = ( r+t rt − iη  ) y, (48) where y is an arbitrary vector of rη  . the matrix s is symmetric since q is symmetric, then we can write s =   (s + st ), (49) and using the property (11) yields vec(s) =   (iη  + uη×η )vec(s) = β∑ i= µici, (50) where • β = rank [ (iη  + uη×η ) ( r+t rt − iη  )] , • ci,i=,...,β are β linearly independent columns of (iη  + uη×η ) ( r+t rt − iη  ) , (51) • µi,i=,...,β are arbitrary values. consequently, the symmetric matrix q verifying (47) is of the following form q = τ t (ds(p)m(a) + m(a) t ds(p))τ + β∑ i= µimat(η,η)(ci). (52) according to (26) and the following lemma [10] lemma 3. let g ∈ rn×m, k(.) a polynomial vectorial function defined in (25) and g.k(.) the resultant product of g by k(.), then one has m(g.k) = gm(k), where g = ds(g) and m(.) the matrix function defined in (13). thus, we can write m(a) = m( f + gk) = m( f ) + gm(k), (53) finally, we obtain the following quadratic form of the symmetric matrix q q = τ t [ds(p)m( f ) + m( f ) t ds(p)]τ + τ t [ds(p)gm(k) + m(k) t gt ds(p)]τ + β∑ i= µimat(η,η)(ci). (54) if q is negative definite, then the derivative v̇ (x ) is negative definite. which ends the proof. an lmi technique for the global stabilization of nonlinear polynomial systems 343 4 stabilizing control synthesis using the lmi approach in this section we show how the stabilization problem stated by the theorem 1 can be formulated as an lmi feasibility problem. let recall that our main problem is to find • gain matrices ki,i=,...,r; • a (n×n) matrix p; • real parameters µi,i=,...,β ; such that p > , (55) τ t [ds(p)m( f ) + m( f ) t ds(p)]τ + τ t [ds(p)gm(k) + m(k) t gt ds(p)]τ + β∑ i= µimat(η,η)(ci) < . (56) note that this problem is nonlinear with respect of the unknown parameters p,ki and µi, since the inequality (56) is bilinear on (p, ki). to overcome this problem we make use of the known schur’s complement [18] and we exploit the separation lemma [26]. in this sequel we transform the bmi problem into lmi problem as it is shown in the following development. making use of the following separation lemma [26] lemma 4. for any matrices a and b with appropriate dimensions and for any positive scalar ε > , one has: at b + bt a ≤ ε at a + ε −bt b, one obtains q ≤ τ t [ds(p)m( f ) + m( f )t ds(p)]τ + β∑ i= µimat(η,η)(ci) +γ τ t ds(p) t ds(p)τ + γ −τ t m(k) t gt gm(k)τ, (57) with γ > . then, to ensure that the matrix q is negative definite, it is sufficient to have τ t [ds(p)m( f ) + m( f ) t ds(p)]τ + β∑ i= µimat(η,η)(ci) −τ t ds(p) t (−γ i)ds(p)τ − τ t m(k) t gt (−γ −i)gm(k)τ < . (58) using the generalized schur’s complement, the inequality (58) is equivalent to   τ t (ds(p)m( f ) + m( f ) t ds(p))τ + β∑ i= µimat(η,η)(ci) (ds(p)τ)t (gm(k)τ)t ds(p)τ −γ −i  gm(k)τ  −γ i   < , (59) when pre-and post-multiplying the inequality (59) by ξ = diag(i, i, γ −i), we get   τ t (ds(p)m( f ) + m( f ) t ds(p))τ + β∑ i= µimat(η,η)(ci) (ds(p)τ)t (gw(k)τ)t ds(p)τ −γ −i  gw(k)τ  −γ −i   < , (60) with w(k) = γ −m(k). this new inequality is linear on the decision variables, and then we can state the following result. 344 m. moez belhaouane, r. mtar, h. belkhiria ayadi, n. benhadj braiek theorem 5. the equilibrium (x = ) of the system (1) is globally asymptotically stabilizable if there exist • a (n×n)-symmetric positive definite matrix p ; • arbitrary parameters µi,i=,...,β ∈r ; • gain matrices ki,i=,...,r; • a real γ >  ; such that p > , (61) and   τ t (ds(p)m( f ) + m( f ) t ds(p))τ + β∑ i= µimat(η,η)(ci) (ds(p)τ)t (gw(k)τ)t ds(p)τ −γ −i  gw(k)τ  −γ −i   < . (62) thus, a stabilizing control law (25) for the considered polynomial system (1) can be characterized by applying the following procedure 1. solve the lmi feasibility problem i.e., find the matrices ds(p), w(k) and the parameters µi and γ such that the inequalities (61), (62) are verified. 2. extract the gain matrices ki from the relation m(k) = γw(k). this optimization problem can be carried out using matlab software. to provide the effectiveness of the proposed approach, we consider the following numerical example. 5 illustrative example our aim in this section is to apply the proposed approach for the global stabilization of the following polynomial system { ẋ = −x + x + x + xx − x   + xx − xx   + x  , ẋ = −x + .x − x − .xx − x   − xx + .xx   − x   + u. (63) using the kronecker product, this system can be described by the following compact state equation ẋ = fx + fx [] + fx [] + gu, (64) with f = [ −  − . ] f = [     − −.   ] f = [ −   −     − −  .    − ] and g = [   ] . we are interested with the stabilization of the origin equilibrium (x = ) of the system (64). let us note that the uncontrolled (u = ) non linear system is unstable since the matrix f has an unstable eigenvalue. an lmi technique for the global stabilization of nonlinear polynomial systems 345 solving the optimization problem formulated by theorem 2, we obtain    µ = −. µ = . µ = . ; p = [ . . . . ] ; γ = .. the searched gain matrices, extracted from m(k), are given by k = [ −. −. ] k = [ . .   ] k = [ −. −. −. −. . −. . . ] then a global stabilizing control law can be characterized for the studied system using the previous developed method. this control law can be expressed as u = kx + kx [] + kx []. (65) the figure 1 shows the behavior of the state variables x(t) and x(t) of the controlled system from initial conditions which were taken sufficiently far from the initial conditions (x() = −, x() = ). it appears that the state variables converge into the origin point which confirm the asymptotic stability of the controlled system. 0 0.2 0.4 0.6 0.8 1 −10 −5 0 5 10 15 t(s) v ar ia bl es x 1 an d x2 dynamic behaviour of the state variable x1 dynamic behaviour of the state variable x2 figure 1: closed-loop responses of the system (64) with the control law (65). 6 conclusion in this paper, an original technique has been proposed for the global and asymptotic stabilization of the nonlinear polynomial systems. this new stabilizing approach is based on the lyapunov direct method and elaborated algebraic developments using the kronecker product properties. this development has allowed the formulation of the system stabilization condition as an lmi feasibility problem, which resolution leads to a polynomial control law ensuring the quadratic stability in the whole state space of the 346 m. moez belhaouane, r. mtar, h. belkhiria ayadi, n. benhadj braiek considered system. further works, will consider extension of these results to the robust stabilization of polynomial uncertain systems. bibliography [1] j. c. doyle, “a review of µ for case studies in robust control,” ifac, pp. 395–402, 1987. 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[24] y. nesterov and a.nemirovsky, “interior point polynomial methods in convex programming: theory and applications,” siam, pp. 101–127, 1994. [25] n. benhadj braiek and f. rotella, “logid: a nonlinear systems identification software,” modelling and simulation of systems, scientific publishing co., pp. 211–218, 1990. [26] zhou and khargonedkar, “robust stabilization of linear systems with norm-bounded time-varying uncertainty,” sys. contr. letters, vol. 10, pp. 17–20, 1988. mohamed moez belhaouane was born in tunis in 1980. he received the electrical engineering bachelor and the master degree in automatic control from école supérieure des sciences et techniques de tunis (esstt), in 2003 and 2005, respectively. currently, he is research member of the processes study and automatic control laboratory (lecap) in the polytechnic school of tunisia and he is working toward ph.d. degree in electrical engineering. his research interests include analysis and control of nonlinear systems, polynomial systems and robust control. riadh mtar was born in tunis in 1974. he received his master degree of systems analysis and numerical treatment from école nationale d’ingénieurs de tunis (enit), in 2005. he is currently an contractual assistant in école supérieure de commerce (esc) and research member of the processes study and automatic control laboratory (lecap) in the polytechnic school of tunisia. actually, he is preparing his ph.d. dissertation in electrical engineering from école nationale d’ingénieurs de tunis (enit). his research interests include analysis and control of nonlinear polynomial discret systems. hela belkhiria ayadi received her engineer diplomas from the école nationale d’ingénieurs de monastir (enim) in 1997 and master degree of automatic control from école supérieure des sciences et techniques de tunis (esstt), in 1998. she completed her ph.d. in electrical engineering in 2004 from école nationale d’ingénieurs de tunis (enit). she is now an assistant professor in école supérieure des sciences et techniques de tunis (esstt) and research member of the processes study and automatic control laboratory (lecap) in the polytechnic school of tunisia. her research interests include robust analysis and control of nonlinear uncertain systems. 348 m. moez belhaouane, r. mtar, h. belkhiria ayadi, n. benhadj braiek naceur benhadj braiek was born in mahdia, tunisia, in 1963. he obtained the master of electrical engineers and the master of systems analysis and numerical processing, both from école nationale d’ingénieurs de tunis (enit) in 1987, the master of automatic control from institut industriel de nord (école centrale de lille) in 1988, the ph.d. degree in automatic control from université des sciences et techniques de lille, france, 1990, and the doctorat d’état in electrical engineering in 1995 from école nationale d’ingénieurs de tunis (enit). now, he is professor of electrical engineering at the university of tunis école supérieure des sciences et techniques de tunis (esstt). he is also director of the processes study and automatic control laboratory (lecap) in the polytechnic school of tunisia. his domain of interest is related to the modeling, analysis and control of nonlinear systems with applications on electrical processes. international journal of computers, communications & control vol. i (2006), no. 4, pp. 21-34 on guaranteed global exponential stability of polynomial singularly perturbed control systems hajer bouzaouache, naceur benhadj braiek abstract: the problem of global exponential stability for a class of nonlinear singularly perturbed systems is examined in this paper. the stability analysis is based on the use of basic results of integral manifold of nonlinear singularly perturbed systems, the composite lyapunov method and the notations and properties of tensoriel algebra. some of the derived results are presented as linear matrix inequalities (lmis) feasibility tests. moreover, we pointed out that if the global exponential stability of the reduced order subsystem is established this is equivalent to guarantee the global exponential stability of the original high order closed loop system. an upper bound ε1 of the small parameter ε , can also be determined up to which established stability conditions via lmi’s are maintained verified. a numerical example is given to illustrate the proposed approach. keywords: nonlinear singularly perturbed system, integral manifold, lyapunov stability, kronecker product, linear matrix inequalities (lmis). 1 introduction stability analysis and control of nonlinear singularly perturbed systems have been widely studied in the literature [2], [6], [7], [12], [13]. in a two time scale framework, the stability study of the controlled systems using the lyapunov stability method [15] and the integral manifold approach as a means for the control of nonlinear systems based on the singular perturbation method have been developed in recent years [10], [11], [14], [16], [17]. the approaches proposed in this direction. differ by imposing different conditions on the smoothness properties of the used functions, different assumptions and different classes of lyapunov functions. in this paper, we are concerned with the global exponential stability of polynomial singularly perturbed systems when the chosen design manifold is an exact integral one. further extension of some previous results [11], [17] are suggested and leads to effective global exponential stability conditions via lmis [8] which can be easily verified when using lmi toolbox of matlab. the contribution of the present paper is based, on one hand, on the use of the lyapunov method which is a powerful tool for combined controller design and stability analysis, the definition of appropriate lyapunov functions for the reduced systems and the corrected system via the integral manifold approach and on the other hand, on the notations and properties of the tensoriel product [9]. our paper is organized as follows: in section 2 we present the considered description of the studied systems which allows important algebraic manipulations and some results from the literature on integral manifolds for nonlinear singularly perturbed systems. some useful notations and needed assumptions are introduced in section 3. exploiting the stability statements about singularly perturbed systems possessing integral manifolds and using the composite lyapunov technique, we propose in section 4 an appropriate control law that insures the existence of an attractive integral manifold and furthermore insures stability of the studied systems when the dynamics are restrictive to the integral manifold. the stability results proving the global exponential stability of polynomial singularly perturbed systems are also given and presented as linear matrix inequalities feasibility tests. finally an illustrative example is treated and some conclusions are drawn. copyright c© 2006 by ccc publications 22 hajer bouzaouache, naceur benhadj braiek 2 studied systems and integral manifolds the class of systems to be considered in this paper are described by the following state equations: { ẋ = f (x, z) (a) ε ż = g(x, z) + l(x, z)u (b) (1) where x ∈ rn1 is the state of the slow subsystem (1-a), z ∈ rn2 is the state of the fast subsystem, u ∈ rpis the input control. ε is a small positive parameter. f , g and l are analytic vector fields which are sufficiently many times continuously differentiable functions of their arguments. using the kronecker power of vectors, these functions can be written in the polynomial form as [7]:    f (x, z, ε) = r ∑ i=1 i+1 ∑ j=1 fi jx [i+1− j] ⊗z[ j−1] g(x, z) = r ∑ i=1 i+1 ∑ j=1 gi jx [i+1− j] ⊗z[ j−1] l(x, z) = r ∑ i=1 i+1 ∑ j=1 li j(im ⊗(x[i+1− j] ⊗z[ j−1])) (2) in general the stability of the reduced order subsystems for a class of nonlinear singularly perturbed systems cannot guarantee the stability of the original full order system even with the additional stability of the boundary layer subsystem but when an attractive manifold is designed, the stability problem of the original system reduces to a stability problem of a low dimensional system on the manifold. subsequently, in the context of control system design, our goal is to find an appropriate control law that insures the existence of an attractive integral manifold and furthermore insures stability of the studied systems (1) when the dynamics are restrictive to the integral manifold. the basic ideas of exploiting the integral manifold method are: – if an integral manifold σ of systems described by (1) is established, so that if the initial states start on σ, the trajectory of the system remains on σ thereafter. – when restricted to the integral manifold σ, the dynamics of the system should insure stability of the equilibrium. – the integral manifold should be attractive so that if the initial conditions are off σ, the solution trajectory asymptotically converges to σ. according to these important issues of the integral manifold method, let’s present the definition, and the properties of integral manifold of nonlinear systems. definition 1. [16] the set σ ⊂ r×rn is said to be an integral manifold (invariant manifold) for the differential equation: ẋ = n(t, x ), x , n ∈rn if for (t0, x0) ∈ σ, the solution (t, x (t)), x (t0) = x0, is in σ for t ∈r. if (t, x (t)) ∈ σ for only a finite interval of time, then σ is said to be a local integral manifold. lemma 1. [11] consider the following system: { ẋ = f (t, x, y, ε) ε ẏ = g(t, x, y, ε) (3) x, f ∈rn, y, g ∈rm,t ∈r, ε a small parameter. and suppose the following hypotheses hold: – the algebraic equation g(t, x, y, 0) = 0 has an isolated solution y = h0(t, x), ∀t ∈r, ∀x ∈ bx – the functions f , g, and h0 are twice continuously differentiable (∈c2) ∀t ∈r,∀x ∈ bx. ∀ε ∈ [0, ε0) and for ‖y−h0(t, x)‖ ≤ ϕ̄y where ε0 and ϕ̄y are positive real constants. on guaranteed global exponential stability of polynomial singularly perturbed control systems 23 – the eigenvalues λi = λi(t, x), i = 1, 2, . . . , m of the matrix z(t, x) := ( ∂ g ∂ y ) (t, x, h0(t, x), 0) satisfy the inequality re [λi] ≤ −2β < 0 ∀t ∈r,∀x ∈ bx (4) then there exists ε ≤ ε1 such that ∀ε ∈ [0, ε1), the singularly perturbed system (3) has an m-dimensional local integral manifold σε : y = h0(t, x) + h(t, x, ε) = h(t, x, ε) (5) where h(t, x, ε) is defined for all x ∈ bx and ε ≤ ε1 and is continuously differentiable (∈ c1) the function h(t, x, ε) ∈ c1 satisfies the so-called manifold equation : ε ∂ h ∂ t + ε ∂ h ∂ x f (t, x, h, ε)g(t, x, h, ε) (6) which is obtained by substituting y by h in equation (3). on this manifold, the flow of systems (3) is governed by the n-dimensional reduced system ẋ = f (t, x, h(t, x, ε), ε) (7) furthermore, if for x ∈ bx and p integer we have f (t, x, y, ε) ∈ c p+1, g(t, x, y, ε) ∈ c p+2 and h0(t, x) ∈ c p+2 then h ∈ c p 3 useful notations and assumptions in our study we make use of the following lemma 2 and assumptions 1-2. the lemma 2 is concerned with a kronecker transformation of vectors. more properties of the kronecker product are given in the appendix. lemma 2. [6] given x = ( x z ) ∈ rn; x ∈ rn1 , z ∈ rn2 and n = n1 + n2 there exists a matrix (i) m ∈ rn i×ni making possible a transformation which introduces the change of coordinates that forms the new following kronecker power of vector: (̂ x z )[i] =   x[i] : x[i− j] ⊗z[ j] : z[i]   ∈rni such that x [i] = ( x z )[i] = (i) m (̂ x z )[i] (8) with    (i) m = ( (i−1) m ⊗in) (i) u (i) v (1) m = in ∣∣∣∣∣∣ 1 ≤ j ≤ (i−1) ni = i ∑ j=0 n i− j 1 n j 2 (9) 24 hajer bouzaouache, naceur benhadj braiek and (i) v =   ini1 a b 0 . . . 0 ab . . . ini2   ︸ ︷︷ ︸∣∣∣∣ (i+1) blocs columns2i blocs rows (i) u =   u n (i−1) 1 ×n . . . 0 u n (i−k) 1 n (k−1) 2 ×n 0 . . . u n (i−1) 2 ×n   f or j ∈ {2, ...i} { a = u n2×n(i− j+1)1 ⊗i n ( j−2) 2 b = i n (i− j+1) 1 ×n ( j−1) 2 (10) the permutation matrix denoted un×m is defined in [9] un×m = n ∑ i=1 m ∑ k=1 ( ei (n) etk (m) )⊗(etk (m) ei (n) ) (11) this matrix is square (nm×nm) and has precisely a single 1 in each row and in each column. to clarify the meaning of (i) m, consider the following example: for n = 3 (n1 = 2, n2 = 1), i = 2; x [2] = ( x z )[2] = (2) m (̂ x z )[2] x [2] = ( x z )[2] =   1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1     x[2] x⊗z z[2]   assumption 1. there exists a continuously differentiable function v1(t, x) : r×rn1 →r+ such that the following inequalities hold: ∀t ∈r, x ∈rn1 α1 ‖x‖2 ≤ v1(t, x) ≤ α2 ‖x‖2 (12) ‖∇xv1(t, x)‖ ≤ α3 ‖x‖2 (13) on guaranteed global exponential stability of polynomial singularly perturbed control systems 25 ∇tv1(t, x) + (∇xv1(t, x))t f (t, x, 0, 0) ≤ −2γ1v1 (14) where α1, α2, α3, and γ1 are positive constants. assumption 2. there exists a continuously differentiable function v2(t, z) : r×rn2 →r+ such that the following inequalities hold: ∀t ∈r, z ∈rn2 β1 ‖z‖2 ≤ v2(t, z) ≤ β2 ‖z‖2 (15) dv2(t, z) dt ≤ −2 ε γ2v2(t, z) (16) (17) where β1, β2 and γ2 are positive constants. 4 main results given the system (1), (2), we have to determine an adequate feedback control u that, starting from any initial states, will attract exponentially the trajectories of the closed loop system along the chosen design manifold to the equilibrium point at the origin. in what follows, we assume that hypothesis of lemma 1 are satisfied, and hence the singularly perturbed system (1) has an n2 dimensional integral manifold: z = h0(x) (18) satisfying the equation (1-b). the flow of system (1), on this manifold, is governed by the n1 dimensional reduced system: ẋ = f (t, x, h0(t, x, ε), ε) (19) this result can be reached by the design of a desired control u satisfying: l(x, z)u = −g(x, z) + a(z−h0) + ε dh0(x) dx f (x, z) (20) where a is a hurwitz matrix. specifically, we choose the design manifold in this paper to be equal to z = h0(x) = 0. then, the control in equation (20) becomes: l(x, z)u = −g(x, z) + az (21) and the nonlinear singularly perturbed system (1) can be written as:    ẋ = r ∑ i=1 i+1 ∑ j=1 fi jx [i+1− j] ⊗z[ j−1] (a) ε ż = az (b) (22) it is then clear, that the fast subsystem and the fast states of the system (22) are attracted toward the manifold as quickly as desired by the choice of the hurwitz matrix a. the reduced order system of (22) is obtained by setting ε = 0 as: ẋ = f (x, 0) = r ∑ i=1 fiix [i] (23) the boundary layer system is given, in the fast time scale τ tε , by: dz∗(τ) dτ = az∗(τ) (24) 26 hajer bouzaouache, naceur benhadj braiek now to study the stability of the system (22), let’s consider that the reduced order system (23) and the boundary layer system (24) have respectively v1(x) and v2(z) as quadratic lyapunov candidate functions verifying assumptions 1-2 and defined as follows : v1(x) = x t p1x (25) v2(z) = z t p2z (26) where p1, p2 are symmetric positive definite matrices solutions of the following lyapunov equations: v̇1(x) ≤ −xt q1x (27) v̇2(z) ≤ −zt q2z (28) q1, q2 are also positive definite matrices. based on results of the stability theory [15] and others derived in previous work [1], [5], (27) and (28) are formulated as follows: τ t1 (m t 1 p1 + p1m1)τ1 ≤ −q1 (29) ( a ε )t p2 + p2 ( a ε ) ≤ −q2 (30) where p1 =   p1 0 p1 ⊗in1 . . . 0 p1 ⊗ins−11   (31) and m1 =   λ11π11 λ12π12 ··· λ1sπ1s ... . . . ... ... . . . ... λs1πs1 ··· ··· λssπss   (32) with πk− j+1, j =   mat (nk− j ,n j ) (f 1 t kk ) mat (nk− j ,n j ) (f 2 t kk ) ... mat (nk− j ,n j ) (f n t kk )   (33) for the corrected system (22), we define the following lyapunov functions: v (x, z, ε) = x t eε px (34) where x = [ x z ] ∈rn; p = ( p1 0 0 p2 ) and eε = ( in1 0 0 ε in2 ) (35) the derivative of v (x, z, ε) along the trajectories of (22) is given by: v̇ (x, z, ε) = x t eε pẋ + ẋ t eε px (36) on guaranteed global exponential stability of polynomial singularly perturbed control systems 27 in the above equation, we need to explicit the derivative of the state vector x . so, we begin by writing the state equations (22) in the following form: ẋ = .( x z ) = r ∑ i=1 λi (̂ x z )[i] (37) with λ1 = ( f11 f12 0 aε ) and for i > 1: λi = ( fi1 ···fi j ···fi(i+1) on2×αi ) (38) using the results given by the lemma 2, it follows from equation (37) that ẋ = r ∑ i=1 λi (i) m +x [i] (39) where (i)+ m is the moore-penrose pseudo inverse of (i) m defined in (a.4). the derivative of the composite lyapunov function (34) is then written: v̇ (x, z, ε) = 2 r ∑ k=1 x t (eε pλk (k) m +)x k (40) using the property of the vec-function (1), we have: v̇ (x, z, ε) = 2 r ∑ k=1 v tk x [k+1] (41) where vk = vec(eε pλk (k) m +) (42) knowing that all polynomials with even degree (2s) can be represented as a symmetric quadratic form. thus, we assume in the following development that r is odd: r = 2s−1, and it comes out: v tk x [k+1] = hk ∑ j=gk λk− j+1, jx [k+1− j]nk− j+1, jx [ j] (43) where λk− j+1, j are arbitrary reals verifying: hk ∑ j=gk λk− j+1, j = 1 (44) and for k = 1, . . . , 2s−1: gk = sup(0, k + 1−s) and hk = inf(s, k) (45) for j = gk, . . . , hk: nk− j+1, j = mat (nk− j+1,n j ) (vk) (46) applying the properties [1], one obtains: nk− j+1, j = mat (nk− j+1,n j ) ( vec(eε pλk (k) m +) ) = unk− j×n(eε p⊗ink− j ).mk− j+1, j (47) 28 hajer bouzaouache, naceur benhadj braiek with mk− j+1, j =   mat (nk− j ,n j ) (b1 t k ) mat (nk− j ,n j ) (b2 t k ) ... mat (nk− j ,n j ) (bn t k )   andbk=λk (k) m + (48) where bik is the i-th row of the matrix bk: bk=   b1k b2k ... bnk   (49) by (47) and from the relation (48), we obtain: x [k− j+1] t nk− j+1, jx [ j] = x [k− j+1] t unk− j×n(eε p⊗ink− j )mk− j+1, jx [ j] = x [k− j+1] t (eε p⊗ink− j )mk− j+1, jx [ j] (50) consequently, we have: v tk x [k+1] = hk ∑ j=gk λk− j+1, jx [k− j+1] t nk− j+1, jx [ j] = x t (pε mk)x (51) with x =   x x [2] ... x [s]   (52) and pε =   eε p 0 eε p⊗in . . . 0 eε p⊗ins−1   (53) let’s note that pε is a symmetric positive matrix, and v̇ (x , ε) (41) can be written as: v̇ (x , ε) = 2 2s−1 ∑ k=1 v tk x [k+1] = x t (pε mε + m t ε pε )x (54) with mε = 2s−1 ∑ k=1 mk =   λ11m11 λ12m12 ··· λ1sm1s ... . . . ... ... . . . ... λs1ms1 ··· ··· λssmss   (55) when considering the nun-redundant form, the vector x can be written as: x = τ x̃ (56) on guaranteed global exponential stability of polynomial singularly perturbed control systems 29 where τ =   t1 0 . . . 0 ts   and x̃ =   x̃ ... x̃ [s]   (57) >from (54) and (56) we easily obtain: v̇ (x , ε) = x̃ t τ t (pε mε +mtε pε )τ x̃ (58) let us denote the largest eigenvalue of the matrix pε by λmax(pε ), the smallest eigenvalue of q by λmin(q). where the matrix q verifies: v̇ (x , ε) ≤−x t qx . the positive definiteness of pε and q implies that these scalars are all strictly positive. since matrix theory shows that: pε ≤ λmax(pε )i; λmin(q)i ≤ q (59) we have x t qx ≥ λmin(q) λmax(pε ) x t [λmax(pε )i] x ≥ λmin(q) λmax(pε ) x t [λmax(eε p)i] x (60) otherwise x t x ≥ ‖x‖2 and eε p ≤ λmax(eε p)i (61) hence, (54) will satisfies the following condition: v̇ (x , ε) ≤ −2γv (x , ε) where γ = 1 2 · λmin(q) λmax(pε ) (62) it comes out v (x , ε) ≤ v (x0)e−2γ(t−t0) (63) considering the previous developments, we state now our main result: theorem 1. assume the following assumptions hold: (i) lemma 1 satisfied (ii) assumptions 1 2 are satisfied the system (1) is globally exponentially stable (ges), if there is, for all ε < ε1, ε1 > 0 a feasible solution to the lmi :    ε > 0 ∃pt1 = p1 > 0 ∃pt2 = p2 > 0 ∃pt = p > 0( a ε )t p2 + p2 ( a ε ) ≤ −q2 τ t1 (m t 1 p1 + p1m1)τ1 ≤ −q1 τ t (mtε pε + pε mε )τ ≤ −q (64) m1, τ1 and p1 are given by (32), (57) and (31). mε , τ and pε are given by (55), (57) and (53). moreover, the lyapunov function that demonstrates the g.e.s is given by: v (x) = x t eε pε x now, let’s evaluate the convergence rate of the full order system (22). in view of (12), (15), and (27), we have for all t ∈r, x ∈rn1 and z ∈rn2 : v1(x) ≤ (α2 ‖x0‖2 + ε β2 ‖z0‖2)e−2γ(t−t0) (65) >from (12) and (15) we have: ‖x‖ ≤   (√ α2 α1 ‖x0‖ )2 +   √ ε β2 α1 ‖z0‖   2   1/2 e−γ(t−t0) ≤   √ α2 α1 ‖x0‖+ √ ε β2 α1 ‖z0‖   e−γ(t−t0) (66) 30 hajer bouzaouache, naceur benhadj braiek identically, using (15) and (16), we obtain: β1 ∥∥z2 ∥∥ ≤ v2(z) ≤ β2 ∥∥z20 ∥∥ e−2(γ2/ε)(t−t0) (67) then ‖z‖ ≤ √ β2 β1 ‖z0‖e−(γ2/ε)(t−t0) (68) >from (66) and (68), we can write in the case γ ≤ (γ2/ε) that ‖x‖2 ≤ 2η e−2γ(t−t0) where η = max     √ α2 α1 ‖x0‖+ √ ε β2 α1 ‖z0‖   2 , (√ β2 β1 ‖z0‖ )2  (69) which implies that the norm ‖x‖ of the state vector converges to zero exponentially, with a rate γ . the convergence rates of the reduced systems can be calculated: γ1 = 1 2 · λmin(q1) α2 , γ2 = λmin(q2) 2β2 >from above, we state the following second result: theorem 2. assume the following assumptions hold: (i) lemma 1 satisfied (ii) assumptions 1 2 are satisfied (iii) there exists a lyapunov function v (t, x , ε) that satisfies equation ((34)) then the original nonlinear singularly perturbed system ((1)) is globally exponentially stable under the proposed control ((21)) and with the convergence rate γ ((63)). moreover, note that when we proves that the limit of γ as ε → 0 , tends to the convergence rate of the reduced order system. this implies that under the proposed control (21), the global exponential stability of the initial studied system is equivalent to that of the reduced order system. 5 illustrative example to illustrate the previous derived results, we consider a third order nonlinear singularly perturbed system defined by the following equations:    ẋ1 = −x1 + x2 + 0.1x1z ẋ2 = −x1 −0.09x2 + 2z + 0.05x1z ε ż = 4x1 −4x2 + z + 0.5x21 −x22 + 10u this system can be described by the following model using the kronecker product and the power of vectors which allowed important algebraic manipulations. { ẋ = f11x + f12z + f21x [2] + f22x⊗z + f23z [2] ε ż = g11x + g12z + g21x [2] + g22x⊗z + g23z [2] + bu on guaranteed global exponential stability of polynomial singularly perturbed control systems 31 where f11 = [ −1 1 −1 −0.09 ] , f12 = [ 0 2 ] , g11 = [ 4 −4 ] , g12 = 1, f21 = 02×4, f22 = [ 0.1 0 0 0.5 ] , f23 = 02×1 g21 = [ 0.5 1 0 ] , g22 = [ 0 0 ] , g23 = 0, b = 10. when implemented using the lmi toolbox of matlab, the proposed lmi’s conditions proves that the numerical studied system which is initially instable can be globally exponentially stabilised by the given controller with the considered a = −1 for all ε < ε1 = 0.5 in view of theorem 1: u = −0.4x1 + 0.4x2 −0.1z−0.05x21 + 0.1x22 figure 1: state trajectories of the controlled studied system hence for all ε < ε1 = 0.5 and from any initial states, the trajectories of the system are steered to the origin along the integral manifold with the convergence rate 0.1 in view of (62). indeed, it is shown in fig. 1 that the state trajectories of the controlled system ( – – –) are bounded by the function (—). 6 conclusion in this paper, the global exponential stabilisation for nonlinear singularly perturbed control systems is investigated. in the stability study, the composite lyapunov method was applied and the global exponential stability of the equilibrium of the full control system was established for all ε < ε1. the upper bound ε1 for which the stability properties are guaranteed can be reached after a number of iterations on ε when resolving the proposed lmi’s conditions via the lmi toolbox of matlab. a numerical example has been provided to illustrate the proposed results. 32 hajer bouzaouache, naceur benhadj braiek 7 appendix notations: the dimensions of the matrices used here are the following: a(p×q), b(r ×s), c(s×h), d(s×h), e(n× p), p(n×n), x (n×1) ∈rm, y (m×1) ∈rm, z(q×1) ∈rq let’s consider the following notations: in: n×n identity matrix; 0n×m: n×n zero matrix; 0zero matrix of convenient dimensions ; at : transpose of matrix a; a > 0 (a ≥ 0): symetric positive definite (semi definite matrix a); ek (q) : q dimensional unit vector which has 1 in the kth element and zero elsewhere. the kth row of a matrix such as a is denoted ak. and the kth column is denoted a.k. the ik element of a will be denoted aik. the kronecker product of a and b is denoted a⊗b a (p.r×q.s) matrix, and the i−th kronecker’s power of a denoted i a[i] = a⊗a⊗···⊗a s a ( pi ×qi) matrix. the nun-redundant j-power x̃ [ j] of the state vector x introduced in [9] is defined as: x̃ [1] = x [1] = x   ∀ j ≥ 2 x̃ [ j] = [ x j 1, x j−1 1 x2,··· , x j−1 1 xn, x j−2 1 x 2 2 x j−2 1 x2x3,··· , x j−2 1 x2xn, ···x j−21 xn2 ,··· , x j−3 1 x 3 2 ,··· , x jn ] (a.1) where the repeated components of the redundant j-power are omitted. then we have the following relation:    ∀ j ∈n ∃!tj ∈rn j×α j ; α j = ( n + j −1 j ) x [ j] = tjx̃ [ j] (a.2) thus, one possible solution for the inversion can be written as: x̃ [ j] = t +j x [ j] (a.3) where t +j is the moore-penrose pseudo inverse of tj given by: t +j = ( t tj tj )−1 t tj (a.4) and α j stands for the binomial coefficients. an important vector valued function of matrix denoted vec(.) was defined as [9]: vecpq×1(a) =   a.1 a.2 ... a.q   (a.5) a matrix valued function is a vector denoted mat(n,m)(.) was defined in [1] as follows: if v is a vector of dimension p = n×n then mmat(n,m)(v ) is the n×m matrix verifying: v = vec(m) (a.6) among the main properties of this product presented in [9], [1], we recall the following useful ones: (a⊗b) (c ⊗d) = (ac)⊗(bd) (a.7) (a⊗b)t = at ⊗bt (a.8) b⊗a = ur×p (a⊗b)uq×s (a.9) x ⊗y = un×m (y ⊗x ) (a.10) vec (eac) = ( ct ⊗e ) vec (a) (a.11) vec ( at ) = up×qvec (a) (a.12) ∀i ≤ k x [k] = uni×nk−i x [k] (a.13) on guaranteed global exponential stability of polynomial singularly perturbed control systems 33 references [1] e. benhadj braiek , f. rotella, m. benrejeb, algebraic criteria for global stability analysis of nonlinear systems, sams journal, vol.17, pp. 211-227, 1995. 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[17] m.w.spong, k. khorasani, p.v. kokotovic, an integral manifold approach to feedback control of flexible joint robots, ieee j. robot. automat.3 (4), pp. 291-300, 1987. 34 hajer bouzaouache, naceur benhadj braiek hajer bouzaouache, naceur benhadj braiek laboratory of study and automatic control of processes address: polytechnic school of tunisia (ept) bp.743, 2078 la marsa, tunisia. e-mail: hajer.bouzaouache,naceur.benhadj@ept.rnu.tn received: november 6, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 2, pp. 224-237 towards structured modelling with hyperdag p systems r. nicolescu, m.j. dinneen, y.-b. kim michael j. dinneen, yun-bum kim and radu nicolescu department of computer science, university of auckland, private bag 92019, auckland, new zealand e-mail: {radu,mjd,yun}@cs.auckland.ac.nz abstract: although p systems are computationally complete, many real world models, such as socio-economic systems, databases, operating systems and distributed systems, seem to require more expressive power than provided by tree structures. many such systems have a primary tree-like structure augmented with shared or secondary communication channels. modelling these as tree-based systems, while theoretically possible, is not very appealing, because it typically needs artificial extensions that introduce additional complexities, inexistent in the originals. in this paper, we propose and define a new model called hyperdag p systems, in short, hp systems, which extend the definition of conventional p systems, by allowing dags, interpreted as hypergraphs, instead of trees, as models for the membrane structure. we investigate the relation between our hp systems and neural p systems. despite using an apparently restricted structure, i.e., a dag instead of a general graph, we argue that hp systems have essentially the same computational power as tissue and neural p systems. we argue that hp systems offer a structured approach to membranebased modelling that is often closer to the behavior and underlying structure of the modelled objects. keywords: hyperdag p systems, tissue and neural p systems, membrane structures. 1 introduction p systems provide a distributed computational model, based on the structure and interaction of living cells, introduced by g. păun in 1998 [10]. the model was initially based on transition rules, but was later expanded into a large family of related models. essentially, all versions of p systems have a structure consisting of cell-like membranes and a set of rules that govern their evolution over time. many of the “classical” versions use a structure, where membranes correspond to nodes in a rooted tree. such a structure is often visualized as a venn diagram, where nesting denotes a parent–child relationship. for example, figure 1 [12] shows the same p system structure with nine membranes, labelled as 1,...,9, both as a rooted tree and as a venn diagram. more, recently, neural p systems [11], abbreviated as np systems (also known as tissue p systems [7]), have been introduced, partially to overcome the limitations of the tree model. essentially, these systems organize their cells in an arbitrary graph. for example, ignoring for the moment the actual contents of cells (states, objects, rules), figure 2 illustrates the membrane structure of a simple np system, consisting of three cells, σ1,σ2,σ3, where cell σ1 is the output cell. a large variety of rules have been used to describe the operational behavior of p systems, such as multiset rewriting rules, communication rules and membrane handling rules. essentially, transition p systems and np systems use multiset rewriting rules, p systems with symport/antiport rules operate by communicating immutable objects and p systems with active membranes combine all three rule types. for a comprehensive overview and more details, we refer the reader to [11, 12]. besides theoretical computer science and biology, p systems have been applied to a variety of other domains, ranging from linguistics [5] to theoretically efficient solutions of np-complete problems [16], copyright c⃝ 2006-2010 by ccc publications towards structured modelling with hyperdag p systems 225 2 3 4 5 6 7 8 9 1 1 2 3 4 5 6 7 8 9 figure 1: a p system structure represented as a tree and as a venn diagram. σ1 σ2 σ3 figure 2: an np system structure represented as a digraph. or to model distributed algorithms [3, 6]. the underlying tree structure provides good support for reasoning and formal verification, good potential for efficient implementation on multi-core architectures, and an excellent visualization, very appealing to practitioners. although the p systems are computationally complete, many real world models seem to require more expressive power, essentially trees augmented by shared or secondary communication channels. for example, the notion of a processing node having a unique parent is not true for (a) computer networks where a computer could simultaneously be attached to several subnets (e.g., to an ethernet bus and to a wireless cell), (b) living organisms may be the result of multiple inheritance (e.g., the evolutionary “tree” is not really a tree, because of lateral gene transfer [4]) and (c) socio-economic scenarios where a player is often connected to and influenced by more than one factor [13, 14, 15]. modelling these as tree-based systems, while theoretically possible, is not very appealing. simulating shared or secondary channels requires artificial mechanisms that will ripple data up and down the tree, via a common ancestor. this could of course limit the merits of using a formal model. models based on general graphs, such as np systems, while allowing any direct communications, also tend to obscure the structures already present in the modelled objects, limiting the advantages that a more structured approach could provide. verification is more difficult without a clear modularization of concerns, practical parallel implementation could be less efficient, if the locality of reference is not enforced, and visualizations are not very meaningful, unless the primary structure is clearly emphasized. we do not think that we have to choose between structure and flexibility. we propose a solution that seems to combine both, i.e., flexibility without sacrificing the advantages of a structured approach. our main contribution in this paper is to propose a new model for p systems, called hyperdag p systems, in short, hp systems, that allows more flexible communications than tree-based models, while preserving a strong hierarchical structure. this model, defined in section 3, (a) extends the tree structure of classical p systems to directed acyclic graphs (dags), (b) augments the operational rules of np systems with broadcast facilities (via a go-sibling transfer tag), and (c) enables dynamical changes of the rewriting modes (e.g., to alternate between determinism and parallelism) and of the transfer modes (e.g., to switch between unicast or broadcast). in contrast, classical p systems, both treeand graph-based p systems, seem to focus on a statical approach. we investigate the relation between our hp systems and np systems. despite using an apparently 226 r. nicolescu, m.j. dinneen, y.-b. kim restricted structure, we show in section 4 that our dag-based model has the same computational power as graph-based tissue p systems and neural p systems. we argue that hp systems offer a structured approach to membrane-based modelling that is often closer to the behavior and underlying structure of the modelled objects. because our extensions address the membrane topology, not the rules model, they can be applied to a variety of p system flavors, such as systems based on symport/antiport rules. we support our view with a realistic example (see examples 11 and 12), inspired from computer networking, modelled as an hp system with a shared communication channel (broadcast channel). classical p systems allow a “nice” planar visualization, where the parent–child relationships between membranes are represented by venn-like diagrams. we show in section 5 that the extended membrane structure of hp systems can still be visualized by hierarchically nested planar regions. 2 preliminaries a (binary) relation r over two sets x and y is a subset of their cartesian product, r ⊆ x ×y . for a ⊆ x and b ⊆ y , we set r(a) = {y ∈ y | ∃x ∈ a,(x,y) ∈ r}, r−1(b) = {x ∈ x | ∃y ∈ b,(x,y) ∈ r}. a digraph (directed graph) g is a pair (x,a), where x is a finite set of elements called nodes (or vertices), and a is a binary relation a ⊆ x × x , of elements called arcs. for an arc (x,y) ∈ a, x is a predecessor of y and y is a successor of x. a length n −1 path is a sequence of n distinct nodes x1,...,xn, such that {(x1,x2),...,(xn−1,xn)} ⊆ a. a cycle is a path x1,...,xn, where n ≥ 1 and (xn,x1) ∈ a. a dag (directed acyclic graph) is a digraph (x,a) without cycles. for x ∈ x , a−1(x) = a−1({x}) are x’s parents, a(x) = a({x}) are x’s children, a(a−1(x))\{x} = a(a−1({x}))\{x} are x’s siblings (siblings defines a symmetric relation). a node x ∈ x is a source iff |a−1(x)| = 0, and x ∈ x is a sink iff |a(x)| = 0. the height of a node x is the maximum length of all paths from x to a sink node. an arc (x,y) is transitive if there exists a path x1,...,xn, with x1 = x, xn = y and n > 2. dags without transitive arcs are here called canonical. a (rooted unordered) tree is a dag with exactly one source, called root, and all other nodes have exactly one parent (predecessor). sinks in a tree are also called leaves. a topological order of a dag is a linear reordering of vertices, in which each vertex x comes before all its children vertices a(x). dags and trees are typically represented with parent-child arcs on the top-down axis, i.e., sources (roots) up and sinks (leaves) down. example dags are shown in figures 3 and 4. we consider a variant hypergraph definition, based on multisets, as an extension of the classical definition [1], which is based on sets. a hypergraph is here a pair (x,e), where x is a finite set of elements called nodes (or vertices), and e is a finite multiset of subsets of x , i.e., e ∈ e ⇔ e ⊆ x . by using a multiset of edges, instead of a more conventional set of edges, we introduce an intensional element, where two extensionally equivalent hyperedges (i.e., hyperedges containing the same nodes) are not necessarily equal. a graph is a set based hypergraph, where hyperedges are known as edges and contain exactly two nodes. alternatively, a graph (x,e) can be interpreted as a digraph (x,a), where a = {(x,y) | {x,y} ∈ e}. hypergraphs (set or multiset based) can be represented by planar diagrams, where hyperedges are represented as regions delimited by images of jordan curves (simple closed curves) [2]. with the above hypergraph definition, a height 1 dag (x,a) can be interpreted as a hypergraph (x,e), where e is the multiset e = {a(x) | |a−1(x)| = 0}. for example, figure 3 represents, side by side, the dag d = ({a,b,c,d,e, f },{(d,a),(d,b),(d,c),(e,b), (e,c),( f ,b),( f ,c)}) and its corresponding hypergraph h = ({a,b,c},{d,e, f }), where d = {a,b,c},e = {b,c}, f = {b,c}. note that the apparently empty differences of regions are needed in the case of multiset based hypergraphs, to support the intensional (as opposed to the extensional) aspect: here e ̸= f , despite containing the same nodes, b and c, and neither e nor f is included in d. generalizing the above hypergraph definition, a height n generalized hypergraph is a system (x,e), recursively built via a sequence of n hypergraphs (x1,e1),...,(xn,en) where x1 = x , xi ∩ ei = /0,xi+1 = towards structured modelling with hyperdag p systems 227 d e a b c a b c d e f f figure 3: a simple height 1 dag and its corresponding hypergraph representation. xi ∪ ei, e ∩ ei ̸= /0 for ∀e ∈ ei+1 and e = ∪ i∈{1,...,n} ei. an arbitrary height n dag can be represented by a height n generalized hypergraph, where the hypergraph nodes correspond to dag sinks, and height i hyperedges correspond to height i dag nodes, for i ∈ {1,...,n}. we will later see that any generalized hypergraph that corresponds to a non-transitive dag can also be represented by hierarchically nested planar regions delimited by jordan curves, where arcs are represented by direct nesting. for example, figure 4 shows a height 2 dag and its corresponding height 2 hypergraph (x,e), where x = x1 = {a,b,c,d,e}, e1 = { f ,g,h}, e2 = {i}, e = { f ,g,h,i}. a b c d e a b c d e f g h i f g h i figure 4: a height 2 dag and its corresponding height 2 hypergraph. an alphabet o is a finite non-empty sets of objects. we will assume that the alphabet o is implicitly ordered. multisets over an alphabet o are represented as strings over o, such as on11 ...o nk k , where oi ∈ o, ni ≥ 0, and, in the canonical form, letters appear in sorted order, i.e., o1 < ··· < ok, and ni ≥ 1. the set of all multisets is denoted by o∗. for this representation, two strings are equivalent if they become equal after sorting, e.g., a2cbd0a and a3bc are equivalent representations of the same multiset {a,a,a,b,c}. under this convention, the empty string λ represents the empty multiset, and string concatenation represents multiset union, e.g., (a2c) ·(ab) = a3bc. 3 hyperdag p systems in this paper we use the definition of np systems, as given in [11], that coincides with an early definition of tissue p systems as given in [7]. our definition includes a small technical correction (slight ambiguity). for details, please see our technical report [8]. as in the mentioned np systems definition, we will use the following sets of tagged objects: ogo = {(a,go) | a ∈ o}, oout = {(a,out) | a ∈ o}, and we set otot = o ∪ ogo ∪ oout . for simplicity, we will use subscripts for these tagged objects, such as ago for (a,go) and aout for (a,out). we also define projection homomorphisms, here denoted in postfix notation: |o, |go, |out : o∗tot → o∗, by o|o = o,ogo|go = o,oout|out = o for o ∈ o, and otherwise λ . for example, a2a3gob4bgo|go = a3b. besides the existing go, out tags, we consider three other object tags: 1. go-parent, abbreviated by the symbol ↑, indicating objects that will be sent to parents; 228 r. nicolescu, m.j. dinneen, y.-b. kim 2. go-child, abbreviated by the symbol ↓, indicating objects that will be sent to children; 3. go-sibling, abbreviated by the symbol ↔, indicating objects that will be sent to siblings. the precise semantics of these tags will be explained below when we detail the hp system object transfer modes. in fact, we could also discard the go tag, as it corresponds to the union of these new target tags (go-parent, go-child, go-sibling); however, we will keep it here, for its concise expressive power. we use similar notation as np systems for these new tags o↑,o↓,o↔, and postfix projections |↑, |↓, |↔. other extension tags, including addressing mechanisms (such as from, to or via tags) are possible, and indeed seem natural, but this is beyond the scope of this article. we will now define hp systems, as an apparent restriction of np systems, where the underlying structure is a dag, with several other adjustments. definition 1 (hyperdag p systems) an hp system (of degree m) is a system π = (o,σ1,..., σm,δ ,iout), where: 1. o is an ordered finite non-empty alphabet of objects; 2. σ1,...,σm are cells, of the form σi = (qi,si,0,wi,0,pi), 1 ≤ i ≤ m, where: • qi is a finite set (of states), • si,0 ∈ qi is the initial state, • wi,0 ∈ o∗ is the initial multiset of objects, • pi is a finite set of multiset rewriting rules of the form sx → s′x′u↑v↓w↔ygozout , where s,s′ ∈ qi, x,x′ ∈ o∗, u↑ ∈ o∗↑ , v↓ ∈ o∗↓ , w↔ ∈ o∗↔ , ygo ∈ o∗go and zout ∈ o∗out , with the restriction that zout = λ , for all i ∈ {1,...,m}\iout , 3. δ is a set of dag parent–child arcs on {1,...,m}, i.e., δ ⊆ {1,...,m}×{1,...,m}, representing duplex communication channels between cells; 4. iout ⊆ {1,...,m} indicates the output cells, the only cells allowed to send objects to the “environment”. the essential novelty of our proposal is to replace the arbitrary arc set used in neural p systems by a more structured arc set δ (dag), or, otherwise interpreted, as a generalized multiset-based hypergraph. this interpretation has actually suggested the name of our proposal, hyperdag p systems, and their abbreviation hp systems. the changes in the rules format are mostly adaptations needed by the new topological structure. here, we have reused and enhanced the rewriting rules used by np systems [11]. however, we could adopt and adapt any other rule set, from other variants or extensions of p systems, such as rewriting, symport/antiport or boundary rules [12]. definitions of configurations, transitions, computations and results of computations in hp systems are similar to definitions used for np systems (see also [8]), with the following essential additions and differences, here informally stated: • the rewriting mode α and transfer mode β may not be fixed from the start, i.e., they may vary, for each cell σi and state s ∈ qi. • if object transfer mode is repl (this is a deterministic step): – the objects tagged with ↑ will be sent to all the parents, replicated as necessary – the objects tagged with ↓ will be sent to all the children, replicated as necessary towards structured modelling with hyperdag p systems 229 – the objects tagged with ↔ will be sent to all the siblings, of all sibling groups, replicated as necessary • if object transfer mode is one (this is a nondeterministic step): – the objects tagged with ↑ will be sent to one of the parents, arbitrarily chosen – the objects tagged with ↓ will be sent to one of the children, arbitrarily chosen – the objects tagged with ↔ will be sent to one of the siblings, of one of the sibling groups, arbitrarily chosen • if object transfer mode is spread (this is a nondeterministic step): – the objects tagged with ↑ will be split into submultisets and distributed among the parents, in an arbitrary way – the objects tagged with ↓ will be split into submultisets and distributed among the children, in an arbitrary way – the objects tagged with ↔ will be split into submultisets and distributed among the siblings and sibling groups, in an arbitrary way figure 5 schematically shows the possible object transfers from a cell σi, having two children, two parents, hence two sibling groups, with one sibling in the first group and two siblings in the other. the above mentioned transfer modes will select one, some or all the illustrated transfer targets, deterministically (repl) or nondeterministically (one, spread). go-parent (↑) go-parent (↑) go-sibling (↔) go-child (↓) go-child (↓) go-sibling (↔) go-sibling (↔) σi figure 5: an annotated hp system indicating possible transfers from cell σi. the parent-child axis is top-down. plain lines indicate parent-child relations and dashed lines indicate siblings. arrows at the end of long thick lines, plain or dashed, indicate possible transfer directions from cell σi. more formal definitions follow. definition 2 (configurations) a configuration of the hp system π is an m-tuple of the form (s1w1,...,smwm), with si ∈ qi and wi ∈ o∗, for 1 ≤ i ≤ m. the m-tuple (s1,0w1,0,...,sm,0wm,0) is the initial configuration of π . definition 3 (rewriting and transfer modes) for an hp system of degree m, • the object rewriting mode is a function α : ∪ i∈{1,...,m} {i}× qi → {min, par,max} . • the object transfer mode is a function β : ∪ i∈{1,...,m} {i}× qi → {repl,one,spread} . 230 r. nicolescu, m.j. dinneen, y.-b. kim definition 4 (rewriting steps) for each cell σi with s,s′ ∈ qi, x ∈ o∗, y ∈ o∗tot , we define a rewriting step, denoted by ⇒α , where α = α(i,s) ∈ {min, par,max}. • sx ⇒min s′y iff sw → s′w′ ∈ pi, w ⊆ x, and y = (x − w)∪ w′; • sx ⇒par s′y iff sw → s′w′ ∈ pi, wk ⊆ x, wk+1 * x, for some k ≥ 1, and y = (x − wk)∪ w′k; • sx ⇒max s′y iff sw1 → s′w′1,...,swk → s′w′k ∈ pi, k ≥ 1, such that w1 ...wk ⊆ x,y = (x−w1 ...wk)∪ w′1 ...w ′ k, and there is no sw → s′w′ ∈ pi, such that w1 ...wkw ⊆ x (note that rules can be combined only if they start from the same state s and end in the same state s′). definition 5 (transition steps) given two configurations c1 = (s1w1,...,smwm) and c2 = (s′1w ′′ 1 ,...,s ′ mw ′′ m), we write c1 ⇒α,β c2, for α and β (as defined in definition 3), if the conditions below are met. first, we apply rewriting steps (as defined in definition 4) on each cell, i.e., siwi ⇒α(i,si) s′i w′i , 1 ≤ i ≤ m. secondly, we define z↑j,k, z↓j,k, z↔j,k, the outgoing multisets from j to k, where j ∈ {1,...,m} and, respectively, k ∈ δ −1( j), k ∈ δ ( j), k ∈ δ (δ −1( j))\{ j}: • if β ( j,s j) = repl, then – z↑j,k = w′j|↑, for k ∈ δ −1( j); – z↓j,k = w′j|↓, for k ∈ δ ( j); – z↔j,k = w′j|↔, for k ∈ δ (δ −1( j))\{ j}. • if β ( j,s j) = one, then – z↑j,k j = w′j|↑, for an arbitrary k j ∈ δ −1( j), and z↑j,k = λ for k ∈ δ −1( j)\{k j}; – z↓j,k j = w′j|↓, for an arbitrary k j ∈ δ ( j), and z↓j,k = λ for k ∈ δ ( j)\{k j}; – z↔j,k j = w′j|↔, for an arbitrary k j ∈ δ (δ −1( j))\{ j}, and z↔j,k = λ for k ∈ δ (δ −1( j))\{ j,k j}. • if β ( j,s j) = spread, then – {z↑j,k}k∈δ −1( j) is an arbitrary multiset partition of w′j|↑; – {z↓j,k}k∈δ ( j) is an arbitrary multiset partition of w′j|↓; – {z↔j,k}k∈δ (δ −1( j))\{ j} is an arbitrary multiset partition of w′j|↔. finally, we set w′′i = w ′ i |o ∪ ∪ j∈δ −1(i) z↑j,i ∪ ∪ j∈δ (i) z↓j,i ∪ ∪ j∈δ (δ −1(i))\{i} z↔j,i , for i ∈ {1,...,m}. definition 6 (halting and results) if no more transitions are possible, the hp system halts. for halted hp system, the computational result is the multiset that was cumulatively sent out (to the “environment”) from the output cells iout . the numerical result is the set of vectors consisting of the object multiplicities in the multiset result. within the family of p systems, two systems are functionally equivalent, if they yield the same computational results. towards structured modelling with hyperdag p systems 231 example 7 consider two hp systems, π1 and π2, (which are functionally equivalent). π1 = (o,σ1,σ2,σ3,δ ,iout), where: • o = {a}; • σ1 = ({s},s,a,{sa → sa↓,sa → saout}); • σ2 = ({s},s,λ ,{sa → sa↑}); • σ3 = ({s},s,λ ,{sa → sa↑}); • δ = {(1,2),(1,3)}; • iout = {1}. s, a sa → sa↓ sa → saout s, λ sa → sa↑ s, λ sa → sa↑ σ1 σ2 σ3 π2 = (o,σ1,σ2,σ3,σ4,σ5,δ ,iout), where: • o = {a}; • σ1 = ({s},s,a,{sa → sa↔,sa → saout}); • σ2 = ({s},s,λ ,{sa → sa↔}); • σ3 = ({s},s,λ ,{sa → sa↔}); • σ4 = ({s},s,λ , /0); • σ5 = ({s},s,λ , /0); • δ = {(4,1),(4,2),(5,1),(5,3)}; • iout = {1}. s, a sa → sa ↔ sa → saout s, λ sa → sa ↔ s, λ sa → sa ↔ σ1σ2 σ3 σ4 σ5 s, λ s, λ 4 relations between p systems, neural p systems and hyperdag p systems theorem 8 (hyperdag p systems include non-dissolving transition p systems). any non-dissolving1 transition p system can be simulated by an hp system, with the same number of steps and object transfers. proof: given a non-dissolving, transition p system πt [12], we build a functionally equivalent hp system πh by the following transformation f . essentially, we use the same elements, with minor adjustments. as the underlying structure, we can reuse the rooted tree structure of the p systems, because any rooted tree is a dag. πt = (o,c, µ,w1,...,wm,r1,...,rm,io), πh = f (πt ) = (o′,σ ′1,...,σ ′ m,δ ,iout). • o′ = o; • σ ′i = (q ′ i ,s ′ i,0,w ′ i,0,p ′ i ), 1 ≤ i ≤ m, where: – q′i = {s}, where s /∈ o; – s′i,0 = s; – w′i,0 = wi; – p′i = {su → sv′ | u → v ∈ ri}, where v′ is a translation of v by the following homomorphism: (o ∪ o ×{here,in,out})∗ → o∗, such that a → a, (a,here) → a, (a,out) → a↑, (a,in) → a↓. 1a dissolving membrane occurs if we allow rules that tell a membrane to disappear where its remaining objects go to its parent membrane; see [12]. 232 r. nicolescu, m.j. dinneen, y.-b. kim • δ = µ ; • iout = {io}; • the object rewriting mode is the max constant function, i.e., α(i,s) = max, for i ∈ {1,...,m},s ∈ qi; • the object transfer mode is the spread constant function, i.e., β (i,s) = spread, for i ∈ {1,...,m},s ∈ qi. tags go-child(↓), go-parent(↑) correspond to p system target indications in,out, respectively. an empty tag corresponds to p system target indication here. object rewriting and transfer modes of hp systems are a superset of object rewriting and transfer modes of p systems. we omit here the rest of the proof that shows the two systems, πt and πh , yield the same computational results, which is now straightforward but lengthy. 2 remark 9 in analogy to the p systems, it is straightforward to extend the hp systems with additional features, such as dissolving membranes, priorities or polarities. however, to keep the arguments simple, we here omit such extensions. proving that hp systems also simulate np systems appears more daunting. however, here we will use a natural interpretation of hp systems, where the bulk of the computing will be done by the sink nodes, and the upper nodes (parents) will function mostly as communication channels. remark 10 the combination of go-sibling (↔) with repl object transfer mode enables the efficient modelling of a communication bus, using only one hyperedge or, in the corresponding dag, n arcs. in contrast, any formal systems that use graph edges (or digraph arcs) to model 1:1 communication channels will need n(n −1) separate edges (or 2n(n −1) arcs) to model the associated complete subgraph (clique). it is expected that this modelling improvement will also translate into a complexity advantage, if we count the number of messages. in hp systems, a local broadcast needs only one message to siblings, while graphor digraph-based systems need n −1 messages. example 11 figure 6 shows the structure of an hp system that models a computer network. four computers are connected to “ethernet bus 1”, the other four computers are connected to “ethernet bus 2”, while two of the first group and two of the second group are at the same time connected to the same wireless cell. in this figure, we also suggest that “ethernet bus 1” and “ethernet bus 2” are themselves connected to a higher level communication hub in a generalized hypergraph. example 12 figure 7 shows the computer network of figure 6, modelled as a graph (if we omit arrows) or as a digraph (if we consider the arrows). note that the graphor digraph-based models, such as np systems, do not support the grouping concept, i.e., there is no direct way to mark the nodes a,b,c,d as being part of the “ethernet bus 1”. we can now mention an important theorem comparing hp systems and np systems. theorem 13 (hyperdag p systems can simulate symmetric neural p systems). any symmetric np system can be simulated by an hp system, with the same number of steps and object transfers. proof: the simulation details are given in our research report [8]. 2 remark 14 we leave here open the case of non-symmetric np systems, which can also be simulated by hp systems, but require additional costs, in terms of steps and object transfers. towards structured modelling with hyperdag p systems 233 ethernet bus 1 ethernet bus 2 wireless cell ethernet bus 1 ethernet bus 2 a b c d e f g h a b c d e f g h wireless cell figure 6: a computer network and its corresponding hypergraph representation. a b c d e f g h (ethernet bus 1) (ethernet bus 2)(wireless cell) figure 7: the digraph representation of the computer network of figure 6. 5 planar representation of hyperdag p systems classical tree-based p systems allow a “nice” planar representation, where the parent–child relationships between membranes are represented by venn-like diagrams. can we extend this representation to cover our dag-based hp systems? in this section, we will show that any hp system, structurally based on a canonical dag, can still be intensionally represented by hierarchically nested planar regions, delimited by jordan curves (simple closed curves). conversely, we also show that any set of hierarchically nested planar regions delimited by jordan curves can be interpreted as a canonical dag, which can form the structural basis of a number of hp systems. we will first show how to represent a canonical dag as a set of hierarchically nested planar regions. algorithm 15 (algorithm for visually representing a canonical dag) without loss of generality, we consider a canonical dag (v,δ ) of order n, where vertices are indexed according to an arbitrary topological order implied by the arcs, by considering parents before the children, i.e., v = {vi | 1 ≤ i ≤ n}, where (vi,v j) ∈ δ implies i < j. figure 8 shows side by side a simple height 1 canonical dag and its corresponding hypergraph representation. note the intensional representation (as opposed to the extensional one), where v2 is not totally included in v1, although all vertices included in v2, i.e., v4 and v5, are also included in v1. a possible topological order is v1,v2,v3,v4,v5. 234 r. nicolescu, m.j. dinneen, y.-b. kim v3 v4 v5 v1 v2 1 2 3 4 5 figure 8: a simple canonical dag and its corresponding hypergraph representation. for each vertex vi, we associate a distance ψi = 12(n−i+1) , for i ∈ {1,...,n}. for figure 8, ψi = 1 32 , 1 16 , 1 8 , 1 4 , 1 2 , for i ∈ {1,...,n}. we process the vertices in reverse topological order vn,...,v1, at each step i representing the current vertex vi by a planar region ri. first, we set parallel horizontal axis xo and xp, vertically separated by distance 3(n −1). secondly, we set points o1,...,on on xo, such that oi and oi+1 are separated by distance 3, for 1 ≤ i ≤ n − 1. we define oi as the origin point of vi, and write oi = origin(vi). finally, we set points p1,..., pn on xp, such that pi and pi+1 are separated by distance 3, for 1 ≤ i ≤ n −1. we define pi as the corridor point of vi. figure 9 shows the construction of xo,xp,oi and pi, for the dag of figure 8, where n = 5. o3 o4 o5o1 o2 p1 p2 p3 p4 p5 xo xp figure 9: construction of xo,xp,oi and pi, for the dag of figure 8, where n = 5. if the current vertex vi is a sink, then ri is a circle with radius 1 2 centered at oi. if the current vertex vi is a non-sink, then ri is constructed as follows. assume that the children of vi are w1,...,wni , and their (already created) regions are s1,...,sni . consider line segments l0,l1,...,lni , where l0 is bounded by oi and pi, and l j is bounded by pi and origin(w j), for j ∈ {1,...,ni}. let l0, l1,...,lni , t1,...,tni be the regions enclosed by jordan curves around l0, l1,...,lni , s1,...,sni , at a distance ψi, and let r′i = l0 ∪ ∪ j=1,...,ni l j ∪ ∪ j=1,...,ni tj. we define ri as the external contour of r ′ i . this definition will discard all internal holes (such as the dashed enclosed regions of figure 10), if any, without introducing any additional containment relations between our regions. the details of our construction guarantee that no internal hole will ever contain an origin point. 2 figure 10 shows the side by side, a dag and its corresponding planar region representation; internal holes are represented by dotted lines. our objective here was not to create “nice” visualizations, but to prove that it is possible to represent an arbitrary canonical dag, i.e., an arbitrary hp system structurally based on a canonical dag, by hierarchically nested planar regions. we will next show that, for any finite set of hierarchically nested planar regions, we can build a corresponding canonical dag (i.e., the underlying structure of an hp system). algorithm 16 (algorithm for building a canonical dag from finite set of hierarchically nested planar regions) towards structured modelling with hyperdag p systems 235 1 2 3 4 r1 r2 r3 r4 xo xp figure 10: a height 2 dag and its corresponding representation, built by algorithm 15. assume that we have n hierarchically nested planar regions, 1. label each planar region by ri, i ∈ {1,...,n}, 2. if ri directly nests r j then draw an arc from a vertex vi to a vertex v j, i, j ∈ {1,...,n}, i ̸= j. 2 we now show that a canonical digraph produced from algorithm 16 does not contain any cycles. our proof is by contradiction. let us assume a digraph g produced from algorithm 16 contains a cycle vi,...,vk,...,vi. then every vertex in a cycle has an incoming arc. if vertex vk is a maximal element in a cycle, with respect to direct nesting, then its corresponding planar region rk have the largest region area among planar regions in a cycle. since no other planar region in a cycle can contain rk, there are no arc incident to vertex vk. hence, there is no cycle in g. remark 17 we present in [9] a solution to the problem of representing dags (that contain transitive arcs) by a set of simple regions, where direct containment denotes a parent–child relation. 6 summary we have proposed a new model, as an extension of p systems, that provides a better communication structure and we believe is often more convenient for modelling real-world applications based on tree structures augmented with secondary or shared communication channels. we have shown that hp systems functionally extends the basic functionality of transition p systems and np systems, even though the underlying structure of hp systems is different. in dag-based hp systems, we can have a natural separation of computing cells (sinks) from communication cells (hyperedges). this model also allows us to easily represent multiple inheritance or to distribute computational results (as specified by a dag) amongst several different parts of a membrane structure. we note that the operational behavior of hp systems is separate from the topological structure of a membrane system. in this paper, we illustrated hp systems using the computational rules of np systems, where multisets of objects are repeatedly changed within cells, by using a fixed set of multiset rewriting rules, or transferred between cells, using several possible transfer modes. finally, we provided an intuitive visualization of hp systems, by showing that any set of hierarchically nested planar regions (which represents any set of cells ordered by containment) is equivalent to, or modelled by, a dag without transitive arcs. we provided simple algorithms to translate between these two interpretations. 236 r. nicolescu, m.j. dinneen, y.-b. kim this paper is part of an ongoing research dedicated to structured modelling and model checking of p systems. dedication this article is dedicated to mario j. pérez-jiménez, on the occasion of his 60th birthday (november 2008). bibliography [1] c. berge, hypergraphs: combinatorics of finite sets, elsevier science publishers, 1989. [2] c. carathéodory, theory of functions of a complex variable, vol.1, chelsea publishing company, 1954. [3] g. ciobanu, distributed algorithms over communicating membrane systems, bio systems, 70(2):123–133, 2003. [4] w. f. doolittle, uprooting the tree of life, scientific american, 282(2):90–95, 2000. [5] t. -o. ishdorj, m. ionescu, replicative-distribution rules in p systems with active membranes, proceeding of the first international colloquium on theoretical aspects of computing (ictac 2004), 68–83, 2004. [6] c. li, validating p system as distributed computing models, master thesis, 2008. [7] c. martín-vide, g. păun, j. pazos, a. rodríguez-patón, tissue p systems, theoretical computer science, 296(2):295–326, 2003. [8] r. nicolescu, m. j. dinneen, y.-b. kim, structured modelling with hyperdag p systems: part a, cdmtcs research report series, cdmtcs-342, 1–24, december 2008. http://www.cs.auckland.ac.nz/cdmtcs/researchreports/342hyperdaga.pdf [9] r. nicolescu, m. j. dinneen, y.-b. kim, discovering the membrane topology of hyperdag p systems, proceeding of the tenth workshop on membrane computing (wmc10 2009), curtea de argeş, romania, august 24–27, 426–451, 2009. [10] g. păun, computing with membranes. journal of computer and system sciences, 61(1):108–143, 2000, (and turku center for computer science, tucs report 208, november 1998). [11] g. păun, membrane computing: an introduction. springer-verlag, 2002. [12] g. păun, introduction to membrane computing, proceeding of the first brainstorming workshop on uncertainty in membrane computing, 17–65, 2004. [13] g. păun, r. a. păun, membrane computing as a framework for modeling economic processes, seventh international symposium on symbolic and numeric algorithms for scientific computing (synasc 2005), 11–18, 2005. [14] m. slikker, a. van den nouweland, social and economic networks in cooperative game theory, kluwer academic publishers, 2001. [15] v. i. voloshin, coloring mixed hypergraphs: theory, algorithms and applications, american mathematical society, 2002. [16] c. zandron, c. ferretti, g. mauri, solving np-complete problems using p systems with active membranes, proceeding of the second international conference on unconventional models of computation, 289–301, 2000. towards structured modelling with hyperdag p systems 237 michael j. dinneen received his phd in 1996 from the university of victoria in canada. he is currently a senior lecturer at the university of auckland. his research interests are in combinatorial algorithms, graph algorithms, uncoventional computing models and network design. yun-bum kim is currently a phd student at the university of auckland, new zealand. in 2007 he has completed a msc thesis in network optimization design problems and currently has interests in molecular and distributed computing. radu nicolescu (phd bucharest, macm, memieee) is currently a senior lecturer at the university of auckland, new zealand. he has research interests in formal languages, information complexity and service oriented computing. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 459-472 inverse kinematics solution for robot manipulator based on neural network under joint subspace y. feng, w. yao-nan, y. yi-min yin feng, wang yao-nan, yang yi-min the college of electrical and information engineering hunan university, changsha, hunan province 410082, p.r.china e-mail: yinfeng83@126.com, yaonan@hnu.cn, yimin-yang@126.com abstract: neural networks with their inherent learning ability have been widely applied to solve the robot manipulator inverse kinematics problems. however, there are still two open problems: (1) without knowing inverse kinematic expressions, these solutions have the difficulty of how to collect training sets, and (2) the gradient-based learning algorithms can cause a very slow training process, especially for a complex configuration, or a large set of training data. unlike these traditional implementations, the proposed metho trains neural network in joint subspace which can be easily calculated with electromagnetism-like method. the kinematics equation and its inverse are one-to-one mapping within the subspace. thus the constrained training sets can be easily collected by forward kinematics relations. for issue 2, this paper uses a novel learning algorithm called extreme learning machine (elm) which randomly choose the input weights and analytically determines the output weights of the single hidden layer feedforward neural networks (slfns). in theory, this algorithm tends to provide the best generalization performance at extremely fast learning speed. the results show that the proposed approach has not only greatly reduced the computation time but also improved the precision. keywords: inverse kinematics, neural network, extreme learning machine. 1 introduction the inverse kinematics (ik) problem for a serial-chain manipulator is to find the values of the joint positions given the position and orientation of the end-effector relative to the base. there are many solutions to solve the inverse kinematics problem [1] , such as geometric, algebraic, and numerical iterative methods. in particular, some of the most popular methods are mainly based on inversion of the mapping established between joint space and task space by the jacobian matrix. this solution uses numerical iteration to invert the forward kinematic jacobian matrix and does not always guarantee to produce all possible inverse kinematics solutions. the artificial neural network, which has significant flexibility and learning ability, has been used in the inverse kinematics problem. one solution followed a closed-loop control scheme where a neural network is used to directly learn the nonlinear relationship between the displacement in the workspace and control signal in the joint angle space to achieve a desired position([2] and [3]) . other schemes used neural networks to learn a mapping function from the world space to joint space. although there are various neural networks, the multi-layer perceptron network (mlpn) and the radial basis function network (rbfn) are the most popular neural network applied to functional approximation problems. in [4], the effects of structural parameters, iteration steps and different numbers of training points on the performance of the inverse kinematics approximation were investigated. the results showed that a more complex mlpn configuration is likely to produce a more accurate inverse kinematics approximation. however, it also leads to the number of iterations increasing significantly to satisfy the required training goal. similarly, the neural networkss generalization ability seems to be improved when the number of training sets is increased. however, if the numbers of hidden neurons or training sets are too large, copyright c⃝ 2006-2012 by ccc publications 460 y. feng, w. yao-nan, y. yi-min the training process can not even converge to an expected error goal in some cases. in [5], an mlpn with various structures of the input layer were proposed to solve the inverse kinematics problem of a 6 dof manipulator. three different forms representing the orientation of the end-effector with respect to the base were defined: a 33 rotation matrix, a set of 3 euler angles and one angle and a 13 unit vector. another solution combining an mlpn and a lookup table to solve an inverse kinematics problem of a redundant manipulator was proposed in [6]. although the use of mlpn in the inverse kinematics problem has a greater extent, there have some significant disadvantages. for example, there is no reasonable mechanism to select a suitable network configuration relating to the system characteristics represented by training sets. in addition, training mlpn using the back-error propagation algorithm is complex and slow. for a complex mlpn structure required for a complex configuration manipulator, or a large set of training data, the training process is slow to converge to a specific goal. therefore, trends towards using rbfn which are conceptually simpler and possess the ability to model any nonlinear function conveniently have become more popular. in [7], a variety of network configurations based on rbfn were developed to explore the effect of various network configurations on the performance of the network. in [8], a novel architecture of rbfn with two hidden layers was developed for a inverse kinematics problem of a 3-link manipulator. a fusion approach was proposed in [9]. the proposed approach used rbfn for prediction of incremental joint angles which in turn were transformed into absolute joint angles with the assistance of forward kinematics relations. another rbfn-based method was presented in [10]. it developed a structure of six parallel rbfn, each of which consists of six inputs which represent a location of the end-effector and one output as the joint angle. thus, the group of six parallel rbfn (one for each joint angle) could perform an inverse kinematics approximation. in addition, some hybrid techniques made use of neural networks along with expert system [11], fuzzy logic [12] and genetic algorithm [13] for solving the inverse kinematics. though these intelligence approaches can be applied for two or three dof planar robots, they often demand high performance computing systems and complex computer programming for complex robotic system. traditionally, all the parameters (weights and biases) of the feedforward networks need to be turned. for past decades gradient descent-based methods have mainly been used in various learning algorithms. it is clear that the learning process often needs many training patterns and times to cover the entire workspace. thus, it is not surprising to see that it may take several hours and several days to train neural networks to solve the inverse kinematics. unlike these traditional implementations, this paper uses a novel learning algorithm called extreme learning machine (elm) for single hidden layer feedforward neural networks (slfn) which randomly chooses the input weights and biases, and analytically determines the output weights of slfn [14,15]. in theory, this algorithm tends to provide the best generalization performance at extremely fast learning speed. another issue of concern for solving the inverse kinematics using neural networks is the training data sets. as we know, the joint space of the robot can be considered as an inverse image of the cartesian space and vice versa. thus, the forward kinematics can be assumed to be an inverse image of inverse kinematics and vice versa [9]. the pose p can be used as an input and the corresponding joint angle q as the output for the neural network training data. in other words, q−→p relationship is used while generating the data whereas p−→q mapping is done while training the neural network. usually, the inverse kinematics problems have multiple solutions. for example, the puma 560 robot has at most eight solutions when there are no joint limits imposed. hence, the inverse kinematics equation is one-tomany mapping.unfortunately,the neural network can not match the actual output with the desired output. so the learning error of neural network is hard to be calculated when training. an effective solution is that the training sets are constrained to only one solution set so that the one-to-one mapping can be achieved. for simple structure, such as two-link planar manipulator, the training sets can be collected based on the inverse kinematics equation which only consist of either the positive or negative solution. however, this solution has the difficulty of how to collect constrained data without knowing the inverse kinematic inverse kinematics solution for robot manipulator based on neural network under joint subspace 461 expressions of the complex robotic system. the present work attempts to resolve this crucial issue by using a novel heuristic algorithm, called electromagnetism-like method (em)[16,17], for determining a joint subspace which includes one and only one inverse kinematics solution for a given pose. for convenience’s sake, a graphic depiction of the proposed method is illustrated by using a 2d example, as shown in figure 1. 1 2 the whole joint space joint subspace 1 d 2 d 1 2 3 figure 1: an illustration of the proposed algorithm. the point 1 and 2 is true solution and point 3 is an approximate solution in figure 1, assume that there are two inverse solutions in the whole space. one approximate solution is denoted by (θ∗1,θ ∗ 2) . if we select appropriate dθ1 and dθ2 such that θ1 ∈ [θ ∗ 1 − dθ1,θ ∗ 1 + dθ1] and dθ2 ∈ [θ∗2 − dθ2,θ ∗ 2 + dθ2], this joint subspace includes just one true solution point 1. based on this, the data required for training of neural network is proposed to be derived from the joint subspace with the forward kinematics relations instead of deriving a set complex inverse kinematics equations from the whole joint space. then the true solution point 1 can be approached by using the trained network. the proposed method can be summarized as follows: 1. given a desired coordinate of position and orientation of the end-effector, making use of em to calculate an approximate solution near to one true solution. 2. specify appropriate value of dθk such that θk ∈ [θ∗k − dθk,θ ∗ k + dθk](k = 1,2, · · · ,n), where n is the number of joint and θ∗k is the approximate solution of the k joint variable calculated with em in step 1. for the sake of simplicity, all dθ can be set to be the same value. 3. collect the training sets from the joint subspace, and train the neural network with elm. for a new coordinate of position and orientation of the end-effector, the neural networks usually need to be retrained following the steps above. fortunately, our results show that the training process is very fast. thus, the retraining procedure appears to be acceptable. 2 calculation of joint subspace with em 2.1 problem formulation as shown in figure 2, the desired position vector and orientation matrix of a manipulator end-effector are denoted by: pd and [rd] = [d1,d2,d3], where dj (j=1, 2, 3) are unit vectors along the xd,yd,zd axes. ph is the current position vector of the end-effector. the current orientation matrix is defined by : [rh] = [h1,h2,h3], where hj (j=1,2,3) are unit vectors along the xh,yh,zh axes and the joint variables are denoted by the n×1 vector, θ= [θ1,θ2, · · · ,θn]t. the error between the current and the desired locations of the end-effector can be described by the following functions [18]: 462 y. feng, w. yao-nan, y. yi-min figure 2: the current and the desired end-effector configurations position error: ∆p(θ) =∥ pd − ph(θ)∥ (1) orientation error: ∆o(θ) = 3∑ j=1 (d j · h j (θ) − 1) 2 (2) the total error: e(θ) =∆p(θ) +∆o(θ) (3) where (·) denotes the vector dot product. furthermore, the total error can be chosen to be a weighted sum of the position and orientation components: e(θ) =wp∆p(θ) +wo∆o(θ) (4) where wp and wo are weighting factors assigned to position and orientation, respectively, such that wp +wo = 1. now the inverse kinematics problem is to find a solution θ∗, such that e(θ∗) ≤ ε(ε−→ 0).it is clear that this problem can be transformed into the following minimization problem: mine(θ) s.t. θ ∈ℜn|lk ≤ θk ≤ uk, i = 1,2, · · · ,n (5) 2.2 brief of electromagnetism-like method (em) to solve the problem in (5), the general scheme of em is given by following procedures: initialize, local search, calculation of charge and total force vector and movement according to the total force. initialization the procedure initialization is used to sample m points, θ1, · · · ,θm, randomly from the feasible domain of the joint variables, where θi = [θi1, · · · ,θ i n](i = 1, · · · ,m). the procedure uniform sampling can be determined by following θik = lk + rand · (uk − lk) k = 1,2, · · · ,n (6) inverse kinematics solution for robot manipulator based on neural network under joint subspace 463 the procedure ends with m points identified, and the point that has the best function value is stored in θbest. local search the local search procedure is used to gather the local information and improve the current solutions. it can be applied to one or many points for local refinement per iteration. the selection of these two procedures, does not affect the convergence result. calculation of charge and total force vector the charges of the points are calculated according to their objective function values, and the charge of each point is not constant and changes from iteration to iteration. the charge of the ith point, qi, is evaluated as following qi = exp[−n (e(θi) − e(θbest)) m∑ k=1 (e(θk) − e(θbest)) ], i = 1,2, · · · ,m (7) in this way, points that have better objective values possess higher charges. notice that, unlike electrical charges, no signs are attached to the charge of an individual point in (7). instead, the direction of a particular force between two points is decided after comparing their objective function values. hence, the total force fi exerted on point i is computed by the following equation fi =  m∑ j,i (θ j − θi) q jqi ∥ θ j − θi ∥2 , i f e(θ j) < e(θi) m∑ j,i (θi − θ j) q jqi ∥ θ j − θi ∥2 , others (8) according to (8), the point that has a better objective function value attracts the other one. contrarily, the point with worse objective function value repels the other. since θbest has the minimum objective function value, it acts as an absolute point of attraction. then it attracts all other points in the population to better region. movement according to the total force after evaluating the total force vector fi, the point i is moved in the direction of the force by a random step length in (8). here the random step length λ is assumed to be uniformly distributed between 0 and 1. θi = θi +λ fi ∥fi∥ rng, i = 1,2, · · · ,m (9) in (9), rng is a vector whose components denote the allowed feasible movement toward the upper bound uk or the lower bound lk of the joint variables. after finishing the above procedures, the positions of points are updated and we have finished one iteration calculation of em. take the figure 3 for example. there are three particles and their own objective values are 15, 10 and 5, respectively. because particle 1 is worse than particle 3 while particle 2 is better than particle 3, particle 1 represents a repulsion force which is f13 and particle 2 encourages particle 3 that moves to the neighborhood region of particle 2. consequently, particle 3 moves along with the total force f. 464 y. feng, w. yao-nan, y. yi-min 1 2 3 ( ) 5f x ( ) 15f x ( ) 10f x f13{repulsion} f f23 {attraction} figure 3: an example of attract-repulse effect on particle number 3 2.3 performance evaluation of em in solving the inverse kinematics this example is used to examine the precision of the approximate solution calculated by em, which directly impact the choice of the interval width dθ. the robot structure for this example is based on puma 560. the link parameters are given in table 1. table 1 the link parameters of the puma 560 robot joint link length (m) twist angle (degree) offset length (m) joint limitations (degree) 1 0 -90 0.6604 [−160,160] 2 0.4320 0 0.2000 [−225,045] 3 0 90 -0.0505 [−045,225] 4 0 -90 0.4320 [−110,170] 5 0 90 0.0000 [−100,100] 6 0 0 0.0565 [−266,266] the desired configuration of the end-effector is given by: pd=[0.7433, 0.3111, 0.7883] (m), and d1= [-0.6366, 0.7712, -0.0084], d2= [0.0227, 0.0296, 0.9993], d3 = [0.7709, 0.6359, -0.0364]. note that there are multiple solutions within the joint limitations shown in table 1. for the sake of simplicity, the joint 1 and 3 limitations are rearranged into [-120,160] and [-45,120], respectively. for the given coordinates of the end-effector, it corresponds to an exact solution of θ = [10 20 30 40 50 60](degree) within the adjusted joint limitations. then the error between an approximate solution and the true solution can be easily calculated. the stopping criterion for em is defined by ε=0.01. in other words, stop calculation when the total error (see (3)) is less than ε. in this example, 100 trials have been conducted for em and the maximum absolute error (absolute value) at each joint angle is shown in figure 4. from figure 4, the widths of the joint limitations are set as about 20◦ at least, i.e.dθ = 10◦, which can guarantee that the one-to-one mapping is achieved. it should be noted that em is not suitable for high precision applications. as can be seen in figure 5, the number of evaluations drastically increases with precision. however, during the early stage of computations, em algorithm is highly efficient. thus, em is suitable for providing a good initial guess. 3 model the inverse kinematics with neural network the architecture used for solving the inverse kinematics problems is shown in figure 6. the single layer network consists of n outputs (joint angles) and 12 inputs [n, s, a, p] which represents a location (position and orientation) of the end-effector. as mentioned earlier, the training set have been constrained to only one solution set so that the one-to-one mapping could be achieved. for the present work, a fast and accurate learning algorithm called as extreme learning machine inverse kinematics solution for robot manipulator based on neural network under joint subspace 465 figure 4: maximum absolute error at each joint angle among 100 trials 0 50 100 150 200 250 300 350 400 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 steps t o ta l e rr o r figure 5: the performance of em 466 y. feng, w. yao-nan, y. yi-min x p y p z p x n y n z n x s y s z s x a y a z a 1 2 1n n position and orientation of the end-effector output: joint angles figure 6: a general structure of the slfn to approximate the inverse kinematics (elm) are used to train the neural network in modeling the inverse kinematics of robot. test results show that the learning speed of elm algorithm is much faster than the traditional methods. for example, the learning speed of elm is at least 1000 and 2000 times faster than bp and svm for solving the benchmark problem of california housing [14]. thus, this new training method is very suitable for solving the inverse kinematics. 3.1 brief of extreme learning machine (elm) elm is a unified with randomly generated hidden nodes independent of the training data. the output of an slfn with l hidden nodes can be represented by fl(x) = l∑ i=1 βig(ai,bi,x), x ∈ rn,ai ∈ rn (10) where ai and bi are the learning parameters of hidden nodes and βi is the weight connecting in ith hidden node to the output node. g(ai,bi,x) is the output of the ith hidden nodes with respect to the input x. additive and rbf hidden nodes are used often in applications. for additive hidden node with the activation function g(x) (e.g. sigmoid,threshold,sin,etc.), g(ai,bi,x) is given by g(ai,bi,x) = g(ai · x + bi), bi ∈ r (11) where ai is the weight vector connecting the input layer to the ith hidden node and bi is the bias of the ith hidden node. ai · x denotes the inner product of vectors. for rbf hidden node with activation function g(x) (e.g. gaussian), g(ai,bi,x) is given by inverse kinematics solution for robot manipulator based on neural network under joint subspace 467 g(ai,bi,x) = g(bi ∥ x − ai ∥) bi ∈ r+ (12) where ai and bi are the center and impact factor of ith rbf node. r+ indicates the set of all positive real values. the rbf network is a special case of slfn with rbf nodes in its hidden layer. each rbf node has its own centroid and impact factor, and its output is given by a radially symmetric function of the distance between the input and the center. for a given set of trainning samples(xi,ti)ni=1 ⊂ r n ×rm,if the outputs of the network are equal to the targets, we have fl(x j) = l∑ i=1 βig(ai,bi,xi) = t j j = 1,2, · · · , n. (13) above equation can be written compactly as hβ= t (14) where h =  g(a1,b1,x1), · · · ,g(al,bl,x1) ..., · · · , ... g(a1,b1,xn ), · · · ,g(al,bl,xn )  n×l (15) β=  βt1 ... βtl  l×m and t =  tt1 ... ttn  n×m (16) βt is the transpose of a matrix or vector β. h is called the hidden layer output matrix of the network; the ith column of h is the ith hidden node’s output vector with respect to input and the jth row of h is the output vector of the hidden layer with respect to input x j. usually, when the number of training data is larger than the number of hidden nodes n > l, one can not expect an exact solution of the system (14).after the hidden nodes are randomly generated and given the training data, the hidden-layer output matrix h is known and need not be tuned. thus, training slfns simply amounts to getting the solution of a linear system (14) of output weights β. under the constraint of minimum norm least square, i.e., min ∥β∥ and∥hβ− t∥ , a simple representation of the solution of the system (14) is given explicitly as β̂= h†t (17) where h† is the moore-penrose generalized inverse of the hidden-layer output matrix h. the simple learning algorithm can be summarized as follows: algorithm elm: given a training set ℜ= (xi, ti)|xi ∈ rn,ti ∈ rm, i = 1, · · · , n, activation function g(x),and hidden neuron number n step 1: assign arbitrary input weight wi and bias bi, i = 1, · · · , n; step 2: calculate the hidden layer output matrix h; step 3: calculate the output wight β : β= h†t where h,β and t are defined as formula (15) and (16). 468 y. feng, w. yao-nan, y. yi-min 4 performance evaluation and discussion example 1: this simple example demonstrates that the neural network trained by the constrained data can produce a better approximation of the inverse kinematics function. an rbfn is used to approximate the inverse kinematics function of two-link manipulator. it consists of two revolute joints and two links that have the same length of 30mm. two coordinate values x, y describe the position of the tip of the manipulator. the forward kinematics is x = l1cosθ1 + l2cos(θ1 + θ2)y= l1 sinθ1 + l2 sin(θ1 + θ2) (18) the inverse kinematics can be described by θ2 = atan2(± √ 1 − ( x2 +y2 − l21 − l 2 2 2l1l2 )2, x2 +y2 − l21 − l 2 2 2l1l2 ) (19) θ1 = atan2(y, x) − atan2(l2 sinθ2, l1 + l2cosθ2) (20) given a desired configuration of the end-effector, there are usually two desired true solutions which correspond with the lower-elbow structure and the upper-elbow structure respectively. we test the neural network with three different cases. 1. the training set randomly sample from the whole joint space. 2. the training set randomly sample from the constrained joint space which only consisted of the positive solution (+sign in (19)). 3. the training set randomly sample from the sub joint space. all the simulation are carried out in matlab 6.5 environment running in an intel(r) core(tm) 2 duo cpu 3.00ghz pc. the training process of the neural network can be executed using matlab code "newrb". the root mean squared (rms) error goal is defined by 0.001, and the number of training sets is 1000. 0 100 200 300 400 500 10 −4 10 −2 10 0 10 2 10 4 steps r m s e rr o r case1,training time:84.8590s case2, training time: 10.5620s case3,training time:1.3440s figure 7: the training convergence performance as can be seen in the figure 7, the same network trained by the constrained training data produces a better convergence performance.moreover, the neural network trained within the sub joint space produces the best performance. for case 1, the training sets contain the many-to-one mapping from the joint space to the cartesian space. it may be one reason that leads to training failure. inverse kinematics solution for robot manipulator based on neural network under joint subspace 469 example 2: in this example, the performance comparison of the new proposed elm algorithm and the gradient-based learning algorithm has been conducted for an inverse kinematics of puma robot. the desired configuration of the end-effector is the same with section 2.3. test 1:training the network with traditional algorithm first, the approximate solution is calculated by em. set the dθ = 30◦, then one of the sub space is determined for each joint, as shown in table 2. table 2 one group of joint subspace joint number joint 1 joint 2 joint 3 joint 4 joint 5 joint 6 subrange (degree) [-20.1, 39.9] [-8.7, 51.3] [2.1, 62.1] [2.9, 62.9] [11.9, 71.9] [37.6, 97.6] next, different training size, which is 100, 500 and 1000 respectively, sample randomly from the sub joint space. other 500 data set is used for testing the performance of neural network. the root mean squared error goal and the maximum number of neurons are set as 10−8 and 500, respectively. and the spreads in three cases are experimentally selected as 3, 1.2 and 1.2 so that the rbfn can produce an appropriate performance. the training steps are repeated until the network’s root mean squared error falls below goal or the maximum number of neurons are reached. figure 8 shows the training convergence performance obtained by using different training size. the training time increases greatly with the number of the training data, as can be seen in the figure 8. although the training error fail to reach the goal 10−8 using 500 and 1000 training data, all three trained networks are considered to achieve a good approximate performance. since the training error successfully reach the 10−4 in three case, which is an accepted result for inverse kinematics. these conclusions can also be confirmed by the following results. figure 9, 10 and 11 shows the testing root mean square (rms) error at each joint angle using the corresponding networks trained above. it can be seen that the rms error is very small. in addition, the network trained using 500 and 1000 size performs similarly. and the generalizations of both of them are better than the network trained using 100 data size. this occurs because less training data reduces the generalization of the network. however, taking into account training time, the network trained with less data size appears to be a better choice. 0 100 200 300 400 500 10 −8 10 −6 10 −4 10 −2 10 0 10 2 10 4 steps r m s e rr o r training size=1000, training time=100.0780s training size=500 training time=44.320s training size=100 training time=0.8750s figure 8: network training convergence with different training size. figure 9: absolute error at each joint angle using 100 training set test 2: training the network with elm in this example, a single feedforward network with sigmoidal additive activation function is used. for elm, the input weights and biases are randomly chosen from the range [-1, 1].to compare the results of elm and gradient-based learning algorithm in test 1, two groups of tests use the same training/testing sets. figure 12 shows the training rms errors with different hidden nodes number in three cases. the corresponding testing rms errors are plot in figure 13. the average training time is 0.0014 s, 0.0056 s and 0.012 s, respectively. as observed from figure 12 and 13, in general, the network trained using three 470 y. feng, w. yao-nan, y. yi-min 1 2 3 4 5 6 0 0.5 1 1.5 2 2.5 3 3.5 x 10 −3 joint number t ra in in g r m s e rr o r( d e g re e ) figure 10: absolute error at each joint angle using 500 training set. 1 2 3 4 5 6 0 0.5 1 1.5 2 2.5 3 3.5 x 10 −4 joint number t ra in in g r m s e rr o r (d e g re e ) figure 11: absolute error at each joint angle using 1000 training set groups of training data performs similarly. furthermore, the lowest validation error is achieved when the numbers of hidden nodes are within the ranges [15, 50]. the results show that the generalization performance obtained by the elm algorithm is very close to the generalization performance of gradientbased learning algorithm. however, the elm algorithm can be simply conducted and runs much faster. according to our results, the average learning speed of elm algorithm is at least 1000 times than the gradient-based learning algorithm. 0 5 10 15 20 25 30 35 40 45 50 0 0.005 0.01 0.015 0.02 (a) number of hidden nodes t ra in in g r m s e rr o r 0 5 10 15 20 25 30 35 40 45 50 0 0.005 0.01 0.015 0.02 (b) number of hidden nodes t ra in g r m s e rr o r 0 5 10 15 20 25 30 35 40 45 50 0 0.005 0.01 0.015 0.02 (c) number of hidden nodes t ra in g r m s e rr o r figure 12: the training rms error (degree) with elm, the training size: (a) 100, (b) 500 and (c) 1000. example 3: this example demonstrates that the proposed method can be used for continuous joint space trajectory planning. the robot structure for this example is still based on puma 560 robot. the desired trajectory of the end-effector is a circle centered at (0.2, 0.05, 0.5) (m) with respect to the base coordinate frame and a radius equal to 0.2(m). the trajectory is discretized into 72 equally spaced points. to ensure the existence of solution, the joint limitations are released in this example. moreover, noting that multiple solutions do exit, in order to prevent a sudden jump to another solution, a unique orientation is assigned and the approximate solution for each of the successive points is given by the solution of the preceding point. for example, if the calculated solution of the k point is denoted by θk, the joint variable limitations are set as [θk − dθ,θk + dθ] for the k+1 point, instead of re-computing the approximate solution. the computed joint trajectories and the corresponding total error (sum of the position and orientation error) are plotted in figure 14 and figure 15, respectively. it should be noted inverse kinematics solution for robot manipulator based on neural network under joint subspace 471 0 5 10 15 20 25 30 35 40 45 50 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 number of hidden nodes t e st in g r m s e rr o r (d e g re e ) training data size=100 training data size=500 training data size=1000 figure 13: the testing rms error with elm. that the trajectory in figure 14 is just one of the multi-trajectory for puma robot. 0 10 20 30 40 50 60 70 80 −100 −50 0 50 100 150 200 trackpoint jo in t v al ue (d eg re e) theta1 theta2 theta3 theta4 theta5 theta6 figure 14: computed trajectories of the joints. 0 10 20 30 40 50 60 70 80 0 0.5 1 1.5 2 2.5 x 10 −9 trackpoint t ot al e rr or ( th e po si tio n an d or ie nt at io n er ro r) figure 15: total error at each track point the results in figure 15 show that the idea of using a neural network has produced an excellent approximation of the inverse kinematics function. although neural network solutions are usually not suited for high precision robotic application, high precision results are achieved here. this occurs because the joint varies are limited within a small space when training network. 5 conclusions the proposed hybrid approach combined the electromagnetism-like method and the neural network to solve the inverse kinematics problem. unlike the traditional neural network approaches that generate the training data from the whole joint space, the neural network in the proposed approach collects the training data from a sub joint space, in which the training set is constrained to only one solution set so that the one-to-one mapping is achieved. another important feature of the proposed approach is to use an efficient learning algorithm, elm, to train the neural network. the learning speed of this novel training algorithm can be thousands of times faster than traditional feedforward network learning algorithms while obtaining better generalization performance. the results show that the proposed hybrid approach has not only greatly reduced the computation time but also improved the precision. 472 y. feng, w. yao-nan, y. yi-min bibliography [1] bruno siciliano, oussama khatib, springer handbook of robotics,springer press,2008. 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[13] karlra p, prakash nr, a neuro-genetic algorithm approach for solving inverse kinematics of robotic manipulators, ieee international conference on systems, man and cybernetics,2:1979-1984,2003. [14] guang-bin huang, qin-yu zhu, chee-kheong siew, extreme learning machine: a new learning scheme of feedforward neural networks, in proceedings of ieee international joint conference on neural networks,2:985-990,2004. [15] guang-bin huang, lei chen, enhanced random search based incremental extreme learning machine,neurocomputing, 71(16-18):3460-3468,2008. [16] birbil si, fang sc, an electromagnetism-like mechanism for global optimization,journal of global optimization, 23(3):263-282,2003. [17] birbil si, fang sc, sheu rl, on the convergence of a population-based global optimization algorithm,journal of global optimization, 30:301-318,2004. [18] wang lct, chen cc, a combined optimization method for solving the inverse kinematics problem of mechanical manipulators,ieee transaction on robotics and automation, 7(4):489-499,1991. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 3, pp. 304-316 surface roughness image analysis using quasi-fractal characteristics and fuzzy clustering methods tiberiu vesselenyi, ioan dzitac, simona dzitac, victor vaida abstract: in this paper the authors describe the results of experiments for surface roughness image acquisition and processing in order to develop an automated roughness control system. this implies the finding of a characteristic roughness parameter (for example ra) on the bases of information contained in the image of the surface. to achieve this goal we use quasi-fractal characteristics and fuzzy clustering methods. keywords: image processing, surface roughness, quasifractal parameters, fuzzy clustering. 1 introduction surface roughness of manufactured products is defined in sr iso 4287/2001 standard and other international standards. simple and complex characterization parameters are explained in works like [1], which are considering the use of stylus devices to measure roughness after a linear or curved path [3, 7, 7, 12]. although these devices had been continuously upgraded in order to increase measuring precision [3], they are not efficient enough used in automated measuring systems, due to the fact that the stylus must make contact with the measured surface and also due to the very long time of measurement. a newer technique in surface roughness measurement is the employment of digital image acquisition and processing [4, 6]. in this case the camera is coupled to a microscope (bellow a magnification of x100) and the acquisitioned images are processed with specially designed computer programs. so one image of 24 mm2, corresponds to 100 stylus scanning. in the paper [10] a method of summit and directionality identification of textured images is defined using surface image analysis. in [2] there is shown that surfaces obtained by turning, milling and grinding presents a high complexity when they are analyzed by optical, electron microscopes or afm (atomic force microscope). isotropic surfaces obtained by machining can be characterized by the mandelbrotweierstrass function using fractal methods. for the characterization of anisotropic surfaces some authors are proposing methods based on the two dimensional fft algorithm. this method had been used for characterization of grinded surface. the basic idea in [11] is to decompose the surface roughness (described in terms of amplitude, wave length and direction) in convex elements (summits) and then analyze these structures with morphologic trees. the authors claim that clear correlations can be obtained between the obtained morphologic tress and the tribologic proprieties of the surfaces. in [4] the correspondence between surface roughness obtained with a certain manufacturing method and its image had been studied based on functional dependence between roughness height and the grey level values of the image. the authors had studied these correspondences with the help of polynomial nets, trained with experimental results, which has as inputs cutting speed, feed and cutting depth and also the mean values of image grey levels. the polynomial net is capable to estimate the surface roughness with an acceptable accuracy, which had been validated with a series of experimental measurements. the advantage of this method is the possibility to apply it to online roughness estimation in turning processes. the paper [9] proposes a new method to analyze and characterize the surface roughness. on the basis of an algorithm in three steps the classification of textured images of some manufactured surfaces is made, surfaces obtained by casting, milling and grinding. in the first step the image is processed by a frequency normalized wavelet transform, obtaining a set of images at different scales and phases. in the copyright © 2006-2008 by ccc publications surface roughness image analysis using quasi-fractal characteristics and fuzzy clustering methods 305 second step characteristic parameter values are extracted and in the third step the image classification is obtained using the set of extracted features. from the synthesis of presented works, it can be concluded that the method of surface image processing is better than the methods using the stylus type measurement, because it is faster and there is no contact between the measuring instrument and the surface. it also has been shown that there is a correlation between the height of the roughness and the image grey levels and the estimation of surface roughness can be done in similar ways as texture analysis. 2 acquisition and preprocessing of surface images for image acquisition purposes several manufactured roughness probes with known roughness parameters were used (stalî dovodca gost 9378-80 e15718) obtained by manufacturing operations as: cylindrical milling, plane milling, shaping, frontal grinding, plane grinding and polishing. in this paper only surface roughness images representing shaping, plane grinding and polishing will be analyzed, because these images are more similar to each other and harder to classify. four non-overlapping images, of every probe’s surface were taken using a ccd camera mounted on a citival microscope at magnifications of x10 and x25. the resolution of the images was 640x480 pixel. the correlation between surface roughness and surface image had been studied in a large number of papers [4] showing a certain functional dependency between asperity height and image intensity. during experiments however, we observed that this correlation is more complex and depends in a very high degree on the illumination conditions of the probe. usual image processing phases, of non-object representation images are presented in [14]. after the image acquisition, a number of preprocessing operations had to be made in order to obtain better image quality. the used preprocessing steps were as follows: filtering eliminate inherent image noises; establishing region of interest keep only high information regions of the image; uneven illumination effects elimination eliminate effects of higher intensity in the middle of the image, which is characteristic for images taken with microscopes (figure 1 and 2) ; correction of probe rotation and position variations as the images are anisotropic, rotation of the probe can alter the analysis results. here 2d fast fourier transforms described in [2] or oriented gabor filters described in [5], can be used. the authors had tested several automatic image rotation algorithms and finally a 1d fft method was used. on the base of tested preprocessing methods a program module had been developed, which can perform al preprocessing steps automatically and which can be eventually included in an automated quality control system. after preprocessing, the image quality was fair enough to perform the next step of image processing. studying recent researches in texture analysis and image processing a number of statistical methods (co-occurrence, statistical moments) and frequency domain methods (gabor filters, wavelet analysis), had been tested in order to obtain automated recognition of surface roughness parameters, but these methods didn’t yield the wanted results. so we focused our research on fractal methods. 3 fractal image processing characterization considering the goal of our research, computation of fractal dimension is less important from a practical point of view. it is more important to use fractal or pseudo-fractal parameters in order to discriminate surfaces with different roughness characteristics. fractal dimension computation of rough 306 tiberiu vesselenyi, ioan dzitac, simona dzitac, victor vaida figure 1: surface image with higher center intensity. figure 2: image after preprocessing (uneven lighting and rotation has been eliminated). surface roughness image analysis using quasi-fractal characteristics and fuzzy clustering methods 307 surfaces using the weierstrass-mandelbrot function is described in [2] and others. when this function is correlated to power spectral density, the fractal dimension is correlated to the slope of the spectrum represented in logarithmic scale. the weierstrass-mandelbrot function is difficult to apply in practice. that is why we had to use methods, which are easier to implement as computer algorithms. these methods are the box counting method (bc) and the frequency domain fractal parameter (using power spectral density diagrams). both methods had been tested on the roughness probes images. the box counting method (bc) had been derived from the “compass dimension” and is closely related to fractal dimension as it has been stated by mandelbrot with the relation: d = log n log(1/r) (1) the compass dimension is obtained measuring a curve (which can represent a section through a surface) with decreasing measuring units (ri, i = k...1) and storing the number of measures ni for each ri. the diagram of log(ni) as function of log(1/ri) is drawn obtaining a so called richardson plot. if the richardson plot is a straight line then the measured object is fractal and the slope of the plot is it’s compass dimension. the bc method uses rectangular boxes of decreasing edges instead the linear measure ri. fractal dimension can also be computed on the bases of power spectral density (psd), as it is stated in [1]. if the psd amplitude is represented as a function of spatial frequency (f) in a logarithmic diagram then the fractal parameter can be considered as the slope (p′1) of the log(psd) approximation line and the (p2’) as the intersection of this line with the ordinate axis. log(psd) = p′1 log( f ) + p ′ 2 (2) first we have developed a 2d box counting algorithm and then a 3d algorithm (which uses 3d boxes on 3d matrix as shown in figure 2) both yielding satisfactory results. although the obtained results show that the analyzed images do not have true fractal behavior (the resulted richardson plot is not a rigorously straight line), the goal is to find correlations between obtained parameters on one hand and the surface roughness on the other hand. in this research we had used the bc3d (3 dimensional box counting) method, but in the richardson plot, instead of using only linear approximation in order to define parameters, we also used second and third degree polynomials. if we denote y = log(ni) and x = log(1/ri), we will have the following relations: y = p1x + p2 (3) y = p3x 2 + p4x + p5 (4) y = p6x 3 + p7x 2 + p8x + p9 (5) examples of curve fitting for relations (3), (4), (5) are given in figures 3, 4 and 5. we had considered then to use coefficients p1ěp9 to characterize the roughness of the studied manufactured surfaces. these coefficients had been named by us “quasi fractal parameters” (qfp, meaning fractal-like parameters) highlighting the fact that they are not rigorously correlated to fractal dimension. in order to study the possibilities to classify images representing rough surfaces, with the above defined parameters, we have made two dimensional representations that we denoted as qfp diagrams (quasi fractal parameter diagrams). each of these diagrams represents a bi-dimensional space having as dimensions two of the qfps defined above. 308 tiberiu vesselenyi, ioan dzitac, simona dzitac, victor vaida figure 3: linear curve fitting for points obtained with the bc3d algorithm applied on an image of shaped surface. figure 4: second order polynomial curve fitting for points obtained with the bc3d algorithm applied on an image of shaped surface. surface roughness image analysis using quasi-fractal characteristics and fuzzy clustering methods 309 figure 5: third order polynomial curve fitting for points obtained with the bc3d algorithm applied on an image of shaped surface. 4 experimental considerations in order to establish how qfps can characterize surface roughness we had acquired and processed four different images from each surface roughness probe, for three types of cutting operations: shaping with 4 different roughness values, grinding with four different roughness values and polishing with three different roughness values. table 1 shows sample categories, roughness values, sample codes and diagram symbols. in diagram symbols the color represents the operation and the symbol represents a specific roughness value. some examples of analyzed images are shown in figure 6. there had been also acquired images for magnification of x25. for both magnifications, the images had been studied using qfp diagrams. two examples of such diagrams are shown in figure 6.a and 6.b. comparing diagrams in figures 6.a. and 6.b., it can be observed that some of the same studied samples have different locations in the parameter space. for example the black squares (representing s4 samples) have one location for x10 magnification and other location for x25 magnification. this shows that p1 and p2 parameters are not “true fractal” parameters because they do not exhibit an invariance to scale, but this also shows that if in x10 magnification we can not really distinguish the s4 samples from the g group samples, at x25 magnification the s4 samples are well distinguishable. this lead to the conclusion that acquiring and analyzing sample images at different magnifications can help to better discriminate the samples. the selection of optimum magnifications to be used needs to be experimented for large sets of images. in this paper we will present discrimination algorithms only for samples with magnification of x10. the establishing of box dimension range is also an issue, but this can be also solved with experiments on large sets of calibration images. the authors plan to present algorithms for these issues in a future work. we had mentioned before that there is a closed link between fractal dimension 310 tiberiu vesselenyi, ioan dzitac, simona dzitac, victor vaida figure 6: examples of studied images (magnification x10, coding as stated in table 1). figure 7: qfp diagrams for parameters p1 − p2, a. magnification x10; b. magnification x25. surface roughness image analysis using quasi-fractal characteristics and fuzzy clustering methods 311 table 1: machining roughness code diagram operation (ra) symbol 12.5−6.3 s1 black diamond shaping 6.3−3.2 s2 black up pointing triangle 3.2−1.6 s3 black down pointing triangle 1.6-0.8 s4 black square 3.2−1.6 g3 blue down pointing triangle grinding 1.6−0.8 g4 blue square 0.8−0.4 g5 blue circle 0.4−0.2 g6 blue star 0.2−0.1 p7 red plus sign polishing 0.1−0.05 p8 red x 0.05−0.025 p9 red dot and power spectral density parameters, obtained with fft methods [1] and [2]. the authors had made experiments with the psd method too, in order to compare the two methods. for the psd method the same probes were used as in the first case. the obtained results were very close to results obtained with the box counting method. here an issue to solve is the automated selection of the analysis frequency range. establishing samples discrimination can be made automatically by a series of clustering methods, like fuzzy c-means or artificial neural networks. the goal of applying clustering methods is to find the cluster center for each sample with known roughness parameter (training phase). after finding the cluster central point this will be used to classify unknown roughness samples (recognition phase). in this phase we will discuss only the training phase. regardless what kind of clustering method we use, a good practice is to make the discrimination in successive steps: 1. plot the qfp diagrams for combinations of quasi-fractal parameters taken by two (it is also possible to use higher order qfp spaces, but these can not be properly represented in diagrams); 2. apply a clustering algorithm and find cluster centers; 3. observe which samples are well discriminated, store the cluster centers for these samples and eliminate them from the data set; 4. restart from step 1. with the remaining samples, until all the samples are discriminated. 5 fuzzy c-means clustering of quasi-fractal parameters in order to perform sample discrimination, we choose to use the fuzzy c-means clustering method, which has been implemented in some programming environments as for example in matlab. this method does not need large sets of data for training and was suitable for our purpose. in this method each sample is considered to be a part of a cluster, in some degree, defined by a membership degree. this method has been introduced in [13], as an enhancement of existing clustering methods. the method solves the problem of clustering by grouping data sets in multi-dimensional spaces in a number of specified clusters. the method starts with an estimation of the cluster centers marking the central value of each cluster. in the same time, for each sample is assigned a membership degree, which reflects the 312 tiberiu vesselenyi, ioan dzitac, simona dzitac, victor vaida belonging of that sample to certain cluster. the initial cluster center is then successively modified in order to achieve the minimum of an objective function which is the distance of a sample to the center, weighted by the samples membership degree. we are presenting in the followings the results obtained applying the fuzzy c-means clustering method to the qfp spaces of the described samples. final clustering is achieved in 4 phases. figure 8: qfp diagram (a) and clustering (b) for phase 1. the black diamond markers in (b) represent the cluster center and each color represents one cluster. figure 9: qfp diagram (a) and clustering (b) for phase 2. the black diamond markers in (b) represent the cluster center. in the first phase (figure 8) we could find a well discriminated clustering of samples s1, s3, s4, in the second phase (figure 9) we discriminate s2, in the third (figure 10) p1, p2, p3 and g6 and eventually in phase four (figure 11) g3, g4, g5. the found cluster centers will serve to classify any unknown sample later on. in order to increase clustering precision, a combination of different diagrams can also be used. 6 conclusions in this paper new quasi-fractal parameters (p1 . . . p9) were defined and a new type of diagram (qfp diagram) was proposed to achieve roughness image recognition. surface roughness image analysis using quasi-fractal characteristics and fuzzy clustering methods 313 figure 10: qfp diagram (a) and clustering (b) for phase 3. figure 11: qfp diagram (a) and clustering (b) for phase 4. 314 tiberiu vesselenyi, ioan dzitac, simona dzitac, victor vaida the presented method is not an optimal one since the selection of parameters for the different phases was made by hand, but this selection is needed to be done only once for a group of roughness samples obtained with certain manufacturing methods. the algorithms must also be tested on larger sets of images. as further developments the selection of box dimension range for bc3d method and frequency range for psd method will be studied. we will also study the influence of magnification on quasi-fractal parameter discrimination, and a method to find optimal parameter selection. bibliography [1] m. a. costa fractal description of rough surfaces for haptic display phd thesis, stanford university, 2000. [2] z. jiang; h. wang; b. fei research into the application of fractal geometry in characterising machined surfaces, international journal of machine tools and manufacture, elsevier science ltd, 2001. [3] r. k. leach nanosurf iv: traceable measurement of surface texture at the national physical laboratory, international journal of machine tools and manufacture, elsevier science ltd, 2001. [4] b. y. lee; y. s. tangsurface roughness inspection by computer vision in turning operations, international journal of machine tools and manufacture, elsevier science ltd, 2001. [5] d. m. tsai; s. k. wu; m.c. chenoptimal gabor filter design for texture segmentation using stochastic optimization, image and vision computing, elsevier science, 2000. 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[11] h. zahouani, m. assoul, r. vargiolu, t. mathia the morphological tree transform of surface motifs. incidence in tribology, ecole centrale de lion, universite france comte, france, international journal of machine tools and manufacture, elsevier science ltd, 2001. surface roughness image analysis using quasi-fractal characteristics and fuzzy clustering methods 315 [12] m. wieczorowski spiral sampling as a fast way of data acquisition in surface topography, international journal of machine tools and manufacture, elsevier science ltd, 2001. [13] j. c. bezdecpattern recognition with fuzzy objective function algorithms, plenum press, new york, 1981. [14] c. aguilera, m. ramos, g. roaan automatic grading system for panels surfaces using artificial vision, international journal of computers, communications and control, vol. i (2006), no. 2, pp. 15-22, 2006. tiberiu vesselenyi university of oradea universitatii st. 1, 410087, oradea, romania e-mail: tvesselenyi@yahoo.co.uk ioan dzitac department of economics agora university of oradea piata tineretului 8, oradea 410526, romania e-mail: idzitac@univagora.ro simona dzitac university of oradea universitatii st. 1, 410087, oradea, romania e-mail: simona.dzitac@gmail.com victor vaida university of oradea universitatii st. 1, 410087, oradea, romania sc electrocentrale deva sa str. santierului, nr.1, mintia, romania e-mail: vaida@termodeva.ro tiberiu vesselenyi was born in oradea, romania in 1957, he finished the university “politehnica” from timişoara in 1983. from 1983 to 1991 he worked at a machine building company in oradea as designer and cnc programmer. from 1991 till 1994 he was a research engineer at the “geothermal energy research center” in oradea and from 1994 till today is assoc. prof. at the university of oradea, where he teaches robot and cnc programming. he had earned a phd in robotics at the university “politehnica” at timişoara. he had published over 150 papers in national and international conferences and journals, and is author or coo author of 4 books. 316 tiberiu vesselenyi, ioan dzitac, simona dzitac, victor vaida ioan dzitac received m. sc. in mathematics (1977) and ph.d in information sc. (2002) from “babes-bolyai” university of clujnapoca. at this moment, he is associate professor and head of economics department at agora university, oradea, romania. his current research interests include different aspects of parallel and distributed computing, applied mathematics and economic informatics. he has edited 6 conference proceedings, published 15 books and more than 50 scientific papers in journals and conferences proceedings. he was member of the program committee of 27 international conferences. simona dzitac received b.sc. (2000) and m. sc. (2001) in mathematics-physics, b.sc. (2005) and m. sc. (2007) in energy engineering from university of oradea and b.sc. in economic informatics (2007) from university of craiova, romania. at this moment, she is phd student in energy engineering field and researcher at university of oradea. her current research interests include reliability, applied mathematics and computer science in engineering fields. she published 5 books and 45 scientific papers in journals and conferences proceedings. victor vaida graduated from the technical university of timisoara, faculty of electrotechnics, being awarded a b.sc degree in electrical power engineering in 1968. in 1997 he was awarded a phd in technical sciences by transilvania university of brasov. he completed several specialization trainings and courses: in automation, electric and information systems in power plants at siemens company in germany (1972 1974), in the quality of electricity supply (2007 2008) at sier bucharest and in business management at the bucharest institute of management (1994). at present, he is the technical manager of sc electrocentrale deva and an associate professor of the university of oradea, faculty of power engineering. he is also an associate researcher at the economy and industry institute of the romanian academy and the president of the society of power engineers of romania. his research activity includes: 7 specialized books and 160 articles in the power field published in romania and abroad, 27 contract/grant-based research projects and some other r and d. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 4, pp. 439-451 mpm job-shop under availability constraints n. zribi, l. duţă, a. el kamel nozha zribi école centrale de lille france luminiţa duţă state university "valahia tirgoviste", romania abstract: a large part of scheduling literature assumes that machines are available all the time. in this paper, the mpm job-shop scheduling problem, where the machine maintenance has to be performed within certain time intervals inducing machine unavailability, is studied. two approaches to solve the problem are proposed. the first is a two-phase approach where the assignment and the sequencing are solved separately. the second is an integrated approach based on the exact resolution of the 2-job problem using the geometric approach. keywords: genetic algorithm, geometric approach, assignment heuristic most of scheduling literature assumes that machines are available all the time. however, in many realistic situations, e.g., in typical industrial settings, machine breakdowns and preventive scheduled maintenance have rather quietly common occurrences. these considerations increase the complexity of any scheduling problem but make the problem closer to the industrial reality [26]. in this paper, we consider the job-shop scheduling with multi purpose machines and availability constraints. we consider the deterministic model where the unavailability periods corresponding to maintenance tasks are known in advance. we also assume that preemption of operations is not allowed. more precisely, an operation oi j of job ji on machine mk starts only if its execution can be finished before mk becomes unavailable. the problem considered is a generalization of the classical job-shop problem and the multi-purpose machine problem studied in [12], where machines are available all times. as compared to the literature dedicated to classical scheduling problems, studies dealing with limited machine scheduling problems are rather rare. availability constraints have been firstly introduced in single machine [1], [28] and parallel machines [24], [25]. lee extensively investigated flow-shop scheduling problems with two machines [15], [18], [19]. in particular, the author defined the resumable, non-resumable and semi-resumable models. an operation is called resumable if it can be interrupted by an unavailability period and completed without penalty as soon as the machine becomes available again. if the part of the operation that has been processed before the unavailability period must be partially (respectively fully) re-executed, then the operation is called semi-resumable (respectively non-resumable). recently, flow-shop scheduling problems with two machines and resumable jobs have been treated in [9] and [14]. job-shop problem under unavailability constraints has also been considered recently [30], [3] where authors proposed a branch and bound algorithm for the job-shop problem with heads and tails and unavailability periods. the problem considered here is strongly np-hard since problem without unavailability periods is already strongly np-hard [12]. in this paper we propose two different approaches to solve this problem. the remainder of this paper is organized as follows. after a description of the considered problem in the following section, we propose first a two-phase method where a heuristic is used to solve the assignment problem and a genetic algorithm is developed for the sequencing problem. an integrated method, based on the exact resolution of the 2-job problem, is then developed. a comparison between the two algorithms is given in section 4. copyright c© 2006-2009 by ccc publications 440 n. zribi, l. duţă, a. el kamel 1 problem formulation the mpm job-shop (job shop with multi purpose machines) with availability constraints ( j(mpm)ncwin | cmax : terminology defined in [29]) may be formulated as follows. there are n jobs j, ..., jn to be processed on a set of m machines r = (m, ..., mm). each machine mr can process at most one job at a time. each job ji consists of a sequence of ni operations, that must be accomplished according to its manufacturing process. each operation oi j (i = , ..., n; j = , ..., ni) can be performed by any machine mr in a given set µi j ⊂ r for pi j time units. each operation is non-preemptive, i.e., it must be accomplished without interruption. moreover, we assume that machine mr is unavailable during giving periods corresponding to preventive maintenance. the starting times and durations of these tasks are fixed and known in advance. we note kr the number of maintenance tasks on machine mr. arl and drl represent respectively the starting and the finishing time of the lth maintenance task on machine mr. the objective is to find a schedule, defined by the starting time si j and the completion time ci j of each operation oi j, with a minimum makespan (maxci j). the scheduling problem in j(mpm)ncwin | cmax can be decomposed in two subproblems: • a routing subproblem that consists in assigning operations to machines; • an operation scheduling subproblem associated with each machine to minimize the makespan. this is a job-shop scheduling problem with availability constraints j, ncwin | cmax. 2 two-phase approach for the problem j(mpm)ncwin | cmax 2.1 the routing problem since the precedence constraints could be relaxed following the decomposition of the problem in two separate stages, the assignment problem may be treated as a parallel machine problem with the two additional constraints: • an operation can be performed by a machine belonging to a subset of the set of the available machines: partial flexibility • machines are subjected to several maintenance constraints: the planning horizon is divided into subintervals we propose a heuristic based on several priority rules taking into account these two additional constraints. assignment heuristic we use a list algorithm based on priority rules in order to construct an initial assignment solution. let us define the following parameters: • ri j: earliest starting time of operation oi j (definition 1) • t sk availability date of machine mk at iteration s, where s denotes the iteration number • erk set of operations which can be performed on machine mk • eask set of operations which can be assigned to machine mk at iteration s • cmsk load of machine mk at iteration s mpm job-shop under availability constraints 441 definition 1. to each operation oi j, we associate an earliest starting time ri j calculated by the following formula: { ri, =  ∀  ≤ i ≤ n , ri, j+ = ri, j + pi, j ∀  ≤ j ≤ ni − , ∀  ≤ i ≤ n. (1) in step 1, the different parameters are initialized. in step 2, for each machine we determine the set eask of operations such that: ri j ≤ t sk . in step 3, we evaluate (the potential) starting time of each operation on each possible machine. the availability periods are taken into account. in fact we test if the operation can be scheduled before the next availability period on that machine. a pair (operation/machine) is selected using the following priority rule: the less flexible machine mk (min cmsk ) is selected. operations in eak are sorted in non decreasing order of card(µi j) (priority is accorded to the less flexible operation). mk is assigned to the first operation in eak which can be scheduled before the next unavailability period. this priority rule allows occupying the time intervals before the unavailability periods and takes into account the load of machines and the flexibility degree of each operation. in order to ensure a high level of the solution quality, we have chosen to improve the assignment given by the assignment heuristic. to this end, a local improvement search has been studied. such search is based on a tabu algorithm , an adapted routing move technique and an adapted criteria for the studied problem. in next section, we give a description of the tabu algorithm. a tabu search algorithm for the assignment problem optimization criteria: for a classical routing problem, where machines are available all times, we choose, in general, to minimize the workload of the most loaded machine, since it provides a lower bound for the makespan. we define, for each assignment s, a lower bound denoted lb(s) for the makespan corresponding to s. this lower bound is based on relaxation into a set of single-machine problems taking into account the unavailability periods. the objective of the tabu search algorithm presented here is to minimize cr = lb(s) and hence to preoptimize the makespan. given an assignment s, we associate with each machine mk, a single-machine problem πk with ready times (definition 1), tails (definition 2) and unavailability periods. a lower bound for πk is the makespan of a preemptive schedule with unavailability periods, based on the jackson preemptive schedule (jps) algorithm. such schedule is calculated for each machine and lb(s) is the maximum makespan value of these schedules. definition 2. after the finishing of operation oi j , a time of qi j has to go before job ji is finished completely. qi j is called the tail of operation oi j the procedure of preemptive schedule allows constructing the optimal schedule when preemptresume applies and hence to obtain a lower bound for πk due to the two following reasons: 1. the unavailability period is treated as an operation, so the problem here is equivalent to the preempt-resume case where jps gives the optimal solution. 2. the unavailability period will start right on its ready time and will never be preempted since it has the largest tail among the available operations. preemptive schedule is calculated for each machine and lb(s) is the maximum makespan value of these schedules. 442 n. zribi, l. duţă, a. el kamel description of the tabu search (ts) algorithm ts was introduced by glover as a general iterative meta-heuristic for solving combinatorial optimization problems [16]. the ts algorithm is as follows. the initial solution is obtained by applying the assignment heuristic described above. the solution is described as a list of operations with their corresponding machines. a routing move is defined by the relocation of a critical operation (operation that belongs to the critical machine) to a feasible machine position. for a given solution, we consider every possible relocation of every reroutable critical operation. the routing move is based on the following steps: 1. find the critical machine mkc . 2. find an operation oi j that can be assigned to another machine mk ∈ µi j without increasing the criterion value. 3. reassign oi j to mk if possible. the tabu list consists of pairs (op; mo), where op denotes the operation that is moved from machine mo to a different machine. the choice of the move is based on the value of cr which is the maximum makespan value of the preemptive schedules. 2.2 genetic algorithm for the sequencing problem after the assignment step, each operation is assigned to a fixed machine. thus the mpm job-shop problem is reduced to a job-shop problem with availability constraints (jspac). the problem is then to assign a starting time si j and a completion time ci j to each operation oi j (ci j = si j + pi j). the considered objective is to minimize the makespan (cmax = maxi, jci j ). we propose a genetic algorithm to optimize the makespan in a jspac. coding according to the literature, two types of approaches exist. in the first, the schedule is directly coded in the chromosome. in the second, a scheduler is associated to the ga to transform the chromosome into actual schedule. in this paper, the latter approach is used to code ga chromosomes. in fact, we use a representation based on job operation. it consists in representing the schedule in a chain of nt operations ( ∑ ≤i≤n ni) where operations of the same job are represented by the same symbol ji, the job number. each job ji appears exactly ni times (ni is the number of operations of ji) in the chain. for example , for a job-shop problem of dimension 3*3 (3 jobs and 3 operations per job), an example of a chromosome is (1 2 1 3 1 3 2 2 3). the computation of the starting time and the completion time (si j , ci j) is obtained according to the order z of each task in the chain (chromosome) and taking into account the unavailability periods of the machines. crossover and mutation we use gox generalized order crossover , a swap based mutation and an "intelligent" mutation operator. mpm job-shop under availability constraints 443 "intelligent" mutation operator: this operator consists in reducing the idle time before unavailability periods in order to improve the makespan. this mutation heuristic is described by algorithm 1 and consists in: for each unavailability period of the critical machine, we exchange an operation scheduled before a maintenance period with another operation which can begin before this maintenance period, but is scheduled after this one. all possible permutations are tested. the permutation minimizing the makespan is selected. algorithm 1 heuristic for the "intelligent" mutation operator choose a chromosome x aleatory calculate the schedule and find the critical machine mmax find ommax : set of operations oi j assigned to mmax for each maintenance period d of mmax for each operation oi j in ommax finishing before d for each operation oi′ j ′ de ommax which can begin before d and having been scheduled after d test the permutation of oi j and oi′ j ′ choose the permutation minimizing the makespan 2.3 application example let us consider an example of a mpm job-shop. it is made of 15 jobs, 5 operations per job and 5 machines. each machine is subject to two maintenance periods as follows: m: [201 250],[463 512] means that the machine m is unavailable between the dates 201 and 250 and between the date 463 et 512. m: [104 139] , [520 588] m: [233 331], [499 528] m: [137 186], [507 556] m: [129 187], [783 881] 1. assignment step we apply first the assignment heuristic to obtain an initial solution. in order to evaluate the solution given by the assignment heuristic, we report in table 1 the load of the different machines as well as the makespan value of the preemptive schedule constructed using jackson rule. m m m m m load 714 782 868 789 839 preemptive 812 907 995 887 997 schedule table 1: results of the assignment schedule 2. sequencing problem 444 n. zribi, l. duţă, a. el kamel m m m m m load 815 812 790 818 757 preemptive 913 915 917 916 915 schedule table 2: results of the ts algorithm 0 500 1000 1500 2000 2500 in s t 1 in s t 2 in s t 3 in s t 4 in s t 5 in s t 6 in s t 7 in s t 8 in s t 9 in s t 1 0 in s t 1 1 in s t 1 2 in s t 1 3 in s t 1 4 in s t 1 5 in s t 1 6 in s t 1 7 in s t 1 8 in s t 1 9 in s t 2 0 instances m ak es p a n cmax lbjmpm figure 1: simulation tests table 3 gives the value of the lower bounds based on preemptive schedule for the initial solution of assignment (solution 1) and the solution obtained after applying ts (solution 2) as well as the makespan found by the genetic algorithm. these results show that ts improves the global solution of the problem and ga gives a good solution comparing to the lower bound. we propose in the next section an integrated approach based on the exact resolution of the 2-job problem. solution 1 solution 2 preemptive 997 917 schedule makespan 1023 968 table 3: results of the genetic algorithm other experiments were performed on randomly generated instances with more or less availability periods. we report in this section different results for three classes of instances. each class has five instances. the number of jobs for each class is 10 , 15, 10 and 20 respectively. the number of machines is equal to 5 , 10, 10 and 10 respectively. the processing time for each operation is randomly selected in [|40,150|]. for each machine, the maintenance tasks occur after at least one operation. the starting time for each maintenance task differs from machine to another. the duration of a maintenance task on a machine is the average of the processing times of operations. for the ga, the mutation and crossover probabilities are fixed to: (pcrossover = ., pmutation = .). in figure 1 we compare the result of the two-phases approach with a lower bound denoted lbjmpm that we have developed for the problem [31]. comparing with lbjmpmac our approach gives interesting results (rdjmpmac .%) with a short computational time. it’s worth noting that lbjmpm , which is based on a lower bound for the parallel machine problem, is more interesting for instances with high flexibility. mpm job-shop under availability constraints 445 3 an integrated approach for the j(mpm), ncwin | cmax this approach is based on the exact resolution of the problem j(mpm), ncwin|n = |cmax. a polynomial algorithm is developed to solve the problem. 3.1 polynomial algorithm for j, ncwin|n = |cmax state of the art the geometric approach has been firstly introduced by akers and friedman (1955) in [2]. it consists in reducing the two-job job-shop scheduling problem in the search of a shortest path and thus gives a polynomial algorithm to solve it. the first step of the geometric approach is the representation of the scheduling problem in a 2dimensional plane with obstacles, which represent the machine conflict between operations of the two jobs [22]. more precisely, each job ji is represented by an axe with ni intervals according to its manufacturing process. each interval corresponds to an operation oi j and has a length of pi j (fig.2). intervals o j and ok form an obstacle if o j and ok share the same machine (fig.2). the horizontal and the vertical crossing the final point f, which corresponds to the completion of the two jobs, are considered as the final obstacle. a feasible solution of the scheduling problem is then a path going from the origin o to the final point f . such a path consists of horizontal, vertical and diagonal legs. a horizontal (resp. vertical) leg represents the exclusive progression of job j (resp. j), whereas diagonal legs correspond to simultaneous executions of the two jobs. moreover, any path must avoid the interior of the obstacles. this is due to the fact that two operations can not be executed simultaneously on the same machine and are not preemptable. the length of a horizontal or vertical segment is equal to its usual length while the length of a diagonal segment is equal to the length of its projection in any axe, which is the time spent for the simultaneous processing of two operations. the shortest path problem in the plane can be transformed into an unrestricted shortest path problem in an acyclic network (see fig.2 ), where the set of vertices corresponds to the origin o, the final point f and the north-west and south-east corners of the obstacles. each vertex has at most two successors obtained by going diagonally until hitting an obstacle d. if the obstacle d is the final obstacle, the vertex f is the only successor of node, otherwise the nw and se corners of obstacle d, are immediate successors of node (see fig.2 ). the temporized geometric approach (tga), developed by aggoune [3], [4] is an extension of the geometric approach which exactly solve the problem j, ncwin|n = |cmax. it allows integrating the evolution of time and so the availability of the machines, based on the definition and the introduction of new vertices, as well as a new and dynamic way to progress from one vertex to its successors. vertices characterization and definitions in tga: in the classical geometric approach, vertices of the network are the north-west (nw) and south-east (se) corners of the obstacles hit when going diagonally in the plane. these corners are located at the extremities of the intervals corresponding to operations in conflict. each vertex can then be defined thanks to its coordinates in the plane: the x-coordinate (resp. 446 n. zribi, l. duţă, a. el kamel j1= {(m1, 1), (m2, 2), (m3, 1)} j2= {(m2, 2), (m3, 1), (m1, 1)} nw1 se1 o f o nw1 se1 f 2 3 3 4 figure 2: classic geometric approach y-coordinate) of the vertex corresponds to operation of job j (resp. j) to be executed. in tga [4], some vertices can be located between two lines bounding an operation, i.e., inside the intervals. for each vertex, each coordinate is additionally attributed by information related to the duration of already processed part of the associated operation. moreover, an earliest starting time h(s) is associated with each vertex s. h(s)is the length of the shortest path from the origin to s. the set of vertices of the network constructed by tga is composed by the three following types of vertices: • regular vertices, located at the intersection of horizontal and vertical lines, to which nw and se corners of obstacles belong. • singular vertices, located on a horizontal (resp. vertical) line, which means that the execution of the operation of job j (resp. j) has not started yet • waiting vertices, also located at the intersection of two lines, for which the execution of operations of jobs j and j has not started yet. a singular vertex is created if the progression of only one job is possible (availability problem for the other job), whereas waiting vertices are created if the progression is possible for none of the two jobs. a waiting vertex is always a duplication of the regular vertex having the same geometric coordinates but not the same earliest starting time. the progression works as follow: • if the operations of the two jobs cannot start at time h(s), the earliest starting time of vertex s, a waiting vertex is then created. • if there is an availability problem in the direction j (resp. j), the progression is made along the vertical (resp. horizontal) line, what means that the execution of the operations of job j (resp. j) only, until job j (resp. j) becomes available. a singular vertex , from which a diagonal progression is possible, is added as successor of s. • if there is no availability problem, that is to say if the operations of the two jobs can be executed at time h(s), the progression works as in the classical geometric approach. 3.2 an extended approach we propose a generalization of tga (gtga) in order to deal with the flexibility property of the j(mpm), ncwin | n =  | cmax. this generalization is based on the works of aggoune [4] and mati and al. [21] for the flexible job-shop without availability constraints. as for the job-shop problem, the mpm job-shop under availability constraints 447 scheduling of mpm job shop can be represented in the 2-dimensional plane with potential obstacles that depend on the assignment of machines for the two jobs. let us define the vertices of the network describing the progression in the plane, the successors of each vertex and the distance between any two vertices. we develop the algorithm succvertex allowing to find the successors of each vertex s = ((k, ∆ k ), (k, ∆  k )). the algorithm succvertex is in three steps. • step 1 is an initialization step. set ph (resp pv) is defined to keep the machines of job j (resp j) allowing to progress until meeting an horizontal and \ or a vertical. this set is used to progress in the next iteration of the program in the case of diagonal progression. e is the set of the possible machines of ov and e is the set of the possible machines of oh. • in step 2, first we check the availability of the two machines for ov et oh. if these machines are available, we progress diagonally until a vertical (end of operation of job 1) and or an horizontal (end of operation of job 2) is met depending on the duration of the current operations. set ph or pv is defined to keep the machines of job j or j respectively in the case of diagonal progression. if an availability problem occurs in one of the two directions, the algorithm succvertexavailability is used to define the successors of s in this case. the machines concerned by this case are memorized in sets pv_ availavility, rh_ availability, ph_ availavility and rv_ availability. these sets are used by algorithm succvertexavailability. the corners se and nw of the potential obstacles that could be reached from s are added as successors of s during the diagonal progression. • in step 3, we update the current time current_ time, the sets e, e, v and h. the algorithm succvertex is stopped if the final obstacle is hit or when the diagonal progression is not possible because of availability problems or unavoidable obstacle. the algorithm succvertexavailability allows progressing horizontally or vertically (availability problem) until the progression in the two directions becomes possible (end of the unavailability period) , in this case a singular or a regular vertex is added as successor to s ; another availability problem occurs, and in this case a waiting vertex is added as successor of s or an unavoidable obstacle is hit. distance between two vertices s and s′: the distance between a vertex vi and its successor vi′ is calculated inside the two developed algorithms using the variable current_ time. in fact, all machines used to progress from vi until vi′ are fixed. remark: if f is a successor of s, the distance between s and f is calculated using only available machines, if possible, or machines becoming available first. in fact we neglect the other paths. theorem 1. the set of vertices constructed by applying algorithm succvertex is sufficient to determine the optimal schedule. proof. the correctness of theorem 1 is due to the fact that tga gives the optimal schedule in the case of classical job-shop [3] and the developed algorithm checks all possible machines for each operation. 3.3 the general job shop problem with multi purpose machine and availability constraints from the result of the previous section we can deduce a greedy heuristic to calculate a solution for j(mpm), ncwin | cmax. this heuristic works as follows: 1. the two first jobs are optimally scheduled using the gtga algorithm. 2. additional unavailability periods, corresponding to the execution of operations of the two scheduled jobs, are fixed on each machine. 448 n. zribi, l. duţă, a. el kamel instance size (nxmxnt) two-phase approach integrated approach inst1 10x5x50 852 833 inst2 10x5x50 641 631 inst3 10x5x50 697 707 inst4 10x5x50 710 703 inst5 10x5x50 633 625 inst6 15x5x75 945 983 inst7 15x5x75 897 891 inst8 15x5x75 1023 1017 inst9 15x5x75 1001 997 inst10 15x5x75 998 1002 table 4: comparison of the two approaches 3. the algorithm is applied to the next two jobs of the sequence, taking into account the initial and the new unavailability periods. this procedure continues until all jobs are treated. if the number of job is odd we need an insertion procedure to schedule the last job. it is based on the following rule: an operation of the last job is inserted in such a way, that it begins as early as possible, if we have the choice between two machines we choose the machine giving the smallest idle time. 4 experimental results to perform an experimental evaluation of the proposed approaches, we present, in this paper, a ten classical flexible job-shop instances [13]. in order to provide proper experimental settings, two availability periods are generated randomly for each machine. it is worth noting that these results were also confirmed by several other experiments based on randomly generated instances with more or less sizes and/or availability periods [31]. in table 4, we report the values of the makespan given respectively by the two-phase approach and the integrated approach. these simulations show that the two-phase approach gives interesting results comparing with the integrated approach. the main advantage of the two-phase approach is related to the computation time. the integrated approach is rather more complicated mainly in terms of computing time. besides, we note that solutions given by the integrated approach vary with the initial sequence of jobs. hence, this approach can be improved by adding an optimization algorithm for the initial sequence of jobs. meanwhile, it appears clearly worth applying this polynomial algorithm in a branch and bound algorithm, as done by jurisch for the classical mpm job-shop without availability constraints [17]. 5 conclusion we have investigated in this paper mpm job shop scheduling problems under availability constraints. we have proposed two kinds of methods. the first one solves the assignment and the sequencing problems separately. the second one is based on an extension of the geometric approach to deal with the machine availability and the flexibility property of the problem. we are now working on the development of a maintenance management system with an industrial partner including these optimization in a multi-objective environnement. in fact, the analysis of performance of scheduling problem involves mpm job-shop under availability constraints 449 more than one criteria [7]. we are focusing on two main criteria : the maintenance total costs and the delivery processes. other aspects that we will focus on is the real-time approaches to solve schedule problem with unanticipated interruptions [5]. bibliography [1] adiri i., bruno j., frostig e., and rinnooy kan a. h. g. : single machine flow-time with a single breakdown. acta informatica, 26, 679-696. 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[31] zribi n. : ordonnancement des job-shop flexibles sous contraintes de disponibilité des machines. phd thesis, ecole centrale de lille, france, 2005. nozha zribi has obtained an engineer diploma in 2002, a master of science and european master in computer engineering in 2002, and a phd in operational research from ecole centale of lille in 2005. since 2007, she is a research engineer. her interests are logistics, software design and implementation and sea. luminiţa duţă is currently associate professor (lecturer) at "valahia" university of targoviste, romania. she took the phd from université franche comté, besancon (france) and from technical university "politehnica" of bucharest in automation and control field. her research interests include the use of decision support systems in complex process controlling and meta-heuristics. she is a member of ifac technical committees 5.2 and 5.4. mpm job-shop under availability constraints 451 abdelkader el kamel (m’96-sm’00) received the engineering diploma in 1990, the ph.d. in 1993 and the "habilitation ŕ diriger des recherches" in 2000. he is currently professor at the ecole centrale de lille and visiting professor in different countries (china, chili, india and tunisia). he is the author or coauthor of more than 100 journal articles, book chapters, plenary sessions, and communications in international conferences. his current research interests include intelligent control of complex systems under uncertainty; modeling, design, real-time monitoring of autonomous dynamical systems and mobile cooperative robots. dr. el kamel was the president of the ieee-smc conference held in tunisia in 2002. he was program chair or ipc member of several ieee, ifac and wac conferences, member of the adcom of ieee france section, president of the ieee-smc tc on "complex systems under uncertainty", member of the ifac tc 3.2 and president of the ieee-smc france chapter. international journal of computers, communications & control vol. i (2006), no. 4, pp. 110-125 ant colony with dynamic local search for the time scheduling of transport networks salah zidi, salah maouche, slim hammadi abstract: this article presents an ant colony optimization for the time scheduling of public transport traffic. in fact, the assistance of a decision support system becomes necessary for the real-time regulation of this transport networks since the size of the search space increases exponentially with the number of vehicles and stops. so, we propose an ant colony algorithm with dynamic local search, in the case of unpredictable disturbance. this approach consists in applying a local search window with increasing dimension according to the iterations. it treats the regulation problem as an optimization and provides the regulator with relevant decisions. a regulated timetable is proposed as solution aiming at minimizing the waiting time of passengers. we insure the three most important criteria of regulation which are the punctuality, the regularity and the correspondence. keywords: urban transportation systems, real-time scheduling, ant colony optimization. 1 introduction in real time, the transport system can be affected in an unpredictable way by incidents which cause a delay between these theoretical time schedules and the real time schedules. in these conditions, we have re-adjust the planning (time table) made at previous time to return quickly to the theoretical schedules. it is a real-time regulation. within this context we developed an algorithm of scheduling which allows us to ensure the most important three criteria: the punctuality, the regularity of passing of vehicles by stops as well as the correspondence between the lines of the different transport modes. it is an ant colony algorithm where the main goal is to find in every movement between two successive stops the delay which it is necessary to apply to concerned vehicles to optimize these three criteria of regulation. at the beginning, our regulation algorithm encountered difficulties to escape from local optima. in fact, the optimization problem which we have to resolve is complicated and the research space is important. to manage these difficulties we propose the idea of the dynamic local search. it is the new method which consists in applying a local search window with increasing dimensions according to the iterations. this article contains five parts. in the first, we will present the regulation problem. the second part is a presentation of the ant colony algorithm. in the third part we will detail our algorithm of regulation and we will also explain the simulation results of this approach in the urban transport network of lille. we will finish by a conclusion. 2 real-time regulation the planning process of a public transport company is made by establishing different timetables that describe trips according to the lines, frequencies, transport demand, and travel times in the network. these trips are then transformed into blocks and assigned to vehicles. a crew scheduling process finally follows this vehicle scheduling [1]. hence, the vehicle schedules are fixed for every timetable period. this type of vehicle scheduling is in fact called predictive scheduling. it is based on a periodic review copyright c© 2006 by ccc publications ant colony with dynamic local search for the time scheduling of transport networks 111 of demand and resource availability in order to create arrival and departure times for the vehicles at the different stops of the network. however, in reality, travel times and transport demands are not fixed because of random external influences that affect the traffic within the network and cause disturbances. these disturbances can be, for example, caused by traffic jams, accidents, or strikes. consequently, the theoretical schedules resulting from the planning process cannot be followed exactly, which compels trips to start late and makes customers wait longer. therefore, to reduce the effects of the disturbances, the theoretical schedules have to be adapted to the real traffic conditions through regulation, or rescheduling tasks [2]. this process is then called reactive scheduling. it consists in creating new schedules that increase the level of service by undertaking operational decisions, such as, the injection of an extra vehicle in the network, or the deviation of the routes of some vehicles. the real-time traffic management of an urban transport system is presented by the figure1. figure 1: process of real time management of public transport traffic presently, it’s a human operator, regulator, who performs these real time tasks and controls the global network traffic by treating the information provided by the automatic vehicle monitoring (avm) system and the vehicle drivers. the level of service can be represented by different regulation criteria such as, the regularity, punctuality, and connection criteria. the choice of the criteria depends on the regulation objectives obtained from the nature of the disturbances. however, the regulator is usually overloaded with information, which complicates its decision-making task. in addition, despite the avm system assistance, the regulator spends more than 50% of his work time in communication with the vehicle drivers. hence, the regulator has to carry out difficult tasks that are often inaccessible for the human scale especially if many disturbances occur simultaneously, which involves the assistance of a decision support system [3]. within this context, researchers began to think about the development of a computer system for the regulators by using different tools such as the fuzzy logic [4], the multi-agent system [5] and an evolutionary algorithm [6]. in our work we will apply an approach of ant colony optimization and we will introduce a new idea of dynamic local search. 112 salah zidi, salah maouche, slim hammadi 3 ant colony optimization (aco) ant colonies are able to organize their foraging behavior in a seemingly efficient way without any centralized control[7]. this self-organizing structure is carried out via stigmergic communication, i.e. communication by changing the environment, in this case by laying down pheromone trails. initially ants have no idea of where food is in the environment, so they wander randomly, leaving a pheromone trail. when an ant finds food it wanders back to the nest. initially these paths will be arbitrary, but when an ant follows a shorter path it will be able to follow that path more often within the same time period than an ant following a longer path, so there is a positive reinforcement process whereby the shorter paths get stronger. a simple version of this is illustrated in figure 2, where ants have two possible routes from a nest to a food source. if two ants set out at the same time, one taking route a and one route b, which is twice as long, then the ant taking a will have traveled back and forth between the food source twice in the same time that the other ant has traveled back and forth once. therefore there will be a stronger pheromone trail on route a compared to route b. this idea can be effectively scaled up to solving route finding problems such as the tsp, with performance as good as or better than existing heuristics. figure 2: a simple ant foraging problem. the natural ants inspired "ant colony algorithms" invented in 1992 by marco dorigo from free brussels university, in his ph.d. thesis [8]. in an iteration of this algorithm, n ants build n solutions according to decisions based on heuristics criteria and on the quantity of pheromone. this quantity is updated by examining the solutions. it is strengthened for the decisions having given better solutions and decreased for the others. this mechanism allows to improve gradually the solutions during the iterations. the ants colony optimization were applied to divers problems of optimization such as the salesman traveling problem [9], the problems of robotics [10], the industrial problems [11] as well as the carp (capacitated arc routing problem) in [12] and the rucksack problem [13]. and there are several versions of the ants colony algorithm such as the ant system [14], the min-max ant system [15], the asrank [16] and the ant colony system [9]. 4 rescheduling algorithm 4.1 notation we use even notations in the horizon of regulation. sh : set of stops of the regulation horizon. v h : set of the vehicles in the horizon of regulation. srk: k th stop situated on the line r. v li : i th vehicle in the line l . veh(v li , s m j , s r k): first successor of v l i at traveling s m j to s r k. almi j : stop variable of v l i ats m j . ant colony with dynamic local search for the time scheduling of transport networks 113 x lmri jk : destination variable of v l i from s m j to s r k. talmi j : arrival time of v l i at s m j . tdlmi j : departure time of v l i from s m j . nmont lmi j : number of persons who go aboard v l i in s m j . ndesclmi j : number of persons who come down of v l i in the stop s m j . ρ ll ′m ii′ j : rate of correspondence from v l i to v l′ i′ in s m j . µ(∆ti, smj , s r k): arriving rate of the passengers traveling between these stops during ∆ti. nl ′mr i′ jk : number of passengers in v l′ i′ traveling from s m j to s r k. y ll ′m ii′ j : variable of correspondence from v l i to v l′ i′ in the stop s m j (equal 1 if a correspondence is possible and 0 otherwise). wll ′m ii′ j : number of transferring persons from v l i to v l′ i′ at s m j . clmi j : load of v l i at its departure from s m j . at: total time of waiting of the passengers in the regulation horizon. at0: initial waiting time of passengers at the different stops of the regulation horizon according to the disturbed schedules. t t : total duration of transfers in the regulation horizon. t t0: initial transfer or connection time between the different vehicles of the regulation horizon according to the disturbed schedules. rt : total duration of roads in the regulation horizon. rt0: initial route time for the different vehicles of the regulation horizon according to the disturbed schedules. 4.2 criteria of regulation to have a good quality of service in a transport network, several criteria must be assured during the off-line planning and the on-line regulation such as the safety, the regularity, and the punctuality. in the present problem, we chose the criteria of the regularity, the punctuality and the correspondence. they are the most important and the most used by regulators and researchers. regularity this criterion expresses the preservation of the regularity of the time intervals which separate the successive passings of vehicles. it concerns the minimization of the passenger wait in stops. the calculation of the traveler wait in a stop smj depends on the interval separating two successive vehicles and the number of travelers at this stop. we suppose that in a given period of the day, v l ′ i′ is the vehicle succeeding v l i in the stop s m j . the time interval which separates both passages is: ∆t = tal ′m i′ j −tdlmi j (1) we consider the distribution of the passenger arrivals, µsmj (t), to the stop s m j . we can then calculate, according to the figure 3, the passenger wait, during t. (equation 2) attente(∆t, smj ) = ∫ ∆t 0 µsmj (t)(∆t −t)dt (2) the distribution of the passenger arrivals to stops is often considered as a non-stationary process [6]. besides, if we have reduced intervals (2 to 4 minutes) or situated in the homogeneous periods, we can consider a constant passenger flow,µsmj . consequently, the number of persons who arrive in s m j during ∆t is µsmj ×∆tand the average wait becomes: 114 salah zidi, salah maouche, slim hammadi attente(∆t, smj ) = µsmj × ∆t 2 2 (3) figure 3: distribution of the passenger arrivals to a stop in the case of a wider interval not belonging to a homogeneous period, it can be divided into several reduced intervals to simplify the calculation of the traveler waits, ∆t = ⋃ i=1...n ∆ti . the number of persons arriving in smj during ∆ti with a rate of arrival equal to µi is µi ×∆ti . their average wait is then: attente(∆ti , smj ) = µi ×∆ti ×( ∆ti 2 + n ∑ i′=i+1 ∆ti′) (4) indeed, the averages wait of the travelers who arrived during ∆ti is ( ∆ti2 + ∑ n i′=i+1 ∆ti′). we can so calculate the average wait during t at the stop smj : attente(∆t, smj ) = n ∑ i=1 µi ×∆ti ×( ∆ti 2 + n ∑ i′=i+1 ∆ti′) (5) we can now formulate, in the following equation, the total wait, at the stop smj , of the travelers who go to srk during the interval ∆t which separates the successive passages of both vehicles v l i and v l ′ i′ (v l′ i′ = veh +(v li , s m j , s r k)). attente(∆t, smj , s r k) = n ∑ i=1 µ(∆ti , smj , s r k)×∆ti ×( ∆ti 2 + n ∑ i′=i+1 ∆ti′) (6) the duration of the wait of all the passengers at smj is then the sum of the waits for all the vehicles which pass by this stop, as described below: attente(smj ) = ∑ v li ∈v h (almi j × ∑ srk>s m j attente(tal ′m i′ j −tdlmi j , smj , srk)) (7) finally, because the criterion of regularity is concerning the total wait, at, of passengers at stops in the regulation horizon, this at is then formulated in equation 8 and 9 by adding the wait at the different concerned stops. at = ∑ smj ∈sh attente(smj ) (8) at = ∑ smj ∈sh ∑ v li ∈v h (almi j × ∑ srk>s m j attente(tal ′m i′ j −tdlmi j , smj , srk)) (9) ant colony with dynamic local search for the time scheduling of transport networks 115 correspondence the correspondence criterion is associated to the duration of transfers between vehicles in the disrupted zone. we can suppose that the number of persons in transfer at stop smj is proportional to the passenger number who go to this stop with the rate ρ ll ′m ii′ j so w ll′m ii′ j = ρ ll′m ii′ j ×ndesclmi j . we can deduct the total duration of transfers, tt, who is equal to a sum of the durations of the correspondences between the various vehicles at the concerned stops of the network (equation 10). t t = ∑ v li ∈v h ∑ v l ′ i′ ∈v h ∑ smj ∈sh y ll ′m ii′ j ×wll ′m ii′ j ×(tdl ′m i′ j −talmi j ) (10) punctuality the punctuality criterion deals with the route duration of the different vehicles. it is computed after an estimation of the vehicle loads via the arrival rates of the passengers at the stops, and also the initial real loads that are assumed known. hence, rt = ∑ v li ∈v h ∑ smj ∈sh almi j ×clm ′ i j′ ×(tdlmi j −tdlm ′ i j′ ) (11) in fact, the loads are determined by the number of the alighting and boarding persons, according to the arrival rates or to the origin destination matrix. it can be written as clmi j = c lm′ i j′ −ndesclmi j + nmont lmi j (12) 4.3 principle of the rescheduling algorithm at first, we have a set of stops and routes of the disrupted zone. we add for every inter-stop (route chosen between two successive stations) a set of fictitious arcs which don’t have a physical existence but we consider them as delays to be applied to this road. in the figure 4, for example, we dispose of 5 arcs between every two successive stops. the first arc presents the real arc with 0 minutes of delay and the others possess 1, 2, 3 or 4 minutes of delay witch we can consider in one of both stops or in a route between these stops. figure 4: graphic presentation of the decision arcs the objective of the ant colony algorithm is to find the delay to be applied for every vehicle, with the aim to finding the schedules that satisfy the different criteria then propose a regulated timetable. so, for every vehicle ants move between stops and search the best arcs (delay) to be taken, until the arrival terminus. an ant has to propose all the delays which it is necessary to apply between the departure and the arrival. 116 salah zidi, salah maouche, slim hammadi 4.4 objective function the objective function to optimize is an aggregation of three criteria representing the total travel time, total waiting time, and total transfer time. this aggregation relies on weight parameters representing the relative importance of the criteria according to the disturbances and the regulator objectives. for instance, if no connection is involved in the disturbance, the weight parameter associated with the transfer criterion would be surely null. hence, the objective function to maximize is written as f = c=3 ∑ c=1 αccrc (13) where cr1 = (at0 −at ): for the regularity, cr2 = (t t0 −t t ): for the transfer (correspondence), cr3 = (rt0 − rt ): for the punctuality, α1, α2andα3 weight parameters for the regularity, transfer and punctuality criteria, respectively and ∑−13αc = 1. 4.5 generation of a new solution from a stop s and with the probability p, an ant uses the first method witch chooses an arc i with the probability p1 described by the equation (14). in that case we give the same probability to the k arcs (in our case k=5) to be chosen. this choice allows us a better exploration of the research space. p1 = { 1 k if i ∈ ωs 0 otherwise (14) with the probability 1-p, the ant uses the second method, more intelligent than the first. that is by regard to the pheromone trail, τ(t). and it chose an arc according to the probability given by the equation (15). p1 = { [τi(t)] ∑kj=1[τ j (t)] if i ∈ ωs 0 otherwise (15) where ωs is a set of arcs witch have a stop s as departure. 4.6 update of pheromone trail by analogy with the nature, every ant leaves a quantity of pheromone on every chosen arc. we reinforce the pheromone trail on the chosen arcs by taking into account vaporization. that is represented by the equation (16), which includes a term of persistence ρ and a term of strengthening ∆τa, it is a quantity added by an ant a. τi(t + 1) = ρ τi(t) + ∑ allants ∆τa (16) where ∆τa = fa is the objective function of the solution proposed bay the ant a. 4.7 algorithm structure every ant moves from a stop to the other, by using the method of generation of solution described previously, and it adds this arc to its road, until the terminus stop, then the ant begins again from the departure stop for all the vehicles of the disrupted zone. as soon as the ant chose the regulation decision (the delay) to apply to every vehicle, we build the new regulated timetable and we calculate the three criteria: the regularity, the punctuality and the transfer then the objective function which we compare ant colony with dynamic local search for the time scheduling of transport networks 117 with those found by the previous ants and we keep the best (maximal). when all the ants treat all the transport services we update the pheromone trail on every arc. the algorithm structure is presented by the figure 5. figure 5: ants colony algorithm for the regulation we stop the algorithm when the best solution found by ants is unchanged since a number of iteration i1 or a maximum number of iteration i2 is attained. 4.8 dynamic local search to escape from local optima, the methaheuristic algorithms showed an important efficiency. but, many difficulties persist for the more intricate problems and the wide research areas. we proposed in the first algorithm, in the paragraph iv, the second solution based on research idea. according to our early execution tests related to the second algorithm, in the paragraph v. c, we noticed that the unequal decisions to zero (the delays to be applied) are always located in a part of the disrupted spatiotemporal zone. so, we adopted another way based on local research, which is one of the most applied ideas with ant colony algorithms [12] as well as other heuristics such as the genetic algorithms [17]. this idea, which shows promising results, is based on a procedure that aims to improve the found solutions. in fact, this idea deals with a restricted research area, where the early solutions are located. nevertheless, we still have the problem of bumping into local optimums areas, which leads us to poor quality solutions. so, we used a new idea of dynamic local research. this idea aims at restricting the research area by means of spatiotemporal windows with increasing dimension according to the iterations. we begin the investigation in a small zone of the research space and we look for decisions, which can be different from zero on this zone, but we apply only the zero 118 salah zidi, salah maouche, slim hammadi figure 6: rescheduling ant colony algorithm with dynamic local search ant colony with dynamic local search for the time scheduling of transport networks 119 decision delay on the rest of the research space. after some iteration, we increase the spatiotemporal dimensions of this zone and we investigate the research space for new solutions. after that, we compare the results and we continue to increase the local search zone, after some iteration, until we get a dimension equals to the complete space. the figure 6 shows our improved algorithm comparing to the one illustrated in figure 5. this algorithm includes the research process with increase of the zone. in fact, we try to look for optimal decisions included between s1 and s2 stations (spatial limitation), and t1 and t2 times (temporal limitation). we affect the decision zero outside of this zone. after some iteration, we decrease a station for s1 and we add a station for s2 to increase the spatial zone and we make same for the temporal window. without initializing the pheromone trails, the solutions of the first zone will be more strengthened than the others and therefore they will have more chance to be chosen. 5 simulation and result we applied this work to scenarios inspired from a real transportation system existing in lille, in the north of france. we used an algorithm with 100 ants and two maximal number of iteration i1=10 and i2=500 and a probability p=10%. 5.1 scenario1 in the line 0 which has a frequency of one bus every 10 minutes. a disturbance, caused by the vehicle v 03 , is detected at tpert=12:01am, at its departure from the stop s 0 2. the incident consists of a traffic accident between two cars, what slows down vehicles. the delay of the disrupted vehicle at its arrival in s03 is estimated to 5 minutes. we suppose that no correspondence is involved in the disturbance. so, we are only interested in the criteria of regularity and punctuality. we assume that the studied horizon is included in a homogenous period of the day. then, we can have a constant arrival rate of passengers at all the stations. let µ = 2 passengers per minute. s00 s 0 1 s 0 2 s 0 3 s 0 4 s 0 5 s 0 6 s 0 7 v 00 0 0 0 0 0 0 0 0 v 01 0 0 0 0 0 0 0 0 v 02 0 0 0 0 0 0 0 0 v 03 0 0 0 0 0 0 0 0 v 04 0 0 0 0 0 0 0 0 v 05 0 0 0 0 0 0 0 0 v 06 0 0 0 0 0 0 0 0 example 1: α1 = α2 = 0andα3 = 1 f = 0 v 00 0 0 0 0 0 0 0 0 v 01 0 0 0 0 0 0 0 0 v 02 0 0 0 0 0 2 1 0 v 03 0 0 0 0 0 0 0 0 v 04 0 0 0 3 0 0 0 0 v 05 0 0 0 1 0 0 0 0 v 06 0 0 0 0 0 0 0 0 example 2: α1 = 1andα2 = α3 = 0 f = 160 table 1: results of the monocriterion regulation 120 salah zidi, salah maouche, slim hammadi the table 1 shows the delays to be applied to the vehicles of the regulation horizon. they involve decisions proposed by our ant colony algorithm for a regulation. for the first two examples, we consider a monocriterion regulation where we optimize only the punctuality in the first and the regularity in the second. in the example1, the optimal solution for the punctuality can correspond only to null decisions in all the compartments of table. so, we had a null objective function f. in the example 2 the algorithm can allow to delay vehicles to adjust the intervals which separate them. we notice that vehicles v 02 and v 0 4 , before and after the disrupted vehicle, were delayed 3 minutes and v 05 was delayed 1 minute. figure 7: vehicles schedules representation for the example 2 scenario 1 the figure 7 shows the efficiency of the scheduling algorithm to insure the regularity of vehicles in the perturbed zone. s00 s 0 1 s 0 2 s 0 3 s 0 4 s 0 5 s 0 6 s 0 7 v 00 0 0 0 0 0 0 0 0 v 01 0 0 0 0 0 0 0 0 v 02 0 0 0 0 1 0 0 0 v 03 0 0 0 0 0 0 0 0 v 04 0 0 0 1 0 0 0 0 v 05 0 0 0 0 0 0 0 0 v 06 0 0 0 0 0 0 0 0 exp 3: α1 = 0.9α2 = 0α3 = 0.1 f = 20.89 v 00 0 0 0 0 0 0 0 0 v 01 0 0 0 0 0 0 0 0 v 02 0 0 0 0 3 0 0 0 v 03 0 0 0 0 0 0 0 0 v 04 0 0 0 3 0 0 0 0 v 05 0 0 0 1 0 0 0 0 v 06 0 0 0 0 0 0 0 0 exp 4: α1 = 0.98α2 = 0α3 = 0.02 f = 132.78 table 2: results of the multicriteria regulation the example 3 favors the punctuality criterion, although α1 < α3. we notice a difference with the example 2 in the number of treated decisions. indeed, only vehicles v 02 and v 0 4 have to be delayed 1 minute and we find an objective function f = 20,89. in the example 4 the result is very similar to the ant colony with dynamic local search for the time scheduling of transport networks 121 example 2. indeed, v 02 and v 0 4 were delayed 3 minutes as in the example 2, but for the vehicle v 0 2 the delay is applied in s04 instead of and because the passenger number in these stops is more important thus a delay in is more interesting for the punctuality. we had f=132.78. in this first scenario the time of execution is always between 7 and 8 seconds. 5.2 scenario2 the disturbance is detected at tpert=12:24. it is caused by a technical problem at the tram line t, obliging the tram v t3 to stand still 7 min at stop s t 2 . this tram-line has a frequency of one vehicle per 10 min. the stop is situated at 10 min from a connection node, n, where a connection is planned at 12:40 with a bus from line b, which has a frequency of one bus every 20 min. however, because of the disturbance, it would arrive at 12:43 at n, so the connection would not occur. we assume that the studied horizon is included in a homogenous period of the day. then, we can have a constant arrival rate of passengers at all the stations (µ = 2). additionally, we assume that the connection rates between the two concerned lines are constant. hence, we suppose that the number of passengers in the tram arriving at the node and willing to take a bus on line is proportional to the load of the tram with a rate of 10%. and, the connection rate from the buses to the trams is 20%. we applied our algorithm for a first example which takes into account the regularity criterion (table 3). the best solution is obtained within 20 s with a maximal value of the objective function f=362 passengers-minute, that is a decrease of wait of 10 minutes for more than 36 passengers. we notice the important number of decisions (delays) for the disrupted line (t) to adjust the intervals before and after the disrupted vehicle. line t st0 s t 1 s t 2 s t 3 n s t 5 s t 6 s t 7 s t 8 v t0 0 0 0 0 0 0 0 0 0 v t1 0 0 0 3 0 0 3 0 0 v t2 0 0 0 0 0 0 0 0 0 v t3 0 0 3 2 0 0 0 1 0 v t4 0 0 0 3 0 0 1 0 0 v t5 0 0 0 0 0 0 0 2 2 v t6 0 0 0 0 0 0 0 0 0 line b sb0 s b 1 s b 2 s b 3 n s b 5 s b 6 s b 7 s b 8 v b0 0 0 0 0 0 0 0 0 0 v b1 0 0 0 0 0 0 0 0 0 v b2 0 0 0 0 0 0 0 0 0 v b3 0 0 0 0 0 0 0 0 0 v b4 0 0 0 0 0 0 0 0 0 α1 = 1α2andα3 = 0 table 3: results of example1 for scenario 2 the second example concerns the transfer criterion (table 4). so, the delays were applied before the stop n and we had an objective function f=208. the figure 8 shows the efficiency of the scheduling algorithm to insure the correspondence in the perturbed zone. we executed the algorithm also in other multicriteria example where α1 = 0.4, α2 = 0.58 and α3 = 0.02. we had a decrease of the decisions number (not equal to zero) because the punctuality criterion 122 salah zidi, salah maouche, slim hammadi line t st0 s t 1 s t 2 s t 3 n s t 5 s t 6 s t 7 s t 8 v t0 0 0 0 0 0 0 0 0 0 v t1 0 0 0 0 0 0 0 0 0 v t2 0 0 0 0 0 0 0 0 0 v t3 0 0 0 3 3 0 0 0 0 v t4 0 0 0 0 0 0 0 0 0 v t5 0 3 0 3 0 0 0 0 0 v t6 0 0 0 0 0 0 0 0 0 line b sb0 s b 1 s b 2 s b 3 n s b 5 s b 6 s b 7 s b 8 v b0 0 0 0 0 0 0 0 0 0 v b1 0 0 0 0 0 0 0 0 0 v b2 0 0 0 2 0 0 0 0 0 v b3 0 0 0 0 0 0 0 0 0 v b4 0 0 0 0 0 0 0 0 0 α1 = 0α2 = 1andα3 = 0 table 4: results of example 2 for scenario 2 figure 8: vehicles schedules representation for the example 2 scenario 2 ant colony with dynamic local search for the time scheduling of transport networks 123 and we had f=104. 5.3 comparison between both rescheduling ant colony algorithms we applied both rescheduling algorithms, without local search and with dynamic local search, for real transport scenarios. we present in the table 5 the results of these simulations. we notice that the algorithm with dynamic local search is faster. its execution time is always lower than the first algorithm, with same number of iteration. the ant colony algorithm with the dynamic local search space proposes an important improvement of solutions with regard to the algorithm without local search. ant colony algorithm (aca) aca with dynamic local search scenario example f execution time (ms) f execution time (ms) 1 1 0 7390 0 7390 2 160 7157 182 6922 3 20.89 7047 20.89 6890 4 132.78 7468 132.94 6860 2 1 192 10094 212 10000 2 526.4 10150 533.99 8290 3 394 10000 391.956 9370 3 1 362 28000 362 29000 2 208 31000 232 22800 3 104.88 28930 104.88 28060 4 114.39 28.31 115.23 26040 4 1 192 10500 192 7828 2 290 10109 310 8578 3 0 5000 0 5000 4 74.8 8700 74.8 8672 table 5: results of the both rescheduling ant colony algorithms for the found optima, we notice that it is also effective. in fact, the results of the algorithm with dynamic local search are better than the author 7 times on 15 examples. the local search allows us to win in execution times and in search space exploitation. in fact, it is easier and faster to look for optima in a smaller zone. this parameter time is very important for our real time problem of regulation. but also the idea of the dynamic dimension allows us more investigation than the idea of the classic local search. 6 conclusion the disturbance of a transport system affects, first of all, the vehicle schedules. in this article we presented an ant colony algorithm for the time-based regulation of a multimodal transport network, in real-time. the results of this algorithm applied to real scenarios of transportation system existing in lille, showed the efficiency of our approach. the execution time is also important. indeed our objective consists in proposing quickly a solution before the propagation of the disturbance. for a set of five stops, for example, for every vehicle there are 44 = 254 possible solutions. so it is a complex problem. and we also noticed problems of convergence of our algorithm because of the important dimension of the research space. the idea of the dynamic local search allowed us to win in the execution times but also the improvement of the solutions without decreasing the search space exploration and exploitation. 124 salah zidi, salah maouche, slim hammadi references [1] d. huisman, r. freling, and a. p. m.wagelmans, "a dynamic approach to vehicle scheduling," econometric inst., erasmus university, rotterdam, the netherlands, rep. ei2001-17, 2001. begin comment [2] s. maouche, h. laichour, s. hayat, "amélioration de la qualité de correspondances dans les réseaux de transport", rapport final grrt, avril 2001. [3] p. borne, b. fayech, s. hammadi and s. maouche, "decision support system for urban transportation networks", ieee smc part c: applications and reviews, special issue on decision technologies in honour of prof madan singh, vol.33, no.1, pp.67-77, 2003. [4] a. soulhi, "contribution de l’intelligence artificielle à l’aide à la décision dans la gestion des systèmes de transport urbain". thèse de doctorat, université des sciences et technologies de lille, france, 2000. [5] h. laichour, " modélisation multi-agent et aide à la décision : application à la régulation des correspondances dans les réseaux de transport urbain". thèse de doctorat, université des sciences et technologies de lille, france, 2002. [6] b. fayech, "régulation des réseaux de transport multimodal : systèmes multi-agent et algorithmes évolutionnistes", thèse de doctorat, université des sciences et technologies de lille, france, 2003. [7] j. d. moss and c. g. johnson, "an ant colony algorithm for multiple sequence alignment in bioinformatics", in david w. pearson, nigel c. steele, and rudolf f. albrecht, editors, artificial neural networks and genetic algorithms, pages 182-186. springer,april 2003. [8] m. dorigo,"optimization, learning and natural algorithms". ph.d.thesis, politecnico di milano, italy, in italian, 1992. [9] m. dorigo and l. m. gambardella, "ant colonies for the traveling salesman problem". biosystems, 43:73-81. also tecnical report tr/iridia/1996-3, iridia, université libre de bruxelles, 1997. [10] n. monmarché, "algorithme de fourmis artificielles : application à la classification et à l’optimisation", thèse à l’université de françois rabelais tour, 2000. [11] c. gagné and w. l. price, "optimisation par colonie de fourmis pour un problème d’ordonnancement industriel avec temps de réglages dépendants de la séquence". 3e conférence francophone de modélisation et simulation mosim01, troyes (france), 2001. [12] p. lacomme, c. prins, a. tanguy, "optimisation par colonies de fourmis pour les tournées sur arcs". 4e conférence francophone de modélisation et simulation mosim03, toulouse (france), 2003. [13] i. alaya, c. solnon and k. ghédira, "algorithme fourmi avec différentes stratégies phéromonales pour le sac à dos multidimensionnel", mhosi’05, hammamet, tunisie, 2005. [14] m. dorigo, v. maniezzo, and a. colorni. the ant system: optimization by a colony of cooperating agents. ieee transactions on systems, man and cybernetics, part b, 26(1):2941, 1996. [15] t. stutzle and h. hoos. max-min ant system and local search for combinatorial optimization problems. in s. vos, s. martello, i.h. osman, and c. roucairol, editors, meta-heuristics: advances and trends in local search paradigms for optimization, pages 137-154. kluwer, boston, 1998. ant colony with dynamic local search for the time scheduling of transport networks 125 [16] b. bullnheimer, r. f. hartl, and c. strauss. "a new rank-based version of the ant system: a computational study". technical report pom-03/97, institute of management science, university of vienna, accepted for publication in the central european journal for operations research and economics, 1997. [17] p. lacomme, c. prins and w. ramdane-chérif "competitive genetic algorithms for the capacitated arc routing problem and its extensions", in e.j.w. boers and al. (ed.), applications of evolutionary computing, pp. 473-483, lecture notes in computer science 2037, springer, 2001. acknowledgement: the authors want to acknowledge the support of the eu thought the feder grant # obj2-2005/3-4.1-253presrge-7820 salah zidi, salah maouche and slim hammadi université des sciences et technologies de lille and ecole centrale de lille ufr i.e.e.a. bâtiment p2, bureau 308 ufr i.e.e.a. cité scientifique 59655 villeneuve dáscq cedex france e-mail: salah.zidi@ed.univ-lille1.fr, salah.maouche@univ-lille1.fr, slim.hammadi@ec-lille.fr received: november 11, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 634-641 parallel simulation of quantum search s. caraiman, v. manta simona caraiman, vasile manta “gheorghe asachi” technical university of iasi romania, 700050 iasi, 27 dimitrie mangeron e-mail: {sarustei,vmanta}@cs.tuiasi.ro abstract: simulation of quantum computers using classical computers is a computationally hard problem, requiring a huge amount of operations and storage. parallelization can alleviate this problem, allowing the simulation of more qubits at the same time or the same number of qubits to be simulated in less time. a promising approach is represented by executing these simulators in grid systems that can provide access to high performance resources. in this paper we present a parallel implementation of the qc-lib quantum computer simulator deployed as a grid service. using a specific scheme for partitioning the terms describing quantum states and efficient parallelization of the general singe qubit operator and of the controlled operators, very good speed-ups were obtained for the simulation of the quantum search problem. keywords: quantum computer simulation, parallel computing, quantum search. 1 introduction the research in quantum informatics has gained an immense interest due to the remarkable results obtained for the factorization [11] and search [6] problems. these results prove the huge computational power of a quantum machine with respect to the classical computers. however, building quantum computers represents an immense technological challenge and, at present, the quantum hardware is only available in research labs. under these circumstances quantum simulators have become valuable instruments in developing and testing quantum algorithms and in the simulation of physical models used in the implementation of a quantum processor. according to feynman’s paper [3], classical computers will never be able to simulate quantum systems in polynomial time. the simulation of 29 qubits (quantum bits) uses 32 gb of memory [1] and any additional qubit doubles the resources needed: time, memory, computational power and space. in this paper we present a solution based on grid computing for the quantum simulation problem. our simulator relies on paralel processing for storing quantum states and applying quantum operators. the deployment of this solution in grid systems provides access to high performance computing devices for simulation and availability in the context of collaboration through the means of virtual organizations. our quantum simulator, gqcl, partitions the terms coresponding to a quantum state between several procesing nodes using a scheme that minimizes communication between nodes during the application of quantum operators. in a previous paper [1] we describe the development of a grid service that provides this functionality to client applications by enabling the quantum computation language [9] through a parallel implementation of the qc-lib simulator [8]. the results recorded for the application of the hadamard transform illustrate the performances of this approach [1]. in the following we present the parallelization of the general single qubit operator, the conditional operators and of the measurement process. this allows us to study the performance of our simulator regarding the quantum search problem. copyright c⃝ 2006-2010 by ccc publications parallel simulation of quantum search 635 2 basic concepts in quantum computing the quantum analogous of the classical bit is the qubit. a qubit is a quantum system whose states can be completely described by the superposition of two orthonormal basis states, labeled |0⟩ and |1⟩ (in a hilbert space h = c2, |0⟩ = (1 0)t , |1⟩ = (0 1)t ). any state |ψ⟩ can be described by: |ψ⟩ = α|0⟩ + β|1⟩, |α|2 + |β|2 = 1, (1) where α and β are complex numbers. thus, unlike the classical bit, the qubit can also be in a state different from |0⟩ and |1⟩: linear combinations of states can be formed, called superpositions (eq. 1). when measuring a qubit either the result 0 is obtained, with probability |α|2, or 1 with probability |β|2. the sum of the probabilities must be 1, so the state of a qubit represents a unit vector in a complex bi-dimensional vector space. a collection of n qubits is called a quantum register with dimension n. the general state of a n-qubit register is |ψ⟩ = 2n−1∑ i=0 ai|i⟩, (2) where ai ∈ c, ∑2n−1 i=0 |ai| 2 = 1. this means that the state of a n-qubit register is represented by a complex unit vector in hilbert space h2n. the quantum analogous of the classical not gate is labeled x and can be defined such that x|0⟩ = |1⟩ and x|1⟩ = |0⟩. the quantum not gate acts similarly with its classical counterpart, although, unlike in the classical case, its action is linear: state α|0⟩ + β|1⟩ is transformed in a coresponding state β|0⟩+α|1⟩. a convenient way of representing the action of the quantum not gate is in matrix form: x = ( 0 1 1 0 ) . (3) controlled gates are quantum logical gates acting on more than one qubit. the notion of controlled gate allows the implementation of the if − else constructs. quantum controlled gates use a control qubit to determine whether a specific unitary action is applied to a target qubit. the controlled-not operator (cnot) is the prototypical multi-qubit gate. the first parameter of a cnot gate is the control qubit. if this qubit is in state |0⟩, the target qubit is left unchanged and if the control qubit is in state |1⟩, the target qubit is flipped: |00⟩ → |00⟩; |01⟩ → |01⟩; |10⟩ → |11⟩; |11⟩ → |10⟩. the cnot operator is a generalization of the classical xor, since its action can be summarized as |x, y⟩ → |x, x ⊕ y⟩, where ⊕ is addition modulo two. the matrix representation of cnot is: cnot =   1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0   . (4) there are several other multi-qubit gates, nevertheless, the controlled-not gate and the single qubit gates represent the prototypes for any other quantum gate because of the folowing remarkable universality result: any multi-qubit gate can be built out of cnot gates and single qubit gates. the proof of this statement represents the quantum analogous to the universality of the classical nand gate. 636 s. caraiman, v. manta 3 parallel simulation of quantum computation the state of a n-qubit register is represented by a complex unit vector in hilbert space h2n . storing a complex number a = x + iy on a classical computer requires storing the pair of real numbers (x, y) for which the 8 byte representation is prefered. thus, in order to store an n-qubit quantum register using a conventional (classical) computer, at least 2n+4 bytes are required. the memory needed for simulating a n-qubit quantm computer grows exponentially with respect to the number n. for example, when n = 24 (n = 36) at least 256 mb (1 tb) of memory is required to store a single arbitrary state |ψ⟩. the time evolution of a n-qubit quantum register is determined by a unitary operator defined in the h2n space. the matrix dimension is 2n x 2n. in general, 2n x 2n space and 2n(2n+1 − 1) arithmetic operations are needed to execute such an evolution step. thus, the simulation of a quantum computer using a classical device represents a computationally hard problem and the memory and processor generate drastic limitations on the size of the quantum computer that can be simulated. because of the exponential behavior of quantum systems, simulation using classical computers enforces the use of exponential memory space and the execution of an exponential number of operations. it is obvious that the simulation of quantum problems of interesting sizes enforces the use of high performance computing devices. parallel computing can represent a solution to this problem [5, 7, 10, 13]. nevertheless, the development of a quantum simulator must consider another important aspect: it has to be easily accessible. but this contradicts the first requirement, that it is parallel, which deeply restricts the group of potential users. a solution based on the concept of grid systems ca be used to solve this contradiction and to provide the scientific community with an useful and easily accessible instrument. the grid concept adresses the problem of coordinated resource sharing and problem solving in dinamic, multi-institutional virtual organisations [4]. a grid enabled quantum computer simulator is gqcl [1]. this simulator allows the use of the qcl [9] quantum programming language to implement quantum algorithms and the quantum programs are executed using a parallel version of the qc-lib simulation library [8]. using a specific data partitioning scheme and efficient storing of quantum states allowed very good speedups and efficiency of this parallel implementation which will be discussed in the following. 3.1 overview of gqcl grid service for quantum simulation our quantum computer simulator is based on the qc-lib simulation library which provides a framework to execute programs written in a quantum programming language, qcl, in the absence of quantum hardware. the reasons that lead to this choice for a quantum programming language are detailed in [1] and mainly consider the representation of the quantum state using complex numbers, the possibility to write complex quantum operators, the classical extension and its universality. qcl was conceived by ömer [9] and the first version appeared in 1998 and the last one in 2004. it is open-source running under linux operating system and it is a procedural high level language with a c like syntax. qc-lib is a c++ library for the simulation of quantum computers at an abstract functional level [8], and it is used as the back end interpreter for qcl. for the execution of quantum programs written in qcl we developed a parallel version of the qc-lib simulator in which the terms representing quantum states are distributed across multiple processing nodes. we have chosen to expose the parallel implementation of qc-lib through a globus toolkit 4 (gt4 for short) grid service. parallelization has been achieved through the use of lam 7.1.2/7.1.4 implementation of the mpi-2 standard. in gqcl, the execution of parallel simulation of quantum search 637 figure 1: distribution of the 16 basis states of a 4-qubit register using 23 = 8 proccesing nodes. the processing nodes represent the corners of a 3-dimensional hypercube. the mpi implementation of qc-lib is enabled through the means of a wrapper service based on the factory/instance architecture [12]. in gqcl, the instance service, is responsible for actually managing the quantum simulation as a grid job. through the use of a ws-resource, the instance service starts and monitors the job state, notifying the client application on any relevant changes. also, file staging is automated and when the job finishes, the client is given access to the results of the simulation. the architectural details of gqcl and its advantages are presented in [1]. in the following we discuss the complete parallel implementation of the simulation library, addressing the issues regarding the representation of quantum states using multiple processing nodes, the application of singleand multi-qubit quantum operators and measurements. 3.2 parallel implementation of the qc library a quantum register in qcl contains a number of basis vectors, each with a corresponding amplitude. when the qubits forming the quantum register are in a superposition of states, the number of vectors grows exponentially. in qc-lib, the superposition state of a 1-qubit register is represented by two basis vectors (terms) for which the correponding complex amplitudes must be stored: 2 x complex < double >= 32 bytes. when appplying a quantum operator, two term lists are created: one for storing the terms in the current state and one to accumulate the result of an operation on the state. this gives a total of 64 bytes/term which for a n-qubit register requires the use of 2n+6 bytes. thus for n = 25 (n = 29) qubits 2 gb (32 gb) of memory is necessary. in order to provide an efficient parallel execution of qc-lib we take advantage of the specific form of the quantum computation process and distribute the 2n basis states of a quantum register to 2p processors based on the p least significant bits. in this representation, the processing nodes and the basis vectors are actually considered the coordinates of a n-dimensional hypercube (figure 1). each processing node applies quantum operators only to local terms and communicates the generated terms to corresponding processing nodes if necessary. another feature of our implementation is that for a quantum state only non-zero amplitude terms are stored thus diminishing the communication costs in early stages of some operator execution and the space required to store a quantum state. the general single qubit operator. communication between processing nodes is only necessary when applying the operator to a qubit determining the data distribution. applying a 638 s. caraiman, v. manta general single qubit operator u = ( u11 u12 u21 u22 ) on a single qubit with state |ψ⟩ = α|0⟩ + β|1⟩ yelds u|ψ⟩ = (αu11 + βu12)|0⟩ + (αu21 + βu22)|1⟩ (5) if 2 processors are used, then each processor holds the amplitude of one basis state. applying operator u locally on each processor, terms are created that are not owned by the processor, and so communication of these terms is needed: p0 : α|0⟩ u−→ u11α|0⟩ + u21α|1⟩ p1 : β|1⟩ u−→ u12β|0⟩ + u22β|1⟩ comm−→ u11α|0⟩ + u12β|0⟩ u21α|1⟩ + u22β|1⟩ for each term in the initial state at most two terms are created, out of which at most one needs to be communicated. if working, for example, with an n-qubit register and 2p processors, communication is necessary only when applying a single qubit operator on any of the qubits that form the distribution key. for the rest of the qubits, all the terms needed for computing the amplitude of the resulting state are locally owned by each processor. moreover, in the first case, for each processor, all the remotely owned terms are owned by the same other processor as a single bit is flipped in the distribution key. the parallel implementation of the general single qubit operator allows the parallel execution of not, hadamard, phase shift of the amplitude and exponentiation gates. controlled gates. cnot operator (controlled-not) in the parallel implementation of qc-lib, when the control qubit is in state |0⟩, the state of the target qubit doesn’t change, so no new terms are generated and the amplitudes of existing terms are left unchanged. when the control qubit is in state |1⟩, the state of the target qubit is flipped. in this case new terms are generated that need to be communicated to another processing node if the target qubit is part of the distribution key. for example, working with 4 processing nodes and applying cnot to the least significant qubit in a 3-qubit register initially in the state α|100⟩ + β|011⟩, and the control qubit is qubit 2, the following evolution is obtained: p0 : α|100⟩ cnot−−−−→ α|101⟩ p1 : − p2 : − p3 : β|011⟩ cnot−−−−→ β|011⟩ comm−→ − α|101⟩ − β|011⟩ in qc-lib the cnot operator can act on two registers: the control register and the target register. these registers can represent substates of the quantum basis state (sub-registers). in this case, the cnot gate inverts the state of the target (sub-)register if the control (sub-)register is in the state |1⟩c, where c is the number of qubits in the control register. for example, let a be a 1-qubit register in state |0⟩, b a 2-qubit register in state α0|01⟩ + β0|10⟩ and c a 2-qubit register in state α1|10⟩+β1|11⟩. applying cnot to target register b with control register c, the state of the entire quantum memory distributed to 8 processing nodes will be: p2 : |10010⟩, |11010⟩ cnot−−−−→ |10010⟩, |11100⟩ p4 : |10100⟩, |11100⟩ cnot−−−−→ |10100⟩, |11010⟩ comm−→ |10010⟩, |11010⟩|10100⟩, |11100⟩ similar to the case of the general single qubit operator, each processing node communicates with at most one other process node. the index of the process involved in communication is determined by the qubits of the target (sub-)register that make part of the distribution key of the whole quantum memory. parallel simulation of quantum search 639 the cphase operator is another example of 2-qubit quantum gate implemented in qc-lib and allows for a controlled phase shift of the amplitudes. its inputs are a rotation angle, θ, and a control qubit that acts in the same manner as in the cnot case. the amplitudes of the basis states where the control qubit is |0⟩ are left unchanged, an if the control qubit is in state |1⟩, the phase of the amplitudes of the basis states are multiplied by eiθ. the matrix form of the cphase operator is: cphase =   1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 eiθ   . (6) operator cphase can also act on (sub-)registers of the quantum memory and its parallel implementation is analogous to that of the cnot operator. one important difference between the two implementations is that when applying the cphase operator communication between processing nodes is not necessary as the action of this operator doesn’t generate new terms, and only the amplitudes of the local terms are modified. measurement of quantum states. in qc-lib, the measurement of a n-qubit quantum register is simulated in o(2n) time. let |ψ⟩ = ∑2n−1 j=0 αj|j⟩ be the state of a n-qubit quantum register. the measurement step is simulated in the following manner: 1. rrandomly generate a number p, 0 ≤ p < 1, 2. randomly generate a positive integer x, smaller than the number of terms with non-zero amplitude, 3. determine an integer i, 0 ≤ i < 2n − 1, such that ∑i−1 j=x |αj| 2 ≤ p < ∑i j=x |αj| 2. integer i is the representation of the measured state. after measurement, the state of the register becomes |i⟩. because the terms of the quantum register are distributed across processing nodes, the measurement operation requires communication between these nodes in order to correctly select the term i, but also to colapse the register in state |i⟩. thus, a master process is responsible with the random generation of numbers p and i and with computing the sum. sincronization between processing nodes is achieved using mpi_bcast operations such that the master process could receive the norm of the amplitude of a term j from the owning processing node. after selecting number i, all processing nodes know this number and can pass the state of the quantum register in |i⟩. 4 simulation of quantum search in gqcl many problems in classical computing can be reformulated to express the search of a unique element that satisfies a certain predefined condition [2]. if there is no additional information about the search condition, the best classical algorithm is a brute-force search, meaning that the elements are sequentially tested against the search condition. for a list of n elements, this algorithm executes an average of n/2 comparisons. by exploiting the advantages of quantum parallelism and interference of quantum states, grover formulated a quantum algorithm that can find the searched element in an unstructured database in only o( √ n) steps [6]. grover’s algorithm is based on the concept of amplitude amplification and its principle is to encode the elements in the data set as quantum states of a quantum register and to apply an operator, g, whose effect is to raise the probability that the system finds itself in the marked state (the state encoding the solution of the search problem). because only unitary transformations are used to act upon the system, the probability conservation takes place. this allows that as the probability that the system finds itself in the desired state grows, the probability of all 640 s. caraiman, v. manta (a) (b) figure 2: (a) execution time for grover’s algorithm; (b) speed-up for grover’s algorithm. other (unmarked) states are correspondingly diminished. applying grover’s operator g a certain number of times will determine the probability of the marked state to be very close to 1. in order to acheive this behavior of the quantum system, a grover iteration first inverts the phase of the amplitude of the marked state and then inverts the phase of the amplitude of all states around the mean. the inversion of the solution state can be obtained using a so-called "black box" function (known as a quantum oracle) which must be able only to identify wheather a certain record is member of the solution set and thus the mechanism is very general. after one application of grover operator, the amplitude of the marked state grows with a factor of o( 1√ n ), while the amplitudes of the unmarked states lower correspondingly. to obtain o(1) probability for the solution state, grover iteration must be applied o( √ n) times. there is a finite probability that the search operation doesn’t end in success, in which case, grover’s algorithm must be repeated. the performance of our gqcl quantum simulator with respect to the quantum search problem is evaluated. in order to eloquently compare the running times for different problem sizes, we only measure the execution time for one application of grover’s algorithm. in figure 2 we present the results obtained for different problem sizes on various numbers of processing nodes. it can be observed from figure 2(a) that the run time grows with an average factor of about 2.8 for each additional qubit. this is due to the fact that each extra qubit represents a doubling of the problem size and that the number of applied grover iterations grows with a factor of √ 2 for an additional qubit. variation of the speedup with the number of processing nodes is presented in figure 2(b). for only 19 qubits we obtain a speed-up of 7.9 and the measurements reveal a growing trend of the speed-up with the increase of the problem size. 5 conclusions classical sequential computers enforce drastic limitations over the quantum computation simulation process. quantum computer simulators have become an attractive alternative for experimentation with quantum algorithms, but their purpose cannot be achieved without significant computing resources. a promising approach is represented by executing these simulators in grid systems that can provide high performance resources. the quantum computer simulator described in this paper relies on parallel processing implemented in qc-lib. besides the parallelization of the general single qubit operator, we also described the parallelization of the control gates (cnot, cphase) and of the measurement process. the efficient representation and partitioning of the quantum states using the distributed memory of a computer cluster allowed parallel simulation of quantum search 641 very good speed-ups to be recorded at the execution of grover’s search algorithm. bibliography [1] s. caraiman, a. archip, and v. manta. a grid enabled quantum computer simulator. in proc. of synasc’09. ieee computer society, 2009. [2] s. caraiman and v. manta. new applications of quantum algorithms to computer graphics: the quantum random sample consensus algorithm. in proc. of acm cf ’09, pages 81–88, new york, ny, usa, 2009. acm. [3] r. feynman. simulating physics with computers. int. j. theor. phys., 21(6):467–488, 1982. [4] i. foster, c. kesselman, and s. tuecke. the anatomy of the grid: enabling scalable virtual organizations. int. j. high perform. comput. appl., 15(3):200–222, 2001. [5] i. glendinning and b. omer. parallelization of the qc-lib quantum computer simulator library. in proc. of ppam 2003, volume 3019 of lncs, pages 461–468. springer, 2004. [6] l. grover. a fast quantum mechanical algorithm for database search. in proc. of 28th acm annual stoc, pages 212–219, 1996. [7] j. niwa, k. matsumoto, and h. imai. general-purpose parallel simulator for quantum computing. in proc. of umc ’02, pages 230–251, london, uk, 2002. springer-verlag. [8] b. ömer. simulation of quantum computers, 1996. http://tph.tuwien.ac.at/ oemer/doc/qcsim.ps. [9] b. ömer. structured quantum programming. phd thesis, tu vienna, 2003. [10] k. d. raedt, k. michielsen, h. d. raedt, b. trieuc, g. arnoldc, m. richterc, t. lippertc, h. watanabed, and n. itoe. massively parallel quantum computer simulator. computer physics communications, 176(2):121–136, 2007. [11] p. shor. algorithms for quantum computation: discrete logarithms and factoring. in proc. of sfcs ’94, pages 124–134. ieee computer society, 1994. [12] b. sotomayor. the globus toolkit 4 programmer’s tutorial, 2005. http://gdp.globus.org/gt4-tutorial/multiplehtml/index.html. [13] f. tabakin and b. juliá-díaz. qcmpi: a parallel environment for quantum computing. computer physics communications, 180(6):948–964, 2009. international journal of computers, communications & control vol. ii (2007), no. 1, pp. 48-55 advances in intelligent tutoring systems: problem-solving modes and model of hints alla anohina abstract: the paper focuses on the issues of providing an adaptive support for learners in intelligent tutoring systems when learners solve practical problems. the results of the analysis of support policies of learners in the existing intelligent tutoring systems are given and the revealed problems are emphasized. the concept and the architectural parts of an intelligent tutoring system are defined. the approach which provides greater adaptive abilities of systems of such kind offering two modes of problem-solving and using a two-layer model of hints is described. it is being implemented in the intelligent tutoring system for the minimax algorithm at present. in accordance with the proposed approach the learner solves problems in the mode which is the most appropriate for him/her and receives the most suitable hint. keywords: intelligent tutoring system, problem-solving mode, hint 1 introduction emerging of a knowledge society and growing demands for highly skilled and educated labor force claim for changing traditional teaching and learning processes. one way of changes is related with an integration of various kinds of computer-based learning systems as supplements to conventional teaching methods. however, it is necessary to provide intelligent and adaptive abilities of a software system in order it could take over a role of a teacher in effective way. this idea is not new one as it is exploited in intelligent tutoring systems for more than 30 years since the earliest scholar system [1] appeared. during this time a huge amount of intelligent tutoring systems has been implemented for different areas, for example, for mathematics, physics, medicine, informatics and computer science [2, 3, 4, 5, 6, 7, 8, 9, 10]. nowadays developments of systems of such kind have received new breath with the appearance of the agent paradigm [11]. however, adaptive abilities of intelligent tutoring systems still are not high enough, particularly regarding modes of practical problems solving and support of a learner in this process. solving of domain problems is an important part of intelligent tutoring systems, as it allows to deepen the acquired theoretical knowledge in practice, but the mere solving is unlikely to lead to improved skills or deeper understanding of a subject matter. learning often takes place best when the learner receives feedback from the system. feedback is a way to improve the learning process on the basis of continuous assessment of learning results, the analysis of their quality and performance of necessary corrections. feedback encourages desired learning behavior and discourages undesired one, allows to understand how successfully the learner acts, whether he/she applies relevant knowledge, and it provides opportunities to correct misconceptions. in case of intelligent tutoring systems feedback is the various reactions of the system to learner’s learning behaviour. in its turn, a hint is only one form of feedback. unfortunately, little prior researches have been done which are devoted to the general issues of hints formation in intelligent tutoring systems. the most significant work is [12], containing the description of the results of studying hints used by experienced tutors and an attempt to formulate a strategy for using hints in intelligent tutoring systems. according to [12] hints encourage the student to engage in active cognitive processes that are thought to promote deeper understanding and long-term retention. as it is pointed in [13], the developed intelligent tutoring systems have relatively simple and inflexible hinting policies, which more often demand from the learner to follow a prescribed problem-solving strategy and, therefore, hints are always aimed at the next step which should be taken accordingly to the copyright c© 2006-2007 by ccc publications selected paper from icvl 2006 advances in intelligent tutoring systems: problem-solving modes and model of hints 49 strategy. the authors draw attention to two problems: inflexible choice of the steps targeted by hints and proceeding of hints from the most general to the most specific. the analysis of the existing intelligent tutoring systems allows to make the following conclusions about reactions of a system to actions of a learner. typically the system gives the learner an immediate feedback after each performed action or step during problem-solving irrespective of the fact whether the action or the step was correct or incorrect. such policy prevents the learner from proceeding along a wrong solution path. the examples of immediate feedback are found in [5, 8, 9, 14, 15]. but may be a learner would like to make a series of steps and after that to receive feedback about correctness and to find by his/herself what step has led to the incorrect solution? the system usually provides a special button or tool, which the learner can use to request a hint. in algebrain [5] such tool is an animated agent. the system responds with two types of support: generalized "here’s what i’m expecting you to do at this point" help text and a hint specific to the current state of the problem. in andes [9] there are two buttons. one of them gives help "what’s wrong with that?" on an incorrect entry. other button provides a hint about the next step in problem-solving. typically hints are organized in a range from the most general to the most specific. the general hint as a rule contains a minimum information on an error. further the informativeness of hints increases. the most specific hint clearly specifies or shows what should be done. hints are given sequentially. there is a number of systems which use this approach, for example, [5, 8, 10, 14, 16, 17]. the organization of hints from the most general to the most specific is not flexible enough. the insufficient amount of information in a hint can cause frustration and desire to request the subsequent hints without attempts to solve a problem by the learner. information, which specifies necessary actions after first request of a hint, in its turn, is contradictory to the learning process. thus, mechanisms, which will allow to implement individual system’s reactions for each learner giving such amount of information, which will help and at the same time will provide certain cognitive load, are necessary. the example of adaptive hinting is described in [18]. the authors use learner’s proficiencies to select an appropriate hint. the learner with high proficiency at a particular skill receives the more subtle hint. the less proficient learner is presented with a more obvious hint. the authors point out that this is better than require learners to wade through several levels of hints before they receive material that is appropriate to their knowledge level. the paper describes an approach which provides greater adaptive abilities of intelligent tutoring systems supporting two modes of problem-solving and using a two-layer model of hints. thus, the learner solves problems in the mode which is the most appropriate for him/her and receives the most suitable hint. the aforementioned approach is being implemented in the intelligent tutoring system for the minimax algorithm at present. the paper is organized as follows. section 2 defines the concept and the architectural parts of an intelligent tutoring system. the developed approach based on two modes of problem-solving and a twolayer model of hints is discussed in section 3. section 4 describes the intelligent tutoring system for the minimax algorithm in which the proposed approach is being implemented. finally, conclusions are presented, and some directions for future work are outlined. 2 intelligent tutoring systems despite of a broad variety of the developed systems an unequivocal and exhaustive definition of an intelligent tutoring system still does not exist. however, it is possible to list the most often mentioned characteristics of systems of such kind [10, 19, 20, 21, 22, 23, 24]. thus, the intelligent tutoring system is a computer-based system. it is an intelligent system because it uses principles and methods of artificial intelligence [25] such as knowledge representation, inference mechanisms and machine learning in its structure and operation. an intelligent tutoring system is an adaptive system as it alters aspects of its structure, functionality or interface for the particular user and his/her changing needs over time [26]. it 50 alla anohina emulates a human teacher, tries to provide benefits of individual (one-on-one) tutoring, and is based on the theory of learning and cognition. furthermore, intelligent tutoring systems are characterized by the fact that they store three basic kinds of knowledge [20, 27]: domain knowledge, knowledge about learners, and pedagogical knowledge. the knowledge types determine three main parts of the system’s architecture: the domain knowledge, the student diagnosis module, and the pedagogical module. an intelligent tutoring system, as any other software intensively communicating with users, needs a part of the architecture responsible for the interaction between the system and the learner. it is a communication module or interface which controls screen layouts, interaction tools, etc. however, each system can contain additional components the presence of which depends on the following factors: features of problem domain, locking down of separate functions of the basic constituent parts in the isolated components of the structure, technology used for system implementation, and additional functional capabilities of the system. the general architecture of an intelligent tutoring system is shown in figure 1. figure 1: the general architecture of an intelligent tutoring system (adopted from [28]) the domain knowledge is the knowledge the system is teaching. most often it is incorporated in the expert model which represents skills and expertise that an expert in a particular domain holds. the model serves as a standard for evaluating the learner’s performance and the knowledge level. the domain knowledge can include fragments of theoretical materials, texts of practical tasks and attributes related with them, explanatory units, rules and principles used in the domain, etc. typically, it is represented within the system using logical, procedural, network or structured knowledge representation schemes [29]. moreover, the domain knowledge is organized in a certain way, commonly, as a hierarchy, for example, a topic includes some units, which consist of several chapters. the expert module generates solutions of problems for their further comparison with solutions of the learner. the student diagnosis module carries out the student diagnosis process that collects information about the learner, analyzes it and stores in the student model. the student model is formed for a particular learner and serves as an input to the pedagogical module which tailors the learning process to the needs of the learner. the model contains learner’s identifying information, information on the current knowledge level of the learner, information about learner’s cognitive, emotional and psychological features, his/her past experience, interests, and system’s options usage by the learner. the pedagogical module provides a knowledge infrastructure for adaptation of the learning process to characteristics and needs of a learner without interventions of a human-teacher. it implements the advances in intelligent tutoring systems: problem-solving modes and model of hints 51 learning process on the basis of teaching strategies and instructions held in the pedagogical model. the primary tasks of this module are selection and sequencing of learning material that is the most suitable for the learner, determining of the type and content of feedback and help, and answering questions from the learner. 3 the proposed approach 3.1 the problem-solving modes generally, there are two possibilities regarding moments of feedback delivering: an immediate feedback after each step or action in problem-solving and feedback after submission of a whole solution to the problem. it is a basis for two modes of problem-solving in the proposed approach. in the completeness mode a learner chooses the moments of feedback presentation to check correctness of a series of steps. so, he/she can perform one or more steps solving a problem and then to require checking of the performed steps. the system provides feedback about correctness of his/her previous actions and the learner by his/herself should determine what step has led to the incorrect solution. this mode is similar to reinforcement learning [30] which is widely used in artificial intelligence. in the step-by-step mode the system monitors each problem-solving step and gives feedback about its correctness. there are four variations of the step-by-step mode regarding a kind of information given to the learner: • the learner receives both positive and negative feedback. in the case when the learner has performed the correct action he/she is praised (receives a positive feedback, or a reward). if the step was incorrect, criticism (negative feedback) is given to the learner. moreover, negative feedback can be given in two different forms: only as a text, which informs that the action was incorrect, and as a text about the incorrect step together with a hint about how to improve his/her operation. • the learner receives only negative feedback. in this case negative feedback also can be given in two different ways described above. in the completeness mode the learner is not praised or criticized for each performed step. instead of it he/she receives a total estimation of all performed actions. the estimation specifies how far the learner is from his/her goal: the correct solution of a problem. the total estimation can have a positive or negative deviation regarding difference between a number of correctly and incorrectly performed steps. in case when the mentioned difference exceeds some admissible value, a hint can be given to the learner. thus, there are two variations of the completeness mode regarding a kind of feedback: • the learner receives only a total estimation of the performed steps. • the learner receives a total estimation of the performed steps together with a hint about how to improve his/her operation. it is obvious, that it is necessary to provide an opportunity to change the problem-solving mode and a kind of feedback by the learner, as well as, to request a hint in case when he/she receives only the text of feedback. thus, the general scheme of the problem-solving modes and kinds of feedback is displayed in figure 2. it is necessary to stress, that the described problem-solving modes can be implemented only if the process of finding of a problem solution consists of several (homogeneous or heterogeneous) steps. 52 alla anohina figure 2: the problem-solving modes and kinds of feedback for an intelligent tutoring system 3.2 the model of hints the model of hints in the proposed approach defines two layers (figure 3): a layer of the general hint categories and a layer of hints within the general categories. there are three general hint categories: general hints, hints of average informativeness, and specific hints. each category contains one or more hint, which also are ranged from less informative to more informative. the model allows the learner to receive a hint that is the most suitable for him/her. before the learner starts to work in problem-solving mode testing should be taken with the purpose to determine a general hint category which is suitable for the learner. further requesting help during problem-solving the learner will receive an average by number hint from the hint category suitable for him/her. if after receiving of a hint the learner is not capable to execute a correct action, he/she is presented with a subsequent hint. the process proceeds while he/she will not reach last hint for the given error. such approach spares the learner from being presented with informativeless hints. contrary, the learner timely receives a hint providing help and certain cognitive load, therefore, reducing an opportunity of frustration, floundering and loss of interest to learning. figure 3: the two-layer model of hints (adopted from [28]) 4 the intelligent tutoring system for the minimax algorithm the described approach is being implemented in the intelligent tutoring system for a topic of the learning course "fundamentals of artificial intelligence" at the faculty of computer science and information technology at riga technical university. the topic is related with the algorithm for implementing advances in intelligent tutoring systems: problem-solving modes and model of hints 53 two-person games with full information, i.e., the minimax algorithm [29] which example is given in [31] in details. all practical problems directed to the development of skills of applying of the minimax algorithm consist of a sequence of homogeneous steps. let’s consider one of them. a game tree is presented to the learner. it has arcs which are inadmissible for a game with full information: arcs between nodes at the same level or through levels. as a rule, there are from 3 up to 5 wrong arcs in the tree. the learner should find them and to remove from the tree. thus, a removal of one (wrong or correct) arc is a step in this task. it is obvious, that tasks for the minimax algorithm are a fine example of an opportunity to provide two problem-solving modes in the system. at present the following tasks regarding the developed approach are completed: possible mistakes and hints corresponding to them are defined for each problem, sets of hints for each mistake are divided into categories according to the two-layer model of hints described in the previous section, and the user interface of both problem-solving modes is developed. the architecture of the system corresponds to the general architecture of an intelligent tutoring systems displayed in figure 1. the system carries out assessment of an initial learner’s knowledge level on the topic, provides the theoretical knowledge acquiring mode and the practical problem-solving mode with preliminary determining of the problem-solving mode and a category of hints most suitable for the learner, and gives a final assessment of the achieved knowledge level. 5 conclusions and future work adaptive abilities of intelligent tutoring systems are not high enough especially regarding problemsolving modes offered to a learner and ordering of hints from the most general to the most specific. the paper presents an approach which allows the learner to work in the problem-solving mode that is the most appropriate for him/her and to receive the most suitable hint. the proposed two-layer model of hints can reduce frustration, floundering and loss of interest to learning that are inevitable in case when the learner receives too little support during problem-solving. at present, the proposed approach is in a stage of development. firstly, it is necessary to determine, how testing for the problem-solving mode and a category of hints most suitable for the learner may be implemented. secondly, both psychological and pedagogical foundations of the proposed approach should be specified. after the implementation of the approach in the intelligent tutoring system for the minimax algorithm the testing of the system will be done in the corresponding learning course together with questioning of learners and the subsequent interpretation of the received results. references [1] j. 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[26] d. r. benyon and d. m. murray, adaptive systems: from intelligent tutoring to autonomous agents, knowledge-based systems, vol. 6, no. 4, pp. 197-219, 1993. [27] c. frasson, t. mengelle and e. aimeur, using pedagogical agents in a multi-strategic intelligent tutoring system, proceedings of the 8th world conference on artificial intelligence in education ai-ed97, workshop v: pedagogical agents, kobe, japan, pp. 40-47, 1997. [28] a. anohina, the problem-solving modes and a two-layer model of hints in the intelligent tutoring system for minimax algorithm, proceedings of the 1st international conference on virtual learning, bucharest, romania, pp. 105-112, 2006. [29] g. f. luger, artificial intelligence: structures and strategies for complex problem solving, addison wesley, 2001. [30] s. russell and p. norvig, artificial intelligence: a modern approach, prentice hall, 2003. [31] a. anohina, intelligent tutoring system for minimax algorithm, scientific proceedings of riga technical university, computer science, 5th series, vol. 22, pp. 122-130, 2005. alla anohina riga technical university department of systems theory and design kalku street 1, riga, latvia, lv-1658 e-mail: alla.anohina@cs.rtu.lv received: november 8, 2006 editor’s note about the author: alla anohina is an assistant at the department of system theory and design of riga technical university in latvia. she got m.sc.ing. in 2002 from riga technical university and received werner von siemens excellence award, award of latvian fund of education and award and memorial medal of latvian academy of sciences, lattelecom ltd. and latvian fund of education for the best master’s thesis in the year 2002. her main research fields are intelligent tutoring systems, computer-assisted assessment systems and artificial intelligence. now she is finishing her ph.d. thesis which main topic is related with the development of the intelligent supporting system for adaptive learning and knowledge assessment. she has five years’ experience of teaching in the field of computer science both in riga technical university, and in other educational institutions of latvia. she has participated in several research projects related with the development of knowledge assessment software. international journal of computers, communications & control vol. ii (2007), no. 2, pp. 121-131 modelling of wastewater treatment plant for monitoring and control purposes by state – space wavelet networks adam borowa, mietek a. brdys, krzysztof mazur abstract: most of industrial processes are nonlinear, not stationary, and dynamical with at least few different time scales in their internal dynamics and hardly measured states. a biological wastewater treatment plant falls into this category. the paper considers modelling such processes for monitorning and control purposes by using state space wavelet neural networks (sswn). the modelling method is illustrated based on bioreactors of the wastewater treatment plant. the learning algorithms and basis function (multidimensional wavelets) are also proposed. the simulation results based on real data record are presented. keywords: neural network models, model approximation, learning algorithms, waste treatment. 1 introduction biological wastewater treatment plants (wwtp) are very important due to their ability of neutralising results of human activity. figure 1: activated sludge reactor (bioreactor) with secondary clarifier typical wwtp consists of three phases of treatment: mechanical, biological and chemical. example of biological part of wwtp (bioreactor with secondary clarifier) is shown in figure 1. before biological treatment the wastewater passes through mechanical treatment where coarse particle, inorganic solids and suspended particulate matter are removed. chemical treatment may be implemented before, after or into biological treatment. activated sludge is responsible for nitrogen and phosphorus removing in bioreactor. biological treatment, due nitrogen and phosphorous removing, consists of three phases: anaerobic, anoxic and aerobic. in figure 1 recirculation (from secondary clarifier to anoxic zone and from anoxic to anaerobic) are shown. control of that recirculation, the air flow rate to aerobic zone and excessive sludge flow rate is very important for the process quality [8]. wastewater treatment process is very complex due to its specific features such as: highly non-linear and multiple time scale dynamics, varying influent flow, high dimension of state vector with many states not accessible by hard sensors; see [4] for details. due to nationwide regulations, which force high standards on treated wastewater quality, a need for better treatment still exists. there are two solutions of this problem. the first is to enlarge the wwtp and the second, to implement an intelligent control system. itšs obvious that the last proposition is better in an economic sense. the main purpose of control of wwtp is to keep all quality parameters of treated waste under specific norm. because of complexity of the process, the advanced control technologies are required. the multilevel-multilayer hierarchical control structure was recently derived [1] and further developed in [4]. copyright © 2006-2007 by ccc publications 122 adam borowa, mietek a. brdys, krzysztof mazur figure 2: multilevel-multilayer hierarchical control structure this structure (figure 2) consists of 3 control levels: supervisory control level, optimising control level and follow up control level; each with different control objectives. optimising control level (ocl) uses a robust model predictive control (rmpc) algorithm and it is decomposed into three layers each with different control horizon and time scale. this work assembles on modeling the wwtp for rmpc and supervisory control. a good candidate for modelling a dynamical, non-linear system with multiple time scales is a state space wavelet network (sswn). using the wavelets as the basis functions solves the multiple time scale problem. the rest of article order goes as follow. section 2 is a description of problem statement. wavelet networks are presented in section 3. a learning algorithm is proposed in section 4. stability of sswn is mentioned in section 5. application and results are shown in section 6 and 7. 2 problem statement the paper aim is to verify the possibility of applying the sswn modelling of a wwtp for monitoring and control purposes. 2.1 monitoring of wwtp for supervisory control purposes monitoring the wwtp state is essential for the supervisory control purposes [4]. most of the wwtp states are not measurable. even though the sswn for state monitoring can be made. first model of wwtp in simba has to be calibrated. methods for simba model calibration are presented in [2]. when calibrated model in simba is available then it can be used as a data generator for learning the sswn. 2.2 progress of the hierarchical intelligent control structure the layer decomposition of the ocl raises a loss on optimality and certain problems with accommodating the constraints into the layer optimisation tasks. as the new sswn model has an ability to combine the two time scales (fast and medium), the fast and medium layers can be combined into one layer, hence eliminating the above problems. modelling of wastewater treatment plant for monitoring and control purposes by state – space wavelet networks 123 3 wavelet network wavelet network is a neural network with one hidden layer consisting wavelets as the basis function. wavelets are specific mathematical functions and are described below. neurons made up of wavelets are called wavelons. 3.1 wavelets any function satisfying the conditions (1) and (2), where ψ(ω) is a fourier transform of ψ(t), is called a mother wavelet. it is required that cψ = ∫ ∞ 0 |ψ(ω)|2 ω dω < ∞ (1) ∫ ψ(t)dt = 0 (2) examples of wavelets are haar wavelet or morlet wavelet shown in figure 3 and defined by the equations (3) and (4) respectively. ψ(x) =    1, 0 ≤ x < 0.5 −1, 0.5 ≤ x < 1 0, elsewere (3) ψ(x) = exp(−x2/2)∗cos(5x) (4) a family of wavelets is made by dilating and translating mother wavelet. see equation (5), where d and figure 3: haar and morlet mother wavelets. t represent the dilation and translation parameters respectively. ψdt (x) = √ dψ(d(x−t)) (5) the translation and dilation parameter determine the position (time) and scale (frequency) domain. the wavelets represented by equation (5) may be design as orthonormal. 3.2 multidimensional wavelets the multidimensional wavelet (6) was presented first by zhang and beneveniste in [11]. in equation (6) ψs(x) is a one–dimensional wavelet, x is a vector and ψ(x ) is a scalar ψ(x ) = ψ(x1, ..., xn) = πnj=1ψs(x j) (6) 124 adam borowa, mietek a. brdys, krzysztof mazur few months later zhang [12] presented new multidimensional wavelet radial wavelet (7) and (8) where ψ(x ) is a one–dimensional wavelet. ψ(x ) = ψs(||x||) (7) ||x|| = (xt x) 12 (8) it is logical that for a multivariable function approximation the multidimensional wavelets are desirable. the multidimensional wavelet (6) was tested and some problems were encountered. first, often the optimisation problem to be solved by the learning algorithm was illűconditioned. second, a large n relatively easily led to overparameterization. only when radial wavelet (equations (7) and (8)) was used the network learning was successfully finalized. the radial multidimensional wavelet, used in the paper, is given by equations (9) (13) and it is shown in figure 4. figure 4: multidimensional radial wavelon. z=[x,u] (9) d j = [d1, j, ..., dk+m+n, j] (10) t j = [t1, j, ...,tk+m+n, j] (11) a= diag(d j)∗(z−t j)t (12) a j = r(a(z,d,t)) = (at a) 1 2 (13) a one ű dimension wavelet used in this application was a morlet wavelet (4). proposed structure for a wavelon was described in [12]. 3.3 feed-forward and input-output wavelet networks. it was presented in [5] that feed–forward wavelet network (ffwn) in the form (14) is a universal approximator for any function in sobolev space. wavelet networks have more freedom then other neural networks because of number of optimised parameters for each wavelon. z = σni=1wiψ(di(x−ti)) (14) the wavelet network for one-dimensional input network is described by equation (14), where di and ti are dilation and translation parameters respectively, wi are linear weights and n is a number of wavelons. a feed forward wavelet network (figure 5) was presented first by zhang and beneveniste [11]. a dynamical wavelet network (dwn) was presented in [7]. the dwn structure is shown in figure 6. it is the input– output structure, which is commonly used but it has some drawbacks that do not permit this dwn to be used for wwtp modelling. since 1992 many wavelet networks were presented and used in different places of human activity. modelling of wastewater treatment plant for monitoring and control purposes by state – space wavelet networks 125 figure 5: feed-forward wavelet network. figure 6: dynamical wavelet nertwork. 3.4 state space wavelet network state space wavelet network (sswn) is not as well known as ffwn or dwn but it has specific advantages over mentioned networks. due to its of internal state space component the sswn, better captures the modeled plant structure. therefore the modeling error is smaller and the learning process is faster. nevertheless, there is no proof for such a network to be the universal approximator. sswn with multidimensional wavelets is shown in figure 7 and described by the equations (15) and (16), where n is a number of outputs, m + n is a number of state variables, k is a number of inputs (control and disturbance), l is a number of wavelons. a number of parameters to be estimated during learning process are then (2∗l∗(n + m + k) + l∗(n + m)). it combines the state space architecture of dynamic neural network [10] with the multidimensional wavelons as the processing nodes. figure 7: structure of state space wavelet network with multidimensional wavelons ∀n+mi=1 xi(k + 1) = σlj=1wi, j ∗ψ j(x(k), u(k)) (15) ∀ni=1yi(k) = σlj=1wi, j ∗ψ j(x(k), u(k)) (16) 4 learning algorithm during searching for the right structure of wavelet network, selecting the learning algorithm was also investigated. searching over a large set of combinatorial optimization algorithms included: simple genetic algorithm (sga), sga with elitism, evolutionary algorithm (ea), ea with sbx crossover, 126 adam borowa, mietek a. brdys, krzysztof mazur simulated annealing (sa) and parallel hybrid of sa and ea with the use of computer grid technology. the sa algorithm turned out to be the best for our problems. 4.1 simulated annealing whilst inspiration for ga and ea was in biology (genetic), the sa was inspired by thermodynamics (statistical mechanics). the algorithm was motivated by the growing mechanism of a single crystal from a melt [6]. it was found that slow cooling (annealing) of melted metal goes to low state of energy while fast cooling does not. a simple algorithm based on monte carlo search was proposed in (metropolis 1953) which then became an important part of the sa algorithm [6]. this algorithm works on a chain of atoms (s). in each step one atom from the chain is disturbed and the new chain energy e(s′) is calculated. a difference between the chain s and s′ energies is calculated by equation (17). ∆e = e(s′)−e(s) (17) if ∆e < 0 then new chain s′ is accepted; otherwise the new chain is conditionally accepted with a probability given by boltzmann probability factor (18), where kb is a boltzmannšs constant and t is a temperature factor. whilst t is not real temperature and kb is a constant then product kb ∗ t may be replaced for practical implementation by single factor t . p(∆e) = exp(−∆e/(kbt )) (18) the annealing schedule was added in [6] in order to formalise the sa algorithm. the sa algorithm works iteratively as follow. for given temperature t0 the metropolis monte carlo (mmc) method is applied. when chain of atoms is said to be stable then new t is computed and mmc is applied; this procedure goes as long as the temperature reaches 0 or the energy obtains optimum. for the new t the computation-annealing schedule is given by equation (19), where t0 is initial temperature and k is sa iteration counter. for practical matter chain of atoms is said to be stable when mmc iteration counter obtain an established number. t (k) = t0 1 + ln(k) (19) 4.2 a simple method for sswn learning with simulated annealing algorithm in the paper the learning procedure was implemented as follow: – initialize a chain of atoms, – set the initial temperature, – establish a number of mmc iterations needed to obtain a stable chain, – run iterative sa algorithm. initialization of chain of atoms is made using a random number generator with upper and lower constrains for weights and wavelet coefficients. the initial temperature is chosen by the user (between 0 and 1). number of mmc iterations is proportional to length of the chain of atoms. in optimisation only one chain of atom should be used because convergence of sa is independent of initialization due to exploring nature of the method. before the sa is applied the energy function must be described. energy function is given by equation (20), where y is the plant output and y κ is a sswn output, n is a number of sswn outputs, j is a number of samples taken for network learning. e = 1 n ∗j σ n i=1σ j j=1|yi, j −y κi, j| (20) modelling of wastewater treatment plant for monitoring and control purposes by state – space wavelet networks 127 it is important to normalize the data before learning the network. 5 stability of sswn it was proved that under certain conditions the state space neural networks (ssnn) can be made stable [9]. the sufficient conditions for the stability suitable constrain the network weights. it is not a subject of this work to proof the sswn stability. however, we shall demonstrate by simulation that the stability is in place if the network parameters are constrained. a discharge of a randomly chosen initial state of the network is illustraed in figure 8. after the discharge has been finished the network output accurately follows the plant output. figure 8: discharge of randomly chosen initial state of sswn 6 application after the sswn structure and learning algorithm have been chosen the inputs, outputs, size of the state vector and the number of wavelons must be fixed. figure 9: learning results for different m and l the presented sswn was applied to wastewater treatment plant modelling. the modelled plant consists of 4 control inputs (flow rate to aerobic zone, 2 recirculation flow rates and excessive sludge flow rate), 4 measured or estimated disturbance inputs, including inflow and quality (cod, bod, t ss), and 4 outputs: outflow (q), concentrations of nitrate and nitrite nitrogen (sno) and nh + 4 + nh3 nitrogen (snh ) in effluent and concentration of oxygen in aerobic zone (so). still the number of state space variables m and number of wavelons l are unknown. fortunately we know the size of state of the modelled plant, which is 14 for each zone of biological treatment. therefore m was searched in a set of values: 14, 21 and 28; the larger m implied a huge set of optimized parameters. in order to reduce the computational burden m wavelons were applied. the network was parameterized by 980, 1911 and 3136 parameters, respectively. results of the learning (limited to single output) for these three parameterization examples are shown in figure 9. finally m and l were selected as 28 to give small modelling error and acceptable learning time. 128 adam borowa, mietek a. brdys, krzysztof mazur 7 results the results of long term learning are shown in figure 10. the learning time was around 36 hours with simulated annealing algorithm. the bold line shows the modelled plant output while the dashed line illustrates the network output. the mean modelling error was about 1.84 figure 10: comparison of model and plant outputs 8 conclusions a structure of a dynamical wavelet network called state space wavelet network (sswn) has been proposed. the multidimensional radial wavelon has been proposed as the network processing nodes. the simulated annealing for the sswn learning has been derived and validated by application to modelling the wastewater treatment plant. it has been demonstrated that such a network is able to approximate this dynamic, nonlinear, not stationary process with several different time scales. references [1] m. a. brdys, m. grochowski, k. duzinkiewicz, w. chotkowski, y. liu "design of control structure for integrated wastewater treatment plant űsewer systems", international conference on technolog, automation and control of wastewater and drinking water systems tiaswik’02 gdansk – sobieszewo, poland, june 19-21 2002. [2] w. chotkowski, j. mźkinia, m.a. brdys, k. duzinkiewicz, k. konarczak "mathematical modelling of the processes in integrated municipal wastewater systems", proc. of the 9th ifac/ifors/imacs/ifip symposium on large scale systems: theory and applications, bucharest, july 18-20 2001. [3] i. daubechies ten lectures on wavelets cbms-nsf regional series in applied mathematics, siam, philadelphia, 1992. modelling of wastewater treatment plant for monitoring and control purposes by state – space wavelet networks 129 [4] m. grochowski, m.a. brdys, t. gmiñski "intelligent control structure for control of integrated wastewater systems" ifac 10th symposium large scale systems: theory and applications osaka ű japan july 26-28 2004. [5] a. juditsky, q. zhang, b. delyon, p-y. glorennec, a. beneveniste "wavelets in identification" rapport de recherche nř2315 1994. [6] s. kirkpatrick, c.d. gelatt, m.p. vecchi "optimization by simulated annealing" science vol. 220 pp. 671-680, 1983. [7] y. oussar, i. rivals, l. personnaz, g. dreyfus "training wavelet networks for nonlinear dynamic input-output modeling" neurocomputing, vol. 20, pp. 173-188, 1998. [8] g. olsson, r newell wastewater treatment systems. modelling, diagnosis and control. iwa publishing, london, 1999. [9] e.n. sanchez, j.p. perez "input-to-state stability (iss) analysis for dynamic neural networks" ieee transactions on circuits and systems ű i: fundamental theory and applications, vol. 46, no. 11, pp 1395 ű 1398, november 1999. [10] j.m. zamarreno, v. pastora "state space neural network. properties and applications." neural networks, vol. 11, pp 1099-1112, 1998. [11] q. zhang, a. beneveniste "wavelet networks" ieee trans. on neural networks, vol. 3, num. 6, pp 889-898, nov. 1992. [12] q. zhang "wavelet network: the radial structure and an efficient initialization procedure" technical report of linköping university, lith-isy-i-1423, october 1992. [13] j. zhao, b. chen, j. shen "multidimensional non-orthogonal wavelet basis function neural network for dynamic process fault diagnosis" computer and chemical engineering vol. 23 pp. 83-92, 1998. adam borowa, krzysztof mazur gdansk university of technology, department of automatic control, ul. g. narutowicza 11/12, 80 952 gdansk, poland e-mail: aborowa@ely.pg.gda.pl, kmazur@ely.pg.gda.pl mietek a. brdys the university of birmingham, school of engineering, department of electronic, electrical and computer engineering, birmingham b15 2tt, uk e-mail: m.brdys@bham.ac.uk received: march 12, 2007 130 adam borowa, mietek a. brdys, krzysztof mazur adam borowa received his m.sc. degree in control engineering in 2002 from electrical and control engineering department at gdansk university of technology. soon after he became a ph.d. student in this department. during the period of 2001 to 2002 he served one’s apprenticeship on wastewater treatment plant at swarzewo. he published 6 publications. mainly he focuses on modelling and monitoring of large scale systems, especially processes with many time scales. mietek a. brdys received the m.sc. degree in electronic engineering and the ph.d. and the d.sc. degrees in control systems from the institute of automatic control at the warsaw university of technology in 1970, 1974 and 1980, respectively. from 1974 to 1983, he held the posts of assistant professor and associate professor at the warsaw university of technology. in 1992 he became full professor of control systems in poland. between 1978 and 1995, he held various visiting faculty positions at the university of minnesota, city university, de montfort university and university polytechnic of catalunya. since january 1989, he has held the post of senior lecturer in the school of electronic, electrical and computer engineering at the university of birmingham. since february 2001 he has held the post of full professor of control systems in the department of automatic control at gdansk university of technology. he has served as consultant for honeywell systems and research center in minneapolis, gec marconi and water authorities in uk, france, spain, germany and poland. he is head of interdisciplinary research network on decision support and control systems at the university of birmingham and head of intelligent decision support and control system group at technical university of gdansk. his research is supported by the uk and polish research councils, and industry and european commission. he is author and co-author of about 200 refereed papers and six books. his current research includes intelligent decision support and control of complex uncertain systems, robust monitoring and control, softly switched robustly feasible model predictive control. the applications include environmental systems, technological processes, autonomous intelligent vehicles and defence systems. he is a chartered engineer, a member of the iee, a senior member of ieee, a fellow of ima and a vice-chair of ifac technical committee on large scale complex systems. he the ipc chair of the 11th ifac symposium on large scale complex systems, gdansk, july 23-25, 2007. modelling of wastewater treatment plant for monitoring and control purposes by state – space wavelet networks 131 krzysztof mazur received his m.sc. degree in control engineering from electrical and control engineering department at gdansk university of technology in 2005. currently a ph.d. student in this department. his research interests are in the areas of modelling, control and monitoring of large scale systems. coauthor of 4 publications. international journal of computers, communications & control vol. i (2006), no. 1, pp. 25-32 one more universality result for p systems with objects on membranes gheorghe păun abstract: we continue here the attempt to bridge brane calculi with membrane computing, following the investigation started in [2]. specifically, we consider p systems with objects placed on membranes, and processed by membrane operations. the operations used in this paper are membrane creation (cre), and membrane dissolution (dis), defined in a way which reminds the operations pino, exo from a brane calculus from [1]. for p systems based on these operations we prove the universality, for one of the two possible variants of the operations; for the other variant the problem remains open. keywords: membrane computing, brane calculi, matrix grammar, universality 1 introduction this paper is a direct continuation of [2], where a first step was made to bridge membrane computing [4], [5], [6] and brane calculi [1]. the main point of this effort is to define p systems which work with multisets of objects placed on the membranes rather than inside the compartments defined by membranes, and to process these multisets by means of operations with membranes rather than by multiset rewriting rules acting only on objects. the operations pino, exo, mate, drip were formalized in [2] as membrane computing rules, and used in defining p systems based on them. the universality of mate, drip operations was proved in [2] (for systems using simultaneously at any step of a computation at most eleven membranes). we give here an universality result for other two operations, membrane creation (cre), and membrane dissolution (dis), which have the same syntax as pino, exo operations, but a different interpretation in what concerns the contents of the handled membranes – details can be found in section 3 below. actually, as it was the case in [2] with pino, exo, we have two variants of each of the operations cre, dis. for one of these variants, we prove the turing completeness, while the case of the other variant remains open (we believe that a similar result holds true). 2 prerequisites all notions of formal language theory we use are elementary and standard, and can be found in any basic monograph of formal language theory. for the sake of completeness, we introduce below only the notion of matrix grammars with appearance checking – after specifying that by re we denote the family of recursively enumerable languages, and by psre the family of parikh images of languages from re (the parikh mapping associated with an alphabet v is denoted by ψv ). a matrix grammars with appearance checking [3] is a construct g = (n, t, s, m, f), where n, t are disjoint alphabets (of non-terminals and terminals, respectively), s ∈ n (axiom), m is a finite set of matrices, that is sequences of the form (a1 → x1, . . . , an → xn), n ≥ 1, of context-free rules over n ∪t , and f is a set of occurrences of rules in the matrices of m. for w, z ∈ (n ∪ t )∗ we write w =⇒ z if there is a matrix (a1 → x1, . . . , an → xn) in m and the strings wi ∈ (n ∪t )∗, 1 ≤ i ≤ n + 1, such that w = w1, z = wn+1, and, for all 1 ≤ i ≤ n, either (1) wi = w′iaiw′′i , wi+1 = w′ixiw′′i , for some w′i, w ′′ i ∈ (n ∪ t )∗, or (2) wi = wi+1, ai does not appear in wi, and the rule ai → xi appears in f . (if applicable, the rules from f should be applied, but if they cannot be applied, then we may skip them. that is why the rules from f are said to be applied in the appearance checking mode.) if f = /0, then the grammar is said to be without appearance checking. the language generated by g is defined by l(g) = {w ∈ t ∗ | s =⇒∗ w}, where =⇒∗ is the reflexive and transitive closure of the relation =⇒. the family of languages of this form is denoted by matac; it is known that matac = re. we say that a matrix grammar with appearance checking g = (n, t, s, m, f) is in the z-binary normal form if n = n1 ∪ n2 ∪{s, z, #}, with these three sets mutually disjoint, and the matrices in m are in one of the following forms: 1. (s → x a), with x ∈ n1, a ∈ n2, 2. (x → y, a → w), with x ,y ∈ n1, a ∈ n2, w ∈ (n2 ∪t )∗,|w| ≤ 2, copyright c© 2006 by ccc publications 26 gheorghe păun 3. (x → y, a → #), with x ∈ n1,y ∈ n1 ∪{z}, a ∈ n2, 4. (z → λ ). moreover, there is only one matrix of type 1, f consists exactly of all rules a → # appearing in matrices of type 3, and, if a sentential form generated by g contains the symbol z, then it is of the form zw, for some w ∈ (t ∪{#})∗ (that is, the appearance of z makes sure that, except for z, all symbols are either terminal or the trap-symbol #). the matrix of type 4 is used only once, in the last step of a derivation. for each language l ∈ re there is a matrix grammar with appearance checking g in the z-binary normal form such that l = l(g). as usual, we represent multisets over an alphabet v by strings over v , with the obvious observation that all permutations of a string represent the same multiset. 3 p systems using the cre/dis operations we start by recalling from [2] the formalization of the operations pino, exo in terms of membrane computing. a membrane is represented, as usual, by a pair of square brackets, [ ], but we associate here with membranes multisets of object (corresponding to the proteins embedded in the real membranes). a membrane having associated a multiset u (represented by a string) is written in the form [ ] u; we also use to say that the membrane is marked with the multiset u. the following four operations were defined in [2]: pinoi : [ ] uav → [ [ ] ux] v, (1) exoi : [ [ ] ua] v → [ ] uxv, (2) pinoe : [ ] uav → [ [ ] v] ux, (3) exoe : [ [ ] u] av → [ ] uxv. (4) in all cases with a ∈ v , u, x ∈ v ∗, v ∈ v +, with ux ∈ v + for pino rules, where v is a given alphabet of objects. in each case, multisets of proteins are transferred from input membranes to output membranes as indicated in the rules, with protein a evolved into the multisets x (which can be empty). the subscripts i and e stand for “internal" and “external", respectively, pointing to the “main" membrane of the operation in each case. it is important to note that the multisets u, v and the protein a marking the left hand membranes of these rules correspond to the multisets u, v, x from the right hand side of the rules; specifically, the multiset uxv resulting when applying the rule is precisely split into ux and v, with these two multisets assigned to the two new membranes. the rules are applied as follows. assume that we have a membrane [ ] zuav, for a ∈ v, u, v, z ∈ v ∗. by a pinoi rule as in (1), we obtain any one of the pairs of membranes [ [ ] z1ux ] z2v such that z = z1z2, z1, z2 ∈ v ∗, and by a pinoe rule as in (3), we obtain any one of the pairs of membranes [ [ ] z1v ] z2ux such that z = z1z2, z1, z2 ∈ v ∗. in the case of the two exo operations, the result is uniquely determined. from a pair of membranes [ [ ] z1ua] z2v, by an exoi rule as in (2) we obtain the membrane [ ] z1z2uxv, and from [ [ ] z1u] z2av, by an exoe rule as in (4) we obtain the same membrane [ ] z1z2uxv. the contents of membranes involved in these operations is transferred from the input membranes to the output membranes in the same way as in brane calculi (p,q represent here the possible contents of the respective membranes): pinoi : [ p ] uav → [ [ ] ux p ] v, exoi : [ [ p ] ua q ] v → p [ q ] uxv, pinoe : [ p ] uav → [ [ ] v p ] ux, exoe : [ [ p ] u q ] av → p [ q ] uxv. here we change the interpretation of these rules, as suggested below (because the new semantics do not correspond to the operations pino, exo, we change the name of operations to cre, dis, for “membrane creation" and one more universality result for p systems with objects on membranes 27 “membrane dissolution"): crei : [ p ] uav → [ [ p ] ux ] v, disi : [ [ p ] ua q ] v → [ p q ] uxv, cree : [ p ] uav → [ [ p ] v ] ux, dise : [ [ p ] u q ] av → [ p q ] uxv. that is, when a membrane is created inside an existing membrane, the new membrane contains all previously existing membranes, and while dissolving a membrane, its contents remains inside the membrane where it was placed before the operation. the interpretation of the latter operation is rather similar to the usual dissolution operation in membrane computing, while the membrane creation is understood as doubling the existing membrane, with a distribution of the multiset marking the initial membrane to the two new membranes. using rules as defined above, we can define a p system as π = (a, µ, u1, . . . , um, r), where: 1. a is an alphabet (finite, non-empty) of objects; 2. µ is a membrane structure with m ≥ 2 membranes; 3. u1, . . . , um are multisets of objects (represented by strings over a) bound to the m membranes of µ at the beginning of the computation; the skin membrane is marked with u1 = λ ; 4. r is a finite set of cre, dis rules, of the forms specified above, with the objects from the set a. for a rule of any type, with u, a, v as above, |uav| is called the weight of the rule. in what follows, the skin membrane plays no role in the computation, no rule can be applied to it. also, we stress the fact that there is no object in the compartments of µ ; a membrane can contain other membranes inside, but in-between membranes there is nothing. when using any rule of any type, we say that the membranes from its left hand side are involved in the rule; they all are “consumed", and the membranes from the right hand side of the rule are produced instead. similarly, the object a specified in the left hand side of rules is “consumed", and it is replaced by the multiset x. the evolution of the system is defined in the standard way used in membrane computing, with the rules applied in the non-deterministic maximally parallel manner, with each membrane involved in at most one rule. thus, the parallelism is maximal at the level of membranes – each membrane which can evolve has to do it – but each multiset of objects evolves in a sequential manner, as only one rule can act on any multiset in a transition step. more precise details can be found in [2]. a computation which starts from the initial configuration is successful if (i) it halts, that is, it reaches a configuration where no rule can be applied, and (ii) in the halting configuration there are only two membranes, the skin (marked with λ ) and an inner one. the result of a successful computation is the vector of multiplicities of objects which mark the inner membrane in the halting configuration. the set of all vectors computed in this way by π is denoted by ps(π). the family of all sets of vectors ps(π) computed by p systems π using at any moment during a computation at most m membranes, and crei, disi rules of weight at most p, q, respectively, is denoted by psopm(crep, disq). when one of the parameters m, p, q is not bounded we replace it with ∗. we end this section by pointing out some relations which follow directly from the definitions (and from turingchurch thesis). lemma 1. (i) psopm(crep, disq) ⊆ psopm′(crep′, disq′), for all m ≤ m′, p ≤ p′, q ≤ q′. (ii) psop∗(cre∗, dis∗) ⊆ psre. we also recall the main result from [2]: psop11(mate5, drip5) = psre (the notation is self-explanatory). 28 gheorghe păun 4 universality for the cre/dis operations in the case of cre, dis operations as defined above, we cannot generate vectors of norm 0 or 1: in each rule [ ] uav → [ [ ] ux] v, [ [ ] ua] v → [ ] uxv (necessary in the last step of any computation in order to get only one internal membrane) we have imposed to have |uxv| ≥ 2. that is why the universality below is obtained modulo vectors of the form (0, . . . , 0) and (0, . . . , 0, 1, 0, . . . , 0). we denote by ps′re and ps′opm(crep, disq) the sets of vectors from psre and psopm(crep, disq) having the sum of elements greater than or equal to 2. theorem 2. ps′re = ps′opm(crep, disq) for all m ≥ 7, p ≥ 4, and q ≥ 4. proof. let us consider a language l ∈ re = matac, l ⊆ v 2v ∗, for an alphabet v with n symbols. we write this language in the form l = ⋃ a,b∈v {ab}∂ lab(l). let gab = (nab,v, sab, mab, fab) be a matrix grammar with appearance checking such that l(gab) = ∂ lab(l), for a, b ∈ v . we consider these grammars gab in the z-normal form, with the notations from section 2 (hence nab = nab,1 ∪nab,2 ∪{sab, zab, #}), and we construct the matrix grammar g = (n,v, s, m, f) with n = n1 ∪n2 ∪{zab | a, b ∈ v}∪{s, #}, n1 = ⋃ a,b∈v nab,1, n2 = ⋃ a,b∈v nab,2, m = {(s → x a) | for (sab → x a) ∈ mab, a, b ∈ v} ∪ {(x → y, a → w) | for (x → y, a → w) ∈ mab, a, b ∈ v} ∪ {(zab → ab) | for (z → λ ) ∈ mab, a, b ∈ v}. obviously, l(g) = l. we assume that all two-rules matrices from m are injectively labeled, in the form ml : (x → y, a → x), l ∈ lab, for a set of labels lab. starting from the grammar g we now construct a p system π = (a, [ [ ] ], λ , s1s2, r), with the alphabet a = {y,y ′,y ′′,y ′′′,y iv,y v,y vi,y vii,y viii,y ix,y x | y ∈ n1} ∪ {α, α′, α′′ | α ∈ n2 ∪v} ∪ {ā | a ∈ n2} ∪ {zab, z′ab, z′′ab, z′′′ab | a, b ∈ v} ∪ {e, h, h′, s1, s2, s3, c1, . . . , c11, c0, c′0, c′′0 , c′3, c′′3 , d1, d2, d′1, d′2, f ′, f ′′, #}, and the rules from the set r as constructed below. any computation starts from the configuration [ [ ] s1s2 ] λ , by using the following rules: step 1 : [ ] s1s2 → [ [ ] x ] s2 , step 2 : [ [ ] x ] s2 → [ ] x c0d1s2 , step 3 : [ ] x s2c0d1 → [ [ ] x s3 ] c0d1 , step 4 : [ ] s3x → [ [ ] eā ] x , [ ] c0d1 → [ [ ] c′0 ] d1 , step 5 : [ [ ] eā ] x → [ ] eāx , [ [ ] c′0 ] d1 → [ ] c′′0 d1 , step 6 : [ ] x āe → [ [ ] x a ] e , [ ] c′′0 d1 → [ [ ] c1 ] d1 , for each matrix (sab → x a) ∈ mab, for a, b ∈ v . one more universality result for p systems with objects on membranes 29 the rules are used as indicated in the table above, with two rules simultaneously applied in steps 4, 5, 6. the only possible branching is in step 3, when instead of the rule [ ] x s2c0d1 → [ [ ] x s3 ] c0d1 , we can also use the rule [ ] c0d1 → [ [ ] c′0 ] d1 . in this way we obtain the membranes [ [ ] c′0 ] d1 , with x s2 distributed among them. because s3 will be never introduced, we continue only with rules which process membranes marked with ci and d1, namely, the rules from the third column of table 1; in this way, the computation will never stop, both because we can return again and again to a pair of membranes of the form [ [ ] c1 ] d1 , and because pairs of membranes marked with c ′ 3 will appear and introduce trap objects/membranes – see also below. the evolution of the membrane structure is indicated in figure 1. initial [ [ ] s1s2 ] λ step 1 [ [ [ ] x ] s2 ] λ step 2 [ [ ] x c0d1s2 ] λ step 3 [ [ [ ] x s3 ] c0d1 ] λ step 4 [ [ [ [ [ ] eā ] x ] c′0 ] d1 ] λ step 5 [ [ [ ] eāx ] c′′0 d1 ] λ step 6 [ [ [ [ [ ] x a ] e ] c1 ] d1 ] λ figure 1: the evolution of membranes at the beginning of computations. thus, we end with a configuration of the form [ [ [ [ [ ] x a ] e ] c1 ] d1 ] λ . the rules for simulating the two-rules matrices from m are indicated in table 1; by w′ we denote here the string obtained from w by priming one symbol; if w = λ , then w′ = f ′, hence α′ = f ′, α′′ = f ′′ and, in row 6, α = λ . step ml : (x → y, a → w) ml : (x → y, b → #) 1 [ [ ] x ] e → [ ] xl e [ [ ] x ] e → [ ] xl e [ [ ] c1 ] d1 → [ ] c2c3d1 2 [ ] aexl → [ [ ] w′ ] exl [ ] xl be → [ [ ] xl ## ] e [ ] c3c2d1 → [ [ ] c′3 ] c2d1 3 [ [ ] exl ] c′3 → [ ] ey ′c′3 [ [ ] xl e ] c′3 → [ ]y vi hec′3 [ ] c2d1 → [ [ ] c4 ] d1 4 [ ] c′3y ′e → [ [ ] c′3y ′′ ] e [ ]y vic′3eh → [ [ ] c′3y vii ] eh [ [ ] c4 ] d1 → [ ] c5d1 5 [ [ ] α′ ] c′3y ′′ → [ ] α′′c′3y ′′ [ ]y vii c′3 → [ [ ]y viii ] c′3 [ ] c5d1 → [ [ ] c6 ] d1 [ ] he → [ [ ] h′ ] e 6 [ [ ] α′′c′3y ′′ ] e → [ ] α c′3y ′′e [ [ ] c′3 ] h′ → [ ] c′′3 h′ [ [ ] c6 ] d1 → [ ] c7d1 7 [ ] c′3y ′′e → [ [ ] c′3y ′′′ ] e [ [ ]y viii ] c′′3 h′ → [ ]y ix c′′3 h′ [ ] c7d1 → [ [ ] c8 ] d1 8 [ [ ] c′3y ′′′ ] e → [ ]y ′′′e [ [ ]y ix c′′3 h′] e → [ ]y ix h′e [ [ ] c8 ] d1 → [ ] c9d1 9 [ ]y ′′′e → [ [ ]y iv ] e [ ]y ix h′e → [ ]y x ] e [ ] c9d1 → [ [ ] c10 ] d1 10 [ [ ]y iv ] e → [ ]y ve [ [ ]y x ] e → [ ]y x e [ [ ] c10 ] d1 → [ ] c11d1 11 [ ]y ve → [ [ ]y ] e [ ]y x e → [ [ ]y ] e [ ] c11d1 → [ [ ] c1 ] d1 table 1: rules for simulating two-rules matrices. we also consider the rules [ ] xl e → [ [ ] ## ] e , for each matrix ml : (x → y, a → w), [ [ ] h′ ] e → [ ] ##e , [ ] ## → [ [ ] # ] #, [ [ ] # ] # → [ ] ##. the simulation of matrices in g is performed by modifying the marking of the central membranes, those emerging from the initial membranes with markings x a and e, with these operations being assisted by the two membranes with markings c1 and d1 and their successors, which are external to the central membranes where the sentential form of g is produced. always during the computation, the membranes remain embedded one in another, in a linear manner, never having two membranes on the same level (here stands the essential difference between the interpretation of the cre, dis operations and the interpretation of the pino, exo operations from [1], [2]). 30 gheorghe păun the evolution of the membranes and of their relevant markings can be followed in figure 2. if in the second step the rule [ ] aexl → [ [ ] w′ ] exl is not applicable (hence the matrix ml cannot be applied), then the rule [ ] xl e → [ [ ] ## ] e will be applied, introducing the trap-object #, and the computation will never halt. starting [ [ [ [ [ ] x ] e ] c1 ] d1 ] λ step 1 [ [ [ ] xl ea ] c2c3d1 ] λ step 2 [ [ [ [ [ ] w′ ] exl ] c′3 ] c2d1 ] λ step 3 [ [ [ [ [ ] α′ ] ey ′c′3 ] c4 ] d1 ] λ step 4 [ [ [ [ [ ] α′ ] c′3y ′′ ] e ] c5d1 ] λ step 5 [ [ [ [ [ ] α′′c′3y ′′ ] e ] c6 ] d1 ] λ step 6 [ [ [ ] α c′3y ′′e ] c7d1 ] λ step 7 [ [ [ [ [ ] c′3y ′′′ ] e ] c8 ] d1 ] λ step 8 [ [ [ ]y ′′′e ] c9d1 ] λ step 9 [ [ [ [ [ ]y iv ] e ] c10 ] d1 ] λ step 10 [ [ [ ]y ve ] c11d1 ] λ step 11 [ [ [ [ [ ]y ] e ] c1 ] d1 ] λ figure 2: the evolution of membranes when simulating ml : (x → y, a → w). the evolution of membranes in the case of the simulation of a matrix ml : (x → y, b → #) can be followed in figure 3. this time, if b is present, in step 2 we have to use the rule [ ] xl be → [ [ ] xi## ] e , and the computation will never halt. if no copy of b is present, then the central membrane does not evolve, waiting for the membrane marked with c′3 to be produced; this membrane can be used in the next step for evolving the central membrane. starting [ [ [ [ [ ] x ] e ] c1 ] d1 ] λ step 1 [ [ [ ] xl e ] c2c3d1 ] λ step 2 [ [ [ [ ] xl e ] c′3 ] c2d1 ] λ step 3 [ [ [ [ ]y vihec′3 ] c4 ] d1 ] λ step 4 [ [ [ [ ] c′3y vii ] eh ] c5d1 ] λ step 5 [ [ [ [ [ [ [ ]y viii ] c′3 ] h′ ] e ] c6 ] d1 ] λ step 6 [ [ [ [ [ ]y viii ] c′′3 h′ ] e ] c7d1 ] λ step 7 [ [ [ [ [ ]y ix c′′3 h′ ] e ] c8 ] d1 ] λ step 8 [ [ [ ]y ix h′e ] c9d1 ] λ step 9 [ [ [ [ [ ]y x ] e ] c10 ] d1 ] λ step 10 [ [ [ ]y x e ] c11d1 ] λ step 11 [ [ [ [ [ ]y ] e ] c1 ] d1 ] λ figure 3: the evolution of membranes when simulating ml : (x → y, b → #). another step when we can apply a rule different from that indicated in table 1 is step 4, when we can also use the rule [ ] he → [ [ ] h′ ] e . in this way, we pass to the configuration of membranes [ [ [ [ ] h′w1 ] ew2 ] c5d1 ] λ , where w1w2 = y vic′3. no rule can be applied to the two inner membranes other than [ [ ] h′ ] e → [ ] ##e , and again the computation will never stop. therefore, the simulation of matrices in g should be done as above, and in this way we return to a configuration as that we have started with, with four membranes marked with x , e, c1, d1, respectively (the central membranes also having on them the symbols of the current sentential form of g which is simulated in π). note that the rules used for simulating a matrix ml : (x → y, a → w) cannot be mixed with the rules used for simulating a matrix ml′ : (x ′ → y ′, a′ → #), because of the injective labeling of matrices from m and because of the priming of symbols from n1. the process can be iterated, hence at some moment we introduce the symbol zab identified by the symbols from n1 used. the respective configuration is of the form: [ [ [ [ [ ] zab ] e ] c1 ] d1 ] λ . the central membrane will one more universality result for p systems with objects on membranes 31 “swallow" all other membranes, also removing all auxiliary objects. to this aim, we use the following rules: step 1 [ [ ] zab ] e → [ ] z′abe , step 2 [ [ ] z′abe ] c2c3 → [ ] z′abbc2c3 , step 3 [ ] z′abc2c3d1 → [ [ ] z′ab ] c3d1 , step 4 [ [ ] z′ab ] c3d1 → [ ] z′′abc3d1 , step 5 [ ] z′′abc3d1 → [ [ ] z′′ab ] d1 , step 6 [ [ ] z′′ab ] d1 → [ ] z′′′abd1 , step 7 [ ] z′′′abd1b → [ [ ] z′′′ab ] b, step 8 [ [ ] z′′′ab ] b → [ ] ab, for all a, b ∈ v . furthermore, we consider the rules [ ] z′abe → [ [ ] ## ] e , [ [ ] c′3 ] c′3 → [ ] ##c′3 , [ ] #a → [ [ ] ## ] a, for all a ∈ v. the first of these rules is used in step 2 if the rule [ [ ] z′abe ] c2c3 → [ ] z′abbc2c3 is not used – the objects c2c3d1 might be used at that time by the rule [ ] c3c2d1 → [ [ ] c′3 ] c2d1 from table 1. similarly, if this last rule is used in step 3 instead of the rule [ ] z′abc2c3d1 → [ [ ] z′ab ] c3d1 , then a membrane marked with c ′ 3 is introduced, which will never be removed. in particular, after 11 steps, we introduce another membrane marked with c′3, and then the rule [ [ ] c′3 ] c′3 → [ ] ##c′3 is used, preventing the termination of the computation. in conclusion, the evolution of the membranes in the final stage of the computation is as indicated in figure 4. starting [ [ [ [ [ ] zab ] e ] c1 ] d1 ] λ step 1 [ [ [ ] z′abe ] c2c3d1 ] λ step 2 [ [ ] z′abbc2c3d1 ] λ step 3 [ [ [ ] z′ab ] c3d1 ] λ step 4 [ [ ] z′′abc3d1 ] λ step 5 [ [ [ ] z′′ab ] d1 ] λ step 6 [ [ ] z′′′abd1 ] λ step 7 [ [ [ ] z′′′ab ] b ] λ step 8 [ [ ] ab ] λ figure 4: the evolution of membranes in the end of computations. the equality ψv (l(g)) = ps(π) follows from the previous explanations. with the observation that the maximal number of membranes present in the system is seven, in step 5 from figure 3 (during the simulation of matrices with a rule to be used in the appearance checking mode), and that the rules have the weight as specified in the theorem, we conclude the proof. 5 final remarks the case of using the operations cree, dise remains as a task for the reader, and the same with other operations from brane calculus – see also [2] for related problems. improvements of the result in theorem 2 are also plausible in what concerns the degree of context-sensitivity of the rules (and maybe also in what concerns the number of membranes). the same problems can be formulated for the result from [2]. as a general research topic, it remains to systematically investigate p systems with multisets of objects placed on membranes (maybe also in the compartments), processed by membrane handling operations like in brane calculi (maybe also by local multiset rewriting rules). 32 gheorghe păun references [1] l. cardelli, brane calculi. interactions of biological membranes, proc. computational methods in systems biology, 2004, springer-verlag, berlin, to appear. [2] l. cardelli, gh. păun, an universality result for a (mem)brane calculus based on mate/drip operations, intern. j. foundations of computer sci., 17, 1 (2006), 49–68. [3] j. dassow, gh. păun, regulated rewriting in formal language theory, springer-verlag, berlin, 1989. [4] gh. păun, computing with membranes, journal of computer and system sciences, 61, 1 (2000), 108–143 (and turku center for computer science–tucs report 208, november 1998, www.tucs.fi). [5] gh. păun, membrane computing. an introduction, springer-verlag, berlin, 2002. [6] the membrane computing web page: http://psystems.disco.unimib.it. institute of mathematics of the romanian academy po box 1-764, 014700 bucureşti, romania and research group on natural computing department of computer science and artificial intelligence university of sevilla avda. reina mercedes s/n, 41012 sevilla, spain e-mail: george.paun@imar.ro, gpaun@us.es editor’s note about the author: gheorghe păun (born on december 6, 1950) graduated the faculty of mathematics of the bucharest university in 1974 and got his phd at the same faculty in 1977. he has won many scholarships, in germany, finland, the netherlands, spain, etc. presently he is a senior researcher at the institute of mathematics of the romanian academy, bucharest, and a ramon y cajal research professor at sevilla university, spain. since 1997 he is a corresponding member of the romanian academy, and since 2006 a member of academia europaea. his main research fields are formal language theory (regulated rewriting, contextual grammars, grammar systems), automata theory, combinatorics on words, computational linguistics, dna computing, membrane computing (this last area was initiated by him in 1998). he has (co)authored and (co)edited more than fifty books in these areas, and he has (co)authored more than 400 research papers. in the last two decades he has visited many universities from europe, usa, canada, japan, also participating to many international conferences, several times as an invited speaker. he is a member of the editorial board of numerous computer science journals and professional associations. international journal of computers, communications & control vol. i (2006), no. 2, pp. 61-71 solution to the reliability problem of radial electric distribution systems through artificial intelligence e. lópez, j. campos, c. tardon, f. salgado, j. tardon, r. lópez abstract: in this paper the distribution electric system reliability is recognized like an artificial intelligence problem. how this idea is applied in evaluation of reliability is detailed. concepts as intelligence matrix and inter-feeder route are defined. from the last one a reliability prediction strategy for medium voltage networks is proposed and tested. keywords: artificial intelligence, agent, searching, primary route, secondary route 1 introduction it has been established that within an electric system, nearly 80% of the faults occur in the distribution system, (billinton and allan,1998 ). legal aspects protect the costumers from faults in the electric system. so it’s necessary, for electrical companies, to guarantee a high level of security, quality and reliability in the service. companies are not only affected by the demanding norms but also by high financial lost due to energy non-sell and penalties. the reliability is evaluated using the markovian models (brown and gupta, 1998; asgapoor and mathine, 1997 brown, et al., 1997 brown and ochoa, 1998) or different analytic methods (billinton y wagn, 1999), that, according to the past behavior, they make an extrapolation of its future one. this paper is based in developing a method that makes possible the prediction of distribution system’s behavior in terms of reliability, addressing it to the artificial intelligence (ai). this is capable of giving information in terms of representative indexes in the points of loading and overall system, including alternative supply in case of the principal feeding’s failure. when considering the ai, we intent to model the distribution system’s reliability and developing concepts to be used in the solution of a generic problem that requires topological analysis. therefore, emphasis is made in searching problems as ait’s tasks. within the ai’s wide world we find the searching’s problems (russell and norvig, 1996). once the problem is defined (this action is called the "abstraction") the process starts from an initial state. then, operators are applied to the present state, resulting in a new set of states. this process is called "expansion" state. the essence of the search is to choose one option and to put all the others aside to be expanded afterwards, just in case the first election does not reach solution. the dilemma of which state to be expanded first is determined by the "strategy of search". the process of searching can be included as the construction of a search tree that is superposed on the space of states where the node of roots is the initial state whereas the nodes leaves are states that do not have successor in the tree. (because they have not been expanded yet or because they have already been expanded but they generate an empty state). an efficient strategy of search is named "depth-first search". this strategy always expands one of the nodes at the deepest level of the tree and only when the search finds its end (a goal state or one without expansion) the search continues expanding nodes of less deeper level. 2 proposed method the algorithm has the mission or purpose of finding the failure modes of the system. the hypotheses are: the elements of protection are a 100% reliable. the fault is determined by first order cut. the first step of the method is to make a protocol of nodal assignation. it consists in labelling a bus or node of distribution with number 1 and the branches with ascendant (rising) numbers from the departure node to full stop of load, in the form shown by the arrows in figure 1. copyright c© 2006 by ccc publications 62 e. lópez, j. campos, c. tardon, f. salgado, j. tardon, r. lópez figure 1: basic system next the space of state is defined, the one that corresponds to the environment in which the agent can move to find the solution. according to the above for the basic system of test (bs), figure 1, the states are defined by each one of the buses of the system. the bs consists of 16 buses and 5 points of load (lp). there are 16 possible states by which the agent can "travel" with the purpose of satisfying the test of goal. the information related with the topology of the bs is in the so called "intelligence matrix"(im) which forms the base of the algorithm of search. part of the im corresponding to the bs is shown in figure 2 figure 2: part of im for the basic system the numbers for the rows and columns are the nodes or buses. the procedure to enter the data consists of entering the components between two consecutive nodes, identifying it with a letter and associating it with the respective element of the im. for bs, between the node 1 and 2 there is a line, indicated by an l in the position of the element [1,2] of the matrix. the same for the element [2,1]. when there is a switching or protection device (as between nodes 2 and 3) or when there is a line and a disconnects, the element, [2,3], is defined with an l (line). in the position [3,2] you enter an s (disconnects) indicating that the switching device is closer to bus 3. this directional quality or differentiation of the common elements of the system with those of switching and/or protection is conclusive in the optima resolution of the problem, this is the reason of the name "intelligence matrix". specially, for the evaluation of the indicator of future reliability, en the bs are identified: initial state: the search begins from the point of load to evaluate. operators: the agent can "jump" or "travel" from a particular state towards the next closer, i.e., from [3,2] to [2,1]. in direction to the node of generation (ascending form) or towards the other point of load (descending form). state space: the states extended by the agent from the load point (initial state). route: the route from one state to another within the space of states. goal test: it corresponds to have extended all the possible states in which any eventual fault may produce a lost in supply energy to the load point evaluated. solution to the reliability problem of radial electric distribution systems through artificial intelligence 63 cost of route: unit cost in order to travel from one state to the next nearest one. solution: it corresponds to the result of the algorithm of search once the goal is fulfilled. in accordance to the above the complete im for the bs is shown in figure 3. the non-existence of elements in the system is indicated with blank spaces in figure 3. in order to find the failure modes of system, the algorithm must identify the primary route and the secondary route (lópez et al, 1998). the im of the bs shows that the diagonal divides the information of the different routes. the last one, due to the arranged assignation of the number of nodes. on the other, the algorithm uses the "best first search", then, the best expanding option occurs at first. in this case, it corresponds to the primary route. it leaves, in "waiting queue", the secondary routes for the next expansion. 2.1 getting the primary route the primary route is the minimal cost one from the load point to the feeding node. the information about the primary route is contained under the principal diagonal of the im. the primary route of each load point is stored in a matrix called " matrix of primary route" (mpr). though each row will contain the primary route corresponding to a load point. the mpr is of order m x n (load point x nodes) and initially is a null matrix. its formation is ordered and it is starting with the agent in the first load point (lp1). in the intelligence matrix the row corresponding to the node number, that has the load point, is checked. then the elements contained under the diagonal are examined. when finding an element within the search sector, in the row of the matrix of primary routes, the number of the next node that corresponds is entered. the number of the column that corresponds to the found element gives this node. then, is verified the row corresponding to the next node of the primary route and so on, until arriving at node 1 of the feeding. the figure 4 outlines the way of how to find the primary route for the load point lp1. it corresponds to the node 16. figure 5 shows the bs’s matrix of the primary route (mpr of bs). regarding to the agent like an "problem-solving agent" (russell and norvig, 1996) and one state as a structure of simple data, the agent is placed in a problem of determining the route. the agent corresponds to an algorithm of resolution that uses a sequence of actions to follow in order to obtain its goal. the "resolutor-agent" of the simplified problem, in order to find the primary route, corresponds to figure 6. this agent is executed for each load point (lp) and ends or gets to the goal, when finding the feeding node (node 1). the route of the agent corresponds to the one shown above in figure 4. it is observed in figure 6 that the agent has two rules (if condition, then action).they take step from a basic and incipient system to an expert one that has the particularity to provide a great flexibility by the time of incorporating new knowledge. the incorporation of a new rule is enough for it, which don’t need to change the algorithm’s behavior. 2.2 secondary routes at the moment the "resolutor agent" of the primary route crosses the system and full the matrix of primary route, mpr. a vector is added that stores the quantity of nodes including each primary route this vector is called "cont". by knowing the dimensions of each primary rout (cont) the agent can track the secondary routes ’looking’ for it in the superior diagonal of the im. the last one for each one of the nodes that the primary route contains. extending the first secondary routes nearer to the feeding and leaving in delay the rest of the routes in the nearest nodes to the load point does this. for this, the mpr in inverse form is crossed. for example, for the load point lp1 the first secondary routes that are originated in node 1, then those of node 2, 7, and 8 are extended, to arrive to node 16 which corresponds to the node that contains the load point. the search of these routes is similar to which is made in the primary routes, but with some restrictions. one of them is that the agent must finish the search at the moment a fuse is find and must begin to extend the following one that is in delay. as secondary routes are subdivided in different branches, it is 64 e. lópez, j. campos, c. tardon, f. salgado, j. tardon, r. lópez figure 3: intelligence matrix corresponding to bs figure 4: searching the primary route for lp1 (node 16) necessary to store the nodes that at the moment of the distance traveled (on the diagonal of the matrix) will have to wait until the previous one in its deeper level extends (point of neighboring or fusible load ). for this, it is strictly necessary to store four data of the node to expand: figure 5: matrix of primary route (mpr) for the bs • the preceding node is required in case that there is a switching or protection device in the arrival of the node. these elements can involve the end of the route searching or a change in the repairing time to switching time. • the node to expand, is the node that indicates in which row of the intelligence matrix, to look for the elements that form the secondary route and if there are more elements that will be needed to expand more ahead. • the node towards which the agent travels, gives the direction in which the search takes place. • condition in which it is found, considering the time of repairing or switching, essential condition for the calculus of the unavailability. these data are stored in a matrix of 4 rows and a undetermined number of columns. it is filled during the route and it is an uncertain number at first. this matrix has the name of "extend" and determines the end of the process of searching as the last element in delay expands. the simplified agent to find the secondary routes is summarized in figure 7. this agent is executed for each primary route, i.e., for each load point it finds all the corresponding secondary routes. it is also observed that it conditions on switching time the rest of the route in case of finding a disconnects (s) and ends the route when finding a fuse (f). the end of this agent corresponds to the extension of the last secondary route (on the primary route) and that there aren’t any nodes in delay waiting to be extended. solution to the reliability problem of radial electric distribution systems through artificial intelligence 65 figure 6: simplified agent to find the primary route 2.3 rate of failure and unavailability for a load point it is possible to calculate its rate of failure and its unavailability by adding those that correspond to primary or secondary routes. the time of shut down is the quotient between the total unavailability of the load point and its corresponding rate of failure. rate o f f ailure : λ (l p) = λpr(l p) + λsr(l p) (1) u navailability : u (l p) = upr(l p) + usr(l p) (2) outage time : r(l p) = u (l p) λ (l p) (3) in the primary route: the information stored is checked in the mpr. a rate of failure is assigned to each element. this information has been entered in a routine of parallel data entrance, to the im. the sum of the rates individual failures provide the rate of failure of the primary route for the load point λpr(l p). the unavailability of the primary route is the sum of individual unavailability of the elements involved in it, without considering the presence of the switching devices. in the secondary routes: while the search is made the rates of individual failures of the elements in this route are added. at the same time, the unavailability of the secondary routes is being calculated. at the beginning, the unavailability is related to the repair time of the element, when a switching device is found the unavailability is associated to the switching time and this is kept from this node to the end of the corresponding secondary route. as long as one advances in the search the individual unavailability’s are added. at the end of the process the results are the unavailability, usr(l p) and the rate of failure λsr(l p) of the secondary routes by load point. the way in which the algorithm is worked is shown in figure 9 (getting the mode of failure for the lp1). with a different color the point where the disconnects is indicated, which conditions to use the time of switching for the rest of the route. 2.4 considering the alternative feeding a system with alternative feeding diminishes the times of unavailability to the customer. before making any calculus, it necessary to know where the alternative feeding is. for the previous, the "inter66 e. lópez, j. campos, c. tardon, f. salgado, j. tardon, r. lópez feeding route" that joins the main feeding with the alternative feeding is defined. the corresponding agent is similar to the "resolutor agent" that finds the primary route (under the diagonal of the matrix). but instead of beginning at the load point (lp), it starts its nodal search where the alternative feeding is. this route is stored in a vector called "ralt". in the first place, the inter-feeding route is located. then, it is compared with the primary routes for each load point settling down the intersection point among them. then, a pursuit between the main feeding and the intersection point is done, where the agent has to realize if there is a switching device in this sector. if not in this route, a calculus is made in a traditional way, without affecting the alternative feeding. if there is a switching device between the main feeding and the intersection point of the routes, this conditions all the elements that are found between the switching device and the main feeding, in case of a possible fault to only consider the time of switching, since this one is isolated, connecting the alternative supplies. this lightens the search, since the agent leaves the search of other switching devices in that sector to assign times. the rest of the system is analyzed as if there is no alternative feeding. considering the bs with alternative feeding in the node 11, nodes-1 2 3 4 11 form the inter-feeding route for that configuration. for the lp3 (node 15), the intersection of the primary routes, the inter-feeding and the switching device in [3,2] are shown in figure 11 the existing switching device conditions a great part of the system to a time of switching. this same example is considered, in terms of evaluating the lp1 (node 16). the primary route of this load point only intersects in node 2 with the inter-feeding route and there is no switching device between node 2 and the main feeding, in which the alternative supply does not influence the index calculus. figure 7: simplified agent finding the secondary routes solution to the reliability problem of radial electric distribution systems through artificial intelligence 67 3 applications the algorithm was applied to several distribution systems. in this document 4 of them are presented: the basic system (bs), two rbts feeders (billinton and kumar, 1989; allan et al., 1991) and a real system. 3.1 basic system this system was created to replace the necessity of having a small distribution system (ds) that had the complexity and diversity of a real one. the system has 5 load points and a primary feeding in node 1. the elements corresponding to a conventional ds are also present, such as: line, cables, fuses, transformers, switching devices, and so on. the modes of failure, the times of switching and repairing used correspond to those indicated in the ieee st. 493 (1990). the results are indicated in table 1. figure 8: mode of failure lp1 (node 16) figure 9: inter-feeding route. alternative supplies in 11 3.2 rbts system (roy billinton test system) this is a test system that is divided en 6 buses. bar 1 is the feeding of the system, whereas bus 2 to 6 correspond to the load buses. the whole system shows different level of tension which it works 220, 138, 33, and 11 kv. with the purpose of proving the algorithm, the reliability of the feeder 1 of buses 2 and 4 are evaluated, corresponding to a level of 11 kv. table 2 gives the result of the reliability indexes. 3.3 the cge’s feeder this feeder is inserted in the cge’s distribution system. this has 70 elements and 33 load points. for its evaluation the same considerations of the experimental feeder are applied. three evaluations to the system were made. case 1: future reliability evaluation. case 2: future reliability evaluation considering the alternative feeding in node 27. case 3: future reliability evaluation considering the alternative feeding in node 55. table 3, show the results of the reliability evaluation for the three different cases given. 68 e. lópez, j. campos, c. tardon, f. salgado, j. tardon, r. lópez figure 10: switching zone and lp3-alternative supplies alt node 5 alt node 11 nerc indexes saifi 0.3963 0.3963 0.3963 saidi 4.4847 4.0171 4.1588 caidi 11.3159 10.1359 10.4935 ens mwh/year 21.396 19.102 19.759 cier indexes f 0.3964 0.3964 0.3964 t 4.4576 3.9616 4.1166 d 11.2442 9.9931 10.3841 end 21.396 19.102 19.759 (cier: regional electric inter-american council) (nerc: national electric reliability council) table 1: reliability indexes of the basic system 4 analysis of results the index of future reliability for each system as the nerc presents was obtained, those that are associated to the client. the index corresponding to the cier; that referring to the power (kva) were calculated. these last ones are of great importance due to the fact that these are the authorized by the cne (national energy council) in chile through the general law’s regulation of the electric service (1980). table 1 shows the difference between the alternative feeding in some of the bs’s nodes in respect to the same system with only a main feeding. an important improvement is observed in the reliability’s index when considering the alternative feeding. if the analysis is centered in one of the index as the average time of fault is, when applying the alternative feeding in node 5 a diminish in time of 11%, can be proved. by applying the same alternative feeding in point 11 the diminishing time of fault was around 7.6%. this is justified whereas node 5 is in the biggest point load of the feeder and there is a switching device immediately before the connection of the new feeding. the rbts system was useful in confirming the good behavior of the proposed algorithm. this system, though being small, was very easy to follow with a hand calculator, in a addition to the complete results base already given in the brown et al., (1997). referring to the results (table 2) the most important is to see the effect that is produced in the reliability the connection to the client from the main one with a disconnects (bus 2) or with two disconnects isolating the client in both sides (bus 4). a this difference is only remarked by the fact that both bus’s feeders consider an alternative feeding. with this specific fact and in similar conditions the solution to the reliability problem of radial electric distribution systems through artificial intelligence 69 rbts 2.1 rbts 4.1 nerc indexes saifi 0.2488 0.3021 saidi 3.6193 3.4694 caidi 14.5443 11.4849 ens 13.155 12.196 cier indexes f 0.2477 0.3031 t 3.6090 3.4746 d 14.5696 11.4630 end 13.155 12.196 table 2: reliability indexes of the rbts systems best reliability is obtained in the bus 4’s feeder. this is clear since with the possibility of having an alternative feeding from both ends, the most advisable it is to maintain the client with the switching. this means that before a possible fault, the client can isolate himself of the extreme in which the fault is produced and can keep connected with the other end in which the feeding remains, diminishing therefore the times of unavailability and improving the reliability of the system. for the cge’s feeder just like the previous cases, got good results being within the established limits for f and t (ministry of mining, 1980). in table 3 it is possible to see that the unavailability index decreases when considering alternative feeding. for the alternative feeding en node 27, the algorithm obtained a decrease of 15.6% en the unavailability. in as much as if the alternative feeding is in node 55, the decrease in the unavailability is of 24,5%. the differences between values is directly related with the amount of the existing switching devices. all results were checked with theoretical or manual analysis to assure the correct execution of the developed algorithm. case 1 case 2 case 3 nerc indexes saifi 0.3222 0.3222 0.3222 saidi 2.8079 2.3575 2.1948 caidi 8.7139 7.3162 6.8112 ens 12.410 10.470 9.3700 cier indexes f 0.3198 0.3198 0.3198 t 3.2547 2.7458 2.4573 d 10.176 8.5849 7.6830 end 12.410 10.470 9.3700 table 3: reliability indexes of cge system 5 conclusions this paper deals with the problem of the future reliability of a distribution system through ai. the solution considers the process of "searching" placed in the field of the ai. the approach presented in this paper is a powerful tool in distribution system reliability prediction. its kindness was demonstrated in many evaluations that were made in systems with completely different characteristics. as it was expected, the results obtained were consequent with the theoretical and practical considerations of the 70 e. lópez, j. campos, c. tardon, f. salgado, j. tardon, r. lópez problem. the model proposes the concepts of "intelligence matrix" and "agent". a very remarkable aspect of the conjunction matrix-agent is the facility with which it deals with the elements of protection and switching devices to value the importance of the strategic location of this elements. furthermore the "intelligence matrix" gathers a condition so that the "agent" works in an efficient way within the topological search. his connections make the run in an efficient and rapid way to complete the layout of the routes that involves the distribution reliability’s calculus. this point is the clue of success in the search tree-failure modes. a second significant contribution corresponds to the route concept "inter-feeding" helping to harness the model in which it says to be related with the option of alternative feeding. with the help of this a decrease in the unnecessary expenses of time and computer memory was obtained. this, as well, becomes a potential tool of evaluation of the ideal positioning of an alternative point of feeding in a real system of distribution. from a more general perspective, we can conclude that the use of this model prevents important economical measures, in which the electric companies could commit or incur when not having a suitable control. finally, the investigation’s development resulted in the necessity to deepen in ordaining the switching and protection devices, that can lead to obtain the best reliability of the system. references [1] asgarpoor s. y m. mathine, reliability evaluation of distribution systems with non-exponential down times. ieee transactions on power systems. vol. 12, no. 2, 1997. [2] allan r., r. billinton, i. sjarief, l. goel and k. s. so, a reliability test system for educational purposes basic distribution system data and results, ieee transactions on power system, vol. 6, no. 2, 1991. [3] billinton, r. y allan, r., reliability assessment of a large electric power systems. kluwer academic publishers, boston, usa, 1988. [4] billinton r y s. kumar, a reliability test system for educational purposes basic data ieee transactions on power systems, vol. 4, no. 3, 1989. [5] billinton r., y s. jonnavithula, a test system for teaching overall power system reliability assessment, ieee transactions on power systems, vol. 11, no. 4, 1996. [6] billinton r., y p. wang, teaching distribution system reliability evaluation using monte carlo simulation, ieee transactions on power systems, vol.14, no. 2, 1999. [7] brown r., s. gupta, r. d. christie, s. s. venkata y r. fletcher, distribution system reliability assessment using hierarchical markov modeling, ieee transactions on power delivery, vol. 11, no. 4, 1996. [8] brown r., s. gupta, r. d. christie, s. s. venkata y r. fletcher, distribution system reliability assessment momentary interruptions and storms, ieee transaction on power delivery, vol. 12, no. 4, 1997. [9] brown r., y j. ochoa, distribution system reliability: default data and model validation, ieee transactions on power systems, vol. 13 no. 2, 1998. [10] ieee st 493, ieee recommended practice for the design of reliable industrial and commercial power systems, 1990. solution to the reliability problem of radial electric distribution systems through artificial intelligence 71 [11] lópez e., tardon c., contreras v., reliability evaluation of primary distribution systems via search tree, procc. of international congress of acca/ifac automatic control association 2000, santiago of chile, 1998. [12] ministry of mining, general law of electric services. official newspaper of the republic of chile, 1998. [13] russell s y p. norvig, artificial intelligence. a modern approach, editorial prentice hall & hispanoamericana s.a., 1996. e. lópez, j. campos, c. tardon, f. salgado, university of concepción, chile department of electrical engineering e-mail: elopez@die.udec.cl j. tardon sts-saesa-frontel e-mail: jtardon@saesa.cl r. lópez supelec. lep-university of paris xi. e-mail: rodrigo.lopez@supelec.fr international journal of computers communications & control issn 1841-9836, 10(3):298-307, june, 2015. threshold based best custodian routing protocol for delay tolerant network q. ayub, m. s. mohd zahid, s. rashid, a. hanan abdullah qaisar ayub*, soperi mohd zahid, sulma rashid, abdul hanan abdullah faculty of computing universiti teknologi malaysia utm skudai, 81310 johor,malaysia sheikhqaisar@gmail.com, soperi@utm.my, sulmaqaiser@gmail.com, hanan@utm.my *corresponding author:sheikhqaisar@gmail.com abstract: delay tolerant network (dtn) is a kind of network in which the source may not be able to establish the stable and uninterrupted path to destination due to network partitioning, dynamic topology change and frequent disconnections. in order to dealt disruption and disconnections a store, carry and forward paradigm is used in which node stores the incoming messages in its buffer, carries it while moving and forward when comes within the transmission range of other nodes. message forwarding contributes and important role in increasing its delivery. for instance, probabilistic routing protocol forwards message to a node having high probability value to meet message destination. these protocols cannot handle a situation in which the node continually transmits messages even the probability difference is very small. in this paper, we have proposed a routing protocol known as threshold based best custodian routing protocol (tbbcrp) for delay tolerant network. we have proposed a threshold-based method to compute the quality value which is the ability of node to carry message. a self-learning mechanism has been used to remove the delivered messages from the network. moreover, a buffer aware mechanism has been used that make sure availability of buffer space at receiver before message transmission. we have compared the performance of tbbcrp with epidemic, prophet and delegated forwarding. the proposed tbbcrp outperforms in terms of maximizing the delivery probability, reducing number of transmissions and message drop. keywords: delay tolerance network , store-carry-forward, routing protocols , algorithms. 1 introduction with advancement in communication technologies [1-2] it is now possible to interconnect mobile nodes, stand-alone computers and provide an innovative way to join the social and business communities. despite, other communication architectures such as lan, wlan, the mobile ad hoc networks have gained more popularity. in ad hoc networking routing protocols [3-6], the source and destination establishes the end-to-end path prior to the transmission of data. this prerequisite is impossible in highly disrupted wireless applications such as wildlife monitoring, deep-space communication and military networks. such environments suffer frequent disconnections, dynamic teleology change and network partitioning due to node mobility. in addition, limited network resource, for instance, buffer space, bandwidth, energy and processing power of nodes makes routing a real challenge. delay tolerance network (dtn)[7] is a kind of network that aims to provide the communication via opportunistically connected mobile nodes. a novel method called as the store, carry and forward is used in which nodes stores the message in their buffers carries them while moving and forwards when connected to other nodes. the dtn routing protocols can be classified as copyright © 2006-2015 by ccc publications threshold based best custodian routing protocol for delay tolerant network 299 single copy and multi copy. in single copy protocols, the unique copy of the message exists in entire network [8,21]. these protocols are capable to operate under limited resource but reduce delivery ratio and raises the delivery delay. multi copy routing protocols transmits the redundant copies of each message to all connected nodes [9-13]. therefore, the message can reach to its destination via multiple intermediate nodes. as a result, multi copy routing protocols minimize the delivery delay and maximize the delivery [14-18]. the multi copy routing protocols are more robust, but unreliable due to consumption of high volume of network resources. the probabilistic routing protocols were proposed to reduce the resource consumption that considers node behavior such as its movement pattern, encounter history [12,14,27] before the transmission of the message. a carrier node with probabilistic protocol continues to forward message to high probable relay nodes. this issue was addressed in [19] where vijay erramilli et al. proposed a new message forwarding technique called as delegated forwarding. in this method, each node maintains a quality metric and forward the message to another node only if it has high quality metric which have been seen by the message. later, in [20][23-24] the authors present the variations of delegated forwarding. the previous works has not defined a method to compute quality value of nodes. the contribution to this paper is as follows • we have proposed a routing protocol called as threshold based best custodian routing protocol for delay tolerant network (tbbcrp). • we have used a self-learning method to remove the previously delivered messages from the network. • a threshold-based method has been used to assign the quality value to network nodes. • we have compared the performance of tbbcrp with epidemic, prophet and delegated forwarding in terms of minimizing number of transmissions, number of drops, overhead while raising the deliver probability. 1.1 review of dtn routing protocols in dynamic surroundings like intentions topology change, node mobility and frequent network partitions, problem is to select a suitable relay node for a message. in addition, scarce network resources such as limited buffer space, bandwidth, and low power of nodes makes data routing even more challenging. therefore, prototype of a good routing protocol must focus on minimizing the consumption of network resources and delivery of more messages to their destinations. in flooding based routing protocols such as epidemic protocol [13] each node encounter results in the exchange of messages. the encountering nodes further diffuse the message copy and process continues. despite the fact that in epidemic protocol, each message may have more than one path to reach destination, epidemic protocol consumes high volume of network resources. the control on creation of message copies can reduce the resource requirement. in this background, qouta based routing protocols were emerged where each node was given the opportunity to transmit the n number of message copies for example spray and wait[10], spray and focus[11], spray and wait binary qon spray and wait[26]. the spray and wait algorithm consist of a spray phase, where node spread n message copies to its neighbors called as relays. if the destination is not found in the spray phase then each node wait until contacted to message destination. in spray and wait protocol, message transmission was limited only to the neighboring nodes. this problem was solved in binary spray and wait protocol in which a source node on encountering forwards the n/2 message copies to the connected 300 q. ayub, m. s. mohd zahid, s. rashid, a. hanan abdullah node while keeps n/2. in addition, the receiver node was also privileged to distribute n/2 message copies. this hierarchical forwarding improves the performance of spray and wait and increases message delivery. the spraying protocols work well when the node movement is independent and identically distributed (iid) which is not possible for real world scenarios where each node exhibits its own movement pattern. these challenges motivate the researchers and various utility functions were introduced in spraying algorithms. for example, spray and focus [11] protocol starts by distributing the n/2 message copies like spray and wait binary, however when the node left only one copy of message then it shifts to the focus phase where the nodes forwards the message to neighbors by observing its suitability to meet the destination. the suitability is determined by the time since two nodes last saw each other. quality of node spray and wait [26] improves the performance of binary spray and wait algorithm by introducing qon (quality of node). the qon is represented by an integer number which describes encountering frequency of one node to encounter with other node in a given time interval. the primary objective of quota based routing protocols was to control the transmission of message copies. despite the fact that spraying algorithms has exploited the encountering history of nodes, other factor such as mobility patterns or positional coordinates can improve the routing procedure. for example in [28], the author designs a mobility based spraying strategy most social first, most mobile first (mmf), last seen first (lsf). the shen ling et [22] observe the node mobility and introduce a influence factor which is determined by the mobility of the nodes. in [12] lindgren et al. introduces prophet protocol which reduces the number of message transmissions by introducing a new metric called as delivery predictability and transitive connectivity. the nodes are capable receive a message only if they constitute a high value of predictability and transitivity. as expected, prophet protocol compared to epidemic protocol minimizes the number of transmissions. the variation of prophet such as procs [29] introduces a new message forwarding method which observes movement pattern of nodes and their time sequence in [19] vijay erramilli et al. propose a new message forwarding technique called as delegated forwarding. in delegated forwarding, each node maintains a quality metric and forwards the message to another node only if it has high quality metric which have been seen by the message. the protocol works well to and controls the transmission of the message on the relay nodes. however, it does not provide any solution to control the transmission of the source messages. in [20] author modified the algorithm by updating source message with the probability value of high quality node. in this way, the replication was also controlled from the source messages. in [23] the author defines the delegated forwarding by designing cost-based drop and transmission methods. according to this, the message that is close to their destinations are assigned the high priorities by defining a replication count number called as the delegated number. yunsheng wang et.al [30] proposed for single copy multicast, multi copy multicast and delegated forwarding multicast algorithms. 1.2 the proposed threshold based best custodian routing protocol in proposed tbbcrp, each network node maintains the time information about encountering to other nodes in a vector called as previous encounter vector (pev). the pev consists of node id nid and encounter time (et). we have used et to assign the quality value which describes the future encountering likelihood among same nodes. the high quality value indicates that node is more likely to delver message. the quality information is stored in the quality vector. the quality vector consists of node id and quality value (qv). principal-1. when nodes contact, they assigns quality values by using upstream and downthreshold based best custodian routing protocol for delay tolerant network 301 stream time threshold: td = currenttime − et(ni). (1) when the node encounters first time, they initializes the default quality in the quality vector and current time in the et of pev. if nodes have encountered previously, then the time difference (td) is computed by subtracting the current time from the et by using equation 1 and threshold streams module is invoked. the threshold stream module updates the quality value for a node by mapping the td in the pre-defined collection of threshold queue. table 1 shows the meaning of variables used in tbbcrp. table 1: meaning of variables used in tbbcrp symbol description ni , nj node i and node j pev,et previous encounter vector, encounter time qv quality value td time difference qp quality points vd vector deliver figure 1: structure of threshold queue figure 1 show the structure of threshold queue which is divided into upstream time thresholds and downstream time thresholds. the upstream time thresholds further defines its lower bound of upstream limit (lbul) and upper bound of upstream limit (ubul). the upstream thresholds are used to decrement quality value while downstream thresholds are used to increase quality values. the nodes encountering after large interval of time shows high td value and relevant quality points (qp) are subtracted from quality value of node. when the time difference is above the ubul then quality value of node is initialized to zero. the downstream threshold starts after lbul and defines its lower bound of downstream limit (lbdl). the downstream threshold is used to increment the quality value of nodes. for instance, nodes encountering after small interval of time are expected to encounter again. the td is mapped and relevant quality points (qp) are incremented in the quality value. when time difference is lower than the lbdl then maximum quality points ( qp) are assigned to the quality value (qv). figure 2 shows the algorithmic flow of threshold based method in which node x and node y have established connectivity. the node x and y has maintained previous encounter vector 302 q. ayub, m. s. mohd zahid, s. rashid, a. hanan abdullah figure 2: threshold based best custodian routing protocol example (pev) and quality vector. in step one, x map tdy which represents time elapsed since x has seen y. in step two, the relevant quality values qvy will be given to x. the same steps are followed by node y. 1.3 self learning method to remove delivered messages since, dtn is a highly disrupted environment where it is not possible to keep the track of the transmitted messages via central administration. hence, most of the time the message even after finding their destinations cannot convey their delivery status to the other nodes, and message replication continues even it is delivered. when the network resources scarce then replications of delivered messages produce high overhead on the buffer space, bandwidth and energy. despite the influence of other factors, these messages also produce the congestion that a node overcomes by dropping its stored messages. hence, a solution is required to remove the delivered message from network. inspired by immunity based routing protocol, we have defined a de-centralized mechanism to remove the previously delivered messages. principal-2. the algorithm states that when a node deliverers a message to the current connection as a final recipient then it stores the message id in vector delivered (vd) and remove it from the buffer. the tbbcrp , each network node maintains a vector called vector delivered (vd). when a node forwards the message copy to a connection as final recipients it inserts the message id in vd and removes the message from the list of its carried messages. this module is invoked after the threshold computation and before the transmission of messages. figure 3: exchange of previously delivered messages figure 3 show the technical flow of removing delivered messages. accordingly, on encounterthreshold based best custodian routing protocol for delay tolerant network 303 ing, ni forward vdi [16-17] to nj. vdi hold ids of delivered messages known by ni. nj subtracts vdi from vdj to get vdrequired that holds list of delivered messages not known by nj and send it to ni by using eq. (2). v drequired = (v di − v dj) (2) ni computes v dremove by intersecting vdrequired from vdi and send it to nj by using eq. (3). v dremove = (v drequired ∩ v di) (3) finally, nj removes the v dremove messages from buffer and update vdj by using eq. (4). v dj = v dremove ∪ v dj (4) 1.4 control on message replication by buffer space the dtn message consists of message header and payload header. the payload header contains the actual contents of message. the message header is collection of control information such as message identification, hop count, and time to live. the dtn node utilizes control information to forward and drop messages. in tbbcrp, we have included a new data field in message header called as recent quality (rq). the rq is an integer value initialized with zero for the messages generated by the source. principal-3 when a transmitter forward the message copy it updates the rq of message with the of quality value of receiver, while the receiver will update the rq of message to its own. the principal 3 is about the implementation of delegated forwarding in message header. we have used the same concept in df++[24]. briefly, after transmitting message copy, the sender node updates the rq of message header to quality value of node which receives message. similarly, receiving node update the message rq to its own quality value. the idea is that the receiver or transmitter will not replicate the message until the encountered node has higher quality value than the rq. principal-4. the transmitter will forward the message only if the qv of receiver to meet with the message destination is greater then the rq and available buffer space at receiver is capable to store the message. the high quality nodes are likely to encounter the message destination. however, if we forward a message to a congested high quality node then this forwarding decision may degrade the network performance. since, the congested node will drop its previously stored messages to accommodate the new one. in our previous work, df++ [24] and cfbarp [25] an adaptive mechanism has been defined to dealt with buffer space. hence, the node forward the message only if the quality value of receiver is high as well as available buffer is able to accommodate the incoming message. after transmission, the transmitter will subtract the message size from available buffer. 2 simulation and results this section provides the performance analysis of existing and proposed routing protocols in terms of minimizing the message transmissions, message drop and raising the delivery probability by one simulator [31]. one is event driven simulator written in java and has been designed to evaluate the dtn applications. the reality of simulation has bee increased by using a city based environment which consist on pedestrian, cars and trains. the pedestrians were divided into two groups with 40 members at each group. the pedestrian are moving with shortest path map based movement model at the speed between 0.5km/h and 1.5 km/h. each pedestrian has been 304 q. ayub, m. s. mohd zahid, s. rashid, a. hanan abdullah carrying mobiles with 2mb of buffer size. the transmission range of mobile nodes is 10 meters. the 40 cars are moving via map route movement at the speed between 10km/h-50km/h. finally, six trains are moving via map route movement at the speed between 7km/h and10km/h. the random size message generated from the sample of 100k-300k and the inter message creation interval is 25s-35s. the bandwidth if equally distributed at 2mbps. figure 4: transmissions by varying number of nodes figure 4 represents the results of exiting prophet, epidemic and delegated forwarding as compared to proposed tbbcrp routing protocol in terms of number of message transmissions. the flooding based epidemic protocol has been showing high number of transmissions. the delegated forwarding has controlled the message diffusion as compared to epidemic protocol but still forwards high quantity of messages compare to prophet and tbbcrp protocols. the message transmissions are getting higher with increasing number of nodes. the reason is that, at high number of nodes, message exchange gets higher. it is possible to sustain such traffic under the infinite buffer space. however, in the current environment the buffer is limited resource thus a better quality node when receive a message by having no space mechanically triggers the drop event. further, due to the multi copy of each message the same high quantity node may reputedly receive the dropped messages, thus cause high transmissions, message drop and waste of node energy. the proposed tbbcrp routing protocol has reduced the message transmissions. figure 5: message dropped by varying number of nodes the figure 5 depicts the results message drop by increasing the number of nodes. we can see that increasing the number of nodes has raised the message dropped. this is because the buffer space is finite, and nodes cannot accommodate all incoming messages. for instance, at increasing threshold based best custodian routing protocol for delay tolerant network 305 number of nodes such as 186, 216, and 246, even the protocols like prophet and delegated forwarding has dropped large number of messages. the reason is that when the encounter rate among nodes is high then multiple nodes became highly probable to receive the message. the proposed tbbcrp has shown the constant stance for all network traffic. figure 6: delivery by varying number of nodes the figure 6 plot the results of existing and proposed routing protocols in terms of message delivery probability by increasing number of nodes. it can be observed that at less number of nodes such as 126, 156 the protocols such as prophet, epidemic and delegated forwarding has delivered more messages. nevertheless, as the number of nodes gets higher like 186,216 and 246 less number of message find their destinations. the reason is that messages were dropped before reaching destination. 3 conclusion in this paper we have proposed a routing protocol called as threshold based best custodian routing protocol (tbbcrp) for delay tolerant network. a threshold based method has been proposed to compute the quality value of nodes, which is the ability of nodes to carry message. moreover, a self learning method has been used to remove previously delivered messages from network. the proposed protocol out performs well as compared to existing strategies in terms of maximizing the delivery probability, reducing number of transmissions and message drop. acknowledgments this work is financed by institution scholarship provided by utm and ministry of higher education of malaysia. bibliography [1] ariyavisitakul, s. l. 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(2009); the one simulator for dtn protocol evaluation. proc. of the 2nd international conference on simulation tools and techniques. icst (institute for computer sciences, social-informatics and telecommunications engineering), 1-10. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 135-148 feedback gain design method for the full-order flux observer in sensorless control of induction motor abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi abstract: this paper deals with a feedback gain design method for the full-order flux observer with adaptive speed loop, which enables the minimizing the unstable operation region of this observer to a line in the torque-speed plane. the stability in regenerating mode is studied using necessary condition of stability based on determinant of matrix and a linearized model. simulations results where the proposed observer is compared with an exiting solution (where the unstable region is not totally removed) are presented to validate the proposed observer design. keywords: induction motor, full-order flux observer, sensorless control, stability analysis, adaptive speed estimator, regenerating mode 1 introduction the speed-sensorless control of induction motor drives have developed significantly during the last number of years. speed adaptive full observers introduced by [8], [15] are promising flux estimators for inductions motors drives. the speed adaptive observer consists of a state variable observer augmented with a speed adaptation loop. the observer gain and the speed adaptive law determine the properties of the observer. the speed adaptation law is based on the component of the current estimation error with the estimated rotor flux. the adaptation law was originally derived using the lyapunov stability theory [8]. however, the stability of the adaptation law is not guaranteed and stability problem exist in the regenerating mode. the derivation in [8] neglects a term including the actual rotor flux (which is not measurable). the positive-realness condition is not satisfied as shown [5]. some limits of operation were quickly highlighted [9], [13]. in particular, a well known instability region was described in regenerating mode. thus, the drive stability can’t be guaranteed when this type of observer is associated with a field oriented control. there was many work in order to reduce this region of instability which is due to inadequate observer design [1, 2, 5, 14]. in this paper, we describe the design of an adaptation law that minimizes the instability region of an adaptive speed estimator. the paper is organized as follows. the induction motor model and the speed adaptive flux observer are first defined in section 2 and 3 respectively. we introduce the observer gain design in section 4 leading to a reduced instability region limited to a line. finally, simulations results are presented and discussed in section 5, where the proposed observer is compared with an exiting solution [5, 13]. 2 induction motor model the induction motor is described by the following state equations in the synchronous rotating reference frame with complex notations: d dt x = a (ω, ωs) x + bus (1) is = cx (2) where x = [ ψ r is ] t a =   −( 1 tr + jωsl ) lm tr lm b ( 1 tr − jω) −(a + jωs)   , b =   0 1 σ ls   , c = [ 0(2×2) i ] , i = [ 1 0 0 1 ] (3) copyright © 2006-2008 by ccc publications 136 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi and the mechanical equation is: d dt ω = p2 lm jlr ℑ(isψ ∗ r )− p tl j (4) where ∗ means a conjugate, j a complex number and ℑ an imaginary part. ψ r : rotor flux; is : stator current; us : stator voltage; ωs : stator angular frequency; ω : motor angular speed; ωsl = ωs − ω : slip angular frequency; rs, rr : stator and rotor resistance; ls, lr : stator and rotor self-inductance; lm : mutual inductance; tl : load torque; j : rotor inertia ; p : number of pole pairs; tr = lr/rr : rotor time constant; a = (l2r rs + l 2 mrr)/(σ lsl 2 r ); b = σ lslr; σ = 1−(l2m)/(lslr) : leakage coefficient. 3 adaptive observer the conventional full-order observer, which estimates the stator current and the rotor flux together [10, 11], is written as the following state equation. d dt x̂ = â (ω̂, ω̂s) x̂ + bus + g(is − îs) (5) îs = cx̂ (6) where ̂ means the estimated values and g = [ g1 g2 ]t is the observer gain matrix. we assume that all machine parameters are perfectly known except the motor speed. using the assumption of constant angular rotor speed ω̇ = 0 (i.e. the speed variations are slow with respect to electrical mode) [8], [5], the speed adaptive law is [8]: d dt ω̂ = λ lm b (eid ψ̂rq −eiqψ̂rd ) (7) where λ is a positive constant and will be tuned in (7) to improve observer dynamics. in practice, proportional-integral action is used in order to improve the dynamic behavior of the estimator. d dt ω̂ = kp d dt (eid ψ̂rq −eiqψ̂rd ) + ki(eid ψ̂rq −eiqψ̂rd ) (8) where eid = isd − îsd , eiq = isq − îsq, (isd , isq) are (d,q) components of stator current, (ψrd , ψrq) are (d,q) components of rotor flux. the speed adaptive observer scheme with the speed adaptation mechanism is presented in fig. 1. 4 observer gain design 4.1 linearized model the nonlinear and complicated dynamics of the speed adaptive observer can be studied via smallsignal linearization. it is useful to proceed with a local analysis based in the principle of stability in the first approximation [12, 7]. we will choose the particular form g1 = g1i2×2 where i2×2 is the identity matrix and g2 = 02×2. the complete adaptive observer may be written as equation (10). note that according the assumption ω̇ = 0, the motor model (1) may be written as (9)    d dt ψ r = −( 1 tr + jωsl )ψ r + lm tr is d dt is = lm b ( 1 tr − jω)ψ r −(a + jωs)is + 1 σ ls us d dt ω = 0 (9) feedback gain design method for the full-order flux observer in sensorless control of induction motor 137 induction motor b â speed adaptive law eq. (8) + + + ∫ ω̂ g c i s x̂ î s − + u s 1 figure 1: speed adaptive observer    d dt ψ̂ r = −( 1 tr + jω̂sl )ψ̂ r + lm tr îs + g1δ ei d dt îs = lm b ( 1 tr − jω̂)ψ̂ r −(a + jω̂s)̂is + 1 σ ls us d dt ω̂ = kp d dt (eid ψ̂rq −eiqψ̂rd ) + ki (eid ψ̂rq −eiqψ̂rd ) (10) we investigate the stability of the observer by linearizing the two systems (10) and (9) around an equilibrium operating point.defining the new state vectors x = xo + δ x with xo = [ ψ ro iso ωo ] t , δ x = [ δ ψ r δ is δ ω ]t and x̂ = x̂o + δ x̂ with x̂o = [ ψ̂ ro îso ω̂o ] t , δ x̂ = [ δ ψ̂ r δ îs δ ω̂ ] t . the reference frame is synchronized with the estimated rotor flux (ψ̂rqo = 0), then its two components are ψ̂rd = ψ̂o + δ ψ̂rd and ψ̂rq = δ ψ̂rq. in these two systems, the stator frequencies are regarded as identical : ωs = ω̂s [5]. preserving only dynamic parts, the two systems (9), (10) become after linearization,:    d dt δ ψ r = −( 1 tr + jωslo )δ ψ r + lm tr δ is − jψoδ ωsl d dt δ is = lm b ( 1 tr − jωo)δ ψ r −(a + jωso)δ is + 1 σ ls δ us − j lm b ψoδ ω − jisoδ ωs d dt δ ω = 0, (11)    d dt δ ψ̂ r = −( 1 tr + jω̂slo )δ ψ̂ r + lm tr δ îs − jψ̂oδ ω̂sl + g1δ ei d dt δ îs = lm b ( 1 tr − jω̂o)δ ψ̂ r −(a + jω̂so)δ îs + 1 σ ls δ us − j lm b ψ̂oδ ω̂ − jîsoδ ω̂s d dt δ ω̂ = −kp(− lm b ωoψ̂oδ ψ̂ rd + lm btr ψ̂oδ ψ̂ rq −ωsoψ̂oδ îsd −aψ̂oδ îsq − lm b ψ̂oδ ω̂s) −ki(−eidoδ ψ̂ rq + eiqoδ ψ̂ rd + ψ̂ oδ eiq). (12) 138 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi defining δ e = [ δ eψ δ ei δ eω ]t , the system describing the estimation error is as follows:    d dt δ eψ = −( 1 tr + jωslo )δ eψ + ( lm tr −g1)δ ei − jeψo δ ωsl + jeωo δ ψ̂ r + jψ̂oδ eω d dt δ ei = lm b ( 1 tr − jωo)δ eψ −(a + jωso)δ ei − j lm b eψo δ ω − j lm b eωo δ ψ̂ r − jeioδ ωs − j lm b ψ̂ o δ eω d dt δ eω = kp(− lm b ωoψ̂oδ eψ d + lm btr ψ̂oδ eψ q −ωsoψ̂oδ eid −aψ̂oδ eiq − lm b ψ̂oδ eω ) +ki(−eidoδ ψ̂ rq + eiqoδ ψ̂ rd + ψ̂ oδ eiq). (13) separating each state in d and q components, we obtain the corresponding state matrix â1: â1 =   − 1 tr ωslo lm tr −g1 0 0 −ωslo − 1 tr 0 lm tr −g1 ψ̂o lm btr lm b ωo −a ωso 0 −lm b ωo lm btr −ωso −a − lm b ψ̂o −lm b kpωoψ̂o lm btr kpψ̂o −kpωsoψ̂o (ki −akp)ψ̂o − lm b kpψ̂o   (14) note the dependency of the dynamic matrix â1 by the operating condition. in order to obtain analytic conditions about the local stability using the necessary condition for stability based on the determinant of (14) [4], it is possible to obtain a relevant result as reported in the next section. 4.2 stability criterion we use the following property: det(â1) = 5 ∏ i=1 λi (15) where λi are the eigenvalues of matrix â1. the determinant of matrix â1 is: det(â1) = −lmψ̂ 2o kiωso((ωso −ωo)abtr + l2mωo −lmωog1tr + ωsob)/(b2tr) (16) the condition det(â) = 0 leads to: ωso = 0, (17a) ωso = ωo g1lm + rslr (rrls + rslr) . (17b) these conditions of stability may be expressed in the torque/speed plane. let us consider the mechanical equation: d dt ω = p2 lm jlr ℑ(isψ ∗ r )− p tl j . (18) under rfoc conditions and steady state (ψ̂rqo = ψrqo = 0), we obtain: 0 = p lm lr ψ̂oisqo −tlo (19) feedback gain design method for the full-order flux observer in sensorless control of induction motor 139 then isqo = lr plmψ̂o tlo. (20) from system (1), in the same conditions, we find : ωslo = lm trψ̂o isqo. (21) finally using ωso = ωslo + ωo, equations (17a) and (17b) become tlo = − pψ̂ 2o rr ωo (22a) tlo = − pψ̂ 2o rr (1− g1lm rrls )ωo (1 + ts tr ) (22b) with ts = ls/rs. above relations describe respectively two lines, defining two well known instability regions in regenerating mode. an sufficient condition for instability is then: det(â1) > 0. (23) the condition (23) defines a set whose the instability region is a subset. in order to complete the study of local stability, we plot for each eigenvalue, the locus in the torque/plane where conditions (ℜ(λi) > 0, i = 1 . . . 5) are verified. in one hand, if we chose a zero observer gain, as in [9], g1 = 0 (24) we obtain the instability region limited by lines d1 and d2, (fig. 1) where ℜλi > 0, i = 1...5, are the positive real part of the eigenvalues λi of the state matrix â1. the eigenvalues correspond respectively to the states variables δ eψrd , δ eψrq , δ eid , δ eiq and δ eω . tlo = − pψ̂ 2o rr ωo (1 + tr ts ) (25) in other hand, in order to reduce (not totally remove) the unstable region, a real valued observer gain was considered in [13] which corresponds to the region limited by lines d1 and d3, (fig. 3). the value of the parameter g1 selected is: g1 = −0.25rs (26) it is be noted that the curves corresponding to zero observer gain are similar, except that the unstable region is larger. tlo = − pψ̂ 2o rr (1 + 0.25rslm rrls )ωo (1 + tr ts ) (27) the principle of the instability reduction proposed here consists in the calculation of the feedback gain so that the unstable region will be limited to the inobservabilité line (d1). we can note that, whatever the structure of the matrix g, (d1) is always defined by ωso = 0. from equation (16), we can write the 140 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi −200 −150 −100 −50 0 50 100 150 200 −50 −40 −30 −20 −10 0 10 20 30 40 50 ω o (rad/s) t l o (n m ) region (ω o ,t lo ), zero observer gain ℜλ 2 of δψ rq ℜ 0λ 3 of δi sd reλ 4 of δi sq ℜλ 5 of δω tlo − tlo d1, wso=0 d2 figure 2: torque/speed plane,g1 = 0, ℜ(λi) > 0, i = 1...5 −200 −150 −100 −50 0 50 100 150 200 −50 −40 −30 −20 −10 0 10 20 30 40 50 ω o (rad/s) t l o (n m ) region (ω o , t lo ), observer gain g 1 = − 0.25 r s ℜλ 2 of δψ rq ℜ 0λ 3 of δi sd ℜλ 4 of δi sq ℜλ 5 of δω t lo − t lo d 1 , (ω wso =0) d 2 d 3 figure 3: torque/speed plane, g1 = −0.25rs, ℜ(λi) > 0, i = 1...5 feedback gain design method for the full-order flux observer in sensorless control of induction motor 141 condition ωso = 0 in equation (28) (ωso −ωo)abtr + l2mωo −lmωog1tr + ωsob = 0 (28) which can be achieved by choosing the following observer gains g1 = − lrrs lm (29) the straight line (d1) correspond to zero synchronous speed ωs = 0. it is known in the literature as the inobservability line (normally referred as dc-excitation) [6, 3] and seems to be a generic problem for sensorless control of induction motors. 5 simulations results in order to validate the proposed design, the regenerating mode low speed operation of the speed adaptive observer was investigate by means of simulations. a rotor flux oriented control (rfoc) is simulated using matlab/simulink software. the block diagram of the control system is shown in fig.4. the flux reference is fixed to the nominal value ψ re fo where ref denotes the reference value. the proposed usd -+ ω ref ω̂ + i ref sq -+ ψ ref o ψ̂r + i ref sd cω(s) cψ(s) ci(s) ci(s) isd isq usq motor adaptive observer eq. (5) usd usq isd isq ψ̂r ω̂ i sd i sq ψrd ψrq ω model eq. (1) 1 figure 4: block diagram of sensorless rfoc induction motor simulator. observer is compared with an exiting solution [13]. in order to validate the proposed design, we studied a conventional test used by industrial drive designers: very low and progressive load torque increase under constant speed; fig. 5 depicts results in regenerating mode obtained using the observer gain g1 = −0.25rs, [13], [5]. the speed reference was set to (−25rad/s) (dashed line) and a rated-load torque ramp was applied at t=0. after applying the load progressively, the drive should operate in the regenerating mode. however, the actual angular speed et actual flux of the motor collapse and the system becomes unstable. fig. 6 present results obtained using the proposed observer design. the system behaves stably. on fig. 7, the observer gain g1 = −0.25rs was used. real speed diverges. first subplot shows reference (dashed line) and actual angular speed. second subplot shows rated-load torque ramp. third subplot present actual flux components (ψrα , ψrβ ) in stator reference frame. fouth subplot, shows control voltages. in the fifth and six subplot respectively, we present current and current norm. we note that when the load torque increases, the control voltage, the current and the current norm increase too. on fig.8, the proposed observer design was used. the system becames stable. real rotor angular speed converges well towards the reference value in response to the same rated-load torque. note the behavior of the actual flux at (t ≈ 3.75 s) when the real angular rotor speed crosses the line (d1 = d3). the system becomes unobservable at this time. 142 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi −50 −45 −40 −35 −30 −25 −20 −15 −10 −5 0 0 5 10 15 20 25 t l o ( n m ) region (ω o ,t lo ) d 1 d 3 −50 −45 −40 −35 −30 −25 −20 −15 −10 −5 0 0 5 10 15 20 25 ω o (rad/s) t l o ( n m ) figure 5: a rated-load torque ramp is applied with the observer gain g1 = −0.25rs. first subplot shows region (ωo, tlo) with the two lines d1 and d3. second subplot shows the actual angular speed. feedback gain design method for the full-order flux observer in sensorless control of induction motor 143 −50 −45 −40 −35 −30 −25 −20 −15 −10 −5 0 0 5 10 15 20 25 t l o ( n m ) region (ω o ,t lo ) d 1 , (ω so =0) d 3 −50 −45 −40 −35 −30 −25 −20 −15 −10 −5 0 0 5 10 15 20 25 ω o (rad/s) t l o ( n m ) figure 6: a proposed observer design was used. first subplot shows region (ωo, tlo) with the line d1 = d3. second subplot shows the actual angular speed. 144 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi 0 2 4 6 −40 −30 −20 −10 0 ω o (r a d /s ) 0 2 4 6 0 5 10 15 20 25 t l o ( n m ) 0 2 4 6 −3 −2 −1 0 1 2 time(s) ψ rα , ψ rβ ( w b ) 0 2 4 6 −400 −200 0 200 u sα , u sβ ( v ) 0 2 4 6 −30 −20 −10 0 10 20 i s α , i s β (a ) 0 2 4 6 0 10 20 30 c u rr e n t n o rm ( a ) time(s) figure 7: instability phenomenon with observer gain g1 = −0.25rs. feedback gain design method for the full-order flux observer in sensorless control of induction motor 145 0 2 4 6 −40 −30 −20 −10 0 ω o (r a d /s ) 0 2 4 6 0 5 10 15 20 25 t l o ( n m ) 0 2 4 6 −3 −2 −1 0 1 2 time(s) ψ rα , ψ rβ ( w b ) 0 2 4 6 −400 −200 0 200 u sα , u sβ ( v ) 0 2 4 6 −30 −20 −10 0 10 20 i s α , i s β (a ) 0 2 4 6 0 10 20 30 c u rr e n t n o rm ( a ) time(s) figure 8: the instability was removed by the proposed observer design g1 = −lrrs/lm. 146 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi 6 appendix 6.1 induction motor parameters voltage rating : 380 v , current rating: 2.2 a, number of phases: 3, rated power: 1.1 kw , frequency: 50 hz, rated speed: 1430 r pm/min, p = 2, ls = 0.472 h, lr = 0.4721 h, lm = 0.4475 h, rs = 9.65 ω, rr = 4.3 ω. 7 conclusions the feedback gain design method proposed in this paper reduces the instability region of adaptive observer to a inobservability line (d1) (ωso = 0). the observer using the proposed gain does not have the unstable region, which was shown by means of speed/torque plane and a linearized model. the stability of the regenerating-mode operation was also confirmed by simulations. bibliography [1] a. bouhenna, c. chaigne, n. bensiali, e. etien and g. champenois, design of speed adaptation law in sensorless control of induction motor in regenerating mode, simulat. modell. pract. theory, elsevier, doi:10.1016, simpat.2007.04.005, vol. 15, no.7, pp. 847-863, 2007. [2] a. bouhenna, contribution à la commande sans capteur mécanique de la machine asynchrone en mode générateur à basse vitesse, thèse de doctorat en sciences, université des sciences et de la technologie d’oran, algérie, octobre, 2007. [3] c. canudas de wit and a. youssef and j. p. barbot and ph. martin and f. malrait, observability conditions of induction motors at low frequencies, proc. cdc, sydney, pp. 1-7, 2000. [4] e. etien and n. bensiali and c. chaine and g. champenois, adaptive speed observers for sensorless control of induction motors: a new criterion of stability, international review of electrical engineering, vol. 1, pp. 36-43, 2006. [5] m. hinkanen, stabilization of regenerating-mode operation in sensorless induction motor drives by full-order flux observer design, ieee trans. ind. electron., vol. 51, pp. 1318-1328, 2004. [6] h. hofmann and s. sanders, speed-sensorless vector torque control of induction machine using a two-time-scale approach, ieee trans. on ind. appl., vol. 34, pp. 169-177, 1998. [7] h. k. khalil, nonlinear systems, macmillan, new york, 1983. [8] h. kubota and k. matsuse, dsp-based speed adaptive flux observer of induction motor, ieee trans. ind. appl., vol. 29, pp. 344-348, 1993. [9] h. kubota and i. sato, regenerating mode low speed operation of sensorless induction motor drive with adaptive observer, ieee trans. ind. appl., vol. 38, pp. 1081-1086, 2002. [10] a. mansouri and m. chenafa and a. bouhenna and e. etien, powerful nonlinear observer associated with field-oriented control of an induction motor, international journal of applied mathematics and computer sciences, vol. 14, no. 2, pp. 209-220, 2004. feedback gain design method for the full-order flux observer in sensorless control of induction motor 147 [11] m. chenafa and a. mansouri and a. bouhenna and e. etien and a. belaidi and m. a. denai, global stability of linearizing control with a new robust nonlinear observer of the induction motor, international journal of applied mathematics and computer sciences, vol. 15, no. 2, pp. 235-243, 2005. [12] m. montanarri and s. peresada and a. tilli, observeless scheme for sensorless control of induction motor: stability analysis and design procedure, proc. of the 10th mediterranean conference and automation, med’02, lisbon, 2002. [13] s. suwankawin and s. sangwongwanich, speeds sensorless im drive with decoupling control and stability analysis of speed estimation, ieee trans. ind. electron., vol. 49, pp. 444-455, 2002. [14] s. suwankawin and s. sangwongwanich, design strategy of an adaptive full order observer for speed sensorless induction motor drives-tracking performance and stabilization, ieee trans. ind. electron. vol. 53, pp. 96-119, 2006. [15] g. yang and t. chin, adaptive-speed identification scheme for a vector-controlled speed sensorless inverter-induction motor drive, ieee trans. ind. appl. electron., vol. 29, pp. 820-825, 1993. a. bouhenna1, a. mansouri1, m. chenafa1, and a. belaidi1 1 e.n.s.e.t. d’oran, laboratoire d’automatique et d’analyses des systèmes, (l.a.a.s) département de génie électrique, b.p 1523, el m’naouer, oran, algérie e-mail: bouhenna @ enset-oran.dz, (abouhenna @ yahoo.fr) received: october 10, 2007. 148 abderrahmane bouhenna, abdellah mansouri, mohammed chenafa, abdelkader belaidi abderrahmane bouhenna was born in 1955. he receive the dipl. eng. degree in electronic engineering, the m. s degree and the doctorat in automatic from u.s.t.o, oran, algeria in 1980, 1987 and 2007 respectively. he is currently a professeur searcher in the laboratory of automatic and analysis systems at enset of oran (algeria). he work on the subject of sensorless control and observers of induction motors and obtained some results in this domain for the stabilisation of the observers and the control of the mas in regenerating mode at low speed. abdellah mansouri was born in oran in algeria, in 1953. he received his bs degree in electronic engineering from usto (algeria) in 1979, the ms degree in engineering control from usto (algeria) in 1991, and the phd degree in engineering control from usto (algeria) in 2004. he is currently professor of automatic control at enset of oran (algeria). his research interests are non linear control and observers applied in induction motor and manipulator robot. mohammed chenafa was born in oran in algeria, in 1954. he received his bs degree in electronic engineering from usto (algeria) in 1979, the ms degree in signal processing and robotic from usto (algeria) in 1998, and the phd degree in engineering control from usto (algeria) in 2005. he is currently professor of automatic control at enset of oran (algeria). his research interests are non linear control and observers applied in induction motor and manipulator robot. abdelkader belaidi, professor in 1981 he obtained a ph.d in radiation physics at the university of east anglia, norwich england. his fields of interest are collision damage in materials and neural and fuzzy logic. now, he is a professor of physics and applied computing at the higher school of education oran algeria. he is also head of automatic and system analysis laboratory (laas). international journal of computers, communications & control vol. ii (2007), no. 4, pp. 375-387 fuzzy and neural controllers for a pneumatic actuator tiberiu vesselenyi, simona dziţac, ioan dziţac, mişu-jan manolescu abstract: there is a great diversity of ways to use fuzzy inference in robot control systems, either in the place where it is applied in the control scheme or in the form or type of inference algorithms used. on the other hand, artificial neural networks ability to simulate nonlinear systems is used in different researches in order to develop automated control systems of industrial processes. in these applications of neural networks, there are two important steps: system identification (development of neural process model) and development of control (definition of neural control structure). in this paper we present some modelling applications, which uses fuzzy and neural controllers, developed on a pneumatic actuator containing a force and a position sensor, which can be used for robotic grinding operations. following the simulation one of the algorithms was tested on an experimental setup. the paper also presents the development of a narma-l2 neural controller for a pneumatic actuator using position feedback. the structure had been trained and validated, obtaining good results. keywords: fuzzy control, neural control, force-position feedback, pneumatic actuator. 1 introduction there is a great diversity in which fuzzy inference and neural networks can be used in robotics operation control either in the place it has in the control scheme or in the type of fuzzy or neural controller. from the studied references the conclusion can be drawn that fuzzy inference is used (among others) in trajectory generation [3], robot model design [2], instead of p.i.d. controllers [4] or in combination with these [6]. a detailed presentation of general purpose fuzzy controllers is given in [5]. in the same work, it is shown that there can be made fuzzy controllers similar to classical ones (quasi p.i.d.). in other researches the importance of parameter adjustment is emphasized and also that fuzzy controllers can be adjusted more easily [6]. due to the fact that a large part of fuzzy inference systems had been implemented on heuristic basis (usually the membership functions are chosen upon the educated guess of specialists) there is no guarantee of a reliable operation or stability of the system in unforeseen conditions. due to this, experimental tests must be considered. a great number of researches in this field have as goal the development of methodologies of synthesis and analysis of fuzzy inference systems, in the field of robotics [2], [4] or in the larger field of control systems [5] (i.e. study of stability of fuzzy controllers). also there are works regarding the elaboration of fuzzy models of robots (used in direct and inverse kinematics [3] or in inverse dynamics [1], which can replace analytical models, and shorten the computing times. many researches try a systematic approach of fuzzy systems design (development of a design methodology), which can eliminate the subjectivity in choosing the membership functions and rule sets, as in [2], in which a clear method is presented for a rigorous selection of fuzzy inference parameters. in order to develop a model and test fuzzy and neural control in the design phase an adequate programming environment must be selected. for this purpose we had chosen the matlab programming environment, because it offers predefined functions to develop fuzzy and neural control systems. these functions are linked to extern modules like the "inference system" and the "fuzzy engine", and the simulink module can also use these functions. user applications can be linked to these modules using the predefined functions. the typical base structure of fuzzy systems develop a model which make the correspondence: copyright © 2006-2007 by ccc publications 376 tiberiu vesselenyi, simona dziţac, ioan dziţac, mişu-jan manolescu • crisp value input membership functions inference rules • output characteristics output membership functions crisp output value. also, a typical fuzzy inference system, supposes a user defined set of parameters which try to encrypt the model’s variables characteristics. if instead the development of a process model is wanted for which certain experimental input-output data sets exists, the fuzzy system parameters can be automatically generated that is the system identification can be done. in this case the identification strategy can be a neural-fuzzy approach, which has at its base acquiring knowledge from the presented data set in order to generate membership function parameters. in the matlab environment the adjustment of these parameters can be done with a module which works similar to a neural network named anfis (adaptive neural fuzzy inference system). as teaching algorithm error back propagation is used and the optimization is made by a gradient method, followed by error minimization (by the quadratic sum method). in [10], a methodology is presented for designing an adaptive fuzzy logic controller. "the neurofuzzy controller is first trained using data from an approximate analytical model of a cellular network then the controller is fine tuned and adapted to the unique cell dwell time and call holding time distributions of a particular cell in the network". the ability of neural networks to simulate non-linear systems, is used in some researches [13], in order to develop industrial processes control systems. when using neural networks in controlling processes there have to be two steps: system identification (development of process neural model) and control design (development of neural control system). in the system identification step, the neural model of the controlled process is developed and in the second step this model is used to obtain the neural net that will control the process. the training of process neural model is made "offline" (or "batch processing"), but the training and optimization of neural control must be made "online" using training data sets. a chaos search immune algorithm is proposed in [12] by integrating the chaos optimization algorithm and the clonal selection algorithm: "first, optimization variables are expressed by chaotic variables through solution space transformation. then, taking advantages of the ergodic and stochastic properties of chaotic variables, a chaos search is performed in the neighborhoods of high affinity antibodies to exploit local solution space, and the motion of the chaotic variables in their ergodic space is used to explore the whole solution space. furthermore, a generalized radial basis function neuro-fuzzy controller [...] is constructed and designed automatically". 2 simulation of pneumatic system with fuzzy controller 2.1 general considerations the general scheme of the automated grinding system is presented in figure 1. this system is used to grind metal probes for microscope observations. the "command module" represents a programmable computing unit on which the control algorithm is running (fuzzy or neural controller in this case a pentium iv pc) and it has the possibility to transmit signals to the execution unit and to receive data from sensors (by means of a daq card with analogical and digital channels). the "execution module" contains the execution elements, which are simple and proportional electro valves, "force sensor" and "position sensor" are the sensor used to generate the feedback signals. the pneumatic setup of the force-position feedback system (fpfs) is shown in figure 3. for the position feedback (pfs) system the force sensor is missing. in the position feedback case the system has to move the probe, approaching the grinding surface to an approximate distance, and then to move it with smaller speed to touch the surface. fuzzy and neural controllers for a pneumatic actuator 377 figure 1: scheme of automated metal probe grinding system in the force position feedback case the system has to fulfil the objectives represented in diagram presented in figure 2, that is to move the probe near the grinding wheel surface and then to move it with reduced speed until the force reaches a reference value and then again to maintain the probe pushed on the surface with the reference force. above described strategies can be graphically expressed in the diagram shown in figure 3. figure 2: scheme of pneumatic setup for the fuzzy fpfs case (the complete case) there have to be defined: • two input linguistic variables "position error" and "forces error" and one output linguistic variable named "control value". • position reference is given by the superior limit of the uncertainty domain d2; • the final control will be made by the "force" variable; • for position there are two ranges which must be defined (big and small) and for the force seven ranges (big negative, medium negative, small negative, zero, big positive, medium positive, small positive). 378 tiberiu vesselenyi, simona dziţac, ioan dziţac, mişu-jan manolescu figure 3: functional diagrams for position (a) and force (b) control 2.2 fuzzy position feedback system the model for fuzzy pfs is presented in figure 4. in this case the system is used without force reaction. this model has been made only as a preliminary study. in figure 5 the systems inference diagrams are shown: • the gaussian input membership functions represents 5 linguistic ranges of the "position error"; • the output triangular membership functions represent also 5 linguistic ranges of the output variable "control value"; deffuzyfication is made by a "centroid" function. figure 4: fuzzy pfs model results of simulation is given in figure 6 for some random step reference values. we can observe how the actual controlled position is following the reference position. the delays are relatively large but considering that the pneumatic system is acting similar to a damper, this range of delay is acceptable. 2.3 fuzzy force position feedback system the fuzzy fpfs is presented in figure 7. fuzzy and neural controllers for a pneumatic actuator 379 figure 5: inference diagram of the fuzzy pfs model figure 6: reference (r) and actual (a) signals diagram resulted from system simulation (fuzzy pfs case) figure 7: fuzzy fpfs model 380 tiberiu vesselenyi, simona dziţac, ioan dziţac, mişu-jan manolescu for both inputs (as for "force error" as for "position error") the membership functions are "gaussian" type and for output membership function the "triangular" type had been used. simulation results for step type references are given in figures 8 and 9. analyzing the result diagram we can conclude that the control is working correctly. figure 8: reference and response force values figure 9: reference and response position values 3 experimental setup for fuzzy fpfs the experimental study in order to test fuzzy fpfs operation is shown in figure 10. the base idea of this concept was the use of a pci6023e daq card (from national instruments), for which there are predefined acquisition functions in the matlab "data acquisition toolbox". this fact makes possible the fuzzy and neural controllers for a pneumatic actuator 381 use of data acquisition, fuzzy inference engine and command signal generation from the same program. even the designed system gives birth to considerable delays in comparison with a true real time system (which would use xpctarget modules), it is good enough for testing of controllers accuracy. figure 10: experimental setup scheme the daq card has 8 analogical input channels (ach1...8) on 12 bits, from which ach1 and ach2. were used. a major disadvantage of pci6023 daq card is the absence of analog output, which would be used to command the pressure regulator proportional electro-valve (figure 10). this issue was solved by using 6 out of 8 digital channels of the digital i/o port (dio in figure 10) and a d/a (digital to analog) converter. so we can obtain 64 values of pressure for the 6 bits available, which suffice for the experiments. we cannot use al the 8 cannels of the dio port because 2 channels must be used to command the 2 on-off electro-valves used to change the piston’s movement direction. 4 neural position feedback system in the case of neural pfs there must be two steps to complete: system identification (development of process neural model) and control design (development of neural control system) [3]. in the system identification step, the neural model of the controlled process is developed and in the second step this model is used to obtain the neural net that will control the process. 4.1 identification step in the identification step a convenient structure for the process model must be found and then the neural network will be trained in order to obtain the value of weights, using training data sets. a largely used standard structure, representative for nonlinear discrete systems is narma (nonlinear autoregressive moving average) [3], given by the relation: y(k + d) = n[y(k), y(k −1), . . . , y(k −n + 1), u(k), u(k −1), . . . , u(k −n + 1)] (1) in which: u(k) is the system’s input and y(k) is the output. in order to identify the process, the network will be trained with the non-linear function n. 4.2 control step if the goal of the system is to follow a reference trajectory, y(k + d) = yr(k + d), a non-linear controller will have to be developed: u(k) = g[y(k), y(k −1), . . . , y(k −n + 1), yr(k + d), u(k −1), . . . , u(k −m + 1)] (2) 382 tiberiu vesselenyi, simona dziţac, ioan dziţac, mişu-jan manolescu in order to generate the function g, which minimizes the quadratic mean error, a dynamic backpropagation learning algorithm should be used which is hard to implement and very slow. that is the reason why some approximate models are usually used. such an approximate model is given by relation: y(k + d) = f [y(k), y(k −1), . . . , y(k −n + 1), u(k −1), . . . , u(k −m + 1)] (3) +g[y(k), y(k −1), . . . , y(k −n + 1), u(k −1), . . . , u(k −m + 1)]·u(k) this model is in a form in which the input u(k) is not contained in the non-linear term and if y(k + d) = yr(k + d), it can then be written that: u(k) = yr(k + d)− f [y(k), y(k −1), . . . , y(k −n + 1), u(k −1), . . . , u(k −n + 1)] g[y(k), y(k −1), . . . , y(k −n + 1), u(k −1), . . . , u(k −n + 1)] (4) in this form it is necessary to find the input values u(k), based on the output in the same step y(k), which is inconvenient and is better to use the form: y(k + d) = f [y(k), y(k −1), . . . , y(k −n + 1), u(k), u(k −1), . . . , u(k −n + 1)] (5) +g[y(k), y(k −1), . . . , y(k −n + 1), u(k), . . . , u(k −n + 1)]·u(k + 1) for d ≥ 2. the structure of the process model neural network neural network is given in figure 11. figure 11: process neural model based on relation (5) the controller expression from relation 5, will be: u(k + 1) = yr(k + d)− f [y(k), . . . , y(k −n + 1), u(k), . . . , u(k −n + 1)] g[y(k), . . . , y(k −n + 1), u(k), . . . , u(k −n + 1)] (6) for d ≥ 2. in figure 12 is presented the control scheme in which yr is generated by the neural model ("reference model"). in this case the controller has to make only a few computations and the neural model can be trained off-line. the method can be applied in industrial robot control, but it was not tested yet with pneumatic actuators. the real advantage of using this kind of controllers for industrial robots, would be the use of parallel system for each axes of the robot. as support of computational implementation of the simulation the simulink module of matlab was used. simulink, contains predefined blocks to generate the neural model as well a gui for parameter settings, training and validation of the controller. fuzzy and neural controllers for a pneumatic actuator 383 figure 12: control scheme designed on the relation (6) 4.3 neural position feedback system simulation for this case a proportional valve and two on-off electro-valves have been used. the pneumatic design scheme is the same as it was presented in figure 2, only the controller structure is different. this model is very complex and even if the pc used for simulation (intel pentium iv 2ghz), has a significant computing power training cycles are very long (about hundreds of minutes). that is why we have used the simplified model of the process shown in figure 13. the simplification is made by neglecting the air compressibility terms and the elimination of air from cylinders inactive chamber. figure 13: simplified process scheme the presented model is then integrated with the controller model given the final version shown in figure 14. figure 14: process with controller scheme 384 tiberiu vesselenyi, simona dziţac, ioan dziţac, mişu-jan manolescu after model design, the training, validation and test data sets were generated with the help of process model. on this basis the neural model of process and controller were obtained. the training error as function of training epochs is presented in figure 15. a number of 3000 data in 300 epochs were used as training parameters. as it can be seen from the diagram the convergence of the network is good. simulations were carried out for different reference values for position from 0.025 to 0.06. in figure 16 a sample of obtained response is shown for position reference value of 0.042. figure 15: training of the neural network figure 16: reference (r) and actual (a)value of simulation results 5 results and conclusions the structure of the narma-l2 neural controller for a pneumatic actuator has been trained and validated, obtaining good results. after the analysis of simulation results and the experimental system operation it can be said that the fuzzy controller is working in a proper manner for this application but needs further adjustments in order to increase the robustness of the control. fuzzy and neural controllers for a pneumatic actuator 385 in actual operation there are unwanted effects as: • position control error is constant, but the actual position remains under the reference value with about 6% for a value of reference from 6 to 9; • the uncertainty domain in which the probe can meet the grinding wheel and the domain in which the control is acceptable is small (about 0.003 m), but in practice this is about 0.005 to 0.006 m; • the force control error is increasing with the increase of reference, limiting the application domain in which the error is still acceptable. although in some domains the control is acceptable further adjustments of the control parameters are needed. in the study of neural controller simulation it resulted that the overshoot error of the system was only of 3%. in this case further studies must be carried out to implement the neural controller for the fpfs case and to test it experimentally. bibliography [1] amann p., perronne j.,m., gissinger g.,l., frank p., m., identification of fuzzy relational models for fault detection, control engineering practice 9, 555, 2001. [2] emami m.r., goldenberg a.a., burhan t.r., systematic design and analysis of fuzzy-logic control and application to robotics, part i. modeling, robotics and autonomous systems 33, pp. 65-88, 2000. [3] jang, r., matlab fuzzy toolbox the mathworks, inc. revision: 1.12, date: 2000, 15. [4] novakovic, b., scap, d., novakovic d., an analytic approach to fuzzy robot control synthesis, engineering applications of artificial intelligence 13, pp. 71-83, 2000. [5] preitl, st., precup, e., introducerea în conducerea fuzzy a proceselor, ed. tehnicã, bucuresti, 1997. [6] reznik l., ghanayem o., bourmistrov a., pid plus fuzzy controller structures as a design base for industrial applications, engineering applications of artificial intelligence 13, pp. 419-430, 2000. [7] vesselenyi t., automated flexible cell for microstructure recognition, phd thesis, universitatea "politehnica" timisoara, 2005. [8] m.m., chen, j.a., fairwather, s.a., green, edumech. mechatronic instructional systems. case study: pneumatics systems, production of shandor motion systems, inc., 1999. [9] harbick k., sukhatme s., speed control of a pneumatic monopod using a neural network, www.harbick-ann.com, 2002. [10] raad r., raad i., neuro-fuzzy admission control in cellular networks. communication systems, (10th ieee singapore international conference on communication systems.), pp. 1-7, 2006. [11] wenmei h., yong y., yali t., adaptive neuron control based on predictive model in pneumatic servo system, 2002. [12] zuo x.q., fan y.s., a chaos search immune algorithm with its application to neuro-fuzzy controller design. chaos, solitons & fractals, vol. 30, issue 1, pp. 94-109, 2006. 386 tiberiu vesselenyi, simona dziţac, ioan dziţac, mişu-jan manolescu [13] zhang j., knoll a., schmidt r., a neuro-fuzzy control model for fine-positioning of manipulators, robotics and autonomous systems, 32, pp. 101-113, 2000. tiberiu vesselenyi university of oradea universităţii st. 1, 410087, oradea, romania tvesselenyi@yahoo.co.uk simona dziţac university of oradea universităţii st. 1, 410087, oradea, romania sdzitac@rdslink.ro ioan dziţac department of economic informatics agora university of oradea piaţa tineretului 8, oradea 410526, romania idzitac@univagora.ro mişu-jan manolescu agora university piaţa tineretului 8, 410526 oradea, romania rectorat@univagora.ro received: february, 14, 2007 fuzzy and neural controllers for a pneumatic actuator 387 tiberiu vesselenyi was born in oradea, romania in 1957, he finished the university "politehnica" from timişoara in 1983. from 1983 to 1991 he worked at a machine building company in oradea as designer and cnc programmer. from 1991 till 1994 he was a research engineer at the "geothermal energy research center" in oradea and from 1994 till today is assoc. prof. at the university of oradea, where he teaches robot and cnc programming. he had earned a phd in robotics at the university "politehnica" at timişoara. he had published over 150 papers in national and international conferences and journals, and is author or coo author of 4 books. simona dziţac received b.sc. (2000) and m. sc. (2001) in mathematics-physics, b.sc. (2005) and m. sc. (2007) in energy engineering from university of oradea and b.sc. in economic informatics (2007) from university of craiova, romania. at this moment, she is phd student in energy engineering field and researcher at university of oradea. her current research interests include reliability, applied mathematics and computer science in engineering fields. she published 4 books and 38 scientific papers in journals and conferences proceedings. ioan dziţac received m. sc. in mathematics (1977) and ph. d in information sc. (2002) from "babes-bolyai" university of cluj-napoca. at this moment, he is associate professor and head of economic informatics department at agora university, oradea, romania. his current research interests include different aspects of parallel and distributed computing, applied mathematics and economic informatics. he has edited 4 conference proceedings, published 12 books and 47 scientific papers in journals and conferences proceedings. he was member of the program committee of 18 international conferences. mişu-jan manolescu received m. sc in electro-mechanics (1984) from the university of craiova, ph. d in microwave (1994) from the university of oradea, and ph. d in human resources management (2000) from the university of craiova. now, he is professor and president of agora university, oradea, romania. his current research interests include different aspects of knowledge management, and knowledge engineering. he has edited 4 conference proceedings, published 9 books and 63 scientific papers in journals and conferences proceedings. he has been member of program committee of the 5 international conferences. int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 776-784 stability of discrete-time systems with time-varying delay: delay decomposition approach s.b. stojanovic, d.lj. debeljkovic, n. dimitrijevic sreten b. stojanovic university of nis, faculty of technology serbia, 16000 leskovac, bulevar oslobodjenja 124 e-mail: ssreten@ptt.rs dragutin lj. debeljkovic, nebojsa dimitrijevic university of belgrade, faculty of mechanical engineering serbia, 11120 beograd, kraljice marije 16 e-mail: ddebeljkovic@mas.bg.ac.rs, ndimitri@verat.net abstract: this article deals with the problem of obtaining delay-dependent stability conditions for a class of discrete-time systems with interval time-varying delay. using the decomposition the delay interval into two unequal subintervals by tuning parameter α, a new interval delay-dependent lyapunov-krasovskii functional is constructed to derive novel delay-dependent stability conditions which are expressed in terms of linear matrix inequalities. this leads to reduction of conservatism in terms of the upper bounds of the maximum time-delay. the numerical examples show that the obtained result is less conservative than some existing ones in the literature. keywords: time-delay systems, interval time-varying delay, asymptotic stability, delay-dependent stability, lyapunov-krasovskii methods. 1 introduction time-delay frequently occurs in many practical systems, such as manufacturing systems, telecommunication and economic systems etc. since time-delay is an important source of instability and poor performance, considerable attention has been paid to the problem of stability analysis and controller synthesis for continuous time-delay systems (see e.g. [3-5, 10, 11, 17-21, 23-26] and the reference therein). inversely, less attention has been drawn to the corresponding results for discrete-time delay systems (see e.g. [1, 2, 6-9, 12-15, 22, 24]). this is mainly due to the fact that such systems can be transformed into augmented systems without delay. this augmentation of the system is, however, inappropriate for systems with unknown delays and for systems with time-varying delay which are the subject analysis in this work. recently, increasing attention has been devoted to the problem of delay-dependent stability of linear systems with time-varying delay, including continuous-time (see e.g. [5, 10, 11, 18-21, 23], 25, 26]) and discrete-time systems (see e.g. [1, 2, 7-9, 12, 14, 15, 24]) and a great number of delay-dependent stability criteria were derived. the key point for deriving the delay-dependent stability criterions is the choice of an appropriate lyapunov–krasovskii functional (lkf). it is known that the existence of a complete quadratic lyapunov-krasovskii functional (cqlkf) is a sufficient and necessary condition for asymptotic stability of the time-delay system. using the cqlkf, one can obtain the maximum allowable upper bound (maub) of delay which is very close to the analytical delay limit for stability. however, the cqlkf leads to a complicated system of partial differential equations, yielding infinite dimensional linear matrix inequalities (lmis). therefore, to develop simpler stability criteria, many authors have used special forms of lkf rather than cqlkf, which give lmis with finite order and a reduced value of maub. further, to reduce the conservativeness of the existing results, some new analysis methods have been proposed, such as descriptor system transformation method [3-5], free weighting matrix copyright c⃝ 2006-2012 by ccc publications stability of discrete-time systems with time-varying delay: delay decomposition approach 777 method [8, 11, 23], matrix inequality method [10, 17, 18] and input–output approach [19]. using these methods, many stability criteria were derived by checking a variation of lkf in a whole interval of the time-delay. contrary to this approach, in [24, 25], in order to obtain some less conservative stability conditions, the interval of the time delay is divided into multiple equidistant subintervals and interval delay-dependent lkf (id-d lkf) is constructed. by checking the variation of the id-d lkf defined on the subintervals, some new delay-dependent stability criteria are derived. it is worth pointing out that the main difference between lkf and id-d lkf lies in that the former allows taking different weighing matrices on different subintervals. therefore, id-d lkf, as expected, will yield less conservative delay-dependent stability criteria. inspired by the idea of zhu and yang [26] on splitting the delay of continuous-time systems into two unequal subintervals, a new method is developed in this paper for stability analysis for discrete-time systems with time-varying delay. the delay interval [k −hm, k −1] in the id-d lkf is divided into two unequal subintervals: [k −hm, k −α−1] and [k −α, k −1], where 0 < α < hm is a tuning parameter. the new id-d lkf is constructed with different weighing matrices in various subintervals. free-weighting matrices and model transformation are not used in order to derive delay dependent criterion. it is shown that the presented stability condition is much less conservative than the existing ones [1, 2, 6-9, 13-15, 22, 24], because it has a lower value of maub. the derived condition can be seen as an extension of the methods in [24, 25], wherein the whole delay range is divided to n ≥ 2 equal subintervals. as the number of subintervals in [24, 25] is greater than two, the decomposition approach is more complex and the resulting stability conditions are more conservative and difficult to implement. to demonstrate the effectiveness of the proposed method, numerical examples are given in section 3. notation: ℜn and z+ denote the n-dimensional euclidean space and positive integers. notation p > 0 (p ≥ 0) means that matrix p is real symmetric and positive definite (semidefinite). for real symmetric matrices p and q, the notation p > q (p ≥ q) means that matrix p − q is positive definite (positive semi-definite). i is an identity matrix with an appropriate dimension. superscript “t” represents the transpose. in symmetric block matrices or complex matrix expressions, we use an asterisk (∗) to represent a term which is induced by symmetry. if dimensions of matrices are not explicitly given, then they are assumed to be compatible for algebraic operations. 2 main results consider the following system with an interval time-varying delay: x(k + 1) = ax(k) + bx(k −h(k)) (1) where x(k) ∈ℜn is the state at instant k, matrices a ∈ℜn×nand b ∈ℜn×n are constant matrices and h(k)is the positive integer representing the time delay of the system that we assume to be time dependent and satisfies the following: 0 ≤ h(k) ≤ hm (2) where hm is known to be a positive and finite integer. the aim of this article is to establish the sufficient condition that guarantee the delaydependent stability of the system (1), which is less conservative than the existing results in literature. we first introduce the following result, which will be used in the proof of our main results. 778 s.b. stojanovic, d.lj. debeljkovic, n. dimitrijevic lemma 1. let y(k) = x(k + 1)−x(k). for any matrix r > 0 [24] −(hm −hm) k−1−hm∑ m=k−hm yt (m)ry(m) ≤ [ x(k −hm) x(k −hm) ]t [ −r r r −r ] [ x(k −hm) x(k −hm) ] = − [x(k −hm)−x(k −hm)] t r [x(k −hm)−x(k −hm)] (3) theorem 2. for given scalars hm(hm > 0) and α(0 < α < hm), the system described by (1)-(2) is asymptotically stable if there exists matrices p = pt > 0, qi = qti ≥ 0 and zi = z t i ≥ 0 (i = 1,2,3), such that the following lmis hold: φ =   φ11 φ12 0 0 φ15 ∗ φ22 φ23 0 φ25 ∗ ∗ φ33 φ34 0 ∗ ∗ ∗ φ44 0 ∗ ∗ ∗ ∗ φ55   < 0 (4) ψ =   ψ11 ψ12 ψ13 0 ψ15 ∗ ψ22 ψ23 ψ24 ψ25 ∗ ∗ ψ33 0 0 ∗ ∗ ∗ ψ44 0 ∗ ∗ ∗ ∗ ψ55   < 0 (5) where φ11 = a t pa−p + q1 + q3 − 1α (z1 + z3) , φ12 = a t pb + 1 α (z1 + z3) , φ15 = (a− i)t u1, φ22 = b t pb −q3 − 1α (2z1 + z3) , φ23 = 1 α z1, φ25 = b t u1, φ33 = −q1 + q2 − 1αz1 − 1 hm −α z2, φ34 = 1 hm −α z2, φ44 = −q2 − 1hm −αz2, φ55 = −u1, ψ11 = φ11, ψ12 = a t pb, ψ13 = 1 α (z1 + z3) , ψ15 = (a− i)t u2, ψ22 = b t pb −q3 − 1hm −α (2z2 + z3) , ψ23 = 1 hm −α (z2 + z3) , ψ24 = 1 hm −α z2, ψ25 = b t u2, ψ33 = −q1 + q2 − 1α(z1 + z3)− 1 hm −α (z2 + z3) , ψ44 = −q2 − 1hm −αz2, ψ55 = −u2, u1 = αz1 + (hm −α)z2 + αz3, u2 = αz1 + (hm −α)z2 + hmz3 proof: construct the interval delay-dependent lkf as v (k) = v1(k) + v2(k) + v3(k) (6) where v1(k) = x t (k)px(k) (7) v2(k) = k−1∑ i=k−α xt (i)q1x(i) + k−1−α∑ i=k−hm xt (i)q2x(i) + k−1∑ i=k−h(k) xt (i)q3x(i) (8) v3(k) = −1∑ i=−α k−1∑ j=k+i yt (j)z1y(j) + −1−α∑ i=−hm k−1∑ j=k+i yt (j)z2y(j) + −1∑ i=−h(k) k−1∑ j=k+i yt (j)z3y(j) (9) stability of discrete-time systems with time-varying delay: delay decomposition approach 779 where p = pt > 0, qi = qti ≥ 0 and zi = z t i > 0 (i = 1,2,3). note, the delay interval [k −hm, k −1] in the lkf is divided into two unequal subintervals: [k −hm, k −α−1] and [k −α, k −1], where 0 < α < hm is a tuning parameter. taking the difference of ∆vi(k) = vi(k + 1)−vi(k), we can obtain ∆v1(k) = x t (k) ( at pa−p ) x(k) + 2xt (k)at pbx(k −h(k)) +xt (k −h(k))bt pbx(k −h(k)) (10) ∆v2(k) = x t (k)q1x(k)−xt (k −α)q1x(k −α) +xt (k −α)q2x(k −α)−xt (k −hm)q2x(k −hm) +xt (k)q3x(k)−xt (k −h(k))q3x(k −h(k)) (11) ∆v3(k) = −1∑ i=−α [ yt (k)z1y(k)−yt (k + i)z1y(k + i) ] + −1−α∑ i=−hm [ yt (k)z2y(k)−yt (k + i)z2y(k + i) ] + −1∑ i=−h(k) [ yt (k)z3y(k)−yt (k + i)z3y(k + i) ] = αyt (k)z1y(k)− −1∑ i=−α yt (k + i)z1y(k + i) + (hm −α)yt (k)z2y(k)− −1−α∑ i=−hm yt (k + i)z2y(k + i) +h(k)yt (k)z3y(k)− −1∑ i=−h(k) yt (k + i)z3y(k + i) = yt (k) [αz1 + (hm −α)z2 + h(k)z3]y(k) − k−1∑ m=k−α yt (m)z1y(m)− k−1−α∑ m=k−hm yt (m)z2y(m) − k−1∑ m=k−h(k) yt (m)z3y(m) = yt (k) [αz1 + (hm −α)z2 + h(k)z3]y(k) − k−1∑ m=k−α yt (m)z1y(m)− k−1−α∑ m=k−hm yt (m)z2y(m) − k−1∑ m=k−h(k) yt (m)z3y(m) (12) it is know from (2) that, for any k ∈ z+, h(k) ∈ [0,α−1] or h(k) ∈ [α,hm]. define two sets ω1 = { k : h(k) ∈ [0,α] , k ∈ z+ } (13) ω2 = { k : h(k) ∈ [α + 1,hm] , k ∈ z+ } (14) in the following, we will discuss the variation of ∆v (k) for two cases (k ∈ ω1 and k ∈ ω2). case 1. for k ∈ ω1, i.e. 0 ≤ h(k) ≤ α. k−1∑ m=k−α yt (m)z1y(m) = k−1−h(k)∑ m=k−α yt (m)z1y(m) + k−1∑ m=k−h(k) yt (m)z1y(m) (15) 780 s.b. stojanovic, d.lj. debeljkovic, n. dimitrijevic ∆v3(k) = y t (k) [αz1 + (hm −α)z2 + h(k)z3]y(k)− k−1−h(k)∑ m=k−α yt (m)z1y(m) − k−1∑ m=k−h(k) yt (m) (z1 + z3)y(m)− k−1−α∑ m=k−hm yt (m)z2y(m) (16) because z1 + z3 > 0, h(k) ≤ α and α−h(k) ≤ α, using lemma 1, it follows − k−1∑ m=k−h(k) yt (m) (z1 + z3)y(m) ≤− 1 h(k) [x(k)−x(k −h(k))]t (z1 + z3) [x(k)−x(k −h(k))] ≤ 1 α xt (k) (−z1 −z3)x(k) + 1α2x t (k) (z1 + z3)x(k −h(k)) + 1 α xt (k −h(k)) (−z1 −z3)x(k −h(k)) (17) − k−1−h(k)∑ m=k−α yt (m)z1y(m) ≤− 1 α−h(k) [x(k −h(k))−x(k −α)] t z1 [x(k −h(k))−x(k −α)] ≤ 1 α xt (k −h(k)) (−z1)x(k −h(k)) + 1α2x t (k −h(k))z1x(k −α) + 1 α xt (k −α) (−z1)x(k −α) (18) − k−1−α∑ m=k−hm yt (m)z2y(m) ≤− 1 hm −α [x(k −α)−x(k −hm)] t z2 [x(k −α)−x(k −hm)] ≤ 1 hm −α xt (k −α) (−z2)x(k −α) + 1hm −α2x t (k −α)z2x(k −hm) + 1 hm −α xt (k −hm) (−z2)x(k −hm) (19) combining (10)-(19), it yields ∆v (k) ≤ ξt (k)φ̂ξ(k) φ̂ =   φ11 + (a− i)t u1(a− i) φ12 + (a− i)t u1b 0 0 ∗ φ22 + bt u1b φ23 0 ∗ ∗ φ33 φ34 ∗ ∗ ∗ φ44   ξ(k) = [ xt (k) xt (k −h(k)) xt (k −α) xt (k −hm) ]t (20) obviously, ∆v (k) < 0 for k ∈ ω1 if φ̂ < 0. using the schur complement, it is easy to see that ∆v (k) < 0 holds if φ < 0 and h(k) ∈ [0,α]. case 2. similarly, for k ∈ ω2, i.e. α + 1 ≤ h(k) ≤ hm , using the schur complement, it is easy to see that ∆v (k) < 0 holds if ψ < 0. from the above discussions, we can see that for all k ∈ z+ if (4)-(5) hold, ∆v (k) < 0, which completes the proof. 2 remark 3. theorem 2 presents a stability result which depends on the maximum delay bound hm . the conditions in theorem 2 are expressed in terms of lmis, and therefore, they can be easily checked by using standard numerical software. remark 4. the delay interval [k −hm, k −1] in the id-d lfk is divided into two unequal subintervals: [k −hm, k −α−1] and [k −α, k −1], where 0 < α < hm is a tuning parameter. stability of discrete-time systems with time-varying delay: delay decomposition approach 781 consequently, the different weighing matrices in the lyapunov functional are used in various subintervals and the information of delayed state x(k −α) can be taken into full consideration. further, using the subintervals and lemma 1, the upper bounds of some terms in ∆v3(k) are more accurately estimated than by using the previous methods since the upper bound hm of delay h(k) on the interval 0 ≤ h(k) ≤ hm is substituted with two less conservative upper bounds α and hm on the subintervals 0 ≤ h(k) ≤ α and α < h(k) ≤ hm , respectively. so the decomposition method presented in theorem 2 can reduce the value of maub. an algorithm for seeking a corresponding values of α (0 < α < hm) subject to (4)-(5), such that the maub of hm has maximal value, can easily be obtained. algorithm 5. step 1. let h = 0 and α = 0. step 2. h = h + 1. step 3. α = α + 1. step 4. if inequalities (4)-(5) is feasible, then αm = α, α = 0 and go to step 2; otherwise, go to step 5. step 5. if α = h−1, go to step 6; otherwise, go to step 3. step 6. the maximal delay is hm = h−1 and the minimal value of tuning parameterαis αm. 3 numerical examples in this section, two examples are presented. the obtained results have been compared with several existing criteria in the literature. example 1. consider system (1) with the time-varying delay h(k) satisfying (2) and a = [ 0.8 0 0 λ ] , b = [ −0.1 0 −0.1 −0.1 ] , λ ∈{0.91, 0.97} case 1 (λ = 0.91). this system was considered in [13] and [22]. table 1 lists the maub of delay obtained from theorem 2 of this paper. for comparison, results from [13, 22] are also listed in the table. it is clear that theorem 2 leads to better results than those in [13, 22]. table 1. comparison of maub of delay based on different existing methods for λ = 0.91 method hm [13] 41 [22, corollary 1] 42 theorem 2 in this paper 46 for α = 19, · · · ,30 case 2 (λ = 0.97). for comparison, the results from [2, 6, 7, 9] and this paper are listed in table 2. it is clear that theorem 2 gives much better results than the existing delay-dependent criteria. example 2. consider system (1) with the time-varying delay h(k) satisfying (2) and case 1. [1, 14, 15] a = [ 0.6 0 0.35 0.7 ] , b = [ 0.1 0 0.2 0.1 ] case 2. [8, 9, 15, 24] a = [ 0.8 0 0.05 0.9 ] , b = [ −0.1 0 −0.2 −0.1 ] 782 s.b. stojanovic, d.lj. debeljkovic, n. dimitrijevic table 2. comparison of maub of delay based on different existing methods for λ = 0.97 method hm [9, theorem 1] 4 [7, theorem 3] 8 [6, lemma 2] 8 [2, theorem 1] 10 theorem 2 in this paper 17 for α = 9,10,11 for the above systems, the lmis conditions for delay-independent stability [16] p = pt > 0, q = qt > 0,   −p + q 0 at p ∗ −q bt p ∗ ∗ −p   < 0 are feasible, from which we deduce that both systems are stable for 0 ≤ h(k) < ∞. using the existing delay-dependent criteria, it can be obtained only the finite value of maub, which will guarantee the stability of the given systems. tables 3 (case 1) and 4 (case 2) list the intervals of time delay for different methods. based on theorem 2 in this paper, large numerical values of maub are obtained (hm → ∞). hence, using of theorem 2 leads to better results than those in [1, 8, 9, 14, 15, 25]. table 3. interval of time delay for case 1 method interval [1, th. 3.1] 2 ≤ h(k) ≤ 10 [15, theorem 1], [15, theorem 2] 2 ≤ h(k) ≤ 13 [14, theorem 3.2] 0 ≤ h(k) ≤ 12 [2, theorem 1] 2 ≤ h(k) ≤ 15 theorem 2 in this paper 0 ≤ h(k) ≤ 10 ·1021 table 4. interval of time delay for case 2 method interval stability [9, theorem 1] 0 ≤ h(k) ≤ 6 [15, theorem 2] 0 ≤ h(k) ≤ 10 [8, theorem 1] 0 ≤ h(k) ≤ 12 [24, theorem 5] 2 ≤ h(k) ≤ 19 [24, theorem 7] 2 ≤ h(k) ≤ 20 theorem 2 in this paper 0 ≤ h(k) ≤ 9.61 ·108 stability of discrete-time systems with time-varying delay: delay decomposition approach 783 4 conclusion in this paper, the problem of obtaining delay dependent stability conditions for a class of systems with interval time-varying delay is discussed. a new interval delay-dependent lyapunov– krasovskii functional is constructed by splitting the delay interval into two unequal intervals by tuning parameter α. the free-weighting matrices and model transformation are not used in order to derive delay-dependent criteria. numerical examples show that the results proposed in this paper are much less conservative while comparing the maximum allowable upper bound of delay with the existing results in the literature. bibliography [1] e.k. boukas, discrete-time systems with time-varying time delay: stability and stabilizability, mathematical problems in engineering, article id 42489:1-10, 2006. 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[26] x.l. zhu and g.h. yang, new results of stability analysis for systems with time-varying delay, international journal of robust and nonlinear control 20: 596–606, 2010. international journal of computers, communications & control vol. ii (2007), no. 3, pp. 279-287 lorenz system stabilization using fuzzy controllers radu-emil precup, marius l. tomescu, ştefan preitl abstract: the paper suggests a takagi sugeno (ts) fuzzy logic controller (flc) designed to stabilize the lorentz chaotic systems. the stability analysis of the fuzzy control system is performed using barbashin-krasovskii theorem. this paper proves that if the derivative of lyapunov function is negative semi-definite for each fuzzy rule then the controlled lorentz system is asymptotically stable in the sense of lyapunov. the stability theorem suggested here offers sufficient conditions for the stability of the lorenz system controlled by ts flcs. an illustrative example describes the application of the new stability analysis method. keywords: chaotic systems, fuzzy control, lyapunov functions, nonlinear equations and systems. 1 introduction chaotic systems exhibit exponential sensitivity to small perturbations and also have a large variety of distinct possible dynamical motions. these properties will be reviewed in this paper along with their consequences and implications to active control of chaotic systems using small control signals. chaos control refers to a process wherein a tiny perturbation is applied to a chaotic system in order to achieve a desirable (chaotic, periodic, or stationary) behavior [9]. the idea of chaos control was formulated in 1990 at the university of maryland [5]. in [5] and a method for stabilizing an unstable periodic orbit was suggested. the basic idea is in the fact that a significant change in the behavior of a chaotic system can be made by a very small correction of its parameters. there exist three historically earliest and most actively developing directions of research in chaos control: open-loop control based on periodic system excitation referred to also as nonfeedback control, the method of poincaré map linearization called also the ott, grebogi and yorke (ogy) method [5] and the method of time-delayed feedback (pyragas method) [7, 8]. lima and pettini [3] proposed a disturbance-based technique of stabilizing the chaotic system towards a periodic state. in this case the periodicity is fixed by the frequency of a control signal disturbing the parameter space. such a technique was called "suppression of chaos" or "nonfeedback control". its implementation can be complicated by the fact that it needs a preliminary learning task of the system response to possible disturbances of variable amplitude. the ogy method [5] stabilizes unstable periodic orbits (upos) found in the chaotic regime via small feedback disturbances to an accessible parameter. the control disturbance is offered when the orbit crosses a given poincaré section such that the trajectory will be close to the stable manifold of the desired upo. in this method in the limit of zero noise the orbit of the controlled system is identical to the upo of uncontrolled system and the feedback disturbance vanishes. a drawback of the ogy method is that it becomes difficult to apply for very fast systems since it requires the detailed computer-aided analysis of the system at each crossing of the poincaré section. also, noise can result in occasional bursts where the trajectory moves far away from the controlled periodic orbit. an alternative method of feedback stabilization of upo’s, introduced by pyragas [7], consists of a continuous linear feedback applied at each computational time step. as in the ogy case, in this method the controlled orbit coincides with the upo of the uncontrolled system and the feedback vanishes for zero noise when control is achieved. the feedback procedure can be applied without a priori knowledge on the location of the periodic orbit for a version in which the feedback term contains a delayed variable in which the delay corresponds to the period of the upo. moreover, it is expected that it can be used for fast systems, since no parameters are changed on a fast time-scale, and the method does not require copyright © 2006-2007 by ccc publications 280 radu-emil precup, marius l. tomescu, ştefan preitl a computer-aided analysis of the system. for some systems this method is robust even in the presence of considerable noise [7]. a disadvantage of pyragas’s method is that it achieves control only over a limited range of the parameter space i.e. a given orbit will become eventually unstable in the controlled system as the parameters are varied more deeply into the chaotic regime. the use of delayed feedback also increases the dimensionality of the system. the paper is organized as follows. the accepted class of fuzzy logic control systems with takagisugeno (ts) fuzzy logic controllers (flcs) is described in the next section. section 3 is focused on the design of stable fuzzy logic control systems based on the new stability analysis method expressed in terms of a theorem formulated on the basis of the barbashin-krasovskii theorem. then, section 4 performs an analysis of the lorentz equation that exhibits chaotic behavior. section 5 is dedicated to the stable design of a ts flc to stabilize the lorentz chaotic system, and section 6 concludes the paper. 2 accepted class of fuzzy logic control systems fuzzy logic control has become an important methodology in control engineering because it can offer superior performance indices and better trade-off to system robustness and sensitivity, which results into handling nonlinear control better than traditional methods. calvo and cartwright [1] introduced the idea of fuzzy control in chaotic systems. hua o. wang and kazuo tanaka proposed a stability design approach to lorenz system [10], based on ts fuzzy models using a linear matrix inequality (lmi) technique. in [11] oscar calvo proposed a mamdani flc for control of chaos in chua’s circuit. ahmad m. harb and issam al-smadi presented in [11] a mamdani flc to control the lorenz equation and chua’s circuit to be a stable constant or periodic solution, where a single tuning parameter is chosen in case of lorenz system and the flc adjusts this parameter. in this paper the fuzzy logic control system is accepted to consist of a process and a ts flc as shown in figure 1. the flc consists of r fuzzy rules. the process of extracting the knowledge from human operators in the form of fuzzy control rules is by no means trivial, nor is the process of deriving the rules based on heuristics and good understanding the process and control systems theory. figure 1: fuzzy logic control system structure. let x be a universe of discourse. consider the nonlinear autonomous system of the following form representing the state-space equations of the controlled process: ẋ = f (x) + b (x) u, x (t0) = x0 (1) where: − x ∈ x , x = [x1, x2, ..., xn]t is the state vector, − f (x) = [ f1 (x) , f2 (x) , ..., fn (x)]t , b (x) = [b1 (x) , b2 (x) , ..., bn (x)]t are functions describing the dynamics of the plant, lorenz system stabilization using fuzzy controllers 281 − u is the control signal applied to the process calculated by the weighted sum defuzzification method, − the time variable, t, has been omitted to simplify the further formulation, − x (t0) is the initial state vector at time t0 . the i-th fuzzy rule / fuzzy control rule in the fuzzy rule base of t-s flc is of the form (2): rule i : if xi is xi,1 and ... and xn is xi,n then u = ui (x) , i = 1, r, r ∈ in∗, (2) where xi,1, xi,2, .., xi,n are fuzzy sets that describe the linguistics terms (lts) of input variables, u = ui (x)is the control output of rule i, and the function and is a t-norm and can be a single value or a function of the state vector, x. each fuzzy rule generates an activation degree: αi (x (t)) = and ( µi,1 (x1 (t)) , µi,2 (x2 (t)) ...µi,n (xn (t)) ) , αi ∈ [0, 1] , i = 1, r (3) it is assumed that for any x ∈ x in the input universe of discourse x there exists at least one αi ∈ [0, 1] , i = 1, r, among all rules that is nonzero. the control signal u, which must be applied to the process, is a function of αi and ui . applying the weighted sum defuzzification method, the output of the flc will be: u = ∑ri=1 αiui ∑ri=1 αi (4) where r is the total number of rules. 3 stability analysis of fuzzy logic control systems the stability analysis presented in this section is based on barbashin-krasovskii theorem presented in [2]. this section is concentrated on the formulation and proof of theorem 3 that ensures sufficient conditions for the stability of nonlinear systems controlled by ts flcs. the function v (x) = xt px is considered, where p ∈ irn×n is a positive definite matrix. from this it results that v is positive definite and has continuous partial derivatives. the derivatives of v in the conditions (1) are: v̇ (x) = ẋt px + xt pẋ = ( f (x) + b (x) u (x))t px + xt p ( f (x) + b (x) u (x)) = f (x) + b (x) u (x) (5) where f (x) = f (x)t px + xt p f (x) and b (x) = b (x)t px + xt pb (x). definition 1. if v (x) = xt px is defined on domain x containing the origin containing the origin, then for any fuzzy rule the derivative v̇i = f + bui is defined. proposition 2. for any input x0 ∈ x it results that umin (x0) ≤ u (x0) ≤ umax (x0), where umin (x0) = min (u1 (x0) , ..., ur (x0)) and umax (x0) = max (u1 (x0) , ..., ur (x0)) . proof. let x0 ∈ x , than among all rules two rules can be found, with indices p and q, such that up (x0) = umin (x0) and uq (x0) = umax (x0). hence the following result is valid: umin (x0) = r ∑ i=1 αi (x0) umin (x0) r ∑ i=1 αi (x0) ≤ r ∑ i=1 αi (x0) ui (x0) r ∑ i=1 αi (x0) ≤ r ∑ i=1 αi (x0) umax (x0) r ∑ i=1 αi (x0) = umax (x0) ⇒ umin (x) ≤ u (x) ≤ umax (x) ,∀x ∈ x (6) this property permits the formulation of theorem 3 that outlines the stability analysis approach. 282 radu-emil precup, marius l. tomescu, ştefan preitl theorem 3. let x = 0 be an equilibrium point for (1). let v (x) = xt px be a positive function on domain x containing the origin, such that v̇i (x) ≤ 0, i = 1, r, x ∈ x . let s = { x ∈ x ∣∣v̇ (x) = 0 } and suppose that no solution can stay identically in s excepting the trivial solution x (t) ≡ 0. then, the origin is asymptotically stable. proof. it should be proved that v̇ is negative semidefinite in the conditions (1). from the conditions of theorem 3 one may write: v̇ (x) = f (x) + b (x) ui < 0, i = 1, r, x ∈ x (7) from proposition 2 it is obtained that for ∀x ∈ x there exist two rules, with indices p and q, such that up (x0) = umin (x0) and uq (x0) = umax (x0). three possible cases should be considered as follows: case 1: if b (x) is strictly positive, from proposition 2 the result is: up (x) ≤ u (x) ≤ uq (x) ⇒ ⇒ f (x) + b (x) up (x) ≤ f (x) + b (x) u (x) ≤ f (x) + b (x) uq (x) ≤ 0 ⇒ ⇒ v̇p (x) ≤ v̇ (x) ≤ v̇q (x) ≤ 0, (8) therefore v̇ (x) ≤ 0. case 2: if b (x) is strictly negative, proposition 2 yields: up (x) ≤ u (x) ≤ uq (x) ⇒ ⇒ 0 ≥ f (x) + b (x) up (x) ≥ f (x) + b (x) u (x) ≥ f (x) + b (x) uq (x) ⇒ ⇒ 0 ≥ v̇p (x) ≥ v̇ (x) ≥ v̇q (x) (9) therefore once more v̇ (x) ≤ 0. case 3: if b (x) = 0 from (8) we have v̇ (x) = f (x) < 0. from the above cases it is justified to conclude that, whatever the value of is, the result will be v̇ ≤ 0. consequently, theorem 3 satisfies all conditions of barbashin-krasovskii theorem [2], so the equilibrium point at the origin will be globally asymptotically stable. 4 properties of lorenz equations this section presents an overview on dynamic chaotic processes with focus on the lorenz system referred to also as lorenz equation or attractor [4]. modern discussions of chaos are mainly based on the works about the lorenz attractor. the lorenz equation is commonly defined as three coupled ordinary differential equations expressed in (10) to model the convective motion of fluid cell, which is warmed from below and cooled to above: dx dt = σ (y−x) dy dt = x (ρ −z)−y dz dt = xy−β z (10) where the three parameters σ , ρ, β > 0 are called the prandtl number, the rayleigh number and the physical proportion, respectively. these constant parameters determine the behavior of the system and the three equations exhibit chaotic behavior i.e. they are extremely sensitive to initial conditions. a lorenz system stabilization using fuzzy controllers 283 small change in initial conditions leads quickly to large differences in corresponding solutions. the classic values used to demonstrate chaos are σ = 10 and β = 83 . it is important to note that x, y, z are not spatial coordinates. the variable x is proportional to the intensity of the convective motion while y is proportional to the temperature difference between the ascending and descending currents; similar signs of x and y denote that warm fluid is rising and cold fluid is descending. the variable z is proportional to the distortion of vertical temperature profile from linearity, a positive value indicating that the strongest gradients occur near the boundaries. the essential properties of lorenz equation can be summarized as follows: nonlinearity. the two nonlinearities are xy and xz. symmetry. equations are invariant under (x, y) → (−x,−y). in other words, if (x(t),y(t),z(t)) is a solution, (−x (t) ,−y (t) , z (t)) will be also a solution. volume contraction. the lorenz system is dissipative i.e. volumes in phase-space contract under the flow. fixed points. in order to solve (10) for the fixed points let f (x) =   σ (y−x) x (ρ −z)−y xy−β z   and it is necessary to solve f (x) = 0. it is clear that one of those fixed point is s0 = (0, 0, 0) and with some algebraic operations one may determine that s1,2 = ( ± √ β (ρ −1),± √ β (ρ −1), (ρ −1) ) are equilibrium points and real when ρ > 1. invariance. the z-axis is invariant, meaning that a solution that starts on the z-axis (i.e. x = y = 0) will remain on the z-axis. in addition, the solution will tend towards the origin if the initial conditions belong to the z-axis. solutions stay close to origin. if σ , ρ, β > 0 then all solutions of the lorenz equation will enter an ellipsoid centered at (0, 0, 2b) in finite time. in addition, the solution will remain inside the ellipsoid once it has entered. it follows by definition that the ellipsoid is an attracting set. 5 design of stable fuzzy logic control system the design of the fuzzy logic control system with ts flc starts with rewriting the ordinary differential equation (10) as the following form representing the state-space equations of the controlled process: ẋ =   σ (x2 −x1) x1 (ρ −x3)−x2 x1x2 −β x3   +   1 0 0   u, x (t0) = x0 (11) next, the fuzzification module of ts flc is set according to figure 2 showing the membership functions that describe the linguistic terms (lts) of the linguistic variables x1 and x2. the lts representing "positive", "zero" and "negative" values are noted by p, z and n, respectively. the inference engine employs the fuzzy logic operators and and or implemented by the min and max functions, respectively. the inference engine is assisted by the complete set of fuzzy control rules illustrated in table 1, and the weighted sum defuzzification method is utilized. summarizing, the only parameters to be calculated are the consequents ui, i = 1, 9 , in the 9 fuzzy control rules. 284 radu-emil precup, marius l. tomescu, ştefan preitl figure 2: membership functions of x1 and x2. table 1 fuzzy control rule base rule antecedent consequent x1 x2 u 1 p p u1 2 n n u2 3 p n u3 4 n p u4 5 p z u5 6 n z u6 7 z p u7 8 z n u8 9 z z u9 theorem 3 will be applied as follows to find the values of ui for which the system (11) can be stabilized with the above described ts flc. let x = [−40, 40]× [−40, 40]× [−40, 40] that contain the origin. the lyapunov function candidate v (x) = 12 ( x21 + x 2 2 + x 2 3 ) is considered, which is a continuously differentiable positive function on domain x . the total derivative of v with respect to time using (11) is: v̇ (x) = −σ x21 −x22 −β x23 + x1x2 (σ + ρ) + x1u (12) from (12) it is obvious that v̇ (0) = 0 ⇔ x = 0 and this implies s = {0}. further on, each fuzzy control rule will be analyzed here: • for rule 1 it is obtained x1 is p and x2 is p. then v̇1 (x) = −σ x21 −x22 −β x23 + x1x2 (σ + ρ) + x1u1. in these conditions to satisfy v̇ (x) ≤ 0 it is chosen u1 = −40 (σ + ρ). • for rule 2 it is obtained x1 is n and x2 is n. then v̇1 (x) = −σ x21 −x22 −β x23 + x1x2 (σ + ρ) + x1u2. in these conditions to satisfy v̇ (x) ≤ 0 it is chosen u2 = 40 (σ + ρ). • for rule 3 it is obtained x1 is p and x2 is n. then v̇1 (x) = −σ x21 −x22 −β x23 + x1x2 (σ + ρ) + x1u3. in these conditions to satisfy v̇ (x) ≤ 0 it is chosen u3 = 0. • for rule 4 it is obtained x1 is n and x2 is p. then v̇1 (x) = −σ x21 −x22 −β x23 + x1x2 (σ + ρ) + x1u4. in these conditions to satisfy v̇ (x) ≤ 0 it is chosen u4 = 0. • for rule 5 it is obtained x1 is p and x2 is z. then v̇1 (x) = −σ x21 −x22 −β x23 + x1x2 (σ + ρ) + x1u5. in these conditions to satisfy v̇ (x) ≤ 0 it is chosen u5 = −10 (σ + ρ). lorenz system stabilization using fuzzy controllers 285 • for rule 6 it is obtained x1 is n and x2 is z. then v̇1 (x) = −σ x21 −x22 −β x23 + x1x2 (σ + ρ) + x1u6. in these conditions to satisfy v̇ (x) ≤ 0 it is chosen u6 = 10 (σ + ρ). • for rule 7 it is obtained x1 is z and x2 is p. then v̇1 (x) = −σ x21 −x22 −β x23 + x1x2 (σ + ρ) + x1u7. in these conditions to satisfy v̇ (x) ≤ 0 it is chosen u7 = −x2 (σ + ρ). • for rule 8 it is obtained x1 is z and x2 is n. then v̇1 (x) = −σ x21 −x22 −β x23 + x1x2 (σ + ρ) + x1u8. in these conditions to satisfy v̇ (x) ≤ 0 it is chosen u8 = −x2 (σ + ρ). • for rule 9 it is obtained x1 is z and x2 is z. then v̇1 (x) = −σ x21 −x22 −β x23 + x1x2 (σ + ρ) + x1u9. in these conditions to satisfy v̇ (x) ≤ 0 it is chosen u9 = −x2 (σ + ρ). concluding, due to theorem 3 it results that the system composes by this ts flc and the lorenz process described by (11) is globally asymptotically stable in the sense of lyapunov at the origin. considering the values of process parameters σ = 10, ρ = 28, β = 83 , the initial state x1 (0) = 1, x2 (0) = −1and x3 (0) = 1, the responses of x1, x2 and x3 versus time in the closed-loop system are shown in figures 3-7. figure 3: state variable x1 versus time of lorenz system without flc (a) and with flc (b). figure 4: state variable x2 versus time of lorenz system without flc (a) and with flc (b). figure 5: state variable x3 versus time of lorenz system without flc (a) and with flc (b). 286 radu-emil precup, marius l. tomescu, ştefan preitl figure 6: phase portraits of lorenz system without control (a) and with flc (b). 6 conclusions the paper has proposed a simple and efficient fuzzy logic control solution employing a ts flc meant for stabilizing the lorenz system. the fuzzy logic controller design is assisted by the stability approach stated and proved in terms of theorem 3. theorem 3 has general character guaranteeing sufficient stability conditions for fuzzy logic control systems with ts flcs. this approach decomposes the stability analysis to the analysis of each rule so the complexity is reduced drastically. the new stability analysis approach is different to lyapunov’s theorem and allows more applications. in particular, it is well suited to controlling processes where the derivative of the lyapunov function candidate is not negative definite, therefore applying theorem 3 to nonlinear processes controlled by ts flcs can be successful in case of a wide area of nonlinear dynamic systems. digital simulations illustrated in this paper prove that the proposed stability analysis method is simpler than the nonfeedback control method proposed by lima and pettini [3], than ogy method proposed by ott, grebogi and yorke [5] and than pyragas method [7]. besides, the controller structure presented in section 5 can be implemented 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[2] khalil h. k., nonlinear systems, 3rd edition, prentice hall, englewood cliffs, nj, 2002. [3] lima r., pettini m., suppression of chaos by resonant parametric perturbations, physical letter a, vol. 41, pp. 726-733, 1990. [4] lorenz e. n., the essence of chaos, university of washington press, 1993. [5] ott e., grebogi, c., yorke, j. a, controlling chaos, physical review letter, vol. 64, pp. 1196-1199, 1990. [6] precup r.-e., preitl s., optimisation criteria in development of fuzzy controllers with dynamics, engineering applications of artificial intelligence, vol. 17, no. 6, pp. 661-674, 2004. [7] pyragas k, continuous control of chaos by self-controlling feedback, physical letter a, vol. 170, pp. 421-427, 1992. lorenz system stabilization using fuzzy controllers 287 [8] pyragas k., tamaževièius a., experimental control of chaos by delayed self-controlling feedback, physical letter a, vol. 180, pp. 99-102, 1993. [9] schuster h. g., handbook of chaos control: foundations and applications, wiley-vch verlag gmbh, 1999. [10] wang h. o., tanaka k., fuzzy modeling and control of chaotic systems, in integration of fuzzy logic and chaos theory, springer-verlag, berlin, heidelberg, 2006. [11] zhong l., halang w. a., chen, g. (eds.), integration of fuzzy logic and chaos theory, springerverlag, berlin, heidelberg, 2006. radu-emil precup "politehnica" university of timisoara department of automation and applied informatics bd. v. parvan 2, ro-300223 timisoara, romania e-mail: rprecup@aut.utt.ro marius l. tomescu "aurel vlaicu" university computer science faculty complex universitar m, str. elena dragoi 2, ro-310330 arad, romania e-mail: tom_uav@yahoo.com ştefan preitl "politehnica" university of timisoara department of automation and applied informatics bd. v. parvan 2, ro-300223 timisoara, romania e-mail: spreitl@aut.utt.ro received: december 24, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 1, pp. 6-7 editorial – special issue on collaboration support systems (css) h.s. ko, s. y. nof recent and emerging advances in computer and information science and technology have realized a powerful computing and communication environment. it enables effective interactions and collaboration among groups of people and systems (and systems-of-systems) beyond traditional restrictions of time and space. the evolution in hardware (e.g., pervasive computing devices, wireless sensor networks, nano-electronics) and software (e.g., multi-agent systems, workflow and information integration, interaction models and protocols) technology, and their flexible teaming have further enabled diverse forms of collaboration approaches. it has been observed during the last few decades that numerous collaboration methodologies, tools and applications in various domains have emerged to provide better quality services, helping to solve domain-specific, highly complex problems. the development of collaboration tools and methodologies has increased the domain knowledge that can be discovered and shared by individuals, and the level and intensity of interactions and collaboration that can dramatically decrease problem complexity and increase solution quality. at the same time, inefficient interactions, task and information overloads, and ineffective collaboration are prevalent. in spite of the considerable progress in collaboration tools and methods, a sound foundation of collaboration science is yet to be established; lacking such foundation is a major obstacle in leveling up sophistication and anticipated benefits of collaboration methods. recently, however, collaboration control theory (cct) models and techniques have been proposed as the foundation of designing collaboration support systems (css), and new features of css are being investigated in various areas based on cct. in order to provide readers with a significant opportunity of investigating novel and forthcoming problems in collaboration research, the guest editors have invited authors from various disciplines and focused on collaborative design and modeling features in the respective areas. after careful and rigorous review and revision processes, eight articles have been selected for their special quality and relevance to css. the selected articles are organized along two main topics in css: 1) css models and theories; and 2) css methods and applications. in the area of css models and theories: • "swarming models for facilitating collaborative decisions" – zamfirescu and filip introduce the use of swarming models (stigmergic mechanisms) to build collaborative support systems for complex cognitive tasks, exemplifying them through an experiment for group decision processes (gdp) in e-meetings. • "design of protocols for task administration in collaborative production systems" – ko and nof investigate the design of task administration protocols for collaboration support in production system, where those protocols, as control mechanisms, can manage complicated events in the collaborative task workflow environment and overcome limitations of coordination protocols. • "mining authoritativeness of collaborative innovation partners" – engler and kusiak, present a novel approach to automatically determine the authoritativeness of entities for collaboration and demonstrate the use of mining schema for identifying collaboration partners over the internet. • "reference architecture for collaborative design" – huang, yang, chen, and nof present reference architecture for collaborative design (cd) as a framework for analyzing and supporting cd, then describe and illustrate dimensions forming the architecture as a cube of design aspect, design stage, and collaboration scope. the next group of articles focuses on css methods and applications: copyright c© 2006-2010 by ccc publications editorial – special issue on collaboration support systems (css) 7 • "coordinating aerial robots and unattended ground sensors for intelligent surveillance systems" – de freitas, heimfarth, allgayer, wagner, larsson, pereira, and ferreira present a system solution to enable interoperability and coordination support for heterogeneous sensor networks composed of low-end ground sensor nodes and mobile sensors carried by autonomous aerial robots. • "introducing collaborative practices in small medium enterprises" – antonelli and chiabert propose a methodology to evaluate the possibility of using plm as a framework which exploits collaboration links within an enterprise, based on an exhaustive analysis of the plm impact on different aspects of the enterprise. • "a software system development life cycle model for improved stakeholders’ communication and collaboration" – cohen, dori, and de haan describe a collaborative software system development life cycle model using object process methodology (opm), which includes various stakeholders and variables, and considers multiple aspects in collaboratively developing off-theshelf software. • "gaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system" – wachs describes the design of an intelligent, collaborative system framework, which involves the integration of machine vision, voice recognition and computer graphics-projection techniques to improve operation rooms for surgery by highly intuitive, natural and multimodal interactions. these articles are presented based on related scientific investigations around the world and reflect well on the broad and challenging area of css. the guest editors wish to thank all the contributing authors, the referees, and the editorial office colleagues who have all endeavored to bring this special issue on css to you. h.s. ko and s.y. nof west lafayette, indiana, usa special issue guest editors int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 2, pp. 167-177 hierarchical distributed reasoning system for geometric image generation nicolae ţăndăreanu, mihaela verona ghindeanu, sergiu andrei nicolescu university of craiova, romania department of mathematics and computer science a.i. cuza st, no. 13, 200585 e-mail: ntand@rdslink.ro, mghindeanu@yahoo.com abstract: the concept of hierarchical reasoning system was introduced in [5], where an intuitive method to build such systems based on their inputs is given. in this paper we formalize several concepts which open a possible research line concerning the use of these structures. a hierarchical reasoning system h is a directed graph organized on several levels such that each node of the level j is a hyper-schema of order j. as a mathematical structure, h is an abstract one and a special kind of formal computation is introduced. as a result of this computation we obtain a set f(h) of formulas. we explain what we understand by an interpretation of h and define its corresponding semantical computation. by means of an interpretation i(h) for h and applying the rules of the semantical computation, each element of w ∈ f(h) becomes some object i(w) of a given space. we exemplify these concepts and we show that for two distinct interpretations i1(h) and i2(h) for the same system h, a given formula w ∈ f(h) is transformed into a sentence i1(w) of a natural language whereas i2(w) is a geometric image. a short description of a java implementation of a hierarchical system generating images is also given in a separate section. by examples we show that the mechanism introduced in this paper allows us to model the distributed knowledge. finally several open problems are specified. keywords: semantic schema, interpretation, hyper-schema, distributed reasoning system, geometrical image generation 1 introduction various kinds of mechanisms for image synthesis were presented and implemented on computer. the panel of the mathematical models for this subject includes the rewriting systems and graph-based models. picture-processing grammars ([2]), picture grammars ([3]), stochastic grammars ([14]) and l-systems are some of the rewriting systems used to process images. the l-systems are a class of string rewriting mechanism originally developed by a biologist, a. lindenmayer, in 1968 ([7]). the original emphases were on plant topology spatial relations between cells or larger plant modules. the l-systems are a practical tool for generating fractal forms. today these models are applied in architecture, physiology ([1]) and music. in order to interpret the l-system as music, lmuse system ([9]) maps any of the turtle’s 3d movement, orientation directions (forward, up, and left), its drawing line length, and thickness into musical pitches, note durations and volume. a great number of research works and practical implementations have confirmed the interest of mathematicians and computer scientists in developing and applying the methods of graph theory. these methods were applied to obtain new knowledge representation models and to process images. a very productive notion with large applications in knowledge representation is that of conceptual graph, a copyright © 2006-2009 by ccc publications 168 nicolae ţăndăreanu, mihaela verona ghindeanu, sergiu andrei nicolescu notion introduced in literature by j.f.sowa ([8],[10]). we can find several applications of the graphbased methods in [6] (low-level processing of digital images, learning algorithms for high-level computer vision and pattern recognition). the concept of semantic schema was introduced in [11] as an extension of semantic networks. this structure is obtained by means of a labeled graph and a peano algebra built over the edge labels. since then many applications of this structure were presented (new semantics in logic programming, knowledge representation for intelligent dialog systems etc). in [12] we defined a new mechanism for generating images similar with the edge rewriting in the way that both approaches can be used to define complex images based on some simple other images. in the mentioned paper the concept of hierarchical distributed reasoning system was introduced. each leaf of the system is given by a semantic schema. the other nodes are hyper-schemas ([12]). we presented an intuitive method to obtain geometrical images. the leaves represent the input of the system in semantic schemas and, by appending proper interpretations, they obtain the graphical illustrations of the received inputs. in this manner the leaves obtains the initial images. then, at the upper levels, these images are combined by hyper-schemas to obtain complex images. we obtained a bottom-up method to obtain images from initiators. in this paper we obtain the following results: • starting with the concept of hierarchical distributed and reasoning system (hgr system) introduced in [12] in section 3 we define a formal computation in such a structure. as a result of this computation we obtain a set f(h) of formulas for an arbitrary hdr system h. this is the formal computation in an hdr system. • an hdr system h is an abstract structure. in section 4 we introduce the concept of interpretation for h. by means of an interpretation i(h) for h each element of f(h) becomes some object of a given space. this gives the semantical computation. both the formal and semantical computations are exemplified. we show that for two distinct interpretations i1(h) and i2(h) for the same system h we can generate sentences in a natural language giving the reasoning conclusions and geometrical images respectively. • a short description of a java implementation of an hdr system is also given in section 5. • by examples we show that the mechanism introduced in this paper allows us to model the distributed knowledge. • the last section contains the conclusions and future works. several open problems are specified in this section. 2 basic concepts consider a symbol θ of arity 2. a θ-semantic schema ([11]) or shortly, θ-schema is a system s = (x, a0, a, r), where: • x is a finite non-empty set of symbols named object symbols; • a0 is a finite non-empty set of elements named label symbols and a0 ⊆ a ⊆ a0, where a0 is the peano θ-algebra generated by a0; • r ⊆ x × a × x is a non-empty set of relations which fulfills the following conditions: 1. (x, θ(u, v), y) ∈ r ⇒ ∃z ∈ x : (x, u, z) ∈ r, (z, v, y) ∈ r 2. θ(u, v) ∈ a, (x, u, z) ∈ r, (z, v, y) ∈ r ⇒ (x, θ(u, v), y) ∈ r hierarchical distributed reasoning system for geometric image generation 169 3. {α | ∃(x, α, y) ∈ r} = a an element from r ∩ (x × a0 × x) is a regular arc of s. we denote by ded(s) the least set satisfying the following properties ([13]): • if (x, a, y) ∈ r0 then ([x, y], a) ∈ ded(s) • if ([xi, . . . , xk], u) ∈ ded(s) and ([xk, . . . , xr], v) ∈ ded(s), i < k < r and θ(u, v) ∈ a then ([xi, . . . , xr], θ(u, v)) ∈ ded(s). an element of ded(s) is a deductive path of s. let us consider the schemas s1 = (x1, a01, a1, r1) and s2 = (x2, a02, a2, r2). in the remainder of this section we describe a new structure which relieves a special kind of cooperation between s1 and s2. if d1 = ([x, . . . , y], u) ∈ ded(si) and d2 = ([y, . . . , z], v) ∈ ded(s3−i), where i ∈ {1, 2}, then we say that d1 is connected to right by d2 or d2 is connected to left by d1. we say that d1 is connected by d2 if d1 is connected to right or to left by d2. we consider the sets of deductive paths l1 ⊆ ded(s1) and l2 ⊆ ded(s2). we say that l1 ∪ l2 is a pairwise connected set of deductive paths if every deductive path of li is connected by some deductive path of l3−i. for each i ∈ {1, 2} we consider a set vi of symbols such that vi ∩ (a1 ∪ a2) = ∅. we consider also a set ei such that ei ⊆ xi × vi × xi, card(ei) = card(li) and e1 ∩ e2 = ∅. consider also a bijective mapping gi : li −→ ei such that gi(d) = (x, e, y), where d = ([x, . . . , y], θ(u, v)) ∈ li. this mapping transforms each deductive path ([x, . . . , y], θ(u, v)) from li into a regular arc (x, e, y). shortly, we say that the path ([x, . . . , y], θ(u, v)) is designated by (x, e, y). we can define now a cooperating structure of hyper-schemas. a hyper-schema of order zero is a semantic schema. consider the hyper-schemas s1 and s2 of order zero. a hyper-schema of order one over s1 and s2 obtained by means of l1 and l2 is a θ-schema s which includes the regular arcs obtained from l1 and l2 ([12]). we denote by hyp1({s1, s2}) the set of all hyper-schemas of first order over s1 and s2. in general we write s ∈ hypk({s1, s2}) and we name s a hyper-schema of order k if s1 and s2 are hyper-schemas of order j ≤ k − 1 and at least one of them has the order k − 1. an hdr system ([12]) is the tuple h = (q1, q2, . . . , qk) where k ≥ 2 and • q1 = {s1, . . . , sn1 }, n1 > 1, constitutes the first level of the system. the entities {s1, . . . , sn1 } are hyper-schemas of order zero. the set q1 gives the leaves of h. • q2 = {sn1+1, . . . , sn2 }, n2 ≥ n1 + 1, gives the second level of the system and sn1+1, . . . , sn2 are hyper-schemas of order 1. more precisely, for every m ∈ {n1 + 1, . . . , n2} there are m1, m2 ∈ {1, . . . , n1}, m1 6= m2 such that sm ∈ hyp1({sm1 , sm2 }). • for j ∈ {3, . . . , k}, qj = {snj−1+1, . . . , snj } represents the j-th level of the system, where nj ≥ nj−1 + 1. for every m ∈ {nj−1 + 1, . . . , nj} there is m1 ∈ {nj−2, . . . , nj−1} and there is m2 ∈ {1, . . . , nj−1} such that sm ∈ hypj−1({sm1 , sm2 }). 3 formal computations in hdr systems suppose that h = (q1, q2, . . . , qk) is an hdr system. the components of h are the hyper-schemas s1, . . . , snk . we can visualize h as a graph structure. in order to obtain this structure we represent each hyper-schema by a node and we draw two directed arcs from sr to sj and to sm if sr ∈ hypp({sj, sm}) for some p. the structure obtained in this manner is not a tree. this can be observed in figure 1: there are two distinct paths from s7 to s2 and there is no root of this structure. 170 nicolae ţăndăreanu, mihaela verona ghindeanu, sergiu andrei nicolescu à ^ s1 s2 s5 s3 s6 à ^ à ^ s7 s8 à ^ s4 figure 1: the graph structure of h for each i ∈ {1, . . . , nk} we consider that si is given by the tuple si = (xi, a0i, ai, ri) and we denote r0i = ri ∩ (xi × a0i × xi). for each r ∈ {n1 + 1, . . . , nk} such that sr is a hyper-schema over sj and sm in h we consider: • the connected sets lj,r ⊆ ded(sj) and lm,r ⊆ ded(sm); • the sets ej,r, em,r and the transformational mappings gj,r : lj,r −→ ej,r, gm,r : lm,r −→ em,r. by the assumptions of the previous section we have r0r ⊇ ej,r ∪ em,r. we denote n0r = ej,r ∪ em,r. obviously we have the following property: proposition 1. n0i = ∅ if and only if si is a leaf of h. for a symbol h of arity 1 we consider the set: m = nk⋃ i=1 { h([x, y], a) | (x, a, y) ∈ r0i \ n0i} where we used the notation h([x, y], a) instead of h(([x, y], a)). we consider the symbols σ1, . . . , σnk of arity 2 and denote by hh the peano {σ1, . . . , σnk }-algebra generated by m. we consider the alphabet z including the symbols σi, the elements of xi, the elements of ai, the left and right parentheses, the square brackets [ and ], the symbol h and comma. as in the theory of formal languages, the set z∗ defines all the words over z. because a hyper-schema is a semantic schema we have the following property: proposition 2. if si is a hyper-schema of h and ([x1, . . . , xk+1], θ(u, v)) ∈ ded(si) then there is r uniquely determined such that ([x1, . . . , xr+1], u) ∈ ded(si) and ([xr+1, . . ., xk+1], v) ∈ ded(si). definition 1. let be w1, w2 ∈ z∗. we define the following binary relation on z∗, denoted by ⇒h: • for i ∈ {1, . . . , nk}, if (x, e, y) ∈ r0i \ n0i then w1([x, y], e)w2 ⇒h w1h([x, y], e)w2; • for i ∈ {1, . . . , nk}, if (x, e, y) ∈ n0i then w1([x, y], e)w2 ⇒h w1dw2, where d is the deductive path designated by (x, e, y); • suppose that ([x1, . . . , xk+1], θ(u, v)) ∈ ded(si), i ∈ {1, . . . , n(h)}, ([x1, . . . , xr+1], u) ∈ ded(si) and ([xr+1, . . ., xk+1], v) ∈ ded(si) then: w1([x1, . . . , xk+1], θ(u, v))w2 ⇒h w1σi(([x1, . . . , xr+1], u), ([xr+1, . . ., xk+1], v))w2 the reflexive and transitive closure of ⇒h is denoted by ⇒∗h. we denote f(si) = {w ∈ h(h) | ∃d ∈ ded(si) : d ⇒∗h w} and f(h) = ⋃nk i=1 f(si). let us exemplify this computation. we consider the hyper-schemas s1 and s2 of order zero from figure 2 and the hyper-schema of order 1 from figure 3. if we take hierarchical distributed reasoning system for geometric image generation 171 x1 x2 x3 x4 ? a b a ? θ(a, b) x3 x6 x5 y1 6 b c a ? θ(b, c) s1 s2 figure 2: semantic schemas s1 and s2 of order zero x1 x3 x5 z1e1 e2 a? θ(e1, e2) 6 θ(θ(e1, e2), a) figure 3: hyper-schema s3 ∈ hyp1({s1, s2}) • l1,3 = {([x1, x2, x3], θ(a, b))}, l2,3 = {([x3, x6, x5], θ(b, c))} • e1,3 = {(x1, e1, x3)}, e2,3 = {(x3, e2, x5)} • g1,3([x1, x2, x3], θ(a, b)) = (x1, e1, x3), g2,3([x3, x6, x5], θ(b, c)) = (x3, e2, x5) then we obtain the following computations: • ([x1, x3, x5], θ(e1, e2)) ⇒h σ3(([x1, x3], e1), ([x3, x5], e2)) • ([x1, x3], e1) ⇒h ([x1, x2, x3], θ(a, b)) ⇒h σ1(([x1, x2], a), ([x2, x3], b)) ⇒∗h σ1(h([x1, x2], a), h([x2, x3], b)) ∈ f(s1) • ([x3, x5], e2) ⇒h ([x3, x6, x5], θ(b, c)) ⇒h σ2(([x3, x6], b), ([x6, x5], c)) ⇒∗h σ2(h([x3, x6], b), h([x6, x5], c)) ∈ f(s2) in conclusion, ([x1, x3, x5], θ(e1, e2)) ⇒∗h σ3(σ1(h([x1, x2], a), h([x2, x3], b)), σ2(h([x3, x6], b), h([x6, x5], c))) and the last formula is an element of f(h), where h = (q1, q2), q1 = {s1, s2} and q2 = {s3}. 4 semantical computations in hdr systems the semantical computation in an hdr system h transforms every formula of f(h) into an object of some space. in this section we describe this transformational process. let us consider the hdr system h = (q1, q2, . . . , qk) and an element w ∈ f(h). if d = ([x1, . . . , xk], θ(u, v)) ∈ ded(si and d ⇒∗h w then we write sort(w) = θ(u, v). definition 2. an interpretation for h is a system i = (ob, ob, alg): • ob is a set of objects; • ob : x −→ ob, where x = ⋃nki=1 xi, is a mapping that "interprets" each node as an object; • alg = ⋃nki=1{algiu}u∈ai , where algiu is an algorithm with two input arguments and one output argument such that if gj,k([x, . . . , y], θ(u, v)) = (x, e, y) then algke = alg j θ(u,v) . definition 3. the valuation mapping valh of the hdr system h is defined as follows: • if w = h([x, y], a) ∈ f(h) then valh(w) = ⋃nk i=1{alg i a(ob(x), ob(y))}. 172 nicolae ţăndăreanu, mihaela verona ghindeanu, sergiu andrei nicolescu • if w = σj(w1, w2) ∈ f(h), w1 ∈ f(h), w2 ∈ f(h) and sort(w) = α then valh(w) = {alg j α(o1, o2) | ok ∈ valh(wk), k = 1, 2} in order to exemplify the computations we consider again the hdr system h from section 3. we define an interpretation of h by means of some sentential forms. such a structure is a sentence containing two variables. if we substitute each variable by an object then a sentential form becomes a sentence in a natural language. we shall consider the following sentential forms: p1(x, y)="x is the father of y"; p2(x, y)="x is the mother of y"; p3(x, y)="x is the brother of a y"; p4(x, y)="x likes to eat y"; q1(x, y)="x is the grandmother of y"; q2(x, y)="a brother of x likes to eat y"; r(x, y)="a nephew of x likes to eat y"; we consider the following algorithms: algorithm alg1a(o1, o2) { return p1(o1, o2)}; algorithm alg 1 b(o1, o2) { return p2(o1, o2)}; algorithm alg2b(o1, o2) { return p3(o1, o2)}; algorithm alg 2 c(o1, o2) { return p4(o1, o2)}; algorithm alg1 θ(a,b) (o1, o2) { if o1 = p1(t1, t2), o2 = p2(t2, t3) then return q1(t1, t3)} algorithm alg2 θ(b,c) (o1, o2) { if o1 = p3(t1, t2), o2 = p4(t2, t3) then return q2(t1, t3)} algorithm alg1e1 (o1, o2) { return q1(o1, o2)}; algorithm alg1e2 (o1, o2) { return q2(o1, o2)}; algorithm alg1b(o1, o2) { return p2(o1, o2)}; algorithm alg3 θ(e1,e2) (o1, o2) { if o1 = q1(t1, t2), o2 = q2(t2, t3) then return r(t1, t3)} consider the interpretation i1 = (ob1, ob1, alg1) of the system h, where we specify only the useful entities allowing to exemplify the computation: • ob1 = {peter, helen, john, sorin, pizza} • ob1(x1) = peter, ob1(x2) = helen, ob1(x3) = john, ob1(x6) = sorin, ob1(x5) = pizza • alg1 = {alg1a, alg1b, alg2b, alg2c, alg1θ(a,b), alg2θ(b,c), alg3e1 , alg3e2 , alg3θ(e1,e2)} where alg3e1 = alg 1 θ(a,b) , alg3e2 = alg 2 θ(b,c) . it is not difficult to observe that for the formula w = σ3(σ1(h([x1, x2], a), h([x2, x3], b)), σ2(h([x3, x6], b), h([x6, x5], c))) = σ3(α, β) from the last part of the previous section we obtain the following computations: valh(α) = {alg 1 e1 (o3, o4) | o3 ∈ valh(h([x1, x2], a)), o4 ∈ valh(h([x2, x3], b))} valh(h([x1, x2], a)) = {alg 1 a(peter, helen)} = {p1(peter, helen)} valh(h([x2, x3], b)) = {alg 1 b(helen, john), alg 2 b(helen, john)} = {p2(helen, john), p3(helen, john)} therefore valh(α) = {alg1e1 (p1(peter, helen), p2(helen, john)), alg 1 e1 (p1(peter, helen), p3(helen, john))} = {q1(peter, john)} valh(β) = {alg 2 e2 (o5, o6) | o5 ∈ valh(h([x3, x6], b)), o6 ∈ valh(h([x6, x5], c))} valh(h([x3, x6], b)) = {alg 1 b(john, sorin), alg 2 b(john, sorin)} = {p2(john, sorin), p3(john, sorin)} valh(h([x6, x5], c)) = alg 2 c(sorin, pizza)} = {p4(sorin, pizza)} therefore valh(β) = {alg2e2 (p2(john, sorin), p4(sorin, pizza)), alg 2 e2 (p3(john, sorin), p4(sorin, pizza))} = {q2(john, pizza)} finally, from valh(α) and valh(β) we deduce valh(w) = {alg 3 θ(e1,e2) (q1(peter, john), q2(john, pizza))} = {a nephew of peter likes to eat pizza} we observe that the conclusion obtained by h can not be obtained neither by s1, neither by s2. this explains why h is named a distributed system. hierarchical distributed reasoning system for geometric image generation 173 figure 4: the image generated by i2 we give now a short description of another interpretation i2 for the same system h. as a result we obtain geometrical images. • ob2 = {1, (3, 3), (3, 1.5)} • ob2(x1) = 1, ob2(x2) = ob2(x6) = (3, 3), ob2(x3) = 1, ob2(x5) = (3, 1.5) • alg1a(p, q){return the interior of circle with radius p and center q} • alg1b(p, q){return the interior of the square centered in p and the sides of length 2*q parallel with coordinate axes } • alg1 θ(a,b) (α, β){ if α = alg1a(p, q) and β = alg 1 b(q, r) then return β \ α } • alg2b(p, q){return the exterior of circle with radius p and center q} • alg2c(p, q){return the interior of the rectangle centered in p and the sides of lengths specified by q, parallel with coordinate axes } • alg2 θ(b,c) (α, β){ if α = alg1 θ(a,b) (p, q) and β = alg2 θ(b,c) (q, r) then return β ∩ α } • alg3 θ(e1,e2) (α, β){ if α = alg2a(p, q) and β = alg 2 c(q, r) then return β ∪ α } for the same formula w ∈ f(h) as in the previous computation, the object valh(w) given by i2 is shown in figure 4. 5 a java implementation if we note by a the set consisting of some geometrical objects names then each system’s input is an word w = a1 . . . ak over the alphabet v = a ∪ {+, −} having the following properties: • ai = +/− means a left/right rotation with a specific angle, denoted by δ and to draw a line on the current direction • ai = oj means to draw the graphical illustration of the object oj such that its entry direction is on the current direction. in our implementation, each geometrical object used in the generation method is an instance of the a class named object. graphically, it is a representation of a figure inside a square. every instance of this class can have one of the following types: circle, triangle, star and square corresponding to the figure it consists of. other members of this class are the entry direction and the exit direction related to some corner of the object. the corner corresponding to the entry direction becomes the entry point of the object. similar for the exit point. the main routine of the algorithm is createhdrs (algorithm 2). the construction of the system starts by defining the schemas of the agents (steps 1 ÷ 4). the hyper-schemas of order one corresponding to the managers of the second level are constructed using the steps 7 ÷ 14. the condition for existing a hyper-schema over two schemas is that their maximal paths are connected deductive paths. this property is verified using the routine connectedpaths (algorithm 3). if the second level of the system was successfully defined (if condition of step 15) then the process of creating new levels in hdrs continues using the while loop of step 17. the hyper-schemas of orders greater than 2 are created using the routine createhypschs (algorithm 4). the geometrical objects that are used for the image generation process are introduced using the first 174 nicolae ţăndăreanu, mihaela verona ghindeanu, sergiu andrei nicolescu figure 5: first window of the application window of the application. for each object the user must specify the type, the entry and exit points (the corners are numbered starting from the down-left) and related to them the entry and the exit direction. also, using the controls of the first window, the input descriptions can be edited(see figure 5). the second window of the application gives the outputs provided by the system’s reasoning components (see figure 6). it consists of three buttons and a panel. the application can draw maximum 1000 images with maximum 50 geometrical objects per image. 6 conclusions and future works in this paper we formalized the syntactical and semantical computations in an hdr system. we exemplified these computations and for some hdr system h we gave two interpretations: one interpretation generates phrases and the other generates geometrical images. this examples give an idea concerning the generative power of our mechanism. we relieved also by these examples the fact that the distributed reasoning can be modeled by an hdr system. a short description of a java implementation for an hdr system generating images is also given. we intend to develop the applications of an hdr system. first, we intend to use the mobile agents to process such systems ([4]). second, we intend to use the hdr systems in e-learning. the basic idea comes from the fact that a link in an html document gives a reference to another document of the similar structure. hierarchical distributed reasoning system for geometric image generation 175 (a) the initiators and some images obtained by the managers of q2 and q3 levels (b) images obtained at the 4th level in the system figure 6: second window of the application 176 nicolae ţăndăreanu, mihaela verona ghindeanu, sergiu andrei nicolescu algorithm 2 procedure createhdrs procedure createhdrs 1. for i ← 1, nocmd 2. call create−schema(commands[i], schema[i], agent[i]) 3. maximalpath[i] ← schema[i].getmaximalpath() 4. endfor 5. noag ← nocomd 6. nokm ← noag + 1 7. for i, j ← 1, noag; j 6= i 8. if connectedpaths(maximalpath[i], maximalpath[j]) 9. call create−hypersch(hypsch[nokm], schema[i], schema[j]) 10. hypsch[nokm].order ← 1 11. maximalpath[nokm] ← hypsch[nokm].getmaximalpath() 12. nokm ← nokm + 1 13. endif 14. endfor 15. if nokm > noag + 1 16. order ← 2 17. while createhypschs(order) 18. order ← order + 1 19. endwhile 20. endif endprocedure algorithm 3 function connectedpaths function connectedpathspath1, path2 1. if path1.lastnode=path2.firstnode 2. return true 3. endif 4. if path1.firstnode=path2.lastnode 5. return true 6. endif 7. return false endfunction algorithm 4 function createhypschs functioncreatehypschsorder 1. newhypsch ← false 2. for i ← nokm − 1, noag 3. if hypsch[i].order 6= order − 1 4. continue 5. endif 6. for j ← 1, nokm − 1; j 6= i 7. if connectedpaths(maximalpath[i], maximalpath[j]) 8. newhypsch ← true 9. if j ≤ noag 10. call create−hypersch(hypsch[nokm], hypsch[i], schema[j]) 11. else 12. call create−hypersch(hypsch[nokm], hypsch[i], hypsch[j]) 13. endif 14. hypsch[nokm].order ← order 15. maximalpath[nokm] ← hypsch[nokm].getmaximalpath() 16. nokm ← nokm + 1 17. endif 18. endfor 19. endfor 20. return newhypsch endfunction hierarchical distributed reasoning system for geometric image generation 177 bibliography [1] allen m., prusinkiewicz p., dejong t. (2004) systems for modeling the architecture and physiology of growing trees: the l-peach model, proceedings of the 4thinternational workshop on functional-structural plant models, pp. 220-225 [2] chang shi-kuo (1970) the analysis of two-dimensional patterns using picture processing grammars, annual acm symposium on theory of computing archive, proceedings of the second annual acm symposium on theory of computing, p. 206-216 [3] drewes f., ewert s., klempien-hinrichs r., kreowsky h.j. (2003) computing raster images from grid picture grammars, journal of automata, languages and combinatorics, vol.8, issue 3, p. 499-519 [4] dzitac i., bărbat b. e. (2009) artificial intelligence + distributed systems = agents, int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844, vol. iv, no. 1, pp. 17-26 [5] ghindeanu m. (2008) constructing architectures for an hierarchical distributed reasoning system based on its inputs, international multi-conference on engineering and technological innovation, usa, p. 231-234 [6] kandel a., bunke h., last m. (eds) (2007) applied graph theory in computer vision and pattern recognition, springer, studies in computational intelligence 52 [7] lindenmayer a. (1968) mathematical models for cellular interaction in development, parts i and ii, journal of theoretical biology (18), p. 280-315. [8] priss u., corbett d., angelova g. (eds.) (2002) conceptual structures: integration and interfaces, 10th int. conf. on conceptual structures, iccs 2002 [9] sharp d. (1998) lmuse version 0.7b, http://www.geocities.com/athens/academy/8764/ lmuse/lmusetxt.html [10] sowa j.f. (1984) conceptual structuresinformation processing in mind and machine, addisonwesley [11] ţăndăreanu n. (2004). semantic schemas and applications in logical representation of knowledge, proceedings of the 10th international conference on cybernetics and information technologies, systems and applications, usa, vol.iii, p. 82-87 [12] ţăndăreanu n., ghindeanu m. (2008) hierarchical semantic structures applied in automatic image generation, proceedings of 11th iasted international conference on intelligent systems and control, isbn: 978-0-88986-777-2 [13] ţăndăreanu n., ghindeanu m. (2008) path-based reasoning in semantic schemas, annals of university of craiova, mathematics and computer science series, vol.35, p.171-181 [14] zu song-chun , mumford d. (2006) a stochastic grammar of images, foundations and trends in computer graphics and vision, vol. 2, issue 4, p. 259-362 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 3, pp. 318-318 book review florin gheorghe filip introduction to discrete event systems by christos g. cassandras and stèphane lafortune, 2008, springer science + business media llc, xxiv + 776 p., isbn: 978-0-387-33332-8. discrete event systems (des) represent a class of dynamic, discrete time systems whose states can take values only in a discrete-value space and can change only as a result of asynchronous occurrences of planned or spontaneous events. at present, there are ever more numerous subclasses of discrete event systems. queuing systems, computer and communication systems, manufacturing and traffic systems are only a few examples of des which are described in the book. this is a second revised and added edition of a book written by professors cassandras of boston university and lafortune of the university of michigan. it builds upon the cassandras’ book entitled “discrete event systems: modelling and performance analysis”, which was published in 1993 by irwin and aksen associates and received in 1999 the “harold chesnut” prize of ifac (international federation of automatic control) for the best text book in control engineering. the necessity of the book was perceived by the authors as a consequence of remarking several facts as it follows. there are more and more various complex real-life technical objects, which can be viewed as instances of des class. they are to be understood, their operations are to be controlled and, if it is possible, optimized. the traditional methods, which are based on differential or difference equations, though have been proved effective in controlling many systems which show a continuous in time function to represent the state trajectories, are not adequate for the des, whose sample paths (state trajectories) are described by piece-wise constant functions of time. consequently, there is a need to develop new modelling frameworks, analysis techniques, design tools, testing methods, and systematic control, and optimization procedures for this new generation of highly complex systems. to fulfil their objective the authors adopt a multidisciplinary approach by building on control theory (“for performance optimization via feed back control”), computer science (“for modelling and verification of event-driven processes”), and operations research (“for analysis and simulation of stochastic des”). also the authors propose modelling frameworks and describe new analysis and control methods which are specific for des and introduce new paradigms to allow combining mathematical tools with processing experimental data. in the preface, the authors emphasize the critical role of the electronic computer in performing various activities such as system analysis, design, and control. the authors are well aware of the existence of numerous books and papers addressing various subclasses of des such as: language and automata theory, petri nets , queuing models, markov chains, discrete time simulation, perturbation analysis and so on , all using specific representations of the objects studied. the authors aim at proposing in the book a unified modelling framework with a view to enabling a coherent and systematic study of the objects which belong to almost all des subclasses mentioned above. consequently, two discrete event modeling formalisms are utilized throughout the book to represent the state transition structures: automata and, to a lesser extent, petri nets. in section 1.3.3, copyright © 2006-2008 by ccc publications 318 florin gheorghe filip the authors introduce three levels of abstraction in the study of des (untimed, or logical, timed, and stochastic) to describe sequences of events. the levels of abstraction are used to gradually refine the presentation of the notions and methods contained in the book. the first chapter of the book contains an introduction to system concepts and parallel presentations of the main concepts of continuous variable dynamic systems, (cds) and discrete event driven systems. it also introduces hybrid systems; which most of the time be have as cds, but in certain time moments when discrete events cause discontinuities in the state trajectory. chapter 2, 3 and 4 contain a study of des at the logical (or untimed) level of abstraction. language models of des and the representation of languages by automata are described in chapter 2. software tools for analysis of dss are presented too. supervisory control issues are studied in chapter 3, which contains also a detailed presentation of decentralized control. in chapter 4, petri nets concepts are addressed. chapter 5 refines the models presented in chapter 2 (automata) and 4 (petri nets) to include time through the clock mechanism and gives an introduction to hybrid systems. chapters 6-11 utilise the third (stochastic) level of abstraction. the first three chapters (7, 8, and 9) contain aspects which are presented in the "traditional" manner of stochastic models based on probability theory. markov chains, and classical queuing theory models are presented in chapters 7 and 8, respectively. control and decision models based on markov chains are described in chapter 9. chapters 10 and 11 presents several concepts and techniques which heavily rely on the use of computer and do not require adopting the assumptions which were necessary when using classical stochastic models. chapter 10 contains an introduction to discrete event simulation including a presentation of languages and corresponding software products (in section 10.4.). chapter 11 presents sensitivity analysis and concurrent estimation methods, including the new “infinitesimal perturbation analysis” (ipa). the book contains also auxiliary material such as: a) a review of probability theory, and b) a description of ipa estimator. a web site (http://vita.bu.edu/cgc/book ), which is continuously maintained, can help the reader in his/her study. this is a high quality book, rich in content, up-to-date, and well written. the presentation style utilized throughout the book is a formal one. various examples presented and references made to relevant web sites increase value and usability of the book. the book can be of great value for various categories of senior undergraduate and postgraduate students and of the people which are interested in control, communications, computer science as well as in manufacturing and industrial engineering. therefore, i warmly recommend it to the readers of the international journal of computers, communications, and control (ijccc). florin gheorghe filip romanian academy 125, calea victoriei, 010071 bucharest-1, romania e-mail: ffilip@acad.ro int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 1, pp. 125-137 swarming models for facilitating collaborative decisions c.b. zamfirescu, f. g. filip constantin-bala zamfirescu lucian blaga university of sibiu, faculty of engineering, department of computer science and automatic control romania, 69121 sibiu, 17 emil cioran e-mail: zbc@acm.org florin gheorghe filip 1. romanian academy ince and bar romania, 010071 bucharest, 125 calea victoriei, and 2. nat. institute for informatics -ici romania, 011455 bucharest, 8-10 b-dul maresal al. averescu e-mail: filipf@acad.ro abstract: the paper highlights the computational power of swarming models (i.e., stigmergic mechanisms) to build collaborative support systems for complex cognitive tasks such as facilitation of group decision processes (gdp) in e-meetings. unlike traditional approaches that minimize the cognitive complexity by incorporating the facilitation knowledge into the system, stigmergic coordination mechanisms minimize the complexity by providing the system with emergent functionalities that are shaped by the environment itself through the possibility to structure it in terms of high-level cognitive artefacts. this is illustrated by conducting a socio-simulation experiment for an envisioned collaborative software tool that acts as a stigmergic environment for modelling the gdp. the results show superior results when the users are allowed to increase the representational complexity of a gdp model with cognitive artefacts that support guidance and action in the conceptual problem space. keywords: collaborative working environments, group decision support systems, facilitation, social simulation, stigmergy, swarming models of computation. 1 introduction the simplicity of swarming models is becoming ever more popular in the design of decentralized systems that are developed to run in open, dynamic, and unknown environments. inspired from the behaviour of social insects, marine animals, birds, and even humans, the concept covers a broad spectrum of mechanisms able to generate an intelligent collective behaviour. these mechanisms are simply identified as stigmergic coordination[1]. the most cited example of swarming models is the food foraging behaviour in ant colonies [2]. each ant senses the signs (pheromones) in its environment and acts in accordance with them without any direct communication with other ants from the colony. if there is no sign in the environment the ant executes a randomized search for food. when an ant discovers a food source, it drops a smelling chemical substance (pheromone) on its way back to the nest while carrying a bit of food. thus the ant creates a pheromone trail between nest and food source. when an ant senses the pheromone trails, it will follow the most intense one to the food source, the intensity of the pheromone signifies the shortest path toward the food that was discovered until that point in time; when the ant arrives at the food source, it will return with food, while depositing more pheromones, intensifying the pheromone trail. the above copyright c© 2006-2010 by ccc publications 126 c.b. zamfirescu, f. g. filip simple reactive behaviour of each ant results in an emergent intelligent behaviour of the colony that is able to find the shortest path from the nest to the food source without any central coordination. this behaviour is constrained by the limited sensorial aptitude of an ant to sense the local pheromone trails with no mental plan on how to find the shortest path or knowledge about the environment in which they act. consequently, the ant’s behaviour is an emergent property induced at the same time by two exogenous factors: 1) the environment (the surface of the terrain and the evaporation mechanism for the pheromones), and 2) the ants’ actions over the environment (the pheromone trails). despite its behavioural simplicity (i.e., stimuli-response rules), a single stigmergic agent can emulate any turing machine and can execute any symbolic or sub-symbolic algorithm proposed by the ai (artificial intelligence) research mainstream [3]. nevertheless, most of the computer applications are outside the domain of human cognitive abilities, serving merely as cognitive support systems through an active and semantically rich environment [4]. therefore swarming models of computation are seen as a feasible approach to construct systems that are not limited to the classical optimization problem of finding the shortest route [5], but are able to support human decisions as well [6, 7, 8]. one application domain with a high level of cognitive complexity is the facilitation of gdp in emeetings. the complexity associated with the construction, coordination and execution of gdp is well recognized in the research field of group decision support system (gdss) [9]. for this reason gdss has seldom been a full success story and a widely adopted technology as it has been foreseen by its pioneers. all the applications developed to support the group facilitation in e-meetings follow the traditional centralized approach where the system explicitly codifies the facilitation knowledge. examples include software tools that embed knowledge about the collaborative patterns of interaction [10] and workflows [11] for the most frequently used gdps. these applications basically suffer from the same obstacles met in the traditional ai mainstream such as [12]: 1) the restrictions to codify the human’s knowledge into the computing system; 2) the lack of self-development capabilities for this knowledge; 3) the blackbox perspective over a system disconnected from the environment where the relevant knowledge are extracted. in contrast to the traditional approach that codifies the facilitation knowledge into the gdss, this paper illustrates how the stigmergic mechanisms of swarming models may be employed to build emergent and self-organizing functionalities that support group facilitation in e-meetings. in this particular case, the stigmergic coordination mechanisms are implemented over the conceptual environment of the gdp modelling space which is exploited by the users of an e-meeting system. unlike conventional approaches that minimize the cognitive complexity associated with the construction and execution of a gdp by incorporating the facilitation knowledge into the system, stigmergic coordination mechanisms minimize it by structuring a shared conceptual environment populated with the cognitive artefacts that represents the basic skills of conducting an e-meeting. in this way the users are collectively constructing and interpreting the facilitation knowledge through the successive uses of the system. the remaining part of this paper is organized as follows. the next section presents a brief analysis of the innate relationship between distributed cognition and stigmergy as it has been presented by many authors within the web 2.0 technology mainstream. section 3 describes the main components of an envisioned collaborative software tool that act as a stigmergic environment for modelling the gdp: the structure of the semantic environment, the low-level behaviour of the users in interacting with this environment, and the high-level of cognitive constructs that may be employed by the users to structure the gdp. these components are implemented and tested in a socio-simulation experiment which is described in section 4. the experimental results show clear self-organizing capabilities, but simultaneously high dependability of system’s performance on the user’s ability to structure the stigmergic environment. from the engineering standpoint of constructing purposeful facilitation tools for e-meetings, these results are discussed and summarized in the last section. swarming models for facilitating collaborative decisions 127 2 stigmergy and cognition most of the research in cognitive science is rooted in the basic assumption that cognition is purely an internal process of representation and manipulation of knowledge disconnected from the environment. nevertheless, recent studies in cognitive sciences reveal the essential role of the environment (physical or artificial) in mediating the knowledge by facilitating their external representation and information exchange among these representations [13]. it becomes clear ’that individuals are socially and culturally situated and that the environment needs to be considered in order to understand cognition’ [14]. by admitting the critical role of the environment in cognition, the researchers became increasingly aware of the relationship between cognition and stigmergy. even if the term stigmergy has been primarily used for typically reactive (non-rational) agents, its relationship with cognition was investigated for the first time by susi and ziemke [15]. the authors conclude with the assertion that the conceptual framework of stigmergy offers a common denominator for the social sciences theories (i.e., activity theory; situated and distributed cognition). for example a cave painting is an emblematic case of stigmergic coordination where people used the physical environment to indirectly communicate their knowledge in ancient human society. the relevance to cognition of the stigmergic coordination mechanisms in human society is illustrated by parunak in several examples of social activities [6]. albeit the use of stigmergic mechanisms may be observed in various social activities, it is more evident in the digital world realm. in a comprehensive study of collaborative support systems, elliot [16] found that stigmergy is a coordination mechanism inherent not only in collaborative processes over physical environments, but also in a range of collaborative support systems. for instance the plethora of applications that are considered to be web 2.0 technology (especially media such as wiki and community blogging) is generally recognized to be stigmergic systems. moreover, applications such as google’s pagerank system, ebay’s online auctioning, amazon’s recommender systems [6], wikipedia.org, open source software and multiplayer social environment second life [17] are employing the stigmergic coordination mechanisms to exhibit the functionalities of an intelligent collective behaviour. in these stigmergic systems the users exploit their digital environment through the use of engineered artefacts that may be annotated with symbolic information representing the human’s cognition [7]. given that the essential capability of any stigmergic system is to transfer the cognitive complexity from the humans to the environment [18], the problem-solving capabilities of the users decisively depend on how the problem is represented in the digital environment. a standard representation of the problem in the environment is realized as a composition of cognitive artefacts linked in a weighted graph. basically, this graph signifies a navigation map that supports the cognitive effort to find and reach any artefact from the place where it is needed. the artefacts commonly stand for the possible states of the problem, while the links are the set of possible actions that guide the decision process (the conceptual navigation) from one state to another of the problem space. similar with the intensity of the pheromone trails in the case of real ants, these actions are weighted in order to discriminate the most effective ones. as a result, the improvement of problem-solving ability requires two corresponding processes [8]: 1) the augmentation of the environment with additional states and actions to increase the accuracy of problem representation, and 2) the improvement of the preference function for an action in order to compensate the expansion of the exploration space. the wide employment of the stigmergic patterns of interaction in collaborative working environments has been triggered by its fundamental advantage of preventing the humans’ cognition to be exposed to the complexity of the environment [19]. as users interact only locally, there is no need for tasks allocation, the tasks being preferentially performed by the most expert since they are the most attracted to act and finalize the task with minimal effort [20]. moreover there is no need for prediction since the environment records actions in the problem space and the unexpected events are automatically traced through the outcome of the users’ actions over the environment. all these advantages make the stigmergic models of computation a suitable approach to support the cognitive complexity of facilitating gdp, a process that 128 c.b. zamfirescu, f. g. filip runs in a dynamic, open and uncertain environment. 3 a simulation model for group decisions in e-meetings the wide range of tools that support group decisions in e-meetings falls under the gdss general umbrella term. gdss is defined as an interactive computer-based environment that supports a concerted and coordinated team effort towards completion of joint tasks [21]. a gdss is composed of a set of highly configurable collaborative "tools" (e.g., brainstorming, voting and ranking, multi-criteria analysis, etc.) that requires a high level of expertise for an effective use for complex decisions [22]. the strong relationship between the gdp outcome and the presence of a skilful facilitator to direct the joint decision process is thoroughly presented in many field studies of gdss research [23]. the inaccessibility of many organizations to a well-trained gdss facilitator is recognized to be one of the main obstacles which limit the adoption of gdss technology [24]. to reduce the dependence on the facilitator, the participant-driven gdss was proposed as the most promising research direction to leverage the skills and abilities of each group member [25]. however, this approach is highly constrained by the cognitive complexity associated with the construction, coordination and execution of gdp by inexperienced users. to overcome the problem of cognitive complexity briggs and de vreede [24] introduced the thinklet (tl) concept as a discrete facilitation unit that integrates a specific tool, its configuration and a script to use it a predefined interaction protocol among users that is enforced and mediated by a specific collaborative tool. this concept was anticipated by the declarative model of the experienced decision maker (medm), which was proposed in late 80’s to help the user of a dss to build the model, select the appropriate solver, and evaluate various solutions provided by the computerized algorithms in so called "mixed knowledge" dss [22, 26]. the tls are considered to be the smallest piece of essential knowledge to design collaborative processes. examples include [24]: strawpoll (evaluate) used to reveal the agreements or disagreements within a group; leafhopper (diverge) used when the participants does not known in advance the topics for discussion or they have different interests or level of expertise; expertchoice (organize) used when the ideas of the group are organized by a single participant; reviewreflect (converge) used when a different reviews from group members for a document should reach an agreement etc. with tls, the conceptual model for a gdp takes the form of a shared plan of collaborative actions [24, 27]. each collaborative action is an interaction protocol embodied in a tl. as any plan, the model for a gdp may be hierarchically decomposed in sub-plans at different levels of abstraction. this conceptual structure of a gdp model is acknowledged in any application domain of the gdss technology, such as project management [28], user requirement elicitation [29], crisis response management [30], scenario design [31], risk identification [32], etc. in the view of envisioning a collaborative software tool that acts as a stigmergic environment for modelling the gdp (in the same way in which a collaborative cad software acts as a stigmergic environment for architectural design [33]), we developed a socio-simulation model that mimics the users’ conceptual ’navigation’ over the semantic structure of the problem space for facilitating the e-meetings. as for any stigmergic system the simulation model entails the description of agents’ behaviour and the structure of the shared environment where the agents are localized and moved over it. for the gdss domain, the agents are the users responsible to define, execute and evaluate a gdp model which is a path through the conceptual space of the available tls. the environment is the collaborative facilitation tool that supports the conceptual representation of the problem space comprising all the tls discovered and documented by the users’ community (so far there are over 70 tls acknowledged in literature [34]). swarming models for facilitating collaborative decisions 129 3.1 the semantic environment for facilitating the e-meetings according to parunak [6], a stigmergic environment assumes the definition of three main components: 1) topology, 2) states, and 3) processes. structurally, the topology may be viewed as a fully connected weighted graph that codifies the facilitation knowledge of group decision in e-meetings. this knowledge presumes correlated information among the users and the tls, reflecting the users’ evaluation of the performance for a tl (a node in the graph) relative to a problem type. the performance is stored for each problem type in a variable associated with each edge of the graph. the problem type is simply codified through a unique id to distinguish among different performances when they are read, during the modelling phase of the gdp, or modified, after the gdp has been executed and evaluated by agents. evaluation of a gdp model entails a subjective assessment of the model against some performance criteria after its execution. regularly, gdp’s performance may be quantified in terms of efficiency, effectiveness and users’ satisfaction, as illustrated in several studies from the gdss research field [35]. the performance from all the graph’s edges describes the state of the environment over time. usually, the environment executes a set of processes on the variables (as aggregation end evaporation in the case of ants). for our case, we apply a simple weighted additive rule to simulate the aggregation of performances: pjk(t lk,t) = pjk(t lk,t − ) + u pjk(t lk)/w (1) where: t represents the temporal component of the model which is incremented by one for each successive use of the gdss; k is the tl’s identification index from the set of tls used to model the gdp;u pjk(t lk) is the user’s performance of the k-th tl evaluated from the side of tl j at moment t;pjk(t lk,t) and pjk(t lk,t − ) are the new and previous values of the (collective) performance stored on the edge between the tls j and k; w is a tuning parameter, arbitrarily chosen, to weight the impact of the last evaluation. 3.2 the agents’ behaviour over the semantic environment the agents are the users who interact with the envisioned collaborative tool to model the gdp. conceptually, in any point in time an agent is "located" in a node (tl) of the cognitive environment of the problem space, performing one of the following basic actions: 1) evaluates the preference for the next possible tl (or tls) that are going to be executed given the current execution context of the gdp; 2) selects the next best tl (or a group of tls) for further completing the gdp model; 3) executes the tl (or the group of tls) from the model, and finally; 4) evaluates the performance for the executed tls. the evaluation activity is simulated using the formula (1), while the first three actions with luce’s selection axiom [36]: p jk = e p jk(t lk)/t / m∑ i= ep ji(t li)/t , (2) where p jk represents the preference for an alternative tl, i.e. the selection probability of the tl k from the tl j; i is the index of tls connected from the side of node j (in fact all the m tls available in the problem space as long the graph is fully connected); and t is a parameter used to define the deviation from a pure rational behaviour. the above formula is the most common model of stochastic decisions due to its correlation with the psycho-social observations of human behaviour in several domains. as a result of normalization, the preferences for the unexploited tls are diminishing after each performance update. this mechanism replicates the pheromone evaporation process of the real ants (e.g., even if a tl has been positively evaluated after an execution of a gdp model, the associated preference will decrease once a better alternative is discovered and more frequently used). the uncertainty associated with the construction of preferences is generally modelled in equation (2) with the parameter t that range between 0 (when 130 c.b. zamfirescu, f. g. filip selection is deterministic as is the ideal case of a perfectly informed decision) and 1 (when the selection is completely random as in the case of a completely irrational decision). note that luce’s selection axiom does not specify the reasons of uncertainty which for the modelling of gdp may cover any aspect of complexity, unfeasibility, cost or even refusal to evaluate the performance of a tl after its execution. 3.3 navigation strategies over the semantic environment the agents, who are reflecting the users in modelling a gdp, are engaging in means-ends reasoning activities to achieve the group decision goal. during the execution of the model they must be able to adapt to the changes and uncertainties associated with both the execution and the decision goal. moreover, when the decision problem is not completely clear or too complex to be fully tackled, the users are defining intermediate sub-goals to be subsequently achieved. as a result, the design of a gdp model is most often done incrementally and interleaved with its execution. table 1: different modelling strategy to construct a gdp model design strategies execution problem’s goal codification (ds) ds1 certain stable the problem type is codified through a unique id ds2 uncertain stable the problem type is codified as a variation from the current state of execution to the desired one ds3 uncertain unstable the problem type is codified as a variation from the current state of execution to any future possible state in table 1 we have summarized three basic design strategies (ds), with direct implications on the way the gdp model is decomposed on different levels of abstractions. these are: • ds1 which corresponds to the traditional use of gdss when the gdp model is predefined by the facilitator. in this case, the user is providing a complete structure of the gdp model, the facilitator having a complete vision over the execution plan for the gdp. it includes all the necessary collaborative actions (in the form of tls) together with their precedence constraints. this design strategy relates to the hypothetical conditions when the execution context remains stable in time as regards the tl execution’s outcome and the decision’s objectives. thus, each problem type is identically codified in all edges that connect the tls used in modelling the gdp and relates to a low level of semantically structured environment. • ds2 which corresponds to problem types with stable objectives but uncertain tl execution’s outcome from the gdp model. in this case, after the execution of each tl, adjusting the remaining gdp is needed. this design strategy is codified in the conceptual graph of gdp modelling with different ids for each sub-problem that corresponds to the variance from the current state of execution to the desired one. • ds3 which relates to complex plans of actions for the gdp model, where both the outcome of an activity and the decision’s objectives are unstable during the execution. in this case, when the decision problem is not entirely clear or too complex for designing an entire gdp model, the users are defining intermediate sub-goals that are subsequently dealt with. this design strategy is codified in the conceptual graph for the gdp modelling with different ids for each sub-problem that corresponds to the variance from the current state of execution to any future possible one. swarming models for facilitating collaborative decisions 131 4 experimental results to evaluate the design strategies for modelling the gdp we conducted a virtual experiment following the research methodology proposed by carley [37], implementing in the netlogo multi-agent simulation environment [38] the model described in the previous section. in the experiment the users ("turtles") are to engage themselves in the facilitation of e-meetings, trying to define the gdp model for a problem type by moving in the conceptual graph of tls (the nodes and edges are implemented also as "turtles"). the number of tls that compose the graph is arbitrary chosen from the interface (the "num-tls" variable in figure 1), while their utilities for a certain problem type is predefined with random values between 0 and 1 when the experiments are initialized. note that the netlogo implementation includes some additional variables required to analyse if the model presents similar results to those reported in the traditional ethnographical studies. this issue is beyond the scope of this paper and is detailed in [39]. figure 1: the interface of the model in the netlogo environment in the next sections the normalized performance of the gdp models and its associated entropy for 100 successive explorations (iterations) in the three design strategies (the "planning-degree" variable in figure 1): ds1, ds2 and ds3 are presented. an exploration stands for a complete execution cycle of a gdp. it includes three consecutive phases: 1) finding a suitable model through the successive selection (using the equation (2)) of tls that compose the gdp for the given problem type; 2) executing the identified model and assessing its performance by reading and averaging the predefined utility values of all the tls that compose the gdp model; 3) evaluating the model by updating the performance value (using the equation (1)). the statistics are aggregated from 30 experiments for a relatively simple problem type of 5 successive tls. the parameter t from equation (2) is set to 0.7 (the "pheromonesensitivity" variable from figure 1 to favour a faster convergence rate in finding a suitable solution in the problem space composed of 70 tls. from the engineering viewpoint, the design strategies have direct implications on the way in which the cognitive environment for modelling the gdp is structured. as described before, the structure of 132 c.b. zamfirescu, f. g. filip the environment is simply reflected in the possibility to semantically decompose the problem in subproblems on different levels of abstractions. this design issue implies in our implementation to record with specific ids the performance for each sub-problem type that emerges from the decomposition process. 4.1 the impact of the modelling strategies over the model’s performance in figure 2 the aggregate performance (a relative number between 0 and 1 as resulted from averaging the predefined utility values of all the tls that compose the gdp model) from 30 experiments of a gdp modelling process for the defined design strategies ds1, ds2 and ds3 are shown. as may be expected, the performance fits an exponential function, a typical behaviour for a stigmergic system [40, 41, 42]. figure 2: the gdp models’ performance for the defined modelling strategies figure 3: the distribution of gdp models’ performance for the three modelling strategies like any heuristic model, the stigmergic coordination mechanisms do not guarantee finding an optimal solution, but a near-optimal or acceptable one. as a consequence, from one experiment to another there are some variations in performance for the convergence values. figure 3 illustrates in whiskers diagrams the distribution of performance for the same experimental data that are depicted in figure 2. the three strategies show different performance and convergences to an optimal solution. contrasting with ds1, ds2 takes benefit from the prior experiences not only in relation with the entire problem type but also from the intermediate solutions to model the sub-problems in which the initial problem has been decomposed. in addition, ds3 increases the granularity by adding the opportunity to decompose the problem’s objective. as a consequence, the figures show the influence of problem type decomposition on the gdp model’s performance. as may have been expected, ds3 shows the best performance results and a low inconsistency among the identified solution for a gdp model. but, ds2 converge faster to an optimal solution and a lower swarming models for facilitating collaborative decisions 133 inconsistency among the feasible solutions (the deviation of the average solution from those with maximal and minimal performances). this can be explained by the additional constraints of having stable objectives during the modelling process. 4.2 the cognitive complexity associated with each modelling strategy the auto-organization of relations between tls (i.e., the performance update after successive evaluations) entails a decrease of freedom due to the emergence of contextual constraints that reduce the probability to select some tls (i.e., the preference for the available tl as defined in equation (2)). for a problem type, the degree of freedom corresponds to the probabilistic distribution of preferences for the selection alternatives that is equivalent with the shannon normalized entropy [40, 43]. the shannon normalized entropy for the selection of a tl is given by: e(pjk) = − m∑ k= pjk ·ln(pjk)/ln(m) (3) where pjk represents the preference, the selection probability of the tl k from the tl j; k is the index for the tls connected from the node j (in fact, all the m tls available in the problem space). when the recorded performance is equal for all the available modelling alternatives, the user is considering the entire problem space when he selects a feasible tl (the probabilities from equation (3) being equally distributed entail an entropy equal with 1). contrary, when the recorded performance favours a single alternative, the user will have no freedom in the selection of the best tl (all the probabilities from formula 2 being 0 except the best alternative that is 1, entails an entropy equal with 0). thus, the entropy associated with tl’s selection is a measure of cognitive complexity for modelling the gdp. moreover, it is a local metrics that can be computed for each tl’s selection activity for modelling the gdp. figure 4 shows the cognitive complexity associated with gdp modelling for the design strategies: ds1, ds2 and ds3. the data are obtained for the same experimental settings as introduced in the beginning of this section. because this measure is computed on the basis of the local data for each selection action (the performances available on the edges from the current tl), the figure corresponds to the average of entropies for all the tl selection actions needed to complete the gdp model (5 successive tls, for this case). similar to the distribution of gdp models’ performance, comparing with ds1 the normalized entropy for ds2 and ds3 converge faster to 0. for ds3 the gradual increase in the complexity of the representations for a gdp model in the conceptual problem space (through the definition of different sub-plans on different semantic levels) makes the design of the gdp process more manageable in the sense that it reduces the overall complexity of representing the gdp model. nevertheless to reduce the cognitive complexity for modelling the gdp, the users should be supported in using this representation for guidance and action in the conceptual problem space. this entails the reflection of the abstract representations into the conceptual environment through an increased semantic complexity (by adding new problem types, codified with distinct ids in our implementation) which facilitates the synergy among partially overlapping gdp. only in this way the users may move from one sub-plan to another or from low level detailing with tls to high level of plan and to be fully supported by the stigmergic coordination mechanisms at the same time. 5 summary and conclusions stigmergic mechanisms are widely employed in human society, from the classical examples of largescale complex adaptive systems such as stock markets, supply logistics and cultural memes to the more 134 c.b. zamfirescu, f. g. filip figure 4: the normalized entropy of the gdp modelling for the three design strategies recent range of collaborative working environments such as wiki, google, social networking and opensource software. continuing our work in achieving inter-paradigmatic synergy between symbolic and sub-symbolic reasoning for large-scale complex systems [44, 45], the paper highlighted the computational power of stigmergic coordination mechanisms to build collaborative support systems for complex decisions such as facilitation of gdp in e-meetings. unalike conventional approaches that minimize the cognitive complexity by incorporating the facilitation knowledge into the system, stigmergic coordination mechanisms minimize it by offering emergent functionalities that are made up not only by the user’s actions (through the selection of the right modelling components) but also by the environment itself (through the possibility to structure it in terms of high-level cognitive artefacts such as plans or sub-plans). this was demonstrated by implementing and testing in a socio-simulation experiment an envisioned collaborative software tool that act as a stigmergic environment for modelling the gdp. the results show superior results when the users are supported to gradually increase the complexity of the representations for a gdp model which is reflect on its turn in the relational complexity of the 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[45] f.g. filip, k. leiviskä. large-scale complex systems. handbook of automation ( s. nof, editor), 619-638, springer, 2009. constantin-bala zamfirescu is currently associate professor (lecturer) at "lucian blaga" university of sibiu, romania. he took the phd from "politehnica" university bucharest in automation and control field. his research interests include the employment of agent-oriented engineering paradigm in group decision support and manufacturing control systems. he is a member of ifac technical committees 5.4, eu cost action 298 and nd eu noe for the advancement of artificial cognitive systems, interaction and robotics. florin gheorghe filip member of the romanian academy (elected in 1991), received his m. sc and ph. d. in control engineering from the technical university "politehnica" bucharest in 1970 and 1982, respectively. he has been with the national r & d institute for informatics since 1970 and was the managing director of ici from 1991 to 1997. he was elected in 2000 (and re-elected in 2002 and 2006) as a vice-president of the romanian academy. he has been the chair of the ifac technical committees 5.4 (2002-2008). his main scienti?c interests include: control and optimization of large-scale systems, decision support systems, technology management, and models for knowledge-information society. he is author/co-author of six books, editor of nineteen volumes and the author/co-author of over two hundred articles published in contributed volumes and scientific journals. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 125-133 ontology model of a robotics agents community h. latorre, k. harispe, r. salinas, g. lefranc homero latorre karina harispe universidad tecnológica metropolitana de chile departamento de informática y computación josé pedro alessandri 1242, ñuñoa. santiago – chile e-mail: helatorre@gmail.com renato salinas universidad de santiago de chile departamento de ingenieria mecánica avda. bernardo o’higgins 3363. estación central. santiago – chile e-mail: renato.salinas@usach.cl gastón lefranc pontificia universidad católica de valparaíso escuela de ingenieria eléctrica avda. brasil 2147. valparaiso – chile e-mail: glefranc@ucv.cl abstract: this paper presents an ontology model of necessities and decisions for a cooperative community of heterogeneous robotic agents. based on an ant community, it defines the characteristics of a collaborative and cooperative community of robots, using multi agent theory where each robot shares information with others robots in the community, to accomplish a common objective. keywords: robotics, intelligent agents, multi agents, collaborative and cooperative robotics, long-range-dependent, network layer, network traffic, selfsimilar process. 1 introduction collaborative and cooperative tasks are used in robotic agents, where the knowledge is shared; there exist communication to talk to the others robots where the area to the tasks is located and which decision that they have make, according to the necessities. these works have the hierarchy characteristics similar to that presented in nature, especially in the animal kingdom, where the most interesting are the behavior of bees, ants and termites, communicating where are the foods and the decisions that they have to take, according to the necessities. based on this group behavior, these insects are intelligent agents designed by nature. the agents can be simulated in a computer and put in group of robots that will be able to do tasks in coordinating way, where the decisions are taken with the group characteristics. the tasks in a community of collaborative and cooperative robots have increased in complex way, meaning the tasks cannot be done with only one robot, but needing a heterogeneous community of robots. this heterogeneity of collective robots produces new challenges, adds a new difficulty to collective robotics, such as the aptitude to coordinate individuals with multiple qualities of an intelligent way; and to solve a specific goal working together and taking into account all the characteristics. to solve a certain task, each of them fulfills a specific function with which the copyright c⃝ 2006-2011 by ccc publications 126 h. latorre, k. harispe, r. salinas, g. lefranc rest must be able to coexist, the satisfactory results by means of the union of their qualities, and work together [1]. there exist several methodologies for building multi agent systems. few of them address the information domain of the system. it is as important as the representation of the system in the information domain is the various agents’ information domain view. heterogeneous systems can contain agents with differing data models, a case that can occur when reusing previously built agents or integrating legacy components into the system. most existing methodologies lack specific guidance on the development of the information domain specification for a multi agent system and for the agents in the system. an appropriate methodology for developing ontology must be defined for designers to use for specifying domain representations in multi agent systems. the existing methodologies for designing domain ontologies are built to describe everything about a specific domain; however, this is not appropriate for multi agent systems because the system ontology should only specify the information required for proper system execution. the system ontology acts as a prerequisite for future reuse of the system, as the ontology specifies the view of the information domain used by the multi agent system. any system that reuses the developed multi agent system must ensure that the previously developed system ontology does not conflict with the ontology being used in the new system [2]. once the system ontology is constructed, a multi agent system design methodology should allow the analyst to specify objects from the data model as parameters in the conversations between the agents. to ensure the proper functionality of the multi agent system, the designer must be able to verify that the agents have the necessary information required for system execution. since the information is represented in the classes of the data model, the design of the methodology must show the classes passed between agents [3,4]. in this paper is presented an ontology model of necessities and decisions for a cooperative community of heterogeneous robotic agents based on an ant community. it defines the characteristics of collaborative and cooperative community of robots, using multi agent theory where each robot shares information with others robots in the community, to accomplish a common objective. 2 description of the problem in general, the complexity of the tasks added to the lack of constant structure in the environment, does not allow a homogeneous and structured community should be capable to fulfill the aims raised of an ideal way. nevertheless, these limitations of capacities can be covered if different technologies are integrated; applying the heterogeneity in the architecture added to intelligent behaviors handled by means of multi agent systems – mas. it is important to create mas capable of controlling a heterogeneous community of robots in order to exhibit cooperation and collaboration among them, reaching the proposed aim. there are two concepts that have to be clarified, how the members of the community must be interrelated to the moment to make the work; and cooperation and collaboration. cooperation and collaboration is not the same thing. to collaborate is to contribute, is to give help (in knowledge or work) to do something, is to share the same proposition and the common goals. to cooperate is a collective work with a common goals, it means working together simultaneously, to have interactions of the collective work and interchange of ideas [5]. the cooperative and collaborative environments seek to transform heterogeneous groups into intelligent, flexible and autonomous communities. the community does not need having subordination; this means that the community has no teachers and apprentices nor masters and slaves; the agency is a mere way of communication towards the exterior, to send of results or requests of support [6, 7]. ontology model of a robotics agents community 127 in some communities, the robots communicate to a robot agency, to send reports outside of the community. in a heterogeneous community of robots, the communication between the robots must be in an egalitarian form, without intermediaries. each of them has to have the capacity of direct connection with its partners, without establishing first the communication with the agency. briefly, it is important to have a mas capable of provoking cooperation and collaboration, generating intelligence, flexibility and autonomy, without subordination, to arrive to results obtained from actions realized as consequence of consensus decisions [1, 8–10]. 3 description of the organization of the community the community is formed by heterogeneous robots; each one is specializing in specific functions, which it helps in the achievement of the common goals. inside the community, every robot must report its characteristics to the rest of the community, in order to generate knowledge that it allows to determine which of its qualities are adapted to a specific works. every robot has responsibilities, which will be resolved by consensus of the community, and it will have to realize its actions without being an obstacle to its companions, and giving any help that is necessary in unforeseen moments. to realize the work, and unlike the existing robotic communities, a leader does not exist hence, the communication, transfer of information, and capture of decisions is of "all with all", where every contribution has the same value for the community. if we base the study on a community of ants, these have behaviors that lead them to achieving its goals, though an ant alone is not capable of feeding and defending its anthill, millions of them can do it. in turn, a robot is not capable of realizing tasks that need major capacities that those that it possesses, but a group of them that brings together as a whole all the characteristics necessary, it can realize it. considering a group of agents, natural or artificial, which must come to an aim it can say that: (a) the ants (natural agents) possess intrinsic behaviors of communication, work and capture of decisions. (b) the robots (artificial agents) possess behavior learned or incorporated by means of programming which is based on predefined ontologies. to manage to obtain a "natural behavior" in a robotic community modularization of every action realized by the natural agents in an artificial agent is based in the observation of the nature. 4 systems of ontologies of communication every robot must know the directions of its community, or detect what signs are available, to be able to send the requests of connection and initiate the communicative act that will coordinate the execution of the tasks to realize. for this connection, three agents are needed: one agent capable of detecting signs, and to check the existing directions, one agent to request connection and other one to accept the request (fig. 1). on the other hand, whenever a new member joins the community, it has to report to its companions which are its characteristics and the functions for those that it was designed. for that purpose, it needs to have an agent that it should allow it to deliver its "curriculum" to the rest of the community, and in turn, it needs an agent capable of catching the information relating to each of its new collaborators. (fig. 1). during the accomplishment of the tasks, certain needs arise when in an individual is not capable of solving the task. in this case, it requests help to others. this implies that every 128 h. latorre, k. harispe, r. salinas, g. lefranc individual has the aptitude to communicate its needs and in turn to process requests raised from others. a way of solving this problem is by means of agents in charge of realizing these tasks (fig. 2). besides, it is necessary to have an agent capable of trying the need and to generate an action that could cover this need, based on the knowledge that it has, or to declare itself unable to realize something of usefulness. in brief, there needs an agent capable of making an individual decision. 5 ontology system of making of decisions in the execution of a cooperative and collaborative work, the communities of robotic agents face problems that need of rapid solution, even if its behavior is intrinsic in them and present certain characteristics of collective memory. they have to take decisions that in some cases involve to sacrifice some members of the colony. for it, when a need is communicated and it receives several individual answers to that aptitude to cover the need, the community must decide which of the offers is better, or how to coordinate them. for it, there can be applied different concepts based on the ontologies of decision in which the robotic mas is sustained. the last instance, before realizing the pertinent assignments to cover a need, is the taking of decisions (d), where the conducts of the community meet reflected with major force, from the coordination up to the cooperation. unlike the traditional focus in the collective robotics, in the community of robotic agents there appears a new concept, the consensus. any decision taken must be realized by mutual agreement for the whole community that it is directly faced to a problem – fig. 3. the making of decisions carries out three different forms, commonly used among human groups: decision for similarity (ds): this one is taken when more than one individual presents the same solution, expressed of different way; the community may take both as different, and nevertheless it has to have the aptitude to detect the similarities among them and to obtain the unique solution. yet, when there are significant differences between the offers realized, it must be feasible to unite the similarities among them and to optimize the differences to obtain the final solution that will be applied. decision for quality (dc): this one turns out to be one of the most complexes since the robots must be capable of determining which of all the offers is the optimal, or the best solution. this requires that to community possesses another human characteristic, the figure 1: fipa protocol for community of robotic agents. ontology model of a robotics agents community 129 figure 2: architecture of knowledge behaviors for community of robotic agents. intelligence, which brings the artificial intelligence, besides needing a major quantity of resources assigned to the making decisions. decision for majority (dm): this one refers to the choice of a solution from the generated offer more time, this solution goes of the hand in the majority of the times of the decision for similarity, since it turns out slightly probable that two or more agent components of robots with different characteristics and particular knowledge generate exactly equal solutions. the decision for majority implies reducing the number of offers by means of the assimilation, then there determining which is the accepted solution by means of the acceptance of the members’ major quantity. the behavior (similar to the human being) assigned to the community of robotic agents, goes directly related to the solution of problems. it is for it that the actions to take realize in a sequenced way being careful not to omit any of them in order not to present faults to the moment of it determines the suitable way of handling a certain situation arisen during the palliation of pertinent labors for the fulfillment of an aim. on the other hand, the satisfactories generally they turn out to be inactive individuals of the colony, already be that they are in the nest, or that have stopped its labors inside the community. these agents robots are capable of solving problems and of covering the needs of its companions as they were described in the previous point. the general way of obtaining the satisfactory determined by means of the process that culminates in the making of decisions, is by means of communicative acts which allow to deliver information to other members of the colony on what it is looked, in order that these could provide it. in a graphical form this sequence of actions is represented in fig. 4. figure 3: architecture for making decisions in a community of robotic agents. 130 h. latorre, k. harispe, r. salinas, g. lefranc figure 4: sequence of actions for the search of a satisfactory decision. in a robotic community, every individual must have a small base of knowledge handled by an agent which will determine those actions can be used in the future and which not, beside to determine which are those situations that must be reported to the rest of the community to create a collective memory. likewise, the robots must possess an agent capable of making a record of mistakes and correct them, registering also the above mentioned alteration. also it has to have the aptitude to eliminate the knowledge that is unnecessary or is obsolete, to avoid information garbage inside the robot and its community. 6 ontologies for needs these types of ontologies define the particularities of every type of existing need. nowadays, there are four basic needs, the nourishment, affiliation, assignment and repair. each of them possesses certain points by means of which there can be associated an individual and to a labor, bequeathing this way to facilitating the take of decisions in the active community. each of these needs possesses a particular class, these are: class nourishment: this one describes the levels of discharge of energy that produce some problem in a robot, and defines the possible solution to the above mentioned mishap. the table 1 shows one of the subclasses that shape the class nourishment. class affiliation: this one describes the most particular need of all, the affiliation. this one takes place at the arrival of an individual to the active community and defines what must be fulfilled in order that this one is accepted by who will be its companions of making place the affiliation of the new individual to the group. the table 2 shows one of the subclasses that shape the class affiliation. class assignment: this one describes the need of union between an individual and a labor. when it arises a certain task must be looked the one who realizes it, of compatibility exists with the agents robots inactive these they will be assigned, of not being like that a request ontology model of a robotics agents community 131 will realize to the agency robots. they can be given in combination when more than one individual needs, and the total coverage of the satisfactory does not exist in the active community. the table 3 shows one of the subclasses that shape the class assignment. class repair: this one describes possible damages somewhere or a piece of an individual, defining possibilities of repair, change or to send to workshop, depending on the severity of the fault. the table 4 shows one of the subclasses that shape the class repair. subclass definition level of hunger measure in per cent. it refers at the level of discharge that presents the battery of nourishment of the robot. intermediate state it adduces to the state to which there will change the individual while it is recharged (if it is left or it is still active or in wait) satisfactory it will determine depending on the unload if a loader or a derrick is necessary. way of connection it determines the type of connector between the individual uncharged and the individual charger. table 1: subclass of the class nourishment subclass definition community activates it determines the identificator of the active community to which an individual is sent. individual it determines the identificator of the individual who is sent. minimal percentage of acceptance it determines the minimal percentage of approval that must exist in order that the individual be accepted as member of the community. table 2: subclasses of the class affiliation. subclass definition labor definition of the labor to realize. satisfactory determination of the type of suitable individual. compatibility with oi existence of compatibility with the agents robots inactive inside the active community. (alphanumeric chain which first character is the number of agents compatible robots, followed by its identificator). request to agency robots package of request of compatible individuals to send to the agency (chain of characters where the first character defines the quantity followed by the description of the satisfactory). table 3: subclasses of the class assignment in the communities of robotic agents, it is necessary to define each and every of the ontologies that there are forming the base of knowledge of the robotic agents, which are fundamentals to define the task that the community is capable of realizing. in this work, it presents a proposition of the ontologies of connection, of decision and of needs, for a community of robotic agents in particular, which joined the development of intelligent agents, manages to form a type of social behavior very similar to the behaviors in the conducts presented by social groups such as the ants, which possess a highly developed instinct of collaboration and cooperation. 132 h. latorre, k. harispe, r. salinas, g. lefranc subclass definition damaged piece it determines which is the piece that presents damage. level of damage it determines of percentage form the severity of the fault. capacity of change it determines if it is possible to replace the damaged piece or not. intermediate state it adduces to the state to which there will change the individual while it is repaired (if it is left or it is still active or in wait) satisfactory it determines that repairer is the most suitable, or if it turns out to be more suitable if a derrick is requested. table 4: subclasses of the class repair figure 5: ontology of needs the following image (fig 5) presents a graph of the components of the classes of the ontology of needs. 7 conclusions in the communities of robotic agents, it is necessary to define each and every one of the ontologies that there are forming the base of knowledge of the robotic agents, which are fundamental to define the task that the community is capable of realizing. in this work, it presents a proposition of an ontology model for a cooperative community of heterogeneous robotics agents. it defines the characteristics of collaborative and cooperative community of robots, using multi agent theory where each robot shares information with others robots in the community, to accomplish a common objective. the ontologies are for connection, for decisions and for needs, for a community of robotic agents in particular, which joined the development of intelligent agents, manages to form a type of social behavior very similar to the behavior by social groups such as the ants. bibliography [1] lefranc, g. “colony of robots: new challenge”, int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844, vol. iii (2008), suppl. issue: ijccc 2008, pp. ontology model of a robotics agents community 133 92-107. [2] ellips masehian, and davoud sedighizade, “classic and heuristic approaches in robot motion planning – a chronological review”. proceedings of world academy of science, engineering and technology volume 23, august 2007. [3] mcguinness, d.l., fikes, r., rice, j. and wilder, s. an environment for merging and testing large ontologies. principles of knowledge representation and reasoning: proceedings of the seventh international conference. a. g. cohn, f. giunchiglia and b. selman, editors. san francisco, ca, morgan kaufmann publishers. 2000. [4] supnithi t., inaba a., ikeda m., toyoda j., mizoguchi r., 1999. “learning goal ontology supported by learning theories for opportunistic group formation”. proc. of aied99, pp. 67-74. [5] brophy s., biswas g., katzlberger t., bransford j. and schwartz d., 1999. “teachable agents: combining insights from learning theory and computer science”. in s. p. lajoie and m. vivet (eds.), artificial intelligence in education, pp. 21-28. [6] sloczinski, h. lucila maria costi santarosa, “aprendizagem coletiva em curso mediado pela web”, vii congresso iberoamericano de informática educativa, 2003. [7] nwana h., 1996. “software agents: an overview”. vol. 11, no 3, pp. 205-244. cambridge university press. [8] paquette g., 1999. “meta-knowledge representation for learning scenarios engineering”. proceedings of ai-ed, 1999. [9] posadas j., 2003. “arquitectura para el control de robots móviles mediante delegación de códigos y agentes”. tesis doctoral. universidad politécnica de valencia. [10] soller, a. linton, f., goodman, b., and lesgold, a., 1999. “toward intelligent analysis and support of collaborative learning interaction”. proceedings of the ninth international conference on artificial intelligence in education, le mans, france, 75-82. [11] russell, s., norvig, p., 1995. “artificial intelligence: a modern approach”. ed. prentice hall. [12] wooldridge m. and jennings r., 1995. “intelligent agents: theory and practice”. the knowledge engineering review, 10(2): pp. 115–152. international journal of computers, communications & control vol. i (2006), no. 4, pp. 35-44 numerical aspects and performances of trajectory planning methods of flexible axes jean-yves dieulot, issam thimoumi, frédéric colas, richard béarée abstract: adequate path planning design is an important stage for controlling flexible axes because it may allow to cancel vibrations induced by oscillating modes. among bang-bang profiles which are linked to optimal control, jerk assignment (acceleration derivative) and input shapers have been investigated. theoretical results show the performance and robustness with respect to natural frequency mismatch. practical validations on a real robot arm show the relevance of the jerk algorithm which is more robust with the same productivity performances as input shaping techniques. keywords: flexible axes, vibrations, bang-bang laws, input shapers, motion planning. 1 introduction vibration control of flexible cartesian robots or axes is strongly related to productivity. indeed, lighter structures allow material savings and higher performances in terms of speed, acceleration. . . which, in turn, cause undesirable oscillations. in practice, classical servo control fails to reduce vibrations, which, in many cases, restrains the operating speed [1]. the only way to damp the oscillations consists of designing proper smooth reference trajectories, which should be however as fast as possible [2]. many methods have been introduced using the inversion motion equations or a combination of smooth trigonometric or polynomial functions, e.g. [3]. very often, though, these methods face the robustness problem with respect to uncertainties in the dominant frequency, which is the main model parameter to be taken into account into anti-vibration algorithms design. near-optimal bang-bang methods have been designed to solve the trade-off between productivity and vibrational behavior. these mainly consist of convolving a series of impulses with the control input, where the inter-pulse duration is adequately chosen in a way that the resulting oscillations are in phase opposition [4]. these techniques are known as input shapers, and have proven to be quite effective on linear systems e.g. [5, 6]. on another hand, the most popular damping scheme consists of a bang-bang profile in jerk (which is the derivative of acceleration), and has been proven to be related to input shapers with negative weights [7]. the goal of the paper is to compare different bang-bang laws (shapers, jerk. . . ) in terms of productivity and sensitivity to parameter uncertainty, for a classical model of flexible axis controlled in closed-loop. in a first part, different trajectory planning methods are presented and theoretical results, in terms of vibration attenuation and robustness, are presented. the second part deals with an experimental validation on a real industrial pick-and-place robot which will draw a fair comparison in terms of productivity between these methods. 2 preshaping laws 2.1 architecture of cnc axes control of a cnc axis or a cartesian robot arm is decomposed into three phases [1]: • geometric motion planning which provides the desired path within the whole workspace (in 2or 3-d) copyright c© 2006 by ccc publications 36 jean-yves dieulot, issam thimoumi, frédéric colas, richard béarée figure 1: control strategy of a cnc axis • a time-dependent path planning stage which should, of course, take into account for saturations, but, which will be shown later, should be designed to cancel undesirable vibrations • a servo controller which should help to reject disturbances and to track the reference trajectory. in practice, the model of a flexible axis controlled with cascaded loop will be [1] x(s) xre f (s) = 1 + k fvkv s 1 + skv 1 + 2ζω s 1 + 2ζω s + 1 ω 2 s 2 (1) where x is the position of the motor, xre f is the reference trajectory, kv, k fv are servo gains (position and speed gains, respectively), ζ , ω are respectively the damping ratio (assumed as very small, ζ << 1) and the natural frequency of the axis modeled as a two-mass-spring damper unit. the main purpose of this paper will be the design of the time-dependent path-planning of the closed-loop system represented by equation (1). 2.2 smooth continuous laws a first way of designing the reference trajectory, would be to invert the whole path planning, such as in the so-called flatness approach [3]. in practice, though, model parameters vary since the structure consists of an assembly of flexible beams. the linear approximation of equation (1) is indeed valid for one set-point, which is the case during the short periods corresponding to the start and stop procedures, but not during the whole course. for point-to-point displacements, a stage of acceleration and deceleration is necessary. in cnc control, these stages are driven in a very sharp way, commonly a bang-bang in acceleration, which consequently excites the flexible mode of equation (1) and generates high-amplitude vibrations. smooth trajectories based upon the natural frequency of the closed-loop system (1) can thus be designed, and an obvious method consists of introducing such laws as trigonometric trajectories (fig. 2). the maximal vibratory error for a "sine" profile is (εvib)max = x̄re f sin ( π τ 2 ) π τ (τ 2 −1) (2) for a square sine [2] profile ( τ = tf ω/2π ; tf is the motion duration, x̄re f is the target position). in practice, the error is significantly reduced for a movement of duration superior to 4 times the natural period. an alternative consist of building smooth polynomials which would verify smooth constraints on the dynamics such as minimizing the amplitude of the jerk during the whole displacement, the corresponding polynomial is: xre f = 30t 3x̄re f t 5f ( t 2 5 − tf t 2 + t 2f 3 ) (3) numerical aspects and performances of trajectory planning methods of flexible axes 37 figure 2: smooth control laws and the corresponding maximum error decreases again after a few periods [2]: (εvib)max = 15x̄re f π 5τ 5 [ 3π τ cos (π τ) + ( −3 + π 2τ 2 ) sin (π τ) ] (4) 2.3 jerk laws contrary to previous laws, it is also possible to use "bang-bang" or discontinuous laws to achieve the feedforward control of cnc axes, which are related to time-optimal control as proven in [8]. figure 3: bang-bang profile (jerk-limited) the maximum theoretical error for a bang-bang in acceleration is max t≥t f (ε(t)) x̄re f = ( sinc ( π.τ 2 ))2 , τ = tf ω/2π . for a profile with a bang-bang in jerk, one obtains [2]: max t≥tf ∣∣∣∣ ε(t) x̄re f ∣∣∣∣ = 4am ω 2 · √ 1 + k f 2v ω 2/k2v 1 + ω 2/k2v · ∣∣∣∣sinc ( tjω 2 )∣∣∣∣ ψ (tj, ta, tv) (5) where tf = 4tj + 2ta + tv (see fig. 3), am is the maximum acceleration, and: ψ (tj, ta, tv) = ∣∣sin ((ta + tj) ω/2) sin ((ta + 2tj + tv) ω/2) ∣∣ in theory, it is possible to find a value of jerk for zero-vibration which will be: tj = 2π ω and j = 2π ω am (6) the theoretical robustness with respect to an uncertainty in the natural period t is shown below: 38 jean-yves dieulot, issam thimoumi, frédéric colas, richard béarée figure 4: residual vibrations 2.4 input shapers the principle of input shapers was introduced by singer and seering [4] and consists of convolving the reference or the input with a series of well-chosen impulses. jerk motion planning can be considered as input shaping with negative weights. the underlying idea is that the oscillation will be compensated figure 5: input shaper principle by that induced by a shifted pulse as shown fig. 6: a shaper is thus a series of shifted pulses which can figure 6: superposition of oscillations be written f (s) = a0 + n ∑ i=1 aie −sti (7) where n ∑ i=1 ai = 1. the response to a second order system will be: x(t) = n ∑ i=1 x̄re f [ aiω√ 1−ζ 2 e−ζ ω(t−ti) ] sin(ω √ 1−ζ 2(t −ti)) numerical aspects and performances of trajectory planning methods of flexible axes 39 • the zero-vibration (zv) shaper, which is a two pulse filter for which the vibration at the stop stage should be zero; one obtains the following equations:    a0 + a1eζ ω t1 cos(ω √ 1−ζ 2t1) = 0 a1e ζ ω t1 sin(ω √ 1−ζ 2t1) = 0 a0 + a1 = 1 • the zero-vibration derivative (zvd) shaper which is a three pulse filter for which the position and its derivative at the stop stage should be zero; equations are: [ ai ti ] =   1 d(ζ ) 2e −ζ π√ 1−ζ 2 d(ζ ) e −2ζ π√ 1−ζ 2 d(ζ ) 0 π ω √ 1−ζ 2 2π ω √ 1−ζ 2   where d(ζ ) = 1 + 2e −ζ π√ 1−ζ 2 + e −2ζ π√ 1−ζ 2 figure 7: shaper robustness figure 7 shows shaper robustness where the percentage of residual vibration is plotted versus the ratio ω1/ω where ω1 = 2π/t1 (a specified insensitive is a 4-pulse shaper [9]). the more pulses, the more the shaper will be robust with respect to natural frequency uncertainty. however, it can be understood that adding pulses leads to higher cycle times, as will be shown later on. one can see that the zv shaper, which is quite performing, is not really robust with respect to natural frequency mismatch. when applied to equation (1), the zv shaper gives a maximal error of: max t≥tf ∣∣∣∣ ε(t) x̄re f ∣∣∣∣ = a 2 + 2t 2ω 2 √√√√√√   ( 1 + cos ( π ω ω1 ) −t ω sin ( π ω ω1 ))2 + ( t ω + t ω cos ( π ω ω1 ) + sin ( π ω ω1 ))2   with a theoretical cycle time of: tf = 1 2 (√ 16x̄re f a −t1 ) the zvd shaper gives a maximal error of max t≥tf ∣∣∣∣ ε(t) x̄re f ∣∣∣∣ = a 2 + 2t 2ω 2 √√√√√√   ( t ω + t ω cos ( π ω ω1 ) + cos ( π ω ω1 ) sin ( π ω ω1 ) + sin ( π ω ω1 ))2 + ( 1 + cos ( π ω ω1 ) + t ω sin ( π ω ω1 ) + t ω cos ( π ω ω1 ) sin ( π ω ω1 ))2   40 jean-yves dieulot, issam thimoumi, frédéric colas, richard béarée and the cycle time is tf = 12 (√ 16x̄re f a + t1 ) , which is much longer than precedingly, and comparable to the bang-bang of jerk cycle time. 3 experimental comparisons of path planning laws 3.1 material and methods the experimental validations are carried out on a 3-axes robot (fig. 8). it has been equipped with a real-time "dspace 1103" control card. the available measurements on the motor part come from the actuator axis encoders [10]. when the horizontal axis is only moving, it will be assumed that the axis stiffness remains almost constant (with an actual variation of 30 %) and the system can be considered as linear. the validation was undertaken for a displacement on the x axis, with x2 varying from x20 = 0 to x2 = 900mm and y2 = 0mm with a height z = 315mm. according to experimental results, the natural figure 8: overview of the first test-setup prototype (stroke [mm]: x-1000 y-400 z-800, max. feedrate: 120 m.min−1, max. acceleration: 4 m.s−2). frequency was calculated as fn = 9.8hz, the damping ratio was taken as ς = 0.0132. in this case, the optimal value of the jerk given by equation (6), which limits the amplitude of vibrations, is of 30 m.s−3. parameters of the industrial control loop were kp = 14s−1, kv = 2a.rd.s−1. 3.2 experimental results one can see that, from fig. 9, the curve follows a sinc (.) evolution. fine jerk tuning is indeed effective. when the jerk parameter is not well-tuned, the vibrational behavior deteriorates slightly. as for the zvd shaper (fig. 10) , one can find out what was simulated, one obtains zero vibration when a = ω1/ω is even, and the curve is flat around points cancelling vibrations. results for the zv shaper are alike and not shown here. 3.3 comparison and discussion one can compare experimentally the maximal error for the three laws: figure 11 shows that the zv shaper is not robust at all. the jerk law is quite robust with respect to input shapers, because the jerk is a continuous derivative of the acceleration during the whole motion, whereas the zvd cancels vibrations only at the point where zero vibrations are desired. for a value of a = 1, the three curves have the same percentage of residual vibration (same performances). when a = 0, one finds again the classical bang-bang in acceleration for which by definition 100% vibrations occur (fig. 11). the cycle time is numerical aspects and performances of trajectory planning methods of flexible axes 41 figure 9: jerk-limited residual vibrations (%)-(a) : variation of maximal residual vibrations as a function of a = ω j/ω, ω j = 2πtj (b) : amplitude of vibrations at jerk j=100 (c) : amplitude of vibrations at jerk j=30 figure 10: zvd residual vibrations (%)-(a) : variation of maximal residual vibrations as a function of a = ω1/ω , (b) : amplitude of vibrations at a = 0.5 (c) : amplitude of vibrations at a = 1 42 jean-yves dieulot, issam thimoumi, frédéric colas, richard béarée figure 11: comparison of planning laws: percentage of maximum vibrations figure 12: comparison of planning laws: cycle time numerical aspects and performances of trajectory planning methods of flexible axes 43 defined as the instant for which the motor position reaches its reference by 0.2 mm. figure 12 shows the evolution of the cycle time of the different laws. one can see that the zv shaper is quicker by 13%. as a conclusion, the jerk bang-bang algorithm is more robust to parameter variations than a zv shaper which has approximately the same cycle time. another advantage is that this algorithm is now implemented in most industrial cnc control devices. when the frequency is always the same, fixed and well-estimated, one can expect to gain some productivity using a zv shaper instead. a possibility to cope with non-linearities consists either of using "gain scheduling" or adaptive/iterative algorithms e.g. [11, 12]. 4 summary and conclusions the design and computation of near-optimal reference trajectories for cnc devices is a difficult challenge which should examine the trade-off between cancellation of undesirable vibrations and rapidity. this design can be achieved using continuous "jerk" (derivative of acceleration) laws or input shapers, which consist of convolving pulses with the control input. theoretical and empirical results show the relevance of using jerk laws due to their productivity performances at comparable robustness properties (with respect to natural frequency uncertainties). a good calibration of the model parameters would allow to use a two-pulse shaper, which is faster but less robust. in practice, however, these algorithms have to be adapted to be implemented into cnc units, because of neglected dynamics (such as dry friction) which may cause static errors. references [1] béarée, r., barre p.j., bloch s., influence of high feed rate machine tool control parameters on the contouring accuracy, j. intell. robotic systems, vol. 40, 2004, pp 321-342. 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[7] singhose w., singer n., seering w., time-optimal negative input shapers, j. dynam. syst., meas., contr., vol. 119, 1997, pp. 198-205. [8] lau m. a., pao, l. y. , input shaping and time-optimal control of flexible structures, automatica, vol. 39, 2003, pp 893-900. [9] singhose w. e., derezinski s. j., singer n. c., extra insensitive input shaper for controlling flexible spacecraft, j. guidance control dynamics, vol. 19, 1996, pp 385-391. 44 jean-yves dieulot, issam thimoumi, frédéric colas, richard béarée [10] dumetz e., dieulot j.y., barre p.j., colas f., control of an industrial robot using acceleration feedback, j. intell robotic systems, vol. 46, 2006, pp 111-128. [11] md zain m.z., tokhi m.o., mohamed z., hybrid learning control schemes with input shaping of a flexible manipulator system, mechatronics, vol. 16, 2006, 209-219. [12] cutforth, c. f., pao l. y., adaptive input shaping for maneuvering flexible structures, automatica, vol. 40, 2004, pp 685-693. jean-yves dieulot ensam de lille research technological team cemodyne 8 bd louis xiv, 59000 lille cedex e-mail: jean-yves.dieulot@polytech-lille.fr received: november 6, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 719-726 a homogeneous algorithm for motion estimation and compensation by using cellular neural networks c. grava, a. gacsádi, i. buciu cristian grava, alexandru gacsádi, ioan buciu university of oradea faculty of electrical engineering and information technology oradea, romania e-mail: {cgrava,agacsadi,ibuciu}@uoradea.ro abstract: in this paper we present an original implementation of a homogeneous algorithm for motion estimation and compensation in image sequences, by using cellular neural networks (cnn). the cnn has been proven their efficiency in real-time image processing, because they can be implemented on a cnn chip or they can be emulated on field programmable gate array (fpga). the motion information is obtained by using a cnn implementation of the well-known horn & schunck method. this information is further used in a cnn implementation of a motion-compensation method. through our algorithm we obtain a homogeneous implementation for real-time applications in artificial vision or medical imaging. the algorithm is illustrated on some classical sequences and the results confirm the validity of our algorithm. keywords: cellular neural networks, motion estimation, horn & schunck method. 1 introduction the motion estimation and compensation algorithms were developed for different applications as artificial vision, video information compression, medical imaging, digital and high-definition television, video-telephony, virtual-reality and multimedia techniques. motion estimation allows one to reduce the temporal redundancy in a sequence of images in order to reduce the transmission rate and has been widely used in television signal coding (e.g. motion-compensated (mc) prediction, mc interpolation) and videoconference services [1]. to avoid this limitation in this paper we propose a fully parallel solution in order to realize the motion estimation and compensation, using cnn [2], as a competing alternative to classical computational techniques. the advantage of the algorithms that can be implemented on cnns is that these kinds of neural networks already exists in hardware version [3], [4] and thus we can obtain real-time applications. in our case, because the motion estimation and compensation methods have generally a great computational cost, we develop homogeneous algorithms (that is the estimation part and compensation part are implemented in the cnn environment) for real-time applications that can be after that applied in artificial vision or medical imaging. after a small introduction, in the second part of this paper we present an overview of motion estimation and compensation methods, followed by a section that presents the cnn implementation of the horn and schunck motion estimation method and a section that presents the cnn implementation of the motion compensation. in the section dedicated to experimental results we present results that confirm the validity of our algorithm and we finalize our paper with a section of conclusions giving also some perspectives to our work. copyright c⃝ 2006-2010 by ccc publications 720 c. grava, a. gacsádi, i. buciu 2 overview on motion estimation and compensation methods the most used motion estimation methods are [5]: • differential methods (or gradient methods); in this case the motion being estimated based on the spatial and temporal gradients of images [6], [7], [1]; • block-based methods (or correlative methods). these methods could be classified in phasecorrelation methods and block-matching methods. in the case of phase-correlation methods, the motion is estimated based on the fourier phase-difference between two blocks from two successive images. these methods are less used in practice because of the high noise-sensitivity. in the case of block-matching methods the location of the block (in the following or previous images) that best matches the reference block in the current image is searched, based on a certain matching or difference criteria. both methods are usually applied in the case of a translation movement, but could be also adapted for other spatial models of the movement [8]. the principle of almost all motion estimation methods is that the brightness intensity of each pixel is constant along the motion trajectory or is modifying in a predictable way. this hypothesis of brightness intensity preservation of each point (x,y,t) along the motion trajectory can be expressed through the equation of displaced frame difference (dfd), between the t and t − 1 = t − ∆t instants [9]: dfd(x, y) = φ(x − dx, y − dy, t − dt) − φ(x, y, t), (1) where φ(x, y, t) denote the brightness distribution of the image at the moment t and d = [dx, dy]t is the displacement vector between the moments t and t′ = t − ∆t (dx and dy being the displacement vectors on x and y direction, respectively). differential motion estimation methods are based on spatial and temporal gradients of a sequence of images. if the brightness intensity of a pixel is not varying in time, then dφ/dt = φt = 0 [1]. the first order taylor development of this last relation, has as result the “equation of movement constraint” emc (or “optical flow equation” ofe) that links the spatial and temporal gradients of brightness intensity [9]: ∂φ ∂x ∂x ∂t + ∂φ ∂y ∂y ∂t + ∂φ ∂t = 0. (2) we can rewrite: φx · νx + φy · νy + φt = 0, (3) where φx and φy are the spatial gradients, φt is the temporal gradient of the brightness intensity and νx = dx/dt, νy = dy/dt are the velocities on x and y directions [1]. as it can be observed in eq. 3, the optical flow equation has two unknowns (νx, νy), hence the system is under-determined leading to an ill-posed issue. in order to obtain both movement components (νx, νy), it has to be introduced a second constraint, to obtain a fully determined system (two equations with two unknowns). one of the possible constraints is offered by the well-known horn & schunck motion estimation method [1], which assumes that all the neighbor pixels have similar movement (we say that the velocity field is uniform or smooth). it results that it has to be minimized an energy: e2 = e2flow + γe 2 uniformity (4) the first term correspond to the difference related to the projection of the velocity vectors on the spatial gradient (as in the emc) and the second term correspond to the difference related a homogeneous algorithm for motion estimation and compensation by using cellular neural networks 721 to a smooth field, g being the weighting term between the two terms. the uniformity constrain is expressed by the equation: e2uniformity = ( ∂νx ∂x )2 + ( ∂νx ∂y )2 + ( ∂νy ∂x )2 + ( ∂νy ∂y )2 (5) e2flow = ( φx · νx + φy · νy + φt )2 (6) the solution is obtained after a gauss-seidel minimization [1]. equation 6 will be minimized when the error (that means the difference between two successive values of (νx, νy), will be considered as being the minimal or when the maximum chosen number of iterations will be reached. this method is not limited to translations as block-matching method and the computations are shorter, but the movement amplitude has to be small (less than three pixels) because of the considerations regarding taylor development. the solution to avoid the constraint regarding small amplitude of movement is to use the multi-resolution technique [1]. using two consecutive frames (φi(x, y, ti) and φf(x, y, tf)) of a sequence (fig. 1), after the application of a motion estimation algorithm, as horn and schunck method, for each pixel it results an estimation of its movement in the both two directions (x,y) of the system of co-ordinates that is attached to image plane. figure 1: the illustration of motion compensation. the purpose of motion compensation is that based on estimated motion information and starting from a reference image (the initial image φi, in fig. 1) to obtain an estimation of the real comparison image (the final image φf) that was used in the process of motion estimation [10]. 3 the cnn implementation of the horn & schunck motion estimation method regarding cnn gray-scale image processing generally, variational computing based template design is possible if the design constrains are respected [11], [12]. in order to analytically determine the template, cost functions or energies are used in the designing step. an important designing aspect is represented by the way to associate each energy function with one of the a, b, c or d template, as well as the way to chose the layers number of the cnn. taking into account the characteristics of the existing cnn chip it is recommended to use only mono-layer cnn and only a and b templates. in the cost functions some weights can be introduced in order to maintain the state values in the linear zone of the state-output transfer characteristic. the motion estimation results using the two images, φ1(x, y, t) and φ2(x, y, t + ∆t), a two-layer cnn structure and the hosch.tem (see fig. 2). after the cost function minimization [13], for the hosch.tem it results: • polarization images z1 = φxφt and z2 = φyφt, 722 c. grava, a. gacsádi, i. buciu figure 2: two-layer cnn structure for the proposed horn & schunck motion estimation method. • nonlinear templates a1 and a2: 0 a 0 a 1-4a a 0 a 0 where the parameter a also includes the γ parameter from the equation (6), obviously weighted with the constants that results at the energy minimization. nonlinear d − type template d is: 0 0 0 0 dkl 0 0 0 0 where each element dkl is expressed as follows: d11(νx) = (φx)2 · νx, d21(νy) = φxφy · νy, d22(νy) = (φ y)2 · νy, and d12(νx) = φxφy · νx, respectively. in fig. 3 the images with the estimated motion in the case of the “taxi” real sequence are presented. the first two images represent two images of the well-known sequence in motion estimation reference, “hamburg-taxi”, and in the last two images the motion νx and νy (or displacement) images are presented. the two last images represent the displacement in the two spatial directions. the combination between these two images could be also represented as a single image with vectors corresponding to the displacement of each pixel of the reference image, as we will present in the section presenting other experimental results. for a better visualization, the motion images, νx and νy, are not calibrated in the cnn domain. if we want to use these images in image compensation we have to calibrate them in the cnn domain [−1, +1]. figure 3: two images of the hamburg-taxi real sequence, used in our experiments. a homogeneous algorithm for motion estimation and compensation by using cellular neural networks 723 4 the cnn implementation of motion compensation in order to develop a motion compensation algorithm that can be directly implemented on a cnn chip, we have to decompose such an algorithm as elementary steps that can be then implemented on the existing hardware. as a result of the motion estimation process, a pixel could be stationary or can change its position in one of the eight elementary directions: n, n-e, e, s-e, s, s-w, w, n-w. after the application of a motion estimation technique, the pixels of the intermediary image frame φ(x, y, t), that corresponds to any given moment t ∈ (ti, tf), could be classified in the following categories (see fig. 1), where t is the time-position of the intermediary image, between the initial image and the final image: • pixels of “a” type that has an identical position in the two consecutive images. these pixels does not change, at a given moment t ∈ (ti, tf), neither their positions nor their values; • pixels of “b” type, that will moves as a result of the fact that the corresponding pixels in the two images that contains the motion information has a value greater than a current elementary value (that could be view as a quantum or a threshold). the value of these pixels is not changing, but inserting intermediary images between the initial and final image, φi and φf, their positions are changing successively with one elementary value (one quantum) corresponding to the spatial discretization. the maximum number of intermediary images that could be inserted equals the maximum number of elementary values (quantum) that could be identified in the images that contains motion (or displacement) information; • pixels of “c” type are those pixels that will change their values because will be covered by the pixels that will arrive in that position, overlapping the initial pixel: c(t) = shift(b(t)) (7) • pixels of “d ” type, with unknown values, that are the result of the displacement of “b” type pixels, that liberates a location but there is no pixel arriving in that location. in each step of the movement, the value of these pixels could be determined through spatial cnn spline-cubic interpolation [10]: d(t) = ⌊d(t − 1) · c(t) + b(t) · c(t)⌋ · b(1) (8) • pixels of “e” type that at the current time during the processing will have the same value as in the initial image φi, due to the movement of the pixels (arrivals and departures of the pixels). the values of these pixels will be restored from the initial image: e(t) = c(t − 1) · b(t) · c(t) · d(t) (9) each intermediary step has as result an associate image and to create this intermediary image it has to be done the following operations: • determine the “c” type pixels, that is the displacement with one pixel in the direction resulting from the motion information; • interpolation, in order to determine the values of unknown pixels, that is the “d ” type pixels. for each intermediary image, the value of a pixel results after the determination of the type of that pixel. the state of a pixel could change during the processing. the initial and final image, 724 c. grava, a. gacsádi, i. buciu φi and φf, and the images that contain the motion information have the same dimensions. in this paper, all images are converted to standard cnn gray-scale images, taking values between -1 to +1 (see fig. 5). the convention is that pixels with negative value are coding a displacement to the left (fig. 5 a) or to up, respectively (fig. 5 b) and the pixels having positive values are coding the displacements to the right (fig. 5 a) or to down, respectively (fig. 5 b). the values of the pixels coding the motion are multiples of the minimum detectable value of the motion. in order to detect the pixels that will change their position and to move the pixels, the treshold.tem and shift.tem templates family are used [14]. the determination of the value of pixels of “d ” type could be made for each intermediate image or only to the final image. in order to avoid the modification of the pixels of “a” type or in order to restore the pixels of “e” type, some mask-images are created during the processing, using the equations (7), (8) and (9). figure 4: conventions in the images containing the motion information. 5 experimental results in this section some experimental results obtained by using the "cadetwin" (cnn application development environment and toolkit under windows [14]) are presented. the images containing the motion estimation, νx and νy, are calibrated in the cnn domain [−1, +1]. the processing time depends on the number of interpolations and on the number of the motion estimation quantum, that results after spatial discretization, that is on the total number of images inserted between the initial and final image, φi and φf. due to parallel processing, this total processing time is independent by the dimensions of the original images or by the number of moving pixels. in order to illustrate the implemented method in the case of real images and in the case of a complex movement, starting from an initial image of a well-known “tennis-table” sequence, we have simulated a complex motion, using the free form deformation principle, resulting (see fig. 5) three real images of a sequence (φ1, φ2, φ3). in figure 5 we also represented the “motion estimation field” obtained after applying our cnn implementation of the horn and schunck motion estimation method. starting from the first image of the real sequence (φ1) and this “motion estimation field” we can obtain the “motion compensated image” (φ̂2), that represent an estimation of the real image (φ2). as it can be observed, the biggest errors between the real image and the motion compensated image (φ2 − φ̂2) are located in the region with a high gradient of intensity that usually corresponds to the regions with different motion. another cause oh these errors could also be the discrete nature of the image spatial support and the interpolations that are necessary. 6 conclusions generally, in the case of serial implementation of a motion compensation algorithm, the processing time depends on the image dimensions. in the case of our cnn motion estimation and compensation algorithm, that uses only 3×3 linear templates, the algorithm can be directly a homogeneous algorithm for motion estimation and compensation by using cellular neural networks 725 figure 5: motion estimation and compensation using cnn: principle and results. implemented on the cnn-universal chip [4] and thus the image processing become completely parallel. the advantage of using the cnn hardware platform is that the total processing time doesn’t depend on image dimensions, being dependent only on the number of displacement steps that has to be performed and thus we can obtain real-time applications with applications in artificial vision and medical imaging. taking into account that our cnn motion estimation and compensation algorithm is based on the cost functions minimization (usually partially differential equations) resulting nonlinear templates, our attention is focused on the fpga implementation of our algorithm, on a digital emulator of the cnn [11], [12]. 7 acknowledgement this work was partially supported by a grant from the romanian national university research council, pncdi program id-668/2008. bibliography [1] horn b.k.p. and schunck b.g, determining optical flow, artificial intelligence, vol.17, pp. 185-203, 1981. [2] chua l.o. and yang l, fuzzy control rules in convex optimization, ieee transactions on circuits and systems, vol.35, pp.1257-1290, 1998. 726 c. grava, a. gacsádi, i. buciu [3] cembrano g.l., rodríguez-vázquez a., espejo-meana s., and domínguez-castro r., ace16k: a 128×128 focal plane analog processor with digital i/o, int. j. neural syst., vol.17, issue 6, pp. 427-434, 2003. [4] roska t. and chua l.o., the cnn universal machine: an analogic array computer, ieee transactions on circuits and systems, vol.40, pp. 167-173, 1993. [5] konrad j., motion detection and estimation, image processing handbook, networking and multimedia, pp. 207-227, 2000. [6] bruhn a., weickert j., feddern c., kohlberger t., schnorr c., real-time optic flow computation with variational methods, computer analysis of images and patterns, pp. 222-229, 2003. [7] brox t., bruhn a., papenberg n., weickert j., high accuracy optical flow estimation based on a theory for warping, eccv, pp. 25-36, 2004. [8] wei w., hou z.-x., guo y.-c., a displacement search algorithm for deformable block matching motion estimation, proc. of ieee international symposium on communications and information technology, pp. 457-460, 2005. [9] barron j.l., fleet d.j., beauchemin s., performance of optical flow techniques, international journal of computer vision, vol. 12, issue 1, pp. 43-77, 1994. [10] grava c., gacsádi a., gordan c., maghiar t., bondor k, motion compensation using cellular neural networks, proc. of the european conference on circuit theory and design (ecctd), vol. i, pp. i-397-i-400, krakow, poland, 2003. [11] kincses z., nagy z., szolgay p, implementation of nonlinear template runner emulated digital cnn-um on fpga, proc. of the 10th international workshop on cellular neural networks and their applications, pp. 186-190, istanbul, turkey, 2006. [12] nagy z., vörösházi zs., szolgay p., emulated digital cnn-um solution of partial diferential, int. journal of circuit theory and applications, vol. 34, issue 4, pp. 445-470, 2006. [13] gacsádi a., grava c., tiponut v., szolgay p., a cnn implementation of the horn & schunck motion estimation method, proc. of the 10th international workshop on cellular neural networks and their applications, pp. 381-385, istanbul, turkey, 2006. [14] *** cadetwin, cnn application development environment and toolkit under windows. version 3.0, analogical and neural computing laboratory, hungarian academy of sciences, budapest, 1999. international journal of computers, communications & control vol. ii (2007), no. 2, pp. 195-204 robust pid decentralized controller design using lmi danica rosinová, vojtech veselý abstract: the new lmi based method for robust stability analysis for linear uncertain system with pid controller is proposed. the general constrained structure of controller matrix is considered appropriate for both output feedback and decentralized control and the respective guaranteed cost control design scheme is presented. the sufficient robust stability condition is developed for extended quadratic performance index including first derivative of the state vector to damp oscillations. the obtained stability condition is formulated for parameter-dependent lyapunov function. keywords: uncertain systems, robust stability, decentralized control, linear matrix inequalities (lmi), lyapunov function 1 introduction robust stability and robust control belong to fundamental problems in control theory and practice; various approaches in this field have been proposed to cope with uncertainties that always appear in real plant ([2];[8];[7];[5];[4]). the development of linear matrix inequality (lmi) computational techniques has brought an efficient tool to solve a large set of convex problems in polynomial time (e.g. [2]). significant effort has been therefore made to formulate crucial control problems in algebraic way ([12]), so that the numerical lmi solution can be adopted. this approach is advantageously used in solving control problems for linear systems with convex (affine or polytopic) uncertainty domain. however, many important problems in linear control design, such as decentralized control, simultaneous sof or more generally structured linear control problems have been proven as np hard ([1]). intensive research has been devoted to overcome nonconvexity and transform the nonconvex or np-hard problem into convex optimisation problem in lmi framework. various techniques have been developed using inner or outer convex approximation of the respective nonconvex domain. the common tool in both inner and outer approximation is the use of linearization or convexification. in ([6]; [3]) the general convexifying algorithm for the nonconvex function together with potential convexifying functions for both continuous and discrete-time case have been proposed. linearization approach for continuous and discrete-time system design was independently used in ([13];[11]). proportional-integral-derivative (pid) controllers belong to the most popular ones in the industrial world. the derivative part of the controller, however, causes difficulties when uncertainties are considered. in multivariable pid control schemes using lmi developed recently ([14]) the incorporation of the derivative part requires inversion of the respective matrix, which does not allow including uncertainties. the other way to cope with the derivative part is to assume the special case when output and its derivative are state variables, robust pid controller for first and second order siso systems are proposed for this case in ([7]). in this paper a state space approach to designing decentralized (multi-loop) pid robust controllers is proposed for linear uncertain system with guaranteed cost with a new quadratic cost function. the major contribution is in considering the derivative part in robust control framework. we adopt the new pid control problem formulation using lmi that is appropriate for polytopic uncertain systems. the robust pid control scheme is proposed for structured control gain matrix, thus enabling decentralized pid control design. in section 2 the robust control design problem with structured control gain matrix is formulated in general. the robust optimal control design procedure in state space with the extended cost function is proposed in section 3. the main result-robust pid controller design approach is provided in section copyright © 2006-2007 by ccc publications 196 danica rosinová, vojtech veselý 4, the developed approach is appropriate for decentralized control structure. in section 5 the results are illustrated on the examples. 2 problem formulation and preliminaries consider a linear affine uncertain system: δ x(t) = (a + δ a)x(t) + (b + δ b)u(t) y(t) = cx(t) (1) where δ x(t) = ẋ(t) for continuous time system δ x(t) = x(t + 1) for discrete time system x(t) ∈ rn, u(t) ∈ rm, y(t) ∈ rl are state, control and output vectors respectively; a, b,c are known constant matrices of appropriate dimensions corresponding to the nominal system, δ a, δ b are matrices of uncertainties of the respective dimensions.the uncertainties are considered to be affine of the form δ a = p ∑ j=1 ε jã j, δ b = p ∑ j=1 ε jb̃ j (2) where ε j ≤ ε j ≤ ε̄ j are unknown uncertainty parameters; ã j, b̃ j, j = 1, 2, ..., p are constant matrices of uncertainties of the respective dimensions and structure. the uncertain system (1), (2) can be equivalently described by a polytopic model given by its vertices {(a1, b1,c), (a2, b2,c), ..., (an , bn ,c)}, n = 2p. the decentralized feedback control law is considered in the form u(t) = fcx(t) (3) where f is a matrix corresponding to decentralized controller. the uncertain closed-loop polytopic system is then δ x(t) = ac(α)x(t) (4) where ac(α) ∈ { n ∑ i=1 αiaci, n ∑ i=1 αi = 1, αi ≥ 0 } , aci = ai + bifc. (5) to assess the performance quality a quadratic cost function known from lq theory is often used. however, in practice the response rate or overshoot are often limited. therefore we include into the cost function the additional derivative term for state variable to open the possibility to damp the oscillations and limit the response rate. jc = ∞∫ 0 [x(t)t qx(t) + u(t)t ru(t) + δ x(t)t sδ x(t)]dt for a continuous-time and jd = ∞ ∑ k=0 [x(t)t qx(t) + u(t)t ru(t) + δ x(t)t sδ x(t)] (6) robust pid decentralized controller design using lmi 197 for a discrete-time system, where q, s ∈ rn×n, r ∈ rm×m are symmetric positive definite matrices. the concept of guaranteed cost control is used in a standard way: let there exist a feedback gain matrix f0 and a constant j0 such that j ≤ j0 (7) holds for the closed loop system (4), (5). then the respective control (3) is called the guaranteed cost control and the value of j0 is the guaranteed cost. the main aim of this paper is to develop a decentralized pid control algorithm that stabilizes the uncertain system (1), with guaranteed cost with respect to the cost function (6). we start with basic notions concerning lyapunov stability and convexifying functions. in the following we use d−stability concept ([4]) to receive the respective stability conditions in more general form. definition 1. (d-stability) consider the d-domain in the complex plain defined as d = {s is complex number : [ 1 s ]∗ [ r11 r12 r∗12 r22 ] [ 1 s ] < 0}. the considered linear system (1) is d-stable if all its poles lie in the d-domain. (to simplify the reading of formulas we use in the definition 1 scalar values of the parameters ri j, in general the stability domain can be defined using matrix values of parameters ri j with the respective dimensions.) the standard choice of ri j is r11 = 0, r12 = 1, r22= 0 for a continuous-time system; r11 = −1, r12 = 0, r22= 1 for a discrete-time system. the quadratic d-stability is equivalent to the existence of one lyapunov function for the whole set that describes the uncertain system model. definition 2. (quadratic d− stability) the uncertain system (4) is quadratically d−stable if and only if there exists a symmetric positive definite matrix p such that r12pac(α) + r∗12a t c (α)p + r11p + r22a t c (α)pac(α) < 0 (8) instead of quadratic stability, a robust stability notion is considered based on the parameter dependent lyapunov function (pdlf) defined as p(α) = n ∑ i=1 aipi where pi = p t i > 0 (9) to obtain less conservative results than using quadratic stability with unique lyapunov function. definition 3. ([5]) system (4) is robustly d-stable in the convex uncertainty domain (5) with parameterdependent lyapunov function (9) if and only if there exists a matrix p(α) = p(α)t > 0 such that r12p(α)ac(α) + r∗12a t c (α)p(α) + r11p(α)+ +r22atc (α)p(α)ac(α) < 0 (10) for all α such that ac (α) is given by (5). the sufficient robust d-stability condition which can be considered as not too conservative has been proposed in ([9]), recalled in the following lemma. lemma 4. if there exist matrices e ∈ rnxn, g ∈ rnxn and n symmetric positive definite matrices pi ∈ rnxn such that for all i= 1,. . . ,n: [ r11pi + atcie t + eaci r12pi −e + atcig r∗12pi −e t + gt aci r22pi −(g + gt ) ] < 0 (11) then system (4) is robustly d−stable. 198 danica rosinová, vojtech veselý note that matrices e and g are not restricted to any special form; they were included to relax the conservatism of the sufficient condition. to transform nonconvex problem of structured control (decentralized control in our case) into convex form, the convexifying (linearizing) function can be used ([6]; [3];[11];[13]). the respective potential convexifying function for x−1 and xwx has been proposed in the linearizing form: the linearization of x−1 ∈ rnxn about the value xk > 0 is φ(x−1, xk) = x−1k −x−1k (x −xk)x−1k (12) the linearization of xw x ∈ rnxn about xk is ψ(xw x , xk) = −xkw xk + xw xk + xkw x (13) both functions defined in (12) and (13) meets one of the basic requirements on convexifying function: to be equal to the original nonconvex term if and only if xk = x . however, the question how to choose the appropriate nice convexifying function remains still open. in the sequel, x > 0 denotes positive definite matrix; * in matrices denotes the respective transposed term to make the matrix symmetric; i denotes identity matrix and 0 denotes zero matrix of the respective dimensions. 3 robust optimal controller design in this section the new design algorithm for optimal control with guaranteed cost is developed using parameter dependent lyapunov function and convexifying approach employing iterative procedure. the proposed control design approach uses sufficient stability condition inspired by the result of ([9]). the next theorem provides the new form of robust stability condition for linear uncertain system with guaranteed cost. theorem 5. consider uncertain linear system (1), (2) with static output feedback (3) and cost function (6). the following statements are equivalent: i) closed loop system (4) is robustly d-stable with pdlf (9) and guaranteed cost with respect to cost function (6): j ≤ j0 = xt (0)p(α)x(0). ii) there exist matrices p(α) > 0 defined by (9) such that r12p(α)ac(α) + r∗12a t c (α)p(α) + r22a t c (α)p(α)ac(α)+ +r11p(α) + q + ct f t rfc + atc (α)sac(α) < 0 (14) iii) there exist matrices p(α) > 0 defined by (9) and h, g and f of the respective dimensions such that [ r11p(α) + atc (α)h t + hac(α) + q + ct f t rfc r∗12p(α)−h t + gt ac(α) ] . [ . ∗ r22p(α)−(g + gt ) + s ] < 0 (15) aci = (ai + bifc) denotes the i−th closed loop system vertex. matrix f is the guaranteed cost decentralized control gain for the uncertain system (4), (5). robust pid decentralized controller design using lmi 199 proof. for brevity the detail steps of the proof are omitted where standard tools are applied. (i) ⇔(ii): the proof is analogous to that in ([10]). the (ii) ⇒(i) is shown by taking v (t) = x(t)p(α)x(t) as a candidate lyapunov function for (4) and writing δv (t), where δv (t) = v̇ (t) for continuous time system δv (t) = v (t + 1)−v (t) for discrete time system δv (t) = r∗12δ x(t) t p(α)x(t) + r12x(t)t p(α)δ x(t) + r11x(t)t p(α)x(t) + r22δ x(t)t p(α)δ x(t) (16) substituting for δ x from (4) to (16) and comparing with (14) provides d−stability of the considered system when the latter inequality holds. the guaranteed cost can be proved by summing or integrating both sides of the following inequality for t from 0 to ∞: δv (t) < −x(t)t [q + ct f t rfc + atc (α)sac(α)]x(t) the (i) ⇒(ii) can be proved by contradiction. (ii)⇔(iii): the proof follows the same steps to the proof of lemma 1: (iii) ⇒(ii) is proved in standard way multiplying both sides of (15) by the full rank matrix: [ i atc (α) ] {l.h.s.(15)} [ i ac(α) ] < 0. (ii) ⇒ (iii) follows from applying a schur complement to (14) rewritten as r12p(α)ac(α) + r∗12a t c (α)p(α) + q + c t f t rfc+ +r11p(α) + atc (α)[r22p(α) + s]ac(α) < 0 therefore [ x11 x12 x t12 x22 ] < 0 where x11 = r11p(α) + r12p(α)ac(α) + r∗12a t c (α)p(α) + q+ +ct f t rfc x12 = atc (α)[r22p(α) + s] x22 = −[r22p(α) + s] which for h = r12p(α), g = [r22p(α) + s] gives (15). the guaranteed cost control design is based on the robust stability condition (15). since the matrix inequality (15) is not lmi we use the inner approximation for the continuous time system applying linearization formula (13) together with using the respective quadratic forms to obtain lmi formulation, which is then solved by iterative procedure. 4 pid robust controller design for continuous-time systems control algorithm for pid is considered as u(t) = kpy(t) + ki t∫ 0 y(t)dt + fdcd ẋ(t) (17) the proportional and integral term can be included into the state vector in the common way defining the auxiliary state z = t∫ 0 y(t), i.e. ż(t) = y(t) = cx(t). then the closed-loop system for pi part of the controller is ẋn = [ ẋ ż ] = [ a + δ a 0 c 0 ] [ x z ] + [ b + δ b 0 ] u(t) 200 danica rosinová, vojtech veselý and u(t) = fcx(t) + fdcd ẋ(t) (18) where fcx(t) and fdcd ẋ(t) correspond respectively to the pi and d term of pid controller. the resulting closed loop system with pid controller (17) is then ẋn(t) = ac(α)xn(t) + b(α) [ fdcd 0 ] ẋn(t) (19) where the pi controller term is included inac(α). (for brevity we omit the argument t.) to simplify the denotation, in the following we consider pd controller (which is equivalent to the assumption, that the i term of pid controller has been already included into the system dynamics in the above outlined way) and the closed loop is described by ẋ(t) = ac(α)x(t) + b(α)fdcd ẋ(t) (20) let us consider the following performance index js = ∞∫ 0 [ x ẋ ]t [ q + ct f t rfc 0 0 s ] [ x ẋ ] dt (21) which formally corresponds to (6). then for lyapunov function (9) we have the necessary and sufficient condition for robust stability with guaranteed cost in the form (14), i.e. for continuous time system: [ x ẋ ]t [ q + ct f t rfc p(α) p(α) s ] [ x ẋ ] < 0. (22) the main result on robust pid control stabilization is summarized in the next theorem theorem 6. consider a continuous uncertain linear system (1), (2) with pid controller (17) and cost function (21). the following statements are equivalent: • closed loop system (19) is robustly d-stable with pdlf (9) and guaranteed cost with respect to cost function (21): j ≤ j0 = xt (0)p(α)x(0). • there exist matrices p(α) > 0 defined by (9), and h, g, f and fd of the respective dimensions such that [ atcih t + haci + q + ct f t rfc pi −mtdih + gt aci ∗ −mtdig−gt mdi + s ] < 0 (23) aci = (ai + bifc) denotes the i−th closed loop system vertex, mdi includes the derivative part of the pid controller: mdi = i −bifdcd . proof. owing to (20) for any matrices h and g: ( −xt h − ẋt gt ) (ẋ−ac(α)x−b(α)fdcd ẋ)+ +(ẋ−ac(α)x−b(α)fdcd ẋ)t ( h t x−gẋ ) = 0 (24) summing up the l.h.s of (24) and (22) and taking into consideration linearity w.r.t. α we get condition (23). theorem 6 provides the robust stability condition for the linear uncertain system with pid controller. notice that the derivative term does not appear in the matrix inversion and allows including the uncertainty in control matrix b into the stability condition. robust pid decentralized controller design using lmi 201 considering pid control design, there are unknown matrices h, g, f and fd to be solved from (23). (recall thataci = (ai + bifc), mdi = i −bifdcd .) then the inequality (23) is not lmi, to cope with the respective unknown matrices products the linearizing approach using (13) has been adopted and the pid iterative control design algorithm based on lmi (4x4 matrix) has been proposed. the resulting closed loop system with pd controller is ẋ(t) = (i −bifdcd )−1(ai + bifc)x(t), i = 1, ..., n (25) the extension of the proposed algorithm to decentralized control design is straightforward since the respective f and fd matrices are assumed as being of the prescribed structure, therefore it is enough to prescribe the decentralized structure for both matrices. 5 examples in this section the major contribution of the proposed approach: design of robust derivative feedback is illustrated on the examples. the results obtained using the proposed new iterative algorithm based on (23) to design the pd controller are provided and discussed. the impact of matrix s choice is studied as well. we consider affine models of uncertain system (1), (2) with symmetric uncertainty domain: ε j = −q, ε j = q (26) example 1. consider the uncertain system (1), (2) where: a =   −4.365 −0.6723 −0.3363 7.0880 −6.5570 −4.6010 −2.4100 7.5840 −14.3100   b =   2.3740 0.7485 1.3660 3.4440 0.9461 −9.6190   c = cd = [ 0 1 0 0 0 1 ] uncertainty parameter q=1; uncertainty matrices: ã1 =   −0.5608 0.8553 0.5892 0.6698 −1.3750 −0.9909 3.1917 1.7971 −2.5887   b̃1 =   −0.1602 −0.3521 0.1162 −2.4839 −0.1106 −4.6057   ã2 =   0.6698 −1.3750 −0.9909 −2.8963 −1.5292 10.5160 −3.5777 2.8389 1.9087   b̃2 =   0.1562 0.1306 −0.4958 4.0379 −0.0306 0.8947   the uncertain system can be described by four vertices; the corresponding maximal eigenvalues in the vertices of open loop system are respectively: -4.0896 ± 2.1956i ; -3.9243 ; 1.5014; -4.9595 notice that the open loop uncertain system is unstable (see third vertex). the stabilizing optimal pd controller has been designed. optimality is considered in the sense of guaranteed cost w.r.t. cost function (21) with matrices r = i2x2,q = 0.001∗i3x3 . the results summarized in the tab. 1 indicate the differences between results obtained for different values of s 202 danica rosinová, vojtech veselý s f (proportional part) fd (derivative part) max eig in vertices 1e-6 *i -1.0567 -0.5643 -2.1825 -1.4969 -0.3126 -0.2243 -0.0967 0.0330 -4.8644 -2.4074 -3.8368 ± 1.1165i -4.7436 0.1*i -1.0724 -0.5818 -2.1941 -1.4642 -0.3227 -0.2186 -0.0969 0.0340 -4.9546 -2.2211 -3.7823 ± 1.4723i -4.7751 table 1: example 1, example 2 consider the uncertain system (1), (2) where: a =   −2.9800 0.9300 0 −0.0340 −0.9900 −0.2100 0.0350 −0.0011 0 0 0 1 0.3900 −5.5550 0 −1.89   , ã1 =   0 1.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0   , b =   −0.0320 0 0 −1.6000   , b̃1 =   0 0 0 0   , c = [ 0 0 1 0 0 0 0 1 ] the results are summarized in tab. 2 for r = 1, q = 0.0005 ∗ i4x4 for various values of matrix s in cost function. as indicated in tab.2, increasing values of s slow down the response (max.eig.cl shifted to zero) as assumed. 6 conclusion the new robust pid controller design method is proposed for uncertain linear system based on lmi. the important feature of this pid design approach is that the derivative term appears in such form that enables to consider the model uncertainties. since the structured feedback matrix is assumed, this approach is appropriate for decentralized pid control design. the guaranteed cost control is proposed with a new quadratic cost function including the derivative term for state vector as a tool to influence the overshoot and response rate. the obtained results are illustrated on the examples: to show the robust pid control design and the influence of the choice of matrix s in the extended cost function. acknowledgment the work has been supported by slovak scientific grant agency, grant n 1/3841/06. robust pid decentralized controller design using lmi 203 s=10e-8*i4x4 qmax 1.1 max.eig.cl -0.189 s=0.1*i4x4 qmax 1.1 max.eig.cl -0.1101 s=0.2*i4x4 qmax 1.1 max.eig.cl -0.0863 s=0.29*i4x4 qmax 1.02 max.eig.cl -0.0590 table 2: comparison for various s example 2 references [1] blondel, v. and j.n. tsitsiklis (1997). np-hardness of some linear control design problems. siam j. control optim., 35, pp.2118-2127. 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[8] gyurkovics, e., takacs, t.( 2000): stabilisation of discrete-time interconnected systems under control constraints. iee proceedings control theory and applications, 147, no. 2, 137-144 [9] peaucelle, d., d. arzelier, o. bachelier and j. bernussou (2000). a new robust d-stability condition for real convex polytopic uncertainty. systems and control letters, 40, pp. 21-30. [10] rosinová, d., v. veselý and v. kučera (2003). a necessary and sufficient condition for static output feedback stabilizability of linear discrete-time systems. kybernetika, 39, pp. 447-459. [11] rosinová, d. and v. veselý (2003). robust output feedback design of discrete-time systems – linear matrix inequality methods. in: 2th ifac conf. csd’03 (cd-rom), bratislava, slovakia. 204 danica rosinová, vojtech veselý [12] skelton, r.e., t. iwasaki and k. grigoriadis (1998). a unified algebraic approach to linear control design, taylor and francis. [13] veselý, v. (2003). robust output feedback synthesis: lmi approach. in: 2th ifac conference csd’03 (cd-rom), bratislava, slovakia. [14] zheng feng, qing-guo wang, tong heng lee (2002). on the design of multivariable pid controllers via lmi approach. automatica, 38, pp.517-526. danica rosinová and vojtech veselý slovak university of technology institute for control and industrial informatics ilkovičova 3 81219 bratislava , slovakia {danica.rosinova;vojtech.vesely}@stuba.sk received: december 24, 2006 danica rosinová was born in bratislava, slovak republic in 1961. she received msc. and phd from slovak university of technology in 1985 and 1996. since 1985 she has been with the department of automatic control systems, now institute for control and industrial informatics at the faculty of electrical engineering and information technology stu bratislava. since 2006 she has been associate professor. her research interests concentrate on robust control, large scale systems theory and optimization. students. vojtech veselý was born in 1940. since 1964 he has worked at the department of automatic control systems at the faculty of electrical engineering and information technology, slovak university of technology in bratislava. since 1986 he has been full professor. his research interests include the areas of power system control, robust control, decentralized control of large-scale systems, proces control and optimization. he is author and coauthor of more then 270 scientific and technical papers, he successful supervised up today 19 phd international journal of computers communications & control issn 1841-9836, 11(2):179-193, april 2016. design of a fuzzy networked control systems. priority exchange scheduling algorithm h. benítez-pérez, j. ortega-arjona, j.a. rojas-vargas, a. durán-chavesti h. benítez-pérez universidad nacional autónoma de méxico apdo. postal 20-726, admón. 20, del. a. obregón, méxico d. f., cp. 01000. hector.benitez@iimas.unam.mx jorge ortega-arjona facultad de ciencias unam av. universidad 3000, c. u., méxico d. f. jloa@ciencias.unam.mx jared a. rojas-vargas iimas unam cto. escolar 3000, c. u., méxico d. f. jared_36_23@hotmail.com a. durán-chavesti* universidad nacional autónoma de méxico apdo. postal 20-726, admón. 20, del. a. obregón, méxico d. f., cp. 01000. *corresponding author: adrian.chavesti@iimas.unam.mx abstract: this work presents a supervisory control strategy for networked control systems (ncss). this shows the identification and control of the plant using fuzzy theory. the fuzzy model incorporates the delay dynamics within the fuzzy rules based upon a real-time hierarchical scheduling strategy. a hierarchical scheduling priority exchange algorithm is used based upon codesign strategy following mutual correlation among control and network algorithms in order to bounded time delays. a system of magnetic levitation is presented as a case study. keywords: fuzzy control, networked control system, time delay codesign. 1 introduction the control design and stability analysis of network-based control systems (ncss) have been studied in recent years [14], [8] and [24] based upon codesign strategy. the main advantages of this kind of systems are their low cost, small volume of wiring, distributed processing, simple installation, maintenance and reliability. in a ncs, one of the key issue is the effect of network-induced delay in the system performance. the delay can be constant, time-varying, or even random, this depends on the scheduler, network type, architecture, operating systems, etc [24]. one strategy to be followed is the codesign since it takes both desired procedures to be followed. nilsson analyzes several important facets of ncss [15]. it introduces models for the delays in ncs, first as a fixed delay, after as an independently random, and finally like a markov process. the author introduces optimal stochastic control theorems for ncss based upon the independently random and markovian delay models. in [18], introduces static and dynamic scheduling policies for transmission of sensor data in a continuous-time lti system. they introduce the notion of the maximum allowable transfer interval (mati), which is the longest time after a sensor should transmit a data. [18] derived bounds of the mati such that the ncs is stable. this mati ensures that the lyapunov function of the system under consideration is strictly decreasing at all times. in [22] extends the copyright © 2006-2016 by ccc publications 180 h. benítez-pérez, j. ortega-arjona, j.a. rojas-vargas, a. durán-chavesti work of walsh., he developed a theorem which ensures the decrease of a lyapunov function for a discrete-time lti system at each sampling instant, using two different bounds. these results are less conservative than those of walsh, because he doesn’t require the system’s lyapunov function to be strictly decreasing at all time. besides, following the work presented by [13] although the strategy is similar as well as the case study in here, the proposed fuzzy control follows each local time delay produced by the scheduling algorithm which is dynamic and reactive to external tasks modification (as priority exchange proposes). although the results are stable in both cases, in here the challenging strategy is to dismiss dynamic local time delays without forcing system bounds. it is important to mention that this work follows the expressions designed in [3], [4] and [3] with the characteristic of real local time delays and local gain control design following eqn. 10 and lmi procedure as presented in section 4. in [7], [17], [20] and [21] introduce a number of different linear matrix inequality (lmi) tools for analyzing and designing optimal switched ncss. [23] takes into consideration both the network-induced delay and the time delay the plant, a controller design method is proposed by using the delay-dependent approach. an appropriate lyapunov functional candidate is utilized to obtain a memoryless feedback controller, this is derived by solving a set of linear matrix inequalities (lmis). in [19] models the network induced delays of the ncss as interval variables governed by a markov chain. using the upper and lower bounds of the delays, a discrete-time markovian jump system with norm-bounded uncertainties is presented to model the ncss. based on this model, the h∞ state feedback controller can be constructed via a set of lmis. recently [9] introduced a new (descriptor) model transformation for delay-dependent stability for systems with time-varying delays in terms of lmis, and she also refines recent results on delay-dependent h∞ control and extend them to the case of time-varying delays. alternatively [10] takes into consideration both the network-induced delay and the time delay in the plant, and thus, introduces a controller design method, using the delay-dependent approach. an appropriate lyapunov functional candidate is used to obtain a memoryless feedback controller, derived by solving a set of linear matrix inequalities (lmis) [6]. [11] models the network induced delays of the ncss as interval variables governed by a markov chain. using the upper and lower bounds of the delays, a discrete-time markovian jump system with norm-bounded uncertainties is presented to model the ncss. based on this model, a h∞ state feedback controller can be constructed via a set of lmis. an interesting approximation has been presented by [2] where time delays incorporation has been proposed following state space representation. 2 systems proposal based on this review, this paper defines a model (fig. 1) that integrates the time delays for a class of nonlinear system, where the actual proposal it is the enhancement of states in order to represent of control and plant states to fulfill a complete modeling of time delays according to priority exchange dynamic schedulling algorithm. it comprises two types of fuzzy rules, one that models the dynamics of the plant and another that introduces the networked-induced time delay. it involves estimating the time delay based scheduling behaviour where the fuzzy rules are such as: if xi(k) is µij then x n j (k + 1) = ajx(k) + b0u(k) (1) i=1...n j=1...r h=1...s design of a fuzzy networked control systems. priority exchange scheduling algorithm 181 figure 1: fuzzy model proposed. the overall system is: x̂(k + 1) = r∑ j=1 xnj + s∑ h=1 xdh = r∑ j=1 rjajx(k) + s∑ h=1 sh(b0)u(k) (2) where xi is the ith state of the plant, µij is the membership function of the ith state and s is the total number of local time delays and the jth rule. aj ∈ rnxn, b0,h ∈ rn, x ∈ rn, u(k) ∈ r, with n states, and r nominal fuzzy rules, sh is he nominal selection of current fuzzy rule. where n and d denote nominal and delayed model respectively. the fire strength ψj is defined as the function multiplication between the membership functions µij. µij = exp ( − (xi − cij)2 σ2ij ) (3) ψj = m∏ i=1 µij (4) rj = ψj∑r k=1 ψk (5) 0 < rj ≤ 1, r∑ j=1 rj(x) = 1 (6) for the s fuzzy rules with delay τcah, νh is the gaussian membership function of the time delay with center αh and standard deviation βh. sh = νh∑s k=1 νk (7) νh = exp ( − (τcah −αh)2 β2h ) (8) the proposed decomposition in terms of feedback state space representation has been reviewed by [4], where the indexing is defined by the time delays as local and bounded situations through the network. firstly, as augmented states and the related bounded time delays of plant and controller, following the strategies presented in [3], [4] and [3] the control structure is modified according to a particular gain control per local time delays scenarios and different local operational points from a particular case study. in here the strategy is modified by designing local control laws as 182 h. benítez-pérez, j. ortega-arjona, j.a. rojas-vargas, a. durán-chavesti gain rather them a dynamic state feedback control. the results as shown in section 5 are quite promising in that respect, it is presented as such: x = [ xc xp ] (9) xc(k + 1) xp(k + 1) = ∑n j=1 ∑n i=1 [ hjhi [ b p j (xc(k − tcai)) ] + hja p jxp(k) ] ∑n j=1 ∑n i=1 [ hjhi [ fcj ( cipxp(k − tsci) )] + hjf c j ] (10) where the delays are independent based upon the time obtained from scheduling approximation: tca1 + tsc1 < tca2 + tsc2 < ... < tcam + tscm < t (11) now from the derivative of a candidate lyapunov function is expressed as: ∆u(k) = v (k + 1)−v (k) (12) and the related lyapunov function is: v (k) = x(k)t px(k) (13) each of the fuzzy rules is given as an expression of local delays from current condition from plant towards controller, and vice versa. [ xc xp ] =   xc(k) xc(k − tca1) xc(k − tca2) ... xc(k − tcam) xp(k) xp(k − tsc1) xp(k − tsc2) xp(k − tsc3) ... xp(k − tscm)   (14) for each rule, there is a delay related to a particular condition to the plant and controller. each of the rules maybe updated through learning procedure or lmi process. each of the rules is unique on every specific time. in this case, these are associated to a particular relationship of last equation. in terms of the lyapunov candidate, this is expressed as in eqn 15 which is consistent to eqn. 8. v (k + 1)−v (k) = [ xc(k + 1) xp(k + 1) ]t p [ xc(k + 1) xp(k + 1) ] − [ xc(k) xp(k) ]t p [ xc(k) xp(k) ] (15) v (k + 1)−v (k) =   ∑mj=2 ∑mi=2 ( hjhi ( b p j (xc(k − tcaj)) ) + hja p jxp(k) ) ∑m j=2 ∑m i=2 ( hjhi ( fcj (xp(k − tscj)) ))  t p design of a fuzzy networked control systems. priority exchange scheduling algorithm 183   ∑mj=2 ∑mi=2 ( hjhi ( b p j (xc(k − tcaj)) ) + hja p jxp(k) ) ∑m j=2 ∑m i=2 ( hjhi ( fcj ( cipxp(k − tscj) )))  t − [ xc(k) xp(k) ]t p [ xc(k) xp(k) ] (16) therefore: v (k + 1)−v (k) = [ xc(k + 1) xp(k + 1) ]t p [ xc(k + 1) xp(k + 1) ] −   xc(k) xc(k − tca1) xc(k − tca2) ... xc(k − tcam) xp(k) xp(k − tsc1) xp(k − tsc2) xp(k − tsc3) ... xp(k − tscm)   t p   xc(k) xc(k − tca1) xc(k − tca2) ... xc(k − tcam) xp(k) xp(k − tsc1) xp(k − tsc2) xp(k − tsc3) ... xp(k − tscm)   (17) tcaj and tscj are the related time delays. considering the fuzzy system representation: v (k + 1)−v (k) =   ∑mj=2 ∑mi=2 ( hjhi ( b p j ( cicxc(k − tcaj) )) + hia p i xp(k) ) ∑m j=2 ∑m i=2 ( hjhi ( fcj ( cipxp(k − tscj) )))  t p (18)   ∑mj=2 ∑mi=2 ( hjhi ( b p j ( cicxc(k − tcaj) )) + hia p i xp(k) ) ∑m j=2 ∑m i=2 ( hjhi ( fcj ( cipxp(k − tscj) )))  −   xc(k) xc(k − tca1) xc(k − tca2) ... xc(k − tcam) xp(k) xp(k − tsc1) xp(k − tsc2) xp(k − tsc3) ... xp(k − tscm)   t p   xc(k) xc(k − tca1) xc(k − tca2) ... xc(k − tcam) xp(k) xp(k − tsc1) xp(k − tsc2) xp(k − tsc3) ... xp(k − tscm)   (19) if only one of the time delays is considered: 184 h. benítez-pérez, j. ortega-arjona, j.a. rojas-vargas, a. durán-chavesti 0 > [ xc(k + 1) xp(k + 1) ]t p [ xc(k + 1) xp(k + 1) ] −   xc(k) xc(k − tcaj) xp(k) xp(k − tscj)  p   xc(k) xc(k − tcaj) xp(k) xp(k − tscj)   (20) in here every time delay is local, independent and bounded according to dynamic scheduling algorithm which is based upon the structural codesign section. 3 structural codesign the codesign proposal follows the iteration between schedulability and stability analysis following online approximation. in fact, according to dynamic scheduling algorithm proposal which is based upon structural codesign strategy, these time delays can be seen like a phase modification within the communication period from the involved processes. this scenario presents a complete phase modification at the entire system. the communication network plays a key role in order to define the behavior of the dynamic system in terms of time variance giving a nonlinear behavior. in order to understand such a nonlinear behavior, time delays are incorporated by the use of real-time system theory that allows time delays to be bounded even in the case of causal modifications due to external effects, based upon priority exchange [4]. this algorithm bounds time delays through a real-time scheduling algorithm within communication network. according to fig. 3, structural reconfiguration takes place as a result of priority exchange scheduling algorithm and the associated user request. this reconfiguration causes a control law modification [19] which is the actual control law reconfiguration. scheduling approach potentially modifies frequency execution and communication of tasks in order to give certain priority to some of them during a bounded time as shown in fig. 3. furthermore, in this kind of strategy tasks modifies their priority, it does not imply that neither the period nor the consumption times are modified. therefore the tasks would have a bounded delay within the sampling time which is reflected as changing on the phase. potential modifications onto scheduling approach deploy change in the priorities that affects time delays and the respective control law. the delays are measured as ∆ t and bounded into the inherent control period of time according to eqn. 11. now by taking partial results from scheduling algorithm like tsj and the related ∆t, the actual time delays are used at the control law for parameters design. the involved time delays are depicted as τij and come from this scheduling design. other delays like actuators and control delays are not used in the design of the control law, although play an important role. therefore scheduling and control analysis merge together when time delays are complete bounded even in the case of time variance. the main restriction is in terms of predictable time delays. the objective here is to present a reconfiguration control strategy developed from the time delay knowledge, following scheduling approximation where time delays are known and bounded according to used scheduling algorithm. the scheduling strategy proposed here pursues to tackle local faults in terms of fault tolerance. in this situation, current time delays would be inevitable. classical earliest deadline first (edf) plus priority exchange (pe) [4] algorithm are used here to decompose time lines and the respective time delays when present. for instance, time delays are supervised for a number of tasks as follows: c1 → cnt1 → tn (21) design of a fuzzy networked control systems. priority exchange scheduling algorithm 185 priority is given as the well-known edf algorithm, which establishes that the process with the closest deadline has the most important priority [12]. however, when an aperiodic task appears, it is necessary to deploy other algorithms to cope with concurrent conditions. to do so, the pe algorithm is used to manage spare time from the edf algorithm. the pe algorithm [6] uses a virtual server that deploys a periodic task with the highest priority in order to provide enough computing resources for aperiodic tasks. this simple procedure gives a proximity, deterministic, and dynamic behavior within the group of included processes. in this case, time delays can be deterministic and bounded. as an example, consider a group of tasks as shown in table 1. in this case, consumption times as well as periods are given in terms of integer units. remember: the server task is the time given for an aperiodic task to take place on the system. name consumption (in units) period (in units) task 1 2 9 task 2 1 9 task 3 2 10 server 1 6 table 1: first example for pe algorithm. the result of the ordering based upon pe is presented in fig. 2. figure 2: related organization for pe of tasks in table 1. based on this dynamic scheduling algorithm, time delays are given as current calculations in terms of task ordering. in this case, every time that the scheduling algorithm takes place, the global time delays are modified in the short and long term. for instance, consider the following example, in which four tasks are set, and two aperiodic tasks take place at different times, giving different events with different time delays. the following task ordering is shown in fig. 3, using the pe algorithm, where clearly time delays appear. now, from this, a resulting ordering of different tiny time delays is given for two scenarios, as shown in fig. 4. these two scenarios present two different local time delays that need to be taken into account before hand, in order to settle the related delays according to scheduling approach and control design. these time delays can be expressed in terms of local relations between both dynamical systems. these relations are the actual and possible delays, bounded as marked limits of possible and current scenarios. then, delays may be expressed as local summations with a high degree 186 h. benítez-pérez, j. ortega-arjona, j.a. rojas-vargas, a. durán-chavesti name consumption (in units) period (in units) task 1 2 9 task 2 1 9 task 3 2 10 server 1 6 aperiodic task 1 (ap1) 0.9 it occurs at 9 aperiodic task 2 (ap2) 1.0 it occurs at 13 table 2: second example of pe. figure 3: related time delays are depicted according to both scenarios. of certainty (as presented in [13]). in this last example, during the second scenario, a total delay is given as: total delay = consumption_time_delay_aperiodic_task1 + consumption_time_delay_task1 + tsc2 + consumption_time_delay_task2 + consumption_time_delay_aperiodic_task2 + consumption_time_delay_task3 now, from this example, lp is equal to 2 and lc is equal to 3. lp and lc are the total number of local delays within one scenario from sensor to control and from control to actuator respectively. in this case, local time delays as presented in the general eqn. 14 are the result of the iteration of scheduling algorithm. in the approximation presented in this paper the local delays are around four time delays as expressed as last expression called total delays. the approach followed at the control reconfiguration does not take into account scheduler decision in a direct manner. it takes the time delays as bounded values already defined and used to design a suitable control law. therefore, according to current state plant values, the related fuzzy rule is selected. for a ncs, the communication network strongly affects the dynamics of the system, expressed as a time variance that exposes a nonlinear behaviour. such nonlinearity is addressed by incorporating time delays. from real-time system theory, it is known that time delays are bounded even in the case of causal modifications due to external effects. design of a fuzzy networked control systems. priority exchange scheduling algorithm 187 4 case of study the case of study consists of a simulation from magnetic levitation system whose sensors and actuators are operated by a "host", the signals from the sensors are sent by the host through a ethernet 10/100 network and received by a "server" where the control input is calculated and sent over ethernet network to the host. fig. 5 shows current system configuration as in real state. figure 4: current configuration of magnetic levitation system. the system consists of a coil inside a cabin, the coil levitates a steel ball that rests on a black post. the elevation of the ball is measured from the post using a light sensor inside the post. the issue of the experiment is to design a controller that does levitate the steel ball following a desired trajectory. figure 5: maglev system. the nonlinear equations for the magnetic levitation system are: 188 h. benítez-pérez, j. ortega-arjona, j.a. rojas-vargas, a. durán-chavesti ẋ1 = x2 ẋ2 = −kmx23 2mb(x1) 2 + g ẋ3 = 1 lc (−rx3 + u) were r = rc + rs and u = vc input voltage and rc electromagnet resistance rs resistor in series wiht the coil km constant of electromagnet force mb mass of the ball g gravitacional constant lc coil inductance the values of the parameters are provided in [16]. the method generated three rules for the nominal fuzzy control and the range of delay was divided in six parts then the delayed fuzzy control has six fuzzy rules. for the fuzzy model three feedback vector f j were designed to ensures the stability of the overall system. following eqn. 10 and resolving eqn. 20 through lmi it is possible to verify the stability in an asymptotic procedure. 5 results once the fuzzy control laws are designed according to equations (17)-(20) where the objective is to find a common positive definite matrix p satisfying the linear matrix inequality. two tests are performed to prove the effectiveness of the method proposed. in all tests the reference trajectory signal applied is a sine signal to be followed by the steel ball. three fuzzy rules are defined to approximate the magnetic levitation system by means of three linear models, as follows: rule 1: if x1(t) is about 0.006m, then x(k + 1) = a1x(k) + b1u(k) rule 2: if x1(t) is about 0.009m, then x(k + 1) = a2x(k) + b2u(k) rule 3: if x1(t) is about 0.013m, then x(k + 1) = a3x(k) + b3u(k) where x1 is the ball position in meters and design of a fuzzy networked control systems. priority exchange scheduling algorithm 189 a1 =  1.0016 0.0010 03.2718 1.0016 −0.055 0 0 0.9737   a2 =  1.0011 0.0010 02.1808 1.0011 −0.0175 0 0 0.9737   a3 =  1.0012 0.0010 02.3774 1.0012 −0.0212 0 0 0.9737   b1 = b2 = b3 =   00 0.0024   the control gains obtained by means lmi matlab’s toolbox are: f1 = [ −51650 −1102 379 ] f2 = [ −48530 −1058 341 ] f3 = [ 22546 −479 128 ] these control gain values guarantee the stability of the system during the presence of local time delays according to table 3. in this case local time delays are responsive in terms on an periodic external task, that is presented every determined seconds. with the next positive definite matrix p : p =   0.1980 0.0042 −0.00070.0042 0.0001 −0.0000 −0.0007 −0.0000 0.0000   (22) in order to prove the effectiveness of the metod proposed, two experiments were performed, in the first scenario the plant tracks a reference signal (sine wave) and the transmission task were the following (table 3) name consumption (in milliseconds) period (in milliseconds) task 1 2 10 task 2 1 12 task 3 2 14 aperiodic task 1 90 table 3: pe the activation task was performed using stateflow as shown in fig. 6 where according to table 3 the task 1 is the controller transmission task and has the priority one, the task 2 is the sensor transmission task an has the priority two and the task 3 and 4 are the transmissions task 190 h. benítez-pérez, j. ortega-arjona, j.a. rojas-vargas, a. durán-chavesti and the sporadic transmission task from others nodes. the system response obtained in this first experiment is shown in fig. 7 (without time delays). figure 6: activation tasks following table 3 figure 7: ball position response in the first scenario the second test is to apply a time delay less than the sampling period according to the total time delay. fig. 8 shows the behavior of the system which maintains stability with a delay of 2 ms and a sampling period of 90 ms related to the aperiodic task. the behavior is very similar to the system without time delay. design of a fuzzy networked control systems. priority exchange scheduling algorithm 191 figure 8: ball position response in the second scenario 6 conclusion it has been established a supervisory fuzzy control to minimize the effects caused by the time delay due to communication into the network which is designed through codesign strategy. this approach introduces the time delay produced by scheduling approach named priority exchange procedure. with this fuzzy model a fuzzy control is designed and the stability analysis is proposed for this controller. this approach shows that the system with a time delay smaller than sampling period but with a complex behaviour maintains the stability, the stability analysis for time varying delay and a bound for this delay remain a work in the future. although the example related to the time delays is fairly demonstrative it becomes challenging in terms of the dynamic scheduling approach where local time delays is pursued according to eqn. 10 in a general form and implemented through state flow tool in each node. acknowledgments the authors acknowledge the support of unam-papiit in100813, conacyt 176556 and picco 10-53. bibliography [1] almeida, l. et al (2002); the ftt-can protocol: why and how. ieee transactions on industrial electronics, 49(6):1189-1201. 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(2007); h-inf control of networked control systems via lmi approach, international journal of innovative computing, information and control, 3(2):343-352. [20] xiao, l. et al (2000); control with random communication delays via a discrete-time jump system approach, american control conference, 3:2199-2204. design of a fuzzy networked control systems. priority exchange scheduling algorithm 193 [21] yu, m. et al (2003); an lmi approach to networked control systems with data packet dropout and transmission delays, international journal of hybrid systems, 3(2):3545 3550. [22] zhang, w. (2001); stability analysis of networked control systems. ph.d. thesis, case western reserve university, dept. of electrical engineering and computer science. [23] zhu, x. et al (2008); state feedback control design of networked control systems with time delay in the plant, international journal of innovative computing, information and control, 4(2): 283-290. [24] http://www.mathworks.com/help/robust/lmis.html (retrieved on july 20, 2015). ijcccv4n1draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 1, pp. 82-91 development journey of qadpz a desktop grid computing platform monica vlădoiu, zoran constantinescu monica vlădoiu petroleum-gas university of ploieşti, departament of informatics bd. bucureşti, nr. 39, ploieşti, romania e-mail: mmvladoiu@acm.org zoran constantinescu zealsoft ltd. str. tg. neamţ, nr. 60, bucureşti, romania e-mail: zoran@unde.ro abstract: in this paper we present qadpz, an open source system for desktop grid computing, which enables users of a local network or internet to share resources. qadpz allows a centralized management and use of the computational resources of idle computers from a network of desktop computers. qadpz users can submit compute-intensive applications to the system, which are then automatically scheduled for execution. the scheduling is performed according to the hardware and software requirements of the application. users can later monitor and control the execution of the applications. each application consists of one or more tasks. applications can be independent, when the composing tasks do not require any interaction, or parallel, when the tasks communicate with each other during the computation. the paper describes both qadpz functionality and the process of design and implementation, with focus on requirements, architecture, user interface and security. some future work ideas are also presented. keywords: desktop grid computing, distributed and parallel computing. 1 introduction grid computing and peer-to-peer (p2p) are both concerned with the pooling and coordinated use of resources within distributed communities, and are constructed as overlay structures that operate largely independently of institutional relationships [1]. the grid is foreseen as a system that coordinates distributed resources using standard, open, general-purpose protocols and interfaces to deliver nontrivial qualities of service [1, 2]. grid computing systems can be classified into two broad types: heavy-weight, feature-rich systems that provide access to large-scale, intraand inter-institutional resources, such as clusters or multiprocessors, and desktop grids, in which cycles are scavenged from idle desktop computers. p2p networks are typically used for connecting nodes via largely ad-hoc connections. a pure p2p network does not have the notion of clients or servers but only equal peer nodes that simultaneously function as both “clients” and “servers” to the other nodes on the network [3]. this paper deals with qadpz [’kwod ’pi: ’si:], an open source system for desktop grid computing, which enables users from a local network or internet to share their resources [4, 5]. qadpz (quite advanced distributed parallel zystem) is a system for heterogeneous desktop grid computing that allows a centralized management and use of the computational resources of idle computers from a network of desktop computers. qadpz users can submit compute-intensive applications to the system, which are then automatically scheduled for execution. applications can be independent, when the composing tasks do not require any interaction, or they can be parallel, when the tasks communicate with each other during the computation. thus, the system provides support for both taskand data-parallelism. here are some important features of qadpz [4]: • native support for multiple operating systems: linux, windows, macos and unix; • support for legacy applications, which for different reasons could not be rewritten; • object-oriented development framework that supports either low-level programming languages as c and c++, or high-level language applications (such as lisp, python, or java), and that provides for using such applications in a computation; copyright © 2006-2009 by ccc publications development journey of qadpz a desktop grid computing platform 83 • master worker-model that is improved with some refined capabilities: pushing of work units, pipelining, sending more work-units at a time, adaptive number of workers, adaptive timeout interval for work units, and the use of multithreading [6]; • a master can act as a client to another master. that makes it possible to create a distributed master, which consists of independent master nodes which communicate with each other, thus creating a virtual master; • extended c/c++ api, which supports creation of lightweight tasks and parallel computing, using the message passing paradigm (mpi) [7]; • low-level optimizations: on-the-fly compression and encryption for communication. to increase performance, an experimental, adaptive compression algorithm, which can transparently choose from different algorithms, is also provided; • efficient communication by using two different protocols (udp and tcp/ip); • autonomic computing characteristics: self-knowledge, self-configuration, self-optimization and self-healing [8]. 2 justification for a new desktop grid system the idea of using the idle computational resources from existing desktop computers is not new, though the use of such distributed systems, especially in a research environment, has been limited. this is due to the lack of supporting applications, and challenges regarding security, management, and standardization. the need to develop qadpz has arisen from the following main reasons: o many existing systems were highly specialized in a very limited set of computationally challenging problems, and hence did not allow the execution of a general application. for example, seti@home was programmed to solve one specific task: the analysis of data from telescopes [9, 10]. similarly, distributed.net aimed to test the vulnerability of some particular encryption schemes [11]; o at the time of the development, the source code was generally not available, hence making difficult the extension or analysis of any new, non-standard application. commercial systems such as entropia, office grid and united devices offered numerous features, but they were not free [4, 12]. on the other hand, some open source systems were available, e.g. xtremweb [13], boinc [14, 15], condor [16], but they were limited in functionality; o very few existing systems allowed specific considerations to be made wrt. challenges of computationally intensive applications, especially those of scientific computing and visualization [4]. systems like boinc and bayanihan [12] allowed only task parallelism, where there was no communication between the running tasks during a computation. most computationally intensive applications need such communication; o most of the existing systems usually had a complicated deployment procedure, requiring high-level, privileged access to the desktop computers, which made very hard to use such systems on a larger scale, and also made further maintenance of the computers complicated e.g. condor and globus toolkit [12, 17, 18]; o many of today’s networks are heterogeneous, thus requiring a distributed computing system with support for various architectures and different type of operating systems. the java language provides the incentives for such requirements, and many java based systems emerged: jxta, bayanihan, xtremweb, javelin [12]. there were very few systems supporting different architectures and operating systems in native mode, some of them being condor and boinc. there were also systems, which run only on one type operating system, either windows or unix, thus limiting their usability in heterogeneous environments for instance, entropia [12]. 84 monica vlădoiu, zoran constantinescu 3 qadpz requirements given the reasons mentioned in the previous section, we have set up a set of requirements that a successful desktop grid computing system should satisfy to support computationally intensive applications. the overall goal of the system was to be friendly, flexible and suitable to a variety of needs. the main prerequisite has therefore been an open architecture that could evolve in pace with the needs and challenges of the real world. two sets of requirements for qadpz have been specified: one for the system as a whole, mostly from a functional point of view, and another for the system interface. additionally, a set of non-functional requirements that concern the development of the platform itself has been established. system requirements are concerned mainly with sharing and management of both resources and application jobs, in a heterogeneous environment. they also involve performance and usability of the system, as required by our conceptual model (extended master-worker). the system interface covers both user interfaces and programming interfaces [4, 6]. the system requirements are listed further on: o resource sharing: idle computational cycles, storage space, specific data, etc. of the desktop machines which contribute to the system; o resource management: efficient management of the available shared resources, which remain under the control of their owners via use policies and specific mechanisms; o job management: users should be able to submit, monitor and control the execution of computational jobs on the system; o heterogeneity: ability to work on a network of heterogeneous desktop computers, with different architectures (intel, risc, etc.) and different operating systems (unix, windows, mac os, linux); o simple installation and minimal maintenance; o parallel programming support: support for different parallel programming paradigms, for example both taskand data-parallelism, by using well known standards; o network support: ability to work both in a lan environment and in internet; o communications: the higher level communication protocol should rely on both tcp/ip and udp/ip, this dual support increasing the efficiency; o autonomous features: support for different autonomicity aspects: self-management, self-optimization, self-healing, self-configuration, and self-knowledge; o provide performance measurements, which could be exploited for better usage of the available resources; o on-line/off-line support for both batch (the user submits jobs which will be executed at a later time) and interactive applications (the user can inspect the partial result and interact with the execution of the application). the interface requirements can be split up into two parts: first, the user interfaces that is the graphical interface, which the human users use to access the system. using this interface, the users can either monitor or control the behavior of the system. the other interface is the programming interface (api), which allows different user applications to interact with the system. the interface requirements are enlisted beneath: o personalization: different levels of access for various users, according to their personal skills and preferences; o job management interface: a simple, platform independent, graphical user interface, to allow submission, monitoring and control of the different computational jobs; o resource sharing interface: a simple, intuitive graphical user interface, which allows the control of shared resources. the main non-functional requirements concern object oriented programming, for its well-known advantages, and open source development, which is a natural choice for modern research, as it encourages integration, cooperation and boosting of new ideas [19]. development journey of qadpz a desktop grid computing platform 85 figure 1: the qadpz close-up architecture 4 qadpz architecture the qadpz system has a centralized architecture, based on the client-server model, which is the most common paradigm used in distributed computing. in our case, the server manages the available computational resources of the desktop computers. the client is a process that needs computational services in order to accomplish a certain work. it sends a request to the server, in which it asks for the execution of a concrete task that is covered by the services. usually, the server carries out the task and sends back the result to the client. in our situation, the server has two parts: a single master, which accepts new requests from the clients, and multiple slaves, which handle those requests. the system consists of three types of entities: master, client, and slave (figure 1). the control and data flow in the system are separated. the data files (represented by binary, input, and output files) that are necessary to run the applications are not sent to the master. they are stored on one or more data servers. the smallest independent execution unit of the qadpz is called a task. to facilitate the management, multiple tasks can be grouped into jobs. different types of jobs can be submitted to the system: programs written in scripting languages (e.g. lisp, java, python), legacy applications and parallel programs (mpi). a job can consist of independent tasks, which do not require any kind of communication between them. this is usually called task parallelism. jobs can also consist of parallel tasks, where different tasks running on different computers can communicate with each other. inter-slave communication is accomplished using a mpi subset. the current implementation of qadpz considers only one central master node. this can be an inconvenience in certain situations, when computers located in different networks are used together. however, our high-level communication protocol between the entities allows a master to act as a client to another master, thus making possible to create a virtual master, consisting of independent master nodes, which communicate with each other. 4.1 master the main role of the master is to start and control the tasks, and to keep track of the availability, capabilities and configuration of the slaves. the master is responsible for managing the available resources and it has always an up-to-date overview of the system resources. it knows which slaves can accept jobs for execution and how to contact them. it schedules also the computational tasks submitted by any authorized user. jobs are sent to the appropriate slave based on the hardware and software requirements from the job description. tasks can be started, stopped, or re-scheduled by the master. users create tasks that can be submitted to the master by using a client, which acts as an interface to the system. to make 86 monica vlădoiu, zoran constantinescu figure 2: simplified uml diagram of qadpz’s architecture this possible, the master keeps a database of authorized users (figure 2). 4.2 slave each computer contributing with computing resources to the system is called a slave and has two roles: first, it has to report the shared resources to the master. these are mainly computational resources (cpu cycles), but can also be storage space, input or output devices etc. the slave periodically sends to the master information about the system, which describes the hardware architecture of the slave (cpu type, cpu speed, physical memory, etc.), the software environment available on that architecture (operating system, available application or libraries), and the resources available on that slave (figure 5). secondly, the slave can accept computational jobs from the master. after accepting a computational request from the master, the slave downloads the corresponding binaries and data files for the task, executes the task, and uploads the result files after finishing. this can be done only when the slave is free, and any interactive, local user is not using the resources. the presence of a user logging into a slave computer is automatically detected and the task is killed or moved to another slave to minimize the disturbance to the regular computer users. the slave decides for itself whether or not to accept a computational job to be run (by setting some configuration parameters). the user can configure different times of day when the slave may accept computational jobs. it can also disable the slave at any time. the slave component runs as a small background process on the user’s desktop. it starts automatically when the system starts. the program does not need any special privileges to run, which makes it very easy to install and control by an ordinary user. below we present a simple example on how to create a computational application to be executed on a slave. // slavedumb simple example of how to create a computational job #include "slaveserv.h" // callback functions for notification from the slave service void taskstop () { istaskstop = 1; dbug_print("taskstop"); } void taskctrl (const char *arg) { istaskctrl = 1; dbug_print("taskctrl arg=%s", arg); } // this is the exec loop on each task-thread int taskexec (char *data, char *datares, char *userdata) { int isfinished = 0; dbug_print("task started"); // set callback functions q2adpz_slv_setcb_task_stop (taskstop); q2adpz_slv_setcb_task_ctrl (taskctrl); development journey of qadpz a desktop grid computing platform 87 dbug_print("input data ’%s’", data); // start main task loop while (! isfinished) { //do some crunching of the data { ... if (ok) isfinished = 1; } //task needs to be stopped if (istaskstop) { ... dbug_print("task stop executed"); break; } if (istaskctrl) { ... q2adpz_slv_task_status (task_ok, "task ctrl"); dbug_print("info", ("task ctrl executed"); } //if crunching finished if (isfinished) { dbug_print("task finished res=’%s’", datares); break; } } // while } 4.3 client the client represents the interface for submitting jobs in the system. there are two execution modes for the client: a batch mode and an interactive mode. in the batch mode, a project file describes a job by specifying the required resources and how to start the tasks. this information is sent to the master, which is responsible for scheduling the tasks. the client can detach from the master and connect later for the results. each project is described by using the xml language. in the interactive mode, the client remains connected to the master for the entire execution of the job. also, the client can get direct connection to each of the slaves involved in the computation. the client has a lot of freedom over the creation and controlling of new tasks: it can dynamically create new tasks, send messages to the tasks already in execution, and receive feedback from the running tasks, either through the master node, or by means of direct communication with the slaves running the respective tasks. an example of a job description in xml is listed beneath. <job name="executable_example"> <task id="1" type="executable"> <runcount>3</runcount> <datapathprefix>./datafiles/</datapathprefix> <filesurl>http://www-data/qadpz/cgi-bin</filesurl> <inputfile constant="yes">simple/source.txt</inputfile> <outputfile>simple/dest.txt</outputfile> <taskinfo> <memory unit="mb">1</memory> <disk unit="mb">1</disk> <timeout>3600</timeout> <os>linux</os> <cpu>i386</cpu> <url> http://www-data/qadpz/app/lib/linux/i386/libslv-app.so </url> <executable type="file">simple</executable> </taskinfo> <taskinfo> <memory unit="mb">1</memory> <disk unit="mb">1</disk> <timeout>3600</timeout> <os>win32</os> <cpu>i386</cpu> <url> http://www-data/qadpz/app/lib/win32/i386/slv_app.dll </url> <executable type="file">simple.exe</executable> </taskinfo> <taskinfo> <memory unit="mb">1</memory> <disk unit="mb">1</disk> <timeout>3600</timeout> <os>sunos</os> <cpu>sun4u</cpu> <url> http://www-data/qadpz/app/lib/sunos/sun4u/libslv-app.so 88 monica vlădoiu, zoran constantinescu </url> <executable type="file">simple</executable> </taskinfo> </task> </job> 4.4 jobs, tasks and subtasks the qadpz users can submit, monitor, and control computing applications to be executed on the computers that share resources. tasks can be binary programs, which can run on any of the sharing computers. a task comes in the form of an executable program, compiled for a specific architecture and operating system. for better performance, a task can be also in the form of a shared (dynamic) library, which can be more efficiently loaded by the slave program. as an alternative to native binary programs for a specific platform, a task can also be an interpreted or precompiled program. for example, it can be a compiled java application or an interpreted program (e.g. perl, python), which further needs, respectively, a java virtual machine or a specific interpreter, on the host computer. multiple tasks, which are related to each other, can be grouped into a job that is actually what a user submits to the system (see job life in figure 3). a job can be composed of one or more tasks. using jobs provides for easier structuring and management of the computational applications for both the user and the system. each job is assigned uniquely to one user, however, a user can have multiple jobs submitted at the same time to the system. the tasks that correspond to a job can be independent or not at execution time. tasks can further be divided into subtasks, consisting of finer work units that are executed within a task. subtasks are used for interactive applications, which require permanent connection between a client and the slaves. they are usually generated at run-time at the client, and sent for execution to an already running task, which can solve them. the main reason for having subtasks is to improve the efficiency of smaller execution units without the overhead of starting a new task each time. figure 3: qadpz job life 4.5 user interface the qadpz user interface provides for a user-friendly environment, in which the user can interact with the system. this interaction involves mainly the submission, the monitoring, and the management of the submitted computational applications, along with the resource monitoring and control. the first interface is the job-monitoring interface that is a web-based interface that provides detailed information about all existing jobs in the system. the user can browse the jobs, see their status, and view their component tasks. s/he can also easily create new jobs and tasks. using this interface, each job can be stopped or deleted (figure 4). the second interface is also web-based and provides information related to the resources in the system. basically it gives a list of the slaves registered in the system and their current status (figure 5). the owner of a desktop computer running a slave is given an interactive application, development journey of qadpz a desktop grid computing platform 89 which permits easy configuration of the slave. the user has complete control over the slave running on her computer. figure 4: job-monitoring web interface figure 5: slave information/configuration interface 5 security because of the unreliability of the udp protocol, which is our first option for the low-level communication protocol due to its benefits, it is not guaranteed that the execution tasks arriving to the slave computers are undoubtedly sent by the master. this is a serious security threat since it allows for a malicious hacker to submit any piece of code to the slave nodes (ipspoofing). for that reason, and on the cost of a decreased performance, all communication from clients to master and from master to slaves is encrypted and/or signed. particularly, the data flow from client to master has to be authorized by the name and the password of a qadpz user, and encrypted using a master public key. a master private key signs the data flow from master to slaves and the authenticity is verified using a master public key on slave nodes. it is important to note that the data flow from slaves to master and from master to clients is neither encrypted nor signed, which means that a malicious hacker can monitor (packet sniffing) or alter (ipspoofing) the data or control information arriving back to master or client nodes, and thus put the slave nodes and/or the master node out of operation, modify the resulted data that are submitted by the slaves, or do any other kind of harm to the computational process. in other words, the current qadpz security scheme is designed to protect the security of the computers in the network, i.e. a malicious hacker cannot submit an alien piece of code to be executed instead of a user computational task. however, this scheme does not protect the qadpz user data. we plan to provide optional data integrity in the future versions of the system. security of the system is handled in two ways. on the one hand, only registered users are allowed to submit applications for execution. this is done by using a user/password scheme, and allows a simple access control to the computational resources. the qadpz system manages its own user database, completely independent of any of the underlying operating systems, thus simplifying users’ access to the system. the qadpz administrator can create new users by using some supporting tools. on the other hand, security involves the encryption of messages exchanged between various components of qadpz. this is done by using public key encryption, and provides an additional level of protection against malicious attacks. 6 conclusions and future work the present paper reveals the development experience of qadpz, a desktop grid computing environment. we summarized the main features of the system that make it a powerful platform for running computationally intensive applications. the reasons that have justified the endeavor of developing a new desktop grid platform are also presented. the qadpz requirements have included all the core capabilities that a successful desktop grid system should provide [12]. we presented the detailed architecture of the system, along with some of the design details. when we started this work, our main goal was to build an easy to use, open source system that provides the complex functionality that users expect from 90 monica vlădoiu, zoran constantinescu such a platform [4, 12]. it is worth mentioning that qadpz has over a thousand users who have already downloaded it [20]. many of them use it for their daily tasks and we have got valuable feedback from them [4]. further on we present some future work ideas that aim to improve the qadpz system: o many areas of the qadpz system are incomplete. for example, many large scale parallel problems require checkpointing: running a parallel application for hours or even days and loosing all results due to one failing node is unacceptable; o data integrity is an important issue, especially in an open environment (internet); o improved support for user data security: computation results data can be encrypted and/or signed so that the user of the system can be sure the received data is correct; o users could be provided with different scheduling algorithms to choose from, according to the type of their problem; o more complete implementation of the mpi layer and development of a complete library of the collective communication routines; o adding a set of transparent profiling tools for evaluating the performance of the different components, which is crucially important when running parallel applications; o decentralizing the system by employing p2p services, which would permit to a group of users to form an ad-hoc set of shared resources; moving towards a p2p architecture; o interconnection with a grid computing environment that must be decentralized, robust, highly available, and scalable [21], while efficiently mapping application instances to available resources in the system. these future developments of qadpz subscribe to the belief that the vision that motivates both grid and p2p, i.e. that of “a worldwide computer within which access to resources and services can be negotiated as and when needed, will only become real if we are successful in developing a technology that combines important elements of p2p and grid computing” [1]. 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[19] j. cassens, z. constantinescu, free software: an adequate form of software for research and education in informatics?, in linuxtag 2003 conference, pp. 5-10, 2003. [20] sourceforge, [online] available: http://sourceforge.net [accessed april 1, 2008]. [21] f. berman, g. fox, a.j.g. hey, grid computing: making the global infrastructure a reality, new york: j. wiley, 2003. monica vlădoiu got her msc (1991) and phd (2002) in the department of computer science of the polytechnic university of bucharest, romania. since then, she has been with the dept. of informatics, petroleum-gas university of ploieşti (upg), romania. her main research interests include digital libraries, learning objects, multimedia databases, reflective and blended learning, desktop grid computing and e-society. she has published over 30 research papers concerning these topics and she has (co-) authored 3 books. zoran constantinescu got his msc (1997) in the dept. of computer science of the polytechnic university of bucharest, romania. since then, he has been working both in the software engineering industry and in higher education. he got his doctoral degree in computer science (2008), from the norwegian university of science and technology, trondheim, norway. his research interests include parallel and distributed computing, desktop grid computing, gps systems and embedded systems. he has published over 20 research papers dealing with the above mentioned topics. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 149-160 a neural approach of multimodel representation of complex processes nesrine elfelly, jean-yves dieulot, pierre borne abstract: the multimodel approach was recently developed to deal with the issues of complex processes modeling and control. despite its success in different fields, it still faced with some design problems, and in particular the determination of the models and of the adequate method of validities computation. in this paper, we propose a neural approach to derive different models describing the process in different operating conditions. the implementation of this approach requires two main steps. the first step consists in exciting the system with a rich (e.g. pseudo random) signal and collecting measurements. these measurements are classified by using an adequate kohonen self-organizing neural network. the second step is a parametric identification of the base-models by using the classification results for order and parameters estimation. the suggested approach is implemented and tested with two processes and compared to the classical modeling approach. the obtained results turn out to be satisfactory and show a good precision. these also allow to draw some interpretations about the adequate validities’ calculation method based on classification results. keywords: complex processes, modeling, multimodel approach, kohonen map 1 introduction nowadays, technological developments increase the complexity of systems. this complexity can be caused by non linearity, non stability, wide operating domain, variations of system parameters or external perturbations. as a result, it is often difficult or even impossible to propose a simple model which could reckon with the whole process complexity by using physical laws. consequently, it is very useful to focus on advanced and practical approaches in order to handle this complexity. the multimodel approach has recently been developed and applied in several science and engineering domains. it was proposed as an efficient and powerful method to cope with modeling and control difficulties when complex non linear and/or uncertain processes are concerned. the multimodel approach supposes the definition of a set of models. then, it becomes possible to replace the unique model by a set of simpler models thus making a so-called models’ base. each model of this base describes the behavior of the considered process at a specific operating point. the multimodel approach objective is to decrease the process complexity by its study under certain specific conditions. several researchers have been interested in multimodel analysis and control approaches [7, 8, 19] and many applications have been proposed in different contexts. in spite of its success in many fields (academic, biomedical, . . . ), the multimodel approach remains confronted with several difficulties such as the calculation of models’ validities, the adequate technique of fusion or switching between models as well as the determination of the models’ base. indeed, in 1985, takagi and sugeno [21] suggested a fuzzy process representation by the contribution of local models. this approach has been applied in many fields of activities but often faced problems related to the lack of information about the system structure or the incertitude of its parameters. besides, it can sometimes lead to a large number of models which generates a high computational burden when designing the control algorithm. this has led several researchers to develop other approaches in order to cope with these difficulties. for uncertain complex systems with bounded parameters, some approaches were developed for the determination of the models’ base [9, 11]. an extension for uncertain discrete systems has been proposed by mezghani [13]. the case of multivariable systems was addressed by raissi [16]. the approaches, copyright © 2006-2008 by ccc publications 150 nesrine elfelly, jean-yves dieulot, pierre borne previously named, require the knowledge of parameters’ variations limits, which is generally not possible, in particular in case of uncertain systems for which parameters variations domains are unknown or ill-known. other related studies [12, 19] suggest the system be described by a set of local models often defined by using a reference model and some linearization methods. in another context, both fuzzy logic and neural networks were carried out for the multimodel control [1, 18]. an idea was to use neural approaches for complex systems modeling. these methods have the advantage of requiring very little information about the considered process and are useful for uncertain systems. within this context, very few studies [2, 3, 15, 17, 20, 22] were proposed but they don’t address strongly non-linear systems. in this paper, a neural approach for the determination of the models’ base for uncertain complex systems is proposed, in particular those which exhibit strong non-linearities. the proposed approach requires a priori little knowledge about the considered system; only input/output information can be sufficient. in the following section, the different steps of the proposed modeling approach are detailed. two simulation examples and some interpretations are then presented. the evaluation of the suggested modeling strategy is the topic of the last section. we finish the present paper by a conclusion. 2 complex systems’ modeling: neural approach in this section, a models’ base-determination approach for uncertain complex systems is described. this approach requires neither a global model definition nor the knowledge of parameters variations domains; only input/output information are needed. the suggested approach allows the generation of the base-models’ structure and parameters. the application of this approach requires first to classify the numerical data by exploiting a kohonen map and to determine the number of models. secondly, a structural and parametric identification of different base-models is carried out by using classification results. then, the validity index of each model is computed. finally, the multimodel output is obtained by the fusion of the models’ outputs weighted by their validity indexes. 2.1 classification of the numerical data by using a kohonen map as a first step, the output or input/output measurements collected from the considered process have to be classified in order to identify operating clusters from which the models’ base will be deduced. this classification is carried out by using a self-organizing kohonen map. self-organizing kohonen map methodology the self-organizing map (som) represents a specific kind of neural networks. in its original form, the som was invented by the founder of the neural networks research center, professor teuvo kohonen in 1981-82. the special property of the som is that it effectively creates spatially organized internal representations of various features of input signals and their abstractions [10]. the schematic representation of this network is given in figure 1. neurons in the target layer are ordered and correspond to cells of a bi-dimensional map. every neuron of the input layer is connected to every neuron of the output layer. the classification strategy consists in applying the kohonen rule. this rule is characterized by an unsupervised competitive learning where a competition takes place before the modification of the network-weights. only the neurons which win the competition have the right to change their weights. the kohonen rule works as follows: a neural approach of multimodel representation of complex processes 151 figure 1: kohonen map weights are initialized to random values; an input vector is presented to the network; the distance between the input vector and weights connecting inputs to each output neuron is computed; the neuron corresponding to the smallest distance, i.e. the nearest to the input vector, wins the competition, weights connecting inputs to this neuron are modified accordingly [5]. this procedure is repeated several times until weights stabilize. at the end of the learning stage, the representative vectors of different clusters and their centers are obtained. determination of the operating-system clusters the determination of the operating-system clusters requires firstly that the considered system be excited. secondly, the number of clusters has to be determined. the third step consists in classifying data by using a kohonen neural network. the first step consists in applying an input signal and then collecting (output or input/output) measurements that will be used for classification. the excitation signal must be rich enough and persistently exciting with well-chosen parameters in order to allow a full excitation of the operating dynamics, and to take in consideration the non-linear aspect of the considered process. for the second step, the method proposed by talmoudi has been adopted for the determination of the adequate number of clusters which corresponds to the number of models [22]. within this scope, a kohonen network, with an important number n of neurons in the output layer, has been considered. at the end of the learning procedure, if the repartition of classes is not good, the clusters i having a number of elements nci verifying the relation (1), will have to be removed [22]. nci ≤ 1 2 nh n , (1) where nh represents the number of the considered measures. else, the number of neurons in the output layer is increased and training is restarted. the same procedure is repeated over and over until the satisfactory number of clusters is obtained. afterwards, the data classification is tackled by using a kohonen network for which the number of neurons in the input layer equals the number of system-variables to be considered and the number of neurons in the output layer equals the number of clusters determined with the help of the method previously described. the classification results will then be exploited for the identification of the different base-models. 2.2 parametric identification of the base-models in this section, the orders of the models are estimated in a first step. the chosen method is the socalled instrumental determinants’ ratio-test. this method is mainly based on the conditions concerning 152 nesrine elfelly, jean-yves dieulot, pierre borne a matrix called "information matrix" which contains the input/output measurements [4]. this matrix is described as follows: qm = 1 nh nh ∑ k=1   u(k) u(k + 1) u(k −1) ... u(k −m + 1) u(k + m)   [ y(k + 1) u(k + 1) ··· y(k + m) u(k + m) ] , (2) where nh is the number of observations. the instrumental determinants’ ratio (rdi) is given by: rdi(m) = ∣∣∣∣ det(qm) det(qm+1) ∣∣∣∣ . (3) for every value of m, the determination procedure of the order consists in building the matrices qm and qm+1 and in evaluating the ratio rdi(m), the retained order m is the value for which the ratio rdi(m) quickly increases for the first time. as a second step, the parametric identification issue consists in calculating the values of the parameters of the corresponding model-equation, given several experimental measures which describe the dynamic behavior of the system. as previously mentioned, the data classification gives a certain repartition of clusters. for each cluster, input/output measurements are collected. these measurements allow the identification of the corresponding model. for this, the recursive least-squares method (rls) [4] was applied to achieve the parameters estimation. 2.3 computation of validities the validity coefficient is a number belonging to the interval [0 1]. it represents the relevance degree of each base-model calculated at each instant. in literature, several methods have been proposed to deal with the validity issue. in our study, the residues’ approach was adopted for the calculation of validities. this method is based on the distance measurement between the process and the considered model. for example, the residue can be given by the following expression: ri = |y−yi| i = 1, . . . , n (4) where: n: number of base-models; y: process output; yi: output of the model mi. if this residue value is equal to zero, the corresponding model mi perfectly represents the process at that time. on the contrary, a non null value translates the fact that the model mi represents the system partially. the normalized residues are given by: r′i = ri ∑nj=1 r j . (5) within the context of the residues’ approach, several methods have been proposed for the calculation of validities [6, 13, 14]. only two methods will be considered: the simple and the reinforced validities. the validities are given by: vi = 1−r′i . (6) the simple and reinforced validities are defined by using the following formulas. a neural approach of multimodel representation of complex processes 153 simple validities: the normalized simple validities are defined so that their sum must be equal to 1 at each time: vsimpi = vi n −1 . (7) reinforced validities: for this type of validities, the reinforcement expression is introduced as: v ′ren f i = vi n ∏ j =1, j 6=i (1−v j) . (8) the normalized reinforced validities could be written as follows: vren fi = v ′ren f i ∑nj=1 v ′ren f j . (9) 2.4 computation of the multimodel output the multimodel output is calculated by a fusion of the models’ outputs weighted by their respective validity indexes, as illustrated by the following expression: ymm(k) = n ∑ i=1 yi(k) vi(k) . (10) vi(k) could be a simple or a reinforced validity for which ∑ni=1 vi(k) = 1. 3 simulation examples in order to underline the interest and the performance of the proposed approach, some simulation examples are carried out. 3.1 example 1: second order discrete system the considered system is a complex discrete system whose evolution is described by the following equation: y(k) = −a1(k) y(k −1)−a2(k) y(k −2) + b1(k) u(k −1) + b2(k) u(k −2) . (11) the variation laws of different parameters of the process are given by: a1(k) = 0.04 sin (0.035k)−0.8 , a2(k) = 0.005 sin (0.03k) + 0.1 , (12) b1(k) = 0.02 sin (0.03k) + 0.5 , b2(k) = 0.01 sin (0.035k) + 0.2 . (13) first, the system is excited by a uniform random signal u(k ). then, the measurements y(k ) and y(k−1) are collected at different instants. the adequate number of clusters determined by using the method described in paragraph 3.1.2, is equal to three. the numerical data are fed into a kohonen network which presents an input layer with two neurons and three neurons in the output layer. the classification results are given in figure 2. from each of the three data sets relative to the various clusters, the orders and the parameters of the transfer functions relative to the three base-models are estimated. figure 3 shows the evolutions of the rdii(m) for the three models. 154 nesrine elfelly, jean-yves dieulot, pierre borne 2.5 3 3.5 4 4.5 2 2.5 3 3.5 4 4.5 y(k−1) y (k ) clusters’ centers figure 2: classification results 1 2 3 4 0 0.5 1 1.5 2 2.5 x 10 5 rdi 1 (m) 1 2 3 4 0 2 4 6 8 x 10 4 rdi 2 (m) 1 2 3 4 0 1 2 3 4 x 10 4 rdi 3 (m) m 1 =2 m 2 =2 m 3 =2 figure 3: evolutions of the rdi for the three base-models it appears clearly that the estimated orders of the three models are equal to two. this result is predictable since the considered system is a second order one. the application of the recursive leastsquares method allows us to write the following transfer functions: f1(z −1) = 0.33549 z−1 −0.047793 z−2 1−0.83697 z−1 −0.039754 z−2 , (14) f2(z −1) = 0.41086 z−1 −0.021659 z−2 1−0.8633 z−1 −0.055068 z−2 , (15) f3(z −1) = 0.36985 z−1 −0.0079934 z−2 1−0.9234 z−1 −0.060727 z−2 . (16) in order to evaluate the obtained global model, a validation step is worked out where other inputs which are different from those used for classification are fed into the system. then, the real and the multimodel outputs are compared. let us consider the following input sequence: u(k) = 1 + sin (0.08k) . (17) the validation results are given in figures 4 and 5. y: real output of the system. ymmr, ymms: multimodel outputs obtained by using respectively the methods of reinforced and simple validities. ymmr (k) = n ∑ i=1 yi(k) v ren f i (k) , ymms (k) = n ∑ i=1 yi(k) v simp i (k) . (18) er, es: relative errors between the real and the multimodel outputs. er(k) = ∣∣∣∣ y(k)−ymmr (k) y(k) ∣∣∣∣ , es(k) = ∣∣∣∣ y(k)−ymms (k) y(k) ∣∣∣∣ . (19) a neural approach of multimodel representation of complex processes 155 0 100 200 300 400 500 0 1 2 3 4 5 6 time y (t ), y m m r( t) , y m m s (t ) ymm s (t) y mm r (t) y(t) figure 4: real and multimodel outputs 0 100 200 300 400 500 0 5 10 time e r( t) , e s (t ) e s (t) e r (t) figure 5: evolutions of the relative errors it can be seen that the multimodel output, obtained by the fusion of the base-models’ outputs weighted by the reinforced validities, follows the real output with a negligible error (er(t)). this error is more important when applying the simple validities method (es(t)). this allows to conclude that, for this kind of system, the reinforced validities method is more appropriate than the simple validities one. 3.2 example 2: second order continuous system with input/output-dependent parameters as a second simulation example, we consider the system whose evolution is described by the following equations: a0y + a1ẏ + ÿ = u + bu̇ , (20) a0(y) = 0.3 + sigm(y−2) , a1(y) = sat(y2) , b(u) = sat(u) , (21) sigm is the sigmoid function; sat is the saturation function. the considered system is complex and strongly non-linear with parameters being functions of both the input and the output, which makes the modeling task difficult. the chosen excitation signal (figure 6) is a sine curve distorted by a random uniform signal since this input is richer than a simple random signal and allows considering the complex and non-linear aspects of the system. it is worthy to note that signal parameters (frequency, amplitude) need to be adjusted in order to obtain good results. 0 2 4 6 8 10 −2 −1 0 1 2 time u (t ) figure 6: evolution of the excitation signal moreover, in order to describe the system dynamics, the number of variables used for classification is increased. in fact, not only the output data y(k ) and y(k − 1) are considered but also the input data 156 nesrine elfelly, jean-yves dieulot, pierre borne u(k − 1). after generating the output, a sampling of the input and output signals followed, with an adequate sampling period, in order to collect the different measurements: y(k ), y(k−1), and u(k−1). once the number of clusters determined, the numerical data are presented to a kohonen network owning three neurons in both input and output layers. at the end of the learning procedure, three data sets (figure 7) are obtained, each of which being used for the identification of the corresponding model. −0.5 0 0.5 1 1.5 2 −1 0 1 2 −2 −1 0 1 2 y(k−1)y(k) u (k − 1 ) clusters’ centers figure 7: three data sets relative to the different clusters the application of the instrumental determinants’ ratio-test method allows us to determine the three models’ orders which are respectively: two, three and two. the corresponding transfer functions are given by the following expressions: f1(z −1) = 0.058303 z−1 + 0.052302 z−2 1−0.43246 z−1 −0.45748 z−2 , (22) f2(z −1) = −0.023815 z−1 + 0.0013593 z−2 + 0.031481 z−3 1−0.68802 z−1 −0.24025 z−2 −0.072128 z−3 , (23) f3(z −1) = 0.12995 z−1 −0.014359 z−2 1−0.64023 z−1 −0.37958 z−2 . (24) the application of the following input sequence is the purpose of the validation step: u(k) = 1 + sin (k) . (25) the validation results are illustrated by the figures 8 and 9. 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 time y (t ), y m m r( t) , y m m s (t ) ymm s (t) y(t) ymm r (t) figure 8: real and multimodel outputs 0 2 4 6 8 10 0 0.2 0.4 0.6 0.8 time e r (t ), e s( t) e s (t) e r (t) figure 9: evolutions of the relative errors contrary to the first example, the results obtained by the application of the simple validities’ method are much better than those given by the reinforced validities’ method. in fact, the figures 8 and 9 show that the multimodel output ymms (t), deducted by fusion of the base-models’ outputs weighted by the simple validities, follows almost perfectly the real output with an error es(t) nearly null compared to the a neural approach of multimodel representation of complex processes 157 error er(t) which is relatively important. in order to give prominence to the capacity of the identified models to reproduce the operating system in different domains, let us consider another input sequence given by: u(k) = 1.2 + 1.5 sin (2k) . (26) the multimodel output is generated by application of the simple validities’ method. the result is given in figure 10. 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 time y( t), y m m s(t ) y mm s (t) y(t) figure 10: real and multimodel outputs (second validation) 3.3 interpretations referring to the obtained simulation results, it can be noticed that the application of the suggested approach allows a good modeling of the considered systems. we can also make an important interpretation about the use of the validities’ calculation methods. as remarked before, in some cases simple validities give better results and in other cases it is preferable to use reinforced validities. so, more simulations and observations were worked out to conclude that the choice of the validities’ calculation method depends on the classification results i.e. the clusters structure and repartition. thus, it can be noted that when there are several variations in the same cluster and when an overlapping between clusters occurs, which is the case in the second simulation example (figure 7), it is worth to use the simple validities’ method since it takes account of different models’ outputs refering to the expression (7). in this case, no model could represent ideally the process at any time. but when the clusters present very few variations and are well separated (figure 2), the reinforced validities’ method is better-adapted. the application of this method, thanks to the reinforcement expression (8), promotes the contribution of the most dominant model which represents at best the process behavior. 4 evaluation of the suggested modeling strategy in order to highlight the interest and the performance of the proposed modeling strategy, the classical modeling approach involving the identification of a global model was carried out. then, the results given by the suggested approach were compared to those given by classical modeling strategy. let us consider the second simulation example described by the equations (20) and (21) and the excitation signal given by figure 6. by using the instrumental determinants’ ratio-test method for the estimation of the order and the recursive least-squares method for the parametric identification, the transfer function f(z−1) of the global model is given by the following expression: f(z−1) = 0.03601 z−1 + 0.0035236 z−2 1−0.55726 z−1 −0.42595 z−2 . (27) by using the same numerical data, the classification and the identification steps give the three models described by equations (22), (23) and (24). the input sequence given by the equation (25) was applied 158 nesrine elfelly, jean-yves dieulot, pierre borne again. figure 11 represents the evolutions of the real, the multimodel and the global model outputs. the multimodel output is obtained by fusion of the models’ outputs weighted by their simple validities degrees calculated at each instant. 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 3 time y( t), y m m s(t ), y g (t) y g (t): global model output y(t): real output y mm s (t): multimodel output figure 11: evolutions of the real, multimodel and global model outputs the adopted multimodel approach offers a very satisfactory precision compared to the case of the classical modeling approach based on the unique global model. 5 conclusion in this paper, a neural approach of multimodel representation is proposed. this approach is applicable when dealing with complex, strongly non-linear and uncertain processes. it allows the determination of the models’ base by using a kohonen network and two methods of structural and parametric identification. the different steps were detailed. the multimodel output is obtained by using the technique of fusion and the adequate validities’ computation method. the suggested approach has been implemented and tested for different complex systems. simulation results, two of which were described in this paper, prove the efficiency and the precision of the proposed modeling strategy and show that the method works well with various processes even when highly complex. some interpretations have been made about the choice of the adequate validities’ calculation method to be applied. furthermore, in order to demonstrate the performance and the relevance of the suggested approach, a comparison with the classical modeling approach has been made. bibliography [1] m. a. al-akhras, g. m. aly, r. j. green, neural network learning approach of intelligent multimodel controller, iee proceedings on control theory and applications, vol. 143, pp. 395-400, 1996. [2] i. s. baruch, r. l. beltran, j-l. olivares, r. m. garrido, a fuzzy-neural multi-model for mechanical systems identification and control, mexican international conference on artificial intelligence n. 3, vol. 2972, pp. 774-783, 2004. [3] i. s. baruch, f. thomas, j-m. flores, e. gortcheva, a multimodel recurrent neural network for systems identification and control, ieee international joint conference on neural networks, vol. 2, pp. 1291-1296, 2001. [4] r. ben abdennour, p. borne, m. ksouri, f. m’sahli, identification et commande numérique des procédés industriels, editions technip, paris, france, 2001. a neural approach of multimodel representation of complex processes 159 [5] p. borne, m. benrejeb, j. haggège, les réseaux de neurones, editions technip, paris, france, 2007. [6] f. delmotte, l. dubois, p. borne, a general scheme for multi-model controller using trust, mathematics and computers in simulation, vol. 41, pp. 173-186, 1996. [7] a. el kamel, m. ksouri-lahmari, p. borne, contribution to multimodel analysis and control, studies in informatics and control, vol. 9, pp. 29-38, 2000. [8] t. a. johansen, b. a. foss, editorial: multiple model approaches to modelling and control, international journal of control, vol. 72, pp. 575, 1999. [9] z. kardous, a. el kamel, n. ben hadj braiek, p. borne, on the quadratic stabilization in discrete multimodel control, ieee conference on control applications, vol. 2, pp. 1398-1403, 2003. [10] t. kohonen, the self-organizing map, ieee proceedings, vol. 78, pp. 1464 1480, 1990. [11] m. ksouri-lahmari, p. borne, m. benrejeb, multimodel: the construction of model bases, studies in informatics and control, vol. 3, pp. 199-210, 2004. [12] d. j. leith, w. e. leithead, analytic framework for blended multiple model systems using linear local models, international journal of control, vol. 72, pp. 605-619, 1999. [13] s. mezghani, a. el kamel, p. borne, multimodel control of discrete systems with uncertainties, international journal of studies in informatics and control, 2000. [14] k. s. narendra, j. balakrishman, adaptative control using multiple models, ieee transactions on automatic control, vol. 42, pp. 171-187, 1997. [15] j. c. principe, l. wang, m. a. motter, local dynamic modeling with self-organizing maps and application to nonlinear system identification and control, ieee proceedings, vol. 86, pp. 22402258, 1998. [16] t. raissi, a. el kamel, w. byrski, p. borne, multimodel analysis and control of multivariable systems, ieee international conference on systems man and cybernetics, vol. 1, pp. 640-645, 2001. [17] m. ronen, y. shabtai, h. guterman, hybrid model building methodology using unsupervised fuzzy clustering and supervised neural networks, biotechnology and bioengineering, vol. 77, n. 4, pp. 420-429, 2002. [18] n. sadati, a. talasaz, robust fuzzy multimodel control using variable structure system, ieee conference on cybernetics and intelligent systems, vol. 1, pp. 497-502, 2004. [19] r. schorten, r. marry-smith, r. bjorgan, h. gollee, on the interpretation of local models in blended multiple model structures, international journal of control, vol. 72, pp. 620-628, 1999. [20] r. r. selmic, f. l. lewis, multimodel neural networks identification and failure detection of nonlinear systems, ieee conference on decision and control, vol. 4, pp. 3128-3133, 2001. [21] t. takagi, m. sugeno, fuzzy identification of systems and its applications to modelling and control, ieee transactions on systems man and cybernetics, vol. 15, pp. 116-132, 1985. [22] s. talmoudi, r. ben abdennour, a. kamel, p. borne, a systematic determination approach of a models base for uncertain systems: experimental validation, ieee international conference on systems man and cybernetics, vol. 6, pp. 73-81, 2002. 160 nesrine elfelly, jean-yves dieulot, pierre borne nesrine elfelly1, jeans-yves dieulot2, pierre borne3 1université des sciences et technologies de lille (ustl), 2ecole polytechnique de lille, 3ecole centrale de lille (ec lille) laboratoire d’automatique, génie informatique et signal ecole centrale de lille, cité scientifique bp 48 59651 villeneuve d’ascq cedex, france e-mail:nesrine.elfelly@ed.univ-lille1.fr,jean-yves.dieulot@polytech-lille.fr,pierre.borne@ec-lille.fr received: december 4, 2007 nesrine elfelly was born in monastir, tunisia in 1982. she received the engineer diploma degree in computer engineering from the "ecole nationale d’ingénieur de tunis" (tunisia) in 2006. she obtained the master of automatic control, computer engineering and image processing from the "ecole centrale de lille" (france) in 2007 and the master of automatic control and signal processing from the "ecole nationale d’ingénieur de tunis" in the same year. she is currently a phd student in automatic control and applied computing within the framework of lagisustl and lara-enit cooperation. her current research interests are the applications of neural approaches for complex systems analysis and control. jean-yves dieulot graduated from french ecole d’ingénieurs institut industriel du nord in 1990, and obtained a phd from the university of lille in 1993. he is currently associate professor at ecole polytechnique universitaire de lille, france, and with lagis (laboratory of automatic control). his main interests are in fuzzy, nonlinear and robot control. pierre borne was born in corbeil, france in 1944, he received the master degree of physics in 1967, the masters of electronics, of mechanics and of applied mathematics in 1968. the same year he obtained the diploma of "ingénieur idn" (french "grande ecole"). he obtained the phd in automatic control of the university of lille in 1970 and the dsc in physics of the same university in 1976. he became doctor honoris causa of the moscow institute of electronics and mathematics (russia) in 1999, of the university of waterloo (canada) in 2006 and the polytechnic university of bucharest (romania) in 2007. he is author or coauthor of about 200 journal articles and book chapters, of 35 plenary lectures and of about 250 communications in international conferences. he has been the supervisor of 69 phd thesis and is author of 20 books. he is fellow of ieee and has been president of the ieee/smc society in 2000 and 2001. he is presently professor "de classe exceptionnelle" at the ecole centrale de lille and director of the french "plan pluriformation" of research in automatic control. ijcccv4n3draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 3, pp. 234-243 p–lingua 2.0: a software framework for cell–like p systems manuel garcía-quismondo, rosa gutiérrez-escudero, miguel a martínez-del-amor enrique orejuela-pinedo, i. pérez-hurtado research group on natural computing dpt. of computer science and artificial intelligence university of sevilla avda. reina mercedes s/n, 41012 sevilla, spain e-mail: mangarfer2@alum.us.es, {rgutierrez, mdelamor, orejuela, perezh}@us.es received: april 5, 2009 accepted: may 30, 2009 abstract: p-lingua is a programming language for membrane computing. it was first presented in edinburgh, during the ninth workshop on membrane computing (wmc9). in this paper, the models, simulators and formats included in p-lingua in version 2.0 are explained. we focus on the stochastic model, associated simulators and updated features. finally, we present one of the first applications based on plingua: a tool for describing and simulating ecosystems. keywords: programming languages, software development, p systems, membrane computing, p-lingua 1 introduction membrane computing (or cellular computing) is a branch of natural computing that was introduced by gh. păun [10]. the main idea is to consider biochemical processes taking place inside living cells from a computational point of view. the initial definition of this computing paradigm is very flexible and many different models have been defined. each model displays characteristic semantic constraints that determine the way in which rules are applied. hence, the need for software simulators capable of taking into account different scenarios when simulating p system computations comes to the fore. an initial approach could be defining inputs for each simulator specifically. nevertheless, this approach involves defining new input formats for each simulator, so designing simulators would take a great effort. a second approach could be standardizing the simulator input, so all simulators need to process inputs specified in the same format. these two approaches raise up a trade-off: on the one hand, specific simulator inputs could be defined in a more straightforward way, as the used format is closer to the p system features to simulate. on the other hand, although the latter approach involves analyzing different p systems and models to develop a standard format, there is no need to develop a new simulator every time a new p system should be simulated, as it is possible to specify it in the standard input format. moreover, researches would not have to devise a new input format every time they specify a p system and would not need to change the way to specify p systems which need to be simulated every time they move on to another model, as they would keep on using the standard input format. this second approach is the one considered in p-lingua project, a programming language whose first version, presented in [3], is able to define p systems within the active membrane p system model with division rules. the authors also provide software tools for compilation, simulation and debug tasks. from now on, we will call p-lingua 1.0 this version of the language and its associated tools. as p-lingua is intended to become a standard for p systems definition, it should also consider other models. in this paper, we present p-lingua 2.0 as a framework to define cell-like p system models, including several algorithms to simulate p system computations for the supported models (from now on, copyright c© 2006-2009 by ccc publications p–lingua 2.0: a software framework for cell–like p systems 235 simulators), as well as different formats to represent p systems with associated parsers to translate from each other. this paper is structured as follows. in section 2 the supported models at this stage are enumerated. the next section introduces some algorithms used to simulate p systems, focusing on the stochastic and the probabilistic p system models. in section 4 different file formats to representate cell-like p systems are presented, for example, p-lingua 2.0 programming language. model definitions, simulators and parsers have been encoded in a java library, plinguacore c©, presented in section 6, this library is free software and it can be easily expanded. command-line tools to compile files and simulate p systems have been slightly modified in section 5 these changes are presented. the next section introduces one of the first applications of p-lingua, a software tool to describe and simulate ecosystems. finally, some conclusions and future work are enumerated in section 8. 2 supported p system models the supported models developed so far are enumerated below, but a standard mechanism for defining new cell-like models has been included on the p-lingua 2.0 framework. each model displays characteristic semantic constraints entailing the rules applied, such as the number of objects specified on the left-hand side, membrane creation, polarization, and so on. it is possible to define additional models by including the corresponding semantic constraints within the plinguacore java library. this mechanism has been used on all the existent models. the supported p system models in p-lingua 2.0 are transition p systems, symport/antiport p systems, p systems with active membranes, with membrane division and membrane creation rules, probabilistic p systems and stochastic p systems. more details on those models can be found in [13], except for stochastic p systems, which are described in [10]. 3 simulators in p-lingua 1.0, only one simulator was supported, since there was only one p system model definition. however, as new models have been included, new simulators have been developed, providing at least one simulator for each supported model. all simulators in p-lingua 2.0 can step backwards (as well as the simulator in p-lingua 1.0), but this option should be set before the simulation starts. p-lingua 2.0 also takes into account the existence of different simulation algorithms for the same model and provides a means for selecting a simulator among the ones which are suitable to simulate the p system, by checking its model. so far, only the stochastic p system model provides several simulation algorithms to choose, but the plugin-oriented architecture of the plinguacore java library allows easily to encode new simulators. 3.1 simulators for stochastic p systems in the original definitions p systems evolve in a non-deterministic and maximally parallel manner (that is, all the objects in every membrane that can evolve by a rule must do it [10]). when trying to simulate biological phenomena, like living cells, the classical non-deterministic and maximally parallel approach is not valid anymore. first, biochemical reactions, which are modelled by rules, occur at a specific rate (determined by the propensity of the rule), therefore they cannot be selected in an arbitrary and non-deterministic way. second, in the classical approach all time steps are equal and this does not represent the time evolution of a real cell system. the strategies to replace the original approach are based on gillespie’s theory of stochastic kinetics 236 manuel garcía-quismondo, rosa gutiérrez-escudero, miguel a martínez-del-amor enrique orejuela-pinedo, i. pérez-hurtado [6]. a constant c is associated to each rule, which provides p systems with a stochastic extension. the constant c depends on the physical properties of the molecules involved in the reaction modeled by the rule and other physical parameters of the system. besides, it represents the probability per time unit at which the reaction takes place. also, it is used to calculate the propensity of each rule which determines the probability and time needed to apply the rule. two different algorithms based on the principles stated above have been implemented and integrated in plinguacore. multicompartimental gillespie algorithm the gillespie [6] algorithm or ssa (stochastic simulation algorithm) was developed for a single, well-mixed and fixed volume/compartment. p systems generally contain several compartments or membranes. for that reason, an adaptation of this algorithm was presented in [10] and it can be applied in the different regions defined by the compartmentalised structure of a p system model. the next rule to be applied in each compartment and the waiting time for this application is computed using a local gillespie algorithm. the multicompartimental gillespie algorithm can be broadly summarized as follows: repeat until a prefixed simulation time is reached: 1. calculate for each membrane i,  ≤ i ≤ m, and for each rule r j ∈ rli the propensity, a j, by multiplying the stochastic constant clij associated to r j by the number of distinct possible combinations of the objects and substrings present of the left-side of the rule with respect to the current contents of membranes involved in the rule. 2. compute the sum of all propensities a = m∑ i= ∑ r j∈rli a j 3. generate two random numbers r and r from the uniform distribution in the unit interval and select τi and ji according to τi =  a ln(  r ) ji = the smallest integer satisfying ji∑ j= a j > ra in this way, we choose τi according to an exponential distribution with parameter a. 4. the next rule to be applied is r ji and the waiting time for this rule is τi. as a result of the application of this rule, the state of one or two compartments may be changed and has to be updated. the multicompartimental next reaction method the gillespie algorithm is an exact numerical simulation method appropriate for systems with a small number of reactions, since it takes a time proportional to the number of reactions (i.e., the number of rules). an exact algorithm which is also efficient is presented in [5], the next reaction method. it uses only a single random number per simulation event (instead of two) and takes a time proportional to the logarithm of the number of reactions. we have adapted this algorithm to make it compartimental. the idea of this method is to be extremely sensitive in recalculating a j and ti, trying to recalculate them only if they change. in order to do that, a data structure called dependency graph [5] is introduced. let r : u[v]l c −→ u′[v′]l be a given rule with propensity ar and let the parent membrane of l be labelled with l ′. we define the following sets: p–lingua 2.0: a software framework for cell–like p systems 237 • dependson(ar) = {(b,t) | b is an object or string whose quantity affect the value. ar, t = l if b ∈ v and t = l ′ if b ∈ u}. generally, dependson(ar) = {(b, l) | b ∈ v} ∪ {(b, l ′) | b ∈ u} • affects(r) = {(b,t) | b is an object or string whose quantity is changed when the rule. r is excuted, t = l if b ∈ v ∨ b ∈ v′ and t = l ′ if b ∈ u ∨ b ∈ u′}. generally, affects(r) = {(b, l) | b ∈ v ∨ b ∈ v′} ∪ {(b, l ′) : b ∈ u ∨ b ∈ u′}. definition 1. given a set of rules r = rl ∪ ··· ∪ rlm , the dependency graph is the directed graph g = (v, e), with vertex set v = r and edge set e = {(vi, v j) | affects(vi) ∩ dependson(av j ) 6= /0} in this way, if there exists an edge (vi, v j) ∈ e and vi is executed, as some objects affected by this execution are involved in the calculation of av j , this propensity would have to be recalculated. the dependency graph depends only on the rules of the system and is static, so it is built only once. the times τi, that represent the waiting time for each rule to be applied, are stored in an indexed priority queue. this data structure, discussed in detail in [5], has nice properties: finding the minimum element takes constant time, the number of nodes is the number of rules |r|, because of the indexing scheme it is possible to find any arbitrary reaction in constant time and finally, the operation of updating a node (only when τi is changed, which we can detect using to the dependency graph) takes log |r| operations. the multicompartimental next reaction method can be broadly summarized as follows: 1. build the dependency graph, calculate the propensity ar for every rule r ∈ r and generate τi for every rule according to an exponential distribution with parameter ar. all the values τr are stored in a priority queue. set t ←  (this is the global time of the system). 2. get the minimum τµ from the priority queue, t ← t + τµ . execute the rule rµ (this is the next rule scheduled to be executed, because its waiting time is least). 3. for each edge (µ, α) in the dependency graph recalculate and update the propensity aα and • if α 6= µ , set τα ← aα,old (τα − τµ ) aα,new + τµ • if α = µ , generate a random number r, according to an exponential distribution with parameter aµ and set τµ ← τµ + r update the node in the indexed priority queue that holds τα . 4. go to 2 and repeat until a prefixed simulation time is reached. both multicompartimental gillespie algorithm and multicompartimental next reaction method are the core of the direct stochastic simulator and efficient stochatic simulator, respectively. one of them, which can be chosen in runtime, will be executed when compiling and simulating a p-lingua file that starts with @model<stochastic>. 238 manuel garcía-quismondo, rosa gutiérrez-escudero, miguel a martínez-del-amor enrique orejuela-pinedo, i. pérez-hurtado 3.2 a simulator for probabilistic p systems next, we describe how the simulator for probabilistic p systems implements the applicability of the rules to a given configuration. (a) rules are classified into sets so that all the rules belonging to the same set have the same left–hand side. (b) let {r, . . . , rt } be one of the sets of rules. let us suppose that the common left-hand side is u [v] α i and their respective probabilistic constants are cr , . . . , crt . in order to determine how these rules are applied to a given configuration, we proceed as follows: – one computes the greatest number n so that un appears in the parent membrane of i and vn appears in membrane i. – n random numbers x such that  ≤ x <  are generated. – for each k ( ≤ k ≤ t) let nk be the amount of numbers generated belonging to interval [ ∑k− j= cr j , ∑k j= cr j ) (assuming that cr = ). – for each k ( ≤ k ≤ t), rule rk is applied nk times. 4 file formats to define p systems together with models and simulators, new formats have been included in p-lingua 2.0. p-lingua 1.0 provided a programming language to define p systems and an xml file format [3]. both have been upgraded to allow representations of p systems which have a cell-like structure. it also supports backwards compatibility, so any file which defines a p system by using p-lingua 1.0 is also recognized by p-lingua 2.0 tools. a detailed description of the syntax of p-lingua programming language, including the new extensions added in order to support the new models, can be found in [4]. a new format has been included as well, the binary format, whose purpose is to use less disk space than the xml format. at this point, the concepts input format and output format should be introduced. an input format is a file format which, if a p system is specified in a file by following that format, the p system specified can be processed by the plinguacore java library. an output format is a file format which, if a p system is specified in a file by following that format, that file can be generated by the library. these concepts are similar to the source code and object code concepts [3]. for p-lingua 2.0 framework, p-lingua programming language is an input format, the binary format is an output format and, eventually, xml is both an input and an output format. this means that plingua programs can be processed by plinguacore, binary files can be generated by plinguacore and xml files can be both processed and generated by the library. 5 command-line tools p-lingua 1.0 provided command-line tools for simulating p systems and compiling files which specify p systems [3]. in p-lingua 2.0, the command-line tool general syntax has changed but, as it provides backwards compatibility, all valid actions in p-lingua 1.0 are still valid in p-lingua 2.0, as well. p–lingua 2.0: a software framework for cell–like p systems 239 5.1 the compilation command-line tool the command-line tool general syntax for compiling input files is defined as follows: plingua [-input_format] input_file [-output_format] output_file [-v verbosity_level] [-h] the command header plingua requests the system to compile the p system specified on a file to a file specified on another, whereas the file input_file contains the programme that we want to be compiled, and output_file is the name of the file that is generated [3]. optional arguments are in square brackets: • the option -input_format defines the format followed by input_file, which should be an input format. • at this stage, valid input formats are p-lingua and xml. • if no input format is set, the p-lingua format is assumed. • the option -output_format defines the format followed by output_file, which should be an output format. • at this stage, valid output formats are xml and bin. • if no input format is set, the xml format is assumed by default. • the option -v verbosity level is a number between 0 and 5 indicating the level of detail of the messages shown during the compilation process [3]. • the option -h displays some help information [3]. 5.2 the simulation command-line tool the simulations are launched from the command line as follows: plingua_sim input_file -o output_file [-v verbosity level] [-h] [-to timeout] [-st steps] [-mode simulatorid] [-a] [-b] the command header plingua_sim requests the system to simulate the p system specified on a file, whereas input_xml is an xml document where a p system is formatted on, and output file is the name of the file where the report about the simulated computation will be saved [3]. optional arguments are in brackets: • the option -v verbosity level is a number between 0 and 5 indicating the level of detail of the messages shown during the compilation process [3]. if no value is specified, it is 3 by default. • the option -h displays some help information [3]. • the option -to sets a timeout for the simulation defined in timeout (in milliseconds), so when the time out has elapsed the simulation is halted. if the simulation has reached a halting configuration before the time out has elapsed this option has no effect. • the option -st sets a maximum number of steps the simulation can take (defined in steps), so when the time out has elapsed the simulation comes to a halt. if the simulation has reached a halting configuration or the time out has elapsed (in case the option -to is set) before the specified number of steps have been taken this option has no effect. 240 manuel garcía-quismondo, rosa gutiérrez-escudero, miguel a martínez-del-amor enrique orejuela-pinedo, i. pérez-hurtado • the option -mode sets the specific simulator to simulate the p system (defined in simulatorid). this option reports an error in case the simulator defined by simulatorid is not a valid simulator for the p system model. • the option -a defines if the simulation can take alternative steps. this option reports an error if the simulator does not support alternative steps. • the option -b defines if the simulation can step backwards. as every simulator supports stepping backwards, this option does not report errors. 6 the plinguacore java library plinguacore c© is a java library which performs all functions supported by p-lingua 2.0, that is, models definition, simulators and formats. this library reports the rules and membrane structure read from a file where a p system is defined, detects errors in the file, reports them. if the p system is defined in p-lingua programming language, it locates the error in the file. this library performs simulations by using the simulators implemented as well as taking into account all options defined. it reports the simulation process, by displaying the current configuration as text and reporting the elapsed time. eventually, this library translates files that define a p system between formats, for instance, from p-lingua language format to binary format. this library is free software published under lgpl license [12], so everyone who is interested can upgrade, change and distribute it respecting the license restrictions. 7 a tool for simulating ecosystems based on p-lingua the bearded vulture (gypaetus barbatus) is an endangered species in europe that feeds almost exclusively on bone remains of wild and domestic ungulates. in [1], it is presented a first model of an ecosystem related to the bearded vulture in the pyrenees (ne spain), by using probabilistic p systems where the inherent stochasticity and uncertainty in ecosystems are captured by using probabilistic strategies. in order to validate experimentally the designed p system, the authors have developed a simulator that allows them to analyze the evolution of the ecosystem under different initial conditions. that software application is focused on a particular p system, specifically, the initial model of the ecosystem presented in [1]. with the aim of improving the model, the authors are adding ingredients to it, as new species and more complex behaviour for the animals. the improved model, together with results of virtual experiments made with this software application, is exhaustively described in [2]. a new gpl [11] licensed java application with a friendly user-interface sitting on the plinguacore java library has been developed. this application provides a flexible way to check, validate and improve computational models of ecosystem based on p systems instead of designing new software tools each time new ingredients are added to the models. furthermore, it is possible to change the initial parameters of the modelled ecosystem in order to make the virtual experiments suggested by experts. these experiments will provide results that can be interpreted in terms of hypotheses. finally, some of these hyphoteses will be selected by the experts in order to be checked in real experiments. 8 conclusions and future work creating a programming language to specify p systems is an important task in order to facilitate the development of software applications for membrane computing. in [3] p-lingua was presented as a programming language to define active membrane p systems with division rules. the present paper extends that language to other models: transition p systems, p–lingua 2.0: a software framework for cell–like p systems 241 symport/antiport p systems, active membranes p systems with division or creation rules, probabilistic p systems and stochastic p systems. we have developed a java library which recognizes the models, implements several simulators for each model and defines different formats to codify p systems, like the p-lingua one or a new binary format. this library can be expanded to define new models, simulators and formats. it is possible to select different algorithms to simulate a p system, for example, there are two different algorithms for stochastic p systems. the library can be used inside other software applications, in this sense, we present a tool for virtual experimentation of ecosystems. an internet website [14], still under construction, will be available to download the applications, libraries, source-code and technical reports, as well as provide information about the progress of the p-lingua project. in addition, this site aims to be a meeting point for users and developers through the use of web-tools as forums. the syntax of the p-lingua programming language is sufficiently standard for specifying different models of cell-like p systems. however, a new version of the language is necessary in order to specify tissue-like p systems but this will be the aim of a future work. although p-lingua 2.0 provides a way to simulate and compile p-systems, command-line tools are usually not user-friendly. it means it is not easy and intuitive for people to use them. for this purpose, p-lingua 1.0 provided an integrated development environment (ide) [3], which eased the way people could use p-lingua 1.0. for p-lingua 2.0, a new ide, called plinguaplugin, is being developed. such an application is integrated into the eclipse platform [13], so it makes the most of eclipse’s capabilities to provide a framework for translating, developing and testing p systems. it aims to be user-friendly and useful for p system researchers. 8.1 acknowledgement the authors acknowledge the valuable assistance given by mario j. perez-jimenez whose vast experience and human quality was essential for us in taking our first steps in scientific research. the authors also acknowledge the support of the project tin2006–13425 of the ministerio de educación y ciencia of spain, cofinanced by feder funds, and the support of the project of excellence with investigador de reconocida valía of the junta de andalucía, grant p08-tic-04200. bibliography [1] m. cardona, m.a. colomer, m.j. pérez–jiménez, d. sanuy, a. margalida. modeling ecosystems using p systems: the bearded vulture, a case study. lecture notes in computer science, 5391, 137–156, 2009 [2] m. cardona, m.a. colomer, a. margalida, i. pérez–hurtado, m.j. pérez–jiménez, d. sanuy. p system based model of an ecosystem of the scavenger birds, proceedings of the 7th brainstorming week on membrane computing, vol. i, 65–80, in press. [3] d. díaz–pernil, i. pérez–hurtado, m.j. pérez–jiménez, a. riscos–núñez. a p-lingua programming environment for membrane computing, proceedings of the 9th workshop on membrane computing, 155–172, 2008. [4] m. garcía–quismondo, r. gutiérrez–escudero, i. pérez–hurtado, m.j. pérez–jiménez. p-lingua 2.0: new features and first applications, proceedings of the 7th brainstorming week on membrane computing, vol. i, 141–168, in press. [5] m.a. gibson and j. bruck. efficient exact stochastic simulation of chemical systems with many species and many channels, j. phys. chem., 104, 1876–1889, 2000. 242 manuel garcía-quismondo, rosa gutiérrez-escudero, miguel a martínez-del-amor enrique orejuela-pinedo, i. pérez-hurtado [6] d.t. gillespie. exact stochastic simulation of coupled chemical reactions, j. phys. chem., 81, 2340–2361, 1977. [7] gh. păun. membrane computing. an introduction, springer–verlag, berlin, 2002. [8] gh. păun. computing with membranes, journal of computer and system sciences 61(1) 108–143, 2000. [9] gh. păun. p systems with active membranes. journal of automata, languages and combinatorics, 6, 1, 75–90, 2001. [10] f.j. romero–campero. p systems, a computational modelling framework for systems biology, doctoral thesis, university of seville, department of computer science and artificial intelligence, 2008. [11] the gnu general public license: http://www.gnu.org/copyleft/gpl.html [12] the gnu lesser general public license: http://www.gnu.org/copyleft/lgpl.html [13] the eclipse project: http://www.eclipse.org [14] the p-lingua website: http://www.p-lingua.org manuel garcía-quismondo fernández was born in june 11, 1985. he got his degree in ingeniería técnica en informática de sistemas in the university of sevilla in june 2007. currently, he is about to get another degree, this time in ingeniería en informática, at the same university. since september 2008, he has been a granted student at the department of computer science and artificial intelligence. he has developed a software application called plinguaplugin and co-developed another one called plinguacore, directed by agustín riscos-nuñez and ignacio pérez-hurtado. rosa gutiérrez-escudero was born in august 16, 1984. she received her degree in computer science in june 2008 from the university of sevilla. since september 2008, she has been a phd student at the department of computer science and artificial intelligence of the university of sevilla (spain). she is also a member of the research group on natural computing in the same university. her main research interests within the membrane computing area are computer simulation and complexity theory. miguel a. martínez-del-amor was born in july 10, 1984. he received his degree in computer science from the university of murcia (spain) in june 2008. currently, he is a phd student at the department of computer science and artificial intelligence in the university of sevilla (spain). he is also a member of the research group on natural computing at the same university, and his main research interest is to joint membrane computing and high performance computing by using efficient computer simulations. enrique orejuela-pinedo was born in june 7, 1979. he received his degree in biology in 2005, from the university of sevilla (spain). he has cooperated as internal student at the departament of ecology and vegetal biology in the university of sevilla. currently, he is a phd student at the department of computer science and artificial intelligence in the university of sevilla. he is also a member of the research group on natural computing at the same university, and his main research interests are natural computing and membrane computing, specially computational models of ecosystems. ignacio pérez-hurtado was born in september 21, 1977. he received his degree in computer science in october 2003. he was systems analyst in a company for three years. since september 2006, he has been a phd student at the department of computer science and artificial intelligence in the university of sevilla (spain). he is an associate professor at the same department. he is also p–lingua 2.0: a software framework for cell–like p systems 243 a member of the research group on natural computing at the said university, and his main research interests within the membrane computing are computer simulation, models for biological processes and complexity theory. international journal of computers, communications & control vol. i (2006), no. 1, pp. 81-99 grigore c. moisil (1906 1973) and his school in algebraic logic george georgescu, afrodita iorgulescu, sergiu rudeanu abstract: we present in the paper a very concise but updated survey emphasizing the research done by gr. c. moisil and his school in algebraic logic. keywords: n-valued lukasiewicz-moisil algebra, θ -valued lukasiewicz-moisil algebra, post algebra the mathematical logic is one of the domain in which the creative spirit of gr.c. moisil manifested plenary. his work in logic stands out by the the novelty, the variety and the depth of treated subjects. his first works are connected to the top results of the time and wear an algebraic seal. the young professor from jassy came after a rich experience in mechanics and differential equations. van der waerden treatise of algebra has decisively influenced his entry in logic by the algebraic gate. in the same time, these works have a powerful philosophical imprint. from this vast creation, the contributions in multiple-valued logics represent the part with the most intense impact on today researches. the first system of multiple-valued logic was introduced by j. łukasiewicz in 1920. independently, e. post introduced in 1921 a different multiple-valued logic. for łukasiewicz, the motivation was of philosophical nature he was looking for an interpretation of the concepts of possibility and necessity while for post, the research was intended as a natural mathematical generalization of bivalent logic. in 1930, łukasiewicz and tarski studied a logic whose truth values are the real numbers from the interval [0, 1]. 1 łukasiewicz-moisil algebras in 1940, gr. c. moisil has defined the 3-valued and the 4-valued łukasiewicz algebras and in 1942, the nvalued łukasiewicz algebras (n ≥ 2). his goal was to algebrize łukasiewicz’s logic. boolean algebras, algebraic models of classical logic, are particular cases of that new structures. in the description of a logical system, the implication was traditionally the principal connector. the n-valent system of łukasiewicz had as truth values the elements of the set ln = { 0, 1 n−1 , 2 n−1 , . . . , n−2 n−1 , 1 } and was built around a new concept of implication, on which are based the definitions of the other connectors. for moisil, the basic structure is that of lattice, to which he adds a negation (getting the so called "de morgan algebra") and also some unary operations (called by moisil "chryssipian endomorphisms"), representing the "nuancing". the łukasiewicz implication was let on a secondary plane and, in the case of an arbitrary valence, was completely lost. further axiomatizations were suggested by a. monteiro, r. cignoli, c. sicoe, s. rudeanu and others. an example of a. rose from 1956 established that for n ≥ 5 the łukasiewicz implication can no more be defined on a łukasiewicz algebra. consequently, only for n = 3 and n = 4 the structures introduced by moisil are models for łukasiewicz logic. the lost of implication has lead to another type of logic, called today "moisil logic", distinct from łukasiewicz system; the logic corresponding to n-valued łukasiewicz-moisil algebras was created by moisil in 1964. the fundamental concept of moisil logic is the nuancing. nowadays we feel it appropiate to call these algebras łukasiewicz-moisil algebras or lm algebras for short. for complete information and references on łukasiewicz-moisil algebras see [25] the work of moisil on lm algebras covers two periods of time: a first period, during 1940-1942, when he introduces the n-valued lm algebras with negation and studies special classes of these structures, as centered and axed lm algebras and a second one, during 1954-1973, when he introduces the θ -valued lm algebras without negation, applies multiple-valued logics to swiching theory and study algebraic properties of lm algebras (representation, ideals, reziduation). copyright c© 2006 by ccc publications 82 george georgescu, afrodita iorgulescu, sergiu rudeanu moisil’s works traced research directions for many romanian and foreign mathematicians. in argentina, at bahia blanca, antonio monteiro and his school (roberto cignoli, luiz monteiro, luiza iturrioz, maurice abad etc.) have contributed decisively to consolidate lm agebras as a domain of algebra of logic and to disseminate them in the mathematical world. in his phd thesis from 1969 [29], r. cignoli makes a very deep study of n-valued moisil algebras (the name he first gives to the n-valued łukasiewicz algebras introduced by moisil). 1.1 n-valued łukasiewicz-moisil algebras the structure called "de morgan algebra" was first studied by moisil; the name was given by antonio monteiro [142]; a duplicate name is "quasi-boolean algebra" given by a. bialynicki-birula and h. rasiowa. definition 1.1. a de morgan algebra is a structure (a,∨,∧,−, 0, 1) such that (a,∨,∧, 0, 1) is a distributive lattice with 0 and 1 and the unary operation −, called negation, verifies: (dm0) 1 = 0−, (dm1) (x−)− = x, (dm2) (x∧y)− = x− ∨y−. remark 1.2. in a de morgan algebra we also have: (dm3) (x∨y)− = x− ∧y−. definition 1.3. let j = {1, 2, . . . , n−1}. an n-valued łukasiewicz-moisil algebra (n ≥ 2) or an lmn bf algebra for short is an algebra a = (a,∨,∧,−, (r j) j∈j , 0, 1) of type (2, 2, 1, (1) j∈j , 0, 0) such that: (i) (a,∨,∧,−, 0, 1) is a de morgan algebra. (ii) the unary operations r1, r2, . . . , rn−1 fulfil the following axioms: for every x, y ∈ a and every i, j ∈ j, (l1) r j(x∨y) = r j x∨r j y, (l2) r j x∨(r j x)− = 1, (l3) r j ◦ri = ri, (l4) r j(x−) = (rn− j x)−, (l5) r1x ≤ r2x ≤ ··· ≤ rn−1x, (l6) if r j x = r j y for every j ∈ j, then x = y; this is the determination principle. if a fulfils (i) and only (l1)–(l5) we shall say that a is an lmn pre-algebra. proposition 1.4. in every lmn algebra a, the following properties are verified: for every x, y ∈ a and every j ∈ j, (l7) r j(x∧y) = r j x∧r j y; (l8) r j x∧(r j x)− = 0; (l9) x ≤ y if and only if (r j x ≤ r j y, for every j ∈ j); (l10) r1x ≤ x ≤ rn−1x; (l11) r j 0 = 0, r j 1 = 1; (l12) let c(a) be the set of complemented elements of a, i.e. c(a) = {x ∈ a | ∃x′ ∈ a, x∨x′ = 1, x∧x′ = 0}. let k j be the set of all elements of a left invariant by r j , j ∈ j, i.e. k j = {x ∈ a | r j x = x}. grigore c. moisil (1906 1973) and his school in algebraic logic 83 then: (i) r j x ∈ c(a), for every j ∈ j, x ∈ a and (ii) c(a) = k j , for every j ∈ j; (l12’) (c(a),∨,∧,−, 0, 1) is a boolean algebra, where x− = x′; (l12”) if z ∈ c(a), then for every x ∈ a: x∧z = 0 ⇐⇒ x ≤ z−, z∨x = 1 ⇐⇒ z− ≤ x; (l13) x− ∨rn−1x = 1; (l14) x∧(rn−1x)− = 0. example 1.5. the algebra ln = l (lmn) n = (ln,∨,∧,−, (r j) j∈j , 0, 1), where ln = { 0, 1 n−1 , 2 n−1 , . . . , n−2 n−1 , 1 } and    x∨y = max(x, y), x∧y = min(x, y), x− = 1−x, r j ( i n−1 ) = { 0, if j + i < n, 1, if j + i ≥ n, i ∈ {0}∪j, j ∈ j, is an lmn algebra, that we shall call the canonical lmn algebra. the proper subalgebras of ln have the form: s = ln − ⋃ x∈ln−{0} {x, x−}. they are lmn algebras. the smallest subalgebra of ln (with respect to ⊆) is c(ln) = {0, 1}, which is also a boolean algebra, cf. (l12’). for instance, the subalgebras of l3 are l2 and l3, l4 are l2 and l4 and l5 are l2, l3, {0, 1/4, 3/4, 1} and l5. remark 1.6. lm2 algebras coincide with boolean algebras. proposition 1.7. in every lmn pre-algebra, the determination principle (l6) is equivalent to each of the following conditions: for every x, y ∈ l, (a) x∧(r j x)− ∧r j+1y ≤ y, for every j ∈ j −{n−1}; (b) x∧ n−1∧ j=1 ((r j x)− ∨r j y) ≤ y. þ (representation theorem of moisil) every lmn algebra can be embedded in a direct product of copies of the canonical lmn algebra ln. corollary 1.1. every lmn algebra is a subdirect product of subalgebras of the canonical lmn algebra ln. ∗∗∗ in 1968, gr. c.moisil introduced the θ -valued łukasiewicz algebras or lmθ algebras for short (without negation), where θ is the order type of a chain with first and last element. the concept of θ -valued łukasiewicz algebra is obtained from that of n-valued, on the one hand, by dropping the negation − and on the other hand, by replacing the set ln by a totally ordered set i with first and least elements and by adapting the axioms to this case; the determination principle is preserved. these structures were thought by moisil as models of a logic with an infinity of nuances. according to a confession done by moisil, he imagined lmθ algebras (without negation) long 84 george georgescu, afrodita iorgulescu, sergiu rudeanu time ago, but the care of finding a strong motivation for them delayed the announcement; the motivation was found when moisil met zadeh’s fuzzy set theory, in which he saw a confirmation of his old ideas. in 1969, marek and traczyk [110] introduced the notion of generalized łukasewicz algebra (with negation), in an attempt to generalize to the infinite case the lmn algebras; but their generalization is not a natural one. in his phd thesis from 1972 [64], g. georgescu studied duality theory for moisil’s lmθ algebras (without negation), the injective objects (and their characterization), monadic and poliadic algebras. in his phd thesis from 1981 [53], a. filipoiu studied the lmθ algebras (without negation) and their associated logic. he gives a representation theorem for lmθ algebras by aids of θ -valent moisil field. in his master thesis from 1981 [12] also, l. beznea studies a generalization of lmθ algebras (without negation) obtained by eliminating the determination principle. later on, in his phd theses from 1984 [21], v. boicescu introduced and studied the n-valued lm algebras without negation, as a particular case of moisil’s lmθ algebras (without negation). following the inverse way, a. iorgulescu, in her phd thesis from 1984 [90] also, introduced and studied a natural generalization of moisil’s lmn algebras to the infinite case, called θ -valued lm algebras with negation or lmθ algebras with negation for short; any lmθ algebra with negation is a moisil’s lmθ algebra without negation. 2 connection with logic gr. c. moisil invented lm algebras in order to create an algebraic structure playing the same role with respect to the multiple-valued logic as boolean algebras play with respect to classical, bivalent logic. however, as shown by the example of a. rose, this only happens for the cases n = 3 and n = 4. the algebraic structures adequate to the infinite-valued logic of łukasiewicz (truth valued in the real interval [0, 1]) are the mv-algebras introduced by c.c. chang in 1958 or, equivalently, the wajsberg algebras introduced by font, rodriguez and torrens in 1984; d. mundici proved in in 1986 that mv algebras are categorically equivalent to lattice-ordered abelian groups with strong unit. r. grigolia’s mvn algebras, introduced in 1977, and cignoli’s proper łukasiewicz algebras, introduced in 1982, are algebraic structures corresponding to n-valued logic of łukasiewicz. the logic corresponding to lmn algebras was created by moisil himself in 1964. łukasiewicz logic has implication as its primary connector, while moisil logic is based on the idea of nuance, expressed algebraically by the chrysippian endomorphisms. the “engine" of the latter logic is moisil’s determination principle, according to which an n-valued sentence is determined by its boolean nuances. the determination principle realizes a transfer from the multiple-valued logic to the classical logic. this determination brings moisil logic much closer to classical logic than łukasiewicz logic. one could say that moisil logic is derived from classical logic by the idea of nuancing. algebraically, this tight relationship is expressed by the fundamental adjunction between the categories of boolean and łukasiewicz algebras. v. boicescu in 1971 and a. filipoiu in 1981 introduced and studied logics appropiate to lmθ algebras without negation. (i.e. infinite-valued lm algebras). a. filipoiu generalized smullyan’s method of analytic tableaux to θ -valued logic without negation and studied the θ -valued predicate calculus as well, with applications to systems of recording and retrieval of information. łukasiewicz logic, post logic and moisil logic consitute the three directions in the classical theory of multiplevalued logic. their corresponding algebraic models are mv algebras, post algebras and lm algebras. 3 connections with other structures of algebraic logic moisil introduced in 1941 the centered lm3 algebras. post algebras (cf. p. rosenbloom (1942), g. epstein (1960), t. traczyk (1963)) turn out to be centered lm algebras, cf. r. cignoli (1969) and g. georgescu c. vraciu (1969). the θ -valued post algebras were studied by t. traczyk (1967) and g. georgescu (1971). gr. c. moisil, r. cignoli, l. iturrioz, a. monteiro and v. boicescu studied lm algebras as particular cases of heyting algebras. v. boicescu also studied lm algebras as stone algebras. grigore c. moisil (1906 1973) and his school in algebraic logic 85 lm3 algebras and lm4 algebras are polynomially equivalent to mv3 algebras and mv4 algebras, respectively, since they are the algebraic counterpart of the 3-valued łukasiewicz logic and the 4-valued łukasiewicz logic, respectively. d. mundici was first to point out the equivalence between lm3 algebras and mv3 algebras, in 1989. then a. iorgulescu, in 1998-2000 [91] [94], pointed out the isomorphism between the categories of lmk algebras and of mvk algebras, for k = 3, 4 and also studied the categories lmn and mvn for n ≥ 5, showing that every mvn can be made into an lmn algebra. she then studied those lmn algebras that can be viewed as mvn algebras: 3.1 connections between lmn algebras and mvn algebras mv algebras were introduced by c.c. chang, in 1958 [26]. a simplified list of axioms of mv algebras was given by mangani [109], as follows: definition 3.1. an mv algebra is an algebra a = (a,⊕,−, 0) of type (2, 1, 0), where the following axioms are verified: for every x, y, z ∈ a, (mv1) (a,⊕, 0) is an abelian monoid, (mv2) x⊕0− = 0−, (mv3) (x−)− = x, (mv4) (x− ⊕y)− ⊕y = (y− ⊕x)− ⊕x, where x ·y = (x− ⊕y−)−. definition 3.2. for any m ∈ in, we have: (i) 0x = 0 and (m + 1)x = mx⊕x, (ii) x0 = 1 and xm+1 = xm ·x. the mvn algebras were introduced by revaz grigolia in 1977 [87], as follows. definition 3.3. an mvn algebra (n ≥ 2) is an mv algebra a = (a,⊕,−, 0), whose operations fulfil the additional axioms: (m1) (n−1)x⊕x = (n−1)x, (m1’) xn−1 ·x = xn−1 and, if n ≥ 4, the axioms: (m2) [( jx)·(x− ⊕[( j −1)x]−)]n−1 = 0, (m2’) (n−1)[x j ⊕(x− ·[x j−1]−)] = 1, where 1 < j < n−1 and j does not divide n−1. corollary 3.1. mv2 algebras coincide with boolean algebras. example 3.4. the mv algebra ln = l (mvn) n = (ln,⊕,−, 0), where ln = { 0, 1 n−1 , 2 n−1 , . . . , n−2 n−1 , 1 } and for any x, y ∈ ln: x⊕y = min(1, x + y), x ·y = max(0, x + y−1), x− = 1−x and x∨y = max(x, y), x∧y = min(x, y), is an mvn algebra. we shall call it the canonical mvn algebra. note that b(ln) = {0, 1}. the subalgebras of ln are of the form: sm = { 0, k n−1 , . . . , (m−2)k n−1 , 1 } , 86 george georgescu, afrodita iorgulescu, sergiu rudeanu where k = n−1m−1 , if m−1 divides n−1. the subalgebras sm of ln are isomorphic to lm = { 0, 1m−1 , . . . , m−2 m−1 , 1 } , if m − 1 divides n − 1, and they are mvn algebras. hence lm = (lm,⊕,·,−, 0, 1) (m ≤ n) is an mvn algebra if and only if m−1 divides n−1. for instance, the subalgebras of: l3 are l2 and l3, l4 are l2 and l4 and l5 are l2, l3 and l5. þ every mvn algebra is a subdirect product of subalgebras of the canonical mvn algebra ln. d. mundici was the first to prove in 1989 [152] that mv3 algebras coincide with lm3 algebras. a. iorgulescu has proved in 1998-2000 [91] [94] that: 1 mv4 algebras coincide with lm4 algebras, 2 the canonical mvn algebra coincides with the canonical lmn algebra (n ≥ 2), 3 for n ≥ 5, any mvn algebra is a lmn algebra, 4 those lmn algebras which are mvn algebras, for every n ≥ 5, are exactly cignoli’s proper n-valued łukasiewicz algebras. here are for short the results 1-3: ∗∗∗ to obtain the transformation of an mvn algebra into an lmn algebra, for any n ≥ 3, iorgulescu used suchoń’s transformation [174]: suchoń defines moisil operators (σ j) j∈j ( σ j = rn− j ) of the canonical lmn algebra (n ≥ 3) starting from the łukasiewiczian implication → and from the negation −. he puts b3(x) = (x −) → x and b j+1(x) = (x−) → b j(x), j ≥ 3. (1) then he defines: σ1x = bn(x) (2) and for 1 < j ≤ [n/2], σ j x = { σn−1(bl+1(x)), l j ≥ n−1 σl j(bl+1(x)), l j < n−1, (3) where l = max{m | m( j −1) < n−1}, while σn− j(x) = (σ j(x−))−, for 1 ≤ j ≤ [n/2]. (4) suchoń’s moisil operators verify: σ1 ≥ σ2 ≥ . . . ≥ σn−1. remark 3.5. if we want to use suchoń’s construction, it is convenient to consider not the mv algebra (a,⊕,−, 0), but the wajsberg algebra, (a,→,−, 1), introduced by j. m. font, a. j. rodriguez and a. torrens in 1984; mv algebras and wajsberg algebras are isomorphic structures: • if a = (a,→,−, 1) is a wajsberg algebra and if we define α(a) = (a,⊕,−, 0) by x⊕y = x− → y, 0 = 1−, (5) then α(a) is an mv algebra. • conversely, if a = (a,⊕,−, 0) is an mv algebra and if we define β (a) = (a,→,−, 1) by x → y = x− ⊕y, (6) 1 = 0−, then β (a) is a wajsberg algebra. • the maps α, β are mutually inverse. grigore c. moisil (1906 1973) and his school in algebraic logic 87 it follows immediately by (1) that b3(x) = x⊕x = 2x and b j+1(x) = x⊕b j(x) = jx, j ≥ 3. (7) by using suchoń’s construction, iorgulescu then gave the following definition 3.6. let a = (a,⊕,−, 0) be an mvn algebra (n ≥ 3). define φs(a) = (a,∨,∧,−, (r j) j∈j , 0, 1) by x∨y = x ·y− ⊕y, x∧y = (x− ∨y−)−, rn−1x = (n−1)x, (8) rn− j x = { r1(lx), l j ≥ n−1 rn−l j(lx), l j < n−1, (9) for 1 < j ≤ [n/2], l = max{m | m( j −1) < n−1} , r j x = (rn− j(x−))−, 1 ≤ j ≤ [n/2]. (10) proposition 3.7. if ln is the canonical mvn algebra (n ≥ 3), then φs(ln) is the canonical lmn algebra. þ if a is an mvn algebra (n ≥ 3), then φs(a) is an lmn algebra. ∗∗∗ proposition 3.8. 1) given the canonical lmn algebra (n ≥ 3) ln = (ln,∨,∧,−, (r j) j∈j , 0, 1), define ψ(ln) = (ln, ⊕n ,−, 0) by : if n = 2k + 1, x⊕2k+1 y = (x∨r2ky)∧(y∨r2kx) (11) ∧ (x∗ ∨r2k−1y)∧(y∗ ∨r2k−1x) ... ∧ (x(k−1)∗ ∨rk+1y)∧(y(k−1)∗ ∨rk+1x), if n = 2k, x⊕2k y = (x∨r2k−1y)∧(y∨r2k−1x) (12) ∧ (x∗ ∨r2k−2y)∧(y∗ ∨r2k−2x) ... ∧ (x(k−1)∗ ∨rky)∧(y(k−1)∗ ∨rkx), where x∗ is the successor of x and x2∗ = (x∗ )∗ , xm∗ = ( x(m−1)∗ )∗ (13) then ψ(ln) is the canonical mvn algebra. 2) the maps φs, from proposition 3.7, and ψ are mutually inverse. since for n = 3, in the canonical lm3 algebra l3, the operation ⊕ is: x⊕y = (x∨r2y)∧(y∨r2x) and for n = 4, in the canonical lm4 algebra l4, the operation ⊕ is defined by: x⊕y = (x∨r3y)∧(y∨r3x)∧(x∗ ∨r2y)∧(y∗ ∨r2x) = = (x∨r3y)∧(y∨r3x)∧(x− ∨y− ∨r2x∨r2y), it follows that the transformation ψ is not polynomial for n ≥ 5). those lmn algebras which are mvn algebras (i.e. for which the transformation ψ is defined), for every n ≥ 5, are exactly cignoli’s proper n-valued łukasiewicz algebras [34], but the proof is very technical [94]. 88 george georgescu, afrodita iorgulescu, sergiu rudeanu 4 representation theorems numerous representation theorems have been given for lm algebras. the first is due to moisil himself and is reminiscent of the representation theorem for boolean algebras: every lmn algebra can be embadded into a cartesian power of ln. in a modern vision [25], every lm algebra is a subdirect product of subalgebras of the algebra in(i, l2) of incresing functions from i to l2. in particular every lmn algebra is a subdirect product of subalgebras of ln (cignoli). l is a direct product of subalgebras of ln if and only if it is complete and atomic (boicescu 1984). the representation by continuous functions studied by cignoli, boicescu and filipoiu, means that for every lmθ algebra without negation l there is a unique boolean space x such that l is isomorphic to the algebra of all continuous functions f : x → in(i, l2), where in(i, l2) is endowed with the topology having as basis the principal ideals and the principal filters generated by the characteristic functions of the sets {k | k > α}, α ∈ θ . the representation of lmθ algebras without or with negation by moisil fields of sets is due to filipoiu. the stone duality was extended from boolean algebras to lmθ algebras without or with negation with the aid of a suitable concept called lmθ -valued stone space (cignoli, georgescu, iorgulescu), while the priestley duality is based on a suitable adaptation of the concept of priestley space (filipoiu). the representation of lm algebras as algebras of fuzzy sets was studied by d. ponasse, j.l. coulon and j. coulon, s. ribeyre and s. rudeanu. the representation of lmn algebras by lm3 algebras is legitimated by the "good" properties of the latter and was studied by a. monteiro, l. monteiro, f. coppola, v. boicescu and a. iorgulescu. 5 categorial aspects the stone and priestley dualities are in fact equivalences of categories. other categorial properties of lm algebras were studied. here are a few samples. the association of l with the boolean alegebra c(l) of complemented elements of l is extended to a functor c :lmθ →b, while the association of a boolean algebra b with the algebra in(i, b) is extended to a functor t : b →lmθ . then c and t are adjoint functors, c is faithful and t is fully faithful. this yields in particular the representation theorem of moisil. the construction of the functors c and t was given by moisil himself. the injective and projective objects have also been studied, for instance, an lmθ algebra is injective if and only if it is a complete post algebra (whose center is a complete boolean algebra), cf. l. monteiro, r. cignoli, g. georgescu and c. vraciu, v. boicescu. 6 ideals and congruences the study of the appropiate ideal and congruence theory for lm algebras was undertaken by gr. c. moisil, a. monteiro, r. cignoli, c. sicoe. v. boicescu introduced the concepts of θ -ideal and θ -congruence, the prime spectre. for instance, in the case of lmn algebras without negation, the congruence lattice of l is a boolean algebra (a stone algebra) if and only if l is finite (c(l) is a complete boolean algebra). 7 monadic and polyadic algebras l. monteiro and g. georgescu studied the generalization to lm algebras of the monadic and polyadic boolean algebras introduced by p.r. halmos. sample results: the representation of monadic lm algebras by functional monadic lm algebras and the semantic completeness for polyadic lm algebras. a paper of g. geogescu, a. iorgulescu and i. leuştean investigates monadic mvn algebras and closed mvn algebras. grigore c. moisil (1906 1973) and his school in algebraic logic 89 8 miscellanea various other topics have also been studied. thus: v. boicescu proved that the lattice of equational subclass of lmn is a finite heyting algebra. the study of atomic algebras and the characterization of simple algebras as subalgebras of in(i, l2) and the property that lmn algebras without negation form an equational class, are also due to boicescu. the study of irredundant algebras and of exactly n-valued algebras is due to boicescu as well. a. iorgulescu introduced and studied m-complete lmθ algebras with negations, generalizing many of the properties of m-complete boolean algebras. g. georgescu and i. leuştean studied probabilities on lm algebras. l. beznea studied a generalization of lm algebras, obtained by dropping the determination principle. let us also mention m. sularia’s theory of d algebras. these structures are subdirect products between a heyting algebra and a brouwer algebra and represent the algebraic counterpart of a logic of problem solving. in [44], [48], the authors study łukasiewicz bck algebras endowed with moisil oparators. in [163], [164], c. sanza introduced and studied (monadic) n×m-valued łukasiewicz algebras with negation. in a very recent paper [103], i. leuştean proposes a unifying framework for lmn algebras, mv algebras and post algebras; essentially, an lmn+1 algebra is charcterized by a string of n boolean ideals of his boolean center. the necessary and sufficient conditions are given that such a string must satisfy to define a mvn+1 algebra or a post algebra of order n + 1. this result could be seen as a generalization of moisil’s determination principle. as an application, in paper [75], some special cauchy completions of mvn+1 algebras are characterized by using the properties of corresponding strings of boolean ideals. in another very recent paper [75], g.georgescu and a. popescu introduced the notion of n-nuanced mv algebra, by performing a łukasiewicz-moisil nuancing construction on top of mv-algebras. these structures extend both mv-algebras and łukasiewicz-moisil algebras, thus unifying two important types of structures in the algebra of logic. on a logical level, n-nuanced mv algebras amalgamate two distinct approaches to manyvaluedness: that of the infinitely valued łukasiewicz logic, more related in spirit to the fuzzy approach, and that of moisil n-nuanced logic, which is more concerned with nuances of truth rather than truth degrees. they study n-nuanced mv algebras mainly from the algebraic and categorial points of view and also consider some basic model-theoretic aspects. the relationship with a suitable notion of n-nuanced ordered group via an extension of the γ construction is also analyzed: 8.1 n-nuanced mv algebras usually, mv algebras are defined only in terms of ⊕, − and 0. however, in order to point out the symmetry of these structures, the authors prefered the following slightly redundant definition: definition 8.1. an mv algebra is a structure (a,⊕,¯,−, 0, 1), satisfying the following axioms: (mv1’) (a,⊕, 0) and (a,¯, 1) are commutative monoids, (mv2’) x¯0 = 0 and x⊕1 = 1, (mv3’) (x−)− = x, (mv4’) (x⊕y)− = x− ¯y−, (mv5’) (x¯y−)⊕y = (y¯x−)⊕x. definition 8.2. a generalized de morgan algebra is a structure l = (l,⊕,¯,−, 0, 1), where ⊕, ¯ are binary operations, − is a unary operation, and 0, 1 are constants such that the following conditions hold: (i) (l,⊕, 0), (l,¯, 1) are commutative monoids; (ii) (x⊕y)− = x− ¯y− and (x−)− = x for all x, y ∈ l; . remark 8.3. if l is a generalized de morgan algebra, then (x¯y)− = x− ⊕y− for all x, y ∈ l. definition 8.4. an n-nuanced mv-algebra (nmvn algebra for short) is a structure l = (l,⊕,¯,−, r1, . . . , rn−1, 0, 1) such that (l,⊕,¯,−, 0, 1) is a generalized de morgan algebra and r1, . . . , rn−1 satisfy the following axioms: (a0) rix⊕((rix)− ¯riy) = riy⊕((riy)− ¯rix), for i ∈ {1, . . . , n−1}, 90 george georgescu, afrodita iorgulescu, sergiu rudeanu (a1) ri(x⊕y) = rix⊕riy, ri(x¯y) = rix¯riy, ri(0) = 0, ri(1) = 1, for i ∈ {1, . . . , n−1}, (a2) rix⊕(rix)− = 1, rix¯(rix)− = 0, for i ∈ {1, . . . , n−1}, (a3) ri ◦r j = r j , for i, j ∈ {1, . . . , n−1}, (a4) ri(x−) = (rn−ix)−, for i ∈ {1, . . . , n−1}, (a5) (determination principle:) if rix = riy for each i ∈ {1, . . . , n−1}, then x = y, (a6) r1x ≤ r2x ≤ . . . ≤ rn−1x. remark 8.5. nmvn algebras provide a common generalization of mvand łukasiwicz-moisil algebras. indeed, if n = 2, then, because of the determination principle, r1 is the identity, thus an nmvn algebra can be identified with an mv-algebra; if (l,⊕,¯,−, 0, 1) is a de morgan algebra, then (l,⊕,¯,−, r1, . . . , rn−1, 0, 1) becomes an lmn algebra. example 8.6. let a = (a,⊕,¯,−, 0, 1) be an mv-algebra. consider the set t (a) = {(x1, . . . , xn−1) ∈ an−1 | x1 ≤ . . . ≤ xn−1}. since an−1 is an mv-algebra (with operations taken component-wise from a) and t (a) is closed under the operations 0, 1, ⊕, ¯ (where 0 and 1 are the constant vectors), then we can consider these operations on t (a). we furthermore define −, r1, . . . , rn−1 by: (x1, . . . , xn−1)− = (x−n−1, . . . , x − 1 ), ri(x1, . . . , xn−1) = (xi, . . . , xi), for i ∈ {1, . . . , n−1}. then (t (a),⊕,¯,−, r1, . . . , rn−1, 0, 1) is an nmvn algebra. define m(l) = {x ∈ l | rix = x for all i ∈ {1, . . . , n−1}}. then m(l), together with the operations ⊕,¯,−, 0, 1 induced by l, is an mv algebra, called the mv-center of l. in the mv-algebra m(l) we have a canonical order ≤. let us define an extension of this order to l by: x ≤ y iff, for each i ∈ {1, . . . , n−1}, rix ≤ riy. because of the determination principle, this is indeed an order and because of (a3), it is indeed an extension of the order on m(l). moreover, the compatibility properties listed in the following lemma are obvious: proposition 8.7. the following properties are true in a l: (1) 0 is the greatest and 1 is the least element in l w.r.t. ≤; (2) for each x, y ∈ l, x ≤ y iff y− ≤ x−; (3) for each x, x′, y, y′ ∈ l, if x ≤ x′ and y ≤ y′, then x⊕y ≤ x′ ⊕y′ and x¯y ≤ x′ ¯y′, (4) r1x ≤ x ≤ rn−1x, for any x ∈ l, (5) for x, y ∈ l, if x⊕y = 1 and x¯y = 0, then x, y ∈ m(l) and y = x−, (6) m(l) = {x ∈ l | x⊕x− = 1′, x¯x− = 0}. 9 applications to switching theory whereas boolean algebra is a suitable tool for the study of networks made up of binary devices, the study of networks involving multi-positional devices and the so-called hazard and race phenomena have imposed the use of other algebraic tools, namely galois fields, łukasiewicz-moisil algebras and the theory of discrete functions. moisil investigated circuits involving devices such as polarized relays with unstable neutral, ordinary relays under low self-maintaining current, valves, resistances, multi-positional relays, as well as transistors and other electronic devices. see also [125]. moisil has described the operation of such devices by characteristic equations of the form xk+1 = ϕ(ξk, xk), where the variable x associated with the relay contact takes values in ln, where n ≤ 5 grigore c. moisil (1906 1973) and his school in algebraic logic 91 depends on the type of the 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[182] l.a. zadeh, fuzzy sets, inform. and control, 8, 1965, 338-353. george georgescu university of bucharest address: str. academiei no. 14, romania e-mail: georgescu@funinf.cs.unibuc.ro afrodita iorgulescu academy of economic studies address: piaţa romană nr. 6 r 70167, oficiul poştal 22, bucharest, romania e-mail: afrodita@ase.ro sergiu rudeanu university of bucharest address: str. academiei no. 14, romania e-mail: rud@funinf.cs.unibuc.ro international journal of computers, communications & control vol. iii (2008), no. 1, pp. 69-79 olc, on-line compiler to teach programming languages cayetano guerra artal, maria dolores afonso suarez idafen santana perez, ruben quesada lopez editorial note: the program committee of icvl 2007 sent to ijccc nine of the best papers presented at the conference, with the recommendation to be published in an extended form. after a new evaluation, our reviewers decided that five of those papers can be published in ijccc, two of which in this issue. abstract: the advance of internet towards web 2.0 conveys the potential it has in a wide range of scopes. the ongoing progress of the web technology and its availability in teaching and learning, as well as a students’ profile increasingly more used to managing an important amount of digital information, offers lecturers the opportunity and challenge of putting at students’ disposal didactic tools making use of the internet. programming is one of the essential areas taught in university studies of computer science and other engineering degrees. at present, it is a knowledge acquired through tutorial classes and the practice with different tools for programming. this paper shows the acquired experience in the development and use of a simple compiler accessible through a web page. in addition it presents a teaching proposal for its use in subjects that include programming languages lessons. olc on-line compiler is an application which greatly lightens the student’s workload at the initial stage of programming. during this initial period they will neither have to deal with the complexities of the installation and the configuration of these types of tools, nor with the understanding of multiple options which they present. therefore students can concentrate on the comprehension of the programming structures and the programming language to be studied. keywords: compiler, e-learning, interactivity, virtual laboratory, web 2.0, video tutorials. 1 introduction new information and communication technologies (ict’s) still imply changes in a wide range of society scopes. a society in which the information, its use and distribution in digital format are part of a number of tasks carried out every day. new generations understand their environment in a natural way as well as the use of these technologies. this is reflected in university students who are increasingly getting used to managing this kind of information [1]. from the beginning internet has meant a milestone in communication at a world wide level [2]. it provides net users with an hypertext document system with a rich content and hyperlinks that allow to reference another resource: text document, image, animation, audio or video. in its beginning, information was contained in html pages that were not updated very frequently. from here its evolution was directed to dynamic html pages that make use of cms with data bases. next, it was directed to web 2.0, a concept that has turned into reality in internet, through which contents and the way of interaction with these contents in internet have changed. users, from sharing these contents, have turned their role into the edition and classification of them. 2 web 2.0 this term, web 2.0 [3], coined for the first time in 2004, makes reference to a new vision of the web which has evolved towards users’ communities, interaction and social networks. amongst the technologies that bring the web 2.0 into reality we find: the ones that use the web platform to host desktop copyright © 2006-2008 by ccc publications 70 cayetano guerra artal, maria dolores afonso suarez idafen santana perez, ruben quesada lopez applications; the ones that separate content and design using style sheets; the ones that support rss or the ones that allow the management of users and communities. in the same way, there exists a set of web services that can be considered indicators of the advance towards web 2.0 [4]. these services provide communication mechanisms that make it possible to present information to users actively. amongst these services we can find advertising services, shared photo-database, community-generated tag database, delivery of contents, encyclopaedias or personal web pages. web navigators have experienced an important progress with regard to their function. they have been evolving from their beginnings, in which their function consisted in showing the contents of static web pages and their hyperlinks. later on they went further, interpreting the interfaces that show one or various data bases’ content. at present they support newest technologies that allow not only the interaction with dynamic web pages but also a wide range of web services. amongst them social networks, that promotes information interchange between users. internet has undergone a change which has made it become a relevant instrument for teaching and knowledge spreading, no matter the subject area. the presence of e-learning in internet has allowed to increase education quality and its disposition. its use to create teaching applications is fully interesting, being able to use, among other features, interactivity. 3 olc: learning programming nowadays, programming is one of the essential areas taught in university studies of computer science and other engineering degrees, as well as in diplomas of computer science. at present, it is a knowledge acquired through theoretical classes and the practice with different tools for programming such as editors, compilers, linkers, debuggers or interpreters. in addition to this we find teacher tutorial classes. being included in all programming subjects, compilers generate the executable programs made of the instructions written by the programmers. compilation is a process carried out by means of the succession of a set of operations through which, using the instructions written in a programming language, we obtain the code written in another language that is understood by the computer. this is how we obtain executable programs. the web 2.0 is at its very peak, this fact and the change in the web navigators’ function allow to make use of them as production tools such as text editors, spreadsheets or, as we expound in the present work, a compiler. in this way we can move a multilanguage ide from a computer to a web page. olc -on line compileris an application which greatly lightens the student’s workload at the initial stage of programming. its use is especially intended for subjects where the students must learn how to make programs. during this initial period they will neither have to deal with the complexities of the installation and the configuration of these types of tools, nor with the understanding of multiple options which they present. therefore students can concentrate on the comprehension of programming structures and the programming language to be studied. the use made of the compiler in this initial stage is restricted to create programs from a set of instructions free of errors. the student will have written these instructions and the compiler will have helped to correct them. this way in later stages of the learning process and once the students have acquired the basic knowledge of the programming structures and the programming language, they can start using compilers with more complexities in the installation and configuration tasks as well as with a higher number of options. the knowledge acquired by the students in this first stage of the learning process helps them to get a better comprehension of the use and handling of the compilers. 3.1 functionality olc interface is simple and intuitive.an interface defines the communication boundary between two entities, in this case between the students and the application. after the student identification, s/he must choose the programming language with which s/he is going to work. this can be performed from the selection language menu. figure 1. olc, on-line compiler to teach programming languages 71 figure 1: welcome screen. where the student chooses the programming language to work with. once the programming language is chosen, the screen on figure 2 will be displayed. the tabs menu located at the top of the screen, offers users the navigation through the editor, the files, the tutorials and other sections. in the section "my files" a list with the user’s files located on the server for that language is shown. these files are displayed by the last modified date. the students can manage their files using the available options. they can also load files in the server and work with them. under the tabs menu, the tools bar can be found, whose options make it possible to work with different files and the code. below the screen is divided into two text areas: • one for the code, at the top, where students will write the code of their programs. • one for the results, at the bottom, only for reading, that will give information about the different actions performed in the files. local files are shown as a list with a colour code used to identify them. figure 3. apart from the local files the application has a set of tutorials in video. by using them the information transmition can be carried out in a more efficient and interesting way for students. through these videos figure 4, a learning process can be established. these video tutorials have got a clear purpose: to assist users in learning how to manage the compiler and to follow the practical classes. these video tutorials are delivered through files embedded in the web page of olc. this kind of material has got an increasingly didactic interest, since students will have the possibility of viewing again those chapters of the video where some doubts may have arisen. they could do this as many times as necessary [5] and from any device [6] with an internet connection. in the server we can find also a set of local files with examples. the teachers after the lessons can propose practical exercises using the different examples that can be found in the folders on-line. during the teaching period they can check the results of students’ work, directing them in their learning process. 72 cayetano guerra artal, maria dolores afonso suarez idafen santana perez, ruben quesada lopez figure 2: olc work screen. with the tabs menu, the tools bar, and two text areas: one for the code and the other one for the results. figure 3: list of local files, with a colour code used to identify them. olc, on-line compiler to teach programming languages 73 figure 4: multimedia tutorials. 4 teaching proposal with olc the subject "fundamentals of computer science" is taught in the first year of various engineering degrees, amongst them the degree of "chemical engineering" of the "school of industrial engineering" that belongs to the university of las palmas de gran canaria. this subject, in this degree, is taught in the second term of the first academic year. its aim is that students acquire basic knowledge of computer science in the theoretical classes and basic knowledge of programming in the practical classes. the theoretical hours are taught in the theory classrooms and the practical hours are taught in the laboratory. this is a classroom equipped with computers, one for each student, and connected to internet. programming knowledge is necessary for the use of certain tools that students will use in the following academic years, such as programmable calculators or software like matlab. the number of teaching hours is three of theoretical lessons per week using traditional methods and one hour of practical lessons. this means fifteen practical hours during the term. • in the first five hours of practical classes basic knowledge of programming, tools to use and c language are introduced. in the first hour of these five an introductory class of olc will be taught. the following four hours the lessons will be taught through the video tutorials that will indicate how to make use of olc and will ask the students to work with the examples proposed by the teachers. these first exercises will consist of simple programs that students will have to edit, modify and compile. • in the following five hours, students will work in the modification and creation of computer programs. the proposal of modification made by the teachers will consist of making changes in programs that make use of a bit more complex structures than the ones used in the previous phase. a group of ten exercises could be completed, two for each practical hour, with an increasingly difficulty. 74 cayetano guerra artal, maria dolores afonso suarez idafen santana perez, ruben quesada lopez • for the last five hours the students must work in a practical exercise proposed by themselves. teachers must give their approval to this proposal computer program. this will be the final exercise to evaluate and should be made with a more powerful compiler. at this stage of the learning process, students must already control the use of the compiler and its different options. the independence acquired by the student after these stages using olc and their control in the comprehension and use of other compiles should be noticeable. the assessment process will take into account the valuation of the supervision of their work with olc and the final exercise proposed. according to this approach, using olc compiler the role of teachers is greatly restated. they could dedicate time to transmitting motivation about the subject being studied and directing the students in their studies. besides, the supervision of their work and the evaluation is a continuous process that could be done completely on-line. the role of students has also changed. the use of this kind of applications gives them more autonomy than in previous academic courses. 5 didactic perspective the work developed has been exposed to a group of five teachers [7] whose subjects deal with programming. all these subjects are included in the first academic year of several engineering studies. different opinions about the exposition have been found. the features that the teachers have taken into account to give their viewpoint about olc have been: the professional profile their students will have in the future; the use they will make of programming both in the academic and professional field, and the level of knowledge they must achieve during the academic course. to a lesser extent they have considered the percentage that programming has in the final evaluation and the number of hours they must dedicate to the practical part of the subject. • teacher one. he teaches "basic knowledge of computer science" belonging to the first course of "chemical engineering". to me the teaching proposal is the right one. the students find many difficulties in the understanding of compilers at the initial period of learning programming, he thinks. furthermore, sometimes even the use of computer is complex for them, although they learn quickly. professionally they will unlikely need to make computer programs. nevertheless, in the following academic years, surely, they will need to understand what variables are and how to make use of them in small computer programs as well as using them in small functions created for matlab. according to the teacher in their senior thesis, it is very probable that they will only need to manage statistics information and perhaps make some update or modification in a small computer program in order to obtain some of this information. • teacher two. she is the responsible for the practical part of the subject "fundamentals of computer science". this subject corresponds to the group of training complements in the degree in industrial organization engineering. in my opinion the teaching proposal with olc is very well structured. maybe the level of knowledge shown in the video tutorials is quite high, since generally the students who apply for this degree do not have a high interest in programming. the number of students that choose the subject is low. they try to pass the subject without paying attention to the tools they are using. professionally it is not probable that they will make use of these programming skills in the future. that is why the percentage of the final evaluation is low, although the use of multimedia material could be proposed for other subjects, since these multimedia tools are ideal for teaching. • teacher three. he is the coordinator of the subject "programming methodology" belonging to the first course of "technical computer science engineering". he thinks it is a good proposal. in his department they have thought to develop a similar tool. however, they have not considered using olc, on-line compiler to teach programming languages 75 multimedia tutorials, just only help files. we should take advantage of these multimedia capacities. his students will surely use the programming knowledge both at an academic level and in their professional life. • teacher four. she has taught "fundamentals of computers" in the first year of "computer science systems engineering". she finds the teaching proposal with olc interesting. in fact in "fundamentals of computer" she uses a program developed for students to use in order to create computer programs in assembly language . thus they will not have to make use of the machine compiler. according to her, the students are well trained in computer science; therefore they do not need to begin with a tool so simple. however there exists a fundamental aspect of this tool that should be exploited: to have production tools in the web. undoubtedly they mean a great advantage, she thinks. proposals with more functions and accessible from the web would be very interesting for her students. • teacher five. she teaches "basic knowledge of computer science" belonging to the first course of "public works technical engineering" in all its three specialities: "hydrology", "civil building", and "urban transport and services". she thinks that the proposal is of no interest for what she needs, bearing in mind the number of practical lessons per week and the teaching content, which do not match together. she believes that in the future students will not make much use of this programming knowledge. it is quite complex to make them understand some concepts. we are just in a period in which not all the students who reach university studies are equally trained in computer science, and only some of them have got a computer at home. maybe the practical lessons should be concentrated on removing programming knowledge from the contents of the subject. some students have also given their opinion about the environment. their profile extends from students of computer science engineering in their second year at the university to some professional that already have finished the bachelor of "computer science". • student one. he studies the 2nd year of the degree "industrial engineering" (basic knowledge). the possibility of make my programs without having to install any software in my computer (i have had problems trying to install some c compilers before), makes me save time and allows me to work in the same conditions independently of the place -laboratory or home-. furthermore with no knowledge about linux, i could compile my first program in that operating system. • student two. he studies the 2nd year of the bachelor degrees "computer science engineering" (learning c programming language) the use of olc is ideal to learn a programming language. firstly, it hides tiresome options that do not add anything to a beginner user, as path specifications or debug options. moreover the availability of the video tutorials while using the editor is much handy that making programs in a conventional programming environment, where you should read the tutorial in a web page or using paper format. finally, the operations i can carry out with "my files" option are the necessary ones for a beginner (save, compile, indent, and download the .exe file), and can be made in an easy way. • student three. he has finished the bachelor degree "computer science engineering" and in a few weeks will defend his senior thesis (high level of programming language knowledge). i like the idea implemented, to have the compiler in the web allow access from any computer with an internet connection and it is useful to compile in a specific moment with no need to download and install a compiler. for students that just make a brief immersion in the programming studies is ideal in fact, but if you study computer science at a professional level you find limitations. apart of this, to download the .exe file with the result of the compilation every time is not much useful cause it is more handy the characteristic exe button of an ide. 76 cayetano guerra artal, maria dolores afonso suarez idafen santana perez, ruben quesada lopez • student four. he still has to study three subjects to finish the degree "computer science engineering" (high level of programming language knowledge). for me it wasn’t a trouble to install the c compiler, this tool is not the compiler i would have liked to use at the beginning. i think that when i began to make programs in c language i installed the compiler in my computer at home so i had not used olc at all. on the other hand i cannot execute the programs made on-line and this is something that i do not like. when you compile a program the first thing you want to prove is the result, and using olc you must download the .exe file, i prefer to compile with my ide. the great advantage could be to compile and execute on-line. • student five. he has finished the bachelor degree "computer science engineering" and is developing his senior thesis (high level of programming language knowledge). advantages: easy to use. you do not have to install any software, just only an internet connection is necessary. you can work from everywhere: home, laboratories, any place with an internet connection. disadvantages: to download the .exe file. just a few options of debug i do not like that my programs be in the web, for security reasons. in resume: it is interesting at an initial stage of programming or for easy programs in computers where you do not have a compiler installed but apart from that it does not offer much more. it could be more attractive if you could execute your programs on -line. 6 conclusions this work proposes an interesting initiative in the field of e-learning: to portray interactive applications on-line for didactic use [8]. a simple compiler accessible from the web provides students with an easy way to learn at the initial stage of programming studies. olc makes use of multimedia contents to support teachers in the teaching process. these contents show how to work with the on-line compiler and with the programming language [9]. apart from these there are further advantages, such as having at students and teachers’ disposal [10] the compiler through the web and the possibility of making use of it at any time and from any computer with an internet connection. thus barriers of time and space are eliminated. students will have access to the compiler, the contents and the files with which they have been working. besides, there is no need to save the work in store devices nor to install software. to have remote virtual laboratories increases their efficiency [11] and reduces the costs of the resources used in learning [12]. olc could not compete with the powerful traditional ide’s due to its limitations for the expert software development, but undoubtedly it fills the void that exists in the area of teaching programming at the initial stage. this experience has already passed from the development phase to the experimental one. at present we are working on a research of those subjects that include programming languages lessons, and the use of olc could be suitable. as many other initiatives, to promote its acceptance and to achieve its use depend on programming teachers, who have already valued the application positively and favourably. according to their opinion, to introduce the students in the field of programming is hard in the initial stage. not only for the complexity of the control and data structures and the programming language to learn, but also for the complexity of the tools they need to use, such as compilers, bearing in mind that students only need to make use of a reduced number of functions at their initial period of their programming studies. using this kind of applications with multimedia contents allow teachers to dedicate time to activities which improve the quality of teaching, transmitting a further motivation about the subject to be studied and the results they can obtain, as well as directing students closely in their studies. [13]. in the near future, we will continue this line of work. our new projects involve the development of other applications accessible from the web [14], used as olc, in e-learning field. some of these applications will count on a key feature: interactivity [15]. thus students will develop new working methods [16] in order to ensure participation [17]. olc, on-line compiler to teach programming languages 77 7 acknowledgment this article is an extended version of our paper [18], published in proceedings of the 2nd international conference on virtual learning ű icvl 2007 (m. vlada, g. albeanu, d.m. popovici, eds). we wish to thank the program committee of icvl 2007 that select and recommended the publication of our extended work in ijccc. bibliography [1] p. brey. "evaluating the social and cultural implications of the internet". volume 35, issue 3 (september 2005). acm press. [2] c. mchugo, t. hall. "enhancing ict learning with real-life problems". emrc university of limerik.". [3] t. o’reilly. "what is web 2.0 design patterns and business". models for the next generation of software [4] a. yong-yeol, h. seungyeop, k. haewoon, m. sue, j. hawoong. "semantic web and web 2.0: analysis of topological characteristics of huge online social networking services". proceedings of the 16th international conference on world wide web www’07. acm press [5] t. hartsell, s. chi-yin yuen. "video streaming in online learning". aace journal. 14(1), pp. 3143. chesapeake, va: aace. [6] l. neal. "predictions for 2006: e-learning experts map the road ahead". volume 2006 issue 1 (january 2006). acm press. new york. [7] s. anderson. "working together to develop a professional learning community". herdsa 2002 [8] g. lautenbach. "engaging with e-learning: emerging epistemologies and increased pedagogic vision in higher education". iadis inter national conference e-learning 2007 [9] r. rheeder, r. diseko, g. lautenbach. "the design of interactivity for a web based learning environment at a higher education institution." iadis international conference e-learning 2007 [10] h. samaras, t. giouvanakis, d bousiou, k. tarabanis. "towards a new generation of multimedia learning research". aace journal. 14 (1), pp. 3-30. chesapeake, va: aace [11] lawson, e.a. stackpole, w. (2006). it education online education: does a virtual networking laboratory result in similar student achievement and satisfaction?. proceedings of the 7th conference on information technology education sigite’06. (october 2006). acm press.w. nejdl, m. wolpers "european e-learning: important research issues and application scenarios". l3s research center and university of hannover.) [12] w. nejdl, m. wolpers "european e-learning: important research issues and application scenarios". l3s research center and university of hannover. [13] k. fraser, "a blended learning approach to teaching" introduction to multimedia, the e-bit!. aishe conference 2006 [14] a. leene. "the microweb". using content in theory and practice microlearning 2006, innsbruck 7 june 2006 78 cayetano guerra artal, maria dolores afonso suarez idafen santana perez, ruben quesada lopez [15] p. valente, p. sampaio. "defining goals for the design of interactive multimedia documents". in proceedings of world conference on educational multimedia, hypermedia and telecommunications 2006 (pp. 955-962). chesapeake, va: aace. [16] n. sala. "web based learning environment: an example". in proceedings of 2000 (pp. 807-808). chesapeake, va: aace. [17] c. gráinne, "making sense of today’s technology-enhanced environment for learning: rethinking student and teacher roles" iadis international conference e-learning 2007 [18] cayetano guerra artal, maria dolores afonso suarez, idafen santana perez, ruben quesada lopez, "olc, on-line compiler to teach programming languages", in (m. vlada, g. albeanu, d.m. popovici, eds) proceedings of the 2nd international conference on virtual learning icvl 2007, october 26-28, 2007, constanta, romania, pp. 283-290, 2007. cayetano guerra artal, maria dolores afonso suarez idafen santana perez, ruben quesada lopez instituto universitario de sistemas inteligentes y aplicaciones numéricas en ingeniería iusiani universidad de las palmas de gran canaria departamento de informatica y sistemas edif. de informatica y matematicas. campus universitario de tafira. 35017. las palmas. spain. e-mail: cguerra@iusiani.ulpgc.es, mafonso@dis.ulpgc.es idafensp@gmail.com, rubenql@gmail.com received: november 4, 2007 cayetano guerra artal, graduated at the university of las palmas de gran canaria in computer science. he has been teaching multimedia for seven years at the computer science and systems department of the university of las palmas de gran canaria. he received his ph.d. in computer vision. his main research fields are computer vision, computer graphics, multimedia and e-learning. olc, on-line compiler to teach programming languages 79 maria dolores afonso suarez, graduated at the university of las palmas de gran canaria in computer science. she has worked in private enterprises developing software and as associated professor at the computer science and systems department of the university of las palmas de gran canaria. her main research fields are software engineering, multimedia, and e-learning. idafen santana pérez, graduated at the school of computer science of the university of las palmas de gran canaria. he is a pre-graduated student of the faculty of computer science of this university. his current research interests are in e-learning aplications and natural language processing. ruben quesada lopez, graduated at the school of computer science of the university of las palmas de gran canaria. he is a pre-graduated student of the faculty of computer science of this university. his current research interests are in e-learning aplications and natural language processing. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 871-883 natural language based on-demand service composition f.-c. pop, m. cremene, j.-y. tigli, s. lavirotte, m. riveill, m. vaida florin-claudiu pop, marcel cremene, mircea vaida technical university of cluj-napoca, faculty of electronics, telecommunications and it, cluj-napoca, romania e-mail: {florin.pop, marcel.cremene, mircea.vaida}@com.utcluj.ro michel riveill, jean-yves tigli, stéphane lavirotte université de nice sophia antipolis, sophia antipolis cedex, france e-mail:{riveill, tigli, lavirott}@unice.fr abstract: the widespread of web services in the ubiquitous computing era and the impossibility to predict a priori all possible user needs generates the necessity for on-demand service composition. natural language is one of the the easiest ways for a user to express what he expects regarding a service. two main problems need to be solved in order to create a composite service to satisfy the user: a)retrieval of relevant services and b) orchestration/composition of the selected services in order to fulfill the user request. we solve the first problem by using semantic concepts associated with the services and we define a conceptual distance to measure the similarity between the user request and a service configuration. retrieved services are composed, based on aspect oriented templates called aspects of assembly. we have tested our application in an environment for pervasive computing called ubiquarium, where our system composes a service according to the user request described by a sentence. the implementation is based on the wcomp middleware that enables us to use regular web services but also web services for devices. keywords: natural language, service composition, on-demand, middleware, templates 1 introduction background. since web 2.0 marked it’s appearance as a concept in the fall of 2004 and introduced the principle of the internet as a platform [19], the complexity and diversity of this platform grew together with the more enhanced features it was providing to its users. given the fact that the software in the internet era is delivered as a service, not as a product and there is no release cycle for the services, it is the user who’s in charge of finding a service and using it. in a near future services will be more diverse and widespread as computers will become ubiquitous. in the same way the information across the web is structured, classified and then presented to a user that sends a natural language request to a search engine, so a collection of applications should be assembled, classified and deployed using the services that are found within a given context based on a similar unrestricted language request coming from the user. the problem. in a context where various services with different functionality are available, it’s possible to compose new services on-demand, based on a user request, only when the right selection of components is used. on-demand service composition involves two operations: service retrieval and service orchestration. service retrieval refers to identifying those specific services that are addressed by the user copyright c⃝ 2006-2010 by ccc publications 872 f.-c. pop, m. cremene, j.-y. tigli, s. lavirotte, m. riveill, m. vaida request or the closest functional match to the request. the transition from a natural language request to a list of services is a challenge that is even more difficult when no restrictions are added to control the request. service orchestration or service assembly is the process of linking the retrieved services in a functional flow so that the user demand is fulfilled. both mentioned problems make the object of this paper, but while service retrieval was the field of our research, for service orchestration we used an existing approach. scenario. we consider the following scenario to illustrate the purpose of dynamic service composition based on natural language requests. a handicapped person lives inside an intelligent house, surrounded by intelligent devices, sensors and actuators. each device has its own inputs and outputs and is able to process different types of data, leading to a large number of possible functional combinations of those devices. the person in the intelligent house wants to use those devices by combining their functionality (e.g. link the output from a sensor to the input of an actuator), but his disability prevents him to physically interact with the devices or he simply lacks any technical knowledge. therefore, he expresses his need using the natural language (either written or spoken): "i want to use my remote control on the wheel chair to turn off the light, change the channel on tv and play some music on the media center". each device the user addresses through his request (remote control, light, tv, media center) provides a different service with specific actions that can be used in various configurations. finding a way to sort these configurations by relevance to the user’s request is a key requirement for the imagined scenario. also, when one of the devices the user wants to use is not present in the intelligent house or it was replaced with an updated version, the system should adapt and assemble a service that is the closest match to the user’s need. approach. dynamic service composition solves the problem of adaptation to different contexts and user preferences. also, by composing services on demand, the learning curve required for the user to work with new configurations is reduced as the user "gets what he wants" from the application. existing systems for dynamic service composition based on natural language requests either provide a restricted natural language interface or don’t offer support for adaptation to structural and behavioral changes of the service configuration. we start with an initial set of services that are discoverable across a network. the user requests a completely new service using an unrestricted natural language sentence. in order to find specific devices to satisfy the request, we use semantic concepts and define a conceptual distance between the request and a service configuration. concepts are leafs on a lexical tree that is generated by deriving and generalizing a notion. once the services that match the request are identified, some aspect oriented advices are used to connect the services so that when a service disappears from the context or a new service is made available, the service configuration adapts. outline. this paper is organized as follows: the next section examines the problems a dynamic service composition system should solve in order to be usable in the modern context of web services. section 3 describes the solution we propose including the principles that lead to this solution, while section 4 focuses on the design and implementation patterns we used, along with the test results. section 5 examines some of the existing dynamic service composition approaches. the paper ends with conclusions and further research. 2 an overview of the problem on-demand dynamic service composition based on natural language requests raises some challenges that need to be studied before a solution is to be proposed. service retrieval. the first problem we approach in this paper is finding and retrieving a particular service. the large variety of web services useable for composition needs to be classified natural language based on-demand service composition 873 in a way that would make it machine meaningful and semantic-rich for the search to provide the best results. one web extension, called semantic web [3] is focused on enabling better web service interoperation. the semantic web’s purpose is to bring structure to the meaningful content of web pages, creating an environment where software agents roaming from page to page can readily carry out sophisticated tasks for users. one dialect of the daml [14] family of semantic web markup languages was proposed in [17] for the markup of web services. this so-called semantic markup of web services creates a distributed knowledge base (kb) that provides a means for agents to populate their local kbs so that they can reason about web services to perform automatic web service discovery, execution and composition and interoperation. but the semantic mark-up uses a narrow, predefined vocabulary as identified in [16], which makes possible only the retrieval of those web services for which the vocabulary is known. queries or requests from web services or user requests, using another vocabulary than the predetermined vocabulary are not suitable to find or retrieve such a web service. therefore, a narrow vocabulary for the semantic mark-up of web services is not appropriate to be used in combination with natural language requests. service composition. the second problem that needs to be solved is the actual composition of the retrieved services. there are 3 types of systems for dynamic service composition according to [12]. template-based systems [18,21] are using a service template to compose an application. they can handle complex interactions between components and allow some level of flexibility by choosing different sets of components. the drawback of this approach is that they cannot compose applications for which templates are not available. interface-based systems [7] allow the user to submit a set of inputs and outputs for the application he is requesting. these systems have a higher adaptability than the template-based systems, but certain applications cannot be represented as a set of inputs and outputs (e.g. an email sending service does not output any data). logic-based systems [20,23] extend the interface-based approach by adding extra information into interface information using first order logic or linear logic. a user requests an application by submitting a first order formula representing the logic that must be satisfied by the application. they are more adaptable than the template-based systems since they donőt require service templates and offer support for more varieties of services than the interface-based systems. their main disadvantage is given by the fact that they are not extensible and are not suitable for a distributed environment. adaptation. the last, but the most important problem that needs to be solved by a dynamic service composition system is adaptation. we distinguish two types of changes that require adaptation [2]: structure changes and behavior changes. structural adaptation consists in modifying the retrieved services while preserving the global behavior of the application. the behavior describes the sequence of operations to be executed to fulfill the user request. structural changes are triggered when a retrieved services disappears from the context or is replaced by another (for example the analog tv is replaced by a digital tv). behavior changes are related to the user who may decide that the current service does no longer satisfy his need. 3 proposed solution our dynamic service composition system was inspired out of the user’s need to interact with the intelligent devices that surround him. this user-machine interaction should be as natural as human interaction, through unrestricted natural language. intelligent devices are entities that provide services to the user and have networking capabilities. roughly, there are two types of 874 f.-c. pop, m. cremene, j.-y. tigli, s. lavirotte, m. riveill, m. vaida intelligent devices: basic devices that are simple service producers (e.g. a light that offers the illuminating service, a tv that offers the tuning service that allows changing the tv channels) and controller devices that can consume services by other devices, acting as interfaces for a composite service (e.g. a mobile phone, an ultra-mobile pc, a netbook). hybrid devices that implement both previously mentioned functionalities can also be imagined. the intelligent devices are connected to form an ad-hoc network inside the intelligent house, which they use to exchange information. this means that the devices can appear and disappear from the network structure on-the-fly: a device can auto-configure when it joins the network and then leave the network without notice. network and device management is a task for the middleware that runs the intelligent house. 3.1 semantic descriptions for web services wsdl [10] is intended for the functional description of web services and the semantic web mark-up is limited to a narrow vocabulary, which is not suited for natural language requests. a lexical tree [16] would add too much semantic information to a service and would not be suited for embedded devices. to overcome these limitations, we propose the use of general notions, called concepts, to describe the utility of a service. a service is not entirely identified by a single concept, but by an infinite number of concepts that are determined through the generalization of a notion. this notion will serve as a semantic description for an intelligent device that offers a service. we use the television notion to describe a tv, for example. through generalization we find that both the television and electronic equipment concepts refer to the same device. to increase the precision, a lexical analysis is also conducted for the service description and the user request by the composition system. this way, the service description suffers little or no modifications due to the extra semantic information. 3.2 linguistic processing we consider the scenario where the user interacts with appliances and he expresses his need through a sentence: "i want to use my phone to turn off the light, turn on the tv and play some music on hifi". in order to retrieve the services required to satisfy the user need, the request goes through a linguistic processing module, responsible for: • text segmentation required to separate the words in the phrase (e.g. switch off the light is transformed into switch, off, the, light); • removing stop words that are considered to be irrelevant (e.g. the, to, and); • stemming (e.g. lights is transformed into light, using becomes use); • spell-checking to correct the misspelled words and the words "damaged" during stemming. the output text segments for the user request in the considered scenario are: want, use, phone, switch, light, turn, tv, play, music, hifi. 3.3 the graph of concepts the text segments together with the service descriptions are nodes in a graph, called the graph of concepts. the arcs in this graph connect each text segment to each service description. the weight of each arc represents the conceptual distance between the text segment and the natural language based on-demand service composition 875 service description. we introduced the conceptual distance to measure the relationship between two notions. measures of semantic similarity or relatedness are found in linguistic processing literature. according to the study published in [6], the jiang and conrath’s measure proved to be best for practical usability. all compared distances are based on the information content of the lexical terms (the probability of encountering an instance). while this information may be valuable for other applications, it is of less importance for service composition and it adds to the complexity of the implementation, therefore making it slower. our approach, the conceptual distance, is faster and less complex than jiang and conrath’s measure, while the last is more accurate. the conceptual distance is a numerical evaluation of how accurate two notions refer to the same concept. for example the words phone and telephone describe the same concept a communication device, therefore the conceptual distance is null. on the other hand, the words phone and electronic equipment can describe the same concept a communication device, but one of them is more general, therefore it can address more concepts, which leads to a non-null distance between these words. figure 1 shows an example of a graph of concepts where the text segments are tv, light, hifi, phone and the service semantic descriptions are television, light, dvd, hifi, mobile phone and pda. figure 1.a. represents all the distances and figure 1.b. represent only the arcs with minimum conceptual distance. figure 1: the graph of concepts 3.4 knowledge structure in order to evaluate the conceptual distance we need to find a way to classify the lexical basis of the english language. we used for this purpose a specialized dictionary called wordnet [11]. wordnet groups nouns, verbs, adjectives and adverbs in sets of synonyms, called synsets. each synset describes a different concept. different senses of a word are in different synsets. most synsets are connected to other synsets via a number of semantic relations. for example, the semantic relations for nouns include: 876 f.-c. pop, m. cremene, j.-y. tigli, s. lavirotte, m. riveill, m. vaida • hypernyms: y is a hypernym of x if every x is a (kind of) y (mobile phone is a hypernym of phone); • hyponyms: y is a hyponym of x if every y is a (kind of) x (phone is a hyponym of mobile phone); • coordinate terms: y is a coordinate term of x if x and y share a hypernym (mobile phone is a coordinate term of cellular phone, and cellular phone is a coordinate term of mobile phone); • holonym: y is a holonym of x if x is a part of y (mobile phone is a holonym of transmitter); • meronym: y is a meronym of x if y is a part of x (transmitter is a meronym of mobile phone). while semantic relations apply to all members of a synset because they share the same meaning, words can also be connected to other words through lexical relations, including antonyms and derivationally related, as well. both nouns and verbs are organized into hierarchies, defined by hypernym or is a relationships. for example, the hierarchy for mobile phone is: • cellular telephone, cellular phone, cellphone, cell, mobile phone • radiotelephone, radiophone, wireless telephone • telephone, phone, telephone set • electronic equipment • equipment the words at the same level in hierarchy are synonyms of each other. 3.5 the concept hierarchy the algorithm that evaluates the conceptual distance uses the wordnet lexicon to create concept hierarchies. a concept hierarchy is generated in 4 steps: 1. find the synset that contains the concept for which the hierarchy is generated. each word in the synset becomes a root for a tree in the concept hierarchy. 2. for each tree root, find the synsets that are in a relationship with the rootős synset. each word in the related synset becomes a leaf for the tree, on the next level in hierarchy, branching from the root. 3. for each word on the current level in hierarchy, find the synsets related to the wordős synset and add the words in the found synsets as leafs for the tree on the next level. 4. repeat step 3 until the hierarchy is big enough so that the degree of generalization for the notion for which the hierarchy is built, corresponds to an accepted accuracy that produces best results. the bigger the hierarchy the longer it takes to generate it, but the smaller the hierarchy the more confusion can occur among concepts. the hierarchy for the notion mobile phone is shown in figure 2. the roots of each the tree are part of the same synset and each level in a tree represents the words from the synsets that are related to the word they are branching from. natural language based on-demand service composition 877 figure 2: the concept hierarchy for the notion mobile phone 3.6 the conceptual distance in order to evaluate the conceptual distance for two notions, a concept hierarchy is built for each notion. then, the conceptual distance is calculated as follows: • the minimum difference of levels between the common node of the 2 hierarchies and the node that represents the notion on which the hierarchy is built for, if such a common node exists. • the maximum number of levels a hierarchy can have if thereős no common notion among the two. examples: • d (mobile phone, cell) = 0 • d (radiotelephone, radiophone) = 0 • d (mobile phone, radiophone) = 1 • d (mobile phone, telephone) = 2 3.7 service retrieval finding and retrieving the closest services to the user request resumes to identifying the couples (text segment, service description) in the graph of concepts that have a minimum sum of conceptual distances. this makes it easier to take advantage of a service that only partially matches a request. 878 f.-c. pop, m. cremene, j.-y. tigli, s. lavirotte, m. riveill, m. vaida in order to find mentioned couples we need to apply tow transformations to the graph of concepts: • finding the sub-graph that has the minimum distance path that includes all the nodes. after this transformation each service description will be connected to 2 text segments. • for each triplet (service description, text segment 1, text segment 2) remove the arc that has the maximum weight. in order to find the minimum weight sub-graph, we use the kruskal [15] algorithm that calculates the minimum spanning tree (mst). the nodes that contain the service descriptions resulted after the two transformations are applied, represent the services that are used to generate the composite service requested by the user. the transformed sub-graph for the graph in figure 1 contains the nodes that are connected with the thick continuous line. 3.8 service composition we used a template-based service composition system because of its capability to handle complex interactions between components and the flexibility of choosing different sets of components. the system we used, called aspects of assembly (aoa) [9] is part of the wcomp [8] middleware for ubiquitous computing and besides the benefits that derive from the fact that is template-based, also offers support for auto-adaptation. these templates can be automatically selected either by the service composition system when satisfying a user request or triggered by context changes in a self-adaptive process and composed by a weaver with logical merging of high-level speciţcations. the result of the weaver is projected in terms of pure elementary modiţcations (pems) ð add, remove components, link, unlink ports. the aoa architecture consists of an extended model of aspect oriented programming (aop) for adaptation advices and of a weaving process with logical merging. an aoa template is structured as an aspect with a list of components involved in composition (called pointcut) and adaptation advice (a description of the architectural reconfigurations), which is specified using a domain specific language (dsl). we will examine some aoa templates and the composition process in detail in the next section. 4 implementation and results we used the wcomp [8] platform for ubiquitous computing as the middleware of our intelligent house. wcomp uses the upnp protocol to achieve device interconnectivity and interoperability. each upnp device has a software proxy that acts like a software component. using this proxy, we can treat web services for devices similar to the ui components of a gui designer. we added some meta-data to the upnp service description for each device to serve as semantic description. using wcomp, we have simulated the following devices/services: tv set, described by the television notion; dvd recorder, described by the dvd notion; mobile phone, described by the mobile phone notion; pda, described by the pda notion; hifi, described by the hifi notion; lighting system, described by the light notion. the interactions between these components were specified using aoa templates. following, is an example of such a template that is used to connect the mobile phone to the tv: pointcut inputdev:=/mobilephone.*/ natural language based on-demand service composition 879 outputdev:=/television.*/ advice keytochannel(inputdev, outputdev): input.^key_pressed -> ( output.set_channel ; call ) the first 3 lines describe (defining the pointcut as in aspect programming), using filters in the awk language, the components involved in the interaction: a mobile phone (inputdev) and a television (outoutdev). the filters of type /instancename.*/ will find components that have their name prefixed by instancename. line 4 declares a composition schema that uses the previously described components. lines 4-5 specify the composition mechanics: call the tv.set_channel method when the mobile phone fires the event key_pressed. the service composition system implements a upnp device that offers the service of designing composite services for the user in order to fit seamlessly with the wcomp middleware. requests from the user are captured by a wcomp assembly of components, and then sent using the upnp protocol to the service designer along with a description of the context where the devices are located. the service designer queries the devices for service descriptions (semantic meta-data), and then finds only those services that are relevant to the user request. instances of the devices that provide the named services are interconnected based on the rules described in the aoa templates. scenario 1. "i want to use my phone to turn off the light, turn on the tv and play some music on hifi". this phrase contains many irrelevant words to the service composition system, but the relevant words are identical (except tv) to the service semantic descriptions. irrelevant words have an effect of increasing the time required to process the graph of concepts. all the relevant services are identified and then composed. figure 3: the dynamically composed service for scenario 1 scenario 2. "use pda for broadcasting". this user request is challenging for any composition system because it doesn’t address the tv directly, but through the abstract concept of broadcasting. due to the use of the specialized dictionary, the tv is found and then connected to the pda. figure 4: the dynamically composed service for scenario 2 880 f.-c. pop, m. cremene, j.-y. tigli, s. lavirotte, m. riveill, m. vaida the wordnet (free download) dictionary is installed on a local machine thus the word search is fast. the main complexity of the algorithm is given by the conceptual distance computation. the time necessary in order to compute the distance between two concepts (similarity), based on the english dictionary, was about 6 ms (on a dell latitude 830 laptop, cpu dual core 2.2ghz, 2g ram) with the fastest algorithm that we have found (a wordnet similarity implementation, the java package edu.sussex.nlp.jws.jiangandconrath). thus, if we have m user concepts and n service concepts, we need a time equal to m∗n 2 ∗ 6 ms. for instance, we can create the concept graph for 10 user concepts and 64 services in about 2 seconds. the aoa application is very fast also (less than 1 sec. for up to 350 components). in this section we have shown that we are able to create, in practice, new services, ondemand, using real devices, by applying the patterns available for the selected set of services. the scenarios discussed above were tested in a dedicated environment for ambient computing, called ubiquarium [24]. real devices were used (a part of the services are real devices and another part are virtual ones). 5 related work composing web services on the basis of natural language requests. the solution described in [4] and [5] assumes that the user requests are expressed with a controlled subset (a narrow vocabulary) of natural language. the sentence that represents the userős request is transformed into a flow model using templates (e.g. if . . . then . . . else, when . . . do). verbs are used to identify the action and its parameters. each available service is paired with a well-defined set of keywords. owl-s annotations are used to provide operation semantics and an ontological classification of web services. the operations act as nodes of a direct acyclic graph and the relations among their iopes (inputs, outputs, preconditions and effects) establish arcs. the graph is translated into an executable service at invocation time. the example the authors use to sustain the proposed solution uses the phrase "if there is any cinema showing "big fish" in turin then send a sms to dario containing "lets go to the movies tonight!"" an if . . . then template is used to identify the flow model. in the next step, called context focus, the service types are identified: cinema, sms. the verb (send) triggers the parameter retrieval stage where iotypes recognizers based on format (date, time, telephone numbersé) and values (city namesé) are used to extract the actionős parameters. this solution establishes a synergy between the semantic service descriptions and the natural language interpretation of user requests, through a common ontology and a consistent lexical vocabulary. therefore it canőt be used in active environments where new components that act as black-boxes appear and disappear from the context dynamically. also, the use of a controlled subset of natural language makes it non intuitive for the user as he is restricted to the use of templates when expressing a request. semantics-based dynamic service composition. papers [12] and [13] propose the cosmos model and the segsec platform for dynamic service composition. their idea is to transform the semantics of the user request into a semantic graph. nodes in the semantic graph represent operations, inputs, outputs and properties of a component, as well as their data types and concepts. arcs (labeled links) represent the relationships among the nodes. concepts, entities representing abstract ideas actions are used to annotate the semantics of the operations, inputs, outputs and properties of components. the user request is parsed and the components addressed by the user form a workflow. the example in [13] uses the phrase "print directions from home to restaurant". the semantic graph contains the predicate (print), the target of the action (direction) and the parameters (home, restaurant). the workflow, containing the retrieved components, is executed as soon as natural language based on-demand service composition 881 it satisfies the user request. this analysis takes place in a step called semantic matching and consists in a test that verifies that all the links that appear in the user request also appear in the graph that models the workflow. the authors of [13] admit that their solution is not suited for environments where a large number of components are deployed. the platform lacks the feature of providing a solution in the case where the workflow doesnőt satisfy the userős request. if the generated workflow doesnőt match exactly the user request, then the dynamic service composition fails. also, the ability of the implementation to discover certain components is to be questioned because itős limited to work with a narrow set of keywords and it lacks a vocabulary. web service with associated lexical tree. the invention claimed by alcatel [16] relates to a method to mark-up a web service in order to allow finding and retrieving said service via a natural language request. a lexical tree, built by deriving the service description, finding synonyms and related forms of the derived keywords, is associated to each service. finding a service based on the user request resumes to comparing the natural language query to the lexical tree of each web service. this method of retrieving a web service proves to be the most appropriate when dealing with natural language requests. the invention however doesnőt exploit the full potential of this finding, as it lacks service composition. 6 conclusion this paper proposes a new method for assembling services on demand, starting from the user request expressed in natural language. we use a semantic analysis of the user request, in order to identify the services described by concepts that are related to concepts from the user request. retrieved services are then composed, based on composition patterns, called aoa (aspects of assembly). the uses of patterns, which assure that the new service is always valid, compensate the ambiguity of the natural language. another important advantage of the aoa patterns, comparing to other existent pattern-based approaches, is the fact these patterns may be superposed by composition. thus, a large number of combinations are possible using a given set of patterns. additionally, for a given set of services, the aoa mechanism applies only the patterns that lead to valid services. the solution was implemented on the wcomp platform and tested in a dedicated ambient computing environment, called ubiquarium, using real and virtual intelligent devices/services. the service composition is user driven, by natural language (voice) and allows the user to get the service on-demand. from this point of view, our solution is less restrictive than the other solutions described in the state of the art section. an important advantage of our solution is the reuse of wordnet free dictionary, which is acting like ontology. due to this, we can relax very much the limitations for the natural language, imposed by solutions where an ontology (usually restricted) must be created by the developer. otherwise, the creation of a rich ontology is a very costly task and our solution succeeded to avoid it by reusing wordnet. this choice has another important advantage: it solves the problem of dealing with different ontologies and does not need to impose a common ontology (the only requirement is to use english). a particular aspect of our proposal is the mixed approach: semantic and pattern-based. this approach combines the advantages of the both: thanks to composition patterns, it allows us to build complex composite services, which are always valid and functional. with other approaches (interface, logic, semantic based), that are not using patterns/templates, it is very difficult to create complex architectures that are valid and work correctly. as future work, we intend to extend our solution for dynamic service adaptation (at runtime) and this should be feasible because wcomp was designed for dynamic service reconfiguration in 882 f.-c. pop, m. cremene, j.-y. tigli, s. lavirotte, m. riveill, m. vaida pervasive environments. acknowledgments this work was supported by the econet project code 18826ym and the romanian national project pncdi ii code 1062, and 1083 financed by uefiscsu. thanks to other members of the rainbow team for fruitful discussions and feedback: vincent hourdin, daniel cheung-foo-wo, eric callegari. bibliography [1] a. v. aho, b.w. kernighan, p. j. weinberger, the awk programming language, addisonwesley, 1988. [2] anastasopoulos m.; klus h.; koch j.; niebuhr d.; werkman e., doami a middleware platform facilitating (re-)conţguration in ubiquitous systems, in system support for ubiquitous computing workshop. at the 8th annual conference on ubiquitous computing (ubicomp 2006), sep 2006. [3] berners-lee, t.; hendler, j.; lassila, o., the semantic web, scientific american magazine, may 17 2001. [4] bosca, a.; ferrato, a.; corno, f.; congiu, i.; valetto, g., composing web services on the basis of natural language requests, ieee international conference on web services (icws’05), pp. 817-818, 2005. [5] bosca, a.; corno, f.; valetto, g.; maglione, r., on-the-fly construction of web services compositions from natural language requests, journal of software (jsw), issn : 1796-217x, vol. 1 issue 1, pag 53-63, july 2006. [6] e. budanitsky, g. hirst, semantic distance in wordnet: an experimental, application-oriented evaluation of five measures, 2001. [7] chandrasekaran s.; madden s.; ionescu m., ninja paths: an architecture for composing services over wide area networks, cs262 class project writeup, uc berkeley, 2000. [8] cheung-foo-wo, d.; tigli, j.-y.; lavirotte, s.; riveill, m., wcomp: a multi-design approach for prototyping applications using heterogeneous resources, in 17th ieee intern. workshop on rapid syst. prototyping, pag 119ð125, creta, 2006. [9] cheung-foo-wo, d.; tigli, j.-y.; lavirotte, s.; riveill, m., self-adaptation of eventdriven component-oriented middleware using aspects of assembly, in 5th international workshop on midd leware for pervasive and ad-hoc computing (mpac), california, usa, nov 2007. [10] christensen, e.; curbera, f.; meredith, g.; weerawarana, s. web services description language (wsdl) 1.1, website, 2001 http://www.w3.org/tr/wsdl [11] cognitive science laboratory, princeton university, wordnet ð a lexical database for the english language, website, 2006 http://wordnet.princeton.edu/ natural language based on-demand service composition 883 [12] fujii, k.; suda, t., component service model with semantics (cosmos): a new component model for dynamic service composition, saint-w ’04: proceedings of the 2004 symposium on applications and the internet-workshops (saint 2004 workshops). washington, dc, usa: ieee computer society, 2004. [13] fujii, k.; suda, t., semantics-based dynamic service composition, ieee journal on selected areas in communications, vol 23(12), pag 23612372, dec 2005. [14] hendler, j.; mcguinness, d., the darpa agent markup language, ieee intelligent systems, vol. 15, no. 6, nov./dec. 2000, pp. 72ð73. [15] kruskal, j. b., on the shortest spanning subtree of a graph and the traveling salesman problem, proc. amer. math. soc., vol 7, 1956. [16] larvet, p., web service with associated lexical tree, european patent, ep1835417. [17] mcilraith, s. a.; cao son, t.; zeng h., semantic web services, ieee intelligent systems, vol. 16, no. 2, mar./apr. 2001, pp. 46-53. [18] molina a. j.; koo h.-m.; ko i.-y., a template-based mechanism for dynamic service composition based on context prediction in ubicomp applications, in proceedings of the international workshop on intelligent web based tools (iwbt’2007), 2007. [19] o’reilly, t. what is web 2.0, website, 2005 http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20. html [20] rao j.; kungas p.; matskin m., logic-based web services composition: from service description to process model, proceedings of the ieee international conference on web services, p.446, june 06-09, 2004. [21] sirin, e.; parsia, b.; hendler j., template-based composition of semantic web services, in aaai fall symposium on agents and the semantic web, 2004. [22] upnp forum, website, 2008 http://www.upnp.org/ [23] wu d.; parsia b.; sirin e.; hendler j.; nau d., automating daml-s web services composition using shop2, in proceedings of 2nd international santic web conference (iswc2003), sanibel island, florida, october 2003. [24] hourdin, v.; cheung-foo-wo d.; s.l. ; j.y.t., ubiquarium informatique: une plate-forme pour l’etude des equipements informatiques mobiles en environnement simule., in proccedings of 3-eme journees francophones mobilite et ubiquite (ubimob), paris, september 2006. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 473-481 optimal bitstream adaptation for scalable video based on two-dimensional rate and quality models j. hou, s. wan junhui hou, shuai wan northwestern polytechnical university school of electronics and information xi’an 710129, china e-mail: houjunhuihn@gmail.com swan@nwpu.edu.cn abstract: in this paper, a two-dimensional (2d) rate model is proposed considering the joint impact of spatial (i.e., the frame size) and snr (i.e., the quantization step) resolutions on the overall rate-distortion performance. a related 2d quality model is then proposed in terms of perceptual quality. then the two proposed models are applied to scalable video to address the problem of optimal bitstream adaptation. experimental results show that the proposed rate and quality models fit the actual data very well, with high coefficients of determination and small relative root mean square errors. moreover, given the bandwidth constraint and required display resolution of the end users, the optimal combination of snr and spatial layers that provides the highest perceptual quality can be achieved using the proposed models. keywords: 2d rate model, 2d perceptual quality model, scalable video, bitstream adaptation. 1 introduction recent multimedia applications are featured by various resolutions designed for a variety of devices with different computational and display capabilities. these devices range from cell phones and pda’s with small screens and restricted processing power to high-end work stations with high-definition displays. the related video services or applications are connected to different types of networks with various bandwidth limitation and loss characteristics. a highly attractive approach to address the vast heterogeneity is known as scalable video, which allows for spatial, temporal, and snr scalabilities [1]. in scalable video coding (svc), the video signal can be encoded into a base layer (bl) and one or more enhancement layers (els), with each enhancement layer improving the resolution (either temporally or spatially) or the fidelity of the video sequence. as a result, certain parts of the scalable bitstream can be removed for adaptation to various capabilities of end users as well as varying network conditions. at the network proxy or gateway, a bitstream adaptor is usually employed to extract the bitstream to meet particular constraints, e.g., targeted bit-rates and/or spatial or temporal resolutions. for a given set of constraints, the solution can be varieties of resolution combinations, leading to different visual qualities. the challenging problem of bitstream adaptation is therefore how to determine the combination of the spatial resolution (i.e., the frame size (s)), temporal resolution (i.e., the frame rate (t)) and snr (signal-to-noise ratio) resolution (i.e., the quantization step (q)) to be used for bitstream extraction under a given targeted bit-rate to maximize the resulting quality. many efforts have been devoted to bitstream adaptation for scalable video. a basic and content independent extractor is provided in the reference software of the joint scalable video model (2) [2]. in [3], an alternative extraction method is proposed based on rate-distortion optimization. this technique utilizes the concept of quality layers and improves the performance of the jsvm basic extractor by arranging the priority of layers based on their contributions to the global improvement in quality. a more copyright c⃝ 2006-2012 by ccc publications 474 j. hou, s. wan efficient method for extraction is proposed in [4], using an accurately and efficiently estimation of the quality degradation resulting from discarding an arbitrary number of network abstraction layer (nal) units from multiple layers taking drift into account. however, the methods in [3] and [4] are executed only within a single resolution, e.g., the snr plane. in [5], an effective method is proposed to quickly solve the problem of spatial resolution selection based on an analysis on the content information. however, the peak-signal-to-noise ratio (psnr) is used as the distortion criterion, which does not correlate well with the perceptual quality especially with regard to spatial scalability. in [6], two-dimensional (2d) rate and perceptual quality models in terms of the frame rate and the quantization step are built, and then the two models are applied to optimal bitstream extraction in svc. however, the spatial resolution is not considered in [6] and the parameters in the quality model are difficult to obtain. in [7], the video quality under different spatial, temporal and snr combinations is quantitatively and perceptually assessed, based on which an efficient adaptation algorithm is proposed. however, there is lack of a rate model to estimate the related bit-rate. on the other hand, performance improvement can be achieved by resorting to network-related technologies, such as using a priority mechanism [8], or self optimization of networked communications [9] presents a model for self optimization of network communications in order to improve cluster performance by shortening the data transfer time. in this paper, 2d rate and quality models are proposed for optimal bitstream adaptation for scalable video under given bandwidth constrains and required display resolutions at the end user. assuming that the frame rate is determined, the two 2d models are applied to extract bitstream to achieve the optimal combination of spatial and snr resolutions. the rest of the paper is organized as follows: section 2 presents the 2d rate and perceptual quality models considering the impact of spatial and snr resolutions. their application in constrained scalable video adaptation is introduced in section 3. section 4 presents the experimental results. section 5 concludes this paper and discusses future directions. 2 two-dimensional rate and perceptual quality models in this section, the impact of the spatial and snr resolutions on the bit-rate and the perceptual quality is analyzed, based on which a 2d rate model and a 2d perceptual quality model are respectively derived. 2.1 two-dimensional rate model considering snr and temporal scalabilities, we have proposed an analytical 2d rate model for h.264/svc [10]. in this paper, this model is extended to the spatial domain where a product of a power function of the quantization step q and a power function of the spatial resolution index s are used, given as r(q, s) = cqαsγ, (1) where α and γ are both content-dependent model parameters. the values of α and γ characterize how fast the bit-rate reduces with the increase of q and how fast the bit-rate increases with the refinement of the spatial resolution, respectively. usually a sequence with richer texture has larger absolute values of α and γ. here s is computed through dividing the frame size of the current spatial resolution by the frame size of the lowest spatial resolution. in order to evaluate the model accuracy, sequences, with qcif, cif and 4cif resolutions were encoded into 3 dyadic spatial layers using jsvm9.19.7 [11], respectively. each spatial layer contained 5 quality layers. the base quality layer in a lower spatial layer was used to perform inter-layer prediction to avoid drifting at the decoder. the gop (group of picture) size was set to 1 to avoid the effect of temporal scalability. 120 frames were encoded for each sequence. the difference of the quantization parameter (qp) between adjacent quality layers and adjacent spatial layers optimal bitstream adaptation for scalable video based on two-dimensional rate and quality models 475 were set to 5 and 6, respectively, following [12]. the qp of the base quality layer of the lowest spatial level was set to 38. the model parameters were obtained by minimizing the root mean square error (rmse) between the actual and predicted bit-rates. the actual values and those predicted using (1) are plotted in figure 1. it is clear that the proposed 2d rate model fits the actual data very well. table 1 gives the used parameters and the model accuracy in terms of the rrmse (rrms e = rms e/rmax, where rmax denotes the maximum bit-rate in the actual data) and the coefficient of determined (cod), defined as: cod = 1 − ∑ i ( xi − x̂i )2 ∑ i ( xi − x )2 , (2) where xi and x̂i are the actual and the predicted values of the bit-rate, respectively, and x is the mean of all actual bit-rates shown in figure 1. it is once again demonstrated that the proposed rate model is very accurate in prediction, where high cod and small rrmse values can be observed for all tested sequences. specifically, the average cod and rrmse are 0.9892 and 2.81%, respectively. and as expected, the “city" sequence with the richest texture among the tested sequences has the largest absolute values of α and γ. 0 50 100 150 200 250 0 500 1000 1500 2000 2500 3000 3500 quantization step b it ! ra te ( k b /s ) soccer actual data qcif actual data cif actual data 4cif predicted values using (1) 0 50 100 150 200 250 0 500 1000 1500 2000 2500 3000 3500 quantization step b it ! ra te ( k b /s ) crew actual data qcif actual data cif actual data 4cif predicted values using (1) 0 50 100 150 200 250 0 500 1000 1500 2000 2500 3000 quantization step b it ! ra te ( k b /s ) city actual data qcif actual data cif actual data 4cif predicted values using (1) 0 50 100 150 200 250 0 500 1000 1500 quantization step b it ! ra te ( k b /s ) ice actual data qcif actual data cif actual data 4cif predicted values using (1) figure 1: actual bit-rates and predicted values using (1). table 1: the values of parameters in (1) and model accuracy soccer crew city ice ave. c×10 3 9.67 13.58 10.76 4.23 α -1.276 -1.365 -1.462 -1.048 γ 0.970 0.965 1.078 0.756 cod 0.9958 0.9860 0.9792 0.9929 0.9892 rrmse 1.71% 3.31% 3.97% 2.27% 2.81% 476 j. hou, s. wan 2.2 two-dimensional quality model it has been widely acknowledged that the quality metrics of the psnr and the mean square error (mse) do not correlate well with the perceptual quality. on the other hand, the subjective quality can be well captured by the mean opinion scores (mos) and video quality metric (vqm) [13], at the cost of high complexity in testing and computations. trading off between the complexity and the consistency with the human perception, the structural similarity (ssim) [14] is used as the quality measure in this paper. the ssim measures the structural similarity as well as the luminance and contrast similarity between two images block by block. in this paper, the ssim values have been measured with regard to different combinations of spatial and snr resolutions, where the layers of lower spatial resolutions were upsampled to 4cif using a set of 6-taps filters provided by the jsvm. according to empirical observations, a logarithmic function in terms of the spatial resolution index and the quantization step is used to model the perceptual quality regarding different spatial and snr resolutions, which is expressed as qmssim(q, s) = a0 + a1 ln q + a2 ln s + a3 ln q ln s, (3) where a0,a1,a2 and a3 are all content-dependent model parameters. here the second and third terms indicates the impact of the snr and the spatial resolution on perceptual quality, respectively. the forth term models the joint impact of the snr and the spatial resolution. the model parameters can be derived easily by minimizing the rmse between the actual and predicted values. the actual and predicted qualities are shown in figure 2. table 2 lists the used parameters and the model accuracy in terms of the cod and rrmse. it can be concluded from figure 2 and table 2 that the proposed 2d perceptual quality model is very accurate in prediction with high cod and small rrmse values for all tested sequences. 0 50 100 150 200 250 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 quantization step q m s s im soccer actual data qcif actual data cif actual data 4cif predicted values using (3) 0 50 100 150 200 250 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 quantization step q m s s im crew actual data qcif actual data cif actual data 4cif predicted values using (3) 0 50 100 150 200 250 0.4 0.5 0.6 0.7 0.8 0.9 1 quantization step q m s s im city actual data qcif actual data cif actual data 4cif predicted values using (3) 0 50 100 150 200 250 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 quantization step q m s s im ice actual data qcif actual data cif actual data 4cif predicted values using (3) figure 2: actual qualities and predicted values using (3). optimal bitstream adaptation for scalable video based on two-dimensional rate and quality models 477 table 2: the values of parameters (3) in and model accuracy soccer crew city ice ave. a0 0.7709 0.9112 0.7268 0.9395 a1 -0.0614 -0.0569 -0.0791 -0.0193 a2 0.2292 0.1454 0.2918 0.0739 a3 -0.0393 -0.0251 -0.0444 -0.0141 cod 0.9871 0.9842 0.9754 0.9561 0.9757 rrmse 1.39% 1.11% 2.49% 0.8% 1.45% 3 optimal bitstream adaptation for scalable video using proposed models the proposed models are applied to constrained bitstream adaptation for scalable video. figure 3 provides a systematical view of the adaptation problem. for each video, a single full-resolution scalable bitstream is available at a server, where the bitstream will be adapted at a network proxy or gateway according to the user channel conditions and viewing preferences (i.e., the displayed spatial resolution). when a user requests the video from the server, the adaptor (at the proxy) will determine an appropriate bit-rate rt for extraction based on the channel condition. based on rt and the user’s settings of viewing preference (embedded in the user profile and sent to the adaptor), the adaptor determines the optimal set of spatial and snr layers to extract, so as to provide the best perceptual quality. for a given targeted bit-rate rt and the required display spatial resolution, the adaptation problem can be formulated as the following constrained optimization problem: determine q, s to maximize qmssim(q, s) subject to r(q, s) ≤ rt u(s)|s < s, (4) where rt and s denote the targeted bit-rate and the required display spatial resolution index, respectively. by u(s)|s < s it is indicated that up-sampling is executed if the extracted spatial resolution is less than the required display spatial resolution. assume that both the spatial resolution and the quantization step may take on any effective value. by setting r(q, s) = rt, it can be obtained that q = α √ rt csγ , (5) which describes the feasible q for a given s, to satisfy the rate constraint rt. substituting (5) into (3) yields qmssim(s) =− a3γ(ln s)2 α + (a3 ln rt/c +αa2 − a1γ) ln s α + a0 + a1 ln rt/c α . (6) equation 6 is the achievable quality with different spatial resolutions under the targeted bit-rate rt. clearly, this function has a unique maximum, which can be derived by setting its first order derivative with respect to s to be zero. this yields s = e(a3 ln (rt/c)+αa2−a1γ)/2a3γ. (7) 478 j. hou, s. wan for any given rt and s , we can solve (7) numerically to determine the optimal spatial resolution. then using (5) and (6) the optimal quantization step can be determined, and the corresponding quality can be maximized. the parameters for the rate model, i.e., c, α and γ, can be easily derived from the bit-rates corresponding to several different (q, s)combinations using least square fitting. the quality model parameters, i.e., a0, a1, a2 and a3, can be derived using the least square fitting at the encoder, and then embedded in the header field of the video stream. based on the simulations, only several bytes are required to represent those parameters which can be neglected compared to the actual video stream payload. ������ ��� �� ���� ����� ��� ������������������ ��� ������������������ ����������������������� �������� ��������� � �����!������ �����"������ #��� $��� ��������!������� ��������� ���� ����%�&� $ ' � � � � ��! � � � � �� � �( �# � � �� � �� "��� �!������������ �� ���� )) ) )) figure 3: constrained scalable video adaptation. 4 experimental results the experimental results are presented in this section to evaluate the performance of the proposed extraction method. firstly, assuming that the spatial resolution can be any positive values, and then the practical case where spatial resolutions to be discrete is considered. 4.1 optimal solutions assuming q and s taking continuous values assuming that both the spatial resolution and the quantization step can take continuous values. figure 4 shows the optimal spatial resolution, quantization step and quality as functions of the targeted bitrate rt. as expected, as the targeted bit-rate increases, the optimal s increases while the optimal q reduces, and the achievable best quality continuously improves. notice that the optimal s increases more rapidly for the “city" sequence than for the other sequences because of its richer texture. the up-sampling introduces more severe quality decrease than the quantization step. therefore, under the bit-rate constraint, a larger spatial resolution with a larger quantization step is a better choice. 4.2 optimal solutions under dyadic spatial resolution scalability the h.264/svc includes three profiles [15], i.e., the “scalable baseline" profile, the “scalable high" profile, and the “scalable high intra" profile. while the latter two profiles support full spatial svc scalability, the scalable baseline profile imposes some constraints to enable simplified application scenarios. for example, dyadic spatial scalability is provided in the baseline profile, where the scaling ratio of the width and height between adjacent spatial layers is equal to 2. from a practical point of view, it will be interesting to see the optimal combination of the spatial resolution and quantization step for different optimal bitstream adaptation for scalable video based on two-dimensional rate and quality models 479 0 500 1000 1500 2000 2500 3000 3500 4000 0 10 20 30 40 50 60 70 80 90 100 targeted bit!rate (kb/s) soccer optimal s optimal q optimal (100*qm ssim ) 0 500 1000 1500 2000 2500 3000 3500 4000 0 10 20 30 40 50 60 70 80 90 100 targeted bit!rate (kb/s) crew optimal s optimal q optimal (100*qm ssim ) 0 500 1000 1500 2000 2500 3000 3500 4000 0 20 40 60 80 100 120 140 targeted bit!rate (kb/s) city optimal s optimal q optimal (100*qm ssim ) 0 500 1000 1500 2000 2500 3000 3500 4000 0 20 40 60 80 100 120 targeted bit!rate (kb/s) ice optimal s optimal q optimal (100*qm ssim ) figure 4: optimal quantization step, spatial resolution index and the corresponding quality versus the targeted bit-rate by assuming the quantization step and the spatial resolution to be continuous. targeted bit-rates under this svc structure. to obtain the optimal solution for this svc structure, we first determine the optimal s using (7), and then choose two spatial resolutions up and down around the value from the candidates. finally, compute the quality using (6) corresponding to the two spatial resolutions and choose the spatial resolution that leads to a better quality. the experimental results are shown in figure 5. because the spatial resolution can only increase in a discrete step, the optimal quantization step does not decrease monotonically with the bit-rate. whenever the optimal s jumps to the next higher value, the optimal q first increases to meet the rate constraint, and then decreases while the optimal s is held constant, as the rate increases. consistent with the previous results in figure 4, for the “city" sequence with richer texture, the optimal s is 16 (corresponding to 4cif) at a low bit-rates, whereas for other sequences, the optimal s stays 4 (corresponding to cif) even at high bit-rates. in practice, the svc encoder with quality scalability does not allow the quantization step to change continuously. the finest granularity in quality scalability is a decrement of qp by 1 with each additional quality layer. this means that the quantization step reduces by a factor of 2−1/6 with each additional layer. in practice, much coarser granularity is typically used, with a decrement of qp by 3 to 6 typically [11]. when we limit the values of q to be discrete in addition to allow only dyadic spatial resolutions, a rate constraint cannot be always exactly met. however, one may still obtain the optimal q and s for any given constrains using the proposed scheme by estimating the bit-rate and quality of each combination in the finite set of feasible values for q and s. 5 conclusions and future works in this paper, a 2d rate model and a 2d quality model have been proposed, based on which a modeldriven method for optimal bitstream adaptation is developed. experimental results have demonstrated the accuracy of the two models. using the proposed extraction method, the optimal combination of quality and spatial layers can be determined, providing the highest perceptual quality for a given bandwidth 480 j. hou, s. wan 0 500 1000 1500 2000 2500 3000 3500 4000 0 20 40 60 80 100 120 targeted bit!rate (kb/s) soccer optimal s optimal q optimal (100*qm ssim ) 0 500 1000 1500 2000 2500 3000 3500 4000 0 10 20 30 40 50 60 70 80 90 100 targeted bit!rate (kb/s) crew optimal s optimal q optimal (100*qm ssim ) 0 500 1000 1500 2000 2500 3000 3500 4000 0 20 40 60 80 100 120 140 targeted bit!rate (kb/s) city optimal s optimal q optimal (100*qm ssim ) 0 500 1000 1500 2000 2500 3000 3500 4000 0 20 40 60 80 100 120 targeted bit!rate (kb/s) ice optimal s optimal q optimal (100*qm ssim ) figure 5: optimal quantization step, spatial resolution index and the corresponding quality versus the targeted bit-rate by assuming that the q varies continuously and the spatial resolution takes qcif/cif/4cif. constraint and required display frame rate of the end user. future work may include an extension of the proposed models to three-dimension, taking temporal scalability into account. moreover, the proposed models can be applied to other applications, e.g., advanced multidimensional rate control for video coding. acknowledgement this work was supported by the national science foundation of china (60902052, 60902081), the doctoral fund of ministry of education of china (no.20096102120032), and the npu foundation for fundamental research (jc201038). bibliography [1] h. schwarz, d. marpe, t. wiegand, overview of the scalable video coding extension of the h.264/avc standard, ieee trans. circuits syst. video technol., vol. 17, no. 9, pp.1103-1120, 2007. [2] j. reichel, h. schwarz, and m. wien, joint scalable video model 11 (jsvm 11), joint video team, doc. jvt-x202, 2007. [3] i. amonou, n. cammas, s. kervadec, s. pateux, optimized rate distortion extraction with quality layers in the scalable extension of h.264/avc, ieee trans. circuits syst. video technol., vol. 17, no. 9, pp.1186-1193, 2007. [4] ehsan maani, aggelos k. katsaggelos, optimized bit extraction using distortion modeling in the scalable extension of h.264/avc, ieee trans. image process., vol. 18, no. 9, pp.2022-2029, 2009. optimal bitstream adaptation for scalable video based on two-dimensional rate and quality models 481 [5] yu wang, lap-pui chau, kim-hui yap, spatial resolution decision in scalable bitstream extraction for network and receiver aware adaptation, proceedings of the 2008 ieee international conference on multimedia and expo, pp.577-580, 2008. [6] y. wang, z. ma, y.-f. qu, modeling rate and perceptual quality of scalable video as function of quantization and frame rate and its application in scalable video adaptation, proceedings of the ieee 17th packet video workshop, pp.1-9, 2009. [7] guangtao zhai, jianfei cai, weisi lin, xiaokang yang, wenjun zhang, three dimensional scalable video adaptation via user-end perceptual quality assessment, ieee trans. broadcasting, vol.54, no.3, pp.719-728, 2008. [8] a. rahim, z.s. khan, f.b. muhaya, m. sher, m.k. khan, information sharing in vehicular adhoc network, int. j. of computers, communications and control, 5(5):892-899, 2010. [9] a. rusan, c.-m. amarandei, a new model for cluster communications optimization, int. j. of computers, communications and control, 5(5):910-918, 2010. [10] junhui hou, shuai wan, fuzheng yang, "frame rate adaptive rate model for video rate control," proceedings of the 2010 ieee international conference on multimedia communication, pp.226229, 2010. [11] h.264 svc reference software (jsvm 9.19.7) and manual cvs sever, jvt, 2010 [online]. available: garcon.ient.rwth-aachen.de. [12] xiang li, peter amon, andreas hutter, andr kaup, performance analysis of inter-layer prediction in scalable video coding extension of h.264/avc, ieee trans. broadcasting, vol. 57, no. 1, pp6674, 2011. [13] m. pinson, s. wolf. a new standardized method for objectively measuring video quality, ieee transactions on broadcasting, vol. 50, no.3, pp.312-322, 2004. [14] z. wang, a. c. bovik, h. r. sheikh, and e. p. simoncelli, image quality assessment: from error visibility to structural similarity, ieee trans. image process., vol. 13, no. 4, pp.600-612, 2004. [15] t. wiegand, g. j. sullivan, j. reichel, h. schwarz, m. wien, eds., amendment 3 to itu-t rec. h.264 (2005) | iso/iec 14496-10:2005, scalable video coding, 2007. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 799-812 discussion of the analysis of self-similar teletraffic with long-range dependence (lrd) at the network layer level g. millán, h. kaschel, g. lefranc ginno millán, héctor kaschel universidad de santiago de chile departamento de ingeniería eléctrica avda. libertador bernardo o’higgins #3363. estación central. santiago chile e-mail: {ginno.millan,hector.kaschel}@usach.cl gastón lefranc pontificia universidad católica de valparaíso escuela de ingeniería eléctrica avda. brasil #2147. valparaíso chile e-mail: glefranc@ucv.cl abstract: traffic streams, sources as well as aggregated traffic flows, often exhibit long-range-dependent (lrd) properties. this paper presents the theoretical foundations to justify that the behavior of traffic in a high-speed computer network can be modeled from a self-similar perspective by limiting its scope of analysis to the network layer, since the most relevant properties of self-similar processes are consistent for use in the formulation of traffic models when performing this specific task. keywords: long-range-dependent, network layer, network traffic, self-similar process. 1 introduction still rooted in the genesis of the design of present day high speed computer networks is the trend to scalable development with a base prepared for the primary support of processing applications which, although requiring a reliable transport service, are not demanding in terms of other quality of service (qos) parameters such as delay, flow rate, latency, and loss rate. it is a reality that is subordinated to financial justifications, unable to reflect both the behavior and the operation of present day network environments, most of them characterized not only by their scalability and support of services and added value applications with high band width and availability requirements, but also by their convergence, complementarity, and interoperability. on the other hand, sustained development in the fields of optical, nanometric, and quantum technologies with greater emphasis has allowed the evolution of computer networks, providing them with the capacity needed to satisfy simultaneously the requirements of diverse traffic, creating the scenario inherent to the appearance of new services and applications for which this characteristic is essential; we are in the presence of services that involve real time traffic and which, because of their nature, have highly demanding needs to and from the available bandwidth. therefore, the new high speed networks must be capable of providing a service that not only ensures the availability of the resources, but is also provided under quality conditions that are well defined, parameterizable, adaptable, and dynamic in their assignment, because the requirements of present day real time applications and services cannot be satisfied using the high-level protocols if the carrier networks do not offer the necessary guaranties. it is crucial, therefore, to make quantitative analyses that evaluate the service quality offered by the new technologies, leaving copyright c⃝ 2006-2010 by ccc publications 800 g. millán, h. kaschel, g. lefranc aside the unsubstantial bases, arguments, and assumptions. the main problem that appears at the time of making a rigorous evaluation of the performance of a communications network is that of modeling the input traffic to the network. in fact, to many authors, traffic modeling is the most critical problem related to the evaluation of the performance of communications networks, because the success of the analyses depends directly on how representative of reality are the traffic models used. historically, traffic modeling had its origin in conventional telephony systems and has been based almost exclusively on assumptions of independence between the times of arrival of successive grids and exponential durations in the use of the resources. concretely, the acceptance of both assumptions implies a restriction toward the stochastic processes so that they obey a universe of poisson or markov processes. in that respect, the usefulness of their use by network designers as well as by systems analysts for planning capacities and predicting performance is not questioned [1]. however, in a wide range of real world cases it has been verified that the results predicted from the analysis of tails differ significantly from the actual observed performance, and this marked discrepancy has its origin in the fact that traffic processes often present lrd on many or all the temporal scales, while poisson or markov models, which have no memory or show short-range dependence (srd), present traffic flow over much shorter time scales. as a result, there is a tendency to produce highly optimistic forecasts of performance due to the use of distributions with finite variance for characterizing the periods with the presence and absence of burst packets. in view of the above arguments, the following working hypothesis is proposed: “it is completely feasible to restrict the evolution of a statistically self-similar process to a well defined application setting without altering its nature and its more important properties, in that way highlighting the validity of its postulates and giving greater plausibility to its physical interpretation.” in this context, the plausibility refers to the action of conferring an admissible character, therefore worthy of consideration, to one or several parameters that are components of an analytical model, whose interpretations do not constitute only a mathematical idealization. this paper presents a detailed discussion of the theoretical bases that justify the fact that the behavior of a high speed computer network traffic in the presence of long-range dependence can be modeled from a selfsimilar perspective limiting its analysis setting to the network layer level, stressing that all the most relevant properties of the self-similar processes are consistent for use in the formulation of traffic models when this distinction is made, since the need for its concept is justified to describe the traffic that is registered in the settings of present day computer networks. 2 bibliographic discussion since kleinrock’s publication [2], later expanded in [3], which establishes the mathematical theory that governs networks with packet switching, the existence of temporal dependence in the performance of the different types of data traffic flows has become an exciting field of research with countless discoveries, with the huge impact and influence that they have on the performance of tail systems standing out among them. the latter fact accounts for both the current existence and coexistence of a wide variety of input traffic models that show structures with rather complex correlations, which are applied to cases in which the models of the communications systems that are being studied allow adequate analytic handling. in any case, these models, basically markovian, neglect the temporal correlations starting from a given temporal separation, even if the latter can be increased arbitrarily at the expense of complicating the models with additional parameters. since 1993 an increasing number of published studies have documented that the data traffic pattern is well modeled by self-similar processes in a wide range of real world and network situations [1], with their top reference point and foundational work in the research of leland, willinger, taqqu, and wilson [4], presented originally at acm sigcomm ’93, and discussion of the analysis of self-similar teletraffic with long-range dependence (lrd) at the network layer level 801 then amended and extended in [5]. in spite of the existence of some previous papers that provide informal descriptions of this performance, such as [6][8], and to an exception from mandelbrot himself [9], no one had submitted the idea of self-similarity applied to the analysis of data traffic in itself, and that paper shattered the illusion that a simple tail analysis, based on the assumption that the traffic follows a poisson distribution can model adequately all the network traffic [1], showing that traffic in ethernet has a self-similar or fractal nature and therefore requires new modeling and analysis statements. in this respect, the methodology followed by the authors involves a massive compilation of traffic samples from 1898 through 1992, from different ethernet lans of the research and engineering center of bellcore in morristown, usa [5], which resulted in a detailed temporal high resolution collection totaling more than 100 million packets with 10 µs precision, grouped in four sets of measures available in [10], and in the application of a rigorous and exhaustive statistical analysis based on the modeling of the traffic sources using hyperbolic tail distributions, in particular pareto’s, comparing the results with the behavior of the traffic flow of the real traces, and in the observation of the estimated value of hurst’s parameter (h) for each of the four sets of traffic samples, expressed for processing as a series of ordered pairs of data composed of the time of arrival and the size of the ethernet packet, as well as for each level of temporal traffic aggregation considered. a complete analysis of both the statement and the methodology followed by the authors is found in [11] and [12], and their proofs in [13]. specifically, this research shows that: • it is possible to model ethernet traffic producing results similar to those of real ethernet traffic using few parameters (parsimony), and with the fundamental added value of being physically plausible. • ethernet lan traffic can be modeled through the superposition of many sources that vary between a state of burst transmission and one of inactivity, using for their characterizations infinite variance distributions. in particular it is proved using a pareto distribution. • ethernet traffic is statistically self-similar regardless of the place and time at which it is checked. • the degree of self-similarity measured in terms of hurst’s parameter (h) is a function of the use factor of ethernet and can be used to get the magnitude of the traffic bursts. • traditional traffic models are unable to capture the property of self-similarity. these results and their deep implications, as can be noted from the preceding paragraphs, produced a host of researchers seeking to observe that same behavior associated with the largest variety of communications and applications scenarios. what follows is an exhaustive literature review of research results in relation to their field of application, that approaches the treatment of systems communications systems and applications from a self-similar perspective. it should be noted that the idea is not only to show the application of this view, but also to present results that dissent from it. the self-similar or fractal behavior of traffic in wan networks is shown in [14][16], pointing out the failure of poisson models to represent the strong correlations that exist at different temporal scales. evidence and conclusions on this behavior in traffic due to the www are provided in [17][19], considering interconnection scenarios as well as traffic patterns in browsers. on the other hand, [20] and [21] point out the fractal nature of the data flow of the protocols that compose the signaling system 7 in common channel signaling networks [22], showing that the traditional methods are not adequate to interpret their behavior, and the duration of the calls are better characterized if hyperbolic tail distributions are used. in another setting, [9] and [23][25] show that the lrd 802 g. millán, h. kaschel, g. lefranc is a characteristic inherent to vbr video traffic that does not have any relation with the type of codec or the number of special effects that contain the recorded scenes. specifically, the vbr video traffic flow transmitted through b-isdn, atm, and internet networks are studied, showing that the behavior of the distribution’s tail that represents the marginal band width can be described exactly if hyperbolic tail distributions (like pareto) are used; that the self-correlation function of the video sequences decays hyperbolically (this is equivalent to long-range dependence) and it can be modeled using self-similar processes, and finally, that the use of models that only capture the srd is inappropriate for characterizing this kind of traffic because it overestimates the performance, leading to insufficient resource assignments, which finally derives in poor perceptions by the users of the networks when difficulties appear to achieve the service quality expected by them. in the field of wireless communications, in [26] it is shown that traffic in cdpd networks has an lrd behavior. using the r/s and variance-time methods, it estimates values for hurst’s parameters of h = 0.8 and h = 0.9, respectively, thus disregarding the use of predictive models based on poisson arrival processes. in [27] an investigation is made of the impact of mobility on the added traffic in wireless networks in the city of bristol, uk, and of whether the applications of voice and data together produce self-similar traffic. it is concluded that the added traffic generated by the mobile users that use voice and data services together show a self-similar lrd behavior that has no relation with the rate of penetration of the services. they warn on the drastic changes that wireless multimedia service implementations must undergo in terms of the traffic profiles used in their models to be able to capture that characteristic. from another perspective, in [28] it is shown that traffic in wireless networks with ad hoc topology is self-similar and forecastable as a consequence of the fact that subjacent temporal self-similar series are, in essence, predictable. the required data are captured using a wireless test network with ad hoc topology. the analysis of traffic and the design of wireless ip networks describing tcp traffic as dominant in present day internet is approached in [29], indicating that its statistical nature shows the same behavior over all the temporal scales. it also presents an analysis of traffic traces which shows the statistically self-similar nature of the traffic due to www and to the vbr video over these types of networks. in [30] develops a new model for traffic in wireless networks that has its origin in the alternating fractal renewal processes (afrp) proposed as traffic models in [14], and in the wide band network traffic model using the extended alternating fractal renewal processes (eafrp) proposed in [31]. with the incorporation of a limiting rate for alternation between the two states, called extended limiting rate, the rate-limited eafrp model is formulated, which assumes an advance with respect to the wireline models used traditionally for model traffic in wireless networks, since it takes care of its two main deficiencies: omission of the effects of the lrd temporal correlations, and inability to make reliable performance forecasts due to the high dependence on short-range processes. in [32] there is an extensive discussion of the problem of modeling the data traffic that flows from and to the wireless networks with respect to the internet, taking care of large scale wireless communication structures. based on the methodology presented in [33] and tested extensively in [34], [35], it is concluded that the circulation of traffic flows cannot be treated using poisson models, and that their behavior is statistically self-similar. in [36] two solutions are proposed to create an interconnecting bridge between wimax and wifi links. the former is based on maintaining a certain level of qos from one end to the other regardless of the wireless technology used, while the latter is aimed at the reduction of the complexity in its physical implementation at the expense of not providing any qos guaranty. in both cases the performance of the system is compared with computer simulations that consider real time traffic with long-range dependence that is manifested trough a polynomial type decay of the self-correlation function. to model the traffic generated by the many terminals within the wlan, recourse is made to the on/off methodology presented in [14], supporting its foundations on [37]. finally, in the same context, discussion of the analysis of self-similar teletraffic with long-range dependence (lrd) at the network layer level 803 but in another field of application, [38] deals with the use of fractal geometry in the antenna design process, while in [39] a general synthesis methodology is proposed that covers the efficient design of fractal antennas and their wimax applications. in the field of optical networks, [40] deals extensively with the use of self-similar traffic models, in particular that proposed in [15], as the only way to represent reality faithfully and to evaluate the performance of ethernet passive optical networks. in [41] a new protocol is proposed for labeled switching in optical networks with optical burst switching (obs), with time/space labeling that allows to keep the signaling always joined with the addressing functions. the results obtained are based on simulations under the premise of accepting the self-similarity of the traffic in the networks with wavelength division multiplexing. finally, [42] reports on the implications of reducing the self-similarity of ip traffic by the obs assembly algorithms. in spite of what all the above arguments imply in terms of putting in evidence the merits and advantages of the use of parsimonious models and which also provide a plausible physical interpretation to its parameters, the question arises on the degree of prevalence of these self-similar traffic patterns and on which are the conditions for the analysis of performance to depend critically on considering self-similarity. in this sense it is valid to inquire not only about the origin of the data that have been analyzed with respect to the synthetic traces generated, but also on what is the context or the setting in which these comparisons have been made, and where are the results aimed at. it is no less true that in the light of all the research presented, it seems even irrelevant to think of a traditional analysis of tails to represent data traffic flow in present day high speed networks. but this is neither categorical nor restrictive; this methodology cannot be disregarded flatly, and neither is its proper to think that the former is the unified solution by simply arguing it ubiquitousness in all the temporal scales, because this, analyzed from a higher perspective, and even though the presence of the correlation in the traffic is not under discussion, brings about the question of whether the correlation structure alone is sufficient to characterize traffic over self-similar processes. the context described in this way is very extensive, and therefore the results must be limited. there are several reports that put in evidence the lack of consensus on the application field of self-similar models and of the impact of lrd on the performance of communications systems, and even though their number is much more limited than that of those whose results support exactly the opposite, their conclusions must be analyzed carefully because they bring forth, in essence, a critical and fundamental matter in common: “since the traditional tail models are unable to put in evidence the self-similarity characteristic, its validity for predicting performance would be supported if it is shown that self-similarity does not have a measurable impact on performance,” and even more so if it is shown that the models based on self-similar stochastic processes fail when considering the impact of important characteristic parameters in each particular network case. precisely based on this last point, [43] presents a detailed analysis of a fault detected in self-similar models by considering that they are incapable of reflecting the impact of the range of temporal scales of interest for evaluating the performance and the prediction of problems, and those first order statistics such as the marginal distribution of the process. based on traces generated by a jpeg encoder of an ntsc television channel and the traces of [6] it is reported that: • there is a correlation horizon such that the rate of loss does not affect performance beyond it. • the correlation level considered for evaluating performance depends not only on the structure of the source traffic correlation, but also on the temporal scales belonging to the system that is being studied. • the scale factor considered has a considerably greater impact on the rate of loss than hurst’s parameter or the size of the buffers. 804 g. millán, h. kaschel, g. lefranc • increasing the size of the buffers helps reduce the rate of loss if we are dealing with srd traffic. on lrd traffic it does not have a considerable impact. the in-depth analysis of this work shows that the impact of the lrd is dealt with over the contribution of the single server model in relation to the size of the input buffer, using for that purpose a model of fluid [44], [45], that presents a hyperbolic drop down to a given cut coefficient from which it drops to zero. based on the results from many simulation experiments using video traces as well as ethernet traces for different values of hurst’s parameter, cut coefficients and buffer sizes, and a wide range of marginal distributions, the authors discovered the existence of a critical cut coefficient that they call “correlation horizon,” such that the rate of loss is not affected if the cut coefficient is increased above it. therefore, the correlation horizon separates the relevant from the irrelevant correlation coefficients with respect to the rate of loss. finally, in their conclusions the authors argue that because of the existence of a finite cut horizon (which in the case of the finite buffers is a function of their size), any model that captures the correlation structure up to that horizon will be valid to represent the system. on the contrary, if the correlation horizon is infinite, i.e., the system’s time scale cannot be determined clearly, then selfsimilar models must be used. based on the above point, [46] states that in the case of considering a finite buffer, the effects of the lrd are detectable only if it makes the occupation periods become sufficiently long, since the behavior of their tail is affected largely by the characteristics of the traffic that arrives during those periods. in that respect, this appraisal is based on [47], where through the definition of the concept of “relevant temporal scale” as the typical duration at which all the arrivals at a tail interact and affect collectively their behavior, it is deduced that for the large buffers the size of the tail can be large, so many arrivals interact in the tail and cause the long-range correlation to cause more losses than those predicted by the models that are not capable of considering it or do not consider it. however, in the case of small buffers, where few arrivals interact, the effect of the lrd is imperceptible. therefore, the standpoint on the appraisal of the effects of the lrd in terms of its impact on the occupied periods is depicted by the authors through the concept of “reset effect,” which involves that as the buffer in question is emptied, the system forgets everything. in this way, in the case of vbr video servers, since they are sensitive to the delay and to the loss of meshes, the intensity of the traffic flow will not be very large, giving rise to short periods of occupation and a very pronounced reset effect in the regions of practical operation, an effect that will also be reinforced in the case of finite buffers, due to the truncating effect of these types of buffers. the latter is due to the fact that an occupied period in which there is overflow is shorter than the corresponding one in an infinite buffer model, or similarly, there can be several periods occupied in the finite buffer version before the corresponding occupation period ends in the infinite buffer version. then, through the use of markov models, the authors finally report that when the srd is strong and parameter h is moderate, the lrd has no impact on the occupation of the buffer, and therefore their models give rise to good estimations, and in the case in which the srd shows a slightly pronounced behavior and parameter h has a high value, the truncating effect is sufficiently strong for their models to estimate well the rate of loss, even though it is admitted that for a high traffic intensity and a large buffer size the estimation of the mean size of the tail is bad. in [48], a research having characteristics similar to those of the previous one, the concept of critical temporal scale (cts) is introduced as follows. given the size of the buffer and the marginal distribution of mesh sizes, the cts of a vbr video source is defined as the number of mesh correlations that contribute effectively to the rate of loss of cells. using models of the video traces of [9], the authors state that for markovian models as well as with lrd, the cts is finite and decreases with the size of the buffer. consequently, and under the assumption that the size of the buffer required to multiplex a large number of vbr video source is typically small due to the restrictions that correspond to real time applications, it is concluded that for buffer sizing scenarios in atm discussion of the analysis of self-similar teletraffic with long-range dependence (lrd) at the network layer level 805 networks it is not necessary to capture the correlations with the lrd of a video source even if the traffic shows this behavior markedly, because for all practical cases the srd has a dominant impact. the following can be argued with respect to this last conclusion and to the research in general: • the result is based on an analysis that relies completely on marginal gaussian distributions, and even though it can be explained from the perspective of keeping an adequate analytic treatment, it is not sufficient for validating absolute generalizations of the “in all cases” type nor to attenuate the fact of working with distributions independent of the behavior of others. • the behavior of the models if hyperbolic tail distributions are used or if they are tested with distributions without margins is not reported. an extended version of this research is [49], where not only these two considerations are taken up, but attention is also given to the relevance of the srd and the lrd in real time vbr video traffic in wide band networks (particularly atm) and in the integrated internet services, and through a theoretical and simulations approach, the authors tackle the problem of determining admissible ranges for the probability of cell loss and its relation with the size of the buffers in terms of the maximum delay. it should be noted that this research takes place within the context of validating the use of markovian models and srd for video applications, and for that purpose the authors show basically that: • the long-range correlations have no impact on the probability of cell loss. • an adequately implemented markov model that captures the relevant range of correlation provides good predictions of performance. • the capture of the lrd by itself can lead to an underestimation of the necessary resources in the network. • the cts explains the strong relation existing between the probability of cell loss in atm and the srd at the expense of the lrd. that is, for the applications of interest, the cts is small and more sensitive to the short-range than to the long-range traffic correlations. • analytically simple markovian models are feasible and have the capacity to harmonize marginal distributions and correlations over a given critical temporal scale. as a last example of the already mentioned lack of consensus in the field of video traffic, [61] presents a model of a vbr traffic source that uses finite states markov chains, and states that although the original model presented in [26] is good in terms of its parsimony, it does not lend itself adequately to analytic studies. summarizing, six reports have been presented so far with a common denominator: dissenting on the blind applicability of the self-similar model and its implications in performance, considering as examples systems that involve video traffic, mainly of the vbr type. but as expected, this is not the only field in which discrepancies appear, and more acutely and in direct relation with the working hypothesis stated there is a number of reports that question the validity of the use of hurst’s parameter as a single descriptor to characterize the lrd of a self-similar stochastic process. but before dealing with this topic a last group of reports are mentioned that have a critical position toward self-similar models in areas other than video traffic. in the field of wireless networks and their associated technologies, [51] studies the behavior of the self-similarity characteristic of traffic when it goes from a wired to a wireless network through a gateway, concluding that the device can change the traffic’s degree of selfsimilarity as a direct consequence of the reassembly and repacking operations on the self-similar 806 g. millán, h. kaschel, g. lefranc input traffic, even reaching its annulment. in [52] the above behavior is reaffirmed considering the study of the influence of the mac mechanism of ieee 802.3e on lrd traffic when it goes through one or several links, suggesting that the traffic transported through a wlan interface undergoes deep structural changes in its statistical model, and showing that the fractional gaussian traffic model is inadequate to describe its behavior. finally, in the field of obs networks, [53] reports the development of an algorithm for the assembly of bursts that has the purpose of reducing the degree of self-similarity of ip traffic. it is admitted that this is a characteristic inherent to www traffic, but its presence causes an important disadvantage in terms of the performance of the tails, so it must be reduced in favor of a random srd traffic. the first paragraph of this page mentions the existence of some research that questions the use of hurst’s parameter as a single indicator to capture the self-similarity characteristic of a stochastic process that boasts of being such, in addition to stressing the importance that this fact has to prove the stated work hypothesis. in this respect, and recalling its statement, it is specified that the idea is not to validate exhaustively the self-similarity parameter or hurst’s parameter, but only to obtain an indicator of the presence of the characteristic in representative traffic series. consider the following definition: the valuated real process x(t) , t ∈ r is self-similar with h > 0 if for all a > 0 the finite-dimensional distributions of x(at) , t ∈ r are identical to the finite-dimensional distributions ah x(t), t ∈ r, i.e., {x(at), t ∈ r} =d {ahx(t), t ∈ r} ∀a > 0 (2.1) where =d means equality for all the finite-dimensional distributions [54], [55]. the property defined by (1), is usually known as the scaling property, and a direct consequence of its definition is that a self-similar process preserves its distribution, and thereby its statistics, since it is subjected to a temporal scaling. also, from the same standpoint parameter h, or hurst’s, is known as the self-similarity parameter for the stochastic process x(t) to which it is associated. the first report that recognized and approached the need to have additional parameters to characterize the variability of the traffic is [26], where although it is accepted that h is necessary for that purpose, it is not sufficient. through a detailed statistical analysis of vbr video samples, the authors conclude that the self-correlation of the vbr video sequences decays hyperbolically, equivalent to the lrd. but since the lrd is related to the frequency of the components of the process and not to the distribution of the bandwidth requirements, if the marginal distribution is compressed because the coefficient of variability (coefficient between the mean bandwidth and the standard deviation) tends to zero when the number of multiplexed input sources that give rise to the traffic tend to infinity, the traffic, as the number of sources increases, is confined within narrow statistical limits, and although within these frontiers the behavior continues to be long range (result confirmed through h = 0.7), in the range in which the standard deviation is much less than the product of the mean of the bandwidth distribution and the number of multiplexed input sources, the traffic does not depend on h. therefore, h is necessary to characterize the variability, but it is not sufficient. so an adequate characterization of video traffic must consider at least the following four parameters: the mean of the bandwidth distribution, the number of multiplexed input sources, the standard deviation, and the coefficient of variability derived from them. the authors make it clear, finally, that these results are valid only if they follow the central limit theorem [56], i.e., when the standard deviation is finite. although the above result may seem to be expected because the parsimony can lead to imprecision in both the interpretations and the results, it is not so in relation to the effect produced by assuming that if the detailed behavior of the components of a given stochastic process, p1, that shows some degree of self-similarity h1 is not known, then a biunivocal correspondence between h1 and p1 is clearly established. in other words, processes that show clearly differentiated behaviors are possible, but their correlation structures must be characterized by the same h parameter. in this discussion of the analysis of self-similar teletraffic with long-range dependence (lrd) at the network layer level 807 respect, [57] approaches this problem considering the asymptotic behavior of an unlimited buffer of a multiplexor under different self-similar input traffic models, in particular, cox’s infinite server models, or m/g/∞ [58], and fractional gaussian noise models [59], [60]. in this way the authors report getting two completely different behaviors for the buffer’s probability tail, namely that while with the former the drop is mostly hyperbolic, with the latter it presents a wiebull asymptotic behavior [61], simply showing that hurst’s parameter is insufficient as a single descriptor to characterize the lrd in input traffic models [62], [63]. in [64] it is also shown how synthetic traces that have identical self-similarity parameters and means differ significantly from one another. finally, in [65] the insufficiency of hurst’s parameter by itself as a precise descriptor of the long-range dependence of the traffic in an ethernet network is reported. in this research it is shown convincingly, through an analysis of tails applied to a series of representative data of the traces of the real traffic registered in the ethernet network of the department of computer science of the university of california at los angeles, ca, usa, that hurst’s parameter does not provide a precise prediction of the performance of the tails for a given lrd traffic, and that its behavior is not monotonic with respect to the presence or absence of bursts if the original series is disaggregated into smaller ones, which also implies that h does not serve to characterize the relative importance of groups within a whole. it is clearly seen that both results are opposed to the conventionalism of [6] since h cannot be used to size the traffic bursts in ethernet. questions on where, when, and under what set of circumstances the use of self-similar processes in modeling communications systems and related applications is completely valid, as well as the uncertainty on the existence of a scenario that brings together criteria and its influence on the basic characteristics of the processes remain unanswered. however, they seem to have an answer derived from the analysis of the research of ryu and lowen, [66], [67], on the use of fractal point processes (fpp) for modeling and analyzing self-similar traffic in networks [68]. concretely, this research proposes to make a distinction between self-similarity at the application level and self-similarity at the network level for the purpose of the design and administration of wideband networks in terms of a correct provision of the qos required by the applications, and their best results are the proofs that self-similarity of vbr traffic can frequently be ignored in the face of the sizing of the buffers in atm, and that self-similar traffic at the applications level can be managed effectively in the context of the admission control for assigning resources with service quality guarantees, because it is independent of the network conditions under which it is sent. 3 conclusions a detailed discussion has been presented of the theoretical bases that support the position of considering that high speed computer network traffic shows self-similar behavior and long-range dependence. with respect to the above, and considering the arguments in favor and against this position, it is believed that traffic in present day computer network settings is of a statistically self-similar nature and presents a pronounced long range dependence (lrd). accepting the above singularities as inherent to the traffic flow of present day high speed network settings, it is proposed that their behaviors are amenable to being modeled by limiting their applicability to the network layer level, estimating that the most relevant properties of self-similar stochastic processes are consistent to be used in the formulation of traffic models when that distinction is made, since the concepts of self-similarity and long-range dependence are justified by the need to describe faithfully the real traffic processes in present day computer network settings. to depict the concepts with which this research deals, the following work hypothesis is proposed: “it is completely feasible to restrict the evolution of a statistically self-similar process to a well defined application setting without altering its nature and its more important properties, in that 808 g. millán, h. kaschel, g. lefranc way highlighting the validity of its postulates and giving greater 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[68] s.b. lowen and m.c. teich, estimation and simulation of fractal stochastic point processes, fractals. complex geometry patterns and scaling in nature and society, vol. 3, no. 1, pp. 183-210, mar. 1995. international journal of computers, communications & control vol. i (2006), no. 4, pp. 126-138 generic modeling and configuration management in product lifecycle management souheïl zina, muriel lombard, luc lossent, charles henriot abstract: the plm (product lifecycle management) is often defined as a set of functions and procedures which allows one to manage and to exploit the data defining at the same time the products and the processes implemented for their developments. however, the installation of a plm solution remains a difficult exercise taking into account the complexity and the diversity of the customer requirements as well as the transverse utilization of this solution in all the company’s’ functions. the issues faced by both editors and integrators of plm applications arise from the specific aspect of customers’ projects, even tough most functional needs are often generic. in this paper we are focused on product modeling in plm applications, more particularly on configuration management that traces product evolutions throughout its lifecycle. we will insist on the links between the configuration needs and the multi-view approach models and we release problems related to plm applications deployment. our work concerns the plm generic solutions based on the concept of generic models. this generic model takes into account the configurations specification associated to the managed product and can be extended to cover specific needs. keywords: plm, configuration management, generic modeling. 1 introduction monitoring the technical information is one of the main preoccupation of the companies. indeed, the increasingly constraining regulations and the higher competition level require being more rigorous and reactive to the customers’ requests. the product quality improvement and the reduction costs cycles require applying technical data management rules and means. the plm (product lifecycle management) appeared to answer the need of managing growing volumes of data in more and more complex environments. the plm covers product life cycle integrality, without stopping itself at the design stages; it extends today to a wide scope of fields such as the aerospace-defense, food, drugs, public sector, engineering, manufacturing, etc. product term thus indicates diverse entities in very varied trades. the product life cycle management by a plm solution allows including, not only all the necessary elements to ensure its traceability, like modeling, document management, numerical analysis, know-how capitalization, etc. but all the information system components making it possible to ensure the product monitoring from its manufacture to its marketing until its disappearance or likely its recycling. in plm applications, the technical data are organized within configurations. the configuration management is used to manage products complexity and knowledge diversity resulting from various business cases in the company. indeed, the growing number of plm applications users, the technical data volume and the various evolutions associated to these data require: • controlling and checking the exchanged technical data consistency, unicity and safety, • taking into account data evolutions and all their effects on the product and its components. copyright c© 2006 by ccc publications generic modeling and configuration management in product lifecycle management 127 this is why the configuration management is a fundamental component in plm applications, making it possible to control and manage complexity related to the data. in the literature, several researches treat various types of problems around the plm. this work is especially interested in the problems related to the data exchange and sharing [32, 26], the process management problems [19, 12, 33] and the product configuration management problems [20, 21, 18, 4]. in this paper, we are centered on technical data management and particularly on configuration management. in the first part of this document, we present problems related to the configurations modeling and a treatment example on given configurations. in the second part, we will show why the plm solution deployment in a company remains a difficult exercise with the showing off of first problems. the study carried out at lascom1 consisted, starting from existing plm applications, to set up a step of reverse engineering which made it possible to formalize the concepts used in plm applications within a uml meta-model. this fundamental stage made it possible to validate the handled concepts at the conceptual level, then to underline the advantages of working on this modeling level to capitalization. 2 the product modeling in plm applications a technical object is thus a business object on which data management requirements are expressed in regard to the management and the handling of complex objects. the technical data concerns the design, the manufacture, the maintenance, the recycling and the marketing [3]. the definition of various business or technical objects and the links between them strongly depends on the company needs, organization and working methods. the diversity of plm applications (diversity related to the customer’s specificities), the data increasing complexity and the need for evolving and flexible systems (due to the evolution of the needs as time goes by) implies that there is not an universal plm application model being able to meet all the customers conflicting needs. in the literature several research tasks are interested in product modeling with different approaches and technologies: use rdf (resource description framework) [17], topic maps [16], or the orientedobject approaches [9] which aim to model and implement pdm (product data management) systems using uml (unified modeling language). 2.1 configuration management in plm applications research works are interested in methods and tools development for managing the product configuration; they deal especially with generic product modeling. in the literature, several methods, based on artificial intelligence techniques, propose solutions to solve the products configuration problems [29]. these methods are based on rules and constraints [1, 31, 10]. the product configuration is based on a configuration model (often called product generic structure); the model describes the components (called also configuration elements or objects members of a configuration) that can be included in the product configuration. the configuration model also includes the combination rules between these components. various tools, called configurators (e.g. engcon [14], wecotin [2]), make it possible to obtain a precise product description, which satisfies the needs and which is validated by the compliance with the rules of constraints defined in the configuration model. these tools can be integrated in plm applications to 1lascom (www.lascom.com) is an editor of life cycle management solutions concerning company product and processes. 128 souheïl zina, muriel lombard, luc lossent, charles henriot contribute to configure the product. in our work we are centered particularly on the configuration management. like configuration tools, the configuration management tools are also based on product models making it possible to formalize technical objects, links and constraints which express managed and traced needs. indeed, the configuration management is a discipline of management which consists in applying technical and administrative rules to the development, the production and maintenance, in all configuration article life cycle. it consists in managing the technical description of a system (and its different components), like managing the whole of the modifications made during the system evolution. the configuration management finds out an interest when it concerns a product management that have a lot of variants and a long lifecycles or unit complex products like the production system itself or special machines [22]. figure 1 presents a configuration example of a production site composition modeled in a plm application. this configuration follow the evolution of the productions lines and their adaptability to the product. figure 1: example of a production site configuration. the configuration management consists in controlling information of product structure, especially its decomposition in elementary subsets, parts and addition to the whole of this information, functional and physics characteristics. the standard [15] presents recommendations for using configuration management in industry. it provides the detailed process, organization and procedures of management. according to this standard, the configuration management is an integral part of the plm; it provides a clear vision of the configuration state, associated to products or projects, as well as theirs evolutions by guaranteeing a total traceability. configuration management tools integrate functions and mechanisms allowing audit and control of all actions carried out on the product configuration. generic modeling and configuration management in product lifecycle management 129 2.2 product multi-point of view modeling the concepts of view and point of view were studied in several fields related to the data processing: databases, analyzes and design, programming languages, etc. in the literature several research tasks integrated the concepts of view and point of view in products modeling [24, 23, 13, 25, 28], these views are generally used to express different trades’ needs on the product. figure 2 presents an example of different business views around a product. figure 2: various business views associated with a product. the notion of point of view, classically used in the literature, has for main goal the description of a complex entity having several facets. the points of view make it possible to structure information starting from various criteria related with trade or product. this make it more representative and understandable and so easier to exploit. each actor of the company handles particular view of the product that corresponds to his specific needs: functional view, technical view, industrial view, etc. there are two principal approaches which were used to take into account the actors points of view in the product model: multi-view and multi-model approaches. the multi-view approach is based on the development of a single model starting from different views. this unique model is accessible according to several points of view. the main advantage of the use of a single model is that modifications made on a sub-model are reflected in the other sub-models. consequently, the problems of inconsistency due to the division of the data between the partial models are avoided [24, 23]. this approach is very much used in works to product representation in cad (computer-aided design) systems. research of million [23] relates to the problem of designing technical information systems in an industrial multi-actors context in order to visualize information according to various views. the suggested method, called vim (viewpoints information modeling), makes it possible to build up, by successive adjustments, a total data model starting from an initial model centered on the technical object according to the points of view considered. the principal interest of the multi-view approach lies in the fact that there is a unique model to manage, which facilitates the exchanges management and information sharing. however, it leads to a static product representation because the models handled by the various actors are fixed and do not vary at the same time as the product representation [8]. the multi-model approach consists in creating a model associated with each actor’s view on the 130 souheïl zina, muriel lombard, luc lossent, charles henriot product. thus, there as exist many models as of different points of view on the product. each model contains the technical objects and the relations which correspond to the given point of view. the management of the relations between these various models imposes using a whole of coherence rules which must apply to the models whole. the multi-models approach makes possible to structure the data following specific models to each point of view on the product. these separated models can evolve independently. however, coherence maintenance and information sharing between these models are much more difficult to ensure. the problem of coherence is attenuated by the use of rules of coherence, but their identification and their formalization remain difficult. 2.3 configuration models and data processing to be coherent with the different actors needs on the product, the configuration models must take into account these concepts of view and points of view associated to the product. according to the application needs, advitiumtm, software package developed by the lascom company, define several types of configuration for a product (a given technical object). we define, for example, design configuration, documentary configuration, configuration carried out according to the stages reached in the project, etc. these configuration types correspond to different points of view on the product. each configuration is based on its own model and can evolve independently of the other types of configuration associated with the product. these configurations structure the data necessary to the product definition. thus, each actor will gather, treat on a hierarchical basis and complete the technical objects according to his own needs. it is for this purpose that various structures of the product are managed. each one corresponds to a particular configuration type. we defined the concept of “context” related to the configuration elements in order to take into account the specific use of context data. the application of a context allows defining contextual views on the configuration [34]. starting from existing plm applications, the study carried out at lascom consisted in setting a step of reverse engineering (step 1) which made it possible to formalize the concepts (step 2 & 3) used in advitiumtm within an uml meta-model [27] (this meta-model is not presented in this paper). the employed methodology is illustrated by figure 3, the plm applications deployment (step 4 & 5) is described by figure 7 section 3.1. generic modeling and configuration management in product lifecycle management 131 figure 3: the employed methodology. we developed tools for plm applications retro-modeling, these tools allow formalizing configuration links between various technical objects classes. our problematic is located in the formalization of the technical objects and links existing between them to answer: • a technical data presentation matter to user, • optimization of existing links in data base considering semantic studies produced according to customers requirements, • easy access to configuration data and an optimization of the treatments on the configuration elements. 132 souheïl zina, muriel lombard, luc lossent, charles henriot our tool allows, from advitiumtm relational databases, to generate an xsd (xml schema definition) of configurations definition. figure 4 represents the hierarchical structure of classes in an example of a technical object (ot01_site) configuration model. an example of this class instance is given by figure 1. figure 4: example of configuration xml schema. in this model, configuration elements (buildings, drawings, documents, folders, etc.) are linked between them and organized to form configurations. technical objects can be related for various reasons: to express a composition link, to make a specialization, membership, etc. links between the technical objects can be static (specific to a technical object instance) or dynamic (evolve with the technique objects versions) [4]. majority of configuration models are recursive. it is noted, in figure 4, that the class “folder” is re-used at various places in the hierarchy. this led, at the physical level, to infinite hierarchical structures. our tool, associated with graphviz [11], allows the visualization of configuration links graph of an existing plm application. this representation informs us about the configuration structure and characteristics (depth, degree of objects re-uses, etc.). figure 5 presents the graph of product study configuration. we noted that the whole of the technical objects which constitute the product structure can be represented using a dag (directed acyclic graph). each graph consists of nodes set (technical objects); these nodes are connected by links. links have properties (link type, beginning and expired dates, etc.) allowing to describe more precisely relationships among the objects and authorizing, thus, the configuration traceability evolutions. generic modeling and configuration management in product lifecycle management 133 nomenclature d’étude 1763 1648 1773 1582 1784 1681 1805 1588 1568 1576 1791 1792 1794 1795 1796 1682 1671 1672 1678 1595 1597 1567 1570 1640 1641 1552 1553 1639 1642 1564 17551738 1739 1743 1546 1511 17251729 1732 1518 1524 1713 1528 1717 1721 1932 1983 1985 19882877 28781530 1534 1722 1689 1699 1708 1624 17011700 17021710 1711 16871690 1693 16941696 16031599 1600 1601 1602 16041616 1617 1618 1619 1620 1621 16221625 1659 1631 1632 1633 16341638 1703 1704 1705 1706 1707 1691 1692 1606 1607 1608 1609 1610 1611 1612 1613 1626 1627 1628 figure 5: example of study configuration graph in existing plm applications. the nodes can contain additional informations (order, descriptions, etc.), which lead the need for defining an entity node as presented at figure 6. in order to implement applications according to customer requirements, this model includes a set of basic concepts (figure 6.a) independent of any applicability use (e.g. part, configuration, user, group, etc.). these concepts can be specialized to complet the model according to application needs (figure 6.b). the derivation of this conceptual model gives a part of the physical model (relational model) currently implemented in the advitiumtm software package. data treatments related to the configurations can be inspired from techniques and methods resulting from the graphs theories. indeed, in the configurations handling and management, certain basic user’s functions are commonly used: • search, in a given technical object configuration, the whole of descendants of this object (to obtain the configuration elements). • search, in a given technical object configuration, the whole of the ascending of this object (to obtain the employment cases). • make search with criteria related on technical objects and their links properties (e.g. validity dates). in certain research, the criteria on links properties must be checked for the whole links constitute the way between the starting and arrival technical object. in order to improve application performances related to configurations management (in particular search and navigation functions in configurations), we studied certain technical solutions which aim im134 souheïl zina, muriel lombard, luc lossent, charles henriot figure 6: a part of generic model. plementing hierarchical structures in relational databases. among these solutions, we quote the “nested sets” method [5] (well adapted to handle the trees structures in the relational databases). other research tasks are interested in the resolution of this problems type which consists in managing hierarchical data structures in relational databases [7, 30]. 3 the reference model construction plm applications are based on complex and evolving product structures. the issues faced by both editors and integrators arise from the specific aspect of customers’ projects. as company’s needs are often specific, a plm solution implementation requires heavy investments mainly regarding development aspects. these developments require huge implementation timetables and massive resources. one explanation lays on the fact that developments are very specific, and stress applied by plm solution integrators on physical aspect rather than on conceptual aspect. however, the consideration of the needs at the conceptual level, by plm applications editors, permit to capitalize knowledge related to their products engineering and to rationalize the design and development teams working methods. indeed, the knowledge capitalization helps to preserve, to share and above all to re-use know-hows generated through customers’ projects. this re-use thus allows: • offering a better times control of engineering and a more flexible software offer, • limiting plm applications maintenance costs, • facilitating the evolution plm applications and allowing a greater users autonomy. generic modeling and configuration management in product lifecycle management 135 3.1 the generic models so as to capitalize, it is recommended to study a set of reference models by sector or trade, in order to have a base of standard models or generic models. in fact, it is rather easier to particularize a model dedicated to a sector of activity than to reinvent it each time. thereby, we reveal two levels of trade: • generic level: in this level a person is in charge of creating generic models, by studying certain number of similar cases already encountered and modeled in reference models. these models can be enriched, if necessary, with generic specificities even if these specificities are not used till now but they are useful regarding the preceding businesses. • particular level: in this level a person is particularizing this generic level suited to trade to obtain the particular desired model. this model is particularized according to the specification elements. thus, this person will have to particularize a partially defined model existing, and will have only to complete this initial model so as to provide the model to be implemented in the company, consequently making a gain of time. figure 7: process of model instantiation. figure 7 represents the interest that can be disposed by generic models per activity sector to simplify the work of plm solutions integrators or developer. 3.2 functions deployment it has already been showed, compared to database modeling approaches, that a good data organization (coherent models and adapted to the needs) allows to considerably simplify the treatments and 136 souheïl zina, muriel lombard, luc lossent, charles henriot thus to improve the performances of the concerned applications. indeed, a generic implementation is often too general when it is used in a specific situation; this generalization often causes an ineffective execution. to increase the performances, this implementation must be adapted in order to keep preserving only the necessary functionalities in a specific situation. it is thus suitable to define and associate elementary functions to the generic models. these functions can be combined and organized to meet the specific needs expressed during models particularization. otherwise, for better plm application appropriation by users, a “trade translation” of the functions is also necessary. 4 summary and conclusions the product life cycle management is a recent field, the perimeter of plm applications is in constant evolution. this evolution implies that there is not a data model able to meet all the customer requirements. our work concerns the generic solutions of technical data management based on the concept of generic model. this generic model takes into account the configuration specification associated to technical objects. disposing of generic models by sector or by trade permits to facilitate the work of the plm solutions integrators or developers. this work can be done through an audit to extract the modeling invariants. it is advisable to well determine the trade sectors. thus, in the objective of the plm tools appropriation by the users, this classification propose solutions practically ready-made for its deployment in term of modeling of the technical data. this dimension allows decreasing or eliminating the specific developments. in these models, the terminology thus is well taken into account, since particularized with a given industrial sector. references [1] m. aldanondo, h. fargier, and m. véron. configuration, configurateurs et gestion de production, hermès science, traité ic2 productique, pp.179-209, 2001. 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[32] l. yesilbas, b. rose, m. lombard. specification of a repository to support collaborative knowledge exchanges in ippop project. computers in industry, vol.57, issue 8-9, pp.690-710. [33] h. zhuge : a process matching approach for flexible workflow process reuse, information and software techology, vol.44, pp.445-450, 2002. [34] s. zina, m. lombard, l. lossent. integration of contextual views in configuration management for plm applications. 9th ifac symposium on automated systems based on human skill and knowledge, 2006. souheïl zina 1,2, muriel lombard 1, luc lossent 1, charles henriot 2 e-mail: s.zina@lascom.com 1 centre de recherche en automatique de nancy umr 7039 université henri poincaré, nancy i faculté des sciences b.p. 239 54 506 vandoeuvre-lès-nancy, france 2 lascom burospace antélia 4, route de gisy 91 571 bièvres cedex, france received: november 11, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 4, pp. 349-362 generalized modus ponens using fodor’s implication and t-norm product with threshold i. iancu ion iancu university of craiova department of mathematics and computer science 13 a. i. cuza street, 200585, romania e-mail: i_iancu@yahoo.com abstract: using generalized modus ponens reasoning, we examine the values of the inferred conclusion depending on the correspondence between the premise of the rule and the observed fact. the conclusion is obtained using fodor’s implication in order to represent a fuzzy if-then rule with a single input single output and the tnorm with threshold generated by t-norm product, as a compositional operator. a comparison study with the case when the standard t-norm product is used is made. some comments and an example are presented in order to show how the obtained results can be used. keywords: t-norm, t-conorm, negation, implication, fuzzy number, generalized modus ponens rule 1 introduction the database of a rule-based system may contain imprecisions which appear in the description of the rules given by the expert. the imprecision implies the difficulty of representing the rules expressed, generally, by means of natural language. another difficulty is the utilization of these rules in approximate reasoning when the observed facts do not match the condition of the rule. in order to obtain an imprecise conclusion from imprecise premises, zadeh extends the traditional modus ponens rule obtaining generalized modus ponens (gmp). an investigation of gmp inference was made by many papers: [2], [3], [4], [5], [7], [9], [14], [15], [27], [28], [29], [30], [33], [34], [35]. also, we analyzed this type of inference in some papers: [19] , [22], [24] , [25], [26]. the proposition x is a can be understood as the quantity x satis f ies the predicate a or the variable x takes its values in the set a. the semantic content of the proposition x is a can be represented by πx = µa, where πx is the possibility distribution restricting the possible value of x and µa is the membership function of the set a. because the majority of practical applications work with trapezoidal or triangular distributions and these representations are still a subject of various recent papers ([1], [13] and [16], for instance) we copyright c© 2006-2009 by ccc publications 350 i. iancu will work with membership functions represented by trapezoidal fuzzy numbers. such a number n = (a, b, α, β ) is defined as µn (x) =     f or x < a − α x − a + α α f or x ∈ [a − α, a]  f or x ∈ [a, b] b + β − x β f or x ∈ [b, b + β ]  f or x > b + β let x and y be two variables whose domains are u and v , respectively. a causal link from x to y is represented as a conditional possibility distribution [35, 36] πy /x which restricts the possible values of y for a given value of x . for the rule i f x is a then y is b we have ∀ u ∈ u, ∀ v ∈ v, πy /x (v, u) = µa(u) → µb(v) where → is an implication operator and µa and µb are the possibility distributions of the propositions "x is a" and "y is b", respectively. if µa′ is the possibility distribution of the proposition x is a′ then from the rule i f x is a then y is b and the fact x is a′ generalized modus ponens rule computes the possibility distribution µb′ of the conclusion y is b′ as µb′ (v) = sup u∈u t ( µa′ (u) , πy /x (v, u) ) , where t is a t-norm. 2 basic concepts the main concepts used in gmp are presented below, using the terminology of [8], [17] and [32]. definition 1. a function t : [, ] → [, ] is a t-norm iff it is commutative, associative, non-decreasing and t (x, ) = x ∀x ∈ [, ]. definition 2. a function s : [, ] → [, ] is a t-conorm iff it is commutative, associative, non-decreasing and s(x, ) = x ∀x ∈ [, ]. definition 3. a function n : [, ] → [, ] is a strong negation iff it is an involutive and continuous decreasing function from [, ] to itself. generalized modus ponens using fodor’s implication and t-norm product with threshold 351 in order to represent a rule, the notion of fuzzy implication is used. we recall an axiomatic approach (formulated by fodor in [10, 11, 12]) to the definition of fuzzy implication. definition 4. an implication is a function i : [, ] → [, ] satisfying the following conditions: i1: if x ≤ z then i(x, y) ≥ i(z, y) for all x, y, z ∈ [, ] i2: if y ≤ z then i(x, y) ≤ i(x, z) for all x, y, z ∈ [, ] i3: i(, y) =  (falsity implies anything) for all y ∈ [, ] i4: i(x, ) =  (anything implies tautology) for all x ∈ [, ] i5: i(, ) =  (booleanity) the following properties could be important in some applications: i6: i(, x) = x (tautology cannot justify anything) for all x ∈ [, ] i7: i(x, i(y, z)) = i(y, i(x, z)) (exchange principle) for all x, y, z ∈ [, ] i8: x ≤ y if and only if i(x, y) =  (implication defines ordering) for all x, y ∈ [, ] i9: i(x, ) = n(x) for all x ∈ [, ] is a strong negation i10: i(x, y) ≥ y for all x, y ∈ [, ] i11: i(x, x) =  (identity principle) for all x ∈ [, ] i12: i(x, y) = i(n(y), n(x)) for all x, y ∈ [, ] and a strong negation n i13: i is a continuous function. the most important families of implications are given by definition 5. a s-implication associated with a t-conorm s and a strong negation n is defined by is,ns (x, y) = s(n(x), y) ∀x, y ∈ [, ] a r-implication associated with a t-norm t is defined by itr(x, y) = sup{z ∈ [, ]|t (x, z) ≤ y} ∀x, y ∈ [, ] a ql-implication is defined by it,s,nql (x, y) = s(n(x), t (x, y)) ∀x, y ∈ [, ] one of the most important implications is the fodor’s implication if (x, y) = {  i f x ≤ y max( − x, y) otherwise which is [5] a r-implication for t = min, a s -implication for s = max and a ql-implication for t = min and s = max, where min (x, y) = {  i f x + y ≤  min(x, y) i f x + y >  and max (x, y) = {  i f x + y ≥  max(x, y) i f x + y <  352 i. iancu and n(x) =  − x. besides, the fodor’s implication verifies the properties i1-i12. an important class of t-norms (t-conorms) is given by the t-norms (t-conorms) with thresholds, obtained from standard t-norms (t-conorms); the number of thresholds is an integer n ≥ . first example of operators with 1-threshold were given by pacholczyk in [31]. various families of such t-operators can be found in [18, 20, 21, 23], where the advantage of their usage to represent the uncertain knowledge is justified. in this paper we analyze the results obtained by reasoning with imprecise knowledge using a t-norm with threshold as a composition operator. finally we will compare these results with those obtained using the corresponding standard operators. we consider the following t-norm with a single threshold k ∈ (, )[31] tk(x, y) = { k  − k t (  − k k x,  − k k y) i f x ≤ k and y ≤ k min(x, y) i f x > k or y > k obtained from the t-norm t (x, y). we will work with the t-norm generated by tp(x, y) = xy, which is one of the most used; it results tk(x, y) = {  − k k xy i f x ≤ k and y ≤ k min(x, y) i f x > k or y > k 3 main results taking into account the following reasons, we shall work with rules having a single input single output: a) a rule with multiple consequent can be treated as a set of rules with a single conclusion; for instance, the rule i f antecedent then c and c and ....... and cn is equivalent to the rules i f antecedent then c i f antecedent then c ................................. i f antecedent then cn. b) a rule with multiple premise can be broken up into simple rules [6] when the rules are represented with any s-implication or any r-implication and the observations are normalized fuzzy sets. our aim is to obtain the conclusion "y is b′" from the rule i f x is a then y is b and the fact x is a′ where the fuzzy sets a, a′, b and b′ are represented by trapezoidal possibility distributions. the set b′ is computed as µb′(v) = sup u∈u tk(µa′(u), if (µa(u), µb(v))), analyzing five cases, depending on the relation between µa and µa′. generalized modus ponens using fodor’s implication and t-norm product with threshold 353 theorem 6. if the premise contains the observation, i. e. µa′(u) ≤ µa(u) ∀u ∈ u , then µb′(v) = µb(v) i f µb(v) ≥ . µb′(v) ∈ [µb(v),  − µb(v)) i f µb(v) < . proof. i1) value on the set u = {u ∈ u /µa(u) ≤ µb(v)} because if (µa(u), µb(v)) = , we have µb′(v) = sup u∈u tk(µa′(u), ) = sup u∈u µa′(u) ≤ µb(v). i2) value on the set u = {u ∈ u /µa(u) > µb(v) ≥ .}∪{u ∈ u /µa(u) >  − µb(v) > .} we have if (µa(u), µb(v)) = µb(v). if k < µb(v) then µb′(v) = sup u∈u tk(µa′(u), µb(v)) = sup u∈u min(µa′(u), µb(v)) = µb(v). for k ≥ µb(v) and u  = {u ∈ u/µa′(u) ≤ k} we have µb′(v) = sup u∈u  tk(µa′(u), µb(v)) = sup u∈u   − k k µa′(u)µb(v) ≤ ( − k)µb(v) < µb(v). for k ≥ µb(v) and u  = {u ∈ u/µa′(u) > k} we obtain µb′(v) = sup u∈u  tk(µa′(u), µb(v)) = sup u∈u  min(µa′(u), µb(v)) = µb(v). i3) value on the set u = {u ∈ u /µb(v) < µa(u) ≤  − µb(v)} in this case if (µa(u), µb(v)) =  − µa(u) and therefore µb′(v) = sup u∈u tk(µa′(u),  − µa(u)). for k < µb(v) we have  − µa(u) ≥ µb(v) > k and tk ≡ min. it results µb′(v) = sup u∈u min(µa′(u),  − µa(u)) <  − µb(v). for µb(v) ≤ k ≤  − µb(v) we analyze the cases: i): value on the set u  = {u ∈ u /µb(v) ≤ µa(u) <  − k} because k <  − µa(u) we obtain µb′(v) = supu∈u  tk(µa′(u),  − µa(u)) = supu∈u  min(µa′(u),  − µa(u)) < min( − k,  − µb(v)) =  − k. i): value on the set u  = {u ∈ u /µb(v) <  − k ≤ µa(u) ≤  − µb(v)} in this case,  − µa(u) ≤ k and we study three possibilities, depending on µa′(u). i): on the set u ,  = {u ∈ u  /µa′(u) = } we obtain µb′(v) =  i): on the set u ,  = {u ∈ u  /µa′(u) ∈ (, k]} we have µb′(v) = sup u∈u ,  − k k µa′(u)( − µa(u)) < k( − k). 354 i. iancu i): on the set u ,  = {u ∈ u  /µa′(u) > k} we get µb′(v) = sup u∈u , min(µa′(u),  − µa(u)) <  − µb(v). for k >  − µb(v) we consider the set u  = {u ∈ u/µb(v) >  − k} = {u ∈ u / − k < µb(v) < µa(u) ≤  − µb(v)} and we work with the subsets of u  for which µa′(u) = , µa′(u) ∈ (, k] and µa′(u) > k, respectively; we obtain the following corresponding results: µb′(v) = , µb′(v) < k( − k) and µb′(v) <  − µb(v). synthesizing the previous results, one obtain the conclusion formulated in the theorem. theorem 7. if the premise and the observation coincide, i. e. µa(u) = µa′(u) ∀u ∈ u , then µb′(v) = µb(v) i f k > . and µb(v) ≥  − k, µb′(v) ∈ [µb(v),  − k) i f k > . and µb(v) <  − k, µb′(v) = max(., µb(v)) i f k ≤ .. proof. in this case one repeat the proof of the theorem 6 taking account the equality µa(u) = µa′(u) ∀u ∈ u . it results: 1) if . < k ≤ µb(v) then µb′(v) = µb(v) 2) if k ≤ . ≤ µb(v) then µb′(v) = µb(v) 3) if k ≤ µb(v) < . then µb′(v) = . 4) if µb(v) ≤ k ≤ . then µb′(v) = . 5) if . ≤ µb(v) < k then µb′(v) = µb(v) 6) if µb(v) ≤ . < k then µb′(v) = µb(v) i f µb(v) ≥  − k and µb′(v) ∈ [µb(v),  − k) i f µb(v) <  − k from which we get the conclusion. theorem 8. if the observation contains the premise, i. e. µa(u) ≤ µa′(u) ∀u ∈ u , then µb′(v) ≥ max(µb(v),  − k k µb(v)( − µb(v))) i f µb(v) ≤ min(., k) µb′(v) ≥ µb(v) otherwise. proof. i1) value on the set u = {u ∈ u /µa(u) ≤ µb(v)} because if (µa(u), µb(v)) =  we have µb′(v) = sup u∈u min(µa′(u), ) = sup u∈u µa′(u) ≥ µb(v). i2) value on the set u = {u ∈ u /. ≤ µb(v) < µa(u)}∪{u ∈ u /µa(u) >  − µb(v) > .} in this case if (µa(u), µb(v)) = µb(v) and i) for k < µb(v) we obtain µb′(v) = sup u∈u tk(µa′(u), µb(v)) = sup u∈u min(µa′(u), µb(v)) = µb(v) i) for k ≥ µb(v) we consider two subsets of u: generalized modus ponens using fodor’s implication and t-norm product with threshold 355 i) on the subset u   = {u ∈ u/µa′(u) ≤ k} we have µb′(v) = sup u∈u  tk(µa′(u), µb(v)) = sup u∈u   − k k µa′(u)µb(v)) ≤ ( − k)µb(v) < µb(v) i) on the subset u   = {u ∈ u/µa′(u) > k} we have µb′(v) = sup u∈u  tk(µa′(u), µb(v)) = sup u∈u min(µa′(u), µb(v)) = µb(v). i3) value on the set u = {u ∈ u /µb(v) < µa(u) ≤  − µb(v)} . in this case if (µa(u), µb(v)) =  − µa(u) and we analyze the following cases. i) if k < µb(v) then µb′(v) = sup u∈u tk(µa′(u),  − µa(u)) = min(µa′(u),  − µa(u)) <  − µb(v). ii) if k ≥ µb(v) we consider the following subcases: ii) µb(v) ≤ k ≤  − µb(v) ii) on the set u   = {u ∈ u/µb(v) ≤ µa(u) <  − k} we have µb′(v) = sup u∈u  min(µa′(u),  − µa(u)) <  − µb(v) ii) on the set u   = {u ∈ u/ − k ≤ µa(u) ≤  − µb(v)} we consider two subsets: • u , = {u ∈ u  /µa′(u) ≤ k} for which we obtain µb′(v) = supu∈u ,  − k k µa′(u)( − µa(u)) ≥ supu∈u ,  − k k µa(u)( − µa(u)) ≥ max(( − k),  − kk µb(v)( − µb(v))) ≥  − k k µb(v)( − µb(v)) • u , = {u ∈ u  /µa′(u) > k} for which we have µb′(v) = sup u∈u , min(µa′(u),  − µa(u)) <  − µb(v). ii) k >  − µb(v) which defines the set u  = {u ∈ u/ − k < µb(v)} = {u ∈ u / − k < µb(v) < µa(u) ≤  − µb(v)} • for µa′(u) ≤ k we obtain µb′(v) = supu∈u   − k k µa′(u)( − µa(u)) ≥ supu∈u   − kk µa(u)( − µa(u)) ≥  − k k µb(v)( − µb(v)) • for µa′(u) > k it results µb′(v) = sup u∈u  min(µa′(u),  − µa(u)) <  − µb(v). finally we obtain the conclusion formulated in the theorem. theorem 9. if there is a partial overlapping between the sets a and a′ then µb′(v) =  i f core(a′)∩(u − aµb(v)) 6= /0 and µb′(v) ≥ µb(v) otherwise where aα denotes the α -cut of a. 356 i. iancu proof. i1) the case core(a′)∩(u − aµb(v)) 6= /0. on the set u = {u ∈ u /µa(u) ≤ µb(v)} we have if (µa(u), µb(v)) =  and therefore µb′(v) = sup u∈u tk(µa′(u), ) = . i2) the case core(a′)∩(u − aµb(v)) = /0. on the set u = {u ∈ u /µa(u) > µb(v) ≥ .} we have if (µa(u), µb(v)) = µb(v) and therefore µb′(v) = sup u∈u tk(µa′(u), µb(v)) ≥ tk(, µb(v)) = µb(v). if µb(v) < . we analyze three cases. let ũ = {u ∈ u /µa(u) = µb(v)}; card(ũ ) =  if  < µb(v) < . i) the case ũ ∩supp(a′) = /0 and core(a′)∩core(a) 6= /0. on the set u = {u ∈ u /µa(u) ≥  − µb(v) > .} it results µb′(v) = sup u∈u tk(µa′(u), µb(v)) ≥ tk(, µb(v)) = µb(v). i) the case ũ ∩supp(a′) = /0 and core(a′)∩core(a) = /0. we consider the set u = {u ∈ u /µb(v) < µa(u) ≤  − µb(v)}; on the set u = u ∪u we have µb′(v) = sup u∈u tk(µa′(u), if (µa(u), µb(v))) ≥ sup u∈u tk(µa′(u), µb(v)) ≥ tk(, µb(v)) = µb(v). i) the case ũ ∩supp(a′) 6= /0. on the set u we obtain µb′(v) ≥ µb(v), as in the previous case. it results that, in the case i2), µb′(v) ≥ µb(v). the same result is obtained for µb(v) ∈ {, }. we consider the negation with threshold k ∈ (, ) [31] nk(x) = {  −  − kk x i f x ≤ k k  − k ( − x) i f x ≥ k obtained from the standard negation n(x) =  − x. theorem 10. if the premise and the observation are contradictory, i.e. µa′(u) = nk(µa(u)) ∀u ∈ u , then µb′(v) =  ∀v ∈ v. proof. on the set u = {u ∈ u /µa(u) ≤ µb(v)} we have µb′(v) = sup u∈u tk(µa′(u), ) = sup u∈u min(µa′(u), ) = sup u∈u µa′(u) = sup u∈u nk(µa(u)) =  because there is u ∈ u with µa(u) = . 4 interpretation and utilization of results in this section we will compare the results given by the common operators (t-norm product tp(x, y) = xy and negation n(x) =  − x) with those obtained by the corresponding operators with threshold and we will indicate some possibility of their utilization in a fuzzy reasoning system. an example of working with these results is also presented. in the case of standard operators tp and n, according to [24] we have: generalized modus ponens using fodor’s implication and t-norm product with threshold 357 theorem 11. if the premise contains the observation, i.e µa′(u) ≤ µa(u) ∀u ∈ u , then µb′ (v) = µb(v) i f µb(v) ≥ . or (. ≤ µb(v) < .) µb′ (v) < . i f µb(v) < . theorem 12. if the premise and the observation coincide, i.e. µa(u) = µa′(u) ∀u ∈ u , then µb′ (v) = max(µb(v), .) theorem 13. if the observation contains the premise, i.e. µa(u) ≤ µa′(u) ∀u ∈ u , then µb′(v) ≥ µb(v) ∀v ∈ v. theorem 14. if there is a partial overlapping between the sets a and a′, then µb′(v) =  i f core(a′)∩(u − aµb(v)) 6= /0 and µb′(v) ≥ µb(v) otherwise where aα denotes the α−cut of a. theorem 15. if the premise and the observation are contradictory, i.e. ∀u ∈ u µa′(u) =  − µa(u), then µb′(v) =  ∀v ∈ v . if the observation is more precise than the premise of the rule then it gives more information than the premise. however, it does not seem reasonable to think that the generalized modus ponens allows to obtain a conclusion more precise than that of the rule. the result of the inference is valid if µb′(v) = µb(v), ∀v ∈ v . sometimes, the deduction operation allows the reinforcement of the conclusion, as is specified in [28], [19] and [25]: rule: if the tomato is red then the tomato is ripe. observation: this tomato is very red. if we know that the maturity degree increases with respect to color, we can infer "this tomato is very ripe". on the other hand, in the example rule: if the melon is ripe then it is sweet observation: the melon is very ripe we do not infer that "the melon is very sweet" because it can be so ripe that it can be rotten. this examples show that if the expert has not supplementary information about the connection between the variation of the premise and the conclusion, he must be satisfied with the conclusion µb′(v) = µb(v). the theorem 6 gives a valid result if we choose µb′(v) = µb(v) for µb(v) < .. as opposite, the corresponding theorem 11 from the case of the standard t-norm tp does not allow to obtain a valid result if µb(v) < .. when the observation and the premise of the rule coincide the convenient behavior of the fuzzy deduction is to obtain an identical conclusion. a different conclusion indicates the appearance of an uncertainty in the conclusion. the both theorems, 7 and 12, give an uncertain conclusion, but we can choose k > . in the theorem 7 and we obtain a better result, because the uncertainty is smaller in comparison with the result from the theorem 12. if the observation contains the premise, because max(  − kk µb(v)( − µb(v)), µb(v)) ≥ µb(v) it results that theorem 8 gives a better result that theorem 13. in this case the inferred conclusion b′ is a superset of b; we can choose the first superset. 358 i. iancu if there is a partial overlapping between the premise and the observation or the premise and the observation are contradictory then the two t-norms give the same results for the inferred conclusion. the value µb′(v) =  obtained in these cases represents an indeterminate conclusion, all elements v ∈ v having a possibility equal to 1. in the case of "partial overlapping" we propose a "mediation" between the two possible values: µb′(v) =  and µb′(v) ≥ µb(v); if b, b, ..., bk are the supersets of b with µbk (v) ≥ µbk− (v) ≥ .... ≥ µb (v), we can choose b′ = b [ k  ] , where [x] is the greatest integer which is smaller than or equal to x. the theorem 10 gives a waited result, that represents one of the basic properties of gmp reasoning. the results from theorems 6-10 can be used in a fuzzy inference system as in the following example. a customer is interested to buy a computer. the quality of the computer depends on its price as is specified by the rules: rule1: if the price is very low then the quality is below average rule2: if the price is very very high then the quality is very good. rule3: if the price is middle then the quality is good. the variable price has values in the following set of linguistic terms lp = {very very low, very low, low, midle, high, very high, very very high} and the variable quality has values in the set lq = {poor, below average, average, above average, good, very good}. we consider the universes of discourse [, ] for price and [, ] for quality. the linguistic terms are represented by the following trapezoidal fuzzy numbers: very very low = (, , , ) very low = (, , , ) low = (, , , ) middle = (, , , ) high = (, , , ) very high = (, , , ) very very high = (, , , ) poor = (, , , ) below average = (, , , ) average = (., ., ., .) above average = (, , , ) good = (., ., ., .) very good = (., ., ., .). these fuzzy numbers are depicted in the figures 1 and 2. we consider the observations: observation1: the price is very very low observation2: the price is very high observation3: the price is high the theorems 6-10, used together with the comments from this section, give the following results: 1) the conclusion obtained from rule1 and observation1 is "the quality is below average"; this result is obtained with theorem 6 2) theorem 8 is applied for rule2 and observation2 and gives the conclusion "the quality is good" generalized modus ponens using fodor’s implication and t-norm product with threshold 359 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 0 0.2 0.4 0.6 0.8 1 de gr ee o f m em be rs hi p v.v.low vlow low v.v.highmiddle high vhigh figure 1: fuzzy sets for linguistic terms from the list lp 0 1 2 3 4 5 6 7 8 9 10 0 0.2 0.4 0.6 0.8 1 de gr ee o f m em be rs hi p poor baverage average aaverage good vgood figure 2: fuzzy sets for linguistic terms from the list lq 3) using theorem 9 for the rule3 and observation3 one obtain the conclusion "the quality is average". as it can be observed from this example, our results allow us to obtain the inferred conclusion by a very simple calculus in comparison with the standard formula used in gmp. 5 summary and conclusions the results obtained in this paper explain how the generalized modus ponens rule works with the fodor’s implication and the t-norm product with threshold. combining these results with the approximations proposed in the previous section we obtain a fast answer for the value of the conclusion inferred by gmp reasoning. we worked with the t-norm product because it is one of the most used in practical applications. as it results from the previous sections, one obtain better results in the case of t-norm with threshold. in a future paper we will analyze the results given by another t-norms with threshold and 360 i. iancu another implications. bibliography [1] t. allahviranloo, m. adabitabar firozja, note on “trapezoidal approximation of fuzzy numbers”, fuzzy sets and systems 7, 2007, 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[36] l. a. zadeh, a theory of approximate reasoning, machine intelligence (eds: j. e. hayes, d. mikie and l. i. mikulich), john wiley & sons, new york, 1979, pp. 149–194. 362 i. iancu ion iancu received his degree in mathematics from the university of craiova and ph. d. in artificial intelligence from the university of bucharest. currently, he is titular professor of computer science and artificial intelligence at the university of craiova. his main topics of interest include fuzzy sets, modeling of uncertain and imprecise knowledge, approximate reasoning, soft computing. a several number of research articles in the above areas have also been published in various journals and proceedings; also, he is the author of eight books. international journal of computers, communications & control vol. i (2006), no. 2, pp. 85-94 coordinated control of mobile robots based on artificial vision carlos m. soria, ricardo carelli, rafael kelly, juan m. ibarra zannatha abstract: this work presents a control strategy for coordination of multiple robots based on artificial vision to measure the relative posture between them, in order to reach and maintain a specified formation. given a leader robot that moves about an unknown trajectory with unknown velocity, a controller is designed to maintain the robots following the leader at a certain distance behind, by using visual information about the position of the leader robot. the control system is proved to be asymptotically stable at the equilibrium point, which corresponds to the accomplishment of the navigation objective. experimental results with two robots, a leader and a follower, are included to show the performance of the vision-based control system. keywords: mobile robots, coordinated robots, vision based control, artificial vision. 1 introduction during the last years, efforts have been made to give autonomy to single mobile robots by using different sensors, actuators and advanced control algorithms. this was mainly motivated by the necessity to develop complex tasks in an autonomous way, as demanded by service or production applications. robots have thus become highly sophisticated systems. in some applications, a valid alternative (or even the mandatory solution) is the use of multiple simple robots which, operating in a coordinated way, can develop complex tasks ([1]; [2]; [7]). this alternative offers additional advantages, in terms of flexibility in operating the group of robots and failure tolerance due to redundancy in available mobile robots [6]. as the number of robots increases, the task of controlling the system becomes more complex. control strategies can be classified as centralized and decentralized. in a centralized control system, all planning and control functions are developed in a single control unit. each mobile robot has few simple sensors, the actuators and the communication system with the single control unit. every motion and conflict between robots is solved by the control unit. nevertheless, the system becomes vulnerable to any failure that may occur in the control unit. on the other hand, with the decentralized control approach, each robot is equipped with multiple sensors and a controller thus becoming capable of recognizing the environment and taking its own control actions ([3]; [5]). the function of the control center -if availableis to assign tasks to each robot and to govern the information flow in the system. the coordinated control of robots allows a team of robots to perform missions not easy or viable to be achieved by a single robot. the guidance of a mobile robot requires its localization in the environment [4]. a precise localization is needed when multiple robots share a common environment. odometric sensors, sonar sensors, gyros, laser and vision and its fusion are commonly used for robot localization and environment modeling. vision sensors are increasingly applied because of its ever-growing capability to capture information. this work proposes a strategy based on capturing vision information with single cameras mounted on the follower robots, to obtain the relative posture of a leader robot and to guide a specified robots formation. the controller design is then obtained using non linear control theory, and the overall control system is proved to be asymptotically stable. the paper is organized as follows. the concept of robot formation is briefly discussed in section 2 and the robot kinematics model in section 3. the visual measurement of the posture of a leader robot is presented in section 4. the controller design and the stability analysis are given in section 5. some experimental results are discussed in section 6. finally, section 7 presents some concluding comments. copyright c© 2006 by ccc publications 86 carlos m. soria, ricardo carelli, rafael kelly, juan m. ibarra zannatha 2 robots formation in this work the following formation strategy is considered. one of the robots is defined as the leader robot, and moves in an unknown trajectory to the other follower robots. the leader has a pattern mounted on its back, which is observed by the follower robots to obtain information about their relative posture to the leader. this information is used to control their position to reach the specified formation. the visual information is obtained by a looking ahead camera mounted on each follower robot. figure 1 shows typical triangle and convoy configurations that can be defined for three robots by using this strategy. figure 1: a) triangle formation. b) convoy formation. 3 mobile robot model in this work it is considered the unicycle-like mobile robot, with is described by the following kinematics equations: . x = v cos ϕ (1) . y = v sin ϕ . θ = ω where (x, y) are the cartesian coordinates of robot position, and ϕ the robot heading or orientation angle; v and ω are the robot’s linear and angular velocities. the non-holonomic restriction for model (1) is . y cos ϕ − .x sin ϕ = 0 (2) which specifies the tangent trajectory along any feasible trajectory for the robot. the reference point of the robot is assumed to be the middle point between the two driven wheels, fig. 2. v1 and v2 denote linear speeds of the left and the right wheels, respectively. linear and angular velocities of the robot can be expressed as v = (v1 + v2)/2 and ω = (v1 − v2)/l respectively, where l represents the distance between the two driven wheels. coordinated control of mobile robots based on artificial vision 87 figure 2: geometric description of the mobile robot. 4 visual measurement of the leader posture the control objective is that the follower robots follow the leading robot evolving with unknown motion in the working area. the robots have been equipped with a looking-ahead fixed vision camera. this camera captures the image of a pattern mounted on the leading vehicle that features four marks on a square of known side length e[m]. in order to ease the calculations, and without losing generality, the height of the horizontal median of this square is made to coincide with the height of the image’s camera center. the projected pattern on the camera image will appear with a projection distortion as represented in fig. 3. the positions of the pattern’s marks on the image are expressed in pixels as (xi, yi) with i = a, b,c and d. these variables are considered as the image features: figure 3: image of the pattern’s marks. from these image features, as measured by the vision system, it is possible to compute the posture of the leading or target vehicle (xt , zt , ϕt ), measured on a coordinate system associated to the camera (xc, zc). fig. 4 shows a diagram with the horizontal projection of the vision system, showing the posture of the leader or target vehicle on the camera’s coordinates of a follower robot, from which the following expressions are obtained: 88 carlos m. soria, ricardo carelli, rafael kelly, juan m. ibarra zannatha xt = xr + xl 2 zt = zr + zl 2 (3) ϕt = cos−1( xr −xl e ) by resorting now to the model of reverse perspective, expressions are obtained to compute the leading vehicle’s posture of (3) as a function of the measurements supplied by the vision system, fig.4: xl = zl − f f xa, xr = zr − f f xb (4) zl = f ( e hl + 1 ) , zr = f ( e hr + 1 ) using the variables from (3) and (4), and assuming that the camera is mounted on the robot’s center, the relative posture of the follower robot with respect to the leading robot (ϕ, θ , d) can then be calculated. the relative posture is defined as in figs. 4 and 5. these variables are calculated by the following expressions: φ = tan−1( zr −zl xr −xl ) ϕ = tan−1 ( xt zt ) (5) θ = ϕ + φ d = √ x2t + z 2 t figure 4: location of the leader respecting the camera. coordinated control of mobile robots based on artificial vision 89 figure 5: relative position between the leader and a follower robot. 5 servo visual control of the mobile robot 5.1 controller figure 6 defines the control structure to be used in this work, where h represents the relationship between the variables defining the relative posture of the follower and the leading robot, and the image features ξ . figure 6: control structure. the control objective is defined as follows: assuming a case where a leading robot moves along an unknown trajectory, with unknown velocity as well, make the follower robot keep a desired distance dd to the leader and pointing to it (that is ϕd = 0), using only visual information, fig. 5. more specifically, the control objective can be expressed as lim t→∞ e(t) = lim t→∞ (dd −d) = 0 (6) lim t→∞ ϕ̃(t) = lim t→∞ (ϕd −ϕ) = 0 the evolution of the posture of the follower robot relative to the leader will be stated by the time derivative of the two error variables. the variation of distance error is given by the difference between the projection of the leader’s velocity and the follower robot velocity on the line connecting both vehicles, that is: 90 carlos m. soria, ricardo carelli, rafael kelly, juan m. ibarra zannatha . e = −vt cos θ + v cos ϕ̃ (7) likewise, the variation of angle error has three terms: the angular velocity of the follower robot, and the rotational effect of the linear velocities of both robots, which can be expressed as: . ϕ̃ = ω + vt sin θ d + v sin ϕ̃ d (8) for the system dynamics expressed by (7) and (8), the following nonlinear controller is proposed to achieve the control objective given in (6), v = 1 cos ϕ̃ (vt cos θ − f (e)) (9) ω = − f (ϕ̃)−vt sin θ d −v sin ϕ̃ d in (9), f (e), f (ϕ̃) ∈ ω , with ω the set of functions that meet the following definition: ω = { f : ℜ → ℜ/ f (0) = 0 and x f (x) > 0 ∀x ∈ ℜ} in particular, the following functions are considered: f (e) = ke tanh(λee) and f (ϕ̃) = kϕ̃ tanh(λϕ̃ ϕ̃). these functions prevent the control actions becoming saturated. the variables used by this controller (θ , ϕ, d) as given by (5) are calculated from the image captured by the vision system. by combining (7), (8) and (9), the closed-loop system is obtained: . e = − fe(e) (10) . ϕ̃ = − fϕ̃ (ϕ̃) 5.2 stability analysis considering the system of (10) with its single equilibrium point at the origin, the following lyapunov candidate function, v = e2 2 + ϕ̃ 2 2 (11) has a time-derivative on the system trajectories given by . v = −e f (e)−ϕ̃ f (ϕ̃) (12) it is then concluded the asymptotic stability of the equilibrium, that is: e(t) → 0, ϕ̃(t) → 0, with t → ∞. it should be noted that the controller of (9) requires knowing the linear velocity vt of the leader vehicle. this variable should be estimated with the available visual information. by approximating the derivative of the position error as given in (7) by the discrete difference between successive positions, and considering a 0.1s sampling period, the objective velocity can be approximated as follows: v̂t = (dk −dk−1)/0.1 + v cos ϕ̃ cos θ coordinated control of mobile robots based on artificial vision 91 6 experiments in order to evaluate the performance of the proposed coordination control algorithm, experiments were carried out with two pioneer 2dx mobile robots (fig. 7). each robot has its own control system. the vision system includes a frame grabber pxc200 that allows capturing the images from a camera sony ev-d30 mounted on the follower robot. these images are transmitted from the follower robot to a pentium ii-400 mhz pc, in charge of processing the images and of calculating the corresponding control actions. figure 8(a) shows the image captured by the camera; this image is processed to obtain the image shown in fig. 8(b). from this image, the centroids of the four projected pattern’s marks are calculated and used to compute the variables (θ , ϕ, d) needed by the controller. finally, the computed control actions are sent by a transmitter to the follower robot. for the experiences, the following parameter values were used: ke = 200, λe = 0.005, kϕ̃ = 10 and λϕ̃ = 0.1. the follower robot has to follow the leading robot by keeping a desired distance of dd = 0.50m and ϕd = 0. figure 9 shows the evolution of the distance between both robots. figure 10 shows the evolution of angles ϕ and θ . from these figures, the accomplishment of the control objective can be verified. fig. 11 depicts the control actions that are calculated and sent to the follower robot. the estimation of the leader robot’s velocity is shown in fig. 12, and the trajectory of the follower robot is depicted in fig. 13. figure 7: robots used in the experiment. figure 8: (a) image captured by the robot’s camera. (b) image processed to find the centroid of each mark. 92 carlos m. soria, ricardo carelli, rafael kelly, juan m. ibarra zannatha figure 9: evolution of the distance to the objective. figure 10: evolution of angles ϕ and θ . figure 11: calculated control actions. coordinated control of mobile robots based on artificial vision 93 figure 12: estimated velocity of the objective. figure 13: trajectory followed by the follower robot. 7 conclusions in this work, a non-linear vision-based controller for coordinated motion of mobile robots following a leader robot has been presented. the nonlinear controller has been designed with state dependent control gains that allow avoiding the saturation of the control actions. by using the lyapunov method, it has been proven that the resulting control system is asymptotically stable. through experiences, it has been demonstrated that the proposed control system accomplishes the control objective with a good performance, leading to a specified formation in which the follower robots follow a leader robot by using visual information. 8 acknowledgment this work was partially supported by anpcyt and conicet, argentina. the authors thank the science and technology for development program (cyted) for promoting the research cooperation between their groups. references [1] r. alami, multirobot cooperation in the martha project, sieee robotics and automation magazine, vol. 5, no. 1, pp. 36-47, 1998. 94 carlos m. soria, ricardo carelli, rafael kelly, juan m. ibarra zannatha [2] r. c. arkin, integrating behavioural, perceptual and world knowledge in reactive navigation, journal of robotics and autonomous systems, vol. 6, no. 1, pp. 36-47, 1990. [3] h. asama, operation of cooperative multiple robots using communication in a decentralised robotic system, proc. conf. from perception to action, switzerland, 5-7 sept., 1994. [4] j. borestein, navigating mobilerobots systems and techniques, a k peters, wesley, ma, usa, 1996. [5] r. a. brooks, a robust layered control system for a mobile robot, ieee journal of robotics and automation, vol. 2, no. 1, pp. 14-23, 1986. [6] y. ishida, functional complement by co-operation of multiple autonomous robots, proc. ieee int. conf. on robotics and automation, pp. 2476-2481, 1994. [7] m. j. mataric, learning in multi-robot systems, lecture notes in artificial intelligence – adaptation and learning in multi-agent systems, vol. 10, 1996. carlos m. soria, ricardo carelli universidad nacional de san juan instituto de automática av. san martín oeste 1109 5400, san juan, argentina e-mail: csoria@inaut.unsj.edu.ar, rcarelli@inaut.unsj.edu.ar rafael kelly centro de investigación científica y de educación superior de ensenada ensenada, baja california, 22800 méxico e-mail: rkelly@cicese.mx juan m. ibarra zannatha laboratorio de robótica del departamento de control automático centro de investigación y de estudios avanzados av. ipn n 2508, lindavista, 07360 méxico, df e-mail: jibarra@ctrl.cinvestav.mx international journal of computers, communications & control vol. ii (2007), no. 2, pp. 132-142 predictive control of a wastewater treatment process sergiu caraman, mihaela sbarciog, marian barbu abstract: the paper deals with the design of a predictive controller for a wastewater treatment process. in the considered process, the wastewater is treated in order to obtain an effluent having the substrate concentration within the standard limits established by law (below 20 mg/l). this goal is achieved by controlling the concentration of dissolved oxygen to a certain value. the predictive controller uses a neural network as internal model of the process and alters the dilution rate in order to fulfill the control objective. this control strategy offers various possibilities for the control law adjustment by means of the following parameters: the prediction horizon, the control horizon, the weights of the error and the command. the predictive control structure has been tested in three functioning regimes, considered essential due to the frequency of their occurrence in current practice. keywords: predictive control, wastewater treatment, neural network, bioreactor 1 introduction the issue of wastewater treatment belongs to a larger area, namely the environment protection. the environment protection generally and biological wastewater treatments particularly is essential for the life of the human communities and received lately a lot of attention from specialized international organizations. in this context, european laws envisage a series of specific orientations for treating and maintaining the water quality within legal limits (eg. surface water directives, 75/440/eec and 79/869/eec, drinking water directives 80/778/eec/15july1980 and 98/83/eec/3 november 1998, urban wastewater treatment directive 91/271/eec etc.). complementary to what has been already stated, the wastewater treatment processes are very complex, non-linear and characterized by many uncertainties w.r.t. the influent parameters, the structure and the coefficients of the model. moreover, many wastewater treatment plants do not have measurement and control equipments. therefore, there is a need in designing control strategies for the good operation of the process, strategies that may consider various types of models. process modelling: there has been a long transition between adopting the procedure of wastewater treatment using active sludge and setting up the theoretical framework to closely describe the procedure. the delay was mainly caused by the conflicting hypotheses related to the explanation of process mechanisms and their difficult translation into mathematical models [3, 7]. in 1983, international water association (iwa) formed a working group destined to promote and facilitate the practical methods of designing and operating the biological wastewater treatment systems. as a result, the activated sludge model 1 (asm1) has been presented in 1987 (see [8]). the model used 13 state variables and described the elimination of organic carbon and nitrogen. the same working group extended the model afterwards by adding the biological process of phosphorus elimination, and named this model the activated sludge model 2 (asm2) [9]. two other improved versions of asm2, named asm2d and asm3 appeared [10]. the major shortcoming of asm1 is its complexity, which makes it difficult to be used in a control system. a simplified alternative of the asm1 model was obtained by taking into consideration the significant variables on a medium time-scale (a few hours to several days). this is why the variables with a slow evolution were considered constant and those having a fast evolution were neglected [13]. these simplifications allowed the usage of asm1 model in designing control laws. lately, iwa established two major research areas: copyright © 2006-2007 by ccc publications predictive control of a wastewater treatment process 133 • modelling of different industrial wastewater treatment processes: cellulose and paper industry, agricultural farms, spun glass industry, etc. the research team tries to model each process, depending on the substances involved in the process. contrary to the case of domestic wastewater treatment which is made naturally by the microorganisms, the industrial wastewater treatment is done by cultivation of microorganisms, sometimes genetically modified, which consume a certain organic substrate [17, 5]. • conditioning the excess of active sludge in order to use it in other industrial activities, especially as a fertilizer in agriculture [16, 20]. process control: the wastewater treatment systems are complex, non-linear processes, with multiple inputs and outputs (multivariable), which determine equivocal information about the influent’s characteristics, the model’s structure and parameters. two approaches can be distinguished in choosing the control structure for such a process: the first one is process-driven and the second one is model-based. the first approach deals with the separate control of the most important variables. within this category, the well-known problem of controlling the dissolved oxygen level is one of the most important issues for a good operation of the wastewater treatment plants. thus, a good level of dissolved oxygen allows the optimal growth of microorganisms used in the process [12]. recently, the control of nitrogen and phosphor level received also a lot of attention [21]. the second approach has been improved a number of times. these improvements are related to the type of the mathematical model used, as it is the case for state estimators. using simplified models allowed the application of advanced control techniques (e.g. precise linearizing or adaptive control, robust control techniques etc.) [19]. however, when using more complex models, such as the asm1 model, the issue of automatic control became very complicated and the established results were less numerous. for the asm1 model classic control techniques are usually used (pi, pid controllers), arranged hierarchically, in a three-level structure [2]: at the higher level, a stable trajectory for the process is calculated for a certain period of time; the medium level deals with the trajectory optimization for the dissolved oxygen, the flow of the recycled active sludge and the recycled inflow for nitrogen removal; at the lower level, the control of dissolved oxygen concentration is achieved, based on the medium level reference. a well-suited approach for this type of process is the control based on artificial intelligence strategies. thus the intelligent control exploits the knowledge and experience accumulated from managing the process and puts it across the control structures like expert, fuzzy, neuro-fuzzy systems [1, 15, 18]. the present study considers a simplified model of the biological wastewater treatment plant [19]. the process is controlled using a model-based predictive control (mpc) strategy. the predictive controller uses a neural network as internal model of the process. this offers various possibilities for the control law adjustment by means of the following parameters: the prediction horizon, the control horizon, the weights of the error and the command. the control purpose is to maintain the substrate concentration below an admissible limit, which is indirectly achieved by controlling the dissolved oxygen concentration, considering as control input the dilution rate d. the predictive control structure has been tested during several functioning regimes, which are essential due to their frequent occurrence in current practice. the paper is structured as follows: the second section describes the process components and the mathematical model of the plant, the third section introduces theoretical considerations about the control structure used in the paper, while the fourth section refers to the neural network used as internal model of the predictive controller. the fifth section presents the simulation results of the proposed control structure and the last section is dedicated to conclusions. 2 the model of the wastewater treatment process the mathematical model considered in this paper has been proposed in [19]. the model is based on the following assumptions: 134 sergiu caraman, mihaela sbarciog, marian barbu • the system runs in steady-state regime (fin = fout = f , d = f/v ); • the recycled sludge is proportional to the process flow (f ): fr = r · f , where r is the recycled sludge rate; • the flow of the sludge removed from the bioreactor (sludge that is in excess) is considered proportional to the process flow (f ): fβ = β ·f , where β is the removed sludge rate; • there is no substrate or dissolved oxygen in the recycled sludge flow of the bioreactor; • the output flow of the aerated tank is equal to the sum between the output flow of the clarifier tank (settler) and the recycled sludge flow. aerated bioreactor settler influent effluent waste sludge settled sludge recycled sludge s, x , dosin, doin, d x , do, s, (1+r)·d xr , r·d xr , (r+β )·d xr s, (1−β )·d xr , β·d figure 1: the schematic representation of the wastewater treatment process figure 1 presents the schematic representation of the wastewater treatment process. the aeration tank is a biological reactor containing a mixture of liquid and suspended solid, where a microorganism population is grown in order to remove the organic substrate from the mixture. the clarifier tank is a gravity settlement tank where the sludge and the clear effluent are separated. a part of the removed sludge is recycled back to the aeration tank and the other part removed [14]. under these conditions the process model is given by the following mass balance equations: dx dt = µ(t)x (t)−d(t)(1 + r)x (t) + rd(t)xr(t) (1) ds dt = −µ(t) y x (t)−d(t)(1 + r)s(t) + d(t)sin (2) ddo dt = −k0 µ(t) y x (t)−d(t)(1 + r)do(t) + αw (domax −do(t)) + d(t)doin (3) dxr dt = d(t)(1 + r)x (t)−d(t)(β + r)xr(t) (4) µ(t) = µmax s(t) ks + s(t) do(t) kdo + do(t) (5) where x (t) biomass, s(t) substrate, do(t) dissolved oxygen, domax maximum dissolved oxygen, xr(t) recycled biomass, d(t) dilution rate, sin and doin substrate and dissolved oxygen concentrations in the influent, y biomass yield factor, µ biomass growth rate, µmax maximum specific growth rate, ks and kdo saturation constants, α oxygen transfer rate, w aeration rate, k0 model constant, r and β ratio of recycled and waste flow to the influent. the model coefficients have the following values: y = 0.65; β = 0.2; α = 0.018; kdo = 2 mg/l; k0 = 0.5 µmax = 0.15 mg/l; ks = 100 mg/l; domax = 10 mg/l; r = 0.6. predictive control of a wastewater treatment process 135 figure 2: the systemic diagram of the wastewater treatment process the systemic diagram of the process is given in figure 2. figure 3 illustrates the open loop response of the system for a step input d = 0.1 h−1(w = 80 h−1). the initial conditions considered in this simulation are: x (0) = 200 mg/l, s(0) = 88 mg/l, do(0) = 5 mg/l, x r(0) = 320 mg/l, doin = 0.5 mg/l, sin = 200 mg/l. 0 50 100 175 180 185 190 195 200 time [h] x [ m g /l] 0 50 100 55 60 65 70 75 80 time [h] s [ m g /l] 0 50 100 4.5 5 5.5 6 time [h] d o [ m g /l] 0 50 100 320 330 340 350 360 time [h] x r [m g /l] figure 3: open loop system response during the normal functioning of the wastewater treatment process, three regimes have been identified: rain (d = 1/20 h−1, w = 80 h−1), normal (d = 1/35 h−1, w = 60 h−1) and drought (d = 1/50 h−1, w = 20 h−1). the first case is characterized by maximum values for the aeration and dilution rates, the second regime considers medium values for w and d and the third case is characterized by small values for the same parameters. in this study special attention has been paid to the predictive controller, such that it provides good performances for all the three functioning regimes. 3 predictive control predictive control algorithms belong to the class of model-based control strategies, using a process model to incorporate the predicted future behavior of the process into the controller design procedure [6]. independent of the type of model used and of the cost index minimized, the principle of mpc is the same. at each sampling instant t [4]: • use the process model to predict the future output of the process over the prediction horizon n2 , {y(t + k/t), k = 1 . . . n2}, based on past inputs and outputs and postulated future inputs; • minimize the cost index, taking into account possible constraints on input, output and states, in order to determine the optimal control sequence {u(t + k/t), k = 0 . . . nu −1}, where nu is the control horizon; 136 sergiu caraman, mihaela sbarciog, marian barbu • use the receding-horizon control mechanism that introduces feedback into the optimization problem by applying to the process the first optimal control action and discarding the consequent ones. as it is straightforward from the description above (the model is the central element of the entire strategy), the success of mpc strategy is highly dependent on a reliable process model, that is a model which approximates well the process dynamics. a lot of research has been carried out up to now in the area of mpc based on linear models, however many of the real-life processes are characterized by complex non-linearities, the necessity of having a non-linear model of the process becoming straightforward. therefore, emphasis is placed nowadays on using nonlinear models in the framework of predictive control that would lead to improved control performances. the concept of the predictive control algorithm used in this work is illustrated in figure 4. + w u process model step response coefficients min j u g(t) y y m figure 4: predictive control structure at each sampling instant t, the increment of the control input ∆u(t) is calculated by minimizing the cost function j = n2 ∑ k=1 δ 2(k) [w(t + k/t)−y(t + k/t)]2 + nu−1 ∑ k=0 λ 2(k) [∆u(t + k/t)]2 (6) where w(t + k/t) is the setpoint prediction, δ (k) and λ (k) are respectively the weighting coefficients of the prediction errors and of the control input increments. in order to calculate the output prediction, the step response of the model must be determined. to this end, it is necessary to admit that the model can be linearized around the current operating point. then y(t + k/t) = y f ree(t + k/t) + y f orced (t + k/t) (7) where { y f ree(t + k/t), k = 1 . . . n2 } is the model output produced by the control input sequence {u(t + k/t) = u(t −1), k = 0 . . . n2 −1} and y f orced (t + k/t) = k ∑ i=1 gi∆u(t + k −i/t) (8) with {gi, i = 1 . . . n2} the unit step response coefficients. in matrix notation, equation (7) becomes y = gu + yf ree (9) where y = [ y(t + 1/t) . . . y(t + n2/t) ]t (10) u = [ ∆u(t/t) . . . ∆u(t + nu −1/t) ]t (11) yf ree = [ y f ree(t + 1/t) . . . y f ree(t + n2/t) ]t (12) g =   g1 0 . . . 0 g2 g1 . . . 0 ... ... ... ... gn2 gn2−1 . . . gn2−nu−1   (13) predictive control of a wastewater treatment process 137 using the model (9) in (6), a quadratic relation with respect to u is obtained: j = [∆·w −∆(g·u + yf ree)]t [∆·w −∆(g·u + yf ree)] + (λ·u )t (λ·u ) (14) where w = [ w(t + 1/t) . . . w(t + n2/t) ]t (15) ∆ = diag [ δ (1) . . . δ (n2) ] (16) λ = diag [ λ (0) . . . λ (nu −1) ] (17) only the first component of the vector u , ∆u(t/t) = ∆u(t), is used. at the next sampling instant the whole procedure is repeated. 4 the internal model of the process artificial neural networks form an important class of nonlinear systems, with many applications in modeling and control. as mathematically proven (see [11]), any static continuous nonlinear function can be approximated arbitrary well over a compact interval by a multilayer neural network with one or more hidden layers. in this contribution a feedforward neural network is used to model the behavior of the wastewater treatment process. the proposed neural network has three layers: the first one contains 15 neurons, the second one 7 neurons and the output layer 4 neurons. to appropriately capture the interconnections between all variables, up to four time-delayed values of the inputs and states were supplied to the network. the neural model predicts x (t), s(t), xr(t) and do(t) as functions of: d(t −1), d(t −2), d(t −3), w (t −1), w (t −2), w (t −3) x (t −1), x (t −2), x (t −3), x (t −4), s(t −1), s(t −2), s(t −3) xr(t −1), xr(t −2), xr(t −3), xr(t −4), do(t −1), do(t −2), do(t −3) the data used to train the neural network was obtained by integrating the differential equations (1)(4), considering randomly varying dilution rates in the interval [0, 0.1] and randomly varying aeration rates in the interval [0, 100]. before training, the data was scaled to the interval [0, 1]. in the same manner, a second data set was generated and used to validate the accuracy of the model. as figure 5 shows, there is hardly any difference between the measured values from the process and the ones predicted by the neural network for the dissolved oxygen and substrate concentrations. there is a noticeable shift between the biomass calculated based on the differential equations of the process and the biomass predicted by the neural network, but this is not going to affect the performance of the predictive controller since the neural network is used to predict the dissolved oxygen level. 5 simulation results figure 6 illustrates the control principle. the predictive controller calculates the dilution rate, which forces the dissolved oxygen concentration to follow the setpoint. controlling the concentration of dissolved oxygen has a benefic effect on the substrate concentration, which is brought within the limits imposed by the law (below 20mg/l). various configurations of the predictive controller parameters can be chosen in order to fulfill the control requirements. in these simulations a fast control was pursued, which can be generally achieved for a small prediction horizon, and less attention was paid to the magnitude of control input variations. the controller parameters were: n2 = 5, nu = 1, ∆ = i5, λ = 0. 138 sergiu caraman, mihaela sbarciog, marian barbu 0 0.05 0.1 d 0 50 100 w 0 5 10 d o 0 100 200 s 0 200 400 x 0 100 200 300 400 500 600 700 800 900 1000 0 500 x r time [h] figure 5: validation of the neural network model: process continuous line, model dash-dotted line air flow rate (containing do ) consumed do measured do here intervenes the designed controller 1) if the water stays in the tank long enough, the do concentration will increase, because the oxygen is not any longer consumed (treated water is equivalent to the substrate being biologically degraded); 2) if the water does not stay in the tank long enough, the do will decrease, thus the water is not treated (the explanation is that aerobe metabolism reactions are taking place). figure 6: the principle of the dissolved oxygen concentration control 0 50 100 w 4 6 8 d o 0 10 20 s 0 0.05 d 260 280 300 x 0 50 100 150 200 250 300 350 400 450 500 500 550 600 x r time [h] figure 7: simulation results for do constant setpoint (7.5) predictive control of a wastewater treatment process 139 the results of the dissolved oxygen concentration control are presented in figures 7, 8 and 9. figure 7 considers a constant dissolved oxygen setpoint (7.5), while figure 8 shows the case when the dissolved oxygen setpoint is variable. in both cases, the aeration rate w varies such that the process covers all three functioning regimes (rain, normal and drought). 0 50 100 w 4 6 8 d o 0 10 20 s 0 0.05 d 260 280 300 x 0 50 100 150 200 250 300 350 400 450 500 500 550 600 x r time [h] figure 8: simulation results for do variable setpoint figure 9 considers a constant dissolved oxygen setpoint but a variable concentration of the substrate in the influent. at time t = 150 h, sin was changed from 200 mg/l to 300 mg/l and was kept constant to the new value until t = 250 h, when it was changed to 150 mg/l. the controller adjusts the dilution rate and do is brought back to the setpoint value. 0 50 100 w 4 6 8 d o 0 10 20 s 0 0.05 d 200 300 400 x 0 50 100 150 200 250 300 350 400 450 500 500 600 700 x r time [h] figure 9: variable substrate concentration in influent 6 conclusions wastewater treatment is a complex process, which needs control for a good operation. this paper introduces a predictive controller for such a system and evaluates the control performances. the success of the mpc strategy is highly dependent on a reliable process model, that is a model which approximates well the process dynamics. taking into account the complexity of the wastewater treatment process, a neural network has been chosen as internal model for the predictive controller. 140 sergiu caraman, mihaela sbarciog, marian barbu the simulation results show a good performance of the control loop. the controller manipulates the dilution rate and forces the dissolved oxygen concentration to follow the imposed setpoint. this has a benefic effect on the substrate concentration, which is maintained within the limits established by law. the control is effective for various operational regimes, defined by the aeration rate. moreover, it is able to reject disturbances that might appear on the substrate concentration in the inflow. references [1] z. boger, application of neural networks to water and wastewater treatment plant operation, isa trans., vol. 31, pp. 25-33, 1992. [2] m. a. brdys and y. zhang, robust hierarchical optimising control of municipal wastewater treatment plants,preprints of the 9th ifac/ifors/imacs/ifip symposium large scale systems: theory & applications ű lssš2001, bucharest, romania, pp. 540-547, july 18-20, 2001. [3] a. m. buswell and h. l. long, microbiology and theory of activated sludge, journal of american pollution wks. assn., vol. 10, 1923. [4] e. f. camacho and c. bordons, model predictive control, springer-verlag, london, 1999. [5] e. choi et al., high strength nitrogen removal from nightsoil and piggery wastes, proc. of the 6th iwa speciality symposium on strong nitrogenous and agro-wastewater, seoul, korea, 11-13 june, 2003. [6] r. de keyser, model based predictive control, invited chapter in "unesco encyclopedia of life support systems (eolss)", eolss publishers co. ltd., oxford (www.eolss.net), 2003. [7] b. l. goodman and a. j. englande, a unified model of the activated sludge process, journal of water pollution control fed., vol. 46, pp. 312-332, 1974. [8] m. henze et. al., activated sludge model no. 1, iawq scientific and technical report no. 1, iawq, london, great britain, 1987. [9] m. henze et al., activated sludge model no. 2, iawq scientific and technical report no. 3, iawq, london, great britain., 1995. [10] m. henze et al., activated sludge models asm1, asm2, asm2d and asm3, iwa publishing, london, great britain, 2000. [11] k. hornik, m. stinchcobe and h. white, multilayer feedforward networks are universal approximators, neural networks, vol. 2, pp. 359-366, 1989. [12] p. ingildsen, realising full-scale control in wastewater treatment systems using in situ nutrient sensors, ph.d. thesis, dept. of industrial electrical engineering and automation, lund university, sweden, 2002. [13] u. jeppsson, modelling aspects of wastewater treatment processes, ph.d. thesis, dept. of industrial electrical eng. and automation, lund university, sweden, 1996. [14] m. r. katebi, m.a. johnson and j. wilke, control and instrumentation for wastewater treatment plant, springer-verlag, london, 1999. predictive control of a wastewater treatment process 141 [15] r. e. king and a. stathaki, a multi-layer perceptron for the control of a wastewater treatment plant, 12th mediterranean conference on control and automation med2004, kusadasi, turkey, proc. cd-rom, june 6-9, 2004. [16] h. kroiss, what is the potential for utilizing the resources in sludge, water science and technology, vol. 49, pp. 1-10, 2003. [17] g. langergraber et al., monitoring of a paper mill wastewater treatment plant using uv/vis spectroscopy, water science and technology, vol. 49, pp. 9-14, 2003. [18] s. a. manesis, d. j. sapidis and r. e. king, intelligent control of wastewater treatment plants, artificial intelligence in engineering, vol. 12, pp. 275-281, 1998. [19] f. nejjari, a. benhammou, b. dahhou and g. roux, non-linear multivariable adaptive control of an activated sludge wastewater treatment process, int. j. adapt. control signal process., vol. 13, pp. 347-365, 1999. [20] j. t. novak and c. park, chemical conditioning of sludge, proc. of int. conf. on wastewater sludge as a resource biosolids 2003, trondheim, norway, 23-25 june, 2003. [21] s. yagi, h. kohara, y. nakamura and s. shiba, fuzzy control of a wastewater treatment plant for nutrients removal, proc. of the int. conf. on artif. intell. in engineering & technology, sabah, malaysia, june 17-18, 2002. sergiu caraman & marian barbu "dunarea de jos" university of galati, romania department of automatic control and electronics domneasca street, no. 47, 800008-galati, romania sergiu.caraman, marian.barbu@ugal.ro mihaela sbarciog ghent university, belgium department of electrical energy, systems and automation technologiepark 913, zwijnaarde-ghent, 9052, belgium mihaela@autoctrl.ugent.be received: february 19, 2007 sergiu caraman (born on september 30, 1955) received the license degree in 1980 from polytechnical university of bucharest and the phd degree in 1996 from “dunarea de jos” university of galati, romania, both in control systems. presently he is professor in the faculty of electrical engineering and electronics, “dunarea de jos” university. his research interest includes topics such as modelling and control of biotechnological processes, intelligent control. 142 sergiu caraman, mihaela sbarciog, marian barbu mihaela sbarciog(born on may 21, 1976) graduated the faculty of naval and electrical engineering of “dunarea de jos” university of galati in 1999. presently she is a research assistant at faculty of engineering, ghent university. her main research field is the analysis and control of biochemical reaction systems. marian barbu (born on march 22, 1978) received the license degree in 2001 and the phd degree in 2006 from “dunarea de jos” university of galati, both in control systems. presently he is lecturer in the faculty of electrical engineering and electronics, “dunarea de jos” university. his research interest includes topics such as modelling and control of wastewater treatment processes, robust and intelligent control. international journal of computers, communications & control vol. i (2006), no. 1, pp. 33-39 symbolic computations based on grid services dana petcu, cosmin bonchiş, cornel izbaşa abstract: the widespread adoption of the current grid technologies is still impeded by a number of problems, one of which is difficulty of developing and implementing grid-enabled applications. in another dimension, symbolic computation, aiming to automatize the steps of mathematical problem solving, has become in the last years a basis for advanced applications in many areas of computer science. in this context we have recently analyzed and developed grid-extensions of known tools for symbolic computations. we further present in this paper a case study of a web service-based grid application for symbolic computations. keywords: grid computing, web services, mathematical software 1 introduction it is widely recognized that grid computing is the computer buzzword of the decade. many of the greatest challenges for software systems lie in how to enable automated software components deployed by different organizations to dynamically discover one another, communicate and coordinate their actions and form sound, robust and effective compound applications and services. the grid promises to solve these problems. the grid underlying problems are multi-disciplinary and cover a wide range of issues from service discovery, management, robust management of dependencies and system-system communication to security, legal or ethical frameworks, methodologies, verification, testing, and finally deployment of infrastructure in a shared and accessible environment. after the forerunner first-generation grid systems, the second-generation proposed the vision of computing resources to be shared like content on the web over the internet. the associated layered architecture abstracts fundamental system components, their purpose, and their interaction with each other. moreover the current, third-generation grid is aligned with web services. comparing to the conventional distributed computing environments, the usual grid environment focuses on the user: users working on their home machines see the illusion of a single computer, with access to all kinds of data and physical resources. moreover, the specific machines that are used to execute an application are chosen from the user’s point of view, maximizing the performance of that application, regardless of the effect on the system as a whole. the two basic approaches to computational solution of mathematical problems are numerical and symbolic. for a long time, the numerical approach had an advantage of being capable of solving a substantially larger set of problems. developments in symbolic computing are lagging relative to numerical computing, mainly due to the inadequacy of available computational resources: most importantly computer memory, but also processor power. continuous growth in the capabilities of computer hardware led to an increasing interest in symbolic calculations. a transition from numerical modeling to analytical modeling can be observed today in various spheres of science and technology; a motivation consists in the desire to construct more accurate and faithful simulations. currently software tools for symbolic computations, known as computer algebra systems (cas), allow users to study computational problems on the basis of their mathematical formulations and to focus on the problems themselves instead of spending time transforming the problems into forms that are numerically solvable. there are three ways in which a cas can interact with the grid, that are detailed in the next section: the cas uses a grid service to improve its own services, the cas uses the grid infrastructure to improve its response time, or the cas is presented to a client application as a grid service. we have studied the first cases in [11] respectively [10]. in this paper we look to the third approach. the paper is organized as follows. section 2 presents a state-of-the-art in the field. section 3 underlines the benefits of using grid services for symbolic computations. in the context of the current grid and cas technologies, a case study is presented in section 4. further work directions are identified in section 5. 2 the grid and the cas 2.1 approaches for cas-grid interaction a grid-extension of a cas system can tackle one or more of the following approaches: copyright c© 2006 by ccc publications 34 dana petcu, cosmin bonchiş, cornel izbaşa ability to accept services from the grid: the cas must be capable to augment its facilities with external modules, in particular it should be able to explore computational grid facilities, to connect to a specific grid service, to use it, and to translate its results for the cas interface. this approach is taken into consideration by netsolve/gridsolve [1], geodise [4], mathgridlink [14], our maple2g [9]. ability to communicate and cooperate over the grid: several kernels of cass must be able cooperate within a grid when solving problems; in order to have the same cas on different computational nodes a grid-version must be available; in the case of different cass, appropriate interfaces between them must be developed and implemented or a common language for inter-communication must be adopted. this approach is taken into account by maple2g [10]. being a source of grid or web services: the cas or some of its facilities must be reachable as grid or web services and allowed to be activated by remote users under appropriate security and licensing conditions; furthermore, deployment of the services must be done in an easy way from the inside of the cas. this approach is taken into account by the several projects described bellow. more details about the first two approaches can be found in [12]. details about the third approach follows. 2.2 web service-based cas extensions in number theory there exist a number of successful internet projects [6] aiming, among others, at finding large prime numbers, factoring large numbers, computing digits of π , finding collisions on known encryption algorithms etc. a cas web-wrapper component that can be used by multiple systems was reported in [15]. another online system, ogb (for gröbner basis computations), has been recently deployed [5]. maplenet [7] offers a software platform to enhance mathematics and related courses over the web. the client machine must be able to run java applets. a publisher machine is responsible for creating and editing the content of the web pages and, when complete, uploading them to the server. the server is the machine to which clients will connect to access web pages and the applets associated with them. the server also respond to publishing requests from the publisher machine for the transfer of content between the publisher and the server. it manages concurrent maple instances as required to serve client connections for mathematical computation and display services. the server can also provide some additional services including user authentication, logging information, and database access. webmathematica [16] offers access to mathematica applications through a web browser or other web clients. mathematica can be seen as a development environment for webmathematica sites. standard java technologies are used: java servlet and javaserver pages. webmathematica allows a site to deliver html pages that are enhanced by the addition of mathematica commands. when a request is made for one of these pages, the mathematica commands are evaluated and the computed result is inserted into the page. input can come from html forms, applets, javascript, and web-enabled applications. it is also possible to send data files to a server for processing. output can use different formats such as html, images, mathematica notebooks, mathml, svg, xml, postscript, pdf. the monet project [8], funded by the european commission, was a two-year (april 2002-march 2004) investigation into mathematical web services aiming to demonstrate the applicability of the semantic web to the world of mathematical software. the principal objective was to develop a framework for the description and provision of web-based mathematical services. the key to such a framework is the ability to discover services dynamically based on published descriptions which describe both their mathematical and non-mathematical attributes. such discovery and subsequent interaction are mediated by software agents capable of recognizing the criteria which should determine how particular kinds of problems are solved, and extracting them from the user’s problem description. a symbolic solver wrapper tool architecture was designed to provide an environment that encapsulates cass and expose their functionalities through symbolic services deployed. all symbolic services are running as independent web services, each reachable at its own unique url, all of them are enclosed within the symbolic server and they are managed by the wrapper tool symbolic solver environment. each symbolic service is assigned to several instances, such as a service core java class, a source code implementing the service with a mathematical solving software (a cas), and a msdl file. the principal information about each service is provided by the service configuration file that contains tree parts: service’s msdl, service interface to mathematical solving system and the actual service implementation. the technologies used for symbolic solver services implementation are java, axis, tomcat, soap, wsdl, jsp, msdl. maple was chosen as an example of the solving engine for the first implementation and axiom was used to validate the architecture and to demonstrate abilities to adopt different solving engine without performing major changes. symbolic computations based on grid services 35 2.3 grid service-based cas extensions there exist a number of grid-oriented projects that involve cass. the project grid enabled numerical and symbolic services [3], genss (march 2004-february 2006), in the frame of uk e-science programme, addressed the combination of grid computing and mathematical web services, their extension to deliver mathematical problem analysis, the code and the resources to compute the answers, using a common open agent-based framework. the main research focus lied on matchmaking techniques for advertisement and discovery of mathematical services. the project involved the design and implementation of an ontology for symbolic mathematical problems and used to support service specification and registration of services. the ontology has been extended based on work undertaken in monet [8]. the grid execution management for legacy code architecture, gemlca [2] is a recent solution to deploy existing legacy code applications written in any programming language as a grid service without modifying or even requiring access to the legacy code (source or binary). the access point for a client to the gemlca architecture is the front-end layer composed of a number of grid services offering interfaces in order to deploy, query, submit, check the status of, and get the results back from computational jobs. the front-end layer is described in wsdl and can be used by any grid service client to bind and make use of its functionality through soap. in order to access a legacy code program, the user executes the gemlca grid service client that creates a legacy code instance with the help of a legacy code factory. following this, the system submits the job to the compute server through globus toolkit version 3 using a particular job manager. a specific xml format, lcid (legacy code interface description file) is necessary to be used. 3 benefits of using symbolic-computing services based on globus-wsrf the recent version 4 of globus toolkit, de-facto standard for grid technologies, is based on standard web services technologies such as soap and wsdl. it is written according the wsrf specification (web services resource framework). the basic requirements addressed by wsrf is the ability to create, address, inspect, discover, and manage statefull resources. grid services extends web services (usually stateless services) by providing these extra functionalities. wsrf approach is more flexible than the previous ones implemented in globus toolkit (e.g. ogsi implementation) allowing many-to-many mappings between web services (the message processor) and any associated statefull resource (the statefull service instance). 3.1 statefull services the wsrf approach simplifies the development of grid-service wrappers for cass. the cas can take now the role of the statefull service instance. if we go back to the case of the web-wrapper of a cas, we can identify several problems solved by the grid environment. successive related requests to the service hiding the cas will need the maintenance of the service instance in a command waiting cycle, without releasing the connections. when the connection is interrupted, but the client come back to the system it must start as a any new incomer. if a statefull service is used, the latest state of the cas can be registered. despite the fact that the connection was closed, the client can come back and resume the computation at any time before the service instance destruction. 3.2 service instances on remote grid nodes using a appropriate scheduler the cas service instance can run on a different grid hardware resource than the one where the container for web-based grid services resides, primarily addressed by the service client. this approach solves the problem of the server overload of a classic client-server architecture. 3.3 service discovery the user is confronted with thousands of packages are available to perform all kinds of mathematical computations. a standard way to categorize, explore, discover, invoke and compose them is needed. grid computing has 36 dana petcu, cosmin bonchiş, cornel izbaşa awake high expectations for its potential as a discovery accelerator. grid wsrf-based services are described in wsdl standard format that can be easily inspected by any potential client. 3.4 security standards globus’s gsi offers two message-level protection schemes, and one transport-level scheme [13]. gsi secure message scheme that provides message-level security can be used in the case on proprietary cas usage. globus toolkit implements three authentication methods: x.509 certificates, username-password, and anonymous authentication. the first two authentication methods are recommended in the case of a proprietary cas usage. gsi supports also authorization in both the server-side and the client-side. the server has six possible authorization modes: none, self, gridmap, identity-authorization, host authorization, saml callout authorization. depending on the authorization mode that if will be chosen, the server will decide if it accepts or declines an incoming request. identity-authorization or saml callout authorization are recommended in the case of a proprietary cas usage. 4 a case study: grid-based services using maple we proceed with a practical example of how a cas can be made available as grid-wsrf service and for this purpose maple became our cas of choice. the main reason is that, despite its robustness and ease of use, we were not able to locate efforts to link maple with the grid, accept ours, namely maple2g. furthermore, it is well known that maple excels other cass in solving selected classes of problems e.g. systems of nonlinear equations or inequalities. finally, maple has already a build-in socket library for communicating over the internet, and a library for parsing xml. these capabilities match very well with our goal as they suffice to make maple a client for an grid computational service. maple2g (maple-to-grid) was build recently as a grid-wrapper for maple. maple2g consists of two parts a cas-dependent and a grid-dependent one. therefore, any change in the cas or in the grid will be reflected only in one part of the proposed system. furthermore, the cas-dependent part is relatively simple and easy to be ported to support another cas or legacy software. maple2g covers all three approaches described in section 2.1. we describe here the newest component, the one that presents maple as grid service. it is based on the wsrf implementation from globus toolkit 4. figure 1: the four elements of the application, the client, the factory, the instance, and the resource. their interaction in time at the server side, the application has two main components: the factory service, the instance service. a resource is created by the factory service and used by the instance service. the interaction pushed by the client follows the steps (figure 1): 1. the client request the service instance and resource creation; 2. the factory service creates the resource; 3. the key of the resource is identified by the factory; 4. the factory creates the service instance; symbolic computations based on grid services 37 5. the service reference is returned to the factory; 6. the endpointreference (instance and resource identifiers) is send to the client; 7. the client contacts the service instance and request an operation involving the resource; 8. the service instance serves the request using the resource; 9. the service instance sends the result to the client. the client can reiterate the steps (7-9) using the endpointreference. the application components were written in java. the factory service is activated once the globus service container is activated on the resource were the service package was deployed. the instance service launches maple as a concurrent thread on the same hardware resources on which it runs. the communication between the java-based instance service and maple is done via sockets. at start maple thread reads a temporary file that specify the socket channel that should be open and then the cycle in which it accepts any string coming on that communication channel, evaluate it as maple command, and sends the result via the socket connection to the instance service. for example, after the maplefactoryservice activation, the mapleclientperform can send maple commands to the instance service in form of strings: $ mapleclientcreate http://194.102.62.15:8080/wsrf/services/maplefactoryservice >ref $ mapleclientperform ref "ifactor(123456789056789098765098765432100);" (2)^2*(5)^2*(7)*(43)*(113)*(331)*(683)*(12067378391)*(65837)*(202087) $ mapleclientperform ref "p:=12*x^6+84*x^4-54*x^5-270*x^3+168*x^2-216*x+96:" $ mapleclientperform ref "solve(p);" 1/2, 4, i, -i, 2*i, -2*i we have build also a web interface that is depicted by figure 2. we used ajax (asynchronous javascript and xml) for sending and receiving dates to the server. after the user authentification, the interface can be use in few simple step: 1. start the globus container on the server (figure 2.a); 2. choose the proper service (mathfactoryservice) from the list of available services (figure 2.b); 3. start the mathfactoryservice which will launch the maple instance (figure 2.c); 4. fill and execute a maple command (figure 2.d and e). 5. the maple instance executes the command and returns an mathml document as answer. the mathml document will be transform to a svg file which is send back to the interface (figure 2.f). 5 conclusions and further work at this stage maple2g exists as a demonstrator system with some of the functionalities described above. in the near future it will be further developed to include facilities existing in other systems, in order for it to become more robust. currently if the access to maple service is granted, any maple commands can be used. restricted access to a subset of commands (e.g. no access to shell commands revealing the host characteristics or establishing socket connections) should by implemented. specialized services based on maple should be developed and deployed as grid services. experiments on the wide-area grids will help guide further development of the system. deployment of grid services based on other cass than maple using the same codes must be take also into consideration. acknowledgment this work was partially supported by the european project science (fp6-2004-infrastructure-5-026123) and the romanian project compgrid (cncsis-2004-949). 38 dana petcu, cosmin bonchiş, cornel izbaşa figure 2: using a web interface to access symbolic computing services symbolic computations based on grid services 39 references [1] s. agrawal, j. dongarra, k. seymour, s. vadhiyar, “netsolve: past, present, and future – a look at a grid enabled server", in making the global infrastructure a reality, wiley, berman f. (ed.), pp. 613-622, 2003. [2] t. delaitre, a. goyeneche, p. kacsuk, t. kiss, g.z. terstyanszky, s.c. winter, “gemlca: grid execution management for legacy code architecture design”, in procs. of the 30th euromicro conference, special session on advances in web computing, august 2004, rennes, france, pp. 305-315, 2004. [3] genss, available on-line at: http://genss.cs.bath.ac.uk/index.htm. [4] geodise, available on-line at: http://www.geodise.org/. [5] m. gettrick, “ogb: online gröbner bases”, http://grobner.it.nuigalway.ie, 2004. [6] internet-based distributed computing, available on-line at: http://www.aspenleaf.com/ distributed/apmath.html. [7] maplenet, available on-line at: http://www.maplesoft.com/maplenet/. [8] monet, available on-line at: http://monet.nag.co.uk. [9] d. petcu, d. dubu, “an extension of maple for grid and cluster computing”, procs. iccc 2004, i. dziţac, t. maghiar, c.popescu (eds.), oradea, băile felix spa, may 27-29 2004, ed. metropolis, pp. 355-360, 2004. [10] d.petcu, d.dubu, m.paprzycki, “grid-based parallel maple”, lncs 3241, procs. pvmmpi 2004, budapest, hungary, september 19-22, 2004, d. kranzmüller, p. kacsuk, j. dongarra (eds.), springer, pp. 215-223, 2004. [11] d.petcu, m.paprycki, d.dubu, “design and implementation of a grid extension of maple”, scientific programming, vol. 13, no. 2, ios press, pp. 137-149, 2005. [12] d. petcu, d. ţepeneu, m. paprzycki, t. ida, “symbolic computations on grids”, chapter 27, engineering the grid: status and perspective, b. di martino, j. dongarra, a. hoisie, l. yang, and h. zima (eds.), 2006. [13] b. sotomayor, l. childers, globus toolkit 4 : programming java services, morgan kaufmann, 2005. [14] d. ţepeneu, t. ida, “mathgridlink connecting mathematica to the grid”, in procs. ims’04, banff, alberta, canada, 2004. [15] a. weber, w. küchlin, b. eggers, v. simonis, “parallel computer algebra software as a web component”, available on-line at: http://www.cs.ucsb.edu/conferences/ java98/papers/algebra.pdf, 1998. [16] webmathematica, available on-line at: http://www.wolfram.com/products/webmathematica/. dana petcu institute e-austria timişoara and western university of timişoara computer science department address: b-dul vasile pârvan 4, 300223 timişoara, romania e-mail: petcu@info.uvt.ro cosmin bonchiş institute e-austria timişoara, and vasile goldiş university of arad computer science department cornel izbaşa institute e-austria timişoara, and western university of timişoara computer science department international journal of computers, communications & control vol. i (2006), no. 2, pp. 5-5 preface gastón lefranc hernández it is an honor for me to introduce you a list of papers selected from the xvi congress of acca (asociación chilena de control automático, chilean association of automatic control), done in santiago of chile at the end of 2004, in its 30 years of existence. since 1974, acca organize congress (every 2 years), tutorial courses for continuing education, seminars, workshops, exposition of systems and equipments and our magazine automatica and innovation. all of these activities have results: important influence in all ambits, consolidating to acca as a point of meeting of people from industries, from private and public institutions, from the academics world, from suppliers of systems and equipments, and professional from all latin america . in 1976, acca had its first program committee to select papers, and its first proceedings. this congress was organized by acca, ieee chilean chapter on control, robotics and cybernetics, ifac and its technical committees, and the universidad de las américas, chile. acca is nmo of ifac since 1984. acca congress had keynotes speakers for plenary: dr. françois vernadat from france, dr. florin filip from romania, dr. shimon nof from usa and dr. philippe dupont, from france. a round table was organised to discuss about productivity, interested in chile and latin american professionals. the 46 accepted papers selected by the international program committee, were presented in technical sessions where new ideas, critical comments, and the beginning of cooperation leading to future projects will take place. the papers come from argentina, brazil, chile, cuba and méxico. the international program committee selected 10 papers as the best of this congress, to be published in “international journal of computers, communications & control”. the papers are in areas of artificial intelligence, vision, manufacturing, pattern recognition and robotics. acca wants to say thanks to dr. florin filip to permit to show the activities in chile and latin america, oriented to improve and to solve our own problems. it is pleasure to introduce you to this special issue conference dedicated to acca in its 30 years. gastón lefranc hernández chairman of international program committee xvi congress of acca guest editor of this issue copyright c© 2006 by ccc publications int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 2, pp. 144-147 acad. solomon marcus at 85 years (a selective bio-bibliography) f. g. filip (editor in chief of ijccc), i. dzitac (associate editor in chief of ijccc) professor solomon marcus is one of the seniors of the romanian science and culture, with a tremendously diverse and intensive activity, with numerous, fundamental, and many times pioneering contributions to mathematics, mathematical linguistics, formal language theory, semiotics, education, and to other areas, with an impressive internal and international impact and recognition, since several decades very actively involved in the scientific and cultural life – and still as active as ever at his age. that is why the short presentation below is by no means complete, it is only a way to pay our homage to him at his 85th birthday anniversary. we are honored and we thank to professor solomon marcus for having the kindness to publish, in the first issue of this journal, an article occasioned by the moisil centenary1. 1 biography and general data born at march 1, 1925, in bacău, romania. elementary school and high school in bacău, classified the first at the school-leaving examination (“bacalaureat"), in 1944. faculty of science, mathematics, university of bucharest, 1945-1949. assistant professor since 1950, lecturer since 1955, associate professor since 1964, and professor since 1966. professor emeritus since 1991, when he retired. all positions held in the faculty of mathematics, university of bucharest. phd in mathematics in 1956 (with the thesis about “monotonous functions of two variables"), state doctor in sciences in 1968, both at the university of bucharest. corresponding member of romanian academy since april 1993, and full member of the romanian academy (academician) since december 2001. 1grigore c. moisil: a life becoming a myth, international journal of computers, communications & control, 1(1),73-79, 2006 copyright c⃝ 2006-2010 by ccc publications acad. solomon marcus at 85 years (a selective bio-bibliography) 145 research and teaching in the fields of mathematical analysis, theoretical computer science, measure theory, general topology, linguistics, history and philosophy of mathematics, poetics, semiotics, applications of mathematics to natural and social sciences. has published over 50 books, as single author or in collaboration, and over 400 research articles, as well as several hundreds of articles on various cultural topics. has edited over 40 collective volumes. more than 250 invited lectures at various universities from europe, americas, and asia. in his papers and books has solved many open problems in mathematical analysis and has formulated a still larger number of problems and research topics, has introduced new ideas, notions and models, which were addressed and continued by a large number of researchers. just an example: the contextual grammars he has introduced in 1967-68 are now called marcus contextual grammars and were subject of many papers and phd theses, as well as of two monographs. many students and collaborators, a really “contagious" professor and researcher. cited and followed by thousand of authors, from romania and abroad. his books were reviewed and commented by many personalities, among whom, we mention a few: emanuel vasiliu, mike holcombe, sheng yu, yehosua bar-hillel, maurice gross, l. nebesky, michel janot, i.i. revzin, ferenc kiefer, h.p. edmundson, ju.a. sreider, jacob l. mey, carlo tagliavini, gr.c. moisil, matei călinescu, nicolae manolescu, sorin stati, k. sgallova, p. sgall, gabriela melinescu, jean-marie klinkenberg, virgil nemoianu, adrian marino, miron nicolescu, alexandru ivasiuc, wladimir krysinski, mircea mihăieş, şerban cioculescu, eugen simion, constantin negoiţă, tudor octavian, dinu flamând, stelian tănase, alin teodorescu, mircea scarlat, smaranda vultur, cristian calude, gheorghe păun, tudorel urian, paul cernat, irina mavrodin, rosa del conte, jean-pierre descles, umberto eco, marius iosifescu, roman jakobson, gheorghe mihoc, octav onicescu, cesare segre, gheorghe tomozei, iorgu iordan, constanţa buzea, cornel ungureanu, bogdan suceavă, basarab nicolescu, alexandru t. balaban, mihai zamfir, and many others. member of the editorial board of over three dozens of professional journals. among the numerous honors received, we recall: doctor honoris causa of the universities of bacău, constanţa, and craiova. vice president of the international association for semiotic studies (19891999). honorary president of the balkan semiotic society (since 2001). member of the executive committee of the international association for semiotic studies. honorary member of the international association for visual semiotics. honorary member of the toronto semiotic circle, of como (italy) semiotic circle and of the hungarian semiotic society. honorary member of the romanian mathematical society. permanent invited professor, sao paulo catholic university. member of the romanian national mathematical committee. member of the leading council of the romanian linguistic society. member of the romanian union of writers and member (1990-1995) of the leading council of this union. vice-president of the romanian semiotic society. member of the romanian committee for the club of rome. honorary member of the society eratosthene, switzerland. he has also received many prizes, in romania and abroad. with various occasions, special issue of journals and collective volumes were devoted to him. here are some of them: the international journal of computer mathematics (great britain) and revue roumaine de mathematiques pures et appliquées devoted some of their issues in 1985 to his 60th anniversary, while two collective volumes were dedicated to his 70th anniversary, in 1995, mathematical aspects of natural and formal languages, world scientific, singapore, 1994, and mathematical linguistics and related topics, ed. academiei, bucharest, 1995. in the year 2000, two volumes were dedicated to his 75th anniversary: finite vs infinite. an eternal dilemma, springer, london, and recent topics in mathematical and computational linguistics, ed. academiei, bucharest. in the year 2005, volume 64, numbers 1-4 of fundamenta informaticae were published as a “special issue in honor of the 80th birthday of professor solomon marcus", with papers by 70 authors from 16 countries. 146 f. g. filip (editor in chief of ijccc), i. dzitac (associate editor in chief of ijccc) 2 books, authored or co-authored 1. lingvistica matematică. modele matematice în lingvistică. ed. didactică şi pedagogică. bucureşti, 1963. 2. gramatici şi automate finite. ed academiei, bucureşti, 1964. 3. analiza matematică. vol. i. ed. didactică şi pedagogică, bucureşti, 1st edition 1962, 2nd edition 1963, 3rd edition 1966, 4th edition 1971, 5th edition 1980 (in collab. with m. nicolescu and n. dinculeanu). 4. lingvistica matematică (2nd edition, revised and completed with 4 new chapters). ed. didactică şi pedagogică, bucureşti, 1966. 5. introducere în lingvistica matematică. ed. ştiinţifică, bucureşti, 1966 (in collab. with e. nicolau and s. stati) 6. noţiuni de analiză matematică. originea, evoluţia şi semnificaţia lor. ed. ştiinţifică, bucureşti, 1967. 7. limbaj, logică, filozofie. ed. ştiinţifică, bucureşti, 1968 (in collab. with al. boboc, gh. enescu, c. popa, and s. stati). 8. analiza matematică. vol. ii. ed. didactică şi pedagogică, bucureşti, 1st edition 1966, 2nd edition 1971, 3rd edition 1980 (in collab. with m. nicolescu and n. dinculeanu). 9. introduction mathematique a la linguistique structurale. dunod, paris, 1967. 10. algebraic linguistics; analytical models. academic press, new york, 1967. 11. poetica matematică. ed. academiei, bucureşti, 1970. 12. teoretiko-mnozestvennye modeli jazykov. ed. nauka, moscova, 1970 (translation of the first five chapters of book 10 and of the last chapter of book 9). 13. algebraicke modely jazyka. ed. academia, prague, 1969 (translation in czech of book 4, and of a part of book 2). 14. introduzione alla linguistica matematica. casa editrice riccardo patron, bologna, 1970 (revised and completed translation of book 5; in collab. with e. nicolau and s. stati). 15. mathematische poetik. ed. academiei, bucureşti – athenaum-verlag, frankfurt am main, 1973 (revised and completed translation of book 11). 16. matematicka poetika. ed. nolit, belgrad, 1974 (revised and completed serbo-croatian translation of book 11). 17. din gândirea matematică românească. ed. ştiinţifică şi enciclopedică, bucureşti, 1975. 18. semiotica folclorului. abordare lingvistico-matematică. ed. academiei, bucureşti, 1975 (coauthor). 19. matematicka analyza ctena podruhe. ed. academia, prague, 1976 (revised and completed czech translation of book 6). 20. a nyelvi szepseq matematikaja. ed. gondolat, budapesta, 1977. 21. metode distribuţionale algebrice în lingvistică. ed. academiei, bucureşti, 1977 (coauthor). 22. la semiotique formelle du folklore. approche linguistico-mathematique. ed. klincksieck, paris – ed. academiei, bucureşti, 1978 (revised and completed translation of book 18; co-author). 23. introduccion en la linguistica matematica. ed. teide. barcelona, 1978 (revised and completed spanish translation of book 5). 24. semne despre semne. ed. ştiinţifică şi enciclopedică, bucureşti, 1979. acad. solomon marcus at 85 years (a selective bio-bibliography) 147 25. contextual ambiguities in natural & artificial languages. vol. 1, communication and cognition, ghent, belgium, 1981 (revised and completed translation of a part of book 21). 26. snmeia gia ta snmeia. ed. pneumatikos, atena, 1981 (greek translation of book 24). 27. metode matematice în problematica dezvoltării. ed. academiei, bucureşti, 1982 (co-author). 28. gândirea algoritmică. ed. tehnică, bucureşti, 1982. 29. semiotica matematică a artelor vizuale. ed. ştiinţifică şi enciclopedică, bucureşti, 1982 (coordinator and co-author). 30. simion stoilow. ed. ştiinţifică şi enciclopedică, bucureşti, 1983 (in collab. with cabiria andreian cazacu). 31. paradoxul. ed. albatros, bucureşti, 1984. 32. timpul. ed. albatros, bucureşti, 1985. 33. artă şi ştiinţă. ed. eminescu, bucureşti, 1986. 34. analiza matematică. vol. ii, univ. bucureşti, 1986 (co-author). 35. to paradocso. ed. pneumatikos, atena, 1986 (greek version of book 31). 36. şocul matematicii. ed. albatros, bucureşti, 1987. 37. moduri de gândire. colecţia “ştiinţa pentru toţi", ed. ştiinţifică şi enciclopedică, bucureşti, 1987. 38. provocarea ştiinţei. seria “idei contemporane", ed. politică, bucureşti, 1988. 39. invenţie şi descoperire. ed. cartea românească, 1989. 40. analiza matematică. materiale pentru perfecţionarea profesorilor de liceu iii. universitatea din bucureşti, facultatea de matematică, bucureşti, 1989 (co-author). 41. dicţionar de analiză matematică. ed. ştiinţifică şi enciclopedică, bucureşti, 1989 (co-author). 42. controverse în ştiinţă şi inginerie. ed. tehnică, bucureşti, 1991. 43. language, logic, cognition and communication; a semiotic,computational and historical approach. report 9/96. grup de recerca en linguistica matematica i enginyeria del llenguatge. reports universitat rovira i virgili, tarragona, spain, 1996. 44. matematica, manual pentru clasa a ix-a, licee teoretice specializarea filologie (in colab. with mihaela singer). ed. sigma, bucureşti, 1999; 2nd edition, 2000. 45. matematica, manual pentru clasa a xii-a (in colab. with p. alexandrescu, m. radulescu, s. radulescu). ed. paralela 45, bucureşti – piteşti, 2002. 46. jocul ca libertate. ed. scripta (colecţia ludică), bucureşti, 2003. 47. mathematics in romania. cub press 22, baia mare, 2004. 48. intâlnirea extremelor. scriitori în orizontul ştiinţei. ed. paralela 45, bucureşti – piteşti, 2005. 49. paradigme universale. ed. paralela 45, piteşti – bucureşti, 2005. 50. paradigme universale ii. pornind de la un zâmbet. ed. paralela 45, piteşti – bucureşti, 2006. 51. paradigme universale iii. jocul. ed. paralela 45, piteşti – bucureşti, 2007. 52. words and languages everywhere. polimetrica academic publisher, milano, 2007. 53. solomon marcus (protagonist): educaţia în spectacol. spandugino publ. house., bucureşti, 2010. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 2, pp. 238-250 solving problems in a distributed way in membrane computing: dp systems gh. păun, m.j. pérez-jiménez gheorghe păun institute of mathematics of the romanian academy po box 1-764, 014700 bucureşti, romania, and department of computer science and artificial intelligence university of sevilla avda. reina mercedes s/n, 41012 sevilla, spain e-mail: gpaun@us.es mario j. pérez-jiménez department of computer science and artificial intelligence university of sevilla avda. reina mercedes s/n, 41012 sevilla, spain e-mail: marper@us.es abstract: although p systems are distributed parallel computing devices, no explicit way of handling the input in a distributed way in this framework was considered so far. this note proposes a distributed architecture (based on cell-like p systems, with their skin membranes communicating through channels as in tissue-like p systems, according to specified rules of the antiport type), where parts of a problem can be introduced as inputs in various components and then processed in parallel. the respective devices are called dp systems, with the case of accepting strings called dp automata. the communication complexity can be evaluated in various ways: statically (counting the communication rules in a dp system which solves a given problem), or dynamically (counting the number of communication steps, of communication rules used in a computation, or the number of objects communicated). for each measure, two notions of “parallelizability" can be introduced. besides (informal) definitions, some illustrations of these idea are provided for dp automata: each regular language is “weakly parallelizable" (i.e., it can be recognized in this framework, using a constant number of communication steps), and there are languages of various types with respect to chomsky hierarchy which are “efficiently parallelizable" (they are parallelizable and, moreover, are accepted in a faster way by a dp automaton than by a single p automaton). several suggestions for further research are made. keywords: membrane computing, p system, distributed computing, communication complexity, chomsky hierarchy. 1 introduction p systems are by definition distributed parallel computing devices, [11], [12], [17], and they can solve computationally hard problems in a feasible time, [13], but this efficiency is achieved by a trade-off between space and time, based on the possibility of generating an exponential workspace in a linear time, by means of biologically inspired operations, such as membrane division and membrane creation. however, no class of p systems was proposed where a hard problem can be solved in a distributed parallel copyright c⃝ 2006-2010 by ccc publications solving problems in a distributed way in membrane computing: dp systems 239 way after splitting the problem in parts and introducing these subproblems in components of a p system which can work on these subproblems in parallel and produce the solution to the initial problem by interacting/communicating among each other (like in standard distributed computer science). in particular, no communication complexity, in the sense of [2], [9], [16], was considered for p systems, in spite of the fact that computation (time) complexity is very well developed, [13], and also space complexity was recently investigated, [14]. some proposals towards a communication complexity of p systems were made in [1], but mainly related to the communication effort in terms of symport/antiport rules used in so-called evolution-communication p systems of [5]. (note that in communication complexity theory the focus is not on the time efficiency of solving a problem, but the parties involved in the computation just receive portions of the input, in general, distributed in a balanced manner, “as fair as possible" – this distribution introduces an inherent difficulty in handling the input – and then mainly the complexity of the communication needed to parties to handle this input is investigated.) this note tries to fill in this gap, proposing a rather natural framework for solving problems in a distributed way, using a class of p systems which mixes ingredients already existing in various much investigated types of p systems. namely, we consider p systems with inputs, in two variants: (i) like in p automata, [6], [10], where a string of symbols is recognized if those symbols are brought into the system from the environment and the computation eventually halts (it is important to note that the string is “read" during the computation, not before it), and (ii) in the usual manner of complexity investigations, [13], where an instance of a decision problem is introduced in a p system in the form of a multiset of symbols (this operation takes no time, the computation starts after having the code of the problem inside), and the system decides that instance in the end of a computation which sends to the environment one of the special objects yes or no. several such systems, no matter of what type, are put together in a complex system which we call dp system (from “distributed p system"); the component systems communicate through channels linking their skin membranes, by antiport rules as in tissue-like p systems. when accepting strings by dp systems with p automata as components, the device is called a dp automaton. such an architecture was already used, with specific ingredients, for instance, in the investigations related to eco-systems, where “local environments" are necessary to be delimited and communication possibilities exist, linking them; details can be found in the recent paper [4]. the way to use a dp system is obvious: a problem q is split into parts q1,q2,...,qn, which are introduced in the n components of the dp system (as in p automata or as in decision p systems), these n systems work separately on their problems, and communicate to each other according to the skin-toskin rules. the solution to the problem q is provided by the whole system (by halting – in the case of accepting strings, by sending out one of the objects yes or no, etc.). like in communication complexity, [9], we request the problem to be distributed in a balanced way among the components of the dp system, i.e., in “as equal as possible" parts (also an almost balanced way to distribute the input among two processors is considered in [9] – no partner takes more than two thirds of the input – which does not seem very natural to be extended to the general case, of n processors). several possibilities exist for defining the communication complexity of a computation. we follow here the ideas of [1], and introduce three measures: the number of steps of the computation when a communication rule is used (such a step is called communication step), the number of communication rules used during a computation, and the number of objects transferred among components (by communication rules) during a computation. all these three measures are dynamically defined; we can also consider a static parameter, like in descriptional complexity of chomsky languages (see a survey in [8]), i.e., the number of communication rules in a dp system. a problem is said to be “weakly parallelizable" with respect to a given (dynamical) communication complexity measure if it can be split in a balanced way, introduced in the dp system, and solved using a number of communication steps bounded by a constant given in advance; a problem is “efficiently parallelizable" if it is weakly parallelizable and can be solved by a dp system in a more efficient way than by a single p system; more precise definitions are given in the next sections of the paper. 240 gh. păun, m.j. pérez-jiménez various possibilities exist, depending on the type of systems (communicating systems, e.g., based on symport/antiport rules, systems with active membranes, catalytic systems, etc.) and the type of problem we consider (accepting strings, decision problems, numerical problems, etc.). in this note we only sketch the general formal framework and give an illustration, for the case of accepting strings as in p automata. we only show here that all regular languages are weakly parallelizable (only one communication step suffices, hence the weak parallizability holds with respect to all three dynamical measures), and that there are regular, context-free non-regular, context-sensitive noncontext-free languages which are efficiently parallelizable with respect to the first two dynamical measures mentioned above (in view of the results in [9], there are linear languages which are not efficiently parallelizable with respect to the number of communicated objects/bits among components). if we use extended systems (a terminal alphabet of objects is available) and the communication channels among the components of a dp automaton are controlled, e.g., by states, as in [7], or created during the computation, as in [3], then the power of our devices increases considerably: all recursively enumerable languages are weakly parallelizable in this framework. many research problems remain to be explored, starting with precise definitions for given classes of p systems, continuing with the study of usefulness of this strategy for solving computationally hard problems (which problems are weakly/efficiently parallelizable and which is the obtained speed-up for them?), and ending with a communication complexity theory of dp systems, taking into account all measures of complexity mentioned above (for the number of objects communicated among components, which corresponds to the number of bits considered in [9], we can transfer here the general results from communication complexity – note however that in many papers in this area one deals with 2-party protocols, while in our framework we want to have an n-party set-up, and that we are also interested in the time efficiency of the distributed and parallel way of solving a problem). 2 dp systems – a preliminary formalization the reader is assumed familiar with basics of membrane computing, e.g., from [11], [12], and of formal language theory, e.g., from [15], hence we pass directly to introducing our proposal of a distributed p system. the general idea is captured in the following notion. a dp scheme (of degree n ≥ 1) is a construct ∆ = (o,π1,...,πn,r), where: 1. o is an alphabet of objects; 2. π1,...,πn are cell-like p systems with o as the alphabet of objects and the skin membranes labeled with s1,...,sn, respectively; 3. r is a finite set of rules of the form (si,u/v,s j), where 1 ≤ i, j ≤ n, i ̸= j, and u,v ∈ o∗, with uv ̸= λ ; |uv| is called the weight of the rule (si,u/v,s j). the systems π1,...,πn are called components of the scheme ∆ and the rules in r are called intercomponents communication rules. each component can take an input, work on it, communicate with other components (by means of rules in r), and provide the answer to the problem in the end of a halting computation. (a delicate issue can appear in the case of components which can send objects to the environment and bring objects from the environment – this happens, for instance, for symport/antiport p systems; in this case we have to decide whether or not the components can exchange objects by means of the environment, or the only permitted communication is done by means of the rules in r. for instance, solving problems in a distributed way in membrane computing: dp systems 241 a “local environment" for each component can be considered, disjoint from the “local environments" of other components, thus preventing the interaction of components by means of other rules than those in r. actually, the rules in r themselves can be defined between these “local environments" – which is a variant worth to explore. we point out here that also the need of a “local environment" has appeared in the applications of membrane computing to eco-systems investigations, see [4] and its references.) now, we can particularize this notion in various ways, depending on the type of systems πi,1 ≤ i ≤ n, and the type of problems we want to solve. for instance, we can define dp systems with active membranes, as dp schemes as above, with the components being p systems with active membranes, each of them having a membrane designated as the input membrane. having a decision problem – consider, e.g., sat for n variables and m clauses – we can split a given instance of it in parts which are encoded in multisets which are introduced in the components of the dp system. for example, we can introduce the code of each separate clause in a separate component of the dp system. the components start to work, each one deciding its clause, and in the end they communicate to each other the result; if one of the components will find that all m clauses are satisfied, then the whole sat formula is satisfied. intuitively, this is a faster way than deciding the formula by means of a single p system with active membranes – but a crucial aspect has been neglected above: in order to say that the formula is satisfied, all the m clauses should be satisfied by the same truth-assignment, and this supposes that the m components communicate to each other also which is the assignment which turns true the clauses. that is, besides the usual time complexity of solving the problem we have now to consider the cost of communication among the components and the trade-off between these two parameters should be estimated. another interesting case, which will be briefly investigated in the subsequent section, is that of accepting strings in the sense of p automata, [6], [10]; we will come back immediately to this case. on the other hand, we have several possibilities for estimating “the cost of communication", and we adapt here the ideas from [1]. let us consider a dp system ∆ , and let δ : w0 =⇒ w1 =⇒ ... =⇒ wh be a halting computation in ∆ , with w0 being the initial configuration. then, for each i = 0,1,...,h −1 we can write: comn(wi =⇒ wi+1) = 1 if a communication rule is used in this transition, and 0 otherwise, comr(wi =⇒ wi+1) = the number of communication rules used in this transition, comw (wi =⇒ wi+1) = the total weight of the communication rules used in this transition. these parameters can then be extended in the natural way to computations, results of computations, systems, problems/languages. we consider below the case of accepting strings (by l(∆ ) we denote the language of strings accepted by ∆ ): for comx ∈ {comn,comr,comw } we define comx(δ ) = ∑h−1 i=0 comx(wi =⇒ wi+1), for δ : w0 =⇒ w1 =⇒ ... =⇒ wh a halting computation, comx(w,∆ ) = min{comx(δ ) | δ : w0 =⇒ w1 =⇒ ... =⇒ wh is a computation in ∆ which accepts the string w}, comx(∆ ) = max{comx(w,∆ ) | w ∈ l(∆ )}, comx(l) = min{comx(∆ ) | l = l(∆ )}. similar definitions can be considered for more general decidability problem than accepting strings, then complexity classes can be defined. we do not enter here into details for this general case; in the next section we will briefly consider the specific case of dp automata and of languages. the previously sketched approach should be investigated in more details. which is the speed-up for a given problem or class of problems? clearly, comn(α) ≤ comr(α) ≤ comw (α), for all valid 242 gh. păun, m.j. pérez-jiménez α . moreover, in one communication step one can use arbitrarily many communication rules, which therefore move from a component to another one arbitrarily many objects. anyway, independently of the communication cost, presumably, only a linear speed-up can be obtained by splitting the problem in a given number of parts. are there problems which however cannot be solved in this framework in a faster way than by using a single p system (with active membranes) provided that the communication cost is bounded (e.g., using communication rules in r only for a constant number of times)? which is the communication complexity for a given problem or class of problems? finding suggestive examples can be a first step in approaching such issues. a case study will be considered in the next section, not for dp systems with active membranes (which, we believe, deserve a separate and detailed examination), but for a distributed version of p automata. 3 dp automata we consider now the distributed version of p automata, [6], [10], which are symport/antiport p systems which accept strings: the sequence of objects (because we work with strings and symbol objects, we use interchangeably the terms “object" and “symbol") imported by the system from the environment during a halting computation is the string accepted by that computation (if several objects are brought in the system at the same time, then any permutation of them is considered as a substring of the accepted string; a variant, considered in [6], is to associate a symbol to each multiset and to build a string by such “marks" attached to the imported multisets). the accepted string can be introduced in the system symbol by symbol, in the first steps of the computation (if the string is of length k, then it is introduced in the system in the first k steps of the computation – the p automaton is then called initial), or in arbitrary steps. of course, the initial mode is more restrictive – but we do not enter here into details. as a kind of mixture of the ideas in [6] and [10] for defining the accepted language, we can consider extended p automata, that is, with a distinguished alphabet of objects, t , whose elements are taken into account when building the accepted string (the other objects taken by the system from the environment are ignored). here, however, we work with non-extended p automata. a dp automaton is a construct ∆ = (o,e,π1,...,πn,r), where (o,π1,...,πn,r) is a dp scheme, e ⊆ o (the objects available in arbitrarily many copies in the environment), πi = (o, µi,wi,1,...,wi,ki,e,ri,1,...,ri,ki) is a symport/antiport p system of degree ki (without an output membrane), with the skin membrane labeled with (i,1) = si, for all i = 1,2,...,n. a halting computation with respect to ∆ accepts the string x = x1x2 ...xn over o if the components π1,...,πn, starting from their initial configurations, using the symport/antiport rules as well as the intercomponents communication rules, in the non-deterministically maximally parallel way, bring from the environment the substrings x1,...,xn, respectively, and eventually halts. the dp automaton is synchronized, a universal clock exists for all components, marking the time in the same way for the whole dp automaton. it is also important to note that we work here in the non-extended case, all input symbols are recorded in the string. in this way, at most context-sensitive languages can be recognized. the three complexity measures comn,comr,comw defined in the previous section can be directly introduced for dp automata (and they were formulated above for this case). with respect to them, we can consider two levels of parallelizability. a language l ⊆ v ∗ is said to be (n,m)-weakly comx parallelizable, for some n ≥ 2,m ≥ 1, and x ∈ {n,r,w }, if there is a dp automaton ∆ with n components and there is a finite subset f∆ of l such that each string x ∈ l − f∆ can be written as x = x1x2 ...xn, with ||xi|− |x j|| ≤ 1 for all 1 ≤ i, j ≤ n, each solving problems in a distributed way in membrane computing: dp systems 243 component πi of ∆ takes as input the string xi,1 ≤ i ≤ n, and the string x is accepted by ∆ by a halting computation δ such that comx(δ ) ≤ m. a language l is said to be weakly comx parallelizable if it is (n,m)-weakly comx parallelizable for some n ≥ 2,m ≥ 1. two conditions are here important: (i) the string is distributed in equal parts, modulo one symbol, to the components of the dp automaton, and (ii) the communication complexity, in the sense of measure comx , is bounded by the constant m. we have said nothing before about the length of the computation. that is why we also introduce a stronger version of parallelizability. a language l ⊆ v ∗ is said to be (n,m,k)-efficiently comx parallelizable, for some n ≥ 2,m ≥ 1,k ≥ 2, and x ∈ {n,r,w }, if it is (n,m) weakly comx parallelizable, and there is a dp automaton ∆ such that lim x∈l,|x|→∞ timeπ (x) time∆ (x) ≥ k, for all p automata π such that l = l(π) (timeγ (x) denotes here the smallest number of steps needed for the device γ to accept the string x). a language l is said to be efficiently comx parallelizable if it is (n,m,k)-efficiently comx parallelizable for some n ≥ 2,m ≥ 1, k ≥ 2. note that in the case of dp automata, the duration of a computation may also depend on the way the string is split in substrings and introduced in the components of the system; in a natural way, one of the most efficient distribution of the string and shortest computation are chosen. of course, as larger the constant k as better. moreover, while time∆ (x) is just given by means of a construction of a suitable dp automaton ∆ , timeπ (x) should be estimated with respect to all p automata π . an example is worth considering in order to illustrate this definition. let us examine the dp system from figure 1 – the alphabet of objects is o = {a,b,c,d,c1,c2,#}, and e = {a,b}. clearly, component π1 (in the left hand side of the figure) can only bring objects a,c inside, and component π2 (in the right hand side of the figure) can only bring objects b,d inside. in each step, only one of a,c, alternately, enters π1 and only one of b,d, alternately, enters π2 (note that we do not need objects c,d to be present initially in the environment, while one copy of each a and b is sufficient). the computation of each component can stop only by “hiding" the “carrier objects" c,d inside an inner membrane, and this means releasing c1 in π1 and c2 in π2. if these objects are not released at the same time in the two components, so that the exchange rule (s1,c1/c2,s2) can be used, then, because of the maximal parallelism, the object c1 should enter membrane (1,3), and object c2 should enter membrane (2,3); in each case, the trap-object # is released, and the computation never stops: the object # oscillates forever across membrane (1,2) in π1 and across membrane (2,2) in π2. consequently, the two strings accepted by the two components of ∆ should have the same length, that is the language accepted by the system is l(∆ ) = {(ac)s(bd)s | s ≥ 0}. note the crucial role played here by the fact that the system is synchronized, and that a computation which accepts a string xs = (ac)s(bd)s, hence of length 4s, lasts 2s + 2 steps (2s steps for bringing objects inside, one step when objects c,d are introduced in an inner membrane, and one inter-components communication step), with one of these steps being a communication between components. obviously, if we recognize a string xs = (ac)s(bd)s as above by means of a usual symport/antiport p system, then, because no two symbols of the string can be interchanged, no two adjacent symbols can be introduced in the system at the same step, hence the computation lasts at least as many steps as the length of the string, that is, 4s. this shows that our language is not only (2,r)-weakly comx parallelizable, but also (2,r,2)-efficiently comx parallelizable, for (r,x) ∈ {(1,n),(1,r),(2,w )}. this conclusion is worth formulating as a theorem. 244 gh. păun, m.j. pérez-jiménez ' & $ % ' & $ % # " !# " ! # " ! # " ! s1 (1,2) (1,3) c1 # (c1,out;c,in) (#,in) (#,out) (#,out;c1,in) c d (s1,c1/c2,s2) (c,out;a,in) (a,out;c,in) s2 (2,2) c2 # (2,3) (c2,out;d,in) (#,in) (#,out) (#,out;c2,in) (d,out;b,in) (b,out;d,in) figure 1: an example of a dp automaton theorem 1. the language l = {(ac)s(bd)s | s ≥ 0} is efficiently comx parallelizable, for all x ∈ {n,r,w }. note that this language is not regular (but it is linear, hence also context-free). the previous construction can be extended to dp automata with three components: π1 inputs the string (ac)s, π2 inputs (bd)s, and π3 inputs (ac)s, then π1 produces the object c1, π2 produces two copies of c2, and π3 produces the object c3. now, c1 is exchanged for one copy of c2 from π2 and c3 for the other copy, otherwise the computation never stops. the recognized language is {(ac)s(bd)s(ac)s | s ≥ 0}. this language is not context-free, hence we have: theorem 2. there are context-sensitive non-context-free languages which are efficiently comx parallelizable, for all x ∈ {n,r,w }. the previous two theorems show that the distribution, in the form of dp systems, is useful from the time complexity point of view, although only one communication step is performed and only one communication rule is used at that step. moreover, the proofs of the two theorems show that, in general, languages consisting of strings with two well related halves (but not containing “too much" information in each half of the string, besides the length), are weakly parallelizable, and, if no two adjacent symbols of the strings can be interchanged, then these languages are efficiently parallelizable. we have said nothing above about regular languages – this is the subject of the next section. solving problems in a distributed way in membrane computing: dp systems 245 4 all regular languages are weakly parallelizable the assertion in the title of this section corresponds to theorem 2.3.5.1 in [9], which states that for each regular language there is a constant k which bounds its (2-party) communication complexity. the version of this result in terms of weak comx parallelizability is shown by the following construction. consider a non-deterministic finite automaton a = (q,t,q0,f,p) (set of states, alphabet, initial state, final states, set of transition rules, written in the form qa → q′, for q,q′ ∈ q,a ∈ t ). without any loss of generality, we may assume that all states of q are reachable from the initial state (for each q ∈ q there is x ∈ t ∗ such that q0x =⇒∗ q with respect to transition rules in p). we construct the following dp automaton: ∆ = (o,e,π1,π2,r), where : o = q ∪ t ∪{d} ∪ {(q,q′) | q,q′ ∈ q} ∪ {⟨q,q f ⟩ | q ∈ q,q f ∈ f} ∪ {⟨q⟩ | q ∈ q}, e = o −{d}, π1 = (o,[s1[1,2 ]1,2 ]s1,q0,λ ,e,rs1,r1,2), rs1 = {(q,out;q ′a,in) | qa → q′ ∈ p} ∪ {(q,out;⟨q′⟩a,in) | qa → q′ ∈ p}, r1,2 = {(⟨q⟩,in), (⟨q⟩,out) | q ∈ q}, π2 = (o,[s2 ]s2,d,e,rs2), rs2 = {(d,out;(q,q ′)a,in) | qa → q′ ∈ p,q ∈ q} ∪ {((q,q′),out;(q,q′′)a,in) | q′a → q′′ ∈ p,q ∈ q} ∪ {((q,q′),out;⟨q,q f ⟩a,in) | q′a → q f ∈ p,q ∈ q,q f ∈ f}, r = {(s1,⟨q⟩/⟨q,q f ⟩,s2) | q ∈ q,q f ∈ f}. the first component analyzes a prefix of a string in l(a), the second component analyzes a suffix of a string in l(a), first guessing a state q ∈ q from which the automaton starts its work. at some moment, the first component stops bringing objects inside by taking from the environment a symbol ⟨q′⟩ for some q′ ∈ q, reached after parsing the prefix of the string in l(a). this object will pass repeatedly across the inner membrane of π1. the second component can stop if a state q′ is reached in the automaton a for which no rule q′a → q′′ exists in p (and then ∆ never stops, because its first component never stops), or after reaching a state in f , hence introducing an object of the form ⟨q,q f ⟩ for some q f ∈ f . note that q is the state chosen initially and always stored in the first position of objects (q1,q2) used by π2. the computation can halt only by using a communication rule from r, and this is possible only if q = q′ – the first component has reached the state of a which was the state from which the second component started its work. consequently, the concatenation of the two strings introduced in the system by the two components is a string from l(a). thus, the language l(a) is weakly parallelizable. now, consider a regular language such that no two adjacent symbols in a string can be permuted (take an arbitrary regular language l over an alphabet v and a morphism h : v ∗ −→ (v ∪{c})∗, where c is a symbol not in v , such that h(a) = ac for each a ∈ v ). then, clearly, if the two strings accepted by the two components of the dp automaton ∆ are of equal length (note that the strings of h(l) are of an even length), then the time needed to ∆ to accept the whole string is (about) half of the time needed to any p automaton π which accepts the same language. this proves that the language h(l) is efficiently parallelizable, hence we can state: 246 gh. păun, m.j. pérez-jiménez theorem 3. each regular language is weakly comx parallelizable, and there are efficiently comx parallelizable regular languages, for all x ∈ {n,r,w }. of course, faster dp automata can be constructed, if we use more than two components. however, it is not clear whether dp automata with n + 1 components are always faster than dp automata with n components – this might depend on the structure of the considered language (remember that the distribution of the input string to the components of the dp automaton must be balanced). more specifically, we expect that there are (n,m) weakly parallelizable languages which are not, e.g., (n + 1,m) weakly parallelizable; similar results are expected for efficiently parallelizable languages. a natural question is how much the result in theorem 3 can be extended. for instance, is a similar result true for the linear languages, or for bigger families of languages? according to theorem 2.3.5.4 in [9], this is not true for measures comr and comw , the recognition of context-free languages (actually, the language lr at page 78 of [9] is linear) have already the highest communication complexity (in 2party protocols), a linear one with respect to the length of the string. thus, the number of communication rules used by a dp automaton during a computation cannot be bounded by a constant. the case of measure comn remains to be settled: is it possible to have computations with a bounded number of communication steps, but with these steps using an unbounded number of rules? we conjecture that even in this case, languages of the form {x mi(x) | x ∈ {a,b}∗}, where mi(x) is the mirror image of x (such a language is minimally linear, i.e., can be generated by a linear grammar with only one nonterminal), are not weakly comn parallelizable. many other questions can be raised in this framework. for instance, we can consider families of languages: (n,m)-weakly comx parallelizable, weakly comx parallelizable, (n,m,k)-efficiently comx parallelizable, and efficiently comx parallelizable. which are their properties: interrelationships and relationships with families in chomsky hierarchy, closure and decidability properties, hierarchies on various parameters, characterizations and representations, etc. then, there is another possibility of interest, suggested already above: the static complexity measure defined as the cardinality of r, the set of communication rules. there is a substantial theory of descriptional complexity of (mainly context-free) grammars and languages, see [8], which suggests a lot of research questions starting from coms(∆ ) = card(r) (with “s" coming from “static") and extended to languages in the natural way (coms(l) = min{coms(∆ ) | l = l(∆ )}): hierarchies, decidability of various problems, the effect of operations with languages on their complexity, etc. 5 the power of controlling the communication in the previous sections the communication rules were used as any rule of the system, non-deterministically choosing the rules to be applied, with the communication rules competing for objects with the inner rules of the components, and observing the restriction of maximal parallelism. however, we can distinguish the two types of rules, “internal evolution rules" (transition rules, symport/antiport rules, rules with active membranes, etc.) and communication rules. then, as in [1], we can apply the rules according to a priority relation, with priority for evolution rules, or with priority for communication rules. moreover, we can place various types of controls on the communication channel itself. for instance, because the communication rules are antiport rules, we can associate with them promoters or inhibitors, as used in many places in membrane computing. a still more natural regulation mechanism is to associate states with the channels, like in [7]. in this case, the communication rules associated with a pair (i, j) of components πi,π j are of the form (q,u/v,q′), where q,q′ are elements of a given finite set q of states; initially, the channel is assumed in a given state q0. a rule as above is applied only if the cannel is in state q – and the antiport rule (i,u/v, j) can be applied; after exchanging the multisets u,v among the two components πi,π j, the state of the channel is changed to q′. solving problems in a distributed way in membrane computing: dp systems 247 an important decision should be made in what concerns the parallelism. in [7], the channel rules are used in the sequential mode, but we can also consider two types of parallelism: (i) choose a rule and use it as many times as made possible by the objects in the two components, or (ii) apply at the same time all rules of the form (q,u/v,q′) for various u and v (but with the same q and q′), in the non-deterministic maximally parallel way. in the result discussed below, any of these two possibilities works – and the result is somewhat surprising: theorem 4. any recursively enumerable language l is (2,2)-weakly comn and comr parallelizable and has coms(l) ≤ 2, with respect to extended dp automata with channel states. we do not formally prove this assertion, but we only describe the (rather complex, if we cover all details) construction of the suitable dp automaton. take a recursively enumerable language l ⊆ t +, for some t = {a1,a2,...,an}. for each string w ∈ t +, let valn+1(w) be the value of w when considered as a number in base n + 1, using the digits a1,a2,...,an interpreted as 1,2,...,n, without also using the digit zero. we extend the notation to languages, in the natural way: valn+1(l) = {valn+1(w) | w ∈ l}. clearly, l is recursively enumerable if and only if valn+1(l) is recursively enumerable, and the passage from strings w to numbers valn+1(w) can be done in terms of p automata (extended symport/antiport p systems are universal, hence they can simulate any turing machine; this is one of the places where we need to work with extended systems, as we need copies of a and b – see below – to express the values of strings, and such symbols should be taken from the environment without being included in the accepted strings). construct now a dp automaton ∆ with two components, π1 and π2, working as follows. the component π1 receives as input a string w1 ∈ t ∗ and π2 receives as input a string w2 ∈ t ∗, such that w1w2 should be checked whether or not it belongs to the language l. without loss of generality, we may assume that |w1| ∈ {|w2|, |w2|+1} (we can choose a balanced distribution of the two halves of the string). in the beginning, the state of the channel between the two components is q0. both components start to receive the input symbols, one in each time unit; the component π1 transforms the strings w1 in valn+1(w1) copies of a symbol a, and π2 transforms the string w2 in valn+1(w2) copies of a symbol b. when this computation is completed in π1, a special symbol, t, is introduced. for this symbol, we provide the communication rule (q0,t/λ ,q1), whose role is to change the state of the channel. we also consider the rule (q1,a/λ ,q2). using it in the maximally parallel way, all symbols a from π1 are moved to π2, in one communication step. because we have considered w1 at least of the length of w2 and we also need two steps for “opening" the channel and for moving the symbols a across it, we are sure that in this moment in π2 we have, besides the valn+1(w1) copies of a, valn+1(w2) copies of b. the second component takes now these copies of a and b and computes valn+1(w1w2), for instance, as the number of copies of an object c. after that, π2 checks whether or not valn+1(w1w2) ∈ valn+1(l). if the computation halts, then the string w1w2 is accepted, it belongs to the language l. note that the dp automaton ∆ contains two communication rules (hence coms(l) ≤ 2) and that each computation contains two communication steps (hence comn(l) ≤ 2), in each step only one rule being used (hence comr(l) ≤ 2). these observations complete the proof of the theorem. of course, comw (∆ ) = ∞. (similarly, if we define comr taking into account the multiplicity of using the rules, then also comr can be considered infinite – hence the assertion in the theorem remains to be stated only for the measure comn.) instead of changing channel states as above, we can assume that the channel itself switches from “virtual" to “actual", like in population p systems, [3]: the channel is created by object t produced by π1, and then used for moving a from π1 to π2 by a usual communication rule (which, by definition, is used in the maximally parallel way). anyway, the conclusion of this discussion is that the results we obtain crucially depend on the ingredients we use when building our dp systems (as well as on the chosen definitions for complexity 248 gh. păun, m.j. pérez-jiménez measures and types of parallelizability). 6 closing remarks the paper proposes a rather natural way (using existing ingredients in membrane computing, bringing no new, on purpose invented, stuff into the stage) for solving problems in a “standard" distributed manner (i.e., splitting problems in parts, introducing them in various component “computers", and constructing the solution through the cooperation of these components) in the framework of membrane computing. so called dp schemes/systems were defined, and two notions of parallelizability were proposed and briefly investigated for the case of dp automata (accepting strings). a lot of problems and research topics were suggested. the reader can imagine also further problems, for instance, transferring in this area notions and questions from the communication complexity theory, [9], considering other types of p systems (what about spiking neural p systems, where we have only one type of objects and no antiport-like rules for communicating among components?), maybe using unsynchronized p systems, non-linear balanced input, and so on and so forth. we are convinced that dp systems are worth investigating. note. during the recent brainstorming week on membrane computing, 1-5 of february 2010, sevilla, spain, several comments about the definitions and the results of this paper were made, especially by erzsébet csuhaj-varú, györgy vaszil, rudolf freund, and marian kögler. several continuations of this paper are now in preparation; the interested reader is requested to check the bibliography from [17], in particular, the brainstorming proceedings volume. acknowledgements this work is supported by proyecto de excelencia con investigador de reconocida valía, de la junta de andalucía, grant p08 – tic 04200. useful discussions with henry adorna are gratefully acknowledged. bibliography [1] h. adorna, gh. păun, m.j. pérez-jiménez: on communication complexity in evolutioncommunication p systems. manuscript, 2009. 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[17] the p systems website: www.ppage.psystems.eu. gheorghe păun graduated the faculty of mathematics, university of bucharest, in 1974 and received his ph.d. from the same university in 1977. from 1990 he is a senior researcher at the institute of mathematics of the romanian academy. he (repeatedly) visited numerous universities in europe, asia, and north america. his main research areas are formal language theory and its applications, computational linguistics, dna computing, and membrane computing; this last research area was initiated by him, in 1998, and the respective models are now called p systems, see http://ppage.psystems.eu). he has published a large number of research papers, has lectured at many universities, and gave numerous invited talks at recognized international conferences. he has published eleven monographs in mathematics and computer science, has (co)edited over seventy collective volumes and special issues of journals, and also published many popular science books, books on recreational mathematics (games), and fiction books. he is a member of the editorial board of more than a dozen international journals and was/is involved in the program/steering/organizing committees for many recognized conferences and workshops. in 1997 he was elected a member of the romanian academy and from 2006 he is a member of academia europaea. he also got other honors, in romania or abroad. he is an isi highly cited researcher (see http://isihighlycited.com/). 250 gh. păun, m.j. pérez-jiménez mario j. pérez-jiménez received his degree in mathematics from the barcelona university and the doctor degree in mathematics from the sevilla university. in the past, he was associated professor at the university of barcelona. currently, he is full professor of computer science and artificial intelligence at the university of sevilla, where is the head of the research group on natural computing. his main research fields are computational complexity theory, natural computing, membrane computing, bioinformatics, and computational modeling for systems biology and population biology. he has published twelve books in computer science and mathematics, and over 200 scientific papers in international journals (collaborating with many researchers worldwide). he is the main researcher in various european, spanish and andalusia research grants. he has been an independent expert to the evaluation of nest (new and emergent science and technology) proposals under the sixth framework programme of the european community, and from may 2006 he is an european science foundation peer reviewer. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 961-967 secure data retention of call detail records f. vancea, c. vancea, d. popescu, d. zmaranda, g. gabor florin vancea, codruţa vancea, daniela elena popescu doina zmaranda, gianina gabor university of oradea, romania 410087 oradea, st. universităţii 1 e-mail: {fvancea,cvancea,depopescu,zdoina,gianina}@uoradea.ro abstract: in today’s world communication is relying heavily on electronic means, both for voice and other native data. all these communication sessions leave behind journaling information by the very nature of the underlying services. this information is both sensitive with respect to user’s rights and important for law enforcement purposes, so proper storage and retrieval techniques have to be taken into consideration. the paper discusses such techniques in relation with recent eu recommendations and suggests some methods for achieving good performance while preserving the required security levels. keywords: call detail records, secure retention, row chaining. 1 introduction during the last years we have seen an increasing interest in secure storage and retrieval of journaling archives of various transactions. the possibility of fraud or inappropriate usage of resources have led to laws and regulations regarding storage of transaction history for phone calls, sms, e-mail traffic and other forms of communication. specifically, the european parliament and the council of the european union have adopted a directive (2006/24/ec) [1] which gives guidelines for retention of data collected from publicly available communication services or public communication networks, in order to ensure that the data is available for investigation, detection and prosecution of serious crime. usually, the devices that perform the actual communication function are generating logging data that can identify the peers, time attributes and other relevant details of the transaction. these logs have been traditionally used for billing and eventually for statistical purposes by the network operators themselves. since this information was stored anyway by the operators, the law enforcement agencies could make use of them for specific purposes within a frame more or less regulated by each country laws. currently, many states are requiring the voice and data operators to store these logs for a well-defined period of time and to present them timely when legally requested by various law-enforcement entities. in order for this information to be useful for investigation and furthermore for prosecution, there is an implicit requirement that the information contained by the logs cannot be tampered with. even if the operators should not (or actually may not) capture and store the actual content of the communication, there are also increasing concerns about the privacy of the service users related to their communication partners. wholesale collection of user communication actions and habits can be viewed as a breach of privacy, so strong protection against casual browsing or unauthorized intentional usage of this information should be provided. when combining the long-term storage and efficient retrieval requirements with privacy, nonrepudiation, integrity, performance and cost requirements, the task of data retention becomes non-trivial. copyright c⃝ 2006-2010 by ccc publications 962 f. vancea, c. vancea, d. popescu, d. zmaranda, g. gabor 2 system structure, main requirements and challenges the communication network operator has to manage during normal operation several pieces of information about each communication session. at the bare minimum this information is used to establish communication channels and to ensure correct traffic flow but it may also be stored and used for billing purposes. we will call such information about one user session call detail record (cdr) as this is the name commonly used by voice service providers. for simplicity we will also name the communication session call but all reasoning below applies also for data sessions (e-mail, internet sessions). the records originate in the network equipment (central switch, access infrastructure), which have limited ability to store data, so periodically the records are transferred into database systems where they are processed (e.g. for billing) and finally discarded. if cdr retention is desired, the records are also stored for a rather extended period of time in some sort of semi-persistent storage until lawfully required through a query or until the retention period expires. network equipment billing process data is discarded legitimate query cdr retention system figure 1: cdr data flow and data retention for the purpose of this discussion we will not consider the billing path because this is part of the existing business model of the operator and is already well-established and less likely to accept any change. user data may be exposed there but this is an old threat, which is supposedly already handled by operator’s procedures. furthermore, the lifecycle of user sensitive data there is relatively short (one month or at most a couple of months) and the impact of integrity loss is only eventually financial to the operator but not deeply legal to the user. the cdr data can be attacked: • before entering the retention system; • while stored in the retention system; • during query; • after supposed deletion. we will consider that the attacks performed before entry in the retention system have a low risk because the data stays in transit between network equipment and storage for a short period of time, the path is internal to the operator (thus is reasonably secure) and more important, the attacks are likely to take place only after a reasonable long time after the call is placed (otherwise the attacker would have refrained from making the call at all or would have attacked directly the network infrastructure to obtain an untraceable call). the attacks on cdr data during the query may be: secure data retention of call detail records 963 • impersonating authorized entities; • query alteration; • result alteration; • result disclosure. all those can be stopped by proper signing and encryption of the request and the result. the attacks after supposed deletion are disclosure attacks and would attempt to extract protected information from media used to store cdr data in the retention system. encrypting the data is of course the main protection method but ciphertext-extensive attacks may exist which exploit the very large amount of encrypted data. to avoid these attacks write-once media should be destroyed properly and rewritable media should be properly erased after retention time expires. we will focus on data stored in the retention system, because the cdr data stays there a rather long time (at least 6 months, according to eu recommendation) and the attacks may be either against the confidentiality or against the integrity of the data. the cdr retention system has to take steps to prevent at least the following attack types: • unauthorized disclosure of call detail, either for a particular call, a particular originator or a particular termination • alteration of call detail (originator, termination, start/end time, other details) • complete removal of one call or of all calls matching a particular originator, termination or time interval • complete denial of service against the retention system when evaluation the potential solutions we should also consider the size of the problem. one reasonably large operator may easily originate or terminate in excess of 10m calls per day (probably at least one order of magnitude more in the case of data services). at a minimum, a cdr will contain the originating subscriber number or identifier (at least 10 characters), termination or forwarding subscriber number (at least 10 characters), starting timestamp and duration (about 6 characters). to cover all situations (international dial numbers) additional space has to be reserved. in the case of mobile services additional location data is required and data services may have longer identification tokens. we estimate therefore the raw cdr to require at least 32 bytes of storage, leading to a minimum figure of 320mb of raw data to be stored for each day of traffic. this extends to about 10gb monthly and 120 gb yearly. the above figures may appear small by today’s standards in memory and storage capacity, but they are absolute minimum estimates of raw data. in a working system there should be protection which is obviously introduced by encryption and a working system should offer some sort of direct random access to the data, preferably optimized like an indexed database query. both features have storage overhead and we consider it to be tenfold from extrapolating previous experiences. in the end the retention system will probably have to deal securely with about 500gb of data for a 6 month retention period on the assumption of 10m calls per day. the above estimation may still appear small by comparison to a large business platform, but we should keep in mind that the cdr retention system is a non-profitable investment for the operator so one cannot expect it to use significant resources in terms of purchased hardware or software. fortunately, the criteria for common queries are limited: by originator identifier (directly or indirectly mapped through user identity), by termination identifier and by time interval. 964 f. vancea, c. vancea, d. popescu, d. zmaranda, g. gabor 3 potential solutions according to the problem description we should figure how to store the data and how to efficiently retrieve the required cdr according to the typical query criteria. 3.1 direct worm storage one obvious approach is to use worm media, given that the write-once feature will provide tampering protection. this approach will not automatically solve the confidentiality issue, so some additional measures have to be taken. the main disadvantage of passive worm media is capacity. considering the above estimations, plain passive worm media is not sufficient unless the time span for data written on one support is short. the time span should also be as short as possible, because data is protected only after write, and data in transit will be vulnerable to integrity attacks. short time spans on one support will translate into many media units for the entire retention period, making the query process difficult and time-consuming. the speed of passive worm media is also limited, affecting the query performance. a better performing approach is to use special active worm devices. these are special storage units presenting the worm feature at the access port (ethernet, scsi) but backed by conventional magnetic storage. the worm feature is protected by dedicated interface and firmware and the whole device is tamper-proof. this kind of device offers good protection but is usually expensive and its main use case is append to log. our goal is to map database-like search capabilities over the protected data and the append to log restricted primitive is not friendly with common database engines and their storage management. the lesser version of active worm is a hybrid software system that achieves the write once feature by modification of the operating system block drivers or by a custom operating system [2]. the protection it provides may be reasonable but far weaker than the one offered by true passive optical media. this hybrid alternative fails to be database-friendly, too. all the solutions based mainly on worm devices will amount to periodically storing the cdr logs and sequentially scanning those for each query. this is not very efficient; especially considering that all the data that is sequentially scanned will be encrypted for confidentiality purposes. 3.2 database backed by worm storage a slightly better solution would be to use a true database for query purposes and a passive worm reference for result validation. the cdr data would both be stored in the database and consolidated with a reasonable short interval to worm storage (passive media should be ok). the initial query would be performed over the regular database and the integrity of the database itself might be checked against some hash checks stored on worm media. the solution is fragile and may not provide either the required level of integrity or the desired level of performance. some dbms (e.g. oracle) offer the possibility to split the storage segments over read-write and read-only media. in such a simple scenario, the retention time window would be split in several fragments out of which only the last one is residing on read-write storage. of course, management of splitting would be rather complicated and there are certain limitations in place which would limit the reasonable unsafe fragment to at least one week. the solution has the disadvantage of poor query performance for time frames located on read-only media, but the main disadvantage is that the unsafe fragment cannot be protected even if conventional rowlevel encryption is applied. little can be done to actually prevent the rows in the read-write partition from being maliciously created, changed or deleted. security mechanisms at database secure data retention of call detail records 965 level do exist but we are looking for an additional layer of trust, which would rule out any database authority from tampering the data. by using encryption at row level one can simply prevent row changing and unauthorized row creation [3]. efficient query is possible by index-scan over plaintext time attributes or by index-scan over pre-encrypted source or target fields. unfortunately row deletion can be always performed at some authority level without being even detected. source target start time duration source target start time duration source target start time duration symmetrical encryption sig sig sig row-level signature figure 2: row content is protected but rows can be deleted 3.3 database with cross-linked rows backed by worm storage an improved solution is using results of our previous work, namely row chaining [4] [5]. beside symmetrical encryption for confidentiality protection and row-level signature for row integrity and authenticity protection we use a chaining scheme that links one row to the previous one. the scheme can detect row deletion unless a whole block of rows from the end are collectively deleted. this would be solved by building the chain two ways, at the cost of revisiting previous records. given the volume and the dynamic of data we consider a single chain is providing enough protection. dur .. dur .. s’. . s’. . s’. . source target start time dur .. source target start time source target start time symmetrical encryption sig sig sig row-level signature figure 3: rows protected by chaining cannot be deleted using the chaining scheme has the great advantage of relieving us from calling the worm storage upon each query. the cdr data can now reside entirely in plain read-write database storage as its integrity is now protected by chains and signatures. when a query is presented, the results are quickly determined at regular database speed, and then the result-set is validated by walking the chain one or two steps for each record. the worm storage is still required in case tampering is detected in any form and as a backup to avoid denial-of-service attacks (complete corruption of the database). the above scheme has however a flaw and can be improved. one can maliciously alter the database indexes to skip certain records, and those records will never be returned in the result 966 f. vancea, c. vancea, d. popescu, d. zmaranda, g. gabor set even if they are present in the database and the storage structure is still cryptographically intact. all checks will succeed, even for queries returning rows adjacent to the masked ones. the solution is to build the chains not on subsequent rows as they are added to the database but on rows belonging to the same source or the same target. this means that the database will contain a chain of rows for each source and a chain of rows for each target. there is a new efficiency issue here, because we will need two chaining fields, one for source and one for target. the issue of protecting the last record in the chain becomes also apparently more sensitive, because the open ends of the chains are living longer. however, undetected deletion of a row requires that it is at the open end of both source and target chains, which is unlikely to happen for long in regular traffic. 4 aggregation attack and countermeasures so far we have considered that encrypting the source and the target fields offers appropriate protection against disclosure of the actual participants to a call. unfortunately, in order to benefit from indexed search a particular source or target identifier has to be encrypted always with the same key, yielding the same encrypted value. the key does not have to be the same for all encryptions (actually should be changed over the source-target population) but once an identifier is encrypted in the system it should use the same ciphertext representation. this invariance property required for indexing opens a path for aggregation attacks. say the attacker has access to the database (either online or in archived form) and is looking for source a, but does not know the key. actually it does not need to. if the attacker knows that a has placed calls at some particular moments in time it can aggregate through the data looking for that particular call pattern. once it finds it all the past and future cdr involving a (at least as source) are accessible. the required number of such fixed points is not very high, as it was previously proven in aggregation attacks on blinded statistical data. the success of the attack depends on the traffic volume around the known time-points and on the number of the time-points available. the attacker may even optimize its chances by explicitly placing calls to a at low-traffic time intervals or if the attacker is itself a, by making calls at such well-chosen moments. to provide some level of protection against this kind of attack we have to dilute the resolution the attacker has in the time domain when building a candidate set based on a given timestamp. when the attacker has a timestamp it can effectively build a set of sources or destinations that have placed or received a call at that moment in time. if the timestamp is say 11:00:00 am, the set will be likely large (high traffic at busy hours) but if the timestamp is 03:00:00 am the set will be considerably smaller. the time resolution dilution has to be made by sacrificing performance at some level. we will store in the start time field a down-rounded timestamp and in an additional encrypted field the offset of the actual start time. this will reduce the resolution of the time searches and will entail some sequential processing to achieve exact query results. however, since the query specifies typically also the source or the target the performance penalty will be small (will use source or target indexes). on the other hand, the attacker has only the degraded start time to work with. to further refine the protection, the rounding should be made according to traffic patterns. lower traffic intervals should be larger and higher traffic intervals may be smaller. this helps reducing the performance degradation we mentioned before and can be achieved by a hamming-like algorithm applied once in a while to determine the optimal rounding intervals. even when using the rounding method to protect against data aggregation, special care should be taken with respect to the data storage pattern. all records have to be added incrementally with the starting timestamp (or eventually the end timestamp) because that is the way they come secure data retention of call detail records 967 from the network device logs. if the rounding algorithm performs for example the rounding at 23:00, 01:00, 05:00 the attacker may place a call shortly after 01:00 am and look for records after but in close proximity of the start time switch from 23:00 to 01:00. the solution is to randomly shift the rounding boundaries each day with at least 10% of the theoretical interval size. 5 conclusions and future works designing and implementing a system which provides secure data retention of call detail records is not a trivial task. there are several inter-related issues ranging from storage methods to data protection by encryption, and from efficient query to safe implementation. unfortunately we have to note that in the current form, secure data retention has little driving force. the operators that manage the data have little to gain by properly implementing such a system and proper implementation is not necessarily cheap. the laws mandating the presence of such systems are rather vague on the required security and performance levels for such systems. the potential direct users have no direct means to encourage proper (read fast) implementations and the real owners of the call data (the subscribers) have also little means to encourage proper (read secure) implementations. we however tried to outline within the limited size of this paper some of the requirements and the challenges related to such a system together with means to achieve a safe system with good performance. of course, each link in the chain has its very important contribution to the overall strength and there are many details left uncovered. some directions may be: the proper protocol to be implemented between the retention system and the authorized partner requesting a query, proper and efficient protocol for destruction of cdr data at the end of retention period, implementation details on the chaining and on the time dilution algorithm, general implementation notes regarding secure split-role administration. some of our future work may be related to these directions. bibliography [1] ***, directive 2006/24/ec of the european parliament and of the council of 15 march 2006, official journal of the european union, l105/54, 13.04.2006 [2] y.wang, y.zheng, fast and secure worm storage systems, proceedings of the ieee security in storage workshop (sisw), pages 11-19, 2003 [3] b. schneier, applied cryptography, john wiley & sons, 1996. [4] f. vancea, c. vancea, f. vancea, c. vancea, practical security issues for a real case application, int. j. of computers, communication and control 3(s), pages 11-16, 2008 [5] f. vancea, c. vancea, protecting data integrity with chained rows and public key cryptography. comments on a real case, proceedings of rsee 2008 international journal of computers, communications & control vol. ii (2007), no. 1, pp. 56-65 advancing electronic assessment nikolaos doukas, antonios andreatos abstract: a computer-aided assessment system is presented that has been designed to produce and deliver tests to the hellenic air force academy students and assess their performance. the system is called e-xaminer and is intended for use in both undergraduate courses and distance learning post-graduate programs of the academy. the e-xaminer uses meta-language concepts to automatically generate tests, based on parametrically designed questions. tests intended for different students may entail differences in the arithmetic parameters. additionally, different tests may be composed from different but equivalent and randomly chosen sub-questions. the system may also present each student with a scrambled sequence of the same questions, as a counter-measure against cheating. examinations are delivered via a webbased interface; an automatically generated program marks the answers submitted by each student. e-xaminer allows the implementation of question parameterisation and counter cheating measures, so that electronic tests become significantly different and more powerful than traditional ones. sample problems are presented which show the additional features of the e-xaminer, intended to facilitate the work of the course organiser in issuing and marking the tests, as well as in combating cheating. this paper focuses on some new, advanced types of questions enabled by electronic assessment; it then compares paper-and-pencil exams to electronic exams; results from a small student poll on the electronic exams are also presented. finally, the directions for planned future work are outlined. keywords: e-xaminer, computer-assisted assessment, domain specific languages, hafa. 1 introduction 1.1 the revolution of e-learning e-learning may be defined as the technology and the academic services related to the teaching and learning processes [1]. it is a wide term covering all the range of previous educational applications such as computer based training (cbt) and web based training (wbt), as well as more recent technologies such as lms, virtual classrooms or labs and digital collaboration [2]. historically, the rush of e-learning may be located in the decade 1990-2000, a period characterised by a boom in the information & communication technologies (ict) and the invent and evolution of the web. for better exploitation of the above technologies, standards which will allow the interoperability of various platforms and the re-use of educational material are being developed. the first such standards appeared in 2001. the above evolution, along with other reasons, not only led to the foundation of many open universities around the world [3], but also pushed many traditional universities to offer distance learning courses [1]. it is estimated that, as far as continuing education in higher education institutions is concerned, distance learning will grow ten times faster than on-campus learning over the next ten years [4]. according to burns, “up to 45 percent of colleges and university enrolment is from adult learners, many of which sign-up for distance learning classes rather than on-campus classes. revenues for continuing education rose 67% at responding institutions since the previous survey in 2004. the trend is expected to grow distance learning 10 times faster than campus classes over the next decade. the growth in distance learning is driven by the growth of interactive marketing” [4]. the web has been considered a means of education and knowledge since its early days. today it is widely used for educational purposes due to its worldwide spread and penetration and because it supports a lot ways for the representation of information [5]. copyright c© 2006-2007 by ccc publications selected paper from icvl 2006 advancing electronic assessment 57 the recent proliferation of distance learning (delivered by open universities worldwide, as well as traditional institutions) has encouraged many higher education organisations to develop software for supporting this type of learning. a well known such package is claroline [6], which is freeware and supports 32 languages. with the expansion of e-learning, the need for electronic examinations has become more imperative. a significant number of computer aided assessment (caa) systems has appeared as a result of the work of both academic institutions and commercial companies. examples of such systems are given in [6], [7], [8], [9], [10], [11] and [12]. these systems have already been extensively tested and are being widely used. 1.2 the merits of electronic testing in ref. [13], the public-private coalition known as the ‘partnership for 21st century skills’ gives a vision of how students should be prepared to face the challenges of the 21st century. within this report, the benefits of using technology in order to give immediate feedback on student assessments is underlined. electronic testing has been accused of bringing non-technology students to a dissadvantage when they are forced to use a keyboard to type their answers, rather than writing them on paper [14]. in response, public and private sector experts state in [13] that 21st century literacy is much more than the basic computer skills required for typing an answer. it is pointed out that the new tools for learning including computers, telecommunications and audio or video based media are critical enablers of learning in a number of realms, even for subjects that have nothing to do with technoology. they hence conclude that there is a need for assessment tools that measure those essential skills that will never be captured by traditional tests. while the value of traditional testing (like portfolio assessment) as a means for classroom level assessment of individual students is not questioned, electronic tests provide rapid feedback on students performance that can be compared across classrooms or schools [13]. furthermore, computer based training can enable the evolvment of novel concepts that instructors would have never contemplated of delivering a few years ago [15]. the authors present a training platform which automatically designs courses that not only adapt to the students capabilities and previous knowledge, but also dynamically adjusts the contents, according to the students performance during the progress of the course. the statistical results presented show that computer delivered courses are extremely efficient in promoting learning. an automated computer-assisted assessment (caa) system has already been presented [12], [16], which has been designed for the needs of the hellenic air force academy (hafa). this system is called e-xaminer, a title which emphasises the fact that this system extends e-learning in the field of examinations. the principle reasons which dictated the development of a new system from scratch, in order to cover the needs of the hafa, where the following: (1) the limited number of types of questions supported by the existing systems. (2) the intention of hafa to experiment with cheating countermeasures so that the final system could eventually be used in its distance learning program [17]. (3) most existing systems do not support the greek language, while teaching in hafa is done exclusively in greek. this fact creates some special requirements that needed to be met. in ref. [16] we have presented the nine different problem types and partial credit possibilities supported by the e-xaminer so far and we have briefly discussed the main advantages of caa. in ref. [12] we have presented the dsl approach adopted by the e-xaminer, cheating countermeasures and some experimental results obtained by the pilot application of the e-xaminer. 1.3 making the difference multiple choice tests suffer from the drawback that examinees may aquire free-marks by taking lucky guesses [18]. furthermore, a way to exploit electronic exams in order to overcome this problem is proposed. given the evolution of web technologies since then, the need to make electronic exams 58 nikolaos doukas, antonios andreatos significantly different is currently more pronounced. another challenge for the design of electronic examination systems is to make exams more pleasant for students. students are naturally reluctant to sit exams, since exams are a source of stess for them. on the contrary, they are keen to spend large periods of time in front of the computer screen, surfing the internet. the question is hence posed: if exams are delivered using a friendly web interface, can they become a less stressful experience? this paper focuses on the implementation of test parameterisation and counter-cheating measures on the e-xaminer, that distinguish e-xams from paper and pencil exams and make them a better tool from promoting learning; it also lists some empirical results and presents a comparison between conventional paper-and-pencil exams and electronic exams given by means of the examiner (and therefore, referred to as ‘e-xams’). the way in which questions are parameterised is firstly presented. examples of parametric questions are shown. counter-measures against exam-time cheating are listed and the way in which these have been implemented in the new system is described. the new system has been used for mid-term tests of students of all four years of the academy, for all specialisations. statistical results from these tests are given. finally conclusions are drawn and the directions for the planned future work are presented. 2 design of the system 2.1 system architecture the architecture of the e-xaminer is depicted in figure 1. the system operates as follows. the instructor sets examination questions and model answers using a domain specific language (dsl) [19]. these files are given as input to the core of the e-xaminer which produces two programs, the examination agent and the marking agent. the examination agent is installed in an appropriate http server and is the only part of the system that is ever exposed to a public terminal. the examinees open the ‘e-xam’ pages and answer the test questions by filling in forms, using their favorite web browser. student answers are stored and finally passed as input to the examination agent that runs on the instructors terminal (e.g. a laptop computer only temporarily connected to the student network). a marking report and statistics are produced. figure 1: architecture of the e-xaminer advancing electronic assessment 59 2.2 the examination procedure figure 2 depicts the examination procedure. the correspondance to the system architecture described earlier on is straightforward. it is interesting to note however that it is possible for the instructor, to reiterate the process with a revised marking scheme. a marking (grading) policy is implicitely determined by the instructor and is included in his/her marking scheme. the marking scheme may include alternative answers that carry different marks. for example, the correct answer to the question “what is the maximum value that can be represented in an n-bit unsigned binary number”, is 2n − 1. the instructor may however decide to grant students a partial credit for the (wrong) answer 2n . this iterative procedure, which would have been more tedious for a paper-and-pencil test, represents a significant advantage of e-assessment. as already pointed out, the main drawback of existing systems, as far as their use by the hafa is concerned, was the fact that they supported only a limited number of question types; furthermore, these questions were often fixed (same for all students). it was required that the new system supported parameterisation. additionally, the whole process needed to be automated. by using the e-xaminer, the entire class is examined simultaneously, but each student sees a different worksheet [12]. figure 2: flowchart of the examination procedure 2.3 question parameterisation apart from the innovative types of questions, a key feature of the e-xaminer is question parameterisation. it was required that the system be able to receive as input from the course instructor a skeleton question and produce a series of different questions from it. the process of defining and designing such skeleton questions is the question parameterisation incorporated in the e-xaminer [12]. in order to implement this parameterisation, our system employs the concept of domain specific languages or dsl [19]. a skeleton question can hence be defined by its textual parts, a series of parameters that it requires and a rule which is used for assembling the final question. an additional parameterisation for the entire test is question order scrambling, i.e., the randomisation of the order in which the questions in the test of a particular student is arranged. this randomisation may be expanded to the arrangement of sub-questions of a particular question. two examples of parameterised questions which were used during the past academic year (2005-6) are given below. the first question was designed to examine a chapter on binary arithmetic. each student was given 60 nikolaos doukas, antonios andreatos six different random numbers. each student was hence required to perform a number of operations on the numbers assigned to them. these operations were selected at random for each student from a set of predefined ones (binary to decimal conversion, decimal to binary conversion, addition, subtraction and negation). the setup of this question eliminated any chance that a student might have copied from their collegue sitting next to them. the second question was designed as part of a course on computer networks [20]. the skeleton of question was the following: “a company owns a set of class b ip addresses. the company has x lans with an increase rate of y% during the next 10 years. each lan connects w computers, with an increase rate of z% during the next 10 years. which is the best way of sharing the class b host bits between subnets and computers?”. all students sitting this examination had to answer this question, however each student had a different set of values for the x, y, z and w parameters. these values where automatically assigned by the e-xaminer. tests delivered during the pilot application phase, were just automated versions of what a paper and pencil test would have looked like. in order to better exploit the potentials of the caa web interface, the decision was made to include various innovative features in the tests. the first such feature was the electronic processing of an (electronic) document, linked to the e-xam by a simple hyperlink. the question had as follows [20]: “given the specification of http 1.1 [rfc 2616] in electronic format, answer the following questions: 1. what is the signalling mechanism used to terminate a persistent connection? 2. who may terminate such a connection? (a) the client, (b) the server, (c) both of them 3. what does an error code 415 signify? 4. what encryption services are provided by http?” rfc 2616 is a 176-page document describing the http standard. students are obviously not asked to memorise this text and it is impractical to copy the entire document for each student for the purpose of the exam. they are however expected to be familiar with such documents and able to extract information from them using various text/word processing tools (such as ‘search’ or ‘find’). testing of this skill is only possible via electronic exams. these questions may further combined with parameterisation, in order to produce a different error code for each student. the next planned test requires the students to use some special services available on the internet, get the answer on their browser and copy it on their ‘e-xam’. telecommunication engineering students in hafa are required to be able to write, assemble and run machine language programs. in another type of question therefore, examinees were required to assemble their programme and submit the assembler listing file. this is another example showing how e-assessment can test skills that would have been impossible to examine with paper-and-pencil exams. 2.4 facilitating the process of in-classroom examinations an additional requirement for the system was that it should shield both instructors and students from having to comprehend any complicated software engineering concepts. as far as students are concerned, the target was met by virtue of the fact that a plain web interface was used for delivering the tests. students were pleasantly surprised to notice that each one of them had a different set of numbers. this made them adopt an attitude such as “i will mind my own business and finish my questions in time”, rather than the more traditional “i will try to see if one of my colleagues sitting next to me can help me with the exam questions”. hence learning was promoted and student and instructor psychology was boosted. in order to maximise the benefits for course instructors as well, the concept of domain advancing electronic assessment 61 specific languages was employed. instructors compose the skeleton examination questions and their model (correct) solutions in html format (figures 1, 2). they are not required to be familiar with html (low-level) coding [12]. they can use their favourite html editor to complete the task, as current editors have a simple user interface (similar to a word processor such as microsoft word). the html files are processed by the e-xaminer core and two sets of programs are produced: the examination agent and the marking agent (figure 1). the examination agent is an active page combining html and perlscript. the testing agent is a perl script which processes the answers submitted by students. the outputs are the marked tests and statistics of the class performance. 3 contribution to the virtual classroom the fact that questions are parameterised has several positive implications for the virtual classroom. firstly, tests can become personalised (a different set of questions for each student) without compromising test fairness. additionally, tests can be given more often than usual (the customary midterm exam may be replaced by weekly exams), since the effort required by the instructor is minimal. both the above achievements are recommended practices against examination-time cheating and in favour of promoting learning [21], [22]. it has additionally been observed that the web interface makes the idea of frequent exams, more agreeable than usual for students. marking papers is a fairer process, since, even at early stages, the system has caught grading errors made by humans. as it was mentioned earlier, the electronic tests in particular have been accused of bringing some students to a disadvantage [14]. the e-xaminer experience has shown that the e-test marks have large correlation to both the oral mark awarded by the instructor, as well as the mark attained during the paper-and-pencil semester final exam. furthermore the hafa, being a technically oriented institution, demands from its students (which, as officers of the hellenic air force, are going to handle electronic equipment worth millions of euros) to be familiar and at ease with technology. accusations have been extended to state that “test-takers are unable to underline text, scratch out eliminated choices and work out math problems all commonly-used strategies" [14]. current web technology allows not only underlining on-screen texts, but also much more elaborate formatting to take place by incorporating a simple html editor (a practice used by many web-based email services). as far as scratching is concerned, we have provided special scratch-pad areas next to each question (implemented as html textareas). we have also encouraged our students to get better help by using electronic dictionaries and calculators, as well as their old paper notebook which is sitting next to their keyboard. 4 pilot application and statistics the e-xaminer is being used in testing digital electronics, computer science, microprocessors and computer network courses taught in hafa since the beginning of the past academic year (2005-6). the caa tests were addressed to students of all disciplines taught in hafa (pilots, aircraft, telecommunications and civil engineers, as well as air-traffic controllers). during the first six months all student answers were being marked by both the e-xaminer and the instructor. during this first period, automatically assigned marks deviated by up to 10% from the instructor marks. this deviation declined steadily as the system evolved and staff acquired experience in assigning better model answers. currently, only a limited number of tests, selected at random, are being manually graded and the deviation between the two sets of marks is is less than 1%. the grading resolution (that is, the minimum grade associated to questions), was set to 0.5% during recent e-xams. after the initial 6-month deployment period, the e-xaminer has been systematically catching human marking errors in up to 10% of student papers. instructors have very often exploited the fact that the system is automatic and have revised their model 62 nikolaos doukas, antonios andreatos answers (along with their marking scheme) so as to better assess class performance. 4.1 comparing paper exams to electronic exams in this subsection electronic exams delivered by the e-xaminer will be compared to traditional paperand-pencil exams. rather than answering a question of the form ‘which one is better’, our experience from the use of the e-xaminer during the current (2006-7) and the past academic year (2005-6) will be presented. each approach has its own merits that need to be considered by course instructors before making a choice. the aim of this section is to support such a choice. the advantages of paper-and-pencil exams include: • they can be easily adopted to any subject. • they may be used by digitally illiterate students. • no technological infrastructure is necessary. • no computer-related background skills are necessary. their major disadvantages are: • the grading process is usually tedious and time-consuming. • it is difficult to embed in them multimedia (other than figures). on the other hand, the advantages of electronic assessment are: • tests may be generated automatically from a pool of questions and problems. • grading is easy, automatic and hence tests may be delivered more frequently. • it is easy to embed multimedia (such as designs, circuits, video and sound clips for courses such as signal processing, electronics and telecoms). • students with bad, illedgible handwriting can produce a well formed document. • results and statistics are immediately generated automatically and students obtain rapid feedback on their performance. • counter-cheating methods may be employed. • supplementary programs may be used (such as dictionaries, assemblers, compilers, matlab etc.). • exams may be easily stored and retrieved, results may be further processed with other computer programs such as excel and spss. • finally it should be noted that e-xams are environment-friendly in that little paper need ever be used. the disadvantages of electronic assessment are [16]: • it might be difficult to adopt it to some types of questions (such as math formulae, where special equation editors are needed for typing the formulae and complex software for interpreting them). • it might be difficult to adopt it to some subjects. • it requires additional equipment such as pcs, lan, software etc. and is hence more complicated to give, as well as, vulnerable to power or system failures. an instructor must take all the above factors into account before making a decision. advancing electronic assessment 63 4.2 student opinions it is important to consider the opinions of users in evaluating any e-learning system [22], [23]. the authors of ref. [22] define such an evaluation procedure based on hypothesis testing. the hypotheses that needed to be tested in the case of the e-xaminer where (1) “e-xams are perceived as equally difficult to the corresponding paper ones”, (2) “e-xams promote learning by making tests more agreable experience for students” and (3) “e-xams promote learning by helping students accept their test marks as a fair assessment of their performance”. in order for us to investigate these hypotheses, three extra questions were added at the end of the most recent tests. the questions where: 1. do you consider the electronic test to be more difficult than what you would expect from a paper test? 2. do you believe that automatic assessment you will get for this test will be fairer than the one you would have got if the test was marked by your instructor? 3. do you prefer this test to a traditional paper one? hafa students answered by an overwhelming majority (over 90%) that the electronic test was equally difficult and more preferable to the traditional test, while they expected their automatically assigned marks to better reflect their performance. answers to these questions are still being collected, as more classes sit electronic exams and will be presented in future publications once the required statistical significance is attained. 5 summary and conclusions the implementation of parametric questions and counter cheating measures of the ‘e-xaminer’, our caa system, was outlined. this implementation was shown to offer advantages to university course organisers, in that it facilitated their work and eliminated much of the tediousness involved in the grading process. the new system was also shown to promote learning by the students, by making exam taking a frequent, fair and agreeable procedure. some contributions were also made to theoretical issues concerning the electronic classroom and its acceptance in general. additionally, e-xaminer was shown to offer a number of advantages in the fight against examination-time cheating. statistics from the pilot application were given, were it was shown that e-xaminer was capable of effectively marking student answers. innovative types of exam questions were used, that could not have been set without the existence of an e-assessment platform. initial statistical results were presented on the acceptance of the system by students. these results are part of a study on the usefulness of this system in promoting learning. during the next academic year we plan to further develop the system in order to: • include some additional types of questions/problems. • support multimedia in both questions and answers. this is a clear advantage of caa over paperand-pencil exams. • include a timer and enforce automatic submission upon time-out. • strengthen security and fight cheating attempts by adding an authentication module that will monitor users login and logout [12]. 64 nikolaos doukas, antonios andreatos references [1] the univ. of iowa in cooperation with hbo systems inc., e-learning assessment summary report, available online at: www.hbosystems.com, 2004, retrieved on march 31, 2006. 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[20] j. f. kurose and k. w. ross, computer networking a top-down approach featuring the internet, 3rd ed., addison-wesley, 2005. [21] a. angeletou et al., assessment techniques for e-learning process, proceedings of the 3rd international conference on open & distance learning, vol. b, pp. 47-54, patra, greece, 2005. [22] d. spinellis, p. zaharias and a. vrechopoulos, coping with plagiarism and grading load: randomized programming assignments and reflective grading, computer applications in engineering education, to appear in 2007. [23] r. guidorzi and m. l. giovannini, e-learning tools in higher education: users’ opinions, proceedings of eden’03, rhodes, greece, pp. 201-206, 2003. nikolaos doukas, antonios andreatos dept. of aeronautical sciences div. of computer engineering and informatics hellenic air force academy dekeleia, attica, tga-1010 greece e-mail: nikos@doukas.net.gr, andreatos@hafa.gr received: november 18, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 2, pp. 178-184 optimization for date redistributed system with applications mădălina văleanu, smaranda cosma, dan cosma, grigor moldovan, dana vasilescu mădălina văleanu university of medicine and pharmacy "iuliu hatieganu" medical informatics and biostatistics department cluj-napoca e-mail: mvaleanu@umfcluj.ro smaranda cosma babes-bolyai university business department, faculty of business cluj-napoca e-mail: smaranda.cosma@tbs.ubbcluj.ro dan cosma university of medicine and pharmacy "iuliu hatieganu" department of pediatric surgery and orthopedics cluj-napoca e-mail: dcosma@umfcluj.ro grigor moldovan babes-bolyai university computer systems department cluj-napoca e-mail: moldovan@cs.ubbcluj.ro dana vasilescu university of medicine and pharmacy "iuliu hatieganu" department of pediatric surgery and orthopedics cluj-napoca e-mail: dana.vasilescu@umfcluj.ro abstract: in this paper we intend to define a strategy for managing databases with mobile structures, taking into account their redistribution in the nodes of a computer network. the minimal cost of the redistribution is highlighted and some applications for medical and business databases are presented. keywords: distributed database, costs, medical databases, economical databases, wireless network 1 introduction the paper presents a generalization and extension for mobile environments of the context of the problem expressed in paper [1]. it also presents some applications of the problem. let us consider the mobile databases (tables) bi, i = 1, n distributed in r nodes of a network of computer stations with own memories si, i = 1, r. hence, we have: copyright © 2006-2009 by ccc publications optimization for date redistributed system with applications 179 b = {b1, b2, . . ., bn} and s = {s1, s2, . . ., sr}, where b is a distributed database. now we identify the nodes, that is the stations of the computer network considered with the same symbols as for the memory supports si in s. the architectures of the fixed computer networks with nodes s = {s1, s2, . . ., sr} can differ, but on the horizontal level, in general, they are modeled through a relative graph. particular computer network architectures can exist, such as the hypercube or generalizations of the hypercube [2] [3], which provide simple and efficient routing means but whose complexity in number of nodes and restrictions make them inefficient in the end. modeling a computer network in a graph, in general, will give a static network with fixed and well defined geographical locations. in our previous paper [1] we have supposed that subbases bi of the database b have a well defined location and that this location is maintained fixed during the running of a distributed application. at present, mobile databases characterized by allocation in permanent change are known. consequently, dynamism characterizes all their aspects. they are searched for (selected), accessed and processed from a mobile environment made up of laptops, mobile phones etc. they become stronger and stronger elements of data processing. they are connected by means of wireless stations in special fixed points (nodes) belonging to a computer network. the traditional model of transactions migrates towards a mobile transaction model. the following presents a strategy for this kind of databases processing. databases bi, i = 1, n and other soft resources (programs) shall be stored in fixed hosts, noted fh and identified through si (fixed host computers!), in a well established network, s = {s1, s2, . . ., sr}. the mobile environment will inherit the properties of the distributed environment. the mobile environment with ever increasing storing spaces will be able to take over the mobile databases for processing data, by duplicating procedures, and the results will turn back to a fixed host. the fixed hosts will be those that permanently preserve the data subbases bi. databases bi could be preserved in mobile hosts, but any update (modification) should end with their replication to a fixed host mentioned with a new version number. in the fixed network, it is necessary to update all duplicated subbases bi, using the most recent version numbers allotted in order to maintain the consistency of the distributed database b. the update of the databases should occur instantly after the alteration of their version number. the update of duplicated data subbases bi can also be achieved with the help of a jeton function periodically passing through the nodes s of the network in consideration. it is obvious that between the mobile hosts noted mh and fixed hosts noted fh belonging to a computer network, there is a fixed interface, called mobile support station, noted mss (mobile support station) or base station. the connection between a mobile support station (mss) and a mobile host (mh) is wireless. each mss allotted to a node si controls a cell of mobile hosts identified by {i1, i2, . . ., ik} . a mobile host can disconnect from a mss and possibly reconnect to another mss just while running a distributed application. the disconnection and connection of mobile stations occurs frequently. disconnection establishes a new distribution of subbases in the network nodes. the figure below presents the general architecture of a mobile platform. in the design of a mobile database, in each node si we shall highlight for bi an often modifying and dynamic component that frequently is accessed from other nodes of the network, noted bi and other components, noted mi(s) associated to mobile units, quite often accessed locally to mobile units but where the modification of data is less frequent. 180 mădălina văleanu, smaranda cosma, dan cosma, grigor moldovan, dana vasilescu figure 1: mobile platform we have bi = (bi; mi(1), mi(2), . . ., mi(k)) where we used for indices ip notation i(p) and p = 1, k. we can consider that we made a decomposition of the subbases bi by selection and/or projection operations so that by further union operations we get updated subbase bi. at a given moment in the computer network stations we witness a certain distribution, respectively a grouping of databases bi, i = 1, n. in general, if n ≥ r, then, more subbases bi will exist in the memory si. for reasons of simplification, in our study, we will suppose that consecutively, in each station si of the r station, we will have d subbases si , hence n = d.r. we also suppose that we have removed, by means of a certain conveniently selected strategy, the duplications of subbases bi, therefore bi are distinct. a distributed application supposing programs running in the network under consideration leads to the access, from the nodes si, of the subbases bj in a defined succession, until the result needed is obtained. we note the successively accessed data subbases (some of them more often accessed in a distributed application) in a vectorial form, as follows: bl = (bm1 , bm2 , . . ., bms ) then l = (m1, m2, . . .ms); mk ∈ {1, 2, . . ., n} remember that mk identifies the place in the succession of accesses of the subbases in b performed. in general, in the case of an access from si network node to a subbase found in sj, a so-called penalty should also be considered (for instance: time, cost) noted with pij for all i, j = 1, r which will be composed of a fixed penalty pfij established in a fixed computer network and possibly from a penalty due to the specific working manner with mobile databases noted pmij. we will obtain pij = pfij + pmij. 2 grouping data subbases on the network fixed nodes let distributed database be b = {b1, b2, . . ., bn}. the indices of subbases build the set in = {1, . . ., n}. the reorganisation of these data is defined by permutation indices σ = ( 1 2 ...k...n i1i2...ik...in ) where ik ∈ in; k = 1, n are distinct. the permutation σ is also written as σ = (σ(1), σ(2), . . ., σ(n)). if we have two permutations σ and τ , optimization for date redistributed system with applications 181 their produce στ is obtained by the composition of the two functions, so that στ = (σ(τ(1)), σ(τ(2)), . . ., σ(τ(n))). let us mark with supp(σ), the permutation support σ, i.e. the set of the elements i ∈ {1, 2, . . ., n} having the property σ(i) 6= j. a permutation σ is called cyclic of length m, m ≥ 2 if elements i1, i2, . . ., im ∈ supp(σ) exist so as σ(i1) = i2, σ(i2) = i3, . . .σ(im−1) = im, σ(im) = i1. it is known that any permutation that is different from the identical one can be written as a produce of distinct cycles. [4] let us consider that n = d.r, i.e. on any si station in the r computer network we have d data subbases. let us take the following reorganisation (distribution) of the n data subbases on the r workstations, successively: bσ−1(1) bσ−1(2) bσ−1(d) in s1 . . . . . . . . . . . . bσ−1(1+(i−1)d) bσ−1(2+(i−1)d) bσ−1(id) in si . . . . . . . . . . . . bσ−1(1+(r−1)d) bσ−1(2+(r−1)d) bσ−1(rd) in sr note: σ is a bijective application, σ : {1, 2, . . ., n} → {1, 2, . . ., n} where σ(k) = ik, respectively σ−1(ik) = k; k = 1, n. consider the sequence of successive accesses ld = (m1, m2, . . ., ms) of the subbases in b and the indices of these stations are. with these notations, we define the cost of a distributed application with the relationship c(s, b, ld, σ) = s−1∑ k=1 (prσ(mk),rσ(mk+1) + qrσ(mk)) where qrσ(mk) represents the cost of the activities in station srσ(mk). let us note with aij the number of the times (mk, mk+1) = (i, j), k = 1, s − 1 ; i, j ∈ {1, 2, . . ., n}, and with ci , how many times mk = i, k = 1, s; i, j ∈ {1, 2, . . ., n}. then, c(s, b, ld, σ) = n∑ i=1 n∑ j=1 (aijprσ(mk),rσ(mk+1) + ciqrσ(mk)) = n∑ i=1 n∑ j=1 (aσ−1(i),σ−1(j)pri(i),ri(j) + ciqri(i)) the cost of a distributed application will be: c(s, b, ld, σ) = n∑ i=1 n∑ j=1 (aσ−1(i),σ−1(j)p ∗ ij + ciq ∗ i ), where pri(i),ri(j) = p ∗ ij; qri(i) = q ∗ i a way to find out this sums can see in [5]. notes 1. in practice, we can consider that pij is symmetrical, hence p∗ij will also be symmetrical, i.e. p∗ij = p ∗ ji. penalties pij can be determined, for instance, with the help of statistical data after more run182 mădălina văleanu, smaranda cosma, dan cosma, grigor moldovan, dana vasilescu nings of the programs of the distributed application d. 2. if permutation σ is decomposed in a produce of cyclical permutations, the formula for the application cost can be simplified accordingly. 3 applications 3.1 medical databases the medical field is one of the most important fields of social concern. it is defined by high level dynamism. nowadays the healthcare system from every country have to faced to many challenges of the 21st century which influenced significant the financial aspects of the organizations’ activities. in these new conditions the competitions increased and pressed on the costs to acquire and maintain a high quality of the technology and capital outlay. today, the data needed for research are registered in medical documents, in a written form, but not in a unique form and the work necessary to retrieve and process such data is enormous. it is for this reason that it is required to design general interest databases, for doctors, patients, researchers and health units. such databases are characterised by the fact that they will include an enormous volume of data distributed among the nodes of a computer network. the medical field activities can be divided into primary level care, secondary level care activities and the division can go even further. primary care refers to family medicine that is to the first contact and consultation point for the patient. secondary care is the service provided by a specialist, who does not have the first contact with the patient, in general. usually, a doctor providing secondary care services treats patients previously consulted by a family doctor. to establish a correct diagnosis and a proper treatment it is necessary to handle the databases in question that, often mean to return to their redistribution in the nodes of the computer network. a concrete examples are given in papers [6]. in such an application it is equally important to find an answer to a query to the databases in as short a time as possible. a valuable component of a heath care computer-based system is its capacity to work with information on the patients stored in various locations: the national health authority, the social insurance system, providers of primary and secondary care services. in order to find solutions, a distributed approach should be taken into consideration. [7] [8] health care information, such as medical records, x-rays, lab tests results are more are more kept and processed by computers. that is why standards to send such data away in an unambiguous manner among computers are required. in this way, uniform health care-related information shall also be available. a standard represents a conceptual lexis common for all the stakeholders in the healthcare network. standards of this kind are already available for many medicine subfields. optimization for date redistributed system with applications 183 3.2 economical databases the economic applications of distributed databases are diverse and numerous and their importance is also valuable. it is enough to take as an example a system in the field of international trade involving the handling of very large volumes of data relative to the varied goods distributed among shop networks. the management information system of a company becomes decisive for international corporations of the learning-company type that extend more and more their activities using varied international market penetration strategies. e-commerce develops and includes larger and larger scopes. that is why it is necessary to establish standards related to marketed products, in this field too. the queries put to distributed and increasing in size databases should be made more efficient in time. it is sure that in the near future the optimising of costs (query types) of the used databases will have to be considered. marketing research identifies, collects, processes, analyses, interprets and communicates information relevant for a specific marketing situation to make a decision. all the data are organised in distributed databases in more locations. marketing research mainly aims at reducing risk and uncertainty in the conception and grounding of marketing-related decisions and at implementing and controlling the practical putting into application of these decisions. [7] 4 conclusions the fundamental problem, with respect to the distributed application d under consideration, consists in the determination of a permutation σ in the set of possible permutations p having elements {1, 2, . . ., n}, indices of the b data subbases so that the cost of the use of the distributed application d programs would be minimal, i.e. min{c(s, b, ld, σ); σ ∈ p}. it is obvious that the problem relates to combination, its solution is important when the distributed application d is used repeatedly. the problem is solved once and the advantage remains operational all along the use of the respective distributed application. the applications for this are multiple. here are describes some applications in medical and economical domain, that can use distributed databases and where the problem of cost minimizing (time need to transferred information from one point of the network to another) is very important. bibliography [1] g. moldovan, m. văleanu, the performance optimization for date redistributing system in computer network, international journal of computer, communications & control, issn 1841-9836, e-issn 1841-9844, vol iii, supl. issue, p. 116-118, 2008. [2] g. moldovan, m. văleanu, redistributing databases in a computer network, analele univ. bucureşti, ser. math.-info., 56, 2006. 184 mădălina văleanu, smaranda cosma, dan cosma, grigor moldovan, dana vasilescu [3] g. moldovan, i. dzitac, sisteme distribuite modele matematice, ed. univ. agora, 2006. [4] l. aspinall, data base. re-organisation –algorithms, ibm, uksc – 0029, 1972. [5] a. gog, h. grebla, evolutionary tuning for distributed database performance, the 4th international symposium on parallel and distributed computing (ispdc), lille, france, ieee computer society, 2005, p 275-281. [6] s. cosma, d. cosma , a. negrusa, m. văleanu, g. moldovan, d. vasilescu, implementation of the communication system for clubfoot, wseas transactions on communications, issn:1109-2742, issue 9, vol 7, sept 2008, p.932-941 [7] d.e. vasilescu, d. cosma, m. văleanu, i. negreanu, d. vasilescu, the results of the early conservative orthopedic treatment in the congenital talipes equinovarus, applied medical informatics 2004, vol 15, p 34-43. [8] d. cosma, s. cosma, m. văleanu, d.e. vasilescu, g. moldovan, web based guideline for clubfoot: patient orientated materials, journal of international business and economics, issn 1544-8037, 8-1, 2008. int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 785-790 clustering-based energy-efficient broadcast tree in wireless networks j. yu, h. jiang, g. wang, q. guo jiguo yu, honglu jiang school of computer science, qufu normal university, rizhao, 276826, p.r. china e-mail: jiguoyu@sina.com; jianghonglu88@163.com guanghui wang school of mathematics, shandong university, jinan, 250100, p.r. china e-mail: ghwang@sdu.edu.cn qiang guo key laboratory for computer networks of shandong province shandong computer science center, jinan, 250014, p.r. china e-mail: guoq@keylab.net abstract: the characteristics of wireless networks present formidable challenges to the study of broadcasting problem. a crucial issue in wireless networks is the energy consumption, because of the nonlinear attenuation properties of radio signals. another crucial issue is the trade-off between reaching more nodes in a single hop by using higher power versus reaching fewer nodes in that single hop by using lower power. given a wireless network with a specified source node that broadcasts messages to all other nodes in the network, the minimum energy broadcast (meb) problem is np-hard. in this paper, we propose a hybrid approach cbeeb(clustering-based energy-efficient broadcast) for the meb problem based on clustering. theoretical analysis indicates the efficiency and effectiveness of cbeeb. simulation results show that cbeeb has better performance compared with the existing heuristic approaches. keywords: wireless network, energy-efficient, broadcast, clustering 1 introduction nodes in wireless networks are usually powered by batteries with limited capacity. therefore, energy efficiency is one of the most important design issues for wireless networks. the energy consumption in transmission can be further reduced by using one or more intermediate nodes instead of transmitting directly. broadcasting in wireless networks is different from that in wired networks, since all nodes that are within the transmission range of the sender can receive the transmission without any additional cost at the sender. this characteristic of wireless transmission is known as wireless multicast advantage (wma) [1]. this allows us to seek power optimal range assignment for the energy-efficient broadcast problem. the minimum energy broadcast (meb) problem is to find a broadcast scheme with minimum energy consumption. the problem is also known as minimum power broadcast (mpb) problem or minimum energy consumption broadcast subgraph (mecbs) problem [2]. we use energy and power alternatively throughout this paper. in this paper, we study the problem of broadcasting in wireless networks, where every node is equipped with omni directional antennas. we propose a hybrid algorithm based on clustering and prove its correctness. theoretical analysis shows the effectiveness of our approach. we compare it with the broadcast incremental power (bip) [1] algorithm and the best heuristic approaches known in [2–4] by simulation. copyright c⃝ 2006-2012 by ccc publications 786 j. yu, h. jiang, g. wang, q. guo 2 related work most previously developed models for broadcasting and multicasting problems were linkbased models, which does not properly reflect the properties of the all-wireless network environment. to solve the mpb problem, wieselthier et al. first proposed the node-based approach (bip)which is more suitable for wireless environment than the link-based algorithms [1]. it was subsequently shown in [5] that the bip algorithm has an approximation ratio between 13/3 and 12. das et al. gave an improvement procedure called r-shrink for meb problem [6]. cagalj et al. [7] proposed another improvement procedure called embedded wireless multicast advantage (ewma). kang et al. [8] generalized the ewma into another heuristic called largest expanding sweep search(less). in [9], an ant-colony based approach for the problem was presented. an algorithm called cm using ant colony optimization approach was also presented in [3]. furthermore, in [4] a simple simulated annealing algorithm with metropolis chains of dynamic length was presented. the algorithm is in fact a probabilistic version of the 1-shrink tree-improvement algorithm described in [9]. kang et al. [10] gave a perturbation based iterated local optimization method that uses less as local search. in [11], montemanni et al. presented a simulated annealing approach based on r-shrink. they used sweep procedure to improve the solution obtained through simulated annealing. al-shihabi et al. [12] developed a hybrid approach combining nested partitioning with lp relaxation and r-shrink method. in [13], wolf et al. proposed an evolutionary local search, which uses modified r-shrink as local search and random increase in transmission power of some nodes as mutation operator. recently, wu et al. [14] developed a generational genetic algorithm that uses permutation encoding to represent a broadcast scheme. hashemi et al. presented a simulated annealing algorithm using a special node selection mechanism in its neighborhood structure [2]. in [15], singh et al. proposed a hybrid approach to the meb problem combining a genetic algorithm with a local search heuristic, which is a modified version of r-shrink improvement procedure [6]. 3 preliminaries and model we consider static multi-hop ad hoc wireless networks with omni-directional transmitters, and each node can adjust its transmitting power based on the distance to the receiving node. in the most common power attenuation model, received signal power varies as d−α, where d is the distance from the transmitter and α is an environment-dependent constant typically between 2 and 5. therefore, the transmitter power required to support the direct communication is proportional to dα. to represent one wireless ad hoc network, let g = (v,e) be a directed graph, where v denotes the set of nodes and e denotes the set of edges, and a special node s ∈ v that broadcasts messages to all other nodes of v. the transmission energy required by a node in an arborescence is determined by the longest edge among all edges of that arborescence. leaf nodes do not relay messages to any other node. therefore, the transmission energy required by leaf nodes is zero. the total transmission energy required for the broadcast can be computed by adding the energy which is required by each node in that arborescence. definition 1. min-energy broadcast problem(meb): let g = (v,e) be a directed graph, find a broadcast tree t ⊆ e rooted at s, with the minimum energy cost ∑ u∈v max(u,v)∈t d(u,v) α, where d(u,v) is the distance between the nodes u and v. clustering-based energy-efficient broadcast tree in wireless networks 787 4 hybrid approach–cbeeb we now propose a hybrid approach cbeeb (clustering-based energy-efficient broadcast) to meb problem, which includes a clustering algorithm and the ibip (improved bip) algorithm. a special node s broadcasts messages to all other nodes of v , and each node except s has one unique identifier id. for convenience, the notation and terminology used in the rest of this paper are summarized in table 1. definition 2. priority of nodes: for two nodes i,j ∈ v\{s},i.prio > j.prio ⇔ d(i) > d(j), or d(i) = d(j) & &id(i) > id(j) table 1. notation and terminology id(i) the unique identifier of node i i.prio the priority of node i d(i) the degree of node i n1i the set of one-hop neighbors of node i i.ch the clusterhead elected by node i i.type the type of node i, which is a clusterhead or a leaf node the clustering algorithm is as follows: step 1. each node except s collects the information of all its one-hop neighbors n1i . such information can be acquired by each node exchanging message including its id and priority to its one-hop neighbors. then each node sorts the priority for all nodes within its one-hop neighborhood. step 2. a node i decides to become a clusterhead if either one of the following criteria is satisfied: (1)node i has the highest priority in its one-hop neighborhood. (2) node i is an one-hop neighbor of a node b. furthermore, i has the highest priority in the one-hop neighborhood of b. step 3. all the clusterheads are added to the set d. assign to each clusterhead v ∈ d the transmission range rv = maxv′∈n1i d(v,v′). then, all the cluster members are the leaf nodes of the broadcast tree. the following figure 1 gives an example of the clustering algorithm in a twenty-node network. 1 2 3 5 6 7 4 8 1 0 9 1 2 1 3 1 4 1 1 1 5 1 6 1 7 1 8 1 9 2 0 1 2 3 5 6 7 4 8 1 0 9 1 2 1 3 1 4 1 1 1 5 1 6 1 7 1 8 1 9 2 0 1 2 3 5 6 7 4 8 1 0 9 1 2 1 3 1 4 1 1 1 5 1 6 1 7 1 8 1 9 2 0 f i g u r e 1 a . r a n d o m n e t w o r k ( n = 2 0 ) f i g u r e 1 b . r e d n o d e s a r e c l u s t e r h e a d s e l e c t e d b y t h e c l u s t e r i n g a l g o r i t h m f i g u r e 1 c . c o n n e c t t h e c l u s t e r m e m b e r s t o t h e c l u s t e r h e a d figure 1. an example of clustering in a twenty-node network. we give the ibip(improved bip) algorithm. the ibip algorithm runs on the clusterhead set d. the cluster members elected by the clustering algorithm consist of the leaf nodes of the broadcast tree. step 1. initially, the tree consists of only the source node s. we begin by determining the node that node s can reach with minimum expenditure of power. 788 j. yu, h. jiang, g. wang, q. guo step 2. determine which new clusterhead can be added to the tree at minimum additional power in the set of d. if node i is already in the tree, and node j is not yet in the tree, (i,j ∈ d). if pi,j < rαi , then we add j to the tree, where pi,j is the power required to support the transmission between node i and j, and ri is the transmission range of node i which is assigned in the clustering algorithm. otherwise let p ′i,j = pi,j −max{rαi ,p(i)}, where p ′i,j represents the incremental power associated with adding j to the set of nodes to which node i is already transmitting. the pair {i,j} that causes the minimum value of p ′i,j is selected. node i transmits at a power level sufficient to reach node j. thus, one new node is added to the tree. step 3. this process continues until all nodes are included in the tree. the total power required to maintain this tree is the sum of the transmitted powers at each of the transmitting nodes. the following figure2 gives an example of the ibip algorithm. s 1 2 3 4 9 6 7 5 8 s 1 2 3 4 9 6 7 5 8 s t e p 1 : s 9 s t e p 2 : s 6 f i g u r e 2 a . f i g u r e 2 b . s 1 2 3 4 9 6 7 5 8 s 1 2 3 4 9 6 7 5 8 s t e p 3 : 6 7 f i n a l t r e e f i g u r e 2 c . f i g u r e 2 d . figure 2. an example of ibip algorithm in a nine-clusterhead network with source node s. 5 algorithm analysis and simulation theorem 3. the set of clusterheads elected by the clustering algorithm cover all network nodes. proof: because each node either has the highest priority among its one-hop neighbors or has a neighbor with the highest priority among one-hop neighbors of the node. by the definition of the dominating set, a node either becomes a dominator itself or elects the neighbor with the highest priority among one-hop neighbors of the node as a dominator. thus the network has a dominating set elected by the algorithm. therefore, we conclude that the sets of clusterheads elected by the clustering algorithm can cover all network nodes. 2 theorem 4. the cbeeb algorithm has time complexity of o(m2), where m ≤ n dmin+1 is the number of clusterhead elected by the clustering algorithm and dmin is the smallest degree of nodes in the network. proof: the clustering algorithm adopts a distributed clustering strategy. thus, the time complexity of the entire network equals that of a single node o(1). in the algorithm ibip, it runs on the set of clusterheads. obviously, the number of the clusterheads is smaller than n dmin+1 . moreover, the ibip algorithm is based on the prim’s algorithm. thus the time complexity is o(( n dmin+1 )2). therefore, the total time complexity of the cbeeb algorithm is o(1 +m2), that is o(m2). 2 theorem 5. the overhead complexity of control messages in the network of the cbeeb algorithm is o(n + m3), where m ≤ n dmin+1 is the number of clusterhead elected by the clustering algorithm and dmin is the smallest degree of nodes in the network. clustering-based energy-efficient broadcast tree in wireless networks 789 proof: at the beginning of the clustering algorithm, each node broadcasts a message to collect its information of its one-hop neighbors. therefore, the overhead complexity of control messages in the network is o(n). the ibip runs on the set of clusterheads elected by the clustering algorithm based on the prim’s algorithm. because we need to update the power p ′i,j at each step of the algorithm, the message complexity is o(m3) when a straightforward implementation is used [1]. therefore, we can conclude that the overhead complexity of control messages in the network of the cbeeb algorithm is o(n + m3). 2 comparing with the time complexity of the bip algorithm is o(n2), and the message complexity is o(n3) [1]. obviously, our algorithm cbeeb shows better effectiveness. we now compare the performance of cbeeb algorithm with other six broadcast algorithms blimst [1], bip [1], cm [3], sa [4], esa [2]and ga [15] by simulations. the simulations are carried out in an ideal network generated over a 100 × 100m2 area without consideration for packet lost. the number of network nodes changes from 25 to 250 with each increment of 25. fig 3a. the original network topology with 150 nodes figure 3b.the topology after running clustering algorithm figure 3c.the topology after running ibip figure 4.energy cost comparison among seven algorithms figure 3 illustrates the backbone topology construction process using cbeeb. in figure 4, we compare the energy cost of our algorithm with other six algorithms. the simulation results show that our algorithm makes further improvements on the energy cost compared with the blimst and bip algorithm. cm, sa, esa and ga algorithm can improve the energy cost over the bip algorithm. moreover, we can see cbeeb performs better than the ga algorithm especially when the number of nodes is large. 790 j. yu, h. jiang, g. wang, q. guo 6 conclusions in this paper, we propose a hybrid approach for the meb problem in wireless networks. compared with existing approaches, theoretical and experimental results show the superiority. as further work, we will develop fully distributed algorithms for meb problem. acknowledgments this work was partially supported by the national natural science foundation of china for contract (11101243, 60373012), natural science foundation of shandong province for contract(zr2009gm009, zr2009am013), stpu of shandong province for contract (j10lg09) and tkp of shandong province for contract (2009gg10001014). bibliography [1] j. e. wieselthier, g. d. nguyen, a. ephremides, on the construction of energy efficient broadcast and multicast trees in wireless networks, in: proc. of infocom 2000, 585-594, 2000. [2] s. m. hashemi, mohsen rezapour, ahmad moradi, two new algorithms for the min-power broadcast problem in static ad hoc networks, applied math. and compu., vol.190, pp. 1657-1668, 2007. [3] a. k. das, r. j. marks, m. ei-sharkawi, p. arabshi, a. gray, a cluster-merge algorithm for solving the minimum power broadcast problem in large scale wireless networks, in proc. of the milcom conference, 13-16, 2003. [4] a. k. das, r. j. marks, m. ei-sharkawi, p. arabshi, a. gray, the minimum power broadcast problem in wireless networks: a simulated annealing approach, in proc. of wcnc, 2057-2062, 2005. [5] p. j. wan, g. calinescu, x. y. li, o. frieder, minimum-energy broadcast routing in static ad hoc wireless networks, in proc. of infocom, 1162-1171, 2001. [6] a. k. das, r. j. marks, m. ei-sharkawi, p. arabshai, a. gray, r-shrink: a heuristic for improving minimum power broadcast trees in wireless networks, in proc. of globecom 523-527, 2003. [7] m. cagalj, j. p. hubaux, c. enz, minimum-energy broadcast in all-wireless networks: npcompleteness and distribution issues, in proc. of mobicom’02, 172-182, 2002. [8] i. kang, r. poovendran, broadcast with heterogeneous node capability, in proc. of globecom 4114-4119, 2004. [9] a. k. das, r. j. marks, m. ei-sharkawi, p. arabshi, a. gray, the minimum power broadcast problem in wireless networks: an ant colony system approach, in proc. of the ieee cas workshop on wireless communications and networking, 5-6, 2002. [10] i. kang, r. poovendran, iterated local optimization for minimum energy broadcast, in: proc. of wiopt, 332-341, 2005. [11] r. montemanni, l. m. gambardella, a. k. das, the minimum power broadcast in wireless networks: a simulated annealing approach, in proc. of wcnc, 2057-2062, 2005. [12] s. al-shihabi, p. merz, s. wolf, nested portioning for the minimum energy broadcast problem,in proc. of lion 2, lncs 5313, 1-11, 2008. [13] s. wolf, p. merz, evolutionary local search for the minimum energy broadcast problem,in proc. of evocop’08, lncs 4972, 61-72, 2008. [14] x. wu, x. wang, r. liu, solving minimum power broadcast problem in wireless ad hoc networks using genetic algorithm,in proc. of cnsr, 203-207, 2008. [15] alok singh, wilson naik bhukya, a hybrid genetic algorithm for the minimum energy broadcast problem in wireless ad hoc networks, applied soft computing, vol.11, pp. 667-674, 2011. international journal of computers, communications & control vol. i (2006), no. 3, pp. 41-59 formation control of mobile robots dang binh nguyen, khac duc do abstract: a constructive method is presented to design cooperative controllers that force a group of n mobile robots to achieve a particular formation in terms of shape and orientation while avoiding collisions between themselves. the control development is based on new local potential functions, which attain the minimum value when the desired formation is achieved, and are equal to infinity when a collision occurs. the proposed controller development is also extended to formation control of nonholonomic mobile robots. keywords: formation control, mobile robot, local potential function, nonholonomic mobile robot. 1 introduction over the last few years, formation control of multiple vehicles has received a lot of attention from the control community. applications of vehicle formation control include the coordination of multiple robots, unmanned air/ocean vehicles, satellites, aircraft and spacecraft [1]-[28]. for example, a cooperative mobile sensor network, where each mobile robot serves as a mobile sensor, is expected to outperform a single large vehicle with multiple sensors or a collection of independent vehicles when the objective is to climb the gradient of an environmental field. the single, heavily equipped vehicle may require considerable power to operate its sensor payload, it lacks robustness to vehicle failure and it cannot adapt the configuration or resolution of the sensor array. an independent vehicle with a single sensor may need to perform costly maneuvers to effectively climb a gradient, for instance, wandering significantly to collect rich enough data much like the "run and tumble" behavior of flagellated bacteria. in military missions, a group of autonomous vehicles are required to keep in a specified formation for area coverage and reconnaissance. in automated highway system, the throughput of the transportation network can be greatly increased if vehicles can form to platoons at a desired velocity while keeping a specified distance between vehicles. research on formation control also helps people to better understand some biological social behaviors, such as swarm of insects and flocking of birds. in the literature, there have been roughly three methods to formation control of multiple vehicles: leader-following, behavioral and virtual structure. each method has its own advantages and disadvantages. in the leader-following approach, some vehicles are considered as leaders, whist the rest of robots in the group act as followers [1], [2], [3], [4]. the leaders track predefined reference trajectories, and the followers track transformed versions of the states of their nearest neighbors according to given schemes. an advantage of the leader-following approach is that it is easy to understand and implement. in addition, the formation can still be maintained even if the leader is perturbed by some disturbances. however, a disadvantage is that there is no explicit feedback to the formation, that is, no explicit feedback from the followers to the leader in this case. if the follower is perturbed, the formation cannot be maintained. furthermore, the leader is a single point of failure for the formation. in the behavioral approach [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], few desired behaviors such as collision/obstacle avoidance and goal/target seeking are prescribed for each vehicle and the formation control is calculated from a weighting of the relative importance of each behavior. the advantages of this approach are: it is natural to derive control strategies when vehicles have multiple competing objectives, and an explicit feedback is included through communication between neighbors. the disadvantages are: the group behavior cannot be explicitly defined, and it is difficult to analyze the approach mathematically and guarantee the group stability. in the virtual structure approach, the entire formation is treated as a single entity [15], [16], [17], [18]. when the structure moves, it traces out desired trajectories for each robot in the group to copyright c© 2006 by ccc publications 42 dang binh nguyen, khac duc do track. some similar ideas based on the perceptive reference frame, the virtual leader, and the formation reference point are given in [14], [17], [19]. the advantages of the virtual structure approach are: it is fairly easy to prescribe the coordinated behavior for the group, and the formation can be maintained very well during the manoeuvres, i.e. the virtual structure can evolve as a whole in a given direction with some given orientation and maintain a rigid geometric relationship among multiple vehicles. however requiring the formation to act as a virtual structure limits the class of potential applications such as when the formation shape is time-varying or needs to be frequently reconfigured, this approach may not be the optimal choice. the virtual structure and leader-following approaches require that the full state of the leader or virtual structure be communicated to each member of the formation. in contrast, behavior-based approach is decentralized and may be implemented with significantly less communication. formation feedback has been recently introduced in the literature [18], [20], [21], [22]. in [18], a coordination architecture for spacecraft formation control is introduced to incorporate the leader-following, behavioral, and virtual structure approaches to the multi-robot coordination problem. this architecture can be extended to include formation feedback. in [20], a lyapunov formation function is used to define a formation error for a class of robots (double integrator dynamics) so that a constrained motion control problem of multiple systems is converted into a stabilization problem for one single system. the error feedback is incorporated to the virtual leader through parameterized trajectories. in terms of information from the robots in the group used for feedback in the control design for each robot, there are two main approaches to solve the problem of motion planning/control of a group of mobile robots: centralization and decentralization. in the centralized approach, see for example [18], a single controller and collision free trajectories are constructed in a workspace. the centralized approach has a drawback of computation complexity but guarantees a complete solution. the decentralized approach, see for example [23], requires less computational effort, and offers an easy way to scale the size of the robot group. this approach usually involves a combination of robot based local potential fields [14], [24], [25]. the main problem with the decentralized approach is that it is unable or extremely difficult to predict and control the critical points. basically, the closed loop system under a controller designed by the decentralized approach has multiple equilibrium points. it is rather difficult to design a controller such that all the equilibrium points except for the desired equilibrium one are unstable/saddle points for a group of many robots. moreover even the formation control system is designed in a centralized manner, the tuning constants in several aforementioned papers (e.g. [26], [27], [28], [29]), which are crucial to guarantee that the only desired equilibrium points are asymptotic stable and that the other critical points are unstable, are extremely difficult to obtain for practical implementation. in most of the above papers, point-robots with simple (single or double integrator) dynamics (e.g. [14], [24], [29]) or fully actuated vehicles [19] (which can be converted to a double integrator dynamics via a feedback linearization) were investigated. vehicles with nonholonomic constraints were also considered (e.g. [5]). however, the nonholonomic kinematics are transformed to a double integrator dynamics by controlling the hand position instead of the inertial position of the vehicles. consequently, the vehicle heading is not controlled. in addition, in the tracking control of single nonholonomic mobile robots (e.g. [30], [31], [32]) the tracking errors are often converted into special forms to deal with nonholonomic constraints using several non-trivial coordinate transformations. if these techniques are migrated to formation control of a group of nonholonomic mobile robots, it is extremely difficult to incorporate collision avoidance between the robots. the above problems motivate the contribution of this paper. in this paper, we propose a constructive method to design a cooperative formation control system for a group of n mobile robots. the simple point-mass robots are first considered to clarify the design philosophy. the proposed technique is extended to mobile robots with nonholonomic constraints (nonholonomic mobile robots). new local potential functions are constructed to design gradient based cooperative controllers to achieve almost global asymptotic convergence of a group of mobile robots to a particular formation in term of both shape and orientation, and guarantee no collisions between themselves. formal proof of the results is given. formation control of mobile robots 43 2 problem statement we consider a group of n simple point-mass mobile robots, of which each has the following dynamics q̇i = ui, i = 1, ..., n (1) where qi ∈ rn and ui ∈ rn are the state and control input of the robot i. we assume that n > 1 and n > 1. the assumption that each robot is represented as a point is not as restrictive as it may seem since various shapes can be mapped to single points through a series of transformations [26], [27], [28]. our task is to design the control input ui for each robot i that forces the group of n robots to stabilize with respect to their group members in configurations that make a particular formation specified by a desired vector l(η) = [lt12(η), l t 23(η), ..., l t n−1,n (η)] t , where η ∈ rm is the formation parameter vector to specify the formation change, while avoiding collisions between themselves. the parameter vector η is used to specify rotation, expansion and contraction of the formation such that when η converges to its desired value η f , the desired shape of the formation is achieved. in addition, it requires all the robots align their velocity vectors to a desired bounded one ud ∈ rn, and move toward specified directions specified by the desired formation velocity vector. the control objective is formally stated as follows: control objective: assume that at the initial time t0 each robot initializes at a different location, and that each robot has a different desired location, i.e. there exist strictly positive constants ε1, ε2 and ε3 such that ||qi(t0)−q j(t0)|| ≥ ε1, ||li j(η)|| ≥ ε2, ||∂ li j(η)/∂ η|| ≤ ε3, ∀i, j ∈ {1, 2, ...n}, ∀η ∈ rm. (2) design the control input ui for each robot i , and an update law for the formation parameter vector η such that each robot (almost) globally asymptotically approaches its desired location to form a desired formation, and that the robots’ velocity converges to the desired (bounded) velocity ud while avoiding collisions with all other robots in the group, i.e. limt→∞(qi(t)−q j(t)−li j(η(t))) = 0, limt→∞(η(t)−η f ) = 0, limt→∞(ui(t)−ud ) = 0, ||qi(t)−q j(t)|| > ε4, ∀i, j ∈ {1, 2, ...n}, ∀t ≥ t0 ≥ 0 (3) where ε4 is a strictly positive constant, and η f is a vector of constants that determine the desired formation. the desired formation can be represented by a labeled directed graph ([29], [34]) in the following definition. definition 1. the formation graph, g = {v, e, l} is a directed labeled graph consisting of: -a set of vertices (nodes), v = {ϑ1,··· , ϑn} indexed by the mobile robots in the group, -a set of edges, e = {(ϑi, ϑ j) ∈v ×v}, containing ordered pairs of vertices that represent inter-robot position constraints, and -a set of labels, l = {γi j|γi j = ||qi − q j − li j||2, ∀(ϑi, ϑ j) ∈ e}, li j = qi f − q j f ∈ rn indexed by the edges in e. indeed, when the control objective is achieved, the edge labels become ||qi−q j −li j||2 = 0, ∀(ϑi, ϑ j) ∈ e, i.e. the relative distance between the robots i and j is li j. 3 control design we consider the following local potential function ϕi = γi + δ βi (4) 44 dang binh nguyen, khac duc do where δ are positive tuning constants, the functions γi and βi are the goal and related collision avoidance functions for the robot i specified as follows: -the goal function γi is essentially the sum of all distances from the robot i to its adjacent group members, ni. a simple choice of this function is γi = ∑ j∈ni γi j, γi j = 1 2 ||qi −q j −li j||2. (5) -the related collision function βi should be chosen such that it is equal to infinity whenever any robots come in contact with the robot i, i.e. a collision occurs, and attains the minimum value when the robot i is at its desired location with respect to other group members belong to ni, which are adjacent to the robot i. this function is chosen as follows: βi = ∑ j∈ni ( β ki j β 2ki jl + 1 β ki j ) (6) where k is a positive constant to be chosen later, βi j and βi jl are collision and desired collision functions chosen as βi j = 1 2 ||qi −q j||2, βi jl = 1 2 ||li j||2. (7) it is noted from (7) that βi j = β ji and βi jl = β jil . remark 1. 1. the above choice of the potential function ϕi given in (4) with its components specified in (5)-(6), has the following properties: 1) it attains the minimum value when the robot i is at the desired location with respect to other group member belong to ni, which are adjacent to the robot i, i.e. qi − q j − li j = 0, j ∈ ni, and 2) it is equal to infinity whenever one or more robots come in contact with the robot i, i.e. when a collision occurs. 2. the potential function (4) is different from the ones proposed in [14] and [33] in the sense that the ones in [14] and [33] are centralized and do not put penalty on the relative distance between the robots, i.e. do not include the goal function γi. therefore, the controllers developed in [14] and [33] do not guarantee the formation converge to a specified configuration but to any configurations that locally minimize the potential functions (these potential functions in [14] and [33] are nonconvex). 3. our potential function (4) is also different from the navigation functions proposed in [26] and [29] in the sense that our potential function is of the form of sum of collision avoidance functions while those navigation functions in [26] and [29] are of the form of product of collision avoidance functions . this feature makes our potential function "more decentralized". furthermore, our potential function is equal to infinity while those in [14], [26] and [29] is equal to a finite constant when a collision occurs. however, those in [26] and [29] also cover obstacle and work space boundary avoidance. although these issues are not included in this paper for clarity, considering these issues is possible and is the subject of future work. 4. our potential function does not have problems like local minima and non-reachable goal as listed in [24]. to design the control input ui, we differentiate both sides of (4) along the solutions of (1) to obtain ϕ̇i = ∑ j∈ni [ωti j(ui −u j)−ψti jη̇] = ∑ j∈ni [ωti j(ui −ud −(u j −ud ))−ψti j η̇] = ∑ j∈ni ωti j(ui −ud )− ∑ j∈ni ωti j(u j −ud )− ∑ j∈ni ψti jη̇ (8) formation control of mobile robots 45 where ωi j = qi −q j −li j + δ k ( 1 β 2ki jl − 1 β 2ki j ) β k−1i j (qi −q j) ψi j = [( qi −q j −li j + 2δ kβ ki j β 2k+1i jl li j )t ∂ li j ∂ η ]t . (9) from (8), we simply choose the control ui for the robot i and the update law for η as follows: ui = −c ∑ j∈ni ωi j + ud η̇ = −γ(η −η f ) (10) where c ∈ rn×n+ and γ ∈ rm×m+ are symmetric positive definite matrices. substituting (10) into (8) yields ϕ̇i = − ∑ j∈ni ωti jc ∑ j∈ni ωi j − ∑ j∈ni ωti j(u j −ud ) + ∑ j∈ni ψti jγ(η −η f ). (11) substituting (10) into (1) results in the closed loop system q̇i = −c ∑ j∈ni ωi j + ud , i = 1, ..., n. (12) since the desired formation is specified in terms on relative distances between the robots, we write the closed loop system of the inter-robot dynamics from the closed loop system (12) as q̇i j = −c ( ∑ a∈ni ωia − ∑ b∈n j ω jb ) , (i, j) ∈ {1, ..., n}, i 6= j (13) where qi j = qi −q j. we now state the main result in the following theorem. theorem 1. under the assumptions stated in the control objective, the control for each robot i given in (10) with an appropriate choice of the tuning constants δ and k, solves the control objective. proof. see appendix. 4 simulations we carry out a simulation example in two-dimensional space to illustrate the results. the number of robots is n = 4. the initial positions of robots are chosen randomly in the circle with a radius of 0.5 centered at the origin. the design constants are chosen as c = diag(0.4, 0.4), k = 0.5, δ = 0.1. it is noted that this choice satisfies the conditions in the proof of theorem 1. we run two simulations with ud = [1 0.2]t (linear formation motion meaning that each robot will move on a rectilinear line to form the desired formation) and ud = [sin(0.5t) cos(0.5t)] (circular formation motion meaning that each robot will move on a circle to form the desired formation). for clarity, we take the formation parameter η as a scalar to implement formation expansion. the desired formation is depicted in figure 1. these simulations are motivated by gradient climbing missions in which the mobile sensor network (each mobile robot serves as a mobile sensor) seeks out local maxima or minima in the environmental field. the network can adapt its configuration in response to the sensed environment in order to optimize its gradient climb. for example, gradients in temperature fields (among others) can be estimated from the data collected by the 46 dang binh nguyen, khac duc do mobile robots; these are of interest for enabling gradient climbing to locate and track features such as fronts and eddies. these gradients can be used to compute the desired reference velocity vector ud in our simulations in this section. in the first 4.5 seconds (for the linear formation motion case) and 15 seconds (for the circular formation case), η is set to zero then is updated to η f = 3 for the rest of simulation time. the update gain is chosen as γ = 2 (scalar). robot 1 robot 4 robot 3 robot 2 2( 1 ) 2 (1 ) 2(1 ) 2( 1 )2(1 ) 2 (1 ) figure 1: desired formation for simulation. figures 2 and 3 plot simulation results for the linear formation motion and circular formation cases, respectively. for clarity, we only plot the control u1 = [ux1 uy1]t and distances from the robot 1 to other members in the group, i.e. ‖q12‖,‖q13‖ and ‖q14‖. it is seen from these figures that the desired formation shapes are nicely achieved and there are no collisions between any robots, see the bottom right figures in figures 2 and 3, where the distances from the robot 1 to other members in the groups are plotted. clearly, these distances are always larger than zero. it is also seen from figures 2 and 3 that at the beginning all the robots rapidly move away from each other to avoid collisions since they start pretty close to each other. 5 extension to formation control of nonholonomic mobile robots control of single nonholonomic mobile robots receives considerable attention, and is complicated due to the fact that they have less controls than the outputs to be controlled, see for example [30], [31], [32] and references therein. indeed, control of a group of nonholonomic mobile robots is more complicated due to some nonholonomic (non-integral) constraint. however, in this section we show that the control method developed in section 3 can be readily extended to force a group of n nonholonomic mobile robots of unicycle type to move in such a way that a desired formation is achieved. for clarity, we consider only the kinematic model of the nonholonomic mobile robots. designing the control system at the dynamic level even without requiring robot velocities be measured can be carried out using one more "backstepping" step [35] and our proposed exponential observer in [31]. consider the kinematic model of the unicycle mobile robot i, whose only two wheels are actuated and the third wheel is not actuated formation control of mobile robots 47 0 2 4 6 8 10 12 −2 0 2 4 6 8 x y t=9 s 0 2 4 6 8 10 −1 0 1 2 3 4 5 time [s] c o n tr o l u 1 0 2 4 6 8 10 0 1 2 3 4 time [s] d is ta n ce s −2 0 2 4 6 8 −2 0 2 4 6 8 x y t=4.5 s ||q 12 || ||q 13 || ||q 14 || u x1 u y1 robot 1 robot 2 robot 3 robot 4 figure 2: linear formation motion: simulation results. 2 0 2 4 6 4 2 0 2 4 x y t=30 s 0 10 20 30 2 0 2 4 6 time [s] c o n tr o l u 1 0 10 20 30 0 1 2 3 4 time [s] d is ta n c e s 2 0 2 4 6 4 2 0 2 4 x y t=15 s u x1 u y1 ||q 12 || ||q 13 || ||q 14 || robot 3 robot 2 robot 1 robot 4 figure 3: circular formation motion: simulation results. 48 dang binh nguyen, khac duc do (see figure 4), given by ẋi = ri 2 (cos(θi)ω1i + cos(θi)ω2i) ẏi = ri 2 (sin(θi)ω1i + sin(θi)ω2i) θ̇i = ri 2bi (ω1i −ω2i) (14) where (xi, yi) denote the coordinates of the middle point, p0i, between the left and right driving wheels, and θi denotes the heading of the robot i coordinated in the earth-fixed frame oxy , see figure 4, ω1i and ω2i denote the angular velocities of the wheels of the robot i. moreover ri and bi are defined in figure 4. the task now is to design the control inputs ω1i and ω2i to achieve the control objective stated in section 3. we require an additional assumption on the desired formation velocity vector ud that limt→∞ ||ud (t)|| 6= 0, i.e. we do not consider the stabilization/regulation problem. for convenience, we convert the angular velocities of the wheels to the linear and angular velocities (vi and ri) of the robot i by the following relationship: [ vi ri ] = ( 1 ri [ 1 bi 1 −bi ])−1 [ ω1i ω2i ] . (15) with (15), we can write (14) as ẋi = vi cos(θi) ẏi = vi sin(θi) θ̇i = ri (16) y x o surg e ax is i y i x 2 i b 0i p i sway axis actuated wheel passive wheel 2 i r robot i figure 4: geometric description of a nonholonomic mobile robot. indeed, the kinematic model (14) or (16) possesses the following nonholonomic constraint: ẋi sin(θi)− ẏi cos(θi) = 0. (17) moreover, we will consider the the linear and angular velocities (vi and ri) of the robot i as the control inputs. after these inputs are designed, ω1i and ω2i are calculated from (15). formation control of mobile robots 49 5.1 control design the control design consists of two steps. at the first step, we consider the control vi and the yaw angle θi as a virtual control to steer the robot position (xi, yi) to its desired location. at the second step, the control ri will be desired to force the virtual yaw angle to converge to its actual yaw angle. step 1. define θei = θi −αθi (18) where αθi is a virtual control of θi. with (18), we can write (16) as q̇i = ui + λθei (19) where qi = ∣∣∣∣ xi yi ∣∣∣∣ , ui = ∣∣∣∣ cos(αθi ) sin(αθi ) ∣∣∣∣ vi, λθei = ∣∣∣∣ (cos(θei)−1) cos(αθi )−sin(θei) sin(αθi ) sin(θei) cos(αθi ) + (cos(θei)−1) sin(αθi ) ∣∣∣∣ vi. (20) it is seen that (19) is almost of the same form as (1). however, the problem is that the controls vi and αθi are not solvable directly from the control ui if ui is not designed properly. we therefore present briefly how ui is designed to tackle that problem. consider the following potential function (the same form as (4)) ϕi = γi + δ βi (21) where δ , γi and βi are defined in section 3, see (6) and (7). differentiating both sides of (21) along the solutions of (19) gives ϕ̇i = ∑ j∈ni [ωti j(ui + λθei −u∗d −(u j + λθe j −u∗d ))−ψti jη̇] (22) where ωi j and ψi j are defined in (9), and u∗d = √ 1 + n ∑ i=1 || ∑ j∈ni ωi j||2 ud . it is noted that we use u∗d instead of ud in (22) to overcome the nonholonomic problem of the mobile robot under investigation. indeed, limt→∞ ∑ j∈ni ωi j(t) = 0 implies that limt→∞ u∗d (t) = ud . from (22), we choose the control ui and the update law for η as ui = −c||ud|| ∑ j∈ni ωi j + u∗d η̇ = −γ(η −η f ) (23) where c and γ are diagonal positive definite matrices. again, ||ud|| is included in the control ui to overcome the nonholonomic problem. defining θd = arctan(udy/udx), then from the first equations of (23) and (20), we have cos(αθi )vi = −c1||ud|| ∑ j∈ni ωxi j + √ 1 + n ∑ i=1 || ∑ j∈ni ωi j||2||ud||cos(θd ) sin(αθi )vi = −c2||ud|| ∑ j∈ni ωyi j + √ 1 + n ∑ i=1 || ∑ j∈ni ωi j||2||ud||sin(θd ) (24) where ωxi j and ωyi j are defined as ωi j = [ ωxi j ωyi j]t , c1 and c2 are defined as c = diag(c1, c2). we now need to solve (24) for vi and αθi . to do this, multiplying both sides of the first and second equations of (24) with cos(θd ) and sin(θd ), respectively, then adding them together result in cos(αθi −θd )vi = −c1||ud|| ∑ j∈ni ωxi j cos(θd )− c2||ud|| ∑ j∈ni ωyi j sin(θd ) + √√√√1 + n ∑ i=1 || ∑ j∈ni ωi j||2||ud||. (25) 50 dang binh nguyen, khac duc do on the other hand, multiplying both sides of the first and second equations of (24) with sin(θd ) and cos(θd ), respectively, then subtracting from each other result in sin(αθi −θd )vi = c1||ud|| ∑ j∈ni ωxi j sin(θd )−c2||ud|| ∑ j∈ni ωyi j cos(θd ). (26) from (25) and (26), we have αθi = θd + arctan   c1 ∑ j∈ni ωxi j sin(θd )−c2 ∑ j∈ni ωyi j cos(θd ) −c1 ∑ j∈ni ωxi j cos(θd )−c2 ∑ j∈ni ωyi j sin(θd ) + √ 1 + n ∑ i=1 || ∑ j∈ni ωi j||2   . (27) it is seen that (27) is well-defined if the positive constants c1 and c2 are chosen such that c1 + c2 < 1. (28) the control vi is found by solving (24) as vi = cos(αθi )||ud|| ( −c1 ∑ j∈ni ωxi j + √ 1 + n ∑ i=1 || ∑ j∈ni ωi j||2 cos(θd ) ) + sin(αθi )||ud|| ( −c2 ∑ j∈ni ωyi j + √ 1 + n ∑ i=1 || ∑ j∈ni ωi j||2 sin(θd ) ) . (29) substituting (23) into (22) results in ϕ̇i = −||ud|| ∑ j∈ni ωti jc ∑ j∈ni ωi j + ∑ j∈ni [ωti j(λθei −(u j + λθe j −u∗d )) + ψti jγ(η −η f )]. (30) step 2. to design the control ri, differentiating both sides of (18) along the solutions of the third equation of (16) and choosing the control ri as ri = −diθei −α̇θi − ∑ j∈ni ωti jλθei /θei (31) where di is a positive constant, and the term ∑ j∈ni ωti jλθei /θei is to cancel the cross term ∑ j∈ni ωti jλθei in (30), result in θ̇ei = −diθei − ∑ j∈ni ωti jλθei /θei. (32) note that λθei /θei is well defined since sin(θie)/θie = 1∫ 0 cos(θieλ )dλ and (cos(θie)−1)/θie = 1∫ 0 sin(θieλ )dλ are smooth functions. 4.2 stability analysis we consider the following function ϕtot = log(1 + n ∑ i=1 (ϕi + θ 2ei)) + 1 2 (η −η f )t γ(η −η f ) (33) whose derivative along the solutions of (30), (32) and the second equation of (23) satisfies ϕ̇tot = −2 ||ud|| n ∑ i=1 ∑ j∈ni ωti jc ∑ j∈ni ωi j + n ∑ i=1 diθ 2ei 1 + n ∑ i=1 (ϕi + θ 2ei) + n ∑ i=1 ∑ j∈ni ψti jγ(η −η f ) 1 + n ∑ i=1 (ϕi + θ 2ei) −(η −η f )t γ(η −η f ) (34) formation control of mobile robots 51 where we have used − n ∑ i=1 ∑ j∈ni [ωti j(u j + λθe j −u∗d ) = n ∑ i=1 ∑ j∈ni [ωti j(ui −u∗d + λθei ) = −||ud|| n ∑ i=1 ∑ j∈ni ωti jc ∑ j∈ni ωi j + n ∑ i=1 ∑ j∈ni ωti jλθei . (35) the rest of stability analysis can be carried out in the same lines as in proof of theorem 1 since (34) is of the same form as (40) and limt→∞ ||ud (t)|| 6= 0 by assumption. finally, note that limt→∞ θei(t) = 0 and limt→∞ ∑ j∈ni ωi j(t) = 0 implies that limt→∞(θi(t)−θd ) = 0 , i.e. the yaw angle of all robots converge to the desired angle θd = arctan(udy/udx). 5.2 simulation results we now perform a simulation to illustrate the results in the previous subsection. the number of robots, initial conditions of the robot positions, control gains, desired formation velocity and desired formation shape are the same as in section 4. the robot heading angles are initialized randomly in the circle with a radius of 0.5 centered at the origin. for clarity, we only simulate the circular formation motion, and we do not include simulation results on the formation expansion as in section 4, i.e. the formation parameter η is set to zero in all the simulation time. the other design constants are chosen as di = 5. simulation results are plotted in figure 5. again, it is seen that the robots are forced to move to nicely achieve the desired formation and no collisions between the robots occur. moreover, the yaw angle of all robots converges to the desired value θd , see the top-right figure in figure 5, where the yaw angle errors are plotted. a close look at figure 5 shows that the main different between simulation results in this subsection and those in section 4 is that the robots take a longer time to approach the desired formation. this is because we use the heading angles θi as the virtual controls to steer the robots to overcome the noholonomic constraint. 6 summary and conclusions this paper has contributed the method to construct local potential functions, based on which gradientlike cooperative controllers were designed for a group of mobile robots both with and without nonholonomic constraints to perform certain formation missions. formal analysis of the convergence and feasibility of the control solutions have also been provided. in the near future, it is of interest to apply the proposed control design method combined with the control design scheme for other single underactuated robots with second order nonholonomic constraints such as underactuated ships [37] to achieve a desired formation for a group of underactuated systems. 7 appendix: proof of theorem 1 we prove theorem 1 in two steps. at the first step, we show that there are no collisions between any robots and the solutions of the closed loop system exist. at the second step, we prove that the equilibrium point of the inter-robot dynamics closed loop system (13), at which qi − q j − li j = 0, is asymptotically stable. finally, we show that all other equilibrium(s) of (13) are either unstable or saddle. step 1. proof of no collision and existence of solutions: we consider the following common potential function ϕ given by ϕ = n ∑ i=1 ϕi (36) 52 dang binh nguyen, khac duc do 2 0 2 4 6 3 2 1 0 1 2 3 4 x y 0 2 4 6 8 10 12 1 0.5 0 0.5 1 time [s] h e a d in g a n g le e rr o rs 0 5 10 15 20 5 0 5 10 time [s] c o n tr o ls v 1 , r 1 0 5 10 15 20 0 0.5 1 1.5 2 time [s] d is ta n c e s 1 d 2 d 3 d 4 d v 1 r 1 ||q 12 || ||q 13 || ||q 14 || robot 1 robot 2 robot 3 robot 4 figure 5: mobile robot circular formation motion: simulation result. formation control of mobile robots 53 whose derivative along the solutions of (11) is ϕ̇ = − n ∑ i=1 ∑ j∈ni ωti jc ∑ j∈ni ωi j − n ∑ i=1 ∑ j∈ni ωti j(u j −ud ) + n ∑ i=1 ∑ j∈ni ψti jγ(η −η f ). (37) since li j = −l ji and ωi j = −ω ji, we have n ∑ i=1 ∑ j∈ni ωti j(u j −ud ) = − n ∑ i=1 ∑ j∈ni ωti j(ui −ud ). (38) substituting (38) into (37) gives ϕ̇ = −2 n ∑ i=1 ∑ j∈ni ωti jc ∑ j∈ni ωi j + n ∑ i=1 ∑ j∈ni ψti jγ(η −η f ). (39) we now consider the following total function ϕtot = log(1 + ϕ) + 0.5||η − η f ||2 whose derivative along the solutions of (39) the second equation of (10) satisfies ϕ̇tot = − 2 1 + ϕ n ∑ i=1 ∑ j∈ni ωti jc ∑ j∈ni ωi j + 1 1 + ϕ n ∑ i=1 ∑ j∈ni ψti jγ(η −η f )−(η −η f )t γ(η −η f ) (40) which implies that ϕ̇tot ≤ − 2 1 + ϕ n ∑ i=1 ∑ j∈ni ωti jc ∑ j∈ni ωi j + λmax(γ) 4ε(1 + ϕ)2 ∥∥∥∥∥ n ∑ i=1 ∑ j∈ni ψti j ∥∥∥∥∥ 2 − (λmin(γ)−ε λmax(γ))||η −η f ||2 (41) where ε is a positive constant, λmin(γ) and λmax(γ) denote the minimum and maximum eigenvalues of γ respectively. from (9), and definition of the function ϕ , it can be readily shown that there exists a positive constant ωmax such that 1 (1 + ϕ)2 ∥∥∥∥∥ n ∑ i=1 ∑ j∈ni ψti j ∥∥∥∥∥ 2 ≤ ωmax (42) with (42) in mind, picking ε = λmin(γ)/λmax(γ) we can write (41) as ϕ̇tot ≤ λ 2max(γ) 4λmin(γ) ωmax ∆ = ϖmax. (43) integrating both sides of (43) results in ϕtot (t) ≤ ϕtot (t0) + ϖmax(t −t0). (44) where ϕtot (t) and ϕtot (t0) are (from the definition of ϕtot ) ϕtot (t) = log [ 1 + n ∑ i=1 ( γi(t) + δ ∑ j∈ni ( β ki j (t) β 2ki jl + 1 β ki j (t) ))] + 12||η(t)−η f ||2 ϕtot (t0) = log [ 1 + n ∑ i=1 ( γi(t0) + δ ∑ j∈ni ( β ki j (t0) β 2ki jl + 1 β ki j (t0) ))] + 12||η(t0)−η f ||2. (45) the right hand side of (44) cannot escape to infinity unless when t = ∞ since βi jl > 0 and βi j(t0) > 0 (see definition of βi jl and βi j given in (7)). therefore the left hand side of (44) cannot escape to infinity for 54 dang binh nguyen, khac duc do all t ∈ [t0, ∞). this implies that βi j(t) cannot be zero for all t ∈ [t0, ∞), i.e. no collisions can occur for all t ∈ [t0, ∞). on the other hand, it is true from the second equation of (10) that ||η(t)−η f || ≤ ||η(t0)−η f ||e−λmin(γ)(t−t0) (46) which means that the desired formation shape is achieved exponentially. using (42) and (46), we can write (41) as ϕ̇tot≤ λmax(γ) √ ωmax||η(t0)−η f ||e−λmin(γ)(t−t0). (47) integrating both sides of (47) from t0 to t results in ϕtot (t) ≤ ϕtot (t0) + λmax(γ) √ ωmax||η(t0)−η f ||/λmin(γ). (48) it is seen that the right hand side of (48) is bounded. therefore the left hand side of (48) must also be bounded. this implies that βi j(t) must be larger than a strictly positive constant for all t ∈ [t0, ∞), which in turn means that there exists a strictly positive constant ε4 such that the last inequality of (3) holds. to prove that the solutions of the closed loop system (12) exist, we consider the function w = 0.5 n ∑ i=1 ||qi||2 whose derivative along the solutions of (12), after some simple manipulation, satisfies ẇ ≤ ρ1(1 + 1/ min(βi j))w + ρ2 , where ρ1 and ρ2 are some positive constants, which implies that the solutions of (12) exist since βi j(t) is larger than a strictly positive constant for all t ∈ [t0, ∞). furthermore, applying barbalat’s lemma found in [36] to (41) gives lim t→∞ 1 1 + ϕ(t) n ∑ i=1 ∑ j∈ni ωti j(t)c ∑ j∈ni ωi j(t) = 0 (49) which implies that { limt→∞ ∑ j∈ni ωi j(t) = 0 limt→∞ ϕ(t) = χ1 or { limt→∞ ∑ j∈ni ωi j(t) = χ2 limt→∞ ϕ(t) = ∞ (50) where χ1 and χ2 are some constants. from definitions of ωi j and ϕ , the second limit set in (50) cannot be true. therefore, the first limit set in (50) implies that limt→∞ ∑ j∈ni ωi j(t) = 0. step 2. behavior near equilibrium points. at the steady state, the equilibrium points are found by solving the following equations ∑ j∈ni ωi j = ∑ j∈ni ( qi j −li j + δ k ( 1 β 2ki jl − 1 β 2ki j ) β k−1i j qi j ) = 0, i = 1, ..., n. (51) it is directly verified that q̄ = l̄ where q̄ and are stack vectors of qi j and li j, respectively, i.e. q̄ = [qt12, q t 13..., q t n−1,n ] t and l̄ = [lt12, l t 13..., l t n−1,n ] t , is one root of (51). in addition there is (are) another root(s) denoted by q̄c = [qt12c, q t 13c..., q t n−1,nc] t of (51) different from l̄ satisfying ∑ j∈ni ωi j ∣∣∣∣∣ q̄=q̄c = ∑ j∈ni ( qi jc −li j + δ k ( 1 β 2ki jl − 1 β 2ki jc ) β k−1i jc qi jc ) = 0, i = 1, ..., n (52) where βi jc = 0.5||qic − q jc||2. in the following, we will show that the equilibrium point q̄ = l̄ is asymptotically stable, and the equilibrium point(s) q̄ = q̄c is (are) unstable or saddle. we now write the closed loop system of the inter-robot dynamics (13) as ˙̄q = −c̄f(q̄, l̄). (53) formation control of mobile robots 55 where c̄ = diag(c,··· ,c︸ ︷︷ ︸ e ) with e the number of edges of the formation graph, and f(q̄, l̄) = [ ∑ a∈n1 ωt1a − ∑ b∈n2 ωt2b, ∑ a∈n1 ωt1a − ∑ b∈n3 ωt3b, ...., ∑ a∈ni ωtia − ∑ b∈n j ωtjb, ...., ∑ a∈nn−1 ωtn−1,a − ∑ b∈nn ωtnb] t . (54) since (52) holds for all i = 1, ..., n, at the steady state we have ∑ a∈ni ωia− ∑ b∈n j ω jb = 0, ∀(i, j) ∈{1, ..., n}, i 6= j. therefore the equilibrium points q̄ = l̄ and q̄ = q̄c are also the equilibrium points of (53). the general gradient of f(q̄, l̄) with respect to q̄ is given by ∂ f(q̄, l̄) ∂ q̄ =   ∂ ξ12 ∂ q12 ∂ ξ12 ∂ q13 ··· ∂ ξ12∂ qn−1,n ... ... ... ... ∂ ξi j ∂ q12 ··· ∂ ξi j∂ qi j ∂ ξi j ∂ qn−1,n ... ... ... ... ∂ ξn−1,n ∂ q12 ··· ··· ∂ ξn−1,n∂ qn−1,n   , ξi j = ∑ a∈ni ωia − ∑ b∈n j ω jb, (i, j) ∈ {1, ..., n}, i 6= j. (55) it can be checked that ∂ ξi j ∂ qi j = nin×n + 2δ k ( 1 β 2ki jl − 1 β 2ki j ) β k−1i j in×n + 2δ k ( (k −1) ( 1 β 2ki jl − 1 β 2ki j ) β k−2i j + 2k β k+2i j ) qi j q t i j ∆ = hi j ∂ ξi j ∂ qcd = sδ k ( 1 β 2kcdl − 1 β 2kcd ) β k−1cd in×n + sδ k ( (k −1) ( 1 β 2kcdl − 1 β 2kcd ) β k−2cd + 2k β k+2cd ) qcd q t cd , (56) where (c, d) ∈{1, ..., n}, (c, d) 6= (i, j), c 6= d , and s = 1 or s = −1 depending on value of c, d, i and j. however, we do not need to specify the sign of s for our next task. we now investigate properties of the equilibrium points q̄ = l̄ and q̄ = q̄c based on the general gradient ∂ f(q̄, l̄)/∂ q̄ evaluated at those points. step 2.1 proof of q̄ = l̄ being the asymptotic stable equilibrium point: at the equilibrium point q̄ = l̄ , we have ∂ ξi j ∂ qi j ∣∣∣∣ q̄=l̄ = nin×n + 4δ k2 β k+2i jl li jl t i j , ∂ ξi j ∂ qcd ∣∣∣∣ q̄=l̄ = s 2δ k2 β k+2cdl lcd l t cd , (57) where βcdl = 0.5||lcd||2 . with (57), let ξ ∈ rne we have ξ t ∂ f(q̄, l̄) ∂ q̄ ∣∣∣∣ q̄=l̄ ξ ≥ ( n − 4δ k2ne max(l2i ja) min(β k+2i jl ) ) ξ t ξ , (i, j) ∈ {1, ..., n}, i 6= j (58) where li ja is the a th element of li j. therefore, for any given constant k if we choose the tuning constant δ such that n − 4δ k2ne max(l2i ja) min(β k+2i jl ) > 0 → δ < n min(β k+2i jl ) 4k2ne max(l2i ja) , (i, j) ∈ {1, ..., n}, i 6= j (59) then the matrix ∂ f(q̄, l̄)/∂ q̄ ∣∣ q̄=l̄ is positive definite, which in turn implies that the equilibrium point q̄ = l̄ is asymptotically stable. 56 dang binh nguyen, khac duc do step 2.2. proof of q̄ = q̄c being the unstable/saddle equilibrium point(s): the idea is to consider block matrices on the main diagonal of the matrix ∂ f(q̄, l̄)/∂ q̄ ∣∣ q̄=q̄c and show that there exists at least one block matrix whose determinant is negative. define hi jc = ∂ ξi j/∂ qi j ∣∣ q̄=q̄c and let φa and φb be the ath and bth elements of qi jc, (a, b) ∈ {1, ..., n}, a 6= b. we form the matrices h abi jc from the matrix hi jc as follows h abi jc = [ h11 h12 h21 h22 ] h11 = n + 2δ kπi jcβ k−1i jc + 2δ k[(k −1)πi jcβ k−2i jc + 2k/β k+2i jc ]φ 2a h12 = 2δ k[(k −1)πi jcβ k−2i jc + 2k/β k+2i jc ]φaφb h21 = 2δ k[(k −1)πi jcβ k−2i jc + 2k/β k+2i jc ]φaφb h22 = n + 2δ kπi jcβ k−1i jc + 2δ k[(k −1)πi jcβ k−2i jc + 2k/β k+2i jc ]φ 2b (60) where πi jc = 1/β 2ki jl −1/β 2ki jc . the determinant of h abi jc is given by det(h abi jc) = (n + 2δ kπi jcβ k−1 i jc )∆ ab i jc (61) where ∆abi jc = n + 2δ kπi jcβ k−1 i jc + 2δ k[(k −1)πi jcβ k−2i jc + 2k/β k+2i jc ](φ 2a + φ 2b ) (62) let us consider the sum: n−1 ∑ a=1 n ∑ b=a+1 ∆abi jc = n(n−1)n + 2δ k(n−1)(2(k−1) + n)β k−1i jc /β 2ki jl + 2δ k(n−1)(2(k + 1)−n)/β k+1i jc . (63) since n > 1, picking k > n/2 − 1 ensures that n−1 ∑ a=1 n ∑ b=a+1 ∆abi jc > 0. therefore, there exists at least one pair (a, b) ∈ {1, ..., n} denoted by (a∗, b∗) such that ∆a∗b∗i jc > 0. now for all (i, j) ∈ {1, ..., n}, i 6= j let us consider the sum: n−1 ∑ i=1 n ∑ j=i+1 det(h a ∗b∗ i jc ) ∆a∗b∗i jc βi jc = n−1 ∑ i=1 n ∑ j=i+1 (nβi jc + 2δ kπi jcβ ki jc). (64) on the other hand, multiplying both sides of f(q̄c, l̄) = 0 with q̄tc results in q̄ t c f(q̄c, l̄) = 0, which is expanded to n−1 ∑ i=1 n ∑ j=i+1 (nqti jc(qi jc −li j) + 2δ knπi jcβ ki jc) = 0. (65) substituting (65) into (64) results in n−1 ∑ i=1 n ∑ j=i+1 det(h a ∗b∗ i jc ) ∆a∗b∗i jc βi jc = n−1 ∑ i=1 n ∑ j=i+1 (n −2)βi jc + n−1 ∑ i=1 n ∑ j=i+1 qti jcli j. (66) the term n−1 ∑ i=1 n ∑ j=i+1 ( qti jcli j ) is strictly negative since at the point where qi j = li j (the point f in figure 6) all attractive and repulsive forces are equal to zero while at the point where qi j = qi jc (the point c in figure 6) the sum of attractive and repulsive forces is equal to zero (see section 2 for discussion of a simple case). therefore the point qi j = 0 (the point o in figure 6) must locate between the points qi j = li j and qi j = qi jc , see figure 6. furthermore if we write (65) as 2 n−1 ∑ i=1 n ∑ j=i+1 βi jc + δ k(β ki jc/β 2k i jl −1/β ki jc) = n−1 ∑ i=1 n ∑ j=i+1 qti jcli j (67) formation control of mobile robots 57 12 q 13 q 1n q 21 q 2 n q 1,1n q 1,n n q o f c the point where all attractive and repulsive forces are zero the point where sum of attractive and repulsive forces are zero. figure 6: illustration of location of critical points. we can see that deceasing δ results in decrease in βi jc since βi jl is a bounded constant and the right hand side of (67) is negative. therefore, choosing a sufficiently small δ ensures that the right hand of (64) is strictly negative since βi jc = 0.5||qi jc||2 . that is n−1 ∑ i=1 n ∑ j=i+1 det(h a ∗b∗ i jc ) ∆a∗b∗i jc βi jc < 0 (68) which implies that there exists at least one pair (i, j) ∈ {1, ..., n} denoted by (i∗, j∗) such that det(h a ∗b∗ i∗ j∗c) < 0. 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[37] k. d. do and j. pan, global path-tracking of underactuated ships with non-zero off-diagonal terms, automatica, vol. 41, pp. 87-95, 2005. dang binh nguyen thai nguyen university of technology rector’s office 3-2 street, thai nguyen city, vietnam e-mail: nguyen.d.binh.tnut@gmail.com khac duc do school of mechanical engineering the university of western australia 35 stirling highway, crawley, wa 6009, australia e-mail: duc@mech.uwa.edu.au http://horos.rdsor.ro/ijcccv3n4draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 4, pp. 395-411 mccls: certificateless signature scheme for emergency mobile wireless cyber-physical systems zhong xu, xue liu, guoqing zhang, wenbo he abstract: mobile ad hoc network is a self-configurable and self-organizing wireless network of mobile devices without fixed infrastructure support, which makes it a good candidate as underlying communication network for the cyber-physical systems in emergency conditions such as earthquake, flood, and battlefields. in these scenarios, efficient communication schemes with security support are especially desired. two cryptography approaches, the public key cryptography and the identitybased cryptography, face the costly and complex key management problem and the “key escrow" problem in the real-life deployment. recently, the certificateless public key cryptography (cl-pkc) was introduced to address these problems in previous approaches. however, the efficiency of the schemes based on cl-pkc is not high and can be improved further. in this paper, we present an improved certificateless signature scheme (mccls) based on bilinear pairings. first, we theoretically compare the efficiency of mccls with that of existing certificateless signature schemes (cls). second, an empirical study is conducted to compare the traditional aodv with the mccls scheme based on aodv (mcdv) in their efficiency and effectiveness against two most common attacks (i.e. redirection attack and rushing attack). results from theoretical analysis show that the new mccls scheme is more efficient than existing cls solutions, and results from empirical studies show that the mcdv is able to resist the two common attacks without causing substantial degradation of the network performance. keywords: certificateless signature, manets, cyber-physical systems, security; 1 introduction a salient feature of cyber-physical systems (cps) is that it integrates computing, monitoring, and communication capabilities, and constantly interacts with the physical environment. as a result, cyberphysical system must be dependable, safe, secure and efficient [16]. many emergency applications such as earthquake, flood and battlefields [10] proposed for cps will be implemented on networked environments where computing devices are connected through wireless links. for many applications such as the military applications, fixed infrastructure may not be available in the environment or even be destroyed [9]. it is important to solve the connectivity problems with self-configurable and self-organizing characteristics. a possible solution for the lack of communication means is deployment of the mobile ad hoc networks (manets). while manets provide a great flexibility for establishing communications, they are particularly prone to the security threats of eavesdropping, interception and routing attacks. some of these problems may be solved or mitigated with the use of cryptographic schemes [7]. in the recent literature many papers make specific proposals on determining how to use public key infrastructure (pki) [27, 23, 4, 15] and identity-based public key cryptography (id-pkc) [20, 13, 8, 25] cryptographic techniques to secure manets. the traditional pki signature scheme uses a centralized certificate authority to issue a digital certificate that binds a user with the corresponding public key. the requirement of certificate authority inevitably leads to complex certificate management problems in practice. copyright © 2006-2008 by ccc publications 396 zhong xu, xue liu, guoqing zhang, wenbo he the id-pkc which was introduced by shamir [20] is developed from traditional pki to simplify the certificate management process. in the id-pkc based scheme, user’s public key is derived directly from certain aspects of his identity such as email address which is assumed to be publicly known. a private key is generated by a trusted third party – private key generator (pkg). however, a new inherent problem is brought by this approach, namely the “key escrow" problem since the private key of user is known to the pkg. as a result, the pkg is able to impersonate any user of its choice, or decrypt messages. in order to solve the costly and complex key management problems in pki and the “key escrow" problem in id-pkc respectively, al-riyami and paterson [1] proposed the first certificateless public key cryptography (cl-pkc) scheme. in the certificateless signature (cls) scheme, key generation center (kgc) only provides user with a partial private key, which is related to the user’s identity and the master private key only known by pkc. then the user generates the remaining part of the private key and the corresponding public key. as a result, the kgc does not know the user’s private key because the user’s private key is generated by user itself, thereby solving the “key escrow" problem in id-pkc based schemes. however, cls schemes are usually computationally intensive, and hence they are not readily applicable in practical applications. in this paper, we present mccls scheme, a new cls scheme for mobile wireless cyber-physical systems. compared with existing cls schemes, mccls scheme only requires one pairing operation in the verification phase, and none in the signing phase. since the pairing operation is the most time-consuming computation in pairing-based cryptosystems, our mccls scheme has less computation overhead and therefore is more efficient than those schemes proposed previously in [1, 12, 14, 26]. we also provide a detailed security proof for mccls scheme based on the computational diffie-hellman problem (cdhp) [6]. then an empirical study is conducted to compare the mccls based on aodv (mcdv) with the traditional aodv in their efficiency and effectiveness against the two most common attacks, redirection attack and rushing attack, based on qualnet simulation software [19]. results show that our scheme is efficient in terms of computation overhead and it can resist redirection attack [18] and rushing attack [11]. the remainder of the paper is organized as follows. section 2 provides a brief description on the related work. section 3 introduces the preliminaries and the background on the security model and the attack model. section 4 presents our efficient mccls scheme. section 5 analyzes the security of mccls scheme in detail. section 6 evaluates the performance of mccls scheme under the redirection attack and the rushing attack. finally, section 7 concludes this paper with summaries and the directions of future work. 2 related work cyber-physical systems (cps) are physical and engineered systems whose operations are integrated, monitored, and controlled by a computational core [17]. cps integrate the communication and computation with the physical process [2]. since cyber-physical systems constantly interact with the physical environment, they must be dependable, safe, secure and efficient [16]. cps is a new active research area. the position papers published in the nsf workshop on cyberphysical system [16] presents a good overview of the different aspects of cps research. though security is an important research issue of cps , little work has been done [3] so far for the security of cps. since many emergency applications proposed for cps will be implemented on mobile ad hoc networks (manets), it is natural to ask the question if security schemes proposed for manets are practical for cps. to overcome the security problems in manets due to their infrastructure-less nature, we need some new methods to solve these problems. one of these methods is the lightweight and efficient key management scheme. recently, in order to solve the key management problem in public key mccls: certificateless signature scheme for emergency mobile wireless cyber-physical systems 397 cryptography and “key escrow" problem in identity-based cryptography schemes, al-riyami and paterson [1] proposed the first certificateless signature (cls) scheme but fail to provide the security proof. later, huang et al. [12] found that this cls scheme was insecure against a type i forger attack. a modified cls scheme was proposed with security proved under the random oracle model [5]. however, the scheme requires more pairing operations than the original scheme proposed in [1]. in [14], li et al. therefore, proposed another cls scheme, with a formal security analysis omitted. another shortcoming of this scheme is that the verification algorithm requires four quite expensive pairing operations. zhang et al. [26] presented a cls scheme with a formal security analysis but it still needs four pairing operations in the verification phase. following that, yap et al. [22] proposed a new cls scheme, which requires no pairing operation in the signing phase but requires two pairing operations in the verification phase. however, since pairing operations are costly in computation and are usually time consuming, using more pairing operations in the scheme will make it difficult to be applied for emergency cyber-physical systems, because cps need constantly interact with the physical environment and with stringent timing requirements. in this paper, we present mccls scheme, which is more efficient and hence is a good alternative to be used in cyber-physical systems. a good security protocol must be resilient against security attacks. in the following, we briefly introduce two most commonly studied attacks. later in this paper, we prove that the proposed mccls scheme is resilient against these two attacks. redirection attack [18] is one of the many possible attacks in manets. in this attack, a malicious node sends a forged route reply (rrep) packet to a source node by altering control message fields with falsified values. when a source node receives multiple rreps, by comparing the destination sequence numbers contained in rrep packets, it regards the largest one as the most recent routing information and selects the route through which that rrep packet has been sent. if the attacker sends the rrep with destination sequence number higher than that of the real destination node to the source node, the data traffic will be directed toward the attacker. it then drops all data packets it receives instead of forwarding them to the next node on the routing path. consequently, the source and destination nodes will lose communication with each other. rushing attack [11] usually aims at a reactive routing protocol. every node in the network only forwards the first route discovery packet that it receives and drops the rest. malicious nodes can “rush" the route request packets towards the destination. as a result, the nodes that receive these “rushed" request packets forward them and discard other route requests that arrive later. the resulted routes would then include the malicious nodes. in this way, the attacker is placed in an advantageous position. 3 preliminaries in this section, we present some mathematical background which helps in realizing cls based on the bilinear pairing. it is commonly used in cls schemes to realize signature and verification [1, 12, 14]. we define two cyclic groups g1, g2, where g1 is an additive group and g2 is a multiplicative group, where both groups have a prime order p. let e be a computable bilinear map e : g1 × g1 → g2. we have the following conditions: 1. bilinearity: for any p, q, r ∈ g1, we have e(p + q, r) = e(p, r)e(q, r). for a, b ∈ z∗p and p, q ∈ g1, we have e(ap, bq) = e(p, q) ab = e(p, abp) = e(abp, p). 2. non-degeneracy: there exists p, q ∈ g1, such that e(p, q) 6= 1. 3. computability: there is an efficient algorithm to compute e(p, q) for all p, q ∈ g1 the map e will be derived from either the weil or tate pairing on an elliptic curve over a finite field. 398 zhong xu, xue liu, guoqing zhang, wenbo he an efficiently computable bilinear map e provides an algorithm for solving the decision diffiehellman problem (ddhp) [6]. that is, given (p, ap, bp, cp) ∈ g1 and a, b ∈ z∗p, decide whether c ≡ ab ∈ z ∗ p. in bilinear pairing, decision diffie-hellman (ddh) problem is easy and computational diffiehellman (cdh) problem [22] is still hard. that is, for a, b ∈ z∗p, given (p, ap, bp), computing abp is infeasible. 3.1 certificateless scheme usually, a certificateless signature (cls) scheme consists of five polynomial time algorithms [1]: • setup. kgc runs a probabilistic algorithm to initialize the system. it receives a security parameter k and returns a randomly chosen master key and a list of public parameters param. • extract partial private key. kgc takes the master key and an identity id ∈ {0,1}∗ as inputs, and outputs a private partial key did. • generate key pair. the user takes a list of public parameters param as inputs, outputs a private key sid and a public key pid. • cl-sign. the user takes a list of public parameters param, full private keys (did, sid), and a message m to produce a signature σ on m. • cl-verify. anyone in this algorithm may take {param, id, pid} and a message m as inputs, and outputs true if and only if σ is the valid signature, or a symbol ⊥ to indicate a failure. we may note that, once the user received the public parameters, such as public key of kgc, user chooses secret value to generate his key pair including user’s private key and user’s public key. thus the user’s full private key is composed of the partial private key generated by kgc and the user’s private key generated by user himself. neither the kgc nor the user can generate the full private keys by himself, therefore solving the “key escrow" problem. 3.2 adversarial model as defined in [1, 22], there are two types of adversaries, type i and type ii, with different capabilities. in cls, type i adversary ai acts as a third part who tries to impersonate a user. it is not allowed to know the kgc’s master private key. however, ai can replace the public key pid with values of its choice due to nature of the public key generated by the user. this means the adversary is able to fool the user accepting the signature, which is signed by the adversary’s public key. type ii adversary aii represents a malicious kgc who knows the master private key. that is, aii can compute the partial private key by itself. but aii does not know the user’s private key sid and it cannot replace the user’s public keys pid . definition 1. a cls scheme is secure against existential forgery on adaptive chosen message and id attacks against adversary a, of type i or type ii if no polynomial time algorithm has a non-negligible advantage against a challenger c in the following game [1]: 1. the challenger c takes a security parameter k and runs the setup algorithm. challenger c gives a the system parameters param. if a is of type i, the challenge c keeps the master private key to itself. otherwise, c gives the master private key to a. 2. a can request c to answer the following types of queries: mccls: certificateless signature scheme for emergency mobile wireless cyber-physical systems 399 • partial key extraction (for type i adversary only). c returns to a’s partial private key did as the result of running extract partial private key algorithm. • secret value and public key extraction. c returns to a’s private key sid associated with a’s public key pid as the result of running extract partial private key and generate key pair algorithms. in the case of type i adversary, c returns if the user’s public key pid has been replaced. • public key replacement (for type i adversary only). a can replace the associated public key pid to a new public key p ′ id which is chosen by itself. • sign. c returns a valid signature σ using cl-sign algorithm regardless whether the public key pid has been replaced or not. 3. eventually, a outputs a signature (id∗, m∗, σ ∗). a wins game if veri f y(param, pid∗ , m ∗, σ ∗) = true and the generated output fulfills the following conditions: • cl-sign(id∗, m∗) has never been queried. • if adversary a is type i, id∗ has not been submitted to partial key extraction. • if adversary a is type ii, id∗ has not been submitted to secret value and public key extraction. 4 mccls scheme mccls scheme is motivated by the identity-based signature from [24]. our verification phase algorithm requires one pairing operation only, hence mccls scheme outperforms the other existing cls schemes in terms of efficiency. besides, message signing in mccls scheme is fast as it involves no pairing computation. mccls scheme is comprised of the following five stages. • setup. given a cyclic group g1 of prime order p, with an admissible pairing e and its generator p, kgc picks s ∈ z∗p and sets ppub = sp. then chooses two hash functions h1 : {0,1}∗ → g1 and h2 : {0,1}∗ × g1 → z∗p. the public system parameter list is (p, ppub, h1, h2), and the master private key is msk = s. • extract partial private key. given an identity id, kgc computes qid = h1(id) and did = sh1(id) . output did as the partial private key corresponding to qid = h1(id). • generate key pair. the user generates a secret value x ∈ z∗p, the public key is pid = xppub. the user’s private key is sid = x. • sign. given the user’s full private keys (did, sid) and a message m, user picks a number r ∈ z∗p and outputs a signature σ = (v, s, r) where s = 1 sid did, r = (r − sid)p and v = h2(m, r, pid)rp. • verification. given the signature (v, s, r) of a message m for the identity id, anyone in this algorithm can act a verifier to compute h = h2(m, r, pid). then checks whether (ppub,v −hr, s/h, qid) is a valid diffie-hellman tuple, that is, computes whether e(ppub, qid) = e(v − hr, s/h). if yes, accept the signature. otherwise, reject it. 5 analysis of mccls scheme in this section, we analyze the correctness, performance and security proof of mccls scheme. 400 zhong xu, xue liu, guoqing zhang, wenbo he 5.1 correctness the correctness of mccls scheme can be verified as follows: e(v − hr, s/h) = e(hrp − hrp + xhp, s/h) = e(xhp, did/xh) = e(ppub, qid). note that e(ppub, qid) is independent of the message, and only needs to be computed once and for all. so mccls scheme is more efficient than other previous schemes. 5.2 performance mccls scheme only requires two scalar multiplication in signature phase and two scalar multiplication computations and one pairing operation in verification phase. the pairing operations are expensive comparing with scalar multiplication and exponentiation. the comparison between the exiting schemes and mccls scheme according to efficiency of sign and verification algorithms and the length of public keys is shown in table 1. it shows that mccls scheme has the lowest pairing operations requirement and has the same length of public key as other cls schemes. table 1: comparison of the cls schemes ap [1] lcs [14] zwxf [26] yhg [22] mccls sign 1p+3s 2s 3s 2s 2s verify 4p+1e 4p+2s 4p 2p+3s 1p+3s pklen 2 points 2 points 1 points 1 point 1 point pklen: the public key length; s: the scalar multiplication computation; p: the pairing operation; e: the exponential computation. 5.3 security proof in this section we discuss the security of mccls scheme under the security model discussed in section 3. the main theorems concerning the security of our scheme are: theorem 2. our certificateless signature scheme is existentially unforgeable against a type i adversary ai in the random oracle model under the assumption that the cdh problem in g1 is infeasible. proof. suppose there exists an adversary ai which has an advantage in attacking mccls scheme. we build a challenger c that uses ai to solve the cdh problem. c receives an instance (p, ap, bp) of the cdhp. its goal is to compute abp. on the setup phase, c sets p as the generator of the group, and sets ppub = ap where a is the master key, which is unknown to ai. in order to avoid collision, c maintains a list l = (idi, didi , sidi , pidi ) throughout the game. the list is initially empty. c then starts to answer oracle queries with the following procedures [26]: • h1 queries. suppose ai makes qh1 queries to h1 oracle, where qh1 denotes the maximum number of queries. randomly choose j ∈ [1, qh1 ]. when an identity idi is submitted to oracle h1 where i ∈ [1, qh1 ], if i = j, assume that idi = id∗ at this point, c saves a list l1 = (idi, qi, yi) where qi = bp, yi = ⊥ (indicate to failure). otherwise, c generates a random number yi and lets qi = yip, then saves l1 = (idi, qi, yi). mccls: certificateless signature scheme for emergency mobile wireless cyber-physical systems 401 • partial key extraction (idi) queries. when ai makes the query on idi, if idi = id ∗, then c aborts and halts the simulation. otherwise c finds l and performs as follows: – if the list l contains (idi, didi , sidi , pidi ), c checks whether didi = ⊥. if didi 6= ⊥, c returns didi to ai. if didi = ⊥, and idi 6= id∗, c answers with didi = yippub = yi(ap) as partial private key. c then returns didi to ai and adds it to l. – if the list l does not contain (idi, didi , sidi , pidi ), c sets (didi = yippub = yi(ap)). then challenger c sets (sid, pid) = ⊥ and adds idi, didi , sidi , pidi to the list l. • public key extraction (idi) queries. when ai makes the query on idi, c finds l and performs as follows: – if the list l contains (idi, didi , sidi , pidi ), c checks whether pidi = ⊥. if pidi 6= ⊥, c returns pidi to ai. otherwise, c picks a random xi ∈ z∗p, and sets pidi = xippub, sidi = xi. c then returns pidi to ai and adds (sidi , pidi ) to l. – if the list l does not contain (idi, didi , sidi , pidi ), c picks a random xi ∈ z∗p, and sets pidi = xippub, sidi = xi. c then returns pidi to ai and adds (sidi , pidi ) to l. • secret value extraction (idi) queries. when ai makes the query on idi, if idi = id ∗, then c aborts and halts the simulation. otherwise c finds l and performs as follows: – if the list l contains (idi, didi , sidi , pidi ), c checks whether didi = ⊥. if didi = ⊥, c executes partial key extraction queries to obtain didi . if pidi = ⊥, c makes public key extraction queries to obtain sidi = xi, pidi = xippub. then c saves the value and adds full private keys (didi , sidi ) to the list l. – if the list l does not contain (idi, didi , sidi , pidi ), c executes partialkeyextractionqueries to obtain didi and makes publickeyextractionqueries to obtain (sidi , pidi ). then c saves the value and adds full private keys (didi , sidi ) to the list l. • public key replacement(idi, p ′ id) queries. when ai makes the query on (idi, p ′ idi ), c finds l and performs as follows: – if the list l contains (idi, didi , sidi , pidi ), c sets pidi = pid′i and sidi = ⊥. – if the list l does not contain (idi, didi , sidi , pidi ), c sets didi = ⊥, pidi = pid′i and sidi = ⊥. and then c adds to the list l. • h2 queries. when ai makes the query on (m, r, pidi ), c first scans if a list l2 = (m, r, pidi , h j) has been defined. if defined, return the list to ai. otherwise, c picks a random h j ∈ z∗p as the hash value and returns h j, and adds it to l2. • sign queries(idi, m j). when ai asks for a signature by user idi on message m j. c finds (idi, didi , sidi , pidi ). if didi not found, c runs partial key extraction queries. if (pidi , sidi ) not found, c runs public key extraction queries. note that if idi 6= id∗, ai is able to generate signature on any messages using corresponding full private keys (didi , sidi ). as far as idi = id ∗, assume that pidi is current public key and corresponding private key sidi = x, where x ∈ z∗p, additionally submits through the ai. this is because the public key has been replaced earlier by ai, then c cannot know the corresponding private key and thus the signing oracle’s answer may not be correct. on receiving sign queries, c does the following: 402 zhong xu, xue liu, guoqing zhang, wenbo he 1. choose random r j ∈ z∗p and look up the list l2 for h j, if not found, c runs h2 queries to get h j. 2. compute vj = h j(x + a r j )p and s j = r jqi = r jbp, r j = xp; 3. return the signature σ = (vj, s j, r j). now, σ is returned to ai, which appears to be valid signature since e(vj − h jr, s j/h j) = e(h j(x + a r j )p) − h jxp, r jbp/h j) = e(h jap/r j, r jbp/h j) = e(ap, bp) = e(ppub, qid). finally, ai will output a valid forgery r = (id j, m j, r j, s j,vj). if id j 6= id∗, c outputs the fail and aborts the simulation. otherwise, we can compute r j through r j = ah j vj−h j x [21], since (ppub,vjp − h jr j, s j/h j, qi) is a valid diffie-hellman tuple. apply r j to s j, we have s j = ah j vj − h jx qi s j = ah j vj − h jx bp abp = s j(vj − h jx)/h j . so abp = s j(vj − h jx)/h j is the answer to our cdhp instance. if the ai can break our scheme, then the attacker solves the cdh problem. theorem 3. our certificateless signature scheme is existentially unforgeable against the aii adversary in the random oracle model under the assumption that the cdh problem in g1 is infeasible. proof. suppose there exists an adversary aii which has advantage in attacking mccls scheme. we build a challenger c that uses aii to solve the cdh problem. c receives an instance (p, ap, bp) of the cdhp. its goal is to compute abp. on the setup phase, c sets p as the generator of the group, and sets ppub = sp where s is the master key, which is known to aii. in order to avoid collision, c maintains a list l = (idi, sidi , pidi ) throughout the game. the list is initially empty. c then starts to answer oracle queries with the following procedures: • h1 queries. suppose aii makes qh1 queries to h1 oracle, where qh1 denotes the maximum number of queries. randomly choose j ∈ [1, qh1 ]. when an identity idi is submitted to oracle h1 where i ∈ [1, qh1 ], if i = j, assume that idi = id∗ at this point, c saves a list l1 = (idi, qi, yi) where qi = ap, yi = ⊥ (indicate to failure). otherwise, c generates a random number yi and lets qi = yip, and saves l1 = (idi, qi, yi). • public key extraction (idi) queries. when aii makes the query on idi, c finds l and performs as follows: if the list l contains (idi, sidi , pidi ), c checks whether pidi = ⊥. if pidi 6= ⊥, c returns pidi to aii. otherwise, c picks a random xi ∈ z∗p, and sets pidi = bppub, sidi = xi. c then returns pidi to aii and adds (sidi , pidi ) to l. if the list l does not contain (idi, sidi , pidi ), c picks a random xi ∈ z∗p, and sets pidi = bppub, sidi = xi. c then returns pidi to aii and adds (sidi , pidi ) to l. mccls: certificateless signature scheme for emergency mobile wireless cyber-physical systems 403 • secret value extraction (idi) queries. when aii makes the query on idi, if idi = id ∗, then c aborts and halts the simulation. otherwise c finds l and performs as follows: if the list l contains (idi, sidi , pidi ), c checks whether pidi = ⊥. if pidi = ⊥, c makes public keyextractionqueries to obtain (sidi = xi, pidi = xippub). then c saves the value and adds user’s private keys sidi to the list l. if the list l does not contain (idi, sidi , pidi ), c executes public key extraction queries to obtain (sidi , pidi ). then c saves the value and adds user’s private keys sidi to the list l. • h2 queries. when aii makes the query on (m, r, pidi ), c first scans whether a list l2 = (m, r, pidi , h j) has been defined. if defined, return the list to aii. otherwise, c picks a random h j ∈ z∗p as the hash value of hand returns h j, and adds it to l2. • sign queries(idi, m j). when aii asks for a signature by user idi on message m j. c finds (idi, sidi , pidi ). if (pidi , sidi ) not found, c runs public key extraction queries. on receiving sign queries, c does the following: 1. choose random r j ∈ z∗p and look up the list l2 for h j, if not found, c runs h2 queries to get h j. 2. compute vj = ( sh j +bh j r j xi )p and s j = r jxiqi = r jxiap, r j = bp r j xi ; 3. return the signature σ = (vj, s j, r j). now, σ is returned to aii, which appears to be valid signature since e(vj − h jr, s j/h j) = e(( sh j +bh j r j xi p) − h j bpr j xi , r jxiap/h j) = e( sph j r j xi , r jxiap/h j) = e(sp, ap) = e(ppub, qid). finally, aii will output a valid forgery r = (id j, m j, r j, s j,vj). if id j 6= id∗, c outputs the fail and aborts the simulation. otherwise, we can compute r through r j = sh j +bh j xvj , since (ppub,vjp−h jr j, s j/h j, qi) is a valid diffie-hellman tuple. apply r j to s j, we have s j = sh j + bh j xivj xiqi s j = qish j + bh jap v abp = vjr j − qis jh j h j . so abp = vj r j−qis j h j h j is the answer to our cdhp instance. if the aii can break our scheme, then the attacker solves the cdh problem. 404 zhong xu, xue liu, guoqing zhang, wenbo he table 2: general parameters parameter value transmitter 250m bandwidth 2mb/s simulation time 600s environment 900m×900m traffic type cbr (constant bit rate) packet rate 4 packets/s packet size 512 bytes node maximum speed 0, 5,10,15,20 m/s pause time 0s attack nodes 1,2 and 4 redirection, 1,2 and 4 rushing queuing policy at routers first-in-first-out 6 evaluation and analysis in this section, an efficient mccls scheme named mcdv based on the ad hoc on-demand distance vector routing (aodv) is proposed. we start the simulations using qualnet [19] in order to compare the original aodv protocol without any security requirements with mcdv based on the cls with routing authentication extension. we also evaluate the performance of two schemes under 1, 2 and 4 nodes redirection attacks and 1, 2 and 4 nodes rushing attacks, as this is more realistic in the real emergency applications. our implementation retains most of the aodv mechanisms, such as route discovery, reverse path setup, forwarding path setup, route maintenance, and so on. in our experiments, 20 nodes move around in a rectangular area of 900×900m according to a mobility model, i.e., the random waypoint model. the nodes spread randomly over the network. each node starts its journey from a random location to a random destination. we vary the nodes speed from 0m/s to 20m/s, and set the nodes pause time as 0s. table 2 lists the values of the common parameters used in all experiment. other parameters will be given in the description of each specific experiment. the performance of mcdv is compared using the following performance metrics. • packet delivery ratio: ratio of the number of packets received by the destination over the number of packets sent by the source. • rreq ratio: ratio of sum number of rreq initiated, forwarded and retried over the sum of number of data packets sent as source and data packets forwarded. present the number of rreq packets transmitted through the network. • end-to-end delay: the average time experienced by each packet when traveling from the source to the destination. • throughput: ratio of the total bytes sent by all sources nodes over the total time. • packet drop ratio: ratio of the number of packets discarded by attacking nodes over the total number of packets sent by all sources. effects of variousmetrics on differentprotocols: experiments in this section are used to study the performance between mcdv and aodv. the results are shown in fig. 1. the packet delivery ratio and the rreq ratio are shown in fig. 1(a) and fig. 1(b), respectively. we can see that mcdv could work well in the experiment because the packet delivery ratio and rreq ratio in aodv are very similar to that of mcdv, without causing any substantial degradation of the network mccls: certificateless signature scheme for emergency mobile wireless cyber-physical systems 405 !""#$% & !"'# !""()*+ ,-% ./012301 "4# !"'-5 (6% 7( "8# !"4 !"4#9 ,:; ( !"8 !"8# # $ $# < =>??@abc=d !"# !$ !$#% &'( ) ! # !" # " "# $ %%* + ,-../012,3 45678967 (a) comparison with aodv and mcdv, packet delivery 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"# ! "$ ! %&'( )* " ! "" ! "+,. /0 & ! 1# ! 1$ ! ",2 34 , 56789 78 ! 1 ! 1" ! 1+4 , 9:78 ! 1 ; 1 1; " <=>>? @ab<c<=>>? @ab<c ! """ ! #"" !"#"" ! """$% & '( ') *+,./#"" !""""01 23&4$ 56,./""" ./#""71 8930 .:#"" " # " # ."" # " # .";<==>?@a;b (c) comparison with aodv and mcdv, end-to-end delay(s) (d) comparison with aodv and mcdv, throughput(bit/s) figure 1: effects of various metrics on different protocols performance. as nodes speed increases, the number of data packets reaching the destination decreases and the number of rreq packets transmitting through the networks increases. end-to-end delay of mcdv scheme is shown in fig. 1(c). our scheme has a little bit higher delay than that of aodv due to the exchange of packets during authentication phase of the security process. result shows that mcdv has a similar end-to-end delay with aodv at a relatively low speed, however, when the maximum speed of nodes is higher than 15m/s, aodv outperforms mcdv scheme. more specifically, our scheme needs authentication operation, and those additional operations are computed in our scheme but not in aodv. we only measure delays for data packets that survived to reach their destination. throughput of mcdv works well as result shown in fig. 1(d) because the effect of throughput of the network is very small (around 0.16%). however, if this scheme in other real scenarios such as disaster scenarios, battlefield scenarios, or even very high-speed scenarios, the effect of throughput of the network may reduce more than this. effects of multiple attackers with redirection attacks: we simulated aodv and mcdv under redirection attacks by varying the nodes speed from 0m/s to 20m/s while setting the number of attack nodes to 1, 2 and 4 nodes, respectively. we first study the packet delivery ratio and packet dropped ratio. from the results of fig. 2(a), we can see that packet delivery ratio drops as the speed increases when we use aodv routing protocol under redirection attacks. meanwhile, we observe that given the same speed of nodes, the higher the number of attackers, the lower the packet delivery ratio in aodv. the packet delivery ratio in the case of 4 attackers declines dramatically to 43% as the speed of nodes 406 zhong xu, xue liu, guoqing zhang, wenbo he ! "!# $%& ' (")("*%+ ,-& .,/0 1234 567 8998:;<=> ?("!("@a $bc ,% 1234 567 8998:;<=> d ?1234 567 8998:;<=> d e1234 567 8998:;<=> dfg:34 567 8998:;<=> d ?g:34 567 8998:;<=> d e( ( h ( h !(g:34 567 8998:;<=> dfijkkl mnopq ! "#$% && ' ( )*+ )!, ./0 1 "#$% 234 5&&56'789 : ("#$% 234 5&&56'789 : ;"#$% 234 5&&56'789 :<=6$% 234 5&&56'789 : (=6$% 234 5&&56'789 : ; )>+ )*/? @1aab, =6$% 234 5&&56'789 ;=6$% 234 5&&56'789 :< ) + )>c .de b/ + > >+ * fghhi jklmn (a) packet delivery ratio under multiple redirection attacks (b) rreq ratio under multiple redirection attacks !" #" $ ! $ !"%&' () $ *" $ # $ #"+, -. / 01 % 2345 678 9::9;<=>? @ a $ " $ * $ *", -. b + 2345 678 9::9;<=>? @ c2345 678 9::9;<=>? @de;45 678 9::9;<=>? @ ae;45 678 9::9;<=>? @ c " * *" # e;45 678 9::9;<=>? @dfghhi jklmn !"""" """"" #"""" !""""$ %& '( )""""*"""" """""+,.+%/0 1234 567 8998:;<=> ? @1234 567 8998:;<=> ? a#""""!"""")""""bcd 1234 567 8998:;<=> ? a1234 567 8998:;<=> ?ef:34 567 8998:;<=> ? @f:34 567 8998:;<=> ? af:34 567 8998:;<=> ?e" " g " g #"f:34 567 8998:;<=> ?ehijjk lmnop (c) end-to-end delay under multiple redirection attacks (d) throughput under multiple redirection attacks figure 2: effects of multiple attackers with redirection attacks increases to 15m/s. in contrast, the packet delivery ratio of mcdv maintains from 94% to 98% even the number of attackers increases to 4 which is slightly lower than normally packet delivery ratio as we can see in fig. 1(a). all of these are brought by the fact that our routing scheme retains most of the aodv’s mechanisms and the extra operations of sign phase and verification phase are very low. as we would expect from fig. 2(b), mcdv is able to detect all redirection attacks and the packet dropped ratio is zero. on the contrary, as the attack nodes increase, the packet dropped ratio also rises at the same speed when using aodv. especially, the highest packet dropped ratio of aodv is almost 25% at speed of 15m/s. mcdv can detect all the attacks because the node will verify the signature when it receives the packet. only if this packet passes the verification, the receiving node updates its routing table entry according to the information carried in the packet. otherwise, the node will drop this packet. readers may note that in fig. 2(c), given the same speed of nodes, the end-to-end delay in the mcdv under redirection attacks are slightly higher than the end-to-end delay in the aodv under redirection attacks. this is simply due to our definition of the end-to-end delay, which is defined as the time a packet takes to travel from the source to the destination. given the same network size, the same number of senders and the same number of receivers, as attacker or more attackers are added to the network, the number of available nodes forwarding packet decreases, making the average end-to-end delay decrease. the result in fig. 2(d) shows the throughput in the network. we can see that the higher the attackers, the lower the throughput at the same speed in aodv. as the speed goes up in aodv, the throughput of network decreases. when the speed is 15m/s, the throughput of aodv drops to 76% comparing with that of original protocol. in contrast, our scheme has the similar trend as the original aodv protocol. as the speed is 15m/s and the network is under 4 redirection attackers, the most effect of throughput is mccls: certificateless signature scheme for emergency mobile wireless cyber-physical systems 407 ! "!#$ %&' ( )"*)"+&, -.' /-0# 1234 567 8998:;<=> ? @)"!)"ab %cd -& 1234 567 8998:;<=> @1234 567 8998:;<=> ? e1234 567 8998:;<=> ?fg:34 567 8998:;<=> ? @g:34 567 8998:;<=> ? e) ) h ) h !)g:34 567 8998:;<=> ?fijkkl mnopq (a) packet delivery ratio under multiple rushing attacks (b) rreq ratio under multiple rushing attacks !" #" $ ! $ !"%&' () $ *" $ # $ #"+, -. / 01 % 2345 678 9::9;<=>? @ a $ " $ * $ *", -. b + 2345 678 9::9;<=>? @ c2345 678 9::9;<=>? @de;45 678 9::9;<=>? @ ae;45 678 9::9;<=>? @ c " * *" # e;45 678 9::9;<=>? @dfghhi jklmn !"""" """"" #"""" !""""$ %& '( )""""*"""" """""+,.+%/0 1234 567 8998:;<=> ? @#""""!"""")""""abc 1234 567 8998:;<=> @1234 567 8998:;<=> ? d1234 567 8998:;<=> ?ef:34 567 8998:;<=> ? @f:34 567 8998:;<=> ? d;" " g " g #"f:34 567 8998:;<=> ?ehijjk lmnop (c) end-to-end delay under multiple rushing attacks (d) throughput under multiple rushing attacks figure 3: effects of multiple attackers with rushing attacks around 0.9%. effectsofmultipleattackerswithrushingattacks: in this section, we compare the varying metrics of aodv and that of mcdv under 1, 2 and 4 rushing attacks, respectively. the graph in fig. 3(a) shows that, the higher the nodes speed, the lower the packet delivery ratio is when using aodv. however, the packer delivery ratio declines dramatically to 24% as the number of attackers increases to 4 nodes and at the speed of 20m/s. on the other hand, the lowest packet delivery ratio in mcdv still maintains 95% when the nodes are at the speed of 15m/s. the fig. 3(b) shows that, given the same speed, the higher the attacker node(s), the higher the packet dropped ratio is under aodv. in contrast, mcdv can detect all the rushing attacks, thus the packet dropped ratio is zero. these results indicate that the aodv protocol performs worse under the rushing attacks than under the redirection attacks. this is because we set the transmit distance as 740m to simulate the rushing attacks. in this situation, the malicious nodes may readily access to the forwarding group and discard all data packets. with the number of attackers increasing, the packet delivery ratio decreases and the packet dropped ratio rises. in contrast, mcdv maintains high packet delivery ratio and the packet dropped ratio is zero. this is due to its less computation overhead and efficient implementation of signature and verification. the fig. 3(c) shows that the end-to-end delay in rushing attacks. mcdv end-to-end delay is slightly higher than aodv end-to-end delay. the explanation for this is similar to the situation discussed in the case of redirection attacks. the difference is that when a node is converted to attacker, the probability of this attacker being selected into the forwarding group increases, and the average end-to-end delay decreases. 408 zhong xu, xue liu, guoqing zhang, wenbo he fig. 3(d) shows the throughput of two protocols under rushing attacks. although mechanisms of redirection attacks and rushing attacks are different, they have a similar way to affect the throughput. the throughput drops more severely under rushing attacks than under redirection attacks. in particular, the lowest throughput almost drops to 63% under 4 rushing attacks when nodes at the speed of 20m/s. in contrast to the aodv protocol under rushing attacks, our scheme has very similar throughput to the original protocol. 7 conclusion an efficient certificateless signature scheme named mccls is proposed in this paper. this scheme is based on the bilinear diffie-hellman assumption in the random oracle model for emergency mobile wireless cyber-physical systems. since mccls only requires one pairing operation in the verification phase, and none in the signing phase, theoretically it is more efficient than existing certificateless signature schemes. we also present simulation of mcdv which is based on mccls scheme and compare its performance under two most common attacks (i.e. redirection attack and rushing attack) with typical protocol-aodv providing no protection mechanism. these results show that mcdv can completely resist the two kinds of attacks without causing substantial degradation of network performance. in the future, we will further investigate security schemes in the wide physical environment. thereby we can find schemes which either prevent more comprehensive external attacks or resist internal attacks from the compromised nodes. 8 acknowledgment this work was supported in part by an nserc discovery grant 341823-07 and a national studyabroad scholarship of p.r.china under grant no. [2007] 3020. part of this work has been published in preliminary form in the proceedings of the first international workshop on cyber-physical systems, in conjunction with icdcs 2008, beijing, china. bibliography [1] s. s. al-riyami and k. g.paterson. certificateless public key cryptography. in asiacrypt: advances in cryptology – asiacrypt: international conference on the theory and application of cryptology. lncs, springer-verlag, 2003. [2] e. a.lee. cyber-physical systems are computing foundations adequate. technical report, uc berkeley, 2006. [3] m. anand, e. cronin, and m. sherr. security challenges in next generation cyber physical systems. technical report, university of pennsylvania, 2007. http://www.truststc.org/scada/papers/paper33.pdf. 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[27] l. zhou and z. haas. securing ad hoc networks. network, ieee, 13(6):24–30, nov/dec 1999. zhong xu1,2, xue liu 1mcgill university school of computer science 3480 university street, montreal, quebec, canada, h3a 2a7 e-mail: {zhongxu,xueliu}@cs.mcgill.ca guoqing zhang 2northwestern polytechnical university college of automation xi’an, shaanxi, china e-mail: gqzhang@cs.mcgill.ca wenbo he university of illinois at urbana-champaign dept. of computer science urbana, il, usa. e-mail: wenbohe@uiuc.edu zhong xu received the b.e degree in automation from xi’an technological university in 2001 and the m.e degree in computer science from xidian university in 2005. currently, zhong is a joint ph.d student in mcgill university, montreal, canada and northwestern polytechnical university, xi’an, china. from august 2001 to august 2002, he was an assistant lecturer in xi’an technological university, china. his research interests include security of ad hoc networks, information security, embedded systems and cyber-physical systems. mccls: certificateless signature scheme for emergency mobile wireless cyber-physical systems 411 dr. xue (steve) liu is an assistant professor in the school of computer science at mcgill university. he is also affiliated to the centre for intelligent machines (cim). xue obtained his ph.d. in computer science from the university of illinois at urbana-champaign in 2006. he obtained his b.s. degree in mathematics and m.s. degree in automatic control both from tsinghua university, china. he worked briefly in the hewlettpackard labsibm t. j. watson research center. he received the ray ozzie fellowship, the saburo muroga fellowship, the mavis memorial fund award, and the c. w. gear outstanding graduate award, from the university of illinois at urbana-champaign. he has filed 5 patents, and published more than 50 research papers in international journals and major peer-reviewed conference proceedings. guoqingzhang received the b.e degree and m.e. degree in automation both from northwestern polytechnical university. currently, he is a ph.d student in northwestern polytechnical university, xi’an, china. his research interests include vehicular ad-hoc networks, information security and embedded systems. wenbo he is currently a ph.d student at department of computer science, in university of illinois at urbana-champaign, where she is advised by professor klara nahrstedt. she received the mavis memorial fund scholarship award from college of engineering of uiuc in 2006, and c. w. gear outstanding graduate award from department of computer science in 2007. she is also a recipient of vodafone fellowship in 2005-2008. wenbo received the m.s. degree in electrical and computer engineering from the university of illinois at urbana-champaign in 2000. she received the m.eng. degree in automatic control theory from tsinghua university, beijing, china, in 1998, and the b.e. degree in automatic control from the harbin engineering university, heilongjiang, china, in 1995. from august 2001 to january 2005, she was a software engineer with cisco systems inc., champaign, il. her research interests include pervasive and mobile computing, and network security and privacy. international journal of computers, communications & control vol. i (2006), no. 4, pp. 45-52 the fast fourier and hilbert-huang transforms: a comparison denis donnelly abstract: the conversion of time domain data via the fast fourier (fft) and hilbert-huang (hht) transforms is compared. the fft treats amplitude vs. time information globally as it transforms the data to an amplitude vs. frequency description. the hht is not constrained by the assumptions of stationarity and linearity, required for the fft, and generates both amplitude and frequency information as a function of time. the behavior and flexibility of these two transforms are examined for a number of different time domain signal types. keywords: fast fourier transform, hilbert-huang transform, data analysis 1 introduction a common approach in spectrum analysis for extracting frequency information from time series data is to use the fast fourier transform (fft) [don, 05]. a more recent method [hua, 98], [hua, 05], which generates amplitude and frequency vs. time spectra, is the hilbert-huang transform (hht). these two approaches are fundamentally different. the fft assumes stationarity and linearity of the data and relies on globally defined orthogonal basis states. the hht does not require the same assumptions of the data. in order to represent nonlinear and nonstationary data, global basis states must be replaced with adaptive, locally determined ones, a process the first stage of the hht does perform. the resulting basis states are, in general, not strictly orthogonal. after a very brief review of the fft, the hht is described. the comparative behavior of the two transforms is then explored. various data sets with different signal characteristics are examined. finally, the chirp of a bat is analyzed. 2 the fast fourier transform the fast fourier transform (fft) provides an efficient algorithm for converting data from the time domain into the frequency domain. typically, the data to be transformed consists of n uniformly spaced points x j = x(t j) where n = 2n with n an integer, and t j = j∆t where j ranges from 0 to n − 1. the discrete fourier transform can be expressed in several ways. a commonly used form is the following (with i = √ −1): xk = n−1 ∑ j=0 x jex p(−2π i j n k) (1) where k = −n/2, . . . ,−1, 0, 1, . . . n/2 − 1 and where x j represent the time domain data and xk their representation in the frequency domain. the algorithm for the fft conversion process (cooley-tukey or any of several other comparable algorithms) makes the fft widely applicable as it reduces the number of computations from something on the order of n2 to nlogn which obviously provides an enormous reduction in computation time. the frequency data are typically displayed in one of two ways: an amplitude spectrum or a power spectrum. the amplitude spectrum is typically expressed by the relation ak = 2 n |xk|. (2) copyright c© 2006 by ccc publications 46 denis donnelly whereas the power spectrum is typically expressed by the relation pk = 1 n ∣∣x 2k ∣∣ . (3) where k = 0, 1, . . . , n/2. 3 the hilbert-huang transform the hilbert-huang transform is carried out in two stages: 1) the empirical mode decomposition (emd) process, which deconstructs the signal into a set of intrinsic mode functions (imf) and 2) the extraction of frequency vs. time information from each of the imf’s in combination with its hilbert transform (ht). a brief summary of the process for discretely sampled signals follows. the emd process deconstructs the original signal into a set of imf’s. each imf, extracted from the signal by a series of siftings, has two fundamental properties: 1) the number of extrema and the number of zero crossings differ, at most, by one and 2) the mean value of the envelopes defined by the local maxima and local minima is zero. unlike the harmonic functions of a fourier series, these oscillatory functions may vary in both amplitude and frequency over time. in this decomposition process, the first imf contains the highest frequencies associated with the original signal; each subsequent imf contains lower-frequency components. amplitudes and frequencies are extracted from these imf’s in the second stage of the hht process. the instantaneous amplitude and angular frequency associated with each imf depend on the amplitude and phase of a complex number that the imf and its hilbert transform (ht) define. the real part of the complex number is the imf; the imaginary part of the number is the imf’s ht. the instantaneous amplitude is the amplitude of this complex number. the instantaneous angular frequency associated with that imf is the derivative of the unwrapped phase. the entire process is repeated for each imf to extract the complete frequency versus time information from the original data. the computation of the ht is essentially a convolution of an imf, x(t), with 1/t. the effect of this convolving is to emphasize the local properties of x(t). this locality preserves the time structure of the signal’s amplitude and frequency. the emd process is designed to deconstruct the complete signal into a set of imfs, each of which is extracted from a starting data set via a sifting process. this sifting process is repeated until the criteria listed above are satisfied and the difference between successive siftings is suitably small (there is some discussion as to what the terminating difference between siftings should be; it depends to some extent on the data set being examined). the process of extracting imf’s terminates when the residual contains no significant frequency information. this sifting process comprises several steps. given a discretely sampled signal y(t), 1. determine the location of all maxima, ymax(t), and minima ymin(t) y(t). 2. fit a cubic spline through the ymax(t) and another through the ymin(t). 3. calculate the mean of the spline curves at each point m(t) = (ymax(t) + ymin(t))/2 4. remove the trend, m(t). let d(t) = y(t)−m(t). 5. is d(t) an imf? if d(t) meets the criteria defining an imf, let ci(t) = d(t) and advance i by 1. extract the residual r(t) = y(t)−d(t). if d(t) doesn’t meet the criteria, further sifting is required. repeat steps 1 through 5, substituting d(t) for y(t). the fast fourier and hilbert-huang transforms: a comparison 47 6. repeat steps 1 through 5 until the residual no longer contains any useful frequency information. the original signal is, of course, equal to the sum of its parts. if we have n imfs and a final residual rn (t), y(t) = n ∑ i=1 ci(t) + rn (t) (4) the second stage of the hht process extracts the amplitude and frequency information from each imf. the steps for amplitude and frequency extraction from a given discrete imf are as follows: 1. compute the imf’s discrete fourier transform (dft) using the series expression (1) for the transform. 2. compute the ht. use the real and imaginary parts of step 1’s dft as coefficients (m = n/2): y j = 1 n m ∑ k1=0 ( re(xk1 sin(2π k1 j n + im(xk1 cos(2π k1 j n ) + (−1 n ) n−1 ∑ k2=m+1 ( re(xk2 sin(2π k2 j n + im(xk2 cos(2π k2 j n ) (5) 3. form the complex number z j = x j + iy j, extract the phase φ j = tan−1(y j/x j). 4. unwrap the phase so that it becomes a monotonically increasing function. 5. determine the frequency. take the derivative of the phase f j = 1 2π dφ j dt 6. determine the amplitude. a j = √ x2j + y 2 j 4 examples the first examples treat cases where the signal in the time domain has the properties of an imf and no sifting is required. the examples are chosen to demonstrate the responses of the fft and hht to changes in frequency and amplitude of the time domain signal. the first signal considered consists of two cycles of a sine wave with frequency equal to one followed after a brief pause by four cycles of a sine wave with a frequency equal to two. fig. 1 shows both the signal and its hht. the oscillations in the hht are typical and vary only slightly with the amplitude of the original signal. the low frequency end of the fft for this example is shown in fig. 2. while it does show the major peaks at frequencies equal to one and two, it has a rather rough appearance. the complex structure of the fft arises because, in effect, the fft has to create a fourier series to fit the signal comprising both the two frequencies as well as the portions where the signal is zero. the second signal has a fixed frequency sine wave with amplitude increasing with time. the hht, fig. 3, is not daunted by this amplitude variation. the fft on the other hand has an amplitude which is consistent with an average value of the peak amplitudes for the time domain data. the third signal, a chirp, has a constant amplitude and a variable frequency which increases over time by one order of magnitude. the hht provides an accurate representation of the frequency as long as the signal is sampled with sufficient frequency. if the number is too small the oscillations in the frequency curve grow and can have negative values. fig. 4 shows the frequency response; here n=2048 and there 48 denis donnelly are more than twenty computed sample points in the last (highest frequency) cycle. the fft (fig. 5.) is nearly constant for the frequency spanned but drops off near the ends. an example which clearly contrasts the behavior of the fft and hht and which demonstrates which process is more correct physically, is that of a sum of two sine waves with frequencies close enough to readily show a beat pattern (f1 = 1, f2 = 1.08). the fft of such a pattern is shown in fig. 6. the two original frequencies are clearly present in the amplitude spectrum. the hht on the other hand generates a single frequency of 1.04 which is an average of the two sine wave frequencies. (when dealing with signals that show a beat pattern, for example, artifacts are likely to appear in the frequency representation near the nodal points of the signal.) the hht amplitude function follows the beat pattern as an envelope. while the description of the beat signal as a sum of two individual frequency signals is mathematically equivalent to a single signal with a frequency equal to the average of the two frequencies used to construct the signal, times an amplitude modulating function with a frequency equal to the difference in frequency of the two signals, the two different representations are not equivalent physically. the hht provides the physically correct frequency description; the instantaneous frequency is the average frequency. this is an important notion that is worth emphasizing. we are used to talking about the harmonic content of signals, which is a mathematically correct description, but it may not be the best representation of what is actually taking place. it is interesting to observe what happens to the amplitude spectrum of the fft, as a smaller and smaller number of cycles of the pattern are subjected to analysis. if the number of cycles is small enough that the signal does not go beyond the first node of the envelope, then the fft shows only one peak at the average value of the two frequencies. the hht cannot treat functions such as a single cycle of a square wave as there are no maxima and minima to fit, while the fft can readily analyze such a signal. if a small amplitude sine wave is made to ride on top of a square wave, for example, the hht will recognize the sine wave away from the transition region. near the step transition, the frequency response shows a large spike. it is useful to know how differing frequencies are distributed during the emd process. an estimate can be obtained by taking the fft’s of the first and second imf’s as the difference between two frequencies is increased. from this, we can observe the behavior of the sorting process. for example, a factor of two or greater in the frequencies is needed to shift the lower frequency signal well into the next imf (see table 1). for example, if the emd process is applied to a signal comprising frequencies 1 and 0.5, the first imf yields an average frequency value of 1 with fluctuations ranging approximately from 0.96 to 1.05; the second imf yields an average value of 0.5 with the fluctuations ranging from 0.49 to 0.51. frequency 2 peak amplitude peak amplictude peak amplitude frequency 1 = 1 frequency 2 imf1 frequency 2 imf2 0.95 526 489 0.9 521 482 0.8 516 444 14 0.7 513 362 55 0.6 510 188 178 0,5 507 34 327 0.4 508 409 for a final application, we look at the chirp of a bat: 400 data points sampled at 7 intervals. the bat’s chirp does consist of frequencies decreasing over time, consequently, an fft of the entire signal would not yield useful information. instead, to use the fft, a sliding window approach is used [don, 06]. for example, look at 64 point subsets of the entire data set. in this time range, the inherent frequency structure is revealed. as this short window slides along, the changing frequencies can be tracked. the hht of the chirp, shows information of interest in the first and second imf’s. results are shown in fig. 7. the the fast fourier and hilbert-huang transforms: a comparison 49 second harmonic had too small an amplitude for the hht process to recognize it. both analysis forms would benefit from a shorter sampling interval. in the figure, the fit to the fundamental is proportional to ex p(−const √ t) . the fit to the first harmonic is simply two times that of the fundamental. because of the complexity of the signals in the figure, the fft results for the sliding window technique are not shown. but they approximate the fitted lines shown in the figure. there is a weak second harmonic which can be extracted from the data when using the fft process. however, that signal is too weak for the hht to recognize. 5 final comments the combination of sifting associated with the emd process, and then taking the ht, unwrapping the phase, and taking the derivative of each imf is computationally much more demanding than taking the fft. however, the hht not only provides instantaneous frequency and amplitude descriptions, it results in representations that are more meaningful physically. acknowledgments: i thank curtis condon, ken white and al feng of the beckman institute of the university of illinois for the bat chirp data and for permission to use it in this article. i thank edwin rogers for helpful comments. 0 2 4 6 8 2 0 2 time f re q u e n c y figure 1: hht frequency representation (solid line) of the time domain signal (dashed). 0 1 2 3 0 50 100 150 frequency a m p li tu d e figure 2: fft amplitude spectrum (solid line) of the (dotted) signal shown in fig. 1. only the lower frequencies are displayed for emphasis. 50 denis donnelly 0 2 4 6 8 5 0 5 time a m p li tu d e figure 3: hht amplitude spectrum (solid line) of the (dotted) signal. 0 2 4 6 8 10 0 5 10 time f re q u e n c y / a m p li tu d e figure 4: hht frequency representation of a chirp signal. the hht follows the increase in chirp frequency as it scales from one to ten. the amplitude one chirp signal is also shown. 0 10 20 0 100 200 frequency a m p li tu d e figure 5: fft of a chirp signal with a frequency ranging from one to ten. only the lower frequencies are displayed for emphasis. the fast fourier and hilbert-huang transforms: a comparison 51 0.8 1 1.2 0 500 1000 1500 frequency a m p li tu d e 1 1.08 figure 6: the fft of a signal consisting of the sum of two sine waves with frequencies 1 and 1.08. only a portion of the fft is shown for emphasis. 0 1 2 0 1 10 5 2 10 5 3 10 5 4 10 5 time f re q u e n c y figure 7: frequency vs. time of a bat chirp as determined via hht. the fluctuating curves represent the hht results. the solid curve corresponds to the fundamental. the dotted curve is the first harmonic. the two smooth curves are fits to the data. 52 denis donnelly references [1] denis donnelly and bert rust, "the fast fourier transform for experimentalists: part i concepts," computing in science & eng., vol. 7, no. 2, pp. 80-88, 2005. [2] denis donnelly, "the fast fourier transform for experimentalists: part vi chirp of a bat," computing in science & eng., vol. 8, no. 2, pp. 72-78, 2006. [3] norden e. huang et. al., "the empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis," proc. r. soc. lond. a., vol. 454, pp. 903-995, 1998. [4] norden e. huang and samuel shen, eds. new jersey: world scientific, 2005. denis donnelly siena college department of physics loudonville, ny 12211 e-mail: donnelly@siena.edu received: november 8, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 642-653 tense θ-valued moisil propositional logic c. chiriţă carmen chiriţă university of bucharest faculty of mathematics and computer science romania, 010014 bucharest, 4 academiei e-mail: stama@funinf.cs.unibuc.ro abstract: in this paper we study the tense θ-valued moisil propositional calculus, a logical system obtained from the θ-valued moisil propositional logic by adding two tense operators. the main result is a completeness theorem for tense θ-valued moisil propositional logic. the proof of this theorem is based on the representation theorem of tense θ-valued łukasiewicz-moisil algebras, developed in a previous paper. keywords: łukasiewicz-moisil algebras, tense moisil logic. 1 introduction the first contribution to the algebraic logic of finite-valued łukasiewicz propositional calculus is moisil’s paper [18], where n-valued łukasiewicz algebras (named today łukasiewicz-moisil algebras) were introduced. according to an example given by a. rose (1957), for n ≥ 5 the łukasiewicz implication cannot be defined in an n-valued łukasiewicz-moisil algebra. hence, moisil discovered a new many-valued logical system (named today moisil logic), whose algebraic models are n-valued łukasiewicz-moisil algebras. in 1969, moisil defined the θ-valued łukasiewicz algebras, where θ is the order type of a bounded chain. these structures extend a part of the definition of n-valued łukasiewicz algebras, but they differ from these by accepting many negation operations ( [3], [10], [16], [23]). the logic corresponding to the θ-valued łukasiewicz-moisil algebras was developed by boicescu [1] and filipoiu [10] (see also [2]). this logical system is called the θ-valued moisil propositional logic. the chrysippian endomorphisms of θ-valued łukasiewicz-moisil algebras are reflected in the syntax of the θ-valued moisil propositional logic by chrysippian operations. this paper is devoted to the tense θ-valued moisil propositional calculus, a logical system obtained from the θ-valued moisil propositional calculus by adding the tense operators g and h. the algebraic basis of this logic consists of tense θ-valued łukasiewicz-moisil algebras (tense lmθ-algebras), algebraic structures studied in our paper [7]. we extend some of the results of [8], where a tense n-valued propositional logic was studied. the tense θ-valued moisil propositional calculus unifies two logical systems: the classical tense logic and the θ-valued moisil logic. the connection between these logics is realized by axioms that express the behaviour of the tense operators with respect to the chrysippian operations. the paper is organized as follows. in section 2 we recall some definitions and basic facts on θ-valued łukasiewicz-moisil algebras and θ-valued moisil logic, with emphasis on the connectives →k and ↔k and their algebraic counterparts. section 3 deals with tense θ-valued łukasiewicz-moisil algebras (tense lmθ-algebras), algebraic structures obtained from θ-valued łukasiewicz-moisil algebras by adding the two tense operators g and h. section 4 contains the syntactical construction of the tense θ-valued moisil propositional calculus. we establish some properties regarding the inferential structure of this logical system. copyright c⃝ 2006-2010 by ccc publications tense θ-valued moisil propositional logic 643 the lindenbaum-tarski algebra associated with the tense θ-valued moisil propositional calculus is studied in section 5. we obtain the structure of tense lmθ-algebra. the syntactical properties of the tense θ-valued moisil logic are reflected in this tense lmθ-algebra, thus we use the algebraic framework in order to obtain results for the logical system. in section 6 we define the interpretations of tense θ-valued moisil propositional calculus and the k-tautologies of this logic. our main result is the completeness theorem proved in this section (theorem 26). its proof uses the representation theorem of tense lmθ-algebras applied to the lindenbaum-tarski algebra constructed in the previous section. 2 θ-valued moisil logic and θ-valued łukasiewicz-moisil algebras let (i, ≤) be a totally ordered set, with first and last element, denoted by 0 and 1 respectively, and of order type θ, through this paper. we fix an element k ∈ i, through this paper. in this section, we recall the θ-valued moisil logic mθ described in [2]. the axiomatization of θ-valued moisil propositional calculus uses the system of axioms of θ-valued calculus introduced by boicescu [4] and filipoiu [10]. the basic results are taken from filipoiu [10](see also [2]). the alphabet of mθ has the following primitive symbols: an infinite set v of propositional variables; the logical connectives ∨, ∧, φi, φi for all i ∈ i and the parantheses (,). the set prop(v) of propositions of mθ is defined by canonical induction. for each i ∈ i, we shall use the following abbreviations: p →i q = φip ∨ φiq and p ↔i q = = (p →i q) ∧ (q →i p). the θ-valued propositional calculus has the following k-axioms: (2.1) p →k (q →k p), (2.2) (p →k (q →k r)) →k ((p →k q) →k (p →k r)), (2.3) p ∧ q →k p, (2.4) p ∧ q →k q, (2.5) (p →k q) →k ((p →k r) →k (p →k q ∧ r)), (2.6) p →k p ∨ q, (2.7) q →k p ∨ q, (2.8) (p →k q) →k ((r →k q) →k (p ∨ r →k q)), (2.9) φi(p ∧ q) ↔k φip ∧ φiq, for every i ∈ i, (2.10) φi(p ∨ q) ↔k φip ∧ φiq, for every i ∈ i, (2.11) φjp ↔k φiφjp, for every i, j ∈ i, (2.12) φjp ↔k φiφjp, for every i, j ∈ i, (2.13) φjp ↔k φiφjp, for every i, j ∈ i, (2.14) φjp ↔k φiφjp, for every i, j ∈ i, (2.15) φip →k φjp, for every i, j ∈ i, i ≤ j. 644 c. chiriţă the notion of formal proof in mθ is defined in terms of the above k-axioms and the k-modus ponens inference rule: p, p →k q q . for briefness, we will say "modus ponens" (m.p) instead of "k-modus ponens" from now on. we shall denote by ⊢k p that p is a k-theorem. we remind some k-theorems of mθ, which will be used in our proofs. proposition 1. ( [2], p. 491, example 3.12) the following propositions are k-theorems of mθ: (2.16) ⊢k p →k p, (2.17) ⊢k p ↔k φkp, (2.18) ⊢k (φip ∨ φip), for every i ∈ i, j ∈ i, (2.19) ⊢k (φj(p ∨ q) ↔k φjp ∨ φjq), j ∈ i, (2.20) ⊢k (φj(p ∧ q) ↔k φjp ∨ φjq), j ∈ i, (2.21) ⊢k ((p →k q) →k (φkq →k φkp)), (2.22) p φjp , j ≥ k, (2.23) φkp →k φkq p →k q . proposition 2. the following propositions are k-theorems of mθ: (2.24) ⊢k p →k (q →k (p ∧ q)), (2.25) ⊢k (p ∧ q →k r) →k (p →k (q →k r)), (2.26) ⊢k (p →k (q →k r)) →k ((p ∧ q) →k r), (2.27) ⊢k (p →k q) →k ((q →k r) →k (p →k r)), (2.28) ⊢k (p →k q) → ((r →k t) →k (p ∧ r →k q ∧ t)). proof: we shall establish only the k-theorems (2.24), (2.25) and (2.28). (2.24) we shall use (2.5), (2.16), (2.1), modus ponens and the deduction theorem (see [2], p. 495, proposition 3.17). {p, q} ⊢k (p →k p) →k ((p →k q) →k (p →k p ∧ q)) (2.5) {p, q} ⊢k p →k p (2.16) {p, q} ⊢k (p →k q) →k (p →k p ∧ q)) (m.p) {p, q} ⊢k q →k (p →k q) (2.1) {p, q} ⊢k q {p, q} ⊢k p →k q (m.p) {p, q} ⊢k p →k (p ∧ q) (m.p) {p, q} ⊢k p {p, q} ⊢k p ∧ q (m.p) {p} ⊢k q →k (p ∧ q) (deduction theorem) ⊢k p →k (q →k (p ∧ q)) (deduction theorem) tense θ-valued moisil propositional logic 645 (2.25) we shall apply (2.24), modus ponens and the deduction theorem. {p ∧ q →k r, p, q} ⊢k p →k (q →k (p ∧ q)) (2.24) {p ∧ q →k r, p, q} ⊢k p {p ∧ q →k r, p, q} ⊢k q →k (p ∧ q) (m.p) {p ∧ q →k r, p, q} ⊢k q {p ∧ q →k r, p, q} ⊢k p ∧ q (m.p) {p ∧ q →k r, p, q} ⊢k p ∧ q →k r {p ∧ q →k r, p, q} ⊢k r (m.p) {p ∧ q →k r, p} ⊢k q →k r (deduction theorem) {p ∧ q →k r} ⊢k p →k (q →k r) (deduction theorem) ⊢k (p ∧ q →k r) →k (p →k (q →k r)) (deduction theorem) (2.28) we shall use k-axioms (2.3), (2.4), modus ponens, k-theorem (2.24) and the deduction theorem. {p →k q, r →k t, p ∧ r} ⊢k p ∧ r {p →k q, r →k t, p ∧ r} ⊢k p ∧ r →k p (2.3) {p →k q, r →k t, p ∧ r} ⊢k p (m.p) {p →k q, r →k t, p ∧ r} ⊢k p →k q {p →k q, r →k t, p ∧ r} ⊢k q (m.p) {p →k q, r →k t, p ∧ r} ⊢k p ∧ r →k r (2.4) {p →k q, r →k t, p ∧ r} ⊢k r (m.p) {p →k q, r →k t, p ∧ r} ⊢k r →k t {p →k q, r →k t, p ∧ r} ⊢k t (m.p) {p →k q, r →k t, p ∧ r} ⊢k q →k (t →k (q ∧ t)) (2.24) {p →k q, r →k t, p ∧ r} ⊢k t →k (q ∧ t) (m.p) {p →k q, r →k t, p ∧ r} ⊢k q ∧ t (m.p) applying the deduction theorem three times we obtain that ⊢k (p →k q) → ((r →k t) →k (p ∧ r →k q ∧ t)). the rest of the proof is straightforward. 2 the θ-valued łukasiewicz-moisil algebras constitute the algebraic counterpart of the θ-valued moisil logic. the lindenbaum-tarski algebra of the θ-valued moisil propositional calculus is an θ-valued łukasiewicz-moisil algebra (see [2], p. 500, theorem 3.30). we shall recall the definition of θ-valued łukasiewicz-moisil algebras. definition 3. a θ-valued łukasiewicz-moisil algebra (lmθ-algebra) is an algebra l = (l, ∧, ∨, {φi}i∈i, {φi}i∈i, 0l, 1l) of type (2, 2, {1}i∈i, {1}i∈i, 0, 0) such that for all x, y ∈ l, (2.29) (l, ∧, ∨, 0l, 1l) is a bounded distributive lattice, (2.30) φi is a bounded distributive lattice endomorphism for all i ∈ i, (2.31) φix ∧ φix = 0l; φix ∨ φix = 1l for all i ∈ i, (2.32) φi ◦ φj = φj for all i, j ∈ i, (2.33) if i ≤ j then φi ≤ φj for all i, j ∈ i, (2.34) if φix = φiy for all i ∈ i, then x = y (this is known as moisil’s determination principle). 646 c. chiriţă let l = (l, ∧, ∨, {φi}i∈i, {φi}i∈i, 0l, 1l) be an lmθ-algebra. we say that l is complete if the lattice (l, ∧, ∨, 0l, 1l) is complete. l is completely chrysippian if for every {xk}k∈k (xk ∈ l for all k ∈ k) such that ∧ k∈k xk and ∨ k∈k xk exist, the following properties hold: φi( ∧ k∈k xk) = ∧ k∈k φixk, φi( ∨ k∈k xk) = ∨ k∈k φixk (∀i ∈ i). example 4. let b = (b, ∧, ∨,− , 0b, 1b) be a boolean algebra. the set d(b) = b[i] = {f|f : i → b, i ≤ j ⇒ f(i) ≤ f(j)} of all increasing functions from i to b can be made into a lmθ-algebra d(b) = (d(b), ∧, ∨, {φi}i∈i, {φi}i∈i, 0d(b), 1d(b)) where 0d(b), 1d(b) : i → b are defined by 0d(b)(i) = 0b and 1d(b)(i) = 1b for every i ∈ i, the operations of the lattice (d(b), ∧, ∨, 0d(b), 1d(b)) are defined pointwise (cf. [2], p.6, example 1.10) and (φif)(j) = f(i), (φif)(j) = (f(i))− (∀j ∈ i) (∀i ∈ i). let l = (l, ∧, ∨, {φi}i∈i, {φi}i∈i, 0l, 1l) be an lmθ-algebra. for each j ∈ i we consider the binary operation →j on l defined by (2.35) a →j b = φ̄ja∨φjb = (φja∧φ̄jb)− for all a, b ∈ l. this implication is associated to ∧ (like for boolean algebras), but like for boolean algebras also, there exists the following implication: a ;j b = φ̄ja ∧ φjb, associated to ∨. the notion of morphism of lmθ-algebras is defined as usual ( [2]). of course, a morphism of lmθ-algebras preserves the operation →j. 3 tense θ-valued łukasiewicz-moisil algebras in this section we shall recall some definitions and basic results on tense θ-valued łukasiewiczmoisil algebras from [7]. definition 5. a tense lmθ-algebra is a triple at = (a, g, h), where a = (a, ∧, ∨, {φi}i∈i, {φi}i∈i, 0a, 1a) is an lmθ-algebra and g, h : a → a are two unary operations on a such that for all x, y ∈ a, (3.1) g(1a) = 1a, h(1a) = 1a, (3.2) g(x ∧ y) = g(x) ∧ g(y), h(x ∧ y) = h(x) ∧ h(y), (3.3) g ◦ φi = φi ◦ g, h ◦ φi = φi ◦ h, for any i ∈ i, (3.4) g(x) ∨ y = 1a iff x ∨ h(y) = 1a. definition 6. let (a, g, h) be a tense lmθ-algebra. for any i ∈ i, let us consider the unary operations pi, fi defined by pix = φihφix and fix = φigφix, for any x ∈ a. proposition 7. let a = (a, ∧, ∨, {φi}i∈i, {φi}i∈i, 0a, 1a) be an lmθ-algebra and g, h be two unary operations on a that satisfy conditions (3.1), (3.2) and (3.3). then, the condition (3.4) is equivalent with (3.4′) φi ≤ g ◦ pi and φi ≤ h ◦ fi for all i ∈ i. thus, if we replace in definition 5 the axiom (3.4) with the condition (3.4′), we obtain an equivalent definition of tense lmθ-algebra. proposition 8. let a = (a, ∧, ∨, {φi}i∈i, {φi}i∈i, 0a, 1a) be an lmθ-algebra and g, h be two unary operations on a that satisfy conditions (3.1) and (3.3). then, the condition (3.2) is equivalent to (3.2′) g(a →k b) ≤ g(a) →k g(b); h(a →k b) ≤ h(a) →k h(b) for all k ∈ i where→k is defined by (2.35). tense θ-valued moisil propositional logic 647 thus, if in definition 5 we replace the axiom (3.2) by (3.2’), we obtain an equivalent definition for tense lmθ-algebra. definition 9. a frame is a pair (x, r), where x is a nonempty set and r is a binary relation on x. let (x, r) be a frame and l = (l, ∧, ∨, {φi}i∈i, {φ̄i}i∈i, 0l, 1l) be a complete and completely chrysippian lmθ-algebra. lx has a canonical structure of lmθ-algebra. let’s us define for all p ∈ lx and x ∈ x: g∗(p)(x) = ∧ {p(y)|y ∈ x, xry}, h∗(p)(x) = ∧ {p(y)|y ∈ x, yrx}. proposition 10. for any frame (x, r), (lx, g∗, h∗) is a tense lmθ-algebra. let (b, g, h) be a tense boolean algebra. we define on d(b) the unary operations d(g) and d(h) by: d(g)(f) = g ◦ f, d(h)(f) = h ◦ f for all f ∈ d(b). lemma 11. if (b, g, h) is a tense boolean algebra then (d(b), d(g), d(h)) is a tense lmθalgebra. theorem 12. (the representation theorem for tense lmθ-algebras) for every tense lmθ-algebra (a, g, h) there exist a frame (x, r) and an injective morphism of tense lmθ-algebras α : a → (d(l2))x, where l2 = {0, 1}, the standard boolean algebra. 4 tense θ-valued moisil logic (the syntax) in this section we introduce the tense θ-valued moisil propositional calculus tmθ, a logical system obtained from the θ-valued propositional calculus (see [2]) by adding the two tense operators g and h. we define the notion of k-theorem and k-deduction then we establish some syntactical properties of tmθ. the alphabet of tmθ has the following primitive symbols: an infinite set v of propositional variables; the logical connectives ∨, ∧, φi, φi for all i ∈ i; the tense operators g and h and parantheses (, ). the set e of propositions of tmθ is defined by canonical induction. definition 13. we shall use the following abbreviations: for all α, β ∈ e and i ∈ i, we define α →i β = φiα ∨ φiβ; α ↔i β = (α →i β) ∧ (β →i α); fiα = φigφiα; piα = φihφiα. definition 14. we call a k-axiom of tense θ-valued moisil propositional calculus a proposition of one of the following forms: (4.1) the k-axioms of θ-valued moisil propositional calculus ((2.1)-(2.15) in section 2); (4.2) g(α →k β) →k (gα →k gβ); h(α →k β) →k (hα →k hβ); (4.3) gφiα ↔k φigα; hφiα ↔k φihα, for all i ∈ i; (4.4) φiα →k gpiα; φiα →k hfiα, for all i ∈ i. the notion of formal k-proof in tmθ is defined in terms of the above axioms and the following inference rules: α, α →k β β (modus ponens); α gα α hα (temporal generalizations) definition 15. we say that a proposition α is a k-theorem of tmθ if there exists a k-proof of it. we will denote by ⊢k α the fact that α is a k-theorem of tmθ. definition 16. let γ ⊆ e and α ∈ e. we say that α is a k-deduction from γ and write γ ⊢k α if there exist n ∈ n = {0, 1, 2, ...} and α1, ..., αn ∈ γ such that ⊢k n∧ i=1 αi →k α. 648 c. chiriţă we remark that the logical structure of tmθ (k-theorems and k-deduction) combines the logical stuctures of two logical systems: the θ-valued moisil logic and tense classical logic. further we shall prove some syntactical properties. lemma 17. let γ ⊆ e and α ∈ e. then γ ⊢k α iff there exist n ∈ n and α1, ..., αn ∈ γ such that ⊢k α1 →k (α2 →k ...(αn →k α)...). proof: by definition 16 and k-theorems (2.25) and (2.26). 2 lemma 18. let γ ⊆ e and α ∈ e. then γ ⊢k α iff there exists γ ′ ⊆ γ, γ ′ finite, such that γ ′ ⊢k α. proof: by definition 16 and lemma 17. 2 proposition 19. let γ, σ ⊆ e and α, β ∈ e. the following properties hold: (i) if ⊢k α then γ ⊢k α; (ii) if γ ⊆ σ and γ ⊢k α then σ ⊢k α; (iii) if α ∈ γ then γ ⊢k α; (iv) {α} ⊢k β iff ⊢k α →k β; (v) if γ ⊢k α and {α} ⊢k β then γ ⊢k β; (vi) if γ ⊢k α and γ ⊢k α →k β then γ ⊢k β; (vii) γ ⊢k α ∧ β iff γ ⊢k α and γ ⊢k β. proof: (i) using definition 16 for n = 0. (ii) by applying definition 16. (iii) using k-theorem (2.16) and definition 16. (iv) we assume that ⊢k α →k β. then, by definition 16, we obtain that {α} ⊢k β. conversely, if {α} ⊢k β then there exists n ∈ n such that ⊢k (α ∧ ... ∧ α︸ ︷︷ ︸ n ) →k β. by using k-axioms (2.4) and (2.5), we get that ⊢k (α ∧ ... ∧ α︸ ︷︷ ︸ n ) ↔k α, so ⊢k α →k β. (v) we suppose that γ ⊢k α and {α} ⊢k β. then there exist n ∈ n and α1, .., αn ∈ γ such that ⊢k n∧ i=1 αi →k α. using (iv), it follows that ⊢k α →k β and by applying k-theorem (2.27) and modus ponens, we obtain that ⊢k n∧ i=1 αi →k β, so γ ⊢k β. (vi) let γ ⊢k α and γ ⊢k α →k β. by applying lemma 18, there exist γ1, γ2 ⊆ γ such that γ1 ⊢k α and γ2 ⊢k α →k β. by (ii), it follows that γ1 ∪ γ2 ⊢k α and γ1 ∪ γ2 ⊢k α →k β. if we consider γ1 ∪ γ2 = {γ1, ..., γn}, we obtain that ⊢k n∧ i=1 γi →k α and ⊢k n∧ i=1 γi →k (α →k →k β). by applying k-axiom (2.2) and modus ponens, we get that ⊢k n∧ i=1 γi →k β, so γ ⊢k β. (vii) we assume that γ ⊢k α ∧ β. by using k-axioms (2.3) and (2.4) and applying (i) and (vi), we obtain that γ ⊢k α and γ ⊢k β. conversely, we assume that γ ⊢k α and γ ⊢k β. by using k-theorem (2.24) and (i), we obtain that γ ⊢k α →k (β →k α ∧ β). by applying twice (vi), we get γ ⊢k α ∧ β. 2 theorem 20. (the deduction theorem) let γ ⊆ e and α, β ∈ e. then γ ∪ {α} ⊢k β iff γ ⊢k α →k β. proof: we assume that γ ∪ {α} ⊢k β. then there exist n ∈ n and α1, ..., αn ∈ γ such that ⊢k ( n∧ i=1 αi ∧ α) →k β. by applying k-theorem (2.25) and modus ponens, it follows that tense θ-valued moisil propositional logic 649 ⊢k n∧ i=1 αi →k (α →k β). using definition 16, we obtain that γ ⊢k α →k β. conversely, we suppose that γ ⊢k α →k β. thus, by proposition 4.1 (ii), we get γ ∪ {α} ⊢k α →k β. also, by proposition 4.1 (iii), we have that γ ∪ {α} ⊢k α, hence by applying proposition 4.1 (vi), it results that γ ∪ {α} ⊢k β. 2 proposition 21. in tmθ, the following properties hold: (4.5) if ⊢k α ↔k β, then ⊢k gα ↔k gβ, (4.6) ⊢k g(α ∧ β) ↔k (gα ∧ gβ). proof: (4.5) by using k-axioms (2.3), (2.4), k-theorem (2.24) and modus ponens, we obtain that: ⊢k α ↔k β iff ⊢k α →k β and ⊢k β →k α. applying the temporal generalization rule g, we get that ⊢k g(α →k β) and ⊢k g(β →k α). then, by k-axiom (4.2) and modus ponens, it follows that ⊢k gα →k gβ and ⊢k gβ →k gα, hence ⊢k gα ↔k gβ. (4.6) we shall prove that ⊢k g(α ∧ β) →k (gα ∧ gβ) and ⊢k (gα ∧ gβ) →k g(α ∧ β). by applying proposition 21 (4.5) for k-axioms (2.3), (2.4), we obtain that ⊢k g(α ∧ β) →k gα and ⊢k g(α ∧ β) →k gβ. using k-axiom (2.5) and modus ponens, it results that ⊢k g(α ∧ β) →k (gα ∧ gβ). by k-teorem (2.24) and the temporal generalization rule g, we obtain that ⊢k g(α →k (β →k α∧β)). applying k-axiom (4.2), modus ponens and k-theorem (2.27), it follows that ⊢k gα →k (gβ →k g(α∧β)). using k-theorem (2.26) and modus ponens, we get that ⊢k (gα ∧ gβ) →k g(α ∧ β). thus ⊢k g(α ∧ β) ↔k (gα ∧ gβ). 2 we remark that there exists a similar proposition concerning h. 5 the k-lindenbaum-tarski algebra of tense θ-valued moisil logic in this section we shall prove that the k-lindenbaum-tarski algebra of tmθ is a tense θvalued łukasiewicz-moisil algebra. therefore, the tense θ-valued łukasiewicz-moisil algebras constitute the algebraic structures of tmθ and the properties of tense lmθ-algebras reflect the syntactical properties of tmθ. we consider the binary relation ∼k on the set of all propositions e, defined by: α ∼k β iff ⊢k φiα ↔k φiβ for all i ∈ i. lemma 22. ∼k is an equivalence relation on e. for any proposition α ∈ e, we denote by [α]k the equivalence class of α. we can define the following operations on the set e/∼k : [α]k∨[β]k = [α∨β]k; [α]k∧[β]k = [α∧β]k; φi[α]k = [φiα]k; φi[α]k = [φiα]k for all i ∈ i; g([α]k) = [gα]k; h([α]k) = [hα]k; 0k = [φkα]k, 1k = [φkα]k, where α is a k-theorem of tmθ. proposition 23. (e/∼k, ∧, ∨, {φi}i∈i, {φi}i∈i, 0k, 1k, g, h), the k-lindenbaum-tarski algebra of tmθ, is a tense lmθ-algebra. proof: by ( [2], p.500, theorem 3.30), we have that (e/∼k, ∧, ∨, {φi}i∈i, {φi}i∈i, 0k, 1k) is an lmθ-algebra. what is left to prove is that the operations g and h are well defined and the conditions (3.1)-(3.4) are satisfied. due to the symmetrical position of g and h we shall only include the proofs for g. let α, β ∈ e such that α ∼k β. thus, ⊢k φiα ↔k φiβ for all i ∈ i. applying proposition 21 (4.5), we obtain that ⊢k gφiα ↔k gφiβ for all i ∈ i. using k-axiom (4.3), it follows that ⊢k φigα ↔k φigβ for all i ∈ i, so gα ∼k gβ. 650 c. chiriţă (3.1) we have to prove that g([φkα]k) = [φkα]k i.e. by definition of ∼k that ⊢k φigφkα ↔k φiφkα for every α such that ⊢k α and for all i ∈ i. let α ∈ e such that ⊢k α and i ∈ i. by k-theorem (2.22), we obtain that ⊢k φkα and by applying the temporal generalization rule g, we obtain that ⊢k gφkα. using k-axiom (2.1) and modus ponens, it results that ⊢k φkα →k gφkα and ⊢k gφkα →k φkα. thus, we get that (i) ⊢k φkα ↔k gφkα. using k-axiom (2.11), we have that (ii) ⊢k φiφkα ↔k φkα and by using proposition 21(4.5), we obtain that (iii) ⊢k gφiφkα ↔k gφkα. using k-axiom (4.3) and the conditions (i),(ii), (iii), it results that ⊢k φigφkα ↔k φiφkα. (3.2) let α, β ∈ e. we must prove that g([α]k ∧ [β]k) = g([α]k) ∧ g([β]k) i.e. g(α ∧ β) ∼k gα ∧ gβ which is equivalent with ⊢k φig(α ∧ β) ↔k φi(gα ∧ gβ) for all i ∈ i. let i ∈ i. by using proposition 21(4.6) for α = φiα and β = φiβ, we obtain that (i) ⊢k g(φiα ∧ ∧φiβ) ↔k (gφiα ∧ gφiβ). by using k-axiom (2.9) and proposition 21(4.5), we get that (ii) ⊢k gφi(α ∧ β) ↔k g(φiα ∧ φiβ). by conditions (i) and (ii), we obtain that (a) ⊢k gφi(α∧β) ↔k (gφiα∧gφiβ). by k-axiom (4.3), we have: ⊢k gφiα ↔k φigα and ⊢k gφiβ ↔k φigβ. applying k-theorem (2.28), it follows that (b) ⊢k (gφiα ∧ gφiβ) ↔k↔k (φigα∧φigβ). by conditions (a), (b) and k-axiom (4.3), we obtain that ⊢k φig(α∧ ∧β) ↔k φi(gα ∧ gβ). (3.3) we have to prove that ⊢k φjgφiα ↔k φjφigα for all i, j ∈ i. let i, j ∈ i. by kaxiom (2.11), we obtain that (a) ⊢k φjφigα ↔k φigα. using k-axiom (4.3), we have that (b) ⊢k φjgφiα ↔k gφjφiα. by k-axioms (2.11) and proposition 21(4.5), it follows that (c) ⊢k gφjφiα ↔k gφiα. by (a), (b), (c) and k-axiom (4.3), we get that ⊢k φjgφiα ↔k φjφigα. (3.4) since by proposition 7, the condition (3.4) is equivalent with (3.4’), we shall prove that [φiα]k ≤ [gpiα]k for all i ∈ i, i.e. ⊢k φjφiα →k φjgpiα for all i, j ∈ i. let i, j ∈ i. by k-axiom (2.13), we have that ⊢k piα ↔k φjpiα. applying proposition 21 (4.5), it follows that ⊢k gpiα ↔k gφjpiα. using k-axiom (4.3), it results that (1) ⊢k gpiα ↔k φjgpiα. also, by k-axiom (2.11), we have that (2) ⊢k φiα ↔k φjφiα. by (1), (2) and k-axiom (4.4), we get that ⊢k φjφiα →k φjgpiα. 2 6 semantics and completeness theorem of tense θ-valued moisil logic this section concernes with the semantics of tmθ, which combines the properties of kripke semantics for t and the algebraic semantics for mθ. we establish a completeness theorem for tmθ by using the representation theorem of tense θ-valued łukasiewicz-moisil algebras [7]. definition 24. let (x, r) be a frame. a valuation of tmθ is a function v : e × x → l[i]2 such that for all α, β ∈ e and x ∈ x, the following equalities hold: v(α →k β, x) = v(α, x) →k v(β, x); v(α ∧ β, x) = v(α, x) ∧ v(β, x); v(α ∨ β, x) = v(α, x) ∨ v(β, x); v(φiα, x) = φiv(α, x) for any i ∈ i; v(φiα, x) = φiv(α, x) for any i ∈ i; v(gp, x) = ∧ {v(p, y)|xry}; v(hp, x) = ∧ {v(p, y)|yrx}. the first five conditions of the previous definition reflect "the many-valued past" of tmθ (see [2], p.487) and the last two conditions correspond to "the tense past" of tmθ (see [5], p.93). definition 25. we say that a proposition α is a k-tautology and we write |=k α if for every frame (x, r), for any valuation v : e × x → l[i] 2 and for all x ∈ x, we have v(α, x)(k) = 1. tense θ-valued moisil propositional logic 651 the following result establishes the equivalence between the k-theorems and the k-tautologies of tmθ. the proof of the main implication is based on the representation theorem for tense θvalued łukasiewicz-moisil algebras (theorem 12). theorem 26. (completeness theorem). for any proposition α of tmθ, we have: ⊢k α iff |=k α. proof: (⇒). we shall prove by induction on the definition of ⊢k α that for every frame (x, r) and for any valuation v : e × x → l[i] 2 , we have v(α, x)(k) = 1, for all x ∈ x. let (x, r) be a frame, v : e × x → l[i] 2 be a valuation and x ∈ x. • we suppose that α is a k-axiom. (a) let α be g(p →k q) →k (gp →k gq) with p, q ∈ e. it is known that a →k (b →k c) = = (a ∧ b) →k c ( [7], p.6, proposition 2.1 (l)). we have: v(α, x)(k) = = v(g(p →k q) →k (gp →k gq), x)(k) = [v(g(p →k q), x) →k (v(gp, x) →k v(gq, x))](k) = = [(v(g(p →k q), x) ∧ v(gp, x)) →k v(gq, x)](k) = [∧ xry ((v(p, y) →k v(q, y)) ∧ v(p, y)) →k →k ∧ xry v(q, y)](k) = [φk ∧ xry ((v(p, y) →k v(q, y)) ∧ v(p, y)) ∨ φk ∧ xry v(q, y)](k) = = [( ∧ xry (v(p, y) →k v(q, y))∧v(p, y))(k)]−∨(∧ xry v(q, y))(k) = [ ∧ xry ((v(p, y)(k))−∨v(q, y)(k))∧ ∧v(p, y)(k)]− ∨ ( ∧ xry v(q, y)(k)) = [ ∧ xry (v(q, y)(k) ∧ v(p, y)(k))]− ∨ ( ∧ xry v(q, y)(k)). since v(q, y)(k), v(p, y)(k) ∈ l2 and v(q, y)(k) ∧ v(p, y)(k) ≤ v(q, y)(k), we obtain that∧ xry (v(q, y)(k) ∧ v(p, y)(k)) ≤ ∧ xry v(q, y)(k). since in a boolean algebra we have a ≤ b iff ā ∨ b = 1, we get that [ ∧ xry (v(q, y)(k) ∧ v(p, y)(k))]− ∨ ( ∧ xry v(q, y)(k)) = 1. (b) let α be gφip ↔k φigp with p ∈ e and i ∈ i. then v(α, x)(k) = = v(gφip ↔k φigp, x)(k) = v((gφip →k φigp) ∧ (φigp →k gφip), x)(k) = = [(v(gφip, x) →k v(φigp, x)) ∧ (v(φigp, x) →k v(gφip, x))](k). since l[i]2 is complete and completely chrysippian, it follows that v(gφip, x) = ∧ xry φiv(p, y) = φi( ∧ xry v(p, y)) = = v(φigp, x). we know that a →k a = 1 ( [7], p.6, proposition 2.1 (f)), hence v(α, x)(k) = 1. (c) let α be φip →k gpip with i ∈ i. we have: v(α, x)(k) = v(φip →k gpip, x)(k) = = (v(φip, x) →k v(gpip, x))(k) = (φiv(p, x) →k ∧ xry v(pip, y))(k) = = (φiv(p, x) →k ∧ xry ∨ zry φiv(p, z))(k) = φk(φiv(p, x))(k) ∨ φk( ∧ xry ∨ zry φiv(p, z))(k) = = [v(p, x)(i)]− ∨ ∧ xry ∨ zry v(p, z)(i). let y ∈ x such that xry. then v(p, x)(i) ≤ ∨ zry v(p, z)(i), hence v(p, x)(i) ≤ ∧ xry ∨ zry v(p, z)(i). we obtain that [v(p, x)(i)]− ∨ ∧ xry ∨ zry v(p, z)(i) = 1. • we assume that α was obtained by applying the modus ponens rule. we have that v(β, x)(k) = 1 and v(β →k α, x)(k) = 1. but v(β →k α, x)(k) = (v(β, x) →k v(α, x))(k) = = (φkv(β, x) ∨ φkv(α, x))(k) = φk(v(β, x))(k) ∨ φk(v(α, x))(k) = [v(β, x)(k)] − ∨ v(α, x)(k). we deduce that v(α, x)(k) = 1. 652 c. chiriţă • we suppose that α = gβ such that ⊢k β. we have that v(β, x)(k) = 1, for every x ∈ x. then v(gβ, x)(k) = ( ∧ xry v(β, y))(k) = ∧ xry v(β, y)(k) = 1. (⇐). we shall prove that if ̸⊢k α then ̸|=k α. assume that ̸⊢k α, so [α]k ̸= 1k. by using proposition 23, we have that the k-lindenbaum-tarski algebra (e/∼k, g, h) of tmθ is a tense lmθ-algebra. applying the representation theorem for tense lmθ-algebras (theorem 12), there exist a frame (x, r) and an injective morphism of tense lmθ-algebras d : (e/∼k, g, h) →→ (d(l2)x, g∗, h∗). let us consider the function v : e × x → l[i]2 defined by v(α, x) = d([α]k)(x), for all α ∈ e and x ∈ x. it is straightforward to prove that v is a valuation. since d is injective and [α]k ̸= 1k, we obtain that d([α]k) ̸= 1d(l2)x, hence there exists a ∈ x such that v(α, a) = d([α]k)(a) ̸= 1d(l2). thus α is not a k-tautology. 2 7 concluding remarks the tense θ-valued moisil propositional calculus tmθ can be viewed as a common generalization of the θ-valued moisil propositional logic mθ and the classical tense logic t. tmθ combines the logical structures of these logical systems and its semantic is inspired from the semantics of t and mθ. the main result of this paper is a completeness theorem for tmθ. its proof is derived from the representation theorem of tense θ-valued łukasiewicz-moisil algebras [7]. an open problem is to obtain a proof of the representation theorem for tense θ-valued łukasiewicz-moisil algebras by using theorem 26. the next step in the study of tense aspects of moisil logic is to define the tense θ-valued predicate logic (the syntax and the semantic) and the algebras corresponding of this logic (polyadic tense θ-valued łukasiewicz-moisil algebras). we hope to prove a completeness theorem for tense θ-valued moisil predicate logic and a representation theorem for the corresponding algebras. the tense logics corresponding to the lmθ-algebras with negations [16] will be the subject of another paper. bibliography [1] v. boicescu, sur les systèmes déductifs dans la logique θ-valente, publ. dép. math. lyon, 8, 123-133, 1971. [2] v. boicescu, a. filipoiu, g. georgescu and s. rudeanu, łukasiewicz-moisil algebras, northholland, 1991. [3] v. boicescu, contributions to the study of łukasiewicz-moisil algebras (romanian), ph.d. thesis, university of bucharest, 1984. [4] v. boicescu, sur une logique polyvalente, rev. roumaine sci. soc., sér. philos. et logique, 17, 393-405, 1973b. [5] j. p. burgess. basic tense logic. in: dov gabbay and f. guenthner, eds., handbook of philosophical logic, chapter ii.2, reidel, 89-134, 1984. [6] r. cignoli, i.m.l. d’ottaviano and d. mundici, algebraic foundations of many-valued reasoning, kluwer, 2000. [7] c. chiriţă, tense θ-valued łukasiewicz -moisil algebras, to appear in journal of multiplevalued logic and soft computing. tense θ-valued moisil propositional logic 653 [8] d. diaconescu, g. georgescu, tense operators on mv-algebras and łukasiewicz-moisil algebras, fundamenta informaticae xx: 1-30, 2007. [9] i. dziţac, l. andrei, 65 years from birth of prof. gheorghe s. nadiu (1941-1998), international journal of computers, communications & control vol. i, no. 3, pp. 93-98, 2006. [10] a. filipoiu, θ-valued łukasiewicz-moisil algebras and logics (romanian), ph.d.thesis, university of bucharest, 1981. [11] g. georgescu, a. iorgulescu, s. rudeanu, grigore c. moisil (1906-1973) and his school in algebraic logic, int. journal of computers, communications & control, vol. i, no. 1, pp. 81-99, 2006. [12] g. georgescu, a. iorgulescu, s. rudeanu, some romanian researches in algebra of logic, in: grigore c.moisil and his followers, editura academiei romane, 86-120, 2007. [13] g. georgescu, a. iorgulescu, i. leuştean, monadic and closure mv-algebras, multiplevalued logic, 3, 235-257, 1998. [14] r. goldblatt, logics of time and computation, csli lecture notes no. 7, 1992. [15] p. hájek, metamathematics of fuzzy logic, kluwer acad.publ., dordrecht, 1998 [16] a. iorgulescu, 1 + θ -valued łukasiewicz-moisil algebras with negation (romanian). ph.d. thesis, university of bucharest, 1984. [17] j. łukasiewicz, on three-valued logic, ruch filozoficzny (polish), 5, 60-171, 1920. [18] gr. c. moisil, recherches sur les logiques non-chrysippiennes, ann. sci. univ. jassy, 26, 431-466, 1940. [19] gr. c. moisil, notes sur les logiques non-chrysippiennes, ann. sci. univ. jassy, 27, 86-98, 1941. [20] gr. c. moisil, logique modale, disquis. math. phys, 2, 1942. [21] gr. c. moisil, łukasiewiczian algebras, computing center, university of bucharest (preprint), 311-324, 1968. [22] gr. c. moisil, essais sur les logiques non-chrysippiennes, ed. academiei, bucharest, 1972. [23] gh. s. nadiu, cercetări asupra logicilor necryssipiene/research about necryssipiene logics, 1972 (phd thesis, supervisor grigore c. moisil). [24] h. rasiowa, an algebraic approach to non-classical logics, north-holland publ., amsterdam, polish scientific publ., warszawa, 1974. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 150-157 intelligent management of the cryptographic keys g. moise, o. cangea gabriela moise, otilia cangea petroleum-gas university of ploiesti romania, 100680 ploiesti, 39 bvd. bucuresti e-mail: {gmoise,ocangea}@upg-ploiesti.ro abstract: with the continuous development of the computers networks, new problems have been posed in the process of keys management in the cryptographic systems. the main element in the cryptographic technologies is the keys management, as the cryptographic algorithms are known, while the keys have to be either secret (for unauthorized users that do not need them), or public (for users that need them). with an efficient cryptographic keys management system and the existing encryption techniques, there may be implemented a proper security system in the informational systems of the organizations. the process of cryptographic keys management consists in the following operations: keys generation, distribution, update, revocation, storage, backup/ recovery, import and export, usage control, expiration, and destruction. the cryptographic keys management techniques depend on the type of the keys, i.e. symmetric or public. nowadays, the efforts of the researches in the cryptographic keys management are focused on the standardization and interoperability of the keys management. in this paper, the authors analyze the existing keys management systems and standards available for the keys management techniques, emphasizing the advantages and disadvantages of different systems. they also propose a cryptographic keys management model based on the ideas and principles of the interrap architecture (a conceptual model developed by jőrg műller for intelligent agents). also, there are incorporated some intelligent techniques to manage emergency situations, such as keys losing or their improper usage. keywords: cryptographic key management, intelligent agents, key management model. 1 introduction the key management is the core of a cryptographic system. the processes related to the key management consist in generation, distribution, update, revocation, storage, backup/recovery, import and export, usage control, expiration, and destruction of the cryptographic keys. practically, the security of the information is assured by keeping secret the private cryptographic keys. key management consists in a set of protocols that enable to establish and maintain the keying relationships between the entities of a network [6]. the concept of “keying relationship”is defined in [6] as the state wherein parties of the cryptosystems share keying material. according to the type of cryptographic algorithm used in a cryptosystem, there are two situations: key management used in a symmetric cryptosystem and key management used in an asymmetric cryptosystem. in the former case, the sender and the receiver share the same secret key or two keys computationally feasible and in the latter case, there are involved two transformations: one to generate the public key and the other to generate the private key. [6] [11] the techniques used to distribute confidential keys are: key layering, key translation center, and symmetric copyright c⃝ 2006-2011 by ccc publications intelligent management of the cryptographic keys 151 key certificate techniques. key layering comprises the following techniques: master key, key encrypting keys and data keys. key translation center (ktc ) consists in a trusted server, which allows two entities to establish a secure communication using long-term keys. techniques used to distribute public keys are: point to point delivery over a trusted channel, direct access to a trusted public file (public-key registry), use of an online trusted server, use of an off-line server and certificates, and use of systems implicitly guaranteeing authenticity of public parameters [6]. the advantages of using the keys management in the situation of a public key are: use of a simple key management, on-line trusted server not-required and enhancing the functionality of the system. in this paper, there is studied the problem of the cryptographic key management in large distributed systems, more specifically, the problems of keys distribution and generation. the main concepts used in this paper are the security domain and the keys graph. the concept of security domain is defined in [7] as “a collection of systems (servers, devices, and so on) that share a common set of keys and are attached to an administered network”. in this paper the concept is used in the sense of a collection of entities (to allow an abstractive interpretation) which share a private key. the concept of key graph was introduced in [10] as an arrangement of the keys into a hierarchy and a key server manages all keys. a particular keys hierarchy is the keys tree, which enables to define key management scheme. in this paper it is proposed an intelligent key management model suited to the structure of the network. the new ideas introduced in this paper are: combining the behaviour agent architecture into the key distribution problem and distribution based on crt (chinese reminder theorem [13]). the paper is structured as follows: • formalism of the key management problem, that studies the existing key management systems and introduces the concept of security domain graph; • backgrounds of the intelligent key management model, referring to the chinese remainder theorem, as an important calculation algorithm used in order to generate the key management model; • intelligent key management, proposing a model of the cryptographic key management that may be used in a sdg type architecture, based on the principles of the interrap architecture, introducing intelligent agents responsible with the key management in the cryptographic system; • conclusions, that emphasize the importance of the key management and the advantages offered by the proposed model. 2 formalism of the key management problem to formalize the problem of keys management in a computers communication system, there are defined the security domain and a partially ordered relation between security domains. a security domain is a set of the entities (users, data, hardware devices, etc.) which share the same secret key. so, it can be established an equivalence relation between two entities e1, e2, according to the following statement: e1 ≡e2 if e1, e2 share the same secret key. the equivalence relation between two entities produces equivalence classes, called security domains. let us consider n security domains sd={sd1,sd2, ..., sdn}. the set sd contains all the entities of the computer communications system. on the set of the security domains, it can be defined 152 g. moise, o. cangea a binary partially-ordered relationship, using the operator ≺. the relation sdj≺sdi means that the entities of the security domain sdi have a security clearance higher or equal than that of the entities of the security domain sdj. for example, the entities from sdi can decrypt the message received from the entities that belong to sdj. also, it is used the expression that the security domain sdi dominates the security domain sdj. in this way, one may determine a partially ordered set (sd, ≺) , shortly called poset. messages (data, plain texts) from the security class sdi are encrypted with the cryptographic key ski and data from the security domain sdj are encrypted with the cryptographic key skj. if there is the relation sdj≺sdi , the entities of the security domain sdi have the right to decrypt the cipher text using the cryptographic key ski. in contrast, the entities which are parts of the security domains, sdj , cannot decrypt the messages received from the entities of the security domain sdi . also, if the following relations between three security domains sdk≺sdj and sdj≺sdi, are true, that means that the entities of sdj can decrypt the cipher texts received from the entities of sdk and the entities of sdi can decrypt the cipher texts received from the entities of sdj . consequently, the entities of sdi can decrypt the cipher texts received directly from the entities of sdk. in this manner, there are generated domains hierarchies. a hasse diagram can represent a poset. this diagram is called in [1] a security class privilege graph (scpg), in this paper it is used the term of security domain graph (sdg). an example of sdg is shown in figure 1 (a security domains tree (sdt), b a general security domain graph (gsdg)). figure 1: security domain graph where the following representations have the similar sense (figure 2) the most simple keys management model assumes the existence of a keys server and if an entity figure 2: graphical representation of a partially-ordered relationship between two security domains needs to decrypt a message, it has to ask the proper key over a secure channel, or each security domain has to store all successors secret keys. these models are not really feasible, because the tendencies of increasing the networks dimensions require large storage spaces and a lot of secure channels. in this way, there is quite a challenge to define a keys management model properly adjusted to the security domain graph. intelligent management of the cryptographic keys 153 3 backgrounds of the intelligent key management model there were proposed a lot of cryptographic keys management architecture. to implement data encryption standard, ibm proposed a key management scheme for des in the 70’ years. the cryptographic keys management architecture consists in cryptographic systems connected via a communications network. each cryptographic system has a cryptographic facility, a cryptographic key data set, a cryptographic facility access program and is using application programs. one solution based on control vector is proposed in [5]. the scheme uses a control vectors which facilities the implementation of owner key management policy and rules. this technique enables key distribution in different environments: peer-to-peer distribution, key distribution center, and key translation center. a list of keys management architecture can be found at [8]. akl and taylor proposed the first cryptographic keys assignment scheme to solve problems related to access control in hierarchies (at scheme). according to at scheme [1], each security class (the security class contains data and users with the same rights) has associated a secret key and a public parameter. for a relation sc j≺sc i , sc i use the public parameter t j and the secret key ki to derive the secret key k j. the secret key ki is computed according the formula ki = k ti 0 (modm), where k 0 is the secret key of the central authority and m is the product of two secret large prime numbers. t i is a public parameter with the following property: sc j≺sc i , if t j is a multiple of t i. in order to generate the public parameters, for t i it is used the formula ti = ∏ scinot≻sck pk, where pk are prime numbers related to each security class.the major inconvenient of the method is the fact that the value of t i increases and will become impractical (figure 3). another problem is represented by the question: is the arrangement of the security figure 3: the diagram of generating the t parameter in an at scheme domains according to the sdg? (that is, when a security domain has more than one parent). in order to generate a model for the key management, the authors studied the chinese remainder theorem that is an ancient, but important calculation algorithm in modular arithmetic. it enables one to solve simultaneous equations with respect to different moduli in a considerable generality. here is the statement of the problem that the chinese remainder theorem solves. theorem 1. chinese remainder theorem [12]. let m1, m2, ..., mk ∈z with gcd(mi, mj)=1,any i,j=1, k, i ̸=j . let m be the product m=m1×m2×...mk . let a1, a2, ...,ak ∈z . consider the system: x≡a1(mod m1) x≡a2(mod m2) ... x≡ak(mod mk) 154 g. moise, o. cangea then there exists exactly one x∈zmsolution of the system. the solution to the system above may be obtained using the following algorithm: step 1 for each to i=1 to k calculate z i=m1×m2×...×mi−1×mi+1×...mk step 2 for each to i=1 to k calculate yi=z −1 i (mod mi) step 3 calculate x=a1×y1×z 1+...+ak×yk×z k, return x. 4 intelligent key management in this paper it is proposed a model of cryptographic keys management that may be used in the architecture of sdg type. each entity of the security domains has associated an intelligent agent (sdkma) responsible with the cryptographic key management in the system (figure 4). where sdi=ei1uei2uei3..., where eij use k i and each sdi is organized according to the schema figure 4: security domains graph and intelligent key management agents shown in figure 5. figure 5: the entities relations in a security domain each sdkma is structured according the interrap intelligent agent architecture defined by műller [9]. interrap architecture is a layered bdi model (belief-desire-intention model) with three layers: behavior based layer, local planning layer, and cooperative planning layer. the behavior based layer contains the reactivity and procedural knowledge used in routine tasks, the local planning layer provides reasoning to realize the local tasks and to produce goal oriented behaviors, and the cooperative planning layer enables and facilities collaborative work with other agents. the structure of sdkma is presented in figure 6. behavior planning layer acts in the emergency situations (renewal keys, delete entity, add entity, destroy keys). local planning layer manages the cryptographic keys within the framework of the security domain. cooperative planning layer manages the cryptographic keys between security domains. world kb contains the procedures and functions used in the emergency situations (structure intelligent management of the cryptographic keys 155 figure 6: the structure of sdkma of the networks and security domains, new arrivals and leaving of the entities). planning kb contains procedures and functions responsible with the key management within the framework of the security domain and social kb contains procedures and functions used to key management between security domains. these knowledge bases are self-updated; therefore, the keys management model has the property of flexibility that is modifications in the structure of the network cause modifications in the key management model. keys management within the framework of security domains can be realized according to key server schema. for each security domains, it is selected, in a randomized way, an entity. its sdkma will play the role of key server. the server stores the key and the associated list of the entities from the security domain. entities request the key to the server. upon entity authentication, the server sends the keying material if the entity is authorized. key management between security domains can be realized according toat scheme if the node corresponding to the security domain has one parent. the arrangement in security domains offers the advantage of calculating not greater values of parameters t. there were provided solutions to optimize the at schema in order to compute smaller values of parameters t [4]. if the relations between security domains are according to the scheme presented in figure 7, then one has the following situation: a node has more than one parents and a node has more than two ancestors. figure 7: security domain sd3 with two parents and two ancestors if an entity of sd3 broadcasts a message m, than the entities of sd1 and sd2 can decrypt the 156 g. moise, o. cangea cipher text c. so, the entities have to know a single decryption key. the asymmetric keys generator has to send to each security domains a private key or each sdkma can compute (cooperative planning layer) the private key using the chinese remainder theorem. for each entity (sd1 and sd2) it will select a large secret prime number n1, respectively n2. the secret key is the solution (in the space zn1×n2 ) of the system: x≡a1(mod n1) x≡a2(mod n2), where a1 , a2 are public parameters. to calculate the solution of the system, it can be used the lagrange method: step 1 find cofactors u12, u21 using extended euclidian algorithm step 2 calculate l1=u21×m2 , l2=u12×m1 step 3 calculate the solution x=a1×l2+a2×l2 if one entity of sd4 and one entity of sd5 broadcast in the same time two encrypted messages c4 and c5, the sdkma (the cooperative planning layer) of each entity of sd3 manage a waiting queue and decrypt the messages using randomized priorities. the advantages offered by the proposed model are: more securely, because the intelligent property changes any time the parameters used in keys generation, and more efficiently, through the use of a security domain based structure of the network. the dynamism of the network can be easily implemented because the knowledge bases related to each layer of the sdkma are updated with actual information in any moment. also, in order to generate the keys, the procedures and functions may contain different algorithms and the sdkma can change any time the key distribution algorithms according to the dimensions of the networks or the necessary security level. 5 conclusions starting from the analysis of the existing key management systems and standards available for the keys management techniques, in this paper it is proposed an intelligent cryptographic key management model between security domains, sdkma type, based on the ideas and principles of the interrap architecture, emphasizing the advantages referring to security, efficiency and feasibility. as future directions, one may consider designing a fuzzy model of the cryptographic key management system, but only if this approach would bring important improvements for the model, regarding to the procedures and functions used to generate the keys. nevertheless, the most secure situation is to hold the keys in secure hardware and perform all processing there [3], being impossible to achieve this goal in large scale networks. bibliography [1] akl, s.g., taylor, p.d., cryptographic solution to a problem of access control in a hierarchy,acm transactions on computer system,3 (1), 1983. 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[13] zhou, j., ou, o. h., key tree and chinese remainder theorem based group key distribution scheme, proceedings of the 9th international conference on algorithms and architectures for parallel processing, isbn:978-3-642-03094-9, 2009. international journal of computers, communications & control vol. ii (2007), no. 3, pp. 288-298 optimization of queries with conjunction of predicates nicoleta livia tudor abstract: a method to optimize the access at the objects of a relational database is through the optimization of the queries. this article presents an approach of the cost model used in optimization of select-project-join (spj) queries with conjunction of predicates and proposes a join optimization algorithm named system ro-h (system rank ordering heuristic). the system ro-h algorithm for optimizing spj queries with conjunction of predicates is a system r dynamic programming algorithm that extends optimal linear join subplans using a rank-ordering heuristic method as follows: choosing a predicate in ascending order according to the h-metric, where the h-metric depends on the selectivity and the cost per tuple of the predicate, using an expression with heuristic constants. the system rank-ordering heuristic algorithm finds an optimal plan in the space of linear left deep join trees. the system ro-h algorithm saves not a single plan, but multiple optimal plans for every subset, one for each distinct such order, termed interesting order. in order to build an optimal execution plan for a set s of i relations, the optimal plan for each subset of s, consisting of i-1 relations is extended, using the lemma based on a h-metric for predicates. optimal plans for subsets are stored and reused. the optimization algorithm chooses a plan of least cost from the execution space. keywords: optimal join subplans, cost function, query tree, join optimization algorithm, h-metric, heuristic method 1 introduction this article proposes a select-project-join query optimization algorithm, based on a heuristic function and suggests an approach of the cost model used in optimization of queries with conjunction of predicates. the paper is organized as follows: • the section prior work presents the improved alternatives of join optimization algorithms • the section system rank ordering heuristic algorithm: algorithm shows a different improved system r dynamic programming algorithm based on utilization of a heuristic function and the mathematical expression of the system rank ordering heuristic algorithm • the section performance evaluation presents the performance results and implementation details. 2 prior work there are known many improved alternatives for optimizing queries with user defined predicates: system r dynamic programming algorithm [1] illustrates the system r dynamic programming algorithm that finds an optimal plan in the space of linear (left-deep) join trees [2]. the algorithm proceeds by building optimal execution plans for increasingly larger subsets of the set of all relations in the join. in order to build an optimal plan for a set s of i+1 relations, the optimal plan for each subset of s, consisting of i relations is extended, and the cheapest of the extended plans is chosen. the system-r algorithm copyright © 2006-2007 by ccc publications optimization of queries with conjunction of predicates 289 saves not a single plan, but multiple optimal plans for every subset s, one for each distinct such order, termed interesting order. the enumeration complexity of the algorithm is o(n2n−1), where r1, r2,. . . , rn relations. the optimization algorithm for the space of bushy join trees is similar to the system-r algorithm, except that the both inputs of a join operator can be an intermediate result. the number of optimal subplans that must be stored for a join with n tables is 2n times the number of interesting orders. the complexity is o(3n). ldl algorithm was used in the ldl project at mcc [3] and subsequently at the papyrus project at hp laboratories [4]. the ldl algorithm treats expensive predicates and relations alike and may produce plans that are significantly worse than plans produced by the traditional optimization algorithm where all selections are evaluated as early as possible. ldl algorithm cannot consider all plans in the space of unconstrained linear execution trees. hellerstein [5] shows that the ldl algorithm fails to consider plans that evaluate expensive predicates on operands of a join prior to taking the join. in order to optimize a query that consists of a join of n relations and k expensive predicates, the dynamic programming algorithm will need to construct 2n+k optimal subplans. the predicate migration algorithm improves on the ldl approach. predicate migration approach, which given a linear join tree, chooses a way of interleaving the join and the user-defined predicates and is integrated with a system r style optimizer [5]. the algorithm places the user-defined predicates in their optimal position relative to the join nodes. this approach has serious drawbacks that limit its applicability [6]: it cannot guarantee an optimal plan because it uses a heuristic to estimate the rank of join predicates that influence the choice of the plan. the naive optimization algorithm for the space of unconstrained linear join trees behaves exactly like the system r algorithm. the total number of stored plans per each distinct set of relations increases to 2k, and the number of plans that need to be stored increases to 2k+n, where k is the number of user-defined predicates. the complexity of this algorithm is exponential in the number of user-defined predicates and in the number of relations in the query. optimization algorithms with complete rank-ordering [7] use the ability to order the execution of predicates (called ranks or rank-order), that were applied prior to application of any other operators. when the join methods are regular, the algorithm restricts the sequence in which the user-defined predicates may be applied and reduces the complexity of enumeration from exponential to polynomial in the number of user-defined predicates. optimization algorithm with pruning [7] compares and prunes plans that have different tags. the "udp-pushdown" rule provides a sufficient condition for a predicate to be pushed down and it can be used to pin the selections as soon as they are evaluable and helps avoid constructing plans where the predicates are pulled up. the "udp-pullover" rule allows avoid generating alternative plans that push down user-defined predicates and are suboptimal. the conservative local heuristic algorithm can choose among plans that result from application of a sequence of udp-pushdown and udp-pullover rules. the two plans picked by the conservative local heuristic complement each other, and the heuristic can guard against the choice of a plan resulting from greedily pushing down a predicate by the pull-rank algorithm. thus, conservative local heuristic can find optimal plans that pull-rank and other global heuristics fail to find due to their greedy approach, but incurs only low computational overhead. as the following lemma states, the conservative local heuristic algorithm produces an optimal plan in several important special cases [7]. in the next section, we present a different improved system r dynamic programming algorithm for optimizing select-project-join queries with conjunction of predicates, based on a heuristic method. we implement the optimization algorithm by extending a system r style optimizer. 290 nicoleta livia tudor 3 system rank ordering heuristic algorithm in this section, we discuss an approache proposed for optimizing select-project-join (spj) queries with conjunction of predicates, using an algorithm system ro-h (system rank ordering heuristic), based on a heuristic method. this algorithm extends optimal linear join subplans by choosing one predicate in ascending order according to the h-metric (heuristic metric). h-metric determines minimum value between the rank of the predicate and the ratio between selectivity minus 1 and cost per tuple. to define the heuristic method for extending optimal linear join subplans, the following shall be considered. 3.1 regular join methods let us consider a class of spj queries on relations r1, r2, . . . , rn, n ∈n* and an implementation of the join operator with conjunction of k predicates p1, . . ., pk, k∈n*. definition 1. a join method is called regular if the cost f(r1, r2) of joining two relations of sizes r1 and r2 depends on the sizes of the relations as follows: f (r1, r2) = a + br1 + cr2 + dr1r2 where the constants a, b, c, d are independent of the sizes of relations r1 and r2 [7]. if join operators follow the assumption of being regular joins, we can restrict enumeration to execution trees where all predicates are ordered by rank order heuristic. 3.2 tags for plan representation the following definition states formally how we associate a tag with a join tree [7]: definition 2. let t be an unconstrained linear join tree that consists of a join among a set r of relations and evaluation of a set u ⊆ s of user defined predicates where s is the set of all user defined predicates in the query that can be evaluated over the subexpression of the query that consists of the join among relations in r. then, the tag associated with the tree t is the ordered set of predicates s−u, sorted by rank order. figure 1 illustrates the execution plans that need to be considered when there are three relations r1, r2, r3 and two selection predicates p1, p2 on r1. t and t’ are possible plans for r1 ./ r2 ./ r3 (each with differing tags). the tags for t and t’ are < > and < p1> respectively. figure 1: the execution plans 3.3 predicate order in optimal plans let us consider a class of spj queries on relations r1, r2, . . . , rn and an implementation of the join operator with conjunction of k predicates p1, . . ., pk. the problem of join ordering, addressed in [8], [9], [10] and [11] utilizes the notion of a rank. the rank of a predicate pi, (i = 1, . . ., n), rank(pi) = costpertuple(pi) / (1 − selectivity(pi)). chaudhuri and shim refer to such execution trees as rank ordered [7], as defined below: optimization of queries with conjunction of predicates 291 definition 3. the user-defined predicates in an unconstrained execution tree t are rank ordered if for any two user-defined predicates p and p’ in t such that rank(p) < rank(p’), either p precedes p’ in the tree t, or p is not evaluable in the tree t’ obtained by exchanging the positions of p and p’ in t. hellerstein et al. [12] consider expensive predicates, i.e., where the computation needed for evaluating whether the predicate is true or false dominates the overall cost [12]. in that context, it is shown that predicates should be ranked in ascending order according to the metric (selectivity−1)/(cost pertuple). hellerstein et al. consider that the processor is perfect in its prediction, and it predicts the branch to the next iteration of the query will be taken when the selectivity ≤ 0.5, and will not be taken when selectivity > 0.5. in system rank ordering heuristic algorithm, we refer to such execution trees as rank ordered, according to the h-metric based on a heuristic method. definition 4. we call the h-metric (heuristic metric) of predicate pi, i = 1, . . . ,k having selectivity si, the pair ( si, c1 ∗si + c2 ∗(1−si) cost pertuple(pi) ) , where c1 and c2 are heuristic constants, defined as follows: c1 = 0, c2 = -1, if 0.5 < si ≤ 1 and c2 = 0, c1 = -1, if 0 ≤ si ≤ 0.5. observations: 1. the utility of the h-metric is that we avoid generating a large number of intermediate-quality plans, that improve on the currently computed best cost, without being optimal. 2. the heuristic method is not guaranteed to find the optimal solution, but we will demonstrate that it finds good solutions. lemma 5. let t be an unconstrained linear join tree that consists of a join among a set of n relations and let si and s j be the selectivities for pi and p j respectively. the plan of the execution tree t cannot be optimal if si ≥ s j and c1 ∗si + c2 ∗(1−si) cost pertuple(pi) > c1 ∗s j + c2 ∗(1−s j) cost pertuple(p j) , where c1 and c2 are heuristic constants, defined as follows: c1 = 0, c2 = -1, if 0.5 < si ≤ 1 and c2 = 0, c1 = -1, if 0 ≤ si ≤ 0.5. proof: let t be an unconstrained linear join tree that consists of a join among a set r of n relations, two predicates p1, p2 with s1 ≥ s2 and let τ be a subexpression among execution tree t and let us refer to this subexpression by τ (r), where the parameter r refers to the input relation of τ . then: τ(r) = τ(σp1(r1)) we can relate the execution trees in figure 2 and the following correspondence holds: t = σp2 (τ) the following parameters shall be defined: • cost(p1) = cost of predicate τ per tuple • |r1| = number of tuples in relation r1 292 nicoleta livia tudor figure 2: τ : subexpression of execution tree t • cost(τ ) = cost of execution tree τ • size(τ ) = size of the output of execution tree τ • si = selectivity of the predicate pi • τ0 = τ (r1) we now estimate the cost of the execution tree t as follows: cost(t ) = cost(p1)|r1|+ cost(τ) + cost(p2)size(τ), where size(τ ) = size(τ0) s1, then cost(t) = cost(p1) |r1| + cost(τ ) + cost(p2) size(τ0) s1 we can represent that the cost of an expression τ (r1) to be the sum of the following three components: 1. cost of evaluating predicates in the expression τ (r1): the sum of all such costs is ∑ i costisizei(r1) where costi is the cost of applying the predicate per tuple and sizei is the size of the relation preceding the i-th application of a predicate. 2. cost of evaluating join nodes in the expression τ (r1) that are ancestors of r1: the sum of all such costs is ∑ j cost(join) j(r1) 3. cost of evaluating all other operators that are not affected by the input relation r1: we denote this cost by cost0. then the cost of an expression τ (r1) can be computed as follows: cost(τ(r1)) = ∑ i costisizei(r1) + ∑ j cost(join) j(r1) + cost0 cost(t ) = cost(p1)|r1|+ ∑ i costisizei(r1) + ∑ j cost(join) j(r1) + cost0 + cost(p2)size(τ0)s1 if s1 ≥ s2 and cost(p1) = cost(p2), then c1 ∗s1 + c2 ∗(1−s1) cost pertuple(p1) > c1 ∗s2 + c2 ∗(1−s2) cost pertuple(p2) optimization of queries with conjunction of predicates 293 if we change p1 with p2, let t’ be a join tree that consists of a join among a set r of n relations, two predicates p1, p2 with s1 ≥ s2, then cost(t ′) = cost(p2)|r1|+ ∑ i costisizei(r1) + ∑ j cost(join) j(r1) + cost0 + cost(p1)size(τ0)s2 cost (t) > cost (t’) ⇒ the plan of the execution tree t cannot be optimal a corollary of this lemma is that whenever two consecutive terms appear anywhere as conjunctions in an optimal plan, then the one with lower selectivity must appear first if it has the same h-metric. we use lemma 5. in the system rank-ordering heuristic algorithm below. 3.4 system rank-ordering heuristic algorithm the system rank-ordering heuristic algorithm finds an optimal plan in the space of linear (leftdeep) join trees. the cost function assigns a real number to any given plan in the execution space and satisfies the principle of optimality [13]. an optimal plan for a set of relations must be an extension of an optimal plan for some subset of the set. the optimization algorithm chooses a plan of least cost from the execution space. definition 6. the system rank-ordering heuristic algorithm for optimizing spj queries with conjunction of predicates is a system r dynamic programming algorithm that extends optimal linear join subplans using a rank-ordering heuristic method as follows: • choosing a predicate in ascending order according to the h-metric • h-metric depends on the selectivity and the cost per tuple of the predicate, using an expression with heuristic constants. the system rank-ordering heuristic algorithm saves not a single plan, but multiple optimal plans for every subset, one for each distinct such order, termed interesting order [14]. in order to build an optimal execution plan for a set s of i relations, the optimal plan for each subset of s, consisting of i 1 relations is extended, using the lemma 1 based on a h-metric for k predicates. optimal plans for subsets are stored in the optplan() array and are reused. the system rank-ordering heuristic algorithm is presented as follows: procedure system rank-ordering heuristic for i = 2 to n do for all s from {r1, ..., rn} with | s | = i bestplan = a plan with infinite cost for all rj, sj, where s = union ({rj}, sj),intersect({rj},sj) = null for all p from optplan(sj, t) with all different tag t nr1 = |p|; nr2 = | rj | r1 = array with evaluable predicates on p r2 = array with evaluable predicates on rj euristicorder(r1); euristicorder(r2) // ascending ordering of predicates // in the p, rj according to the h-metric for i = 0 to nr1 for j = 0 to nr2 p’ = extendjoinplan( p, rj, r1[i], r2[j]) if cost (p’) < cost(bestplan[tag(p’)]) 294 nicoleta livia tudor bestplan[tag(p’)] = p’ endif repeat repeat repeat repeat optplan(s) = bestplan repeat repeat finalplan = a plan with infinite cost for all plan p from optplan({r1, ě, rn}) if complete_cost(p) < cost(finalplan) finalplan = completed plan of p endif repeat return(finalplan) end function extendjoinplan( p, rj, r1[i], r2[j]) let r1[i] be a predicate applied to the plan p for sj, t r2[j] = a predicate for the relation rj p’ = the join plan between sj, t and rj, for the subset of predicates (r1[i], r2[j]) extend optplan(sj, t) with the plan p’ return procedure euristicorder(r1) for i = 0 to nr1 1 for j = i + 1 to nr1 // si = selectivity for r1[i] // cost (pi) = cost of the predicate r1[i] if r1[i] > r1[j] and (c1*si + c2*(1-si)) / cost(pi) > (c1*sj + c2*(1-sj)) / cost(pj) change r1[i] with r1[j] endif repeat repeat return observation: arrays r1 and r2 are useful for ascending ordering of the predicates according to the h-metric. when the join methods are regular, the system rank-ordering heuristic algorithm enables us to further restrict the sequence in which the predicates may be applied and reduces the complexity of enumeration in the number of predicates. 4 performance evaluation in this section we present the results of performance evaluations on our implementation. optimization of queries with conjunction of predicates 295 4.1 experimental framework we used an experimental framework similar to that in [15] and [16] and [7]. we performed experiments using an amd athlon(tm)xp 1600+ machine with 256 mb of ram and running windows xp professional version 2002. the algorithms were run on queries consisting of equijoins. relation cardinalities ranged from a hundred to a thousand tuples, and the numbers of unique values in join columns varied from 25o/o to 100o/o of the corresponding relation cardinality. the selectivities of predicates were randomly chosen from 10−4 to 1.0 and the cost per tuple of predicates was represented by the number of i/o accesses and selected randomly from 1 to 1000. we considered nested-loop, merge-scan, and simple and hybrid hash joins as join methods [17]. in our experiments, only the cost for number of i/o accesses was accounted for. for our experiments, we generated 3 join (join among four relations) queries, 5 join queries, and 7 join queries. we performed two sets of experiments. in the first set, we varied the number of selection predicates that apply on one relation. in the second set, we varied the distribution of the selection predicates among multiple relations in the query, i.e., we kept the number of selection predicates fixed, but varied how these predicates are distributed among the relations in a query. 4.2 candidate algorithms for each query instance, we ran the following optimization algorithms: • system r dynamic programming algorithm: the system r style optimization algorithm that evaluates all predicates as early as possible • optimization algorithms with complete rank-ordering: it compares plans that have the same tag over the same set of relations • opt-rank-conservative algorithm: this algorithm uses conservative local heuristic with complete rank-ordering • system rank-ordering heuristic algorithm that extends optimal linear join subplans using a rankordering heuristic method. 4.3 effect of number of predicates in this set of experiments, the number of predicates was varied from 1 to 5 and the number of join queries was varied from 3 to 7 (7 joins for 8 relations). the results presented for each data point represents an average over 100 queries. these queries were generated by randomly choosing one relation on which all the predicates apply and then randomly picking the cost and selectivities of the predicates as well. table 1 shows the average number of enumerated plans for the algorithms: system r dynamic programming, optimization with complete rank-ordering, opt-rank-conservative and system rankordering heuristic algorithm. figures 3, 4 and 5 show a comparison of the performances (average number of enumerated plans) for the 4 algorithms (system r dynamic programming, optimization with complete rank-ordering, opt-rank-conservative and system rank-ordering heuristic algorithm). the results obtained for queries with 3, 5 or 7 joins show a similar trend: • the enumerations necessary in the system r dynamic programming algorithm is independent of the number of predicates 296 nicoleta livia tudor table 1. worst-case estimates for enumerated plans ( 3 join query) figure 3: 3 join query figure 4: 5 join query figure 5: performance on a varying number of predicates optimization of queries with conjunction of predicates 297 • the optimization algorithms with complete rank-ordering generated more plans than the optrank-conservative algorithm and system rank-ordering heuristic algorithm • the average number of enumerated plans for the system rank-ordering heuristic algorithm is approximately linear • the gap in the number of enumerated plans between the complete rank-ordering algorithm and system rank-ordering heuristic algorithm increases significantly as the number of predicates grows 5 conclusion this article presents an approach of the cost model used in optimization of select-project-join (spj) queries with conjunction of predicates and proposes a join optimization algorithm named system ro-h (system rank ordering heuristic). the system ro-h algorithm is a system r dynamic programming algorithm that extends optimal linear join subplans using a rank ordering heuristic method. the comparison of the performances of algorithms shows that our proposed system rank-ordering heuristic techniques are extremely effective and are guaranteed to generate optimal plans. bibliography [1] s. ganguly, w. hasan, r. krishnamurthy, query optimization for parallel execution, in proceedings of the acm sigmod international conference on management of data sigmod, ed. acm press, new york, pp. 9-18, 1992. [2] p. g. selinger, m. m. astrahan, r. a. lorie, t. g. price, access path selection in a relational database management system, in proceedings of acm sigmod international conference on management of data sigmod, boston, ma, acm press, new york, pp. 23-34, 1979. [3] d. chimenti, r. gamboa, r. krishnamurthy, towards an open architecture for ldl, in proceedings of the 15th international conference on very large data bases, vldb, netherlands, ed. morgan kaufmann publishers inc., san francisco, ca, pp. 195-203, 1989. [4] s. chaudhuri, k. shim, query optimization in the presence of foreign functions, in proceedings of the 19th international conference on very large data bases, ireland, morgan kaufmann publishers inc., san francisco, ca, pp. 526-541, 1993. [5] j. m. hellerstein, practical predicate placement, in proceedings of the acm sigmod international conference on management of data, minneapolis, acm press, new york, pp. 325-335, 1994. [6] j. m. hellerstein, m. stonebraker, predicate migration: optimizing queries with expensive predicates, in proceedings of the acm sigmod international conference on management of data, washington, acm press, new york, pp. 267-276, 1993. [7] s. chaudhuri, k. shim, optimization of queries with user-defined predicates, acm transactions on database systems, vol. 24, no. 2, pp. 177-228, 1999. [8] j. m. hellerstein, j. m., m. stonebraker, predicate migration: optimizing queries with expensive predicates, in proceedings of the acm sigmod international conference onmanagement of data, washington, dc, eds. acm press, new york, ny, pp. 267-276, 1993. 298 nicoleta livia tudor [9] r. krishnamurthy, h. boral, c. zaniolo, optimization of nonrecursive queries, in proceedings of the 12th international conference on very large data bases, kyoto, japan, vldb endowment, berkeley, ca, pp. 128-137, 1986. [10] c. l. monma, j. sidney, sequencing with series-parallel precedence constraints, math. oper. res. 4, pp. 215-224, 1979. [11] k. y. whang, r. krishnamurthy, query optimization in a memory-resident domain relational calculus database system, acm trans. database syst. 15, pp. 67-95, 1990. [12] j. m. hellerstein, m. stonebraker, predicate migration: optimizing queries with expensive predicates, in proceedings of the acm sigmod conference, 1993. [13] t. h. cormen, c. e. leiserson, r. l. rivest, introduction to algorithms, mit press, cambridge, ma, 1990. [14] p. g. selinger, m. m. astrahan, r. a. lorie, t. g. price, access path selection in a relational database management system, in proceedings of acm sigmod international conference on management of data, boston, ma, acm press, new york, pp. 23-34, 1979. [15] y. e. ioannidis, y. c. kang, randomized algorithms for optimizing large join queries, in proceedings of the 1990 acm sigmod international conference on management of data, ed. acm press, new york, ny, pp. 312-321, 1990. [16] s. chaudhuri, k. shim, including group-by in query optimization, in proceedings of the 20th international conference on very large data bases, vldb endowment, berkeley, ca, 1994. [17] l. d. shapiro, join processing in database systems with large main memories, acm trans. database syst. 11, pp. 239-264, 1986. tudor nicoleta liviana petroleum-gas university of ploiesti department of computer science 39 bucuresti avenue, 100680, ploiesti, romania e-mail: tudorl@upg-ploiesti.ro received: january 25, 2007 revised: june 13, 2007 nicoleta liviana tudor (born on july 15, 1968) graduated the faculty of mathematics of the bucharest university in 1992. she has 15 years experience of teaching in the field of computer science both in petroleum-gas university of ploiesti, and in other educational institutions of romania. since 2006 she is lecturer in the computer science department at petroleum-gas university of ploiesti, romania. she is a phd student at the petroleumgas university of ploiesti, control engineering and computers department. her main research fields are middle-tier business objects, xml web services, data processing, relational databases. she has authored 2 books and more than 14 research papers in the area of relational databases, data structure, and published in international journals, and in the proceedings of prestigious international conferences. international journal of computers, communications & control vol. ii (2007), no. 3, pp. 209-216 60 years from birth of academician f.g. filip ioan dziţac, mişu-jan manolescu, horea oros, emma văleanu “to whom it may concern ... dr. filip has shown himself to be a very innovative and productive researcher whose papers are equivalent in quality and cutting-edge findings to those from the best research organizations of the world. witness his publications in automatica, computers in industry and other leading journals in the field. the establishment of dr. filip’s stature as an internationally recognized researcher in his field and the acceptance of his work are shown by the large number of international conferences where he has served as session chairman and/or program committee member and many invited seminars he has presented in other countries... 1” 1 introduction born in bucharest on the 25th of july 1947, the academician florin gheorghe filip, editor in chief of our journal (ijccc), turns 60, this is why the operative/executive editorial staff of ijccc wants to 1prof. theodore j. williams professor of engineering and director, purdue lab. for applied industrial control, purdue university; former president of instrument society of america; former president of the american federation of information processing societies; chairman of ifac/ifip task force on “architecture for enterprise integration” (august 1994) copyright © 2006-2007 by ccc publications 210 ioan dziţac, mişu-jan manolescu, horea oros, emma văleanu dedicate this short biographical-sketch in order to pay him homage. his life is under the sign of creation in science, of generosity in his relationship with his co-workers, of his innovative courage in the coordination of the projects he has worked on, of the stimulating energy in the community he has worked for a longer period of time or just collaborated occasionally, and under the sign of modesty in his relation with other people with whom he was in contact. most of the people who know him through their work acknowledge his good and stimulating effects on their work or career. in order to show him respect, lots of similar journals dedicated him special numbers in this period. not willing to be in competition with other journals, we would like to dedicate only this short biographical sketch, but in no 4/2007 of ijccc, we are going to publish some of the scientific articles of authors who expressed their wish to dedicate them to academician filip when he turns his 60. we will present in this paper a short digest of the biography and work of f.g. filip 2 opera omnia prof. f. g. filip is author/coauthor of 6 monographs published in ed. tehnica (http://www.edituratehnica.ro/) and he is editor/coordinator of 9 contribution volumes published in ed. tehnica, romanian academy editing house (www.ear.ro), ed. expert and elsevier. also, he publish more than 200 scientific papers in several international journals (computers in industry, ifac j. automatica, ifac j control engineering practice, large scale systems, j. of human systems management, systems analysis, modeling and simulation, studies in informatics and control etc.) and some contribution volumes printed in the publishing houses: pergamon press, north holland, chapmann and hall, springer, kluwer, ed. tehnica and ed. academiei române. more than 50 articles of f.g. filip are registered in thomson isi web of knowledge database and 22 in scopus and elsevier databases. 3 academic qualifications, scientific titles and professional positions f.g. filip graduated the faculty of automatics, university politehnica of bucharest, obtaining the engineer diploma in 1970 and received his ph. d (1982) in automatics (electrical processes automation field). later on, he participated at several courses of professional forming and improvement: “complex informatics systems, tele-processing systems” (sweden, at the universities from goeteborg, uppsala, malmoe, stockholm 1974); “research and technology management” (germany, fraunhofer iitb karlsruhe, 1995); “decisional systems” (germany, fraunhofer first, berlin, 1998) etc. f.g. filip is corresponding member of the romanian academy since 1991 and full member of the romanian academy since 1999. since 2000 acad. filip is vicepresident of the romanian academy. last professional titles obtained by f.g. filip are first degree researcher (1990) and university professor (1998). he jobs and positions held is following: engineer (1970-1976); scientific researcher (1976-1979); main researcher iii-(1979-1990); head of the research laboratory (1985-1991); main researcher i (1990); directorthrough contest at ici (1991-1997); scientific directorresponsible with the international cooperation (1997-2001); president of the scientific committee (1995-2003); vice-president of the romanian academy (2000-now). 4 awards, diplomas and distinctions f.g. filip received the following award, diplomas and distinctions: 60 years from birth of academician f.g. filip 211 1. “grigore moisil” award and the diploma “man of the year 1999” granted by inforec (the association of economical informatics). 2. doctor honoris causa of "lucian blaga" university, sibiu (2000). 3. the award “copy ro for informatics” for the year 2003 for the monograph “computer aided decision: base methods and techniques” (ed. tehnica, bucharest, 2002, in romanian). 4. honorary professor of “dunarea de jos” university, galati (from 2003). 5. excellency diploma “the most prominent romanian personality of the decade in it&c” granted by aries (romanian association for the electronically and software industry). 6. honorary member of the romanian technical sciences academy astr (www.astr.ro) (from 2006). 7. honorary member of moldavian academy of science (from 2006). 8. decorated with the national order “loyal service” in rank of “great cross” (granted by the romanian president on 30.11.2000) 9. honorary citizen of campeni city(2002). 5 research activities research projects coordination regarding: • the elaboration of the dimensioning methods for the equipments configurations; • participating on projects regarding the informatics systems in industry; • researches regarding the designing of discrete automate systems; research projects coordination regarding: • hierarchical leadership methods and hierarchical compute methods; • real time hierarchical informatics systems; applicative projects coordination regarding: • implementation and design of real time informatics systems and decision support systems (dss); • contribution to the design of the dispecer ssd (exported in 1996) researcher leadership in conceiving ssd with combined knowledge (numerical models and artificial intelligence elements) ici teams coordination participating to projects/networks of excellence (“network of excellence”noe) or international “working group”-wg: • for the ec-peco program: esatt + (“european science and technology transfer network+”), • for the inco program: a) noe ametmas (cp 96-26), b) inids (“information dissemination in european rtd”ic-1030) 212 ioan dziţac, mişu-jan manolescu, horea oros, emma văleanu • for the esprit program: orbit (“object-oriented decision assistance for continuous operations scheduling and coordination”-24487), noe icims (9251), si e (simulation europe) wg (cod: 8467) general coordination of the research activities from ici. coordination of the rnc (national computer network for research) project accomplishment, the first romanian network connected to the internet (1993). coordination of the projects for introducing the informatics in the cultural institutes (libraries, museums). founding (in 1989) and coordination of the international journal “studies in informatics and controlsic” (http://www.ici.ro/ici/revista/sic.html) (registered from 1995 the ieee inspec database, indexed in isi web of knowledge, the first romanian journal that had an on-line version from 1992). founding (in 2006) and coordination of the international journal “international journal of computers, communications and control-ijccc” (http://www.journal.univagora.ro). participating to some ici international projects: a) interbit (“interbalkan net of technology”ist 1999-14022), b) mobitech (“european sme challenge in mobile telecom technologies”-ips1999-50125), c) idealist 5fp, d) prolearn (as “associated partner”) coordination of the following romanian academy sections: • technical sciences • geological sciences • sociological, juridical and economical sciences • informatics science and technology managerial coordination of the romanian academy library (bar) supervision of the construction activities at the romanian academy library coordination of two fundamental projects of the romanian academy • informational society knowledge society (2001-2002) http://www.academiaromana.ro/pro_pri/pag_com01socinf_prpri.htm • inter-disciplinary program for preventing the major risks phenomenon at a national scale (2004-2005) http://www.icmpp.ro/institute/p_fundam_ar.htm coordination of four international projects of the romanian academy: • forsociety “laying the foundations for an era-net on foresight and society” (contract erasct-2003-003231) • rointera “romanian research community integration in the european research area” (contract inco-ct-2003-510469), (www.rointera.ro) • romob “romanian mobility center” (contract fp 6 513461) • forsociety “transnational foresight era net” (http://195.251.117.130/forsociety/partners/index.html) coordinator of projects: the programs ec-peco, esprit, copernicus, ist of ue the responsible of the romanian teams from ici at 10 projects/networks of excellence/workgroups (1993-2000: see the above no. 8) and at 2 projects of the romanian academy in fp6 (2003-): a) rointera (nco-ct-2003-510469), b) forsociety (eranet 011832) evaluator: 1992 (in the first team with invited experts from the central and east europe), 1996, 1999, 2004, 2005. member in : 60 years from birth of academician f.g. filip 213 • program committee of the ist (“information society technologies”) research program of ue (1999-) national representative named. • consultative committee of the european committee in the priority domain ist from fp6 (istaginformation society technologies advisory group) (invited directly by the european committee) (2003-2004) nato science for peace • associate member of the sub-committee computer networking (2000-2001) (selected by nato) cdi national program • member of the consultative board for research development innovation (1992-) and general secretary (1998-) member of the “informatica” 5th committee of the consultative board (from 1991), president of the “informatica” 5th committee (1993-1994) and vice-president (from 1995) romanian academy grants (gar) • vice-president of the gar committee (1995-2007) romanian academy priority and fundamental programs • coordinator of the project “informational society knowledge based society” http://www.academiaromana.ro/pro_pri/pag_com01socinf_prpri.htm (2001-2002) • leadership of the program “major risk phenomenon and processes at national scale” http://www.icmpp.ro/institute/contributii_stiintifice.htm (2004-2005) 6 didactical activities doctorate guidance at • upb (university politehnica of bucharest), faculty of automatics specialization “automatics” (from 1993) 8 finalized doctorates from which one is made in association with franche conte university) • romanian academy specialization “computer sciences” (from 2002) professor of “applied informatics” at “valahia” university, targoviste (1998-2001) courses of: • “artificial intelligence” • “computer aid decision” • “computer aid enterprise engineering” (master level) visiting professor at • university politehnica of bucharest, faculty of mechanics (mecatronica department). course: “expert systems” (1998) • hyperion university of bucharest (1999-2003). courses of: a) "computer aided decisions" and b) "artificial intelligence" • agora university (2006now) “decision support systems” 214 ioan dziţac, mişu-jan manolescu, horea oros, emma văleanu master courses at: • “lucian blaga” university sibiu. course: “industrial management” (1998) • upb-cpru: course: "enterprise re-engineering" (1998) and "computer aided decision"(2005, 2006) • university of bucharest, faculty of letter. course: “computer aided decision” (2004) • ase bucharest. the modules “computer aided decision” from the course “project management” (2000) • ecole centrale de lille (france, 2006) 7 member in the editorial staff of some scientific journals/conferences member in international program committees (ipc) at more than 50 international conferences and congresses from europe, sua, south america, asia and africa. member in the editorial board of the following journals: 1. sams (systems analysis, modeling and simulation) (taylor and francis) (1993-2004) 2. international j. of critical infrastructures (interscience publishers: www.interscience.com ) (2004) 3. computer journal of moldova , chisinau (1993), information technologies and control (issn 1312-2622), sofia (din 1998) 4. information technologies and control, sofia (2003) 5. romjist (romanian journal of information science and technology) of the romanian academy 6. studies in informatics and control -sic (founder and chief-editor, from 1989) 7. international journal of computers, communications and control (http://www.journal.univagora.ro/)chief-editor (from 2006) 8. control engineering and applied informatics (http://www.upg-ploiesti.ro/srait/publicatii.html) 9. romanian journal of informatics and automatics (http://www.ici.ro/ici/revista/ria.html): founder 10. romanian journal of automatics (http://www.ipa.ro/web3-8re.html) invited at conferences and seminars at universities and research institutes from: england (1984), austria (1996), brazil (1995), chile (2004), china (1983, 1988, 2006), france (1992, 2006), germany (1991), kuwait (1986), moldova republic (1995, 2006), sweden (1970), tunisia (1998). 8 member in professional organizations • ifac (“international federation of automatic control”): vice-president of the technical committee (tc) 5.4 “large-scale complex systems”, http://www.academiaromana.ro/ifac/ifac_tc54.htm), (1998-2002), president of tc 5.4 (20022005), confirmed for a new mandate (2005-2008) • founder member of sie (simulation europe) (1993-1997) 60 years from birth of academician f.g. filip 215 • honorary member of atic (association for communication and informatics technology, from 1996) (www.atic.ro) • romanian forum for the informational society committee of the romanian academy, executive president (from 1997) • http://www.academiaromana.ro/forum_info/fpsc.htm • srait (society of automatics and technical informatics), founder member (1992), vice-president (1992-2001), now member of the director committee (http://www.upg-ploiesti.ro/srait/comitet.html) • member of ifac ifip (“international federation of information processing”) task-force “architectures for entreprise integration” (from 1995) • honorary member of pitch (“association for the promotion of international technological cooperation for humanistic ends”-) (2000-2006) bibliography [1] corina pascu, f g filip (eds). (2005). visions on information society future in an enlarged europe, ed academiei, bucharest. [2] filip. f.g. (2004). decision support systems, ed tehnica, bucharest. [3] filip, f.g. (2002). computer aided decision. ed tehnica, bucharest [4] filip f.g., dumitrache i., iliescu, s.s. (eds) (2002). large scale systems: theory and applications 2001 (lss’01). elsevier science ltd. (pergamon), oxford. [5] filip f.g. (coordinator) (2001). informational society knowledge society. ed. expert, bucuresti. [6] filip f.g., boldur b. (1999). industrial informatics: new paradigms and applications, ed. tehnica. [7] filip f.g. (1998). optimization models with sparse matrices and relatively constant parameters. systems analysis modeling and simulation, 33, 407-430. [8] filip f.g., alexandru a. and socol i. (1997). technology management and international cooperation: several success stories. j. human systems management, 16, 223-229. [9] filip f.g., neagu g. and donciulescu d. (1996). dssfm: from technology to decision making. in proceedings 14th ifac world congress, san francisco vol b, 367-372. [10] filip f.g. (1995). it culture dissemination in romania: experiments and achievements. in a. inzelt and r. coenen (eds.) knowledge technology transfer and forecasting, kluwer academic, dordrecht, 149 160. [11] filip f.g. (1995). towards more humanized dss. in: l.m. camarinha matos and h. afsarmanesh (eds.) balanced automation systems: architectures and design methods. chapman & hall, london, 230 240. [12] filip f.g. (1994). evolutions in systems analysis, modeling and simulation in computer based industrial automation, systems analysis, modeling and simulation, 15, 135 149. [13] filip f.g. and neagu g. (1993). cim in continuous and discrete manufacturing: object-oriented generic modeling, ifac j.control eng. practice, 1(5), 815 825. 216 ioan dziţac, mişu-jan manolescu, horea oros, emma văleanu [14] filip f.g. (1992). system analysis and expert systems techniques for operative decision-making. in: a. sydow (ed.), computational systems analysis: topics and trends, elsevier, amsterdam, 285 304. [15] filip f.g. (1988), operative decision-making in the process industry. in: preprints, 12th imacs world congress, paris, vol.4, 523 525. [16] guran m. and filip f.g. (1986). hierarchical and real time systems with distributed data processing. ed tehnica, bucuresti. [17] filip f.g. and donciulescu d.a. (1983). on on-line direct dynamic coordination method in process industry, ifac j. automatica, 19(1), (pergamon press , oxford), 317 320. [18] filip f.g., neagu g. and donciulescu d. (1983). job shop scheduling optimization in real time production control. computers in industry, 4(4) (north holland, amsterdam), 395 403. [19] filip f.g., donciulescu d., muratcea m., oraseanu l. and predoiu d. (1980). hierarchical control of large-scale chemical and metallurgical systems. in a. titli and m. g. singh (eds.). large scale systems: theory and application. pergamon press, oxford, 563 575. [20] http://www.ici.ro/ici/revista/sic.html [21] http://www.journal.univagora.ro/ [22] http://www.ici.ro/ici/homepage/filipf.html [23] http://www.academiaromana.ro/cv2006/birou_vice02filip.htm [24] http://romania-on-line.net/whoswho/filipfloring.htm ioan dziţac executive editor of ijccc agora university piata tineretului 8, 410526 oradea romania idzitac@univagora.ro mişu-jan manolescu managing editor of ijccc agora university piata tineretului 8, 410526 oradea romania rectorat@univagora.ro horea oros technical editor and editorial secretary of ijccc university of oradea universitatii st. 1, 410087, oradea romania horos@uoradea.ro emma văleanu webmaster and editorial secretary of ijccc agora university piata tineretului 8, 410526 oradea romania evaleanu@univagora.ro int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 813-818 software solution for monitoring street traffic and generating optimum routes using graph theory algorithms m. moise, m. zingale, a.i. condea maria moise, marilena zingale, alexandru ioan condea romanian american university, romania e-mail: maria.moise@rau.ro, zingale@un.org, zander.aq@gmail.com abstract: nowadays, big cities are facing traffic jams, generated by the great number of automobiles in regard to the limited infrastructure capacity. drivers are being presented with these problems: increased time spent between areas of interest, a higher risk of having an accident and of course stress suffering. in order to solve the urban traffic-jam problem a number of solutions have been developed. one of these is tomtom , which offers, free of charge, the possibility to generate navigation indications for a route. unfortunately, the traffic monitoring service is limited to a few countries, but some countries are not on their coverage area. at this time there isn’t a complete method to calculate the optimum route from a destination to another, taking into account street traffic. the only way to get relevant information is represented by the drivers personal expetraffic jams a series of solutiontraffic jams a series of solutionrience and the news on tv/radio. thus, the choice for generating an optimum route is up to the driver/client and, as a consequence, this method is not a scientific one, being certified only empirically. in this context, the paper presents a software solution, which determines the optimum route, taking street traffic into regard, thus contributing to a substantial reduction of time spent in traffic by drivers. the information needed for the application regarding the state of the street traffic can be supplied by the agents that check all available information sources(news bulletins, radio, police announcements) and the mobile agents that patrol the streets. the aim of this paper is to present a solution for determining the optimum route choice for cars. the solution is composed of two applications: first is mapmaker, which designs a street map. second is bestroute which can add traffic coefficients to streets and calculate the optimum route. in order to choose the best route two criteria are used: the minimum distance and the street traffic coefficients. the data regarding the streets map and the traffic situation is taken from a mysql database; the optimum route from destination a to the destination b is calculated using a modified dijkstra algorithm. keywords: optimum routes, graph theory, dijkstra algorithm. 1 introduction today most big cities are facing traffic jams that are generated by the exponential increase of automobiles versus the original limited traffic flow design. in order to solve the urban traffic jams a series of solutions have been proposed and used worldwide. one of these is tomtom , which offers free of charge the possibility to generate a navigation path on their website. unfortunately, the traffic monitoring service is limited to a few countries, and romania is not on their supported list. copyright c⃝ 2006-2010 by ccc publications 814 m. moise, m. zingale, a.i. condea in romania such a solution has not been developed yet; existing solutions in bucharest are limited to the synchronization of the traffic lights and the existence of a reduced number of intelligent traffic lights. at this time a complete solution to calculate the optimum route from a destination to another, with the traffic taken into account, does not exist. the only information available represents the personal experience and/or the news on tv or radio. thus calculating the optimum route is up to the driver(client) and as a consequence it is certified only empirically. in this context, the paper presents a software solution, which determines the optimum route, taking street traffic into regard, thus contributing to a substantial reduction of time drivers spend in traffic. the information needed regarding the state of street traffic can be acquired either by agents that check all the available information sources or by agents that patrol the streets. 2 solutions description the solution for determining the optimum route is composed of two applications. first is mapmaker a map designer with the ability to use a background satellite image for easier plotting. for example, it could use a selection of satellite images from bucharest, covering the doamna ghica area and the adjacent neighborhoods. after the background image is selected the map is designed node by node with the use of the mouse. the streets are made by connecting the desired nodes. the resulting map can be saved both on the local hard-disk and on an external mysql database. the second application is bestroute, used to set traffic coefficients and calculate optimal routes. the necessary data is taken from a mysql database. the application has two modes of usage: • the client mode, which is used to calculate the optimal route based on the selection of two nodes. the result is shown both graphically and in text mode; • the admin mode, which is used for altering the traffic coefficients of a street. in this mode, you can add, modify and delete user accounts. this mode also provides a set of reports about customers or streets. the bestroute application uses elements and algorithms from the graph theory. a modified dijkstra algorithm has been used in order to calculate the minimum distance path between two nodes of the graph. the features of the algorithm’s dijkstra are the following: • there are established a list of distances, a list of previous nodes, a list of visited nodes and a current node; • the values from the list of distances are initialized with an infinite value, excluding the home node, which is set with value "0"; • all values in the list of visited nodes are set with value "false"; • all values in the list of previous nodes are initialized with the value "-1"; • the home node is set as the current node; • the current node is marked as visited; • the distances are updated based on nodes that can be viewed immediately from the current node; • the current node is updated to the unvisited node, which may be visited by means of the shortest path from the home node; • to be repeated (from point f) until all nodes are visited. formalization of the dijkstra algorithm consists of: step 1. initialization of the initial peak having a value of 0: w(x0) = 0; step 2. setting up the lot a comprised of the initial node: a = x1; step 3. analysis of nodes outside the lot a, namely: software solution for monitoring street traffic and generating optimum routes using graph theory algorithms 815 i. if the nodes can be reached by direct arcs from nodes in a, then for these nodes, we calculate: w(xi) = min(w(xj + v(xj, xi)) for problems of minim (2.1) xj ∈ a ∃(xj, xi) and it is added to the lot a only that node for which the minimum value is obtained, then step 4 is initialized. step 4. analysis of a multitude: i. if xn ∈ a, then its value represents the value of the optimum value path from x1 to xn; in order to find this path we start backwards from the final node xn and we find the node xk1, xk2, ..., xkr q which form the path searched for, where xk1 = xn, xkr = x1, and each other index ki+1 is the one for which: w( ) + v(xki+1, xki) = w(xki); stop. ii. ifxn /∈ a, the algorithm is resumed from step 3. w( ) + v(xki+1, xki) = w(xki); stop. iii. if xn /∈ a, the algorithm is resumed from step 3. the application bestroute uses data from public sources and its own agents. on its execution the program queries the mysql database and retrieves the map and its traffic coefficients, then it shows a color coded visual representation of the traffic map. upon requesting a route from address a to address b a modified dijkstra algorithm is used. it is also possible to select whether one wants the fastest route (traffic wise) or the shortest route (geographically). this application also has the possibility to generate reports on customers (useful for the administrator) and on the state of the streets. the reports generated by the application can be exported in excel worksheets and/or pdf document format. for generating reports microsoft report viewer component within the net framework was used and the reports were created using the report wizard plus a manual tweak for better presentation of data. 3 solution output presentation 3.1 mapmaker in fig. 1 we have the main screen. figure 1: main screen of module mapmaker. figure 2: the menu background image. by clicking on file we can: • create a new map new; 816 m. moise, m. zingale, a.i. condea • save the existing map save; • open a saved map open; • save the map to the database save to db; • open a saved map from the database open from db. selecting a background image from the hard-disk is done by clicking on background image (fig. 2). to design the nodes, we use the buttons from fig.3, which enable: • add a new node to the map nod nou; • show details about a selected street detalii muchie; • delete a selected node sterge; • clear the map scale clear scara. figure 3: buttons for creating, modifying and deleting nodes and clearing the maps scale. also mapmaker uses a system which automatically determines the scale for the map. it is necessary to introduce the distance between nodes only the first time. the calculation is done by the ratio between the initially introduced distance and the actual pixel distance between the two points. 3.2 bestroute the applications main screen is illustrated in fig. 4. figure 4: options of the file menu. figure 5: options of the reports menu. by clicking on file we can download map and user data from the server or quit. by clicking on clienti while in administrator mode one can add new users, modify old ones, delete them or set whether a specific user is restricted or not. by clicking on reports (fig.5) one can request reports on clients, restricted clients and street traffic. on opening the application, the system displays the map in img. 6, and selecting the start node is illustrated in fig. 7. generating a non-optimized route in fig.8; after selecting the destination node the application calculates the shortest path available. optimized route provided by the application is illustrated in fig.9. software solution for monitoring street traffic and generating optimum routes using graph theory algorithms 817 figure 6: initial state of the map on opening the application. figure 7: selecting the start node. figure 8: image (red one) of nonoptimized route. figure 9: image of optimized route, enabling avoiding crowded streets. if the optimize option is checked the application will generate an optimum route to avoid heavy traffic areas. also a time estimate is offered. for calculating the optimal route, the following distance modifiers based on traffic coefficients were used: 1, 1.25, 1.66, 2.50, 5 and 999 for closed streets. to calculate the estimated time, the following maximum speeds were assumed: for excellent traffic conditions a speed of 50kph, reducing speed by 10kph to the minimum of 10kph for very bad traffic. thus, a relatively accurate time can be calculated for the generated route. for the best estimation possible, a interval of plus/minus 20% is assumed. on both generating options a street by street text solution is also supplied. 4 conclusions using such an application by drivers leads to avoiding stress and fatigue generated by traffic jam, thus reducing car crash risks. also, the application provides information on routes unknown to drivers thus leading to a better awareness of the city. implementing the application for a taxi or currier company offers an edge on competitors, generating a shorter delivery or reply time. on the whole, large-scale use of the application in partnership with the town hall and police may lead to general fluidization of traffic, equally improving the environment. 818 m. moise, m. zingale, a.i. condea bibliography [1] moise, m., zingale, m., condea, a., informatics application which determines the optimum routes for the cars, in proc. of the e-comm-line 2009 conference, section v 35, pp. 5. [2] moise, m. data base informatics systems, prouniversitaria publishing house, 2008, bucharest [3] *** http://msdn.microsoft.com microsoft developer network [4] *** http://stackoverflow.com/ stackoverflow [5] *** http://dev.mysql.com/usingmysql/dotnet/ documentation mysql int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 727-734 towards low delay sub-stream scheduling w. guofu, d. qiang, w. jiqing, b. dongsong, d. wenhua wu guofu, dou qiang, wu jiqing, ban dongsong,wenhua dou national university of defense technology school of computer science changsha, hunan, p.r.china e-mail: {gfwu,qdou,jqwu,dsban,whdou}@nudt.edu.cn abstract: peer-to-peer streaming is an effectual and promising way to distribute media content. in a mesh-based system, pull method is the conventional scheduling way. but pull method often suffers from long transmission delay. in this paper, we present a novel sub-stream-oriented low delay scheduling strategy under the push-pull hybrid framework. first the sub-stream scheduling problem is transformed into the matching problem of the weighted bipartite graph. then we present a minimum delay, maximum matching algorithm. not only the maximum matching is maintained, but also the transmission delay of each sub-stream is as low as possible. simulation result shows that our method can greatly reduce the transmission delay. keywords: p2p streaming, scheduling, sub-stream, weighted bipartite graph, matching 1 introduction the emerging peer-to-peer (p2p) systems have appeared to be the most promising driving force for video streaming over the internet. by distributing the workload to a large number of low-cost computing hosts such as pcs and workstations, one can eliminate the need for a costly centralized server and at the same time improve the system’s scalability. there are already commercial products emerging, e.g., pplive [1], coolstreaming [3]. any p2p streaming system consists of two distinct but related components: (i) an overlay construction mechanism, and (ii) a content scheduling mechanism. to improve the performance of p2p streaming systems, many studies focus on the overlay construction. however, the content scheduling mechanism also has greatly impaction on the performance. carefully designed scheduling mechanism could have a better tradeoff among maximum streaming rate, minimum transmitting delay and control overhead. the overlay construction falls into two categories: tree(s) and mesh. in the mesh overlay, the pull method which is very similar to that of bit-torrent protocol [2]is widely used. recent result [5]shows that the protocol of unstructured mesh overlay outperforms the traditional multi-tree approaches much in many aspects. several media content scheduling mechanisms for mesh-based system have been proposed. coolstreaming/donet [3] and chainsaw [4] proposes pull-based scheduling framework. jm li [6] designs a scheduling algorithm to manipulate the order of data blocks to improve the transmission efficiency in p2p streaming system. m zhang [7] defines priorities for different blocks according to their rarity property and their emergency property, and tries to maximize the average priority sum of each node. based on the traffic from each neighbor, a node in gridmedia [8] subscribes the pushing packets from its neighbors at the end of each time interval. pulsar [9] combines push-based operations along a structured overlay with flexibility of pull operations. lstreaming [10] uses sub-streams in the push-pull streaming system. copyright c⃝ 2006-2010 by ccc publications 728 w. guofu, d. qiang, w. jiqing, b. dongsong, d. wenhua this paper presents an effectual content scheduling strategy which can largely reduce the content transmission delay meanwhile remaining the main advantage of the pure pull method. first, the original stream is divided into k sub-streams, and each sub-stream has the same rate. a node requests one sub-stream instead of one content block from its neighbors. then the sub-stream scheduling problem is transformed into the matching problem of weighted bipartite graph. the well known hungarian algorithm which solves the maximum matching problem is ameliorated. not only maximum matching is reserved by the new improved algorithm, but also the transmission delay of each sub-stream is as low as possible. the main difference between our scheme and lstreaming is the choice of available neighbors. the following paper is organized as follows: section 2 describes our motivation. in section 3, we present our algorithm for the sub-stream scheduling problem. simulation result shows in section 4. finally, we conclude our work in section 5. 2 motivation 2.1 delay analysis of the pull-based scheduling strategy the main drawback of pull-based scheduling strategy is that content blocks suffer from long delay. now we give a quantitative analysis showing why this strategy resulting in long transmission delay. in such system, the media content is divided into equal size blocks, each of which has a unique sequence number. every node (including the source node) periodically broadcasts all of its neighbors a bit vector called buffer map (bm) which represents the availability of useful blocks in its buffer pool. according to the announcement, each node decides from which neighbor to ask for which blocks, and periodically sends requests to its neighbors for the desired blocks. when a neighbor receives the request, it puts the desired blocks into its output queue, waiting to send out. for the efficiency of information exchange, bm and requests will only be sent periodically so that dozens of packets can be mapped into a single packet. we denote the interval between two buffer map packets or two request packets as t. figure 1 shows a typical process that a content block goes from one node to a neighbor. at the time t1, a fresh block arrives in node a. because the bm packets will only be sent at the beginning of each time slot, the useful information will wait until the time t2, when a new time slot begins. after a period of time td1, at the time t3, the bm packet reaches at node b. time td1 contains two components: propagation latency and transmit latency. the propagation latency lies on the length of links between node a and node b, while the transmit latency lies on the length of packet and the available bandwidth. the width of the shadow represents the transmit latency. because the length of bm and request packets is small, we ignore the latency. at the time t3, node b would make a design whether or not to request the fresh block from node a. as the same reason, node b sends the request packet at the time t4. at the time t5, node a receives the request packet, and then it puts the fresh block to the output queue. after a queue delay tw, the fresh block is sent out. because the length of content block is large, we can’t ignore the transmit latency of the packet. at the time t7, the fresh block is received by node b. now we can compute the one hop delay, which is the interval between the time t1 and t7. apparently, tp and tr are uniform random variable on the length t of time slot, and they are independent. their average values are both t/2. we suppose the packet with largest waiting time will be sent first, the blocks before the time t1 reached at node a at the same time slot will be sent before the fresh block, so the queue delay tw is t −tp. thus, the average latency t1−delay for one packet in one hop can be computed as t1−delay = e[tp + td1 + tr + td2 + tw + td3] = 3t 2 + 3td + l u (1) towards low delay sub-stream scheduling 729 where td is the average end-to-end delay, and l is the length of content block, and u is the average upload bandwidth of nodes. figure 1: one hop latency 2.2 using sub-stream to reduce the one hop latency t1−delay we can reduce the latency by adopting a shorter cycle t ′, but this will bring in more information packets. meanwhile the three end-to-end delays can’t be avoidable. a naive solution is that when node a receives the fresh content block, it directly sends it to node b. in this case, the one hop latency is only td + lu . but node b may receive repeated blocks from different neighbors under blind packets scheduling. an efficient way is that node a sends specifically sub-stream(s) to node b, while other neighbors send another sub-stream(s) to node b. different sub-streams are not intersected. node b decides to subscribe which sub-stream from which neighbor, and requests for sub-stream(s) instead of content block(s). theoretically, node b requests for substream(s) only once, and then just waits to receive media content. figure 2 shows the creation figure 2: content blocks in different sub-streams figure 3: the information peerp collects of sub-streams. the original media content is divided into blocks with equal size, and each block has a unique sequence number, counting from number 1. suppose we divide the original stream into k sub-streams, then what content blocks each sub-stream has is expressly described in figure 2. which sub-stream a content block with sequence number m belonging to is determined by the following function f(m). f(m) = { m mod k if x mod k ̸= 0 k else (2) 3 sub-stream scheduling in our framework, participating peers adopt gossip protocol to self-organize into unstructured mesh overlay. in this section, we study the problem of sub-stream scheduling, that is to say a 730 w. guofu, d. qiang, w. jiqing, b. dongsong, d. wenhua node subscribe which sub-stream from which neighbor. we suppose in the initial period, the node uses pull-based method to get the content blocks. after that, push-based method works. 3.1 exchanging information peers periodically broadcast sub-stream information and available upload bandwidth to neighbors. the period can be long, because other cases may trigger a node to broadcast the information or send the information to chosen neighbors. when a node joins the system, all of its neighbors send information packets to it actively. the information includes the sub-streams the neighbor can provide {sij}, latency of each sub-stream from the source to the neighbor {hij} and the available upload bandwidth of the neighbor {ci}. sij =1 presents neighbori can provide sub − streamj, else sij = 0. hij presents the number of hops of sub − streamj from the source to neighbori. if sij = 0 then hij = +∞. ci is not the real bandwidth, but it presents the multiples of one sub-stream rate. for example, neighbori’s available upload bandwidth can support transmitting two sub-streams to local node, then ci = 2. after a short period, the local node gets information from all of its neighbors. suppose the original stream is divided into 4 sub-streams. figure 3 shows the information that peerp collects from all of its neighbors. the information on the left side of every connection presents the sub-streams neighbors can provide and their latency starting from the stream source node, while the number on the right side presents how many sub-streams the neighbor’s available upload bandwidth can support. for example, in figure 3, neighbor p1 can provide sub-stream 1, 2, 3 respectively, and the corresponding latency is 3, 4, 4 hops respectively. meanwhile the available bandwidth between p1 and p can support 2 sub-streams. 3.2 constructing weighted bipartite graph more than one neighbors can provide the same sub-stream, so we should make a decision which neighbor is more suitable as a provider for one sub-stream. here we transform the substream scheduling problem into the matching problem of weighted bipartite graph. first, we construct the weighted bipartite graph, according to the information collecting from neighbors. the weighted bipartite graph can be expressed by a quadruple group g = (x, y, e, w), where x presents the set of sub-stream providers, y presents the set of sub-streams, e presents the set of connections between x and y, and w presents set of weight on each connection. there is at most one connection between any two elements in x and y respectively. so if a neighbor’s upload bandwidth can support several sub-streams, we should characterize this situation in our bipartite graph. here, we allow same elements to coexist in set x to deal with this situation. if neighbori’s available upload bandwidth can support c sub-streams, then we copy c same elements of neighbori into set x. when all neighbors are put into set x, we complete the construction of set x. next we build the connections between x and y. if any element x∈ x can provide sub-stream y∈ y, then a connection element e = (x, y) is added into connection set e. the corresponding weight of connection e is the hops of sub-stream y from stream source to provider x. we use the example in figure 3 to illustrate the construction of weighted bipartite graph. neighbor p1’s available upload bandwidth can support 2 sub-streams, so there are 2 p1 elements in the set x, as shown in figure 4. neighbor p1 can provide sub-stream 1, 2, 3 respectively, and the corresponding latency is 3, 4, 4 hops respectively. accordingly, in the weighted bipartite graph, both of the two provider p1 have connections with sub-stream 1, 2, 3 respectively, and the weight on each connection is 3, 4, 4 respectively. the whole weighted bipartite graph transformed from figure 3 is shown in figure 4. the number on each edge presents the weight of each edge. towards low delay sub-stream scheduling 731 3.3 minimum delay, maximum matching algorithm (mdmma) after the weighted bipartite graph has been constructed, we try to find the best matching. here we mainly consider two targets: first, more sub-streams should be transmitted in parallel, because this can make use of peers’ upload bandwidth as much as possible, speeding up the transmission of the media content; second, hops of each sub-stream from source to the node should be as little as possible, because this can lead to lower delay. existent matching algorithms of bipartite graph only care one of the two targets. in this paper, we enhance the hungarian algorithm which is the well known algorithm to solve the maximum matching of bipartite graph. the ameliorated hungarian algorithm first insures that the matching is the maximum matching, and then it tries to find the lowest latency matching. the algorithm in detail is described in table 1. the main changes occur in step 1.2 and step 1.4. in step 1.2, when there are several candidate "uncheck" providers, we choose the provider whose connected edge has the minimum weight. in step 1.4, to find the augment chain, we start from the vertex whose connected edge has the minimum weight, making sure that the edge with the lowest weight is put into the matching. although the running time of the algorithm is o(n3), n is usually is less than 30, so the peer can find the appropriate neighbors quickly. table 1 min delay max matching algorithm min delay max matching input g = (x, y, e, w) output m 0 set m = ϕ , and set all vertexes in g with no label. 1.1 let s = {x/x ∈ x, and exist y ∈ y, (x, y) ∈ m}. if x/s = ϕ , then finish; else label every elements x ∈ x/s with "-1" and "uncheck". 1.2 if all x ∈ x is checked, finish; else choose the "uncheck" xi whose connected edge has the minimum weight. 1.3 if all y ∈ {y/y ∈ y, and (xi, y) ∈ e} have been labeled, then label xi with "checked", goto step 1.2. 1.4 let p = {y/y ∈ y, and (xi, y) ∈ e, and y is not labeled}, label all y ∈ p with "i". let q = {y/y ∈ y, and exist x ∈ x, (x, y) ∈ m}. if p/q ̸= ϕ, then choose yj ∈ p/q whose connected edge has the minimum weight, goto step 2; else for every yj ∈ p, label xp((xp, yj) ∈ m) with "j" and "unchecke", label xi with "checked", goto step 1.2. 2 find the augment chain c starting from yj until found x(∈ s) with label "-1", m = m ⊕ c, cancel labels of all vertex in g, goto step 1.1. figure 4: weighted bipartite graph figure 5: min delay, max matching the ameliorated hungarian algorithm is heuristic. it can insure that the matching is the maximum matching, but it can’t insure that the matching is minimum delay matching theoretically. applying the min delay max matching algorithm to the weighted bipartite graph in figure 4, we get the following matching as shown in figure 5. according to the result, neighbor p1 provide sub-stream 1, 2 to peer p, and neighbor p3 provides sub-stream 3, 4 to peer p 732 w. guofu, d. qiang, w. jiqing, b. dongsong, d. wenhua respectively. 3.4 broadcasting new sub-stream information when a node decides to subscribe which sub-stream from which neighbor, it sends sub-stream request to the appointed neighbor. if the neighbor rejects the request, it cuts the matching connections in the weighted bipartite graph, and finds another provider with lowest latency. it requests again until it subscribe the sub-stream successfully or there is no useful connection. then it broadcasts the subscribed sub-streams to its neighbors. the hop of each sub-stream in the information packets is adding the accepted hops by 1. when a neighbor accepts the request, it sends out the content blocks belonging to the desired sub-stream in the push window. if there is sub-stream(s) which can’t find proper provider, the node should find more new neighbors. 3.5 other discussion monitor neighbor’s available upload bandwidth. we measure the interval of consecutive packets of the same sub-stream monitor neighbor’s upload bandwidth. if the interval remains changeless (or change slightly), we suppose the bandwidth of the neighbor is affluent. if the interval changes greatly, we suppose the bandwidth of the neighbor is deficient. the rational reason behind this conclusion is that: if the bandwidth is affluent, there will no queue in the neighbors, little burst will happen, so the interval is changeless; if the bandwidth is deficient, burst will often happen, which leading to changing intervals. request missing blocks from neighbors with affluent bandwidth. packets in transmission may be lost. when the missing content blocks enter into the pull widow, we request the block explicitly from the neighbor with affluent available upload bandwidth. sub-stream re-scheduling. due to network dynamics, neighbors’ upload capacity may fluctuate. when a neighbor is not competent as a sub-stream provider, a new provider should be chosen. also we choose the appropriate provider from the neighbors with affluent. the neighbor who can provide sub-stream with fewer hops has higher priority. 4 performance evaluation 4.1 simulation settings we use the random model of gt-itm [11] to generate the underlying topology with 5000 routers. each link transmitting delay is set with a uniform random variable within [10ms, 100ms]. we randomly choose 2000 routers and connect one peer with one router. a peer randomly selects 10 to 15 other nodes as its neighbors to construct the mesh overlay. we set the playback rate of the original stream is 512kbps, and divide the stream into 32 sub-streams, each sub-stream with rate of 16kbps. the size of each content block is 1k bytes. the upload capacity of video source is 5 mbps. all peers are assumed to be dsl users with three type of available upload bandwidth of 1 mbps, 512 kbps and 256 kbps. these three types of peer represent 15%, 60% and 35% of the total peers. we suppose peer’s download capacity is infinite. we run the simulation in the environment of matlab7.0. 4.2 simulation result here we compare our algorithm mdmma with the pure pull-based method. we mainly care about the transmission delay of the stream. figure 6 shows the cumulative distribution function of the content block transmission delay of the two methods in steady state. we can see that our towards low delay sub-stream scheduling 733 figure 6: content block transmission delay cdf algorithm mdmma reduce the transmission delay greatly. for example, when 90% of the nodes get the content blocks, it only costs less than 50 seconds in our method, while in the push-based method, it costs more than 80 seconds. we can conclude that our proposed method is much better than pure pull-based method in the delay performance. 5 conclusion p2p streaming system consists of two components: (i) overlay construction mechanism, and (ii) content scheduling mechanism. studies show that mesh-based system is better than tree-based system especially in high churn rate of node. in this paper, we present new scheduling mechanism to improve the performance of mesh-based p2p streaming systems. our contribution includes two folds: first, we transform the sub-stream scheduling problem into the matching problem of the weighted bipartite graph; second, we present a minimum delay, maximum matching algorithm. not only the maximum matching is maintained, but also the transmission delay of each sub-stream is as low as possible. simulation shows that our method can reduce the transmission delay greatly. bibliography [1] pplive, http://www.pplive.com/. [2] bittorrent, http://bitconjuer.com/. [3] x.zhang, j.liu, and et al. "coolstreaming/donet: a data-driven overlay network for efficent media streaming". in proc. of infocom 2005, us, pp.2102-2111, mar.2005. [4] v.pai, k.kumar, and et al. "chainsaw: eliminating trees from overlay multicast". peer-topeer system ˘ô, pp.127-140, nov.2005. [5] n.magharei, r.rejaie, and y.guo. "mesh or multiple-tree: a comparative study of p2p live streaming services". in proc. of infocom 2007, usa, pp.1424-1432, may.2007. [6] jm.li, c.k.yeo, and b.s.lee. "peer-to-peer streaming scheduling to improve real-time latency". in proc. of multimedia and expo, china, pp.36-39, jul.2007. [7] m.zhang, y.q.xiong, and et al. "optimizing the throughput of data-driven peer-to-peer streaming". ieee transactions on parallel and distributed systems, vol.20, no.1,pp.97110, may.2008 734 w. guofu, d. qiang, w. jiqing, b. dongsong, d. wenhua [8] m.zhang, j.g.luo, and et al. "a peer-to-peer network for live media streaming using a push-pull approach". in proc. of the 13th annual acm internatioan conference on multimedia, singapore, pp.287-290, 2005. [9] t.locher, r.meier, and et al. "push-to-pull peer-to-peer live streaming". in proc. of disc 07, germany, pp.388-402, 2007. [10] z.j.li, y.yu, and et al. "towards low redundancy push-pull p2p live streaming". in proc. of of acm sigcomm 2008 demo, usa, aug. 2008. [11] k.c.ellen, w.zegura and s.bhattacharjee. "how to model an internetwork". in proc. of infocom 1996, usa, pp.594-602, 1996. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 149-160 a neural approach of multimodel representation of complex processes nesrine elfelly, jean-yves dieulot, pierre borne abstract: the multimodel approach was recently developed to deal with the issues of complex processes modeling and control. despite its success in different fields, it still faced with some design problems, and in particular the determination of the models and of the adequate method of validities computation. in this paper, we propose a neural approach to derive different models describing the process in different operating conditions. the implementation of this approach requires two main steps. the first step consists in exciting the system with a rich (e.g. pseudo random) signal and collecting measurements. these measurements are classified by using an adequate kohonen self-organizing neural network. the second step is a parametric identification of the base-models by using the classification results for order and parameters estimation. the suggested approach is implemented and tested with two processes and compared to the classical modeling approach. the obtained results turn out to be satisfactory and show a good precision. these also allow to draw some interpretations about the adequate validities’ calculation method based on classification results. keywords: complex processes, modeling, multimodel approach, kohonen map 1 introduction nowadays, technological developments increase the complexity of systems. this complexity can be caused by non linearity, non stability, wide operating domain, variations of system parameters or external perturbations. as a result, it is often difficult or even impossible to propose a simple model which could reckon with the whole process complexity by using physical laws. consequently, it is very useful to focus on advanced and practical approaches in order to handle this complexity. the multimodel approach has recently been developed and applied in several science and engineering domains. it was proposed as an efficient and powerful method to cope with modeling and control difficulties when complex non linear and/or uncertain processes are concerned. the multimodel approach supposes the definition of a set of models. then, it becomes possible to replace the unique model by a set of simpler models thus making a so-called models’ base. each model of this base describes the behavior of the considered process at a specific operating point. the multimodel approach objective is to decrease the process complexity by its study under certain specific conditions. several researchers have been interested in multimodel analysis and control approaches [7, 8, 19] and many applications have been proposed in different contexts. in spite of its success in many fields (academic, biomedical, . . . ), the multimodel approach remains confronted with several difficulties such as the calculation of models’ validities, the adequate technique of fusion or switching between models as well as the determination of the models’ base. indeed, in 1985, takagi and sugeno [21] suggested a fuzzy process representation by the contribution of local models. this approach has been applied in many fields of activities but often faced problems related to the lack of information about the system structure or the incertitude of its parameters. besides, it can sometimes lead to a large number of models which generates a high computational burden when designing the control algorithm. this has led several researchers to develop other approaches in order to cope with these difficulties. for uncertain complex systems with bounded parameters, some approaches were developed for the determination of the models’ base [9, 11]. an extension for uncertain discrete systems has been proposed by mezghani [13]. the case of multivariable systems was addressed by raissi [16]. the approaches, copyright © 2006-2008 by ccc publications 150 nesrine elfelly, jean-yves dieulot, pierre borne previously named, require the knowledge of parameters’ variations limits, which is generally not possible, in particular in case of uncertain systems for which parameters variations domains are unknown or ill-known. other related studies [12, 19] suggest the system be described by a set of local models often defined by using a reference model and some linearization methods. in another context, both fuzzy logic and neural networks were carried out for the multimodel control [1, 18]. an idea was to use neural approaches for complex systems modeling. these methods have the advantage of requiring very little information about the considered process and are useful for uncertain systems. within this context, very few studies [2, 3, 15, 17, 20, 22] were proposed but they don’t address strongly non-linear systems. in this paper, a neural approach for the determination of the models’ base for uncertain complex systems is proposed, in particular those which exhibit strong non-linearities. the proposed approach requires a priori little knowledge about the considered system; only input/output information can be sufficient. in the following section, the different steps of the proposed modeling approach are detailed. two simulation examples and some interpretations are then presented. the evaluation of the suggested modeling strategy is the topic of the last section. we finish the present paper by a conclusion. 2 complex systems’ modeling: neural approach in this section, a models’ base-determination approach for uncertain complex systems is described. this approach requires neither a global model definition nor the knowledge of parameters variations domains; only input/output information are needed. the suggested approach allows the generation of the base-models’ structure and parameters. the application of this approach requires first to classify the numerical data by exploiting a kohonen map and to determine the number of models. secondly, a structural and parametric identification of different base-models is carried out by using classification results. then, the validity index of each model is computed. finally, the multimodel output is obtained by the fusion of the models’ outputs weighted by their validity indexes. 2.1 classification of the numerical data by using a kohonen map as a first step, the output or input/output measurements collected from the considered process have to be classified in order to identify operating clusters from which the models’ base will be deduced. this classification is carried out by using a self-organizing kohonen map. self-organizing kohonen map methodology the self-organizing map (som) represents a specific kind of neural networks. in its original form, the som was invented by the founder of the neural networks research center, professor teuvo kohonen in 1981-82. the special property of the som is that it effectively creates spatially organized internal representations of various features of input signals and their abstractions [10]. the schematic representation of this network is given in figure 1. neurons in the target layer are ordered and correspond to cells of a bi-dimensional map. every neuron of the input layer is connected to every neuron of the output layer. the classification strategy consists in applying the kohonen rule. this rule is characterized by an unsupervised competitive learning where a competition takes place before the modification of the network-weights. only the neurons which win the competition have the right to change their weights. the kohonen rule works as follows: a neural approach of multimodel representation of complex processes 151 figure 1: kohonen map weights are initialized to random values; an input vector is presented to the network; the distance between the input vector and weights connecting inputs to each output neuron is computed; the neuron corresponding to the smallest distance, i.e. the nearest to the input vector, wins the competition, weights connecting inputs to this neuron are modified accordingly [5]. this procedure is repeated several times until weights stabilize. at the end of the learning stage, the representative vectors of different clusters and their centers are obtained. determination of the operating-system clusters the determination of the operating-system clusters requires firstly that the considered system be excited. secondly, the number of clusters has to be determined. the third step consists in classifying data by using a kohonen neural network. the first step consists in applying an input signal and then collecting (output or input/output) measurements that will be used for classification. the excitation signal must be rich enough and persistently exciting with well-chosen parameters in order to allow a full excitation of the operating dynamics, and to take in consideration the non-linear aspect of the considered process. for the second step, the method proposed by talmoudi has been adopted for the determination of the adequate number of clusters which corresponds to the number of models [22]. within this scope, a kohonen network, with an important number n of neurons in the output layer, has been considered. at the end of the learning procedure, if the repartition of classes is not good, the clusters i having a number of elements nci verifying the relation (1), will have to be removed [22]. nci ≤ 1 2 nh n , (1) where nh represents the number of the considered measures. else, the number of neurons in the output layer is increased and training is restarted. the same procedure is repeated over and over until the satisfactory number of clusters is obtained. afterwards, the data classification is tackled by using a kohonen network for which the number of neurons in the input layer equals the number of system-variables to be considered and the number of neurons in the output layer equals the number of clusters determined with the help of the method previously described. the classification results will then be exploited for the identification of the different base-models. 2.2 parametric identification of the base-models in this section, the orders of the models are estimated in a first step. the chosen method is the socalled instrumental determinants’ ratio-test. this method is mainly based on the conditions concerning 152 nesrine elfelly, jean-yves dieulot, pierre borne a matrix called "information matrix" which contains the input/output measurements [4]. this matrix is described as follows: qm = 1 nh nh ∑ k=1   u(k) u(k + 1) u(k −1) ... u(k −m + 1) u(k + m)   [ y(k + 1) u(k + 1) ··· y(k + m) u(k + m) ] , (2) where nh is the number of observations. the instrumental determinants’ ratio (rdi) is given by: rdi(m) = ∣∣∣∣ det(qm) det(qm+1) ∣∣∣∣ . (3) for every value of m, the determination procedure of the order consists in building the matrices qm and qm+1 and in evaluating the ratio rdi(m), the retained order m is the value for which the ratio rdi(m) quickly increases for the first time. as a second step, the parametric identification issue consists in calculating the values of the parameters of the corresponding model-equation, given several experimental measures which describe the dynamic behavior of the system. as previously mentioned, the data classification gives a certain repartition of clusters. for each cluster, input/output measurements are collected. these measurements allow the identification of the corresponding model. for this, the recursive least-squares method (rls) [4] was applied to achieve the parameters estimation. 2.3 computation of validities the validity coefficient is a number belonging to the interval [0 1]. it represents the relevance degree of each base-model calculated at each instant. in literature, several methods have been proposed to deal with the validity issue. in our study, the residues’ approach was adopted for the calculation of validities. this method is based on the distance measurement between the process and the considered model. for example, the residue can be given by the following expression: ri = |y−yi| i = 1, . . . , n (4) where: n: number of base-models; y: process output; yi: output of the model mi. if this residue value is equal to zero, the corresponding model mi perfectly represents the process at that time. on the contrary, a non null value translates the fact that the model mi represents the system partially. the normalized residues are given by: r′i = ri ∑nj=1 r j . (5) within the context of the residues’ approach, several methods have been proposed for the calculation of validities [6, 13, 14]. only two methods will be considered: the simple and the reinforced validities. the validities are given by: vi = 1−r′i . (6) the simple and reinforced validities are defined by using the following formulas. a neural approach of multimodel representation of complex processes 153 simple validities: the normalized simple validities are defined so that their sum must be equal to 1 at each time: vsimpi = vi n −1 . (7) reinforced validities: for this type of validities, the reinforcement expression is introduced as: v ′ren f i = vi n ∏ j =1, j 6=i (1−v j) . (8) the normalized reinforced validities could be written as follows: vren fi = v ′ren f i ∑nj=1 v ′ren f j . (9) 2.4 computation of the multimodel output the multimodel output is calculated by a fusion of the models’ outputs weighted by their respective validity indexes, as illustrated by the following expression: ymm(k) = n ∑ i=1 yi(k) vi(k) . (10) vi(k) could be a simple or a reinforced validity for which ∑ni=1 vi(k) = 1. 3 simulation examples in order to underline the interest and the performance of the proposed approach, some simulation examples are carried out. 3.1 example 1: second order discrete system the considered system is a complex discrete system whose evolution is described by the following equation: y(k) = −a1(k) y(k −1)−a2(k) y(k −2) + b1(k) u(k −1) + b2(k) u(k −2) . (11) the variation laws of different parameters of the process are given by: a1(k) = 0.04 sin (0.035k)−0.8 , a2(k) = 0.005 sin (0.03k) + 0.1 , (12) b1(k) = 0.02 sin (0.03k) + 0.5 , b2(k) = 0.01 sin (0.035k) + 0.2 . (13) first, the system is excited by a uniform random signal u(k ). then, the measurements y(k ) and y(k−1) are collected at different instants. the adequate number of clusters determined by using the method described in paragraph 3.1.2, is equal to three. the numerical data are fed into a kohonen network which presents an input layer with two neurons and three neurons in the output layer. the classification results are given in figure 2. from each of the three data sets relative to the various clusters, the orders and the parameters of the transfer functions relative to the three base-models are estimated. figure 3 shows the evolutions of the rdii(m) for the three models. 154 nesrine elfelly, jean-yves dieulot, pierre borne 2.5 3 3.5 4 4.5 2 2.5 3 3.5 4 4.5 y(k−1) y (k ) clusters’ centers figure 2: classification results 1 2 3 4 0 0.5 1 1.5 2 2.5 x 10 5 rdi 1 (m) 1 2 3 4 0 2 4 6 8 x 10 4 rdi 2 (m) 1 2 3 4 0 1 2 3 4 x 10 4 rdi 3 (m) m 1 =2 m 2 =2 m 3 =2 figure 3: evolutions of the rdi for the three base-models it appears clearly that the estimated orders of the three models are equal to two. this result is predictable since the considered system is a second order one. the application of the recursive leastsquares method allows us to write the following transfer functions: f1(z −1) = 0.33549 z−1 −0.047793 z−2 1−0.83697 z−1 −0.039754 z−2 , (14) f2(z −1) = 0.41086 z−1 −0.021659 z−2 1−0.8633 z−1 −0.055068 z−2 , (15) f3(z −1) = 0.36985 z−1 −0.0079934 z−2 1−0.9234 z−1 −0.060727 z−2 . (16) in order to evaluate the obtained global model, a validation step is worked out where other inputs which are different from those used for classification are fed into the system. then, the real and the multimodel outputs are compared. let us consider the following input sequence: u(k) = 1 + sin (0.08k) . (17) the validation results are given in figures 4 and 5. y: real output of the system. ymmr, ymms: multimodel outputs obtained by using respectively the methods of reinforced and simple validities. ymmr (k) = n ∑ i=1 yi(k) v ren f i (k) , ymms (k) = n ∑ i=1 yi(k) v simp i (k) . (18) er, es: relative errors between the real and the multimodel outputs. er(k) = ∣∣∣∣ y(k)−ymmr (k) y(k) ∣∣∣∣ , es(k) = ∣∣∣∣ y(k)−ymms (k) y(k) ∣∣∣∣ . (19) a neural approach of multimodel representation of complex processes 155 0 100 200 300 400 500 0 1 2 3 4 5 6 time y (t ), y m m r( t) , y m m s (t ) ymm s (t) y mm r (t) y(t) figure 4: real and multimodel outputs 0 100 200 300 400 500 0 5 10 time e r( t) , e s (t ) e s (t) e r (t) figure 5: evolutions of the relative errors it can be seen that the multimodel output, obtained by the fusion of the base-models’ outputs weighted by the reinforced validities, follows the real output with a negligible error (er(t)). this error is more important when applying the simple validities method (es(t)). this allows to conclude that, for this kind of system, the reinforced validities method is more appropriate than the simple validities one. 3.2 example 2: second order continuous system with input/output-dependent parameters as a second simulation example, we consider the system whose evolution is described by the following equations: a0y + a1ẏ + ÿ = u + bu̇ , (20) a0(y) = 0.3 + sigm(y−2) , a1(y) = sat(y2) , b(u) = sat(u) , (21) sigm is the sigmoid function; sat is the saturation function. the considered system is complex and strongly non-linear with parameters being functions of both the input and the output, which makes the modeling task difficult. the chosen excitation signal (figure 6) is a sine curve distorted by a random uniform signal since this input is richer than a simple random signal and allows considering the complex and non-linear aspects of the system. it is worthy to note that signal parameters (frequency, amplitude) need to be adjusted in order to obtain good results. 0 2 4 6 8 10 −2 −1 0 1 2 time u (t ) figure 6: evolution of the excitation signal moreover, in order to describe the system dynamics, the number of variables used for classification is increased. in fact, not only the output data y(k ) and y(k − 1) are considered but also the input data 156 nesrine elfelly, jean-yves dieulot, pierre borne u(k − 1). after generating the output, a sampling of the input and output signals followed, with an adequate sampling period, in order to collect the different measurements: y(k ), y(k−1), and u(k−1). once the number of clusters determined, the numerical data are presented to a kohonen network owning three neurons in both input and output layers. at the end of the learning procedure, three data sets (figure 7) are obtained, each of which being used for the identification of the corresponding model. −0.5 0 0.5 1 1.5 2 −1 0 1 2 −2 −1 0 1 2 y(k−1)y(k) u (k − 1 ) clusters’ centers figure 7: three data sets relative to the different clusters the application of the instrumental determinants’ ratio-test method allows us to determine the three models’ orders which are respectively: two, three and two. the corresponding transfer functions are given by the following expressions: f1(z −1) = 0.058303 z−1 + 0.052302 z−2 1−0.43246 z−1 −0.45748 z−2 , (22) f2(z −1) = −0.023815 z−1 + 0.0013593 z−2 + 0.031481 z−3 1−0.68802 z−1 −0.24025 z−2 −0.072128 z−3 , (23) f3(z −1) = 0.12995 z−1 −0.014359 z−2 1−0.64023 z−1 −0.37958 z−2 . (24) the application of the following input sequence is the purpose of the validation step: u(k) = 1 + sin (k) . (25) the validation results are illustrated by the figures 8 and 9. 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 time y (t ), y m m r( t) , y m m s (t ) ymm s (t) y(t) ymm r (t) figure 8: real and multimodel outputs 0 2 4 6 8 10 0 0.2 0.4 0.6 0.8 time e r (t ), e s( t) e s (t) e r (t) figure 9: evolutions of the relative errors contrary to the first example, the results obtained by the application of the simple validities’ method are much better than those given by the reinforced validities’ method. in fact, the figures 8 and 9 show that the multimodel output ymms (t), deducted by fusion of the base-models’ outputs weighted by the simple validities, follows almost perfectly the real output with an error es(t) nearly null compared to the a neural approach of multimodel representation of complex processes 157 error er(t) which is relatively important. in order to give prominence to the capacity of the identified models to reproduce the operating system in different domains, let us consider another input sequence given by: u(k) = 1.2 + 1.5 sin (2k) . (26) the multimodel output is generated by application of the simple validities’ method. the result is given in figure 10. 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 time y( t), y m m s(t ) y mm s (t) y(t) figure 10: real and multimodel outputs (second validation) 3.3 interpretations referring to the obtained simulation results, it can be noticed that the application of the suggested approach allows a good modeling of the considered systems. we can also make an important interpretation about the use of the validities’ calculation methods. as remarked before, in some cases simple validities give better results and in other cases it is preferable to use reinforced validities. so, more simulations and observations were worked out to conclude that the choice of the validities’ calculation method depends on the classification results i.e. the clusters structure and repartition. thus, it can be noted that when there are several variations in the same cluster and when an overlapping between clusters occurs, which is the case in the second simulation example (figure 7), it is worth to use the simple validities’ method since it takes account of different models’ outputs refering to the expression (7). in this case, no model could represent ideally the process at any time. but when the clusters present very few variations and are well separated (figure 2), the reinforced validities’ method is better-adapted. the application of this method, thanks to the reinforcement expression (8), promotes the contribution of the most dominant model which represents at best the process behavior. 4 evaluation of the suggested modeling strategy in order to highlight the interest and the performance of the proposed modeling strategy, the classical modeling approach involving the identification of a global model was carried out. then, the results given by the suggested approach were compared to those given by classical modeling strategy. let us consider the second simulation example described by the equations (20) and (21) and the excitation signal given by figure 6. by using the instrumental determinants’ ratio-test method for the estimation of the order and the recursive least-squares method for the parametric identification, the transfer function f(z−1) of the global model is given by the following expression: f(z−1) = 0.03601 z−1 + 0.0035236 z−2 1−0.55726 z−1 −0.42595 z−2 . (27) by using the same numerical data, the classification and the identification steps give the three models described by equations (22), (23) and (24). the input sequence given by the equation (25) was applied 158 nesrine elfelly, jean-yves dieulot, pierre borne again. figure 11 represents the evolutions of the real, the multimodel and the global model outputs. the multimodel output is obtained by fusion of the models’ outputs weighted by their simple validities degrees calculated at each instant. 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 3 time y( t), y m m s(t ), y g (t) y g (t): global model output y(t): real output y mm s (t): multimodel output figure 11: evolutions of the real, multimodel and global model outputs the adopted multimodel approach offers a very satisfactory precision compared to the case of the classical modeling approach based on the unique global model. 5 conclusion in this paper, a neural approach of multimodel representation is proposed. this approach is applicable when dealing with complex, strongly non-linear and uncertain processes. it allows the determination of the models’ base by using a kohonen network and two methods of structural and parametric identification. the different steps were detailed. the multimodel output is obtained by using the technique of fusion and the adequate validities’ computation method. the suggested approach has been implemented and tested for different complex systems. simulation results, two of which were described in this paper, prove the efficiency and the precision of the proposed modeling strategy and show that the method works well with various processes even when highly complex. some interpretations have been made about the choice of the adequate validities’ calculation method to be applied. furthermore, in order to demonstrate the performance and the relevance of the suggested approach, a comparison with the classical modeling approach has been made. bibliography [1] m. a. al-akhras, g. m. aly, r. j. green, neural network learning approach of intelligent multimodel controller, iee proceedings on control theory and applications, vol. 143, pp. 395-400, 1996. [2] i. s. baruch, r. l. beltran, j-l. olivares, r. m. garrido, a fuzzy-neural multi-model for mechanical systems identification and control, mexican international conference on artificial intelligence n. 3, vol. 2972, pp. 774-783, 2004. [3] i. s. baruch, f. thomas, j-m. flores, e. gortcheva, a multimodel recurrent neural network for systems identification and control, ieee international joint conference on neural networks, vol. 2, pp. 1291-1296, 2001. [4] r. ben abdennour, p. borne, m. ksouri, f. m’sahli, identification et commande numérique des procédés industriels, editions technip, paris, france, 2001. a neural approach of multimodel representation of complex processes 159 [5] p. borne, m. benrejeb, j. haggège, les réseaux de neurones, editions technip, paris, france, 2007. [6] f. delmotte, l. dubois, p. borne, a general scheme for multi-model controller using trust, mathematics and computers in simulation, vol. 41, pp. 173-186, 1996. [7] a. el kamel, m. ksouri-lahmari, p. borne, contribution to multimodel analysis and control, studies in informatics and control, vol. 9, pp. 29-38, 2000. [8] t. a. johansen, b. a. foss, editorial: multiple model approaches to modelling and control, international journal of control, vol. 72, pp. 575, 1999. [9] z. kardous, a. el kamel, n. ben hadj braiek, p. borne, on the quadratic stabilization in discrete multimodel control, ieee conference on control applications, vol. 2, pp. 1398-1403, 2003. [10] t. kohonen, the self-organizing map, ieee proceedings, vol. 78, pp. 1464 1480, 1990. [11] m. ksouri-lahmari, p. borne, m. benrejeb, multimodel: the construction of model bases, studies in informatics and control, vol. 3, pp. 199-210, 2004. [12] d. j. leith, w. e. leithead, analytic framework for blended multiple model systems using linear local models, international journal of control, vol. 72, pp. 605-619, 1999. [13] s. mezghani, a. el kamel, p. borne, multimodel control of discrete systems with uncertainties, international journal of studies in informatics and control, 2000. [14] k. s. narendra, j. balakrishman, adaptative control using multiple models, ieee transactions on automatic control, vol. 42, pp. 171-187, 1997. [15] j. c. principe, l. wang, m. a. motter, local dynamic modeling with self-organizing maps and application to nonlinear system identification and control, ieee proceedings, vol. 86, pp. 22402258, 1998. [16] t. raissi, a. el kamel, w. byrski, p. borne, multimodel analysis and control of multivariable systems, ieee international conference on systems man and cybernetics, vol. 1, pp. 640-645, 2001. [17] m. ronen, y. shabtai, h. guterman, hybrid model building methodology using unsupervised fuzzy clustering and supervised neural networks, biotechnology and bioengineering, vol. 77, n. 4, pp. 420-429, 2002. [18] n. sadati, a. talasaz, robust fuzzy multimodel control using variable structure system, ieee conference on cybernetics and intelligent systems, vol. 1, pp. 497-502, 2004. [19] r. schorten, r. marry-smith, r. bjorgan, h. gollee, on the interpretation of local models in blended multiple model structures, international journal of control, vol. 72, pp. 620-628, 1999. [20] r. r. selmic, f. l. lewis, multimodel neural networks identification and failure detection of nonlinear systems, ieee conference on decision and control, vol. 4, pp. 3128-3133, 2001. [21] t. takagi, m. sugeno, fuzzy identification of systems and its applications to modelling and control, ieee transactions on systems man and cybernetics, vol. 15, pp. 116-132, 1985. [22] s. talmoudi, r. ben abdennour, a. kamel, p. borne, a systematic determination approach of a models base for uncertain systems: experimental validation, ieee international conference on systems man and cybernetics, vol. 6, pp. 73-81, 2002. 160 nesrine elfelly, jean-yves dieulot, pierre borne nesrine elfelly1, jeans-yves dieulot2, pierre borne3 1université des sciences et technologies de lille (ustl), 2ecole polytechnique de lille, 3ecole centrale de lille (ec lille) laboratoire d’automatique, génie informatique et signal ecole centrale de lille, cité scientifique bp 48 59651 villeneuve d’ascq cedex, france e-mail:nesrine.elfelly@ed.univ-lille1.fr,jean-yves.dieulot@polytech-lille.fr,pierre.borne@ec-lille.fr received: december 4, 2007 nesrine elfelly was born in monastir, tunisia in 1982. she received the engineer diploma degree in computer engineering from the "ecole nationale d’ingénieur de tunis" (tunisia) in 2006. she obtained the master of automatic control, computer engineering and image processing from the "ecole centrale de lille" (france) in 2007 and the master of automatic control and signal processing from the "ecole nationale d’ingénieur de tunis" in the same year. she is currently a phd student in automatic control and applied computing within the framework of lagisustl and lara-enit cooperation. her current research interests are the applications of neural approaches for complex systems analysis and control. jean-yves dieulot graduated from french ecole d’ingénieurs institut industriel du nord in 1990, and obtained a phd from the university of lille in 1993. he is currently associate professor at ecole polytechnique universitaire de lille, france, and with lagis (laboratory of automatic control). his main interests are in fuzzy, nonlinear and robot control. pierre borne was born in corbeil, france in 1944, he received the master degree of physics in 1967, the masters of electronics, of mechanics and of applied mathematics in 1968. the same year he obtained the diploma of "ingénieur idn" (french "grande ecole"). he obtained the phd in automatic control of the university of lille in 1970 and the dsc in physics of the same university in 1976. he became doctor honoris causa of the moscow institute of electronics and mathematics (russia) in 1999, of the university of waterloo (canada) in 2006 and the polytechnic university of bucharest (romania) in 2007. he is author or coauthor of about 200 journal articles and book chapters, of 35 plenary lectures and of about 250 communications in international conferences. he has been the supervisor of 69 phd thesis and is author of 20 books. he is fellow of ieee and has been president of the ieee/smc society in 2000 and 2001. he is presently professor "de classe exceptionnelle" at the ecole centrale de lille and director of the french "plan pluriformation" of research in automatic control. international journal of computers, communications & control vol. ii (2007), no. 4, pp. 388-402 a toolbox for input-output system inversion antonio visioli, aurelio piazzi abstract: in this paper a matlab-based toolbox for the input-output system inversion of linear systems is presented. different methods, based either on analytical or numerical approaches, are implemented. the toolbox can be exploited in the design of a feedforward action for control systems in different contexts in order to improve performances in the set-point regulation. the use of a pre-actuation and a post-actuation time can be easily analyzed as well as the role played by the choice of the desired output function. keywords: cacsd, input-output inversion, feedforward, set-point regulation, optimization. 1 introduction it is well-known that a (properly designed) feedback controller provides robustness to a control system with respect to parameter variations and allows to compensate for external disturbances. on the other side, a high performance in the set-point following task can be achieved by adopting a suitable feedforward action. indeed, the proper design of a control system consists of suitably combining feedback and feedforward control. different techniques have been developed for the synthesis of a feedforward controller for a linear system (see e.g. [1, 2, 3]). when the set-point regulation is of concern, a (noncausal) system inversion approach has been proven to be effective in this context [4]-[13]. basically, the approach consists of selecting a desired output function in order to achieve a transition from a current output value y0 to a new one y1 and then to determine the corresponding input function by applying a stable inversion procedure. then, the calculated input function is adopted as a reference command input to the (closed-loop) system, instead of the typical step signal. actually, while many software packages are available for the synthesis of feedback controllers (for example, via root locus techniques or bode plots), they are not available for the synthesis of a system inversion based feedforward action. indeed, the presence of a computer aided control systems design tool makes the applicability of these (somewhat complex) techniques much easier and it can be exploited to understand deeply the role of the command function in the context of set-point regulation. in this paper a matlab-based toolbox for the input-output system inversion of a linear system is presented. different techniques in this context are considered, related both to a noncausal and a causal approach. the toolbox allows to evaluate the role of the use of a pre-actuation and a post-actuation time as well as the role of the choice of the desired output function. it can be adopted as a useful tool in different fields, such as robust control, process control and control of mechatronic systems. the paper is organized as follows. in section 2 the input-output system inversion approach is briefly reviewed and the different methodologies implemented in the toolbox are presented. the functions implemented in the toolbox are described in section 3 and application examples are shown in section 4. conclusions are drawn in section 5. notation. ci denotes the set of scalar real functions that are continuous till the ith derivative and bci denotes the subset of ci of the scalar real functions that are bounded. the ith order differential operator is di. copyright © 2006-2007 by ccc publications a toolbox for input-output system inversion 389 2 input-output system inversion consider a general asimptotically stable nonminimum-phase linear system σ described by the following rational transfer function (note that this might represent the transfer function of a feedback control system): h(s) = k1 b(s) a(s) = k1 sm + bm−1sm−1 +···+ b0 sn + an−1sn−1 +···+ a0 , k1 6= 0 (1) where it is assumed that polynomials a(s) and b(s) are coprime (no pole-zero cancellations occur) and that σ has not purely imaginary zeros. the input and output of σ are u ∈ r and y ∈ r respectively and the relative order (or relative degree) of σ is ρ := n−m. the set of all cause/effect pairs associated with σ is denoted by b := {(u(·), y(·)) ∈ pc ×pc : dny + an−1dn−1y +···+ a0y = k1(dmu + bm−1dm−1u +···+ b0u)} (2) where pc denotes the set of piecewise continuous functions defined over (−∞, +∞), i.e. the real field r. in the framework of the behavioral approach, b is the behavior set of σ that can be rigorously introduced by means of the so-called weak solutions of the differential equation associated to σ [14]. the following proposition [14] is useful in the development of the subsequent analysis. proposition 1. consider any pair (u(·), y(·)) ∈ b. then, u(·) ∈ cl (r) if and only if y(·) ∈ cρ+l (r) with l being a nonnegative integer. the considered regulation problem consists of obtaining an output transition from a previous value y0 to a new value y1. without loss of generality, in the following we will consider y0 = 0. define yd (·) ∈ bck with yd (t) = 0 for t < 0 as the desired output function to obtain the transition. from a practical point of view, a transition time τ has to be defined, i.e. the desired output function is defined as yd (t) :=    0 for t < 0 y01(t) for 0 ≤ t ≤ τ y1 for t > τ. (3) then, the following stable input-output inversion (sioi) problem can be formulated. sioi problem. determine an input function ud (·) ∈ bck−ρ such that (ud (·), yd (·)) ∈ b. (4) the general solution to the sioi problem can be derived as follows [15]. first, express the inverse of the transfer function (1) as h−1(s) = 1 k1 a(s) b(s) = ξρ sρ + ξρ−1sρ−1 +···+ ξ0 + h0(s) (5) where h0(s) is a strictly proper rational transfer function representing the zero dynamics of σ. by using the fraction expansion, h0(s) can be decomposed as h0(s) = h − 0 (s) + h + 0 (s) = d(s) b−(s) + e(s) b+(s) (6) where b−(s) and b+(s) are the monic polynomials containing the roots of b(s) with negative and positive real parts, respectively. define η−0 (t) and η + 0 (t) as the analytic extensions of l −1[h−0 (s)] and l−1[h+0 (s)] over the space of the bohl functions for which η − 0 (t)1(t) = l −1[h−0 (s)] and η + 0 (t)1(t) = 390 antonio visioli, aurelio piazzi l−1[h+0 (s)] respectively. then, the solution of the sioi problem is derived as: ud (t) = ξρ dρ yd (t) +···+ ξ1dyd (t) + ξ0yd (t) + ∫ t 0 η−(t −v)yd (v)dv− ∫ +∞ t η +(t −v)yd (v)dv. (7) it is worth noting that, in general, ud (t) is defined over the time interval (−∞, +∞) and therefore, in order to practically use it, it is necessary to truncate it. thus, the input function exhibits a pre-actuation (associated with the unstable zeros) and a post-actuation (associated with the stable zeros) time intervals (see for example [16]), denoted as tp and t f respectively. they can be calculated with arbitrary precision by selecting two arbitrary small parameters ε0 and ε1 and by subsequently determining t0 := max{t′ ∈r : |ud (t)| ≤ ε0 ∀t ∈ (−∞,t′]} (8) and t1 := min { t′ ∈r : ∣∣∣∣ud (t)− 1 h(0) ∣∣∣∣ ≤ ε1 ∀t ∈ [t′, ∞) } . (9) then, it has to be fixed tp = min{0,t0} t f = max{τ,t1}. (10) hence, the actual input function to be applied to the system is given by ũd (t) :=    0 for t < tp ud (t) for tp ≤ t ≤ t f 1 y1 for t > t f . (11) alternatively, the pre-actuation and post-actuation time intervals can be calculated as [4] tp = − 10 drhp t f = 10 dlhp (12) where drhp and dlhp are the minimum distance of the right and left half plane zeros respectively from the imaginary axis of the complex plane. it is worth noting that the pre-actuation time is zero when there are no unstable zeros and the postactuation time is zero when there are no stable zeros. in general, the integrals in expression (7) can not be solved analytically and therefore a numerical solution has to be determined (in this context the cavalieri-simpson’s rule can be conveniently exploited to found an accurate solution with a small computational time [15]). a notable exception occurs when the selected desired output function is a polynomial function [17], i.e. yd (t; τ) =    0 for t < 0 y1 (2k+1)! k!τ 2k+1 ∑ k i=0 (−1)k−i i!(k−i)!(2k−i+1) τ it 2k−i+1 for 0 ≤ t ≤ τ y1 for t > τ (13) note that function yd (t; τ), parameterized by the transition time τ is a ck-function over (−∞, +∞) and is strictly increasing in the interval [0, τ] so that neither overshooting nor undershooting appear in this output planning for set-point regulation. in this case an analytical solution can be found [18] and this fact can be exploited in speeding up the computational time and most of all in avoiding numerical problems. a very interesting application of the analytical stable inversion procedure is for the improvement of the set-point following performance of proportional-integral-derivative (pid) controllers. specifically, if a pid controller is employed for a first-order plus dead-time (fopdt) process, described by the following transfer function, p(s) = k t s + 1 e−ls, (14) a toolbox for input-output system inversion 391 or for an integrator plus dead-time (ipdt) process, described by the transfer function p(s) = k s e−ls, (15) then a closed-form solution of the stable input-output inversion applied to the closed-loop system can be exploited (a rational closed-loop transfer function is obtained by adopting a padè approximation) [13]. indeed, the actual command signal to be applied for a given process and a given pid controller is determined by substituting the actual value of the process and pid parameters into the resulting closedform expression. in this context the pid transfer function is expressed as c(s) = kp ( 1 + 1 tis + td s ) 1 tf s + 1 , (16) where kp is the proportional gain, ti is the integral time constant, td is the derivative time constant and tf is the time constant of the filter that is adopted to render the system proper. a polynomial output function can be also usefully exploited in determining a causal input-output inversion despite the presence of unstable zeros [9]. in particular, the order of the polynomial function is selected in order to satisfy boundary conditions so that yd (·) ∈ bck with k ≥ ρ and at the same time to have a number of free coefficients equal to the number of the unstable zeros of the system σ. then, the free parameters are determined in order to annihilate the unstable modes in the input function determined by the inversion procedure. in this way there is no need of a pre-actuation time interval and the resulting inversion is causal. it can be therefore employed when a preview time is not available in a given application. however, this is paid by the possible presence of undershoots and overshoots in the resulting output function. note that the approach can be easily extended in order to avoid also the presence of a post-actuation time interval. 3 toolbox description the designed matlab-based toolbox implements the methods described in section 2. it requires the control system toolbox and the symbolic math toolbox of matlab. the following main functions are available. [time,command,preaction,postaction]=numdyninvcs(sys,yd,tau,st) this function determines the input command function of a system that causes a desired output function by means of an input-output inversion scheme based on the use of the cavalieri-simpson’s rule for the determination of the integrals in (7) [15]. in particular, the meaning of the parameters is the following one: • sys is the transfer function of the system expressed in symbolic form (with s as a symbolic variable); • yd is the desired output function (for t ∈ [0, τ]) expressed as an array of numerical values from 0 to τ corresponding to the time instant equally spaced by the sampling time; • tau is the transition time; • st is the sampling time; • time is the output time vector; it starts from the preaction time tp but for convenience the zero time is shifted to tp; 392 antonio visioli, aurelio piazzi • command is the determined input function expressed as a numerical array corresponding to the time array time; • preaction is the pre-actuation time calculated with formula (12); • postaction is the post-actuation time calculated with formula (12). [time,command,preaction,postaction]=outdyninvcs(sys,yd,tau,st) this function operates basically as numdyninvcs with the difference that the desired output function yd is expressed as a symbolic expression with symbolic variable t. [time,command,preaction,postaction]=numdyninv(sys,yd,tau,st) this function is very similar to numdyninvcs but it performs the numerical integration by applying a rectangular rule. in order to obtain an accurate result, a small value of the sampling time has to be selected. this might result in a high computational time. [time,command,preaction,postaction]=outdyninv(sys,yd,tau,st) this function is very similar to outdyninvcs but it performs the numerical integration by applying a rectangular rule. also in this case, in order to obtain an accurate result, a small value of the sampling time has to be selected. this might result in a high computational time. [time,command,preaction,¯postaction]=dyninv(sys,y1,tau,threshold0, threshold1,st) this function solves the input-output inversion problem when the desired output function is a polynomial function (13). the order of the polynomial is automatically selected, according to proposition 1, in order to obtain a continuous input function, i.e. such as ud (·) ∈ bc0. since the input function is determined analytically, the pre-actuation and post-actuation time intervals are conveniently determined by adopting formulae (10). the function parameters that are different from those that have been already described have the following meaning: • y1 is the desired new output steady-state value (it is assumed, without loss of generality, that the current input and output steady-state values are zero); • threshold0 is the parameter ε0 in formula (8), which is adopted to calculate the pre-actuation time; • threshold1 is the parameter ε1 in formula (9), which is adopted to calculate the post-actuation time. [time,command,taunum,preaction,postaction]= optdyninv(sys,y1,constraints,threshold0,threshold1,tc) this function solves the minimum-time inversion problem that consists of finding the minimum output transition time subject to constraints posed on the input function and its derivatives until an arbitrary order l. formally, the optimisation problem is posed as follows [18]: min τ∈r+ τ (17) such that, i = 0, 1, . . . , l, |diud (t; τ)| ≤ u(i)m ∀t ∈ (−∞, +∞) (18) a toolbox for input-output system inversion 393 where the positive values u(i)m , i = 0, 1, . . . , l, are given bounds of the problem. note that the problem admits a solution if u(0)m > 1/|h(0)| and u (i) m > 0, i = 1, . . . , l. the optimisation problem is solved by applying a simple bisection algorithm in conjunction with a gridding of the time axis [17]. with respect to the function dyninv there are the following different parameters: • constraints is the array (of l + 1 elements) of the constraints for the input derivatives until the lth order; note that the user-chosen dimension of the array automatically determines the order of the constrained derivatives and therefore the order of the output polynomial function (which is determined as l −1 + ρ so that ud (·) ∈ bcl−1); • taunum is the resulting optimal transition time. it is worth noting that, if a rigorous determination of the transition time is sought, the posed optimisation problem should be approached with the tools of global optimisation. in this context the presented inputoutput inversion toolbox can be easily integrated with the b4m toolbox that allows to handle interval arithmetic, which is a well-known effective tool for global optimisation [19]. [time,command,out,postaction]=causaldyninv(sys,y1,tau,st,hbc,pa) this function implements the causal approach proposed in [9]. the resulting pre-actuation time is always zero despite the possible presence of unstable zeros. in particular, the function deals with the following parameters: • hbc is the order h of the boundary conditions to be satisfied for the polynomial output function at time t = 0 and t = τ , so that yd (·) ∈ bc2h+1. note that it has to be h ≥ ρ in order to ensure that the input function is at least continuous, i.e. ud (·) ∈ bc0; • pa is a string that, if set to ‘nopostaction’, avoid also the use of a post-actuation time even if the system has stable zeros. in other words, in this case the system attains an equilibrium point at t = τ . if the parameter is not adopted or if it is set to another value, then a post-actuation time is present and it is determined by means of formula (12). [time,command,preaction,¯postaction]=piddyninvfopdt(k,t,l,kp,ti,td, tf,tau,st) this function determines the input command function to unitary feedback closed-loop system in which a process described by a fopdt transfer function is controlled by a pid controller. the following parameters are adopted: • k, t, l are the process gain, time constant and dead time respectively (see (14)); • kp, ti, td, tf are the pid parameters (see (16), where the meaning of the different parameters is obvious). [time,command,preaction,postaction]=piddyninvipdt(k,t,l,kp,ti,td,tf, tau,st) this function determines the input command function to unitary feedback closed-loop system in which a process described by a ipdt transfer function is controlled by a pid controller. the meaning of the parameters can be straightforwardly derived by considering those of the previous function together with expression (15). 394 antonio visioli, aurelio piazzi 4 application examples in order to better understand the usage of the input-output inversion toolbox, a few examples are given. consider the system h(s) = 4 (1−s)(s + 1) (s + 2)(s2 + 2s + 2) . (19) to insert the system in symbolic form in the matlab workspace, the following two commands can be applied: syms s h=4*(1-s)*(s+1)/(s+2)/(s^2+2*s+2) then, suppose that an output transition from 0 to y1 = 1 is required to be performed in τ = 3 by means of the following output function: y(t) = 4796646617206209 562949953421312 t 63 25 − 4658008624178539 562949953421312 t 127 50 + (20) 6313836048447483 5316911983139663491615228241121378304 t 39. the output function can be inserted in the matlab workspace (denote the variable as yt) either in symbolic form or as an array whose elements are the values of y(t) for t = 0, ts, 2ts, . . . , τ , where ts is the sampling time (in this case it has been selected ts = 0.001). in the first case the command to be adopted is: [time,command,preaction,postaction]=outdyninvcs(h,yt,3,0.001) while in the second case it has to be [time,command,preaction,postaction]=numdyninvcs(h,yt,3,0.001) in both cases the pre-actuation time results to be tp = −10 s and the postaction time is t f = 10 s (according to expressions (12)). the resulting input function and the output function obtained by applying it to the actual system are reported in figure 1 (note that the result is the same in both cases). select now a polynomial output function (13) to perform again an output transition from 0 to y1 = 1 and select the parameters ε0 = ε1 = 10−3. in this case the matlab command to be adopted is [time,command,preaction,postaction]=dyninv(h,1,3,0.001,0.001,0.001) the resulting pre-actuation and post-actuation times preaction and postaction (determined by means of formula (10)) are respectively tp = −6.256 s and t f = 3.547 s. the determined input and the corresponding system output are plotted in figure 2. note that the resulting output function is a cubic polynomial, i.e. yd (t; τ) = y1 ( − 2 τ 3 t 3 + 3 τ 2 t 2 ) t ∈ [0, τ] as it is k = ρ = 1 in formula (13). consider now the minimum time problem (17)-(18) and set the limits on the input derivatives as u(0)m = 2, u(1)m = 10 and u (2) m = 20. this means that the following matlab command has to be executed: limits=[2 10 20] then, the following function has to be employed (note that the sampling time is 0.001 s as before and again it is ε0 = ε1 = 10−3): [time,command,taunum,pre,post]=optdyninv(h,1,limits,0.001,0.001,0.001) the resulting optimal transition time taunum is equal to 0.875 s, while the pre-actuation and postactuation times are tp = −7.111 s and t f = 4.402 s. the determined command input and the corresponding system output are shown in figure 3, while the first and second derivatives of the command input are plotted in figure 4. it can be deduced that the active constraint is the one related to the second time derivative of the input. if the causal approach is desired, i.e. the pre-actuation time is to be avoided, then the function a toolbox for input-output system inversion 395 causaldyninv has to be adopted. in particular, we select the order of the boundary conditions as h = ρ = 1 and the desired transition time as τ = 3. then, if a post-actuation time interval is allowed, the matlab command to be adopted is: [time,command,out,postaction]=causaldyninv(h,1,3,0.001,1) in this case the resulting command input and the corresponding output are plotted in figure 5. note that the post-actuation time interval is t f = 10 s. conversely, if a post-actuation is not allowed, the matlab function to be employed is [time,command,out,postaction]=causaldyninv(h,1,3,0.001,1,‘nopostaction’) the resulting command input and the corresponding output function are shown in figure 6. it can be seen that in both cases the output function is not monotonic. indeed, the possible overshoots and undershoots are more and more significant when the selected transition time decreases. in order to verify the effectiveness of the inversion approach for pid control, consider first the system p(s) = 2 10s + 1 e−5s, (21) controlled by a pid controller (see (16)) with kp = 1.2, ti = 10, td = 2.5, tf = 0.01. the transition time is fixed to 15 s and the sampling time to 0.01 s. then, the following matlab command is adopted: [time,command,pre,post]=piddyninvfopdt(2,10,5,1.2,10,2.5,0.01,15,0.01) the resulting command input (applied to the closed-loop system) and output functions are plotted in figure 7. the pre-actuation and post-actuation time intervals are tp = −16.67 s and t f = 50.01 s. note that the output function is slightly different from the desired one because of the padè approximation. then, a ipdt transfer function is considered: p(s) = 0.1 s e−5s. (22) in this case the tuning of the pid parameters is kp = 0.12, ti = 10, td = 2.5, tf = 0.01. the transition time and the sampling time are the same as before, so that the following function is employed: [time,command,pre,post]=piddyninvipdt(0.1,10,5,0.12,10,2.5,0.01,15,0.01) figure 8 reports the determined closed-loop command input and the resulting system output. also in this case the pre-actuation and post-actuation time intervals are tp = −16.67 s and t f = 50.01 s. 5 conclusions a matlab-based toolbox for the input-output inversion of linear scalar systems has been described in this paper. the toolbox is very useful to understand and to verify the effectiveness of a feedforward action in a control scheme and, in general, of a combined feedback/feedforward synthesis. in this context, the role played by the planned output function can be easily evaluated as well as the influence of the noncausal approach with respect to a causal one. indeed, all the aspects related to the use of an inputoutput inversion-based control design can be thoroughly investigated and analyzed. the toolbox can be exploited in different fields such as motion control, robust control, and process control. it can be downloaded from the website http://www.ing.unibs.it/∼visioli/ioitoolbox.htm 396 antonio visioli, aurelio piazzi 0 2 4 6 8 10 12 14 16 18 20 0 0.2 0.4 0.6 0.8 1 time [s] command input resulting output figure 1: command input and resulting system output with system (19) and desired output function (20) 0 2 4 6 8 10 12 14 0 0.2 0.4 0.6 0.8 1 time [s] command input resulting output figure 2: command input and resulting system output with system (19) and a polynomial desired output function (13) a toolbox for input-output system inversion 397 0 2 4 6 8 10 12 0 0.2 0.4 0.6 0.8 1 1.2 time [s] command input resulting output figure 3: optimal command input and resulting minimum-time system output with system (19) and a polynomial desired output function (13) 0 2 4 6 8 10 12 −20 −15 −10 −5 0 5 10 15 20 time [s] first derivative second derivative figure 4: first and second derivative of the optimal command input with system (19) and a polynomial desired output function (13) 398 antonio visioli, aurelio piazzi 0 1 2 3 4 5 6 −0.2 0 0.2 0.4 0.6 0.8 1 time [s] command input resulting output figure 5: command input and resulting system output with system (19) and a causal approach with post-actuation 0 0.5 1 1.5 2 2.5 3 3.5 4 −1 −0.5 0 0.5 1 1.5 2 time [s] command input resulting output figure 6: command input and resulting system output with system (19) and a causal approach without post-actuation a toolbox for input-output system inversion 399 0 10 20 30 40 50 60 70 0 0.2 0.4 0.6 0.8 1 time [s] command input resulting output figure 7: command input and resulting system output with system (21) with a pid controller 0 10 20 30 40 50 60 70 −3 −2 −1 0 1 2 3 4 5 6 time [s] command input resulting output figure 8: command input and resulting system output with system (22) with a pid controller 400 antonio visioli, aurelio piazzi bibliography [1] b. c. kuo, automatic control systems, prentice hall, englewood cliffs (nj), 1995. [2] a. wallen, k. j. åström, pulse-step control, preprints of the 15th ifac world congress on automatic control, barcelona (spain), 2002. [3] a. visioli, a new design for a pid plus feedforward controller, journal of process control, vol. 14, no. 4, pp. 455-461, 2004. [4] h. perez, s. devasia, optimal output transitions for linear systems, automatica, vol. 39, pp. 181-192, 2003. [5] q. zou, s. devasia, preview-based optimal inversion for output tracking: application to scanning tunneling microscopy, proceedings ieee international conference on decision and control, las vegas (usa), pp. 79-85, 2002. [6] d. iamratanakul, h. perez, s. devasia, feedforward trajectory design for output transitions in discrete-time systems: disk-drive example, proceedings of the american control conference, denver (usa), pp. 3142-3147, 2003. [7] a. piazzi, a. visioli, minimum-time system-inversion-based motion planning for residual vibration reduction, ieee/asme transactions on mechatronics, vol. 5, no. 1, pp. 12-22, 2000. [8] a. piazzi, a.visioli, optimal inversion-based control fo the set-point regulation of nonminimumphase uncertain scalar systems, ieee transactions on automatic control, vol. 46, no. 10, pp. 16541659, 2001. [9] m. benosman, g. le vey, stable inversion of siso nonminimum phase linear systems through output planning: an experimental application to the one-link flexible manipulator, ieee transactions on control systems technology, vol. 11, no. 4, pp. 588-597, 2003. [10] c. guarino lo bianco, a. piazzi, a servo control system design using dynamic inversion, control engineering practice, vol. 10, no, 8, pp. 847-855, 2002. [11] a. piazzi, a. visioli, optimal dynamic inversion based control of an overhead crane, iee proceedings control theory and applications, vol. 149, no. 5, pp. 405-411, 2002. [12] a. visioli, a. piazzi, improving set-point following performance of industrial controllers with a fast dynamic inversion algorithm, industrial engineering and chemistry research, vol. 42, pp. 13571362, 2003. [13] a. piazzi, a. visioli, a noncausal approach for pid control, journal of process control, vol. 16, pp. 831-843, 2006. [14] j. w. polderman, j. c. willems, introduction to mathematical systems theory, springer-verlag, new york, 1998. [15] d. pallastrelli, a. piazzi, stable dynamic inversion of nonminimum-phase scalar linear systems, preprints of the 16th ifac world congress on automatic control, prague (cz), 2005. [16] q. zou, s. devasia, preview-based inversion of nonlinear nonminimum-phase systems: vtol example, proceedings of the ieee international conference on decision and control, paradise island (the bahamas), pp. 4350-4356, 2004. a toolbox for input-output system inversion 401 [17] a. piazzi, a. visioli, optimal noncausal set-point regulation of scalar systems, automatica, vol. 37, no. 1, pp. 121-127, 2001. [18] a. piazzi, a. visioli, using stable input-output inversion for minimum-time feedforward constrained regulation of scalar systems, automatica, vol. 41, no. 2, pp. 305-313, 2005. [19] e. hansen, g. w. walster, global optimization using interval analysis 2nd edition, marcel dekker, 2003. antonio visioli university of brescia dipartimento di elettronica per l’automazione via branze 38, i-25123 brescia, italy e-mail: antonio.visioli@ing.unibs.it aurelio piazzi university of parma dipartimento di ingegneria dell’informazione parco area delle scienze 181a, i-43100 parma, italy received: january 2, 2007 402 antonio visioli, aurelio piazzi antonio visioli received the laurea degree in electronic engineering from the university of parma, parma, italy, and the ph.d. degree in applied mechanics from the university of brescia, brescia, italy, in 1995 and 1999 respectively. his ph.d. dissertation was on control strategies for industrial robot manipulators. he is currently an associate professor of automatic control, department of electronics for automation, university of brescia, italy. his research interests include industrial robot control and trajectory planning, dynamic-inversion-based control and process control. he is the author or co-author of more than 100 papers in international journals and refereed conference proceedings and he is the author of the book practical pid control published by springer. dr. visioli is a senior member of ieee and a member of ifac and anipla (italian association for the automation). aurelio piazzi received the laurea degree in nuclear engineering in 1982 and the ph.d. degree in system engineering in 1987, both from the university of bologna, italy. from 1990 to 1992, he was research associate in system theory, deis, university of bologna. since 1992 he has been at the university of parma where he is currently full professor of automatic control at the department of informatics engineering. his main research interests are in system and control theory and related engineering applications. his recent research activities have focused on optimization and dynamic inversion techniques for autonomous vehicle guidance and for the design of high-performance control systems. scientific coordinator of bilateral research programs in collaboration with various industries (among them cnr, enel, rfi -ferrovie dello stato), in 2002 and 2003 he has directed the european project cookies within the eu cluster eutist-imv (integrated machine vision) in collaboration with gruppo colussi (perugia) for the artificial vision-based control of food industrial ovens. he is a member of ieee and siam and has published over 90 scientific papers in international journals and conference proceedings. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 892-899 information sharing in vehicular adhoc network a. rahim, z.s. khan, f.b. muhaya, m. sher, m.k. khan aneel rahim, zeeshan shafi khan 1. prince muqrin chair for it security, king saud university, saudi arabia 2. international islamic university, pakistan e-mail: aneelrahim,zeeshanshafi@ksu.edu.sa fahad bin muhaya prince muqrin chair for it security, king saud university, saudi arabia e-mail: fmuhaya@ksu.edu.sa muhammad sher international islamic university, pakistan e-mail: m.sher@iiu.edu.pk muhammad khurram khan center of excellence in information assurance, king saud university, saudi arabia e-mail: mkhurram@ksu.edu.sa abstract: relevance technique broadcast the useful information and removes the redundant data. 802.11e protocol implementation has certain flaws and is not suitable for vanets scenarios. main issue in 802.11e protocol is internal sorting of packets, no priority mechanism within the queues and often lower priority traffic get more medium than high priority traffic. in this paper, the mathematical model of relevance scheme is enhanced so that it can consider the network control in real scenario by considering the impact of malicious node in network. problems of 802.11e protocol can be resolved by making virtual queue at application level. we analyze the comparison of simple virtual queue with the over all impact of virtual queue and mathematical model. similarly we compare the mathematical model with over all impact of virtual queue and modified mathematical model using ns-2 simulator. keywords: vanets, broadcast, 802.11e, malicious 1 introduction vehicle to vehicle (v2v) communication enhances the safety of passenger and driver [1]. v2v communications is unreliable because of shadowing, doppler shifts and multi-path fading. [2] security is an important concern in mobile adhoc network [3] [4] [5]. attacks are easily launched on vanets [6] because of high speed [7], no infrastructure and topology changes frequently [8] [9]. several security attacks are possible on safety application, which includes denial of service [10], masquerade [11], fake information, false position information and id disclosure [12]. vehicular communication vulnerabilities are explained in [13] which include jamming, forgery, in-transit traffic tampering, impersonation, privacy violation and on-board tampering. malicious data in vanet is because of distributed environment and unreliable components of data generation. [14] we in this paper resolve the problems of 802.11e protocol by making virtual queue at application and enhance the mathematical model of message benefit to consider copyright c⃝ 2006-2010 by ccc publications information sharing in vehicular adhoc network 893 the network traffic. we measure the global benefit in real scenario by considering the impact of malicious node. we also show the comparison of simple virtual queue with the over all impact of virtual queue and mathematical model. similarly we compare simple mathematical model with over all impact of virtual queue and mathematical model. this paper is organized as follows: in section 2, we discuss relevance based approach, its characteristics and its implementation using cross layer, 802.11e and 802.11e with virtual queue. in section 3, proposed study and results are presented using ns-2. lastly in section 4 conclusions is given. 2 related work relevance technique disseminates the useful information and removes the redundant data [16]. vehicle contains huge information that can’t be shared to due the high speed of vehicles. technique which gives high priority traffic more medium as compare to low priority traffic is a suitable approach for vanets. so relevance technique is the only option as it forward data according to its relevance. relevance techniques based upon the calculation of relevance value of message and its distribution according to its priority [15, 16]. altruism, application-oriented information differentiation, controlled unfairness is the basic characteristics of relevance based approach [15, 17, 18]. 2.1 cross layer and 802.11 e implementation relevance technique can be implemented through cross layer design or by 802.11e protocol. in cross layer design, relevance value of every packet is measured at application layer and pass to link layer through packet header. modified medium access control and interface queue broadcast the high priority traffic with help of application layer information. [15, 17]. 802.11e protocol implementation has certain flaws and is suitable for vanets scenarios [18]. main issue in 802.11e protocol is that it does not provide internal sorting of packets, no priority mechanism within the queues and performance of the network degrades as lower traffic some times get more medium than high priority traffic [15]. 2.2 802.11e implementation with virtual queue 802.11e protocol problems are overcome by adding four virtual queues at application level. packets are sorted according to their priority and most importance messages are near the head of queues. sorting is done by getting the current information from application layer and length of 802.11e is set to be one. when 802.11e is empty, one high priority packet is moved from virtual queue to 802.11e queue. packet in 802.11e queue does not mean that it always broadcast. if get a packet in virtual queue that has higher relevance than packet in 802.11e queue than we swap both the packets in order to achieve higher global benefit [19]. 2.3 mathematically model for relevance based approach the mathematical model for relevance based approach is given below. message benefit = 1∑n i=0 αi ∗ n∑ i=0 αi ∗ bi(m, v, i) − − − − − − − − − [18]. to determine the relevance value of message, message (m), vehicle (v), and information (i) context parameters are used. the n parameters are computed with the help of application dependent function bi. the n parameters are then weighted with application dependent factors ai. in the end, all parameters are sum up and divided by the sum of all ai. 894 a. rahim, z.s. khan, f.b. muhaya, m. sher, m.k. khan 2.4 enhanced mathematically model for relevance based approach as the existing model does not have the support for network control traffic. so network performance can improve by adding the network control in the mathematical model [20]. enhanced message benefit = 1∑n i=0 αi ∗ n∑ i=0 αi ∗ bi(m, v, i) + n∑ i=0 pi a) n∑ i=0 pi = 0 if it is user traffic where as n∑ i=0 pi = 1 for operational level network problem, n∑ i=0 pi = 2 for administrative level, n∑ i=0 pi = 3 for maintenance level b) message benefit = n∑ i=0 pi (for network traffic only) if 0 n∑ i=0 pi � 3 then 1∑n i=0 αi ∗ n∑ i=0 αi ∗ bi(m, v, i) = 0 user and network traffic is assigned a value between zero and three, in order to handle them easily with four queues (q0, q1, q2, and q3) of 802.11e. high priority traffic is assigned q0 so that it can be forward before the packets in q1, q2 and q3. queues are assigned to user and network control traffic according to there relevance value. but in the existing approach there is no mechanism for priority for network control traffic. so the global benefit is enhanced by considering network traffic. 3 proposed study and results in this study we simulate the relevance based approach and calculate global benefit in ideal scenario that all nodes are doing their properly and there is no malicious node in the network. in the second scenario we consider the impact of malicious node and measure how much global benefit is decreased. the malicious nodes forward the relevant messages first but also inject some surplus information. in last scenario malicious node forward the surplus message first and ignore the relevant message. in order to validate the proposed study, we compare the performance of relevance based approach in real and ideal scenario with 802.11e protocol. ns2, a network simulator [21], is used to simulate the behavior for relevance based approach in vanets scenarios. we use manhattan mobility model and traffic is generated by generic mobility simulation framework [22]. vehicles are moving at a speed of 72km/hr to 108 km/hr within an area of 3000m x 3000m with transmission range of 300m. performance of relevance based approach is measured by calculating the global benefit. information sharing in vehicular adhoc network 895 table 1: simulation parameters parameters settings channel wireless vehicles 50,100,150 mac protocol 802.11e time 50s routing protocol dsdv network simulator is used for the simulation and different parameter used in the following study is given in table 1. 3.1 improvement due to mathematical model in this study we simulate enhanced mathematically model of message benefit shown above with existing relevance based approach. figure 1(a) shows that 50 vehicles are moving at high speed and share safety and comfort information with each other. relevance based approach consider only user traffic and ignore network traffic. so its global benefit can be improved by improving the mathematical model. we now evaluate the performance of relevance based approach by adding the network control parameter in the existing formula. figure 1(a) shows the global benefit with enhanced relevance based approach. it is clear from figure 1(a) that global benefit is low by using existing relevance based approach because network control traffic set lower priority and get less bandwidth than user traffic. so lower priority traffic can get more bandwidth than higher priority traffic. that’s why the global benefit is improved by adding the network parameter in relevance based approach. figure 1: (a) improvement due to mathematically (b) improvement due to virtual queue 3.2 improvement due to virtual queue figure 1(b) shows simple 802.11e and virtual queue with 802.11e, safety messages and route messages are forwarded by vehicles. in this study 150 vehicles exchanging information with each other. in simple 802.11 e, there is no mechanism of priority assignment. this problem is resolved by virtual queue. so its global benefit is greater than simple 802.11 e because it does not allow lower priority traffic to get more medium than higher priority traffic. 3.3 improvement due to virtual queue and mathematically model first we check the improvement due mathematical model and virtual queue separately but now we consider the impact of both on the global benefit of network. figure 2 shows that global benefit of existing and enhance relevance based approach due to virtual queue and mathematically 896 a. rahim, z.s. khan, f.b. muhaya, m. sher, m.k. khan model. enhanced relevance based approach has higher global benefit because it resolves the problem of priority mechanism and ignorance of network control traffic. figure 2: improvement due to virtual queue and mathematically model 3.4 comparison this study shows the comparison of simple virtual queue with the over all impact of virtual queue and mathematical model. similarly we compare simple mathematical model with over all impact of virtual queue and mathematical model. fig 3(a) shows the global benefit due to message benefit (mb), enhance message benefit (emb) and virtual queue + emb. it is clear from figure that global benefit by using virtual queue + emb is greater than simple emb because within a queue a there is no priority mechanism available. fig 3(b) shows the global benefit due to 802.11e, virtual queue and emb + virtual queue. it is clear from figure that global benefit by using emb + virtual queue is greater than 802.11e and simple queue because in simple queue we don’t have discriminate between user traffic and network traffic. figure 3: (a)comparison of mathematically model with both virtual queue and mathematical model (b) comparison of virtual queue with both mathematical models 3.5 impact of malicious node in this study we consider the impact of malicious node on emb, virtual queue and both (emb + virtual queue).figure 4(a) shows that 50 vehicles are moving at high speed and share safety and comfort information with each other. first we simulate the mb and emb in ideal information sharing in vehicular adhoc network 897 scenario that no malicious node exists and all nodes try to improve the benefit of network rather than their own benefit. after that we simulate emb in real scenario that malicious exist and damage the performance of the network. figure 4(a) shows that global benefit of emb in real scenario lies between the emb mb in ideal scenario. figure 4(b) shows that 150 vehicles exchanging information with each other. first we simulate the 802.11e and virtual queue in ideal scenario that no malicious node exists and all nodes try to improve the benefit of network rather than their own benefit. after that we simulate virtual queue in real scenario that malicious exist and damage the performance of the network. figure 4(b) shows that global benefit of emb in real scenario lies below than 802.11e and virtual queue in ideal scenario. figure 5 shows that 150 vehicles are moving at high speed and share safety and comfort information with each other. first we simulate the mb and vq + emb in ideal scenario that no malicious node exists and all nodes try to improve the benefit of network rather than their own benefit. after that we simulate vq+ emb in real scenario that malicious exist and damage the performance of the network. figure 5 shows that global benefit emb + vq in real scenario lies between the emb +vq and mb in ideal scenario. figure 4: (a) impact of malicious node on emb (b)impact of malicious node on virtual queue (vq) figure 5: impact of malicious node on enhanced message benefit and virtual queue 4 conclusion relevance scheme rely on intermediate node for communication so it consider there is no selfish node exist in network. however it is not possible in real scenario. we in this paper simulate 898 a. rahim, z.s. khan, f.b. muhaya, m. sher, m.k. khan the relevance based approach using 802.11e, virtual queue with 802.11e and enhance message benefit in real and ideal scenario. simulation results shows that global benefit is improved by using virtual queue with enhance mathematical model. 5 acknowledgments this research is supported by the prince muqrin chair (pmc) for it security at king saud university, riyadh, saudi arabia. bibliography [1] y. wu, l. yang, g. wu, j. guo, an improved coded repetition scheme for safety messaging in 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[16] c. adler, s. eichler, t. kosch, c. schroth, m. strassberger, self-organized and contextadaptive information diffusion in vehicular ad hoc networks, 3rd international symposium on wireless communication systems, 2006. [17] s. eichler, c. schroth, t. kosch, m. strassberger, strategies for context-adaptive message dissemination in vehicular ad hoc networks, second international workshop on vehicle-tovehicle communications, july 2006. [18] c. schroth, r. eigner, s. eichler, m. strassberger, a framework for network utility maximization in vanets, acm international conference on mobile computing and networking, usa september 29, 2006. [19] a. rahim, m. yasin, i. ahmad, z. s. khan, m. sher, relevance based approach with virtual queue using 802.11e protocol for vehicular adhoc networks , 2nd international conference on computer, control and communication, karachi, 14 feb 2009. [20] a. rahim, f. b. muhaya, z. s. khan, m.a. ansari, m. sher, enhance relevance based approach for network control relevance, accepted in infomatica journal issn: 0350-5596. [21] network simulator, ns2 http://www.isi.edu/nsnam /ns [22] r. baumann, f. legendre, p. sommer, generic mobility simulation framework (gmsf), acm mobilitymodels’08, hong kong sar, china, may 26, 2008 http://horos.rdsor.ro/ijcccv3n4draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 4, pp. 324-335 on dpa-resistive implementation of fsr-based stream ciphers using sabl logic styles reza ebrahimi atani, sattar mirzakuchaki, shahabaddin ebrahimi atani, willi meier abstract: the threat of dpa attacks is of crucial importance when designing cryptographic hardware. this contribution discusses the dpa-resistant implementation of two estream finalists using sabl logic styles. particularly, two feedback shift register (fsr) based stream ciphers, grain v.1 and trivium are designed in both bsim3 130nm and typical 350nm technologies and simulated by hspice software. circuit simulations and statistical power analysis show that dpa resistivity of sabl implementation of both stream ciphers has a major improvement. the paper presents the tradeoffs involved in the circuit design and the design for performance issues. keywords: dpa attack, stream cipher, grain v.1, trivium, sabl, standard cmos. 1 introduction the term of security for a cryptographic primitive can be considered from two points of view: mathematical security (resistance against classical cryptanalysis) and the second one is physical security. physical attacks on cryptographic devices take advantage of implementation-specific characteristics to recover the secret parameters. they are therefore much less general since they are specific to a given implementation but often much more powerful than classical cryptanalysis, and are considered very seriously by cryptographic devices implementors. a side-channel attack occurs when an attacker is able to use some additional information leaked from the implementation of a cryptographic function to cryptanalyze the function. clearly, given enough side-channel information, it is trivial to break a cipher. one side channel attack in particular, namely the differential power analysis (dpa) is of great concern. it was first reported by kocher et al. in 1998 that the power consumption of a smart card could reveal the secret key of the cryptographic algorithm [1]. dpa is a well-known and thoroughly studied threat for implementations of block ciphers (des and aes), public key algorithms (rsa) and recently stream ciphers (grain and trivium [4]). stream ciphers as part of the symmetric key cryptography family, have always had the reputation of efficiency in hardware and speed. they have attracted much attention since the beginning of the estream project in 2004. although there is vast literature about dpa on implementations of block ciphers and public key algorithms, only few publications can be found about dpa attacks on stream ciphers ([2], [3], [4], [8], [13], [14]). in power analysis attacks, it is assumed that the power consumption of a circuit is correlated to the data handled. an attacker can therefore recover secret information by simply monitoring the power signals of a running device. stream ciphers require frequent synchronization to prevent synchronization loss between sender and receiver. normally the initialization will be done with the same secret key and with a different initial value iv. so an attacker can disrupt the synchronization and apply a new known iv and measure the power traces in the initialization phase to apply a dpa on the embedded system of the stream cipher. so far, there is only one report on a practical dpa targeting hardware implementations of stream ciphers [4]. in that paper, a chosen iv dpa attack on grain and trivium stream ciphers has been described and executed. protecting implementations against dpa attacks is usually difficult and expensive. the goal of countermeasures against dpa attacks is to make the power consumption independent of intermediate values of the stream cipher. in general, there are three basic groups into which these countermeasures can be copyright © 2006-2008 by ccc publications on dpa-resistive implementation of fsr-based stream ciphers using sabl logic styles 325 characterized: protocol countermeasures, algorithmic countermeasures, and hardware countermeasures [11]. the principles of the countermeasures can be implemented at different levels in a cryptographic device. in general, these techniques are theoretical countermeasures and only reduce the side channel leakage and do not fundamentally prevent a dpa. but the advantage of these countermeasures is to make the attack significantly harder. in this article, we provide a brief overview of hiding and masking logic styles (hardware countermeasures) and particularly we will use sense amplifier base logic (sabl) for secure implementation of stream ciphers. sabl is a logic style that uses a fixed amount of charge for every transition, including the degenerated events in which a gate does not change state. in every cycle, a sabl gate charges a total capacitance with a constant value. so far, there has not been a unified architecture which can be used as a test bench for applicability of logic styles on stream ciphers. regarding this, two fsr-based stream ciphers grain v.1 and trivium stream ciphers are implemented in cell level to find out the tradeoffs involved in designing the architecture and performance issues. power traces of the resulting circuits exhibit that sabl significantly reduces signal to noise ratio (snr). the rest of the paper is structured as follows: a general model of power analysis attack on stream ciphers is given in section 2. section 3 describes an overview of dpa countermeasures on cell level. in sections 4 and 5 the descriptions of grain v.1 and trivium are explained. design and simulation issues are described in section 6 and finally, conclusions are drawn in section 7. 2 differential power analysis of stream ciphers dpa is based on the fact that cmos logic and application specific details cause logic operations to have power characteristics that depend on the input data. it relies further on statistical analysis and error correction to extract the information from the power consumption that is correlated to the secret key [1]. in a dpa a hypothetical model of the device under attack is used to predict the power consumption. the classical setup for a dpa on stream ciphers is illustrated in fig. 1. output power traces are determined by the input data, iv, private key, output of the device and by many other parameters. an attacker to some extent has the potential knowledge of some of them (e.g. iv, input data and output data) while others are unknown. regarding a dpa attack, multiple measurements of the power consumption of a cryptographic device are made. for each measurement, different chosen iv’s are sent to the device. since the cryptographic algorithm is known, a hypothesis on intermediate values can be used to calculate the targeted data values based on the random input values. if the correct hypothesis is used, the targeted data values are calculated correctly for all measurements. according to (1), the total power consumption of an embedded device depends on 3 factors: ptotal = pcons. + pnoise + pdd. (1) with the help of statistical methods (calculation of correlations, mean values, etc.), the randomness of the data values that are not targeted (pconst. : leakage currents and data independent power consumption and pnoise: which comes from electrical noise) is exploited to reduce their effects on the power consumption traces. pdd is the data dependent power consumption and is targeted in statistical analysis. after all, the result of the statistical operation indicates which key hypothesis is correct. normally, a hamming distance power model is used to map the transitions that occur at the outputs of cells of a netlist to power consumption values. in cmos gates, it is reasonable to assume that the main component of the data dependent power consumption is the dynamic power consumption which is the power dissipation of charging and discharging of output capacitance nodes (p0→1 or p1→0). in a cmos gate, we can express dynamic power consumption by: pdynamic = n ·cl · f ·v 2dd (2) 326 reza ebrahimi atani, sattar mirzakuchaki, shahabaddin ebrahimi atani, willi meier where cl is the gate load capacitance and n is the probability of a 0 → 1 or 1 → 0 output transition and f is the clock frequency. this equation shows that the power consumption of cmos circuits is data dependent. note that n is the most important factor in the hypothetical model. there are different techniques for calculation of it. for example, a variable gate delay model can be used for measuring the number of transitions and glitches of a circuit [7]. this technique can be easily applied to circuits by using a vhdl simulator in register transfer level. figure 1: differential power analysis model of stream ciphers. 3 dpa countermeasures on cell level so far, several methods in different ways have been proposed to counteract dpa attacks. in this section different known dpa countermeasures on cell level (hiding and masking techniques) are briefly presented and then their merits and disadvantages will be discussed. the first structured approach to counteract dpa attacks at the cell level was the use of hiding logic styles. these styles try to break the correlation between an algorithm’s intermediate results and the power consumption of the cryptographic device that executes this algorithm by making the instantaneous power consumption of the cells either random or the same in each clock cycle. the three major types of hiding logic styles are: dual-rail precharge (drp), asynchronous, and current mode logic (cml). drp logic styles are the most popular types. for instance, sabl [10] and wave dynamic differential logic (wddl) [15] are dual rail precharge logic styles whose logic gates are driven by a precharge signal to prevent glitches, and each logic signal is represented by two complementary wires. other examples of drp logic styles are dual spacer dual rail logic (dsdr), three-phase dual-rail precharge logic (tdpl) [16], and three state dynamic logic (3sdl). data dependent time of evaluation of the wddl and its memory effect made it vulnerable to dpa attacks. one of the major drawbacks of hiding logic styles is the balancing of the cells and interconnect layouts to achieve constant power consumption. since the charge and discharge of output nodes of dynamic and differential styles follow simple rc charge and discharge, cells and wires must mainly be balanced in a capacitive and resistive manner. but due to process variations, complex cross-coupling effects, and area limitations it is a hard task. besides hiding, masking at the cell level has become popular during the past few years. using a masked logic style, designers also break the correlation between an algorithm’s intermediate values and the power consumption of the cryptographic device that executes this algorithm. all intermediate values are masked by a random value. the cells then process only the masked intermediate values and their corresponding mask. because the unmasked values and the masked value are uncorrelated, power consumption of the cell also remains uncorrelated to intermediate values. generally there are two types of masking operation: boolean masking or arithmetic masking. if the masked cells are not activated in a data or operation dependent manner, masked logic styles counteract dpa attacks. there are two different on dpa-resistive implementation of fsr-based stream ciphers using sabl logic styles 327 possible masking schemes: one mask per circuit (single masking) or one mask per signal. these masked bits are normally prepared by some random number/sequence generators. before, masking was mainly used at the architecture level. as a result, only a few practical results are available for this type of cell level countermeasure. for examples masked dual rail precharge logic (mdpl) [17] and dual rail random switching logic (drsl) [18] were introduced by combining the masking scheme and dual rail precharge logic in order to use semi custom design tools without routing constrains. designers can implement mdpl cells using commonly available conventional single rail standard cells. only sequential cells are connected to the clock signal, and combinational cells precharge their outputs when their inputs have been set to the precharge value. the memory effect can reduce the dpa resistance of masked logic styles. practical evaluations of the manufactured chips have also shown that early propagation is also a major threat to the dpa resistancy of masked logic styles. although all these efforts, it has been shown ([19], [20], [21]) that mdpl leaks information. for example in [19], it has been shown that mdpl is susceptible to the early propagation effect. in order to combat the early propagation issues, the designers of mdpl introduced a so called improved mdpl (imdpl). in each imdpl gate there is an evaluation precharge detection unit, which consists of three (cmos) and gates and two (cmos) or gates. hence it is not surprising that the area requirements for imdpl gates increased significantly compared to mdpl gates. another threat to masked circuits is the detection of the mask value, which lets attackers completely cancel out the effect of masking in a dpa attack. in particular, such an attack is dangerous for single masked circuits, where only one mask value is used for all signals in the circuit. increasing the number of mask values per circuit is an option but it is impractical regarding its high complexity and area utilization. 4 sense amplifier based logic in this paper we will concentrate on sabl [10] for dpa resistive implementation of stream ciphers. sabl is part of the drp logic styles. fig. 2 shows the transistor schematic of standard sabl gate library used for implementation of ciphers. equation (3) illustrates the power consumption of a sabl gate, p = cl · f ·v 2dd +cclk · f ·v 2dd (3) where cl represents the total output capacitance of the gate and cclk is the clock propagation circuitry capacitance. as can be seen in the fig.2, sabl gates can be designed using differential pull down networks (dpdn) or differential pull up networks (dpun), controlled respectively by clk and clk. this allows two modes for cascading sabl gates: domino connection (by connecting the outputs of the gate to the inputs of the next gate through inverters) or np-connection (n-gates followed by p-gates like in np-logic). in sabl, the concepts of dual rail and precharge logic are combined to achieve constant power consumption. precharging breaks a signal’s sequence of values by splitting each clock cycle into precharge and evaluation phases. in the precharge phase, the complementary wires encoding a signal are set to a predefined precharge value, such as 1. in the subsequent evaluation phase, one of the two complementary wires is set to 1 according to the actual value that is processed. as a result, for each signal in a circuit, exactly one 0 → 1 transition and one 1 → 0 transition occur in a clock cycle. by ensuring a balance between the complementary wires between cells on the one hand and a balance of the internal structure of the cells on the other hand, designers can achieve constant power consumption. the price is high power consumption and high current spikes of these gates which appear at the beginning of the precharge phase. by the use of delayed clock mechanism introduced in [13] and [14] we can reduce the peak of these spikes. but in practice the throughput is highly dependent on layout design of the chip to have balanced complementary wires. since the charge and discharge of output nodes of differential styles follow simple 328 reza ebrahimi atani, sattar mirzakuchaki, shahabaddin ebrahimi atani, willi meier figure 2: (i) sabl d-flip flop (ii) sabl nand2 gate (iii) sabl xor2 gate rc charge and discharge, cells and wires must mainly be balanced in a capacitive and resistive manner. but due to process variations, complex cross-coupling effects, and area limitations this is hard to achieve. avoiding these effects often requires custom cell design, which involves considerably more design effort than using available standard cells. 5 grain stream cipher grain v.1 [5] is a stream cipher introduced in 2005 as a candidate for the hardware profile of estream project. grain v.1 is a binary additive synchronous stream cipher with an internal state of 160 bits si, si+1,. . . , si+79 and bi, bi+1,. . . , bi+79 residing in a linear feedback shift register (lfsr) and a nonlinear feedback shift register (nlfsr), respectively. the design of the algorithm mainly targets hardware environments where gate count, power consumption and memory is very limited. the key size of grain is 80 bits (ki, 0 ≤ i ≤ 79). additionally, an initial value of 64 bits (ivi, 0 ≤ i ≤ 63) is required. in initialization phase, all 80 nlfsr elements are loaded with the key bits, (bi = ki, 0 ≤ i ≤ 79), then the first 64 lfsr elements are loaded with the iv bits, (si = ivi, 0 ≤ i ≤ 63). the last 16 bits of the lfsr are filled with ones. f (x) and g(x) are two polynomials used as feedback function for the lfsr and nlfsr. f : si+80 = si+62 ⊕ si+51 ⊕ si+38 ⊕ si+23 ⊕ si+13 ⊕ si (4) g : bi+80 = si ⊕ bi ⊕ bi+9 ⊕ bi+14 ⊕ bi+21 ⊕ bi+28 ⊕ bi+33 ⊕ bi+37 ⊕ bi+45 ⊕ bi+52 ⊕ bi+60 ⊕ bi+62 ⊕ bi+63 · bi+60 ⊕ bi+37 · bi+33 ⊕ bi+15 · bi+9 ⊕ bi+60 · bi+52 · bi+45 ⊕ bi+33 · bi+28 · bi+21 ⊕ bi+63 · bi+45 · bi+28 · bi+9 ⊕ bi+60 · bi+52 · bi+37 · bi+33 ⊕ bi+63 · bi+60 · bi+21 · bi+15 ⊕ bi+63 · bi+60 · bi+52 · bi+45 · bi+37 ⊕ (5) bi+33 · bi+28 · bi+21 · bi+15 · bi+9 ⊕ bi+52 · bi+45 · bi+37 · bi+33 · bi+28 · bi+21 the output function h(x) uses as input selected bits from both feedback shift registers: h(x) = x1 ⊕ x4 ⊕ x0 · x3 ⊕ x2 · x3 ⊕ x3 · x4 ⊕ x0 · x1 · x2 ⊕ + x0 · x2 · x3 ⊕ x0 · x2 · x4 ⊕ x1 · x2 · x4 ⊕ x2 · x3 · x4 (6) on dpa-resistive implementation of fsr-based stream ciphers using sabl logic styles 329 where the variables x0, x1, x2, x3, and x4 corresponds to the tap positions si+3, si+25, si+46, si+64 and bi+63 respectively. the output of the filter function is masked with the some state bits from the nfsr to produce the keystream zi: zi = bi+1 ⊕ bi+2 ⊕ bi+4 ⊕ bi+10 ⊕ bi+31 ⊕ bi+43 ⊕ bi+56 ⊕ h(si+3, si+25, si+46, si+64, bi+63) this output is used during the initialization phase as additional feedback to lfsr and nlfsr. during normal operation this value is used as key stream output. the generated output bits per clock cycle is called radix. by implementing the small feedback functions, f (x) and g(x), and the output function several times the speed of grain can easily reach up to radix-32. 6 trivium stream cipher trivium [6] is a stream cipher introduced in 2005 as a candidate for the hardware profile of the estream project. trivium has an internal state of 288 bits ai, ai+1, . . . , ai+92, bi, bi+1, . . . , bi+83 and ci, ci+1, . . . , ci+110 residing in three coupled nlfsrs a, b, and c of 93, 84, and 111 bits respectively. trivium has a key k = (k0, . . . , k79) of 80 bits as well as an initial value iv = (iv0, ..., iv79) of 80 bits. the initialization of the key and iv is done as follows:    (a0, . . . , a92) = (0, . . . ,0, k79, . . . , k0) (b0, . . . , b83) = (0,0,0,0, iv79, . . . , iv0) (c0, . . . , c110) = (1,1,1,0,0, . . . ,0,0) (7) then, the state is updated over 4 full cycles, according to (3), but without generating key stream bits. after 1152 clocking it outputs a key stream bit zi.    ai+93 = ai+24 ⊕ ci ⊕ (ci+1 · ci+2) ⊕ ci+45 bi+84 = bi+6 ⊕ ai ⊕ (ai+1 · ai+2) ⊕ ai+27 ci+111 = ci+24 ⊕ bi ⊕ (bi+1 · bi+2) ⊕ bi+15 (8) zi = ai ⊕ bi ⊕ ci ⊕ ai+27 ⊕ bi+15 ⊕ ci+45 (9) trivium has a very simple structure that is well suited for different radix implementations from radix-1 to radix-64 without noticeable hardware penalties. the basic structure of the grain v.1 and trivium stream ciphers are shown in fig. 3. in april 15, 2008, the estream competition was finished and according to the final report [12] both ciphers were selected among the four finalists of the h/w profile. 7 design and simulation results both estream candidates are modeled at transistor level using a spice netlist. circuit design of grain v.1 and trivium are mainly based on the techniques presented in [13] and [14]. in order to specify the impact of minimum feature size on the design, ciphers are designed using two technologies: typical bsim3 0.13µ m cmos soi technology and typical 0.35µ m cmos soi technology. spice simulations were run to test the circuits by test vectors provided by the inventors of the ciphers using hspice circuit simulator and c compiler. domino cascading scheme is used for all sabl gate connections to make sure having a 0 → 1 transition in the input of all cascaded gates to prevent possible glitches. first a new standard gate library based on sabl logic is designed. minimum possible sized transistors are used to lower the total capacitance to get lower dynamic power in (2). this will also minimize the charging time 330 reza ebrahimi atani, sattar mirzakuchaki, shahabaddin ebrahimi atani, willi meier figure 3: (i) grain stream cipher (ii) trivium stream cipher. during precharge phase. besides, this will help to cut the current spikes in the beginning of the precharge phase of each cycle. in order to get rid of the spikes, a delayed clocking mechanism is used [13], [14]. in order to increase security and speed of the initialization phase, a parallel data loading scheme is used, since in case of serial bit loading a straight forward simple power analysis attack is very likely to be successful in recovering all key bits. in parallel loading, key and iv will be loaded in the state bits after the first rising edge of the clk signal. the gate level architecture of the parallel data loading scheme for standard cmos is shown in fig. 4. note that in case of sabl all the wires and gates are dual rail. the area overhead is three nand2 gates for each flipflop of the fsrs in the ciphers. since all the components in the architecture need a clk signal for switching from precharge phase into evaluation and vice versa, a chained buffer clock signal is needed. for the standard cmos implementation of a the stream ciphers, standard two input nand gates (4 transistors), 8 transistor two input xor gates, and the former 24 transistor, edge triggered d-flipflops (using eight nand2), are used. in order to monitor all current variations, one sample has been taken every 50ps. both simulations were run for four different 80-bit keys and iv’s (64 bit iv for grain v.1) in both sabl and standard cmos designs. all power simulations are observed by 5mhz clock signal. the average power consumption per cycle was extracted by averaging the power consumption on 100 consecutive clock cycles. then, the mean power consumption (mpc), the power consumption standard deviation (pcsd), the normalized energy deviation (ned) and normalized standard deviation (nsd) were extracted for each simulated logic style (10). for example supply current traces for standard cmos design of grain v.1 for the choice of k2, iv3 (in table 1) in initialization phase is shown in fig. 5. ned = max(energy/cycle) − min(energy/cycle) max(energy/cycle) , nsd = pcsd mpc (10) in terms of transistor cost, the complete trivium (including parallel data loading and clock buffering circuitary) required ≈ 23000 transistors for the sabl and ≈ 8500 transistors for the standard cmos. in case of grain v.1, ≈ 13500 transistors for the sabl and ≈ 6000 transistors for the standard cmos are needed, confirming more than two times higher hardware cost for sabl styles. table 1 shows the summary of final statistical power analysis results. for example in 0.13µ m technology, and for k1, iv1, for grain v.1, pcsdsabl pcsdscmos = 0.016 which shows that the power consumption fluctuations of sabl implementation is nearly 1.6% of standard cmos (power = current ×costant supply voltage). this is a on dpa-resistive implementation of fsr-based stream ciphers using sabl logic styles 331 figure 4: parallel data loading scheme in fsrs (standard cmos) figure 5: supply current variation of standard cmos design of grain v.1 in 350nm technology 332 reza ebrahimi atani, sattar mirzakuchaki, shahabaddin ebrahimi atani, willi meier table 1: statistical power analysis of trivium and grain v.1 for different 80 bit hexadecimal key and iv’s (k1 = aa . . . a, k2 = 80. . .0, iv1 = 55. . .5, iv2 = f f . . . f, iv3 = 00. . .0, iv4 = 11. . .1). [note that in case of grain v.1 the iv’s are 64 bits] stream cipher trivium grain trivium mpc [µw ] pcsd [µw ] ned nsd mpc [µw ] pcsd [µw ] ned nsd sabl 0.35µ m,vdd = 3.3v,vt n = 0.6v,vt p = −0.85v k1, iv1 949 1.2632 0.0091 0.00133 616 0.9497 0.0136 0.00154 k1, iv2 940 1.2469 0.0106 0.00132 605 0.9375 0.0111 0.00155 k2, iv3 938 1.2403 0.0089 0.00131 601 0.9318 0.0122 0.00155 k2, iv4 943 1.2531 0.0117 0.00133 611 0.9393 0.0120 0.00153 s-cmos 0.35µ m,vdd = 3.3v,vt n = 0.6v,vt p = −0.85v k1, iv1 641 49.1 0.3292 0.0766 421 26.8 0.2784 0.0636 k1, iv2 637 19.3 0.2351 0.0303 402 18.5 0.1905 0.0460 k2, iv3 629 23.5 0.3139 0.0374 397 17.1 0.2187 0.0430 k2, iv4 635 41.7 0.2842 0.0657 415 29.6 0.3882 0.0713 sabl 0.13µ m,vdd = 1.2v,vt n = 0.4v,vt p = −0.39v k1, iv1 545 0.8435 0.0061 0.0015 378 0.7610 0.0124 0.0020 k1, iv2 537 0.7927 0.0054 0.0014 371 0.7424 0.0082 0.0020 k2, iv3 536 0.7831 0.0050 0.0014 369 0.7291 0.0079 0.0019 k2, iv4 541 0.8237 0.0059 0.0015 374 0.7482 0.0101 0.0020 s-cmos 0.13µ m,vdd = 1.2v,vt n = 0.4v,vt p = −0.39v k1, iv1 337 31.20 0.8945 0.0926 258 22.50 0.7889 0.0872 k1, iv2 321 16.12 0.8191 0.0190 246 14.14 0.8026 0.0545 k2, iv3 319 17.14 0.8614 0.0537 242 12.31 0.8402 0.0509 k2, iv4 326 29.94 0.9218 0.0918 252 21.72 0.7924 0.0862 major improvement but still pcsdsabl 6= 0 and very small current variations are detectable. the overall comparison between sabl and standard cmos design for 4 different key and iv choices (table 1) is shown in table 2. dpa resistivity factor is calculated in (11): dpa resistivity ∝ 1 pcsd (11) one of the fundamental parameters of a cryptographic algorithm is the amount of data it can process within a given period. the total throughput of the algorithm is expressed as mbits/s and can be calculated from t = f × radix where f is the clock frequency of the design (e.g. 5mhz). since trivium throughput rate for sabl and s-cmos designs are equal, in order to make a fair comparison, a new normalized table 2: overall comparison for sabl and s-cmos design of trivium (all data are normalized) cipher trivium grain logic style sabl s-cmos sabl s-cmos sabl s-cmos sabl s-cmos technology 0.13µ m 0.35µ m 0.13µ m 0.35µ m transistor cost (a) 1 0.37 1 0.37 1 0.44 1 0.44 power consumption (p) 1 0.60 1 0.67 1 0.66 1 0.67 dpa resistancy (dr) 1 0.0374 1 0.0435 1 0.045 1 0.043 qualifying factor (qf) 1 0.062 1 0.065 1 0.068 1 0.064 on dpa-resistive implementation of fsr-based stream ciphers using sabl logic styles 333 qualifying factor (qf) is defined in (12): qf = dr × t p × a (12) where, a, p, and dr corresponds to transistor cost, power consumption, and dpa resistancy respectively. at the end of the simulations and data analysis we exhibited that sabl logic styles allow to significantly decrease the supply current variations of both estream circuits. but still in both designs very small current variations are detectable. as a disadvantage this could be a start of a dpa attack since the predictability of the energy variations is more critical than their amplitude. it is clear that decreasing the power consumption variations will affect all the stream cipher design components in exactly the same way, and therefore not affect the snr. since dpa efficiency depends on the possibility to predict the power consumption of a device in function of its input data and the value of the correlation coefficient, the attack is still theoretically feasible against sabl circuits. but these current differences are due to the presence of parasitic capacitances in the design and therefore, they cannot be predicted without a precise transistor level knowledge of the circuit. as a consequence, an attacker can only target one specific implementation and preliminarily needs to build a table containing the power consumption differences in function of the circuit input data. these informations are not usually made available to the users in full custom design. moreover, under the assumption that we can perfectly predict and measure the power consumption, a circuit resistance is equal for any logic style. nevertheless, in practice, measurements are not perfect and induce noise, independently of the logic style considered. this will cause a reduction of the correlation values, depending on the power consumption variances, although it is hard to evaluate and highly depends on the attacker measurement setup. as can be seen in the table 1 and table 2 the dpa resistancy is improved for smaller minimum feature sized designs. although, current variations do not follow the scaling rules. this is mainly because of clock feedthrough effect and also the former subthreshhold leakages which play a big roll in deep submicron designs. stream ciphers always had the reputation of efficiency in hardware. their smaller architecture helps to use full custom design flow in order to have balanced routing of component wires. so simpler stream cipher designs would have lower design costs. regarding design flow, trivium has lower hardware complexity and circuit design is easier. although trivium has bigger architecture, timing constraints and clock distribution of trivium are the same as grain. comparing resistance against dpa attacks of the two estream candidates, simulations show grain has lower current spikes and smaller current variations. this is thanks to the higher circuit complexity of grain which combines different current variation of gates to achieve a semi random supply current variation. current spikes in trivium are due to the higher number of flip flops. another disadvantage of trivium is its large number of iterations in initialization phase (1152 rounds) which let attackers to have more power traces. 8 summary and conclusions this paper investigated the use of sabl logic to counteract power analysis attacks. in particular, an efficient dpa resistive circuit for grain v.1 and trivium stream ciphers have been designed and compared with their standard cmos implementations. first we exhibited that sabl allow to significantly decrease the circuit energy variations. this is due to equal amounts of power consumption in each clock cycle of sabl gates. all implementations have been done on transistor level but in practice the cipher itself is part of a system on chip with lots of other circuits which can increase pcons. +pnoise in (1) to achieve lower snr. although sabl cannot be completely tamper resistant, this logic probably presents acceptable security margins for general applications of stream ciphers. for future work interested researchers can investigate some circuit changes in sabl styles to counteract other side channel attacks such as fault attacks to obtain more security. 334 reza ebrahimi atani, sattar mirzakuchaki, shahabaddin ebrahimi atani, willi meier acknowledgment reza ebrahimi atani wishes to thank the iran telecommunication research center (itrc) for their financial support (www.itrc.ac.ir). bibliography [1] p. c. kocher, j. jaffe, and b. jun, “differential power 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[20] p. schaumont, and k. tiri, “masking and dual-rail logic dont add up,” in cryptographic hardware and embedded systems ches 2006, vol. 4249 of lncs, springer-verlag, 2006, pp. 95–106. [21] b. gierlichs, “dpa-resistance without routing constraints?” in cryptographic hardware and embedded systems ches 2006, vol. 4249 of lncs, springer-verlag, 2006, pp. 107–120. reza ebrahimi atani electrical engineering department iran university of science and technology (iust), narmak,16846, tehran, iran. e-mail: rebrahimi@iust.ac.ir sattar mirzakuchaki electrical engineering department iran university of science and technology (iust), narmak,16846, tehran, iran. e-mail: m-kuchaki@iust.ac.ir shahabaddin ebrahimi atani mathematics department university of guilan p.o.box 1914, rasht, iran. e-mail: ebrahimi@guilan.ac.ir willi meier iast institute fhnw ch 5210, windisch, switzerland. e-mail: willi.meier@fhnw.ch int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 161-171 antenna arrays principle and solutions: robust control approach florin hutu, sebastien cauet, patrick coirault abstract: this paper treats solutions on the ability of a chain of non identical oscillators to drive antenna arrays. frequency approaches were studied in order to solve the problem of synchronization of the oscillators. however, in this article, a new structure of chain of oscillators is introduced. secondly, lyapunov theory of stability is used to design a dynamical controller guarantying the oscillators synchronization. the problem of synchronization is transformed into a problem of asymptotic stabilization for a nonlinear system. it is formulated as a system of linear matrix inequalities where the parameter variations of the two oscillators and their differences are modeled by polytopic matrices. the theoretical result is successfully applied to an array of transistor-based oscillators used in "smart antenna" systems. keyword : nonlinear systems, control applications, antenna arrays 1 introduction the demand of mobile communication services is in a continuous growth, moreover, it is estimated that the rate will be maintained in the next years. this continuous development has stimulated the research of new hardware and software solutions in order to increase the volume of exchanged data and a better management of the emitted or received electromagnetic field. smart antenna arrays comprise a number of antennas that work in conjunction with an intelligent system that processes the received and transmitted data. the processing can be realized in a hardware or in a software way and allows smart antenna arrays to focus beams into particular directions. this problem can be partially solved by using several directional antennas. this solution divides the 360-degree coverage area into sectors. however, smart antenna arrays provide a much more effective solution by focusing the transmitted power toward user and only looking in the direction of the user for the up link signal. this ensures that the user receives the optimum quality of service and the maximum coverage from a base station. the new technologies development increases the antenna array performances and minimizes the costs of production and the occupied space. it makes them implementable in domains like wireless or satellite communications, radar systems, missile defense systems, automobile industry, etc. smart antennas or antenna arrays are a part of communication systems that can improve their global performances. this technique can increase the spectral efficiency and reduce the multi path fading, bit error rate (ber), the co-channel interferences (cci) and the system complexity [1]. this is possible by electronically adjusting the beam pattern of the antenna array in order to provide important gain for the desired signals and small gain for interference signals. at emission, the purpose of smart antennas is to minimize the interference between the different transmitters who works on the same communication channel and, thus to more efficiently use the emitted power. for this reason, the beam shape must be controlled in order to minimize the amplitude of the side lobes and to maximize the energy in the main lobe. moreover, the direction of the main lobe must be controlled. the focused application is inter-vehicle communication. the bandwidth will be, first of all, in the area of 24ghz and finally around 79ghz. at these frequencies, technique like "software defined radio" can not be used. one of the main objectives that is pointed out is to develop a structure which will extend in both space and time the safety information available to drivers by using the infrastructure and vehicles as sources. copyright © 2006-2008 by ccc publications 162 florin hutu, sebastien cauet, patrick coirault when the smart antennas are used in reception systems, the signals coming from interference directions must be rejected and those which comes from the desired directions must be privileged. for this reason, different phases and amplitudes must be assured by the carrier signals locally generated [2, 3, 4]. the work that has been done in the field of dynamics of coupled nonlinear systems using the frequency approach [5][6, 7][8] shows that they offer methods of phase control among array elements and beam scanning capabilities but also implies problems of stabilization. the proposed structure of the array of antennas is based on unidirectional coupled oscillators. in details, this paper treats the synchronization of a system made by two oscillators with an unidirectional coupling and this problem of synchronization is transformed in a problem of stabilization for a nonlinear system. the strategy chosen is to find an output feedback dynamic controller using lyapunov functions that assures a robust synchronization despite parameters variations of the oscillators. the problem of computing dynamic output feedbacks on lti (linear time-invariant) systems in term of matrix inequalities is difficult to solve. there are two known techniques: the iterative algorithms and the cancellation of variable products by using the matrix separation lemma. the reader can found some papers on treatment of this problem by lmi (linear matrix inequality)-s who can be numerically solved [9, 10, 11, 12, 13]. the variations of the parameters of the oscillators are taken into account by considering the state matrix as a polytopic one. the polytopic structure is easily tractable by linear matrix inequalities. once the stability of the vertices, defined for the polytope is proved, the stability and the synchronization of the two oscillators is assured for all systems inside the polytope. the nonlinear character of the oscillators allows the synchronization (if their free running frequencies are in a certain domain [6]) but also makes them dependent of initial conditions. the main objective is to cancel the nonlinear effect and to maintain the synchronization when the physical parameters of the oscillators and external conditions are modified. the originality of the method comes from the inclusion of the non-linear term and the undesired variations in a perturbation. this problem is transformed in a h∞ optimization. in section 2 is presented an overview of the antenna array theory. in section 3 a model of master and slave oscillators and the controller synthesis are introduced. section 4 presents the numerical results in the case of an array of coupled nonlinear oscillators. 2 antenna array theory overview 2.1 theoretical background a smart antenna is composed by an array of individual radiative elements (elementary antennas), which are placed in a particular configuration (linear, circular or matrix). by gathering these elementary antennas in such arrays and by changing the characteristics of the signals associated to each element, the array can present different gains according to the direction. let us consider an uniform linear array of n identical patch antennas placed at the same distance d between them as in fig. 1. for the theoretical study of this configuration, it is assumed that in the elementary antennas, harmonic signals of the same frequency but different amplitudes and phases are injected. the mathematical expression of the total electromagnetic field generated by the array in far-field regions (fraunhofer regions) can be written as the product of the electromagnetic field of the reference antenna (which is considered the first antenna in the array) and a term which depends on the amplitudes and phases of the injected signals etotal = ere f ∗ f (θ). (1) thereafter, the gain of the antenna array will be considered, knowing that it is a normalization of the antenna arrays principle and solutions: robust control approach 163 wm sin 2π f0tw1 sin 2π f0t w2 sin 2π f0t wn sin 2π f0t θ z0 d figure 1: an uniform antenna array amplitude of the electromagnetic field. the array factor can be written as f (θ) = n ∑ m=1 wme− j(m−1)k0d cos θ, (2) where wm = ame jϕm . in the easiest emission case, the amplitudes have the same value, the mathematical expression of the radiation pattern is f (θ ) = 1 n sin nγ2 sin γ2 , (3) where γ = ϕ − k0d cos(θ ), n the number of antennas, d the distance between them. it can be seen that a quantity of the radiated energy is lost in the side lobes which implies a certain weakening of the antenna array gain. it is obvious that in the emission case, both variations of the amplitudes and the phases of the carrier signals are in a large interval. this paper proposes a new technique to generate such signals, which have the same frequency and different phases and amplitudes. it can be concluded that both variations of the amplitudes and phases are in a large interval. 3 problem formulation 3.1 problem statement if the amplitude variation can be easily solved by using variable gain amplifiers, the problem of the phase variation is more constraining. there are several techniques which permits solving this problem. the main technical problem with beamforming for transmit is realizing the phase and amplitude of the signals in each antenna channel. the first step will be generating the reference signal with the selected or required frequency. it has to be modulated with the information to be transmitted (e.g. radar, communication). then it has to be amplified and distributed to all transmit channels. then the individual signals have to be weighted for beamforming, that means amplified or attenuated, according to the desired weighting amplitude. the desired phase has to be realized by a suitable steerable phase shifter or delay line. digital devices are now available, under the headline "software defined radio". this technique is not usable on frequencies over 400 mhz. here, techniques, which are pointed out, are designed for applications over 2ghz. techniques can be divided into both main approaches. the first approach uses the signal generated by one oscillator and the second which uses signals generated by array of coupled oscillators. signals with the same frequency but different phases and amplitudes can be built by delaying the signal generated with one master oscillator using high-frequency power dividers and variable delay lines or butler couplers. this approach is very useful when discrete-time systems are built. another approach 164 florin hutu, sebastien cauet, patrick coirault is to use polyphasic oscillators and a multiplexing system [5]. but in these techniques, it can not be obtained continuous phase variations. the second approach is based on the synchronization of arrays of oscillators having their free running frequencies with a weak dispersion. in [6, 8] it was demonstrated that arrays of coupled nonlinear oscillators can synchronize. moreover, according to the coupling strength and to free-running frequencies, phase variations can be made. recent works [14] shows how the phase variation can be guaranty by changing only the free-running frequencies of all coupled oscillators in the array. in order to generate these carrier signals, the following general schematic fig. 2 is proposed. ( f0 , an , ϕn ) + + + slave3slave2master slaven σcτn−1σcτ2σcτ1 gain1 gain2 gain3 gainn σσς ( f0 , a1 , ϕ1 ) ( f0 , a2 , ϕ2 ) ( f0 , a3 , ϕ3 ) figure 2: unidirectional coupling of a chain of oscillators this schematic is a variation of the york’s approach shown in [15]. because of the unidirectional coupling, each slave oscillator is driven only by its left neighbor. hence, the study of this configuration is reduced to the study of a pair of two non-linear systems (master-salve synchronization). the purpose is to design the parameters of the controller system σc in order to make the output ye(t) tends toward zero. when this objective is fulfilled, the delayed output of the master oscillator and the output of the slave oscillator become identical, so both oscillators are synchronized. generally, because of the technological realization, the oscillators don’t have the same free running frequencies. this is the reason why the feedback loop was introduced to guarantee the robust synchronization between both oscillators. the delay element and the variable gain amplifier will guaranty different phases and amplitudes for the output signals. the difference between the oscillators will be modeled as a variation of the slave oscillator parameters around those of the master oscillator parameters, which is considered as the reference. the variations due to the temperature or at the ageing of the components are modeled by a polytopic uncertainty of the master oscillator parameters around the nominal values. the oscillators are built using a double differential pair structure. in order to determine the parameters of the dynamical controller, the nonlinear oscillators is modeled using the van der pol model. in this article, we consider that the system is a perturbed van der pol model as: { ẋ = a(θ1)x + g(x,t, θ1) + bu y = cx , (4) where x = [ il v0 ] a(θ1) =   0 1 l0 − 1 c0 0   u = iin j c0 g(x,t, θ1) =   0 α c0 x2 − β c0 x2 3   b = [ 0 1 ] c = [ 0 1 ] , (5) with the uncertain parameters θ1 = [α, l0,c0]. antenna arrays principle and solutions: robust control approach 165 3.2 master-slave synchronization the structure is made up by two different systems which belong to the class previously described. the master system is considered independent (u = 0) and the dynamical controller σc drives the slave system using the error signal as reference. the error signal is constituted by the difference between a delayed version of the master output and the slave output. the state-space representation of the master system can be written as follows σm : { ˙xm = am(θ1)xm + gm(xm,t, θ1) ym = cxm . (6) for the slave system, the state-space representation can be written as σs : { ẋs = as(θ1)xs + gs(xs,t, θ1) + b2u ys = cxs . (7) between the parameters of the master and the slave oscillators, it is considered that there is the same difference δ    ls = lm (1 + δ ) cs = cm (1 + δ ) αs = αm (1 + δ ) βs = βm (1 + δ ) . (8) in that case, this notation can be introduced am(θ1) = as(θ1) + b1(θ2). (9) this difference is transformed into the difference between the state matrix of the master and the slave. with the assumptions in (8), b1(θ2) can be written as follows b1(θ2) =   0 − δ lm (1 + δ ) δ cm (1 + δ ) 0   . (10) if an error state is defined as e(t) = xm(t −τ)−xs(t), (11) a state-space representation can be written σe : { ė = am(θ1)e−b1(θ2)xs + eg(xm, xs,t, θ1)−b2u ye = ce , (12) where eg(xm, xs,t, θ1) = gm(xm,t, θ1)−gs(xs,t, θ1). (13) 3.3 nonlinear bound determination in order to determine the bounds of the nonlinearities difference, the scalar function f : d1 7→ d2 f (x) = −α x + β x3 is used. the bounds can be considered as the slopes of the tangents passing through x = xm and x = 0 of f (x). −α(x2 −x1) ≤ ( f (x2)− f (x1)) ≤ (−α + 3β xm2)(x2 −x1) ∀x1, x2 ∈ d1 (14) 166 florin hutu, sebastien cauet, patrick coirault consider both nonlinear oscillators and the domain d1 = [−1.35v, 1.35v ], then the bound of the nonlinearities difference (13) can be written as follows   0 − 1 c0 ( α + 3β−0.22 )   ≤ eg(xm, xs,t, θ ) ≤   0 α c0   . (15) 3.4 controller synthesis assume that am(θ1) resp. b1(θ2) are two matrices that belong to a polytope of matrices and it is represented by a convex combination of the extreme matrices ai resp. b1i with i = 1 . . . 2m . am = { am(θ1)| am(θ1) = 2m ∑ i=1 ξiai; ξi ∈ ∆1 } (16) and consider that the matrix eg(xm, xs,t, θ1) can be bounded with nb(θ1) being its upper bound eg(xm, xs,t, θ1) ≤ nb(θ1)e. (17) the worst case for our system is the superior limit, then the matrix an (θ ) = am(θ1) + nb(θ1). assume that the dynamical output controller of the system (12) is described by the following statespace representation and its dimension is nc. σc : { ẋc = acxc + bcye u = ccxc + dcye (18) the purpose of this controller is to make the slave system follow the delayed output of the master system. this condition is performed when the error signal defined in (11) tends toward zero. the term b1(θ2)xs, representing the difference between both systems, acts as a perturbation on the error state e. the synthesis of this controller has been made with a technique similar to [9, 10] the following theorem solves the problem of variable matrices product in the synthesis problem by introducing extra unknown variable matrices. theorem 1. if there exists a set of matrices pi > 0, a state feedback controller k0, an unknown variable square and nonsingular matrix g ∈ rnu+nc , an unknown variable matrix h ∈ r(nu+nc)×(nu+nc) and four unknown variables matrices f1, f4 ∈ r(nx+nc)×(nx+nc), f2 ∈ rnx×(nx+nc) and f3 ∈ r(n∞+nc)×(nx+nc) such that the inequality (19) is verified, then the dynamical controller k = g−1l makes the error system (12) asymptotically stable for all matrices an (θ1) and b1(θ2) described as a polytope. φ2 + 1sym      f1 f2 f3 f4 o   [ o o o o b̃2 ]    + sym      o o o o i   l [ c̃ o o o o ]    +sym      o o o o i   g [ −k0 o o o −i ]    < o; (19) antenna arrays principle and solutions: robust control approach 167 the matrix φ2 is defined as follows φ2 =   o o ccl t pi o o −γi o o o ccl o −γi o o pi o o o o o o o o o   + sym      f1 f2 f3 f4 o   [ ã0i b̃1 j o −i o ]    ∀i ∈ {1 . . . 2m} and ∀ j ∈ {1 . . . 2p} , (20) where ã0i = ãni + b̃k0 and k = [ dc cc bc ac ] . (21) the expression (16) can be numerically solved using matlab’s© "lmi toolbox". 4 numerical results in order to check the theoretical result, a transistor-based simulation has been done using agilent’s ads© software and mosfet transistors in 0.35µ m silicon technology. it was considered that all the parameters of an (θ ) have ±5% variation around their nominal values. this variation can be seen as the variation depending on the temperature of the oscillators that are built on the same integrated circuit substrate. this is mathematically transformed into the variation of the state matrix an (θ1) inside the polytope. using the matlab’s ”lmi toolbox” applied to the 8 vertices of the polytope, the following output-feedback controller was found k = [ dc cc bc ac ] = [ 2.66588·1011 22.3994 7.51499·109 −1.1885 ] . (22) it assures the synchronization of oscillators having δ = ±5% difference between parameters. this difference is represented by variation of the perturbation matrix b1(θ2) inside the polytope. variations between lm , cm and ls, cs parameters, corresponds to a possible difference between the free-running frequencies of both oscillators f0s ∈ [ f0m (1−|δ|)2 f0m (1 +|δ|)2 ] . (23) the difference between αm , βm and αs, βs stands for a possible difference between the transistor operating points of both nonlinear oscillators. this controller was applied to a pair of both non-linear oscillators. their free running frequencies are f0m = 2ghz and f0s = 2.2ghz. it has been chosen those frequencies in order to build a discrete component platform. in fig. 4 are presented both output voltages for master and slave oscillators and is divided into three sequences. a first sequence in which, the controller σc is not activated, both oscillators oscillates from their free-running frequencies. the second step at t = 55ns, the controller σc is activated. the obtained delay is closed to the imposed value (τ = t /4 = 1.25·10−10s). this delay will correspond to a orientation of the main lobe in θ = 120◦. finally, at t = 60ns, in order to verify the robustness of the dynamical controller, the free running frequency of the master oscillator was changed to fm = 2ghz. in fig. 3 the error between both output signals is presented. it can be seen that the error tends toward zero after a short period of time when the controller is started. 1sym{x} = x t + x ; ∀x ∈rn 168 florin hutu, sebastien cauet, patrick coirault figure 3: output voltages of both oscillators figure 4: the error between both signals provided by the oscillators 4.1 array of oscillators consider the situation where the θp = 60◦ direction must be privileged and θi1 = 90◦ and θi2 = 120◦ must be rejected. in table 4.1, the necessary and final values of the amplitudes and phases are shown for n = 8 antennas. figure fig.5 depicts the corresponding radiation pattern. the orientation of the main lobe is closed to the desired value θp = 58.14 and both interference directions θi1 = 90◦ and θi2 = 120◦ are rejected. 5 conclusion this paper presents a novel method to drive antenna arrays. it is based on unidirectionally coupled oscillators. an output feedback controller has been designed to assure synchronization with advanced control theory using lmi (linear matrix inequalities) tools. the result was successfully extended to a chain of eight unidirectionally coupled oscillators. additional research will be made to constrain the dynamical controller to realize the desired delay in order to eliminate the delay element. antenna arrays principle and solutions: robust control approach 169 amplitudes [v ] phases [◦] necessary simulated necessary simulated 0.269 0.21 0 0 0.21 0.2 143 138 0.14 0.14 180 179 0.4 0.4 -162 160 0.08 0.08 0 -2 0.14 0.13 63.1 57 0 0 0 7 0.3 0.29 -24.4 -30 table 1: computed and final values for the amplitudes and phases figure 5: the radiation pattern in the particular case of θp = 60◦ 170 florin hutu, sebastien cauet, patrick coirault bibliography [1] l. c. godara (ed.), applications of antenna arrays to mobile communications, part i: performance improvement, feasability and system consideration, vol. 85, proceedings of the ieee, 1997. [2] s. chandran, adaptative antenna arrays trends and applications, springer-verlag, 2004. [3] s. j. orfanidis, electromagnetic waves and antennas (2004). http://www.ece.rutgers.edu/ orfanidi/ewa/ [4] r. c. hansen, phased array antennas, wiley-interscience, 2001. [5] x. guan, h. hashemi, a. hajimiri, a fully integrated 24 ghz eight-element phased-array receiver in silicon, ieee journal of solid-state circuits 39 (12) (2004) 2311–2320. [6] p. liao, r. a. york, a six-element beam scanning array, ieee microwave and guided wave letters 4 (1) (1994) 20–22. [7] a. tombak, a. mortazawi, a novel low-cost beam-steering technique based on the extended resonance power dividing method, ieee transactions on microwave theory and techniques (2003) 1–7. [8] r. a. york, t. itoh, injection and phase-locking techniques for beam control, ieee transactions on microwave theory and techniques 46 (11) (1998) 1920–1929. [9] d. arzellier, d. peaucelle, s. salhi, robust static output feedback stabilization for polytopic uncertain systems: improving the guaranteed performance bound, in: rocond milan-italy, 2003. [10] d. mehdi, e. boukas, o. bachelier, static output feedback design for uncertain linear discrete time system, ima journal of mathematical control and information. [11] t. iwasaki, r. e. skelton, k. grigoriadis., a unified algebraic approach to linear control design, taylor and francis, 1998. [12] d. peaucelle, d. azellier, an efficient numerical solution for h2 static output feedback synthesis, europeen control conference. [13] s. boyd, l. e. ghaoui, e. feron, v. balakrishnan, linear matrix inequalities in system and control theory, vol. 15, studies in applied mathematics, usa, 1994. [14] t. heath, simultaneous beam steering and null formation with coupled, nonlinear oscillator arrays, ieee transactions on antennas and propagation 53 (6) (2005) 2031–2035. [15] r. a. york, nonlinear analysis of phase relationshiops in quasi-optical oscillator arrays, ieee transactions on microwave theory and techniques 41 (10) (1993) 1799–1809. florin hutu university of poitiers department : laii-esip 40 avenue du recteur pineau e-mail: florin.hutu@etu.univ-poitiers.fr received: november 28, 2007 antenna arrays principle and solutions: robust control approach 171 florin hutu was born in romania in 1977 and received the ph.d. degree in automatic control in 2007. he is currently a post doc at the university of poitiers where his interests are currently involved with the design of microwave antenna array. sébastien cauet was born in france in 1971 and received the ph.d. degree in automatic control in 1999. he is currently an associated professor of electrical and computer engineering at the university of poitiers where his interests are currently involved with the control of electrical power rotating motors, the design of microwave antenna array and application of control to chaos transmission. patrick coirault was born in poitiers, france, in 1965. he received the ph.d. degree from the university of poitiers in 1991. he was an assistant professor at the institute of technology of poitiers from 1992 to 1997. he is now professor at the institute of technology of chatellerault. his current research interests are in nonlinear identification and control, with applications to chaotic systems. ijcccv4n1draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 1, pp. 92-98 a note on the generative power of axon p systems xingyi zhang, jun wang, linqiang pan huazhong university of science and technology, department of control science and engineering key laboratory of image processing and intelligent control wuhan 430074, hubei, people’s republic of china e-mail: xyzhanghust@gmail.com, junwangjf@gmail.com, lqpan@mail.hust.edu.cn abstract: axon p systems are a class of spiking neural p systems. in this paper, the axon p systems are used as number generators and language generators. as a language generator, the relationships of the families of languages generated by axon p systems with finite and context-free languages are considered. as a number generator, a characterization of the family of finite sets can be obtained by axon p systems with only one node. the relationships of sets of numbers generated by axon p systems with semilinear sets of numbers are also investigated. this paper partially answers some open problems formulated by h. chen, t.-o. ishdorj and gh. păun. keywords: membrane computing, sn p systems, axon p systems 1 introduction the spiking neural p systems (in short, sn p systems) are a class of bio-inspired computing devices introduced in [6], which attempts to incorporate the idea of spiking neurons into the area of membrane computing. the resulting models are a variant of tissue-like and neural-like p systems, with specific ingredients and way of functioning inspired from spiking neurons. in sn p systems, the main “informationprocessor" is the neuron, while the axon is only a channel of communication without any other role – which is not exactly the case in neurobiology. so, recently, a special form of spiking neural p systems, called axon p systems, is introduced in [3], which corresponds to the activity of ranvier nodes of neuron axon. actually, axon p systems are a sort of linear sn p systems. spikes are transmitted along the axon, to the left and to the right, from one node to another node, and an output is provided by the rightmost node. a symbol bi is associated with a step when i spikes exit the system, in this way a string is associated with a computation. in [3], the language generating power of axon p systems under the above definition was investigated, and many open problems and research topics were formulated. in this paper, we continue the study of axon p systems, specifically, the number generative power and the language generative power are investigated, and in this context we answer some open problems formulated in [3]. sn p systems can be used as number generators (e.g., [2, 4, 6, 7]) or language generators (e.g., [1, 2, 5, 10, 12]). as a variant of sn p systems, axon p systems can also be used as number generators and language generators. as a number generator, we do not care whether or not the computation halts, but we only request that the output node spikes exactly twice during the computation, and the result of a computation is the number of steps elapsed between the two moments when the output node spikes. in this case, a characterization of the family of finite sets is given by axon p systems with one node. also, the relationships of sets of numbers generated by axon p systems with semilinear sets of numbers are also investigated. as a language generator, each configuration is described by a corresponding string, and the result of a halting computation is defined as the strings associated with configurations where the system emits a spike. in this case, the relationships of the families of languages generated by axon p systems with finite and context-free languages are considered. the paper is organized as follows. in section 2, formal language theory prerequisites useful in the following sections are recalled. in section 3, the definition of axon p systems and the problems considered in this paper are given. axon p systems as number generators and language generators are investigated respectively in section 4 and section 5. conclusions and remarks are drawn in section 6. 2 formal language theory prerequisites we assume the reader to be familiar with basic language and automata theory, as well as basic membrane computing [8] (for more updated information about membrane computing, please refer to [11]), so that we specify here only a few notations and basic definitions. copyright © 2006-2009 by ccc publications a note on the generative power of axon p systems 93 let us start by mentioning the following convention: when comparing two generative or accepting devices, number zero is ignored (this corresponds to the usual convention in language theory of ignoring the empty string). for an alphabet v , v ∗ denotes the set of all finite strings over v , with the empty string denoted by λ . the set of all nonempty strings over v is denoted by v +. when v = {a} is a singleton, then we write simply a∗ and a+ instead of {a}∗, {a}+. the length of a string x ∈ v ∗ is denoted by |x|. for a language l ⊆ v ∗, the set length(l) = {|x| | x ∈ l} is called the length set of l. the families of finite, regular, linear and context-free languages are denoted by fin, reg, lin, cf, respectively. the families of length sets of languages in fin, reg, lin and cf are denoted by nfin, nreg, nlin, ncf, respectively. the family of languages generated by l systems is denoted by l, and we add the letter e in front of l if extended l systems are used. we also denote by slin the family of semilinear sets of numbers (the subscript indicates that we work with one-dimensional vectors, not with semilinear sets of vectors in general). it is known that the following equalities hold true: nreg = nlin = ncf = slin (see, e.g., [8]). a regular expression over an alphabet v is defined as follows: (i) λ and each a ∈ v is a regular expression, (ii) if e,e are regular expressions over v , then (e)(e), (e)∪(e), and (e)+ are regular expressions over v , and (iii) nothing else is a regular expression over v . with each expression e we associate a language l(e), defined in the following way: (i) l(λ ) = {λ } and l(a) = {a}, for all a ∈ v , (ii) l((e) ∪ (e)) = l(e) ∪l(e), l((e)(e)) = l(e)l(e), and l((e)+) = l(e)+, for all regular expressions e,e over v . non-necessary parentheses are omitted when writing a regular expression, and also (e)+ ∪ {λ } can be written as e∗. a chomsky grammar is given in the form g = (n,t,s,p), with n being the nonterminal alphabet, t the terminal alphabet, s ∈ n the axiom, and p is the finite set of productions. for regular grammars, the productions are of the form u → v, for some u ∈ n,v ∈ t ∪ tn (in regular grammars, we also allow productions of the form u → λ , but only when this is useful for simplifying the grammar: because of the convention that the empty string is not counted when comparing the languages generated by two grammars, such productions are not necessary in regular grammars). 3 axon p systems we now introduce the axon p systems. an axon p system of degree m ≥  is a construct of the form π = (o, ρ, . . . , ρm), where: 1. o = {a} is the singleton alphabet (a is called spike); 2. ρ, . . . , ρm are (ranvier) nodes, of the form ρi = (ni,ri),  ≤ i ≤ m, where: a) ni ≥  is the initial number of spikes contained in ρi; b) ri is a finite set of rules of the form e/a c → (al,ar), where e is a regular expression over a, c ≥ , and l,r ≥ , with the restriction that r contains only rules with l = . the nodes are arranged along an axon in the order ρ, . . . , ρm, with ρm at the end of the axon; this means that node ρm is the output node of the system. a rule e/ac → (al,ar) ∈ ri is used as follows. if the node ρi contains k spikes, and ak ∈ l(e),k ≥ c, then the rule can be applied, and this means consuming (removing) c spikes from ρi (thus only k − c spikes remain in ρi), and it sends l spikes to its left hand neighbor and r spikes to its right hand neighbor; the first node, ρ does not send spikes to the left, while in the case of the rightmost node, ρm, the spikes sent to the right are “lost” in the environment. a global clock is assumed, marking the time for the whole system, hence the functioning of the system is synchronized. if a rule e/ac → (al,ar) has e = ac, then we will write it in the simplified form ac → (al,ar). if several rules can be used at the same time, then the one to be applied is chosen non-deterministically. 94 xingyi zhang, jun wang, linqiang pan during the computation, a configuration of the system is described by the number of spikes present in each node; thus, the initial configuration is 〈n, . . . ,nm〉. using the rules as described above, one can define transitions among configurations. a transition between two configurations c,c is denoted by c ⇒ c. if c = 〈k,k, . . . ,km〉, then c = 〈k′,k′, . . . ,k′m〉, where k′i = (ki − ci) + ri− + li+, i is the current node, ci is number of spikes consumed in this node, and ri−, li+ are the numbers of spikes sent to this node by the neighboring nodes,  ≤ i ≤ m − . in the case of i =  or i = m, there is only one neighbor. any sequence of transitions starting in the initial configuration is called a computation. a computation halts if it reaches a configuration where no rule can be used. in this paper, we consider the following two ways of defining the result of a computation for axon p systems. (i) similar to [6] and [10], we do not care whether or not the computation halts, but we only request that the output node spikes exactly twice during the computation. then, the number of steps elapsed between the two spikes is the number computed by the axon p system along that computation. we denote by n(π ) the set of numbers computed in this way by the axon p system π , and by spikapm(rulek,consp) the family of sets n(π ) generated by axon p systems with at most m nodes, at most k rules in each node, each rule consuming at most p spikes. as usual, any of these parameters is replaced by ∗ if it is not bounded. (ii) as formulated in [3], a language is associated with a computation of axon p systems in the following way: for each node ρi we consider a symbol ci and a configuration 〈k, . . . ,km〉 is described by the string ck . . .c km m ; then, the result of a halting computation is defined as the strings associated with configurations where the system emits a spike. all these strings form the language generated by the system. we denote by l(π ) the language generated in this way by the axon p system π , and by lapm(rulek,consp) the family of languages l(π ) with m,k, p having the same meaning as above. 4 axon p systems as number generators in this section, we investigate the number generative power of axon p systems. 4.1 a characterization of nfin in [6], it has been proved that sn p systems can characterize nfin by using only one neuron. for axon p systems, we have a similar result. theorem 1. nfin = spikap(rule∗,cons∗). proof. the inclusion spikap(rule∗,cons∗) ⊆ nfin can be easily proved: in each step, the number of spikes in an axon p system with only one node decreases by at least one, hence any computation lasts at most as many steps as the number of spikes present in the system at the beginning. thus, axon p systems with only one node can only compute finite sets of numbers. to prove the opposite inclusion nfin ⊆ spikap(rule∗,cons∗), let us take a finite set of numbers, f = {n, . . . ,nk}, and we assume that we have n < n < . . . < nk. an axon p system that generates f is shown in figure 1. initially, the node contains nk +  spikes, hence, only the rule a nk+/a → (λ ,a) can be used in the first step. it consumes one spike and immediately sends a spike to the environment. in the next step, we have to continue with rules ank+−t/a → (λ , λ ), for t = , and then for the respective t = , . . . ,n. but for t = n (in step n + ), there is another rule a nk+−t → (λ ,a) which can be used, non-deterministically chosen. if we choose the second rule, the system will send a spike to the environment again and then the computation halts. therefore, the number n will be generated. if we choose the first rule, the process will continue, and in a similar way the numbers n, . . . ,nk− can be generated in turn. in step nk +  (the last step) only the rule ank+−t → (λ ,a) with t = nk can be used, therefore, the number nk can be generated and this concludes the computation. consequently, all the numbers in f can be generated by the above axon p system, which concludes the proof. a note on the generative power of axon p systems 95 ank+ ank+/a → (λ ,a) ank+−t/a → (λ , λ ) t = , , . . . ,nk −  ank+−t → (λ ,a) t = ni,  ≤ i ≤ k figure 1: an axon p system generating a finite set of numbers 4.2 relationships with semilinear sets of numbers as mentioned in section 4.1, sn p systems with one neuron generate exactly the family of finite sets of numbers. actually, sn p systems with two neurons also characterize the family of finite sets. so, the following results on axon p systems are unexpected: such systems with two nodes generate all semilinear sets of numbers. theorem 2. slin ⊆ spikap(rule∗,cons∗). proof. consider a regular grammar g = (n,t,s,p) with n = {a,a, . . . ,an},n ≥ , s = an, t = {b,b, . . . ,bs}, and the productions in p are of the forms ai → bka j, ai → bk,  ≤ i, j ≤ n,  ≤ k ≤ s. then length(l(g)) can be generated by an axon p system as shown in figure 2. in the initial configuration we have n +  spikes in node ρ and n +  spikes in node ρ, therefore, in the first step, only the rule an+/a → (λ ,a) can be used in node ρ, and the rule an+/a → (λ , λ ) is used in node ρ. thus, a spike is sent to the environment and n spikes remain in node ρ and n spikes in node ρ. in the next step, node ρ fires by a rule a n+i/an+i− j → (an, λ ) or an+i → (λ ,a) associated with a production an → bka j or an → bk from p, for i = n. if the second rule is used, another spike is sent to the environment and the computation halts. in this way, the generated number is . if the first rule is used, n − j spikes are consumed from node ρ. in this step node ρ also fires and sends n spikes to node ρ. it will send n spikes back to node ρ as long as it receives n spikes from node ρ. assume that in some step t, the rule an+i/an+i− j → (an, λ ), for ai → bka j, or an+i → (λ ,a), for ai → bk, is used, for some  ≤ i ≤ n, and n spikes are received from node ρ. if the first rule is used, then n spikes are sent to node ρ, n + i − j spikes are consumed, and j spikes remain in node ρ. then in step t + , we have n + j spikes in node ρ, and a rule for ai → bka j or ai → bk can be used. in step t +  node ρ also receives n spikes. in this way, the computation continues. if the second rule is used, then no spike is sent to node ρ, all spikes in node ρ are consumed, and n spikes are received from node ρ, which will remain in node ρ forever. moreover, in this step another spike is sent to the environment again. then the computation halts. we know that, if we use a production ai → bka j or ai → bk one time, then the length of a string generated by the grammar g increases by one, and this corresponds to the number of steps increasing by one by using the associated rule an+i/an+i− j → (an, λ ) or an+i → (λ ,a) one time in the axon p system. moreover, the second spike is sent to the environment and the computation halts whenever a rule an+i → (λ ,a) is used, which corresponds to a production ai → bk being used in g. therefore, the length set of all the strings in l(g) can be generated, and the proof is complete. the inclusion in the previous theorem is proper. theorem 3. spikap(rule,cons) − slin 6= /0. proof. let us consider the axon p system from figure 3. in the initial configuration, only node ρ contains  spikes, hence it fires in the first step by the rule a(a)+/a → (λ ,a) and sends  spikes to node ρ. in the next step, the rule a → (a, λ ) or a → (λ ,a) can be used in node ρ, non-deterministically chosen. if we use the rule a → (a, λ ), then we get a number of spikes of the form k +  in the second node, hence the first rule a(a)+/a → (a, λ ) in node ρ is applied as many times as possible, thus returning 96 xingyi zhang, jun wang, linqiang pan -¾ 1 2 an+ an+/a → (λ , λ ) an → (λ ,an) an+ an+/a → (λ ,a) an+i/an+i− j → (an, λ ) for ai → bka j ∈ p an+i → (λ ,a) for ai → bk ∈ p figure 2: an axon p system generating a semilinear set the spikes to node ρ. to end this returning, we have to use the rule a → (a, λ ) in node ρ, which makes again the number of spikes from node ρ be of the form k +  (note that no rule can be applied in any node when the number of spikes is multiple of 4). this process can be iterated any number of times, thus multiplying by  the number of spikes present in node ρ. assume that we have a configuration 〈n + , 〉 in step t, for some n ≥ ; initially, this is the case, with n = . in node ρ, the rule a(a )+/a → (λ ,a) can be used as many times as possible until the node remains with only one spike. it moves all spikes to the second node, multiplied by . therefore, in step t + n− + , we have a configuration 〈, n+〉. in the next step, node ρ can also use the rule a → (λ ,a) instead of a → (a, λ ). then the number of spikes from node ρ is of the form k +  (n+ +  spikes), hence the rule a(a)+/a → (λ ,a) should be applied in step t + n− +  and a spike is sent to the environment. at the same time, the number of spikes decreases by one and will be of the form k +  (n+ +  spikes). therefore, the rule a(a)+/a → (λ , λ ) should be applied in step t + n− + . this rule does not change the form of the number of spikes and it remains in the form of k + , hence it is used as many times as possible. in this way, no spike is sent to the environment until the number of spikes from ρ becomes  and this process needs  n −  steps. then, in the step t + n− + n + , the rule a → (λ ,a) should be used and the second spike is sent to the environment. therefore, the number of steps elapsed between the two spikes which were sent to the environment is n and the computation halts. consequently, the set {n | n ≥ } can be generated by this system, which, obviously, is not a semilinear set. -¾ 1 2 a a(a)+/a → (λ ,a) a → (λ ,a) a → (λ ,a) a(a)+/a → (a, λ ) a → (a, λ ) a(a)+/a → (λ ,a) a(a)+/a → (λ , λ ) a → (λ ,a) figure 3: an axon p system generating a non-semilinear set {n|n ≥ } in [6], it has been shown that semilinear sets of numbers can be characterized by sn p systems with a bound on the number of spikes present in any neuron, but the number of neurons is not bounded. the following theorem also gives a characterization of semilinear sets of numbers, but here only two nodes are used in the axon p systems. theorem 4. slin = spikap(rule∗,cons∗,bound∗), where bound∗ indicates that axon p systems have a bound on the number of spikes present in any node, but this bound is not specified. proof. from theorem 2, it is enough to prove the inclusion spikap(rule∗,cons∗,bound∗) ⊆ slin. the proof is similar to the one of lemma 9.1 in [6], so we do not recall it here. a note on the generative power of axon p systems 97 5 axon p systems as language generators we now pass to considering the language generative power of axon p systems. all strings generated by an axon p system in the way mentioned in section 3 are of the form ck c k  . . .c km m , where 〈k,k, . . . ,km〉 is a configuration of the system. therefore, if there is a bound on the number of spikes in any node of axon p systems, we can directly have the following result. remark 5. lapn(rule∗,cons∗,bound∗) ⊆ fin, for n ≥ . moreover, as a direct consequence of this restrictive way of defining the languages associated with axon p systems, we cannot find a characterization of finite languages for the general axon p systems. for instance, it is not difficult to find that the string ccc cannot be generated by any axon p system. 5.1 beyond cf theorem 6. there exists at least a language l ∈ el −cf, which can be generated by axon p systems. proof. let us consider the language l = {cn c n  c n  | n ≥ }; it is obvious that l ∈ el −cf. we construct the axon p system whose initial configuration is shown in figure 4. 1 2 3 a (a)+/a → (λ ,a) a (a)+/a → (a,a) a (a)+/a → (a, λ ) (a)+/a → (a,a) -¾-¾ figure 4: an axon p system generating the language {cn c n  c n  | n ≥ } initially, each node of the system contains two spikes. hence, each node can be activated, and in node ρ both the rule (a )+/a → (a, λ ) and the rule (a)+/a → (a,a) can be applied, non-deterministically chosen. if the first rule is applied, then the two spikes in each node are consumed and no spike is sent to the environment. at the same time, each node accumulates 4 spikes (the 4 spikes in nodes ρ and ρ are received from node ρ; the 4 spikes in node ρ are received from both node ρ and node ρ, two from node ρ and two from node ρ). in this way, all the nodes can be activated in the next step. if in node ρ we continue using the rule (a)+/a → (a, λ ), then in a similar way each node obtains 6 spikes. this process can be iterated until the second rule is applied. in this case, the number of spikes in each node increases by two in each step. therefore, each node of the system accumulates n spikes in step n, for n ≥ . at any moment, we can also apply the second rule in node ρ. assume that it is applied in step n + , then node ρ sends a spike to the environment, which means that the string c n  c n  c n  is generated by this system. at the same time, node ρ accumulates n +  spikes, node ρ accumulates n +  spikes, and node ρ accumulates n +  spikes. in the next step, only node ρ can be activated. thus, it consumes two spikes and sends two spikes to node ρ. this process can be iterated until all the spikes in node ρ are moved to node ρ, and then the computation halts. therefore, the language {cn c n  c n  | n ≥ } can be generated by the axon p system and this concludes the proof. 6 conclusions and remarks in this paper, the number generative power and the language generative power of axon p systems are investigated. as a variant of sn p systems, there remain many open problems and research topics about axon p systems to be considered. one important aspect is suggested by the research about sn p systems. for instance, as usual in sn p systems, an arbitrary delay can be considered in axon p systems. what about considering the spike trains (finite or infinite) themselves as the result of a computation in axon p systems, as investigated in [10]. the various applications for axon p systems also deserve to be considered. as suggested by gheorghe păun in [9], a more general and probably more interesting problem is combining neurons and axons in a global model; maybe also astrocytes can be added, thus obtaining a 98 xingyi zhang, jun wang, linqiang pan more complex model, closer to reality. please refer to [3, 9] for more open problems and research topics related to axon p systems. acknowledgements comments and suggestions from gheorghe păun are greatly acknowledged. the project was supported by national natural science foundation of china (grant nos. 60674106, 30870826, 60703047, 60503002, and 60533010), 863 program of china (2006aa01z104), program for new century excellent talents in university (ncet-05-0612), ph.d. programs foundation of ministry of education of china (20060487014), chenguang program of wuhan (200750731262), and hust-srf (2007z015a). bibliography [1] h.m. chen, m. ionescu, m. j. pérez-jiménez, r. freund, and gh. păun, on string languages generated by spiking neural p systems, fundamenta informaticae, vol. 75(1–4), pp. 141–162, 2007. [2] h. m. chen, m. ionescu, t.-o. ishdorj, a. păun, gh. păun and m. j. pérez-jiménez, spiking neural p systems with extended rules, in: m. a. gutiérrez-naranjo, gh. păun, a. riscos-núñez, f. j. romero-campero, eds., fourth brainstorming week on membrane computing, vol. i, rgnc report 02/2006, research group on natural computing, sevilla university, fénix editora, pp. 241–266, 2006. [3] h. m. chen, t.-o. ishdorj and gh. păun, computing along the axon, progress in natual science, vol. 17(4), pp. 417–423, 2007. [4] o. h. ibarra, s. woodworth, f. yu and a. păun, on spiking neural p systems and partially blind counter machines, natural computing, vol. 7(1), pp. 3–19, 2008. [5] o. h. ibarra and s. woodworth, characterizing regular languages by spiking neural p systems, international journal of foundations of computer science, vol. 18(6), pp. 1247–1256, 2007. [6] m. ionescu, gh. păun and t. yokomori, spiking neural p systems, fundamenta informaticae, vol. 71(2–3), pp. 279–308, 2006. [7] m. ionescu, gh. păun and t. yokomori, spiking neural p systems with exhaustive use of rules, international journal of unconventional computing, vol. 3(2), pp. 135–154, 2007. [8] gh. păun, membrane computing – an introduction, springer-verlag, berlin, 2002. [9] gh. păun, twenty six research topics about spiking neural p systems, in: m. a. gutiérreznaranjo, gh. păun, a. romero-jiménez, a. riscos-núñez, eds., fifth brainstorming week on membrane computing, rgnc report 01/2007, research group on natural computing, sevilla university, fénix editora, pp. 263–280, 2007. [10] gh. păun, m. j. pérez-jiménez and g. rozenberg, spike trains in spiking neural p systems, international journal of foundations of computer science, vol. 17(4), 975–1002, 2006. [11] the p system web page: http://ppage.psystems.eu [12] x. y. zhang, x. x. zeng and l. q. pan, on string languages generated by spiking neural p systems with exhaustive use of rules, natural computing, to appear. xingyi zhang was born in china on june 6, 1982. he received his master degree in applied mathematics from huazhong university of science and technology in 2006. currently, his main research fields are formal language theory and its applications, unconventional models of computation, especially, membrane computing. he has published several scientific papers in international journals. linqiang pan was born in zhejiang, china on november 22, 1972. he got phd at nanjing university in 2000. since 2004, he is a professor at huazhong university of science and technology, china. his main research fields are graph theory and membrane computing. international journal of computers communications & control issn 1841-9836, 11(2):209-223, april 2016. influence model of user behavior characteristics on information dissemination s.c. han, y. liu, h.l. chen, z.j. zhang shaochun han beijing jiao tong university beijing, 100044, china 15620009060@126.com yun liu beijing jiao tong university beijing, 100044, china liuyun@bjtu.edu.cn huiling chen* 1. school of pharmaceutical science and technology tianjin university 2. tianjin university of traditional chinese medicine *corresponding author: 15022613010@163.com zhenjiang zhang beijing jiao tong university beijing, 100044, china zhjzhang1@bjtu.edu.cn abstract: quantitative analysis on human behavior, especially mining and modeling temporal and spatial regularities, is a common focus of statistical physics and complexity sciences. the in-depth understanding of human behavior helps in explaining many complex socioeconomic phenomena, and in finding applications in public opinion monitoring, disease control, transportation system design, calling center services, information recommendation. in this paper,we study the impact of human activity patterns on information diffusion. using sir propagation model and empirical data, conduct quantitative research on the impact of user behavior on information dissemination. it is found that when the exponent is small, user behavioral characteristics have features of many new dissemination nodes, fast information dissemination, but information continued propagation time is short, with limited influence; when the exponent is big, there are fewer new dissemination nodes, but will expand the scope of information dissemination and extend information dissemination duration; it is also found that for group behaviors, the power-law characteristic a greater impact on the speed of information dissemination than individual behaviors. this study provides a reference to better understand influence of social networking user behavior characteristics on information dissemination and kinetic effect. keywords: sir, behavior dynamics, scaling laws, information dissemination. 1 introduction the analysis target of user behavior time characteristics is the statistical regularities manifested when humans repeatedly engaged in certain things, which was firstly proposed by poisson introduced the concept of probability in his work of case judgment management, namely poisson distribution. when human data collection capabilities are limited, the poisson distribution is widely used as a classic means to depict human activity patterns. in recent years, with the emergence of high-performance processors and constant enhancement of computer parallel computing power, making the massive social network data processing copyright © 2006-2016 by ccc publications 210 s.c. han, y. liu, h.l. chen, z.j. zhang become possible. at present, through empirical analysis, research and mining user behavior characteristics of large data and use simulation technology [1][8], a large number of scholars make analysis of network relationships and identify potential objective law. by analyzing massive data of various networks, more and more facts have proven that, user behavior corresponded time interval distribution has obvious heavy-tailed effect, which can be well fitted by power function [16][18]. at the same time, scholars use massive data of social networks, from many fields, multi-angle and multi-dimensional human behavior characteristics were studied. for example, x.song et al. [9] analyzed the geographical distribution of twitter users, user’s neighbor nodes and the degree of correlation coefficient, and twitter users were grouped. h.kwak et al. [10] studied the average shortest interval and length of twitter micro blog, posts survival time, maximum repost depth and user grouping sorting features, the text sorted twitter users according to the number of fans and page-rank value, the final results of the two sorting methods are substantially the same, which is obviously different from the final sorting result obtained by users information forwarding number, indicating that there is not tight dependencies between users information forwarding number and their owned neighbor node. m.cha et al. [11] by comparing correlation coefficient of twitter users posts forwarded number, post reply number and the number of neighbor nodes, studied the effect of core users on information dissemination. the article [12] conducted further analysis of twitter posts forwarded relevant factors. the article [13] also conducted data analysis of scholarly articles downloads from an economy physics web site, and found that download rate of different papers show exponential decrease per unit time,and the average download rate f and its variance approximately satisfy f ∝ oα, of which, α is located between 0.6 to 0.9. the paper [14] extracted sina blog user’s interaction data, through network degree distribution analysis, it can be found that in the sina blog, the in-degree and out-degree obeys power-law distribution, but the exponent of out-degree is larger than in-degree, which explains that part of the blog user do not add more friends, and even of users do not have friends, and studies have found that the correlation coefficient of blog network in-link and out-link degree distribution is positive; while the correlation coefficient of out-link and in-link degree distribution is negative. in this paper, firstly carry out mathematical statistical analysis of users publish information and information reply time intervals in qq space data set, and by means of sir [15] model to investigate influence of time characteristics of user behavior on information dissemination process in the social network, and make comparative analysis of even stepping model. 2 data in this paper, the author utilized qq spatial data set. this data set is obtained by the use of crawling program wrote by python language. the program logins qq space by the way of simulated browser, automatically go to access to interaction information between user and his friends, and write into the corresponding xml file, then read xml into the database by using python parsing, remove user hidden qq number and other abnormal data, to get the final data set of qq space. topological statistics characteristics of the data set is shown: v denotes the number of nodes(4800), e denotes border coefficient(66475), d is the network diameter(5), c is network clustering coefficient(0.423), kmax is the maximum node degree(854), k is average node degree(30.882). influence model of user behavior characteristics on information dissemination 211 2.1 all nodes interval features all nodes features refers to the overall behavior features of all nodes in data set. figure 1 is analysis of group posting behavior characteristic in each board; figure 2 is analysis of group reply behavior characteristics in each board. it can be found from the figures that, all group behaviors are consistent with power-law distribution, and power exponent can be obtained through simulation fitting. in posting behavioral characteristics analysis,the exponent of message board is the largest, reaching 1.1223, while the exponent of talk board is only 0.816, with exponent of group posting and log board is at the average;however, in analysis of reply behavioral characteristics, the exponent of talk board is the largest, is 1.1041, and the smallest is exponent of log board; so even posting and replying behavioral characteristics are different in the same board. figure 1: analysis of group posting behavior characteristic in each board 2.2 individual node interval features the paper also analyzes the features of individual behavior, firstly the data sets are sorted in accordance with the number of nodes posting and the number of reply’s in descending order, and then select the first rank (denoted by a), three-quarters (denoted as b ), intermediate (denoted as c) and fourth (denoted as d) four-node data respectively, post and reply node behavior characteristics were analyzed. as shown in figure 3 and 4, the actual post and reply number are at a high level at data set node a and b, so the power index of posting and reply are relatively close, which shows that for active nodes, dealing with things usually by a specific behavior pattern, which is consistent with the literature conclusions; while at data set node c and node d, the actual post number is few but reply of a larger number, so causing power exponent of its post behavior is small, while power exponent of reply behavior is large. to explain this phenomenon, author of this paper used nc algorithm to calculate network binding targets of these four nodes, found that nodes a and b have greater binding, while the binding of node c and d are small, seen from the above exponential distribution of post and reply two types of nodes and the calculation result of nc index value, for node a and b such very active nodes, because of its dominant position in 212 s.c. han, y. liu, h.l. chen, z.j. zhang figure 2: analysis of group reply behavior characteristics in each board the message, the posting content has high credibility and timeliness, resulting in follow-up reply interaction increases, while for nodes c and d, as a result of information disadvantage, it can only attract other nodes to forward information by replying behavior. therefore, the posting and replying behaviors of active nodes are positively related, while posting and replying behaviors of non-active nodes are negatively related. 2.3 cluster interval features according to the above sort results, data sets are divided into 20 equal portions in this paper, which generate 20 clusters (the higher ranking clusters have higher degree of activity), distribution exponent of each cluster posting and reply behavior interval age calculated. in figure 5 and figure 6, from left to right are the eighth cluster, the tenth cluster and twelfth cluster posting and reply behavior time interval distribution. it can be found that with the lower degree of cluster activity, power index of the three cluster posting and reply behaviors also decreased, figure 7 is exponential distribution figure of 20 cluster posting behavior, from curve trend of the figure, we can draw a conclusion that cluster activity degree is positively correlated with power exponent,which is consistent with analysis results of the literature. 2.4 bm phase diagram analysis when the time interval between incidents follows power-law distribution, means that many events will concentrated occur in a relatively short period of time, followed by a long idle period, this situation is called event paroxysmal feature. from incident time interval distribution, for system with strong paroxysmal feature, most of the time interval will be less than the average event interval, but relatively large time interval may also occur, this phenomenon means that the standard deviation of its time distribution is relatively large. event paroxysmal feature can be measured by variation coefficient b of time interval, as shown in formula 1: b ≡ στ mτ −1 στ mτ + 1 = στ −mτ στ −mτ (1) influence model of user behavior characteristics on information dissemination 213 figure 3: characteristics of individuals posting behavior figure 4: characteristics of individual reply behavior figure 5: analysis of three cluster post behavior characteristics 214 s.c. han, y. liu, h.l. chen, z.j. zhang figure 6: analysis of three cluster reply behavior characteristics figure 7: relation diagram of 20 cluster exponential distribution and degree of activity of which στ and mτ represent standard deviation and average value of pτ , the value range of b is (-1 to 1), with respect to the poisson distribution, the average value and standard deviation are equal, the paroxysmal is 0 therefore, can be seen as an equilibrium point set between goh and barabasi; for recurring events, the time interval distribution is actually a δ function,the standard deviation is 0, b value is -1. for the power-law distribution, the standard deviation is much larger than average value, b is close to 1, that is, the closer to 1 indicates the stronger paroxysmal, close to 0 indicates a neutral, belonging to random events series, close to -1 indicates no paroxysmal, is cyclical periodic events. in addition to paroxysmal feature of events, events characteristics may also be depicted by memory description: time sequence of events has a certain memory, a long interval is also followed by a longer time interval, and a short interval is also followed by a shorter time interval, then all of the time intervals form a sequence according to occurring time sequence (time interval sequence of two successive behaviors), assume that this sequence has nτ elements, i.e., nτ +1 events occurred,define the previous nτ -1 elements constitute sequence 1, and define latter nτ -1 elements constitute sequence two, as shown in formula 2, pearson correlation of the two sequences can be used to measure the sequence memory. m ≡ 1 nτ −1 nτ −1∑ i=1 (τi −m1)(τi −m2) σ1σ2 (2) m1 and m2 are the mean of sequence 1 and sequence2 respectively, σ1 and σ2 are standard deviation of sequence 1 and sequence 2. obviously, the value range of m is also between (-1 to 1): m > 0 represents memory effect, m < 0 represents anti-memory effect. when m is close to 1, indicates a long (short) time interval is more inclined to corresponding long (short) time interval after another; when closes to 0 indicates a neutral; when closes to -1 indicates a long (short) time interval is more inclined to corresponding short (long) time interval after another. figure 8 is influence model of user behavior characteristics on information dissemination 215 figure 8: bm phase diagram of posting behavior bm phase diagram of posting amount exceed 10, 30, 50, 100, 200, 400, 600, 800 and 1000 posts respectively in data set. it can be found from the figure that, although there are great variance in number of posts, their average point location in bm phase diagram are stable between at the horizontal coordinates (0.244-0.26) and vertical coordinates (0.416-0.464), indicating that in qq space, user posting behavior has obvious paroxysmal and memory features. 3 model building in this paper, the information dissemination model involved utilized sir model of infectious disease dynamics to complete. define users of social network as nodes, friends relationship between nodes is defined as side, and nodes are divided into three categories: stifler,ignorant and spreaders. stiflers indicates that the node receives and knows contents of the information, but it does not take the initiative to disseminate this information; ignorant indicates that the node has not received the information sent by the neighboring nodes, so it will not conduct information dissemination, but it will receive this information at a certain probability; spreaders indicates that the node has received information from one of its neighboring nodes, at the next timestepping, the node will take the initiative to disseminate information to its neighboring nodes. for spreaders, in information dissemination process, if the neighbor node receiving information belongs to ignorant, then the neighboring node may become spreaders, and the node will conduct information dissemination at next time; if neighbor node receiving information belongs to spreaders or stiflers, i.e.the receiver has received the information, then the information receiving party will abandon to continue to spread this information, and if the neighbor node is spreaders, it will change its own state into stiflers. it can thus be seen that for social network node, its transfer between stiflers, ignorant and spreaders states depends not only on the node’s state at 216 s.c. han, y. liu, h.l. chen, z.j. zhang the current time-stepping step, also relates to state of its neighbor nodes for information interaction. at the same time this model assumes: social network structure is relatively static, that is number of nodes, sides relationship and weight between nodes do not change with time; in the social network, node send messages to its neighbor nodes at certain time-stepping,and after neighbor node receiving the information, it will be forwarded at a certain degree of probability;information will only spread among neighbors. to sum up, let the state transition rules as follows: 1. infection probability λ : after spreaders sending message to its neighbor nodes, ignorant will become spreaders at λ probability, and this information will be disseminated at the next time-stepping; 2. stiflers probability µ: spreaders will become stiflers at µ probability, if and only if spreaders contact with other neighbors spreaders or stiflers; 3. attenuation probability υ: with time attenuation, spreaders will no longer take the initiative to disseminate information, so define attenuation probability υ, namely, when spreaders has no interaction with any neighbor nodes, it will automatically become stiflers at υ probability. the probability λ of ignorant i to believe and actively disseminate information is associated with its neighbor node spread influence, so the spread probability of node i is: λ = 1− ∏ j∈ϕi (1− ωij∑ki m=1 ωij) (3) where in ϕi, is defined as the set of spread nodes of neighbor nodes to node i, ωij is defined as the connection weight between node i and node j. according to sir propagation model, it shows the entire information dissemination process: spreaders(j) + ignorant(i) λ−→ spreaders(j) + spreaders(i) spreaders(m) + spreaders(n) µ −→ stiflers(m) + spreaders(n) spreaders(m) + stiflers(k) µ −→ stiflers(m) + stiflers(k) spreaders(p) υ−→ stiflers(p) (4) 3.1 propagation model according to the evolution rule of formula 3, build mean-field differential evolution equations of information dissemination model. define s, i and r to represents spreaders, ignorant and stiflers states; for nodes of k degree, define the total number of nodes in spreaders, ignorant and stiflers states asmk,s,mk,i and mk,r, the total number of all nodes k degree is mk, and define: mk = mk,s + mk,i + mk,r (5) assuming in social network, the node i is in ignorant state at t time, in [t,t+△t] period of time, define piii as the probability of i will remain in ignorant state, definep i is as the probability of i to change from ignorant state to spreaders state, andpiis = 1 −p i ii.if j remains in ignorant state in △t period of time, indicating that the neighbor node of j in the spreaders state fail to disseminate information to j. so piiican be expressed as: piii = g∏ m=0 (1−△tλi) (6) influence model of user behavior characteristics on information dissemination 217 where in g=g(t) is defined as the total number of nodes neighboring i at t time, specifically as follows: ∏ g,t = ϕ(k,t)g(1−ϕ(k,t))k−g (7) where in, ϕ(k,t) is defined as probability of ignorant of k degree has adjacency relationship with certain spreaders at t time: ∏ (g,t) = ∑ k′ p(k′ | k)p(sk′ | ik) ≈ ∑ k′ p(k′ | k)ρs(k′, t) (8) in formula 8, p(k′ | k)is the correlation function of degree, that is conditional probability of node of k degree and node of k′ degree have adjacency relationship; p(sk′ | ik) is defined when node of k′ degree has connection relationship with uninfected node of k degree, the node belongs probability of propagation state; ρs(k′, t)is defined as spreaders density at t time and k′ degree. by traversing all possible g and i, the mean of piii can be calculated, i.e. pii(k,t), which is used to describe the mean field dynamics equation of the model: pii(k,t) = 1 mk ∑ i k∑ g=0 g∏ m=0 (1−△tλmi)ϕ(k,t)g[1−ϕ(k,t)]k−g (9) where in, mk is the total number of all nodes of k degree in the network. in [t,t+△t] period of time, piss is defined as the probability of spreaders to maintain spread state, traverse all possible g, the average probability pii(k,t) for node to maintain spread state: pii(k,t) = k∑ g=0 (1−µ△ t)gϕ(k,t)g(1−ϕ(k,t))k−g(1−υ △ t) = k∑ g=0 (1−µ△ t)ϕ(k,t)g(1−ϕ(k,t))k−g(1−υ △ t) = (1−µ△ t)ϕ(k,t) + 1−ϕ(k,t)k(1−υ △ t) = (1−µ△ tϕ(k,t)k)(1−υ △ t) = (1−µ△ t ∑ k′ p(k′ | k)[ρs(k′, t) + ρrs(k′, t)])k(1−υ △ t) (10) the probability for spreaders to become stiflers (contact) can be expressed as psr(k,t) = 1−pss(k,t). on the basis of average state transition probability, according to node change rules of formula 5, changing situation of nodes of k degree in three states in[t,t+△t]time period can be obtained, as shown in equation 11, 12 and equation 13: mk,i(t +△t) = mk,i(t)−mk,i(t)(1−pii(k,t)) = mk,i(t)−mk,i(t)[1− 1 mk ∑ i k∑ g=0 g∏ m=0 (1−△tλmi)ϕ(k,t)g[1−ϕ(k,t)]k−g] (11) 218 s.c. han, y. liu, h.l. chen, z.j. zhang mk,i(t +△t) = mk,s(t) + mk,i(t)(1−pii(k,t))−mk,s(t)(1−pss(k,t)) = mk,s(t) + mk,i(t)[1− 1 nk ∑ i k∑ g=0 g∏ m=0 (1−△tλmi) ϕ(k,t)g[1−ϕ(k,t)]k−g]−mk,s(t)[1− (1−µ△ t ∑ k′ p(k′ | k) [ρs(k′, t) + ρrs(k′, t)])k(1−υ △ t)] (12) mk,r(t +△t) = mk,r(t) + mk,s(t)(1−pss(k,t)) = mk,r(t) + mk,s(t)[1− (1−µ△ t ∑ k′ p(k′ | k) [ρs(k′, t) + ρrs(k′, t)])k(1−υ △ t)] (13) to simplify the calculations, denoteφ(k,g,t) = ϕ(k,t)g(1 −ϕ(k,t))k−g, for formula 11, the following variants can be realized: mk,i(t +△t)−mk,r(t) mk = mk,i(t) mk [1− 1 mk ∑ i k∑ g=0 g∏ m=0 (1−△tλmi)φ(g,k,t)] (14) denote ρi(k,t) = mk,i(t) mk . both ends of formula(14)take△t →0. since lim△t→0 ∏g m=0(1−△tλ mi) = ∑g m=0(−λ mi), so formula (14) can be defined as: φρi(k,t) φ(t) = ρi(k,t) mk ∑ j g∑ m=1 k∑ g=1 φ(g,k,t)λmi (15) similarly, it can be derived from formula (13) mk,r(t +△t)−mk,r(k,t) nk = mk,s(k,t) nk (1−pss(k,t)) = mk,s(k,t) mk [1− (1−µ△ t ∑ k′ p(k′ | k) [ρs(k′, t) + ρrs(k′, t)])k(1−υ △ t)] (16) at right end of formula(16) (1−µ△ t ∑ k′ p(k′ | k)[ρs(k′, t) + ρrs(k′, t)])k = ∑ k=0 nc(k,n)(−µ△ t ∑ k′ p(k′ | k[ρs(k′, t) + ρrs(k′, t)])n = ck0 + c k 1 (−µ△ t ∑ k′ p(k′ | k[ρs(k′, t) + ρrs(k′, t)]) = 1−kµ△ t ∑ k′ p(k′ | k[ρs(k′, t) + ρrs(k′, t)] (17) therefore, formula(16) is varied: influence model of user behavior characteristics on information dissemination 219 mk,r(t +△t)−mk,r(k,t) nk = ρs(k,t)[1− (1−kµ△ t ∑ k′ p(k′ | k)[ρs(k′, t) + ρrs(k′, t)])(1−υ △ t)] = ρs(k,t)(kµ△ t ∑ k′ p(k′ | k) + υ △ t + kµ△ t2 ∑ k′ p(k′ | k))[ρs(k′, t) + ρr(k′, t)] (18) both ends of formula (18) divide △t and take the limit △t → 0. φρr(k,t) φ(t) = kµρs(k,t) ∑ k′ [ρs(k′, t) + ρr(k′, t)]p(k′ | k) + υρs(k,t) (19) since φρ i(α,k,t) φ(t) + φρs(α,k,t) φ(t) + φρr(α,k,t) φ(t) = 0,so it is easy to obtain: φρs(k,t) φ(t) = ρi(k,t) mk ∑ j g∑ m=1 k∑ g=1 φ(g,k,t)λmi −kµρs(k,t) ∑ k′ [ρs(k′, t) + ρr(k′, t)]p(k′ | k)−υρs(k,t) (20) by formula (15), (19) and (20) simultaneous,obtain the dynamical evolution equations of information dissemination in social network for depicting changes in the relationship between spreaders,ignorant and stiflers density over time. 4 simulation analysis figure 9 is the propagation and evolution diagram of stiflers, ignorant and spreaders density under different power exponent α and even time-stepping( α = 0), and from left to right are spreads density evolution diagram, ignorant evolution diagram and stiflers evolution diagram. it can be seen from spreads density evolution diagram that, with the decrease of power exponent α, peak of wave of spreads density continues to decline, but the final information dissemination duration has been prolonged, indicating that the smaller heterogeneity of temporal characteristics of user behaviors, breadth of information dissemination may be affected, but the final information dissemination duration will be extended. it can be seen from ignorant evolution diagram and stiflers evolution diagram that, for the former, with the decrease of power exponent α, the greater proportion of ignorant; while for the latter, with the decrease of power exponent α, the smaller proportion of stiflers. figure 10 is respectively represent evolution diagram of the proportion of new spreaders number per time step and proportion of cumulative spreaders number, which also confirmed from edgewise that the power-law characteristic of user activity time distribution will have a huge impact on information dissemination. when the power exponent is small, although there are many new spreaders, the information dissemination comes and goes fast, which may not have a greater impact; but when the power exponent is large, although the number of people implementing information dissemination is small, it lasts longer, it will produce more lasting influence, which explains the phenomenon why some information gets spread again after a long silence. relaxation time refers to the time required for model to start from evolution to tended to be 220 s.c. han, y. liu, h.l. chen, z.j. zhang figure 9: influence of time-order character on propagation figure 10: left:evolution diagram of proportion of new spreaders number per time step. right: evolution diagram of proportion of cumulative spreaders number stable, figure 11 are the relationship between power exponent and the relaxation time and respectively represent when the number of spreads goes over half of time t, it can be seen from the figure that, as the power exponent increases, the relaxation time shows obvious linear downward trend. since the power-law distribution of user behavior can be expressed in group and individual nodes, so the paper conducted simulation analysis of impact of power-law characteristic at groups and individual-level on information dissemination process. the so-called power-law distribution at group level refers to the behavior time distribution sequence of each node is regular, namely the time interval of node behavior remains unchanged,but the degree of activity between nodes varies greatly, and meets power-law distribution. power-law distribution at individual-level refers to the time interval distribution of individual’s own behavior meets the power-law characteristic, but the degree of activity and time intervals satisfied different distribution between individuals are the same. the center and right respectively represent when the number of spreads goes over half of time t, impact of power-law distribution characteristics group and individual level on speed of information dissemination relationship diagram. it can be seen from the above figures that, at the group level, the power-law distribution has greater impact on the dissemination of information, while power-law distribution at individual level has less effect on the dissemination of information. figure 12 respectively represent relation diagram between the maximum propagation range of information dissemination cmax and the maximum node propagation density smax and power exponent, as it can be seen from the figure,with continued exponential increases, the maximum propagation range of information dissemination cmax and smax the maximum node propagation density simultaneously increase, but when the power index α > 1.5, the maximum propagation range of information dissemination has been stabilized, while it will still have some impact on the maximum node propagation density smax. influence model of user behavior characteristics on information dissemination 221 figure 11: left:relationship between power exponent α and relaxation time. center:impactof power-law distribution characteristics group-level on speed of information dissemination. right: impact of power-law distribution characteristics individual-level on speed of information dissemination figure 12: left:relation diagram between the maximum propagation range of information dissemination and power exponent. right: relation diagram between the maximum node propagation density and power exponent 5 conclusion with empirical data of social networks, here conduct quantitative analysis of user behavior time interval characteristics, which is conducive to explain many complex networks information propagation phenomena, and can generate social benefits and application value in the public opinion monitoring, disease prevention, information recommendation and other aspects. firstly, the paper makes use of real social network user behavior data, to analyze information posting and reply behaviors of networking groups, network individuals and network groups respectively, then use the bm phase diagram to analyze paroxysmal and memory characteristics of user’s information posting and reply behaviors. by means of sir propagation model and empirical data, implement quantitative study of impact of user behavior time interval characteristic on information dissemination process, and found that user behavior time interval meets the characteristics of power-law distribution, although it will slow speed of information dissemination to a great extent, it will also extend the duration of the information dissemination and can increase the ultimate scale of information dissemination, and thus more likely to have a greater impact on the society; it also found that at the group level, power-law characteristic has a greater impact on speed of information dissemination, while at individual level, the speed of information dissemination is less affected by power-law characteristic. acknowledgment this research is supported by: 1. national natural science foundation of china (no.61271308); 2. beijing natural science foundation (no.4112045); 222 s.c. han, y. liu, h.l. chen, z.j. zhang 3. beijing city science and technology project (no.z121100000312024); 4. specialized research fund for the doctoral program of higher education of china (no. w11c100030). bibliography [1] zhou, t.; han, x.p.et al(2013); statistical mechanics on temporal and spatial activities of human, journal of university of electronic science and technology of china, issn 1674862x, 4(4):481-540. 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[18] medina, j.r.; lorenz,t.; hirche, s.(2015); synthesizing anticipatory haptic assistance considering human behavior uncertainty, robotics ieee transactions on, 31(1):180-190. ijcccv4n3draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 3, pp. 244-252 first steps towards a cpu made of spiking neural p systems miguel a. gutiérrez-naranjo, alberto leporati miguel a. gutiérrez-naranjo university of sevilla, department of computer science and artificial intelligence avda. reina mercedes s/n, 41012, sevilla, spain e-mail: magutier@us.es alberto leporati università degli studi di milano – bicocca, dipartimento di informatica, sistemistica e comunicazione viale sarca 336/14, 20126 milano, italy e-mail: alberto.leporati@unimib.it received: april 5, 2009 accepted: may 30, 2009 abstract: we consider spiking neural p systems as devices which can be used to perform some basic arithmetic operations, namely addition, subtraction, comparison and multiplication by a fixed factor. the input to these systems are natural numbers expressed in binary form, encoded as appropriate sequences of spikes. a single system accepts as inputs numbers of any size. the present work may be considered as a first step towards the design of a cpu based on the working of spiking neural p systems. keywords: spiking neural p systems, arithmetic operations, membrane computing 1 introduction spiking neural p systems (sn p systems, for short) have been introduced in [3] as a new class of distributed and parallel computing devices. they were inspired by membrane systems (also known as p systems) [12, 13, 7] and are based on the neurophysiological behavior of neurons sending electrical impulses to other neurons. in sn p systems the processing elements are called neurons and are placed in the nodes of a directed graph, called the synapse graph. the contents of each neuron consist of a number of copies of a single object type, namely the spike. each neuron may also contain rules which allow to remove a given number of spikes from it, or to send spikes (possibly with a delay) to other neurons. the application of every rule is determined by checking the contents of the neuron against a regular set associated with the rule. formally, an sn p system of degree m ≥ , as defined in [4], is a construct of the form π = (o, σ, σ, . . . , σm, syn, in, out), where o = {a} is the singleton alphabet (a is called spike); σ, σ, . . . , σm are neurons, of the form σi = (ni, ri), with  ≤ i ≤ m, where: ni ≥  is the initial number of spikes contained in σi; ri is a finite set of rules of the following two forms: (1) e/ac → a; d, where e is a regular expression over a, and c ≥ , d ≥  are integer numbers. if e = ac, then it is usually written in the simplified form ac → a; d; similarly, if a rule e/ac → a; d has d = , then we can simply write it as e/ac → a. hence, if a rule e/ac → a; d has e = ac and d = , then we can write ac → a; (2) as → λ , for s ≥ , with the restriction that for each rule e/ac → a; d of type (1) from ri, we have as 6∈ l(e) (where l(e) denotes the regular language defined by e); syn ⊆ {, , . . . , m}×{, , . . . , m}, with (i, i) 6∈ syn for  ≤ i ≤ m, is the directed graph of synapses between neurons; in, out ∈ {, , . . . , m} indicate the input and output neurons of π . the rules of type (1) are called firing (also spiking) rules, and are applied as follows. if the neuron σi contains k ≥ c spikes, and ak ∈ l(e), then the rule e/ac → a; d ∈ ri can be applied. the execution of this rule removes c spikes from σi (thus leaving k − c spikes), and prepares one spike to be delivered to all the neurons σ j such that (i, j) ∈ syn. if d = , then the spike is immediately emitted, otherwise it is emitted after d computation steps of the system. (observe that, as usually happens in membrane computing, a global clock is assumed, marking the time for the whole system, hence the functioning of the system is synchronized.) if the rule is used in step t and d ≥ , then in steps t,t + ,t + , . . . ,t + d −  the neuron is closed, so that it cannot receive new spikes (if a neuron has a copyright c© 2006-2009 by ccc publications first steps towards a cpu made of spiking neural p systems 245 synapse to a closed neuron and tries to send a spike along it, then that particular spike is lost), and cannot fire new rules. in the step t + d, the neuron spikes and becomes open again, so that it can receive spikes (which can be used starting with the step t + d + ) and select rules to be fired. rules of type (2) are called forgetting rules, and are applied as follows: if the neuron σi contains exactly s spikes, then the rule as → λ from ri can be used, meaning that all s spikes are removed from σi. in what follows we will use an extended version of forgetting rules, written in the form e/as → λ ; d. the application of these rules is analogous to that of firing rules. with respect to their basic version, extended forgetting rules are controlled by a regular expression, and may compete against firing rules for their application. it is possible to prove that the use of extended forgetting rules does not modify the computational power of sn p systems. in each time unit, if a neuron σi can use one of its rules, then a rule from ri must be used. in case two or more rules can be applied in a neuron at a given computation step, only one of them is nondeterministically chosen. thus, the rules are used in the sequential manner in each neuron, but neurons function in parallel with each other. the initial configuration of the system is described by the numbers n, n, . . ., nm of spikes present in each neuron, with all neurons being open. during the computation, a configuration is described by both the number of spikes present in each neuron and by the number of steps to wait until it becomes open (this number is zero if the neuron is already open). a computation in a system as above starts in the initial configuration. a positive integer number is given as input to a specified input neuron. usually, the number is specified as the time elapsed between the arrival of two spikes. however, as discussed in [4], other possibilities exist: for example, we can consider the number of spikes initially contained in the input neuron, or the number of spikes read in a given interval of time. all these possibilities are equivalent from the point of view of computational power. to pass from a configuration to another one, for each neuron a rule is chosen among the set of applicable rules, and is executed. generally, a computation may not halt. however, in any case the output of the system is usually considered to be the time elapsed between the arrival of two spikes in a designated output cell. defined in this way, sn p systems compute (partial) functions of the kind f : n → n; they can also indirectly compute functions of the kind f : nk → n by using a bijection from nk to n. it is not difficult to show that sn p systems can simulate register machines [4], and hence are universal. if we do not specify an input neuron (hence no input is taken from the environment) then we use sn p systems in the generative mode; we start from the initial configuration, and we look at the output produced by the system. note that generative sn p systems are inherently nondeterministic, otherwise they would always reproduce the same sequence of computation steps, and hence the same output. dually, we can neglect the output neuron and use sn p systems in the accepting mode; for k ≥ , the natural numbers n, n, . . . , nk are read in input and, if the computation halts, then the numbers are accepted. also in these cases, sn p systems are universal computation devices [3, 4]. in this paper we consider sn p systems in a different way. we will use them to build the components of a restricted arithmetic logic unit in which one or several natural numbers are provided in binary form, some arithmetic operation is performed and the result is sent out also in binary form. the arithmetic operations we will consider are addition, subtraction and multiplication among natural numbers. each number will be provided to the system as a sequence of spikes: at each time step, zero or one spikes will be supplied to an input neuron, depending upon whether the corresponding bit of the number is  or . also the output neuron will emit the computed number to the environment in binary form, encoded as a spike train. the paper is organised as follows. in section 2 we present an sn p system which can be used to add two natural numbers expressed in binary form, of any length (that is, composed of any number of bits). in section 3 we present an analogous sn p system, that computes the difference (subtraction) among two natural numbers. section 4 contains the description of a very simple system that can be used to compare two natural numbers. section 5 first extends the system presented in section 2 to perform the addition of any given set of natural numbers, and then describes a spiking neural p system that performs the multiplication of any natural number, given as input, by a fixed factor embedded into the system. finally, section 6 concludes the paper and suggests some possible directions for future research. 2 addition in this section we describe a simple sn p system that performs the addition of two natural numbers. we call such a system the sn p system for 2-addition. it is composed of three neurons (see figure 1): two input neurons and an addition neuron, which is also the output neuron. both input neurons have a synapse to the addition neuron. each input neuron receives one of the numbers to be added as a sequence of spikes, that encodes the number in 246 miguel a. gutiérrez-naranjo, alberto leporati time step input input add output t =  0 0 0 0 t =  0 1 0 0 t =  0 0 1 0 t =  1 1 0 1 t =  1 0 2 0 t =  1 1 2 0 t =  0 0 3 0 t =  0 0 1 1 t =  0 0 0 1 table 1: number of spikes in each neuron of πadd , and number of spikes sent to the environment, at each time step during the computation of the addition  +  =  binary form. as explained above, no spike in the sequence at a given time instant means  in the corresponding position of the binary expression, whereas one spike means . note that the numbers provided as input to the system may be arbitrarily long. the input neurons have only one rule, a → a, which is used to forward the spikes to the addition neuron as soon as they arrive. the addition neuron has three rules: a → a, a/a → λ and a/a → a, which are used to compute the result. figure 1: an sn p system that performs the addition among two natural numbers theorem 1. the sn p system for 2-addition outputs the addition in binary form of two non-negative integers, provided to the neurons σinput and σinput in binary form. proof. at the beginning of the computation, the system does not contain any spike. during the computation, neuron σadd may contain , ,  or  spikes. we can thus divide the behavior of σadd in three cases: • if there are no spikes, no rules are activated and in the next step 0 spikes are sent to the environment. this encodes the operation  +  = . • if there is 1 spike, then the rule a → a is triggered. the spike is consumed and one spike is sent out. this encodes  +  =  +  = . • if there are 2 spikes, then the rule a/a → λ is triggered. no spike is sent out and one spike (the carry) remains in the neuron for the next step. • if there are 3 spikes, then the rule a/a → a is applied. one spike is sent to the environment, two of them are consumed and one remains for the next step. from this behavior, it is easily seen that the output is computed correctly. at the third computation step, the first of the spikes in the spike train that encodes the output in binary form is emitted by σadd . as an example, let us consider the addition  +  = , that in binary form can be written as  +  = . table 1 reports the number of spikes contained in each neuron of πadd , as well as the number of spikes sent to the environment, at each time step during the computation. the input and the output sequences are written in bold. 3 subtraction the subtraction sn p system, illustrated in figure 2, consists of ten neurons. the first input number, the minuend, is provided to neuron σinput in binary form, encoded as a spike train as described above. similarly, the first steps towards a cpu made of spiking neural p systems 247 figure 2: an sn p system that performs the subtraction among two natural numbers time step input input aux aux aux aux aux sub output t =  0 0 0 0 0 0 0 0 0 t =  0 1 0 0 0 0 1 0 0 t =  0 1 0 0 0 1 1 1 0 t =  1 0 0 0 0 1 1 2 0 t =  1 0 1 1 1 0 1 3 1 t =  0 1 1 1 1 0 1 5 0 t =  1 1 0 0 0 1 1 4 0 t =  1 0 1 1 1 1 1 2 1 t =  0 0 1 1 1 0 1 6 1 t =  0 0 0 0 0 0 1 5 1 t =  0 0 0 0 0 0 1 1 0 table 2: number of spikes in each neuron of πsub, and number of spikes sent to the environment, at each time step during the computation of the subtraction  −  =  second input number (the subtrahend) is supplied in binary form to neuron σinput . the set of neurons σaux , σaux and σaux act as a multiplier of the minuend: they multiply by 3 the number of spikes provided by neuron σin put . the system contains also a subsystem composed of neurons σgen, σaux_ f low and σaux , whose target is to provide a constant flow of spikes to σsub. all the neurons mentioned up to now have only one rule: a → a. the neurons σauxi , for  ≤ i ≤ , are connected with neuron σsub; this is both the output neuron and the neuron in which the result of the subtraction is computed, by means of six rules: a → λ , a/a → a, a/a → λ , a → a, a → λ and a/a → a. at the beginning of the computation all neurons are empty except σgen, which contains one spike. theorem 2. the subtraction sn p system outputs the subtraction, in binary form, of two non-negative integer numbers, provided in binary form to neurons σinput (the minuend) and σinput (the subtrahend). the result can be easily checked by direct inspection of all possible cases. a detailed proof of this theorem — not given here, due to the lack of space — can be found in [2]. as an example let us calculate  −  = , that in binary form can be written as  −  = . table 2 reports the number of spikes that occur in each neuron of πsub, at each time step during the computation. note that at each step only one rule is active in the subtraction neuron, and thus the computation is deterministic. the first time step in which the output starts to be emitted by the system is t = . 248 miguel a. gutiérrez-naranjo, alberto leporati figure 3: an sn p system that compares two natural numbers of any length, expressed in binary form 4 checking equality checking the equality of two numbers is a different task with respect to computing addition or subtraction. when comparing two numbers the output should be a binary mark, which indicates whether they are equal or not. since an sn p system produces a spike train, we will encode the output as follows: starting from an appropriate instant of time, at each computation step the system will emit a spike if and only if the two corresponding input bits (that were inserted into the system some time steps before) are different. so doing, the system will emit no spike to the environment if the input numbers are equal, and at least one spike if they are different. stated otherwise, if we compare two n-bit numbers then the output will also be an n-bit number: if such an output number is 0, then the input numbers are equal, otherwise they are different. bearing in mind these marks for equality and inequality, the design of the sn p system is trivial. it consists of three neurons: two input neurons, having a → a as the single rule, linked to a third neuron, the checking neuron. this checking neuron is also the output neuron, and it has only two rules: a → λ and a → a. the system is illustrated in figure 3. 5 multiplication in this section we present a first approach to the problem of computing the multiplication of two binary numbers by means of sn p systems. the main difference between multiplication and the addition or subtraction operations presented in the previous sections is that in addition and subtraction the n-th digit in the binary representation of the inputs is used exactly once, to compute the n-th digit of the output, and then it can be discarded. on the contrary, in the usual algorithm for multiplication the different digits of the inputs are reused several times; hence the design of a device that executes this algorithm needs some kind of memory. other algorithms for multiplication, such as booth’s algorithm (see, for example, [1]) also need some kind of memory, to store the intermediate results. we propose a family of sn p systems for performing the multiplication of two non-negative integer numbers. in these systems only one number, the multiplicand, is provided as input; the other number, the multiplier, is instead encoded in the structure of the system. the family thus contains one sn p system for each possible multiplier. in the design of our systems, we exploit the following basic fact concerning multiplication by one binary digit: any number remains the same if multiplied by 1, whereas it produces a 0 if multiplied by zero. bearing this fact in mind, an sn p system associated to a fixed multiplier only needs to add different copies of the multiplicand, by feeding such copies to an addition device with the appropriate delay. before presenting this design, we extend the 2-addition sn p system from section 2 to an n-addition sn p system. 5.1 adding n numbers in this section we present a family {πadd (n)}n∈n of sn p systems which allows to add numbers expressed in binary form. precisely, for any integer n ≥  the system πadd (n) computes the sum of n natural numbers. in what follows we will call πadd (n) the sn p system for n-addition. for n =  we will obtain the sn p system for 2-addition that we have described in section 2. the system πadd (n) consists of n +  neurons: n input neurons and one addition neuron, which is also the output neuron. each input neuron has only one rule, a → a, and is linked to the addition neuron. this latter neuron computes the result of the computation by means of n rules ri, i ∈ {, . . . , n}, which are defined as follows: ri ≡ a i/ak+ → a if i is odd and i = k + , whereas ri ≡ ai/ak → λ if i is even and i = k. as an example, figure 4 shows πadd (), the sn p system for 5-addition. first steps towards a cpu made of spiking neural p systems 249 figure 4: an sn p system that performs the addition among five natural numbers theorem 3. the sn p system for n-addition outputs the addition in binary form of n non-negative integer numbers, provided to the neurons σinput , . . . , σinputn in binary form. proof. let a, . . . , an be the n numbers to be added, and let a p i a p− i . . . a  i be the binary expression of ai,  ≤ i ≤ n, padded with zeros on the left to obtain (p + )-digit numbers (where p +  is the maximum number of digits among the binary representations of a, . . . , an). hence we can write ai = ∑p k= a k i  k for all i ∈ {, , . . . , n}. for each i ∈ {, . . . , n}, let a′i be the number with binary expression a p i . . . a  i , i.e., a ′ i = ∑p k= a k i  k−. moreover, let u = ∑n i= a  i and let k ∈ n and α ∈ {, } such that u = k + α (α =  if u is odd and α =  if u is even). the addition of a, . . . , an can be written as: n∑ i= ai = n∑ i= p∑ k= a k i  k = ( n∑ i= p∑ k= a k i  k ) + n∑ i= a  i =  ( n∑ i= a ′ i + k ) + α according to this formula, if br . . . b is the binary expression of ∑n i= ai, then b = α and br . . . b is the binary expression of ∑n i= a ′ i + k. let us assume now that at the time instant t there are i spikes in neuron σadd . these spikes can come from the input neurons, or they may have remained from the previous computation step. let us compute bt , the t-th digit of the output, dividing the problem in the following cases. • let us assume that i is odd and i = k + . then, according to the previous formula, bt =  and k units should be added to the computation of the next digit. this operation is performed by the rule ai/ak+ → a. by applying this rule, one spike is sent to the environment (bt = ) and k +  spikes are consumed, so that i − (k + ) = k +  − (k + ) = k spikes remain. • let us assume that i is even and i = k. then, according to the previous formula, bt =  and k units should be added to the computation of the next digit. this operation is performed by the rule ai/ak → λ . by applying this rule, no spike is sent to the environment (bt = ) and k spikes are consumed, so that i − k = k − k = k spikes remain for the next step. as an example, let us consider the addition of the numbers 3, 4, 2, 7 and 1, whose binary representations are , , ,  and , respectively. table 3 shows the evolution of the number of spikes in the neurons of the sn p system πadd () (illustrated in figure 4), as well as the number of spikes sent to the environment at each computation step, when performing such an addition. the input and the output sequences are written in bold. according with the computation, the result of the addition is  = . 5.2 multiplication by a fixed multiplier we now describe a family {πmult (n)}n∈n of sn p systems, one for each natural number n, that operate as multiplier devices. precisely, the system πmult (n) takes as input a number in binary form, and outputs the input multiplied by n. the output is also expressed in binary form. given a natural number n, the sn p system πmult (n) is defined as follows. it consists of one input neuron, σinput , linked to k neurons σaux , . . . , σauxk , where k is the number of occurrences of the digit 1 in the binary 250 miguel a. gutiérrez-naranjo, alberto leporati time step input input input input input add output t =  1 0 0 1 1 0 0 t =  1 0 1 1 0 3 0 t =  0 1 0 1 0 4 1 t =  0 0 0 0 0 4 0 t =  0 0 0 0 0 2 0 t =  0 0 0 0 0 1 0 t =  0 0 0 0 0 0 1 table 3: number of spikes in each neuron of πadd () (the system illustrated in figure 4) and number of spikes sent to the environment, at each time step during the computation of the addition  +  +  +  +  =  representation of n. for each i ∈ {, . . . , k}, neuron σauxi is connected with a new neuron σauxi , which is connected with σauxi , etc. this sequence of neurons is a path of linked neurons that extends until reaching σauxi j i , where ji is the number of order of the corresponding digit in the binary representation of n, where the first digit corresponds to , the second one corresponds to , and so on. all the last neurons of the k sequences are connected with a final neuron σadd , which is the same as the output neuron of the k-addition sn p system πadd (k) described above. this neuron has the rules for the addition of k natural numbers. all the other neurons have only the rule a → a. for example, let us consider n = , whose binary representation is . such a representation has three digits equal to 1, at the positions 2, 4 and 5. the system πmult (), illustrated in figure 5, has 13 neurons: σinput , σadd , and three sequences of neurons associated with the three digits equal to 1: σaux and σaux , corresponding to the 1 in the second position (corresponding to the power ); σaux , σaux , σaux and σaux , corresponding to the 1 in the fourth position (corresponding to the power ); σaux , σaux , σaux , σaux and σaux , corresponding to the 1 in the fifth position (corresponding to the power ). the last neurons of these sequences, namely σaux , σaux and σaux , are linked to neuron σadd , which is also the output neuron. the rules of this neuron are a → a, a/a → λ and a/a → a, which are the same as in the addition neuron of the 3-addition sn p system πadd () described in the previous section. figure 5: an sn p system that computes the product among the natural number given as input (in binary form) and the fixed multiplier  = , encoded in the structure of the system theorem 4. the sn p system πmult (n) built as above takes as input a number m in binary form and outputs the result of the multiplication m · n in binary form. proof. since we already proved that the neuron σadd performs the addition of several numbers in binary form, it only remains to transform the multiplication m · n (where n is a fixed parameter) into an appropriate addition. to this aim, let n = ∑q j= n j j . then we can write m · n = m ·   q∑ j= n j j   = q∑ j= ( m ·  j ) n j = ∑ ≤ j≤q ∧ n j = ( m ·  j ) according to this expression, m · n can be calculated as the addition of as many copies of m as the number of digits n j equal to  that appear in the binary representation of n. such copies have to be padded with j zeros first steps towards a cpu made of spiking neural p systems 251 time step input aux aux aux add out t =  1 0 0 0 0 0 t =  0 0 0 0 0 0 t =  1 1 0 0 0 0 t =  1 0 0 0 1 0 t =  1 1 1 0 0 1 t =  0 1 0 1 2 0 t =  0 1 1 0 3 0 t =  0 0 1 1 3 1 t =  0 0 1 1 3 1 t =  0 0 0 1 3 1 t =  0 0 0 0 2 1 t =  0 0 0 0 1 0 t =  0 0 0 0 0 1 table 4: number of spikes in neurons σaux , σaux , σaux and σadd of πmult () (the system illustrated in figure 5) and number of spikes sent to the environment, at each time step during the computation of the multiplication  ·  =  on the right (that is, they have to be multiplied by  j ), to take into account the correct weight of n j . hence, if k = ∑q j= n j then to compute m · n it suffices to provide k copies of m — each shifted in time of a number of steps that corresponds to the weight of a bit n j equal to  — to a neuron that computes the addition of k natural numbers. 6 conclusion and future work in this paper we have presented some simple sn p systems that perform the following operations: addition, multiple addition, comparison, and multiplication by a fixed factor. all the numbers given as inputs to these systems are expressed in binary form, encoded as a spike train in which at each time instant the presence of a spike denotes 1, and the absence of a spike denotes 0. the outputs of the computations are also expelled to the environment in the same form. the motivation for this work lies in the fact that we would like to implement a cpu using only spiking neural p systems. to this aim, the first step is to design the arithmetic logic unit of the cpu, and hence to study a compact way to perform arithmetical and logical operations by means of spiking neural p systems. ours is certainly not the unique possible way to approach the problem; other two possibilities are: (1) implementing the cpu as a network composed of and/or/not boolean gates, and (2) simulating the cpu by means of register machines. in both cases, using techniques widely known in the literature, one could design an sn p system that simulates the boolean network (resp., the register machine), thus implementing the cpu. in any case, an interesting extension to the present work is to try to design an sn p system for the multiplication, where both the numbers m and n to be multiplied are supplied as inputs. and, of course, we would also need a system to compute the integer division between two natural numbers; probably, this last system is the most difficult to design. acknowledgement the first author wishes to acknowledge the support of the project tin2006–13425 of ministerio de educación y ciencia of spain, cofinanced by feder funds and the support of the project of excellence with investigador de reconocida valía of the junta de andalucía, grant p08-tic-04200. the second author was partially supported by the miur project “mathematical aspects and emerging applications of automata and formal languages" (2007). 252 miguel a. gutiérrez-naranjo, alberto leporati bibliography [1] m.j. flynn. advanced computer arithmetic design. john wiley publisher, 2001. [2] m.a. gutiérrez-naranjo and a. leporati. performing arithmetic operations with spiking neural p systems. in proc. of the seventh brainstorming week on membrane computing, vol. i, fénix editora, seville, spain, 2009, 181–198. availble at http://www.gcn.us.es. [3] m. ionescu, gh. păun and t. yokomori: spiking neural p systems. fundamenta informaticae, 71(2-3):279– 308, 2006. [4] m. ionescu, a. păun, gh. păun, m.j. pérez-jiménez. computing with spiking neural p systems: traces and small universal systems. in dna computing, th international meeting on dna computing (dna12), revised selected papers, lncs 4287, springer, 2006, 1–16. [5] gh. păun. computing with membranes. journal of computer and system sciences, 61:108–143, 2000. see also turku centre for computer science — tucs report no. 208, 1998. [6] gh. păun. membrane computing. an introduction. springer–verlag, 2002. [7] the p systems web page: http://ppage.psystems.eu/ miguel a. gutiérrez-naranjo is an associate professor at the department of computer science and artificial intelligence of the university of seville in spain. he obtained his doctoral degree in mathematics in 2002. his main research area is natural computing, with a special interest in membrane computing. alberto leporati obtained a ph.d. in computer science from the university of milano (italy) in 2002. since 2004, he is assistant professor at the university of milano – bicocca. his research interests are in membrane computing, theoretical computer science and computational complexity. international journal of computers, communications & control vol. iii (2008), no. 1, pp. 80-89 adaptive compensation of friction forces with differential filter kouichi mitsunaga, takami matsuo abstract: in this paper, we design an adaptive controller to compensate the nonlinear friction model when the output is the position. first, we present an adaptive differential filter to estimate the velocity. secondly, the dynamic friction force is compensated by a fuzzy adaptive controller with position measurements. finally, a simulation result for the proposed controller is demonstrated. keywords: nonlinear friction, adaptive controller, fuzzy basis function expansion, adaptive differential filter. 1 introduction friction is one of the greatest obstacles in high precision positioning systems. since it can cause steady state and tracking errors, its influence on the response of the systems must be considered seriously ([10]). many friction models have been proposed that differ on the friction effects that are modeled in a lubricated contact. these models are divided into two categories: the kinetic and dynamic friction models. the kinetic friction models take into account the friction effects such as the viscous friction, the coulomb friction, and the stribeck effect. another category of friction model includes dynamic friction model that embody the natural mechanism of friction generation such as the lugre model ([2]). the coefficients of the various friction-related effects are usually very difficult to measure. a number of methods for friction estimation and compensation have been proposed for these models: adaptive control, joint torque control, learning control, variable structure control, and so on. friedland et al.[5] proposed a reduced-order nonlinear observer to estimate the velocity-dependent coefficient of the classical nonlinear friction model with velocity measurements. huang et al.[9] presented an adaptive radial basis function observer to compensate for the effects of the dynamic friction model full-states measurements. tomei[15] considered the tracking problem for robot manipulators with unknown parameters and dynamic lugre model using full-states information. ge et al.[6] presented adaptive controllers by combining neural network parameterization, dual observer for state estimation/stability and adaptive control techniques based on the dynamic lugre friction model. sato et al.[14] proposed an adaptive friction compensation method with an h∞ performance using the neural network approximation that is equivalent to the radial basis function approximation or fuzzy basis function approximation. they used the neural network approximation to parameterize the nonlinear characteristics of the dynamic lugre model. a sliding-mode type error function is introduced that requires full-states measurements. canudas de wit et al.[3] designed an observer-based adaptive friction compensation scheme for systems with generalized position/velocity static characteristics based on full stats measurement. the proposed controller guarantees the global asymptotic stability of the tracking error while preserving boundedness of all the internal signals. the nonlinear friction is approximated either by a linear span of set of continuous known function, or by a neural network of bounded basis function. putra et al.[12] proposed an observer-based friction compensation for a class of the kinetic friction models with known system parameters based on the strictly positive real condition. in the case of partial-states measurements, an adaptive estimator that does not require the strictly positive realness of the plants is needed. ray et al.[13] presented a non-model-based friction estimation method using extended kalman-bucy filtering. the filter is used to estimate a friction force with the full-states measurements. for the classical nonlinear friction model without velocity measurements, xia et al.[17] employed a velocity observer based on state space nonlinear friction model and designed an adaptive controller that achieved a semi-global asymptotically stability. the velocity observer is a model-based estimator that requires plant parameters to estimate the velocity. park et al.[11] proposed a non-model-based differential filter of a nonlinear function based on copyright © 2006-2008 by ccc publications adaptive compensation of friction forces with differential filter 81 the adaptive control theory. the proposed differential filter is applied to estimate the time-derivative of a nonlinear function of the intensity for each pixel to detect moving objects within a scene acquired by a stationary camera. in estimating the friction forces, the differential filter is applicable to estimate the velocity with the position measurements. in this paper, we design a friction compensator with the position measurement. to begin with, an adaptive differential filter that is a non-model based adaptive algorithm to estimate the derivative of a signal, is designed to estimate the velocity signal. next, the velocity signal in friction compensator proposed by canudas de wit et al.[3] is replaced by the estimate of the adaptive differential filter. finally, a simulation result of the proposed compensator is presented. from the simulation result, it is shown that the proposed compensator is robust against additive noises. 2 adaptive differential filter the velocity information is the most important data to estimate and compensate the friction forces. most of the conventional papers assume that the velocity is measurable. if the velocity information is not available, we have to estimate the velocity signal. park et al.[11] proposed the adaptive estimator of the time-derivative of an output that is called the adaptive differential filter. their estimator causes the estimation error in the case of fast time-varying signals. in this paper, we present another adaptive differential filter applicable to fast time-varying signals. let y(t) be a measurement signal at a time t and f (y(t)) be its nonlinear function. we define θt (t) as the derivative with respect to time of the nonlinear function f (y(t)), i.e. θt (t) = d f (y(t)) dt = d f (y) dy dy(t) dt . (1) if d f (x)dx has the inverse ( d f (x) dx )−1 , we have ẏ(t) = θt (t)ξ (t), (2) ξ (t) = ( d f (y) dy )−1 . (3) the problem is defined as follows: problem 1. design a differential filter to estimate the derivative, θt (t), of the nonlinear function f (y(t)) with available signal y(t) and without any knowledge of its dynamics. if the signal ξ (t) is available, problem 1 is equivalent to the estimation problem of the time-varying parameter θt in eq.(2) with the available signals y(t) and ξ (t). it is assumed that the time-varying parameter in (2) is satisfied the following inequality: |θt (t)−θt 0| ≤ εt 0 where θ0 is an unknown constant, and ε0 is a known constant. eq.(2) can be rewritten as ẏ = θt 0ξ (t) + εt (t)ξ (t) (4) where εt (t) = θt (t)−θt 0. we give an adaptive observer as ˙̂yt = −k(ŷt −y) + θ̂t (t)ξ (t)− ε̂t (t)sgn(ŷt −y)|ξ (t)| (5) where k > 0. defining the observer error as et (t) = ŷt (t)−y(t), we obtain the error system as ėt = −ket + θ̃t (t)ξ (t)− ε̂t (t)sgn(et )|ξ (t)|−εt (t)ξ (t) (6) 82 kouichi mitsunaga, takami matsuo where θ̃t (t) = θ̂t (t)−θt 0. the parameter update laws are selected as ˙̃θt (t) = ˙̂θt (t) = −γ et (t)ξ (t) (7) ˙̂εt (t) = |et (t)||ξ (t)| (8) where γ > 0. we can prove the following lemma: lemma 1. consider the error system (6). the parameter update laws guarantee the stability of the origin of the error system as follows: et (t) → 0 (t → ∞). moreover, if limt→∞ėt (t) = 0, then limt→∞θ̃t ε (t) = 0, where θ̃t ε (t) = θ̂t (t)−θt (t). proof: the whole system can be written by ėt = −ket + θ̃t (t)ξ (t)− ε̂t (t)sgn(et )|ξ (t)|−εt (t)ξ (t) ˙̃θt = −γ et ξ (t) ˙̂εt = |et ||ξ (t)|. define a lyapunov-like function as v1 = 1 2 ( e2t (t) + 1 γ θ̃ 2t (t) + ε̃ 2 t (t) ) where ε̃t (t) = ε̂t (t)−εt 0. its time derivative is given by v̇1 = et ėt + 1 γ θ̃t ˙̃θt + ε̃t ˙̂εt = et (−ket + θ̃t (t)ξ (t)− ε̂t (t)sgn(et )|ξ (t)| −εt (t)ξ (t)) + 1 γ θ̃t ˙̃θt + ε̃t ˙̂εt = −ke2t + θ̃t ( et ξ (t) + 1 γ ˙̃θt ) −et ε̂t sgn(et )|ξ (t)| −et εt ξ (t) + ε̂t ˙̂εt −εt 0 ˙̂εt = −ke2t −(et εt ξ (t) + et εt 0sgn(et )|ξ (t)|) = −ke2t −(et εt ξ (t) + εt 0|et ||ξ (t)|) ≤ −ke2t +|et ||εt ||ξ|−εt 0|et ||ξ (t)| ≤ −ke2t ≤ 0. hence, we have et ∈ l2 ∩ l∞, θ̃t ∈ l∞, ε̃t ∈ l∞. since et ∈ l∞, θ̃t ∈ l∞, ε̃t ∈ l∞, we have ėt ∈ l∞. from barbalat’s lemma, we conclude that limt→∞ et (t) = 0. moreover, setting u as ut = θ̃t (t)ξ (t)− ε̂t (t)sgn(et )|ξ (t)|−εt (t)ξ (t), the error dynamics can be expressed in a first-order system: ėt (t) = −ket (t) + ut (t). adaptive compensation of friction forces with differential filter 83 if limt→∞ėt (t) = 0, then lim t→∞ (θ̂t (t)−θt 0 −εt (t)) = lim t→∞ (θ̂t (t)−θt (t)) = 0. we obtain the estimator for the differential of the signal y(t) by selecting the nonlinear function f (y(t)) as the linear function f (y(t)) = y(t). in this case, the regressor signal ξ (t) is equal to 1. the adaptive observer and the update laws are given by ˙̂yt = −k(ŷt −y) + θ̂t (t)− ε̂t (t)sgn(ŷt −y) (9) ˙̂θt (t) = −γ et (t), ˙̂εt (t) = |et (t)| (10) where et (t) = ŷt (t)−y(t). thus, we get the estimate of the differential of the signal y(t) as ˆ̇y = θ̂t = − ∫ t 0 γ et (τ)dτ. (11) we call this estimator (11) the adaptive differential filter. 3 nonlinear friction model canudas de wit et al. present a new dynamic friction model that captures dynamic friction effects (the gahl effect, frictional memory, stick-slip motion) as well as steady state friction effects, including the stribeck effect ([2, 8]). the interface between two surfaces is modeled by contact between sets of bristles. if z represents the average bristle deflection, ẏ the velocity between two surfaces, the friction force ff can be expressed as ff = σ0z + σ1ż + σ2ẏ ż = −α(ẏ)|ẏ|z + ẏ α(ẏ) = σ0 fc + ( fs − fc) exp{−(ẏ/ẏs)2} where σ0 is the stiffness for deflection angle, σ1 the damping coefficient for slip, σ2 the viscous friction coefficient, fc the coulomb friction level, fs the level of the stiction force, and ẏs the stribeck velocity. the system under discussion is the following equation: mÿ(t) + ff (t) = u(t) (12) where m is known and ff is unknown. as in canudas de wit et al.[3], the nonlinear function α(ẏ) is approximated by fbfe as α(ẏ) = kt ζ (ẏ) + ε f (13) k = [ k1, ··· , km ]t (14) ζ (ẏ) = [ ζ 1(ẏ), ··· , ζ m(ẏ) ]t (15) where ε is the approximation error and ζ j is defined as ζ j(ẏ) = µa j (ẏ(t)) ∑mj=1 µa j (ẏ(t)) . where the membership functions are selected as the gaussian functions: µa j (v) = exp(− (v− v̄ j)2 2σ 2j ) σ j = σ , j = 1,··· , m. the approximation error is assumed to be bounded and sufficiently small. 84 kouichi mitsunaga, takami matsuo 4 controller design consider the tracking control problem of system (12) under the assumption that (i) the parameters m, σ0, σ1, σ2 are known and the nonlinear function α(ẏ) in unknown. the desired trajectory yd is smooth enough and bounded. the tracking error and its derivatives are defined as e = yd −y, ė = ẏd − ẏ, ë = ÿd − ÿ. 4.1 adaptive controller with full-states measurements we review the adaptive controller proposed by canudas de wit et al.[3] with full-states measurements. they presented the internal state observer for the average bristle deflection as: ˙̂z = ẏ− k̂t ζ (ẏ)|ẏ|ẑ + k0z̃ + k1e (16) z̃ = m σ1 ė + kd −mρ σ1 e + e f (17) ė f = −ρ e f + cρ e (18) where ẑ is the estimate of the average bristle deflection and k0 > 0, k1 > 0, c = kp−ρ kd +mρ 2 σ0 . defining the estimation error of the average bristle deflection as z̃ = z− ẑ, (19) we have the following estimation error equation: ˙̃z = −α(ẏ)|ẏ|z̃− k̃t ζ (ẏ)|ẏ|ẑ−k0z̃−k1e (20) where k̃ = k − k̂. the controller is given as follows[3] u = mÿd + h(p)e + f̂f (21) f̂f = σ0ẑ + σ1 ˙̂z + σ2ẏ (22) where h(p) is the following differentiator: h(p) = kd p + kp, p = d dt . (23) moreover, we obtain the tracking error equation as më + kd ė + kpe = σ1 ˙̃z + σ0z̃. (24) we give the same adaptive update law as in [3]: ˙̂k = −γ 1 k1 ζ (ẏ)|ẏ|z̃ẑ. (25) 4.2 adaptive controller with position measurements in the position measurement case, we use the estimate ˆ̇y of the adaptive differential filter instead of the velocity signal ẏ. they presented the internal state observer for the average bristle deflection as: ˙̂z = ˆ̇y− k̂t ζ ( ˆ̇y)| ˆ̇y|ẑ + k0z̃ + k1e (26) z̃ = m σ1 ˆ̇e + kd −mρ σ1 e + e f (27) ė f = −ρ e f + cρ e (28) adaptive compensation of friction forces with differential filter 85 where ˆ̇y is the estimate of the velocity by the adaptive differential filter and ˆ̇e = ẏd − ˆ̇y. defining the estimation error of the adaptive differential filter ε as ε = ˆ̇y− ẏ, we have ˆ̇e = ė−ε. we have the following estimation error equation: ˙̃z = −α(ẏ)|ẏ|z̃− k̃t ζ ( ˆ̇y)| ˆ̇y|ẑ−k0z̃−k1e−kt ε 0ẑ−ε (29) where k̃ = k − k̂ and ε 0 is caused by estimation error of the adaptive differential filter. moreover, we obtain the tracking error equation as më + kd ė + kpe = σ1 ˙̃z + σ0z̃ + (kd −σ2)ε. (30) the controller is given as follows: u = mÿd + kd ˆ̇e + kpe + f̂f (31) f̂f = σ0ẑ + σ1 ˙̂z + σ2 ˆ̇y. (32) replacing the velocity signal by its estimate of the adaptive differential filter, the adaptive update law is given by: ˙̂k = −γ 1 k1 ζ ( ˆ̇y)| ˆ̇y|z̃ẑ. (33) 4.3 simulation example next example is the following lugre model: m = 10, σ0 = 5, σ1 = 2 √ 5, σ2 = 0.01, fc = 1, fs = 1.5, vs = 0.1. the initial conditions are selected as y(0) = 0, ẏ(0) = 0.2, z(0) = 1. the desired reference signals yd , ẏd , ÿd are given by yd = 3 sin 0.02πt, ẏd = 0.06π cos 0.02πt, ÿd = −0.0012π sin 0.02πt. the parameters of the fuzzy basis function are selected as follows: σ j = 0.5( j = 1,··· , 5), v̄1 = −1, v̄2 = −0.5, v̄3 = 0.0, v̄4 = 0.5, v̄5 = 1. the parameters of the adaptive differential filter is given by k = 1, γ = 0.5. the simulation parameters of the compensator are selected as follows: kd = kp = 1, k0 = 1, k1 = 0.1, γ = 50   1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1   . 86 kouichi mitsunaga, takami matsuo 0 50 100 150 200 250 300 −3 −2 −1 0 1 2 3 time[sec] o u t p u t a n d i t s r e f e r e n c e [ m ] 0 50 100 150 200 250 300 −3 −2 −1 0 1 2 3 time[sec] o u t p u t a n d i t s r e f e r e n c e [ m ] figure 1: the output y(solid line) and its desired reference yd (dotted line) in the time range [0, 300]. left: noise free ,right: additive noise n(0, 10−4). 0 20 40 60 80 100 −3 −2 −1 0 1 2 3 time[sec] o u t p u t a n d i t s r e f e r e n c e [ m ] 0 20 40 60 80 100 −3 −2 −1 0 1 2 3 time[sec] o u t p u t a n d i t s r e f e r e n c e [ m ] figure 2: the output y(solid line) and its desired reference yd (dotted line) in the time range [0, 100]. left: noise free ,right: additive noise n(0, 10−4). the simulation is carried out in the noise free case and in the case where the output yd with the additive noise n(0, 10−4). figure 1 shows the outputs y(solid line) and its desired references yd (dotted line) in the time range [0, 300] in both cases. figure 2 shows the outputs y(solid line) and its desired references yd (dotted line) in the time range [0, 100] in both cases. figure 3 shows the velocities ẏ(solid line) and its estimates ŷ by the adaptive differential filter (dotted line) in both cases. figure 4 shows the inputs u in both cases. figure 5 shows the friction force(solid line) and its estimate(dotted line). from these figures, the proposed friction compensator is robust against the additive noise. 5 conclusion we proposed the adaptive differential filter and its application to friction compensation. specifically, the velocity information in the friction compensator proposed by canudas de wit et al.[3] is replaced by the estimate with the adaptive differential filter. from the simulation results, the proposed friction compensator is robust against the additive noise. adaptive compensation of friction forces with differential filter 87 0 50 100 150 200 250 300 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 time[sec] v e l o c i t y a n d i t s e s t i m a t e [ m / s e c ] 0 50 100 150 200 250 300 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 time[sec] v e l o c i t y a n d i t s e s t i m a t e [ m / s e c ] figure 3: the velocity ẏ(solid line) and its estimate ˆ̇y(dotted line). left: noise free ,right: additive noise n(0, 10−4). 0 50 100 150 200 250 300 −3 −2 −1 0 1 2 3 4 time[sec] i n p u t [ n ] 0 50 100 150 200 250 300 −3 −2 −1 0 1 2 3 4 time[sec] i n p u t [ n ] figure 4: the input signal. left: noise free ,right: additive noise n(0, 10−4). 0 50 100 150 200 250 300 −3 −2 −1 0 1 2 3 4 5 time[sec] f r i c t i o n f o r c e a n d i t s e s t i m a t e [ n ] 0 50 100 150 200 250 300 −3 −2 −1 0 1 2 3 4 5 time[sec] f r i c t i o n f o r c e a n d i t s e s t i m a t e [ n ] figure 5: the friction(solid line) and its estimate(dotted line). left: noise free ,right: additive noise n(0, 10−4). 88 kouichi mitsunaga, takami matsuo bibliography [1] b. armstrong-helouvry, p. dupont, c. canudas de wit, “ a survey of models, analysis tools and compensation methods for the control of machines with friction”. automatica, vol.30, pp.1083– 1138, 1994. 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[12] d. putra, l.p.m. moreau, and h. nijmeijer, “ observer-based compensation of discontinuous friction”. proc. of the 43rd ieee conference on decision and control, pp.4940–4945, 2004. [13] l. r. ray, a. ramasubramanian, and j. townsend, “ adaptive friction compensation using extended kalman-bucy filter friction estimation”. control engineering practice, vol.9, pp.169–179, 2001. [14] k. sato, y. mishima, k. tsuruta, and k. murata, “ adaptive positioning control for linear slider with friction compensation”. trans. of society of the instrument and control engineers,japan, vol.40-2, pp.275–277, 2004 (in japanese). [15] p. tomei, “ robust adaptive friction compensation for tracking control of robot manipulators”. ieee transaction on automatic control, vol.45-6, pp.2164–2169, 2000. [16] j. wang, s.s. ge and t.h. lee, “ adaptive friction compensation for servo mechanism”. in: adaptive control of nonsmooth dynamic systems g. tao and f.l. lewis (eds.), springer, pp.211– 248, 2001. adaptive compensation of friction forces with differential filter 89 [17] q.h. xia, s.y. lin, m.h. ang jr. and t.m. lim, “ adaptive joint friction compensation using a model-based operational space velocity observer”. proc. of ieee international conf. on robotics and automation, pp.3081–3086, 2004. kouichi mitsunaga oita institute of technology control engineering department 407-27 higashihama, nakatsu, oita, japan 871-0006 e-mail: mitu@oita-it.ac.jp takami matsuo oita university department of architecture and mechatronics 700 dannoharu, oita, japan 870-1192 e-mail: matsuo@cc.oita-u.ac.jp received: august 4, 2007 kouichi mitsunaga was born in japan on july 19,1963. he received the b.s. degree in electrical engineering from oita university, oita, japan, in 1986. he is currently an assistant professor in the control engineering department, oita institute of technology, japan. he has worked in control system engineering. takami matsuo was born in japan on march 24,1958. he received the doctoral degree in engineering from kyushu university, fukuoka, japan, in 1985. he is currently a professor in the department of architecture and mechatronics, oita university, japan. he has worked in control system theory and its applications. matsuo is a member of ieee, and siam. international journal of computers, communications & control vol. i (2006), no. 2, pp. 95-101 improvement and extension of virtual reality for flexible systems of manufacture flavio véliz vasconcelo, gastón lefranc hernández abstract: in this work it presents the improvement and extension of the virtual reality software created by the robotics, artificial intelligence and automatization outpost laboratory in the school of electrical engineering of the pontificia universidad católica de valparaíso. keywords:software, virtual reality, java, fms. 1 introduction the development of this program has as it bases the version 1,0 on which a virtual model is created prototype of a cartesian manipulator pertaining to a cell of storage as/rs of a flexible system of manufacture. version 2,0, version created in this project bases its modifications on three aspects: • the 3d model, is made using support tools that facilitate the work and allow a better design, in this one case 3dstudio. • extends software adding to the virtual reality new objects from the flexible system of complete manufacture that is in the laboratory • settles down a control of the scara manipulator and the virtual reality of the work imagines that is made in an assembled flexible cell of, constituted by a robotic manipulator, a vision system and a automated transport system. the reason for this one project is to maintain the control of a process and also to make modifications in the cell doing the simulation in the virtual reality to optimize the work of the flexible system reducing to the died time of the cell produced by the halting of the process when making modifications and tests of the same ones. can be tested in the virtual reality then this account with all the factors that affect a process, for example, gravity and restriction of movement of the objects. 2 state of the art the generation of interactive tridimensional graphs has advanced immensely in the last years, now we can simulate real worlds with a good degree of reality in real time. but the techniques of interface man-machineare still in investigation. the first virtual reality (vr) systems appeared at the end of 80’ and beginnings of 90’, but the investigation in this field begun at end of the 60’ decade, especially in the aerospace military industry, and in investigation center as the massachusetts institute of technology, and de university of north of carolina. between the 90’ decade ant the beginning of the xxi century, the vr has experimented a big development united to the advances in communications, computers and all the digital technology. now the virtual reality is considered as a work tool for the industry, like the automotive industry, robotic, etc., in universities like universidad autónoma de méxico (unam), pontificia universidad católica de valparaíso and universidad politécnica de cataluña. even in the art universities like pompeu fabra and museum like the gugenheim museum from new york. copyright c© 2006 by ccc publications 96 flavio véliz vasconcelo, gastón lefranc hernández in the pucv the virtual reality began in 2001 with a prototype of this type of software. this was the representation of a manipulator in the space. this manipulator is part of a flexible manufacturer system (fms) made in the robotic, automation and artificial intelligence laboratory of the engineering electrical school. the first idea was represent a cell from the fms, the software must be flexible and must work in any operative system so was made completely with java. in the v2.0 of this software, is represent all the system that exist in the laboratory, using 3d development tools. this version can control both manipulators in the fms, the cartesian manipulator, part of the as/rs cell, and the scara manipulator that work with artificial stereo vision and the assembled cell. at this moment, the laboratory is working in the creation of the environment in real time, using the 3d vision, and the final idea is add this application to the virtual reality v2.0 software to make this more flexible. 3 structure of the system the represented cell is a manufacture cell of storage that is made up of three stages, the first in charge one of the administration of information (base it of data), the second interface for the administrator and third, the mechanism that will execute the indicated tasks. the storage system is conformed by a warehouse in rectangular form that denominated matrix and a cartesian manipulator of 4 degrees of freedom. with respect to the design of the virtual world a virtual universe was structured so that it was possible to be added other devices of a sfm, integrating of this form in which are different cells from a flexible production system as it is in figure 1. figure 1: 4 representation and control of the graphical devices the established movements of the manipulator in the classes of cartesian manipulator settle down by means of the incorporation of the inverse kinematics of the manipulator, defined as a general characteristic of the manipulating class, which will inherit each manipulator i specify. for the cartesian manipulator simulated, the inverse kinematics the position given by the system of data base is applied directly, since or a user, will be carried out on the base of several final positions. since the manipulator is cartesian, the final position and the position of each one of their joints are referred a single system of coordinates, unlike another type of manipulators. improvement and extension of virtual reality for flexible systems of manufacture 97 the information that gives the data base working in automatic way, with respect to the task to make is the following one: • move manipulator to one position (x1,y1, z1) • storage the "pallet" • take out the "pallet" • take the "pallet" from the transport system (x ,y, z) • put the "pallet" on the transport system (x ,y, z) the information entered by the user when working in manual way updates the data base so that this one issues the action order to the manipulator. the orders that can be sent are the same ones that gives the data base to him when working in automatic way, that is to say, to keep "pallet", to remove "pallet", etc. the real manipulator is implemented with sensors that confirm their position when executing a movement and this one information is realimentada to the program of virtual reality so that it makes sure that what it is happening in the virtual reality is just like it is happening in the true manipulator. 5 interface with the reality an interface with the data base must exist, whose the objective is to receive information of this one or to send information towards this to modify it. this interface is implemented by means of the program net8, which allows to the connection between the program of virtual reality and the data base to be able to extract the information stored by means of order tables or position. the program must be able to execute the control of a new cell of manufacture, corresponding to the cell of assembled and vision stere. the cell is composed by a manipulator of the type scara 7547, the work table, the conveyor belts, "pallets" that are used for the transport of products and the system of vision by computer. this flexible system of production imagines in a virtual reality of with the intention of having the total control on the same one, being able to represent everything what it is happening in the reality, in a virtual reality, thus is possible to be detected any fault in the real system while the behavior of the virtual reality is being observed. the system of virtual reality has communication with the assembled system of through the port of com1 communications of interface rs-232, through which the coordinates are transmitted towards which the manipulator is due to move when he is commanded from the assembled station of or to order a manual control of the scara manipulator when controlling the interface of virtual reality. 6 tools of programming and design the described program previously was developed programming in java language using the api of design 3d, java3d. the programming language was chosen by the following reasons: 1. the possibility must exist of creating an interface between the graphical generator and the source program. because java3d is a api of java, this one function already is built-in. 2. the availability and facility of manual and tutorial obtaining about java of these programs. 3. the advantage to create a program multiplatform, since java works on a virtual machine. 4. as version 1,0 of the program of virtual reality exists, created with the tools of java, it will use its code of source to reuse it in new versions. the great advantage of java is that it is a programming language oo and of neutral architecture, and the advantage of the programming oriented to objetos (poo) is the capacity to reuse code. another one of the great advantages of java is that not they must creates applications different to use the application 98 flavio véliz vasconcelo, gastón lefranc hernández in different platforms. with java we can to develop an application that automatically can be used in diverse platforms, like windows, unix or macintosh systems. using jbuilder, of the borland company, it is possible to be made an express and easy joint of the elements of a graphical interface for java applications. to construct user interfaces is not of great complexity because it is counted on several blocks of components, which are selected from a trowel that contains components such as: text bellboys, areas, lists, pictures of dialogues, etc. soon the values of the properties of these components are selected and al is enclosed component event del the code that will treat this event, saying al program how to respond to an event in the user interface. in order to obtain a better design of virtual reality that resembles more the real fms it uses the design program 3dstudio max5 version in spanish supported by the tool of autocad design 2002 version in spanish. to the support of these tools and using the class "loader" of j3d it is possible to be related the archives of objects 3d of 3dstudio and java. the design is begun creating the objects in the autocad program thus to be able to handle the measures of the objects and to give with greater facility the different forms from the objects, soon the archives of autocad are exported towards 3dstudio and here the design is completed giving colors and textures to these. 7 3d model the great improvement that incorporates version 2,0 of the program of virtual reality bases on the generation of objects since in version 1,0 the manipulator was created in his totality having used the api of java 3d, which implies to have to write great amount of lines of code for a simple object and without great definition. although using j3d a scene can be created, also it is possible to be done generating the objects of the scene in a program of design like for example autocad or 3dstudio max. this is a powerful form to bring objects towards the virtual world being concerned the information from a file. to load the data from a file allows to accede to the data created in another application. the program 3dstudio max5 provides the tools necessary to be able to create the objects that will compose the flexible system of manufacture. the simulation of a universe is generated using a modelador. the development of a modelador in 3-d includes three main phases, as it is in figure 2. figure 2: generation of primitive: the simple geometric objects are created, as it bases of any geometric model. the simple objects are created by primitive parametric of generation, or linear or radial sweeping. composition: the compound objects are created assembling simple objects. the composition generates more complex objects with rigid or articulated behavior. animation: the animation consists of equipping with movement to the objects within the environment. this can be improvement and extension of virtual reality for flexible systems of manufacture 99 applied to the objects composed in its totality or one of its joints. the animation includes movements of camera (point of view of the observer) and manipulation of the lights. through different techniques from design the images of figures 3 and 4 were obtained: figure 3: simple object figure 4: compound object in order to be able to obtain these one better quality in the design the support for the design looks for using the programs corel draw and their application with scanner to make ocr of vectorization. the data from measures of the manipulator are taken, that are in the manual of this one, where are two views that are sufficient to be able to make a design 3d of the manipulator. similarly all the components are being created that comprise of the flexible system which they were mentioned in the previous points, arriving at a final design of the version 2,0 that can be seen in figure 5. 8 inverse kinematics when we select a position in the cartesian plane inside of the work space, the scara is instruct to move to the selected position. with this information we can calculate the inverse kinematics to generate the animation of the 3d manipulator, moving the articulations or joints to the set point. figure 5: virtual sfm in every joint, the z axis is selected as rotation axis, as x axis the junction of the joints, and the y axis the third perpendicular axis. 100 flavio véliz vasconcelo, gastón lefranc hernández to describe the position of the joints from the angles of joints we can describe a transformation matrix that it relates the frame assigned to a joint with the frame of the previous joint. to the scara manipulator, the transformation matrix between the ended frame and the base frame is: t =   cosθ12 −sinθ12 0 l1cosθ1 sinθ12 cosθ12 0 l1sinθ1 0 0 1 0 0 0 0 1   (1) where: θ12 = θ1 + θ2 the problem of the inverse kinematic is obtain the angle from the position in the cartesian coordinates (x,y) of the greeper. this angles can be obtain in algebraic way or geometrically. in this case we chose the geometrical method, because is more simple. in this way, we obtain: cosθ2 = (r2 −l21 + l22)/(2l1l2) (2) θ1 −β + δ , i f θ2 < 0 (3) θ1 = β −δ , i f θ2 > 0 (4) where, r2 = x 2 + y 2 (5) tan β = y /x (6) cosδ = (r2 + l21 −l22)/(2l1r) (7) l1 and l2 are know values, because are the length of the two part of the arm of the scara manipulator, so is necessary to know the value of r to could calculate δ . like the point of destiny of the greeper is know too, and know in cartesian coordinates, the x and y values are replaced in the equation number 5, then we can obtain the r value. x and y value are data entered by the user, using the virtual reality software interface, or trough the database that command all the flexible manufacturer system. like we have the values of l1, l2, and r, we can obtain the β value working with equation 7. with the x and y values replaced in equation 6 we can obtain the value of β . with equation 2 we obtain the θ2, that indicate the rotation angle of l2 and that define the equation of rotation of the extremity l1, equations 3 and 4 depending of the case. this information is passed to the animation class, class that have the capacity to move the 3d design using the behavior class of j3d. 9 conclusions the use of the virtual reality in the industrial area allows to increase the production and to keep us to the moved away personnel of the accomplishment of dangerous tasks. with the simulation of the flexible system tests in a simulated system are reduced to the production costs when doing that it counts on all the factors that affect to the real system, in such a way that the best option can be selected to make a task without having to stop the system for a long time, but so single the necessary thing to make the modification. the advantage to make the virtual reality in java is to allow to implement the application in independent platforms without no problem then this runs on a virtual machine, and as java were born oriented to the internet, also it allows us to adapt software so that service to the production can be used in the web rendering and allowing the control of the flexible system, from any part of the world. improvement and extension of virtual reality for flexible systems of manufacture 101 figure 6: virtual scara, joints and rotation axis virtual reality can join two communities very different as artist and scientist, because this projects need informatics and engineering experts and designers and expert in visual arts. references [1] zepeda, roberto (lefranc, gastón, p.g.), modelación en redes de petry y simulación en realidad virtual de sistemas flexibles de manufactura, informe final de proyecto de titulación, pontificia universidad católica de valparaíso. agosto, 2001. [2] flores, javier (lefranc, gastón, p.g.), realidad virtual a sistemas flexibles de manufactura, informe final de proyecto de titulación, pontificia universidad católica de valparaíso. mayo, 2004. [3] aaron e. walsh, doug gehringer, java 3d, api jump start. [4] selman, daniel, java 3d programming. [5] burgos, daniel, 3dstudio max práctico. guía de aprendizaje. [6] lemay cadenhead, aprendiendo java en 21 días. flavio véliz vasconcelo, gastón lefranc hernández pontificia universidad católica de valparaíso escuela de ingeniería eléctrica av. brasil 2147, valparaíso, chile e-mail: flavio.veliz@vtr.net, glefranc@ieee.org international journal of computers, communications & control vol. ii (2007), no. 2, pp. 143-148 self-organizing maps for analysis of expandable polystyrene batch process mikko heikkinen, ville nurminen, yrjö hiltunen abstract: self-organizing maps (som) have been successfully applied in many fields of research. in this paper, we demonstrate the use of som-based method for the analysis of expandable polystyrene (eps) batch process. to this end, a data set of eps-batch process was used for training a som. reference vectors of the som were then classified by k-means algorithm into six clusters, which represent product types of the process. this som could also be used for estimating the optimal amounts of the stabilisation agent. the results of a validation data set showed a good agreement between the actual and estimated amounts of the stabilisation agent. based on this model a web application was made for test use at the plant. the results indicate that the som method can also be efficiently applied to the analysis of the batch process. keywords: neural networks, self-organizing maps, process control, batch process 1 introduction batch processes are typically based on predefined process recipes. if process circumstances, chemicals and recipes are constant, the product should basically be always the same. a batch process is also commonly used for producing expandable polystyrene (eps). however, in practice this polymerisation reaction is a very sensitive process and numerous variables affect it, which makes the process difficult to control. the eps production has to be able to follow fast the aims and quality requirements of the market, which causes additional demands on the process control. archived process data is an important resource for the knowledge management of the process and it can be used for the optimization and improvement of productivity. recent applications have demonstrated that artificial neural networks can provide an efficient and highly automated method for modelling industrial data [1], [2]. in particular, studies, which use standardized protocols, are most likely to benefit from automated ann analysis [1], [2]. self-organizing maps [1], [3]-[5] have also been successfully applied in many areas of research and are thus a tool for process optimization. the som method offers an efficient means of handling complex multidimensional data, which is typically the situation in industrial applications. in addition, the som method is robust for missing values of data. here, we apply self-organizing maps to the analysis of an eps-batch process. the optimal amounts of the stabilisation agent can be estimated using the som model. in the study we have also included some features of the supervised approach in the designed unsupervised method. 2 methods 2.1 the process and the data the studied process was a typical suspension polymerisation batch process, which is commonly used for producing eps (expandable polystyrene). the polymerisation stage is executed in a pressuretemperature range below the boiling point of styrene-water suspension system. after the polymerisation stage the process continues into the impregnation stage, where the blowing agent is impregnated into the beads. the impregnation stage assumed to be negligible in the means of bead size distribution. the biggest challenge in the suspension polymerisation process is to achieve the required bead size distribution. it is common knowledge that the basic variables in the term of the bead size are the mixing copyright © 2006-2007 by ccc publications 144 mikko heikkinen, ville nurminen, yrjö hiltunen properties and the amount and quality of the suspension stabilizers. however the suspension polymerisation of styrene is a very sensitive process and numerous variables affect it. most of these variables cannot be measured or followed by in a reasonable way. for example to analyse all impurities from all raw materials is too heavy a task for any industrial laboratory. some variables are quite easily measurable, but have not been traced due to the assumption that they would not have a significant contribution to the process. to be able to model the process the studied system required elimination of the variables, which were assumed to be inessential. the data for the model were divided into three groups: recipe, results and process parameters. process parameters, such as actual reactor temperature, were measured and stored automatically from each batch every one minute. process parameter data was not used for modelling in the first part of this study and the target is to add it later to the model. table i shows the recipe and result variables. the data contained 15 production campaigns and about 450 batches in 4 reactors. the data had to be divided into two separate groups due to the process changes in the stabilisation system. these changes limited the size of useful data to 251 batches, which is a low amount for accurate modelling. fortunately more production campaigns will be executed almost every month and more data will be available for further studies. 2.2 computational methods self-organizing maps self-organizing maps (soms) are an artificial neural network methodology, which can transform an n-dimensional input vector into a oneor two-dimensional discrete map. the input vectors, which have common features, are projected to the same area of the map e.g. (in this case described as şneuronsť). each neuron is associated with an n-dimensional reference vector, which provides a link between the output and input spaces. during learning, the input data vector is mapped onto a particular neuron (best matching unit, bmu) based on the minimal n-dimensional distance between the input vector and the reference vectors of the neurons. then the reference vectors of the activated neurons are updated. when the trained map is applied, the best matching units are calculated using these reference vectors. in this unsupervised methodology, the som can be constructed without previous a priori knowledge [1]. the data were coded into 11 inputs for the som. all input values were variance scaled. the som having 676 neurons in a 26x26 hexagonal arrangement was constructed. the linear initialization and table 1: recipe and result variables of the particular styrochem suspension polymerisation process. recipe variables u nit result variables u nit amount of stabilisation agent % from ms under sized % mixing speed rpm product a % polymerisation temperature °c product b % reactivity min product c % amount of styrene kg product d % additional stabilisation kg product e % product type product f % batch number in campaign over sized % reactor mean particle size mm delta mm narrowness self-organizing maps for analysis of expandable polystyrene batch process 145 batch training algorithms were used in the training of the map. a gaussian function was used as the neighbourhood function. the map was taught with 10 epochs and the initial neighbourhood had the value of 6. the som toolbox [7] was used in the analysis under a matlab-software platform (mathworks, natick, ma, usa). the data set of an eps-batch process (n= 251 batches) was divided into two subsets. the first subset (the first 190 batches) was the training set, which was used for training the map. the other subset (the last 61 batches) was the test set. variables of each batch and amounts of the stabilisation agent were used as an input for the som in the training phase. however, the amounts of the stabilisation agent were missing values in the test phase, i.e. best matching neurons for the test set were sought using only those other batch variables specified above. estimated values for the amounts of the stabilisation agent were obtained from the reference vectors of neurons. k-means method the k-means algorithm was applied to the clustering of the map. the k-means method is a wellknown non-hierarchical cluster algorithm [8]. the basic version begins by randomly picking k cluster centers, assigning each point to the cluster whose mean is closest in a euclidean distances sense, then computing the mean vectors of the points assigned to each cluster, and using these as new centers in an iterative approach. 3 results and discussion the map was obtained by training a self-organizing network with the training set of an eps-batch process. the map and the six clusters calculated by the k-means method are shown in figure 1. these clusters represent different types of the products. the brief descriptions of the clusters are also illustrated in figure 1. the method was validated by using the test set, i.e. the last 61 data vectors were in test. the results, when the som was trained to estimate the amounts of the stabilisation agent, are illustrated in fig. 2. the correlation coefficient between the actual amount of the stabilisation agent and the estimated one was 0.851 (fig 2 a). in fig. 2 b) the actual and estimated amounts of the stabilisation agent are shown as a function of the batch number. table 2: variables used for modelling. variables under sized product a product b product c product d product e mean particle size batch number in campaign amount of stabilisation agent in previous batch amount of stabilisation agent a web application based on this som model was made for estimating the optimal amount of stabilisation agent in practice at the plant. figure 3 illustrates the interface of this application. the same 146 mikko heikkinen, ville nurminen, yrjö hiltunen figure 1: som using the data of an eps-batch process. the background colours visualize the six clusters of the map. short descriptions for each cluster are also shown. figure 2: a) the correlation between the actual amount of the stabilisation agent and the one estimated by the som analysis. the results are from the test set (the last 61 batches) and the value for the correlation coefficient is 0.851. b) the actual and estimated amounts of the stabilisation agent as a function of the batch number. self-organizing maps for analysis of expandable polystyrene batch process 147 figure 3: the interface of the web application. variables, which are shown in table 2, have been used in this application. a user gives as many as possible out of the ten upmost variables and the application calculates the lowest one. the first seven variables define the target amounts of the product. our earlier results showed that the som method could be successfully applied to process state monitoring and optimization of nox emissions in the case of a continuous process [6]. the results presented here illustrate also the advantages of using som method in the analysis of a batch process. figure 3 shows that the method can be used for estimation of optimal parameters of the process. it seems to reduce varieties of the process and so helps to get better products. because our som method is also nonsensitive for the presence of missing values, it is feasible in the analysis of industrial data. furthermore, som analysis does not require extensive knowledge of neural nets and it can easily be included in any kind of software. an attractive property of som is also that it can be retrained, if new product types of the process are to be analysed. 4 conclusion the som analysis provides an efficient and automated method for data analysis in the process industry. the present study shows that this kind of data-driven approach is a fruitful way of analysing a batch process. 5 acknowledgement this research was supported by styrochem ltd. references [1] t. kohonen, self-organizing maps, springer-verlag, berlin heidelberg new york, 2001. [2] s. haykin, neural networks: a comprehensive foundation, upper saddle river, nj: prentice hall, 1999. 148 mikko heikkinen, ville nurminen, yrjö hiltunen [3] j. kaartinen, y. hiltunen, p. t. kovanen, m. ala-korpela, classification of human blood plasma lipid abnormalities by 1h magnetic resonance spectroscopy and self-organizing maps, nmr biomed, vol. 11, pp. 168-176, 1998. [4] m. t. hyvönen, y. hiltunen, w. el-deredy, t. ojala, j. vaara, p. t. kovanen, m. ala-korpela, application of self-organizing maps in conformational analysis of lipids, journal of the american chemical society, vol. 123, pp. 810-816, 2001. [5] m. heikkinen, m. kolehmainen, y. hiltunen, classification of process phases using self-organizing maps and sammonšs mapping for investigating activated sludge treatment plant in a pulp mill, proceedings of the fourth european symposium on intelligent technologies and their implementation on smart adaptive systems, pp. 281-297, 2004. [6] m. heikkinen, a. kettunen, e. niemitalo, r. kuivalainen, y. hiltunen, som-based method for process state monitoring and optimization in fluidized bed energy plant, icann 2005, lecture notes in computer science 3696, eds. w. duch, j. kacprzyk, e. oja, s. zadrozny, springer-verlag berlin heidelberg, pp. 409-414, 2005. [7] homepage of som toolbox, helsinki university of technology, laboratory of computer and information science (cis), http://www.cis.hut.fi/projects/somtoolbox/. [8] j. macqueen, some methods for classification and analysis of multivariate observations, in proceedings of the fifth berkeley symposium on mathematical statistics and probability, vol. i: statistics, university of california press, berkeley and los angeles, pp. 281-297, 1967. mikko heikkinen university of kuopio, department of environmental sciences p.o. box 1627, fin 70211 kuopio, finland e-mail: mikko.heikkinen@uku.fi ville nurminen styrochem ltd p.o. box 360, fin 06101 porvoo, finland e-mail: ville.nurminen@styrochem.com yrjö hiltunen university of kuopio, department of environmental sciences p.o. box 1627, fin 70211 kuopio, finland e-mail: yrjo.hiltunen@uku.fi received: march 6, 2007 international journal of computers, communications & control vol. i (2006), no. 1, pp. 41-46 an exact algorithm for steiner tree problem on graphs milan stanojević, mirko vujošević abstract: the paper presents a new original algorithm for solving steiner tree problem on graph. the algorithm is simple and intended for solving problems with relatively low dimensions. it is based on use of existing open source software for solving integer linear programming problems. the algorithm is tested and shown very efficient for different randomly generated problems on graphs of up to 50 nodes, up to 10 terminals and average node degree 7. keywords: steiner tree problem on graph, branch and cut, algorithm, optimization 1 introduction the steiner tree problem (stp) is met in telecommunication and energetic systems, vlsi technologies and in other network planning tasks. the problem is to find a minimal length tree which connects all terminal nodes of a given graph, and contains arbitrary number of other nodes. the problem is similar to the well known shortest spanning tree problem (sstp), but unlike that it doesn’t necessary contain all nodes of given graph. also, a very important difference is that stp is much harder problem than sstp. the usual formulation of stp is the following: a connected undirected graph g = (n, e), where n = {1, . . . , n} is a set of nodes and e ⊆ {{i, j} | i ∈ n, j ∈ n, i < j} denotes a set of edges, is given. a positive value (length, weight, etc.) ce is associated to every edge e ∈ e. also, a set t ⊂ n of so called terminal (steiner) nodes is given. definition 1. steiner tree for t in g is a subgraph st = (n′, e′), n′ ⊆ n, e′ ⊂ e which satisfies one of the following statements: 1. t ⊆ n′ and st is a tree; 2. ∀s,t ∈ t in st exists exactly one path from s to t. the length of steiner tree is the sum of lengths of all edges which it consists of. the steiner tree problem is to find the shortest steiner tree. this is a np-hard problem and its decision problem variant belongs to np-complete problem class [6]. the problem is presented in details in surveys: [4], [5], [9] and [11]. in papers [2], [8] and [7] the problem was solved to optimality using sophisticated branch and cut methods. in this paper an original exact algorithm, generally based on branch and cut procedure, for solving steiner tree problems on graph is proposed. it uses code from an available open source project which develops software for solving linear and integer linear programming problems. an intention was to formulate an algorithm which can be implemented in relatively short time and which will be able to solve stp of “reasonable” dimensions in “reasonable” time. to be more precise, the implementation of proposed algorithm lasted about 3-4 days. the program could solve problems of dimensions which are larger of the most of real life problems in, at most, several minutes. in this paper, in the next section, a mathematical model of the steiner tree problem and its explanation are given. in section 3, the algorithm and the process of implementation are explained in details. in section 4 the experiments are described and some conclusions about the algorithm behavior are made. in section 5, the conclusion of the whole paper is given. 2 mathematical model a steiner tree can be represented by a vector of binary variables x = (xe)|e|. each element of the vector is assigned to one edge from the set e. the value of a variable xe indicates whether the corresponding edge e is in the steiner tree (xe = 1) or not (xe = 0). a mathematical model of the stp on undirected graphs has the following copyright c© 2006 by ccc publications 42 milan stanojević, mirko vujošević form: min ∑ e∈e cexe subject to (i) ∑ e∈δ (m) xe ≥ 1 ∀ m ⊂ n, m ∩t 6= /0, (n\m)∩t 6= /0 (ii) xe ∈ {0, 1} ∀ e ∈ e (1) where n, e and t are as in definition 1, ce is a positive value associated to each edge e and δ (m) denotes a graph cut defined by subset of nodes m ⊂ n, i.e. the set of edges with one end node in m and the second one in complement set n \m, δ (m) = {{i, j} ∈ e | i ∈ m, j ∈ n \m}. the mathematical model (1) is linear and integer. constraints (i) ensure that in every cut, for which terminal nodes are on the both sides (m and n\m), at least one edge exists. in other words, they ensure that between each two terminal nodes exists at least one path. a feasible solution of model (1) is not necessary a steiner tree. but the optimal solution will be a steiner tree because any edge constructing a contour would violate the optimality condition. so, although the formulation is rather comprehensive, it can be applied only for those problems where the goal is to minimize the length of the steiner tree. a disadvantage of model (1) is that the number of constraints grows exponentially in the problem size. on the other hand, in branch and cut methods, the relaxation of the formulation may give acceptable results. 3 implementation two main challenges in solving the model (1) are: (i) the exponential number of constraints, and (ii) the exponential time needed to solve the integer program even with smaller number of constraints. to overcome the first challenge, the proposed algorithm uses a relaxation of model (1). the idea was inspired by the paper [7] and partly published at [10]. namely, because of a big number of constraints, solving a model which includes all the constraints is very hard and in many cases impossible. on the other hand, in the most cases it is not necessary to include all the constraints in order to obtain an optimal solution. only the constraints which are active in the optimal solution are really necessary. of course, we cannot predict which of them will be active, but we can start with some smaller number of constraints, giving priority to those which are “more likely” to be necessary to obtain a feasible solution (steiner tree). if we solve a model with smaller number of constraints and the solution is a steiner tree of the given graph, then this solution will be the optimal for the starting problem, i.e. by adding more constraints we cannot improve the solution. otherwise, the obtained solution will consist of two or more subtrees. then, we iteratively add those constraints for which we find they are violated and which would probably lead to feasible solution until we finally obtain a steiner tree. the most common way to solve a binary linear programming problem is implementation of branch and bound method in combination with simplex method. according to the one of main features of the algorithm we intended to formulate – quick and easy implementation, development of simplex and branch and bound algorithms from a scratch wouldn’t be appropriate. although it would finally give better performance if they would be incorporated in the essence of complete procedure, the development of these procedures would last very long. an alternative was found among open source projects. the project used in this implementation was lp_solve. lp_solve [1] is an open source project that realize very robust procedures and techniques for solving linear programming problems. beside that, it implements the branch and bound method for solving binary, integer and mixed integer linear problems. it has a lot of options by which it is possible to influence the branch and bound procedure changing its strategies, so it is possible to significantly improve its performance [13]. lp_solve can be used as independent application when it can read problem files in lp and mps formats, and as a set of functions, when it can be incorporated into other programs and controlled from the host code. the project itself doesn’t have any restrictions of the problems dimensions. some successful applications on mixed integer programming problems with several thousands variables were reported. the license of the project is glgpl (gnu lesser general public license) [12] and it allows free download, using, changing and redistribution of the source code of lp_solve project. the algorithm is formally formulated as follows: an exact algorithm for steiner tree problem on graphs 43 algorithm 1 simplified branch and cut algorithm for stp 1. formulation of initial integer linear mathematical model: (a) goal function formulation: one variable is introduced for each edge and corresponding edge length is associated as a parameter to each variable. (b) for each terminal node, one constraint of type (i) is formulated, so the terminal node is a single node on one side of the cut and the rest of the nodes are on the other side, i.e. ∑ e∈δ (m) xe ≥ 1 ∀m ∈ {{t} | t ∈ t}. the number of constraints after step 1 will be |t|. 2. solve the current mathematical model. 3. check if the obtained solution is a steiner tree, i.e. if there is a path between all pairs of terminal nodes. if so, the optimal solution was found in step 2; the end of the procedure. otherwise, next step. 4. if solution is not a steiner tree, then it represents two or more unconnected subtrees. for every subtree add one type (i) constraint defined by cut δ (m) where the nodes of that subtree belong to set m. go to step 2. the proposed algorithm can be qualified as a simplified version of branch and cut method, i.e. a combination of branch and bound and cutting planes. the steps of the algorithm will be illustrated by an example. suppose, we have to obtain the minimal steiner tree for the given graph, illustrated in figure 1, where four terminal nodes are marked with bigger circles. the mathematical model created in step 1 and updated in step 4 will have one column for every edge. the constraints added in the first step are necessary to provide that every terminal is connected to, at least, one edge. in the example, four constraints will be added in the first step – one for every terminal. they will ensure that at least one edge is connected to each terminal. in figure 2, the four cuts are marked as open curves surrounding each terminal node, and the edges marked by dashed lines are candidates to be in the first solution. figure 1: initial graph figure 2: first step the possible solution after step 2 could be like one in figure 3. as mentioned above, if the obtained solution is not a steiner tree, it will consist of several subtrees. the number of subtrees generally can be between 2 and |t|. for determining if the obtained solution is a steiner tree (in step 3), dijkstra’s shortest path algorithm was used. in step 4 a new constraint for each subtree is added. in the example, three new constraints, corresponding to three subtrees shown in figure 3, are added. on each image of figure 4, one cut (represented by the curve surrounding the subtree) and edges (dashed lines) among which, at least one will be in the next solution are shown. after the next optimization, a possible solution could be like the one shown in figure 5. the solution satisfies all added constraints and the graph structure is a steiner tree. without further checking, we can claim that it is an 44 milan stanojević, mirko vujošević figure 3: possible solution after the first iteration figure 4: three new constraints optimal solution of the given steiner tree problem. figure 5: final – feasible and optimal solution the main criteria for determining data structures were access speed and simplicity of implementation. the amount of used memory was not considered because of relatively small graph dimensions of target instances. the logical choice were static structures (vectors and matrices). the realized structures enabled fast data access and mapping between graph structure (realized through neighborhood matrix) and vector of edges with all corresponding attributes. the algorithm was implemented in c language and it is compiled and tested on linux operation system (with gcc gnu c compiler) on pc with pentiumr iii processor on 600 mhz and 384 mb of ram. the implemented program supports so called stp format [15] of steiner tree problems, and it is compatible with the library of standard steiner tree problems [14]. 4 experimental results the developed program was tested on different problem instances. a characteristic of the steiner tree problem is that the complexity of a procedure for its solving depends on three attributes: number of nodes (n), number of edges and number of terminal nodes (t). the largest dimensions of instances successfully solved by the program were [n/t]: 20/10, 32/8 and 50/5, with the average node degree 7. the solving procedures lasted between 2 seconds and 2 minutes for the most of the instances. these dimensions may look modest in comparison with those with several thousands nodes which were successfully solved as it was reported in papers [2] and [7]. however, comparing the procedure complexity, simplicity of implementation and the fact that in many real-life telecommunication planning processes, even smaller size problems may appear, the proposed implementation could be very useful. to get a more precise insight in the behavior of the algorithm, experiments were performed with instances where some parameters were varied. in the following table, results (mean values and standard deviation) obtained by experiments where every dimension was tested on 15 randomly generated instances are given. in the columns named “no. of rows” the number of constraints needed to obtain an optimal solution (the optimization in the last iteration) is given. the columns named “no. of iterations” represent a number of iterations of algorithm 1 when an exact algorithm for steiner tree problem on graphs 45 passing through steps 2-4, i.e. the total number of solved binary subproblems (in step 2). the columns “time” represent cpu time spent to solve the problem. the column “ancai” shows the average number of constraints added per iteration. it’s obtained by formula: no.rows−tno.iterations where t represents the number of terminal nodes, i.e. the number of constraints added in initial mathematical model in step 1. table 1: the complexity analysis of the algorithm instance dimensions no. of rows no. of iterations time [sec.] an[n/t] x σ x σ x σ cai 1 2 3 4 5 6 7 8 1 20/5 25 8.9 7.5 3.0 0.2 0.3 2.7 2 32/5 38 22.1 12.3 8.0 3.0 9.7 2.7 3 50/5 60 28.3 20.0 11.3 7.2 13.3 2.8 4 20/8 65 20.3 19.5 8.5 11.5 27.5 2.9 5 32/8 88 31.9 26.3 12.0 34.5 46.7 3.0 6 20/10 83 15.5 23.6 5.2 13.4 9.9 3.1 on the basis of data given in table 1, some conclusions can be made. the fact that complexity of stp grows with the number of nodes is obvious from the first three rows. although the speed of the growth cannot be determined exactly on the basis of so small sample, it is obviously nonlinear – probably exponential. the more interesting conclusion is that the growth of complexity is faster by changing the number of terminal nodes than the total number of nodes in graph. comparing rows 1, 4 and 6, a kind of “explosion” of complexity can be seen: when the number of terminals was increased two times, execution time was increased 67 times. similar conclusion can be made observing rows 2 and 5: addition of three terminals resulted in execution time increase of more than 11 times. the most important analysis here concerns the number of constraints needed to get an optimal solution. according the column 2, that growth is almost linear in problem size. we cannot be certain if it is linear, but it is definitely not exponential. finally, we can conclude that, although the number of constraints in model (1) grows exponentially, the number of constraints necessary to obtain an optimal solution grows much slowlier. the number of iterations grows even less. the parameter in column 8 is also interesting. the average number of constraints added in each iteration also represents the average number of subtrees obtained in each sub solution. it seems that the relatively small values in column 8 doesn’t depend much on the number of terminal nodes. the explanation could be that the current solution of the solving procedure relatively quickly forms a structure which consists of a few subtrees each containing several terminals. this may contradict to the previous statement that complexity depends more on the number of terminals than on the total number of nodes, because, what influences the number of iterations is the number of unconnected subtrees, and not the number of terminals. one possible explanation is that the structure of subtrees changes, so subtrees contain different terminals in different iterations. it is also important to have in mind that every iteration lasts more than the previous one because every mathematical model has more rows (constraints) than the previous one. yet another interesting thing from table 1 is relatively big dispersion (represented by standard deviation) of data obtained by different randomly generated instances with same characteristics. this is a consequence of a nature of the algorithm (which is nondeterministic polynomial). it is impossible to predict the number of iterations necessary to obtain final solution. in the worst case it can be exponential. 5 conclusion the first impressions and experiment conclusions indicate that the proposed algorithm can be efficiently used when there is a need for rapid development of an algorithm for solving smaller size steiner tree problems. although the worst case number of constraints needed to obtain a final solution remains exponential, the algorithm have shown a kind of “good behavior” – in all solved examples the number stayed relatively low. although the exponential complexity of the branch and bound method (step 2 of the algorithm) remains, instances with acceptable dimensions can be solved in real time. 46 milan stanojević, mirko vujošević the idea of successive adding violated constraints could be also applied to some other problems whose mathematical models have an exponential number of constraints. concerning that, some new researches have been planed, where the idea would be applied to the traveling salesman problem (tsp). namely, the so called dfj formulation of tsp [3] also has exponential number of constraints, but it has shown a good behavior in relaxation based algorithms. references [1] m. berkelaar, k. eikland, p. notebaert, lp_solve, files and discussion group, ftp://ftp.es.ele.tue.nl/pub/lp_solve, http://groups.yahoo.com/group/lp_solve/, 1994-2006. [2] s. chopra, e. gorres, m. r. rao, “solving a steiner tree problem on a graph using branch and cut”, orsa journal on computing, vol. 4, pp. 320-335, 1992. [3] g. b. dantzig, d. r. fulkerson, s. m. johnson, “solution of a large-scale traveling-salesman problem”, operations research, vol. 2, pp. 393-410, 1954. [4] f. k. hwang, d. s. richards, “steiner tree problems”, networks, vol. 22, pp. 55-89, 1992. [5] f. k. hwang, d. s. richards, p. winter, the steiner tree problem, north-holland, amsterdam, 1992. [6] r. m. karp, “reducibility among combinatorial problems”, r. e. miller, j. w. thatcher (ed.), complexity of computer computations, pp. 85-103, plenum press, new york, 1972. [7] t. koch, a. martin, “solving steiner tree problems in graphs to optimality”, networks, vol. 32, pp. 207-232, 1998. [8] a. lucena, j.e. beasley, “a branch and cut algorithm for the steiner problem in graphs”, networks, vol. 31, pp. 39-59, 1998. [9] n. maculan, “the steiner tree problem in graphs”, surveys in combinatorial optimization, s. martello, g. laporte, m. minoux, c. c. ribeiro (ed.), annals of discrete mathematics, vol. 31, pp. 185-212, 1987. [10] m. stanojević, m. vujošević, “a new algorithm for solving steiner tree problem on graph” (in serbian), 12th telecommunications forum telfor 2004, belgrade, http://www.telfor.org.yu/telfor2004/e-index.html (http://www.telfor.org.yu/telfor2004/radovi/tm-2-4.pdf), 2004. [11] p. winter, “steiner problem in networks: a survey”, networks, vol. 17, pp. 129-167, 1987. [12] gnu lesser general public license, http://www.gnu.org/copyleft/lesser.html [13] lp_solve reference guide, http://www.geocities.com/lpsolve/ [14] steinlib testsets – the library of standard steiner problems, http://elib.zib.de/steinlib/testset.php [15] stp – description of the stp data format, http://elib.zib.de/steinlib/format.php milan stanojević, mirko vujošević university of belgrade faculty of organizational sciences address: jove ilića 154, belgrade, serbia and montenegro e-mail: {milans,mirkov}@fon.bg.ac.yu int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 4, pp. 363-373 fuzzy logic in genetic regulatory network models c. muñoz, f. vargas, j. bustos, m. curilem, s. salvo, h. miranda carlos muñoz poblete, francisco vargas parra, jaime bustos gomez millaray curilem saldias, sonia salvo garrido, horacio miranda vargas university of la frontera avenue francisco salazar 01145, temuco, chile e-mail: comunoz@ufro.cl abstract: interactions between genes and the proteins they synthesize shape genetic regulatory networks (grn). several models have been proposed to describe these interactions, been the most commonly used those based on ordinary differential equations (odes). some approximations using piecewise linear differential equations (pldes), have been proposed to simplify the model non linearities. however they not allways give good results. in this context, it has been developed a model capable of representing small grn, combining characteristics from the ode’s models and fuzzy inference systems (fis). the fis is trained through an artificial neural network, which forms an adaptive nertwork-based fuzzy inference system (anfis). this network allows to adapt the membership and output functions from the fis according to the training data, thus, reducing the previous knowledge needed to model the specific phenomenon. in addition, fuzzy logic allows to express their rules through linguistic labels, which also allows to incorporate expert knowledge in a friendly way. the proposed model has been used to describe the lac operon in e. coli and it has been compared with the models already mentioned. the outcome errors due to the training process of the anfis network are comparable with those of the models based on odes. additionally, the fuzzy logic approach provides modeling flexibility and knowledge acquisition advantages. keywords: genetic regulatory network, fuzzy logic, anfis, differential equations, lac operon 1 introduction factors in charge of regulating the expression of a gene can be both environmental (such as produced by other factors genes) or those produced by other genes or even the same gene under regulation. the latter is the basis for understanding the so-called genetic regulatory networks (grn), since they are real networks of interaction between genes in a cell. being able to accurately predict these interactions using mathematical and / or computational models would benefit a wide variety of applications such as medicine or agriculture. there have been many approaches to model these networks, such as bayesian networks, boolean networks, models based on ordinary differential equations, piecewise linear models, stochastic models and others [13], [14], [13], [17]. from them, we can to highlight the models based on ordinary differential equations (odes) since they describe biological phenomena in large detail, being used primarily for modeling small regulatory networks. a disadvantage of these models is the large number of parameters (to be known a priori) acceptable for a biological description, requiring an exhaustive study copyright c© 2006-2009 by ccc publications 364 c. muñoz, f. vargas, j. bustos, m. curilem, s. salvo, h. miranda of the available literature to specify the parameters and/or designing experiments to estimate each of the parameters as required. it is now possible, using the necessary experimental data, to use optimization tools or artificial intelligence to solve this problem [2], [25]. an approach to the ode-based models is based on a piecewise linear differential equations (pldes) [3], [5], [7]. on the other hand, artificial intelligence techniques, including fuzzy logic, have been incorporated primarily to the classification and analysis of data obtained through microarrays [8], [11], [21], [15], [23]. moreover, the techniques of fuzzy logic have also been considered for describing biological systems of which some a priori knowledge exists [12], [18], [19]. in this context, it is necessary to propose models that attempt to get good predictions, reducing the need for prior knowledge. we must also consider that these models should have the ability to easily incorporate the knowledge of experts in the field of genomics, as well as experimental information thus complementing previous work with the new developments. similarly, the steady states of the model should be analyzed in order to ensure a biologically acceptable description. in this work we propose the development of a model that integrates the fuzzy inference with differential equations. we have chosen the differential equations because they constitute a model that describes with sufficient fidelity the regulatory processes; we also incorporate fuzzy logic because they have the ability to work with nonlinear systems. the proposed model can reduce the need for prior knowledge of the phenomenon due to the training of the network anfis, transforming it into a grey box model [1], combining differential equations, and network training. in addition, the proposed model makes it possible to express their fuzzy rules across linguistic labels, which gives the ability to incorporate expert knowledge in a friendly language. 1.1 biological background a gene is active when it is able to synthesize one or more (depending on the body) types of proteins, which can play a regulatory role in the expression of the same or other genes, and also can catalyze chemical reactions within a cell. therefore the function of a cell in an organism depends on the genes that are active, or in other words, it depends on the expression of its genes. the process of synthesis of a protein consists of 2 phases, the first of them is known as transcription. at this stage the segment of dna that contains the information of the gene is transcribed into a string of messenger rna (mrna) through the enzyme called rna polymerase. the action of this enzyme is regulated by a series of molecules called transcription factors (tf), which use certain areas of dna, called zones cis-regulatory that are specific to this end. then, when we have the chain of mrna with the information on the protein synthesis it comes a second stage called translation. here an internal organelle called a ribosome reads the information chain mrna and, together with the transfer rna (trnas), it links the amino acids needed to form the protein that indicates the information of the gene. this protein may regulate the expression of the same or other genes, and can also participate in metabolic processes of the cell. 2 materials and methods this section defines the model based on a fuzzy inference system (fis), presenting also the characteristics of the first models based on odes and its piecewise linear approximations in order to compare all these models in a real system. 2.1 odes based models these models are based on a series of ordinary differential equations that relate mrna molecules with proteins that they synthesize, the action of other molecules present in the regulation can also be fuzzy logic in genetic regulatory network models 365 incorporated. usually this kind of differential equations presents the form: dxi dt = ∑ j αi j fi j(x j) − γixi (1) where xi represents a molecule produced in the process, ai j is the production rate of the molecule i due to the molecule j, γi is the degradation rate of the molecule i, and fi j is a function that determines the interaction of the molecule x j with the molecule xi, which is called regulation function. this is a nonlinear function, which provides for realism from a biological point of view. it is generally defined as a function of sigmoidal type, commonly the hill function [3], [5]: fi j = h + i j (x j, θi j, mi j) = x mi j j θ mi ji j + x mi j j (2) this equation shows that for values x j well over the threshold, θi j, the function tends to a value of 1, whereas when x j tends to values below the threshold, θi j, the function is close to the value 0, as seen in figure 1.a. the speed with which the function passes from the value 0 to 1 (while x j varies) depends on the slope at the point threshold. this slope changes depending on the value of mi j. 2.2 plde-step models due to the nonlinear nature of the odes-based models, piecewise linear approximations have emerged that attempt to simplify the odes-based model to a set of linear models. such models are based on a piecewise linear differential equations (pldes). the number of potential resulting linear models depends on the amount of regulation functions to approximate and the amount of linear segments on each approximation. a widely used approach approximates the regulation function, fi j, to only 2 cases: fi j = si j(x j, θi j) = { , if x j > θi j , if x j ≤ θi j (3) the regulation function is then approximated to a step function [1], so this model has been named piecewise linear differential equations-step (plde-step) [20]. in this case, the value of the threshold, θi j, is the only parameter to estimate for each regulation function. the curve is shown in figure 1.b. 2.3 plde-logoid models in addition to the step function, there are other features to approximate a nonlinear model to a piecewise linear. thus, we find in the literature approaches that use the ramp function as part of the linear segments of the model [5]. this approximation of the regulation function is also known as a logoid function [3] calling such models as based on piecewise linear differential equations-logoid(plde-logoid) [20]. in this case, the curve takes the form shown in figure 1.c, and the regulation function is defined as: fi j = li j(x j, θi j, δi j) =    , if x j > θi j + δi j   δi j (x j − θi j) +  , if θi j − δi j  < x j ≤ θi j + δi j , if x j ≤ θi j − δi j (4) where the new parameter, δi j, corresponds to the piece at which the function moves from 0 to the value set to 1, corresponding to the inverse of the ramp function in that segment. as shown in (4), there are 3 possible cases for every regulation function, which increase the number of potential linear differential equations to solve compared to the model plde-step. however this also increases the accuracy of the approximation. 366 c. muñoz, f. vargas, j. bustos, m. curilem, s. salvo, h. miranda figure 1: different regulation function: a) the hill function, b) step function and c) logoid function. 2.4 proposed model in this case each regulation function is approximated by a fuzzy inference system (fis), of the takagi-sugeno type. this fis is capable of representing the nonlinear behavior of the regulation function making it possible to define linguistic labels to determine the concentrations of molecules. moreover, one can assume the production rates as unknown and include their action within the fis, which diminishes the prior knowledge required for modeling. thus, the differential equations take the form: dxi dt = ∑ j fisi j(x j) − γixi (5) by comparing (5) to (1) we observe that it has been replaced αi j fi j by the fuzzy inference system f isi j, i.e. not only approaching the regulation function but also including the production rates. for the design of the fis it should mainly be considered the characteristics of the membership functions, the number of them, the fuzzy rules, and the output functions. the latter implies that when designing the fuzzy inference system a good knowledge of the phenomenon is required, including aspects such as the ranges of concentrations, the fuzzy rules, and so on. that is why we use a training network called anfis [16] to get a fuzzy system that approximates the odes-based model. this network is trained with experimental data and is capable of adapting, using a hybrid learning algorithm, the characteristics of membership functions and output functions so to reduce the error between the experimental data and data generated by the fis. figure 2, shows a fuzzy inference system with 2 inputs x and y, 2 membership functions for each input (a, a, b and b), 2 fuzzy rules which consequences are f  and f , and 1 output f . among the most important factors to be considered for the training of the network are the type and number of membership functions, the maximum amount of training epochs and the error goal. we must also consider that the data for training must provide sufficient information to model the dynamics of the system. the training network allows complementing the phenomenological model with the information obtained from the experimental data. consequently, the model is considered a hybrid model or grey box model. in this work the proposed model is called ode-fis model due to its characteristics. 2.5 implementation in a real system all models are compared representing the lac operon in e. coli. the lac operon is a very well studied process in the bacterium escherichia coli [10], [22], [24], and although broadly it appears simple, in reality it can be modeled so detailed that you can include more than 100 biochemical reactions. in this paper we use the model of the lac operon shown in [24]. fuzzy logic in genetic regulatory network models 367 figure 2: structure of a anfis network: a) takagi-sugeno type fuzzy inference system, b) anfis representation of the system. the description of the process is as follows. the main source of carbon for the bacteria e. coli is glucose. when glucose is not present in the environment cell, the bacterium is able to form glucose through lactose. for this, there is a regulatory mechanism that allows to synthesize the enzymes necessary to obtain glucose. this mechanism is called lac operon. this model describes the synthesis of glucose from lactose by the bacterium e coli. this is due to the fact that in the absence of glucose, but in the presence of lactose, the bacteria activates the synthesis of β -galactosidase and permeases. the β -galactosidase breaks down the lactose into alolactosa, glucose and galactose, being the alolactosa the inducer in the operon regulation. moreover permeases allow the passage of external lactose towards the cell. an outline of the system as used in [7], is shown in figure 3. the model presented in [24]is based on 5 nonlinear differential equations, where we can find 6 hill functions. these equations describe the production of mrna, β -galactosidase, alolactosa, lactose and permeases, also allowing to manipulate the external lactose and feed phosphate. figure 3: simplified outline of the lac operon in e. coli as the model used is based on hill functions, for comparison purposes it should be approximated by the models plde and ode-fis. in the case of plde-step model we use the same threshold values presented by the odes-based model, and therefore it does not require a new design parameter. in the case of plde-logoid model the thresholds do not vary, however we must design the value δi j, shown in (4). for doing this, the coefficient k must be calculated, such that δi j j = ki j, which in turn delivers the lowest value of steady state error with respect to the ode models. in the case of the ode-fis model we train 6 regulation functions with data obtained from the model shown in [24], and which entries correspond to 368 c. muñoz, f. vargas, j. bustos, m. curilem, s. salvo, h. miranda states of the system. to cover a wide range of training conditions we reduce the external lactose concentration of 0.08 mm to 0 mm, through 4 negative steps, which in turn allows obtaining more information on the dynamics of the system. the structure of the model corresponds to 3 membership functions per entry, each one of the gauss type, labeled low, medium and high referring to the concentration level of each entry. the training epochs are 80 and the error goal is 0. for comparison purposes we reproduce the 2 experiments shown in [24] using the 4 models described previously. in each case, both the expression of β -galactosidase and permeases are plotted. the first of these experiments consists on monitoring the changing states of the system in time for a given set of initial conditions, and where the external lactose level and the feeding phosphate rate are kept constant. in the second experiment we maintain a constant level of external lactose changing periodically the phosphate feeding. for the comparison of β -galactosidase we also count with the experimental data [9] and [14] for the experiment 1 and data from [6]to experiment 2, which were provided by the authors of [24]. to compare, on a quantitative basis the approximations of models pldes and ode-fis, we have replicated experiment 1 and formulated a table with the steady state error (sse) and the integrated square error (ise) with respect to the odes-based model, considering a simulation time of 500. it was also implemented an experiment to assess whether the training of the network anfis was able to capture the main equilibrium points of the system. thus, we performed a sweep sampling of the values of the external lactose and the initial conditions to see if the model ode-fis has the same equilibrium points as the odes model. all the experimental work is developed in the matlab software, using primarily the simulink, fuzzy logic and optimization toolboxes. 3 results in the work of yildirim and mackey [24] the expression of β -galactosidase for two types of experiments is presented, being reproduced for all models. the standard profile of β -galactosidase, is shown in figure 4 for the odes-based model, the plde-step model, the plde-logoid model, the ode-fis model, and the experimental data of the work of [9] and [14]. it is noted that all models evolve to the same steady state except plde-step, which clearly does not represent adequately the profile of β galactosidase. in addition to the β -galactosidase we present the dynamics of the permeases, standardized and shown in figure 5. once again the poor performance of the plde-step model is repeated. we also stress the monitoring overshoot achieved by the ode-fis model. figure 6 shows the normalized profile of β -galactosidase for all models, in addition to the experimental work of [6]. this figure shows the similarity between the experimental data, and the ode and ode-fis models, but not pldes models (having a lower yield), particularly the plde-step model. for the experiment 2, the permeases level is followed properly for the ode-fis model, as shown in figure 7. in addition, the models pldes again show a poor performance. in order to determine which of the approaches better represents the model based on odes we obtain error rates by simulating the experiment 1. table 1 shows the steady state error (sse) and the integrated square error (ise) for a time simulation of 500. fuzzy logic in genetic regulatory network models 369 figure 4: comparison of the dynamics of β -galactosidase for different models depending on the conditions of the experiment 1. figure 5: comparison of the dynamics of permeases for different models depending on the conditions of the experiment 1. 370 c. muñoz, f. vargas, j. bustos, m. curilem, s. salvo, h. miranda figure 6: comparison of the dynamics of β -galactosidase for different models depending on the conditions of the experiment 2. figure 7: comparison of the dynamics of permeases for different models depending on the conditions of the experiment 2. sse ise β -gal permease β -gal permease plde step 7.300 e-4 1.503 e-2 2.272 e-4 1.100 e-1 plde logoid 2.739 e-8 3.349 e-7 1.030 e-6 9.080 e-4 ode fis 5.257 e-7 1.080 e-5 3.314 e-9 6.677 e-6 table 1: steady state error with respect to odes model fuzzy logic in genetic regulatory network models 371 to demonstrate the use of rules and linguistic labels we mention the case of a term describing the behavior of the alolactosa, which depends on 2 inputs; internal lactose (l) and β -galactosidase (b). given that we define 3 fuzzy sets ( low, medium, high), there are 8 possible rules () that connect the 2 inputs. as an example we mention 3 rules that were obtained from the training process: r1-if l is high and b is low then the influence of the term is null r2-if l is medium and b is low then the influence of the term is null r3-if l is low and then b is low then the influence of the term is null the label null is associated to the output function of the takagi-sugeno system, and corresponds to a value of 0. when analyzing these rules we observe that if the concentration of β -galactosidase is low, the associated term does not influence the production of alolactosa, which is consistent with reality. due to the fact that these 3 rules depend mainly on the value of b, they can be edited and replaced by a single one having the form: rthen the influence of the term is null in addition to editing rules, you can edit the features of the membership functions. the toolbox of matlab allows you to graphically edit the membership functions, without the need of a detailed mathematical knowledge of these functions. in addition, using simulink you can see the level of activation of each rule and the degree of membership of the states in the fuzzy sets in the inference systems, all this while the simulation takes place. figure 8: equilibrium points of odes model and its comparison with the equilibrium points found for ode-fis model. with regards to the stability of the ode-fis model, figure 8 shows the equilibrium points for alolactosa as external lactose level, as shown in [24]. additionally the figure also shows the steady states of the ode-fis model. most of the equilibrium points of odes model are also equilibrium points of the ode-fis model. it must be noticed that for each value of the external lactose in the ode-fis model we do not see more than 1 equilibrium point. on the contrary, in the odes model we find up to 3 points. 372 c. muñoz, f. vargas, j. bustos, m. curilem, s. salvo, h. miranda 4 summary and conclusions the flexibility that delivers the fuzzy logic and the capacity of training provided by anfis allowed to represent the non-linear system behaviour with enough similarity to the obtained with ode, showing a better performance than the plde models, even without describing the dynamic transient problems of interacting molecules. it should be mentioned that the plde models can be enhanced with optimization techniques and/or artificial intelligence tool to better design parameters θi j and δi j as needed. however this requires additional algorithms, not necessarily trivial. the anfis network allowed to obtain (from the training data) all the information needed to describe not only the transient state of the performed experiments, but also was able to achieve a large quantity of stable points of odes. this shows the great training capacity of the network, and the flexibility of the fuzzy inference system. the fuzzy logic has proved to be an important tool due to its ability to represent non-linear systems, its friendly language to express knowledge and the ability to incorporate and edit fuzzy rules. in addition, complementing the fuzzy logic with an artificial neural network for training (anfis) turns to be a powerful tool for obtaining knowledge from experimental data, suggesting the development of new techniques based both on fuzzy logic as in the networks of training and differential equations. the natural step now is to work with larger regulatory networks, adressing the criteria for 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[26] x. zhu, and j. xu, estimation of time varying parameters in nonlinear systems by using dynamic optimization, industrial electronics society. iecon 2005. 31st annual conference of ieee, 2005. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 968-975 robust 2-dof pid control for congestion control of tcp/ip networks r. vilanova, v. m. alfaro ramón vilanova department de telecomunicació i enginyeria de sistemes universitat autònoma de barcelona 08193, bellaterra, spain, e-mail: ramon.vilanova@uab.cat víctor m. alfaro escuela de ingeniería eléctrica universidad de costa rica san josé, 11501-2060, costa rica. e-mail: victor.alfaro@ucr.ac.cr abstract: this paper presents how robust pid control can improve the performance of congesion control on tcp/ip networks. the proposed approach is compared with other control methods, such as pi control or red/aqm, showing the advantages of the proposed technique. keywords: congestion control, active queue management, pid control. 1 introduction internet congestion control and congestion avoidance have been active research interests in the area of networking (see, for example [1] [2] [3]) during the last two decades. it has two components: (1) the end-to-end congestion control protocol, such as tcp [4], and (2) an active queue management (aqm) scheme implemented in routers. aqm signals congestion by discarding or marking packets. when congestion is detected by tcp, it will take actions to reduce the source sending rate. normally, aqm objectives are: to stabilize the buffer queue length at a given target, thereby achieving predictable queueing delay, and to minimize the occurrences of queue overflow and underflow, thus reducing packet loss and maximizing link utilization. thus, it is necessary to reduce as much as possible this problem. at present, there are methodologies to deal with this issue [5]: congestion control which is used after the network is overloaded and congestion avoidance which takes action before the problem appears. this paper deals with congestion control because it is where feedback control techniques can be openly and easily applied. recently several mathematical models of active queue management (aqm) schemes supporting transmission control protocol (tcp) flows in communication networks have been proposed [2] [3]. from these models a control theory-based approach can be used to analyze or to design aqm schemes. the more well known aqm scheme is probably red [1]. red can detect and respond to long-term traffic patterns, but it cannot detect congestion caused by short-term traffic load changes. in addition, it is well known that an appropriate tuning of red parameters is not an easy task an may result in a non stabilizing controls scheme. this fact has motivated the research for alternative control approaches. this paper presents the application of a robust pid approach as an aqm controller. the controller can be easily tuned from the network parameters and it is compared with red and the pi controller proposed in [3]. the performance under different load conditions shows the robustness and superiority of the presented approach. in addition the simple formulation of the pid controller also constitutes a motivation for implementation. copyright c⃝ 2006-2010 by ccc publications robust 2-dof pid control for congestion control of tcp/ip networks 969 the rest of the paper is organized as follows. next section presents the nonlinear model for a tcp router as well as the tcp control problem formulation. section 3 reviews the red and pi controller approaches for aqm control whereas section 4 presents the robust 2-dof pid approach. in section 5 a discussion and comparison is conducted. the paper ends with drawing some conclusions on the reported results. 2 aqm router dynamic model and control problem statement in this section the dynamic nonlinear/linearized equations of tcp behavior developed in [3] are briefly reviewed as well as the purposes of aqm control stated. 2.1 dynamic tcp model as in the literature, a network configuration consisting of a single congested router with a transmission capacity c is considered in this paper. tcp timeout mechanisms have been ignored for simplification. using fluid-flow and stochastic differential equation analysis, the following coupled, nonlinear differential equations have been proposed as the dynamic model of the tcp behavior: ˙w(t) = 1 r(t) − w(t) 2 w(t − r(t − r(t))) r(t − r(t)) p(t − r(t)) (2.1) ˙q(t) = { −c + n(t) r(t) w(t) q(t) > 0 max 0, −c + n(t) r(t) w(t) q(t) = 0 where w . = average tcp window size (packets) q . = average queue length (packets) r(t) . = round-trip time= q(t) c + tp (secs) c . = link capacity tp . = propagation delay (secs) n . = number of active tcp sessions p . = probability of packet mark the queue length q, and window-size w, are positive bounded quantities; i.e., q ∈ [0, q̄] and w ∈ [0, w̄] where q and w denote buffer capacity and maximum window size respectively. in this formulation, the congestion window size w(t) is increased by one every round-trip time if no congestion is detected, and is halved upon a congestion detection. moreover it has been assumed that the aqm scheme implemented at the router marks packets using explicit congestion notification (ecn) to inform the tcp sources of impending congestion. to linearize (2.1), it is assumed that the number of active tcp sessions and the link capacity are time-invariant, i.e., n(t) ≡ n and c(t) ≡ c. in addition the dependence of the time delay argument t − r on queue length q, is ignored and it is assumed to be fixed to t − ro. then, local linearization of (2.1) about the operating point results in the following equation: 970 r. vilanova, v. m. alfaro figure 1: aqm block diagram of the linearized model along with high frequency uncertain dynamics ˙δw(t) = − n r2oc (δw(t) + δw(t − ro)) (2.2) − 1 r2oc (δq(t) − δq(t − ro)) − r2oc 2n2 δq(t − ro) ˙δq(t) = n ro δw(t) − 1 ro δq(t) where δw(t) = w(t) − wo and δq(t) = q(t) − qo are the incremental variables with respect to an operating point. the operating point for a desired equilibrium queue length qo is given by: ro = qo c + tp wo = roc n po = 2 w2o (2.3) this leads to a low order nominal model of the network dynamics that is accurate at a particular operating point (ro, qo, co, wo, po) given by: p(s) = po(s)e −ros = c2/(2n) (s + 2n r2oc )(s + 1 ro ) (2.4) by modelling the high frequency dynamics using a block ∆(s) such that ∆(s) = 2n2 r3oc 3 s(1 − e−ros) (2.5) this term represents the high frequency, necessarily parasitic, network uncertainty in the model. computational experience has shown that this can adequately capture certain deviations from nominal network performance. these considerations lead to the generation of a simplified feedback control system as shown in fig. (1) 2.2 aqm control problem the function of an aqm control law is to mark packets (with probability p) as a function of measured queue length q. marking of packets is consecutively used by the sender to throttle the amount of data sent; if no marked packets are received the window size is increased. upon reception of a marked packet the window size is halved. the principal performance objectives for an aqm control law are: 1. efficient queue utilization, to avoid overflow or emptiness of the queue buffer. robust 2-dof pid control for congestion control of tcp/ip networks 971 2. regulated queueing delay, to minimize (optimize) the time required for a data packet to be serviced by the routing queue. the queueing delay is equal to q/c. 3. robustness, to maintain closed-loop performance in spite of plant uncertainties, n, ro and c. 3 red and pi approaches to aqm this section reviews two of the more well established approaches for aqm control, say red and the pi approach presented in [3]. 3.1 red approach to aqm random early detection (known as red) was presented by [1]. a red gateway calculates the average queue size, using a low-pass filter with an exponential weighted moving average. the average queue size is compared to two thresholds (minimum and maximum). when the average queue size is less than the minimum threshold, no packets are marked. when the average queue size is greater than the maximum threshold, every arriving packet is marked. if marked packets are in fact dropped, or if all source nodes are cooperative, this ensures that the average queue size does not significantly exceed the maximum threshold. when the average queue size is between the minimum and the maximum threshold, each arriving packet is marked with probability p, where p is a function of the measured queue length q. hollot et al. in [3] proposed the following transfer function model for the red controller: cred(s) = klred s + k = kred s/kred + 1 (3.1) where lred = pmax (maxth − minth) kred = r3oc 2 (2n)2 kred = −c ln(1 − αred) (3.2) and where αred is reds queue-averaging weight. the corresponding controller block diagram is shown in figure (2). figure 2: block diagram of red as a cascade of low-pass filter and nonlinear gain element. 3.2 pi approach to aqm according to [3] the transfer function of a pi controller can be written as: cpi = kpi s/z + 1 s (3.3) this controller is very well known by the control community. its parameters can be tuned following methods proposed in the control literature. for example, [3] gave guidelines based on the bode diagram tuning technique: 972 r. vilanova, v. m. alfaro figure 3: 2-dof control system z = 2n r2on kpi = ωgz ∣∣∣∣jωg + 1/roc2/(2n) ∣∣∣∣ ωg = βro (3.4) where β determines the phase margin of the resulting nominal control system. the pi proposed in [3] is designed for a phase margin of about 30o. 4 robust pid control this section briefly presents the approach for analytical robust tuning (art) of 2-dof pid controllers recently presented in [6]. consider the two-degree-of-freedom (2-dof) control system of fig. 3, where p(s) is the controlled process transfer function, cr(s) the set-point controller transfer function, cy(s) the feedback controller transfer function, and r(s) the setpoint, d(s) the load-disturbance, and y(s) the controlled variable. the output of the 2-dof controller is given by u(s) = cr(s)r(s) − cy(s)y(s) (4.1) for a pid2 [7] it is u(s) = kc ( β + 1 tis ) r(s) − kc ( 1 + 1 tis + tds ) y(s) (4.2) where kc is the controller gain, ti the integral time constant, td the derivative time constant, and β the set-point weighting factor (0 ≤ β ≤ 1). we will start right now with a second-order-plus-dead-time (sopdt) model of the form p(s) = kpe −l ′′ s (t ′′ s + 1)(at ′′ s + 1) , τo = l′′ t ′′ (4.3) the pid controller parameters are determined by the following equations for processes with parameters in the range 0.1 ≤ τo ≤ 1.0 and 0.15 ≤ a ≤ 1.0. κc = 10τi 21τc + 10τo − 10τi (4.4) τi = (21τc + 10τo)[(1 + a)τo + a] − τ 2 c(τc + 12τo) 10(1 + a)τo + 10a + 10τ2o (4.5) τd = 12τ2c + 10τiτo − (1 + a)(21τc + 10τo − 10τi) 10τi (4.6) robust 2-dof pid control for congestion control of tcp/ip networks 973 β = min { 1 kc , τct ′′ ti , 1 } (4.7) the controller normalized parameters κc, τi and τd, and β depend on the model normalized dead-time τo and time constants ratio a, and on the design parameter τc. a minimum system robustness level is incorporated into the design process estimating a recommended maximum speed (τcmin) of the resulting closed-loop control system parameterized in terms of the maximum sensitivity function (ms) by using τcmin = k11(ms) + k12(ms)a k13(ms) (4.8) k11(ms) = 2.442 − 2.219ms + 0.515ms 2 k12(ms) = 10.518 − 8.990ms + 2.203ms 2 k13(ms) = 0.949 − 0.197ms combining the performance and robustness consideration above the design parameter may be selected in the range τcmin ≤ τc ≤ 1.25 + 2.25a. the range limits for the design parameter selection then combine the necessary restriction so that all controller parameters are positive and the accomplishment of a specified maximum sensitivity, with the necessity that the obtained response does not deviate too much away from the desired response, due of the dead-time approximation used in obtaining the tuning equations. for a more detailed presentation and discussion of the method please see [6]. 5 discussion in order to illustrate the effectiveness of the robust 2-dof pid method, a numerical situation will be presented by taking the network simulation parameters of [3] where qo = 175 packets, tp = 0.2 seconds, c = 3750 packets/s (this corresponds to a 15mb/s link with an average packet size of 500 bytes.). for a load of n = 60 tcp sessions we have wo = 15 packets, po = 0.008, and ro = 0.246. therefore p(s) = po(s)e −0.246 = 1.17126 105 (s + 0.53)(s + 4.1) e−0.246 ∆(s) = 2.24 10−6s(1 − e−0.246s) (5.1) the corresponding red controller parameters are (see [3]) k = 0.005 and lred = 1.86 10−4 whereas those of the pi controller kpi = 9.64 10−6 and z = 0.53. on the other hand, for the application of the art method the parameters of the soptd model are needed. these result to be a = 0.1297, l′′ = 0.2467 and t ′′ = 1.9014. and the controller parameters depend upon the desired robustness level expressed in terms of te ms level. a minimum robustness is assured by ms = 2.0 whereas highly robust systems are designed with ms = 1.4. here we take an intermediate level with ms = 1.6. the resulting 2-dof pid controller parameters are: kc = 4.4241 10 −5, ti = 2.1443, td = 0.3436 and β = 0.6172. figure (4) shows the performance of the three presented control strategies applied to the nonlinear system (2.1). as it can be seen the red controller cannot reach the new references. this is an inherent drawback because it lacks the corresponding integrator. on the other hand both the pi and art-pid controllers reach the desired targets. on the right part of figure (4) it can de observed that the art-pid controller reaches the desired set-point faster and without any overshoot. in addition, a test under different load conditions has been performed. in this case the number of tcp sessions 974 r. vilanova, v. m. alfaro 0 50 100 150 200 160 180 200 220 240 260 red (dash−dot) pi (dash) art (solid) time (sec) 0 50 100 150 200 5 6 7 8 9 10 11 x 10 −3 red (dash−dot) pi (dash) art (solid) time (sec) 10 12 14 16 18 20 160 180 200 220 240 260 red (dash−dot) pi (dash) art (solid) time (sec) q u e u e l e n g th 10 12 14 16 18 20 5 6 7 8 9 10 11 x 10 −3 red (dash−dot) pi (dash) art (solid) time (sec) p a c k e t m a rk in g figure 4: changes on desired queue length. comparison of pi, pid and red performance. plots on the right side show a zoomed version to better compare pi and pid is a signal of the form n(t) = n(1 + 0.01 ∗ sin(0.05t)) + v(t) being v(t) a normally gaussian distributed random number of zero mean and variance 0.01 ∗ n. v(t) is assumed to change with a sampling time of 5 sec. it can be seen that the art-pid provides faster response to the load variation and faster recuperation of the desired queue length. table (1) shows the mean value and standard deviation of the queue length error computed with respect to the desired target qo = 175packets. as a proof of performance, as we achieve a lower variation of the queue, a predictable performance will be expected. therefore better qos. table 1: mean and standard deviation of the queue length error controller red pi art mean -0.43 -0.27 -0.1 std 10.9 9.2 5.0 6 conclusions in this paper the suitability of applying robust pid controllers for the purpose of improving internet congestion control has been presented. the main advantage of the art pid tuning is its one-parameter tuning. in addition this parameter is a direct specification of the desired robustness level. therefore suitable for the sittuations with changes in load and system parameters. the performance has been compared to that of red and a pi controller previously proposed in the literature. acknowledgment this work has received financial support from the spanish cicyt program under grant dpi2007-63356. support from the universidad de costa rica is greatly appreciated. robust 2-dof pid control for congestion control of tcp/ip networks 975 0 20 40 60 80 100 120 140 160 180 200 150 160 170 180 190 200 red (dash−dot) pi (dash) art (solid) time (sec) 0 20 40 60 80 100 120 140 160 180 200 5 6 7 8 9 10 11 12 x 10 −3 red (dash−dot) pi (dash) art (solid) time (sec) figure 5: regulation performance on a desired queue length of qo = 175packets facing random variations of the number of tcp sessions bibliography [1] s. floyd and v. jacobson, “random early detection gateways for congestion avoidance.,” ieee/acm transactions on networking, vol. 1, pp. 397–413, 1993. [2] f. kelly, “mathematical modeling of the internet, in mathematics unlimited-2001 and beyond,” b. enquist and w. schmid, eds, berlin, germany, springer-verlag., 2001. [3] c. hollot, v. misra, d. towsley, and w. gong, “analysis and design of controllers for aqm routers supporting tcp flows,” ieee trans. automat. contr., vol. 47, pp. 945–959, 2002. [4] j. postel, “transmission control protocol,” rfc 793, 1981. [5] s. ryu, c. rump, and c. qiao, “advances in active queue management (aqm) based tcp congestion control,” telecommunication systems, vol. 25, pp. 317–351, 2004. [6] v. alfaro, r. vilanova, and o. arrieta, “a single-parameter robust tuning approach for twodegree-of-freedom pid controllers,” the european control conference,budapest, hungary, august 23-26, 2009, 2009. [7] k. åström and t. hägglund, advanced pid control. isa the instrumentation, systems, and automation society, 2006. international journal of computers, communications & control vol. ii (2007), no. 1, pp. 66-73 development of an algorithm for groupware modeling for a collaborative learning ikuo kitagaki, atsushi hikita, makoto takeya, yasuhiro fujihara abstract: this paper reports an algorithm for forming groups of students with regard to a computer system for collaborative learning designed to give a cue for debate utilizing mobile terminals. with this system, questionnaires which should be used as the seeds for debates are prepared in advance on the web and all students attending the class answer to the questionnaires on the web through their mobile terminals. following this, the computer assigns students to appropriate groups based on the results of answers, and transmits each of answers and information of the group member to terminals of the students. based on the information, students form groups and each group starts debate. in this study, system composition is dealt with first and algorithm for forming groups using answers by the students is discussed. keywords: group forming, algorithm, collaborative learning, mobile terminal, debate system, groupware 1 introduction this study relates to university class in japan. in many cases, lectures are given in one-sided manner, and it is pointed out that some schemes are necessary to encourage students to express their opinions or to induce discussions by students themselves[5][8]. considering the recent trend that almost all students carry their own mobile terminals[6][7], this study relates to a learning system that utilizes mobile terminals as an ancillary tools for debate. students are divided into groups making use of the information of answers by the students against questionnaires presented beforehand. on this occasion, information necessary for grouping and results of grouping are transmitted together with contents of each of answers to mobile terminals of the students. students are then requested to form groups based on these information and to initiate debates in each group. in other words, this system is one sort of blended learning in which face-to-face debate is carried out utilizing electronic educational information communication. in this study, algorithm for grouping based on information of answers by the students is dealt with. with collaborative learning as mentioned, two points can be expected as mentioned below. 1. groups are being formed considering education effects. therefore, every student can know why he/she is assigned to the current group. 2. students are instructed to start debate using the information on their answers as the seeds. therefore, they can easily grasp a cue for debates. according to the conventional method of grouping of students, groups are formed simply mechanically in the order of student id numbers or those seating nearby are assigned to the same group. meanwhile, in this study, it is attempted to form groups appropriately based on answers against some sort of assignments including tests. teaching by groups which were formed based on answers of the students was attempted by the prior study[2][3]. at the time of this study, since possession of a mobile terminal by every student was not practical, test results were collected and marked by the teacher, groups were formed based on the results. test papers were returned to the students, groups were formed, and students in each of the groups are requested to discuss primarily incorrect answers. for this reason, it took enormous time from answering to the questionnaires by the students to group discussion. this time, fully making use of information technology available today, we configured a new collaborative learning copyright c© 2006-2007 by ccc publications selected paper from icvl 2006 development of an algorithm for groupware modeling for a collaborative learning 67 system. thanks to assistance by mobile terminals, it is expected that from answering by the students till execution of discussions can be made promptly and smoothly. this system is currently under development. in section 2, outline of system composition will be described, and an algorithm for forming groups will be introduced in section 3 onward. although the algorithm is one portion of the system, it is significant to make that in order to put the system into practical use, which may promote the practical e-learning in universities[1]. 2 system flow total four files are prepared and used in this system, student file, questionnaire file, aggregation file and groupware file. these files are used in this order. 1. student file basic information such as mail address, age, gender or the like of students are summarized in this file. 2. questionnaire file questionnaire or the like concerning discussion theme are summarized in this file. this file can be consisted of any of three types, multiple-choice method that allows only one selection, multiplechoice method that allows a plurality of selections, and free composition. (multiple-choice method alone is subjected to processing of item 4. onward.) 3. aggregation file answers by the students for questionnaires in 2. are aggregated in this file. 4. groupware file the computer forms student groups appropriately based on results of answers by the students in 3. this file also includes information on, in addition to answers by the students, which students form one group. execution procedures for preparing and utilizing above-mentioned files are shown in fig.1. (1) a student accesses the predetermined url, enters basic information such as gender, id number or the like and transmits it. his/her file (excel) is then completed while the teacher may execute input and deletion of the information by operating excel directly. basic information of a certain class is stored here and this file can be utilized at every round-table discussion held by the class. (2) with regard to a discussion planned by a certain teaching, a group of questionnaires which may benefit grouping is prepared and is registered to the system as a questionnaire file. (3) several groups of questionnaires are being registered in the questionnaire file. the teacher picks up specific groups of questionnaires and transmits url for browsing questionnaire items to mail addresses of all students. (4) students answer the questionnaires and transmit them to create an aggregation file. (5) based on the information in the aggregation file, students are assigned appropriately to a certain group. (6) information on grouping is transmitted to mobile terminals of the students. upon completion of above-mentioned procedures, the students are grouped in the class and start discussions using results of answers as the cue. 68 ikuo kitagaki, atsushi hikita, makoto takeya, yasuhiro fujihara (6)mending students groupware information instruction of group discussion (3)sending url for browsing questionnaire items (4)assembling answer data procudure questionnaire maker regst. of personal inform.(ftp) web server command of mail sending assembled data internet mail web browser mail receiving url keying in the answer excel excel student data csv hp for generating questionnaire hp for kicking mails data assembly program csv regst. of qustionnaire item download(ftp) making groups program starting the duscuss student (5)making groups sending personal information (1) reg. student data (2)reg.questio nnaires download(ftp) figure 1: system flow development of an algorithm for groupware modeling for a collaborative learning 69 3 construction of students groups suppose that there are four students a, b, c and d, and three questions a, b and c are given. results of evaluation of answers to these questions are shown by o and x as shown in table 1. one group consists of two students and discussion is made within the group primarily for questions which resulted in incorrect answers. in this case, two types of grouping shown in the table are compared. with grouping p, students a and b and students c and d constitute one group, respectively. however, in both groups, each of group members presented the same correct/incorrect pattern. namely, in group 1, both members gave incorrect answer for question b and a possibility that they can teach each other for problem solving is low. the same also applies to group 2. contrary, with grouping q, students a and c and students b and d constitute one group, respectively, and the said problem does not occur in this case. from above-mentioned cases, it may be said that grouping q is superior to grouping p. when we focus on problem solving of correct answers and incorrect answers of the test, possibility of problem solving is considered to be higher for such a case where there are different patterns of answers rather than a case where there is only one pattern in the same group. table 1: example of results of evaluation of answers and grouping student result of result of result of grouping p grouping q evaluation of evaluation of evaluation of (group id) (group id) question a question b question c a © × © 1 1 b © × © 1 2 c × © × 2 1 d × © × 2 2 suppose that the number of members in each of groups is the same. then excellence of grouping ug in a certain group g (g ∈ g) is defined. question j and question set are expressed by m j and m (m ∈ m), respectively, and student i in one group g and the student sets is expressed by si and sg (si ∈ g), respectively. further, result e of evaluation of answer for question mi and for student sk are expressed by e (mi,sk). • answer is correct → e(mi,sk) = 1 • answer is incorrect → e(mi,sk) = 0 ug = ∑ sk∈g ∑ mi∈m (1−e(mi, sk)) ⋃ s j∈g e(mi, s j) |m ‖ sg| (1) where, |m| denotes the number of elements of sets m. grouping u of entire class is expressed by equation (2) u = ∑ g∈g ug |g| (2) although two ug’s obtained by equation (1) are comparable to each other only for the case that both groups consist of the same number of the students, the proposed idea in this paper will be able to be 70 ikuo kitagaki, atsushi hikita, makoto takeya, yasuhiro fujihara extended to the different number of groups in a classroom by equation (3) where both equations (1) and (2) are combined: ug = ∑ g∈g ∑ sk∈g ∑ mi∈m (1−e(mi, sk)) ⋃ s j∈g e(mi, s j) |m ‖ sg| (3) where n means the number of the students in a classroom. actually there could be that several groups are different in the number of group members because the total number of the classroom cannot be well divided into an integer by appropriate integers. whether the number of the students in a group is equal or not, it is necessary to calculate equation (2) or (3) for all the combination of student group in order to obtained the maximum solution. although it is possible to do that theoretically, it is difficult to obtain the strict solution because of the enormous number of calculations. in reality, if the number of group members is supposed to be 4, the computer can execute computations of a class consisting of 50 peoples at the most. then more simplified method should be used. in other words, with the relevant algorithm, suppose that students of one class are re-expressed as s1, . . , sn , the following replacement is made which results in calculations of n (n-1) times in total, and a group in which u becomes the maximum is judged to be the optimum grouping. s1 and s2, s1 and s3,··· , s1 and sn s2 and s3, s2 and s4,··· , s2 and sn ······ ············sn−1 and sn (4) it is already known that when one class consists of 20 and several members and one group consists of four members, excellence of grouping by the simplified algorithm as used in the current proposal is, compared with a case where the same is obtained for all combinations without simplification, approximately 60 ∼ 100% optimization rate depending upon the answer pattern[4]. 4 discussion in a classroom two kinds of discussion were administrated concerning the proposed method. the first kind is the one for discussing test answers, where the evaluation either of correct or wrong was determined. digital mathematics served as the subject. several test problems were given to the students. all the test problems had multiple choice type of answers thus the students selected an answer among them. because the evaluation was already done, the instructor, after group making, told them to start by telling the evaluated result then discuss the answer which was evaluated wrong. the second kind is the one for discussing opinions, where the evaluation either of correct or wrong cannot be determined. training program for job hunting served as the subject. the example of a question done by an interview and the answer by the interviewee was presented followed by several opinions for the answer then the students selected an opinion which they thought the closest to their impression. the instructor, after group making, told them to start by telling their own choice, discuss a better answer then make the report as group co-working. through several administrations, the method was thought to give the students learning motivation because, every time when the proposed discussions were done, group member changed for clear reason leading to their refreshment. in addition to that, we thought that, if the more detail direction in what point the discussion ought to be done was given to the students, they may think it easier to start. development of an algorithm for groupware modeling for a collaborative learning 71 5 consideration according to the algorithm for forming groups used in the current proposal, grouping is made so that results of evaluation of answers of the test by students may become different as much as possible. for the sake of establishment of more generalized algorithm, overall investigations are necessary which includes what sort of group discussions are actually available, what sort of information are effective for forming groups, what judgment criteria should be used to determine good or bad of grouping based on the said information. although in the present study, results of evaluation of answers are digitized to either 0 or 1, the authors intend to investigate another algorithm capable of coping with more diversified evaluation results. at the same time, utility of this group discussion system and points to be improved will be checked from both software and hardware aspects through practicing. 6 aknowledgements the present study has been promoted under the grant partly from grant-in-aid for scientific research, germination study no.17650260, fundamental study (b) no.18300288, and germination study no.17300275, each sponsored by the ministry of education, culture, sports, science and technology. we are sincerely thankful to all the persons and bodies helping our works. references [1] styliadis d. a., karamitsos d.i. and zachariou i.d: personalized e-learning implementation-the gis case international journal of computers, communications & control, vol. 1,1, pp. 59-66, 2006. [2] kitagaki i., shimizu y. and suetake k., an instructional method which permits the students to critically discuss their own test answers, jour.of educ. technol., vol.5, no.1, pp.23-33, 1980. [3] kitagaki i., shimizu y., consideration on an educational system which permits the students to discuss over their own test answers, res. sci.educ., vol.5, no.1, pp.22-28,1981. [4] kitagaki i, a consideration on an educational groupware algorithm using fuzzy integral, ieice, tech.rep.,vol.et94-101, pp.61-66,1994. [5] nagai m., kitazawa t., koshikawa h., kato h., akahori k, eevelopment and verification of the formative evaluation system with utilizing mobile phone for web-based collaborative learning, japan journal of educational technology , vol.28, no.4, pp.333-342, 2005. [6] nagamori m., ueno m., ando m., pokpong s., endo k., nagaoka k., response analyzer system using mobile phones for distance education, japan journal of educational technology , vol.29, suppl., pp.57-60, 2005. [7] otuska k., yahiro t., the input interface of the value of evaluation in an evaluation system of instruction using mobile phones, japan journal of educational technology, vol.30, no.2, pp.125134, 2006. [8] ozawa s., mochizuki t., egi h., kunifuji s, facilitating reflection in collaborative learning using formative peer evaluation among groups, japan journal of educational technology vol.28, no.4, pp.281-294, 2005. 72 ikuo kitagaki, atsushi hikita, makoto takeya, yasuhiro fujihara ikuo kitagaki hiroshima university, japan graduate school of education the research institute for higher education 1-2-2 kagamiyama, higashi-hiroshima, 739-8512, japan e-mail: kitagaki@hiroshima-u.ac.jp atsushi hikita hiroshima university, japan community cooperation center e-mail: hikita@hiroshima-u.ac.jp makoto takeya takushoku university, japan faculty of engineering department of computer science 815-1 tate-machi, hachioji, tokyo, 193-0985 japan e-mail: takeya@cs.takushoku-u.ac.jp yasuhiro fujihara iwate prefecutural university faculty of software and information science fuji@iwate-pu.ac.jp received: november 7, 2006 editor’s note about the authors: ikuo kitagaki, born in aichi on august 9, 1947, received his be and me degrees in electronics in 1970 and 1972, and his doctor engineer degree in 1981 from tokyo institute of technology. joining tokyo institute of technology in 1973 and employment promotion corporation in 1986, he was engaged in various research areas including the development of computer applications in education, fuzzy science, science of laughter, and so on. since 2000, he has been belonging to hiroshima university, hiroshima, japan. presently he is professor of graduate school of education/the research institute for higher education, hiroshima university. one of his most important recent works is "university authority (in japanese)". his career is shown also in "who’s who in science and engineering, marquis 9th ed.(2006-2007), usa". development of an algorithm for groupware modeling for a collaborative learning 73 atsushi hikita (born on january 30, 1968) graduated the faculty of physics of sophia university, japan in 1991, and got his master degree at the same faculty in 1993. he worked in mitsubishi research institute as a media and information planner and researcher (1993-2000). his main research fields are information design (information and its places, especially map and pictograms as a nonverbal communications), and media design (media communications for society, museum, library and classroom). he has coauthered and co-edited, several books and papers for these fields. he is a chair of apng (asia pacific networking group) education and live e! wg, committee member of chugoku-shikoku internet council, hiroshima region ipv6 deploy committee. makoto takeya, born in tokyo on november 2, 1941, received his be and me degrees in applied physics in 1966 and 1968, and his doctor of science degree in 1981 from waseda university. joining nec corporation in 1968, he was engaged in various research areas including the development of computer applications in education. since 1986 he has been belonging to takushoku university, tokyo, japan. during 1992-1993 he was a visiting scholar of department of educational psychology, university of illinois. presently he is professor of department of computer science, takushoku university. among his most important books are "a new test theory: structure analysis methods for educational information" (in japanese) and "structure analysis methods for instruction: theory and practice of instructional architecture, design and evaluation". he is the recipient of the 1976 yonezawa memorial award from the institute of electronics and communications engineers of japan, the 1989 excellent research award from behaviormetrics society of japan, the 1996 winning paper award from japan society of educational technology, and the 1999 engineering education award from japanese society for engineering education. yasuhiro fujihara (born on january 8, 1971) graduated the faculty of education of kobe university in 1993. presently he is an assistant professor at faculty of software and information science, iwate prefectural university, japan. his main research fields are educational technology (educational evaluation, e-learning) and ict education. he is a member of japan society for educational technology, and the institute of electronics, information and communication engineers. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 325-335 consensus problem of second-order dynamic agents with heterogeneous input and communication delays c.-l. liu, f. liu cheng-lin liu, fei liu jiangnan university institute of automation wuxi 214122, jiangsu, people’s republic of china e-mail: liucl@jiangnan.edu.cn, fliu@jiangnan.edu.cn abstract: consensus problem of second-order multi-agent systems with velocity damping term in agent’s dynamics is investigated. based on frequency-domain analysis, decentralized consensus condition, which depends on the input delays, is obtained for the system based on undirected and symmetric graph with heterogeneous input delays. for the system based on directed graph with both heterogeneous input delays and communication delays, decentralized consensus condition, which is dependent on the input delays but independent on the communication delays, is also obtained. simulations illustrate the correctness of the results. keywords: coordination control, consensus, second-order multi-agent systems, communication delay, input delay 1 introduction in the last decade, distributed coordination of multiple autonomous agents has attracted more and more attention from various research communities for its broad application including automated highway systems, air traffic control, congestion control in internet, etc. consensus problem, which is one of the most fundamental and important issues in coordination control of multi-agent systems, requires that the outputs of several spatially distributed agents reach a common value without recurse to a central controller. for the first-order multi-agent systems with agents’ dynamics modeled by single integrators and second-order multi-agent systems with agents’ dynamics modeled by double integrators, consensus algorithms have been proposed to solve the consensus problem, and sufficient conditions have been obtained for the system converging to the consensus with static or switched interconnection topology [1–4]. recently, more and more attention has been paid on the delay effect on consensus convergence of multi-agent systems. generally speaking, two kinds of time delays cannot be negligible in the multiagent systems. one is communication delay, which is related to the information transmission between neighboring agents. the other is input delay, which is related to the processing and connecting time for the packets arriving at each agent [5]. consensus problem under communication delays has been extensively studied for the first-order multi-agent systems based on different analysis methods, such as lyapunov functions analysis [6, 7], frequency-domain analysis [2, 8], method based on delayed and hierarchical graphs [9, 10], method based on difference of maximum value and minimum value [11, 12], etc. however, consensus analysis of second-order multi-agent systems with communication delay is much more difficult, and many existing results are mostly on the synchronous consensus algorithm [13–15], in which self-delays equaling to the corresponding communication delays are introduced for each agent in the coordination control part. compared with the first-order multi-agent systems, the consensus algorithm without any self-delay, which is called asynchronous consensus algorithm, has not been studied extensively for the second-order multi-agent systems. using small-µ stability theorem, yang et al. [16] obtained the frequency-domain copyright c© 2006-2010 by ccc publications 326 c.-l. liu, f. liu consensus conditions for the second-order multi-agent systems with time-varying communication delays. based on frequency-domain analysis [17] and lyapunov-krasovskii functional method [18], spong et al. proved that, by choosing proper consensus protocol and control parameters, the second-order multiagent systems with heterogeneous communication delays can converge to a stationary consensus without any relationship to the delays. using the properties of nonnegative matrices, lin and jia [19] obtained delay-independent sufficient conditions for the second-order discrete-time multi-agent systems with heterogeneous communication delays converging to the stationary consensus under dynamically changing topologies. to our knowledge, however, the consensus problem under input delays has not attracted much more attention. in some reports, the identical communication delay introduced in the synchronous consensus algorithm can be treated as the identical input delay [2, 14, 15]. using frequency-domain analysis method, tian and liu [5] considered the consensus problem of the first-order multi-agent systems with heterogeneous input delays based on undirected graphs, and obtained the decentralized consensus criterion depending on the input delays. moreover, the decentralized consensus condition, which depends only on the input delays, is also obtained for the first-order multi-agent systems with both heterogeneous communication delays and input delays based on the digraph in [5]. in [20], tian and liu investigated the leader-following consensus problem of the second-order multi-agent systems with heterogeneous input delays and symmetric coupling weights, and the decentralized consensus condition with some prerequisites is obtained for the system converging to the states of the dynamic leader asymptotically. furthermore, the robustness of the symmetric system with asymmetric weight perturbation is also investigated in [20], and a bound of the largest singular value of the perturbation matrix is obtained as the robust consensus condition. in this paper, we consider the consensus problem of second-order multi-agent systems with velocity damping term in the agent’s dynamics, and analyze the consensus conditions for the system with heterogeneous delays converging to the stationary consensus. firstly, we investigate the consensus problem for the system based on undirected and symmetric graph with heterogeneous input delays, and a decentralized consensus condition, which is delay-dependent, is obtained by using some early results for the internet congestion control with heterogeneous communication delays [21]. then, we study the consensus problem for the system based on general directed graph with both heterogeneous input delays and communication delays by using greshgorin disc theorem, and another decentralized consensus condition, which depends on the input delays only, is also obtained. this consensus condition is more conservative than the former for the existence of heterogeneous communication delays and the asymmetry of coupling weights, but it can be applied to the systems based on directed graph with asymmetric weights. 2 preliminaries on graph theory a weighted directed graph (digraph) g = (v, e, a) of order n consists of a set of vertices v = {, ..., n}, a set of edges e ⊆ v×v and a weighted adjacency matrix a = [ai j] ∈ rn×n with nonnegative adjacency elements ai j. the node indexes belong to a finite index set i = {, , ..., n}. an edge of the weighted diagraph g is denoted by ei j = (i, j) ∈ e, i.e., ei j is a directed edge from i to j. we assume that the adjacency elements associated with the edges of the digraph are positive, i.e., ai j >  ⇔ ei j ∈ e. moreover, we assume aii =  for all i ∈ i. the set of neighbors of node i is denoted by ni = { j ∈ v : (i, j) ∈ e}. in the digraph g, if (i, j) ∈ e ↔ ( j, i) ∈ e, we usually say g is undirected graph or bidirectional graph. the out-degree of node i is defined as: degout (i) = ∑n j= ai j. let d be the diagonal matrix with the out-degree of each node along the diagonal and call it the degree matrix of g. the laplacian matrix of the weighted digraph is defined as l = d − a. if there is a path in g from one node i to another node j, then j is said to be reachable from i. if not, then j is said to be not reachable from i. if a node is reachable from every other node in the digraph, then consensus problem of second-order dynamic agents with heterogeneous input and communication delays 327 we say it globally reachable. a digraph is strongly connected if every node in the digraph is globally reachable. an undirected graph is connected if it contains a globally reachable node. 3 problem formulation in a multi-agent system composed of n agents, each agent can be regarded as a node in a digraph, and information flow between neighboring agents can be considered as directed paths between the nodes in the digraph. thus, the interconnection topology of multi-agent systems can be described as a diagraph g = (v, e, a). in this paper, we just consider static topology g, i.e., the connection of the nodes in the diagraph g does not change with time. consider the second-order dynamic agents modeled by ẋi = vi, miv̇i = fi, i ∈ i, (1) where xi ∈ r and vi ∈ r are the position and the velocity of the agent i respectively, mi >  is the mass of the agent i, and fi is the total force on the agent i. analogous to [22], the total force fi in the second-order model (1) consists of two components: fi = −αivi + ui, where ui is the external control input, and −αivi with αi >  denotes the velocity damping term caused by the resistance, e.g., the friction. then, the second-order model (1) becomes ẋi = vi, miv̇i = −αivi + ui, i ∈ i. (2) with non-negligible input delays for the external control, the agents (2) become ẋi(t) = vi(t), miv̇i(t) = −αivi(t) + ui(t − ti), i ∈ i, (3) where ti >  is the input delay of the agent i. for the system (3), we take a consensus protocol based on the agents’ position states as follows ui = κi ∑ j∈ni ai j(x j − xi), (4) where κi > , ni denotes the neighbors of agent i, and ai j >  is the adjacency element of a in the digraph g = (v, e, a). under the communication delays, the protocol (4) becomes ui(t) = κi ∑ j∈ni ai j(x j(t − τi j) − xi(t)), (5) where τi j is the communication delay from agent j to agent i. with the protocol (5), the closed-loop form of the system (3) is ẋi(t) = vi(t), miv̇i(t) = −αivi(t) + κi ∑ j∈ni ai j(x j(t − ti − τi j) − xi(t − ti)), i ∈ i. (6) remark 1. in [20], tian and liu has studied the leader-following consensus problem of the second-order multi-agent systems with heterogeneous input delays under double-consensus algorithm, and obtained the consensus conditions for the system with symmetric and asymmetric weights respectively. different from [20], we consider the stationary consensus of the second-order dynamic agents (6) with velocity damping term, and analyze the consensus conditions for the system with heterogeneous communication delays and input delays. 328 c.-l. liu, f. liu 4 consensus criterion 4.1 consensus under heterogeneous input delays in this section, we investigate the consensus problem of multi-agent systems (6) just with heterogeneous input delays only as follows ẋi(t) = vi(t), miv̇i(t) = −αivi(t) + κi ∑ j∈ni ai j(x j(t − ti) − xi(t − ti)), i ∈ i. (7) firstly, we give an assumption on the velocity damping coefficient αi, the mass mi and input delay ti in the following. assumption 2. (ti αimi − tj α j m j )(ti − tj) ≤ , ∀i, j ∈ i, i 6= j. now, we present some sufficient conditions for second-order multi-agent systems with heterogeneous input delays. theorem 3. consider the network of n dynamic agents (7) with a static interconnection topology g = (v, e, a) that is undirected (or bidirectional) and connected, and the topology graph has symmetric weights, i.e., ai j = a ji. then, under assumption 2, all the agents in system (7) asymptotically converge to a stationary consensus, i.e., limt→∞ xi(t) = c, limt→ vi(t) = , ∀i ∈ i, where c is a constant, if ∑ j∈ni ai j < mi κi(gmi )− ,∀i ∈ i, (8) where gmi is the gain margin of the transfer function wi(s) = e−sti s+ αi mi s . before proving theorem 3, we list two useful lemmas as follows. lemma 4. [23] let q ∈ cn×n, q = q? ≥  and t = diag{ti,ti ∈ c}. then λ (qt ) ∈ ρ(q)co(∪{ti}), where λ (·) denotes matrix eigenvalue, ρ(·) denotes the matrix spectral radius, and co(·) denotes the convex hull. based on remark 4 and claim 1 in [21], we obtain the following lemma. lemma 5. suppose that assumption 2 holds for the frequency response of a family of systems described by gi(jω) = gmi s + j αimi ω e−jtiω , i ∈ i, where gmi is the gain margin of the transfer function wi(s) = e−sti s+ αi mi s . then, γ co(∪{gi(jω), i ∈ i}) does not contain the point (−, j) for any given real number γ ∈ [, ) and any ω ∈ (−∞, ∞). now, we give the proof of theorem 3. the system (7) is rewritten as follows ẋi(t) = vi(t), v̇i(t) = −ᾱivi(t) + κ̄i ∑ j∈ni ai j(x j(t − ti) − xi(t − ti)), i ∈ i, (9) consensus problem of second-order dynamic agents with heterogeneous input and communication delays 329 where ᾱi = αimi and κ̄i = κi mi . taking the laplace transform of the system (9), we obtain the characteristic equation of the system (9) about x(t) = [x(t),··· , xn(t)]t as follows det(diag{s + ᾱis, i ∈ i} + diag{κ̄ie−tis, i ∈ i}l) = . define d(s) = det(diag{s + ᾱis, i ∈ i} + diag{κ̄ie−tis, i ∈ i}l), and we will prove that all the zeros of d(s) are on the open left half complex plane or s =  in the following. when s = , d() = det(diag{ + ᾱi, i ∈ i} + diag{κ̄ie−ti, i ∈ i}l) = det(diag{κ̄i, i ∈ i}) det(l), because the interconnection graph g = (v, e, a) is connected,  is a simple eigenvalue of l [24], i.e., rank(l) = n − . hence, d(s) has only one zero at s = . when s 6= , define f(s) = det(i + diag{ κ̄is+ᾱis e −tis, i ∈ i}l). we will prove that the zeros of f(s) lie on the open left half complex plane. according to the general nyquist stability criterion [25], the zeros of f(s) are on the open left half complex plane, if λ (diag{ κ̄i (jω)+jω ᾱi e−jω ti , i ∈ i}l) does not enclose the point (−, j) for ω ∈ r. for the symmetric weights (ai j = a ji), we get l = lt ≥  according to the definition of the laplacian matrix. based on lemma 4, we get λ (diag{ κ̄i −ω  + jᾱiω e−jtiω }l) = λ (diag{ gmi −ω  + jᾱiω e−jtiω diag{ √ κ̄i(gmi )−}ldiag{ √ κ̄i(gmi )−}) ∈ ρ(diag{ √ κ̄i(gmi )−}ldiag{ √ κ̄i(gmi )−})co(∪ gmi −ω  + jᾱiω e−jtiω ). since the spectral radius of any matrix is bounded by its largest absolute row sum, it follows from the condition (8) that ρ(diag{ √ κ̄i(gmi )−}ldiag{ √ κ̄i(gmi )−}) = ρ(diag{κ̄i(g m i ) −}l) ≤ max i∈i κ̄i(gmi ) −( ∑ j∈ni ai j) < . therefore, from lemma 5, we obtain that (−, ) 6∈ ρ(diag{ √ κ̄i(gmi )−}ldiag{ √ κ̄i(gmi )−})co(∪ gmi −ω  + jᾱiω e−jtiω ), i.e., λ (diag{ κ̄i (jω)+jω ᾱi e−jω ti , i ∈ i}l) does not enclose the point (−, j) for ω ∈ r, which implies that the zeros of f(s) are all on the open left half complex plane. now, we have proved that d(s) has its zeros on the open left half complex plane except for one zero at s = . thus, the state xi(t) of the system (7) converges to a steady state, i.e., limt→∞ xi(t) = x∗i , i ∈ i, and limt→∞ vi(t) = ,∀i ∈ i holds for (7). it is obtained from (7) that l[x∗,··· , x∗n]t = . since rank(l) = n − and l[,··· , ]t =  from the definition of the laplacian matrix l, the roots of lx∗ =  can be expressed as x∗ = c[,··· , ]t , where c is a constant. theorem 3 is proved. 2 remark 6. obviously, the consensus condition (8) in theorem 3 depends strictly on the assumption 2 and the symmetry of the coupling weights between agents. 330 c.-l. liu, f. liu 4.2 consensus under heterogeneous input and communication delays in multi-agent systems, the interconnection topology composed of dynamic agents is usually asymmetric, and the communication delays caused by information transmission always exist between neighboring agents. thus, the lemma 4 and lemma 5 which play important roles in the proof of theorem 3 cannot be applied in these cases. in this section, we will analyze the consensus of the second-order dynamic agents (6) with both heterogeneous input delays and communication delays under general directed interconnection topology. theorem 7. consider the network of n dynamic agents (6) with a static interconnection topology g = (v, e, a) that has a globally reachable node. if ∑ j∈ni ai j < α i κi(mi + αiti) , ∀i ∈ i, (10) all the agents in the system (6) converge to a stationary consensus asymptotically. proof: firstly, rewrite the system (6) as ẋi(t) = vi(t), v̇i(t) = −ᾱivi(t) + κ̄i ∑ j∈ni ai j(x j(t − ti − τi j) − xi(t − ti)), i ∈ i, (11) where ᾱi = αimi and κ̄i = κi mi . taking the laplace transform of the system (12), we obtain that the characteristic equation of the system (12) about x(t) = [x(t),··· , xn(t)]t is det(diag{s + ᾱis, i ∈ i} + diag{κ̄ie−tis, i ∈ i}l(s)) = , where the n×n matrix l(s) = {li j(s)} is defined by li j(s) =    −ai je−τi j s, j ∈ ni;∑ j∈ni ai j, j = i; , otherwise, and l() = l, which is the laplacian matrix. define d̃(s) = det(diag{s + ᾱis, i ∈ i} + diag{κ̄ie−tis, i ∈ i}l(s)), and we will prove that all the zeros of d̃(s) are on the open left half plane or s =  in the following. when s = , d̃() = det(diag{ + ᾱi, i ∈ i} + diag{κ̄ie−ti, i ∈ i}l()) = det(diag{κ̄i, i ∈ i) det(l), because the interconnection topology g = (v, e, a) has a globally reachable node,  is a simple eigenvalue of l [24]. hence, d̃() = , i.e., d̃(s) has only one zero at s = . when s 6= , define f̃(s) = det(i + diag{ κ̄is+ᾱis e −tis, i ∈ i}l(s)). we will prove the zeros of f̃(s) lie on the open left half complex plane. according to the general nyquist stability criterion [25], the zeros of f̃(s) are on the open left half complex plane, if λ (diag{ κ̄i (jω)+jω ᾱi e−jω ti , i ∈ i}l(jω)) does not enclose the point (−, j) for ω ∈ r. based on the greshgorin’s disc theorem, λ (diag{ κ̄i −ω  + jᾱiω e−jtiω , i ∈ i}l(s)) ∈ ⋃ i∈i {ζ : ζ ∈ c, |ζ − κ̄i( ∑ j∈ni ai j) −ω  + jᾱiω e−jtiω | ≤ ∑ j∈ni | κ̄iai j −ω  + jᾱiω e−j(ti+τi j )ω |} = ⋃ i∈i {ζ : ζ ∈ c, |ζ − κ̄igi −ω  + jᾱiω e−jtiω | ≤ | κ̄igi −ω  + jᾱiω e−jtiω |} consensus problem of second-order dynamic agents with heterogeneous input and communication delays 331 holds for ω ∈ r, where gi = ∑ j∈ni ai j. then, λ (diag{ κ̄i (jω)+jω ᾱi e−jω ti , i ∈ i}l(jω)) does not enclose the point (−, j) for ω ∈ r as long as the point (−a, j) with a ≥  does not in the disc {ζ : ζ ∈ c, |ζ − κ̄igi −ω +jᾱiω e−jtiω | ≤ | κ̄igi −ω +jᾱiω e−jtiω |} for all ω ∈ r, i.e., | − a + j − κ̄igi −ω +jᾱiω e−jtiω | > | κ̄igi −ω +jᾱiω e−jtiω | holds for all ω ∈ r when a ≥ . by calculating, we obtain | − a + j − κ̄igi −ω  + jᾱiω e−jtiω | − | κ̄igi −ω  + jᾱiω e−jtiω | = a(a − κ̄igi cos(ω ti) + ᾱi sin(ω ti) ω ω  + ᾱ i ). because cos(ω ti) ≤  and sin(ω ti)ω ≤ ti hold for ω ∈ r, it follows from (11) that κ̄igi cos(ω ti) + ᾱi sin(ω ti) ω ω  + ᾱ i ≤ κ̄igi( + ᾱiti) ᾱ i < . hence, | − a + j − κ̄igi −ω +jᾱiω e−jtiω | > | κ̄igi −ω +jᾱiω e−jtiω | holds for all ω ∈ r when a ≥ . now, we have proved that d̃(s) has its zeros on the open left half complex plane except for a zero at s = . thus, the state xi(t) of the system (6) converges to a steady state, i.e., limt→∞ xi(t) = x∗i , i ∈ i, and limt→∞ vi(t) = ,∀i ∈ i holds for (6). then, analogous to the proof of theorem 3, the system (6) converges to a stationary consensus for the digraph that has a globally reachable node. theorem 7 is proved. 2 remark 8. in the networks composed of interconnected dynamic systems, the scalability is a important property that needs to be maintained [5, 20, 21, 23]. obviously, the decentralized consensus conditions (8) and (11) maintain the scalability of the multi-agent network. without having to redesign the entire network whenever an agent is added or removed, the networks (6) and (7) can achieve the desired collective behavior as long as the local conditions for the agent and its neighbors hold respectively, and the connectedness of the interconnection topology is maintained. remark 9. in the consensus analysis of the multi-agent systems, greshgorin’s disc theorem has been extensively used to obtain the communication delay-independent consensus condition for the system with heterogeneous communication delays [8, 17]. in [17], decentralized frequency-domain consensus conditions have been obtained for the multi-agent systems with agents’ dynamic modeled by strictly stable linear systems under heterogeneous communication delays. then, by transformation, the system (6) can be expressed as a special case of the system studied in [17]. however, (11) gives a concrete algebraic criterion, which is convenient for the design of the consensus algorithm. remark 10. according to [20] (the inequality (24) there in), the consensus condition (8) in theorem 3 satisfies mi κi(gmi )− > αi κiti > α i κi(mi + αiti) . thus, under the same conditions, the consensus condition (11) in theorem 7 is more conservative than the consensus condition (8) given in theorem 3. 5 simulation example 11. consensus under input delays based on symmetric graph. consider a multi-agent network of five dynamic agents described by (7). the interconnection topology is described in figure 1, and the graph is undirected and connected. the symmetric weights of the edges are: a = a = ., a = a = ., a = a = ., a = a = ., a = a = .. the input delays of the agents are: t = .(s), t = .(s), t = .(s), t = .(s) and t = .(s). the velocity 332 c.-l. liu, f. liu damping coefficients of the agents are: α = , α =  , α = ., α = , α = , and the mass of each agent is assumed to be , i.e., mi = , i = ,··· , . thus, the assumption 2 holds for all the agents. for the transfer functions wi(s) = e−sti s+ αi mi s , i = ,··· , , by using the matlab simulator, we obtain the gain margins: gm ' ., gm ' ., gm ' ., gm ' . and gm ' .. according to the condition (8), we obtain that the control parameters κi satisfy: κ ∈ (, .), κ ∈ (, .), κ ∈ (, .), κ ∈ (, .), κ ∈ (, .), and we choose κ = , κ = ., κ = ., κ =  and κ = . then, with the initial states generated randomly, the agents in the system (7) converge to a stationary consensus (see figure 2). 31 2 45 figure 1: undirected graph with symmetric weights 0 5 10 15 20 25 30 35 40 45 −0.5 0 0.5 1 1.5 time/second x i 0 5 10 15 20 25 30 35 40 45 −1 −0.5 0 0.5 1 1.5 time/second v i figure 2: consensus convergence under input delays example 12. consensus under input and communication delays based on asymmetric digraph. consider a network of five agents described by (6). the interconnection topology is a digraph described in figure 3, and the globally reachable node set of the digraph is {, }. the weights of the directed edges are: a = ., a = ., a = ., a = ., a = ., a = ., and the corresponding communication delays are: τ = .(s), τ = .(s), τ = .(s), τ = .(s), τ = .(s), τ = .(s). the velocity damping coefficients of the agents are: α = ., α = , α = , α = , α = , and the mass of each agent is assumed to be . choosing the control parameters: κ = , κ = , κ = ., κ = , κ = , we obtain from the condition (11) that the constraints on the input delays are: t ∈ (, .)(s), t ∈ (, )(s), t ∈ (, )(s), t ∈ (, .)(s) and t ∈ (, .)(s). with t = (s), t = .(s), t = .(s), t = (s), t = .(s), the agents in the system (6) converge to a stationary consensus (see figure 4). 6 conclusions in this paper, we investigate the consensus problem of second-order multi-agent systems with velocity damping term in the agent’s dynamic. based on the frequency-domain analysis, two sufficient consensus problem of second-order dynamic agents with heterogeneous input and communication delays 333 3 1 2 45 figure 3: digraph composed of 5 agents 0 5 10 15 20 25 30 35 40 45 −0.5 0 0.5 1 1.5 time/second x i 0 5 10 15 20 25 30 35 40 45 −0.5 0 0.5 1 1.5 time/second v i figure 4: consensus convergence under input and communication delays decentralized consensus conditions are obtained. one consensus condition is for the system with heterogeneous input delays based on undirected and symmetric graph, and is dependent on the input delays. the other consensus condition is for the system with both heterogeneous input delays and communication delays based on general directed graph, and depends on the input delays only. although the later consensus condition is more conservative than the former, it can be applied to the systems based on directed graph with asymmetric coupling weights. acknowledgements this work was supported by specialized research fund for the doctoral program of higher education of china (grant no. 20090093120006). bibliography [1] a. jadbabaie, j. lin, a.s. morse, coordination of groups of mobile autonomous agents using nearest neighbor rules, ieee transactions on automatic control, 48(6):988-1001, 2003. 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[25] c.a. desoer, y.t. wang, on the generalized nyquist stability criterion, ieee transactions on automatic control, 25(2):187-196, 1980. cheng-lin liu was born in china in 1981. he got phd at southeast university in 2008. since 2008, he is a faculty member at institute of automation, jiangnan university, china. his current research interests include internet congestion control and coordination control of multi-agent systems. fei liu was born in china in 1965. he is a professor at institute of automation, jiangnan university, china. his research interests include the theory and application of advanced process control, process monitoring and diagnose on industrial system, and integrated automatic system for fermentation process. ijcccv11n4.dvi international journal of computers communications & control issn 1841-9836, 11(4):567-579, august 2016. an abnormal network traffic detection algorithm based on big data analysis h.p. yao, y.q. liu, c. fang haipeng yao* 1. state key laboratory of networking and switching technology beijing university of posts and telecommunications no 10, xitucheng road, haidian district, beijing, prc 2. beijing advanced innovation center for future internet technology beijing university of technology 100 ping le yuan, chaoyang district, beijing, prc *corresponding author: yaohaipeng@bupt.edu.cn yiqing liu state key laboratory of networking and switching technology beijing university of posts and telecommunications no 10, xitucheng road, haidian district, beijing, prc colin617@qq.com chao fang 1. beijing advanced innovation center for future internet technology beijing university of technology 100 ping le yuan, chaoyang district, beijing, prc fangchao.bupt@gmail.com 2. college of electronic information and control engineering beijing university of technology 100 ping le yuan, chaoyang district, beijing, prc fangchao.bupt@gmail.com abstract: anomaly network detection is a very important way to analyze and detect malicious behavior in network. how to effectively detect anomaly network flow under the pressure of big data is a very important area, which has attracted more and more researchers’ attention. in this paper, we propose a new model based on big data analysis, which can avoid the influence brought by adjustment of network traffic distribution, increase detection accuracy and reduce the false negative rate. simulation results reveal that, compared with k-means, decision tree and random forest algorithms, the proposed model has a much better performance, which can achieve a detection rate of 95.4% on normal data, 98.6% on dos attack, 93.9% on probe attack, 56.1% on u2r attack, and 77.2% on r2l attack. keywords: anomaly traffic detection, big data, k-means, decision tree, random forest. 1 introduction with the improvement of network, storage, calculation and transmission,the internet is interacting more closely with people than ever before. while the internet is making our life more convenient, it brings about some potential risks. for example, malicious attacks involving user privacy and security become more and more frequent. the changes of how people use the internet is a new challenge for traditional abnormal network event detection techniques. it is more hard for researchers to get aware of some new kinds of attacks. to resolve these problems, some abnormal network traffic detection methods copyright © 2006-2016 by ccc publications 568 h.p. yao, y.q. liu, c. fang have been proposed. traditional abnormal traffic detection method can be classified into two categories [1–3]. one is misuse detection, and the other is abnormal detection. the two methods have their own pros and cons. misuse detection has a high accuracy but needs support from known knowledge. abnormal detection do not need known knowledge, but cannot categorize the type of attacks, the accuracy is also lower. for example, hari om [4] designes a hybrid detection system, which is a hybrid anomaly detection system considering k-means, k-nearest neighbor and naïve bayes methods. however, the explosive increase of network traffic has directly or indirectly pushed the internet into the big data era, which makes anomaly traffic detection more difficult to deal with because of high calculation volume and constant changes of network data distribution caused by big data [5–8]. because the speed of network data generation is fast, it makes the volume of normal traffic and abnormal traffic differ a lot, and the distribution of the data change. besides, with big data, the difference between normal traffic and abnormal traffic is increasing. it makes the traditional methods unable to effectively detect abnormal traffic. therefore, to increase the accuracy of abnormal traffic and avoid the loose caused by false negative detection, we propose a novel model based on big data analytics, which can avoid the influence brought by adjustment of network traffic distribution, increase dectection accuracy and reduce the false negative rate. the core of the proposed model is not simply combination of traditional detection methods, but a novel detection model based on big data. in the simulation, we use k-means, decision tree and random forest algorithms as comparative objects to vertify the effectiveness of our model. simulation results reveal that the proposed model has a much better performance, which can achieve a detection rate of 95.4% on normal data, 98.6% on dos attack, 93.9% on probe attack, 56.1% on u2r attack, and 77.2% on r2l attack. the rest of this paper is organized as follows. in section 2, related work of this paper is presented. the system model is given in section 3. simulation results are presented and discussed in section 4. finally, we conclude this study in section 4.3. 2 related work 2.1 k-means k-means is a classic clustering algorithm [9,10], which uses simple iteration algorithm to cluster the data set into certain amount of categories.commonly, the number of clusters is annotated to be k. the four steps of k-means are: 1. initialization: randomly select k data points from the data set as the centers of the kclusters; 2. distribution: assign each point in the data set to the nearest center; 3. update: calculate new centers according to the cluster assignment, the new center is the average point of all the points in a cluster; 4. repeat: repeat these steps until no center is updated in this round, and the clustering is converged. k-means needs the number of classification k to be specified. if k is not chosen properly, it will lead to an improper result of classification. so choose a proper cluster number is crucial to the result of k-means. another disadvantage of k-means is that, k-means can only use euclidean distance. even though euclidean distance is convenient to calculate, but it cannot take the difference between two features into consideration, it means it treats all features as same. in the reality, it will sometimes lead to poor performance. anyway, k-means has its own advantages when dealing with big data. an abnormal network traffic detection algorithm based on big data analysis 569 1. k-means is simple. the time complexity is n ( nd∗k+1 log n ) , it can be fast when the number of clusters and the number of features are small; 2. k-means can be well adjusted to big data set and has high performance. 2.2 decision tree decision tree [9] is a common algorithm used in machine learning. a complete decision tree is composed by three kind of elements: 1. decision node, indicating which feature is used in split; 2. chance nodes, indicating possible values of each features; 3. leaf node, indicating which category is the record in. there are two steps needed to use a decision tree: 1. tree generation: generate a tree according to training set. need to determine which feature need to use in the split, and determine which category the result is in. 2. classification: classify new records from the root of the decision tree, and compare the record with each of the decision node, move to corresponding branch with the result. repeat this process, and after a data reaches the leaf node, the category of leaf node is the new category of the node. quinlan proposed c4.5 algorithm in [11], which is a well known decision tree algorithm.the main method is to generate the decision tree from root to leaf, in order to reduce the level of uncertainty. therefore, this algorithm can be described as follows. gain ratio is the index c4.5 used to select feature. define a feature in the feature set to be ak, the training set to be t��definition of information gain is defined like this: gain(t,ak) = info(t)− infoak(t) (1) where info(t) = − n ∑ i=1 freq(ci,t) |t | log2 freq(ci,t) |t | (2) infoak(t) = − ∑ ak∈d(ak) |takak | |t | info(t ak ak ) (3) freq(ci,t) means the number of records belongs to ci in t . takak express that subset which ak is ak, and domain of ak is d(ak). splitinfo(ak) is defined to be: splitinfo(t,ak) = − ∑ ak∈d(ak) |takak | |t | log2 |takak | |t | (4) gain ratio is gainratio(t,ak) = gain(t,ak) splitinfo(ak) (5) the advantages of decision tree are: 1. the tree generated is easy to generate and easy to explain; 2. performs well when dealing with large data set. 570 h.p. yao, y.q. liu, c. fang 2.3 random forest random forest algorithm [9, 12] is a classification algorithm and contains multiple decision trees, where each tree has a vote, and result is the one with highest vote. when generating decision tree, feature selection and pruning can be used to avoid over fitting. but when the number of features is large, the problems can hardly be avoided. random forest consists of multiple decision trees, which can effectively avoid those problems. random forest has following advantages: 1. it can be used in various situation with a pretty high accuracy on classification; 2. it can effectively support multi-feature situation without feature selection; 3. it can report the importance distribution of features. 3 system model influenced by big data, network data distribution is gradually changing. this paper try to solve the problem that caused by the increasing difference between normal traffic and abnormal traffic. therefore, we proposed a new abnormal traffic detection model based on big data analysis, and this model includes three sub-models. 3.1 normal traffic selection model normal traffic selection model uses classification and clustering algorithm to distinguish normal and anomaly behaviors, rather than involved specific anomaly behaviors. this model includes two stages: 1. training stage: training model uses data that labeled normal or abnormal, and the model applies in test stage. 2. test stage: test stage is similar to detection in practice. using unlabeled date, the model classifies traffic data into normal or abnormal, and labels them. normal traffic selection model uses k-means clustering algorithm, knn, decision tree and random forest classification algorithms. traditionally, before using k-means algorithms, it is very important to set the number of categories, because we don’t know how many categories. but in order to distinguish normal and abnormal behavior, the normal traffic selection model uses k-means as following way. in training stage, using labeled information classify data into normal and abnormal. these two categories use k-means separately instead of clustering all data at once, getting the center of the data set respectively. then using the center of the data set, knn clustering algorithm classifies test data. decision tree and random forest classification algorithms train with labeled normal and abnormal data. 3.2 abnormal traffic selection model the purpose of abnormal traffic selection model is avoid influence caused by too many normal traffic than abnormal traffic. this model classifies anomaly traffic into specific categories, and includes two stage as well: 1. training stage: this stage only use abnormal data to train classification model, and every data label specific attack group. using classification algorithms learns classified rules. 2. test stage: test stage is similar to detection in practice, using unlabeled data (including normal behavior data). the classification model classifies anomaly traffic into specific categories according to the classified rules, and gives specific label to every data. an abnormal network traffic detection algorithm based on big data analysis 571 table 1: distribution of kddcup99 data set data set normal dos probe r2l u2r 10 percent of training data set 97278 391458 4107 1126 52 test data set 60593 229853 4166 16189 228 abnormal traffic selection model uses decision tree and random forest classification algorithms. abnormal traffic selection model and normal traffic selection model are independent, without order of priority in training stage or test stage. mixed compensation model combines the result from normal traffic selection model and abnormal traffic selection model to produce a final result. although abnormal traffic selection model is more effective because without influence of normal traffic data, the model has high false negative rate due to this characteristic. therefore use normal set produced by normal traffic selection model to compensate abnormal set a = {a1,a2, · · · ,ak} produced by abnormal traffic selection model. ai, i ∈ [1,k] denote specific attack category. if c denote detection result, rule of compensation as follow: { if c ∈ ai, c ∈ n, then c ∈ n if c ∈ ai, c /∈ n, then c ∈ ai. (6) 4 simulation results and discussions before using three sub-models of anomaly detection based on big data analysis, data set needs be preprocessed with label for training model. it should be noted that rightly selecting feature is a good way to reduce dimension and increase efficiency of running. in the simulation, three different algorithms are used to verify validity of the proposed model. 4.1 data set in the simulation, we use kddcup99 [13] data set to test my model. kddcup99 data set is widespread use for testing abnormal detection model, which is obtained and processed from kddcup99 [14]. kddcup99 data set has 41 features and been sorted into three group: basic feature, content feature and time feature [15]. the distribution of data set is shown as table 1, where training data has 5 million records, 10 percent of training data has 494021 records, and test data has 311029 records. every record is labeled to be normal or abnormal, and abnormal data can be classified into four groups: dos, u2r, r2l and probe. from table 1, we find that normal data in training data set is more than abnormal data in test data set. therefore, this data set can be used to test the performance of the proposed model under different circumstances. 4.2 simulation results as shown in table 2, we have done eight experiments with the model based on big data analysis, and three control experiments which used k-means, decision tree or random forest respectively. in the control groups, training classify model uses all training data set with five categories, then classifying test data into five categories. another control group is winner of kddcup99. in the simulation, prediction accuracy is used as a simulation metric of detection effect, which is shown in table 3. besides, we adopt way of sorting and grading for every type. for example, 572 h.p. yao, y.q. liu, c. fang table 2: number of experiments no. normal traffic selection model abnormal traffic selection model no. of control group algorithm 1 k-means1∗ random forest 9 k-means 2 k-means1∗ decision tree 10 decision tree 3 k-means2∗ random forest 11 random forest 4 k-means2∗ decision tree 12 winner of kddcup99 5 decision tree decision tree 6 decision tree random forest 7 random forest decision tree 8 random forest random forest *note: in the normal traffic selection model, the number of cluster of normal and abnormal respectively is 4 and 30 in k − means1, and the number of cluster of normal and abnormal respectively is 100 and 300 in k − means2. table 3: prediction accuracy no. experiment normal dos probe u2r r2l 1 k-means1+random forest 0.632 0.814 0.939 0.561 0.679 2 k-means1+decision tree 0.656 0.791 0.878 0.500 0.772 3 k-means2+random forest 0.945 0.983 0.910 0.513 0.510 4 k-means2+decision tree 0.946 0.979 0.852 0.500 0.504 5 decision tree+decision tree 0.951 0.984 0.829 0.500 0.512 6 decision tree + random forest 0.951 0.986 0.831 0.550 0.517 7 random forest + decision tree 0.954 0.980 0.861 0.500 0.521 8 random forest + random forest 0.952 0.985 0.872 0.520 0.510 9 k-means 0.938 0.968 0.785 0.500 0.528 10 decision tree 0.951 0.983 0.793 0.500 0.500 11 random forest 0.952 0.985 0.875 0.522 0.507 12 winner of kddcup99 0.995 0.971 0.833 0.132 0.084 all experiments are sorted by prediction accuracy of normal. the first grades 1 point, the second grades 2 points, and so on. finally, adding grade of five groups is final grade. as shown in table 4, the experiment group and winner of kddcup99 are sorted by final grade. while the later has high detection rate in normal data, as for four attack types, the result of model based on big data analysis is better than winner of kddcup99. algorithm of winner of kddcup99 is c5 decision tree [16–19]. training data of winner of kddcup99 is a little different with my experiment. thus for evaluating detection effect of the proposed mode, we did three control experiments with same training data and test data, used with k-means, decision tree or random forest respectively. the number of these experiments is noted as 11, 10 and 9. sorting result shows that detection effect of algorithm of the proposed model is better than no use, as shown as table 5. we will discuss experiments results, compared no.8 with no.11, no.7 with no.5 and no.3 with no.4. discussing result of no.8 and no.11 score of top three are same. judging no.8 and no.11 with final grade, detection result of two experiments are almost same. and both of them use random forest algorithm. but the difference is: an abnormal network traffic detection algorithm based on big data analysis 573 table 4: compared with winner of kddcup99 no. experiment normal dos probe u2r r2l final socre rank 8 random forest+random forest 3 2 2 2 4 13 1 6 decision tree+random forest 4 1 6 1 2 14 2 7 random forest +decision tree 2 5 3 4 1 15 3 2 k-means2+random forest 7 4 1 3 5 20 4 5 decision tree+decision tree 4 3 7 6 3 23 5 4 k-means2+decision tree 6 6 4 5 6 27 6 12 winner of kddcup99 1 7 5 7 7 27 6 table 5: compared with control group no. experiment normal dos probe u2r r2l final socre rank 6 decision tree+random forest 4 1 6 1 3 15 1 8 random forest + random forest 2 2 3 3 5 15 1 11 random forest 2 2 2 2 7 15 1 7 random forest + decision tree 1 7 4 5 2 19 4 3 k-means2+ random forest 8 5 1 4 5 23 5 5 decision tree + decision tree 4 4 7 5 4 24 6 10 decision tree 4 5 8 5 9 31 7 9 k-means 9 9 9 5 1 33 8 4 k-means2+ decision tree 7 8 5 5 8 33 8 1. importance of variable used in classifying is different; 2. no.8 has lower false negative rate. • importance of variable as shown as fig. 1, variables chosen by random forest in no.8 and no.11 are different. random forest algorithm can output importance of variables, noted gini index [9]. fig. 1 shows that top 20 have important variables in comparison with top 1, whose value is higher and more important. in no.8, rank of variables is different between normal traffic selection model and abnormal traffic selection model. this means that variable used for predicting normal or abnormal and specific attack is different. therefore, choosing variable in no.11 is influenced by both sides, and output a compromised result when choosing variables, that’s why prediction of model in no.11 has deviation. • comparison of false negative rate in order to evaluate effect on predicting abnormal behavior, false negative rate is used as an important index, which can measure how many attack events are omitted. table 6 shows confusion matrix of results of experiments no.8 and no.11 when using random forest. row express information of prediction, and column express actual information. false negative rate of no.8 in normal type is very low, but high in u2r and r2l type. in no.8, false negative rate of normal selection model in normal is low. without influence of normal training data, false negative rate of abnormal selection model in four specific attack types are lower than no.11. discussing result of no.5 and no.7 no.5 and no.7 respectively compare with no.6 and no.8 by using same algorithm in normal traffic selection model, and their ranks are lower when using decision tree in abnormal traffic 574 h.p. yao, y.q. liu, c. fang (a) no.11 (b) no.8 normal traffic selection model (c) no.8 abnormal traffic selection model figure 1: importance of variables in random forest. an abnormal network traffic detection algorithm based on big data analysis 575 table 6: confusion matrix no.11 prediction normal dos probe u2r r2l normal 60287 5967 847 159 15839 dos 69 223814 191 8 0 probe 233 72 3128 50 104 u2r 1 0 0 10 5 r2l 3 0 0 1 241 false negative 0.00505 0.026273 0.24916 0.95614 0.985113 no.8 normal traffic selection model prediction normal abnormal normal 60289 22853 abnormal 304 227583 false negative 0.005017 0.091253 no.8 abnormal traffic selection model prediction dos probe u2r r2l dos 229231 769 20 4693 probe 297 3393 135 5646 u2r 0 0 39 32 r2l 325 4 34 5818 false negative 0.002706 0.18555 0.828947 0.64062 table 7: confusion matrix of abnormal traffic selection model with decision tree predition dos probe u2r r2l dos 227792 589 34 6245 probe 1434 3192 20 283 u2r 0 0 0 0 r2l 627 385 174 9661 selection model. table 7 is confusion matrix of abnormal traffic selection model with decision tree algorithm. it shows that u2r can not be detected and false negative rate of r2l is higher. in order to find the reason, classify tree is checked in fig. 2, where the classification model prefers dos and probe attack, then r2l attack, and no result point of u2r attack. distribution of training data can explain this phenomenon, which can be shown in fig. 3. when generating decision tree, the obtained information will cause results in favor of feature which have more samples. therefore, if the number of training data set in every group is different enough, it cannot get efficient classification model for small samples. moreover, because the number of between training data is comparatively equal, classification result is better, such as no.6, when normal traffic selection model uses decision tree. discussing result of no.3 and no.4 no.3 and no.4 use k-means in normal traffic selection model to choose clustering center. table 8 shows final prediction accuracies in no.3 and no.4. because final results are lower than that of normal traffic selection model or abnormal traffic selection model, we find that this problem is caused by using k-means in normal selection model. table 9 shows confusion matrix 576 h.p. yao, y.q. liu, c. fang figure 2: classify tree of abnormal traffic selection model. figure 3: distribution of training data. an abnormal network traffic detection algorithm based on big data analysis 577 table 8: accuracy of no.3 and no.4 no. model algorithm accuracy no.3 normal traffic selection k-means 0.926 abnormal traffic selection random forest 0.948 mixed compensation model 0.923 no.4 normal traffic selection k-means 0.925 abnormal traffic selection decision tree 0.961 mixed compensation model 0.918 table 9: confusion matrix of normal traffic selection model of no.3 and no.4 no. prediction normal abnormal no.3 normal 59189 21663 abnormal 1404 228773 no.4 normal 59428 22221 abnormal 1165 228215 of normal traffic selection model of no.3 and no.4. many abnormal records are predicted as normal, which cause high false negative rate. therefore, many abnormal records predicted by abnormal traffic selection model will be regarded as normal after mixed compensation model. nowadays, many novel attacks are unknown to researchers, and many attacks will be disguised as normal. it’s very dangerous to have high false negative rate, and it does not fit the proposed model. because the effect of k-means has great correlation with the number of centers chosen to cluster, and we can fine tune the strength of clustering, and lower the false negative rate to establish a strict normal selection model. in no.3 and no.4, the number of centers for normal traffic and attacks is 100 and 300, respectively. although it can achieve a good overall accuracy, its false negative rate is higher than other model. however, according to table 10, by choosing 4 and 30 in no.1 and no.2, it has lower false negative rate, and only classify four kinds of attacks. besides, a strict normal detection model is established. by adjusting the parameters and reducing false negative rate in no.1 and no.2, we can find that the rank has increased rapidly compared with no.3 and no.4. especially, when kmeans combines with random forest, it has a very high accuracy on probe, u2r and r2l attack. therefore, we can draw the conclusion that by adjusting the parameters of k-means, the strength of abnormal traffic detection can be controlled by adjusting the strength of normal traffic identification. 4.3 summary based on the results analyzed above, as shown in table 11, the following conclusions can be drawn: 1. random forest classification algorithm can adapt to the change of distribution of network data, and this algorithm by using the proposed model can reduce false negative rate. 2. if the number of training data in different group is largely different with each other, the classify model built by decision tree will prefer to attack types, which have more training data. so we should avoid using decision tree in abnormal traffic selection model. however, in the normal traffic selection model, the difference between different groups is comparatively small. in this 578 h.p. yao, y.q. liu, c. fang table 10: results of experiments no. experiment dos probe u2r r2l final rank 1 k-means1+random forest 10 1 1 2 14 1 6 decision tree+ random forest 1 8 2 5 16 2 8 random forest + random forest 2 5 4 7 18 3 11 random forest 2 4 3 9 18 3 3 k-means2+ random forest 5 2 5 7 19 5 2 k-means1+ decision tree 11 3 6 1 21 6 7 random forest + decision tree 7 6 6 4 23 7 5 decision tree + decision tree 4 9 6 6 25 8 9 k-means 9 11 6 3 29 9 4 k-means2+ decision tree 8 7 6 10 31 10 10 decision tree 5 10 6 11 32 11 table 11: summary of model model 1 model 2 model 3 normal traffic selection model k-means1 decision tree random forest abnormal traffic selection model random forest random forest random forest situation, using decision tree can fast get classify model, and the results have higher accuracy. 3. there are more and more unknown abnormal events in the future. in order to avoid loss of false negative prediction, we can change the number of clustering in the normal traffic selection model with k-means algorithm to reduce false negative rate and increase the accuracy of detecting abnormal events. conclusion with the change of distribution of network data, traditional anomaly traffic detection techniques can not fit this situation. in order to solve the problem, we propose an anomaly traffic detection model based on big data analysis. simulation results show that the proposed model achieves a detection rate of 95.4% on normal data, 98.6% on dos attack, 93.9% on probe attack, 56.1% on u2r attack, and 77.2% on r2l attack.therefore, the model can increase the accuracy of attack behavior, and reduce false negative rate. acknowledgment this work was supported by nsfc (61471056) and china jiangsu future internet research fund (by2013095-3-1, by2013095-3-03). bibliography [1] patcha, a.; park, j.m. 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[19] prasad, k. et al (2016); a knowledge-based system for end mill selection, advances in production engineering & management, issn 1856-6250, 11(1): 15-28. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 2, pp. 251-260 implementing web services using java technology m. pirnau mironela pirnau “titu maiorescu” university bucharest romania, 22 dambovnicului street, district 4, 040051, bucharest, romania e-mail: mironela.pirnau@utm.ro abstract: there are several essential activities that need to take place in any service-oriented environment such as: a web service has to be created, to have its interfaces and invocation methods defined, its service has to be published to one or more intranet or internet repositories to locate potential users. a web service needs to be located in order to invoke potential different users. the technologies offered by web services enable us to cast older applications, we can use them and the packages already existing in a certain enterprise. the infrastructure associated with older applications can also be wrapped as a serial set of services. in the following pages i tried to present general characteristics of the web services up to present, as well as the usage of java technologies for the implementation of web services. the present paper targets a systematisation of the main web services, as well as highlighting and testing the most important technologies used to develop them. keywords: web service, java, xml, soap and semantic web. 1 introduction web services are software applications that conform to the web service interoperability organization. they are becoming more and more popular and reveal a model in which discrete tasks within e-business processes are widely distributed throughout a value net. the label for "web services" has two levels of meaning one specific and one conceptual. web services represent standards, which describe services oriented to applications with architecture having a base of components. these are software components, which can be reusable, having included functionality [11, 12, 16]. the web services use standard xml languages. they use http for sending messages and they are independent of the platform and of the language. web services are freer systems, and the customer may not have knowledge of the web service up to the time it invokes, being suited to join requests of applications meeting demands of an internet-wide [22]. 2 web services structure web services systems maintain important decoupling and dynamic bonds of the elements. the entire elements in a system represent and publish an application programming interface (api) message to different components in the network that work together. services are comprised among applications by employing service discovery for the dynamic bonds of the partners. the web services show such architecture full of service orientated approach by orchestrating and finding out available network services, or even precise integration of the incidences that are based on the term of constructing applications. the web services’ architecture need three basic performs such as publish, find and bind. the providers publish services toward a certain service interloper [1, 4]. the requesters "find" demands services using an interloper and "bind" to them. the architecture of web services describes certain principles for building and also creating dynamic systems with no single involving. there are many ways to instantiate a web service, choosing various implementation techniques for the roles and operations of the web services structure. from the structure of web services we may mention: the service processes that involve more than one web service, including discovery belongs to this part of the architecture as it allows us to locate one particular service from among a collection of web services. the architecture of web service invokes many layers and joins technologies [22] as we can see in the figure 1. web services can support other network protocols and distributed computing technologies. the implementation, according to the soa principles, is based on the following protocols: http, soap, wsdl, and uddi [17]. copyright c⃝ 2006-2010 by ccc publications 252 m. pirnau http[3] represents the most used internet protocol and also the leading method of transmitting the information within the web. protocol of demand-answer type between the clients and the servers is another type of method to send data through web. simple object access protocol (soap) is a format used to transmit xml message [1, 2]. it also represents a certain type of communication protocol among applications within internet. it is a platform independent protocol no matter the language used and it permits the avoiding firewalls. web services description language (wsdl) is also a xml language that permits the specification of the accessing manner within the web services. it allows the description of the web services, too. universal description discovery and integration has the following acronym uddi. it is a system of registers that is based on xml; it is independent of the platform. uddi is requested by the soap messages that demand a web service [10]. it offers access towards the wsdl descriptive document within the web service, too. figure 1: the architecture layers of the web services the soap mainly specifies the manner we should format demands towards the server and the manner the server could build its own reply. soap represents a popular selection for web services. along the transport, messages might be transmitted within a specific manner between servers and clients. the protocol of choice is represented by the http architecture. the pile depicted above (figure 2) enables web services to leverage the existing internet infrastructure. it creates a low cost of entry to an omnipresent environment. the flexibility is not compromised by the interoperability requirement provided as alternative and value-add technologies [1]. an action that makes a wsdl document available is the service provider sending a wsdl document directly to a service requestor, named "direct publication". the service provider can publish the wsdl document describing the service to a host local wsdl registry, a private uddi registry or to the uddi operator. figure 2: the interoperable base web services stack implementing web services using java technology 253 3 java technologies web services represent web-based enterprise applications which use transport protocols that are xmlbased, open standards in order to exchange data to their clients [18]. from the java technologies we mention the following, presented in the table below [20]. table 1: java technologies wsit web services interoperability technology jax-ws10 the java api for xml web services, package javax.xml.ws jax-rpc the java api for xml processing jaxp provides a java interface to dom, sax, and xslt jaxb java architecture for xml binding, package javax.xml.bind saaj the soap with attachments api for java, package javax.xml.soap jaxr the java api for xml registries jstl javaserver pages standard tag library jsr 181 java web services metadata, package javax.jws as an example of these technologies i shall present the saaj [22] technology that stands for soap with attachments api for java and that provides a standard set of apis in order to send xml documents including attachments over the internet. it is similar to jax-rcc but it needs additional effort both on the client’s and on the server’s side [15, 21]. the saaj apis provides a standard means for handling soap messages. it can be used to create, to inspect and to alter soap messages. saaj also contains methods for creating and sending attachments with the soap message. the attachments may contain data in the shape of any format, not being limited to xml as in the case of the soap messages. the attachments can be used to send inappropriate data in xml format as images for example. the saaj apis offers the attachmentpart class in order to represent the attachment part of a soap message [16]. any soap message object has automatically a soap part object and requires sub-elements but because the attachmentpart objects are optional, we must create and add them ourselves. saaj contains the api in order to create, populate and access soap messages, according to soap and soap attachment specifications. saaj contains api needed to send non-provider-model messages [21]. we present the package relationship in figure 3 . figure 3: saaj package relationship saaj represents a part from jaxm (java api for xml messaging) which was evolved from the jsr expert group [1, 13, 21]. the final form of the specifications show two different aspects: the core functionality concerned with manipulating of the soap messages and a very high level of messaging facility. regarding saaj his an api of a very low standard, which is completely aware of the messages that are exchanged within the web service and its customers [16]. an application that uses saaj needs to construct soap messages little by little and take data out of the reply messages. the usage of saaj demands a great deal of work deposed from the developer’s side than jax-rpc. we present a few cases in which it would be better to be helped by saaj and its relative jaxm instead of using jax-rpc [1, 7]. 254 m. pirnau jax-rpc can be accessed just when the customer and the service are active at the same specific time and we also assume that there is a free network way to bond them. in the case the service is not free the moment the customer accesses it, there is no possibility of using jax-rpc [17, 20]. jaxm is a service that provides a confident service able to deliver without need to demand the customer elements to be included in the way the confidence is offered. saaj as well as jaxm deliver a wide result disposal for messages based on xml. the api from the javax.xml.soap package permits specifications such as: creating a piece of xml; creating access, adding, modifying parts of the soap message; creating a point-to-point contact to a certain endpoint; creating, adding, modifying a certain soap fault of information type etc. the table 2 includes the javax.xml.soap package and includes the api for building soap messages [4, 20]. the package is defined and included in soap with attachments api for javatm is named saaj. saaj a tool for creating and sending messages to allow sellers to bring up the implementations, the entire saaj api is built of interfaces and some classes that are abstract. in order to create a soap message, there is need to be used a messagefactory type message. part of the javax.xml.soap package represents an abstract class and that might be obtained by using the newinstance method. this type of method is represented by the public static messagefactory newinstance that propagates soapexception. the message factory implies two methods to create the soap messages. the first method is represented by one public soapmessage createmessage that throws soapexception. one public soapmessage createmessage(mimeheaders headers, inputstream) which throw soapexception. the second of the methods is used to non-serialize messages in a link of an input. table 2: methods included in the javax.xml.soap package class summary attachmentpart single attachment to a soapmessage object messagefactory factory for creating soapmessage objects mimeheader object that stores a mime header name and the value soapconnection connection that a client might use for transmitting messages to a certain remote party (url) soapconnectionfactory factory for creating soapconnection objects soapelementfactory creating soapelements soapfactory creating various objects existing in the soap xml stack soapmessage soap messages root class the default messages that comes back by the messagefactory createmessage method contains a soappart but also contains no types of attachments. an object of soapenvelope style is included in the soappart. in the middle of the envelope there are certain parts such as the header, made by an object able to implement the soapheader interface and a soap message body in the shape of an object of soapbody classification. the body and the header, both contain certain xml elements. the java technologies represent methods for constructing web services [8]. 4 related work the web semantic implemented and proposed use of the machinery to extend the web represents a common goal. the multiple demands on the web create a semantically temptation having a highly expressive formalisms because the web represents a principled architecture of standards, formalisms and languages [6, 14]. what is specific for web 2.0 is that it connects humans among them and for web 3.0 it is specific that it connects humans to information in a very innovative new manner. web 3.0 functions as an intelligent assistant, which perceives the concepts of the web pages and transmits important information of one individual. it not only looks for robotically through keywords. tim berners-lee is the person who founded the world wide web. he defines the future online environment as being the semantic implementing web services using java technology 255 web [5, 8, 9]. this is the same web 3.0 a type of internet that perceives information and not just transmits it to the client. web 3.0 will be capable to offer for visitors complete data on any subject. the communities that are based on the same complete data exchange will certain develop. mobile web which is supported by mobile intelligent terminals would be able to enable the data access everywhere and anytime. rich internet applications (ria) would appear more and more. web 3.0 represents a much more direct, closer, measurable, reliable and honest communication. the metro style is made up from: jax-ws, jaxb, and wsit and within it is possible to create web services that are secure, transactional, confident and interoperable, also certain customers [15]. the metro elements are a certain part of the project metro, which part of glassfish, java ee (java platform, enterprise edition), and only part in java se (java platform, standard edition). both glassfish and java ee represent support for jax-rpc api [15, 17]. 5 web applications. java web start technology jws (java web start) represents a technology for installing, launching, and updating the java applications directly from the web and uses the jnlp (java network launching protocol). for java applications, that use of the jws technology launches a range of order of javaws application as the following syntax: javaws [ options ] [url], and the options are those in figure 4. figure 4: options for java web start the role of the programmer: creates a java application and archives the program along with its all resources; signs the jar archive (if necessary) and creates a configuration folder (jnlp) that describes the application; creates a web page having a link to the configuration folder and copies on the web server, the jar folder, the jnlp and html one. the role of the user: accesses the link to the application from the programmer’s web page; the application will be installed automatically, locally and installs a jre if necessary; launches in execution the application; for each execution of the application, it verifies if a new version on the programmer’s new web page exists. as a practical application, i have created a project that contains java classes with administration role of the students. the purpose of the application is to use the java start technology and to realize "a following" in using the application for this technology as well as other technologies that i have tested (for example the servlets technology etc). to realise the source, i also used the classes from the swing, awt, sql etc packages. if we open the application in the netbeans ide 6.7 [23] environment, the project window looks like the one in figure 5. 256 m. pirnau figure 5: the window of the application opened in netbeans helped by java web start, the users may access one java application only with click of a mouse on an html link towards a jnlp file for the certain applications from inside the web browser [19]. this should be installed onto the customer machine. it is not necessary to install jdk (java development kit). in order to permit our java application to be ran with java web start, we have to configure the main properties of the manner the ide could construct the project. the moment java web start is accessed, the ide creates automatically a jnlp file and an html page along with the link to the jnlp file, along with the jar file. for the beginning, we must configure the project in order to activate java web start and to locally test the application. so we must choose the properties option from the project’s shortcut menu, and in the window from the figure 6 the suitable settings are set. the application’s project must be configured so that the java web start technology may be used at the launching.certain sources are mixed together and then the main class of the project is launched in execution. the ide mixes sources and we might see the java screen starts, and in the warning window we are asked if the application might be executed and also to select the checkbox and click run inside the proper warning window. the "application" application begins, the same as in figure 7. after activating the application, the dist folder is created and contains for java web start, the following two additional folders: launch.jnlp it is an xml file having special parts and specifications instructing browsers how to run the application. the attributes for the jnlp files might include the jnlp version, application title, the seller’s name, a link of the application of the jar file etc. the configuration folder jnlp is in an xml format and offers information regarding the application that will be installed using jws. its structure is presented in figure 8. launch.html it is generated in an automatic html page a link to the jnlp file. the users might click the link in order to begin applications through java web start (figure 9). how to run an application from a distant location the moment we are sure that the application begins in a successfully manner using java web start from local sources, we might upload this to a distant location and start launching it from far there. in order to develop applications using java web start inside the web, the server of the web is able to manipulate jnlp files. the web server can be configured in order to recognize jnlp files as applications, such as the mime type for jnlp. each web server has a certain manner for adding mime types. to develop our application to the implementing web services using java technology 257 figure 6: project properties web start figure 7: the main menu of the application 258 m. pirnau figure 8: the structure of the launch.jnlp file figure 9: the structure of the launch.html file implementing web services using java technology 259 customer, we should be sure that a web server accesses all the files that contain our application. this may represent typically amounts for copying one or many of the jar files, with a jnlp file, to the directories of the web server. the settings needed to enable the web site sustain that java web start is quite the same to develop a content based on html. in order to launch the application out of the web, we have to use a certain link of the files of applications source in the jnlp file. regarding this, the project is chosen and in the "properties" window the codebase section is defined. the url where the files are loaded has the jnlp extension and also the jar and html extension (figure 10). figure 10: settings for running applications on the web at this moment we may run our application, in the window of a browser, we type the url towards the launch.html file and then press the "launch the application" connection. our "application" application begins with java web start. after analyzing the current application, we may observe that the integration of the applications and of the data sources must be realized without significant changes of the applications and of the data. because the web allows the easy access to information and services, it became a main element of communication having the condition to be used for applications that run it, adequate technologies such as those implemented on the java platform. 6 conclusions the web services represent a wide range of technologies used in the application of distributed implementation at the web system level. these technologies develop a program-to-program communication pattern using the xml standard, which assures a neutral shape report to platforms, programming languages and software specifications. a web services application is an application that interacts with the world using xml for data definition, wdsl for service definition and soap for communication with another software. the fundamental idea of the web services is the integration [16]. this concept represents a set of standard technologies that facilitate the interoperability among heterogenic systems at an organizational level or on the internet. for the optimization of the web aplications, the companies head more and more to the programming environment of java and of the relevant platforms. java represents an independent language regarding the platform that is projected for the web and, at the same time, it is a developing platform of various aplications, no matter the company’s specific. next to the java platform the xml technology has developed having the goal to exchange data among the aplications. as future research i intend to run more applications on java different platforms and to compare both the manner of using these for working in the environment and the security level implemented the platforms. 260 m. pirnau bibliography [1] e. hewitt, java soa cookbook, o’reilly, 2009. [2] j. governor, d hinchcliffe, d , web 2.0 architectures, o’reilly, 2009. [3] j. zukowski, java 6 platform revealed, springerlink date, 2006. [4] *** , soap with attachments api for java (saaj), sun microsystems, tech. rep., 2004. [5] r. volz, s. handschuh, s. staab, l. stojanovic, and n. stojanovic, unveiling the hidden bride: deep annotation for mapping and migrating legacy data to the semantic web, j. web semantics, vol. 1, no. 2, pp. 187-206, 2004 [6] i. dzitac, b. e. barbat, artificial intelligence + distributed systems = agents, international journal of computers, communications and control, 4(1):17-26, 2009. [7] r. chinnici, java api for xml-based rpc (jaxrpc), java community process, tech. rep., 2003. [8] r. nagappan, r. skoczylas, r. sriganesh, developing java web services, wiley 2003. [9] i. dzitac, s. dzitac, e. valeanu, web distributed computing for landscape architecture, in eleftheriadis, n., styliadis, a., paliokas, i. (eds), proceedings of the lant07, international conference landscape architecture and new technologies, 25-26 may 2007, drama, greece, pp. 25-36, 2007. [10] ***, java 2 platform, enterprise edition specification, sun microsystems, tech. rep., 2003; [11] l. duta, f.g. filip, control and decision making in disassembling used electronic products, studies in informatics and control, 17 (1):17-28, 2008. [12] l.a. zadeh, d. tufis, f.g. filip, i. dzitac ( eds), from natural language to soft computing: new paradigms in artificial intelligence, editing house of the romanain academy, bucharest, 2008. [13] n. kassem, a. vijendran, and rajiv.mordani, java api for xml messaging (jaxm), sun microsystems, tech. rep., 2003, http://java.sun.com/xml/downloads/jaxm.html. [14] http://www.nowpublishers.com/. [15] http://java.sun.com/webservices/technologies/index.jsp. [16] http://en.wikipedia.org/wiki/service-oriented_architecture. [17] http://java.sun.com/developer/technicalarticles/webservices/soa/. [18] http://java.sun.com/javase/technologies/desktop/javawebstart/index.jsp. [19] http://java.sun.com/javase/6/docs. [20] http://java.sun.com/webservices/docs/1.6/tutorial/doc/. [21] http://www.javapassion.com/webservices. [22] http://www.w3.org/tr/ws-arch/. [23] http://www.netbeans.org. mironela pirnau (1966) has a m.sc. in automatics, faculty of automatics and computing, politechnical university in bucharest ( 1992) and phd in electronics (2003) from university of pitesti. her current interests include applied informatics. since 2008 she has been a lecturer phd at the faculty of science and technology of information, titu maiorescu university in bucharest. she has published books and articles on informatics. she has recently been interested in researches on the use of java technologies in developing applications of informatics. international journal of computers communications & control issn 1841-9836, 9(4):471-481, august, 2014. networked predictive fuzzy control of systems with forward channel delays based on a linear model predictor s.w. tong, d.w. qian, g.p. liu shiwen tong∗ college of automation beijing union university beijing 100101, china ∗ corresponding author: shiwen.tong@buu.edu.cn dianwei qian school of control and computer engineering north china electric power university beijing 102206, china dianwei.qian@ncepu.edu.cn guo-ping liu 1.school of engineering university of south wales portypridd cf37 1dl, uk 2.ctgt center harbin institute of technology harbin 150001, china guoping.liu@southwales.ac.uk abstract: this paper presents a novel networked control framework, using fuzzy logic control, for systems with network delays which are known to greatly weaken the control performance of the controlled system. to deal with the network delays, the predicted differences between the desired future set-points and the predicted outputs from a model predictor are utilized as the inputs of a fuzzy controller, thus a series of future control actions are generated. by selecting the appropriated control sequence in the plant side, the network delays are compensated. the simulative results demonstrate that the proposed method can obviously reduce the effect of network delays, and improve the system dynamic performance. keywords: networked control, model prediction, fuzzy control, delay compensation. 1 introduction the emergence of the network technology has changed the communication architecture of control systems from traditional point-to-point to current common bus. sensors, actuators and controllers are connected through network, formed a feedback control system (namely, networked control system). this architecture has injected fresh blood to the classic and modern control theories and also arises higher challenges to the controller design at the same time. on the one hand, the introduction of the network to the control system brings many advantages such as low cost, easy maintenance and high reliability. on the other hand, the unavoidable time delay, data dropout and other complicated phenomenon existing in the network should be considered. in recent years, networked control theory and technology have become an important and hot research area. scholars from different countries have made a lot of breakthroughs in the networked control [1][8]. network delay has become one of the most concerned issues in the networked control system. because the network delays can dramatically degrade the control performance of the systems copyright © 2006-2014 by ccc publications 472 s.w. tong, d.w. qian, g.p. liu even makes the systems unstable. from recent published literatures, it can be seen that the treatment of the network delays can be summarized in the following: the first one is to integrate delay information into the controller design by designing a robust controller to decrease the effect of time delay [3], [6][8]; the second one is to estimate delay information in the backward or forward channel by using reason-rule-table, average-value or delay windows (dw) method [9,10]; the third one is to eliminate the network delays in the return path by using a cascade control structure with p (proportion) control in the inner loop and fuzzy adaptive control in the outer loop [4]; as we all known, one of the obvious characteristics of the networked control system is that the communication networks can transmit a packet of data simultaneously. this feature provides another solution to compensate for network delays in the forward channel [11,12]. based on it, liu et al. [1, 2] proposed networked predictive control (npc), using the strings of future control actions, to compensate for the forward channel delays. in this paper, we try to design a networked controller by using fuzzy control theories. different from networked predictive control method, we separate the model prediction from the controller design. the function of the model predictor is just to produce future predicted outputs. a fuzzy controller is designed to generate a series of future control sequence based on the errors between the desired future outputs and the model predicted outputs. then the strings of future control actions are packed and sent to the plant side through the communication channel. thus, the effect of delays in the forward channel is lessened by using a delay compensator in the plant side. predictive control and fuzzy control are powerful tools. they have been used in the design of the networked controller [1][5], [7, 8]. some researchers have connected the prediction to the fuzzy control and have proposed ’predictive fuzzy control’ method [13, 14]. they use ’future error’ and ’future error change’ as inputs of the fuzzy controller to produce the control actions at the current time. to my knowledge, the combination of networked control system and the predictive fuzzy control has not been reported except one paper written by us in 2007 [15]. we call it ’networked predictive fuzzy control’ with abbreviation npfc. the core idea of the npfc is producing ’future control actions’ by fuzzy controller design according to the ’future error’ and ’future error change’ from a model predictor. then the network delays can be compensated by choosing the ’future control actions’. this paper is organized as follows: the architecture of networked predictive fuzzy control (npfc) is firstly presented. secondly, a model predictor based on diophantine equation is proposed. thirdly, the fuzzy controller using ’future errors’ and ’future error changes’ as inputs to derive ’future control actions’ is designed. then the delay compensation mechanism is discussed and the method is implemented in a servo control system. finally, the conclusions are drawn in section 4. 2 design of networked control systems 2.1 structure of networked predictive fuzzy control systems the networked predictive fuzzy control system as shown in fig. 1 mainly consists of three key parts: the model predictor, the fuzzy controller and the delay compensator. the model predictor is used to predict future outputs of the controlled system y(t|t), y(t + 1|t), · · · , y(t + n − 1|t) according to the delayed output y(t − 1) of the controlled system in the backward channel and the control actions u(t − d − 1), u(t − d − 2), · · · , u(t − d − nb) in the past. the errors e(t|t), e(t + 1|t), · · · , e(t + n − 1|t) between the desired future outputs r(t|t), r(t + 1|t), · · · , r(t + n − 1|t) and the predictive future outputs y(t|t), y(t + 1|t), · · · , y(t + n − 1|t) of the controlled system can be used to design a fuzzy controller to produce the future control sequences u(t|t), u(t + networked predictive fuzzy control of systems with forward channel delays based on a linear model predictor 473 1|t), · · · , u(t+nu −1|t). then the future control sequences are packed and sent to the plant side through network. in the plant side, a delay compensator is used to compensate for the forward network delays by selecting appropriate control sequence. figure 1: structure of networked predictive fuzzy control system 2.2 model predictor the function of the model predictor is to produce a series of future outputs of the controlled object according to the delayed output in the backward channel and the control actions in the past. models, no matter linear or nonlinear model, of such functions can all be the predictor. to simplified the issue and focus on the research work step by step, we only consider forward channel delays in this paper. this consideration is reasonable in some cases. for example, in a network that the transmission velocity in the backward channel is faster than the forward channel thus the delays in the backward channel can be neglected. therefore a linear model predictor based on the diophantine equation is proposed. consider a single-input and single-output process with the following form a(z−1)y(t) = z−db(z−1)u(t) (1) where, a(z−1) = 1 + a1z −1 + · · · + anaz −na b(z−1) = b0 + b1z −1 + · · · + bnbz −nb introducing a diophantine equation to derive the model predictor. ∆a(z−1)ei(z −1) + z−ifi(z −1) = 1 (2) where, ei(z−1) is of order i − 1 and fi(z−1) is of order na. ei(z −1) = 1 + i−1∑ j=1 ei,jz −j, fi(z −1) = na∑ j=0 fi,jz −j. 474 s.w. tong, d.w. qian, g.p. liu define n as predictive horizon, nm as model control horizon, from (1) and (2), the predicted value yp(t + 1) of the controlled system can be obtained. yp(t + 1) = p∆u(t − d) + q∆u(t − d − 1) + fy(t − 1) (3) where, p =   g0 0 · · · 0 g1 g0 · · · 0 ... ... . . . ... gnm−1 · · · · · · g0 ... ... · · · ... gn−1 gn−2 · · · gn−nm   yp(t + 1) = [yp(t|t), yp(t + 1|t), · · · , yp(t + n − 1|t)]t ∆u(t − d) = [∆u(t − d), ∆u(t − d + 1), · · · , ∆u(t − d + nm − 1)]t ∆u(t − d − 1) = [∆u(t − d − nb), ∆u(t − d − nb + 1), · · · , ∆u(t − d − 1)]t f = [f1(z −1), f2(z −1), · · · , fn(z−1)]t q =   gnb · · · g1... · · · ... gnb+n−1 · · · gn   let gi(z−1) = b(z−1)ei(z−1) = g0+g1z−1+· · ·+gnb+i−1z −(nb+i−1), we can construct matrix p and q by solving the diophantine equation. it should be noted that the item ∆u(t−d) = [∆u(t−d), ∆u(t−d+1), · · · , ∆u(t−d+nm−1)]t in equation (3) can not be obtained at the current time t. we assume ∆u(t − d + i) = 0, i = 0, 1, · · · , nm − 1. this assumption is reasonable. on the one hand, the dynamic response of the future time can be reflected by using the past control actions and the previous process outputs. on the other hand, the item y(t − 1) is corresponding to the correction of the predicted model. that is y(t − 1) = ym(t − 1) + e(t − 1), where ym(t − 1) is the model output and e(t − 1) is the error between the process output and the model output. 2.3 fuzzy controller the successful implementation of networked predictive fuzzy control relies on an assurance that the fuzzy controller must calculate all the pre-defined control actions in every sample time. so the fuzzy control algorithm should be simple and suitable to the real-time control. a realtime simplified fuzzy controller [16] is used to generate the future control actions. unlike the ordinary fuzzy control, which uses error and error change at the present time to produce the current control action, the networked predictive fuzzy controller uses ’future error’ and ’future error change’ to derive ’future control actions’. the ordinary fuzzy control can be regarded as a special condition of the networked fuzzy control when the predictive step is equal to zero. this networked predictive fuzzy controller has two-input one-output. one input is the error e between the desired future output and the predicted output. the other one is the change of the error ec. the output of the fuzzy controller is the change of the future control action ∆u. the membership functions of e and ec are adopted as triangular forms and the membership function of the output ∆u is adopted discrete form as fig. 2 shown. the ’simplification’ of the fuzzy controller relays on the inference process. for the typical two-input-one-output fuzzy controller, only four control rules are excited at each cycle time with networked predictive fuzzy control of systems with forward channel delays based on a linear model predictor 475 figure 2: membership functions of e, ec and ∆u. (a) is the membership function of input e, (b) is the membership function of input ec, (c) is the discrete form membership function of output ∆u. the form ’if e is le and ec is lec, then ∆u is l∆u’, where l is the linguistic variables pb, pm, ps, ze, ns, nm, nb. due to the characteristic of the triangular membership function, e is at most belong to two membership functions µie and µ i+1 e , ec is at most belong to two membership functions µjec and µ j+1 ec , thus ∆u has 2 × 2 combinations, that is four control rules: if e is l(i)e and if ec is l (j) ec then ∆u is l (i,j) ∆u if e is l(i)e and if ec is l (j+1) ec then ∆u is l (i,j+1) ∆u if e is l(i+1)e and if ec is l (j) ec then ∆u is l (i+1,j) ∆u if e is l(i+1)e and if ec is l (j+1) ec then ∆u is l (i+1,j+1) ∆u from fig. 2(c) shown, output ∆u is adopted discrete form membership function. it is assumed that output domain has been divided into cm(k), m = 1, 2, · · · , n. do minimum and maximum operator, calculate µ̃ (m) l (i,j) ∆u = ∧(µ l (i) e , µ l (j) ec , µ (m) l (i,j) ∆u ) µ̃ (m) l (i,j+1) ∆u = ∧(µ l (i) e , µ l (j+1) ec , µ (m) l (i,j+1) ∆u ) µ̃ (m) l (i+1,j) ∆u = ∧(µ l (i+1) e , µ l (j) ec , µ (m) l (i+1,j) ∆u ) µ̃ (m) l (i+1,j+1) ∆u = ∧(µ l (i+1) e , µ l (j+1) ec , µ (m) l (i+1,j+1) ∆u ) (4) and ˜̃µ(m)∆u = ∨(µ̃(m) l (i,j) ∆u , µ̃ (m) l (i,j+1) ∆u , µ̃ (m) l (i+1,j) ∆u , µ̃ (m) l (i+1,j+1) ∆u ) (5) where m = 1, 2, · · · , n it is not easy to directly get the inference rules of the future; however, the typical dynamic of the second order system can be obtained ahead of time. fig. 3 presents the phase plane of 476 s.w. tong, d.w. qian, g.p. liu −0.04 −0.02 0 0.02 0.04 0.06 0.08 0.1 −0.08 −0.06 −0.04 −0.02 0 0.02 0.04 e ec pb nb ps ns ze nb nm ns pb pm ps ze ze ze figure 3: phase plane and fuzzy control rules analysis of typical second order linear system. the typical second order linear system with the x-axis standing for variable e and y-axis standing for variable ec. from this figure, it can be seen that the points in x-axis and y-axis are crucial. if these points are controlled very well, the control performance is guaranteed. so the main inference rules are deduced as shown in table 1. table 1: main control rules of the fuzzy controller in npfc no. control rules 1 if e = pb and ec = ze then ∆u = pb 2 if e = ze and ec = nb then ∆u = nb 3 if e = nb and ec = ze then ∆u = nb 4 if e = ze and ec = pb then ∆u = pb 5 if e = ps and ec = ze then ∆u = ps 6 if e = ze and ec = nm then ∆u = nm 7 if e = ns and ec = ze then ∆u = ns 8 if e = ze and ec = pm then ∆u = pm 9 if e = ze and ec = ns then ∆u = ns 10 if e = ze and ec = ps then ∆u = ps 11 if e = ze and ec = ze then ∆u = ze thus, the incremental control actions ∆u(t + η) = n∑ m=1 ˜̃µ(m)∆u ∗ cm n∑ m=1 ˜̃µ(m)∆u (6) and the predicted control actions u(t + η) = u(t + η − 1) + ∆u(t + η), η is integer and 0 ≤ η ≤ nu − 1 (7) can be given, where nu is the control horizon. if e is the error and ec is the error change at present time, then ∆u is the incremental control action at present time. if e and ec are the values of the future time, then the future incremental control actions can be derived. networked predictive fuzzy control of systems with forward channel delays based on a linear model predictor 477 as paper [17][19] presents, variable domain implemented in the fuzzy control can greatly improve the control accuracy, which has successfully been applied to the control of a quadruple inverted pendulum [20] and the wing rock phenomenon [21]. this is achieved by the domain contraction and expansion. domain contraction is equivalent to the increase of the control rules. although the real-time algorithm has considered only 11 rules, together with the variable domain strategy, the fuzzy controller can acquire satisfactory control performance. the varies of domain can be achieved by multiplying flex factors α(e), β(ec) and γ(e, ec) of domains e, ec and ∆u, respectively. there are many different forms of flex factors. in this paper, we adopt the exponential form of functions: α(e) = [ |e| e ]τ1 , 0 < τ1 < 1 β(ec) = [ |ec| ec ]τ2 , 0 < τ2 < 1 γ(e, ec) = [[ |e| e ]τ1 [ |ec| ec ]τ2]τ3 , 0 < τ1, τ2, τ3 < 1 (8) to summarized, the networked predictive fuzzy controller has eleven parameters to be designed. four parameters are related to the model predictor. they are the order of the predictive model na and nb, the predictive horizon n, and the model control horizon nm. seven parameters are belong to the networked fuzzy controller. they are the control horizon nu, the scaling gains ke, kec, k∆u of error e, error change ec and incremental control action ∆u, and the variable domain parameters τ1, τ2 and τ3. the program steps for the networked predictive fuzzy control plus variable domain strategy are summarized below: step 1 : use equation (3), calculate the future outputs yp(t|t), yp(t + 1|t), · · · , yp(t + n − 1|t) of the controlled system according to the delayed output of the feedback channel and the previous control actions. step 2 : calculate the differences between the desired future outputs r(t|t), r(t + 1|t), · · · , r(t + n −1|t) and the model predicted values yp(t|t), yp(t+1|t), · · · , yp(t+n −1|t) to get e(t|t), e(t+ 1|t), · · · , e(t + n − 1|t) and ec(t|t), ec(t + 1|t), · · · , ec(t + n − 1|t). step 3 : adjust input and output domain using equation (8) in terms of e(t + η) and ec(t + η). step 4 : calculate membership functions of input e(t + η) and ec(t + η) and output ∆u(t + η). step 5 : use minimum-maximum inference method [see equation (4) and (5)]. step 6 : calculate the predicted control actions u(t + η) using equation (6) and (7). step 7 : let η = 0 to nu − 1, repeat step 3-6. step 8 : send the control actions ∆u(t),∆u(t + 1),· · · ,∆u(t + nu − 1) with a packet to the plant side. step 9 : select the control action u(t|t − k) and add to the controlled process. step 10 : in the next sample time, repeat step 1-9. 2.4 network delays compensation it is assumed that the network communication delay in the forward channel is not greater than the length of the predicted control horizon. to make use of the ’packet transmission’ characteristic of the network, a string of future control actions which contain u(t), u(t+1), · · · u(t+ nu − 1) at sample time t are sent to the plant side at the same time. then the control value from the latest control sequence available on the plant side is chosen as a control input of the plant to compensate for the forward channel delay. for example, if the latest control sequence 478 s.w. tong, d.w. qian, g.p. liu on the plant side is   u(t − k|t − k) u(t − k + 1|t − k) ... u(t − k + nu − 1|t − k)   then the output selected control signal will be u(t) = u(t|t − k) 0 2 4 6 8 10 12 −5 0 5 10 15 20 25 30 35 40 45 time(s) a n g le (d eg re e) npc npfc reference no delay compensation figure 4: the step responses of npc and npfc with 1-step forward channel delay. 3 simulations consider the servo control system as reference [2] shown. the system model with discrete form is as follows: g(z−1) = −0.00886z−1 + 1.268227z−2 1 − 1.66168z−1 + 0.6631z−2 (9) where sample time is 0.04 second. suppose there is one step delay in the forward channel, following section 2.2 and section 2.3, we design a linear model predictor and a real-time fuzzy controller. the parameters of the model predictor are: na = 2, nb = 2, n = 12, nm = 10. the parameters of the networked fuzzy controller are: nu = 10, ke = 0.00125, kec = 0.02, k∆u = 0.6, τ1 = 0.1, τ2 = 0.1 and τ3 = 0.01. the npc parameters are set to n = 12, nu = 10, ρ = 1500. fig. 4 shows the control performance of npfc and npc. the dot line is the set-point. the solid line stands for the npfc method. the dash line stands for npc method. the dash-dot line stands for the nfpc method without delay compensation. from the figure, it can be seen that the npfc can be regulated better than the npc in control performance with rapid dynamic and small overshot. the delay compensation mechanism is very effective. suppose the case that six step delays exist in the forward channel. the npfc controller parameters are adjusted as: ke = 0.0004, kec = 0.008, k∆u = 0.08, and the npc parameters networked predictive fuzzy control of systems with forward channel delays based on a linear model predictor 479 are set to n = 25, nu = 10, ρ = 100000. to testify the control performance of the networked predictive fuzzy control method, the results of the npc and the npfc are presented in fig. 5. through model prediction, fuzzy controller design and delay compensation, the npfc presents very obviously better performance than npc method. the rising time of npfc is about 1.1 seconds while 1.5 seconds for npc method. moreover, npc has 3.75% overshot while npfc has nearly no overshot. when npfc method not considers delay compensation, static errors can be seen in fig. 5. on the contrary, the dynamic response reaches steady state after 1.4 seconds when the delay compensator is acting. 0 2 4 6 8 10 12 −5 0 5 10 15 20 25 30 35 40 45 time(s) a ng le (d eg re e) npc npfc reference no delay compensation figure 5: the step responses of npc and npfc with 6-step forward channel delay. 4 conclusions this paper proposes a network predictive fuzzy controller structure. by solving the diophantine equation, the linear model predictor generates a series of predictive outputs of the controlled system according to the outputs and the control sequence in the past time. the errors between the desired future outputs and the predictive outputs from the linear model predictor and the error change are used to design a real-time fuzzy controller. so a series of future control sequence is produced in the controller side. by selecting the appropriate control sequence in the plant side, the delays in the forward channel are compensated. because npfc has more parameters which can be regulated, the control performance can be adjusted better than npc method. acknowledgments this work was supported in part by the beijing municipal excellent talents funded project (grant no. 2013d005022000003), the korea foundation for advanced studies fellowship to dianwei qian, and the national natural science foundation of china (grant no. 61273104 and 61333003). 480 s.w. tong, d.w. qian, g.p. liu bibliography [1] liu g. p.; rees d.; chai s. c.; nie x. y. 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(1999); variable universe adpative fuzzy controller, science in china (series e), issn 1006-9321, 20(1):32-42. [20] li h. x.; miao z. h.; wang j. y. (2002); variable universe adaptive fuzzy control on the quadruple inverted pendulum, science in china (series e), issn 1006-9321, 45(2):213-224. [21] liu z. l.; su c. y.; svoboda j. (2004); control of wing rock phenomenon with a variable universe fuzzy controller, proceeding of the american control conference. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 2, pp. 160-170 ewma algorithm in network practice p. čisar, s. bošnjak, s. maravić čisar petar čisar telekom srbija prvomajska 2-4, subotica, serbia e-mail: petarc@telekom.rs saša bošnjak faculty of economics segedinski put 9-11, subotica, serbia e-mail: bsale@eccf.su.ac.yu sanja maravić čisar subotica tech marka oreškovića 16, subotica, serbia e-mail: sanjam@vts.su.ac.rs abstract: abstract: intrusion detection is used to monitor and capture intrusions into computer and network systems which attempt to compromise their security. many intrusions manifest in changes in the intensity of events occuring in computer networks. because of the ability of exponentially weighted moving average (ewma) control charts to monitor the rate of occurrences of events based on their intensity, this technique is appropriate for implementation in control limits based algorithms. the paper also gives a review of a possible optimization method. the validation check of results will be performed on authentic network samples. keywords: intrusion detection, ewma, control limits, optimization, autocorrelation 1 introduction the exponentially weighted moving average is a statistic for monitoring the process that averages the data in a way that gives less and less weight to data as they are further removed in time. for the ewma control technique, the decision regarding the state of control of the process depends on the ewma statistic, which is an exponentially weighted average of all prior data, including the most recent measurements. by the choice of weighting factor λ , the ewma control procedure can be made sensitive to a small or gradual drift in the process. the statistic that is calculated is the following: ew mat = λyt +(1− λ )ew mat−1 t = 1,2,...,n (1) where • ew ma0 is the mean of historical data (target) • yt is the observation at time t • n is the number of observations to be monitored including ewma0 • 0 < λ ≤ 1 is a constant that determines the depth of memory of the ewma. copyright c⃝ 2006-2010 by ccc publications ewma algorithm in network practice 161 this equation has been established by roberts as described in [4]. the parameter λ determines the rate at which "older" data enter into the calculation of the ewma statistic. a value of λ = 1 implies that only the most recent measurement influences the ewma. thus, a large value of λ = 1 gives more weight to recent data and less weight to older data a small value of λ gives more weight to older data. the value of λ is usually set between 0.2 and 0.3 [2] although this choice is somewhat arbitrary. lucas and saccucci [3] have shown that although the smoothing factor λ used in an ewma chart is usually recommended to be in the interval between 0.05 to 0.25, in practice the optimally designed smoothing factor depends not only on the given size of the mean shift δ , but also on a given in-control average run length (arl). the estimated variance of the ewma statistic is approximately: σ 2ew ma = λ 2− λ · σ 2 (2) where σ is the standard deviation calculated from the historical data. the center line for the control chart is the target value or ew ma0. the upper and lower control limits are: ucl = ew ma0 + kσew ma (3) lcl = ew ma0 − kσew ma where the factor k is either set equal 3 (the 3-sigma control limits) or chosen using the lucas and saccucci tables (arl = 370). in addition to the aforementioned authors, the publications [6]-[13] have also dealt with the topic of ewma statistics and statistical anomaly detection in computer networks. control charts are specialized time series plots, which assist in determining whether a process is in statistical control. some of the most widely used forms of control charts are x-r charts and individuals charts. these are frequently referred to as "shewhart" charts after the control charting pioneer walter shewhart who introduced such techniques. these charts are sensitive to detecting relatively large shifts in the process (i.e. of the order of 1.5σ or above). in computer network practice, shifts can be caused by intrusion or attack, for example. two types of charts are primarily used to detect smaller shifts (less than 1.5σ ), namely cumulative sum (or cusum) charts and ewma charts. a cusum chart plots the cumulative sums of the deviations of each sample value from a target value. an alternative technique to detect small shifts is to use the ewma methodology. this type of chart has some very attractive properties, in particular: 1. unlike x-r and individuals charts, all of the data collected over time may be used to determine the control status of a process. 2. like the cusum, the ewma utilizes all previous observations, but the weight attached to data exponentially decreases as the observations become older and older. 3. the ewma is often superior to the cusum charting technique due to the fact that it better detects larger shifts. 4. ewma schemes may be applied for monitoring standard deviations in addition to the process mean. 5. ewma schemes can be used to forecast values of a process mean. 6. the ewma methodology is not sensitive to normality assumptions. 162 p. čisar, s. bošnjak, s. maravić čisar in real situations, the exact value of the shift size is often unknown and can only be reasonably assumed to vary within a certain range. such a range of shifts deteriorates the performance of existing control charts. one of the algorithms for determining the maximal shift in normal traffic is described in [17]. the paper describes the process of application of ewma algorithm for one major user, given as an example. it can be shown that the obtained results are valid for the other analyzed users as well. this research uses samples of authentic network traffic (i.e. traffic intensity in a unit of time). traffic analysis is realized in the form of statistical calculations on samples which derives from traffic curve. from the appropriate pattern of internet traffic, 35 samples of local maximums are taken in order to ensure that the statistical analysis is performed on a large sample (number of samples n > 30), thus supporting and leading to general conclusions. the aim of this research is to determine those allowed ewma values of traffic, that when they are exceeded, it will be considered as the appearance of a statistical anomaly suspected to attack. in this sense, the choice of only local maximums for analysis can be accepted as logical, because the critical point of maximum value of aggregate traffic is in this way also included. the proposed method of calculating the overall optimal value λ is applied to traffic patterns, on the basis of which the lower and upper control limits of traffic are determined. for statistical detection of an attack the primary interest is the appearance of a situation in which the upper control limit is exceeded. the overstepping of the lower control limit can be understood as a statistical anomaly, but in the case of this research, it is only related to the local maximum (and not to the aggregate network traffic) and as such does not endanger the security of the computer network in general. therefore, the situation in which the value of network traffic falls below some lower limit is not considered to be a suspicious event or attack, because the initial presumption of this research is the increase of traffic during an external attack. for the observed pattern of traffic, ewma values are calculated and if these values are outside of the control limits, that situation is interpreted as a statistical anomaly. emphasis in this work is placed on determining the occurrence of false alarms, as an important security feature of applied algorithm. 2 optimized exponential smoothing calculating the optimal value of parameter λ is based on the study of authentic samples of network traffic. random variations of network traffic are normal phenomena in the observed sample. in order to decrease or eliminate the influence of individual random variations of network traffic on occurrence of false alarms, the procedure of exponential smoothing is applied, as an aspect of data preprocessing. for any time period t, the smoothed value st is determined by computing: st = λ yt−1 +(1− λ )st−1 where 0 < λ ≤ 1 and t ≥ 3 (4) this is the basic equation of exponential smoothing. the formulation here is given by hunter [2]. this smoothing scheme begins by setting s2 to y1, where si stands for smoothed observation or ewma, and yi stands for the original observation. the subscripts refer to the time periods 1, 2, ..., n. for example, the third period is s3 = λ y2 +(1− λ )s2 and so on. there is no s1. the optimal value for λ is the value which results in the smallest mean of the squared errors (mse). the initial ewma plays an important role in computing all the subsequent ewma’s. there are several approaches to define this value: 1. setting s2 to y1 2. setting s2 to the target of the process 3. setting s2 to average of the first four or five observations ewma algorithm in network practice 163 it can also be shown that the smaller the value of λ , the more important is the selection of the initial ewma. the user would be well-advised to try several methods before finalizing the settings. for different input values of initial parameter s2, an application in "matlab" is created which calculates and plots the dependence of sse and partial value of λ in range of 0 ÷1, with adjustable step. in addition, the optimal value λopt is also calculated. for the optimal value, in accordance with the smoothing scheme, that particular value is taken for which the sse is minimal. the following figure shows an example for calculating the optimal value of the parameter λ for a specific s2. figure 1: calculation λopt (sse) due to the lack of an exact method of calculation in available publications about the determination of the initial s2 in the procedure of exponential smoothing, the authors of this paper have dealt with researching the link between selection of s2 = y1 and λopt , i.e. s2(λopt). in that sense, the range of s2 is determined using the lowest to the highest sample value during the period of observation. this research was conducted on an authentic sample of network traffic of an internet service provider and the segment of observation was the range of values of local maximums (in this concrete case from 8 to 34 mb/s), with a large enough number of values n = 33 > 30, taking into account the generality of conclusions. the period of observation was one month. the next table (table 1) shows the numerical and graphical dependence s2(λopt). since a set of different results has been obtained for partial values of λopt , the authors suggest for the overall optimal parameter λopt to accept the average of all the partial results (in this particular case it is 0.75). 3 autocorrelation autocorrelation or serial correlation of time series means that the value of the observed variable in a time unit depends on values which appear prior to or later in series. in practical situations, autocorrelation of the first order is usually examined, which may be shown by a simple correlation coefficient or so-called autocorrelation coefficient. let rt be the time series data, where t = 1,2,...,t , then the autocorrelation coefficient of the first order is given by: 164 p. čisar, s. bošnjak, s. maravić čisar s2 λo pt 1 8 0.72 2 9 0.72 3 10 0.72 4 11 0.72 5 12 0.71 6 13 0.71 7 14 0.72 8 15 0.72 9 16 0.72 10 17 0.72 11 18 0.72 12 18.5 0.73 13 19 0.73 14 19.5 0.73 15 20 0.73 16 20.5 0.73 17 21 0.74 18 21.5 0.74 19 22 0.74 20 22.5 0.75 21 23 0.75 22 23.5 0.75 23 24 0.75 24 25 0.76 25 26 0.77 26 27 0.77 27 28 0.78 28 29 0.79 29 30 0.8 30 31 0.8 31 32 0.81 32 33 0.82 33 34 0.82 table 1: calculation of s2(λopt) ρ(r) = t∑ t=2 rt rt−1√√√√ t∑ t=2 r2t t∑ t=2 r2t−1 -1 ≤ ρ ≤ 1 (5) one of the standard features of traffic time series is that increasing rates of traffic rt are not mutually significantly autocorrelated, ie. the value of autocorrelation coefficient is near zero. at the same time, this means that the distribution of positive and negative values of increasing rates is random and that does not follow a specific systematic regularity. positive autocorrelation implicates that the positive values are followed by mainly positive values and negative values by negative ones and then is ρ ≈ +1. in the case of negative autocorrelation, there is often a change of sign, i.e. the positive rate in most cases leads to a negative rate and vice versa and then is ρ ≈ −1. since there is no typical scheme, on the basis of positive rate in particular time period there is now way of concluding (it can not be concluded) with a significant probability that in the next period either a growth or decline will appear. the same applies to the situation for the negative rate. researchers in [5] dealt with the influence of autocorrelated and uncorrelated data on the behavior of intrusion detection algorithm. in their work they came to conclusion that ewma algorithm for autocorrelated and uncorrelated data works well in the sense of intrusion detection in some information systems. the advantage of ewma technique for uncorrelated data is that this technique (as opposed to the case of autocorrelated data) can detect not only rapid changes in the intensity of events, but also small changes in the mean value realized through the gradual increase or decrease of the intensity of events. however, in ewma for uncorrelated data, the initial value of smoothed intensity events is to be reset after intrusion detection, in order to avoid the impact of current values of parameters on future results (carry-over effect). in case of ewma for autocorrelated data this reset is not necessary, because ewma automatically adjusts the upper and lower control limits. generally, the smoothing constant should not be too small, so that a short-term trend in the intensity of events in the recent past can be detected.other ewma algorithm in network practice 165 publications have also shown the need for taking into account the autocorrelation of input data. as it is emphasized in [18], in the case of dynamic systems the autocorrelation in variables is taking into account incorporating time lags of the time series during the modeling stage. samples of network traffic were obtained by network software "mrtg" (multi router traffic grapher) version 2.10.15. this software generates three types of graphs: • daily with calculation of 5-minute average • weekly with calculation of 30-minute average • monthly with calculation of 2-hour average the graphs also enable numerical information on the maximum and average traffic for the appropriate period of time. daily, weekly and monthly graphs of the first measurement will be used for calculation of initial historical data, while the application of ewma statistics, with aim of checking the validity of certain parameters, will be realized on daily, weekly and monthly traffic graphs of the second measurement. for the application of exponential smoothing method to network traffic it is necessary first to determine the historical values: ew ma0 and standard deviation σ0. for this purpose, it is necessary to collect appropriate traffic samples to perform adequate calculations. this study will use a total of 105 samples of local maximum: 35 samples from the daily traffic graph, 35 samples from the weekly traffic graph and 35 samples from the monthly traffic graph (table 2). time yt(daily) yt(weekly) yt(monthly) 1 12 21 23 2 10.5 22.5 30 3 8.5 23 27 4 10.5 20 27 5 18 20.5 25 6 22 23.5 27 7 25.5 24 22 8 20 21 24 9 33.9 23 23 10 25 25 20 11 24 25.5 24.5 12 26.5 24.5 26.5 13 27.5 22 28 14 23 25.5 27 15 25 27 23 16 24 28 22.5 17 23 27 26.5 18 23 28 31 19 22 25.5 22.5 20 23 30 22.5 21 23 29 27 22 23 26.5 25 23 23 29 26 24 16 26.5 28 25 16 27.5 21 26 9 26 24 27 11.5 25 22 28 8.5 24 22 29 8.5 23.5 22 30 14 22 23 31 23 22.5 27 32 23 24 29 33 20 24 25 34 23 25 25 35 23 23 22 table 2: network samples on the basis of the data presented in the table the following can be calculated: ew ma0 = 23.10 and σ0 = 4.87. in accordance with the method described above and to justify the usage of ewma statistics, it is important to determine the statistical independence of samples, which will be examined by checking the existence of correlation between data. for this purpose, pearson’s correlation coefficient will be used, which is supplied as a ratio of covariances of two variables and the product of their standard deviations: 166 p. čisar, s. bošnjak, s. maravić čisar ρxy = cov(x,y ) σxσy −1 ≤ ρxy ≤ 1 (6) other authors proposed different interpretation ways of correlation coefficient. cohen [1] noted that all the criteria are based on the greater or lesser extent of arbitrariness and should not be kept too strictly. yet, one often used interpretation of these coefficients is given below, as described in [16]: • ρ between 0 and 0.2 no correlation or is insignificant • ρ between 0.2 and 0.4 low correlation • ρ between 0.4 and 0.6 moderate correlation • ρ between 0.6 and 0.8 significant correlation • ρ between 0.8 and 1 high correlation the value of correlation coefficient ρxy can be calculated using the statistical function correl (array1, array2) in ms excel. when examining the table above, it is possible to identify three series of data (daily, weekly and monthly) and in this sense three different correlation coefficients can be calculated: • correlation coefficient for daily weekly series: ρ1 = 0.28 → low correlation • correlation coefficient for daily monthly series: ρ2 = 0.04 • correlation coefficient for weekly monthly series: ρ3 = −0.04 beside testing the correlation coefficient within a single measurement, it is important to check the existence of correlation between corresponding periods from different measurements. for that purpose, values of correlation coefficient of two daily (weekly, monthly) intervals are checked and the following results are obtained: • correlation coefficient for daily daily series: ρ4 = −0.15 • correlation coefficient for weekly weekly series: ρ5 = 0.11 • correlation coefficient for monthly monthly series: ρ6 = −0.02 as all calculated coefficients are with low degree of correlation, or without it, it can be concluded that the used data are statistically independent and that the application of ewma statistics is justified. 4 network practice values ew ma0 and σ0 are calculated for the period of one month. it can be reasonably assumed that these values in another monthly period of observation would be different, having in mind the various unpredictable traffic situations and accidental nature of network traffic as a process. therefore, in the further phase of research, the extent of change of these values will be studied. the determination of maximum changes of characteristic traffic values (maximum and average) is based on the analysis of numerical data of several larger internet users that derives from the popular network software mrtg, which is related to the period of one day, week and month. without the loss of generality, the graphical presentation of curves from three users is given below, noting that the observed traffic curves of other users do not deviate significantly from the forms shown here. ewma algorithm in network practice 167 figure 2: traffic curves of different users daily1 daily2 diff. weekly1 weekly2 diff. monthly1 monthly2 diff. [mb/s] [mb/s] [%]) [mb/s] [mb/s] [%] [mb/s] [mb/s] [%] user 1 max 33.9 33.1 -2.4 29.7 33.4 12.4 9.7 9.8 1 average 16.5 19.1 15.8 17.0 21.1 24.1 6.01 6.6 9.8 user 2 max 3.94 3.63 -7.8 3.98 3.68 -7.5 48.2 49.2 2 average 2.35 2.09 -11 2.28 2.09 -8.3 30.9 30 -3 user 3 max 9.31 10.0 7.4 9.71 9.99 2.9 9.9 9.7 -2 average 5.71 6.01 5.2 5.63 6.64 17.9 5.4 4.9 -9.2 user 4 max 9.69 9.99 3.1 10.0 9.91 -0.9 10 10 0 average 4.96 5.14 3.6 5.2 4.94 -5 7.4 7.6 2.7 user 5 max 48.2 46.3 -3.9 48.5 45.2 -6.8 1.8 1.8 0 average 29 24.4 -15.9 30.4 26.4 -13.1 0.14 0.14 0 user 6 max 10.1 10.1 0 10.0 10.0 0 3.94 3.66 -7.1 average 7.78 7.95 2.2 7.43 8.14 9.6 1.9 2.03 6.8 user 7 max 3.98 3.97 -0.02 3.94 3.99 1.2 3.9 3.9 0 average 1.74 1.79 2.9 1.88 1.99 5.9 1.9 2 5.2 table 3: differences in characteristic values of traffic the maximum and average values of traffic are calculated twice in the period of a month. the results are arranged in the following table: by analysis of the numerical data it can be concluded that the maximum change of mean value p is not greater than 25%. namely, that is the largest deviation of size in both measurements. since the main idea of this research is to find out the maximum and minimum tolerant ewma values of local maxima, in further calculations the following values will be used (for the accepted p = 0.25): ew ma0max = (1+ p)ew ma0 ≈ 1.25ew ma0 ew ma0min = (1− p)ew ma0 ≈ 0.75ew ma0 similarly, σ0max ≈ 1.25σ0 and σ 2ew ma = (λ /(2− λ ))σ 2 0max. previously calculated values of parameters ew ma0 and λopt , as well as the values of ucl and lcl from the first measurement will be verified in different situations of daily, weekly and monthly traffic from the second measurement. ucl value is given by ew ma0max + kσew ma, while lcl is ew ma0min − kσew ma. the following results are obtained: it is important to notice that according to the previous figure in case of appropriately determined ewma parameters, there is no situation of threshold exceeding, which eliminates the appearance of false alarms. 168 p. čisar, s. bošnjak, s. maravić čisar figure 3: verification (daily traffic, second measurement) figure 4: verification (weekly traffic, second measurement) 5 conclusions the aim of this research was to examine the possibility of applying ewma statistics in intrusion detection in network traffic. the research has shown that direct application of this algorithm on computer network traffic, as applied in industrial processes, does not provide acceptable results. namely, often proposed values for exponential smoothing factor in case of network application of the algorithm, may in some circumstances lead to the creation of false alarms, thus endangering the security level of system. due to the lack of an acceptable precise method for determination of initial value of the coefficient in exponential smoothing procedure in available publications, this research has been directed towards establishing a relation between the choice of initial ratio and optimal value for smoothing. by creating the appropriate application, the practical way was presented for testing the impact of different values of parameters on the level of anomaly detection. this enabled the establishment of graphical presentation of input depending on output sizes, which all contributed to the creation of proposed method for calculating the optimal value of smoothing factor. before the start of the implementation of statistical analysis of traffic, the extent of autocorrelation between the used data has to be examined, by calculating the correlation coefficients. one of the imporewma algorithm in network practice 169 figure 5: verification (monthly traffic, second measurement) tant results is that it is shown that analysis of properties of network traffic based on individual patterns of daily traffic only is not recommended, because of the increased level of autocorrelation. for this reason, when calculating the historical parameters, network traffic must be viewed in a wider context of time, taking into account the weekly and monthly periods. using the network monitoring software, it is also necessary to determine the maximum variations of basic traffic characteristics (average and maximum). to make this algorithm properly applicable in network environment it is necessary to perform previous processing of historical data, in order to obtain initial values of key parameters. based on the proof lent by the obtained results it can be concluded that the choice of ewma parameters significantly affects the operation of this algorithm in network environment. therefore, the optimization process of parameters before the application of the algorithm is of particular importance. bibliography [1] j. cohen, statistical power analysis for the behavioral sciences (2nd ed.), lawrence erlbaum associates, hillsdale, new jersey, 1998. [2] j.s. hunter, the exponentially weighted moving average, journal of quality technology 18: 203210, 1986. [3] j.m. lucas, m.s. saccucci, exponentially weighted moving average control schemes: properties and enhancements, technometrics 32, 1-29., 1990. [4] s.w. roberts, control chart tests based on geometric moving averages, technometrics, 1959. [5] ye et al., computer intrusion detection through ewma for autocorrelated and uncorrelated data, ieee transactions on reliability vol. 52, no. 1, 2003. [6] g. fengmin, deciphering detection techniques: part ii anomaly-based intrusion detection, white paper, mcafee security, 2003 [7] s. sorensen, competitive overview of statistical anomaly detection, white paper, juniper networks, 2004. [8] v. a. mahadik, x. wu and d. s. reeves, detection of denial-of-qos attacks based on χ2 statistic and ewma control charts, http://arqos.csc.ncsu.edu/papers/2002-02-usenixsec-diffservattack.pdf 170 p. čisar, s. bošnjak, s. maravić čisar [9] a. s. neubauer, the ewma control chart: properties and comparison with other quality-control procedures by computer simulation, clinical chemistry, http://www.clinchem.org/cgi/content/full/43/4/594 [10] d. seibold, enterprise campus security-addressing the imploding perimeter, http://www.itsa.ufl.edu/2003/presentations/intsec.ppt [11] a. vasilios, s. and f. papagalou, application of anomaly detection algorithms for detecting syn flooding attacks, http://www.ist-scampi.org/publications/papers/siris-globecom2004.pdf [12] j. viinikka and h. debar, monitoring ids background noise using ewma control charts and alert information, http://viinikka.info/viideb2004.pdf [13] y. zhao, f. tsung and z. wang, dual cusum control schemes for detecting a range of mean shifts, ieee transactions, http://qlab.ieem.ust.hk/qlab/download/papers/paper%2035.pdf, 2005 [14] engineering statistics handbook-ewma control charts, http://www.itl.nist.gov/div898/handbook/pmc/section3/pmc324.htm [15] engineering statistics handbook−single exponential smoothing, http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc431.htm [16] savannah state university, office of institutional research & planning, http://irp.savstate.edu/irp/glossary/correlation.html [17] p. čisar, s. maravić čisar, a first derivate based algorithm for anomaly detection, international journal of computers, communications & control, 3(s):238-242, 2008 [18] j. mina, c. verde, fault detection for large scale systems using dynamic principal components analysis with adaptation, international journal of computers, communications & control, 2(2):185194, 2007.. petar čisar was born on september 08, 1965. he graduated at the faculty of electrical engineering in belgrade. master’s study completed in information engineering at the faculty of economics in subotica. currently works on phd thesis. the spheres of his interest are mobile technologies, as well as the development of security methods in network environments. saša bošnjak was born on december 31, 1961. he is a associate professor of computer science at the faculty of economics subotica. he holds a range of courses in information engineering. his research interests are database, software development, computer networks, reuse methodology, ebusiness and internet technology. he earned a phd degree in information system faculty of economics subotica in 1995. sanja maravić čisar was born on august 03, 1970. she graduated at the faculty of electrical engineering in belgrade. master’s study completed at the technical faculty in zrenjanin. she works as lecturer at subotica tech at following courses: visual programming, object-oriented based programming, java and multimedia systems. currently works on phd thesis. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 819-824 adaptive web applications for citizens’ education. case study: teaching children the value of electrical energy. i. moisil, s. dzitac, l. popper, a. pitic ioana moisil, alina pitic lucian blaga univeristy of sibiu, romania e-mail: im25sibiu@gmail.com simona dzitac, laurentiu popper university of oradea, romania e-mail: simona.dzitac@gmail.com, director@perfect-service.ro abstract: “the foundation of every state is the education of its youth.” diogenes laertius long-term energy saving and reduction of environmental consequences of energy consuming are among the most challenging objectives of our time. people are prone to routine and habit. to change these habits is almost a sisif’s work. in spite of continuous efforts from environmental specialists, we are witnessing an increase in electricity (and gas) consumption, at least at the level of households. studies carried on have shown that consumers have in many cases an irrational behaviour. to correct that, researchers are studying consumers’ decision making behaviour and try several intervention measures. in our paper we are presenting the design and development of a web based adaptive system aimed to educate citizens for an electrical energy saving behaviour. the system is composed of three subsystems: adaptation system, user’s profile and knowledge base. we have used a user-centered design approach. for adults, users’ profiles are build taking into account age group, educational level, gender, income, professional aspects, consuming behaviour. a set of questionnaires have been designed in order to collect users’ data. for children, the standard profiles are more complicated and, in function of the age group, can be obtained off line through interviews or/and through online activities (games, quizzes etc.). the knowledge base is build for the electrical energy domain. the adaptation sub-system will present information to the user based on s/he profile. the system is populated with data for users of 6 to 10 years of age. for this users group a social and affective interaction design approach was used. keywords: adaptive web, electrical energy saving, citizens education, interaction design, user-centred design. 1 introduction the european union (eu) has established that reducing energy consumption and eliminating energy wastage are among the main goals to be achieved in the near future. “at the end of 2006, the eu pledged to cut its annual consumption of primary energy by 20% by 2020. to achieve this goal, it is working to mobilise public opinion, decision-makers and market operators and to set minimum energy efficiency standards and rules on labelling for products, services and infrastructure”. [1] it seems more than natural, in the information society, to use the web as a mean to make people aware of the energy saving problem. in fact there are many web sites informing about the ways of reducing energy consumption [1–6]. copyright c⃝ 2006-2010 by ccc publications 820 i. moisil, s. dzitac, l. popper, a. pitic analysing the content of these web presentations we have found out that though very informative and scientifically correct, they are not always answering to some users’ expectations, being too serious, and too boring. this was the opinion of 56% of 200 users of age 6 to 22 investigated in 2009. it is clear that different actors that have a word to say in the process of reducing energy consumption must be approached in different ways. it is also important to note that it is a problem of changing the behaviour, the mentality and this is a very sensitive process. having this in mind we have tried to design and develop of a web based adaptive system aimed to educate citizens for an electrical energy saving behaviour. the system is composed of three subsystems: adaptation system, user’s profile or model and knowledge base (domain model). we have used a user-centered design approach. for adults, users’ profiles are built taking into account age group, educational level, gender, income, professional aspects, consuming behaviour. a set of questionnaires have been designed in order to collect users’ data. for children, the standard profiles are more complicated and, in function of the age group, can be obtained off line through interviews or/and through online activities (games, quizzes etc.). the knowledge base is build for the electrical energy domain. the adaptation sub-system will present information to the user based on s/he profile. at present the system is populated with data for users of 6 to 10 years of age. 2 design considerations in our paper we are presenting a first case study with the educational software for electrical energy saving. the case study is considering users of 6 to 10 years of age. for this category of users we had use a hybrid user-centred design methodology, blending different kinds of usercentred designs with interaction (social interaction, affective interaction) and participatory design and taking into account learning objectives and learners age and preferences. the design research phase had two steps. firstly we identified the need of computerbased educational products, the interest of the users towards computers, the level of satisfaction concerning other computer-based educational products, what characteristics of human-computer interaction they prefer (sounds, colours, mediating agents) and also the general level of computer literacy. we have also investigated users’ behaviour towards the educational/formative software market (are they buying educational software, if so, on what subjects, who is the buyer, how often, etc.). the second step aimed to investigate users’ behaviour towards electrical energy consumption aspects (how interested they are, did they know what is electricity, how they perceive other educational software on this topic, are their parents concerned by energy saving, etc). 130 young pupils from both rural and urban locations participated in the design. 63% were girls and 37% boys, the age interval being from 6 to 10 years old. 84% had a computer at home. 82% have internet connexion. • children in the first and forth grade (6 years old and 10 years old) are at the beginning of an educational cycle and therefore more exposed to change, with distributed interests and less interested than the others (50% are satisfied by the use of computers in class). • 75% of the subjects that have a computer at home are very interested in using an educational software, but only 40% of those that do not have a computer at home showed a maximum interest. • 86% of the subjects prefer computer games and 80% like also cartoons, movies, etc.; 66% of the girls choose stories and only 43% of the boys; attraction to music is growing with the age, from 60% at the age of 6 to 85% at the age of 10. • most parents are restricting children access to internet. • interest to something new is decreasing with age, from 93% at the age of 7 to 33% at the age of 11. adaptive web applications for citizens’ education. case study: teaching children the value of electrical energy. 821 • the influence of the family environment is very important. • 27% of the subjects voted for a software based mediator agent. • 94% of the subjects prefer software with sound incorporated. • for 57% of the subjects software products are provided by parents, 27% are buying themselves these products. • 87% of the children do not know the most important sources of electrical energy (“electricity is coming from the cable”). • 73% of the subjects appreciate the idea of quizzes on how to save electrical energy. • children with intellectual parents are more aware of the aspects concerning energy saving especially linked to global warming. children from rural area are more concerned by the cost of energy than by global warming. some of them are thinking that this is not their problem. • 76% of the subjects are attracted by computers. based on these results it has been decided how to design the software interface and how to structure the content. the software has to use the children’ preferred colours, to have sound facilities, to enable the use of the mouse but also of the keyboard, the concepts introduced have to be transparent, the children being able to navigate freely through the software, games have to be included in order to make more attractive the product and less boring, if the case, and the mediator has to be included in the software. all these data have been obtained during working meetings with the children. for example, we have discussed the video-clip energy, let’s save it! [1]. the video-clip has been found attractive by children 8 to 10 years of age. for the others it was not interesting. one observation was that “it goes too quickly”. in fact the video clip is presenting in less than 4 minutes a global view of energy waste in the day by day activities. the presentation is user-friendly, in an attractive way and it raises worries, but no more. another observation, concerning a different web application was that “it sounds like papa!” (citing: “don’t leave lights on when no one is in the room. if you are going to be out of the room for more than five minutes, turn off the light.” [2]). so it was very clear that the site must be designed taking into account the children preferences. as a result we have structured the web application in four main chapters: 1. what is electricity a. short history b. experiments c. sources of electrical energy i. classical ii. alternative 2. using and saving electrical energy 3. games, activities, quizzes, puzzles, enigmas 4. resources for teachers, parents and educators the system architecture is composed of three subsystems: adaptation system, user’s profile or model and knowledge base (domain model). the domain model for electrical energy is a set of concepts, each concept consisting of a set of topics. topics are linked to each other thus forming a semantic network. a topic is presented usually on a web page. a page is divided in sub-pages (chunks) that will differ in function of the user type [7–9]. the adaptation module is providing different interfaces and contents in function of the user profile (model). for the moment each page has implemented two variants: user in the age group 6 to 10, and children older than 10 years. we have used plants and trees images ant the beginning because these were interesting for children of all ages (fig.2) 822 i. moisil, s. dzitac, l. popper, a. pitic figure 1: system architecture. figure 2: and 2b . accessing the web application the user is identified by a user name and a password. this is in fact the link to the user profile. the user can freely navigate in the site but also s/he can choose a certain topic looking at the icons on the screen. for example in fig.4 there several icons (buttons) giving access to information on electrical energy sources. each button is also explained by voice. the children can experience the effect of using too much light (fig.5 and 6). adaptive web applications for citizens’ education. case study: teaching children the value of electrical energy. 823 figure 3: access to the web application. figure 4: buttons giving access to information on electrical energy sources. figure 5: all electrical bulbs are off (the tree has all leaves) figure 6: four electrical bulbs are on (the tree has lost most of the leaves) the client is implemented as web pages. there is a number of linked frames. we have used flash, php, mysql and javascript language that made possible to overcome html limitations. evaluation the current interface has been evaluated from the point of view of presentation aspects and functionalities by the children participating in the design and also by other 34 children (6 to 11 years of age). 92% were very satisfied with the web application. 7% would have liked more games and 1% declared that he is not interested in the subject. two teachers and two parents participated also in the evaluation process. the first appreciation was positive. 3 conclusion and further research in this paper we have described the design and development of an adaptive webbased system for citizens’ education in respect to electrical energy consumption reduction. the system is a complex one. the core system is implemented and also several modules. we had presented a first case study with the educational software for electrical energy saving. the case study is considering users of 6 to 10 years of age. for this category of users we had use a hybrid user-centred design methodology, blending different kinds of user-centred designs with interaction (social interaction, affective interaction) and participatory design and taking into 824 i. moisil, s. dzitac, l. popper, a. pitic account learning objectives and learners age and preferences. the results of the first evaluations were positive. future research will continue to develop the system with other users’ profiles (other age category, in the first stage) and to evaluate the educational effectiveness of the system’s adaptation. bibliography [1] http://ec.europa.eu/energy/efficiency/index_en.htm. [2] http://www.energyquest.ca.gov/saving_energy/index.html. [3] http://tonto.eia.doe.gov/kids/energy.cfm?page=3. [4] http://www.kyotoinhome.info/. [5] www.eais.info. [6] thttp://www.oppapers.com/subjects/energy-saving-page1.html. [7] ayersman, d.j. & minden, a.v. (1995). individual differences, computers, and instruction. computers in human behavior, 11(3-4), 371-390. [8] brusilovsky, p.(1996). methods and techniques of adaptive hypermedia. user modeling and user-adapted interaction. 6, 87-129. [9] jonassen, d. & wang, s. (1993). acquiring structural knowledge from semantically structured hypertext. journal of computer-based instruction, 20(1), 1-8. [10] liu, y., ginther, d. (1999). cognitive styles and distance education. on-line journal of distance learning administration, 2,3. [11] c.y. huang, t.t. yang, w.l chen, s. y. nof, reference architecture for collaborative design, int. j. of computers, communications & control, issn 18419836, e-issn 18419844 vol. v (2010), no. 1, pp. 71-90. [12] antonios andreatos, virtual communities and their importance for informal learning international journal of computers, communications & control, vol. ii (2007), no. 1, pp. 39-47. international journal of computers, communications & control vol. i (2006), no. 4, pp. 53-60 an iterative method for the design process of mode handling model nadia hamani, nathalie dangoumau, etienne craye abstract: this paper focuses on formal verification and validation of a model dedicated to mode handling of flexible manufacturing systems. the model is specified using the synchronous formalism safe state machines. a structured framework for the design process is presented. the obtained model is characterized by a strong hierarchy and concurrency that is why within the design process an iterative approach for specification, verification and validation is propose in order to improve this process. the main properties being verified are presented and the approach is illustrated through an example of a manufacturing production cell. keywords: flexible manufacturing systems, supervision, mode handling, functional and behavioral modeling, verification, validation. 1 introduction according to our design approach of fault tolerant control systems, mode handling is a function of supervision. in view of a disturbance (failures, breakdowns) mode handling allows implementing the decisions about mode and configuration changing. the design of mode handling function needs to provide a model representing the operating modes of the production system and its subsystems. to this aim, it is important to use an adequate modeling method and powerful specification formalisms. those characterizing the most significant approaches are compared in [9]. we proposed in our early work a modeling approach for reactive mode handling of flexible manufacturing systems (fms) [10]. this approach is based on a functional modeling [11] and a synchronous reactive approach using safe state machines (ssm) [1] [2]. due to increasing complexity and flexibility of fms, some problems characterize mode changing if coherence and safety constraints are not taken into account in the specification / modeling stages. so it is necessary to verify and validate the proposed models at the early stages of the design process. a greater interest has been allocated for few years to formal verification methods, which guarantee that for all possible evolutions of a model, several properties are satisfied. for our modeling approach, one the interests of using ssm formalism relies on its strong semantics leading to the possibility of using analysis tools in order to formally prove that the behavior of the system respects some properties. the purpose of this paper is to present the iterative approach used in the design process of the model dedicated to mode handling. the paper is organized as follows. in section 2 the basic concepts of v&v are reminded and the design process based on formal methods is introduced. the stages of this design process including v&v are detailed in section 3 and section 4. the iterative approach is introduced and the main properties being verified within the design process are presented. finally the approach is illustrated using an example of a flexible manufacturing cell. 2 the design process a design process should involve many intermediary stages of v&v. this enables to detect and eliminate as soon as possible the specification or modeling errors. the modifications are thus carried out with a lower cost in particular in the final step. indeed, v&v characterize any correct development process. according to [14] verification is the proof that the internal semantics of a model is correct independently of the modeled system whereas validation determines if the model corresponds to the copyright c© 2006 by ccc publications 54 nadia hamani, nathalie dangoumau, etienne craye attempts of the designer formulated in the functional requirements. the required properties are either generic, they depend on the formalism we use, or specific to the modeled system. v&v consists of analyzing the properties that should be satisfied by the final model using some analysis tools. two complementary methods exist, simulation and formal verification. we are interested in our study only in formal methods for v&v. for formal verification, we have to build a formal model representing the behavior of the system and formal specifications of the properties. fig. 1 shows the design process including v&v: the formal specification as well as the structured modeling process that we remind in the next section, allow ensuring a correct development process. v&v of the obtained model is an iterative approach characterized by some corrections, which require reconsidering the specification/modeling stages as needed. based on the verified and validated model the implementation is ensured through automatic code generation. figure 1: the design process 3 specification/modeling the functional requirements provide the informal specifications about mode handling function: the intended behavior of the fms and the properties that should be satisfied. other properties such as determinism are also mandatory. the designer formalizes those informal requirements to provide formal specifications of the model, of the required properties and, if necessary, some assumptions about the environment. the formalization task provides the following models. 3.1 the formal model representing the behavior of the system the specifications of the model dedicated to fms mode handling are detailed in our earlier work [10][11]. we remind in the following the main characteristics of the modeling process. the fms subsystems. in our approach, an operating sequence characterizes each part. the specification method of fms subsystems was described in [11]. the idea is to decompose and identify the functional subsystems, which take part in the realization of its missions. a mission corresponds to a set of operating sequences that the system should produce simultaneously. the fms functional model is obtained by a hierarchical decomposition leading to the elementary machining and transfer operations that the fms performs. the structural aspect completes this specification by associating the resources to the elementary operations they perform. the obtained subsystems are organized in six layers as shown in fig. 2. an iterative method for the design process of mode handling model 55 the functional representation is a graph composed of functional subsystems (called entities) linked by logical relationships (and, or). or nodes correspond either to an inclusive or an exclusive logical operator according to the constraints given in the functional requirements. figure 2: the fms functional model behavior specification. given the fms functional model, one should provide behavioral specifications. it is necessary to represent the activation and deactivation mechanisms at all the hierarchy levels of the specification as well as the availability of the resources and the functions of the fms. the activation/deactivation of an entity is represented by the working mode. the functioning mode represents the availability/unavailability of an entity. the aim is to handle concurrently the information flows downwards to transmit high-level control and reconfiguration orders and upwards to follow up the reports and failures detected by the monitoring function. the reactivity needed for this bi-directional exchange of information, in addition with the characteristics of concurrency and preemption, require a synchronous approach. the behavior of the identified fms subsystems and the logical relationships between them are specified using the synchronous formalism ssm [1][2]. ssm inherits many features from statecharts [12] but offers several forms of preemption and benefits of more strict semantics fully compatible with that of esterel synchronous language [4]. ssm supports also, with a very rigorous semantics, hierarchy, communication, concurrency, and various forms of preemption, which characterize our modeling approach. in addition, ssm takes advantage of an industrial development environment [7], which provides necessary tools for a design process. the behavior of each entity of the model, whatever its level, is represented by a ssm model. this model allows knowing, at any time, if an entity belonging to the current mode of the fms is in normal state e_n, degraded state e_d or out of order state e_out according to the functioning mode point of view; and if it is activated x_active or inactive (in stopping state) e_off according to the working mode point of view. the change-of-state of the entities of the model belonging to successive levels is carried out according to precise rules which depend on the kind of the logical relation and the number of child entities. several cases are studied in [10]. for the representation of the model, we proposed some reference models specified using ssm [10]. these are generic models depending on the number of the child entities and the logical relationships that connect them. the reference models are instantiated for each entity of the model. the proposed method is iterative, it is necessary to specify the models of level i , then we encapsulate the ssm of level i in the ssm of level i+1 until completing the specification of the model (see fig. 3). the encapsulation of the models enables representing the hierarchical levels which characterize the model. the aim is to improve the legibility in the specification/modeling stage. the hierarchy is represented using the encapsulation of successive levels whereas the entities belonging to the same level are separated by dashed lines; these are used to represent concurrency in ssm syntax (see [2]). 56 nadia hamani, nathalie dangoumau, etienne craye figure 3: the modular specification 3.2 the formal specification of properties predictability and dependability are the main characteristics of reactive systems. in our work, we consider only properties of logical behavior such as determinism and safety properties. as we adopt a reactive synchronous approach based on ssm formalism, generic properties of reactivity and determinism should be verified. the rigorous semantics of ssm enable to provide formal verification mechanisms ensuring such properties. several properties specific to mode handling function can be verified. the main properties are presented in section 4. 4 verification and validation in the verification stage (fig. 1), one checks if the model satisfies generic properties. ssm specifications are automatically translated into esterel program which is compiled in a system of boolean equations (logical circuits) an implicit form of finite state machines (fsms). the properties of logical correctness of esterel programs can then be verified on this system. the analysis technique used to this aim is the constructive causality analysis carried out by esterel compiler v5_x. causality analysis is a semantic analysis which allows accepting or rejecting esterel programs according to whether they are constructive or not [5]. the processor which implements the algorithms of constructive analysis builds the reachable state space using symbolic techniques based on binary decision diagrams (bdd). one checks in the validation stage if the model satisfies the specific properties formalized in the specification stage. the analysis techniques used are model-checking and/or theorem proving. several model-checking tools were developed for esterel language. for instance built-in verifier tool an evolution of xeve (xesterel verifier) [6] is currently integrated into esterel studio. xeve takes as input the system of boolean equations and built the reachable state space using bdd (symbolic model-checking [13]) in order to check the statutes of output signals of the model or the observers [8] representing the properties. failing of v&v stage implies that there is a specification or a modeling error. it is then necessary to go back over this model as needed. the formal specifications are may be incorrect. the errors are either in the specifications of: the model, the property, the correspondence between the model and the property or the assumptions related to the environment. if the specification/modeling stages are considered to be correct then we should reconsider informal requirements. indeed, within the formal specification of the model or the properties, errors are may be caused by the ambiguity or the incompleteness of the informal requirements provided in the functional an iterative method for the design process of mode handling model 57 requirements. in this case, these requirements must be improved and formalized again. we propose to complete the modular and hierarchical specification shown in fig. 3 by introducing an iterative method of v&v. this method is presented in the following. the aim is to reduce the specification/modeling errors and to enable their early correction by introducing intermediate stages of v&v into the multi level specification stage. 4.1 an iterative method for v&v we propose at first to check each single reference model used for the specification of the model. the aim is to prove reactivity and determinism. the verification of these reference models is performed using the analysis tools integrated into esterel studio environment. the single verification of the reference models: for each reference model write the model verify the model if the verification fails, correct the specification and repeat the verification as needed. the single reference models checked in this way are instantiated in order to obtain the behavioral models of the fms subsystems. then the integration of these models allows editing progressively the whole model. using the ssm graphical editor, the best way is to follow a bottom up approach based on the encapsulation of ssm of level i in the ssm of level i+1. at each level we should be sure that the intermediary ssm satisfies some properties and the accumulation of errors will be avoided. that is why we propose to associate with this specification an iterative method of v&v. if the v&v does not converge then it is necessary to correct the specification and to repeat the v&v process as needed. indeed, v&v is carried out in an iterative way until the whole model (the final ssm) will be verified and validated. the method described in the following is represented in fig. 4. figure 4: the iterative v&v repeat for all the models of level i (i = 0, n) (1) write the models of level i the specification is carried out by instantiation of the predefined and verified models (2) verify then validate the ssm of level i if the v&v fails, correct the specification and repeat the previous step as needed if i = n go to (4) if not (3) encapsulate the models of level i ’ssm of level i+1 is then created’ i = i + 1 end of the procedure 58 nadia hamani, nathalie dangoumau, etienne craye (4) the ssm represents the fms model (the top level corresponds to the whole model) if the previous steps converge, we conclude that the final ssm is verified and validated. when the final ssm is verified and validated, the code could be generated. the properties being verified within this process are presented in the following. 4.2 the properties the model dedicated to mode handling is translated automatically into esterel language. esterel compiler is composed of several processors for type checking, syntactical and lexical analysis. semantic analysis also called causality analysis allows checking generic properties (deadlock freeness, reactivity, determinism). deadlock freeness. causality analysis makes it possible to prove that the program is causal according to the constructive causality defined by berry [5]. constructive causality of esterel programs ensures deadlock freeness, which characterizes synchronous languages. deadlocks are due to the synchronism assumption (see causality problems in [3]). reactivity and determinism. if an esterel program is constructive then it is reactive and deterministic. a program is said to be reactive if it provides a well-defined solution for each input. it is deterministic if this solution is unique [5]. the aim of specific properties is to ensure coherence and safety constraints of mode handling. we have proposed three kinds of safety properties: mode reachability, the mission uniqueness of a fms and mutual exclusion of incompatible modes. states reachability. we can check the reachability of the states specified by the model (according to the specifications, an output signal is associated with each control state of the model). to this aim, the statutes of output signals that correspond to control states of the model are tested. the mission uniqueness of a fms. this property ensures operation safety of the fms according to the selected mission. indeed, selecting a mission implies performing some operations and activating the resources, which take part in this mission. thus, the operations and the resources, which do not take part in the current mission, should not be activated for safety and coherence reasons. the property is guaranteed by construction of the model. incompatibility of the modes. in order to ensure mode coherence, the proposed specifications guarantee mutual exclusion of incompatible states (each state is belonging to a distinct mode) of the entities of the model. it is about mutual exclusion of working states of the entities related with the logical relation xor (for example redundant resources). the previous properties are verified within the iterative approach of v&v. the intermediate verifications are important because if causality cycles are detected early their correction will be easier. contrary to verification, intermediate validations are may be expensive and some properties are checked only on the final model, in particular the properties related to the uniqueness of the fms mission. in addition to the main properties, other properties of mode handling model can be checked. they deal with the conditions of mode changing and the respect of modes sequence [10]. 5 illustration we have applied our approach to the example of the cell represented in fig. 5. this cell consists of two machines m1, m2 and input/output buffers (i/o). the transport system is composed of two robots r1, r2. we suppose that m1 is loaded by r1 and m2 is loaded by r2, this later will be used to load the an iterative method for the design process of mode handling model 59 two machines when r1 is failing. the machining functions performed by this system are turning and milling. turning (t) is carried out by m1, milling (f) by m1 or m2. the cell has three missions, m1, m2 and m3. the corresponding operating sequences are respectively os1 (t), os2 (f) and then os1 and os2 simultaneously. figure 5: illustration example the obtained model using our modeling method is specified using 4 reference models and 29 instances for 809 lines of esterel code generated from the ssm models. this model needs 34 input signals and 145 output signals. we simulated at first some scenarios of behavior in order to correct the specification errors so that the intended behavior will be in conformity with the functional requirements. the interactive simulator xes [7] is used. the studied cell can be extended with addition of machining resources (simple or polyvalent) and redundancies of the transport system (robots, conveyor). this allows studying the problem with increasing complexity. for the specification, adding or removing reference or instantiated models enable integrating easily the changes on the model thanks to the modularity and the hierarchy of the specification approach. however, due to increasing complexity of the cell some tests performed by the model-checker could not be concluded (out of memory problem). we try to solve this problem in a future work. 6 summary and conclusions this paper deals with v&v within the design process of a model dedicated to mode handling of fms. the first contribution of this study is a structured process for carrying out v&v. it is an extension of our control system design approach in order to take into account v&v stage. an iterative method is used for specification and v&v stages of the design process. the aim is to refine the model construction by introducing intermediate steps of v&v for an early correction of errors. within the proposed framework the required properties (determinism, reachability, mutual exclusion of incompatible modes,..) are given. the analysis tools integrated into esterel studio are used in this process. the proposed approach can be extended to deal with environment constraints in the specification stage. in addition, further work aims at studying some examples of increasing complexity. references [1] c. andré, representation and analysis of reactive behaviors: a synchronous approach, in proc. of the ieee-smc computational engineering in systems applications (cesa 96), lille, france, july, 1996, pp. 19-29. [2] c. andré, semantics of ssm (safe state machines), guyancourt, france, april 2003, available at: http://www.esterel-technologies.com [3] a. benveniste, p. caspi, s. edwards, n. halbwachs, p. le guernic, and r. de simone, the synchronous languages twelve years later, in proc. of the ieee, 91(1), special issue on embedded systems, pp. 64-83, 2003. 60 nadia hamani, nathalie dangoumau, etienne craye [4] g. berry, g. gonthier, the esterel synchronous programming language: design, semantics, implementation, science of computer programming, vol. 19(2), pp. 87-152, 1992. [5] g. berry, the constructive semantics of pure esterel draft version 3, 1999, available at: http://www.esterel-technologies.com [6] a. bouali, xeve an esterel verification environment, in proc. 14th int. conf. on computer aided verification cav’98, lncs, ubc, vancouver, canada, june 1998. [7] esterel studiotm, available at: http://www.esterel-technologies.com. [8] n. halbawachs, f. lagnier and p. raymond, synchronous observers and the verification of reactive systems, in proc. of amast’93, 1993. [9] hamani n, dangoumau n, craye e, a comparative study of mode handling approaches, in proc. of the 35th international conference on computers & industrial engineering (cie 05), istanbul turkey, june 19-22, 2005. [10] n. hamani, a contribution to modeling and verification for mode handling of manufacturing systems, phd dissertation, ecole centrale de lille, france, 2005 [in french]. [11] n. hamani, n. dangoumau, e. craye, functional modeling for mode handling of flexible manufacturing systems, in proc. 12th ifac symposium on information control problems in manufacturing, saint etienne, france, 2006. [12] d. harel, statecharts: a visual approach for complex systems, science of computer programming, vol. 8, nř3, pp. 231-275, 1987. [13] k.l. mcmillan, symbolic model checking, kluwer academic publishers, 1993. [14] j.m. roussel, and j.j. lesage, validation and verification of grafcet using finite state machines, in proc. of imacs-ieee multiconference on computational engineering in systems applications, lille, france, 1996, pp. 758-764. nadia hamani, nathalie dangoumau, etienne craye laboratoire d’automatique, génie informatique et signal (lagis), cnrs umr 8146 ecole centrale de lille, bp. 48, villeneuve d’ascq, f-59650 france e-mail: nadia.hamani@ec-lille.fr received: november 6, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 158-165 heuristic algorithms for solving the generalized vehicle routing problem p.c. pop, c. pop sitar, i. zelina, v. lupşe, c. chira petrică claudiu pop, ioana zelina, vasile lupşe north university of baia mare, department of mathematics and informatics romania, v. babeş , 430083, baia mare e-mail: petrica.pop@ubm.ro, ioanazelina@yahoo.com, vasilelupse@yahoo.co.uk corina pop sitar north university of baia mare, department of economics romania, v. babeş , 430083, baia mare e-mail: sitarcorina@yahoo.com camelia chira “babes-bolyai” university of cluj-napoca romania, m. kogalniceanu, 400084, cluj-napoca e-mail: cchira@cs.ubbcluj.ro abstract: the vehicle routing problem (vrp) is one of the most famous combinatorial optimization problems and has been intensively studied due to the many practical applications in the field of distribution, collection, logistics, etc. we study a generalization of the vrp called the generalized vehicle routing problem (gvrp) where given a partition of the nodes of the graph into node sets we want to find the optimal routes from the given depot to the number of predefined clusters which include exactly one node from each cluster. the purpose of this paper is to present heuristic algorithms to solve this problem approximately. we present constructive algorithms and local search algorithms for solving the generalized vehicle routing problem. keywords: network design, combinatorial optimization, generalized vehicle routing problem, heuristic algorithms. 1 introduction combinatorial optimization is a lively field of applied mathematics, combining techniques from combinatorics, linear programming, and the theory of algorithms, to solve optimization problems over discrete structures. the study of combinatorial optimization owes its existence to the advent of modern digital computer. most currently accepted methods of solution to combinatorial optimization problems would hardly have been taking seriously 30 years ago, for the simple reason that no one could have carried out the computations involved. moreover, the existence of digital computers has also created a multitude of technical problems of a combinatorial character. combinatorial optimization problems can be generalized in a natural way by considering a related problem relative to a given partition of the nodes of the graph into node sets, while the feasibility constraints are expressed in terms of the clusters. in this way, it is introduced the class of generalized combinatorial optimization problems. in the literature one finds generalized problems such as the generalized minimum spanning tree problem [15], the generalized traveling salesman problem, the generalized vehicle routing problem, the generalized (subset) assignment problem, etc. these generalized problems belong to the class of np-complete problems, copyright c⃝ 2006-2011 by ccc publications heuristic algorithms for solving the generalized vehicle routing problem 159 are harder than the classical ones and nowadays are intensively studied due to the interesting properties and applications in the real world, even though many practitioners are reluctant to use them for practical modeling problems because of the complexity of finding optimal or nearoptimal solutions. the generalized vehicle routing problem (gvrp) is an extension of the vehicle routing problem (vrp) and was introduced by ghiani and improta [4]. the gvrp is the problem of designing optimal delivery or collection routes, subject to capacity restrictions, from a given depot to a number of predefined, mutually exclusive and exhaustive node-sets (clusters). the gvrp can be viewed as a particular type of location-routing problem (see, e.g. laporte [7], nagy and salhi [10]) for which several algorithms, mostly heuristics, exist. ghiani and improta [4] showed that the problem can be transformed into a capacitated arc routing problem (carp) and baldacci et al. [1] proved that the reverse transformation is valid. recently, pop [14] provided a new efficient transformation of the gvrp into the classical vehicle routing problem (vrp). in 2003, kara and bektas [5] proposed an integer programming formulation for gvrp with a polynomially increasing number of binary variables and constraints and in 2008 kara and pop [6] presented two integer linear programming formulations for gvrp with o(n2) binary variables and o(n2) constraints, where n is the number of customers which are partitioned into a given number of clusters. as far as we know, the only specific algorithm for solving the gvrp was developed by pop et al. [13] and was based on ant colony optimization. the complexity of obtaining optimum or even near-optimal solutions for the generalized combinatorial optimization problems may lead to the development of: • efficient transformations of the generalized combinatorial optimization problems into classical combinatorial optimization problems [4, 13]; • heuristic and metaheuristic algorithms [11]. the aim of this paper is to describe three classes of heuristic algorithms for solving approximately the generalized vehicle routing problem. 2 definition of the gvrp let g = (v, a) be a directed graph with v = {0, 1, 2, ...., n} as the set of vertices and the set of arcs a = {(i, j) | i, j ∈ v, i ̸= j}. a nonnegative cost cij associated with each arc (i, j) ∈ a. the set of vertices (nodes) is partitioned into k + 1 mutually exclusive nonempty subsets, called clusters, v0, v1, ..., vk (i.e. v = v0 ∪ v1 ∪ ... ∪ vk and vl ∩ vp = ∅ for all l, p ∈ {0, 1, ..., k} and l ̸= p). the cluster v0 has only one vertex 0, which represents the depot, and remaining n nodes belonging to the remaining k clusters represent geographically dispersed customers. each customer has a certain amount of demand and the total demand of each cluster can be satisfied via any of its nodes. there exist m identical vehicles, each with a capacity q. the generalized vehicle routing problem (gvrp) consists in finding the minimum total cost tours of starting and ending at the depot, such that each cluster should be visited by exactly once, the entering and leaving nodes of each cluster is the same and the sum of all the demands of any tour (route) does not exceed the capacity of the vehicle q. an illustrative scheme of the gvrp and a feasible tour is shown in the next figure. 160 p.c. pop, c. pop sitar, i. zelina, v. lupşe, c. chira 2 3 1 0 7 6 4 5 10 9 8 v 1 v 0 v 2 v 3 v 4 d1=3 d2=5 d3=4 d0=0 d10=3 d9=4 d8=2 d7=2 d4=5 d5=4 d6=5 q1=12 q2=9 q0=0 q4=11 q3=5 11 v 5 d11=7 q5=7 m=2 and q=25 figure 1 an example of a feasible solution of the gvrp in figure 1, it is presented a feasible solution consisting of a collection of two tours (routes): 0-3-5-0 and 0-11-7-6-0 satisfying the capacity restrictions and the condition that from each cluster is visited exactly one node. the cost of this feasible solution is obtained summing the costs of the arcs belonging to the selected tours. the gvrp reduces to the classical vehicle routing problem (vrp) when all the clusters are singletons and to the generalized traveling salesman problem (gtsp) when m = 1 and q = ∞. the gvrp is np -hard because it includes the generalized traveling salesman problem as a special case when m = 1 and q = ∞. several real-world situations can be modeled as a gvrp. the post-box collection problem described in laporte et al. [8] becomes an asymmetric gvrp if more than one vehicle is required. furthermore, the gvrp is able to model the distribution of goods by sea to a number of customers situated in an archipelago as in philippines, new zeeland, indonesia, italy, greece and croatia. in this application, a number of potential harbours is selected for every island and a fleet of ships is required to visit exactly one harbour for every island. several applications of the gtsp may be extended naturally to gvrp. 3 heuristic algorithms for solving the gvrp two fundamental goals in computer science are finding algorithms with provably good run times and with provably good or optimal solution quality. a heuristic is an algorithm that abandons one or both of these goals; for example, it usually finds pretty good solutions, but there is no proof the solutions could not get arbitrarily bad; or it usually runs reasonably quickly, but there is no argument that this will always be the case. heuristics are typically used when there is no known method to find an optimal solution, under the given constraints (of time, space etc.) or at all. several families of heuristic algorithms have been proposed for the classical vrp, see for example laporte et al. [9]. these can be classified into two main classes: classical heuristics and metaheuristics. most standard construction and improvement procedures in use belong to the first class. these methods performs a relatively limited exploration of the solution space and generally produce good quality solutions in reasonable computational times. in what it follows we will provide three classes of heuristic algorithms for solving the gvrp: heuristic algorithms for solving the generalized vehicle routing problem 161 • constructive heuristics: nearest neighbour and a clarke-wright based heuristic; • improvement heuristics: string cross (sc), string exchange (se), string relocation (sr) and string mix (sm); • a local-global heuristic. 3.1 constructive heuristics nearest neighbour perhaps the most natural heuristic for the gvrp is the famous nearest neighbour algorithm (nn). in this algorithm the rule is always to go next to the nearest as-yet-unvisited customer subject to the following restrictions: we start from the depot, from each cluster is visited exactly one vertex (customer) and the sum of all the demands of the current tour (route) does not exceed the capacity of the vehicle q. if the sum of all the demands of a current tour (route) exceeds the capacity of the vehicle then we start again from the depot and visit next the nearest customer from an unvisited yet cluster. if all the clusters are visited, then the algorithm terminates. a collection of routes traversing exactly one city from each cluster in the constructed order represents the output of the algorithm. the nearest neighbour algorithm is easy to implement and executes quickly, but it can sometimes miss shorter routes, due to its greedy nature. the running time of the described nearest neighbour algorithm is o(n2). a clarke-wright based heuristic algorithm the clarke and wright [2] savings algorithm is perhaps the most well known heuristic for the vrp. it applies for the problems for which the number of vehicles is a decision variable, and works in the case of directed and undirected problems. the algorithm in the case of the gvrp works as follows: step 1 (savings computation). for each i ∈ vl and j ∈ vp, where l ̸= p and l, p ∈ {1, ..., k} compute the savings: sij = ci0 + c0j − cij. it is obviously that sij ≥ 0 and sij = sji. we order the savings in a nonincresing fashion. at the beginning we create k routes denoted (0, il, 0), l ∈ 1, ..., k as follows for each cluster vl we define c0il = min{c0j | j ∈ vl}. there will be as many routes as the number of clusters and total distance of the routes is: d = c0i1 + c0i2 + ... + c0ik. step 2 (route extension). consider in turn each route (0, i, ..., j, 0). determine the first saving sui or sjv that can feasibly be used to merge the current route with another route ending with (u, 0) or starting with (0, v), for any u ∈ vl and v ∈ vp, where l ̸= p and l, p ∈ {1, ..., k} and vl and vp are clusters not visited by the route (0, i, ..., j, 0). because at a given moment there can exist more feasible route extensions, the priority will have that one that produces the biggest reduction of the total distance of the route. we implement the merge and repeat this operation to the current route. if no feasible merge exists, consider the next route and reapply the same operations. stop when no route merge is feasible. the clarke-wright based algorithm for solving the gvrp is easy to implement and its running time is o(n2 log n). 162 p.c. pop, c. pop sitar, i. zelina, v. lupşe, c. chira 3.2 improvement heuristics the improvement heuristics algorithms for the gvrp are based on simple routes modifications and may operate on each vehicle route taken separately, or on a several routes at a time. in the first case, any improvement heuristic for traveling salesman problem (tsp) can be applied, such as 2-opt, 3-opt, etc. in the second case, procedures that exploit the multi-route structure of the gvrp can be developed. we can see these improvements as a neighbourhood search process, where each route has an associated neighborhood of adjacent routes. the heuristics algorithms for the gvrp that we are going to describe are based on the classification of the van breedam [16] of the improvement operations as string cross, string exchange, string relocation and string mix. a) string cross (sc): two strings of vertices are exchanged by crossing two edges of two different routes. v0 v0 vp vq vi vj vj vi vp vq figure 2 an example of a possible string cross. in the left side are presented the routes before the exchange of the vertices strings and in the right side the routes after the exchange in the above picture there were presented just the clusters of the string of vertices that are exchanged in order to have a clearer figure. it is important to mention that we investigate all the possible connections of the exchanged vertices within the clusters in order to get improved routes, as is shown in figure 2: the nodes belonging to the marked clusters after the exchange may be diferrent. b) string exchange (se): two strings of at most r vertices are exchanged between two routes. v0 v0 vp vq vi vj vq vp vi vj figure 3 an example of a possible string exchange c) string relocation (sr): a string of at most k vertices is moved from one route to another (k = 1 or k = 2). v 0 v 0 vj vq vp vi vj vi vq vp figure 4 an example of a possible string relocation heuristic algorithms for solving the generalized vehicle routing problem 163 d) string mix (sm): consists in selecting the best move between the string exchange and string relocation. 3.3 a local-global heuristic for gvrp the last heuristic algorithm for solving the gvrp that we are going to describe is based on local-global approach and it aims at distinguishing between global connections (connections between clusters) and local connections (connections between nodes from different clusters). as we will see, having a global collection of routes connecting the clusters it is rather easy to find the corresponding best (w.r.t. cost minimization) solution of the gvrp. there are several generalized collection of routes, i.e. routes containing exactly one node from a cluster, corresponding to a global collection of routes. between these generalized collection of routes there exist one called the best generalized collection of routes (w.r.t. cost minimization) that can be determined either by dynamic programming or by solving an linear integer program. the local-global approach was applied succesfully to other generalized combinatorial optimization problems such as: generalized minimum spanning tree problem (gmstp) and generalized traveling salesman problem (gtsp) in order to provide exact exponential time algorithms, strong mixed-integer programming formulations, solution procedures based on these mixedinteger programming formulations and a heuristic algorithm for solving the gmstp, see [?, 12]. let g′ be the graph obtained from g after replacing all nodes of a cluster vi with a supernode representing vi. we will call the graph g′ the global graph. for convenience, we identify vi with the supernode representing it. edges of the graph g′ are defined between each pair of the graph vertices v1, . . . , vk. in the next figure we present the collection of generalized routes corresponding to the a global collection of routes. v 0 v 2 v 1 v 5 v 4 v 3 v 0 v 2 v 1 v 5 v 4 v 3 figure 5 example showing a generalized collection of routes corresponding to a global collection of routes given a global route and a sequence (v0, vk1, ..., vkp) in which the clusters are visited, we want to find the best feasible route r∗ (w.r.t cost minimization), visiting the clusters according to the given sequence. this can be done in polynomial time, by solving the following shortest path problem as we will describe below. we construct a layered network, denoted by ln, having p + 2 layers corresponding to the clusters v0, vk1, ..., vkp and in addition we duplicate the cluster v0, containing the vertex denoted 0 and representing the depot. the layered network contains in addition the extra node denoted by 0′ for each. there is an arc (i, j) for each i ∈ vkl and j ∈ vkl+1 (l = 1, ..., p − 1), having the 164 p.c. pop, c. pop sitar, i. zelina, v. lupşe, c. chira cost cij and an arc (i, h), i, h ∈ vkl , (l = 2, ..., p) having cost cih. moreover, there is an arc (i, 0 ′) for each i ∈ vkp having cost ci0′ . v 0 v v vk vk k v 0 1 2 p ... figure 6 example showing a route in the constructed layered network ln we consider paths from 0 to 0′, that visits exactly one node from each cluster vk1, vk2, ..., vkp , hence it gives a feasible route. conversely, every route visiting the clusters according to the sequence (v0, vk1, ..., vkp) corresponds to a path in the layered network from 0 to 0′. therefore, it follows that the best (w.r.t cost minimization) route r∗ visiting the clusters in a given sequence can be found by determining all the shortest paths from 0 to the corresponding 0′ with the property that visits exactly one node from each of the clusters (vk1, vk2, ..., vkp). the overall time complexity of the above procedure is o(m + log n), where by m we denoted the number of edges and n number of nodes. therefore, given a global collection of routes connecting the clusters we can find efficiently the best corresponding collection of generalized routes. in order to provide global collections of routes we may use any improvement heuristics for the classical vrp. 4 conclusion and future work the generalized vehicle routing problem is an extension of the vehicle routing problem (vrp) and consists in designing optimal delivery or collection routes, subject to capacity restrictions, from a given depot to a number of predefined, mutually exclusive and exhaustive node-sets (clusters). the gvrp is an np -hard problem and finds many interesting real-world applications. the aim of this paper was to present three classes of heuristic algorithms: constructive heuristics including nearest neighbour and a clarke-wright based heuristic; improvement heuristics including string cross (sc), string exchange (se), string relocation (sr) and string mix (sm) and a local-global heuristic for the gvrp. computational results are planned in order to assess the effectiveness of the proposed heuristic algorithms. acknowledgement. this work was partially supported by cncsis-uefiscsu, project number pnii idei 508/2007. bibliography [1] r. baldacci, e. bartolini and g. laporte, some applications of the generalized vehicle routing problem, le cahiers du gerad, g-2008-82, 2008. [2] g. clarke and j.w. wright, scheduling of vehicles from a central depot to a number of delivery points, operations research, vol. 12, pp. 568-581, 1964. heuristic algorithms for solving the generalized vehicle routing problem 165 [3] m. fischetti, j.j. salazar and p. toth, a branch-and-cut algorithm for the symmetric generalized traveling salesman problem, operations research, 45:378-394, 1997. [4] g. ghiani, g. improta, an efficient transformation of the generalized vehicle routing problem, european journal of operational research, vol. 122, pp. 11-17, 2000. [5] i. kara and t. bektas, integer linear programming formulation of the generalized vehicle routing problem, in proc. of the 5-th euro/informs joint international meeting, 2003. [6] i. kara and p.c. pop, new mathematical models of the generalized vehicle routing problem and extensions, in proc. of the international conference on applied mathematical programming and modelling, bratislava, slovakia, may 27-30, 2008. [7] g. laporte, location-routing problems, in b.l. golden and a.a. assad (eds.), vehicle routing: methods and studies, north-holland, amsterdam, pp. 163-197, 1988. [8] g. laporte, s. chapleau, p.e. landry and h. mercure, an algorithm for the design of mail box collection routes in urban areas, transportation research b, vol. 23, pp. 271-280, 1989. [9] g. laporte, m. gendreau, j-y. potvin and f. semet, classical and modern heuristics for the vehicle routing problem, international transactions in operational research, volume 7, issue 4-5, pp. 285 300, 2006. [10] g. nagy and s. salhi, location-routing issues: models and methods, european journal of operational research, vol. 177, pp. 649-672, 2007. [11] c.m. pintea, d. dumitrescu and p.c. pop, combining heuristics and modifying local information to guide ant-based search, carpathian journal of mathematics, vol. 24, no. 1, pp. 94-103, 2008. [12] p.c. pop, c. pop sitar, i. zelina and i. tascu, exact algorithms for generalized combinatorial optimization problems, lecture notes in computer science, vol. 4616, pp. 154-162, 2007. [13] p.c. pop, c.m. pintea, i. zelina and d. dumitrescu, solving the generalized vehicle routing problem with an acs-based algorithm, american institute of physics (aip), conference proceedings: bics 2008, vol.1117, no.1, 157–162, 2009. [14] p.c. pop, efficient transformations of the generalized combinatorial optimization problems into classical variants, proceedings of the 9-th balkan conference on operational research, constanta, romania, 2-6 september 2009. [15] p.c. pop, a survey of different integer programming formulations of the generalized minimum spanning tree problem, carpathian journal of mathematics, vol. 25, no. 1, pp. 104-118, 2009. [16] a. van breedam, an analysis of the behavior of the heuristics of the vehicle routing problem for a selection of problems, with vehicle-related, customer-related and time-related constraints, ph.d. dissertation, university of antwerp, belgium, 1994. international journal of computers, communications & control vol. i (2006), no. 3, pp. 61-69 bringing iptv to the market through differentiated service provisioning cathryn peoples, petre dini, sally mcclean, gerard parr, michaela black abstract: the world of telecommunications continues to provide radical technologies. offering the benefits of a superior television experience at reduced long-term costs, iptv is the newest offering. deployments, however, are slow to be rolled out; the hardware and software support necessary is not uniformly available. this paper examines the challenges in providing iptv services and the limitations in developments to overcome these challenges. subsequently, a proposal is made which attempts to help solve the challenge of fulfilling real-time multimedia transmissions through provisioning for differentiated services. initial implementations in opnet are documented, and the paper concludes with an outline of future work. keywords: transport layer, real-time communication, qos, multimedia protocols, reconfigurable stacks, opnet simulation. 1 introduction it is anticipated that iptv will be enthusiastically accepted [1] [2; cited by 4] by early adopters, those with a passion for technology. however, while the desire for rich multimedia-based information grows, it is necessary to remove any service impairments to maximise the possibilities of widespread attraction. iptv, with its high bandwidth and strict quality of service (qos) requirements, will currently not operate to a satisfactory quality in many networks throughout the world. investments and improvements are therefore required. the potential for iptv to become a killer application will depend on such developments. without an efficient and reliable service, demand for it cannot grow. as with previous internet applications with widespread demand, it is anticipated that iptv will have a similar appeal. the race is now on to make the necessary hardware and software upgrades to ensure an end-user experience of quality. 2 iptv a report by accenture [3] revealed that there is a lack of consumer awareness on the definition of iptv. for those who claim to understand, some exhibited widely varying interpretations. in the context of this paper, it is our (the authors’) understanding that iptv describes the transmission of traditional television over an ip network. in addition, iptv seeks to further build upon the user experience. the intention is to enable a customised and interactive service [4] [5]. it will be possible to search for ondemand viewing, obtain access to exclusive content, and succumb to product recommendation. to attract initial customers, it is not thought imperative to provide a service which is fully reliable or which exceeds the current viewing experience. in the early stages, iptv is likely to be used as a supplement to the traditional television service, adopted by those with a passion for new technologies. however, to ensure longevity, it is necessary to provide a service which exceeds the current experience. this demands a service which is uninterrupted, reliable, and ideally offers cost advantages. in addition, availability through minimal operational efforts is advantageous in terms of encouraging widespread appeal. copyright c© 2006 by ccc publications 62 cathryn peoples, petre dini, sally mcclean, gerard parr, michaela black 2.1 challenges of transmitting iptv there are numerous challenges to providing iptv. voice and video are applications with stringent qos requirements, demanding an end-to-end delay of less than 100 milliseconds and a bit error rate (ber) of less than 10−6. in addition, they require minimal variation on the amount of end-to-end delay; jitter has a negative effect on the ability to stream the service smoothly. indeed, the accumulation of a sufficient amount can render the communication useless [6]. ensuring high levels of qos is difficult, especially given the increasing popularity of the internet and resulting pressure on restricted resources. in addition, increased roll-out of wireless links creates connections with notoriously error-prone links. rejaie et al (1999) [7] have the opinion that, "the quality of delivered service to real-time operations is neither controllable nor predictable." while this fact may have been true in 1999, developments are ongoing to remove the occurrence of such limited network performance. the ability to achieve certainty in the reliability and quality of transmissions is required. customers purchase internet packages according to different levels of qos, which guarantee that they will receive a certain level of performance. therefore, the ability to ensure the quality of a real-time service is required. 3 multimedia protocols numerous multimedia protocols have been designed in recent years to meet the challenges involved in achieving real-time communication. in addition to considering those developed specifically for multimedia transmissions, protocols with rate-based control mechanisms are also applicable to multimedia transmissions. flow control is an important attribute where applications require a steady rate of throughput and where reliability is required. window-based flow control, as in the case of tcp [8], is inappropriate for multimedia applications. variable file sizes result in transportation through the network at different rates. setting a retransmission time-out which does not match the time-out appropriate to the media can increase jitter, and hence qos. in addition, rate-based transport protocols are considered to be more reliable than window-based approaches. the design of the window-based approach is highly constrained in terms of window-size and retransmission time-out, and any changes in the external environment will cause the protocol to fail. rate-based approaches, in contrast, adapt based on real-time, and not pre-defined, information. the broadband application transport system (bats) [9] was proposed in 1994 as a rate-based transport protocol. supporting four classes of service, provisions are made for a variety of application requirements. while ensuring throughput of class a applications (those which are time-sensitive, connection-oriented, and which require a constant bit rate) by blocking all subsequent communications, there is a limitation of this approach in terms of coping ability when there is more than one class a transmission. as all connections are blocked when a class a application is transmitting to ensure sufficient bandwidth, the implication is that a second class a transmission must wait until the first has completed. this approach, therefore, has significant limitations. rt-ring [10] was developed in 2002 to transmit multimedia in real-time. it offers three qualities of service and a fairness control mechanism which provisions for traffic with both real-time and non real-time requirements. however, a limitation also exists with this approach. the principle behind this protocol is that a non-satisfied station (one which has not achieved its real-time transmission) can maintain priority until it is satisfied. what happens, however, if there are multiple stations with the same priority? in large-scale deployments, this approach is likely to be unsuitable. the rate adaptation protocol (rap) [7] was created to provide qos for multimedia applications by adapting the quality of a flow of data. quality is adapted by using layered encoding, and simultaneously delivering a number of layers which can fit inside the available bandwidth. however, despite the benefits of bandwidth-provisioning, doubts exist regarding the direct provisions made for the transmission of bringing iptv to the market through differentiated service provisioning 63 real-time traffic. each packet is acknowledged and losses are detected by missing acks. lost data is not retransmitted, but the information is used to update the rate at which traffic flows. in addition, rap incorporates a feature of adapting the rate of flow depending on the feedback delay. adjusting the rate of flow does not seem like a suitable response to take for video applications, where such actions will result in varying amounts of network jitter. the rate control scheme (rcs) is designed for environments with high error rates and high bandwidthdelay products. it uses dummy packets to perform congestion control and an amid algorithm to adjust its transmission rate. a flaw exists in this protocol in relation to its approach to flow and congestion control. due to the protocol’s inability to perform retransmissions, if congestion occurs between a dummy packet being received at the source and the actual transmission being sent, the data will be lost without the ability to be recovered. in addition, it appears as though this protocol could cause the application to experience variable jitter. if a dummy packet is not received, the transmission will cease at least until the period when the next dummy segment is to be sent. providing sufficient bandwidth exists, the transmission will continue. however, a gap in the transmission will have been introduced, and bringing with it jitter. a qos-guaranteed transport system (qts) [11] provides differentiated qos based on the needs of the application. a bandwidth allocator determines the rate at which data is transported through the network, and the transport protocol module performs rate-based congestion control at a rate which has been determined by the bandwidth allocator. as with bats, the limitation is that there is no mechanism to cope with multiple applications having the same qos requirements. it therefore seems difficult to ensure that applications will receive the required qos. the main criticism of the current protocols which have been developed for multimedia applications is that they do not take network resource information into account when making protocol choices. while the qos requirements of the application are important, there are benefits in also incorporating network information. in this way, the available resources can be maximised for all applications and not only those with class a (in bats [9] language) requirements. 3.1 reliable transportation from the application or transport layer? it is appropriate at this stage to discuss the location of transportation reliability within the protocol stack. provisions for transmission reliability are increasingly being incorporated within the application layer. the real-time protocol (rtp) [12] is one such example, used in the application layer of the osi (open systems interconnection) [13] protocol stack for the transmission of applications with real-time requirements. cfdp [14], the ccsds [15] file delivery protocol developed by the delay tolerant network research group (dtnrg) [16], is another example, deployed within the ccsds protocol stack [17]. flow and error control functions are combined within the application layer in an attempt to compress the layered approach to solving the communication problem as introduced by the iso (international standards organisation) [18]. however, it is the opinion of the authors that it makes greater sense to retain the traditional layered approach to solving the communication problem, and have each layer solve an individually complex problem. this explains why the protocols under investigation are located within the transport, and not the application, layer. 4 reconfigurable protocol stacks the concept of reconfigurable protocol stacks has long been under investigation [19] [20] [21], and it is believed that there are significant performance improvements to be achieved in the real-time multimedia transmission challenge through development of this technique. there are obvious benefits to operating a reconfigurable protocol stack in 21st century networks due to increasing pressures on resources and the need to provide quality of service. a dynamic network architecture allows exactly 64 cathryn peoples, petre dini, sally mcclean, gerard parr, michaela black the right combination of protocols to be chosen for each application. however, reconfigurable protocol stacks remain to be deployed in networks today. an obvious concern which arises from the consideration of reconfigurable protocol stacks is the risk of increasing processing time when choosing the optimum stack configuration. there is also the risk that, in incorporating multiple choices into the stack, memory increases, and hence hardware costs, will be required. there are several approaches to incorporating reconfigurability into the protocol stack. one approach is to empower the message with the intelligence to decide the path through the stack and the functions necessary in relation to the requirements of the packet [22] [23]. another approach is to group the functions specific to an application at all layers [24] [25]. while there may be multiple functions within each layer, only one from each will be relevant to an application and the grouping will be determined in advance. the final approach investigated is to build a middleware layer into the stack, which makes provisions for allowing adaptability [26] [27]. the approaches presented are diverse and thorough, and yet many more exist in addition to those examined here [28] [29] [30]. however, each has certain features with specific application for various environments and applications. as with the multimedia protocols, the main criticism is that the majority of the proposed reconfigurable protocol stacks do not take environmental information into account when making protocol choices, but rely on the needs of the application. as transmissions depend heavily on the environmental constraints in the network, it is important that explicit network information is used in the decision-making process. 5 research proposal current transport layer protocols are incapable of achieving the stringent qos requirements of realtime multimedia applications. they neither possess the capabilities to characterise applications nor adapt performance to maximise the possibility that qos is achieved. such functionalities, however, are thought paramount to the achievement of application qos in tomorrow’s networks. the research proposal incorporates application and environmental information at the top of the stack to influence decisions made in the lower layers. in addition, the functionalities which are available in each layer will be increased. currently, within the transport layer, either tcp [8] or the user datagram protocol (udp) [31] will be available according to the nature of the application. each protocol has welldefined and contrasting approaches to communication. however, network conditions can be extremely variable and dynamic. therefore, my proposal incorporates a greater amount of variability into the transport layer, different protocols being more appropriate to use in different circumstances. the protocol stack will be evaluated on a hop-by-hop basis. variability of protocol functions will be introduced in terms of connection orientation of the communication, flow control and the rate of flow control, error control, and the retransmission strategy. the proposed protocol stack is shown in figure 1. environmental information is passed to the protocol stack via a connecting network diagnostic and management system. the diagnostic system will not be discussed in depth in this paper; this represents future work and may be documented at a later date. several attributes are passed between the protocol stack and the diagnostic system. this is in keeping with bashir et al’s (2005) [32] opinion that network performance is typically measured using metrics for throughput, connectivity, end-to-end and round-trip packet delay, and packet loss. several of these parameters have been selected to combine environmental and application information. the subsequent result will be used to select the transport protocol. the formula is shown in equation 1. result = √√√√ √ ber( √ u d2e2 ∗(db + dt ) ) ∗tos (1) bringing iptv to the market through differentiated service provisioning 65 context-aware protocol stack network agent & fault diagnosis system real-time contextual information sending and receiving timestamp source address destination address ip traffic dropped (packets/sec) ip background traffic delay (sec) jitter (sec) data drop rate (buffer o’flow) (bps) data drop rate (retry threshold exceeded) (bps) load (bps) packet delay (sec) packet delay variation (sec) tcp delay (sec) tcp segment delay (sec) tcp retransmission count application c1 c2 c3 network c1 c2 c3 data link c1 c2 c3 bundling context-aware middleware application classification bw mc rt p tcp rcs transport parsing & normalisation real-time analysis fault identification fault diagnosis action b1 b2 … bx physical c1 c2 c3 qts rtp rap udp multi-hop backbone data collectors figure 1: proposed protocol stack and network management function combination o f application and environmental in f ormation where: u link utilisation de2e end-to-end delay db data drop (buffer overflow) dt data drop (retry threshold exceeded) ber bit error rate tos type of service the type of service (tos) attributes refers to the qos required by an application. the scale being used is 0 best effort application, 3 excellent effort, and 6 interactive voice. the qos therefore increases in parallel with the tos value. as an example of how this protocol stack will be used, it is the intention that where the link is under-performing (i.e. low link utilisation, high end-to-end delay, high number of data drops, and high bers), the transport protocol deployed is one with a reduced amount of overhead. the rationale behind this approach is that, where the network is operating in a manner which is sub-optimal, a protocol with less control overhead should be used to maximise the opportunity that some of the data transmitted will be received (taking into account that iptv is an application which can cope with some data loss). if a protocol with more stringent error-control requirements was used in such an environment, there is a greater opportunity that the data will not be received due to an inability to cope with data loss. it should also be noted that, while it is shown in the stack that the complete protocols are deployed at each hop when the conditions are evaluated, it is the intention that the separate functionalities of each protocol can be called. for example, it may be decided that the four classes of service from bats should 66 cathryn peoples, petre dini, sally mcclean, gerard parr, michaela black be deployed alongside the qts error-control scheme (error-control only for loss-sensitive applications). it is the intention to call the current multimedia protocol functions to maximise their potential and the success of the transmission, given the application requirements and the environmental constraints. the calling of individual error, flow, and retransmission strategies will depend on individual formulaic conditions designed. 6 implementation implementation of the research proposal has partially occurred. the functionality being documented in this paper is the integration of adaptability in terms of the connection-orientation of the communication. within opnet [33], this has involved integrating environmental information, which has not previously been provisioned for. the environmental information and the evaluation criteria discussed in section 5 have been inserted into the application layer. defined within the function block, the changes are subsequently called from the process model states when spawning the application profiles. dependencies between the layers have also been provisioned for. this has involved integrating the changes made in the application layer within the subsequent modules called. opnet uses a global data structure from which it determines the transport protocol being used for any application. it is referred to several times in the initiation of a communication for the purpose of populating data structures with the transport protocol. one of the functions of this implementation was therefore to remove all reliances on the global transport protocol service, and to ensure that all references were instead made to the intelligently calculated protocol. changes were required in the tpal layer and the process models generated by the spawning of the application process, the video_calling_manager and gna_profile_mgr models. after the tpal layer, the transport protocol module and the lower layers of the stack are called. as the changes are being made to the choice of transport protocol, all changes must therefore be made before this layer. figure 2 shows the dependencies between the process modules, and the span of changes required to achieve implementation at the client. the changes implemented in a top-down fashion between the application and tpal layers have ensured developments at the client. developments must now be made in a bottom-up approach between the same layers to allow incorporation of the same changes at the server. 7 conclusions and future work the research proposal has been thoroughly investigated and it is believed that the proposed scheme can have a realistic existence in future networks. reconfigurable protocol stacks have been widely investigated in the literature, but remain to be deployed. given the dynamism of network conditions, it seems sensible to incorporate flexibility into the protocol stack. conditions in the external environment are pivotal in enabling the transport protocol’s operation, and hence the fulfilment of the application’s requirements. therefore, its incorporation into the decision-making process is thought fundamental to providing future service differentiation. it is the challenge to design one which is technically and commercially feasible. this research has been conducted during a four-month internship with cisco systems in silicon valley. the protocol stack developments discussed in this paper are also simultaneously being developed for a phd research project. this internship has allowed research to occur specifically on the transmission of multimedia, although the applicability of all application types to this proposal will be considered for the final project. this period has seen the initiation of the task of delving into opnet’s code to modify how functions are performed at present. future work will involve incorporating the other techniques with an adaptable status. these include flow control, error control, and the retransmission strategy. bringing iptv to the market through differentiated service provisioning 67 destroy_profile spawn_profile svc_compl arrival client_closed rtrv_complete close open remote_ profile wait start init gna_clsvr_mgr ace gna_profile_mgr init idle spawn end close gna_video_calling_mgr open connect end idle receive send_ close send tpal_v3 pre_init init wait req reg error_ trap open gna_profile_spawn app_pk_to_tpal_send figure 2: data flows from application to tpal layers references [1] multimedia research group, "ip tv global forecast 2006 2009: semiannual ip tv global forecast", march 2006. [2] gartner group, "iptv prediction", gartner group research report, winter 2005. [3] zadeh, a; douglass, g, k; dogra, r; "communications and high tech: "infinite possibilities" television?", accenture, may 2006. available at: http://www.accenture.com/global/research_and_insights/outlook/by_issue/y2006/ infinitetelevision.htm [4] shin, d, h; "potential user factors driving adoption of iptv. what are customers expecting from iptv?", technological forecasting and social change, 2006. [5] harris, a; "white paper of enabling iptv: what carriers need to know to succeed", may 2005, idc. available at: http://www.emc.com/analyst/pdf/idc_iptv_whitepaper_jun_9_05.pdf 68 cathryn peoples, petre dini, sally mcclean, gerard parr, michaela black [6] chan, s-p; kok, c-w; wong, a, k; "multimedia streaming gateway with jitter detection", ieee transactions on multimedia, volume 7, number 3, june 2005, pages 585 592. 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[11] yuang, m, c; liu, j, c; "qts: a qos-guaranteed transport system for broadband multimedia communications", ieee transactions on industrial electronics, volume 45, number 1, february 1998, pages 69 77. [12] schulzrinne, h; casner, s; frederick, r; jacobson, v; "rtp: a transport protocol for real-time applications", network working group, request for comments 3550, july 2003. [13] international organisation for standardisation, "open systems interconnection basic reference model: the basic model", iso/iec 7498-1: 1994, available at: http://isotec.iso.org/livelink/livelink/fetch/2000/2489/ lttf_home/publiclyavailablestandards.htm [14] consultative committee for space data systems, "ccsds file delivery protocol (cfdp): recommendation for space data system standards", blue book, october 2002. [15] consultative committee for space data systems, available at: www.ccsds.org [16] delay tolerant network research group. available at: www.dtnrg.org [17] consultative committee for space data systems; "overview of space link protocols", ccsds 130.0-g-1, green book, june 2001. [18] international standards organisation, available at: www.iso.org [19] curran, k; parr, g; "the use of dynamically reconfigurable protocol stacks for streaming multimedia to mobile devices", proceedings of the 8th international conference on communication systems, 25 28 november 2002, volume 2, pages 947 951. [20] johnny, t, f; "adaptive protocol suite for next generation wireless internet", proceedings of the ieee 2005 international conference on personal wireless communications, 23 25 january 2005, pages 509 513. [21] akyildiz, i; altunbasak, y; fekri, f; sivakumar, r; "adaptnet: an adaptive protocol suite for the next-generation wireless internet", ieee communications magazine, march 2004, pages 128 136. [22] o’malley, s, w; paterson, l, l; "a dynamic network architecture", acm transactions on computer systems, volume 10, number 2, may 1992, pages 110 143. bringing iptv to the market through differentiated service provisioning 69 [23] schöler, t; müller-schloer, c; "design, implementation and validation of a generic and reconfigurable protocol stack framework for mobile terminals", proceedings of the ieee 24th international conference on distributed computing systems workshops, 2004. [24] haas, z; "a protocol structure for high-speed communication over broadband isdn", ieee network magazine, january 1991, pages 64 70. [25] zitterbart, m; stiller, b; tantawy, a, n; "a model for flexible high-performance communication subsystems", ieee journal on selected areas in communications, volume 11, number 4, may 1993, pages 507 518. [26] curran, k; parr, g; "the use of dynamically reconfigurable protocol stacks for streaming multimedia to mobile devices", proceedings of the 8th international conference on communication systems, 25 28 november 2002, volume 2, pages 947 951. [27] zhang, c, h; chin, t, k; koh, k, y; ong, g, m; suthon, s; peng, c, h; pung, h, k; "octopus: an adaptive group communication environment for multimedia application", the ieee 10th international conference on networks, 27 30 august 2002, pages 145 150. [28] akyildiz, i; altunbasak, y; fekri, f; sivakumar, r; "adaptnet: an adaptive protocol suite for the next-generation wireless internet", ieee communications magazine, march 2004, pages 128 136. [29] an, l; pung, h, k; zhou, l; "design and implementation of a dynamic protocol framework", computer communications 29, 2006, pages 1309 1315. [30] guan, s-u; lim, s-s, "modeling adaptable multimedia and self-modifying protocol exceution", future generation computer systems 20, 2004, pages 123 143. [31] postel, j, "user datagram protocol", request for comments 768, august 1980. [32] bashir, o; parish, d; sandford, m; phillips, i; "optimising data processing in network performance monitoring systems", proceedings of the iee communications, volume 152, issue 5, 7 october 2005, pages 633 642. [33] opnet, available at: www.opnet.com cathryn peoples, gerard parr, sally mcclean, michaela black university of ulster school of computing and information engineering coleraine, northern ireland bt52 1sa e-mail: {si.mcclean,gp.parr,mm.blackg}@ulster.ac.uk petre dini cisco 170 west tasman drive san jose, ca 95134 usa e-mail: pdini@cisco.com ijcccv4n1draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 1, pp. 6-16 incremental improvement of the evaluation algorithm in the concept map based knowledge assessment system alla anohina, marks vilkelis, romans lukasenko riga technical university, department of systems theory and design kalku street 1, riga, latvia, lv-1658 e-mail: alla.anohina@rtu.lv, markvilkel@inbox.lv, lrexpress@inbox.lv abstract: the paper is devoted to the knowledge assessment system that has been developed at the department of systems theory and design of riga technical university for the last four years. the system is based on concept maps that allow displaying the knowledge structure of a particular learner in the form of a graph. teacher’s created concept maps serve as a standard against which learner’s concept maps are compared. however, it is not correct to compare teacher’s and learners’ concept maps by examining the exact equivalence of relationships in both maps, because people construct knowledge in different ways. thus, an appropriate mechanism is needed for the flexible evaluation of learners’ concept maps. the paper describes the algorithm implemented in the concept map based knowledge assessment system and its evolution through four prototypes of the system. keywords: knowledge assessment system, concept maps, evaluation algorithm 1 introduction rapid development of information and communication technologies and availability of huge amount of electronic information resources has led to changes in the roles of the main actors of the educational process, namely, a teacher and a learner. nowadays teachers should guide learners through the learning process by advising them and providing necessary stimuli while learners search information in different places, inter alia in e-learning environments (for example, [1, 2]) and turn it into knowledge. special methods are needed for the evaluation of learners’ knowledge structures created in such conditions and one of them is concept maps that allow displaying the knowledge structure of a particular learner in the form of a graph. the department of systems theory and design of the faculty of computer science and information technology of riga technical university has been developing the concept map based knowledge assessment system since the year 2005. the system has twofold goals in the context of the integration of technology into the traditional educational process: 1) to promote learners’ knowledge self-assessment, and 2) to support the teacher in the improvement of the learning course through systematic assessment of learners’ knowledge and analysis of its results. the goals are reached by the use of concept maps as an assessment tool. three prototypes of the system have been already implemented and the fourth one is under development at the moment [3]. the paper is a logical continuation of the description of the system’s working principles presented in [4] by focusing on a different aspect of its functionality, namely, on the algorithm for the comparison of learner’s and teacher’s concept maps and its incremental improvement through system’s prototypes. the paper is organized as follows. section 2 gives an overview of the system. section 3 discusses related works concerning scoring systems of concept maps. evolution of the algorithm for the comparison of learner’s and teacher’s concept maps is described in section 4. finally, conclusions are presented. copyright © 2006-2009 by ccc publications incremental improvement of the evaluation algorithm in the concept map based knowledge assessment system 7 2 overview of the system as it was mentioned in introduction concept maps are used as an assessment tool in the developed knowledge assessment system. according to [5] they can foster the learning of well-integrated structural knowledge as opposed to the memorization of fragmentary, unintegrated facts and externalize the conceptual knowledge (both correct and erroneous) that learners hold in a knowledge domain. a concept map is a graph with labeled nodes corresponding to concepts in a problem domain and with arcs indicating relationships between pairs of concepts. arcs can be directed or undirected and with or without linking phrases on them. a linking phrase specifies the kind of a relationship between concepts. concept map based tasks can be divided in 1) "fill-in-the map" tasks, where the structure of a concept map is given to the learner and he/she must fill it using the provided set of concepts and/or linking phrases, and 2) "construct-a-map" tasks, where the learner must decide on the structure of the concept map by him/herself. the developed concept map based knowledge assessment system is used in the following way. the teacher defines stages of knowledge assessment and creates concept maps for all of them by specifying relevant concepts and relationships among them in such a way that the concept map of each stage is nothing else then an extension of the previous one. thus, the concept map of the last stage includes all concepts and relationships among them. teacher’s created concept maps serve as a standard against which the learners’ concept maps are compared. during knowledge assessment the learner solves a concept map based task corresponding to the assessment stage. after the learner has submitted his/her solution, the system compares the concept maps of the learner and the teacher, calculates the score of the learner’s result and generates feedback. the system offers five concept map based tasks that are ranged from the easiest to the most difficult (figure 1) [6]. eight transitions between tasks are implemented allowing the learner to find a task most suitable for his/her knowledge level. four transitions increase the degree of task difficulty. they are carried out after the analysis of the learner’s solution if the learner has reached the teacher’s specified number of points in the current assessment stage without reducing the degree of difficulty of the original task. so, this is a system’s adaptive reaction to the learner’s behavior. other four transitions reduce the degree of task difficulty and they are carried out by the voluntary request from the learner during the solving of the task. thus, the system supports knowledge self-assessment as it makes the analysis and evaluation of learners’ concept maps, as well as provides feedback about the learner’s errors. it promotes systematic knowledge assessment because it allows the extension of the initially created concept map for other assessment stages. moreover, statistical information about differences between learners’ concept maps and teacher’s concept map is collected providing opportunities for the teacher to improve the learning course [4]. the system is implemented as a web-based three-tier client-server application [4, 7] consisting of the following architectural layers (figure 2): 1) a data storage layer represented by data base management system (dbms); 2) an application logics layer composed of the application server and the server side code running on it; a special persistence and query framework is used to communicate with the dbms; and 3) the representation layer or graphical user interface. as it is shown in figure 2 the concept map based knowledge assessment system can be divided into three logical domains: administrator, teacher and student. each domain has its own goal, but they are strictly linked together. functionality of each domain can be used by one of three user roles which names correspond to the names of the domains. an administrator is responsible for the administration and maintenance of the whole system using such functions as input, editing and deleting of data about users (teachers and students), courses and student groups. the teacher domain provides all necessary functions for the creation of concept maps and defining of their attributes, as well as for the viewing of learners’ results. functionality of the student domain includes all things related to the completion of the 8 alla anohina, marks vilkelis, romans lukasenko concept map based tasks by learners and providing of feedback after the completion of the task. figure 1: tasks offered in the concept map based knowledge assessment system figure 2: the three-tier architecture of the system 3 related works there are a lot of research related to scoring systems of concept maps. in [14] three main approaches are highlighted: evaluation of components of a concept map, comparison with the expert concept map, or combination of both previously mentioned kinds. novak and gowin [11] have offered a scheme in which different number of points are assigned to such components of a concept map as propositions, levels of hierarchy, cross-links, specific examples of concepts. in [13] the previously described set of components is extended by number of branchings in the concept map. for the comparison of the learner’s concept map with the expert map goldsmith [8] has offered "the closeness index" indicating the degree of similarity between the expert and the learner’s concept map. in turn, herl and colleagues [9] use a matching algorithm that includes several expert maps evaluating each learner’s concept map. in their work two evaluation indicators are calculated: (a) stringent semantic content score on the basis of the exact relationships matches between learner’s and expert’s concept incremental improvement of the evaluation algorithm in the concept map based knowledge assessment system 9 maps, and b) categorized semantic content score, when learner’s defined relationship matches some set of possible relationships in the expert concept map. in [10] concept maps are scored by comparing each learner’s concept map with two expert maps. the learner receives half a point for each relationship that matches with an expert relationship and a full point, if the relationship matches with both experts’ relationships. learners also receive additional points for relationships that are more valued by experts ("critical relationships") and relationships that are less like what experts might include. both the evaluation of components of concept maps and the comparison with the expert concept map are combined in [12], where three different approaches to the scoring of concept maps are used: (a) a total score, defined as a number of valid learner’s relationships, (b) a congruence score, defined as a proportion of valid learner’s relationships to all relationships in the expert map, (c) a salience score, defined as a proportion of valid learner’s relationships to all relationships in the learner’s concept map. regardless of the diversity of schemes for scoring of concept maps, it is necessary to note that basically they are developed for the evaluation of tasks, in which learners must create their own concept maps. thus, a question about the evaluation of fill-in-the-map tasks remains open. moreover, the greater part of the offered schemes are not considered in the context of computer-assisted assessment systems, thus, it is very difficult to evaluate, whether they are feasible and useful in the concept map based knowledge assessment system. these factors motivated the authors of the paper to develop an algorithm suitable for the evaluation of both fill-in-the-map tasks and construct-a-map tasks in the concept map based knowledge assessment system. 4 evaluation algorithm the developed algorithm is sensitive to the arrangement and coherence of concepts in the learners’ concept maps. it is based on the assumption that the learner’s understanding of the presence of a relationship between concepts has the primary value, while other aspects such as the type of the relationship, the linking phrase, the direction of the arc and the places of concepts are secondary things. the algorithm is capable of recognizing different patterns of the learner’s solution. two kinds of relationships are used in concept maps: 1) important relationships which show that relationships between the corresponding concepts are considered as important knowledge in the learning course, and 2) less important relationships that specify desirable knowledge. for each correctly provided important relationship the learner receives 5 points, but for each less important relationship only 2 points are assigned. in the first system’s prototype developed in 2005 only one fill-in-the-map task was offered to learners. thus, all learners received the same structure of a concept map and a list of concepts. learners must insert provided concepts in correct nodes of the concept map structure. arcs were undirected and did not have semantics. taking into account that the value of a completely correct relationship is 100 %, the following contributions of its constituent parts were defined: the presence of the relationship in the learner’s concept map 50% (a fact that the learner understands the presence of the relationship between concepts has the primary value), the correct type of the relationship 30% (the learner should be able to distinguish, what is important and what is less important in the learning course), both concepts related by the relationship are placed in the correct places 20% (this factor has the greatest subjectivity). thus, the patterns of the learner’s solution which the algorithm was capable of recognizing are the following [15, 16]: • pattern 1. the learner has interrelated concepts in the same way as they are related in the teacher’s concept map. in this case the learner receives the maximum score regarding the type of the relationship. figure 3.b. shows that the concepts a and e in the learner’s concept map are interrelated in the same way as they are interrelated in the teacher’s concept map (figure 3.a). • pattern 2. the learner has defined a relationship that does not exist in the concept map of the teacher. in this case he/she does not receive any points. figure 3.c demonstrates that the learner 10 alla anohina, marks vilkelis, romans lukasenko figure 3: patterns of the learner’s solution that the first system’s prototype was able to recognize: a) the teacher’s concept map; b)-f) the patterns in the learner created concept map has made a relationship between the concepts a and h that are not interrelated in the teacher’s concept map (figure 3.a). • pattern 3. the learner’s defined relationship exists in the teacher’s map, the type of the relationship is correct, but at least one of the concepts is placed in an incorrect place. the learner receives 80% of the maximum score for the correct relationship of this type. figure 3.d shows that the learner has interrelated the concepts b and d. a similar relationship exists in the teacher’s concept map (figure 3.a). however, both concepts are located in incorrect places, although the type of the relationship is correct. • pattern 4. the learner’s defined relationship exists in the teacher’s map, the type of the relationship is wrong, and at least one of the concepts is placed in an incorrect place. the learner receives 50% of the maximum score for the correct relationship of this type. this pattern is displayed in figure 3.e. comparing the learner’s defined relationship between the concepts a and f with the same teacher’s relationship (figure 3.a) it is possible to notice that the concept f is located in the incorrect place, as well as the type of the relationship is a less important relationship instead of an important relationship. • pattern 5. a concept is placed in a wrong place, but its place is not important. the learner receives the maximum score for a corresponding relationship. figure 3.f demonstrates that the learner has changed places of the concepts m and l. new tasks 3 fill-in-the-map and 2 construct-a-map tasks (figure 1)were introduced in the second system’s prototype developed in 2006. moreover, linking phrases were added to concept maps causing modification of points received for relationships and extension of the set of patterns recognized by the system. assuming that a value of the fully correct relationship is 100%, the following contributions of its constituent parts were defined: the presence of the relationship in the learner’s concept map 40%, a correct linking phrase provided for the relationship 30% (semantics of relationships are important knowledge units), a correct type of the relationship 20%, both concepts related by the relationship are placed in the correct places 10%. therefore, the extended set of patterns of the learner’s solution was the following [6]: incremental improvement of the evaluation algorithm in the concept map based knowledge assessment system 11 • pattern 1. the learner has defined a completely correct relationship. in this case the learner receives the maximum score regarding the type of the relationship. figure 4.b demonstrates that the concepts a and e in the learner’s concept map are interrelated in the same way as they are interrelated in the teacher’s concept map (figure 4.a). • pattern 2. the learner has defined a relationship that does not exist in the concept map of the teacher and he/she does not receive any points. figure 4.c shows that the learner has related the concepts a and h which are not related in the teacher’s map (figure 4.a). • pattern 3. the learner’s defined relationship exists in the teacher’s map, both the type of the relationship and the linking phrase are correct, but at least one of the concepts is located in an incorrect place. the learner receives 90% of the maximum score for that relationship. this pattern is displayed in figure 4.d. the learner has defined the relationship between the concepts b and d by providing the correct type of the relationship and the linking phrase. however, both concepts are located in different places as compared to the teacher’s concept map (figure 4.a). • pattern 4. the learner’s defined relationship exists in the teacher’s map, but the type of the relationship is incorrect. the learner receives 80% of the maximum score for the correct relationship. this pattern is valid only for construct-a-map tasks (tasks 4 and 5 in figure 1) where places of concepts are not important. assuming that figure 4.e displays the learner’s created concept map, one can see that the learner has interrelated the concepts b and c by defining the correct linking phrase, but providing the important relationship instead of a less important one as it is in the teacher’s map (figure 4.a). • pattern 5. the learner’s defined relationship exists in the teacher’s map, but the linking phrase is incorrect. the learner receives 70% of the maximum score for the correct relationship. figure 4.f shows that the learner has located the concepts a and k in the same places as they are located in the teacher’s concept map (figure 4.a), but the linking phrase differs. • pattern 6. the learner’s defined relationship exists in the teacher’s map, the type of the relationship is incorrect, and at least one of concepts is placed in the incorrect place. the learner receives 70% of the maximum score. figure 4.g shows that the learner has related the concepts a and f using the less important relationship instead of the important relationship (figure 4.a) and has located the concept f in other place. • pattern 7. the learner’s defined relationship exists in the teacher’s map, the linking phrase is incorrect, and at least one of concepts is located in the incorrect place. the learner receives 60% of the maximum score. this pattern is displayed in figure 4.h, where one can see that the learner has defined the relationship between the concepts g and f, but has pointed out the linking phrase that differs from the linking phrase in the teacher’s concept map (figure 4.a), as well as has located both concepts incorrectly. • pattern 8. the learner’s defined relationship exists in the teacher’s map, but both the type of the relationship and the linking phrase are incorrect. the learner receives 50% of the maximum score for the correct relationship. this pattern is valid only for construct-a-map tasks (tasks 4 and 5 in figure 1), where places of concepts are not important. assuming that figure 4.i displays the learner’s created concept map, one can see that the learner has interrelated the concepts o and b that are also interrelated in the teacher’s concept map (figure 4.a). the learner has provided an incorrect type of the relationship (an important relationship instead of a less important relationship) and an incorrect linking phrase. 12 alla anohina, marks vilkelis, romans lukasenko figure 4: patterns of the learner’s solution that the second system’s prototype was able to recognize: a) the teacher’s concept map; b)-j) the patterns in the learner created concept map (numbers on links display different relationships and represent linking phrases) incremental improvement of the evaluation algorithm in the concept map based knowledge assessment system 13 • pattern 9. the learner’s defined relationship exists in the teacher’s map, both the type of the relationship and the linking phrase are incorrect, in addition at least one of the concepts is placed in the incorrect place. the learner receives 40% of the maximum score for that relationship. this pattern is shown in figure 4.j. by comparing the learner’s defined relationship between the concepts j and n with the same relationship in the teacher’s concept map (figure 4.a), one can see that both concepts are located in incorrect places, and the type of the relationship and the linking phrase are incorrect. in the third prototype implemented in 2007 directed arcs were introduced causing the following modifications of points received for relationships: the presence of the relationship in the learner’s concept map 40%, a correct linking phrase provided for the relationship 30%, a correct direction of the arc corresponding to the relationship 15%, a correct type of the relationship 10%, both concepts related with the relationship are placed in the correct places 10%. totally 36 patterns were acquired for all tasks and they are summarized in figure 5. no modifications of points have been made in the fourth system’s prototype. thus, the evaluation algorithm is based on the set of patterns specified in figure 5. however, improvement of the algorithm is continued by studying possibility to reveal extra relationships in learners’ concept maps [17]. figure 6 displays a situation when some relationships are "hidden": there are only 3 relationships in the teacher’s concept map (figure 6.a), but 2 more relationships can be derived (figure 6.b). these derived relationships are correct too and could appear in learners’ concept maps, so it is necessary to define a mechanism according to which the system could detect extra relationships and, thus, make assessment more flexible and automated. with aim to determine extra relationships 6 types of relationships are considered [17]:is a a relationship between concepts meaning that one of the concepts is a sub-class of other, part of a relationship between concepts meaning that one of the concepts is a part of other, attribute a relationship between a concept and its attribute, example a relationship between a general concept and a particular example of it, value a relationship between an attribute and its value, kind of a relationship between levels of hierarchy. figure 7 shows the structure of a pattern further explained in figure 8. the pattern has two main relationships (relation 1 and relation 2) that have types mentioned previously. in some cases combination between relationships is not allowed. in other cases an extra relationship (relation 3) can be formed. column "combination allowed" identifies either combination between relation 1 and relation 2 is allowed or not. the entry "cannot be specified" in the column "relation 3" indicates that there can be situations when an extra relationship can be added, but not always. in turn, the entry "no extra relationship" in the same column points out that no additional relationship of considered 6 types can be added. additional relationships that can be derived from more than three concepts and two relationships between them can be revealed as well. in such case the algorithm must iteratively go through the concept map searching for patterns and adding extra relationships whenever it is possible. the algorithm stops when no new relationship has been added during the last iteration. 5 conclusions the use of concept maps for the evaluation of learners’ knowledge structures demands an appropriate mechanism for the comparison of learners’ concept map with the teacher’s one. the mechanism should consider that people construct knowledge in different ways and there can be differences between concept maps that are not indicators of incorrect knowledge. the paper presents the algorithm capable of recognizing several patterns of the learner’s solution on the basis of such criteria as location of concepts, types of relationships, direction of arcs and correctness of linking phrases allowing the learner to receive 14 alla anohina, marks vilkelis, romans lukasenko figure 5: the extended set of patterns of the learner’s solution in the third system’s prototype figure 6: hidden relationships (adopted from [17]) figure 7: structure of the pattern (adopted from [17]) incremental improvement of the evaluation algorithm in the concept map based knowledge assessment system 15 some points in case if his/her relationships match teacher’s ones only partly. moreover, detection of extra relationships that can appear in learners’ concept maps makes assessment more automated. figure 8: patterns containing three concepts and two relationships (adopted from [17]) bibliography [1] a.styliadis, i.karamitsos, d.zachariou,personalized e-learning implementation-the gis case, international journal of computers,communications & control, vol.1, no.1, pp.59-67, 2006. 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[15] a. anohina, j. grundspenkis, prototype of multiagent knowledge assessment system for support of process oriented learning, proceedings of the 7th international baltic conference on databases and information systems, july 3-6, vilnius, lithuania, pp. 211-219, 2006. [16] a. anohina, v. graudina, j. grundspenkis, using concept maps in adaptive knowledge assessment, advances in information systems development "new methods and practice for the networked society", springer, pp. 469-480, 2006. [17] j. grundspenkis, m. strautmane, usage of graph patterns for knowledge assessment based on concept maps. scientific proceedings of riga technical university, rtu publishing, riga, 2008 (submitted). alla anohina is a lecturer at the department of systems theory and design of riga technical university in latvia. she obtained her doctoral degree in computer systems from riga technical university in 2007. her main research fields are intelligent tutoring systems, computer-assisted assessment systems and artificial intelligence. she has seven years’ experience of teaching in the field of computer science both in riga technical university, and in other educational institutions of latvia. she has participated in several research projects related to the development of of educational software. marks vilkelis is currently developing the master thesis "the knowledge assessment system as a client-server java application" at the institute of information technology of riga technical university. in 2005 he obtained the bachelor degree from the mentioned institution. his scientific interests cover java open source technologies and applications of artificial intelligence. he has experience of working as a programmer in some research projects. his professional activities are related to the programming of complex business applications in the international software development company. romans lukasenko is a phd student of computer systems at the riga technical university in latvia. he obtained the master degree in computer systems from the mentioned institution in 2006. his main research fields are intelligent tutoring systems, computer-assisted assessment systems and student modelling. his thesis is related to the implementation of a student model in the knowledge assessment system. romans lukashenko works also as a researcher at the department of systems theory and design of riga technical university. he has participated in several research projects related to the development of educational software. international journal of computers, communications & control vol. ii (2007), no. 3, pp. 217-228 reactive control using behavior modelling of a mobile robot imen ayari, abderrazak chatti abstract: this paper deals with the reactive control of an autonomous robot which should move safely in a crowded unknown environment to reach a goal. a behavior based approach is used to realize obstacle avoidance within a neural model conceived from a set of examples of perception/action relations; supervised learning is used for the aim; while goal-reaching task is realized using a fuzzy rule-based system. a task activation module is used to generate the overall command, resulting from the fuzzy controller and the neural model. real time simulation examples of generated path with proposed techniques are presented. keywords: reactive control, mobile robots, neural networks, learning,fuzzy control. 1 introduction one of the major challenges in the development of intelligent mobile robotic systems is endowing them with an ability to plan motions and to navigate autonomously in a crowded environment avoiding any type of obstacles. different kinds of the path planning problem can be illustrated; the simplest one is to find a continuous path from a starting position to a goal position given the exact description of the environment. different global approaches were applied for the purpose such as decomposition, road-map, and retraction methods. when dealing with unknown environment, much fewer approaches are used; obstacles in that case are either detected locally during the robot movement and dynamically incorporated into the path generation process, or approximated in the workspace (miura, uozumi, and shirai [9]; yu and su [10]; bennewitz, burgard and thrun [11]). [12] a way to deal with the navigation problem is within behavior based navigation approaches. the main idea is to subdivide the navigation task into small subtasks. several behavior-based control schemes have been achieved inspired by the subsumption architecture of brooks[13][14]such us reactive behaviors defined by arkin using motor schemes (arkin [15][16][17]); damn architecture presented by rosenblatt et al.[18][19][12] in which a centralized module of votes provided by independent behaviors combines into a voted output and fuzzy logic approach to manage behaviors used by saffiotti [13], seraji et al. [14][15] and others [16][17].[18] this framework provides an overview on the contribution of soft computing to the field of behavior based robot control, a hybrid approach is adopted using both neural and fuzzy logic methods to realize the specified task of reaching a goal with obstacles avoiding in a crowded unknown environment. a neural model is conceived to synthesize avoiding obstacle behavior from a set of examples representing the perception/action relation using a supervised learning strategy. goal-reaching task is realized using a fuzzy rule-based system. a task activation module is used to generate the overall command, resulting from the fuzzy controller and the neural model. simulation examples of generated path with proposed techniques are presented. 2 neural networks in robotics historically, robots for industrial purposes involved little or no learning. recently, a growing interest in unstructured environments has encouraged learning intensive design methodologies. the emerging class of robots must be able to interact responsively with people and other robots providing assistance and service that will increasingly affect everyday life. robots may learn by adjusting parameters, building copyright © 2006-2007 by ccc publications 218 imen ayari, abderrazak chatti environmental models such as maps, exploiting patterns, evolving rule sets, generating entire behaviors, devising new strategies, predicting environmental changes, recognizing the strategies of opponents or exchanging knowledge with other robots. essentially, neural networks deal with cognitive tasks such as learning, adaptation, generalization and optimization. indeed, recognition, learning, decision-making and action constitute the principal navigation problems. to solve these problems fuzzy logic and neural networks are used. they improve the learning and adaptation capabilities related to variations in the environment where information is qualitative, inaccurate, uncertain or incomplete. artificial neural networks (anns) are algorithms based very loosely on the neural phenomenon of spreading activation. anns can encode knowledge and skill implicitly as associative connections between nodes. stimulation introduced to the input nodes of a neural network travels between layers of the network to produce some output on the other end. this output is evaluated by a trainer who applies supervised or unsupervised learning to alter the weights of synaptic connections and thereby change the way the network will respond. in this way, anns allow human knowledge and guidance to orchestrate the learning process. such techniques, where learning is applied by a knowledgeable teacher, are often referred to as robot shaping. usually robot shaping involves symbolic interaction between a human and robot and may even involve a high-level tasking language. such an interface allows humans to supply high-level assistance and allows the robot to accomplish the low-level learning necessary to achieve the goal. [19] 3 robot description the robot used in this work has been conceived in our laboratory [20][21] around a hardware architecture that facilitates the communication with a remote computer in different ways. it was based on different modules as shown in figure (1). every module has processing capabilities that allows updating the type of the low level current controller for the motor driver (2), test different dead reckoning approximations for the wheel encoders that determines the position (3) or compute different averaging methods for the infrared sensors (4). the robot can be totally free with an embedded master controller while transmitting wireless information to a computer, or the computer can be its master taking inputs and giving orders. a mechanical structure handling this architecture was based on two dc motors controlling through gears two differential wheels. wheels are provided with encoders that allows after processing determining an approximate relative position of the robot. eight infrared sensors are placed on a circle every 45◦, they sense for obstacles by measuring the reflected light. figure 1: robot architecture reactive control using behavior modelling of a mobile robot 219 figure 2: ir sensors (ir diode + phototransistor) figure 3: robot parameters the figure (3) shows the robot parameters where c is the center of the wheels and g is the gravity center, d separate these two centers as the in the general case d is always different than zero. the movement vector q containing variables of the robot has the cartesian coordinates of the center, the orientation and angle of each of left and right wheels: q = (xc,yc, φ , θr, θl ) (1) given the kinematics equations of this model: ẏc cos φ − ẋc sin φ −dφ̇ = 0 ẋc cos φ + ẏc sin φ −bφ̇ = rθ̇l ẋc cos φ + ẏc sin φ + bφ̇ = rθ̇r we can obtain the pfaffian kinematics constraint: a(q)q̇ = 0 with a(q) =   −sin φ cos φ −d 0 0 −cos φ −sin φ −b r 0 −cos φ −sin φ b 0 r   220 imen ayari, abderrazak chatti deriving the kinetic energy equation and considering the system with the pfaffian constraint, we obtain the matrix differential system: m(q)q̈ + v (q, q̇) = e(q)τ −at (q)λ a(q)s(q) = 0 st (msυ̇(t) + mṡυ(t) + v ) = τ the resulting system is based on non-stationary matrices that will be re-computed every simulation step: ẋ = [ sυ 0 ] + [ 0 i ] u thus we have an approximate model of the robot dynamics. matrices of this differential equation have parameters measured on the real robot and some others are variables computed on the run with the simulation like position and speed. the input of this equation is the torque applied on every wheel. resolving theses equations provide the speed and position of the robot. figure 4: robot dynamic model input output 4 proposed approach a hybrid approach is adopted using both neural and fuzzy logic methods to realize the specified task of reaching a goal point while avoiding obstacles. the control structure is made of two tasks as seen in the figure (5), a task for reaching the goal and a second one for avoiding obstacles; the input of the control system is sensors data and the outputs are the actuators command. obstacle avoidance goal reaching (main task) actuators sensors figure 5: control structure 5 goal reaching task the goal reaching task is expected to align the robot’s heading with the direction of the goal; goal is the orientation difference between the robot axe and the goal. a fuzzy controller is used to accomplish the task as shows the figure (7), with fc-lw and fc-rw the fuzzy command respectively of the left wheel and the right wheel. reactive control using behavior modelling of a mobile robot 221 θgoal goal point r figure 6: goal orientation resolving fuzzy controller θgoal fc_lw fc_rw figure 7: goal reaching controller fuzzy sets the variable ’ goal ’ has 8 fuzzy sets: ( left, front left, front, front right , right, right back, back, left back ) the variables fc-lw and fc-rw have 6 fuzzy sets: (go ahead, go more on left, go more on right, quick turn left, quick turn right, go back ) fuzzy inference system the inference system id defined by the following rules: • if goal is on front then go ahead • if goal is on left then go more on left • if goal is right then go more on right • if goal is back then go back • if goal is on front left then quick turn left • if goal is left back then go back and go more on left • if goal is on right back then go back and go more on right • if goal is on front right then quick turn right -180° -135° -90° -45° 0° 45° 90° 135° 180° b lb l fl f fr r rb b θgoal figure 8: inputs and outputs membership functions 222 imen ayari, abderrazak chatti 6 obstacle avoidance task using neural robot behavior modelling for the task of avoiding obstacles, a neural model is utilized, reproducing the robot behavior in the used environment in order to avoid obstacles. it is a way of learning the environment with obstacle existence by affecting command values to each vector data from sensors. in our application the model inputs are the 8 infrared sensors data and the outputs are the commands of each wheel motor. robot behavior model c1 c2 nc_lw nc_rw c8 figure 9: obstacle avoidance strategy c1 c2 c3 c4 c5 c6 c7 c8 figure 10: infrared sensors position the corresponding neural network model is a normalized multilayer perceptron (mlp) composed of 5 hidden neurons with sigmoidal activation functions and two output linear neuron representing wheels command. the training algorithm used is the backpropagation algorithm with simple gradient. the simple gradient version consists in modifying the weights w according to the following formula: w( k) =w(k −1) + ∆w(k) with : ∆w(k) = −µ ∂ j ∂ w where µ is the training step. ∑ nc_lw ∑ nc_rw c1 c8 . . . . . figure 11: neural network architecture the network training is made using a conceived indoor environment with many obstacles of different shapes. the training data was collected using a joystick to guide the robot navigation in the environment reactive control using behavior modelling of a mobile robot 223 while avoiding obstacles. a sequence of the 8 sensors data and the corresponding wheels command is registered in a file used for the training and test of the achieved model. the model validation is made by computing the prediction error and by testing the robot behavior in the same environment after its training. the training examples describing the obstacle avoiding behavior should be chosen carefully in order to be representative and can be generalized for other environments. a first training was made in a learning environment endowed with simple obstacles shape; we use a more type of the learning environment. the training principle is to learn the robot how to dodge obstacles; the robot goes forward, its front should be free otherwise it turns and follows the obstacle border as seen in figures (12) and (13). as a basic environment was used the one shown in the figure1.a; the test result was satisfactory in the same environment but collisions were detected when adding particular shapes of obstacles like corners or u traps. a second training is then achieved to learn avoiding this particular type of obstacles as seen in figure (13); the navigation test was satisfactory for corners but not for u traps; the robot begins to oscillate between the right and left side without having an outlet, in fact the same command is alternately applied for left and right wheel which causes the oscillation behavior. this situation was treated in the task activation module that gives the overall command for reaching goal and avoiding obstacles tasks. figure 12: behavior robot learning in simple environment 7 task activation the overall command, resulting from the fuzzy controller for reaching goal and the neural model for avoiding obstacles, is computed by a task activation module as shows the figure (14). a third unit is used at the input of the task activation module; it determines the situation (context) concerning the robot localization. three situations are considered according to sensors data giving information about obstacles existence. s1: if c1 is far and c2 is farěand c8 is far then obstacle is far s2: if c1 is near or c2 is nearěor c8 is near then obstacle is near s3: if c1 is near and c2 is near and c4 is near and c6 is near and c7 is near and c8 is near then trap situation 224 imen ayari, abderrazak chatti figure 13: behavior robot learning in complex environment (particular shaped obstacles) sensors configuration (c1…c8) θgoal obstacle avoidance (neural model) goal reaching (fuzzy system) situation selection (s1, s2, s3) task activation (if situation then taski ) nc_lw nc_rw fc_lw fc_rw sk c_lw c_rw task1 task2 figure 14: task activation module reactive control using behavior modelling of a mobile robot 225 the input fuzzy sets describing the distances of each sensor to the nearest obstacle is defined in the figure (15). 0 20 100 far near 1 figure 15: membership functions for distance to obstacle trap situation is activated when the robot is trapped in a narrow corridor; the neural controller gives in this case a bad behavior; the robot begins to oscillate between the right and left side without having an outlet. the figure (16) shows an example of a trap situation. figure 16: example of a trap situation the task activation module is made up of a fuzzy rule base: if situation then task i three rules are considered as follows: • if obstacle is far then goal reaching • if obstacle is near then obstacle avoidance • if trap situation and 0 < goal < pi then turn left else turn right 8 simulation results to verify the validity of the proposed approach, some cases were illustrated using a real time simulation of the upper presented robot; it has to move from a given current position to a desired goal position in an unknown environment endowed with different obstacle shapes: simple shapes as seen in figure (17) and particular ones depicted by figure (18.2). the learned environment and a new one were both tested for the robot navigation depicted respectively by figures (18) and (17). the robot is any case, able to reach the goal placed behind obstacles while avoiding obstacles successfully. 9 summary and conclusions this framework presented a behavior based approach making use of the neural network and fuzzy control to realize the task of reaching goal in a crowded environment. a neural network modelling the robot behavior when encountering obstacles is conceived using supervised learning. different shapes of obstacles were learned; the neural model should associate a particular sensor data to a given action. the goal reaching task was achieved using a fuzzy rule-based system; coordination of the two tasks was addressed using a task activation module giving the overall action depending on the context. the simulation examples of the generation of the collision free path with goal reaching show that designed strategy is acceptable as solution of this problem. 226 imen ayari, abderrazak chatti figure 17: collision free goal reaching in an unlearned environment figure 18: collision free goal reaching in a learned environment reactive control using behavior modelling of a mobile robot 227 bibliography [1] j.miura, h.uozumi and y.shirai, mobile robot motion planning considering the motion uncertainty of moving obstacles, in proceedings of ieee international conference on systems, man, and cybernetics, pp.692-697, 1999. [2] h.yu and t. su, a destination driven navigator with dynamic obstacle motion prediction, in proceedings of ieee international conference on robotics and automation, pp. 2692-2697, seoul, korea: ieee, 2001. [3] m.bennewitz, w.burgard and s. thrun, adapting navigation strategies using motions patterns of people, in proceedings of ieee international conference on robotics and automation, pp.20002005, taipei, taiwan: ieee, 2003. [4] dmitry v. lebedev;1, jochen j. steil, helge j. ritter,the dynamic wave expansion neural network model for robot motion planning in time-varying environments, university of bielefeld, faculty of technology, neuroinformatics group, 2005 [5] r. c. arkin, towards cosmopolitan robots: intelligent navigation in extended man-made environments, phd thesis, university of massachusetts, department of computer and information science, 1987. [6] r. c. arkin, motor schema-based mobile robot navigation, int. j. of robotic research, vol 8, pp. 92-112, 1989. 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[11] j. rosenblatt and d.w. payton, a fine-grained alternative to the subsumption architecture for mobile robot control, proceedings of the ieee/inns international joint conference on neural networks, washington dc, vol. 2, pp. 317-324, june 1989. [12] d. langer, j.k. rosenblatt, and m. hebert, a behavior-based system for off-road navigation, ieee journal of robotics and automation, vol. 10, no. 6, pp. 776-782, 1994. [13] saffiotti, the uses of fuzzy logic for autonomous robot navigation: a catalogue raisonn’e, soft computing research journal, vol. 1, no. 4, pp. 180-197, 1997. [14] h. seraji and a. howard, behavior-based robot navigation on challenging terrain: a fuzzy logic approach, ieee trans. rob. autom., vol. 18, no. 3, pp. 308-321, 2002. [15] simon x. yang, hao li, , max q.-h meng, and peter x liu, an embedded fuzzy controller for a behavior-based mobile robot with guaranteed performance, ieee transactions on fuzzy systems, vol. 12, no. 4, pp.436-446,august 2004. 228 imen ayari, abderrazak chatti [16] x. yang, m. moallem, and r. v. patel, a layered goal-oriented fuzzy motion planning strategy for mobile robot navigation, ieee transactions on systems, man, and cybernetics, vol. 35, no. 6, 1214-1224, december 2005. [17] majura f. selekwa, damion d. dunlap, and emmanuel g. collins, jr., implementation of multivalued fuzzy behavior control for robot navigation in cluttered environments, proceedings of the 2005 ieee international conference on robotics and automation barcelona, spain, pp.3699-3706, april 2005. [18] fatmi, a. al yahmadi, l. khriji, and n. masmoudi, a fuzzy logic based navigation of a mobile robot, transactions on engineering, computing and technology, vol. 15, issn 1305-5313, 2006 [19] d. janglová, neural networks in mobile robot motion, inernational journal of advanced robotic systems, vol.1 n.1, pp. 15-22,2004. [20] w. filali, i. ayari, a.chatti, modelling, real time simulation and fuzzy control of a differential wheels mobile robot, congrès international en sciences et techniques de l’automatique sta, hammamet, 2006. [21] w. filali, i. ayari, a. chatti, research platform on mobile robotics with modular architecture, tunisian-japanese seminar on culture, science and technology tjcst, sousse, 2005. [22] i. rivals, l. personnaz, g. dreyfus and d. canas, real-time control of an autonomous vehicle: a neural network approach to the path following problem, in neuro-nimes, pp 219-229, nimes, 1994. imen ayari, abderrazak chatti institut national des sciences appliquées et de technologie (insat) centre urbain nord, bp. 676, 1080 tunis cedex, tunisie e-mail : ayari.imen@gmail.com, abderrazak.chatti@insat.rnu.tn received : february 12, 2007 revised : june 26, 2007 imen ayari (born on july 10, 1979) received the engineer diploma degree in 2003 and the master degree in 2005, both in automatic and industrial computing from the "institut national des sciences appliquées et de technologie", tunisia. she is currently preparing the ph.d. degree in automatic and computer science within the framework of lagis-ec-lille and lara-enit (tunisia) cooperation. her research is related to the appliance of soft computing methods especially neural networks for mobile robots control. abderrazak chatti received the master of science degree in 1988 from polytechnical school of saint-pétersbourg from russia and the phd degree in technical sciences in 1994 from polytechnical university of saint-pétersbourg, russia, both in automatics and industrial computing. presently he is associate professor in the "institut national des sciences appliquées et de technologie", tunisia. his research interest includes topics such as modelling and control of non linear dynamic processes, robust and intelligent control. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 664-664 a fuzzy control heuristic applied to non-linear dynamic system using a fuzzy knowledge representation f.m. cordova, g. leyton felisa m. cordova university of santiago of chile ecuador 3769. estacion central chile, santiago e-mail: felisa.cordova@usach.cl guillermo leyton university of la serena benavente 980 e-mail: gleyton@userena.cl abstract: this paper presents the design of a fuzzy control heuristic that can be applied for modeling nonlinear dynamic systems using a fuzzy knowledge representation. nonlinear dynamic systems have been modeled traditionally on the basis of connections between the subsystems that compose it. nevertheless, this model design does not consider some of the following problems: existing dynamics between the subsystems; order and priority of the connection between subsystems; degrees of influence or causality between subsystems; particular state of each subsystem and state of the system on the basis of the combination of the diverse states of the subsystems; positive or negative influences between subsystems. in this context, the main objective of this proposal is to manage the whole system state by managing the state combination of the subsystems involved. in the proposed design the diverse states of subsystems at different levels are represented by a knowledge base matrix of fuzzy intervals (kbmfi). this type of structure is a fuzzy hypercube that provides facilities operations like: insert, delete, and switching. it also allows boolean operations between different kbmfi and inferences. each subsystem in a specific level and its connectors are characterized by factors with fuzzy attributes represented by membership functions. existing measures the degree of influence among the different levels are obtained (negatives, positives). in addition, the system state is determined based on the combination of the statements of the subsystems (stable, oscillatory, attractor, chaos). it allows introducing the dynamic effects in the calculation of each output level. the control and search of knowledge patterns are made by means of a fuzzy control heuristic. finally, an application to the co-ordination of the activities among different levels of the operation of an underground mine is developed and discussed. keywords: fuzzy systems, knowledge representation, heuristics, nonlinear dynamic systems. 1 introduction organizations can be visualized as complex systems composed of various subsystems that respond to different problems and have their own dynamics. this process in turn is recursive, so each subsystem has a particular dynamics. such is the case of managements, business areas, departments, primary and support activities of the value chain, activities plans, besides many copyright c⃝ 2006-2010 by ccc publications a fuzzy control heuristic applied to non-linear dynamic system using a fuzzy knowledge representation 665 other systems and subsystems existing in the organization. each subsystem is characterized by its variables and by inputs that can alter its performance and its outputs, which are the inputs of other subsystems, whose dependent effects are known only approximately. this constitutes a situation of a set of highly dynamic subsystems and with clearly nonlinear characteristics. usually, these factors are not considered in the decision making processes. it is clear that a universe of this kind is quite heterogeneous, dynamic, and growing. also, because of the nature of the stated problem, it must be considered that these subsystems represent inputs among themselves, giving the problem a high dose of parallelism. insofar as these subsystems serve as inputs among themselves, feedback is taking place continuously, making the system’s dynamics difficult to control, predict, manage and administer [1]. it is also necessary to take into account the increasing number of data, information and knowledge that current systems must administer, in particular their adequate representation [9]. if we consider that the problem of knowledge-based management and decision making must be carried out in organizations having these characteristics, then it is ever more important to support conceptual models and tools adequate for the planning, management and control processes of this dynamics. on the other hand, the representation knowledge is a fundamental component in any intelligent system that allows coding knowledge, objects, objectives, actions, and processes. the scheme for the chosen representation of knowledge determines the reasoning process and its efficiency. numerous studies on the representation of knowledge show that a representation can be more adequate than another one for a particular case or it can be capable of covering a greater number of cases [8]. the more traditional methods used are semantic networks, frames, production rules, trees, and bits matrices. cazorla et al. [3] suggest that knowledge can be classified according to the specific application to be used that develops knowledge: procedural, declarative, meta-knowledge, heuristic, or structural. however, the theory of diffuse sets proposed by zadeh [12], [13] allows the generation of knowledge representations that are closer to the nature itself of what it is desired to represent. the conceptual models of systems, their representation based on knowledge, and the tools for supporting management and decision making must then consider in their design factors such as high dynamism, parallelism, feedback, incompleteness, handling of uncertainty, nonlinearity, vagueness, qualitative definitions and behaviors, personal opinions, etc. along this line, some authors [1], [16], [15] make a profound development of various concepts such as fuzzy function approximations, chaos and fuzzy control, and processing of fuzzy signals. however, his greatest contribution refers to the calculation and representation of knowledge by means of fuzzy cubes and fuzzy cognitive maps. mcneill [6] also works with fuzzy theory as a means of representing environments with uncertainty usually characterized by their nonlinearity. welstead, on the other hand, supported by one of kosko’s results [11] suggests that fuzzy rules can be represented by one or more fuzzy associative memory matrices (fam); combining the above with genetic algorithms he proposes a model to approach prediction problems. they also use fuzzy representations centered mainly on the interaction of fuzzy theory, neural networks, and genetic algorithms, supporting a new line of work known as computational intelligence. tsoukalas [10] is more centered on the interaction and creation of fuzzy theory and neural network hybrids. to approach these kinds of problems, models are designed making use mainly of causal diagrams or knowledge maps with a series of nodes that would represent the concepts that are relevant to the system, and links between them that show the causal relation (influence) between concepts. in this context, the objective of this paper is to make a study and analysis that will allow modeling some types of dynamic systems, representing knowledge by means of a knowledge base matrix of fuzzy intervals and fuzzy cognitive maps [4], [14] and [15] with the purpose of achieving their categorization and fuzzy weight, as well as the levels of incidence in other subsystems, in this way characterizing the complete system with its levels of fuzzy incidence [5], [10]. 666 f.m. cordova, g. leyton 2 modeling of the diffuse knowledge base matrix in this proposal each of the map’s concepts corresponds to a fuzzy set, and it is specifically a particular knowledge base matrix of fuzzy intervals (kbmfi). the connections between concepts will have an associated value in the [-1,1] range that represents the degree of influence of a (kbmfi) node on another. if the value is positive, it indicates that an increase on the evidence of the origin concept increases the meaning, the evidence or the truth value of the destination concept. if it is negative, an increase of the evidence of the source causes a decrease of that of destination. if the value is 0, there is no connection, and no causal relation. in this way it is possible to get blurred cognitive maps from the opinion of one or various experts on the relations between some aspects of the evaluation process of a hypothetical case. also, the clear recursiveness involved in these types of systems is considered, and a vision of granularity is proposed that allows overcoming the various levels of abstraction subjacent in the dissimilar subsystems. on the other hand, internally each subsystem can be represented by kbmfis, allowing their incidence weight to be obtained with respect to other subsystems and at the same time represent their particular behavior. definition 1. let x be a classical set of objects, called the universe. belonging to a subset a of x can be defined in terms of the characteristic function: µa : x −→ [0, 1] x −→ µa(x) (1) where: µa(x) = { 1 x ∈ a 0 x ̸∈ a if the evaluation set 0, 1 is extended to the real interval [0,1], then it is possible to talk about the partial belonging in a, where µa(x) is the degree of belonging of x in a, and the values 0 and 1 are interpreted as "non-belonging" and "total belonging", respectively. clearly, a is a subset of x, which has no defined boundaries. this leads to the following definition. definition 2. let x be an object’s space. a fuzzy set a of x is characterized by the set of pairs: a = {(x, µa(x))/x ∈ x} where µa : x −→ [0, 1] (2) the fuzzy concept proposed by zadeh [11] is based on the fact of allowing the partial belonging in a set for certain elements of a given universe. definition 3. a fuzzy hypercube can be considered as a unit hypercube, i.e., a hypercube in = [0, 1]n. the n fuzzy cube has two vertices or binary subsets. a fuzzy cube contains all the fuzzy sets of a set x of n objects. the non-fuzzy sets are found at the vertices of the cube. the continuum of fuzzy sets is in the cube. definition 4. knowledge base matrix of fuzzy intervals (kbmfi) means the hypercube that is constituted by the various knowledge e1, e2, e3, ..., en, relative to a domain of knowledge, considering also the different weight or importance that each of them has in the particular domain. the kbmfi is a fuzzy hypercube where e1, e2, ..., en, represent the various contingencies or characteristics of the area under discussion, according to the opinion of the experts. ej, with j = 1, 2, ..., n, do not necessarily have the same relevance or weight, they can be in particular a fuzzy control heuristic applied to non-linear dynamic system using a fuzzy knowledge representation 667 fuzzy frames consisting of s1, ..., sm, where s1, s2, ..., sm, are the possible factors, not necessarily disjoint, such that each characteristic ei can be expressed by means of some particular union of s1, s2, ..., sm factors. now the particular determination of each ei through its particular factors s1, s2, ..., sm, model systems composed of a range of nodes n1, n2, ..., nn, continually influencing each other if and where the incidence of one with respect to others is completely dynamic. in particular, this outlines a vision of dynamic nonlinear systems which in similar but not equal versions are seen as causality maps. if the map is adjusted to the opinions of several experts, one would have to get the assessments of all of them and therefore establish the definitive values associated with the causality relations. it must be noted that in general the causalities mentioned by the experts with respect to the various influences exerted by the nodes of the maps are more attributable to qualitative than quantitative concepts. as already stated, nonlinear dynamic systems involve nonlinear and feedback behaviors. in these systems the output of a process or node is used as input for the following node or iteration, and the output of this can again be the input of the same previous node, i.e., self-recurrent behaviors. this behavior corresponds to the following equation: f(x0) =   xn−1 xn xn+1 assuming that the following situation occurs when modeling the system: x1, x2, x3, ..., xn. definition 5. let x0 be an arbitrary starting node, then the above sequence is called the trajectory. considering these definitions, several behaviors can occur, such as, for example: fixed points; periodic trajectories; behaviors given by attractor nodes, and chaos. 3 case study the case study corresponds to the situation of an underground mine which has three levels: production level, reduction level, and transport level. the problem consists in "providing support to activity scheduling management". the problem consists in "providing support to activity scheduling management" [2]. the total system shown by figure 1 consists of these three subsystems and the dynamics that exists between them. this situation is denoted as level 1. figure 1: production, reduction and transport levels. 668 f.m. cordova, g. leyton n1: production level considers n11, n12, n13, as subsystems; n2: reduction level considers n21, n22, as subsystems; n3: transport level considers no subsystems. looking at it at a more particular abstraction level, level 2 appears, as shown in figure 2. from the particular situation shown, in figure 1 it is seen that: n1 influences n2 negatively and n3 positively, n2 influences n1 and n3 positively, n3 influences n2 negatively and n3 positively. however, figure 2 shows that the information obtained at level 1 of abstraction of the system does not have the sensitivity or reliability that is obtained at level 2 of abstraction, whose granularity or disaggregation is slightly higher. figure 2: diagram of influence at the different levels. if both levels are confronted, it may be incorrectly deduced that apparently contradictory information is obtained. for example, if we look at level 1 and level 2 for the case of n3 with n2, at level 1 it was stated that n3 influences n2 negatively, but at level 2 it could be concluded that both have the opposite influence, n31 influences n22 negatively and n21 influences n31 positively. this apparent contradiction can be explained, for example, by saying that when production at the reduction level decreases, there is less pressure on the demand for trains or cars, and on the other hand, if there is not sufficient transport from n31 there is an impact due to accumulation of material at the reduction level, which is considered a negative influence. then the question is, which of the two situations has greater incidence weight? according to figure 3, and only as an example, it can be stated that the negative impact from n31 to n22 is greater than the influence of n22 on n31. the main observations to the system are: it is clear that it is a dynamic fuzzy system. in turn, every ni is a dynamic fuzzy subsystem. the connections between the various ni are fuzzy. these connections can be positive or negative. if positive, ni influences positively on nj. if negative, ni influences negatively on nj. 4 design and implementation of the kbmfi matrix going more deeply into table 1, the experts draw these kbmfi as causal tables. they do not state equations, but make links between subsystems. the kbmfi systems convert each pictograph into a fuzzy rules weight matrix. the nodes of the kbmfi can model the complex nonlinearities between the input and output nodes. the kbmfi can model the dynamics that occur in the multiple iterations that take place in these dynamic systems. the kbmfis with n nodes have nn arcs. since ni(t) nodes are fuzzy concepts, their values ∈ [0, 1]; a state of a kbmfi is the ni(t) = (n1(t), n2(t), ..., nn(t)) vector, so it is a point of the a fuzzy control heuristic applied to non-linear dynamic system using a fuzzy knowledge representation 669 hypercube in = [0, 1]n. an inference in a kbmfi is a road or sequence of points in in, i.e., it is a fuzzy process or an indexed family of fuzzy sets n(t). it is clearly seen that the kbmfis can perform "forward chaining," and whether they can perform "backward chaining" (nonlinearity inverse causality) is an open question. the kbmfis form, as nonlinear dynamic systems, semantic fuzzy networks and act as neural networks. the kbmfis can converge to a fixed point, to a limited cycle, that can be a stable or oscillating state or a chaotic attractor in the fuzzy cube in. in this context, one of the basic questions to be answered is: what happens if the input to the (kbmfi) system is known? in this sense, each kbmfi stores a set of global rules of the form: if n(0) then attractor a (3) a kbmfi with a single fixed global point has only one global rule. the size of the attractor regions in the fuzzy cube governs the number of these global regions or hidden patterns. the kbmfis can have large and small attractor regions in in, each of them with a different degree of complexity. therefore an input state can lead to chaos and a relatively close input state can end up in a fixed point or limited cycle or a stable state. since the kbmfis correspond to a semantic fuzzy network structure, it is possible to associate a matrix m. this matrix lists the causal links between ni nodes. as an example, if it is considered again the case described by figure 2, the corresponding kbmfi is presented where a row is the incidence of ni on nj; columns are nodes influence ni and α, β, γ, δ, η, τ, are values. fuzzy function: [little, moreorless, much, etc.]. n11 n12 n13 n21 n22 n31 n11 0 −αµ +αµ 0 0 +αµ n12 0 0 0 +βµ 0 0 n13 +γµ 0 0 0 −γµ 0 n21 0 −ηµ −ηµ 0 −ηµ 0 n22 +δµ 0 0 0 0 +δµ n31 +τµ 0 0 0 −τµ 0 the proposed model is decomposed in diverse abstraction levels and at each level is represented by a corresponding kbmfi. initially, observing figure 2, the abstraction level 0 appears. only the influence shapes are observed. a node ni can influence positively or negatively to the node nj. abstraction level 0 appears: n1 n2 n3 n1 0 + n2 + 0 n3 + 0 experts are asked to qualify the degree of influence between: µ = [nothing, irrelevant, few, influence, regular, alter, a lot, very much, so much] as shown in the incidence graphic of figure 3. applying the incidence graphic, a second level of abstraction 01 is obtained: n1 n2 n3 n1 0 −µ +µ n2 +µ 0 −µ n3 +µ −µ 0 it is observed that the degree of incidence between a node ni with a node nj, this means that it exists a bigger degree of specificity (granulation) between them. this enhancement of 670 f.m. cordova, g. leyton figure 3: incidence graphic. specificity is explicit in the following level, it exist a "slot" between ni with nj. in this case different situations are denoted: n1 influences in a negative way to n2; n1 influences in a positive way to n3; n2 influences in a positive way to n1; n2 influences in a negative way to n3; n3 influences in a positive way to n1; n3 influences in a negative way to n2. if it is considered that a node ni can be decomposed in ni1, ni2, ..., nik, in where those nim, m = 1, 2, ..., k, with a particular dynamic conforms a ni, the situation in the analyzed case is as follows: n1 = (n11, n12, n13); at level 0; node or subsystem n1. n1 at level 01, node or subsystem n1i is defined by: n11 n12 n13 n11 0 + n12 0 0 0 n13 + 0 0 n1 at level 011 is defined by: n11 n12 n13 n11 0 −αµ +αµ n12 0 0 0 n13 +αµ 0 0 n2 = n21, n22; at level 0; node or subsystem n2. n2 at level 01, node or subsystem n2i is defined by: n21 n22 n21 0 n22 0 0 n2 at level 011 is defined by: a fuzzy control heuristic applied to non-linear dynamic system using a fuzzy knowledge representation 671 n21 n22 n21 0 −ηµ n22 0 0 applying the same procedure to node n3 and it is only characterized by n31, n3 at level 011 is defined by: n31 n31 0 at this point only the fuzzy subsystem cohesion is developed. so, it is necessary to visualize what it happens with the external dynamic between subsystems, in order to obtain the fuzzy matching inter systems. continuing with the fuzzy cohesion procedure, links between nodes n1, n2 and n3, at level 0 by nodes are obtained: n1 n2 n3 n1 0 + at level 01 by node n1: n1 n2 n3 n1 0 −αµ +αµ at level 011 by node n1: n1 n21 n22 n31 n11 0 0 0 +αµ at level 02 by node n1: n1 n21 n22 n31 n11 0 +βµ 0 0 in this way, influences are obtained allowing the fuzzy matching. 5 heuristic control for the kbmfi each ni level has fij factors that determine it, with i = 1, 2, 3; j = 1, 2, ..., m. table 1 shows relevant characteristics, factors, attributes and fuzzy functions at production level. table 2 shows relevant factors, attributes and fuzzy functions at production level. each fij factor has aijs attributes that determine it, where i = 1, 2, 3; j = 1, 2, ..., m; s = 1, 2, ..., k (see table 1). each aijs has attribute metrics associated with its nature. these metrics are functions of fuzzy membership (see table 2). for the above points, it is possible to state that the degrees of influence (negative or positive) that exist between the various levels can be measured, allowing the calculation of the existing dynamics of the system to achieve an intelligent fuzzy control with the purpose of keeping the system in a desirable state (stable). 672 f.m. cordova, g. leyton characteristics of level 1 (production) attributes (metrics or fuzzy functions table 1.1) fuzzy functions factors 1 rel. card. (crc) 1 2 3 4 5 6 7 8 9 1. number of workmen present 2. drilling, agents, and resources 3. blasting, agents and resources 4. technologies involved 5. number of equipments 6. lectures relative cardinality of level 1 (crn1) table 1: relevant characteristics of level 1 at production level. 6 heuristic the proposed heuristic consists of the following stages: stage 1: obtaining the fij factors of each level ni. stage 2: obtaining the aijs attributes of each fij factor. stage 3: determining the metrics associated with each aijs attribute, i.e., determining the fuzzy membership functions for each aijs. stage 4: determining the "formula" that corresponds to each fij from the aijs, for example: fij = λ1aij1 ⊕ λ2aij2 ⊕ ... ⊕ λkaijk (4) where ⊕ is the operator to be determined (=>, ∨, ∪, etc.) and ∑ λk = 1. stage 5: determining ni from the fij, for example: ni = λ1fi1 ⊕ λ2fi2 ⊕ ... oplusλmfim (5) where ⊕ is the operator to be determined (=>, ∨, ∪, etc.) and ∑ λm = 1. note that the output of all nj must be between 0 and 1. stage 6: determining whether the "influence" of the output of ni to other levels is negative or positive. stage 7: recalculating the nt output, with its internal values, considering the influence exerted on it by the recursive dynamics of the nodes ni at stages 1, 2,..., 5. stage 8: determining the output of nt, input of nl, and determining whether we feed nl or ni, and specifying the times. note that in this step we distinguish between what influences what, or we make a push, we make a pull, or both at the same time, with a delay of one with respect to the other, etc. a fuzzy control heuristic applied to non-linear dynamic system using a fuzzy knowledge representation 673 factors and attributes fuzzy functions project system 1. number of workmen present. (decision making complexity). 1.1 number of engineers. µ11(x) = 1 − 25−x 25 10 ≤ x ≤ 25 1.2 number of technicians. µ12(x) = 1 − ( 75−x 75 )2 30 ≤ x ≤ 75 1.3 number of miners. µ13(x) = 1 − ( 150−x 150 )2 60 ≤ x ≤ 150 1.4 number of equipments µ14(x) = 1 − 30−x 30 12 ≤ x ≤ 30 x = amount of engineers, miners, ... 2. drilling, agents and resources. for evaluating this characteristic, first the predominant factor must be identified and then the calculation can be made. for example, if x = 25 or 30 or 90 or 21, for respective: 2.1 planned drillings. µ1i : µ 1 1(25) = 1; µ 1 2(30) = 0.64; µ 1 3(90) = 0.84; µ 1 4(21) = 0.91 2.2 direct agents involved. µ21(x) = 1 − √ 50−x 50 20 ≤ x ≤ 50 2.3 indirect agents involved. µ22,3(x) = 1 − ( 15−x 30 )3 6 ≤ x ≤ 15 table 2: factors, attributes and fuzzy functions at production level. 7 conclusions and future works the work done in the paper allows the characterization of a complex system through subsystems considering the dynamics and the incidence of each subsystem on the others. from the display of the complexity of the system and subsystems, the kbmfi is constructed, which allows an adequate representation of diffuse knowledge and the dynamics associated with the system. a fuzzy control heuristic is also designed that allows managing the kbmfi. in the case of the planning of mining operations, the kbmfi and the associated heuristic allow the evaluation of the impact of the incidence of various factors such as reduction of the number of planned workers in a shift, faults in load haul and dump lhd equipment, rock breakers, shafts, and trains, among others. if someone is considering developing software from this proposal, it should be kept in mind that in the tool there should be an agent module that is informed (alert) of the acceptable critical values for each node, so that this node does not alter acceptable states (experts) of the nodes with which it interacts. in such case the agent must learn about the acceptable critical values, know and learn preventive measures; know and learn mitigation measures, and know and learn corrective measures. bibliography [1] d. alahakoon, s. k. halgamuge, and b. srinivasan, dynamic self organizing maps with controlled growth for knowledge discovery, ieee trans. neural networks, vol.11:601-614, 2000. [2] f. cordova, l. canete, l. quezada, f. yanine, an intelligent supervising system for the operation of an undergound mine, international journal of computers, communications and control, vol. iii: 259-269, 2008. [3] m. gupta, r.k. ragade,yager, advances in fuzzy sets theory and applications, north holland, amsterdam, 1979. 674 f.m. cordova, g. leyton [4] b. kosko, fuzzy ingineering. prentice hall, 1997. [5] g. martinez, servente and pasquini,sistemas inteligentes, nl nueva libreria, argentina, 2003. [6] t. mcneill, fuzzy logic a practical approach. academic press, 1997. [7] h. roman, sobre entropias fuzzy, tesis de doctorado, universidad de campinas, brasil, 1989. [8] e. schnaider, a. kandel, applications of the negation operator in fuzzy production rules, fuzzy sets and systems, vol. 34: 293-299, noth holland, 1990. [9] w. silder, j. buckley, fuzzy expert system and fuzzy reasoning, john wiley and sons inc., new jersey, 416, 2005. [10] u. tsoukalas, fuzzy and neural approaches in engineering. wiley interscience, 1997. [11] s. welstead, neural network and fuzzy logic applications in c++. wiley interscience, 1994. [12] l.a. zadeh, the role of fuzzy logic in the management of uncertainly in expert systems, aproximate reasoning in expert systems, elservier science pub., north holland, 3-31, 1985. [13] l.a. zadeh et al (eds.), from natural language to soft computing: new paradigms in artificial intelligence, editing house of romanian academy, 2008. [14] zadeh, l.a., outline of a new approach to the analysis of a complex systems and decision processed, ieee trans. syst. man cybern., vol. 3: 28-44, 1973. [15] l.a. zadeh, fuzzy sets and fuzzy information: granulation theory, beijing normal university press, beijing, 1997. [16] l.zhong, w.a. halang,g. chen, integration of fuzzy logic and chaos theory, springerverlag, berlin heidelberg, 2006. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 735-743 full-text search engine using mysql c. gyorodi, r. gyorodi, g. pecherle, g. m. cornea cornelia gyorodi, robert gyorodi, george pecherle, george mihai cornea department of computer science faculty of electrical engineering and information technology university of oradea, str. universitatii 1, 410087, oradea, romania e-mail: cgyorodi@uoradea.ro, rgyorodi@rdsor.ro, gpecherle@uoradea.ro, generalmip@yahoo.com abstract: in this article we will try to explain how we can create a search engine using the powerful mysql full-text search. the ever increasing demands of the web requires cheap and elaborate search options. one of the most important issues for a search engine is to have the capacity to order its results set as relevance and provide the user with suggestions in the case of a spelling mistake or a small result set. in order to fulfill this request we thought about using the powerful mysql full-text search. this option is suitable for small to medium scale websites. in order to provide sound like capabilities, a second table containing a bag of words from the main table together with the corresponding metaphone is created. when a suggestion is needed, this table is interrogated for the metaphone of the searched word and the result set is computed resulting a suggestion. keywords: full-text, search, mysql, index, search engine, ranking, metaphone, levenstein 1 introduction before the advent of the search engine, users had to search manually through dozens or hundreds of articles to find the ones that were right for them. nowadays, in our more usercentered world, we expect the results to come to the user, not the other way around. the search engine gets the computer to do the work for the user. full-text search is widely used for various services of the internet. a high-speed and a more efficient full-text search technology are necessary because of the amount of increasing handled document and corresponding document data every day [6]. according to the mysql manual, full-text is a "natural language search"; the words are indexed and appear to represent the row, using the columns you specified. as an example, if all your rows contain "mysql" then "mysql" won’t match much. it’s not terribly unique, and it would return too many results. however, if "mysql" were present in only 5% of the rows, it would return those rows because it doesn’t appear too often to be known as a keyword that’s very used. mysql full-text search doesn’t have too many user-tunable parameters. if you want more control over your search you will have to download the sources and compile them yourself after you’ve made the changes you wanted. anyway, mysql full-text is tuned for best effectiveness. modifying the default behaviour in most cases can actually decrease effectiveness [1]. our implementation comes to bring a plus of functionality to the basic search capabilities that mysql offers by returning better quality results to the user. for small data sets to be searched, its performance can be easily compared with the one of the more advanced dedicated full-text search engines. copyright c⃝ 2006-2010 by ccc publications 736 c. gyorodi, r. gyorodi, g. pecherle, g. m. cornea 2 full-text search in text retrieval, full-text search refers to a technique for searching a computer-stored document or database. in a full-text search, the search engine examines all of the words in every stored document as it tries to match search words supplied by the user. when dealing with a small number of documents it is possible for the full-text search engine to directly scan the contents of the documents with each query, a strategy called serial scanning. this is what some rudimentary tools, such as grep, do when searching. however, when the number of documents to search is potentially large or the quantity of search queries to perform is substantial, the problem of full-text search is often divided into two tasks: indexing and searching. the indexing stage will scan the text of all the documents and build a list of search terms, often called an index, but more correctly named a concordance. in the search stage, when performing a specific query, only the index is referenced rather than the text of the original documents. some indexers also employ language-specific stemming on the words being indexed, so for example any of the words "drives", "drove", or "driven" will be recorded in the index under a single concept word "drive" [2]. before passing on to the search, we would like to talk a little about how mysql full-text indexes work. a mysql full-text query returns rows according to relevance. but what is relevance? it is a floating-point number based on formulas. researchers have shown that these formulas produce results that real users want. for every word that isn’t too short or isn’t a too common one, mysql calculates a number to determine its relevance inside the text. to be noticed that mysql will not increase weight if two keywords are close to each other. local weight, global weight, and query weight are the only things that matter. mysql can work with stemming but as we’ve seen from different tests it isn’t working very well, usually bombarding the user with tons of irrelevant results. there are three formulas we have to mention for full-text index [3]. local weight = (log(dtf)+1)/sumdtf * u / (1+0.0115*u) global weight = log((n-nf)/nf) query weight = local weight * global weight * qf where: dtf how many times the term appears in the row sumdtf -the sum of "(log(dtf)+1)" for all terms in the same row u how many unique terms are in the row n how many rows are in the table nf how many rows contain the term qf how many times the term appears in the query notice that local weight depends on a straight multiplier, the term-within-row frequency times the unique frequency. but most simply: if a term appears many times in a row, the weight goes up. why does local weight depend on how many times the term is in the row? think of the document you are reading now. i inevitably mention "mysql" and "full-text" several times. that’s typical: if words appear several times, they are likely to be relevant. notice that global weight depends on an inverse multiplier, the count of rows minus the count of rows that the term appears in. put most simply: if a term appears in many rows, the weight goes down. to illustrate this better we’ve chosen to give a practical example. for this example, we’ve used a smaller database in order to be able to calculate the relevancies. to see what is in a fullfull-text search engine using mysql 737 text index, we can use the myisam_ftdump program. it comes with the standard distribution. the name of owner table is demo and it is located in dmp database. the table consists in 2 columns, the second one being the one with full-text index. in order to see the local weights of the different words we have to use the command dump index. this command will return the data offsets and the word weights (fig. 1). figure 1: dump index in order to see the global weights of the words we have to use the command calculate per-word stats. this command will return for us a word count and the global weight (fig. 2). figure 2: calculate per-word stats for the first formula: (log(dtf)+1)/sumdtf * u/(1+0.0115*u); where dtf is how many times the term appears in the row "mysql" appears 2 times in row 0 so log(dtf()+1) = 0.6931472 + 1 = 1.6931472 sumdtf the sum of "(log(dtf)+1)" for all terms in the same row "mysql" appears 2 times in row 0, so add log(2)+1 "world" appears 1 times in row 0, so add log(1)+1 "popular" appears 1 times in row 0, so add log(1)+1 "open" appears 1 times in row 0, so add log(1)+1 "source" appears 1 times in row 0, so add log(1)+1 "database" appears 1 times in row 0, so add log(1)+1 "free " appears 1 times in row 0, so add log(1)+1 so sumdtf = log(2)+1 + (log(1)+1)*6 = 7.6931472 738 c. gyorodi, r. gyorodi, g. pecherle, g. m. cornea u how many unique terms are in the row there are 7 unique terms in row 0 so u/(1+0.115*u) = 7/(1+0.0115*7) = 6.478482 local weight = 1.6931472 / 7.6931472 * 6.478482= 1.4258175 (check figure 2 for the first occurrence of the term “mysql” * ) for the second formula: log((n-nf)/nf); where: n how many rows are in the table; there are 4 rows in the ’demo’ table nf how many rows contain the term; the term "special" occurs in 3 rows log((4-3)/3)= -1.0986123 (check fig. 3 for the term “mysql” ** ) note that because this term appears in more than 50% of the rows it has a negative global weight. in an actual search, this term will practically be ignored, only the adjacent terms being representative. 3 implementation the primary table is the table where we keep all the data that has to be searched and ranked. also, from this table we will create later the “bag of words” together with their metaphones in order to provide correction suggestions to the user. the primary table will consist of 6 columns, the first one being the row id and the others all being meant for full-text search. the structure of the table is: id unique identifier for every row url the url where the text was taken from title the title of the page the text was taken from content everything on the page that is not part of the formatting and functionality (only text, no html or other scripts, including the dealers and strong text) headers h1,... contents from the page. they will have higher relevancy than strong and content but lower than url and title strong words that appear between b or strong tags. they will have higher relevancy than ordinary text from content but lower than all the others. during the construction of the table in the indexing process we decompose every page to obtain the required fields. first we will obtain the url and the title fields. after we have those two we can get rid off the head part of the web page and move on to the body. before parsing out the entire html and other script tags from the text, we will have to take out the header and the strong keywords that will have a higher relevancy in our future search. after we’ve done that we can parse all the remaining script tags and send the resulting data into the mysql database. an improvement to this table would be a new column that contains all the keywords linking to this page. this way you can extend the article relevancy behind the actual page. those linking keywords can be considered the ones between the anchor tag or the most relevant keywords from that page (calculated by their density after we’ve previously removed the stop words) or a combination of both. however, this approach requires many more pages to be scanned and many of them could come from outside our website. after the first indexing is complete we can create the full-text index on the table. from now on we can make full-text searches on that table. any new insert into the database will full-text search engine using mysql 739 be attached on the fly internally by mysql so that we don’t have to worry about this [9]. the sql syntax for doing this is: alter table ‘content‘ add fulltext (‘url‘,‘title‘, ‘content‘,‘headers‘,‘strong‘) searching the database is not very hard. actually we will only use a query to apply a search on the database and let mysql do the rest. it is a lot harder to determine the best values for the weights of the results. depending on those constants, we will have different search results as we will show in the following examples. in order to query the database we will use the following query. the query is virtually composed of three parts. the first part is meant to determine the relevancies of every column in part, the second one is meant for determining the matching rows and the last one is for ordering. you can see the query in fig. 3. figure 3: search query relurl, reltitle, relcontent, relheaders and relstrong are the different relevancies returned by mysql after the preliminary search of every different column in part. the parameters u, t, c, h and s represent the importance of those relevancies in part. the query will execute like this: first, different relevancies will be returned from the search on the individual columns. those relevancies will be later used to calculate the order of the results. after we have got the relevancies we have to move on to the retrieval of all the rows that contain the terms that we searched. in order to do this we have to select all the matches inside the text fields (‘content‘, ‘url‘, ‘title‘, ‘headers‘, ‘strong‘). once we have those results we will have to reorder them by relevance. in order to do this we will determine the order by argument using the following formula. relevance = relurl *u + reltitle *t + relcontent *c+ relheaders *h+ relstrong *s figure 4: relevance formula the relevance for every column should be carefully selected in order to achieve the best result order. the url relevance is not so important in our opinion. we have added it because all the big search engines have it. we will treat it as it would have the same relevance as the page title. the next column on the relevance scale, after the url and the title would be the headers relevance. let’s think about a certainty search. we want to search for an article about “mysql fulltext”. the script will first determine the pages that contain the keywords inside the title and url of the page because those are the most suitable for us. the next in importance is the headers column. the last two of the columns are the content column and the strong column. the strong column contains keywords that the writer of that page thought they are of a higher importance, with strong relevance to the subject. 740 c. gyorodi, r. gyorodi, g. pecherle, g. m. cornea the order of the relevancies is clear. the plain content relevance is the lower one, followed by the keywords relevance (strong), headers, title which is about the same to the url relevance. the problems appear when we have to determine the best magnitude for those relevance parameters. if the difference of the relevance parameters is too small, we will not have effective search results ordering. but if we fall into the other side, we can provide excessive ordering based only on the hierarchy we’ve determined for the parameters and less based on the relevancies of the results. a schematisation of the proposed indexing and search algorithm is provided below: a) indexing 1. the web page is processed by reading its contents. 2. the internal links from the page are determined. 3. the unique internal links and full page contents are inserted in a ’pages’ table. 4. the next link to follow is taken from the ’pages’ table and the new corresponding page is processed by going to step 1, until there are no more unprocessed pages. 5. the ’pages’ table is processed to determine the page title, headings and strong text and the results are placed in a new ’contents’ table. b) full-text search 1. query the ’contents’ table against a search term. 2. determine the relevancies of every column in part. 3. determine the matching rows. 4. order the results by relevance, calculated using the formula in fig. 4. the parameters u, t, c, h and s are chosen by us and they are an indicator of the importance of each of the relevancies (url, title, content, headers and strong text). 4 testing to test the algorithm, we have set up a database of 320 mb (including data and index) and a total number of 11,670 records. the database was built by indexing a subset of the wikipedia.com website, using the indexing algorithm described in the previous section. for testing purposes, we have chosen the following values for the u, t, c, h and s parameters. the user can set his own values. a higher value means a higher importance of that element: u=1.14, t=1.14, c=1, h=1.3, s=1.2 the testing process was done by asking different users to give ratings to each of the search results. we have performed a total of 143 tests. the ratings given by the users proved that our algorithm is more relevant than the default mysql method by 19.47% . here are some examples of tests done with specific keywords: keyword: "programming" results: our algorithm (the left panel) returned relevant results in positions 1 and 2 (an article titled "computer programming" and another one "application programming interface"). this was because our algorithm assigned a higher importance to the page title and url (1.14) than the page contents (1). the right panel did not return many relevant results (a result which was close to our expectations was in position 2, but it was not very specific because it was about a specific programming language and not about programming languages in general). keyword: "universal serial bus" results: our algorithm (the left panel) returned a relevant result in position 1 (an article titled full-text search engine using mysql 741 "universal serial bus"). the right panel did not return any relevant results (instead it returned pages that contain "universal" only and not the whole search term). 5 search suggestions in many cases, there appears the necessity to correct the spelling mistakes made by the users when they don’t know exactly how to spell something or are just making a typing mistake. to do this, we will have to give them the closest correct form that best matches the initial search. metaphone is a phonetic algorithm, an algorithm published in 1990 for indexing words by their english pronunciation. the algorithm produces variable length keys as its output, as opposed to soundex’s fixed-length keys. similar sounding words share the same keys. metaphone was developed by lawrence philips as a response to deficiencies in the soundex algorithm. it is more accurate than soundex because it uses a larger set of rules for english pronunciation. metaphone is available as a built-in operator in a number of systems, including later versions of php. the original author later produced a new version of the algorithm, which he named double metaphone, that produces more accurate results than the original algorithm. however i will use the first version of the script because it returns accurate enough keys with a much better performance than the second one. [4] in order to provide fast enough processing for the suggestions we will create a “bag of words” containing all the distinct words contained inside the content column. for each one of them we will attach the metaphone key. when a search is done we will select the candidates by calculating the metaphone of every word inside the search string and select the matches inside the metaphone table. after that operation we will have about 3 to 8 possible words to choose from. the levenshtein distance is defined as the minimal number of characters you have to replace, insert or delete to transform str1 into str2 . the complexity of the algorithm is o(m*n), where n and m are the length of str1 and str2 (rather good when compared to similar_text(), which is o(max(n, m)**3), but still expensive). in its simplest form the function will take only the two strings as parameters and will calculate just the number of insert, replace and delete operations needed to transform str1 into str2. [5] we will use this algorithm to determine the closest form from the list of candidates by selecting the candidate with the smallest levenshtein distance to the searched keyword. to be noticed that this algorithm only works for word spelling mistakes. it does not work for mistakes like missing space, or composed words. missing space example: “mysqlfultext” will not suggest “mysql fultext” composed words example: “pine apple” will not suggest “pineapple” although some of the suggestion results can be hidden from the users because of the use of metaphone equivalence, this approach gives an incredible boost of performance. with a suggestion table containing over 130,000 rows it would take way too long to determine the levensthein distance between the searched form and the correct form regarding the fact that, as we have said before, the levenshtein algorithm has a complexity o(m*n). in owner case we would have a complexity o(m*n)*nrrows, where nrrows exceeds 130,000. also, by applying this direct approach, we wouldn’t solve the problems we’ve illustrated above. by applying owner algorithm we can reduce the candidates from 130,000 to approximately 4-7 words. this represents a significant improvement of performance with only little disadvantages. furthermore, those disadvantages could be counteracted by completing the suggestion table according to the newly searched term if the suggestion we provided is wrong and if there 742 c. gyorodi, r. gyorodi, g. pecherle, g. m. cornea is a sufficient similarity between the previous and the actual search, similarity which can be determined by dividing the levenshtein distance with the string length. a schematisation of the proposed search suggestions algorithm is provided below: 1. create a table containing all the words in the contents column and their attached metaphone keys. 2. the metaphone of the searched keyword is determined using the metaphone function [4]. 3. the levenshtein distance is calculated as in [5]. 4. the word with the smallest levenshtein distance is chosen. 6 comparison with other similar technologies in comparison with other similar technologies we have strong points and weak points at the same time. it is impossible to have all of them at the same time and there has to be a compromise between costs, speed, maintenance and quality of the results. in comparison with mysql basic full-text search we have the advantage of a better ordered result set. however, this comes with the price of an increased search time because of the extra computation needed for the relevance ordering, according to fig. 4. both methods have the advantages of being free, with easy maintenance (the update of the full-text index is done on the fly) and they don’t require special permissions on the server. if we compare it with the free open-source sql full-text search engine sphinx, we can mention other differences. the common part of the two solutions is that they are both free. the strong points of our solution are that we have a better ordered result set and easier maintainability. the strong point for sphinx is the search speed making it suitable for searching bigger databases. another week point for sphinx is the maintainability. the full-text index is not able to be updated on the fly so that it has to be recomputed periodically, task that can take from 15 minutes for small databases to a few hours for large ones. 7 conclusions by using our implementation it is possible to offer a complete and powerful search option. for the future, we would like to improve the search speed by using a dedicated full-text search engine. another improvement we would like to implement would be a new parameter which will determine the popularity of a result based on determining if a user found what he wanted or not. this will use a combination of the time the user spent on that certain page divided by the content length, how many times the user returned to this page or if a page is or is not a stop page (which can indicate that the user found what he wanted to know). also, in addition to search, we would also like to implement high-speed insert and delete, allowing full-text search to be used in the same way as other types of database search in which data can be searched right after data is inserted [7]. another idea for future improvement is handling scientific document searches, especially mathematical text and mathematical operations [8]. for the suggestion tool we want to improve the algorithm in such a way to be able to correct as many mistakes as possible. full-text search engine using mysql 743 bibliography [1] fine-tuning mysql full-text search http://dev.mysql.com/doc/refman/5.0/en/fulltextfine-tuning.html [2] full text search by wikipedia http://en.wikipedia.org/wiki/full_text_search [3] mysql’s full-text formulas by database journals http://www.databasejournal.com/ features/mysql/article.php/3512461/mysqls-full-text-formulas.htm [4] metaphone by wikipedia http://en.wikipedia.org/wiki/metaphone [5] the levenstein distance http://us2.php.net/levenshtein [6] atlam, e.-s., ghada, e.-m., fuketa, m., morita, k., aoe, j., a compact memory space of dynamic full-text search using bi-gram index, computers and communications, 2004. proceedings iscc 2004. ninth international symposium [7] ikeda, t., mano, h., itoh, h., takegawa, h., hiraoka, t., horibe, s., ogawa, y., "trmeister: a dbms with high-performance full-text search functions", data engineering, 2005. icde 2005. proceedings. 21st international conference [8] misutka, j., galambos, l., "mathematical extension of full text search engine indexer", information and communication technologies: from theory to applications, 2008. ictta 2008. 3rd international conference [9] d. zmaranda, g. gabor, issues on optimality criteria applied in real-time scheduling, international journal of computers communications & control, issn 1841-9836, suppl.s, 3(s):536-540, 2008. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 161-171 antenna arrays principle and solutions: robust control approach florin hutu, sebastien cauet, patrick coirault abstract: this paper treats solutions on the ability of a chain of non identical oscillators to drive antenna arrays. frequency approaches were studied in order to solve the problem of synchronization of the oscillators. however, in this article, a new structure of chain of oscillators is introduced. secondly, lyapunov theory of stability is used to design a dynamical controller guarantying the oscillators synchronization. the problem of synchronization is transformed into a problem of asymptotic stabilization for a nonlinear system. it is formulated as a system of linear matrix inequalities where the parameter variations of the two oscillators and their differences are modeled by polytopic matrices. the theoretical result is successfully applied to an array of transistor-based oscillators used in "smart antenna" systems. keyword : nonlinear systems, control applications, antenna arrays 1 introduction the demand of mobile communication services is in a continuous growth, moreover, it is estimated that the rate will be maintained in the next years. this continuous development has stimulated the research of new hardware and software solutions in order to increase the volume of exchanged data and a better management of the emitted or received electromagnetic field. smart antenna arrays comprise a number of antennas that work in conjunction with an intelligent system that processes the received and transmitted data. the processing can be realized in a hardware or in a software way and allows smart antenna arrays to focus beams into particular directions. this problem can be partially solved by using several directional antennas. this solution divides the 360-degree coverage area into sectors. however, smart antenna arrays provide a much more effective solution by focusing the transmitted power toward user and only looking in the direction of the user for the up link signal. this ensures that the user receives the optimum quality of service and the maximum coverage from a base station. the new technologies development increases the antenna array performances and minimizes the costs of production and the occupied space. it makes them implementable in domains like wireless or satellite communications, radar systems, missile defense systems, automobile industry, etc. smart antennas or antenna arrays are a part of communication systems that can improve their global performances. this technique can increase the spectral efficiency and reduce the multi path fading, bit error rate (ber), the co-channel interferences (cci) and the system complexity [1]. this is possible by electronically adjusting the beam pattern of the antenna array in order to provide important gain for the desired signals and small gain for interference signals. at emission, the purpose of smart antennas is to minimize the interference between the different transmitters who works on the same communication channel and, thus to more efficiently use the emitted power. for this reason, the beam shape must be controlled in order to minimize the amplitude of the side lobes and to maximize the energy in the main lobe. moreover, the direction of the main lobe must be controlled. the focused application is inter-vehicle communication. the bandwidth will be, first of all, in the area of 24ghz and finally around 79ghz. at these frequencies, technique like "software defined radio" can not be used. one of the main objectives that is pointed out is to develop a structure which will extend in both space and time the safety information available to drivers by using the infrastructure and vehicles as sources. copyright © 2006-2008 by ccc publications 162 florin hutu, sebastien cauet, patrick coirault when the smart antennas are used in reception systems, the signals coming from interference directions must be rejected and those which comes from the desired directions must be privileged. for this reason, different phases and amplitudes must be assured by the carrier signals locally generated [2, 3, 4]. the work that has been done in the field of dynamics of coupled nonlinear systems using the frequency approach [5][6, 7][8] shows that they offer methods of phase control among array elements and beam scanning capabilities but also implies problems of stabilization. the proposed structure of the array of antennas is based on unidirectional coupled oscillators. in details, this paper treats the synchronization of a system made by two oscillators with an unidirectional coupling and this problem of synchronization is transformed in a problem of stabilization for a nonlinear system. the strategy chosen is to find an output feedback dynamic controller using lyapunov functions that assures a robust synchronization despite parameters variations of the oscillators. the problem of computing dynamic output feedbacks on lti (linear time-invariant) systems in term of matrix inequalities is difficult to solve. there are two known techniques: the iterative algorithms and the cancellation of variable products by using the matrix separation lemma. the reader can found some papers on treatment of this problem by lmi (linear matrix inequality)-s who can be numerically solved [9, 10, 11, 12, 13]. the variations of the parameters of the oscillators are taken into account by considering the state matrix as a polytopic one. the polytopic structure is easily tractable by linear matrix inequalities. once the stability of the vertices, defined for the polytope is proved, the stability and the synchronization of the two oscillators is assured for all systems inside the polytope. the nonlinear character of the oscillators allows the synchronization (if their free running frequencies are in a certain domain [6]) but also makes them dependent of initial conditions. the main objective is to cancel the nonlinear effect and to maintain the synchronization when the physical parameters of the oscillators and external conditions are modified. the originality of the method comes from the inclusion of the non-linear term and the undesired variations in a perturbation. this problem is transformed in a h∞ optimization. in section 2 is presented an overview of the antenna array theory. in section 3 a model of master and slave oscillators and the controller synthesis are introduced. section 4 presents the numerical results in the case of an array of coupled nonlinear oscillators. 2 antenna array theory overview 2.1 theoretical background a smart antenna is composed by an array of individual radiative elements (elementary antennas), which are placed in a particular configuration (linear, circular or matrix). by gathering these elementary antennas in such arrays and by changing the characteristics of the signals associated to each element, the array can present different gains according to the direction. let us consider an uniform linear array of n identical patch antennas placed at the same distance d between them as in fig. 1. for the theoretical study of this configuration, it is assumed that in the elementary antennas, harmonic signals of the same frequency but different amplitudes and phases are injected. the mathematical expression of the total electromagnetic field generated by the array in far-field regions (fraunhofer regions) can be written as the product of the electromagnetic field of the reference antenna (which is considered the first antenna in the array) and a term which depends on the amplitudes and phases of the injected signals etotal = ere f ∗ f (θ). (1) thereafter, the gain of the antenna array will be considered, knowing that it is a normalization of the antenna arrays principle and solutions: robust control approach 163 wm sin 2π f0tw1 sin 2π f0t w2 sin 2π f0t wn sin 2π f0t θ z0 d figure 1: an uniform antenna array amplitude of the electromagnetic field. the array factor can be written as f (θ) = n ∑ m=1 wme− j(m−1)k0d cos θ, (2) where wm = ame jϕm . in the easiest emission case, the amplitudes have the same value, the mathematical expression of the radiation pattern is f (θ ) = 1 n sin nγ2 sin γ2 , (3) where γ = ϕ − k0d cos(θ ), n the number of antennas, d the distance between them. it can be seen that a quantity of the radiated energy is lost in the side lobes which implies a certain weakening of the antenna array gain. it is obvious that in the emission case, both variations of the amplitudes and the phases of the carrier signals are in a large interval. this paper proposes a new technique to generate such signals, which have the same frequency and different phases and amplitudes. it can be concluded that both variations of the amplitudes and phases are in a large interval. 3 problem formulation 3.1 problem statement if the amplitude variation can be easily solved by using variable gain amplifiers, the problem of the phase variation is more constraining. there are several techniques which permits solving this problem. the main technical problem with beamforming for transmit is realizing the phase and amplitude of the signals in each antenna channel. the first step will be generating the reference signal with the selected or required frequency. it has to be modulated with the information to be transmitted (e.g. radar, communication). then it has to be amplified and distributed to all transmit channels. then the individual signals have to be weighted for beamforming, that means amplified or attenuated, according to the desired weighting amplitude. the desired phase has to be realized by a suitable steerable phase shifter or delay line. digital devices are now available, under the headline "software defined radio". this technique is not usable on frequencies over 400 mhz. here, techniques, which are pointed out, are designed for applications over 2ghz. techniques can be divided into both main approaches. the first approach uses the signal generated by one oscillator and the second which uses signals generated by array of coupled oscillators. signals with the same frequency but different phases and amplitudes can be built by delaying the signal generated with one master oscillator using high-frequency power dividers and variable delay lines or butler couplers. this approach is very useful when discrete-time systems are built. another approach 164 florin hutu, sebastien cauet, patrick coirault is to use polyphasic oscillators and a multiplexing system [5]. but in these techniques, it can not be obtained continuous phase variations. the second approach is based on the synchronization of arrays of oscillators having their free running frequencies with a weak dispersion. in [6, 8] it was demonstrated that arrays of coupled nonlinear oscillators can synchronize. moreover, according to the coupling strength and to free-running frequencies, phase variations can be made. recent works [14] shows how the phase variation can be guaranty by changing only the free-running frequencies of all coupled oscillators in the array. in order to generate these carrier signals, the following general schematic fig. 2 is proposed. ( f0 , an , ϕn ) + + + slave3slave2master slaven σcτn−1σcτ2σcτ1 gain1 gain2 gain3 gainn σσς ( f0 , a1 , ϕ1 ) ( f0 , a2 , ϕ2 ) ( f0 , a3 , ϕ3 ) figure 2: unidirectional coupling of a chain of oscillators this schematic is a variation of the york’s approach shown in [15]. because of the unidirectional coupling, each slave oscillator is driven only by its left neighbor. hence, the study of this configuration is reduced to the study of a pair of two non-linear systems (master-salve synchronization). the purpose is to design the parameters of the controller system σc in order to make the output ye(t) tends toward zero. when this objective is fulfilled, the delayed output of the master oscillator and the output of the slave oscillator become identical, so both oscillators are synchronized. generally, because of the technological realization, the oscillators don’t have the same free running frequencies. this is the reason why the feedback loop was introduced to guarantee the robust synchronization between both oscillators. the delay element and the variable gain amplifier will guaranty different phases and amplitudes for the output signals. the difference between the oscillators will be modeled as a variation of the slave oscillator parameters around those of the master oscillator parameters, which is considered as the reference. the variations due to the temperature or at the ageing of the components are modeled by a polytopic uncertainty of the master oscillator parameters around the nominal values. the oscillators are built using a double differential pair structure. in order to determine the parameters of the dynamical controller, the nonlinear oscillators is modeled using the van der pol model. in this article, we consider that the system is a perturbed van der pol model as: { ẋ = a(θ1)x + g(x,t, θ1) + bu y = cx , (4) where x = [ il v0 ] a(θ1) =   0 1 l0 − 1 c0 0   u = iin j c0 g(x,t, θ1) =   0 α c0 x2 − β c0 x2 3   b = [ 0 1 ] c = [ 0 1 ] , (5) with the uncertain parameters θ1 = [α, l0,c0]. antenna arrays principle and solutions: robust control approach 165 3.2 master-slave synchronization the structure is made up by two different systems which belong to the class previously described. the master system is considered independent (u = 0) and the dynamical controller σc drives the slave system using the error signal as reference. the error signal is constituted by the difference between a delayed version of the master output and the slave output. the state-space representation of the master system can be written as follows σm : { ˙xm = am(θ1)xm + gm(xm,t, θ1) ym = cxm . (6) for the slave system, the state-space representation can be written as σs : { ẋs = as(θ1)xs + gs(xs,t, θ1) + b2u ys = cxs . (7) between the parameters of the master and the slave oscillators, it is considered that there is the same difference δ    ls = lm (1 + δ ) cs = cm (1 + δ ) αs = αm (1 + δ ) βs = βm (1 + δ ) . (8) in that case, this notation can be introduced am(θ1) = as(θ1) + b1(θ2). (9) this difference is transformed into the difference between the state matrix of the master and the slave. with the assumptions in (8), b1(θ2) can be written as follows b1(θ2) =   0 − δ lm (1 + δ ) δ cm (1 + δ ) 0   . (10) if an error state is defined as e(t) = xm(t −τ)−xs(t), (11) a state-space representation can be written σe : { ė = am(θ1)e−b1(θ2)xs + eg(xm, xs,t, θ1)−b2u ye = ce , (12) where eg(xm, xs,t, θ1) = gm(xm,t, θ1)−gs(xs,t, θ1). (13) 3.3 nonlinear bound determination in order to determine the bounds of the nonlinearities difference, the scalar function f : d1 7→ d2 f (x) = −α x + β x3 is used. the bounds can be considered as the slopes of the tangents passing through x = xm and x = 0 of f (x). −α(x2 −x1) ≤ ( f (x2)− f (x1)) ≤ (−α + 3β xm2)(x2 −x1) ∀x1, x2 ∈ d1 (14) 166 florin hutu, sebastien cauet, patrick coirault consider both nonlinear oscillators and the domain d1 = [−1.35v, 1.35v ], then the bound of the nonlinearities difference (13) can be written as follows   0 − 1 c0 ( α + 3β−0.22 )   ≤ eg(xm, xs,t, θ ) ≤   0 α c0   . (15) 3.4 controller synthesis assume that am(θ1) resp. b1(θ2) are two matrices that belong to a polytope of matrices and it is represented by a convex combination of the extreme matrices ai resp. b1i with i = 1 . . . 2m . am = { am(θ1)| am(θ1) = 2m ∑ i=1 ξiai; ξi ∈ ∆1 } (16) and consider that the matrix eg(xm, xs,t, θ1) can be bounded with nb(θ1) being its upper bound eg(xm, xs,t, θ1) ≤ nb(θ1)e. (17) the worst case for our system is the superior limit, then the matrix an (θ ) = am(θ1) + nb(θ1). assume that the dynamical output controller of the system (12) is described by the following statespace representation and its dimension is nc. σc : { ẋc = acxc + bcye u = ccxc + dcye (18) the purpose of this controller is to make the slave system follow the delayed output of the master system. this condition is performed when the error signal defined in (11) tends toward zero. the term b1(θ2)xs, representing the difference between both systems, acts as a perturbation on the error state e. the synthesis of this controller has been made with a technique similar to [9, 10] the following theorem solves the problem of variable matrices product in the synthesis problem by introducing extra unknown variable matrices. theorem 1. if there exists a set of matrices pi > 0, a state feedback controller k0, an unknown variable square and nonsingular matrix g ∈ rnu+nc , an unknown variable matrix h ∈ r(nu+nc)×(nu+nc) and four unknown variables matrices f1, f4 ∈ r(nx+nc)×(nx+nc), f2 ∈ rnx×(nx+nc) and f3 ∈ r(n∞+nc)×(nx+nc) such that the inequality (19) is verified, then the dynamical controller k = g−1l makes the error system (12) asymptotically stable for all matrices an (θ1) and b1(θ2) described as a polytope. φ2 + 1sym      f1 f2 f3 f4 o   [ o o o o b̃2 ]    + sym      o o o o i   l [ c̃ o o o o ]    +sym      o o o o i   g [ −k0 o o o −i ]    < o; (19) antenna arrays principle and solutions: robust control approach 167 the matrix φ2 is defined as follows φ2 =   o o ccl t pi o o −γi o o o ccl o −γi o o pi o o o o o o o o o   + sym      f1 f2 f3 f4 o   [ ã0i b̃1 j o −i o ]    ∀i ∈ {1 . . . 2m} and ∀ j ∈ {1 . . . 2p} , (20) where ã0i = ãni + b̃k0 and k = [ dc cc bc ac ] . (21) the expression (16) can be numerically solved using matlab’s© "lmi toolbox". 4 numerical results in order to check the theoretical result, a transistor-based simulation has been done using agilent’s ads© software and mosfet transistors in 0.35µ m silicon technology. it was considered that all the parameters of an (θ ) have ±5% variation around their nominal values. this variation can be seen as the variation depending on the temperature of the oscillators that are built on the same integrated circuit substrate. this is mathematically transformed into the variation of the state matrix an (θ1) inside the polytope. using the matlab’s ”lmi toolbox” applied to the 8 vertices of the polytope, the following output-feedback controller was found k = [ dc cc bc ac ] = [ 2.66588·1011 22.3994 7.51499·109 −1.1885 ] . (22) it assures the synchronization of oscillators having δ = ±5% difference between parameters. this difference is represented by variation of the perturbation matrix b1(θ2) inside the polytope. variations between lm , cm and ls, cs parameters, corresponds to a possible difference between the free-running frequencies of both oscillators f0s ∈ [ f0m (1−|δ|)2 f0m (1 +|δ|)2 ] . (23) the difference between αm , βm and αs, βs stands for a possible difference between the transistor operating points of both nonlinear oscillators. this controller was applied to a pair of both non-linear oscillators. their free running frequencies are f0m = 2ghz and f0s = 2.2ghz. it has been chosen those frequencies in order to build a discrete component platform. in fig. 4 are presented both output voltages for master and slave oscillators and is divided into three sequences. a first sequence in which, the controller σc is not activated, both oscillators oscillates from their free-running frequencies. the second step at t = 55ns, the controller σc is activated. the obtained delay is closed to the imposed value (τ = t /4 = 1.25·10−10s). this delay will correspond to a orientation of the main lobe in θ = 120◦. finally, at t = 60ns, in order to verify the robustness of the dynamical controller, the free running frequency of the master oscillator was changed to fm = 2ghz. in fig. 3 the error between both output signals is presented. it can be seen that the error tends toward zero after a short period of time when the controller is started. 1sym{x} = x t + x ; ∀x ∈rn 168 florin hutu, sebastien cauet, patrick coirault figure 3: output voltages of both oscillators figure 4: the error between both signals provided by the oscillators 4.1 array of oscillators consider the situation where the θp = 60◦ direction must be privileged and θi1 = 90◦ and θi2 = 120◦ must be rejected. in table 4.1, the necessary and final values of the amplitudes and phases are shown for n = 8 antennas. figure fig.5 depicts the corresponding radiation pattern. the orientation of the main lobe is closed to the desired value θp = 58.14 and both interference directions θi1 = 90◦ and θi2 = 120◦ are rejected. 5 conclusion this paper presents a novel method to drive antenna arrays. it is based on unidirectionally coupled oscillators. an output feedback controller has been designed to assure synchronization with advanced control theory using lmi (linear matrix inequalities) tools. the result was successfully extended to a chain of eight unidirectionally coupled oscillators. additional research will be made to constrain the dynamical controller to realize the desired delay in order to eliminate the delay element. antenna arrays principle and solutions: robust control approach 169 amplitudes [v ] phases [◦] necessary simulated necessary simulated 0.269 0.21 0 0 0.21 0.2 143 138 0.14 0.14 180 179 0.4 0.4 -162 160 0.08 0.08 0 -2 0.14 0.13 63.1 57 0 0 0 7 0.3 0.29 -24.4 -30 table 1: computed and final values for the amplitudes and phases figure 5: the radiation pattern in the particular case of θp = 60◦ 170 florin hutu, sebastien cauet, patrick coirault bibliography [1] l. c. godara (ed.), applications of antenna arrays to mobile communications, part i: performance improvement, feasability and system consideration, vol. 85, proceedings of the ieee, 1997. [2] s. chandran, adaptative antenna arrays trends and applications, springer-verlag, 2004. [3] s. j. orfanidis, electromagnetic waves and antennas (2004). http://www.ece.rutgers.edu/ orfanidi/ewa/ [4] r. c. hansen, phased array antennas, wiley-interscience, 2001. [5] x. guan, h. hashemi, a. hajimiri, a fully integrated 24 ghz eight-element phased-array receiver in silicon, ieee journal of solid-state circuits 39 (12) (2004) 2311–2320. [6] p. liao, r. a. york, a six-element beam scanning array, ieee microwave and guided wave letters 4 (1) (1994) 20–22. [7] a. tombak, a. mortazawi, a novel low-cost beam-steering technique based on the extended resonance power dividing method, ieee transactions on microwave theory and techniques (2003) 1–7. [8] r. a. york, t. itoh, injection and phase-locking techniques for beam control, ieee transactions on microwave theory and techniques 46 (11) (1998) 1920–1929. [9] d. arzellier, d. peaucelle, s. salhi, robust static output feedback stabilization for polytopic uncertain systems: improving the guaranteed performance bound, in: rocond milan-italy, 2003. [10] d. mehdi, e. boukas, o. bachelier, static output feedback design for uncertain linear discrete time system, ima journal of mathematical control and information. [11] t. iwasaki, r. e. skelton, k. grigoriadis., a unified algebraic approach to linear control design, taylor and francis, 1998. [12] d. peaucelle, d. azellier, an efficient numerical solution for h2 static output feedback synthesis, europeen control conference. [13] s. boyd, l. e. ghaoui, e. feron, v. balakrishnan, linear matrix inequalities in system and control theory, vol. 15, studies in applied mathematics, usa, 1994. [14] t. heath, simultaneous beam steering and null formation with coupled, nonlinear oscillator arrays, ieee transactions on antennas and propagation 53 (6) (2005) 2031–2035. [15] r. a. york, nonlinear analysis of phase relationshiops in quasi-optical oscillator arrays, ieee transactions on microwave theory and techniques 41 (10) (1993) 1799–1809. florin hutu university of poitiers department : laii-esip 40 avenue du recteur pineau e-mail: florin.hutu@etu.univ-poitiers.fr received: november 28, 2007 antenna arrays principle and solutions: robust control approach 171 florin hutu was born in romania in 1977 and received the ph.d. degree in automatic control in 2007. he is currently a post doc at the university of poitiers where his interests are currently involved with the design of microwave antenna array. sébastien cauet was born in france in 1971 and received the ph.d. degree in automatic control in 1999. he is currently an associated professor of electrical and computer engineering at the university of poitiers where his interests are currently involved with the control of electrical power rotating motors, the design of microwave antenna array and application of control to chaos transmission. patrick coirault was born in poitiers, france, in 1965. he received the ph.d. degree from the university of poitiers in 1991. he was an assistant professor at the institute of technology of poitiers from 1992 to 1997. he is now professor at the institute of technology of chatellerault. his current research interests are in nonlinear identification and control, with applications to chaotic systems. international journal of computers, communications & control vol. ii (2007), no. 4, pp. 303-313 domino: trivalent logic semantics in bivalent syntax clothes boldur e. bărbat abstract: the paper describes a rather general software mechanism developed primarily for decision making in dynamic and uncertain environments (typical application: managing overbooking). domino (decision-oriented mechanism for "if" as non-deterministic operator) is meant to deal with undecidability due to any kind of future contingents. its description here is self-contained but, since a validation is underway within a much broader undertaking involving agent-oriented software, to impair redundancy, several aspects explained in very recent papers are here abridged. in essence, domino acts as an "if" with enhanced semantics: it can answer "yes", "no" or "undecidable in the time span given" (it renders control to an exception handler). despite its trivalent logic semantics, it respects the rigours of structural programming and the syntax of bivalent logic (it is programmed in plain c++ to be applicable to legacy systems too). as for most novel approaches, expectations are high, involving a less algorithmic, less probabilistic, less difficult to understand method to treat undecidability in dynamic and uncertain environments, where postponing decisions means keeping open alternatives (to react better to rapid environment changes). keywords: undecidability; open, heterogeneous, dynamic and uncertain environments (ohdue); decision-making; trivalent logic semantics; agent-oriented software engineering. 1 introduction despite the major changes taking place in the internet and globalization era and their increasing speed because of the geometrically increasing computing power (due to moore’s law, that is expected to hold at least other ten years) -, basic software mechanisms advanced much too slowly. as regards uncertain knowledge processing, the hindrance is obvious: available software tools are either hardly affordable (because of high complexity both cognitive and structural) or rather ineffective (designed for other environments, applied to ill-defined problems or lacking expected functionality). for instance, the very concept of "uncertainty" was treated inadequately regardless of its growing relevance for application domains (mainly where real-time decision-making is involved [14]), environments (even more dynamic and uncertain [19]), end-user expectations (requesting anthropocentric interfaces [4]), it paradigms (predominantly "computing as interaction" [1]) and so on. the main weaknesses: a) insufficient theoretical rigour (undecidability is considered primarily atemporal keeping its initial mathematical meaning); b) poor practical effectiveness (confusing "unknown" with "unknowable" and applying sophisticated prediction methods in inappropriate contexts); c) unfit apparatus (algorithmic approaches implying determinism, decidability, and bivalence). since the first two issues are investigated in very recent papers [8] [6] [9] [7], to reduce redundancy and to keep focusing on the very mechanisms, here the approach is an explicitly software engineering one: computer science aspects are dealt with only at the beginning (what for new mechanisms?) and at the end (what are the expectations?). in particular, the paper presents in detail domino, a mechanism developed primarily for decision making in dynamic and uncertain environments (a typical example for potential application area comes from the overbooking policy of carrier companies). the decision-oriented mechanism for "if" as non-deterministic operator (domino) is meant to deal with undecidability due to any kind of future contingents. as a rather general software mechanism, its description here is self-contained. however, since a validation is underway within a much broader undertaking involving agent-oriented software (including non-algorithmic approaches to treat uncertainty), several aspects explained in the papers mentioned above are here abridged or skipped copyright © 2006-2007 by ccc publications 304 boldur e. bărbat over. as a result, the paper is structured as follows: section 2 shows the rationale (i.e., premises and diagnosis) for developing new methods to handle uncertainty in decision support systems (dss). section 3 delimits the problem in both meanings: firstly it restricts it (narrowing the scope to undecidability due to future contingents) and secondly it defines it (as software engineering task). section 4 outlines the architecture, basically in search of the third value semantics. on this groundwork, section 5 explains the structure, in search of bivalent syntax clothes. since conclusions are prohibited for a bottom-up project, before its validation, section 6 lists the expectations, ranged in three time horizons (in fact, they are first conclusions). 2 rationale. premises and diagnosis since in this section the paper is to some extent also a position paper, for the sake of simplicity, the premises, opinions, criteria, motives, and their corollaries are not separated in conceptual categories but asserted clustered around two "attractor words": premises and diagnosis. thus, consensus is not mandatory to assess the research results. in other words, the utility of domino can be evaluated even when some of the claims made here are rejected. most of the assertions below are valid in any modern it context but dss are explicitly referred to because "if" is the basic tool (again in both senses: essential and simple) for decision-making. likewise, the emphasis is on the main changes affecting the essence of "if". to preserve both text autonomy and shortness, for topics dealt with recently, details should be found in the paper the quotation comes from. premises. they reflect the general context, representing a very simplified input vector: • environment. "present-day it environments, except for some irrelevant applications, are open and heterogeneous (the resources involved are unalike and their availability is not warranted), dynamic (the pace of exogenous and endogenous changes is high) and uncertain (both information and its processing rules are revisable, fuzzy, uncertain and intrinsically non-deterministic" [6]. • system. except trivial applications, the system exposed to decision-making is "man-machine system [...], highly complex (multi-distributed mainly in space, but also in time and organization), under real-time control (the subsystems act on each other at least, communicate intensely), almost always online" [7]. • user expectations. "since most decision makers are already familiar with google, most available general information is either known or easy accessed; now they need acceptable good answer, but very fast and with incomplete or even uncertain information" [7]. "intelligent system behaviour whatever that could mean becomes a crucial user expectation" [8]. • task. most aim at "managing situations"; main attributes are: "high complexity, multicriterial, pareto optimality, approximate, online, parallel, critical response time, high risk" [7]. • dss architectonics. the software process (not package) is "vaguely specified, validated against end-user expectations" and has "two new fundamental design-space dimensions: time and uncertainty" [7]. • it infrastructure and paradigms. "the it infrastructure is sufficiently advanced (in both facts and trends: nanoelectronics, broadband communication, semantic web, multimodal interfaces, etc.) to allow anthropocentrism for most it applications" [8]. the leading paradigm is "computing as interaction" [1] [19]. second echelon paradigms (prevalent in modern artificial intelligence) are assessed through an "affordability filter" in [6]. as regards software engineering, the paradigm that becomes dominant is agent-orientation. domino: trivalent logic semantics in bivalent syntax clothes 305 diagnosis. the main weaknesses of current it systems are investigated in [7], where the focus is on conventional modelling. since dss weaknesses are very similar, they are stated here adapted and abridged from [7]: they stem from inappropriate conceptualising, based on rigid, algorithmic (i.e., deterministic, almost sequential, "computational", and atemporal processing), meant for decision making as "step by step solving of arising sub-problems", not for decision making as "continuous process of dealing with unexpected, potentially risky, fast changing situations requesting immediate albeit not optimal response". sectorial aspects are: • poorly reflected (or absent) temporal dimension. limited parallelism (if any), ineffective multithreading, poor reactivity (scarce interrupt handling impairing proper reaction to environment stimuli); no exception propagation; no dynamic priorities; no proper thread synchronization). since the agent is a process now acknowledged as such by a formal standard [13] its temporality cannot be disregarded anymore. • poor concurrent programming. often such situations are handled by resource wasting "active wait loops" or counterproductive "mutexes" instead of a simple wait (for an event) with timeout. • misunderstood uncertainty. even if the fact that accurate numeric data are hard to get is accepted, the emphasis is on approximated, predicted, evaluated by rule of thumb, or even on intrinsically fuzzy data, rather than on missing ones (lacking sensor information, delayed previous decisions, server crash etc.). • distorted prediction. bayesian inferences are considered unsuitable to decision-making because "even if decision-makers could get all [ě] answers in due time, would they believe them strongly enough to make critical decisions only on their basis? humans are not "probabilistic beings" and are very prone to any sort of "gambler’s fallacy"." [9]. in short, the real-world decision-making challenge is: the situations are such that you have no time to solve (accurately, complex) problems. 3 delimiting the problem the multifaceted issue of handling uncertainty in dss must be first restricted to arrive at manageable complexity and afterwards defined as software engineering task. restricting the scope. the target was narrowed to deal with undecidability due to future contingents because of five reasons: • unaffordable complexity. uncertainty as epistemic concept, together with its species and degrees, was investigated in [9] starting from the 28 definitions found on the web. beside the overwhelming diversity of those definitions, ranging from "doubt" to "statistically defined discrepancy", the very meaning of uncertainty "depends on the professional background and on the task to carry out (better said, mostly on the time available to complete it)" [9]. thus, "uncertain" means practically (mainly, subconsciously) for mathematicians, unknowable, for software developers, undependable, and for end users (decision-makers), undecidable. recent related work attests the link between uncertainty and complexity: a terminology and typology of uncertainty "together with the role of uncertainty at different stages in the modelling processes. brief reviews have been made of 14 different (partly complementary) methods commonly used in uncertainty assessment and characterisation" is presented in [17]. a rare case when undecidability is discussed outside mathematics is in a sociological context where the authors "suggest that as well as being able to consider organizational decision-making as an instance of (albeit bounded) rationality or calculability, it can 306 boldur e. bărbat also be regarded as a process of choice amongst heterogeneous possibilities" [12]. "in this context could be found a common denominator for a general definition of uncertainty at least, acceptable to the three categories mentioned above? uncertainty, in its widest sense, comprises any unsure link in the chain of steps necessary to fulfil a task" [9]. much too complex to cope with. • avoidable complexity. on the other hand, from the 28 definitions "only a few are interesting, since they are anthropocentric, mirroring the common user (mainly decision-maker) stance: a) "doubt" [...]; b) "the fundamental inability to make a deterministic prognosis" [...]; c) "lack of knowledge of future events"." [9]. • intrinsic importance. "real-world problems show that the most important and ill-treated kind of uncertainty is that due to future contingents: decisions are difficult to make because a relevant event not happened yet, not because a result is imprecise. moreover, its pragmatic corollary highlights a key aspect in decision-making: since any statement about a future event is undecidable, how to proceed in this case? should it be predicted, circumvented, waited?" [9]. • time pressure. the geometrically increasing computing power due to moore’s law (mentioned in section 1) promotes factors tending to reduce radically the role of algorithms and (bivalent) logic in it [7]. two reasons are already manifest: "since deterministic applications are vanishing (because of ohdue), the conventional algorithm is not anymore program backbone. even when still useful, the conventional algorithm is not anymore the main programming instrument (being hidden in procedures easily reached in a host of libraries or being generated by 4gl)" [8]. • risky side effects. an algorithm is almost unable to feel time: no sense of future events, no such step as: "warning: i don’t know yet". worse: often the algorithm cheats, confusing undecidability with negation. defining the task. "since here the issue is to design a mechanism not a particular application, the cardinal concern, from a clear-cut software engineering perspective, is about reducing complexity, both structural (to make the mechanism useful to legacy systems too) and cognitive (to motivate system designers as well as to increase user acceptance)" [9]. software engineering constraints imply: simple tools, with immediate applicability in current designs; no sophisticated concepts or instruments (such as agents, temporal logics, explicit uncertain information processing, computability theory and computational complexity theory, bayesian methods, certainty factors, etc.); bottom-up design and testing; as much as possible conventional development (prevalent algorithmic reasoning, usual api functions, straightforward implementation, downward compatibility, etc.). while most restrictions are comprehensible albeit very tough -, prohibiting both temporal logics and explicit uncertain information processing seems counterproductive, since one of the premises claimed time and uncertainty as fundamental design-space dimensions. unfortunately, even most responsive and appropriate approaches ([14] included) are less applicable because they are sectorial (e.g., treating time without uncertainty or vice versa). other interesting temporal logics are too sophisticated: for instance, the "linear time" logic (used for specifying reactive systems) is based on the two modalities "since" and "until"; in [16], extensions of this logic are investigated from the perspective of undecidability for "x will happen within t unit of time" showing that the extension is undecidable, whenever t is irrational. although aspects of a primitive temporal dimension could be implemented using common api functions e.g., wait (for an event) with timeout as well as synchronization methods used in multithreading, no similar mechanism is available for uncertainty. here lies the innovative core of the undertaking with all its potential, openings and risks: the only mechanism on hand in common operating systems, able to shift initiative from algorithm to environment, is exception handling. thus, the algorithm gives up a morsel of its proactiveness deterministic par excellence to gain a touch of reactivity; this "small domino: trivalent logic semantics in bivalent syntax clothes 307 amount of non-determinism" is mandatory to be able to mirror (in real time) its epistemic facet: uncertainty. (in domino, when a conditional expression in if is undecidable, an exception is raised and control is handed over to its handler.) the idea to assign a new semantic value i.e., enabling a software entity (labelled or not as agent) to react prompt to asynchronous environment stimuli to a mechanism meant to increase robustness was advanced (and defended in detail) in [3] after testing it in expressing emotions of pathematic agents. later, using exceptions to achieve agent reactivity was suggested in the context of sub-symbolic inferences [8] and of emergence as leverage [9]: "even primeval animals move "algorithmically" ("if gap then avoid, else go on") only a few steps, in very hostile environments. moreover, reaction to stimuli cannot mean perpetual looking for the stimulus. the cardinal hindrance stems [...] from the mechanisms employed: neither nature, nor technology can afford in the long run mechanisms involving large amount of testing because they are too time-consuming tools". (currently exceptions are tested to help self-referencing agents to show some primitive form of self-awareness [5]. however, in recent related work exceptions are still regarded solely as a "mechanism for structuring error recovery in software systems so that errors can be more easily detected, signaled, and handled" [11]. one of the "fundamental motivations for employing exception handling in [...] robust applications" is to "improve the maintainability of the normal code and promote reuse of error handling code" [11]. in [10], the exception handling mechanism of a state-of-the-art industrial embedded software system is analysed and the fault-proneness of the return-code idiom for dealing with exceptions is evaluated.) as a result, since a conditional expression in an ordinary if has now a third exit variant (the exception), the very if acquires a trivalent logic semantics. then, why stay at the stage of "trivalent logic semantics" and not go further towards "trivalent logic"? because such logics are far to meet the challenge, despite the huge effort to conceptualise, develop and implement them. a three-valued logic is a "logical structure which does not assume the law of the excluded middle. three truth values are possible: true, false, or undecided. there are 3072 such logics" [18]. to avoid the 3073rd one1 , "for the sake of simplicity, the trivalent semantics should be grounded on a usual bivalent software infrastructure" [9]. indeed, the proposed solution for domino considers the revisited concept of undecidability [7] [9] filtered through the double sieve of relevance to usual decision-making (section 4) and design simplicity as vital software engineering request (section 5). 4 in search of the third value semantics since the key aspect in decision-making is to handle "don’t know"-like uncertainty [7] (e.g., when a relevant component of an if condition is undecidable because an expected event not happened yet), it is appropriate to a third value meaning something like "caveat: i don’t know (yet)", "unknowable" or "unknown". in many-valued logics it "is general usage [...] to assume that there are two particular truth degrees, usually denoted by "0" and "1", respectively, which act like the traditional truth values "falsum" and "verum"." [15]. "obviously, any decision-making needs those pillars of bivalence" [9]. thus, the third value should be added to the two boolean constants of if. in short, the three outputs are: yes, no, undecidable, where it must still be decided what should "undecidable" really mean. the investigation starts with the first and more relevant three-valued logics and their respective motivations and meanings of the third truth value: • łukasiewicz. the first intention was to use the third value for "possible" (to model modalities) to deal with future contingents (first of all, the "paradox of the sea battle" posed by diodorus cronus). ignoring the philosophical motivation and context (the ontological status of the future, is it determined or not, does free will actually exist, etc.), the third value "i" for "indeterminate", interpreted 1at least (supposing that in this century no other trivalent logic has been concocted) 308 boldur e. bărbat as "unknowable" or "problematical" is semantically very close to the real-world decision-making problems. (on the contrary, the "1/2" notation although elegant from a (meta)mathematical perspective is totally unacceptable in software because, for both theoretical and practical reasons, interpreting it as intermediate value of "half true and half false" is at least confusing and useless; moreover, it could lead to computational disaster.) • kleene. the third value in this logic is "u", interpreted as "undefined" or "undetermined" or "unknown" with two connotations: "permanently unknown" or "temporary lack of knowledge". the second meaning is helpful for postponing decisions and is actually used in data base applications. for instance, "sql implements ternary logic as a means of handling null field content. sql uses null to represent missing data in a database. if a field contains no defined value, sql assumes this means that an actual value exists, but that value is not currently recorded in the database. [...] comparing anything to null even another null results in an unknown truth state. for example, the sql expression "city = ’paris’" resolves to false for a record with "chicago" in the city field, but it resolves to unknown for a record with a null city field. in other words, to sql, an undefined field represents potentially any possible value: a missing city might or might not represent paris." (en.wikipedia.org/wiki/ ternary_logic). moreover, the kleene logic, as a natural sublogic of belnap’s four-valued logic [2], is an important framework for many paraconsistent logics major feature for application in computer science. • bochvar. inspired by the examination of semantic paradoxes, the third truth value "m" means "meaningless" or "paradoxical". this logic is useful when syntactic meaningfulness (rather than semantic one) is looked for (crucial for program verification but dispensable for decision-making). hence the semantics of the third value in a "three-output if" should be based on a blend of a łukasiewicz "i" interpreted as "unknowable" or "problematical"2 and a kleene "u" interpreted as "temporary lack of knowledge". thus, the semantics of "undecidable" is refined to "undecidable in the time span given". in fact, it gives a chance to the "yet" in "i don’t know (yet)", postponing the verdict of "undecidable" as much as possible (depending heavily on both the problem to solve and the decision-making strategies applied). of course, to be effective, such a procrastination strategy must be put to work only in a distributed environment (reflected in software through multithreading); otherwise, the user would be frustrated by the frequent wait periods. 5 in search of bivalent syntax clothes domino respects the rigours of structural programming and the syntax of bivalent logic, is programmed in plain c++ (to be applicable to legacy systems too), is based on a few functions of the windows32 api, was tested only within the toy problems (regarding real-time decisions in industrial control or in medical informatics) the examples are taken from, and is currently tested in a real-world problem (a simplified version of implementing overbooking policy). its flowchart is in figure 1 (the only symbol that is not among the common flowchart notation is the one for raising exceptions). the (windows 2000) api functions involved are: a) bool setevent(hevent) handle hevent; /* event-object handle */ example: setevent(g_hevent_phlimit); 2according to the ancient stoic perspective, updated by łukasiewicz domino: trivalent logic semantics in bivalent syntax clothes 309 exception handler raise exception resetevifcond domino evifcond ? yes no wait evifcond timeout? no yes usual if yes no usual then usual else usual if yes no usual then usual else undecidable (in the timespan given) warning or action end domino figure 1: domino: architecture expressed in bivalent logic 310 boldur e. bărbat b) bool resetevent(hevent) /* likewise */ c) dword waitforsingleobject(hobject, dwtimeout); handle hobject; /* handle of the event waited for*/ dword dwtimeout; /* maximal wait duration (ms) (infinite: unlimited wait) (0: testing the event state) */ example from an alarm-bell program: dwresult = waitforsingleobject(g_heventoverheating, infinite); d) dword waitformultipleobjects(cobjects, lphobjects, fwaitall, dwtimeout); dword cobjects; /* number of objects (maximum 64) */ const handle *lphobjects; /* handle table address */ bool fwaitall; /* flag for conjunction /disjunction (true: all events are waited) (false: only the first event is waited)*/ dword dwtimeout; /* likewise*/ example from a domotics program (for 5 boilers; uses multiple events): dwresult = waitformultipleobjects(5, hboilerpressure, true, 4000); remarks. 1. the exception handler can: a) give control to the human decision maker (after a warning); or b) act (for instance, propagating dynamically the exception from the callee to the caller). 2. time is not just explicitly present in "wait" but, more important, hidden in thread synchronization (setevent is employed in the thread providing information needed to make a decision). 3. uncertainty is dealt with through the intrinsically uncertain interrupts generated by the environment stimuli, expressed at the program level through the exception handler. 4. if dwtimeout = 0, the mechanism is a conventional "if" (however, a more robust one since evaluation is preceded by a real-time validation test). 6 expectations the three time horizons are roughly delimited by: a) validating domino in the overbooking application; b) designing and validating other (similar) mechanisms of the agorithm toolbox [9]; c) api functions for domino-like primitives. short range. a) domino addresses the main problem of current "if" in applications running in ohdue: "if" is inadequate not because it cannot say "probably 80b) it solves this problem in a simple way (for both designer and user). c) it is easy to implement due to its straightforward structure involving only common api functions: despite its enriched semantics the emulated "if" does not need a modified compiler. domino: trivalent logic semantics in bivalent syntax clothes 311 evifcond yes no usual then usual else undecidable (łukasiewicz semantics) raise exception figure 2: domino: semantics interpreted in trivalent logic middle range. a) corollary 1: being downward compatible, it should be useful for legacy systems. b) corollary 2: emulating a language primitive, it should be useful not only for dss, but for any application in ohdue. c) corollary 3: dealing with both time and uncertainty, it should be useful for any agent-oriented application: other agorithm mechanisms could be outlined similarly. d) hopefully it will start a "virtuous circle" able to adapt the dss to the "information age" requirements changing both the decision-makers expectations and the programming paradigm. long range. a) a new interpretation of domino: a three-valued if within the frame of bivalent logic b) the badly needed shift in software engineering towards exception handling: the stimulus causes an interrupt that is treated as exception. (real-world applications cannot afford large amount of "if temperature > n 0c then alarm" testing c) motivating applied research in ai logics, avoiding the gap between new logics (dealing at highly abstract mathematical levels with either time or non-determinism but rarely with both) and decisionsupport applications (either remaining at the level of rigid algorithmic approach or using approaches that ignore the fundamentals of human decision making). d) a new, "procrastination logic": less algorithmic, less probabilistic, less difficult to understand and to apply in ohdue, where postponing a decision means keeping open alternatives (to react better to rapid environment changes). bibliography [1] agentlink roadmap: overview and consultation report, agentlink iii. agent based computing, university of southampton, 2004. [2] anderson, a.r, n.d. belnap, j.m. dunn. entailment: the logic of relevance and necessity, volume 2. princeton university press, princeton, 1992. 312 boldur e. bărbat [3] barbat, b.e. agent-oriented intelligent systems. romanian academy publishing house, bucharest, 2002 (in romanian, "grigore moisil" prize of the romanian academy). [4] barbat, b.e. the impact of broad-band communication upon hmi language(s). (chapter 7.) communicating in the world of humans and icts. (chapter 8.) in cost action 269. e-citizens in the arena of social and political communication (l. fortunati, ed.), pp. 113-142, eur21803, office for official publications of the european communities, luxembourg, 2005. [5] barbat, b.e., a. moiceanu, i. pah. gödelian self-reference in agent-oriented software. proc. of the 11th wseas international conference on computers (iccomp ’07) (n.e. mastorakis et al, eds.), 92-97, agios nikolaos, crete, 2007. [6] barbat, b.e, a. moiceanu, s. plesca, s.c. negulescu. affordability and paradigms in agent-based systems. computer science journal of moldova, 2007. (in print.) [7] barbat, b.e., r.s. muntean, r. fabian. approximation versus undecidability in economic modelling. proc. of the international workshop new approaches, algorithms and advanced computational techniques in approximation theory and its applications (d. simian, ed.), 2007. (in print.) [8] barbat, b.e., s.c. negulescu. from algorithms to (sub-)symbolic inferences in multi-agent systems. international journal of computers, communications & control, 1, 3, 5-12, 2006. (paper selected from the proc. of icccc 2006.) [9] barbat, b.e., s.c. negulescu, s. plesca. emergence as leverage and non-algorithmic approaches in agent-oriented software. studies in informatics and control journal, 16, 4, 2007. (in print.) [10] bruntink, m., a. van deursen, t. tourwé. discovering faults in idiom-based exception handling. proc. of the 28th international conference on software engineering, 242 251, acm press, new york, 2006. [11] castor filho, f., a. garcia, c.m.f. rubira. error handling as an aspect. proceedings of the 2nd workshop on best practices in applying aspect-oriented software development, acm press, new york, 2007. [12] clegg, s., kornberger, m., rhodes, c. organizational ethics, decision making, undecidability. the sociological review, 55, 2, 393-409(17), blackwell publishing, 2007. [13] fipa tc agent management. fipa agent management specification. standard sc00023k (2004/18/03). http://www.fipa.org/specs/fipa00023/sc00023k.pdf, 2004. [14] fisher, m. temporal development methods for agent-based systems. autonomous agents and multi-agent systems, 10, 41-66, springer science + business media inc., 2005. [15] gottwald, s. many-valued logic. in stanford encyclopedia of philosophy (e.n. zalta, ed.). http://plato. stanford.edu/entries/logic-manyvalued/, 2004. [16] rabinovich, a. temporal logics with incommensurable distances are undecidable. information and computation, 205, 5, 707-715, elsevier, 2007. [17] refsgaard, j.c. et al. uncertainty in the environmental modelling process-a framework and guidance. environmental modelling & software, 1543-1556, elsevier, 2007. [18] weisstein, e.w. three-valued logic. from mathworld–a wolfram web resource. http://mathworld. wolfram.com/three-valuedlogic.html. crc press llc, wolfram research, 1999. domino: trivalent logic semantics in bivalent syntax clothes 313 [19] zambonelli, f., a. omicini. challenges and research directions in agent-oriented software engineering. autonomous agents and multi-agent systems, 9, 253-283, kluwer academic publishers, 2004. boldur e. bărbat "lucian blaga" university of sibiu department of computer science 5-7 ion raţiu st., 550012, sibiu, romania email: bbarbat@gmail.com received: march, 24, 2007 boldur barbat m.sc. in electronic engineering, postgraduate specialising in programming, ph.d. in digital computers ("politehnica" university bucharest). he is with "lucian blaga" university sibiu, faculty of sciences (full professor) and "politehnica" university timisoara, faculty of automation and computers (advisor for doctoral studies in computer science). over 20 books (romanian academy it prize, 2002). about 50 papers/articles in english in the last five years. current research interests: emergence in agent-based systems (self-awareness, stigmergy), human-agent interaction (transdisciplinary), agentoriented software engineering (non-algorithmic real-time software, uncertainty). international journal of computers communications & control issn 1841-9836, 10(5):746-759, october, 2015. multi-attribute collaborative filtering recommendation c. yu, y. luo, k. liu changrui yu school of information management and engineering, shanghai university of finance and economics, shanghai 200433, china yucr@sjtu.edu.cn yan luo* shu-uts silc business school, shanghai university, 20 chengzhong rd, shanghai 201800, china *corresponding author: luoyan@shu.edu.cn kecheng liu informatics research centre, henley business school, university of reading, reading, rg6 3xa, u.k. k.liu@reading.ac.uk abstract: currently researchers in the field of personalized recommendations bear little consideration on users’ interest differences in resource attributes although resource attribute is usually one of the most important factors in determining user preferences. to solve this problem, the paper builds an evaluation model of user interest based on resource multi-attributes, proposes a modified pearson-compatibility multiattribute group decision-making algorithm, and introduces an algorithm to solve the recommendation problem of k-neighbor similar users. this study addresses the issues on preference differences of similar users, incomplete values, and advanced converge of the algorithm, and realizes multi-attribute collaborative filtering. the algorithm is proved to be effective by an experiment of collaborative recommendation among multi-users in a virtual environment. the experimental results show that the algorithm has a high accuracy on predicting target users’ attribute preferences and has a strong anti-interference ability on deviation and incomplete values. keywords: personalized recommendation, group decision-making, multi-attribute, collaborative filtering, pearson-compatibility. 1 introduction a recommender system aims to generate meaningful recommendations to users for items or products that might interest them [1]. in many markets, consumers face a wealth of products and information from which they can make choices. to alleviate this problem, many web sites attempt to help users by incorporating a recommender system that provides users with a list of items and/or web pages that are likely to interest them. there are real-world operations of industrial recommender systems, such as the recommendations for books on amazon, or movies on netflix. as one of the most successful approaches to building recommender systems, collaborative filtering (cf) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users [2]. the developers of one of the first recommender systems, tapestry [3] coined the phrase "collaborative filtering (cf)", which has been widely adopted regardless of the facts that recommenders may not explicitly collaborate copyright © 2006-2015 by ccc publications multi-attribute collaborative filtering recommendation 747 with recipients and recommendations may suggest particularly interesting items, in addition to indicating those that should be filtered out [4]. the fundamental assumption of cf is that if users x and y rate n items similarly, or have similar behaviors (e.g., buying, watching, and listening), and hence will rate or act on other items similarly [5]. studies in literature [3] and [4] have shown that users’ interest in a product or service is affected by user topic preferences, content preferences, user habits, public evaluation and other factors, and that these factors are decided by the different attributes of items. for example, users liking a new movie may be caused by one or more attributes of the movie, such as the director, star, theme, content, style, public comment and so forth. thus, in the application of collaborative filtering algorithm, it is necessary to use a multi-attribute analysis model, in which the user rating to an item is based on a different perspective (attributes) to describe their interest preferences. although user and resource clustering based on the attribute information has been widely discussed in the multi-attribute collaborative filtering literatures, the recommend method is still traditional [6]. such methods can only obtain a set of potential interest items of target users, but the reasons of such a recommendation are not given to the target user. in addition, the present studies scarcely consider the characteristics differences of similar users interested in the item attributes, which can lead to recommendation deviation [6]. for example, in a traditional way, user b is the most similar one to the target user a because a and b have the same degree of interest in the same film. however, if the film properties they prefer are completely different, it will lead to recommendation deviation when we give greater weight to b. based on our previous research, we propose that multi-attribute collaborative filtering can be treated as a group decision making process. by building the rating matrix of target items for the similar users, we remove the user who has a large attribute preference difference to target user from the nearest user set, and save the problem of recommendations deviation. in addition, we can analyze the user’s interest performance from the view of item attributes and give the descriptions for the recommendation. accordingly, this paper proposes a modified pearsoncompatibility multi-attribute group decision-making algorithm and introduces the algorithm to solve the recommendation problem of k-neighbor similar users. this paper has extended our previous research significantly with much more details of the theoretical model, such as the characters of the multi-attribute evaluation, the selection of the nearest neighbor of target user, and the group decision-making model of personalized recommendation. more importantly, we have enriched and refined the collaborative filtering recommendation algorithm which is the core of this paper. this paper is organized as follows. we review recommender systems and multi-attribute utility theory. we then introduce the establishment of user interest model using applied ontology method to describe user profile and illustrate the algorithm in specific steps. finally, we do an experiment and conclude with findings and discussions. 2 descriptions of basic model 2.1 user rating matrix a user’s comment on a certain item is usually an integration of multi-attribute comments made from different angles [6]. suppose an item is shown as follows: p = {a1,a2,a3, . . .an} based on the revised rating model, the paper establishes the user rating matrix. suppose the user set is denoted as u = {u1,u2, . . .up} and the user uj rating for item pi is denoted as 748 c. yu, y. luo, k. liu a(uj,pi): a(uj,pi) = a1 a2 a3 an−1 an    ω11 ω12 ω13 . . . ω1n−1 ω1n a1 ω21 ω22 ω23 . . . ω2n−1 ω2n a2 ω31 ω32 ω33 . . . ω3n−1 ω3n a3 . . . . . . . . . . . . . . . . . . . . . ω(n−1)1 ω(n−1)2 ω(n−1)3 . . . ω(n−1)(n−1) ω(n−1)n an−1 ωn1 ωn2 ωn3 . . . ωn(n−1) ωnn an where ωxy is the importance of attribute ax of product pi in comparison with attribute ay for user uj. here we use the 1-9 scale paired comparison method to analyze the compared importance level of each attribute of the product that a user evaluates [7]. the rating matrix of an item is mainly acquired through user scoring, or acquired through user behavior analysis, or acquired with the approaches of web semantic digging and fuzzy mathematics [6]. the present user rating system, such as movielens, only asks a user to make a synthetic rating for the movie he or she watched and give a quantitative scoring between 1 and 5. this approach is not accurate in identifying the similarity in the preference of two users. for example, if two users are interested in the same movie. when they rate it, they give it the same score. however, the angles of their preference for the movie are totally different. a user may like the star and the style. the other user prefers the theme and the content of the movie. therefore, we propose to build a multi-attribute rating system to evaluate a product, i.e., a product has many attributes. when evaluating the product, a user mainly gives the preference ratings of each product attribute. another notable character of the multi-attribute evaluation is the absolute sparsity of the judgment matrix. this is caused by users who only know a few attributes of the product. this matrix is like the incomplete value judgment matrix of group-decision making, which easily lead to obvious deviations for the prediction results. so we should take some adjusted measures to solve the problem. 2.2 user interest model suppose user uj has rated several items and the rating matrix set is as = {a(uj,p1),a(uj,p2), . . .a(uj,pt)}, where a(uj,pi)(i = 1,2, . . . t) is user uj rating matrix for product i (i.e., pi). this paper applies the rating matrix set to establishing the user interest model [6]. the specific steps are as follows. 1. calculating the feature weight vector of each rating matrix, and then acquire the feature weight vector set v s ={v p1uj (w1,w2,w3, . . .wsize(a(uj,p1))),v p2 uj (w1,w2,w3, . . .wsize(a(uj,p2))) . . . v ptuj (w1,w2,w3, . . .wsize(a(uj,pt)))}. where v piuj (w1,w2,w3, . . .wsize(a(uj,pi))) denotes the feature weight vector of the user rating matrix a(uj,pi)(i = 1,2, . . . t) and size(a(uj,pi)) denotes the length of the feature weight vector. 2. according to the category of each attribute, calculate the user interest weights of the relavant attribute in the related resource category. referring to the methods proposed multi-attribute collaborative filtering recommendation 749 by [8] and [9], we propose the following formula for calculating the degree of the user interest. v a(uj,ay,n) = n∑ k=1 a(uj,pk)×v pk uj (wy) n (1) where v a(uj,ay,n) denotes the degree to which user uj is interested in attribute ay. n is the number of the items which has attribute ay and user uj have rated. a(uj,pk)×v pk uj (wy) (k = 1,2,3, . . .n) denotes the degree of user uj interest in attribute ay of product pk, which indicates how user uj preference on item pk is mostly determined by attribute ay. 2.3 selection of the nearest neighbor of target user we define the target user as the online user which requires evaluations and preliminary recommendations. the set of nearest neighbors is composed of the users who have the most similar interest and preference to item with the target user. traditionally, researchers use k-nearest neighbor (knn) algorithm and pearson correlationbased similarity formula to do cluster analysis on the target user and the similar users, according to their similar interest and preference. through these methods, the similar user set with different group standards can be obtained. in the process of collaborative filtering recommendation with group decision-making method, we consider the characteristics of the target user preference and search the similar user sets. the specific steps are as follows. step 1. search the user set which has similar interest distribution with target user ut , i.e., to obtain the intersection set of the interest distribution of all users and the target user. two users can rate the same attributes in a number of categories and have similar interest weights. this approach can be applied to obtain the initial nearest neighbor set: iu = {(u1,ω1),(u2,ω2),(u3,ω3) . . . ,(uw,ωw)} where w is the number of users totally in the initial nearest neighbor set. uk denotes the kth user whose interest set is sk. ωk denotes the interest intersection between user uk and the target user ut . step 2. use pearson correlation-based similarity formula to calculate the similarity between target user ut and a random user uk. when the degree of similarity reaches the threshold, uk can be divided into sut which is the similar user set of ut . finally, sut = {u1,u2, . . .us} is obtained, i.e., s similar users meet the threshold. the set of the interest intersection between the target user ut and s similar users that meet the threshold is seut = ω1∪ω2∪ω3 . . . . . .∪ωs. 3 collaborative filtering recommendation algorithm based on multi-attribute group decision-making 3.1 group decision-making model of personalized recommendation after acquiring a similar user set sut , we need predict and recommend the items that target user ut has not commented yet. suppose the item set is source = {p1,p2,s3, . . .pn} and pk (k = 1,2, . . .n.) is the item that the target user has not commented yet. the traditional collaborative filtering recommendation algorithm is applied to calculate the overall evaluation value of the item a(ut ,pk) given by similar users and then obtain the initial recommended source sinitial. suppose a random item pk ∈ sinitial has n attributes denoted as a set spk = 750 c. yu, y. luo, k. liu {a1,a2, . . .an}, the set satisfies the condition spk ⊆ seut . the comment matrix is denoted as a1,a2,a3, . . .ap, which means similar users in sut commend pk. we suppose p users are similar with the target user and have made comments to the item. the algorithm proposed in this paper requires that p >= 3. when p < 3, refer to the article [10]. after obtaining the evaluation matrix a1,a2,a3, . . .ap, it vital for this research to use the information of comments to get preference matrix of the target users. the collaborative filtering recommendation of multi-attribute similar users is a typical group decision problem, in which many similar users make their decisions independently without any discussions and then the computer synthesizes their opinions to make recommendations to the target user. the result requires that all similar users’ comprehensive evaluation matrixes and comprehensive characteristics weight vector {w1,w2,w3, . . .wn } be calculated. wi denotes the comprehensive preference of the similar users to attribute ai, which is compared with other attributes of the product that has not been recommended. according to comprehensive characteristics weight vector, we can know which attribute determines the users’ interest on a specific item. applying the semantic analysis method and the semi-structured description language to the attributes, we can make better recommendation to users. a recommendation model can be transferred to group decision-making model in order to solve a problem. however, the traditional group decision-making algorithm still need be improved when applied since there exists differences between similar users and decision-making expert. the differences mainly exist in the following aspects: 1. it is hard to use the weight vectors to measure the influence of similar users in the recommendation processes mainly because of the complexity of user preferences. according to the traditional method, two users have a high similarity in their interest preferences. however, it is hard to identify the deviation existing in the preferences of the two users on a specific item. 2. there are a large number of incomplete values in user comment information because some users may make no comments on the unfamiliar attributes to ensure the accuracy of evaluations. in this situation, the application of some traditional group decision-making algorithms, such as weighted average method or weighted least squared logarithm method may result in a great deviation of the final result. to solve the two problems mentioned above, this paper makes improvements in group decisionmaking compatibility test algorithm and builds a collaborative correction algorithm based on pearson-compatibility model. the core of this algorithm is to simulate the expert group-decision making process, discover the common opinions of most experts via data analysis, and revise the opinions of each expert to realize the final consistent compatibility. moreover, to solve the problem of premature convergence, this paper adopts a mode of multi-user collaborative correction. in addition to the usual algorithms, when it calculates the value of each user’s impact, this paper uses the common opinion of all users rather than target user‘s opinion as a measurable index. the user whose opinion is more similar to the common opinion would be given a higher weight, which is different to traditional method. 3.2 the related definitions firstly, we introduce the calculation of the value of user impact weight. this value is an important indicator to measure the degree of evaluation information consistency between a user and the others [6]. the user matrix with higher group evaluation consistency will get higher multi-attribute collaborative filtering recommendation 751 weight. vise versa. this paper adopts the concept of user rating similarity [11, 12]. we turn all the similar n×n user rating matrixes into n2×1 one dimensional vector. the user uk judgment matrix ak could be denoted as v k = {ωk11,ω k 12,ω k 13 . . .ω k 1n,ω k 21,ω k 22,ω k 23 . . .ω k 2n . . .ω k n1 . . . . . .ω k nn}. pearson similarity formula to calculate the rating matrix between the user uk and the user ul is show as follows: si(ak,al) = n2∑ i=1 (v k(i)−v k)× (v l(i)−v l)√ n2∑ u=1 [v k(i)−v k]2 × √ n2∑ u=1 [v l(i)−v l]2 (2) v k is the average value of all elements of user uk rating matrix. v k = ωk11 + ω k 12 + . . . + ω k nn n2 . the similarity between user k and other users could be calculated as follows: sik = p∑ l=1,l ̸=k si(ak,al)/(p−1). where p denotes the number of users. we propose a formula dk = 1 −sik as the approximate measure of variance, which indicates the deviation degree of evaluation matrix. the approximate influence weight of user uk is shown as follows: θk = (1−max{sil, l = 1,2, . . .p})2 d2k (3) after acquiring the similar user influence weight, we suppose the group integrated approximate evaluation matrix of p users is a∗, and the value of each element ω∗ij in matrix a ∗ is as following: ω∗ij = p∑ k=1 θk ×ωkij/ p∑ k=1 θk (4) matrix a∗ is not a positive reciprocal matrix. suppose x is a positive reciprocal matrix composed of xij. this paper uses the least square method to modify x and propose the following formula: f(x) = min n∑ i=1 n∑ j=1 (xij −ω∗ij) 2 s.t  xij ×xji = 1xij > 0 (i,j = 1,2, . . .n) (5) the definition of compatibility and comprehensive compatibility are as follows: definition 1. suppose x is the group user comprehensive evaluation matrix obtained by using the method of the least squares. then the judgment matrix compatibility between user k and the other users is as follows: s(ak,x) = n∑ i=1 n∑ j=1 ω (k) ij ×xij max((ω (k) ij ) 2,(xij)2) n2 −α (6) 752 c. yu, y. luo, k. liu although paper [13] has defined expert judgment matrix compatibility in usual cases, it does not consider the incomplete value. formula (6) is a modified approach to solve the problem. firstly, the block that the user does not rate is processed and given the value 0. then α is used to indicate the number of 0. the aim of this approach is to eliminate the influence of user judgment matrix on compatibility indicator. definition 2. suppose a1′, a2′ . . .ap′ are the compatibility correction of matrixes of p users’ judgment matrixes. then we get the comprehensive consistency indicator s̄, as follows: s̄ = p∑ k=1 s(a,ak′) p (7) readers can refer to the simulation result of article [7]. when s(a,b) >= 0.8, the two evaluation matrixes is considered nearly compatible. when s̄ ≥ 0.8, evaluation matrixes of all p similar users is considered compatible. 3.3 collaborative correction algorithm based on pearson-compatibility pearson-compatibility model could be used to simulate the process of experts doing group discussions and finally making group decisions. the model mainly consists of two indicator calculation formulae: pearson similarity calculation and compatibility test. pearson similarity calculation formula is mainly used to calculate the information reservation degree of user rating matrix after the matrix has been revised. and compatibility test is used to test degree of consistency of user rating matrix after matrixes has been revised. the mainly function of pearsoncompatibility model is to build the associations between two variational indicators. based on this association, compatibility correction algorithm will choose the best revised matrix in each iteration process, which could make the user matrixes unanimous and most similar to the true value. referring to [14], based on the related method [15, 16], this paper proposes a pearsoncompatibility model as follows: ω(t) = si(t) 1−β ×s(t) 1+β (8) where ω(t) denotes the calculation result of pearson-compatibility model after t times iteration. with all the continuous revision of user matrixes, si(t) decreases from 1 to 0, and s(t) increases from 0 to 1. so each time of modification generates a better revised scheme which minimizes the change of si(t) while maximizes the change of s(t). this indicates that all the experts acquires a better compatibility with the least possible original information lose. this method is similar to the real decision processes [16] and can provide an effective way to approach the true value. in pearson-compatibility model, the best revised scheme in each iteration process is the one that maximizes ω(t). β is used to control the marginal diminishing rate and the marginal increasing rate of si(t) and s(t). the efficiency of this algorithm is low when the consistency of the evaluation matrixes is low. moreover, it is possible that the algorithm converges in advance, i.e., ω(t) reaches the max value at the beginning of calculation. at that time, if the value of β increases, the efficiency of the algorithm can be improved and simultaneously solve the problem of pre-mature convergence to some degree. regarding the designation and realization of compatibility correction algorithm based on pearson-compatibility, readers could refer to [17, 18]. in order to solve the problem of premature convergence, this paper introduces a multi-user collaborative correction mechanism which multi-attribute collaborative filtering recommendation 753 is based on simulated annealing algorithm (saa). the following experiments in this paper testify that this method can solve the problem of pre-mature convergence in advance, and further improves the accuracy of the experiment result. this algorithm is described as follows: step1: suppose u is the iteration times of compatibility test. zs is the number of users whose evaluation matrix could not be revised further in each step, which is given the original value 0. suppose original user matrixes of all p users are a1(0),a2(0) . . . . . .ap(0). then after u times compatibility correction iteration, p users evaluation matrixes are: a1(u),a2(u) . . . . . .ap(u) step2: use formula (2) and (3) to calculate the influence weights of the evaluation matrixes of p users. step3: use formula (4) and (5) to calculate the user comprehensive rating matrix x(u) after the uth compatibility correction. step4: use formula (6) to calculate the compatibility between the kth user ak(u) and x(u). then apply formula (7) to calculate the comprehensive consistency degree of s̄(u). step5: when zs = p and s̄(u) < 0.8, go to step7. when zs ̸= p and s̄(u) < 0.8, go to step6. when zs ̸= p and s̄(u) ≥ 0.8, we believe p users comprehensive consistency could pass the test, go on with step8. step6: referring to [17], we can revise x(u) and ak(u). suppose the deviation matrix of ak(u) is dk(u) = {ek(u)ij }, where e k(u) ij = x (u) ij −ω k(u) ij . suppose e k(u) wr = max(|x (u) ij −ω k(u) ij |) is the maximum deviation item after u times iteration, where ωk(u)ij is nonzero term. let u = u+1 and begin a new round of revision. the revised rating matrix is: ak(u) =   x (u−1) ij −e k(u−1) ij ×0.95 µ, when i = w,j = r 1/[x (u−1) ij −e k(u−1) ij ×0.95 µ], when i = r,j = w x (u−1) ij −e k(u−1) ij , when i ̸= w,i ̸= r,j ̸= r,j ̸= w (9) where u is the step length adjusting factor. this paper gives a limitation between 0 and 10 to u. thus, the range of 0.95µ is [0.5987, 1]. the larger u is, the faster the rate of convergence of rating matrix of user k will be. in order to prevent an oversized adjustment of a single user matrix and thus lose the information in the initial rating matrix, the maximum adjustment extent is set as 0.5987. if u is given an appropriate value in each iteration, the calculating speed and the accuracy of the algorithm result will be highly improved. however, the existing algorithm is not effective enough. this paper proposes an approach of building a multi-user collaborative filtering model based on simulated annealing algorithm (saa), i.e., applying saa to revise the maximum deviation item in each iteration and calculate the optimal correction factor µk for each user k(k = 1,2 . . .p). in the simulated annealing algorithm (saa), each point s of the search space is analogous to a state of some physical system. the goal is to bring the system, from an arbitrary initial state, to a state with the minimum possible energy. according to the principle of metropolis, the probability of solid particle at temperature t changed from the disordered state to the steady state is exp(− ∆e k×t ). ∆e is the internal energy change quantity when the solid temperature change to t and k means the boltzm-ann constant. when we apply saa method to solve the 754 c. yu, y. luo, k. liu combinational optimization problem, internal energy e means the value of target function and temperature t means the control factor. then we can get the simulated annealing algorithm. in the saa, we suppose the initial energy value of target function in the model is e and control factor is t . then we can control the attenuation amplitude in each iteration and use the random function to simulate the random motion of particle. and we calculate the objective function difference as ∆e, and according to metropolis principle to choose the result. when t decrease to the critical value or the model solution could not be better, we stop this algorithm and get the approximate optimal result. the saa applied to the multi-user collaborative correction algorithm could be described as following: step6.1: suppose it is the uth iteration. let formula (8) be the energy function. suppose c = 0 is the number of iterations for calculating µk, and sc = 0 is the verdict factor to judge whether to stop the iteration. step6.2: for user ak(u),(k = 1,2, . . .p), apply formula (9) to confirm the maximal deviation item and to calculate the evaluation set a1(u)c ,a 2(u) c ,a 3(u) c , . . .a p(u) c . randomly choose the initial state set [µ1c,µ 2 c, . . .µ p c] between 0 and 1. step6.3: run matlab software to generate p random numbers ∆1,∆2, . . .∆p, which are between 0 and 1. suppose µkc = ∆k (k = 1,2 . . .p, and ∆k in each iteration is different). let c = c + 1 and calculate the energy function ω(u)c . suppose t is the temperature schedule. its initial value t0 = 100. the attenuation function is tc = tc−1 × 0.95. suppose the counting variable is rt and rt = rt + 1. let ∆f = ω(u)c −ω (u) c−1. then we make the following judgment: when ∆f > 0, µ1c,µ 2 c, . . .µ p c is accepted as the new state solution and go on with step4. when ∆f < 0, there are two possibilities. if e( ∆f tc ) > random[0,1] holds, µ1c,µ 2 c, . . . µ p c is accepted as the new state solution and go on with step4. otherwise, let µkc = µ k c−1, sc = sc +1. step6.4: when sc > 10 or tc < 0.01, which means we reach the end condition and export the result µ1c,µ 2 c, . . .µ p c. otherwise, go on with step2 after the value of u is obtained. step7: popup dialog prompt whether to set a new threshold sxy which means the user acceptable level. if the user choose to reset, zs value would be zero. and we will let the value of pearson in each loop iteration compared with sxy and get the value of zs. then we execute step5. if the user choose not reset, we execute step8. step8: export the calculation result x(u), s (u) and p user final evaluation matrixes ak(u),(k = 1,2, . . .p). we have testify the algorithm is effective in the previous studies. if you are interested in the test process, you can refer to article [16] and [17]. 4 experimentation we build an experiment environment to execute our algorithm at current conditions to validate the effectiveness of this algorithm [6]. the environment is described as follows: we adopt ontology and the relevant methods in order to design and develop the movie information database. jena 2.6.2 is applied to store the movie information in rdf format and arq-2.2 is used to manage the movie information. we have imported 300 movies which involve 10 categories. a semantic analysis of each movie is conducted to get key words and form the initial attribute set. then the synonyms and the similar words in the initial set are combined. take some topical words as the characteristic attributes and use them to represent multi-attribute collaborative filtering recommendation 755 these movies. finally, 15 attributive categories and 282 concrete attributes are extracted. then an online multi-attribute rating system based on the movie database and a collaborative filtering recommendation system based on group-decision making are designed and developed. the concrete process that tests the algorithm is as follows [6]: 1. select four evaluated movies in which g(u,p) is comparatively big and use them as the testify set. they respectively include 6, 7, 8 and 9 attributes. then, use the target user evaluation matrixes which are further used as the real weight vectors to calculate the user interest vectors for each movie. 2. based on the user-evaluated movies set (excluding the 4 movies in the test set), apply the methods in sections 2.3 and 2.4 to searching the most similar user set for the target user (i.e., the similar interest distributions). take movie 1 with 6 attributes as an example. the real interest vectors are s = [3.7288, 2.7053, 1.9627, 0.4657, 0.3293, 0.3293]. the total score of this movie is 4.5 which indicates that the target user has a high preference to this movie. moreover, the preference is mainly determined by the first three attributes. totally, 9 similar users have evaluated this movie. firstly, the traditional collaborative filtering algorithm is applied to obtaining the weighted average of the total score of this movie and gets the result 3.94. we are not sure whether the target users have interests in this movie. thus, we need use the similar user evaluation matrixes to make judgments. the evaluation matrixes of six similar users are listed as follows: a =   1 2 2 5 7 9 1/2 1 1 6 5 6 1/2 1 1 7 7 6 1/5 1/6 1/7 1 1 2 1/7 1/5 1/7 1 1 3 1/9 1/6 1/6 1/2 1/3 1   b =   1 2 3 3 7 7 1/2 1 0 4 7 6 1/3 0 1 0 4 5 1/3 1/4 0 1 1 2 1/7 1/7 1/4 1 1 3 1/7 1/6 1/5 1/2 1/3 1   c =   1 2 2 4 0 0 1/2 1 1 3 0 0 1/2 1 1 2 0 0 1/4 1/3 1/2 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1   d =   1 2 3 4 6 8 1/2 1 2 2 4 6 1/3 1/2 1 1/2 4 7 1/4 1/2 2 1 3 2 1/6 1/4 1/4 1/3 1 2 1/8 1/6 1/7 1/2 1/2 1   e =   1 2 0 7 8 0 1/2 1 2 8 7 7 0 1/2 1 0 7 8 1/7 1 0 1 1 2 1/8 1/7 1/7 1 1 1 0 1/7 1/8 1/2 1 1   f =   1 2 4 0 7 0 1/2 1 2 6 7 7 1/4 1/2 1 0 2 2 0 1/6 0 1 0 2 1/7 1/7 1/2 0 1 1 0 1/7 1/2 1/2 1 1   3. apply the four algorithms to calculate the score of the four movies and make comparisons on the deviations of the real weight vectors of the target users. the result is listed as follows: table 1: the comparison between algorithms movie 1 (6 order) movie 2 (7 order) movie 3 (8 order) movie 4 (9 order) arithmetic weighted average method 0.1589 0.0564 0.1985 0.1132 logarithmic least squares method 0.1054 0.0534 0.1398 0.0831 compatibility correction algorithm 0.0877 0.0556 0.1042 0.0687 our algorithm 0.0780 0.0543 0.0885 0.0683 756 c. yu, y. luo, k. liu when the scores of a part of similar users have a large deviation from those of the other users, the algorithm proposed in this paper can solve the problem of early convergence better than the other algorithms and obtain an accurate result, as shown in table 1. the core of our algorithm is the revised values of the comprehensive evaluation matrix determined by the majority of users. accordingly, the highly deviated evaluation values are revised. the result of seven order matrix experiment shows that the deviations of the result of any algorithms are not notable when all the similar users have unanimous evaluation matrixes, the result of nine order matrix experiment shows that the result of the proposed algorithm is similar to that of compatibility correction algorithm when all the similar users have unanimous evaluation matrixes, while still have some incomplete values, and is better than the other two algorithms obviously. when there are 5 similar users and six order evaluation matrix is executed with our algorithm, the change tendencies of the main indicators are shown in figure 1: 0 2 4 6 8 10 12 14 16 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 iterations p e a rs o n -c o m p a ti b il it y v a lu e compatibility value of five similar users pearson value of five similar users pearson-compatibility value of five similar users figure 1: the main indicators change in our example 4. for example, for the first movie with the six order evaluation matrix, the influence of the user number on the accuracy of recommendation results is examined. suppose the user number is 3, 5, 7 and 9. the accuracy and the number of iterations are calculated with different means of permutation and combination. part of the result is shown in table 2: table 2: the comparison of different similar user numbers user number 3 5 7 9 initial indicator of comprehensive consistency degree 0.5872 0.6890 0.6872 0.7081 deviation of result 0.1680 0.1093 0.0828 0.0780 number of iterations 12 16 28 37 more deviation items are generated as the user number increases. therefore, the iterations of this algorithm rise. this test indicates that the effectiveness of this algorithm is highly related to the initial consistency degrees of all users and the number of users. in general, when the initial consistency degree is low and the similar user set is limited (e.g. there are 3 users), it is hard for the algorithm to dig out the common information among the users. therefore, the result deviation is huge. however, when the number of similar users increases to a certain degree (e.g. the number is equal or bigger than 7), the algorithm still remains a good accuracy, even if the initial compatibility is low. regarding the provision of personalized services to the target users, this paper calculates the comprehensive evaluation weight vectors of each movie with the group-decision making model. multi-attribute collaborative filtering recommendation 757 take the first movie with 6 attributes as an example. the comprehensive evaluation score of nine similar users is g(u,p) = 3.94. the comprehensive evaluation vectors are v = [4.0653 2.9492 1.7630 0.3972 0.3044 0.3607]. each value of the weight vector represents the potential interest degree of the target user on the corresponding product attributes. thus, the total score calculation formula is tscore = g(u,p)× n∑ i=1 vi/n (10) where tscore denotes the total score, n denotes the number of attributes, and vi denotes the comprehensive evaluation value of the (i)th attribute of the product. the recommendation set can be fixed through the way of ranking or threshold setting. the total scores of the four movies is shown in figure 2: 0 1 2 3 4 5 6 7 t s c o re 6.4616 5.9400 5.6734 4.6622 movie1 movie1 movie3 movie4 figure 2: the tscore of four movies the first movie has the highest score with six attributes, as shown in figure 2. the characters of this movie are analyzed as follows using the user interest model. firstly, attribute vi of weight vector v is normalized and generate vector v = [0.4131,0.2997,0.1792,0.0404,0.0309,0.0367]. the three attributes whose values are bigger than the average value 0.1666 are picked out. when the attribute value is bigger than 0.1666, the majority of users have evident preference on movie one. in the target user interest model, there are 124 attributes totally. the 3 attributes of movie one that are bigger than the average value are connotation, characteristic and special efficiency are still larger than the average value (1/124 = 0.0081) among 124 target user attribute preferences. this result indicates that target user has evident preference to these 3 attributes and the popularity of this movie is mainly determined by these attributes. therefore, we could introduce movie one to target user and provide the reasons why this movie is introduced. we also could use semantic analysis technique to describe each attribute in detail and provide more personal service to target user. the comparison analysis histogram is shown in figure 3. m bar means the percentages which attributes are ranking at top 3 in movie one, u bar means percentages which attributes are ranking at top 3 in target user interest model. the analysis show us that target user may have larger interest preference to attribute one in movie one. 5 conclusion the traditional collaborative filtering personal recommended algorithms seldom consider the multi-attribute problem. our approach is based on group-decision making. we propose an 758 c. yu, y. luo, k. liu 0 0.1 0.2 0.3 0.4 0.5 0 0.01 0.02 0.03 0.04 0.05 u barm bar connotation special efficacyplot 0.4131 0.2997 0.1792 0.0450 0.0254 0.0326 figure 3: comparison analysis histogram of movie one with target user interest. improved pearson-compatibility algorithm which is applied to the collaborative filtering recommend field. we then build a virtual recommend environment and testify the effectiveness and feasibility of this algorithm. the advantages of the collaborative filtering personal recommended algorithm based on group-decision include: identifying a more suited similar users set for the target user. an accurate target user model could be set up via field subdivision according to field attributes. then the users who have similar interest distribution with target user can be found. the similar user set is generated. providing more accurate and personal recommend service to the target user. the traditional collaborative filtering method could neither recommend a result set to target user, nor provide analysis service [6]. this weakness is overcome by making an information integration to know what are mainly factors determining the user preference, so that we could handle the user need more accurate. considering evaluation deviation between the similar users and revising the user evaluation. instead of weighted mean, group-decision making method is used to calculate the comprehensive evaluation score. deleting the deviation item and revising the evaluation matrix could make the result have a better fitting effect [6]. the collaborative filtering method based on pearsoncompatibility is applied to the personal recommended field. the result of the experiment shows that the algorithm is stable when dealing with the deviation items and identifies the common preference information between similar users. acknowledgment this research work is supported by innovation program of shanghai municipal education commission (no. 12zz070, no. 12ys018) and humanity and social science youth foundation of ministry of education, china (no. 12yjc630136). bibliography [1] g. adomavicius, a. tuzhilin (2005); toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, ieee trans. on knowl. and data eng., 17(6):734-749. multi-attribute collaborative filtering recommendation 759 [2] xiaoyuan su, taghi m. khoshgoftaar(2009); a survey of collaborative filtering techniques, advances in artificial intelligence, 2009:1-19. 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[9] a. ypma, t. heskes (2002); categorization of web pages and user clustering with mixtures of hidden markov models, proc. of the webkdd 2002 workshop: web mining for usage patterns and user profiles, sigkdd 2002, edmonton, alberta, canada. [10] z.s. hua, b.g. gong, x.y. xu (2008); a ds–ahp approach for multi-attribute decision making problem with incomplete information, expert systems with applications, 34(3):22212227. [11] h. jeon, t. kim, j. choi (2010); personalized information retrieval by usingadaptive user profiling and collaborative filtering, aiss: advances in information sciences and service sciences, 2(4):134-142. [12] liu, a., yang, z.(2010); watching, thinking, reacting: a human-centered framework formovie content analysis, international journal of digital content technology and its applications, 4(5):23-37. [13] y. dong , y. chen, s. wang (2004); algorithm of solving weights for group decision making by improving compatibility, systems engineering-theory & practice, 2004-10. [14] c.-s. yu (2002); a gp-ahp method for solving group decision-making fuzzy ahp problems, computers & operations research, 29(14): 1969-2001. [15] y. zhang, h. wang (2002); development and application of p-s aided decision system, systems engineering-theory methodology application, 2002-04. [16] l. liang, l. xiong, g. wang (2004); a new method of determining the reliability of decisionmakers in group decision, systems engineering, 22(6):91-94. [17] j. barzilai, f. a. lootsma (1994); power relations and group aggregation in the multiplicative ahp and smart, proc. 3rd int. symp. ahp, 157-168. [18] j. sun, w. xu, q.d. wu(2005); a new algorithm for incomplete matrixes’ compatibility improvement and group ranking in group decision making, systems engineering-theory & practice, 10(10):89-94. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 910-918 a new model for cluster communications optimization a. rusan, c.-m. amarandei andrei rusan, cristian-mihai amarandei “gheorghe asachi” technical university of iaşi department of computer science and engineering, adress: bd. dimitrie mangeron, nr. 53a, 700050, iaşi, romania e-mail: andrei.rusan@tuiasi.ro, camarand@cs.tuiasi.ro abstract: performance losses of cluster applications can arise from various sources in the communications network of computer clusters. typically, cpu intensive applications generate a small amount of network traffic the overall influence of the network subsystem is minimal. on the other hand, a dataintensive and network aware application generates a large amount of network traffic and the influence of the network subsystem is significantly greater. this paper presents a model that aims to improve the cluster’s network performance by reducing the data transfer time, this solution having the advantage that doesn’t imply modifications of the original applications or of the kernel. keywords: cluster communications optimization, network performance 1 introduction the computing performance of a cluster is dependent on the performance of cluster components: computing nodes and communication infrastructure. the cluster communication infrastructure was built using network devices whose performance can be modified only by hardware changes, i.e. replacing 100mbps ethernet switches with gigabit switches; therefore this component of the cluster will be neglected. the performance of a computing node is defined by hardware and software performance, where the hardware performance can be considered a constant value and it can be influenced by the hardware changes only. the software can be separated in components: application and operating system. the computing performance of the cluster can be influenced by each of these components. cluster performance increasing by performing modifications at the applications level is a goal very hard to achieve, some applications requiring a complete rewrite for this task. of course, there are exceptions too, like network-aware applications, which are built exactly with this goal in mind and they do not require any optimizations. the last component that can influence the performance is the operating system through the kernel configuration and at the network layer optimizations. taking into account that clusters typical contains a large number of computing nodes, the solution for cluster performance improvements must be done with minimal modifications in the systems. these requirements are necessary to keep the administrative tasks at a decent level. there is a research work involving the network tuning mechanisms or network-aware applications trying to solve these issues. the projects developed so far, like wad (work around daemon) [1] or enable [2] don’t meet the imposed requirements, as for wad a modified kernel must be used, and for enable the applications must be rewritten. the wad project provides a transparent mechanism to work around a variety of network issues, including tcp buffer size, mtu size, packet reordering, and leaky network loss [1]. the wad goal is to eliminate the "wizard gap" representing the difference between the network performances achievable by manually handcrafting of the optimal tuning parameters, compared copyright c⃝ 2006-2010 by ccc publications a new model for cluster communications optimization 911 to an untuned application [1]. to attain this goal, wad requires a modified kernel from the web100 project. this solution could not be applied in our case, because of the different linux kernel versions the web100 project provides a kernel patch starting from the 2.6.12. through the use of a different kernel version that the provided one by the linux distribution, centos 4.5 in our case, problems in maintaining the operating system across clusters can occur. therefore our solution works fine no mater the kernel version used. the enable project, includes monitoring tools, visualization tools, archival tools, problem detection tools, and monitoring data summary and retrieval tools. enable provides an api that makes it very easy for application or middleware developers to determine the optimal network parameters [2]. however, the solution provided by this project was not applicable in our case, because applications could not be rewritten. this paper presents a model for self optimization of the network communications in order to improve cluster performance by shortening the data transfer time. the model implementation does not require applications and kernel structure modifications or adding new modules to the existing ones. also, the implementation uses only the tools provided by the operating system for runtime configuration and therefore the automatic operating system and kernel updates can be applied immediately. in the next section a brief review of the tcp transport protocol issues, linux kernel network subsystem and the netpipe network performance measurement tool are presented. section 3 describes the network self optimization model and the proposed algorithm. section 4 presents the test environment and the experimental results. the final section summarizes author’s efforts on tuning the cluster communications network and considers future extensions of the work. 2 background tcp protocol transmits new data into the network when old data has been received as indicated by acknowledgments from the receiver to the sender. the data rate is determined by the window size and is limited by the application, the buffer space at the sender or the receiver and by the congestion window. tcp adjust the congestion window to find an appropriate share of the network capacity of the path between source and destination. missing or corrupted data segments are repaired by tcp by retransmitting the data from the sender’s buffer. this process requires an entire window of data to fit into both sender and receiver buffers [1]. the largest tcp window can be 216=65kb because the tcp header uses 16 bits to report the receive window size to the sender. the window scale option was introduced defining an implicit scale factor used to multiply the windows size value from tcp header in order to obtain the real tcp window size, as described in [3]. these buffers have default values that may either be changed by the applications using system calls or by using tools provided by the operating system, i.e. sysctl tool from linux/unix. the second part of this section is focused on the network subsystem of the linux kernel. starting from the version 2.4 of linux kernel, an auto tuning technique is used to perform memory management. this technique simply increases and decreases buffer sizes depending on available system memory and available socket buffer space. by increasing buffer sizes when they are full of data, tcp connections can increase their window size performance improvements are an intentional side-effect [4]. on the other hand, this is done within the limitation of the available system memory and socket buffer space, and on the busy cluster that are valuable resource. the network subsystem of the linux operating system should be tuned in order to obtain an optimal performance of a computing system. in order to do that, changes can be operated at the following levels: network interface and kernel parameters. the kernel parameters allowing to change the network behavior can be tuned by modifying the following files located in 912 a. rusan, c.-m. amarandei /proc/sys/net: /proc/sys/net/core/rmem_max /proc/sys/net/core/rmem_default /proc/sys/net/core/wmem_max /proc/sys/net/core/wmem_default /proc/sys/net/ipv4/tcp_stack /proc/sys/net/ipv4/tcp_timestamps /proc/sys/net/ipv4/tcp_keepalive_time /proc/sys/net/ipv4/tcp_mem /proc/sys/net/ipv4/tcp_rmem /proc/sys/net/ipv4/tcp_wmem /proc/sys/net/ipv4/tcp_window_scaling the network interface can also be tuned by modifying the speed and duplex settings and the mtu size. two problems have to be addressed while setting up the cluster: • the default kernel values don’t provide the best performance for the custom environment, and • the number of communication devices needed to be set. since solutions to solve these two problems are missing an optimal value for each system in cluster to get the best possible performance is proposed. by using the right tools, the network settings related changes are available immediately, the optimization algorithm presented in this paper being based on these features. the values of the send/receive buffers (tcp_wmem and tcp_rmem) can be changed by specifying minimum size, initial size, and maximum size as follows: sysctl -w net.ipv4.tcp_rmem="4096 87380 8388608" sysctl -w net.ipv4.tcp_wmem="4096 87380 8388608" the third value must be the same as or less than the wmem_max and rmem_max values. the first value can be increased on high-speed, high-quality networks so that the tcp window starts out at a sufficiently high value [5]. also, the tcp window scaling is an option to enlarge the transfer window. performance measurements of the cluster network can be done using a wide area of tools like iperf [6], netperf [7] or netpipe (network protocol independent performance evaluator). because the performance measurements must be done for both tcp and mpi layer and the netpipe provides a complete measurement of the communication performance on both of them, the tests were performed using this tool. the netpipe utility performs simple ping pong tests, bouncing messages of increasing size between two computers. to provide a complete test, netpipe modifies the message size, with a slight perturbation, at regular intervals and measures the point-to-point communications performance between nodes [8]. because we want to determine of the maximum bandwidth available for different use cases, usage of different message size is a must. from all the performance measurements tools available, only netpipe had this feature by default, which defines it as a right tool for this kind of tests. an in depth description of the netpipe utility can be found in [8][10]. linux kernel network subsystem information gathered by running netpipe on the cluster were used in order to improve the throughput. 3 proposed model the proposed model implies the computation of a best possible set of values for a given set of parameters. figure 1 presents the model schematics composed from three parts: ”control logic”, a new model for cluster communications optimization 913 ”parameter computation” and ”network test tool”. the first component is responsible for sending starting set of values to ”parameter computation” and keeps the running flux under control. the second component computes the sets of values based on previously known sets of data and on the results of the current run received from the ”network test tool” and send it to the kernel in order to set the network subsystem. the ”network test tool” is responsible to run set of tests and provide the results to ”parameter computation” component. the optimization process is started by ”control logic”, which sends starting values to ”parameter computation” that are set in the kernel network subsystem and starts the first set of tests through the ”network test tool”. after the tests are finished, the results are sent to "parameter computation" which computes a new set of values and the process continues until meeting the end condition. compute node compute node network test tool network subsystem network device compute node network test tool network subsystem application kernel hardware control logic parameter computation network device network infrastructure network test tool network subsystem network device figure 1: the cluster communication optimization model the process provides self optimization of the kernel network subsystem, the only necessary interaction with the application being the configuration file that contains necessary values for the application startup and for the components behavior. these values can be set by administrators to meet the specific needs. an algorithm implementing model functionality is proposed. this algorithm performs the bandwidth measurements and adjusts the sets of parameters to obtain the highest bandwidth usage for each case and running tests. given l, the number of tests to be performed, let it be n = {n1, n2, . . . , nk} the set of nodes in cluster, t = {t1, t1, . . . , tl} the set of test variables (i.e. tcp_rmem, tcp_wmem, tcp_window_scalling), i = {i1, i2, . . . , il} the kernel network subsystem parameters start values and e = {e1, e1, . . . , el} be the set of computed values for each test ti. also, given m as the number of messages, ms = {ms1, ms2, . . . , msm} is defined as the set of message sizes used by the testing tool, x = {x1, x2, . . . , xm} as the best set of result value for each test tt, the results set ri = {r1, r2, . . . , rk}i one for each cluster node, s = {s1, s2, . . . , sm}, where si = ∑k j=i rij, and b = {b1, b2, . . . , bm} as the set of best values for each test ti. the algorithm computes the values for the test variables 914 a. rusan, c.-m. amarandei as follows: the algorithm has two main components: the network test component corresponding to ”network test tool” in figure 1 and implemented by lines 3-19 from the algorithm, and the computational component corresponding to ”parameter computation” in figure 1 and implemented by lines 20-30. the methods used in the algorithm implements the following actions: • generate_set: produce a new set of parameters used for network testing; • start_remote_testing_program: launches the remote component of the testing application (netpipe in our case); • prepare_local_testing_program: prepare the local component of the testing application, necessary to maximize the accuracy of the measured values; • execution_of_local_testing_programs: runs the testing application; • get_max_count: extract the parameter value corresponding to the maximal throughput. finally, the line 31 sequentially sets the kernel parameters to the best computed values on the entire cluster. 4 implementation and experimental results to improve the cluster communications, dynamic tests and adjustments for the following linux kernel network parameters are performed: tcp_window_scalling, tcp_rmem and tcp_wmem. bandwidth measurements and tcp parameters adjustments were carried out to obtain the highest bandwidth usage for each case and to determine the maximum bandwidth available for different use cases. the environment used to test the proposed model consists in a grid cluster with the following configuration: one front-end computer with 4 x 3.66 ghz intel xeon processors, 4 x 146 gb hard drive and 8 gb of ram and 12 computing nodes with 1 x 2.33ghz intel core2 duo cpu, 1 x 160gb hard drive and 2gb of ram with gigabit ethernet card connected with cat6 cables via a gigabit switch. first implementation of the algorithm was made in bash, but due the dificulties in working with data structures was switched to perl. also, to preserve measurement accuracy and performance the tests results were saved to files for further usage, like graphical presentation. the performance results were obtained based on a test array with three elements: one for a new model for cluster communications optimization 915 tcp windows scaling, a second one for tcp read buffer and the third one for the tcp write buffer kernel parameters. for each of this there is a graphical presentation, where on the x and y-axis the message size used during tests and the resulted bandwidth values are, respectively, presented. the results for the tcp windows scaling parameter are showed in figure 2(a), where one line is for net.ipv4.tcp_window_scaling=0, and the other one is for net.ipv4.tcp_window_scaling=1. for an easier reading of the results graph, we apply a bezier function in order to obtain the presentation from figure 2(b). the influence of tcp read/write buffer size over the available bandwidth are presented in figure 3(a)/figure4(a). in this case, there are a large number of graphic representations and the observation is very difficult, so a bezier function was applied on the results values to make the graphical presentation more readable, as shown in figure3(b)/figure4(b). the red line in the graphical representations corresponds to the default value for tcp_rmem (4kb) and the blue dotted line is the best value resulted using this model, with 100mbps more than the default value. by applying different values for tcp buffers and running tests bandwidth variation for each of them is presented (figure 5). tcp buffers size starts from 4kb and each algorithm step doubles the previous value. 0 100 200 300 400 500 600 700 1 10 100 1000 10000 100000 1e+06 1e+07 b a n d w id th i n m b p s message size in bytes "scaling_disabled" "scaling_enabled" (a) 0 100 200 300 400 500 600 700 1 10 100 1000 10000 100000 1e+06 1e+07 b a n d w id th i n m b p s message size in bytes "scaling_disabled" "scaling_enabled" (b) figure 2: tcp window scaling influence over bandwidth 0 100 200 300 400 500 600 700 800 900 1 10 100 1000 10000 100000 1e+06 1e+07 b a n d w id th i n m b p s message size in bytes "4096" "8192" "16384" "32768" "65536" "131072" "262144" "524288" (a) 0 100 200 300 400 500 600 700 800 900 1 10 100 1000 10000 100000 1e+06 1e+07 b a n d w id th i n m b p s message size in bytes "4096" "8192" "16384" "32768" "65536" "131072" "262144" "524288" (b) figure 3: tcp read buffer influence over bandwidth 916 a. rusan, c.-m. amarandei 0 100 200 300 400 500 600 700 800 900 1 10 100 1000 10000 100000 1e+06 1e+07 b a n d w id th i n m b p s message size in bytes "4096" "8192" "16384" "32768" "65536" "131072" "262144" "524288" (a) 0 100 200 300 400 500 600 700 800 900 1 10 100 1000 10000 100000 1e+06 1e+07 b a n d w id th i n m b p s message size in bytes "4096" "8192" "16384" "32768" "65536" "131072" "262144" "524288" (b) figure 4: tcp write buffer influence over bandwidth figure 5: bandwidth achieved for different tcp buffer size a ram drive on all computers was built in order to test the results without any delays introduced by the hard disk drive. because of the 2gb memory limits on the computing nodes, the file transferred and the ram drive size was 512 mb. in the figure 6(a) transfer time for the file is shown. in this test the tcp buffers size was changed from 4kb to 512 kb and data transfer starts in both directions for each value, from the frontend to cluster nodes and back. 54.00 54.50 55.00 55.50 56.00 56.50 57.00 57.50 58.00 58.50 59.00 tr a n s fe r ti m e ( m s ) 40 96 81 92 16 38 4 32 76 8 65 53 6 13 10 72 26 21 44 52 42 88 buffer size tcp_wmem tcp_rmem (a) 547 724 480 715 371 695 0 100 200 300 400 500 600 700 800 before after b a n d w id th wscalling wmem rmem (b) figure 6: (a)transfer time for a 512mb file between cluster; (b)the benefits of parameters adjustments the best transfer time was obtained when both tcp_rmem and tcp_wmem values were 16kb a new model for cluster communications optimization 917 or 32kb. during the tests, this solution provides all values for the considered tcp parameters, which can be useful for other scenarios. in figure 6(b), the bandwidth improvement is presented. using only the default values, the cluster internal network available bandwidth is not optimally used with a strong impact on the overall computing performance. using this optimization model, the bandwidth available in the cluster is efficiently used. 5 conclusions and future work using the proposed model, the communication between cluster nodes has been improved. all results are considered for the specific needs of mentioned cluster, where a significant amount of data needs to be transferred between cluster nodes. for other applications, like a web server farm, the final results may be slightly different, but can be optimized by adjusting the test tool for those specific needs. the network kernel parameters computed can be used later if the use case is changed, i.e. a web server farm. the algorithm can be used for ipv6 too, however the authors doesn’t implemented nor tested. the further development of presented application will follow two directions: one is to extend its capabilities to support udp traffic performance adjustment; and the second one is to support tuning parameters other than the ones related to the linux kernel. bibliography [1] t. dunigan, m. mathis, b. tierney , a tcp tuning daemon, conference on high performance networking and computing, proceedings of the 2002 acm/ieee conference on supercomputing, baltimore, maryland, 2002 [2] b.l. tierney, tcp tuning guide for distributed application on wide area networks, usenix;login. http://www-didc.lbl.gov/tcp-wan-perf.pdf., 2001 [3] v. jacobson, r. braden, d. borman, rfc1323 tcp extensions for high performance, may 1992 [4] b.l tierney, d. gunter, j. lee, m. stoufer, j.b. evans, enabling network-aware applications, proceedings of the 10th ieee international symposium on high performance distributed computing, page: 281-288, 2001, isbn: 0-7695-1296-8 [5] netpipe, webpage: http://www.scl.ameslab.gov/netpipe/ [6] a. tirumala, l. cottrell, "iperf quick mode", http://www-iepm.slac.stanford.edu/bw/ iperfres.html [7] netperf homepage, http://www.netperf.org/netperf/netperfpage.html [8] d. turner, a. oline, x. chen, and t. benjegerdes,integrating new capabilities into netpipe, lecture notes in computer science, springer-verlag, september 2003, pp. 37-44. [9] d. turner, x. chen, protocol-dependent message-passing performance on linux clusters, proceedings of the ieee international conference on cluster computing, september 2002, pp. 187-194. [10] q.o. snell, a. mikler, j.l. gustafson, netpipe: a network protocol independent performance evaluator, asted international conference on intelligent information management and systems, june 1996. 918 a. rusan, c.-m. amarandei [11] h. sivakumar, s. bailey, r. l. grossman,psockets: the case for application-level network striping for data intensive applications using high speed wide area networks, proceedings of ieee supercomputing 2000, nov., 2000, http://www.ncdm.uic.edu/html/psockets. html [12] j. postel, rfc793 transmission control protocol, september 1981 [13] r. braden , rfc1122 requirements for internet hosts – communication layers, october 1989 [14] v. paxson, g. almes, j. mahdavi, m. mathis, rfc 2330 framework for ip performance metrics, may 1998 [15] e. ciliendo, t. kunimasa, b. braswell, linux performance and tuning guidelines, ibm, july 2007. http://horos.rdsor.ro/ijcccv3n4draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 4, pp. 336-342 fuzzy szpilrajn theorem through indicators irina georgescu abstract: in this paper there are studied some numerical indicators which measure the degree to which a fuzzy relation verifies some properties (reflexivity, transitivity, etc. ).the main result is a fuzzy generalization of the szpilrajn theorem in terms of such numerical indicators and is applied to any fuzzy relation. keywords: fuzzy relation, szpilrajn theorem, similarity 1 introduction the classical szpilrajn theorem [10] asserts that any partial order can be extended to a total order. this result has been followed by refinements and generalizations and has been used in applications too (e. g. consumer theory [8]). the first fuzzy version of the szpilrajn theorem has been established by zadeh [11]. other fuzzy versions of this theorem can be found in [4], [6]. in [3] the topic is systematically studied in the framework of the fuzzy orders with respect to a left–continuous t–norm ∗ and a ∗–similarity relation ω. the idea of this paper is the following: instead of studying a property p of a fuzzy relation r (e. g. reflexivity, transitivity, etc.) to define numerical indicators which should express "the degree to which the fuzzy relation r verifies the property p". in this way, instead of considering a fuzzy order r on a set x we will have a number ord(r) which should measure "the degree to which r is a fuzzy order". the main result of the paper is a generalization of the szpilrajn theorem expressed in terms of such numerical indicators. it is a refinement of theorem 6.2 in [3] and it is applied to any fuzzy relation. 2 preliminaries in this section we shall recall some basic facts on the residuum associated with a left–continuous t–norm and on fuzzy relations ([1], [2], [4], [5], [7], [9]). for any a, b ∈ [0,1] we denote a ∨ b = max(a, b) and a ∧ b = min(a, b). more generally, for any set {ai}i∈i ⊆ [0,1] we denote ∨ i∈i ai = sup{ai|i ∈ i} and ∧ i∈i ai = inf{ai|i ∈ i}. let ∗ be a left–continuous t–norm [7], [5]. the residuum → associated with ∗ is introduced by a → b = ∨ {c ∈ [0,1]|a ∗ c ≤ b}. the biresiduum ↔ is denoted by a ↔ b = (a → b) ∧ (b → a). we fix a left–continuous t–norm ∗. lemma 1. [1], [5] for any a, b, c ∈ [0,1] the following properties hold: (1) a ∗ b ≤ c iff a ≤ b → c; (2) a ∧ b = a ∗ (a → b); (3) a ≤ b iff a → b = 1; (4) a = 1 → a; (5) 1 = a → a; (6) a ≤ a ∗ (a ↔ b). copyright © 2006-2008 by ccc publications fuzzy szpilrajn theorem through indicators 337 lemma 2. [1], [5] for any {ai}i∈i ⊆ [0,1] and a ∈ [0,1] the following properties hold: (1) ( ∨ i∈i ai) ∗ a = ∨ i∈i (ai ∗ a); (2) a → ( ∧ i∈i ai) = ∧ i∈i (a → ai); (3) ( ∨ i∈i ai) → a = ∧ i∈i (ai → a). let x be a non–empty subset. a fuzzy subset of x is a function a : x → [0,1]. denote by f(x ) the family of the fuzzy subsets of x. for any a, b ∈ f(x ) denote a ⊆ b if a(x) ≤ b(x) for any x ∈ x. a fuzzy relation on x is a function r : x 2 → [0,1]. r is said to be • reflexive if r(x, x) = 1 for any x ∈ x; • symmetric if r(x, y) = r(y, x) for any x, y ∈ x; • ∗–transitive if r(x, y) ∗ r(y, z) ≤ r(x, z) for all x, y, z ∈ x; • strongly complete if r(x, y) ∨ r(y, x) = 1 for any x, y ∈ x. a reflexive, symmetric and ∗–transitive fuzzy relation ω on x will be called ∗–similarity relation. let ω be a ∗–similarity relation on x and r a fuzzy relation on x. r is said to be: • ω–reflexive if ω(x, y) ≤ r(x, y) for any x, y ∈ x; • (∗, ω)–antisymmetric if r(x, y) ∗ r(y, x) ≤ ω(x, y) for any x, y ∈ x. a (∗, ω)–order is ∗–transitive, ω–reflexive and (∗, ω)–antisymmetric fuzzy relation r on x. let r, q be two (∗, ω)–orders on x. we say that q is an extension of r if r ⊆ q, i. e. r(x, y) ≤ q(x, y) for all x, y ∈ x. the following fuzzy generalization of the szpilrajn theorem was proved in [3]: theorem 3. let ω be a ∗–similarity relation on x . then any (∗, ω)–order on x has a strongly complete extension. 3 some indicators let ∗ be a left–continuous t–norm and ω be a ∗–similarity relation on x. definition 4. for any fuzzy relation r on x let us define: re f (r) = ∧ x∈x r(x, x); trans(r) = ∧ x,y,z∈x [r(x, y) ∗ r(y, z) → r(x, z)]; re fω(r) = ∧ x,y∈x (ω(x, y) → r(x, y)]; antω(r) = ∧ x,y∈x [r(x, y) ∗ r(y, x) → ω(x, y)]; sc(r) = ∧ x,y∈x (r(x, y) ∨ r(y, x)); ordω(r) = re fω(r) ∧ antω(r) ∧ trans(r). lemma 5. for any fuzzy relation r the following equivalences hold: (1) re f (r) = 1 iff r is reflexive; (2) trans(r) = 1 iff r is ∗–reflexive; 338 irina georgescu (3) re fω(r) = 1 iff r is ω–reflexive; (4) antω(r) = 1 iff r is (∗, ω)–antisymmetric; (5) sc(r) = 1 iff r is strongly complete; (6) ordω(r) = 1 iff r is a (∗, ω)–order. re f (r) will be called the degree of reflexivity of r, trans(r) the degree of ∗–transitivity of r, etc. the indicators introduced above refine the properties of reflexivity, transitivity, etc. of fuzzy relations. thus, instead of saying that the fuzzy relation r is reflexive, the real number re f (r) will measure "the degree to which r is reflexive". proposition 6. let r be a fuzzy relation on x and x, y, z ∈ x . then (1) re f (r) ≤ r(x, x); (2) trans(r) ∗ r(x, y) ∗ r(y, z) ≤ r(x, z); (3) re fω(r) ∗ ω(x, y) ≤ r(x, y); (4) antω(r) ∗ r(x, y) ∗ r(y, x) ≤ ω(x, y); (5) ordω(r) ≤ ω(x, y) ↔ (r(x, y) ∗ r(y, x)); (6) ordω(r) ≤ r(x, x). 4 main result in this section we shall prove a generalization of the theorem of szpilrajn formulated in terms of the indicators introduced in the previous paragraph. the result will be valid for any fuzzy relations and in particular one will obtain theorem 3. let ∗ be a left–continuous t–norm and ω a ∗–similarity relation on x. if r and q are two fuzzy relations on x then we denote r 4 q iff r ⊆ q and ordω(r) ≤ ordω(q). it is easy to see that 4 is a partial order on the set of the fuzzy relations defined on x. if q is a fuzzy relation on x then we denote by ext(q) the set of all fuzzy relations r on x with the property that q 4 r. lemma 7. if q is a fuzzy relation on x then the partially ordered set (ext(q), 4) admits a maximal element. proof. we prove that (ext(q), 4) is inductive. we consider a chain (ri)i∈i in ext(q): for any i, j ∈ i we have ri 4 r j or r j 4 ri. of course q 4 ri for any i ∈ i. we will denote r = ⋃ i∈i ri. it suffices to prove that r ∈ ext(q). it is obvious that q ⊆ r therefore we have to prove that ordω(q) ≤ ordω(r). we show first that (a) ordω(q) ≤ re fω(r). let x, y ∈ x and i ∈ i. since q ⊆ ri it follows immediately ordω(q) ≤ re fω(q) ≤ re fω(ri) ≤ ω(x, y) → ri(x, y). by applying lemma 1 (2) and the previous inequality ordω(q) ∗ ω(x, y) ≤ ω(x, y) ∗ (ω(x, y) → ri(x, y)) = = ω(x, y) ∧ ri(x, y) ≤ ri(x, y) ≤ r(x, y) from where according to lemma 1 (1), ordω(q) ≤ ω(x, y) → r(x, y). it follows fuzzy szpilrajn theorem through indicators 339 ordω(q) ≤ ∧ x,y∈x (ω(x, y) → r(x, y)) = re fω(r). we intend to prove that (b) ordω(q) ≤ antω(r) let x, y ∈ x. then ordω(q) ∗ r(x, y) ∗ r(y, x) = ordω(q) ∗ [ ∨ i∈i ri(x, y)] ∗ [ ∨ j∈i r j(y, x)] = = ∨ i, j∈i ordω(q) ∗ ri(x, y) ∗ r j(y, x). let i, j ∈ i. assume that ri 4 r j therefore ri ⊆ r j and ordω(ri) ≤ ordω(r j). then, according to q 4 r j and proposition 6 (4): ordω(q) ∗ ri(x, y) ∗ r j(y, x) ≤ ordω(r j) ∗ r j(x, y) ∗ r j(y, x) ≤ ≤ antω(r j) ∗ r j(x, y) ∗ r j(y, x) ≤ ω(x, y). since this inequality is valid for any i, j ∈ i it follows ordω(q)∗ r(x, y)∗ r(y, x) ≤ ω(x, y), therefore, according to lemma 1 (1), ordω(q) ≤ r(x, y) ∗ r(y, x) → ω(x, y). from here we deduce ordω(q) ≤ ∧ x,y∈x [r(x, y) ∗ r(y, x) → ω(x, y)] = antω(r). we still have to prove (c) ordω(q) ≤ trans(r). let x, y ∈ x. then ordω(q) ∗ r(x, y) ∗ r(y, z) = ∨ i, j∈i ordω(q) ∗ ri(x, y) ∗ r j(y, x) let i, j ∈ i. assume ri 4 r j therefore ri ⊆ r j and ordω(ri) ≤ ordω(r j). according to proposition 6 (2): ordω(q) ∗ ri(x, y) ∗ r j(y, z) ≤ ordω(r j) ∗ r j(x, y) ∗ r j(y, z) ≤ ≤ trans(r j) ∗ r j(x, y) ∗ r j(y, z) ≤ r j(x, z) ≤ r(x, z) from where, ordω(q) ∗ r(x, y) ∗ r(y, z) ≤ r(x, z). by applying lemma 1 (1) it follows that for any x, y, z ∈ x we have ordω(q) ≤ r(x, y) ∗ r(y, z) → r(x, z), from where ordω(q) ≤ ∧ x,y,z∈x [r(x, y) ∗ r(y, z) → r(x, z)] = trans(r). from (a), (b) and (c) one obtains ordω(q) ≤ ordω(r). we have shown that (ext(q), 4) is inductive. according to zorn’s axiom a maximal element exists in ext(q). in the following we will situate ourselves in the case of the gödel t–norm ∧. theorem 8. let q be a fuzzy relation on x . then there exists a fuzzy relation r on x such that q 4 r and ordω(q) ≤ sc(r). proof. according to lemma 7 there exists a fuzzy relation r on x maximal in (ext(q), 4). then q 4 r. it remains to prove that ordω(q) ≤ sc(r). we assume by absurdum that ordω(q) 6≤ sc(r) = ∧ x,y∈x (r(x, y) ∨ r(y, x)). therefore there exist a, b ∈ x such that ordω(q) 6≤ r(a, b) ∨ r(b, a) from where ordω(q) 6≤ r(a, b) and ordω(q) 6≤ r(b, a). assume r(a, b) ≤ r(b, a). according to the lines above, r(b, a) < ordω(q). we define a new fuzzy relation r′ on x by r′(x, y) = r(x, y) ∨ (r(x, b) ∧ r(a, y)) for any x, y ∈ x. we intend to prove that r 4 r′. it is obvious that r ⊆ r′ therefore it remains to prove that ordω(r) ≤ ordω(r′). from r ⊆ r′ it follows immediately (1) ordω(r) ≤ re fω(r′) we establish now the inequality: (2) ordω(r) ≤ antω(r′) let x, y ∈ x. then 340 irina georgescu ordω(r) ∧ r′(x, y) ∧ r′(y, x) = = ordω(r) ∧ [r(x, y) ∨ (r(x, b) ∧ r(a, y))] ∧ [r(y, x) ∨ (r(y, b) ∧ r(a, x))] = = [ordω(r) ∧ r(x, y) ∧ r(y, x)] ∨ [ordω(r) ∧ r(x, y) ∧ r(y, b) ∧ r(a, x)]∨ ∨[ordω(r) ∧ r(y, x) ∧ r(x, b) ∧ r(a, y)] ∨ [ordω(r) ∧ r(x, b) ∧ r(a, y) ∧ r(y, b) ∧ r(a, x)] we will establish the following inequalities: (a) ordω(r) ∧ r(x, y) ∧ r(y, x) ≤ ω(x, y); (b) ordω(r) ∧ r(x, y) ∧ r(y, b) ∧ r(a, x) ≤ ω(x, y); (c) ordω(r) ∧ r(y, x) ∧ r(x, b) ∧ r(a, y) ≤ ω(x, y); (d) ordω(r) ∧ r(x, b) ∧ r(a, y) ∧ r(y, b) ∧ r(a, x) ≤ ω(x, y). in order to obtain (a) we use proposition 6 (4): ordω(r) ∧ r(x, y) ∧ r(y, x) ≤ antω(r) ∧ r(x, y) ∧ r(y, x) ≤ ω(a, b). we treat now the other three cases. according to proposition 6 (5) we have ordω(r) ≤ ω(a, b) ↔ (r(a, b) ∧ r(b, a)) = ω(a, b) ↔ r(a, b) we consider first the case r(x, y) ≤ r(y, x). then ordω(r) ≤ ω(x, y) ↔ r(x, y) with the same argument as above. (b) results like this: ordω(r) ∧ r(x, y) ∧ r(y, b) ∧ r(a, x) ≤ r(x, y) ∧ [ω(x, y) ↔ r(x, y)] ≤ ω(x, y) now we treat cases (c) and (d). first we notice that according to proposition 6 (2): ordω(r) ∧ r(y, x) ∧ r(x, b) ∧ r(a, y) ≤ trans(r) ∧ r(a, y) ∧ r(y, x) ∧ r(x, b) ≤ r(a, b) therefore ordω(r) ∧ r(y, x) ∧ r(a, y) ≤ r(a, b) ∧ [r(a, b) ↔ ω(a, b)] ≤ ω(a, b). analogously we obtain: ordω(r) ∧ r(x, b) ∧ r(a, y) ∧ r(y, b) ∧ r(a, x) ≤ ω(a, b). we consider the possible subcases: (i) ω(a, b) ≤ r(x, y); (ii) ω(a, b) > r(x, y). according to the proof above, in case (i) the inequalities (c) and (d) are immediate. we are situated now in case (ii). one notices that ordω(r) ∧ r(x, b) ∧ ω(a, b) ∧ r(a, y) ≤ ω(a, b) ∧ [ω(a, b) ↔ r(a, b)] ≤ r(a, b) ≤ r(b, a) therefore ordω(r) ∧ r(x, b) ∧ ω(a, b) ∧ r(a, y) ≤ ordω(r) ∧ r(x, b) ∧ r(b, a) ∧ r(a, y) ≤ trans(r) ∧ r(x, b) ∧ r(b, a) ∧ r(a, y) ≤ r(x, y) from ordω(r) ∧ r(x, b) ∧ ω(a, b) ∧ r(a, y) ≤ r(x, y) and ω(a, b) > r(x, y) it follows ordω(r) ∧ r(x, b) ∧ r(a, y) ≤ r(x, y). by using this last inequality we have ordω(r) ∧ r(y, x) ∧ r(x, b) ∧ r(a, y) ≤ ordω(r) ∧ r(x, b) ∧ r(a, y) ≤ r(x, y) from where ordω(r) ∧ r(y, x) ∧ r(x, b) ∧ r(a, y) ≤ r(x, y) ∧ [r(x, y) ↔ ω(x, y)] ≤ ω(x, y) thus (c) was proved and (d) follows analogously. the case r(y, x) ≤ r(x, y) is treated analogously. therefore the inequalities (a)–(d) are true, so ordω(r) ∧ r′(x, y) ∧ r′(y, x) ≤ ω(x, y). cf. lemma 1 (1) for any x, y, z ∈ x we have ordω(r) ≤ (r′(x, y) ∧ r′(y, x)) → ω(x, y) therefore ordω(r) ≤ ∧ x,y∈x [(r′(x, y) ∧ r′(y, x)) → ω(x, y)] = antω(r′) now we establish the inequality (3) ordω(r) ≤ trans(r′). let x, y, z ∈ x. we prove (4) ordω(r) ∧ r′(x, y) ∧ r′(y, z) ≤ r′(x, z). we notice that ordω(r) ∧ r′(x, y) ∧ r′(y, z) = fuzzy szpilrajn theorem through indicators 341 = ordω(r) ∧ [r(x, y) ∨ (r(x, b) ∧ r(a, y))] ∧ [r(y, z) ∨ (r(y, b) ∧ r(a, z))] = = [ordω(r) ∧ r(x, y) ∧ r(y, z)] ∨ [ordω(r) ∧ r(x, y) ∧ r(y, b) ∧ r(a, z)]∨ ∨[ordω(r) ∧ r(y, z) ∧ r(x, b) ∧ r(a, y)] ∨ [ordω(r) ∧ r(x, b) ∧ r(a, y) ∧ r(y, b) ∧ r(a, z)]. then to prove (4) is equivalent with establishing the following inequalities: (e) ordω(r) ∧ r(x, y) ∧ r(y, z) ≤ r′(x, z); (f) ordω(r) ∧ r(x, y) ∧ r(y, b) ∧ r(a, z) ≤ r′(x, z); (g) ordω(r) ∧ r(y, z) ∧ r(x, b) ∧ r(a, y) ≤ r′(x, z); (h) ordω(r) ∧ r(x, b) ∧ r(a, y) ∧ r(y, b) ∧ r(a, z) ≤ r′(x, z). (e) follows by applying proposition 6 (2): ordω(r) ∧ r(x, y) ∧ r(y, z) ≤ trans(r) ∧ r(x, y) ∧ r(y, z) ≤ r(x, z) ≤ r′(x, z). (f) and (g) follow like this: ordω(r) ∧ r(x, y) ∧ r(y, b) ∧ r(a, z) ≤ (trans(r) ∧ r(x, y) ∧ r(y, b)) ∧ r(a, z) ≤ r(x, b) ∧ r(a, z) ≤ r′(x, z); ordω(r) ∧ r(y, z) ∧ r(x, b) ∧ r(a, y) ≤ (trans(r) ∧ r(a, y) ∧ r(y, z)) ∧ r(x, b) ≤ r(x, b) ∧ r(a, z) ≤ r′(x, z). (h) follows similarly. we established (e)–(h), therefore (4) is true. cf. lemma 1 (1) for any x, y, z ∈ x we have ordω(r) ≤ (r′(x, y) ∧ r(y, z)) → r′(x, z), from where ordω(r) ≤ ∧ x,y,z∈x [(r′(x, y) ∧ r′(y, z)) → r′(x, z)] = trans(r′) from (1), (2) and (3) we deduce ordω(r) ≤ ordω(r′) therefore r 4 r′. we can see that r′(a, b) = r(b, a) ∨ (r(b, b) ∧ r(a, a)) and r(b, a) < ordω(q) ≤ ordω(r) (since q 4 r). then, by applying proposition 6 (6): r(b, a) < ordω(r) ≤ r(b, b) ∧ r(a, a) ≤ r′(a, b). it follows r 6= r′, contradicting the maximality of r. we conclude ordω(q) ≤ sc(r), therefore the theorem is proved. remark 9. by applying lemma 5 we see that theorem 3 is a particular case of theorem 8. bibliography [1] r. bělohlávek, fuzzy relational systems. foundations and principles, kluwer, 2002. [2] u. bodenhofer, similarity–based generalizations of fuzzy orderings preserving the classical axioms, international journal of uncertainty, fuzziness and knowledge based systems, vol. 3, pp. 593–610, 2000. [3] u. bodenhofer, f. klawonn, a formal study of linearity axioms for fuzzy orderings, fuzzy sets and systems, vol. 145, pp. 323–354, 2004. [4] s. gottwald, fuzzy sets and fuzzy logic, vieweg, braunschweig, 1993. [5] p. hájek, methamathematics of fuzzy logic, kluwer, 1998. [6] u. höhle, n. blanchard, partial ordering in l–undeterminate sets, information sciences, vol. 35, pp. 135–144, 1985. [7] e. p. klement, r. mesiar, e. pap, triangular norms , kluwer, 2000. [8] m. richter, revealed preference theory, econometrica, vol. 34, pp. 635–645, 1966. [9] i. j. rudas, j. fodor, information aggregation in intelligent systems using generalized operators, international journal of computers, communications and control, vol. 1, pp. 47–57, 2006. 342 irina georgescu [10] e. szpilrajn, sur l’extension de l’ordre partiel, fundamenta mathematicae, vol. 16, pp. 386–389, 1930. [11] l. a. zadeh, similarity relations and fuzzy orderings, information sciences, vol. 3, pp. 177–200, 1971. irina georgescu academy of economic studies department of economic cybernetics piata romana no 6, r 70167, oficiul postal 22 bucharest, romania e-mail: irina.georgescu@csie.ase.ro irina georgescu received her phd in economics (information systems) from abo akademi university, turku, finland in 2005. since then she is a teaching assistant at the department of economic cybernetics, academy of economic studies, bucharest, romania. between 2007–2008 she was a postdoctoral researcher at abo akademi university, turku, finland. she is the author of about 30 scientific publications and of a monograph issued by springer. her research interests are mainly in the area of soft computing techniques, consumer theory and social choice and welfare economics. international journal of computers, communications & control vol. iii (2008), no. 1, pp. 6-20 robust fuzzy sliding mode controller for discrete nonlinear systems hafedh abid, mohamed chtourou, ahmed toumi abstract: in this work we are interested to discrete robust fuzzy sliding mode control. the discrete siso nonlinear uncertain system is presented by the takgisugeno type fuzzy model state. we recall the principle of the sliding mode control theory then we combine the fuzzy systems with the sliding mode control technique to compute at each sampling time the control law. the control law comports two terms: equivalent control law and switching control law which has a high frequency. the uncertainty is replaced by its upper bound. inverted pendulum and mass spring dumper are used to check performance of the proposed fuzzy robust sliding mode control scheme. keywords: nonlinear systems, sliding mode, t-s fuzzy systems, reaching law. 1 introduction many of the industrial plants include nonlinearities or/and uncertainties. to reach the wanted performances, using the classical theories, nonlinearities must be identified to calculate the appropriate controller. the robust control theories is one of the techniques that permits to reach the desired performances in presence of external or/ and internal disturbances. in addition to the stability, the tracking problem must be solved independently of uncertainties. in the literature, many methods have been developed in continuous as well in discrete-time to solve the tracking problem for nonlinear systems. in the last decade many researches combine classical techniques with intelligent one, such as sliding mode with neuronal systems or sliding mode with fuzzy systems [3][4], to benefit from the advantages of the two control techniques. the sliding mode control (smc) was originally developed for variable structure systems in continuous domain. utkin [12] gives a thorough description of the sliding mode theory in continuous time. also, slotine and li [16] describe in detail continuous sliding mode controllers. at the end of the twentieth century, the research of discrete time smc has been attracted more attention, such as [6],[8],[9],[10], as for the implementation of the controller on a digital computer requires a sampling time and the assumption of an infinite switching time does not hold any more. the fuzzy systems have been combined with classical sliding mode control to provide robust stability to the fuzzy controller. the combination of the two control principles, is called fuzzy sliding mode control (fsmc), it provides an alternative to design a robust controller for nonlinear systems with uncertainties [15],[14]. our contribution in this work consists in presenting a new robust fuzzy sliding mode controller based on the takagi-sugeno fuzzy state model for discrete nonlinear systems. this paper is organised as follow. in section 2, we recall the discrete takagi-sugeno type fuzzy state model for nonlinear systems. then, we describe the sufficient and necessary reaching conditions of sliding mode control for discrete nonlinear systems in the first part of the third section ten, a fuzzy sliding mode controller for discrete time of nonlinear systems is developed in the second part and tracking robust fuzzy sliding mode control law is described in the third part. the simulation results of two nonlinear systems show performances of the proposed fsmc in section 4. conclusions are drawn in the final section. copyright © 2006-2008 by ccc publications robust fuzzy sliding mode controller for discrete nonlinear systems 7 2 problem statement and fuzzy systems 2.1 problem statement consider a class of discrete nonlinear siso systems described by the following equations:    x1 (k + 1) = x2(k) xi (k + 1) = xi+1(k) xn (k + 1) = f (x (k)) + g (x (k)) u(k) y (k) = cx (k) (1) where, c=[1, 0, ..., 0], x (k) = [ x1(k) . . . xn (k) ]t ∈ rn it is the state vector that is assumed to be observable. we note that f (x (k)) and g(x (k)) represent two discrete bounded nonlinear functions of the nonlinear siso systems . they can be obtained from the continuous form by the first order discretized system using eulers approximation. in order for (1) to be controllable, it is required that g(x (k)) 6=0. if both functions f (x (k)) and g(x (k)) in (1) are available for feedback, the feedback linearization control can be used to design a well-defined controller, which is usually given in the form: u (k) = 1 g (x (k)) ( −kt e (k)− f (x (k)) + xnd (k + 1) ) (2) where, the state vector x (k)and the desired state vector xd (k) are defined as: x (k) = [ x1(k) . . . xn (k) ]t ∈ rn ; xd (k) = [ x1d (k) . . . xnd (k) ]t ∈ rn e (k) represents the state tracking error, it is defined as: e (k) = x (k)− xd (k), the vector k (k) =[ kn kn−1 . . k1 ]t ∈ rn will be chosen such that all roots of the following polynomial h (s) = sn +k1sn−1 +...+ kn are situated inside the unit complex disc. in general case, the functions f (x (k)) and g(x (k)) are badly known nonlinear functions so, the control law (2) cannot be implanted. to overcome this difficulty, many approaches are used such as adaptive control, linearization around operating points, fuzzy control. . . etc. 2.2 discrete takagi-sugeno type fuzzy systems the advantage of the t-s type fuzzy models is that their description permits the utilization of the state representation, and by consequence to exploit the maximum of the potential relative to this representation. the takagi-sugeno (t-s) type fuzzy model can be viewed as a natural expansion of piecewise linear partition for nonlinear systems. the nonlinear system is represented as a collection of the fuzzy if-then rules, where each rule describes the local dynamics by a linear system model. the general fuzzy model is achieved by fuzzy amalgamation of the linear systems models [1][2]. the ith rule of the discrete fuzzy model has the following form: ith plant rule : if z1is µi1and...and zn is µin t hen x (k + 1) = (adi + ∆adi) x (k) + (bdi + ∆bdi) u (k) (3) where { µi j } are the fuzzy sets, adi ∈ rnxn and bdi ∈ rnxm are recpectively the ith state matrix and the input matrix, c is the number of the if-then fuzzy rules; u(k) is the input vector, z (k) =[ z1(k) . . . zn (k) ] are the premise variables they represent some measurable system variables, they can be chosen as a state variables. for each rule ri is attributed a weight wi(z(k)) which depends on the grade of the membership function of the premise variables z j(k) in fuzzy sets µi j : 8 hafedh abid, mohamed chtourou, ahmed toumi wi (z (k)) = n ∏ j=1 µi j (z j (k)) ; wi (z (k)) â 0 ;for i = 1, ..., c; c ∑ i=1 wi (z (k)) â 0. where, µi j (z j (k)) is the grade of the membership function of z j (k) to the fuzzy set µi j. the discrete takagi-sugeno type fuzzy model is inferred as follows: x (k + 1) = c ∑ i=1 wi (z (k)) ((adi + ∆adi) x (k) + (bdi + ∆bdi) u (k)) c ∑ i=1 wi (z (k)) (4) the normalized weight is defined as which is presented as follow: hi(z (k)) = wi(z(k)) c ∑ i=1 wi(z(k)) ; 0 ≺ hi(z (k)) ≺ 1; i = 1, ..., c; c ∑ i=1 hi (z (k)) = 1. the output of the discrete takagi-sugeno type fuzzy model for the uncertain nonlinear systems can be inferred as:    x (k + 1) = c ∑ i=1 hi (z (k)) ((adi + ∆adi) x (k) + (bdi + ∆bdi) u (k)) y (k) = c ∑ i=1 hi (z (k))cix (k) (5) where ci = [1, 0, 0, . . . , 0]. it is required that all bdi(k) are different from zero to assure the controllability of (5). 3 sliding mode control law and reaching conditions sliding mode control, first appeared in the soviet literature, it has been widely recognized as a potential approach to uncertain dynamical non-linear systems that are subject to external disturbances and parameter variations [12]. in sliding mode control (smc), the control action forces the system trajectories to cross a manifold of the state space which is called the sliding surface designated by the designers [12]. the system trajectories are then constrained to the sliding surface for all subsequent time via the use of high speed switching controls. the most significant advantage of the sliding mode is robustness against changes in system parameters or disturbances. the major disadvantage associated to the sliding mode control is the chattering phenomena, because it can excite undesirable high frequency dynamics. the sliding mode control comports three modes, namely, the reaching mode (rm), sliding mode (sm), and steady-state mode (ss). let us describe the discrete sliding mode control however, only a few researches are interested by discrete-time systems. a discrete version of smc has a big importance when the implementation of the control is realized by numerical components which need a sampling period to compute the appropriate controller. it must be pointed out that the discrete version of smc cannot be obtained from their continuous counterpart by means of simple equivalence. among the first which are interested by smc problem and used an equivalent form of the continuous reaching condition to give a discrete reaching condition are dote and hoft [5]. [s (k + 1)−s (k)] s (k) ≺ 0 (6) milosavljevic [6] recommended the concept of the quasi-sliding mode and signalled hat condition (6) is not sufficient for a discrete sliding mode control. robust fuzzy sliding mode controller for discrete nonlinear systems 9 sarpturk, et al. [7], used the following reaching condition. |s (k + 1)| ≺ |s (k)| (7) furuta [8] used the equivalent form of a lyapunov-type of continuous reaching condition to give the discrete version. v (k + 1)−v (k) ≺ 0 with v (k) = 1 2 (s (k))2 (8) weibing gao et al. [9] pointed out that all these forms of reaching conditions are incomplete for a satisfactory characterization of a discrete-time sliding mode. he suggests that the state trajectory of a discrete sliding mode control system must have some attributes which form the basis of the discrete sliding mode control, for more information see [9]: 3.1 discrete fuzzy sliding mode control law for a discrete smc the following reaching law has been chosen: s (k + 1) = s (k)−qt s (k)−ε t sgn (s (k)) , with1−qt â 0, ε â 0, q â 0. (9) the sliding surface is defined as: s (k) = gt x (k) where gt is a constant row vector gt = [g1, ..., gn−1, 1] such that all the roots of the following polynomial are situated in the left-half open complex plane: h (s) = sn−1 + gn−1sn−2 + ... + g1 the sliding mode control comports two terms which are:equivalent control term and switching control term[3][5][7][12]. ug = ue + us (10) the equivalent control law in the first part we assume that : ∆adi = 0nxn and ∆bdi = [0, ..., 0]t . the switching function is defined as: s (k) = gt x (k) the ideal quasi sliding mode satisfies: s (k + 1) = s (k) = 0 we deduct : 0 = gt c ∑ i=1 hi (z (k)) (adi (k + 1) x (k) + bdi ue (k)) ; k = 0, 1, ... (11) the equivalent control term is given by: ue (k) = − ( c ∑ i=1 hi (z (k)) g t bdi )−1 [ gt c ∑ i=1 hi (z (k))adi (k + 1) x (k) ] (12) we assume that condition is hold: ( c ∑ i=1 hi (z (k)) gt bdi ) 6= 0 the switching control law from the reaching law we can write:s (k + 1)−s (k) = −qt s (k)−ε t sgn (s (k)) s (k + 1)−s (k) = gt c ∑ i=1 hi (z (k)) (adi (k + 1) x (k) + bdi u (k))−gt x (k) (13) 10 hafedh abid, mohamed chtourou, ahmed toumi if we compare the two latest equations we deduct the global control law: ug (k) = −z−1 [ c ∑ i=1 hi (z (k)) ( gt adi (k + 1) ) x (k)−(1−qt ) s(k) + ε t sgn (s (k)) ] (14) where z = ( c ∑ i=1 hi (z (k)) gt bdi ) from equations and we obtain the switching control term: us (k) = − ( c ∑ i=1 hi (z (k)) g t bdi )−1 [−(1−qt ) s (k) + ε t sgn (s (k))] (15) 3.2 robust fuzzy sliding mode control law consider the discrete system in the perturbed condition. it will be described by the t-s type fuzzy model . where ∆adi represents system parameters variation and ∆bdi is the external disturbance for each sub-model. we assume the matching conditions are satisfied: ∆adi = bdi ^ adi and ∆bdi = bdi ^ bdi where, ^ adi is a row vector and ^ bdi is a scalar . they should be written as: ^ adi = [ −∆ai1 −∆ai2 . . . −∆ain ] ^ bdi = −∆bi. then the equation (5) becomes: x (k + 1) = c ∑ i=1 hi (z (k)) ( adix (k) + bdiu (k) + ^ adix (k) + ^ bdi ) (16) however, the global control law will be expressed as: ug (k) = −z−1 [ c ∑ i=1 hi (z (k)) ( gt adi (k + 1) ) x (k) + γ−(1−qt ) s (k) + ε t sgn (s (k)) ] (17) where γ = gt bdi ( ^ adix (k) + ^ bdi ) in general case and are unknown, but their upper bound are known, so the last global control law can not be implemented. however, to over com this difficulty we replace respectively the unknown terms and by the following expressions: ai = (√( eig ( ^ atdiup ^ adiup )))t ; bi = ∆bi max. ^ adiup = [ −∆ai1 max −∆ai2 max . . . −∆ain max ] ^ bdiup = −∆bi max we define a new set of perturbations and control parameters as follow:. sig = gt bdiaix (k); fig = gt bdibi; the choice of sig and fig is done to ensure that the sign of the incremental s(k) is opposite to the sign of s(k). the global control law will be expressed as: ug (k) = −z−1 [ c ∑ i=1 hi (z (k)) ( gt adi ) x (k) + qi −(1−qt ) s (k) + ε t sgn (s (k)) ] (18) where qi = (sig + fig)−(sig + fig) sgn (s(k)) robust fuzzy sliding mode controller for discrete nonlinear systems 11 3.3 tracking robust fuzzy sliding mode control law the tracking problem will be transformed into the stability problem. indeed,s (k) = 0 represents an equation whose unique stationary solution is . the tracking problem of the desired vectorxd comes back to locate inside the quasi-sliding band width the sliding surface for all sampling time. the sliding surface will be expressed as: s (k) = gt x̃ (k) where, x̃ (k) = x (k)−xd (k) the control law will be expressed as: ug (k) = −z−1 [ c ∑ i=1 hi (z (k)) ( gt adi ) x̃ (k) + qi −(1−qt ) s (k) + ε t sgn (s (k)) ] (19) 4 illustration to illustrate the performance of the presented approach, we choose inverted pendulum and mass spring damper which are widely used in the control literature of nonlinear system. 4.1 inverted pendulum the equations of system in continuous form are given by (20) [18]: where, x1 is the angle in radian of the pendulum from the vertical axis; x2 is the angular velocity in rad/s; g is the gravity acceleration; m and 2l are respectively the mass and the length of the pendulum; m is the mass of the cart and u is the force applied to the cart. the nominal values of the parameters are: g= 9.81m/s2, m = 2 kg, m = 8 kg, 2l =1m.    . x1(t) = x2(t) . x2(t) = f (x1, x2) + g(x1, x2)u + d(t) f (x1, x2) = mlx22 sin x1 cos x1−(m+m)g sin x1 ml cos2 x1− 4l3 (m+m) ; g(x1, x2) = cos x1 ml cos2 x1− 4l3 (m+m) (20) the membership functions for xi ∈ ]−π/2, π/2[are: µ1i(xi) = 1− ∣∣∣ xi(k)π/2 ∣∣∣ and µ2i(xi) = ∣∣∣ xi(k)π/2 ∣∣∣ , the state matrices and input vectors for sub-systems are: ad1 = [ 1 0.01 0.1729 1 ] ; ad2 = [ 1 0.01 0.0936 1 ] , bd1 = [ 0 0.0018 ] , bd2 = [ 0 0.000052 ] ∆ad1u p = [ 0 0 0.05229 0 ] , ; ∆ad2up = [ 0 0 0.028 0 ] , ∆bd1up = 0.0005; ∆bd2up = 0.00002; we have been chosen: q = 70; t = 0.01; ε = 0.1; gt = [10 1]; the figure 1 presents the simulation results of the behavior of variable state x1(k)and s(k) of inverted pendulum for nominal system. the initial conditions are given by:x (0) = [π/3; 0]. we present by the figures2, 3, 4, 5, 6 and 7 the simulation results of the behavior of the state variables x1(k), x2(k), the position and velocity error e1(k) and e2(k), the sliding surface and the control law respectively of the inverted pendulum with parameters vary of an uncertain way in time.the initial conditions are given by: x (0) = [−π/60; 0] ; gt =[15 1], q=80, ε =1.5; the function sign is replaced by the well known sat function which is defined as: { if s ≺ 1φ ; sat = s if s º 1φ ; sat = sgn(s) 12 hafedh abid, mohamed chtourou, ahmed toumi 0 50 100 150 200 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 evolution of x1(k) and s(k) (k) x1(k) s(k) figure 1: stabilization of x1(k) and the sliding surface s(k) 4.2 mass spring damper the mass-spring-damper system is described in continuous time by the following equation [17]: m1ẍ1(t) + c1x1 (t) + c2 · x1 (t) + c3x1 (t) + c4x21 (t) = ( 1 + c5 ( · x1 )3 (t) ) u (t) thet-s fuzzy model is discribe by the following rules: rule 1: ifx1 is m11 and x2 is m12 then x (k + 1) = ad1x (k) + bdi1u (k) rule 2: if x1 is m21 andx2 is m22 then x (k + 1) = ad2x (k) + bdi2u (k) rule 3: if x1 is m31 and x2 is m32 then x (k + 1) = ad3x (k) + bdi3u (k) rule 4: if x1 is m41 and x2 is m42 then x (k + 1) = ad4x (k) + bdi4u (k) for nominal values of m1, c2, c3 and c4, matrices ai and bi are given by: ad1 = ad2 = [ 1 0.01 −0.0001 0.99 ] ; ad3 = ad4 = [ 1 0.01 −0.0023 0.99 ] ; bd1 = bd3 = [ 0 0.0143 ] , bd2 = bd4 = [ 0 0.0056 ] ; ∆ad1up = ∆ad2up = [ 0 0 0 0.003 ] , ∆ad3up = ∆ad4up = [ 0 0 0.0007 0.003 ] , ∆bd1up = ∆bd3up = 0.043; ∆bd2up = ∆bd4up = 0.0017; the initial condition and parameters are chosen as:x (0) = [−π/60; 0] ; gt =[15 1], t=0.01; q=70, ε =0.15. we present by the figures 8and 9 the simulation results of the behavior of the state variables x1(k), x2(k), of the mass spring damper with parameters vary of an uncertain way in time. we present by the figures 10, 11, 12 and 13 the simulation results of the behavior of the position and velocity error e1(k) and e2(k), the sliding surface and the control law respectively of the mass spring damper with parameters vary of an uncertain way in time. robust fuzzy sliding mode controller for discrete nonlinear systems 13 0 1000 2000 3000 4000 5000 6000 7000 −0.2 −0.15 −0.1 −0.05 0 0.05 0.1 0.15 evolution of x1 and x1d k x1(k) x1d(k) figure 2: evolution of x1 and x1d 0 1000 2000 3000 4000 5000 6000 7000 −0.2 −0.15 −0.1 −0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 evolution of x2 and x2d k x2(k) x2d(k) figure 3: evolution of x2 and x2d 14 hafedh abid, mohamed chtourou, ahmed toumi 0 1000 2000 3000 4000 5000 6000 7000 −0.01 −0.005 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 evolution of the position error k figure 4: evolution of the position error 0 1000 2000 3000 4000 5000 6000 7000 −0.2 −0.15 −0.1 −0.05 0 0.05 evolution of the speed error k figure 5: evolution of the speed error robust fuzzy sliding mode controller for discrete nonlinear systems 15 0 1000 2000 3000 4000 5000 6000 7000 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 evolution of the sliding surface k figure 6: evolution of the sliding surface 0 1000 2000 3000 4000 5000 6000 7000 −50 −40 −30 −20 −10 0 10 20 30 40 50 evolution of the control law u k figure 7: evolution of the control law 16 hafedh abid, mohamed chtourou, ahmed toumi 0 200 400 600 800 1000 1200 1400 1600 1800 2000 −0.2 −0.15 −0.1 −0.05 0 0.05 0.1 0.15 evolution of x1 and x1d k x1(k) x1d(k) figure 8: evolution of x1 and x1d. 0 200 400 600 800 1000 1200 1400 1600 1800 2000 −0.5 0 0.5 1 1.5 evolution of x2 and x2d k x2(k) x2d(k) figure 9: evolution of x2 and de x2d. robust fuzzy sliding mode controller for discrete nonlinear systems 17 0 20 40 60 80 100 120 140 160 180 200 −0.25 −0.2 −0.15 −0.1 −0.05 0 0.05 evolution of the position error k figure 10: evolution of the position error 0 200 400 600 800 1000 1200 1400 1600 1800 2000 0 0.5 1 1.5 2 2.5 evolution of the speed error k figure 11: evolution of the speed error 18 hafedh abid, mohamed chtourou, ahmed toumi 0 200 400 600 800 1000 1200 1400 1600 1800 2000 −2 −1.8 −1.6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 evolution of the sliding surface k figure 12: evolution the sliding surface 0 200 400 600 800 1000 1200 1400 1600 1800 2000 −20 0 20 40 60 80 100 evolution of the control law u k figure 13: evolution of the control law robust fuzzy sliding mode controller for discrete nonlinear systems 19 5 conclusions in this paper we present a robust fuzzy sliding mode controller for discrete nonlinear systems. first, we recall the discrete takagi-sugeno type fuzzy model, then the principe of the sliding mode control in discrete time. the uncertainty are assumed to be verifie the matching conditions. we develop a robust controller based on the sliding mode and the dynamic t-s fuzzy state model. the uncertainties are replaced bye the bigger eigen-value of the upper bound matrices of uncertainties. the expressions in discrete time of both equivalent control term and hitting term are developed. the tracking control law is developed. simulation results for inverted pendulum and mass spring damper with parameters variation show the performance of the proposed control law. bibliography [1] michio sugeno "on stability of fuzzy systems expressed by rules with singleton consequents, (ieee transaction on fuzzy systems,vol 7 n 2 feb 1999). [2] t.takagi and m.sugeno, fuzzy identification of systems and its applications to modeling and control ieee trans syst. man,cybern. vol15. 116-132 jan/feb1985. [3] w.chang, j.bae park, y.hoan joob and g.chen design of robust fuzzy model-based controller with sliding mode control for siso non-linear systems fuzzy sets and systems 125 (2002) pp 1-22. [4] hafedh abid mohamed chtourou et ahmed toumi, a sliding mode based robust fuzzy controller for a class of uncertain systems ssd05, mrach 2005 sousse tunisia. [5] y. dote and r.g..hoft, microprocessor based sliding mode controller for dc motor drives presented at the industrial application. soc. annu. meeting, cincinnati, oh, 1980. [6] d. milosavljevic, general conditions for the existence of a quasi-sliding mode on the switching hyperplane in discrete variable systems automat. remote contr., vol. 46, pp(307-314) 1985. [7] s. z. sarpturk, y istefanopulos, and o. kaynak, on the stability of discrete-time sliding mode control systems ieee trans. automat. contr., vol. 32, n 10, pp(930-932) 1987.. [8] k. furuta, sliding mode control of a discrete system" , systems and control letter, volume 14, pp145-152. [9] weibing gao, yufu wang and abdollah homaifa, discrete-time variable structure control systems ieee transactions on industrial, electronics, volume 42, n 2, april 1995, pp(117-122). [10] weibing gao., and hung, j.c., variable structure control of nonlinear systems: a new approach ieee transactions on industrial electronics, 40, 45, 1993 [11] h. lee, e. kim, h.j. kang et m. park, a new sliding-mode control with fuzzy boundary layer fuzzy sets and systems 120 (2001), pp. 135-143 [12] utkin, v. i, sliding modes and their application in variable structure systems (moscow: nauka) 1974 (in russian, and also, 1978, mir, in english). [13] utkin, v. i, variable structure systems with sliding mode” ieee transactions on automatic control, vol. ac-22, no. 2, pp 212-222, april., (1977). [14] yu, x. h., man, z. h. and wu, b. l design of fuzzy sliding-mode control systems, fuzzy sets and systems, 95, pp.295-306. ., (1998) 20 hafedh abid, mohamed chtourou, ahmed toumi [15] ting, c. s., li, t. h. s, and kung, f. c., an approach to systematic design of the fuzzy control system fuzzy sets and systems, 77, pp. 151-166. (1996). [16] slotine, j. j. e. and li. w., applied nonlinear control , prentice hall, englewood cliffs, nj.(1991), [17] kazuo tanaka, takayuki ikeda and hua o.wang, robust stabilization of a class of uncertain nonlinear systems via fuzzy control: quadratic stability, h∞ control theory, and linear matrix inequalities ieee trans on fuzzy system, vol 4 n 1 feb 1996. [18] mehrdad hojati and saeed gazor hybrid adaptive fuzzy identification and control nonlinear systemsieee transactions on fuzzy systems vol 10, n 2. april 2002 pp 198-210. hafedh abid1,3, mohamed chtourou2, ahmed toumi3 1institut supérieure des études technologiques de sfax laboratoire d’automatique, génie informatique et signal cité scientifique, bp 48, 59651 villeneuve d’ascq, france 2unité de commande intelligente, design et optimisation des systèmes complexes(icos) enis, b.p. w, 3038 sfax, tunisie 3unité de procédés industriels unité de commande automatique (ucpi) enis,b.p. w, 3038 sfax, tunisie e-mail: hafedh.abid@isetso.rnu.tn mohamed.chtourou@enis.rnu.tn ahmed.toumi@sta-tn.com received: june 13, 2007 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 172-182 analytical model for a multiprocessor with private caches and shared memory angel vassilev nikolov abstract: we develop an analytical model of multiprocessor with private caches and shared memory and obtain the following results: the instantaneous state probabilities and the steady-state probabilities of the system. both transient behaviour and equilibrium can be studied and analyzed. we showed that results can be applied to determine the output parameters for both blocking and non-blocking caches. keywords: invalidate cache-coherence protocol, queuing system, discrete transform 1 introduction shared memory multiprocessors are widely used as platforms for technical and commercial computing [2]. performance evaluation is a key technology for design in computer architecture. the continuous growth in complexity of systems is making this task increasingly complex [7]. in general, the problem of developing effective performance evaluation techniques can be stated as finding the best trade-off between accuracy and speed. the most common approach to estimate the performance of a superscalar multiprocessor is through building a software model and simulating the execution of a set of benchmarks. since processors are synchronous machines, however, simulators usually work at cycle-level and this leads to enormous slowdown [9]. it might take hours even days to simulate. for memory structures relatively accurate analytical models were developed [3, 7, 9, 10] through extensive use of various queuing systems. open queue system with poisson arrivals and exponential service times is considered quite good for description of memory hierarchies [7]. our focus is on the impact of the cache-coherence protocols on the overall system performance. the most commonly used technique for this purpose is the mean value analysis (mva) [3, 5, 7, 8, 9]. it allows the total number of the customers to be fixed (closed queue system), and this seems to be more adequate representation of the processes of self-blocking requestors [5]. calculations of output parameters such as residency times, waiting times and utilization are shown in [3, 8, 9]. mva is based on the forced flow that means in equilibrium output rate equals input rate. however, instantaneously, we can have input rate different from output rate, so that the instantaneous probabilities could be different from equilibrium [7]. mva offers no possibility to study transient effects. moreover, the assumption of exponential service times is not realistic, in fact all bus access times and memory access times are constants. it will be seen later in this paper that state probabilities depend on the server’s time density function. we use the technique of markov processes to describe the behaviour of the multiprocessor implementing cache-coherence protocols. 2 definition and analysis of the model a multiprocessor consists of several processors connected together to a shared main memory by a common complete transaction bus. each processor has a private cache. when a processor issues a request to its cache, the cache controller examines the state of the cache and takes suitable action, which may include generating bus transaction to access main memory. coherence is maintained by having all cache controllers "snoop" on the bus and monitor the transaction. snoopy cache-coherence protocols fall in two major categories: invalidate and update [2, 3, 10]. invalidating protocols are studied here but the concepts can be applied with some modifications to updating protocols too. transactions may copyright © 2006-2008 by ccc publications analytical model for a multiprocessor with private caches and shared memory 173 or may not include the memory block and the shared bus. typical transaction that does not include memory block is invalidate cache copy which occurs when a processor requests writing in the cache. all other processors simply change the status bit(s) of their on copies to invalid. if the memory block is uncached or not clean it can be uploaded from the main memory, but in today’s multiprocessors it is rather uploaded from another cache designated as owner (o) (cache-to cache transfer). memory-tocache transfer occurs when the only clean copy is in the main memory. a cache block is written back (wb) in the main memory (bus is used) when a dirty copy is evicted [6]. the bus and the main memory are also used when synchronization procedures are executed [2]. apparently the bus can be considered as the bottleneck of the system. in terms of the queuing theory processors can be viewed as customers (clients) and the bus can be viewed as a server. inter-arrival times are exponentially distributed with parameter λ . this assumption is adequate for most applications [7]. requests are served on first come first served (fcfs) basis. immediately after issuing a request for cache-to-cache transfer or synchronization procedure the customer blocks itself. the service time for blocking request has a density function f1(x). when service is completed the processor (customer) resumes processing with probability p or resumes processing and generates a new request with probability q (p+q=1). details on how to obtain the input parameters are given in [2, 3, 8, 9]. this new request has a different density function f2(x) and corresponds to wb transaction. it does not block the customer but the server is held until completion of wb transaction therefore adding to the queue. the system can be in one of the following states: 1) n: all n customers are doing internal processing; 2) j, 1: j customers are doing internal processing (n-j are blocked respectively) and all requests are of type 1(0≤j≤n-1), 3) j ,2: j customers are doing internal processing , the server is serving request of type 2, and n-j customers are waiting in the queue for service of type 1 (0≤j≤n). the transitions between these states are illustrated in fig. 1. throughout this paper we use the following notations pn (t) probability[all n customers are doing internal processing at time t] p j,i(t,x) probability[j customers are doing internal processing, n-j are in the queue and/or in the server, and the server is busy doing service of type i at time t and the elapsed service time lies between x and x+dx ] p j,i(x) probability[in the equilibrium state j customers are doing internal processing, n-j are in the queue and/or in the server, the server is busy doing service of type i and the elapsed service time lies between x and x+dx ] p j,i(t) probability[j customers are doing internal processing, n-j are in the queue or in the server, the server is busy doing service of type i at time t] pn , p j,i steady-state probabilities. pn = limt→∞ pn (t), pj,i = ∫ ∞ 0 pj,i(x)dx βi = jλ fi(x) cumulative distribution function (c.d.f.) of the service time of type i ; i=1,2 fi(x) probability density function (p.d.f.) of the service time of type i ; i=1,2 δ m,n kronecker delta 1 µi = ∫ ∞ 0 xfi(x)dx i=1,2 hi(x) = fi(x) 1−fi(x) service rate for type i; i=1,2 fi(s), fi(s+βn), fi(βn) laplace transforms (lt) of fi(x) t.u. time unit viewing the nature of the system, we obtain the following set of integro-differential equations [ d dt + βn ] pn = p ∫ t 0 pn−1(t, x)h1(x)dx + ∫ t 0 pn,2(t, x)h2(x)dx (1) 174 angel vassilev nikolov figure 1: state-transition diagram of the model. 1 ≤ j ≤ n [ d dt + ∂ ∂ t + βn−1 + h1(x) ] pn−1,1(t, x) = 0 (2) [ d dt + ∂ ∂ t + βn + h2(x) ] pn,2(t, x) = 0 (3) [ d dt + ∂ ∂ t + β j + hi(x) ] pj,i(t, x) = β j+1pj+1,i(t, x) (4) for i = 1, 1 ≤ j ≤ n −1; i = 2, 1 ≤ j ≤ n [ d dt + ∂ ∂ t + hi(x) ] p0,i(t, x) = β1p1,i(t, x) (5) for i=1,2 having the following boundary and initial conditions pj,1(t, 0) = (1−δ j,0)p ∫ ∞ 0 pj−1,1(t, x)h1(x)dx + ∫ ∞ 0 pj,2(t, x)h2(x)dx + δ j,n−1βn pn (t) (6) for 0 ≤ j ≤ n −1 pj,2(t, 0) = q ∫ ∞ 0 pj−1,1(t, x)h1(x)dx (7) for 1 ≤ j ≤ n pn (0) = 1, p0,2(t, 0) = 0, pj,i(0, 0) = 0 (8) for i = 1, 1 ≤ j ≤ n −1; i = 2, 1 ≤ j ≤ n by using laplace transform and discrete transform [4, 8] the above equations are transformed as follows (s + βn) pn = 1 + p ∫ ∞ 0 pn−1(s, x)h1(x)dx + ∫ ∞ 0 pn,2(s, x)h2(x)dx (9) analytical model for a multiprocessor with private caches and shared memory 175 [ s + d dx + β j + hi(x) ] u j,i(s, x) = 0 (10) for i = 1, 1 ≤ j ≤ n −1; i = 2, 1 ≤ j ≤ n [ s + d dx + +hi(x) ] p0,i(s, x) = β1p1,i(s, x) (11) for i=1,2 where u j,1(s, x) = ∑n−1n= j ( n j ) pn,1(s, x), pj,1(s, x) = ∑n−1n= j (−1)n− j ( n j ) un,1(s, x) for 1 ≤ j ≤ n − 1, and u j,2(s, x) = ∑nn= j ( n j ) pn,2(s, x), pj,2(s, x) = ∑nn= j(−1)n− j ( n j ) un,2(s, x) for 1 ≤ j ≤ n. let v j,i(s, x) = u j,i(s,x) 1−fi(x) and p ′ 0,1(s, x) = p0,1(s,x) 1−fi(x) . then from (10 and 11) we have after some transformations [ s + d dx + βi ] v j,i(s, x) = 0 for i = 1, 1 ≤ j ≤ n −1; i = 2, 1 ≤ j ≤ n and [ s + d dx ] p ′ 0,i(s, x) = β1p1,i(s, x) for i = 1, 2. hence the solutions of (9-11) are u j,i(s, x) = [1−fi(x)]u j,i(s, 0)e−(s+βi)x (12) pn (s) = 1 + p f1(s + βn−1)un−1,1(s, 0) + f2(s + βn )un,2(s, 0) s + βn (13) p0,1(s, x) = [1−f1(x)]β1e−sx [ p0,1(s, 0) + n−1 ∑ n=1 (−1)n−1n 1−e −βnx βn un,1(s, 0) ] (14) p0,2(s, x) = [1−f2(x)]β1e−sx [ n ∑ n=1 (−1)n−1n 1−e −βnx βn un,2(s, 0) ] . (15) by integrating (12, 14, and 15) we obtain the lt of the instantaneous probabilities pj,1(s) = n−1 ∑ n= j (−1)n− j ( n j ) [ 1− f1(s + βn) s + βn ] un,1(s, 0) (16) for 1 ≤ j ≤ n −1 pj,2(s) = n ∑ n= j (−1)n− j ( n j ) [ 1− f2(s + βn) s + βn ] un,2(s, 0) (17) for 1 ≤ j ≤ n p0,1(s) = p0,1(s, 0) [ 1− f1(s) s ] + β1 n−1 ∑ n=1 (−1)n−1n [ 1− f1(s) s − 1− f1(s + βn) s + βn ] un,1(s, 0) βn (18) 176 angel vassilev nikolov p0,2(s) = β1 n ∑ n=1 (−1)n−1n [ 1− f2(s) s − 1− f2(s + βn) s + βn ] un,2(s, 0) βn . (19) taking lt of (6-7) and using (8 and 12-15) we get after some transformations the following system of linear equations n−1 ∑ n= j (−1)n− j ( n j ) un,1(s, 0) = p n−1 ∑ n= j (−1)n− j+1 ( n j −1 ) f1(s + βn)un,1(s, 0) (20) + n ∑ n= j (−1)n− j ( n j ) f2(s + βn)un,2(s, 0) + δ j,n−1βn pn for 2 ≤ j ≤ n −1 n ∑ n= j (−1)n− j ( n j ) un,2(s, 0) = q n−1 ∑ n= j−1 (−1)n− j+1 ( n j −1 ) f1(s + βn)un,1(s, 0) (21) for 2 ≤ j ≤ n n−1 ∑ n=1 (−1)n−1 ( n j ) un,1(s, 0) = pp0,1(s, 0) f1(s) + pβ1 [ n−1 ∑ n=1 (−1)n−1n f1(s)− f1(s + βn) βn un,1(s, 0) ] (22) n ∑ n=1 (−1)n−1 ( n j ) un,2(s, 0) = qp0,1(s, 0) f1(s) + qβ1 [ n−1 ∑ n=1 (−1)n−1n f1(s)− f1(s + βn) βn un,1(s, 0) ] (23) coefficients u j,i(s,0) can now be determined from the above equations. we can apply the final-value theorem to (16-19) to obtain the steady-state probabilities but it will require use of the l’hopital rule and seems difficult and impractical [11]. instead we set the following differential equations βnpn = p ∫ ∞ 0 pn−1(x)h1(x)dx + ∫ ∞ 0 pn,2(x)h2(x)dx (24) [ d dx + βn−1 + h1(x) ] pn−1,1(x) = 0 (25) [ d dx + βn + h2(x) ] pn,2(x) = 0 (26) [ d dx + β j + hi(x) ] pj,i(x) = β j+1pj+1,i(x) (27) for i = 1, 1 ≤ j ≤ n −1; i = 2, 1 ≤ j ≤ n [ d dx + hi(x) ] p0,i(x) = β1p1,i(x) (28) for i=1,2. equations (24-28) are to be solved under the following boundary conditions and normalizing condition pj,1(0) = (1−δ j,0)p ∫ ∞ 0 pj−1,1(x)h1(x)dx + ∫ ∞ 0 pj,2(x)h2(x)dx + δ j,n−1βn pn (29) analytical model for a multiprocessor with private caches and shared memory 177 for 0 ≤ j ≤ n −1 pj,2(0) = q ∫ ∞ 0 pj−1,1(x)h1(x)dx (30) for 1 ≤ j ≤ n −1 p0,2(0) = 0 (31) pn + n−1 ∑ j=0 pj,1 + n ∑ j=0 pj,2 = 1. (32) the solutions of (2.29-2.32) are pn = 1 + p f1(βn−1)un−1,1(0) + f2(βn )un,2(0) βn (33) pj,1 = n−1 ∑ n= j (−1)n− j ( n j ) [ 1− f1(βn) βn ] un,1(0) (34) for 1 ≤ j ≤ n −1 pj,2 = n ∑ n= j (−1)n− j ( n j ) [ 1− f2(βn) βn ] un,2(0) (35) for 1 ≤ j ≤ n −1 p0,1 = p0,1(0) µ1 + n−1 ∑ n= j (−1)n− jn [ 1 µ1 − 1− f1(βn) βn ] un,1(0) (36) p0,2 = n ∑ n= j (−1)n− jn [ 1 µ2 − 1− f2(βn) βn ] un,2(0) (37) for u j,i(0) and p0,1(0) we have n−1 ∑ n= j (−1)n− j ( n j ) un,1(0) = p n−1 ∑ n= j (−1)n− j+1 ( n j −1 ) f1(βn)un,1(0) + δ j,n−1βn pn (38) for 2 ≤ j ≤ n −1 n−1 ∑ n= j (−1)n− j ( n j ) un,2(0) = q n−1 ∑ n= j (−1)n− j+1 ( n j −1 ) f2(βn)un,2(0) (39) for 2 ≤ j ≤ n −1 p0,1(0) = β1 n ∑ n=1 (−1)n−1n [ 1− f2(βn βn ] un,2(0) (40) n−1 ∑ n=1 (−1)n−1nun,1(0) = pp0,1(0) + pβ1 n ∑ n=1 (−1)n−1n [ 1− f1(βn βn ] un,1(0) + n ∑ n=1 n f2βnun,2(0) (41) n ∑ n=1 (−1)n−1nun,2(0) = qp0,1(0) + qβ1 n ∑ n=1 (−1)n−1n [ 1− f1(βn βn ] un,1(0) (42) the coefficients u j,i(0) can be determined from (32) and (38-42). 178 angel vassilev nikolov 3 examples in order to obtain the transient state probabilities first we have to determine pn (s) and pj,i(s) from (1619) and (20-24) and then to apply the inverse laplace transform to them. we used the packages of maple 8 on a standard pc platform under windows xp for these computations [12]. results were produced and printed in less than a second. for n=4 the instantaneous probabilities are listed in appendix a. various performance characteristics can be computed using the steady-state probabilities. for example, the average number of blocked customers (anbc) in the case of blocking caches will be given by anbc = 2 ∑ i=1 n ∑ j=0 (n − j)pj,i. (43) in the case of non-blocking caches anbc will be anbc = n ∑ j=0 (n − j −1 + k)pj,1 + n−1 ∑ j=0 (n − j)pj,2. (44) where k is the ratio of average memory stall time [2] . k depends strongly on the application. (1-k) actually refers to the fraction time the processor is consuming data while cache-to-cache or memory-tocache transfer is in progress. in appendix b we list the anbc for two popular service time distributions: exponential and erlangian [1], for blocking and fully non-blocking caches (k=0). the time to solve (33-42) and calculate anbc was meaninglessly short. 4 concluding remarks this work presented a model for a shared bus, shared memory multiprocessor with private caches and captures the whole spectrum of invalidate type cache coherence protocols. although we started with fairly sophisticated set of integro-differential equations, the output of the model is a set of few linear equations from which the state probabilities can be determined. the approach eliminates the main drawbacks of the most commonly used mva analysis: inability to deal with transients and constraint on the service time distribution. the model gives insights into the transient behaviour of the system. moreover, the assumption of exponentially distributed service times can be dropped; any continuous distribution can be used. the ease of obtaining performance measures in a meaningless time makes very feasible the incorporation of the model in a multiprocessor design tool. bibliography [1] s. k. bose, introduction to queuing systems, kluwer/plenum publishers, 2001 [2] j. l. hennessy, d. a. patterson; computer architecture: a quantitative approach, pearson publishers, 2003 [3] m. c. chiang, memory system design for bus based multiprocessor, phd thesis, university of wisconsin, 1991 [4] t. itoi, t. nishida, m. kodama and e. ohi, n-unit parallel redundant system with correlated failures and single repair facility, microelectronics and reliability, vol. 17, pp. 279-285, 1978 analytical model for a multiprocessor with private caches and shared memory 179 [5] e. lazowska, j. zahorjan, g. graham, and k. sevcik, quantitative system performance, computer system analysis using queuing network models, prentice-hall, englewood clis, nj, may 1984 [6] a. louri, a.k. kodi, an optical interconnection network and a modifying snooping protocol for the design of large-scale symmetric multiprocessors (smps), ieee transactions on parallel and distributing systems, vol. 15, no. 12, dec. 2004, pp. 1093-11047. [7] r. e. matick, comparison of analytic performance models using closed mean-value analysis versus open-queuing theory for estimating cycles per instruction of memory hierarchies, ibm journal of research and development, jul 2003 [8] d. j. sorin et. al., a customized mva model for ilp multiprocessors, technical report no.1369, university of wisconsin-madison, 1998 [9] d. j. sorin et. al., evaluation of shared-memory parallel system with ilp processors , proc. 25th int’l symp. on computer architecture, june 1998, pp. 180-191 [10] j. sustersic, a. hurson, coherence protocol for bus-based and scalable multiprocessors, internet and wireless distributed computing environments: a survey , advances in computers, vol.59, 2003, pp. 211-278 [11] schiff, joel l., the laplace transform, springer, 1999 [12] waterloo maple inc., introduction to maple 8, 2002 appendix a for n=4, λ =0.001[1/t.u.], f1(x)=0.1exp(-0.1x), and f2(x)=0.01exp(-0.01x) the instantaneous probabilities are p4(t) =0 .9211361286+0.8058476879e-2*exp(-0.1248619627*t) +0.8535072295e-2*exp(-0.1089825679*t)+0.9049529656e-2*exp(-0.9494144284e-1*t) +0.9696074769e-2*exp(-0.8072343638e-1*t)+0.1774027054e-3 exp(-0.1510201407e-1*t) +0.1728181365e-2*exp(-0.1398702636e-1*t) +0.5618533851e-2*exp(-0.1256085210e-1*t) +0.1211910345e-1 exp(-0.1067946234e-1*t)+0.2388149701e-1*exp(-0.8161235321e-2*t), p31(t) =0.3792913471e-1-0.1093143496e-1*exp(-0.1248619627*t) -0.1007354818e-1 *exp(-0.1089825679*t)-0.9271731350e-2*exp(-0.9494144284e-1*t) -0.8405607569e-2*exp(-0.8072343638e-1*t)+0.2658572506e-2 *exp(-0.1510201407e-1*t)-0.2212663963e-5*exp(-0.1398702636e-1*t) -0.3015750621e-3*exp(-0.1256085210e-1*t)-0.6739486112e-3 *exp(-0.1067946234e-1*t)-0.9276492013e-3*exp(-0.8161235321e-2*t), p21(t) = 0.1420742616e-2+0.2324288902e-2*exp(-0.1248619627*t) +0.2986557798e-3* exp(-0.1089825679*t)-0.1243034332e-2 *exp(-0.9494144284e-1*t)-0.2544948528e-2*exp(-0.8072343638e-1*t) -0.5329230583e-2*exp(-0.1510201407e-1*t)+0.6737760872e-2 *exp(-0.1398702636e-1*t)+0.4442015626e-3*exp(-0.1256085210e-1*t) -0.5688666731e-3*exp(-0.1067946234e-1*t)-0.1539483112e-2 *exp(-0.8161235321e-2*t), p11(t) =0 .7684028624e-4-0.2290986748e-3*exp(-0.1248619627*t) +0.3160128043e-3*exp(-0.1089825679*t)+0.2148505581e-3 *exp(-0.9494144284e-1*t)-0.3252577041e-3 180 angel vassilev nikolov *exp(-0.8072343638e-1*t)+0.3775725072e-2*exp(-0.1510201407e-1*t) -0.8400171763e-2*exp(-0.1398702636e-1*t)+0.4974831098e-2 *exp(-0.1256085210e-1*t)+0.5143708805e-3*exp(-0.1067946234e-1*t) -0.9181578239e-3*exp(-0.8161235321e-2*t), p01(t) = 0.9242283829e-5*exp(0-.1248619627*t)-0.3513395647e-4 *exp(-0.1089825679*t)+0.4257071327e-4*exp(-0.9494144284e-1*t) -0.1688587212e-4*exp(-0.8072343638e-1*t)-0.9200675435e-3 *exp(-0.1510201407e-1*t)+0.2810623081e-2*exp(-0.1398702636e-1*t) -0.3183584754e-2*exp(-0.1256085210e-1*t)+0.1611954912e-2 *exp(-0.1067946234e-1*t)-0.3239077071e-3*exp(-0.8161235321e-2*t) +0.5218152790e-5, p42(t) = 0.2709223908e-1+0.9859983387e-3*exp(-.1248619627*t) +0.1060558367e-2*exp(-0.1089825679*t)+0.1145474465e-2 *exp(-0.9494144284e-1*t)+0.1259769099e-2*exp(-0.8072343638e-1*t) -0.2412466943e-1*exp(-0.1510201407e-1*t)-0.1705507775e-2 *exp(-0.1398702636e-1*t)-0.2095511260e-2*exp(-0.1256085210e-1*t) -0.2029637013e-2*exp(-0.1067946234e-1*t)-0.1588776483e-2 *exp(-0.8161235321e-2*t), p32(t) = -0.2421204825e-3*exp(-0.1248619627*t)-0.7509940526e-4 *exp(-0.1089825679*t)+0.9576676158e-4 *exp(-0.9494144284e-1*t)+0.3013803504e-3 *exp(-0.8072343638e-1*t)+0.7126069503e-1*exp(-0.1510201407e-1*t) -0.6135152996e-1*exp(-0.1398702636e-1*t)-0.8971987351e-2 *exp(-0.1256085210e-1*t)-0.5950006752e-2*exp(-0.1067946234e-1*t) -0.4494935895e-2*exp(-0.8161235321e-2*t)+0.9428952497e-2, p22(t) =0 .2421271696e-2+0.2626333487e-4*exp(-0.1248619627*t) -.3154175021e-4*exp(-0.1089825679*t)-0.2945613244e-4 *exp(-0.9494144284e-1*t)+0.3412946115e-4*exp(-0.8072343638e-1*t) -0.8108903801e-1*exp(-0.1510201407e-1*t)+0.1349032466 *exp(-0.1398702636e-1*t)-0.4071010637e-1*exp(-0.1256085210e-1*t) -0.9622074403e-2*exp(-0.1067946234e-1*t)-0.5904604182e-2 *exp(-0.8161235321e-2*t), p12(t) = -0.1765308077e-5*exp(-0.1248619627*t)+0.4800731626e-5 *exp(-0.1089825679*t)-0.4448905932e-5*exp(-0.9494144284e-1*t) +0.1599282603e-5*exp(-0.8072343638e-1*t)+0.4177917201e-1 *exp(-0.1510201407e-1*t)-0.9973555226e-1*exp(-0.139870263e-1*t +0.7256040480e-1*exp(-0.1256085210e-1*t)-0.9747995399e-2 *exp(-0.1067946234e-1*t)-0.5300997812e-2*exp(-0.8161235321e-2*t) +0.4449749927e-3, p02(t) = -0.4618227199e-6*exp(-0.1248619627*t)-0.2203030325e-6 *exp(-0.1089825679*t)+0.4881890483e-7*exp(0-.9494144284e-1*t) -0.1699392257e-7*exp(-0.8072343638e-1*t)-0.8188760719e-2 *exp(-0.1510201407e-1*t)+0.2501502200e-1*exp(-0.1398702636e-1*t) -0.2833447693e-1*exp(-0.1256085210e-1*t)+0.1434663085e-1 *exp(-0.1067946234e-1*t)-0.2882912592e-2*exp(-0.8161235321e-2*t) +0.4449749927e-4. in the above expressions e-i means 10-i for i=1,7. analytical model for a multiprocessor with private caches and shared memory 181 appendix b table 1: n=8, f1(x)=0.1exp(-0.1x), f2(x)=0.01exp(-0.01x) λ [1/t.u.] p anbc for blocking anbc for fully caches nonblocking caches 0.001 0.9 0.154099881194466 0.075640880006411 0.002 0.9 0.441552853804251 0.290383910880334 0.003 0.9 0.822750601431095 0.607433119474025 0.004 0.9 1.253944990222998 0.984102789831906 0.001 0.8 0.230012889507952 0.152313018403034 0.002 0.8 0.729883782777377 0.584481458432927 0.003 0.8 1.382033782478873 1.183494795953230 0.004 0.8 2.063720956300253 1.826269794552253 table 2: n=8, f1(x)=0.13x2ex p(−0.1x)/2!, f2(x)=0.013x2ex p(−0.01x)/2! λ [1/t.u] p anbc for blocking anbc for fully caches nonblocking caches 0.001 0.9 0.384839057891723 0.211437492029451 0.002 0.9 1.313451009452606 0.582993712839022 0.003 0.9 2.390481400874492 1.782339618354729 0.004 0.9 3.691834116720534 2.882438452093385 0.001 0.8 0.614956120345239 0.400820549913285 0.002 0.8 2.611487230549326 1.722034656332087 0.003 0.8 4.062557145097248 3.429652938504840 0.004 0.8 5.899361833023557 5.394204692051840 angel vassilev nikolov national university of lesotho department of mathematics and computer science roma 180 lesotho e-mail: av.nikolov@nul.ls received: december 17, 2007 182 angel vassilev nikolov angel vassilev nikolov received the beng degree in electronic and computer engineering from the technical university of budapest, hungary in 1974 and the phd degree in computer science from the bulgarian academy of sciences in 1982 where he worked as a research associate. in 1989 he was promoted to associate research professor in bulgaria. dr nikolov also served as a lecturer of computer science at the national university of science and technology, bulawayo, zimbabwe and at the grande prairie regional college, alberta, canada and as an associate professor at sharjah college, united arab emirates. his research interests include computer architecture, performance evaluation of multiprocessors, and reliability modeling. he has published numerous journal and conference articles and holds four patents on the above topics. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 1, pp. 8-19 introducing collaborative practices in small medium enterprises d. antonelli, p. chiabert dario antonelli, paolo chiabert politecnico di torino department of production systems and economics corso duca degli abruzzi 24, 10129 torino, italy e-mail: {dario.antonelli,paolo.chiabert}@polito.it abstract: in an enterprise, collaborative working schemes are obtained only by adopting a suitable organization of the enterprise functions. at the informative level collaboration can be enabled by using suitable project management tools for the exchange of information that is the basis of collaboration. one of these tools is product lifecycle management (plm). on the basis of an exhaustive analysis of the plm impact on the different aspects of the enterprise, the paper proposes an original methodology to evaluate the possibility of using plm as framework in which exploit collaboration links within the enterprise. the methodology is not founded on the formal declared organization but on the actual data flows that are induced by the relations among the documents used to develop a project. data are collected on the field using questionnaires. the links are made explicit by applying the hierarchical clustering with single linkage agglomerative technique. the attitude to the implementation of plm is then analysed with respect to the organization and to the informative system. such methodology is general and can be profitably applied to assist the plm implementation in the enterprises. keywords: product life-cycle management, concurrent engineering, business process management, cluster analysis, collaboration. 1 introduction small and medium enterprises (smes) are companies with a turnover of less than 50meuro and a number of employees below 250, as for the eu recommendation 2003/361/ec. in the european countries they are the backbone of the economic system [7]. concurrent engineering (ce) is an effective strategy to shorten ’time to market’ of innovative products. the key for its accomplishment stays in the collaboration among all the enterprise personnel appointed to the project. the goal is accomplished by the parallel development of different activities obtained by the integration of several company functions in a multidisciplinary group in order to anticipate the constraints generated by the industrial dynamics [14]. the core aspect of ce is therefore the creation of tight co-operation links among all the industrial actors involved in the definition of product and process. a significant tool to assist the ce effectiveness is plm. unfortunately, plm implementation inside smes is scarce. the reason goes beyond implementation difficulties or excessive costs of it systems. it derives from the poor conformity of the ordinary sme organization of work to the requirements of ce in term of strategy, organization and technology [13]. it is useful to remark that often in literature the term collaboration is given the same significance as the term coordination. collaboration means working with the others for the success of an activity. coordination can refer to optimisation of the work by finding the best division of activities to be performed by humans and machines. the definition put forth by malone and crowston [15] is: "coordination is managing dependencies between activities". coordination also, recalling the theory of systems, implies the presence of a coordinator, then some hierarchical shape on the organisation [18]. instead, collaboration is the process of various individuals, groups or systems working together on a voluntary basis without the need for a manager or a work program [16]. obviously collaboration works only if some behaviour rules are respected, as the ones proposed in [21] in the case of collaborative software agents. in the organization oriented to ce, different coordination mechanisms have been adopted [10]. they differ mainly on the instant of application of the coordination: predetermined before starting the work or determined during the work as a result of a negotiation. a discussed list of co-ordination systems is given in [20]: information workflow, copyright c© 2006-2010 by ccc publications introducing collaborative practices in small medium enterprises 9 negotiated objectives, milestones, professional rules, mapped parameters, design space sharing, joint parameters, data coherency. some coordination systems need the active participation of a coordinator, e.g. the project manager. conversely other systems like design space sharing and data coherency must be enforced by the use of appropriate software tools, e.g. the plm. a comprehensive definition of product lifecycle management is "a strategic business approach for the effective management and use of corporate intellectual capital" where "corporate intellectual capital" consists of products definition, products history and best practices" [6]. several plm systems already operates in large companies demonstrating their effectiveness. they range from the ford project c3p (cad/cae/cam and product information management) started in 1996 and based on sdrc’s i-deas and metaphase software to the boeing 7e7 dreamlinear airplane entirely designed in the dassault systemes plm software platform, to the siemens teamcenter suite largely adopted in automotive industries. presently new lighter software suites, based on the "out of the shelf" commercial policy, make plm systems affordable to smes. despite the availability of software, plm do not have an adequate diffusion among them. in authors’ opinion the most important justification of enterprises’ interest in plm should be the competitive edge provided by collaboration. as a matter of fact, plm assures the most effective control of enterprise’s projects by increasing the efficiency in information management along three directions: • increase of efficiency in the management of technical product information. product data represent the core of manufacturing enterprise. they should be protected against unauthorized accesses but, at the same time, should be promptly shared for design activities as well as for many other functions in the enterprise. • easiness of product data exchange by establishing proper interfaces towards enterprise functions (manufacturing, maintenance ...) and, vice versa, by defining communicational channels to gather any relevant information for the upgrading. • management of the enterprise knowledge database for storing and retrieving historical products. the carry over process, widely adopted to speed up the time to market of new products, benefits from the use of plm tools. the reasons of its inadequate diffusion are uniformly spread over enterprise organization and information technology system: • accessing product data from enterprise functions not directly involved in product development is a functionality provided by it tools like erp that are seldom present inside the smes. • product/process development is historically a self-governed function, with human resources, activities and hardware/software tools sharply separated by other industrial functions. • the implementation of it tools linking different enterprises areas requires a clear definition of decisional and informational processes. the idea behind this research is that the work organization inside a sme is based on spontaneous collaboration that overcomes the attempts to organize the activities by introducing coordination mechanisms, as in the case of plm implementation. spontaneous collaboration works well as far as the dimensions of the firm are small enough to allow everyone to know where to find the information they need. as far as the enterprise grows a technology like plm becomes necessary to assist the information retrieval. to assess the validity of this supposition we need to find a way to measure the extent of collaboration. the paper illustrates an original approach to the analysis of management processes of sme’s product data. using simple data-driven procedures, it is possible to forecast the impact of collaboration on sme organization without inferring formal models in any case available in literature [4]. in detail, the proposed analysis evaluates the sme readiness to plm implementation, by matching the collaborative network with the functional organization of the enterprise. 2 product and process information in sme: dataflow it is useful to distinguish among products and processes to develop products. innovative and competitive products increase enterprise’s market share whilst efficient and robust industrial processes allows the enterprise emerging over the competitors. as a consequence, the information on enterprise products and processes, in terms of data and metadata (table 1), has to be as efficient as possible along the whole product lifecycle. 10 d. antonelli, p. chiabert table 1: enterprise information product data processes business metadata (step, pdm) enterprise content management engineering data (gps, step) manufacturing planning pdm/plm systems were born to control data proliferation in design activities, therefore their primary function is the control of product data in terms of integrity, protection and modification. a second important function regards data availability: user-friendly, simultaneous and multi-point access to product data [20]. plm systems do not limit their function to database repository, but actively support product data development by tracking design activities, maintaining configuration of evolving products, and allowing the re-use of the data in the development of new products [9]. the enlargement of the scope of product data, which are no more property of design function but become capital of the whole enterprise, requires a redefinition of their accessibility and modifiability, with regard to other it systems like erp. the redistribution of the responsibilities on product data among enterprise functions represents the most innovative aspect of plm systems. obviously it is also the most difficult aspect to be addressed in plm implementation and it justifies the lateness of plm diffusion in the smes. plm systems have an impact on the entire enterprise and require a structured organization where informative flows, interfaces and decision makers are well identified. this is normal in large companies, but it is not predictable in smes, where the same person often provides different functions and where decision makers are individuated more on the basis of capital share than on the basis of organizational role and technical competence. the intrinsic complexity of plm systems and the informal organization of small companies represent a real difficulty in the implementation of plm in smes [1]. 2.1 sme organizational model the technical literature widely illustrates case studies related to the implementation of plm in large and well structured organizations. unfortunately few experiences are available on plm implementation in smes, where informal organizational structure does not allow the application of standard methodologies, based on the development of a detailed ontological model (sap, uml, ...), in order to perform an efficient description of enterprise processes [8], [17], [25]. a preliminary analysis of enterprise organization should rely on the generic product lifecycle functions [2] schematized in figure 1: • direction: the overall project management, this function is transversal to the others. • design concept: idea for new product or, more often, product design enhancement is recognized, based on market knowledge. • market demand analysis: manufacturer studies need for the new product design and estimates demand and feasibility of meeting demand. • engineering: design engineers create the product design using all available information from the plm system, including after-market factors, manufacturability data, customers’ needs/preferences, and more. • sourcing: procurement carry out preliminary work required to acquire parts, materials, components, equipment, and anything else is required to manufacture the product. • production: product is built to design specifications established by engineering and using parts and materials acquired by sourcing. conformance to specification is checked through quality control/assurance or process control methodologies. • distribution: product is shipped to either distributors, who store it until order is received from customer, or directly to final customer. • after market: products are maintained, serviced, or repaired under warranty or as a value-added service. using a unique repository of various after-market data ensures that after-market factors are taken into account in subsequent design projects, increasing the value of the product to the customer. introducing collaborative practices in small medium enterprises 11 figure 1: product lifecycle analysis (from aberdeen [1]). the experimental data used in the paper were collected from a sme which designs, manufactures, installs and maintains industrial choppers all around the world. the enterprise structure, according to a functional point of view, adheres to the proposed model. 2.2 sme investigation procedure there is a widespread commonplace saying that every sme works without any notable organization. actually, the organization exists but it is usually not explicitly formalized: it is a natural organization based on significance relationships and spontaneous collaboration. therefore, it makes no sense to look at classical organization devices, like organization charts, information systems, team management tools (pert, gantt) [23]. there are four basic principles which make the organization of a sme efficient: • process-based cooperation, without a fixed separation of tasks. • extended sharing of knowledge, unfortunately transmitted in a informal way. • small teams with continuous communications and interchanges of information. • potential for outside development by having recourse to the networks of enterprises. these same principles represent the hardest obstacle to an efficient description of the enterprise data, functions, processes and related managing tools. researchers adopt a simple data-driven approach focused on the investigation of enterprise documents in order to overcome the obstacle. a set of questionnaires submitted to enterprise people are used to collect information on produced and consulted documents thus providing an unbiased objective description of the true organization, processes and hierarchies operating in the enterprise. the questionnaires play a fundamental role in diminishing the noise involuntarily introduced by interviewed people: the focus on documents and their management and not on sensible data regarding the enterprise organization, defines the search field and the required information. researchers submitted questionnaires to enterprise personnel during the development of a new project and transformed the enterprise in a living laboratory where researchers interacted with employers during the questionnaires filling, observed the product development process and analyzed the documents identifying their dependence relationships as well as their format. 12 d. antonelli, p. chiabert 2.3 sme information reorganization several operations applied to the information collected by questionnaires, provide a more structured description of enterprise and its organization, functions, processes and hierarchies: • analysis and evaluation of plm functionalities in the enterprise context; • analysis and definition of product data: quantity, relationships, applicability, supporting media; • definition of enterprise functions and analysis of their role in product data management; • analysis of enterprise’s acquaintance with informatics; • development of the dlsm (data lifecycle simulation matrix) representing the lifecycle of product data and their dependencies within the enterprise functions; • development of the dism (data interchange simulation matrix) representing the interfaces used to communicate the product data among the different functions; • creation of a metric to exploit the enterprise position, according to technological and organizational aspects, against the best practice. the dlsm and dism matrices provide a complete picture of product data management across the enterprise functions and along the product lifecycle. on this basis it is possible to evaluate the organizational and informational structure by detailing the interactions with each enterprise function. moreover, analyzing the dlsm and dism matrix it is possible to extrapolate useful indicators which address the reorganization of the enterprise. 3 reorganizing the data: dlsm and dism matrices the core of the methodology is the building of a dlsm (data lifecycle simulation matrix), a square matrix containing a complete description of the enterprise organization based on its documents. the dlsm matrix represents the dependence relationship among the data/documents inside different enterprise functions. the rows of the dlsm matrix, contain the enterprise documents classified according to the enterprise function responsible for their production and maintenance. the columns of the dlsm matrix contain the same documents presented in the same order. the dependence relationships among documents are made explicit by activating the cells at the intersection of the row and of the column corresponding to the associated documents. when a cell is activated, the document along the column will make use/reference to the document identified in the row. a formal description of dlsm matrix is: dlsm(i, j = ) = {enterprise documents source} dlsm(i = , j) = {enterprise documents access} (1) dlsm(i, j) = {  when document i is accessed by document j  otherwise figure 2 shows a pattern view of the 160x160 sparse dlsm matrix representing the documents required to describe product lifecycle within the analysed case study. the grey areas placed on the matrix diagonal highlight the enterprise functions: direction, marketing, administration, design, planning, supply, manufacturing and assembling, inventories, after sale services. figure 3 shows the upper and left corner of dlms matrix including some documents produced by the two functions direction and marketing. some interesting results can be extracted from simple operations performed on the dlsm matrix. the dism (data interchange simulation matrix) is a square matrix having the same structure of dlsm, but now the cells are activated when the format and the support used to save two inter-dependent documents are the same. the core of the method is the transformation of the dlsm into the dism. for every intersection activated in the dlsm, the document formats and supports are compared: if they agree (i.e. both ms word format, or excel format, or paper support, ...) the cell in the dism is activated. obviously there are quite less occurrence in the dism with respect to dlsm. figure 4 represents the upper and left corner of the dism matrix including some documents produced by the introducing collaborative practices in small medium enterprises 13 s tr a te g y : m a rk e ts a n d c o m p e ti to rs a n a ly s is m a rk e t o p p o rt u n it ie s a n a ly s is n e w c o n c e p ts d e s ig n a n a ly s is t a rg e t m a rk e ts a n a ly s is t a rg e t p ri c e s a n a ly s is t a rg e t c o s ts a n a ly s is t im e t o m a rk e t a n a ly s is e c o n o m ic a l ri s k s a n a ly s is f u n d in g s o u rc e s b u d g e ti n g a ll o c a ti o n a n a ly s is p ro to ty p e t a rg e ts c li e n ts r e q u ir e m e n ts r e p o rt in g c li e n t in te rv ie w s k e y p e rf o rm a n c e c o m p e ti to r p ro d u c ts t a rg e t s e ll in g p ri c e d e m a n d f o re c a s t h is to ri c a l e c o n o m ic a l a n a ly s is e c o n o m ic f o re c a s t p ro d u c ts p o rt fo li o m a rk e t s h a re e x te rn a l lo g is ti c s p ri c e p o li c ie s p ro d u c ts g u a ra n te e s a p p li c a ti o n p ro d u c ts s e ll in g p la n p o rt fo li o a c tu a l o rd e rs o ff e ri n g , s a le s p ro d u c t a g re e m e n t m t o : c o n fi g u ra ti o n , ti m e to m a rk e t a d v e rt is in g m a te ri a ls p ro d u c ts e c o n o m ic a l p e rf o rm a n c e h is to ri c a l e c o n o m ic a n a ly s is e c o n o m ic a n a ly ti c a l re p o rt e c o n o m ic e v a lu a ti o n o f p ro to ty p e s a n a ly s is p ro d u c t c o s ts s e n s it iv it y a n a ly s is p ro d u c t b il li n g s u p p li e rs p a y m e n ts a rc h iv io p ro g e tt i c o n c lu s i e s tu d i e le n c o p ro g e tt i in e s s e re s it u a z io n e d e i p ro g e tt i in e s s e re l is te " m e tr ic h e " p e r le s p e c if ic h e o b ie tt iv o d i p ro d o tt o s p e c if ic h e o b ie tt iv o c o n c e tt i d i p ro d o tt o a lt e rn a ti v i d is ti n ta m a te ri a li a p p ro x d e i m o d e ll i c o n c e tt u a li d is ti n ta a p p ro x l a v o ra z io n i d e i m o d e ll i c o n c e tt u a li p ia n o a p p ro x d i a s s e m b la g g io d e i m o d e ll i c o n c e tt u a li c a ra tt e ri s ti c h e f u n z io n a li d e i m o d e ll i c o n c e tt u a li c a ra tt e ri s ti c h e t e c n ic h e d e i m o d e ll i c o n c e tt u a li d e fi n iz io n e p ro to ti p o s p e c if ic h e t e c n ic h e f in a li s p e c if ic h e e s te ti c h e f in a li s p e c if ic h e f in a li d i m a n u te n ib il it à s p e c if ic h e f in a li d i a ff id a b il it à s p e c if ic h e f in a li d i d u ra ta a rc h iv io a n a li s i e c o n o m ic h e s ti m a e c o n o m ic a ( d i c o m p e te n z a ) d e i c o n c e tt i e la b o ra ti p ia n o d i c o n d iv is io n e d e l p ro d o tt o d is e g n i 3 d p a rt ic o la ri d e l p ro to ti p o d is e g n i 2 d p a rt ic o la ri d e l p ro to ti p o d is e g n i 3 d c o m p le s s iv o p ro to ti p o d is e g n i 2 d c o m p le s s iv o p ro to ti p o d is e g n i e s p lo s i d e l p ro to ti p o m a te ri a li p a rt ic o la ri p ro to ti p o d e fi n iz io n e d e ll e la v o ra z io n i s u l p ro to ti p o d e fi n iz io n e d e ll e t o ll e ra n z e p a rt ic o la ri p ro to ti p o a rc h iv io r is u lt a ti d e i te s t e le n c o t e s t d a e ff e tt u a re r is u lt a ti t e s t s u l p ro to ti p o c o s ti p ro to ti p o d is e g n i 3 d p a rt ic o la ri p ro d o tt o d is e g n i 2 d p a rt ic o la ri p ro d o tt o d is e g n i 3 d c o m p le s s iv o p ro d o tt o d is e g n i 2 d c o m p e s s iv o p ro d o tt o d is e g n i d e g li e s p lo s i e le n c o e d e s c ri z io n e m o d if ic h e d is e g n i d e l p ro d o tt o d e fi n iz io n e d e ll e t o ll e ra n z e d e l p ro d o tt o c o d ic i p a rt ic o la ri c o d ic e p ro d o tt o d e n o m in a z io n e p a rt ic o la ri d is ti n ta m a te ri a li p ro d o tt o d im e n s io n i e p e s o d e i g re z z i d im e n s io n e e p e s o d e i fi n it i c a ra tt e ri s ti c h e p a rt ic o la ri c o m m e rc ia li t ra tt a m e n ti te rm ic i/ g a v a n ic i/ e tc . d is ti n ta b a s e e le n c o t e s t d a e ff e tt u a re s u l p ro d o tt o ( p re s e ri e ) r is u lt a ti t e s t s u l p ro d o tt o ( p re s e ri e ) p ro c e d u ra c o n tr o ll o q u a li tà in te rn a ( p a rt ic o la ri / a s s e m b la ti / p ro d o tt o ) p ro c e d u re c o n tr o ll o q u a li tà in a c c e tt a z io n e ( p a rt ic o la ri / a s s e m b la ti / p ro d o tt o ) p ro c e d u ra c o n tr o ll o q u a li tà p re s s o i l fo rn it o re s c h e m i e le tt ri c i c e rt if ic a z io n i d e l p ro d o tt o n o rm a ti v e v ig e n ti s c h e m a a s s e m b la g g io p ro d o tt o d e fi n iz io n e d e ll e c o n fi g u ra z io n i m a n u a li d 'u s o e m a n u te n z io n e d is ti n ta e c a ra tt e ri s tc h e d e ll e m a c c h in e u te n s il i d a u ti li z z a re p e r o g n i s in g o lo p ro d o tt o s e le z io n e /p ro g e tt a z io n e d e ll e a tt re z z a tu re u te n s il i d a u ti li z z a re p e r le la v o ra z io n i p a ra m e tr i d i la v o ra z io n e c ic li d i la v o ra z io n e p ro g ra m m i p e r i c n c d e fi n iz io n e d e i c o s ti d i la v o ra z io n e r is u lt a ti a n a li s i "m a k e o r b u y " c o s ti d i p ro d u z io n e b u d g e t d i p ro d u z io n e p ia n o p ri n c ip a le d i p ro d u z io n e ( m p s ) p ia n if ic a z io n e d e i fa b b is o g n i ( te c n ic h e p u s h o p u ll ) p ro g ra m m a z io n e o p e ra ti v a ( g a n tt , e tc . ) p o rt a fo g li o f o rn it o ri c o d ic e f o rn it o re a n a g ra fi c a f o rn it o re p re v e n ti v i fo rn it o ri o rd in e d i a c q u is to d o c u m e n ta z io n i te c n ic h e d e i fo rn it o ri c o s to d e l p a rt ic o la re c o m m e rc ia le ( c o n c o rd a to c o n i l fo rn it o re ) c o s to d e l p a rt ic o la re n o n c o m m e rc ia le ( c o n c o rd a to c o n i l fo rn it o re ) l o tt o m in im o d i a c q u is to d e l p a rt ic o la re ( c o n c o rd a to c o n i l fo rn it o re ) d a ta d i c o n s e g n a r ig a d 'o rd in e l e a d t im e f o rn it o re c o n d iz io n i d i p a g a m e n to c o n d iz io n i p e r il t ra s p o rt o c o n d iz io n i d i g a ra n z ia e v e n tu a li p e n a li tà p e r ri ta rd a ta c o n s e g n a t ip o lo g ia d 'im b a ll o s e q u e n z a f o rn it o ri s u i p a rt ic o la ri r ie p il o g o s o ll e c it i d is ti n ta d o c u m e n ti i n v ia ti a fo rn it o ri a n a li s i te m p i e c o s ti d i a s s e m b la g g io p ia n o a s s e m b la g g io c o m m e s s e p ia n o a s s e m b la g g io p ro d o tt o s c h e d a a v a n z a m e n to la v o ro s c h e d a r il e v a m e n to d if e tt o s it à b o ll a d i c a ri c o b o ll a d i s c a ri c o b o ll a d i re s o b u o n o d i p re li e v o i n te rn o b u o n o d i v e rs a m e n to in te rn o q u a n ti tà d is p o n ib il e d i o g n i s in g o lo p a rt ic o la re q u a n ti tà i m p e g n a ta d i o g n i s in g o lo p a rt ic o la re v a lo re q u a n ti tà d is p o n ib il e d i o g n i s in g o lo p a rt ic o la re s ta tu s d e l p a rt ic o la re ( c o n tr o ll a to -n o n c o n tr o ll a to ) s ta tu s d e l m a te ri a le ( g re z z o -s e m il a v o ra to la v o ra to -c o m m e rc ia le ) c o d ic e d i p re li e v o la v o ra z io n e i n te rn a c o d ic e d i p re li e v o la v o ra z io n e e s te rn a c o d ic e d i p re li e v o v e n d it a c o d ic e r e s o c o o rd in a te d i s to c c a g g io d i o g n i s in g o lo p a rt ic o la re a n a li s i a b c s to ri c o i n te rv e n ti s u i p ro d o tt i s im il i, g ià c o m m e rc ia li z z a ti s to ri c o i n te rv e n ti s u l p ro d o tt o d u ra n te i l p e ri o d o d i a tt iv it à a rc h iv io m o d if ic h e s u i p ro d o tt i s im il i, g ià c o m m e rc ia li z z a ti a rc h iv io m o d if ic h e s u l p ro d o tt o d u ra n te i l p e ri o d o d i a tt iv it à a rc h iv io d if e tt o s it à s u i p ro d o tt i s im il i, g ià c o m m e rc ia li z z a ti a rc h iv io d if e tt o s it à s u l p ro d o tt o d u ra n te i l p e ri o d o d i a tt iv it à s ta ti s ti c h e d e i g u a s ti s u i p ro d o tt i s im il i, g ià c o m m e rc ia li z z a ti s ta ti s ti c h e d e i g u a s ti s u l p ro d o tt o d u ra n te i l p e ri o d o d i a tt iv it à a rc h iv io d e i re c la m i in e re n ti a i p ro d o tt i s im il i, g ià c o m m e rc ia li z z a ti r e p o rt d e i re c la m i in e re n ti a l p ro d o tt o a rc h iv io o s s e rv a z io n i s u i p ro d o tt i s im il i, g ià c o m m e rc ia li z z a ti r e p o rt o s s e rv a z io n i s u l p ro d o tt o ,d e i c li e n ti strategy: markets and competitors analysis market opportunities 1.0 analysis new concepts design 1.0 1.0 analysis target markets 1.0 analysis target prices 1.0 analysis target costs 1.0 1.0 analysis time to market 1.0 1.0 1.0 analysis economical risks 1.0 1.0 1.0 1.0 1.0 1.0 analysis funding sources budgeting allocation analysis prototype targets 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 clients requirements 1.0 1.0 1.0 1.0 reporting client interviews key performance competitor products 1.0 target selling price 1.0 1.0 1.0 demand forecast 1.0 1.0 1.0 historical economical analysis economic forecast 1.0 1.0 1.0 1.0 1.0 products portfolio 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 market share external logistics price policies products guarantees application 1.0 1.0 1.0 1.0 products selling plan 1.0 portfolio actual orders offering, sales product agreement mto: configuration, time to market advertising materials products economical performance historical economic analysis economic analytical report 1.0 1.0 1.0 1.0 1.0 1.0 economic evaluation of prototypes analysis product costs sensitivity analysis product billing suppliers payments projects historical library projects currently active active projects status qfd analysis for active projects 1.0 1.0 1.0 target functionalities / requirements 1.0 1.0 1.0 1.0 1.0 collection of conceptual alternative solutions 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 bill of materials of conceptual solutions 1.0 1.0 1.0 1.0 manufacturing bom of conceptual solutions 1.0 engineering bom of conceptual solutions 1.0 1.0 functional characteristics of conceptual solutions 1.0 technical characteristics of conceptual solutions 1.0 design prototype 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 final technical specifications 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 final shaping specifications 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 final maintenance specification 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 final reliability specifications 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 final product lifecycle specifications 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 economic analysis stima economica ( di competenza ) dei concetti elaborati 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 piano di condivisione del prodotto 1.0 1.0 1.0 1.0 1.0 disegni 3d particolari del prototipo 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 disegni 2d particolari del prototipo 1.0 1.0 disegni 3d complessivo prototipo 1.0 1.0 disegni 2d complessivo prototipo 1.0 disegni esplosi del prototipo 1.0 materiali particolari prototipo 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 definizione delle lavorazioni sul prototipo 1.0 1.0 1.0 definizione delle tolleranze particolari prototipo 1.0 1.0 1.0 1.0 1.0 archivio risultati dei test elenco test da effettuare 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 risultati test sul prototipo costi prototipo 1.0 1.0 1.0 1.0 1.0 disegni 3d particolari prodotto 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 disegni 2d particolari prodotto 1.0 1.0 disegni 3d complessivo prodotto 1.0 1.0 1.0 disegni 2d compessivo prodotto 1.0 disegni degli esplosi 1.0 elenco e descrizione modifiche disegni del prodotto 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 definizione delle tolleranze del prodotto codici particolari 1.0 1.0 1.0 codice prodotto denominazione particolari distinta materiali prodotto 1.0 1.0 1.0 1.0 1.0 1.0 dimensioni e peso dei grezzi 1.0 1.0 1.0 dimensione e peso dei finiti 1.0 1.0 1.0 caratteristiche particolari commerciali 1.0 1.0 1.0 1.0 1.0 1.0 trattamenti termici/gavanici/etc. 1.0 1.0 1.0 1.0 1.0 1.0 distinta base elenco test da effettuare sul prodotto ( pre serie ) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 risultati test sul prodotto ( pre serie ) procedura controllo qualità interna ( particolari / assemblati / prodotto ) 1.0 1.0 1.0 1.0 1.0 1.0 procedure controllo qualità in accettazione ( particolari / assemblati / 1.0 1.0 1.0 1.0 1.0 1.0 procedura controllo qualità presso il fornitore schemi elettrici 1.0 certificazioni del prodotto 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 normative vigenti schema assemblaggio prodotto 1.0 1.0 1.0 1.0 1.0 1.0 1.0 definizione delle configurazioni 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 manuali d'uso e manutenzione 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 distinta e caratteristiche delle macchine utensili da utilizzare 1.0 1.0 1.0 1.0 1.0 1.0 selezione/progettazione delle attrezzature 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 utensili da utilizzare per le lavorazioni 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 parametri di lavorazione 1.0 1.0 1.0 1.0 1.0 1.0 cicli di lavorazione 1.0 1.0 1.0 1.0 1.0 1.0 1.0 programmi per i cnc 1.0 1.0 1.0 1.0 definizione dei costi di lavorazione 1.0 1.0 1.0 1.0 1.0 1.0 1.0 risultati analisi "make or buy" 1.0 1.0 1.0 costi di produzione 1.0 1.0 budget di produzione 1.0 1.0 1.0 piano principale di produzione ( mps ) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 pianificazione dei fabbisogni ( tecniche push o pull 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 programmazione operativa ( gantt, etc. ) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 portafoglio fornitori codice fornitore anagrafica fornitore preventivi fornitori 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ordine di acquisto 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 documentazioni tecniche dei fornitori costo del particolare commerciale ( concordato con il fornitore ) 1.0 1.0 1.0 1.0 costo del particolare non commerciale ( concordato con il fornitore ) 1.0 1.0 1.0 1.0 lotto minimo di acquisto del particolare ( concordato con il fornitore ) data di consegna riga d'ordine lead time fornitore condizioni di pagamento condizioni per il trasporto condizioni di garanzia eventuali penalità per ritardata consegna tipologia d'imballo sequenza fornitori sui particolari riepilogo solleciti distinta documenti inviati al fornitori analisi tempi di assemblaggio piano assemblaggio commesse piano assemblaggio prodotto scheda avanzamento lavoro scheda rilevamento difettosità bolla di carico bolla di scarico bolla di reso documento di prelievo interno documento di versamento interno quantità disponibile quantità impegnata valore quantità disponibile status del particolare ( controllato-non controllato ) status del materiale ( grezzo-semilavoratolavorato-commerciale ) codice di prelievo lavorazione interna codice di prelievo lavorazione esterna codice di prelievo vendita codice reso coordinate di stoccaggio analisi abc storico interventi sui prodotti simili, già commercializzati storico interventi sul prodotto archivio modifiche sui prodotti simili, già commercializzati archivio modifiche sul prodotto archivio difettosità sui prodotti simili, già commercializzati archivio difettosità sul prodotto statistiche dei guasti sui prodotti simili, già commercializzati statistiche dei guasti sul prodotto archivio dei reclami inerenti ai prodotti simili, già commercializzati report dei reclami inerenti al prodotto archivio osservazioni sui prodotti simili, già commercializzati report osservazioni sul prodotto dei clienti dlsm figure 2: overview of the complete dlsm. s tr a te g y : m a rk e ts a n d c o m p e ti to rs a n a ly s is m a rk e t o p p o rt u n it ie s a n a ly s is n e w c o n c e p ts d e s ig n a n a ly s is t a rg e t m a rk e ts a n a ly s is t a rg e t p ri c e s a n a ly s is t a rg e t c o s ts a n a ly s is t im e t o m a rk e t a n a ly s is e c o n o m ic a l ri s k s a n a ly s is f u n d in g s o u rc e s b u d g e ti n g a ll o c a ti o n a n a ly s is p ro to ty p e t a rg e ts c li e n ts re q u ir e m e n ts r e p o rt in g c li e n t in te rv ie w s k e y p e rf o rm a n c e p ro d u c ts t a rg e t s e ll in g p ri c e strategy: markets and competitors analysis market opportunities 1 analysis new concepts design 1 1 analysis target markets 1 analysis target prices 1 analysis target costs 1 1 analysis time to market 1 1 1 analysis economical risks 1 1 1 1 analysis funding sources budgeting allocation analysis prototype targets 1 1 1 1 1 1 1 1 1 clients requirements 1 1 1 reporting client interviews key performance products 1 target selling price 1 1 d i r e c t i o n dlsm m a r k e t i n g figure 3: sample of a portion of dlsm including some documents of the function "direction". 14 d. antonelli, p. chiabert enterprise function "direction". for every intersection activated in the dism, the compatibility of the adopted applications and their publication on the firm’s network is verified and the sum over the rows and the columns occurrence are generated in order to deduce the integration in the product data flow. s tr a te g y : m a rk e ts a n d c o m p e ti to rs a n a ly s is m a rk e t o p p o rt u n it ie s a n a ly s is n e w c o n c e p ts d e s ig n a n a ly s is t a rg e t m a rk e ts a n a ly s is t a rg e t p ri c e s a n a ly s is t a rg e t c o s ts a n a ly s is t im e t o m a rk e t a n a ly s is e c o n o m ic a l ri s k s strategy: markets and competitors analysis market opportunities 1 analysis new concepts design 1 analysis target markets analysis target prices analysis target costs 1 analysis time to market 1 analysis economical risks 1 1 d i r e c t i o n dism figure 4: sample of a portion of dism including some documents of the function "direction". the activated cells in the dism play a relevant role from a plm perspective when the document format and the support are electronic. taking in account only the interfaces based on electronic format, the distance of dism from dlsm addresses the investments in the informational infrastructure the enterprise needs. 3.1 analysing the dlsm matrix the sum α and β of the occurrences along the rows and the columns of dlsm allows to determine quantitatively the amount of functional dependencies among the documents in the information flow. table 2 presents the α and β values for the document "analysis target prices" produced by the enterprise function "direction": table 2: dependencies of "analysis target prices" document. document α β price target 1 7 the document is consulted by only another document ("analysis of market opportunities", produced by the function direction) and requires the existence of other seven documents belonging to the direction, marketing and design functions. it is noteworthy that the same document can be shared among different functions inside the enterprise, therefore another important parameter is the percentage of occurrences of every document (i) inside every function (j), associated to the matrix dlsm(i,j). as an example figure 5 shows the percentage use of all the documents generated by the function "direction". in the collaboration network, the function "direction" behaves as a supplier, the other enterprise functions are then the clients. the analysis can be profitably reverted in figure 6 that shows the percentage use of documents, generated by other functions, through the function "direction", which now acts as the client for the other functions. different analyses of dlsm structure bring to light unpredicted outcomes. 4 clustering the dlsm: multivariate analysis the scope of this section is illustrating the motivations behind the choice of the clustering method used to group the project documents. clustering of the documents is the main application of the dlsm matrix. produced documents have already a taxonomy, belonging to one of the product lifecycle functions, that correspond directly introducing collaborative practices in small medium enterprises 15 analysis engineering design production direction sourcing figure 5: use of direction documents by different enterprise functions including direction (labels represents the destinations). analysis engineering design production assembly sourcing direction ’ figure 6: use of enterprise data by the direction function (labels represents the origins). to the enterprise departments. nevertheless, a typical sme structure is by far different and usually simpler than what appears from the organizational chart. therefore it is noteworthy to test if the clustering of the document based on their correlations matches the official taxonomy. we remark that the clustering performed here has nothing to do with the known study field of information science that goes under the name of document classification. this latter can be defined in the following way [24]: "document clustering is a fundamental operation used in unsupervised document organization, automatic topic extraction, and information retrieval. it provides a structure for organizing large bodies of text for efficient browsing and searching". in our case, document clustering, based on the dlsm matrix of correlations, is only a pretext to analyse the enterprise organization from an unbiased point of view. in common with the ordinary document classification is the choice of unsupervised clustering methods. as there are reasonable doubts that the real structure of the collaboration network would match the enterprise structure it is convenient not to use the knowledge about the existing organization and leave the classification of the documents to an unsupervised method: the statistical cluster analysis. cluster analysis investigates grouping by minimizing a suitable distance measure among the data. the distance function should give a way to measure the similarity between two documents. euclidean distance is obviously improper for measuring the similarity in the correlations among documents. for the research aims the best suited is the hamming distance that calculates the percentage of attributes that differ between two objects. given the dlsm, the element dlsm(i,j) represents the attribute j of the document i. the number of documents and of attributes is the same, n. for every two documents r, s the hamming distance dh is: dh = dlsm(r, j) − dlsm(s, j) n (2) 16 d. antonelli, p. chiabert hierarchical clustering is the more appropriate technique for working with ordinal metrics. it creates a cluster tree, which is not a single set of clusters, but rather a multi-level hierarchy, where clusters at one level are joined as clusters at the next higher level. the decision of the most appropriate level of clustering is found by choosing the threshold of the inconsistency coefficient for each link of the hierarchical cluster tree. there are different agglomerative techniques [3], among which the nearest neighbour or single linkage. two clusters are joined together on the basis of the minimum distance between the two nearest elements among all the existing clusters. if cluster a is made of documents ai (i=1,...,m) and cluster b is made of documents b j (j=1,...,r), the distance between the two clusters is: dsl = min(dh(ai, b j))∀i,∀ j (3) single linkage is seldom selected among the techniques because it has the drawback of creating elongated cluster (in a spatial visualization of the distance measure) if there is a long chain of documents each one differing from the successive for some attribute. in present study the attributes are the dependencies of documents and it is reasonable to expect that every document be the starting point for a successive document. before to apply the clustering, a pre-processing has been executed on the dlsm to reduce the search space [5]. after a binary sorting, the void rows and columns have been excluded from the clustering. these represent documents which have been produced independently the ones from all the others. it is possible that most of them be useless documents and be requested by internal bureaucratic procedures. the result of the application of the hierarchical clustering to the modified dlsm is the dendrogram graph represented in figure 7. the dashed line represents a threshold inconsistency value chosen to group the documents in ten clusters which correspond to the nine divisions actually present in the enterprise. figure 7: dendrogram of the hierarchical tree for the dlsm matrix. a number of considerations can be drawn on the result of this clusterization: • one cluster (document index from 1 to 67) is by far larger than the others and includes documents belonging to nearly all the divisions; • with the exception of the fourth cluster (document index from 71 to 74), all the others have only one document inside; • these one-document clusters have an high unconsistency value, therefore they cannot be attributed to a larger one changing the agglomerative technique. the first consideration describe a positive situation. the majority of documents have strict reciprocal dependencies. a possible explanation is that the sme applies implicitly a concurrent engineering strategy in the introducing collaborative practices in small medium enterprises 17 management of new projects. as a matter of fact every project is carried on by an unstructured working team made usually of few persons if not just one, who are responsible for the production of the key documents which are used by all the divisions to produce their own documents. second and third considerations indicate the presence of dead end documents, i.e. documents that get their data from other documents but are no longer used to produce new documents. this is not necessarily an issue as it seems natural that every division produces a final report on the project done. the fourth cluster instead represents a small number of documents totally independent from the others. the issue is not in the presence of a cluster but in the high value of the inconsistency index (measured by the length of the dendrogram branches) that denotes an excessive distance between this cluster and every other. from a plm perspective the results of the dlsm clustering suggests the need of investments in the sharing of documents. under the hypothesis of a working team operating in a concurrent engineering framework is essential to increase the accessibility of documents by all the enterprise functions. in a sme, the issues related to the condivision of product data are strictly related to the possibility of access to the different database and data formats. inside the large majority of the smes, the integration of the enterprise documentation is not considered a critical issue [11]. 5 evaluating the attitude to change the questionnaires provide a picture of product data management across the enterprise functions and along the product lifecycle. such information can be reorganized according to the dism in order to gain a better knowledge on enterprise organization. on this base it is possible to evaluate the impact of plm on the informational structure by detailing the effects on each enterprise function. the evaluation of attitude to collaborative working in the ce context should be expressed in terms of costs and benefits, which depend on enterprise readiness and propensity to accept innovation [19]. the test case focused on the analysis of two attitude indexes: attitude to organizational changes and attitude to informational changes. the attitude to innovation assumes different characteristics depending on enterprise functions. authors extracted from technical literature a set of criteria to be applied to the most relevant functions, as identified on the basis of dslm and dism. different evaluating scales and different weights have been applied according to criteria nature [17]. the results of evaluation of both organizational and informational indexes for the most relevant enterprise functions (p1 marketing, p2 design, and p3 supply) are summarized in figure 8, which represents a pictorial description of the enterprise global attitude to innovation. figure 8: global attitude index for the test case. 18 d. antonelli, p. chiabert 6 conclusions the paper focuses on the development of a method for the evaluation of enterprise readiness to collaborative product and process design, with particular attention to the implementation of plm in the smes. data collection relies on questionnaires focused on the document management process adopted in the enterprise. the questionnaires are submitted to enterprise personnel while researchers integrates knowledge and analysis of decisional processes. in order to evaluate the amount of business process management required to upgrade the enterprise organisation to plm pre-requisites, the authors developed an analysis tool consisting of a classification of product data flows and their supporting formats. subsequent refinements of the gathered information allow to benchmark the enterprise propensity to the implementation of plm. the simple approach proposed in the paper seems to solve some of the problems related to the introduction of information technology systems in smes: insufficient awareness of organizational issues, insufficient involvement of end users, inadequate traininig of users. bibliography [1] aberdeen, "plm it’s for any manufacturer striving for product excellence", an executive white 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[2] r.w. bagshaw, s.t. newman, "structured approach to the design of a production data analysis facility part 1: conceptual design and information requirements", int. j. prod. res., vol. 39, no. 10, pp. 2121-2141, 2001. [3] a. cevahir, "clustering of correlated documents into designated number of clusters: a practical approach", www.cs.bilkent.edu.tr, 2005. [4] d. chen, g. doumeingts, "developing a theory of design a tentative approach using mathematics", studies in informatics and control, vol.11, no.1, 2002, ici, bucharest. [5] g. czibula, g. serban, "hierarchical clustering based design patterns identification", int. j. of computers, communications & control, vol. iii, pp. 248-252, 2008, ccc publications. [6] datamation limited, "understanding product lifecycle management", rep. no. plm-11, rev. no. 1.0, september 2002, cambridge, uk. [7] european commission, sme and cooperation, observatory of european smes, 2003, enterprise pubblication, brussels. [8] b. eynard, t. gallet, p. nowak, l. roucoules, "uml based specifications of pdm product structure and workflow", computers in industry, vol.55, pp. 301-316, 2004. [9] b. fagerström, l. olsson, "knowledge management in collaborative product development", systems engineering, vol. 5, no. 4, pp. 274-285, 2002. [10] f.g. filip, "decision support and control for large-scale complex systems", annual reviews in control, vol.32, no.1, pp. 61-70, 2008. [11] f. franceschini, advanced quality function deployment, boca raton, st. lucie press, 2002. [12] h. kaschel, l.m. sánchez y bernal, "importance of flexibility in manufacturing systems", int. j. of computers, communications & control, vol. i (2), pp. 53-60, 2006, ccc publications. [13] j. krauth, introducing information technology in small and medium sized enterprises, studies in informatics and control, vol.8, no.1, 1999, ici, bucharest. [14] a. kusiak, concurrent engineering: automation, tools, and techniques, wiley, usa, 1992. [15] t.w. malone, k. crowston, "the interdisciplinary study of coordination", acm computing surveys (csur), vol. 26, no. 1, pp.87-119, 1994. [16] s.y. nof, g. morel, l. monostori, a. molina, f. filip, "from plant and logistisc control to multi-enterprise collaboration", annual reviews in control, vol.30, no.1, pp. 55-68, 2006. introducing collaborative practices in small medium enterprises 19 [17] k. schmid, i. john, "a customizable approach to full lifecycle variability management", science of computer programming, vol.54, pp. 259-284, 2004. [18] m.g. singh, a. titli, systems decomposition, optimisation and control, pergamon press, 1988. [19] r. sudarsan, s.j. fenves, r.d. sriram, f. wang, "a product information modelling framework for product lifecycle management", computer aided design, vol.37, pp.1399-1411, 2005. [20] j. stark, product lifecycle management 21st century paradigm for product realisation, 2005, springerverlag, london, uk. [21] s.n. talukdar, "collaboration rules for autonomous software agents", decision support systems, vol. 24, pp.269-278, 1999. [22] s. tichkiewitch, d. brissaud, "co-ordination between product and process definitions in a concurrent engineering environment", annals of cirp, vol. 49, pp. 75-78, 2000. [23] a. tiwana, b. ramesh, "a design knowledge management system to support collaborative information product evolution", decision support system, vol.31, pp.241-262, 2005. [24] c. xiaohui, g. jinzhu, t.e. potok, "a flocking based algorithm for document clustering analysis", j. of systems architecture, vol.52, pp. 505-515, 2006, elsevier. [25] s. zina, m. lombard, l. lossent, c. henriot, "generic modeling and configuration management in product lifecycle management", int. j. of computers, communications & control, vol. i (4), pp. 126-138, 2006, ccc publications. dario antonelli italy 1966, holds a m.s. degree in mechanical engineering from the politecnico di torino, 1990. he worked at fiat research center until 1992. he is currently associate professor at the department of production systems and economics of politecnico di torino. his scientific activity is mainly related to numeric finite element simulation of metalworking processes, to experimental identification of process parameters and to supply chain management. paolo chiabert (b. june 28, 1963). m.sc. degree in electronic engineering, politecnico di torino (1993), ibm consultant (1994), researcher in design and methods of industrial engineering at the politecnico di torino (1995-2003), associate professor in technology and manufacturing systems (2004). according to the product lifecycle management perspective, the research interests range from design methods, to manufacturing technologies, to production planning and control, to quality management. since 1997 italian expert in the iso technical committee 213 geometrical product specifications and verification, he is member of the gps technical commission at uni. international journal of computers, communications & control vol. ii (2007), no. 2, pp. 149-158 blind steganalysis: estimation of hidden message length sanjay kumar jena, g.v.v. krishna abstract: steganography is used to hide the occurrence of communication. discovering and rendering useless such covert message is an art of steganalysis. the importance of techniques that can reliably detect the presence of secret messages in images is increasing as images can hide a large amount of malicious code that could be activated by a small trojan horse type of virus and also for tracking criminal activities over internet. this paper presents an improved blind steganalysis technique. the proposed algorithm reduces the initial-bias, and estimates the lsb embedding message ratios by constructing equations with the statistics of difference image histogram. experimental results show that this algorithm is more accurate and reliable than the conventional difference image histogram method. it outperforms other powerful steganalysis approaches for embedded ratio greater than 40% and comparable with rs steganalysis technique for shorter hidden message length. keywords: steganography, steganalysis, hidden message extraction 1 introduction steganography is the art of passing information through apparently innocent files in a manner that the very existence of the message is unknown. the term steganography in greek literally means, “covered writing”. the innocent files can be referred to as cover text, cover image, or cover audio as appropriate. after embedding the secret message it is referred to as stego-medium. a stego-key is used to control the hiding process so as to restrict detection and/or recovery of the embedded data. while cryptography is about protecting the content of messages (their meaning), steganography is about hiding the message so that intermediate persons cannot see the message. historically, steganography has been a form of security through obscurity where the security lies in that only sender and receiver know the method in which the message is hidden. this is in violation of kirchoff’s principle, which states that the security should lie in key alone. steganography can be either “linguistic steganography”or “technical steganography”[1]. the ancient techniques that hide messages physically are called as technical steganographic systems. they include microdots, tattoos, invisible inks and semagrams. recent techniques belong to the linguistic steganography. these techniques hide message in the cover images, which are of digital form. steganalysis is the process of detecting the existence of the steganography in a cover medium and rendering it useless. current trend in steganalysis [4] seems to suggest two extreme approaches (a) little or no statistical assumptions about the image under investigation where statistics are learnt using a large database and (b) a parametric model is assumed for the image and its statistics are computed for steganalysis detection. the messages embedded into an image are often imperceptible to human eyes. but there exists some detectable artifacts in the images depending on the steganographic algorithm used [2,5]. the steganalyst uses these artifacts for the detection of the steganography. by far the most popular and frequently used steganographic method is the least significant bit embedding (lsb). it works by embedding message bits as the lsb’s of randomly selected pixels. several techniques for the steganalysis of the images for lsb embedding are present. fridrich and goljan [6,7] proposed lsb steganography dual detection method, named rs method, based on probability statistics in the color or grayscale images. the basic idea is that lsb plane seems random in the typical cover images, but to some extent the other 7 bit planes could predict it. this method is suitable for detection of the non-sequential steganography reliably. pfitzmann and westfeld [8] introduced a method based on statistical analysis of pairs of values (povs) copyright © 2006-2007 by ccc publications 150 sanjay kumar jena, g.v.v. krishna that are exchanged during message embedding this method, which became known as the χ 2 attack, is quite general and can be applied to many embedding paradigms besides the lsb embedding. it provides very reliable results when the message placement for sequential embedding. fridrich et al. [9] developed a steganographic method for detecting lsb embedding in 24-bit color images-the raw quick pairs (rqp) method. the new method is based on analyzing close pairs of colors created by lsb embedding. on the condition that the number of unique colors in the cover image will be less than 30 percent that of the total pixels, it works reasonably well. when the number of unique colors exceeds about 50 percent that of total pixels, the results gradually become unreliable. this frequently happens for high resolution raw scans and images taken with digital cameras stored in an uncompressed format. another disadvantage of the rqp method is that it can’t be applied to grayscale images. there are few papers in the field of the detecting pixels that contain the hidden message. ian davidson and goutam paul [10] proposed the hidden message location problem as outlier detection using probability/energy measures of images. pixels contributing the most to the energy calculations of an image are deemed outliers. though results for grayscale images are quite accurate; they are not as good as for color images. the algorithm can be defeated if the steganography algorithm has knowledge of probability/energy function or if the message is carefully embedded in the high-energy regions of an image. the difference image histogram proposed by t.zhang and x.ping [10] consists of an initial-bias. the proposed algorithm constructs the embedding ratio-estimate equations using the difference image histogram and reduces the initial bias. experimental results show that the novel algorithm is more accurate than the conventional difference image histogram method and other steganalysis techniques. in the following section, we review principle of the difference image histogram method, and then in section 3, describe the improved difference image histogram method (idih) algorithm. sections 4 show the experimental results and conclude this paper in section 5. 2 principles of difference image histogram tao zhang and xijian ping introduced the difference image histogram method, which uses the measure of weak correlation between successive bit planes to construct a classifier for discrimination between stego-images and cover images. considering the property of lsb steganography, the difference image histogram is used as a statistical analysis tool. the difference image is defined as d(i, j) = i(i + 1, j)−i(i, j). (1) where i(i, j) denotes the value of the image i at the position (i, j). t.zhang and x.ping found that there exists difference between the difference image histograms for normal image and the image obtained after flipping operation on the lsb plane. this fact is utilized to realize the steganalysis technique. to explain the details of difference image histogram method (dih), we need to define some notions, let i be the test image, which has m ×n pixels. the embedding ratio p is defined as the percentage of the embedded message length to the maximum capacity. if the difference image histogram of an image is represented by hi, that of the image after flipping all bits in the lsb plane by fi, and that of the image after setting all bits in the lsb plane to zero by gi, there exist the following relationships between hi, fi and gi. h2i = f2i = a2i,2ig2i, h2i+1 = a2i,2i+1g2i + a2i+2,2i+1g2i+2, (2) f2i+1 = a2i,2i−1g2i + a2i+2,2i+3g2i+2 in which a2i,2i+ j is defined as the transition coefficient from the histogram gi to hi. when j = 0, 1,−1 then 0 < a2i,2i+ j < 1, otherwise a2i,2i+ j = 0, and they satisfy a2i,2i−1 + a2i,2i + a2i,2i+1 = 1 (3) blind steganalysis: estimation of hidden message length 151 h2i a2i,2i−1 a2i,2i 2i+1h a2i,2i+1 g2i h2i−1 g2i f 2i+12i−1f f 2i a2i,2i a2i,2i−1a2i,2i+1 figure 1: the transition diagram from gi to hi, fi starting from the approximate symmetry of the difference histogram about i = 0, we first get a0,1 ∼ a0,−1 from the above equations (2) we obtain the following iterative formula for calculating transition coefficients for all positive integers i: a0,1 = a0,−1 = g0 −h0 2g0 , a2i,2i = h2i g2i , (4) a2i,2i−1 = h2i−1 −a2i−2,2i−1g2i−2 g2i , a2i,2i+1 = 1−a2i,2i −a2i,2i−1. assuming the embedded hidden message forms a random bit sequence, for the stego-image with the lsb plane fully embedded (i.e.p = 100%) the lsb plane is independent of the neighboring bit planes. therefore, for such stego-images we have a2i,2i+1 ≈ 0.25, a2i,2i+1 ≈ 0.5, a2i,2i+1 ≈ 0.25. the h2i+1 consists of two parts: a2i,2i+1g2i and a2i+2,2i+1g2i+2. statistical tests show that for natural images these two parts make an approximately equal contribution to h2i+1, that is a2i,2i+1g2i ≈ a2i+2,2i+1g2i+2 (5) if αi = (a2i+2,2i+1)/(a2i,2i+1), βi = (a2i+2,2i+3)/(a2i,2i−1) and γi = g2i/g2i+2 then the statistical hypothesis of the steganalytic method is that for a natural image the following equation should be satisfied: αi ≈ γi (6) while for stego-images with the lsb plane fully embedded αi ≈ 1. (7) the physical quantity αi, can be viewed as the measure of the weak correlation between the lsb plane and its neighboring bit planes. from further experiments they got that for a given i the value of αi, decreases monotonically with the increasing length of embedded secret messages (p) and when the embedding ratio p increases to 100%, αi decreases to 1 approximately.figure-2 shows the functional relation between αi and the embedding ration p when i = 0 for the “lena”image. the relationship between α , and the embedding ratio p will be modeled using a quadratic equation y = ax2 + bx + c. the following four critical points p1 = (0, γi), p2 = (p, αi), p3 = (1, 1), and p4 = 152 sanjay kumar jena, g.v.v. krishna 0 0.5 1 1.5 2 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 1.3 embedded ratio(p) α i (0,γ i ) (p,γ i ) (1,1) (2−p,β i ) α i = 0.0624p2−0.2885p+1.2366 figure 2: the functional relation between αi and p(i = 0) (2− p, β i). now the following equation set is obtained: c = γi, ap2 + bp + c = αi, (8) a + b + c = 1, a(2− p)2 + b(2− p) + c = βi. assume d1 = 1−γi, d2 = αi −γi, d3 = βi −γi, then above equation set (8) can be simplified to 2d1 p 2 + (d3 −4d1 −d2)p + 2d2 = 0 (9) the embedding ratio p can be obtained from the root of above whose absolute value is smaller. if the discriminant is smaller than zero, then p ≈ 1. 3 principles of improved difference image histogram steganalysis the difference image histogram algorithm was primarily based on the statistical hypothesis that for the natural images αi ≈ γi (10) and for a stego-images with the lsb plane fully embedded αi ≈ 1. (11) obviously, the hypotheses given in equations(10) and (11) will affect the precision of the difference image histogram method. once in these hypotheses there exists some initial bias, the estimate value via the equation(9) will not be reliable. when the embedding ratio is low, the bias of these hypotheses will lead the incorrect decision, and if there are no embedding messages in images, the false alarm rate is high. table 1 will show the mean and variance of the γi to α i value. with the increase in i the variance increases and the mean begins to deviate from 1. in some cases the detection lead to an incorrect decision blind steganalysis: estimation of hidden message length 153 i=0 i=1 i=2 i=3 mean 1.0013 1.0034 1.0079 1.0443 variance 7.6e-04 1.4e-03 2.8 e-03 4.9e-03 table 1: statistical data on the ratio of γi to α i for natural images of estimating more than 1% embedding, for the normal images. the figure "proposed" shows the initial value of difference between αi and γi for a lena image and figure "proposed (b)" shows a close look at the αi and γi at p = 0 values. this initial deviation may lead a serious estimate error. the initial-bias in detection of the message in the normal image is affecting the detection of stego-images, as the error present in the normal image will effect the estimation of the hidden message length for stego-image. if the stego-image created with embedded embedding ratio p is denoted as sp, and the image created by flipping all bits in the lsb plane of sp as rp, the value of αi can be calculated for the images sp and rp(note that the value of αi for the images rp is equal to the value of βi for the image sp). moreover, we note that in sp only p/2 of the pixels are flipped by message embedding, while in rp about 1−(p/2) of pixels are flipped. therefore, rp is equivalent to a “stego-image”with “embedding ratio”2− p. so given a stego-image we can calculate the values of αi at p and 2 − p/2, as the value of βi at p is equal to the value of αi at value 2−(p/2). 0 0.5 1 1.5 2 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 embedded message ratio(p) α i γ i α i figure 3: proposed let αi(0) be the initial value of αi and γi(0) be the initial value of γi (i.e. when embedding ratio is zero). the error ‘ε ’be the initial-bias between the γi and αi. so we have ε = γi(0)−αi(0). (12) from the difference image histogram method, γi = g2i/g2i+1 where g is the difference image histogram after setting all bits in lsb plane to zero. the grayscale value of pixels in the image will be even as the lsb are set to zero. when the image is embedded with hidden message using the lsb insertion and then performing the operation of setting all lsb plane bits to zero for the stego-image will result in the values of g2i and g2i+1 unmodified. hence γi(0) = γi ∀ p (13) so the value of error ε will become ε = γi −αi(0). (14) 154 sanjay kumar jena, g.v.v. krishna 0 0.05 0.1 0.15 0.2 0.25 1.21 1.215 1.22 1.225 1.23 1.235 1.24 1.245 1.25 embedded message ratio(p) α i γ i α i figure 4: proposed-(b) the difference image histogram models the relationship between αi and the embedded ratio (p) using a quadratic equation y = ax2 + bx + c. considering the statistical hypotheses given in equation (15) to be correct initially, we can find (p,αi), (1,1), (2 − p,βi) are the three points on the curve y = ax2 + bx + c. now we obtain the following equation set: ap2 + bp + c = αi, a + b + c = 1, (15) a(2− p)2 + b(2− p) + c = βi. assume e1 = 1− p, e2 = 1−αi, e3 = 1−βi and then constant value “c”in equation 4.21 can be simplified to c = 2e21 −2e1e2 −(e2 + e3)(1−e1) 2e21 . (16) the value of ‘c”in equation (15) will give the αi(0) for an image. hence subtracting the error from the estimated ratio p will remove the initial bias in the image. hence the new estimated ratio “pmodi f ied ”will be pmodi f ied = p−ε (17) 4 description of idih algorithm we now describe our detection algorithm input: a set of bmp images for detecting. output: the embedded ratio estimate pmodi f ied for each image. step 1. select one image in the image set; step 2. obtain difference image histogram of the image before (hi) and after flipping the lsb bit planes to “zero”(gi); step 3. do from the step 4 to 8 for each value of i= 0,1,2; step 4. calculate the statistical values for the image i.e αi = (a2i+2,2i+1)/(a2i,2i+1), βi = (a2i+2,2i+3)/(a2i,2i−1) blind steganalysis: estimation of hidden message length 155 and γi = g2i/g2i+2, where the transition co-efficient can be estimated using the following equation a0,1 = a0,−1 = g0 −h0 2g0 , a2i,2i = h2i g2i , a2i,2i−1 = h2i−1 −a2i−2,2i−1g2i−2 g2i , a2i,2i+1 = 1−a2i,2i −a2i,2i−1. step 5. obtain the value of “p” from the root of the below equation whose absolute value is smaller. 2d1 p 2 + (d3 −4d1 −d2)p + 2d2 = 0 where d1 = 1−γi, d2 = αi −γi, d3 = βi −γi; step 6. calculate the value αi(0) which represents the estimation of αi for zero embedded message length using the following equation αi(0) = 2e21 −2e1e2 −(e2 + e3)(1−e1) 2e21 where e1 = 1− p, e2 = 1−αi and e3 = 1−βi; step 7. calculate the initial bias ‘ε ”as ε = γi −αi(0). step 8. subtract the error ‘ε ’from the p to obtain the modified estimation ratio pmodi f ied (i). pmodi f ied (i) = p−ε step 9. the average of pmodi f ied (i) for i= 0,1,2 will give the final embedded ratio pmodi f ied . 5 experimental results we select 150 standard 512 × 512 test images (such as lena, peppers and so on). applying random and sequential lsb replacement to embed the images with the ratio of p= 0, 10%, 20%,. . . , 90%,100% respectively with 10% increments we created two databases. then we have use the rs method [7], dih method [11] and gefr method [12] to estimate the embedding ratio of secret information respectively. the mask used in the rs method is [1,0; 0,1]. the testing results of the test images got by dih method and the proposed method (idih) are shown in table 2. the leftmost column in table 2 is the real embedding ratio, and column “idih”, “dih”represent the estimate embedding ratio got by improve difference image histogram method (proposed method) and difference image histogram method (dih) respectively. it can be seen in table 2 that the estimate precision of idih is higher than dih obviously. figure [4] and [5] shows the corresponding plot of the embedded message length to the mean absolute error of the estimated values for random embedding and sequential embedding. figure [4] indicates that the proposed algorithm (idih) algorithm outperforms the other three steganalysis techniques for embedded ratios greater than 40%. improved difference image histogram algorithm has performance comparable to the rs steganalysis for short messages (when p is smaller than 40%). however, because it is harder to detect smaller messages than large messages, the accuracy of the estimate is far more important for smaller message embedding. the proposed algorithm proves to be effective and reliable when complete range of embedding lengths is considered and compared to the existing algorithms. 156 sanjay kumar jena, g.v.v. krishna embedding random sequential ratio(%) idih dih idih dih 0% 0.3052 1.6855 0.3052 1.6855 10% 14.7804 15.3881 15.6703 16.0368 20% 20.38 20.80 27.98 28.11 30% 20.3764 20.8017 27.9818 28.1124 40% 40.1524 42.9062 44.3258 44.922 50% 48.6793 52.2864 49.7154 48.5228 60% 62.245 63.8 60.5394 56.5979 70% 72.7311 66.67118 69.7919 68.726 80% 84.6388 73.4632 80.8796 72.2582 90% 90.9915 85.8664 84.8516 81.955 100% 98.6088 92.5193 98.6088 92.5193 table 2: comparison between idih and dih 0 10 20 30 40 50 60 70 80 90 100 0 2 4 6 8 10 12 embedded ratio (p) m e a n e rr o r idih dih gefr rs figure 5: comparison with other steganalytic techniques for random lsb embedding in case of sequential embedding (as shown in figure ??), the accuracy is much higher than the case of random embedding for the embedded ratios of greater than 40%. it is having a higher performance to all the other steganalytic techniques for entire range of possible embedding lengths. 6 summary and conclusions this paper proposes a new detection algorithm, which is an improved algorithm to the difference image histogram algorithm and performed tests on a group of raw lossless images. experimental results show that the improved difference image histogram steganalysis method is more accurate and reliable than the conventional difference image histogram method. the proposed algorithm reduces the mean error by 50% for embedding ratios greater than 40% when compared to the dih algorithm. references [1] f.a.p.petitcolas, r.j.anderson and m.g.kuhn, “information hiding-a survey”, proceedings of the ieee, vol.87(7), special issue on protection of multimedia content, june 1999, pp.1062-1018. blind steganalysis: estimation of hidden message length 157 0 10 20 30 40 50 60 70 80 90 100 0 5 10 15 20 25 embedde ratio (p) m e a n e rr o r idih dih gefr rs figure 6: comp. with other steganalytic techniques for sequential lsb embedding [2] n.f.johnson and s.jajodia, “steganalysis of images created using current steganography software”, lecture notes in computer science, vol.1525, springer, berlin, april 1998, pp.273-289. [3] n.f.johnson, and s.jajodia, “exploring steganography: seeing the unseen ”, ieee, computer, february 1998, pp. 26-34. [4] r.chandramouli and k.p.subbalakshmi, “current trends in steganalysis: a critical survey ”, international control, automation, robotics and vision conference 2004, volume 2, december 2004, pp.964 967 [5] neil f. johnson and sushil jajodia, “steganalysis: the investigation of hidden information”, ieee information technology conference, september 1998, pp.113-116. [6] j.fridrich, m.goljan and r.du, “detecting lsb steganography in color and grayscale images”, ieee, vol.8(4) multimedia, october-december 2001, pp.22-28. [7] j. fridrich, m. goljan, r. du, “reliable detection of lsb steganography in grayscale and color images”, proceeding of acm, special session on multimedia security and watermarking, ottawa, canada, 2001, pp. 27-30. [8] a.westfeld, a. pittzmann, “attacks on steganographic systems”, information hiding, third international workshop, ih’99, dresden, germany, 29 september-1 october, 1999. [9] j.fridrich, r.du and m.long, “steganalysis of lsb encoding in color images’, in proceedings of icme 2000, july-august 2000. [10] t.zhang and x.ping, “reliable detection of lsb steganography based on the difference image histogram”, ieee international conference on acoustics, speech, and signal processing, volume 3, april 2003, pp.545-548. [11] li zhi and sui ai fen, “detection of random lsb image steganography”, vehicular technology conference ieee 60th , vol.3, september 2004, pp.2113 2117. [12] a.pinet, securengine professional v1.0, 2004, http://securengine.isecu qrelabs.com. [13] cbir image database, university of washington, available at http://www.cs.washington.edu/research/imagedatabse/groundtruth/. 158 sanjay kumar jena, g.v.v. krishna [14] the usc-sipi image database, available at: http://sipi.usc.edu/ser vices/databse/database.html. sanjay kumar jena national institute of technology department ff computer science and engineering rourkela, orissa india, 769008 e-mail: skjena@nitrkl.ac.in g.v.v. krishna national institute of technology department ff computer science and engineering rourkela, orissa india, 769008 e-mail: gvvkrishna@yahoo.co.in received: august 18, 2006 dr. s.k. jena was born in 28 april, 1954. he received his ph.d. from indian institute of technology, bombay and m.tech from indian institute of technology, kharagpur. he has joined national institute of technology as professor in the department of computer science and engineering in 2002. currently he is working as professor and head of computer science and engineering department. he has more than 35 publications in international journals and conferences. his research areas of interest are database engineering, distributed computing, parallel algorithm, information security and data compression. mr. g.v.vamsi.krishna was born in 17 june, 1985. he received his m.tech in computer science & engineering from national institute of technology, rourkela in 2006 and b.tech in computer science & engineering from sarada institute of science, technology and management, hyderabad in 2004. currently he is working as software engineer in ibm india pvt. ltd., bangalore. international journal of computers communications & control issn 1841-9836, 11(2):259-272, april 2016. a multi-objective approach for a multihoming load balancing scheme in whn c. lozano-garzon, m. molina, y. donoso carlos lozano-garzon* universidad de los andes, bogotá, colombia, south america & universitat de girona, girona, spain. *corresponding author: calozanog@ieee.org miguel molina, yezid donoso universidad de los andes bogotá, colombia, south america mf.molina35@uniandes.edu.co, ydonoso@uniandes.edu.co abstract: the telco operators face up to challenges related to the need of ensuring a quality of service to the user in a planning, maintenance and resource allocation in their complex networks. these challenges are directly related with the need to ensure an user’s service with a good level of quality in a highly dynamic environment in terms of changes in the radio access technologies, growth in the number of mobile users, technical requirements of the new services and applications, and the possibility to connect to different networks at the same time, among others. in this paper, we address the problem of the user’s service allocation into the different feasible networks in order to reduce the network overloading. we present a multihoming load balancing scheme that allows the re-allocation of services according to their qos requirements and the availability of network resources. we propose a multi-objective optimization model of this problem together with an evolutionary algorithm to solve it. through simulation in different scenarios, we show that our algorithm is efficient, sensitive, scalable and provides optimal solutions. keywords: heterogeneous networks, load balancing, multihoming, multi-objective optimization, multi-objective evolutionary algorithms, vertical handover. 1 introduction given the continuous advances in network technologies, the growth in the number of mobile users and the increasing demand of the new services and applications, the mobile network operator are confronted with multiple challenges in the planning, maintenance and operation of their complex infrastructure. in many cases, these networks are composed by multiple radio access technologies, which allows to the user the access to different services by using simultaneously one or more their network interfaces. in some cases during the network operation, it is possible that some radio access channels could be overloaded because of a traffic growth caused by the increased of the number of user’s services connected to these channels. therefore, the mobile operators need some mechanisms that allows a balanced distribution of the traffic load over the available networks. note that this process could involve could involve the reallocation of some users. the main goal of this mechanism is the optimal use of the available network resources whilst the technical requirements needs of each service are guaranteed. from the viewpoint of the mobile network operators, one of the most appropriate ways to achieve this goal is that the deployed infrastructure must be capable to perform the user’s reallocation for one network to another, this process is called vertical handover (vho). due to standards such as ieee 802.21 [1] only provide the framework for vho, the decision making copyright © 2006-2016 by ccc publications 260 c. lozano-garzon, m. molina, y. donoso algorithm that allow establish the best connection for each user is an open challenge [2]. in this algorithms, the re-allocation must be based on different metrics obtained from the mobile devices and/or from the performance parameters of the available networks [3]. also, it is important that this network changes process should be transparent for the user. based on the recent advances in the mobile phones, nowadays these devices could establish connections to multiple networks in a simultaneously way, which is referred to as multihoming [4]. this characteristic facilitates a seamless vho process while it is seamless to the user [5] and allows the simultaneous transmission of multiple services across multiple network interfaces [6]. several research projects use the multihoming strategy over a heterogeneous environment in order to achieve a load balancing [4]; [7–9] or to make a better distribution of the bandwidth charge [10,11]. in other studies this strategy was used as a decision tool for the vho process [12–15]. considering the aforementioned problems, we addressed our study about the always best connected (abc) problem in heterogeneous wireless networks (hwn) in [16]. in this work we designed a proactive vertical handover decision algorithm (vho-da) based on user preferences, qos requirements, and network conditions. later in [17], we presents a load balancing optimization scheme; this scheme is composed by one mathematical model and a two-step algorithm based on the anchor-adjustment heuristic. in this paper, we also address the problem of load balancing across heterogeneous networks from the viewpoint of the operator. we present a multi-objective optimization model to solve the traffic load balancing problem into hwn using a multihoming strategy, and an evolutionary algorithm to solve it. the remainder of this paper is structured as follows. in section 2 we introduce the mathematical model that encodes the multi-objective function in order to obtain a global load balancing among hwn. in section 3 the load balancing algorithm based on strength pareto evolutionary algorithm is presented. the experimental results about the performance of our proposal are shown in section 4. finally, concluding remarks and directions for further research are given in section 5. 2 load balancing mathematical model as it was mentioned in [17], the load balancing is an important strategy used by the mobile operators in order to allocate, in a fair way, the available resources in a network. however, this strategy implies, in many cases, the reallocation of mobile devices; therefore, it is necessary to consider the cost of connecting services to the new networks and the energy consumption of the mobile device. considering the above statements and the possibility of the simultaneous use of multiple network interfaces by each mobile device [6], in this section we proposed a multiobjective multihoming mathematical model. 2.1 decision variable by assuming that mobile devices are able to perform multihoming in the network, we define the decision variable x as a binary variable that specify if the service s of the mobile k is connected to the network j or not (see figure 1). the variable is represented as follows: x k,s j = { 1 if the service is connected to the network 0 otherwise a multi-objective approach for a multihoming load balancing scheme in whn 261 figure 1: multihoming cellular system 2.2 objective functions in order to design an efficient load balancing algorithm, the mathematical model is formulated under the premise of achieving an overall load balance in the wireless heterogeneous networks, whilst the connection cost and the energy consumption is also minimized. this function is expressed as: min(α,β,γ), where α represents the load balancing function, β the connection cost function, and γ the energy consumption function. load balancing function the load balancing function (α) is the main function of this model. this function determines the network traffic load by considering the demand of the services of the mobile devices in relation to the theoretical available bandwidth of the network. for this model, the load function is defined as: α = max(αj),∀j ∈ n, where j represents the destination network of the mobile device, n the set of access networks and αj the load of the network j. we calculate αj as the sum of demanded bandwidth (ds) of each connected service (s), for each mobile (k) over the theoretical available bandwidth of the network (bwj) αj = ∑ k ∑ s ds ·x k,s j bwj ,∀j connection cost function the function determines the maximum monetary cost of the mobile devices that are connected to the network. if a mobile device has at least one service connected to the network, its cost is taken into account to access the network. the connection cost function is expressed as: β = max(βj),∀j ∈ n, where j represents the network and βj the maximum cost of connected mobile devices to this network (costj). βj is defined as: βj = ∑ k maxs(costj ·x k,s j ),∀j 262 c. lozano-garzon, m. molina, y. donoso energy consumption function this function determines the energy consumption of the mobile devices that are connected to the network. if a mobile device has at least one service connected to the network, the consumption that generates for being connected to the network is taken into account in our model. we defined the energy function as: γ = max(γj),∀j ∈ n. whilst γj is determined as the maximum consumption that is generated by mobile device k for being connected to the jth network γj = ∑ k maxs{cons(rssk,j) ·x k,s j },∀j finally, in order to guarantee consistency in our model, we consider that when a services s is active in the device, it will generate a traffic demand ds. following the work presented in [18], the values of received signal strength are discretized at three levels: low, medium and high. note that the power consumption is inversely proportional to the received signal strength (rss) and therefore, a high signal level results in a low power consumption by the mobile radio interface through which communication is established. because multiple network interfaces can be used in a multihoming scenario, it is a privilege to be connected to those network which you receive better signals from, i.e. where less energy is consumed for being connected. consumption levels derived from the rss are modeled in the cons(rssk,j) function. cons(rssk,j) =   1 rssk,j > rssth2 2 rssth1 ≤ rssk,j < rssth2 3 0 < rssk,j < rssth1 0 rssk,j = 0 2.3 model constraints through the model constraints, we intend to guarantee the adjustment of this model to the real-life telco networks. in this case, the model only allows the service connection to those networks that are in the coverage area, comply with the cost the user can assume to connect to the network, and offer enough bandwidth to meet the demand of service and an adequate power consumption according to the level of battery charge that the mobile device has. demand constraint the demand constraint states that the service can be only connected to a network that has enough bandwidth to meet its demand. ds ·x k,s j ≤ bwj,∀j cost constraint the cost constraint states that the overall cost to access network j (costj), i.e. the cost to connect any service to network j must be less or equal to the cost incurred by the user in the contract of the mobile device k (costk). costj ·x k,s j ≤ costk,∀k a multi-objective approach for a multihoming load balancing scheme in whn 263 access constraint through the access constraint it is ensured that each service s that is active on the mobile device k is connected to a n network and can only be connected to one network. ∑ j x k,s j = yk,s,∀k,∀s reach constraint this constraint ensures that only networks that exceed the defined signal strength threshold (rssth) are considered in the model assessment. rssk,j ≥ rssth,∀j,∀k power consumption constraint the power consumption constraint ensures that services only can be connected to those networks that offer lower power consumption, according to the current battery level of the mobile device. cons(rssk,j) ·x k,s j ≤ batk,∀j,∀k where batk is defined as: batk =   1 chargek < batth1 2 batth2 ≤ chargek ≤ batth1 3 chargek > batth2 a service s of a mobile device k is considered active when the constraints are satisfied at least for one of the networks; i.e. if the service can be connected to at least one of the networks available for the device. when the service s is active, it consumes the traffic demand in the network that it is connected. 3 multi-objective evolutionary algorithm in order to solve the multi-objective model, several strategies can be used. one of them is to evaluate each objective function in the model separately (mono-objective approach). the weight sum method is one of these strategies. however, it has several disadvantages, including not finding all optimal solutions if the solution set is not convex, and the need to normalize the functions so that no one predominates over the others [7]. for this reason we propose the use of an multi-objective evolutionary algorithm (moea) to find the best set of solutions for all objective functions at the same time. the chosen algorithm is the elitist type evolutionary algorithm spea (strength pareto evolutionary algorithm) proposed by zitzler and thiele [7,19]. the time complexity of this algorithm is upper bounded by o(nm2) in each generation, where m is the population size and n is the number of objectives. 264 c. lozano-garzon, m. molina, y. donoso algorithm 1: spea algorithm pseudo-code 1: generate a random population m 2: while not max number of generation do 3: evaluate population according objective function 4: calculate the fitness of each of the individuals 5: classification population based on fitness (m,m′) 6: generate new population mt+1 7: apply binary tournament selection 8: apply crossover operator 9: mutation operator 10: end while 11: find pareto optimal set. 3.1 chromosome representation as starting point for implementing the evolutionary algorithm it is necessary to define the chromosome, i.e. the data representation of the solutions in the model. in the proposed mathematical model, the solutions can be expressed in a matrix representation (see fig. 3). the rows represent mobile devices and the columns represent services; whilst the cell values represent the network in which the service will be connected, the value of zero is given when the service is not active and it is not connected to any network. node s node i1 node s node i1 node s node i1 node s node i1 ... ... node s node i1 node s node i1 node 1 ... node i2 node i3 node 2 ... node i2 node 3 ... node i2 ... node 4 node i2 node i3 node n-1 node i2 ... node n figure 3: chromosome representation of the solution the decision variables of the mathematical model can be directly obtained from the chromosome, for example if service 2 of the mobile device 3 is connected to network 4, it means that x 3,2 4 = 1 and for the remaining j networks the variable is 0. 3.2 genetic operators the crossover function takes two initial solutions (i.e. chromosomes), called parent solutions, and then created a new one from them. we define a crossover function based on the well-known a multi-objective approach for a multihoming load balancing scheme in whn 265 (a) crossover function (b) mutation function figure 4: genetic operators single-point strategy proposed in [19]. the new solutions are generated by combining the first p rows of the first parent solution with the last k −p rows of parent second parent solution, and vice-versa. the figure 4(a) shows an example of this operator. since the solution of each mobile device in the parent chromosomes meets the constraints of the model, the solutions of the mobile devices that are part of the child solutions also meet the constraints since each mobile device represented in a row conserves its signal strength and battery level conditions as it moves from one chromosome to another. also note that the inactive services are the same in the different solutions so they are conserved. the mutation strategy is based on a random function that takes a mobile device service from its current to a new one as long as the constrains are met, i.e. the network will be connected to a feasible network. the function takes into account only the services that are activated and have more than one feasible network. figure 4(b) shows an example of the mutation strategy. in this case, the service 3 of the mobile 2 is moved from network 3 to network 4 3.3 generation of scenario parameters once the genetic operators are defined, the algorithm starts to generate pseudo-randomly parameters for each mobile. this parameters are the maximum cost that the user can assume to connect to one network (costk), the percentage values and battery charge levels (batk and chargek), the signal strength values that each mobile perceives to each network j (rssk,j), with its corresponding power consumption level parameter (consk,j). when the scenario is created, we proceed to validate all constraints of model for each available network: demand, cost, reach and power consumption. after this validation, we store in a vector the networks that can be used to connect each service of each mobile device or the zero value ([0]) 266 c. lozano-garzon, m. molina, y. donoso if the service cannot be connected to any network. the result is a feasibility matrix (factk,s), this matrix is used to computed a matrix of active services yk,s, where it is defined the probability of use for a mobile service, for each service that can be connected to at least one network. once the active services are defined, a population of m initial solutions and an m′ elite (or external) population are randomly generated from both the feasibility and active services matrix. in our implementation, the value of m and m′ are set to 20 and 4, respectively. 4 experimental results in order to verify the correct operation of the proposed algorithm, we define four different experimental scenarios. for each one of them, we execute 500 iteration of the algorithm with different number of networks, mobile devices and services. 4.1 first experimental scenario the aim of this scenario is to compare the quality of the solution obtained by our algorithm with respect to the optimal solutions obtained by solving each function separately. the optimal solutions were computed using gams system [20]. for this purpose, the scenario is composed by five mobile devices that will connect three services in three different available access networks. tables 1 and 2 describe the parameters of bandwidth, cost, distribution of active services and network coverage for each mobile device. table 1: network parameters network technology theoretical bandwidth (mbps) costj (monetary units) lte 70 80 wifi g 54 0 hspa+ 15 40 table 2: bandwidth demand parameters service voice video web demand (mbps) 0.1 3 0.5 to solve a multi-objective mathematical model through a general optimization software such as gams [20], it is necessary to convert the optimization problem into a single objective one. in this case, each objective function was optimized separately obtaining the solutions shown in table 3. on the other hand the proposed algorithm converges rapidly to a set of 4 optimal solutions; three of them are unique. the results are presented in table 4. based on the feasible solutions obtained, we plot three different graphics in order to identify the pareto-optimal front. these graphics are: load (α) vs. cost (β), cost (β) vs. consumption (γ), and load (α) vs. consumption (γ), as you can see in figure 4:(a). in the load (α) vs. cost (β) graph, the optimal pareto front can be seen, because as the load decreases the cost grows. the cost (β) vs. consumption (γ) graph shows that solutions move in cost values of 80, 120 and 160, and zero-cost solution is quite atypical. finally, in the load (α) vs. consumption (γ) graph can be seen that some optimal solutions are on value 4 and others on power consumption value 7. making a comparison between the two sets of solutions obtained (table 3 and 4), the proposed algorithm found several intermediate solutions belonging to the pareto optimal front. these solutions cannot be found under mono-objective approaches because they are clearly not a linear a multi-objective approach for a multihoming load balancing scheme in whn 267 table 3: mathematical model solutions obtained using gams lte (load) wifi g (load) hspa+ (load) α β γ solution 1 0.086 0.080 0.080 0.086 160 4 solution 2 0 0.213 0 0.213 0 7 solution 3 0.103 0.080 0 0.103 160 4 table 4: solution results for the first study case lte (load) wifi g (load) hspa+ (load) α β γ solution 1 0.051 0.115 0.113 0.115 120 7 solution 2 0.051 0.124 0.080 0.124 80 4 solution 3 0.086 0.080 0.080 0.086 160 4 combination of the objective functions. by looking at the solutions obtained by gams, it is possible to see that the solution 3 is dominated by solution 1, so that the solution 3 is not part of the pareto optimal front. moreover, it is highly improbable that the zero solution cost (found with gams) can be found by our algorithm, because this solution must connect all the services to the same network that goes against the main objective of our proposal. finally, in order to evaluate the quality of the solutions found by the algorithm, a performance metric called spacing was calculated. this metric, as its name implies, analyze the distribution of the solutions in a pareto front. s = √√√√ 1 |q| · |q|∑ i=1 (di −d)2 where: di = mink∈q∧k∧≠i m∑ m=1 |fim −f k m| d = |q|∑ i=1 di |q| it is important to note that di are the distance measure, d is the mean value of the above distance measure, and fkm is the mth objective function value of the kth member of the population [7]. for our implementation the value of s obtained was 21.505. this value was influenced in a strongest way by the difference in the cost values. 4.2 second experimental scenario we proceed to perform of the algorithm with the same external and elite population size m = 20, m′ = 4; but changing to 20 mobile devices, 5 services for each device, and 7 access networks. this scenario was randomly generated according to the characteristics presented in table 5 and 6. the feasible solutions obtained are presented in table 7. the solutions obtained show that the services present a tendency to be allocated into the networks with the highest capacity restricted only by the cost that each mobile could pay. the 268 c. lozano-garzon, m. molina, y. donoso table 5: network parameters network technology lte wifi n wifi g wimax hspa+ hsdpa umts theoretical bandwidth (mbps) 70 300 54 15 15 2 0.3 costj (monetary units) 80 0 0 60 40 20 10 table 6: bandwidth demand of services for second study case service voice video web game chat demand (mbps) 0.1 3 0.5 2 0.2 table 7: network parameters lte wifi n wifi g wimax hspa+ hsdpa umts α β γ solution 1 0.057 0.205 0.652 0.527 0.447 0.4 0.667 0.667 160 29 solution 2 0.086 0.205 0.585 0.587 0.493 0.467 0.587 0.587 240 27 solutions obtained by our algorithm show that if the model constrains are met, then the algorithm trends to allocate the mobile services in those networks with the highest capacity and low cost in addition, we plot three same graphics as the previous scenario (see figure 5:(a)). in the first two graphs it is observed the optimal pareto fronts. these first two graphs shows that in order to achieve a better load distribution across networks and to reach a lower power consumption of mobile devices, the services should be grouped into the higher-cost networks. however, it can be said that the load variations are little in comparison with the cost difference that can be obtained; so that the operator may prefer the solution which means lower cost. the value of the spacing metric (s) was 35.542, despite having three repeated solutions. the difference in the values of cost and load shows that the solutions are not equally distant. 4.3 third experimental scenario for this scenario, we only change the number of mobile devices to 500 in comparison with the latest scenario. the solutions obtained are shown in table 8. based on the achieved solutions for this scenario we can assert that our algorithm present a good grade of sensitivity. we can observe that small changes in the main parameters are reflected in the services allocation, which mean changes in the set of optimal solutions. in figure 6:(a) you can see, well defined, the optimal pareto fronts in the first two graphs. this scenario also shows how small changes in load balancing produce appreciable changes in the cost function. the value of s for this scenario was 30.168, again due to the wide differences in cost values. 4.4 fourth experimental scenario in this last scenario we increased the number of mobile devices to 10000 in order to validate the scalability of the algorithm. the other scenario parameters are kept as the scenario 2. the feasible solutions are shown in table 9. figure 6:(b) shows the optimal solutions found by our algorithm. it is possible to see that solutions trend to maintain the the same power consumption in both graphs, cost (β) vs. consumption (γ) and load (α) vs. consumption (γ). the s metric value was 1.0, so that the solutions are fairly well spaced, being an unique solutions. the algorithm is fully adaptable to any situation within the proposed mathematical model designed. a multi-objective approach for a multihoming load balancing scheme in whn 269 (a) first study case (b) second study case figure 5: 2d perspectives of solution distribution table 8: solution results for the third study case solution 1 solution 2 solution 3 solution 4 lte 1.531 1.517 1.544 1.544 wifi n 3.161 3.163 3.153 3.165 wifi g 18.113 18.219 18.302 18.237 wimax 16.62 16.58 16.533 16.547 hspa+ 27.247 26.947 26.747 26.747 hsdpa 48.6 48.467 48.6 48.533 umts 64.667 64.667 65.333 65 α 64.667 64.667 65.333 65 β 7560 7500 7440 7440 γ 732 734 732 733 after running the algorithm on this scenario, we prove that the algorithm maintains its characteristics of high sensitivity in the searching for optimal solutions regardless of the number of mobile devices on the problem. that means our algorithm is scalable. 270 c. lozano-garzon, m. molina, y. donoso table 9: solution results for the fourth study case solution 1 solution 2 solution 3 solution 4 lte 30.46 30.26 30.263 30.463 wifi n 65.56 65.63 65.62 65.57 wifi g 358.61 358.64 358.61 358.55 wimax 329.43 328.86 328.69 329.6 hspa+ 537.59 537.41 537.79 537.34 hsdpa 934.93 936.67 937.13 934.53 umts 1730 1730.33 1729 1731.33 α 1730 1730.33 1729 1731.33 β 152760 152520 152600 152680 γ 15024 15027 15027 15022 (a) first study case (b) fourth study case figure 6: 2d perspectives of solution distribution a multi-objective approach for a multihoming load balancing scheme in whn 271 5 conclusions and future work we have presented a multihoming load balancing model in heterogeneous wireless networks based on a multi-objective approach. in this model we take as objective functions the network load, connection cost, and energy consumption; with the aim of performing an efficient use of the capacity resources in the available networks. based on this model it was designed a vertical handover algorithm (vho) using evolutionary algorithms, specifically the strength pareto evolutionary algorithm (spea). through the proposed environments we validate the correct operation of our algorithm; in the first scenario we validate the exactitude of the feasible solutions obtained by our algorithm in comparison to the solutions of the mathematical model using gams. in the other scenarios, we validate the sensibility and scalability of our evolutionary algorithm. the results obtained by our proposal were satisfactory and provided a starting point for the mobile network operator to run a vho processes in their networks. with this process they could get an efficient use of their network resources, reduce the connection costs, and extend the battery life of mobile devices. due to we proposes a multi-objective optimization algorithm, the model is opens up to incorporate additional parameters as objective functions; these parameters could be obtained from both the available access networks and the mobile devices. as future work we propose to continue this research, we want to introduce the concept of fairness in the load balance optimization and also include the concept of quality of experience (qoe) in the objective functions. 6 acknowledgment the authors would like to thank to administrative department of science, technology and innovation (colciencias) for the financial support to carlos lozano-garzon through the 528 2011 national call for doctoral studies in colombia bibliography [1] ieee computer society (2008); 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(2015); gams a user’s guide, gams development corporation., available at http://www.gams.com/dd/docs/bigdocs/gamsusersguide.pdf. ijcccv4n3draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 3, pp. 253-262 mutation based testing of p systems florentin ipate, marian gheorghe florentin ipate the university of pitesti department of computer science, faculty of mathematics and computer science str targu din vale 1, 110040 pitesti e-mail: florentin.ipate@ifsoft.ro marian gheorghe the university of sheffield department of computer science regent court, portobello street, sheffield s1 4dp, uk e-mail: m.gheorghe@dcs.shef.ac.uk received: april 5, 2009 accepted: may 30, 2009 abstract: although testing is an essential part of software development, until recently, p system testing has been completely neglected. mutation testing (mutation analysis) is a structural software testing method which involves modifying the program in small ways. in this paper, we provide a formal way of generating mutants for systems specified by context-free grammars. furthermore, the paper shows how the proposed method can be used to construct mutants for a p system specification. keywords: mutation testing, p systems, kripke structures, context-free grammars 1 introduction membrane computing, the research field initiated by gheorghe păun in 1998 [12], aims to define computational models, called p systems, which are inspired by the behaviour and structure of the living cell. since its introduction in 1998, the p system model has been intensively studied and developed: many variants of membrane systems have been proposed, a research monograph [13] has been published and regular collective volumes are annually edited. furthermore, a comprehensive bibliography of p systems can be found at [16]. of the many variants of p systems that have been defined, in this paper we consider cell-like p systems without priority and membrane dissolving rules [13]. testing is an essential part of software development and all software applications, irrespective of their use and purpose, are tested before being released. testing is not a replacement for a formal verification procedure, when the former is also present, but rather a complementary mechanism to increase the confidence in software correctness [5]. although formal verification has been applied to different models based on p systems [1], until recently testing has been completely neglected in this context. the main testing strategies involve either (1) knowing the specific function or behaviour a product is meant to deliver (functional or black-box testing) or (2) knowing the internal structure of the product (structural or white-box testing). in black-box testing, the test generation is based on a formal specification or model, in which case the process could be automated. a number of recent papers devise black-box testing strategies for p systems based on rule coverage [4], finite state machine [8] and stream x-machine [7] conformance techniques. in this paper, we propose an approach to p system testing based on mutation analysis. mutation testing (mutation analysis) is a structural software testing method which involves modifying the program in small ways [14], [9]. the modified versions of the program are called mutants. copyright c© 2006-2009 by ccc publications 254 florentin ipate, marian gheorghe consider, for example, the following fragment of a java program: if (x ≥ &&a) y = y + ; else y = y + ; then mutants for this code fragment can be obtained by substituting: (i) && with another logic operator, e.g., ||; (ii) ≥ with another comparison operator, e.g., >, =; (iii) + with another arithmetic operators, e.g., −; (iv) substituting one variable (e.g., x) with another one, e.g., y (we assume that the two variables have the same type). some (not all) mutants of the above code fragment are given below. if (x ≥ ||a) y = y + ; else y = y + ; if (x > &&a) y = y + ; else y = y + ; if (x ≥ &&a) y = y − ; else y = y + ; if (x ≥ &&a) y = y + ; else y = y − ; if (x ≥ &&a) x = y + ; else y = y + ; if (x ≥ &&a) y = y + ; else x = y + ; a variety of mutation operators (ways of introducing errors into the correct code) for imperative languages are defined in the literature [9], [10] (a few examples are given above). these are called traditional mutation operators. beside these, there are mutation operators for specialised programming environments, such as object-oriented languages [10]. a popular tool for generating mutants for java programs is mujava [15], [10]. the underlying idea behind mutation testing is that, in practice, an erroneous program either differs only in a small way from the correct program or, alternatively, a bigger fault can be expressed as the summation of smaller (basic) faults and so, in order to detect the fault, the appropriate mutants need to be generated. if the test suite is able to detect the fault (i.e., one of the tests fails), then the mutant is said to be killed. two kinds of mutation have been defined in the literature: weak mutation requires the test input to cause different program states for the mutant and the original program; strong mutation requires the same condition but also the erroneous state to be propagated at the end of the program. mutation analysis has been largely used in white-box testing, but only a few tentative attempts to use this idea in black-box testing have been reported in the literature [11]. offutt et al. propose a general strategy for developing mutation operators for a grammar based software artefact, but the ideas that outline the proposed strategy for mutation operator development are rather vague and general and no formalisation is provided. in this paper we provide a formal way of generating mutants for systems specified by context-free grammars. given such a specification, a derivation (or parse) tree can be associated with it. based on the tree, we formally describe the process of generating the mutants for the given specification. furthermore, the paper shows how the proposed method can be used to construct mutants for a p system specification. 2 preliminaries for an alphabet v = {a, ..., ap}, v ∗ denotes the set of all strings over v ; λ denotes the empty string. for a string u ∈ v ∗, |u|ai denotes the number of ai occurrences in u. each string u has an associated vector of non-negative integers (|u|a , ..., |u|ap ). this is denoted by ψv (u). the concept of context-free grammar is assumed to be known, for details we refer to a classical textbook [6]. only proper context-free grammar, i.e., with no useless symbols and no λ or renaming productions, will be used in this paper. for any derivation from the start symbol to a string of terminal symbols, w, a derivation (or parse) tree with the yield, the string of terminals obtained by concatening the leaves from left to right, w, is associated. the set of terminal strings derived from the start symbol mutation based testing of p systems 255 is called the language generated by the language. a grammar is said to be ambiguous if there exists a string and in any leftmost derivation (always the leftmost nonterminal is rewritten) this can be generated by more than one derivation (parse) tree. in the sequel possibly ambiguous grammars will be considered. 2.1 p systems a basic cell-like p system is defined as a hierarchical arrangement of membranes identifying corresponding regions of the system. with each region there are associated a finite multiset of objects and a finite set of rules; both may be empty. a multiset is either denoted by a string u ∈ v ∗, where the order is not considered, or by ψv (u). the following definition refers to one of the many variants of p systems, namely cell-like p systems, which uses non-cooperative transformation and communication rules [13]. we will call these processing rules. since now onwards we will refer to this model as simply p system. definition 1. a p system is a tuple π = (v, µ, w, ..., wn, r, ..., rn), where v is a finite set, called alphabet; µ defines the membrane structure, which is a hierarchical arrangement of n compartments called regions delimited by membranes these membranes and regions are identified by integers 1 to n; wi,  ≤ i ≤ n, represents the initial multiset occurring in region i; ri,  ≤ i ≤ n, denotes the set of processing rules applied in region i. the membrane structure, µ , is denoted by a string of left and right brackets ([, and ]), each with the label of the membrane it points to; µ also describes the position of each membrane in the hierarchy. the rules in each region have the form u → (a,t)...(am,tm), where u is a multiset of symbols from v , ai ∈ v , ti ∈ {in, out, here},  ≤ i ≤ m. when such a rule is applied to a multiset u in the current region, u is replaced by the symbols ai with ti = here; symbols ai with ti = out are sent to the outer region or outside the system when the current region is the external compartment and symbols ai with ti = in are sent into one of the regions contained in the current one, arbitrarily chosen. in the following definitions and examples all the symbols (ai, here) are used as ai. the rules are applied in maximally parallel mode which means that they are used in all the regions in the same time and in each region all the symbols that may be processed, must be. a configuration of the p system π , is a tuple c = (u, ..., un), where ui ∈ v ∗, is the multiset associated with region i,  ≤ i ≤ n. a derivation of a configuration c to c using the maximal parallelism mode is denoted by c =⇒ c. in the set of all configurations we will distinguish terminal configurations; c = (u, ..., un) is a terminal configuration if there is no region i such that ui can be further derived. for the type of p systems we investigate in this paper, multi-membranes can be equivalently collapsed into one membrane through properly renaming symbols in a membrane. thus, for the sake of convenience, subsequently we will only focus on p systems with only one membrane. 2.2 kripke structures definition 2. a kripke structure over a set of atomic propositions ap is a four tuple m = (s, h, i, l), where s is a finite set of states; i ⊆ s is a set of initial states; h ⊆ s × s is a transition relation that must be left-total, that is, for every state s ∈ s there is a state s′ ∈ s such that (s, s′) ∈ h; l : s −→ ap is an interpretation function, that labels each state with the set of atomic propositions true in that state. usually, the kripke structure representation of a system results by giving values to every variable in each configuration of the system. suppose var, . . . , varn are the system variables, vali denotes the set of values for vari and vali is a value from vali,  ≤ i ≤ n. then the states of the system are s = {(val, . . . , valn) | val ∈ val, . . . , valn ∈ valn}, and the set of atomic predicates are ap = {(vari = vali) |  ≤ i ≤ n, val ∈ vali}. naturally, l will map each state (given by the values of variables) onto the corresponding set of atomic propositions. additionally, a halt (sink) state is needed when h is not lefttotal and an extra atomic proposition, that indicates that the system has reached this state, is added to ap. for convenience, in the sequel ap and l will be omitted from the definition of a kripke structure. 256 florentin ipate, marian gheorghe 3 mutation testing from a context-free grammar in this section we provide a way of constructing mutants for systems specified by context-free grammars. given the system specification, in the form of a parse tree, we formally describe the generation of mutants for the given specification. consider a context-free grammar g = (v, t, p, s) and l(g) the language defined by g. we assume that, for every production rule p : a −→ x . . . xk, we have defined a set mut(p), called the set of mutants of p. a mutant p′ of p is a production rule of the form a −→ x ′ . . . x ′ n such that each symbol x ′ , . . . , x ′ n is either a terminal or is found among x, . . . , xk. furthermore, p ′ is either a production rule of g itself or has the form a −→ a, a ∈ v ; this condition ensures that the yield of the mutated tree is syntactically correct. among the mutants of p, the following types of mutants can be distinguished: • a terminal replacement mutant is a production rule of the form a −→ x ′ . . . x ′ k if there exists j,  ≤ j ≤ k, such that x j, x ′ j ∈ t , x j 6= x ′ j and x ′ i = xi,  ≤ i ≤ n, i 6= j. • a terminal insertion mutant is a production rule of the form a −→ w where w is obtained by inserting one terminal into the string x . . . xk (at any position). • a string deletion mutant is a production rule of the form a −→ w where w is obtained by removing one or more symbols from x . . . xk. • a string reordering mutant is a production rule of the form a −→ w where w is obtained by reordering the string x . . . xk. given any parse tree tr for g, the set of mutants of tr is defined as follows: • a one-node tree has no mutants. • let tr be the tree with root a and subtrees tr, . . . , trk having roots, nodes x, . . . , xk, respectively and p ∈ p the corresponding production rule of g, of the form a −→ x . . . xk. this is denoted by tr = maketree(a, tr, . . . , trk). let tr ′ denote a mutant of tr. then either – (subtree mutation) tr ′ = maketree(a, tr ′, . . . , tr ′ k ), where there exists j,  ≤ j ≤ k, such that tr ′j is mutant of tr j and tr ′ i = tri,  ≤ i ≤ k, i 6= j, or – (rule mutation) tr ′ = maketree(a, tr ′, . . . , tr ′ n), where there exists a mutant p ′ of p of the form a −→ x ′ . . . x ′ n such that for every i,  ≤ i ≤ n, there exists ji,  ≤ ji ≤ k, such that tr ′i = tr ji . according to [11] these operations can be made such as to keep the result produced by them in the same language or in a larger one. in the first case a much simpler approach can be considered whereby each rule having a certain nonterminal in the left hand side is replaced by another different rule having the same nonterminal as left hand side. however the above set of operations provide a two stage method which generates mutants by considering first the rule level and then the derivation (parse) tree. if these operations are restricted to produce strings in the same language then we have the following result. lemma 3. every mutant of a parse tree from g is also a parse tree from g. proof. follows by induction on the depth of the tree. thus, the yield of any mutant constructed as above belongs to the language described by g and so only syntactically correct mutants will be generated. syntactically incorrect mutants are useless (they do not produce test data) and so the complexity of the testing process is reduced by making sure that these are ruled out from the outset. mutation based testing of p systems 257 let us consider the grammar g = (v, t, p, s) where v = {s}; t = {, . . . , n} ∪ {+, −}, with n a fixed upper bound; p = {p, p}∪{p i  |  ≤ i ≤ n}, with p : s −→ s + s, p : s −→ s − s, p i  : s −→ i,  ≤ i ≤ n. suppose we have the following rule mutants: • for p : s −→ s − s (terminal replacement), s −→ s (string deletion) • for p : s −→ s + s (terminal replacement), s −→ s (string deletion) • for pi : s −→ i −  and s −→ i +  if  < i < n, s −→  if i =  and s −→ n −  if i = n. the mutants of pi are of terminal replacement type and are based on a technique widely used in software testing practice, called boundary value analysis. according to practical experience, many errors tend to lurk close to boundaries; thus, an efficient way to uncover faults is to look at the neighbouring values consider the string  +  −  and a parse tree for this string as represented in figure 1 (leaf nodes are in bold). the construction of mutants for the given parse tree is illustrated in figures 2, 3 and 4. thus, the mutated strings are  +  − ,  +  − ,  +  − ,  +  − ,  − ,  − ,  +  − ,  +  − ,  +  + ,  −  − ,  + , . some of these produce the same result as the original string; these are called equivalent mutants. since no input value can distinguish these mutants from the correct string, they will not affect the test suite when strong mutation is considered. s + s s 3 1 2 s s figure 1: example parse tree 4 p system mutation testing consider a 1-membrane p system π = (v, µ, w, r), where r = {r, . . . , rm}; each rule ri,  ≤ i ≤ m, is of the form ui −→ vi, where ui and vi are multisets over the alphabet v . in the sequel, we treat the multisets as vectors of non-negative integers, that is each multiset u is replaced by ψv (u) ∈ nnn k, where k denotes the number of symbols in v . in order to keep the number of configurations finite we will assume that each component of a configuration u cannot exceed an established upper bound denoted max. we denote u ≤ max if ui ≤ max for every  ≤ i ≤ k and n k max = {u ∈ nnn k | u ≤ max}. analogously to [3], the system is assumed to crash whenever u ≤ max does not hold (this is different from the normal termination, which occurs when u ≤ max and no rule can be applied). under these conditions, the 1-membrane p system π can be described by a kripke structure. in order to define the kripke structure equivalent of π we use two predicates, maxparal and apply, defined by: 258 florentin ipate, marian gheorghe s 1 s 2 s 3 tree 1 st level mutants s 0 s 1 s 2 s 2 s 3 s 4 figure 2: 1st level mutants maxparal(u, u, v, n, . . . , um, vm, nm), u ∈ n k max, n, . . . , nm ∈ nnn signifies that a derivation of the configuration u in maximally parallel mode is obtained by applying rules r : u −→ v, . . . , rm : um −→ vm, n, . . . , nm times, respectively; apply(u, v, u, v, n, . . . , um, vm, nm), u ∈ n k max, n, . . . , nm ∈ nnn, denotes that v is the result of applying rules r, . . . , rm, n, . . . , nm times, respectively. then the kripke structure equivalent m = (s, h, i, l) of π is defined as follows: s = nkmax ∪{halt,crash} with halt,crash /∈ nkmax, halt 6= crash; i = w; h is defined by: • (u, v) ∈ h, u, v ∈ nkmax, if ∃n, . . . , nm ∈ nnn · maxparal(u, u, v, n, . . . , um, vm, nm) ∧ apply(u, v, u, v, n, . . . , um, cm, nm); • (u, halt) ∈ h, u ∈ nkmax, if ¬∃v ∈ n k max, n, . . . , nm ∈ nnn· apply(u, v, u, v, n, . . . , um, vm, nm); • (u,crash) ∈ h if ¬∃v ∈ nkmax ∪ {halt} · (u, v) ∈ h; • (halt, halt) ∈ h, (crash,crash) ∈ h. it can be observed that the relation h is left-total. in order to use mutation analysis in p system testing we first have to describe an appropriate contextfree grammar, such that the p system specification can be written as a string accepted by this grammar. the parse tree for the string is then generated and the procedure presented in the previous section is used for mutant construction. the grammar definition will depend on the level at which testing is intended to be performed. at a high level (for instance in integration testing) the predicates maxparal and apply will normally be assumed to be correctly implemented and so they will be presented as terminals in the grammar; obviously, they can be themselves described by context-free grammars and appropriate mutants will be generated in a similar fashion. on the other hand, it is possible to incorporate the definitions of the two predicates mutation based testing of p systems 259 s s + s 1 2 tree 2 nd level mutants s s + s 0 2 s s + s 2 2 s s + s 0 1 s s + s 2 3 s s + s 1 2 s s 1 s s 2 figure 3: 2nd level mutants into the definition of the transition relation h; in this case the corresponding grammar will be much more complex and system testing will be performed in one single step. the following (simplified) example illustrates the above strategy for high-level testing of p systems. example 4. consider a 1-membrane p systems with 2 rules r : u −→ v, r : u −→ v. then the transition of the kripke structure representation of π is given by the formulae: • (u, v) ∈ h, u, v ∈ nmax, if ∃n, n ∈ n · maxparal(u, u, v, n, u, v, n) ∧ apply(u, v, u, v, n, u, c, n); • (u, halt) ∈ h, u ∈ nmax, if ¬∃v ∈ n  max, n, n ∈ nnn· apply(u, v, u, v, n, u, v, n); • (u,crash) ∈ h if ¬∃v ∈ nmax ∪ {halt} · (u, v) ∈ h; • (halt, halt) ∈ h, (crash,crash) ∈ h; then such a system can be described by a context-free grammar g = (v, t, p, s) where v = {s, s, s,u,v,u,v,u,v}; t contains (bounded) vectors from nnn , the additional states halt and 260 florentin ipate, marian gheorghe s s 3 left tree mutant s + s 1 2 s s right tree mutant s + s s 3 1 2 + s s s s 3s + s 1 2 s s 3 rd level mutants of the original tree figure 4: 3rd level mutants crash, predicates maxparal and apply, the "true" logical value, logical operators, quantifiers and other symbols, i.e., t = nmax ∪ {halt,crash, maxparal, apply,true, ∧, , ∨, ¬,∃,∀, n, n,·, (, )}. the set of production rules consists of: p : s −→ ¬s; p : s −→ s ∧ s; p : s −→ s ∨ s; p : s −→ true; p : s −→ ∃n · s; p : s −→ ∃n · s; p : s −→ s ∧ s; p : s −→ apply(u,v,u,v, n,u,v, n); p : s −→ maxparal(u,u,v, n,u,v, n); rules that transform nonterminals u,u,v,u,v into vectors from nnn. the following mutants can be defined for the rules p to p: p ′  : s −→ s; p ′  : s −→ s ∨s, p ′′  : s −→ s; p′ : s −→ s ∧ s, p ′′  : s −→ s; p ′  : s −→ ¬true; p ′  : s −→ ∀n ·s; p ′  : s −→ ∀n ·s. p ′  : s −→ s ∨ s, p′′ : s −→ s. for p mutants can be defined by negating de predicate, changing parameters such that the obtained formula is syntactically correct, e.g., switch u and u. similarly, mutants for p are obtained by negating de predicate, changing parameters such that the obtained formula is syntactically correct. for the remaining rules mutants are generated by adding  to or subtracting  from each integer value. mutation based testing of p systems 261 5 conclusions in many applications based on formal specification methods the test sets are generated directly from the formal models. the same applies to formal models based on grammars. however the approach presented in [11], although novel and with many practical consequences, lacks a rigorous method of defining the process of generating the mutants. in this paper a formal method is introduced to rigorously define operations with rules and subtrees of derivation trees for context-free grammar formalisms. this is then extended to p systems and some examples are provided to illustrate the approach. in this paper, the mutation operators are applied to the kripke structure equivalent of the p system rather than to the p system itself. the advantage of this approach is that test values can be simply generated using a model checking tool (these are the counterexamples returned by the tool). future work may investigate the application of the mutation operators directly to the p system and the associated test generation process. acknowledgment. this work is supported by the cncsis grant idei 643/2009 (evomt). the authors are grateful to reviewers for their comments. bibliography [1] f. bernardini, m. gheorghe, j. j. romero-campero, n. walkinshaw, a hybrid approach to modelling biological systems, workshop on membrane computing 2007, lecture notes in computer science, vol. 4860, pp. 138–159, 2007. [2] g. ciobanu, gh. păun, m. j. pérez-jiménez (eds.), applications od membrane computing, springer, 2006. [3] z. dang, o. h. ibarra, c. li, g. xie, on the decidability of model-checking for p systems, journal of automata, languages and combinatorics, vol. 11, pp. 279–298, 2006. [4] m. gheorghe, f. ipate, on testing p systems, workshop on membrane computing, lecture notes in computer science, vol. 5391, pp. 204–216, 2008. [5] m. holcombe, f. ipate, correct systems: building a business process solution, springer, 1998. [6] j. e. hopcroft, r. motwani, j. d. ullman, introduction to automata theory, languages, and computation (2nd edition), addison-wesley, 2001. [7] f. ipate, m. gheorghe, testing non-determinstic stream x-machine model and p systems, electronic notes in theoretical computer science, vol. 227, pp. 113–126, 2009. [8] f. ipate, m. gheorghe, finite state based testing of p systems, natural computing, to appear, 2009. [9] j. offutt, a practical system for mutation testing: help for the common programmer, international test conference, pp. 824–830, 1994. [10] y.-s. ma, j. offutt, y. r. kwon, mujava: an automated class mutation system, software testing, verification and reliability, vol. 15, pp. 97–133, 2005. [11] j. offutt, p. ammann, g. mason, l. (ling) liu, mutation testing implements grammar-based testing, proceedings of the second workshop on mutation analysis, 2006. [12] gh. păun, computing with membranes, journal of computer and system sciences, vol. 61, pp. 108–143, 2000. [13] gh. păun, membrane computing: an introduction, springer-verlag, berlin, 2002. 262 florentin ipate, marian gheorghe [14] http://en.wikipedia.org/wiki/mutation_testing [15] http://cs.gmu.edu/ offutt/mujava/ [16] http://ppage.psystems.eu florentin ipate was born on 4th december 1967 in constanta. fi holds a phd and msc degrees with the university of sheffield and a bsc with politehnica university of bucharest, all in computer science. he is now a professor of computer science and phd supervisor with the university of pitesti. he has been awarded in hoc signo vinces prize for research and publications, by the national research council for higher education, romania, 2002 and copyro publishing prize for computer science, romania, 2000. fi’s research interests are in specification and model based testing, formal specification languages for software systems, agile modelling and testing, modelling and testing biology-inspired computing systems. his main research results have been published in a research monograph with springer and in high profile journals. marian gheorghe was born on 2nd february 1953 in bucharest. mg holds a phd and a bsc with the university of bucharest. he is now senior lecturer with the university of sheffield and head of the verification and testing group. mg’s research interests are in formal computational models, verification and testing, modelling biological systems, agent technologies, artificial life, empirical software engineering. he has published in important international journals and is featured in the main computer science publications database, dblp, with around 60 items. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 4, pp. 374-385 an immuno-genetic hybrid algorithm e. nabil, a. badr, i. farag emad nabil misr university for science and technology,information technology faculty, computer science department. e-mail: emadnabilcs@gmail.com amr badr, ibrahim farag cairo university,computers and information faculty, computer science department. 5 dr. ahmed zewail street, postal code: 12613, orman, giza, egypt e-mail: ruaab@rusys.eg.net, i.farag@fci-cu.edu.eg abstract: the construction of artificial systems by drawing inspiration from natural systems is not a new idea. the artificial neural network (ann) and genetic algorithms (gas) are good examples of successful applications of the biological metaphor to the solution of computational problems. the study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (nis) in order to develop problemsolving techniques. in this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. we proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from gas to increase the exploration of the search space. we also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the wellknown wisconsin breast cancer diagnosis problem (wbcd). our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable. the hybrid algorithm overcomes both the gas and the ais, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. the learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. the hybrid algorithm overcomes also gas and ais and reached the convergence point before them. keywords: genetic algorithms, artificial immune system, fuzzy logic, breast cancer diagnosis, memory acquisition. 1 introduction computing and engineering have been enriched by the introduction of the biological ideas to help developing solutions to various problems. this can be exemplified by the artificial neural networks (ann), evolutionary algorithms (ea) [11], artificial life (alife), and cellular automata (ca) [13]. there exist three different approaches; the first is: biologically motivated computing, under this umbrella the ea, ann and artificial immune system (ais) [21]; the second is computationally motivated biology, where computing provides models and inspiration for biology (i.e. alife and ca). the third approach is computing with biological mechanisms, which involves the use of information processing capabilities of biological systems to replace or supplement the current silicon-based computers (e.g. membrane copyright c© 2006-2009 by ccc publications an immuno-genetic hybrid algorithm 375 computing, quantum computing and dna computing) [8], [9] [14], [18]. our research point will be under the umbrella of the first approach. in this paper, we combine two methodologies which are genetic algorithms and artificial immune system (ais), so as to automatically produce a fuzzy system for breast cancer diagnosis. the major advantage of fuzzy systems is that they favor interpretability [3], [4] and provide what is called confidence measure which means, in our case, the degree of benignity or malignancy. finding good fuzzy systems is quite a hard task. so, this is where ga and ais algorithms work, enabling the automatic production of fuzzy systems, based on a database of training cases. in this paper we also ensure the ability of memory acquisition and learning of the algorithm by applying it to a binary pattern recognition problem. the paper is organized as follows: in the next two sections we provide an overview of the clonal selection algorithm and the genetic algorithm. in section [4] we present our proposed hybrid algorithm between ga and ais that will be tested on the wisconsin breast cancer diagnosis (wbcd) problem described in section [5]. evolving fuzzy system of the wbcd, parameters setup and testing also included in section [5]. section [6] speaks about the learning and memory acquisition of the hybrid algorithm. the algorithm testing is delineated also in section [6], followed by concluding remarks in section [7]. 2 the clonal selection algorithm the standard clonal selection algorithm clonalg [5], [6], [15], [16], [17] can be summarized as follows. begin t=0; initialize the initial population p(t) randomly; identify antigen s; evaluate affinity p(t)versus s; while (not finished) do begin t= t+1; select c(t) from p(t-1); proportional cloning of c(t) forming c’(t); mutation c’(t) forming c"(t); select p(t) from c"(t) and p(t-1); select memory cell from p(t); metadymanics; end. end. 3 the genetic algorithm (ga) the standard genetic algorithm [7], [23] can be summarized as follows. begin t=0; initialize the initial population p(t) randomly; evaluate structures in p (t); while (not finished) do begin t= t+1; select parents c(t) from p(t-1); crossover and mutate structures in c (t) forming c’ (t); 376 e. nabil, a. badr, i. farag replace c’ (t) by p (t-1); end. end 4 the proposed hybrid algorithm d = l∑ i= δ where { , abi 6=agi , otherwise } (1) the affinities of individuals are measured using the hamming distance depicted in equation 1. the proposed algorithm modifies clonal selection algorithm mutation method. the mutation in nature occurs at small percentage value = 0.002 and this is rational from the computational point of view to ensure that the good solutions are not distorted too much. however, researches have shown that an initial large mutation rate that decreases exponentially as a function of the generation number improves the convergence speed and accuracy [1]. the initial large mutation rate ensures that a large space is covered, while the mutation rate becomes smaller when the individuals start to converge to the optimum. this is accepted solution for the trade off between the exploration and exploitation we used the time-decaying formula in equation (2) [18], [22], [24] where τ is a positive constant, m() is the initial large mutation rate and t is the generation number. the equation is depicted in figure 1. we have imported the crossover operator from the genetic algorithms in order to increase the exploration of the landscape and to add a recombination operator in the clonal selection algorithm. m(t) = m()e−t/τ  (2) figure 1: the effect of the degraded function on mutation value the proposed algorithm can be summarized as follows. begin t=0; initialize the initial population p(t) randomly; identify antigen s; evaluate fitness p (t) versus s; while (not finished) do begin 1. t= t+1; 2. select c(t) from p(t-1); 3. proportional cloning of c(t) forming c’(t); an immuno-genetic hybrid algorithm 377 table 1: the wcbd data representation case v v ... v diagnosis 1 1 2 ... 8 benign 2 2 4 ... 3 benign ... ... ... ... ... ... 683 4 8 ... 1 malignant 4. degraded proportional mutation c’(t) forming c"(t); 5. crossover c"(t) forming c*(t); 6. select p(t) from c*(t) and p(t-1); 7. select memory cell from p(t); 8. metadymanics; end. end. the proposed algorithm will be tested on the famous wisconsin breast cancer diagnosis problem (section 5) and simple binary pattern recognition problem (section 6) to ensure the memory acquisition ability of the algorithm. 5 the wisconsin breast cancer diagnosis problem in this section, we present the wisconsin breast cancer diagnosis problem [3] which is the test case of our proposed algorithm. breast cancer is the most common cancer among women, excluding skin cancer. the presence of a breast mass is an alert sign of a cancer, but it does not always indicate a malignant one. fine needle aspiration (fna) is an outpatient procedure that involves using a small-gauge needle to extract fluid directly from a breast mass. fna procedure over breast masses is a cost-effective, non-traumatic, and mostly non-invasive diagnostic test that obtains information needed to evaluate malignancy. the wisconsin breast cancer diagnosis (wbcd) database [4] is the result of the efforts made at the university of wisconsin hospital for accurately diagnosing breast masses based solely on an fna test. nine visually assessed characteristics of an fna sample considered relevant for diagnosis were identified, and assigned an integer value between 1 and 10. the measured variables are as follows: 1. clump thickness (v); 2. uniformity of cell size (v); 3. uniformity of cell shape (v); 4. marginal adhesion (v); 5. single epithelial cell size (v); 6. bare nuclei (v); 7. bland chromatin (v); 8. normal nucleoli (v); 9. mitosis (v). 378 e. nabil, a. badr, i. farag the database itself consists of 683 cases. the general form of the database is described in table 1. there exist some previous systems that achieved high classification ration, but these systems look like black boxes and with no explanation or interpretation about how the decision was taken. further, the degree of benignity or malignancy is not provided. these two points are covered in this study besides high performance classification ratio. 5.1 evolutionary fuzzy modeling evolutionary algorithms are used to search large, and often complex, search spaces. they have proven worthwhile on numerous diverse problems and are able to find near-optimal solutions with an adequate performance measure. fuzzy modeling can be seen as an optimization problem where part or all of the parameters of a fuzzy system constitute the search space. 5.2 applying evolution to fuzzy modeling three of the four types of fuzzy parameters can be used to define targets for evolutionary fuzzy modelling: structural parameters, connective parameters, and operational parameters. logical parameters are usually predefined by the designer based on experience. the evolutionary algorithm is used to tune the knowledge contained in the fuzzy system by finding membership function values (p, d values) and the relevant variables. evolutionary structure learning is carried out by encoding within the genome an entire fuzzy system. this is known as the pittsburgh approach. figure 2: the proposed diagnosis system. note that the fuzzy subsystem displayed to the left is the fuzzy inference system in figure 3. figure 3: basic structure of a fuzzy inference system 5.3 evolving fuzzy systems for the wbcd problem the solution scheme we propose for the wbcd problem is depicted in figure 2, note that the fuzzy subsystem displayed to the left of figure 2 is the fuzzy inference system of figure 3 [10], [20]. figure 2 consists of a fuzzy system and a threshold unit. the fuzzy system computes a malignancy value of the an immuno-genetic hybrid algorithm 379 malignancy of a case, based on the input values, the threshold unit then outputs a benign or malignant diagnostic according to the fuzzy system’s output. if the malignancy value is less than or equals 3, it is considered a benign case. other than that, it is diagnosed as a malignant one. 5.4 fuzzy system parameters according to information obtained from previous work [3], we have deduced the following points. • small number of rules: systems with no more than four rules have been shown to obtain high performance [2], [19]. • small number of variables: rules with no more than 4 antecedents have proven to be adequate[2]. • nature of the input variables: higher-valued variables are associated with malignancy. some fuzzy models forgo interpretability in the interest of improved performance. where medical diagnosis is concerned, interpretability, also called linguistic integrity, is the major advantage of fuzzy systems. this motivated us to take into account the following semantic criteria, defining constraints on the fuzzy parameters [12]: • distinguishability: to what extent the system is understood and has interpretability. • justifiable number of elements: the number of membership functions of a variable. this number should not exceed the limit of ± distinct terms. the same criterion is applied to the number of variables in the rule antecedent; this is to be familiar for humans. • orthogonality. for each element of the universe of discourse, the sum of all its membership values should be equal to one. 5.5 the fuzzy system setup logical parameters • reasoning mechanism: singleton-type fuzzy system, i.e. output membership functions are real values, rather than fuzzy ones. • fuzzy operators: min. • input membership function type: orthogonal, trapezoidal. • defuzzification method: weighted average. structural parameters • relevant variables: there is insufficient a priori knowledge to define them; therefore, this will be one of the algorithm’s objectives. • number of input membership functions: two membership functions denoted low and high. • number of output membership functions: two singletons are used, corresponding to the benign and malignant diagnostics. • number of rules: in our approach, this is a user-configurable parameter. will there be only one rule? the rule itself is to be found by the genetic algorithm. connective parameters 380 e. nabil, a. badr, i. farag • antecedents of rules: to be found by the algorithm. • consequent of rules: the algorithm finds rules for the benign diagnostic; the malignant diagnostic is an else condition. • rule weights: active rules have a weight of value 1 and the else condition has a weight of 0.25. operational parameters • input membership function values: to be found by the evolutionary algorithm. • output membership function values: following the wbcd database, we used a value of 2 for benign and 4 for malignant. 5.6 the evolutionary algorithm setup we apply pittsburgh-style structure learning, using our algorithm to search for three parameters. the relevant variables, the input membership function values, and the antecedents of rules. they are constructed as follows: • membership function parameters. there are nine variables (v-v), each with two parameters p and d, defining the start point and the length of the membership function edges, respectively. • antecedents. the ith rule has the form: if (v is ai )and...and (v is ai ) then (output is benign) where aij represents the membership function applicable to variable vj. a i j can take the values: 1 (low), 2 (high), or 0 or 3 (other). • relevant variables are searched for implicitly by letting the algorithm choose non-existent membership functions as valid antecedents; in such a case, the respective variable is considered irrelevant. table 2: parameters encoding of a genome, total genome length is 54+18= 72 parameter values bits quantity total bits p 1-8 3 97 27 d 1-8 37 9 27 a 0-3 2 9 18 the parameters encoding are described in table 2, which form a single individual’s genome. table 3 shows a sample genome. we used a genetic algorithm with a fixed population size of 200 individuals to evolve the fuzzy inference system, and fitness-proportionate selection. the algorithm terminates when the maximum number of generations is reached. an example of a genome for a rule system depicted in table 3, the first 18 positions encode the parameters p and d for the nine variables v -v. the rest encode the membership function applicable for the nine antecedents of the rule; table 4 is an interpretation of the database and the rule base of the rule system encoded in table 3. 5.7 testing the proposed algorithm has been tested on the wsbc problem. the three algorithms have been implemented and have been tested in wisconsin database. the three algorithms have reached a valid classification ratio equal to 97.36% i.e. 665 valid diagnosis cases from 683 cases. and the results of the an immuno-genetic hybrid algorithm 381 three algorithms were depicted in figure 4. it is clear that the hybrid algorithms reached the maximum classification ratio in the earlier generations before the ga and the ais. also the ais reached before the ga. table 3: database p d p d p d p d p d p d p d 3 5 4 1 2 8 5 1 7 7 2 5 5 5 p d p d a a a a a a a a a 7 2 4 7 1 1 3 3 3 1 3 1 1 table 4: rule base rule if ((v is low) and (v is low) and (v is low) and (v is low) and (v is low)) then( output is benign ) default else(output is malign) figure 4: the execution of the three algorithms 6 the pattern recognition problem the learning and memory acquisition was verified through its application to a binary character recognition problem. in this case, we assumed that the antigen population was represented by a set of ten binary characters (n = 10) to be learned. each character is represented by a bit string of length l = 121. the population size =200. the original characters are depicted in figure 5. figure 6 illustrates the initial memory set. figure 7 illustrates the input patterns. (i.e. the antigens) for which the learning will takes place. figures 8,9 and 10 represent the maturation of the memory set through 200 generations. the affinity here refers the degree of matching of the antigens, i.e. the affinity measure is the hamming distance (discussed in section 4) between the antigens and antibodies, the. note that the exact matching is not important for recognition; partial matching is enough. the hybrid algorithm converged at generation 200. figure 8, 9 and 10 presents the application of the ga, ais and the hybrid algorithm to the binary character recognition problem respectivily where (a) presents memory set after 50 cell generations; (b) 382 e. nabil, a. badr, i. farag figure 5: the original digits figure 6: the initial input patterns figure 7: the input patterns (i.e. the antigens) for which the learning will take place figure 8: application of the ga to the binary character recognition problem figure 9: application of the ais to the binary character recognition problem an immuno-genetic hybrid algorithm 383 figure 10: application of the hybrid algorithm to the binary character recognition problem. presents memory set after 100 cell generations; (c) presents memory set after 150 cell generations and (d) presents memory set after 200 cell generations.it is clear that the hybrid algorithm overcomes the other two algorithms. we have used static mutation values, also proportional to affinity, in the binary recognition problem because results showed that static mutation is better than dynamic one. figure 11 represents the affinity of ga, ais and the hybrid algorithms in recognition of the pattern zero, note that the affinity refers the degree of matching with antigens. i.e. the hamming distance with antigens. figure 11: the affinity of ga, ais and the hybrid algorithms in recognition of the pattern zero 7 conclusions in this research, artificial immune system is combined with genetic algorithms in one hybrid algorithm. a modification is proposed to the clonal selection algorithm which is inspired from the clonal selection principle and affinity maturation of the human immune responses. the adaptability of the mutation rate is introduced by simple degrading function. also, the crossover is merged into the clonal selection algorithm, two-point crossover applied after the mutation process, to increase the exploration of the landscape. the hybrid algorithm is combined with fuzzy logic and applied to the well-known wisconsin breast cancer diagnosis problem. we claim that our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is interpretable. the hybrid algorithm overcomes both the genetic algorithm and the artificial immune system and reached the highest classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. the proposed system also applied to a binary character recognition problem. the mutation in the hybrid algorithm was adapted using a degraded function so that the mutation decreases with time, but in 384 e. nabil, a. badr, i. farag the binary character recognition problem, the results showed that it is better to keep the mutation value small and static through all generations. the hybrid algorithm overcomes the other two algorithms. the hybrid system can solve the wbcd problem with more than one fuzzy rule. this is to increase the classification accuracy. also, there are many gene representation techniques that can be used instead of the pittsburgh approach like the michigan approach, the iterative rule learning approach and hybridization between them. this can be considered as a future work. we claim that our hybrid algorithm is highly effective and better than gas and ais, for sure not in all cases, future experiments may prove that gas or ais separately is better, but at least in memory acquisition, the wcdb and similar problems we claim that our algorithm is better. bibliography [1] a.p. engelbrecht, computational intelligence: an introduction, england, john wiley & sons; 2003. [2] c.a. pena reyes, m.a. sipper, evolving fuzzy rules for breast cancer diagnosis, proc nonlinear theory and applications, 2, pp 369-372, 1998. [3] c.a. pena reyes, m.a. sipper, fuzzy-genetic approach to breast cancer diagnosis,artificial intelligence in medicine; vol: 17, num:2, 131-155, 1999. [4] c.j. merz, p.m. murphy, uci repository of machine learning database, http:/www.ics.uci.edu/m̃learn/mlrepository.html, 1996. [5] d. dasgupta , artificial immune systems and their applications, springer-verlag, inc., 1999. [6] d. dasgupta, n. attoh-okine, immunity-based systems, ieee international conference on systems, man, and cybernetics, orlando, florida, pp 363-374, october 12-15,1997. [7] d.a. coley, an introduction to genetic algorithms for scientists and engineers, world scientific publishing co.,inc., 2001. [8] e. gutuleac, descriptive timed membrane petri nets for modelling of parallel computing, international journal of computers, communications & control, vol. i, no. s: suppl. issue, pp. 256-261, 2006. [9] g. ciobanu, a programming perspective of the membrane systems,international journal of computers, communications & control, vol. i, no. s: suppl. issue, pp.13-22, 2006. [10] h. zhang, d. liu, fuzzy modeling and fuzzy control, birkhauser, 2006. [11] j. rennard, genetic algorithm viewer: demonstration of a genetic algorithm, http://www.rennard.org/alife/english/gavgb.pdf, 2000. [12] j.j. espinosa, j. vandewalle, constructing fuzzy models with linguistic integrity, ieee transactions on fuzzy systems; vol. 7, no. 4, pp. 377-393, 1999. [13] l.n. de castro, fundamentals of natural computing: basic concepts, algorithms, and applications, crc press llc; 2007. [14] l.n. de castro, f.j. zuben, artificial immune systems: part i – basic theory and applications, eec/unicamp, campinas, sp, tech. rep. – rt dca 01/99, p. 95. 1999. [15] l.n. de castro, f.j. zuben, learning and optimization using the clonal selection principle ,ieee transactions on evolutionary computation , vol.:6, num.:3, pp 239-251, jun, 2002. an immuno-genetic hybrid algorithm 385 [16] l.n. de castro, f.j. zuben, the clonal selection algorithm with engineering applications, artificial immune system workshop, genetic and evolutionary computation conference , a. s. wu (ed.), pp. 36-37, 2000. [17] l.n. de castro, j. timmis, artificial immune systems (a new computational approach) , springer verlag, 2002. [18] l.n. de castro, natural computing,information science and technology, idea group, inc., 2005. [19] r. setiono, extracting rules from pruned neural networks for breast cancer diagnosis,artificial intelligence in medicine, vol. 8, no. 1, pp. 37-51, feb. 1996. [20] r.r. yager, l.a. zadeh, fuzzy sets, neural networks, and soft computing, new york, van nostrand reinhold, 1994. [21] s. forrest, s.a. hofmeyrt, a. somayajit, architecture for an artificial immune system,evolutionary computing, vol. 8, no. 4, pp 443-473, 2000. [22] t. back, d. fogel, z. mechalewicz, glossary, evolutionary computation 1: basic algorithms and operators, institute of physics publishing, bristol and philadelphia, 2000. [23] t. back, the interaction of mutation rate, selection & self-adaptation within a genetic algorithm, in proc. 2nd int. conf. on parallel problem solving from nature, north-holland, amsterdam, pp. 85-94, 1992. [24] w. m. spears, adapting crossover in genetic algorithms, artificial intelligence center internal report aic-94-019, naval research laboratory, washington, dc 20375, 1994. emad nabil received his bsc and msc degrees in computer science from computers and information faculty, cairo university in 2004 and 2008 respectively; his main research interests are in the natural computing area including p systems, ga, ais and ann.now i am working in my ph.d. in p systems and their applications in optimization. amr badr currently he is an associate professor of computer science, computers and information faculty,cairo university. his research interests include computational intelligence, petri nets, bioinformatics and medical imaging. he has published about 50 journal research papers in these areas and supervised nearly 50 msc and phd students. he is currently on the editorial boards and a reviewer at several journals. ibrahim farag professor of computer science, computers and information faculty,cairo university. he is the founder of the faculty of computers and information at cairo university and one of the pioneers of computer science in egypt. he has supervised more than 200 msc and phd students at cairo university. international journal of computers, communications & control vol. iii (2008), no. 1, pp. 90-102 a formal description of the systemic theory based e-learning gabriela moise abstract: this paper aims at presenting the systems theory-based approach to the learning (instructional) process. such as approach is necessary, if one takes into consideration the the complex and holistic nature of the learning process. the presented modeling is based on the definition of an automatic regulation system and artificial intelligence techniques. for this purpose, an intelligent instructional system (iis) is defined using the concepts: inputs, outputs, perturbations factors, regulation, feedback and ai learning techniques. the contextual environment of learning plays the major role in this system and it establishes the perturbation factors. the regulation consists in ai learning techniques, so the iis is a flexible and adaptive system. the process’ behaviour is described using pseudo-boolean and linear equations. this kind of approach enables solving the problems of the complexity and uncertainty of the learning (instructional) systems and, particularly, those of the e-learning (online and offline). applications of the proposed approach may be found in e-learning courses for mathematics, computing, architecture, archaeology, cultural heritage, etc. keywords: online learning, system theory, feedback, intelligent instructional system. 1 introduction the theoretical bases of the instructional process design are formed of a set of theories such as the instruction and learning theory, the communication theory, the systems’ theory. the system theory and the systemic thinking have a major influence on the field of the instructional design. the systemic approach of the instruction process design was issued by james finn [7], who applied the systems theory in the educational technology. in 1978, walter dick and lou carey published the book entitled "the systemic design of instruction". the model built by dick and carey [5] doesn’t follow a linear approach, each level of the instructional process being revised for certainty of achieving the objectives of instructional process. the model is presented in figure no. 1. figure 1: dick and carey’s model any learning (instructional) form may be tackled from the point of view of the general systems theory, distance learning forms included. a system is defined by a set of elements that interact and work together in order to achieve an objective. moore and kearsley [15] present the importance of the copyright © 2006-2008 by ccc publications a formal description of the systemic theory based e-learning 91 systemic approach of the distance educational process. the distance educational system has to be viewed as a system within which all the elements are interdependent and interconnected. the consequence of this fact is that any change of an element of the system will affect the whole instructional process. moore (1983) defined the concept of transactional distance, in that he rigorously presented the relation between the teacher and the student: the distance between teacher and student is a geographical, an educational and a psychological one.[16] saba [17] refined the theory of moore and proved trough experiments, that the transactional distance is a measure of the relation existing between the teacher and the student. saba asserts: "transactional distance varies by rate of dialogue and structure", so that when dialogue increases, the structure and transactional distance decrease; when the structure increases, the transactional distance increases and the dialogue decreases (see figure 2). figure 2: stella modeling of dynamic of saba’s system transactional distance(t) = transactional distance(t −dt) + (dialogue−structure)∗dt norbert wiener [20] has introduced the cybernetic concept in the paper "the human use of human beings": "when we desire a motion to follow a given pattern the difference between this pattern and the actually performed motion is used as a new input to cause the part regulated to move in such a way as to bring its motion closer to that given by the pattern." the notion of instructional system was introduced by robert glaser in 1962. the levels of the system defined by glaser are represented in the figure no. 3. the individualized instruction is viewed as a continuous cycle of diagnose, specification and evaluation. the output condition is defined as follows: the student has learned all the proposed objectives. figure 3: glaser’s instructional model with feedback banathy (1996) [2] comprised the educational system in the human activity systems (has). he defined the term of "system view of education": "we learn to think about education as a system, we can understand and describe it as a system, we can put the systems view into practice and apply it in educational inquiry, and we can design education so that it will manifest systemic behavior". the 92 gabriela moise systemic approach of the educational system facilitates the exploration of the process according to the subsystems constituents: teacher, student, institution, administration etc. banathy (1992) [1] proposed two phases of the systemic development of education, which are considered the principles of systems’ design. (see figure 4) figure 4: the phases of the development of a systemic approach of education (banathy) in 1992, banathy [1] proposed three models of social systems namely the systems-environmental model, the functions-structure model and the process-behaviour model. these models were be used to describe the educational systems "open, dynamic and complex social systems". the systems-environmental model enables one to see the learning (instructional) systems into the context of the relationships, interactions and interdependencies between the system and the environment. the functions-structure model enables one to describe the goals of the systems and the functions that have to be implemented in order to achieve these goals. also it defines the components of the system and the relations between them, thus defining the very structure of the system. the process-behavior model enables one to understand the behaviour of the system: which are the inputs of the system, how these inputs have transformed to produce the expected output. also, banathy identified four domains: systems’ philosophy, theory, methodology, and application, constituting the conceptual system of systems inquiry in educational systems. a detailed report of the systems view of the distance learning may be found in [6]. the e-courses have to be design to incorporate active and cooperative learning techniques in a systematic way that addresses all aspects of the course: delivery, management, and assessment.[9] in this paper, the author proposes a new model of the learning (instructional) process based on the systems theory. the model focuses on the regulation, so that the system will produce the expected results. the behaviour of the system is described using a mathematical model, based on the pseudoboolean equations and linear equations, which was called "quasi-boolean system". the concept of the "quasi-boolean system" is introduced to portray the behaviour of the e-learning system. the genesis of the "quasi-boolean system" is the idea that the students are evaluated at the end of a training session and they get a grade: a number or a truth value (true or false). in the traditional learning (instructional) process, the teacher takes care of achieving the objectives of the learning (instructional) process. in the e-learning system, one uses a combined regulation: a feedback regulator and a predictive regulator. the predictive regulator uses ai learning techniques (q-learning [13]). the remaining part of the paper is organized as follows: in section 2, a systematic model of the learning (instructional) process is presented; in section 3, a mathematical model is given; in section 4, an application is developed; in section 5 the complexity of the instructional (learning) process is identified and finally, in section 6 summary and conclusions, the advantages using the proposed model are presented. a formal description of the systemic theory based e-learning 93 2 a systemic model of the learning (instructional) process the multidisciplinary nature of the systems theory facilities the implementation of the system thinking in the learning (instructional) process.[3] in this paper, the learning (instructional) process is modeled starting from the definition of an automatic regulation system. definition 1. an automatic regulation system (s,r,a) is defined according to the schema presented in figure no. 5.[11] figure 5: an automatic regulation system y∗ is the reference value, y is the output value, v are the perturbation factors, u is the input value, ε is the error between the reference value and the output value. definition 2. an intelligent instructional system (iis) with a feedback regulator is defined according to the schema: figure 6: the intelligent instructional system the instructional system is a system with inverse connection that decides the behavior view to nullify the error. definition 3. an intelligent instructional system (iis) with a combined regulation is defined according to the schema presented in the figure no. 7. 94 gabriela moise figure 7: intelligent instructional system the input of the system represents the reference value, what the students have to realize after the learning (instructional) process has finished. the state at k step (the system is sequential) is defined by the students’ knowledge and skills at step k. the instruction context may be referred as a social, an emotional, a mental, school, technological, knowledge context.[14] in this approach, all factors that affect the learning motivation, goals, previous knowledge, interest, teaching styles, learning styles, classroom climate, parents, preoccupations, hobbies, etc represent the perturbations. definition 4. an intentional model of instruction is a structure with the form: (com, con, cri), where com, con, cri have the following meanings: • com defines the behaviour • con describes the conditions in which the students prove learning • cri is a precise setting of acceptable standards and performance the teacher has to consider the inferences in the learning process, starting from measurable pieces of evidence that can be measured, while the learning can not be measured directly. the goals of learning are formulated according to the domains of learning: the cognitive domain, the affective domain and the psychomotor one. the teacher and the instructional designer have the major role in defining the goals of learning. definition 5. the input values of iis are defined by the goals of the instructional process, the intentional models of behaviour. the goals of a course describe the intention of the teacher referring to the students, who attend the course. the goals define what is expected from the students after the instructional process has finished. y∗re f = (com ∗ re f , con ∗ re f , cri ∗ re f , gradere f ), where • com∗re f defines the reference behaviour • con∗re f describes the reference conditions in which the students prove learning • cri∗re f is the setting of the reference standards and performance • grade∗re f is the reference grade (the accepted grade) a formal description of the systemic theory based e-learning 95 for example: y∗re f = (behaviour : write aprogram, conditions : usingc + + language in 15 minutes, criteria : program runs without errors and display correct results, 8) or y∗re f = (write a program, c + + language, 15 minutes, programs run without errors, display correct results, 10) y∗re f = ( com∗re f , con ∗ re f , cri ∗ re f , grade ∗ re f ) ,    com∗re f = write a program con∗re f = { language = c + + time = 15 min cri∗re f = { run without errors display correct results grade∗re f = { 10 meaning: at the end of the module, the student will be able to write a program to sum the first n integer numbers, using c++ language, in 15 minutes, the program will run without errors and the result will be correct, the reference grade is 10. definition 6. the outputs of the iis system are defined as follows: y = (y1, y2, ..., ym) , where yi are defined according to gagne [8] • declarative knowledge; • procedural knowledge; • cognitive strategies; • attitudes; • psychomotor abilities. the outputs (the students’ knowledge) are measured (y∗) in a way that allows them to be compared with the reference values. in order to evaluate the outputs of the iis system, a measuring system (ms) is to be established. ms : y → m, y is the possible outputs’ sets. the measuring system has to take into account goals of the instruction (the reference value of the iis system). the behaviour of the student has to be evaluated in the conditions defined by the goals of the instruction and has to satisfy a set of criteria. the measuring system evaluates the outputs of the system (what the student realize in the evaluation phase) and produces feedback, so the measuring system establishes an output behaviour model. y∗ = (com∗, con∗, cri∗, grade∗) ,    com∗ = write a program con∗ = { language = c + + time = 30 min cri∗ = { run without errors display incorrect results grade∗ = { 8 meaning: at the end of the module, the student has to write a program to sum the first n integer numbers, using c++ language, in 30 minutes, the program runs without errors and the result is correct, the grade is 8. 96 gabriela moise the output of the iis system is evaluated according to the ms and has the following values: y∗ = ms(y) = (com, con, cri, grade) the error is defined as a difference between two models (patterns). to determine the difference between two behaviour patterns, a patterns’ analyzer (defined through a procedures collection) will be used.(see figure 8) figure 8: patterns’ analyzer the analysis of the behaviors’ patterns imply the following steps: 1. the criteria will be decomposed in primitive criteria; 2. the primitive criteria are analyzed according to some rules based procedures; 3. the analysis procedures are updated according to learning techniques taken from artificial intelligence. in the example above: cri∗ = { run without errors display correct results −cri∗re f = { run without errors display incorrect results the criteria analysis establishes that the program written by the student has a logical error. the value of the error: invalid logical program. definition 7. the perturbation factors are defined by the contextual environment of the learning (instructional) process. the contextual learning is built on all conditions that affect the instructional process. cole and griffin have shown the complexity of this concept.[4] the predictive regulator of the iis system decides what kind of instructional techniques and information will be used in the learning process, analyzing the contextual factors. the predictive regulation contains ai techniques to instruction of the iis system. the predictive commands are rules having the following structure: r = (c1 ∧c2 ∧...∧cn ) → (a1; a2; ...; ak) , where ci, i = 1, ..., n are the conditions and ai, i = 1, ..., k are the actions in the iis system, the conditions are perturbation factors and they are inputs of the system. the feedback regulator of the iis system checks the value of the error and provides a corrective command. while the iis system is not a numeric system, the behaviour of the system cannot be described using equations with real numbers. a formal description of the systemic theory based e-learning 97 3 a mathematical model of the instructional system definition 8. a vector of n elements, where the elements are real numbers and logical values, is called a quasi-boolean vector. x = ( x1, x2, ..., xk, xk+1, .., xn ) where xi ∈ r, i = 1, k and xi ∈ b2, i = k + 1, n the real part of x is noted with r(x) and the boolean part of x is noted b(x) and they are defined as: r(x) = ( x1, x2, ..., xk ) b(x) = ( xk+1, xk+2, ..., xn ) definition 9. it is called a quasi-boolean sequential system a pair of functions ( f = ( f1, f2, f3), g) defined as: xik = f i 1(b(xk−1), b(uk−1), b(vk−1)) + f i 2(r(xk−1), r(uk−1), r(vk−1)), i = 1, n1 xik = f i 3(b(xk−1), b(uk−1), b(vk−1)), i = n1 + 1, n yik = g i 1(b(xk), b(uk)) + g i 2(r(xk), b(uk)), i = 1, p1 yik = g i 3(r(xk), r(uk)), i = p1 + 1, p where: x ∈ x = rn1 ×bn22 , n1 + n2 = n represent the set of the states of the system, u ∈ u = rm1 ×bm22 , m1 + m2 = n represent the set of the input signals of the system, v ∈ v = rr1 ×br22 , r1 + r2 = r represent the set of the perturbations of the system, y ∈ y = rp1 ×bp22 , p1 + p2 = p represent the set of the output signs of the system, f i1, i = 1, n1 are pseudo-boolean functions, f i2, i = 1, n1 are linear functions, f i3, i = n11, n are boolean functions, gi1, i = 1, p1 are pseudo-boolean functions, gi2, i = 1, p1 are linear functions, gi3, i = p1 + 1, p are boolean functions. remarks: the signs of the quasi-boolean system are vectors with real numbers and boolean values elements. the complexity of the system is described through linear, pseudo-boolean and boolean functions. the informational state of the system is described using real numbers and truth values. any boolean function can be expressed using a pseudo-boolean function. in order to model the learning (instructional) process, there will be used integer numbers from an interval, instead of real numbers, so the instructional system will become a finite states system. using the interpolation formula for pseudo-boolean functions [10], the equations of the system become: xki = ∑ cγ x αn1+1 k−1 , ..., x αn k−1, u αm1+1 k−1 , ..., u αm k−1, v αr1+1 k−1 , ..., v αr k−1 + f i 2(r(xk−1), r(uk−1), r(vk−1), i = 1, n, cγ = f i1(αn1+1, ..., αn, αm1+1, ..., αm, αr1+1, ..., αr) yki = ∑ bδ x δn1+1 k−1 , ..., x δn k−1, u δm1+1 k−1 , ..., u δm k−1 + g i 2(r(xk−1), r(uk−1)), i = 1, n, bδ = g i 1(δn1+1, ..., δn, δm1+1, ..., δm) the equations of the system can be expressed using linear polynomial dependent on the states, the perturbations and the inputs of the system. if the pseudo-boolean functions are linear pseudo-boolean functions, they can be expressed as follows: t (x) = c1x1 + c2x2 + ... + cnxn + d, where c1, c2, ..., cn, d are coefficients (in the system described in the paper, the coefficients are integer number). the topic of the paper is not to describe the quasi-boolean systems. these quasi-boolean of systems (in particular cases) are observable, controllable and there may be defined regulation laws (feedback regulation and predictive regulation). the methods of solving linear pseudo-boolean equations could be found in [10]. 98 gabriela moise example: one considers an online course with three modules. the initial state is: x0 = (0, 0, 0) i.e. there is no teaching the inputs (commands) of the system are: u0 = (0, 0, 0) i.e. there is no teaching process u1 = (1, 0, 0) i.e. the system teaches module no. 1 u2 = (0, 1, 0) i.e. the system teaches module no. 2 u3 = (0, 0, 1) i.e. the system teaches module no. 3 the perturbations are: (0, 0) i.e. the electrical power is interrupted (0, 1) i.e. the internet connection failed (1, 0) i.e. the computer is failed the equation of the system is: xk+1 = (x1k v 1 k v 2 k + u 1 k v 1 k v 2 k −x1k u1k v1k v2k , x2k v1k v2k + u2k v1k v2k −x2k u2k v1k v2k , x3k v1k v2k + u3k v1k v2k −x3k u3k v1k v2k ) the reference value is: y∗re f = (1, 1, 1) if there is no perturbation, the evolution of the system is: x1 = (0, 0, 0) x2 = (1, 0, 0) x3 = (1, 1, 0) x(4) = (1, 1, 1) the regulator verifies the obtained value (1, 1, 1) with the reference value (1, 1, 1). if there is a difference between them (considering a perturbation), the output value could be (0, 1, 1). the error formula is: y∗re f = (y 1 re f , y 2 re f , y 3 re f ) y∗ = (y∗1, y∗2, y∗3) e = (y1re f y ∗1, y2re f y ∗2, y3re f y ∗3) the patterns’ analyzer described in section 2 is expressed as a boolean equation. in the former case, the error is e = (0, 0, 0) and the process finished with success. in the latter case, the error is e = (1, 0, 0) and the feedback regulation has to order to teach the first module. 4 an application example the objective of the learning(instructional) process: at the end of the teaching session, students will be able to design a simple web page using html language and the web page will be written correctly. in order to achieve this goal, the system have to teach: (1) module no. 1: the structure of the web pages, (2) module no.2: the < html >, < head >, < title >, < body >, < p > tags, (3) module no. 3: applications. for each module, the learning (instructional) system has a set of pedagogical resources. the reference value is: y∗re f = (1, 1, 1, 10), where 1 is the true value=the student has written a web page, 1 is the true value=the student has used the html language, 1 is the true value=the structure of the web page is correct the perturbation considered are defined by the learning styles (visual, auditive, kinesthetic) and the age categories (three categories). (1, 0, 0, 1, 0, 0) i.e. learning style=visual and age category=1 (0, 1, 0, 1, 0, 0) i.e. learning style=auditive and age category=1 (0, 0, 1, 1, 0, 0) i.e. learning style=kinesthetic and age category=1 (1, 0, 0, 0, 1, 0) i.e. learning style=visual and age category=2 (0, 1, 0, 0, 1, 0) i.e. learning style=auditive and age category=2 (0, 0, 1, 0, 1, 0) i.e. learning style=kinesthetic and age category=2 (1, 0, 0, 0, 0, 1) i.e. learning style=visual and age category=3 a formal description of the systemic theory based e-learning 99 (0, 1, 0, 0, 0, 1) i.e. learning style=auditive and age category=3 (0, 0, 1, 0, 0, 1) i.e. earning style=kinesthetic and age category=3 the instructional system has to teach three modules; for each module there is a set of pedagogical resources (noted with prk=pedagogical resources with identification k). the commands of the system are: (0, 0, 0, 0) i.e. invoke a virtual meeting between the teacher and the student (1, 0, 0, pr1) i.e. teach module no. 1 using the pedagogical resource with identification 1 (1, 0, 0, pr2) i.e. teach module no. 1 using the pedagogical resource with identification 2 (1, 0, 0, pr3) i.e. teach module no. 1 using the pedagogical resource with identification 3 (1, 0, 0, pr4) i.e. teach module no. 1 using the pedagogical resource with identification 4 (1, 0, 0, pr5) i.e. teach module no. 1 using the pedagogical resource with identification 5 (0, 1, 0, pr6) i.e. teach module no. 2 using the pedagogical resource with identification 6 (0, 1, 0, pr7) i.e. teach module no. 2 using the pedagogical resource with identification 7 (0, 1, 0, pr8) i.e. teach module no. 2 using the pedagogical resource with identification 8 (0, 0, 1, pr9) i.e. teach module no. 3 using the pedagogical resource with identification 9 (0, 0, 1, pr10) i.e. teach module no. 3 using the pedagogical resource with identification 10 (0, 0, 1, pr11) i.e. teach the module no. 3 using the pedagogical resource with identification 11 (0, 0, 1, pr12) i.e. teach module no. 3 using the pedagogical resource with identification 12 so, there are five pedagogical resources for module no. 1, three pedagogical resources for module no. 2, and four pedagogical resources for module no. 3. the equations of the system are: x1k = x 1 k−1 + u 1 k−1 −x1k−1u1k−1 x2k = x 2 k−1 + u 2 k−1 −x2k−1u2k−1 x3k = x 3 k−1 + u 3 k−1 −x3k−1u3k−1 x4k = x 4 k−1 + u 1 k−1u 4 k−1 x5k = x 5 k−1 + u 2 k−1u 5 k−1 x6k = x 6 k−1 + u 3 k−1u 6 k−1 a state of the form (1, 1, 0, 4, 7, 0) means that the student has followed the modules no.1 and 2 using the pedagogical resources no. 4 and 7. the output is: y = (x1k x 2 k x 3 k , x 1 k x 2 k x 3 k , x 1 k x 2 k x 3 k , α x 1 k x 2 k x 3 k + β x 1 k x 2 k x 3 k + γ x 1 k x 2 k x 3 k ), where α + β + γ = 10 are given coefficients. this means that a student may obtain at the evaluation the grade=10, if the behaviour has the value 1, the conditions have the value 1 and the criteria have the value 1. a scenario of the system’s behavior is: a new student has to learn web pages. the student has a learning style and an age category. the predictive controller has learned which pedagogical resources fit with the profile of the student. this can be realized using ai learning techniques [13] or the algorithm proposed in [18]. the error is: e = y4∗re f −y4∗ + α y1∗re f y1∗ + β y2∗re f y2∗ + γ y3∗re f y3∗ if the error is too big, the feedback controller provides a command to invoke a virtual meeting between the teacher and the student. 5 problems of the online instructional systems the major problems connected to the online instructional systems are posed by their complexity and uncertainty. generally speaking, the uncertainty refers to the impossibility of exact prediction. the uncertainty is a feature of the human behaviour. the human factor is a part of the online instructional systems and the instructional process act on humans (figure no. 9). so, an online instructional system has 100 gabriela moise to be viewed from the point of its complexity and uncertainty. the uncertainty of the system means that the inputs of the system don’t determine exactly the outputs of the system. the outputs of the system have figure 9: inputs and outputs of the instructional process to satisfy the standards knowledge according to the objectives of the instructional process. the authors of the paper "defining uncertainty a conceptual basis for uncertainty management in model-based decision support" [19] define the uncertainty concept taking into consideration three dimensions: • the location of the uncertainty; • the level of uncertainty; • the nature of the uncertainty the source of uncertainty, the imperfection of the knowledge, the diversity of the values, etc. at a macro level, there may be distinguished two sources of the uncertainty: 1. the variability; 2. the limit of the knowledge. the instructional systems are developing from the new scientific perspective of the analysis of their uncertainty. if we consider an instructional system with perturbation factors having null values, the outputs of the system cannot be predicted with accuracy. the instructional process is unpredictable and can not be reduced at linear sequences to produce predictable outputs. considering the perturbation factors and the complexity of the relations between the parts of the system and the relations between system and the environment, the instructional system is a complex system. to obtain a reference value, the instructional system has to be defined as a complex adaptive system. a complex adaptive system (cas) is a system with a lot of internal and external relations; the system can adapt itself and evaluate according to changes from the environment. it is important to see that there is not a precise boundary between the system and the environment. the system influences and changes the environment. an instructional system is linked with other systems, so that the changes have to be considered in terms of a co-evolution with other systems. an instructional system has to be designed from the viewpoint of the internal variable side and external variable side. the system records information about the environment, this information determines the behaviour of the system, the system transmits information to the environment and acts in accordance with the environment. another viewpoint of instructional process is the hermeneutic aspect, in which the learning is defined as an interpretation act, inseparable from the cultural and historical context. jonassen [12] states that the instructional design theory has to consider the learning as an open system, which receives inputs from many sources, such as individual differences, emotional states, social and economical factors, demographical factors and so forth. the instructional designers have to understand the complex and holistic nature of the learning process and have not to isolate this kind of systems in closed systems. 6 summary and conclusions in the perspective of the evolution of the whole society, it is necessary to approach the instructional process from the viewpoint of the systems theory. this perspective enables to solve the problems of a formal description of the systemic theory based e-learning 101 the instructional process, in special considering the distance learning process. practically, the distance instructional process, speaking about online learning process, can be successfully applied only using the techniques from systems theory and artificial intelligence. in this paper, the author has modeled the learning (instructional) process using the general systems theory. in this paper, the term of "quasiboolean system" was introduced in order to provide a mathematical model describing the behaviour of the instructional system. the ai techniques are used in the regulation of the system. the possible applications of the proposed approach may be found in the instructional system to teach mathematics, computer science, architecture [21], physics, chemistry, etc. this kind of approach enables solving the problems of the complexity and uncertainty of the instructional systems, especially those of the online instructional systems. bibliography [1] b. h. banathy, a systems view of education: concepts and principles for effective practice. englewood cliffs, n.j.: educational technology publications, 1992. [2] b. h. banathy, systems inquiry and its application in education, in d. h. jonassen (ed), handbook of research for educational communication and technology, new york: macmillan library reference, 1996. [3] d. chen and w. stroup, toward a conceptual framework for science and technology education for all, journal of science education and technology, volume 2, no.3, 1993. [4] m. cole., p. griffin, contextual factors in education. information analyses report. prepared for committee on research in mathematics science, and technology education, commission on behavioral and social sciences and education, national research council. wisconsin center for education research, madison, 1987. [5] w. dick, l. carey, the systematic design of instruction, glenview,il.:scott,foresman, 1978. [6] r.r. du mont, distance learning: a systems view an assessment and review of the literature, research report, grant awarded by the ohio learning network in cooperation with research center for educational technology kent state university, 2002. [7] j.d. finn, automation and education: i. general aspects, av communication review, 5(1), 1957. [8] r. gagné, l. briggs, w.w. wager,principles of instructional design,(3rd ed.). new york, ny: holt, rinehart and winston, inc., 1988. [9] g. gonzalez, a systematic approach to active and cooperative learning in cs1 and its effects on cs2, technical symposium on computer science education, proceedings of the 37th sigcse technical symposium on computer science education, 2006. [10] p.l. hammer(ivanescu), s. rudeanu, methodes booleennes en recherche operationelle, dunod, paris, 1970. [11] c. ilas, teoria sistemelor de reglare automata, matrix rom, bucuresti, 2006. [12] d.h. jonassen, et al. certainty, determinism, and predictability in theories of instructional design: lessons from science, educational technology, (37)1, 1997. [13] g. moise, a software system for online learning applied in the field of computer science, ijccc, volume: ii, no: 1, 2007. 102 gabriela moise [14] g. moise, a rules based on context methodology to build the pedagogical resources, proceedings of the 2nd international conference on virtual learning, bucharest university press, 2007. [15] m.g. moore and g. kearsley, distance education: a systems view, belmont, ca:wadsworth publishing co., 1996. [16] m.g. moore,theory of transactional distance, in d. keegan (ed.) theoretical principles of distance education. new york: routledge, 1993. [17] f. saba, r.l. shearer,verifying key theoretical concepts in a dynamic model of distance education, the american journal of distance education, 8 (1), 1994. [18] a.d. styliadis, i. d. karamitsos, d. i. zachariou, personalized e-learning implementation the gis case, international journal of computers, communications & control, vol. i, no.1, 2006. [19] w.e. walker, p. harremöees, j. rotmans, j.p. van der sluijs, m.b.a. van asselt, p. janssen and m.p. krayer von krauss, defining uncertainty a conceptual basis for uncertainty management in model-based decision support, integrated assessment , vol. 4, no. 1, 2003. [20] n. wiener, the human use of human beings : cybernetics and society, second edition revised, doubleday anchor, 1954. [21] a.d. styliadis, e-learning documentation of historical living systems with 3d modeling functionality, informatica, vol. 18, no. 3, pp. 419-446, 2007. gabriela moise petroleum-gas university of ploiesti computer science department no. 39 bd. bucuresti, ploiesti, romania e-mail: gmoise@upg-ploiesti.ro received: february 13, 2007 gabriela moise (born on february 13, 1969) graduated the faculty of mathematics, specialization informatics of the bucharest university. she worked in the software industry, developing it solutions. since 2003 she is lecturer at petroleum-gas university of ploiesti. her research fields are: e-learning, graph theory, pedagogical agents, knowledge representation, e-health. she has (co)authored seven books and more than twenty research papers. she has participated to many international conferences in the elearning and e-business area. international journal of computers, communications & control vol. i (2006), no. 2, pp. 7-14 development of a matlabr toolbox for the design of grey-box neural models gonzalo acuña, erika pinto abstract: a matlab toolbox is developed for the design, construction and validation of grey-box neural network models. this toolbox, available in www.diinf.usach.cl/gacuna has been tested in simulations with a continuously stirred reactor process. the grey-box model performs well for validation data with 5% additive gaussian noise for one-step-ahead (osa) and model-predictive-output (mpo) estimations. keywords: grey-box model, neural networks, one-step-ahead estimation, multiple prediction output, time variant parameter identification. 1 introduction in the development of dynamic system models it is best to take advantage of the a priori knowledge of a process, generally expressed in terms of a set of ordinary differential equations which represent mass or energy balances. in complex biotechnological processes, the most difficult task is the modeling of the time-varying parameters, such as specific kinetics. in order to address this problem [1], proposed the use of grey-box models which combine a priori knowledge expressed in terms of a phenomenological, or white-box model, with a black-box model such as neural network. these models have proved to be satisfactory for dynamic systems, they have better generalization characteristics, and they can be identified with a smaller amount of data [1]. [2] classified these grey-box models into two principal categories: those which deliver intermediate values (of parameters or variables) for use in phenomenological models (serial grey-box models), or those in parallel with the dynamic model, adjusted to compensate for modeling errors (parallel grey-box models). [3] showed that the series strategy resulted in grey-box models with superior results. more recently [4] and [5] have employed and analyzed this type of model demonstrating their performance and their use in complex processes. matlabr is well known as a completely integrated application development environment, oriented to projects that involve complex mathematical calculations and graphic visualizations. this software has a large variety of toolboxes which are specialized packets which can carry out different functions according to the area of development, for example; optimization, image processing, neural networks, simulations and statistics among others. thus, the present work deals with the creation of a group of functions integrated in a matlabr toolbox which allows the development of an grey-box neural model (gbnm) for complex systems in general. the present document is organized as follows: section 2 details some of the aspects of the gbnms used, section 3 examines the design and construction of the toolbox; section 4 looks at the applications of the toolbox; and section 5 outlines the conclusions reached. 2 grey-box neuronal models as mentioned previously gbnms take advantage of the combination of the a priori knowledge of a given process -expressed in terms of a set of differential equations that represent the first principles that govern the processwith neural networks. the latter are responsible for modeling the interaction between variables that are relevant to the system, and certain time-varying. it is a well established fact that neural networks are capable of approximating non-linear functions. in particular, it has been demonstrated that perceptrons, with only one hidden layer and an adequate number of neurons in their internal layer, are copyright c© 2006 by ccc publications 8 gonzalo acuña, erika pinto universal approximators [6]. for the purposes of the present work it is important to distinguish between two training modes for neural networks included in gbnms. the first type, also known as the direct learning mode [5], uses the error generated at the output of the neural network for the correct determination of its weights (figure 1). figure 1: grey-box neural model in its direct learning mode. the second type corresponds to an indirect mode by which the error generated at the output of the gbnm is used for the training of the neural network [5] (figure 2). figure 2: grey-box neural model in its indirect learning mode. the toolbox developed in the present work is based on the direct learning mode of the neural network. the neural networks used are multi-layered perceptrons with only one hidden layer. the training algorithm is error retropropagation combined with a levenberg-marquardt optimization. the validation of the results obtained is carried out with tests that consist in evaluating the error produced when using the gbnm for a one step ahead (osa) prediction, and for model predictive output (mpo) prediction, [7]. the error indices used are three: the root mean square (rms) error, the relative standard deviation (rsd) and the adecuation index (ia), which are presented below [7]: rms = √ ∑ni=1(oi − pi)2 ∑ni=1 o 2 i ; rsd = √ ∑ni=1(oi − pi)2 n ; ia = 1− ∑ n i=1(oi − pi)2 ∑ni=1(|o′i|+|p′i|)2 where oi and pi are the observed and predicted values respectively, in time i, and n is the total number of data. p′i = pi −om and o′i = oi −om, where om is the median value of the observations. 3 development of the matlab toolbox this toolbox consists of two principal parts: a neural network and serial grey box model, either with osa or mpo estimates. development of a matlabr toolbox for the design of grey-box neural models 9 the methodology used corresponds to the modified cascade model which has the following stages: • definition of requirements. • system design. • implementation and testing of units. • integration and testing of system. the details of the methodology can be found in [8]. 3.1 neural network this is the first part of the toolbox, and its principal function is to create and train the network that the serial grey-box model uses for estimating the unknown parameter or parameters which are then combined with the phenomenological part of the model. for this reason it is necessary to run this part of the program before running the serial grey-box model. the principal functions of the neural network are the following: • verify the existence of data: this function’s role is to verify the existence of the data necessary for the functioning of the neural network. • verify dimensions: this function verifies that the quantity of input output data is equal. • verify layers: this function verifies that the total number of neurons in hidden layers is correct. • divide data: this function divides input and output data that have been entered by the user into training and validation data, (70% and 30% respectively). • create and train the network: this function creates and trains the neural retropropagation network based on the data input by the user. this is carry out using the matlabr neural network toolbox functions. for the creation of the neural network the “trainlm” function is used which applies the levenberg-marquardt method for optimizing weights. • simulate network outputs: this function simulates network outputs for the purpose of validation. • graph network inputs: this allows the visualization of the behavior of the entered input. 3.2 serial grey box model this is the second part of the toolbox which allows the development of general serial grey-box models either with osa or mpo estimations. the principal functions of the serial grey-box model applied either to osa or mpo estimations are the following: • verify the existence of the files: this function verifies the existence of the files necessary for the functioning of the serial grey-box model. • verify the existence of data: among these data are the number of iterations, the initial values for the status vector, the value of delta t, and where applicable to input u and the real values of the status vector variables for each instant of time used. 10 gonzalo acuña, erika pinto • verify dimensions: this function verifies that the dimensions of the input vector be the same as the number of iterations. for estimating osa the quantity of real values of the status variables must be equal to the number of iterations. for mpo it only returns the results for the output of the system, but cannot evaluate said output with real data. • develop model: this function is in charge of developing the grey-box model which consists of estimating the unknown parameter or parameters by using the neural network created and trained previously in order to develop the different equations entered. • return different graphs: graphs the inputs of the system, each of the components of the status vector for the total number of iterations, the validation of the unknown parameter or parameters, and the output of the system together with its validations. • calculate the error indices: calculates rsd, rms and ia for the output. • display results: returns the last iteration both for the status vector and the output vector, and for the error indices that correspond to the unknown parameter or parameters or the output or outputs of the system. 4 aplications and results 4.1 description of the process in the simulation of a cstr process a first order exothermic reaction is used [9], in which the inputs to the system correspond to the temperature of the cooling sleeve and the system output corresponds to the degree of completion of the reaction. the systems is described by the following status equations: x′1 = −x1 −da ·(1−x1)·e ( x2 1+ x2 γ ) (4.1) x′2 = −x2 −b.da ·(1−x1)·e ( x2 1+ x2 γ ) + β .(u−x2) (4.2) y = x1 (4.3) where: x1 : degree of completion of the reaction; x2 : adimensional temperature of the reactor contents; u : input that corresponds to the adimensional flow rate of the heat-transfer fluid through the cooling sleeve, and y is the output of the system. for the purposes of this simulation the following values are used for the model’s constants: da = 0, 072; b = 8, 0; β = 0, 3; γ = 20, 0 because grey-box models are made up of two parts, one of which is the phenomenological model represented by the different differential equations, and the other of which corresponds to the empirical model represented by the neural network, the equations that represent the phenomenological model of the serial grey-box model are the following: x′1 = −x1 + 0, 072.(1−x1).ρ (4.4) x′2 = −x2 + 8.0, 072.(1−x1).ρ + 0, 3.(u−x2) (4.5) y = x1 (4.6) development of a matlabr toolbox for the design of grey-box neural models 11 where ρ is the parameter that is difficult to obtain: ρ = e ( x2 1+ x2 20 ) when equations 4.4 through 4.6 are discretized in the interval between t and t+1, the following equations are obtained: x1(t+1) = x1(t) + (−x1(t) + 0, 072.(1−x1(t)).ρ).∆t (4.7) x2(t+1) = x2(t) + (−x2(t) + 8.0, 072.(1−x1(t)).ρ + 0, 3.(u−x2(t))).∆t (4.8) thus the equations that constitute the phenomenological part of the serial grey-box model, either with osa or mpo estimations, are the following: x1(t+1) = x1(t) + (−x1(t) + 0, 072.(1−x1(t)).ρ).∆t (4.9) x2(t+1) = x2t + (−x2(t) + 8.0, 072.(1−x1(t)).ρ + 0, 3.(u−x2(t))).∆t (4.10) y = x1(t+1) (4.11) 4.2 osa estimation with 5% noise the validation obtained for the unknown parameter with 5% noise can be seen in figure 3. figure 3: validation of the unknown parameter with osa estimation and 5% noise. the validation obtained from the output of the system can be seen in figure 4. figure 4: validation of the output of the system with osa estimation and 5% noise. 12 gonzalo acuña, erika pinto the error indices obtained from the serial grey-box model with osa estimation and 5% noise, for parameter ρ and the output of the system are presented in table 4.1. table 4.1. error indices for parameter and the output of the system with an osa estimation and 5% noise. parameter ρ system output rsd 4.368098e-2 2.756748e-3 rms 1.733075e-2 1.944067e-2 ia 9.111975e-1 9.846065e-1 the error indices obtained are quite acceptable given that the acceptability values are rsd < 0.1, rms < 0, 1 and ia > 0.9 , as explained in section 2.6 this indicates that the serial grey-box model developed for the simulation of a cstr process, with osa estimation and 5% noise, is quite good. 4.3 mpo estimation with 5% noise the validation obtained for the unknown parameter with 5% noise can be seen in figure 5. figure 5: validation of the unknown parameter with mpo estimation and 5% noise. the validation obtained from the output of the system can be seen in figure 6. figure 6: validation of the output of the system with mpo estimation and 5% noise. the error indices obtained from the serial grey-box model with mpo estimation and 5% noise, for parameter ρ and the output of the system are presented in table 4.2. table 4.2. error indices for parameter ρ and the output of the system with an mpo estimation and 5% noise. parameter ρ system output rsd 4.368098e-2 3.441815e-3 rms 1.733075e-2 2.427178e-2 ia 9.111975e-1 9.764686e-1 development of a matlabr toolbox for the design of grey-box neural models 13 the error indices obtained are quite acceptable given that the acceptability values are rsd < 0.1 , rms < 0, 1 and ia > 0.9. this indicates that the serial grey-box model developed for the simulation of a cstr process, with mpo estimation and 5% noise, is quite good. 5 conclusions grey-box models constitute a real alternative for those real world processes for which the available a priori knowledge is incomplete, for example in a variety of industrial processes. as in grey-box models only some of the physical and/or chemical laws that represent the model are known, and there are unknown parameters that must somehow be estimated, multi-layered perceptron neural networks have been employed for their notable capacity to approximate complex functions on the basis of observed data. one of the advantages of this toolbox, with reference to the creation and training of neural networks, is that neuron transfer functions for each of the hidden and output layers that make up the neural network, can be determined by the user, who can then create and train the neural network in a manner that is in keeping with the specific problem that is to be modeled. concerning the estimation of unknown system parameters, the developed toolbox has the capacity to estimate as many parameters as necessary as long as the necessary data are available. the developed toolbox (available at www.diinf.usach.cl/gacuna), allows the creation of serial greybox models, either with osa (one step ahead) or mpo (model predictive output) estimations, depending on what is to be modeled. with the creation of this matlabr toolbox, the end user has access to a simple, trustworthy and fast tool which allows the development and subsequent manipulation of different serial grey-box models, either with osa or mpo estimations for solving a particular problem. this tool will become part of the matlabr toolboxes such that the user can access it at will simply by downloading matlabr. thus this new tool becomes a real and simple alternative for modeling those real work processes for which a priori knowledge available is incomplete. the error indices obtained are good, both for the unknown parameters and for each of the outputs of the system, even when the level of noise applied to the various input data was 5%, which indicates that the models developed are of good quality. acknowledgements the authors wish to thank partial financing provided by fondecyt project 1040208. references [1] psichogios, d.; ungar, l. (1992). “a hybrid neural network-first principles approach to process modeling”, computers & chemical engineering, 38(10): 1499-1511. [2] thompson, m.; kramer, m. (1994). “modeling chemical processes using prior knowledge and neural networks”, computers & chemical engineering , 40(8):1328-1340. [3] van can, h; hellinga, c.; luyben, k.; heijnen, j. (1996). “strategy for dynamic process modeling based on neural network in macroscopic balances”, aiche journal, 42:3403-3418. [4] thibault, j, acuña, g., pérez-correa, r., jorquera, h., molin, p., agosin, e., (2000) “a hybrid representation approach for modelling complex dynamic bioprocesses” bioprocess engineering, 22(6):547-556. [5] acuña g.; cubillos, f.; thibault, j.; latrille, e. (1999). “comparison of methods for training greybox neural network models”, computers & chemicals engineering supplement, 23:561-564. 14 gonzalo acuña, erika pinto [6] hornik k. stinchcombe m., white h., (1989) “multilayer feedforward networks are universal approximators”, neural networks, 2:359-366. [7] billings s.a., jamaluddin h. b. y chen s., (1992) “properties of neural network with applications to modeling non-linear dynamical system”, int. j. control, 55(1):193-224. [8] pinto armijo, erika (2004), “a matlab based application for developing grey-box models”, memoria de título de ingeniería civil informática, universidad de santiago de chile (in spanish). [9] hernández e.; arkun, y. (1992). “study of the control-relevant properties of backpropagation neural network models of nonlinear dynamical systems”, computers & chemical engineering, 16(4):227-240. gonzalo acuña, erika pinto universidad de santiago de chile departamento de ingeniería informática avda. ecuador no 3659 casilla 10233; santiago, chile e-mail: gacuna@usach.cl international journal of computers, communications & control vol. ii (2007), no. 1, pp. 74-83 a methodology for providing individualised computer-generated feedback to students michael lambiris abstract: the traditional way of providing feedback to students after tests or assignments is labour-intensive. this paper explains the concepts and techniques used by the author to build computer-based applications that analyse students’ answers and generate individualised, detailed and constructive feedback. the paper explains how the data gathered from a student’s answers can be combined with other knowledge about the subject matter being taught, and the specific test questions, to create computerised routines that evaluate the individual student’s performance. this information can be presented in ways that help students to assess their progress, both in relation to their acquired knowledge in specified areas of study, and with regard to their ability to exercise relevant skills. in this way, appropriate feedback can be provided to large numbers of students quickly and efficiently. the same techniques can be used to provide information to the instructor about the performance of the group as a whole, with a degree of detail and accuracy that exceeds the impressions usually gained through traditional marking. the paper also explains the role of the subject instructor in designing and creating feedback-generating applications. the methodologies described provide insight into the details of the process and are a useful basis for further experimentation and development. keywords: teaching technology, computer-generated feedback, methodology and design, teaching large classes 1 difficulties with providing good feedback it is widely recognised by educators that detailed, constructive, prompt and individualised feedback is an important aspect of good teaching and effective learning. see [1]. but providing feedback to students in the traditional form, that is, by reading the students’ answers, evaluating them and writing comments, can be very time-consuming, especially with large classes. i teach a subject called principles of business law that attracts enrolments of up to 700 students each semester. assessment in this subject consists of four computerised tests, each comprising 30 to 40 multiple-choice questions. the tests are done under examination conditions. scores are posted a day or two afterwards. it was in this context that i wished to provide individualised feedback after each test to the students. with classes of this size, it is impractical for an instructor to write comments for each student. a way was needed to produce feedback by means of a computer program. 2 what should feedback consist of? one way of providing feedback would be to publish the test questions together with the correct answers. this is often what students expect, but it may not be the best approach to learning. thirty or forty questions cannot comprehensively test everything a student should know. a test is usually only a sampling of the student’s knowledge and skills. when students correctly answer questions in a test, this indicates a probability that they know the relevant subject area well. similarly, when they answer questions wrongly, this indicates a probability that they have an inadequate grasp of the subject area. if a student’s answers demonstrate a weakness, they will likely need to revise that whole area of study rather than being given the correct answers to specific questions. accordingly, my aim is to provide feedback in the form of general analysis, comment and advice. see [2]. copyright c© 2006-2007 by ccc publications selected paper from icvl 2006 a methodology for providing individualised computer-generated feedback to students 75 3 extracting useful information from basic data instructors who are knowledgeable in their own specialist area may not also be competent computer programmers and will need to employ specialist help to create application software. but the instructor needs to understand some basic programming concepts and techniques to be able to participate effectively in the process of designing and shaping feedback-generating software appropriately. in this paper i explain in detail one way in which such software can be created. the starting point of is to identify what basic data is available. in each of the assessment tests that i use in teaching my subject, the students answer the questions by selecting a letter that represents their chosen answer (a, b, c, etc.). this letter is recorded in an electronic database so that the student’s record consists of a sequence of 30 to 40 individual letters. a symbol (-) is used to indicate unanswered questions. a typical string of answers looks like this: baabebccaecb-dabbab-abbceabaccaabaeadac. to create effective feedback, techniques are needed to extract more information from such basic data. how can this be done? essentially, the process involves combining three types of information. the first is the particular answer the student chose for each question. the second is what the instructor knows generally about the subject area and skills being tested. the third is the focus or intent of each particular question in the test. the computer application can be designed to take proper account of these three factors and to draw specified conclusions from them. in this way, it is possible to build a useful picture of how the student has performed, and to identify their particular strengths and weaknesses. this information, properly presented with comments and advice, forms the basis for individualised feedback. to fully envisage what is possible, it helps to understand the computer processes that are involved. an easy example of this is working out whether or not a particular answer is correct or incorrect. essentially, the student’s answer must be compared to the correct answer, to see if they are the same. a computer program compares data by using variables. variables can be thought of as an electronic slots in which specified information can be stored. to compare a student’s chosen answer with the correct answer, the data representing a student’s answer can be retrieved from the database where it is permanently stored, and temporarily placed in a specified variable. the data representing the correct answer can be placed in another variable. the computer program then compares the contents of the two variables. if they match, it follows that the student has answered the question correctly and the ok result can be stored in a third variable, in the form of an increasing number or score. if there is no match, the student’s answer is wrong and this not ok conclusion can be stored in a fourth variable (or by adjusting the number in the third variable downwards). using a process like this for all the student’s answers, it is possible to work out how many answers were right or wrong. but a difficulty immediately emerges. it is apparent that the results obtained do not disclose why a student has chosen a particular answer. there can be many reasons for getting an answer wrong. for example, the student may have simply misread the question; or failed to understand the significance of a particular term used in the question; or not have had the necessary knowledge or skill to answer correctly. similar possibilities exist in respect of correct answers. taken individually, therefore, a student’s correct and incorrect answers do not provide a sufficiently reliable basis for giving feedback and advice. but, given sufficient data, it is possible to look for significant patterns in a studentšs right and wrong answers. when all of the student’s answers are analysed in the light of the particular knowledge and skills that the various questions are designed to test, distinct patterns emerge that can be useful as the basis for providing that student with helpful feedback. 4 identifying categories of skill and knowledge to develop a computer-based application that carries out the necessary analysis of the student’s answers requires careful thought and planning. the first step is to analyse each of the questions on the test, to identify, describe and name the particular categories of knowledge and skill involved. to do this 76 michael lambiris the instructor must combine their subject-matter expertise, teaching experience and examining skills. it may initially seem difficult to categorise each question in a specific and uncompromising way some questions defy any neat classification. but, when classifying questions in one or more specified ways, it quite often happens that fresh insight is gained into what a question is truly attempting to do, and how that question might be improved so that it achieves its objectives more clearly and precisely. this is not a bad thing to happen. examples taken from specific tests illustrate the way in which categories may be defined. in the first test written by pbl students, analysis shows that each of the 40 questions involves one of three different generic skills. one is an ability to recall and apply acquired knowledge. another is the ability to find specified information in a statute and a law report. the third is the ability to understand, analyse, and draw conclusions from specific facts. each question can also be categorised according to the area of knowledge involved. in the test being discussed, the areas of knowledge are: (1) constitutional arrangements and the organs of government in australia; (2) the law-making powers of specified organs of government; (3) the processes and procedures for enacting legislation; (4) the hierarchy of the federal and state court systems; (5) the nature and organisation of law; (6) understanding and appropriate use of legal terms and concepts; (7) the interpretation and application of statutory law; (8) the interpretation and application of case-law; and (9) recognition and understanding of judicial reasoning. when each category of skill and knowledge has been identified, it needs to be given a brief but distinctive name. using the example above, the three categories of skill can be named qt1; qt2 and qt3. the nine categories of knowledge can be named: ch; lmp; nol; cs; lc; leg; sti; cl; and jr. these names can be used (with some modification) to identify variables in a computer program. to carry out an analysis, the program will need three separate variables for each named category of skill and knowledge. this allows us to take account of whether the student answered that question rightly (r) or wrongly (w), or left it unanswered (n). using this naming regime, the name of the first category above qt1 is transformed into three variables named qt1r, qt1w or qt1n. similarly, qt2 becomes qt2r, qt2w and qt2n; and so on. further named variables will be needed to track other important aspects of the results, for example: q1; q2; q3, etc to hold the student’s answer to each question; right to hold the correct answer being considered; wrong for the incorrect answer being considered; result for the result of comparing two variables; rans for the total number of correct answers; wans for the total number of incorrect answers; nans for the total number unanswered questions; tans for the total number of questions attempted; and score for the final score for the test. 5 developing routines for analysis to see what sort of information can now be extracted from the basic data requires some understanding of the computer-based processes involved. imagine that we want to begin by analysing a student’s answer to the first question in the test. the computer program begins by finding the particular student’s string of answers in the database. it then selects the answer chosen by that student to the first question, and places the appropriate letter (a,b or c) in the relevant named variable, for example, in q1. next, the program places the letter which represents the correct answer to that question (a, b or c) in the variable right. by comparing the letter stored in q1 with the letter stored in right, the program can decide whether or not the question was correctly answered. this result can then be stored in a third variable where the total number of correct answers is kept: rans. if the question was not answered, this fact can be recorded in the variable that stores the total number of unanswered questions: nans. and if the question was answered wrongly, this conclusion is stored in the variable that stores the total number of incorrect answers: wans. the program can now be made to classify the student’s answer to the first question by reference to a category of skill. for example, assume question 1 tested the student’s ability to understand, analyse, and draw conclusions from specific facts. recall that the relevant variable for this skill was named qt3. a methodology for providing individualised computer-generated feedback to students 77 if the student got the answer right, the program can store this conclusion in the variable that counts the student’s correct answers in this category qt3r. alternatively, if the question was answered wrongly, that conclusion can be recorded in the variable qt3w which shows the total of wrong answers in this category. unanswered questions in this category are recorded in the variable qt3n. the same procedure is followed to classify the student’s answer to this question in relation to the area of knowledge being tested, using the variables lmpr; lmpw; or lmpn. in this way, the student’s answer to question one is being evaluated in various ways. the same routines are then repeated for each of the remaining questions, with appropriate changes to the variables used to store the conclusions. once these basic routines have been carried out, further processes can be used to derive additional information from the data, or to organise it usefully. for example, the total number of questions answered by the student can be calculated by adding together the number of the student’s correct and incorrect answers (rans + wans), and placing the result in the variable tans (for total answers). similar processes add to the value of the available information. so far, the individual questions have been classified as belonging to one of nine different areas of knowledge. for the purpose of generating feedback, the areas of knowledge can usefully be grouped into a smaller number of broader categories. the point of doing this is that it often helps students to understand where their strengths and weakness might lie in general terms, before going on to a more detailed analysis. in the first test written by pbl students, the nine areas of knowledge can be grouped into three broader categories, represented by variables total1, total2 and total3, as shown below: in total1 the broad area of knowledge is organs, powers and processes of government and includes: constitutional arrangements and the organs of government in australia (ch); the law-making powers of specified organs of government (lmp); the processes and procedures for enacting legislation (leg); and the hierarchy of the federal and state court systems (cs). in total2, the broad area of knowledge is legal concepts and language and includes: the nature and organisation of law (nol); and understanding and appropriate use of legal terms and concepts (lc). in total3 the broad area of knowledge is the interpretation and application of law and includes: the interpretation and application of statutory law (sti); the interpretation and application of case-law (cl); and recognition and understanding judicial reasoning (jr). the totals in the relevant variables (shown in brackets above) are added together to show how the student has performed in each broad area of knowledge. this is done separately for right answers, wrong answers and unanswered questions. for example, the numbers in the variables chr; lmpr; legr; and csr are added together in total1r to show the correct answers in this broad area of knowledge, while chw; lmpw; legw; and csw are added together in ’total1w’ to show the incorrect answers in this same area. the variables chn; lmpn; legn; and csn are added together in total1n to show the unanswered questions in this area. the same type of process can be used to produce data in relation to other specified learning objectives. finally, we can calculate the student’s score for the test and place it in score. this is done by taking the number of correct answers (already contained in the variable rans) and doing whatever arithmetic calculation is needed to express it as a final mark. in the test now being discussed, a mark out of 15 is needed because the test counts for 15 per cent of the overall assessment for the subject. the number in rans is therefore divided by 2.667 and the result placed in score. 6 presenting information as feedback using routines to analyse the basic data and extract additional information in the way described above is only the initial stage of actually providing feedback to a student. the next step is to build an interface that presents this data appropriately. the information available is sufficient to provide quite detailed feedback if it is built into a careful sequence of explanation, coupled with comment and advice. this should be presented in a clear, friendly, constructive and flexible way. one possibility is to follow 78 michael lambiris a traditional web-page design, with a list of contents on the left of the screen to indicate the extent and structure of the available feedback, with direct hyperlinks different sections. see figure 2 below. as far as possible, the feedback should be individualised, by displaying the particular student’s own data. in addition, particular comments and advice can be displayed selectively, depending on whether the particular student has a good score, an average score, or a poor score. the screenshots below provide examples. to script a full range of alternative comments and advice requires considerable forethought but the result is worthwhile. the feedback can also include information about how the individual student’s performance compares to the class as a whole. and it can usefully include information and advice about future tests, for example, what new forms of question will be encountered, and what specific preparation may be needed. students are very receptive to such information in the immediate aftermath of a test. the feedback applications can be made available to students either on a local area network, or by providing a downloadable version, or by running them on-line. 7 providing feedback to the instructor so far, this paper has been concerned with providing feedback to the students but it is also important that the instructor get feedback on the effectiveness of their teaching, the validity of the questions set in the test, and the extent and accuracy of student learning. traditional marking, which involves reading the answers, provides this feedback because, if a significant number of students make the same mistake, the instructor quickly becomes aware of the problem. with computer-based testing it is harder to get a clear idea of these matters. the normal output of a computer based test is a list of final marks and these do not tell the instructor much about where specific problems might lie. however, it is possible to use the techniques described above (with appropriate modification) to provide an analysis of the group results. for a group analysis, the program begins by finding each student’s string of answers in the database and carrying out the same sort of analysis already described, classifying the answers as right or wrong, and categorising the right and wrong answers in various ways, for example, by area of knowledge or skill, or in relation to specified learning objectives. as each student’s string of answers is analysed, a cumulative total is built up, so that in the end it is known how many students in the entire group got each question right or wrong; what the distribution of marks is; what percentage of the answers were right or wrong in relation to particular areas of law; and what percentage of students satisfactorily demonstrated competency at particular skills. this type of analysis would be time-consuming to do manually but it is quickly and easily accomplished using the methodologies described. the results give an accurate and clear picture of group performance for example, see figures 6 and 7 below. if too many students appear to be answering a particular question wrongly, the instructor will quickly notice this and be able to investigate the different possibilities. it may be that the question is badly written; or that the topic is poorly taught; or that the student’s have prepared inadequately in that area of study. responding appropriately helps to improve the quality of the teaching and learning process. 8 conclusions by using appropriate techniques, and properly coordinating the skills and experience of instructors and computer-programmers, it is possible to automatically generate and deliver very satisfactory indivdualised feedback for students and instructors. although the examples discussed here use the data obtained from computer-based tests in multiple-choice form, the same ideas could be adapted to tests that are not computer based, or that do not consist of multiple-choice questions. all that is required is to work out a marking scheme where numbers or letters are used to record the marker’s evaluation of what the student has achieved. this data could be digitalised and used as the basis for computer-generated analysis a methodology for providing individualised computer-generated feedback to students 79 figure 1: a sample question from a test. this question involves case-law, more specifically the meaning of coded information in case citations (variable cl). the student must interpret and evaluate the significance of that information (variable qt3). and feedback, in much the same way as described in this paper. in essence, therefore, the techniques explained in this paper could find application in a wide range of situations. the screenshots illustrate various aspects of the ideas explained in this paper. they show how the information generated from the basic data can be presented in a constructive, meaningful and readable style, and within a well-contextualised framework. the last two screenshots present an analysis of group data and show how a clear and detailed overview can be gained by the instructor of class performance as a whole. references [1] johnstone r, patterson j and rubinstein k, improving criteria and feedback in student assessment in law, cavendish publishing, australia, 1998. [2] east r, effective assessment strategies in law, http: //www.ukcle.ac.uk/resources/ assessment/effective.html, 2005. [3] higgins, e. and tatham, l, assessing by multiple choice question (mcq) tests,http:// www.ukcle.ac.uk/resources /trns/mcqs/index.html, 2003. [4] lambiris m, assessment management software, australian law courseware pty ltd, australia, http://www.alcware.com, 2005 2006. 80 michael lambiris figure 2: in the feedback application, the topics are listed on the left of the screen with hyperlinks to the content of each section. this particular screen explains the scoring process, shows the individual student’s final score and grade, and provides an appropriate comment. figure 3: this screen provides a detailed analysis of the individual student’s performance in a specified area of law (organs, powers and processes of government) and selectively provides appropriate comment. the feedback is based on the variables total 1r; chr; lmpr; legr and csr. a methodology for providing individualised computer-generated feedback to students 81 figure 4: this screen uses the variables qt1r; qt2r and qt3r to analyse the individual student’s ability to perform tasks involving specified skills. appropriate comments are also displayed selectively, depending on the values in these variables. figure 5: this screen summarises all of the available data. presented in tabular form, it gives a concise overview of the student’s performance. it also shows how a substantial amount of meaningful information can be generated from the basic data. 82 michael lambiris figure 6: using the same variables as devised for the feedback application, the data for the entire group of students can be generated for the instructor. this screen shows how many students in one group answered particular questions correctly or not. figure 7: group data can also give the instructor an overview of performance in relation to areas of knowledge, or particular skills. this screen shows the percentage of correct answers for the entire group in relation to the eleven areas of knowledge being tested. a methodology for providing individualised computer-generated feedback to students 83 michael lambiris the university of melbourne, faculty of law victoria 3010 australia e-mail: m.lambiris@unimelb.edu.au received: november 6, 2006 editor’s note about the author: michael lambiris (born january 22, 1950) obtained an llb (hons) from the university of london in 1971, and a phd from rhodes university in 1988. he has held positions at the university of zimbabwe (1976-1982 and at rhodes university, south africa (1982-1991). he is presently an associate professor and reader in the faculty of law, the university of melbourne, victoria, australia. his main fields of teaching and research are commercial law and computer-based legal education. in addition to writing computer-based learning materials, he has developed computer-based testing and feedback software, written various papers and books and presented papers at many international conferences. he is the managing director of australian law courseware (pty) ltd which publishes computer-based learning materials for law students. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 2, pp. 185-197 quality control of statistical learning environments and prediction of learning outcomes through reproducible computing patrick wessa k.u.leuven association lessius, dept. of business studies belgium e-mail: patrick@wessa.net abstract: this article introduces a new approach to statistics education that allows us to accurately measure and control key aspects of the computations and communication processes that are involved in non-rote learning within the pedagogical paradigm of constructivism. the solution that is presented relies on a newly developed technology (hosted at www.freestatistics.org) and computing framework (hosted at www.wessa.net) that supports reproducibility and reusability of statistical research results that are presented in a so-called compendium. reproducible computing leads to responsible learning behaviour, and a stream of high-quality communications that emerges when students are engaged in peer review activities. more importantly, the proposed solution provides a series of objective measurements of actual learning processes that are otherwise unobservable. a comparison between actual and reported data, demonstrates that reported learning process measurements are highly misleading in unexpected ways. however, reproducible computing and objective measurements of actual learning behaviour, reveal important guidelines that allow us to improve the effectiveness of learning and the e-learning system. keywords: reproducible computing, learning environment, quality control, statistics education, psychometrics 1 introduction in education-related research it is common practice to investigate learning processes through measurements that are based on questionnaires. reported measures often reveal interesting information about a wide variety of aspects of computing-assisted learning such as: computer attitudes [22]; computer emotions and knowledge [17]; learner experiences and satisfaction [34]; etc... the importance of such measurements has been highlighted by many authors from various perspectives ([7], [15], [12]) especially from the perspective of the constructivist pedagogical paradigm ([35], [30], [11], [24]). these reported measures, while intrinsically interesting, may not always provide us with the information we need to assess and improve systems that support e-learning. moreover, the implementation of new learning technologies and data analysis tools open up a wide array of measurement opportunities which lead to new areas of research. an excellent example is the use of data mining tools in the open source e-learning environment called moodle [28]. even though it seems to be very difficult to measure and empirically prove [25], there is no doubt in my mind that the introduction of computers in homes and classrooms has led to an improvement of overall learning productivity, educational communication mechanisms, social constructivism, and collaboration. however, the use of computers and software in statistics education may unwillingly result in several types of adverse effects because the complex processes that are required to learn and (truly) copyright © 2006-2009 by ccc publications 186 patrick wessa understand statistical concepts are often mystified by technicalities and a variety of practical problems that have nothing to do with mathematics or statistics. it is within this context that i argue that a system for quality control should be embedded into the e-learning system, which is not limited to the virtual learning environment but extends to the statistical software, databases, and learning repositories (statistical learning environment). there is an important, additional benefit for implementing such a monitoring and control system it is directly related to the problem of irreproducible research which has received a great deal of attention within the statistical computing community ([9], [26], [29], [14], [13], [18], [10]). the most prominent citation about the problem of irreproducible research is called claerbout’s principle ([9]): an article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. the actual scholarship is the complete software development environment and that complete set of instructions that generated the figures... several solutions have been proposed ([5], [10], [19]) but have not been adopted in statistics education because they require students to understand the technicalities of scientific word processing (latex) or statistical programming (r code). based on a newly developed statistical learning environment (sle) i propose a solution that is feasible for educational purposes and allows us to monitor, research, and control the learning processes based on the dynamics of between-student communication and collaboration. 2 reproducible computing 2.1 r framework the r framework allows educators and scientists to develop new, tailor-made statistical software (based on the r language) within the context of an open-access business model that allows us to create, disseminate, and maintain software modules efficiently and with a very low cost in terms of computing resources and maintenance efforts [36]. the so-called r modules empower students to perform statistical analysis through a web-based interface that does not require them to download or install anything on the client machine. this permits students to focus primarily on the interpretation of the analysis however, the r framework also allows advanced students and scientists to inspect and change the r code that was coded by the original author. this results in the creation of so-called derived r modules that may be better suited for particular purposes. there are several important reasons why the r framework helps in controlling the quality of the statistical learning processes that are supported by the computer: • the r modules are web applications with an advanced session management which includes all aspects of the computations that are executed. in addition, the session manager uses attributes that identify the student and the course in which (s)he is enrolled. therefore all computations that are performed within the context of a statistics course can be associated with an individual student to implement this feature, the educator only needs to use certain html tags in the hyperlink that is inserted in the virtual learning environment. • every r module is uniquely described by an expandable set of meta data (incl. the actual statistical code) which can be stored and transmitted. this implies that every computation that is executed can be uniquely defined by the r module’s meta data and additional information about the data and the parameters that have been specified by the user. as a consequence, every computation can be uniquely described and archived with meta data. quality control of statistical learning environments and prediction of learning outcomes through reproducible computing 187 • the r framework allows other servers (under certain conditions) to send meta data through an ordinary http request which allows it to rebuild and execute the r module with the specified data and parameters in real time. therefore it is possible to remotely store computational objects and send them back to the r framework such that the original computation can be reproduced and reused. • all the processes that are associated with the above items are automatically stored in a so-called process measurement database. this implies that all computer-assisted learning activities are objectively measured and stored for the purpose of analysis. 2.2 compendium platform if a derived r module contains generic improvements or if a computation needs to be communicated to other students/scientists then it is necessary to have a simple, transparent mechanism that allows one to permanently store the computation in a repository of computational objects that can be easily retrieved, recomputed, and reused. such a repository was recently created within the oof 2007/13 project of the k.u.leuven association and is called the compendium platform. the main reason for creating the r framework and the compendium platform, is that it allows anyone to create and use compendia of reproducible research. a compendium is defined as [37]: a research document where each computation is referenced by a unique url that points to an object that contains all the information that is necessary to recompute it. such documents can be easily created (even by students) and permit any reader to (exactly) recompute the statistical results that are presented therein. a few simple clicks are sufficient to have the r framework reproduce the results and to reuse them in derived work [37]. the practical implications of this technology will become obvious in section 3 because the three figures that are presented can be recomputed and reused through the compendium platform. 2.3 communication, feedback, and learning the concept of reproducible computing was implemented in several undergraduate statistics courses in order to thoroughly test the new system and to measure key aspects of the educational activities and experiences. two different student populations were investigated in detail: a group of (academic) bachelor students, and a group of so-called switching students. the second population is of particular interest because it consists of students who obtained a (professional) bachelor degree and decided to make the switch to an academic master which requires them to complete a preparatory year. on the one hand, switching students are highly motivated and more mature than the bachelor students. a priori, one would expect them to prefer practical activities (such as communication and computing) above theory and critical reflection. on the other hand, one might expect the bachelor students to have a more critical (scientific) attitude and better mathematical background than the switching students. students from both populations took a similar statistics course which covered topics from introductory statistics, regression analysis, and introductory time series analysis. the main learning activities in both statistics courses were based on a weekly series of workshops where each student was required to investigate practical, empirical problems. at the end of each week, students submitted their papers electronically. during the lecture i proposed a series of solutions and illustrated commonly made mistakes. after the lecture, students had to work on the next assignment and complete a series of peer reviews (assessments) about the work that was submitted the week before. the assessment grades did not count towards the final score however, each submitted peer review was accompanied by verbal feedback messages. i graded a (quasi random) sample of these messages in order to provide students with a strong incentive to take the review process seriously. there is strong empirical evidence that this approach had beneficial effects on non-rote learning of statistical concepts [38]. 188 patrick wessa 3 objective measurements versus reported data in a recent paper [37] it is illustrated how the compendium platform’s repository supports “technical” quality control of the statistical software and accompanying documentation for students. on the one hand, reproducible computing allows students to accurately communicate computational problems and questions without the need to understand the underlying technicalities. on the other hand, it allows the educator (and creator of the computational software) to analyze the reported problem (based on the detailed, raw output of the r engine that executed the request) and to transparently communicate the solutions to the students. moreover, the measurement of learning activities and experiences is a conditio sine qua non for controlling the “overall” quality of the sle. this will be illustrated, based on the data that have been collected from both student groups. at the same time, the importance of objective (as opposed to reported) measurements is illustrated based on a simple, comparative diagnostic tool. the reported measurements were obtained through questionnaires on a 5-point likert scale and should consequently be treated as ordinal data. the questions were based on well-known psychological surveys ([12], [8]) and the ibm computer system usability survey [20] which was adapted and extended [27]. useful data was obtained from a total of 111 bachelor students and 129 switching students the response ratio was very high (between 82.9% and 92% depending on the questionnaire). all observations of actual learning activities were measured on a ratio scale (the number of archived computations and the number of submitted feedback messages). a total number of 34438 meaningful, verbal feedback communications and 6587 archived computations were registered. in order to compare the actual and reported data, all measurements were converted to ordinal rank orders. in addition, the pearson’s rho correlations and kendall’s tau rank correlations ([1], [2], [16]) that represent the degree of linear association between the properties under investigation, were computed (these can be consulted in the archived computations about the figures). in electronic versions of this paper, one can simply (ctrl-)click the hyperlinks below figures 1, 2, and 3 to view the archived computation in the repository. readers of the printed version of this document, have to manually enter the respective urls into their internet browser to view the statistical computations that have been stored (at www.freestatistics.org). figure 1 displays the bivariate kernel density [21] between the rank order of the number of feedback messages that have been submitted in peer reviews about the workshops (x-axis) and the rank order of the number of (reproducible) computations that have been archived in the repository (y-axis). the rank orders have been computed within the bachelor population for the top panels, and within the switching population for the bottom panels. this implies that the ranks that are attributed to female and male students are expressed on the same axes and can be compared. figure 1 clearly demonstrates that female bachelor students are much more involved in feedback and computing than their male colleagues. at the same time, female switching students are more computing-oriented whereas the male switching students seem to have a slight preference for feedback communication. this information has important repercussions for controlling the quality of the learning environment and it provides clear guidelines towards actions that should be taken (by me) to improve participatory incentives towards male bachelor students in future courses. would i have been able to gain this insight based on reported measurements alone? the answer is clearly negative (as is illustrated in figures 2 and 3). it is quite obvious that male bachelor students highly over-estimate their performance in terms of feedback submissions (see figure 2) because the rank orders of reported measures (x-axis) are higher than the ranks of actual feedback submissions (y-axis). female bachelor students however, underestimate their involvement (relative to their male colleagues) because they are concentrated above the diagonal line. in the male switching student population several clusters of high density can be detected which leads us to conclude that we cannot treat them as one homogeneous group. in figure 3 the comparison between reported computing measures (x-axis) and actual computing (y-axis) leads to similar conclusions. male bachelor students highly exaggerate their efforts, whereas quality control of statistical learning environments and prediction of learning outcomes through reproducible computing 189 0 20 40 60 80 100 120 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 female bachelor students # submitted messages # r e p ro d u ci b le c o m p u ta tio n s 0.002 0.0 02 0.004 0.006 0.008 0.008 0.01 0.012 0.014 0.016 0.018 0.02 0.022 0 20 40 60 80 100 120 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 male bachelor students # submitted messages # r e p ro d u ci b le c o m p u ta tio n s 0.005 0.01 0.01 0.01 0.01 0.0 1 0.015 0.02 0.025 0.03 0 50 100 150 0 5 0 1 0 0 1 5 0 female switching students # submitted messages # r e p ro d u ci b le c o m p u ta tio n s 0.001 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0 .0 1 0.01 0.01 0.011 0.012 0.013 0 50 100 0 5 0 1 0 0 male switching students # submitted messages # r e p ro d u ci b le c o m p u ta tio n s 0.002 0.004 0.006 0.006 0.008 0.01 0.01 0.01 0.012 0.014 0.014 0.016 0.016 figure 1: submitted feedback versus reproducible computations www.freestatistics.org/blog/date/2008/jun/30/t1214840420q0fyankop4x9ebf.htm female bachelor and switching students underestimate themselves. the group of male switching students is heterogeneous. overall, the testimony of students is extremely misleading and poorly correlated with actual observations. if we would have recomputed figure 1 with reported measures then the conclusions would have been the opposite of what is true. the reader can try out this experiment by simply reproducing the computation of figure 1 with reported measures on both axes. 4 quality control in order to be able to control (and improve) the quality of the sle, it is necessary to estimate the impact of key-aspects of the learning processes that are associated with the sle. the methodology that allows us to do this is based on a mathematical model which is described in [40] and relates the learning outcomes to objectively measured activities and reported experiences. typically, models that predict learning outcomes based on exogenous variables that are related to the learning (and computing) environment have an extremely low percentage of variance explained. in a recent and extensive study [25], six models were discussed that predicted the statistics subtest scores of the massachusetts comprehensive assessment system the variance explained ranged between 4% and 7%. it is obvious that any model that is used to control the quality of an sle should perform much better. there are three important requirements to build high-quality models: 1. high-quality exogenous variables (preferably based on objective measurements) [39]; 190 patrick wessa 0 20 40 60 80 100 120 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 female bachelor students reported feedback submissions a ct u a l f e e d b a ck s u b m is si o n s 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02 −20 0 20 40 60 80 100 120 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 male bachelor students reported feedback submissions a ct u a l f e e d b a ck s u b m is si o n s 0.002 0.002 0.004 0.006 0.008 0.01 0.012 0.012 0.014 0.014 0 50 100 0 5 0 1 0 0 1 5 0 female switching students reported feedback submissions a ct u a l f e e d b a ck s u b m is si o n s 0.001 0.001 0.002 0.003 0.004 0.005 0.006 0.006 0.007 0 .0 08 0.008 0.009 0.01 0. 01 1 0.011 0 50 100 150 0 5 0 1 0 0 male switching students reported feedback submissions a ct u a l f e e d b a ck s u b m is si o n s 0.002 0.002 0.004 0.006 0.008 0.008 0.01 0.01 0.01 0.012 0.012 0.012 0.014 0.014 figure 2: reported versus actually submitted feedback http://www.freestatistics.org/blog/date/2008/jun/30/t12148409608o0dnj2k4s04jil.htm 2. high-quality endogenous variable (c.q. test scores) based on optimal weights of the individual items (section 4.1, [40]); 3. homogeneous sample for which the model is computed. the third condition refers to the fact that student populations may consist of different types of students with specific learning behaviors. in the formentioned statistics course there were 4 groups with distinct characteristics. this is clearly illustrated in section 3 and in figures 1, 2, and 3. instead of computing separate models (for each of the sub populations) section 4.2 presents a comprehensive model with all combinations of interaction effects (male/female and bachelor/switching). this greatly improves the interpretation of the prediction model and allows us to perform differential quality control of the sle. 4.1 model first, a classical regression approach is used to predict the learning outcomes (c.q. exam scores) as a linear function of (k − 1) ∈ n0 exogenous variables of interest. let ~y represent an n × 1 vector for all n ∈ n students (with n > k), containing the weighted sum of g item scores (c.q. scores on individual exam questions): ~y ≡ ∑gj=1 ωj~yj with initial unit weights ωj ≡ 1. in addition, define an n × k matrix x that represents all exogenous variables (including a one-valued column which represents the constant), and a k × 1 parameter vector ~b that represents the weights of the linear combination of all columns in x that is used to describe ~y. the complete model is denoted m1 and is defined by ~y = x~b + ~e where ~e ← iid n(~0, σ2e) represents the prediction error. quality control of statistical learning environments and prediction of learning outcomes through reproducible computing 191 0 20 40 60 80 100 120 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 female bachelor students reported intention to use # r e p ro d u ci b le c o m p u ta tio n s 0.002 0.004 0.006 0.008 0.01 0.01 0.012 0.012 0.014 0.016 0 20 40 60 80 100 120 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 male bachelor students reported intention to use # r e p ro d u ci b le c o m p u ta tio n s 0.002 0.00 2 0.002 0.002 0.004 0.006 0.008 0.01 0.01 0.012 0.012 0.014 0.014 0.016 0 .0 1 8 0 .0 2 0 50 100 0 5 0 1 0 0 1 5 0 female switching students reported intention to use # r e p ro d u ci b le c o m p u ta tio n s 0.002 0.004 0.006 0.008 0.01 0.012 0 50 100 150 0 5 0 1 0 0 male switching students reported intention to use # r e p ro d u ci b le c o m p u ta tio n s 0.002 0.004 0.006 0.00 6 0.008 0.008 0.01 0.01 0.012 0.012 0.012 0.012 0.014 0.014 0.014 0.016 figure 3: reported versus actual reproducible computing http://www.freestatistics.org/blog/date/2008/jun/30/t1214841152sn6jlyhgseclgqm.htm in the second model m2, the prediction of the first model is specified by a linear combination of the individual items (questions) that made up the total exam score. let y represent the n × g matrix that contains all g item scores, then it is possible to define the model ~̂y = y~c + ~a where ~a ← iid n(~0, σ2a). note that there is no constant term in this model. the third model (m3) simply combines m1 and m2 by relating ^̂ ~y to x in the regression model ^̂~y = x~f+~u. the estimator for ~f can be shown to be ~̂f = (x′x)−1 x′^̂~y = (x′x)−1 x′y (y ′y)−1 y ′x (x′x)−1 x′~y ([40]). m3 is likely to yield different results from m1 unless the estimated parameters m2 are (nearly) equal to the original weights ~̂c = (ĉ1, ĉ2, ĉ3, ..., ĉg) ′ ' (ω̂1, ω̂2, ω̂3, ..., ω̂g)′. from a statistical point of view it is not possible to test the improvement that is induced by the objective exam score transformations. the reason for this is that the traditional f-test assumes that the endogenous variables in two models (m1 and m3) to be compared are identical. therefore it is necessary to use an auxiliary model (m∗3) which is based on m3 and includes ~y as an explanatory variable. this extended model ^̂~y = x~f + ~yg + ~u can be shown to be equivalent to ( y (y ′y)−1 y ′x (x′x)−1 x′ − gin ) ~y = x~f + ~u such that it can be concluded that m∗3 is equal to m1 with a transformed endogenous variable. the interesting aspect about this auxiliary regression is the limiting case when g → 0 and y (y ′y)−1 y ′x (x′x)−1 x′ → in because it leads to m1 with ~f = ~b and ~u = ~e. this result is important because it is now easy to test if it is necessary to apply the transformation to the endogenous variable. the null hypothesis is simply h0 : g = 0 versus h1 : g 6= 0 which can be tested with the conventional t-test. in other words, if the null hypothesis is rejected then the transformation is necessary and the estimated parameters ~̂c and ~̂f interpretable. the usefulness of this modeling approach is illustrated in the next subsection. 192 patrick wessa 4.2 empirical evidence the data that was collected from the implemented sle (as described in section 2.3) contained the following exogenous variables: • bcount: actual computations • gender: 0 = female / 1 = male • future: intention to use • pop: 0 = bachelor / 1 = switching • nnzfg: actually submitted feedback messages in peer review • reflection: reported feedback messages in peer review table 1 presents the empirical results of two models (m1 and m3). the endogenous variable in m1 is the sum of all exam questions with unit weights whereas m3 is based on objective exam score transformations (optimal weights of individual questions). table 1: empirical results variable estimate m1 sig. estimate m3 sig. (intercept) 6.935987 * 6.333557 *** bcount 0.033281 0.035939 *** gender -2.166419 -1.465320 pop -4.616769 -0.494553 nnzfg 0.027379 * 0.030161 *** future 0.625812 . 0.639711 *** reflection -0.167591 -0.167980 ** bcount:gender -0.027786 -0.036090 ** bcount:pop 0.018510 -0.007901 gender:pop 3.699211 2.116220 gender:nnzfg -0.001449 -0.013074 ** pop:nnzfg -0.020088 -0.024768 *** gender:future -0.274359 -0.354713 * pop:future -0.038318 -0.143013 gender:reflection 0.161042 0.225622 * pop:reflection 0.289011 0.160574 . bcount:gender:pop 0.019236 0.021735 gender:pop:nnzfg -0.002538 0.009896 . gender:pop:future -0.248325 -0.157158 gender:pop:reflection -0.128991 -0.160408 residual standard error 3.446 0.9593 degrees of freedom 179 179 adj. r-squared 0.1607 0.6626 f-statistic 2.995 *** 21.47 *** signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 from the results in table 1 it is clear that m3 provides unlike m1 a lot of interesting information about the relationship between optimally weighted exam scores and the exogenous variables which are quality control of statistical learning environments and prediction of learning outcomes through reproducible computing 193 under the control of the educator. the percentage of variance explained (adjusted r2) in m3 is more than 66% which allows us to make much better predictions than what is usually reported in otherwise excellent academic articles [25]. as explained before, the traditional f-test cannot be used to test the significance of the improvement. however, the auxiliary regression’s null hypothesis h0 : g = 0 is rejected even if an extremely low type i error is chosen (the p-value is 3.23 × 10−11). this implies that m3 performs significantly better and the objective exam score transformations are necessary. in addition, several diagnostic tests about the final model (m3) are shown in figure 4 they indicate no statistical inadequacies. the most interesting aspects of this analysis are the estimated parameters of m3. with regard to quality control of the sle the following conclusions can be made: • there is a positive effect of performing reproducible, statistical computations (bcount). this effect is significant at the 0.1% type i error level and cannot be measured without optimal weights (m1). however, this effect is only relevant for female students because the parameter that is associated with bcount:gender is also significant and has a negative sign. • submitting feedback messages (in peer review) is very beneficial and improves exam scores (pvalue < 0.01%). this effect is about twice as large for female students than for males (the gender:nnzfg parameter partially offsets the effect for male students). in addition, students from the switching population benefit less from feedback submissions. • the reported “intention to use” (as measured in the usability survey) positively affects exam scores. this effect is strongest for female students. note that previous research has shown that intention is mainly related to student’s perception about the comparative advantage (of the software system) to learn statistics as compared to other alternatives (such as textbooks) [27]. • females who report a high number of submitted feedback messages have significantly lower exam scores. on the other hand, male students who exaggerate their efforts are not in danger of having lower exam scores. this implies that the female exaggeration bias is small but harmful the male exaggeration bias is big and harmless. based on these empirical results it is now possible to control (improve) the quality of the sle: • female students should be encouraged to generate more reproducible computations. • peer review (based on reproducible computing) is highly beneficial to learn statistics especially when it requires students to engage in submitting feedback messages to their peers. male students need to (at least) double their efforts (compared to females) in order to obtain the same effect. students from the switching population also need more feedback submissions than bachelor students. • it is important to explain the sle to students emphasizing the comparative advantages of the system and the potentially improved exam scores. however, male students need more (or better) arguments before they accept the new technology and exhibit an increased degree of “intention to use.” • female students who exaggerate their reported efforts should receive accurate feedback about their real performance which is based on objective measurements. self assessment and reflection about student’s actual efforts (as compared to perceived efforts) should be an integral part of the sle. 194 patrick wessa 6 8 10 12 − 3 − 2 − 1 0 1 2 3 fitted values r e si d u a ls residuals vs fitted 145 31 141 −3 −2 −1 0 1 2 3 − 3 − 2 − 1 0 1 2 3 theoretical quantiles s ta n d a rd iz e d r e si d u a ls normal q−q 31 145 141 6 8 10 12 0 .0 0 .5 1 .0 1 .5 fitted values s ta n d a rd iz e d r e si d u a ls scale−location 31 145 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(2009a), discovering computer-assisted learning processes based on objective exam score transformations, proceedings of the world congress on educational sciences, cyprus int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 2, pp. 104-117 a novel fuzzy artmap architecture with adaptive feature weights based on onicescu’s informational energy răzvan andonie, lucian mircea sasu, angel caţaron răzvan andonie computer science department central washington university, ellensburg, usa and department of electronics and computers transylvania university of braşov, romania e-mail: andonie@cwu.edu angel caţaron department of electronics and computers transylvania university of braşov, romania e-mail: cataron@vega.unitbv.ro lucian mircea sasu applied informatics department transylvania university of braşov, romania e-mail: lmsasu@unitbv.ro abstract: fuzzy artmap with relevance factor (famr) is a fuzzy artmap (fam) neural architecture with the following property: each training pair has a relevance factor assigned to it, proportional to the importance of that pair during the learning phase. using a relevance factor adds more flexibility to the training phase, allowing ranking of sample pairs according to the confidence we have in the information source or in the pattern itself. we introduce a novel famr architecture: famr with feature weighting (famrfw). in the first stage, the training data features are weighted. in our experiments, we use a feature weighting method based on onicescu’s informational energy (ie). in the second stage, the obtained weights are used to improve famrfw training. the effect of this approach is that category dimensions in the direction of relevant features are decreased, whereas category dimensions in the direction of non-relevant feature are increased. experimental results, performed on several benchmarks, show that feature weighting can improve the classification performance of the general famr algorithm. keywords: fuzzy artmap, feature weighting, lvq, onicescu’s informational energy. 1 introduction the fam architecture is based upon the adaptive resonance theory (art) developed by carpenter and grossberg [7]. fam neural networks can analyze and classify noisy information with fuzzy logic, and can avoid the plasticity-stability dilemma of other neural architectures. the fam paradigm is prolific and there are many variations of carpenter’s et al. [7] initial model: art-emap [9], dartmap [8], boosted artmap [27], fuzzy artvar [12], gaussian artmap [28], probart [21], pfam [20], copyright © 2006-2009 by ccc publications a novel fuzzy artmap architecture with adaptive feature weights based on onicescu’s informational energy 105 ordered fam [11], and µartmap [14]. the fam model has been incorporated in the mit lincoln lab system for data mining of geospatial images because of its computational capabilities for incremental learning, fast stable learning, and visualization [25]. one way to improve the fam algorithm is to generalize the distance measure between vectors [10]. based on this principle, we introduced in previous work [2] a novel fam architecture with distance measure generalization: fam with feature weighting (famfw). feature weighting is a feature importance ranking algorithm where weights, not only ranks, are obtained. in our approach, training data feature weights were first generated. next, these weights were used by the famfw network, generalizing the distance measure. potentially, any feature weighting method can be used, and this makes the famfw very general. feature weighting can be achieved, for example, by lvq type methods. several such techniques have been recently introduced. these methods combine the lvq classification with feature weighting. in one of these approaches, rlvq (relevance lvq), feature weights were determined to generalize the lvq distance function [16]. a modification of the rlvq model, grlvq (generalized rlvq), has been proposed in [18]. the srng (supervised relevance neural gas) algorithm [17] combines the ng (neural gas) algorithm [22] and the grlvq. ng [22] is a neural model applied to the task of vector quantization by using a neighborhood cooperation scheme and a soft-max adaptation rule, similar to the kohonen feature map. in [1], we introduced the energy supervised relevance neural gas (esrng) feature weighting algorithm. the esrng is based on the srng model. it maximizes onicescu’s ie as a criteria for computing the weights of input features. the esrng is the feature weighting algorithm we used in [2], in combination with our famfw algorithm . famr is a fam incremental learning system introduced in our previous work [4]. during the learning phase, each sample pair is assigned a relevance factor proportional to the importance of that pair. the famr has been successfully applied to classification, probability estimation, and function approximation. in famr, the relevance factor of a training pair may be user-defined, or computed, and is proportional to the importance of the respective pair in the learning process. in the present paper, we focus on the famr neural network, the esrng feature weighting algorithm, and the distance measure generalization principle. we contribute the following: 1. we introduce a novel famr architecture with distance measure generalization: famr with feature weighting (famrfw), adapting the famfw model for the famr case. 2. compared to [2], we include new experiments on standard benchmarks. we first introduce the basic fam and famr notations (section 2), and the esrng feature weighting algorithm (section 3). in section 4, we describe the new famrfw algorithm, which uses a weighted distance measure. section 5 contains experimental results performed with the famrfw method. section 6 contains the final remarks. 2 a brief description of the famr we will summarize the fam standard architecture and the famr learning mechanism, which differentiates it from the standard fam. 2.1 the fam architecture a detailed fam description can be found in carpenter’s et al. seminal paper [7], but more simplified presentations are given in [26] and [19]. 106 răzvan andonie, lucian mircea sasu, angel caţaron figure 1: fuzzy artmap architecture [7]. the fam architecture consists of a pair of fuzzy art modules, arta and artb, connected by an inter–art module called mapfield (see fig. 1). arta and artb are used for coding the input and output patterns, respectively, and mapfield allows mapping between inputs and outputs. the arta module contains the input layer fa1 and the competitive layer f a 2 . a preprocessing layer f a 0 is also added before fa1 . analogous layers appear in artb. the initial input vectors have the form: a = (a1, . . . , an) ∈ [0, 1]n. a data preprocessing technique called complement coding is performed by the fa0 layer in order to avoid node proliferation. each input vector a produces the normalized vector a = (a, 1 − a) whose l1 norm is constant: |a| = n. let ma be the number of nodes in fa1 and na be the number of nodes in f a 2 . due to the preprocessing step, ma = 2n. the weight vector between fa1 and f a 2 is w a. each fa2 node represents a class of inputs grouped together, denoted as a category. each fa2 category has its own set of adaptive weights stored in the form of a vector waj , j = 1, . . . na, whose geometrical interpretation is a hyper-rectangle inside the unit box. similar notations are used for the artb module. for a classification problem, the class index is the same as the category number in fb2 , thus artb can be substituted with a vector. the mapfield module allows fam to perform associations between arta and artb categories. the number of nodes in mapfield is equal to the number of nodes in fb2 . each node j from f a 2 is linked to each node from fb2 via a weight vector w ab j . the learning algorithm is sketched below. for each training pattern, the vigilance parameter factor ρa is set equal to its baseline value, and all nodes are not inhibited. for each (preprocessed) input a, a fuzzy choice function is used to get the response for each fa2 category: tj(a) = |a ∧ waj | αa + |waj | , j = 1, . . . , na (1) let j be the node with the highest value computed as in (1). if the resonance condition from eq. (2) is not fulfilled: ρ(a, waj ) = |a ∧ waj | |a| ≥ ρa, (2) then the jth node is inhibited such that it will not participate to further competitions for this pattern and a new search for a resonant category is performed. this might lead to creation of a new category in arta. a novel fuzzy artmap architecture with adaptive feature weights based on onicescu’s informational energy 107 a similar process occurs in artb and let k be the winning node from artb. the fb2 output vector is set to: ybk = { 1, if k = k 0, otherwise k = 1, . . . , nb (3) an output vector xab is formed in mapfield: xab = yb ∧ wabj . a mapfield vigilance test controls the match between the predicted vector xab and the target vector yb: |xab| |yb| ≥ ρab (4) where ρab ∈ [0, 1] is a mapfield vigilance parameter. if the test from (4) is not passed, then a sequence of steps called match tracking is initiated (the vigilance parameter ρa is increased and a new resonant category will be sought for arta); otherwise learning occurs in arta, artb, and mapfield: wa(new)j = βa ( a ∧ wa(old)j ) + (1 − βa)w a(old) j (5) (and the analogous in artb) and wabjk = δkk, where δij is kronecker’s delta. with respect to βa, there are two learning modes: i) fast learning for βa = 1 for the entire training process, and ii) fast-commit and slow-recode learning corresponds to setting βa = 1 when creating a new node and βa < 1 for subsequent learning. 2.2 the famr learning mechanism the main difference between the famr and the original fam is the updating scheme of the wabjk weights. the famr uses the following iterative updating [4]: w ab(new) jk =    w ab(old) jk if j 6= j w ab(old) jk + qt qnew j ( 1 − w ab(old) jk ) w ab(old) jk ( 1 − qt qnew j ) if k 6= k (6) where qt is the relevance assigned to the tth input pattern (t = 1, 2, . . . ), and qnewj = q old j + qt. the relevance qt is a real positive finite number directly proportional to the importance of the experiment considered at step t. this wabjk approximation is a correct biased estimator of the posterior probability p(k|j), the probability of selecting the k-th artb category after having selected the j-th arta category [4]. let q be the vector [q1 . . . qna ]; initially, each qj (1 ≤ j ≤ na) has the same initial value q0. na and nb are the number of categories in arta and artb, respectively. these are initialized at 0. for incremental learning of one training pair, the famr mapfield learning scheme is described by algorithm 1. the vigilance test is: nb w ab jk ≥ ρab (7) for a clearer presentation, not to create a confusion between vector relevancies and feature weights, we will assume in all our following experiments that relevancies are set to a constant positive value. since we actually do not use relevances, is this famr equivalent to the standard fam model, as introduced in [7]? the answer is no, because, unlike the standard fam: i) the famr accepts one-to-many relationships; and ii) the famr is a conditional probability estimator, with an estimated convergence rate computed in [4]. 108 răzvan andonie, lucian mircea sasu, angel caţaron algorithm 1 the t-th iteration in the famr mapfield algorithm [4]. step 1. accept the t-th vector pair (a, b) with relevance factor qt. step 2. find a resonant category in artb or create a new one. if |b ∧ wbk|/|b| < ρb, for k = 1, . . . , nb then nb = nb + 1{add a new category to artb} k = nb if nb > 1 then wabjk = q0 nbqj , for j = 1, . . . , na {append new component to wabj } wabjk = w ab jk − wab jk nb−1 , for k = 1, . . . , k − 1; j = 1, . . . na{normalize} end if else let k be the index of the artb category passing the resonance condition and with maximum activation function. end if step 3. find a resonant category in artb or create a new one. if |a ∧ waj |/|a| < ρa, for j = 1, . . . , na then na = na + 1{add a new category to arta} j = na qj = q0 {append new component to q} wabjk = 1/nb, for k = 1, . . . , nb {append new row to w ab} else let j be the index of the arta category passing the resonance condition and with maximum activation function. end if step 4. j, k are winners or newly added nodes. check if match tracking applies. if vigilance test (7) is passed then {learn in mapfield} qj = qj + qt wabjk = w ab jk + qt qj (1 − wabjk ) wabjk = w ab jk ( 1 − qt qj ) , for k = 1, . . . , nb, k 6= k else perform match tracking and restart from step 3 end if 3 the esrng feature weighting algorithm we use the esrng feature weighting algorithm to compute the generalized distance measure in the famrfw. details of the esrng algorithm can be found in [1]. is is based on onicescu’s ie, and approximates the unilateral dependency of random variables by parzen windows approximation. before outlining the principal steps of the esrng method, we review the basic properties of the ie. 3.1 onicescu’s informational energy for a discrete random variable x with probabilities pk, the ie was introduced in 1966 by octav onicescu [24] as e(x) = ∑n k=1 p 2 k. for a continuous random variable y, the ie was defined by silviu guiaşu [15]: e(y) = ∫ +∞ −∞ p2(y)dy, a novel fuzzy artmap architecture with adaptive feature weights based on onicescu’s informational energy 109 where p(y) is the probability density function. for a continuous random variable y and a discrete random variable c, the conditional ie is defined as: e(y|c) = ∫ y m∑ m=1 p(cm)p 2(y|cm)dy. in order to study the interaction between two random variables x and y, the following measure of unilateral dependency was introduced by andonie et al. [3]: o(y, x) = e(y|x) − e(y) with the following properties: 1. o is not symmetrical with respect to its arguments; 2. o(y, x) ≥ 0 and the equality holds iff y and x are independent; 3. o(y, x) ≤ 1 − e(y) and the equality holds iff y is completely dependent on x. this measure quantifies the unilateral dependence characterizing y with respect to x and corresponds to the amount of information detained by x about y. 3.2 the feature weighting procedure esrng is an online algorithm which adapts a set of lvq reference vectors by minimizing the quantization error. at each iteration, it also adapts the input vector feature weights. the core of the method is based on the maximization of the o(y, c) measure. to connect input vector xi with its class j, represented by vector wj, we use a simple transform. we consider a continuous random variable y with its samples yi = λi(xi − wj), i = 1, . . . , n, where: • λ is the vector of weights; • xi, i = 1, . . . , n, are the training vectors, each of them from one of the classes c1, c2, . . . , cm; • wj, j = 1, . . . , p, are the lvq determined class prototypes. assuming that the m class labels are samples of a discrete random variable denoted by c, we can use gradient ascend to iteratively update the feature weights by maximizing o(y, c): λ(t+1) = λ(t) + α n∑ i=1 ∂o(y, c) ∂yi i (xi − wj) . from the definition of o(y, x), we obtain: o(y, c) = e(y|c) − e(y) = m∑ p=1 1 p(cp) ∫ y p2(y, cp)dy − ∫ y p2(y)dy. (8) this expression involves a considerable computational effort. therefore, we approximate the probability densities from the integrals using the parzen windows estimation method. the multidimensional gaussian kernel is [13]: g(y, σ2i) = 1 (2π) d 2 σd · e− yty 2σ2 (9) 110 răzvan andonie, lucian mircea sasu, angel caţaron where d is the dimension of the definition space of the kernel, i is the identity matrix, and σ2i is the covariance matrix. we approximate the probability density p(y) replacing each data sample yi with a gaussian kernel, and averaging the obtained values: p(y) = 1 n n∑ i=1 g(y − yi, σ2i). we denote by mp the number of training samples from class cp. we have: ∫ y p2(y, cp)dy = 1 n2 mp∑ k=1 mp∑ l=1 g(ypk − ypl, 2σ2i) and ∫ y p2(y)dy = 1 n2 n∑ k=1 n∑ l=1 g(yk − yl, 2σ2i), where ypk, ypl are two training samples from class cp, whereas yk, yl represent two training samples from any class. equation (8) can be rewritten, and we obtain the final esrng update formula of the feature weights: λ(t+1) = λ(t) − α 1 4σ2 g(y1 − y2, 2σ2i) · (y2 − y1)i · ·(x1 − wj(1) − x2 + wj(2)), where wj(1) and wj(2) are the closest prototypes to x1 and x2, respectively. the esrng algorithm has the following general steps: 1. update the reference vectors using the srng scheme. 2. update the feature weights. 3. repeat steps 1 and 2, for all training set samples. this algorithm uses a generalized euclidean distance. the updating formula for the reference vectors can be found in [1]; we will not explicitly use this formula in the present paper. the esrng algorithm generates numeric values assigned to each input feature, quantifying their importance in the classification task: the most relevant feature receives the highest numeric value. we use these factors as feature weights in the famrfw algorithm. 4 famrfw – a novel neural model the famrfw is a famr architecture with a generalized distance measure. for an arta category wj, we define its size s(wj): s(wj) = n − |wj| (10) and the distance to a normalized input a: dis(a, wj) = |wj| − |a ∧ wj| = n∑ i=1 dji, (11) a novel fuzzy artmap architecture with adaptive feature weights based on onicescu’s informational energy 111 where (dj1, . . . , djn) = wj − a ∧ wj. in [10] it is shown that: tj(a) = n − s(wj) − dis(a, wj) n − s(wj) + αa (12) ρ(a, waj ) = n − s(wj) − dis(a, wj) n (13) a generalization of dis(a, wj) is the weighted distance: dis(a, wj; λ) = n∑ i=1 λidji, (14) where λ = (λ1, . . . , λn), and λi ∈ [0, n] is the weight associated to the ith feature. we impose the constraint |λ| = n. for λ1 = · · · = λn = 1, we obtain in particular the famr. charalampidis et al. [10] used the following weighted distance: dis(x, wj|λ, ref) = n∑ i=1 (1 − λ)lrefj + λ (1 − λ)lji + λ dji, (15) where lrefj is a function of category j’s lengths of the hyper-rectangle, and λ is a scalar in [0, 1]. in our case, the function dis(a, wj; λ) does not depend on sides of the category created during learning, but on the computed feature weights. this makes our approach very different than the one in [10]. the effect of using distance dis(a, wj; λ) for a bidimensional category is depicted in fig. 2(a). the hexagonal shapes represent the points situated at constant distance from the category. these shapes are flattened in the direction of the feature with a larger weight and elongated in the direction of the feature with a smaller weight. this is in accordance with the following intuition: the category dimension in the direction of a relevant feature should be smaller than the category dimension in the direction of a non-relevant feature. hence, we may expect that more categories will cover the relevant directions than the non-relevant ones. (a) bounds for constant weighted distance dis(a, wj; λ) for various values of λ. the rectangle in the middle represents a category. (b) bounds for constant distance dis(a, wj; λ) for the null feature weight. the rectangle in the middle represents the category. figure 2: geometric interpretation of constant distance when using dis(a, wj; λ) for bidimensional patterns. for a null weight feature (fig. 2(b)), the bounds are reduced to parallel lines on both sides of the rectangle representing the category. in this extreme case, the discriminative distance is the one along the remaining feature dimension. this is another major difference between our approach and the one in [10], where, while using function dis(x, wj|λ, ref), the contours of a constant weighted distance are inside 112 răzvan andonie, lucian mircea sasu, angel caţaron some limiting hexagons. in our method, the contour is insensitive to the actual value of the null weighted feature. 5 experimental results we test the famrfw for several standard classification tasks, all from the uci machine learning repository [5]. the experiments are performed on the famr and the famrfw architectures. the two famrfw stages are: i) the λ feature weights are obtained by the esrng algorithm; ii) these weights are used both for training and testing the famr. a nice feature of the fam architectures and the esrng algorithm is the on-line (incremental) learning capability, i.e., the training set is processed only once. this type of learning is especially useful when dealing with very large datasets, since it can reduce significantly the computational overhead. for famr training and for both famrfw stages we use on-line learning. 5.1 methodology for each experiment, we use three-way data splits (i.e., the available dataset is divided into training, validation, and test sets) and random subsampling. random subsampling is a faster, simplified version of k-fold cross validation: 1. the dataset is randomized. 2. the first 60% of the dataset is used for training and the next 20% for validation (i.e., for tuning the model parameters). the following parameters are optimized using a simple “grid-search” for ρa, ρab ∈ {0, 0.1, . . . , 0.9} and βa ∈ {0, 0.1, . . . , 1}. the goal is to allow both fast learning and fast-commit slow-recode. the optimal parameter values are the ones producing the highest pcc and the lowest number of arta categories. 3. the network with optimal parameters is trained with the joint training + validation data. 4. the last 20% of the dataset is used for testing. as a result, the percent of correct classification (pcc) and the number of generated arta categories are computed. 5. repeat this procedure six times. the ρa value, optimized during training/validation, controls the number of generated arta categories. after training/validation, this number does not change. for ρa > 0, some test vectors may be rejected (i.e., not classified). in all our experiments, after the arta categories were generated, we set ρa = 0 for testing. this has the following positive effects: • all test vectors are necessarily classified. • we obtain experimentally better classification results, both for the famr and the famrfw, compared to the ones with optimized ρa values. this is shown in table 1, for all considered classification tasks. the feature weights values in the famrfw are the ones mentioned in the following sections. a novel fuzzy artmap architecture with adaptive feature weights based on onicescu’s informational energy 113 table 1: average pcc test set results using the optimized ρa (computed in the validation phase) vs. using ρa = 0. famr famrfw optimized ρa ρa = 0 optimized ρa ρa = 0 breast cancer 86.54% 91.22% 91.22% 91.22% balance scale 75.86% 76.53% 75.92% 78.13% wine recognition 83.33% 84.72% 83.79% 89.35% ionosphere 85.44% 88.96% 85.91% 89.43% 5.2 breast cancer classification this dataset (formally called wisconsin diagnostic breast cancer) includes 569 instances. the instances are described by 30 real attributes. the given features are computed from a digitized image of a fine needle aspirate (fna) of a breast mass. the famrfw generated weights are: [0.784, 0.816, 0.795, 2.847, 0.784, 0.784, 0.784, 0.784, 0.784, 0.784, 0.784, 0.784, 0.785, 0.808, 0.784, 0.784, 0.784, 0.784, 0.784, 0.784, 0.784, 0.829, 0.828, 5.047, 0.784, 0.784, 0.784, 0.784, 0.784, 0.784]. in table 2, we observe that the average pcc for the famr and the famrfw is the same, but the famrfw has much less arta categories than the famr. table 2: classification performance for the breast cancer problem. test famr famrfw no. no. of arta categories pcc no. of arta categories pcc 1 61 93.85% 24 87.71% 2 7 90.35% 7 93.85% 3 10 95.61% 8 91.22% 4 39 85.08% 6 88.59% 5 6 92.98% 6 94.73% 6 6 89.47% 5 91.22% average 21.5 91.22% 9.33 91.22% 5.3 balance scale classification this dataset was generated to model psychological experimental results. each example is classified as having the balance scale tip to the right, tip to the left, or be balanced. the attributes are the left weight, the left distance, the right weight, and the right distance. the correct way to find the class is the greater of (left-distance * left-weight) and (right-distance * right-weight). if they are equal, it is balanced. the set contains 625 patterns, with a uneven distribution of the three classes; each input pattern has 4 features. the esrng generated feature weights are λ = [1.002, 1.113, 0.827, 1.058]. the famrfw has better classification accuracy and less arta categories than the famr (table 3). 5.4 wine recognition the wine recognition data are the results of a chemical analysis of wines grown in the same region in italy, but derived from three different cultivars. the analysis determined the quantities of 13 constituents found in each of the 3 types of wines. the dataset contains 178 instances. 114 răzvan andonie, lucian mircea sasu, angel caţaron table 3: classification performance for the balance scale problem. test famr famrfw no. no. of arta categories pcc no. of arta categories pcc 1 95 74.4% 53 75.2% 2 70 80.0% 39 80.0% 3 22 78.4% 54 81.6% 4 75 75.2% 44 85.6% 5 125 71.2% 69 72.0% 6 62 80.0% 107 74.4% average 74.83 76.53% 61 78.13% the esrng algorithm produced the weights λ = [0.900, 0.757, 0.659, 1.668, 2.349, 0.702, 1.028, 0.668, 0.774, 0.874, 0.666, 0.701, 1.253]. the famrfw classification results are better, with less generated arta categories (table 4). table 4: classification performance for the wine recognition problem. test famr famrfw no. no. of arta categories pcc no. of arta categories pcc 1 10 88.88% 6 86.11% 2 15 97.22% 10 97.22% 3 32 69.44% 11 86.11% 4 17 83.33% 11 86.11% 5 55 80.55% 39 94.44% 6 12 88.88% 8 86.11% average 23.5 84.71% 14.16 89.35% 5.5 ionosphere this binary classification problem starts from collected radar datasets. the data come from 16 highfrequency antennas, targeting the free electrons in the ionosphere. “good” radar returns are those showing evidence of some type of structure in the ionosphere. “bad” returns are those passing through the ionosphere. there are 351 instances and each input pattern has 34 features. the esrng generated λ vector is: [0.551,0.520,1.179,1.168,1.301,1.180,0.940,1.272,1.024,0.903, 0.843,0.976,0.870,0.844,0.807,0.877,0.893,1.012,0.994,1.012,0.964,1.061,1.029,1.227,0.978,1.020,0.943, 1.027,1.087,1.032,0.978,1.117,0.999,1.374]. on average, famrfw produced much less arta categories than the famr. this time, the famr produced a slightly better pcc (table 5). 6 conclusions according to our experiments, using the feature relevances and the generalized distance measure may improve the classification accuracy of the famr algorithm. in addition, the famrfw uses less arta categories, which is an important factor. the number of categories controls the generalization a novel fuzzy artmap architecture with adaptive feature weights based on onicescu’s informational energy 115 table 5: classification performance for the ionosphere problem. test famr famrfw no. no. of arta categories pcc no. of arta categories pcc 1 28 81.69% 8 90.14% 2 20 81.69% 8 85.91% 3 17 91.54% 7 83.09% 4 9 94.36% 8 88.73% 5 5 90.14% 5 94.36% 6 9 94.36% 5 94.36% average 14.66 88.96% 6.83 89.43% capability and the computational complexity of a fam architecture. this generalization is a trade-off between overfitting and underfitting the training data. it is good to minimize the number of categories if this does not decrease too much the classification accuracy. the esrng feature weighting algorithm can be replaced by other weighting methods. we have not tested the function approximation capability of the famrfw neural network because the esrng weighting algorithm is presently restricted to classification tasks. lvq methods can be extended to function approximation [23] and we plan to adapt the esrng algorithm in this sense. this would enable us to test the famrfw + esrng procedure on standard feature approximation and prediction benchmarks. our approach is at the intersection of two major computational paradigms: 1. carpenter and grossberg’s adaptive resonance theory, an advanced distributed model where parallelism is intrinsic to the problem, not just a mean to speed up [6]. 2. onicescu’s informational energy and the unilateral dependency measure. to the best of our knowledge, we are the only ones using onicescu’s energy in neural processing systems. bibliography [1] r. andonie and a. caţaron. feature ranking using supervised neural gas and informational energy. in proceedings of ieee international joint conference on neural networks (ijcnn2005), canada, montreal, july 31 august 4, 2005. 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[28] j. williamson. gaussian artmap: a neural network for fast incremental learning of noisy multidimensional maps. neural networks, 9, 1996, 881–897. international journal of computers communications & control issn 1841-9836, 9(4):510-523, august, 2014. study on directed trust graph based recommendation for e-commerce system h.d. zhong, s. zhang, y. wang, y. shu haidong zhong institute of modern logistics, zhejiang wanli university no. 8 south qianhu rd., ningbo, zhejiang, 315100, pr, china zhong_hai_dong@163.com shaozhong zhang institute of electronics and information, zhejiang wanli university no. 8 south qianhu rd., ningbo, zhejiang, 315100, pr, china dlut_z88@163.com yanling wang institute of modern logistics, zhejiang wanli university no. 8 south qianhu rd., ningbo, zhejiang, 315100, pr, china yanling_wang1@yahoo.com.cn yonggang shu* school of tourism and city management, zhejiang gongshang university xuezheng street, hangzhou, 310018, pr, china *corresponding author: zhd_1981@163.com abstract: automated recommender systems have played a more and more important role in marketing and ever increasingly booming e-commerce systems. they provide useful predictions personalized recommendations according to customers characteristics and a variety of large and complex product offerings. in many of these recommendation technologies collaborative filtering (cf) has proven to be one of the most successful recommendation method, which has been widely used in many e-commerce systems. the success of cf recommendation depends mainly on locating similar neighbors to get recommendation items. however, many scholars have found that the process of finding similar neighbors often fail, due to some inherent weaknesses of cf based recommendation. in view of this, we propose a trust feedback recommendation algorithm based on directed trust graph (dtg), which is able to propagate trust relationship. in our approach, there is no need to compute similarity between users, but utilize the trust relation between them to conduct prediction calculation. based on the analysis of human trust perception, we incorporate the process into our recommendation algorithm. experimental evaluation on real life epinions datasets shows that the effectiveness and practicability of our approach. keywords: trust, recommendation, graph, e-commerce, feedback. 1 introduction with the fast development of networking systems, an ever-increasing number of merchants are attracted to swarm into e-commerce all over the world [1]. accordingly, the internet is changing from generally simple information exchange and extraction tool into the biggest virtualized market space presenting a great number of commercial services, ranging from electronic web-stores, on-line booking and service center to other social services [2]. with the popularization of the internet, the amount of available information are exponentially growing and e-commerce systems structure becomes more complicated when it provides more and more choices for users [3,4]. under the circumstances, perform transaction tasks typically involved in e-commerce, customers copyright © 2006-2014 by ccc publications study on directed trust graph based recommendation for e-commerce system 511 have often to spend a large amount of time navigating among thousands of web pages to explore desired products and make their purchases. on the other hand, electronic business suppliers suffer from the problems of promoting their commodities to their potential customers in an effective way, considering their preferences, habits and other personalized characteristics. to deal with this challenge, researchers have advanced a recommendation approach which automatically analyze and mining e-commerce system visitors trading and browsed items data to filter web page information, classified newsgroup messages, and recommend valuable merchandise items [5]. recommender systems, one of the most important computer-based intelligent approaches to find out the most appropriate services or goods from a large amount of products, are proved to be important tools that overcome the information overload by sifting through the large set of data and recommending information relevant to the user [6–10]. typically, in e-commerce environment a recommender system analyzes trading data between consumer and sellers and items to find associations among them, and the items bought by similar users are presented as recommendations. using this technology, some e-commerce systems, such as amazon.com, ebay.com and netflix.com, are reported to have enhanced e-commerce sales by transforming ecommerce system browsers to buyers, increasing cross-selling and building customer loyalty [11]. as early as in the early 1990s intensive studies have been conducted in recommender systems, and many scholars deem them as knowledge discovery in database (kdd) systems or electronic agent systems [12–14]. up to now, the existing recommendation means can be generally classified as content based, collaborative, knowledge-based, demographic, and utility based [15–17], among which collaborative filtering based personalized recommendation is proved to be one of the most successfully used technology [15]. in the newer, narrower sense, collaborative filtering builds correlations between pairs of items by making automatic predictions (filtering) about the interests of a user, and then figure out recommendations by finding items with high similarity to many users (collaborating). the acquiescent assumption of the collaborative filtering approach is that if a person a has the same opinion as a person b on an issue, a is more likely to have b’s opinion on a different issue t than to have the opinion on t of a person chosen randomly. for example, a collaborative filtering recommendation system for laptop preferences could make forecasts about which laptop a user should like given a partial list of that user’s preferences (likes or dislikes) [10]. however there are two main inherent weaknesses in the collaborative filtering based recommendation systems [8]: (1) it is a challenge to find similar user, because the probability of two random users have rated any items in common is very small, and hence they are hardly comparable. (2) if ad hoc user profiles with the goal of being considered as similar to the target user a created, cf based recommendation can be easily attacked and recommendation precision can be greatly influenced. in order to overcome these shortages, massa p. and avesani p. proposed a trust-aware recommendation approach [8]: make use of trust propagation to search for trustable users based on the trust network instead of finding similar users as collaborative filtering does. the products bought or preferred by these users are then recommended to the active user over the trust network. three years later, ray s. and mahanti a. put forward a new idea to improve prediction accuracy for trust aware recommender systems by removing all the trust statements that fall below a threshold correlation value to reconstruct the trust network [18]. they assume that a trust statement passed between two users should imply that similarity between both users will be relatively high, and generally utilize all the trust statements present in the data of similar users, calculated based on ratings, to make predictions. experiment on epinions datasets shows their method has better performance and effectiveness than that of the original approach for different levels of trust propagation and threshold correlation values [18]. however, every trust statement passed between users does not imply the correlation between them will also be high, because one user may pass trust statements to another user on the basis of perceived notion that 512 h.d. zhong, s. zhang, y. wang, y. shu his (or her) predilections match with others, while similarity calculated based on ratings may show that they are different. in our opinion, in trust based recommendation method users similarity calculating is not necessary. and we propose an approach where we construct a directed trust graph of users without considering the similarity between them. based on the dtg we present a trust feed back recommendation algorithm (tfbra) to make recommendations for a user. it shows a substantial good performance for generating predictions through experimental evaluation on epinions datasets [19]. the rest of the paper is organized as fellows. in section 2 we review some existing techniques, which are related to our work. in section 3 the approach of trust based recommendation is explained in detail. section 4 conducts verifying experiment, using real-life epinions datasets, and the results of our evaluations is discussed. the last section draws conclusions and points out the related possible future work. 2 related work 2.1 trust since the birth of human beings and human social interactions trust came into being, and almost every aspect of a persons life is based on some form of trust. undoubtedly, trust is positive and vital to humanity since it is part of love and friendship, and meaningful relationships depend upon it. presently, trust is a research interest of many disciplines including management[18], marketing [21] and information systems [22]. however, scholars have difficult in reaching a consensus what exactly trust is, and they disagree even on the basic definitions. presumptively, definitions of trust generally indicate a situation with characteristics of the following aspects [23–25]: one party (trustor) is willing to depend on the actions of another party (trustee); the situation is directed to the future. moreover, the trustor (voluntarily or forcedly) abandons control over the actions performed by the trustee. as a consequence, the trustor is uncertain about the outcome of the other’s actions; he can only develop and evaluate expectations. the uncertainty involves the risk of failure or harm to the trustor if the trustee will not behave as desired. the rapid expansion of e-commerce conducts the research of trust in social science to new challenges. at the same time increasing the importance of trust and the urgency to know what customers shopping decision or preference [26], such as, interpersonal relationship between customers and sellers has been dis-intermediated by the method, and had to be improved upon. additionally, e-commerce systems should be well designed or be made to imply the sellers are trustable, even if the regardless of seller’s actual trustworthiness is not high. many researchers have intensively studied the structure and formation mechanisms of trust from the aspects of both individual and organizational, and have identified five types antecedents to consumer trust, including institution-based, personality-based and calculation-based sources for trust building [27–30]. 2.2 recommendation system since the first appearance of the first paper on collaborative filtering in the mid-1990s, recommender systems have attracted many scholars’ attention, and become an important research areas [31]. over the last decade new approaches have been proposed to improve the efficiency and practicability of recommender system both in the industry and academia. at present recommender system related researches are still popular issues because they constitute problem-rich study on directed trust graph based recommendation for e-commerce system 513 research area and practical use requirement that help the potential buyers to solve information overload by providing personalized recommendation, and useful services according to their characteristics. over the past decades a lot of research work has been conducted on recommendation technologies (or algorithms), which used a wide range of statistical, artificial intelligence, information science and other techniques. these researches have observably improved the state-of-art in comparison to the previous recommender systems which used collaborativeand content-based heuristics. up to now, algorithmic means adopted in recommender systems can be divided into (1) content-based recommendation, collaborative, or hybrid, based recommendation and (2) heuristic-based or model-based recommendation based on the types of recommendation approaches used for the rating evaluation. some of these methods are utilized in the industrialstrength recommender systems, e.g. the ones deployed at amazon [32], movielens [33] and versifi technologies. however, despite there are many recommendation means, the present used recommender systems still need to be intensively improved, including better approaches for representing the recommended product items, more advanced and efficient recommendation algorithm or methods, utilization of multi-criteria ratings, to make recommendation methods more effective and applicable. in recent years, scholars proposed a new recommendation method: trust based recommendation [8, 34, 35], and proved it more robust against shilling attacks and more capable and effective in generating recommendations for e-commerce system visitors, however they still need to calculate users similarity. trust based recommendation systems are proved to make more accurate recommendation compared with traditional systems, because they utilize a new concept of trust propagation over a trust network. in [8], it has been experimentally shown how trust based recommender system outperform traditional recommendation methods on dataset from epinions.com. 3 trust based recommendation algorithm in this section we start by introducing basic notations about trust cognitive process and concept of dtg. based on the analysis of human trust perception, we incorporate the process into our recommendation algorithm. and then we present the logical architecture of trust based recommendation approach. 3.1 trust cognitive analysis from the viewpoint of recommendation in human society, it is easy to find that a person is usually more inclined to trust the recommendation information from his or her “acquaintance” than that of “strangers”. in e-commerce recommendation systems all the people can be connected by trust relationship, which can be denoted as a dtg. as shown in figure 1 (a), solid line represents the direct trust relationship between users (n1 trustn2, n2 trustn3). through the trust relationship of n2 andn3, n1 trust n3 indirectly. if there is no trust relation between n2 andn3, it is impossible to create the trust relationship between n1 and n3 (as is shown in figure (b)). here we first define some basic trust related definitions in graph theory definition 1 (direct trust) direct trust can be defined as an triple< i, j, dti,j >,stand for the directed edge from node i point to nodej,and direct trust value of node i to node j is defined asdti,j, which is a discrete integer value in [1, 5], and a greater value represents a deeper trust degree. definition 2 (indirect trust) indirect trust can be defined as an triple< i, j, idti,j >,let iandjstand for nodes in the trust graph, iis attainable tojthrough limited hoph(h > 1, h ∈ 514 h.d. zhong, s. zhang, y. wang, y. shu figure 1: trust process in human activity n),and indirect trust value of node i to node j is defined as idti,j, which is a discrete integer value in[1, 5], and a greater value represents a deeper trust degree. theorem 1 (direct trust preference principle) for any node i in the trust graph, its direct trust nodes are more credible than indirect trust nodes, where there are trust edges from i′sdirect trust nodes to its indirect trust nodes. for example, in figure 1 (a) dtn1n2 is more credible than idtn1n3 (or dtn1n2 > idtn1n3). 3.2 dtg based trust feedback on the base of trust perception process presented above, we propose a dtg based trust feedback algrithm (the general principle is shown in figure 2). in figure 2 the edges represents the trust relationship between different nodes. while asking for recommendations of u 0, the node just send a trust query information to its direct trust nodes (u 1 ,u 3 ,u 15 and u 16), and these nodes also send the trust query information to their trusted node similarly, until the end of trust inquiry control process and then all the queried node feedback their trust value of any products. through the trust feedback process, comprehensive score of the items recommended by trusted node (include both direct trust nodes and indirect trust nodes) for u 0 . 3.3 dtg establishment as narrated before, direct trust node and indirect nodes are used to represent the trust relationship based on graph theory. the step of establishing a dtg is instroduced here in detail below. definition 3 (direct trust node, dtn) dtn of a certain node refers to any node that has direct trust relationship with it. definition 4 (indirect trust node, idtn) idtn of a certain node refers to any node that has indirect trust relationship with it. in this paper we adopt a five level value: 1,2,3,4,5 to repersent the trust(driect trust and indriect trust) value between one another. the higher trust value implies a further trust. in our framework the relationship between any node and its neighbor can be devided into three categories: direct trust relationship, indirect trust relationship and irrelevant nodes. for example, in table 1, u0 has 4 dtns. figure 3 shows the process of establishing a dtg. 3.4 dtg based feedback trust recommendation fusion calculation according to the transitivity attribute of trust, recommendated items for a specific node can be caculated on the base of feedback trust in dtg. if node ni trust node nj, ni is more likely to accept the items that nj gives a high rating value. and highly-rated-value items of dtns of ni account for a larger proportion in recommendated items than that of idtns of ni. study on directed trust graph based recommendation for e-commerce system 515 figure 2: dtg based trust feedback principle table 1: relationship between u0 and its neighors peer neighbors dtn u0 u1 yes u0 u3 yes u0 u15 yes u0 u16 yes 516 h.d. zhong, s. zhang, y. wang, y. shu figure 3: the process of establishing a dtg study on directed trust graph based recommendation for e-commerce system 517 definition 5 the recommendated items prv (i)of a certain node ni can be abtained by comprehensive evaluation on the feedback trust value and high-rated-value items of its idtns and dtns. recommedated items from the trusted nodes(include dtns and idtns) of ni can be defined as the fellowing formula: prv (i) = level∑ l=1 w(l, α) ∑ v∈tnll tsv ∗ rvi level∑ l=1 w(l, α) ∑ v∈ntll tsv (1) in the formula level denotes the distance from node ni,w(l, α) denotes weighted control factor function of the trust feedback value, which is related to the level and attenuation factor α. in our opinion w(l, α) be defined as the fellowing formula: w(l, α) =   α , level = 1 α level∏ l=1 1 l , level > 1 (2) in the formula α denotes the trust attenuation factor or initial trust factor. 3.5 trust feedback recommendation algorithm as it has already been stated above, in e-commerce trading process people are more likely to accept the recommendations from direct trust persons. in order to simulate the trust based recommendation idea, we designed the trust feedback recommedation algrithm, the pseudo-code is as follows. //tfbra pseudo-code trustfeedbackrecommedationalgrithm input: s, maxlevel, α /* s denotes the active user needs for recommendations, maxlevel denotes maxium search level, α denotes attenuation factor*/ output: pr /*recommended items list with high predict rating value */ begin while l <= maxlevel do search dtns in the lth level in dtg if (nodes in searched dtns of s is not in the trust nodes list tnll) put the nodes into a trust nodes list tnll endif search the dtns in tnll if (l > maxlevel ) endwhile else l add-self endwhile prv (i) = level∑ l=1 w(l,α) ∑ v∈t nll tsv∗rvi level∑ l=1 w(l,α) ∑ v∈nt ll tsv /∗ calcuate the predict rating value of each item of users in tnl, let prv (i) represent the predict rating value of item i*/ put the items with highest predicted rating value (prv ) into pr end 518 h.d. zhong, s. zhang, y. wang, y. shu 4 experimental evaluation and result discussion 4.1 experiment dataset in order to examine the effectiveness of our algorithm, we perform our experiment on dataset from epinions. the epinions dataset was collected by paolo massa in a 5-week crawl (november/december 2003) from the epinions.com web site. the dataset contains 49,290 users who rated a total of 139,738 different items at least once, writing, 664,824 reviews and 487,181 issued trust statements. users and items are represented by anonymized numeric identifiers [19]. the epinions datasets contain two files: (1) ratings_data.txt.bz2 (2.5 megabytes), which contains the ratings given by users to items. in this file every line has the format “user_id item_id rating_value”. user_id is an integer in [1,49290], item_id is an integer in [1,139738] and rating_value is an integer in the range [1,5]; (2) trust_data.txt.bz2 (1.7 megabytes), which contains the trust statements issued by users. in the file every line has the format “source_user_id target_user_id trust_statement_value”. source_user_id and target_user_id are integer in [1, 49290], and trust_statement_value is always 1 (since in the dataset there are only positive trust statements and not negative ones (distrust)). 4.2 evaluation measures to use the epinions datasets in a more flexible way, we imported the two files (ratings_data.txt and trust_data.txt) into microsoft sql server 2005 to create two tables (rating_data and trust_data). and we add a trust_value column into the trust_data table and set a random integer value in [1, 5] to represent the trust value between two users. evaluation of the approach put forward in chapter 3 is conducted in our self-development recommendation prototype system, which is implemented on microsoft visual 2008 platform in windows 7 ultimate environment with a intel core i3-2310 2.1ghz (4 cpus) processor and 6 gigabyte memory. main interface of the prototype system is shown in figure 4. figure 4: main interface of the self-developed recommendation prototype system 4.3 result and discussion all the results are based on the self-developed trust based recommendation system. there are thousands of combination modes of the trust attenuation factor (α) with the maximum search study on directed trust graph based recommendation for e-commerce system 519 figure 5: ten recommended items for a randomly selected user are calculated according to tfbra with αset to be 0.8 level (maxlevel). we find it really hard to determine how α and maxlevel can be chosen. in order to prove the effectiveness of our recommend algorithm, two values (0.8 and 0.6) are chosen for parameterα, and ten recommended items for a randomly selected user are calculated according to tfbra. figure 5 shows the graphs for trust attenuation factor values with 0.8 and 0.6 respectively. error between recommended value and actual rating value is shown in table 2. in the paper mean absolute erro [36] (mae) is used to evaluate the practical applicability of the trust feedback recommendation algorithm, a smaller mae value means the higher accuracy. the calculation method of mae is listed below: mae = 1 n n∑ i=1 |ei| (3) where ei is the difference between recommended rating value and actual rating value on the same item (product). if a recommendation needed node have very few (or no) dtns, mae may be relatively great (as can be seen from figure 5 and figure 6 whenmaxlevel = 1). with the increasing ofmaxlevel,available feedback trust calculation nodes (include dtns and idtns) grows rapidly, which leads to more accurate recommendations. although there are a great variety of combination modes of α with maxlevel, it can be easily find from table 2 that mae decrease obviously while maxlevel increase. table 2: error between recommended rating value and actual rating value mae maxlevel=1 maxlevel=2 maxlevel=4 maxlevel=5 α = 0.8 0.954 0.957 0.41 0.221 α = 0.6 0.757 0.49 0.4 0.331 520 h.d. zhong, s. zhang, y. wang, y. shu figure 6: ten recommended items for a randomly selected user are calculated according to tfbra with αset to be 0.6 5 conclusions recommendation is an important issue in information science; also it is a hot topic in ecommerce systems. when computing recommendations for users in e-commerce systems, most of the existing researches usually focus on find recommendations from similar users. however, many scholars have proved that there are many challenges in finding similar neighbors. the research tries to overcome the difficulties in traditional recommendation methods and put forward a new idea to get more to reasonable recommendations from trusted users. based on the basic notations about trust cognitive process and dtg, which is constructed according to trust links between users, the paper proposes a trust feedback recommendation algorithm. we show through experiments on secondary processed epinions datasets to better highlight the relative advantages of the different algorithms, and the experimental results indicate good effectiveness of our trust based recommendation approach. one limitation of our method is that it has been test and verified on only one dataset i.e. epinions datasets. in future we would like to prove the effectiveness of our trust feedback recommendation algorithm on trust datasets that exhibit characteristics different from epinions datasets. three important research questions that we would like to examine are: (1) study the effect on recommendation accuracy when the way of setting trust value between two users varies; (2) study the relation between weighted control factor function and the accuracy of our trust feedback recommendation algorithm; (3) study the effectiveness of our approach using other datasets and compare the practicability of our method with other analogous means. we believe our approach can improve the coverage of recommender systems; however there is still much work need to be done. acknowledgements this work is supported by the national science foundation of china (grant no. 71373244), the ningbo natural science fundation project (grant nos. 2013a610068 and 2010a610119), the zhejiang province department of education project (grant no. y201329219), the science foundation of zhejiang province (grant no. ly13g030033), the modern port service industry and culture research center of the key research base of philosophy and social sciences of zhejiang province, the science and technology innovation team of ningbo (grant nos. 2013b82009 and study on directed trust graph based recommendation for e-commerce system 521 2012b82003) and the social development project of ningbo (grant no. 2012c50045). bibliography [1] papazoglou, m. p. 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(2004); evaluating collaborative filtering recommender systems, acm transactions on information systems, issn 1046-8188, 22(1): 5-53. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 2, pp. 171-178 environmental policy and science management: using a scientometric-specific gis for e-learning purposes n.d. hasanagas, a.d. styliadis, e.i. papadopoulou nikolaos d. hasanagas, ahanasios d. styliadis kavala institute of technology, department of landscape architecture city of drama, greece, gr 66100 email: styliadis@ath.forthnet.gr, nikolaos.hasanagas@gmail.com eleni i. papadopoulou aristotle university of thessaloniki, faculty of agricultural science department of agricultural economics, city of thessaloniki, gr 54124 e-mail epapa@agro.auth.gr abstract: who is the "good scientist" in rural-environmental policy? this is not so self-evident as in the case of private high-tech industry. developing e-learning system in environmental science management is a challenging task in the area of forest and general rural development policy. who determines the most "important" scientific information and who controls it? there are algorithms for measuring centrality in information networks. the concepts of closeness and betweenness centrality are used as basic metadata for categorizing the communication type in the rural-environmental policy networks. this paper discusses the development of a gis-based model which includes region-based scientometrics, regarding policy field communication. keywords: region-based scientometrics, gis e-learning, forest policy, integrated rural development policy, environmental policy, complete network analysis, policy making. 1 introduction it is evident that in high-tech policy sector, "good scientist" is the private industry, since impressive products like mobile phones are usually produced by the industry and not by universities or public agencies. however, this is not the case in rural-environmental policy, where the main issues are about "multiple-use forestry", "nature protection", "integrated rural development", "sustainability" etc, as in these abstract issues, success normally means to persuade about who the "trouble-maker" and who the "rescuer" is and not to find concrete solutions. moreover, despite the common belief that universities or research institutes are the most influential suppliers and controllers of "scientific" information, this is also not necessarily the rule in rural-environmental policy. this paper presents the possibility of developing a pattern of scientometric-specific gis -based model, which is going to be applicable in e-learning for various target groups (e.g. students who are specialized in forest policy and rural policy analysis, lobbyists, policy makers). in the context of this paper, "scientometric" differs from the classical measurements of citation rates or implementation of academic bibliography. it is defined as the management of information which is regarded as "scientific" by lobbyists, public administrators and academics in networks of forest policy issues which are used as case studies in this paper. the complete network analysis conducted here by visone software showed that the "scientific" information, which is regarded as most "important" in a network, is often supplied by practitioners, such as environmental and forest agencies, landowner organizations etc, and not necessarily by actors officially entitled as "scientific", such us universities or research centers. this implies that the "scientific" is rather a feeling induced by factors different from the official orientation of an actor. the analysis pointed out that forest policy, environmental issues and rural development are inseparable policy fields and strongly integrated with each other. the actors who produce "scientific" information, which is considered to be "important", can disseminate the information which is favorable for them, in order to support their socio-political positions. thus, a "scientifically important" actor may be an informal but influential decision-maker. additionally, the network will collapse, if an actor who participates in many information paths and thus controls the flow of information, suddenly abandons the network. in this case, the actor is merely a crucial "postman" but not necessarily a "decision-maker". in policy copyright c⃝ 2006-2010 by ccc publications 172 n.d. hasanagas, a.d. styliadis, e.i. papadopoulou planning, implementation and evaluation, it is useful to distinguish the "decision-maker" from the "postman" in order to focus the lobbying activity on the former rather than on the latter. this is especially useful in integrated rural development, incl. forest policy, as there are numerous complex, informal and different procedures among the countries and unclear indicators (rudi research project findings). therefore, there is a point in developing an adaptive gis e-learning system on region-specific information networks in order to make lobbyists, public administrators and students more familiar with science management in policy arena [14]. 2 selecting the gis learning object and the learner target group adaptive learning systems are supposed to require numerous and elaborated rules [5, 6, 18]. however, the existence of tested algorithms which detect the position of an actor in the formal or informal hierarchy of information in a network simplifies the rules and metadata requirements, even in complex social systems [6, 8, 11, 16, 19]. two algorithms, "closeness centrality" (cc) and "centrality of betweenness" (cb), can be used for classifying the actors. the only question is which type of actors interests each learner group [2, 11, 17, 6, 9]. we can distinguish two aggregated and seven specific learner groups: a) aggregated learner groups: 1) state actor employees (civil servants of agencies), 2) private actor employees (lobbyists, managers, experts and employees of interest groups or enterprises) figure 1: learner target groups. b) specific learner target groups (figure 1): these emerge by the combination of three dimensions [12, 13, 15]: 1) the legal character of the actor (public or private), 2) the orientation of the actor (economic or non-profit seeking regarding the management of natural resources, or science-orientation), and 3) the degree of dependence on the liberal market (i. individual enterprises which are directly exposed to the competition, ii. economic interest groups of enterprises which only represent their interests in the policy network, iii. non-profit seeking interest groups such as cultural association for the renovation of villages, forest recreation, and environmental ngos, which only depend on national and eu funds, or private universities and research centres which can be flexible to the market needs, and iv. state actors which are totally independent from the market, such as agencies, public universities and environmental policy and science management: using a scientometric-specific gis for e-learning purposes 173 research units) (rudi findings). the gis learning objects (figure 2) are the seven actor types described in figure 1 (“archimedes” research project findings), in each category of information management function (cc and cb). an enterprise characterized by high cc (strong "decision-maker") in a certain region-specific policy network is a case of learning object, while the same enterprise characterized by a low cb (weak "postman") is a different case of learning object. figure 2: processing model framework. each learner can thus examine whether the actor he/she belongs to is a strong "decision-maker" or "postman" in comparison to the other actors, so as to plan his/her lobbying strategy more effectively. in this way, instead of a standardized instructional design, the learnerpractitioner (or policy analysis student) can be flexibly oriented by using the cc and cb as indicators. 3 visualizing "decision-makers" and "postmen" various socio-informatics software products have been used for measuring and visualizing the invisible structures of policy networks which very often include informal relationships [1, 3]. the insna web-site ("international network for social network analysis"www.insna.org) offers a remarkable variety of such software. however, the implementation and outputs of this software in e-learning through a region-specific gis learning management system is still an open challenge, where the close cooperation of a multi-disciplinary group of social scientists [13, 15], software engineers, forest policy analysts, economists and practitioners such as lobbyists, foresters, agrarians etc is necessary. the informal relations are sometimes much more decisive for the policy output than the formal ones [3, 4, 7, 14]. a usual relation type in such region-specific networks is the exchange of information [8, 9]. the visone software is appropriate for depicting and analyzing both formal and informal information exchange. the algorithms of cc and cb are used for this purpose. the cc is an indicator of importance of a policy actor, while the cb indicates the capacity of controlling the paths of information exchange and thereby the dissemination of "scientific" information [13]. cc measures the distance d (i.e. the shortest number of links) between two actors. if i.e. the royal scottish forestry society gives information to the friends of the loch lomond and the latter to the national trust of scotland (and there is no direct information link from the royal scottish forestry society to the national trust of scotland), then the distance (d) between the royal scottish forestry society and the national trust of scotland is d=2 (links). the sum of all distances from an actor i to any other actor is the closeness of the actor i and then the closeness centrality of i is defined as its inverse closeness [8, 9]: 174 n.d. hasanagas, a.d. styliadis, e.i. papadopoulou cc(i) = ⌊ ∑ j d( j,i)⌋ −1 (1) the fewer links are needed to connect i to any other actor, the higher its cc is. if an actor possesses information that is regarded as crucial by the other actors, then one can expect this actor to have a high cc. for this reason, cc is an indicator of the information importance. cb quantifies the information control potential of an actor i and is defined as the sum of the ratios of shortest paths between other actors that the actor i sits on: cb(i) = ∑ |pi(i, j)| |p(i, j)| (2) where p(i,j) and pi (i,j) are the sets of all shortest paths between i and j, and those shortest paths passing through i, respectively. thus, an actor with high cb plays the role of the "go-between" for many other actors in term of shortest paths and, in this way, controls the dissemination of information. if the actor is also aware of its capability, it can influence the decision-making processes. cb points out the most crucial "postman" of information in a network, while cc shows who is the most important "sender". the "sender" is an information imposer and thereby a "decision-maker" who can directly influence the policy-making. in everyday life, the "sender" (e.g. a bank which makes a decision) may often be clearly differentiated from the person who brings the letter from the bank to the interested client. however, in politics, it is often unclear who the "sender" and who the "postman" is, as these roles are informal. using the two indicators, cb and cc, this difference can be detected. three examples of region-specific networks of "scientific" information flow are presented in figure 3. these are networks investigating issues like uk1: management of loch lomond and trossachs national park, uk2: scottish forestry strategy, and greece: the amendment of the forest-related article 24 of the national constitution law. the actors are landowners’ associations, forest services, agricultural directorates, cultural associations (related to nature aesthetics and cultural landscapes), angling and hunting associations, environmental ngos, technical chambers, tourist enterprises, forest and agricultural industries, municipalities etc (figure 1 can be used as a legend to understand the nodes in the depicted networks). number of actors, number of links and the network density (%) appear in each table cell of figure 3. in the first row, the whole region-specific networks appear (two from scotland and one from greece). the networks of "scientific" information shaped according to the cc and cb of the actors are presented in the second and third row respectively. the difference of the density degree (%) between whole contact networks (first row) and this of "scientific" information networks is remarkable: the density of "scientific" information is drastically lower than the "general contact" density, though issues like forest-environment and integrated rural development policy are supposed to be multi-disciplinary areas. not only the "science"-interested actors are relatively few but the lower degree of density also shows that the "scientific" communication is much weaker than the total activities (e.g. lobbying and institutional pressure, exchange of material support etc) (rudi findings). the analysis suggests that in some networks, economic or profit-seeking, private or state actors appear to be the most "important" actors in the "scientific" arena, while universities or research institutes are quite low in importance. this is particularly true for a science-oriented actor (a university) in the uk1 issue. this university is totally excluded from the "scientific" information network. thus, it is quite subjective which information is regarded as "scientific" (rudi findings). moreover, by comparing the cc with the cb network of the uk2 issue, it becomes obvious that the most influential "decision-maker" is not necessarily the best "postman": a forest service possesses the highest cc, while an environmental protection agency possesses the highest cb. in figure 4, a comparative view of region-specific scientometric data is provided: each learner target group can focus its research work on the respective learning object (actor type and position in information hierarchy). the learners can examine and interpreting the quantitative results: e.g. why the most important "scientist" and "science" controller is the state, why the science-oriented actors are of disputable importance and much weaker in intermediating "science" etc. the e-learning system functions as an "adaptor" which connects the academic knowledge with the practical and "local knowledge". the synergy between field expert experience and academic knowledge can be thereby optimized. possibilities of coalition building or cross-sectoral over-bridging through science management [12, 14] as well as chances for effective coordination through information control [7, 10, 15] can be discussed and analyzed by all learners. such an e-learning system could be useful in research projects which aim at the better understanding of rural-environmental policy design, delivery, monitoring and evaluation environmental policy and science management: using a scientometric-specific gis for e-learning purposes 175 figure 3: region-specific networks of "scientific" information flow. (“archimedes” and rudi findings). in particular, the implementation of forest policy as a holistic "scientific" toolbox for socio-economic planning, financing and development of forestry, evaluation of the protective and economic role of forest, prediction of supply and demand of forest products and forest administration is enabled. 4 summary and conclusions there are different science management patterns among region-specific policy networks. science-oriented actors are not necessary the leading ones in the information networks which are considered as "scientific" ones by their participants. a scientometric-specific gis based on complete network analysis helps all learners understand that forest policy, integrated rural development and environmental policy constitute a multifaceted and cohesive politico-administrative system, where the potential influence regarding information use and distribution becomes quite complex and makes the policy output unpredictable. by the presented gis e-learning system all learner target groups become more familiar with a policy design which takes into account the needs not only of the agricultural population but of a whole integrated rural territory, which is developed in the framework of a new type of urban-countryside relationship (rudi findings). the quantified learning objects of the integrated rural policy arena should be further interpreted through a participative and qualitative approach. thereby, the socio-economic and ecological driving forces which determine the quantitative results will be better understood by the learner target groups, independently of any political interests or personal views. acknowledgements the research initiative proposed by this paper has been supported by the eu-funded research project “archimedes” 176 n.d. hasanagas, a.d. styliadis, e.i. papadopoulou figure 4: scientometricembedded gis. environmental policy and science management: using a scientometric-specific gis for e-learning purposes 177 (department of landscape architecture, kavala institute of technology, drama, greece), by the eu-funded research project "rudi: rural development impactsassessing the impact of rural development policies, incl. leader" (department of agricultural economics, faculty of agricultural science, aristotle university of thessaloniki, greece), and by the institute of forest policy and nature conservation of goettingen university (germany). bibliography [1] k.a. bakar, b. s. doherty, evaluation of the recorded state mechanism for protecting agent integrity against malicious hosts. int. j. of computers, communications & control, 3(1), 60-68, 2008. [2] a.d. styliadis, i.i. akbaylar, d.a. papadopoulou, n.d. hasanagas, s.a. roussa, l.a. sexidis, metadatabased heritage sites modeling with e-learning functionality. journal of cultural heritage, 10, 296-312, 2009. [3] m. a. rajan, m. girish chandra, l.c. reddy, p. hiremath, concepts of graph theory relevant to ad-hoc networks. int. j. of computers, communications & control, 3(s):465-469, 2008. [4] p. borne, m. benrejeb, on the representation and the stability study of large scale systems. int. j. of computers, communications & control, 3(s):55-66, 2008. [5] a.d. styliadis, d.g. konstantinidou, k.a. tyxola, ecad system design applications in architecture. int. j. of computers, communications & control, 3(2), 204-214, 2008. [6] a.d. styliadis, digital documentation of historical buildings with 3-d modeling functionality. automation in construction, 16, 498-510, 2007. 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[11] a. andreatos, virtual communities and their importance for informal learning. int. j. of computers, communications & control, 2(1), 39-47, 2007. [12] m. krott, n.d. hasanagas, measuring bridges between sectors: causative evaluation of crosssectorality. forest policy and economics, 8, 555-563, 2006. [13] km. krott, catalyst for innovation in european forest policy sciences. evaluation of the efi research program 3: policy analysis. forest policy and economics, 5(2), 123-134, 2003. [14] s. shortall, are rural development programmes socially inclusive? social inclusion, civic engagement, participation, and social capital: exploring the differences. journal of rural studies, 24(4), 450-457, 2008. [15] d.j.a. douglas, the restructuring of local government in rural regions: a rural development perspective. journal of rural studies, 21(2), 231-246, 2005. [16] a.d. styliadis, i.d. karamitsos, d.i. zachariou, personalized e-learning implementation the gis case. international journal of computers, communications & control, 1(1), 59-67, 2006 [17] a.d. styliadis, e-learning documentation of historical living systems with 3-d modeling functionality. informatica, 18(3), 419-446, 2007. [18] a.d. styliadis, p.g. patias, n.c. zestas, 3-d computer modeling with intra-component, geometric, quality and topological constraints. informatica, 14(3), 375-392, 2003. 178 n.d. hasanagas, a.d. styliadis, e.i. papadopoulou [19] a.d. styliadis, m.gr. vassilakopoulos, a spatio-temporal geometry-based model for digital documentation of historical living systems. information & management, 42, 349-359, 2005. nikolaos d. hasanagas born in 1974. assistant professor in environment-related subjects at the kavala institute of technology, drama, greece. bsc and msc eq. in environmental sciences (aristotle univ. of thessaloniki, greece), ba and ma eq. in social sciences, phd in environmental policy analysis (goettingen univ., germany). athanasios d. styliadis born in 1956. professor of digital architecture and design computing at the department of landscape architecture at the kavala institute of technology, drama, greece. diploma in surveying engineering, msc in computer science (dundee univ., scotland), phd in caad and gis (aristotle univ. of thessaloniki, greece). eleni i. papadopoulou born in 1957. assistant professor in rural policy at the faculty of agricultural science at the aristotle university of thessaloniki, greece. bsc in agriculture engineering, msc in agricultural economics (univ. of reading, uk), phd (aristotle university of thessaloniki). int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 825-836 a genetic algorithm for multiobjective hard scheduling optimization e. niño, c. ardila, a. perez, y. donoso elías niño, carlos ardila department of computer science universidad del norte km 5, via pto colombia. barranquilla, colombia e-mail: {enino,cardila}@uninorte.edu.co alfredo perez department of computer science and engineering university south florida 4202 e. fowler ave. tampa, florida e-mail: ajperez4@cse.usf.edu yezid donoso department of computing and systems engineering universidad de los andes cra 1 no. 18a-12. bogotá, colombia e-mail: ydonoso@uniandes.edu.co abstract: this paper proposes a genetic algorithm for multiobjective scheduling optimization based in the object oriented design with constrains on delivery times, process precedence and resource availability. initially, the programming algorithm (pa) was designed and implemented, taking into account all constraints mentioned. this algorithm’s main objective is, given a sequence of production orders, products and processes, calculate its total programming cost and time. once the programming algorithm was defined, the genetic algorithm (ga) was developed for minimizing two objectives: delivery times and total programming cost. the stages defined for this algorithm were: selection, crossover and mutation. during the first stage, the individuals composing the next generation are selected using a strong dominance test. given the strong restrictions on the model, the crossover stage utilizes a process level structure (pls) where processes are grouped by its levels in the product tree. finally during the mutation stage, the solutions are modified in two different ways (selected in a random fashion): changing the selection of the resources of one process and organizing the processes by its execution time by level. in order to obtain more variability in the found solutions, the production orders and the products are organized with activity planning rules such as edd, spt and lpt. for each level of processes, the processes are organized by its processing time from lower to higher (plu), from higher to lower (pul), randomly (pr), and by local search (ls). as strategies for local search, three algorithms were implemented: tabu search (ts), simulated annealing (sa) and exchange deterministic algorithm (eda). the purpose of the local search is to organize the processes in such a way that minimizes the total execution time of the level. finally, pareto fronts are used to show the obtained results of applying each of the specified strategies. results are analyzed and compared. keywords: scheduling, process, genetic algorithm, local search, pareto front. copyright c⃝ 2006-2010 by ccc publications 826 e. niño, c. ardila, a. perez, y. donoso 1 introduction the genetics algorithms (ga) are a powerful tool for solving combinatorial problems. nowadays, it exits a lot of algorithms inspired in ga for solving real problems such as design of vehicle suspensions [1], product deployment in telecom services [2], design of the flexible multi-body model vehicle suspensions based on skeletons implementing [3], job-shop scheduling [4], economic dispatch of generators with prohibited operating zones [5], multi-project scheduling [6], inversion analysis of permeability coefficients [7], path planning in unstructured mobile robot environments [8], rough mill component scheduling [9] and power plant control system design [10]. in productive systems is very critical the assignments of resources, for instance, a product has process and the process requires resources. the programming of the execution of the processes affects the overall cost and time of the products. due to this, it is very important try to do the planning and scheduling in the best way. it can be accomplished with a genetic algorithm. 2 preliminaries 2.1 local search local search are techniques that allows finding solutions in a set of solutions. it always tries to improve the actual solution through perturbations. a perturbation is a simple way for changing a solution. the perturbation depends of the way for representing the solutions, for instance in figure 1 can be seen a binary representation of a solution, in this case the perturbation can be done changing ones (1) by zeros (0). on the other hand, in figure 2 can be seen a no-binary representation of a solution (tour of the traveling salesman problem [11] for example), in this case the perturbation can be done swapping two elements of the tour. 001001 001101 perturbation x x’ figure 1: a binary representation of solutions. in this case x is the representation of the decimal number 9. the perturbation was done changing the 4th 0 to 1. due to this, it creates a new solution x’ that is the representation of the decimal number 13. 1-2-3-4 1-3-2-4 perturbation swapping 2 and 3 x x’ figure 2: a no-binary representation of solutions. in this case x is the representation of a tour in tsp. the perturbation was done changing the 2 and 3 in the string. due to this, it creates a new solution x’. a genetic algorithm for multiobjective hard scheduling optimization 827 there exist a many local search algorithms such as tabu search (ts) [12], simulated annealing (sa) [13] and exchange deterministic algorithm (eda) [14]. due to this, it is necessary to find a good representation of the solutions. obviously, it depends of the problem to solve. 2.2 genetic algorithm genetic algorithm (ga) is a search technique used for solving optimizations problem. the most important in ga is the design of the chromosome. it is the representation of the feasible solutions. consequently, the behavior of the ga depends of the chromosome. due to this, a bad chromosome implies a bad behavior of the ga. on the other hand, a good chromosome may imply a good behavior of the ga. the framework of ga can be seen in figure 3. genetic algorithm s = create initial solutions do selection (s) crossover (s) mutation (s) until condition stop = true end genetic algorithm figure 3: the framework of a genetic algorithm. ga has three important steps. first, it selects the solutions for the crossover and mutation step. this selection can be done using a metric, for example the inverse generational distance (igd) [15]. second, it takes pairs of solutions for crossing. it can be done at random. crossover consists in creates new solutions with parts of two solutions. the two original solutions are named parents (father and mother) and the two new solutions sons. it is created with half from father and half from mother. lastly, it takes some sons for mutating it. the mutation is a step that allows creating new solutions. it can be done using a perturbation or a local search. the three steps of ga can be seen in figure 4. 0 1 2 3 1 0 3 2 0 1 3 2 1 0 2 3 3 1 0 2 2 0 1 3 swap(1, 2) swap(0, 3) parents selection crossover mutation sons figure 4: steps of a genetic algorithm. 828 e. niño, c. ardila, a. perez, y. donoso 3 a genetic algorithm for multiobjective hard scheduling optimization we state a new genetic algorithm for multiobjective hard scheduling optimization (gamhso). this algorithm works in scenarios with the following characteristics: there are production orders that are composed by a set of products. each of the products is described by a product tree that contains all the processes needed to build such product. for a product to be ready, it is required the execution of all processes that belongs to its tree. a process may need the execution of another process (precedence) or other subpart before it can be executed. the execution of some processes can be only done in certain times (schedules). to execute a process, a group of resources is required. the defined resources are: machinery, employees, and vehicles. it is not necessary for a group of resources to contain all types of resources. finally, if a subcomponent is required, it has to be constructed by a set of processes or it can be modeled by a process that indicates idle state (the subcomponent has not arrived yet to the system). this scenario can be seen in a domain model in figure 5. figure 5: domain model for scenario of gamhso formally gahmso is defined in figure 6. the two objectives of gamhso is found a set of solutions nondominated of the programming of the processes minimizing the overall time and cost. the initial solutions are created with some heuristics. the heuristics organizes the production orders with rules. gahmso use three rules for creating the initial solutions. it selects the heuristic in at random. the available heuristics for creating the initial solutions are early due date (edd), long process time (lpt) and short process time (spt). edd organizes the production orders from lower to upper respect to the due date. spt organizes the production orders from lower to upper respect to the summation of the processes time of the products. lpt is the contrary to spt. a genetic algorithm for multiobjective hard scheduling optimization 829 once the initial solutions are created, the selection step selects solutions from the overall set of solutions. the selected solutions are named candidates. the candidates are the solutions that can be crossed for creating new solutions. the amount of candidates is defined by the candidate rate cr. it is very important the definition of the chromosome for the crossover step. for instance, consider the product of the figure 7. the representation of two feasible solutions could be to program the execution of processes in the order 8 11 9 10 6 7 5 4 1 2 3 and 11 10 6 5 4 2 8 9 7 1 3. the program indicates a feasible solution for programming the processes. some processes can be executed parallel, so the order indicates the priority in the utilization of the resources. for instance, processes 8 and 9 can be executed at the same time, but in the first solution, if 8 uses a machine that 9 requires, 9 could not executed until 8 free the resource. the problem with this representation is that the crossover step could create unfeasible solutions. for example, if we split the two solutions mentioned in the middle and later we cross those solutions, we will obtain the solutions 8 11 9 10 6 2 8 9 7 1 3 and 11 10 6 5 47 5 4 1 2 3. obviously, those are unfeasible solutions. inputs: c ,r ρr output: a set (s) of nondominated solutions. gamhso s = create initial solutions(edd, lpt, spt) do s’ = selection of candidates (s, c )r c = crossover of candidates (s’, ρ )r x = generated a integer in [0,2] with a uniform distribution. switch(x) case 0: c = tabusearch(c) case 1: c = simulatedannealing(c) case 2: c = exchange deterministic algorithm(c) end s = s u c s = remove domiated solutions(s) until condition stop = true end gamhso figure 6: the framework of gamhso 4 definition of the chromosome the objectives of gamsho are the optimization of overall cost and time. it plays with the programming order of the execution of the processes. so it is very important to provide a good representation of the solutions. consider again the product of the figure 7. it has subparts because it is modeling the reality. but, żwhat does a subpart mean? it means that the process that contains the subpart cannot be executed until the processes that build it have been executed. in other words, there exists a precedence constrain between the process that contain the subpart and the processes 830 e. niño, c. ardila, a. perez, y. donoso that build the subpart. if we applied this to the figure 7, we are going to obtain the tree of the figure 8. in this tree does not exist subpart, we replace the subpart for the precedence between the processes. once the tree is ready, we need to create a chromosome that allows the representation of the programming. process1 process2 process3 process4 process5 process6 process7 process8 process9 process10 process11 prod a subprt1 subprt2 figure 7: tree product a. product a requires of the execution of the processes 1, 2 and 3. process 1 requires the execution of the process 7. process 7 requires the subpart1. subpart1 requires the execution of the processes 8 and 9. process 2 does not require processes. process 3 require the execution of the processes 4 and 5.process 5 requires the execution of the process 6. process 6 requires the subpart 2. subpart 2 requires the execution of process 10 and process 10 requires the execution of process 11. first we group the processes by level. it means that the time execution of a process in a superior level depends on the finalization execution time of the parents in a low level. formally: p.start_time = max(parenti.finalization_time) (4.1) for instance, if process 5 finishes its execution in time 20 and the process 4 finishes its execution in time 40, process 3 will start its execution in time 40. on the other hand, the process 2 can be executed since time 0. once the levels of the processes have been identified, those are grouped in a level structure. it can be seen in figure 9. each process knows its children in the superior level. due to this, the chromosome for gamhso can be seen in figure 10. the production orders are executed from left to the right (from up to down). the products are built from left to the right (from up to down). the processes are executed from right to the left (from down to up). the processes are processed from left to the right (from up to down). the crossover step consists in select two solutions, split in the middle and cross. it can be done by levels. an example can be seen in figure 11. the number of solution that can be crossed a genetic algorithm for multiobjective hard scheduling optimization 831 process1 process2 process3 process4 process5 process6 process7 process8 process9 process10 process11 prod a level 0: processes 1, 2 and 3 level 1: processes 4, 5 and 7 level 2: processes 6, 8 and 9 level 3: process 10 level 4: process 11 figure 8: a view of the precedence tree of product a group by level. process1 process2 process3 process4 process5 process6process7 process8 process9 process10 process11 prod a level0 level1 level2 level3 level4 figure 9: a representation of the product a in a level structure. is specify by the crossover rate (ρr). once the solution is created, it is necessary to schedule the processes of the solution for obtaining the time and cost of the solution. the programming algorithm (pa) is an algorithm that requires a solution for programming all the processes for all the products of the production orders. it verifies is a process can be executed with three validations: first, it verifies the process precedence. second, it verifies if the resource are available for the execution of the process. lastly, it verifies is the process can be executed in the journey. once a process completes its execution, it set the initial time to his children to his finalization time. pa can be seen in figure 12. the mutation step of gamhso consists in the improvement of the solutions through local search (ls). gamhso works with three ls: tabu search (ts), simulated annealing (sa) and exchange deterministic algorithm (eda). 5 experimental settings we tested gamhso in a computer amd turion 64, 2 gb of ram and a hard disk of 120 gb. the test consisted in build 1000 products of type a (figure 7). the parameters were cr = 0.4, 832 e. niño, c. ardila, a. perez, y. donoso sns2s1 s3 o1 o2 o3 om p1 p2 p3 pi l1 l2 l3 lj p1 p2 p3 pk set of solutions s each solution has the same set of productions orders but sorted by edd, spt or lpt each order has a set of products sorted by edd, spt or ltp each product has a set of levels each level has a set of processes figure 10: the chromosome for gamhso. ρr = 0.5. we tested the performance of gomhsa with eda, ts and sa. for making a real comparison, we use the inverted generational distance (igd) metric [15]. it is defined as follows: given a reference set a∗, the igd value of a set a ⊂ rm is defined as: igd(a, a∗) = 1 |a∗| ∑ v∈a∗ {min u∈a d(u, v)} (5.1) 6 results of gamhso the pareto fronts for each ls can be seen in figure 12. the results of igd metric between the lss can be seen in table 1. the running times for gamhso for each ls can be seen table 2. eda sa ts eda sa ts 1137.0605 7485.2654 6157.3669 6388.229 0 0 01134.2321 7367.1584 table 1: the igd-metrics values for each ls against the rest of ls. eda ts sa running time in seconds 32 57 128 table 2: running times of gamhso with each ls. a genetic algorithm for multiobjective hard scheduling optimization 833 s1 o a 0 1 2 3 a b c f d g h e s2 o a 0 1 2 3 b a f c h d g e s3 o a 2 3 h d g e 0 1 a b c f new solution solution 1 e-d-g-h-c-f-a-b solution 2 e-h-d-g-f-c-b-a solution 3 e-h-d-g-c-f-a-b figure 11: an example of the crossover step of gamhso. 7 conclusions and future works we designed a new genetic algorithm for hard scheduling optimization (gamhso). it works with very difficult scenarios of productive systems. also, we define a chromosome for gamhso that avoids the creation of unfeasible solutions. due to this, it is not necessary to verify if a solution (for the crossover step) is a feasible solution. consequently, the performance of the algorithm is satisfactory in comparison with the size of the feasible solutions space. on the other hand, we state a new programming algorithm (pa) for scheduling of a set of production orders. pa is a flexible algorithm that allows the incorporations of new restrictions to the processes. it allows calculate the overall time and cost of a set of production orders. we made a real comparison of the gamhso behavior with some local search strategies such as exchange deterministic algorithm (eda), tabu search (ts) and simulated annealing (sa). the best performance of gamhso was using eda and sa. lastly, we will investigate a new chromosome that allows the crossover between production orders. 834 e. niño, c. ardila, a. perez, y. donoso programming algoritm(o as a set of production orders) cost = 0 time = 0 for each o in oorder for each p in oproduct for each l in plevel for each p in pprocess t = p.initialtime do while p cannot be executed in time t t = t+1 end while it = t while p can be executed in time t t = t+1 end while ft = t for all resources r used for p. r.programtask(it,ft). cost = cost+r.getcost until t = 0 for all processes children pc of p. pc. = max(pc.initialtime,t)initialtime if thent > time time = t end if end for end for end for end for return time,cost end programming algorithm figure 12: the framework of the programming algorithm (pa) for getting 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[15] minzhong liu; xiufen zou; yu chen; zhijian wu, “performance assessment of dmoea-dd with cec 2009 moea competition test instances”, evolutionary computation, 2009. cec ’09. ieee congress on , vol., no., pp.2913-2918, 18-21 may 2009. url: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4983309&isnumber=4982922 [16] h. li and j.d. landa-silva, evolutionary multi-objective simulated annealing with adaptive and competitive search direction, proceedings of the 2008 ieee congress on evolutionary computation (cec 2008), ieee press, pp. 3310-3317, 01-06 june, 2008, hong kong. international journal of computers, communications & control vol. i (2006), no. 1, pp. 47-57 information aggregation in intelligent systems using generalized operators imre j. rudas, jános fodor abstract: aggregation of information represented by membership functions is a central matter in intelligent systems where fuzzy rule base and reasoning mechanism are applied. typical examples of such systems consist of, but not limited to, fuzzy control, decision support and expert systems. since the advent of fuzzy sets a great number of fuzzy connectives, aggregation operators have been introduced. some families of such operators (like t-norms) have become standard in the field. nevertheless, it also became clear that these operators do not always follow the real phenomena. therefore, there is a natural need for finding new operators to develop more sophisticated intelligent systems. this paper summarizes the research results of the authors that have been carried out in recent years on generalization of conventional operators. keywords: t-norm, t-conorm, uninorm, entropyand distance-based conjunctions and disjunctions. 1 introduction information aggregation is one of the key issues in development of intelligent systems. although fuzzy set theory provides a host of attractive aggregation operators for integrating the membership values representing uncertain information, the results do not always follow the modeled real phenomena and it has been shown that in some situations some operations may work better than others. since the pioneering work of zadeh the basic research was oriented towards the investigation of the properties of t-norms and t-conorms and also to find new ones satisfying the axiom system. as a result of this a great number (of various type) of t-operators have been introduced accepting the axiom system as a fixed, unchangeable skeleton. until the last few years no strong efforts were devoted to generalize t-operators by modifying “weakening” this axiom system. on one hand, the sound theoretical foundation as well as their wide variety have given t-norms and t-conorms almost an exclusive role in different theoretical investigations and practical applications. on the other hand, people are inclined to use them also as a matter of routine. the following observations support this statement and . when one works with binary conjunctions and there is no need to extend them for three or more arguments, associativity is an unnecessarily restrictive condition. the same is valid for commutativity if the two arguments have different semantical backgrounds and it has no sense to interchange one with the other. these observations, which are very often left out of consideration, advocate our study and have urged us to revise definitions and properties of operations for information aggregation and reasoning. 2 traditional operations the original fuzzy set theory was formulated in terms of zadeh’s standard operations of intersection, union and complement. the axiomatic skeleton used for characterizing fuzzy intersection and fuzzy union are known as triangular norms (t-norms) and triangular conorms (t-conorms), respectively. for more details we refer to the book [9]. 2.1 triangular norms and conorms definition 1. a non-increasing function n : [0, 1] → [0, 1] satisfying n(0) = 1, n(1) = 0 is called a negation. a negation n is called strict if n is strictly decreasing and continuous. a strict negation n is said to be a strong negation if n is also involutive: n(n(x)) = x for all x ∈ [0, 1]. the standard negation is simply ns(x) = 1 −x, x ∈ [0, 1]. clearly, this negation is strong. it plays a key role in the representation of strong negations. we call a continuous, strictly increasing function ϕ : [0, 1] → [0, 1] with ϕ(0) = 0, ϕ(1) = 1 an automorphism of the unit interval. copyright c© 2006 by ccc publications 48 imre j. rudas, jános fodor definition 2. a triangular norm (shortly: a t-norm) is a function t : [0, 1]2 → [0, 1] which is associative, increasing and commutative, and satisfies the boundary condition t (1, x) = x for all x ∈ [0, 1]. definition 3. a triangular conorm (shortly: a t-conorm) is an associative, commutative, increasing s : [0, 1]2 → [0, 1] function, with boundary condition s(0, x) = x for all x ∈ [0, 1]. notice that continuity of a t-norm and a t-conorm is not taken for granted. in what follows we assume that t is a t-norm, s is a t-conorm and n is a strict negation. clearly, for every t-norm t and strong negation n, the operation s defined by s(x, y) = n(t (n(x), n(y))), x, y ∈ [0, 1] (1) is a t-conorm. in addition, t (x, y) = n(s(n(x), n(y))) (x, y ∈ [0, 1]). in this case s and t are called n-duals. in case of the standard negation (i.e., when n(x) = 1 − x for x ∈ [0, 1]) we simply speak about duals. obviously, equality (1) expresses the de morgan’s law in the fuzzy case. generally, for any t-norm t and t-conorm s we have tw (x, y) ≤ t (x, y) ≤ tm (x, y) and sm (x, y) ≤ s (x, y) ≤ ss (x, y) , where tm (x, y) = min (x, y), sm (x, y) = max (x, y), tw is the weakest t-norm, and ss is the strongest t-conorm. these inequalities are important from practical point of view as they establish the boundaries of the possible range of mappings t and s. 2.2 uninorms and nullnorms uninorms uninorms were introduced by yager and rybalov [19] as a generalization of t-norms and t-conorms. for uninorms, the neutral element is not forced to be either 0 or 1, but can be any value in the unit interval. definition 4. [19] a uninorm u is a commutative, associative and increasing binary operator with a neutral element e ∈ [0, 1 ] , i.e., for all x ∈ [0, 1 ] we have u (x, e) = x. t-norms do not allow low values to be compensated by high values, while t-conorms do not allow high values to be compensated by low values. uninorms may allow values separated by their neutral element to be aggregated in a compensating way. the structure of uninorms was studied by fodor et al. [11]. for a uninorm u with neutral element e ∈ ] 0, 1 ] , the binary operator tu defined by tu (x, y) = u (e x, e y) e is a t-norm; for a uninorm u with neutral element e ∈ [0, 1[, the binary operator su defined by su (x, y) = u (e + (1−e)x, e + (1−e)y)−e 1−e is a t-conorm. the structure of a uninorm with neutral element e ∈]0, 1[ on the squares [0, e ] 2 and [e, 1 ] 2 is therefore closely related to t-norms and t-conorms. for e ∈]0, 1[, we denote by φe and ψe the linear transformations defined by φe(x) = xe and ψe(x) = x−e 1−e . to any uninorm u with neutral element e ∈]0, 1[, there corresponds a t-norm t and a t-conorm s such that: (i) for any (x, y) ∈ [0, e ] 2: u (x, y) = φ−1e (t (φe(x), φe(y))); (ii) for any (x, y) ∈ [e, 1 ] 2: u (x, y) = ψ−1e (s(ψe(x), ψe(y))). on the remaining part of the unit square, i.e. on e = [0, e[×] e, 1 ] ∪]e, 1 ] ×[0, e[, it satisfies min(x, y) ≤ u (x, y) ≤ max(x, y), and could therefore partially show a compensating behaviour, i.e. take values strictly between minimum and maximum. note that any uninorm u is either conjunctive, i.e. u (0, 1) = u (1, 0) = 0, or disjunctive, i.e. u (0, 1) = u (1, 0) = 1. information aggregation in intelligent systems using generalized operators 49 representation of uninorms in analogy to the representation of continuous archimedean t-norms and t-conorms in terms of additive generators, fodor et al. [11] have investigated the existence of uninorms with a similar representation in terms of a single-variable function. this search leads back to dombi’s class of aggregative operators [7]. this work is also closely related to that of klement et al. on associative compensatory operators [15]. consider e ∈]0, 1[ and a strictly increasing continuous [0, 1] →r mapping h with h(0) = −∞, h(e) = 0 and h(1) = +∞. the binary operator u defined by u (x, y) = h−1(h(x) + h(y)) for any (x, y) ∈ [0, 1]2 \{(0, 1), (1, 0)}, and either u (0, 1) = u (1, 0) = 0 or u (0, 1) = u (1, 0) = 1, is a uninorm with neutral element e. the class of uninorms that can be constructed in this way has been characterized [11]. consider a uninorm u with neutral element e ∈]0, 1[, then there exists a strictly increasing continuous [0, 1] → r mapping h with h(0) = −∞, h(e) = 0 and h(1) = +∞ such that u (x, y) = h−1(h(x) + h(y)) for any (x, y) ∈ [0, 1]2 \{(0, 1), (1, 0)} if and only if (i) u is strictly increasing and continuous on ]0, 1[2; (ii) there exists an involutive negator n with fixpoint e such that u (x, y) = n(u (n(x), n(y)))) for any (x, y) ∈ [0, 1]2 \{(0, 1), (1, 0)}. the uninorms characterized above are called representable uninorms. the mapping h is called an additive generator of u . the involutive negator corresponding to a representable uninorm u with additive generator h, as mentioned in condition (ii) above, is denoted nu and is given by nu (x) = h −1(−h(x)). (2) clearly, any representable uninorm comes in a conjunctive and a disjunctive version, i.e. there always exist two representable uninorms that only differ in the points (0, 1) and (1, 0). representable uninorms are almost continuous, i.e. continuous except in (0, 1) and (1, 0), and archimedean, in the sense that (∀x ∈]0, e[)(u (x, x) < x) and (∀x ∈]e, 1[)(u (x, x) > x). clearly, representable uninorms are not idempotent. the classes umin and umax do not contain representable uninorms. a very important fact is that the underlying t-norm and t-conorm of a representable uninorm must be strict and cannot be nilpotent. moreover, given a strict t-norm t with decreasing additive generator f and a strict t-conorm s with increasing additive generator g, we can always construct a representable uninorm u with desired neutral element e ∈]0, 1[ that has t and s as underlying t-norm and t-conorm. it suffices to consider as additive generator the mapping h defined by h(x) =    − f ( x e ) , if x ≤ e g ( x−e 1−e ) , if x ≥ e . (3) on the other hand, the following property indicates that representable uninorms are in some sense also generalizations of nilpotent t-norms and nilpotent t-conorms: (∀x ∈ [0, 1])(u (x, nu (x)) = nu (e)). this claim is further supported by studying the residual operators of representable uninorms in [6]. as an example of the representable case, consider the additive generator h defined by h(x) = log x1−x , then the corresponding conjunctive representable uninorm u is given by u (x, y) = 0 if (x, y) ∈ {(1, 0), (0, 1)}, and u (x, y) = xy (1−x)(1−y) + xy otherwise, and has as neutral element 12 . note that nu is the standard negator: nu (x) = 1−x. the class of representable uninorms contains famous operators, such as the functions for combining certainty factors in the expert systems mycin (see [18, 5]) and prospector [5]. the mycin expert system was one 50 imre j. rudas, jános fodor of the first systems capable of reasoning under uncertainty [2]. to that end, certainty factors were introduced as numbers in the interval [−1, 1]. essential in the processing of these certainty factors is the modified combining function c proposed by van melle [2]. the [−1, 1]2 → [−1, 1] mapping c is defined by c(x, y) =    x + y(1−x) , if min(x, y) ≥ 0 x + y(1 + x) , if max(x, y) ≤ 0 x + y 1−min(|x|,|y|) , otherwise . the definition of c is not clear in the points (−1, 1) and (1,−1), though it is understood that c(−1, 1) = c(1,−1) = −1. rescaling the function c to a binary operator on [0, 1], we obtain a representable uninorm with neutral element 1 2 and as underlying t-norm and t-conorm the product and the probabilistic sum. implicitly, these results are contained in the book of hájek et al. [14], in the context of ordered abelian groups. nullnorms definition 5. [3] a nullnorm v is a commutative, associative and increasing binary operator with an absorbing element a ∈ [0, 1], i.e. (∀x ∈ [0, 1])(v (x, a) = a), and that satisfies (∀x ∈ [0, a])(v (x, 0) = x) (4) (∀x ∈ [a, 1])(v (x, 1) = x) (5) the absorbing element a corresponding to a nullnorm v is clearly unique. by definition, the case a = 0 leads back to t-norms, while the case a = 1 leads back to t-conorms. in the following proposition, we show that the structure of a nullnorm is similar to that of a uninorm. in particular, it can be shown that it is built up from a t-norm, a t-conorm and the absorbing element [3]. theorem 6. consider a ∈ [0, 1]. a binary operator v is a nullnorm with absorbing element a if and only if (i) if a = 0: v is a t-norm; (ii) if 0 < a < 1: there exists a t-norm tv and a t-conorm sv such that v (x, y) is given by    φ−1a (sv (φa(x), φa(y))) , if (x, y) ∈ [0, a]2 ψ−1a (tv (ψa(x), ψa(y))) , if (x, y) ∈ [a, 1]2 a , elsewhere ; (6) (iii) if a = 1: v is a t-conorm. recall that for any t-norm t and t-conorm s it holds that t (x, y) ≤ min(x, y) ≤ max(x, y) ≤ s(x, y), for any (x, y) ∈ [0, 1]2. hence, for a nullnorm v with absorbing element a it holds that (∀(x, y) ∈ [0, a]2) (v (x, y) ≥ max(x, y)) and (∀(x, y) ∈ [a, 1]2) (v (x, y) ≤ min(x, y)). clearly, for any nullnorm v with absorbing element a it holds for all x ∈ [0, 1] that v (x, 0) = min(x, a) and v (x, 1) = max(x, a). (7) notice that, without the additional conditions (4) and (5), it cannot be shown that a commutative, associative and increasing binary operator v with absorbing element a behaves as a t-conorm and t-norm on the squares [0, a]2 and [a, 1]2. nullnorms are a generalization of the well-known median studied by fung and fu [13], which corresponds to the case t = min and s = max. for a more general treatment of this operator, we refer to [10]. we recall here the characterization of that median as given by czogala and drewniak [4]. firstly, they observe that an idempotent, associative and increasing binary operator o has as absorbing element a ∈ [0, 1] if and only if o(0, 1) = o(1, 0) = a. then the following theorem can be proven. theorem 7. [4] consider a ∈ [0, 1]. a continuous, idempotent, associative and increasing binary operator o satisfies o(0, 1) = o(1, 0) = a if and only if it is given by o(x, y) =    max(x, y) , if (x, y) ∈ [0, a]2 min(x, y) , if (x, y) ∈ [a, 1]2 a , elsewhere . information aggregation in intelligent systems using generalized operators 51 nullnorms are also a special case of the class of t -s aggregation functions introduced and studied by fodor and calvo [12]. definition 8. consider a continuous t-norm t and a continuous t-conorm s. a binary operator f is called a t -s aggregation function if it is increasing and commutative, and satisfies the boundary conditions (∀x ∈ [0, 1])(f(x, 0) = t (f(1, 0), x)) (∀x ∈ [0, 1])(f(x, 1) = s(f(1, 0), x)). when t is the algebraic product and s is the probabilistic sum, we recover the class of aggregation functions studied by mayor and trillas [17]. rephrasing a result of fodor and calvo, we can state that the class of associative t -s aggregation functions coincides with the class of nullnorms with underlying t-norm t and t-conorm s. 2.3 the role of commutativity and associativity one possible way of simplification of axiom skeletons of t-norms and t-conorms may be not requiring that these operations to have the commutative and the associative properties. non-commutative and non-associative operations are widely used in mathematics, so, why do we restrict our investigations by keeping these axioms? what are the requirements of the most typical applications? from theoretical point of view the commutative law is not required, while the associative law is necessary to extend the operation to more than two variables. in applications, like fuzzy logic control, fuzzy expert systems and fuzzy systems modeling fuzzy rule base and fuzzy inference mechanism are used, where the information aggregation is performed by operations. the inference procedures do not always require commutative and associative laws of the operations used in these procedures. these properties are not necessary for conjunction operations used in the simplest fuzzy controllers with two inputs and one output. for rules with greater amount of inputs and outputs these properties are also not required if the sequence of variables in the rules are fixed. moreover, the non-commutativity of conjunction may in fact be desirable for rules because it can reflect different influences of the input variables on the output of the system. for example, in fuzzy control, the positions of the input variables the “error” and the “change in error” in rules are usually fixed and these variables have different influences on the output of the system. in the application areas of fuzzy models when the sequence of operands is not fixed, the property of non-commutativity may not be desirable. later some examples will be given for parametric non-commutative and non-associative operations. 3 generalized conjunctions and disjunctions the axiom systems of t-norms and t-conorms are very similar to each other except the neutral element, i.e. the type is characterized by the neutral element. if the neutral element is equal to 1 then the operation is a conjunction type, while if the neutral element is zero the disjunction operation is obtained. by using these properties we introduce the concepts of conjunction and disjunction operations [1]. definition 9. let t be a mapping t : [0, 1] × [0, 1] → [0, 1]. t is a conjunction operation if t (x, 1) = x for all x ∈ [0, 1]. definition 10. let s be a mapping s : [0, 1] × [0, 1] → [0, 1]. s is a conjunction operation if s(x, 0) = x for all x ∈ [0, 1]. conjunction and disjunction operations may also be obtained one from another by means of an involutive negation n: s (x, y) = n (t (n (x) , n (y))), and t (x, y) = n (s (n (x) , n (y))). it can be seen easily that conjunction and disjunction operations satisfy the following boundary conditions: t (1, 1) = 1, t (0, x) = t (x, 0) = 0, s(0, 0) = 0, s(1, x) = s(x, 1) = 1. by fixing these conditions, new types of generalized operations are introduced. definition 11. let t be a mapping t : [0, 1] × [0, 1] → [0, 1]. t is a quasi-conjunction operation if t (0, 0) = t (0, 1) = t (1, 0) = 0, and t (1, 1) = 1. definition 12. let s be a mapping s : [0, 1]×[0, 1] → [0, 1]. s is a quasi-disjunction operation if s(0, 1) = s(1, 0) = s(1, 1) = 1, and s(0, 0) = 0. 52 imre j. rudas, jános fodor it is easy to see that conjunction and disjunction operations are quasi-conjunctions and quasi-disjunctions, respectively, but the converse is not true. omitting t (1, 1) = 1 and s(0, 0) = 0 from the definitions further generalization can be obtained. definition 13. let t be a mapping t : [0, 1] × [0, 1] → [0, 1]. t is a pseudo-conjunction operation if t (0, 0) = t (0, 1) = t (1, 0) = 0. definition 14. let s be a mapping s : [0, 1] × [0, 1] → [0, 1]. s is a pseudo-disjunction operation if s(0, 1) = s(1, 0) = s(1, 1) = 1. theorem 15. assume that t and s are non-decreasing pseudo-conjunctions and pseudo-disjunctions, respectively. then there exist the absorbing elements 0 and 1 such as t (x, 0) = t (0, x) = 0 and s (x, 1) = s (1, x) = 1. 3.1 entropy-based conjunction and disjunction operators the question of how fuzzy is a fuzzy set has been one of the issues associated with the development of the fuzzy set theory. in accordance with a current terminological trend in the literature, measure of uncertainty is being referred as measure of fuzziness, or fuzzy entropy [16]. throughout this part the following notations will be used; x is the universal set, f(x ) is the class of all fuzzy subsets of x , ℜ+ is the set of non negative real numbers, ā is the fuzzy complement of a ∈f(x ) and |a| is the cardinality of a. definition 16. let x be a universal set and a is a fuzzy subset of x with membership function µa. the fuzzy entropy is a mapping e: f(x ) → ℜ+ which satisfies the following axioms: ae 1 e (a) = 0 if a is a crisp set. ae 2 if a ≺ b then e (a) ≤ e (b); where a ≺ b means that a is sharper than b. ae 3 e (a) assumes its maximum value if and only if a is maximally fuzzy. ae 4 e (a) = e ( ā ) , ∀a ∈ x . let ep be equilibrium of the fuzzy complement c and specify ae 2 and ae 3 as follows: aes 2 a is sharper than b in the following sense: µa (x) ≤ µb (x) for µb (x) ≤ ep and µa (x) ≥ µb (x) for µb (x) ≥ ep, for all x ∈ x . aes 3 a is defined maximally fuzzy when µa (x) = ep ∀x ∈ x . let a be a fuzzy subset of x and define the following function fa : x → [0,1] by fa : x 7→ { µa(x) if µa (x) ≤ ep c (µa (x)) if µa (x) > ep (8) denote φa the fuzzy set generated by fa as its membership function. theorem 17. the g (|φa|) is an entropy, where g:ℜ → ℜ is a monotonically increasing real function and g (0) = 0. definition 18. let a be a fuzzy subset of x . fa is said to be an elementary fuzzy entropy function if the cardinality of the fuzzy set φa = {(x, fa (x))|x ∈ x , fa (x) ∈ [0, 1]} is an entropy of a. it is obvious that fa is an elementary entropy function. now we introduce some operations based on entropy. for more details we refer to [1]. definition 19. let a and b be two fuzzy subsets of the universe of discourse x and denote ϕa and ϕb their elementary entropy functions, respectively. the minimum entropy conjunction operations is defined as i∗ϕ = i ∗ ϕ (a, b) ={( x, µi∗ϕ (x) ) |x ∈ x , µi∗ϕ (x) ∈ [0, 1] } , where µi∗ϕ : x 7→    µa (x) , if ϕa (x) < ϕb (x) µb (x) , if ϕb (x) < ϕa (x) min (µa (x) , µb (x)) , if ϕa (x) = ϕb (x) . (9) definition 20. let a and bbe two fuzzy subsets of the universe of discourse x and denote ϕa and ϕb their elementary entropy functions, respectively. the maximum entropy disjunction operation is defined as u∗ϕ = u ∗ ϕ (a, b) ={( x, µu∗ϕ (x) ) |x ∈ x , µu∗ϕ (x) ∈ [0, 1] } , where information aggregation in intelligent systems using generalized operators 53 figure 1: entropy based conjunction operator (left) and entropy based disjunction operator (right) figure 2: the construction of i∗ϕ (left) and the construction of u ∗ ϕ (right). µu∗ϕ :x 7→    µa (x) , if ϕa (x) > ϕb (x) µb (x) , if ϕb (x) > ϕa (x) max (µa (x) , µb (x)) , if ϕa (x) = ϕb (x) . (10) the geometrical representation of the minimum fuzziness conjunction and the maximum fuzziness disjunction operators can be seen in fig. 1. several important properties of these operations as well as their construction can be found in [1]. now we present only two figures about the construction. notice also that i∗ϕ is a quasi-conjunction, u ∗ ϕ is a quasi-disjunction operation, and u ∗ ϕ is a commutative semigroup operation on [0, 1] [1]. 3.2 a parametric family of quasi-conjunctions let us cite the following result, which is the base of the forthcoming parametric construction, from [1]. theorem 21. suppose t1, t2 are quasi-conjunctions, s1 and s2 are pseudo disjunctions and h, g1, g2 : [0,1] → [0,1] are non-decreasing functions such that g1 (1) = g2 (1) = 1. then the following functions t (x, y) = t2 (t1 (x, y) , s1 (g1 (x) , g2 (y))) (11) t (x, y) = t2 (t1 (x, y) , g1s1 (x, y)) (12) t (x, y) = t2 (t1 (x, y) , s2 (h (x) , s1 (x, y))) (13) are quasi-conjunctions. 54 imre j. rudas, jános fodor by the use of this theorem the simplest parametric quasi-conjunction operations can be obtained as follows: t (x, y) = xpyq, (14) t (x, y) = min (xp, yq) , (15) t (x, y) = (xy)p (x + y−xy)q (16) where p, q ≥ 0. 4 distance-based operations let e be an arbitrary element of the closed unit interval [0, 1] and denote by d (x, y) the distance of two elements x and yof [0, 1]. the idea of definitions of distance-based operators is generated from the reformulation of the definition of the min and max operators as follows min(x, y) = { x, if d (x, 0) ≤ d (y, 0) y, if d (x, 0) > d (y, 0) , max(x, y) = { x, if d (x, 0) ≥ d (y, 0) y, if d (x, 0) < d (y, 0) based on this observation the following definitions can be introduced, see [1]. definition 22. the maximum distance minimum operator with respect to e ∈ [0, 1] is defined as min max e (x, y) =    x, if d (x, e) > d (y, e) y, if d (x, e) < d (y, e) min (x, y) , if d (x, e) = d (y, e) . (17) definition 23. the maximum distance maximum operator with respect to e ∈ [0, 1] is defined as max max e (x, y) =    x, if d (x, e) > d (y, e) y, if d (x, e) < d (y, e) max (x, y) , if d (x, e) = d (y, e) . (18) definition 24. the minimum distance minimum operator with respect to e ∈ [0, 1] is defined as min min e (x, y) =    x, if d (x, e) < d (y, e) y, if d (x, e) > d (y, e) min (x, y) , if d (x, e) = d (y, e) . (19) definition 25. the minimum distance maximum operator with respect to e ∈ [0, 1] is defined as max min e (x, y) =    x, if d (x, e) < d (y, e) y, if d (x, e) > d (y, e) max (x, y) , if d (x, e) = d (y, e) . (20) 4.1 the structure of distance-based operators it can be proved by simple computation that if the distance of x and yis defined as d (x, y) = |x−y| then the distance-based operators can be expressed by means of the min and max operators as follows. min max e =    max (x, y) , if y > 2e−x min (x, y) , if y < 2e−x min (x, y) , if y = 2e−x , min min e =    min (x, y) , if y > 2e−x max (x, y) , if y < 2e−x min (x, y) , if y = 2e−x (21) max max e =    max (x, y) , if y > 2e−x min (x, y) , if y < 2e−x max (x, y) , if y = 2e−x , max min e =    min (x, y) , if y > 2e−x max (x, y) , if y < 2e−x max (x, y) , if y = 2e−x (22) the structures of the maxmine and the min min e operators are illustrated in fig. 3. information aggregation in intelligent systems using generalized operators 55 figure 3: maximum distance minimum operator (left) and minimum distance minimum operator (right). 5 summary and conclusions in this paper we summarized some of our contributions to the theory of non-conventional aggregation operators. further details and another classes of aggregation operators can be found in [1]. references [1] i. batyrshin, o. kaynak, and i. rudas, fuzzy modeling based on generalized conjunction operations, ieee transactions on fuzzy systems, vol. 10, no. 5 (2002), pp. 678–683. [2] b. buchanan and e. shortliffe, rule-based expert systems, the mycin experiments of the stanford heuristic programming project, addison-wesley, reading, ma, 1984. [3] t. calvo, b. de baets and j. fodor, the functional equations of frank and alsina for uninorms and nullnorms, fuzzy sets and systems 120 (2001) 385–394. [4] e. czogala and j. drewniak, associative monotonic operations in fuzzy set theory, fuzzy sets and systems 12 (1984), 249–269. [5] b. de baets and j. fodor, van melle’s combining function in mycin is a representable uninorm: an alternative proof. fuzzy sets and systems 104 (1999) 133-136. [6] b. de baets and j. fodor, residual operators of uninorms, soft computing 3 (1999), 89–100. [7] j. dombi, basic concepts for the theory of evaluation: the aggregative operator, european j. oper. res. 10 (1982), 282–293. [8] j.c. fodor, a new look at fuzzy connectives, fuzzy sets and systems 57 (1993) 141–148. [9] j.c. fodor and m. roubens, fuzzy preference modelling and multicriteria decision support, (kluwer, dordrecht, 1994). [10] j. fodor, an extension of fung–fu’s theorem, internat. j. uncertain. fuzziness knowledge-based systems 4 (1996), 235–243. [11] j. fodor, r. yager and a. rybalov, structure of uninorms, internat. j. uncertain. fuzziness knowledge-based systems 5 (1997) 411–427. [12] j. fodor and t. calvo, aggregation functions defined by t-norms and t-conorms, aggregation and fusion of imperfect information (b. bouchon-meunier, ed.), physica-verlag, 1998, pp. 36–48. [13] l. fung and k. fu, an axiomatic approach to rational decision-making in a fuzzy environment, fuzzy sets and their applications to cognitive and decision processes (k. tanaka, l. zadeh, k. fu and m. shimura, eds.), academic press, new york, san francisco, london, 1975, pp. 227–256. 56 imre j. rudas, jános fodor [14] p. hájek, t. havránek and r. jiroušek, uncertain information processing in expert systems (crc press, 1992). [15] e.-p. klement, r. mesiar and e. pap, on the relationship of associative compensatory operators to triangular norms and conorms, internat. j. uncertain. fuzziness knowledge-based systems 4 (1996), 129–144. [16] j.g. klir and t.a. folger, fuzzy sets, uncertainty, and information (prentice-hall international editions, usa, 1988). [17] g. mayor and e. trillas, on the representation of some aggregation functions, proc. internat. symposium on multiple-valued logic, 1986, pp. 110–114. [18] a. tsadiras and k. margaritis, the mycin certainty factor handling function as uninorm operator and its use as a threshold function in artificial neurons, fuzzy sets and systems 93 (1998), 263–274. [19] r. yager and a. rybalov, uninorm aggregation operators, fuzzy sets and systems 80 (1996) 111–120. imre j. rudas, jános fodor budapest tech institute of intelligent engineering systems address: bécsi út 96/b, h-1034 budapest, hungary e-mail: {rudas,fodor}@bmf.hu the research has been supported in part by otka t046762. editor’s note about the authors: imre j. rudas graduated from bánki donát polytechnic, budapest in 1971, received the master degree in mathematics from the eötvös loránd university, budapest, the ph.d. in robotics from the hungarian academy of sciences in 1987, while the doctor of science degree from the hungarian academy of sciences. he received his first doctor honoris causa degree from the technical university of kosice, slovakia and his second honorary doctorate from university polytechnica timisoara, romania. he is active as a full university professor and head of department of intelligent engineering systems. he serves as the rector of budapest tech from august 1, 2003 for a period of four years. he is a fellow of ieee, administrative committee member of ieee industrial electronics society, chairman of the hungarian chapters of ieee computational intelligence and ieee systems, man and cybernetics societies. he is also a registered expert of the united nations industrial development organization and the eu. he is the president of the hungarian fuzzy association and steering committee member of the hungarian robotics association and the john von neumann computer society. he is member of the editorial boards of several leading international scientific journals and founder of the ieee international conference series on intelligent engineering systems and ieee international conference on computational cybernetics, and some regional symposia. his present areas of research activity are: robotics with special emphasis on robot control, soft computing, computed aided process planning, fuzzy control and fuzzy sets. he has published one book, more than 300 papers in various journals and international conference proceedings. information aggregation in intelligent systems using generalized operators 57 jános fodor obtained his master degree in mathematics in 1981, his university doctorate in 1984. in 2000 he habilitated at the roland eötvös university, budapest, hungary. he defended his csc and dsc degrees in 1991 and 2004, respectively, at the hungarian academy of sciences. in 1997 he won the "széchenyi professorship" in the first phase of this award. at the beginning of his researcher’s activity he was involved in the stochastic problems of operations research, later on in the field of fuzzy logics, preference modeling, decision making, computational intelligence, intelligent control, and mathematical foundations of the management of uncertainties. he is author of two monographs (issued by kluwer and springer), and about 200 other scientific publications. he served as the editor of two scientific books, and serves as the editor of various journal issues. his works obtained more than 700 independent citations. he is member of the editorial boards of various leading international scientific journals. jános fodor is the co-chairman of the hungarian fuzzy association, and the eurofuse euro working group on fuzzy sets. he is member of the operation research committee of the hungarian academy of sciences, and the jury of electronics and electrical sciences of the hungarian research fund (otka). he regularly leads various research projects supported by diverse funds and resources. many times he served as invited professor in various universities in italy, spain, belgium, and france. international journal of computers, communications & control vol. i (2006), no. 3, pp. 71-83 multiobjective optimization scheduling problems by pareto-optimality in agro-alimentary workshop fatma tangour, ihsen saad abstract: this paper deals with the multiobjective optimization problem of an agroalimentary production workshop. three criteria are considered in addition to this initial cost of production: the cost of the out-of-date products, the cost of the distribution discount and the makespan, and a new coding is proposed for this type of workshop. the adopted approach consists in generating optimal solutions diversified in the search space of solutions, and to help the decision maker when it cannot give a particular preference to one of the objective functions to make the good decision with respect to the quoted criteria. keywords: agro-alimentary workshop, scheduling problems, genetic algorithms, pareto-optimality, multiobjective optimization, production cost, makespan. 1 introduction multi-objective optimization aims to optimize several components of an objective functions vector. contrary to mono-objective, the multi-objective problem usually does not have a solution optimizing the whole concerned criteria, but a set of solutions, known as the set of the pareto-optimal solutions. any solution of this unit is optimal in the sense that no improvement can be made on a component without degradation of at least another component of the vector [15]. given that a solution chosen by a decision maker can not be acceptable by another, it proves to be useful to envisage several alternatives to the choice of a pareto optimal solution [18]. in this article, the scheduling problems in the agro-alimentary production workshops are dealt [6]. the principal objective is to search a realizable scheduling minimizing the makespan, the cost of the out-of-date products and the cost of the distribution discount. the transformation methods of the multi-objective problems into mono-objective problems are applied [2]. this article is organized as follows. the one machine scheduling problem is formulated in section 2; the resolution approach suggested with this problem is described in section 3. the effectiveness of this approach is tested for some examples in section 4. 2 problem formulation the problem is to build a multi-objective one machine scheduling problem adapted to agro-alimentary industries. among the constraints and the criteria specific to agro-food industry, the out-of-date of the products and the discount of distribution can be distinguished. the objective is then to select among the cases of realisable scheduling the one which presents the best reducing compromise between the various criteria [7]. the goal of this study is, then, to minimise these criteria such as: • c1 : the cost of the out-of-date products, • c2 : the cost of the distribution discount, • c3 : the makespan. copyright c© 2006 by ccc publications 72 fatma tangour, ihsen saad the basic production cost on the one machine problem is supposed independent from the scheduling. the data of the considered case are as follows: we have a set n of operations, each operation is characterised by its earliest starting time, its effective starting time, its processing time and its effective completion time. notations : ti : effective starting time of operation oi, ri : earliest starting time of the operation oi, γi : effective completion time of the operation oi, pi : processing time of the operation oi, pi : finished product of the operation oi, cik : k th component of the components set of the operation oi, vik : validity limit date of the component cik, cpi : completion time of product pi, dlivpi : delivery date of the product pi, dvpi : lifespan of the product pi, drpi : return delay of the product pi, previk : cost price of the component of the cik product pi, pvenpi : unit selling price of the product pi cstkpi : cost of storage per unit of time of a unit of the product pi. 2.1 criteria formulations three criteria are considered. the two first constitute criteria specific to the agro-alimentary production workshops [16]. the last criterion is traditional and used for the optimization of the scheduling problems of a traditional production workshop. the considered objectives relate to minimization: • c1: the cost of the out-of-date products c1 = ∑ i ∑ k previk ( max (0,ti −vik) (ti −vik) ) (1) • c2: the cost of the distribution discount c2 = ∑ i max ( 0, dlivpi −cpi ) × ( pvenpi dvpi −drpi + cstkpi ) (2) • c3: the makespan c3 = ∑ i (ti + pi) (3) 2.2 lower bound formulations proposition 1. ci ≥ 0, ∀i ∈ {1, 2} and cbi = 0; where cbi represents the lower bound of the criteria ci. proposition 2. the lower bound of the makespan, cb3 is defined as follows: cb3 = ∑ i min (ri + pi) (4) multiobjective optimization scheduling problems by pareto-optimality in agro-alimentary workshop73 proof. when: c3 = max 1≤i≤n γi, ti ≥ ri and then cb3 = min  ∑ i (ti + pi)   (5) cb3 = ∑ i min (ri + pi) (6) 3 genetic algorithms application for the scheduling problems 3.1 principle various approaches have been proposed to solve scheduling optimization problems, among them the genetic algorithms (gas) approach can be distinguish. this approach was largely adopted these last years [11], [14]. the use of gas in many fields proved reliable in particular in combinatorial problems such as the scheduling problems [3], [4], [12]. other hybrid algorithms have also been proposed [1] [8].the main difficulty in the resolution of these problems types results in their algorithmic representation form, which constitutes the most significant point in genetic search. several representation approaches and various standard ags operators were proposed, to solve these problems. among them, the representation based on the priority rules [5]. the principle of a simple genetic algorithm is as follows, figure 1. 3.2 proposed genetic algorithm coding proposed coding in the application case is: ordered operations coding lists “oocl”, table 1. inspired from the clo (operation list coding) coding [9] and the cpm (parallel machines coding) coding [10], it consists in proposing ordered lists for the products line. the proposed coding defined the ordered, the starting time and the completion time of the operations. these dates are calculated and updated by the “dates calculation algorithm”, table 2. table 1: oocl coding 1 2 3 4 o2,t2, γ2 o1,t1, γ1 o4,t4, γ4 . . . . table 2: dates calculation algorithm ti : effective starting time of operation oi, ti = max (ri, γ j) γi : completion time of the operationoi, γi = max (ri, γ j) + pi where γ j represents the completion time of the operation o j that preceded oi the operators used for this coding are: mutation, crossover at a point and crossover at two points. the mutation operator chooses two points of the same individual (list), to generate another individual, table 74 fatma tangour, ihsen saad mutation population generation i probability pm selection evaluation crossover p2 p1 p e1 e2 e probability pc population generation i+1 figure 1: genetic algorithm principle multiobjective optimization scheduling problems by pareto-optimality in agro-alimentary workshop75 3. the crossover at a point operator chooses, two individuals’ parents to generate two other individuals’ children starting from only one point, table 4. and the two points crossover operator chooses, two individuals to generate two other individuals starting from two points, table 5. table 3: mutation algorithm beginning 1. choose two positions i and j of the same individual, for each position correspond an operation oi and o j, 2. permute between the operations oi and o j to obtain the child, 3. update the child, 4. calculate c1 ,c2 ,c3 of the new individual according to the “dates calculation algorithm”, end 3.3 multi-objective evaluation approach generally, the considered criteria present nonlinear and complexes relations and do not have the same importance from the point of view of decision maker. thus, much of considerations can be retained to take account of all these difficulties. with this intention, a fuzzy method evaluation is proposed. this method is based on the steps which follow [13]: for each objective function a lower bound is calculated as follow: ci(x) ≥ cbi ∀x ∈ s, 1 ≤ i ≤ nc (7) where s represents the space of realisable solutions and nc the number of objective functions. the fuzzification is applied by the functions described, figure 2. 1 µ c + εc b m ii i i b h c i i figure 2: fuzzy application in the resolution of the scale problem for each realizable solution x, a vector c(x) is associated, c(x) ∈ [ cb1 , +∞ ] × ... × [ cbnc , +∞ ] , then c(x) = (c1(x), ..., cnc (x)) t ; for each vector c(x), a fuzzification of their components is proposed and 76 fatma tangour, ihsen saad table 4: crossover i algorithm beginning 1. choose two individuals p1 and p2, and a crossover point 2. go to all the operations while i < n do • if j < i then – copy the operations of the p1 in the child1 – copy the operations of the p2 in the child2 • else copy from p2 (respectively p1) with the same position, the missing operations in the child1(respectively child2) • end if end while 3. finish the construction of the child1 (respectively of the child2) with the missing operations (by respecting the order) 4. update the child1 and the child2 5. calculate c1, c2, c3 of the two new individuals according to the “dates calculation algorithm” end multiobjective optimization scheduling problems by pareto-optimality in agro-alimentary workshop77 table 5: crossover ii algorithm beginning 1. choose two individuals p1 and p2 and two crossover points i and k 2. go to all the operations while i < n do • copy the operations of the p1, which precede the first crossover point and which follows the second crossover point, in the child1 • copy the operations of the p2, which precede the first crossover point and which follows the second crossover point , in the child2 • copy, with the same position, the missing operations of the p2 in the child1 • copy, with the same position, the missing operations of the p1 in the child2 end while 3. finish the construction of the child1 (respectively of the child2) with the missing operations (by respecting the order) 4. update the child1 and the child2 5. calculate c1, c2, c3 of the two new individuals according to the “dates calculation algorithm” end 78 fatma tangour, ihsen saad considered as two sub-sets bi and mi, figure 2. if ci(x) ∈ [ cbi , c h i + ε ] then µ bi (ci(x)) = chi −ci(x) + ε chi −cbi + ε , else µ bi (ci(x)) = 0 (8) when chi represents the maximum value of the solution given by a considered heuristics according to the ith objective function. µ bi (ci(x)) is considered as the fuzzy measurement of ci(x) in the sub-set b i. then, the quality of each solution is characterized by the vector cb(x) where all the components are homogeneous since they belong to the same interval and are all without dimension. cb(x) = (a1, ..., anc ) t ai = µ bi (ci(x)), ∀i = 1, 2, ..., nc (9) for the multi-objective evaluation, the objective function cg(x) is reduced to the minimization of the balanced sum of the criteria relating to the use of the aggregation operator owa [17]. cg(x) = nc ∑ i=1 wiai (10) a set of pareto-optimal solutions is built without according privilege to a particular search direction, to help the decision maker when it cannot clearly give a particular preference to an objective function. this approach is based on an algorithm in which, the objective function cg(.), defined at the relation (10), is used for the evaluation of solutions. weightings wi (1 ≤ i ≤ nc) are calculated by using a fuzzy rule. the idea is to measure the average quality of the solutions according to each criterion for each iteration and to calculate the various weights according to the degree of this quality. the goal is to study the profits and the possible improvements of the solutions by giving the priority to the optimization of the objective functions whose average values is far from the lower bound. this approach is called aggregative approach with dynamic search direction. let c̄ k i be the solutions average of the ith objective function found at kth iteration. c̄ k i = ∑ x∈pk cki (x) card(pk) (11) where pk represents the solutions population at this iteration. b i 1 ic µ c +i ε k c i b p l i ' k 0 figure 3: criteria membership function for each vector c(x), a fuzzification is applied to its components ci(x) according to their positions in the interval [ cbi , c̄ 0 i +ε ′ ] ; where ε′ is a little positive value introduced to avoid the division by zero, if c̄ 0 i = c b i then ε ′ = 0.1cbi , else ε ′ = 0. multiobjective optimization scheduling problems by pareto-optimality in agro-alimentary workshop79 the evaluation of the solutions quality is done by using the membership functions defined in figure 3, relating to the two fuzzy subsets, “p" and “l" of the lower bound. the membership functions can thus be formulated as follows: if c̄ k i ∈ [ cbi , c̄ 0 i +ε ′ ] then µ lik ( c̄ k i ) = c̄ k i −cbi c̄ 0 i −cbi + ε′ else µ lik ( c̄ k i ) = 1 (12) the calculation of various weightings is carried out by using the two following fuzzy rules: • if (cki is “p” fromcbi ) then (wk+1i decrease) • if (cki is “l” fromcbi ) then (wk+1i increase) which lead to the following expression: wki = µ lik ( c k i ) nc ∑ j=1 µ ljk ( c k j ) , ∀i ∀k (13) where 1 ≤ i ≤ nc and 2 ≤ k ≤ q, with q the total number of iterations and “l” the index relating to the fuzzy subset. w1i corresponds at the first iteration defined as follow: w1i = 1 nc , ∀i = 1, ..., nc (14) the various weighting vectors (w 1,w 2, ...,w q) are gradually calculated from the kth generation pk at the generation pk+1, according to the distance between the lower bounds and the average of the k th generation individuals, represented by a black circle in the figure 4. the objective is to improve of the solutions by giving the priority to the objective functions optimization whose average of the values is far from the lower bound. indeed, by using a fuzzy rule, it is possible to control the search direction in order to build a final set with solutions approaching as much as possible the optimal values. this method, can be used when the decision maker cannot give a particular preference to an objective function, it also makes it possible to generate weights of the different criteria from an iteration to another in a dynamic way according to the average of the solutions. 4 simulation to illustrate the effectiveness and performance of the proposed approach, six representative examples based on practical data have been selected to compute. these examples deal with 5 to 10 operations. the proposed approach is applied to them to optimize three criteria, represented in eqs. (1-3). for example, the data relating to the example which treats 10 operations and which treats 5 operations is represented respectively in table 6 and table 7. by application of the proposed approach, the following experimental results are obtained, table 8. the different results show that the solutions obtained are generally acceptable and satisfactory. the values of the different objective functions show the efficiency of the suggested approach, table 8. moreover, the proposed method enables us to obtain good results in a polynomial computation time. in fact, the various values of the criteria given by the multiobjective optimization method by paretooptimality show its effectiveness, table 8. the values of the criteria for the pareto border are in the neighbourhood of to the lower bounds. indeed, such an approach makes it possible to generate paretooptimal solutions of good quality. 80 fatma tangour, ihsen saad c b 1 2 c b c 1 2 c solutions for various populations lower bound barycentre for the population p 1 p p k q w w 1 k+1 p k figure 4: search direction table 6: data relating to 10 operations o1 o2 o3 o4 o5 o6 o7 o8 o9 o10 rk 0 1 2 3 4 1 3 2 1 3 pk 1 2 4 2 1 2 1 3 2 4 vi1 13 14 13 13 12 7 7 13 9 9 vi2 15 14 5 14 13 15 15 16 15 15 vi3 12 13 12 11 14 14 12 14 14 previ1 2 3 4 3 2 1 1 2 2 2 previ2 1 2 2 4 3 2 2 1 4 1 previ3 4 3 2 2 1 3 2 3 3 dvpk 35 32 35 33 35 36 31 34 36 31 drpk 14 10 9 11 8 12 7 9 11 10 dlivpk 21 22 25 22 21 20 21 24 26 22 cstkpk 3 2 2 5 2 3 4 3 1 2 pvenpk 4 6 6 8 7 5 6 8 3 5 multiobjective optimization scheduling problems by pareto-optimality in agro-alimentary workshop81 table 7: data relating to 5 operations o1 o2 o3 o4 o5 rk 2 3 1 4 3 pk 1 2 4 2 3 vi1 3 3 4 6 5 vi2 4 1 2 4 3 vi3 2 3 4 previ1 1 2 1 2 4 previ2 3 1 2 4 2 previ3 4 3 1 dvpk 14 16 10 11 14 drpk 5 6 4 7 5 dlivpk 10 13 14 16 12 cstkpk 1 2 3 4 5 pvenpk 8 3 8 5 4 table 8: experimental results n scheduling c1 c2 c3 cg (.) 10 o1o3o5o9o4o2o7o6o8o10 14 4 24 0,915 9 o1o2o3o4o5o7o6o8o9 9 4 20 0,95 8 o1o7o5o2o3o8o4o6 14 1 18 0,963 7 o1o3o6o7o4o2o5 12 9 17 0,752 6 o1o4o3o5o2o6 4 10 14 0,977 5 o1o4o5o3o2 12 10 14 0,53 82 fatma tangour, ihsen saad 5 conclusion a new approach based on the hybridization with the pareto-optimality for solving multiobjective problems in agro-alimentary workshop, is presented. the approach developed in this work provides the possibility to determine an optimal scheduling among several realizable ones; this optimal solution generates the minimization of objective function (10). besides, the proposed approach uses pareto to estimate and to classify obtained decisions. indeed, we can avoid the preemption of certain components, the cost of the out-of-date products, the cost of the distribution discount and the completion time(makespan). the proposed hybrid approach presented in this paper can be considered as effective mechanisms from the computation complexity. references [1] s. cavalieri, p. gaiardelli, hybrid genetic algorithms for a multiple-objective scheduling problem, journal of intelligent manufacturing, vol.9, pp.361-367, 1998. 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[18] e. zitzler, l. thiele, multi-objective evolutionary algorithms: a comparative case study and the strength pareto approach. ieee transactions on evolutionary computation, vol.3, no 4, pp. 257-271, november, 1999. fatma tangour1,2, ihsen saad1,2 1ecole nationale d’ingénieurs de tunis unité de recherche lara-automatique bp 37, le belvédère, 1002 tunis, tunisie 2ecole centrale de lille, cité scientifique laboratoire d’automatique, genie informatique et signal bp 48, 59651 villeneuve d’ascq cedex, france e-mail: {fatma.tangour, ihsen.saad}@enit.rnu.tn editor’s note about the authors: fatma tangour was born in 1969 in nabeul, tunisia. she graduated from “ecole normale superieure de l’enseignement technique” and obtain the master of automatic and signal treatment in 2004 at the “ecole nationale d’ingénieur de tunis”. she is currently preparing the ph.d. degree in automatic and computer science within the framework of lagis-ec-lille and lara-enit cooperation. her research interests are in the area of optimization methods for discrete events systems, computer science and operational research. ihsen saad was born in monastir, tunisia, in 1977. he received the engineer diploma degree in electrical automatic control engineering from the “ecole nationale d’ingénieur de gabès”, tunisia, in 2002. he obtain the master of automatic and signal treatment in 2004 at the “ecole nationale d’ingénieur de tunis”. he is currently preparing the ph.d. degree in automatic and computer science within the framework of lagis-ec-lille and lara-enit cooperation. his research is related to the evolutionary optimization methods for discrete events systems, computer science and operational research. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 166-174 human intervention and interface design in automation systems p. ponsa, r. vilanova, b. amante pere ponsa (corresponding author) automatic control department technical university of catalonia epsevg, av. víctor balaguer, 08800 vilanova i la geltrú, barcelona, spain e-mail: pedro.ponsa@upc.edu ramón vilanova department de telecomunicació i enginyeria de sistemes universitat autònoma de barcelona 08193, bellaterra, spain e-mail: ramon.vilanova@uab.cat beatriz amante project department technical university of catalonia etseiat edifici tr5, c. colom 11, 08222, barcelona, spain e-mail: beatriz.amante@upc.edu abstract: human-machine-interfaces are with no doubt one of the constitutive parts of an automation system. however, it is not till recently that they have received appropriate attention. it is because of a major concern about aspects related to maintenance, safety, achieve operator awareness, etc has been gained. even there are in the market software solutions that allow for the design of efficient and complex interaction systems, it is not widespread the use of a rational design of the overall interface system, especially for large scale systems where the monitoring and supervision systems may include hundreds of interfacing screens. it is on this respect hat this communication provides an example of such development also by showing how to include the automation level operational modes into the interfacing system. another important aspect is how the human operator can enter the control loop in different ways, and such interaction needs to be considered as an integral part of the automation procedure as well as the communication of the automation device.in this paper the application of design and operational modes guidelines in automation are presented inside an educational flexible manufacturing system case study. keywords: human-automation interaction, process control, display design. 1 introduction automation can refer to open-loop operation on the environment or closed-loop control. and the human intervention adopts diverse possibilities: human in the loop (human intervention), human out of the loop (controller intervention) and human on the loop (supervisory control mode over the controlled process). the basic automation replaces the human manual control by an automatic controller; however in highly automated systems is necessary human beings for supervision, adjustment, maintenance, expansion and improvement. automation increases complexity. it is difficult to maintain operational skills in a automated environment with the copyright c⃝ 2006-2011 by ccc publications human intervention and interface design in automation systems 167 presence of an abnormal situation when the operator is required to intervene. the complexity of industrial human process supervision makes it necessary to supplement the human-computer interaction approach with a cross-disciplinary cooperation in order to integrate knowledge and methods from other fields, especially automation and artificial intelligence [1]. our view is that complete control systems engineering must encompass all these approaches. this increasing complexity of production systems has also translated to the automation level. the need to face for larger amounts of information and the capability to interact with other subsystems of the production chain requires of the application of appropriate modelling methodologies. on that respect it is worth to mention that, on the academic side, the authors have developed different tools to tackle such problems. what is proposed in this paper, and presented by its application to a laboratory scale flexible manufacturing system (fms), is a complete integrated approach for the design of the hmi (human machine interface) system. the development follows a top-down approach where the different screens that constitute the overall system are conceived and particular methods are used to ensure, within each one of the designed interfaces, appropriate ergonomic usage. one of the most interesting points is the introduction, at the automation level, of considerations related to the different start and stop modes of the process units. the inclusion of such considerations at hmi level will help in achieving a more solid and helpful interaction system on its relation with the safety and maintenance operations. the structure of the paper is as follows. the second section is a brief introduction about the computer architecture, the plant layout in the flexible manufacturing system and brief comments about human-systems interaction. in the next section, the translation from the physical layout to the global design of the hmi is proposed: the human can use the hmi inside a control room or near the machine/process. section four shows explain the relationship between automatic control and human intervention in manual mode. the paper ends by drawing some concluding remarks. 2 human role in the flexible manufacturing system the application of the proposed integrated approach for hmi design is peformed within a concrete educational frame whose base focuses on two subjects from the automatics and electronics engineering degree program: modelling and simulating systems and integrated production systems, at the technical university of catalonia upc, spain, and automatic control and industrial informatics at the "universitat autňnoma de barcelona" uab, spain. the subjects from the technical engineering program make a special point on technical topics of programmable logic controllers (plc) and industrial handler robots. in the remaining subjects of the engineering program, petri net modelling, simulation by means of the arenaš discrete-even simulation software [2], physical distribution of flexible manufacturing systems and production system modelling and simulation are the main topics. here, we focus on training the students with system behaviour analysis and simulation software so that they would therefore be able to apply all these techniques in their professional life. this software allows flow shop, job shop and flexible manufacturing systems simulation [3]. practical exercises in laboratory complement the theory and, with this purpose, a laboratory scale fms has been built up, configured and being operative during the last two years which allows the comparison between simulated models and real plant performance. this paper does not describe more technical details of the role playing 168 p. ponsa, r. vilanova, b. amante methodology and the project-based learning approach, readers interested in more detail about these techniques are directed to prior work ponsa et al. [4]. another step in a foreseeable future is the integration of supervision tools (supervisory control and data acquisition;scada software), management tools (manufacturing execution systems; mes; and enterprise resource planning; erp) as well as production planning tools (planning policy analysis). in this sense it would also be advisable a new subject on automated production management within the study program which integrates mes and erp systems at the highest level of the computer integrated manufacturing cim pyramid. the figure 1 shows the physical distribution of stations in the above referred educational fms, the computers architecture and the human intervention with the fms system: at the top level (human-computer interaction and human in the supervisory task; in the bottom level (human-machine interaction and human in manual mode task). the fms is composed of electropneumatic units controlled by plc’s and pc’s. the main purpose here consists of emulating current manufacturing systems: object mechanization and supply, transfer, product assembling, quality control, checking and storage; and technologies, such as, pneumatics, robotics, plc, monitoring and production supervision come together. a total of 5 stations constitute the fms. figure 1: human intervention in the flexible manufacturing system: high level (supervisory task), low level (machine operation the cell manufactures two types of products: product 1 goes across stations 1, 2, 3 and 4 (palletization); and product 2 goes from station 4 (assembling) to 3 and comes back to station 4 (palletization). the fms system has product and process flexibility. the product 1 has 9 different variations (size, color); the product 2 has 8 different variations (size, color). the fms can produce 17 different products. from the point of view of process flexibility, the system can produce only the product 1, only the product 2 or both; in this last case station 4 performs two tasks (assembling and palletization) but only has a robot so that production planning policy is important in order to prevent an excessive workload in this station. in contrast with other educational manufacturing systems which use pallets as functional trays for the same purposes, piece transport is carried out directly on an item by item way over the conveyor system from one station to the next one,. at cell control level, we make use of different programmable logic controllers(plc’s) such as the cp2ma, cj1m models from omron; industrial communication protocols (rs-232, rs-422, pc-link, device net, ethernet), and a plc industrial network with s7-314 from siemens and communication protocols (rs-485, human intervention and interface design in automation systems 169 profibus dp). regarding programming software which enables local control level and monitoringsupervision linking we are testing products, such as in touchš software and the technological system platform from wonderware [5]. in a future, the authors want to increase the numbers of the stations and add a storage as/rs station after the station 4 [6]. 3 human machine interface design the structure of the hmi application is a distributed interface with six parts (the main application, and one application for each station) [7]. the main application has information about the behavior of the flexible manufacturing system: (type of items, petri net algorithm, and coordination between stations, maintenance, and safety). the programmable controllers of each station have device net modules and send this information to the master plc. each station has an autonomous mode of functioning or an integrated mode of functioning. in the first mode, the global design of the hmi is conformed as a set of five autonomous hmi applications. in a near future, one of the problems to solve is the connection between the hmi single applications and works all the fms in an integrated mode. the full number of screens of the hmi application for the station1 is 17 screens, while the total number for the global hmi design is of more than 100 screens. control engineering students have been working in the development of the hmi application for each station. in this sense, expert students (today are working in air traffic control) are the tutors of the novice students. through the use of project based learning and cooperative learning, the students have made focus groups in order to define a clear and a global structure for all the hmi applications. figure 2: station_i display design. example of a cyclic menu structure. from the main screen to monitoring and supervisory control screens. in order to achieve an effective hmi application some display design guidelines need to be used. here the ergonomic display design guideline called gedis has been employed [8]. the 170 p. ponsa, r. vilanova, b. amante gedis guide is a method that seeks to cover all the aspects of the interface design. from the initial point of view of strategies for effective humancomputer interaction applied to supervision tasks in an industrial control room [9]. the gedis guide offers design recommendations at the time of creating the interface as well as recommendations for improvement of already created interfaces. the gedis guide is composed of two parts: description of ten indicators and measure of ten indicators. the indicators have been defined from extracted concepts of other generic human factors guidelines and from aspects of human interface design in human-computer interaction. the method to continue for the use of the gedis guide is: analyze the indicator, measure the indicator, obtain the global evaluation index and finally offer recommendations for improvement. for the correct use of the gedis guide the collaboration between human factor technicians is necessary since in some cases the expert’s opinion is needed to analyze the indicator. this paper does not describe more details of the gedis, readers interested in more detail about this guideline are directed to prior work ponsa and díaz [10]. for each station we have developed an interface with the same template. the architecture of this interface has two parts: monitoring and supervisory control (see fig. 2). for monitoring a set of screens (graphical layout of the station, automatic control, communication with the programmable controller plc) have been defined. the aim of this design is to consider the automatic control loop and the human control. in human supervisory control a set of high level screens (start and stop modes guideline, alarm system, fault detection, diagnostic) has been defined. the aim of this design is to consider the supervisory control loop, a high level over the automatic control loop. often, the monitoring interface is associated with the activities of the human operator near the plant, or near the industrial machine (automation level). this task is related to the allocation of physical interface functions and human sensory-motor functions (choose manual or automatic control, activate or deactivate devices, use of the teach pendant of the robot, etc.). with the animating objects properties of the scada it is possible to develop a screen with the devices of the station (pneumatic actuators, motors, conveyors and sensors, etc). in this interface it is important the knowledge of the behavior of the devices. in this sense it is important the monitoring interface of station 4. it is a complex interface because it is necessary to develop an animation screen with the activities of the industrial robot (palletization or handling). another important screen within the monitoring interface is the panel screen when the human operator can select manual mode or automatic mode. this screen is the one that permits a clear interaction between the human and the automatic controller plc. on the other hand the supervisory control interface is associated with the activities of the human operator inside the industrial control room (supervisory control level). the human supervisory task is related to the allocation of interface functions and human cognitive functions (information processing, planning). respect to the interface this part develops a set of screens: historical trends, alarm screen, fault detection and diagnosis screen. it is necessary to apply a general framework to define which is the correct interface representation of a risky situation. however, each station has specific devices so a specific framework is necessary in order to define a possible anomaly of the station. human intervention and interface design in automation systems 171 4 human intervention in the automation cycle petri nets (pn) has proved to be a successful approach on a broad range of applications [11], [12], [13]. there is however one point that it is not clear how to deal with by using the pn formalism: the introduction of the human operator. effectively, in every automation problem the fully automated part is just one part of the solution. it is customary that the operator can enter the loop in different ways, for example by tuning a pid controller [14] and such interaction needs to be considered as an integral part of the automation procedure as well as the communication of the automation device usually a (plc) with the operator. a human machine interface (either as a pc display, industrial panel, etc) provides the connection between the human operator action and the input to the control algorithm inside the controller device (control based pc, control based plc). figure 3: the human operator can intervene inside the automation cycle (grafcet transition) with the use of an industrial panel. in this panel the human operator follow the functional operational modes of the gemma guide. on the other side, the industrial counterpart said that other approaches than pn are currently in use. effectively, even pn allow tackling really large and complex problems, other approaches like sequential flow charts; sfc; or state-transition graph; grafcet; can be considered the primary automation design tool found on the industry. in fact, some of the plcs allow direct programming by using grafcet [15]. therefore it seems there is a gap between both of these approaches. what we would like to point out here is that both disciplines should be combined and used. the important point with grafcet; a simpler approach if compared with pn; is that it has a close and clear connection with the design guide for start and stop modes called gemma [16]. even the gemma guide was started to be used twenty-eight years ago and it was introduced to the engineering students in several places, in our opinion we don‘t paid sufficiently attention to the gemma guide. this is the reason this section would like to focus on the advantages of using the gemma guide and using it inside the hmi application explained in the previous section. although grafcet is very useful in describing the detailed operation of a sequential control performed by a plc it does not provide a general approach for the operation of an automated machine. it is still necessary to define general operational modes and conditions. this is usually done at the specifications definition stage. in this sense, gemma is a recommended tool for this task [17]. on the other hand, it has always been difficult in terms of vocabulary to clearly, and precisely, 172 p. ponsa, r. vilanova, b. amante explain how to start with manual mode or a semi-automatic mode or an automatic mode. also what are the consequences of an emergency stop, a safety stop or a fast stop for a production machine? usually, these questions are answered by each designer in relation to his own experience and knowledge but can seldom be related to a systematical analysis, except in large companies which have been able to define design guide and standards for control and instrumentation. from the previous observations, the gemma guide is a valuable tool to introduce automation in a more general setting than only for the automation of the process production cycle. it is really important to understand the role played by the operator and how he interacts with the automated system [18]. the different operation modes need to be established and be interrelated in a clear and well defined way. for example in normal conditions the automation cycle is represented by three gemma modes: from a1 initial state stop, then f1 normal production, and a2 requested stop at the end of the cycle and finally comeback to a1. in abnormal situation the automation cycle include the management of an emergency situation: from a1 initial state stop, then f1 normal production, d1 emergency stop, a5 preparation to restart after a failure, a6 production reset to the initital state and finally a1 again. the main contribution of this section is to advance one step into an integral conception of the automation process: the design of the automation system has to include considerations on operational modes and these have to be reflected in the human interface. with this aim, fig. 4 shows the gemma graphical representation inside the hmi application. when the system is in normal state or in anomaly, the human operator can see an active mode on screen, this facilities the situation awareness. figure 4: gemma graphical representation inside the hmi application. the human operator can observe the changes from production, stop or failure procedures. when the system is in normal state or in anomaly, the human operator can see an active mode on screen, this facilities the situation awareness. in the hmi application there is a clear relationship between maintenance and safety services of the system. inside the plc an emergency grafcet has been designed with instructions in case of emergency. the maintenance recommendations are used in order to reduce the stop of the station, and the stop of the production. the translation of the gemma guide inside the plc needs the use of the grafcet representation. when a problem appears in station3, for example, the human operator can activate the emergency stop: in this moment stations 1, 2 and human intervention and interface design in automation systems 173 figure 5: when the emergency stop is activated the emergency grafcet forced the stop of the other grafcet. in the traded grafcet we have automatic control or human manual control. the production grafcet is the set of machine operations. 3 stop their production while station 4 and transport unit will still be functioning. when the human operator solves the problem, can procede to activate the reset of the system, drive the system to a safety state and begin again the production in stations 1, 2 and 3. 5 conclusions in this work we raised the need for a top-down integrated design for human automation systems, with special emphasis on the use of appropriate design and operational guidelines (gedis, gemma). different actuation levels are first identified going from the flexible manufacturing system level to the automation level at each station. while the interfacing system at the station or global level has to cover, mostly, high level monitoring actions, it is at the automation level where more complex situations may arise. at this point the authors propose to base the use of the start and stop modes guide, gemma guide, to build up a convenient representation of the operational stages of the production machines. by designing the automation level hmi along these lines its integration with the automation control system becomes almost natural. the gemma guide approach is therefore a recommended taxonomy approach for the introduction of the human operator into the automation cycle in complex academic/industrial domains. it provides a natural relationship between the design of the automation system and the operational modes that are to be considered from the industrial panel point of view. acknowledgment this work has received financial support from an internal grant of the technical 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[17] p. ponsa and r. vilanova, automatización de procesos mediante la guía gemma. edicions upc, no. 105, 2005. [18] k. li, s. thompson, p. wieringa, and j. xeng, “a study on the role on human operators in supervisory automation system and its implications,” proceedings of the 4th world intelligent control and automation, pp. 3288–3293, 2002. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 336-350 node availability for distributed systems considering processor and ram utilization for load balancing a. menendez lc, h. benitez-perez antonio menendez leonel de cervantes, hector benitez perez universidad nacional autonoma de mexico instituto de investigaciones en matematicas aplicadas y sistemas departameto de ingenieria de sistemas computacionales y automatizacion ciudad universitaria, mexico d.f. e-mail: toniomlc@gmail.com, hector@uxdea4.iimas.unam.mx abstract: node-availability is a new metric that based on processor utilization, free ram and number of processes queued at a node, compares different workload levels of the nodes participating in a distributed system. dynamic scheduling and load-balancing in distributed systems can be achieved through the node-availability metric as decision criterion, even without previously knowing the execution time of the processes, nor other information about them such as process communication requirements. this paper also presents a case study which shows that the metric is feasible to implement in conjunction with a dynamic load-balancing algorithm, obtaining an acceptable performance. keywords: node-availability, load balancing, distributed systems, highperformance. 1 introduction load-balancing is a technique often used to distribute computational load among processors or other resources in order to get a better performance (i.e. optimal resource utilization and small processing time). when performing load-balancing (lb) for a distributed system (ds) it is expected that the resources (particularly the processors) to be evenly used, therefore obtaining a general system performance increase [4]. several studies have been carried out in terms of performance [10], task allocation [8], communication media [5], dynamic networking [1], mobile performance [5] and so on. however, these strategies depend on previous measures such as execution time of a process or communication requirements, or in standard metrics (e.g. number of processes queued at a node or processor idle time percentage) where availability (i.e. the capacity of a node to process a job) is not observed. in any case the need to measure the performance(i.e. optimal resource utilization and small processing time) of a ds with similar and almost periodic processes is not directly addressed. in this paper a new metric named node-availability is introduced, it takes advantage on existing metrics such as processor and ram utilization, the number of processes queued at a node and processes communications and compose them to create the new metric. by including several existing metrics in its calculation, node-availability is a metric that provides more information of a node in its value, than solely using any of the existing metrics. it compares different workload levels at two or more nodes participating in a distributed system, providing a decision criterion to be implemented in conjunction with a common workload algorithm. dynamic scheduling and load-balancing in distributed systems is achieved through the node-availability metric, even without previously knowing the execution time of the processes, nor other information about them such as process communication requirements. the objective of this paper is to present a metric named node-availability [9], how it is constructed and how it allows a ds to execute a set of processes in a balanced manner obtaining fair utilization of the overall system. one of the advantages of using the node-availability metric resides in its ability to copyright c© 2006-2010 by ccc publications node availability for distributed systems considering processor and ram utilization for load balancing 337 perform the load-balancing of a ds without previously knowing the execution times of the processes involved, because if the processing times where known, the scheduling and execution of the processes could be done using proven algorithms [3], [6]. the rest of this document is organized as follows: the metric is described in section 2. the lb algorithm used is presented in section 3. a case study is in section 4. the conclusions and future work are in section 5. 2 node-availability the execution time of a process in a ds is a function determined by the complexity of the process, the communication strategy and by the resources available within the ds. since this execution time cannot be easily seeing when a ds implementation is performed other strategy needs to be followed. for instance, secondary measurements such as used memory for each node or communication load amongst processors and processes can be followed. the decision of which is the most suitable measure depends entirely on the implementing resources. considering that a metric is a quantitative and periodic measurement interpreted in the context of a series of previous equivalent measurements, the metric to estimate the nodes availability is presented, first from the node resources point of view (2.1), followed by the tasks load (2.2) and the communications costs (2.3). 2.1 availability one of the most used metrics in terms of distributed computing is availability, defined as the capacity of a node to process a job at a specific time, it can be obtained from several secondary measures like the time consumed by each node (processor) or by communication performance per process. a ds can be considered as a set of nodes communicating with each other trough a network, where node is defined as the autonomous processing unit, which consists of one processor and ram (random access memory). the processes that are executed in a ds generally demand to use the processor and/or memory, they are not characterized by a high input-output demand, so common measurements within nodes are the processor idle time or the percentage of free memory available. when a process demands a memory space larger than the physical ram, the operating system provides virtual memory to it, causing the total execution time of the process to be increased. the percentage of processor and memory available (figure 1) during a time sample, allows to realize what the operating system (os) behavior is, in terms of resource allocation to a process. the os tries to allocate all the (ram) memory that a process demands, it also tries to assign the processor all the possible time, to the process being executed. figure 1. shows a process execution which takes about 70 seconds, during this time, the operating system assigns the processor to it, resulting in a 0% processor availability. on the other hand, the ram demand is lower than the capacity, so the availability of it is between 60% and 100%. figure 2. shows a process demanding an amount of memory lager than the physical memory (ram) of the node, the operating system (os) assigns to the process all the available ram and then it provides virtual memory to fulfill the memory demand. as it can be seen in figure 2. the percentage of memory available during the execution of the process is 0%, while the processor availability oscillates at the beginning and towards the end of the process execution. figures 1 and 2 show the same process executed by the same node. it takes longer to be executed when it demands the use of virtual memory (approx. 10 seconds, figure 2) than when it is restricted to use ram (approx. 70 seconds, figure 1). the first step in our proposal is to determine the availability of a node in terms of its idle processor time and its free ram. the highest values for these metrics indicate the most available node and the lowest values indicate the busiest node. these to metrics are multiplied for two reasons, being the first one 338 a. menendez lc, h. benitez-perez figure 1: memory demand within limits of ram figure 2: demanding virtual memory node availability for distributed systems considering processor and ram utilization for load balancing 339 that since they are percentages the outcome of the product is also a percentage the values cannot be added because the outcome would exceed 100%. the second reason is that if the values are averaged instead of multiplied it is possible to obtain unreal outcomes, for example a node with 100% of idle processor time and 0% of available ram averages 50%, which in terms of availability should be 0%, because if any of the two resources (processor and memory) is not available no process can be processed. on the other hand, with the proposed approach the availability is real since the value in this same example is 0%. so the first step to evaluate the node availability of node (i) is calculated by: ai = αiβi (1) where: i ∈ {, , . . . , n} is the node identifier, αi is the idle processor time percentage of node i, and βi is the free ram percentage of node i. as αi and βi, are percentages, they can be multiplied to find the availability of the node, giving a value between 0 and 1. 2.2 number of processes queued at a node the availability of a node depends not only in its respective α and β values, but also on the number of processes queued, so the previous equation of (equation 1) must reflect this situation, considering that the number of processes affects the availability of a node in an exponential way. so equation 1 becomes: bi = αiβi eεi (2) where εi is the number of process queued at node i. 2.3 processes communications from the communications point of view, a process can have communications with another process in the same node that the process is being executed in, with a process that is being executed in a different node or no communications at all. the communication resources availability and the execution time is different in these three scenarios, therefore it is necessary to differentiate the processes queued in a node based on their communication requirements. so two new variables are introduced: κi is the number of processes queued at node i communicating (internally) with processes also queued in node i; and νi is the number of processes queued at node i communicating (externally) with processes queued in any node different to i. as stated previously, the execution time of a process depends (among other things) on its communication requirements, the communication time can be approximated in terms of the type of communication (i.e. internal or external) that a process performs. as each type of communication has a different time impact, two constants are defined: ρ is the internal communication time and τ is the external communication time. the equation of node-availability (equation 2) now becomes: ci = αiβi e(εi+ρ κi+τ νi) (3) from a process communication requirements point of view, all the nodes of the ds are different. a process tends to finish its execution earlier if it communicates with processes running in the same node, therefore depending mostly on the node availability; if the processes are in different nodes, they depend not only on the different nodes availability, but also on the network speed and available bandwidth. 340 a. menendez lc, h. benitez-perez 2.4 number of samples taken and periodicity our proposal is to determine the availability of a node in terms of its idle processor time, its free ram memory, the number of processes queued at the node and their communications. these measures are taken periodically and when δ samples have been read, then, the node availability is calculated obtaining an arithmetic average of the readings. so the final equation of node-availability or φ is: φi, j =  δ δ∑ j= αi, jβi, j e(εi, j +ρ κi, j +τ νi, j ) (4) where: i ∈ {, , . . . , n} is the node identifier, j ∈ {, , . . . , δ } is the sample number, αi, j is the idle processor time percentage of node i considering the jth sample, βi, j is the free ram memory percentage of node i considering the jth sample, εi, j is the number of processes queued at node i considering the jth sample, κi, j is the number of processes queued at node i with internal communications considering the jth sample, νi, j is the number of processes queued at node i with external communications considering the jth sample, ρ is the internal communication time, τ is the external communication time and δ is the number of samples taken of: αi, j, βi, j and εi, j before sending the data to the lb process. 2.5 optimization characteristics of φ in this section we show that the metric φ presents a global minimum dependent on local values. the metric φ is characterized per node where the discrete variables α, β , ε, κ and ν play the role of representing the system behavior based on the sample taken. therefore ρ and τ are the communication time factors that can be distinguish as bounded variables and they can be delimited through a local linear optimization strategy. the time values of ρ and τ help to determine some network characteristics such as the type of protocol or the required speed specifications. the first parameter to be defined is the load-balancing factor of the distributed system(ds), which is defined as: µ j = φmin, j φmax, j (5) where φmin, j is the node-availability value corresponding to the least available node and φmax, j is the value corresponding to the most available node within the ds at sample j. it can be noticed that when φmin, j and φmax, j are similar, µ j tends to be one if the system is balanced. on the other hand, the ds is as unbalanced as µ j tends to zero. based upon this approximation the ds error at sample j can be defined as the ds level of unbalance ω given by: ω j = ( − µ j)  (6) to minimize this error it is necessary to balance the ds around the local loads, having that: i f µ j →  then ω j →  in order to minimize this error, the use of partial derivatives of this equation is pursued in terms of the communication times ρ and τ as shown next: ∂ ω j ∂ ρmax, j , ∂ ω j ∂ ρmin, j , ∂ ω j ∂ τmax, j , ∂ ω j ∂ τmin, j (7) where: ρmax, j and τmax, j are the communication times at the most available node, and ρmin, j and τmin, j are the communication times at the least available node, both cases at sample j. node availability for distributed systems considering processor and ram utilization for load balancing 341 now if we take into account the related node and sample values, the global error ˝ is expressed as follows: ω j =   ( φmin, j φmax, j ) =   (  − αmin, jβmin, j αmax, jβmax, j e(εmax, j −εmin, j +ρmax, j κmax, j −ρmin, j κmin, j +τmax, j νmax, j −τmin, j νmin, j ) ) (8) where: max corresponds to the most available node, min corresponds to the least available node and j is the sample number. reordering this expression in terms of λ ()j and λ () j as follows: λ ()j = αmin, jβmin, j αmax, jβmax, j λ ()j = εmax, j − εmin, j + ρmax, jκmax, j − ρmin, jκmin, j + τmax, jνmax, j − τmin, jνmin, j (9) ω can be expressed as: ω j =   (  − λ ()j e λ ()j ) (10) the partial derivatives are as follows: ∂ ω j ∂ ρmin, j = ( − λ ()j e λ ()j )(λ ()j e λ ()j )(κmin, j) ∂ ω j ∂ ρmax, j = ( − λ ()j e λ ()j )(λ ()j e λ ()j )(−κmax, j) ∂ ω j ∂ τmin, j = ( − λ ()j e λ ()j )(λ ()j e λ ()j )(νmin, j) ∂ ω j ∂ τmax, j = ( − λ ()j e λ ()j )(λ ()j e λ ()j )(−νmax, j) (11) the communication times ρ and τ defined in terms of the next sampling period (j+1) are expressed as: ρmin, j+ = ρmin, j + η ∂ ω j ∂ ρmin, j ρmax, j+ = ρmax, j + η ∂ ω j ∂ ρmax, j τmin, j+ = τmin, j + η ∂ ω j ∂ τmin, j τmax, j+ = τmax, j + η ∂ ω j ∂ τmax, j (12) where: η is a design factor where the metric balances the performance of each node depending on its relations amongst (α, β and ε ). 342 a. menendez lc, h. benitez-perez 2.6 metric optimization examples in order to show the effectiveness of this technique two examples are carried out in which the metric φ is evaluated without performing any load-balancing. the first example has a medium profile processor utilization and is called "relaxed example", the second example has high profile processor utilization and is named "restrictive example". this processor utilization is calculated with the well known metric of processor utilization by a set of periodic tasks called "processor utilization factor" [16]. in both examples a set of 40 periodic tasks is evenly distributed trough 10 nodes (i.e. 4 tasks per node). the theoretical workload that a set of periodic tasks imposes to a processor can be calculated using equation (13), in which the "utilization" of a processor is a value under one. u = n∑ i= ci pi (13) where: u = processor utilization, ci = time consumed by the task i and pi = period of task i and n = number of tasks. in both examples the purpose is to obtain the optimized values for ρ and τ that are used in the next section where the metric φ is implemented within a load-balancing algorithm. the setup for the two examples consists of 10 nodes with 4 periodic tasks per node, the periods and consumption times for each task differ in both cases, table 1 shows the parameters used for the relaxed example and in table 4 are the periods and consumption times for the tasks in the restrictive example. notice that the first three tasks (i.e. tasks numbered 1 to 3) are identical in all the nodes, the difference in the workloads of the nodes resides in the fourth task, in which the period is modified. as it can be seen in table 1, the fourth task at every node has a period equivalent to 8 times the node number (e.g. the period of the fourth task 4 at the node number 1 is 8, the period of task 4 in node 2 is 16 an so on). the number of samples taken during both examples is 1000, and every 8 (i.e. δ = ) samples the value of φ is calculated using equation (4). the number of tasks per node with internal communications is 2 and one task has external communications. table 1: tasks parameters (relaxed example) task number period (p) consumption time (c) 1 8 1 2 16 2 3 32 3 4 8 times the number of node 1 the set of tasks assigned to each node according to table1 has a processor utilization per node as listed in table 2. this example is called "relaxed" because the maximum utilization of a processor corresponds to node number 1, and it is 0.5 (as can be seen in table 2). following the optimization procedure of ρ and τ explained in the previous section, their final values (final ρ and final τ ) after 1,000 samples with δ =8 are shown in table 2. a restrictive example is presented where the processor utilization is between 70% and 81%, as can be seen in table 4. the difference amongst the "relaxed" and "restrictive" example resides in duration of the periods of the first three tasks at every node, as shown in table 3, these periods last half the time for the restrictive case, thus imposing a major workload to the processors as can be seen in table 4. the number of samples taken is again 1000 with a δ of 8. the number of tasks with internal communications is 2 and the number of tasks with external communications is 1. as stated earlier, the main difference with the node availability for distributed systems considering processor and ram utilization for load balancing 343 table 2: processor utilization and final values of ρ and τ per node (relaxed example) node utilization final ρ final τ factor time time 1 0.4688 0.0095 0.0127 2 0.4062 0.0935 0.0673 3 0.3854 0.1033 0.1165 4 0.375 0.1081 0.1406 5 0.3688 0.111 0.1548 6 0.3646 0.1128 0.1642 7 0.3616 0.1142 0.1708 8 0.3594 0.1152 0.1758 9 0.3576 0.1159 0.1797 10 0.3562 0.1166 0.1828 "relaxed" example resides on the "utilization factor" for all the nodes, which is around 0.75 as shown in table 4. based upon these two examples, final values pf ρ & τ represent the communication characteristics or guarantees that the ds must provide in order to have a balanced system, meaning that when a node has more processor utilization, it needs to take less time in its communications. table 3: tasks parameters (restrictive example) task number period (p) consumption time (c) 1 4 1 2 8 2 3 16 3 4 8 times the number of node 1 even tough the value of τ can be seen as a local parameter, it is more common to have or guarantee a global communication time for all the nodes participating in a network, so a unique communication time for the whole network must be considered. if the value chosen for τ is the one corresponding to the minimum value for the τ ’s amongst all the nodes then the network is more restrictive and therefore the external communications need to be faster. on the other hand the maximum value of τ means that the communications are relaxed respect to the time they take, but the counterpart is that they not help to have a balanced ds since the least available nodes need faster external communications in order to have equivalent node-availability values to the most available nodes. it can clearly be noticed that the in order to provide communications at the speed required by the value of τ , the network specifications play an important role. the same reasoning applies to the values of ρ for internal communications. the range of values for ρ and τ listed in tables 2 for the relaxed example or in 4 for the restrictive example, indicate respectively the time that internal (ρ ) and external (τ ) communication must take in order to have a balanced system. further more they show the benefits of using φ as a metric, convenient not only to perform such a task as load-balancing, but also useful to determine the optimal duration for process communications, and in the case of τ providing the speed specifications for the communications network. whether to choose the minimum, maximum or average values from the restrictive or relaxed case 344 a. menendez lc, h. benitez-perez table 4: processor utilization per node (restrictive example) node utilization final ρ final τ factor time time 1 0.8125 0.0167 0.0348 2 0.75 0.1491 0.3455 3 0.7292 0.172 0.4599 4 0.7188 0.1828 0.5138 5 0.7125 0.189 0.5452 6 0.7083 0.1931 0.5657 7 0.7054 0.196 0.5802 8 0.7031 0.1982 0.591 9 0.7014 0.1999 0.5993 10 0.7 0.2012 0.606 for ρ and τ depends strictly on the particular implementation case (i.e. network protocol and processor utilization factor). 3 the high-low (hilo) algorithm. in order to perform lb (load-balancing) and load distribution using the presented metric nodeavailability, a simple and well known algorithm, here named high-low (hilo) is used. the underlying principle in hilo is to determine the most available node and the least available one. the knowledge of these nodes is used by the algorithm to perform its two main methods, the periodic method named balance and the event triggered method named distributor (see figure 3). these two methods are nested depending on the arrival of new processes as shown in figure 4, in this case the periodic balance method is executed every period while distributor is executed only when a new process arrives. figure 3: flow diagram of hilo methods balance an distributor node availability for distributed systems considering processor and ram utilization for load balancing 345 the balance method is performed as follows: the most available node is found by calculating the node-availability of all the nodes participating in the distributed system and selecting the one with the maximum φ value, so the most available node is: max(φ ) and analogously the least available node is min(φ ). in order to be able to balance a ds, the hilo algorithm also requires an aimed balance level for it named ξ having  < ξ ≤ . with this three values (max(φ ), min(φ ) and ξ ), the periodical balance method calculates the actual “load-balancing factor” µ using equation (5). if the obtained value of µ is under ξ then the balance method performs the actual load-balancing. this load-balancing is as simple as removing one process from the queue of the node min(φ ) and migrating it to the node named max(φ ). the second method named distributor is responsible to assign a node to any new process arriving to the distributed system. once a new process arrives, distributor sends it to the node max(φ ). figure 4: hilo methods: balance an distributor 3.1 pseudo-code of hilo the algorithm hilo has two methods the first one is a periodic method named “balance”, the second method which is executed every time a new process arrives to the distributed system is named “distributor”. hilo periodically_execute balance if new process then distributor the balance method: balance if min(φ) / max(φ) < ξ then remove process from min(φ) and send it to max(φ) when activated, the distributor method sends the arriving (new) process, to the node max(φ ): 346 a. menendez lc, h. benitez-perez distributor add_queue max(φ) new process 3.2 simulation the hilo algorithm as well as the metric φ are tested on both: a 16 nodes cluster (case study in next section) and on a simulation using matlab. figure 5 shows the simulation of this process where 500 samples are taken, the processes are generated between samples 50 and 250 using a poisson distribution to simulate both; the number of processes ready to be executed and the duration of each one. it can be seeing that the system reaches an absolute balance around sample 400, but during the whole execution of the set of processes, no single node is over-occupied nor idle. figure 5: simulation of a lb process within a cluster, 500 samples are taken and the processes are generated between samples 50 and 250. the parameters used for the simulation are: number of nodes n =16, total samples(j) = 500, and δ = . the internal communication factor ρ = . and the external communication factor τ = . are obtained by calculating the average times from the minimum respective values from table 2 and table 4. the average consumption time of each process is given by an exponential random distribution with mean = δ ∗  and ξ is .%. the initial value for αi, j is one, this value is updated every time a process is queued at a node i, by decreasing its value by 0.1% per process, the approximated inverse procedure is performed when the process has finished. in terms of βi, j (available memory) a similar procedure is performed with a decrement value of 0.12%. these increments/decrements are according to the availability behavior presented in section 2.1. the value for εi, j is calculated every sample, based on how many processes are queued per node and the values for κi, j and νi, j are random numbers between 0 and εi, j. the impact that φ has on the ds load balance can be seen in figure 5, outlining that the work load was evenly distributed amongst the 16 nodes during the whole simulation. node availability for distributed systems considering processor and ram utilization for load balancing 347 4 case study the case study is based on a real geology-specific application, which consists of several similar processes distributed over a cluster. these processes perform a different number of operations locally. the case study is processed seven times with a different number of processes, each occasion, as shown in table 5. every time the algorithm hilo and two common load distribution algorithms: random and round-robin [12–14] are used to execute these 7 different sets of processes. the processes are ready to be executed based on a poisson distribution, independent for each case study. the implementation details are presented in the 4.1 subsection, and the results in subsection 4.2. 4.1 implementation the case study is implemented in a dedicated cluster, which consists of 16 nodes with the following configuration: one master node with 2 xeon processors at 2.6 ghz, 1.5 gb ram and linux kernel 2.6.8. 15 nodes with pentium iv processor at 2.6 ghz, 512 mb ram and linux kernel 2.6.12. the master node performs the distribution and load-balancing functions of the cluster. the case studies are integrated shown in table 5. table 5: number of processes per case study case study number of processes 1 100 2 200 3 300 4 400 5 500 6 1000 7 1500 these processes are independent amongst each other, and to simulate when a process is ready to be executed, a poisson distribution is used. every process performs a random number of local sums and string concatenations, both random numbers are generated globally using an exponential distribution for each case study [6], [15]. furthermore, as both numbers differ, the demands of processor and memory are also different for every process and case study. for these cases κ and ν are equal to zero since there is no communication between processes. each set of processes is executed using the random, round-robin and hilo algorithms to distribute the load. the random algorithm uses a uniform distribution to select the node in which the arriving process is going to be queued. the round-robin algorithm sends the arriving process to the nodes in a round-robin manner. the algorithm hilo sends the arriving process to the un node. the algorithm hilo uses the following values for the parameters described earlier in this paper, selected (as means of example) in a heuristic manner: δ = , n =  and ξ = .. 4.2 results the total execution times of the seven sets of processes (listed in table 1.) were obtained by executing them in the cluster, using the previously listed algorithms for the load distribution. the presented metric node-availability, implemented in the hilo algorithm outperforms the other two as can be seen in the 348 a. menendez lc, h. benitez-perez figure 6 (algorithm comparison). considering the execution time of hilo as 100%, the other two algorithms (round-robin and random) take more time to complete the execution of the same seven sets of processes, this extra time goes from 10% to 65% (i.e. the execution time with round-robin or random algorithms takes from 110% to 165% compared with hilo which is 100%). it can also be noticed in figure 6 that with the smaller set of processes (i.e. 100) the percentage gain of hilo is larger, meaning that the algorithm is efficient even when the set has a relatively small number of processes. figure 6: percentage compared time-efficiency of the algorithm. hilo=100% . every occasion that the condition µ < ξ was fulfilled, a balance was performed. the total numbers of balances that hilo performed are shown in table 6. the total execution time is expressed in minutes:seconds. table 6: number of balances and total time execution per processes set. # processes # balances total time 100 31 01:32.0 200 86 03:17.8 300 137 04:50.0 400 186 06:27.3 500 226 07:52.0 1,000 456 15:36.0 1,500 676 22:59.9 figure 7. shows the execution times of the processes set listed in table5. using the "random", "round-robin" and hilo algorithms, in every case hilo outperforms the other two. 5 conclusions and future work the metric “node-availability” (φ ), allows performing an efficient lb without previously knowing the execution times of the processes, nor the processes communication requirements. this metric takes into account processor and memory availability every given sample and the estimation of the related communication times per processor and process respectively. an optimization procedure based on the communications protocol performance is carried out in order to guarantee the suitability of this metric. the time values of ρ and τ obtained after this optimization node availability for distributed systems considering processor and ram utilization for load balancing 349 figure 7: total time algorithm comparison. procedure provide the speed specifications of the communications network and the type of protocol required. based upon this information the proposed metric (φ ) and hilo perform an efficient response as shown in figure 6. the results presented here provide a clear idea of the impact of φ as the criterion metric to perform a load-balancing procedure. for the case study taken into account, the load-balancing algorithm has a cost in terms of the time taken which can be neglected and is included in the "total time" column of table 6. in terms of hilo algorithm, the ξ factor gives the possibility of balancing either in a very relaxed or strict manner. for instance, if the desired balance factor (ξ ) is decreased in a significant way, the number of balances decreases towards zero. on the other hand when the value of ξ tends to 1 the system performs a load-balancing process every δ samples. bibliography [1] j. bahi, r. couturier, f. vernier, synchronous distributed load balancing on dynamic networks,journal of parallel and distributed computing, 65 pp.1397-1405, elsevier 2005. 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[10] b. parhami, swapped interconection networks: topological, performance, and robustness attributes, journal of parallel and distributed computing, 65 pp.1443-1452, elsevier 2005. [11] m. perez, a. sanchez, j. pea, v. robles, a new formalism for dynamic reconfiguration of data servers in a cluster, journal of parallel and distributed computing, 65 pp.1134-1145, elsevier 2005. [12] h. sit, k. ho, h. v. leong, w. p. r. luk, l. ho, an adaptive clustering approach to dynamic load balancing, proceedings of the 7th international symposium on parallel architectures, algorithms and networks (ispanő04) 1087-4089 ieee 2004. [13] d. takemoto, s. tagashira, s. fujita, partitioning in content-addressable networks distributed algorithms for balanced zone, proceedings of the tenth international conference on parallel and distributed systems (icpadső04) 1521-9097 ieee 2004. [14] torque resource manager http://www.clusterresources.com/pages/products/torque-resourcemanager.php 2006. [15] z. zeng, b. veeravalli, rate-based and queue-based dynamic load balancing algorithms in distributed systems, proceedings of the tenth international conference on parallel and distributed systems, 1521-9097/04 ieee 2004. [16] liu w.s. jane, real-time systems, prentice hall, usa, 2000. antonio menéndez lc is a computer science engineer from the universidad la salle and currently phd candidate by the universidad nacional autónoma de méxico (unam). more than 25 years of experience in the computer and technology industries, leading multi-million projects, as well as in the academic world. devoted to research for the last years has participate in international congresses of real-time, convergence, hybrid systems, and computer science. héctor benítez pérez is a full time researcher in the iimas unam (méxico). he obtained his bsc in electronic engineering at the engineering faculty unam in 1994 and his phd at sheffield university, uk en 1999. his areas of interest are in real time control and fault diagnosis. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 268-279 adding lifetime to objects and membranes in p systems b. aman, g. ciobanu bogdan aman, gabriel ciobanu romanian academy, institute of computer science and a.i.cuza university of iaşi, romania e-mail: baman@iit.tuiasi.ro, gabriel@info.uaic.ro abstract: membrane systems are computing devices inspired from the cell functioning. a feature of membrane systems is the fact that objects and membranes are persistent. in fact, this is not quite true in the real world: cells and intracellular proteins have a well-defined lifetime. inspired from these biological facts, we define a model of membrane systems in which each membrane and each object has attached a lifetime. some results show that this model is at least as powerful as the usual one. 1 introduction to membrane computing membrane systems are essentially parallel and nondeterministic computing models inspired by the compartments of eukaryotic cells and their biochemical reactions. the structure of the cell is represented by a set of hierarchically embedded regions, each one delimited by a surrounding boundary (called membrane), and all of them contained inside an external special membrane called skin. the molecular species (ions, proteins, etc.) floating inside cellular compartments are represented by multisets of objects described by means of symbols or strings over a given alphabet. the objects can be modified or communicated between adjacent compartments. chemical reactions are represented by evolution rules which operate on the objects, as well as on the compartmentalized structure (by dissolving, dividing, creating, or moving membranes). a membrane system can perform computations in the following way: starting from an initial configuration which is defined by the multiset of objects initially placed inside the membranes, the system evolves by applying the evolution rules of each membrane in a nondeterministic and maximally parallel manner. a rule is applicable when all the objects which appear in its left hand side are available in the region where the rule is placed. the maximally parallel way of using the rules means that in each step, in each region of the system, a multiset of rules is chosen which is maximal and applicable, namely a multiset of rules such that no further rule can be added to the multiset. a halting configuration is reached when no rule is applicable. the result is represented by the number of objects from a specified membrane. several variants of membrane systems are inspired by different aspects of living cells (symport and antiport-based communication through membranes, catalytic objects, membrane charge, etc.). their computing power and efficiency have been investigated using the approaches of formal languages, grammars, register machines and complexity theory. membrane systems (also called p systems) are presented together with many variants and examples in [21]. several applications of these systems are presented in [12]. an updated bibliography can be found at the p systems web page [22]. for an alphabet v = {a, . . . , an}, we denote by v ∗ the set of all strings over v ; λ denotes the empty string and v + = v ∗\{λ }. a multiset over v is represented by a string over v (together with all its permutations), and each string precisely identifies a multiset. a language (over v ) is any subset of v ∗; a language is denoted usually by l. given a language l, we define the set ps(l) = {ψv (x) | x ∈ l} called the parikh image of l. if f l is a family of languages, then psf l denotes the family of parikh images of languages in f l. definition 1. a p system of degree n ≥  is a construct π = (v, t,c, h, µ, w, . . . , wn, (r, ρ), . . . , (rn, ρn), io) where: copyright c© 2006-2010 by ccc publications adding lifetime to objects and membranes in p systems 269 1. v is an alphabet of symbols; its elements are called objects; 2. t ⊆ v is the terminal (or output) alphabet; 3. c ⊆ v , c ∩t = /0 is the alphabet of catalysts; 4. h is a set of membrane labels; 5. µ ⊆ h × h is a tree that describes the membrane structure, such that (i, j) ∈ µ denotes that the membrane labelled by j is contained in the membrane labelled by i; 6. wi ∈ v ∗, for each  ≤ i ≤ n, is a multiset of objects initially assigned to membrane i; 7. ri, for all  ≤ i ≤ n, is a finite set of evolution rules that is associated with membrane i; an evolution rule is a multiset rewriting rule of the form u → v, with u ∈ v +, either v = v′ or v = v′δ , v′ ∈ ((v ×{here, out})∪(v ×{in j |  ≤ j ≤ n}))∗, and δ a special symbol not appearing in v ; 8. ρi, for all  ≤ i ≤ n, is a partial order relationship defined over the rules in ri specifying a priority relation between these rules; 9. io is the label of an elementary membrane of µ that identifies the output region. therefore, a p systems of degree n ≥  consists of a membrane structure µ containing n ≥  membranes where each membrane i gets assigned a finite multiset of objects wi and a finite set of evolution rules ri. an evolution rule is a multiset rewriting rule which consumes a multiset of objects from v and produces a multiset of pairs (a,t), with a ∈ v and t ∈ {here, out} ∪ {in j |  ≤ j ≤ n} a target specifying where to move the objects after the application of the rule. as well as this, an evolution rule can produce the special object δ to specify that, after the application of the rule, the membrane containing the special object δ has to be dissolved. after dissolving a membrane, all objects and membranes previously present in it become elements of the immediately upper membrane, while the rules of the dissolved membrane are removed. we use p systems without lifetimes instead of p systems in order to make a clear distinction from the p systems with lifetimes which are introduced in what follows. 2 p systems with lifetimes the evolution of complicated real systems frequently involves various interdependence among components. some mathematical models of such systems combine both discrete and continuous evolutions on multiple time scales with many orders of magnitude. for example, in nature the molecular operations of a living cell can be thought of as such a dynamical system. the molecular operations happen on time scales ranging from − to  seconds, and proceed in ways which are dependent on populations of molecules ranging in size from as few as approximately  to approximately as many as . molecular biologists have used formalisms developed in computer science (e.g. hybrid petri nets) to get simplified models of portions of these transcription and gene regulation processes. according to molecular cell biology [18]: (i) “the life span of intracellular proteins varies from as short as a few minutes for mitotic cycles, which help regulate passage through mitosis, to as long as the age of an organism for proteins in the lens of the eye.” (ii) “most cells in multicellular organisms . . . carry out a specific set of functions over periods of days to months or even the lifetime of the organism (nerve cells, for example).” 270 b. aman, g. ciobanu it is obvious that lifetimes play an important role in the biological evolution. we use an example from the immune system. example 1 ( [18]). t-cell precursors arriving in the thymus from the bone marrow spend up to a week differentiating there before they enter a phase of intense proliferation. in a young adult mouse the thymus contains around  to × thymocytes. about × new cells are generated each day; however, only about  to  ×  (roughly  − %) of these will leave the thymus each day as mature t cells. despite the disparity between the numbers of t cells generated daily in the thymus and the number leaving, the thymus does not continue to grow in size or cell number. this is because approximately % of the thymocytes which develop in the thymus also die within the thymus. inspired from these biological facts, we add lifetimes to objects and membranes. we use a global clock to simulate the passage of time in a membrane system. definition 2. a p system with lifetimes of degree n ≥  is a construct π = (vt , t,c, ht , µt , w, . . . , wn, (r, ρ), . . . , (rn, ρn), io) where: 1. vt = v ×(n∪∞) is a set of pairs of the form (a,ta), where a ∈ v is an object (as in definition 1) and ta ∈ (n∪∞) is the lifetime of the object a; 2. t , c are as in definition 1; 3. ht = h ×(n∪∞) is a set of set of pairs of the form (h,th), where a ∈ h is a membrane label (as in definition 1) and th ∈ (n∪∞) is the lifetime of the membrane h; 4. µt ⊆ ht ×ht is a tree that describes the membrane structure, such that ((i,ti), ( j,t j)) ∈ µt denotes that the membrane labelled by j, with the lifetime jt , is contained in the membrane labelled by i, with the lifetime it ; 5. wi ⊆ (vt )∗ is a multiset of pairs from vt assigned initially to membrane i; 6. ri, for all  ≤ i ≤ n, is a finite set of evolution rules that is associated with membrane i of the following forms: (a) u → v, with u ∈v +t , either v = v′ or v = v′δ , v′ ∈ ((vt ×{here, out})∪(vt ×{in j |  ≤ j ≤ n}))∗; δ is a special symbol not appearing in v ; (b) (a,t) → (a,t − ), for all a ∈ v and t >  if an object a is not involved in a rule of type (a) and it has a lifetime t > , then its lifetime is decreased. (c) (a, ) → λ , for all a ∈ v if an object a has the lifetime  then the object is replaced with the empty multiset λ , thus simulating the degradation of proteins. (d) [ ](i,t) → [ ](i,t−), for all  ≤ i ≤ n in each evolution step the lifetime of each membrane of the membrane structure is decreased with one. (e) [ ](i,) → [δ ](i,), for all  ≤ i ≤ n if the lifetime of a membrane reaches  the membrane is dissolved. 7. ρi, for all  ≤ i ≤ n, is a partial order relationship defined over the rules in ri specifying a priority relation between these rules; adding lifetime to objects and membranes in p systems 271 8. io is the label of an elementary membrane of µt that identifies the output region. these rules are applied according to the following principles: 1. all the rules are applied in parallel: in a step, the rules are applied to all objects and to all membranes; an object can be used only by one rule, non-deterministically chosen (there is no priority among rules), but any object which can evolve by a rule of any form, should evolve. 2. if a membrane is dissolved, then all the objects in its region are left free in the region immediately above it. because all rules are associated with membranes, the rules of a dissolved membrane are no longer available at the next step. 3. the skin membrane has the lifetime equal to ∞, so it can never be dissolved. 4. if a membrane or object has the lifetime equal to ∞, when applying the rules simulating the passage of time we use the equality ∞ −  = ∞. the computation stops when the membrane system contains only objects and membranes that have the lifetime equal to ∞. example 2. the concentration of a molecule can be adjusted quickly only if the lifetime of the molecule is short [1]. it is natural to think of signaling systems in terms of the changes produced when a signal is delivered. but it is just as important to consider what happens when a signal is withdrawn. during development transient signals often produce lasting effects: they can trigger a change that persists indefinitely, through cell memory mechanisms. but in most cases, especially in adult tissues, when a signal ceases, the response fades. the signal acts on a system of molecules that is undergoing continual turnover, and when the signal is shut off, the replacement of the old molecules by new ones wipes out the traces of its action. it follows that the speed of reaction to shutting off the signal depends on the rate of turnover of the molecules that the signal affects. it may not be as obvious that this turnover rate also determines the promptness of the response when the signal is turned on. the figure 1 shows the predicted relative rates of change in the intracellular concentrations of molecules with differing turnover times when their rates of synthesis are increased suddenly by a factor of 10. the concentrations of those molecules that are normally being rapidly degraded in the cell ( red lines) change quickly, whereas the concentrations of those that are normally being slowly degraded ( green lines) change proportionally more slowly. the numbers (in blue) on the right-hand side are the half-lives assumed for each of the different molecules. consider, for example, two intracellular molecules x and y , both of which are normally maintained at a concentration of  molecules per cell. molecule x has a slow turnover rate: it is synthesized and degraded at a rate of 10 molecules per second, so that each molecule has an average lifetime in the cell of  seconds. molecule y turns over  times as quickly: it is synthesized and degraded at a rate of  molecules per second, with each molecule having an average lifetime of  seconds. if a signal acting on the cell boosts the rates of synthesis of both x and y tenfold without any change in the molecular lifetimes, at the end of  second the concentration of y will have increased by nearly  molecules per cell ( ×  − ) while the concentration of x will have increased by only  molecules per cell. in fact, after its synthesis rate has been either increased or decreased abruptly, the time required for a molecule to shift halfway from its old to its new equilibrium concentration is equal to its normal half-life that is, it is equal to the time that would be required for its concentration to fall by half if all synthesis were stopped (figure 1). the same principles apply to proteins as well as to small molecules and to molecules in the extracellular space as well as to those in cells. many intracellular proteins that are rapidly degraded have short half-lives, some surviving less than  minutes; in most cases these are proteins with key regulatory roles, 272 b. aman, g. ciobanu figure 1: the importance of rapid turnover [1] whose concentrations are rapidly regulated in the cell by changes in their rates of synthesis. likewise, any covalent modifications of proteins that occur as part of a rapid signaling process most commonly the addition of a phosphate group to an amino acid side chain must be continuously removed at a rapid rate to make such signaling possible. example 3. the scenario presented in example 2 can be modelled using p systems with lifetimes. consider the membrane configuration [(xs, ∞)(x , ) . . . (x , )(ys, ∞)(y, ) . . . (y, )](cell,∞) describing the structure of the cell when in equilibrium. xs and ys represent the molecules from which x and y are synthesized, while (x ,t) and (y,t) represent the fact that there are  molecules x which have the lifetime equal to t and  molecules y which have the lifetime equal to t. the rules describing the evolution of this system are: 1. (x , ) → λ a molecule x which is at the end of its lifetime is degraded. 2. (y, ) → λ a molecule y which is at the end of its lifetime is degraded. 3. (xs, ∞) → (xs, ∞)(x , ) each second  new x molecules are synthesized. 4. (ys, ∞) → (ys, ∞)(y, ) each second  new y molecules are synthesized. 5. (x ,t) → (x ,t − ), t >  the lifetime of a molecule x which is not involved in any reaction is decreased. 6. (y,t) → (y,t − ), t >  the lifetime of a molecule y which is not involved in any reaction is decreased. adding lifetime to objects and membranes in p systems 273 after applying rules in a maximal manner after each second we reach the initial configuration [(xs, ∞)(x , ) . . . (x , )(ys, ∞)(y, ) . . . (y, )](cell,∞). if two signals enter the cell (we model the signals by the pairs (cx ,tc) and (cy ,tc)) then we consider two new rules: 7. (cx ,tc)(xs, ∞) → (cx ,tc − )(xs, ∞)(x , ) if an object cx is present in the cell then the rate of synthesis of x is increases  times. 8. (cy ,tc)(ys, ∞) → (cy ,tc − )(ys, ∞)(y, ) if an object cy is present in the cell then the rate of synthesis of y is increases  times. when adding this rules we also add some priorities between rules, namely  >  and  > . after one second the concentration of y increases by nearly  molecules per cell (× − ) while the concentration of x increases by only  molecules per cell. figure 2: a p system with lifetimes in figure 2 an example of a p system with lifetimes is shown. graphically, the boxes represent the membranes and their nesting reflects the hierarchy. membrane  represent the skin membrane. both membranes have a lifetime equal to ∞ meaning that no dissolving rule is not necessary. inside membrane  we have the initial multiset of pairs, the evolution rules and some priorities between them. in the evolution rule we omit the subscript here for objects, in the products, that remain in the same membrane. the defined p system with lifetimes computes the least common multiple of n and n, namely lcm(n, n). the idea is to put a pair (a, n − ) and a pair (b, n − ) at the beginning of the computation, and to produce a pair (c, ∞) (which is send in membrane ) each time the lifetime of the object a is decreased. the first time the objects a and b appear together with the lifetime  is exactly after lcm(n, n) time units. at this moment in membrane  are lcm(n, n) objects c, which represent the output of the system. 3 systems with and without lifetimes the following results describe certain relationships between p systems with lifetimes and p systems without lifetimes, and between similar p systems with lifetimes. proposition 4. for every p system without lifetimes there exists a p system with lifetimes providing the same output by performing an equal number of steps. 274 b. aman, g. ciobanu proof: [proof (sketch)] it is easy to state that the class of p systems with lifetimes includes the class of p systems without lifetimes, since we can assign ∞ to all lifetimes appearing in the membrane structure and evolution rules. 2 a somehow surprising result is that p systems with lifetimes can be simulated by p systems without lifetimes. proposition 5. for every p system with lifetimes there exists a p system without lifetimes providing the same output by performing an equal number of steps. proof: we use the notation rhs(r) to denote the multisets of pairs which appear in the right hand side of a rule r. this notation is extended naturally to multisets of rules: given a multiset of rules r, the right hand side of the multiset rhs(r) is obtained by adding the right hand sides of the rules in the multiset, considered with their multiplicities. each object a ∈v from a p system with lifetimes has a maximum lifetime (we denote it by li f etime(a)), which can be calculated as follows: li f etime(a) = max({t | (a,t) ∈ wi,  ≤ i ≤ n}∪{t | (a,t)∈rhs(ri),  ≤ i ≤ n}) in what follows we present the steps which are required to build a p system without lifetimes starting from a p system with lifetimes, such that both provide the same output. 1. a membrane structure from a p system with lifetimes is translated into a membrane structure of a p system without lifetimes in the following way the lifetimes of elements from a p system with lifetimes are simulated using the membranes , . . ., k in the corresponding p system without lifetimes as we show at the next steps of the translation. the value of k is the maximum from the finite lifetime of objects and surrounding membrane mem, namely k = max({li f etime(a) | a ∈ v, li f etime(a) 6= ∞} ∪ {tmem | tmem 6= ∞}). if an object or the surrounding membrane has the lifetime equal to ∞ in the p system with lifetimes then we do not need to count the passage of time, namely to use the membranes , . . ., k in the p system without lifetimes for the corresponding object or membrane. the object mem placed inside the membrane labelled  is used to simulate the passage of time for the membrane. the initial multiset of objects wt from membrane mem in the p system with lifetimes, in translated into the multiset w which is added into membrane  inside membrane mem in the corresponding p system without lifetimes since all objects from the initial multiset are just starting their life. 2. the rules (a,t) → (a,t − ), for all a ∈ v , t >  and t 6= ∞ from the p system with lifetimes can be simulated in the p system without lifetimes using the following rules: (a) a → (aoai, out) placed inside membrane i, for all  ≤ i ≤ li f etime(a) −  the object oai is used to keep track of the units of time that have past from the lifetime of the object a adding lifetime to objects and membranes in p systems 275 (b) aoa j → (a, in j+), placed inside membrane mem, for all  ≤ j ≤ li f etime(a) −  and a ∈ v this rules together with the previous ones simulate the passage of a unit of time from the lifetime of object a in the p system with lifetimes, by moving object a from a membrane j to a membrane j +  for  ≤ j ≤ li f etime(a) −  in the p system without lifetimes. 3. the rules (a, ) → λ , for all a ∈ v from the p system with lifetimes can be simulated in the p system without lifetimes using the following rules: (a) a → λ placed inside membrane li f etime(a) if the object a reaches the membrane labelled with li f etime(a) in the p system without lifetimes, it means that the lifetime of object a is  in the corresponding p system with lifetimes, so it is replaced by λ . 4. the rules ut → vt from the p system with lifetimes can be simulated in the p system without lifetimes using the following rules: (a) uou j → (v, in) the multiset ou j contains objects of the form oa j, where a ∈ u and  ≤ j ≤ li f etime(a) − . when replacing the multiset u with the multiset v, we also remove the objects oa j that keep track of the life of the objects appearing in u since we do not need them anymore. we send the newly obtained multiset v in membrane  since all objects from this multiset are just starting their life. 5. the rules [ ](mem,t) → [ ](mem,t−), for all t >  and t 6= ∞ from the p system with lifetimes can be simulated in the p system without lifetimes using the following rules: (a) mem → (mem omem i, out) placed inside membrane i, for all  ≤ i ≤ li f etime(mem) −  the object omem i is used to keep track of the units of time that have past from the lifetime of the membrane mem; (b) mem omem j → (mem, in j+), placed inside membrane mem, for all  ≤ j ≤ li f etime(mem)− this rules together with the previous ones simulate the passage of a unit of time from the lifetime of membrane mem in the p system with lifetimes, by moving object mem from a membrane j to a membrane j +  for  ≤ j ≤ li f etime(mem) −  in the p system without lifetimes. 6. a rule [ ](mem,) → [δ ](mem,) from the p system with lifetimes can be simulated in the p system without lifetimes using the following rules: (a) mem → (oδ , out) placed inside membrane li f etime(mem) −  if the object mem reaches the membrane labelled with li f etime(mem) −  in the p system without lifetimes, it means that the lifetime of membrane mem is  in the corresponding p system with lifetimes, so it needs to be dissolved. (b) oδ → δ (δ , in) . . . (δ , ink) once this object is created by the previous rule, an object δ is created inside membrane mem and inside each membrane j,  ≤ j ≤ k, is send an object δ . this means that all these membranes are dissolved, all the rules are deleted, and all objects are send in the parent membrane. the dissolving of the membranes take place after applying all other possible rules. at the moment of the dissolution the only existing objects are found in membrane mem. for each object a there exists an object oa j that keeps track of the life of a, thus being able to continue the increment the life of a in the parent membrane. 276 b. aman, g. ciobanu the output membrane from the p system with lifetimes, is translated in the output membrane from the p system without lifetimes. after performing the same number of evolution steps in both systems, the output membranes contain the same multisets of objects. 2 we are now able to prove the computational power of p systems with lifetimes. we denote by ntpm(coo,tar) the family of sets of natural numbers generated by p systems with lifetimes of degree at most m ≥ , using cooperative rules, and communication of objects through membranes. we also denote by nre the family of all sets of natural numbers generated by arbitrary grammars. proposition 6. ntpm(coo,tar) = nre, for all m ≥ . proof: [proof (sketch)] since the outcome of each p system with lifetimes can be obtained by an p system without lifetimes, we cannot get more than the computability power of p systems. therefore, according to theorem 3.3.3 from [21], we have that the family ntpmof sets of natural numbers generated by p systems with lifetimes is the same as the family nre of sets of natural number generated by arbitrary grammars. 2 remark 3.1. consider the membrane system π = [[ ](,∞)(a, )(a, )](,∞) with the set of rules r = {(a, ) → (a, )((c, ∞), in); (a, ) → (a, ); (a, ) → λ }. since the membranes have the lifetime ∞ it is not necessary to consider rules for decreasing lifetimes to membranes. if we rewrite this as π ′ = [[ ](,∞)(a, )(a, )(d,t)](,∞), with r = {(a, ) → (a, )((c, ∞), in); (a, ) → (a, ); (a, ) → λ ;(d, i) → (d, i − ); (d, ) → λ } we have that after t units of time the membrane system π has the same evolution as the membrane system π ′. in automata theory the problem of optimizing a finite-state machine, meaning to find the machine with the minimum number of states that performs the same function, was addressed by the theorem of myhill and nerode; a fast algorithm doing this is the hopcroft minimization algorithm [16]. using a similar approach we want to optimize a given p system with lifetimes. this can be realized with the passage of time, namely all objects and membranes which are not used in rewriting rules and have a finite lifetime are eliminated. proposition 7. let π and π ′ be two p systems with lifetimes such that: 1. π = (vt , t,c, ht , µt , w, . . . , wn, (r, ρ), . . . , (rn, ρn), io) 2. π ′ = (vt , t,c, ht , µt , w′, . . . , w ′ n, (r, ρ), (r ′ , ρ ′ ), . . . , (r ′ n, ρ ′ n), i ′ o) 3. i′o = io the output membrane is the same for the two membrane systems. 4. wi ⊆ wi, for all  ≤ i ≤ n the initial multiset from membrane i of π ′ contains the same objects as the initial multiset from membrane i of π and some other objects together with their initial lifetimes. 5. r′i = ri ∪{(a,t) → (a,t − ), (a, ) → λ | a ∈ w′i \wi} the set of rules r′i contains all the rules of ri and some additional rules to simulate the passage of time for all the objects appearing in w′i \wi. 6. ρ ′i = ρi, for all  ≤ i ≤ n the priority orders are the same for the two membrane systems. then the p systems with lifetimes π and π ′ have the same membrane structure and evolution after max{t | (a,t) ∈ w′i \wi,  ≤ i ≤ n} units of time. adding lifetime to objects and membranes in p systems 277 proof: [proof (sketch)] after max{t | (a,t) ∈ w′i \wi,  ≤ i ≤ n} units of time all objects which appeared in wi\w′i in the description of the membrane system π ′ are consumed. in this case we have that the contents of the membranes of π is the same as the contents of the membranes of π ′, and the applicable rules for the two membrane systems are only the rules of ri,  ≤ i ≤ n. 2 4 related work and conclusion we introduce a new class of p systems, namely the p systems with lifetimes. lifetimes are assigned to each membrane and to each object. this new feature is inspired from biology where cells and intracellular proteins have a well defined lifetime. in order to simulate the passage of time, we use rules of the form (a,t) → (a,t − ) for objects, and [ ](i,t) → [ ](i,t−) for membranes. if the lifetime of an object reaches  then the object is consumed by applying a rule of the form (a, ) → λ , while if the lifetime of a membrane i reaches  then the membrane is marked for dissolution by applying a rule of the form [ ](i,) → [δ ](i,). after dissolving a membrane, all objects and membranes previously contained in it become elements of the immediately upper membrane. we do not obtain a more powerful formalism by adding lifetimes to objects and to membranes into a p system. according to proposition 4, proposition 5 and proposition 6, p systems with lifetimes and p systems without lifetimes have the same computational power. however the p systems with lifetimes are able to describe more naturally some biological phenomena involving timing, as in example 3. a similar idea appears in the framework of spiking p systems: considering a duration of life for spikes, but not for cells [15]. if a spike is not used in a number of steps larger than its lifetime, then it is removed. there are also some papers using time in the context of membrane computing in a different manner than in this paper. in [8] a timed p system is introduced by associating to each rule a natural number representing the time of its execution. then a p system which always produces the same result, independently from the execution times of the rules, is called a time-independent p systems. the notion of time-independent p systems tries to capture the class of systems which are robust against the environment influences over the execution time of the rules of the system. other types of time-free systems are considered in [7, 9]. time can also be used to “control” the computation, for instance by appropriate changes in the execution times of the rules during a computation, and this possibility has been considered in [11]. moreover, timed p automata have been proposed and investigated in [5], where ideas from timed automata have been incorporated into timed p systems. frequency p systems has been introduced and investigated in [19]. in frequency p systems each membrane is clocked independently from the others, and each membrane operates at a certain frequency which could change during the execution. dynamics of such systems have been investigated. if one supposes the existence of two scales of time (an external time of the user, and an internal time of the device), then it is possible to implement accelerated computing devices which can have more computational power than turing machines. this approach has been used to construct accelerated p systems where acceleration is obtained by either decreasing the size of the reactors or by speeding-up the communication channels [6]. in [10, 17] the time of occurrence of certain events is used to compute numbers. if specific events (such as the use of certain rules, the entering/exit of certain objects into/from the system) can be freely chosen, then it is easy to obtain computational completeness results. however, if the length (number of steps) are considered as result of the computation, non-universal systems can be obtained. time is considered in [17, 20] as the result of the computation by using special “observable” configurations taken in regular sets (with the time elapsed between such configurations considered as output). the authors of the current paper have also considered time to “control” the computation in two other formalisms: mobile ambients [2–4] and distributed π -calculus [13, 14]. timers define timeouts for 278 b. aman, g. ciobanu various resources, making them available only for a determined period of time. the passage of time is given by a discrete global time progress function. acknowledgments this work was partially supported by cncsis research grants idei 402/2007 and cnmp parteneriate d1/1052/2007. bibliography [1] b. alberts, a. johnson, j. lewis, m. raff, k. roberts, p. walter. molecular biology of the cell fifth edition. garland science, taylor & francis group, 2008. [2] b. aman, g.ciobanu. timers and proximities for mobile ambients. lecture notes in computer science, vol.4649, 33–43, 2007. [3] b. aman, g.ciobanu. mobile ambients with timers and types. lecture notes in computer science, vol.4711, 50–63, 2007. [4] b. aman, g.ciobanu. timed mobile ambients for network protocols. lecture notes in computer science, vol.5048, 234–250, 2008. [5] r. barbuti, a. maggiolo-schettini, p. milazzo, l. tesei. timed p automata. electronic notes in theoretical computer science, vol.227, 21–36, 2009. [6] c.s. calude, gh. păun. bio-steps beyond turing. biosystems, vol.77, 175–194, 2004. [7] m. cavaliere, v. deufemia. further results on time-free p systems. international journal of foundations of computer science, vol.17, 69–89, 2006. [8] m. cavaliere, d. sburlan. time-independent p systems. lecture notes in computer science, vol.3365, 239–258, 2005. [9] m. cavaliere, d. sburlan. time and synchronization in membrane systems. fundamenta informaticae, vol.64, 65–77, 2005. [10] m. cavaliere, r. freund, a.leitsch, gh. păun. event-related outputs of computations in p systems. journal of automata, languages and combinatorics, vol.11, 263–278, 2006. [11] m. cavaliere, c. zandron. time-driven computations in p systems. proceedings of fourth brainstorming week on membrane computing, 133–143, 2006. [12] g. ciobanu, gh. păun, m.j. pérez-jiménez (eds.). applications of membrane computing, springer, natural computing series, 2006. [13] g. ciobanu, c. prisacariu. timers for distributed systems. electronic notes in theoretical computer science, vol.164(3), 81–99, 2006. [14] g. ciobanu, c. prisacariu. coordination by timers for channel-based anonymous communications. electronic notes in theoretical computer science, vol.175(2), 3–17, 2007. [15] r. freund, m. ionescu, m. oswald. extended spiking neural p systems with decaying spikes and/or total spiking. international journal of foundations of computer science, vol.19, 1223–1234, 2008. [16] j. e. hopcroft. an nlogn algorithm for minimizing the states in a finite automaton. the theory of machines and computations, academic press, 189–196, 1971. [17] o.h. ibarra, a. păun. computing time in computing with cells. lecture notes in computer science, vol.3892, 112–128, 2006. adding lifetime to objects and membranes in p systems 279 [18] h. lodish, a. berk, p. matsudaira, c. kaiser, m. krieger, m. scott, l. zipursky, j. darnell. molecular cell biology sixth edition. freeman, 2008. [19] d. molteni, c. ferretti, g. mauri. frequency membrane systems. computing and informatics, vol.27(3), 467–479, 2008. [20] h. nagda, a. păun, a. rodríguez-patón. p systems with symport/antiport and time. lecture notes in computer science, vol.4361, 463–476, 2006. [21] gh. păun. membrane computing. an introduction. springer, 2002. [22] web page of the p systems: http://ppage.psystems.eu. international journal of computers communications & control issn 1841-9836, 11(2):292-304, april 2016. numerical p systems with thresholds z. zhang, l. pan zhiqiang zhang key laboratory of image information processing and intelligent control, school of automation, huazhong university of science and technology, wuhan 430074, hubei, china zhiqiangzhang@hust.edu.cn linqiang pan* key laboratory of image information processing and intelligent control, school of automation, huazhong university of science and technology, wuhan 430074, hubei, china *corresponding author: lqpan@mail.hust.edu.cn abstract: numerical p systems are a class of p systems inspired both from the structure of living cells and from economics. in this work, a control of using evolution programs is introduced into numerical p systems: a threshold is considered and a program can be applied only when the values of the variables involved in the production function of the program are greater than/equal to (lower-threshold) or smaller than/equal to (upper-threshold) the threshold. the computational power of numerical p systems with lower-threshold or upper-threshold is investigated. it is proved that numerical p systems with a lower-threshold, with one membrane and linear production functions, working both in the all-parallel mode and in the one-parallel mode are universal. the result is also extended to numerical p systems with an upperthreshold, by proving the equivalence of the numerical p systems with lowerand upper-thresholds. keywords: membrane computing, numerical p system, computation power, universality, register machine. 1 introduction membrane computing is a branch of natural computing, which is inspired from the structure and functioning of living cells. the computing devices considered in membrane computing are called p systems. they are parallel, distributed and non-deterministic computational models. according to their membrane structure, there are two main classes of p systems: cell-like p systems, with a hierarchical arrangement of membranes [7], and tissue-like p systems or neurallike p systems, with a net of processor units placed in the nodes of a directed graph [3, 5]. the present work deals with a class of cell-like p systems, called numerical p systems [10]. numerical p systems are motivated by the cell structure and the economic reality. numerical variables are placed in the regions of a membrane structure. these variables can evolve by means of programs, which are composed of two components, a production function and a repartition protocol. a production value of the region at a given step is computed by means of the production function. this value is distributed to variables from the region where the program resides, and to variables in its upper and lower neighbors according to the repartition protocol. by a synchronized use of production functions, followed by the repartition of the obtained values, a transition is defined between system configurations. the values assumed by a distinguished variable during a computation form the set of numbers computed by the system. many computational properties of numerical p systems have been investigated at both the theoretical level and at the application level [4,9,10,12–17]. several strategies of using productionrepartition programs were considered: sequential (at each step, in each region, only one program copyright © 2006-2016 by ccc publications numerical p systems with thresholds 293 can be applied), all-parallel (all programs in a region of the membrane structure are used simultaneously, with each variable participating in all programs where it appears), one-parallel (the programs are chosen to be used in parallel in such a way that each variable participates in only one of the chosen programs). using a threshold is an interesting strategy of controlling the use of production-repartition programs. the idea was introduced in numerical p systems in [12], under the name of enzymatic control: a distinguished variable, called enzyme, is associated with each program and the program is applied only if the current value of the enzyme is not smaller than the smallest value of the variables involved in the production function of the program. the “enzymatic control" is useful in designing robot controllers based on numerical p systems [13–15]. we here introduce a related but different strategy, similar to the threshold control used in [18] for spiking neural p systems: rules can be used according to the result of the comparison of the number of spikes in the neuron with the constant, which corresponds to the fact that a neuron can fire when its potential is greater than or equal to its threshold. in our case, a constant is associated with the numerical p system and it is used as a control threshold in two natural ways: a program can be applied only when the values of the variables involved in the production function are not smaller than (the lower-threshold case), respectively not greater than (the upper-threshold case) the constant. the computational power of such p systems is investigated. specifically, it is proved that universality results can be obtained for such p systems with one membrane and linear production functions, working both in the all-parallel mode and in the one-parallel mode. the proof is done (by simulating register machines) only for lowerthresholds, then the result is extended to the case of upper-threshold by proving that numerical p systems with upper-thresholds can simulate systems with lower-thresholds. the possible usefulness of the threshold control remains to be examined for applications (in robot control). for the sake of applications, it could be useful to consider its stronger versions, such as taking different thresholds for different membranes or even for different programs in the system (maybe also mixing the way to use the thresholds, in the lower or upper ways). 2 preliminaries readers are assumed to be familiar with basic elements of membrane computing, e.g., from [7, 8, 11]. here we only mention some notions and notations which are used in this paper. we denote by n the set of natural numbers, and the set of real numbers is denoted by r. the family of all recursively enumerable sets of k-dimensional vectors of non-negative integers is denoted by ps(k)re. since numbers can be seen as one-dimensional vectors, we can replace ps(1) by n in the notation, thus obtaining nre. an n-register machine is a construct m = (n,p,m), where n > 0 is the number of registers, p is a finite sequence of instructions bijectively labeled with the elements of the set {0,1, . . . ,m}, 0 is the label of the first instruction to be executed, and m is the label of the halt instruction of p . registers contain non-negative integer values. the instructions of p have the following forms: • j : (inc(r),k, l), with 0 ≤ j < m,0 ≤ k,l ≤ m, and 1 ≤ r ≤ n. this instruction, labeled with j, increments the value contained in register r, then nondeterministically jumps either to instruction k or to instruction l. • j : (dec(r),k, l), with 0 ≤ j < m,0 ≤ k,l ≤ m, and 1 ≤ r ≤ n. if the value contained in register r is positive, then decrement it by 1 and jump to instruction k. if the value of r is zero, then jump to instruction l (without altering the content of the register). 294 z. zhang, l. pan • m: halt. a deterministic register machine is a register machine in which all inc instructions have the form j : (inc(r),k,k); we write these instructions simply as j : (inc(r),k). a register machine m generates a set n(m) of numbers in the following way: the machine starts with all registers being empty (i.e., storing the number zero); the machine applies the instruction with label 0 and continues to apply instructions as indicated by the labels (and made possible by the contents of registers); if it reaches the halt instruction, then the number present in register 1 at that time is said to be generated by m. if the computation does not halt, then no number is generated. it is known that register machines generate all sets of numbers which are turing computable, hence they characterize nre [6]. a register machine can also be used to compute functions. a function f : nα → nβ is computed by a register machine m if, when starting with n1 to nα in registers 1 to α, if f(n1, . . . ,nα) = (r1, . . . ,rβ), then m halts in the final label m with registers 1 to β containing r1 to rβ, and all other registers being empty; if f(n1, . . . ,nα) is undefined, then the final label of m is never reached. a register machine can also be used as an accepting device. a set n of numbers is accepted by a deterministic register machine m if, when starting with x ∈ n in register 1, m halts in the final label m with all registers being empty. the following propositions concerning the computational power of register machines are essential for the main results established in this work [1, 2, 6]. proposition 1. for any partial recursive function f: nα → nβ(α,β > 0), there exists a deterministic register machine m with (max{α,β}+ 2) registers computing f. proposition 2. for any recursively enumerable set n ⊆ ps(α)re of vectors of non-negative integers there exists a deterministic register machine m with (α + 2) registers accepting n. proposition 3. for any recursively enumerable set n ⊆ ps(β)re of vectors of non-negative integers there exists a non-deterministic register machine m with (β + 2) registers generating n. 3 numerical p systems with thresholds we introduce the class of numerical p systems to be investigated in this work. the definition is general, for the computing case. a numerical p system with a threshold is a construct π = (m,h,µ,t,(v ar1,pr1,v ar1(0)), . . . ,(v arm,prm,v arm(0)),v arin,v arout), where • m ≥ 1 is the number of membranes; • h is an alphabet of labels for membranes in µ; • µ is a rooted tree with q nodes labeled with the elements of h; • t is a constant, called threshold; • v ari, 1 ≤ i ≤ m, is the set of variables in region i; • v ari(0), 1 ≤ i ≤ m, is the set of initial values of the variables in region i; numerical p systems with thresholds 295 • pri, 1 ≤ i ≤ m, is the set of programs in region i; each program has the form fl,i(x1,i, . . . ,xki,i)|t → cl,i,1|vl,i,1 + · · ·+ cl,i,li|vl,i,li, where fl,i(x1,i, . . . ,xki,i) is the production function, and cl,i,1|vl,i,1 + · · ·+ cl,i,li|vl,i,li is the repartition protocol of the program; • v arin and v arout are the sets of input and of output variables, respectively. the programs allow the system to evolve the values of variables during computations. each program is composed of two parts: a production function and a repartition protocol. the former can be any function using variables from the region that contains the program. only polynomial functions are considered here. by using the production functions in each region, the system computes a production value from the values of its variables at that time. this value is distributed to variables from the region where the program resides, and to variables in its upper (parent) and lower (children) compartments, as specified by the repartition protocol. the programs are applied under the control of the threshold t , according to two strategies: bounding the values of variables from below (lower-threshold) and bounding them from above (upper-threshold). more precisely, in the first case a program can be applied only when the current value of each variable from its production function is greater than or equal to the threshold t . dually, in the upper-threshold case, a program can be applied only when the current value of each variable from its production function is smaller than or equal to the threshold t . the repartition of the “production" takes place as follows. for a repartition protocol rpl,i, variables vl,i,1, . . . ,vl,i,li come from the membrane i where the program resides, the parent membrane and the children membrane. formally, {vl,i,1, . . . , vl,i,li}⊆ v ari∪v arpar(i)∪( ∪ ch∈ch(i) v arch), where par(i) is the parent of membrane i and ch(i) is the set of children of membrane i. the coefficients cl,i,1, . . . ,cl,i,li are natural numbers (they may be also 0, in which case the terms “+0|x" are omitted), which specify the proportion of the current production value distributed to each variable vl,i,1,. . . ,vl,i,li . at time t, if we denote with cl,i = li∑ s=1 cl,i,s the sum of all coefficients of the repartition protocol, and denote with ql,i(t) = fl,i(x1,i(t), . . . ,xki,i(t)) cl,i (1) the “unitary portion", then the value adl,i,r(t) = ql,i(t)·cl,i,r represents the value added to variable vl,i,r. if variable vl,i,r appears in several repartition protocols, for example, rpl1,i1, . . . ,rplk,ik , all these values adl1,i1,r, . . . ,adlk,ik,r are added to variable vl,i,r. after computing the production function value, the variables involved in the production function are reset to zero. so, if at time t variable vl,i,r is involved in at least one production function, its value at time t+1 is vl,i,r(t+1) = k∑ s=1 adls,is,r(t); otherwise, vl,i,r(t + 1)=vl,i,r(t) + k∑ s=1 adls,is,r(t). such a system evolves in the all-parallel mode (at each step, in each membrane, all programs which can be applied are applied, allowing that more than one program share the same variable) or in the one-parallel mode (apply programs in the all-parallel mode with the restriction that one variable can appear in only one of the applied programs). a configuration represents the values of all system’s variables at a given computation step. initially, the variables have the values specified by v ari(0),1 ≤ i ≤ m. using the programs in the way mentioned above, a transition of the system from a configuration to the next one is defined. a sequence of such transitions forms a computation. if no program in each region can be applied, we say that the system reaches a halting configuration. 296 z. zhang, l. pan in this way, a numerical p system can compute a function f: nα → nβ(α,β ≥ 0): the α values of the arguments are introduced in the system as the initial values of variables in v arin and the β-vector of the function value is obtained in the variables from v arout in the halting configuration of the system. if the system never reaches a halting configuration, then no result is obtained. by ignoring the input variables, (non-deterministic) numerical p systems with thresholds can also be used in the generating mode, whereas by ignoring the output variables we can use (deterministic or non-deterministic) numerical p systems with thresholds in the accepting mode. note that qj,i(t) are integers only if the value of the production functions fj,i(x1,i(t), . . . ,xki,i(t)) is divisible by the respective sums cj,i(t). if at any step, all the values of the production functions are divisible by the respective sums, we associate this kind of systems with the notation div. if a current production is not divisible by the associated coefficients total, then we can take the following decisions [10]: (i) the remainder is lost (the production which is not immediately distributed is lost), (ii) the remainder is added to the production obtained in the next step (the non-distributed production is carried over to the next step), (iii) the system simply stops and aborts, no result is associated with that computation. we denote these three cases with lost, carry, stop, respectively. in this paper, the numerical p systems with thresholds that we construct are of the div type. the set of natural numbers generated or accepted in the way mentioned above by a system π is denoted by nα(π),α ∈{gen,acc}. we use nαtγpdm (polyn(r),β) to denote the family of all sets nα(π) of numbers computed by systems π working in α mode, with at most m membranes, production functions which are polynomials of degree at most n, with integer coefficients, with at most r variables in each polynomial, using the rules in the mode β ∈{all,one}, where all stands for all-parallel, and one stands for one-parallel, and with the threshold used in the γ ∈ {l,u} way, with l indicating the lower-threshold case and u indicating the upper-threshold case; the letter d indicates the use of deterministic systems (we remove d when the systems may also be non-deterministic). if one of the parameters m,n,r is not bounded, then we replace it with ∗. 4 the universality of numerical p systems with lower-thresholds in this section, we investigate the computational power of numerical p systems with lowerthresholds working in the all-parallel mode and in the one-parallel mode. theorem 4. each partial recursive function f : nα → nβ (α > 0,β > 0) can be computed by a deterministic numerical p system with a lower-threshold, with only one membrane, using linear production functions that use each at most three variables, and working in the all-parallel mode. proof: let m = (n,p,m) be a deterministic register machine with n registers, computing function f. the initial instruction of m has the label 0 and the machine halts only if the instruction with label m is reached. according to proposition 1, n = max{α,β} + 2 is enough. before the computation starts, let us assume that the values of the first α registers are equal to r1, . . . ,rα. when the computation halts, the values stored in the registers 1, . . . ,β are the values computed by f(r1, . . . ,rα). we construct the following numerical p system with a lower-threshold: πm = (1,{0}, [0 ]0,1,(v ar0,pr0,v ar0(0)),v arin,v arout), where • v ar0 = {xi,1,xi,2,pj | 1 ≤ i ≤ n,0 ≤ j ≤ m}; numerical p systems with thresholds 297 • v ar0(0) is the vector of initial values of the variables, with: – xi,1 = xi,2 = ri, for all 1 ≤ i ≤ α; – xi,1 = xi,2 = 0, for all 1 + α ≤ i ≤ n; – pj = 0, for all 0 ≤ j ≤ m with the exception of p0 = 1; • pr0 = {3pj|1 → 1|xi,1 + 1|xi,2 + 1|pk, for all instructions j : (inc(i),k) ∈ p} ∪ {pj|1 → 1|pl, xi,1 −xi,2 −pj|1 → 1|pl, xi,1 −xi,2 + pj|1 → 1|pk, 2(xi,1 −pj)|1 → 1|xi,1 + 1|xi,2, for all instructions j : (dec(i),k, l) ∈ p}; • v arin = {x1,i, . . . ,xα,i | 1 ≤ i ≤ 2}; • v arout = {x1,1, . . . ,xβ,1}. note that the threshold is equal to 1. the value of register i (1 ≤ i ≤ n) is encoded by variables xi,1 and xi,2. the values of xi,1 and xi,2 are always equal. the input values r1, . . . ,rα are set as the initial values of variables x1,i, . . . ,xα,i 1 ≤ i ≤ 2, respectively. variables p0, . . . ,pm are used to indicate the instruction to be simulated. during the computation, the values of p0, . . . ,pm are equal to 1 or 0 (at most one of them is equal to 1 in each step, and this indicates that the system starts to simulate the corresponding instruction of m). the increment instruction j : (inc(i),k) is simulated by the program 3pj|1 → 1|xi,1 + 1|xi,2 + 1|pk in one step. when pj = 0, the program cannot be applied because the value of pj is smaller than the threshold 1. when pj = 1, the program can be applied since the value of pj is equal to the threshold. after the application of this program, each of the variables xi,1,xi,2,pk obtains a portion 1, and variable pj is reset to zero. variable pk = 1 indicates that the instruction labeled k will be simulated at the next step, the increment of variables xi,1,xi,2 corresponds to the increase of the number stored in register i by 1. so, the increment instruction j : (inc(i),k) has been correctly simulated. the decrement instruction j : (dec(i),k, l) is simulated in one step by the following four programs: pj|1 → 1|pl, (2) xi,1 −xi,2 −pj|1 → 1|pl, (3) xi,1 −xi,2 + pj|1 → 1|pk, (4) 2(xi,1 −pj)|1 → 1|xi,1 + 1|xi,2. (5) when a decrement instruction j : (dec(i),k, l) starts to be simulated, which means that pj = 1, there are the following two cases. • pj = 1, xi,1 = xi,2 = 0. in this case, only program (2) satisfies the threshold condition. after applying program (2), variable pj is set to zero, and variable pl receives a contribution 1, which indicates the next instruction to be simulated. programs (3)–(5) cannot be applied since variables xi,1 and xi,2 are zero (smaller than the threshold 1). hence the values of xi,1 and xi,2 are not changed, and the computation continues with the simulation of instruction l of register machine m. • pj = 1, xi,1 = xi,2 ≥ 1. in this case, all the four programs satisfy the threshold condition, thus all of them can be applied. program (4) transfers the production value 1 to variable pk, which indicates the next instruction k to be simulated. by using program (5), variables xi,1 298 z. zhang, l. pan and xi,2 are decreased; their values are first zeroed and each of them receives a contribution of their former value minus one. the role of program (3) is to cancel the effect of program (2). program (2) transfers the value of pj to pl, thus pl gets a contribution of 1, which is canceled by program (3) by sending a contribution of −1 to pl. hence the values of variables xi,1 and xi,2 are decremented by one and the next instruction to be simulated is the one labeled with k. after the simulation of any instruction of m, the values of both variables xi,1 and xi,2 are equal to the contents of register i (1 ≤ i ≤ n), while only one of variables p0, . . . ,pm is equal to 1, indicating the next instruction of m to be simulated. when m reaches the halt instruction, the corresponding value of variable pm is equal to 1. since no program contains the variable pm in the production function, πm reaches a final configuration; the result of the computation is the values of variables x1,1, . . . ,xβ,1. 2 according to proposition 2, for any recursively enumerable set n ⊆ ps(α)re of vectors of non-negative integers there exists a deterministic register machine m with (α + 2) registers accepting n. for this register machine m, following the proof in theorem 4, we can construct a numerical p system with a lower-threshold that accepts n. so, the following corollary holds. corollary 5. ps(α)re = nacctlpd1 (poly 1(3),all). for numerical p systems with lower-thresholds working in the one-parallel mode, the following similar results hold. theorem 6. each partial recursive function f : nα → nβ (α,β > 0) can be computed by a onemembrane numerical p system with a lower-threshold working in the one-parallel mode, having linear production functions that use each at most five variables. proof: we proceed like in the proof of theorem 4, with the difference that here we simulate both deterministic and non-deterministic register machines. let m = (n,p,m) be a non-deterministic register machine with n = max{α,β}+2 registers, computing the function f. as usual, the input values r1, . . . ,rα are stored in the first α registers before the computation starts, with all the other registers being empty. when the computation halts, the values f(r1, . . . ,rα) will be found in registers 1, . . . ,β. the numerical p system with a lower-threshold to simulate register machine m is constructed as follows. πm = (1,{0}, [0 ]0,1,(v ar0,pr0,v ar0(0)),v arin,v arout), where • v ar0 = {pj,g,xi,g | 1 ≤ g ≤ 5, 1 ≤ i ≤ m,0 ≤ j ≤ n}; • v ar0(0) is the vector of initial values of the variables, with: – xi,g = ri, for all 1 ≤ i ≤ α,1 ≤ g ≤ 5; – xi,g = 0, for all 1 + α ≤ i ≤ n,1 ≤ g ≤ 5; – pj,g = 0, for all 0 ≤ j ≤ m,1 ≤ g ≤ 5; – p0,g = 1, for all 1 ≤ g ≤ 5; numerical p systems with thresholds 299 • pr0 = {2 ∑5 g=1 pj,g|1 → ∑5 g=1 1|xi,g + ∑5 g=1 1|pk,g, 2 ∑5 g=1 pj,g|1 → ∑5 g=1 1|xi,g + ∑5 g=1 1|pl,g; for all instructions j : (inc(i),k, l) ∈ p} ∪ {5(xi,1 −pj,1)|1 → ∑5 g=1 1|xi,g, 8(xi,2 −xi,3 + pj,2)|1 → ∑5 g=1 1|pk,g + ∑3 g=1 1|pj,g, −5(xi,4 −xi,5 + pj,3)|1 → ∑5 g=1 1|pl,g, 5pj,4|1 → ∑5 g=1 1|pl,g, −3pj,5|1 → ∑3 g=1 1|pj,g; for all instructions j : (dec(i),k, l) ∈ p}; • v arin = {xi,g | 1 ≤ g ≤ 5,1 ≤ i ≤ α}; • v arout = {x1,1, . . . ,xβ,1}. in order to ensure that at each step only one variable can appear in the production functions of the applied programs, the value of register i (1 ≤ i ≤ n) is contained in five variables xi,g1 ≤ g ≤ 5, and the system uses five variables pi,g,1 ≤ g ≤ 5, to control the simulation of the instruction with label i of register machine m (in the following, for brevity, we use xi,g and pi,g to represent all the five variables for 1 ≤ g ≤ 5, respectively). during the computation, variables xi,g are always equal to each other, and the same holds for variables pi,g. the input values ri (1 ≤ i ≤ α) are introduced into the system as the initial values of variables xi,g (1 ≤ i ≤ α), respectively. when the instruction i is simulated, all the five variables pi,g are equal to 1, while all the others are zero. the simulation of an increment instruction j : (inc(i),k, l) is done in one step by the following two programs: 2 5∑ g=1 pj,g|1 → 5∑ g=1 1|xi,g + 5∑ g=1 1|pk,g, (6) 2 5∑ g=1 pj,g|1 → 5∑ g=1 1|xi,g + 5∑ g=1 1|pl,g. (7) if pj,g = 0, then programs (6) and (7) cannot be executed since the values of variables pj,g = 0 are smaller than the thresholds 1. if pj,g = 1, then only one of programs (6) and (7) can be applied because their production functions share the same variables (the system works in the one-parallel mode). if program (6) (resp., program (7)) is applied, then variable xi,g is increased by one, setting pk,g (resp., pl,g) to 1, thus the system starts to simulate instruction k (resp., instruction l), and resetting variables pj,g to zero. if m is deterministic, then the simulation of the instruction j : (inc(i),k) is performed by using the program (6). in this case, no competition occurs between the programs, and so the simulation is deterministic. the simulation of a decrement instruction j : (dec(i),k, l) is done in one step by the 300 z. zhang, l. pan following five programs: 5(xi,1 −pj,1)|1 → 5∑ g=1 1|xi,g, (8) 8(xi,2 −xi,3 + pj,2)|1 → 5∑ g=1 1|pk,g + 3∑ g=1 1|pj,g, (9) −5(xi,4 −xi,5 + pj,3)|1 → 5∑ g=1 1|pl,g (10) 5pj,4|1 → 5∑ g=1 1|pl,g, (11) −3pj,5|1 → 3∑ g=1 1|pj,g. (12) if pj,g = 0, then programs (8)–(12) cannot be applied, because pj,g = 0 are smaller than the threshold. so, when pj,g = 0, no undesirable simulation steps can appear. if pj,g = 1,xi,g = 0, then the values of xi,g should remain unchanged, and the computation should jump to the simulation of instruction l, which is realized by programs (11) and (12) in one step. (note that in this case programs (8) – (10) cannot be applied, for the values of xi,g are smaller than the thresholds.) the effect of program (11) is to reset variables pj,4 to zero and to give a contribution 1 to each of variables pl,g, whose values are 1 after the application of this program, thus correctly simulating the passing to instruction l. at the same time, program (12) is applied, the role of which is to set all the variables pj,g (g ̸= 4) to zero. variable pj,5 appears in the production function, so its initial value is canceled, and it receives no contribution, hence its final value is zero. for variables pj,1, pj,2 and pj,3, their initial values are 1 and receive contribution −1, hence their final values are zero, which is also correct. if pj,g = 1,xi,g ≥ 1, then the values of xi,g should be decremented and the computation should proceed to the simulation of instruction k. in this case, all the five programs (8)–(12) can be applied. programs (8) and (9) decrement the values of xi,g and increment the values of pk,g by 1. the other programs have auxiliary roles. note that all the variables pj,g and xi,g appear in the production functions of programs (8)–(12), so their values are first reset to zero, and their final values will be the sum of all the contributions they receive. variables xi,g only appear in the repartition protocol of program (8), which gives a contribution of their initial value minus 1, thus correctly decrementing their values by one. variables pj,1, pj,2 and pj,3 receive a contribution 1 from program (9) and a contribution −1 from program (12), thus their values will be equal to 0. variables pj,4 and pj,5 do not appear in any repartition protocol of programs (8) – (12), thus their final values are zero. the role of program (10) is to cancel the effect of program (11). program (11) sends a contribution 1, and simultaneously program (10) sends a contribution −1, to each of variables pl,g, whose final values are hence equal to 0. each of variables pj,g receives a contribution 1, thus their final values are 1, which is also correct. after the simulation of each instruction of m, all the variables xi,g are equal to the contents of register i (1 ≤ i ≤ n), while the variables pj,g (0 ≤ j ≤ m) correctly indicate the next instruction of m to be simulated. when the program counter of m reaches the value k, the corresponding values of variables pk,g is equal to 1. when the program counter of m reaches the value m, the corresponding values of variables pm,g are equal to 1. since no program contains variables pm,g in the production function, πm reaches a halting configuration; the result of the computation is values of variables x1,1, . . . ,xβ,1. 2 numerical p systems with thresholds 301 according to propositions 2 and 3, for any recursively enumerable set n ⊆ ps(α)re of vectors of non-negative integers there exists a deterministic (or non-deterministic) register machine m with (α + 2) registers accepting (generating, respectively) n. for this register machine m, following the proof in theorem 6, we can construct a deterministic (or non-deterministic) numerical p system with a lower-threshold that accepts (or generates, respectively) n. corollary 7. ps(α)re = ngentlp1(poly1(5),one) = nacctlpd1 (poly 1(5),one). in conclusion, we obtain the following characterizations of nre. theorem 8. nre = nacctlpd1 (poly 1(3),all) = ngentlp1(poly 1(5),one) = = nacctlp d 1 (poly 1(5),one). proof: the first equation can be obtained according to corollary 5, where α = 1. similarly, letting α = 1 in corollary 7, we can obtain the last two equations. 2 5 the universality of numerical p systems with upper-thresholds in this section we prove that the computational power of numerical p systems with upperthresholds (for short, utnp systems) is equivalent with that of numerical p systems with lowerthresholds (for short, ltnp systems). lemma 9. for any numerical p system with a lower-threshold πl, there is a p system πu with an upper-threshold, with the same variables, such that the corresponding variables of πl and πu have equal values but of opposite sign. proof: let πl be a numerical p system with a lower-threshold of the form considered in the previous sections. we construct a numerical p system with an upper-threshold πu in the following way. πu has the same membrane structure as πl. in the same membrane, the two systems have the same variables. the initial values of variables in πu is the same as in πl multiplied with −1. similarly, for the thresholds of the two systems (they are equal, but of opposite signs). for a program fl(x1, . . . ,xi)|t → c1|v1 + · · ·+ cl|vl in πl, we introduce in πu the program fu(x1, . . . ,xi)|−t → c1|v1 + · · ·+ cl|vl, where fu(x1, . . . ,xi) is constructed as follows: • if the production function fl(x1,x2, . . . ,xn) is an odd function, that is, fl(−x1,−x2, . . . ,−xn) = −fl(x1,x2, . . . ,xn), then fu(x1,x2, . . . ,xn) = fl(x1,x2, . . . ,xn); • if the production function fl(x1,x2, . . . ,xn) is an even function, that is, fl(−x1,−x2, . . . ,−xn) = fl(x1,x2, . . . ,xn), then fu(x1,x2, . . . ,xn) = −fl(x1,x2, . . . ,xn); 302 z. zhang, l. pan • if the production function fl(x1,x2, . . . ,xn) is neither an even function nor an odd function, then it can be expressed as the addition of an even function with an odd function, that is, fl(x1,x2, . . . ,xn) = fl(x1,x2, . . . ,xn) + fl(−x1,−x2, . . . ,−xn) 2 + fl(x1,x2, . . . ,xn)−fl(−x1,−x2, . . . ,−xn) 2 , and then fu(x1,x2, . . . ,xn) = − fl(x1,x2, . . . ,xn) + fl(−x1,−x2, . . . ,−xn) 2 + fl(x1,x2, . . . ,xn)−fl(−x1,−x2, . . . ,−xn) 2 = −fl(−x1,−x2, . . . ,−xn). based on the previous construction of the utnp system πu, we can get that, if the two systems are deterministic, working in the all-parallel mode, then at any step, the program in πl and its corresponding program in πu can simultaneously be applied or cannot be applied, and the two production functions have equal values but of opposite signs. thus at any step the variable in the two systems get equal contributions but of opposite signs; this is true also for the initial values, hence always the values of variables are equal but of opposite signs. the systems halt simultaneously. in conclusion, no matter whether πl,πu work in computing mode or in generating mode, this lemma holds true. 2 corollary 10. ps(α)re = nacctupd1 (poly 1(3),all). proof: according to proposition 2, for any recursively enumerable set n ⊆ ps(α)re of vectors of non-negative integers there exists a deterministic register machine m with (α + 2) registers accepting n. for this register machine m, following the proof in theorem 4, we can construct a numerical p system with a lower-threshold πm that accepts n. for πm , according to lemma 9, we can construct an utnp system πu with “contrary" configurations (equal values of variables, but of opposite signs). now we add the programs 1 + pm −xi,1|−1 → 1|xi,1, 1 ≤ i ≤ β. (13) to πu thus obtaining a new utnp system π′u. the initial value of pm is 0, hence programs (13) cannot be applied. as long as pm = 0, there is no difference between the functioning of π′u and πu. when pm is equal to −1, πu reaches a halt configuration, while π′u continues executing program (13). the effect of programs (13) is transforming the variables xi,1 ≤ −1 to their contrary. thus the system π′u has the same output as πm . 2 in a similar way to the proof of corollary 7, we can prove the following corollary. corollary 11. ps(α)re = ngentup1(poly1(5),one) = nacctupd1 (poly 1(5),one). if we set α = 1 in corollary 10 and corollary 11, then we can get the following characterizations. theorem 12. nre = nacctupd1 (poly 1(3),all) = ngentup1(poly 1(5),one) = = nacctup d 1 (poly 1(5),one). numerical p systems with thresholds 303 6 conclusions and discussions in this work, we have introduced thresholds into numerical p systems, and the computational power of such p systems has been investigated. specifically, we proved that universality can be obtained for such p systems with one membrane and linear production functions working both in the all-parallel mode and in the one-parallel mode. the rules of numerical p systems with thresholds constructed in section 4 are applied in the all-parallel mode and in the one-parallel mode, respectively. it remains open what the computational power of numerical p systems with thresholds working in the sequential mode is. in this work, the polynomial functions used in numerical p systems with the two kinds of thresholds have at most 3 variables for all-parallel systems and 5 variables for one-parallel systems. it is a natural question whether the number of variables can be decremented. the thresholds are used in the sense of lower-bounds and upper-bounds. other ways to use the thresholds could be of interest, for example, applying a program only if the values of all (or part of) the variables are strictly greater (or smaller) than the threshold. numerical p systems and enzymatic numerical p systems have already been used in robot control [4, 10, 16, 17]. it remains to check whether numerical p systems with thresholds are also useful for such applications. acknowledgements this work was supported by national natural science foundation of china (61320106005 and 61472154), ph.d. programs foundation of ministry of education of china (2012014213008), natural science foundation of hubei province (2011cda027), and the innovation scientists and technicians troop construction projects of henan province (154200510012). bibliography [1] freund, r.; oswald, m. 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(2010); spiking neural p systems with weights. neural computation 22(10), 2615–2646. international journal of computers, communications & control vol. ii (2007), no. 3, pp. 229-240 a proposed genetic algorithm coding for flow-shop scheduling problems hela boukef, mohamed benrejeb, pierre borne abstract: a new genetic algorithm coding is proposed in this paper to solve flowshop scheduling problems. to show the efficiency of the considered approach, two examples, in pharmaceutical and agro-food industries are considered with minimization of different costs related to each problem as a scope. multi-objective optimization is thus, used and its performances proved. keywords: genetic algorithm, operations coding, flow-shop problems, multiobjective optimization, pharmaceutical industries, agro-food industries. 1 introduction in shop scheduling, there are three basic models classified according to the structure of a processing route: • the job-shop scheduling problem is an operation sequencing problem on multiple machines subject to some precedence constraints among the operations, • the flow-shop scheduling problem is a set of jobs that flow through multiple stages in the same order, • the open-shop scheduling problem is a problem where the workshop has several resources and the routing of all the operations is free. in pharmaceutical and agro-food industries, the tasks execution needs the use of several resources in a single order. it is thus about a flow-shop problem. flow-shop problems have received considerable attention from researchers during the last decades and the scheduling criterion most frequently used was the maximum completion time [3],[7], [4], [21], [15], [26]... however, the analysis of the performance of a schedule often involves more than one aspect and therefore requires a multi-objective treatment [8], [17], [25]. the aim of multi-objective optimization is to deal with many criteria at the same time. this paper is focused on minimizing the total costs related to manufacturing and delivery processes. to solve this kind of problems, the exact methods and the approached methods can be applied. the exact methods, such as branch and bound [1] and linear programming methods [23], concern small size problems at the contrary of approached methods, such as tabu search [10], simulated annealing [16], genetic algorithms [13] and ants colony methods [6], that concern big size ones [14]. in this paper, we focus on the use of genetic algorithms method. the principle scope of this method, based on natural selection mechanism, is the improvement of robustness and balance between cost and performance [11]. the genetic algorithms became famous due to their efficiency in solving combinatory optimization problems [19]. their application fields are very important. they vary from complex real application such as pipelines flow control or robot planning path to theoretical combinatory problems. the paper is organized as follows. first, the notations are introduced. section 2 deals with the presentation and formulation of pharmaceutical and agro-food scheduling problems. section 3 tackles the choice of the use of multi-objective evaluation. section 4 handles with the presentation of genetic copyright © 2006-2007 by ccc publications 230 hela boukef, mohamed benrejeb, pierre borne algorithms and the proposed structured coding of list operations. in section 5, two examples, concerning agro-food and pharmaceutical industries scheduling, are treated by using this algorithm. notations : pi : finished product after operation oi pik : conditioning time of oi on machine k cpik : ending processing time ofpi on machine k dlivpi : delivery date of pi cstkpi : storage cost by time unit of pi for pharmaceutical industries: t pik : preparation time on machine kbefore oi t arrik : stoppage time during oion machine k ctotprod : total production cost cuk : production cost by time unit on machine k donettik : cleaning operations time on machine k doch fik : size changing operations time on machine k crdi : costs of distribution delays of pi c fi : manufacturing cost of pi for agro-food industries: ti : effective start time of manufacturing oi ri : earliest start time of oi γi : effective end time of oi cik : kthcomponent from the whole components of oi vik : limit validity date of a component cik dvpi : lifespan of pi drpi : back delay of pi previk : income of a component cik pvenpi : unit sale price of pi 2 presentation and formulation of pharmaceutical and agro-food scheduling problem 2.1 pharmaceutical industries scheduling case in pharmaceutical industries, many problems can appear in production workshop. several operations of cleaning and format changes have to be managed jointly with the manufacturing operations. the unproductive times generated by these operations are rather significant, taking into account the fact that the time launching of a product manufacturing depends on that which precede it. from a product to another, the change involves certain modifications on the level of each machine. these problems can brake the beginning of the production and delay its end. moreover, stoppage time due to machines break down, and production time are as many factors that breed important production costs [2]. costs of distribution delays can also be calculated taking account of storage costs, date of production and end delivery date. let consider objective functions f1 and f2. they represent the minimization of total cost of production, ctotprod , and the minimization of distribution delays cost of the product pi, crdi . a proposed genetic algorithm coding for flow-shop scheduling problems 231 the total cost of production has the following global expression: ctotprod = ∑ 1< j<n c f j n is the number of manufactured products [22]. in pharmaceutical industries, this expression becomes: f1 = c tot prod = ∑ k cuk ∑ i wik(pik + t p arr ik ) (1) with: t parrik = do nett ik + do ch f ik wik : the coefficient of use of the machine k for the production of the product i, wi,k = {0, 1} the distribution delays penalties has the following expression: f2 = crdi = max(0, d liv pi −cpik )c stk pi (2) 2.2 agro-food industries scheduling case among the different problems occurring in agro-food industries let distinguish products perishability and distribution discount. products perishability is a major problem in agro-food industries because of products short expiry dates. the distribution discount contains penalties applied to sellers and storage costs of final products before their delivery. this criterion depends on the end-products storage’s period of time before its expedition to the distribution areas [9]. generally, expired product’s costs, g1, and distribution discount costs, g2, are expressed by the following expressions: g1 = ∑ k previk ( max (0,ti −vik) (ti −vik) ) (3) g2 = max ( 0, dlivpi −cpi ) × ( pvenpi dvpi −drpi + cstkpi ) (4) usually, resolution approaches take account of one criterion at once. in this paper, we want to optimize a trade off between several criteria at the same time and to find a mean of global evaluation of these criteria. 3 multi-objective evaluation weighting sum function method, one of the different multi-objective methods, consists in combining linearly the different functions applying a weighted coefficient to each of them and summing them [5]. we can also say that it is a weighted linear combination of the objectives and it is used to aggregate the considered objectives in a single one. pharmaceutical industries case [3] the weighted sum of functions f1 and f2 gives the following function feq: feq = α1 f1 + α2 f2 (5) then: feq = α1 ( ∑ k cuk ∑ i wik (pik + t pik + t arr ik ) ) + α2 ( max ( 0, dlivpi −cpi ) cstkpi ) (6) 232 hela boukef, mohamed benrejeb, pierre borne where αi are the coefficient that privilege one function instead of another, α1 + α2 = 1. agro-food industries case [24] the weighted sum geq of functions g1 and g2 is expressed as following: geq = β1g1 + β2g2 (7) then: geq = β1 ∑ k previk ( max(0,ti −vik) (ti −vik) ) + β2 max(0, dlivpi −cpi )× ( pvenpi dvpi −drpi + cstkpi ) (8) where βi are the coefficients that privilege one function instead of another, β1 + β2 = 1. genetic algorithms are adapted from natural systems and are successfully used in artificial systems. they proof themselves in optimization fields and in multiple other application fields [17]. that’s why they are adopted and then proposed for our scheduling problems resolution. 4 proposed operations coding used in genetic algorithms application genetic algorithms are parts of evolutionary algorithms that are composed of three essential elements: • a population made up of several individuals representing the potential solutions (configurations) of a given problem, • an evaluation mechanism of each individual adaptation regard to his external environment, • evolution operators allowing the elimination of certain individuals and the creation of new ones [12]. 4.1 genetic algorithms genetic algorithms are iterative algorithms whose aim is to optimize a function called fitness [13]. they are exploration algorithms based on natural selection mechanisms and genetics and use at the same time survival of the best adapted structures principles and pseudo-random exchanges information, to form an exploration algorithm which has certain characteristics of human exploration. with each generation, a new set of individuals is created by using best elements parts of the precedent generation as well as innovating parts. the genetic algorithms are not purely random. they effectively exploit information obtained previously to speculate in the position of new points to explore, with the hope to improve the performance [11]. to realize this scope, they use a set of points, called individual population, where each individual represents a possible solution of the given problem and contains different elements, called gene, which can take multiple values [20]. the different operators used in genetic algorithms are selection, crossover and mutation. the aim of selection operator is to pick individuals that can survive and/or reproduce themselves to transmit their characteristics to the next generation. the selection operator is based on conservation principle of the most adapted individuals and elimination of the less ones [12]. no selection operator is absolutely perfect; a risk of favouring a number of individuals always exists and it could be a real drawback. the crossover operator ensures the combination of parental genes to form new descendants with new potentials. this operator works randomly according to a probability fixed by the user keeping count of the optimization problem [20]. a proposed genetic algorithm coding for flow-shop scheduling problems 233 the mutation operator consists in a random changing for an individual certain gene’s values [12]. without this operator, a uniform population is produced. a suitable coding choice is an important task that can guarantee the success of the genetic algorithms application. the classic coding introduced by holland [13] corresponds to the binary alphabet (0/1) where the chromosome is simply represented by a finite 0-1 array. more generally, genes can be real, characters or any expressions or elementary entities [14]. 4.2 structured list operations coding in the case related to pharmaceutical industries scheduling, cleaning and size changing operations as well as stoppage and preparation times generate very high production costs. distribution delays can also occur and generate significant costs. in the case of agro-food industries scheduling, products expiry dates and storage time before delivery have to be taken into account for perishability costs and distribution discount costs evaluation. coding to be implemented must thus be able to deal with management time problems, precedence constraints, assignment resources, and the different costs that result from these problems. sloc’s presentation inspired by parallel machine coding cpm [20] and list operations coding clo [14], the proposed structured list operations coding sloc, offers to solve the previously quoted problems. the one point crossover operator for the sloc, selects two individual parents (according to the technique of the caster) and a crossover point. first child receives from first parent the chromosomes preceding the crossover point (same thing for second child and second parent) and it receives from the second parent chromosomes following the crossover point (same thing for second child 2 and first parent). updates are then carried out. the two points crossover operator for the sloc chooses two individual parents and two crossover points. first child receives from first parent the chromosomes preceding the first crossover point and following the second crossover point (same thing for the second child and second parent) then, it receives from the second parent chromosomes located between the two crossover points without redundancy (same thing for second child and first parent). updates are then carried out. when it is about only one machine, the mutation operator chooses one individual parent and two mutation points, the permutation of the two chromosomes produces a new individual. when they are two (or more) machines, this operator chooses one individual parent and a mutation point, the operation thus consists in changing for the individual concerned the number of the machine and making the necessary updates. the use of these operators has as principle concern the improvement of robustness and the balance between costs and performances. algorithm the genetic algorithm steps, applied to pharmaceutical and agro-food scheduling, is given in figure 1. sloc’s structure in the case of only one machine, an individual is composed of a list of products, and for each product a cost function is associated in table 1 where list i is a set of products placed in a well defined ordered 234 hela boukef, mohamed benrejeb, pierre borne crossover at two points q<p probability choice « p » mutation first individual selection with probability = q yes no crossover at one point r <p yes no individual’s evaluation second individual selection with probability = r initial population best individual’s saving figure 1: genetic algorithm steps passage, i = 1, 2, . . ., n and feq(listi) is an equivalent function corresponding to the weighted sum of the two considered cost functions, i = 1, 2, . . ., n. table 1: : scheduling data in agro-food industries list of products feq list1 feq(list1) list2 feq(list2) . . . . . . listn feq(listn) in the case of two or several machines, an individual is also composed of a list of products but, in this case, to each product corresponds a data structure taking into account the number of the machines on which the product is manufactured, of its beginning execution time and of its end time. as in the case of only one machine, to each individual (list of products) is associated the function corresponding to the weighted sum of both or several cost functions, showed in table 2 and using the following notations: a proposed genetic algorithm coding for flow-shop scheduling problems 235 nmi : machines number in which the product pi is manufactured ti : beginning time of product pi manufacturing ci : time ending of product pi manufacturing feq : equivalent function to optimize indi : given individual of a given generation table 2: sloc coding for n machines and m products for a given individual and a given generation product1 nm1 t1 c1 . . . product j nm j t j c j feq(indi) . . . productn nmn tn cn 5 agro-food and pharmaceutical scheduling resolution in this section, we propose to solve a single machine flow-shop problem in agro-food industries and a two-machines flow-shop problem in pharmaceutical industries. the scope is the minimization of different costs, appropriate to each scheduling problem. 5.1 first example: resolution of a flow-shop scheduling problem in agro-food industries the flow-shop scheduling problem data in agro-food industries in the case of one machine is presented in the following table 3. taking into account the data presented in this table, products perishability and distribution discount costs can be calculated following expressions 3 and 4. starting from geq function, the genetic algorithms are applied to solve the scheduling problem in order to obtain the products best list which minimizes this function, by using structured lists operation coding. indeed, for 200 generations and a 20 individuals population per generation, mutation and crossover operations are carried out, thus generating, new individuals whose new costs are calculated. then, the best individual is progressively generated and saved. 236 hela boukef, mohamed benrejeb, pierre borne table 3: scheduling data in agro-food industries operation name component name ri pi vik previk p ven pi dvpi drpi d liv pi c stk pi op1 c11 1 3 3 8 4 5 11 10 6 op1 c12 1 3 8 10 4 5 11 10 6 op1 c13 1 3 2 7 4 5 11 10 6 op2 c21 2 5 5 13 5 12 10 19 2 op2 c22 2 5 12 11 5 12 10 19 2 op2 c23 2 5 11 4 5 12 10 19 2 op3 c31 2 3 5 6 6 15 12 22 7 op3 c32 2 3 20 2 6 15 12 22 7 op3 c33 2 3 3 9 6 15 12 22 7 op4 c41 3 2 17 11 8 11 15 11 14 op4 c42 3 2 10 2 8 11 15 11 14 op4 c43 3 2 4 5 8 11 15 11 14 op5 c51 3 2 11 14 7 8 10 15 18 op5 c52 3 2 15 12 7 8 10 15 18 op5 c53 3 2 4 23 7 8 10 15 18 51,3 » 54 49,8 47,7 9 16 32 150 local minima global minimum figure 2: total cost evolution through the generations in agro-food industries the figure 2 shows that the cost of the best individual of the initial population is 54, then, the first local minimum observed shows that the cost of the best individual passed to 51,3 and this in the nineth generation. for the second local minimum, the total cost passes to 49,8 in the sixteenth generation to stabilize itself in the thirty second generation with 47,7 which is the observed global minimum. 5.2 second example: resolution of a flow-shop scheduling problem in pharmaceutical industries the flow-shop scheduling problem data in pharmaceutical industries, in the case of two machines m1 and m2, is presented in the following table 4. a proposed genetic algorithm coding for flow-shop scheduling problems 237 table 4: scheduling data in pharmaceutical industries product name pi1 t arri1 t pi1 pi2 t arr i2 t pi2 d liv pi p01 1800 240 120 2880 100 100 3 p02 2400 310 150 — — — 20 p03 2250 240 135 2100 115 100 14 p04 1950 175 130 2300 135 115 11 p05 2800 300 120 — — — 10 p06 1750 150 100 1500 115 110 25 p07 — — — 2400 120 110 15 p08 3500 315 180 3000 250 110 25 p09 2200 270 120 2500 110 150 45 p10 1000 115 165 — — — 32 p11 1850 155 180 1650 155 130 12 p12 2120 180 200 2400 160 120 56 p13 3300 210 140 — — — 15 p14 4500 190 175 3800 150 190 60 p15 — — — 1600 100 100 35 p16 6750 180 120 7000 220 100 45 times of manufacturing, stoppage and preparation are indicated in minutes and the various dates are calculated compared to an initial time t0. times and dates comprising the symbol " — " indicate the impossibility of manufacturing the concerning product on the corresponding machine. taking into account the data presented in the table 4, the production cost and the distribution delays penalties can be calculated while following expressions 1 and 2. 720970 710747,5 705395 700075 8 14 146 local minima global minimum figure 3: total cost evolution through the generations in pharmaceutical industries starting from function feq, the genetic algorithms are applied to solve the scheduling problem in order to obtain the products best list which minimizes this function, by using proposed structured lists operation coding. indeed, for 200 generations and 20 individuals population per generation, mutation and 238 hela boukef, mohamed benrejeb, pierre borne crossover operations are carried out, thus generating, new individuals whose new costs are calculated. then, the best individual is progressively generated and saved. the figure 3 shows that the cost of the best individual of the initial population is 720970, then, the first local minimum observed shows that the cost of the best individual passed to 710747,5 and this in the eighth generation. for the second local minimum, the total cost passes to 705395 in the fourteenth generation to stabilize itself in the hundred forty sixth generation with 700075 which is the observed global minimum. 6 conclusion in this paper, a multi-objective approach was adopted for the flow-shop scheduling problems resolution in agro-food and pharmaceutical industries. the proposed structured list operations coding and its use to implement genetic algorithms showed the capacity of this type of algorithms to find the global minimum aimed. it is interesting to make a comparison of the effectiveness of the choice of this suggested coding with other codings. bibliography [1] p. baptiste and c. le pape “ a 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[24] f. tangour, s. hammadi, p. borne and m. benrejeb “ ordonnancement dynamique dans un atelier de production agroalimentaire ”. séminaire d’automatique-industrie, sai’06, matmata, 2006. [25] f. tangour and i. saad “ multiobjective optimization scheduling problems by pareto-optimality in agro-alimentary workshop ”. international journal of computers, communications & control, ijccc, vol. i, no. 3, pp. 71-83, 2006. [26] j. yuan, a. soukhal, y. chen and l. lu “ a note on the complexity of flow shop scheduling with transportation constraints ”. european journal of operational research, vol. 178, pp. 918–925, 2006. hela boukef1,2, mohamed benrejeb1, pierre borne2 1ecole nationale d’ingénieurs de tunis unité de recherche lara-automatique bp 37, le belvédère, 1002 tunis, tunisie 2ecole centrale de lille, cité scientifique laboratoire d’automatique, genie informatique et signal 240 hela boukef, mohamed benrejeb, pierre borne bp 48, 59651 villeneuve d’ascq cedex, france e-mail: hela.boukef@yahoo.fr, mohamed.benrejeb@enit.rnu.tn, pierre.borne@ec-lille.fr hela boukef was born in carthage, tunisia in 1981. she graduated from "institut supérieur de gestion de tunis" in 2003 and obtain the master of automatic and signal treatment in 2006 at the "ecole nationale d’ingénieur de tunis". she is currently preparing the ph.d. degree in automatic and computer science within the framework of lagis-ec-lille and lara-enit cooperation. her research is related to optimization methods for discrete events systems, computer science and operational research. mohamed benrejeb was born in tunisia in 1950. he obtained the diploma of "ingénieur idn" (french "grande ecole") in 1973, the master degree of automatic control in 1974, the phd in automatic control of the university of lille in 1976 and the dsc of the same university in 1980. full professor at "ecole nationale d’ingénieurs de tunis" since 1985 and at "ecole centrale de lille" since 2003, his research interests are in the area of analysis and synthesis of complex systems based on classical and non conventional approaches. pierre borne was born in corbeil, france in 1944, he received the master degree of physics in 1967, the masters of electronics, of mechanics and of applied mathematics in 1968. the same year he obtained the diploma of "ingénieur idn" (french "grande ecole"). he obtained the phd in automatic control of the university of lille in 1970 and the dsc of the same university in 1976. he became doctor honoris causa of the moscow institute of electronics and mathematics (russia) in 1999, of the university of waterloo (canada) in 2006 and of the polytechnic university of bucarest (romania). he is author or co-author of about 200 journal articles and book chapters, and of 34 plenary lectures and of more than 250 communications in international conferences. he has been the supervisor of 66 phd thesis and is author of 20 books . he has been president of the ieee/smc society in 2000 and 2001. he is presently professor "de classe exceptionnelle" at the ecole centrale de lille and director of the french pluriformations national group of research in automatic control. international journal of computers, communications & control vol. i (2006), no. 4, pp. 61-72 time disturbances and filtering of sensors signals in tolerant multi-product job-shops with time constraints nabil jerbi, simon collart dutilleul, etienne craye, mohamed benrejeb abstract: this paper deals with supervision in critical time manufacturing jobshops without assembling tasks. such systems have a robustness property to deal with time disturbances. a filtering mechanism of sensors signals integrating the robustness values is proposed. it provides the avoidance of control freezing if the time disturbance is in the robustness intervals. this constitutes an enhancement of the filtering mechanism since it makes it possible to continue the production in a degraded mode providing the guarantees of quality and safety. when a symptom of abnormal functioning is claimed by the filtering mechanism, it is imperative to localize the time disturbance occurrence. based upon controlled p-time petri nets as a modeling tool, a series of lemmas are quoted in order to build a theory dealing with the localization problem. keywords: p-time petri net, sensor signal, filtering, time disturbance, localization 1 introduction this paper concerns critical time manufacturing job-shops. for each operation is associated a time interval. its lower bound indicates the minimum time needed to execute the operation. the non respect of this value means that the operation was not achieved. the upper bound fixes the maximum time to not exceed otherwise the quality of the product is deteriorated. such systems have a robustness property in order to maintain product quality when there are time disturbances [1], [2]. the robustness is defined as the ability of the system to preserve the specifications facing some expected or unexpected variations. so the robustness characterizes the capacity to deal with disturbances. the robustness is interpreted into different specializations. the passive robustness is based upon variations included in validity time intervals. there is no control loop modification to preserve the required specifications. on the other hand, active robustness uses observed time disturbances to modify the control loop in order to satisfy these specifications. therefore, the robustness intervals must be integrated in the filtering mechanism of sensors signals. furthermore, the observability of time disturbances occurrence is a fundamental data necessary for the control loop modification. it is also an important aspect of the maintaining task [3], [4]. when an abnormal functioning is claimed, it is important to know the initial occurrence of the disturbance. the localization problem is really difficult in robust systems since the rejection of disturbances may hide them [5]. the first part of this paper presents a filtering mechanism of sensors signals taking into account the robustness values. the second part considers the localization of time disturbances. it is necessary to perform this task when the disturbance value passes through the filter. controlled p-time petri nets are used for modeling the considered workshops. afterward, the localization problem of time disturbances in critical time manufacturing systems is tackled. some definitions and lemmas are quoted in order to build a theory dealing with such problems. 2 robustness integration in the filtering of sensors signals at the occurrence of a dysfunction in a manufacturing workshop, it is crucial to react as soon as possible to maintain the productivity and to ensure the safety of the system. it has been recognized that copyright c© 2006 by ccc publications 62 nabil jerbi, simon collart dutilleul, etienne craye, mohamed benrejeb the real time piloting, without human intervention, has a significant contribution regarding this type of problem [6], [7]. in the category of the workshops concerned by this article, the operations have temporal constraints which must be imperatively respected. the violation of these constraints can blame the health of the consumers. thus, the detection of a constraint violation must automatically cause the stop of the production. on the other hand, when taking into account the system robustness, it is proven that this type of violation did not take place. in this case, we plan to maintain the production while describing it as degraded production [8]. of course, the product is not degraded, but the production is degraded because the deliveries moments of the products are not those envisaged initially. it is this context which we propose to integrate in the generation of symptoms as it was presented in [9]. the finality of this section is not to contribute to the state of the art of the monitoring-diagnosis, but to show how the knowledge of the robustness could make the supervision more efficient. by considering this criterion, the filtering of sensors signals of [9] appears pedagogically interesting. 2.1 symptoms generation the idea consists in modeling any operation from a temporal approach. at each operation ai is associated a sensor signal cri. at each sensor signal cri is associated a temporal window [∆tm/cri , ∆tm/cri ] (figure 1). cri is valid only inside this window. ∆tm/cri and ∆tm/cri are defined relatively to the beginning of the operation ai (start-event). the filtering principle is to position the temporal window of each sensor signal cri when his start-vent was received. two types of symptoms are distinguished [9]. symptoms type i noted s1i : this class of symptoms corresponds to awaited sensor signal which is not received at ∆tm/cri . the detection mechanism of this symptom type corresponds to the traditional mechanism of watchdog, but implemented in a separate way of the control. symptoms type ii noted s2i : it is generated by the occurrence of a sensor signal which is not expected. two cases are considered. the first one corresponds to an action but its sensor signal occurs before the validation interval. the second case corresponds to the occurrence of a sensor signal in absence of any order which can create it. ai (start-event) ∆tm/cri validation interval of cri time 0 ∆tm/cri figure 1: operation associated model [9] 2.2 robustness integration the tool used to represent the filtering mechanism is the interpreted t-time petri net. initially, we point out the definition of the interpreted t-time petri net. after, we give the filtering mechanism of sensors signals integrating the two properties of passive and active robustness. definition 1. a t-time petri net is given by a pair < r; is′ >, where r is a petri net and is′ : t → (q+)×(q+ ∪+∞) [10]. time disturbances and filtering of sensors signals in tolerant multi-product job-shops with time constraints 63 definition 2. interpreted t-time petri net is a t-time petri net including an operative part whose state is defined by a set of variables. this state is modified by the operations associated to the places. it determines the value of the conditions (predicates) which are associated to the transitions. the mechanism of watchdog is easily represented by an interpreted t-time petri net. for example, figure 2 shows the detection of a normal state and an abnormal one. if the sensor signal arrives in [tm, tm[, the system is in a normal state. if the sensor signal arrives at the instant tm , the system is in an abnormal one. p2 t1 p1 t2 p3 [tm, tm[*cri [tm, tm]*cri normal state abnormal state figure 2: watchdog mechanism with interpreted t-time petri net within the framework of the robustness integration in the supervision of manufacturing systems with time constraints, we define, figure 3, five time intervals namely: i1i = [∆tm′′/cri , ∆tm′/cri [, i2i = [∆tm′/cri , ∆tm/cri [, i3i = [∆tm/cri , ∆tm/cri [, i4i = [∆tm/cri , ∆tm′/cri [ and i5i = [∆tm′/cri , ∆tm′′/cri [. the margin of passive robustness is available in (i2i ∪i4i) whereas the margin of active robustness is in (i1i ∪ i5i). from a functional point of view, there are three intervals of use in which it is possible to prove the validity: interval of normal functioning, interval of passive robustness and interval of active robustness. in the case of an abnormal functioning, there is always duality of advance and delay scenarios. the adopted filtering mechanism is described by the interpreted t-time petri net of the figure 4. several cases can arise [11]. • if there are absence of order (not ai) and presence of cri, there are freezing of the control and generation of a symptom s2i (place p3). • if the sensor signal cri arrives in the time interval [0, ∆tm′′/cri [, there are freezing of the control and generation of a symptom s2i (place p3). • if the sensor signal cri arrives in the time interval i1i = [∆tm′′/cri , ∆tm′/cri [, there are change of the control (active robustness to an advance) and memorizing a symptom s2i (place p4). • if the sensor signal cri arrives in the time interval i2i = [∆tm′/cri , ∆tm/cri [, there is no change of the control (passive robustness to an advance) but only a memorizing of a symptom s2i (place p5). • if the sensor signal cri arrives in the time interval i3i = [∆tm/cri , ∆tm/cri [, the behavior of the system is normal (place p6). 64 nabil jerbi, simon collart dutilleul, etienne craye, mohamed benrejeb ai (start-event) time ∆tm/cri ∆tm/cri cri (normal functioning) ∆tm’/cri ∆tm’’/cri ∆tm’/cri ∆tm’’/cri 0 normal functioning interval passive robustness interval active robustness interval figure 3: robustness integration in the operation associated model • at the instant ∆tm/cri (transition t7), there is automatically memorizing of a symptom s1i (place p7). • if the sensor signal cri arrives in the time interval i4i = [∆tm/cri , ∆tm′/cri [, it is a case of passive robustness to a delay (place p8). the symptom s 1 i is already memorized (place p7). • if the sensor signal cri arrives in the time interval i5i = [∆tm′/cri , ∆tm′′/cri [, a change of the control is necessary (active robustness to a delay, place p9). • at the instant ∆tm′′/cri (transition t10), there is freezing of the control (place p10). 3 localization of time disturbances in a given topology when, for example, the filtering mechanism executes a control freezing, it is necessary to know where the initial disturbance was occurred. this task is performed on a model of the workshop which uses p-time petri net in order to integrate the staying time constraints in its structure. this aspect is presented in the following section. 3.1 controlled p-time petri net the formal definition of a p-time petri net is given by a pair < r; is >, where [12]: • r is a marked petri net, • is : p → (q+ ∪0)×(q+ ∪+∞) pi → isi = [ai, bi] with 0 ≤ ai ≤ bi. isi defines the static interval of staying time of a mark in the place pi belonging to the set of places p (q+ is the set of positive rational numbers). a mark in the place pi is taken into account in transition validation when it has stayed in pi at least a duration ai and no longer than bi. after the duration bi the token will be dead. using [15], controlled p-time petri net is defined as a quadruplet rpc = (rp, ϕ , u , u0) such that: • rp is a p-time petri net which describes the opened loop system, time disturbances and filtering of sensors signals in tolerant multi-product job-shops with time constraints 65 p1 p5 p7 p4 p9 p3 p10 p8 p6 p2 t1 t2 t3 t11 t13 t5 t7 t6 t9 t10 t14 t12 t16 t17 t15 t4 normal functioning passive robustness active robustness control freezing generation s 2 i and active robustness generation s 2 i and control freezing generation s 2 i and passive robustness generation s 1 i ai*cri cri [0, ∆tm’’/cri[*cri i2i*cri i1i*cri i3i*cri [∆tm/cri, ∆tm/cri] [0, ∆tm’/cri–∆tm/cri[*cri [∆tm’’/cri–∆tm/cri, ∆tm’’/cri–∆tm/cri] [∆tm’/cri–∆tm/cri, ∆tm’’/cri–∆tm/cri[*cri t8 figure 4: robustness integration in the filtering mechanism of sensors signals 66 nabil jerbi, simon collart dutilleul, etienne craye, mohamed benrejeb • ϕ is an application from the set of places (p) toward the set of operations (γ): ϕ : p → γ, • u is the external control of the set of transitions (t ) built on the predicates using the occurrence of internal or external observable events of the system: u : t → {0, 1}, • u0 is the initial value of the predicate vector. let us denote by: • to : the set of observable transitions, • tu o : the set of non observable transitions, • ts : the set of synchronization transitions, • tns : the set of non synchronization transitions, • tp : the set of parallelism transitions, • t◦i (resp. ◦ti) : the output (resp. the input) places of the transition ti, • p◦i (resp. ◦ pi) : the output (resp. the input) transitions of the place pi, • qie : the expected sojourn time of the token in the place pi, • ste(n) : the nnd expected firing instant of the transition t, • st(n) : the nnd effective firing instant of the transition t. 3.2 functional decomposition a workshop in repetitive functioning mode is modeled by a strongly connected event graph (sceg) [13]. performances of a sceg running in mono-periodic functioning mode are proved to be the same as when using the k-periodic functioning [13]. consequently, a mono-periodic functioning is used in order to decrease the complexity of the supervisory problem [14]. in this case, for each transition t, ste(n + 1) = ste(n) + π0 where π0 is the period of the periodic functioning of the given discrete event system. in this paper, the scheduling task is supposed to be done. therefore, the sceg corresponding to the system is provided. moreover, the setting of transitions firing instants is fixed too. as the sojourn times in places have not the same functional signification when they are included in the sequential process of a product or when they are associated to a free resource, a decomposition of the p-time petri net model into four sets is made using [15]. the assumption of multi-product job-shops without assembling tasks as it was established in [16] is used: • ru is the set of places representing the used machines, • rn corresponds to the set of places representing the free machines which are shared between manufacturing circuits, • transc is the set of places representing the loaded transport resources, • transnc is the set of places representing the unloaded transport resources (or the interconnected buffers). time disturbances and filtering of sensors signals in tolerant multi-product job-shops with time constraints 67 figure 5, shows a p-time petri net (g) modeling a system composed by two sequential processes go1 and go2 with two shared machines (m1, m2), where: ru = {p2, p4, p11, p13, p15}, rn = {p6, p7, p8, p9}, transc = {p1, p3, p10, p12, p14}, transnc = {p5, p16}, go1 = (t12, p10, t6, p11, t7, p12, t8, p13, t9, p14, t10, p15, t11) and go2 = (t5, p1, t1, p2, t2, p3, t3, p4, t4). the intervals (isi) and the expected staying times (qie) associated to the places (pi) are: is1 = [30, 50], q1e = 38, is2 = [5, 12], q2e = 7, is3 = [10, 20], q3e = 15, is4 = [5, 20], q4e = 10, is5 = [1, +∞], q5e = 10, is6 = [0, +∞], q6e = 5, is7 = [0, +∞], q7e = 8, is8 = [8, +∞], q8e = 13, is9 = [8, +∞], q9e = 15, is10 = [5, 15], q10e = 12, is11 = [15, 20], q11e = 17, is12 = [3, 7], q12e = 6, is13 = [2, 20], q13e = 5, is14 = [2, 7], q14e = 5, is15 = [15, 20], q15e = 16, is16 = [1, +∞] and q16e = 19. the initial expected firing instants of each transition are: st1e(1) = 15, st2e(1) = 22, st3e(1) = 37, st4e(1) = 7, st5e(1) = 17, st6e(1) = 12, st7e(1) = 29, st8e(1) = 35, st9e(1) = 0, st10e(1) = 5, st11e(1) = 21 and st12e(1) = 0. the repetitive functioning mode is characterized by the period π0 = 40. definition 3. a mono-synchronized subpath is a path containing one and only one synchronization transition which is its last node. definition 4. an elementary mono-synchronized subpath is a mono-synchronized subpath beginning with a place p such as ◦ p is a synchronization transition. in figure 5, there are eight elementary mono-synchronized subpaths constituting a partition of g: l p1 = (p13, t9, p14, t10, p15, t11, p16, t12, p10, t6), l p2 = (p13, t9, p9, t1), l p3 = (p2, t2, p3, t3), l p4 = (p2, t2, p8, t8), l p5 = (p4, t4, p5, t5, p1, t1), l p6 = (p4, t4, p6, t6), l p7 = (p11, t7, p7, t3) and l p8 = (p11, t7, p12, t8). property 1. a place pmp belonging to a sequential process represents a shared machine if and only if p◦mp ∈ tp or ◦ pmp ∈ ts. property 2. the first node of an elementary mono-synchronized subpath is a place belonging to ru and representing a shared machine. 3.3 time disturbances localization let us remember some definitions. definition 5. a time disturbance is detectable if, when it occurs, there exists at least one transition t ∈ to such as st(n) 6= ste(n). definition 6. a time disturbance is quantifiable if its value can be analytically known. definition 7. a time disturbance is localizable when its occurrence node can be identified. definition 8. a time disturbance is partially localizable when its occurrence node location can be proved to belong to a given subset of p. definition 9. a time disturbance is observable when it is detectable, quantifiable and localizable. definition 10. the time passive rejection capacity interval of a path l p is rc(l p) = [ca(l p), cr(l p)] where: ca(l p) = ∑ pi∈(l p∩ (rn∪transnc )) (qie −bi), (1) 68 nabil jerbi, simon collart dutilleul, etienne craye, mohamed benrejeb a place belonging to transc a place belonging to ru a place belonging to rn a place belonging to transnc is4=[2, 20] q4e=10 is1=[30, 47] q1e=38 is2=[5, 12] q2e=7 is3=[10, 20] q3e=15 t4 p4 t2 p2 t1 p1 t5 t3 p3 is5=[1, +∞] q5e=10 p5 m2 m1 is10=[5, 15] q10e=12 t8 p12 t7 p11 t6 t10 p14 t9 t11 p15 p13 p10 t12 is11=[15, 20] q11e=17 is12=[3, 7] q12e=6 is15=[15, 20] q15e=16 is14=[2, 7] q14e=5 is13=[2, 20] q13e=5 is16=[1, +∞] q16e=19 p6 p7 p8 p9 is6=[0, +∞] q6e=5 is7=[0, +∞] q7e=8 is8=[8, +∞] q8e=13 is9=[8, +∞] q9e=15 m1 m2 m3 p16 figure 5: an hillion like model with functional decomposition time disturbances and filtering of sensors signals in tolerant multi-product job-shops with time constraints 69 cr(l p) = ∑ pi∈(l p∩ (rn∪transnc )) (qie −ai). (2) ca(lp) (resp. cr(lp)) is called the time passive rejection capacity for an advance (resp. a delay) time disturbance occurrence. definition 11. let δ a time disturbance and sn a set of nodes belonging to a p-time petri net. δ ∈ sn (resp. δ /∈ sn) means that the occurrence of δ is (resp. is not) in a node of sn. used notations: • cse is the set of elementary mono-synchronized subpaths. • in(l p) is the first node of the path l p. • ou t (l p) is the last node of the path l p. • l p(t∗,t) is the oriented subpath of l p beginning with t∗ and ending with t. • mn−1(l p(t∗,t)) is the number of tokens in l p(t∗,t) after the completion of the cycle (n−1). • given a time disturbance δ , δ rt (n) is the resulting residue quantified at the transition t which is fired at st(n). • ec(in◦(l p),t) is the set of oriented paths connecting the node in◦(l p) of the path l p to the transition t. • h(in◦(l p),t) = min li∈[ec(in◦(l p),t)\l p(in◦(l p),t)] (cr(li)) + δ rt (n). • h′(in◦(l p),t) = min li∈ec(in◦(l p),t) (cr(li)) + δ rt (n). lemma 12. let l p ∈ cse, t ∈ (l p∩to ∩tns), t∗ ∈ (l p∩to) and δ a time disturbance having a residue δ rt (n) 6= 0 quantified at the transition t. the following results are established [17]: δ rt∗(n−mn−1(l p(t∗,t))) = 0 =⇒ δ ∈ [l p(t∗,t)\{t∗}], (3) δ rt∗(n−mn−1(l p(t∗,t))) 6= 0 =⇒ δ /∈ [l p(t∗,t)\{t∗}]. (4) this lemma discusses the case of two observable transitions, t and t∗, such that t is not a synchronization one. when a disturbance is detected at a downstream transition t and is not detected at t∗, it is generated between these two transitions. otherwise, the disturbance occurrence is outside the restriction of the considered path that connects t∗ to t. lemma 13. let l p ∈ cse, t ∈ (l p∩to), t p ∈ (l p∩tp), il p = {li ∈ cse/ ou t (li) = ◦in(l p)} and δ a time disturbance having a residue δ rt (n) > 0 quantified at the transition t. the following assertion is true [17]: δ rt p(n−mn−1(l p(t p,t))) < h′(t p,t) =⇒ δ /∈ { ⋃ li∈il p { li \{in(li), in◦(li)} } ⋃{◦t p, t p }} . (5) 70 nabil jerbi, simon collart dutilleul, etienne craye, mohamed benrejeb in other words, when the residue of the disturbance at the parallelism transition t p does not justify the residue at the transition t, forcibly the disturbance has not crossed t p. lemma 14. let l p ∈ cse, t ∈ (l p∩to ∩ts), t∗ ∈ (l p∩to) and δ a time disturbance having a residue δ rt (n) > 0 quantified at the transition t. the following results are established [17]: δ rt∗(n−mn−1(l p(t∗,t))) = 0 =⇒ δ /∈ [l p(in◦(l p),t∗)\{in◦(l p)}], (6) { 0 ≤ cr(l p(in◦(l p),t∗)) < h(in◦(l p),t) δ rt∗(n−mn−1(l p(t∗,t))) = 0 =⇒ { δ /∈ [(l p\l p(t∗,t))∪{t∗}] δ rin◦(l p)(n−mn−1(l p(in◦(l p),t))) < h(in◦(l p),t) , (7) { δ rt∗(n−mn−1(l p(t∗,t))) 6= 0 δ rt (n) + cr(l p(t∗,t)) 6= δ rt∗(n−mn−1(l p(t∗,t))) =⇒ δ /∈ [l p(in◦(l p),t)\{in◦(l p)}]. (8) the above lemma discusses the case of two observable transitions, t and t∗, such that t is a synchronization one. several results are given. if the residue at the transition t∗ is equal to zero, the disturbance does not belong to the restriction of l p between its only parallelism transition in◦(l p) and t∗. if the disturbance has crossed the parallelism transition of l p (in◦(l p)) and if its residue at in◦(l p) is greater than the passive rejection capacity of the restriction of l p between in◦(l p) and t∗, the residue at t∗ must be different of zero. otherwise, the disturbance has not crossed in◦(l p). if the residue at t∗ is different of zero and if it does not justify the residue at the transition t, the occurrence of the disturbance is not in the restriction of l p between in◦(l p) and t. lemma 15. let l p ∈ cse, t p ∈ (l p∩tp ∩tu o), t ∈ (l p∩to) and cr(l p(t p,t)) the time passive rejection capacity of l p between t p and t for delay occurrence. let us call dif(t p) the set of paths beginning with t p. let us denote difn(t p) the restriction of dif(t p) such that: ∀l p′ ∈ difn(t p), ∀t′ ∈ l p′, we have st′(n + mt′) < st(n) where mt′ = mn−1(l p′(t p,t′))−mn−1(l p(t p,t)). now, let l p′ ∈ difn(t p), t∗ ∈ (l p′ ∩ to) and cr(l p′(t p,t∗)) the passive rejection capacity of l p′ between t p and t∗. given a delay time disturbance δ , the following results are true [17]:    (t /∈ ts)∧(δ rt (n) > 0) δ rt (n) + cr(l p(t p,t))−cr(l p′(t p,t∗)) > 0 δ rt∗(n + mt∗) = 0 =⇒ δ ∈ [l p(t p,t)\{t p}], (9) { (t /∈ ts)∧(δ rt (n) > 0) δ rt∗(n + mt∗) 6= 0 =⇒ δ /∈ [(l p(t p,t)∪ l p′(t p,t∗))\{t p}], (10)    (t ∈ ts)∧(δ rt (n) > 0) cr(l p′(t p,t)) < h′(t p,t) δ rt∗(n + mt∗) = 0 =⇒ { δ /∈ {◦t p, t p} δ rt p(n−mn−1(l p(t p,t))) < h′(t p,t) , (11) { (t ∈ ts)∧(δ rt (n) > 0) δ rt∗(n + mt∗) 6= 0 =⇒ δ /∈ [l p(t p,t)\{t p}]. (12) time disturbances and filtering of sensors signals in tolerant multi-product job-shops with time constraints 71 when t p is a non observable parallelism transition, the following assertion may be used: if a disturbance modifies the t p firing instant, it must be seen downstream of t p. consequently, when the value of the residual effect of the disturbance is greater than the rejection capacity of a given path, a residual variation has to be observed. the different lemmas formulated constitute a tool aiming to define the set of nodes where the disturbance may occur and the subset where it is proved that it did not occur. then the question of using the above lemmas in order to make them collaborate has to be tackled. in other words, it remains to establish an algorithm using these lemmas while testing all mono-synchronized subpaths of the given p-time petri net model. 4 conclusions this paper deals with supervision in critical time manufacturing job-shops. in such systems operation times are included between a minimum and a maximum value. a filtering mechanism of sensors signals integrating the robustness values is described. it provides the avoidance of control freezing if the time disturbance is in the robustness intervals. therefore, it makes it possible to continue the production in a degraded mode providing the guarantees of quality and safety. it should be noted that the knowledge of robustness intervals is a significant parameter in the proposed mechanism. the assumptions formulated in these lines are very restrictive. it is natural to consider different scenarios where the temporal specifications of the process are not fulfilled, nevertheless the production can continue. it is necessary to introduce a finer classification of abnormal functioning and their impact on the considered systems. in this context, fuzzy logic can be used. when a symptom of an abnormal functioning is claimed by the filtering mechanism, it is imperative to localize the time disturbance occurrence. based upon controlled p-time petri nets as a modeling tool, a series of lemmas are quoted in order to build a theory dealing with localization problem. this is quite useful for the maintenance task. in the near future, it is essential to develop an algorithm using the lemmas results and providing localization of time disturbances. references [1] s. calvez, p. aygalinc, and p. bonhomme, proactive/reactive approach for maintenance tasks in time critical systems, ieee international conference on emerging technologies and factory automation (etfa’2005), catane, vol. 1, pp. 947-953, september 2005. [2] s. collart dutilleul, and e. craye, performance and tolerance evaluation, safeprocess’03, ifac symp. on fault detection, supervision and safety for technical processes, washington, june 2003. [3] a. boufaied, a. subias, and m. combacau, chronicle modeling by petri nets for distributed detection of process failures, ieee conference on systems, man, and cybernetics (smc’02), hammamet, october 2002. [4] p. declerck, and m. k. didi alaoui, modelling and analysis of p-time event graphs in the (min, max, +) algebra, ieee conference on systems, man, and cybernetics (smc’04), the hague, vol. 2, pp. 1807-1812, october 2004. [5] n. jerbi, s. collart dutilleul, e. craye, and m. benrejeb, observability of tolerant multi-product job-shops in repetitive functioning mode, imacs’05, paris, july 2005. 72 nabil jerbi, simon collart dutilleul, etienne craye, mohamed benrejeb [6] a. e. k. sahraoui, contribution à la surveillance et à la commande d’ateliers, ph.d. thesis, université paul sabatier, toulouse, 1987. [7] a. toguyeni, e. craye, and j. c. gentina, a method of temporal analysis to perform on-line diagnosis in the context of flexible manufacturing system, iecon’90, vol. 1, pp. 445-450, pacific grovecalifornia, november 1990. [8] m. nourelfath, extension de la théorie de la supervision à la commande des systèmes à événements discrets: application à la sécurité opérationnelle des systèmes de production, ph.d. thesis, insa de lyon, france, july 1997. [9] a. toguyeni, surveillance et diagnostic en ligne dans les ateliers flexibles de l’industrie manufacturière, ph.d. thesis, université des sciences et technologies de lille, november 1992. [10] m. diaz, les réseaux de petri modèles fondamentaux, ed. hermès, paris, 2001. [11] n. jerbi, s. collart dutilleul, e. craye, and m. benrejeb, intégrat0on de la robustesse dans la supervision de systèmes manufacturiers à contraintes de temps, conférence internationale francophone d’automatique (cifa’06), bordeaux, may 2006. [12] w. khansa, p. aygalinc, and j. p. denat, structural analysis of p-time petri nets, computational engineering in systems applications (cesa’96), lille, pp. 127-136, july 1996. [13] s. laftit, j. m. proth, and x. xie, optimisation of invariant criteria for event graph, ieee trans. on automatic control, vol. 37, no. 5, pp. 547-555, may 1992. [14] s. collart dutilleul, j. p. denat, and w. khansa, use of periodic controlled petri net for discrete event dynamical system control synthesis, ecc’95, rome, pp. 2060-2065, september 1995. [15] j. long, and b. descotes-genon, flow optimization method for control synthesis of fexible manufacturing systems modeled by controlled timed petri nets, ieee international conference on robotics and automation, atlanta, georgia, vol. 1, pp. 598-603, may 1993. [16] h. p. hillion, and j. m. proth, performance evaluation of job-shop systems using timed event graphs, ieee trans. on automatic control, vol. 34, no. 1, pp. 3-9, 1989. [17] n. jerbi, s. collart dutilleul, e. craye, and m. benrejeb, localization of time disturbances in tolerant multiproduct job-shops without assembling tasks, computational engineering in systems applications (cesa’06), beijing, pp. 45-50, october 2006. nabil jerbi1,2, simon collart dutilleul1, etienne craye1, mohamed benrejeb2 1ecole centrale de lille laboratoire d’automatique, génie informatique et signal cité scientifique, bp 48, 59651 villeneuve d’ascq, france 2ecole nationale d’ingénieurs de tunis unité de recherche lara-automatique bp 37, le belvédère, 1002 tunis, tunisie e-mail: nabil.jerbi@isetso.rnu.tn, simon.collart_dutilleul@ec-lille.fr, etienne.craye@ec-lille.fr, mohamed.benrejeb@enit.rnu.tn received: november 11, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 675-683 towards open agent systems through dynamic incorporation c. cubillos, m. donoso, n. rodríguez, f. guidi-polanco, d. cabrera-paniagua claudio cubillos, makarena donoso nibaldo rodríguez, franco guidi-polanco pontificia universidad católica de valparaíso av. brasil 2241, valparaíso, chile e-mail: {claudio.cubillos,nibaldo.rodriguez,fguidi}@ucv.cl makarena.donoso@gmail.com daniel cabrera-paniagua universidad de valparaíso valparaíso, chile e-mail:daniel.cabrera@uv.cl abstract: this work tackles the problem of providing a mechanism and infrastructure for allowing a given multiagent system (mas) to become open, allowing the incorporation of newly incoming agents to participate within the existing society. for this, a conceptual analysis of the so-called conciliation problem is presented, covering the diverse levels and issues involved in such a process. our dynamic incorporation architecture is presented, which implements an infrastructure for allowing the participation of external agents into a specific multiagent system by incorporating the appropriate behaviours upon arrival. our multiagent architecture for dynamic incorporation covers three levels: semantics, communication and interaction and has been applyed in a book-trading e-market scenario. keywords: multiagent system, dynamic incorporation architecture, open agent systems, passi. 1 introduction software agents are defined as autonomous entities capable of flexible behavior denoted by reactiveness, pro-activeness and social ability [1]. multiagent systems (mas) consist of diverse agents that communicate and coordinate generating synergy to pursue a common goal. at present, multiagent systems (mas) raises as a key paradigm for the development of next generation software systems which are required to be distributed, intelligent (autonomous, proactive), open and dynamic. while much work has been done by the research community in solving distribution and intelligence issues, little effort has been devoted to openness and dynamicity under mas settings. in the medium-to-long-term future we will see open multi-agent systems spanning multiple application domains, and involving heterogeneous participants developed by diverse design teams. agents seeking to participate in these systems will be able to incorporate and learn the appropriate behavior for participation in the course of doing so, rather than having to prove adherence before entry (as happens today) [2]. however, up to now agent systems typically center on closed agent systems with ad-hoc designs and predefined communications protocols. in recent years, agent systems have evolved to the use of agreed protocols and languages thanks to a huge standardization effort (fipa, omg, w3c). nowadays agent system openness is limited to the participation of any agent able to satisfy publicly-advertised standards. moreover, typically communication protocols, languages copyright c⃝ 2006-2010 by ccc publications 676 c. cubillos, m. donoso, n. rodríguez, f. guidi-polanco, d. cabrera-paniagua (acls) and domain knowledge model (ontology) are defined by the design team prior to any agent interactions. therefore much work needs to be done for the above scenario to become true. the current work presents a conceptual analysis of the so-called conciliation problem for then presenting the design of a multiagent architecture devoted to facilitate the dynamic participation of external agents into a specific multiagent system by incorporating the appropriate behavior upon arrival. a book-trade e-market has been used as study case to validate the multiagent implementation. this work gives continuity to our previous research in [4] [5]. 2 related work relevant research has been developed around coalition formation; the process to form a group of agents and solve a problem via cooperation [4], some works on dynamic coalition formation (dcf) [5] tackle the issue of dynamically building beneficial coalitions (coalition algorithms) among agents that can cope with environmental changes without restarting the negotiation process. however, at present coalition formation for virtual organizations is limited, with such organizations being largely static. all of the existing work has been devoted to optimizing for a given agent, the decision of when to participate or not in a coalition (conformation/disband) but not in providing an infrastructure supporting such dynamic coalition conformations and their agents’ heterogeneities. the novelty of our work relies on 1) presenting a conceptual approach for conciliating agents’ heterogeneities under an open mas setting 2) present a solution based in dynamic behavior loading rather than in a mediated-architecture approach and 3) implementing a solution that solves heterogeneities at two levels: semantics and interaction (behavior) while leaving the implementation of the communication level (related services and conciliation)as further work. 3 the three conciliation levels for the dynamic formation of agent systems to become true, a set of diverse issues need to be solved first. this concern: 1) the mechanism needed to provide the incorporation of a foreign agent to the society (agent system), 2) the ways to allow the agent to incorporate upon-arrival the society common knowledge (ontology) in order to interact, and 3) the means allowing the foreign agent to learn or incorporate upon-arrival the communication protocols and languages used by the society (aips [6], acls [7], etc) and the inherent business model. each of the above issues regards a different aspect of the communication infrastructure used by the agent society to interact and each one is covered by different areas of informatics and computer science. in general terms, the problem of conciliating the existing divergences on the communication capabilities of the entering agent and the existing society can be organized into three levels: firstly, the interaction level, involving the diverse agent interaction protocols (aip’s) to formalize the conversations among agents. then the communication level, tackling the heterogeneinity at message level, that is, the protocol and language used on the messages. finally, the semantic level, conciliating the possible divergences on the symbols used in the messages and the underlying knowledge models of the new agent and the mas society. each of these is further explained in the following. 3.1 interaction level the highest level is the interaction one, tackling the conversations among pair of agents through agent interaction protocols (aip’s) that specify the underlying coordination / cooperatowards open agent systems through dynamic incorporation 677 tion mechanism. examples can be the contract-net protocol (cnp) [11], the different auctions (e.g. english, dutch, vickrey, etc.), tuple-based negotiations, among others. these specific protocols are grounded into interaction diagrams that specify the roles of the participants and the expected messages to be sent and received under which conditions. in practical terms, the new agent will need to incorporate the behavior needed to perform a certain role within the mas society. for example, turning back to the cooper e-market, the new agent will need to add or load the manager role for initiating a contract-net, which will allow him to make the call for proposals, evaluate and select the best proposal and award it. all these tasks will need to be incorporated at run-time by simply instantiating the corresponding behavior classes and adding it to the agent. by having this solve, the agent will still need to understand the parameters required by each of the tasks it has loaded, moving us to the semantic level. other alternative is to have the agent with the correct interaction protocol and role (e.g. manager of a contract-net) but have divergences on the message format or language used in the content, moving us to the communication level. both are described below. 3.2 communication level nowadays agent systems do use communication protocols of their choice that define the message structure or syntax and language used to express the content of the message. examples of such can be fipa acl [8], kqml, an ad-hoc xml-based message envelope, or even the simple but effective concatenization of the different data in an specific order (e.g such as in lowlevel protocols). it can happen that the new agent does know the fipa acl message format but not the prolog language used in the content of the message or can do not know both. now when considering the newly incoming agent, how can we enable the incorporation of those message formats and content language upon entry? one scenario is when the new agent has already the adequate roles and behavior to interact (interaction level) but with a message format and content language that is different from the ones used within the agent society. in this case, a mediation service can be provided, by establishing the mapping of the different slots used on the two message formats. a more efficient approach is the use of a meta-format (meta-ontology for communication) used as message interchange format [9]. in this way each new format must provide its mapping to the meta-format and vice-versa instead of providing its mapping to all the other existing formats. other scenario is when the new agent does not have incorporated the interaction role nor the corresponding behaviors. in this case are directly adopted the format for messages and language used by the society when incorporating the behaviors, that is, when solving the problems at interaction level. 3.3 semantic level existing coordination strategies rely on standard interaction and communication protocols, assuming that all participants (agents) understand the shared domain knowledge usually modelled in terms of an ontology. this assumption is no longer valid in open systems in which an incoming agent (usually developed by another party and in another moment) needs to interact with a certain agent society to obtain some service (pursuing a specific goal) and does not know in advance such domain knowledge used to interact. some questions to answer are: how can the agent request or get that ontology from the agent society and process it or understand it? or how can the agent society provide the incoming agent with all the required knowledge. the basic problem here is how to conceal the concepts of the new agent with regard to the ones used by the society. therefore the entering agent will need to be provided by a service 678 c. cubillos, m. donoso, n. rodríguez, f. guidi-polanco, d. cabrera-paniagua capable of aligning his knowledge models with the one of the mas society, requiring ontology alignment and mapping techniques to solve it. 4 dynamic participation architecture this project used passi (process for agent societies specification and implementation) as methodology of development, which uses uml as modelling language. for a detailed description please refer to [3]. figure 1 shows our conceived solution in terms of the conciliation levels described before. figure 1: the 3-tier architecture for conciliation firstly, the foreign agent (fa) is the one that wants to participate in a specific mas to which it does not belong beforehand, thus requiring a conciliation process. a coordinator manages the diverse steps in the agent incorporation from the initial request until the agent is ready to start interacting within the mas society. another type of agent is the mediator, existing one for tackling each of the three levels of possible divergences (semantic, communication and interaction). in the first two cases, each one will have subscribed a set of translator agents providing specific bridging services among a couple of ontologies, or a pair of message formats in correspondence with the level. in the case of the interaction mediator, it will be having associated a set of behavior-provider agents, each one containing the code for the roles in diverse interaction protocols (e.g. manager/bidder in contract net, auctioneer/auctionee in auctions, etc.). the figure 2 shows when a new agent requests incorporation to the book-trading mas. for this, the fa agent sends a conciliation request to the coordinator containing: the mas domain name to which the fa wants to contact (booktrading in our case); the identifier of the message protocol that the fa has; the id of ontology that the fa knows; and the role name that the fa wants to perform within the book-trading mas. such request is evaluated by the coordinator checking whether if the fa needs conciliation services at any of the three considered levels; be provided with appropriate behaviors to participate (interaction), message-protocol translators (communication) and ontology translators (semantic). for this it compares the protocols and semantics used by the agent and by the domain mas searching for heterogeneities. the coordinator derivates to the interaction mediator the search for the requested role and related coded-behaviors. on its turn, the mediator contacts specific behavior providers following the contract-net protocol [11]. a similar process is carried out for the communication mediator and semantic mediator. in the first case, the coordinator sends the id of the message protocol towards open agent systems through dynamic incorporation 679 figure 2: scenario showing when a foreign agent requests a conciliation used by the fa and the domain mas, while in the second, sends the ontologies id of the domain mas and fa. in all three cases what is received is a list of possible providers and their proposals(bids), as the coordinator is the one incharge of selecting the final providers, composing the work team and announcing it to the fa. for selecting the appropriate members of the work team, the coordinator should consider diverse aspects such as the cost, quality of service, reliability, etc. of each candidate according to its utility function. on its actual form, the coordinator selects the providers based on the cost of each service. more variables within the objective function together with other selection schemas (e.g. foreign agent selection or a mixed approach) remains a matter of future work. the conciliation process may result in the need of services with any combination of the three levels or even require no services in case of a perfect match. in this way, the coordinator has the responsibility of structuring an appropriate work team for the fa. usually, the coordinator will select an ontologytranslator (ot), a messagetranslator (mt), and a behaviorprovider (bp) agent. it is important to mention that each role that the fa agent wants to perform needs a specific work team, therefore are tackled as different conciliation requests. however, nothing prevents a service provider of participating in diverse work teams, even for a same foreign agent. together with requesting and selecting an appropriate work team for the foreign agent, another important process is the actual enforcement of those services. in practical terms, this means sending the behaviors from the provider to the fa, plus the possible translation services at communication and semantic level that could be needed. the figure 3 shows the services’ enforcement, beginning with the foreign agent which decomposes the received working team. it gets the behaviors from the selected behavior provider while obtaining the corresponding codecs from the message and ontology translators. upon receiving the behaviors that compose the role, the fa takes the list of parameters required by the behaviors to work and translates it with the ontology codec. this codec translates the concepts from the domain-mas ontology to the fa ontology. in this way, the fa can map the values of 680 c. cubillos, m. donoso, n. rodríguez, f. guidi-polanco, d. cabrera-paniagua figure 3: scenario showing a foreign agent in the contract enforcing process the behavior parameters with its own attributes and provide the necessary arguments to them. on its turn, the communication codec is used to translate all the inbound/outbound messages among the message protocols used inside the behavior and by the domain mas. regarding the work team composition, in a most general case we will be having several ways of composing types of bps, cms and sts to conciliate a same fa-to-target-mas situation. however, due to the combinatorial character of the problem a simplification has been made considering only one possible combination of providers’ types. furthermore, the fa will usually need conciliation at two levels: the interaction and semantics, as usually the behaviors provided will already use the communication protocols employed by the target mas. 5 book-trading scenario the study case is based on the well-known book trading mas environment in which seller and buyer agents pursue their goals through the contract-net protocol (cnp) [11]. the book-trading example included in the jade development has been used as baseline. buyer agents initiate towards open agent systems through dynamic incorporation 681 figure 4: performance comparison of the mediation-based and conciliation-based architectures the interaction with a call-for-bids, adopting the manager role of the cnp. the call includes the book title the buyer is looking for. on the other side, seller agents assume as potential contractors, processing the calls and making bids for the requested title. in our implementation, the seller looks in its database for the title and sends a bid in case of having stock. the price within the bid is specific for each seller. the buyer collects the answers and selects the cheapest one. 5.1 experiments and results the objective of the study case was to prove the feasibility of our dynamic participation architecture and evaluate the associated costs mainly in terms of performance. for this, the experiments focused in comparing a traditional book-trading market (as the one above) with a mediation-based approach and with our proposal. in the first case, buyers and sellers interact through a mediator agent while in the last case, buyer agents correspond to foreign agents willing to dynamically participate in the book-trading mas by adopting upon arrival the required behavior. in this particular case, the role requested for conciliation is manager of the cnp with its diverse tasks (e.g. call for proposal, bid selection, task awarding, etc.) the test scenario has been generated through a random generation of book requests from a list of 30 titles. the number of sellers has been fixed to 10, each of which has one unit of each book in the list at a price that distributes uniformly u[50, 120]. a total of 10 sets of 100 requests has been generated. the simulation considers a main agent devoted to managing the creation of buyer agents, seller agents and the mas for dynamic participation. the generation and arrival of buyer agents follows a poisson distribution, hence the time between arrivals distributes exponential, e(λ), with λ = 2 in terms of requests per second. for more details on the architecture design and passi-uml diagrams please refer to [5]. regarding the considered distributed environment, the simulations were carried out over pcs with intel pentium 4 of 2 ghz. with 256 mb ram, connected through a 10/100 mb. router. figure 4 shows the service mean times for the 10 datasets, measuring the time required for the buyer agents to buy their books within the book-trading e-market. for our conciliated approach, the graph shows the time spent for the contract-enforcing part only. the time spent in the first part of the process (obtaining a working team) was around 6 seconds. the mediated approach 682 c. cubillos, m. donoso, n. rodríguez, f. guidi-polanco, d. cabrera-paniagua does not consider the time spent in looking for a mediator as it already knows it. these two aspects were not considered in the comparison as a roaming agent will usually carry out many transactions with the same mas before leaving, hence these initial processes happens only once. in [4] a comparison of a typical closed contract-net-based mas and our proposal is presented. 6 conclusions and future works an agent-based software architecture for allowing the dynamic participation of foreign agents into an existing mas has been described. additionally, an implementation has been carried out under the book-trading domain giving an insight on the viability of the proposal. the use of a mechanism based on dynamic behavior loading (conciliating differences at communication and semantic levels) raises as a feasible approach for obtaining an open mas system. further work considers: extending to other levels of conciliation (e.g. communication protocol stack), different ways of services’ delivery (e.g. codec, mediation, tuple-based) and applying our solution to other application domains such as transportation, robotics and supply-chain. 7 acknowledgement this work has been partially funded by conicyt through fondecyt project no. 11080284 and the pontificia universidad católica de valparaíso (www.pucv.cl), through nucleus project no. 037.115/2008 "collaborative systems". bibliography [1] g. weiss. multiagent systems: a modern approach to distributed artificial intelligence, mit press, massachusetts, usa. 1999. [2] m. luck, p. mcburney, c. preist, 2003. agent technology: enabling next generation computing. a roadmap for agent based computing, agentlink ii. [3] p. burrafato and m. cossentino. designing a multiagent solution for a bookstore with the passi methodology. in fourth international bi-conference workshop on agent oriented information systems (aois-2002). [4] c. cubillos, m. donoso, 2009. towards open agent systems: a book-trading study case. in fourth international conference on computer sciences and convergence information technology (iccit ’09), pp. 950–953. [5] c. cubillos, m. donoso, d. cabrera-paniagua, 2009. designing an open agent system for book-trading. third international symposium on intelligent information technology application (iita 2009), pp. 578–581. 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[10] foundation of intelligent physical agents (fipa). 2002. fipa acl message structure specification. doc. no. sc00061g, 03/12/2002. available at: "http://www.fipa.org/specs/fipa00061/" [11] m. uschold, r. jasper and p. clark. three approaches for knowledge sharing: a comparative analysis. in proceedings of the 12th knowledge acquisition, modelling and management workshop, kaw’99, banff, canada, october 1999. [12] fipa contract net interaction protocol specification. available at: www.fipa.org/specs/fipa00029/sc00029h.pdf ijcccv11n4.dvi international journal of computers communications & control issn 1841-9836, 11(4):522-537, august 2016. data-driven control of the activated sludge process: imc plus feedforward approach j.d. rojas, o. arreta, m. meneses, r. vilanova j.d. rojas, o. arrieta escuela de ingeniería eléctrica, universidad de costa rica, san josé, 11501-2060 costa rica. tel: +506-2511-3892, fax: +506-2511-3920 {jdrojas, oarrieta}@eie.ucr.ac.cr m. meneses, r. vilanova* departament de telecomunicacióó i d’enginyeria de sistemes, escola tècnica superior d’enginyeria, etse, universitat autònoma de barcelona, 08193 bellaterra, barcelona, spain {montse.meneses, ramon.vilanova}@uab.cat *corresponding author: ramon.vilanova@uab.cat abstract: in the work presented in this paper, data-driven control is used to tune an internal model control. despite the fact that it may be contradictory to apply a model-free method to a model-based controller, this methodology has been successfully applied to a activated sludge process (asp) based wastewater treatment. in addition a feedforward controller over the influent substrate concentration was also computed using the virtual reference feedback tuning and applied to the same wastewater process to see the effect over the dissolved oxygen and the substrate concentration at the effluent. keywords: activated sludge process, data-driven control, internal model control, wastewater treatment plants 1 introduction data-driven control is a rather new control approach that does not attempt to find the model of the plant to control, instead, it uses experimental data to directly find a controller, which, generally, is meant to minimize some control performance criterion. some of the most remarkable methods within this control approach are the iterative feedback tuning (ift) [1, 2], the windsurfer approach [3, 4], the correlation approach [5, 6] and the virtual reference feedback tuning (vrft) [7–9]. while the ift and the windsurfer approach are iterative methods (that is, several experiments on the plant have to be performed in order to find the controller) the correlation approach and the vrft are one shot methods (only one set of data is needed to find the controller). ift computes an unbiased gradient of a performance index to iteratively improve the tuning of the parameters of a reduced order discrete time controller, at each iteration three different experiments are performed on the system and based on this data, the gradient is computed; in the windsurfer approach the objective is to find a better model for the plant (and subsequently a better controller) using closed-loop data and internal model control (imc) design [10] in such a way that, with every iteration, the closed loop bandwidth can be increased; data-driven control using the correlation approach is a one-shot methodology that attempts to find the values of a restricted order controller that tries to minimize the correlation between the closed-loop error of the system (based in a desired closed-loop behavior) and the reference for the process output, and the vrft translates the model reference control problem into an identification problem, copyright © 2006-2016 by ccc publications data-driven control of the activated sludge process: imc plus feedforward approach 523 being the controller the transfer function to identify based on some "virtual signals" computed from a batch of data taken directly from an open-loop experiment. in this work, the vrft approach is used and extended in order to be applied to a wastewater treatment plant (wwtp). wwtp are an important case of study within the process control area, while an active research area that involves other disciplines as for example chemistry, biology, and instrumentation. moreover, by it self, wwtps have deep impact in the quality of live in big cities. that is why the constraint on the level of pollution of the treated water before discharging it into the receiving waters, is becoming more stringent [11] and because of that, a correct control and operation of wwtp is one of the top priorities for both, industry and academics. among the types of wwtp, the activated sludge process (asp) is one of the most popular and more studied [12, 13]. this is also true from the automatic control perspective: for example in [14] a parameter and state non-linear estimator is used in an adaptive linearizing control of the dissolved oxygen and substrate concentration of an asp but under the assumption that only the dissolved oxygen is available for measurement. in [15], several multivariable pi control method are applied to the asp by linearizing the nonlinear model and the results are presented, as well as the combination of some of these methods. in [16], predictive control is used to maintain a low concentration of substrate at the output by controlling the dissolved oxygen using the dilution rate. the internal model of the predictive control is a three layer neural network. in [17] the control of the substrate concentration is achieve using an estimation based on the dissolved oxygen measurements, a dynamic controller that cope with the change in reference and a pid controller that corrects the steady state error produced by the use of a linearized model in the first controller. in [18] a decentralized pi approach is presented to show that simple well tuned pi controllers can achieve a similar performance than more complex methodologies for the asp case. some other strategies have been proposed recently such as in [19] where an i-p controller control system with pole-placement design is proposed. in all the cases, some sort of model (non-linear o linearized) is used to computed the controller. in several cases it is supposed that some parameters are known which may no be the case for a real plant. the contribution of this paper is to apply a data driven approach to the tuning of discretetime restricted-order linear controller in a decentralized approach to have good performance for both reference tracking and disturbance rejection in an asp based wwtp. without explicitly computing a model of the process an internal model control approach is used in conjunction with the vrft methodology. even more, the effect of the influent concentration disturbance is taken into account by computing a feedforward control using the vrft as well. it was found that this methodology provide excellent results when compared with a pi approach. the rest of the paper is divided in two parts, in section 2, a short overview on vrft is presented as well as the mentioned extensions for the imc control. in section 3 the results of the application of this data-driven method is presented and compared with a two-degrees of freedom pi controller. the conclusion are presented in section 3. 2 virtual reference feedback tuning extensions in this section, an overview on the vrft is presented as well as some results that extend the capacity of the vrft for different control strategies and structure of controllers is presented. 2.1 virtual reference feedback tunning overview the virtual reference feedback tuning (vrft) is a one-shot data-based method for the design of feedback controllers. the original idea was presented in [7], and then formalized by 524 j.d. rojas, o. arreta, m. meneses, r. vilanova + u(t) y(t) c(z; θ) p (z) r̄(t) figure 1: the vrft set up. the dashed lines represent the "virtual" part of the method lecchini, campi, savaresi and guardabassi (see [8, 9]). in [8], the method is presented for the tuning of a feedback controller. if a the controller belongs to the controller class {c (z;θ)} given by c(z;θ) = βt (z)θ, where β (z) = [β1 (z) · · ·βn (z)]t is a known vector of transfer functions, and θ = [θ1 θ2 · · ·θn]t is the vector of parameters, then the control objective is to minimize the model-reference criterion given by: jmr (θ) = ∥ ∥ ∥ ∥ ( p(z)c(z;θ) 1 + p(z)c(z;θ) −m(z) ) w(z) ∥ ∥ ∥ ∥ 2 2 (1) starting from a batch of open-loop data {u(t),y(t)}, a "virtual" signal is computed in such a way that, if the closed-loop system is feed with this virtual signal and the controllers in the loop were the ideal controllers that would achieve a predefined target transfer function, then the input and output signals of the plant in closed-loop would be the same than the batch of open-loop data. the output of the controller should be equal to u(t) and then, this controller can be found by identifying the transfer function which yields the output u(t) when the input r̄(t) − y(t) is applied to the input as depicted in fig. 1 the original vrft algorithm, as presented by the authors in [8], is as follows: given a set of measured i/o data {u(t),y(t)}t=1,...,n 1. calculate: • a virtual reference r̄(t) such that y(t) = m(z)r̄(t), and • the corresponding tracking error e(t) = r̄ −y(t) 2. filter the signals e(t) and u(t) with a suitable filter l(z): el(t) = l(z)e(t) ul(t) = l(z)u(t) 3. select the controller parameter vector, say, θ̂n , that minimizes the following criterion: jnv r (θ) = 1 n n ∑ t=1 (ul(t) −c(z;θ)el(t))2 (2) if c(z;θ) = βt (z)θ, the criterion (2) can be given by jnv r (θ) = 1 n n ∑ t=1 ( ul(t)−ϕtl(t)θ )2 (3) with ϕl(t) = β(z)el(t) and the parameter vector θ̂n is given by θ̂n = [ n ∑ t=1 ϕl(t)ϕl(t) t ]−1 n ∑ t=1 ϕl(t)ul(t) (4) data-driven control of the activated sludge process: imc plus feedforward approach 525 the authors, also showed that, the filter l(z) should be the one that approximates the criterion (2) to (1). this filter should be designed to accomplish the constraint: |l|2 = |1−m|2 |m| |w |2 1 φu (5) where φu is the spectral density of u(t). the vrft framework have been used in several applications and even have been extended for the mimo case and used for pid tuning, for example see [20–24]. 2.2 internal model control using the virtual reference feedback framework internal model control (imc) is a popular control method that incorporates the model of the process into the controller [10]. the standard structure is depicted in fig. 2. p(z) represents the plant, while ¯p(z) is its model. q(z) is the imc controller. if the output of the model and the output of the plant are the same, and there is no disturbance, the control system behaves as if it was in open-loop. if this is the case, to have perfect tracking, q(z) must try to cancel the dynamics of the plant. on the other hand, if there is a mismatch between the plant and its model or if a disturbance acts on the system, the feedback loop enters into play. this characteristics leads to the well know property that an imc system would be nominally internally stable if q(z) is stable, in case the model is equal to the plant. of course, finding a perfect model is rarely achievable and if it were, q(z) may not be possible to be equal to the inverse of this model due to physical limitations or because the inverse of the plant may lead to an unstable controller. in [10] a two-step design is proposed for this kind of controller: 1. solve the nominal performance criterion given, for example, by min q̄(z) ∥ ∥ ( 1 − p̄(z)q̄(z) ) w(z) ∥ ∥ p (6) where w is a filter chosen to give more importance in certain frequencies and ‖·‖p is a given norm that defines the performance criterion. the optimal solution to this problem yields to a sensitivity function given by s∗(z) = 1 − p̄(z)q̄(z) and the complementary sensitivity function given by m∗(z) = p̄(z)q̄(z), that is, the response to a change in the reference is as if it were in open loop, while the response to a disturbance is in closed-loop. of course this response is not achievable and therefore, the model p̄(z) should be divided in an invertible and non-invertible part to be able to approximate the optimal controller. 2. to introduce robustness conditions, the complementary sensitivity has to be rolled off at high frequencies, therefore, it is necessary to add a low pass filter f(z) to the controller q̄(z), to obtain the final controller q(z) = q̄(z)f(z). suppose that the multiplicative uncertainty is bounded by a frequency dependent function l̄m(ω), ∣ ∣ ∣ ∣ p(eω)− p̄(eω) p̄(eω) ∣ ∣ ∣ ∣ ≤ l̄m(ω) then the closed-loop system is robustly stable if and only if |f(eω)| < 1∣ ∣p̄(eω)q̄(eω)l̄m(ω) ∣ ∣ ∀ω (7) 526 j.d. rojas, o. arreta, m. meneses, r. vilanova figure 2: standard structure of the imc. p̄ represents the plant model and q is the imc controller figure 3: disposition for the vrft experiment using the imc topology. the dashed line represents the virtual signals and components. the reasons why the imc control has become very popular is because, finding the controller and the conditions for robust stability can be cast in a very simple form. using the vrft framework, this constraint are not really necessary, since the methodology does not need any modeling step. interested reader on imc control, can find more information on [10, 25]. it is possible to find an imc controller using the vrft framework without concerning about the modeling of the system. in fig. 3, the experimental setup for the vrft applied to the imc topology is depicted. if the target complementary sensitivity function is given by m(z), then the virtual reference r̄(t) is computed as: r̄(t) = m−1(z)y(t) (8) if the ideal controller were in the loop, then one would have p̄(z) = p(z) and the input to the controller q(z,θ) would be r̄(t) and its corresponding output would be u(t) in order to have y(t) as the output of the closed-loop system. from fig 3, it can be found that the ideal controller would be given by q0(z) = m(z)p(z) −1 p̄0(z) = m(z)q0(z) −1 (9) p0(z) would be the ideal plant model that is derived from the ideal controller. this basic idea leads to the following optimization problem which gives the set of optimal parameters θ∗ (in a least square sense): min θ j(θ) = min θ n ∑ i=1 (u(i) −q(z,θ)r̄(i))2 (10) data-driven control of the activated sludge process: imc plus feedforward approach 527 ����� figure 4: virtual reference setup for feedforward plus imc controller. solid lines are for "real" components and signals while dashed lines are for "virtual" components and signals once q(z,θ∗) has been determined, it is easy to compute the approximation of the process model of the plant from (9): p̄(z,θ) = m(z)q(z,θ)−1 (11) it is important to note that p̄(z,θ) is seen just as an “instrumental model”, that results from the determination of the optimal controller. in fact, it can be seen just as a part of the imc controller that results from the optimization. of course, if a robust check is performed with the obtained controller, this approximation of the plant can be used as if it were the nominal model. in that case, the controller and the nominal model would be found at once. the filter for robust operation presented in (7), is already included in the determination of q(z,θ∗) given the desired m(z). 2.3 vrft approach to feedforward control sometimes, it is possible to measure disturbances that affect the process output. in those cases, it is desirable to use a feed-forward controller that acts before the effects of these disturbances reach the output of the plant. in [26], the idea of using the vrft controller was presented to be used in conjunction with a one-degree of freedom controller. the main difference is that it is assumed that the disturbance is available for measurement and is used in the optimization problem. in this paper this idea was implemented in conjunction with the vrft-imc controller. suppose that the control system can be represented by the diagram in fig. 4, where p1(z) and p2(z) represent the unknown dynamics of the plant from the input u(t) and the disturbance d(t) to the output y(t), respectively. these three signals are measured from an open-loop experiment. the idea of using the feedforward control plus the imc controller is to cope with both measurable and non-measurable disturbances. q(z), pm(z) and cf(z) are the controllers to be found. the “virtual” components and signals (which are presented with dashed lines in fig. 4) are: • m(z), which is the target closed-loop dynamics from the reference signal to the output of the controlled system. • f(z), is the target closed-loop dynamics from the measured disturbance to the output. 528 j.d. rojas, o. arreta, m. meneses, r. vilanova • r̄v, is the virtual reference computed from the data obtained from an open loop experiment and the closed-loop target functions. • ȳd, is the ideal disturbed output in closed-loop, if the virtual reference is applied in the closed-loop and the ideal controllers are set in place. • d, is the measurable disturbance signal that it is suppose to be available in the open loop experiment. the virtual reference signal r̄v has to be computed according to the ideal relationships and the measured and virtual signals: ȳd = mr̄v + fd (12) if one is able to find the ideal controllers, then ȳd = y. since this is exactly what is needed, the virtual signal is computed from (12) as: ȳd = y r̄v = m −1 (yd −fd) (13) the transfer function of the controlled system is y = p1q 1 + (p1 −pm)q r + (1 −pmq)(p1cf + p2) 1 + (p1 −pm)q d (14) note in (14), that the input signals do not have a bar, denoting that these signals are not virtual, but actually are entering to the system. when comparing (12) and (14), one is able to find the ideal controllers that would, theoretically, drive the system to the desired dynamics (if the transfer functions of the plant were known): qo = m p1 −m(p1 −pm) cfo = 1 p1 ( f(1 − (p1 −pm)q) 1 −pmq −p2 ) (15) once q(z) and cf (z) has been obtained by optimization, the best approximation of pm(z) is pm(z) = m(z)q −1(z), just as in (9) where one expects that pm(z) ≈ p1(z) since the virtual reference was computed to achieve this relationship. this optimization is found by following the paths in the diagram of fig. 4 that lead from the measured and virtual inputs to the u(t) signal, it is straightforward to find that the cost function to optimize is given by: j(θ) = 1 n n−1 ∑ i=0 [u(i) − (q(z,θ) r̄v(i) + cf (z,θ) d(i))]2 (16) solving this optimization problem, one is able to find directly the two controllers and the instrumental model, using only one batch of input-output data without any iterative scheme. 3 application to an asp based wwtp in this section a practical example of the imc-vrft method exposed above is presented. the plant considered in this paper is the wwtp given in [27]. it comprises an aerated tank data-driven control of the activated sludge process: imc plus feedforward approach 529 table 1: initial conditions biomass x(0) = 217.79 mg/l substrate s(0) = 41.23 mg/l dissolved oxygen do(0) = 6.11 mg/l recycled biomass xr(0) = 435.58 mg/l influent substrate sin(0) = 200.00 mg/l influent dissolved oxygen doin(0)= 0.50 mg/l table 2: kinetic parameters β = 0.2 kc = 2mg/l r = 0.6 ks = 100mg/l α = 0.018 kdo = 0.5 y = 0.65 dos = 0.5mg/l µmax = 0.15 h −1 where microorganisms act on organic matter by biodegradation, and a settler where the solids are separated from the wastewater and a proportional part is then recycled to the aerator in order to maintain certain amount of biomass in the system. the layout is shown in fig. 5. the component balance for the substrate, biomass, recycled biomass and dissolved oxygen provide the following set of non-linear differential equations: dx(t) dt = µ(t)x(t) −d(t)(1 + r)x(t) −rd(t)xr(t) (17) ds(t) dt = −µ(t) y x(t) −d(t)(1 + r)s(t) + d(t)sin (18) ddo(t) dt = −koµ(t) y x(t) −d(t)(1 + r)do(t) + kla(dos −do(t)) + do(t)doin (19) dxr(t) dt = d(t)(1 + r)x(t) −d(t)(β + r)xr(t) (20) µ(t) = µmax s(t) ks + s(t) do(t) kdo + do(t) (21) kla = αw(t) (22) where x(t) biomass, s(t) substrate, do(t) dissolved oxygen, dos maximum dissolved oxygen, xr(t) recycled biomass, d(t) dilution rate, w(t) aeration rate, sin and doin substrate and dissolved oxygen concentrations in the influent, y biomass yield factor, µ biomass growth rate in a monod like form [28], µmax maximum specific growth rate, ks and kdo saturation constants, kla oxygen mass transfer coefficient, α oxygen transfer rate, ko model constant, r and β ratio of recycled and waste flow to the influent. the influent concentrations are set to sin = 200 mg/l and doin = 0.5 mg/l. the control strategy is a decentralized control as in [18] where the multivariable process is treated as two separate single variable process. the strategy is depicted in fig.6. with respect to the control problem definition, it is considered that the dissolved oxygen, do(t), and substrate, s(t), are the controlled outputs of the plant, whereas the dilution rate, d(t), and aeration rate w(t) are the two manipulated variables. the control of do provides a method to maintain the necessary amount of biomass in the system while controlling s gives a way to keep the pollution 530 j.d. rojas, o. arreta, m. meneses, r. vilanova �������� �� ���� ����� ������ ����� ������� ����� ��������� ����� ������ ��� ������ figure 5: wastewater treatment process ���������� ���������� ���������� �� ��� figure 6: control strategy for the wwtp at the effluent in an acceptable level [14]. the initial conditions and kinetic parameters are taken as in [18, 27] and presented in table1 and 2. the settings of the vrft controller are as follows: for both control loops, the sampling time was selected as ts = 0.5min, the imc controller q(z) has the following parameterization: q(z) = α1 + α2z −1 + α3z −2 β1 + β2z−1 + β3z−2 (23) the target transfer function for the do loop is: mdo(z) = 0.02357z−1 1 −0.9764z−1 (24) which represents a first order transfer function with a constant time of approximately 20min. for the s loop (controlled by manipulating d(t)), the target closed-loop dynamics is a first order transfer function with a constant time of approximately 40min given by: ms(z) = 0.01382z−1 1−0.9862z−1 (25) the input-output data was selected as an additive random signal of 0 mean and variance 90 for the w(t) and variance 7.5e-4 for the d(t) around the operation points given in table 1. the resulting controllers were found as: qdo(z) = 40.69 −19.35z−1 −19.65z−2 1−0.4683z−1 −0.4792z−2 qs(z) = 0.01236 −0.006155z−1 −0.006158z−2 1−0.4863z−1 −0.4924z−2 (26) data-driven control of the activated sludge process: imc plus feedforward approach 531 the imcff-vrft version was also implemented, considering the influent substrate concentration sin as the measurable disturbance. the q controller has the same parameterization as in (23). for the feedforward controller, the parameterization is: cf (z) = γ0 + γ1z −1 1−σ1z−1 (27) the sampling time is the same as for the q controllers, and the desired target transfer function is f(z) = 0, which is normally what is desired with the feedforward control. the experimental data was slightly changed, because the dynamics from the disturbance to the output are slower: the data changes slowly and it was taken into account that a portion of the output data were affected only by the input, while another portion is affected only by the disturbance and, finally, another portion is affected by both. this is helpful to identify correctly both controllers at the same time. the resulting controllers are: qdo(z) = 40.57 −80.37z−1 + 39.8z−2 1 −1.974z−1 + 0.9739z−2 cfdo(z) = −0.0002002 + 0.001138z−1 1−0.9976z−1 qs(z) = 0.01233 −0.02416z−1 + 0.01183z−2 1 −1.947z−1 + 0.9479z−2 cfs(z) = −0.0006154 + 0.0004481z−1 1 −0.7261z−1 (28) the results of this controllers are compared to the two-degrees of freedom, continuous time pi controller of [18]. two different test were performed: a change in the references and a disturbance on the influent substrate sin where it is considered that every 24h, an increase of 10% of the value of sin during 1h takes place. for the change in reference (sref(t) for the substrate concentration reference and doref(t) for the dissolved oxygen reference), the result is as given in fig. 7 and 8. a step change of 10mg/l is applied to sref(t) at time t = 10h while a step change of -2mg/l in doref(t) is applied at time t = 100h. the effect of one loop change in the other loop, due to the process interaction, can be observed as well. in both cases the imc-vrft and the imcff-vrft controller achieves a better performance for both the reference tracking as well as the disturbance rejection when the other loop changes. in table 3 the values of the integral of the squared errors (ise) and the total variation (tv), which measures the aggressiveness of the control effort, are presented. ise and tv are computed as: ise = ∫ t 0 e(t)2 dt tv = n ∑ i=1 |u(i) −u(i−1)| (29) e(t) is the error signal (the reference minus the measured output), and u(i) is the output of the controlled sampled every hour and n is the total number of samples. in the column “reference tracking” it can be seen that for the s loop with the application of the imc-vrft controller the ise is greatly reduced (near the 57%) but with almost the same tv. the do loop is improved in both ise and tv, as can be also seen in fig. 7 and 8, the section that is zoomed details the change in the do, it is clear that the response of the pi controller is much worse than the 532 j.d. rojas, o. arreta, m. meneses, r. vilanova 0 50 100 150 40 42 44 46 48 50 52 54 time (s) s u b s tr a te c o n c e n tr a ti o n ( m g /l ) change in reference, s reference pid imc−vrft imcff−vrft figure 7: step change in the s reference at time t = 10h table 3: comparison of the results between the imc-vrft, imcff-vrft and the pid control reference tracking disturbance rejection s do s do ise pi 77.23 2.94 12.05 0.0021 imc-vrft 32.85 0.64 5.56 6e-005 imcff-vrft 33.25 0.65 0.15 0.0083 tv pi 0.091 89.10 0.15 7.02 imc-vrft 0.083 67.33 0.15 4.69 imcff-vrft 0.082 65.01 0.16 13.07 response of the imc-vrft, which almost has no overshoot. in fig. 9, the plot of the control signals is presented for both the dilution rate and the air flow rate. as it was expected, the performance of the imc-vrft and the imcff-vrft are similar for the reference tracking since no disturbance is present for this simulation, despite the fact that the controllers were found using different optimization methods (in the case of the imc-vrft a simple linear least square problem can be casted, while for the imcff-vrft, the output error (oe) method was applied [29]). for the disturbance in the substrate concentration of the influent, the responses are presented in fig. 10, fig. 11 and fig. 12. pi control is faster to control the disturbance fig. 10, but the overshoot is larger. the imcff-vrft controller performs much better than the imc-vrft controller (the ise is 97% lower) with almost the same tv. the response of the do is greatly improved with a reduction of almost 97% of ise for imc-vrft, but the imcff-vrft performs much worse than the pi controller (the ise is almost 4 times bigger with a tv 2 times grater). it is clear that a feedforward strategy is not adecuate for the do loop, since the effect of the change in sin is much slower than the effect of the aireation. data-driven control of the activated sludge process: imc plus feedforward approach 533 � �� ��� ��� ��� � ��� � ��� � ��� � �� �� � � � � �� � � �� � � � � � �� � � � � � �� � � � � � � �� � ������� �� �!������"��� �!������ #$� $%�&' () $%�((&' () *� *� *+ ��� ��, ��� ��� ��+ ��� ��, ����+ ��* � ��� ��, ��� ��� ��� � �� �� � � � � �� � � �� � � � � � �� � � � � � �� � � � � � � �� � ������� �� �!������"��� �!������ #$� $%�&' () $%�((&' () figure 8: step change in the do reference at time t = 100h 0 50 100 150 0.05 0.1 0.15 0.2 0.25 time (s) d il lu ti o n r a te ( h − 1 ) dilution rate pid imc−vrft imcff−vrft 0 50 100 150 0 50 100 150 time (s) a ir f lo w r a te ( m 3 h − 1 ) air flow rate pid imc−vrft imcff−vrft figure 9: control effort during the change in the reference 534 j.d. rojas, o. arreta, m. meneses, r. vilanova 0 50 100 150 40.6 40.8 41 41.2 41.4 41.6 41.8 42 42.2 42.4 time (s) s u b s tr a te c o n c e n tr a ti o n ( m g /l ) disturbance rejection, s reference pid imc−vrft imcff−vrft figure 10: effect over the substrate concentration when the substrate input is disturbed 0 50 100 150 6.1 6.105 6.11 6.115 6.12 6.125 6.13 6.135 6.14 6.145 time (s) d is s o lv e d o x y g e n c o n c e n tr a ti o n ( m g /l ) disturbance rejection, do reference pid imc−vrft imcff−vrft figure 11: effect over the dissolved oxygen when the substrate input is disturbed data-driven control of the activated sludge process: imc plus feedforward approach 535 0 50 100 150 0.08 0.09 0.1 time (s) d il lu ti o n r a te ( h − 1 ) dilution rate pid imc−vrft imcff−vrft 0 50 100 150 89 89.5 90 90.5 91 time (s) a ir f lo w r a te ( m 3 h − 1 ) air flow rate pid imc−vrft imcff−vrft figure 12: control effort during the disturbance in the substrate concentration of the influent conclusions in this paper, the vrft method has been studied and was applied within the imc framework. also, a feedforward extension to the imc-vrft controller was also presented. both methodologies were successfully applied to a wwtp process, substantially improving the results of a continuous time two-degrees of freedom pi controller using a restricted order discrete time controller in the case of the reference tracking. for the disturbance rejection, the feedforward controller greatly improved the performance for the substrate loop but for the dissolved oxygen loop, it was found that the feedforward component degrades the performance. the difference in the constant time and the little effect that has the influent substrate concentration over the dissolved oxygen may be the reason of this poor performance. data-driven control is a powerful tool that can be easily applied to several control problems and that can be extended to several control structures. how to chose the closed-loop target functions without any knowledge of the plant (only data) and how to guarantee 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[29] l. ljung (1999), system identification, theory for the user, 2nd edition, prentice hall, 1999. ijcccv4n3draft.pdf special issue on membrane computing seventh brainstorming week on membrane computing the present volume contains a selection of papers resulting from the seventh brainstorming week on membrane computing (bwmc7), held in sevilla, from february 2 to february 6, 2009. the meeting was organized by the research group on natural computing (rgnc) from department of computer science and artificial intelligence of sevilla university. the previous editions of this series of meetings were organized in tarragona (2003), and sevilla (2004 – 2008). after the first bwmc, a special issue of natural computing – volume 2, number 3, 2003, and a special issue of new generation computing – volume 22, number 4, 2004, were published; papers from the second bwmc have appeared in a special issue of journal of universal computer science – volume 10, number 5, 2004, as well as in a special issue of soft computing – volume 9, number 5, 2005; a selection of papers written during the third bwmc has appeared in a special issue of international journal of foundations of computer science – volume 17, number 1, 2006); after the fourth bwmc a special issue of theoretical computer science was edited – volume 372, numbers 2-3, 2007; after the fifth edition, a special issue of international journal of unconventional computing was edited – volume 5, number 5, 2009; finally, a selection of papers elaborated during the sixth bwmc has appeared in a special issue of fundamenta informaticae – volume 87, number 1, 2008. membrane computing is an area of natural computing which studies models of computation inspired by the structure and functioning of living cells, and organization of cells in tissues and other structures. the resulting models (called p systems) are distributed parallel computing devices, processing multisets in compartments defined by membranes. most classes of p systems are computationally universal and, if an exponential working space can be produced in polynomial time (e.g., by membrane division), then they are able to solve computationally hard problems in a feasible time. a series of applications were recently reported, especially in biology and medicine, but also in computer graphics, cryptography, linguistics, economics, approximate optimization, etc. several simulation programs (useful in applications) are available by now. a comprehensive information about this research area (considered in 2003 by isi as “fast emerging research front in computer science") can be found at the website http://ppage.psystems.eu. at this web address one can also find the volumes published after each bwmc, with the papers resulting from these meetings, including the volume with all papers related to bwmc7. for the present volume we have selected only a few of these papers; they have been thoroughly reworked after the meeting and then they went through the standard refereeing procedure of the journal. the selection also intended to provide a good image of the research in membrane computing, so that the volume contains both theoretical and applicative papers, dealing with computing power, computational complexity, “classic" cell-like p systems and the recently introduced spiking neural p systems, programming, simulation of biological processes, and so on. * as mentioned above, the meeting was organized by the research group on natural computing from sevilla university (http://www.gcn.us.es)– and all the members of this group were enthusiastically involved in this (not always easy) work. the meeting was supported from various sources: (i) proyecto de excelencia de la junta de andalucía, grant tic 581, (ii) proyecto de excelencia con investigador de reconocida valía, de la junta de andalucía, grant p08 – tic 04200, (iii) proyecto del ministerio de educación y ciencia, grant tin2006 – 13425, (iv) iv plan propio de la universidad de sevilla, (v) consejería de innovación, ciencia y empresa de la junta de andalucía, well as by the department of computer science and artificial intelligence from sevilla university. * the present volume is dedicated to the 60th birthday anniversary of professor mario de jesús pérezjiménez, the head of the research group on natural computing from sevilla university, and one of the most active researchers in membrane computing. a really unique combination of enthusiasm and mathematical talent, of scientific devotion and altruism, mario not only contributed a lot to the research in membrane computing, with fundamental results especially related to computational complexity issues and also related to many other theoretical research questions, to applications in biology and eco-systems, to programming, etc., but he also created one of the strongest research groups in natural computing in general, in membrane computing in special; it also deserves to be mentioned the organization, for already several years in a row, of the brainstorming week on membrane computing – all these making sevilla a place of current “pilgrimage" of researchers in membrane computing, from europe, asia, america. for all those who know mario personally, it is hard to believe that he has already six decades: he is so active, enthusiastic and hard working that he looks as young as decades ago, and the theoretical possibility to get retired (according to spanish regulations, this is possible for mario) looks like a nonsensical joke... and, for all who know mario personally, it is impossible not to own to him a lot, from science to daily life (one of the sayings which circulate around is that if you have a need, it is wiser not to tell it loudly, because mario will try immediately to help you...). happy birthday, mario, and many happy returns! guest editors: giancarlo mauri, milan, italy gheorghe păun, bucharest, romania agustín riscos-núñez, seville, spain (sevilla, june 2009) ijcccv4n1draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 1, pp. 17-26 artificial intelligence + distributed systems = agents ioan dzitac, boldur e. bărbat ioan dzitac “aurel vlaicu” university of arad, faculty of exact sciences, department of mathematics-informatics str. elena dragoi, nr. 2, complex universitar m (micalaca, zona iii), arad, romania e-mail: idzitac@gmail.com boldur e. bărbat “lucian blaga” university of sibiu, “hermann oberth” faculty of engineering, department of research 10, victoriei bd, sibiu, 550024, românia e-mail: bbarbat@gmail.com every established religion was once a heresy henry buckle “essays” abstract: the connection with wirth’s book goes beyond the title, albeit confining the area to modern artificial intelligence (ai). whereas thirty years ago, to devise effective programs, it became necessary to enhance the classical algorithmic framework with approaches applied to limited and focused subdomains, in the context of broad-band technology and semantic web, applications running in open, heterogeneous, dynamic and uncertain environments-current paradigms are not enough, because of the shift from programs to processes. beside the structure as position paper, to give more weight to some basic assertions, results of recent research are abridged and commented upon in line with new paradigms. among the conclusions: a) nondeterministic software is unavoidable; its development entails not just new design principles but new computing paradigms. b) agent-oriented systems, to be effectual, should merge conventional agent design with approaches employed in advanced distributed systems (where parallelism is intrinsic to the problem, not just a mean to speed up). keywords: open, heterogeneous, dynamic and uncertain environments (ohdue); computer-aided decision-making; nonalgorithmic software; bounded rationality; agent-oriented software engineering (aose). 1 introduction. adapting to the time(s) of change three dominant features of the post-industrial society relevant here are: a) growing speed of change (due to the intense positive feedback entailed by moore’s law outcomes: internet, broad-band technology, semantic web, google, [9] etc.); b) growing complexity (architectural, cognitive, structural [4, 5, 10]). c) globalization (expressed in it context mainly through the modern enterprise paradigms). bernerslee who coined also the term ggg (giant global graph) to describe the semantic web as a new stage of www utters it very pointed: “i have gone from using a 300 board connection on one of those telephone couplers to a 3 million board connection, so that is a 10,000 factor. so the technology underneath this has tremendously increased in terms of speed and functionality, and the web technology had happened on top” [http://dig.csail.mit.edu/, 2007]. copyright © 2006-2009 by ccc publications 18 ioan dzitac, boldur e. bărbat thus, modern it environments, except for simple applications, move towards open and heterogeneous (resources are unalike and their availability is not warranted), dynamic (the pace of exogenous and endogenous changes is high) and uncertain (both information and its processing rules are revisable, fuzzy, and uncertain). most situations to be controlled are complex and uncertain, and involve parallel processes. thus, the applications developed to deal with must be intelligent [7] (to manage complexity and uncertainty) and distributed (to handle parallelism) are intrinsically non-deterministic and endusers have to interact with them in manners they are not get used to. moreover, a second (side-effect) vicious circle comes out from the interaction between difficulty to adapt and the follow-on frustration (for instance, the claims regarding “digital manipulation”). in brief, adapting to the speed of change is mandatory and the target of this paper is to show that it entails adopting modern it paradigms. the approach is based on the homomorphism of the addition in the paper title to that in the famous book of wirth [22]. accordingly, section 2 presents the rationale analysing and supporting the evolution from programs to agents, emphasizing the temporal dimension. deepening the investigation, section 3 explores the unavoidable paradigm shift (here, many and diverse paradigms are x-rayed). section 4 includes the core: the misleading term “distributed” is thoroughly revisited because distribution was mostly part of the solution and is now recognized as main part of the problem too. just to give more weight to some assertions in preceding sections, models, methods, and mechanisms based on the paradigms endorsed before, are abridged after earlier papers and commented upon in section 5. conclusions and intentions for future development close the paper (section 6). 2 rationale. from programs to agents like all changes induced by systems with intense positive feedback, the shift from programs to agents is at once a long way (conceptually, since it involves multiple paradigm changes), a swift leap (historically, since as it beings algorithms are active, whereas agents are teenagers) and a hard chaotic fight (epistemically, since ai as a whole is at the same time feared and ridiculed, overrated and denied). misinterpretations are eased because confusing program architecture with code structure some professionals still consider that x-rayed programs are nothing more than implemented algorithms, since in binary form data and instructions are indiscernible. moreover, in the case of ai, the argument about the difference between programs and agents is complicated by two conceptual inflations due to overstated advertising: a) to many programs are labelled as intelligent; b) most such “intelligent” programs are renamed as agents (with or without minor reshaping). thus, the notion of agent (and the very metaphor behind it) are blurred. object orientation as almost one and only software engineering paradigm adds two more hindrances to: a) already in the 1990s, real-time programming showed that even the conceptual equivalence “program = object” exposed intellectual difficulties (e.g., objects like “mutex”, “event”, or “exception”, state “start”, method “execute”); b) almost as a corollary, by transitivity, it is commonsensical that the equivalence “agent = object” is awful, since nobody could be happy to be considered “intelligent like an object”. besides, objects have at most a very primitive temporal dimension. tough, this dimension is fundamental because no software entity lacking it could be able to (inter)act in the e-world with other artificial intelligence + distributed systems = agents 19 entities, neither artificial (its peers), nor natural (its human end-users). tellingly enough, time entered the software universe via data structures like dynamic data (e.g., lists in pascal) or data types for concurrency (from monitors in concurrent pascal to tasks in ada). likewise, in ai, a genuine temporal dimension was entailed by distributed systems (to handle the parallelism involved albeit architectural inefficiency due to approaches based on “light” multithreading and time-sharing). thus, the homomorphism suggested in the paper title is even deeper than supposed at first. on the other hand at least in countries similar to romania it curricula are obviously lagging behind the state of the art. aspects relevant for this paper are: a) ai syllabi show a long-lasting flavour of “gofai” (good old-fashioned ai). b) even when rigid planning-based ai is replaced by “bic” (biologically inspired computing), the focus is not on modelling inspired from biologic (sub-symbolic) paradigms -, but merely on simulating biologic behaviour. c) software engineering syllabi are entirely object-oriented; thus, agents if considered are designed as objects (e.g., using jade). d) distributed systems are approached in still more conventional manner: the only concern is to boost speed, not to reflect real-world parallelism. hence the rationale is threefold, depending on the perspective: a) ai (intelligent software must be process-based, not program-based); b) software engineering (intelligent applications should be agent-oriented, not object-oriented); c) it curricula (ai and distributed systems should be merged based on agent-orientation). 3 paradigm shift. what is certain? here “paradigm” means: “thought pattern in any scientific discipline or other epistemological context. the merriam-webster dictionary defines broadly: a philosophical or theoretical framework of any kind. [...] perhaps the greatest barrier to a paradigm shift, in some cases, is the reality of paradigm paralysis, the inability or refusal to see beyond the current models of thinking” [http://en.wikipedia.org/wiki/paradigm#paradigm_shifts]. a lot of fundamental scientific concepts inside and outside it changed dramatically their intension since it begun to be the dominant “novum organon” of post-industrial technological development. they have been investigated from agent-oriented perspective in [4, 5] and lately in [6] where the manifold paradigmatic shift they contributed to engender was labelled “from kelvin to zadeh” (figure 1) because the focus was on precision (unnatural in real world, hence inadequate in software) and the shift described there referred to swap from the conventional “computing with numbers” (based mainly on measurements) to the modern “computing with words” (based mainly on perceptions). in addition, figure 1 tries to remind or at least to suggest that: • moore’s law epitomised as feedback loop in the way usual in automation and electronics emphasises not only the growing psychological difficulty to adapt to an unprecedented speed of change but also its known side effects: instability, distortion, complexity (mainly, cognitive), frustration (e.g., the irritation about “digital manipulation”). 20 ioan dzitac, boldur e. bărbat figure 1: manifold paradigm shift (adapted and extended from [6]) • “manifold” as “diverse”: in line with common usage, “paradigm” regards also specific areas within a discipline (e.g., “programming paradigm”, mainly when it “leverages a computer language’s power” [21]). in figure 1 the paradigmatic level lowers from a wide-ranging one to a narrow, specialised one usual in software engineering. thus, the “kelvin paradigm” suggests that it must stay firmly based on mathematical precision, while the “zadeh paradigm” replays that it should shift towards semiotic flexibility [23]. on the other hand, shifting from “client-server”, to “computing as interaction” [1, 24]) is focused not involving (im)precision, (non)determinism, (un)certainty, etc. (besides, another dimension of reason diversity was recently shown: the ethnographical one [17].) • “manifold” as “many”: after redressing the balance (i.e., accepting all kinds of paradigms, not just gofai), ai was inundated by sub-symbolic paradigms; among the best known are artificial neural networks (ann) and genetic algorithms (ga). the most nihilist and powerful, i.e. the ethological paradigm (based on the physical-grounding hypothesis [4]), seems to be still in vogue for developing agents (above all since the agent is accredited as process by a formal standard [12]). • “manifold” as “non-exhaustive”: to save space some important paradigm shifts having rather philosophical/epistemological nature are skipped over. just one example: humans can communicate only among themselves or with machines too? here, the problem could be circumvented replacing “communication” by “interaction” but the epistemological facet is still there: it has to be admitted (or rejected) that, in the framework of “computing as interaction”, their connotations are artificial intelligence + distributed systems = agents 21 very similar. perhaps, the definition given by sieckenius de souza could help: “communication is the process through which, for a variety of purposes, sign producers (i.e., signification system users in this specific role) choose to express intended meanings by exploring the possibilities of existing signification systems or, occasionally, by resorting to non-systematized signs, which they invent or use in unpredicted ways” [19]. • bounded rationality. the term is used as defined, explained and endorsed in [20, 18, 14]. since it is a very rich concept, often applied as principle, there are many “models of bounded rationality” [20] including a nobel prize winner one (“psychology for behavioural economics” [16]). it entails that almost any human undertaking to be effective as regards the time required must be imperfect. in ai context, perfection suggests the ideal of mathematicians and conventional software developers too to achieve algorithm-based optimisation. just one unquestionable example: “the problem is neither to admit that for any medical act (and for even stronger reasons as regards nursing) “just in time” is a sine qua non condition, nor that bounded rationality is the only practical means to achieve it [13]. nevertheless, there is a double hindrance, due to a yet prevalent mentality: a) therapeutic decision-making is an exclusively human attribute; b) non-algorithmic software is if not nonsensical applicable at most to toy problems” [5]. hence, bounded rationality explodes in a fascicle of interrelated, versatile, and highly applicationdependent features. examples: learning or negotiation strategies; most features meant to reduce complexity (like the “zero-overhead rule” in generative programming). • time enters the picture threefold: a) from left to right, it represent the very essence of paradigm shift in the sense of kuhn (for some conservatives unable to adapt the time to accomplish the shift can attain infinity; on the other hand, children have no problem to “communicate” with their artificial playmates). b) explicitly, through the need to manage “just in time” rapidly changing situations. c) implicitly, by handling real-world parallelism. here, parallelism is intrinsic to any interaction (even between an interface agent and its owner), because interactants coexist in time. of course, distribution adds new facets to an already complex temporal dimension. in short, to be effectual in e-world both users and software developers have to pass the mental rubicon separating programs from agents. indeed, agents are here to stay (even if not yet very intelligent); “affective computing”, “semantic web”, “ambient intelligence” and so on, are more than slogans (they are recognized as main it development directions by eu-promoted acts [1]). thus, “what is certain?” in the section title goes beyond the necessary enquiry of uncertainty, questioning the very essence of nowadays agent-oriented application development. for instance, even when blending vigorous well established paradigms, research trends are in line with the suggestions in the right part of figure 1: “interpretability is considered to be the main advantage of fuzzy systems over alternatives like neural networks, statistical models, etc. [...] in the recent years, research has started to focus on the trade-off between interpretability and accuracy [...]. analysis of the model interpretability and comprehensibility is always convenient, and it is a necessity when accuracy is not a model feature” [15]. 4 “distributed” revisited. a solution becomes problem to distribute means “to divide and dispense in portions” implying a previous entirety able to be divided [http://www.thefreedictionary.com/distributed]. paradoxically, the epistemic trouble with dis22 ioan dzitac, boldur e. bărbat tributed systems is that they are conceived, designed and implemented corresponding to the common connotation of “distributed” [11, 8]. thus, “in distributed computing a program is split up into parts that run simultaneously on multiple computers communicating over a network. [...] the main goal of a distributed computing system is to connect users and resources in a transparent, open, and scalable way. [...] distributed computing implements a kind of concurrency. it interrelates tightly with concurrent programming so much that they are sometimes not taught as distinct subjects [...] if not planned properly, a distributed system can decrease the overall reliability” [http:// en.wikipedia.org/wiki/distributed_computing]. “distribution”, as “the act of distributing or the condition of being distributed; apportionment” [http://www.thefreedictionary.com/distribution], is related to resource management (if something is plenty, no need to apportion). in short and maybe oversimplified there are four kinds of circumstances where, within the it treatment of the case, distribution in its conventional meaning can help (inside parentheses are examples) [4]: a) in space. spatial distribution is the oldest and most familiar type of resource apportionment (equipment components, process phases, networking, credit-card terminals). b) in time. time-sharing preceded its name (learning in schools, delegating authority, or reading on a ride are much older than unix-like operating systems or parallel buses). c) in organization. any organism is based on distributed order (human body, company, state, flower). for virtual enterprises it becomes a major raison d’étre. d) in problem solving. “divide et impera” was always a foremost strategy to fight complexity, chiefly cognitive one (most reductionist theories, most methodologies from euclid’s algorithm to structured programming). the difficulties begun when “distributed”, and “parallel” were perceived as quasi-synonymic in the syntagm “distributed computing”. moreover, epistemic confusion escalates when other debatable (semantically antinomic) concepts generated even more doubtful pair of antonyms: “sequential”/“simultaneous” (instead of “parallel”), “holistic”/“analytical” (instead of “reductionist”). a relevant case is related to the basic metaphor of ann: despite the same distributed neural network structure in both brain hemispheres, an antinomic pair of functions is stated as “linear algorithmic processing” vs. “holistical algorithmic processing” [http://en.wikipedia.org/wiki/cerebral_hemispheres]. here the reluctance to accept the paradigmatic shift that processing could be also non-algorithmic (even in the left brain) generated a quasi-pleonasm (could a step-by-step procedure be nonlinear?) and a quasi-contradiction (could a deterministic procedure be holistical?). as regards processing, the real opposition is “sequential”/“parallel”, where parallelism involves distribution. for instance, “as regards the learning process as such prefixed with “e-” or not the viewpoint is that human learning is best described by the information-processing approach in cognitive psychology, in line with the ideas endorsed in [2]: “most modern information-processing theories are “learning-by-doing” theories which imply that learning would occur best with a combination of abstract instruction and concrete illustrations”. learning should be considered in both humans and agents -as a process where most effectiveness is reached through a blend of symbolic (“left-hemisphere”-like) and subsymbolic (“right-hemisphere”-like) modi operandi” [5]. though, confusion become delusion when “concurrency” and “distribution” were perceived as conceptually close enough that agent-oriented applications concurrent par excellence could be effective if they are designed using mechanisms conceived (and used successfully) for distributed systems. since that is a central claim of this paper, it must be elaborated a bit. when designing distributed systems examples above show it clearly distribution was mostly part of the solution not part of the problem. indeed, in most cases, the problem was a whole and, iff it artificial intelligence + distributed systems = agents 23 such a problem can be split into subproblems, the entirety is disaggregated, the subproblems are solved and, finally, the partial solutions are re-aggregated. at the programming level they run in parallel and, when accessing shared resources, need mutual exclusion. on the contrary, applications devised in line with the “computing as interaction” paradigm above all agent-based applications entail at least two interactants (the interface agent and its owner), evolving in parallel, autonomously but not independently (since they interact as in any normal communication process: inform, wait, interrupt each other, etc.). briefly, they are concurrent processing threads; their programming entails multithreading. the crucial software engineering problem is that, while an api (application programming interface) able to support multithreading covers all requirements for mutual exclusion, the opposite is true just in very simple cases. worse, in most cases, designing concurrent applications with api intended for distributed systems results in severe ineffectiveness. hence, acknowledging the difference between distribution and concurrency is paramount not just at epistemic level, but at engineering level too. a relevant step into diminishing confusions was the different name given in c# to a instruction existing in java, without changing its semantics: “synchronize” is now called “lock” expressing what it really does (tellingly, almost the opposite of what its previous name claimed to do, since to preserve coherence it reduces parallelism). 5 models, methods, mechanisms beside the structure as position paper, to give more weight to the assertions regarding the paradigm shifts symbolised in figure 1, results of recent research (in line with the paradigms endorsed above) are abridged and commented upon. all software entities mentioned below are models, methods, and mechanisms but they are abridged without grouping them corresponding to their kind, since they are commented upon in software engineering papers, as well as in [6, 5] and papers referred to there. there are three categories of mechanisms developed for affordable non-algorithmic agent-based applications in ohdue: a) innovative mechanisms dedicated to “computer-aided x”, where x stays for almost any intellectual activity, within the software engineering toolbox agorithm (agent-oriented interactive timesensitive heuristic mechanisms). so far they are implemented or in earlier development stages, but only for solving toy problems or even in simple experimental models, for x = decision, learning, semiosis. b) existing mechanisms employed in previous research (before 2005, mainly in experimental models for captologic virtual therapists, carried out as pseudoavatars). their structure is based on common api functions callable from a customary java development environment. c) conceptualized within the framework of some phd theses in preparation. to increase paradigmatic relevance they are ordered in relation to figure 1, that is focusing on the missing “l” in the toolbox name. (of course, a bijection is out of question.): • decision-making with future contingents. domino (decision-oriented mechanism for “if” as non-deterministic operator). developed primarily for decision making (typical application: managing overbooking) it is meant to deal with undecidability due to any kind of future contingents. it is a “three-output if” where the semantics of the third value is a blend of a łukasiewicz “i” interpreted as “unknowable” and a kleene “u” interpreted as “temporary lack of knowledge”. (in fact, the semantics of “undecidable” is re’fined to “undecidable in the time span given”.) • analog input. scrollbar input is proposed for all kind of data: uncertain knowledge, intrinsically fuzzy, roughly estimated, etc. 24 ioan dzitac, boldur e. bărbat • dynamic priorities. are applied for: a) fine-tuning agent priorities (mainly the features of multi-agent systems) to manage situations “just in time”; b) fading out retention in “thick time”; c) boosting response of exception handlers. • exception-driven reactivity. prompt response to asynchronous must be mostly stimulusdriven because interaction between basically reactive entities. to respond promptly, interrupts are reflected asynchronously in exceptions with dynamic propagation. • antientropic self-cloning. developed to implement “strange loops” (via gödelian selfreference) as first step of investigating agent self-awareness, it means spawning an agent identical to itself, preserving self-representation (its “i”), but with an enriched world model (via lamarckian evolution). • flexible cloning. to reach efficient polymorphism the copies are purposely imperfect, spawning an agent quasi-identical to its parent; differences between clones become extensive only after recurring cloning (a clone is just a “slightly altered alter ego”). unfortunately, the mechanisms listed above have beside lacking validation in vivo (some of them not even in ovo) a double vulnerability: they are either incremental as regards the “kelvin way of thinking” or too loosely linked to new paradigms. thus, what is their relevance? to break the vicious circle since there is no “methodology for paradigm shift” to leave behind the rd order ignorance [3], software should be considered “not a product, but rather a medium for the storage of knowledge. [...] the other knowledge storage media being, in historical order: dna, brains, hardware, and books. [...] software development is not a product-producing activity, it is a knowledge-acquiring activity.” (that is neither fact, nor proof, it is just expectation.) 6 conclusions and intentions a) non-deterministic software is unavoidable; its development entails not just new design principles but new computing paradigms. b) agent-oriented systems, to be effectual, should merge conventional agent design with approaches employed in advanced distributed systems (where parallelism is intrinsic to the problem, not just a mean to speed up). c) the agorithm toolbox still not sufficient as technological infrastructure for agent-oriented software is a good framework to go ahead. d) the paradigm shift “from kelvin to zadeh” becomes urgent to keep pace with a rapidly changing e-world. e) almost as corollary, these paradigm shifts entails also attitudinal ones: shifting from multi-, through inter-, towards genuine trans-disciplinarity. as regards the prospects of non-algorithmic agent-oriented software, short-term intentions include enhancing the agorithm toolbox with two mini-ontologies: a) dynamic: i (agent), you (master), and rest of the world. b) visual: visual rules should simulate “the arrow of time”. acknowledgement. this work was partially supported by the ministry of education and research through contract no. 12 092/2007. artificial intelligence + distributed systems = agents 25 bibliography [1] agentlink iii. agent based computing. agentlink roadmap: overview and consultation report. university of southampton. http://www.agentlink.org/roadmap/al3rm.pdf, 2005. [2] j.r. anderson, l.m. reder, h.a. simon. applications and misapplications of cognitive psychology to mathematics education. texas educational review, 2000. [3] p.g. armour, the five orders of ignorance. comm. acm, 43 (10), 17-20, 2000. [4] b.e. bărbat, agent-oriented intelligent systems. romanian academy publ. house, bucharest, 2002 (in romanian, “grigore moisil” prize of the romanian academy). [5] b.e. bărbat, e-maieutics. rationale and approach. international journal of computers, communications & control, 3, supplement: suppl. s, 40-54, 2008. [6] b.e. bărbat, natural time for artificial agents. abstracts of icccc papers, băile felix, may 15-17, 27-27, 2008.(invited paper.) [7] i. dziţac, artificial intelligence. ed. house “aurel vlaicu” university, 2008 (in romanian). [8] i. dziţac, parallel and distributed methods for algebraic systems resolution, ccc publications, agora university publishing house, 2006. (in romanian). [9] i. dziţac, icccc 2008 & ewnlc 2008 celebrates bardeen’s centenary and welcomes professor zadeh, international journal of computers communications & control, 3 (suppl. s):16-25, 2008. [10] i. dziţac, i. moisil, advanced ai techniques for web mining, proc. of 10th wseas international conference on mathematical methods, computational techniques and intelligent systems (mamectis ’08, corfu, greece, 343-346, 2008. [11] i. dzitac, g. moldovan, distributed systems: information models, agora university publishing house 2006 (in romanian). [12] fipa tc agent management. fipa agent management specification. standard sc00023k (2004/18/03). http://www.fipa.org/specs/fipa00023/ sc00023k.pdf [13] g. gigerenzer, a. edwards. simple tools for understanding risks: from innumeracy to insight. british medical journal, 327, 741-744, 2003. [14] g. gigerenzer, r. selten. bounded rationality. mit press, cambridge, 2002. [15] f. herrera, genetic fuzzy systems: taxonomy, current research trends and prospects. evol. intel., 1, 27-46, 2008. [16] d. kahneman, maps of bounded rationality: psychology for behavioral economics. lecture (when receiving nobel prize; revised version). stockholm, nobel foundation, 2002. [17] e. livingston, ethnographies of reason. ashgate, aldershot, uk, 2008. [18] a. rubinstein, modeling bounded rationality. mit press, cambridge, 1998. [19] c. sieckenius de souza, the semiotic engineering of human-computer interaction. the mit press, 2005. [20] h.a. simon, models of bounded rationality. mit press, cambridge, 1997. [21] d. spinellis, rational metaprogramming. ieee software, 25, 1, 78-79, jan/feb, 2008. [22] n. wirth, algorithms + data structures = programs. prentice hall, 1978. [23] l.a. zadeh, d. tufis, f.g. filip, i. dzitac, (eds.), from natural language to soft computing: new paradigms in artificial intelligence, romanian academy publishing house, 2008. 26 ioan dzitac, boldur e. bărbat [24] f. zambonelli, a. omicini. challenges and research directions in agent-oriented software engineering. autonomous agents and multi-agent systems, 9, 253-283, kluwer academic publishers, 2004. ioan dzitac received m. sc. in mathematics (1977) and ph.d in information sc. (2002) from “babes-bolyai” university of clujnapoca. his current research interests include different aspects of artificial intelligence and parallel and distributed computing. he has edited 6 conference proceedings, published 16 books and more than 50 scientific papers in journals and conferences proceedings. he was member of the program committee of more than 30 international conferences. boldur e. bărbat m.sc. in electronic engineering, postgraduate specialising in programming, ph.d. in digital computers (“politehnica” university bucharest). he is with “lucian blaga” university sibiu from 1997, (full professor, faculty of engineering, faculty of sciences) and “politehnica” university timişoara from 2005 (faculty of automation and computers, advisor for doctoral studies in computer science). 25 books (a monograph received the romanian academy it prize, 2002). about 50 papers in english from 2001. current research interests: time in artificial intelligence; nonalgorithmic decision support systems; computer-aided semiosis; selfawareness of bodiless agents; agent-oriented software engineering; logics for agents; emergence in agent-based systems. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 172-182 analytical model for a multiprocessor with private caches and shared memory angel vassilev nikolov abstract: we develop an analytical model of multiprocessor with private caches and shared memory and obtain the following results: the instantaneous state probabilities and the steady-state probabilities of the system. both transient behaviour and equilibrium can be studied and analyzed. we showed that results can be applied to determine the output parameters for both blocking and non-blocking caches. keywords: invalidate cache-coherence protocol, queuing system, discrete transform 1 introduction shared memory multiprocessors are widely used as platforms for technical and commercial computing [2]. performance evaluation is a key technology for design in computer architecture. the continuous growth in complexity of systems is making this task increasingly complex [7]. in general, the problem of developing effective performance evaluation techniques can be stated as finding the best trade-off between accuracy and speed. the most common approach to estimate the performance of a superscalar multiprocessor is through building a software model and simulating the execution of a set of benchmarks. since processors are synchronous machines, however, simulators usually work at cycle-level and this leads to enormous slowdown [9]. it might take hours even days to simulate. for memory structures relatively accurate analytical models were developed [3, 7, 9, 10] through extensive use of various queuing systems. open queue system with poisson arrivals and exponential service times is considered quite good for description of memory hierarchies [7]. our focus is on the impact of the cache-coherence protocols on the overall system performance. the most commonly used technique for this purpose is the mean value analysis (mva) [3, 5, 7, 8, 9]. it allows the total number of the customers to be fixed (closed queue system), and this seems to be more adequate representation of the processes of self-blocking requestors [5]. calculations of output parameters such as residency times, waiting times and utilization are shown in [3, 8, 9]. mva is based on the forced flow that means in equilibrium output rate equals input rate. however, instantaneously, we can have input rate different from output rate, so that the instantaneous probabilities could be different from equilibrium [7]. mva offers no possibility to study transient effects. moreover, the assumption of exponential service times is not realistic, in fact all bus access times and memory access times are constants. it will be seen later in this paper that state probabilities depend on the server’s time density function. we use the technique of markov processes to describe the behaviour of the multiprocessor implementing cache-coherence protocols. 2 definition and analysis of the model a multiprocessor consists of several processors connected together to a shared main memory by a common complete transaction bus. each processor has a private cache. when a processor issues a request to its cache, the cache controller examines the state of the cache and takes suitable action, which may include generating bus transaction to access main memory. coherence is maintained by having all cache controllers "snoop" on the bus and monitor the transaction. snoopy cache-coherence protocols fall in two major categories: invalidate and update [2, 3, 10]. invalidating protocols are studied here but the concepts can be applied with some modifications to updating protocols too. transactions may copyright © 2006-2008 by ccc publications analytical model for a multiprocessor with private caches and shared memory 173 or may not include the memory block and the shared bus. typical transaction that does not include memory block is invalidate cache copy which occurs when a processor requests writing in the cache. all other processors simply change the status bit(s) of their on copies to invalid. if the memory block is uncached or not clean it can be uploaded from the main memory, but in today’s multiprocessors it is rather uploaded from another cache designated as owner (o) (cache-to cache transfer). memory-tocache transfer occurs when the only clean copy is in the main memory. a cache block is written back (wb) in the main memory (bus is used) when a dirty copy is evicted [6]. the bus and the main memory are also used when synchronization procedures are executed [2]. apparently the bus can be considered as the bottleneck of the system. in terms of the queuing theory processors can be viewed as customers (clients) and the bus can be viewed as a server. inter-arrival times are exponentially distributed with parameter λ . this assumption is adequate for most applications [7]. requests are served on first come first served (fcfs) basis. immediately after issuing a request for cache-to-cache transfer or synchronization procedure the customer blocks itself. the service time for blocking request has a density function f1(x). when service is completed the processor (customer) resumes processing with probability p or resumes processing and generates a new request with probability q (p+q=1). details on how to obtain the input parameters are given in [2, 3, 8, 9]. this new request has a different density function f2(x) and corresponds to wb transaction. it does not block the customer but the server is held until completion of wb transaction therefore adding to the queue. the system can be in one of the following states: 1) n: all n customers are doing internal processing; 2) j, 1: j customers are doing internal processing (n-j are blocked respectively) and all requests are of type 1(0≤j≤n-1), 3) j ,2: j customers are doing internal processing , the server is serving request of type 2, and n-j customers are waiting in the queue for service of type 1 (0≤j≤n). the transitions between these states are illustrated in fig. 1. throughout this paper we use the following notations pn (t) probability[all n customers are doing internal processing at time t] p j,i(t,x) probability[j customers are doing internal processing, n-j are in the queue and/or in the server, and the server is busy doing service of type i at time t and the elapsed service time lies between x and x+dx ] p j,i(x) probability[in the equilibrium state j customers are doing internal processing, n-j are in the queue and/or in the server, the server is busy doing service of type i and the elapsed service time lies between x and x+dx ] p j,i(t) probability[j customers are doing internal processing, n-j are in the queue or in the server, the server is busy doing service of type i at time t] pn , p j,i steady-state probabilities. pn = limt→∞ pn (t), pj,i = ∫ ∞ 0 pj,i(x)dx βi = jλ fi(x) cumulative distribution function (c.d.f.) of the service time of type i ; i=1,2 fi(x) probability density function (p.d.f.) of the service time of type i ; i=1,2 δ m,n kronecker delta 1 µi = ∫ ∞ 0 xfi(x)dx i=1,2 hi(x) = fi(x) 1−fi(x) service rate for type i; i=1,2 fi(s), fi(s+βn), fi(βn) laplace transforms (lt) of fi(x) t.u. time unit viewing the nature of the system, we obtain the following set of integro-differential equations [ d dt + βn ] pn = p ∫ t 0 pn−1(t, x)h1(x)dx + ∫ t 0 pn,2(t, x)h2(x)dx (1) 174 angel vassilev nikolov figure 1: state-transition diagram of the model. 1 ≤ j ≤ n [ d dt + ∂ ∂ t + βn−1 + h1(x) ] pn−1,1(t, x) = 0 (2) [ d dt + ∂ ∂ t + βn + h2(x) ] pn,2(t, x) = 0 (3) [ d dt + ∂ ∂ t + β j + hi(x) ] pj,i(t, x) = β j+1pj+1,i(t, x) (4) for i = 1, 1 ≤ j ≤ n −1; i = 2, 1 ≤ j ≤ n [ d dt + ∂ ∂ t + hi(x) ] p0,i(t, x) = β1p1,i(t, x) (5) for i=1,2 having the following boundary and initial conditions pj,1(t, 0) = (1−δ j,0)p ∫ ∞ 0 pj−1,1(t, x)h1(x)dx + ∫ ∞ 0 pj,2(t, x)h2(x)dx + δ j,n−1βn pn (t) (6) for 0 ≤ j ≤ n −1 pj,2(t, 0) = q ∫ ∞ 0 pj−1,1(t, x)h1(x)dx (7) for 1 ≤ j ≤ n pn (0) = 1, p0,2(t, 0) = 0, pj,i(0, 0) = 0 (8) for i = 1, 1 ≤ j ≤ n −1; i = 2, 1 ≤ j ≤ n by using laplace transform and discrete transform [4, 8] the above equations are transformed as follows (s + βn) pn = 1 + p ∫ ∞ 0 pn−1(s, x)h1(x)dx + ∫ ∞ 0 pn,2(s, x)h2(x)dx (9) analytical model for a multiprocessor with private caches and shared memory 175 [ s + d dx + β j + hi(x) ] u j,i(s, x) = 0 (10) for i = 1, 1 ≤ j ≤ n −1; i = 2, 1 ≤ j ≤ n [ s + d dx + +hi(x) ] p0,i(s, x) = β1p1,i(s, x) (11) for i=1,2 where u j,1(s, x) = ∑n−1n= j ( n j ) pn,1(s, x), pj,1(s, x) = ∑n−1n= j (−1)n− j ( n j ) un,1(s, x) for 1 ≤ j ≤ n − 1, and u j,2(s, x) = ∑nn= j ( n j ) pn,2(s, x), pj,2(s, x) = ∑nn= j(−1)n− j ( n j ) un,2(s, x) for 1 ≤ j ≤ n. let v j,i(s, x) = u j,i(s,x) 1−fi(x) and p ′ 0,1(s, x) = p0,1(s,x) 1−fi(x) . then from (10 and 11) we have after some transformations [ s + d dx + βi ] v j,i(s, x) = 0 for i = 1, 1 ≤ j ≤ n −1; i = 2, 1 ≤ j ≤ n and [ s + d dx ] p ′ 0,i(s, x) = β1p1,i(s, x) for i = 1, 2. hence the solutions of (9-11) are u j,i(s, x) = [1−fi(x)]u j,i(s, 0)e−(s+βi)x (12) pn (s) = 1 + p f1(s + βn−1)un−1,1(s, 0) + f2(s + βn )un,2(s, 0) s + βn (13) p0,1(s, x) = [1−f1(x)]β1e−sx [ p0,1(s, 0) + n−1 ∑ n=1 (−1)n−1n 1−e −βnx βn un,1(s, 0) ] (14) p0,2(s, x) = [1−f2(x)]β1e−sx [ n ∑ n=1 (−1)n−1n 1−e −βnx βn un,2(s, 0) ] . (15) by integrating (12, 14, and 15) we obtain the lt of the instantaneous probabilities pj,1(s) = n−1 ∑ n= j (−1)n− j ( n j ) [ 1− f1(s + βn) s + βn ] un,1(s, 0) (16) for 1 ≤ j ≤ n −1 pj,2(s) = n ∑ n= j (−1)n− j ( n j ) [ 1− f2(s + βn) s + βn ] un,2(s, 0) (17) for 1 ≤ j ≤ n p0,1(s) = p0,1(s, 0) [ 1− f1(s) s ] + β1 n−1 ∑ n=1 (−1)n−1n [ 1− f1(s) s − 1− f1(s + βn) s + βn ] un,1(s, 0) βn (18) 176 angel vassilev nikolov p0,2(s) = β1 n ∑ n=1 (−1)n−1n [ 1− f2(s) s − 1− f2(s + βn) s + βn ] un,2(s, 0) βn . (19) taking lt of (6-7) and using (8 and 12-15) we get after some transformations the following system of linear equations n−1 ∑ n= j (−1)n− j ( n j ) un,1(s, 0) = p n−1 ∑ n= j (−1)n− j+1 ( n j −1 ) f1(s + βn)un,1(s, 0) (20) + n ∑ n= j (−1)n− j ( n j ) f2(s + βn)un,2(s, 0) + δ j,n−1βn pn for 2 ≤ j ≤ n −1 n ∑ n= j (−1)n− j ( n j ) un,2(s, 0) = q n−1 ∑ n= j−1 (−1)n− j+1 ( n j −1 ) f1(s + βn)un,1(s, 0) (21) for 2 ≤ j ≤ n n−1 ∑ n=1 (−1)n−1 ( n j ) un,1(s, 0) = pp0,1(s, 0) f1(s) + pβ1 [ n−1 ∑ n=1 (−1)n−1n f1(s)− f1(s + βn) βn un,1(s, 0) ] (22) n ∑ n=1 (−1)n−1 ( n j ) un,2(s, 0) = qp0,1(s, 0) f1(s) + qβ1 [ n−1 ∑ n=1 (−1)n−1n f1(s)− f1(s + βn) βn un,1(s, 0) ] (23) coefficients u j,i(s,0) can now be determined from the above equations. we can apply the final-value theorem to (16-19) to obtain the steady-state probabilities but it will require use of the l’hopital rule and seems difficult and impractical [11]. instead we set the following differential equations βnpn = p ∫ ∞ 0 pn−1(x)h1(x)dx + ∫ ∞ 0 pn,2(x)h2(x)dx (24) [ d dx + βn−1 + h1(x) ] pn−1,1(x) = 0 (25) [ d dx + βn + h2(x) ] pn,2(x) = 0 (26) [ d dx + β j + hi(x) ] pj,i(x) = β j+1pj+1,i(x) (27) for i = 1, 1 ≤ j ≤ n −1; i = 2, 1 ≤ j ≤ n [ d dx + hi(x) ] p0,i(x) = β1p1,i(x) (28) for i=1,2. equations (24-28) are to be solved under the following boundary conditions and normalizing condition pj,1(0) = (1−δ j,0)p ∫ ∞ 0 pj−1,1(x)h1(x)dx + ∫ ∞ 0 pj,2(x)h2(x)dx + δ j,n−1βn pn (29) analytical model for a multiprocessor with private caches and shared memory 177 for 0 ≤ j ≤ n −1 pj,2(0) = q ∫ ∞ 0 pj−1,1(x)h1(x)dx (30) for 1 ≤ j ≤ n −1 p0,2(0) = 0 (31) pn + n−1 ∑ j=0 pj,1 + n ∑ j=0 pj,2 = 1. (32) the solutions of (2.29-2.32) are pn = 1 + p f1(βn−1)un−1,1(0) + f2(βn )un,2(0) βn (33) pj,1 = n−1 ∑ n= j (−1)n− j ( n j ) [ 1− f1(βn) βn ] un,1(0) (34) for 1 ≤ j ≤ n −1 pj,2 = n ∑ n= j (−1)n− j ( n j ) [ 1− f2(βn) βn ] un,2(0) (35) for 1 ≤ j ≤ n −1 p0,1 = p0,1(0) µ1 + n−1 ∑ n= j (−1)n− jn [ 1 µ1 − 1− f1(βn) βn ] un,1(0) (36) p0,2 = n ∑ n= j (−1)n− jn [ 1 µ2 − 1− f2(βn) βn ] un,2(0) (37) for u j,i(0) and p0,1(0) we have n−1 ∑ n= j (−1)n− j ( n j ) un,1(0) = p n−1 ∑ n= j (−1)n− j+1 ( n j −1 ) f1(βn)un,1(0) + δ j,n−1βn pn (38) for 2 ≤ j ≤ n −1 n−1 ∑ n= j (−1)n− j ( n j ) un,2(0) = q n−1 ∑ n= j (−1)n− j+1 ( n j −1 ) f2(βn)un,2(0) (39) for 2 ≤ j ≤ n −1 p0,1(0) = β1 n ∑ n=1 (−1)n−1n [ 1− f2(βn βn ] un,2(0) (40) n−1 ∑ n=1 (−1)n−1nun,1(0) = pp0,1(0) + pβ1 n ∑ n=1 (−1)n−1n [ 1− f1(βn βn ] un,1(0) + n ∑ n=1 n f2βnun,2(0) (41) n ∑ n=1 (−1)n−1nun,2(0) = qp0,1(0) + qβ1 n ∑ n=1 (−1)n−1n [ 1− f1(βn βn ] un,1(0) (42) the coefficients u j,i(0) can be determined from (32) and (38-42). 178 angel vassilev nikolov 3 examples in order to obtain the transient state probabilities first we have to determine pn (s) and pj,i(s) from (1619) and (20-24) and then to apply the inverse laplace transform to them. we used the packages of maple 8 on a standard pc platform under windows xp for these computations [12]. results were produced and printed in less than a second. for n=4 the instantaneous probabilities are listed in appendix a. various performance characteristics can be computed using the steady-state probabilities. for example, the average number of blocked customers (anbc) in the case of blocking caches will be given by anbc = 2 ∑ i=1 n ∑ j=0 (n − j)pj,i. (43) in the case of non-blocking caches anbc will be anbc = n ∑ j=0 (n − j −1 + k)pj,1 + n−1 ∑ j=0 (n − j)pj,2. (44) where k is the ratio of average memory stall time [2] . k depends strongly on the application. (1-k) actually refers to the fraction time the processor is consuming data while cache-to-cache or memory-tocache transfer is in progress. in appendix b we list the anbc for two popular service time distributions: exponential and erlangian [1], for blocking and fully non-blocking caches (k=0). the time to solve (33-42) and calculate anbc was meaninglessly short. 4 concluding remarks this work presented a model for a shared bus, shared memory multiprocessor with private caches and captures the whole spectrum of invalidate type cache coherence protocols. although we started with fairly sophisticated set of integro-differential equations, the output of the model is a set of few linear equations from which the state probabilities can be determined. the approach eliminates the main drawbacks of the most commonly used mva analysis: inability to deal with transients and constraint on the service time distribution. the model gives insights into the transient behaviour of the system. moreover, the assumption of exponentially distributed service times can be dropped; any continuous distribution can be used. the ease of obtaining performance measures in a meaningless time makes very feasible the incorporation of the model in a multiprocessor design tool. bibliography [1] s. k. bose, introduction to queuing systems, kluwer/plenum publishers, 2001 [2] j. l. hennessy, d. a. patterson; computer architecture: a quantitative approach, pearson publishers, 2003 [3] m. c. chiang, memory system design for bus based multiprocessor, phd thesis, university of wisconsin, 1991 [4] t. itoi, t. nishida, m. kodama and e. ohi, n-unit parallel redundant system with correlated failures and single repair facility, microelectronics and reliability, vol. 17, pp. 279-285, 1978 analytical model for a multiprocessor with private caches and shared memory 179 [5] e. lazowska, j. zahorjan, g. graham, and k. sevcik, quantitative system performance, computer system analysis using queuing network models, prentice-hall, englewood clis, nj, may 1984 [6] a. louri, a.k. kodi, an optical interconnection network and a modifying snooping protocol for the design of large-scale symmetric multiprocessors (smps), ieee transactions on parallel and distributing systems, vol. 15, no. 12, dec. 2004, pp. 1093-11047. [7] r. e. matick, comparison of analytic performance models using closed mean-value analysis versus open-queuing theory for estimating cycles per instruction of memory hierarchies, ibm journal of research and development, jul 2003 [8] d. j. sorin et. al., a customized mva model for ilp multiprocessors, technical report no.1369, university of wisconsin-madison, 1998 [9] d. j. sorin et. al., evaluation of shared-memory parallel system with ilp processors , proc. 25th int’l symp. on computer architecture, june 1998, pp. 180-191 [10] j. sustersic, a. hurson, coherence protocol for bus-based and scalable multiprocessors, internet and wireless distributed computing environments: a survey , advances in computers, vol.59, 2003, pp. 211-278 [11] schiff, joel l., the laplace transform, springer, 1999 [12] waterloo maple inc., introduction to maple 8, 2002 appendix a for n=4, λ =0.001[1/t.u.], f1(x)=0.1exp(-0.1x), and f2(x)=0.01exp(-0.01x) the instantaneous probabilities are p4(t) =0 .9211361286+0.8058476879e-2*exp(-0.1248619627*t) +0.8535072295e-2*exp(-0.1089825679*t)+0.9049529656e-2*exp(-0.9494144284e-1*t) +0.9696074769e-2*exp(-0.8072343638e-1*t)+0.1774027054e-3 exp(-0.1510201407e-1*t) +0.1728181365e-2*exp(-0.1398702636e-1*t) +0.5618533851e-2*exp(-0.1256085210e-1*t) +0.1211910345e-1 exp(-0.1067946234e-1*t)+0.2388149701e-1*exp(-0.8161235321e-2*t), p31(t) =0.3792913471e-1-0.1093143496e-1*exp(-0.1248619627*t) -0.1007354818e-1 *exp(-0.1089825679*t)-0.9271731350e-2*exp(-0.9494144284e-1*t) -0.8405607569e-2*exp(-0.8072343638e-1*t)+0.2658572506e-2 *exp(-0.1510201407e-1*t)-0.2212663963e-5*exp(-0.1398702636e-1*t) -0.3015750621e-3*exp(-0.1256085210e-1*t)-0.6739486112e-3 *exp(-0.1067946234e-1*t)-0.9276492013e-3*exp(-0.8161235321e-2*t), p21(t) = 0.1420742616e-2+0.2324288902e-2*exp(-0.1248619627*t) +0.2986557798e-3* exp(-0.1089825679*t)-0.1243034332e-2 *exp(-0.9494144284e-1*t)-0.2544948528e-2*exp(-0.8072343638e-1*t) -0.5329230583e-2*exp(-0.1510201407e-1*t)+0.6737760872e-2 *exp(-0.1398702636e-1*t)+0.4442015626e-3*exp(-0.1256085210e-1*t) -0.5688666731e-3*exp(-0.1067946234e-1*t)-0.1539483112e-2 *exp(-0.8161235321e-2*t), p11(t) =0 .7684028624e-4-0.2290986748e-3*exp(-0.1248619627*t) +0.3160128043e-3*exp(-0.1089825679*t)+0.2148505581e-3 *exp(-0.9494144284e-1*t)-0.3252577041e-3 180 angel vassilev nikolov *exp(-0.8072343638e-1*t)+0.3775725072e-2*exp(-0.1510201407e-1*t) -0.8400171763e-2*exp(-0.1398702636e-1*t)+0.4974831098e-2 *exp(-0.1256085210e-1*t)+0.5143708805e-3*exp(-0.1067946234e-1*t) -0.9181578239e-3*exp(-0.8161235321e-2*t), p01(t) = 0.9242283829e-5*exp(0-.1248619627*t)-0.3513395647e-4 *exp(-0.1089825679*t)+0.4257071327e-4*exp(-0.9494144284e-1*t) -0.1688587212e-4*exp(-0.8072343638e-1*t)-0.9200675435e-3 *exp(-0.1510201407e-1*t)+0.2810623081e-2*exp(-0.1398702636e-1*t) -0.3183584754e-2*exp(-0.1256085210e-1*t)+0.1611954912e-2 *exp(-0.1067946234e-1*t)-0.3239077071e-3*exp(-0.8161235321e-2*t) +0.5218152790e-5, p42(t) = 0.2709223908e-1+0.9859983387e-3*exp(-.1248619627*t) +0.1060558367e-2*exp(-0.1089825679*t)+0.1145474465e-2 *exp(-0.9494144284e-1*t)+0.1259769099e-2*exp(-0.8072343638e-1*t) -0.2412466943e-1*exp(-0.1510201407e-1*t)-0.1705507775e-2 *exp(-0.1398702636e-1*t)-0.2095511260e-2*exp(-0.1256085210e-1*t) -0.2029637013e-2*exp(-0.1067946234e-1*t)-0.1588776483e-2 *exp(-0.8161235321e-2*t), p32(t) = -0.2421204825e-3*exp(-0.1248619627*t)-0.7509940526e-4 *exp(-0.1089825679*t)+0.9576676158e-4 *exp(-0.9494144284e-1*t)+0.3013803504e-3 *exp(-0.8072343638e-1*t)+0.7126069503e-1*exp(-0.1510201407e-1*t) -0.6135152996e-1*exp(-0.1398702636e-1*t)-0.8971987351e-2 *exp(-0.1256085210e-1*t)-0.5950006752e-2*exp(-0.1067946234e-1*t) -0.4494935895e-2*exp(-0.8161235321e-2*t)+0.9428952497e-2, p22(t) =0 .2421271696e-2+0.2626333487e-4*exp(-0.1248619627*t) -.3154175021e-4*exp(-0.1089825679*t)-0.2945613244e-4 *exp(-0.9494144284e-1*t)+0.3412946115e-4*exp(-0.8072343638e-1*t) -0.8108903801e-1*exp(-0.1510201407e-1*t)+0.1349032466 *exp(-0.1398702636e-1*t)-0.4071010637e-1*exp(-0.1256085210e-1*t) -0.9622074403e-2*exp(-0.1067946234e-1*t)-0.5904604182e-2 *exp(-0.8161235321e-2*t), p12(t) = -0.1765308077e-5*exp(-0.1248619627*t)+0.4800731626e-5 *exp(-0.1089825679*t)-0.4448905932e-5*exp(-0.9494144284e-1*t) +0.1599282603e-5*exp(-0.8072343638e-1*t)+0.4177917201e-1 *exp(-0.1510201407e-1*t)-0.9973555226e-1*exp(-0.139870263e-1*t +0.7256040480e-1*exp(-0.1256085210e-1*t)-0.9747995399e-2 *exp(-0.1067946234e-1*t)-0.5300997812e-2*exp(-0.8161235321e-2*t) +0.4449749927e-3, p02(t) = -0.4618227199e-6*exp(-0.1248619627*t)-0.2203030325e-6 *exp(-0.1089825679*t)+0.4881890483e-7*exp(0-.9494144284e-1*t) -0.1699392257e-7*exp(-0.8072343638e-1*t)-0.8188760719e-2 *exp(-0.1510201407e-1*t)+0.2501502200e-1*exp(-0.1398702636e-1*t) -0.2833447693e-1*exp(-0.1256085210e-1*t)+0.1434663085e-1 *exp(-0.1067946234e-1*t)-0.2882912592e-2*exp(-0.8161235321e-2*t) +0.4449749927e-4. in the above expressions e-i means 10-i for i=1,7. analytical model for a multiprocessor with private caches and shared memory 181 appendix b table 1: n=8, f1(x)=0.1exp(-0.1x), f2(x)=0.01exp(-0.01x) λ [1/t.u.] p anbc for blocking anbc for fully caches nonblocking caches 0.001 0.9 0.154099881194466 0.075640880006411 0.002 0.9 0.441552853804251 0.290383910880334 0.003 0.9 0.822750601431095 0.607433119474025 0.004 0.9 1.253944990222998 0.984102789831906 0.001 0.8 0.230012889507952 0.152313018403034 0.002 0.8 0.729883782777377 0.584481458432927 0.003 0.8 1.382033782478873 1.183494795953230 0.004 0.8 2.063720956300253 1.826269794552253 table 2: n=8, f1(x)=0.13x2ex p(−0.1x)/2!, f2(x)=0.013x2ex p(−0.01x)/2! λ [1/t.u] p anbc for blocking anbc for fully caches nonblocking caches 0.001 0.9 0.384839057891723 0.211437492029451 0.002 0.9 1.313451009452606 0.582993712839022 0.003 0.9 2.390481400874492 1.782339618354729 0.004 0.9 3.691834116720534 2.882438452093385 0.001 0.8 0.614956120345239 0.400820549913285 0.002 0.8 2.611487230549326 1.722034656332087 0.003 0.8 4.062557145097248 3.429652938504840 0.004 0.8 5.899361833023557 5.394204692051840 angel vassilev nikolov national university of lesotho department of mathematics and computer science roma 180 lesotho e-mail: av.nikolov@nul.ls received: december 17, 2007 182 angel vassilev nikolov angel vassilev nikolov received the beng degree in electronic and computer engineering from the technical university of budapest, hungary in 1974 and the phd degree in computer science from the bulgarian academy of sciences in 1982 where he worked as a research associate. in 1989 he was promoted to associate research professor in bulgaria. dr nikolov also served as a lecturer of computer science at the national university of science and technology, bulawayo, zimbabwe and at the grande prairie regional college, alberta, canada and as an associate professor at sharjah college, united arab emirates. his research interests include computer architecture, performance evaluation of multiprocessors, and reliability modeling. he has published numerous journal and conference articles and holds four patents on the above topics. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 744-754 complex computer simulations, numerical artifacts, and numerical phenomena d.-a. iordache, p. sterian, f. pop, a.r. sterian dan-alexandru iordache, paul sterian, andreea rodica sterian physics department, university politehnica of bucharest, 313 splaiul independentei, bucharest 060042, romania e-mail: {daniordache2003,paul.sterian,andreea_rodica_sterian}@yahoo.com florin pop lecturer, computer science department, university politehnica of bucharest, 313 splaiul independentei, bucharest 060042, romania e-mail: florin.pop@cs.pub.ro abstract: the study of some typical complex computer simulations, presenting one or more complexity features, as the: a) symmetry breaking, b) nonlinear properties, c) dissipative processes, d) high-logical depth, e) selforganizing processes, etc allows to point out some several numerical artifacts, namely the: (i) distortions, (ii) scattering, (iii) pseudo-convergence, (iv) instability, (v) mis-leading (false) symmetry-breaking simulations and others. the detailed analysis of these artifacts allowed clarifying the numerical mechanisms of some such artifacts, which can be named in following numerical phenomena, because their basic features can be exactly predicted. keywords: computer simulations, numerical artifacts, numerical phenomena, self-organizing processes. 1 introduction we live in a computerized world, our civilization being a "civilization of computers". taking into account that computers control the work of all-present complex installations and devices, the appearance (due to some numerical phenomena) of some important distortions of the simulated processes lead usually to major failures of the technical installations. particularly, the events referring to the erroneous computer (numerical) simulation and design of the flight of the patriot missile which failed (with disastrous results) during the gulf war in 1991 to stop a scud missile [1] and the self-destruction of the european space agency’s ariane 5 rocket, at 37 seconds after its launch [2], were both assigned to computer errors [3] and to their associated numerical artifacts/artefacts/phenomena. given being the computer simulations are considerably cheaper than the experimental studies and they allow the prediction of the physical systems behavior even in inaccessible conditions, the computer simulations are widely used in technical studies. because the modern (optimized) technical systems are complex [4], the computer simulations are also complex, and for this reason they generate specific numerical artifacts. in fact, there is a huge number of publications reporting such phenomena (usually related to some complex numerical simulations), as it can be easily found consulting some search systems, e.g. the google system. as it results from table 1, even eliminating by the use of quotation marks (") the "parasitic" published works entitled as numerical simulations of... boiling phenomena, etc, there remain very large numbers of published works in these fields. this work deals with the study of possibilities to discover the mechanisms of the artifacts intervening in some complex simulations and to predict quantitatively the basic parameters of copyright c⃝ 2006-2010 by ccc publications complex computer simulations, numerical artifacts, and numerical phenomena 745 topics no quotation marks with quotation marks, e.g. "numerical artifacts" numerical artifacts 1,180,000 9,370 numerical artefacts 126,000 2,440 numerical phenomena 4,160,000 1,340 complex simulations 3,000 table 1: numbers of published papers found by the google search system (beginning of 2009) the computer generating errors, transforming so the observed numerical artifacts/artefacts in the so-called numerical phenomena. we have to underline from beginning that the discovery of these mechanisms belong to the field of numbers theory and that many problems in the field of numbers theory are extremely difficult. e.g., the statement of the (pierre de) fermat’s last (greatest) theorem was published (after his death, by his eldest son clément samuel fermat) in 1670 [4], but its solution was found only in 1995 [5] by the professor andrew wiles [6]. we will focus mainly to the study of the main features of the classical [7], [8] and of the newly found [9], [10] numerical phenomena associated to the finite differences (fd) simulations [11] of the pulses propagation through media with sharp interfaces and attenuative character, as well as of other numerical methods (as the random walk method, the gradient one, etc), applied to the study of different physical processes, as diffusion [12], [13], solitary wave propagation, applications to the evaluation of the parameters of some physical systems, etc. 2 symmetry breaking in some computing programs 2.1 symmetry breaking of the wave equation in ideal media the finite differences (fd) discretization of the wave equation in ideal media: ∂2w ∂t̃2 = v2φ ∂2w ∂x2 → wt+1 + wt−1 − 2w τ2 = v2φ wi+1 + wi−1 − 2w ϵ2 (1) which is symmetrical relative to the space steps i − 1, i, i + 1 and the time steps t − 1, t, t + 1, if the fd velocity and the wave propagation one are equal: vfd = ϵ τ = vφ (2) defining the courant’s number [7] by means of the relation: c = vφ vfd (3) one finds easily that if: (i) c > 1, there will intervene instabilities, because the fd schema does not use all information received at the observation point (vφ > vfd), (ii) c < 1, there will intervene distortions, because for vφ < vfd, the fd schema uses more information than it receives, and this additional information acts as a jamming, (iii) c = 1, we have an ideal fd schema (stable and convergent), because this schema has all necessary physical information and nothing more! one finds that the symmetry breaking for values different than 1 of the courant number leads to the "classical" numerical phenomena. 746 d.-a. iordache, p. sterian, f. pop, a.r. sterian 2.2 symmetry breaking of the smoothing model of a sharp interface consider a sharp interface between 2 homogeneous elastic media. if the method of finite differences (fd) is used, then in order to avoid the use of dirac function a certain smoothing of the sharp interface is necessary, spreading it over 2 or 3 fd nodes, whose indices i are denoted as i − 1, i and i + 1 (see figure 1). figure 1: smoothing models of the sharp 1-d interfaces (see also [14]-a). then the differential equation ρ(x)∂ 2w ∂t̃2 = ∂ ∂x [ s(x)∂w ∂x ] of the elastic pulses propagation through an in-homogeneous medium becomes: ρ̃i wt+1 + wt−1 − 2w τ2 = ⟨ ∂s ∂x ⟩ i wi+1 + wi−1 2ϵ + s̃i wi+1 + wi−1 − 2w ϵ2 . (4) where ρ̃i = ⟨ρ⟩i, s̃i = ⟨s⟩i and ⟨ ∂s ∂x ⟩ i are the chosen average values around the fd node i. the chosen expressions of these average values (see table 2) can succeed or not to remake the (apparent) symmetry of the propagation medium in the frame of the fd simulation; e.g., one finds from the examination of table 2, that all required expressions are symmetrical around the sites i − 1, i and i + 1 for model 1, and around the sites i and i + 1 for model 2a, while this general symmetry is not kept for all average expressions of models 2b, 3a and 3b. that is why while the smoothing models 1 and 2a ensure always stable and convergent numerical simulations for the smoothing models 2b 3a and 3b, respectively, there appear the basic types of usual numerical artifacts [7, 8, 14]: a) the instability, b) the pseudo-convergence. the plots of these numerical artifacts for the above-indicated 5 types of studied finite differences (fd) smoothing schemes (models), intended to the simulation of certain elastic pulses propagation through complex materials are presented by figures 2 and 3 below (see also [15]). i ρ̃i s̃i ⟨ ∂s ∂x ⟩ i i − 1 i i + 1 i − 1 i i + 1 ⟨ ∂s ∂x ⟩ i−1 ⟨ ∂s ∂x ⟩ i ⟨ ∂s ∂x ⟩ i+1 1 ρ ρ ′+ρ 2 ρ′ s s+s ′ 2 s′ 0 s ′−s ϵ 0 2a ρ 3ρ ′+ρ 4 3ρ′+ρ 4 s 3s+s ′ 4 s+3s′ 4 0 s ′−s 2ϵ s′−s 2ϵ 2b ρ ρ ρ′ s s s′ 0 s ′−s 2ϵ s′−s 2ϵ 3a ρ ρ ′+ρ 2 ρ′ s s+s ′ 2 s′ s ′−s 4ϵ s′−s ϵ s′−s 4ϵ 3b ρ ρ ′+ρ 2 ρ′ 7s+s ′ 8 s+s′ 2 s+7s′ 8 s′−s 4ϵ s′−s ϵ s′−s 4ϵ table 2: expressions of the average values of the main elastic parameters for the basic smoothing models of one-dimensional (1-d) interfaces (see also [14]-a) complex computer simulations, numerical artifacts, and numerical phenomena 747 figure 2: plots of numerical simulations corresponding to different fd schemes (those of models 3a and 3b are pseudo-convergent, and unstable, respectively). figure 3: convergent numerical simulations corresponding to models 1 and 2a, and pseudoconvergent ones for models 2b, 3a. one finds that while the instabilities can be easily detected and eliminated, the pseudoconvergence is considerably more "dangerous", because: a) the pseudo-convergent simulations have a right shape, while: b) the corresponding wrong displacement values can be considerably more difficult observed, hence the pseudo-convergent simulations could be easily misleading. 3 nonlinear properties of the propagation medium it is well-known [7] that the computer rounding errors are amplified considerably in the frame of some non-linear equations, as those corresponding to certain solitary waves, leading due to some instability numerical artifacts (see figure 4). particularly, the korteweg-de vries equation: ∂u ∂t = −v00u ′ − nuu′ − d1u ′′′ (5) can be discretized as: f(i) = p(i)−γ·[a(i + 1) − a(i − 1)]+α·a(i)·[a(i − 1) − a(i + 1)]+β·[a(i − 2) − a(i + 2)] (6) there were studied the numerical artifacts corresponding to the 2 main types of nonlinear solitary waves (which can propagate keeping their shapes): the bell-shaped (or breathers) and 748 d.-a. iordache, p. sterian, f. pop, a.r. sterian figure 4: fd simulation of a korteweg-de vries (kdv) solitary wave breather propagation the kink-shaped waves [16]. figures 4 and 5 present the basic numerical artifacts intervening in the fd simulations of the breathers propagation (see also [17]). while the artifacts intervening in the simulations of the kdv breathers propagation reduce to a monotonic increase of distortions up to the appearance of the instability (figure 4), the use of the discrete version (dnls) of the cubic nonlinear schrödinger (nls) equation to describe the wave-guide arrays with saturable nonlinearity leads in certain conditions to the artefact corresponding to the merging of two breathers with symmetry breaking (see figure 5 and [17]). figure 5: the symmetry breaking artefact intervening in the merging (bound state formation) of 2 symmetric snls breathers 4 dissipative media because the modulus of the first solution of the attenuation-dispersion relation [15] is larger than 1: |g1| = eϵe > 1, the fd schemes used for the simulation of the acoustic pulses propagation in dissipative media are always unstable. there were pointed out also: (i) the instability of the attenuated wave simulation, even for absolutely exact initial conditions, due to the generation of the amplified wave (mathematically possible, but without a physical meaning) by the stochastic local accumulation of some local "rounding" inaccuracies of the exact values corresponding to such waves, acting as a self-organising process in the computing program run, as well as: (ii) the extremely strong acceleration of the amplified wave generation when the complex wave-functions are used (stability and convergence radii of the magnitude order of 1 db or even smaller). the introduction of some: (i) corrective measures (the use of some analytical expressions of some partial derivatives, particularly), (ii) properly chosen effective parameters, allows the weakening of these unpleasant numerical phenomena, ensuring stability and convergence radii of the magnitude order of 100 db [13], which represent sufficiently high values for accurate descriptions, by complex computer simulations, numerical artifacts, and numerical phenomena 749 means of the finite difference method, of the cases of technical interest. 5 high logical depth as it results from equation (1), the fd scheme of waves propagation in ideal media is not too intricate, even if the courant’s number is less than 1: c < 1. the repeated use [by a large number (n > 105) of successive iterations, e.g. for simulations of the ultrasonic non-destructive examinations of some industrial components] of this equation, leads however to the high logical depth complexity feature of the used computer program, and consequently to several numerical artifacts indicated in the frame of figure 6. the expressions of the main limits are: xe = −ct−2 3 √ t, xp = 1+c(t−1), xp = n+c(t−1), xn = ct + n + 2 3 √ t, while for c ≈ 0.5 and n ≈ 71, the relative (to the incoming pulse one) amplitudes of the echo pulses have the magnitude orders: 0.1 for the rectangular pulse, 0.01 for the sine pulses, and 0.001 for the gaussian pulses. figure 6: structure of fd simulations of the propagation of pulses of different shapes 6 from the numerical artifacts to the numerical phenomena 6.1 difficulties and main methods used to study the numerical artifact mechanisms because many problems in the field of numbers theory are extremely difficult, the aim of this study is to point out the main features of the mechanisms leading to some numerical artifacts intervening in the computer simulations of pulses propagation. the accomplished analysis pointed out that the main methods to study these numerical artifact mechanisms are the methods of the: a) fd transfer coefficients [18], b) fourier’s representation of the exact solutions of the discretized wave equation [19]. 6.2 the method of transfer coefficients in order to explain the results corresponding to the linear fd schemes, a partition of the incoming pulse in n components of amplitudes (in the order of their incoming on the studied 750 d.-a. iordache, p. sterian, f. pop, a.r. sterian material) s1, s2, . . . , sn is considered. the amplitudes of the same components in the previous time step are denoted by s′1, s ′ 2, . . . , s ′ n. the transfer coefficients kti are defined by means of the expressions (4) of the displacement wit corresponding to the space site i at the moment (time step) t: wit = n∑ j=1 kt,n+2+t−i−j · sj − n∑ j=1 kt,n+t−i−j · s′j (7) a simplified definition of the transfer coefficients corresponds to the fd simulations with equal values of the real phase speed vφ and of the fd one: vfd = ϵτ (i.e. for the value 1 of the courant number [7]: c = vφ vfd ), because then the pulse partition components at successive time steps coincide: s′i = si (for any i = 1, 2, . . . , n). in the particular case of a sharp 1-d interface located in the site i, the transfer coefficients describing the transmitted wave are defined as [14]-a: wi+1,t = t−2∑ j=1 kjst−j−1 (8) 6.3 fourier’s representation method of the exact solutions of the discretized wave equation the exact discrete solution of the wave equation is written by means of its fourier expansion, as: wj,t = ∞∑ k=−∞ ck · [g(k)] t · eik·jϵ (9) where g(k) is named the amplification factor. introducing this expression in the wave equation, one obtains the "attenuation-dispersion" relation: g − 2 + 1 g = f(k · ϵ, vfd, w) (10) where f(k · ϵ, vfd, w) is a specific function of the considered wave. according to von neumann’s theorem ( [19]-a, p. 42), the considered fd scheme will be stable if both solutions of the algebraic equation (10) fulfil the requirement: |g1,2| ≤ 1, else this scheme will be unstable. 6.4 applications to the study of mechanisms of some numerical artifacts the method of transfer coefficients. appplying this method to the problem of sharp interfaces (see section 2a and figures 1 3), we will find that the above indicated numerical artifacts belong to the class of numerical phenomena, because they can be identified and described starting from the values of the roots of the characteristic equation [15]: ξ2 − (t_t1 + 2t_t2 + t0t1)ξ − t_t2 = 0. (11) where: ti = ai−1bi − 1, while ai and bi are the coefficients of the fd wave equation (4): wi,t+1 = aiwi+1,t + biwi−1,t − wi,t−1. one finds so that as |ξ| > 1 or |ξ| < 1, the used fd scheme is unstable, or it is stable. the method of fourier’s representation. using the above presented method of the fourier’s representation of the exact solutions to the problem of korteweg-de vries solitons complex computer simulations, numerical artifacts, and numerical phenomena 751 (section 3), one obtains the following expression [19]-b,c of the upper threshold (for the numerical scheme stability) of the time step: τmax = ϵ αa + 4β ϵ2 (12) our study [15] pointed out both the validity of the vliegenthart condition (13), as well as the monotonic improvement of the fd simulations accuracy as the representative point of the fd steps ϵ, τ tends to the vliegenthart’s boarder of the stability and instability regions. one finds so that the stability field of the fd simulations of kdv solitons propagation is rather broad. because the size and borders of the stability domain depend on the: a) strongly (e.g., in the case of some exponential dependencies) or weakly (as in the above studied case) character of the nonlinear dependence, b) number of interacting system components. 7 stability and convergence radii of different numerical schemes the accomplished numerical studies [20], [21] have pointed out that, for given values of the wave frequency (or wavelength) and of the tangent of mechanical losses, beginning from a certain number of space (or time) steps xlim, one finds usually the appearance of large oscillations of the simulated displacements, which lead quickly to instability. because the instability is determined by the value of the factor eex and: e = k tan δ 2 , while the wave intensity is proportional to the square of displacement: i ∝ w2, one finds that the measure (in deci-bells) of the intensity level corresponding to the stability field is: ⟨li,stab⟩db = 2 ⟨lw,stab⟩db = 20exlim = 20kxlim tan δ 2 = 40π xlim λ tan δ 2 . (13) of course, the decrease of the wave intensity corresponding to the stability field (limit) is: ilim i0 = e−2exlim = e ⟨li,stab⟩ 10 (14) table 3 synthesizes the obtained numerical results. type of the wave equation no. of numerical ⟨li,stab⟩db tlim interactions (stability, life, steps) complex stiffness equation 12 0.0321 102 complex stress relaxation time 10 4 12732 complex wave-vector equation 8 20 63622 space evolution equation 6 40.476 128839 real wave, equation 5 80.130 255062 table 3: stability radii and mean life of different numerical simulations [15]. 8 analysis of the obtained results for different studied numerical schemes and physical processes the obtained results (tables 1 and 2) concerning the stability and convergence radii of different numerical schemes intended to the computer simulation of certain physical processes 752 d.-a. iordache, p. sterian, f. pop, a.r. sterian (acoustic pulse propagation, diffusion with drift, absorption, etc) indicate the "accessible" logical depths [22] of the specific studied physical problems, for each of the used numerical schemes. these results present also a considerable importance for the choice and optimization of the numerical schemes [23]. certain numerical schemes, e.g. that corresponding to the complex stiffness s̄ symmetric wave equation of the propagation in dissipative media: ρ ∂2w̄ ∂t′2 = s̄ ∂2w̄ ∂x2 (15) allow multiple solutions; using the fd descriptions: t′ = tτ and: x = iϵ (in terms of the time τ and space ϵ steps) of the real time t and space coordinate x, these solutions can be written as: w̄i,t = a · e±iωtτ · e±(e+ik)iϵ (16) even if the initial conditions launch only the "direct" wave: w̄dir.i,t = a · e −eiϵ · ei(ωtτ+kiϵ) (17) some random accumulations of the rounding errors intervening in the evaluation of the partial derivatives produce a local ("spontaneous") generation of the inverse wave: w̄inv.i,t = a ′ · eeiϵ · ei(ωtτ+kiϵ) (18) leading to the sudden apparition of instabilities. one finds so that the numerical simulations of the waves propagation through dissipative media lead to a typical problem of self-organizing systems, with a spontaneous symmetry breaking. this symmetry breaking corresponds to the "spontaneous" local generation of the inverse wave, launched by the random accumulation of the "garbage" rounding errors and followed by the transition between the attenuated wave and the apparently amplified wave, corresponding to the "inverse" wave. the accomplished study (see tables 1 and 2) points out that the "speed" of this self-organization process crucially depends on the number and intensity of the numerical "interactions" between the components (the values wi,t of the displacement in different sites i, t of the fd grid) of the simulation process. because such numerical "interactions" are achieved mainly by the fd approximate expressions of the partial derivatives, the "spontaneous" breaking of the symmetry appears quicker for (in the decreasing order of importance): • a) large numbers of displacement components involved in the expressions of partial derivatives, e.g. when their expressions with 2 previous time steps (instead of those using an only one previous time step) are used1: ḟ(0) = −f(2τ) + 8f(τ) − 8f(−τ) + f(−2τ)/12τ, f̈(0) = −f(2τ) + 16f(τ) − 30f(τ) + 16f(−τ) − (−2τ)/12τ2 when the instabilities appear after only few tens of iterations, • presence and repeated "mixture" of the values of both real and pure imaginary parts of the complex wave function (displacement) w̄, • more parasitic solutions, • more partial derivatives involved in the expression of the differential equation of the acoustic pulse propagation. 1the formulae in more points are considerably more accurate for rather small numbers of iterations, but they give rise later to spurious solutions and instability (see table 1). complex computer simulations, numerical artifacts, and numerical phenomena 753 for these reasons, the highest "accessible" logical depth [22] is reached (for the simulations of the acoustic pulse propagation through attenuative media) for the numerical scheme using the real wave function equation (see table reftab02), with the usual fd approximations of the first 2 order derivatives: ḟ(0) = f(τ) − f(−τ) 2τ , f̈(0) = f(τ) − 2f(0) + f(−τ) τ2 . 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[23] d. iordache, v. iordache, romanian journal of physics, 48(5-6) 697-704 (2003). int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 919-928 a metrics-based diagnosis tool for enhancing innovation capabilities in smes j. sepulveda, j. gonzalez, m. camargo, m. alfaro juan sepulveda, javier gonzalez, miguel alfaro department of industrial engineering, university of santiago of chile 3769 ecuador ave. santiago, chile. po box 10233 mauricio camargo nancy-universite / erpi (equipe de recherche des processus innovatifs) 8, rue bastien lepage 54010 nancy cedex, france abstract: innovation doubtless represents a main strategic lever for the development of small and medium enterprises (smes) in many industrial sectors and it comprises new techniques, new products and new processes, as well as new services which lead to better customer service and revenue. however, the basic question of how well the company is equipped with the necessary practices, methodologies, people, and beliefs, is far from being completely answered yet. in this paper, a metrics-based diagnosis tool for measuring and enhancing the innovation capabilities in smes is presented along with a set of preliminary results from case-based studies at the local industry. in this paper we propose a new method by studying the competences of smes in concepts tied to innovation and by using a specified framework. as a first step, all of the necessary information by using questionnaires with verbal-scales evaluations is compiled. second, we use a non-compensatory flow-based sorting method with central profiles to identify the current level and to classify the company into predefined levels. third, a detailed analysis of the obtained values is performed in order to make a personalized recommendation. keywords: technological innovation, multicriteria decision making, classification methods. 1 introduction 1.1 innovation process the innovation literature is a fragmented corpus due to the contribution of many scholars with diverse disciplinary backgrounds that try to adopt different ontological and epistemological positions to investigate and analyze this complex and multimensional phenomena. thus, a variety of approaches [1], [2] and many different measurement methods [3], [4], [5] can be found. chiesa et al. in [6] describe process and performance as the two foci of innovation management measures; they overlay core processes with a set of enabling processes, the latter describing the deployment of resources, and the effective use of appropriate systems and tools governed by top management leadership and direction. a close link exists between product and process innovations: the majority of the articles address both type of innovations and only few articles considered only process innovations [7]. we can observe this relationship in the definition made by cormican et al. [8] , that describe the product innovation as a continuous and cross-functional process involving and integrating a growing number of different competences inside the organization. in the area of the variables related to innovation, we can consider important works on frameworks. adams et al. [3] in a survey develops a synthesized framework of the innovation management copyright c⃝ 2006-2010 by ccc publications 920 j. sepulveda, j. gonzalez, m. camargo, m. alfaro process consisting of seven categories: inputs management, knowledge management, innovation strategy, organizational culture and structure, portfolio management, project management and commercialization. boly [9] , based on the literature [7] [8], identified the most used practices by innovative enterprises and he classified them under 13 categories or groups. according to the author, these practices constitute the principal actions performed by the enterprises to define their strategy, to guide and impel the innovation processes and make evolve the organization or its methods of work; they develop these practices completely or partially and in a formal or informal way where the level of use of these practices allows to classify the enterprises according to his innovation potential. 1.2 measuring innovation by assessment of practices corona in [10] defined an index of potential innovation (iip), which is calculated by using multicriteria decision making (mcdm) tools, and uses as criteria the 13 innovation practices defined by boly [9] . these practices are the concrete actions executed by the enterprises to define their strategy, to guide and to impel the innovation processes and to make evolve the organization or its working methods. the index will allow to obtain a classification according to the attitudes and strategies adopted by these enterprises. based on [11] we can classify companies as: proactive, preactive, reactive and passive. on the other hand, morel et al. [12] propose the use of choquet’s integral to consider the interaction between the different innovation practices, defining an aggregated index of potential innovation (apii). finally, assielou in [13], besides of adding two new innovation practices, he makes modifications in the system of treatment and practices. current index-based methods are actually sorting procedures and present limitations to correctly classify enterprises in this field. in this paper we propose a new method to make the evaluation of innovation levels in the small and medium enterprises by using a specified framework. as a first step, all the necessary information by using questionnaires with verbal-scales evaluations is collected. then, a non compensatory flow-based sorting method with limiting profiles is applied. the third step consists of a detailed analysis of the values obtained in each evaluation of an enterprise to perform a personalized recommendation for each company about areas to be improved. an example of its application is given. 2 construction of the gathering tool and reference profiles 2.1 gathering tool based on the works by assielou [13], corona [10] and camargo [14] we use categories of innovation practices ci, each with practices belonging to a similar class, as shown below. i. creation / concept generation (c1) 1.1 use of tools to increase the creativity 1.2 integration of the clients and suppliers in the conception process 1.3 organization, compilation and management of information from the exterior ii. conception activities (c2) 2.1 use of tools of help to the conception 2.2 existence of a methodology of help to the conception 2.3 hardware equipment iii. human resources management (c3) a metrics-based diagnosis tool for enhancing innovation capabilities in smes 921 3.1 management of competences and the skills of the society 3.2 innovation stimulation iv. strategy (c4) 4.1 strategy integrated to favor the innovation 4.2 network operation 4.3 client importance 4.4 financing v. project management (c5) 5.1 project administration 5.2 management of project briefcase 5.3 organization of tasks tied to the innovation vi. capitalization of ideas and concepts (c6) 6.1 continuous improvement of the innovation process 6.2 politics of management of the intellectual property 6.3 knowledge capitalization in order to measure every concept on each category, verbal scales mapped onto five numerical levels are defined with values {0; 0.25; 0.5; 0.75; 1}, where 0 and 1 are the lowest and the highest level , respectively. each level was defined in detail for each concept, in order to avoid ambiguity and make the evaluation easier to the interviewer; a survey with 18 evaluations grouped into six categories is then applied. 2.2 reference profiles by observing the classification established by godet [11] and the six categories above, it is possible to establish intervals in which we can classify the enterprises; passive enterprises have the lowest values in every category whereas the preactive ones obtain the highest values. the division by interval in each category allows us to establish segments of values for each innovation profile in the characteristics to be measured by having into consideration that the values included in the interval belongs to the levels expected for a company of that profile. this can give us the idea that it is possible to determine reference profiles, where for example a proactive company will have all its evaluation values in the highest intervals. thus, each of the four divisions represents to a reference company with passive, reactive, preactive and proactive characteristics (table 1). is it possible to see that these reference profiles represent companies that have homogeneous development levels in each characteristic, which is not always the case; for instance, there exist companies with high levels of development in innovation stimulation, but at the same time poor levels in knowledge capitalization, or a company obtains proactive values in human resources management and reactive values in capitalization of ideas and concepts. we cannot hope that the majority of the companies will be homogeneous, therefore it is necessary to find out a method that allows us to establish a correct classification within the four profiles using a non compensatory mathematical tool. 3 flowsort method based on the ranking methodology of promethee, a new sorting method developed by nemery and lamboray [15] is proposed for assigning actions to completely ordered categories; 922 j. sepulveda, j. gonzalez, m. camargo, m. alfaro category passive reactive preactive proactive i. creation / concept generation [0 a2[ [a2 a3[ [a3 a4[ [a4 1] ii. conception activities [0 b2[ [b2 b3[ [b3 b4[ [b4 1] iii. hr management [0 c2[ [c2 c3[ [c3 c4[ [c4 1] iv. strategy [0 d2[ [d2 d3[ [d3 d4[ [d4 1] v. project management [0 e2[ [e2 e3[ [e3 e4[ [e4 1] vi. capitalization of ideas [0 f2[ [f2 f3[ [f3 f4[ [f4 1] table 1: interval distribution of category values these categories are defined either by limiting profiles or by central profiles (also named centroids). the assignment of an action into a category is based on the relative position of this action with respect to the defined reference profiles in terms or incoming or outgoing net flows. we denote by a : (a1, ..., an) the set of n actions to be sorted. these actions are evaluated on q criteria gj(j = 1, ..., q) that have to be maximized. we denote the categories to which the actions must be assigned by c1, c2, ..., ck. these categories are either delimited by two boundaries, in the case of limiting profiles, or by centroids in the case of central profiles. this categories are ordered as c1 > ...cl > ck, where ch > ck , with h < l, which denotes that ch is preferred to category cl. we denote r = (r1..., rk+1) as the set of limiting profiles in the case when a category is defined by an upper and lower profile, represented as rh+1 . on the other hand, when we define a category by one central profile, the centroid is denoted by r̃ = (r̃1, ..., r̃k), where r̃j is the centroid of category cj. we also define π(x, y) as the preference of action x over an action y, which is used in the same way as in promethee. thus, on the basis of these preference degree, positive, negative and net flows of each action x of ri, are computed by equations ( 3.1),( 3.2),( 3.3), where ri = r ∪ {ai}. ϕ+ ṙi = 1 |ṙi| − 1 ∑ y∈ṙi π(x, y) (3.1) ϕ− ṙi = 1 |ṙi| − 1 ∑ y∈ṙi π(y, x) (3.2) ϕ ṙi = ϕ+ ṙi − ϕ− ṙi (3.3) in this case we use ṙi when no difference can be made between a set of limiting profiles and a set of centroids. the flow-based assignment rules differ in the use of limiting profiles and central profiles. in the case of limiting profiles, the rules of the positive and negative flow assignment are defined as follows: cϕ+(ai) = ch, if ϕ + ri (rh) ≥ ϕ+ri(a1) > ϕ + ri (rh+1) (3.4) cϕ−(ai) = ch, if ϕ − ri (rh) < ϕ − ri (a1) ≤ ϕ−ri(rh+1) (3.5) if we want to strictly impose the assignment to one category, using the net flow we can define the assignment rule by ( 3.6). cϕ(ai) = ch, if ϕri(rh) ≥ ϕri(a1) > ϕri(rh+1) (3.6) a metrics-based diagnosis tool for enhancing innovation capabilities in smes 923 in the case of central profiles, the flow-based assignment rules of positive and negative flows are defined by ( 3.7) and ( 3.8). c̃ϕ+(ai) = ch, if ϕ+ r̃i (r̃h) + ϕ + r̃i (r̃h+1) 2 < ϕ+ r̃i (a1) ≤ ϕ+ r̃i (r̃h) + ϕ + r̃i (r̃h−1) 2 (3.7) c̃ϕ−(ai) = ch, if ϕ− r̃i (r̃h) + ϕ − r̃i (r̃h+1) 2 ≥ ϕ− r̃i (a1) > ϕ− r̃i (r̃h) + ϕ − r̃i (r̃h−1) 2 (3.8) here, we can also strictly impose the assignment to one category using the net flows with the assignment rule ( 3.9). c̃ϕ(ai) = ch, if ϕ r̃i (r̃h) + ϕr̃i (r̃h+1) 2 < ϕ r̃i (a1) ≤ ϕ r̃i (r̃h) + ϕr̃i (r̃h−1) 2 (3.9) 4 application in this section the method is explained in four main steps, by using information obtained from seven smes, ej, with (j = 1, ..., 7) taken from the metalworking industry located at santiago of chile, as follows. e1: appliances manufacturing such as refrigerators, gas and kerosene heaters. e2: appliances manufacturing such as gas and electric stoves and heaters. e3: appliances manufacturing such as home boilers and sinks. e4: faucets and gas valves manufacturing. e5: vehicle transforming maker such as for ambulances and safety vehicles. e6: safety deposit box manufacturing with electronic controls. e7: vending machine refurbishment and adapting for industrial utilization. 4.1 first step: determination of the weights and references profiles. by observing the weight used in each one of the practices in [13], we construct our own weight distribution, considering that our definition of categories and concepts is a grouping and in some cases the division of the innovation practices. this weight distribution can be observed in table 2 , where also for every category the local weight of each concept of a category is indicated. to establish the reference profiles, we will define four areas for each of the six categories using the construction of limiting profiles; this will allow us to establish min and max values in every category ci . in this case we constructed a symmetrical division in each of the categories because the necessary information to establish central profiles was not available (table 3). 4.2 second step: survey application and data processing. in this step we work with the information collected from the surveys made to each enterprise. according to the evaluation of each of the concepts, an evaluation for each category ci is made by the weighted sum ∑ eijwij, where eij is the evaluation between [0, 1] of the j-th concept in the i-th category , and wij is the local weight (table 4). 924 j. sepulveda, j. gonzalez, m. camargo, m. alfaro category (ci) local concept weights (wij) global category weight (wi) i. creation / concept generation {0.26; 0.33; 0.41} 0.175 ii. conception activities {0.43; 0.19; 0.38} 0.107 iii. human resources management {0.47; 0.53} 0.068 iv. strategy {0.05; 0.51; 0.27; 0.17} 0.232 v. project management {0.01; 0.47; 0.52} 0.194 vi. capitalization of ideas and concepts {0.43; 0.29; 0.37} 0.224 table 2: local and global weights concepts limiting profile c1 c2 c3 c4 c5 c6 r1 1 1 1 1 1 1 r2 0.75 0.75 0.75 0.75 0.75 0.75 r3 0.5 0.5 0.5 0.5 0.5 0.5 r4 0.25 0.25 0.25 0.25 0.25 0.25 r5 0 0 0 0 0 0 table 3: limiting profiles defined in flow-sort method a c1 c2 c3 c4 c5 c6 e1 0.31 0.32 0.12 0.13 0 0 e2 0.36 0.59 0.40 0.86 0.15 0.19 e3 0.28 0.16 0.40 0.58 0 0.10 e4 0.17 0.22 0.12 0.43 0.11 0.19 e5 0.26 0.42 0.24 0.34 0.15 0 e6 0.52 0.78 0.52 0.98 0.64 0.42 e7 0.17 0.2 0.18 0.29 0.34 0.11 table 4: evaluation results 4.3 third step: flow-sort aplication by defining the set of actions for the seven enterprises as a = {e1, e2, e3, e4, e5, e6, e7}, which have been evaluated in the six criteria already defined, and the four classification categories {passive, reactive, preactive, proactive} defined by the five limiting profiles of table 3, we start calculating the preference degrees, showed in table 5 between the reference profile and the seven enterprises in order to obtain positive and negative flows. with these calculations we can measure positive, negative, and net flows for each enterprise by using equations ( 3.1), ( 3.2), and ( 3.3).the calculations of all of the flows for the enterprises is shown in table 6, where for example the positive flow of enterprise 1 with respect to limiting profile r4 is calculated as ϕ+ r1 (r4) = ∑ π(r4, rj) + π(r4, e1) |r4| − 1 = 1 + 0.67 6 − 1 = 0.334 the assignment to each category in table 6 were obtained by equations ( 3.4) and ( 3.5); for example for assigning enterprise 1 the flow is between profile limits r4 and r5 as indicated by ϕ+ r1 (r4) ≥ ϕ+r1(e1) ≥ ϕ + r1 (r5) and ϕ−r1(r4) < ϕ − r1 (e1) ≤ ϕ−r1(r5). thus enterprise 1 is classified as passive. in the cases when positive and negative flows difers, for example enterprise 3 and 5, we must apply the equation ( 3.6) to obtain an unique classification. a metrics-based diagnosis tool for enhancing innovation capabilities in smes 925 r1 r2 r3 r4 r5 π(e1, rj) 0 0 0 0.33 0.67 π(rj, e1) 1 1 1 0.67 0 π(e2, rj) 0 0.17 0.33 0.67 1 π(rj, e2) 1 0.83 0.67 0.33 0 π(e3, rj) 0 0 0.17 0.50 0.83 π(rj, e3) 1 1 0.83 0.50 0 π(e4, rj) 0 0 0 0 1 π(rj, e4) 1 1 1 1 0 π(e5, rj) 0 0 0 0.50 0.83 π(rj, e5) 1 1 0 0.50 0 π(e6, rj) 0 0 0 0.50 0.83 π(rj, e6) 1 1 1 0.50 0 π(e7, rj) 0 0 0 0.33 1 π(rj, e7) 0 0 0 0.67 0 table 5: preference degrees between the reference profile and the actions r1 r2 r3 r4 r5 ei class ϕ+ 1 0.8 0.6 0.344 0 0.173 passive r1 ϕ− 0 0.2 0.4 0.656 0.916 0.744 passive ϕnet 1 0.6 0.2 -0.313 -0.916 -0.571 passive ϕ+ 1 0.754 0.532 0.284 0.0 0.431 reactive r2 ϕ− 0 0.246 0.468 0.716 1 0.569 reactive ϕnet 1 0.507 0.064 -0.433 -1 -0.139 reactive ϕ+ 1 0.8 0.554 0.305 0 0.303 passive r3 ϕ− 0 0.2 0.446 0.695 0.961 0.659 reactive ϕnet 1 0.6 0.107 -0.39 -0.961 -0.356 reactive ϕ+ 1 0.8 0.6 0.354 0 0.246 passive r4 ϕ− 0 0.2 0.4 0.646 1 0.754 passive ϕnet 1 0.6 0.2 -0.293 -1 -0.507 passive ϕ+ 1 0.8 0.6 0.297 0 0.258 passive r5 ϕ− 0 0.2 0.4 0.703 0.955 0.697 reactive ϕnet 1.0 0.6 0.2 -0.406 -0.955 -0.439 passive ϕ+ 1.0 0.732 0.445 0.2 0 0.623 preactive r6 ϕ− 0.0 0.268 0.555 0.8 1 0.377 preactive ϕnet 1.0 0.464 -0.11 -0.6 -1 0.246 preactive ϕ+ 1.0 0.8 0.6 0.315 0 0.285 passive r7 ϕ− 0.0 0.2 0.4 0.685 1 0.715 passive ϕnet 1.0 0.6 0.2 -0.37 -1 -0.43 passive table 6: flow-sort results the assignment of an enterprise into one of the four enterprises profiles can be easily displayed using the positive and negative flows diagram. in figure 1, which shows the flows of the enterprise 1, it is possible to view that there is not ambiguity in the classification of this enterprise into the passive profile , since the positive and negative flows allocate this enterprise into the same profile. in other case , in the figure 2 which shows the flows of the enterprise 3, we can observe that the positive flow classifies the enterprise into the passive profile and the negative flow classifies the enterprise into the reactive profile, but the final net flow classify this enterprise into the reactive profile. this final classification can be explained taking into consideration the 926 j. sepulveda, j. gonzalez, m. camargo, m. alfaro proximity of the positive and negative action score to the reactive area classification over the passive area classification. figure 1: flow diagram enterprise 1 figure 2: flow diagram enterprise 3 for the enterprise 6 identified as preactive in this method, the net flow action obtains a positive value, which is opposed to the net values obtained by the enterprises classified as passive which have the most negative value (figure 2). it is important to note that the profile areas where there is not exist any ambiguity depends only on the singular comparison between an enterprise and all of the reference profiles. the former explains the variety of these areas that we see in all these flows diagrams. 4.4 fourth step:analysis of the results. the analysis of the results and the search of possible alternatives of innovation progress in these enterprises can be analyzed by observing the detailed net score flows for each category, which show the net significance of all six categories in obtaining the final net flow value for every enterprise. we can observe that in most cases the category that contributes with the highest negative flows is the category capitalization of ideas, which is responsible for the third part of the total net flow. the second category with the most negative flows is the category project management. in most enterprises, these two categories grouped together represent between the 40% or 50% of the final significance in the net flow action (table 7). this can give us an idea figure 3: flow diagram enterprise 6 a metrics-based diagnosis tool for enhancing innovation capabilities in smes 927 that any improvement in the values obtained in the values of these categories can be critical to obtain a better evaluation. in other words, any improvement in any of the concepts that belongs to the categories above mentioned may produce an advance of the innovation process. thus, these enterprises would be more qualified to move to a higher profile. on the other hand, in the enterprises that obtained a classification as reactive and preactive, the values associated to the category strategy have positive net flows values having a positive significance into the total net flow action; thus we can say that enterprises with good evaluations in the concepts concerning to the strategy can present features of a reactive or a preactive enterprise profile. c1 c2 c3 c4 c5 c6 e1 6.13% 3.75% 7.15% 24.39% 27.19% 31.39% e2 7.61% 4.65% 2.96% 30.26% 25.30% 29.22% e3 7.80% 14.30% 3.03% 10.34% 34.58% 29.95% e4 20.70% 12.66% 8.04% 9.15% 22.95% 26.50% e5 7.97% 4.87% 9.29% 10.56% 26.50% 40.80% e6 10.43% 19.13% 4.05% 41.48% 11.56% 13.35% e7 24.44% 14.94% 9.50% 10.80% 9.03% 31.28% table 7: detailed net flow significance 5 conclusion in this paper a new enterprise classification method by using a non compensatory flowbased sorting method with limiting profiles has been proposed. the method called flow-sort was used to identify the current level of the enterprise and to classify it into four predefined levels of innovation: passive, reactive, preactive, and proactive. the aim of the method, besides classifying the enterprise into a profile, is to give new ideas to formulate an improvement strategy to allow the company to increase its innovation performance. along this work, we established many observations, as (a) the importance of defining a precise framework that allow us to evaluate all the characteristics and concepts in a precise and structured form, (b) the correct construction of the tool for gathering data, since this is a key element for obtaining the necessary information input, (c) the use of a mathematical tool that allows a comparison against an established profile; the tool is independent of the universe of enterprises to be measured so that it can be applied on a reduced or a large number of enterprises without changing its effectiveness, (d) the possibility of establishing the parameters for an innovation improvement strategy for each of the enterprises individually according to their obtained values and the analysis of significance of each of the categories and concepts. as a limitation of the application used in the example above, we have the highly arbitrary definition of the four reference profiles since we used four homogenous zones for the six categories when using limiting profiles, as shown in table 3. more accurate knowledge on a company may allow different values for the profiles, or better the use of central profiles. the latter may be continuously refined as the analysis is repeated in the mid and long term. a classification into new innovation levels can be made by using the proposed method. in such a case, the use of either central or limiting profiles will exclusively depend on the certainty about the characteristics of each new level. another comment is related to the interpretation of results and the creation of incentive policies towards better innovation performance levels; these aspects are left to the policy makers at each company since they cannot be predefined in a standardized way. however, an empirical study 928 j. sepulveda, j. gonzalez, m. camargo, m. alfaro with a broader universe of companies by using the method here proposed could give a better answer on the suitability of best practices of this field. bibliography [1] kerssens-van drongelen, i.c. and bilderbeek, j., r&d performance measurement: more than choosing a set of metrics, r& d management vol 29, nş1, p. 35-46, 1999 [2] koberg c., detienne d., heppard k.,an empirical test of environmental, organizational, and process factors affecting incremental and radical innovation, journal of 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springer-verlag, 2008 international journal of computers, communications & control vol. i (2006), no. 2, pp. 15-22 an automatic grading system for panels surfaces using artificial vision cristhian aguilera, mario ramos, gabriel roa abstract: this work describes an automatic grading system using artificial vision to improve the quality of wood panels surfaces. the objective is to control stains on the surface. artificial vision techniques like thresholding and transformed watershed methods are applied. defects quantitative measures found on the surface are also presented, in particular quantity, area, intensity and distribution. keywords:vision, image processing, quality control, plywood 1 introduction the need of manufacturing companies to maintain high quality product requires an exhaustive production control during and at the end of the process. when control is human visual inspection product control is not completely reliable and it is not guaranty of a total quality control. however, the development of new technologies, and especially image analysis systems (artificial vision), has ostensibly improved the quality control process. since it is a noninvasive technology and is capable to inspect the 100% of the production, artificial vision offers an advantage when compared with the manufactured product quality control techniques like human inspection and sampling. the wood industry and specifically in the panels industry has produced efforts to improve each, every one of the production stages. two main characteristics determine the panel quality: mechanical resistance and appearance. artificial vision (garrido, 2003) is a solution for the appearance control quality. panels like plywood are used in the carpentry industry. decorative paneling is the reason why surface quality must be controlled. since the objective of these products is both functional and esthetic. the main objective of this paper is to propose an analytical artificial vision based method to classify panels according to the quantity, area, intensity, and stains distribution on the surface. it includes among these characteristics quantitative methods to measure the stain distribution on the panels. factors to permit the quantification of these parameters and especially of the stain distribution in this paper are proposed. 2 panels 2.1 definition panels can be defined (corma, 2003). structural panels this term refers to those panels employed as structural elements in the construction and packing industry, included plywood. non-structural panels this term refers to those panels employed in the furniture industry, included decorative plywood, hard panels, and medium density fiberboard, mdf. 2.2 classification traditionally, wood, and consequently wood-derived products, has been classified considering two fundamental criteria: resistance and appearance. in relation to the resistance, the objective is to control the mechanical resistance degree. there are standards that these products should satisfy. tests are related with the mechanical quality evaluation like thickness, density, weight, flexion, traction, elasticity, and humidity. copyright c© 2006 by ccc publications 16 cristhian aguilera, mario ramos, gabriel roa in the case of appearance, the objective is the visual appearance control, considering esthetic more than structural values. these criteria are important in the furniture and paneling industry. defects affecting the surface quality, especially in mdf and fiber panels, are the stains on the surface. as an example, a summary of the characteristics considered by a chilean company to grade surface quality of mdf and fiber panels are presented in table 1. it can be observed that panel grading is divided in three types. • first quality (i). • second quality (ii). • third quality (iii). 3 industrial vision system industrial vision systems are rapidly becoming a key factor in the development of total quality procedures in an industrial automation processes context. industrial vision systems permit to inspect production processes without fatigue or distraction, facilitating the quantification of quality variables and contributing to a continuous improvement (silvén and matti, 2003). the figure 1 presents the outline of the stages of an industrial vision system. figure 1: industrial vision system stages 3.1 image acquisitions and digitalization this is the stage in which sensors and the capacity to digitalize the signal produced by the sensor are used. the sensor is either color or monochrome camera that produces a complete image. after capturing the image, this information is sent to the computer to be analyzed (mery, 2003). 3.2 pre-processing in this process, the acquired image is modified in order to improve it according to the parameters to analyze considering: • noise elimination. an automatic grading system for panels surfaces using artificial vision 17 mdf fiber qual surface surface i both faces free of porosity, cracks, semi-inflated and dark stains both faces free of porosity, cracks, unsanded holes, semi-inflated, and stains falling from the surface i minor stains are permitted when there are no more than 2 per face and they are smaller than 2 cm longer strips that do not surpass 0.5 cm2 in area are permitted. the quantity should be less than 10 per face and presenting a separation greater than 5 cms. between them i good behavior during milling and coating application. minor stains are permitted if they are not greater than 5 cm2 of area. the quantity should be less than 5 per face and with a separation greater than 5 cm ii cracks, thick particles, dust, adhesive, or other agent stains, and semi-inflated porosity, cracks, un-sanded holes, thick particles on the surface, adhesive, dust, or other stains iii without quality requirements without quality requirements table 1: characteristics for panel grading according to the surface defects • accentuate or profile image characteristics (borders, limits, etc). • improve the quality of some parts of the image. 3.3 segmentation the segmentation process divides the digital image in unconnected regions in order to separate region of interest from the rest of the scene. in the last years, diverse segmentation techniques have been developed, which can be grouped in three techniques: pixel-oriented, border-oriented, and region-oriented techniques. 3.4 description this process labels the objects considering information supplied by inspection that can be: quantitative: measuring of areas, lengths, perimeters, etc. qualitative: verification of correct task performance (assembling, bottling, labeling, etc.). 3.5 classification classification orders the segmented regions in classes, assigning to each region a group of many preestablished groups that represent all the possible types of regions that are expected to exist in the image. in this stage, a statistical study is performed on the characteristics that are extracted from the objects whose defects are known a priori. 3.6 decision making frequently, computer vision systems control a mechanical apparatus that manipulate the products after classification. 18 cristhian aguilera, mario ramos, gabriel roa 4 proposed techniques to grade the panel surface defects, image analysis techniques need to be used. this stage differentiates one vision system from another. the analysis is performed on images using a gray scale where the intensity values for black are 0 and 255 for white. one of the techniques to be used is thresholding (guindos, 2001). panel surface stains are considered regions. another segmentation technique is the one based in growth of the regions applying functions under morphological operators, specifically the transformed watershed. 4.1 thresholding the thresholding technique (gonzales and goods, 1996) classifies each pixel in two groups, depending on whether or not the gray level exceeds the given threshold. if it does not exceed, then a lower gray level (color black) is assigned; in the opposite, the upper level (white color) is assigned. the objective is to obtain an image segmentation, creating black stains on a white background in a form that is similar to the objects seen from an upper plane, as can be observed in figure 2. figure 2: (a) original image, (b) i. gray-scaled image (c) thresholded image 4.2 transformed watershed this technique is part of a series of algorithms emerging to analyze images as a topographic surface where height is equal to the intensity. the highest pixel values correspond to the highest land areas and vice versa. the transformed watershed simulates, based in the minimum values of the image of water filling the image, figure 3. also, these points can be randomly selected, called markers. the point where the water comes from two regions is called the watershed and is the point that separates the basins corresponding to two minimums. figure 3: (a) marker selection, (b) flooding process from the markers, (c) finalized segmentation, (d) selection of markers using thresholding the key to segment using the transformed watershed is the markers selection. if each local minimum is taken, it could produce an over-segmentation. in order to reduce over-segmentation, the original image an automatic grading system for panels surfaces using artificial vision 19 is frequently softened before performing segmentation. with the thresholding method, a set of markers can be obtained in which a pre-determined filling level is fixed. figure 4 is an example of the over-segmentation when the transformed watershed is applied to the original image without establishing local minimums. figure 4: watershed results: (a) original image, (b) over-segmented image, (c) segmented image using local minimums 5 extraction of panel characteristics when the stains (defects) are found numeric properties are extracted. these characteristics provide information to grade the panel. the characteristic are quantity, area, intensity and distribution. 5.1 quantity this characteristic indicates the number of stains on the panel surface and whose factor is defined by the quantity of stains. fc = ct (1) where: fc is the quantity factor, ct is the number of stains present on the panels surface. 5.2 area this characteristic indicates the stained surface of the panel. its value is defined by the ratio between the sum of stain areas on the panel surface and the total panel surface area. this characteristic is called "area factor" and this is represented by: fa = 1 at k ∑ 1 an (2) where: fa is the area factor, at is the panel area, an is the stain area and k is the number of stains (ct ). in figure 5, it can be observed that the area factor increases proportionally with the stained area of the panel surface, indicating in this way a lower quality. 5.3 intensity this characteristic indicates the intensity level (gray scale, 0-255) of the stains. this property provides information of the stain type on the panel surface. for example, the humidity stain will have a different intensity than an oil stain. to represent this factor, the stain intensity average value and the stain-free panel intensity average are considered. this characteristic will be called "intensity factor" and is represented by: f i = x i −x im x i (3) 20 cristhian aguilera, mario ramos, gabriel roa figure 5: area factor v/s stained area figure 6: intensity factor v/s average stain intensity where: x i is the stain-free panel intensity average and x im is the stain intensity average. consequently, like can be appreciated in figure 6, the intensity factor will increase when stain intensity average value decrease, indicating in this a lower quality. 5.4 distribution this characteristic determines the spatial location of the stains on the surface. this shows if the stains are grouped in only one panel sector or rather are distributed throughout its surface. the position of the objects is established through cartesian coordinates. the method uses the average of the euclidean distances to the objects geometric centers. the factor is defined by the ratio between the distances average and a reference value. the reference value is the distance from the diagonal to the sample. expressed in percentage: f d(%) = 100∗ [ µ(d) dt ] (4) where: µ(d) is the average of euclidean distances, d is the set of objects, dt is the distance from the diagonal of the sample. the average of distances µ(d) depend how distant these object are from the geometric center. the farther they are, the greater is the value of (d) and consequently, f d (%) also be greater, indicating that the object distribution is greater. in figure 7, three examples of distributions are showed. as an example, table 2 presents numerical values of the factors proposed in this work, in relation to the grading criteria presented in table 1. the panel format was 1.5×2.3 meters. an automatic grading system for panels surfaces using artificial vision 21 figure 7: (a) fd=6.8%, (b) fd=9.05%, (c)fd=17.32% mdf fiber qual fc fa (%) fd fc fa (%) fd (%) i < 2 < 0,01 ne < 5 < 0.07 1,8 ii < 10 ne < 10 7 iii sec sec sec sec sec sec table 2: example of factors (ne: not specified, sec: without quality requirements.) 6 comments and conclusions this work is focused towards image analysis by segmentation of stains present on wood panel surfaces and in the definition of factors to quantify defects properties (quantity, area, intensity and distribution). techniques like thresholding and transformed watershed showed goods results to segment stains in gray scales. thresholding is a useful and simple technique, but when it is used only at one threshold level it is not capable of separating (labeling) the distinct stain intensity values that are below this value. the transformed watershed method offers the capacity of separating regions (stains) in function of the intensity value presented by each defect. however, this technique presents over-segmentation, problem that is solved through the image pre-processing to increase the contrast for the objects of interest and the assignment of local minimums (markers). factors proposed in this work provide the necessary information to define a stain quality index to on -line control of based-wood panels. acknowledgements this work was supported by the project incon-032008 3/r of the research secretary, university of bio-bio. references [1] garrido r., adquisición y tratamiento digital de señales para visión artificial, reporte final ingeniería electrónica, universidad del bío-bio, 2003. [2] corma, corporación chilena de la madera, www.corma.cl, 2004. [3] mery d., inspección visual automática, informe interno, departamento de ingeniería en computación, universidad de santiago de chile, 2003. [4] guindos f., fernandez j., and peralta m., visión artificial con imtdi, almería, 2001. 22 cristhian aguilera, mario ramos, gabriel roa [5] gonzález r.,woods r., tratamiento digital de imágenes, addison-wesley, 1996. [6] olli silvén and matti niskanen, framework for industrial visual surface inspections, 6th international conference on quality control by artificial vision, machine vision group, university of oulu, finland, 2003. cristhian aguilera, mario ramos, gabriel roa universidad del bío-bío, concepción-chile departamento de ingeniería eléctrica y electrónica departamento de ingeniería en maderas laboratorio de sistemas automatizados e-mail: cristhia@ubiobio.cl, mramos@pegasus.dci.ubiobio.cl international journal of computers, communications & control vol. ii (2007), no. 4, pp. 314-327 an implementation of reconfigurable network control based upon automata proposal for three conveyor belt case study benítez-pérez h., cárdenas-flores f., garcía-nocetti f. abstract: online reconfiguration performed by a computer network system needs to be addressed from several perspectives due to complexity onto the system. this paper proposes different modeling approximations to obtain a holistic view of reconfiguration onto complex systems. first model is dynamic system modeling, second is an automaton in order to bound possible scenarios and third model is a real time scheduling algorithm to match possible configurations and related control laws. 1 introduction one of the main issues in fault tolerance is to keep availability even in hazard situations. a way to guarantee this is by reconfiguration where several consequences are expected. in that respect, either fault coverage, masking or tolerance are strategies to use during reconfiguration. reconfiguration is a need in order to keep safety during fault scenarios. the results of this action modify several structures within complex systems like communication, dynamic behaviour and predictable response. in order to cover every aspect of these requirements, modeling becomes a crucial issue to get enough information by performing reconfiguration. in here, three different modeling techniques are followed, dynamic system modeling, automaton modeling and scheduling representation. these three strategies, by there own, lacks of a holistic view of the effects of a process such as reconfiguration. for instance, automaton strategy allows a structural view of reconfiguration without determining the effects of this action into dynamic behaviour of the system. alternatively, dynamic system modeling provides a formal view of the effects of time delays and the loose of certain dynamic elements, however, reconfiguration is not a predetermined action only the effects of it. the novelty of this approximation is to integrate various modeling strategies to accomplish reconfiguration and its effects. firstly, dynamic system is modeled in order to determine where time delays play a key role. secondly, automaton modeling is pursued in order to bound scenarios during fault and fault-free situations. from this automaton representation and taking into account real-time requirements, real-time scheduling through a special representation is implemented. the combination of these three allows system modeling even in complex situations like reconfiguration. the objective of this paper is to present a strategy for control reconfiguration based upon time delay knowledge using a scheduling algorithm. fault effects are local within a distributed system environment. the use of a case study is pursued to accomplish this objective. the novelty of this work is the amalgamation of scheduling and control techniques to get this strategy. in particular, for the case of dynamic system modeling, several strategies for managing time delay within control laws have been studied for different research groups. for instance nilsson [8] proposes the use of a time delay scheme integrated to a reconfigurable control strategy based upon a stochastic methodology. on the other hand, wu [10] proposes a reconfiguration strategy based upon a performance measure from a parameter estimation fault diagnosis procedure. another strategy has been proposed by jiang et al. [6] where time delays are used as uncertainties, which modify pole placement of a robust control law. izadi et al. [5] present an interesting view of fault tolerant control approach related to time delay coupling. reconfigurable control has been studied from the point of view of structural modification since fault appearance as presented by blanke et al. [2] where a logical relation between dynamic variables and faults are established. alternatively reconfigurable control may performs a combined modification of system structure as studied by benítez-pérez et al. [1] and thompson [9]. another technique copyright © 2006-2007 by ccc publications an implementation of reconfigurable network control based upon automata proposal for three conveyor belt case study 315 like gain scheduling (khalil, [7]) may give an interesting approximation to several time delay scenarios, however complexity related to system modeling during fault conditions is out the scope of this paper. some considerations need to be stated in order to define this approach. firstly, faults are strictly local in peripheral elements and these are tackled by just eliminating the faulty element. in fact, faults are catastrophic and local. time delays are bounded and restrictive to scheduling algorithms. global stability can be reached by using classical control strategy for online time delays. 2 case study and control reconfiguration approach due to the complexity of this approach a particular implementation is pursued in order to present the potential of complementary modeling for reconfiguration. the case study is based on three conveyor belts (gudmundsson, [4]) integrated as follows, it comprises 3 conveyors belts, 4 actuators, and 12 sensors. it has 16 computing elements that considers the controller and the bus controller. fig. 1 shows a diagram of this implementation. the procedure of the example is next; conveyor belt 1 detects a box, it modifies its speed up to a certain level to transport it in a faster way the box. mc is stated for micro-controller figure 1: conveyor belt example when this box arrives to conveyor belt 2, its speed is modified up to another level to transport this box in a faster manner. similar behavior is presented at conveyor belt 3. the sensor vector is used to detect the current position of box in any of these conveyor belts. furthermore, actuator 4 has the task of pushing the current box when it arrives at this position. for the case of multiple boxes in this example, the aim is that the boxes never crashed between each other. from this explanation, table 1 shows the modification of speeds. this case study is peculiar in that each conveyor belt has two different speeds as shown in table 1. these speeds are dependent on the sensor situation. this sensor situation is depicted as low and high, which is a semaphore for determining the presence of an object. the second peculiarity is related to the difference between hs as follows: hs1 < hs3 < hs2, (1) where the middle conveyor belt is the fastest, then third conveyor belt, and so on. as the reader may realize, there are four motors, three for the conveyor belts and the fourth is to pull any object presented at its region. based on this case study, the response of the three actuators is shown in fig. 2. different speed-ups are shown assuming that a box is presented during a certain time. for instance, the first conveyor belt 316 benítez-pérez h., cárdenas-flores f., garcía-nocetti f. table 1: speed selection conveyor belt conveyor belt conveyor belt 1 2 3 sensors lowspeed low speed low speed s1∗ = low sensors high speed low speed low speed s1∗ = high hs 1 sensors low speed low speed low speed s2∗ = low sensors low speed high speed low speed s2∗ = high hs 2 sensors low speed low speed low speed s3∗ = low sensors low speed low speed high speed s3∗ = high hs 3 presents a faster speed-up during the first 3000 seconds, in comparison with the low speed-up during 3000 to 6000 seconds. this speed-up is shown as a change of slope of the current graphic. similar behavior is presented for both conveyor belts as 2 and 3 are modified, because hs2 is bigger than hs3. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0 10 20 30 d is p la m e n t o f c o n ve yo r b e lt 1 time 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0 10 20 30 40 d is p la m e n t o f c o n ve yo r b e lt 2 time 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0 10 20 30 d is p la m e n t o f c o n ve yo r b e lt 3 time figure 2: related displacement when a box is present in each conveyor belt 2.1 first modeling approach first modeling approach is based on control law modification taken into account time delay appearance, in particular for current case study. the schematic setup is based on fig. 3 considering system response and control implementation. an implementation of reconfigurable network control based upon automata proposal for three conveyor belt case study 317 figure 3: dynamic system implementation in fig. 3, x is the linear displacement θ∗ is the angular displacement ω∗ is the angular velocity f is the lineal force j∗ is the lineal inercy k is the parameter τ∗ is the torque a is the radius v is the lineal velocity in this case, the plant presents two cases with or without a box per belt. as the second case is trivial, the first case is expressed per belt considering the mass of the box (referred to as m). the first conveyor belt is expressed as [ ẍ1 θ̇1 ] = [ j/m 0 j1 ] [ θ1 ẋ1 ] − [ 1/m 0 ] τ1 y = ẋ1. (2) the second conveyor and the third conveyor belt follow simmilar dynamics from these considerations, discrete plants are defined next by considering the presence of the box x(k + 1) = ax(k) + l ∑ i=0 bki u(k −i) bki = ∫ t ki−1 t ki exp(a(t −τ)bdτ, (3) where l = 1 because the maximum number of sensors with delays is just one. therefore, the a matrix is expressed as: a∗ = [ exp(j/m) 0 0 exp(j∗) ] , (4) 318 benítez-pérez h., cárdenas-flores f., garcía-nocetti f. where t is the inherent sampling period, and t k0 , t k 1 , and t k 2 are the related delays of the plant. for the case of local control laws, these are expressed next as: xc(k + 1) = acxc(k) + bcuc(k) yc(k) = ccxc(x−τc −dcuc(k −τc), (5) giving the delays as a result of decomposition from sensor and actuators, which are expressed as τsc and τca respectively. the augmenting representation is given next: uc(k) = yp(k −τsc) up = yc(k −τca), (6) where states are augmented as: z = [ xp(k) xc(k) ] , (7) and expressed as: z(k + 1) = [ ap 0 0 ac ] z(k) + [ 0 0 bccp 0 ] z(k −τsc) + [ bpdccc 0 0 0 ] z(k −τsc −τca −τc) + [ 0 bpcc 0 0 ] z(k −τca −τc). (8) by modifying eqn. 8 to define the stability of network control, the next configuration is proposed: f j = [ a jp 0 0 a jc ] f j1 = [ 0 0 b jcc j p 0 ] f2 j = [ b jpd j cc j p 0 0 0 ] f j3 = [ 0 b jpc j c 0 0 ] . therefore, the state vector is modified for these time delays where the system is asymptotically stable based on f j + ∑3i=1 f j i . based upon a single control loop is stable (eqn. 8), it is possible to define stability for every loop as shown in eqn. 10. τ < σ δ ∑3i=1 ∣∣∣f ji ( f j + ∑3i=1 f j i )∣∣∣ , (9) where τ is the maximum value from all possible time delays at all loops. the absolute value is used in order to guarantee positve response with respect to current time delay. σ = λmin(q) 2λmax(p) δ = [ λmax(p) λmin(p) ] 1 2 , where λmax(p) and λmin(q) are the maximum and minimum eigenvalues of p and q matrices respectively. λmin and λmax can not be complex values since p and q are bounded to be real values. the proposed configuration is presented ( f j + 3 ∑ i=1 f ji )t p + p ( f j + j ∑ i=1 f ji ) = −q, (10) an implementation of reconfigurable network control based upon automata proposal for three conveyor belt case study 319 where p, q are positive definite symmetric matrices and are eigenvalues of the matrix and where the chosen equation is v (x) = 1 2 x jt (t)px j(t). (11) based upon lyapunov proposed equation (eqn. 11) and by its derivative as shown in eqn. 12, where ẋ is substituted by the enhanced representation of ż which contains both states, from the plant as well as the controller. v̇ (x) = 1 2 ż jt (t)pz j(t) + 1 2 z jt (t)pż j(t) ≤ −1 2 z jt (t)qz j(t)+ ∣∣∣∣∣z jt p 3 ∑ i=1 fi ∫ 0 t ji [ f jz j(t + θ ) + 3 ∑ i=1 f ji z j(t −τ ji + θ ) ] dθ ∣∣∣∣∣ . (12) from control law expression, the related time delays are defined as τsc,τca and τc where their respective values will be incorporated later in this section thorugh spliting the time delay (eqn. 8). 2.2 second modelling approach having shown local control laws structures, second modeling approach is the global structure in terms of an automaton (fig. 4). where reconfiguration is expressed for the formal event manager. in this case, two states are possible with several events, which are managed by the sensor vector for each belt (first, second, and third belts) and expressed as s11≤i≤n ,s 2 1≤i≤n and s 3 1≤i≤n , respectively considering fault free scenario. it is important to mention that s11≤i≤n ,s 2 1≤i≤n and s 3 1≤i≤n are indepdent conditions between conveyors, then, s11≤i≤n = 0 means there is no box on conveyor belt 1. at the same time s 2 1≤i≤n 6= 0 can be stated meaning there is a box in second conveyor belt. other condition can be presented as s11≤i≤n 6= 0, s21≤i≤n 6= 0 and s31≤i≤n 6= 0 and where three boxes are presented on the system, each box per conveyor belt. the same sensor conditions are presented in fig. 5. figure 4: fault-free scenario in terms of global structure the switching effect is neglected in this fault-free scenario. in this scenario two cases are defined, when a box is presented (case ii) or the other case (case i). for the second case the chosen control is to maintain the conveyor belt in zero speedup. for first case, the chosen controller is related to certain speed up depending on each conveyor belt. 320 benítez-pérez h., cárdenas-flores f., garcía-nocetti f. for the case of a fault scenario, a new state appears for global control (fig. 5) related to the action pursued when a fault is presented. the necessary event for reaching such a state is s11≤i≤n 6= 0 , and the fault’s last event is composed of local information given by each local sensor with a relation to the health condition measures. figure 5: local fault scenario for the global structure considering individual modeling, there is one type of local fault to be considered, which is that one of the sensors is faulty with no consideration of the type of fault. it is assumed that the fault is detectable and measurable (this is a condition in this paper). 2.3 third modeling approach third modeling approach is related to the use of scheduling algorithm in order to determine possible time delays between processes. it is important to remember that the fault tolerance strategy is based on the use of consecutive sensors to mask the fault using extra communication to perform lateral agreement. therefore, this approach provides two different time graphs, one for each scenario (fault and fault free), as shown in figs. 6 and 7, respectively. the cases from figs. 4 and 5 are related to bounded time delays from decision maker (benítez-pérez et al., [1] and have an effect into control and plan modeling as shown before. at the end of this section (eqn. 15) it is shown how time delays are modified based on time diagram representation (figs. 6 and 7). the reader should realize that time delays are bounded by the use of scheduling algorithm through art2 network. both scenarios are local with respect to one belt. it is considered that the other two belts do not present faulty conditions. as these two scenarios are bounded (fault and fault-free), the respective total consumption times ( tt and tt f respectively) are shown in eqns. 13 and 14 (figs. 6 and 7, respectively), where variable information is presented. tt = ts ∗4 + t sccm + tc + t cacm + ta (13) where: ts is the consumed time by sensors t sccm is the consumed time by communication between sensor and control tc is the consumed time by control node t cacm is the consumed time by communication between controller and actuator ta is the consumed time by actuator an implementation of reconfigurable network control based upon automata proposal for three conveyor belt case study 321 figure 6: fault-free scenario figure 7: fault scenario considering fault masking 322 benítez-pérez h., cárdenas-flores f., garcía-nocetti f. tt f = ts ∗4 + t sccm + t f sccm + tc + t cacm + ta (14) where: t f sccm is the time consumed for the fault sensor to send messages to its neighbor and produce agreement from this time boundary based on these three modeling approaches, it is feasible to implement the control strategy as presented in eqn. 10. as mentioned before, time delays take place in three representations τsc, τca and τc , therefore, by decomposing eqn. 14, time delays are expressed as: τsc = ts ∗4 + t sccm τc = tcm + tc + t cacm τca = ta, (15) a remarkable issue is related to a particular sensor fault related to any of three belts. considering this configuration, three cases are possible: • one local fault; • two local faults, one per belt; • three local faults, one per belt. based on these three possible configurations, there is a worst-case scenario related to three local faults that has an impact on the global control strategy. the other two configurations present a minor degradation for the global control strategy. despite this performance degradation, the system keeps normal functionality due to the inherent fault tolerance strategy (fault masking between sensors) and the local controllers. taking into account these three possible configurations, the local and global time delays are described in table 2. table 2: time delays related to local and global effects configuration 1 local time delays 110 ms one local fault global time delays 110 ms configuration 2 local time delays 110 ms two local fault global time delays 220ms configuration 3 local time delays 110 ms three local fault global time delays 400 ms 3 results from this implementation several results are presented in terms of fault presence and the related action to overcome system lack of performance. how the system responds to these control strategies is presented in the following graphics taking into account fault-free, one local fault and two local faults, respectively (figs. 8, 9 and 10). first scenario presents a fault free situation where local controller response is shown, it is important to highlight that fourth actuator response presents a normal response. when first fault appears (one local sensor does not response and the masking approach is followed) a local time delay takes place where its effects are shown in first local control response. similar situation an implementation of reconfigurable network control based upon automata proposal for three conveyor belt case study 323 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0 0.5 1 1.5 l o ca l c o n tr o l 1 time 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0 0.5 1 1.5 l o ca l c o n tr o l 2 time 7000 7200 7400 7600 7800 8000 8200 8400 8600 8800 9000 0 0.5 1 f o u rt h a ct u a to r time figure 8: fault-free scenario 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0 0.5 1 1.5 l o ca l c o n tr o l 1 time 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0 0.5 1 1.5 l o ca l c o n tr o l 2 time 7000 7200 7400 7600 7800 8000 8200 8400 8600 8800 9000 0 0.5 1 f o u rt h a ct u a to r time figure 9: first local fault appearance and related global effects 324 benítez-pérez h., cárdenas-flores f., garcía-nocetti f. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0 0.5 1 1.5 l o ca l c o n tr o l 1 time 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0 0.5 1 1.5 l o ca l c o n tr o l 2 time 7000 7200 7400 7600 7800 8000 8200 8400 8600 8800 9000 0 0.5 1 f o u rt h a ct u a to r time figure 10: second local fault appearance and related global effects is presented when a second fault appears and first fault is still active (fig. 10). in this case no relevant modification is shown due to time switching controller takes place. this example presents three local control cases with a global automaton in which control reconfiguration is based on the scheduling algorithm, which is simple because it is dependent on the fault presence and on the related time delays. this reconfiguration approach becomes feasible due to the knowledge of fault presence and the consequence of time delays. it is obvious that fault presence is measurable; if this local fault localization approach cannot detect faults, this strategy becomes useless. 93%−95% no adequate structural selected plans with reconfiguration 5%−7% reconfiguration adequate structural selected plans with figure 11: percentage of selected valid plans for structural reconfiguration moreover, local time delay management refers to the use of a quasi-dynamic scheduler to propose dynamic reconfiguration based on current system behavior rather than on predefined scenarios. the scheduler performs task reorganization based on their consumption times and fault presence. the number of accepted plans is presented taking into those selected with no adequate response from structural reconfiguration (fig. 11). for instance, some tasks would not have enough time to be sampled and executed. this result is presented as the percentage of the adequate use of structural reconfiguration during on-line stage. in this case, current control law is modified according to time delays status. having defined the percentage related to those adequate plans during structural reconfiguration, this is taking as 100 % and is evaluated in terms of control law performance. the results are presented in fig. 12. in here, 97% of the valid plans have a valid response in terms of the mean square error response an implementation of reconfigurable network control based upon automata proposal for three conveyor belt case study 325 97%−98% reconfiguration adequate dynamic selected plans with no adequate dynamic selected plans with reconfiguration 2%−3% figure 12: percentage of selected valid plans for control law reconfiguration figure 13: basic model of true time from the dynamic response of case study. to define the communication network performance, the use of the true-time network is pursued. this strategy achieves network simulation based on message transactions that are based on the real-time toolbox from matlab. extended information from this tool is available at (cervin et al. [3]) the true time main characteristics are shown next. in the true time model, computer and network blocks are introduced in fig. 13. these blocks are event driven, and the scheduling algorithm is managed by the user independently of each computer block. true time simulation blocks are basically two blocks. these have been developed by the department of automatic control, lund institute of technology, sweden. each kernel represents the interface between the actual dynamical model and the network simulation. here, continuous simulation and digital conversion take place to transmit information through the network. this tool provides the necessary interruptions to simulate delay propagation as well as synchronization within the network. 4 concluding remarks present approach shows the integration of three modelling techniques in order to perform reconfiguration. three approaches are followed, control law design, automaton modelling and rt scheduling strategy. although there is no formal verification in order to follow this sequence, it has been adopted since structural reconfiguration provides settle conditions for control reconfiguration. the use of a real-time scheduling algorithm in order to approve or disapprove modifications on computer network behaviour allows time delays bounding during a specific time window. this local time delay bounding allows the design of a control law capable to cope with these new conditions. preliminary results show that control reconfiguration is feasible as long as the use of a switching technique predetermines when one control is the adequate. this goal is reached by a strategy compose of three algorithms, one which is responsible for structural reconfiguration and it has been implemented in this paper as art2a network. second 326 benítez-pérez h., cárdenas-flores f., garcía-nocetti f. algorithm is responsible for dynamic control design and third algorithm is based on an automaton technique to perform switching control. what it is important for this last approach is that control conditions are strictly bounded to certain response. future work is focused to produce certain evaluation metrics that allows feasible comparison between different approaches. 5 acknowledgements the authors would like to thank the financial support of disca-iimas-unam, and unam-papiit (in101307 and in105303) mexico in connection with this work. and the high performance computing proyect within the “macroproyecto tecnologas para la universidad de la información y la computación” of the universidad nacional autónoma de méxico (unam). bibliography [1] benítez-pérez h. and garcía-nocetti f., reconfigurable distributed control, springer verlag, 2005. [2] blanke m. and kinnaert m. and lunze j. and staroswiecki m., diagnosis and fault tolerant control, springer, 2003. [3] cervin a. and henriksson d. and lincoln b. and eker j. and arzén k., how does control timing affect performance, ieee control systems magazine, vol. 23, pp. 16-30, 2003. [4] gudmundsson d. and goldberg k., tuning robotic part feeder parameters to maximize throughput, assembly automation publisher: mcb university press, vol. 19, no. 3, pp216-221, 1999. [5] izadi-zamanabadi r. and blanke m., a ship propulsion system as a benchmark for fault-tolerant control, control engineering practice, vol. 7, pp. 227-239, 1999. [6] jiang j. and zhao q., reconfigurable control based on imprecise fault identification, proceedings of the american control conference, ieee, pp. 114-118, san diego, june, 1999. [7] khalil h., nonlinear systems, prentice hall, 2002. [8] nilsson, j., real-time control with delays, phd. thesis, department of automatic control, lund institute of technology, sweden, 1998. [9] thompson, h., wireless and internet communications technologies for monitoring and control, control engineering practice, vol. 12, pp. 781-791, 2004. [10] wu n., reliability of reconfigurable control systems: a fuzzy set theoretic perspective, proceedings of the 36 th conference on decision & control, ieee, tp15 5:10, pp. 3352-3356, sandiego, usa, 1997. benítez-pérez h. universidad nacional autónoma de méxico departamento de ingeniería de sistemas computacionales y automatización apdo. postal 20-726., admón. no. 20 del. a. obregón, méxico d. f., cp. 01000, méxico. e-mail: hector@uxdea4.iimas.unam.mx an implementation of reconfigurable network control based upon automata proposal for three conveyor belt case study 327 cárdenas-flores f., garcía-nocetti, f. iimas, universidad nacional autónoma de méxico apdo. postal 20-726., admón. no. 20 del. a. obregón, méxico d. f., cp. 01000, méxico received: august 1, 2007 hector benítez-pérez is a full time researcher in the iimas unam (méxico). he obtained his bsc in electronic engineering at the engineering faculty unam in 1994 and his phd at sheffield university, uk en 1999. his areas of interest are in real time control and fault diagnosis. f. cárdenas-flores obtained his bsc in biomedical engineering at uam-iztapalapa 1990. he works in the departamento de ingenieria en sistemas computacionales iimas unam since 1994. he areas of interest are in real time, parallel computing and digital signal and image processing. f. garcía-nocetti bsc in electric engeneering at the engineering faculty at unam in 1984. mbs and phd at university of wales, uk 1988, 1991. he is a full time researcher at iimas unam since 2000. his areas of interest are architectures and algorithms for high performance computing including real time applications. he is member of ieee, iee and ifac. http://horos.rdsor.ro/ijcccv3n4draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 4, pp. 343-352 analysis and design on key updating policies for satellite networks yuxuan ji, hengtai ma, gang zheng abstract: satellite networks are becoming increasingly important because of the exciting global communication services they provide. key management policies have been successfully deployed in terrestrial networks to guarantee the information security. however, long propagation, storage and computation constraints bring new challenges in designing efficient and cost-effective key updating policies for satellite networks. based on the structure and communication features of satellite networks, a dynamic key management model for satellite networks (dkm-sn) is presented, which includes certificates owned by each satellite, primary keys and session keys both of which are shared between two satellites. furthermore, a protocol is designed for updating certificates for satellites; different policies for updating primary and session keys are studied and their efficiency and security are analyzed and compared. in addition, simulation environment for satellite networks is built and the key updating processes are implemented in walker constellation. from the simulation results, further contrasts on key updating time and storage costs between the applications of ibm hybrid key management model (hkmm) and dkm-sn in satellite networks are presented. finally, important suggestions in designing key updating policies are given. keywords: key updating, satellite networks, model, protocol, simulation 1 introduction satellite networks are composed of various kinds of communication satellites, vehicles and constellations. it contains both satellite-to-satellite and satellite-to-ground links. satellite networks integrate terrestrial systems and all sorts of satellites which are deployed in different orbits with diverse tasks. nowadays, satellite networks are increasingly used in the long-distance information transmission services. in order to ensure the message confidentiality, integrity and nonrepudiation, as well as efficiency of communication, a key management mechanism should be used to provide data encryption, authentication and key distribution and updating services for satellite communication. key management model defines the entities in the services, the categories and relationships of the keys, and the key updating protocols and algorithms. in contrast with terrestrial networks, satellite networks are subject to dynamic network topology, long propagation delay, as well as low computing and storage capabilities of satellites. due to these constraints, efforts should be made to decrease the key updating time in order to reduce the communication cost. besides, for those keys used to encrypt large amount of information, the key updating protocols should be based on symmetric encryption techniques for the sake of computation cost. the storage cost on satellites should also be reduced by efficiently lowering the number of the keys. currently, key management policies for terrestrial networks are comparatively sophisticated. there are generally three kinds of key management policies, including those that use symmetric key encryption techniques, public key encryption techniques and combination of the two. for example, kerberos [1] uses symmetric key encryption techniques and a kdc (key distribution center); both symmetric and public key encryption techniques are adopted in ibm hybrid key management model (hkmm) in which three kinds of keys including session key, primary key and public key are used. based on different network features and environments, the number of entities and encryption techniques involved in updating a certain type of key may be very different. for instance, though both for updating the session key, neuman-stubblebine protocol [3] includes three parties while janson-tsudik protocol [4] involves only copyright © 2006-2008 by ccc publications 344 yuxuan ji, hengtai ma, gang zheng two. besides, the encryption technique used to update session key can be either symmetric or public. however, all above key management policies are only applicable in terrestrial networks. some problems concerning key management policies in satellite networks have been studied. after studying a series of possible security threats, ccsds showed the urgency and necessity to implement security policies in satellite networks, such as key management, authentication, access control, etc [5]. ayan roy-chowdhury analyzed some problems that occur during encryption and key distribution processes when applying ipsec and ssl into satellite networks [6]. their discussion was based on a hybrid satellite network with a single satellite component and several ground terminals. cruickshank designed an authentication and key establishment protocol for two satellite users who need to encrypt data and voice information [7]. since the public encryption technique was used, the method was applicable in situations where the satellites are only used to relay messages rather than implement public encrypting operations which entail a large amount of computation cost. tanya vladimirova et al. introduced some security services required on satellites, proposed an on-board security architecture and aes fault-tolerant mechanism [8]. however, current literature seldom studies the key management policy for satellite networks with communication links between satellites. we focus on this issue and mainly discuss the categories of keys, the protocols for updating keys and the key updating efficiencies of different policies. hkmm uses session key, primary key and public key. however, the session key updating in hkmm may cause unendurably long propagation if directly applied in satellite networks. in order to solve this problem, a dynamic key management model applicable in satellite networks (dkm-sn)is presented, which also includes session keys, primary keys and public keys. based on dkm-sn, a protocol is designed for updating public keys in satellite networks. by further studying the protocols such as neumanstubblebine, janson-tsudik and improved beller-yacobi protocols [9], the efficiency and security issues are analyzed in designing policies for updating primary keys and session keys in satellite networks. finally, the differences on time and storage costs between hkmm and dkm-sn are shown through simulations under the satellite network environment. the rest of this paper is organized as follows. in section 2, we discuss dkm-sn, detailing the designing principles of all three parts including public key, primary key and session key updates. section 3 presents the simulation results under satellite network environment. we conclude with a short summary and extensions on future work in section 4. 2 key management model in satellite networks, communication process should be kept secret in order to ensure the message confidentiality. besides, robust and secure protocols are indispensable so that the communication process can resist well-known attacks, such as arrogating, playback, modification and so on. therefore, we design a dkm-sn to ensure the secrecy, authenticity and integrity of messages, and at the same time decrease the time, storage and computation costs with best efforts when providing key updating services. dkmsn consists of public, primary and session keys, which are divided based on their functions. firstly, every satellite has a pair of public and private keys, and a certificate issued by ca (certificate authority), all of which are updated through communication between a certain satellite and ca. secondly, there is a primary key shared between a pair of satellites and it is updated with their public key information (including public and private keys and certificates). besides, two satellites also need to share a session key when they communicate with each other and the session key is updated by the primary key shared between them. the differences between dkm-sn and hkmm are: (1) hkmm uses kdc while dkmsn does not; (2) each primary key in hkmm is shared between satellite and kdc while it is shared between two satellites in dkm-sn; (3) in order to lower storage cost, dynamic primary key updating policy is adopted in dkm-sn, which means that for some pairs of satellites their primary keys exist only when they need to establish session keys to communicate. figure 1 shows hkmm on left and dkm-sn on right. p denotes primary key, s for session key, t for terminal in terrestrial networks, and v for satellite. we can see that in hkmm the primary keys analysis and design on key updating policies for satellite networks 345 are shared between each terminal and kdc while the session keys are shared between every pair of terminals. for instance, in hkmm, session key s1 can be established by terminal 3, terminal 4 and kdc using primary keys p3 and p4. in dkm-sn, both the primary and session keys are shared between two satellites. for instance, session key s21 needs to be established and shared only by two satellites v2 and v3 using their primary key p2. p1 p2 p3 p4 p5 s1 s2 s3 p6p5p4 p3 p2 p1 s21, 22, ... s11, 12, ... hkmm dkm-sn ca kdc/ca t1 t2 t3 t4 t5 v1 v2 v3 v4 v5 figure 1: key management models: hkmm and dkm-sn 2.1 certificate updating generally, an entity’s public key information is updated by itself before it expires. based on pkix standards [10], we design a proper protocol for updating public key information. 1. b → m : b, pkb,cert(b) 2. m → b : m, pkm, [cert(m)], pk ′ m, e(pk −1 m , h(pk ′ m, m, b)) 3. b → m : cert(m)′ b, m ca and satellite m; pkx , pk −1 x , cert(x ) old public key, private key and certificate of satellite x; pk ′ x , (pk ′ x ) −1, cert(x ) ′ new public key, private key and certificate of satellite x; e(k,y ) public key encryption with key k and plaintext y ; h(y ) hush function; [y ] y is optional. the execution of this protocol between ca and satellite proceeds as follows: 1. ca sends its own identifier, public key and certificate to satellite and informs the satellite to start updating its public key information. 2. upon receiving message from ca, the satellite does the followings: (1) check validity of ca’s certificate; (2) generate a new pair of public and private keys for itself; (3) get a signature with its old private key and send this signature, its identifier, certificate and both old and new public keys to ca. 3. upon receiving messages from satellite, ca does the followings: (1) check the validity of satellite’s old certificate; (2) check satellite’s signature; (3) generate a new certificate for the satellite’s new public key. 4. upon receiving new certificate from ca, satellite can check its validity with ca’s public key. 346 yuxuan ji, hengtai ma, gang zheng note that the execution can continue only when the checks in the last step are successful. now we consider some possible attacks on ca and satellite when running the protocol. 1. attacks on ca (1) in the second step of our protocol, it is possible that adversary may arrogate satellite m by sending m’s certificate and a public key the adversary generated. however, the adversary has to send a signature at the same time. since we assume that the adversary doesn’t know the satellite’s old private key before it expires, then the adversary could not get a correct signature and would be detected by ca. (2) also in the second step, the adversary may try to replay a signature signed by the satellite’s private key which has expired. obviously, only if both satellite and ca use their valid public key information that hasn’t expired, our protocol could resist this playback attack. 2. attacks on satellite (1) since satellite m’s new public key is sent without encryption, the adversary may generate a certificate for m by signing m’s identifier and the new public key with its own private key, and then sends this false certificate to m in the third step. however, this attack would fail after satellite has received the certificate and checks its validity using ca’s public key. (2) as an alternative, in the third step the adversary could also replay a certificate that has expired. however, the fact is that in order to keep the freshness of every run of the protocol we require that new public and private keys are different from before. for this reason, the expired certificate would be proved invalid when the satellite checks it. the above discussion is suitable for a satellite. when updating the public key information for ca, we should first update it before it expires and then update certificates for all the satellites using ca’s updated certificate. 2.2 primary key updating although all satellites have their own public key information, we avoid using it to encrypt data due to the high complexity and low efficiency of public key encryption algorithms. in most systems, public key information is used to establish symmetric keys so that encryption on data could be faster. we consider two strategies for establishing symmetric keys with public key information: (1) using public key information, two satellites directly establish a shared session key to encrypt data; (2) they first establish a primary key between them with their public key information and then use this primary key to establish their session keys. both primary key and session keys are symmetric keys shared between the two satellites. in the first strategy, though the cost of data encryption is low, the cost of frequent session key updates remains high. as for the second strategy, since both of frequent data encryption and session key updates adopt symmetric key encryption techniques, it is more efficient than the first one. note that the primary keys are updated much less frequently than session keys. hkmm includes primary keys and a kdc in its system. there is a unique primary key shared between kdc and each terminal (or entity). hence, the number of primary keys in hkmm is n. in dkm-sn, the second strategy is adopted, but no kdc is deployed in our network and each unique primary key is shared between two satellites. we build dkm-sn in this way for the following reasons: (1) kdc may become a bottleneck since all the session key updates have to pass kdc; (2) satellite networks are typical of long propagation. since the session key updating process involving three parties (kdc and two satellites) has to be finished within at least 4 steps while that involving two parties (two satellites) in dkm-sn can be achieved within 3 steps, the former would be much slower than the latter. in analysis and design on key updating policies for satellite networks 347 satellite networks, even one trip may lead to unendurably long propagation due to the multi-hop routing and long space distance between two satellites. as for the communication protocol when updating primary keys, improved beller-yacobi protocol [9] is suggested. this sophisticated protocol is qualified for updating a symmetric key with public key information and engages only two entities. besides, this protocol is suitable for the satellites that have limited computing power. 2.3 session key updating session keys are distributed in the network to encrypt the large amount of data, such as images, languages, commands and so on. according to the previous analysis, session key in dkm-sn is shared between two satellites and updated by the primary key between the same pair of satellites. session keys must be updated more frequently than certificate and primary keys because they are used more often. in this way, the possibility of ciphertext-only attacks can be decreased. obtaining an old session key is not once and for all, since the attacker must intercept and analyze new ciphertexts in order to get new session keys. as for the protocols for updating session keys, security and efficiency are of most importance. first of all, the protocol should be able to resist well-known attacks, such as replay, modification, typing, reflection and so on. besides, it should not cost too much time and storage. based on satellite network features, such as long propagation and limited resources, janson-tsudik 2pkdp [4] is suggested for updating session keys. this protocol is illustrated as follows. 1. a → b : a, nab 2. b → a : au t hkab (nab, kba, b), ekab (n ′ ba ) ⊕ kba 3. a → b : ackkab (nab, kba, a) this key establishment protocol contains the minimum cost of computation and numbers of messages and steps as proved in [4]. a detailed comparison of the time and computation costs between session key updating protocol with kdc and that without kdc is presented as follows. as for session key updates with kdc, we use neuman-stubblebine protocol [3] which also has the minimum number of steps involved. according to janson-tsudik 2pkdp, we get the calculation time c jt in a single entity and overall session key establishment time tjt : tjt = 3∗ tab + 4∗ tmac + 2∗ tes + 2∗ t⊕ c jt = 2∗ tmac + tes + t⊕ (1) tmac calculation time of mac() function; tes calculation time of symmetric encryption; t⊕ calculation time of x or operation; tab propagation delay between satellites a and b. since t⊕ ≤ any other item, so we get: tjt ≈ 3∗ tab + 4∗ tmac + 2∗ tes c jt ≈ 2∗ tmac + tes (2) as for neuman-stubblebine protocol, we get the calculation time cns for a single node and overall session key establishment time tns similarly: tns = (2∗ tab + tac + tbc) + 8∗ tes cns = 8∗ tes (3) 348 yuxuan ji, hengtai ma, gang zheng tac propagation delay between kdc and satellite a; tbc propagation delay between kdc and satellite b. because propagation delay is much larger than the calculation time of either encryption or mac() in satellite networks, we conclude from the above estimation that both the calculation time cost of a single node and overall session key updating time cost of janson-tsudik 2pkdp are much less than those of neuman-stubblebine. for the three-party session key establishment policy, kdc may become a bottleneck in the system. fortunately, the two-party session key establishment policy could avoid this drawback. 3 simulations in order to verify the effectiveness and suitability of dkm-sn in satellite network environment, a proper simulation environment for satellite networks is very important and necessary. based on the simulation system for distributed satellite networks [11], we design a simulation scenario and implement the experiments. we set up a walker constellation consisting of 20 meo satellites. there are 4 orbital planes on each of which 5 satellites are equally deployed. each plane is of an inclination of 75◦ and orbit height 14163km. in certificate updating simulation, the protocol designed in section 2.1 is applied and 160-bit ecdsa [12] is implemented as signature algorithm. in primary key updating, improved beller-yacobi protocol is applied. ecdsa and ecies [13] are used as signature and encryption algorithms respectively. in session key updating, janson-tsudik 2pkdp and 128-bit aes are implemented. 3.1 certificate updating simulation dynamic topology in this part, we examine the influences of topological changes on the certificate updates for a certain satellite. dynamic changes of satellite network topology lead to the variations of routing tables in satellites. therefore, the time cost of certificate updating for a certain satellite varies with time. figure 2 shows both the overall and computation time costs of certificate updates for a certain satellite in different periods of time. we update a satellite’s certificate every 10 seconds. figure 2 shows the time cost during 500 seconds. the longest updating time for the satellite is 834.200ms. routing information shows that in the longest updating data transmission from the satellite to ca entails 5 hops and thus the three-step interaction between them entails 15 hops all together. the minimum time cost is 167.45ms when there is only 1 hop between satellite and ca and so it costs 3-hop effort to finish the certificate updating process. based on the result, we suggest update certificates when communication between satellite and ca needs the minimum number of hops. static topology in this part, we examine the certificate updates of different satellites in walker constellation during a fixed period of time. figure 3 shows both the overall and computation time costs of certificate updates for different satellites during a fixed period of time in walker constellation. we can see that there are huge gaps of time costs among the satellites since the number of hops between ca and the satellites are very different. in the simulation, for example, there are 5 hops between ca and no.6 satellite while 1 hop between ca and no.4. analysis and design on key updating policies for satellite networks 349 0 100 200 300 400 500 600 700 800 900 time (s) overall time cost computation time cost 5 10 15 20 25 30 35 40 45 50 time cost (ms) figure 2: time cost of certificate updating of a certain satellite in different periods of time 0 200 400 600 800 1000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 samples overall time cost computation time cost time cost (ms) figure 3: time costs of certificate updates of all satellites in the same period of time 350 yuxuan ji, hengtai ma, gang zheng besides, from figure 2 and figure 3 we can see that the computation cost for different satellites varies little. it is a small fraction of the overall time cost of certificate updates, ranging from 5% to 40%. 3.2 comparison of computation time costs figure 4 shows the computation costs of all three kinds of key updates in walker constellation. 20 samples are presented for each kind of updates. 0 20 40 60 80 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 samp les p rimary key up dating session key up dating certificate up dating time cost (ms) figure 4: computation costs of three kinds of key updates we see from figure 4 that session key update costs least, which is suitable for the most frequently updated session keys. the average time cost of certificate updates is less than that of primary key updates mainly due to the extra public key encryption in the improved beller-yacobi protocol. 3.3 comparison between key management models comparing dkm-sn with hkmm, we mainly have the following conclusions. firstly, the time costs for session key updates in dkm-sn are much less than that in hkmm. secondly, dkm-sn will contain more primary keys than hkmm if all primary keys are pre-distributed between all pairs of satellites. since the storage capacity is limited in satellite, we recommend that for some pairs of satellites their primary keys exist only when necessary. therefore, if we want to establish a session key between two satellites while they share no primary key, we need to first establish a primary key with their public key information and then establish the session key with their primary key. a much detailed comparison between hkmm and dkm-sn is presented as follows. suppose the total number of satellites is n; the average time cost of primary key updates is z. in dkm-sn, let a denote the maximum number of primary keys, namely a = n×(n−1) 2 ; h denotes the average time cost of session key updates when there is a shared primary key between every pair of satellites; x denotes the practical number of primary keys; y denotes the average time cost of session key updates when x pairs of satellites share primary keys. in hkmm, suppose the average time cost of session key updates is h + δ , δ > 0. based on dkm-sn, we have, y = x × h + (a − x) × (z + h) a = (z + h) − z a × x (4) obviously, given x = 0, we get y = z + h. this means there is no primary key pre-shared in dkmsn and for every two satellites before establishing a session key we should first set up a primary key. similarly, given x = a, we get y = h. under this condition, each pair of satellites owns a primary key. analysis and design on key updating policies for satellite networks 351 if y > h + δ , we get x < a×(z−δ ) z . so we conclude that when n < x < a×(z−δ ) z , hkmm is superior to dkm-sn in both average time cost of session key updates and storage cost. specifically, based on the simulation data, we have z ≈ 400ms, h ≈ 360ms, δ ≈ 120ms, n = 20. under this condition, y = (z + h) − z a × x ≈ 760 − 2 × x. figure 5 shows the relationship between average time cost of session key updates and the number of primary keys in dkm-sn. 760 480 average time cost (ms) number of primary keys 14020 2000 360 40 60 80 100 120 160 180 figure 5: average time cost of session key updates in dkm-sn given y > h + δ , namely 760 − 2 × x > 480, we get x < 140. we can see from the linear function shown in figure 5 that if the number of primary keys ranges from 20 to 140, hkmm is better than dkmsn in both time and storage costs. when 140 < x ≤ 200, average time cost of session key updates in dkm-sn is less than that in hkmm while the number of keys is more than that in hkmm. furthermore, if we can deploy the primary keys for those satellites that communicate most frequently, the average time cost function of the number of primary keys will be a concave function shown in figure 5. we can make estimation and decision given real on-board data in satellite networks. for examples, if satellites could store the maximum number of primary keys, pre-allocating a primary key for every pair of satellites is the best choice, which is especially suitable for a network with a small number of satellites. 4 summary and conclusions in this paper, a new key management model called dkm-sn is designed for satellite networks. a protocol for updating satellite certificate is designed and the efficiency and security of different policies for updating primary and session keys are analyzed. the performance is shown when updating three kinds of keys and a further contrast between dkm-sn and hkmm is given in both time cost and storage cost. the efficiency and applicability of different key updating policies are discussed under different onboard storage constraints and time requirements. in the future, we will further discuss the problems in designing efficient, low-cost and secure key management models for satellite networks. bibliography [1] j. kohl, c. neuman, the kerberos network authentication service (v5), http://www.ietf.org/rfc/rfc1510.txt, rfc 1510, 1993. 352 yuxuan ji, hengtai ma, gang zheng [2] v. le, s. m. matyas, d. b. johnson and j. d. wilkins, a public key extension to the common cryptographic architecture, ibm system journal, vol. 32, pp. 461-485, 1993. [3] b. c. neuman and s. g. stubblebine, a note on the use of timestamps as nonces, acm operating systems reviews, vol. 27, pp. 10-14, 1993. [4] philippe janson and gene tsudik, secure and minimal protocols for authenticated key distribution, computer communications, vol. 18, pp. 645-653, 1995. [5] ccsds, security threats against space missions, washington: informational report, ccsds 350.1-g-1, green book, issue 1, 2006. [6] a. roy-chowdhury et al., security issues in hybrid networks with a satellite component, ieee wireless communications, vol. 12, pp. 50-61, 2005. [7] h s cruickshank, a security system for satellite networks, fifth international conference on satellite systems for mobile communications and navigation, london: iee, pp. 187-190, 1996. [8] tanya vladimirova, roohi banu and martin n. sweeting, on-board security services in small satellites, mapld international conference, washington: nasa office of logic design, 2005. [9] c. boyd and a. mathuria, key establishment protocols for secure mobile communication: a selective survey, lecture notes in computer science, vol. 1438, pp. 344-355, 1998. [10] j. schaad, m. myers, public-key infrastructure (x.509), www.ietf.org/html.charters/pkixcharter.html, ietf, pkix 2797. [11] x. ying, z. gang, modeling and distributed simulation for satellite networks, computer simulation, vol. 25, pp. 65-69, 2008. [12] ansi x9.62, public key cryptography for the financial services industry: the elliptic curve digital signature algorithm (ecdsa), american national standards institute, 1999. [13] ansi. x9.63, public key cryptography for the financial services industry: key agreement and key transport using elliptic curve cryptography, american national standards institute, 2001. yuxuan ji institute of software, chinese academy of sciences national key laboratory of integrated information system technology 4# south fourth street, zhong guan cun, beijing 100190, p.r. china e-mail: jiyuxuan06@gmail.com hengtai ma institute of software, chinese academy of sciences national key laboratory of integrated information system technology 4# south fourth street, zhong guan cun, beijing 100190, p.r. china e-mail: htma@ios.cn gang zheng institute of software, chinese academy of sciences national key laboratory of integrated information system technology 4# south fourth street, zhong guan cun, beijing 100190, p.r. china e-mail: gangzhengcn@yahoo.com.cn international journal of computers, communications & control vol. iii (2008), no. 1, pp. 21-32 electroglottographic measures based on gci and goi detection using multiscale product aicha bouzid, noureddine ellouze abstract: this paper deals with glottal parameter estimation such as local pitch and open quotient from electroglottographic signal (egg). this estimation is based on glottal closing instants and glottal opening instants determined by a multi-scale product of this signal. wavelet transform of egg signal is made with a quadratic spline function. wavelet coefficients calculated on different dyadic scales, show modulus maxima at localized discontinuities of egg signal. the detected maxima and minima correspond to the glottal opening and closing instants called gois and gcis. to improve the estimate precision, we operate the multi-scale product of wavelet transform coefficients of three successive dyadic scales. this processing enhances edge detection. a multi-scale product is a nonlinear combination of successive scales; it reduces noise and spurious peaks. we apply cubic root amplitude on the product to improve the representation of weak amplitudes. the method has a good representation of gci and a best detection of goi. the method was tested on the keele university database; it is effective and robust in multiple cases even for a typical signal showing undetermined gois and multiple peaks at gcis. finally precise measurement of these instants allows accurate estimation of prosodic parameters as local pitch and open quotient. keywords: wavelet transform, multi-scale product, electroglottographic signal, glottal closing instant, glottal opening instant 1 introduction electroglottography is a non-invasive medical exploration technique of a glottal activity. the resulting signal called electroglottogram (egg) is a common and efficient reference signal for pitch estimation. egg signal can be used to determine glottal closing instant (gci) and glottal opening instant (goi). gci is commonly used in speech processing like voiced/unvoiced classification, accurate source parameter estimation and robust instant detection; it is very useful in synchronous speech analysis and synthesis. goi is useful for voice quality estimation and other voice and speaker characterization. in the present work, the estimated gci and goi are used to calculate the local fundamental frequency and the glottal open quotient. referring to childers [1], egg signal presents an important amplitude variation at gci, indicating a rapid behaviour change of the source (glottis). the derivative of the egg signal, called degg shows strong peaks at closing instants and weak ones at opening instants. referring to mallat [2], important information lies in sharp transitions on the signal or its derivative. these singularities are detected by following the wavelet transform modulus maxima at fine scales. as we have shown in previous work [3], wavelet transform is efficient in most cases for detecting singularities in egg signal at closing instants, but not so for glottal opening instant detection. in fact, large variations of amplitude on egg signal can be observed at gcis, however at gois the discontinuities are less obvious and can hardly be detected on the signal [4]. referring to sadler [5], [6], the multiscale product method (mpm) is the product of wavelet transform coefficients on different scales. the scale multiplication gives better results of discontinuity detection than any single scale, especially on the localization performance [7], [8], [9]. this method is used for edge detection in image processing. the aim of this paper is to present a new measurement method of glottal parameters from egg signal, such as goi, gci, the signal fundamental frequency f0 and the open quotient oq. different time and spectral based methods have been proposed for estimating the pitch period and the open quotient oq. in this work, we propose to apply the copyright © 2006-2008 by ccc publications 22 aicha bouzid, noureddine ellouze multiscale product method (mpm) on the egg signal, in order to improve the accuracy of goi and gci measurement, so as to obtain better estimation of pitch and open quotient measures. the present paper is organised as follows. after introduction, section 2 focuses the well-known methods applied on egg signal for gci and goi detection and their related problems. the first method uses various thresholds (or crossing levels) of the egg signal, the second method is based on the derivative of egg signal. besides, it is shown that in many cases, these methods present undetermined goi and in fewer cases ambiguous gci. section 3 presents the multiscale product method and its application to egg signal, in order to improve the detection of gois and gcis. section 4 presents a multiscale product enhancement for gci and goi detection based on the cubic root of the mp (crmp) and its comparison to the mp and the degg methods . section 5 deals with egg parameter measurements given by the crmp, the crossing level and the degg extrema methods. finally, section 6 concludes this work. 2 gci and goi detection methods and limitations glottal opening instants (goi) and closing instants (gci) estimation can be carried out on egg signal, by different methods using time or frequency based methods. here, we present two basic methods: the first method is based on egg amplitude crossing level (with different levels), and the second method is based on maxima and minima detection on the degg signal. egg threshold method rothenberg et al. [10] use the 50% crossing level of the signal amplitude from a base line. this method gives a direct value of goi and gci from egg signal in the case of modal and tense voice. 35% of the maximum amplitude of the egg has been also used as a threshold; this ratio gives also direct values of the gci and goi. howard [11] uses the 3/7 maximum egg amplitude threshold for goi detection. illustrations of these methods are depicted in figure 1. the major drawback of such methods is the lack of accuracy for the gci detection and a missing of goi. 0 50 100 150 −1 −0.5 0 0.5 1 0 50 100 150 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 goi 50 % 3/7 35 % gci gci goi figure 1: egg signal (female voice, speaker f4, vowel [o]), 3 crossing levels, and the degg. the top of figure 1 depicts the case of egg signal of a female voice pronouncing the vowel [o], the crossing levels of 50%, 3/7 and 35% of the amplitude allowing the estimation of goi and gci. degg maximum and minimum detection method a more precise method for gci detection uses the degg signal [12]. childers characterises the egg inflexion points by the degg [13], [14]. experimental investigation shows that degg signal shows two opposite peaks in each period. the strong peak corresponds to gci and the weak one corresponds to goi [15], [16], [17]. an illustration of the derivative method is depicted at the bottom of figure 1. goi and gci detection related problems electroglottographic measures based on gci and goi detection using multiscale product 23 methods based on egg and degg, for goi and gci detection have different problems, such as missing events or duplication. examples are taken from the keele university database. it is a speech database containing acoustics and egg signals simultaneously recorded in a soundproof room. it is made by five adult females and five adult males speakers. each utterance consists of the same phonetically balanced english text. egg and speech signals are given with the same sampling frequency of 20 khz [18]. many cases of missing peaks appear on degg at glottal closing instants [19]. some gcis and gois are indiscernible on the degg. this glottal behaviour is observed by anastaplo and karnell [20]. pérez et al. underline the difficulty of goi detection from the derivative of egg signal [21]. 0 500 1000 1500 2000 2500 3000 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0 500 1000 1500 2000 2500 3000 −0.12 −0.1 −0.08 −0.06 −0.04 −0.02 0 0.02 figure 2: egg signal of a male voice (speaker m2, voiced sound) and degg depicting the case of gci and goi missing. gci missing has a considerable effect on the time period measure. goi missing influences the oq measure. figure 2 illustrates problematical cases where threshold methods on egg or degg fail to detect gcis and gois. this example shows egg signal of a voiced sound of the male speaker m2 and its derivative. in many cases multiple peaks appear on degg at glottal closing instants and opening ones [19]. a typical example is presented in figure 3. in this case, the degg signal shows undetermined open instant and a double closing instant. egg signal used for this example is a vowel [i] of the female speaker f2. we can distinguish that the egg signal presents noise due to the subject movement during the recording. another typical example is shown on figure 4. here the degg signal shows glottal opening and closing instants with a poor precision. the egg signal used in this example is a voiced fricative [z] of the a male speaker m5. we can distinguish that the egg signal presents natural noise of the fricative vowel. 3 goi and gci detection by multiscale products it is commonly known that wavelet transform is an efficient tool for detecting and characterizing signal singularities [2]. singularities of the signal are detected by finding abscissa, where the wavelet modulus maxima converge at fine scales. the singularity type of the signal is characterised by wavelet vanishing moments and the decay of maxima across scales. this is explained by the fact that wavelet transform with n vanishing moments can be interpreted as a differential operator of nth order of the signal, smoothed by the primitive function of the wavelet called smoothing function. so if the wavelet is chosen to have one vanishing moment, modulus maxima appear at discontinuities of the signal, and 24 aicha bouzid, noureddine ellouze 0 50 100 150 200 250 300 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0 50 100 150 200 250 300 −0.3 −0.25 −0.2 −0.15 −0.1 −0.05 0 0.05 0.1 0.15 unclear goi redoubled gci figure 3: egg signal (speaker f2, vowel [i]) and the degg depicting the case of double and imprecise peaks at gci and goi. 0 100 200 300 400 500 600 700 800 900 −1 −0.5 0 0.5 1 0 100 200 300 400 500 600 700 800 900 −0.3 −0.2 −0.1 0 0.1 0.2 figure 4: egg signal (speaker m5, voiced fricative [z]) and the degg depicting the case of double and imprecise peaks at gci and goi. electroglottographic measures based on gci and goi detection using multiscale product 25 represent the maxima of the first derivative of the smoothed signal. in previous work [3], we showed that the local regularity of the egg signal can be characterised by the wavelet transform modulus maxima. modulus maxima of egg signal present two types of located singularities indicating glottal closing and glottal opening instants. the greater peak correspond to gci and the weak one to the goi. modulus maxima give an estimation of the events with a better precision for small scales. but no scale can give an accurate value of gci and goi, so as singularities are too smoothed for large scales, and too weak for small scales, to be well located. figure 5 shows the egg signal of a voiced of speaker m2 followed by its wavelet transforms at the following scales 1/2, 1 and 2. here wavelet transform (as well as degg), can’t detect some singularities of the signal. missed events still exist. that’s why multiscale analysis seems to be necessary to improve the egg edge localisation. the products of coefficients across scales are frequently used for image analysis. witkin [20] provided the foundation for scale space theory by generalizing rosenfeld’s work [22], in which smoothing filters at dyadic scales are used. based essentially on forming multiscale products of smoothed gradient estimates, this approach attempts to enhance the peaks of the gradients caused by true edges, while suppressing false peaks due to noise. the wavelet transform acts as an edge detector, and the detail coefficients should be equivalent to the estimated gradients. to distinguish edge maxima from noise and inappropriate maxima, mallat and zhong [23] analyze the singularity properties of wavelet transform domain maxima across various scales. the first derivative of a gaussian and the quadratic spline are used to play this role. xu et al. rely on the variations in scale of the wavelet transform. direct multiplication of wavelet transform data at adjacent scales are used to distinguish important edges from noise [9]. sadler and swami [6] studied multiscale product method of a signal in presence of noise. in wavelet domain, it is well-known that edge structures are present at each subband while noise decreases rapidly along the scales. it has been observed that multiplying the adjacent scales could sharpen edges while diluting noise [6], [9]. the expression of multiscale product is given by: p(n) = 3 ∏ j=1 ωs j ( f (n)) (1) where ωs j ( f (n)) is the wavelet transform of the function for the dyadic scale sj. the product p(n) has the property to reveal peaks at signal edges, and has relatively small values elsewhere. thus, singularities produce peaks along scale in wavelet transform. these peaks are reinforced by the product p(n). the signal peaks will just align across the first few scales, not all of them because increasing the amount of smoothing will spread the response and cause singularities separated in time to interact. thus, choosing too large scales will result in misaligned peaks in p(n). an odd number of terms in the product preserves the sign of the edge. choosing three dyadic successive scales is an optimal solution in multiscale product for detecting small peaks. as multiscale product improves the edge detection, we apply mpm to egg signal to outperform the detection of gci and goi and to improve the measure precision particularily for weak singularities at goi. at the bottom of figure 5, we find the mp of the egg signal of a voiced sound of speaker m2. we can clearly see the effect of the product on cancelling the additional noise peaks present at its derivative depicted in figure 2 and consequently the best detection of goi. we note the efficiency of the mpm to strengthen the gci and its ability to detect gois that become clear enough. however, some missing events still exist. that’s why multiscale product needs to be enhanced for an accurate gci and goi localisation. 26 aicha bouzid, noureddine ellouze 0 500 1000 1500 2000 2500 3000 −0.5 0 0.5 0 500 1000 1500 2000 2500 3000 −1 0 1 0 500 1000 1500 2000 2500 3000 −2 0 2 0 500 1000 1500 2000 2500 3000 −2 0 2 0 500 1000 1500 2000 2500 3000 −1 0 1 figure 5: egg signal of a voiced sound of speaker m2, the 3 wavelet transforms and their product. 4 multiscale product enhancement for goi and gci detection we have seen that multiscale product gives better results than wavelet coefficients. we use the cubic root of multiscale product to improve results for weak peaks. the cubic root of multiscale product has a zooming effect which enhances peak representation. this section deals with this basic enhancement and the comparative result detection of gci and goi detection by degg, mp and cubic root of mp. specific examples from the keele university database are presented below. figure 6 shows an example of egg signal and its derivative, which has precise peaks at goi and gci. the example is a frame of vowel [o] uttered by the speaker f4. the figure respectively shows the egg signal, its derivative, the multiscale product of the following scales 1/2, 1 and 2 and the cubic root of the products. the multiscale product depicts the resulting cross-scale product p(n) for three scales and shows clean peaks aligned with the degg signal edges. first, we note two types of peaks in the cross-scale product; those corresponding to gci are more distinguishable than those related to goi. the modulus cubic root shows maxima at goi and gci, the weak peaks at gois given by the modulus cubic root of the product are better represented and effectively reinforced than those obtained by the multiscale product. figure 7 illustrates the example of a female utterance of vowel [i] where the gois are undetermined and the peaks of gci present irregular structures. we note double peaks of glottal closing that bring about inaccurate measurements. besides, we can clearly see the effect of the product in cancelling the additional peaks, which, then, gives a better detection of goi. the modulus cubic root is used to reinforce the small peaks corresponding to goi. this figure underlines the importance of the modulus cubic root of the multiscale product. figure 8 shows the egg signal of fricative [z] of speaker m5, the degg signal, the multiscale product and its modulus cubic root. figure 9 shows the same representation for a voiced sound of speaker m2. these examples illustrate the modulus cubic root applied on the multiscale product of egg signal. in figure 9, continuous lines indicate gci and dotted lines indicate goi for missing events. in fact the cubic root takes out peaks at goi and gci that don’t exist either in degg signal or in the product. it’s a complex case. thus, we can see that modulus cubic root of multiscale product can give a better detection of gci and goi from the egg signal than the threshold methods and the degg approach. the mp cubic root reinforces the gci minima indiscernible not only at degg but also at mp. consequently the mpm associated with the cubic root gives the most efficient reference measures of goi and gci. electroglottographic measures based on gci and goi detection using multiscale product 27 0 50 100 150 200 250 300 −1 −0.5 0 0.5 1 0 50 100 150 200 250 300 −1 −0.5 0 0.5 0 50 100 150 200 250 300 −150 −100 −50 0 50 0 50 100 150 200 250 300 0 2 4 6 figure 6: egg signal of vowel [o] (speaker f4), degg, mp and its modulus cubic root. 0 50 100 150 200 250 300 −1 −0.5 0 0.5 1 0 50 100 150 200 250 300 −0.4 −0.2 0 0.2 0.4 0 50 100 150 200 250 300 −100 −50 0 50 0 50 100 150 200 250 300 0 1 2 3 4 figure 7: egg signal of vowel [i] (speaker f2), degg, mp and its modulus cubic root. 0 100 200 300 400 500 600 700 800 900 −1 0 1 0 100 200 300 400 500 600 700 800 900 −0.5 0 0.5 0 100 200 300 400 500 600 700 800 900 −1 −0.5 0 0.5 0 100 200 300 400 500 600 700 800 900 0 0.5 1 figure 8: egg signal of voiced fricative [z] ( speaker m5), degg, mp and its modulus cubic root. 28 aicha bouzid, noureddine ellouze 0 500 1000 1500 2000 2500 3000 −0.4 −0.2 0 0.2 0.4 0 500 1000 1500 2000 2500 3000 −0.15 −0.1 −0.05 0 0.05 0 500 1000 1500 2000 2500 3000 −2 −1 0 1 0 500 1000 1500 2000 2500 3000 0 0.5 1 1.5 figure 9: egg signal of a voiced sound (speaker m2), degg, mp, and its modulus cubic root. 5 egg parameter measurements this section deals with the local pitch period and the local open quotient measurement. these can be calculated by using the goi(k) and gci(k). the glottal closing instant represent the beginning of the pitch period. the glottal opening instant goi (k) corresponds to the beginning of the open phase. measurement of f0 and oq local pitch period is given by the following formula t0(k) = gci(k + 1)−gci(k) (2) the local fundamental frequency f0(k) is given by f0(k) = 1 t0(k) (3) the open quotient is defined as the ratio between the duration of the glottis open phase and the fundamental period. open quotient is given by the following formula oq(k) = gci(k + 1)−goi(k) t0(k) (4) in section 5, it is shown that the cubic root of mp outperforms the other methods in typical cases presented in this work. moreover, this performance can be confirmed by evaluating prosodic parameter measures using the proposed method. figure 10 depicts the local fundamental frequency f0 of a voiced sound of speaker m2 corresponding to a case where the degg method gives imprecise goi and the threshold method fails in determining some ambiguous gci and goi. figure 11 depicts the local open quotient for the same utterance determined by the same methods. the threshold methods mentioned above fail in detecting some gcis and gois leading to aberrant and missing measures of the fundamental frequency and the open quotient. besides, imprecise detection of goi from degg signal leads to aberrant measures of oq as shown in periods 12 and 14 of figure 11. 6 conclusion parameter characterisation of egg signal by glottal closing instant gci and glottal opening instant goi detection from this signal is carried out by a new method called multiscale product mpm. the proposed method consists of computing the product of the wavelet transform of egg signal at three successive dyadic scales. the wavelet used is the quadratic spline. this wavelet has one vanishing moment electroglottographic measures based on gci and goi detection using multiscale product 29 0 2 4 6 8 10 12 14 16 18 20 0 20 40 60 80 100 120 140 160 180 200 number of periods fu nd am en tal fre qu en cy (h z) degg threshold mp figure 10: fundamental frequency f0 given by degg (o), 3/7 threshold (*) and mp (+) methods for a voiced sound of speaker m2. 0 2 4 6 8 10 12 14 16 18 20 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 number of periods op en q uo tie nt degg threshold mp imprecise detection of goi by degg figure 11: open quotient oq given by degg (o), 3/7 threshold (*) and mp (+) methods for a voiced sound of speaker m2. 30 aicha bouzid, noureddine ellouze and the calculated coefficients show modulus maxima at discontinuities of the signal. wavelet transform is calculated at the following scales 1/2, 1 and 2. then the cubic root amplitude of the product is calculated to enhance the resulting signal maxima. this method gives better gci and goi localisation than classical methods obtained by the crossing level or by the signal derivative, especially in typical cases of multiple peaks and undetermined gci and goi on degg signal. the non-linear products reinforce the cross-scale peaks produced at gci and especially at goi, and reduce spurious noisy peaks. efficiency of the proposed method is proved. comparative results are given with the threshold and derivative methods for glottal closing instants, glottal opening instants, and glottal parameter measurements like pitch frequency and open quotient. locating gci and goi efficiently by using the cubic root of mp allows us to constitute a robust pitch and open quotient reference. bibliography [1] d. g. childers, a. m. smith and g. p. moore, relationships between electroglottograph, speech, and vocal cord contact, folia phoniatr., vol. 36, pp. 105-118, 1984. [2] s. mallat, a wavelet tour of signal processing, second edition, academic press, san diego 1999. [3] a. bouzid, and n. ellouze, local regularity analysis at glottal opening and closing instants in electroglottogram signal using wavelet transform modulus maxima, in proc. eurospeech 2003, geneve, pp. 2837-2840, 2003. [4] n. henrich, c. d’alessandro, m. castellongo, on the use of the derivative of electroglottographic signals for characterization of non-pathological phonation, journal of acoustical society of america, vol. 115, pp. 1321-1332, 2004. [5] b. m. sadler, t. pham, and l. c. sadler, optimal and wavelet based shock wave detection and estimation, journal of acoustical society of america, vol. 104, no. 2, pp. 955-963, 1998. [6] b. m. sadler, and a. swami, analysis of multiscale products for step detection and estimation, ieee trans. inform. theory, vol. 45, no. 3, pp. 1043-1051, 1999. [7] l. zhang, and p. bao, edge detection by scale multiplication in wavelet domain, pattern recognition letters, vol. 23, no. 14, pp. 1771-1784, 2002. [8] p. bao, l. zhang, and x. wu, canny edge detection enhancement by scale multiplication, ieee trans. on pattern analysis and machine intelligence, vol. 27, no. 9, pp. 1485-1490, 2005. [9] y. xu, j. b. weaver, d. m. healy, and j. lu, wavelet transform domain filters: a spatially selective noise filtration technique, ieee trans. image processing, vol. 3, no. 6, pp. 747-758, 1994. [10] m. rothenberg, and j. j. mahshie, monitoring vocal fold abduction through vocal fold contact area, journal of speech and hearing research, vol. 31, pp. 338-351, 1988. [11] d. m. howard, variation of electrolaryngographically derived closed quotient for trained and untrained adult female singers, journal of voice, vol. 9, no. 2, pp. 1212-1223, 1995. [12] d. m. howard, g. a. lindsey, and b. allen, toward the quantification of vocal efficiency, journal of voice, vol. 4, no. 3, pp. 205-212, 1990. [13] d. g. childers, and a. k. krishnamurthy, a critical review of electroglottography, crc critical reviews in biomedical engineering, vol. 12, pp. 131-161, 1985. electroglottographic measures based on gci and goi detection using multiscale product 31 [14] d. g. childers, d. m. hooks, g. p. moore, l. eskenazi, and a. l. lalwani, electroglottography and vocal fold physiology, journal of speech hearing research, vol. 33, pp. 245-254, 1990. [15] d. g. childers, and j. n. lara, electroglottography for laryngeal function assessment and speech analysis, ieee trans. on biomedical engineering bme, vol. 31, no. 12, pp. 807-817, 1985. [16] s. anastaplo, and m. p. karnell, synchronized videoscopic and electroglottographic examination of glottal opening, journal of acoustical society of america, vol. 83, no. 5, pp. 1883-1890, 1988. [17] m. h. hess, and m. ludwigs, strobophotoglottographic transillumination as a method for the analysis of vocal fold vibration patterns, journal of voice, vol. 14, no. 2, pp. 255-271, 2000. [18] f. plante, g. f. meyer, and w. a. ainsworth, a pitch extraction reference database, in . eurospeech 1995, pp. 837-840, 1995. [19] a. bouzid, n. ellouze, contribution à la détection des instants d’ouverture et de fermeture de la glotte sur les signaux de parole voisé par transformée en ondelettes, thése de doctorat, enit, juillet 2004. [20] a. witkin, scale-space filtering, proc. int. joint conf. artif. intell., pp. 1019-1021, 1983. [21] j. pérez, and a. bonafonte, automatic voice-source parametrization of natural speech, in proc. icslp 2005, lisboa, portugal, 2005. [22] a. rosenfeld, a non linear edge detection, proc. ieee, vol. 58, pp. 814-816, 1970. [23] s. mallat, and s. zhong, characterization of signals from multiscale edges, ieee trans. on pattern analysis and machine intelligence, vol. 14, no. 7, pp. 710-732, 1992. aicha bouzid enit signal processing lab enit b. p. 37, le belvédère 1002, tunis, tunisia e-mail: aicha.bouzid@enit.rnu.tn, bouzidacha@yahoo.fr noureddine ellouze enit signal processing lab enit b. p. 37, le belvédère 1002, tunis, tunisia e-mail: n.ellouze@enit.rnu.tn received: september 1, 2007 32 aicha bouzid, noureddine ellouze dr. aicha bouzid was born in 29 april, 1975. she has diploma in electrical engineering at ecole nationale d’ingénieurs de tunis (enit) in 1998, master degree on automatic and signal processing in 2000 and ph. d. thesis at signal processing laboratory (lsts), in july 2004. she has joined the institut supérieur des etudes technologiques de sfax as associate professor in 1999. currently she is working as assistant professor in the department of electronic at the institut supérieur d’electronique et de communication de sfax. she has more than 30 publications in international journals and conferences. her research areas of interest are signal processing, speech processing and applied mathematics. pr. noureddine ellouze was born in 19 december, 1945. he received a ph.d. degree in 1977 at inp (toulousefrance), and electronic engineering diploma from enseeiht in 1968 university p. sabatier. in 1978. pr. ellouze joined the electrical engineering department at enit (tunisia). in 1990, he became professor in signal processing, digital signal processing and stochastic process. he was the head of the electrical department from 1978 to 1983 and general manager and president of irsit from 1987-1994. he is now director of research laboratory lsts at enit, and is in charge of ats master degree at enit. pr. ellouze has directed multiple masters and thesis and published more than 300 scientific papers in journals and proceedings, in the domain of signal processing, speech processing, biomedical applications, pattern recognition, and man machine communication. international journal of computers, communications & control vol. ii (2007), no. 1, pp. 84-93 a software system for online learning applied in the field of computer science gabriela moise abstract: the computer-assisted learning is a very modern study area, which can be applied to the learning process. the main objective of this paper is to present a software system for online learning based on the intelligent software agents technologies. the main ideas on which this paper is built are: to any person is associated a learning profile (the idea is based on the existence of multiple intelligences, defined by gardner [3]); the pedagogical resources can be shaped through educational semantic networks or through conceptual maps; a flexible software system in computer assisted learning must be based on the intelligent agents’ technology. the system dedicated to computer-assisted learning must be adapted to the learning profile of each student. the author presents a flexible online teaching software system, which learns to teach according to the learning profile of each student (the author defines this system in the phd thesis and includes: intelligent agent structures, reward learning algorithms, algorithms to generate plans for an agent). the application includes two agents: the supervising agent and the pedagogical agent, which determines the optimal pedagogical resources for teaching the course. the application has been designed in microsoft visual studio 6.0 and uses microsoft agent technology, which allows vocal recognition. also, the protéjé 3.0 software has been used, software that allows building ontology for computer assisted learning. the system has been experimented on the graph theory course, taught at postuniversitary computer science courses, the results proving the necessity of defining a strategy for selecting the pedagogical resources presented to the students according to their learning profile. keywords: intelligent agent, conceptual map, learning style 1 introduction a software system for online learning in the field of the computer science is proposed in this paper, a system called ilearning. the system is based on shaping the pedagogical resources through conceptual maps or semantic networks and on using intelligent agent technology.[6] the main feature of the system is its flexibility.[7] in order to use this system we have to consider the learning styles of each person.[4] donald clark says that teaching in unconformity with the learning style of the students doesn’t mean that the students don’t learn, but students learn better if the teacher presents pedagogical resources according to the learning style of the students. [13] teachers have to follow six phases in order to use the system [7]: 1. establishing the learning style of the students through an interrogation of them; 2. drawing the conceptual map of the courses using a software to build conceptual maps; 3. teaching is done based on a technique as to traversing the conceptual map of the course; 4. the teacher has to build for each node of the conceptual map a set or pedagogical resources and the system will select the best resources conforming to the learning style of students; copyright c© 2006-2007 by ccc publications selected paper from icvl 2006 a software system for online learning applied in the field of computer science 85 5. the students are evaluated in each node of the map and the teacher inputs the note in the software system; 6. the teaching takes place traversing all conceptual nodes and showing the resource to students. the system was proposed and tested by the author of this paper in the doctoral these with the same title[7]. 2 the architecture of ilearning system the system is composed by six functional components: instructional manage system, communication system, informational system, evaluation system, tutoring system, pedagogical system. [7] the figure 1: architecture of ilearning system instructional manage system is implemented by a sql server and manages databases about: faculties, students, marks, credits, entrances, registration, teachers, fees; builds reports and statistics about the educational process; plans the instructional process: study periods, evaluation periods; records educational plans; starts teaching process; takes care about securing. the communication agent is implemented by the software agent and accomplishes the communication between students, teachers, students and teachers and administration staff by e-mail, forum, chat; accomplishes the extern communication: e-mail, browser, chat, messaging tools. the informational system is implemented by the sql server and manages the courses and pedagogical resources: records the pedagogical materials on the servers, offers tools to search courses and information in the course’s databases; offers web searching tools: search engine. the evaluation agent is implemented by the software agent and builds personalized tests; evaluates students according to objectives of the instructional process; evaluates the educational process: reports and statistics. the tutor agent is implemented by the software agent and has the following roles: guides the teacher to build pedagogical resources; guides the students in the instruction process; guides the teacher in the teaching activities. the pedagogic agent is implemented by the software agent and verifies the instructional objectives (according to bloom’s taxonomy); checks the learning style of the student; defines pedagogical strategies; defines the curriculum; checks the pedagogical resources; checks the correctness of the evaluation’s activities. the interface is implemented by the software agent and customizes the interface. the space developed by the ilearning system is a flexible instructional environment. the work scenario of the participants to the educational process is presented in figure 2. 86 gabriela moise figure 2: ilearning work’s scenario 3 the agents of ilearning system the ilearning system contains three kinds of components [7]: 1. "execution" agents which perform teaching-learning process. these kinds of agents don’t have perception sensors; they receive the inputs from the supervisor agent. these kinds of agents are intelligent, reactive and goal oriented. 2. "supervisor agent" is a software module which has some intelligent grade. this kind of agent manages the whole system and it is able to learn how a system can be managed. 3. databases system. this component was implemented through a sql server and vb client. 3.1 the execution agents the execution agents are adapted according to interrap [8], model to which was added a capacity component and a decisional module, which establishes the ability of the agent to solve a situation. the architecture of this kind of agent is presented in the figure 3. the control unit receives messages about the state of the environment from the supervisor agent. the unit establishes the ability of the agent to solve the problem. if the agent is not able to solve the situation then it returns an error message. the capacity is defined by the role of the agent in the system. the module analyzes the information received from the supervisor agent; recognizes the situation (goal oriented job or reactive job) and decides the level that will be activated. finally, the module receives the actions that have to be performed by the actors or returns a message to the supervisor agent.the activities are implemented through three levels: communication level, goal oriented level and reactive level. the control unit has the duty to realize the maintenance of the plans library, the behaviors library and the knowledge base of the system. 3.2 the supervisor agents the supervisor agent performs the following functions: a software system for online learning applied in the field of computer science 87 figure 3: the architecture of execution agent 1. coordinates the whole activity of the system; 2. receives the information from the environment (students, teachers and staff) and decides the agent that will be called; 3. receives the requests from the agents and transmits them to the destination agents or solves them using its owner resources; 4. records the capacities of the agents; 5. manages the agents’ library; 6. manages the databases systems; 7. communicates with the ilearning developments. the supervisor agent has the properties of the facilitator agents, mediator agents and broker agents. the architecture of the agent is shown in the figure no. 4. the structure of the agent is simpler then the one of the execution agent while it performs only two types of situations: social situations and administrative situations. figure 4: the architecture of supervisor agent 88 gabriela moise 3.3 the model of ilearning system the ilearning system is a hybrid system, which contains a multi-agent system, and two databases systems. the multi-agent system is composed by agents that cooperate between them. [2] the agents have different goals and are managed by the supervisor agent.[9] the supervisor agent collaborates with the databases systems and coordinates the whole educational process. the model is presented in the figure 5. figure 5: the model of ilearning system 3.4 the ilearning’s programming and reward algorithm to test the system, it was realized a software with visual studio 6.0 and microsoft agent technology. the algorithm used to learn the agents of ilearning system is a reward algorithm based on the qlearning algorithm. this technique starts with an initial estimation q(s, a) for each pair state-action. when it is selected the action a in the state s, the system receives a reward r(s, a) and it is observed the next state s ′ . the q-learning algorithm (watkins, 1989) [11] appreciates the function value state-action as follows: q(s, a) = q(s, a) + α ×(r(s, a) + γ ×mina′ q(s ′ , a ′ )−q(s, a)) where α ∈ (0, 1) is the learning rate, γ ∈ (0, 1) is the discount factor and s′ is the state reached from state s executing the action a.[1] [5] the conceptual map of the course defines the space of system’s state. a student with a certain learning style is with his studies in a node of the conceptual map and he was examined and received notes during the instruction process. the objective of the system is to maximize the results of students at different evaluations. the contribution of the authors is the adaptation of this algorithm for the pedagogical agents. the reward is established through the student’s evaluation in a node of the conceptual map and is defined according to the equation: r (s, a) = note or r (s, a) = note×pnote+apriorknowledge×pbasetime where: note is the score received by the student at his/her evaluation in a node of the conceptual map, apriorknowledge is the score received by students at the initial evaluation (before starting teaching the course if the student starts the course or the average of the scores received by the student at the evaluations in the nodes before the current node in the conceptual map), pnote and pbase are parameters. the algorithm used is: a software system for online learning applied in the field of computer science 89 1. start with an array q for all possible pairs state-action. each item of the array is initialized with zero or a small value. 2. the optimal policy is initialized with a supervised policy. qoptim is initialized with q. 3. for each student the conceptual map is traversed and the q array is calculated. 4. the q is analyzed 4 the experimental results to test the system there were selected students with different learning style (visual style, auditory style, kinesthetic style) and different ages and a module from the "graph theory" course. the conceptual map [10] of the course with seven nodes is presented in the figure 6., each node has attached three pedagogical resource. the software contains two agents: the supervisor agent and the pedagogical agent.[12] the pedagogical agent establishes the optimal pedagogical resources to teach the course. the state of the system is defined by the combination (age, learning style and number of the node). the learning style no. 1 means the visual learning style, the style no. 2 means auditory learning style and the style no. 3 means the kinesthetic learning style. the age no. 1 means the persons with age between 20 and 30 years, the age no. 2 means the persons with age between 30 and 40 years and the age no. 3 means the persons with age between 40 and 50 years. the software teaches itself to teach better, selecting the best pedagogical resources for each node of the conceptual map. figure 6: the conceptual map of the course graph theory, module shortest path the results obtained for age category no. 2 and learning style no. 1 are presented in the table no. 1. the last column contains the values for the parameter q from the reward algorithm. the greater values of parameter q give the optimal policy (the best pedagogical resources for each node of the map according to the learning styles of students). the optimal policy is described in the table no. 2. the results obtained for age category no. 3 and learning style no. 1 are presented in the table no. 3. 90 gabriela moise age learning style no. node no. of pedagogical resource q 2 1 0 1 8.75 2 1 0 2 0 2 1 0 3 10.27 2 1 1 1 4 2 1 1 2 6 2 1 1 3 7 2 1 2 1 6.37 2 1 2 2 0 2 1 2 3 7.68 2 1 3 1 4.5 2 1 3 2 6.12 2 1 3 3 8.18 2 1 4 1 3.5 2 1 4 2 9.28 2 1 4 3 0 2 1 5 1 4 2 1 5 2 3.5 2 1 5 3 7 2 1 6 1 0 2 1 6 2 5.25 2 1 6 3 5.75 table 1: experimental results for age 2 and learning style 1 age learning style no. node no. of pedagogical resource 2 1 0 3 2 1 1 3 2 1 2 3 2 1 3 3 2 1 4 2 2 1 5 3 2 1 6 3 table 2: the optimal policy for age 2 and learning style 1 a software system for online learning applied in the field of computer science 91 age learning style no. node no. of pedagogical resource q 3 1 0 1 6.25 3 1 0 2 8.75 3 1 0 3 0 3 1 1 1 0 3 1 1 2 6.5 3 1 1 3 0 3 1 2 1 5 3 1 2 2 5.75 3 1 2 3 0 3 1 3 1 4 3 1 3 2 0 3 1 3 3 5 3 1 4 1 0 3 1 4 2 4.5 3 1 4 3 5 3 1 5 1 0 3 1 5 2 4.5 3 1 5 3 3.5 3 1 6 1 3.5 3 1 6 2 0 3 1 6 3 4.5 table 3: experimental results for age 3 and learning style 1 the greater values of parameter q give the optimal policy (the best pedagogical resources for each node of map according to the learning styles of students). the optimal policy is described in the table no. 4. it is true that the best results will be obtained if there are a lot of pedagogical resources for each node of the conceptual map. these pedagogical resources have to be in different formats (text, multimedia, audio, video) containing details, explanations, exercises, references, cases studies, and so forth. there are a lot of software tools which can be used to make different files formats. also, we need to improve the computers performances in order to use these kinds of softwares. age learning style no. node no. of pedagogical resource 3 1 0 2 3 1 1 2 3 1 2 2 3 1 3 3 3 1 4 3 3 1 5 2 3 1 6 3 table 4: the optimal policy for age 3 and learning style 1 the teachers validate the results; the pedagogical resources no. 2 and no. 3 are the best pedagogical resources built for this course. the pedagogical resources no. 2 and 3 contain a lot of details, examples and explanations. the resource no. 3 (means the selection of the third pedagogical resource) is the best choice for persons with age group no. 2 and learning style no. 1. the resources no. 2 and 3 (means the 92 gabriela moise selection of the second and the third pedagogical resources) are the best choices for persons with age no. 3 and learning style no. 1. 5 summary and conclusions the online instruction model can be implemented with the intelligent agent technology. the ilearning system proves this. the system models the teaching-learning process so that it can adapt itself at the learning profile of each person. there are defined two kinds of agents: the execution agent and the supervisor agent. the author in the doctoral study tested the system and proved the importance of using the technology of intelligent agents in the online instruction systems. references [1] bowling, m., veloso m., multiagent learning using a variable learning rate, journal artificial intelligence, vol. 136, pp. 215-250, 2002. [2] buiu, c., albu. m., agenti software inteligenti, editura icpe, 2000. [3] gardner, h., intelligence reframed, multiple intelligence for the 21st century, published by basic books, 1999. [4] joyce, b, weil. , calhoun.e. , models of teaching, published by basic books, 1999. [5] leon, f., sova, i., gâlea, d., reinforcement learning strategies for intelligent agents in knowledge-based information systems, proceedings of the 8th international symposium on automatic control and computer science, iaşi, 2004. [6] moise, g., the role of intelligent agents in online learning environment, cblis conference procedings, pp. 98-105, 2005. [7] moise g., a software system for online learning applied to higher education in the field of computer science, thesis: petroleum-gas university of ploiesti, 2006. [8] müller, jörg p., the design of intelligent agents: a layered approach, lecture notes in computer science, vol. 1177: lecture notes in artificial intelligence, springer-verlag, 1996. [9] rao, a., s., georgeff, m., p. , bdi agents from theory to practice,proceedings of the first international conference on multi agent systems icmas-95, san francisco, 1995. [10] sowa, j.f. ,kowledge representation logical, philosophical, and computational foundations, brooks/cole, thomson learning. [11] watkins,c. , learning from delayed rewards, thesis: university of cambridge, england. [12] wooldridge, m., jennings, r., n. , bdi agents from theory to practice,intelligent agents: theory and practice, knowledge engineering review , vol. 10 no 2, 1995. [13] http://www.nwlink.com a software system for online learning applied in the field of computer science 93 gabriela moise petroleum-gas university of ploiesti computer science department no. 39 bd. bucuresti, ploiesti, romania e-mail: gmoise@upg-ploiesti.ro received: november 11, 2006 editor’s note about the author: gabriela moise (born on february 13, 1969) graduated the faculty of mathematics, specialization informatics of the bucharest university. she worked as software development in the software industry. since 2003 she is lecturer at petroleum-gas university of ploiesti. her research fields are: e-learning, graph theory, intelligent agents, knowledge representation, e-health. she has (co)authored seven books and more than twenty research papers. she has participated to many international conferences in the elearning and e-business area. international journal of computers, communications & control vol. iii (2008), no. 1, pp. 103-109 state analysis of time-varying singular bilinear systems by rk-butcher algorithms v. murugesh, k. batri abstract: the runge-kutta (rk)-butcher algorithm is used to study the timevarying singular bilinear systems with the exact solutions. the results (discrete solutions) obtained using the haar wavelets, single-term walsh series (stws) and rk-butcher algorithms are compared with the exact solutions of the time-varying singular bilinear systems. it is found that the solution obtained using the rk-butcher algorithm is closer to the exact solutions of the time-varying singular bilinear systems. the rk-butcher algorithm can easily be implemented using a digital computer and the solution can be obtained for any length of time, which is an added advantage of this algorithm. keywords: time-varying singular bilinear systems, haar wavelets, runge-kutta butcher algorithm, stws algorithm. 1 introduction the development of singular bilinear systems has been studied by some researchers. campbell [1] had done some preliminary work, but there was no available closed-form solution in that paper. in some analysis of neural networks, both singular systems [2] and bilinear systems [3] have been used. the multipliers and algebraic interconnections between singular systems and bilinear systems are allowed in dynamical systems. for singular bilinear systems, lewis et al. [4] have been discussed extensively in the literature. however, the solution due to lewis et al. only applies for the time-invariant case. hsiao and wang [5] applied the haar wavelets for the solution of time -varying singular bilinear systems. sepehrian and razzaghi [6] applied the stws approach for finding the numerical solution of time-varying bilinear systems. runge-kutta (rk) methods have become very popular both as computational techniques and as a topic for research [7-12]. butcher [8] derived the best rk pair, together with an error estimate, and in all statistical measures this approach is known as the rk-butcher algorithm. this algorithm appears to be of sixth order because it requires six function evaluations, but in practice the ’working order’ is closer to five (fifth order). however, the accuracy of the results obtained is better than that of all other algorithms examined including the rkfehlberg, rk-merson, rk-centroidal mean (rkcem) and rk-arithmetic mean (rkam) algorithms. murugesh and murugesan [13-15] introduced the rk-butcher algorithm in raster and time-multiplexing cnn simulations. recently, [16, 18] the rk-butcher algorithm is used to find the numerical solution of an industrial application problem. in this article, we present a new approach for solving the time-varying singular bilinear systems using the rk-butcher algorithm with more accuracy. 2 the rk-butcher algorithm the normal order of an rk algorithm is the approximate number of leading terms of an infinite taylor series which calculates the trajectory of a moving point [17]. the remainder of the infinite sum, which is excluded, is referred to as the local truncation error (lte). these rk algorithms are forward-looking predictors, i.e. they do not use any information from preceding steps to predict the future position of a point. for this reason, they require a minimum of input data and consequently are very simple to program and use. copyright © 2006-2008 by ccc publications 104 v. murugesh, k. batri the general p-stage runge-kutta method for solving an ivp is y′ = f (x, y) (1) with the initial condition y(x0) = y0 is defined by yn+1 = yn + h p ∑ i=1 biki where ki = f ( xn + cih, yn + h p ∑ j=1 ai jk j ) , i = 1, 2, 3, ......, p and ci = p ∑ j=1 ai j, i = i, 2, 3, ....., p in the preceding equations c and b are p-dimensional vectors and a(ai j) is the p × p matrix. then the butcher array takes the form c1 a11 c2 a21 a22 c3 a31 a32 a33 · · · · · · · · · · · · · · · · · cp ap1 ap2 ap3 app b1 b2 bp−1 bp the rk-butcher algorithm of equation (1) is of the form k1 = h f (xn, yn) k2 = h f ( xn + h 4 , yn + k1 4 ) k3 = h f ( xn + h 4 , yn + k1 8 + k2 8 ) k4 = h f ( xn + h 2 , yn − k2 2 + k3 ) (2) k5 = h f ( xn + 3h 4 , yn + 3k1 16 + 9k4 16 ) k6 = h f ( xn + h, yn − 3k1 7 + 2k2 7 + 12k3 7 − 12k4 7 + 8k5 7 ) 5th order predictor yn+1 = yn + 1 90 (7k1 + 32k3 + 12k4 + 32k5 + 7k6) 4th order predictor y∗n+1 = yn + 1 6 (k1 + 4k4 + k6) the local truncation error estimate (ee) is ee = yn+1 −y∗n+1 then the formation of the butcher array of the above equation (2) takes the following form state analysis of time-varying singular bilinear systems by rk-butcher algorithms 105 0 1 4 1 4 1 4 1 8 1 8 1 2 0 1 2 1 3 4 3 16 0 0 9 16 1 37 2 7 12 7 12 7 8 7 7 90 0 32 90 12 90 32 90 7 90 1 6 0 0 4 6 0 1 6 3 analysis of time-varying singular bilinear systems consider the linear first order time-varying singular system k . x(t) = ax(t) + b(t)u(t) (3) with x0 = x(0), where k is an n×n singular matrix, a is an n×n matrix, b is an n×r matrix. x(t) is n-state vectors and u(t) is an r-input vector. the time-varying singular bilinear system is of the form e(t) . x(t) = a(t)x(t) + q ∑ i=1 ni(t)x(t)ui(t) + b(t)u(t) (4) equation (4)is written in the form (3) as e(t) . x(t) = ( a(t) + q ∑ i=1 ni(t)ui(t) ) x(t) + b(t)u(t) (5) where the singular matrix e(t) ∈ rn×n, the state x(t) ∈ rn , the control u(t) ∈ rq, a(t) ∈ rn×n and b(t) ∈ rn×q. ni(t) ∈ rn×n and ui(t), i = 1, 2, 3, ...., q, are the components of u(t). the response x(t), 0 ≤ t ≤ ti, is required to be found. the time-varying singular bilinear systems are much more difficult to solve than the time invariant singular bilinear systems. therefore, many authors have tried various transform methods to over come these difficulties. in this article, we introduce rk-butcher algorithms with more accuracy to solve these time-varying singular bilinear systems. 4 numerical example consider the time-varying singular bilinear system of the following form (hsiao and wang [5]) and sepehrian and razzaghi[6]). e(t) =   0 −t 0 1 0 t 0 1 0   a(t) =   −2 t 1 0 −4 2 −2t 0 1   n1(t) =   1 −t 1 0 3 −2 2t 0 −2   (6) b(t) = [ 2 1 3 ]t , u(t) = 1, with initial condition x(0) = [ 12 2 5 ]t 106 v. murugesh, k. batri when we solve (5), the analytic solution for x(t) can be shown as x(t) =   (2−t) ( ex p (−t 2 ) + ex p (t) ) + 8 2ex p (−t 2 ) −ex p (t) + 1 ex p (−t 2 ) + ex p (t) + 3   (7) the discrete solutions of equation (5) are evaluated using the rk-butcher algorithms (with step size t = 0.25 ) represented in equation (2) and the results are compared with the solutions obtained by the haar wavelets method by hsiao and wang [5] and the stws method by sepehrian and razzaghi [6]. the results are shown in tables 1 3 along with the exact solutions calculated using equation (7). errors between the exact and discrete solutions are also given in tables 1 3. s.no time discrete solution x1values exact solutions haar solutions haar error stws solutions stws error rkbutcher solutions rkbutcher error 1 0 12.000000 12.0000 0.000000 12.00000 0.000000 12.000000 0.000000 2 0.25 11.886053 11.8861 0.000047 11.88605 0.000003 11.886053 0.000000 3 0.5 11.791414 11.7914 0.000014 11.79142 0.000006 11.791414 0.000000 4 0.75 11.711533 11.7115 0.000033 11.71154 0.000007 11.711533 0.000000 5 1 11.641283 11.6413 0.000017 11.64128 0.000003 11.641283 0.000000 6 1.25 11.574810 11.5748 0.000010 11.57481 0.000000 11.574810 0.000000 7 1.5 11.505362 11.5054 0.000038 11.50537 0.000008 11.505362 0.000000 8 1.75 11.425089 11.4251 0.000011 11.42510 0.000011 11.425089 0.000000 9 2 11.324812 11.3248 0.000012 11.32481 0.000002 11.324812 0.000000 table 1: solutions for the problem at various values of x1. s.no time discrete solution x2values exact solutions haar solutions haar error stws solutions stws error rkbutcher solutions rkbutcher error 1 0 2.000000 2.0000 0.000000 2.00000 0.000000 2.000000 0.000000 2 0.25 1.745678 1.7457 0.000022 1.74568 0.000002 1.745678 0.000000 3 0.5 1.480968 1.4810 0.000032 1.48097 0.000002 1.480968 0.000000 4 0.75 1.203067 1.2031 0.000033 1.20307 0.000003 1.203067 0.000000 5 1 0.908880 0.9089 0.000020 0.90888 0.000000 0.908880 0.000000 6 1.25 0.594985 0.5950 0.000015 0.59498 0.000005 0.594985 0.000000 7 1.5 0.257579 0.2576 0.000021 0.25758 0.000001 0.257579 0.000000 8 1.75 -0.107578 -0.1076 0.000022 -0.10758 0.000002 -0.107578 0.000000 9 2 -0.505221 -0.5052 0.000021 -0.50522 0.000001 -0.505221 0.000000 table 2: solutions for the problem at various values of x2. 5 conclusions the discrete solutions obtained using the rk-butcher algorithm gives more accurate values when compared to the haar wavelets method discussed by hsiao and wang [5] and the stws method by state analysis of time-varying singular bilinear systems by rk-butcher algorithms 107 s.no time discrete solution x3values exact solutions haar solutions haar error stws solutions stws error rkbutcher solutions rkbutcher error 1 0 5.000000 5.0000 0.000000 5.00000 0.000000 5.000000 0.000000 2 0.25 5.072562 5.0726 0.000038 5.07256 0.000002 5.072562 0.000000 3 0.5 5.166522 5.1665 0.000022 5.16652 0.000002 5.166522 0.000000 4 0.75 5.284021 5.2840 0.000021 5.28402 0.000001 5.284021 0.000000 5 1 5.427522 5.4275 0.000022 5.42752 0.000002 5.427522 0.000000 6 1.25 5.599862 5.5999 0.000038 5.59986 0.000002 5.599862 0.000000 7 1.5 5.804289 5.8043 0.000011 5.80429 0.000001 5.804289 0.000000 8 1.75 6.044524 6.0445 0.000024 6.04451 0.000014 6.044524 0.000000 9 2 6.324812 6.3248 0.000012 6.32480 0.000012 6.324812 0.000000 table 3: solutions for the problem at various values of x3. sepehrian and razzaghi [6]. from the tables 1-3, one can observe that the solutions obtained by the rk-butcher algorithm match well with the exact solutions of the time-varying singular bilinear systems, but the haar wavelets and stws methods yields a little error. hence the rk-butcher algorithm is more suitable for studying the time-varying bilinear systems. bibliography [1] campell, s.l., 1987, bilinear nonlinear descriptor control systems, crsc technical report 102386-01, department of mathematics, n.c. state university, raleigh, nc 27695. [2] declaris, n. and rindos, a., 1984, semistate analysis of neural networks in apysia californica, proceedings of the 27th mscs, 686-689. [3] wiener, n., 1948, cybernetics (cambridge, mit press). [4] lewis, f. l., mertzios, b. c., and marszalek, w., 1991, analysis of singular bilinear systems using walsh functions, iee proceedings part-d,138, 89-92. [5] hsiao, c. h., and wang, w. j., 2000, state analysis of time-varying singular bilinear systems via haar wavelets, mathematics and computers in simulation, 52, 11-20. [6] sepehrian, b., and razzaghi, m., 2003, state analysis of time-varying singular bilinear systmes by single-term walsh series, international journal of computer mathematics, 80, 413-418. [7] alexander, r.k. and coyle, j.j.,1990, runge-kutta methods for differential-algebraic systems. siam journal of numerical analysis, 27, 736-752. [8] butcher, j.c., 1987, the numerical analysis of ordinary differential equations: runge-kutta and general linear methods (chichester: johnwiley). [9] butcher, j.c., 2003, numerical methods for ordinary differential equations (chichester: johnwiley). [10] shampine, l.f., 1994, numerical solution of ordinary differential equations (newyork: chapman & hall). 108 v. murugesh, k. batri [11] yaakub, a.r. and evans, d.j., 1999, a fourth order runge-kutta rk(4,4) method with error control. international journal of computer mathematics, 71, 383-411. [12] yaakub, a.r. and evans, d.j., 1999, new runge-kutta starters of multi-step methods. international journal of computer mathematics, 71, 99-104. [13] murugesh, v., and murugesan, k., 2004, comparison of numerical integration algorithms in raster cnn simulation, lecture notes in computer science, 3285, 115-122. [14] murugesh, v. and murugesan, k., 2005, simulation of cellular neural networks using the rkbutcher algorithm, international journal of management and systems, 21, 65-78. [15] murugesh, v., and murugesan, k., 2006, simulation of time-multiplexing cellular neural networks with numerical integration algorithms, lecture notes in computer science, 3991, 115-122. [16] devarajan gopal, murugesh, v., and murugesan, k., 2006, numerical solution of second-order robot arm control problem using runge-kutta butcher algorithm, international journal of computer mathematics, 83, 345-356. [17] shampine, l.f. and gordon, m.k., 1975, computer solutions of ordinary differential equations: the initial value problem (san francisco, ca:w.h. freeman). [18] v. murugesh and k. batri, “an efficient numerical integration algorithm for cellular neural network based hole-filler template design”, international journal of computers, communications & control, vol. ii (2007), no. 4, pp. 367-374. v. murugesh department of information and communication engineering hannam university 133 ojung-dong daeduk-gu, daejeon 306-791, republic of korea e-mail: murugesh72@gmail.com k. batri department of computer science and engineering muthayammal engineering college rasipuram 637 408 india e-mail: krishnan.batri@gmail.com received: november 26, 2007 dr. v. murugesh obtained his bachelor of science in computer science and master of computer applications degree from bharathiar university, coimbatore, india during 1992 and 1995 respectively. completed his phd in computer science from bharathidasan university, tiruchirappalli, india during 2006. he has held various positions at national institute of technology, tiruchirappalli, india and sona college of technology, salem, india. currently, he is working as assistant professor in the department of information and communication engineering at hannam university, daejeon, republic of korea. his fields of interest are in neural network based image processing and scientific computing. he has published more than 30 technical papers in international, national journals and conferences. state analysis of time-varying singular bilinear systems by rk-butcher algorithms 109 krishnan batri received the m.e. from madurai kamarj university in 2003. he is a research scholar with the department of computer science and engineering in the national institute of technology tiruchirapalli, tamil nadu, india. currently he is working as a assistant proffessor with the department of computer science and engineering at muthayammal engineering college,rasipuram, tamilnadu, india . his research interests include information retrieval, data fusion and genetic algorithms. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 837-843 modeling gilliland correlation using genetic programming m. olteanu, n. paraschiv, o. cangea marius olteanu, nicolae paraschiv, otilia cangea “petroleum-gas” university of ploiesti romania, ploiesti, 100680, bucuresti blvd., no.39 e-mail: {molteanu,nparaschiv,ocangea}@upg-ploiesti.ro abstract: the distillation process is one of the most important processes in industry, especially petroleum refining. designing a distillation column assesses numerous challenges to the engineer, being a complex process that is approached in various studies. an important component, directly affecting the efficient operation of the column, is the reflux ratio that is correlated with the number of the theoretical stages, a correlation developed and studied by gililland. the correlation is used in the case of simplified control models of distillation columns and it is a graphical method. however, in many situations, there is the need for an analytical form that adequately approximates the experimental data. there are in the literature different analytical forms which are used taking into account the desired precision. the present article attempts to address this problem by using the technique of genetic programming, a branch of evolutionary algorithms that belongs to artificial intelligence, a recently developed technique that has recorded successful applications especially in process modeling. using an evolutionary paradigm and by evolving a population of solutions or subprograms composed of carefully chosen functions and operators, the genetic programming technique is capable of finding the program or relation that fits best the available data. keywords: gilliland correlation, artificial intelligence, genetic programming. 1 introduction the early pioneers of computer science, like alan turing, john von neumann, norbert wiener studied natural systems as guiding metaphors for their desire to understand nature and create intelligent computer programs capable to learn and adapt to their environment. in the 1950s and 1960s, several scientists from germany and united states independently studied evolutionary systems with the aim to use evolution as an optimization tool for engineering problems. several techniques have been created in this period by different research groups: evolution strategies, evolutionary programming and genetic algorithms (see [5]). the basic idea behind all this techniques was to start with a random population of candidate solutions to the specific problem and by applying a set of genetic operators, inspired from the field of genetics, to modify this candidate solutions in such a way to achieve a better fitness or adequacy of the solution for the engineering problem in an iterative process. in this article we apply a technique of evolutionary algorithms, that of genetic programming, with the aim at finding an analytic expression for a well studied and used correlation, the gililland correlation, applied to the design of control models for distillation columns. the algorithms for implementing genetic programming are characterized by many heuristic tuning parameters, this paper underlines the most important ones as a result of the simulations. copyright c⃝ 2006-2010 by ccc publications 838 m. olteanu, n. paraschiv, o. cangea 2 genetic programming based symbolic regression genetic programming was introduced by john koza (see [4]) and it can be seen as an extension of the genetic algorithms by the increase of the complexity of the structures used to represent the potential solution to the problem. in his 1992 book ([1]), john koza suggested that these potential solutions should be represented as trees of functions and operators, dynamic structures of varying size and shape. the classes of problems that can be best approached using this technique are symbolic regression, in which an analytic expression for a function has to be discovered such that a set of experimental data is fitted and machine learning, domain that uses a set of possible computer programs that produce the desired behavior in the case of some particular input data. in genetic programming, the set of possible structures is determined by the set of nf functions from f = {f1, f2, ..., fnf}, each function can take a specified number of arguments denoted a (fi) called its arity and the set of nt terminals from t = {t1, t2, ..., tnt}. some examples of functions are: arithmetic operations: plus (+), minus (-) , multiply (*), divide (/); mathematical functions: logarithm (log), trigonometric functions sin, cos, etc; logical functions: and, or, not. the terminals are variable atoms that represent input variables, signals form sensors / detectors or constant atoms, for example the number 11.25 or the boolean constant true. an example of a simple function represented by such a tree is given in figure 1, the corresponding analytic expression being: f (x, y) = x + √ y − 2 . figure 1: example of a tree representing a possible solution as a particular aspect of functions and terminals representation is that of closure which assumes that each of the function from the set must be capable to accept as its input arguments any value and data type that could possibly be returned by any function in the set and any value accepted by any terminal. another property is that of sufficiency which states that the set of functions and terminals must be capable of defining a solution for the actual problem, the designer of the algorithm is the one that decides what are the most probable functions and constants that could express best a solution. the adequacy or fitness of a particular member of the population has to be measured for a set of fitness cases, in the case of symbolic regression these are the experimental data. the algorithm for implementing genetic programming has a structure that resembles that of a genetic algorithm: 1. the designers establishes the set of functions / operators and the set of terminals for the specific problem modeling gilliland correlation using genetic programming 839 2. t=0, t being the generation counter 3. a random initial population of solutions p(0) is generated 4. fitness evaluation for all the population 5. a new population is created by applying the genetic operators of cloning, mutation and crossover. 6. if the stop condition is met, than the algorithm stops, else t=t+1 and go to step 4 3 genetic programming applied to the gilliland correlation the gilliland correlation or gilliland plot (figure 2) correlates the reflux ratio and the number of theoretical stages for a distillation column (see [3]): x = r − rmin r + 1 , y = n − nmin n + 1 (3.1) given that the minimum reflux ratio rmin is calculated from underwood’s equation and the minimum number of stages nmin by fenske’s method. the resulting curve stretches form (0,1) coordinate at minimum reflux to (1,0) at total reflux. in the literature (see [3]) there are a series of numerical equations derived for this correlation, but because there is some scatter in the fit of data to the gilliland plot, the expressions that best fits the plot are not always the best reflux-stages correlations. one of the most used expressions is that of molokanov: y = 1 − e 1−54.4x 11+117.2x · x−1√ x (3.2) used for higher precision, alternative relations being that of eduljee: y = 0.75(1 − x0.5668) (3.3) and rusche (see [2]): y = 0.1256 · x − 0.8744 · x0.291 (3.4) also, other correlations have been obtained as a result of research in the domain of optimal control (see [6], [7] polynomial analytical expression). for implementing the genetic programming algorithm it was used a free matlab toolbox created by s. silva, a toolbox well documented and highly modular having many configurable parameters (see [8]). a usual running of the algorithm is started by entering the following command at the mathlab prompt: >> [vars, b]=gplab(g,n); where: g=maximum number of generations as stop condition n=the number of individuals in the population vars=a structure containing all the algorithm variables b=best fitted individual 840 m. olteanu, n. paraschiv, o. cangea figure 2: plot of the original gilliland correlation the main modules that compose the toolbox have the following functions: variables initializing, initial population and generation creating. among the many important features of the toolbox, the parameters that were especially important in the symbolic regression applied for the gililland correlation: • population initialization has three possible methods: fullinit, growinit and rampedinit which was used in the algorithm, and that produces an initial population having very diverse trees (a combination of fullinit and growinit methods, see [4], [8]); • with the purpose of avoiding function bloating, the toolbox uses a parameter called dynamiclevel that specifies if the trees depth or the trees number of nodes has a fixed limit or not. another experimental property, called veryheavy specifies if this dynamic limit can be decreased under the initial value during the running in the case that the best individual has a smaller depth or number of nodes. by using this option, a much simpler expression for the function has been obtained, and also the running time of the algorithm and the memory resources implied were substantially reduced; • the methods available for selecting the most adequate individuals are the classical roulette and tournament methods, in addition there are implemented other methods like lexictour or doubletour that chooses taking into account the shortest (having the smaller depth or number of nodes) individual. the best results were achieved using the lexictour method. the crossover operator (figure 3) randomly choose nodes from both parents and swaps the respective branches creating one or two offspring, in the case of the mutation a random node is chosen from the parent individual and substituted by a new random tree, taking into account the imposed restrictions on the depth and number of nodes (see [1]). a set of six functions have been chosen and two random constants with values between 0 and 1. the functions were: plus, minus, times, custom divide (having protection to divide-by-zero error) also custom square root and custom natural logarithm. modeling gilliland correlation using genetic programming 841 figure 3: example of applying the crossover operator the algorithm ran on a computer with an intel core 2 duo processor, having 2gb of memory and matlab 7.4 for windows xp. for a population with 1000 members and a number of 20 generations the running time was in the range of 4 minutes to 4.5 minutes. if the number of generations is increased too much it always results a very big expression for the final function, making it very hard for implementing and studying, a very slow increase in performance (fitness) being obtained. adding the two final points (0,1) and (1,0) in the data set conducts to a function that does not approximate well the middle data points having a poor general performance because of the relative big scatter of the terminal points from the plot, so for most of the simulations, the two ending points were not included. the fitness function used calculates the sum of the absolute difference between the desired output values and the value computed by the individual on all fitness cases. fitness = n∑ i=1 |yi − f (xi)| (3.5) where n is the number of fitness cases, yi the desired output and f (xi) is the value returned by the individual. with a generation number of 40 and a population of 500 individuals for a running time of 169 seconds, the expression obtained is presented in the following tree plot (figure 4): another common representation is the string representation, used in matlab to represent the function: f=plus(times(minus(mysqrt(mydivide(0.96486,0.56835)),times(plus(0.96486,0.33765),mysqrt (x1))),mylog(mysqrt(mydivide(0.8073,0.22837)))),mysqrt(plus(times(0.33765,times(x1,times (plus(mysqrt(0.8073),0.56835),mysqrt(x1)))),mylog(minus(mysqrt(mydivide(0.9393,0.56835)), times(plus(mysqrt(0.8073),0.56835),mysqrt(x1))))))), from which we can write the following simplified relation: 842 m. olteanu, n. paraschiv, o. cangea 0.96486 0.56835 mydivide mysqrt 0.96486 0.33765 plus x1 mysqrt times minus 0.8073 0.22837 mydivide mysqrt mylog times 0.33765 x1 0.8073 mysqrt 0.56835 plus x1 mysqrt times times times 0.9393 0.56835 mydivide mysqrt 0.8073 mysqrt 0.56835 plus x1 mysqrt times minus mylog plus mysqrt plus figure 4: the tree representation of the final solution 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 original data eduljee rusche genetic programming figure 5: plot of the final solution and other correlations together with the original data modeling gilliland correlation using genetic programming 843 f = 0.631(1.30294 − 1.30251 √ x) + √ |0.495 · x √ x + ln(|1.285 − 1.467 √ x|)| (3.6) from the plot of the classic correlations (figure 5) and that of the genetic programming function we can conclude that a very good approximation is obtained, challenging the methods used in the domain of identification. 4 conclusions and future works the study presented in this article aims at applying a recently and not too well studied technique of artificial intelligence, that of genetic programming to the problem of finding the best function that fits some data. the well known gililland correlation, applied in the domain of process control of the distillation process has been studied. using the classic representation of the potential solutions as trees, many interesting results have been obtained. after running the algorithm with varying parameters for initial population for the method of selection the best individuals, the method of creating the new offspring, the genetic operators of crossover and mutation and ending with the number of generations and of individuals in the population, it can be stated that the technique of genetic programming has a promising future potential, proved by the good estimation of the experimental data. one aspect that proved to be important is the careful chosen of the running parameters which directly influence the quality of the solution. bibliography [1] m. affenzeller, s. winkler, s. wagner, a. beham, genetic algorithms and genetic programming modern concepts and practical applications, crc press, 2009. [2] j.r. couper, w.r. penney, j.r. fair, s.m. wallas, chemical process equipment second edition, elsevier gulf professional publishing, 2005. [3] h.z. kister, distillation design, mcgraw-hill, 1992. [4] j.r. koza, genetic programming on the programming of computer by means of natural selection, mit press, 1992. [5] m. mitchell, an introduction to genetic algorithms, mit press, 1996. [6] n. paraschiv, equipment and programs for optimal control of fractionation processes, phd thesis, “petroleum-gas” university of ploiesti, ploiesti, 1987. [7] n. paraschiv, an analytical form of the gilliland graphical correlation for the advanced control of fractionation processes, chemistry magazine no.7-8, 1990. [8] s. silva, gplab a genetic programming toolbox for matlab user manual, ecos evolutionary and complex systems group, university of coimbra, portugal, 2007 accessible from http://gplab.sourceforge.net/index.html. international journal of computers, communications & control vol. i (2006), no. 1, pp. 5-6 editorial florin gheorghe filip, ioan dziţac, mişu-jan manolescu welcome to the international journal of computers, communications & control (ijccc), a new scientific journal in computer science. 1 why ijccc? nowadays there are many prestigious journals in the field of computer science, so it is only natural to ask the question: “is there any reason for a new journal in this area?” there is a saying "in the world of computers the only thing that stays the same is the changing". we live in the most dynamic era of creation and circulation of information. the transition from the information society to knowledge society is accompanied by the globalization of information. more and more specialists, researchers from institutes, universities and industries of all over the world, write scientific papers in this field. these papers must be put in the scientific circuit as fast as possible and, the existing journals cannot publish the increased number of papers. the increased interest of the participants from many countries for the international conference on computers and communications, initiated and organized by ioan dziţac in 20041 (with constantin popescu and horea oros, http://iccc.rdsor.ro), under the guidance of acad. florin-gheorghe filip and continued in 20062 under the name of international conference on computers, communications & control (www.iccc.univagora.ro) at agora university of oradea (president mişu-jan manolescu), gave us the courage and confidence to start this new journal, entitled international journal of computers, communications & control (ijccc). to this new editorial project have subscribed as members of the editorial board, the following professors and specialists in computer science: • pierre borne, ecole centrale de lille, france; • antonio di nola, university of salerno, italy; • ioan dziţac, agora university, oradea, romania (executive editor); • ömer egecioglu, university of california, santa barbara, usa; • florin gheorghe filip, romania academy, romania (editor in chief); • constantin gaindric, institute of mathematics and computer science of the academy of sciences of the republic of moldova; • kaoru hirota, tokyo institute of technology, japan; • mişu-jan manolescu, agora university, oradea, romania (managing editor); • george metakides, university of patras, greece; • shimon y nof, purdue university, usa; • gheorghe păun, imar, romanian academy, romania; • mario j. perez-jimenez, university of seville, spain; • imre j. rudas, budapest tech, hungary; • athanasios d. styliadis, alexander institute of technology, thessaloniki, greece; • gheorghe tecuci, george mason university, usa; • horia-nicolai teodorescu, institute of computer science, romanian academy, iasi, romania; • dan tufiş, racai, romanian academy, romania. 1in 2004 we received for review 112 papers (only 67 accepted) from authors of australia, austria, china, egypt, finland, france, germany, greece, india, ireland, italy, japan, republic of moldova, romania, spain, united kingdom and usa. 2in 2006 we received for reviews 142 papers (only 91 accepted) from authors of previous countries and from others countries: algeria, hungary, iran, serbia & montenegro, tunisia and thailand. copyright c© 2006 by ccc publications 6 florin gheorghe filip, ioan dziţac, mişu-jan manolescu 2 about ijccc international journal of computers, communications & control (ijccc) is published from 2006 and has 4 issues per year, edited by ccc publications, powered by agora university editing house, oradea, romania (www.journal.univagora.ro). every issue is published in online format (issn 1841-9844) and print format (issn 1841-9836). we offer free online access to the full content of the journal. the printed version of the journal should be ordered, by subscription, and will be delivered by regular mail. ijccc is directed to the international communities of scientific researchers from the universities, research units and industry. ijccc publishes original and recent scientific contributions in the following fields: • computing & computational mathematics; • information technology & communications; • computer -based control. to differentiate from other similar journals, the editorial policy of ijccc encourages especially the publishing of scientific papers that focus on the convergence of the 3 “c” (computing, communication, control). the articles submitted to ijccc must be original and previously unpublished in other journals. the submissions will be revised independently by two reviewers. ijccc also publishes: • papers dedicated to the works and life of some remarkable personalities; • reviews of some recent important published books. also, ijccc will publish as supplementary issues the proceedings of some international conferences or symposiums on computers, communications and control, scientific events that have reviewers and program committee. the authors are kindly asked to observe the rules for typesetting and submitting described in instructions for authors, which are to be found at the end of the journal and on the journal’s website www.journal.univagora.ro. there are no fees for processing and publishing articles. the authors of the published articles will receive a hard copy of the journal. topics of interest include, but are not limited to, the following: applications of the information systems; artificial intelligence; automata and formal languages; collaborative working environments; computational mathematics; cryptography and security; e-activities; fuzzy systems; informatics in control; information society knowledge society; natural computing; network design & internet services; multimedia & communications; parallel and distributed computing. florin-gheorghe filip editor in chief of ijccc romanian academy 125, calea victoriei, 71102 bucharest-1, romania e-mail: ffilip@acad.ro ioan dziţac executive editor of ijccc agora university 8, p-ta tineretului, 410526 oradea, romania e-mail: idzitac@univagora.ro mişu-jan manolescu managing editor agora university 8, p-ta tineretului, 410526 oradea, romania e-mail: rectorat@univagora.ro int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 183-195 comdevalco framework the modeling language for procedural paradigm bazil pârv, ioan lazăr, simona motogna abstract: this work is part of a series referring to comdevalco a framework for software component definition, validation, and composition. its constituents are: a modeling language, a component repository and a set of tools. this is the first paper describing the object-oriented modeling language, which contains finegrained constructions, aimed to give a precise description of software components. the current status of the language reflects the procedural paradigm constructs. keywords: software/program verification, validation, modeling methodologies, computer-aided software engineering, flow charts, object-oriented design methods, testing tools. 1 introduction two main forces drive the software development today: complexity and change. software development community looks for methods and practices to deal with these challenges. complexity in software development [6] is the same as in mathematics, dealing with problem-solving. the usual way of solving it is its reduction by reorganization. brooks [2] makes a distinction between essential and accidental complexity. essential complexity belongs to the problem to be solved and cannot be reduced or eliminated. accidental complexity belongs to its solution, being created when fine-grained, general-purpose abstractions are used to directly implement coarse-grained, problem-specific concepts. it can be reduced or even eliminated by increasing the level of abstraction, i.e. by using more coarsegrained, problem-specific concepts (classes). the other challenge in software development is change management: how to build software systems able to change. software needs to change in response to changes in its operational environment and in its requirements. sources of change are both in the problem domain (invalidating existing requirements, adding new ones) and in the solution domain, due to technological evolution. both challenges can be addressed in a disciplined manner using models, which increase the level of abstraction and allow for the automation of the software development process. model-driven development (mdd) processes propose the creation of extensive models before the source code is written. an example of mdd approach is the object management group’s model driven architecture [12] standard. agile versions of mdd approaches have been defined in order to better deal with change management. agile mdd processes [1] create agile models (models “just barely good enough”) that drive the overall development efforts, instead of creating extensive models before writing source code. another agile approach, agile mda [8], emphasizes on complete executable models [9]. our work follows the idea of executable models, proposing an object-based modeling language that fits the procedural paradigm and allows construction and execution of models. software components considered in our approach to the procedural paradigm are program (the only executable), procedure, and function, and the software development process is component-based and model-driven. the modeling language constructs allow both precise definition of these components (called program units), and their verification and validation (v & v), by simulating their execution. once these components pass the v & v process, they can be stored in a component repository, ready for later use in the development process. the structure of this paper is as follows. after this introductory section, the next one is discussing current status, problems, and ideal properties of a modeling language. the third section presents the proposed modeling language, starting with low-level constructs, followed by statements, and finally copyright © 2006-2008 by ccc publications 184 bazil pârv, ioan lazăr, simona motogna program units. the current status of our work is described in the fourth section, while the last one contains some conclusions and plans further efforts. 2 modeling process: current status, problems, and desired features 2.1 modeling languages it is generally recognized that the use of models raises the level of abstraction and favors the automation of the software development process. unfortunately, as greenfield and short stated in [6], the largest impediment to achieve these tasks was “the promulgation of imprecise general-purpose modeling languages as both de facto and de jure standards”, namely uml. martin fowler [5] identifies three different uml goals: informal design (sketching), model-driven development (blueprinting), and direct execution (model interpretation), noticing a similar and independent opinion made by steve mellor [8]. the conclusion is that uml succeeded in the first goal and failed in the others; the reasons for this failure, as they are discussed in [6], are: (1) lack of precision, (2) poor support for component-based development, and (3) weak extensibility mechanisms. uml 2 and its action semantics [15] provide the foundation to construct executable models, but the standardization efforts for defining a subset of actions sufficient for computational completeness are still in progress [14]. in order to make uml a computational-complete specification language, there are some tools [3, 17, 18, 11] which have defined non-standard subsets of actions. other issues related to uml 2 refer to the graphical notations and textual notations. the current version of uml does not define graphical notations for easy manipulation of uml elements. moreover, there are uml elements (e.g. uml structured activity nodes) without a proposed graphical notation, and textual notations for behavioral elements are still in the process of standardization [14]. 2.2 component-based development and the modeling process the process of component-based software development (or cbd for short) has two sub-processes more or less independent: component development process and system development process. naturally, the requirements concerning the components are derived from system requirements; the absence of a relationship, such as causal, may produce severe difficulties in both sub-processes mentioned above. the system construction by assembling software components [4] has several steps: component specification, component evaluation, component testing, and component integration. the system development sub-process focuses on identifying reusable entities and selecting the components fulfilling the requirements, while in the component development sub-process the emphasis is on component reuse: from the beginning, components are designed as reusable entities. component’s degree of reuse depends on its generality, while the easiness in identification, understanding, and use is affected by the component specification. the sole communication channel with the environment is the component’s interface(s). in other words, the client components of a component can only rely on the contracts specified in the interfaces implemented by the component. thus, it is obvious that component development must be interface-driven. in our opinion, the main cbd challenge is to provide a general, flexible and extensible model, for both components and software systems. this model should be language-independent, as well as programming-paradigm independent, allowing the reuse at design level. the design process of a component-based system [7] follows the same steps as in the classical methods: the design of architecture, which depicts the structure of the system (which are its parts) and the design of behavior (how these parts interact in order to fulfill the requirements). the structural description establishes component interconnections, while behavioral description states the ways in which each component uses the services provided by interconnected components in order to fulfill its tasks. comdevalco framework the modeling language for procedural paradigm 185 2.3 ideal properties of a modeling language our discussion here follows the general uses of a modeling language identified by martin fowler [5] and steve mellor [8]: informal design, model-driven development, and direct execution. in order to fulfill these goals, a modeling language should have: (1) good degree of precision, (2) good support for cbd, (3) good support for agile mda processes, and (4) good extensibility mechanisms. in order to be precise, a modeling language needs to have fine-grained constructs, which allow both the complete definition of computing processes and the simulation of their execution. the language elements should cover low-level constructs referring to data types, expressions, program state and behavior (body of statements). as we stated above, in order to offer a good support for cbd, a modeling language needs to build general, flexible and extensible models for both components and software systems. the resulting models should be both language-independent and programming-paradigm independent. in order to offer a good support for agile mda processes, a modeling language should provide a metamodel, together with graphical and textual notations for easy manipulation of language constructs. the metamodel should satisfy two important properties: computability and completeness. the extensibility of a modeling language means the extensibility of its set of constructs, like data types, expressions, statements, and program units. 3 a modeling language proposal: comdevalco solution this section discusses in more detail our proposal of a modeling language, part of a framework for component definition, validation, and composition. the proposed solution is comdevalco a conceptual framework for software components definition, validation, and composition. its constituents are meant to cover both cbd-related sub-processes described in 2.2: component development and component-based system development. the sub-process of component development starts with its definition, using an object-oriented modeling language, and graphical tools. the modeling language provides the necessary precision and consistency, and the use of graphical tools simplifies developer’s work, which doesn’t need to know the notations of the modeling language. once defined, component models are passed to a v & v (verification and validation) process, which is intended to check their correctness and to evaluate their performances. when a component passes the v & v step, it is stored in a component repository, for later (re)use. the sub-process of component-based system development takes the components already stored in repository and uses graphical tools, intended to: select components fulfilling a specific requirement, perform consistency checks regarding component assembly and include a component in the already existing architecture of the target system. when the assembly process is completed, and the target system is built, other tools will perform v & v, as well as performance evaluation operations on it. constituents of the conceptual framework are: the modeling language, the component repository and the toolset. any model of a software component is described as a compound object, using the elements of the object-based modeling language. the component repository represents the persistent part of the framework and its goal is to store and retrieve valid component models. the toolset is aimed to help developers to define, check, and validate software components and systems, as well as to provide maintenance operations for the component repository. starting in a top-down manner, program units considered are program (the only executable) and proper software components specific to the procedural paradigm procedure and function (see figure 1). each of these software components has a name, a state, and a body; the state is given by all variables local to the component, and the body is, generally speaking, a statement. according to the imperative paradigm, the program execution is seen as a set of state changes, i.e. the execution of a statement changes the value of a variable, usually by evaluating an expression. 186 bazil pârv, ioan lazăr, simona motogna figure 1: comdevalco modeling language. main constructs in the procedural paradigm we describe below, in a bottom-up manner, the language elements as they are considered so far. 3.1 low-level constructs basic language constructs are type and declaration. type class abstracts the concept of data type, while a declaration object is used to associate a specific type object to a name (identifier). this corresponds to explicit variable declaration in imperative programming languages. the next important concept is value. each value object encapsulates a value of a specific type. values are fundamental in our model, because a variable represents an alternate name for a value stored in the memory, a function returns a value, or, more generally, the process of evaluating an expression returns a value. having these facts in mind, we designed the expression class hierarchy shown in figure 2. the root of the hierarchy, expression, is abstract and has a single abstract operation, getvalue(), overriden by subclasses and returning a value result. the concrete specializations of expression are: value, variable, binaryexpression, unaryexpression, and definedfunction. figure 2: expression class hierachy value is the simplest expression descendant, its instances corresponding to typed literals (constants). its proper operations are: getdatatype(), which returns the data type of the value stored in the object, comdevalco framework the modeling language for procedural paradigm 187 getvaluestring(), returning the value as a string, and setvalue(). variable is probably one of the most important subclasses of expression, having multiple uses in our model: (1) a variable is a simple expression; (2) all statement objects deal with variable instances, and (3) the state of a program unit is seen as a collection of variable objects. according to the general definition, each variable object has a name (identifier) and a value (a value object). its own operations are: getname(), getdatatype(), and setvalue(). specific expression classes considered so far are binaryexpression and unaryexpression, having an operator and two, respectively one expression operands. the extensible operator class implements evaluate() operations for all operand types, called by getvalue() code in binaryexpression and unaryexpression. definedfunction object corresponds to a one-argument function call. its instance variables are the name of the function and its actual parameter, an expression. 3.2 statements statement class hierarchy employs composite design pattern, with subclasses simplestatement and compoundstatement. a compoundstatement object contains a list of statement objects; both concrete simple and compound statement objects are treated uniformly. as figure 3 shows, statement class is abstract and represents the root of all simple and compound statement classes. its single abstract operation is execute(), which usually produces a state change. statement’s concrete subclasses will implement this operation accordingly. figure 3: statement class hierachy simplestatement subclasses cover all important control statements in any imperative programming language: assignmentstatement, callstatement, inputstatement, outputstatement, loopstatement, and branchstatement. assignmentstatement object takes an expression and a variable; after its execution, the variable takes the value of that expression. inputstatement takes only a variable, being considered as a special kind of assignment: its execution means reading a value from standard input, evaluating and assigning it to the considered variable. the execution of an outputstatement extracts the value of its variable to standard output. callstatement object corresponds to a procedure call. its instance variables are the procedure being 188 bazil pârv, ioan lazăr, simona motogna called (the callee object) and actual (call) parameters, whose values belong to the caller object. according to the definition of a procedure object (see next subsection), three different parameter lists are needed, corresponding to in-, in-out, and out parameters. the execution of this statement has five steps: (1) extracting the values of inand in-out parameters from the caller state; (2) building the callee state; (3) running the callee object; (4) extracting the values of in-out and out parameters from the callee state and (5) updating the state of the caller object with these values. all loop statements execute repeatedly their body, a statement object. three different loop statements were considered in our design, considered as subclasses of the abstract loopstatement: forstatement, repeatstatement, and whilestatement. in the case of a forstatement object, the number of iterations is known a priori, and its execution uses a counter variable, with values ranging from lower to upper bounds. whilestatement and repeatstatement objects use a test expression to continue the iterative process. their execution differs by the test strategy, i.e. evaluate test then execute body (while), respectively execute body then evaluate test (repeat). branchstatement objects correspond to if-then-else constructs. the condition to be checked is an expression object, and both branches are statement objects. its execution evaluates test expression and then, based on its value, executes the corresponding statement. 3.3 program units as we already stated, the program units considered so far are program, procedure, and function (see figure 4), specific to the procedural paradigm. program is the only executable software component, having a name, a state, and a body; the state is made up by all variables local to the component, and the body is a statement object. the only operation of a program object is its run() method, implemented by a call to the execute() method of its body. figure 4: program units class hierachy proper software components are procedure and function. as in the imperative programming world, a procedure declaration must define its name, formal parameters, local state and procedure body. consequently, it is easy to consider procedure as a program subclass; the proper instance variables of the procedure class are the lists of in-, in-out, and out parameters, needed for a complete implementation of execute() method from callstatement class, discussed in 3.2. the concept of a user-defined function in imperative programming languages considers it without side-effects, i.e. the only result of its execution is the value it produces, without affecting the caller’s state. having this in mind, we designed the function class accordingly, i.e. it has only input (in) formal parameters, and produces as result a value object. comdevalco framework the modeling language for procedural paradigm 189 4 experimental results from a methodological viewpoint, our main concern was to model all theoretical aspects in concrete objects constructs of the modeling language. the idea was to apply an iterative and incremental process: start with simple objects, perform checks after each modeling step, in order to be sure that things work. each modeling step covers both theoretical/analytical activities the abstract model of the concept and practical/applicative ones coding, testing and integrating it in the already existing language. the initial step of our work was to prove that things are working well. so we conducted first a proof-of-concept study, and then we started the development of graphical tools. 4.1 modeling language: proof-of-concept stage the modeling language described in section 3 was implemented in java. the classes containing the current implementation are included in three packages: syntax (expression classes, declaration, declarationlist, operator, state, and type), statements (all classes in figure 3), and programunits (program, procedure, and function). the implementations were tested by building some simple components, like solving of polynomial equations of first and second degree, or computing the integer square root (isqrt()) of a positive integer. as we discuss below, in order to test a proper component p, two program units need to be designed: the component p itself and the corresponding test driver (a program component). in each situation, the building process has two main steps: (1) build the state of the component and (2) build its body. as the state is a set of variable objects, its creation is a sequence of allocate() messages. additionally, in the case of procedure and function components, the parameter lists need to be also defined, in the same way. next, the body of a component is a compoundstatement, so the building process needs to create all the statement objects which describe the computing process, and to include them into the body, preserving the sequence of computing steps. consider the simplest example of designing a component ecgr1 which solves polynomial equations of the first degree, and its corresponding test program. the building process of ecgr1 is defined in the following static method: public static procedure buildecgr1() { declarationlist inp = new declarationlist(); // in params declarationlist outp = new declarationlist(); // out params declarationlist inoutp = new declarationlist(); // in-out params declarationlist locale = new declarationlist(); // local state // input parameters inp.allocate("a", value.tdouble); inp.allocate("b", value.tdouble); // output parameters outp.allocate("cod", value.tint); outp.allocate("x", value.tdouble); compoundstatement body = new compoundstatement("solves the equation a x + b = 0"); procedure proc = new procedure("proc ecgr1", locale, body, inp, outp, inoutp); // body: branchstatement(s1, s2) (a == 0) statement s11 = new assignmentstatement("cod = 2", // infinite solution set proc.getlocalstate().get("cod"), new value(value.tint, "2")); statement s12 = new assignmentstatement("cod = 1", // no solution 190 bazil pârv, ioan lazăr, simona motogna proc.getlocalstate().get("cod"), new value(value.tint, "1")); expression e1 = proc.getlocalstate().get("b"); expression e2 = new value(value.tdouble, "0"); expression e = new binaryexpression(e1, e2, new operator(operator.eq)); // s1: branchstatement(s11, s12) (b == 0) branchstatement s1 = new branchstatement("b == 0", e, s11, s12); compoundstatement s2 = new compoundstatement("unique solution"); s2.add(new assignmentstatement("cod = 0", proc.getlocalstate().get("cod"), new value(value.tint, "0"))); e1 = proc.getlocalstate().get("b"); e2 = new value(value.tint, "-1"); expression e3 = new binaryexpression(e1, e2, new operator(operator.times)); // -b e2 = proc.getlocalstate().get("a"); e = new binaryexpression(e3, e2, new operator(operator.div)); // -b/a s2.add(new assignmentstatement("x = -b/a", proc.getlocalstate().get("x"), e)); e1 = proc.getlocalstate().get("a"); e2 = new value(value.tint, "0"); e = new binaryexpression(e1, e2, new operator(operator.eq)); body.add(new branchstatement("a == 0", e, s1, s2)); return proc; } the method builds a procedure object who implements the well-known algorithm for solving first degree polynomial equations (its body being a branchstatement object) and returns it when the building process is done. this object has two in parameters (a and b) and two out parameters (x and cod). the test driver program object is built as follows: public static program buildprogecgr1() { declarationlist state = new declarationlist(); compoundstatement body = new compoundstatement("test driver for ecgr1"); program prog = new program("demoecgr1", state, body); state.allocate("ca", value.tdouble); // coefficient a state.allocate("cb", value.tdouble); // coefficient b state.allocate("rez", value.tint); // return code state.allocate("sol", value.tdouble); // the solution // resets state prog.setstate(state); // start program body.add(new outputstatement("*** program " + prog.getname() + " started ***", null)); // read coeffs body.add(new inputstatement("read coeff ca", prog.getstate().get("ca"))); body.add(new inputstatement("read coeff cb", prog.getstate().get("cb"))); // calls ecgr1 declarationlist pin = new declarationlist(); pin.allocate(prog.getstate().get("ca")); pin.allocate(prog.getstate().get("cb")); declarationlist pout = new declarationlist(); pout.allocate(prog.getstate().get("rez")); comdevalco framework the modeling language for procedural paradigm 191 pout.allocate(prog.getstate().get("sol")); declarationlist pinout = new declarationlist(); procedure ecgr1 = buildecgr1(); // create the ecgr1 procedure object simplestatement s = new callstatement("call ecgr1", ecgr1, pin, pinout, pout); body.add(s); // print results statement s11 = new outputstatement("unique solution", null); statement s12 = new outputstatement("print solution", prog.getstate().get("sol")); compoundstatement s1 = new compoundstatement("print unique solution"); s1.add(s11); s1.add(s12); expression e1 = prog.getstate().get("rez"); expression e2 = new value(value.tint, "1"); expression e = new binaryexpression(e1, e2, new operator(operator.eq)); s11 = new outputstatement("empty solution set", null); // cod = 1 (a=0, b<>0) s12 = new outputstatement("infinite solution set", null); // cod = 2 (a=0, b=0) branchstatement s2 = new branchstatement("rez == 1", e, s11, s12); e2 = new value(value.tint, "0"); e = new binaryexpression(e1, e2, new operator(operator.eq)); branchstatement st = new branchstatement("rez == 0", e, s1, s2); body.add(st); body.add(new outputstatement("*** program " + prog.getname() + " terminated ***", null)); return prog; } this time, the body of the constructed program object is a sequence of statements which: (1) read the coefficients (using a sequence of inputstatement objects), (2) call the ecgr1 component (using a callstatement object), and (3) print the result (using branchstatement objects in which the condition tests the value of the out parameter rez while the branches are outputstatement objects). the above code also contains statements which create and build the state of the program object, and prepare the call process, i.e. create and populate the actual in, out, and in-out parameter lists and then create the callee procedure object ecgr1 (by invoking buildecgr1() method). the main() method of the demo class first creates the driver program object and then calls its run() method: public static void main(string[] args) { textio.putln("demo program units"); program pecgr1 = buildprogecgr1(); pecgr1.run(); } the program object runs its body by executing sequentially the statements in it: (1) asks the user to enter the values of coefficients, (2) calls the ecgr1 procedure and (3) prints an explanatory message and the unique solution (if this is the case). above-described component definition approach is tedious and error-prone. for example, in order to build a branchstatement object, the process is bottom-up: (1) create the statement objects corresponding to the branches and (2) create the branchstatement object containing them. in a real-world situation, the building process is assisted by graphical tools, as we discuss below. these tools will perform at least the following: (1) graphical or textual building of components, (2) 192 bazil pârv, ioan lazăr, simona motogna saving and restoring component definitions to/from a component repository, (3) component testing and debugging. 4.2 the toolset the comdevalco toolset proposal includes graphical tools for component definition, validation, and composition. this subsection describes current status of these tools. as part of the comdevalco framework, a procedural action language (pal) was defined and it is described in [10]. pal contains all statements included in figure 3, has a concrete textual syntax for uml structured activities, and graphical notations for some uml structured activity actions. the main idea for simplifying the construction of uml structured activities is to use the pull data flow for expression trees. also, we propose new graphical notations for conditionals and loops, following the classical flowchart style. in order to allow the exchange of executable models with other tools, a uml profile is also defined, specifying the mapping between pal and uml constructs. a component definition and validation tool is under development, using both graphical and textual pal notations for building program, procedure, and function program units. this tool is used within an agile mda process which includes test-first component design steps: (1) add a test (in the form of a program component calling a non-existing procedure or function), (2) run the tests (in order to report the missing components), (3) add the production code (i.e. design the missing components), and (4) goto step (2). the process ends during the step (2), when all the tests pass. in the steps (1) and (3), developers are allowed to use either the graphical notation or the concrete syntax of pal; the tool maintains automatically the consistency of the two views. the debugging and testing techniques employed in step (2) are defined according to model-level testing and debugging specification [14, 13]. a detailed description of this tool will be given in a separate paper. 4.3 original elements of the proposed solution the proposed solution brings original elements in at least the following directions: • the object model is precise and fine-grained, because all objects are rigorously defined, and the component behavior is described at statement level. the uml metamodel has no correspondent for modeling constructs more fine-grained than program and procedure; • the models are executable and verifiable because each component can be executed; moreover, one can use tools for validation and evaluation of complexity; • the models are independent of any specific object-oriented language; • the modeling language is flexible and extensible in the following dimensions: the statement set, the component (program units) family, the component definition, the data type definition, and the set of components; • the modeling language allows the use of graphical tools in all the phases: building, validating, and using software component models; • the modeling language allows automatic code generation for components in a concrete programming language, according to model driven architecture (mda) specifications. one can define mappings from the modeling elements to specific constructs in a concrete programming language in a declarative way. comdevalco framework the modeling language for procedural paradigm 193 5 conclusions and further work this paper describes the current status of the modeling language, part of the comdevalco framework. as we discussed above, this version implements a minimal set of elements, corresponding to the procedural programming paradigm. the approach considered was aimed to control the complexity of the problem and of the development process. we started with the simplest programming paradigm, using simple data types and expressions and a small but complete set of statement objects. the development process consisted of small steps, meaning either the implementation of a new concept (transforming the concept into an object), or the extension of a model element. as the experiments were successful, we believe that our approach is feasible. future developments of the modeling language will include: extending type, expression, and operator classes in order to define and manage structured and object types, extending the program units with constructs specific to modular, object-oriented, and component-based paradigms. these steps are considered within the planned evolution of the comdevalco framework, which include steps for defining the structure of component repository and developing the tools aimed to operate in the component definition, validation, evaluation, simulation, and composition. acknowledgements this work was supported by the grant id_546, sponsored by nurc romanian national university research council (cncsis). bibliography [1] s.w. ambler, agile model driven development (amdd): the key to scaling agile software development, http://www.agilemodeling.com/essays/amdd.htm. [2] f.p. brooks, no silver bullet: essence and accidents in software engineering, ieee computer, april 1987, pp. 10-19. [3] k. carter, the action specification language reference manual, 2002. http://www.kc.com/ [4] i. crnkovic, m. larsson, building reliable component-based software systems, prentice hall international, artech house publishers, isbn 1-58053-327-2, available july 2002. http://www.idt.mdh.se/cbse-book/ [5] m. fowler, umlmode, may 2003, http://martinfowler.com/bliki/umlmode.html [6] j. grenfield, k. short, software factories: assembling applications with patterns, models, frameworks, and tools, wiley, 2004. [7] p. henderson, r.j. walters, behavioural analysis of component-based systems, declarative systems and software engineering research group, department of electronics and computer science, university of southampton, southampton, uk, 06 june 2000. [8] s.j. mellor, agile mda, 2005. http://www.omg.org/mda/mda_files/agilemda.pdf [9] s.j. mellor, m.j. balcer, executable uml: a foundation for model-driven architecture, addisonwesley, 2002. 194 bazil pârv, ioan lazăr, simona motogna [10] i. lazăr, b. pârv, s. motogna, i.g. czibula, c.l. lazăr, an agile mda approach for executable uml activities, studia ubb, informatica, lii, no. 2, 2007, pp. 101-114. [11] p.a. muller et al, on executable meta-languages applied to model transformations, model transformations in practice workshop, montego bay, jamaica, 2005. [12] object management group, mda guide version 1.0.1, 2003. http://www.omg.org/docs/omg/0306-01.pdf [13] object management group. uml 2.0 testing profile specification, 2005, http://www.omg.org/cgibin/apps/doc?formal/05-07-07.pdf. [14] object management group, model-level testing and debugging, 2007, http://www.omg.org/cgibin/doc?ptc/2007-05-14/ [15] object management group, uml 2.1.1 superstructure specification, 2007, http://www.omg.org/cgi-bin/doc?ptc/07-02-03/ [16] b. pârv, s. motogna, i. lazăr, i.g. czibula, c.l. lazăr, comdevalco a framework for software component definition, validation, and composition, studia ubb, informatica, lii, no. 2, 2007, pp. 59-68. [17] projtech al: project technology, inc, object action language, 2002. [18] telelogic ab, uml 2.0 action semantics and telelogic tau/architect and tau/developer action language, version 1.0, 2004. bazil pârv, ioan lazăr, and simona motogna babeş-bolyai university faculty of mathematics and computer science department of computer science 1, m. kogalniceanu, cluj-napoca 400084, romania e-mail: {bparv,ilazar,motogna}@cs.ubbcluj.ro received: november 20, 2007 comdevalco framework the modeling language for procedural paradigm 195 bazil pârv is professor at the department of computer science, faculty of mathematics and computer science, babeş-bolyai university, cluj-napoca. 7 books and university courses, and more than 75 papers. his research topics cover: programming paradigms, component-based sofware development, mathematical modeling in experimental sciences, and computer algebra. dr. ioan lazăr is lecturer at the department of computer science, faculty of mathematics and computer science, babeşbolyai university, cluj-napoca. he published 7 books and university courses, and more than 15 papers. his current research topics include: object-oriented analysis and design, modeling languages, and programming methodologies. simona motogna is associate professor at the department of computer science, faculty of mathematics and computer science, babeş-bolyai university, cluj-napoca. she obtained her ph.d. in 2001, with the thesis formal specification of objectoriented languages. her topics of interest are: compilers, semantics, formal specification related to object oriented languages and component based programming. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 2, pp. 118-126 integrated system for stereoscopic cognitive vision, localization, mapping, and communication with a mobile service robot cătălin buiu politehnica university of bucharest department of automatic control and systems engineering spl. independentei 313, 060042 bucharest, romania e-mail: cbuiu@ics.pub.ro abstract: this paper describes a stereo-vision-based mobile robot that can navigate and explore its environment autonomously and safely and simultaneously building a tridimensional virtual map of the environment. the control strategy is rule-based and the interaction with robot is done via bluetooth. the stereoscopic vision allows the robot to recognize objects and to determine the distance to the analyzed objects. the robot is able to generate and simultaneously update a full colour 3d map of the environment that is being explored. the position and type of each detected and recognized object is marked in this 3d map. furthermore, the robot will be able to use a gripper in order to collect detected objects and carry them to dedicated collecting bins, and so will be able to work in commercial waste cleanup applications. this application represents a successful integration of computers, control and communication techniques in mobile service robot control. keywords: control, communication, localization, mapping, mobile robot, stereoscopic vision, virtual reality 1 introduction more than 7 million robots will be sold from 2005 to 2008 according to estimations of the international robotics federation and of the economic commission for europe of the united nations. until 2010 a robust increase of 4% per year in the number of robots is estimated. many of these are service robots which are used to assist or even to replace humans in tedious, dull, dangerous or repetitive tasks. the same sources estimate that by 2010, service robots will be able to fully assist elder people and people with disabilities, will extinct fires, will explore industrial pipes and more [1]. in ecological applications, service robots are used to collect waste and dangerous items in indoor and outdoor environments. for doing that, the robots must be able not only to perceive and act upon the environment by using a wide range of sensors and actuators, but also to manifest human-like cognitive abilities, such as to localize themselves, to recognize and classify objects, to generate maps of the environment, to learn from experience, to interact in a natural way with humans and other robots or to develop physical and cognitive abilities in a kind of developmental process similar to humans. it is often the case that a team of robots is asked to fulfill such a task. there are already a lot of interesting results obtained in collective robotics, see for example [2] where coordinated control based on artificial vision is investigated and [3] where decentralized formation control of mobile robots with limited sensing is addressed. the problem of simultaneous localization and mapping (slam) consists of estimating concurrently the robot’s position and generating a map of its surrounding environment. this is an essential skill for a mobile robot but to this day it has eluded complete and robust solutions because noisy robot dynamics and sensors make solving slam a difficult task. copyright © 2006-2009 by ccc publications integrated system for stereoscopic cognitive vision, localization, mapping, and communication with a mobile service robot 119 slam has been widely used for navigation and typically makes use of laser range-finders or sonars. an advantage of using stereo vision over laser range-finders is the ability to detect obstacles at different heights. the solution presented in [4] is based on learning maps of 3d point-landmarks whose location is estimated using correlation-based stereo and identification is performed using their appearance in images using the scale invariant feature transform (sift) [5]. the authors derive an estimate of the robot’s motion from sparse visual measurements using stereo vision and multiple view geometry techniques [6] known in robotics as visual odometry [7], [8], [9]. a number of professional stereo vision systems and related software systems have been developed and have found a number of interesting applications in various domains, from the control of industrial manipulators for assembly and pick-and-place operations, material handling, collision warning and obstacle detection in robotics, people-tracking, environment modeling, autonomous guidance of corn harvesters, digitizing books, to medical applications, such as ophthalmic diagnostics, ir mammography, and robotic laparoscopy. stereo vision for navigation has a long history and is frequently exploited for autonomous navigation, but has limitations in terms of its density and accuracy in the far field [10]. if landmarks can be placed in the field of view of the camera, the location of a vehicle can be determined by means of stereo vision [11], and if a solid model of the target object is available, a robotic manipulator will have at its disposal a modeled environment for automatic tasks [12]. in [13] it is presented a stereoscopic vision system for a khepera miniature robot. the vision system performs objects detection by using the stereo disparity and stereo correspondence. an adaptive panoramic stereo vision approach for localizing 3d moving objects has been developed in the department of computer science at the university of massachusetts at amherst. in the adaptive stereo model, the sensor geometry can be controlled to manage the precision of the resulting virtual stereo system [14]. other indoor and outdoor stereo vision systems have been developed and tested with satisfactory results and some drawbacks, see [15], [16] and [17]. a novel optical system allows the capture of a pair of short, wide stereo images from a single camera, which are then processed to detect vertical edges and infer obstacle positions and locations within the planar field of view, providing real-time obstacle detection [18]. very few applications concern the problem of waste collecting service robots acting indoor. the application reported in this paper is part of the bigger remaster research project currently under development at the autonomous robotics lab of the politehnica university of bucharest, romania. this project concerns the development of a commercial cognitive service robot to be used in waste cleanup in office buildings. a first prototype (remaster one) has been built. related details on the structure of the prototype and of its cognitive vision system using a monocular vision system are given in [19] and [20]. the acquired expertise has been used to propose the structure and to design a stereoscopic cognitive vision system which is detailed in [21]. the aim of this paper is to present the current phase in the remaster project which consists in the design and implementation of an integrated system for stereoscopic cognitive vision, localization, mapping, and communication with the robot. the goals of this system is to allow the robot to recognize and classify various objects and to determine the distance to the objects. combined with the self-localization ability of the robot, this allows the absolute position of detected objects to be determined and marked on a tridimensional map of the working space that is continously updated. so, stereo vision and slam are integrating in order to create a map of the environment, without using any landmarks. the realization of this vision based mapping is the main contribution of this paper. the paper is structured as follows. section 2 gives an overview of the system architecture, and of the robot control and communication system, while section 3 presents the realization of the stereoscopic cognitive vision system. section 4 describes the way in which the robot is able to generate, maintain and update a tridimensional virtual map of the environment that is being explored. the last section of the paper presents conclusions and some directions for further research and developments. 120 cătălin buiu 2 robot control and communication system the integrated system was implemented on a koala robot (fig. 1) which is a mid-size robot designed for real-world applications and capable of carrying larger accessories. it has been chosen for this application, as koala has the functionality necessary for use in practical applications (like sophisticated battery management), and rides on 6 wheels for indoor and all-terrain operation. it has 16 distance sensors and can be controlled via bluetooth. figure 1: koala mobile robot (www.k-team.com) two commercial webcameras have been mounted on top koala at the same level (fig. 2), at a distance of 95 mm between them, and at a height of 170 mm. the cameras are inclined at 10 degrees, and have cmos 1.3 megapixel sensors (1280*960 pixels images and 640*480 videos), manual focus, and a focal distance of 1/4.8 mm. figure 2: stereoscopic vision system on-board koala on the robot side, there is a bluetooth 333s module installed and directly connected to the serial interface of the robot. the control program runs on a separate laptop which communicates with the robot by using a bluetooth connection. the robot can localize itself by using a dedicated and redundant system consisting of a beacon and two transponders ([19], [21]) and an odometry algorithm. the robot is able to navigate in indoor environments consisting of walls and various objects, such as empty cans and bottles. the control program is implemented in matlab and is based on simple control rules which allow an obstacle avoidance and waste finding behavior (fig. 3). the robot will move forward and will be able of a safe navigation and detection of objects in the workspace (fig. 4). after detecting an obstacle, the robot will stop and action according to the type of the obstacle. if wall, the obstacle will be avoided and the robot will resume moving. if not wall, using the distance sensors on-board the robot, the system will compute the distance to the object and the corresponding angle. using these two measurements, the system will compute the absolute position of the detected object and will compare this with the stored coordinates of previously detected objects. if the object is new (its absolute position is not in the database), it will get more attention from the robot which will turn so that it is facing the object (fig. 5). now, the system is ready to acquire stereoscopic images of the object. the images are processed and analyzed as explained in the next section. the distance to the object is determined and based on integrated system for stereoscopic cognitive vision, localization, mapping, and communication with a mobile service robot 121 figure 3: control algorithm figure 4: robot navigating in a test environment figure 5: robot turning to a detected object 122 cătălin buiu the absolute position of the robot, the absolute position of the object is also determined. then the robot resumes its movement in the workspace after storing the type and coordinates of the object in the database and after having marked its position in the tridimensional map which will be detailed later. 3 stereoscopic cognitive vision system stereoscopy is a technique for infering the 3d position of objects from two or more simultaneously views of the scene. reconstruction of the world seen through stereo cameras can be divided in two steps. first, the correspondence problem means that for every point in one image to find out the correspondent point on the other and compute the disparity of these points. this disparity correlates to distance, and the higher disparity of object pixel means that the object is closer to the cameras. secondly, there is the triangulation step. given the disparity map, the focal distance of the two cameras and the geometry of the stereo setting (relative position and orientation of the cameras) compute the (x,y,z) coordinates of all points in the images. the system presented in this paper solves both steps as will be described below. key advantages of camera based systems include: they offer minimally complex solutions, have very low costs, they are entirely solid state, and colour information can be easily acquired at the same time as range data, helping to build realistic full colour 3d models of the environment. all these advantages are exploited in the application reported in this paper. stereo vision provides realtime, full-field distance information, and is useful in many applications in a wide variety of fields, including robotics. there is a number of dedicated software packages, such as small vision system for realtime stereo analysis from sri’s artificial intelligence center. sentience is a volumetric perception system for mobile robots and uses webcam-based stereoscopic vision to generate depth maps, and from these create colour 3d voxel models of the environment for obstacle avoidance, navigation and object recognition purposes. a "cognitive vision system" is defined in [22] as a system that uses visual information to achieve: recognition and categorization of objects, structures and events, learning and adaptation, memory and representation of knowledge, control and attention. for example, a cognitive monocular vision system for a mobile robot using a cmucam2+ camera is presented in [20]. the visual system’s architecture is presented in fig. 6. all the visual information processing is done on the same separate laptop. given the disparity map, the focal distance of the two cameras and the geometry of the stereo setting (relative position and orientation of the cameras), the system is able to compute the coordinates of all points in the images. the distance to the object is used to determine the absolute position of the object which is marked on the 3d map. figure 6: stereovision system’s architecture integrated system for stereoscopic cognitive vision, localization, mapping, and communication with a mobile service robot 123 screenshots from our application that present two stereoscopic images of a detected object are given in fig. 7. these images will be further processed. figure 7: stereoscopic images of a detected object (left and right hand camera) now the images contain relevant data that will be brought in such a form that contours can be extracted. the images are binarized by extracting colour channels corresponding to the colour of detected objects (yellow, in our case). then, dilatation and errosion filters are applied to the images (see fig. 8). then the images are segmented and objects detected (fig. 9). figure 8: extraction of yellow colour channel and application of dilatation and errosion filters (left and right hand image) figure 9: detected contour for the object in the image using simple scalar descriptors, such as area and perimeter, the detected object is recognized and classified as a can, in our case. 4 generation and update of a tridimensional map of the environment simultaneous localization and mapping (slam) is an essential capability for mobile robots exploring unknown environments. the robot presented in this paper is using a dedicated self-localization 124 cătălin buiu system based on the use of a beacon unit and two transponders (master and slave) [21]. the two transponder units are fixed, while the beacon unit is installed on the robot. half-duplex bidirectional communication between beacon and transponders is realized by using infrared light and ultrasounds. the system is using a atmega8 microcontroller with 16mips at 16mhz. the localization of the robot is realized by triangulation of the distances to the two transponders. more, odometry algorithms contribute to a more precise localization of the robot in the working space. the system is able to generate a a virtual map of the explored environment, in which the space, the robot and the objects are modeled as vrml (virtual reality modeling language) objects. vrml is a standard file format for representing tridimensional interactive vector graphics. it also enables the integration of interactive 3d graphics into the web. by using the virtual reality toolbox from matlab, the system will generate realistic 3d views of the working space and the robot (fig. 10), and objects (fig. 11, in which a question mark means an unknown object). figure 10: vrml models of the working space and robot figure 11: vrml models of objects the robot will explore the working space according to the control strategy presented above and simultaneously will update the tridimensional map. after an object is detected and the robot turns towards it, both cameras are taking images of the scene. further, the images are analyzed and the object recognized. the absolute position of the object is also determined and the object marked on the map (fig. 12). then the robot resumes its movement in the workspace and associated activities: navigation, search, classification and localization of objects. the test results show a good and robust functioning of the stereovision system, and although the processing times are not low, this can be improved by the use of an embedded pc with more computing power. 5 summary and conclusions the main research thrust of this paper has been to demonstrate that an integrated system for communication, control, localization and mapping using stereoscopic vision and 3d maps can be designed and implemented for a mobile service robot which will collect waste in indoor environments. this integrated system will be transferred to a more powerful version of the first prototype (remaster one) of the commercial waste cleanup robot that is the final aim of the remaster project. the new robot will have integrated system for stereoscopic cognitive vision, localization, mapping, and communication with a mobile service robot 125 figure 12: robot taking pictures of an object, recognizing it as a can, and marking it on the map a gripper such that the detected objects can be grasped and carried to dedicated bins. future efforts will address the design and implementation of new navigation strategies based on fuzzy logic. image processing algorithms based on cellular neural networks are currently under investigation and implementation. more research will be done in what regards the interactive aspects of the robotic system so that the robot will be able to interact with humans and other robots in a natural way. acknowledgements we acknowledge the support of the romanian government through the excellence research program (contract 83-ceex-ii-03/31.07.2006) and the work of cristian ionita and laura antochi to the development of the stereoscopic vision system and virtual map. bibliography [1] c. buiu (editor), cognitive robots (in romanian), editura universitara, 2008. 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[18] w. lovegrove, b. brame, single-camera stereo vision for obstacle detection in mobile robots, in intelligent robots and computer vision xxv: algorithms, techniques, and active vision., proceedings of the spie, volume 6764, pp. 67640t, 2007 [19] c. buiu, f. cazan, r. ciurlea, developing of a service robot to recognize and sort waste, in: 16th international conference on control systems and computer science, pp. 298-303. politehnica press, bucharest, 2007. [20] ana pavel, c. vasile, c. buiu, cognitive vision system for an ecological mobile robot, in proceedings of sintes 13, the international symposium on system theory, automation, robotics, computers, informatics, electronics and instrumentation, pp. 267-272, universitaria press, craiova, 2007. [21] c. buiu, design and development of a waste cleanup service robot, in proceedings of the first international eurobot conference, heidelberg, pp. 194-202, 2008. [22] a.g. cohn, d. magee, a. galata, d. hogg, s. hazarika, towards an architecture for cognitive vision using qualitative spatio-temporal representations and abduction, in c. freksa, w. brauer, c. habel, k.f. wender (editors), spatial cognition iii, routes and navigation, human memory and learning, spatial representation and spatial learning, pp. 232-248, springer-verlag, 2003. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 403-416 a joint routing and time-slot assignment algorithm for multi-hop cognitive radio networks with primary-user protection h. chen, q. du, p. ren hao chen 1. department of information and communication engineering, xi’an jiaotong university, xi’an, shaanxi,710049, china 2. state key laboratory of integrated services networks, xidian university, xi’an, shaanxi, 710071, china e-mail: js.sq.chenhao@stu.xjtu.edu.cn qinghe du, pinyi ren department of information and communication engineering, xi’an jiaotong university, xi’an, shaanxi,710049, china e-mail: {duqinghe, pyren}@mail.xjtu.edu.cn abstract: cognitive radio has recently emerged as a promising technology to improve the utilization efficiency of the radio spectrum. in cognitive radio networks, secondary users (sus) must avoid causing any harmful interference to primary users (pus) and transparently utilize the licensed spectrum bands. in this paper, we study the puprotection issue in multi-hop cognitive radio networks. in such networks, secondary users carefully select paths and time slots to reduce the interference to pus. we formulate the routing and time-slot assignment problem into a mixed integer linear programming (milp). to solve the milp which is np-hard in general, we propose an algorithm named rsaa (routing and slot assignment algorithm). by relaxing the integral constraints of the milp, rsaa first solves the max flow from the source to the destination. based on the max flow, rsaa constructs a new network topology. on the new topology, rsaa uses branch and bound method to get the near optimal assignment of time slots and paths. the theoretical analyses show that the complexity of our proposed algorithm is o(n4). also, simulation results demonstrate that our proposed algorithm can obtain near-optimal throughputs for sus. keywords: cognitive radio networks; primary-user protection; joint routing and time-slot assignment. 1 introduction the rapid growth in the number of wireless applications such as wifi, wimax, 3g et al. leads to a big radio spectrum shortage. recent studies by the federal communications commission (fcc) highlight that the average utilizations of some licensed spectrum bands allocated through the current static frequency spectrum assignment policies vary between 15% and 85% [1]. to make sufficient use of the spectrum resources in the environment, the notion of cognitive radio (cr) was proposed by dr. joseph mitola in 1999 [2]. in cognitive radio networks, nodes are allowed to sense and explore a wide range of the frequency spectrum and identify currently underutilized spectrum blocks for data transmission. cr can transparently exploit the licensed spectrum bands and is widely considered as the technique for the next generation of wireless communication [3]. to maximize the advantages of cognitive radio networks (crns)łźit is necessary to update the physical layer, media access control (mac) layer and network layer of the traditional wireless communication system. after the concept of cr was proposed, many studies on spectrum sensing and mac protocol copyright c⃝ 2006-2012 by ccc publications 404 h. chen, q. du, p. ren design have been conducted and a lot of progress has been made [4] [5] [6]. the aim of spectrum sensing is to find the spectrum holes in the crns and the mac protocol is to select the one-hop optimal spectrum bands for sus’ transmitting. however, khalife et al. indicated that mac protocol which gave optimal solutions in a single hop configuration may become largely inefficient in a multi-hop scenario and it was of great importance to design cross-layer protocols capable of scheduling, spectrum selecting and routing [7]. cesana et al. in [8] pointed out the challenges of routing in cognitive radio networks: any routing solutions designed for multi-hop crns must be highly coupled to the entire cognitive cycle of spectrum management and the routing module should be able to make fast route maintenance at the sudden appearance of pus. the authors of [9] extended the routing solution in multi-channel multi-radio networks (mcmrns) to crns and proposed a layered graph framework to address channel assignment and routing jointly. hou et al. in [10] [11] illustrated the difference about routing in mcmrns and crns. in crns the radios could send packets over non-contiguous frequency bands and the authors proposed a mixed integer non-linear programming (minlp) model to minimize the required networkwide radio spectrum resources. filippini et al. in [12] proposed a minimum maintenance cost routing for cognitive radio networks. the authors formulated the maintenance cost problem to be an integer optimization model and by carefully selecting routing metrics the authors designed a heuristic distributing algorithm. among all the above routing solutions the accurate information about spectrum availability sensed by the physical layer is crucial for the routing module. so these routing solutions make severe demand on the spectrum sensing module of cr nodes. to ease this demand, in [13] chowdhury et al. proposed a routing solution which avoided harmful interference to pu receivers by designing proper routing metrics. in that solution, each time sus chose the path that passed through regions having minimum overlap with pus’ transmission coverage areas. in [14] ding et al. proposed a distributed algorithm to maximize the capacity of links without generating harmful interference to other users by performing joint routing, dynamic spectrum allocation and scheduling. in [15] xie et al. proposed a geometric approach for relay selections which avoided causing harmful interference to pus in crns. each time the approach selected the best channels available to transmit data to the nearest neighbors or the farthest ones greedily. in [16] the authors zhou et al. gave a mathematical model aiming at minimizing the interference to pus. by relaxing the model’s constraint conditions, the authors transformed the original optimization problem to a linear programming model and gave a joint channel assignment and path selection algorithm. in spectrum sharing multi-hop crns, to guarantee the pus’ priority on the licensed spectrum bands sus must not generate harmful interference to pus. in this paper, to make sufficient use of spectrums we design the routing module of sus through joint routing and time-slot assignments. firstly, we exploit the protocol model [17] which describes the conditions of successful transmission between two nodes and abstract the routing and time-slot assignment problem to be a kind of mixed integer linear programming (milp) model. in the milp model, our objective is to maximize the throughput of sus and our constraints are avoiding harmful interference to pus and eliminating sus’ conflicts caused by concurrent transmissions. then, to get an approximate solution of the milp which is a np-hard problem we propose a near optimal joint routing and time-slot assignment algorithm named rsaa. the theoretical analysis shows that the complexity of rsaa is o(n4). the simulation results demonstrate that rsaa can obtain near optimal throughput. what is more, through the simulation results we analyze the effect of node density and slot periods on the throughput of multi-hop crns. a joint routing and time-slot assignment algorithm for multi-hop cognitive radio networks with primary-user protection 405 2 network model and problem formulation 2.1 network model in 2004, the fcc proposed to allow unlicensed wireless devices to utilize television channel frequencies under the precondition of causing no harmful interference to pus. under this proposal, sus could use the underutilized broadcasting tv spectrum bands for multi-hop communications. in multihop crns, tdma is very necessary for the avoidance of conflicts among sus and the reduction of interference to pus. in the current wlans, nodes use csma/ca to avoid access conflicts. however, csma/ca cannot assure that sus cause no harmful interference to pus. hence it may be not suitable for spectrum sharing crns. in this paper, we consider a cognitive ad hoc network consisting of p pus, n sus and a cognitive scheduling center (csc). the csc is able to access the data base of pus [8] and gathers the information about the pus’ locations and interference thresholds. the interference threshold is defined as the highest interference power which a pu can tolerate. also the csc is designed to be able to collect the information about sus’ locations and the transmitting power via existing communication networks like gsm. after gathering all these information, the csc computes the optimal routing and time-slot assignments and schedule sus’ access to licensed spectrum by delivering these messages to the corresponding nodes. the network architecture is shown in fig. 1. as is shown in fig. 1, the tv receivers act as pus and have priority to use the spectrum f .the csc coordinates and schedules sus to utilize the band f transparently. for example, the csc assigns the path and slot solution s 1 −→ a 2 −→ b 3 −→ c 1 −→ e 2 −→ d for source node s and destination node d. the symbol "a 1 −→ b" represents that node a transmits to node b in time slot 1. although the path s → a → g→ e → d is of less hops and much more throughput, the csc does not choose it because node g causes harmful interference to pus.this paper focuses on finding the optimal routing and time-slot assignment which maximizes the sus’ throughput while avoiding harmful interference to pus. pu a 1 su s su a su b su g su e su d su c 1 2 3 2 pu c pu b csc figure 1: system model 2.2 problem formulation to describe the network mathematically, we first model the successful transmitting conditions among nodes and introduce binary integral variables xi jt, where xi jt=1 indicates that su node i sends packets to su node j in slot t; otherwise xi jt=0. that is, xi jt = { 1, s u i send packets to s u j in slot t 0,otherwise (1) 406 h. chen, q. du, p. ren we assume that all the sus’ radios use the same transmitting power q and the successful transmitting range among sus are rt . let ai denote the neighbor set of node i. so we have ai = { j|di j < rt } (2) where di j denotes the euclid distance between node i and node j. let ii denote the interfered node set of node i, and ri denote the interference range among sus. so we have ii = { j|di j < ri } (3) note that node i cannot transmit to multiple nodes at the same time. we have∑ q∈ai xiqt 6 1 (4) due to potential interference among nodes in the network, if node i uses slot t for transmitting data to node j ∈ ai, then any other nodes which conflict with node j should not use this slot. so we have xi jt + xpqt 6 1, p ∈ i j, p , i,q ∈ ap (5) this is the difference between our model with the model in [9][10], where the authors state the conflict constraint as xi jt + ∑ p∈i j,p,i,q, j xpqt 6 1 (6) it is important to understand that in the conflict constraint (5), two links which interfere with xi jt but do not interfere with each other can access the channel at the same time slot. constraint (4) is the node’s successful sending condition and constraint (5) is the node’s successful receiving condition. under the constraint (4) and (5), nodes in the network can transmit data without conflicting with each other. to guarantee that two nodes transmit data without bit error, the flow rates on each link must not exceed the link’s capacity. let fi jt denote the flow rate between node i and node j at time slot t and gi j denote the channel gain between node i and node j. we have fi jt 6 xi jt blog2(1 + gi j q η ) (7) where b denotes the bandwidth of spectrum band f and η denotes the noise power in the environment. note that the denominator inside the log function contains only η. this is because the interference constraint (5) assures that the interference power received by the su’s receiver is negligible and this helps to simplify our model to be a linear model. what’s more, to make sure that no drop of packet happens at the intermediate nodes, the aggregate data rates in the slot period should meet the flow conservation constraint. i.e. t∑ t=1 ∑ j∈ai fi jt = t∑ t=1 ∑ i∈ap fpit (8) where t denotes the period of scheduling slots of the network. to make sure that all the transmitting power of sus detected at pus does not exceed pus’ threshold int , we have the following constraints n∑ i=1 xi jtgik q 6 int , j ∈ ai,k ∈ p, (9) a joint routing and time-slot assignment algorithm for multi-hop cognitive radio networks with primary-user protection 407 where p denotes the set of pus. in practice the value of int depends on the sensitivity of pu receivers as well as the noise power in the environment. note the average value of sus’ throughput s is the ratio of aggregate amount of data to the time slots, so we have s = 1 t t∑ t=1 ∑ i∈as fsit (10) where s denotes the source node. when there are a number of source-destination pairs in the network, we can introduce one more virtual source-destination pair and simplify the network to be a single source-destination pair network. in such networks, our aim is to find the optimal routing and time-slot assignments which maximize the throughput of sus. mathematically, we have the following optimization problem. max  1t t∑ t=1 ∑ i∈as fsit  (11) s.t.  ∑ q∈ai xiqt 6 1 xi jt + xpqt 6 1, p ∈ i j, p , i,q ∈ ap fi jt 6 xi jt blog2(1 + gi j q η ) t∑ t=1 ∑ j∈ai fi jt = t∑ t=1 ∑ i∈ap fpit n∑ i=1 xi jtgik q 6 int , j ∈ ai,k ∈ p,0 6 t 6 t xi jt = 0 or 1 fi jt > 0 (12) note that the above optimization problem has both integral and continuous variables and the constraint conditions are all linear. so it is a kind of milp problem. in the above milp problem, the optimization variables consists of continuous variables fi jt and binary variables xi jt , while b, t , q, η and int are all constant. 3 routing and time-slot assignment algorithm the above optimization problem is a kind of milp problem, which is np-hard in general [18]. to solve the milp problem, yuan et al. in [16] proposed a greedy algorithm which relaxed the integral constraints and then simplified the milp to be a linear programming (lp) problem. after solving the lp problem, the algorithm fixed the binary variables in the descending order of their relaxed values one by one. although the complexity of this algorithm is equal to solving just one lp problem, the algorithm cannot guarantee that the solution is a feasible flow and that the result meets the interference constraints (4) (5) absolutely. hou et al. in [10] proposed the sequential fixing (sf) algorithm which solved o(n) lp problems iteratively to fix the binary variables. another method to solve milp is to replace the integral constraint to be the following constraint xi jt(xi jt − 1) = 0. (13) based on constraint (13) we can transform milp to be quadratic programming (qp). but we still cannot get optimal solution under polynomial complexity because constraint (13) is non-convex. sherali et al. in [18] pointed out that to solve the milp problem one should exploit the problem’s inherent special structures in the process of model formulations and in algorithmic developments. taking 408 h. chen, q. du, p. ren a closer look at our milp problem we can find that if we relax the integral constraint (1) to be continuous constraint 0 6 xi jt 6 1, (14) and neglect the interference constraint (4)(5). then the milp problem will reduce to a kind of max flow problem. so the optimal solutions of milp are likely to be the subsets of the max flow. based on this idea, we develop our algorithm rsaa which can solve the milp problem efficiently. let e denote the number of edges and n denote the number of su nodes in the crns. since the original milp consists of et binary variables, to get the optimal solution of the milp problem we should enumerate 2et combinations of xi jt. once the binary variables are fixed, the original milp problem is reduced to be the following lp problem. max  1t t∑ t=1 ∑ i∈as fsit  (15) s.t.  fi jt 6 xi jt blog2(1 + gi j q η ) t∑ t=1 ∑ j∈ai fi jt = t∑ t=1 ∑ i∈ap fpit n∑ i=1 xi jtgik q 6 int , j ∈ ai,k ∈ p,0 6 t 6 t fi jt > 0 (16) we denote the new lp problem as lp1 and it consists of et continuous variables. in rsaa algorithm, we first neglect the constraint conditions (4)(5)(7) and solve the max flow problem from the source to the destination. the max flow problem can be formulated to be the following lp problem max  1t t∑ t=1 ∑ i∈as fsit  (17) s.t.  fi jt 6 blog2(1 + gi j q η ) t∑ t=1 ∑ j∈ai fi jt = t∑ t=1 ∑ i∈ap fpit fi jt > 0 (18) we denote the above lp problem as lp2 and use push-pull flow algorithm [20] to obtain the max flow φ= { fi jt}. after getting φ, we construct a new set of binary variables by xt = {xi jt| fi jt > 0, fi jt ∈φ} (19) and xt is the new enumeration set of rsaa. we denote k as the number of variables of xt , i.e. k = |xt |. when we enumerate the new variables in xt , we use constraints (4)(5) as the branch and bound conditions. the complete rsaa algorithm is given in table 1. in the procedure of rsaa algorithm, we first construct new searching variables or edges by the solution of the max flow problem and then cut off all the nodes and edges which have nothing to do with the max flow in the network. to solve the milp in newly constructed network, we take the condition (4) (5) as bound conditions and simplify the milp problem to be a number of lp1 problems. also when we solve lp1, we take it as a kind of special max flow problem, and use dinic algorithm to solve it. as we can see from the algorithm’s process, the solution of rsaa always meets all the constraints of the original milp problem and so we can conclude that the solution of rsaa is a feasible solution to the original milp problem. a joint routing and time-slot assignment algorithm for multi-hop cognitive radio networks with primary-user protection 409 table 1: rsaa algorithm steps contents step 1: let the csc update the pus’ and sus’ locations and compute the sus’ link capacity and their interference power to pus. introduce a virtual source-destination pair and simplify the network to be a signal source-destination pair network. step 2: set up and solve lp2 and obtain its solution φ. use the equation (19) and φ to construct new binary variable set xt . sort the new binary variables in the ascending order by slot index. step 3: initialize the sus’ throughput s = 0; set the current optimal flow solution as φ∗ = ∅, temp_i = 0. step 4: if temp_i > 2k , then the whole algorithm ends, output the optimal throughput s and the optimal flow solution set φ∗; else transform temp_i into |xt | bit binary digits, each digit represents the assignment of corresponding link and slot. if each digit of temp_i meets the interference condition (4)(5),then go to step 5;else temp_i = temp_i + 2b_last−1, where b_last is the smallest digit index in all temp_i’s transformed digits which violate the interference condition (4)(5). go back to step 4. step 5: after getting one combination of {xi jt},use dinic algorithm to solve lp1 and get the max flow value temp_ fval as well as the corresponding flow rates ft = { fi jt}. in the steps of dinic algorithm, when we search the augmenting flows in the layered networks, we select the augmenting flow according to the ascending order of their interference to pus. if the adding of augmenting flow with the minimum interference exceeds int , then augmenting step of dinic algorithm ends; else continue to find the other augmenting flows. when dinic algorithm ends, if s < temp_ fval, then s = temp_ fval,φ∗ = ft ; else go back to step 4. 410 h. chen, q. du, p. ren 4 performance analysis of rsaa 4.1 complexity of rsaa we now analyze the complexity of rsaa using the random network theory. in the original milp problem, the complexity of obtaining optimal solution is exponential and we should solve o(2|e|) linear programming lp1. by reducing the searching variables, the complexity of rsaa algorithm is reduced to be polynomial. in fact, we have the following theorem. theorem 1. in the network where the average degree of each node is constant, the complexity of rsaa is o(n4). proof: let constant d denote the average degree of each node in the network and in practice the value of d is determined by the node density and the node’s transmitting range. by the er model in random network theory [21], the average number of edges in the network is e = dn 2 (20) let l denote the average path length from the source node to the destination node, and then the number of nodes in the network has the following expression [21], n ∝ dl (21) so the average route length can be written as l =α log2 n log2 d , (22) where α is a constant. note that the capacity of each link in random networks is i.i.d. so the maximum throughput we obtain from lp2 are dc, where c denotes the average capacity of links. and then we can conclude that the maximum number of paths in the max flow for source to destination is d. hence, the number of newly constructed binary variables is k = |xt |= dt l =αt d log2 n log2 d . (23) so rsaa need to solve o(2k ) = o(n) linear programming problems lp1. by dinic algorithm the complexity of solving lp1 is o(n3). hence, the total complexity of rsaa algorithm is o(n • n3) = o(n4). � from theorem 1 we can see that both rsaa and sf need to solve o(n) linear programming problems lp1. the difference between them is that rsaa exploits the flow structures of the network to obtain better performance. 4.2 optimal approximation although rsaa restricts the searching space and this restriction may reduce the throughput of sus, we find that this kind of reduction is almost negligible. in fact we have the following theorem. theorem 2. if φ∗ is the optimal solution to the milp problem and pt is the optimal routing and timeslot assignment, then the intersection set between pt and the rsaa’s new constructed searching set xt is not null. a joint routing and time-slot assignment algorithm for multi-hop cognitive radio networks with primary-user protection 411 proof: we prove this theorem by constructing contradictions. suppose the intersection set between xt and the new constructed searching set xt is null, i.e. pt ∩ xt = ∅. we denote the optimal flow rate of the milp as φ∗ and denote the max flow of lp2 as φ. because φ∗ is the solution of the milp, and so φ∗ satisfies all the constraint flow conditions in the milp. then φ∗ satisfies all the constraint conditions of lp2. so φ∗ is a feasible augmenting flow to lp2. because pt ∩ xt = ∅, and this means pt is an independent augmenting path on which we can augment φ∗ to the original max flow. so the optimal solution of lp2 is φ∗ +φ and this is contradictory to the fact that φ is the optimal solution. so the supposition that the intersection set between pt and the rsaa’s new constructed searching set xt is null is false and our theorem is proven. � however, theorem 2 cannot guarantee that the solution of rsaa is optimal. only when pt is the subset of xt , can we say that the solutions of rsaa are optimal. in fact, we can conclude that pt belongs to the subset of xt with high probability according to theorem 2. especially, in the small networks where the max flow consists of only one path, we can conclude that the solution of rsaa will be optimal according to theorem 2 and the flow conservation condition(8). 4.3 the effect of time-slots and conflicts in our milp model, there is a constant t which represents the slot scheduling period of the crns. we can see that the existing of t increases the complexity of our algorithm, and this is because t decides the number of variables. in the network layer, the routing module should fix the best slot period according to the network parameters such as the node density and pu’s threshold. note that the minimum slot period should be enough to avoid the conflicts among sus and the interferences to pus. so we can get the average minimum value of t , tmin = max{lqg∗/int , l/c} (24) in the above equation, c is the average number of mutual interference edges and l is the average route length from the source to destination, and g∗ denotes the average channel gain between two neighbor nodes. in equation (24), the first item means that to connect the source and the destination at least lqg∗/int slot periods are needed to guarantee no harmful interference to pus. the second item means that to avoid the conflicts among sus at least l/c slot periods are needed. and the equation (14) means that only the slot periods are long enough to avoid harmful interference to pus and eliminate the conflicts among sus, the source and destination pair can set up a successful route. in fact, it is necessary to assure that the slot period is greater than the minimum one, or the slot period will become the bottleneck constraint of su’s throughput. and when the slot periods are smaller than the minimum one the increase of slot period will dramatically increase sus’ throughput. but if the slot periods are greater than the minimum one, the increase of slot period will not necessarily lead to the increase of sus’ throughput. and in this scenario sus’ throughput in crns are the tradeoff between the amount of data and the delay as is shown in equation (10). however, if we divide one fixed length of time into a number of slots, we can find that the more slots we divide the fixed time into, the more throughput sus can obtain. in the best case if we divide the fixed time into infinite slots, and then the solution of milp will approximate the solution of the relaxed lp where the integral constraint is reduced to be constraint (14). to describe the effect of conflict on sus’ throughput, we introduce r denoting the ratio of node’s interference distance to transmitting distance. i.e. r = ri/rt (25) note that as the ratio r increases the number of conflicting edges in the network will increase. and this means the number of conflict constraint inequality (5) will increase and so the solutions of the milp will 412 h. chen, q. du, p. ren decrease. intuitively, the increase of the ratio r will decrease the concurrent transmissions and cut down sus’ throughput. however, as the ratio r increases, the number of paths from the source to destination will decrease because of the concurrent conflicts. so according to theorem 2 we can conclude that the solution of rsaa will become closer to the optimal results as the ratio r increases. from the analysis above, we can find that rsaa algorithm can obtain the near optimal throughput of sus at the complexity of o(n4). in the next section we will verify the performance analysis through simulations. 5 simulation results in this section, we present simulation results for the proposed rsaa algorithm and compare it to sf algorithm and the enumeration algorithm. since the enumeration algorithm can obtain optimal solutions, we denote the results of enumeration algorithm as optimal solutions in the following figures. our simulation scenario is set at the rural and mountainous areas where the tv broadcast spectrum is underutilized and the sus can transparently use these spectrums without generating harmful interference to pus. the simulation parameters are shown in table 2. table 2: simulation parameters simulation parameters values the topology area 1000x1000 m2 the distribution of sus’ location uniform distribution channel propagation model two way ground reflection model the transmitting range of sus 250m the interfering range of sus 300m the band width of pus 1mhz the power of noise -140dbw the transmitting power of sus 2w the number of pu 1 the location of pu (0,0) the length of each slot period 1s simulation times 200 5.1 the sus’ throughput vs pu’s outage probability fig. 2 shows the outage probability of pus in the condition that sus do not take the pu’s threshold into consideration. we count a time of outage of pu when pu detects that sus’ interference power exceeds int . in fig. 2 the slot period is set to be 3 and the ratio of interference to transmitting is set to be 6/5. we use software cplex to get the optimal solution of milp. from fig. 2 we can find that as the pu’s threshold increases the outage probability of pu decreases. also the outage probability decreases as the node density nm in the network decreases. this is because the interference power received by pu is the sum of all sus’ transmitting power and more nodes in the network means more interference. as we can see from fig. 2, the outage probability is very high and unbearable if sus do not carefully select paths and slots. so in the networks with high node density, it is very necessary to protect pu from sus’ interference at network layer. fig. 3 compares the sus’ throughput in two different scenarios: in one scenario sus avoid harmful interference to pus and in the other scenario sus neglect pu’s threshold. from fig. 3 we can see that sus’ throughput are very sensitive to pu’s threshold when sus take the interference to pu into a joint routing and time-slot assignment algorithm for multi-hop cognitive radio networks with primary-user protection 413 −115 −110 −105 −100 −95 −90 0 10 20 30 40 50 60 the threshold of pu (dbw) th e o u ta g e p ro b a b ili ty o f p u (% ) nm=8 nm=16 nm=24 figure 2: the outage probability of pu as the increase of pu’s threshold consideration. and the throughput obtained from neglecting pu’s threshold are the upper bound of those of considering pu’s threshold. what’s more, fig. 3 shows that when pu’s threshold is low enough, pu’s threshold becomes the bottleneck of sus’ throughput. fig. 2 and 3 demonstrate the performance tradeoff between pus and sus in spectrum sharing crns. and so our model can offer valuable reference for the design of multi-hop crns. −115 −110 −105 −100 −95 −90 1 2 3 4 5 6 7 the threshold of pu (dbw) th ro u g h p u ts (m b p s) nm=8 not protecting pu nm=16 not protecting pu nm=24 not protecting pu nm=8 protecting pu nm=16 protecting pu nm=24 protecting pu figure 3: sus’ throughput as the increase of pu’s threshold 5.2 the effect of node density fig. 4 compares sus’ throughput of rsaa algorithm, sf algorithm and the enumerate algorithm. in the simulation, the slot period is 3 and the pu’s threshold is -90dbw. the ratio of interference to transmitting is 6/5. from fig. 4 we can find that the results of rsaa outperform the results of sf and can obtain 97% of the optimal throughput for sus averagely. especially when the node density is low, rsaa can obtain 99% of the optimal throughput, while sf just gets 55% optimal throughput for sus. this is because when the node density is low, the optimal path will almost surely locate in the searching set xt as is shown in theorem 2. what’s more, fig. 4 shows that sus’ throughput decrease as the node density increases which is the same with the results in [16]. the reason is that when the node density increases, sus need more slots which mean more delay to avoid the conflicts among sus and the interference to pu. 414 h. chen, q. du, p. ren 6 8 10 12 14 16 18 20 0 2 4 6 8 10 12 throughputs with different node density node density(nodes/km2) th ro u g h p u ts (m b p s) optimal solution rsaa sf figure 4: sus’ throughput on different node density. 5.3 the effect of time-slots and conflict fig. 5 compares the throughput of sus got from the three algorithms as the slot periods in the network increase. in this simulation the node density is set as 13 nodes per square kilometer. the pu’s threshold int and the ratio r are the same with those in fig. 4. from fig. 5 we can see that the rsaa’s approximation to the optimal solution is not affected by slots and in any slot number condition rsaa can get 98% of the optimal throughput for sus. also we can find that when we fix the length of each slot as 1 second and increase the slot numbers in the network, the sus’ throughput increase dramatically when the number of slot periods is small. if we increase the slot periods from 3 to 5, sus’ throughput decrease because the increase of data cannot offset the increase of delay. but when the slot period is 6 the throughput of sus increase as the increase of data outweighs the increase of delay. the fluctuation in fig. 5 shows the complex relationship between relay and throughput in multi-hop wireless networks. 2 2.5 3 3.5 4 4.5 5 5.5 6 1 2 3 4 5 6 7 slot peroids(s) th ro u g h p u ts (m b p s) optimal solution rsaa sf figure 5: sus’ throughput on different slot periods. fig. 6 shows the throughput of sus vary as the ratio of interference distance to transmitting distance increases under the three algorithms. in this simulation the pu’s node density is set as 16 nodes per square kilometer and the pu’s threshold is the same as that in fig. 4. the slot period is 4. from fig. 6 we can also find that rsaa can get 99% of the optimal throughput in average compared to sf’s 34%. what is more, fig. 6 shows that as the ratio increases the solutions of rsaa become much closer to the optimal results and when the ratio exceeds 2, the two solutions nearly overlap with each other which is identical with our above performance analysis. a joint routing and time-slot assignment algorithm for multi-hop cognitive radio networks with primary-user protection 415 1 1.5 2 2.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 the ratio of interferece and transmitting th ro u g h p u ts (m b p s) optimal solutions rsaa sf figure 6: sus’ throughput on different ratio of interference to transmitting. 6 conclusions in spectrum sharing multi-hop crns, by carefully selecting paths and slots sus can utilize the licensed spectrum band transparently. in this paper, we first formulate a milp model to describe the joint routing and time-slot assignment issue and then develop rsaa algorithm to solve this np-hard problem. theoretical analyses and simulation results demonstrate that rsaa algorithm can obtain near-optimal throughput with a polynomial complexity and so it can be widely used in crns. acknowledgment this work was supported in part by the national natural science foundation of 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maniu, 50 e.ciurea@unitbv.ro abstract: an nontrivial extension of the maximal static flow problem is the maximal dynamic flow model, where the transit time to traverse an arc is taken into consideration. if the network parameters as capacities, arc traversal times, and so on, are constant over time, then a dynamic flow problem is said to be stationary. research on flow in planar static network is motivated by the fact that more efficient algorithms can be developed by exploiting the planar structure of the graph. this article states and solves the maximum flow in directed (1,n) planar dynamic networks in the stationary case. keywords: network flow, planar network, dynamic network, maximum flow. 1 introduction the static network flow models bridges several diverse and seemingly unrelated areas of combinatorial optimization. more often in scientific writing, flow in a network refers to the flow of electricity, phone calls, email messages, commodities being transported across truck routes, or other such kinds of flow. many efficient algorithms have been developed to solve the maximum flows problem in static network [1]. the planar static network also arise in practical contexts such as vlsi design and communication networks, and hence it is of interest to find fast flow algorithms for this class of graphs. the computation of a maximum flow in a planar static network has been investigated by many researchers starting from the work of ford and fulkerson [5] who developed an o(n2) time algorithm for (1,n) networks when the source node 1 and sink node n are on the same face. this algorithm was later improved to o(n log n) time by itai and shiloach [8]. by introducing the concept of potentials, hassin [6] gave an algorithm that run in o(n log0.5 n) time using frederickson′s shortest path algorithm [4]. itai and shiloach [8] also developed an algorithm to find a maximum flow in an undirected planar networks when the source node and sink node are not on the same face. for faster maximum flow algorithms in planar (but not necessarily (1,n) planar) undirected and directed static networks see hassin and johnson [7] and johnson and venkatesan [9]. khuller and naor [10] present the flow in planar static networks with nodes capacities. however, in some other applications, time is an essential ingredient [1]. in this instance, to account properly for the evolution of the underlying system over time, we need to use dynamic network flow models. for dynamic network flow problem see [1], [2], [3]. in this paper, we present the maximum flow problem in directed (1,n) planar dynamic networks. we present the case when the planar dynamic network is stationary. further on, in section 2 the maximum flow in directed (1,n) planar static network is exposed. in section 3 some copyright © 2006-2016 by ccc publications the maximum flows in planar dynamic networks 283 basic dynamic network notations and results are presented, while in section 4 is presented the method for solving the maximum flow in directed (1,n) planar dynamic network. the conclusions are presented in section 5 and an example is given in section 6. 2 the maximum flow in directed (1, n) planar static network research on flow in planar static network is motivated by the fact that more efficient algorithms can be developed by exploiting the planar structure of the digraph. definition 1. a digraph g = (n,a) is said to be planar if we can draw it in a two-dimensional plane so that no two arcs intersect each other. researchers have developed very efficient algorithms (in fact, linear time algorithms) for testing the planarity of a digraph. definition 2. let g = (n,a) be a planar digraph. a face of g is a region of the plane bounded by arcs that satisfies the condition that any two points in the region can be connected by a continuous curve that meets no nodes and arcs. the boundary of a face x is the set of all arcs that enclose it. faces x and y said to be adjacent if their boundaries contain a common arc. the planar digraph g has an unbounded face. recall two well-known properties of planar digraphs: – if a connected planar digraph has n nodes, m arcs and q faces, then q = m−n + 2; – if a planar digraph has n nodes and m arcs, then m < 3n. our discussion in this paper applies to a special class of planar digraphs known as (1,n) planar digraphs (the node source 1 and node sink n lie on the boundary of unbounded face). let g = (n,a,u) be a static network with the set of nodes n = {1, . . . , i, . . . ,j, . . . ,n}, the set of arcs a = {a1, . . . ,ak, . . . ,am}, ak = (i,j) and the upper bound (capacity) function u : a → r+, where r is real number set. to define maximal static flow problem, we distinguish two special nodes in the static network g = (n,a,u): a source node 1 and a sink node n. a static flow is a function f : a → r+, satisfying the following conditions: ∑ j f(i,j)− ∑ k f(k,i) =   v, if i = 1 (1a) 0, if i ̸= 1,n (1b) −v, if i = n (1c) 0 ≤ f(i,j) ≤ u(i,j), (i,j) ∈ a (2) for some v ≥ 0. we refer to v as the value of the static flow f. the maximum flow problem is to determine a flow f which maximizes v. a cut is a partition of the node set n into two subsets s and s̄ = n − s. we represent this cut using notation [ s,s̄ ] . we refer to an arc (i,j) with i ∈ s and j ∈ s̄ as a forward arc of the cut and an arc (j,i) with j ∈ s̄ and i ∈ s as a backward arc of the cut. let ( s,s̄ ) denote the set of forward arcs in the cut and let ( s̄,s ) denote the set of backward arcs. we have that the arc set of cut is [ s,s̄ ] = ( s,s̄ ) ∪ ( s̄,s ) . we refer to a cut[ s,s̄ ] as an 1−n cut if 1 ∈ s and n ∈ s̄. for the maximum flow problem, we define the capacity of the 1−n cut [ s,s̄ ] as: 284 c. schiopu, e. ciurea c[s,s̄] = ∑ (s,s̄) u(i,j) (3) we refer to an 1 −n cut whose capacity is the minimum among all 1 −n cuts as a minimal cut. recall the maximum flow minimum cut theorem. theorem 3. the maximum value of the flow from a source node 1 to a sink node n in network g equals the capacity of minimum 1−n cut. many efficient algorithms have been developed to solve the maximum flows problem in some static network [1]. next we present the maximum flow problem in directed (1,n) planar static network. first, we define the dual directed static network denoted by g ′ = (n ′ ,a ′ ,c ′ ). we add the arc (n,1) with u(n,1) = 0, which divides the unbounded face into two faces: a new bounded face and a new unbounded face. in this case we have n ′ = q+1 faces, with q = m−n+2. then we place a node x ′ inside each face x of the network g. we have n ′ = {1 ′ , . . . ,x ′ , . . . ,y ′ , . . . ,n ′ }. let 1 ′ and n ′ , respectively, denote the nodes in the dual directed static network g ′ corresponding to the new bounded face and the new unbounded face. each arc (i,j) ∈ a lies on the boundary of the two faces x and y. corresponding to this arc, the network g ′ contains two oppositely arcs (x ′ ,y ′ ) and (y ′ ,x ′ ). if arc (i,j) is a clockwise arc in the face x, we define the cost c ′ (x ′ ,y ′ ) = u(i,j) and the cost c ′ (y ′ ,x ′ ) = 0. we define arc costs in the opposite manner if arc (i,j) is a counterclockwise arc in the face x. the network g ′ contains the arcs (1 ′ ,n ′ ) and (n ′ ,1 ′ ) which we delete from the network. we have a ′ = {(x ′ ,y ′ ),(y ′ ,x ′ )|x ′ ,y ′ ∈ n ′ ,(x ′ ,y ′ ) and (y ′ ,x ′ ) correspond to (i,j) ∈ a}. there is an one-to-one correspondence between 1−n cuts in the network g and paths from node 1 ′ to node n ′ in the network g ′ . moreover, the capacity of the cut equals the cost of the corresponding path. consequently, we can obtain a minimum 1 − n cut [s,s̄] and c[s,s̄] in network g by determining a shortest path p ′ and c ′ (p ′ ) from node 1 ′ to node n ′ in the network g ′ . we can solve the shortest path problem in the network g ′ using dijkstra′s algorithm [1]. now, we present an algorithm for finding a maximum flow in a directed (1,n) planar static network g = (n,a,u). let d ′ (x ′ ) denote the shortest path distance from node 1 ′ to node x ′ in the dual directed static network g ′ = (n ′ ,a ′ ,c ′ ). the algorithm for maximum flow in directed (1,n) planar static network (amfdpsn) is presented in figure 1 [1]. 1: amfdpsn; 2: begin 3: compute the network g ′ ; 4: dijkstra (g ′ ,d ′ ); 5: for (i,j) ∈ a do 6: f(i,j) := d ′ (y ′ )−d ′ (x ′ ); 7: end for 8: end. figure 1: algorithm for maximum flow in directed (1,n) planar static network theorem 4. the amfdpsn determines a maximum flow in network g. theorem 5. the amfdpsn determines a maximum flow in o(n2) time. using frederickson′s algorithm (see [4]), the amfdpsn determines a maximum flow in o(n1.5) time. the maximum flows in planar dynamic networks 285 3 the maximum flows in dynamic network let g = (n,a,u) be a static network with the node set n, the arc set a, the upper bound function u,1 the source node and n the sink node. let n be the natural number set and let h = {0,1, . . . ,t} be the set of periods, where t is a finite time horizon, t ∈ n. let us state the transit time function h : a×h → n and the time capacity function uh : a × h → r+, where h(i,j;t) represents the transit time of arc (i,j) at time t, t ∈ h and uh(i,j;t) represents the capacity (upper bound) of arc (i,j) at time t,t ∈ h. the maximal dynamic flow problem for t time periods is to determine a flow function fh : a×h → n, which should satisfy the following conditions in dynamic network gh = (n,a,h,uh): t∑ t=0 ( ∑ j fh(i,j;t)− ∑ k ∑ τ fh(k,i;τ)) = vh, if i = 1 (4a) ∑ j fh(i,j;t)− ∑ k ∑ τ fh(k,i;τ) = 0, if i ̸= 1,n, t ∈ h (4b) t∑ t=0 ( ∑ j fh(i,j;t)− ∑ k ∑ τ fh(k,i;τ)) = −vh, if i = n (4c) 0 ≤ fh(i,j;t) ≤ uh(i,j;t), for all (i,j) ∈ a and for all t ∈ h (5) maxvh, (6) where τ = t−h(k,i;τ), vh = t∑ t=0 v(t), v(t) is the flow value at time t and fh(i,j;t) = 0, (i,j) ∈ a, t ∈{t −h(i,j;t) + 1, . . . ,t}. in other words, a dynamic flow fh from 1 to n is any flow fh from 1 to n in which not more than uh(i,j;t) flow units starting from node i at time t and arriving at node j at time t+h(i,j;t) for all arcs (i,j) and all t. note that in a dynamic flow, units may be departing from the source at time 0,1, . . . ,t ′ , t ′ < t . a maximum dynamic flow for t time periods from 1 to n is any dynamic flow from 1 to n in which the maximum possible number of flow units arrive at the sink node n during the first t time periods. we will show how to transform the maximum dynamic flow problem in the dynamic network gh = (n,a,h,uh) into a static flow problem on a static network g ′ h = (n ′ h,a ′ h,u ′ h), called the reduced time-expanded network. for a given dynamic network gh = (n,a,h,uh), we form the time expanded network gh = (nh,ah,uh) as follows. we make t + 1 copies it, t = 0,1, . . . ,t of each node i in gh. node it in gh represents node i in gh at time t. for each (i,j) in gh, there are arcs (it,jθ), θ = t + h(i,j;t), t = 0,1, . . . ,t − h(i,j;t) with capacity uh(it,jθ) = uh(i,j;t) in gh. the arc (it,jθ) in gh represents the potential movement of a commodity from node i to node j in time h(i,j;t). the number of nodes in gh is n(t + 1), and number of arcs is limited by m(t + 1) − ∑ a h̄(i,j), where h̄(i,j) = min{h(i,j; 0), . . . ,h(i,j;t)}. it is easy to see that any dynamic flow in dynamic network gh is equivalent to a static flow in static network gh from the source nodes 10,11, . . . ,1t to the sink nodes n0,n1, . . . ,nt and vice versa. we can further reduce the multiple source, multiple sink problem in network gh to the single source, single sink problem by introducing a supersource node 1∗ and a supersink node n∗ constructing time superexpanded network g∗h = (n ∗ h,a ∗ h,u ∗ h), where n ∗ h = nh ∪{1 ∗,n∗}, a∗h = ah ∪ {(1 ∗,1t)|t = 0,1, . . . ,t}∪{(nt,n∗)|t = 0,1, . . . ,t}, u∗h(it,jθ) = uh(it,jθ) for all (it,jθ) ∈ ah, u∗h(1 ∗,1t) = u ∗ h(nt,n ∗) = ∞, t = 0,1, . . . ,t . now, we construct the time reduced 286 c. schiopu, e. ciurea expanded network g ′ h = (n ′ h,a ′ h,u ′ h) as follows. we define the function h ∗, h∗ : a∗h → n, h∗(1∗,1t) = h ∗(nt,n ∗) = 0, t = 0,1, . . . ,t , h∗(it,jθ) = h(i,j;t), t = 0,1, . . . ,t −h(i,j;t). let d∗(1∗, it) be the length of the shortest path from the source node 1∗ to the node it in network g∗h and d∗(it,n∗)the length of the shortest path from node it to the sink node n∗, with respect to h∗. the computation of d∗(1∗, it) and d∗(it,n∗) for all it ∈ n∗h is performed by means of the usual shortest path algorithms. we have n ′ h = {1 ∗,n∗}∪ {it|it ∈ nh,d∗(1∗, it) + d∗(it,n∗) ≤ t}, a ′ h = {(1 ∗,1t)|d∗(1t,n∗) ≤ t}∪ {(nt,n∗)|d∗(1∗,nt) ≤ t}∪ {(it,jθ)|(it,jθ) ∈ ah,d∗(1∗, it) + h∗(it,jθ) + d ∗(jθ,n ∗) ≤ t} and u ′ h is restriction of u ∗ h at a ′ h. in network g ′ h we rewrite the nodes 1∗, n∗ by 1 ′ , respectively n ′ . it is easy to see that the network g ′ h is always a partial subnetwork of g∗h. since an item released from a node at a specific time does not return to that location at the same or an earlier time, the networks gh, g∗h, g ′ h cannot contain any circuit, and are therefore acyclic always. in the most general dynamic model, the parameter h(i) = 1 is waiting time at node i, and the parameter uh(i;t) is defined as the capacity of the node i, which represents the maximum amount of flow that can wait at node i from time t to t + 1. this most general dynamic model is not discussed in this paper. the maximum dynamic flow problem for t time periods in dynamic network gh formulated in conditions (4), (5), (6) is equivalent with the maximum static flow problem in static network g ′ h as follows: ∑ jθ f ′ h(it,jθ)− ∑ k τ ′ f ′ h(kτ′, it)) =   v ′ h, if it = 1 ′ , (7a) 0, for all it ̸= 1 ′ ,n ′ , (7b) −v ′ h, if it = n ′ , (7c) 0 ≤ f ′ h(it,jθ) ≤ u ′ h(it,jθ), for all (it,jθ) ∈ a ′ h (8) max v ′ h, (9) where by convention it = 1 ′ for t = −1 and it = n ′ for t = t + 1. it is easy to see that network g ′ h is no planar, in general. a dynamic flow problem is said to be stationary if the network parameters as capacities, arc traversal times, and so on, are constant over time (c : a → r+,h : a → n, and so on). in the stationary case it does not require the construction of the reduce time expanded static network g ′ h = (n ′ h,a ′ h,u ′ h) for solving the maximum dynamic flow problem for any t . a maximum dynamic flow in the stationary case can be generated from a maximum value and minimum time flow f in static network g = (n,a,c,u), where c(i,j) = h(i,j) is the cost for any arc (i,j) ∈ a. the algorithm for stationary maximum dynamic flow (asmdf) problem is presented in figure 2 [5]. 1: asmdf; 2: begin 3: amvmcf (g,f); 4: adfef (f,r(p1), . . . ,r(pk)); 5: arf (r(p1), . . . ,r(pk)); 6: end. figure 2: algorithm for stationary maximum dynamic flow. the procedure amvmcf performs the algorithm for maximum value and minimum cost flow f in network g. for statements we suppose that use the algorithm of klein variant (minimum the maximum flows in planar dynamic networks 287 mean cycle canceling algorithm, see [1]). this algorithm have the complexity o(n2m3 log n). the procedure adfef performs the algorithm for decomposition of flow f in elementary flows with r(p1), ...,r(pk) path flows. is necessary that c(pi) ≤ t . this algorithm have complexity o(m2). the procedure arf performs the algorithm for send r(pi) flow, i = 1 . . . ,k, starting out from source node 1 at time periods 0 and repeat it after each time period as long as there is enough time left in the horizon for the flow along the path to arrive at the sink node n. this algorithm have complexity o(kt). hence, the algorithm for stationary maximum dynamic flow have complexity o(n2m3 log n) (we consider that kt ≤ n2m3 log n). the flow obtained with asmdf is called a temporally repeated flow for the obvious reason that is consists of repeated shipments along the same flow paths from 1 to n. the maximum value of a temporally repeated flow obtained with asmdf is: vh = (t + 1)v − ∑ a h(i,j)f(i,j) (10) where v is the maximum value of the flow f obtained with amvmcf. 4 the maximum flows in planar dynamic networks in this section we consider the maximum flows in planar dynamic networks in the stationary case. hence, we use the asmdf which has presented in section 3. the network g = (n,a,c,u) is planar. the minimum mean cycle canceling algorithm is a special case of the klein′s algorithm (cycle canceling algorithm, see [1]). recall that the mean cost of a directed cycle (circuit) p̊ is ( ∑ (c(i,j)|(i,j) ∈ p̊))/ ∣∣∣p̊∣∣∣, and that the minimum mean cycle is a cycle with the smallest mean cost in the network g. is known that use dynamic programing algorithm to find the minimum mean cycle in o(nm) time, see [1]. in this case, the minimum mean cycle canceling algorithm starts with a maximum flow f in the network g. this flow is computed with algorithm presented in section 2. at every iteration, the minimum mean cycle canceling algorithm identifies a minimum mean cycle p̊ in residual network g̃. if the mean cost of the cycle p̊ is negative, the algorithm augments the maximum possible flow along p̊ , updates g̃, and repeats this process. if the mean cost of p̊ is nonnegative, g̃ contains no negative cycle and f is a maximum value and minimum cost flow, so the algorithm terminates. this algorithm is surprisingly simple to state. theorem 6. the asmds correctly computes the maximum flow in planar stationary dynamic network. proof: the asmds correctly computes the maximum flow in general stationary dynamic network. obviously that algorithm is correctly and for planar network. 2 theorem 7. the asdms applied in planar network has the complexity o(n5logn). proof: the asmdf applied in general network has the complexity o(n2m3logn). in planar network we have m = o(n). hence, the asmdf applied in planar network has the complexity o(n5 log n). 2 5 conclusions the computation of a maximum flow in a general network has been an important and well studied problem, both in the fields of computer science and operations research. many efficient 288 c. schiopu, e. ciurea algorithms have been developed to solve this problem, see, e.g., [1]. research on maximum flow in planar network is motivated by the fact that more efficient algorithms can be developed by exploiting the planar structure of the graph. the planar flow algorithms are not only extremely efficient, but they are also very elegant. planar networks also arise in practical contexts such as vlsi design and communication networks, and hence it is of interest to find fast flow algorithms for this class of networks. in this paper, we have studied a generalization of the maximum flow in directed (1,n) planar networks, to include transit time features encountered in many practical situations. the our model, assumes that all attributes in the problem, including arc capacities and transit times, do not change over time. in this case we have used an efficient procedure to find the maximum value and minimum cost flow in directed (1,n) planar static networks g = (n,a,c = h,u), and then develops a set of temporally repeated flows, with the optimal flow decomposed into a set of path flows. we remark that the problem of maximum flow in (1,n) planar dynamic networks was not studied up to the present. also, we introduce the notion of reduced time expanded network g ′ h = (n ′ h,a ′ h,u ′ h) and show how make this network. future research directions include problems: (1) the maximum flow in directed (1,n) planar dynamic networks, where the transit times, the capacities of arcs are all time-varying; (2) the maximum flow in directed (1,n) planar dynamic networks with lower bounds in stationary case and in nonstationary case. these are more practical features in many real-world problems where we desire to control the speed of flows at different arcs. 6 example the planar dynamic network is presented in figure 3(a) and time horizon being set to t = 4, therefore h = {0,1,2,3,4}. the transit times h(i,j) and the upper bounds (capacities) u(i,j) for all arcs are indicate in figure 3(b). (a) (i, j) (1, 2) (1, 3) (2, 3) (2, 4) (3, 4) h(i, j) 1 3 1 2 1 u(i, j) 3 2 1 2 2 (b) figure 3: the planar dynamic network figure 4 shows the (1 ′ ,4 ′ ) dual network g ′ = (n ′ ,a ′ ,c ′ ) corresponding to network gh. the maximum flows in planar dynamic networks 289 figure 4: dual network g ′ corresponding to the network g the flow obtained with amfdpsn is presented in figure 5(a). (a) (b) figure 5: (a) maximum flow; (b) maximum flow of minimum cost with the minimum mean cycle canceling algorithm we obtain the maximum flow of minimum cost and is presented in figure 5(b). applying the procedure adfef we have the following path: p1 = (1,2,4),h(p1) = 3,r(p1) = 2; p2 = (1,2,3,4),h(p2) = 3,r(p2) = 1; p3 = (1,3,4),h(p3) = 4,r(p3) = 1. with the procedure arf we obtain the maximum dynamic flow which is shown in network g ′ h = (n ′ h,a ′ h,u ′ h) in figure 6. 290 c. schiopu, e. ciurea figure 6: the maximum dynamic flow applying the formula (10) we have vh = (4 + 1)4− (3 + 3 + 1 + 4 + 2) = 7. for s ′ h = { 1 ′ ,10,11,22,33 } , s̄ ′ h = { 21,32,43,44,4 ′} we have [ s ′ h, s̄ ′ h ] = (s ′ h, s̄ ′ h) = {(10,21),(22,44),(33,44)} and v ′ h = vh = f ′ h(s ′ h, s̄ ′ h) = u ′ h(s ′ h, s̄ ′ h) = 3 + 2 + 2 = 7. bibliography [1] ahuja,r.; magnanti,t.; orlin,j. (1993); network flows. theory, algorithms and applications, editing prentice hall, inc.,englewood clifss, new jersey. [2] cai,x.; sha,d.; wong,c. (2007); time-varying network optimization, editing springer. [3] ciurea,e. (1984); les problemes des flots dynamiques, cahiers du cero, 26(1-2): 3-9. [4] frederickson,g. (1987); fast algorithms for shortest path in planar graphs, with applications, siam journal on computing, 16: 1004-1022. [5] ford,l.; fulkerson,d. (1962); flows in networks, princeton university press, princeton, n.j. [6] hassin,r. (1981); maximum flows in (s,t) planar networks, information processing letters, 13: 107. the maximum flows in planar dynamic networks 291 [7] hassin,r.; johnson,d. (1985); an o(n log2 n) algorithm for maximum flow in undirected planar networks, siam journal on computing, 14: 612-624. [8] itai, a.; shiloach, y. (1979); maximum flow in planar networks, siam journal on computing, 8: 135-150. [9] johnson, d.; venkatesan,s. (1982); using divide and conquer to find flows in directed planar networks in o(n1.5 log n) time, proceedings of the 20th annual allerton conference on communication , control and computing, university of illinois, urbana-champaign, il., 898-905. [10] khuller,s.; naor,j. (1994); flows in planar graphs with vertex capacities, algorithmica, 11: 200-225. ijcccv4n3draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 3, pp. 263-272 an algorithm for initial fluxes of metabolic p systems roberto pagliarini, giuditta franco, vincenzo manca university of verona, italy computer science department strada le grazie 15, 37134 verona, italy e-mail: {roberto.pagliarini, giuditta.franco, vincenzo.manca}@univr.it received: april 5, 2009 accepted: may 30, 2009 abstract: a central issue in systems biology is the study of efficient methods inferring fluxes of biological reactions by starting from experimental data. among the different techniques proposed in the last years, the theory of metabolic p systems, which is based on the log-gain principle, proved to be helpful for deducing biological fluxes from temporal series of observed dynamics. according to this approach, the algebraic systems provided by the log-gain principle determine the reaction fluxes underlying a system dynamics when initial fluxes are known. here we propose a heuristic algorithm for estimating the initial fluxes, that is tested in two case studies. keywords: biological modeling, p systems, mp systems, metabolic flux estimation, heuristic algorithms. 1 introduction in the last years, the problem of reverse-engineering of biological phenomena from experimental data has spurred increasing interest in scientific communities. for these reasons, many computational models inspired from biology have been proposed. among these models, the metabolic p systems [11, 12], shortly mp systems, proved to be relevant in the analysis of dynamics of biochemical processes, that is, structures where matter of different type is transformed by reactions. by means of mp systems models of several interesting phenomena were provided, among which we mention: the lotka-volterra dynamics [2, 3, 15], a susceptible-infected-recovered epidemic [2], the leukocyte selective recruitment in the immune response [2], the protein kinase c activation [3], the mitotic cycle [14], the pseudomonas quorum sensing [4] and the non-photochemical quenching phenomenon [16]. the importance of mp systems is their potential applicability to the reverse-engineering problem of biological phenomena. in fact, in the framework of mp systems, a theory called log-gain [10, 11, 12] has been introduced, specifically devoted to the deduction of reaction fluxes, that is, the amount of reactants transformed by the reactions at any step of the system. as we will show, a key point for achieving this task consists in the discovery of the fluxes associated to the passage of a metabolic system from the state at the initial observation instant to the next one. in this paper a heuristic algorithm is proposed for estimating the initial fluxes vector from few steps of observation. in few words, the algorithm first roughly computes the initial fluxes by assuming they have a form recalling the mass action principle, and then solves a system of equations to deduce the corresponding fluxes at the next step. from these values, the algorithm evaluates how much of each substance is necessary to activate the first evolution step, and finally the actual initial fluxes are computed by solving a minimization problem. the present paper is organized as follows. section 2 is devoted to the definition of metabolic p systems, while in section 3 log-gain theory is briefly recalled. in section 4 we describe the algorithm copyright c© 2006-2009 by ccc publications 264 roberto pagliarini, giuditta franco, vincenzo manca which solves the initial fluxes problem. section 5 reports the simulations of a couple of systems obtained by starting with initial fluxes computed by our algorithm. further remarks and some directions for future research are discussed in the last section. 2 metabolic p systems mp systems are a special class of dynamical systems (the reader can find some details concerning dynamical aspects of mp systems in [13]), based on p systems [5, 18, 19], which are related to metabolic processes. mp systems are essentially constituted by multiset grammars where rules are regulated by specific functions depending on the state of the system. from a membrane computing point of view, mp systems can be seen as deterministic mono-membrane p systems where the transitions between states are calculated by a suitable recurrent equation. in an mp system the variation of the whole system is considered in a macroscopic time interval. in this manner, the evolution law of the system includes the knowledge of the contribution of each reaction in the evolution from one state to the next one. therefore, dynamics is given at discrete steps, and in each step, it is ruled by a partition of matter among the reactions transforming it. the principle underlying the partitioning is called mass partition principle, and it defines the transformations of object populations, rather than single objects, according to a suitable generalization of chemical laws [11]. the following definition introduces the mp systems in a formal way (n, z, and r denote the sets of natural, integer, and real numbers, respectively). definition 1 (mp system). an mp system m is specified by the following construct: m = (x , r,v, h, φ, ν, µ, τ) where x , r and v are finite disjoint sets, and moreover the following conditions hold, with n, m, k ∈ n: • x = {x, x, . . . , xn} is a finite set of substances. this set represents the types of molecules; • r = {r, r, . . . , rm} is a finite set of reactions. a reaction r is a pair of type αr → βr, where αr identifies the multiset of the reactants (substrates) of r and βr identifies the multiset of the products of r (λ represents the empty multiset). the stoichiometric matrix a of a set r of reactions over a set x of substances is a = (ax,r | x ∈ x , r ∈ r) with ax,r = |βr|x − |αr|x, where |αr|x and |βr|x respectively denote the number of occurrences of x in αr and βr. of course, a reaction r can be seen as the vector r = (ax,r | x ∈ x ) of r n. we also set rα (x) = {r ∈ r | x ∈ αr}, rβ (x) = {r ∈ r | x ∈ βr}, and r(x) = rα (x) ∪ rβ (x); • v = {v, v, . . . , vk} is a finite set of parameters. this set represents entities which affect the dynamics but are not transformed by reactions; • h = {hv | v ∈ v } is a set of parameters evolution functions. the function hv : n → r states the value of parameter v, and h[i] = (hv(i) | v ∈ v ); • φ = {ϕr | r ∈ r} is the set of flux regulation maps, where, for each r ∈ r, ϕr : r n+k → r. let q ∈ rn be the vector of substance values and s ∈ rk be the vector of parameter values. then (q, s) ∈ rn+k is the state of the system. we set by u (q, s) = (ϕr(q, s) | r ∈ r) the flux vector in the state (q, s), constituted by the state q and by the parameters state s; • ν is a natural number which specifies the number of molecules of a (conventional) mole of m; • µ is a function which assigns, to each x ∈ x , the mass µ(x) of a mole of x (with respect to some measure units). an algorithm for initial fluxes of metabolic p systems 265 • τ is the temporal interval between two consecutive observation steps; let x [i] = (x[i], x[i], . . . , xn[i]), for each i ∈ n, be the vector of substances values at the step i, and let x [] be the initial values of substances. the dynamics of an mp system is completely identified by the following recurrent equation, called equational metabolic algorithm, shortly ema: x [i + ] = a ×u (x [i], h[i]) + x [i] (1) where a is the stoichiometric matrix of reactions having dimension n × m, while ×, +, are the usual matrix product and vector sum. we denote by ema[i] the system (1), which allows us to obtain the vector x [i + ] from vectors x [i] and u (x [i], x [i]). if in an mp system the elements ν , µ , and τ are omitted, then the result is called mp grammar. it is a multiset rewrite grammar where rules are regulated by specific functions. such a grammar is completely specified by: i) reactions, ii) flux regulation functions, iii) parameter evolution functions, iv) substances, which are the elements occurring in the reactions, and their initial values and v) parameters, which are the arguments of flux regulation functions different from substances. parameter evolution maps and/or initial values of substances may be omitted when only the mp grammar structure is specified. 3 log-gain theory: a brief recall the starting point of the log-gain theory [10, 11, 12] for mp systems is the allometry law [1, 7], which has many possible formulations [10], but, in the case here discussed, it can be expressed in a simple way. namely, a proportion can be assumed, at each step, between the relative variations of the flux of a reaction and the sum of relative variations of its reactants, with a possible gap, called offset. given the dynamics of an mp system, we will use the following simplified notations, for i ∈ n, and r ∈ r: ur[i] = ϕr(x [i], h[i]) and u [i] = (ur[i] | r ∈ r). (2) assuming to know the vectors x [i] and x [i + ], the equation (1) can be rewritten in the following form, which we call ada[i] (avogadro and dalton action [12]): x [i + ] − x [i] = a ×u [i]. (3) formula (3) expresses a system of n equations and m variables (n is the number of substances and m the number of reactions) which is assumed to have maximal rank. this assumption is not restrictive. in fact, if it does not hold, the rows which are linearly dependent on other rows can be removed, by keeping the notations a, x [i +] and x [i] for the stoichiometric matrix and the vectors of concentration of substances, respectively. we assume thus that a has maximum rank, which we newly call n. then there exist n linearly independent reactions of r, and we call r such a subset of reactions. from a metabolic point of view, this means that fluxes of each reaction of r can be obtained as linear combination of fluxes of the reactions of r. formally, ada[i] is essentially the system ema[i] introduced in section 2. however, these two systems have dual interpretations. in fact, in ema[i], the vectors u [i] and x [i] are known, and the vector x [i + ] is computed by means of them, while in ada[i], the vector x [i + ] − x [i] is known and u [i] is computed by solving a system comprised of both the equations in ada[i] and further equations, dictated by the following log-gain principle, to state the reaction regulation level, as we will see by formula (6). indeed, since the number of reactions is realistically assumed greater than the number of substances, then system (3) has more than one solution. therefore, fluxes cannot be univocally deduced by means of ada[i]. the log-gain principle allows us to add more equations in order to get a univocally solvable system which could provide the flux vector. 266 roberto pagliarini, giuditta franco, vincenzo manca the two following definitions state the log-gain principle. for the detailed motivations of this principle we refer to papers on mp systems theory [10, 11, 12]. further developments providing theoretical and experimental evidences of this principle will be matter of forthcoming papers. definition 2 (discrete log-gain). let (z[i] |i ∈ n ) be a real valued sequence. then, the discrete log-gain of z, for each step i, is given by the following equation: lg(z[i]) = z[i + ] − z[i] z[i] . (4) principle 1 (log-gain regulation). let u [i] be the vector of fluxes at step i, for i ≥ , and let r ⊂ r be a set of n linearly independent vectors of rn. then, the log-gain regulation can be expressed in terms of matrix and vector operations: (u [i + ] − u [i])/u [i] = b × lg(x [i]) + c ⊗ p[i + ] (5) where: • b = (pr,x |r ∈ r, x ∈ x ) where pr,x ∈ {, } with pr,x =  if x is a reactant of r and pr,x =  otherwise; • lg(x [i]) = (lg(x[i]) |x ∈ x ) is the column vector of log-gains of substances; • c = (cr |r ∈ r ), where cr =  if r ∈ r, while cr = ; • p[i + ] is a column vector of values associated with the reactions and called (log-gain) offsets at step i + ; • × denotes the usual matrix product; • +, −, /, ⊗ denote the component-wise sum, subtraction, division and product of vectors. if we assume to know the flux unit vector at step i and put together the equations (5) and (3) at steps i and i +  respectively, we get the following linear system called offset log-gain adjustment module at step i, shortly olga[i], where the number of variables (reported in bold font) is equal to the number of equations: a ×uuu [iii + ] = x [i + ] − x [i + ] (6) (uuu [iii + ] − u [i])/u [i] = b × lg(x [i]) + ccc ⊗ ppp[iii + ]. given the vector lg(x [i]), for i = , , . . . , l, where l ∈ n, it is possible to prove that olga[i], for i = , , . . . , l − , univocally provides u [i] for i = , , . . . , l − . 4 an algorithm to estimate initial metabolic fluxes the iteration of the olga module, introduced in the previous section in order to deduce the fluxes of reactions, assumes the knowledge of the initial values of fluxes. this leads to the formulation of the following problem. problem 1 (initial fluxes problem). given x [] and x [], find a flux vector u [] such that it satisfies the initial dynamics, that is: x [] ≅ a ×u [] + x [] where ≅ means that we are searching for the vector u [] providing the minimum value of the stoichiometric error, defined as (‖·‖ represents the euclidean norm) ‖a ×u [] − (x [] − x [])‖. the algorithm given below solves the initial fluxes problem by using the knowledge about the dynamics in the first evolution steps in order to evaluate the amount of each substance which is necessary to activate the first evolution step. an algorithm for initial fluxes of metabolic p systems 267 4.1 the proposed algorithm our algorithm consists of three phases, some of which include different computational steps. the first phase consists in the approximation of initial fluxes by assuming that fluxes are proportional to the reactant quantities product. in the second phase an olga module is employed to approximate the amount of substances which needs as a fuel for the first evolution step. in the third phase an optimization problem is solved, which is based on the ada system (3). the details of the algorithm work-flow are described in the following. phase 1. the goal here is to roughly evaluate the initial reaction fluxes by assuming that they are proportional to the reactants for certain initial evolution steps i. this could appear restrictive, but at this stage we require only an initial approximation. therefore, at a given step i, for all r ∈ r, we set: ûr[i] = kryr[i] (7) where kr ∈ r, and yr[i] is the product of all substance quantities, at the step i, which are reactants for r. we suppose that if αr = λ then yr[i] = , and we set û [i] = (ûr[i] | r ∈ r). (8) for example, in a metabolic system having three kinds of substances, a, b, c, and as a set of reactions those given in the first column of table 1, the relationships between the fluxes of these reactions and their reactants are reported in the second column of table 1. for any x ∈ x , let us consider the following system, called local-stoichiometric module at the step i, where a is the stoichiometric matrix: x[i + ] − x[i] = ∑ r∈r(x) ax,rûr[i]. (9) if we assume that the constants kr, with r ∈ r, do not sensibly change in few steps, then by applying the system (9), in at most m − n steps either we obtain a square linear system of dimension m having maximum rank or the algorithm ends without an output. in fact, under the assumption that the rank of local-stoichiometric module is n (that is, the number of equations) and that the number of variables is m, with n < m, then the system is completely determined if we add other m − n equations. assuming to gain at least one new significant equation at each step i, then in at most m − n steps we obtain a system of (m − n)n + n equations with m variables and rank equals to m. in this way, we can obtain a square linear system having unique solution. in the example reported in table 1, we have a local-stoichiometric module of  equations having rank  which initially has  variables. at the second iteration of this module, starting from the step , we reactions maps r : a → bc kr a r : b → a kr b r : c → ab kr c r : c → cc kr c table 1: reactions and corresponding flux regulation maps of the local-stoichiometric module. 268 roberto pagliarini, giuditta franco, vincenzo manca get other  equations finally giving the following system: aaa[] − aaa[] = −kkkaaa[] + kkkbbb[] + kkkccc[] b[] − b[] = ka[] − kb[] + kc[] ccc[] − ccc[] = kkkaaa[] − kkkccc[] + kkkccc[] a[] − a[] = −ka[] + kb[] + kc[] bbb[] − bbb[] = kkkaaa[] − kkkbbb[] + kkkccc[] ccc[] − ccc[] = kkkaaa[] − kkkccc[] + kkkccc[] where a[] = , b[] = , c[] = , a[] = ., b[] = ., and c[] = .. this system has rank , and  linearly independent equations are reported in bold font. thus, we can obtain a system of equations having unique solution. in general, if we start with the local-stoichiometric module at the step  then we can compute the vector û [] = (ûr[] |r ∈ r ) by applying the local-stoichiometric module for a suitable number of steps. the algorithm stops with no output if after m − n iterations of the above technique, the number of equations linearly independent is less than m. phase 2. the aim of this step is to estimate the amount of substance necessary to start the first system evolution step. we describe this step along with two sub-phases. in the first sub-phase we solve olga[] module, with u [] = û [], where û [] is the vector of fluxes computed in the previous step. let us call u ∗ = (u∗r | r ∈ r) the solution of this system. however, if some elements of this vector have a negative value, then we choose a different set of n linearly independent reactions in olga and newly apply the above procedure. the algorithm stops with no answer if a positive solution is not found after a number of attempts equal to the number of such different sets. however, general methods are under investigation which systematically and efficiently search for an unique positive solution u ∗. in the second sub-phase we compute, for each x ∈ x , the amount of substance x̄ occurring for the application of the reactions in the first evolution step. if a− is the activation matrix defined by a−x,r = |αr|x, for x ∈ x , r ∈ r, then the searched values are obtained by computing the vector x̄ = a − ×u ∗. phase 3. in the last step we obtain the actual vector of fluxes u ◦ by solving a norm minimization problem [9] such that u◦ provides the minimum of the following (euclidean) norm ‖a × ξ − (x [] − x [])‖ (10) over all the positive vectors ξ = (ξr | r ∈ r) of r m such that a − × ξ = x̄ , (11) where x̄ is the vector computed at the previous step. 5 experiments in this section, in order to evaluate the performance of our algorithm, we apply it to two case studies: i) a synthetic oscillatory metabolic system and ii) the belousov-zhabotinsky reaction [8, 20, 21]. 5.1 a synthetic metabolic system let us consider the synthetic non-cooperative metabolic system without parameters called sirius [11] and given by table 2. firstly, we compute u [] = (ϕ(x [], ϕ(x []), . . . , ϕ(x []). then, we use our algorithm to approximate the vector of fluxes u ◦. the two vectors are essentially the same. an algorithm for initial fluxes of metabolic p systems 269 reactions flux regulation maps r : a → aa ϕ = ka/(k + kc + kb + k) r : a → b ϕ = kac/(k + kc + kb + k) r : b → λ ϕ = kb/(k + k) r : a → c ϕ = kab/(k + kc + kb + k) r : c → λ ϕ = kc/(k + k) x [] = (  ) k = k = k = , k = k = ., k =  table 2: the sirius mp grammar. 5.2 a biochemical case study in this subsection the application of the algorithm to approximate the initial fluxes of the belousovzhabotinsky reaction, also known as bz reaction, is discussed. this system represents a well-known example of biochemical oscillatory phenomenon, in fact this is the first evidence of a chemical clock. although the stoichiometry of the bz reaction is quite complicated, several simplified mathematical models of this phenomenon have been proposed. in particular, prigogine and nicolis [17] proposed a simplified formulation of the dynamics of the bz reaction, called brusselator, whose oscillating behaviour is represented by only two substances, x and y respectively, and it is governed by the following system of differential equations: dx dt = k − kx + kx  y − kx (12) dy dt = kx − kx  y where k = , k = , k =  − and k =  represent constant rates. we use the oscillatory dynamics obtained by solving the system (12), with initial conditions x =  and y = , as experimental data on which testing our algorithm. mp formulation of the brusselator is expressed by the set of rewriting rules reported in table 3, where, according to the literature, the fluxes of each rule r depend on the concentrations of the reactants of r. in fact, species x has two positive and two negative contributions, while one positive and one negative contributions characterize y. thus, the equations can be mapped into suitable stoichiometry by following the strategy described in [6]. rules r : λ → x r : x → y r : xxy → xxx r : x → λ table 3: a set of rewriting rules that describes the brusselator stoichiometry. in the case of bz we adopt a different strategy of validation of our algorithm. in fact, there is a complete correspondence between the dynamics computed by the differential model and that one computed by the equational metabolic algorithm using the fluxes deduced by olga module (figure 1), starting from the initial fluxes inferred by means of our algorithm. 6 conclusions the study of efficient methods for defining mp systems from experimental data is of crucial importance for systematic applications of mp systems to complex dynamics. an essential component of 270 roberto pagliarini, giuditta franco, vincenzo manca figure 1: the bz reaction fluxes calculated by solving the system (6) with initial vector of fluxes inferred by our algorithm. the regulation level of an mp system can be deduced by applying the log-gain theory to data that can be collected from observations of the system. a crucial task to perform in this context is the reliable determination of the initial vector of fluxes. in this paper we have devised an algorithm to infer the initial reaction fluxes of a biological network. the proposed algorithm has been validated on test cases of a synthetic metabolic oscillator and the brusselator phenomenon. future investigations will be developed with the aim i) to develop the computational features of this algorithm and ii) to show the applicability of our method to complex biological cases. bibliography [1] l. von bertalanffy. general systems theory: foundations, developments, applications. george braziller inc, new york, ny, 1967. [2] l. bianco, f. fontana, g. franco, and v. manca. p systems for biological dynamics. in [5], 81–126, 2006. [3] l. bianco, f. fontana, and v. manca. p systems with reaction maps. international journal of foundations of computer science, 17(1):27–48, february 2006. [4] l. bianco, d. pescini, p. siepmann, n. krasnogor, f.j. romero-campero, and m. gheorghe. towards a p systems pseudomonas quorum sensing model. lecture notes in computer science, 4361:197–214, 2007. an algorithm for initial fluxes of metabolic p systems 271 [5] g. ciobanu, m. j. pérez-jiménez, and g. păun (eds.). applications of membrane computing (natural computing series). springer-verlag new york, inc., secaucus, nj, usa, 2006. [6] f. fontana and v. manca. discrete solution to differential equations by metabolic p systems. theoretical computer science, 372:165–182, 2007. [7] j. s. huxley. problems of relative growth. 2nd ed., dover, new york, 1972. [8] d. s. jones and b. d. sleeman. differential equations and mathematical biology. chapman & hall/crc, february 2003. [9] d. g. luenberger. optimization by vector space methods, john wiley & sons, inc., 1969. [10] v. manca. log-gain principles for metabolic p systems, in a. condon et al. (eds.), algorithmic bioprocesses, chapter 28, natural computing series, springer-verlag, berlin heidelberg, 2009. [11] v. manca. the metabolic algorithm for p systems: principles and applications. theoretical computer science, 404:142–157, 2008. [12] v. manca. fundamentals of metabolic p systems. in g. păun, g. rozenberg, and a. salomaa, editors, handbook of membrane computing, chapter 16. oxford university press, 2009. to appear. [13] v. manca. metabolic p dynamics. in g. păun, g. rozenberg, and a. salomaa, editors, handbook of membrane computing, chapter 17. oxford university press, 2009. to appear. [14] v. manca and l. bianco. biological networks in metabolic p systems. biosystems, 91(3):489–498, 2008. [15] v. manca, l. bianco, and f. fontana. evolution and oscillation in p systems: applications to biological phenomena. lecture notes in computer science, 3365:63–84, 2005. [16] v. manca, r. pagliarini, and s. zorzan. a photosynthetic process modelled by a metabolic p system. natural computing, 2009. doi 10.1007/s11047-008-9104-x. [17] g. nicolis and i. prigogine. exploring complexity. an introduction. freeman ans company, san francisco, ca, 1989. [18] g. păun. membrane computing: an introduction. springer, 2002. [19] g. păun and g. rozenberg. a guide to membrane computing. theoretical computer science, 287(1):73–100, september 2002. [20] k. s. scott. chemical chaos. cambridge university press, cambridge, uk, 1991. [21] a. m. zhabotinsky. proc. acc. sci, usrr. 157:392, 1964. roberto pagliarini (july 23, 1981) earned his m. s. degree in computer science at university of verona, where he is currently a phd student in computer science. his research interests focus on system biology, molecular and membrane computing. he has collaborated with the biochemistry and vegetal physiology group at biotechnological department of verona university, in order to investigate computational models for crucial events related to photosynthetic organisms. he is co-author of scientific papers on this subject. 272 roberto pagliarini, giuditta franco, vincenzo manca giuditta franco (august 5, 1974) graduated in mathematics at the university of pisa and earned her phd in computer science, with a dissertation titled “biomolecular computing — combinatorial algorithms and laboratory experiments”, at university of verona, where she is currently an assistant professor. her research interests focus on discrete mathematics and computational models of biological systems, namely on dna and membrane computing. she gave talks in several international workshops and she is co-author of scientific papers published by prestigious sectoral journals. she is an effective member of both the european molecular computing consortium (emcc) and the international society for nanoscale science, computing and engineering (isnsce). vincenzo manca (march 9, 1949) is a full professor at the computer science department of the university of verona, where he is also chair of the bioinformatics programme. he obtained his degrees from the university of pisa. his research interests cover a wide class of topics from mathematical logic, discrete mathematics, and theoretical computer science to informational analysis and computational models of biological systems. at present, his investigation is focused on “natural computing” (in particular dna computing and membrane computing, synthetic computational biology). he is author of more than 100 scientific publications, appearing in international journals and scientific series. international journal of computers, communications & control vol. i (2006), no. 3, pp. 85-92 coalition formation for cooperative information agent-based systems nacer eddine zarour, sabrina bouzidi abstract: the communication technology evolution led to an increase of the carried out services’ and tasks’ number. the aim of actual research in the cooperation and particularly the negotiation between agents is to reach a coherent global state of the multiagent system by favoring agents’ synergy. in this paper, we propose a coalition formation-based negotiation model for the task allocation in the cooperative information agent-based systems. in this model, the agent that activates a negotiation seeks partners for achieving a complex task. the way that the partners take part in a coalition is done one by one according to the choice of all the coalition members. this choice is based on a multicriterion analysis. some obtained experimental results show the suggested model performances. keywords: negotiation, coalition formation, cooperative information systems, multiagent systems, task allocation. 1 introduction in an open environment as cooperative information systems (ciss), which gathers distributed; heterogeneous; and autonomous information systems (iss), the cooperation requires the intervention of a negotiation process in order to identify and structure the various problems, to propose and defend the solutions, to re-examine the intentions in a conflict, and finally to confirm the commitments [1]. in our context, each is is modelled by an autonomous, rational, and egoistic agent. several negotiation models were developed like market mechanisms [2], contract net protocol [3], social laws [4], multiagent planification [5], and organizational structures (coalitions, teams, congregations, etc.) [6]. the coalition formation is a negotiation technique which is particularly preferred for solving the conflicts between agents whose behavior is economic and thus rational. a coalition is a short-term organization based on specific and contextual commitments of involving agents. this allows agents to profit from their respective competencies. to solve the task allocation problem, several coalition formation-based models were suggested [7] [8] [3]. however, these models are not adapted to the context of ciss because they do not define relations of partnership between agents, but rather relations of subcontracting. generally in these models, the agent that activates a negotiation process will only decides to structure the coalition. it selects its partners separately according to its own desires. consequently, the agents’ autonomy is not respected. the objective of this work is to propose a coalition formation-based model for the task allocation in ciss. the proposed model, named cfotacis1 , is inspired from the reality of the cooperation between entreprises. it respects the agents’ autonomy by allowing them to take part in the coalition formation process. it is recursive and includes four phases, the initialization, the negotiation, the evaluation, and the finalization. the rest of the paper is organized as follows. in the following section, we present some related work to the coalition formation. in section 3, we develop the suggested negotiation model. section 4 enumerates the proposed model properties. some experimental results are presented in section 5. section 6 summarizes the contributions of this paper and the research perspectives. 1coalition formation for task allocation in cooperative information systems. copyright c© 2006 by ccc publications selected paper from icccc 2006 86 nacer eddine zarour, sabrina bouzidi 2 related work according to the literature, we consider two approaches for studying the coalition formation process, the macroscopic approach and the microscopic one. the macroscopic approach, which is based on the game theory, considers the coalition as the basic unit [9]. the microscopic approach considers agents as the basic unit where several models were proposed and applied in many fields, especially the e-business [10] and the task allocation [7] [8] [3]. we focuss our discussion on the models based on the second approach which deal with the task allocation problem. in [7], the authors proposed a model where agents exchange their preferences w.r.t. the possible solutions. although, forcing agents to form alliances ensures the termination of the negotiation process, but the model does not respect agents’ freedom since it imposes a solution which may not satisfy all agents. in [8], the authors propose a model where the agent that activates the negotiation process first filters agents of its environment. it uses a multicriterion analysis to find agents that best satisfy its requirements. then, it starts an argumentative negotiation with the candidates, which are classified according to its preferences. this approach does not ensure the reach of a consensus but limiting the negotiation time seems to be a reasonable strategy. two models are proposed in [3]. the first one is dedicated to the competitive agents. the agent selection is based on a preference model, which is built using several criteria. the major inconvenient of this model is that the representing agent of the coalition has a kind of authority on the coalition members. in the second model, agents cooperate in an altruistic way. the model ensures the reach of a consensus and has some advantages like the integration of the new partners one by one in the coalition. however, the altruistic strategy of agents does not require the gradual integration of agents in the coalition. this approach seems to be interesting in the case of egoistic agents. in [10], the authors proposed a formalization of the trust criteria which an agent grants to the other ones of the environment. this trust modeling ensures the stability in the coalition formation due to its reliability. however, the authors do not specify how to find the value of the current evaluation of trust. in order to response at this question in our proposal, we will deduce the trust from some criteria. 3 cfotacis: a coalition formation-based negotiation model in ciss before describing the suggested model, let us admit the following assumption: an agent which wants to integrate the cis must update the portal of this cis [4]. it must specify its localization, its know-how, the information about its branch of industry, its competencies, its capital, etc. cfotacis is recursive and includes four phases, the initialization, the negotiation, the evaluation, and the finalization. in the beginning of the negotiation process, the manager which is the first coalition member, follows only the model phases. after that, when new partners join the coalition and become members, all the members try to find the next partner. the negotiation process iters until the coalition size will be reached. let us give the essential description of the different phases 3.1 the initialization phase the manager consults the portal of the cis (first assumption) to obtain useful informations on agents. then, it decomposes the global task into subtasks w.r.t. the agents’ industry branches. for each subtask, the manager makes a set of the candidates among agents of the environment. coalition formation for cooperative information agent-based systems 87 3.2 the negotiation phase the manager simultaneously sends the proposals to agents of the same set. the message which the manager sends to the candidates must contain all information about the cooperation project, as well as the waiting period that accords to them. then, the manager receives the responses from the candidates. figure 1 represents the state transitions for a negotiation between a manager (m) and a candidate (c). after receiving a proposal from a manager for cooperation, a candidate can: • refuse to cooperate (refuse(c, m)); • accept to achieve the subtask (accept(c, m)); • accept to achieve the subtask and also propose to achieve another subtask(s) (ok(c, m)); • refuse to achieve the subtask but propose to achieve another subtask(s) (no(c, m)); • not answer. in this case, after the expiry of a waiting period, the manager considers the not reply as a rejection (refuse(c, m)). if the candidate agrees to cooperate, it must send to the manager a message that contains its consideration of the cost and the time for achieving the subtask, as well as the maximum reply time that it grants to the manager to evaluate the proposal. figure 1: state transition graph of the negotiation between the manager (m) and a candidate (c) 3.3 the evaluation phase after receiving the candidates’ responses, the manager evaluates and classifies them according to their importances. the evaluation is made using a multicriterion analysis. this phase includes two stages. the first one deals with the evaluation of the criteria and the second one with the aggregation of the evaluations. criterion evaluation in this stage, the manager evaluates agents which sent their proposals according to various criteria. in [11], the author defines a whole of interesting criteria for the selection of the most adequate partner. we select those which seem most significant to our context. we have classified the criteria into three categories: (a) criteria related to the partner the cooperation degree of the candidate agent with the manager (d1) 88 nacer eddine zarour, sabrina bouzidi the cooperation degree of the manager with the candidate agent (d2) the quality of the relation (d3) deduced from three others subcriteria: (i) respect of the allowed time for achieving the subtask (s1) (ii) the quality of achieving the subtask (s2). (iii) honesty with regard to the profit distribution after the last experiment (s3) the experiment in the cooperation: (i) the cooperation number carried out by the partner (a1) (ii) the cooperation number carried out by the partner without interruption (a2). (b) criteria related to the cooperation the capital (c1). the technical capacity (c2). the technological competencies (c3) the existence of a single capability (c4). (c) criteria related to the candidate agent proposal the candidate must specify in its acceptance message the time t and the cost c for achieving the subtask. the aggregation stage after the evaluation of the different criteria, the manager incorporates the evaluations associated with all the criteria in order to have an overall estimate of each agent. in cfotacis, the aggregation of the criteria is used in two cases: (i) for the trust quantification: the trust e, which results from the current relation with the agent partner, is carried out using the equation e = p1*s1+p2*s2+p3*s3 where p1, p2, and p3 are the weights that the agent must specify and p1+p2+p3=1. (ii) for the evaluation of each candidate: the aggregation operator is defined using a deviation at an aspiration point which gathers the preferred values of the criteria [12]. the evaluations are represented using a vector named b. the deviation b to the aspiration point a is defined by the relation. deviation(a,b) = maxj=1..p(λ j(aj-bj)) with λ j=1/( idealjantiidealj) and p is the number of criteria. ideal is a vector which gathers the maximal criteria values of the evaluation vector and antiideal is a vector which gathers the minimal criteria values of the evaluation vector. the best evaluation d* is that which minimizes the deviation to the aspiration point: d*=min{ deviation(a,b)} for well explaining the evaluation mechanism, let us consider the following example. agents ag1, ag2, and ag3 are evaluated according only to the criteria criterion1, criterion2, and criterion3 in the vectors eval1, eval2, and eval3 (table 1). agents ag1 ag2 ag3 ideal antiideal λ criterion eval1 λ (ideal-eval1) eval2 λ (ideal-eval2) eval3 λ (ideal-eval3) criterion1 0.18 0 0.10 1 0.14 0.50 0.18 0.10 12.5 criterion2 0.16 0 0.12 1 0.15 0.25 0.16 0.12 25 criterion3 0.10 1 0.18 0 0.15 0.375 018 0.10 12.5 deviation 1 1 0.50 d* 0.50 coalition formation for cooperative information agent-based systems 89 table 1: an illustrative example of the adopted aggregation operator 3.4 the finalization phase after evaluating agents’ proposals, the manager classes agents in a list according to their deviations. the manager sends the agent which is at the head of the list a message for inviting it to join the coalition; else it contacts the next agent in the list, and so on. the coalition members merge their deviation vectors to have a unified view of the candidates. this merger allows the coalition members to have supplemented information since agents have only a partial vision of their environment. the merger is carried out by the minimum operator. let us consider an example showing how to make the unified evaluation vector. agent ag3 is evaluated by the members ag1 (eval1) and ag2 (eval2) w.r.t. to the eleven (11) criteria (see section 3.3.1). eval1= (0.32, 0.56, 0.88, 20, 10, 2000, 1500, 5, 1, 0.53, 0.23) eval2= (0.56, 0.12, 0.68, 20, 10, 2000, 1500, 5, 1, 0.53, 0.23) the unified deviation vector is eval= (0.32, 0.12, 0.68, 20, 10, 2000, 1500, 5, 1, 0.53, 0.23) thus, for each subtask, the coalition members together try to choose the adequate partner. the negotiation process will be finished when the coalition size is reached, and therefore, the coalition members start the execution of the tasks. hence, with cfotacis, the decision of the coalition structure is collective. 4 the cfotacis properties the proposed model presents several advantages. the choice of the partners is carried out by the coalition members thanks to the unification process of agents’ estimates carried out by each member. the adopted aggregation operator does not authorize the compensation, which permits to choose a partner which has acceptable values for all the criteria. cfotacis ensures the equity property because it offers the same chances to the candidates by limiting the time of the binary negotiations. when the candidate specifies its waiting time, this avoid the coalition members to send message to an agent that is no more waiting. consequently, a considerable profit of time will be obtained. when a partner disengages, it is obvious that it will be sanctioned. this sanction is expressed by the a2 parameter (see section 3.3.1.a). in this case, the coalition members have two alternatives for replacing the disengaged agent. they propose the subtask to the coalition members to cheek whether it exists a partner which is interested to achieve it. therefore, a new negotiation process is avoided. the second alternative is applied when there are not members which are interested. the coalition is obliged to start a new negotiation process to find another partner. finally, if the manager could not find partners or the required size of the coalition is not reached; the negotiation process will be remade. 5 experimental results we have implemented cfotacis using jade 3.0 (java agent development). we have compared its performances with those of a similar model [3]. we remind that in [3], the author proposed two 90 nacer eddine zarour, sabrina bouzidi negotiation models. we are interesting to the one dedicated to egoistic agents considering the rationality of our agents. we have made several series of experiments. in this paper, we show the effect of varying the agents’ and subtasks’ number on the global time of the negotiation process (figures 2 and 3). figure 2: the negotiation time versus the agents’ number for different subtasks’ number in cfotacis figure 3: the negotiation time versus the agents’ number for different subtasks’ number in the model proposed in [3] in each negotiation process, we fixe the subtasks’ number and we increase the agents’ number to finally deduce the consumed time. the graphs presented in this section are drowning using the origin 6.0 software. on the graph of figure 2 (cfotacis), we observe that we could vary agents’ number from 2 to 100 and subtasks’ number from 2 to 10. whereas, on the figure 4 (the comparative model), we could vary agents’ number only from 2 to 8 and subtasks’ number only from 2 to 7. therefore, we deduce that the cfotacis’ scalability is stronger than the one of the comparative model. one reason of this result is that in the comparative model, all agents are in competition for the coalition formation. so, if we have "n" agents in the system, we have also "n" parallel negotiations which cause a scheduling problem in the system. on the other hand, we observe that the negotiation time in cfotacis increases as agents’ and coalition formation for cooperative information agent-based systems 91 subtasks’ number increase. but this increase stills reasonable and very small in comparing it with the one of the comparative model. this is due to the weak scalability of the last one. also, in the comparative model, there is no limitation of the negotiation time between agents 6 summary and conclusions in this paper, we have proposed a coalition formation-based negotiation model for the cooperative information agent-based systems, named cfotacis. the proposed model is inspired from the reality of the cooperation between enterprises. it includes four phases, the initialization, the negotiation, the evaluation, and the finalization. the essential properties of cfotacis are its consideration of the preferences of the coalition members for choosing the future partners. it does not apply any authority on agents and ensures the equity by limiting the time of the binary negotiations due to the absence of the cycles in the proposed negotiation protocol. however, it does not ensure the reach of a consensus but tries to encourage agents to cooperate. the simulation results show that in cfotacis the spent time for the negotiation is reasonable and the messages’ number is deterministic. these results have been confirmed by comparing the cfotacis’ performances with those of the negotiation model proposed in [3]. in the future work, we will improve cfotacis so that it will ensure the reach of a consensus. also, we will adopt it in other application fields like e-commerce. references [1] l. putnam, m. s. pool, “conflict and negociation in handbook of organizational communication: an intersciplinary perspective,” f.m.jablinetal, eds. saga newbury park,, pp. 549-599, california, 1987. [2] t. sandholm, “an algorithm for optimal winner determinism in combinatorial auctions,” in proc. of the 16th int. joint conf. on ai, stockholm, sweden,, 1999. [3] s. aknine, “modèles et méthodes de coordination des systèmes multi agents,” thesis of doctorate, university paris ix dauphine-ufr science of the organizations,, december 2000. [4] n. zarour, m. boufaida, and l. seinturier, “a negotiation framework for organizational information systems,” int. journal of information technology and decision making (ijitdm). world scientific publishing co,, vol. 3, nř2, pp. 213-238, june 2004. [5] a. e. f. saghrouchni and s. haddad, “recursive model for distributed plannin,” in proc. of the 2nd int. conf. on multi-agent systems (icmas’96),, aaai press. kyoto, japan, 1996. [6] c. brooks and e. durfee, “congregating and market formation,” proc. of the 1st int. joint conf. on autonomous agents and multiagent systems,, acm press, pp 96-103, 2002. [7] a. e .f. saghrouchni and g. vauvert, “formation de coalitions pour agents rationnels,” in proc. of ilipn’2000. viiith days of lipn. multi-agent systemes and formal specification and software technologies ,,viltaneuse, france 09, 11-12, 2000. [8] l. k. soh, c. tsatsouli, and h. servey, “a satisficing negotiated and learning coalition formation architecture,” in v.lesser, c.ortiz, and m.tambe, editors, distributed sensor networks: a multi-agent perspective,,kluwer academic publishers, 04, 25-27, 2001. [9] l. larson and t. sandholm, “anytime coalition structure generation: an average case study,” journal of experiments and therotical al,11, 1-20, 2000. [10] vassileva, j. breban and s. horsch, “agent reasoning mechanism for long-term coalitions based on decision making and trust,” computational intelligence, 18, 4, special issue on agent mediated electronic commerce,, pp 583-595, nov. 2002. 92 nacer eddine zarour, sabrina bouzidi [11] c. li and k. sycara, “a stable and efficient scheme for task allocation via agent coalition formation,” www.2.cs.cmu.edu/ softagents/papers/cco3.pdf,, 2003. [12] ] n. r. jennings, p. faratin, p. johnson, m. j. o’brien, and m. wiegand, “using intelligent agents to manage business processes,” practical application of intelligent agents and multi-agents technology, paam 96,1999. nacer eddine zarour, sabrina bouzidi university mentouri of constantine lire laboratory, 25000 algeria e-mail: nasro_zarour@yahoo.fr, sab_bouzidi@yahoo.fr international journal of computers, communications & control vol. i (2006), no. 3, pp. 5-12 from algorithms to (sub-)symbolic inferences in multi-agent systems boldur e. bărbat, sorin c. negulescu abstract: extending metaphorically the moisilean idea of “nuanced-reasoning logic” and adapting it to the e-world age of information technology (it), the paper aims at showing that new logics, already useful in modern software engineering, become necessary mainly for multi-agent systems (mas), despite obvious adversities. the first sections are typical for a position paper, defending such logics from an anthropocentric perspective. through this sieve, section 4 outlines the features asked for by the paradigm of computing as intelligent interaction, based on “nuances of nuanced-reasoning”, that should be reflected by agent logics. to keep the approach credible, section 5 illustrates how quantifiable synergy can be reached even in advanced challenging domains, such as stigmergic coordination by injecting symbolic reasoning in systems based on sub-symbolic “emergent synthesis”. since for future work too the preferred logics are doxastic, the conclusions could be structured in line with the well-known agent architecture: beliefs, desires, intentions. keywords: nuanced-reasoning logic, multi-agent systems, sub-symbolic inferences, stigmergic coordination, synergy 1 introduction. from chrysippus, via moisil, to agent logics for over 40 years, determinism and bivalence of chrysippean logic were the pillars of computer science; likewise, algorithms were the backbone of computer programs, complying with their etymon: pro-gramma = what is written in advance. they sufficed for both fortran-like number crunching and cobol-like business data processing. when early real-time applications (firstly, operating systems) required less autistic programs, algorithms tried to adapt and bizarre terms, such as “unsolicited input”, were coined to fit the incipient non-determinism due to user free will. bivalence not only survived, but also grew in importance strongly backed by hardware. indeed, in the early 70’s, the role of bivalent logic transcended the borders of narrow data processing, penetrating “computer-aided x”, where x stays for almost any intellectual activity. thus, “algorithmic reasoning”, instead of being perceived as a side effect of “analogue humans loosing the battle with digital computers”, became a paradigm in the very sense of kuhn. emerging within this “digital zeitgeist”, nuanced-reasoning [12] was too anti-paradigmatic to redress the balance at least in it (besides, it was technologically useless, as most fascinating heresies). only after the “pc-windows-www” revolution was this “nuanced” kind of fuzzy logic developed by zadeh as “computing with words” acknowledged as an alternative approach to software development (albeit seldom necessary). on the other hand, after a decade of success stories, within artificial intelligence (ai) the perpetual stronghold of applied logics and symbolic processing -, expert systems (based on the newell-simon hypothesis) began to disappoint, because of their brittleness (in all nuances of the word), showing the actual limits of the symbolic paradigm. the reaction was prompt, overwhelming, and exaggerated: “gofai” (good old-fashioned ai) has to be replaced by “bic” (biologically inspired computing), based on sub-symbolic paradigms. the most nihilist and powerful one, i.e. the ethological paradigm (based on the physical-grounding hypothesis), is, for good reasons, still in vogue. however, paradoxically, new, “much nuanced” logics are already used in modern software engineering, tending to become necessary mainly for non-trivial mas, despite many, major, and obvious adversities. the paper aims to: a) defend not just those logics but also the inexorable need of symbolic processing, copyright c© 2006 by ccc publications selected paper from icccc 2006 6 boldur e. bărbat, sorin c. negulescu even in systems where intelligent behaviour emerges sub-symbolically (because of its synergistic potential); b) after explaining why synergy, show how it can be reached. (that is why the title contains the unusual term “(sub-)symbolic”.) thus, after a short history (section 2), the approach is rendered from an anthropocentric perspective: the agent shall behave naturally (i.e., closer to human behaviour), not the opposite (section 3). through this sieve, section 4 outlines the features and symbolic mechanisms asked for by the paradigm of “computing as intelligent interaction”, based on “nuances of nuancedreasoning”. to keep the approach credible, section 5 sums up recent research showing how quantifiable interparadigmatic synergy can be reached even in advanced challenging domains, such as stigmergic coordination by injecting symbolic reasoning in systems based on sub-symbolic “emergent synthesis”. since for future work the preferred logics are doxastic, the conclusions (section 6) far from being apodictic can be structured in line with the well-known agent architecture: beliefs, desires, intentions. 2 history. in search of synergy the research roots are in over 20 papers/articles published in 1997-2002 and synthesised in [3]. after 2002 there are two history strands having the common denominator “looking for synergy in the world of humans and agents”: • stigmergic coordination. after minor improvements in 2003, in [13] some (less quantifiable) synergy was achieved deviating from the biological model applied in the elitist ant systems by adding symbolic processing components (firstly adapting the environment and secondly instituting limited central coordination). in [7] a refined experimental model attested that in operational research, through “stigsynergy” the same solution quality could be reached with fewer ants than used in common benchmarks, saving thus at least one order of magnitude of processing time. • human-agent communication. user-avatar interaction was illustrated in medical captology, employing pathematic agents as virtual therapists [4]. the framework was widened (in the context of broadband communication) to any anthropocentric interface in [5], focusing on the languages enabled by modern multimodal interfaces. on a more abstract level, [6] showed how trans-disciplinary metaphors, applied in communication procedures, can help humanists and technologists get close. 3 approach. towards natural behaviour of artificial entities two perspectives guide the approach: anthropocentric systems, as non-negotiable goal, and agentoriented software engineering (aose), as amendable means depending on long-range effectiveness. (anthropocentrism means focusing on the human being as user, beneficiary, and, ultimately, raison d’être of any application or, more general, technology [5]. here, “anthropocentric” is synonymous to “humancentred”). the premises are: 1. regarding the goal: • despite their fast rising technological level, most it applications involving intense humancomputer interaction (hci) have low degree of user acceptance, ignoring the very slogan: “computing as interaction” [1]. • that drawback holds mainly for ai systems, widening the gap between humanists and technologists. • the main cause: system development is rather technocentric than anthropocentric. from algorithms to (sub-)symbolic inferences in multi-agent systems 7 • the main neglected human features are: 1f) invariants: humans are intrinsically analogue in information processing and multimodal in perception. 2f) prevalent in hci: humans prefer symbolic communication but sub-symbolic response. 2. regarding the means: • the it infrastructure is sufficiently advanced (in both facts and trends: nanoelectronics, broadband communication, semantic web, multimodal interfaces, etc.) to allow anthropocentrism for most it applications. • intelligent system behaviour whatever that could mean becomes a crucial user expectation. regrettably, in ai neither technology, nor design philosophy were yet able to offer it in a user-relevant manner. • nevertheless, agent technology, as ai flagship, proved to be a significant step towards user acceptance. • aose is not bounded to ai, but tends to become the dominant it development paradigm [11], [15]. while the first premises in each category are generally accepted, the last ones are debatable (e.g., a4b is rather an “author thesis” and b4 is strongly contested by object-oriented designers). the corollaries relevant for the paper are: • c1. the geometrically increasing computing power (due to moore’s law) promotes at least five factors tending to reduce radically the role of any species of logic in it at least for applications affordable on usual configurations: 1. since deterministic applications are vanishing, the conventional algorithm is not anymore program backbone. 2. even when still useful, the conventional algorithm is not anymore the main programming instrument (being hidden in procedures easily reached in a host of libraries or being generated by 4gl). 3. in ai the symbolic paradigm is steadily replaced by several sub-symbolic ones, based on fine-grain parallelism. 4. even when symbols are used, they are stored in and retrieved from huge and cheap memory, rather than processed through sophisticated reasoning schemes (case-based reasoning is just a blatant example). 5. cognitive complexity of new, sophisticated logics is too high for a designer, when “cut and try” is affordable. • c2. the rules for human-agent interaction can and should be set by users (at least while we have the demiurgic privilege of shaping agents as we like it!): 1. since interaction is carried out through the interface, anything behind it is user-irrelevant. 2. since natural and artificial intelligence encounter at interface level, they shall join, not collide. 3. to join closer to human demeanour, users should engage interface agents as naturally as possible. 4. hence, let agents behave more and more naturally (e.g., it is not difficult to go beyond gestures to show emotivity, since not emotion has to be replicated, but its appearance firstly forged, later more genuine [5]). 8 boldur e. bărbat, sorin c. negulescu 5. since interaction involves communication, the communication procedures (the term “procedures” is here a prudent, albeit partial, place holder for “language” or, even, “empathy”) must be those humans are familiar with (e.g., body language can and shall be added to verbal messages). 6. since beside how to communicate (the vehicle), it is vital what (the message), beyond the procedures, there must be a representational compatibility between humans and agents (expressed through common ontologies, primitive surrogate of a yet impossible common “weltanschauung”). if regarding c2.1-c2.4, the blend “symbolic/sub-symbolic” is unclear, c2.5 implies symbols, whereas c2.6 is stronger, implying symbolic inferences. at least some of them shall be based on logic(s). for short, acknowledging the decline of logic (because of c1), its necessity is asserted (in line with c2). anyway, the role of desirable features of new logics could be credibly defended outside largescale systems, were the proof is futile only comparing diverse implemented mas designed with or without employing such logics. because of c1.5, this is impractical. to weaken this main approach drawback, the argument is split to render two complementary paths, both based on the idea that the blend “symbolic/sub-symbolic” yields synergy: a) axiological perspective: why and what symbolic processing (section 4, closer to a position paper); b) praxiological perspective: how can symbolic processing be added in experimental sub-symbolic models (section 5, closer to a technical report). 4 nuances of nuanced-reasoning in human-agent dialogue it would be both arrogant and absurd if authors lacking educational background in both mathematics and logic would utter value judgments in these fields. hence: • without claiming that moisil actually attached to “nuanced” other connotation than “fuzzy”, bearing in mind his gifted baroque way of catalysing brainstorming, it is legitimate to use undertones of three (partial) synonyms “degree”, “gradation (sequence, development)”, “fine distinction” as metaphor sources. • all assertions about existing or desirable logics mirror the angle of potential users of such logics, mainly in interface agents and mas based on stigmergy. they convey “calls for help”, not requests, and are uttered as desires. • since, as regards logics dealing with agent-related aspects, for many basic aose requests, fisher’s logic [9] seems for a non-specialist by far to be the most responsive and appropriate, all desiderata below refer to it. ¥ diversified inferences. smith’s propositional-representation theory should be: a) revisited and thoroughly extended; it shall include all main mechanisms (symbolic or not) employed by humans to infer and to make decisions (even “right-hemisphere based” processes, as educated guess, intuition or gambling); b) applied, depending on the sub-field; such mechanisms should be replicated as “omomorph”, as adequate (not as possible!) in agent decision making schemata. if all of them would reach the elegance and dependability of logic, it would be nice, but let it be yet a kind of “princess lointaine”, because in real-world systems most concepts involved tend to become blurred. for instance, even metalogic is now nuanced: soundness remains crucial (still apart from time-critical applications it can be circumvented through revisable reasoning); completeness is more negotiable (the oversimplified solution: “otherwise, nothing happens”). ¥ “more time for agents”. nowadays, any software piece unable to interact efficiently with unpredictable environments (humans included) and with its peers is hardly useful outside toy-problems. that from algorithms to (sub-)symbolic inferences in multi-agent systems 9 means: parallelism, temporal dimension, non-determinism, reactivity. corollary: any such program entity has to be implemented as execution thread (atomic, sequential, asynchronous and dynamic) [3]. to develop into an agent, the thread needs also non-trivial informational and motivational components. (however, the “dynamic component” is a treble confusing term: a) it is not a component but the very agent nature; b) the “sense of time” refers to much more than activity e.g., “waiting” is rather inactivity; c) “dynamic activity” sounds pleonastic from any stance.). ¥ no “start” and no “synchronous agents”. if for e-commerce, it is conceivable to consider that the entire world restarts with each transaction, for process control (even for discrete manufacturing) such eternal re-birth is practically excluded. moreover, it is against the very spirit of: a) the (still dominant) “client-server” paradigm (the tailor is not spawned every time a client needs new clothes); b) real-time software engineering (to react timely to environment stimuli, the thread must exist to handle the interrupt); c) agency itself: the basic feature of autonomy (implying asynchronous behaviour) is endangered. luckily, current timers permit a “fine-grain universal metronome”, avoiding the costly implication: “asynchronously executing agents → temporal logic of the reals”. thus, “asynchronously executing agents” should be perceived as pleonasm, despite their logic is still based upon a discrete model of time, with both infinite past and future. (in real-world mas, there is no “big-bang”.) ¥ no “negative introspection”. unable to comment upon the advantages of ideal doxastic logics outside large-scale mas, the authors feel that positive introspection is highly desirable but that assuming the negative one is ineffective for both agents and humans. thus, if it makes sense and simplifies the features, maybe kd4, not kd45. ¥ no more certitude. less checking. until agent logics offer mechanisms to deal with uncertainty, at least, in simple expressions, the “ugly chasm” separating formal theory and practical system development [9] cannot be avoided. just a plain example of a badly needed such mechanism: exception handling. even primeval animals move “algorithmically” (“if gap then get round, else go on”) only a few steps, in very hostile environments. moreover, reaction to stimuli cannot mean perpetual looking for the stimulus. (instead, the stimulus causes an interrupt that can be treated as exception.) the cardinal hindrance stems not from logic, but from the mechanisms employed: neither nature, nor technology can afford in the long run mechanisms involving large amount of testing because they are too time-consuming tools: “if temperature > n oc then alarm”. thus, the main problem is not the semantics of “unless”, but the repeated checking of “if ”. from this angle, the semantics of “unless” in reiter’s default logic would be more tempting if it would be rather diachronic than synchronic (a bird is or is not a penguin but will never become one). however, a kind of m operator meaning roughly “while no alarm is heard it is consistent to believe that nothing happened”. indeed, the agent is condemned to be a risk-taker, hearing (reactively) the environment, not listening (proactively) to it: the agent stops performing a task only if he hears the alarm bell. the point is that this “if ” belongs to the metalanguage and does not involve thermometer reading! perhaps a non-monotonic logic with “reiter-unless” inserted in a temporal logic with “fisherunless” is what designers dream of. (since dreams are forward-thinking, maybe more: a graphical “flowchart-like” symbol of this m shall be understood by an interpreter of an “aose-ml” -without “object legacy” that can create code for defining, raising, propagating, and handling exceptions. 5 down to ants: synergy, stigmergy, and symbols since as regards stigmergic coordination the research was recently summarised in [7], [8], [13] and the current results are presented in [7], here, only the approach and some relevant aspects of achieving synergy through grafting symbolic processing onto sub-symbolic systems are emphasised. the and written in capitals emphasises the similarity with the synonymous boolean operator, i.e. synergy is searched for in all possible combinations. the mas that relies on sub-symbolic processing more than any other is the biologically inspired ant system (as) where the sub-symbolic echelon is represented by the pheromones in such a way that global 10 boldur e. bărbat, sorin c. negulescu information is available locally. moreover, this system is not only sub-symbolic by itself but it also manifests autopoiesis (it emerges subsymbolic) and the trouble to understand what is in fact going on at system level, is less upsetting than in the case of more familiar sub-symbolic paradigms (as artificial neural networks or evolutionary algorithms) since ant behaviour is easier to follow due to its simplicity. the stigmergy related to mas, “describes a form of asynchronous interaction and information exchange between agents mediated by an ‘active’ environment”, or “the production of certain behaviour in agents as a consequence of the effects produced in the local environment by previous behaviour”. in this context: “the agents are simple, reactive, and unaware of other agents or of the emerging complex activities of the agent society; the environment is an important mechanism to guide activities of these agents and to accumulate information about ongoing activities of the whole agent society” [13]. whereas in [2], [10], [14], the approach was mainly based on self-organization, the approach is an alternative one by obtaining synergy through adding symbolic processing (firstly adapting the environment and secondly instituting limited central coordination). as shown in [7], the as manifests a threshold and it depends on problem type and complexity; the same solution quality can be obtained with fewer ants than used in common benchmarks, saving thus at least one order of magnitude of processing time. details can be found in [13] (improvements to conventional eas), [8] (motivation, approach and new perspective), and [7] (experimental results about moving the threshold in fact modifying the sigmoid function to improve efficiency). possible scientific openings e.g. whether in real-life problems there are instances when “many starts from four” can be found also in [7]. 6 conclusions: beliefs, desires, intentions the conclusions are presented within the bdi frame not just to keep up the atmosphere, but because: a) the conclusions are far from being apodictic and the logics preferred for mas are doxastic; b) using the meanings given by smets, the belief functions have rather dispersed values, and the plausibility functions have quite low values for section 4 and some assertions of section 3; c) the largest part of section 3 is actually a gathering of desires; d) intentions is more humble than “future work”; d) if we intend to interact keener with agents, we have to make steps towards common ontologies preferably based on success stories. beliefs: • despite the fall of conventional algorithms and the fast rise of sub-symbolic paradigms, symbolic processing is unavoidable in aose and agent logics become necessary even outside large-scale systems. • an essential problem in designing agents is implementing their reactivity; main cause: current development environments admit rather very poor exception handling. • even mas based on the most radical sub-symbolic paradigm (stigmergy being “a-symbolic” par excellence), become more effective grafting upon symbolic processing. • taking into account the increasing weight of mas acting as man-machine systems, the anthropocentric perspective requires that human-agent communication should be the model for agentagent communication. • although the brains-surrogate of current agents is still primitive, it shall have two hemispheres, as human do. the left hemisphere, where logic is king, is designed predominantly to implement pro-activeness, whereas the right one, as realm of its instincts, emerges sub-symbolically, and is the main source of reactivity (again, similar to humans). desires: they are addressed to future agent logics, from an outsider (but outspoken aose) perspective: from algorithms to (sub-)symbolic inferences in multi-agent systems 11 • tackle neglected problems common to all kinds of agent-based systems (dwarfs and trolls welcomed). • give us sectorial solutions. they are just fine to begin with. completeness in its polysemy can follow. (if the mas is sound, nobody minds if agents manifest a bit of schizophrenia.) • don’t give us sectorial approaches. they are less applicable (e.g., time without uncertainty or vice versa). • let mas be lasting, even if some agents are mortal. • don’t condemn mas to act synchronously. both environment and users are too capricious to accept it. (instead, we promise to be happy with discrete time.) • don’t sentence us to perpetual testing. to rephrase dijkstra: (the condition in) if is harmful. (allow us to handle exceptions, and we promise not to exaggerate eliminating all “iffs”.) • help us pass the mental rubicon separating objects from agents. (no agent is fond of being considered “intelligent and responsive like an object”.) intentions: • as regards stimergic coordination, the intentions are those states in [7]: for short, increasing “stigsynergy”. • showing how agent reactivity can be significantly improved, through exception-driven multimodal interfaces. • trying dialectics as inference mechanism for negotiation strategies used by e-commerce agents. acknowledgment. this paper is related to the eu-sponsored action cost-298. references [1] “agentlink roadmap: overview and consultation report”, agentlink iii. agent based computing, university of southampton, 2004. [2] banzhaf, w., “self-organizing systems”, encyclopedia of physical science and technology, academic press, new york, 14, pp. 589-598, 2002. [3] bărbat, b.e., “sisteme inteligente orientate spre agent”, ed. academiei române, bucureşti, 467 pages, 2002. [4] bărbat, b.e., “the avatar a pseudo-human extending the genuine one”, the good, the bad and the irrelevant: the user and the future of information and communication technologies, (l. haddon et al, eds.), pp. 38-42, media lab/university of art and design, helsinki, 2003. [5] bărbat, b.e., “the impact of broad-band communication upon hmi language(s). (chapter 7.) communicating in the world of humans and icts. (chapter 8.)”, cost action 269. e-citizens in the arena of social and political communication, (l. fortunati, ed.), pp. 113-142, eur21803, office for official publications of the european communities, luxembourg, 2005. [6] bărbat, b.e., n. bulz, “e-world and real world. from analysing concepts towards integrating approaches”, proc. of the wosc 13th international congress of cybernetics and systems, university of maribor in cooperation with encyclopaedia of life support systems, 1, pp. 47-57, 2005. 12 boldur e. bărbat, sorin c. negulescu [7] bărbat, b.e., s.c. negulescu, c.b. zamfirescu, “human-driven stigmergic control. moving the threshold”, proc. of the 17th imacs world congress (scientific computation, applied mathematics and simulation), paris, july 11-15, 2005. [8] bărbat, b.e., c.b. zamfirescu, s.c. negulescu, “the best from ants and humans: synergy in agent-based systems”, studies in informatics and control journal, 13, 1, 47-59, 2004. [9] fisher, m., “temporal development methods for agent-based systems”, autonomous agents and multi-agent systems, 10, pp. 41-66, springer science + business media inc., 2005. [10] knyazeva, h., haken, h., “synergetics of human creativity. dynamics, synergetics, autonomous agents”, anonlinear systems approaches to cognitive psychology and cognitive science singapore: world scientific, pp. 64-79, 1999. [11] luck, m., mcburney, p., priest, c., “a manifesto for agent technology: towards next generation computing”, autonomous agents and multi-agent systems, 9, 203-252, kluwer academic publishers, 2004. [12] moisil, gr.c., “lecţii despre logica raţionamentului nuanţat”, ed. ştiinţifică şi enciclopedică, bucureşti, 1975. [13] negulescu, s.c., bărbat, b.e., “enhancing the effectiveness of simple multi-agent systems through stigmergic coordination”, fourth international icsc symp. on engineering of intelligent systems (eis 2004), icsc-naiso academic press canada, 149 (abstract; full paper on cd-rom enclosed), 2004. [14] parunak, h.v.d., brueckner, s., john a. sauter, matthews, r., “global convergence of local agent behavior”, submitted to the fourth international joint conference on autonomous agents and multi-agent systems (aamas’05), 2005, available at http://www.erim.org/ vparunak/ aamas05converge.pdf. [15] zambonelli, f., omicini, a., “challenges and research directions in agent-oriented software engineering”, autonomous agents and multi-agent systems, 9, 253-283, kluwer academic publishers, 2004. boldur e. bărbat “lucian blaga” university of sibiu faculty of science address: 5-7, ion raţiu street, sibiu, 550012, românia e-mail: bbarbat@gmail.com sorin c. negulescu “lucian blaga” university of sibiu faculty of engineering address: 4, emil cioran street, sibiu, 550025, românia e-mail: sorin−negulescu@yahoo.com international journal of computers, communications & control vol. i (2006), no. 1, pp. 59-67 personalized e-learning implementation the gis case athanasios d. styliadis, ioannis d. karamitsos, dimitrios i. zachariou abstract: personalized e-learning implementation is recognized as among one of the most interesting research areas in the distance learning web-based education. in particular, the gis e-learning initiatives that incorporate —by default— a number of sequencing spatial techniques (i.e. spatial objects selection and sequencing), will well benefit from a welldefined personalized e-learning implementation with embedded spatial functionality. this is the case addressed in this paper. the gis e-learning implementation introduced in the current paper is based on a set of teaching (lecturing) rules according to the cognitive style of learning preferences of both the learners and the lecturers as well. it is important to note that, in spite of the fact that most of these teaching rules are generic (i.e. domain, view and user independent), there are no so far well-defined and commonly accepted rules on how the learning spatial gis objects and techniques should be selected and how they should be sequenced to make “instructional sense" in a web-based gis course. keywords: e-learning, distance learning, gis, lis, am/fm, spatial sciences. 1 introduction gis learning object selection is the first step to adaptive space navigation and adaptive course sequencing with gis (space) functionality. adaptive navigation seeks to present the gis learning objects associated with an on-line course in an optimized order, where the optimisation criteria takes into consideration the learner’s background and performance in related learning objects [2], whereas adaptive course sequencing is defined as the process that selects learning objects from a digital repository and sequence them in a way which is appropriate for the targeted gis learning community or individuals [16, 11, 17]. selection and sequencing is recognized as among the most interesting research questions in intelligent web-based gis education [13, 6]. although many types of intelligent learning systems with no gis functionality are available, in the proposed gis case five key components could be identified, which are common in most gis systems, namely: data acquisition, retrieval and analysis, preliminary data processing, database construction, and communication and visualization. figure 1 provides a view of the interactions between these five gis modules [18]. figure 1: the main components of a gis intelligent learning systems in most intelligent learning systems that incorporate course sequencing techniques, the pedagogical module is responsible for setting the principles of content selection and instructional planning. the selection of content (in our case, the gis learning objects) is based on a set of teaching rules according to the topology-based cognitive style or learning preferences of the learners [3, 15]. in spite of the fact that most of these rules are generic (i.e. domain independent), there are no well defined and commonly accepted rules on how the gis learning objects should be selected and how they should be sequenced to make “instructional sense” with gis functionality [14]. moreover, in order to design highly adaptive learning systems a huge set of topology-based rules is required, since dependencies between educational characteristics of gis learning objects and learners are rather complex [8, 12, 9, 10]. copyright c© 2006 by ccc publications 60 athanasios d. styliadis, ioannis d. karamitsos, dimitrios i. zachariou in this paper, the learning object selection problem in gis intelligent learning systems is addressed by proposing a methodology that instead of “forcing” an instructional designer to manually define the set of selection rules; produces a decision model that mimics the way the designer decides, based on the observation of the designer’s reaction over a small-scale learning object selection problem. in the next (second) section the gis learning object selection process is discussed as a part of a spatial-course sequencing. the third section discusses a topology-based filtering process of gis learning objects used for reduction of learning objects searching space and proposes gis metadata elements that can be used for learning object filtering according to the open gis consortium guidelines (standards) for gis functionality. 2 gis learning object selection in spatial-course sequencing in automatic and semi-automatic course sequencing, the main idea is to generate a course suited to the needs of the learners. as described in the literature, two main approaches for automatic and semi-automatic course sequencing have been identified: adaptive courseware generation (acg) and dynamic courseware generation (dcg) [3]. in acg the goal is to generate an individualized course taking into account specific learning goals, as well as, the initial level of the student’s knowledge. the entire course is adaptively generated before presenting it to the learner, instead of generating a course incrementally, as in a traditional sequencing context. in dcg on the other hand, the system observes the student progress during his interaction with the course and dynamically adapts the course according to the specific student needs and requirements. if the student’s performance does not meet the expectations, the course is dynamically re-planned. the benefit of this approach is that it applies as much adaptivity to an individual student as possible. both, the above mentioned techniques, in the case of gis lecturing employ a pre-filtering topology-based mechanism to generate a group (“pool”) of gis learning objects that match the general content requirements. this pool can be generated from both distributed and local gis learning object repositories, provided that the appropriate access controls have been granted. the filtering process is based on general requirements such as topology, gis functionality, characteristics of the teaching language, the media of the targeted gis learning objects, as well as, the use of ontologies and topology for the domain in question (known as the spatial knowledge module). the result of the filtering process falls in a virtual pool of gis learning objects that will act as an input space for the content selector (gis functionality). after the creation of the initial pool of the gis learning objects, the content selection process and the underlined topology is applied based on learner characteristics, such as accessibility and competency characteristics or even historical information about related learning activities, included in the student model module. in the next section some filtering elements based on the open gis consortium (o-gis.c) learning object metadata (lom) standard are presented and the methodology is analyzed for the proposed content selection phase of a semi-automatic gis course sequencing with spatial functionality. 3 gis learning object filtering generally, the main goal of a filtering process is the reduction of the searching space. gis learning object repositories (g.lor) often contain hundreds of thousands of gis learning objects, thus the selection process may require a significant computational time and effort. in most intelligent learning systems, learning object filtering is based, either on the knowledge domain they cover, or on the media type characteristics they contain [11]. in the open gis consortium’s (o-gis.c) spatial metadata model, there exist a number of elements covering requirements such as gis functionality, topology, geometry, subject, teaching language, media type of the targeted gis learning object, etc. figure 2 presents the o-gis.c elements identified for each one of the above mentioned topology-based filtering categories and the conditions required. alternatively, filtering can be based on integration of the o-gis.c metadata model elements and ontologies [19, 16]. those approaches assume that both the domain model and the learning objects themselves use the same ontology [14] and limit the filtering only to knowledge domain filtering with gis functionality. personalized e-learning implementation the gis case 61 figure 2: elements for gis learning object filtering the gis learning object selection procedure typically, the design of highly adaptive learning systems requires a huge set of rules, since dependencies between educational characteristics of learning objects and learners are rather complex. this complexity introduces several problems on the definition of the rules required [20, 4], namely: inconsistency, when two or more rules are conf1icting. conf1uence, when two or more rules are equivalent. insufficiency, when one or more rules required have not been defined. the proposed methodology is based on an intelligent mechanism that tries to mimic an instructional designer’s decision model on the selection of the gis learning objects. for this purpose, a framework that attempts to construct a suitability function that maps gis learning object characteristics over learner characteristics and vice versa is designed. the main advantage of this method is that it requires less effort by the instructional designer, since instead of identifying a huge set of rules related to space and topology, only the designer’s selection from a small set of gis learning objects over a reference set of learners is needed. the machine learning technique will try then to discover the dependence between gis learning object and learner characteristics that produce the same selection of gis learning objects per learner as the instructional designer did [1, 13, 20]. the proposed methodology does not depend on the characteristics used for learning objects and learner modelling, thus can be used for extraction of even complex pedagogy-related dependencies. it is obvious that since characteristics/requirements like the domain are used for filtering, the dependencies produced are quite generic, depending only on the educational characteristics of the content and the cognitive characteristics of the learner gis student [5, 2, 16]. figure 2 presents a graphical representation of the proposed selection model extraction framework, which it is consisting of the following three main steps: 1) the 3-d modelling and selection of gis criteria the selection methodology is generic, independent of the learning object and the learner characteristics used for the selection. in the proposed method experiment, gis learning object characteristics was used derived from 62 athanasios d. styliadis, ioannis d. karamitsos, dimitrios i. zachariou figure 3: selection model extraction framework the o-gis.c standard, and learner characteristics derived from the ims global learning consortium inc. learner information package (lip) specification. there exist many criteria affecting the decision of gis learning objects selection. those criteria that lead to a straightforward exclusion of learning objects, such as the topology, the subject, the language and the media type and the gis functionality are used for filtering. the rest set of criteria such as the educational characteristics of gis learning objects are used for selection model extraction, since the dependencies of those criteria can model the pedagogy applied by the instructional designer, when selecting learning objects. those criteria, due to the complexity of interdependencies between them, are the ones that cannot be directly mapped to rules from the instructional designer. thus an automatic or semi-automatic extraction method, like the proposed one, is needed. 2) the selection model extraction after identifying the set of the characteristics and the criteria (see: step 1) that will be used as the input space of the i/o selector, the extract procedure for each gis learning object characteristic and the expert’s suitability evaluation model over a reference set of lip-based characterized learners is presented. the input to this phase is the o-gis.c characteristics of a reference set of learning objects, the ims lip characteristics of a reference set of learners and the suitability preference of an expert for each of the gis learning objects over the whole reference set of learners. the model extraction methodology has the following formulation: let us consider a set of learning objects, called a, which is valued by a set of criteria g = (g1, . . . , gn). the assessment model of the suitability of each gis learning object for a specific learner, leads to the aggregation of all criteria into a unique criterion that we call a suitability function s(g) = s(g1, . . . , gn). personalized e-learning implementation the gis case 63 we define the suitability function as an additive function of the form s(g) = n ∑ i=1 si(gi) with the following additional notation: • si(gi): marginal suitability of the ith selection criterion valued gi, • s(g): global suitability of a learning object. the marginal suitability evaluation for the criterion gi is calculated using the formula si(x) = ai + bix exp(−cix2), where x is the corresponding value of the gi gis learning object selection criterion. this formula produces, according to parameters a, b and c as well as the value space of each criterion, the main criteria forms, we have identified: • monotonic form: when the marginal suitability of a criterion is a monotonic function. • non monotonic form: when the marginal suitability of a criterion is a non-monotonic function. the calculation of the optimal values of parameters a, b and c for each selection criterion is the subject of the knowledge model extraction step. let us call n the strict preference relation and e the indifference relation. if so1 is the global suitability of a learning object o1 and so2 is the global suitability of a learning object o2, then the fol1owing properties generally hold for the suitability function s: so1 > so2 ⇐⇒ (o1)p(o2), and the relation r = p∪i is a week order relation so1 = so2 ⇐⇒ (o1)i(o2). the expert’s requested information then consists of the weak order r defined on a for several learner instances. using the provided weak order relation r and based on the form definition of each learning object characteristic we can define the suitability differences ∆ = (∆1, . . . , ∆m−1), where m is the number of learning objects in the reference set a and ∆k = sok − sok+1 ≥ 0, depending on the suitability relation of (k) and (k + 1) preferred learning object for a specific learner of the reference set. we can introduce an error function e for each suitability difference: ∆k = sok −sok+1 + ek ≥ 0. using constrained optimization techniques, we can then solve the non-linear problem: minimize m−1 ∑ j=1 ( e j )2 subject to the constraints: ∆ j > 0 if o j po j+1 ∆ j = 0 if o j io j+1 } for each one of the learners of the reference set. this optimisation problem will lead to the calculation of the optimal values of the parameter a, b and c for each gis learning object selection criteria over the reference set of learners. 64 athanasios d. styliadis, ioannis d. karamitsos, dimitrios i. zachariou 3) the extrapolation the purpose of this third phase is to generalize the resulted marginal suitability model from the reference set of learners to all learners, by calculating the corresponding marginal suitability values for every combination of learner characteristics. this calculation is based on the interpolation of the marginal suitability values between the two closest instances of the reference set of learners. suppose that we have calculated the marginal suitability sl1i and s l2 i of a criterion gi matching the characteristics of learners l1 and l2 respectively. we can then calculate the corresponding marginal suitability value for another learner l using interpolation if the characteristics of learner l are mapped inside the polyhedron that the characteristics of learners l1 and l2 define, using the formula: si(g l i ) = si(g l1 i ) + gli −g l1 i gl2i −g l1 i [ si(g l2 i )−si(g l1 i ) ] , if si(g l2 i ) > si(g l1 i ) let ci = bci, c?i c, i = 1, 2, . . . , n be the intervals in which the values of each criterion — for both learning object and learners — are found, then we call global suitability surface the space c = ×ni=1ci. the calculation of the global suitability over the above mentioned space is the addition of the marginal suitability surfaces for each of the learning object characteristics over the whole combination set of learner characteristics. some experimental results and discussion in order to evaluate the total efficiency of the proposed methodology both ïn calculating the suitability on the training set of gis learning objects and on estimating the suitability of gis learning objects external from the reference set, we have designed an evaluation criterion, defined by: success(%) = 100· correct learning objects selected n , where n is the number of the desired gis learning objects from the virtual pool that will act as input to the instructional planner. it is assumed that the number of desired gis learning objects is less than the total number of the gis learning objects in the input space (the learning objects pool) and that both the gis learning object metadata and the learner information metadata have normal distribution over the value space of each criterion. additionally, the gis learning objects are classified, for both testing and estimation set, in two classes according to their functionality and aggregation level, since granularity is a parameter affecting the capability of an instructional designer to select learning content for a specific learner. the classification is based on the value space of the “general/aggregation leíel" element of the o-gis.c standard. in the rest of the paper experimental results are presented of the proposed methodology by comparing the resulting selected gis learning objects with those selected by gis experts. also, the success of the proposed method has been evaluated on both the training set of learning objects (the training success) and on the suitability estimation of learning objects external from the reference set (the estimation success). so, figure 4 present average experimental results for gis learning objects with aggregation level 1 and 2 respectively. if it is considered that for one learner instance, the different combinations of gis learning objects, calculated as the multiplication of the value instances of characteristics with gis functionality, lead to more than 1,000 gis learning objects, it is evident that it is almost un-realistic to assume that an instructional designer can manually define the full set of selection rules which correspond to the dependencies extracted by the proposal method and at the same time to avoid the inconsistencies, conf1uence and insufficiency of the produced selection rules. the proposed topology-based methodology is capable of effectively extracting dependencies between gis learning object and learner characteristics affecting the decision of an instructional designer on the gis learning object selection problem. the analysis on the results, presented in figure 3, shows that when the desired number of gis learning objects (i.e. n) is relatively small (less than 100), the selected learning objects by the extracted decision model are almost similar to those the instructional designer would select. on the other hand, when the desired number of gis learning objects is relatively large (say, about 500) the success of the selection is affected, but remains at acceptable level (i.e. about 90%). another parameter affecting the selection success is proved to be the granularity of gis learning objects. granularity mainly affects the capability of an instructional designer to express selection preferences over learning personalized e-learning implementation the gis case 65 figure 4: average experimental results for gis learning objects objects. learning objects with small aggregation level have bigger possibility of producing “gray” decision areas, where the instructional designer cannot decide which gis learning object matches most the cognitive style or learning preferences of a learner [1, 5]. in paper’s experiments, gis learning objects with aggregation level 2, which can be small or even bigger collections of gis learning objects with aggregation level 1, appear to have less possibility of producing indifference relations, enabling to make secure decisions even for bigger desired number of learning objects (n = 1, 000). 4 summary and conclusions in this paper the gis learning object selection problem is addressed as an intelligent topology-based gis learning systems, by proposing a methodology that instead of “forcing” an instructional designer to manual1y define the set of selection rules; produces a decision model that mimics the way the designer decides, based on the observation of the designer’s reaction over a small-scale gis learning object selection problem. hence, the proposed personalized e-learning method, describes a new methodology about selecting and sequencing learning objects with gis functionality in an instructional-sense and user-friendly way. acknowledgements the current paper is supported by the epeaek ii archimedes i research project (action 2.2.17), “personalized 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[16] a. styliadis, k. pehlivanis & d. zahariou, “personalized e-learning for gis lecturing”. hawaii international conference on education. honolulu, hawaii, usa, january 6th-9th, 2006. [17] a. styliadis, k. pehlivanis & d. zahariou, “e-learning with re-usable gis functionality”. global universities in distance education (guide) 2006 – developing a common platform for global co-operation. rome it, february 13th-14th, 2006. [18] a. styliadis & k. pehlivanis, “personalized e-learning in a re-usable way: a proposed gis system design – the architecture”. international journal on engineering and applied sciences (jeas), 2005. [19] m. s. urban & e. g. barriocanal, “on the integration of ieee-lom metadata instances and ontologies”, learning technology newsletter, vol. 5 (1), 2003. [20] h. wu & p. de bra, “sufficient conditions for well-behaved adaptive hypermedia systems” in proc. of the first asia-pacific conference on web intelligence: research and development, maebashi city, japan, 2001. athanasios d. styliadis, ioannis d. karamitsos, dimitrios i. zachariou the atei of thessaloniki, greece epeaek ii, the arximedes 2-2-17 project e-mail: styl@it.teithe.gr personalized e-learning implementation the gis case 67 editor’s note about the author: athanasios d. styliadis is a professor at the department of information technology at the alexander institute of technology (atei) of thessaloniki, greece. he was born in 1956 in florina, greece and he received a diploma in surveying engineering (aristotle university of thessaloniki, greece, 1980), an m.sc. in computer science (dundee university, scotland, 1987) and a ph.d. in cad/gis/computer modeling (aristotle university of thessaloniki, greece, 1997). he was a fellow research scholar at the department of geomatics, university of melbourne and at the center for gis and modeling (cgism), australia. prof. styliadis is the writer of three books in computer graphics, human-computer interaction (hci) programming and gis-geomatics engineering, and he has over 70 journal and conference proceedings publications. also, has been worked at the hellenic army geographical service (hags, athens) for three years as gis system analyst and programmer. his current research interests include: e-learning & geographic information, computer modeling, web-based caad, temporal gis systems, concurrent engineering & 3-d geometry, and digital documentation of monuments and sites. international journal of computers, communications & control vol. ii (2007), no. 2, pp. 159-173 an enhancement of the random sequence 3-level obfuscated algorithm for protecting agents against malicious hosts kamalrulnizam abu bakar, bernard s. doherty abstract: with the advent of agent technology, the need for security of applications becomes a concern that is beyond classical data security capability and considerations. without proper security protection especially against a malicious host attack, the widespread use of agent technology can be severely impeded. the random sequence 3-level obfuscated algorithm has been proposed by the authors to improve agent security: in this paper, an enhancement to the protection level of this algorithm is proposed. the effectiveness of the obfuscation algorithm is enhanced by addition of noise, which surrounds the true value carried by the agent with false values. a malicious host can thus at best guess the true value carried by the agent. keywords: agent security, malicious host, spying attack, noise code. 1 introduction security is considered as an important factor for an agent used in real world applications. the problems in agent security arise when an agent is used in open and unsecured environments [8, 3]. one kind of attack by an execution host (malicious host) is spying on the agent’s code, data and state [6, 11]. spying attack by the malicious host may invade the agent’s privacy, especially an agent’s critical data, for example a user’s maximum budget carried by the agent. knowledge of an agent’s critical data gives a malicious host an advantage in any competition over other hosts because the malicious host knows what is expected by the agent. for example, a customised agent is sent out (in an open and unsecured environment) to find a suitable flight with the fare price under or equal to 500 pounds. malicious host attack based on a spying attack is to raise the offered price until it meets the maximum price that has been set by the agent’s owner, even though the normal price is much lower. spying attack by the malicious host on an agent’s data or state is difficult to detect, because the attack doesn’t leave any detectable trace [6, 11, 5]. the executing host has to read the agent’s code, must have access to the agent’s data, and must be able to manipulate the agent’s variable data in order to execute the agent [6, 5, 8]. therefore, the executing host can see and access all of the agent’s code including data and state, and is thus able to hide any traces of an attack, which makes any attempt to address spying attack difficult. to reduce the likelihood of this attack succeeding, the authors have proposed the random sequence 3-level (rs3) obfuscated algorithm [1], which obfuscates the actual value of an agent’s critical data to prevent a malicious host spying attack. the rs3 obfuscated algorithm consists of multiple polynomial functions which obfuscating the actual value of the agent’s critical data to produce an obfuscated value that is meaningless to the attacker. only selected polynomial functions are used in the conversion process and in each selected function, multiple random inputs are implied. the main objective of using the rs3 obfuscated algorithm is to enable the comparing of confidential values within an unsecured remote host environment without exposing the actual confidential value to an unauthorised party, using comparison of obfuscated values rather than actual values. unfortunately, implementing the rs3 obfuscated algorithm alone may expose the algorithm to an attack, which could execute multiple copies of the algorithm many times in order to analyse it, and propose actual values that will obfuscate to give the comparison outcome sought by the malicious host. this paper proposes an enhancement of the protection level of the rs3 obfuscated algorithm by adding noise code in the agent application that executes in the remote host environment, to hide the actual obfuscated value among a number of false values. the enhancement makes it more difficult for copyright © 2006-2007 by ccc publications 160 kamalrulnizam abu bakar, bernard s. doherty the attacker to analyse the actual obfuscated value in order to discover the actual value of an agent’s critical data: the attacker can deobfuscate the set of values carried by the agent, but can at best guess which is the correct value. this paper is organized as follows: section 2 present an overview of the random sequence 3-level obfuscated algorithm. section 3 describes the random sequence 3-level obfuscated algorithm with noise code, the implementation of noise code to enhance the obfuscated algorithm protection level and the deobfuscation time of the rs3 obfuscated algorithm. section 4 presents the experimental results on the overhead of implementing noise code with the rs3 obfuscated algorithm. section 5 analyse the strength of the rs3 obfuscated algorithm with noise code. section 6 presents a discussion and the conclusion is presented in section 7. 2 an overview of the random sequence 3-level obfuscated algorithm the random sequence 3-level obfuscated algorithm [1] is an algorithm that is designed to protect the confidentiality of an agent against malicious host spying attack. this algorithm uses three polynomial functions for obfuscating the actual value of the agent’s critical data to an obfuscated value that is meaningless to the malicious host, in order to prevent the malicious host from spying on the agent critical data (figure 1 show the obfuscation process of the rs3 obfuscation algorithm). the obfuscated method used in the random sequence 3-level obfuscated algorithm enables the execution host to execute the process of comparing its offer with the agent budget with both values in an obfuscated format without the execution host having any knowledge of the actual value of the agent’s critical data. this comparing process can be done without needing deobfuscation of the data, unlike cryptographic methods, which require decryption of the data, thus revealing its value, before a comparison can be made. although the rs3 obfuscated algorithm is able to obfuscate an agent’s critical data to make it more difficult for the malicious host to spy, the malicious host can execute multiple copies of the obfuscated algorithm in parallel in order to analyse the algorithm quickly, making this obfuscated algorithm vulnerable. this attack can be addressed by limiting the processing time available to the host before the agent is discarded [6]. however, the problem in determining an effective protection interval that can prevent the malicious host having enough time to execute multiple copies of the obfuscated algorithm also makes it difficult for this obfuscated algorithm to be implemented in real applications, where sufficient time must be allowed for legitimate processing. in order to overcome the problem of a malicious host executing multiple copies of the rs3 obfuscated algorithm and of determining an effective protection interval to protect the algorithm, noise code [9, 10] is introduced for an agent that executes in the remote host environment. the rs3 obfuscated algorithm with noise code is described in the next section. 3 the random sequence 3-level obfuscated algorithm with noise code the objective for implementing the noise code in the agent application is to hide the actual obfuscated value among a numbers of fake obfuscated values so the malicious host can at best guess at the true value of the agent’s critical data [9, 10]. the difference between ordinary rs3 algorithm and rs3 with noise code algorithm is in the number of obfuscated values generated and added by the master agent into the slave agent application before the slave agent is dispatched to the remote host execution environment to execute its tasks. in order to discover the true value of the agent’s critical data, the malicious host must first guess the actual obfuscated value among a number of fake obfuscated values. any wrong decision in choosing the obfuscated value will result in using a wrong true value of the agent’s critical data. for instance, if the agent is equipped only with the actual obfuscated value, x without adding any noise code, the probability that the malicious host could discover the actual obfuscated value by searching a range of values is one, an enhancement of the random sequence 3-level obfuscated algorithm for protecting agents against malicious hosts 161 p o l y n o m i a l f u n c t i o n f i r s t r a n d o m n u m b e r u s e r b u d g e t s e l e c t e d s e q u e n c e n u m b e r l e v e l 1 r a n d o m n u m b e r m o d f i r s t r a n d o m n u m b e r p o l y n o m i a l f u n c t i o n l e v e l 2 r a n d o m n u m b e r m o d f i r s t r a n d o m n u m b e r s e l e c t e d s e q u e n c e n u m b e r f i r s t r a n d o m n u m b e r p o l y n o m i a l f u n c t i o n l e v e l 3 r a n d o m n u m b e r m o d f i r s t r a n d o m n u m b e r s e l e c t e d s e q u e n c e n u m b e r f i r s t r a n d o m n u m b e r o b f u s c a t e v a l u e l e v e l 1 l e v e l 2 l e v e l 3 p o l y n o m i a l r e s u l t f r o m l e v e l 1 p o l y n o m i a l r e s u l t f r o m l e v e l 2 figure 1: the rs3 obfuscation algorithm obfuscation process i.e. p(x ) = 1. however, if noise codes ei (fake obfuscated values) are added to the agent, where i = 1, 2, . . . , 100−1, the probability of discovering the actual obfuscated value is 1100 . the probability of guessing the actual obfuscated value becomes smaller as more noise codes are added. figure 2 illustrates the effect of introducing noise codes into the agent application. in addition, the time needed to guess the actual obfuscated value will delay the malicious host in analysing the obfuscated algorithm. therefore, the use of an effective protection interval in enhancing the obfuscated algorithm protection will be less important. 3.1 implementing rs3 obfuscated algorithm with noise code the operation of the rs3 obfuscated algorithm with noise code is almost the same as the operation of the rs3 obfuscated algorithm without noise code (refer to [1]). the only difference between these two obfuscated algorithms is in the number of obfuscated values generated and added by the master agent into the slave agent application before the slave agent is dispatched to the remote host execution environment to execute its tasks. in the operation of rs3 obfuscated algorithm without noise code, the master agent only has to obfuscate the value of user’s budget and add the obfuscated value into the slave agent application before dispatching the slave agent to execute its tasks in the remote host execution environment. however, in the operation of rs3 obfuscated algorithm with noise code, the master agent has to generate more than one obfuscated value (the extra obfuscated values serve as noise codes) and add these obfuscated values 162 kamalrulnizam abu bakar, bernard s. doherty 1 2 3 4 5 6 7 8 9 1 0 1 2 3 4 5 6 7 8 9 1 0 a n a g e n t w i t h o n e o b f u s c a t e d v a l u e a n a g e n t w i t h a n u m b e r o f o b f u s c a t e d v a l u e s n o i s e c o d e ( a n u m b e r o f f a k e o b f u s c a t e d v a l u e s ) + t h e r e a l o b f u s c a t e d v a l u e figure 2: the effect of adding noise codes into the agent application into the slave agent application before dispatching the slave agent to execute in the remote host execution environment (see figure 3). v e c t o r h o s t a d d r e s s = n e w v e c t o r ( ) ; d o u b l e n e w o f f e r = 0 ; d o u b l e b e s t o f f e r 1 , b e s t o f f e r 2 , b e s t o f f e r 3 ; u r l b e s t s h o p 1 , b e s t s h o p 2 , b e s t s h o p 3 ; i f ( n e w o b f u s c a t i o n v a l u e < = o b f u s c a t i o n v a l u e 1 ) ) / / f a k e o b f u s c a t e d v a l u e { b e s t o f f e r 1 = n e w o f f e r ; b e s t s h o p 1 = h o s t a d d r e s s ; } e l s e i f ( n e w o b f u s c a t i o n v a l u e < = o b f u s c a t i o n v a l u e 2 ) ) / / t r u e o b f u s c a t e d v a l u e { b e s t o f f e r 2 = n e w o f f e r ; b e s t s h o p 2 = h o s t a d d r e s s ; } e l s e i f ( n e w o b f u s c a t i o n v a l u e < = o b f u s c a t i o n v a l u e 3 ) ) / / f a k e o b f u s c a t e d v a l u e { b e s t o f f e r 3 = n e w o f f e r ; b e s t s h o p 3 = h o s t a d d r e s s ; } figure 3: a slave agent program added with noise code (data block) to illustrate, the noise code is a fake obfuscated value that is generated by the master agent from a fake user budget value. this fake user budget value is created by the master agent by adding or subtracting a random number to or from the actual user budget value. for example, say the actual user budget value is č500. to create a fake user budget value, the master agent needs to generate a random an enhancement of the random sequence 3-level obfuscated algorithm for protecting agents against malicious hosts 163 number, e.g. 176. if the master agent chooses to add the random number to the actual user budget value, the fake user budget value becomes 676. this value is then converted to the obfuscated value to represent the fake obfuscated value. on the other hand, if the master agent chooses to subtract the random number with the actual user budget value, the fake user budget value becomes 324. the same conversion process will be applied to convert the fake user budget value to a fake obfuscated value. to generate more fake obfuscated values, the master agent has to generate more random numbers. once the obfuscation process in the home host is completed, the master agent dispatches the slave agent together with all the obfuscated values generated (including fake obfuscated values) to the remote host to execute its given tasks. in the remote host execution environment, the slave agent starts its execution process by converting any offer that was gathered from the remote host into an obfuscated value to be used in the comparing process. for example, if the slave agent has 4 obfuscated values (one actual and three fake values), the slave agent has to execute 4 comparing processes. if any of the obfuscated user budget value matches the obfuscated offer value, the corresponding obfuscated user budget value will be excluded from the obfuscation stage (see figure 4). the next obfuscation stage starts by obfuscating a new remote host offer. in the authors’ work, a maximum of three obfuscation stages are used to searches for a flight offer (the obfuscated user budget value is compared with the first class obfuscated fare value in the obfuscation stage 1, business class obfuscated fare value in the obfuscation stage 2 and economy class obfuscated fare value in the obfuscation stage 3) or less than three times if all the obfuscated user budget values have been excluded. o b f u s c a t i o n v a l u e 1 ( f a k e ) o b f u s c a t i o n v a l u e 2 ( t r u e ) o b f u s c a t i o n v a l u e 3 ( f a k e ) o b f u s c a t i o n v a l u e 4 ( f a k e ) o f f e r 1 r s 3 o b f u s c a t i o n a l g o r i t h m o f f e r 1 � s o b f u s c a t i o n v a l u e o b f u s c a t i o n v a l u e 2 ( t r u e ) o b f u s c a t i o n v a l u e 3 ( f a k e ) o b f u s c a t i o n v a l u e 4 ( f a k e ) o f f e r 2 r s 3 o b f u s c a t i o n a l g o r i t h m o f f e r 2 � s o b f u s c a t i o n v a l u e o b f u s c a t i o n v a l u e 3 ( f a k e ) o f f e r 3 r s 3 o b f u s c a t i o n a l g o r i t h m o f f e r 3 � s o b f u s c a t i o n v a l u e m a t c h n o t m a t c h n o t m a t c h n o t m a t c h n o t m a t c h m a t c h m a t c h n o t m a t c h o b f u s c a t i o n s t a g e 1 o b f u s c a t i o n s t a g e 2 o b f u s c a t i o n s t a g e 3 c o m p a r i n g p r o c e s s c o m p a r i n g p r o c e s s c o m p a r i n g p r o c e s s c o m p a r i n g p r o c e s s c o m p a r i n g p r o c e s s c o m p a r i n g p r o c e s s c o m p a r i n g p r o c e s s c o m p a r i n g p r o c e s s figure 4: the illustration of the obfuscation stages and the comparing process after completing the execution process in the remote host, the slave agent returns to its home host together with the remote host offer. the home host then extracts only the offer that fulfils the requirement of the true obfuscated value for further actions. 164 kamalrulnizam abu bakar, bernard s. doherty 3.2 the deobfuscation time of the rs3 obfuscated algorithm with noise code the experiment on the deobfuscation time of the rs3 obfuscated algorithm with noise code is conducted to examine the time taken by the execution host (assumed to be the malicious host) to deobfuscate a full set of obfuscated value. the experiment is conducted using one 700 mhz personal computer with 128 mb of main memory, which running windows 98 operating system. in this experiment, the deobfuscation time is taken starting from the start of the deobfuscation process for the first obfuscated value and ending when the last obfuscated value from a set of obfuscated value have been deobfuscated. the experiment is performed by the execution host by executing the rs3 obfuscated algorithm using different input value many times until the matching obfuscated value is produced and continue until a full set of the obfuscated value are deobfuscated (see figure 5). twenty sets of obfuscated values are examine with the number of values in each set ranging from five to one thousand obfuscated values. each set of obfuscated values contained one correct value and the remainder false values. r s 3 o b f u s c a t e d a l g o r i t h m a s e t o f i n p u t v a l u e s o b f u s c a t e d v a l u e p r o d u c e d a s e t o f o b f u s c a t e d v a l u e s s u p p l i e d c o m p a r i n g p r o c e s s m a t c h ? e n d s t a r t n o y e s figure 5: the deobfuscation process the deobfucation time results (which were gathered in milliseconds and then converted into seconds) of the rs3 obfuscated algorithm with noise code are shown in table 1 and 2, and illustrated in figure 6 and 7 respectively. an enhancement of the random sequence 3-level obfuscated algorithm for protecting agents against malicious hosts 165 obfuscated values 5 10 15 20 25 30 35 40 45 50 deobfuscation time 8.95 21.57 29.42 37.69 49.96 60.71 62.53 72.46 81.66 85.58 table 1: summary statistics of the random sequence 3-level obfuscated algorithm deobfuscation time for small number of obfuscated values obfuscated values 100 200 300 400 500 600 700 800 deobfuscation time 180.15 376.33 581.28 779.12 982.06 1142.27 1376.64 1568.75 obfuscated values 900 1000 deobfuscation time 1765.94 1887.83 table 2: summary statistics of the random sequence 3-level obfuscated algorithm deobfuscation time for large number of obfuscated values p r o t e c t i o n t i m e f o r t h e r s 3 o b f u s c a t e d a l g o r i t h m w i t h n o i s e 0 2 0 4 0 6 0 8 0 1 0 0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 n u m b e r o f o b f u s c a t e d v a l u e s t e s t e d ti m e i n se co nd s d e o b f u s c a t i o n t i m e figure 6: the deobfuscation time of the random sequence 3-level obfuscated algorithm for small number of obfuscated values from the results given in table 1 and 2, and illustrated in figure 6 and 7 respectively, it can be seen that the deobfuscation time of the rs3 obfuscated algorithm with noise code increases linearly with the number of obfuscated values in the test set. note that even after deobfuscation, the malicious host is left with a set of values of which only one is the correct value, and has no way of knowing which one is the correct value. 166 kamalrulnizam abu bakar, bernard s. doherty p r o t e c t i o n t i m e f o r t h e r s 3 o b f u s c a t e d a l g o r i t h m w i t h n o i s e 0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 n u m b e r o f o b f u s c a t e d v a l u e s t e s t e d ti m e i n se co nd s d e o b f u s c a t i o n t i m e figure 7: the deobfuscation time of the random sequence 3-level obfuscated algorithm for large number of obfuscated values 4 the overhead of implementing noise code with the rs3 obfuscated algorithm the experiments to measure the overhead of implementing the rs3 obfuscated algorithm with noise code were conducted using six 400 mhz sun ultra sparc 5 workstations with 128 mb of main memory. each of the workstations is running the solaris 8 operating system and is connected to the others using 100 mbit/s utp1 cable. all of the workstations involved in this experiment were situated in the same room. in this configuration, one workstation was chosen among the six workstations to be the home host for the agent, and only this host had permission to manage and dispatch the agent. the rest of the workstations were assumed to be remote hosts, having only the capability to receive the agent and to dispatch the agent back to its home host. to examine the security overhead in implementing noise code with the random sequence 3-level obfuscated algorithm in an agent-based application, times are measured starting from sending of the agents to the remote hosts and ending with the home host receiving the agents back from the remote hosts. four different experiments are conducted starting with one remote host, two remote hosts, three remote hosts and five remote hosts using three different kinds of agent: a plain agent2, an agent with the random sequence 3-level obfuscated algorithm (rs3) and an agent with the rs3 obfuscated algorithm with noise code (rs3n). the times were measured using the “system.currenttimemillis()” method in the java language. this method produces a specific instant in time with millisecond precision [7]. the experiment is performed for 20 runs3 and the time for each run gathered in milliseconds. the average result of the 20 runs is taken and converted into seconds. the result is then rounded and presented in two decimal places as shown and illustrated in tables 4 to 7 and figure 8 to 11 respectively. the results of the security overhead of the random sequence 3-level obfuscated algorithm without noise (rs3) and the random sequence 3-level obfuscated algorithm with noise are compared to the plain agent as shown in table 3. from these results, it can be seen that there is not much difference in 1unshielded twisted pair category 5e 2agents without security mechanisms 3the results were observed to be consistent after twenty runs an enhancement of the random sequence 3-level obfuscated algorithm for protecting agents against malicious hosts 167 number of mean remote hosts plain rs3 rs3n 1 1.49 1.5 1.69 2 2.45 2.52 2.67 3 3.36 3.56 3.63 5 5.42 5.43 5.66 table 3: summary statistics of the random sequence 3-level obfuscated algorithm (1 cycle, 1 obfuscation value experiment(without noise code) and 1000 obfuscation value experiment(with noise code)) the security overhead for the rs3 without noise and rs3 with noise for all numbers of remote hosts. the highest difference is for one remote host, where the security overhead for rs3 with noise is just 12.67 % higher than the security overhead of rs3 without noise. therefore, it is concluded that rs3 with noise will add a small and acceptable overhead. more detail of the results now follows: firstly, consider plain and rs3, then plain and rs3n experimental results. number of mean standard error standard deviation remote hosts plain rs3 plain rs3 plain rs3 1 1.49 1.5 0.001 0.001 0.003 0.004 2 2.45 2.52 0.003 0.005 0.011 0.022 3 3.36 3.56 0.003 0.004 0.011 0.02 5 5.42 5.43 0.007 0.006 0.031 0.025 table 4: summary statistics of the random sequence 3-level obfuscated algorithm overhead (1 cycle and 1 obfuscation value experiment(without noise code)) number of mean standard error standard deviation remote hosts plain rs3n plain rs3n plain rs3n 1 1.49 1.54 0.001 0.001 0.003 0.003 2 2.45 2.52 0.002 0.007 0.011 0.031 3 3.36 3.51 0.003 0.005 0.011 0.022 5 5.42 5.44 0.007 0.007 0.031 0.031 table 5: summary statistics of the random sequence 3-level obfuscated algorithm (1 cycle and 100 obfuscation value experiment(with noise code)) from the results given in tables 4 and 5 and illustrated in figure 8 and 9 respectively, it can be seen that the mean of the security overhead for a plain agent is almost the same as the security overhead for rs3 and rs3n, where when comparing with rs3, the highest difference is just 5.95 % and 4.46 % when comparing with rs3n. however, from table 6 and illustrated in figure 10, it can be seen that as the number of noise codes is increased, the difference becomes larger, where the highest difference is 13.42 %, which is still considered acceptable. from table 7 and figure 11, the security overhead for both the agents is almost the same as the 168 kamalrulnizam abu bakar, bernard s. doherty s e c u r i t y o v e r h e a d f o r i m p l e m e n t i n g t h e r a n d o m s e q u e n c e 3 l e v e l o b f u s c a t e d a l g o r i t h m 0 1 2 3 4 5 6 1 2 3 5 n u m b e r o f r e m o t e h o s t s ti m e i n se co nd s p l a i n r s 3 figure 8: security overhead of the random sequence 3-level obfuscated algorithm (1 cycle and 1 obfuscation value experiment(without noise code)) s e c u r i t y o v e r h e a d f o r i m p l e m e n t i n g t h e r a n d o m s e q u e n c e 3 l e v e l o b f u s c a t e d a l g o r i t h m 0 1 2 3 4 5 6 1 2 3 5 n u m b e r o f r e m o t e h o s t s ti m e i n se co nd s p l a i n r s 3 n figure 9: security overhead of the random sequence 3-level obfuscated algorithm (1 cycle and 100 obfuscation value experiment(with noise code)) security overhead given in tables 4 to 6, even though now the number of cycles4 has been increased to 100. these results show that implementation of the random sequence 3-level obfuscated algorithm with noise code does increase the overhead by up to 13.42 % compared to the plain agent, but the noise code adds little to the overhead. 5 the analysis of rs3 obfuscated algorithm strength with noise code implementation to analyse the strength of the rs3 obfuscated algorithm after the implementation of noise code, the authors have listed vulnerabilities and the way to address them as follows: 4a loopings that simulate an agent tasks an enhancement of the random sequence 3-level obfuscated algorithm for protecting agents against malicious hosts 169 number of mean standard error standard deviation remote hosts plain rs3n plain rs3n plain rs3n 1 1.49 1.69 0.001 0.004 0.003 0.016 2 2.45 2.67 0.003 0.006 0.011 0.028 3 3.36 3.63 0.003 0.003 0.011 0.015 5 5.42 5.66 0.007 0.006 0.031 0.028 table 6: summary statistics of the random sequence 3-level obfuscated algorithm (1 cycle and 1000 obfuscation value experiment(with noise code)) number of mean standard error standard deviation remote hosts plain rs3n plain rs3n plain rs3n 1 1.48 1.65 0.0003 0.002 0.001 0.008 2 2.46 2.65 0.002 0.003 0.01 0.014 3 3.45 3.67 0.007 0.005 0.03 0.021 5 5.46 5.67 0.013 0.004 0.06 0.02 table 7: summary statistics of the random sequence 3-level obfuscated algorithm (100 cycle and 1000 obfuscation value experiment(with noise code)) s e c u r i t y o v e r h e a d f o r i m p l e m e n t i n g t h e r a n d o m s e q u e n c e 3 l e v e l o b f u s c a t e d a l g o r i t h m 0 1 2 3 4 5 6 1 2 3 5 n u m b e r o f r e m o t e h o s t s ti m e i n se co nd s p l a i n r s 3 n figure 10: security overhead of the random sequence 3-level obfuscated algorithm (1 cycle and 1000 obfuscation value experiment(with noise code)) 5.1 the vulnerabilities of rs3 obfuscated algorithm with noise code there are two main weaknesses of the rs3 obfuscated algorithm with noise code that have been identified. • if the attacker (malicious host) takes a guess at the correct obfuscated value from many obfuscated values (including noise code) carried by the visiting agent and the attacker is given enough time to execute, the attacker can execute the rs3 obfuscated algorithm many times using different result values and watch the pattern of the rs3 obfuscated algorithm outcomes (which result value the 170 kamalrulnizam abu bakar, bernard s. doherty s e c u r i t y o v e r h e a d f o r i m p l e m e n t i n g t h e r a n d o m s e q u e n c e 3 l e v e l o b f u s c a t e d a l g o r i t h m 0 1 2 3 4 5 6 1 2 3 5 n u m b e r o f r e m o t e h o s t s ti m e i n se co nd s p l a i n r s 3 n figure 11: security overhead of the random sequence 3-level obfuscated algorithm (100 cycle and 1000 obfuscation value experiment(with noise code)) agent accepts and which it rejects) to analyse the chosen obfuscated value in order to discover the actual value of agent’s critical data. • if the noise code generated from the actual value are very much out of range from the reasonable value, the malicious host could easily omit that values and take a guess from fewer values. 5.2 addressing vulnerabilities of rs3 obfuscated algorithm with noise code in order to overcome the weaknesses of the rs3 obfuscated algorithm with noise code, two main points are suggested: • to prevent the attacker (malicious host) from being able to guess a correct obfuscated value among many obfuscated values in a short time, and running many tests on rs3 obfuscated algorithm by analysing which result value the agent accepts and which it rejects, the number of noise codes added in the agent application, could be made bigger. this is due to the fact that the probability to guess the correct obfuscated value becomes smaller as the number of noise code added become bigger, i.e. p(x ) → 0 as n → ∞. the malicious host can at best guess the actual value from among the noise code. • to prevent the malicious host able to omit any of the noise code values, the value of the noise codes must be within a reasonable range of the actual value and this can be done by limiting the range of the random numbers. 6 discussion in this paper, the problem of a malicious host spying on the actual value of an agent’s critical data, such as the user maximum budget, has been discussed. the random sequence 3-level obfuscated algorithm, which is able to obfuscate the actual value of an agent’s critical data in order to make it more difficult for the malicious host to spy on the actual value of the critical data, has been previously proposed by the authors. to address weaknesses in the rs3 obfuscated algorithm, an enhancement has been proposed in this paper. an enhancement of the random sequence 3-level obfuscated algorithm for protecting agents against malicious hosts 171 the implementation of the rs3 obfuscated algorithm alone exposes the obfuscated algorithm to the attacker that could execute multiple copies of the obfuscated algorithm many times to analyse the algorithm. the agent owner could also face the problem of determining an effective protection interval for the obfuscated value that is carried by the agent in order to prevent the value from being analysed and discovered by the malicious host. these problems can be overcome by introducing noise codes carried by the agent application to force a malicious host to guess the actual obfuscated value, thus leaving a malicious host with at best a guess at the actual value of the user maximum budget. the noise code implementation also delays the malicious host in analysing the obfuscated algorithm, which has been shown in section 3.2. therefore, the use of an effective protection interval to enhance the level of obfuscated algorithm protection is less important. on the other hand, based on the experimental results on the overhead of implementing the rs3 obfuscated algorithm with noise code, the implementation of the rs3 obfuscated algorithm does increase the overhead by up to 13.42 % compared to the plain agent, but this is considered acceptable. this suggests the random sequence 3-level obfuscated algorithm can be implemented in “real world” agent applications to protect the agent application from the spying attack by the malicious host. experiment shown that adding noise codes to the rs3 obfuscated algorithm gives little (12.67 %) increase in security overhead. 7 conclusion the random sequence 3-level obfuscated algorithm is an algorithm that improves the level of protection of an agent against malicious host spying attack. this obfuscated algorithm does not protect against all spying attacks by the malicious host, only an attack to the agent’s critical data. however, the implementation of noise code in the agent application prevents the malicious host discovering the actual value of critical data carried by the agent; the malicious host can at best guess the actual value from among a number of noise values. the addition of noise code has strengthened the protection of the obfuscated algorithm and has reduced the likelihood of successful attack on the rs3 obfuscated algorithm, with very small increase in execution time. references [1] abu bakar, k. and doherty, b. s.: a random sequence 3-level obfuscated algorithm for protecting mobile agents against malicious hosts. proceedings of the 2003 international conference on informatics, cybernetics and systems. i-shou university(2003) 525 – 530 [2] divincenzo, d. p., leung, d. w. and terhal, b. m.: quantum data hiding. ieee transactions on information theory, vol. 48, no. 3. ieee(2002) 580–598 [3] farmer, w.m. and guttman, j.d. and swarup, v.: security for mobile agents: issues and requirements. proceedings of the 19th national information system security conference. baltimore (1996) 591–597 [4] harmsen, j. j. and pearlman, w. a.: steganalysis of additive noise modelable information hiding. proceedings of spie electronic imaging 5022. spie (2003) 21–24 [5] hohl, f.: a framework to protect mobile agents by using reference states. in: proceedings of the 20th international conference on distributed computing systems (icdcs 2000). ieee computer society (2000) 410-417 172 kamalrulnizam abu bakar, bernard s. doherty [6] hohl, f.: time limited blackbox security: protecting mobile agents from malicious hosts. in: g. vigna (ed.). mobile agent and security. lecture notes in computer science, vol. 1419. springerverlag, berlin(1998) 92–113 [7] sun microsystems, inc. java 2 platform std. ed. v1.3.1 http://java.sun.com/j2se/ 1.3/docs/api/index.html (2004) [8] mandry, t., pernul, g. and rohm, a.: mobile agents in electronic markets: opportunities, risks, agent protection. international journal of electronic commerce. m.e. sharpe (2001) 47–60 [9] ng, s. k. and cheung, k. w.: protecting mobile agents against malicious hosts by intention spreading. in h. arabnia (ed.), proc. international conference on parallel and distributed processing techniques and applications (pdpta’99). csrea (1999) 725–729 [10] ng, s. k. and cheung, k. w.: intention spreading: an extensible theme to protect mobile agents from read attack hoisted by malicious hosts. in jimming liu, ning zhong(ed.), intelligent agent technology: systems, methodologies and tools. world scientific (1999) 406–415 [11] sander, t. and tschudin, c.: protecting mobile agent against malicious hosts. in: g. vigna (ed.). mobile agent and security. lecture notes in computer science, vol. 1419. springer-verlag, berlin(1998) 44–60 [12] yeh, w. h. and hwang, j. j.: hiding digital information using a novel system scheme. computers and security, vol. 20, no. 6. elsevier science (2001) 533–538 kamalrulnizam abu bakar faculty of computer science and information system universiti teknologi malaysia 81310 utm skudai johor d. t. malaysia e-mail: kamarul@fsksm.utm.my bernard s. doherty school of engineering and applied science aston university aston triangle, birmingham b4 7et united kingdom e-mail: b.s.doherty@aston.ac.uk received: july 22, 2006 kamalrulnizam abu bakar is a lecturer at universiti teknologi malaysia, malaysia. he received the diploma and degree of computer science in 1994 and 1996 respectively from universiti teknologi malaysia, malaysia. he then received masters in computer communication and networks degree from leeds metropolitan university, united kingdom in 1998 and phd in network security from aston university, united kingdom in 2004. his current research interests include computer and network security, distributed systems and parallel processing, grid computing, wireless and cellular network. an enhancement of the random sequence 3-level obfuscated algorithm for protecting agents against malicious hosts 173 bernard s. doherty (born october 2nd, 1945) obtained the degrees of bachelor of engineering (electrical), bachelor of arts and master of engineering science from the university of melbourne in 1967, 1971 and 1981 respectively. he has held positions with the state electricicity commission of victoria, lm ericsson pty ltd, swinburne college of technology (all in melbourne) and, since 1980, at aston university (birmingham, uk), where is presently lecturer in computer science. his main fields of teaching and research are distributed and networked applications and information security. in addition to supervising a number of doctoral students, he has developed computer-based administration and teaching software, written a number of papers and presented papers at international conferences. international journal of computers, communications & control vol. ii (2007), no. 3, pp. 241-251 hybrid control accommodation for water-asset management of hydraulic systems subjected to large operating conditions eric duviella, pascale chiron, philippe charbonnaud abstract: the hybrid control accommodation (hca) strategy was proposed to improve the water-asset management of hydraulic systems by resource allocation and setpoint assignment. hydraulic system dynamics are taken into account during the setpoint assignment step which consists in controlling gates for large operating conditions. for hydraulic systems subjected to strong disturbances, transfer delays are variable, thus different operating modes must be considered. a multimodelling method, associated to a selection technique of transfer delay, allowing for the determination of the number of models, is proposed. the simulation results on the first reach of the neste canal show the effectiveness of the hca strategy. keywords: hybrid control accommodation, resource allocation, setpoint assignment, multimodelling, water management. 1 introduction hydrographic networks naturally convey water quantities upstream to downstream. they are equipped with dams, catchment areas, channels, etc., and are instrumented for water resource management and satisfaction of human activity needs. several control methods have been designed [9], and are still designed [8, 2], to supply hydraulic systems with water quantities corresponding to the management objectives and rejecting disturbances. other techniques, such as lpv regulation [10] or supervised internal multimodel controller [3] were recently proposed in order to consider these systems for different operating points. these control methods are accurate for a local control. however, they are not designed to allocate water quantities in excess towards the catchments areas, and water quantities in lack amongst the users. an original proposition for the management of such situations consists in recalculating setpoints according to the resource value (continuous dynamics) and to the resource state (discrete events) of the hydrographic systems. the supervision and hybrid control accommodation strategy by resource allocation and setpoint assignment proposed in [4] allows the water-asset management to take into account the management constraints. the strategy efficiency has been shown by simulation, in the case of an open-channel hydraulic system considering large operating conditions. the problem addressed in this paper deals with the water-asset management of a hydraulic system subjected to scenarii with large disturbances. in the second section, the supervision and hybrid control accommodation strategy, well adapted to consider large operating conditions, is presented. in the third section, a multimodelling method is proposed for determining the number of operating modes (om) necessary to represent open-surface hydraulic system dynamics, accurately. for each om, the transfer delay identification is done in the fourth section. finally, the effectiveness of this proposed strategy is shown by simulation on the first reach of the neste canal. 2 hybrid control accommodation hybrid control accommodation (hca) strategy (see figure 1) allows the computation of new setpoints for each gate g j of hydraulic systems according to data measured on the measurement points mi (see figure 2). the setpoints are computed according to the resource state ei, and taking into account management objectives q j ob j, λ j and µ j weekly fixed by the management objective generation module. copyright © 2006-2007 by ccc publications 242 eric duviella, pascale chiron, philippe charbonnaud figure 1: hca strategy scheme. figure 2: open-surface hydraulic system equipped with measurement points mi and gates g j. hybrid control accommodation for water-asset management of hydraulic systems subjected to large operating conditions 243 the resource state ei determination is carried out at the detection period te by concurrent hybrid automata (see figure 3) designed for each measurement point mi [4]. the five pertinent states retained correspond respectively to no-discrepancy state e0, two states where the discharge discrepancy is either positive (e+) or negative (e−) and constant c, and two states where the discharge discrepancy is either positive (e+) or negative (e−) and no constant ¬c. transitions between states are defined as conditions on the measured discharge value and variation:    di : [|∆qmi| > thi] , ψi : [∆qmi < 0] , ωi : [∣∣q̇mi ∣∣ < dthi ] , (1) with ∆qmi = qmi − n ∑ j=ni q j ob j, where qmi is the measured discharge, q j ob j is the management objective of the gate j, ni the index of the first gate downstream the ith measurement point mi, n the number of gates, q̇mi the estimate derivative of qmi , thi and dthi respectively the detection and diagnosis thresholds. the discharge discrepancy allocation is carried out according to the resource state. when the state is e0, the gate setpoints correspond to their discharge objective q j ob j. when the state is e±∧c, the discharge discrepancy ∆qmi is allocated amongst gates, according to their weights λ j and µ j, by optimizing, for each measurement point, a cost function. finally, when the state is e±∧¬c, the discharge discrepancy is allocated on gate each one on its turn [4]. at each detection date kte, the discharge discrepancy allocation leads to the allocation vector qkmi : qkmi = [ 0 0 . . . 0 qkni q k ni+1 . . . qkn ]t , (2) figure 3: hybrid automata for the measurement point mi. then, to synchronize the control of the gate with the appearance of lacks or excess of the water due to the disturbances, the setpoints must be assigned at a date taking into account the transfer delays tmi, j between the measurement point mi and the gate g j. it is computed according to the relation (3): tmi, j = tmi,ni + j ∑ r=ni+1 tr , (3) where tr is the time necessary for the water quantity to go from gate gr−1 to gate gr (see figure 4). the delay tr depends on the physical characteristics of the hydraulic system and on the discharge value qr. thereafter, the allocation vector qkmi is associated with an allocation dates vector t arising 244 eric duviella, pascale chiron, philippe charbonnaud figure 4: time delays between mi and g j. from the transfer delay values tmi, j. finally, taking into account the allocation date, the setpoints are sent to gates periodically. the control period tc is chosen as a multiple integer of te. in the next section, a multimodelling method is proposed to identify the system dynamics for several om. 3 multimodelling steps the modelling of the free-surface hydraulic system dynamics is generally carried out starting from the diffusive wave equation (4) which is obtained by simplifying saint venant equations [1]. dq dt + c dq dx −d d2q dx2 = 0 , (4) where c and d are respectively the celerity and diffusion coefficients. the diffusive wave equation can be linearized according to an operating discharge qe [7], and the identified celerity and diffusion parameters, denoted ce and de, are expressed as:    ce = 1l2 ∂ j∂ qe [ ∂ l ∂ x − ∂ jl∂ z ] , de = 1l ∂ j∂ qe , (5) where l is the surface width, z is the discharge depth, j is the friction slope expressed with the manning-strickler relation as j = q 2p 4 3 k2s 10 3 where k is the strickler coefficient, p is the wetted perimeter and s the wetted surface. the open-channel systems dynamics can be modelled by transfer functions, as: f(s) = e −τ s 1+w1s+w2s2 , (6) where the coefficients w1, w2 and the pure delay τ are computed according to the coefficients ce, de, to the open-channel system length x , and to the adimensional coefficient cl = 2cex 9de ; if cl ≤ 49 , w2 = 0 and τ = 0, if 49 < cl ≤ 1, w2 = 0. this modelling method allows the identification of the free-surface hydraulic system dynamics with good accuracy only around an operating point. however, setpoint assignment must be done for hydraulic system subjected to large operating conditions. thus, the hydraulic system dynamics must be identified for different om. based on the previous modelling method, the multimodelling approach consists in determining the number of om and their corresponding operating points. the celerity coefficient c is a very relevant parameter of the hydraulic system dynamics. the model identified for each om is available as soon as the error on the celerity coefficient is less than a predefined percentage πc. thus, a validity domain is defined for each om, and the number of om which are necessary to identify the dynamics with a good accuracy, is determined. to limit the switching between two om, an interval ∆c is shared by two successive om validity domains. the selection of parameters πc and ∆c is carried out taking into account the system dynamics. the multimodelling steps are described hybrid control accommodation for water-asset management of hydraulic systems subjected to large operating conditions 245 table 1: multimodelling algorithm. input: cmin, cmax, πc, ∆c, output: cidr , cin fr , csupr , cmed = cmin+cmax 2 , r = 1, for i: ⌊ ln cmin cmed ln 1+πc 1−πc ⌋ + 1 to ⌊ ln cmaxcmed ln 1+πc 1−πc ⌋ , cidr = ( 1+πc 1−πc )i cmed − sign(i)∆c1−sign(i)πc |i| ∑ j=1 ( 1+πc 1−πc )i−sign(i). j , csupr = (1 + πc) ( 1+πc 1−πc )i cmed −sign(i) (1+πc)∆c1−sign(i)πc |i| ∑ j=1 ( 1+πc 1−πc )i−sign(i). j , cin fr = (1−πc) ( 1+πc 1−πc )i cmed −sign(i) (1−πc)∆c1−sign(i)πc |i| ∑ j=1 ( 1+πc 1−πc )i−sign(i). j , r + +, endfor by an algorithm (see table 1), where the om are determined starting with cmed . this one is computed with the parameters cmin and cmax corresponding respectively to the minimum and maximum discharges of the system. the algorithm requires the definition of bxc which corresponds to the integer part of x, and sign(x) such as sign(x) = |x|x . then, according to cidr , the water elevation zidr of each r th om is determined, with one millimeter accuracy, using the digital resolution of the relation (7) with the newton method. cid = √ jks 5 3 p 2 3 l2 [ −12 ∂ l∂ z − l3p ( 2 ∂ p∂ z −5 ps ∂ s∂ z )] . (7) then, the water elevation zidr value is used to compute the discharge qidr with the relation (8). the same steps are used for parameters cin fr and csupr to obtain the domain validity boundaries of the om: [qin fr ; qsupr [. qidr = √ jks 5 3 p 2 3 . (8) finally, the celerity and diffusion coefficients cidr and didr (5), and the transfer function parameters w1, w2 and τ (6) are computed according to the discharge qidr . these parameters allow for the computation and the selection of the transfer delays of each om. this is described in the next section. 4 transfer delay identification the hydraulic systems consist of several reaches, i.e. a part between two measurement points (see figure 5.a), each reach being composed of open-channel reach section (ocrs), i.e. a part between two gates, or between a measurement point and a gate or between a gate and a measurement point. thus, the reach dynamics are modelled with a concurrent hybrid automaton cha (see figure 5.b) composed of several ha defined for each ocrs. this representation is directly inspired from the concurrent hybrid automata proposed in [5, 6]. in the study case, the cha is deterministic. the input of the first automaton haoi of the first ocrs following mi is the discharge measured on mi. the input of the second automaton, haoi+1 is the discharge downstream haoi minus qni , the discharge setpoint assigned to the gate between this two ocrs. 246 eric duviella, pascale chiron, philippe charbonnaud figure 5: (a) a canal reach and (b) its modelling by concurrent hybrid automaton (cha). the ocrs dynamics (see figure 6.a) are modelled using the multimodelling method described in the previous section, and are represented by a ha (see figure 6.b). each state corresponds to one om identified as transfer function fl given by the relation (6). the transition conditions are defined according to the upstream discharge qo and the lth om boundaries qin fl and qsupl . figure 6: (a) ocrs with index o and (b) its modelling by hybrid automaton (ha). for each om, the transfer delay tr is obtained from the step response of the corresponding transfer function. it is chosen as the time value for which πq percent of step is reached. the percentage πq can be tuned from simulation. in the next section, the hca strategy is used to valorize water quantities of a hydraulic system subjected to large operating conditions. hybrid control accommodation for water-asset management of hydraulic systems subjected to large operating conditions 247 table 2: geometrical characteristics of each ocrs. ocrs b [m] f x [m] j [rad] qmin [m3/s] qmax [m3/s] 1 5.73 0.79 204 7.10−4 0.8 10 2 5.09 0.96 702 7.10−4 0.8 9 3 5.21 0.95 562 6.10−4 0.7 7 4 3.72 0.94 1360 5.10−4 0.6 5 5 simulation results the effectiveness of the proposed strategy is shown by simulation on the first reach of the neste canal located in gascogne, a french southwestern region. it is composed of one measurement point m1, three gates g1 to g3 and one output considered as a non-controlled gate g4 (see figure 7). this canal reach is composed of four ocrs with trapezoidal profile characterized by the bottom width b, the average fruit of the banks f , the profile length x and the reach slope i (see figure 8). the geometrical characteristics and operating conditions of each ocrs are detailed in table 2. the fourth ocrs, between g3 and g4, is not modelled, because gate g4 is not locally controlled. in the case of a trapezoidal profile, celerity and diffusion coefficients c and d are expressed by the following relation: figure 7: first reach of the neste canal. { c = ql2 [ − f + l3 ( 2b pz + 5l s − 2z )] d = q2lj , (9) where l = b + 2 f z, s = zb + f z2, p = b + 2z √ 1 + f 2. figure 8: geometrical characteristics of a trapezoidal profile. the multimodelling steps which are carried out with πc = 15 % and ∆c = 0.1, lead to the determination of three om for each ocrs. their corresponding transfer function parameters are given in table 3. the transfer delays tr are computed with a value πq = 63 %. this value, chosen by simulation, leads to the best water quantity valorization. 248 eric duviella, pascale chiron, philippe charbonnaud table 3: multiple models of each ocrs. ocrs q qmin [m3/s] qmax [m3/s] w1 w2 τ tr [s] 1.6 0.8 2.4 161 0 0 160 1 3.2 1.9 5.2 128 0 0 130 7.7 4.3 10 100 0 0 100 1.4 0.8 2.2 424 24331 145 575 2 3 1.7 4.9 364 0 89 450 7.4 4.1 9 355 0 0 355 1.2 0.7 1.9 377 0 129 505 3 2.4 1.5 4 357 0 29 385 5.8 3.2 7 320 0 0 320 the neste canal is supplied with water corresponding to an objective discharge equal to 7 m3/s. the objective discharges and the weights of each gate are displayed in table 4. the canal is subjected to strong withdrawals of 3 m3/s and 4.5 m3/s upstream to m1 (see figure 9.a). this scenario was selected among several simulated scenarii, because it reveals accurately the inversion phenomenon of the discharge tendency. the hca strategy goals consist in the allocation of the water resource amongst the gates according to their weights, and in the minimization of the discharge discrepancy at gate g4. the detection and control periods are selected as te = 12 s and tc = 120 s. in figure 9, the simulation results are shown in continuous line when the transfer delay selection method is used, and in dashed line when the transfer delays are fixed to 100 s for the first ocrs, to 355 s for the second and to 320 s for the third. these transfer delay values are selected according to the operating point of each ocrs, i.e. 7 m3/s, 5 m3/s and 4 m3/s. they correspond to the third om of each ocrs. 2 4 6 (a) q m 1 [ m 3 /s ] e+ c e+ ~c e0 e− ~c e− c (b) 0 1 2 (c) q 1 [ m 3 /s ] 0.5 1 (d) q 2 [ m 3 /s ] 0 1 2 (e) q 3 [ m 3 /s ] 0 2 4 6 8 10 12 14 16 18 2 2.05 (f) q 4 [ m 3 /s ] time[h] figure 9: discharge qm1 (continuous line) and objective discharge qm1ob j (dotted line) (a), resource states (b), setpoints assigned q j (continuous line when the delay selection method is used, dashed line in other case) and objective discharge q job j (dotted line) to g1 (c), g2 (d), g3 (e) and discharge at g4 ( f ). hybrid control accommodation for water-asset management of hydraulic systems subjected to large operating conditions 249 table 4: gate objective discharges q job j, gate weights λ j and µ j, and minimum and maximum discharges q jmin and q jmax . gate g1 g2 g3 g4 q job j [m3/s] 2 1 2 2 λ j 10 10 4 − µ j 10 4 10 − q jmin [m 3/s] 0.02 0.1 0.02 0.15 q jmax [m 3/s] 3.6 4.5 3.5 3 table 5: criteria computed when the transfer delay selection method is or not used. criterion min(q4 [m3/s]) max(q4 [m3/s]) v [m3] with selection method 1.9778 2.0451 541 without selection method 1.9763 2.0455 602 figure 9 displays the discharges qm1 in (a), the corresponding discharge states in (b), the setpoint assigned at gate g1 in (c), g2 in (d), g3 in (e) and the resulting discharge at g4 in (f). the resource in lack measured on m1 is allocated on gates g1 and g3, as long as their setpoints are upper than their respectively minimum discharge characteristics q jmin , otherwise it is allocated on gate g2 (see figures 9.c, 9.d and 9.e). thus, the gates g1 and g3 are controlled between the 1st and 4th hours, and the gate g2 between the 4th and 7th hours. during all the simulation time, the discharge on g4 is close to the objective value of 2 m3/s (see figure 9. f ). however, when the transfer delay selection method is not used, the setpoints are always assigned too early. consequently at g4, the discharge is in excess when the water resource decreases on m1, and the discharge is in lack when the water resource increases on m1. the tendency of qm1 discharge is inverted at g4 the end of the canal reach. the use of the transfer delay selection method improves the performances of the hca strategy and maintains the tendency. the maximum and minimum discharges reached at g4 and the water volume v which was not allocated, are displayed in table 5. the maximum discharge discrepancy at g4 corresponds to 2.26 % of the objective discharge q4ob j when the transfer delay selection method is used and to 2.28 % in the other case. 6 conclusion the hca strategy is adapted to valorize the water resource by allocation and setpoint assignment of open-surface hydraulic system submitted to strong disturbances. a multimodelling method is proposed to identify by a determined number of linear models the open-surface hydraulic systems. then, a transfer delay selection method is proposed to take into account variable transfer delays by the selection of the right setpoint assignment date according to the system dynamics and to the measured discharge. finally, the hca strategy performances are shown by simulation on the first reach of the neste canal subjected to large operating conditions and to strong disturbances. the study of control stability proof of the hca strategy is a future goal. 250 eric duviella, pascale chiron, philippe charbonnaud bibliography [1] chow, v. t., d. r. maidment and l. w. mays. applied hydrology. mcgraw-hill. new york, paris, 1988. [2] choy, s. and e. weyer (2005). reconfiguration schemes to mitigate faults in automated irrigation channels. cdc-ecc’05, 44 th ieee conference on decision and control and european control conference, seville, spain, 12-15 december, pp. 1875-1880, 2005. [3] duviella, e., p. charbonnaud, p. chiron and f. carrillo. supervised internal multi-model control of a dam-gallery open-channel system. cdc-ecc’05, 44 th ieee conference on decision and control and european control conference, seville, spain, 12-15 december, pp. 1887-1892, 2005. [4] duviella, e., p. chiron, p. charbonnaud and p. hurand. supervision and hybrid control accommodation for water-asset management. control engineering practice. vol. 15, issue 1, pp. 17-27, 2007. [5] funiak, s., l. j. blackmore and b. c. williams. gaussian particle filtering for concurrent hybrid models with autonomous transitions. csail memo, 2006. [6] hofbaur, m. w. and b. c. williams. hybrid diagnosis with unknown behavioural modes. international workshop on principles of diagnosis (dx-02), semmering, austria, may 2-4, pp. 97-105, 2002. [7] litrico, x. and d. georges. robust continuous-time and discrete-time flow control of a dam-river system. (i) modelling. applied mathematical modelling, vol. 23, pp. 809-827,1999. [8] litrico, x. and v. fromion. design of structured multivariable controllers for irrigation canals. cdcecc’05, 44 th ieee conference on decision and control and european control conference, seville, spain, 12-15 december, pp. 1881-1886, 2005. [9] malaterre, p.o., d.c. rogers and j. schuurmans. classification of canal control algorithms. journal of irrigation and drainage engineering, vol.124, issue 1, pp. 3-10, 1998. [10] puig, v., j. quevedo, t. escobet, p. charbonnaud and e. duviella. identification and control of an open-flow canal using lpv models. cdc-ecc’05, 44 th ieee conference on decision and control and european control conference, seville, spain, 12-15 december, pp. 1893-1898, 2005. eric duviella ecole des mines de douai département informatique et automatique 941, rue charles bourseul, bp 10838, 59508 douai cedex, france phone: 33.3.27.71.21.02 ; fax : 33.3.27.71.29.80 e-mail: duviella@ensm-douai.fr pascale chiron laboratoire génie de production ecole nationale d’ingénieurs de tarbes 47, avenue d’azereix, bp1629, 65016 tarbes cedex, france phone: 33.5.62.44.27.69 ; fax : 33.5.62.44.27.08 e-mail: pascale.chiron@enit.fr philippe charbonnaud laboratoire génie de production ecole nationale d’ingénieurs de tarbes 47, avenue d’azereix, bp1629, 65016 tarbes cedex, france hybrid control accommodation for water-asset management of hydraulic systems subjected to large operating conditions 251 phone: 33.5.62.44.27.34 ; fax : 33.5.62.44.27.08 e-mail: philippe.charbonnaud@enit.fr received: january 29, 2007 revised: may 22, 2007 eric duviella was born in france in 1978. in 2001, he received the diploma in engineering from the national engineering school, tarbes, france. he received the ph.d. degree from the institut polytechnique, toulouse, france, in 2005. since 2007, he has been an assitant professor in the department informatic and automatic of the ecole des mines, douai, france. he has published around 15 papers in journals or international conferences. his research interests include modelling, hybrid dynamical systems, supervision, reactive control strategy. pascale chiron was born in france in 1961. she is assistant professor at the ecole nationale d’ingénieurs de tarbes, france since 2000. she has obtained his phd in 1989 at ecole centrale in nantes on ’matching and similarity criterion in medical imaging’. now, she is involved in the theme ’planning, control, supervision and distributed simulation’ in the ’automated production’ group of laboratoire de génie de production. her domains of interest are modelling and simulation for system control. philippe charbonnaud was born in france in 1962. since 2003, he is full professor at the ecole nationale d’ingénieurs de tarbes. he is coordinator of the spanish-french project gepredo dealing with the management of hydrographical networks and the predictive management of the water resource. the main contribution lies in the development of a hybrid control method applied to distributed systems. his main topic of interest concerns the real-time decision support systems, and more particularly supervision and control accommodation of distributed systems. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 684-692 advanced information technology support of improved personalized therapy of speech disorders m. danubianu, s.g. pentiuc, i. tobolcea, o.a. schipor mirela danubianu, stefan gheorghe pentiuc, ovidiu andrei schipor “ştefan cel mare” university of suceava romania, 720229 suceava, 13 universităţii e-mail: {mdanub, pentiuc, schipor}@eed.usv.ro iolanda tobolcea “alexandru ioan cuza” university of iaşi romania, 700506 iasi, 11 bulevardul carol i e-mail: itobolcea@yahoo.com abstract: one of the key challenges of the sustainable development strategy adopted by the european council in 2006 is related to public health whose general objective envisages a good level of public health. one of the specific targets includes better treatments of diseases. it is true that there are affections which by their nature do not endanger the life of a person, however they may have a negative impact on her/his life standard. various language or speech disorders are part of this category, but if they are discovered and treated in due time, they can be often corrected. the difficulty for researchers and therapists is to identify those children who have disorders that show a wide range of issues that cannot be solved spontaneously or which may lead to further significant deficiencies. information technology in the latest years was used by specialists in order to assist and supervise speech disorder therapy. consequently they have collected a considerable volume of data about the personal or familial anamnesis, regarding various disorders or regarding the process of personalized therapies. these data can be used in data mining processes that aim to discover interesting patterns which can help the design and adaptation of different therapies in order to obtain the best results in conditions of maximum efficiency. the aim of this paper is to present the logo-dm system. this is a data mining system that can be associated with terapers system in order to use the data from its database as a source for analysis and to provide new information based on an improved system of therapy. through the use of appropriate techniques of data mining logo-dm realizes predictions on the evolution and the final status of patients undergoing therapy and enriches the knowledge data of expert system embedded in terapers. keywords: personalized therapy, data mining, classification, clustering, associations rules. 1 introduction various forms of speech disorders affect an important percent of people. there are affections which, by their nature, do not endanger the life of a person, however may have a negative impact on her/his life standard. discovered and treated in due time, they can be corrected, most often during childhood. the use of information technology in order to assist and supervise speech disorder ther-apy allows specialists to collect a considerable volume of data about the personal or familial anam-nesis, regarding various disorders or regarding the process of personalized therapy. copyright c⃝ 2006-2010 by ccc publications advanced information technology support of improved personalized therapy of speech disorders 685 even if these data can provide plenty of statistical information little useful knowledge can be obtained from it. in order to get such useful knowledge it is necessary to discover patterns in the data regarding the common characteristics of children with different types of diagnosis, about the connection between antecedents, personal and family behaviour and evolution of the child, or on the connection between the anamnesis and the response to different types of treatments or to different phases of the therapeutic process. these patterns are used to establish such a future strategy so as to maximize the benefits of the therapy and to minimize the costs. what are the speech disorders? a speech disorder is a problem with fluency, voice, and/or how a person utters speech sounds. classifying speech into normal and disorder is complex because the statistics points out that only 5% to 10% of the population has a completely normal manner of speaking, all others suffer from one disorder or another. the most common speech disorders are: stuttering, cluttering, voice disorders, dysartria and speech sound disorders. the speech disorder therapy should begin as soon as possible. children enrolled in therapy early in their development (younger than 5 years) tend to have better outcomes than those who begin therapy later. during the therapy, speech therapists use a variety of strategies including: oral motor or feeding therapy, articu-lation therapy and language intervention activities [2]. during the language intervention activities the therapist will interact with a child by playing and talking. he may use pictures, books, objects, or ongoing events to stimulate language development. the therapist may also model correct pro-nunciation and use repetition exercises to build speech and language skills. in the area of speech disorders there are some european projects developed as part of the eu quality of life and management of living resources program, like: olp (ortho-logo-paedia) pro-ject [8], star speech training, assessment, and remediation [12] [19], speechviewer iii developed by ibm [11] or artur (articulation tutor) [17] [18]. currently, the priorities at the international level focus on the development of information systems that can provide a personalised therapy. at the national level, little research has been conducted on the therapy of speech impairments [13]. terapers project [1] [2], developed with the financial support granted by the national agency for scientific research, contract ref. no. 56-ceex-ii03/27.07.2006 by the research center for computer science in the university "stefan cel mare" of suceava, aims to assist and support the speech disorder therapists in their efforts to develop personalized programs for the therapy of dyslalia. 2 data mining and its application in logopaedic area data mining is defined as the process of discovering non-obvious and potentially useful patterns in large data volumes. as exploration and analysis technique of large amounts of data in order to de-tect patterns or rules with a specific meaning, data mining may facilitate the discovery from appar-ently unrelated data, relationships that can anticipate future problems or might solve the studied problems. data mining represents one phase in the complex process of knowledge discovery in databases (kdd) [5]. according to crisp-dm [15], the reference model for this process, kdd consists of a sequence of steps. these steps are presented in figure 1. using appropriate methods, data mining can solve two broad categories of problems: prediction and description [10] [14]. the most used methods for prediction are classifications and regressions, and for description, clustering, deviation detection or association rules. the specific logopaedic tasks performed by data mining fall into the following categories [3]: • classification which places the people with different speech impairments in predefinited classes. thus it is possible to track the size and structure of various groups. we can use 686 m. danubianu, s.g. pentiuc, i. tobolcea, o.a. schipor figure 1: crisp_dm process of knowledge discovery in databases classi-fication which is based on the information contained in many predictor variables, such as per-sonal or familial anamnesis data or related to lifestyle, to join the patients with different seg-ments. • clustering which groups people with speech disorders on the basis of similarity of different features. it is an important task because it helps therapists understand their patients. cluster-ing aims to finding subsets of a predetermined segment, with homogeneous behavior to-wards various methods of therapy that can be effectively targeted by a specific therapy but it is not based on the previous definition of groups. • association rules aim to find out associations between different data which seem to have no semantic dependence. it may be a way to determine why a specific therapy program has been successful on a segment of patients with speech disorders and on the other was ineffective. to conclude with we state that data mining can be a useful tool. still, there is a limitation we have to consider. data mining applications generate information by analyzing patterns of data ob-tained from the systems which assist and supervise the speech therapy. such patterns can help pre-dict the evolution of the individuals that are currently in the process of therapy, or design a scheme of an appropriate therapy for them. however data mining technology can not provide information about impairments, people or behaviors that are not found in the databases that provide data for analysis. 3 logo-dm system 3.1 objectives the idea of trying to improve the quality of logopaedic therapy by applying some data mining tech-niques started from terapers project developed within the research center for computer sci-ence in the university "stefan cel mare" of suceava. this project has proposed to develop a system which is able to assist speech therapists in their speech therapy of dislalya and to asses how the pa-tients respond to various personalized therapy programs. starting in march 2008 the system is cur-rently used by the therapists from regional speech therapy center of suceava. at present, because of the limited time and the economical aspects involved, information regard-ing the therapy for each particular case is of interest [4]: what is the predicted final state for a child or what will be his/her state at the end of various stages of therapy, which the best advanced information technology support of improved personalized therapy of speech disorders 687 exercises are for each case and how they can focus their effort to effectively solve these exercises or how the family receptivity which is an important factor in the success of the therapy is associated with other as-pects of family and personal anamnesis. all this may be the subject of predictions obtained by ap-plying data mining techniques on data collected by using a computer based therapy system. it is also interesting, as part of the knowledge discovered by data mining algorithms, to be used to enrich the knowledge base of expert system embedded. to achieve this goal we propose the development of logo-dm system. consequently its objectives are: • analysis of data collected and their preprocessing in order to assure a proper quality for data mining algorithms • feature selection for the elimination of those irrelevant or redundant • the use of corresponding data mining methods and algorithms that can be applied in order to find models which can answer to problems raised in speech disorders therapy • models evaluation and their validation on new cases • to find new rules which can enrich the knowledge base of the expert system embedded in terapers 3.2 system architecture data mining aims at deriving knowledge from data. the architecture of a data mining system plays an important role in the efficiency with which data is mined. considering the characteristic of the domain we have proposed for the system a two tier client server architecture. this architecture is presented in figure 2. figure 2: logo-dm architecture on the client side there is the user interface (gui) which allows the user to communicate with the system in order to select the task to perform, to select and submit the datasets on which data mining needs to be applied. pattern evaluation and the post-processing step consisting in pattern visualiza-tion are performed also on the client. the knowledge base is the module where the background knowledge is stored. 688 m. danubianu, s.g. pentiuc, i. tobolcea, o.a. schipor the more difficult computational tasks of data mining operations are carried out on the server. here, the data mining kernel contains modules able to perform classifications and association rule detection. supplementary the pre-processing data module allows data to become suitable for apply-ing data mining algorithms. 3.3 some aspects regarding the system implementation it is well known that the best results of data mining algorithms are obtained by applying on data in data warehouses. but in this case the development of a data warehouse is not appropriate, so, it is used, as the primary source of data, a database that contains data collected from the different speech therapists’ offices. in order to choose the right solution for the implementation of the system we have made an analysis of available data both its structure and content. we have started from a scheme with over 60 tables and after deleting tables with irrelevant content for the intended purpose we have obtained, as underlying tables for the final data set, 27 tables as presented in figure 3. content analysis can reveal interesting issues related to data quality or the need for transformation. we have made a first assessment of data quality through the following measures: completeness, conformity, accuracy, consistency and redundancy. the mechanisms provided by the used da-tabase management system have imposed a minimum, controlled redundancy and have assured data consistency. values stored in fields correspond to reality, but unfortunately in some records useful data for analysis are missing. therefore it is necessary to supplement data gaps, and where not pos-sible, the removal of the record for accurate results is suggested. figure 3: the useful part of database schema proper data for the analysis are subjected to the following types of transformation: transformations of the structure, and changes aimed value. structural transformations are dictated by the fact that there are fields in the database contain-ing data related to a complex of features to be addressed individually in the analysis. values of transformations refer to the replacement of coded data by the rules, enabling, for example, the effective storage with descriptive values of characteristics allowing rapid interpretation of results. an example of these transformations is the following. an issue addressed in the anamnesis form is related to the skills of the child. in figure 4 we can see that there is a complex of skills of interest (verbal, perceptual, numeric, psycho-motor or special skills). in the database, all these skills are in two distinct fields: one for general skills, which groups data regarding verbal, perceptual, numeric, psycho-motor and intelligence skills and one for advanced information technology support of improved personalized therapy of speech disorders 689 figure 4: sample of anamnesis data special skills (figure 5). the field called ’aptitudini’ is numeric and is represented in the table by a string of five bits, as shown in figure 5. these bits, positioned from left to right, have the following meaning: • the first bit verbal skills (1present, 0absent) • the second bit perceptual skills (1present, 0absent) • the third bit numeric skills (1present, 0absent) • the fourth bit psycho-motor skills (1present, 0absent) • the fifth bit intelligence (1normal intelligence, 0 mental deficiency) figure 5: data to be transformed since all these attributes may affect the analysis it is desirable that they can be addressed indi-vidually and explicitly in the final data set. for this purpose the original table structure is changed and values are converted to descriptive values as in figure 6. these changes have conducted to a modified form of the relational database used by terapers. in the first phase, construction of target data sets for each of the methods to be applied in the system is through the application of relational expressions like those presented in (1).∏ ii (t1 ◃ ▹t2 ◃ ▹... ◃ ▹tk) (1) where: • ii is a superset of the attributes regarding the useful characteristics for each method • t1 . . . tk is the set of tables containing the attributes in the list of projection. 690 m. danubianu, s.g. pentiuc, i. tobolcea, o.a. schipor figure 6: transformed data each of these expressions was implemented in sql, and has generated intermediate tables. for example, the target data set necessary to establish the profile of children with speech disorders, can be obtained by joining tables which contain: general data about children, family and personal an-amnesis, data on complex evaluation and diagnosis associated. the statement that performs that is presented in (2). the result is a table that contains 129 features. create table caract_copii as select f.∗, l.diagn_final from fise f, logopat l where f.idc = l.idc; (2) data mining techniques were not designed to process large amounts of irrelevant features. conse-quently before their application, a selection of the relevant features is required [6] [7]. the most im-portant objectives of feature selection are: to avoid over fitting and improve model performance. a variant of the mrmr method [9] for categorical values has been used for feature selection. it is based on mutual information criteria, formally defined, for two discrete random variables x and y, as: i (x; y) = ∑ y∈y ∑ x∈x p (x, y) log ( p (x, y) p1(x)p2(y) ) (3) where p(x,y) is joint probability distribution function of x and y, and p1(x) and p2(y) are the marginal probability distribution functions of x and y respectively. for discrete random variable, the joint probability mass function is: p(x, y) = p(x = x, y = y) = p(y = y|x = x) ∗ p(x = x) = p(x = x|y = y) ∗ p(y = y) (4) since these are probabilities, we have∑ x ∑ y p(x = x, y = y) = 1 (5) the marginal probability function, p (x = x) is: p(x = x) = ∑ y p(x = x, y = y) = ∑ yp(x = x|y = y)p(y = y) (6) the criterion used is related to minimizing redundancy and maximizing relevance to the chosen characteristics. the result of tests performed on data prepared as described in the example mentioned above, revealed that, for classification, the minimum error is obtained if we deal with a number between 20 and 22 features selected. the target data set, obtained after these steps, is advanced information technology support of improved personalized therapy of speech disorders 691 subject to data mining algorithms. for an effective implementation of algorithms we have taken into account, and we tested, two possibilities: to use the oracle data mining kernel (odm) which offers the possibility to apply algorithms for classification, clustering and association rules and to use some open source implementations of relevant algorithms adapted and integrated into our own system. we took into account the types of data included in the set and we used implementations in oracle of adaptive bayes network, seeker model and decision trees build with cart [16] and id3/c4.5 for classification, for clustering the oracle implementation of a-clustering algorithm and for association rules apriori algorithm. it should be noted that for the moment, the volume of data on which work is relatively low, because the system which is the main source of these data is operational for only several months. 4 conclusions and future works considering the opportunity of data mining techniques application on data collected in the process of speech therapy, we have concluded that methods such as classification, clustering or as-sociation rules can provide useful information for a more efficient therapy. consequently, we have designed and we are currently implementing a data mining system that aims to use data provided by terapers system, developed by the research center for computer science in the university "stefan cel mare" of suceava, in order to achieve an optimized personalized therapy of dyslalia. we have tested the modules for data pre-processing and on target data sets obtained from these modules we have applied more algorithms for detecting the most appropriate solutions for the data mining kernel. at present efforts are directed towards the implementation of evaluation patterns and visualization modules and towards building a user friendly interface. bibliography [1] m. danubianu, s.g. pentiuc, o. schipor, i. ungureanu, m. nestor, distributed intelligent system for personalized therapy of speech disorders, in proc. of the third international multi-conference on computing in the global information technology iccgi, july 27august 01, athens, greece, 2008. [2] m. danubianu, s.g. pentiuc, o. schipor, m. nestor, i. ungurean, d.m. schipor, terapers intelligent solution for personalized therapy of speech disorders, international journal on ad-vances in life science, p.26-35, 2009. [3] m. danubianu, t. socaciu, does data mining techniques optimize the personalized therapy of speech disorders?, journal of applied computer science and mathematics, p.15-19, 2009 [4] m. danubianu, s.g. pentiuc, t. socaciu, towards the optimized personalized therapy of speech disorders by data mining techniques, the fourth international multi conference on computing in the global information technology iccgi 2009, vol: cd, 23-29 august, cannes la bocca, france, 2009 [5] f.g. filip, decizie asistata de calculator, ed. tehnica, bucuresti, 2005 [6] i. guyon, a. elisseeff, an introduction to variable and feature selection. j. mach learn res., 3, p.1157-1182, 2003 692 m. danubianu, s.g. pentiuc, i. tobolcea, o.a. schipor [7] h. liu, h. motoda, feature selection for knowledge discovery and data mining. kluwer aca-demic publishers, norwell, ma, 1998 [8] olp (ortho-logo-paedia) project for speech therapy (http://www.xanthi.ilsp.gr/olp);w.-k. chen, linear networks and systems (book style).belmont, ca: wadsworth, p. 123-135, 1993 [9] h. peng, f. long, c. ding, feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy, ieee transactions on pattern analysis and machine intelligence, vol. 27, no. 8, p. 1226-1238, 2005 [10] b. reiz, l. csató, bayesian network classifier for medical data analysis. international journal of computers communications & control vol. 4, p: 65-72, 2009 [11] speechviewer iii (http://www.synapseadaptive.com/edmark/prod/sv3) [12] star speech training, assessment, and remediation (http://www.asel.udel.edu/speech) [13] tobolcea,i., interventii logo-terapeutice pentru corectarea formelor dislalice la copilul normal, editura spanda, iasi, 2002. [14] p. wessa, quality control of statistical learning environments and prediction of learning outcomes through reproducible computing, international journal of computers communications & control vol. 4, p: 185-197, 2009 [15] r. wirth, j. hipp, crisp-dm: towards a standard process model for data mining. in proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, pages 29-39, manchester, uk, 2000 [16] www.salford-systems.com/ last visited october 2009 [17] www.speech.kth.se/multimodal/artur/index.html last visited august 2009 [18] o. balter, o. engwall, a.m. oster, h. kjellstrom, wizard-of-oz test of artur a computer-based speech training system with articulation correction. proceedings of the seventh international acm sigaccess conference on computers and accessibility, baltimore, october, 2005, pp.36-43. [19] h.t. bunnel, m.d. yarrington, b.j. polikoff, articulation training for young children, proceedings of 6th international conference on spoken language processing (icslp 2000), beijing, china, october 16-20, 2000, vol.4, pp. 85-88. international journal of computers, communications & control vol. i (2006), no. 1, pp. 7-8 introduction to the first issue ioan dziţac in this inaugural issue we present: • an editorial article, with a general presentation of ijccc and this introduction with short description of the papers published in this first issue; • one anniversary article dedicated to centenary of kurt gödel (1906-1978) and two anniversary articles dedicated to centenary of grigore c. moisil (1906-1973); • some selected papers from the international conference on computers, communications & control, june 1-3, 2006, băile felix oradea, romania, icccc 2006; • two regular papers. 1 introducing the papers daniel c. doolan and sabin tabirca’s paper1 discusses how a java 2 micro edition (j2me) application may be developed to visualize a wide variety of differing fractal types on a mobile phone. an integrated tool has been developed by authors to generate a variety of two dimensional fractals (mandelbrot set, julia set, prime number fractal and the plasma fractal). banshider majhi, y santhosh reddy, d prasanna babu’s propose in their paper a new feature extraction scheme for offline signature verification. the method uses geometric center for feature extraction and euclidean distance model for classification. this classifier is well suitable for features extracted and fast in computation. the method proposed in this paper leads to better results than existing offline signature verification methods. gheorghe păun initiated membrane computing, most known as p systems (with “p” from “păun”; for more details see http://psystems.disco.unimib.it) by paper “computing with membranes”, published in journal of computer and system sciences, 61, 1 (2000), 108-143 (first circulated as tucs research report no 208, november 1998, http://www.tucs.fi). in the paper published in this issue he continues the attempt to bridge brane calculi with membrane computing, following the investigation started in the paper l. cardelli, gh. păun, an universality result for a (mem)brane calculus based on mate/drip operations, intern. j. foundations of computer sci., 17, 1 (2006), 49-68. dana petcu, cosmin bonchiş, cornel izbaşa present in their paper2, a case study of a web service-based grid application for symbolic computations (known as computer algebra systems (cas)), using maple2g (maple-togrid). imre rudas and janos fodor summarizes in their paper3 the research results of the authors that have been carried out in recent years on generalization of conventional operators. aggregating uncertain information is an important issue in the study of intelligent systems. in their contribution, the authors present an overview on generalized operators from the point of view of information aggregation. in this spirit, a comparative study of triangular norms and conorms, uninorms and nullnorms, generalized conjunctions and disjunctions and distancebased operations is presented. milan stanojevic and mirco vujosevic’s propose in their paper4 a new original more efficient algorithm formulation and implementation for solving relatively low dimensions steiner tree problem on graph. athanasios d. styliadis, ioannis d. karamitsos, dimitrios i. zachariou’s paper5 introduces the gis elearning implementation, based on a set of teaching (lecturing) rules according to the cognitive style of learning preferences of both the learners and the lecturers as well. 1selected from icccc 2006 2selected from icccc 2006 3plenary invited paper of icccc 2006 4invited paper of icccc 2006 5invited paper of icccc 2006 copyright c© 2006 by ccc publications 8 ioan dziţac 2 introducing the anniversary articles gabriel ciobanu’s article presents some impressions about life, work, and foundational views of kurt gödel, after his participation to the international symposium celebrating the 100th birthday of kurt gödel, organized between 27-29 april 2006 by the kurt gödel society and university of vienna. gabriel ciobanu has been accepted to this symposium with a contribution regarding a new characterization of computable real numbers. solomon marcus present in this first issue of ijccc a very well documented article6, devoted to 100 years since the birth of great romanian mathematician and computer pioneer (ieee-1996), grigore c. moisil (19061973). in 1976, 1980 and 1992, acad. solomon marcus has edited and prefaced the following volumes: • gr. c. moisil, opera matematica, vol.i, editura academiei, bucuresti, 1976 (preface, edition and introductory study). • gr. c. moisil, opera matematica, vol.ii, editura academiei, bucuresti, 1980 (preface, edition and introductory study). • gr. c. moisil, opera matematica, vol.iii, editura academiei, bucuresti, 1992 (edition and introductory study). george georgescu, afrodita iorgulescu, sergiu rudeanu, three remarkable moisil’s disciples, presents, in their technical paper7, a very concise and updated survey emphasizing the research done by grigore c. moisil and his school in algebraic logic (n-valued lukasiewicz-moisil algebra, θ -valued lukasiewicz-moisil algebra, post algebra etc.). ioan dziţac executive editor of ijccc agora university 8, p-ta tineretului, 410526 oradea, romania e-mail: idzitac@univagora.ro 6plenary invited talk of icccc 2006 7invited paper of icccc 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 2, pp. 179-192 evolved fuzzy control system for a steam generator d. hossu, i. făgărăşan, a. hossu, s.s. iliescu daniela hossu, ioana făgărăşan, andrei hossu, sergiu st. iliescu university politehnica of bucharest automatic control and industrial informatics no.313 splaiul independentei, district 6, code 060042, bucharest, romania e-mail: andone_dana@yahoo.com, dana@aii.pub.ro, ioana@shiva.pub.ro abstract: poor control of steam generator water level is the main cause of unexpected shutdowns in nuclear power plants. particularly at low powers, it is a difficult task due to shrink and swell phenomena and flow measurement errors. in addition, the steam generator is a highly complex, nonlinear and time-varying system and its parameters vary with operating conditions. therefore, there is a need to systematically investigate the problem of controlling the water level in the steam generator in order to prevent such costly reactor shutdowns. the objective of this paper is to design, evaluate and implement a water level controller for steam generators based on a fuzzy model predictive control approach. an original concept of modular evolved control system, seamless and with gradual integration into the existent control system is proposed as base of implementation of the presented system. keywords: evolved control, nonlinear system, fuzzy model, predictive control. 1 introduction poor control of the steam generator water level in the secondary circuit of a nuclear power plant can lead to frequent reactor shutdowns. such shutdowns are caused by violation of safety limits on the water level and are common at low operating power where the plant exhibits strong non-minimum phase characteristics. difficulties on designing a steam generator (sg) level controller arise from the following factors: reverse dynamics, or non-minimum phase behavior due to swell and shrink effects, particularly at low power, changes in plant dynamics with operating power, dynamics uncertainties, corrupted feed-water flow measurement signal with biased noises. particularly it is difficult to control automatically a steam generator water level during transient period or at low power less than 15% of full power because of its dynamic characteristics. various approaches have been reported in the literature: an adaptive pid level controller using a linear parameter varying model to describe the process dynamics over the entire operating power range [12]; lqg controllers with "gain-scheduling" to cover the entire operating range [15]; a hybrid fuzzypi adaptive control of drum level, a model predictive controller to identify the operating point at each sampling time and use the plant model corresponding to this operating point as the prediction model [14]. a nonlinear physical model with a complexity that is suitable for model-based control has been presented by aström and bell [5]. the model describes the behavior of the system over a wide operating range. a model of the steam generator water level process in the form of a transfer function, determined based on first-principles analysis and expert experience has been presented in [30]. a detailed nonlinear model based on the lumped parameter approach for system modules, developed for a typical pwr power copyright c⃝ 2006-2010 by ccc publications 180 d. hossu, i. făgărăşan, a. hossu, s.s. iliescu plant has been presented in [1]. in the paper trained neural networks are used to predict certain system parameters of the plant for a number of different power demand histories. paper [17] presents a self organizing fuzzy logic controller for the water level control of a steam generator. with the advent of the current generation of high-speed computers, more advanced control strategies not limited to pi/pid, can be applied [11], [18], [20], [23]. model predictive control (mpc) is one such controller design technique, which has gained wide acceptance in process control applications. model predictive control has three basic steps: output prediction, control calculation and closing the feedback loop [6], [7], [16]. in this paper, we apply mpc techniques to develop a framework for systematically addressing the various issues in the sg level control problem. the takagi-sugeno fuzzy model representation often provides efficient and computationally attractive solutions to a wide range of modeling problems capable to approximate nonlinear dynamics, multiple operating modes and significant parameter and structure variations [22], [24]. this paper deals with takagi-sugeno (t-s) fuzzy models because this type of model have a good capability for prediction and can be easily used to design model-based predictive controllers for nonlinear systems [9]. the paper includes simulations of typical operating transients in the sg. a new concept of modular advanced control system designed for a seamless and gradual integration into the target systems is presented. the system is designed in such a way to improve the quality of monitoring and control of the whole system. the project targets the large scale distributed advanced control systems with optimum granularity architecture. 2 fuzzy model fuzzy models have become one of the most well established approaches to non-linear system modeling since they are universal approximations which can deal with both quantitative and qualitative (linguistic) forms of information [8], [9], [21], [26], [27], [28], [29]. these models can broadly be divided into three classes: linguistic models so-called mamdani-type models, fuzzy relational models, and takagi-sugeno (ts) models [14]. both linguistic and fuzzy relational models are linguistically interpretable and can incorporate prior qualitative knowledge provided by experts [25]. however, when linguistic knowledge extraction is not the main purpose of modeling, like in many dynamic system identification and control problems, the use of ts models becomes particularly adequate since they are able to accurately represent a wide class of nonlinear systems using a relatively small number of parameters. in a nutshell, ts models perform an interpolation of local models, usually linear or affine in their arguments, by means of a fuzzy inference mechanism. their functional rule base structure is well-known to be intrinsically favorable for control applications. the ts model representation often provides efficient and computationally attractive solutions to a wide range of modeling problems capable to approximate nonlinear dynamics, multiple operating modes and significant parameter and structure variations. the ability of such model to capture the dynamics of a large class of nonlinear plants has been investigated extensively in the literature [13]. in this paper, we apply mpc techniques to develop a framework for systematically addressing the various issues in the sg level control problem. this paper deals with takagi-sugeno (t-s) fuzzy models because of their capability to approximate a large class of static and dynamic nonlinear systems. in t-s modeling methodology, a nonlinear system is divided into a number of nearly linear subsystems. a quasi-linear empirical is developed by means of fuzzy logic for each subsystem. the whole process behavior is characterized by a weighted sum of the outputs from all quasi-linear fuzzy implication. the methodology facilitates the development of a nonlinear model that is essentially a collection of a number of quasi-linear models regulated by fuzzy logic. it also provides an opportunity to simplify the design of model predictive control. the system is divided into a number of linear or nearly linear subsystems. by takagi-sugeno’s modeling methodology a fuzzy quasi-linear model has to be developed for each subsystem. in such a evolved fuzzy control system for a steam generator 181 model, the cause-effect relationship between control u and output y at the sampling time n is established in a discrete time representation. each fuzzy implication is generated based on a system step response [3, 4, 10]. if y(n) is ai0, y(n −1) is a i 1, ..., y(n − m +1) is a i m−1, and u(n) is bi0, u(n −1) is b i 1, ..., u(n − l +1) is b i l−1 (1) t hen yi(n +1) = y(n)+ t∑ j=1 hij ∆ u(n +1− j) where: aij fuzzy set corresponding to output y(n-j) in the i th fuzzy implication bij fuzzy set corresponding to input u(n-j) in the i th fuzzy implication hij impulse response coefficient in the i th fuzzy implication t model horizon △u(n) difference between u(n) and u(n-1) a complete fuzzy model for the system consists of p fuzzy implications. the system output y(n+1) is inferred as a weighted average value of the outputs estimated by all fuzzy implications y(n +1) = ∑p j=1 µ jy j(n +1)∑p j=1 µ j (2) where µ j = ∧i a j i ∧k b j k (3) considering ω j = µ j∑p j=1 µ j (4) then y(n +1) = p∑ j=1 ω j y j(n +1) (5) 3 fuzzy model predictive control 3.1 problem formulation the design goal of a fuzzy model predictive control is to minimize the predictive error between an output and a given reference trajectory in the next ny steps through the selection of nu step optimal control policies. the optimization problem can be formulated as: min∆ u(n), ∆ u(n+1), ..., ∆ u(n+nu) j(n) (6) j(n) = ny∑ i=1 µi(ŷ(n + i)− yr(n + i)) 2 + nu∑ i=1 vi∆ u(n + i)2 (7) where: 182 d. hossu, i. făgărăşan, a. hossu, s.s. iliescu µi and vi are the weighting factors for the prediction error and control energy; ŷ(n +1) ith step output prediction; yr(n +1) ith step reference trajectory; ∆ u(n + i) ith step control action. the weighted sum of the local control policies gives the overall control policy: ∆ u(n + i) = p∑ j=1 ω j∆ u j(n + i) (8) substituting (2) and (8) into (7) yields (9) j(n) = ny∑ i=1 µi   p∑ j=1 ( ω j ( ŷ j(n + i)− yr(n + i) ))2 + nu−1∑ i=0 vi   p∑ j=1 ω j∆ u j(n + i)  2 (9) to simplify the computation, an alternative objective function is proposed as a satisfactory approximation of (9) [10]. j̃(n) = p∑ j=1  (ω j)2   ny∑ i=1 µi ( ŷ j(n + i)− yr(n + i) )2 + nu−1∑ i=0 vi ∆ u j(n + i)2     (10) the optimization problem can be defined as: min∆ u(n), ∆ u(n+1), ..., ∆ u(n+nu−1) j̃(n) = min∆ u(n), ∆ u(n+1), ..., ∆ u(n+nu−1) p∑ j=1 ( ω j )2 j̃ j(n) (11) where j̃ j(n) = ny∑ i=1 µi ( ŷ j(n + i)− yr(n + i) )2 + nu−1∑ i=0 vi ( ∆ u j(n + i) )2 (12) using the alternative objective function (12), we can derive a controller by a hierarchical control design approach. 3.2 controller design 1. lower layer design: for the jth subsystem, the optimization problem is defined as follows: min∆ u(n), ∆ u(n+1), ..., ∆ u(n+nu−1) j̃ j(n) (13) subject to: r j : { if y(n + k −1) is a j0, ..., y(n + k − m) is a j m−1 t hen y j(n + k) = y j(n + k −1)+ ∑t j=1 h j i ∆ u(n + k − i)+ ε j(n + k − i) (14) where ε j(n + k −1) serves for system coordination and it is determined at the upper layer. 2. upper layer design: the upper layer coordination targets the identification of globally optimal control policies through coordinating ε j(n + k −1) for each local subsystem. evolved fuzzy control system for a steam generator 183 3. system coordination: from the lower layer, the local information of output and control is transmitted to the upper layer. at the upper layer, the error variables are evaluated as: ε j(n + k −1) = y(n + k −1)− y j(n + k −1). these values will be compared with those for the same error variables calculated in the last iteration. if p∑ j=1 ny∑ k=1 ∣∣e j(n + k −1)− ε j(n + k −1)∣∣ > β then the control policies are not optimal and need to be modified at the lower layer; else an optimal control action is found. the whole design is decomposed into the derivation of p local controllers. the subsystems regulated by those local controllers will be coordinated to derive a globally optimal control policy. the objective function defined in (12) can be rewritten in a matrix form: j̃ j(n) = ( ŷ j+(n)−y r(n) )t w j1 ( ŷ j+(n)−y r(n) ) + + ( ∆u j+(n) )t w j2 ( ∆u j+(n) ) (15) where: ŷ j+(n) = ( ŷ j(n +1)ŷ j(n +2)...ŷ j(n + ny) )t (16) y r(n) = (yr(n +1)yr(n +2)...yr(n + ny)) t (17) ∆u j+(n) = ( ∆ u j(n)∆ u j(n +1)...∆ u j(n + nu −1) )t (18) w j1 = diag { µ j1, µ j 2, ..., µ j ny } (19) w j2 = diag { v j1, v j 2, ..., v j nu } (20) the ny step prediction of the output by the jth fi can be rewritten as follows: ŷ j+(n) = a j∆u j+(n)+y (n)+ p j(n)+ e j+(n) (21) where: a j =   a j1 0 0 ... 0 a j2 a j 1 0 ... 0 a j3 a j 2 a j 1 ... 0 ... ... ... . . . ... a jny a j ny−1 a jny−2 ... a j ny−nu+1   (22) a ji = i∑ k=1 h jk (23) y (n) = (y(n) y(n) ...y(n) )t (24) p j(n) = ( p j1 (n) p j 2 (n) ...p j ny (n) )t (25) e j+(n) =  0 2∑ k=1 ε j(n + k −1) ··· ny∑ k=1 ε j(n + k −1)  t (26) p ji (n) = i∑ k=1 t∑ l=k+1 h jl ∆ u(n + k − l) (27) 184 d. hossu, i. făgărăşan, a. hossu, s.s. iliescu the resulting control policy for the jth subsystem can be derived as: j̃ j(n) = ( ∆u j+(n) )t ( a j t w j1 a j +w j2 ) ∆u j+(n)+ + ( ∆u j+(n) )t a j t w j1 z j(n)+ ( z j(n) )t w j1 a j∆u j+(n)+ ( z j(n) )t w j1 z j(n) (28) where: z j(n) = y (n)−y r(n)+ p j(n)+ e j+(n) (29) minimizing (26) yields: δ j̃ j(n) δ ∆u j+(n) = 2(a j t w j1 a j +w j2 )∆u j +(n)+2a jt w j1 z j(n) = 0 (30) the control law by the jth fi can be identified as:( ∆u j+(n) )∗ = −k jz j(n) (31) where k j is: k j = ( a j t w j1 a j +w j2 )−1 a j t w j1 (32) the optimal local control policies at the lower layer are identified through optimization, the optimal global control policies can be accordingly derived at the upper layer. ∆u+(n) = (∆ u(n)∆ u(n +1)...∆ u(n + nu −1)) t (33) 3.3 parameter tuning in controller design, the difficulty encountered is how to quickly minimize the upper bound of the objective function so that the control actions can force a process to track a specified trajectory as close as possible. there has no rigorous solution to the selection of optimal control horizon (nu) and prediction horizon (ny). the model horizon is selected so that t ∆ t ≥ open loop settling time. the ranges of weighting factors w j1 and w j 2 can be very wide, the importance is their relative magnitudes. the following three-step procedure to tune the weighting factors is proposed: (a) select a value for w1 and assign it to all local controllers. determine w j 2 independently for each local controller in order to minimize the objective function for that subsystem (b) identify the largest w2 and assign it to all subsystems. (c) examine the system’s closed-loop dynamic performance. if not satisfied, then reduce the value of w2 gradually until the desirable dynamic performance is identified. 3.4 simulations process modeling the main problem in setting up a signal flow diagram for a level controlled system in a sg can be found in the inhomogeneous contents of the sg. the filling consists of water at boiling temperature, pervaded by steam bubbles. since the volume fraction of the steam bubbles is quite considerable, the mean specific weight of the contents is very strongly dependent on the proportion of steam. this, of evolved fuzzy control system for a steam generator 185 figure 1: responses of water level at different operating power (indicated by %) to (a) a step in steam flow-rate. (b) a step in feed-water flow-rate. course, means that the steam content also strongly influences the level in the sg. the steam content itself depends, in turn, on the load factor, on the changes in feed-water flow, and on feed-water temperature. the presence of steam below the liquid level in the sg causes the shrink-and-swell phenomenon that in spite of an increased supply of water, the water level initially falls. fig. 1 shows responses of the water level to steps in feed-water and steam flow-rates at different operating powers. for generating the responses, it was used the power dependent linear parameter varying model identified by irving [12]. at low loads the non-minimum phase behavior is much more pronounced. the changing process dynamics and the inverse response behavior significantly complicate the design of an effective water level control system. a solution to this problem is to design local linear controllers at different points in the operating regime and then applies gain-scheduling techniques to schedule these controllers to obtain a globally applicable controller. consider a step in feed-water flow rate at 5% operating power because of the strong inverse response. for this system, a fuzzy convolution model consisting of four fuzzy implications is developed as follows: for j=1 to 4 r j: if ydal (n) is a j then y jdal (n +1) = y j dal (n)+ ∑200 i=1 h j dal u(n +1− i) in order to define the fuzzy sets we propose the following strategy: dal1 = min(y 1 dal )·kdal1 , dal2 = min(y2dal )·kdal2 , dal3 = min(y 3 dal )·kdal3 , dal4 = min(y 4 dal )·kdal4 where kdal1 = 0.2, kdal2 = 0.9, kdal3 = 0.9, kdal4 = 0.2. are selected in order to obtain a characteristic as close as possible to the open loop response of water level at 5% operating power to a step in feed-water flow-rate. fig. 2 shows the impulse response coefficients for y1dal , y 2 dal , y3dal , y 4 dal subsystems and fig. 3 shows the definition of fuzzy sets a1, a2, a3 and a4. consider a step in steam flow rate at 5% operating power. for this system, a fuzzy convolution model consisting of fourfuzzy implications is developed as follows: for j=1 to 4 r j: if yd0 (n) is a j then y jd0(n +1) = y j d0 (n)+ ∑200 i=1 h j d0 u(n +1− i) 186 d. hossu, i. făgărăşan, a. hossu, s.s. iliescu figure 2: the impulse response coefficients for y1dal , y 2 dal , y3dal , y 4 dal subsystems. figure 3: definition of fuzzy sets a1, a2, a3 and a4 for fi r1, r2 ,r3 and r4 respectively. in order to define the fuzzy sets we propose the following strategy: d01 = max(y 1 d0)·kd01 ,d02 = max(y2d0)·kd02 , d03 = max(y 3 d0 )·kd03 , d04 = max(y 4 d0 )·kd04 where kd01 = 0.4, kd02 = 0.9, kd03 = 0.9, kd04 = 0.6 are selected in order to obtain a characteristic as close as possible to the open loop response of water level at 5% operating power to a step in steam flow-rate. fig. 4 shows the impulse response coefficients for y1d0 , y 2 d0 , y 3 d0 , y4d0 subsystems, fig. 5 shows the definition of fuzzy sets a1, a2, a3 and a4. controller design the goal in this paper is to study the use of the feed-water flow-rate as a manipulated variable to maintain the sg water level within allowable limits, in the face of the changing steam demand resulting from a change in the electrical power demand. frequent reactor shutdowns are caused by violation of safety limits on the water level and are figure 4: the impulse response coefficients for y1d0 , y 2 d0 , y 3 d0 , y4d0 subsystems. evolved fuzzy control system for a steam generator 187 figure 5: definition of fuzzy sets a1, a2, a3 and a4 for fi r1, r2 ,r3 and r4 respectively. figure 6: normalized water level setpoint. common at low operating power where the plant exhibits strong non-minimum phase characteristics.fig. 6 shows the normalized water level setpoint and safety limits. the goal of fuzzy model predictive control is to minimize the predictive error between an output and a given reference trajectory in the next ny (prediction horizon) steps through the selection of nu (control horizon) step optimal control policies. using the alternative objective function (12), we can design a controller by a hierarchical control design approach. the whole design is decomposed into the derivation of 4 local controllers. the subsystems regulated by those local controllers will be coordinated to derive a globally optimal control policy. the objective function defined in (12) can be rewritten in a matrix form as follows: j̃ j(n) = ( ŷ j+(n)−y r(n) )t w j1 ( ŷ j+(n)−y r(n) ) + + ( ∆u j+(n) )t w j2 ( ∆u j+(n) ) (34) where: ŷ j+(n) = ( ŷ jdal (n +1)ŷ j dal (n +2)...ŷ jdal (n + ny) )t (35) y r(n) = (yr(n +1)yr(n +2)...yr(n + ny)) t (36) yr(n) = yre f (n)− yd0(n) (37) ∆u j+(n) = ( ∆ u j(n)∆ u j(n +1)...∆ u j(n + nu −1) )t (38) w j1 = diag { µ j1, µ j 2, ..., µ j ny } (39) w j2 = diag { v j1, v j 2, ..., v j nu } (40) the simulations are organized around two different power transients: • a step-up in power from 5% to 10% (fig. 7(a)); • a ramp-up in power from 5% to 10% (fig. 7(b)). 188 d. hossu, i. făgărăşan, a. hossu, s.s. iliescu figure 7: water level response to (a) a step power increase from 5% to 10% (nu=2, ny=3, w1=1). (b) a power ramp up from 5% to 10% (w2=0.1, w1=1). the model horizon is t = 200. increasing ny results in a more conservative control action that has a stabilizing effect but also increases the computational effort. the computational effort increases as nu is increased. a small value of nu leads to a robust controller. we can see that the performance is not strongly affected by the presence of the feed-water inverse response, only a slight oscillation is visible in the water level response. all local controllers are used all the time. this means that there is no switch from one local controller to the other in operation. the system output is infered as a weighted average value of the outputs of all subsystems. on the other hand, the overall control policy to the process under control is the weighted sum of all local control policies. this kind of design not only eliminates the controller switch problem and thus possible system instability, but also provides a much more smooth control performance in process operation. the performance is not strongly affected by the presence of the feed-water inverse response, only a slight oscillation is visible in the water level response. the fmpc responses are very satisfactory and not very sensitive to changes in tuning parameters. 3.5 evolved controller client/server architecture an original concept of modular evolved control system, seamless and with gradual integration into the primary control system is proposed. the target systems are the large scale distributed control systems with optimum granularity architecture. the aim of the application is to integrate the concepts of evolved control algorithms, portability of software modules, real time characteristics of the application. we propose an approach of a seamless integration of the evolved control modules into an existing control system. the first part of the life cycle phases of the new control system, from conception to validation stage, the new control system lives hiding in the shadow of the control system it will replace, and after validation the old system will be replaced by the new one. the identification, modeling, control and validation stages of the life cycle of the system, will be done on-line (the new system uses a real image of the i/o process data), without affecting the existing control system. because of high level of interconnectivity between system components, it is necessary to provide the highest independence between communication modules on one-hand and the control modules on the other hand. in order to obtain high ability of integration, the communication modules have to cover the widest possible area of industrial communication interfaces and protocols. one item of the application is to offer a unified api of extended generality and extendibility in order evolved fuzzy control system for a steam generator 189 to unify access and information retrieval from various wireless and wired technology and communication interfaces (rs 232, rs 485, fieldbus: profibus / interbus, ethernet ip, tcp/ip, etc). applications could properly adapt to changes in the network connections. the design and implementation of a solution to hide the embedded communication network problems from the application system programmers is included. a software package for evolved control includes a method based on fuzzy model predictive control. by using the basic concept of decomposition-coordination in a large-scale system theory, the fuzzy model predictive controller design can be accomplished through a two-layer iterative design process. the design is decomposed into the derivation of local controllers. the subsystems regulated by those local controllers will be coordinated to derive a globally optimal control policy. one of the main objectives of the application is to supply an integrated solution of systems, which should support all the phases of the life cycle: modeling, simulation, development and implementation. for parameter tuning, for validation and also for embedding a large number of industrial communication protocols, multi-disciplinary simulation environments are developed which generate instruments for control, i/o data consistency check, and defect detection. in the end, real-time advanced control applications are developed, with seamless and gradual integration into the existing distributed control system. in order to provide the real-time characteristic, we choose a multitasking environment for the application (windows operating system). from structural point of view we propose a client / server architecture for fuzzy controller (fc) [2]: client is a windows application representing the implementation of the graphical user interface (gui). the client enables the operator to control the system in two modes: manual/automatic, to monitor the system response, etc. the client has also the ability to connect and communicate with the server application. server is an activex exe application containing the implementation of the fc kernel. the server includes a collection of objects, these objects cover the tasks of both data processing and the communication between dedicated applications for input and output data. the client application will have a thread pool architecture. the server application will have a real multithreading architecture (each active object having assigned its own execution thread). the server have also a multi-layer structure: at the higher level are implemented upper fc and the communication classes (using different transmission mechanisms dde, opc, hli, activex, winsocket, pipes), at the lower level are implemented the controllers for the subsystems corresponding to the low level fc. the server’s application as real multithreading architecture, provides the fc kernel the real-time response characteristic, required for the industrial process control. 4 conclusions control of sg water level strongly affects nuclear power plant availability. the control task is difficult for a number of reasons, the most important among them being the nonlinear plant dynamics and the non-minimum phase plant characteristics. there has been a special interest in this problem during low power transients because of the dominant reverse thermal dynamic effects known as shrink and swell. the sg level control problem was viewed as a single input/single output control problem with the feed-water as the manipulated variable, the level as the controlled variable and the turbine steam demand as disturbance. the process non-linearity was addressed by scheduling the model (and the controller) with the power level. the sg system is modeled by takagi-sugeno’s fuzzy modeling methodology, where the system output is estimated based on gradient. the complex shrink and swell phenomena associated with the sg water level are well captured by the model. the predictive controller based on fuzzy model is designed in a hierarchical control design. 190 d. hossu, i. făgărăşan, a. hossu, s.s. iliescu an original concept of modular evolved control system, seamless and gradual integration into the existing distributed control system is proposed in the paper. a unified api of extended generality and extendibility in order to unify access and information retrieval from various wireless and wired technology and communication interfaces is developed in order to ensure independence between communication and control modules of the designed systems.a client / server architecture for evolved controller that runs on the windows environment, with real-time characteristics is proposed. bibliography [1] akkurt, colak u., pwr system simulation and parameter estimation with neural networks, annals of nuclear energy, vol. 29, pp. 2087-2103, 2002. 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[13] kiriakidis k., non-linear control system design via fuzzy modeling and lmis, international journal of control, 72(7):676-685, 1999. [14] kothare m., mettler b., morari m., bendotti p., falinower c., level control in the steam generator of a nuclear power plant, ieee trans. on control systems technology, 8(1):55-69, 2000. evolved fuzzy control system for a steam generator 191 [15] menon s.k. and parlos a.g., gain-scheduled nonlinear control of u-tube steam generator water level, nuclear sci. eng., vol. 111, pp. 294-308, 1992. [16] morari m. and lee j. h., model predictive control: past, present, and future, computers & chemical eng., pp. 667-682, 1999. [17] park g. y., seong p. h., application of a self-organizing fuzzy logic controller to nuclear steam generator level control, nuclear engineering and design, vol. 167, pp. 345-356, 1997. [18] pedrycz w. and gomide, f., fuzzy systems engineering: toward human-centric computing, wiley-ieee press, 2007. [19] precup, r. e., tomescu m., preitl s., lorenz system stabilization using fuzzy controllers, international journal of computers communications and control, 2(3):279-287, 2007. [20] ross t.j., fuzzy logic with engineering applications, second ed., wiley & sons, 2004. [21] vesselenyi, t; dzitac, s; dzitac, i, manolescu m.j., fuzzy and neural controllers for a pneumatic actuator, international journal of computers communications & control, 2(4):375-387, 2007. [22] yager r., r., zadeh l. a., an introduction to fuzzy logic applications in intelligent systems, kluwer academic publishers, norwell, ma, 1992. [23] yen j., langari r., zadeh l., a., industrial applications of fuzzy logic and intelligent systems, ieee press, piscataway, nj, 1995. [24] ying h., fuzzy control and modeling: analytical foundations and applications, wiley-ieee press, 2000. [25] zadeh, l. , a new frontier in computation computation with information described in natural language, international journal of computers communications and control, 3(s):26-27, 2008. [26] zadeh l. a., toward a generalized theory of uncertainty (gtu): an outline, information sciencesinformatics and computer science: an international journal, 172(1-2):1-40, 2005. [27] zadeh, l. a., knowledge representation in fuzzy logic, ieee transactions on knowledge and data engineering, 1(1):89-100, 1989. [28] zadeh, l. a., is there a need for fuzzy logic?, information sciences: an international journal, 178(13):2751-2779, 2008. [29] zadeh l.a., tufis d., filip f.g., dzitac i.(eds.), from natural language to soft computing: new paradigms in artificial intelligence, editing house of romanian academy, bucharest, isbn 978973-27-1678-6, 2008. [30] zhao f., ou j., du w., simulation modeling of nuclear steam generator water level process a case study, isa transactions, 39, pp. 143-151, 2000. 192 d. hossu, i. făgărăşan, a. hossu, s.s. iliescu daniela hossu is associated professor at university "politehnica" of bucharest. in 1996 she obtained m.s. degree in electrical engineering, specialty control systems and informatics in power systems at university "politehnica" of bucharest. in 2002 she obtained a ph.d. in automatic control from the university "politehnica" of bucharest. she was involved in several projects for retrofitting the instrumentation & control system of thermal power plants. since 1998 she has been involved in lecturing and teaching assistantships at university "politehnica" of bucharest in the following fields: automatic control, data transmission and data compression. her research interests include power plant control systems, artificial intelligence, industrial communication, data compression, software architectures for industrial control. ioana făgărăşan received the m.s. (1996) degree in electrical engineering, specialty control systems and informatics in power systems, and the phd (2002) degree in automatic control from the university politehnica of bucharest (upb). since 1996 she has been assistant professor, lecturer and is currently associated professor in the department of control engineering and industrial informatics at the upb. she has participated in research cooperation programs between the university and the institute for automatic control, technical university of darmstadt, in the fault detection and identification (fdi) field. between 2002 and 2004 she worked as associate professor at the national polytechnic institute of grenoble (automatic control laboratory). her research interests include control system for technical processes, fault detection and isolation methods, diagnostic algorithms and fault tolerant systems. andrei hossu is professor at university "politehnica" of bucharest. in 1982 he obtained m.s. in electrical and computer engineering, department of automatic control and computers, the polytechnic institute of bucharest, romania. in 2002 he obtained a ph.d. in engineering sciences, the electrical control field from the faculty of automatic control and computers, the university "politehnica" of bucharest, romania. his teaching interests include: human-machine interface, artificial vision and artificial intelligence methods. his research interests include: multiprocessor systems for real time control, industrial robots control systems, algorithms, methods for artificial vision systems for industrial robots, structured programming for industrial robots, software architectures for industrial control. sergiu stelian iliescu has received the m.s. (1965) degree from the power system faculty, automatic control department and ph. d. (1979) degree in computerselectronics from university "politehnica" of bucharest (upb). he was starting his professional activity in execution and implementation of automatic control equipments and systems, within the automatic control system execution company (imia). the next step was to work as design engineer within power systems studies and design institute (ispe). since 1968 he is part of the teaching and research team from automatic control and computers faculty, upb and acts as full professor since 1995. he has experience in process computers (tu dresden, 1971-1972) and coordinates collaborative research activities with tu darmstadt (1992-2004), in two research field: in system theory, modeling, identification and control technical processes (institute of automatic technique, tud) and in power system control and monitoring (institute of electrical power systems, tud). his research and teaching domain includes: automatic control theory, informatics system for supervision and control of industrial processes with concern to power system field and system engineering analysis. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 4, pp. 386-400 high performance computing systems with various checkpointing schemes n. naksinehaboon, m. păun, r. nassar, b. leangsuksun, s. scott nichamon naksinehaboon louisiana tech university computer science department louisiana, 71272, usa e-mail: n.nichamon@gmail.com mihaela păun louisiana tech university mathematics and statistics department louisiana, 71272, usa and finance and banks faculty spiru haret university, romania e-mail: mpaun@latech.edu raja nassar louisiana tech university mathematics and statistics department louisiana, 71272, usa e-mail: ran1@suddenlink.net chokchai box leangsuksun louisiana tech university computer science department louisiana, 71272, usa e-mail: box@latech.edu stephen scott oak ridge national laboratory computer science and mathematics division tn 37831-6173 e-mail: scottsl@ornl.gov abstract: finding the failure rate of a system is a crucial step in high performance computing systems analysis. to deal with this problem, a fault tolerant mechanism, called checkpoint/restart technique, was introduced. however, there are additional costs to perform this mechanism. thus, we propose two models for different schemes (full and incremental checkpoint schemes). the models which are based on the reliability of the system are used to determine the checkpoint placements. both proposed models consider a balance of between checkpoint overhead and the re-computing time. due to the extra costs from each incremental checkpoint during the recovery period, a method to find the number of incremental checkpoints between two consecutive full checkpoints is given. our simulation suggests that in most cases our incremental checkpoint model can reduce the waste time more than it is reduced by the full checkpoint model. the waste times produced by both models are in the range of % to % of the application completion time depending on the checkpoint overheads. keywords: large-scale distributed system, reliability, fault-tolerance, checkpoint/restart model, hpc 1 introduction high performance computing (hpc) systems are used to address many challenging computational problems. however, an hpc system is not required only to solve these problems, but also to solve them in a short time. this copyright c© 2006-2009 by ccc publications high performance computing systems with various checkpointing schemes 387 requirement drives the physical size of an hpc system larger. the reliability of a large hpc system is inversely proportional to its size. for example, the blue gene/l system hosted at lawrence livermore national laboratory (llnl), the world second fastest hpc system, has a failure rate of roughly one failure a week [1]. unfortunately, handling the reliability of such large hpc systems is still problematic. a checkpoint/restart mechanism is widely used to deal with the high failure rate problem. if a failure occurs while an application is running, the portion of the application that has been already computed has to be re-computed again. the checkpoint mechanism is able to reduce this re-computing time of an application after a failure occurrence. however, there is an additional time that is spent to perform the mechanism. the detailed of the checkpoint mechanism will be provided in section 2.1. since the message passing interface (mpi) is one of the most popular parallel programming paradigms, we target our reliability study on the system using the mpi standard [20]. normally, an mpi application is decomposed and executed among a group of nodes, where individual subtasks communicate through the mpi. because of the static view of an mpi environment [17], a single node failure would cause the whole application to fail and requires an application restart. the checkpoint/restart scheme has been widely used in [2], [4], [7], [20] to address application outages. however, applications are practically checkpointed without considering the probability of failures. as such, some checkpoints are useless because, at the checkpoint time, the chance of a failure occurrence is small. thus, it causes unnecessary checkpoint overhead. on the other hand, the re-computing time is large because applications are not checkpointed when the probability of failure is high. ideally, a checkpoint is needed when a failure is going to occur within a certain period of time, so there are no useless checkpoints and expensive recomputing time. so we define the waste time of the checkpoint mechanism as the sum of checkpoint overhead, recomputing time and recovery time. consequently, we aim to minimize the waste time by balancing the checkpoint overhead and the re-computing time. in existing studies, the optimal checkpoint placement strategy is either based on the cost function models [2], [3], [20] or markov availability models [5], [14], [19], [22]. in addition, it is typically assumed that the system failure is a poisson process (with a fixed failure rate). however, in practice, the system failure may not always follow the poisson model [16] and the overall system reliability is more complex than that of its individual components. in this paper, we propose two stochastic models for improving the checkpoint/restart scheme in an hpc environment by reducing the waste time. the reliability function of the system is obtained by analyzing historical failure data from the system event log files. 2 full checkpoint/restart model 2.1 behavior of full checkpoint/restart model a full checkpoint/restart mechanism is a traditional checkpoint/restart mechanism which occasionally saves running application states to a local storage. after a failure occurs, the application can be recovered from the last saved state rather than from the starting point. this results in decreasing the time that is spent to re-compute the application. conversely, there is an additional time to save the application states which is called the checkpoint overhead. to improve the checkpoint mechanism, checkpoints should not be performed too frequently, in such a way to balance of the checkpoint overhead and the application re-computing time. thus, we focus on how to determine checkpoint placements or intervals that minimize the waste time. in this section, we present a checkpoint/restart model with the re-computing time coefficient for fault-tolerant parallel applications. we assume that the application model is mpi and supports a coordinated checkpoint mechanism. the characteristic of the model is directly related to the system reliability function where one of the node outage will result in an application outage because of the mpi standard. for a parallel application, the coordinated checkpoint protocol guarantees that the checkpoint of an hpc system, resulted by the synchronization of the local checkpoint on individual process, is consistent. as a result, each process is checkpointed at almost the same time, so we assume that there is no time difference for each individual process checkpoint, and treat it as a single checkpoint. we consider a failure model that allows more than one failure during the lifetime of a given application. moreover, after each failure, the application will be restarted from the last checkpoint. our checkpoint/restart model is shown in figure 1. it follows a renewal reward process in which ωi denotes the ith time between failures in each repeated cycle. we assume that the waste time (checkpoint overhead, recovery time, and re-computing time) of each cycle is a random variable,w,w,w..., since it depends on when a failure occurs. hence, the total waste time may be expressed as 388 n. naksinehaboon, m. păun, r. nassar, b. leangsuksun, s. scott figure 1: checkpoint/restart as a stochastic renewal reward process ct = m∑ i= wi, (1) where m = max { n ∈ {, , , ...}| (∑ni= ωi) ≤ t}, and ct is called a renewal reward process. from [15] the theorem of a renewal reward process is given as lim t→∞ e [ ∑m i= wi] t = e [w] e [ω] (2) in the checkpoint/restart model, eq.(2) shows that the mean of the overall waste time (left hand side of the equation) can be expressed as a function of the mean waste time of the st cycle. this means that minimizing the overall time lost is equivalent to minimizing the waste time in the st cycle. figure 2: checkpoint/restart as a stochastic renewal reward process table 1: parameters of the checkpoint/restart model parameters meaning of checkpoint overhead rf recovery time tre re-computing time nf (t) checkpoint frequency function of the full checkpoint model f (t) probability density function of tbf ωi the cycle between failure i and failure (i+1) consider a checkpoint restart scheme in the first cycle ω (time between two failures). figure 2 illustrates the model with parameters such as checkpoint overhead of (time that is spent to save an application state), recovery time rf (time that is spent to load the application saved state) and re-computing time tre. through the rest of the paper, our failure model is based on the following assumptions: 1. a running application may be interrupted by a series of random transient failures where the time between failures has a certain probability density function (pdf), f (t). 2. the system failure can be detected by a monitoring mechanism, and we assume that there is no failure during the re-computing and recovery time. 3. each checkpoint overhead of is a constant. in practice, we can take this constant to be the average value of multiple checkpoint overheads. high performance computing systems with various checkpointing schemes 389 4. the application can be recovered from the last checkpoint. this implies that the re-computing time tre is a period between the last checkpoint and the present failure. 5. the recovery time rf is a constant. remark: assumption 2 is satisfied since a well-managed system can be engineered with an efficient mechanism to immediately detect the failure. assumption 5, rf , is satisfied if there is a mechanism in place to replace the failed node with a spared node. the objective of this model is the capability of giving the best checkpoint placement sequence that minimizes the total waste time definition 1. let the sequence of discrete checkpoint placements be  = t < t < ... < tn, and let nf (t) be the checkpoint frequency function for the full checkpoint model defined by: ∫b a nf (t)dt =the number of checkpoints from time a to time b. we then can imply that ∫ti+ ti nf (t) dt = . in figure 2, the waste time wi (checkpoint overhead, re-computing time, and recovery time) in a given cycle ωi can be expressed as wi = of ∫ ωi  nf (τ) dτ + tre + rf (3) from assumption 5, rf is a constant, and, from assumption 5, we suppose that the system can be successfully recovered from the last checkpoint. the relationship between re-computing time tre and checkpoint interval is illustrated in figure 3. figure 3: the relationship between rollback tre and checkpoint interval ling et al. [9] and ozaki et al. [11] considered the recovery cost including both re-computing time and recovery time similar to those in our model. they represented their recovery cost by a function, called the recovery cost function. moreover, they illustrated their model with respect to recovery cost by assuming the recovery function to be linear. assuming linearity may be restrictive and may not lead to optimality. in our model, we consider a re-computing time coefficient k ( < k < ) instead of a recovery cost function and propose an algorithm (end of section 2.2) to estimate this re-computing time coefficient. the re-computing time coefficient is general and can be determined for any system failure distribution. this makes our approach useful for application purposes. since ωi is the value between these checkpoint placements, by the mean value theorem, we can estimate the frequency of this interval by nf (ωi). therefore, tre can be approximated by eq.(4), where k is a re-computing time coefficient variable between (0,1), as seen in figure 3. tre ≈ k/nf (ωi), k ∈ (, ) (4) replacing tre in eq.(3) by its value from eq.(4) gives: wi = of ∫ ωi  nf (t) dt + k nf (ωi) + rf (5) according to the theorem of a renewal reward process in eq.(2), the total waste time in the checkpoint and recovery process can be minimized by minimizing e(w). let f (t) be the probability density function of time between failures. then, the probability that the system fails in the interval [t,t + ∆ t] is f (t) · ∆ t. the expected waste time during a cycle in the checkpoint/restart process is e [w] = ∫ ∞  ( of ∫ t  nf (τ) dτ + k nf (t) + rf ) · f (t) dt. (6) 390 n. naksinehaboon, m. păun, r. nassar, b. leangsuksun, s. scott our interest is in determining the optimal checkpoint frequency n (t) in order to minimize the expected waste time as defined by eq.(6). solution: letting x (t) = ∫t  nf (τ) dτ , then x′ (t) = nf (t) and eq.(6) becomes e [w] = ∫ ∞  ( of ·x (t) + k x′ (t) + rf ) · f (t) dt. (7) let φ (x, x′,t) = ( of ·x (t) + kx′(t) + rf ) · f (t) . in order to have an extremum, the necessary condition for eq.(7) requires that the variation of e[w] (the first derivative regarding to the function x of e[w]) to vanish. consequently, the function ô must satisfy the euler’s equation as the following [6]. ∂ φ ∂ x − d dt · ∂ φ ∂ x′ = . (8) since ∂ φ∂ x = of · f (t) and ∂ φ∂ x′ = − k· f (t) (x′(t)) , eq.(8) becomes: of · f (t) + d dt · k · f (t) (x′ (t)) =  (9) by integrating from 0 to t eq.(9) on both sides, the result is: of ·f (t) + k · f (t) (x′ (t)) = c (10) since lim t→∞ f (t) = and lim t→∞ f (t) = , c = of . moreover, x′ (t) = nf (t), then we obtain the optimal checkpoint frequency function in eq.(11) nf (t) = √ k of · √ f (t)  − f (t) , k ∈ (, ) (11) it is worth mentioning that the probability density function (pdf) and the cumulative distribution function (cdf) can be the joint pdf and joint cdf where each corresponding marginal is the failure distribution of each node in an hpc system. 2.2 estimation of the re-computing-time coefficient k in figure 4, tre is the re-computing time of the application recovered after the failure. it is the time interval between the last checkpoint and the failure, which is a random variable depending on the time when the failure occurs from the checkpoint placement. figure 4: tre (a) without checkpoint, and (b) with checkpoint in an application without checkpoints (figure 4(a)), if a failure occurs at time tf , then tre = tf − t. with checkpoints (figure 4(b)), it is obvious that tre is a random variable which depends on the time the failure occurs. therefore, if we know the distribution of the time between failures, then tre can be estimated. high performance computing systems with various checkpointing schemes 391 definition 2. the re-computing time coefficient k is the ratio between the re-computing time and the checkpoint interval in which a failure occurs. as such, k = tre ti+ − ti = tf − ti ti+ − ti to estimate k, we first obtain the expected re-computing time for each checkpoint interval. definition 3. excess life is a random variable, s ≥ , which denotes system survival until time t + s given that it survives until time t. we denote the cdf, the pdf, and the expected value of the excess life s as f (t + s|t) = p ( tf < t + s|t > t ) , f (t + s|t) = df(t+s|t)ds , e [s] = ∫∞  s f (t + s|t) ds. (12) in our checkpoint model, each checkpoint time ti is the time that we expect a failure to occur. the re-computing time during the interval (ti,ti+), trei is a random variable such that its value is in the interval (,ti+ − ti). according to the excess life definition, the expected value of the re-computing time can be calculated as e [trei] = ∫ ti+−ti  s f (ti + s|ti) ds / ∫ ti+−ti  f (ti + s |ti) ds. (13) therefore, for the expected k of the ith checkpoint interval, k̄i, we obtain k̄i = e [trei] / (ti+ − ti). (14) hence, the expected k, k̄, can be express as k̄ = n∑ i= pik̄i / n∑ i= pi, (15) where pi = p ( ti < tf < ti+| tf > ti ) and n is the number of the checkpoints. to estimate k iteratively, we assume an initial value k̂ between 0 and 1. we then calculate the corresponding checkpoint sequence, t,t, ...,tn , from eq.(11). next, we calculate k̄ corresponding to the checkpoint sequence using eqs. (13)(14)(15). we repeat the above procedure by varying k̂ until we obtain a k̄ value that is equal tok̂. algorithm to estimate k step 1: assume k̂ = a, a ∈ (, ) and set t =  step 2: calculate the checkpoint sequence t,t, ...,tn corresponding to k̂ from step 1. step 3: calculate k̄ from eqs. (13)(14)(15) using the sequence in step 2. step 4: if k̂ = k̄, then set k = k̂ = k̄ done else repeat step 1. 2.3 full checkpoint model evaluation full checkpoint model for the exponential distribution by substituting f (t) = λ e−λ t , and  − f (t) = e−λ t in eq.(11), the checkpoint frequency function for the exponential distribution with the failure rate λ , according to eq.(11), is given by nf (t) = √ k of · √ λ . (16) by the definition of the checkpoint frequency function, for i=0, 1, 2. . . we have that ∫ i+ ti nf (t) dt = , (19) 392 n. naksinehaboon, m. păun, r. nassar, b. leangsuksun, s. scott ∫ i+ ti √ k of · √ λ dt = , (17) ti+ = √ of k · √  λ + ti. (18) using induction whent = , the sequence of the optimal checkpoint placements for the exponential distribution with failure rate λ is given by ti = √ of k · √  λ , k ∈ (, ) (19) where ti is the ith checkpoint placement. the checkpoint interval can be obtained by calculating the formula ti − ti−, where i ∈ {, , ...} and t = . then the checkpoint interval for the exponential distribution is expressed as √ k of · √  λ , k ∈ (, ). therefore, according to the proposed model for the exponential distribution, the checkpoint interval is a constant. this reflects the fact that if failures follow the exponential distribution, the failure rate is a constant. estimation of the re-computing time coefficient k for the exponential distribution according to our re-computing time coefficient estimation in section 2.2, let k̂ = a,  < a <  and let its corresponding checkpoint time sequence be {ti} n i=. the cdf, pdf, and expected value of the excess life following an exponential distribution can be derived from eq.(12) to give f (ti + s) = p (ti + s|ti) =  − e −λ t , (20) f (ti + s) = λ e−λ t , (21) e [trei] =  − (√ k of √ λ /  − e−λ √ λ √ of k ) (22) by substituting eq.(22) into eq.(14), we obtain the expected k of the ith checkpoint interval (k̄i) and for the expected k̄ we use eq.(15). 3 incremental checkpoint/restart model 3.1 behavior of the incremental checkpoint/restart model although full checkpoint/restart mechanism helps to reduce the overall waste time of an application in the case of a failure taking place, we have to spend some time to save the application states, so-called checkpoint overhead. if the checkpoint overhead can be reduced, we may have a cheaper waste time. incremental checkpoint mechanism was introduced to reduce the checkpoint overhead by saving the pages that have been changed instead of saving the whole process [12], [8], [13],[18] . in the incremental checkpoint scheme in figure 5, the first checkpoint is typically a full checkpoint. after that, the mechanism determines which pages have changed since the last checkpoint and saves only those pages and repeats this process until another full checkpoint is performed. in order to recover the application, we will load a saved state from the last full checkpoint and load the changed pages from each incremental checkpoint following the last full checkpoint. this results in more expensive recovery cost than the recovery cost of the full checkpoint mechanism. thus, finding the number of incremental checkpoints between two consecutive full checkpoints that balances the recovery cost and the total checkpoint overhead is crucial. this is because too many incremental checkpoints will lead to unnecessary recovery cost. a challenge in achieving minimum overhead using incremental checkpointing schemes is to find a maximum number of incremental checkpoints while maintaining high performance computing systems with various checkpointing schemes 393 lower waste time than traditional checkpoint mechanism. the behavior of incremental checkpoint/restart model is illustrated in figure 5. the incremental checkpoint model consists of two types of checkpoints (full checkpoints and incremental checkpoints). the meaning of each parameter in the incremental checkpoint/restart model is listed in table 2. figure 5: behavior of incremental checkpoint/restart model table 2: parameters in incremental checkpoint/restart model. parameters definitions of full checkpoint overhead. oi incremental checkpoint overhead. tb re-computing time ri recovery time per an incremental checkpoint. rf recovery time per a full checkpoint m number of incremental checkpoints between two consecutive full checkpoints. ωi the cycle between failure (i-1) and failure i where i = 1,2,3,. . . ni (t) checkpoint frequency function for the incremental checkpoint model in our incremental checkpoint model, the recovery cost is decided by the number of incremental checkpoints. after m incremental checkpoints are performed, either another incremental checkpoint or a full checkpoint can be performed. a full checkpoint is chosen if the cost of performing a full checkpoint is cheaper than the recovery cost for an incremental checkpoint. this is what we call a breakeven point. the main idea is to balance a cost saving function with full and incremental checkpoint overheads and the complexity of the recovery that is introduced by the incremental model. the incremental checkpoint model is an extension of the above full checkpoint model, so the assumptions of the full checkpoint model are applied to this incremental checkpoint model as well. however, there are additional assumptions regarding the factors in the incremental checkpoint scheme which are listed as follows: 1. the first checkpoint in an application is a full checkpoint. after an application is recovered from failure, the first checkpoint is a full checkpoint as well. after m consecutive incremental checkpoints, a full checkpoint may be performed if the overall cost reaches a breakeven point between incremental and full checkpoint. we will determine the value of m in the next section. 2. the incremental checkpoint overhead (oi ) may be viewed as an average of all incremental checkpoint overhead performed. although, for an application, there are both small and large incremental checkpoint overheads, this assumption is reasonable because we aim to minimize the waste time caused by the incremental checkpoint mechanism of an application. 3. the recovery cost of the incremental checkpoint (ri ) and the number of incremental checkpoint between two consecutive full checkpoints m is a constant. the evaluation of m is given in section 3.3. in future work, we will extend the model to consider the incremental checkpoint overhead and the number of incremental checkpoints m as functions of time, in order to better represent a realistic scenario. definition 4. let the sequence of discrete checkpoint placements be  = t < t < ... < tn, and the checkpoint frequency function for the incremental checkpoint model denoted by ni (t) is defined by ∫b a ni (t) dt =the number of full and incremental checkpoints from time a to time b. 394 n. naksinehaboon, m. păun, r. nassar, b. leangsuksun, s. scott we still can imply that ∫ti+ ti ni (t) dt = . in figure 5, the total number of checkpoints in cycle ωi is ∫ ωi  ni (t) dt = nf + ni (23) where nf is the number of full checkpoints in cycle ωi and ni is the number of incremental checkpoints in the same cycle ωi. we note that ni ≈ mnf and nf ≈ ∫ωi  ni (t) dt. thus, nf =  m+ ∫ωi  ni (t) dt where m is the number of incremental checkpoint between two consecutive full checkpoints. we recall that the checkpoint procedure is a renewal process. therefore, whenever a failure occurs, the new cycle starts. we follow the renewal reward theory to derive the optimal incremental checkpoint/restart model similarly to the full checkpoint model. therefore, to minimize the overall waste time of the incremental checkpoint model, it is sufficient to find the checkpoint frequency function ni (t) that minimizes only the waste time of the first cycle which consists of full checkpoint overhead, incremental checkpoint overhead, recovery time, and re-computing time in the first cycle. the waste time of the first cycle can be expressed as w = of  m +  ∫ ωi  ni (τ) dτ + oi ∫ ωi  ni (τ) dτ + (rf + mri ) + tre. (24) we suppose that the system can be successfully recovered from the last checkpoint, and the rollback cost tre can be estimated by k / ni (ω), ( < k < ) as in the full checkpoint model, where ni (ω)is the checkpoint frequency at time ω, and k can be evaluated by the similar method as in the full checkpoint scheme. therefore, we substitute tre in eq. (25) and obtain: w = ( of + moi m +  ) ∫ ωi  ni (τ) dτ + (rf + mri ) + k ni (ω) . (25) by following the stochastic renewal reward process theory, minimizing the overall waste time is equivalent to minimizing waste time in cycle ω. the expected waste time during a cycle in the checkpoint process e [w] is e [w] = ∫ ∞  [( of + moi m +  ) ∫ ωi  ni (τ) dτ + (rf + mri ) + k ni (ω) ] . f (t) dt (26) we are now looking for the solution of the overall checkpoint frequency ni (t) to minimize eq.(26). solution: let x (t) = ∫t  ni (τ)dτ , then x′ (t) = ni (t). from eq. (26), we obtain: e [w] = ∫ ∞  [ of + moi m +  x (t) + (rf + mri ) + k x′ (t) ] · f (t) dt. (27) let the function under the integral in right side of eq.(27) beφ(x, x′,t). then φ ( x, x′,t ) = [ of + moi m +  x (t) + (rf + mri ) + k x′ (t) ] f (t) . (28) by following the same argument as in the full checkpoint model, the checkpoint frequency function of the incremental checkpoint model can be expressed as: ni (t) = √ (m + ) k of + moi √ f (t)  − f (t) , k ∈ (, ) . (29) practically, the incremental checkpoint mechanism is an extension of the full (regular) checkpoint mechanism in the sense that incremental checkpoints are performed additionally in order to reduce the total checkpoint overhead of the full checkpoint mechanism. alternately, we can see that the full checkpoint mechanism is the incremental checkpoint mechanism without any incremental checkpoints. according to eq.(11) and eq.(29), the derived models satisfy the connection between the full and incremental checkpoint mechanisms. that is, when m is equal to 0, the incremental checkpoint frequency function, eq.(29), becomes the full checkpoint frequency function, eq.(11). high performance computing systems with various checkpointing schemes 395 3.2 estimation of the consecutive incremental checkpoint number, m we denote the number of incremental checkpoints between two consecutive full checkpoints as m. the value of depends on the next checkpoint type, either incremental or full checkpoint. as discussed earlier, the incremental checkpoint aims to reduce the checkpoint overhead. on the other hand, the recovery cost will increase as the number of subsequent incremental checkpoints (m) increases. this is because the application reconstruction phase requires information from each and every incremental checkpoint since the last full checkpoint. from the model description of the incremental checkpoint below, we assume that the first checkpoint is a full checkpoint, followed by a sequence of incremental checkpoints. moreover, we will perform m incremental checkpoints after a full checkpoint if the expected waste time of having m +  incremental checkpoint is more expensive than that of having m incremental checkpoints. we follow this idea to find m by comparing the expected waste time in two possible cases. in the first case, as shown in figure 6(a), m continuous incremental checkpoints are followed by a full checkpoint. alternatively, as shown in figure 6(b), after placing m continuous incremental checkpoints, we continue to perform the m + th incremental checkpoint. in each case, we consider the probability of failure. details are discussed in what follows. figure 6: sequential incremental checkpoint scenario case (a): after placing m continuous incremental checkpoints, a full checkpoint is performed next as shown in figure 6(a). let pi is the probability that a failure will occur after the second full checkpoint and before the next incremental checkpoint. hence,  − pi is the probability that failure will not occur in that period. if no failure occurs during this period, the overall cost is ca, ca = (of + moi ) + of . alternatively, if the failure occurs, the cost ca is ca = (of + moi ) + of + rf . therefore, the expected cost is ca = ( − pi )(of + moi ) + pi (of + moi + ri ). (30) case (b): after reaching m consecutive incremental checkpoints, another incremental checkpoint is performed as shown in figure 6(b). we consider that the probability of the failure events is approximately the same as in case (a), pi . when no failure occurs, the cost cb is cb = (of + moi ) + oi . alternatively, if a failure happens, the cost cb is cb = (of + moi ) + oi + (rf + (m + )ri ). therefore, the expected cost in case (b) is cb = ( − pi )[of + (m + )oi ) + pi [of + (m + )oi + (rf + (m + )ri )]. (31) we would like to have the number of incremental checkpoints as much as possible that yields the criteria that if another incremental checkpoint is added the expected waste time is larger than that of having another full checkpoint, and then the solution of m must be satisfied cb ≥ ca. thus, we will choose case (a) and perform a full checkpoint after m sequential incremental checkpoints. therefore, we obtain m ≥ of − oi pi ri − . (32) 396 n. naksinehaboon, m. păun, r. nassar, b. leangsuksun, s. scott inequality (32) suggests us that if m ≥ of −oipi ri −  , the cost in case (b) will be greater than the cost in case (a). thus, we take m as m = ⌈ of − oi pi ri −  ⌉ , (33) where d·e is the ceiling function. according to eq.(33), m is proportional to the difference between the full and the incremental checkpoint overhead and inversely proportional to the incremental recovery cost and the probability of a failure occurrence pi . the following points need to be raised at this point. firstly, if the incremental checkpoint overhead oi is nearly as large as the full checkpoint overhead of , performing incremental checkpoints instead of full checkpoints may not reduce the total overhead. in eq.(33), when oi approaches to of , the nominator approaches 0, then the number of incremental checkpoints m is small. secondly, all incremental checkpoints following the last full checkpoints must be loaded after a failure occurs, causing extra cost in the application recovery period. thus, if the incremental checkpoint recovery cost is expensive, then the number of incremental checkpoints should be small in order to maintain the low recovery cost. thirdly, a full checkpoint should be performed when a chance of a failure occurrence is high because, when a failure happens, there are few incremental checkpoints to be loaded. therefore, if the failure probability pi is high, the number of incremental checkpoints m is small as in eq.(33). lastly, the number of incremental checkpoints m does not depend on the full checkpoint recovery cost because there is only one full checkpoint loaded during the application recovery time. 3.3 incremental checkpoint model evaluation we obtained the general solution for our incremental checkpoint model. eq.(29) gives a checkpoint frequency function which is derived from a probability distribution function of the system time between failures (tbf). for the purpose of the incremental checkpoint/restart study and evaluation, we validate our model results only when the system failure follows the exponential distribution. this assumption will help simplify our validation. however, we plan to use these results as a guidance to further our study with other distributions such as time-varying one for the system failures. incremental checkpoint model for the exponential distribution for the time between failures (tbf) that follows an exponential distribution, we substitute f (t) = λ e−λ t , and  − f (t) = e−λ t , t ≥ , λ >  in eq.(34). the optimal model for an exponential distribution can be written as ni (t) = √ (m + ) k of + moi √ λ . (34) we can find the ith checkpoint placement. for i = , , , ... we have that: ∫ ti+ ti √ (m + ) k of + moi √ λ dt = . (35) solving eq.(35), we have that: ti+ = √ of + moi (m + ) k √ λ + ti. (36) using induction and taking t = , the sequence of the optimal checkpoint placements for the exponential distribution with failure rate λ is given by ti = i √ (m + ) k of + moi √  λ , k ∈ (, ) , (37) high performance computing systems with various checkpointing schemes 397 where ti is the ith checkpoint placement either full checkpoint or incremental checkpoint. in eq.(37), the number of incremental checkpoints can be obtained by eq.(33), and we can obtain the failure probability pi analytically. please note that the checkpoint interval (ti+ − ti) is a constant equal to √ (m+)k of +moi √  λ . from eq.(33), the probability pi of failure during the(ti+ − ti) interval is pi = p ( tf < ti+|tf > ti ) = f (ti+) − f (ti)  − f (ti) (38) for the exponential distribution, the cdf is f(t) =  − e−λ t , and we have pi = (  − e−λ ti+ ) − (  − e−λ ti )  − (  − e−λ ti ) (39) since the checkpoint interval is constant and ti+ − ti = t − t = t, then we have pi =  − e −λ t . (40) in practice, we have to find k (the re-computing time coefficient) and m at the same time. algorithm to find k and m: step 1: initialize of , oi , ri and λ . let k̂ = . and t = . //find m corresponding to k̂. step 2: let m̂ = . step 3: calculate t from eq. (35). step 4: calculate pi by eq. (38). step 5: if m̂ < of −oipi ri −  , then m̂ = m̂ + , and go to step 2. else go to step 6. step 6: set m = m̂ −  //finished finding m step 7: calculate the checkpoint sequence t,t, ...,tn corresponding to k̂ and m from eq. (35) step 8: calculate k̄ from eqs. (11)(12)(13) using the sequence in step 7. step 9: if k̂ = k̄ , then set k = k̂ done else set k̂ = k̄, and go to step 1. 4 comparisons between the full checkpoint model and the incremental checkpoint model in this section, we assume that the waste time of the incremental checkpoint model would be less than that of the full checkpoint model. as we discussed before, the incremental checkpoint scheme aims to reduce the total checkpoint overhead. in contrast, it introduces more expensive recovery cost than the full checkpoint mechanism. therefore, in order to provide evidences to the assumption, the comparisons between the waste times of both models are performed based on the actual failure data of white and frost hpc systems. white and frost were ibm supercomputers at lawrence livermore national laboratory. white consisted of 512 nodes with 16 processors per node and 8192 processors in total, and frost consisted of 68 nodes with 16 processors per node and 1088 processors in total. although white and frost systems had retired already at july 26, 2006 and june 30, 2005, respectively, we choose both systems to analyze because we have the failure timestamps of both systems over the period of four years from 2000 to 2004. before we run the simulations, we process both failure data sets (white and frost) as the following. first, we use the time window of one month to break the failure time stamps, and we then have 52 sets of failure times of white system and 50 sets of failure times of frost system. second, for each data set, we calculate the times between failures (tbfs) and the mean time between failures (mtbf). next, the kolmogorov-smirnov test is used 398 n. naksinehaboon, m. păun, r. nassar, b. leangsuksun, s. scott to test the null hypothesis that the data sets follow the exponential distribution with the corresponding mtbf with the significance level of 0.1. after the test, there are 33 sets of tbfs of white and 42 sets of tbfs of frost that follow the exponential distribution. these are the samples that we will use in the simulations for both systems. furthermore, the mtbf of the white samples is around 23 hours, and the mtbf of the frost samples is around 70 hours. an objective of the simulations is to study the ratio between the waste time and the application completion time, denoted as rwc. the application completion time is the time from the beginning of the application computation to the completion including the checkpoint overhead, recovery time, and re-computing time. rwc can be used as a metric to evaluate the efficiency of the models. for example, if rwc value approaches 0, most of application execution time is used to compute the application, which indicates that the performances of the proposed models are likely to be good. otherwise, most of application execution time is the waste time. however, there is no standard or threshold to indicate the goodness of a checkpoint model yet. to simulate the waste time of both models, for each tbf data set, the following procedure is applied. we determine the checkpoint sequences for the full and incremental checkpoint models, and then, for each sequence of tbfs, we compute the waste times for different completion time values, as the range of 0 to 30 days with the increment of 1 day. next we calculate rwc of each completion time value and find the average of rwc over the completion time. four experiments are conducted with different values of full checkpoint overhead (0.5, 2, 10 and 30 minutes). for each experiment, we calculate the average of rwc for the full checkpoint model and the incremental checkpoint model with 3 different values of incremental checkpoint overheads as 10%, 50%, and 90% of the full checkpoint overhead. therefore, we have 16 values of rwc in total for white and frost systems as shown in figure 7, figure 8, respectively. figure 7: white average of rwcs of the full checkpoint model and the incremental checkpoint model with different values of full checkpoint overhead of 0.5, 2, 10, and 30 minutes and different values of incremental checkpoint overhead of 10%, 50%, and 90% of the full checkpoint overhead values. figure 8: frost average of rwcs of the full checkpoint model and the incremental checkpoint model with different values of full checkpoint overhead of 0.5, 2, 10, and 30 minutes and different values of incremental checkpoint overhead of 10%, 50%, and 90% of the full checkpoint overhead values. according to figure 7 and figure 8, in every case of full checkpoint overheads, we notice that the proportions of the rwcs of the incremental checkpoint model are not implied by the ratio between the incremental checkpoint high performance computing systems with various checkpointing schemes 399 overhead and the full checkpoint overhead. for example, when the incremental checkpoint overhead is 10% of the full checkpoint overhead, the averages of rwcs of the incremental checkpoint model is approximately half of the averages of rwcs of the full checkpoint model. in practice, we expect that the waste time of the incremental checkpoint scheme should be less than the full checkpoint scheme, but the graphs show that, while the incremental checkpoint overhead is 90% of the full checkpoint overhead, the average of rwcs of the incremental checkpoint scheme is slightly greater than that of the full checkpoint scheme. moreover, the behaviors of the rwc in white and frost system are very similar although the mtbfs of both systems are notably different. we may imply that our model is able to give similar results in other systems. additionally, the full checkpoint model for the weibul distribution has been compared with the risk-based model [10] in [21]. in the risk-based model, a checkpoint will be performed if the waste time when the checkpoint is performed is less than the waste time when the checkpoint is not performed. in [10], there are three other models, but the risk-based model was the only model chosen for comparison because it is the best one among all the models in [10]. according to [21], with different values of checkpoint overhead, the full checkpoint model produced less waste time than the risk-based model, except for the case when the full checkpoint overhead is equal to 1 hour. in this case, the waste times of both models were very similar. 5 conclusions in this paper, we have presented near optimal checkpoint/restart models in full and incremental checkpoint schemes in a large-scale hpc environment. in these models, the time sequence of checkpoint placements was derived using the theory of a stochastic renewal reward process. the models are general and can be applied to any distribution of time between failures. however, the given example is for the case of the exponential distribution. the re-computing time which directly relates to the failure time, is an important factor in a checkpoint/restart model. instead of using the re-computing time, we introduced the re-computing time coefficient (k), its estimation approach, and an algorithm to estimate k. for the incremental checkpoint model, there is another significant factor which is the number of incremental checkpoints between consecutive two full checkpoints (m). the derived m yields the event that the expected waste time of performing a full checkpoint after m incremental checkpoints is less than that of performing the (m + )th incremental checkpoint. the proposed algorithm does not provide only the evaluation of m but also the re-computing time coefficient. for the model analysis part, the failure data of white and frost, the supercomputing system owned by llnl were used to simulate the waste time of both proposed models. the comparisons between both models are provided. in most cases, the waste times of the incremental checkpoint model are less than those of the full checkpoint model, especially when the incremental checkpoint overhead is much less than the full checkpoint overhead. furthermore, the proportion of the waste times of the incremental and full checkpoint models seems to not relate to the ratio of the incremental and full checkpoint overheads. lastly, the results of white and frost systems are very similar. this may indicate that the proposed models are able to give similar results for other systems. 6 future work in the near future, we will extend the incremental checkpoint model for the case where the number of incremental checkpoints between two consecutive full checkpoints is not a constant. we expect that the extended model will give less waste time than the proposed one. moreover, we will improve the method to evaluate the number of incremental checkpoints between two consecutive full checkpoints to yield optimality. in addition, we will work on improving the models. for example, in some applications, there are many communications between nodes. if one performs a checkpoint while there is a large amount of communications going on, the checkpoint 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[22] d. zmaranda, g. gabor, issues on optimality criteria applied in real-time scheduling,int. j. of computers, communications and control, issue 3, pp.536-540, 2008. ijcccv4n3draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 3, pp. 206-213 dictionary search and update by p systems with string-objects and active membranes artiom alhazov, svetlana cojocaru, ludmila malahova, yurii rogozhin artiom alhazov, svetlana cojocaru, ludmila malahova, yurii rogozhin institute of mathematics and computer science, academy of sciences of moldova academiei 5, chişinău md-2028 moldova e-mail: {artiom,sveta, mal, rogozhin}@math.md artiom alhazov iec, department of information engineering, graduate school of engineering hiroshima university, higashi-hiroshima 739-8527 japan yurii rogozhin rovira i virgili university, research group on mathematical linguistics av. catalunya, 35, tarragona 43002 spain e-mail: yrogozhin@yahoo.com received: april 5, 2009 accepted: may 30, 2009 abstract: membrane computing is a formal framework of distributed parallel computing. in this paper we implement the work with the prefix tree by p systems with strings and active membranes. we present the algorithms of searching in a dictionary and updating it implemented as membrane systems. the systems are constructed as reusable modules, so they are suitable for using as sub-algorithms for solving more complicated problems. keywords: membrane computing, p systems, active membranes, dictionary, prefix tree 1 introduction solving most problems of natural language processing is based on using certain linguistic resources, represented by corpora, lexicons, etc. usually, these collections of data constitute an enormous volume of information, so processing them requires much computational resources. a reasonable approach for obtaining efficient solutions is that based on applying parallelism; this idea has been promoted already in 1970s. for instance, the possibilities of applying massive parallelism in machine translation are considered in [5, 2]. many of the stages of text processing (from tokenization, segmentation, lematizing to those dealing with natural language understanding) can be carried out by parallel methods. this justifies the interest to the methods offered by the biologically inspired models, and by membrane computing in particular. however, there are some issues that by their nature do not allow complete parallelization, yet exactly they are often those “computational primitives" that are inevitably used during solving major problems, like the elementary arithmetic operations are always present in solving difficult computational problems. among such “primitives" in the computational linguistics we mention handling of the dictionaries, e.g., dictionary lookup and dictionary update. exactly these problems constitute the subject of the present paper. in our approach we speak about dictionary represented by a prefix tree. p (membrane) systems are a convenient framework of describing computations on trees. since membrane systems are an abstraction of living cells, the membranes are arranged hierarchically, yielding a tree structure. copyright c© 2006-2009 by ccc publications dictionary search and update by p systems with string-objects and active membranes 207 2 definitions membrane computing is a recent domain of natural computing initiated by gh. păun in [12]. the components of a membrane system are a cell-like membrane structure, in the regions of which one places multisets of objects which evolve in a synchronous maximally parallel manner according to given evolution rules associated with the membranes. the necessary definitions are given in the following subsection; see also [4] for an overview of the domain and [6] for a comprehensive bibliography. 2.1 computing by p systems let o be a finite set of elements called symbols; then set of words over o is denoted by o∗, and the empty word is denoted by λ . definition 1. a p system with string-objects and input is a tuple π = ( o, σ , h, e, µ, m,··· , mp, r, i ) , where: • o is the working alphabet of the system (the objects are strings over o), • σ is an input alphabet, • h is an alphabet whose elements are called labels, i identifies the input region, • e is the set of polarizations, • µ is a membrane structure (a rooted tree) consisting of p membranes injectively labeled by elements of h, • mi is an initial multiset of strings over o associated with membrane i,  ≤ i ≤ p, • r is a finite set of rules defining the behavior of objects from o∗ and of membranes labeled by elements of h. a configuration of a p system is its “snapshot", i.e., the current membrane structure and the multisets of string-objects present in regions of the system. the initial configuration is c = (µ, m,··· , mp). each subsequent configuration c ′ is obtained from the previous configuration c by maximally parallel application of rules to objects and membranes. this is denoted by c ⇒ c ′ (no further rules are applicable together with the rules that transform c into c ′). a computation is thus a sequence of configurations starting from c, respecting relation ⇒ and ending in a halting configuration (i.e., such one that no rules are applicable). if m is a multiset of strings over the input alphabet σ ⊆ o, then the initial configuration of a p system π with an input m over alphabet σ and input region i is (µ, m, . . . , mi−, mi ∪ m, mi+, . . . , mp). 2.2 p systems with active membranes to speak about p systems with active membranes, we need to specify the rules, i.e., the elements of the set r in the description of a p system. due to the nature of the problem of this paper, the standard model was generalized in the following: • cooperative rules: a rule operates on a substring of an object (otherwise, the system cannot even distinguish different permutations of a string); this feature is represented by a superscript * in the rule types; • string replication (to return the result without removing it from the dictionary); 208 artiom alhazov, svetlana cojocaru, ludmila malahova, yurii rogozhin • membrane creation (to add words to the dictionary). hence, the rules can be of the following forms: (a∗) [ a → b ] e h for h ∈ h, e ∈ e, a, b ∈ o∗evolution rules (associated with membranes and depending on the label and the polarization of the membranes, but not directly involving the membranes: the membranes are neither taking part in the application of these rules nor are they modified by them); (a∗r ) [ a → b||c ] e h for h ∈ h, e ∈ e, a, b, c ∈ o∗ (as above, but with string replication); (b∗) a[ ] e h → [ b ] e h for h ∈ h, e, e ∈ e, a, b ∈ o ∗ communication rules (an object is introduced into the membrane, possibly modified; the polarization of the membrane can be modified, but not its label); (c∗) [ a ] e h → [ ] e h b for h ∈ h, e, e ∈ e, a, b ∈ o ∗ communication rules (an object is sent out of the membrane, possibly modified; also the polarization of the membrane can be modified, but not its label); (d∗) [ a ] e h → b for h ∈ h, e ∈ e, a, b ∈ o∗ dissolving rules (in reaction with an object, a membrane can be dissolved, while the object specified in the rule can be modified); (g∗) [ a → [ b ] e g ] e h for g, h ∈ h, e, e ∈ e, a, b ∈ o ∗ membrane creation rules (an object is moved into a newly created membrane, possibly modified). additionally, we will write /0 in place of some strings on the right-hand side of the rules, meaning that the entire string is deleted. the rules of types (a∗), (a∗r ) and (g ∗) are considered to only involve objects, while all other rules are assumed to involve objects and membranes mentioned in their left-hand side. an application of a rule consists in replacing a substring described in the left-hand side of a string in the corresponding region (i.e., associated to a membrane with label h and polarization e for rules of types (a∗), (a∗r ) and (d ∗), or associated to a membrane with label h and polarization e for rules of type (c ∗), or immediately outer of such a membrane for rules of type (b∗) ), by a string described in the right-hand side of the rule, moving the string to the corresponding region (that can be the same as the source region immediately inner or immediately outer, depending on the rule type), and updating the membrane structure accordingly if needed (changing membrane polarization, creating or dissolving a membrane). only the rules involving different objects and membranes can only be applied in parallel; such parallelism is maximal if no further rules are applicable in parallel. 3 dictionary dictionary search represents computing a string-valued function {ui −→ vi |  ≤ i ≤ d} defined on a finite set of strings. we represent such a dictionary by the skin membrane containing the membrane structure corresponding to the prefix tree of {ui |  ≤ i ≤ d}, with strings $vi$ ′ in regions corresponding to the nodes associated to ui. let a, a be the source and target alphabets: ui ∈ a ∗ , vi ∈ a ∗ ,  ≤ i ≤ d. due to technical reasons, we assume that for every l ∈ a, the skin contains a membrane with label l. we also suppose that the source words are non-empty. for instance, the dictionary {bat −→ flying, bit −→ stored} is represented by [ [ ]  a [ [ [ $ f lying$′ ]  t ]  a [ [ $stored$′ ]  t ]  i ] b [ ]  c ···[ ]  z ]   dictionary search and update by p systems with string-objects and active membranes 209 consider a p system corresponding to the given dictionary: π = ( o, σ , h, e, µ, m,··· , mp, r, i ) , o = a ∪ a ∪ {?, ? ′, $, $′, $, $,fail} ∪ {?i |  ≤ i ≤ } ∪ {!i |  ≤ i ≤ }, σ = a ∪ a ∪ {?, ? ′, !, $, $′}, h = a ∪ {}, e = {, +, −}, i = , µ and sets mi,  ≤ i ≤ p, are defined as described above. so only the rules and input semantics still have to be defined. 3.1 dictionary search to translate a word u, input the string ?u?′ in region 1. consider the following rules. s1 ?l[ ]  l → [ ? ]  l , l ∈ a propagation of the input into the membrane structure, reaching the location corresponding to the input word. s2 [ ??′ ]  l → [ ] − l /0, l ∈ a marking the region corresponding to the source word. s3 [ $ → $||$ ] − l , l ∈ a replicating the translation. s4 [ $ ] e l → [ ]  l $, l ∈ h, e ∈ {−, } sending one copy of the translation to the environment. s5 [ $ → $ ]  l , l ∈ a keeping the other copy in the dictionary. the system will send the translation of u in the environment. this is a simple example illustrating search. if the source word is not in the dictionary, the system will be blocked without giving an answer. the following subsection shows a solution to this problem. 3.2 search with fail the set of rules below is considerably more involved than the previous one. however, it handles 3 cases: a) the target word is found, b) the target word is missing in the target location, c) the target location is unreachable. f1 [ ? → ?||? ]   replicate the input. f2 [ ? → ? ]   delay the second copy of the input for one step. f3 ?l[ ]  l → [ ? ] + l , l ∈ a propagation of the first copy towards the target location, changing the polarization of the entered membrane to +. 210 artiom alhazov, svetlana cojocaru, ludmila malahova, yurii rogozhin f4 ?l[ ] + l → [ ? ]  l , l ∈ a propagation of the second copy towards the target location, restoring the polarization of the entered membrane. f5 [ ?l → [ ? ] − l ]  k , l, k ∈ a if a membrane corresponding to some symbol of the source word is missing, then the first copy of the input remains in the same membrane, while the second copy of the input restores its polarization. creating a membrane to handle the failure. f6 [ ?? ′ → ? ]  l , l ∈ a target location found, marking the first input copy. f7 [ ? ]  l → [ ] − l /0, l ∈ a marking the target location. in either case, some membrane has polarization −. it remains to send the answer out, or fail if it is absent. the membrane should be deleted in the fail case. f8 [ $ → $||$ ] − l , l ∈ a replicating the translation. f9 [ $ ] e l → [ ]  l $, l ∈ h, e ∈ {, −} sending one copy of the translation out. f10 [ $ → $ ]  l , l ∈ a keeping the other copy in the dictionary. f11 [ ? → ? ] − l , l ∈ a the second copy of input will check if the translation is available in the current region. f12 ?l[ ] − l → [ ? ] − l , l ∈ a the second copy of input enters the auxiliary membrane with polarization −. by now the second copy of the input is in the region corresponding to either the search word, or to its maximal prefix plus one letter (auxiliary one). f13 [ ? → ? ] − l , l ∈ a it waits for one step. f14 [ ? → /0 ]  l , l ∈ a if the target word has been found, the second copy of the input is erased. f15 [ ? ] − l → [ ]  l ?, l ∈ a if not, the search fails. f16 [ ? ]  l → [ ]  l ?, l ∈ a sending the fail notification to the skin. dictionary search and update by p systems with string-objects and active membranes 211 f17 [ ?l → ? ]   erasing the remaining part of the source word. f18 [ ?? ′ ]   → [ ]  fail answering fail. f19 [ ? → ? ] − l , l ∈ a f20 [ ? → ? ] − l , l ∈ a f21 [ ? → ? ] − l , l ∈ a if the target location was not found, the first input copy waits for 3 steps while the membrane with polarization − handles the second input copy. f22 [ ? ]  l → /0, l ∈ a erasing the auxiliary membrane. 3.3 dictionary update to add an entry u −→ v to the dictionary, input the string !u$v$′ in region 1. consider the following rules. u1 [ ! → !||! ]   replicate the input. u2 [ ! → ! ]   delay the second copy of the input for one step. u3 !l[ ]  l → [ ! ] + l , l ∈ a propagation of the first copy towards the target location, changing the polarization of the entered membrane to +. u4 !l[ ] + l → [ ! ]  l , l ∈ a propagation of the second copy towards the target location, restoring the polarization of the entered membrane. u5 [ ! → ! ]  l , l ∈ a if a membrane corresponding to some symbol of the source word is missing, then the first copy of the input remains in the same membrane, while the second copy of the input restores its polarization. marking the fist copy of the input for creation of missing membranes. u6 [ !l → [ ! ] + l ]  k , l, k ∈ a creating missing membranes. u7 [ !$ → $ ]  l , l ∈ a releasing the target word in the corresponding location. 212 artiom alhazov, svetlana cojocaru, ludmila malahova, yurii rogozhin u8 [ !$ → /0 ]  l , l ∈ a erasing the second copy of the input. we underline that the constructions presented above also hold in a more general case, i.e., when the dictionary is a multi-valued function. indeed, multiple translations can be added to the dictionary as multiple strings in the region associated to the input word. the search for a word with multiple translations will lead to all translations sent to the environment. the price to pay is that the construction is no longer deterministic, since the order of application of rules s4 or f9 to different translations is arbitrary. nevertheless, the constructions remain “deterministic modulo the order in which the translations are sent out". all constructions work in linear time with respect to the length of the input. the parallelism is vital for checking for the absence of a needed submembrane, or for checking for the absence of a translation of a given word; sending multiple translation results out is also parallel. 4 discussion in this paper we presented the linear-time algorithms of searching in a dictionary and updating it implemented as membrane systems. we underline that the systems are constructed as reusable modules, so they are suitable for using as sub-algorithms for solving more complicated problems. the scope of handling dictionaries is not limited to the dictionaries in the classical sense. understanding a dictionary as introduced in section 3, i.e., a string-valued function defined on a finite set of strings, leads to direct applicability of the proposed methods to handle alphabets, lexicons, thesauruses, dictionaries of exceptions, and even databases. at last, it is natural to consider these algorithms together with morphological analyzer and morphological synthesizer. acknowledgments all authors gratefully acknowledge the support by the science and technology center in ukraine, project 4032. artiom alhazov gratefully acknowledges the support of the japan society for the promotion of science and the grant-in-aid for scientific research, project 20·08364. yurii rogozhin gratefully acknowledges the support of the european commission, project molcip, mif1-ct2006-021666. bibliography [1] g. ciobanu, g. păun, m.j. pérez-jiménez eds., applications of membrane computing, springerverlag, 2006. [2] h. kitano, challenges of massive parallelism, proceedings of the 13th international joint conference on artificial intelligence, chambery, france, 1993, vol. 1, 813–834. [3] gh. păun, computing with membranes, journal of computer and system sciences 61(1), 2000, 108–143. [4] gh. păun, membrane computing. an introduction, springer-verlag, 2002. [5] e. sumita, k. oi, o. furuse, h. iida, t. higuchi, n. takahashi, h. kitano, example-based machine translation on massively parallel processors, proceedings of the 13th international joint conference on artificial intelligence, chambery, france, 1993, vol. 2, 1283–1289. [6] p systems webpage. http://ppage.psystems.eu/. dictionary search and update by p systems with string-objects and active membranes 213 artiom alhazov (born on october 11, 1979), graduated in mathematics and computer science (the state university of moldova, chişinău, moldova) and received ph.d. in languages and information systems (rovira i virgili university, tarragona, spain). a researcher at the institute of mathematics and computer science of the academy of sciences of moldova. he completed a postdoc in åbo akademi university, turku, finland, and currently has a postdoc in hiroshima university, higashi-hiroshima, japan. his main research interests are theoretical computer science, formal language theory, parallel distributed computational models, and in particular the descriptional complexity of p systems with weak forms of interaction. he published over 90 research papers (collaborating with more than 30 researchers from many countries in europe and asia). he won numerous prizes for programming in school and university years, and the national youth prize in science, technics, literature and arts in 2006 for a collection of research works. svetlana cojocaru (born on july 26, 1952), graduated in mathematics (the state university of moldova, chişinău, moldova, 1974), received ph.d. in computer science (institute of cybernetics, ukrainian academy of sciences, kiev, 1982) and doctor in habilitation in computer science (institute of mathematics and computer science, academy of sciences of moldova, 2007). a deputy director at the institute of mathematics and computer science of the academy of sciences of moldova. her main research interests are formal languages and grammars, natural language processing, computer algebra, molecular computing. she published over 120 research papers. ludmila malahova (born on july 22, 1947), graduated in computer science (the state university of moldova, chişinău, moldova, 1970). a researcher at the institute of mathematics and computer science of the academy of sciences of moldova. she has a significant experience in computer science including computer graphics, formal languages, computer algebra, natural language processing, and molecular computing with more than 80 papers published in international journals, books and conference proceedings. yurii rogozhin (born on november 13, 1949), graduated in mathematics (the kuban state university, krasnodar, russia, 1971), received ph.d. in mathematical cybernetics (computer center of the russian academy of sciences, moscow, 1981) and doctor in habilitation in computer science (moscow state university, russia, department of computational mathematics and cybernetics, 1999). a principal researcher at the institute of mathematica and computer science of the academy of sciences of moldova republic and marie curie iif researcher at rovira i virgili university, research group on mathematical linguistics, tarragona, spain. his main research interests are mathematics, theoretical computer science, formal language theory and its applications, natural (biomolecular) computing and nanotechnology. he published over 110 research papers (collaborating with more than 45 researches from many countries in europe). int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 351-361 improving a svm meta-classifier for text documents by using naive bayes d. morariu, r. creţulescu, l. vinţan daniel morariu, radu creţulescu, lucian vinţan "lucian blaga" university of sibiu engineering faculty,computer science department e. cioran street, no. 4, 550025 sibiu, romania e-mail: {daniel.morariu,radu.kretzulescu,lucian.vintan}@ulbsibiu.ro abstract: text categorization is the problem of classifying text documents into a set of predefined classes. in this paper, we investigated two approaches: a) to develop a classifier for text document based on naive bayes theory and b) to integrate this classifier into a meta-classifier in order to increase the classification accuracy. the basic idea is to learn a meta-classifier to optimally select the best component classifier for each data point. the experimental results show that combining classifiers can significantly improve the classification accuracy and that our improved meta-classification strategy gives better results than each individual classifier. for reuters2000 text documents we obtained classification accuracies up to 93.87%. keywords: meta-classification, support vector machine, naive bayes, text document and performance evaluation 1 introduction while more and more textual information is available online, effective retrieval is difficult without good indexing and summarization of document content. document categorization is one solution to this problem. the task of document categorization is to assign a user defined categorical label to a given document. in recent years a growing number of categorization methods and machine learning techniques have been developed and applied in different contexts. documents are typically represented as vectors in a features space. each word in the vocabulary is represented as a separate dimension. the number of occurrences of a word in a document represents the value of the corresponding component in the document’s vector. in this paper we investigate some strategies for combining classifiers in order to improve the classification accuracy. we used classifiers based on support vector machine (svm) techniques and based on naive bayes theory, respectively. they are less vulnerable to degrade with an increasing dimensionality of the feature space, and have been shown effective in many classification tasks. the svm classifiers are actually based on learning with kernels and support vectors. we combine multiple classifiers hoping that the classification accuracy can be improved without a significant increase in response time. instead of building only one highly accurate specialized classifier with much time and effort, we build and combine several simpler classifiers. several combination schemes have been described in the papers [2] and [6]. a usually approach is to build individual classifiers and later combine their judgments to make the final decision. another approach, which is not so commonly used because it suffers from the "curse of dimensionality" [5], is to concatenate features from each classifier to make a longer feature vector and use it for the final decision. anyway, meta-classification is effective only if classifiers’ synergism can be exploited. in previous studies combination strategies were usually ad hoc and are implementing strategies like majority vote, linear combination, winner-take-all [2], or bagging and adaboost [16]. also, some rather complex strategies have been suggested; for example in [4] a meta-classification strategy using svm [15] is presented and compared with probability based strategies. copyright c© 2006-2010 by ccc publications 352 d. morariu, r. creţulescu, l. vinţan section 2 and 3 contains prerequisites for the work that we present in this paper. in sections 4 we present the methodology used for our experiments. section 5 presents the experimental framework and section 6 presents the main results of our experiments. the last section debates and concludes on the most important obtained results and proposes some further work. 2 support vector machine the support vector machine (svm) is a classification technique based on statistical learning theory [13], [15] that was applied with great success in many challenging non-linear classification problems and on large data sets. the svm algorithm finds a hyperplane that optimally splits the training set. the optimal hyperplane can be distinguished by the maximum margin of separation between all training points and the hyperplane. looking at a two-dimensional problem we actually want to find a line that "best" separates points in the positive class from points in the negative class. the hyperplane is characterized by a decision function like: f (x) = sign(〈~w, φ(x)〉+ b) (1) where ~w is the weight vector, orthogonal to the hyperplane, "b" is a scalar that represents the hyperplane’s margin, "x" is the current sample tested, "φ(x)" is a function that transforms the input data into a higher dimensional feature space and 〈·,·〉 represents the dot product. sign is the sign function. if ~w has unit length, then 〈~w, φ(x)〉 is the length of φ(x) along the direction of ~w. generally ~w will be scaled by ‖~w‖ . in the training part the algorithm needs to find the normal vector "~w" that leads to the largest "b" of the hyperplane. 3 naive bayes the bayes classifier uses the bayes theorem which basically computes prior probabilities for a given class based on the probability for a given term to belong to the specified class. the classifier computes the probability for a document to be into a given class. bayesian theory woks as a framework for making decision under uncertainty a probabilistic approach to inference [4] and is particularly suited when the dimensionality of the inputs data is high. bayes theorized that the probability of future events could be calculated by determining their earlier frequency. bayes theorem states that: p(y = yi | x = xk) = p(y = yi)p(x = xk | y = yi) p(x = xk) (2) where: p(y = yi) prior probability of hypothesis yprior p(x = xk) prior probability of training data x-evidence p(x = xk | y = yi) probability of x given ylikelihood p(y = yi | x = xk) probability of y given xposterior probability. the naive bayes classifier is based on the simplifying assumption that the attribute values are conditionally independent given target value. in other words the assumption is that, given the target value of the instance, the probability of observing the conjunction y, y...yn is just the product of the probabilities for the individual attributes: cmap = argmax<i<mp̄(xi | y ) = argmax<i<mp̄(xi) n∏ j= p̄(y j | xi) (3) improving a svm meta-classifier for text documents by using naive bayes 353 we used the notation p̄ for p because we don’t know exactly the values of the parameters p̄(xi) and p̄(y j | xi) . these values can be estimated based on the training set. we can calculate p̄(xi) = |di| |d| , where d is the set of documents and di is a subset of d for each category xi contained in the category set x . for training the classifier we consider v as the words vocabulary from documents contained in d, and for any category xi ∈ x there is a subset of documents contained in d that belongs to xi category. let yi a vector that contains all words extracted form documents from di set and ni numbers of all words occurrence from yi. thus for each word yi ∈ v we noted with ni j the total number of word occurrence yi in yi. we can write: p̄(y j | xi) = ni j +  ni + |v | (4) so, basically the naive bayes classifier ignores the possible dependencies, correlations, among the inputs and reduces a multivariate problem to a group of simple independent problems. it is noticed that in a naive bayes classifier the number of distinct p(y | x j) terms that must be estimated from the training data is just the number of distinct attribute values multiplied by the number of distinct target values. therefore it represents a much smaller number than if we would estimate all the p(y, y...yn | x j) terms that are needed for bayesian theory. for extending the svm and the naive bayes classifiers from two-class classification to multi-class classification, typically one of two methods are used: "one versus the rest", where each topic is separated from the remaining topics, and "one versus the one", where a separate classifier is trained for each class pair as in [17]. we selected the first method for two reasons: first, preliminary experiments shows that the this method gives better performance, which might be explained by the fact that the reuter’s database contains strongly overlapping classes and assigns almost all samples in more than one class. second, overall training time is much shorter for the first method. 4 meta-classifier models in [12] we presented a meta-classifier based on 8 svm classifiers that was used to improve the accuracy of the classification for text documents. we use the 3 models presented in [11] to test the classification accuracy of the meta-classifier in the case of introducing a new classifier based of naive bayes theory. the 3 models are: majority vote, the selection based on the euclidian distance and selection based on the cosine angle. 4.1 majority vote (mv) this first model for meta-classification is a maladjusted model that obtains the same results in time. the idea is to use all selected classifiers to classify the current document. each classifier proposes a specified class for this document incrementing the corresponding class-counter. the mv meta-classifier will select the class with the greatest count. if two or more classes with identical counts are obtained we classify the current document in all proposed classes. 4.2 selection based on euclidean distance (sbed) this model selects a classifier based on the current input data. it will learn only data that is incorrectly classified by the selected classifier, because we are expecting to have a smaller number of incorrectly classified input data than the number of correctly classified input data. thus we create for each classifier a queue which contains all incorrectly classified documents. 354 d. morariu, r. creţulescu, l. vinţan when we have an input document (current sample) that needs to be classified, first we randomly chose one classifier. after that we compute the euclidean distances (equation 5) between the current sample and all the samples that are in that self queue of the selected classifier. if we obtain at least one distance smaller than a predefined threshold we will reject this classifier. instead we will randomly select another classifier, except for the already rejected one. if all component classifiers are rejected, however, we’ll choose the classifier with the greatest euclidian distance. eucl(x, x’) = √√√√ n∑ i= ([x]i − [x′]i) (5) where [x]i represents the value from entry i of the vector x, and x and x’ represent the input vectors. after selecting the optimal classifier we’ll used it to classify the current sample. if the selected classifier succeeds to correctly classify the current document, nothing is done. otherwise we’ll put the current document into the corresponding queue. to see if the document is correctly or incorrectly classified we compare our proposed (computed) class with the reuters proposed class. the training is as follows: the meta-classifier analyzes the training set and each time when a document is incorrectly classified, the pattern is added to the selected classifier queue. in the second step, the validation step, we test the classification accuracy using the validation set. in the testing step the characteristics of the meta-classifier remains unchanged. because after each training part the characteristics of meta-classifier might change, we repeated these two steps many times. after 14 steps we obtain good results and the classification accuracy have not substantially increasing after that. 4.3 selection based on cosine angle (sbcos) the cosine angle is another possibility to compute the document similarity, often used to calculate text similarities. the formula to compute the cosine angle θ between two input vectors x and x′ is: cosθ = 〈x, x′〉 |x| · |x′| = ∑n i= [x]i[x ′]i√∑n i= [x]  i · √∑n i= [x ′]i (6) where [x]i represents the value from entry i of a vector x. this method is similar with the sbed method with two modifications. obvious, the first modification is that the similarity between documents is computed using the cosine angle between input vectors. the second modification is that the classifier is not randomly selected. instead doing this we constantly take into consideration all available classifiers. we compute the cosine between the current sample and all incorrect classified samples that are in the self queues. it is chosen the classifier that obtains the maximum cosine value. 5 experimental framework 5.1 the dataset our experiments are performed on the reuters-2000 collection [14], which has 984mb of newspapers articles in a compressed format. collection includes a total of 806,791 documents, with news stories published by reuters press covering the period from 20.07.1996 through 19.07.1997. the articles have 9822391 paragraphs and contain 11522874 sentences and 310033 distinct root words. documents are pre-classified according to 3 categories: by the region (366 regions) the article refers to, by industry codes (870 industry codes) and by topics proposed by reuters (126 topics, 23 of them contain no articles). due to the huge dimensionality of the database we will present here results obtained using a subset of data. from all documents we selected the documents for which the industry code value is improving a svm meta-classifier for text documents by using naive bayes 355 equal to "system software". we obtained 7083 files that are represented using 19038 features and 68 topics. a document is represented as a vector of words, applying a stop-word filter (from a standard set of 510 stop-words) and extracting the word stem [1]. entire set of 7083 documents is represented as word frequency matrix where each row represents a single document and each column represents a single word [3]. from these 68 topics we have eliminated those topics that are poorly or excessively represented. thus we eliminated those topics that contain less than 1% documents from all 7083 documents in the entire set. we also eliminated topics that contain more than 99% samples from the entire set, as being excessively represented. after doing so we obtained 24 different topics and 7053 documents, that were split randomly in training set (4702 samples) and testing set (2351 samples). in the feature extraction part we take into consideration both the article and the title of the article. 5.2 kernel types for svm the idea of the kernel trick is to compute the norm of the difference between two vectors in a higher dimensional feature space without representing those vectors in the new feature space. in practice we observed that by adding a constant bias to the kernel we obtained improved classifying results. for more details please consult [7] and [10]. we used in our selected svm classifiers two types of kernels each of them with different parameters [10]. for the polynomial kernel we varied the degree and for the gaussian kernel we changed the parameter c according to the following formulas (x and x′ being the input vectors): • polynomial k(x, x′) = (·d +〈x ·x′〉)d (7) d being the only parameter to be modified and represents the degree of the kernel, • gaussian (radial basis function rbf) k(x, x′) = ex p(− ‖x − x′‖ n·c ) (8) c being the classical parameter and n being the new parameter, introduced by us, representing the number of elements from the input vectors that are greater than 0. for feature selection with support vector machine method we used the polynomial kernel with degree 1 [8] and [9]. 5.3 representing the input data also in our selected classifier we used different representation of the input data. after extensive experiments [8], [10] we have seen that different types of kernels work better with different types of data representation. we represented the input data in three different formats. in the following formulas n(d,t) is the number of times that term t occurs in document d, and n(d, τ) is the maximum frequency occurring in document d. • binary representation in the input vector we store "0" if the word doesn’t occur in the document and "1" if it occurs without considering the number of occurrences. • nominal representation we compute the value of the weight using the formula: t f(d,t) = n(d,t) maxτ n(d, τ) (9) 356 d. morariu, r. creţulescu, l. vinţan • cornell smart representation we compute the value of the weight using the formula: t f(d,t) = {  i f n(d,t) =   + log( + log(n(d,t)) otherwise (10) these are later called as bin, nom or cs. 6 experimental results in [11] it is presented a meta-classifier based on 8 of svm classifiers that was used to improve the classification accuracy of text documents. the maximum classification accuracy, 87.11%, was obtained by a single svm type classifier with a second degree for polynomial kernel and cornell smart data representation. in [12] there are presented and tested several types of svm classifiers based on polynomial kernel and gaussian kernel, with different forms of representation. from all the tested classifiers eight distinct svm classifiers were included in the meta-classifier. the eight classifiers were chosen based on the obtained classification accuracy. we decided to incorporate also a bayes classifier in the meta-classifier presented above. as a result the new meta-classifier has 9 classifiers. we run again the tests for all the 3 models of the meta-classifier. we also calculated the maximum theoretical limit that could be reached by the new developed metaclassifier. thus, the introduction of the bayes classifier in the meta-classifier increases this maximum limit to 98.63% (against 94.21% as it was without bayes classifier). this fact provides an opportunity to obtain better classification accuracy. 6.1 selection based on the vote majority using this method, the obtained classification accuracy is 86.09%, for the 9 classifiers system. the classification accuracy dropped against the obtained value with 8 classifiers (86.38%), leading to a fall of 0.29%. this may occur because the bayes classifier obtains on the entire set of tests an accuracy of only 81.32%, incorrectly classifying quite a lot of documents (439). consequently, it seems to "help" the meta-classifier by selecting the 7 cases of wrong categories because bayes reinforced the wrong vote. the results are shown in fig. 1. 6.2 selection based on euclidian distance (sbed) we present the results obtained with the new meta-classifier consisting of 9 classifiers in comparison with the meta-classifier presented in [11]. there are presented only the first 14 steps since after this number of steps the classification accuracy is not substantially amended. as in [11] the threshold for the first 7 steps was chosen equal to 2.5 and the threshold for the last 7 steps was chosen equal to 1.5. a step represents a training process followed by a testing process. fig. 1 shows the results for the meta-classifiers with 8 and 9 classifiers. for the meta-classifier with 9 classifiers, the results are weaker than those obtained by the metaclassifier with 8 classifiers. this poorer accuracy is due to the poor accuracy of the bayes classifier (81.32%) compared with the svm, and due to the fact that the classifiers are randomly selected as we already explained. besides, it could be observed that the classification accuracy for the meta-classifier with 9 classifiers has also decreasing trends. this may be due to the fact that a classifier that correctly classifies a document d could incorrectly classify a document d that is quite similar with d. from this reason when running again the tests set this classifier has not been selected for the classification of d (because it gave poor results for d) and then looking for other classifier (which may in turn be classified wrong). improving a svm meta-classifier for text documents by using naive bayes 357 classification accuracy for meta-classifier 76 78 80 82 84 86 88 90 92 94 1 2 3 4 5 6 7 8 9 10 11 12 13 14 steps a c c u ra c y (% ) majority vote_8 classifiers majority vote_9 classifiers sbed _8 classifiers sbed _9 classifiers figure 1: meta-classifier accuracy for majority vote and sbed 6.3 selection based on cosine (sbcos) fig. 2 shows the results, obtained with the new meta-classifier containing 9 classifiers compared with the meta-classifier with 8 classifiers. it presents only the first 14 steps, because after this number of steps, the classification accuracy is not substantially amended. similar to [11], the threshold for the first 7 steps was chosen equal to 0.8 and the threshold for the last 7 steps was chosen equal to 0.9. in addition, this figure shows the maximum limit that can be achieved by the new meta-classifier (98.63%). classification accuracy for meta-classifier 75 80 85 90 95 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 steps a c c u ra c y (% ) sbcos_ 8 classifiers sbcos_ 9 classifiers upper limit figure 2: meta-classifier accuracy for sbcos and upper limit 6.4 the modification of the meta-classifier when it fails to select a class the meta-classifier presented in [11], in the case of a document dn that needs to be classified, takes each classifier and calculates the distance between dn and each document from the error queue respectively. if the calculated distance is less than the established threshold, the meta-classifier will not use the classifier to classify the document dn. on the other hand, if all classifiers are rejected by this method, the meta-classifier will choose the one that has the greatest distance achieved. thus, the meta-classifier will compute (predict) the class specified by the classifier even if it has a high probability to be wrong. for this reason we modified the meta-classifier in such a way that when all classifiers are rejected, the "forced" selected classifier will not choose the class with the highest value (it will fail anyway because 358 d. morariu, r. creţulescu, l. vinţan it is prone to falsely classify that type of documents). instead, it will choose the class immediately following in the list of classes which it predicts. this can be done because the classifier returns different values for each class. in this case, we will not select (predict) the class with the maximum value; instead of doing this, we will select (predict) the class that have the second value in the list, if the difference between the maximum value and the second one is not greater that 0.5. in this case, the classifier would specify another class for the current document dn. by implementing this change, the results of the metaclassifier consisting of 9 classifiers have been improved. in the following paragraphs, we will call this meta-classifier: the modified meta-classifier with 9 classifiers. 70 75 80 85 90 95 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 steps a c c u ra c y (% ) upper limit sbed _8 clasifiers sbed _9 classifiers sbed _9 clasifiers modified figure 3: classification accuracy for modified meta-classifiersbed fig.3 shows the results obtained with the modified meta-classifier with 9 classifiers vs. the metaclassifier with 8 svm classifiers and the unmodified meta-classifier with 9 classifiers respectively, all using sbed method. for the selection based on majority voting, learning not being involved, the modifications of the meta-classifier will produce no influence on the final outcome. besides each figure it is shown the maximum limit that can be obtained by the meta-classifier with 9 classifiers. the results obtained by the modified meta-classifier with 9 classifiers based on euclidian distance were improved. thus, it has been obtained a classification accuracy of 93.32% compared with the unmodified meta-classifier which achieved only 90.38%. recall that under the same conditions, the metaclassifier with 8 svm type classifiers [11] obtained a maximum classification accuracy of only 92.04%. in the first 7 steps, the classification accuracy of the modified meta-classifier with 9 classifiers is almost identical to that of the unmodified meta-classifier with 9 classifiers. this happens because during the first steps we can find a classifier that can be selected to classify the current document. after the first 9 steps we put in the queue of each classifier the documents with problems and only after these steps all the basic classifiers could be rejected and the proposed modification to the meta-classifier might be applied. therefore, during the first steps the results between the two meta-classifiers are slightly different because they always choose a random classifier of the existing 9. with this random selection at different running time we obtain small different results. fig. 4 exposes results obtained by meta-classifier using sbcos method. in this case, the classification accuracy for the new meta-classifier has improved from 93.10% to 93.87%. note that the meta-classifier with 8 svm classifiers under the same conditions, obtained a classification accuracy of only 89.74%. improving a svm meta-classifier for text documents by using naive bayes 359 75 80 85 90 95 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 steps a c c u ra c y (% ) sbcos_ 8 clasifiers sbcos_ 9 clasifiers sbcos_ 9 clasifiers_modifyed upper limit figure 4: classification accuracy for modified meta-classifier sbcos 7 conclusions and further work building up on the meta-classifier presented in [11], based on 8 svm components, we add to these a new bayes type classifier which leads to a significant improvement of the upper limit that the metaclassifier can reach. thus, the meta-classifier upper limit has increased from 94.21% when using 8 svm classifiers [11] to 98.63% when using the 8 svm classifiers plus the bayes classifier. furthermore, we presented results for all three models of meta-classifiers: majority voting (mv), selection based on euclidian distance (sbed) and selection based on cosine (sbcos). in the case of mv, we obtained a classification accuracy of only 86.09%, which is 0.29% lower than when using only 8 classifiers. moreover, in the case of the 9-classifiers sbed meta-classifier we obtained even lower results, on average dropping from 92.04% to 90.38%. in the case of the 9-classifiers sbcos, the classification accuracy of the meta-classifier has increased from 89.74% to 93.10%. finally, we considered that if there is any suspicion that the class to be predicted will not be the correct one, then the classifier should predict a different class. the latest will be the next class in the list of classes only if it is sufficiently close to the first predicted class. these change led to a substantial improvement of the meta-classifier with 9 classifiers. we have performed only experiments with the meta-classifier with 9 classifiers because only in this situation a maximum accuracy of 98.63% could be reached. in the case of the sbed meta-classifier, we obtained an average classification accuracy of 93.32%. this accuracy is with 2.94% greater than the best accuracy obtained without changing the class selection method. in the case of the augmented sbcos meta-classifier we similarly improved the accuracy from 93.10 in the future, an interesting natural extension of our work may be a more adaptive and intelligent meta-classifier that uses a neural network for choosing the classifier that will be used in classifying the current document. the meta-classifier could learn the incorrect / correct document classified and, without using the error queues, could optimally and faster select the optimal classifier that will be used. acknowledgements this work was partially supported by the romanian national council of academic research (cncsis) through the grant cncsis no. 485/2009-2011. 360 d. morariu, r. creţulescu, l. vinţan bibliography [1] s. chakrabarti, mining the webdiscovering knowledge from hypertext data, morgan kaufmann press, 2003. [2] n. dimitrova, l. agnihotri and g. wei, video classification based on hmm using text and face, proceedings of the european conference on signal processing,vol. xvii, pp. 1373-1376, finland, 2000. [3] j. engler, a. kusiak, mining authoritativeness of collaborative innovation partners, international journal of computers, communications and control, vol. v, no. 1, pp. 42-51, 2010. [4] d. lewis, naive (bayes) at forty: the independence assumption in information retrieval, att lab research, nj, vol. 1398, pp. 4-15, usa, 1998. [5] w.h. lin, a. houptmann, news video classification using svm-based multimodal classifier and combination strategies, proceedings of the tenth acm international conference on multimedia, pp. 323-326, 2002. [6] w.h. lin , r. jin, a. houptmann, a meta-classification of multimedia classifiers, international workshop on knowledge discovery in multimedia and complex data, taiwan, 2002. [7] d. morariu, l. vintan, a better correlation of the svm kernel’s parameters, proceeding of the 5th roedunet international conference, sibiu, pp. 244-249, june 2006. [8] d. morariu, l. vintan, v. tresp, feature selection methods for an improved svm classifier, proceedings of the 14th international conference on computational and information science, pp. 83-89, prague, august 2006. [9] d. morariu, l. vintan, v. tresp, evolutionary feature selection for text documents using the svm, proceeding of the 3rd international conference on neural computing and patter recognition, pp. 215-221, barcelona, october 2006. [10] d. morariu, classification and clustering using support vector machine, 2nd phd report, university "lucian blaga" of sibiu, september, 2005, http://webspace.ulbsibiu.ro/daniel.morariu/html/docs /report2.pdf. [11] d. morariu, l. vintan, v. tresp, meta-classification using svm classifiers for text documents, the 3rd international conference on neural computing and patter recognition, pp. 222-227, barcelona, october 2006. [12] d. morariu, text mining methods based on support vector machine, matrixrom, bucharest, 2008. [13] c. nello, j. swawe-taylor, an introduction to support vector machines, cambridge university press, 2000. [14] reuters corpus: http://about.reuters.com/researchandstandards/corpus/. released in november 2000. [15] b. schoelkopf, a. smola, learning with kernels. support vector machines, mit press, london, 2002. [16] g. siyang, l. quingrui, m. lin, meta-classifier in text classification, http://www. comp.nus.edu.sg/ zhouyong/papers/cs5228project.pdf. improving a svm meta-classifier for text documents by using naive bayes 361 [17] r. stoean, c. stoean, m. preuss, d. dumitrescu, evolutionary multi-class support vector machine for classification, international journal of computers, communications and control, 1(s):423-428, 2006. daniel i. morariu phd, was born at september 17th 1974 in sighişoara, romania. he graduates "lucian blaga" university of sibiu, obtaining a m.sc. in computer engineering, and a ph.d. in computer science from the same university. the phd title is "contributions to automatic knowledge extraction from unstructured data", phd supervisor professor lucian n. vintan. the phd program was partially supported from scientific and financial point of view by siemens corporate technology from munich. at present he is a full-time lecture at "lucian blaga" university of sibiu, engineering faculty, computer science department. he published over 12 scientific papers in international conference from romania, czech republic, and spain. r. creţulescu was born at august 8th 1968 in sibiu, romania. he graduates "babeş-bolyai" university of cluj-napoca and obtained a m.sc. in computer engineering, from the "lucian blaga" university of sibiu. at the present time he is a full-time lecture at university of sibiu, engineering faculty, computer science department. he published over 6 scientific papers in international conferences from romania, finnland and greece. profesor lucian n. vinţan phd ("lucian blaga" university of sibiu, ro) is an active researcher in advanced computer architecture, context prediction in ubiquitous computing systems, text documents classification, etc. he is a member of academy of technical sciences from romania, european commission expert in computer science, and visiting researcher fellow at university of hertfordshire, uk. professor vintan published 8 books and over 100 scientific papers (romania, usa, italy, uk, portugal, hungary, austria, germany, poland, china etc.). for his merits, professor vintan obtained the "tudor tanasescu" romanian academy award. he introduced some well-known original architectural concepts in computer architecture domain (dynamic neural branch prediction, pre-computed branches, value prediction focused on cpu’s context, etc.), recognized, cited and debated through over 80 papers published in many prestigious international conferences and scientific reviews (acm, ieee, iee, etc.) international journal of computers, communications & control vol. i (2006), no. 4, pp. 73-84 switching lpv controllers for a variable speed pitch regulated wind turbine fabien lescher, jing-yun zhao, pierre borne abstract: this paper deals with the control of a variable speed, pitch regulated wind turbine in the whole plant operating area. the wind turbine operating area can be divided into several zones, depending on the wind speed, and the control objectives are different for each operating zone. an hybrid control system composed by several lpv controllers which switches during transitions from one operating area to another is designed in order to ensure asymptotic stability and a good level of performances in the whole operating area. the lpv controllers are calculated from a convex lmi formulation of the problem in order to minimize an h2/h∞ criteria that optimizes the energy conversion of the system and that reduces the mechanical fatigue of the plant mechanical structure. the proposed controller is finally compared with two more conventional ones. keywords: wind energy, lpv modeling, h2/h∞ control, switching systems,mechanical fatigue. 1 introduction wind energy has widely grown during the last decades and is nowadays the most competitive form of renewable energy. nevertheless wind energy is not yet cost effective, and consequently, development of new technologies will be crucial for successful penetration of wind energy into electricity market. implementation of advanced control systems is considered as a promising way to improve wind energy conversion and to decrease wind energy cost. the wind turbine control objectives are mainly to optimize wind energy conversion, and to reduce dynamic loads experienced by the plant mechanical structure. indeed, dynamic loads hardly affect wind turbine lifetime and mainly determine mechanic components design, and consequently their cost [1]. designing a control system for a variable speed, pitch regulated wind turbine presents several issues: the system behavior is both highly non linear and quite uncertain, especially because of blades aerodynamic properties which are sensitive to climatic conditions. moreover, the control purpose is a multiobjective task, because the control system has to optimize a trade off between energy conversion maximization and alleviation of mechanical dynamic loads which result from very lightly damped resonant modes of the structure. the wind turbine operation is also decomposed into several operating zones, depending on the wind speed passing through the rotor: for low wind speeds, the wind energy system has to maximize produced power, whereas for high wind speed, the electric power has to be maintained to the generator nominal power. another difficulty of this wind turbine control problem is the uncontrollable and stochastic nature of the main component acting on the plant: the wind speed. moreover, the effective wind speed acting on the whole turbine rotor is a fictitious quantity and is hence not measurable and not available for control operation. in response to this multivariable and multiobjective control problem, the usual implemented controllers are calculated from a linearization of the model around an operating point by designing several separate and decoupled loops, each loop being tuned for one objective [2][3]. these controllers are therefore non optimal for the multiobjective problem and moreover, this design task has to be repeated for several linearization points on the reference trajectory that the system must follow. in this paper, a gain-scheduled wind turbine control system is designed for the whole operating region using linear matrix inequalities (lmis) optimization and the linear parameter varying (lpv) systems copyright c© 2006 by ccc publications 74 fabien lescher, jing-yun zhao, pierre borne framework [4]. the system non-linearities inherent to the aerodynamics subsystem are taken into account by an affine quasi-lpv model, obtained from a jacobian linearization of the system along the reference trajectory. an optimal time-varying controller, calculated through a lmi problem formulation, is calculated in order to minimize an h2/h∞ criterion composed by the different control objectives. this frequency domain criteria optimization is notably known to be efficient to reduce the fatigue of flexible mechanical structure, and is coupled with the output power regulation objective. this controller synthesis procedure has already demonstrated its efficiency for a simpler wind turbine model, especially for mechanical fatigue reduction [5]. besides, the proposed synthesis methodology also allows to handle the problem of transition between the operating zones of the system, for which the control objectives and the available actuators are not the same. the transitions between the operating zones is a serious problem, which can generate transient phenomena and losses of performance, and moreover this transition occurs frequently during the system operating life. the proposed approach generates an hybrid controller composed by switched lpv controllers which guarantees stability and control performances even during the transition between the controllers, from the construction of a piecewise-affine parameter dependent lyapunov function [6][7]. the wind turbine system is described in the first part of the paper. the control objectives over the whole operation area are then presented. the lpv modeling of the wind turbine is described, before the hybrid controller synthesis part. the performances of the proposed controller are finally compared with the ones of two other more conventional controllers. notations: for two symmetric matrices, a and b, a > b means that a − b is definite positive. at denotes the transpose of a. ? stands for symmetric blocks; • stands for an element that has no influence on the development. 2 system description the structure of a variable speed, pitch regulated wind energy conversion system is presented in fig.1. the system is formed by the rotor, the mechanical structure, and by a generator unit, composed by the generator and the static converter connected to the electrical grid. the control system acts on generator in order to apply the reference electromagnetic torque tg,re f and on the pitch actuator in order to control the pitch angle of the blades β , calculated from the measurements of the rotational speed of the shaft at the generator side, and of the flexion speed of the tower by an accelerometer located at the tower top. the effective wind speed v(t) passing through the rotor is considered as a first order dynamic process disturbed by an exogenous signal mv(t) v̇ = − 1 tv v + mv(t) (1) with the time constant tv calculated from the stochastic properties of the wind speed [8]. the mechanical model describing the structure of the plant has three degrees of freedom: the flexions of the blades (blades flap motion) and of the tower (tower fore-aft motion) in the direction of the wind, and the torsion of the drive train shaft (fig.2). a spring-damper representation is used to describe the flexibility of each component. moreover, the three blades are supposed to move conjointly and to be affected by the same forces at the same time. a linear model of the dynamic behavior of this structure is established using lagrange’s equations. the electrical subsystem, corresponding to the generation unit, composed by the generator and the power electronic components, has very fast dynamics compared with dynamics of the other subsystems. consequently, and considering the study objectives, the electrical dynamics are neglected. hence, electromagnetic torque tg is supposed equal to its reference tg,re f . switching lpv controllers for a variable speed pitch regulated wind turbine 75 figure 1: wind energy conversion system structure. figure 2: wind turbine mechanical structure the pitch actuator subsystem represents the hydraulic or electric system which makes the blades revolve around their lengthwise axis. this system is described by a first order transfer function with a time constant tβ . the aerodynamic conversion process of the turbine rotor is characterized by the extracted torque taero and by the out-of-plane thrust force faero, which are functions of the air mass density ρ , the wind velocity v, the rotational speed of the turbine ωt , the horizontal blades speed ẋp and the power and thrust coefficients cp and ct : taero = 1 2 ρ π r2 (v + ẋp)3 ωt cp(λ , β ), faero = 1 2 ρ π r2(v + ẋp)2ct (λ , β ), (2) with r the length of the rotor blades. the aerodynamic coefficients cp and ct are non-linear functions depending on blades pitch angle β and tip speed ratio λ = ωt rv . 76 fabien lescher, jing-yun zhao, pierre borne 3 control task the controller objectives are to ensure: • stability along the reference trajectory for the whole operation, • good performances of the selected outputs, i.e. energy conversion and alleviation of mechanical loads affecting the plant structure. the wind turbine operation area can be divided into three zones, depending on the wind speed acting on blades. the energy conversion objectives, and thus the control objectives, are different for each zone. for low wind speed, i.e. for v < v1, the main objective is to maximize system energy conversion yield. in this partial load 1 zone, system has to operate at cp(λ , β ) = cp,max. pitch angle β is then maintained constant at βopt and rotational speed ωt is controlled to minimize the criteria δ λ = λ − λopt , by acting only on generator electromagnetic torque tg. for higher wind speed, corresponding to v1 < v < v2, turbine rotational speed ωt is maintained at the nominal generator speed by acting on electromagnetic torque tg. pitch angle β is also maintained at βopt to maximize energy conversion efficiency (partial load 2). for high wind speed, i.e. v > v2, wind turbine operates in full load and electric produced power pelec has to be regulated at nominal generator power. turbine rotational speed is maintained around nominal generator speed and pitch angle β is controlled in order to reduce power coefficient cp(λ , β ). control system is then multivariable in this zone, because it acts on both generator torque and pitch angle. evolution of the main variables in function of wind speed are presented in fig.3. the control system has figure 3: evolution of the main variables in function of wind speed. to operate over the full envelope of wind speeds. hence, transitions between these different operating zones have to be handled by the control system in a smooth manner, which avoids the generation of large transients. switching lpv controllers for a variable speed pitch regulated wind turbine 77 the other main control objective is to reduce mechanical fatigue of most expensive plant components, especially blades, drive train and tower. hence, the control system will be designed in order to reduce the variations of the drive train torsion torque td and the blades and tower flexion forces fb and ft , expressed by: td = dd(ωt −ωg) + kd(θt −θg) fb = dbẋb + kbxb ft = dt ẋt + kbxt with d and k the damper and spring coefficients of the corresponding components. the control system will be designed in order to optimize a trade-off between these objectives by minimizing a criterion expressed in the frequency range. h2 norm optimization permits to reduce the average variations of the energy conversion parameter, i.e. δ λ , δ ωt or δ pelec, in the whole frequency range. h∞ cost minimization is able to alleviate, in the frequency range, the maximum response of the mechanical loads to a variation of wind speed, which is generally the response corresponding to the resonance frequency of the component, and which is the most damaging. 4 lpv modeling the lpv model can be considered as a group of linear local descriptions of nonlinear descriptions. given the nonlinear system describing the wind turbine behavior ẋ = f (x, u, w) y = g(x, u, w) (3) z2 = h2(x, u, w) z∞ = h∞(x, u, w) with x the state of the system, u the control input, w the external disturbance, y the measured output and z2 and z∞ the performance outputs, the jacobian linearization approach can be used to create an lpv system based on the first-order taylor series expansion of the nonlinear model. a family of linear plants is obtained by linearizing the nonlinear system with respect to a set of equilibrium points located on the reference trajectory, which are parametrized by the scheduling parameter ρ and satisfy f (xe(ρ), ue(ρ), 0) = 0. corresponding to a specified family of equilibrium points, the family of the linearized plants can be written in the following form:   δ ẋ δ y δ z2 δ z∞   =   ∂ f ∂ x |e ∂ f ∂ u |e ∂ f ∂ w|e ∂ g ∂ x|e ∂ g ∂ u|e ∂ g ∂ w|e ∂ h2 ∂ x |e ∂ h2 ∂ u |e ∂ h2 ∂ w |e ∂ h∞ ∂ x |e ∂ h∞ ∂ u |e ∂ h∞ ∂ w |e     δ x δ u δ w   (4) where the deviation variables are defined by δ x = x−xe(ρ) δ u = u−ue(ρ) δ y = y−ye(ρ) δ w = w−we(ρ) and where j|e represents the value of the jacobian coefficient j at the equilibrium point (xe(ρ), u(ρ)). before deriving the lpv model of the wind turbine, the scheduling parameters must be selected so 78 fabien lescher, jing-yun zhao, pierre borne that the appropriate equilibrium point can be located on the reference trajectory. because the reference trajectory is decomposed into three parts, corresponding to the three operating areas, the parameter set p = [ρmin, ρmax] is partitioned into three subsets p1 = [ρ1,min, ρ1,max] p2 = [ρ2,min, ρ2,max] p3 = [ρ3,min, ρ3,max] with ρ1,max = ρ2,min and ρ2,max = ρ3,min corresponding to v1 and v2, i.e. the transitions between the different operating regions. hence, by calculating the jacobian coefficients as functions of the scheduling parameter ρ along the reference trajectory for each parameter subset pi, and considering the wind turbine system configuration, the dynamic behavior of the model (4) for the wind turbine system is governed by the equation:   δ ẋ δ y δ z2 δ z∞  =   ai(ρ) bi gi cy,i(ρ) 0 0 cz2,i(ρ) dz2,i(ρ) 0 cz∞,i(ρ) dz∞,i(ρ) 0     δ x δ u δ w   (5) i = {1, 2, 3}. the jacobian coefficients of the expression of the aerodynamic torque and of the thrust force (2) are calculated along the reference trajectory and are approximated by affine functions of the scheduling parameter ρ . hence matrices ai(ρ) have the following form: ai(ρ) = ai,0 + ρ ai,1 (6) and are continuous in the whole parameter set p. 5 lpv control design from the developed family of lpv models of the wind turbine, we aim at designing a family of lpv dynamic output-feedback controllers in the form: [ ẋk u ] = [ ak,i(ρ) bk,i(ρ) ck,i(ρ) dk,i(ρ) ] [ xk y ] , i = {1, 2, 3}. (7) the order of each controller is the same than the plant order, and the controllers dynamics are allowed to be discontinuous at the boundaries of the subsets pi. hence one lpv controller is designed in each subset pi in order to optimize a set of performances, expressed as a multichannel h2/h∞ criteria, corresponding to the control objectives of the operating area. the control synthesis is based on the lmi optimization and on the construction of a continuous piecewise-affine lyapunov function depending on the scheduling parameter ρ , which mimics the parameter dependence of the plant model (6). hence, the parameter dependent lyapunov function is defined as: v (xcl , ρ) = xtcl p(ρ)xcl (8) where xtcl = ( xt xtk ) and p(ρ) = φi(ρ)pi(ρ), with φi(ρ) = 1 if ρ ∈ pi, and φi(ρ) = 0 otherwise. in order to apply the linearizing changing of variables described in [9], matrices pi(ρ) and p−1i (ρ) are partitioned as: pi(ρ) = ( yi(ρ) si(ρ) sti (ρ) • ) p−1i (ρ) = ( x r rt • ) (9) switching lpv controllers for a variable speed pitch regulated wind turbine 79 with i −yi(ρ)x = si(ρ)rt . as explained in [4], matrices x and r are constrained to be constant over the whole parameter set p in order to obtain a family of controllers independent of the gain-scheduling parameter rate ρ̇ , which is not available in real-time. moreover, matrices yi(ρ) are affine in the parameter ρ in each subset pi: yi(ρ) = yi,0 + (ρ −ρi,min)yi,1 (10) and the function y (ρ) = φi(ρ)yi(ρ) is constrained to be continuous over the whole parameter set p: yi(ρ) = yj(ρ)∀ρ ∈ pi ⋂ p j (11) by verifying that for the wind turbine model the triple (ai(ρ), bi(ρ),cy,i(ρ)) is stabilizable and detectable for all ρ ∈ p, and by assuming that the scheduling parameter rate ρ̇ is bounded in a set [−ρ̇max, ρ̇max], the family of controllers can be calculated by applying the following theorem1: theorem:[9][6] suppose there exist symmetric definite positive matrices x and y such as (10) and (11), matrices âi(ρ) = âi,0 + (ρ −ρi,min)âi,1 + (ρ −ρi,min)2âi,2 b̂i(ρ) = b̂i,0 + (ρ −ρi,min)b̂i,1 + (ρ −ρi,min)2b̂i,2 ĉi(ρ) = ĉi,0 + (ρ −ρi,min)ĉi,1 d̂i(ρ) = d̂i,0 + (ρ −ρi,min)d̂i,1 symmetric semi-definite positive matrices mi = ( m1,i ? m2,i m3,i ) and a symmetric matrix q satisfying the following matrix inequalities   u + u t ? âi + (ai + bid̂icy,i)t ρ̇yi,1 + v + v t gt gt yi cz∞ x + dz∞ĉi cz∞ + dz∞ d̂icy,i ? ? ? ? −γ i ? 0 −γ i   + (ρ −ρi,min) 2 ( mi 0 0 0 ) < 0   x ? ? i yi ? dz2ĉi + cz2 x cz2 + dz2 d̂icy,i q   > 0 tr(q) < ν( m1,i ? m2,i + âi,2 m3,i + yiai + b̂i,2cy,i ) > 0 (12) for all pairs (ρ, ρ̇) ∈ {ρi,min, ρi,max}×{ρ̇i,min, ρ̇i,max}, with u = aix + biĉi v = yiai + b̂icy,i 1the dependance on ρ is dropped for the notation conveniance 80 fabien lescher, jing-yun zhao, pierre borne then the family of dynamic controllers (7) with, ∀ρ ∈ p: dk,i = d̂i ck,i = (ĉi −dk,icy,ix )r−t bk,i = s −1 i (b̂i −yibidk,i) ak,i = s −1 i (âi −sibk,icy,ix −yibick,irt −y (ai + bidk,icy,i)x )r−t where matrices r and si verify for all ρ ∈ p the relation rsti = i −xyi, guarantees that: • the elements of the family of closed-loop systems composed by (5) and (7) are asymptotically stable; • the h∞-norm of the transfer w → z∞ is less than √ γ ; • the h2-norm of the transfer w → z2 is less than √ ν . note that the lmi problem formulated in this theorem has a finite number of inequalities, thanks to the introduction of matrices mi, and permits to do without the gridding phase which is commonly used for lpv synthesis and which is very computationally intensive. moreover, in order to facilitate the control system implementation, and to prevent the system from having too fast dynamics, additional lmis constraints are formulated to place the poles of the closed loop systems in a determined region of the state-space domain. placing the poles in a circle centered at the origin of the state-space permits to prevent for obtaining too fast poles and too few damped fast poles. hence if the following lmis are satisfied for ∀ρ ∈ {ρi,min, ρi,max} and for semi-definite positive matrices ni = ( n1,i ? n2,i n3,i ) :   −rx ? ? ? −ri −ryi ? ? aix + biĉi âi −rx ? ai + bid̂icy,i yiai + b̂icy,i −ri −ryi   +(ρ −ρi,min)2 ( ni 0 0 ni ) < 0   n1,i ? ? ? n2,i n3,i ? ? 0 âi,2 n1,i ? 0 b̂i,2cy,i + yi,1ai,1 n2,i n3,i   > 0 (13) then the poles of the closed-loop systems composed by (5) and (7) are located in a circle of rayon r and of center the origin of the state-space. hence, an optimal family of dynamic controllers that minimize the h2-norm of the transfer w → z2 for a given h∞-norm √ (γ) of the transfer w → z∞ can be designed by solving the following convex lmis problem: min ν subject to the lmis (12) and (13) (14) 6 wind turbine control system design and simulation the wind turbine control system is designed from the family of developed lpv systems by calculating lpv controllers which optimize a trade-off between the energy conversion, i.e. the energy yield switching lpv controllers for a variable speed pitch regulated wind turbine 81 maximization in partial load and the output energy regulation in full load, and the reduction of mechanical loads. hence the performance output z2 represents the energy optimization variables δ λ , δ ωt or δ pelec, depending on the operating area, and then the subset of the scheduling parameter, augmented by the corresponding weighting functions. in the full load operation, and in order to reduce the pitch actuator fatigue, the pitch angle deviation δ β is added to this vector. the performance output vector z∞ is composed by the mechanical forces affecting the plant structure, ft , fp and td, augmented by the corresponding weighting functions. in order to reduce the vibrations of the mechanical components for frequencies above their resonance frequencies, the outputs ft , fp and td are multiplied by a high-pass filter, in order to penalize the high-frequency variations of these outputs during the controller synthesis. inversely, the energy optimization variables are multiplied by a low-pass filter in order to ensure to the energy conversion output a good tracking at low frequency. indeed, at high frequency, a good tracking of these components is not reached because it would induce an increase of dynamical loads on the actuators and on the mechanical structure. the scheduling parameter ρ is chosen to be ρ = pelec + kβ 2, k > 0, because it permits to locate the operating point on the reference trajectory and is directly measured, contrary to the wind speed crossing over the turbine rotor. moreover, the bounds of the scheduling parameter rate ρ̇ are derived from the actuators rate limitations. by solving numerically the problem (14), three lpv controllers are designed, one for each operating area. therefore, the control applied to the plant actuators can be discontinuous during controllers switching. because the switchings between partial load 2 and full load occur for β = 0, the pitch control is ensured to be continuous, whereas no continuity guarantees are provided concerning the generator torque. to ensure this, a structural constraint on the controllers matrices is added during the synthesis of the lpv controllers: the lines of the controllers matrices ck,i and dk,i corresponding to the generator torque are constrained to be equal at the boundaries of the subsets pi: ck,i(1)(ρ) = ck, j(1)(ρ) dk,i(1)(ρ) = dk, j(1)(ρ) for all ρ ∈ pi ⋂ p j. the efficiency of the proposed controller is compared, at the sight of simulation results, with the ones of the two other existing controllers, a gain scheduling pi-based controller, and a multivariable gain scheduling lqg one, for the full load operation. indeed, this operating area is the most challenging in the viewpoint of control design because the controller is multivariable, because the mechanical loads affecting the plant are the highest, and because the pitch action is really efficient to alleviate these loads with a carefully designed controller [2]. as mentioned in [2] and [3], the pi-based controller is designed by calculating controllers for two separate loops: firstly, a pi controller is tuned to guarantee power regulation from generator speed measurement. then a tower speed feedback is designed to increase tower fore-aft damping, by calculating a controller which appropriately filters blades flap excitation and which does not interact with the bandwidth of the first loop. unlike the proposed controller, this controller acts only on pitch angle. the gain scheduling lqg controller is designed from the same augmented model and with the same performances outputs as the proposed controller. the lqg methodology permits to design a multivariable controller which optimizes a time domain quadratic criteria representing a trade off between the different control objectives. unlike the proposed lpv design methodology, the lqg design does not provide guarantees of stability and performances along the reference trajectory, but only in several operating points on this trajectory. actually, one lqg controller has to be designed at each linearization point of the trajectory, and a gain scheduling process has to be used to interpolate the different lqg controllers. moreover, and contrary to the proposed method, the lqg design does not provide any specification of performance in the frequency range such as h∞ or h2 norms. 82 fabien lescher, jing-yun zhao, pierre borne 200 205 210 215 220 225 230 235 240 245 250 14 16 18 20 wind speed temporal serie w in d s p e e d ( m /s ) 200 205 210 215 220 225 230 235 240 245 250 3.98 4 4.02 x 10 5 generated electric power e le ct ri c p o w e r (w ) 200 205 210 215 220 225 230 235 240 245 250 8 10 12 14 16 18 pitch angle b e ta ( °) time (sec) figure 4: temporal series. lpv controller; pi-controller; lqg controller. table 1: equivalent load: ratio between the different controllers and pi controller. controller shaft tower blades lpv controller 57.3% 63.8% 82.9% lqg controller 59.0% 73.4% 87.8% pi controller 100% 100% 100% the simulations are based on a dynamic model implemented in matlab-simulink of a three blades 400 kw wind turbine containing: • non linear and stationary aerodynamics, with rotational effects disturbances due to wind shear and tower shadow. • flexible drive train model, flexible tower model including first fore-aft mode, flexible blades in flapwise direction, • pitch actuator limitations on pitch rate (±10◦/s) and pitch amplitude (0◦ 30◦). the simulated wind speed respects stochastic properties of van der hoven spectra with high fluctuations. the controllers performances are compared for both power regulation and alleviation of mechanical fatigue. these controllers have equivalent bandwiths and are tuned in order to guarantee a similar level of performance for power regulation. evaluation of mechanical fatigue is provided by using the rainflow counting algorithm, which calculates the number of load cycles from time domain simulations results, and the fatigue equivalent load for each component [10]. temporal series of wind speed, produced power pelec and pitch angle β are presented in fig.4. fatigue equivalent loads for the shaft, blades and tower are calculated from the simulations, and table 1 presents the ratio between the equivalent loads obtained with the different controllers and with the pi-controller. the proposed lpv controller is seen to be more effective for the two selected control objectives, especially for mechanical fatigue reduction for each component, and despite a pitch activity inferior to the pitch activity caused by the lqg controller. a sensitive gain of lifetime of the mechanical components of the plant, or a sensitive reduction of the mass, and consequently of the cost of these components can switching lpv controllers for a variable speed pitch regulated wind turbine 83 200 250 300 350 400 450 10 12 14 16 w in d s p e e d ( m /s ) 200 250 300 350 400 450 2 2.5 3 3.5 4 4.5 x 10 5 e le ct ri c p o w e r (w ) 200 250 300 350 400 450 0 5 10 p itc h a n g le (° ) time (sec) figure 5: transitions between partial load 2 and full load. then be expected. the behavior of the plant with the proposed control system is also shown during the transition between partial load 2 and full load in fig.5, i.e. for wind speeds around the rated wind speed, which is about 12.2m/s. the control system is then seen to handle efficiently this transition. 7 conclusion in this paper, an hybrid controller composed by several lpv controllers has been proposed for the whole operating area of a variable speed, pitch regulated wind turbine. this control system achieves the optimization of a trade-off between the energy conversion and the reduction of the mechanical loads for the whole envelope of wind speeds acting on the plant, taking into account the different objectives of the energy conversion for the different operating areas. moreover, the employed lpv modeling, as well as the lmi formulation of the problem, provides a good framework for additional possible constraints of the control problem during the synthesis, like actuator saturation, or for ensuring the robustness to the system parameters uncertainties. references [1] t. burton, d. sharpe, n. jenkins, and e. bossanyi, wind energy handbook. john wiley and sons, 2001. [2] e.a. bossanyi, the design of closed loop controllers for wind turbines, wind energy, vol.3, 2001, pp.149-163. [3] w.e. leithead, s. dominguez and c.j. spruce, analysis of tower/blade interaction in the cancellation of the tower fore-aft mode via control, proc. european wind energy conference, london, uk, 2004. 84 fabien lescher, jing-yun zhao, pierre borne [4] p. apkarian and r. j. adams, şadvanced gain-scheduling techniques for uncertain systems,ť ieee transactions on control system technology, vol. 6, pp. 21−32, 1997. [5] f. lescher, j. zhao, and p. borne, şrobust gain scheduling controller for pitch regulated variable speed wind turbine,ť studies in informatics and control, vol. 14, pp. 299−315, 2005. [6] s. lim, analysis and control of linear parameter-varying systems, ph.d. dissertation, stanford university, 1999. [7] b. lu and f. wu, switching lpv control designs using multiple parameter-dependent lyapunov functions, automatica, vol.40, 2004, pp. 1973-1980. [8] c. nichita, d. luca, b. dakyo, and e. ceanga, şlarge band simulation of the wind speed for real time wind turbine simulators,ť ieee transactions on energy conversion, vol. 17, pp. 523−529, 2002. [9] c. scherer, p. gahinet, and m. chilali, şmultiobjective outputfeedback control via lmi optimization,ť ieee transactions on automatic control, vol. 42, pp. 896−910, 1997. [10] m. matsuiski and t.endo, fatigue of metals subjected to varying stress, japan soc. mech. engrg, 1969. f. lescher and p. borne ecole centrale de lille lagis laboratory bp 48, cité scientifique, 59651 villeneuve d’ascq cedex, france e-mail: fabien.lescher@eigsi.fr j.y. zhao eigsi la rochelle erpa laboratory 26 rue vaux le foletier, 17000 la rochelle, france e-mail: jing-yun.zhao@eigsi.fr received: november 2, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 768-774 towards the implementation of computer-aided semiosis a.e. lascu, s.c. negulescu, c. butaci, v. cret alina e. lascu, sorin c. negulescu lucian blaga univeristy of sibiu, romania e-mail: alina.lascu@ulbsibiu.ro, sorin.negulescu@ulbsibiu.ro casian butaci, vasile creţ agora university, oradea and r&d agora ltd. cercetare dezvoltare agora oradea, romania e-mail: casian12@yahoo.com, vcret@univagora.ro motto: in the beginning was the word bible, john 1:1 abstract: computer-aided semiosis (cas) is a concept coined by a team of researchers a couple of years ago. since it is a promising domain due to the fact that responds to actual trans-cultural communication needed in the broad-band society where often the message behind the words does not come clear the subject ought being inquired more detailed as promised in other papers of the same authors. this interesting idea was inspired from eco’s theory of communication which states that the receiver "fills the message with significance"; hence it is vital for any communication and is strongly dependent on the cultures involved. in line with eco’s theory, the research in this area must be trans-disciplinary and anthropocentric. in the intention of narrowing the existing gap between the technological offers and user expectations the macro-architectural feature is that translation will progress from textual, semantically correct, to multimodal, culturally adequate, based on common concepts and "grammar" (rules to combine them into meaningful sentences); thus, this paper will present possible approaches towards the implementation of cas. given the fact the ontologies are considered to be the pillars of semantic web but also a key tool in implementing cas, both will be a subject of this paper in the light of finding an implementation solution. the paper is structured on five sections: the first will present the defining aspects of the concept relating it with previous research; the second section will deal with cas approach and architecture, following with the state of the art regarding ontologies and their relation with semantic web. among the conclusions, one is already noticeable: cas could not be possible without a trans-cultural ontology. keywords: computer-aided semiosis (cas), human-computer interaction (hci), ontologies, semantic web, interfaces. 1 introduction the key to effective online cross-cultural communication is a well-designed transcultural ontology which to help in disambiguating between concepts that seem alike but their meaning differs from culture to culture; this tool is intended to be developed in the following years by a team of young researchers in order to substantiate and implement a new and innovative concept in the field of hci: computer-aided semiosis (cas). copyright c⃝ 2006-2010 by ccc publications towards the implementation of computer-aided semiosis 769 university of colorado, usa developed a study regarding cross-cultural communication strategy and came up with the conclusion that often intermediaries who are familiar with both cultures can be helpful in cross-cultural communication situations. they can translate both the substance and the manner of what is said. for instance, they can tone down strong statements that would be considered appropriate in one culture but not in another, before they are given to people from a culture that does not talk together in such a strong way. they can also adjust the timing of what is said and done. some cultures move quickly to the point; others talk about other things long enough to establish rapport or a relationship with the other person. if discussion on the primary topic begins too soon, the group that needs a "warm up" first will feel uncomfortable. a mediator or intermediary who understands this can explain the problem, and make appropriate procedural adjustments [4]. the results of this study can be as well applied in ict due to the fact that online cross-cultural communication could also use virtual intermediaries which to have access to a transcultural ontology assisting thus the user grasping the right meaning of a certain message, i.e. in written (chat), spoken (voice) and/or visual form. in the light of the earlier scan, this paper will present possible approaches towards the implementation of cas. since the ontologies are thought to be the pillars of the semantic web but also an important tool in implementing cas, both will be key-subjects of this paper in the quest of finding an implementation solution. the paper is structured on five sections: the second will present the defining aspects of the concept relating it with previous research; the third will deal with cas rational and approach, following with the state of the art regarding ontologies and their relation with semantic web. among the conclusions, one is already easily remarked: at the online level, communication can be impaired not only by the cultural differences but also by a wide range of differences such as race, age, sex, profession, religion or disabilities; in this regard, if the transcultural ontology will prove its efficiency in disambiguating cultural concepts, consequently other ontologies could be also implemented for aiding the online communication process between different users. 2 defining the concepts in line with history according to modern paradigms, the goal of using icts is "obtaining a service from a huge palette of available ones" and the means is "interacting with an entity". the "entity" is either a human (e.g. when speaking via mobile phones) or a device (e.g. when buying travel documents via computers) [1]. therefore there are three possible ways of communication as described in [1]: a) "face to face", b) "face to interface", c) "interface to interface" in the near future context of semantic web, domain ontologies and so forth. very agent metaphor. thus, when users employ agents (in whatever domain of activity) they expect: a) personalization (agents act considering the specific momentary needs of their clients); b) authorization (agents act on behalf of their clients, within the limits stipulated by the hiring agreement); and c) competence. in short, the agent metaphor suggests that "i hire an agent when i do not have enough time or lack competence to handle the problem myself" [1]. as regards the interface agents, the emphasis is on the interface, entailing that the agent remains hidden (i.e., the users perceive just a "smarter functionality", no "pseudoavatars" [10] intervening in a human-to-human dialogue) [1]. considering these features, one can say that an agent, or more specific an interface agent can act like a smart mediator which to have access to knowledge (i.e. ontology) and by using it efficiently to "translate" to the user only the meaningful messages, saving a lot of time that otherwise would have been wasted. in the 21st century time is money therefore it is vital mostly in the business sector but not restricted to. online communications nowadays means more than a showy website and a newsletter. if 770 a.e. lascu, s.c. negulescu, c. butaci, v. cret new web-based technologies are joint with a society that is rapidly getting to think of online interactions as just as authentic as face-to-face ones, one has the possibility of radically easing the communication even in difficult (but frequent) circumstances, like those involving crosscultural interaction ( [2], [6], [7], [8], [9], [10], [11], [12], [13]) as debated and probed in previous paper of the authors. in our previous researches we approached the transcultural interfaces’ subject which could be explained in human-to-human communication as a progression from textual (semantically correct) conversions to multimodal (culturally adequate) ones [2], based on the concept of cas. since at this research stage the experimental models presented in the related papers are not agent-oriented, the reference to "agents" is found only in [1] and regards conceptual aspects as well as future development. thus far, the emphasise in our research was put on transcultural which represents the ability of people belonging to different cultures to communicate efficiently preserving in the same time their cultural identity [2]. this concept will be further on related with ontology which together, i.e. transcultural ontology, will embody the tool by which cas could be validated as a possible new and challenging domain of hci. 3 rationale and approach as depicted from the title this paper intends to draw the sketch of what it should be the onward study on the implementation/ validation of cas. since the prime motives because of which we started this endeavour was already stated in the previous papers of this team, it is redundant to re-state them; instead this paper is set to focus on the approach and methods. since cas was designed from an anthropocentric perspective, meaning to provide an assistance (i.e. an interface agent which to access a transcultural ontology, transfering the user only meaningful messages), lessening the linguistic hurdles (such as the traduttore-traditore effect), the logocratic pressure of (spoken or written) text, response time criticality, as well as the danger of distortions and noise, via a major upgrade in communication granularity: (one) idea instead of (many) words [9]. the ongoing study should be approached from a trans-disciplinary perspective, in respect with both humanists (i.e. linguists, psychologists) and technologists (i.e. ontology, interface designers and so on). when creating the ontology, the designer should bear in mind the way users and agents may "think"; how an user creates meaning from a piece of image of some sings (i.e. words) and how an interface agent does the same job effortlessly on the contrary. though, do an agent depict the meaning same accurate as a human does? or maybe detecting the true meaning of a message is even more endangered by human’s scrambled mind which is very much contextual, in opposition with an interface agent which will act and respond based only on the given ontology and some very specific rules, and hence the probability to fail giving the expected meaning will be smaller. these are all questions which can not be answered at this point, some answers will be empirically uncovered, some may prove to be exactly contradictory with those thought in the first place. after all, this is what academically we call exploratory research. the approach will be adapted step by step base on the further findings. 4 ontology. state of the art people are able to use the web in order to complete tasks such as finding the icelandic word for "alphabet", reserving a plane ticket, or searching for a low price for an e-book. still, computers cannot perform the same tasks without human direction because web pages are designed to be towards the implementation of computer-aided semiosis 771 read by people, not machines. the semantic web is a vision of information that is understandable by computers, so that they can perform more of the dull work involved in finding, sharing, and combining information on the web [3]. the vision of tim berners-lee regarding semantic web [3]: "i have a dream for the web (in which computers) become capable of analyzing all the data on the web the content, links, and transactions between people and computers. a ’semantic web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. the ’intelligent agents’ people have touted for ages will finally materialize" is related in this study with eco’s semiosis theory (which states that the receiver "fills the message with significance" [5]), meaning that on the same features’ as semantic web’s cas could be implemented but using instead a dedicated/personalized ontology based on which to develop the "translation process". an ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts [17]. the ontology envisioned by this research team is based on the idea of maya script (a logographic type of script which used both logograms and syllabic characters [16]) by replacing, where possible, the words with images as in the catchphrase: "a picture it is worth a thousand words". figure 1 illustrates the way the correspondence between a word-based ontology and an image-based ontology can be created, of course, the example given in this paper is very much simplified. the demarcation line was traced in order to separate the abstract and the concrete synonyms of the word apple. on one side of the line there can be easily remarked every-day interpretations of the word apple, which can without any doubt be recognized by anyone. on the other side, the other interpretations depict more abstract representations of the word apple which require a higher level of knowledge in order to be grasped, i.e. the apple polisher, the temptation (religiously), wilhelm tell representation (historically), newton’s gravitation theory (physical sciences), "an apple a day keeps the doctor away" (health idiom); the point is that the ontology must be trans-disciplinary and trans-culturally created in order to gather all the possible meanings and definitions for a word/concept. disambiguation can further on continue and the figure presented above be expanded with other concepts and relation between them. an example at hand for expanding the ontology would be adding other characteristics for the fruit apple such as variety name (e.g. granny smith, pink lady, red delicious, golden delicious and so on), colour (e.g. red, green, pink, yellow), taste (e.g. sour, sweet, etc.). also, in order to disambiguate applež’s logo there can be made a separation between the old and the new logos. an important disambiguation which must be made in the first place is between english word apple and swedish word äpple which happens to mean the same thing but they belong to different cultures which can generate later on other misunderstanding problems. briefly, the implementation of cas will follow this ontology framework where the icon position has syntactic role (in line with ontology rules) and semantic role for cas (to reduce the differences between "intentio auctoris" and "intentio lectoris") [2]. this kind of ontology based on visual rules can be further enhanced by using animations instead of images. considering this idea, the role of the interface agent will be to "translate". in german, the verb "to translate" means "übersetzen", the reason for this association is that in its most basic visualization, the german word means "to carry something from one side of the river to the other side of the river". in opposition the english word "to translate" does not immediately evoke the same image in the mind. in german, one can say: "mit der fähre den fluss übersetzen" which would literally mean: "to translate the river on a ferry boat". anyway the translator so to name it must take in consideration both cultures from which to which the translation is being made; one must know that ad literam translation doesn’t apply especially when a more subtle message (culturally dependent) must be transmitted, therefore the visualisation of the word "übersetzen" (to move from one side of the river to the other side of the river) leads to 772 a.e. lascu, s.c. negulescu, c. butaci, v. cret figure 1: example of architecture for cas1 several important insights into the nature of translation. in this context, the role of the agent is carrying something across the river, whether it is from here to there or from there to here. the cargo can have a multiplicity of shapes: the description of a technological object, a cultural or historical phenomenon, a poetic image, a metaphorical expression, or a human emotion, to name only a few; the parameters of each word are quite fragile; no two people will take the exact same meaning from a word [14], so the interface agent will have the hardest ever job, to extract the meaning of a message taking into consideration the transcultural ontology in line with the cultural background of both users involved in the communication process. in order to validate cas concept as a possible new subdomain of hci, the researches have to focus from now on, on the implementation by creating a transcultural ontology on the framework presented in the previous section involving in this process preferable a trans-disciplinary team of researchers (linguists, psychologists, anthropologists, designers, programmers and so on) by keeping in mind the fact that an ontology deals with the nature of existence so it is a too impressive domain to be approached only by a team of thrilled researchers, willing to take the burden of exploratory research. the future work will consider refining the ontology framework and hopefully in three-year time span (during the phd studies of the first author) the objectives to be fulfilled involving european teams of researchers interested in this kind of projects. 5 conclusions and future work in order to validate cas concept as a possible new subdomain of hci, the researches have to focus from now on, on the implementation by creating a transcultural ontology on the framework presented in the previous section involving in this process preferable a trans-disciplinary team of researchers (linguists, psychologists, anthropologists, designers, programmers and so on) by keeping in mind the fact that an ontology deals with the nature of existence so it is a too impressive domain to be approached only by a team of thrilled researchers, willing to take the burden of exploratory research. the future work will consider refining the ontology framework and hopefully in three-year towards the implementation of computer-aided semiosis 773 time span (during the phd studies of the first author) the objectives to be fulfilled involving european teams of researchers interested in this kind of projects. acknowledgements: this research is fully supported by the posdru/88/1.5/s/60370 project which is co-financed by the european social fund through the sectoral operational programme for human resources development 2007-2013. we thank mr. cătălin boaru from apple imc romania for kindly granting us the permission to use the appler logo. copyright notice: apple and the apple logo are trademarks of apple computer, inc., registered in the u.s. and other countries. disclaimer: this article is an independent publication and has not been authorized, sponsored, or otherwise approved by apple computer, inc. images were taken from public domain graphic sites with their own disclaimers, and a very few were taken from sites that contained no copyright information or terms of use. bibliography [1] bărbat, b.e. 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(2009). cultural differences in decision-making. a transcultural interface for gambler’s fallacy. in sapio, b. et al (ed.), future of information and communication technologies (pp.656-661). copenhagen, denmark: cost action 298 participation in the broadband society. [13] prundurel, a., negulescu, s.c., lascu, a.e. (2007). mini-ontology for trans-cultural interfaces. in sapio, b. et al (ed.), the good, the bad and the unexpected: the user and the future of information and communication technologies (pp.on cd). moscow, russian federation: cost action 298 participation in the broadband society. [14] rainer schulte (1999). the translator as mediator between cultures. retrived 11.2009, from translation studies. web site: http://translation.utdallas.edu/translationstudies/mediator_essay1.html. [15] russell s., norvig p. (2003). artificial intelligence: a modern approach. nj: prentice hall. 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[17] wikipedia (2009). ontology (information science). retrived 11.2009, from wikipedia. web site: http://en.wikipedia.org/wiki/ontology_(information_ science). int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 3, pp. 270-280 disassembly line scheduling with genetic algorithms luminiţa duţă, florin gheorghe filip, jean-michel henrioud, ciprian popescu abstract: disassembly is part of the demanufacturing and it is meant to obtain components and materials from end-of-life products. an essential performance objective of a disassembly process is the benefits it brings, that is the revenue brought by the retrieved parts and material, diminished by the cost of their retrieval operations. a decision must be taken to balance an automatic disassembly line. a well balanced line will decrease the cost of disassembly operations. an evolutionary (genetic) algorithm is used to deal with the multi-criteria optimization problem of the disassembly scheduling. keywords: control, scheduling algorithms, evolutionary programming, genetic algorithms 1 introduction in the second half of the 20th century, the people and governments started to become aware that the "take make waste" system, which resulted from the industrial revolution, is not sustainable. the assumptions on infinite material "sources" of raw materials and "sinks" to absorbe industrial and domestic wastes could not hold any longer because of the exponential growth of world population and accelerated widespreding of industry and consumption. in their inspiring book on system engineering, blanchard and fabrycky [1, p.555] state that "green manufacturing should be an objective adopted by producers to reduce the environmental impact of their products and production operation continuously. real environmental improvement requires a system life-cycle approach to guide design decisions and operational policies". several driving factors such as: a) competitive differentiation, b) customer consciousness, c) legal regulations, and d) fight for improving profitability have led to this new way of thinking and even to new standards such as iso 14,000 series. a new science of sustainability called "industrial ecology" was born [2,frasch ], [3, graedel, allenby]. within it, research directions such as "design for environment" (with a proactive character), and "environmental management" (a reactive, remedial aproach) compose a new design trend called "environmentally conscious design and manufacturing" [1, blanchard and fabrycky, p. 558]. at present, both designers and manufacturers show an increasing tendency to consider product and system entire life-cycle starting with perceiving customers needs, and continuing with design and development, manufacturing, product utilization, maintenance, phase-out, and disposal. in this context, the term "remanufacturing" includes the set of planning and processing activities (such as checking in, disassembling, cleaning, inspection and sorting, reconditioning, reassembling, testing and packaging) for recycling obsolete products. the first steps of remanufacturing process can be grouped under the name "demanufacturing". a new industry for demanufacturing has shown up to provide with new usage of expensive modules, components and materials and, at the same time, to prevent excessive wastes. also industrial producers tend to add their demanufacturing departments to existing manufacturing facilities. sometimes, chains of firms show up to implement new paradigms such as "extended", "networked", and "virtual" enterprise [3, filip, bărbat]. this paper aims at proposing the usage of genetic algorithms for optimal scheduling of disassembly lines. the remaining part of this paper is organised as it follows. first, the main concepts and research directions in modelling, optimization and control in disassembly processes are reviewed. then, the optimal scheduling problem of a disassembly line is formulated. a short description of genetic algorithms is given next. experimental results on solving the optimal disassembly problem by a genetic algorithm are given before presenting the paper conclusions. copyright © 2006-2008 by ccc publications disassembly line scheduling with genetic algorithms 271 2 disassemblya main stage in recycling several types of recycling of end-of-life manufactured goods are possible such as : a) reusing of components and modules of high value, b) refurbishing and partial reconstruction of a good returned from the market, c) recovery of the raw material or energy by incineration and melting. disposal, which is the last solution to resort to, is done only if the other alternatives are not possible. the choice between these different types determines the recycling process and allow for defining the end-of-life destinations for components of manufactured goods. different recycling loops are different approaches of the process. the simplest approach is that of dismantling a product. by applying dismantling operations, a discarded good can be broken down faster and with small costs. in this case more pure fractions can be obtained with less efforts. this simple approach exploits only the value of the raw material. it does not take into account the functional value of the product or of its components. the regain of the functional values needs a recycling process that minimizes the destroying effects on the product. this means to reuse, refurbish and capitalize the components of the used product in order to remanufacture a new one. remanufacturing is a superior form of reusing, since its objective is to maximize the value of repaired parts and to minimize the disposal quantity. central to remanufacturing is the disassembly process that decomposes a product into parts and/or subassemblies. disassembling is a nondestructive technique and implies the extraction of the desired components and/or materials. if the parts are not reusable after reconditioning, partial or total destructive operations are to be applied: drilling, cutting, wrenching, and shearing. these techniques are used in view of the material or energetically recovery. there are several research directions in relation with disassembly processes such as: modeling, planning, and control. the chapter 16 on "design for produceability and disposability" of the book on system engineering of blanchard and fabrycky [1] contains an excellent introduction to product and system design to facilitate remanufacturing. since disassembly processes can be viewed as discrete, event driven systems [5, cassandras, lafortune], petri nets can be a natural solution. there are lots of reported results. for example, moore, gungor and gupta [6] propose disassembly petri nets to take into account operation precedence constraints in planning aplications. penev and ron [7] proposed disassembly graphs. kuo, zhang and huang [8] describe disassembly trees which associate for each branch the direction of the disassembly operation by adapting the assembly trees proposed by henrioud [9]. the usage of object oriented petri nets proposed by lakos [10], to model disassembly processes is analysed by duta, filip and henrioud [11]. for balancing the operation of the disassembly line, duta, filip and henrioud [12] utilize the method of equal piles approach proposed rekiek [13]. to model products associated with incomplete, imprecise, and, sometimes, wrong information, fuzzy reasoning petri nets are proposed by gao and zhou [14] to make real-time disassembly scheduling decisions possible. a review of state-of-art implementations of control structures for disassembly line is given by duta and filip [15]. 3 problem formulation disassembly of manufactured products induces both disassembly costs and revenues from the parts saved by the process. thus, at the planning stage a good trade-off has to be found that depends, both on the "depth" of the disassembly, and on the sequence of operations. the optimization of the ratio between gain and cost can be accomplished by using an appropriate distribution of the disassembly tasks on workstations, an assignment that provides a maximal value for the total profit. the optimization problem depends upon the structure of the disassembly system: if it is made up of a single workstation, the costs depend mainly upon the process duration. if the system is a line, the costs depend mainly 272 luminiţa duţă, florin gheorghe filip, jean-michel henrioud, ciprian popescu upon the line balancing, all the more if it is highly manual. another problem that occurs during a disassembly process is how deep the disassembly sequence must go so as to maximize the outcome of this process. in [11, duta, filip and henrioud], [16, duta, henrioud and caciula] it was shown that an incomplete disassembly sequence can be more profitable than a complete one. destructive and dismantling operations have to be taken into consideration, as well. hence, we have to deal with a multi-criteria optimization problem of a disassembly process: maximizing the benefit it brings deciding how deep the disassembly sequence can go and minimizing the costs using an optimal scheduling along the line. a decision in a scheduling problem upon many criteria is a np-hard to solve problem [17, filip]. stochastic algorithms have already been used to fulfill a multi-criteria optimization problem in [18, minzu and henrioud]. in this paper we consider that the line structure was given and propose an algorithm which will allow finding a disassembly sequence and its assignment on workstations that optimizes a very simple function which integrates the income from the parts and the cycle time of the disassembly line. in this paper we address the case of disassembly lines where the cycle-time is not merely the sum of all operative and logistic times but it also depends strongly upon the line balancing. the objective is to find the most profitable disassembly sequence taking into account, on one hand the end-of-life options for each part or subassembly of a given product, and on the other hand the operational times for a given assignment of the tasks on the disassembly workstations. a cost function which combines both disassembly costs and revenues was proposed in [19, duta, filip and henrioud]. f = r tcy (1) where r is revenue associated to each disassembled part and tcy is the cycle time. the global revenue is the sum of partial revenues obtained according to the end-of-life destinations of the disassembled parts. these partial revenues are established by experts after repeated disassembly processes. r = ∑ k rk, k = 1..nc (2) where nc is the number of final components or the number of subassemblies obtained after the disassembly process. the cycle time can be defined like the operational time of the slowest workstation on the line tcy = max wi ∑ j∈(tasks on wi) t j (3) where wi is the workstation i, and j is one disassembly operation and t j the operational times. we make the following assumptions: • the disassembly line is linear (flow-shop type); • end-of-life revenues of the subassemblies are known; • operational costs are included in the final incomes; • the criterion of maximizing the outcome depending of the success rate of disassembly operations has been taken into consideration; • the failure of the disassembly process is an event that can also occur since certain parts of the product could be deformed and impossible to separate without destroying; disassembly line scheduling with genetic algorithms 273 • the disassembly line is not starving; it works in a continuous flow. evaluating the function from equation (1) reveals the profit on a time unit, which is an important indicator for the productivity of the disassembly system. this function also takes into account the value of the cycle time obtained for a well-balanced line. the optimization can be made both for the manual and automatic disassembly lines. 4 genetic algorithms genetic algorithms are optimization solvers used in many areas due to their capacity to reduce the combinatorial complexity of np-complete problems. they do not give the global optimal solution, but a local optimal one by exploiting a defined search space. a genetic algorithm starts with a set of randomly generated possible solutions called initial population. each member of a population is encoded as a chromosome. chromosomes are represented by a combination of numbers or characters which contain information about the solution. a score named fitness coefficient is assigned to each chromosome based on the viability of the solution. chromosomes with high scores are chosen as parents to create a new population. the objective is to obtain children with better scores. to avoid the uniformity of the population and to increase the space of research, at each step of creation, two processes may occur namely: crossover and mutation. crossover combines the features of two or more parents into one child chromosome. mutation generates a child similar with his parent with one or more genes altered. these operations ensure the diversity of the new generated population [ 20, goldenberg]. once established the initial population and defined the three types of operations (reproduction, crossover, mutation), a genetic algorithm provides new members of population until a stop condition is fulfilled. usually, this criterion is given by a maximal/minimal value of the objective function obtained after a number of iterations of the genetic algorithm. 5 example to test the genetic algorithm, consider the example of the disassembly of a motorola radio set described by salomonski and zussman, [21]. the corresponding disassembly petri net is given in the figure 1. the disassembly process is represented four distinct possible sequences of transitions: {t1 → t3 → t5 → t7}, {t1 → t3 → t6 → t8}, {t2 → t4 → t5 → t7} and {t2 → t4 → t6 → t8}. to take into account additional destructive disassembly methods, in the correspondent petri net there are three alternative tasks represented by transitions {t1,1,t4,1,t6,1}. for the present study we considered that there are three workstations on the line. an alternative destructive task is done only on a station that can perform both destructive and non-destructive operations. we say that this kind of workstation is a "mixed" one. thus, workstations 2 and 3 are considered mixed. in other words, when a task is moved from one station to another, the type of the operation is changed together with its operational time. we supposed that the tasks t1 and t4 can be performed in a non-destructive way on the first workstation and in a destructive way on the second one. for the task t6 a destructive disassembly is done on the third station and a non-destructive disassembly is performed on the second workstation. according to the petri net given in figure 1 the correspondent operational times on the initial partition of the tasks on the workstations are: ti = {0.92, 0.07, 0.07, 0.12, 0.95, 0.75, 0.75, 0.95} taking into consideration the times for the alternative destructive operations the set above becomes: 274 luminiţa duţă, florin gheorghe filip, jean-michel henrioud, ciprian popescu figure 1: disassembly petri net of a radio set tf = {0.95, 0.07, 0.07, 0.15, 0.95, 0.90, 0.75, 0.95} the final revenues are calculated with the method proposed in [11, duta, filip and henrioud] by using the data from the petri net given in the figure 1. the corresponding sets of revenue values are: ri = {−1.36, 0.27, 0.54, 0.54, 0.62, 0.67, 2.75, 2.75} r f = {−1.32, 0.27, 0.54, 0.50, 0.62, 0.60, 2.75, 2.75} the objective is to maximize the value of the function from the equation (1) by finding the sequence that maximize the final revenue and in the same time ensuring a well-balance of the disassembly line (e.g. minimizing the cycle time). in our problem, a chromosome is represented by the possible tasks assignment matrix s which elements are: si j = { 1 i f the task j can be assigned to the workstation i 0 i f the task j can′t be assigned to the workstation i i = 1..n and j = 1..m (n is the number of workstations and m the number of disassembly operations). one s matrix can be the solution of our optimization problem only if it satisfies the following constraints: 1. the non-divisibility constraint that does not allow a task to be assigned to more than one station. si j ∈ {0, 1} (4) disassembly line scheduling with genetic algorithms 275 2. the assignment constraint that it requires that each task be assigned to exactly one station. ∑ i si j = 1 (5) 3. the precedence constraint that invokes technological order so that if task i is to be done before task j (i<j ), then i cannot be assigned to a station downstream from task j n ∑ k=1 k ·ski − n ∑ k=1 k ·sk j ≤ 0 (6) the steps of the genetic algorithm are: step 1 generating the initial population. there are three workstations and eight operations (tasks). the matrix of the possible task assignment is: m =   1 0 0 1 0 0 1 1 0 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1   we generated 24 matrices s that fulfill the constraints (4)(6) by using the method presented in [19, duta, filip, and henrioud]. we randomly chose three of them as shown bellow: we observe that the three matrices can be obtained by moving an operation from one workstation to the next workstation. the repartition of tasks on the workstations is represented in the figure 2. step 2 evaluating by the value of the objective function. the fitness coefficients f1 and f2 are defined by calculating the value of the objective function from the equation (1) for each complete disassembly sequence of the radio set given in the figure 1. table 1: evaluation of the initial population fitness strength average f1 f2 (f1+f2)/2 fitness % s1 0.5 0.6 0.55 33% 1 s2 0.5 0.43 0.46 27% 0.80 s3 0.75 0.6 0.675 40% 1.20 sum 1.685 100% average 0.5616 33% step 3 selection. the selection of the individuals is made after their robustness so as to generate a more robust and healthy population. we can not utilize the roulette method of selection because it generates non valid individuals (that don’t respect the three constraints specified before). matrices s1 and s3 are the strongest. 276 luminiţa duţă, florin gheorghe filip, jean-michel henrioud, ciprian popescu figure 2: assignment of tasks on the three stations for the initial population step 4 crossover if we have two matrices a and b of the same dimensions a = {ai j} i = 1..n , j = 1..m b = {bi j} i = 1..n , j = 1..m the crossover operator ⊕ is defined as ai j ⊕bi j = { 0 i f (ai j = 0 and bi j = 0) or i f (ai j = 1 and bi j = 1) 1 i f (ai j = 1 and bi j = 0) or i f (ai j = 0 and bi j = 1) we also define a special matrix called mask matrix msk =   0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0   calculating s1 ⊕msk and s3 ⊕msk we obtained two chromosomes that have two genes crossed: a new chromosome and a copy of chromosome s2: step 5 mutation a mutation can be made by the movement of one task between two neighboring stations. a new individual is obtained from s4 disassembly line scheduling with genetic algorithms 277 step 6 replacing the initial population performances of the new population are represented in the table 2. table 2: evaluation of the new population fitness strength average f1 f2 (f1+f2)/2 fitness % s3 0.75 0.6 0.675 31% 0.92 s5 0.95 0.75 0.85 38% 1.16 s6 0.75 0.6 0.675 31% 0.92 sum 2.20 100% average 0.73 33% step 7 iteration as a result the genetic algorithm is iterated until a stop condition is accomplished. after 20 iterations the maximal value of the objective function remains 0.95. so the optimal assignment of tasks is given by the matrix s5. the value of the function from the equation (1) is given in euro/second. 6 conclusions a new computation method in the problem of the optimization of the disassembly sequences is proposed in this paper. the method used has the advantage that it takes into account the operational durations, as well as the profit achieved after a disassembly process from the valorization of the obtained components or subassemblies. in a balanced disassembly line, the cycle time has the lowest value so the operational costs are minimized. the algorithm does not optimize the balance of the disassembly line, but give a solution that improves this balance. applying an evolutionary algorithm some undetectable solutions of the problem can be taken into account. however, using genetic algorithms implies a lot of information. the result is obviously faster obtained than using the backtracking method. in the disassembly process a local and fast solution for the optimal disassembly sequence is preferred to the complex and slower algorithms. in the future work evolutionary programming will be applied to disassembly more complex industrial products. acknowledgement this work was partially supported from grant ceex 13-ceex f03 of ince-iei funded by the national authority for scientific research (ancs). bibliography [1] b.s. blanchard, w.j. fabrycky, systems engineering and analysis, 4th edition. prentice hall, upper saddle river, n.j., 2006. 278 luminiţa duţă, florin gheorghe filip, jean-michel henrioud, ciprian popescu [2] r.a. frasch, "industrial ecology. a philosophical introduction". proc. of nas 89. [3] t.e. graedel, b.r. allenby, industrial ecology. prentice hall, upper saddele river, n.j, 2003. [4] f.g. filip, b.e. bărbat, informatica industrialã (industrial informatics), ed. tehnică, bucureşti (in romanian), 1999. [5] c.g. cassandras, s. lafortune. introduction to discrete event systems. springer science, 2008. [6] k. moore, a. gungor, s. gupta, "disassembly process planning using petri nets". proceedings of the ieee international symposium on electronics andenvironment, oak brook, illinois, 1998. [7] k.d. penev, a.j. de ron, determination of a disassembly strategy international journal of production and research, vol. 34, no. 2, 1996. [8] t.c. kuo, h.c. zhang, s.h. huang, disassembly analysis for electromechanical products: a graph based heuristic approach. international journal of production research, vol. 38, no.5, 2000. [9] j.m. henrioud j.m, contribution a la conceptualisation de i’assemblage automatise: nouvelle approche en vue de la determination des processus d’assemblage, these de doctorat, université de franche comté, france, 1989. [10] c.a. lakos, from coloured petri nets to object petri nets, spring verlag, vol. 935, 1995. [11] l. duţă, f.g. filip, j.m. henrioud, “determination of the optimal disassembly sequence using decision trees”, intelligent assembly and disassembly, elsevier ltd, pp. 43-48, uk, 2003. [12] l. duţă, f.g. filip, j.m. henrioud, "applying equal piles approach to disassembly line balancing problem", proceedings, ifac world congress, prague, 2005. [13] b. rekiek, b. assembly line design. ph.d. thesis, university of brussels. [14] m. gao, m.-c. zhou, "fuzzy reasoning petri nets for demanufacturing process design", proceedings , ieee symp. electronics and the environment, 2001 [15] l. duţă, f.g. filip, "control and decision-making process en disassembly used electronic products", studies in informatics and control sic, vol. 17, no.1, 17-26, 2008. [16] l. duţă, j.m. henrioud,i. caciula, “a real time solution to control disassembly processes”, proceedings of the 4th ifac conference on management and control of production and logistics, mcpl ’07, p.289-294, sibiu, september 2007. [17] f.g. filip, sisteme suport pentru decizii (decision support systems), editura tehnica, bucuresti, (in romanian), 2004. [18] v. minzu, j.m. henrioud, “stochastic algorithm for tasks assignments in single or mixed-model assembly lines”, apii-jesa,vol. 32, no. 7-8, pp. 831-851, 1998. [19] l. duţă, f.g. filip, j.m. henrioud, “a method for dealing with multi-objective optimization problem of disassembly processes”, proceedings of the ieee international symposium of assembly and task planning isatp03, pp. 163-168, july, france, 2003. [20] d.e. goldenberg, genetic algorithms in search, optimisation and machine learning, adisson wesley, usa, 1989. [21] n. salomonski, e. zussman, “on-line predictive model for disassembly process planning adaptation”, robotics and computer integrated manufacturing, vol. 15, pp. 211-220. disassembly line scheduling with genetic algorithms 279 luminiţa duţă valahia state university automatics and computer science department targoviste, romania and ince-centre for economy of industry and services(iei) bucharest, romania e-mail: duta@valahia.ro florin gheorghe filip the romanian academy-ince and the national institute for r&d in informatics (ici) bucharest, romania e-mail: ffilip@acad.ro jean-michel henrioud université de franche comté besancon, france e-mail: jean-michel.henrioud@univ-fcomte.fr ciprian popescu valahia state university automatics and computer science department targoviste, romania e-mail: nicoc_ro@yahoo.com luminita duţă took her phd in control engineering at franchecomte university, besançon, france coordinted by prof. dr. jean-michel henrioud. she is currently associate professor at valahia university of targoviste, romania, where she is in charge with courses on object oriented programming and automatic control. her research interests include control evolutionary computation, heuristics and optimization. florin gheorghe filip, member of the romanian academy (elected in 1991), received his m. sc and ph. d. in control engineering from the technical university "politehnica" bucharest (pub) in 1970 and 1982, respectively. he has been with the national r & d institute for informatics (ici bucharest) since 1970 and was the managing director of ici from 1991 to 1997. he was elected in 2000 (and re-elected in 2002 and 2006) as a vice-president of the romanian academy. he has been the chair of the technical committee "large scale complex systems" of ifac ("international federation of automatic control") since 2002, and the executive president of the "forum for information-knowledge society" since 1997. his main scientific interests include: control and optimization of large-scale systems, decision support systems, technology management, and models for knowledge-information society. he is author/co-author of five books, editor of nine volumes and the author/co-author of over two hundred articles published in contributed volumes and scientific journals (http://www.acad.ro/cv2006/birou_vice02filip.htm). 280 luminiţa duţă, florin gheorghe filip, jean-michel henrioud, ciprian popescu jean-michel henrioud is full professor at the university of franche comté, besançon, france. he was the president of the doctoral science school for engineers of the franche comté university and the adjoint director of the automation laboratory from besancon. he coordinated more than 30 phd thesis in assembly and disassembly research fields. under his coordination important studies in conception, analyze, modeling and control of the production systems, particularly in those of automatic assembly and disassembly processes were done. nicolae ciprian popescu, (teaching assistant, second degree), received his m. sc in modern systems for process control, information processing and transmission from the “valahia” university of targoviste in 2004 and bachelor of computer science from the “politehnica” university of bucharest (pub) in 1999. he has more than 8 years practical experience in software development, and 5 years practical experience in teaching at university level. as a software engineer of “the group of electricity transmission and distribution” of targoviste from 1999 to 2003 he developed software packages for studying the m.v. (medium voltage) and l.v. (low voltage) electric power networks and for the implementation of the electricity billing systems. in 2003 he was accepted in the teaching staff of automation and informatics department, “valahia” university of targoviste. his main scientific interests include: decision support systems, environment management and technology management. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 175-186 a new linear classifier based on combining supervised and unsupervised techniques l. state, i. paraschiv-munteanu luminiţa state university of piteşti faculty of mathematics and computer science romania, 110040 piteşti, 1 târgu din vale st. e-mail: lstate@clicknet.ro iuliana paraschiv-munteanu university of bucharest faculty of mathematics and computer science romania, 010014 bucharest, 14 academiei st. e-mail: pmiulia@fmi.unibuc.ro abstract: the aim of the research reported in the paper is to obtain an alternative approach in using support vector machine (svm) in case of nonlinearly separable data based on using the k-means algorithm instead of the standard kernel based approach. the svm is a relatively new concept in machine learning and it was introduced by vapnik in 1995. in designing a classifier, two main problems have to be solved, on one hand the option concerning a suitable structure and on the other hand the selection of an algorithm for parameter estimation. the algorithm for parameter estimation performs the optimization of a convenable selected cost function with respect to the empirical risk which is directly related to the representativeness of the available learning sequence. the choice of the structure is made such that to maximize the generalization capacity, that is to assure good performance in classifying new data coming from the same classes. in solving these problems one has to establish a balance between the accuracy in encoding the learning sequence and the generalization capacities because usually the over-fitting prevents the minimization of the empirical risk. keywords: support vector machine, classification, unsupervised learning, supervised learning, k-means algorithm. 1 introduction in addition to its solid mathematical foundation in statistical learning theory, svm’s have demonstrated highly competitive performance in numerous real-world applications, such as bioinformatics, text mining, face recognition, and image processing, which has established svm’s as one of the state-of-the-art tools for machine learning and data mining, along with other soft computing techniques. in training support vector machines the decision boundaries are determined directly from the training data so that the separating margins of decision boundaries are maximized in the high-dimensional space called feature space. this learning strategy, based on statistical learning of the training data and the unknown data. the method based on support vectors aims to increase the efficiency in approximating multidimensional functions. the basic idea in a svm approach is twofold. on one hand it aims to determine a classifier that minimizes the empirical risk, that is to encode the learning sequence as good as possible with respect to a certain architecture, and the other hand to improve the copyright c⃝ 2006-2011 by ccc publications 176 l. state, i. paraschiv-munteanu generalization capacity by minimizing the generalization error. in case of non-linear separable data the svm is combined with kernel based technique which transforms the data in a linear separable data by mapping the initial data on to higher dimensional space of features. this mapping is performed in terms of special tailored kernels that allow to keep the computations at a reasonable complexity level. the svm approach proves useful in classifying linear separable data as well as non-linear separable data because the mapping of the initial data onto a higher dimensional space of features determines that these classifiers behave as non-linear classifiers. the basic idea of a svm approach is to obtain higher dimensional representations of the initial data by mapping them using a technique based on kernels in a feature space, such that for a non-linear separable learning sequence, its representation in the feature space becomes linearly separable. being given that the representation of the learning sequence in the feature space is linearly separable, several techniques can be applied to determine in this space a separating hyperplane. obviously, in case of linearly separable learning sequence the set of solutions is infinite, different algorithms yielding to different hyperplane solutions. since a solution that keeps at distance as much as possible all examples assures good generalization capacities, this can be taken as a criterion in selecting a best solution among the available solutions. at first sight, it seems unreasonable to combine a supervised technique to an unsupervised one, mainly because they refer to totally different situations. on one hand, the supervised techniques are applied in case the data set consists of correctly labeled objects, and on the other hand the unsupervised methods deal with unlabeled objects. however our point is to combine the svm and k-means algorithms, in order to obtain a new design of a linear classifier. a new linear classifier resulted as a combination of a supervised svm method and 2-means algorithm is proposed in the paper, and its efficiency is evaluated on experimental basis in the final part of the paper. the tests were performed on simulated data generated from multi-dimensional normal repartitions yielding to linearly separable and non-linearly separable samples respectively. 2 supervised learning using svm let us assume that the data is represented by examples coming from two categories or classes such that the true provenance class for each example is known. we refer to such a collection of individuals as a supervised learning sequence, and it is represented as s= { (xi, yi) | xi ∈ rd , yi ∈{−1, 1} , i = 1, n } . (1) the values 1, −1 are taken as labels corresponding to the classes. we say that the data are linearly separable if there exists a linear discriminant function g : rd −→ r, g(u) = b + w1u1 + . . . + wdud , (2) where u = (u1, . . . , ud) ∈ rd, such that yig (xi) > 0, ∀ (xi, yi) ∈s. denoting by w = (w1, . . . , wd) t the vector whose entries are the coefficients of g, we say that s is separated without errors by the hyperplane hw,b : w t u + b = 0 , (3) and hw,b is called a solution of the separating problem because all examples coming from the class of label 1 belong to the positive semi-space, and all examples coming from the class of label a new linear classifier based on combining supervised and unsupervised techniques 177 −1 belong to the negative semi-space defined by hw,b. obviously a hyperplane is a solution of separating problem if the functional margin min { yi ( wt xi + b ) , 1 ≤ i ≤ n } > 0. in a svm-based approach, by imposing that the functional margin is 1, the search for a solution yields to a constrained quadratic programming problem imposed on the objective function φ(w) = 1 2 ∥w∥2, { min φ(w) yi ( wt xi + b ) ≥ 1 , i = 1, n . (4) if w∗ is a solution of (4), then hw∗,b∗ is called an optimal separating hyperplane. the computation of w∗ and b∗ is carried out using the sv m1 algorithm. algorithm sv m1 ( [9]) input: the learning sequence s; step 1. compute the matrix d = (dik) of entries dik = yiyk (xi) t xk , i, k = 1, n ; step 2. solve the constrained optimization problem  α∗ = arg ( max α∈rn ( αt 1− 1 2 αt dα )) , αi ≥ 0 , ∀ 1 ≤ i ≤ n , n∑ i=1 αiyi = 0 , (5) if α∗i > 0 then xi is called the support vector. step 3. select two support vectors xr, xs such that α∗r > 0 , α ∗ s > 0 , yr = −1 , ys = 1. step 4. compute the parameters w∗, b∗ of the optimal separating hyperplane, and the width of the separating area ρ (w∗, b∗),  w∗ = n∑ i=1 α∗i yixi , b ∗ =− 1 2 (w∗) t (xr + xs) , ρ (w∗, b∗) = 2 ∥w∗∥ (6) output: w∗, b∗, ρ (w∗, b∗). a linear separable sample is represented in figure 1a. the straight lines d1, d2, d3 and d4 are solutions for the separating problem of s, d4 corresponds to the optimal separating hyperplane. the examples placed at the minimum distance to the optimum separating hyperplane are the support vectors. in case of non-linearly separable samples the idea is to determine a separating hyperplane that minimizes the number of misclassified examples. the problem of finding a optimal hyperplane in case of non-linearly separable samples has been approached several ways. the approach introduced by cortes and vapnik ( [3]) uses the error function φσ(ξ) = n∑ i=1 ξσi , (7) where the slack variables ξi , 1≤ i≤n, are taken as indicators for the classification errors (see figure 1b), and σ is a positive real number. the optimality is expressed in terms of the objective function φ : rd ×rn −→ [0, +∞) φ(w, ξ) = 1 2 ∥w∥2 + c f ( n∑ i=1 ξσi ) , (8) 178 l. state, i. paraschiv-munteanu where c > 0 is a given constant, ξ = (ξ1, . . . , ξn), and f is a monotone convex function, f(0) = 0. the idea is to compute a subset of s, say {(xi1, yi1) , . . . , (xik, yik)}, by minimizing φσ(ξ), such that there exists an optimal hyperplane for s\{(xi1, yi1) , . . . , (yik, yik)}. such an optimal hyperplane is referred to as a soft margin hyperplane ( [3]). figure 1: a) optimal separating hyperplane; b)classification errors. a soft margin hyperplane is a solution of the constrained optimization problem  arg ( min w∈rd, b∈r, ξ∈rn (φ(w, ξ)) ) yi ( wt xi + b ) ≥ 1− ξi , ∀ 1 ≤ i ≤ n , ξi ≥ 0 , ∀ 1 ≤ i ≤ n , (9) the samples represented in figure 1b, correspond to the non-linearly separable case. a soft margin hyperplane, the separating area, and the slack variables are indicated in figure 1b. the computation of a soft margin hyperplane is carried out by the algorithm sv m2. algorithm sv m2 ( [9]) input: the learning sequence s; c ∈ (0,∞). step 1. compute the matrix d = (dik) of entries, dik = yiyk (xi) t xk , i, k = 1, n ; step 2. solve the constrained optimization problem  α∗ =arg ( max α∈rn ( αt 1− 1 2 αt dα − (αmax) 2 4 c )) , αi ≥ 0 , ∀ 1 ≤ i ≤ n , n∑ i=1 αiyi = 0 , (10) where αmax = max{α1, . . . , αn} step 3. select two support vectors xr, xs such that α∗r > 0 , α ∗ s > 0 , yr = −1 , ys = 1. step 4. compute the parameters w∗, b∗ of the soft margin hyperplane, and the width of the separating area ρ (w∗, b∗), according to (6). output: w∗, b∗, ρ (w∗, b∗). 3 unsupervised learning using the k-means method center-based clustering algorithms are very efficient for clustering large and high-dimensional databases. they use objective functions to express the quality of any clustering solution, the a new linear classifier based on combining supervised and unsupervised techniques 179 optimal solution corresponding to a solution to a constrained/unconstrained optimization problem imposed of the particular objective function. usually the clusters found have convex shapes and a one of more centers are computed for each cluster. the k-means algorithm was introduced by macqueen ( [8]) for clustering numerical data, each of the produced clusters having a center referred as the cluster mean. let d = {x1, . . . , xn}⊂ rd be the data, k a given positive integer. the classes of any partition {c1, . . . ,ck} of d are called clusters. for any {µ (c1) , . . . , µ (ck)}⊂ rd where each µ (ci) in taken as the center of ci, then the inertia momentum is, ε = k∑ i=1 ∑ x∈ci d2 (x, µ (ci)) , (11) where d is a convenable distance function on rd. in the following we take d as being the euclidean distance, d(x, y) = ∥x−y∥. the k-means method proceeds by iteratively allocate the individuals to the nearest clusters and re-computation of the centers is performed to minimize the inertia momentum, the computation ending when non-significant changes of the centers/value of inertia momentum/membership functions of individuals to clusters are obtained. the k-means algorithm can be treated as an optimization problem where the goal is to minimize a given objective function under certain constraints. let c be the set of all subsets of rd of cardinal k, any particular q = {q1, . . . , qk}∈ c is a possible set of cluster centers. any partition of d into k classes can be obviously represented by a n ×k matrix w = (wil) where (i) wil ∈{0, 1} , i = 1, n , l = 1, k (ii) k∑ l=1 wil = 1 , i = 1, n . (12) the k-means algorithm can be formulated as the constrained optimization problem on the objective function p(w, q) = n∑ i=1 k∑ l=1 wil ∥xi −ql∥ 2 as follows:   min w∈mn×k(r), q∈c p(w, q) wil ∈{0, 1} , i = 1, n , l = 1, k , k∑ l=1 wil = 1 , i = 1, n , q = {q1, . . . , qk} . (13) the ’hard’ problem (13) can be solved by decomposing it into two simpler problems p1 and p2, and then iteratively solving them, where p1. fix q = q̂ ∈c and solve the reduced constrained optimization problem for p ( w, q̂ ) . p2. fix w = ŵ ∈ mn×k (r) and solve the reduced unconstrained optimization problem for p ( ŵ, q ) . the solutions of these problems can be derived by straightforward computations, and they are given by the following theorems: 180 l. state, i. paraschiv-munteanu theorem 1. for any fixed set of centers q̂ = {q̂1, . . . , q̂k} , the function p ( w, q̂ ) is minimized in w (0) = ( w (0) ij ) if and only if w (0) satisfies the conditions w (0) il = 0 ⇐⇒∥xi − q̂l∥ > min 1≤t≤k ∥xi − q̂t∥ , w (0) il = 1 =⇒∥xi − q̂l∥ = min 1≤t≤k ∥xi − q̂t∥ , k∑ j=1 w (0) ij = 1 , for any i = 1, n , l = 1, k. note that in general, for any given q̂ there are more solutions because in general there exist individuals xi at minimum distance to more than one center of q̂. theorem 2. for any fixed ŵ satisfying the constraints of (13), the function p ( ŵ, q ) is minimized there exist an unique point q(0) = { q (0) 1 , . . . , q (0) k } if and only if q (0) l = ( n∑ i=1 ŵilxi )/( n∑ i=1 ŵil ) , l = 1, k. the scheme of the k-means algorithm viewed as search method for solving the optimization problem (13) is: the algorithm k-mop input: d the data set, k the pre-specified number of clusters, d the data dimensionality, t threshold on the maximum number of iterations. initializations: q(0), t ←− 0 solve p ( w, q(0) ) and get w (0) sw ←− false repeat ŵ ←− w (t) solve p ( ŵ, q ) and get q(t+1) if p ( ŵ, q(t) ) = p ( ŵ, q(t+1) ) then sw ←− true output ( ŵ, q(t+1) ) else q̂ ←− q(t+1) solve p ( w (t), q̂ ) and get w (t+1) if p ( w (t), q̂ ) = p ( w (t+1), q̂ ) then sw ←− true output ( w (t+1), q̂, ) endif endif t ←− t + 1 until sw or t > t . output: ŵ, q̂. a new linear classifier based on combining supervised and unsupervised techniques 181 note that the computational complexity of the algorithm k-mop is o(nkd) per iteration. the sequence of values p ( w (t), q(t) ) where w (t), q(t) are computed by k-mop is strictly decreasing, therefore the algorithm converges to a local minima of the objective function. 4 the combined separating technique based on svm and the k-means algorithm at first sight, it seems unreasonable to compare a supervised technique to an unsupervised one, mainly because they refer to totally different situations. on one hand the supervised techniques are applied in case the data set consists of correctly labeled objects, and on the other hand the unsupervised methods deal with unlabeled objects. however our point is to combine svm and k-means algorithm, in order to obtain a new design of a linear classifier. the aim of the experimental analysis is to evaluate the performance of the linear classifier resulted from the combination of the supervised svm method and the 2-means algorithm. the method can be applied to data, either linearly separable or non-linearly separable. obviously in case of non-linearly separable data the classification can not be performed without errors and in this case the number of misclassified examples is the most reasonable criterion for performance evaluation. of a particular importance is the case of linearly separable data, in this case the performance being evaluated in terms of both, misclassified examples and the generalization capacity expressed in terms of the width of separating area. in real life situations, usually is very difficult or even impossible to established whether the data represents a linearly/non-linearly separable set. in using the sv m1 approach we can identify which case the given data set belongs to. for linear separable data, sv m1 computes a separation hyperplane optimal from the point of view of the generalization capacity. in case of a non-linearly separable data sv m2 computes a linear classifier that minimizes the number of misclassified examples. a series of developments are based on non-linear transforms represented be kernel functions whose range is high dimensional spaces. the increase of dimensionality and the convenable choice of the kernel aim to transform a non-linearly separable problem into a linearly separable one. the computation complexity corresponding to kernel-based approaches is significantly large, therefore in case the performance of the algorithm sv m1 proves reasonable good it could be taken as an alternative approach of a kernel-based sv m. we perform a comparative analysis on data consisting of examples generated from two dimensional gaussian distributions. in case of a non-linearly separable data set, using the k-means algorithm, we get a system of pairwise disjoint clusters together with the set of their centers representing a local minimum point of the criterion (13), the clusters being linearly separable when k = 2. consequently, the sv m1 algorithm computes a linear classifier that separates without errors the resulted clusters. our procedure is described as follows input: s = the learning sequence; step 1. compute the matrix d = (dik) of entries, dik = yiyk (xi) t xk , i, k = 1, n ; sh ←− true step 2. if the constrained optimization problem (5) does not have solution then sh ←− false input c ∈ (0,∞), for soft margin hyperplane solve the constrained optimization problem (10) endif step 3. select xr, xs such that α∗r > 0 , α ∗ s > 0 , yr = −1 , ys = 1; 182 l. state, i. paraschiv-munteanu compute the parameters w∗, b∗ of the separating hyperplane, and the width of the separating area, ρ (w∗, b∗) according to (6); step 4. if not sh then compute nr err1 = the numbers of examples incorrectly classified; compute err1 = classification error; endif step 5. split the set d = { xi | xi ∈ rd , i = 1, n } into two clusters c1 and c2 using the 2-means algorithm and label the data belonging to c1 by y′i = 1, and label by y′i = −1 the data belonging to c2. step 6. apply the algorithm svm1 to s′ = {( xi, y ′ i )∣∣ xi ∈ rd, y′i ∈{−1, 1}, i = 1, n} and obtain the parameters of optimal separating hyperplane: w∗1, b ∗ 1, ρ (w ∗ 1, b ∗ 1); compute nr err2 = the number of data incorrectly classified by the algorithm 2−means; compute err2 = classification error resulted after the 2−means splitting ; output: w∗, b∗, ρ (w∗, b∗), nr err1, err1; w∗1, b ∗ 1, ρ (w ∗ 1, b ∗ 1), nr err2, err2. 5 comparative analysis and experimental results the experimental analysis is based on a long series of tests performed on linear/non-linear separable simulated data of different volumes. the analysis aims to derive conclusions concerning: 1. the statistical properties (the empirical means, covariance matrices, eigenvalues, eigenvectors) of the clusters computed by the 2-means algorithm as compared to their counterparts corresponding to the true distributions they come from. 2. the comparison of the performances corresponding to the linear classifier resulted as a combination of svm and the 2-means algorithm described in section 4 and sv m2 in terms of the empirical error. 3. the analysis concerning the influences of the samples sizes on the performance of the procedure described in section 4. 4. the quality of cluster characterization in terms of the principal directions given by a system of unit orthogonal eigenvectors of the sample covariance and empirical covariance matrices of the computed clusters. the analysis aimed to derive conclusions concerning the contribution of each principal direction, and for this reason, some tests were performed on data whose first principal component is strongly dominant, and when the principal directions are of the same importance respectively. the tests were performed on data generated from two-dimensional normal distributions n (µi, σi) , i = 1, 2 of volumes n1 and n2, respectively. the sample covariance matrices are denoted by µ̂i, σ̂i , i = 1, 2. the centers and the empirical covariance matrices corresponding to the clusters computed by the 2-means algorithm are denoted by µi, σi , i = 1, 2. we denote by zi , ẑi , zi , i = 1, 2 orthogonal matrices having as columns unit eigenvectors of σi , σ̂i , σi , i = 1, 2 respectively. test 1: n1 =n2 =50, µ1 = ( 1 1 ) , σ1 = ( 1 0 0 0.25 ) , µ2 = ( 2 3 ) , σ2 = ( 0.5 0 0 0.5 ) . the matrices z1 , z2 and their eigenvalues are λ (1) 1 =0.25 , λ (1) 2 =1, z1 = ( 0 1 1 0 ) , λ(2)1 =0.5 , λ (2) 2 =0.5, z2 = ( 1 0 0 1 ) . a new linear classifier based on combining supervised and unsupervised techniques 183 the set is non-linear separable and it is represented in figure 2i)a. in this case we get µ̂1 = ( 0.92 1.0004 ) , σ̂1 = ( 0.85 0.08 0.08 0.25 ) , µ̂2 = ( 1.98 2.87 ) , σ̂2 = ( 0.44 0.09 0.09 0.63 ) . the matrices ẑ1 , ẑ2 and their eigenvalues being λ̂ (1) 1 =0.24 , λ̂ (1) 2 =0.86, ẑ1 = ( 0.14 −0.98 −0.98 −0.14 ) , λ̂(2)1 =0.40 , λ̂ (2) 2 =0.67, ẑ2 = ( −0.92 0.38 0.38 0.92 ) . using the svm2 with c = 70 we get the classification error class error = 14.70, the number of misclassified samples n errors = 13 and the width of separating area is ρ = 0.61. the value of the error coefficient defined as the ratio of the number of misclassified samples and total volume of the data is c error = 0.13%. the soft margin line d1 is represented in figure 2i)b. −1 0 1 2 3 0 1 2 3 4 −1 0 1 2 3 0 1 2 3 4 −1 0 1 2 3 0 1 2 3 4 −1 0 1 2 3 0 1 2 3 4 dc b d 1 d 2 a i) −1 0 1 2 3 4 5 0 1 2 3 4 −1 0 1 2 3 4 5 0 1 2 3 4 −1 0 1 2 3 4 5 0 1 2 3 4 −1 0 1 2 3 4 5 0 1 2 3 4 a c d d 1 b d 2 ii) figure 2: i) the classification of the data set in test 1; ii) the classification of the data set in test 2. by applying the 2-means algorithm we get clusters whose sample means and covariance matrices are µ1 = ( 0.88 1.06 ) , σ1 = ( 0.64 0.05 0.05 0.30 ) , µ2 = ( 2.13 2.96 ) , σ2 = ( 0.41 −0.06 −0.06 0.56 ) . the matrices z1 , z2 and their eigenvalues are λ (1) 1 =0.29 , λ (1) 2 =0.65, z1 = ( 0.14 −0.98 −0.98 −0.14 ) , λ (2) 1 =0.39 , λ (2) 2 =0.58, z2 = ( −0.92 −0.37 −0.37 0.92 ) , the number of misclassified samples is 10 and the clusters are represented in figure 2i)c. note that the computed centers and clusters are not influenced by the choice of the initial centers. the clusters computed by the 2-means algorithm for randomly selected initial centers are reprezented in figure 2i)c. the separating line d2 resulted by applying the sv m1 algorithm to the data represented by the clusters computed by the 2-means algorithm is represented in figure 2i)d. test 2: n1 = 100 , n2 =200, µ1 = ( 1 1 ) , σ1 = ( 1 0 0 0.25 ) , µ2 = ( 2 3 ) , σ2 = ( 1 0 0 0.25 ) , λ (1) 1 =0.25 , λ (1) 2 =1, z1 = ( 0 1 1 0 ) , λ(2)1 =0.25 , λ (2) 2 =1, z2 = ( 0 1 1 0 ) , µ̂1 = ( 1.12 0.92 ) , σ̂1 = ( 1.35 0.04 0.04 0.26 ) , µ̂2 = ( 2.01 3.00 ) , σ̂2 = ( 0.86 0.05 0.05 0.25 ) , 184 l. state, i. paraschiv-munteanu λ̂ (1) 1 =0.26 , λ̂ (1) 2 =1.35, ẑ1 = ( 0.03 −0.99 −0.99 −0.03 ) , λ̂(2)1 =0.25 , λ̂ (2) 2 =0.87, ẑ2 = ( 0.09 −0.99 −0.99 −0.09 ) . the data set is non-linear separable and it is represented in figure 2ii)a. applying the sv m2 for c = 5 we obtain the soft margin line d1 represented in figure 2ii)b and class error = 19.12, n errors = 13, ρ = 0.25, c error = 0.043%. the clusters computed by the 2-means algorithm are represented in figure 2ii)c and their statistical characteristics are µ1 = ( 0.96 1.004 ) , σ1 = ( 1.19 −0.10 −0.10 0.38 ) , µ2 = ( 2.10 3.007 ) , σ2 = ( 0.76 −0.02 −0.02 0.28 ) , λ (1) 1 =0.37 , λ (1) 2 =1.20, z1 = ( −0.12 −0.99 −0.99 0.12 ) , λ (2) 1 =0.27 , λ (2) 2 =0.76, z2 = ( −0.05 −0.99 −0.99 0.05 ) . in this case the number of misclassified samples is 18. note that the initial choice of the centers does not influence significantly the computed centers and clusters. for instance in figure 2ii)c are represented the resulted clusters in case of randomly selected initial centers. the separating line d2 computed by the algorithm sv m1 applied to the data represented by these clusters is represented in figure 2ii)d. test 3: n1 = n2 =50, µ1 = ( 1 1 ) , σ1 = ( 1 0 0 0.25 ) , µ2 = ( 3 4 ) , σ2 = ( 0.5 0 0 0.5 ) , λ (1) 1 =0.25 , λ (1) 2 =1, z1 = ( 0 1 1 0 ) , λ(2)1 =0.5 , λ (2) 2 =0.5, z2 = ( 1 0 0 1 ) , µ̂1 = ( 0.76 1.008 ) , σ̂1 = ( 1.17 −0.06 −0.06 0.21 ) , µ̂2 = ( 2.87 4.03 ) , σ̂2 = ( 0.56 0.009 0.009 0.31 ) , λ̂ (1) 1 =0.214 , λ̂ (1) 2 =1.180, ẑ1 = ( −0.07 −0.99 −0.99 0.07 ) , λ̂(2)1 =0.31 , λ̂ (2) 2 =0.56, ẑ2 = ( 0.03 −0.99 −0.99 −0.03 ) . the data set is linearly separable and it is represented in figure 3i)a. the soft margin line d1 computed by the sv m1 algorithm is represented in figure 3i)b, the value of the resulted margin being ρ = 1.196429. −2 −1 0 1 2 3 4 0 1 2 3 4 5 −2 −1 0 1 2 3 4 0 1 2 3 4 5 −2 −1 0 1 2 3 4 0 1 2 3 4 5 −2 −1 0 1 2 3 4 0 1 2 3 4 5 a b c d d 2 d 1 i) −1 0 1 2 3 4 0 1 2 3 4 5 −1 0 1 2 3 4 0 1 2 3 4 5 −1 0 1 2 3 4 0 1 2 3 4 5 −1 0 1 2 3 4 0 1 2 3 4 5 ba c d d 1 d 2 ii) figure 3: i) the classification of the data set in test 3; ii) the classification of the data set in test 4. the clusters computed by the 2-means algorithm are represented in figure 3i)c and they are the same as in initial data set whatever the initial choice of the centers is. so, the statistical characteristics are a new linear classifier based on combining supervised and unsupervised techniques 185 µ1 =µ̂1, σ1 =σ̂1, µ2 =µ̂2, , σ2 =σ̂2, λ (1) 1 =λ̂ (1) 1 , λ (1) 2 =λ̂ (1) 2 , z1 =ẑ1, λ (2) 1 =λ̂ (2) 1 , λ (2) 2 =λ̂ (2) 2 , z2 =ẑ2, and the separating line d2 computed by the algorithm sv m1 and represented in figure 3i)d coincides with d1. test 4: n1 = 100 , n2 =150, µ1 = ( 1 1 ) , σ1 = ( 1 0 0 0.25 ) , µ2 = ( 3 4 ) , σ2 = ( 0.5 0 0 0.5 ) . λ (1) 1 =0.25 , λ (1) 2 =1, z1 = ( 0 1 1 0 ) , λ(2)1 =0.5 , λ (2) 2 =0.5, z2 = ( 1 0 0 1 ) . µ̂1 = ( 1.22 1.03 ) , σ̂1 = ( 1.04 −0.03 −0.03 0.24 ) , µ̂2 = ( 2.98 3.99 ) , σ̂2 = ( 0.48 −0.01 −0.01 0.43 ) . λ̂ (1) 1 =0.24 , λ̂ (1) 2 =1.04, ẑ1 = ( −0.04 −0.99 −0.99 0.04 ) , λ̂(2)1 =0.42 , λ̂ (2) 2 =0.49, ẑ2 = ( −0.27 −0.96 −0.96 0.27 ) . the data set is linear separable and it is represented in figure 3ii)a. applying the sv m1 we obtain the soft margin line d1 represented in 3ii)b and ρ = 0.552508. the clusters computed by the 2-means algorithm are represented in figure 3ii)c and their statistical characteristics are µ1 = ( 1.20 1.04 ) ,σ1 = ( 0.98 −0.04 −0.04 0.26 ) , µ2 = ( 3.00 3.98 ) , σ2 = ( 0.48 −0.04 −0.04 0.45 ) , λ (1) 1 =0.26 , λ (1) 2 =0.98, z1 = ( −0.05 −0.99 −0.99 0.05 ) , λ (2) 1 =0.42 , λ (2) 2 =0.51, z2 = ( −0.60 −0.79 −0.79 0.60 ) . in this case the number of misclassified samples is 2 and the initial centers are randomly selected. the separating line d2 computed by the algorithm sv m1 applied to the data represented by these clusters is represented in figure 3ii)d. 6 conclusions and future work although to combine a supervised technique to an unsupervised one seems to be meaningless, mainly because they refer to totally different situations, the combined methodology resulted by putting together k-means and svm methods yielded to an improved classifier. the experimental results point out good performance of the proposed method from both points of view, accuracy and computational complexity. we are optimistic that the research aiming to obtain refined methods by combining supervised, unsupervised and semi-supervised technics has good chances to provide a class of new powerful classification schemes. it has been already performed a series of tests on different types of classifiers obtained by combining pca (principal component analysis), ica (independent component analysis) and kernel based svm’s the results being quite encouraging. bibliography [1] s. abe, support vector machines for pattern classification, springer-verlag, 2005. [2] c.j.c. burges, a tutorial on support vector machines for pattern recognition, data mining and knowledge discovery, 2, pp. 121-167, 1998. [3] c. cortes, v. vapnik, support-vector networks, machine learning, 20(3):273-297, 1995. 186 l. state, i. paraschiv-munteanu [4] n. cristianini, j. shawe-taylor, an introduction to support vector machines and other kernel-based learning methods, cambridge university press, 2000. [5] g. gan, c. ma, j. wu, data clustering: theory, algorithms and applications, siam, 2007. [6] s.r. gunn, support vector machines for classification and regression, university of southampton, technical report, 1998. [7] l. state, i. paraschiv-munteanu, i., introducere in teoria statistică a recunoaşterii formelor, editura universitaţii din piteşti, 2009. [8] j.b. macqueen, some methods for classification and analysis of multivariate observations, proceedings of 5-th berkeley symposium on mathematical statistics and probability, berkeley, university of california press, pp. 281-297, 1967. [9] i. paraschiv-munteanu, support vector machine in solving pattern recognition tasks, proceedings of first doctoral student workshop in computer science, university of piteşti, may 2009. [10] i. paraschiv-munteanu, theoretical approach in performance evaluation of a classsification system, scientific bulletin of the university of piteşti, series mathematics and informatics, no. 14, pp. 203-218, 2008. [11] n. popescu-bodorin, fast k-means image quantization algorithm and its application to iris segmentation", scientific bulletin of the university of piteşti, series mathematics and informatics, no. 14, 2008. [12] r. stoean, c. stoean, m. preuss, d. dumitrescu, evolutionary multi-class support vector machines for classification, international journal of computers communications & control, vol.1 supplement: suppl. s, pp. 423-428, 2006. [13] v.n. vapnik, the nature of statistical learning theory, new york, springer verlag, 1995. [14] v.n. vapnik, statistical learning theory, new york, wiley-interscience, 1998. [15] r. xu, d.c.ii wunsch, clustering, wiley&sons, 2009. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 929-938 network coded transmission in a wireless grid network with an energy constraint r. stoian, a.v. raileanu, l.a. perisoara rodica stoian, adrian victor raileanu, lucian andrei perisoara politehnica university of bucharest romania, 061071 bucharest, 1-3 iuliu maniu e-mail: {rodicastoian2004, adrianrai, lperisoara}@yahoo.com abstract: in wireless networks, routing based on packet forwarding does hardly yield optimum transmission performance in terms of network utilization and throughput. as an alternative to routing, network coding has been introduced in the recent years, where nodes are mixing the data instead of forwarding. in applications, random linear network coding is the most used method, due to its decentralized mode, and due to preserving the achievability of multicast capacity bounds. in this paper, we study the performance of network coding used for multicast transmission of messages in a wireless grid network with an energy constraint. several energy saving schemes have been proposed in the literature, but in this study we will focus on duty cycling scheme, in which nodes are not always in on state. the performance is measured as the end-to-end delay, i.e. the duration until each node can decode the message sent by the source, and the cdf of observations is used to make analysis. keywords: network coding, energy efficient, end-to-end delay, duty cycling. 1 introduction energy saving is an important factor in wireless transmissions, especially in autonomous devices, i.e. battery operated nodes. in applications like battlefield surveillance, environment and habitat monitoring, sometimes it is hostile, hazardous or impractical to replace or recharge the batteries. the performance of wireless network applications highly depends on the lifetime of the network. for practical applications we expect the lifetime to be from several months to several years, so energy saving is crucial in designing the network. energy consumption in a network node can be due to useful sources (transmitting, receiving or processing data) or wasteful sources (channel idle listening, retransmissions due to packet collisions, overhearing, control packets used for errors control). the critical issue is to minimize the energy consumption of network nodes while meeting the application requirements. this paper is organized as follows. in section ii we explain how network coding is applied for wireless networks, marking some advantages of using it. in section iii we describe the scenario of a general multicast transmission in a wireless network. in section iv we present the problem of energy consumption optimization using duty cycling. finally, in section v we present simulation results. 2 network coding and wireless networks network coding is a recent field of information theory that breaks the classical assumption about the routing in the networks. instead of simply forwarding the packets, the intermediate nodes recombine several input packets into one or several output packets. in [1], ahlswede et copyright c⃝ 2006-2010 by ccc publications 930 r. stoian, a.v. raileanu, l.a. perisoara al. showed that network coding achieves the multicast capacity, which is defined as a maximum data rate which is achieved for a multicast transmission. in [2], it is shown that the maximum multicast capacity can be achieved by using linear encoding functions at each node, which implies to solve linear equations at the receiver. in figure 1 we show a simple example of using network coding to reduce the number of transmissions used to exchange two bits b1 and b2, the operation applied being xor. with network coding, the first node can recover the bit b2 from the received bit b1+b2 and the known bit b1. similarly, b1 can be recovered at the second node. network coding can reduces the traffic without increasing delay and so it can save energy by reducing the amount of transmitted data. figure 1: an example of decreasing the transmission time using network coding let suppose for a network that source node s emits k information packets x1, x2, ..., xk, each of length l symbols from a finite field gf(q) to n receivers t1, t2, ..., tn. for linear network coding, each node combines a number of received packets into one or several output packets: y = k∑ i=1 αixi (2.1) where the summation is applied for every symbol position. for random linear network coding, the coefficients αi of the linear combination are generates in a random manner, which assures with high probability a linear independence of the output packets from a node for a sufficiently large size q = 2m of the finite field gf(q), as it was proved in [3]. the encoding coefficients forms the encoding vector α = (α1, α2, ..., αk), which belongs to a k-dimensional vector space over gf(q). all encoding vectors associated with the output edges of all intermediate nodes from s to a specific node t forms the encoding matrix. when we refer to a network code we must specify the all encoding vectors which should be used for the encoding process, for all edges of the network. the encoding coefficients are send to the destination in the packet header, so the destination nodes can decode the packet without knowing the network topology or the encoding rules, even if the nodes are added or removed in an ad-hoc manner. assume a node t has received m coded packets y1, y2, ..., ym and the encoding vectors α1, α2, ..., αm. to decode the packets, it need to solve a linear system with m equations and k unknowns xi, derived from (2.1). to solve this system, the node wait until he receives at least m ≥ k linearly independent packets, equivalently with m linearly independent encoding vectors. network coded transmission in a wireless grid network with an energy constraint 931 this condition is assured using buffers for each input of the node. the buffer stores only the innovative packets (packets which are not a linear combination of the already stored packets in the buffer). non-innovative packets are discarded by gaussian elimination because they do not provide any new information to the node. some advantages of using network coding in wireless networks are throughput and capacity improvements [1], bandwidth and energy savings [4], robustness to noise [5], reduced traffic. 3 a scenario of general multicast we introduce a grid wireless mesh network, which is a generalization of a local network (e.g. office), or a special purpose network deployed over a rectangular area for monitoring or as point of presence (e.g. information panels). the type of communication is one to many, as we consider one original source (e.g. a gateway to another network, a controller, etc.), that sends messages to all the other points, called nodes. this is called general multicast, as one message is sent to a number of participants (e.g. all participants), but because not all nodes are in the radio range of the source, the message is relayed node by node. transmission over radio is simplified, considering only one channel, and without collisions. the radio signal is affected by distance attenuation and a simple exponentially distributed noise floor. the network stack is reduced to simple mac/ip layers, with the purpose of taking into consideration only mac latency and for identifying nodes by addresses. the position of the source is at one corner of the rectangle (see figure 2), as it simulates a gateway or a controller. at the same time, the position was chosen as it provides the worst case scenario, where the source has the lowest number of neighbors possible in the given situation. figure 2: general multicast relay in a wireless grid network, with source in corner position. the radio range of the source is not large enough for broadcast the message unit is considered 1 byte. the entire transmission is an m byte message generated by the source. in normal networks, this would take m consecutive transmissions from the source, and some additional ones when there is no acknowledge received. our model uses random network coding to disseminate information, so that the source emits linear combination of all the message bytes at each transmission. a number of k = 32 random coefficients from gf(256) is used for messages with m < k. the original message is padded with zeros and each 932 r. stoian, a.v. raileanu, l.a. perisoara byte is multiplied the corresponding coefficient ki and summed (i.e. xor-ed). the result is one mixed byte and a list of coefficients that are used for decoding at the receiving nodes. for bandwidth saving, coefficients can be chosen from lower galois fields, e.g. gf(2), reducing the size of the coefficient list. however, using lower galois fields increases the probability that two packets will be linear dependent, and will not contribute to the decoding process. each receiving node accumulates linear independent packets (novel packets), based on analysis of the coefficient list included in each packet. when it has received at least k such packets, it decodes the message using matrix inversion. a node that receives a novel packet will re-encode and send it using a linear combination of all its received packets. if a node receives a packet that is linear dependent to its received packets, it will discard it, without forwarding it. when a node decodes the entire message, it becomes a source, generating packets with its own generated random coefficients. generating acknowledge messages is out the scope of this paper. we evaluate transmission performance using the end-to-end delay (eted) metric, calculated as the time since the source emitted the first packet until a specific node received and decoded the entire message. the overall end-to-end delay (oeted) is the maximum end-to-end delay, i.e. calculated at the last node that received and decoded the message. the minimum endto-end delay is the time until the first successful transmission (i.e. first node that decodes the full message). the average end-to-end delay is the arithmetic average of all the successful transmission times. 4 energy consumption optimization the two main operations that require optimizations of energy are transmission and reception. transmission energy is optimized through radio power adjustment, so that a tradeoff is done between the range and battery life. in our scenario, all the nodes use the same transmission power. in non-centralized networks, however, reduction of transmission energy can be achieved also by reducing the number of redundant sending operations in nodes. in flooding based routing, nodes tend to forward each packet received, creating a lot of duplicated information inside the network. widmer et. al [12] proposed network coding algorithms that reduce the number of transmissions per each node compared to flooding. in [13], fragouli et al. studied distributed algorithms to achieve optimum number of transmissions in grid and random topologies. the results show that network coding can achieve up to 30% energy reduction compared to probabilistic routing. the current study focuses on the optimization of reception and decoding, analyzing how classical energy saving schemes affect performance of network coded transmissions. idle listening and overhearing are major sources of energy consumption in a wireless network. while the first one can be easily optimized, the second one is of great importance to the network coding overall architecture. reducing overhearing in a network coded system may reduce its performance. our article studies the degradation of performance when implementing different levels of energy saving. the method is a simplified version of the s-mac protocol, which use a listen/sleep cycle. the energy consumption is reduced by switching on and off each node independently, so that reception is performed only in some discreet time windows. during power on states, a node is able to receive, decode and retransmit data. during power off states, a node is not able to receive any packet, so it will be lost. this should not be an issue with our current wireless network coding scenario, as neighbor nodes can be in different power states, and take advantage of all the packets. denote t, a full power on power off time interval. we consider an energy duty cycle, the percentage of power on states. for example, a duty cycle of 25% means that the node is on for network coded transmission in a wireless grid network with an energy constraint 933 time t/4, then off for time 3/4t, then again on for t/4, and so on. the power states have random time offsets, so that to avoid situations when all the nodes are in power off state, see figure 3. figure 3: transmission from node a to neighbors b and c. packets that were missed by b in off state (dotted arrow), are recovered later from neighbor c 5 simulations and results this article studies the effects of energy optimizations on the end-to-end delay in the wireless grid network, using random network coding. based on [11], we developed a wireless network simulator, which uses network coding for transmission. we chose a fixed number of nodes n = 100, that are arranged on a 10 × 10 square area. the source message is made of packets of length m = 32 bytes, equal to the number of coefficients, k = 32. the main simulator functional modules are: source, node and scheduler. the source module is responsible to encode the original message and continuously send differently encoded packets to the other nodes. the other nodes contain logic for packet decoding and rank calculation, but also include a packet re-encoder and sender, for relay. the scheduler is responsible for sequencing the packet transfer in the network. transmission hops are determined based on a physical model (as defined in [14], [15]), where instant noise level and attenuation decide whether a node is in the transmission range or not. for studying energy efficiency algorithms, the scheduler has been enhanced to support on-off node states. the simulator engine is event based, so that each transmission and reception has a different timestamp. the link rate is constant for all transmissions, and is simulated as an inter-sending time interval. the time difference is made more realistic by introducing mac latency, propagation time and a random jitter for all other factors that are not explicitly simulated. the first series of simulations were used to analyze the impact of the distance between nodes on the end-to-end delay (eted). we run simulations for distance d = 40 m to d = 179 m. for d < 40 m the networks is close to a broadcast network, (i.e many more than 50% of the nodes are in the source radio range), so it is out of the scope of the current paper. at d = 180 m all the nodes lost connectivity. each simulation was repeated three times. figure 4 shows that for large and medium range coverage, given by d = 80 m and d = 120 m, the nodes completion time increases almost linearly. at very short range, where connectivity is available only with closest neighbors, the last set of nodes is completed in almost exponential time. due to the geometry of the network grid, figure 5 shows two flat regions, for both average and overall eted. 934 r. stoian, a.v. raileanu, l.a. perisoara figure 4: end-to-end delay measured for each node in the network, for different values of d figure 5: end-to-end delay dependency on different values of d these are explained by the number of nodes that are available in one range, for a given d. choosing an optimum d that would maximize the area covered by the network, would be the largest value in any of the flat regions, e.g. 80 m, 120 m, 170 m. now, observing the ratio between average and overall eted between different flat regions, we can note that only the third region (120 170m) has a linear node completeness behavior, while the other ones tend to be logarithmic. in a network where there should not be too many nodes late than the average, choosing d < 130 m would be the right choice. the ratio between the areas covered by the network for the three values of d (80, 120, 170), is 0.22:0.5:1. the ratio observed on average eted is 0.67:0.82:1 and for overall eted is 0.54:0.67:1. if average eted is of interest, network coded transmission in a wireless grid network with an energy constraint 935 maximizing the area leads to a value of d = 170 m. if the overall eted is important, than the value of d = 120 m is offering half of the maximum area but with a 30% reduction of delivery time. in the next stage, for a fixed d = 120 m, we have added to the simulation scenario a power cycle with a duty of 50%. we performed simulations for different values of the power on-off time interval, with t in a range from 10 ms to 500 ms. values lower than 10 ms may not be efficient due to electrical / logical reasons in the device, i.e. network processing chip switching on-off too fast. values above 500 ms are of no interest to the current study, as it reduces the number of switches per experiment to less than 10. in figure 6, at t = 0 are drawn the results when there is no power cycle, for reference. as, expected results show better behavior at lower t values, as the nodes have a quicker opportunity to recover lost packets from their neighbors. figure 6: end-to-end delay dependency on different values of t in the next experiment, we studied the effect of different duty cycles, ranging from 10% to 90%. we observed the behavior of the network for three values of t = [0.010, 0.100, 0.500] s. sometimes, the energy advantage comes from using a duty cycle lower than 50% (i.e. less than half of the energy used). figures 7 and 8 show that it is possible to preserve low values of eted even at a duty cycle of 30%. however, for t > 0.100 s, the optimal duty cycle is close to 60%. we performed another set of measurements with fixed t = 0.100 s, for different duty cycles, and for different values of distance d = [80, 120, 170] m. figures 9 and 10 show that the 80 m and 120 m networks are more sensitive to values lower than 30%, while the 170m network has a higher threshold of 70%. with network coding, the fact that there are only a few nodes connected to each other, does not allow improvement of energy savings. as soon as the number of connections per node doubles, the energy consumption can be optimized up to 30%. 936 r. stoian, a.v. raileanu, l.a. perisoara figure 7: average end-to-end delay dependency on different values of the duty cycle, for t = [0.010, 0.100, 0.500] s figure 8: overall end-to-end delay dependency on different values of the duty cycle, for t = [0.010, 0.100, 0.500] s 6 conclusions this article presents methods for saving energy in a wireless network, in the case of general multicast transmission. the routing of packets inside the network is not store and forward, but mix and forward, namely network coding. we observed the end-to-end delay inside a wireless network coded transmission in a wireless grid network with an energy constraint 937 figure 9: average end-to-end delay dependency on different values of the duty cycle, for d = [80, 120, 170] m figure 10: overall end-to-end delay dependency on different values of the duty cycle, for d = [80, 120, 170] m grid network that uses network coding, for different area sizes (i.e. different node density) and for various reception windows for energy saving. a good tradeoff between area and end-toend delay is to choose half of the maximal area, while obtaining at least 30% improvement in speed. simulations show that energy consumption can be reduced to between 30% and 50%, depending on the duration of the full power on power off cycle. for sparse networks, the energy 938 r. stoian, a.v. raileanu, l.a. perisoara consumption can be reduced up to 70%. further study may reveal important results for other network topologies, other type of transmissions (e.g. unicast), other energy saving schemes or other realistic features (variable range, transmission power, interference, multiple radio channels, etc). bibliography [1] r. ahlswede, n. cai, s. y. r. li, and r. w. yeung, network information flow, ieee trans. inform. theory, vol. 46, no. 4, pp. 1204-1216, july 2000. [2] s. y. r. li, r. w. yeung, and n. cai, linear network coding, ieee trans. inform. theory, vol. it-49, no. 2, pp. 371-381, feb. 2003. [3] y. wu, p. a. chou, and k. jain, a comparison of network coding and tree packing, in proc. isit 2004, chicago, june 2004. [4] y. wu, p. a. chou, and s. y. kung, minimum-energy multicast in mobile ad-hoc networks using network coding, ieee trans. communications, vol. 53, no. 11, pp. 1906-1918, nov. 2005. [5] rodica stoian, l. a. perisoara, and radu stoica, random network coding for wireless adhoc networks, in proc. on int. symp. on signals, circuits and systems (isscs 2009), iasi, romania, vol. 2, pp. 469-472, july 9-10, 2009. [6] g. lu, n. sadagopan, b. krishnamachari, and a. goel, delay efficient sleep scheduling in wireless sensor networks, in proc. of ieee infocom 2005, march 2005. [7] ming xiao, tor m. aulin, energy-efficient network coding for the noisy channel network, isit 2006, seattle, usa,pp. 778-782, july 9-14, 2006. [8] w. ye, j. heidemann, and d. estrin, an energy-efficient mac protocol for wireless sensor networks, in proc. of ieee infocom, june 2002. [9] y. yang, b. krishnamachari, and v.k. prasanna, energy-latency trade offs for data gathering in wireless sensor networks, in proc. of ieee infocom, march 2004. [10] j. h. chang, and l. tassiulas, energy conserving routing in wireless ad-hoc networks, in proc. of ieee infocom, march 2000. [11] slimsim simulator, http://cs.anu.edu.au/ aaron/sim.php [12] j. widmer, c. fragouli, and j.y. le boudec, low-complexity energy-efficient broadcasting in wireless ad-hoc networks using network coding, first network coding workshop (netcod), riva del garda, italy, 2005. [13] c. fragouli, j. widmer, j.y. le boudec, a network coding approach to energy efficient broadcasting: from theory to practice, in ieee infocom, barcelona, spain, apr. 2006 [14] p. gupta, and p. r. kumar., the capacity of wireless networks, ieee transactions on information theory, 46(2):388ďż˝404, march 2000. [15] rodica stoian, and a. raileanu, how to choose a model for wireless networks, scientific bulletin of the “politehnica” university of timişoara, romania, tom 51(65), fasc. 2, pp 109-112, sept. 2006. international journal of computers, communications & control vol. ii (2007), no. 1, pp. 94-102 mas_up-uct: a multi-agent system for university course timetable scheduling mihaela oprea abstract: many real-world applications are mapped into combinatorial problems. an example of such problem is timetable scheduling. in this case, the two basic characteristics can be defined by its distributed and dynamic environment. one efficient solution to solve this problem could be provided by an agent-based approach. a timetable scheduling problem can be modelled as a multi-agent system that provides the final schedule by taken into account all the restrictions. in this paper it is presented a preliminary research work that involves the development of a multi-agent system for university course timetable scheduling, named mas_up-uct. we focus on the architecture of the multi-agent system, and on the evaluation of the communication process by using the interaction diagrams. keywords: intelligent agents, multi-agent systems, timetable scheduling 1 introduction in the last decade, several artificial intelligence (ai) technologies and methods have been applied in the educational domain, at high school or university level. most of the applications that use ai are solving the tutoring/teaching and/or examination tasks, while less of them try to solve also the administrative tasks (e.g. course timetabling, examination timetabling, students presence control, student registration) related to an educational institution. a recent developed educational system that models also such tasks is the e-class personalized prototype system presented in [10]. this system is an extension of the widely available open source learning content management system, e-class, which has a component named school administration, that deals with the tools that handle timetables, financial matters, personal student data, student registration etc. in this paper, we shall focus on the timetabling problem, and we shall discuss about mas_up-uct, an agent-based system that we have designed, which provide solutions to university course timetabling. the general task of solving timetable scheduling problems is iterative and time consuming. in real world applications, the participants to the timetable scheduling have conflicting preferences, which make the search for an optimal solution an np-hard problem. in order to solve the problem it is necessary to find a compromise between all the professors’ requirements, usually conflicting (e.g. day, time). the constraints are related to the availability, timetabling and preferences of each professor, to rooms availability, number of students, and curricula. in order to solve this problem for the particular case of university course timetable scheduling we have adopted the agent-based approach. multi-agents systems (mas) are concerned with coordinating behavior among a collection of autonomous intelligent agents (e.g. software agents) that work in an environment. sometimes, software agents are designed to reconcile their own interests with the constraints implied by other agents. one type of software agents is given by expert assistants who enable us to automate certain manual tasks and who work more efficiently. expert assistant is a term given to an intelligent software agent that performs certain tasks on our behalf [15], [14]. for example, our daily organiser is an assistant. the complexity of multi-agent systems is generally higher than that corresponding to conventional software systems and their success rely on properly designed and well tested subsystems. also, in the particular case of timetable scheduling, the mas could find an optimal or a sub-optimal solution using mainly inter-agent communication (with minimal message passing). in this paper, it is presented the architecture of a multi-agent system, mas_up-uct, that is under development, and has as main purpose the modelling of the university courses timetable scheduling. we copyright c© 2006-2007 by ccc publications selected paper from icvl 2006 mas_up-uct: a multi-agent system for university course timetable scheduling 95 shall describe the architecture of the multi-agent system, focusing on the mapping of a course timetable scheduling in terms of intelligent agents, and finally, we shall make a preliminary evaluation of the multi-agent system. 2 university course timetabling problem the scheduling problem can be defined as a problem of finding the optimal sequence for executing a finite set of operations (tasks or jobs) under a certain set of constraints that must be satisfied. a scheduler usually attempts to maximize the utilization of individuals and/or resources and minimize the time required to complete the entire process being scheduled. there exist a number of different types of scheduling problems, such as job shop problems, sport leagues games scheduling, timetabling, service timetable problem for transportation networks, etc. many scheduling problems share some features with the timetabling problem. in [12] it is presented a survey of automated timetabling. in the educational context, scheduling is the problem of assigning a set of events (courses and/or exams) to limited lengths of periods and to rooms, subject to certain conditions. there are two types of academic schedules: the course schedule and the examination schedule. for both types of problems the resources includes students, staff, rooms, courses, time, equipments. several ai-based educational scheduling systems were reported in the literature (see e.g. the system presented in [13] for examination scheduling in universities). the formulation of the university course timetabling problem (as given in [4] and [12]) is the following: input data: q courses, k1, ..., kq, for each i, course ki consists of ki lectures, r curricula which are groups of courses that have common students, s1, ..., sr, p the number of periods, lk the maximum number of lectures that can be scheduled at period k (i.e. the number of rooms available at period k). goal: find yik (i=1, ..., q; k=1, ..., p), so that (1) ∑pk=1 yik = ki (2) ∑qi=1 yik ≤ lk (3) ∑i∈sl yik ≤ 1 (4) yik = 0 or 1 where i=1, ..., q; l=1, ..., r. the constraints are the following: each course is composed by the correct number of lectures (relation (1)); each time there aren’t more lectures than rooms (relation (2)); avoid conflicting lectures to be scheduled at the same period (relation (3)). the objective function: max ∑qi=1 ∑ p k=1 dikyik where dik is the desiderability of having a lecture of course ki at period k. different solutions, manual or automated, were proposed in the literature. some automated solutions are given by tabu search [3], constraint satisfaction [12], genetic algorithms [2], logic programming [5], and combination of different methods [9]. 96 mihaela oprea 3 the architecture of mas_up-uct system we have designed the architecture of a multi-agent system, mas_up-uct, that tries to solve optimally the university courses timetable scheduling. in figure 1 it is shown the architecture of the multi-agent scheduling system, while figure 2 presents the general overview of the university course timetabling. figure 1: the architecture of mas_up-uct we briefly describe how it is usually made the manual university course timetabling. suppose the university includes five faculties, each of them having a number of specializations. the timetabling for each specialization is done by a person who is dedicated to this job, which we shall name specialization course scheduler. this person will provide five, four or three timetablings corresponding to the specialization’s number of study years. the specialization course scheduler will receive a list of options from each professor that is teaching a course to a certain year of study at that specialization. the list of options will include the professor’s options ordered by their desirability, and will include also, the list of impossible timetable schedulings. after course timetable scheduling is done at every faculty, it is started the activity of rooms allocation at university level. so, the university course timetable scheduling problem is divided in two subproblems: 1. faculty course timetable scheduling (which involves only allocation of course day and time), and 2. university course rooms allocation (which involves allocation of rooms for courses). when all courses have allocated time intervals (day and time) and rooms, the university course timetable scheduling is ended with success. whenever a problem occur, it is started a communication process which will involve mainly a negotiation activity. in most romanian universities, the university course timetable scheduling is done either manually or partial automatically. in order to improve the efficience of the whole activity, we have mapped the course timetabling in terms of autonomous intelligent agents. each faculty has a scheduler multi-agent system (mas-fi), which has to schedule the courses of that faculty. the main scheduler agent (the university scheduler agent) which will allocate the rooms is mscheduler agent. because most professors teach courses to different faculties, every faculty scheduler agent has to communicate with the others scheduler agents, in order to solve some critical situations that may arise. the negotiation strategy used by the agents is similar to that described in [8]. in figure 3 it is presented the mas at faculty level, which includes a faculty scheduler agent, and expert assistants (ea) for each specialization of that faculty. for each specialization it is developed an expert assistant which has to do all the activities connected with that specialization (e.g. evidence of students, course curricula etc). an important activity that should be done by an expert assistant is course timetable scheduling (day and time). a lot of constraints mas_up-uct: a multi-agent system for university course timetable scheduling 97 figure 2: the general overview of the university course timetabling task figure 3: mas-at faculty level 98 mihaela oprea should be satisfied in order to solve the course timetabling. for example, one constraint is that all courses of a specialization are teached for all the groups of that specialization, and this constraint may become more severe in the case of courses that are teached for more than one specialization (this case appear for specializations that have courses with the same curricula). the faculty scheduler agents, who act autonomously, can schedule university course timetable on professor’s individual behalf. ideally, all profesor’s preferences should be accepted. unfortunately, we cannot reach an agreement among agents taking in consideration all professor’s preferences. in course timetable scheduling, agents must quantify the professor’s subjective preferences. in the worst cases (when a classical negotiation will have no results), we can reach a collective agreement by using a persuasion protocol (similar to that presented in [7]). the persuasion protocol is based on the rationality of agents. agents should satisfy some criteria of rationality (e.g. maintaining logical consistency). the advantage is that negotiation using persuasion protocol can reach more agreements compared with existing negotiation protocols and it can improve the rate of agreement in course timetable scheduling. the analysis and design phases of the mas_up-uct development were done by using the methodology gaia v.2 [1]. in this methodology, during the design step three models are built: agent model, service model, and acquaintance model. the agent model specifies the types of agents that compose the system. basically, the assignment of roles to agent types creates the agent model. the service model specifies the services that has to be implemented by the agent types. in gaia, a service is a coherent block of functionality, neutral with respect to implementation details. the acquaintance model shows the communication links between agent types. also, it is built a model of the environment. summarizing, four types of agents are used by our system: main scheduler agent (msa), faculty scheduler agent (fsa), expert assistant agent (eaa), and personal agent (pa). figure 4 shows the roles and responsibilities of each type of agent that compose the system mas_up-uct. figure 4: roles for the agents that compose the system mas_up-uct we make a brief discussion of two critical situations that may arise during a course timetabling: 1) at faculty course timetabling: day and time timetable conflict (two or more professor’s options are identical) solution: start a negotiation process between the expert assistant of that specialization and the professors involved (or their personal agents). a message is sent by the specialization expert assistant to all those professors that are involved in a conflict, and will wait for a solution of the negotiation. if it will receive an answer it will do a rescheduling. if it will receive no solution, it will start a persuasion process of negotiation, suggesting a solution. 2) at university course timetabling: no room is available for a certain day and time course. in this case the mscheduler agent will start a negotiation process between faculty scheduler agents that are involved in the conflict, by given some options. each faculty scheduler involved in the conflict will pass the message to the corresponding expert assistants, or, in mas_up-uct: a multi-agent system for university course timetable scheduling 99 some cases will continue to pass the message to professor’s personal agents, who will than negotiate directly. if after this negotiation no solution will be find out (e.g. some courses cannot be moved in other module or day), the main scheduler agent will start a persuasion dialog between the faculties agents that are in conflict, which in turn will transfer the problem at the lower level. 4 evaluation of the multi-agent system as an evaluation method of our mas we have chosen the interaction diagram method [11]. an interaction diagram is a graph showing the processing of each agent symbollically as one or more vertical bars, and the messaging between agents as horizontal or oblique arrows between agents (from sender to receiver), decorated with message indications. in figure 5 it is presented an example of interaction diagram, which illustrates a negotiation process at faculty level, between two expert assistants (eai and eaj). in figure 6 it is shown the interaction diagram in the case of a critical situation. figure 5: example of interaction diagram in order to evaluate the multi-agent system we can use interaction diagrams to design the communication process between agents (expert assistants, personal agents etc) and to verify that the system executes the correct communication sequences. we have used message flow fragmentation in order to realize an analysis of the communication process. the direct sequence and a part of the inverse sequence of the message flow fragmentation that corresponds to the negotiation process shown in figure 6 is given bellow. beg(mas), beg(f sal ), beg(f sak), beg(ealt), beg(eal r), beg(eaki), beg(eak j), snd(mas, m1), split(m1, m1k, m1l), rcv(f sak, m1k), rcv(f sal , m1l), split(m1k, mki, mk j), split(m1l, mlt, ml r), rcv(eaki, mki), rcv(eak j, mk j), rcv(ealt, mlt), rcv(eal r, ml r), snd(eaki, mki−1), snd(eak j, mk j−1), join(mki−1, mk j−1, m1k−1), ..., end(mas), end(f sal ), end(f sak), end(ealt), end(eal r), end(eaki), end(eak j) the inter-agent communication is done by using the agent language fipa acl. figure 7 shows an example of such a message ma12 ea-ime-ac exchanged during a negotiation. let’s consider a course timetabling conflict at the level of a faculty. this conflict consists in the situation of day and time identical options for two professors (pa1, pa2) that teach at the same specialization (computer science) different courses. this situation is described in figure 8. information exchanges during the conflict solve can be modeled with protocol diagrams using auml notation [6]. figure 8 shows a sample negotiation protocol for day and time timetable conflict solve. as it can be seen, the expert assistant of the computer science specialization (eacs) will inform the two personal agents corresponding to the two professors about the conflict. after this message is sent to both personal agents, between them it will start a negotiation protocol that involves a sequence of proposals and counter-proposals till a solution is accepted by the two agents. at the end of the negotiation process, 100 mihaela oprea figure 6: an example of negotiation in a critical situation figure 7: example of a fipa acl message figure 8: negotation protocol for a day and time conflict in course timetabling mas_up-uct: a multi-agent system for university course timetable scheduling 101 pa1 will inform the expert assistant agent about the solution found. 5 conclusion the paper presented the current state of a research work that involve the development of a multiagent system for university course timetable scheduling. the purpose of our work was to analyse the benefits of using an agent-based approach for the university course timetable scheduling, which involves a lot of communication, cooperation and negotiation processes. we have described the architecture of a multi-agent system for university course timetable scheduling, mas_up-uct, and briefly discussed about the evaluation of the multi-agent system. we can conclude that the main benefits of the agent-based approach adopted for university course timetabling are given by the possibility of doing negotiation between agents as a solution to the conflicts that may arise, and by the analysis of the exchanged messages flow between agents with the interaction diagrams. references [1] l. cernuzzi, t. juan, l. sterling, f. zambonelli, the gaia methodology basic concepts and extensions, methodologies and software engineering for agent systems, eds. bergenti, f., gleizes, m.-p., zambonelli, f., kluwer academic publishers, pp. 69-88, 2004. [2] d. corne, p. ross, h.-l. fang, fast practical evolutionary timetabling, lecture notes in computer science, lncs 865, pp. 251-263, 1994. [3] d. costa, a tabu search algorithm for computing an operational timetable, european journal of operational research, vol. 76, pp. 98-110, 1994. [4] d. de werra, an introduction to timetabling, european journal of operational research, vol. 19, pp. 151-162, 1985. [5] r. fahrion, g. dollanski, construction of university faculty timetables using logic programming techniques, discrete applied mathematics, vol. 35, no. 3, pp. 221-236, 1992. [6] m. -f. huget, j. odell, b. bauer, the auml approach, methodologies and software engineering for agent systems, eds. bergenti, f., gleizes, m.-p., zambonelli, f., kluwer academic publishers, pp. 237-257, 2004. [7] t. ito, t. shintani, an agenda-scheduling system based on persuasion among agents, technical report: nagoya institute of technology, 1997. [8] m. oprea, the use of adaptive negotiation by a shopping agent in agent-mediated electronic commerce, lecture notes in artificial intelligence, lnai 2691, springer-verlag, berlin heidelberg, pp. 594-605, 2003. [9] g. picard, c. bernon, m.-p. gleizes, cooperative agent model within adelfe framework an application to a timetabling problem, proceedings of the 3rd international joint conference on autonomous agents and multi agent systems, new york, usa, pp. 1506-1507, 2004. [10] e. g. prodromou, n. avouris, e-class personalized: design and evaluation of an adaptive learning content management system, artificial intelligence applications and innovations, eds. maglogiannis, i., karpouzis, k., bramer, m., springer, pp. 409-416, 2006. 102 mihaela oprea [11] r. ronnquist, c. k. low, analysing expert assistants through interaction diagrams, proceedings of autonomous agents 97, acm press, pp. 500-501, 1997. [12] a. schaerf, a survey of automated timetabling, technical report: cs-r9567, centrum voor wiskunde en informatica, 1995. [13] c. c. wei, a. lim, automated examination scheduling problem, proceedings of the iasted international conference applied informatics, innsbruck, acta press, pp. 93-96, 2002. [14] g. weiss, multiagent systems, the mit press, cambridge, massachusetts, 1999. [15] m. wooldridge, n. r. jennings, intelligent agents: theory and practice, the knowledge engineering review, vol. 10, no. 2, pp. 115-152, 1995. mihaela oprea university petroleum-gas of ploiesti department of informatics address: b-dul bucuresti 39, 100680 ploiesti, romania e-mail: mihaela@upg-ploiesti.ro received: november 8, 2006 editor’s note about the author: mihaela oprea (born on february 20, 1967) graduated the faculty of automatics and computer science at the university politehnica bucharest in 1990, and got her phd at the department of automatics and computer science of the university petroleumgas of ploiesti in 1996. currently, she is a full professor at the department of informatics of the university petroleum-gas of ploiesti. her main research interests include pattern recognition algorithms, machine learning, knowledge modelling, applications of multi-agent systems and artificial intelligence techniques in various domains such as environmental protection, engineering, and education. she has published more than 70 research papers in the area of artificial intelligence in international journals, and in the proceedings of prestigious international conferences, printed by important publishers (springer, kluwer academic, ios press, ieee computer society press). since 1995 she has visited as a research visitor universities and artificial intelligence research institutes from uk, austria, spain, greece, sweden, czech republic, hungary, and france. she is a member of some professional associations, and of the international programme committees of several conferences held in europe, usa, canada, south america, also participating at some of them as a tutorial presenter. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 1, pp. 20-41 a software system development life cycle model for improved stakeholders’ communication and collaboration s. cohen, d. dori, u. de haan shalom cohen, uzi de haan technion, israel institute of technology haifa, israel e-mail: {shalom1,uzid}@tx.technion.ac.il dov dori 1. technion, israel institute of technology haifa, israel 2. massachusetts institute of technology cambridge, ma, usa e-mail: dori@ie.technion.ac.il abstract: software vendors and entrepreneurs, who try to introduce an innovative software product to a specific organization or an entire market, enter a long and tedious process. during this process, the market and various organizations evaluate the product from different perspectives, such as software robustness, manufacturer reliability, and corporate need for the product. the vendors and entrepreneurs engaged in this process encounter decision crossroads for which no relevant guidance exists in the literature. the research closely monitored the processes associated with the introduction and assimilation of an innovative off-the-shelf (ots) software product into five different organizations in different vertical market segments. observations were carried out to assess organizational and marketing processes and to document and analyze what the software product undergoes before it is accepted for acquisition or full implementation within the organization. the research outcomes offer a unified, collaborative multi-tier system development life cycle (sdlc) framework and methodology for packaged ots software products that greatly improves communication and collaboration among the stakeholders. each tier addresses a different force or stakeholder involved in the software market: vendor, customer, consultants and integrators. all stakeholders refer to the same time-line thus; tasks of various stakeholders are streamlined. adherence to the unified time-line brings about an increased amount of stakeholder interaction, communication and collaboration. newly found tasks that improve communication and collaboration among stakeholders include (1) offering of the ots software product together with personnel as a bundle, (2) an improvisation-intensive iterative task of weaving potential customers’ requirements into the prototype, and (3) a third sale milestone, representing the successful diffusion of the product. the significance of this interdisciplinary research stems from its unique position at a crossroad between software engineering, marketing, and business administration, which has not yet been sufficiently explored or cultivated. keywords: collaboration, system development life cycle model, stakeholders. 1 introduction two major trends dominate the software development world today. the first is the shift of organizations from fulfilling their own software requirements in-house to buying it on the market, either as an copyright c© 2006-2010 by ccc publications a software system development life cycle model for improved stakeholders’ communication and collaboration 21 off-the-shelf packaged software product or from a company tailoring a specific solution.[1] the second trend is the shift from developing tailor-made software to purchasing packaged software from vendors either in stores or directly from the vendors.[2] here we assume that acquisition of packaged software is done by an organizational consumer.1 when relating to a packaged software product, which may be seen as a system by its own accord, one should make a clear distinction between a software product and an information system (is). [3] an is is made up of a number of software products or modules put together.[1] this research examines an off-the-shelf (ots) packaged software product as a system2 which goes through the various stages of system development life cycle (sdlc)3. many software development processes and models use stages outlined in sdlc.[4] we relate to sdlc not only in its traditional "waterfall" sense, but also to other models outlining the stages in software development. since these models, including the spiral and rapid application development (rad) are not as broadly known as the "waterfall" model and are less useful for explaining the market effects on software development delineated hereafter [1], we do focus on the waterfall model as a reference. the lifecycle of an information system includes the various phases that a software product goes through starting with its conception all the way to the stage when it is no longer available for use.[5] the software lifecycle, depicted in figure 1, typically includes the following phases: requirements, analysis, design, construction (or coding), testing (validation), installation, operation, maintenance, and the less emphasized retirement.[6] figure 1: the traditional phases of the system development life cycle model these basic phases have also been adopted for is acquisition purposes. although the names of the phases were changed where appropriate, the basic structure and timeline have been kept. the phases of is acquisition, shown in figure 2, are project justification, financial evaluation of the project, preparations for acquisition, request for proposals, vendor evaluation, contract negotiations and, implementation and maintenance.[7] figure 2: the sdlc model adapted to the acquisition process of information systems the literature research, summarized below, indicates that no significant attempt has been made to extend the sdlc model to other situations encountered by many software vendors and software developing entrepreneurs.[2] software vendors and entrepreneurs, who try to introduce an innovative software product to a specific organization or an entire market, enter a long and tedious process. during this process, the market and various organizations evaluate the product from different perspectives, such as 1the scope of the research is limited to describing the organizational consumer and not the private home user which is a discussion in its own right and differs in many ways from that outlined hereafter. 2"the system," "ots software product," and "packaged software product" will be used from now on interchangeably 3the acronym sdlc will denote here the system development lifecycle model as it relates to software products. its implementation with regards to ots software products will be discussed here. 22 s. cohen, d. dori, u. de haan software robustness, manufacturer reliability, and corporate need for the product. the vendors and entrepreneurs engaged in this process encounter decision crossroads for which no relevant guidance exists in the literature. this lack of guidance is somewhat surprising, since information systems development is best understood as a market phenomenon. it is a perspective which highlights how software is developed, who performs the development, who sells the related products, and how they are introduced to users.[1] 2 research goal and objectives the goal of this research is to develop and evaluate a collaborative multi-tier lifecycle development model for packaged off-the-shelf (ots) software products. the proposed model accounts for market and organizational factors and the way they are woven into the traditional phases of software development. to this end, the research has monitored, outlined, characterized, defined, and mapped specific phases which ots software products typically go through. the resulting comprehensive model relates to the development, marketing, assimilation, and other organizational aspects of the ots software product. the research has identified and defined new, modifiable software lifecycle processes, the adoption of which might benefit various stakeholders under various marketing conditions. our hope that the prevailing model will entail this aim by creating a task-based learning community which is a group of people who are organized around a task i.e. stakeholders, collaborating for a specified period of time to produce a product.[8] here, we attempt to create a unified exhaustive sdlc framework on one timeline with a number of tiers, creating a new collaborative multi-tier system development life cycle methodology. each tier addresses a different force or stakeholder involved in the software market, such as producers, consumers, consultants, and integrators.[1] the basic time frame of the sdlc, especially the beginning (inception) and end (implementation and maintenance) is kept. the various milestones along the sdlc time line indicate an appropriate task for each tier and an explanation of that task. tasks on the same vertical axis are to be performed concurrently and collaboratively. figure 3 depicts a possible scheme of the proposed collaborative multi-tier marketand organizationoriented sdlc model to be fleshed out as a result of the field study outcomes. the list of stakeholders stacked in figure 3 is by no means exhaustive. figure 3: a possible scheme of the proposed collaborative multi-tier marketand organization-oriented sdlc model to be fleshed out as a result of the experiment outcomes the main novelty of this research is that it is a first field-based study that is aimed at the establishment of a collaborative multi-tier sdlc model and a methodology based upon it. in addition, in most is field studies, researchers have access to a limited amount of evidence and observations in participating a software system development life cycle model for improved stakeholders’ communication and collaboration 23 organizations. in contrast, this research takes advantage of the fact that the software that was examined has been developed at the technion and was fortunately being positioned at the point of time required for this research. due to the special ties between the technion and the software vendor, this study has had access to evidence and observations that are normally out of reach to researchers. the validity of these unique findings was tested against the more abridged findings of the control case studies. 3 literature review the only academic development model incorporating any type of market effects is that of carmel and becker.[9] they developed a process model for packaged software development which was partly empirical. carmel and becker [9] point to a few market-related actions necessary to be performed in some, but not all of the described stages. actions like "assessing product differentiation considerations" are attached to the "initial screening stage" of the "requirements loop" with no explanation how they can be achieved. in summary, carmel and becker [9] were the first to attempt a complete process model which adds marketing tasks. their model, however, was only partially based on empirical findings, and instead of having the market define software market needs and sdlc phases, they suggested them a priori. moreover, as cusumano et al.[10] noted, their reliance and justification of a pivotal "freeze specification" stage is problematic in a highly volatile market.[10] as carmel [2] noted, no major study had been conducted on market introductory effects on packaged software innovations before 1995.[2] nevertheless, the idea of introducing a market-based perspective into information systems development was introduced later on.[1] he juxtaposed a market-oriented approach with a simplification of the traditional "waterfall" model. at the basis of his idea is a separation of the traditional sdlc model (from development to user introduction) into two separate parallel models, one for the software developer and the other for the software consumer. in addition sawyer had several interesting assertion as to the growing importance of additional stakeholders in the development process. for example: • third parties (consultants, vendor representatives, etc.) have an increasing role in the initial stages of sdlc. consultants/integrators are now also part of the information system development (isd) process, as they enable and mediate the software market. this contributes to widening the chasm between users and vendors. this chasm is bridged only by indirect links between customers and developers via intermediaries or customer surrogates.[11] • system installation requires a third party in charge of installing the product, customization, and training. • the development process is of smaller importance to the consumer than the final product. although most of the assertions in sawyer’s model may make sense, they are in no way based on empirical evidence and do not have a direct connection to an sdlc model currently in use. his model lacks due reference to the producer’s side, an aspect which this research has elaborated on. in order to cover a large number of organizational and market-related sdlc influencing factors, we searched for academic and professional models in seven domains. we began by looking at the above mentioned few existing market based is/software development models to see how an innovative ots software product is produced in the market. we then continued by looking at works on software cycles and structured development studies to uncover the new ots software manufacturing methods. leaving the is domain, we followed moore’s technology diffusion theory to look for models on technology adoption, innovation introduction and marketing diffusion theory which may relevantly describe the 24 s. cohen, d. dori, u. de haan diffusion process of an ots software product too.[12] we then reviewed research in the cross domain of organizational decision making on is/software related issues to learn how the common is/software related decision-making processes in various organizations are performed. the maturity of the software product, as well as that of the organization, is a matter of much interest to industrial engineers and business administrators and it influences entrepreneurial vendors tremendously and therefore reviewed here too. the relatively young academic field of entrepreneurship was searched for adequate models and research on innovation, innovation-exploration and entrepreneurship in the software market. finally, we surveyed the market for best practices and existing methodologies for ots software development by entrepreneurial vendors. table 1 summarizes the main studies related to this research topic under the various categories. from the above literature review we learnt about the possible variables and added them to the examined research’s model as described in the following section. tabel 1. summary of main studies sdlc and related subjects. aspect article empirical market based is/ carmel and becker[9] very partial software development keil and carmel[11] yes sawyer[1] no sing and kotze[34] partial software cycle and cusumano et al.[10] yes structured development cusumano[35] no carmel[2] yes clark and wheelwright[36][37] partial wheelwright and clark[38] partial boehm and bose[39] yes fine[40] no avison and fitzgerald[4] no ebert[42] yes technology/ innovation introduction/ mustonen-ollila and lyytinen[43] yes diffusion theory marketing4 lucas and spitler[44] yes davis[45] yes moore and benbasat[46] partial brancheau and wetherbe[47] yes cooper and zmud[48] yes fichman and kemerer[25] yes is related decision-making and verville and halingten[49] yes software acquisition processes nelson et al.[50] yes iivari and ervasti[51] yes software product and paulk[52] no organization maturity nordman [53] report lee and o’connor[54] no montaguti et al.[55] no hi tech entrepreneurship shane[56] partial shane[57] no murray and tripsas[58] yes baker et al.[23] yes vera and crossan[27] yes best practices nih matrix[59] best practice agile[61] best practice 4for the most part, the writings in this discipline have not distinguished between the more general definition of an it product and a specific is/software-like product. a software system development life cycle model for improved stakeholders’ communication and collaboration 25 4 methods and experiments in this section, we first provide a short explanation to the research method used (a), and then we describe the case study sites selected (b). data collection efforts are described in (c) and finally in (d) we cover the preliminary research model. a. case study methodology yin identified three main types of case studies based on the purpose for which they are used [13]: (1) explanatory a case study intending to explain the casual links in real-life interventions that are too complex for survey or experimental strategies. (2) descriptive a case study that emphasizes the formation of hypotheses of cause-effect relationships, where a descriptive theory must cover the depth and scope of the case under study.[14] (3) exploratory a case study in which the fieldwork and data collection may be undertaken prior to definition of the research questions and hypotheses. the framework of the study must be created ahead of time to maximize what can be learned, knowing that time is limited. the selected cases should be easy and should include willing subjects.[14] the research strategy utilizes a combination of exploratory and descriptive case studies. the study does not explain an existing theory, so it cannot be categorized as explanatory. rather, it tries to describe and explore emerging software development and marketing processes. fieldwork and data collection were done prior to exact definition of the research questions and hypotheses generations.[15] the research includes multiple exploratory/descriptive case studies, using replication logic. replication logic is a logic by which case studies are selected to create a multiple-case design. cases are selected so that they can either produce typical, negative or disconfirming results or exceptional/discrepant results. this form of case selection is also known as a theoretical sampling of cases as opposed to the normal sampling logic used in quantitative methods. the outcomes of this design are improved theory, generalization ability and cross-case analysis. the latter is achieved by the use of two additional case studies which serve as control or baseline studies. each case study is treated as an independent experiment, and the entire study is comprised of and based upon a sequence of multiple experiments.[16] when a case study strategy is agreed upon, it permits for both qualitative and quantitative sources of evidence to be collected and analyzed.[15] the collection and analysis of these two complementary forms of evidence has enabled triangulation. various methods of data collection and fact retrieval were utilized, as described in section (c) of this chapter. the field-study strategy, by which this research obtained insights into the processes that innovative software products go through, is an empiric study with multiple case studies. the opcat software was introduced into five organizations operating in mostly different vertical market sectors, so that the lessons learned from them cut across sectors. the list of sectors included banking, military, avionic, software and banking-software. one additional off-the-shelf software product (product b) was introduced to a telecom company, and the final product (product c) was introduced to a software organization. the number of case studies chosen (7) reflects a practical balance between the need for sufficient ground for generalization of the findings and the research time and capacity constraints. the number corresponds to the recommended range of 4 to 10 cases for theory building purposes.[15] b. case study sites as the choice of organizations in which to perform the case studies is not a pure random sample, we tried to compensate for this by using theoretical sampling [13] designed to cover a broad spectrum of sectors, company sizes and locations, as depicted in table 2. the customer organizations chosen for the case study sites were: org.1-e is a large airborne avionics systems manufacturer that employs over 3000 employees. 26 s. cohen, d. dori, u. de haan table 1: profile of firms in the exploratory/descriptive multi-firm study company sector size(employees) location introduced product org.4-b banking >10.000 israel opcat org.1-e military/avionics >10.000 israel opcat org.2-el military <1.500 israel opcat org.5-q software/cellular -100 israel opcat org.3-s software/banking -1.000 singapore opcat org.6-c telecom >1.000 israel non opcat org.4-b software >7.000 israel non opcat org.2-el is a medium sized division (<1000 employees) of a high-technological military products manufacturer of ground, air and space-related products. org.3-s: six of the seven case studies were held in the same country and the remaining study took place abroad, where the sale procedure to a large asian banking software developer employing around 1000 people was followed. org.4-b is one of the largest banks in israel, employing around 10,000 employees. the attempt was made to the bank’s business software applications division. org.5-q is a small software company with less than 100 employees, developing software for the cellular phone industry. in addition to introducing opcat into five organizations, two additional case studies org.6-c, org.7-bm were held in which similar ots software products were followed by means of their introductory phases into the market. these two additional case studies served as baseline case studies and assisted with both building the validity and analysis of the findings from the first five studies and with building a more robust and accurate sdlc model4. the case study sites were monitored periodically according to the type of evidence that was collected. for routine correspondence and product-related documentation, ongoing collection was used. for researcher observations, such as meeting attendance with adopting organizations, they were held according to the case study’s natural timetable. evidence collection sessions that is pushed by the researcher, such as questionnaires/surveys and interviews, were held at fixed time intervals across all case studies so that a matrix of observations period versus company was created. these mixed monitoring methods enabled the evaluation of evidence versus specific reference points in time and the description of continuous events as they were unfolding. c. data collection the software products that were introduced into each of these organizations, and the processes that they underwent thereafter until successful adoption installation and acquisition, or possibly rejection by the organization, were monitored and meticulously documented. four different types of evidence collection were utilized for the monitoring and documentation of the abovementioned processes: direct passive observations, documentation collection, open-ended and focused interviews and physical artifacts i.e. generated computer code or diagrams. table 3 summarizes the data collection efforts in the five main case studies held with the opcat vendor. the table also clearly indicates that the most extensive case studies as far as data collection was concerned were org.1-e and org.3-s. two of the remaining 3 studies were shorter studies, mainly as they represent failed attempts of implementation by opcat, and thus spanned a shorter life-cycle. 4due to strict non-disclosure restrictions, the information regarding the two baseline case studies, as well as the products, organizations, and customers examined has been kept confidential notwithstanding its use for hypothesis building and generalization purposes. a software system development life cycle model for improved stakeholders’ communication and collaboration 27 table 2: general data collection types and data collection summary statistics case duration interdirect questiondocumentation other site (months) views passive aires emails/ physical meeting hard copy/ artifacts documenppts/other tation org.1-e 14 4 12+5 day 2/ 5/ 10/ 5 3 brainstorming session org.2-el 14 4 7 1 22/ 3/ 6/ 30 org.3-s 20 6 12+5 day 0 157/ 3/ 8/ 3 8 session course org.4-b 9 2 8 1 12/ -/ 4/ 2 org.5-q 6 2 5 1 10/ -/ 3/ 2 the evidence gathered from these case studies was then compared and analyzed and eventually enabled the identification and definition of common phases that the software went through in the various organizations and industries. d. the preliminary model a general view per the research method described above we defined our own set of a priori basic constructs for this research. the various preliminary variable groups and their interaction are modeled using object process methodology (opm), which provides a variety of complexity management tools that help diagram the model clearly and efficiently.[17] the top-most diagram, seen in figure 4, demonstrates the main process of successful ots software product implementation, which this research examined. this process and its impact on the sdlc of ots software product innovations was our dependent variable. this process is handled and impacted by the various stakeholders in today’s ots software product market, i.e., the vendor, adopting organization, third party integrators/consultants and, indirectly, other market and industry effects that constitute the intervening and contextual variables of this model, respectively. the stakeholders interact via external non-systemic, environmental social networking process, in which they exchange leads, assign projects, etc. the impact of this process as a whole was of concern to this research, but its internal components and intricacies were not further elaborated, as the issue of social networking has already attracted extensive writing and research.[18] the independent variable of this model is the ots software product. for the sake of simplicity, the object representing the product includes only four basic states: specified, developed, acquired, and implemented. these are the most important states in a software product’s lifecycle from the initial undeveloped product state, i.e., product in specification format only, to the successfully implemented product by an adopting organization. research model links in general represent possible hypotheses resulting from these relationships. thus, the bidirectional effect links connecting the various attribute groups in figure 4 mark the possible influence each group may have on others. the bidirectional links generalize unidirectional and bidirectional influences and suggest the variable group undergoes certain changes once the process is performed. the preliminary model spanned 38 variables brought together from the various is, or and marketing domains discussed in section 3. for the sake of brevity we do not bring here a full discussion regarding the reasons for their inclusion, and the variables comprising each variable group|footnotethe full expla28 s. cohen, d. dori, u. de haan legend proces result / consumption link environmental object object effect link instrument link agent link environmental process bi-directional general purpose structure object state figure 4: the proposed preliminary logical model nation is readily available from the authors.. furthermore, the full list of initial variables, as well the final ones, is given in table 4. 5 intermediate findings and conclusions the findings of this research include the final collaborative multi-tier sdlc research model and specifications of how the original research model has been modified throughout the research by omitting, adding and merging variable groups and variables. the aim here was to show how the model and its variables were validated through the various case studies conducted. a table is used to show the original model’s variables vs. the final model’s variables, and how each variable gained or lost validity based on evidence from the case studies. therefore, in this section, we begin by describing the changes to the original research model in (a), and continue with explaining about lead-driven development (ldd) described in (b) brought as a partial downscaled example of the much larger and full collaborative multi-tier system development lifecycle model. we end the section by giving a short explanation as to the contents of the full collaborative multi tier system development lifecycle model in(c). a. changes to the original research model following the guidelines of the case study research methodology [16], we entered the case sites using a preliminary suggested research model described in section 4. as a case study proceeds, the research model is often updated by adding previously missed-out variables and deleting unnecessary or irrelevant ones. table 4 lists the variables in the original and final models, their inclusion or exclusion in the preliminary and final models, the case studies upon which the exclusion or inclusion were based, and the a software system development life cycle model for improved stakeholders’ communication and collaboration 29 nature of the impact of the variable. the nature is depicted using three symbols: +, and ot, as explained next. the plus symbol is used to denote a positive influence on the successful sale and implementation of the ots software product. the minus sign is used to denote a negative influence on the successful sale and implementation of the ots software product. ot, which denotes "other" reasons, is used when the impact is of a compound or qualitative nature. the last "based on" column provides a supporting reason from the literature for the inclusion or exclusion of a specific variable. explanation of the unique impacts and findings of the findings mentioned above: the point in time where the implementation process is completed has been found to be, in the discussed product type, the third sale point. this is so as an initial first sale is either an impulsive buy or an exploratory attempt and is bundled with a human implementer. the second sale is a post-sale buy to try and achieve the product’s associated benefits independently of external vendor human resources attached. the third sale constitutes reconfirmation of the product’s benefits to the adopting organization and is characterized by purchase of licenses and a long-term support plan. the political factors were observed in only one case study which accidentally took part during the second lebanon war, which took place in northern israel during the summer of 2006. however, we attributed this to coincidence and did not otherwise find any political issues effecting the market or industry and therefore deleted these two variables from the final model. a unique influence was found in two of the larger case study sites, i.e., the partially-governmentowned organizations. in these sites, a very strong influence of outsourced personnel, sometimes even positioned within the adopting organization’s decision making units, was noticed. the addition of the outsourced human resources as a descriptive characteristic of the customer’s users was done in tandem with the addition of the same variable in the vendor’s descriptive variables. this is possible, as in many closed and highly specialized industries, many of the organizational employees today are outsourced employees, who are often sent from a common pool of hr outsourcing companies and employees. b. lead-driven development based on the case studies carried out as part of this research, a new approach to software development for off-the-shelf (ots) products of entrepreneurial vendors has been identified. the new model, called lead driven development (ldd) includes detailed guidelines for entrepreneurial vendors developing ots software. these include directions for pure development procedures (at the coding level) along with organizational steps to be held in conjunction with the coding process to support successful product implementation. this model relies on and revolves around an innovative procedure of improvisation, which is new to this industry. improvisation counters many current trends which state that increased formality yields successful implementation. as table 5 indicates, ldd may be highly beneficial to the vendor. examining table 5, we see that in all the five case studies, some form of ldd was followed. the classification level of ldd correspondence of each vendor per each case study site was scored on a scale of 1 to 5, where 5 represents complete correspondence. the classification was made by a number of uninvolved parties who checked for a clear-cut correspondence of the software introduction and development process to ldd. since we examined the development process performed by the vendors and did not follow other stakeholders, we isolated the benefit associated with the use of ldd to be the influence of ldd on ots product sales in the corresponding case study. benefit was therefore observed as the influence of ldd in achieving a preliminary sale with an organization (first sale). a higher level of benefit was achieving a second sale and the highest level a third sale. as explained later, a third sale is a measure of successful implementation. since the observed vendor is of an entrepreneurial character, associated benefits of an efficient development process, such as shorter coding times or increased flexibility were not accounted for. 30 s. cohen, d. dori, u. de haan table 4. original vs. final research model variables top variable variable original final case nature of based group group model model studies impact/ on significance product sdlc introduction maturity stage x x el,e,s,b,q product sdlc growth maturity stage x x el,b,q + product sdlc maturity maturity stage x x na + product sdlc decline maturity stage x x na product trialability x x el,b,q + product complexity x x el,e,s,b,q product compatibility x x el,e,s,b + product relative advantage functional x x el,e,s,b,q + product relative advantage economic x x el,e,s,b,q ? product relative advantage emotional x × not in any na product whole product factor x x el,e,s,b + product specification flexibility x x e,s + vendor hr dmu/ structure stakeholders × x el,b,q ot vendor hr personnel/ structure outsourced hr × x* el,b,q ot [22] vendor service x x el,b,q + vendor business model lock-in x x el,e,s,b,q + vendor business model novelty x x el,e,s,b,q + vendor business model efficiency x x el,e,s,b,q + vendor business complemenmodel tarities x x el,e,s,b,q + vendor marketing strategy-4ps price x x el,e,s vendor marketing strategy-4ps promotion x x el,e,s,b,q + vendor marketing strategy-4ps place x x el,e,s,b,q ot vendor marketing strategy-4ps product x *x el,e,s,b,q a software system development life cycle model for improved stakeholders’ communication and collaboration 31 table 4. original vs. final research model variables (cont.) top variable variable original final case nature of based group group model model studies impact/ on significance market and pest industry political x × el na [19] market and pest industry economic x x el,e,s,b,q + market and pest industry sociological x × not in any na [19] market and pest industry technological x x el,e,s,b,q + market and industry industry type x x el,e,s,b,q ot adopting type organization innovators x x el,e,s + adopting type early organization adopters x x b,q + adopting type early organization majority x x na +/adopting type late organization majority x x na adopting type organization laggards x x na adopting users change organization resistance × x e,b [20] adopting users organization profession x x el,e,s,b,q + adopting users organization position x x el,e,s,b,q + adopting users learning organization curve × x e,b + [21] adopting users outsourced organization hr* × x b,e ot [22] adopting dmu key organization events x x el,s ot adopting dmu time organization x x el,e,s,b,q + adopting dmu power position organization of employees x x el,e,s,b + adopting dmu no. of decision organization makers involved in process x x el,e,b,q 3nd party integrator/ outsourced consultant hr* × x b,e ot [22] table 5: level of lead-driven development implemented by vendors vs. benefit in sales. site ldd level sale 2nd sale 3nd sale org.1-e 4 + + + org.2-s 3 + + org.3-el 5 + not yet not yet org.4-q 1 org.5-b 1 32 s. cohen, d. dori, u. de haan we entered the more comprehensive table figures of table 5 into a statistical software tool and found that the correlation between sales, second sales and third sales (i.e. vendor benefit) and the level of ldd implementation is clearly significant, positive and high. after establishing that the use of ldd is beneficial for the vendor we carefully documented this process and generalized it over all the case studies. the description of the full ldd process now follows. the hereunder elaborated emerging process for software development includes 12 main steps of which at least 4 include some form of improvisation. moreover, the most unique phase of this suggested model, the lead gathering task is improvisation intensive. in addition, the model may serve as a strict continuous model similar to the waterfall model or may be used as a spiral model involving repetitive tasks. at the core of this model, are 12 steps as follows: step 1: initiation this stage is a formal stage in the regular standard sdlc model. however, with entrepreneurial firms this step tends to be an informal one with no accurate start point in time. this stage includes structuring the will and intent to begin with the project and giving the go-ahead instruction as well as providing the limited resources necessary to start exploring the venture. step 2: high level concept development this second step includes forming the high level concept of the product, the problem which it comes to solve, its associated benefit etc. depending on the scope of the project/system this development phase may be fulfilled using limited resources, spare time, and sometimes even academic resources. a substantial level of improvisation is used at this early stage as well as part of the founding process.[23] improvisation is carried out in the development of the suggested product in a quick and result-oriented fashion while using minor or no documentation and testing at all. step 3: prototype the first important milestone of the entrepreneurial vendor is the ability to deliver a functional prototype. the prototype should convey clearly the problem it is solving, its abilities and associated benefits in an easy and understandable manner with a friendly user interface. the number of moderate bugs, missing features as well as load balancing issues is not of much importance at all at this stage as the product shall be used mostly for demo and pilot purposes in the near future. this first prototype release is called by us a "bugged release". the prototype should further include a number of working examples from various domains. step 4: minor testing in non profit environments, academic demo and use after completing the prototype, which should by this time be a powerful demonstration tool, the vendor should strive to demonstrate the tool in non-profit environments. the aim of these demonstrations is finding a limited installation bed for the product. these installations provide the developers with important feedback on bugs, missing features and general use of the tool a preliminary focus feedback group for the tool. the academic scenery is extremely beneficial for these purposes as it also hosts great uncovered commercial potential through conventions, conferences and to a vast number of current or to be professionals. see also penetration attempts in academia by established firms like philips, ibm and sap. step 5: market introduction, benefit oriented demonstrations and mini pilots an additional task which is improvisation intensive is demonstrating the tool to potential customers. the suggested form of product demonstration which we call "benefit oriented demonstration" is a special type of marketing method unused so far in the world of software. the equivalent in the non-software world is that of a vacuum cleaner demonstration in the customer’s home to show him immediate benefits of the product.[24] thus, in this situation we suggest vendors demonstrate their new tools by implementing a form of improvisation at the customer’s site. the vendor should use the tool to perform an on the spot real work task brought in by the customer for which neither the vendor nor the customer were prepared. if this session exceeds one meeting it may be considered as a mini pilot. it is also in this step that the main improvisational task of the entire proposed model is undertaken. from meeting to meeting the vendor’s marketing representatives must try to anticipate using preliminary talks, phone conversations, social networking ties or emails the needs of the potential customer a software system development life cycle model for improved stakeholders’ communication and collaboration 33 as well as his existing environment. this highly informal improvisational task is used to build software requirements for the development coding team. the requirements gathered are for features which will be required in the marketing sessions with the potential customers. these requirements are then addressed and coded immediately by the development team. the new features developed are neither documented nor tested thoroughly as they will be mainly used for demonstration purposes and may be ultimately dropped. however, a mentioning and documentation of the added features is required at least in a "what’s new" file accompanying the product. this step is of a strict repetitive improvisational nature and involves the gathering of lead requirements between marketing meetings and converting them into semi-operational software features. step 6: offer ots + hr = project. after one of the leads materializes the vendor is asked to prepare a formal proposal for sale. many of our case studies have indicated that ots products for the professional organizational realm are rarely sold if they come from unknown vendors due to risk factors mainly of product abandoning. hence, offering the ots with human resources (an implementer) which will assist the adopting organization, prepare the initial material and then tutor its users is usually beneficial to reduce the uncertainty in these situations. after the customer agrees upon the terms of the project (and not only the product) contract engaging commences and the customer is now considered as the baseline customer. hence, we call this sale the "first sale" en route to successful implementation. step 7: bug fixing due to baseline customer requirements and marketing requests after a certain amount of work is done using the tool at the customer’s site, either by the customer’s users or by the hr which was coupled to the ots, important feedback regarding the products begins to accumulate. this information enables bug fixing and tool robustness improving. in addition, important missing features required by the baseline customer and marketing department are added to the software and provide for the first commercial release of the software. this release is still highly saturated by bugs but is already a commercial useable "non-frustrating" version of the product. step 8: constrain features, further commercial releases and support plan the unbridled adding of new features, in the format suggested in steps 5 and 7 above, creates overwhelming monstrous software. at this point the vendor should start to funnel out some features which cater to a smaller audience and which have not been found to be part of the vendor’s targeted audience needs. furthermore, the vendor should try to find a common thread or theme connecting and guiding all other features. this decision enables further product releases each containing additional noteworthy features, bugs correction and feature enhancements. with the continuous use of the product in at least one baseline customer and before the move to the next implementation step, a support plan (or plans) for the product should be created. step 9: develop complexity management tools, train integrators personnel, interface with customer’s software, find additional benefit oriented projects, embed within organizational deliverables within the baseline customer’s everyday work, issues of model complexity very quickly arise. these issues, which are different from testing the product or load-balancing it, should be addressed and solved early on. in addition, this is also the time to deepen the roots within the customer by both trying to embed the product deliverables within the customer’s overall deliverables, interfacing (physically) with the company’s organizational iss and by finding additional projects within the customer’s company to be involved with. deepening the vendor’s role within the customer’s site is a highly improvisational task in nature and hence requires adequate skills. step 10: second sale licenses: the first sale is by no means any indication of a successful implementation or diffusion of the innovation within the adopting organization.[25] moreover, the coupling of the hr with the ots does not really enable a real diffusion of the independent product. hence, a second sale to the same organization marks an important future commitment of the organization to drop the tutoring relationship and proceed to license purchases for independent use. license purchases signify that the organization now associates positive benefit to the use of the product. step 11: maintenance, begin user training, tutoring, software support after a second sale is 34 s. cohen, d. dori, u. de haan made, the relationship between the vendor and customer moves into the maintenance phase. as the previous phase was coupled by hr this is where the customer will be taking his first steps with the software alone. these first steps include: formal user training sessions, one on one user tutoring sessions and general software support. step 12: third sale, support the earliest point one might consider as the point of successful diffusion or implementation of the innovation is, as seen in this study, the point of third sale. after the second sale, the customer independently used the software by himself, learnt the product’s advantages and disadvantages and may now associate self benefit to product more accurately. therefore, a third sale is the first true mark that the customer is truly realizing the benefits of the product and is preparing to use it in the long term. support of the product continues now on an annual basis with milestones for new upgrades and releases. given that a vendor adopts this new 12 stage development process including and especially the improvisation-intensive stages, the question which should arise is how does the organization build and enhance the skills required for improvisation and what are these skills? in the context of our study we identified three main factors which influence improvisational skills: teamwork skills the ability of the entrepreneurial team to communicate with one another and relay timely information, get things done easily and quickly with no inhibitors and outside impeding factors. figure 5: lead driven development: the 12 step timeline experience the entrepreneurial team members’ experience in similar circumstances and their memory to recall their right and wrong doings there. experimental culture the culture of the team, which encourages trying out many a time risky and/or innovative solutions. these three main factors, measured and calibrated according to characteristics described in [26], coincide with improvisational skills factors in the literature. for example, vera and crossan [27] create a theoretical framework based on improvisation and innovative performance in teams. identifying variables from improvisational theatre, they tested the impact of the 16 different related variables on improvisational skills in an environment of a local municipality. they found 4 of the 16 variables to be of higher influence than others. the four factors they isolated were: expertise, teamwork skills, experimental culture and real-time information and communication. a software system development life cycle model for improved stakeholders’ communication and collaboration 35 in the context of our study we can therefore translate and apply their insights as follows: expertise gaining a higher level of software expertise in the intricacies of the development environments enables software developers to find out of the sleeve solutions and bypasses for many software unpredicted difficulties encountered. teamwork skills in general software teams, with a rather higher sense of collaboration usually tend to innovate more. to further clarify this point, we can propose the software teams the following teamwork skills which we encountered: development collaboration, information sharing via email, shared drives, knowledge management portals, inner group dynamics and communication etc. experimental culture the experimental culture includes the ability to import new ideas and procedures from the world wide web, forums, groups and software development associates and try them out. furthermore, experimentation in the software industry, which is not usually backed up by management should be backed up by top management and should also include experimentation on code developed using a number of alternate mechanisms. real-time information and communication the need for real-time information and communication in the software industry is ever more compelling than any other industry. this is so because the software industry is built upon and relies heavily on the backbone of internet. therefore, when improvising it is crucial to gain real-time updated information over the lan or internet and have a variety of channels for communicating with the customer and other team members. each of these channels specializes in a different type of content that may be passed: audio, video, documents, emails etc. c. the collaborative multi-tier system development life cycle the lead-driven development (ldd) paradigm described above represents a list of tasks from the vendor’s point of view. this list helped build the vendor’s tier in the complete collaborative multi-tier system development life-cycle task matrix that caters to all stakeholders. the collaborative multitier model takes into consideration, through the nature of the tasks suggested, pure is development tasks (e.g., prototyping, bug fixing), market influences (e.g., market introduction techniques for entrepreneurial ots software vendors, such as offering the first sale of such a product as a combined project with human resources), and organizational recommendations aimed mainly to avoid customer internal organizational obstacles. the collaborative multi-tier system development life-cycle (cmsdlc) model, which combines common tasks from the various case studies, is consistent as it avoids contradicting tasks. one of its unique features is that it makes a distinction between benefit-oriented tasks and standard waterfalltype formal tasks and milestones. when a task or a set of tasks is performed in an iterative manner it is marked as an on-going task or specifically mentioned in the explanation section as one that needs to be performed iteratively. one such example is the task dealing with lead requirements gathering from potential customers for the purpose of prototype and feature building. the cmsdlc model is also suitable for mature organizations that seek to develop a new ots software product. it is even more suitable when the mature organization separates this entire operation from its existing core operations through various methods, such as founding a new subsidiary. this is akin to an entrepreneurial firm from the development and market perspectives. however, the financial backing of the parent organization and its reputation may shorten the duration of many of the tasks, and may make them more easily achievable. in cases where a mature organization seeks improvement to existing product development methods, the suggested model may not be as applicable, since the level of uncertainty such an organization encounters regarding market and organizational effects (especially regarding the customers), is lower. the chart of the full model depicts in a single, poster style view the multi-tier model. the timeline of the system development lifecycle and the tasks for each stakeholder are stacked on separate horizontal lines. since all the stakeholders share one timeline, the interaction between the stakeholders and the interdependency of tasks is clearly visible. only through extensive collaboration can the effort proceed 36 s. cohen, d. dori, u. de haan as a whole. for each task, a list of case studies which the task was based upon is provided. a list of one-letter abbreviations corresponding to the case study sites which demonstrated the use of the specific task appear below each task. the logic behind gathering all the tasks into the model includes checking tasks for contradictions in the various case studies and merging similar tasks to unifying generic tasks. 6 conclusion and future research at this point we would like to introduce the hypotheses which were derived from the findings of our case studies. the hypotheses suggest a plethora of possible future research to be held on the process of validating them. the first hypothesis which was emerging was based on research question 2. we found that the two parts of an ots software product, the product and its underlying methodology, played an important role in all the case studies and therefore justified the hypothesis. we therefore phrased the first hypothesis as follows. h1: the more a customer is inclined to adopt a methodology, the more he or she is inclined to adopt the ots software tool associated with that methodology. in other words, we will be looking for a possible correlation on a "bundling" relationship between the methodology and its supporting tool. we can explain this phenomenon by looking both at the marketing literature regarding characteristics of products and at the is literature which explains and recommends various modes for is product sale. the marketing literature [28] and [12] and [29] defines clearly the "whole product concept" which was introduced by theodore levitt.[30] levitt defined the whole product as follows: a product is, to the potential buyer, a complex cluster of value satisfactions. the whole product factor denotes the completeness of the product being marketed at a certain point in time with regard to a complete solution. the components of the whole product include the core product, the tangible product, augmented product and total solution. the relationship between the ots product used in our study and its methodology typifies the product as being close to an augmented product. the second hypothesis involved a distinction between organizations using a formal software development procedure and others who use improvisation-intensive development techniques. as we started noticing in the case studies, this distinction is related to the level of uncertainty the vendor organization runs into. hence we defined the following hypothesis: h2: the higher the level of uncertainty is, the higher the vendor’s use of improvisation intensive development methods. improvisation has been shown to be used by entrepreneurial vendors when faced by time pressure, complexity, and uncertainty.[31] future research may extend this assertion to include and emphasize its relevance in the development process too. the third hypothesis concerns the direct benefits associated with utilizing improvisation within the software development process, i.e., the time-to-market of the product and increased sales. the hypothesis was defined as follows. h3: when uncertainty is high, the more a vendor uses improvisation the more his market response time shortens and his ability to make a first sale improves. the organizational change resistance to new technologies in general and software in particular was found in our case studies to be solved by applying a marketing technique which couples a human resource implementer to the product.[32] the human resource implementer escorts the implementation and even performs most of the initial work for the customer using the tool. this triggered the following hypothesis: h4: the higher the level of hr participation in the ots software sale attempt to the change resistant customer is, the higher are the chances for successful implementation. a software system development life cycle model for improved stakeholders’ communication and collaboration 37 similarly to the entrepreneurial vendor’s efforts to overcome change resistance within the adopting organization users, the vendor has to build its legitimacy with the adopting organization’s dmu.[33] we started noticing that this legitimacy buildup was being done via affiliation with accredited scientists/academics and/or through established third party integrators. stinchcombe [33] also specified three reasons for the impediments companies have from entering into a business relationship or buying a product from a new organization. he calls this effect the "liability of newness". the reasons cited for this liability are lack of experience, lack of size and lack of legitimacy. the latter was addressed by opcat, who built its legitimacy in all the case studies through the use of a reputable scientist to improve its lack of legitimacy and external reputation. furthermore, some legitimacy build-up was achieved through the use of large third party integrator with proven reputation and experience in the industry. this gave rise to the following hypothesis: h5: the more a new vendor firm affiliates with a distinguished scientist, or an established third party, the more the chances for successful implementation are higher. 7 summary and recommendations we have proposed and evaluated a software system development life cycle model which aims to improve successful system implementation and adoption by use of communication and collaboration amongst stakeholders. the new model for software development that emerged lead-driven development was discovered, validated against the case studies, and refined via observations in five industry case studies and two additional control studies regarding successful implementations of ots products of entrepreneurial developers. the proposed lead-driven development model accounts for market and organizational factors and the way they are woven into the traditional phases of software development. it offers the basis for the unified, comprehensive multi-tier sdlc framework and methodology that contributes to improved stakeholders’ communication and collaboration through the use of a common reference model for all stakeholders. each tier addresses a different force or stakeholder involved in the software market: vendor, customer, consultants and integrators. the model is potentially beneficial for improving communication and collaboration among life cycle stakeholders in that it embeds action items from the is, marketing and organizational realms. many of these action items are performed using improvisational skills. to excel in lead-driven development in general, and in software development improvisation in particular, entrepreneurial 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[61] agile software development: a definition from whatis.com viewed on:http://whatis.techtarget.com/ definition/0„sid9_gci936457,00.html, november 2006. shalom cohen (b. december 16, 1973) completed his msc in operations research at tel aviv university, israel and his phd in the faculty of industrial engineering and management at the technion, israel. in addition to lecturing at the technion on information system related topics shalom holds the position of chief system architect in a high tech company in the homeland security sector. dov dori (b. september 2, 1953) is information and systems engineering professor at the faculty of industrial engineering and management, technion, israel, and research affiliate with the engineering systems division at massachusetts institute of technology. he received his bsc in industrial engineering and management from technion in 1975, msc. in operations research from tel aviv university in 1982 and phd in computer science from weizmann institute of science in 1988. uzi de haan (b. september 5, 1943) completed his msc in aeronautical engineering at the university of delft, holland and his phd in the faculty of industrial engineering and management at the technion, israel. he joined the technion as a professor in the area of strategic management and entrepreneurship at the faculty of industrial engineering after many years in the high-tech industry. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 196-203 virtual training centre for computer numerical control m. sahin, s. yaldiz, f. unsacar, b. yaldiz, n. bilalis, e. maravelakis, a. antoniadis abstract: it is a fact that virtual training has been a scope of interest for vocational training for a very long time. however, it needs more time to be more common in all specific training fields. focusing on computer numerical control (cnc) training, new developments in the cnc machinery produce a continuous demand on enhancing the programming and technical capabilities of the involved personnel. training on cnc should follow similar developments and in particular in their programming capabilities, automation they offer and their technical capabilities. based on these main objectives a virtual training center (vtc) for cnc has been developed and it is presented in this paper. the vtc is the main result of a multilateral leonardo da vinci project witch aims to promote and reinforce vocational training in cnc machines. keywords: virtual training center, cnc, vet. 1 introduction in most member states the past two decades there has been a growing awareness of the importance of quality in vocational education and training (vet). the changing demands of the knowledge-based society and the overall trend to increase the efficiency and effectiveness of vet systems, constitute major driving forces behind these developments. major european funding frameworks and programmes, such as leonardo da vinci (ldv) have contributed to improving education and vet systems by raising the level of the services they offer. one of the objectives of the innovative vet systems is the transparency and distribution of information. this function concerns the potential and actual use of information. there may be different systems and structures of information distribution among the various actors, and in the public and there are preconditions for creating transparency in the vet system. to improve quality there must be systems for distributing information and certain mechanisms to ensure the circulated information can be used by the various actors in the policy process. the more widespread the distribution, the better the potential use of the data will be and as a reversal effect, better quality data can be expected, as the actors are able to check the information against their experience and will provide feedback to the systems for gathering data. one of the concrete future strategic objectives of education and training systems in the eu (council of the european union, 2001) is the improvement of the quality and the effectiveness of education and training systems in the eu. this includes improving education and training for teachers and trainers, developing skills for the knowledge society, ensuring access to ict for everyone, increasing recruitment to scientific and technical studies, and making the best use of resources. the second strategic objective is facilitating the access of all to education and training systems. this objective includes open learning environment, making learning more attractive, and supporting active citizenship, equal opportunities and social cohesion. this paper describes the main results of a ldv project that addresses the strategic objectives mentioned above. 2 from the blackboard to the virtual training centre during the 60’s and 70’s, teaching and learning tools were nothing but a piece of chalk and a blackboard eraser, teachers and students who met each other face to face inside the classroom during class. copyright © 2006-2008 by ccc publications virtual training centre for computer numerical control 197 in the 80’s, videotape programs were used as teaching aids. in the 90’s, one-way teaching by computer arrived. and finally today’s advanced computer and information network technology has introduced radical innovative breakthroughs in our teaching and learning methods as well as in the learning environment. students can listen to their teacher or trainers in distant classrooms through pc’s and get a simultaneous view of their teachers and texts as well. they can ask questions and record the "class" for repeated viewing. training organizations can conduct professional training directly via the computer network. these learning environments are not so different from a teacher-guided class with discussions and tests as well. in the report "studies in the context of the e-learning initiative: virtual models of european universities" [1] a key concern was how virtual mobility is being supported in european universities through ict integration and e-learning. the study found that the majority of universities face major challenges in promoting ict integration. ict strategy is very important and those universities that have an ict strategy are significantly ahead in integration of ict in administration and organisation and networking. integration of ict and e-learning is politically important in the eu in terms of internationalisation and globalisation of education, student demand and interest in increasing the quality of education through ict [2], [3], [4]. at the national level, integration of ict should become a key priority with national and regional institutions making a commitment to itc and the development of networks. there must be increased national flexibility with a commitment to support common standards of quality and assessment and to develop national and international metadata standards. in the last 3 decades, a large number of vocational training centres and technical universities are giving priority to cnc training. new developments on cnc machines are providing a continuous need for updated cnc training curriculum. training on cnc should follow similar developments and in particular in their programming capabilities, automation they offer and their technical capabilities. in addition, cnc programming is becoming more and more automated through the use of cad/cam systems. this requires from the programmers to acquire cad operation capabilities, on top of their cnc operation and programming knowledge. the major objective in the field of cnc training is to improve the qualifications and competences of the trainees, which is directly related to a well-designed and effective curriculum to be carried out on cncs. the facilities for cnc training vary a lot and this has had direct impact on the experience that the trainee is acquiring during his/her apprentice. this paper presents the development and promotion of a virtual training centre (vtc), an internet based e-learning facility, specifically based on computer numerical control (cnc) training. this centre includes a virtual space (a cnc training portal) on the internet which allows the constant sharing of e-learning based cnc teaching material, which is created so as to foster the further development of e-learning based cnc educational contents. this virtual training centre aims at setting the standard cnc virtual learning in vocational training systems [5]. 198 m. sahin, s. yaldiz, f. unsacar, b. yaldiz, n. bilalis, e. maravelakis, a. antoniadis 3 developing a common cnc curriculum during the first stages of the project, the equipment, methods, curriculum and techniques currently used for cnc training by the organisations in the partner countries were observed, collected and evaluated [6], [7]. the selected materials were used to create a new and common curriculum. five important factors that contribute to learning were taken into account in order to prepare the cnc curriculum: • motivation • aptitude • presentation • repetition • practice with reinforcement the approach for developing the appropriate training material was based on the following key concepts: • motivation • know your machine (from a programmer’s viewpoint) • prepare to write programs • understand the motion types • know the compensation types • format your programs in a safe, convenient, and efficient manner • know the special features of programming • know your machine (from an operator’s viewpoint) • understand the three modes of operation • know the procedures related to operation • you must be able to verify programs safely this approach combined with the important learning factors finally led to a cnc training curriculum including 28 sessions: 1. machine configuration 2. speeds and feeds 3. visualizing program execution 4. understanding program zero 5. measuring program zero 6. assigning program zero 7. flow of program processing 8. introduction to programming words virtual training centre for computer numerical control 199 9. preparation for programming 10. types of motion 11. introduction to compensation 12. dimensional (wear) tool offsets 13. geometry offsets 14. tool nose radius compensation 15. program formatting 16. the four kinds of program format 17. simple canned cycles 18. rough turning and boring multiple repetitive cycle 19. more multiple repetitive cycles 20. threading multiple repetitive cycle 21. subprogramming techniques 22. control model differences 23. other special features of programming 24. control model differences 25. machine panel functions 26. three modes of operation 27. the key operation procedures 28. verifying new programs safely 4 the structure of the virtual training centre to develop the virtual training centre, firstly, a communication website was developed in order to manage the activities and tasks to be carried out by the partners. then, an interactive teaching program was developed and put into a website to form a virtual training centre (figure 1). the common curriculum developed for this purpose was the base of this training centre. the site, along with the interactive teaching program, was divided into four main areas, "news", "exchange of views", "projects and networks", and "information resources". with these, users would be able to access a newsletter, a bulletin board, online surveys and survey reports, information on vet networks, an electronic library with references, a bookshop with downloadable publications and a number of databases. 200 m. sahin, s. yaldiz, f. unsacar, b. yaldiz, n. bilalis, e. maravelakis, a. antoniadis figure 1: the interface of the interactive cnc training centre (http://www.vtcforcnc.com) figure 2: example of 3phase animated cnc training material virtual training centre for computer numerical control 201 in the main core of the cnc training material, simulations and practical exercises are included into the interactive training centre (figure 2). the feedback of the implementation of the vtc in training centres has been recorded and evaluated in order to produce the final version. the evaluation procedure included content (topics, language used, modules), methods (progress, different levels of difficulty, and range of resources, situations and practical cases) and technology (ease of installation, interactive nature and use without a tutor). the main aim of the vtc for cnc aims is to be an interactive platform, a meeting point for policymakers, social-partners, practitioners, researchers and all those with an interest in cnc field of vocational education and training. experts in the field are able to share and exchange knowledge and experience with associates within and outside the european union. this will foster the long-term viability of the centre. 5 aims and target groups of the vtc the vtc aims to improve the skills and competences of people to promote and reinforce the contribution of vocational training to the process of innovation, with a view to improving competitiveness and entrepreneurship, also in view of new employment possibilities. the specific aims of vtc can be defined as follows: • training the trainers, trainees, technicians and apprentices and all enthusiastic about cnc. • preparing technicians as intermediates having common measurable qualities the industry is seeking. • helping to form a labour force that can use current knowledge and technology, and thus, in search for life-long learning. • supporting the sectoral communication through the national centres in partners. • setting up a website to publish the data collected. • adapting the collected materials to enhance the new curriculum satisfying the requirements in a modern sense. • helping to improve and upgrade competences and skills of the involving institutions’ didactic staff and exchange experiences over the virtual training centre. • enabling the participants to extend the common educational qualifications of cnc technologies, the accreditation of the skills and knowledge of cnc technologies acquired within the network created between participating institutions and organizations. • increase the quality of employment through qualified workers. • helping to increase active use of technology acquired and thus to increase the standards. • contributing to individuals by behaving through life long learning. • having a labour power in accordance with common design and production standards. • contributing to labour market by using the common technology and equipment effectively. 202 m. sahin, s. yaldiz, f. unsacar, b. yaldiz, n. bilalis, e. maravelakis, a. antoniadis • helping to enhance available potential of human sources. target groups include trainers, trainees, technicians, apprentices and all enthusiasts about cnc. the final and potential users of the project’s results are the training organisations, the smes dealing with metal products by cnc usage, and the universities, colleges, vocational schools, training centres. 6 conclusions the integration of itc in this virtual learning environment for cnc, the development of the vtc and the common training curriculum are focused on the eu goals of of internationalisation and globalisation of education, student demand and interest in increasing the quality of education through ict. at the national level, integration of ict should become a key priority with national and regional institutions making a commitment to itc and the development of networks. there must be increased national flexibility with a commitment to support common standards of quality and assessment and to develop national and international metadata standards. this centre addresses the priorities expressed here. furthermore, this virtual training centre addresses the strategic objectives mentioned above: improving the quality and effectiveness of education and training systems in the eu by developing skills for the knowledge society, ensuring access to ict for everyone, increasing recruitment to scientific and technical studies, and making the best use of resources. facilitating the access of all to education and training systems by providing open learning environment, making learning more attractive, and supporting active citizenship, equal opportunities and social cohesion is the other strategic objective that can be achieved through this virtual training centre. the experiences and knowledge gained during the implementation of this centre can be used in developing and improving other training programmes in particular in the area of new information technology applications in related sectors. bibliography [1] ramboll, pls: studies in the context of the e-learning initiative: virtual models of european universities (lot1). draft final report to the european commission, dg education and culture. available at http://elearningeuropa.info available at http://elearningeuropa.info, 2004. [2] a. andreatos, virtual communities and their importance for informal learning, international journal of computers, communications & control, vol. ii, no. 1, pp. 39-47, 2007. [3] a. styliadis, i. karamitsos, d. zachariou, personalized e-learning implementation the gis case, international journal of computers, communications & control, vol. i, no. 1, pp. 59-67, 2007. [4] c. zhengxin, learning about learners: system learning in virtual learning environment, international journal of computers, communications & control, vol. i (2008), no. 1, 2007. [5] m. sahin, n. bilalis, s. yaldiz, a. antoniadis, f.unsacar, e. maravelakis, revisiting cnc traininga virtual training centre for cnc international conference on e-portfolio process in vocational education-epvet, bucharest, romania, 2007. [6] w. xiaoling, z. peng, w. zhifang, s. yan, l. bin, l. yangchun, development an interactive vr training for cnc machining, international conference on virtual reality continuum and its applications in industry, proceedings vrcai 2004 acm siggraph, pp. 131-133, 2004. [7] l. yadong, g. xingui, l. wei, y. kazuo , k. keizo and m. fujishimab, an intelligent nc program processor for cnc system of machine tool, robotics and computer-integrated manufacturing, vol 23 (2), pp 160-169, 2007. virtual training centre for computer numerical control 203 mehmet sahin, suleyman yaldiz, faruk unsacar, burak yaldiz technical science college selcuk university 42031 konya, turkey e-mail: mesahin,syaldiz,funsacar@selcuk.edu.tr nikolaos bilalis department of production engineering & management technical university of crete 73100, chania, greece e-mail: bilalis@dpem.tuc.gr emmanuel maravelakis, aristomenis antoniadis department of natural resources & environment design & manufacturing laboratory technological educational institute of crete 73133 chania, greece e-mail: marvel,antoniadis@chania.teicrete.gr received: december 20, 2007 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 280-291 extreme data mining: inference from small datasets r. andonie răzvan andonie computer science department central washington university, ellensburg, usa and department of electronics and computers transylvania university of braşov, romania e-mail: andonie@cwu.edu abstract: neural networks have been applied successfully in many fields. however, satisfactory results can only be found under large sample conditions. when it comes to small training sets, the performance may not be so good, or the learning task can even not be accomplished. this deficiency limits the applications of neural network severely. the main reason why small datasets cannot provide enough information is that there exist gaps between samples, even the domain of samples cannot be ensured. several computational intelligence techniques have been proposed to overcome the limits of learning from small datasets. we have the following goals: i. to discuss the meaning of "small" in the context of inferring from small datasets. ii. to overview computational intelligence solutions for this problem. iii. to illustrate the introduced concepts with a real-life application. 1 introduction small dataset conditions exist in many applications, such as disease diagnosis, fault diagnosis or deficiency detection in biology and biotechnology, mechanics, flexible manufacturing system scheduling, drug design, and short-term load forecasting (an activity conducted on a daily basis by electrical utilities). in this section, we describe a computational chemistry problem, review a class of neural networks to be used, and summarize our previous work in this area. 1.1 a real-world problem: assist drug discovery current treatments for hiv/aids consist of co-administering a protease inhibitor and two reverse transcriptase inhibitors (usually referred to as combination therapy). this therapy is effective in reducing viremia to very low levels; however, in 30-50% of patients it is ineffective due to resistance development often caused by viral mutations. due to resistance and poor bioavailability 1 profiles, as well as toxicity associated with these therapies, there is an urgent need for more efficient design of drugs. we focus on inhibitors to the hiv-1 protease enzyme, using the ic as the target value. a detailed description of the problem, from a computational chemistry point of view, can be found in our papers [1–3]. the ic value represents the concentration of a compound that is required to reduce enzyme activity by 50%. a low ic value indicates good inhibitory activity. the available dataset consists of 196 compounds with experimentally determined ic values. twenty of these molecules are used as an external test set after the training is completed. the remaining 176 molecules are used for training and cross-validation. our practical goal is to predict the (unknown) ic values for 26 novel compounds which are candidates for hiv-1 protease inhibitors. we use two ic prediction accuracy measures: the rmse (root mean squared error) and the symmetric mean absolute percentage error (smape). 1bioavailability is the rate at which the drug reaches the systemic circulation. copyright c© 2006-2010 by ccc publications extreme data mining: inference from small datasets 281 the easiest way to represent a molecule is by a vector of features (molecular descriptors) which may be both topological indices and physico-chemical properties. the resulting features may be numerous and inter-correlated. using the complete set of descriptors may lead to overfitting, if it is too large compared to the size of the training set. we select 35 molecular descriptors based on their contribution to molecular entity. although biological activity data has been obtained for many more chemical structures at various pharmaceutical companies and academic laboratories, they are not available in the public domain. actually, most classical studies for a specific enzyme system have been performed on small datasets, due to limited experimentally determined biological activity values in the public domain. the dimensionality (the number of physico-chemical features) characterizing these molecules is relatively high. our dataset shares these undesired characteristics: it is small, with relatively many features, and highly overlapping. 1.2 prerequisites: famr for ic prediction the famr is a fuzzy artmap (fam) incremental learning system used for classification, probability estimation, and function approximation. we review the basic famr notation. details can be found in [4]. a fam consists of a pair of fuzzy art modules, arta and artb, connected by an inter-art module called mapfield. the fuzzy arta module contains the input layer, f a , and the competitive layer, f a  [5]. a preprocessing layer, f a , is also added before f a  . the art modules create stable recognition categories in response to arbitrary sequences of input patterns. the arta and artb vigilance parameters, ρa and ρb, control the matching mechanism inside the modules. during learning, the mapfield weights are updated: the strength of the weight projecting from the selected arta category to the correct artb category is increased, while the strengths of the weights to other artb categories are decreased. a mapfield vigilance parameter ρab calibrates the degree of predictive mismatch necessary to trigger the search for a different arta category. if the weight projecting from the active arta category through the mapfield to the active artb category is smaller than ρab (vigilance test), then the system responds to the unexpected outcome through the so-called match tracking. this triggers an arta search for a new input category. after choosing an arta category whose prediction of the correct artb category is strong enough, match tracking is disengaged, and the network is said to be in a resonance state. in this case, mapfield learns by updating the weights wabjk of associations between each j-th arta category and each k-th artb category. the famr uses the following iterative updating scheme: wab(new)jk =    wab(old)jk if j 6= j wab(old)jk + qt qnewj (  − wab(old)jk ) wab(old)jk (  − qt qnewj ) if k 6= k (1) where qt is the relevance assigned to the tth input pattern (t = , , . . . ) and qnewj = q old j + qt . the relevance qt is a real positive finite number directly proportional to the importance of the experiment considered at step t. this wabjk approximation is a correct biased estimator of the posterior probability p(k| j), the probability of selecting the k-th artb category after having selected the j-th arta. fam (and famr) networks map subsets of rn to rm and can be used for function approximation. the fam has been proven to be a universal function approximator [6]. we use the famr to predict functions that are known only at a certain number of points. more specifically, we predict ic values. 1.3 our previous work the present paper is based on a sequence of results, each describing new computational intelligence tools for biological activity (ic) prediction. in [7], we investigated the use of a fuzzy neural network 282 r. andonie (fnn) for (ic) prediction. in [1] and [2], we improved this model by adding a two-stage genetic algorithm (ga) optimizer: the first for selecting the best subset of features and the the second for optimizing the fnn parameters. we will refer to this ga-optimized fnn as fs-ga-fnn. in [8] we also focused on the ic prediction task, using the famr model. during the learning phase, each sample pair is assigned a relevance factor proportional to the importance of that pair. the prediction method consists of two stages. first, ga-optimization incorporating cross-validation is used to modify the training dataset. this modification consists of finding the best relevances for the data, according to some fitness criterion. the fitness criterion measures the famr ic prediction accuracy for a given training/validation dataset with given relevances. in stage two, the final famr is obtained by training it using the dataset with optimized relevances. in other words, stage one improves the generalization capability of the famr which will be obtained in stage two. we will refer to this model with gaoptimized relevances as ga-famr. we compared the ga-famr and the ordered famr (a famr algorithm which optimizes the order of training data presentation) in [9]. both methods compensate for insufficient training data by additional optimizations. a trade-off between computational overhead and generalization capability is obtained. recently, we performed rule extraction from the trained famr model [10]. we post-processed the set of generated rules in order to improve generalization. we eliminated overfitting by heuristic generalization of rules and by adding new rules. this method proved to be efficient for small training sets. the present paper results from several invited talks [9, 11, 12]. in section 2, we discuss the capability of neural network to infer from rare samples. section 3 describes two methods for neural training on small datasets. after presenting and discussing experimental results in section 4, we conclude with our final remarks (section 5). 2 neural networks trained on small datasets we aim to discuss the difficulties of inferring a neural network (nn) from small, or non-representative, training sets. we will look closer at the overfitting and generalization aspects of the network. but first, we need to define formally what we understand by "small training set". 2.1 what is "small"? in many multivariable classification or regression (e.g., estimation or forecasting) problems we have a training set tp = (xi,ti) of p pairs of input/output vector x ∈ <n and scalar target t, and the unfortunate circumstance that tp is small. the vc (vapnik-chervonenkis) dimension is a measure of the capacity of a classificator, defined as the cardinality of the largest set of points that the algorithm can shatter. according to vapnik: "for estimating functions with vc dimension h, we consider the size p of data to be small if the ratio p/h is small (say p/h < )" [13]. the main reason why small datasets cannot provide enough information is that there exist gaps between samples, even the domain of samples cannot be ensured. for a small training set, even a simple neural network can have a complexity (e.g., number of connections/parameters) that is comparable to, or exceeds, the training size p. in such a case, we may expect to fit tp very well. however, we can also expect poor generalization to new data identically distributed as the data in tp. in effect, the vc dimension is too large relative to the size of the training set. a completely different definition for “small” sets comes from algorithmic information theory. the kolmogorov complexity of an object such as a string is a measure of the computational resources needed extreme data mining: inference from small datasets 283 to specify the object. more formally, the complexity of a string is the length of the string’s shortest description in some fixed universal description language. it can be shown that the kolmogorov complexity of any string cannot be too much larger than the length of the string itself. a string is considered to be "random" if the length of the shortest problem that generates the string is the same as that of the string itself. strings whose kolmogorov complexity is small relative to the string’s size are considered to have small information content [14]. kolmogorov’s complexity has been studied in the context of inductive inference [15, 16]. it is an open problem how to relate the kolmogorov complexity of a training set and the generalization capability of the inferred nn. we will use a simplified definition: a training set is small if p and n are comparable. in accordance with this definition, the training set for our chemistry problem is small. there is no universally optimal solution to the problem of inferring from small datasets. we only can state some very general principles one can follow. for instance, one principle would be to extract from the training data the maximum useful information available. if not done thoroughly, this may lead to overfitting, and/or to a time-prohibitive training process. a principle for controlling the generalization capability of a nn is to design a network with much fewer connections than the size of the training set. to overcome the limits of learning from small datasets, several general techniques have been proposed [17–26]: generate artificial training samples, feature selection, and parameter fine-tuning of the inferred model. a special learning method designed for small training sets is adaptive learning with domain range expansion. in this case, additional information is used to dynamically improve training. such an approach is, for instance, the central location tracking method [25, 26]. this algorithm attempts to explore the predictive information through the generation of trend value of each datum. the extra information extracted from the data trend stabilizes the learning task and improves the derived knowledge from the occurrence of the latest data. the domain range is expanded to obtain the probable change of the small training data behavior. the choice of specific technique is domain dependent. in computational chemistry, only feature selection and parameter fine-tuning have been used [27–29]. it is very difficult to generate artificial samples because, most probably, they will not physically exist. 2.2 overfitting vs. generalization inference is based on a strong assumption: using a representative training set of samples to infer a model. in this case, we select a subset of the population, perform a statistical analysis on this sample, and use these results as an approximation to the desired statistical characteristics of the population as a whole. the more representative the sample, the larger our confidence that the statistical results obtained by using this sample are indeed a good approximation to the desired population statistics. we gauge the representativeness of a sample by how well its statistical characteristics reflect the statistical characteristics of the entire population. many standard techniques may be used to select a representative sample set [30]. however, if we do not use expert knowledge, selecting the most representative training set from a given dataset was proved to be computationally difficult (np-hard) [31]. the problem is actually more difficult, since in most applications the complete dataset is unknown or too large to be analyzed. therefore, we have to rely on a more or less representative training set. another problem may arise from the training process itself. especially in cases where learning was performed too long or where training the training samples are rare, the inferred model may adjust to very specific random features of the training data, that have no causal relation to the target function. in this process of overfitting, the performance on the training examples still increases while the performance on unseen data becomes worse (the generalization performance is poor). in nn learning, overfitting generally occurs when excessive number of neurons is generated; the network overestimates the complexity of the problem and it cost more resources to train and implement. 284 r. andonie there are three major strategies to avoid overfitting: 1. before learning. before being used, training samples are pre-processed, or new training samples are artificially created. a widely used before learning technique is to artificially extend the training set by introducing new training samples with additive noise [32–34]. it helps to enhance the generalization performance, speed up the training algorithm, and reduce the possibility of local minima entrapment [33–36]. 2. after learning. the network is trained (with possible overfitting) and processed afterwards. such techniques include pruning, weight sharing, weight decay, ensemble neural networks, and complexity regularization [35, 37, 38]. pruning is the process of eliminating nodes and connections from the trained network. the reduced size network has to be sometimes retrained. nn pruning algorithms have practically developed for all major nn architectures [39]. 2.3 how to detect overfitting beside preventing overfitting, a major question is how to detect it. it is desirable to have a measure that can quantify underfitting or overfitting of a network on a given learning problem. we do have again two general strategies: before and after learning. the most common after learning technique is to perform learning/validation iteratively and optimize the learning/validation generalization error by adjusting the parameters and/or architecture of the network. several constructive/destructive algorithms were adopted to incrementally increase or decrease the parameter to be optimized [40]. during the constructive/destructive process, cross-validation is commonly used to check the network quality and the design parameter is chosen using early stopping [41]. the training data is usually divided into two independent sets: a training set and a validation or testing set. only the training set participates in the nn learning, and the validation set is used to compute a validation error, which approximates the generalization error. the inferred nn performance during training and validation is measured, respectively, by training error etrain and validation error evalid presented. once the validation performance stops improving as the target parameter continues to increase, it is possible that the training has begun to fit the noise in the training data, and overfitting occurs. therefore, the stopping criterion is set so that, when evalid starts to increase, or equivalently, when etrain and evalid start to diverge, it is assumed that the optimal value of the target parameter has been reached [36]. cross-validation + early stopping are the common techniques used in finding optimal network structure up to date. an alternative to cross-validation is bootstrapping. more flexible stopping criteria based on early stopping were proposed by prechelt [41]. it helped the users to choose stopping criterion in a systematic and automatic way, based on efficiency, effectiveness, or robustness. liu et al. have introduced an algorithm which, on a given nn is able to recognize the occurrence of overfitting by examining the training error without using a validation set [36]. the algorithm also shows where the recycling of the training samples can be safely stopped so that the optimal structure of the nn is found. a signal-to-noise-ratio figure (snrf) is defined to measure the goodness-of-fit using the training error. based on the snrf measurement, an optimized approximation algorithm is proposed to avoid overfitting in function approximation. an open problem is how to detect before learning the generalization capability, without even knowing the nn to be used. in this case, one should be able to determine the generalization capability of a given training set before using it! for instance, we should determine if a training set is sufficiently smooth and covers sufficiently well the input space in order to produce a reasonably good approximation of an unknown function. such a regression problem depends on the quality of available samples. can we determine if the training set is good enough for being used? can we do this independent of the nn model? extreme data mining: inference from small datasets 285 3 two efficient methods we will illustrate the concepts introduced in section 2 with two famr methods which work well with small training sets. since the methods have been previously described in [9], we will only review them here. 3.1 the ga-famr the relevances attached to the input data are considered as adaptive parameters to be optimized by a ga. the ga-famr operates on an initial population of relevance vectors. each relevance vector has a single relevance associated with a specific training datum in accordance with the famr. because the relevance of specific data is not known beforehand, this population must be optimized using the following ga: step one. initialize a population of popsize chromosomes. each chromosome is composed of n genes, where n equals the size of the training dataset. each gene is a real value in the range (0, 10), defining the relevance of one of the training molecules. step two. for each chromosome, train and validate the famr using cross-validation. compute the fitness value of each chromosome: fit = /smape. step three. establish the next generation. 1. find fitlow, which is the smallest fitness value in the population. 2. subtract fitlow from the fitness value of each chromosome. 3. sum the fitness values of all chromosomes to calculate the total fitness, fitall , of the population. 4. divide each chromosome’s fitness value by fitall . 5. generate popsize new chromosomes to replace the current population. each new chromosome is created by one of two methods: breeding or elitism. (a) breeding: i. for each child, two parents are selected according to the concept of the survival of the fittest. ii. each parent is selected by first generating a random number,  < s < . iii. iterate through the chromosomes in the population. if fit ≥ s, the chromosome is selected. else, subtract fit from s, and continue to the next chromosome. the probability that a chromosome will be selected for reproduction at any given time is given by: (fit − fitlow)/(fitall − fitlow ∗popsize). iv. when two parents have been selected for each child, perform crossover to generate the new chromosome. for each child, one of two crossover methods is chosen with equal probability: a. for each gene, copy the genetic material from one or the other parent; the parent copied for each gene is selected randomly. b. average the genes of the two parents. because the effect of switching specific bits in a real value can be extremely unpredictable, it may be more effective to average two real values. v. before the new child is introduced into the next generation, there is a 0.25 probability that it will undergo mutation in one of its genes, by randomly generating a new real value. 286 r. andonie (b) elitism: at all times, eight global best chromosomes are retained as a possible source of members of the new generation. there is a 1/500 probability that a new chromosome is generated by selecting one of these elite, rather than by crossover of two members of the current population. 3.2 ordered famr for optimizing the fam training data ordering, dagher et al. [42, 43] and tan et al. [44] have introduced efficient procedures. essentially, the training data is preprocessed to identify a fixed order of pattern presentation. we refer to this procedure as the ordering algorithm. when the training input patterns are presented to the famr according to this fixed order, we obtain a famr with improved generalization capability. preprocessing consists of clustering input data. each cluster center will be a molecule in the training set. the ordering of the training data is determined by the order in which the cluster centers are obtained. it is noteworthy that this clustering is different than the formation of arta categories, which is also a clustering of the same input dataset. the ordering algorithm is controlled by a pre-defined parameter, nclust , which is the number of input data clusters, and consists of the following three stages: 1. determine the first pattern to be presented. this pattern corresponds to the first cluster center of the training data. 2. determine the next nclust − patterns to be presented. these patterns correspond to the next nclust −  cluster centers of the training data, and are identified through the max-min clustering algorithm [45]. 3. determine the order of the remaining patterns. these patterns are chosen according to the minimum euclidean distance criterion from the nclust centers defined in stages 1 and 2. stage 1. we start with an m-dimensional input pattern a = (a,··· , am) and obtain 2m-dimensional input pattern a = (a,··· , am,  − a,··· ,  − am) by complement coding [5]. input pattern a, which maximizes the sum in eq (2), is selected as the first pattern to be presented. this pattern is also treated as the first cluster center of the training patterns. m∑ i= |am+i − ai| (2) stage 2. the next nclust −  input patterns are identified for presentation during network training. these patterns represent the next cluster centers of the training patterns. they are determined consecutively using the max-min clustering algorithm. in this stage, the euclidean distances between the remaining input patterns and the existing cluster centers a,··· , ak (k ≤ nclust ) are computed. the minimum euclidean distance between each remaining input pattern a and the existing cluster centers is identified: damin = min dist(a, a j) ( ≤ j ≤ k). the input pattern which maximizes damin is selected as the next cluster center. stage 3. the presentation order of the remaining input patterns is determined by finding the minimum euclidean distances between these patterns and the nclust cluster centers. the whole procedure of stage 3 is repeated until the order of all input patterns for the network training phase has been identified. the value of nclust not only influences the input data ordering, but also has a major impact on the number of arta categories created. thus, nclust controls the generalization capability of the network. successive optimization of relevances and ordering is not a good strategy. the two optimizations can possibly cancel each other out, since they may influence each other. therefore, we do not optimize both extreme data mining: inference from small datasets 287 table 1: prediction performance analysis on the training set [9]. fs-ga-fnn standard famr ga-famr ordered famr smape 89.28 89.99 77.65 86.04 rmse 1132.12 1401.94 1332.53 1366.04 table 2: prediction performance analysis on the test set [9]. fs-ga-fnn standard famr ga-famr ordered famr smape 111.91 99.01 105.17 84.51 rmse 506.08 43.45 56.99 25.49 relevances and ordering for the same network. we will refer in the following to the ordered famr a famr with equal (not optimized relevances) and optimized training data ordering. 4 experimental results in our experiments, all networks were trained with the same set of 176 molecules, using twentyfold cross-validation. thus, we improve the generalization performance on this small training set by introducing some computational overhead. in all experiments we used on-line (incremental) learning: the training set is processed only once. when estimating the quality of a prediction model, the prediction accuracy obtained both for training data and new data is important. one is interested not only in how accurately the model approximates the learning data, but also how the model generalizes on new data. the test set, which is not used for training, consists of twenty molecules. this set is from a different group of molecules than the one used for training, making prediction more difficult. we investigate the ga-famr and the ordered famr. the results are compared to the standard famr model (with no optimizations and equal relevances), and to the fs-ga-fnn. the parameters of the network are determined experimentally, and are fixed for all famr models considered. the ρa and ρb parameters control the number of generated famr categories. it is important to limit the number of categories to prevent overfitting. maintaining constant famr parameters for all tested models simplifies comparison. for the standard famr, the number of arta categories is 13 and the number of artb categories is 8. the experimentally optimized number of arta categories is close to the number of scaffold subtypes, which is a significant match. the statistical results for the test sets are in tables 1 and 2. as expected, the optimized famr models adjust better than the standard famr to the training data (table 1). of the three famr models, the ga-famr adjusts best to the training data. does the ga-famr overfit? we may find the answer by analyzing the prediction performance for test data. from table 2 we conclude that the ordered famr improves the standard famr over the test set. the ga-famr appears to overfit the training data and has therefore a less performant generalization. overall, from tables 1 and 2, we conclude that the ordered-famr performs better than the other models. for low ic values, all three famr models exhibit a similar prediction pattern and they clearly overpredict the target values of the test molecules, which is good in our particular application. we have predicted the ic value of 26 novel potential inhibitors using all four models (see [3]). the fs-ga-fnn and the famr are two radically different neural paradigms. the training datasets are the same, but the number of descriptors is different: famr uses 35, while fs-ga-fnn uses a feature selected subset of 22 descriptors. for some of the novel molecules, all methods predicted very low ic 288 r. andonie table 3: ga-famr prediction performance analysis on the training and test sets for different number of ga generations [9]. 25 generations 50 generations 100 generations training smape 87.78 86.48 84.66 training rmse 1389.54 1381.47 1360.18 test smape 95.56 101.42 106.20 test rmse 41.03 48.80 63.09 values. since radically different methods indicate high inhibitory activity, these are the molecules we consider as excellent candidates for organic synthesis and further drug discovery. it is interesting to analyze the way the ga optimization performs for different numbers of generations (fig. 3). with an increasing number of generations, the network adjusts better to the training set, but it also reduces its generalization capability with respect to the test set. thus, the number of generations controls overfitting. in our experiments (tables 1 and 2), we have used 2000 generations and this explains the relatively poor generalization obtained. we could use less generations and thus improve generalization with the cost of adjusting less to the training data. to determine the optimal number of generations and establish the best trade-off between generalization and overfitting, we may use an early stopping technique, or liu’s et al. algorithm [36]. the generalization capability of the ordered famr is good, but depends on an appropriate selection of the nclust parameter, which is a weakness of this algorithm. the ga-famr is also a good choice, but early stopping should be used to avoid overfitting. the computational overhead of the two algorithms is insignificant when compared to the value of the results. a computationally intensive solution is acceptable because drug synthesis requires years of time and great expense. therefore, obtaining an accurate prediction is more important than execution time. 5 conclusions we have discussed and illustrated how to infer from small datasets. we do not have a nice mathematical solution to the general problem of learning from small datasets. but why? here is our answer: if the vc dimension is too large relative to the size of the training set and we do not have any information about the quality of our training data and how representative it is, then the problem is ill-posed. we only can state the general rule of thumb: from the available samples, extract maximum information, without overfitting. there is no free lunch and we have to balance overfitting and generalization. both presented techniques 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[45] j. tou and r. gonzales, pattern recognition principles. reading, ma: addison-wesley, 1976. international journal of computers, communications & control vol. i (2006), no. 2, pp. 23-33 medium term electric load forecasting using tlfn neural networks danilo bassi, oscar olivares abstract: this paper develops medium term electric load forecasting using neural networks, based on historical series of electric load, economic and demographic variables. the neural network chosen for this work is the time lagged feedforward network (tlfn), which combines conventional network topology (multilayer perceptron) with good handling of time dependencies by means of gamma memory. this is a versatile mechanism that generalizes the short term structures of memory, based on delays and recurrences. this scheme allows smaller adjustments without requiring changes in the general network structure. the neural model gave satisfactory results exceeding those obtained by classical statistical models like multiple linear regression. keywords: neural network model, forecasting, gamma memory, electric load 1 introduction electric load is one of the key variables for electric power companies, since it determines its main source of income, particularly in the case of distributors. according to the foreseen load, the company makes investments and decisions on buying energy from the generating companies, and planning for maintenance and expansion. it is therefore absolutely necessary to have some knowledge of future power load: electric power distributors require a tool that allows them to predict the load in order to support its management and make more efficient its planning formulation. accurate prediction of electric load is difficult, because it is determined largely by variables that involve "uncertainty" and whose relation with the final load is not deduced directly. the load is also characterized as a nonlinear and nonstationary process that can undergo rapid changes due to weather, seasonal and macroeconomic variations. a large number of the classical prediction models are inappropriate for this modelling because of their requirement of linearity and seasonality. on the other hand, an important contribution of artificial neuronal networks (ann) is their elegant capacity for approaching arbitrary nonlinear functions, which makes them adequate for solving this kind of problem. this paper proposes a solution to the problem of predicting monthly electric loads (medium term) of low voltage and low installed power (bt1) customers of distributing companies in chile, based on both historical load values and on the country’s economic and demographic growth data. to solve this forecasting problem various neuronal network models were analyzed and applied to a real case with information from four electric distribution companies in the north of chile. different methodologies for forecasting time series in various areas were considered, particularly financial. 2 background of the forecasting problem 2.1 theoretical aspects of the forecast in general, one problem of forecasting time series consists in predicting future values for a time series based on past information. formally, the forecasting can be defined as the search for or synthesis of a function f, such that (moro 2002) ~x(t + 1) = ~f(~x(t),~x(t −1), . . . , π1, . . . , πk) (1) where, π1, . . . , πk are a set of independent parameters of the time variable. other exogenous timedependent variables can also be included in equation 1. in practice, the forecasting becomes a problem copyright c© 2006 by ccc publications 24 danilo bassi, oscar olivares of approximating a given function as precisely as possible (bengio et. al. 1995). to be able to quantify the performance there are various metrics for the forecasting error (khotanzad & abaye 1997). traditionally, conventional (parametric) statistical models have been used to carry out the forecasting tasks, although artificial neuronal networks (ann) can be far more adequate for problems having nonlinearities, in addition to their capacity for extrapolating and approximating multivariable data and generalizing from examples (costa & ribeiro 1999). 2.2 application of neuronal networks to forecasting problems since the forecasting problem consists in predicting the future value of one or a set of variable based on past patterns, interest must be focused on temporal processing with anns. basically, the anns carry out input/output mapping, and to that end they have the ability of representing nonlinear functions arbitrarily (martín del brío & sanz 2001). to carry out the temporal handling of the variables there are two main schemes, one that uses the external handling of the temporal part (delay line) plus a static conventional network (multilayer perceptron (mlp)), and another one that makes use of some properly dynamic network (with internal handling of the temporal variable). diverse forecasting works are known using mlps, but they have been limited in temporal processing because they occupy as many input neurons as present and past samples are needed, which is inefficient in terms of network design because of the large number of parameters. on the other hand, dynamic networks, whose output also depends on previous inputs or the system’s history, are based on storing a state of the system, either in local internal memories (neuronal) or in the same network recurrence (with feedback connections). recurrent systems are based on their connection topology which incorporates delays. there are spatially recurrent networks, with connections between neurons, and locally recurrent networks, with internal connections at one neuron. there are various elemental memory mechanisms for temporal neuronal processing (principe et. al. 2000). the simplest is the "delay line pe", which implements in memory a window of length d samples, including the current one. after the limit of samples that the time window can hold (d), the stored values are lost. other memory mechanisms alter the value of what is stored, but are much more flexible in the temporal aspect, because they can change their effect, modifying parameters without having to alter the topology. for example, there is the "context pe," which has a feedback connection whose value weights the previous y(n-1) output and produces an exponential memory. also, there are more advanced memory mechanisms such as the "gamma memory pe" (principe et. al. 2000), which combine the basic memory mechanisms (delays) with an exponential filter type feedback. normally, the local dynamic capacities (neuronal) are integrated within a network with more classical topologies, such as the mlp. this is the case of the "time lagged feedforward networks" (tlfn), which are locally recurrent dynamic anns that integrate linear filter structures within a feedforward ann to extend the network’s nonlinear capacity with the representation of time. this paper details solutions using tlfn topologies, which are easier to train than spatially recurrent networks and handle the temporal dependence flexibly. 2.3 forecasting electric load in the case of the forecasting of electric load, some typologies must be considered, depending on the time interval involved (gavrilas 2002): long term (intervals of years, systems development planning); medium term (months to one year, maintenance and expansion planning); short term (one day to one week; for a balance between energy load and generation as an efficient way of controlling the system’s operation and the electric market); and very short term (for example, ten minutes, for a balance between load and generation during shorter time intervals). medium term electric load forecasting using tlfn neural networks 25 2.4 methods of analysis of input variables carrying out an analysis of input variables consists in studying the contribution of each variable to the result of the forecasting model. this is always useful in practice because it makes it possible to eliminate inputs with little information to describe the output or redundancy between the variables (bishop, 1995). the steps to be followed are: • determine all the candidate variables: based on what an expert in the area of the problem to be solved indicates or on what is stated in the literature. • determine linear correlations and statistical analysis: relations between the candidate variables and the output variable, and between themselves, and the behavior of the variable to be forecast is visualized. if there is no linear correlation between two variables, it does not mean that that there are no nonlinear correlations between them (spiegel, 1997). following that, what is mentioned by (refenes et. al. 1997) can be used, which consists in: • building a model that captures the influence of time on the variable to be forecast and the most significant linear dependencies on exogenous variables: it implies identifying the temporal structure using univariate analysis of time series (hagan & behr 1997) (garcía & garcía 2002) and identifying exogenous influences using multiple linear regression. • capturing residual nonlinear dependencies: it is verified whether added value is produced by including additional variables which, even though they may have been rejected by previous linearity criteria, have nonlinear influence. it uses iterative search strategies (forward, backward) and selection criteria, using ann as a tool. 2.5 conceptual aspects of preprocessing although in theory all anns have arbitrary mapping capacities between sets of variables, it is convenient to normalize the data before carrying out the training, to compensate for the inevitable scaling and variability differences between the variables. the above can have a significant impact on the system’s performance, so it has to be considered (bishop 1995). 3 neuronal modelling of the problem 3.1 selection of candidate variables the a priori knowledge of the models determined the following candidate variables: historical electric loads, meteorological (temperature), macroeconomic (gdp, cpi, hsi (hourly salary index)) (torche 1998) (gavrilas 2002), demographic (population, housing) (instituto nacional de estadística (ine) 2002b), and referential (month of the year, kind of month) (karady 2001). 3.2 selection of input variables the first phase of the selection method was applied to the candidate variables of the four companies by means of a linear model. an initial analysis was carried out (linear correlation and basic statistics to inspect the series). to determine the past load samples having the greatest influence on the present sample, autocorrelation and partial autocorrelation were used based on the model obtained, exogenous variables were added (with a forward search strategy). multiple linear regression was used, measuring the performance with the corrected determination coefficient, (r 2 ) (pérez 2001), which the bigger it is, the 26 danilo bassi, oscar olivares better the quality of the regression. the exogenous variable that yields the largest r 2 in the corresponding iteration is chosen, provided the improvement achieved is equal to or better than 5% (improvement and value added criterion). the linear models obtained for each company are (with linear function g): • company a: c(t) = g(c(t −1),c(t −2),c(t −12), hsi(t)) (2) • company b: c(t) = g(c(t −1),c(t −2), popul(t)) (3) • company c: c(t) = g(c(t −1),c(t −13), popul(t), temp(t) (4) • company c: c(t) = g(c(t −1),c(t −2),c(t −10), hous(t)) (5) where c (load) corresponds to the electric load, and the other variables were defined in section 3.1. as can be seen, for the candidate variables to generate a single "universal and portable" model for the four companies that are studied, they have variable incidence in each case. a general model is developed that has as inputs the past loads and the exogenous variables having the largest linear impact. • the "cpi" variable was excluded because it does not appear in the linear models. in spite of not appearing in the linear models, the "gdp" variable was not excluded because of a priori knowledge. the "temperature" variable was excluded because even though it appears in one of the linear models, in general it has a low linear correlation with the load. • for the input values, gamma memory was used in the input layer to reflect delay. • in view of the delay with which bt1 customers react to changes in the macroeconomic and demographic variables, the initial neuronal model assumes that the present load has at least one month of delay with respect to the exogenous variables. 3.3 definition of training and test sets the following sets of data are defined: • learning set. inputs: historical load data and exogenous variables (january 1999 through november 2001). outputs: historical load data from february 1999 through december 2001. • cross-validation set. inputs: historical load data and exogenous variables (december 2001 through may 2002). outputs: historical load data from january 2002 through june 2002. • test set. inputs: historical load data and exogenous variables (june 2002 through november 2002). outputs: historical load data from july 2002 through december 2002. 3.4 data preprocessing this preprocessing consisted in depurating the available information both for training and for testing the ann. it was applied to the defined candidate variables (section 3.1). medium term electric load forecasting using tlfn neural networks 27 3.5 construction of neuronal network for electric load forecasting by low voltage clients based on the selection of variables made, the network was modeled with short term memory mechanisms. a training and test were made separately for each company, but applying a general model to the four companies. it consisted in a focalized tlfn (ftlfn) with gamma memory mechanisms in the input layer, in view of its versatility. since gamma is a unifying system of the two basic memory mechanisms (delay line and context pe), the use of their respective potential is not discarded a priori, since both can be seen as special cases of gamma memories. a hiden layer was used. it will be trained with backpropagation (static bp with optimization method of descent along the gradient) (freeman & skapura 1993) (martín del brío & sanz 2001), since it is a locally recurrent and focalized topology, and also because the µ values (for the gamma memory) will be fixed in an arbitrary but convenient manner, i.e. they will not be adapted, using the criterion of (principe et al. 2000). the training parametrs are: a momentum of value 0.7 and a "path size" of value 0.1 for the weights that connect the input layer with the hidden layer, and 0.01 for the weights that connect the hidden layer with the output layer (chosen on the basis of the initial tests). in the input layer there will be as many input neurons as there are input variables. the hidden layer is nonlinear, with a "hyperbolic tangent" activation function. the number of hidden neurons will be adjusted based on successive tests to optimize the model. the output layer was chosen to be linear. this layer has one neuron (since there is only one output, the load at instant t) and it will be connected to the hidden layer. the output (still normalized) will be postprocessed. cross-validation was used to test the quality of learning and avoid overtraining. in this case the training is stopped if the mean square error (mse) in the cross-validation set does not improve after 100 time-periods (martín del brío & sanz 2001). if the condition is not fulfilled, every component will be submitted to the ann a maximum of 1000 time-periods. the cross-validation was applied every five training time-periods to measure the corresponding error with a certain stability and avoid sudden oscillation biases. since the initial weights are random, the results of the training can differ when carried out more than once. for that reason, the network will be trained five times, saving the best weight combination (minimum mse). to assess the model’s viability, a final evaluation is made with the test set. the forecast horizon will be one month (forecasting the following month noniteratively, so as not to carry over probable forecasting errors that may distort the results). thus, for the training set, a forecast will be made for thirty five months. for the test set the forecast will be made for six months, all this with the forecast horizon mentioned earlier. the forecast is always for the following month. for the training set with bp, the mse will be calculated successively, defined as follows: mse = ( p ∑ j=0 n ∑ i=0 (desiredi j − f orecasti j)2)/(n ∗p) (6) where p is the number of output neurons; n is the number of data set components; f orecasti j is component i output in output neuron j; desiredi j is desired component i output in output neuron j. together with a good approximation, it is desirable for the ann to give as a result an adequate correlation (absolute value between 0.5 and 1.0), thus reflecting the similarity in the shapes of the desired and forecast output curves. in this way, mse, mape, mae and correlation (r) will be measured for the cross-validation and test sets (khotanzad & abaye 1997). the single initial neuronal model proposed for the four companies is (with nonlinear h function (includes operations performed by gamma memory and ann on inputs)): c(t) = h(c(t −1), gdp(t −1), hsi(t −1), popul(t −1), hous(t −1)) (7) in the initial model, greater memory depth m (greater number of samples) will be given to past loads and to the exogenous variable of highest linear impact (linear correlation) on the load. for the four companies, this variable was the population. based on this and on the tests made, the values of for the gamma memories of the corresponding input neurons were fixed as shown in table 1. after testing 28 danilo bassi, oscar olivares the performance of this model, adding the rest of the candidate variables to the network is evaluated, considering that nonlinear relations with the load could still be captured. variable µ historical load 0.8 hsi 1 gdp 1 population 0.8 housing 1 table 1: values of µ for the gamma memories of the initial neuronal model as definition of the model’s requirements we have: cross-validation mse and test less than 0.1; training mse less than that of cross-validation; for the cross-validation and test the correlation must be greater than 0.5; for cross-validation and test mape must be less than 5%. the number of hidden layer neurons was adjusted making successive tests, getting an optimum number of five neurons. the initial neuronal model is illustrated in figure 1. when three output taps were used in the gamma memories, for the "resolution vs. memory depth" compromise, the optimum values shown in table 2 were obtained. figure 1: initial neuronal model the results were satisfactory. also, the model (single) was portable in topology and µ values (fixed) for the four companies. the only difference is that of the final synaptic weights. to depurate the initial neuronal model and get residual nonlinear relations of load with other candidate input variables not included so far, the following alternatives were tested: initial model adding "cpi;" initial model adding "temperature;" initial model adding referential variable "month of the year;" initial model adding referential variable "kind of month;" and alteration of initial model using as basis the current samples of the exogenous variables. in each case, different combinations of µ were tried, but the results showed no improvement. this depuration attempt was made, in the first place, with a forward search strategy (see section 2.4). no improvement was obtained when either the backward strategy (refenes et. al. 1997) was used or when variables were removed from the initial model. therefore, the final neuronal model is the same as the initial neuronal model of equation 7. medium term electric load forecasting using tlfn neural networks 29 variable resolution (r =µ ) taps (d) depth (m = d/r) historical load 0.8 3 3.75 hsi 1 3 3 gdp 1 3 3 population 0.8 3 3.75 housing 1 3 3 table 2: resolution vs. memory depth compromise initial neuronal model 3.6 multiple linear regression model for the case studied this model makes an estimation of the dependent variable y , given the values for a set of independent variables xi. the general model is given by (pérez 2001) (spiegel 1969): y = b0 + b1 ∗x1 + b2 ∗x2 + . . . + bk ∗xk + u (8) where the coefficients bi represent the effect of the explanatory variables xk on the independent variable; b0 is a constant (or independent) term of the model, and u is the residue or error term of the model. when t observations are available in time, equation 9 can be written as follows: y = b0 + b1 ∗x1t + b2 ∗x2t + . . . + bk ∗xkt + ut (9) where, t = 1, 2, 3, . . . , t . a multiple linear regression model was constructed using as input the same variables of equation 7. that is, for this case the regression model is defined formally with a dependent variable: load(t); independent variables: load(t − 1), gdp(t − 1), hsi(t − 1), population(t − 1), housing(t − 1). when the regression is carried out, the coefficients of table 3 are obtained. variable company a company b company c company d constant b -105058393.5 -27666403.6 -29892228 -28500210.8 hist. load -9.02e-02 -0.119 7.65e-02 -0.19651476 gdp 0.803 -0.18 2.036 -1.29619841 hsi -97417.432 207055.599 -97524.597 83108.4594 population 1068.58 0 0 0 housing -1718.429 211.062 447.505 455.258917 table 3: regression coefficients by company 4 comparative analysis with linear regression model the results obtained with the neuronal model are compared below with those of the multiple linear regression model already defined. the mean absolute percent error (mape) of the sets representative of the ann model and the regression model were compared. in this way, for each company: • the mape of the regression model was calculated based on all the existing samples so as not to bias (avoid harming or favoring the results of this model) the results in particular samples. • the mape of the neuronal model was calculated for the samples of the cross-validation and for the samples of the test set. 30 danilo bassi, oscar olivares • the mape of the neuronal model does not consider the training set, because the network, due to the learning reinforcement itself and the constant error minimization, will produce an almost exact forecast, introducing considerable bias in the results and distorting the comparison. table 4 shows a comparison of the results by company: neuronal cross-validation neuronal test regression company a 2.71 2.54 2.77 company b 1.74 3.03 3.17 company c 6.95 3.08 3.69 company d 3.18 5.38 2.71 table 4: comparison of results (mape) for the companies studied the previous comparison and its results show that the neuronal model developed was in general superior in accuracy to the regression model, except in specific cases in which the ann had slight forecasting problems (company c cross-validation and company d test). the cross-validation of company d shows a larger error, but with a slight difference compared to the regression. in the remaining cases the ann approximation was better than that of the regression model. it may be concluded that the ann, by using nonlinear relations that affect the electric load, can decrease the forecasting error, giving a better approximation of the variable that is being studied than the statistical model used. 5 conclusions this paper and its results show that the ann represent a powerful tool for decision making in electric distribution companies. this is based on a methodological selection of variables, a prior study of the problem to be solved, and the processing that the ann can make in the temporal aspect. in this respect, it was decided to construct the neuronal model based on a typology that potentiates the nonlinear static mapping capacity of conventional neuronal topologies (such as the multilayer perceptron), with the handling of time series by means of the "time lagged feedforward network" (tlfn). to implement it, a versatile memory mechanism (gamma memory) was used, which allows minor adjustments to be made without making changes in the network’s general structure, and which generalizes the basic short term memory structures. as future development, forecasting the load of high voltage (at) customers can be attempted. this is more sensitive to price changes and is more exposed to unexpected functional changes (for example, industry breakdowns) and changes in the macroeconomic variables. the results obtained fulfilled the main and specific objectives of the work, which implied having a reliable ann model for predicting the monthly electric load of bt1 customers. also, the ann model had better accuracy than the classical statistical model used. all this means that the model constructed makes it possible to determine the medium term load with acceptable accuracy, as required by the electric distribution companies for the system’s planning and expansion. the gamma networks are more versatile for modelling nonstationary time series. handling of the feedback parameter gives the developer an additional element for adjusting the network, disregarding the increase or decrease of the neurons of the hidden layers, which often is ambiguous. support by linear statistical techniques and time series is an important tool for selecting variables, making it possible to detect linear dependencies prior to the construction of the ann, leading to focusing the test/error of such network on finding nonlinear dependencies, with a large part of the linear modelling already solved. the electric customer reacts with delay or inertia to changes in the macroeconomic variables, and the load reacts with inertia to changes in the demographic variables. it was shown that the present electric load has a delay of at least one month in its "reaction" to exogenous variables. from the above it is seen that if short term memory mechanisms were used for the neuronal medium term electric load forecasting using tlfn neural networks 31 modelling, the delays that must be considered should be handled carefully. otherwise the training would not be useful. references [1] bengio, s., fesant, f., collobert d., a connectionist system for medium term horizon time series prediction, international workshop on applications of neural networks to telecommunications, iwannt, stockholm, sweden, 1995. [2] bishop, c., neural networks for pattern recognition, oxford university press, 1995. [3] costa, n., ribeiro, b., a neural prediction model for monitoring and fault diagnosis of a plastic injection moulding process, centro de informática e sistemas da universidade de coimbra, 1999. [4] freeman, j. and skapura d., redes neuronales: algoritmos, aplicaciones y técnicas de programación, versión en español de garcía-bermejo, r. joyanes, l. addison wesley / díaz de santos, wilmington, delaware, eua, 1993. [5] garcía, j. and garcía, f., econometría. tema 5. autocorrelación, curso 2002/2003. universidad de huelva, 2002. [6] gavrilas, m., neural network based forecasting for electricity markets, technical university of iasi, romania, 2002. [7] hagan, m. and behr, s., "the time series approach to short term load forecasting", ieee transactions on power systems, vol. pwrs-2, no. 3, 1997. [8] instituto nacional de estadísticas (ine 2002b), censo 2002. síntesis de resultados. [9] karady, g., short term load forecasting using neural networks and fuzzy logic, arizona state university. power zone, 2001. [10] khotanzad, a., abaye a., "annstlf a neural network based electric load forecasting system". ieee trans. on neural networks, vol. 8, no. 4, 1997. [11] martín del brío, b., sanz molina a., redes neuronales y sistemas borrosos, 2da. edición, ra-ma, madrid, españa, 2001. [12] moro, q., series temporales y redes neuronales artificiales, departamento de informática universidad de valladolid. pérez, c. (2001), técnicas estadísticas con spss, prentice hall, madrid, españa, 2002. [13] principe j., euliano n., lefebvre w., neural and adaptive systems: fundamentals through simulations, john wiley and sons, new york, 2000. [14] refenes, a., burgess, a., bentz, y., "neural networks in financial engineering: a study in methodology". ieee transactions in neural networks, vol 8; no. 6, 1997. [15] spiegel, m., estadística, serie de compendios schaum. libros mcgraw-hill, colombia, 1969. [16] torche, a., contabilidad nacional. númerosindices. desestacionalización y trimestralización, trabajo estadístico número 63. pontificia universidad católica de chile. insituto de economía. santiago, 1998. 32 danilo bassi, oscar olivares danilo bassi and oscar olivares universidad de santiago de chile departamento de ingeniería informática santiago, chile e-mail: dbassi@ieee.org, oolivare@yahoo.com international journal of computers, communications & control vol. ii (2007), no. 1, pp. 5-16 crossing the rubicon: a generic intelligent advisor răzvan andonie, j. edward russo, rishi dean abstract: recommender systems (rs) are being used by an increasing number of e-commerce sites to help consumers find the personally best products. we define here the criteria that a rs should satisfy, drawing on concepts from behavioral science, computational intelligence, and data mining. we present our conclusions from building the wiseuncle rs and give its general description. rather than being an advisor for a particular application, wiseuncle is a generic rs, a platform for generating application-specific advisors. keywords: recommender systems, electronic commerce, user interface, user modeling. 1 introduction e-commerce sites use rs to guide visitors through the buying process by providing customized information and product recommendations. some actual online recommender systems are described in [26, 31, 32]. several well-known e-commerce businesses use, or have used, rs technology on their web sites: amazon, travelocity, bmw, moviefinder, and dell among them. although commercial rs use began several years ago, we are still only beginning to use such systems on a large scale. overviews of the relatively short history of rs and the techniques used may be found in [16, 30]. what is an “intelligent rs"? we will consider as intelligence the use of artificial intelligence features, such as adaptation, integration of learning algorithms, explanation, and case-based planning. such an intelligent product search engine for online catalog sales is analog devices [1], developed at university of kaiserslautern. we begin by specifying the performance goals of a rs. this delineation of what a successful rs needs to be able to do leads to an analysis of the criteria that a successful rs must meet. then we describe our own rs, named wiseuncle. the paper concludes with the results of the preliminary tests of wiseuncle performed by three e-commerce businesses. a preliminary paper describing our results may be found in [2]. 2 goals the most telling sign of a rs’s success is that it is used. on the one hand, the seller finds enough benefits to mount it on the company’s website. on the other hand, site visitors find it both intelligent and trustworthy enough to purchase its recommended product. both consumers and seller have goals that a rs must help them achieve. many of these goals are closely linked because unless the consumer is satisfied by the interaction with the rs, a sale will not be made and the rs’s main benefit to the seller is lost. thus, the seller must provide a rs that meets the consumer’s goals. 2.1 consumer goals consumers vary greatly in their product knowledge. yet no matter how little they know and how few of the rs’s queries they can respond to, they want an accurate, believable, and customized recommendation of the product they should buy. the overarching task of any rs is to start with the knowledge a consumer can provide and end with a recommendation of the best product tailored to that particular individual. said differently, a rs bridges the gap between what consumers know and the one fact that copyright c© 2006-2007 by ccc publications 6 răzvan andonie, j. edward russo, rishi dean they want to know, the single best product for them. to accomplish this task, the rs must be built with knowledge from experts about both consumers and products and from mining the data of past purchases. even after all this knowledge has been embedded in a rs, the resulting recommendation must not only be accurate, but also credible to the potential buyer. it will do neither buyer nor seller any good if the former doesn’t believe the recommendation, even one that is truly insightful and fully matches the buyer’s needs. 2.2 seller goals sellers also want the rs to deliver an accurate recommendation. (one reason, infrequently acknowledged, is the need to minimize exposure to lawsuits by buyers who feel that they were misled by an incompetent rs.) because the rs is mounted on a website, sellers need it to function independently of any human assistance (sometimes just because humans are expensive). this desired independence requires that the rs must deal with whatever information the consumer can provide, however sparse or inconsistent. it must also function in real time. and it must do all this for the complete set of (possibly) millions of available products, as for automobiles, personal computers, multi-location vacations, and other products constructed from components. for a related kind of large problem for a rs, see [15]. finally, sellers have goals beyond an immediate purchase. they also want a rs to help improve the long-term business relationship. thus, an ideal rs should facilitate site visitors’ return to the website (even if they did not purchase), incite customers to recommend the website to acquaintances, and build a positive image for the company. a technologically sophisticated and customer-friendly website is naturally associated with a technologically sophisticated and customer-friendly company. in addition, a rs can bring valuable information to the company’s marketers, enabling them to improve their products, prices and promotions. for instance, through customer profiling or market segmentation, a rs can help a business decide to whom to send a customized offer or promotion. 3 criteria in this section we list the main criteria that a rs must satisfy. we partition them into two parts, those relating to system design and those more pertinent to the user “conversation". however, because the two are linked, our distinctions are necessarily imprecise. a different set of criteria can be found in [12]. 3.1 system design criteria knowledgeable a successful rs must utilize both global and local knowledge of both products and customers. by global customer knowledge we mean knowledge that is independent of the particular product domain, such as knowing how people make purchase decisions or how best to “converse" with them online. global product knowledge includes the product’s attributes, their functionalities, and anything else that is independent of the particular seller’s offerings. a domain expert in personal computers, for instance, knows video cards, their performance capabilities (e.g., which type of card is needed for displaying photographs, playing video games, or showing movies), their approximate prices, and even how rapidly their technology is changing. local customer knowledge refers to the ability to link a customer’s personal needs, uses, and preferences to the focal product. thus, a consumer may have a high need for status among coworkers that might influence many purchases. however, local consumer knowledge enables a rs to direct the consumer toward a particular product, such as a personal computer with the image of the latest and greatest technology or an automobile that signals status (at least to the target audience of coworkers). local product knowledge focuses on the vendor’s offerings. such knowledge not only crossing the rubicon: a generic intelligent advisor 7 includes models, styles, components, add-ons, etc., but also which components can be configured with others and the moment-to-moment availability of any recommended product or special offers. thus, a rs should be connected to the vendor’s product database and to the pertinent marketing reporting systems. the volatile nature of both real-world products like personal computers and vendors’ information systems requires adequate database maintenance in the rs. finally, a successful rs must not only integrate all four types of knowledge, but it must make all of this knowledge visible to the user. that is, this knowledge should not only be “under the hood" of a good recommender system, but users must be able to see how the full array of knowledge is used and why the resulting recommendation has been intelligently customized to their personal needs. viable rs can be constructed from less than the full array of sources of knowledge just described. however, some functionality will be sacrificed. for instance, global consumer knowledge is used by rs that require users to input numerical estimates like the importance weights of the main product attributes. such systems more or less ignore the decision process that consumers prefer to use (i.e., a part of global consumer knowledge). more extremely, a rs can be built with essentially no expert knowledge relying on data mining alone. using only a record of past products purchased and the assignment of consumers to market segments, a rs can recommend products that were sold successfully in the past to each segment, a kind of popularity poll based on market share. however, the customization to a single consumer is likely to be crude. note that recommendations based on expert knowledge are much better at dealing with new products, whereas market share rankings can only be used for products that have been on the market and changed little over time. customization the task of making a customized recommendation requires the rs to know how to draw multiobjective comparisons among products. this is a classic challenge for any decision maker, human or software. it is made more complex for a rs because the information extracted from a potential purchaser is limited compared to that needed for at least some ideal solutions [13, 6, 11]. nonetheless, the rs must somehow trade off the relative importance of different product attributes or features in order to achieve the best customization. many automobile buyers face a difficult tradeoff between gas mileage and safety. the former points toward smaller and lighter vehicles, while the latter is associated with larger and heavier ones. scalability and real-time performance in order to meet the requirements of an internet application, a rs should be scalable and able to work in real-time, even for very large problem sizes. for instance, a recommender system connected to a large web site must produce each recommendation within a few tens of milliseconds while serving thousands of consumers simultaneously and searching through potentially millions of possible product configurations. imperfect data a rs should be robust in the face of data that are uncertain, noisy, sparse, or missing. one source of value in an intelligent rs lies in the fact that most customers have not deeply considered many of the available products and their product features, or are unable to recognize and express all of their personal needs related to the product. however, the downside to this accommodation to users’ limited knowledge is that we must often deal with sparse or missing data that result from a customer responding, “i don’t know", “it doesn’t matter to me", or “i don’t want to answer this question". in addition, the less consumers know about the product, about their personal needs, and especially about the links between them, the more often the rs will have to resolve inconsistent input. 8 răzvan andonie, j. edward russo, rishi dean connection to database rs should be connected to the vendor’s product database and the marketer’s reporting systems. only products that can be configured in a feasible manner should be recommended. and of these legitimate products, only those that are currently in stock should be recommended. the highly volatile nature of real-world products and information systems requires adequate database maintenance in the rs. retention of customer data during each interaction with a customer, the rs extracts knowledge from the customer that should be used to build and update a customer profile. the rs should save this profile so that it, and aggregations of profiles, can be “mined" for marketing-relevant knowledge. that knowledge may be as straightforward as a record of products purchased by different customer segments. alternatively, it may be as complex as inferring underserved segments in a space of consumers. it may even include the results of deliberate experimentation with, for example, the bundling of components, price discounts, complementary products, and so forth. the point that is relevant to a successful rs is that it must be able to retain enough of the knowledge that it gains, such as customer profiles, to be able to provide useful answers to a range of questions asked by marketing and product managers. learning the rs should be able to improve its functionality by continually learning from its interactions with consumers. this learning can be thought as reward and penalty feedback. after selling a recommended product, the rs will enhance that type of recommendation for customers with similar profiles. when the recommended product is not purchased, the rs will make that recommendation less probable for the next similar customers. this learning depends the availability of sales data. another type of interactive learning comes from the conversation with the customer. if customers customers find the conversation too long or uninteresting, they will not complete the recommendation process and force a partial recommendation. the rs should adapt to this situation and improve future conversations with similar customers. domain independence ideally, a rs should be largely domain-independent, so that with minimum modification, one should be able to customize the same platform for other applications (e.g., selling computers, cars, financial services, or vacations). inter alia, this means that the system design should clearly separate the generic part from the domain-specific knowledge modules. 3.2 user conversation criteria the rs’s customer interface should be based on the psychology of the consumer and the purchase decision process [14]. therefore, behavioral science techniques should guide the customer dialog. that conversation with the user should be based on how consumers actually think, rather than forcing them to feed an optimization algorithm. by speaking the language of the user, a successful rs provides welcome relief to those users from some of the complexity of the purchase decision. the customer-recommender interface is usually based on a series of interactive questions presented to the customer by the rs, accompanied by multiple-choice options for the customer to input their answers. for an alternative conversational structure, see [10]. one task is to devise an optimal strategy to select the next question to be posed to the user. an intelligent dialog should be personalized, with a subsequent question based on the responses to all prior ones. that still leaves the challenge of determining what exactly is meant by optimal. one reasonable proposal is to minimize customer effort, typically measured crossing the rubicon: a generic intelligent advisor 9 as the duration of the dialog, while still being able to make accurate predictions [34, 35, 36]. however, this strategy may be based on too simple a behavioral criterion. we believe that the standard should not be customer effort only, but overall customer satisfaction. the quantification of overall satisfaction must be derived from longer-term statistics on system usage and surveys of customers. one twist on the optimization of question sequence is inserting a small amount of randomness when selecting the questions. this may help to extend the space over which the recommender understands the customer’s interests and ensures that all questions are occasionally presented to customers. during the conversation, the wiseuncle rs adopts a five-stage process for making the purchase decision described by horowitz and russo [25]. the stages are: opening; utilitarian needs; hedonic preferences; optional features or add-ons; and endgame. stage 1, opening, frames the buyer (e.g., the buyer’s level of knowledge of the product category and extent of product search to date). it also identifies the buyer’s preference among gross product characteristics (e.g., a desktop versus a laptop personal computer or a one-week versus a two-week cruise). stages 2 and 3 encompass, respectively, the utilitarian and hedonic or emotional needs [3]. the former include the functional uses of the product, such as an automobile’s seating capacity or environmental friendliness or whether a personal computer is going to be used by teenagers or for showing movies. stage 3’s hedonic needs, like the image of a car’s brand name or body style, are often harder for a buyer to express. thus, it can be a substantial behavioral challenge not only to use such knowledge to identify the best product but also to extract hedonic knowledge from the consumer. stage 4, optional features or add-ons, captures the remaining, minor product specifications, like an automobile’s audio system or aspects of its interior. the final stage, endgame, covers such external elements as the local availability of reliable repair service for an automobile, a pc’s warranty, or travel insurance for a vacation cruise. these five stages are sufficient to structure the process of a purchase decision for even the most complex products. the following factors contribute to the success of such conversations in internet-mediated dialogs [24]. they form the remaining criteria in the design of a rs. the benefits of a conversation exceed its costs people use information only if it is perceived as adding benefits or as reducing costs. if (expected) costs exceed (expected) benefits at any point, there is a clear risk of the customer terminating the dialog. thus, a criterion for a successful conversation is site visitors continually receiving some benefit during, not only after, the interactions/dialog. credibility and trust information and advice must be credible, and the source must be trustworthy [7, 8, 5]. an internetdelivered rs cannot provide the face-to-face cues of trustworthiness that a human can. however, although a rs may have no initial reputation for trust (based on past experience), such an image can be built over time by personal usage, word-of-mouth recommendations, or public endorsements (e.g., the endorsement of the system’s knowledge and disinterestedness in consumer-oriented columns in newspapers and magazines). one alternative is to add a confidence metric (like the average customer rating used by amazon), and this has the potential to improve user satisfaction and alter user behavior in the rs [27]. a second alternative is to make the rs adaptive. this would reduce the risk of manipulation: users can detect systems that manipulate their predictions, something that that has a negative impact on their trust [28]. 10 răzvan andonie, j. edward russo, rishi dean intelligence the rs must display intelligence in the conversation. for instance, it must know what kinds of information people can validly provide and how to successfully extract that information from buyers. consumers can usually say what they need or want the product to do and can often articulate such personal preferences as style and color. however, they may have difficulty specifying the product features that meet those needs. intelligence can also be demonstrated by prompting users about needs that might be overlooked. such needs may include those of others (family members who will use the new personal computer), future needs (an automobile that suits the needs of an elderly parent who may visit regularly), and unanticipated needs (especially first time product users like vacationers to an “adventure" destination). intelligence is manifest in many small ways, such as clearly remembering the answer from a previous query and incorporating it into a subsequent question. it can also appear as the rephrasing of questions to reflect the consumer’s level of knowledge in the product domain. control customers should be able to request additional or explanatory information. or as the conversation proceeds, the customer may learn something that requires returning to an earlier point in the dialog and changing a preference stated there. buyers who feel impatient should be able to request a recommendation at any time, even before the rs would normally feel comfortable providing one. finally, the buyer might even like to suspend the conversation and return later. more control in any situation is empowering, and more so in situations where control is expected. providing satisfactory conversational control is a special challenge to rs. feedback a desire for feedback in a conversation is natural, so appropriate kinds enhance user acceptance of a dialog with a rs. specific feedback might include (a) how much progress has been made toward identifying the best product, and (b) how much longer the conversation is expected to take. whatever specific feedback options are provided, however, users do not want to receive feedback only after they have answered every question (as they must in many static surveys). how might we know that a rs fully meets all of the above criteria? that question has two answers, a real and an ideal. the real answer is that it depends on customer acceptance. this is, we can try to build into the rs all of the desired capabilities, but only users can validate success. we may believe that our system is intelligent or provides plenty of feedback, but if its users believe that the system has failed to do so, then it has. working in the rs’s favor is that users often judge a system’s performance relative to that of the other systems they know. they do not expect the wiseuncle system described below to be as good as a genuine human wise uncle, someone who is very knowledgeable and is on their side. thus, superior performance rather than perfection may be sufficient for success. the ideal criterion is a kind of turing test for rs. that is, ideally the potential buyers would not be able to determine whether they were interacting with a rs or with a (very fast) human advisor who was knowledgeable and on their side (sometimes called a human concierge [9]). this ideal is not as impossible to achieve as it may first sound. no human advisor is perfect, so the rs doesn’t have to be either. we do not expect this ideal goal to be achieved soon by any rs. however, it may be worth keeping in mind as an ideal for system designers. 4 system description rubicon is a generic domain-independent advisor, recommending products from an existing set. each product is configurable, meaning it is comprised of several components, which may each be decrossing the rubicon: a generic intelligent advisor 11 scribed in turn by several attributes. building a rs depends largely on the knowledge representation model, and we chose a computational intelligence framework. our rs is a classifier that “learns" to make good recommendations. this classifier is an expert system, able to explicitly expose its acquired knowledge. the main characteristics of rubicon are: • the inferential process from the customer’s needs to the best product is constructed in two stages, called bridges, one from needs to product attributes, and the second from attributes to the products themselves. • it can easily be customized for different applications since the interface to the application-specific knowledge domain is separated from the main system. • the front-end dialog is dynamic and changes based on user responses. it elicits information from users to determine a location in a needs space which is then used to find optimal (sub-optimal) solutions in a products space. • it accepts imprecise input from users. • it provides a justification for all recommendations. • reversibility: the system can reverse the decision process from effect to cause. this allows forecasting the adoption of new products or services using real customer decision data. figure 1: rubicon high level system description the rubicon system diagram (fig. 1) shows the following main modules. 12 răzvan andonie, j. edward russo, rishi dean 4.1 conversation engine (ce) the ce is responsible for dialog management, presenting questions to the user and processing the resulting responses sent to it by the user. questions and their associated responses are processed to accomplish the following two results: i) propagate the knowledge gained from a response to the subsequent inference mechanisms and ii) determine the next question to pose to the user. in doing so, the dialog management occurs subject to the following constraints: • presents the appropriate questions for the system to confidently determine an intelligent, personalized recommendation. • presents questions conforming to users’ expectation of a real dialog with respect to flow, organization, and coherence. • minimizes the number of questions presented. • it enables scalable addition/ subtraction/ modification of questions. • allows users and administrators to reproduce particular dialogs. • uses proper constructs for further data mining. from a computational standpoint a rule-based expert system is used to implement the ce’s dialog management process. questions and responses are linked by sets of predetermined rules, and a number of other intermediary constructs. in this way, the questions, responses, and rules can be specified, along with goals (i.e., knowledge to be gathered) independently of knowing the dialog flow in advance. the system at runtime determines, based on the behavioral and informational goals, which question to present next to the user. when all appropriate questions have been presented, the conversation is determined to be complete. however, the user may intervene at any time to ask for the system’s current best recommendation based on the information provided thus far. 4.2 inference engine (ie) the purpose of the ie is to map the user’s profile of needs (the output of the ce) to the attributes necessary to comprise the appropriate recommendation. given a set of responses resulting from the dialog, the ie can indicate a set of recommendations, ordered by the degree of their preference. these recommendations are not concrete (physical) product recommendations yet, but a mapping from the user needs space to the space of attributes, yielding generic descriptions of the product, like “ram amount" (e.g., standard, large, maximum) and “network card type". collectively this inference is called the first bridge. the ie is taught by a human expert. however, it can learn incrementally as well: new teaching examples can be added without restarting the teaching process from the beginning. conditional rules can be extracted to describe the behavior of the ie and justify recommendations, market research and performance improvement. the ie is stable under noisy inputs and user uncertainty. such “noise" may be produced by “i don’t know" answers, or by contradictory answers in the dialog. a neuro-fuzzy network incorporates fuzzy, rather than crisp, membership functions in its structure and has the ability to learn when presented with training data [33]. after training, such a network can be used as a classifier. the fuzzy neural network classifier builds decision boundaries by creating subsets of the pattern space. it is a model-free estimator and does not make assumptions of how outputs depend on inputs. instead, it adjusts itself to a given training set by learning algorithms and decides the boundaries of classes. when given an unknown pattern, the fuzzy inference network classifier uses the learned crossing the rubicon: a generic intelligent advisor 13 knowledge to estimate the membership value of this pattern in each class and classify the input pattern according to the membership values. to implement the ie, we have used the fuzzy neural net architecture introduced in [33], trained to represent the expert knowledge of a particular product domain. for instance, in the case of a personal computer rs, experts develop training patterns to represent the varying needs profiles of customers along with their corresponding feature sets for a recommended pc. the inference process is fast, online. 4.3 product search engine (pse) the pse is the second bridge, a mapping from the space of attributes to the space of (physical) products. it is an optimization module interfacing with the retailer’s product database to select the best, valid product configurations that match the criteria specified by the user, such as the minimum cost, the maximum likelihood of success, or a number of other simultaneous criteria. the inputs to the pse are the levels of the attributes (the output of the ie), the configuration constraints (i.e., incompatibilities among components), and a user’s criteria for optimization (e.g., a desired price point). these criteria for the algorithm can be set by the ie and ce and are, therefore, uniquely tailored to a given user. the pse can sort though billions of options in real time, allowing searches to be completed online. the products with the highest degree of fit are passed to the justification engine for further processing. the response to a question is subsequently used to provide more information that adds to rubicon’s knowledge base during a user-experience. this, in turn, leads to a recalculation and optimal selection of the next most appropriate question. this question-response model continues until rubicon is either asked, or has sufficient confidence, to make a recommendation. the pse navigates a vast search space, taking into account different optimization criteria. we used a genetic algorithm approach for this (np-complete) optimization problem. in the initial phases, the pse operates on abstractions of the real world, and then through an adaptor layer translates these abstractions into concrete items. this information is capable of being read and processed at runtime. the pse remains independent of constant updating of the “real world" items. this adaptor level is implemented as an xml data bridge. 4.4 justification engine (je) recommendations are run through the je to provide a plain english explanation of why the system has provided a specific recommendation. this justification is delivered in the same vernacular as the dialog, personalized to the user, and is present to facilitate user understanding and adoption of the recommendation. this can be accomplished by transforming the iešs mathematical equations into linguistic rules more easily understood by the human. a nice feature of the fuzzy-neural architecture used in the ie module is that it can be expressed by a set of fuzzy if/then inference rules and these rules can be easily extracted automatically [33]. the je takes the set of if/then rules from the ie and the set of recommended configurations from the pse and develops a rationale for selecting each product. the value of the je is that it creates confidence in the recommendation. 5 preliminary tests rubicon is implemented using a complimentary modular software approach that encapsulates the individual computational blocks, as well as the necessary software architecture emphasizing a stable and reusable model that is compliant with the j2ee technology standard. although still under development, rubicon was sufficiently developed to be submitted to usability testing by two major pc manufacturers. each test involved about a dozen users and compared three rs. 14 răzvan andonie, j. edward russo, rishi dean one was the manufacturer’s current online rs, one was an attractive competitor, while the third was rubicon. the results made available revealed that rubicon was judged clearly superior in both tests. for instance, in one test, when asked which of the three rs the user would “be most likely to use again", nine of eleven respondents chose rubicon. rubicon was tested online by a webhosting services provider. of 2200 online users who began a conversation, 83% completed it to the point of receiving a recommendation (which was the only result made available to us). this was judged by the host company to be an extraordinary high completion (i.e., non-abandonment) rate. 6 conclusions we have built rubicon to meet the criteria described in section 3. we have used principles and techniques from artificial intelligence and behavioral sciences. since we have focused on the core system, other modules of rubicon, used for prediction, customer profiling, and marketing segmentation were omitted. it was a challenging task to build rubicon, especially because of its generic character. making the system largely independent of a specific e-commerce application required greater complexity and abstraction. but do we really need a generic rs? from a user perspective this may be a non-issue. however, for the rs designer and software engineer this is a critical requirement. we should think not only in terms of how to use a rs, but also how to build it and how to adapt it fast for 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[36] d. mcsherry, minimizing dialog length in interactive case-based reasoning, proceedings of the 17-th international joint conference on artificial intelligence (ijcai 2001), pp. 993-998, 2001. răzvan andonie computer science depart., central washington university, ellensburg, usa, andonie@cwu.edu j. edward russo johnson graduate school of management, cornell university, ithaca, usa, jer9@cornell.edu rishi dean visible measures corp., cambridge, ma, usa, rdean@visiblemeasures.com received: december 2, 2006 int j comput commun, issn 1841-9836 9(3):370-378, june, 2014. comparison and weighted summation type of fuzzy cluster validity indices k.l. zhou, s. ding, c. fu, s.l. yang kaile zhou*, shuai ding, chao fu, shanlin yang school of management hefei university of technology hefei 230009, china *corresponding author: zhoukaile@mail.hfut.edu.cn e-mail: dingshuai@hfut.edu.cn, wls_fuchao@163.com, yangsl@hfut.edu.cn abstract: finding the optimal cluster number and validating the partition results of a data set are difficult tasks since clustering is an unsupervised learning process. cluster validity index (cvi) is a kind of criterion function for evaluating the clustering results and determining the optimal number of clusters. in this paper, we present an extensive comparison of ten well-known cvis for fuzzy clustering. then we extend traditional single cvis by introducing the weighted method and propose a weighted summation type of cvi (wscvi). experiments on nine synthetic data sets and four real-world uci data sets demonstrate that no one cvi performs better on all data sets than others. nevertheless, the proposed wscvi is more effective by properly setting the weights. keywords: fuzzy clustering, fuzzy c-means (fcm), cluster validity indices (cvis), wscvi. 1 introduction clustering [1] is an unsupervised learning process to discover significant patterns in a given data set by partitioning a data set into groups (i.e., clusters) such that the elements assigned to the same group are as similar as possible while those in different groups are dissimilar in some sense. clustering is an unsupervised process, the data objects in a data set are typically unlabeled and no structural knowledge about the data set is available [2]. therefore, evaluating the quality of clustering results and determining the optimal number of clusters are difficult tasks. also, the number of clusters is a prerequisite input parameter for many clustering algorithms [3]. cluster validity index (cvi) is a kind of criterion function to determine the optimal number of clusters [3]. currently, a large number of cvis have been proposed [4, 5]. so it is necessary to evaluate and compare the performances of these cvis. extensive comparisons of crisp cvis have been presented [6, 7], while few studies have focused on the performance comparison of cvis for fuzzy clustering. most comparison studies of fuzzy cvis are presented when a new fuzzy cvi was proposed [8, 9], but the extent of these comparisons was limited. in this paper, we present an extensive comparative study of ten well-known fuzzy cvis. previous studies on cvis have demonstrated that there is no single cvi that can deal with any data sets and always perform better than the others [10, 11]. the idea of weighted cvis has been mentioned in literature [12]. however, few studies have focused on the weighted summation type of cvis for fuzzy clustering. hence, in this paper we propose a weighted form of fuzzy clustering cvis which is the weighted sum of ten well-known fuzzy cvis. the remainder of this paper is organized as follows. section 2 reviews the fuzzy c-means clustering algorithm and ten well-known cvis for fuzzy clustering. then, in section 3, we introduce the weighted summation type of fuzzy clustering cvi. finally, experimental results are presented in section 4. the conclusions are drawn in section 5. copyright © 2006-2014 by ccc publications comparison and weighted summation type of fuzzy cluster validity indices 371 2 fuzzy c-means and fuzzy cvis 2.1 fuzzy c-means algorithm fuzzy c-means (fcm) algorithm [13] starts with determining the number of clusters followed by guessing the initial cluster centers. each cluster center and corresponding membership degrees are updated iteratively by minimizing the objective functions until the termination criterion is met. the objective function of fcm is defined as: jm(u, v ) = c∑ i=1 n∑ j=1 µmij d 2 ij (1) where u denotes the membership, v is the cluster center matrix, n is the number of data objects, c is the number of clusters, m is the fuzzifier in fcm, vi is the center of cluster i, µij is the membership degree of the jth data object xj to vi, d2ij is the euclidean distance of xj to vi, and d2ij = ||xj − vi|| 2. see ref. [13] for the iterative formulas of membership degree µij and cluster centers vi. 2.2 cvis for fuzzy clustering proposed by bezdek [14] in 1974, pc is the first cvi used for fcm clustering, which was defined as pc = 1 n c∑ i=1 n∑ j=1 µmij (2) the optimal cluster number c∗ is obtained with the maximum value of pc. to reduce the monotonic trend of pc index with the increase of the cluster numbers, a normalized form of pc, called npc [15], was defined as npc = 1 − c c − 1 (1 − pc) (3) the optimal number of cluster c∗ is also found when npc reach the maximum value. the concept of entropy was introduced in pe index by bezdek [16]. much like pc index, pe index is defined as pe = − 1 n c∑ i=1 n∑ j=1 µij logα µij (4) where α is the base of the logarithm. the optimal cluster number c∗ is determined by the minimum value of pe index. like npc index, npe [17] was the normalized form of pe index to reduce the monotonic tendency of pe index and was defined as npe = n n − c pe (5) like pe index, the optimal cluster number is corresponding to the minimum value of npe index. different from pc, npc, pe and npe index which only considered the membership degree elements in u, the xb index [18] consists of both the membership degree values and the information about data set itself. xb index is defined as 372 k.l. zhou, s. ding, c. fu, s.l. yang xb = c∑ i=1 n∑ j=1 µ2ijd 2 ij n × min i ̸=j ||vi − vj||2 (6) the optimal cluster number is found at the minimum value point of xb index. kwon [19] extended xb index and proposed a new cvi, vk, which is defined as v k = c∑ i=1 n∑ j=1 µ2ijd 2 ij + (1/c) c∑ i=1 ||vi − v||2 min i ̸=j ||vi − vj||2 (7) in order to determine the best clustering results and the optimal cluster number c∗, we should find the most compact and separate partition, that is, the minimum value of vk index. pakhira et al. [20] proposed a cvi, known as pbm index, for crisp clustering, and a corresponding form for fuzzy clustering, called pbmf index. the definition of pbmf index is pbmf = ( 1 c × e1 ec × dc)2 (8) where ec = c∑ i=1 ei, ei = n∑ j=1 µijdij, dc = c max i,j=1 ||vi − vj||2 the optimal cluster number is found when the maximum value of pbmf index is achieved. different from the ratio type of cvis, fukuyama and sugeno [21] proposed a summation type of cvi called fs index. its definition is fs = c∑ i=1 n∑ j=1 µmij (d 2 ij − ||vi − v|| 2) (9) the optimal cluster number c∗ is achieved at the minimum value of fs index. a new cvi, referred to vt index, was proposed by tang et al. [22] based on the idea of penalty function of kwon’s index vk. vt index is defined as v t = c∑ i=1 n∑ j=1 µ2ijd 2 ij + {1/[c(c − 1)}] c∑ i=1 c∑ k=1;k ̸=i ||vi − vk||2 min i̸=k ||vi − vk||2 + 1/c (10) the optimal cluster number c∗ is also found at the minimum value of vt index. a cvi proposed by bensaid et al. (sc) [23] is defined as sc = c∑ i=1 n∑ j=1 µ2ijd 2 ij n∑ j=1 µij c∑ k=1 ||vi − vk||2 (11) the optimal cluster number c∗ is determined by the minimum value of sc index. comparison and weighted summation type of fuzzy cluster validity indices 373 3 weighted summation type of fuzzy cvis in order to take advantage of each fuzzy cvi and weaken its limitation, we proposed a weighted summation type of cvi (wscvi), which is the weighted sum of the above ten fuzzy cvis. wscvi is defined as wscv i = n∑ i=1 ωi · cv ii (12) where n is the number of cvis. ωi is the weight of index cv ii, which represents the relative importance of the ith cvi. ω satisfies 0 ≤ ωi ≤ 1, n∑ i=1 ωi = 1. cv ii is one of the ten above cvis for fuzzy clustering. among them, pc, npc and pbmf index are maximum type indices, i.e., the optimal cluster number c∗ is achieved at the maximum value of these cvis, while other seven indices are minimum type. in order to obtain c∗ when wscvi is minimum, we convert the three maximum type indices into their corresponding reciprocal types, which are presented as pcr = 1/pc, npcr = 1/npc, pbmfr = 1/pbmf . the values of different cvis change in different range. to overcome the dominate influence of cvis in large values, all the cvis are normalized so that all of their values range in [0, 1]. the corresponding cluster number is optimal cluster number c∗ when the values of the normalized cvis equal to 0, and the minimum value of wscvi is achieved. 4 experimental results nine synthetic data sets (six 2-d data sets and three 3-d data sets) and four real-world data sets were used in the experiments. the value of fuzzifier in fcm is set m=2. we suggest ω1 = ω2 = · · · = ωn = 1/n when there is no prior knowledge available. in the experiments, we also set some other weights to obtain the optimal cluster numbers. the synthetic data sets are expressed as data_d_c_n, in which d is the dimension of the data set, c is the number of clusters in the data set, and n is the total number data objects in the data set. 4.1 data sets three well-known 2-d synthetic data sets, butterfly, example_01 and example_02 presented in [19] and the other three 2-d synthetic data sets, data_2_3_60, data_2_3_70, and data_2_4_110 [24], are shown in figure 1 (a) to (f), respectively. figure 2 shows the three 3-d synthetic data sets, data_3_3_200, data_3_3_300, and data_3_4_320, respectively. we also use four real-world data sets, bupa, wdbc, iris and glass data set, from uci machine learning repository [25] to test the performance of wscvi and the ten single cvis. 4.2 results the optimal cluster numbers found by the ten single cvis and the proposed wscvi with equal weights of each cvi are shown in table 1. it can be seen from table 1 that there is no one cvi that can find the optimal cluster numbers for all of the data sets. wscvi with equal weights, 1/n, found the correct optimal cluster numbers except data_3_3_300, data_3_4_320, iris, and glass. in order to find the optimal cluster numbers of data_3_3_300, data_3_4_320, iris, and glass using wscvi, we adjust the weights of each cvi in wscvi. 374 k.l. zhou, s. ding, c. fu, s.l. yang (a) butterfly (b) eample_01 (c) example_02 (d) data_2_3_60 (e) data_2_3_70 (f) data_2_4_110 0 2 4 6 0 1 2 3 4 -4 -2 0 2 4 -1 0 1 2 3 4 -4 -2 0 2 4 -4 -2 0 2 4 0 2 4 6 8 0 1 2 3 4 5 6 0 2 4 6 0 0.5 1 1.5 2 0 1 2 3 0 0.5 1 1.5 2 2.5 figure 1: six synthetic 2-d data sets (a) data_3_3_200 (b) data_3_3_300 (c) data_3_4_320 0 1 2 0 1 2 -0.5 0 0.5 1 0 0.5 1 0 0.5 1 0 0.5 1 0 0.5 1 0 0.5 1 0 0.5 1 figure 2: three synthetic 3-d data sets comparison and weighted summation type of fuzzy cluster validity indices 375 for data_3_3_300 and data_3_4_320, we set the weight of sc index ωsc=0.5, the weights of the other cvis are all equal to 0.056. the changes of wscvi values with equal weights and with adjusted weights are shown in figure 3. as figure 3 shows, the minimum values of wscvi with adjusted weights are achieved at c=3 for data_3_3_300 and c=4 for data_3_4_320. therefore, the optimal cluster number c∗= 3 for data_3_3_300 and c∗= 4 of data_3_4_320 are both found. for iris data set, the optimal cluster number c∗= 2 considering the geometric structure is found by wscvi when all the weights are equal to 0.1. now we consider an adjusted weights case, in which the weight of pbmf index ωpbmf =0.7, and the weights of other indices are equal to 0.025. the changes of wscvi values with equal weights and adjusted weights on iris data are shown in figure 4 (a). as shown in figure 4 (a), the optimal cluster number c∗= 3 for iris can be found when one cvi dominate other cvis. since six classes in glass data set are heavily overlapped, it is difficult to find six clusters. the changes of wscvi values with equal weights and adjusted weights for glass data are shown in figure 4 (b). table 1: optimal cluster numbers preferred by each cvi c∗ pc npc pe npe xb vk pbmf fs vt sc wscvi butterfly 2 2 2 2 2 2 2 2 2 2 2 2 example_01 3 3 3 3 2 3 3 2 3 3 3 3 example_02 4 4 4 4 2 4 4 2 4 4 4 4 data_2_3_60 3 3 3 3 3 3 3 2 7 3 3 3 data_2_3_70 3 2 3 2 2 2 2 2 4 2 4 3 data_2_4_110 4 2 4 2 2 2 2 2 4 2 10 4 data_3_3_200 3 3 3 2 2 3 3 2 10 3 3 3 data_3_3_300 3 2 2 2 2 2 2 2 15 2 3 2 data_3_4_320 4 3 4 2 2 3 3 2 5 3 4 3 bupa 2 2 2 2 2 2 2 3 4 2 2 2 wdbc 2 2 2 2 2 2 2 5 4 2 2 2 iris 3 2 2 2 2 2 2 3 5 2 2 2 glass 6 2 2 2 2 2 2 6 6 2 5 2 (a) data_3_3_300 (b) data_3_4_320 figure 3: values of wscvi 1 3 5 7 9 11 13 15 17 0 0.2 0.4 0.6 0.8 1 number of clusters v a lu e s o f w s c v i with equal weights with adjusted weights 1 3 5 7 9 11 13 15 17 0 0.2 0.4 0.6 0.8 1 number of clusters v a lu e s o f w s c v i with equal weights with adjusted weithts figure 3: values of wscvi from figure 4 (b), the minimum value of wscvi with adjusted weights achieved at c=4, and its value is the second smallest when c=6. also, there is a large increase when c is greater than 6. therefore, wscvi index with adjusted weights offers the information that c∗=6 is a 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[25] a. asuncion, d.j. newman (2007). uci machine learning repository, university of california, school of information and computer science, irvine, ca, http://www.ics.uci.edu/mlearn/mlrepositor-y.html. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 844-851 a microcontroller-based intelligent system for real-time flood alerting m. oprea, v. buruiana, a. matei mihaela oprea, vasile buruiana, alexandra matei university petroleum-gas of ploiesti, department of informatics bdul bucuresti no 39, 100680, ploiesti, romania e-mail: mihaela@upg-ploiesti.ro, bvasea@gmail.com, matei.alexandra@hotmail.com abstract: the development of efficient flood alerting systems became more demanding in the last years. in this paper it is presented the first version of a prototype intelligent system for flood forecasting and real-time alerting. the system is implemented by using a microcontroller from the arm family, marvell 88f6281, and has a user interface realized under the free unixbsd operating system. also, a knowledge base is integrated in the system. the real-time alert is sent to the decision making factors via a communication channel (such as, the internet, a mobile phone, or radio communication). some experimental results obtained so far are also discussed in the paper. keywords: microcontroller, flood, artificial intelligence. 1 introduction the development of an efficient hydrologic monitoring system requires the use of an automated data acquisition system, the analysis of several parameters that are monitored (water level, water flow, rainfall fell etc), and a real-time alerting system in case of flood production. as hydrologic monitoring is very important in flood prevention, in the last years, different modern techniques were applied for flood forecasting, including some artificial intelligence based approaches, such as expert systems, artificial neural networks, intelligent agents and multiagent systems (see [8]). the systems that were reported in the literature are specific to a given hydrographic basin or to a dam or river, and provide particular solutions, which hardly can be adapted and used in other situations. our long term research purpose is to design and implement a prototype intelligent system for flood forecasting that provides real time alert in case of flood production, and can be adapted to any hydrographic area (basin, river or dam) with minimal infrastructure changes. the objective is to develop a flexible and robust intelligent system by keeping it as simple as possible. some related work reported by the research community in the area of real time alert systems provides solutions such as the embedded controller systems (see [2]), the microprocessor systems (see [3]), and the mobile sensor nodes for alert systems applications (see [9]). a preliminary report on the use of the microcontroller-based system, described in this paper, was given in (see [5]), where the application was in the area of real time seismic alert, and no artificial intelligence technique was used. we have started the development of an intelligent prototype system from our previous work described in (see [5]) (data acquisition and alert transmition), (see [6]) (implementing a low power, high performance bsd unix microcontroller-based system), (see [7]) (intelligent algorithms) and (see [8]) (agent-based modelling). in this paper it is presented the first version of a prototype intelligent system for flood forecasting and real-time alerting. the system is implemented by using a microcontroller from the arm-family, and has a user interface realized under the unixbsd operating system, and a knowledge base integrated in the system. the real-time alert is sent to the decision making factors copyright c⃝ 2006-2010 by ccc publications a microcontroller-based intelligent system for real-time flood alerting 845 via internet or mobile phone or by radio communication. the experimental results obtained so far are also discussed in the paper. 2 the architecture of the microcontroller-based intelligent system the microcontroller-based intelligent system was designed arround the marvell 88f6281 microcontroller by using the marvell sheevaplug development platform (see [11]) , and the unix free bsd 8.0 beta 2 operating system (os) (see [12]) . the architecture of the system is presented in figure 1. figure 1: the architecture of the system as interfaces with the environment, the intelligent system has a data acquisition system (das), and an alert transmission system (ats), both systems being developed within the microcontroller marvel 88f6281. the data acquisition system takes the measurements of different parameters that are monitored from a variety of sensors (si) and measurement devices (e.g. rain gauges). the alert transmission system will send the hydrologic alert code through a communication channel (internet, radio or mobile phone) to the decision factors. we have included also in the microcontroller a data processing module, a knowledge base, and an analysis module, those based on the knowledge of the intelligent system will provide the hydrologic alert code to ats. the microcontroller 88f6281 is built under the soc form, i.e. system on chip, and has a large variety of interfaces as shown in figure 2. its main destination is the mobile telephone market due to its multimedia facilities. we have compiled on this system the unix freebsd 8.0 operating system, available in the beta version, due to its stability and simple programming (see [4]). this os provides support for the arm9 architecture, and it was compiled with the specifications of the 88f6281 microcontroller according to (see [6]). moreover, the os that was chosen is a good option for embedded real time applications (see [1]). the hydrologic alert transmission could be sent by email, sms (on the mobile phone) or by radio transmission (e.g. to a pda personal digital assistant). 3 hydrographic monitoring and analysis system the monitoring and analysis system of a catchment basin structure consists by a complex of sensors that acquire information about the monitored river parameters, as well as rainfalls in the catchment area. another important set of information is given by the catchment physiographic 846 m. oprea, v. buruiana, a. matei figure 2: the marvell sheeva 88f6281 interfacing options characteristics that influence the flow regime during the flood (see [10]). our hydrographic monitoring and analysis system was designed for the monitoring of the evolution process of rainfall transformations into collecting streams by the hydrographic network from one river basin. the system is able to acquire and store the database information on river water level (h), current river speed (v), water flow rates (q), the rainfalls level and frequency (x) etc and transmit them to higher level of the analysis tasks. after the information processing is done by using the analysis algorithms (see [7]), the system has the ability to make decisions and to transmit alerts to the committee of emergency situations in case of flood risks. the input data used by the system can be obtained from parameters measurements timeseries. the analyzed parameters are as follows: • the river water level (h cm), measured by the gauging stations that are located in different regions of the catchment; • the rainfalls quantity (x l/m2); • the river water velocity (v m/s); • the air temperature (t oc); • the depth (a), width (l) and slope (p) of the river; • the thickness of the ice/snow (g cm). the output of the system is given by the numerical expressions that characterize the interdependence of the hydrological process and the factors that determine it: the maximum flow rate (qmax) and the average precipitation. the main steps followed by the hydrographic monitoring and analysis system are given bellow. 1. parameters_measurement (h, q, x, v, t, a, l, p, g); 2. parameters_processing (h, x); 3. parameters_transmission (xaverage); 4. parameters_analysis (); // use of the knowledge base a microcontroller-based intelligent system for real-time flood alerting 847 5. if flood_risk = yes then (a) hydrologic alert code transmission. the parameters measurement step requires periodic measurements of specific parameters such as water flow, water level, river flow rate, temperatures. the equipment used for performance measurements and processing of hydrological parameters of the water courses are the gauging stations. the instrumentation used for parameters measuring vary from groom gauging, pluviometric devices to measure the rainfall, river ratchets gauging for speed measurements, to automatic instrumentation that can measure most of the hydrological parameters that are monitored. the parameters processing step involves the form factor computing for each sub-catchment coefficient of flood form, and the determination of the coefficient of leakage in the warning subbasins. the parameters transmission step is realized through a communication channel, such as internet, radio or mobile phone. the parameters analysis step is the most important step because the subsequent correct decisions are taken in case of flood waves. it consists in the following three steps: • determination of the average level of precipitations; • calculation of the maximum flow in each sub-basin of the downstream warning sub-basins; • based on the calculations made in the previous two steps, the system analyses the risk of flood production by using the rules from the knowledge base that is incorporated in the intelligent system; the rules are given the hydrological alert code (yellow, orange and red). as an example, the main characteristics of the knowledge based system are presented in table 1. table 1: the rules of the knowledge base (selection) rule 1 if flow < attention and precipitations < 40l/m2 then hydrologic_code = green; rule 2 if flow ≥ attention and flow < alert and precipitations < 40l/m2 then flood_risk = yes and hydrologic_code = yellow; rule 3 if flow ≥ attention and flow < alert and precipitations > 40l/m2 then flood_risk = yes and hydrologic_code = orange; rule 4 if flow ≥ alert and flow < danger and precipitations < 40l/m2 then flood_risk = yes and hydrologic_code = orange; rule 5 if flow ≥ alert and flow < danger and precipitations > 40l/m2 then flood_risk = yes and hydrologic_code = red; rule 6 if flow ≥ danger then flood_risk = yes and hydrologic_code = red; the precipitations are occurring predominantly as rain or snow. the fall of rain is the largest factor contributing to increased river flows. snow versus rain is the second source of precipitation. melting snow during spring has a considerable impact on river basin management. tracking the level and the periodicity of precipitation involves measuring them by using existing sensors and transducers gauging stations. warning about the risk of flood is used to take appropriate decisions from the analysis of all information related to the maximum river flow. if the analysis step indicates a possible flood production then the prevention system will send signals to the members of the emergency situations committee. the hydrological alert (containing the alarm severity code of flooding) can be transmitted online to pdas, smart terminals with a gps and 848 m. oprea, v. buruiana, a. matei figure 3: the structure of the monitoring and analysis system gprs (general packet radio service) or internet. in figure 3 it is shown the structure of the monitoring and analysis system for a catchment river basin. x1, x2, . . . , xn are the rainfalls in each sub-catchment; h1, h2, . . . , hn are the rivers level. the system meets three important functions: • the function of monitoring the evolution of the hydrographic basin real time measuring of the river level and rainfall level and periodicity; • the analysis function parameters processing and comparing them with the references parameters of the catchment; • the decision and warning function. 4 experimental results of flood alerting case study as a case study we have experimented the microcontroller-based intelligent system for flood alerting in the prahova catchment basin presented in figure 4. the hydrographic network forms a highly developed basin in a palm form flowing nw-se. the main rivers that compose the prahova sub-basin are the prahova river and its main tributaries: azuga, doftana, teleajen, and cricovul sarat. the water recources in the prahova country have increased significantly due to two large lakes, paltinu (doftanei valley) and maneciu-streams (teleajen valley). the prahova river is the largest collection of water in the prahova county, with a length of 193 km, of which the first 6 km and the last 16 km are located in the counties brasov and ilfov. the prahova river basin is characterized by three types of climates: mountain, hill, and plain. the annual quantity of precipitation is 1000-1400 mm in the mountains, 500-1000 mm in the hills, and 550-600 mm in the plain. summer rainfall is more abundant, where the flood may occur in the gauging stations deepened moara domneasca and adancata. for the visualization of the hydrological process evolution in time we have used the program multi router trafic grapher (mrtg) that generates graphics as a function of time. an example of prahova river level evolution and flow in 24 hours during 7 days is given in figure 5. for the prahova hydrometric station the attention (atc), alert (ac) and danger (dc) cotes for the rivers level are the following: atc = 250 cm, ac = 350 cm and dc = 400 cm and for water flow are: atc = 105 m3/s, ac = 230 m3/s and dc = 320 m3/s. our research goal was to use the microcontroller-based intelligent system, that was presented in the previous sections, a microcontroller-based intelligent system for real-time flood alerting 849 figure 4: the prahova catchment basin figure 5: the evolution of prahova river level and flow in 24 hours during 7 days for the prahova river basin with the purpose of sending warning in real time to the emergency situations committee, and to the possible affected population in case of flood waves. in the experiments that were run so far, the data acquisition was simulated. the experiments made so far used for alert transmission two motorola mx300 radio transmission stations, those were modified to work in the 146 mhz bandwidth. figure 6 shows the motorola mx300 radio stations that were used. figure 6: the two motorola mx300 radio stations the main characteristics of the radio stations are that they can be automatically activated when they receive a radio signal through the incorporated microphone or through the microphone input. therefore, the sound output of the microcontroller-based system was connected to the microphone input of the transmitter and according to the hydrologic alert code provided by the analysis module will run audio alert records according to following script: the evolution of the simulated hydrological process is shown on the microcontroller-based 850 m. oprea, v. buruiana, a. matei ♯ !/bin/bash ♯ script receives argument via parameter {$1} which is received at startup time. ♯ {$1} may be as following: ’green’, ’yellow’, ’orange’, ’red’ and are sent on via the main program ♯ alert on system console echo "alerting code {$1}. activating radio transmitter" ♯record alert via syslog (unix system logger) echo " ‘date |cut -c1-19‘: receiving alerting code {$1} |logger -s ♯playing one of the audio files: ’green.wav’, ’yellow.wav’, ’orange.wav’, ’red.wav’ /usr/local/bin/esdplay -s localhost {$1}.wav intelligent system screen as shown in figure 7. three hydrometric gauging stations were included in the simulation, buşteni, cîmpina and prahova, all for the prahova river basin. figure 7: rain/level/flow evolution for the case study figure 8: the experimental system figure 8 shows the experimental system. in the experiments made, for each hydrological alert code it was sent via radio communication the corresponding audio alert signal (attention, alert and danger), according to the results of the analysis step. 5 conclusions as most of the hydrological processes require real time monitoring and online alerting, we have developed a microcontroller-based intelligent system that can be connected to various sensors and measuring devices for hydrological parameters measurements, and can transmit online hydrological alert codes to the decision making factor (such as emergency situations committee that exist in each county from romania). the system can be adapted to any hydrographic basin with minimal changes in its configuration. at the time the system was developed, all the data coming in was simulated, but at any time its knowledge base system can be fed with data coming from real sensors. during long time hard condition tests under simulated air humidity, simulated power fluctuations, while running a cpu stress testing program and also our simulation software, the intelligent system kept its main characteristics of flexibility and robustness, as no indicators of system failure have occurred at unix freebsd 8.0 operating system kernel level. as a future work we shall extend our experiments by using simultaneously with the radio hydrological alert transmission other communication channels such as the internet (by sending an e-mail) and the a microcontroller-based intelligent system for real-time flood alerting 851 mobile phone (by sending a sms). also, we shall analyze some complex hydrological situations by taking into account the interdependencies between different rivers flows and levels from a hydrographic basin and the meteorological forecasts, improving flood prevention due to an early hydrological alert sent to the decision making factors in order to take more efficient prevention measurements. bibliography [1] d. abbot, linux for embedded real-time applications, newnes, usa, 2003. [2] k. arnold, embedded controller hardware design, llh technology publishing, usa, 2001. [3] s. r. ball, embedded microprocessor systems real world design, 3rd edition, newnes, usa, 2002. [4] bsdi, the freebsd handbook, berkeley university of california, usa, 2000. [5] v. buruiana, wireless seismic sensor, proceedings of process control symposium spc-09, ploiesti, 2009. [6] v. buruiana, experimental research regarding the development of a microcontroller-based intelligent monitoring and alert system, research report, university petroleum-gas of ploiesti, department of informatics, 2009. [7] a. matei, intelligent algorithms for hydrographic monitoring, analysis and prediction, research report, university petroleum-gas of ploiesti, department of informatics, 2009. [8] m. oprea, a. matei, e. petre, agent-based modeling of a dam monitoring system, proceedings of 17th international conference on control systems and computer science cscs 2009, politehnica press, 509-514, 2009. [9] d. popescu, r. varbanescu, a. iordan, s. arghir, interconnection of mobile sensor nodes for alert system applications, proceedings of cscs 2009, politehnica press, bucharest, 249-254, 2009. [10] i. watson, a. d. burnett, hydrology an environmental approach, taylor & francis, crc press, 1995. [11] http://www.tgdaily.com/hardware-opinion/41525-marvells-plug-computer-a-tiny-discretefully-functional-5-watt-linux-server [12] ftp://ftp.freebsd.org/pub/freebsd/iso-images-i386/8.0/8.0-rc2-i386-disc1.iso international journal of computers, communications & control vol. i (2006), no. 1, pp. 69-71 “logic will never be the same again” kurt gödel centenary gabriel ciobanu kurt gödel (1906 1978) kurt gödel was born on 28th of april 1906 in brno, which was at that time a city of the austrian-hungarian monarchy. this year we celebrate the 100th birthday of kurt gödel, perhaps the greatest logician of the twentieth century. we mark this event by a short article on his life and scientific results. kurt gödel enrolled at the university of vienna in 1923, and his scientific evolution is strongly related to the cultural environment of vienna. as a student, gödel became interested in mathematical logic, the field to which he made his major contributions. during that period, under the strong influence of david hilbert, the mathematicians were concerned about the consistency and completeness of the formal systems used in mathematics. hilbert has noticed the strong relationship between logic and mathematics; he argued in favor of an axiomatic approach, believing that all of mathematics can be formulated based on a logical foundation, and each result in mathematics follows from a system of axioms. moreover, such an axiom system can be proved to be consistent. a formal system s is consistent whenever no contradiction is provable from s, and it is complete whenever every sentence a is decided by s in the sense that either s proves a, or s proves a. if neither a nor a is provable in s, then a is undecidable by s, and s is said to be incomplete. in his doctoral dissertation, gödel proved the completeness of first-order predicate logic, that states that any logically valid formula is provable, i.e., any sentence that holds in every model of the first-order predicate logic is derivable in the logic. the completeness theorem is an important property of first-order predicate logic. it does not hold for all logics; for instance, second-order predicate logic does not have a completeness theorem. this result was published in 1930, in “die vollstandigkeit der axiome des logischen funktionenkalkuls”, monatshefte f.math. 37, p.349-360. in 1930 gödel became a member of the university of vienna (where he remains until 1938, the year when austria became part of nazi germany). in 1931, at only 25 years old, gödel proved his most famous results, the incompleteness theorems. the first incompleteness theorem is perhaps the most surprising result in mathematical logic. it states that for any consistent formal theory that proves basic arithmetical truths, it is possible to construct an arithmetical statement copyright c© 2006 by ccc publications 70 gabriel ciobanu that is true, but not provable in the theory. that is, any consistent theory of a certain expressive strength cannot prove everything which is true, i.e. such theories are necessarily incomplete. the second incompleteness theorem states that for any formal theory s including basic arithmetical truths and certain truths about formal provability, s includes a statement of its own consistency if and only if s is inconsistent. these results showed that hilbert’s program is impossible. they were first announced by gödel at a meeting in 1930 at koenigsberg, where also hilbert and von neumann were present, and it was published in 1931 as “uber formal unentscheidbare satze der principia mathematica und verwandter systeme”, in i.monatsh.math.phys. 38, p.173-198. this paper is elegantly organized and clearly presented, progressing efficiently with no wasted energy devoted to collateral aspects. godel’s incompleteness results were simply unexpected, and their proofs, though involving new techniques, are not very difficult. their relevance to mathematical logic and in the theory of computation is preeminent and dominant. their relevance to philosophy is important, even though it is not clear yet in what way and how the things will evolve. in the philosophy of mind, lucas-penrose arguments that the human mind does not work on mechanical principles in mathematics use the incompleteness theorem. the second incompleteness theorem has prompted theologians and postmodernists to reflect why mathematics cannot prove its own consistency. gödel’s results has also interesting interpretations in the language of computer science. on the other hand, the impact of gödel’s incompleteness results among working mathematicians is not impressive. the mathematicians, although generally aware of the theoretical possibility that a problem they are working on may be unsolvable in the current axiomatic framework of mathematics, do not have difficulties in using their field results in order to find a solution. in mathematics, gödel’s incompleteness results show that in many cases, such as in number theory or real analysis, it is not possible to create a complete and consistent finite list of axioms, or even an infinite list that can be produced by a computer program. each time you add a statement as an axiom, there are other true statements which still cannot be proved as true, even with the new axiom. furthermore if the system can prove that it is consistent, then it is inconsistent. in his book “from here to infinity”, ian stewart expresses this by saying that “gödel showed that if anyone finds a proof that arithmetic is consistent, then it isn’t!”. in 1935 gödel established the relative consistency of the axiom of choice, and in 1938 that of the generalized continuum hypothesis; these results are published in “the consistency of the axiom of choice and of the generalized continuum-hypothesis”, proc.nat.acad.sci.usa 24, p.556-557, 1938. in 1940 gödel took a position at the institute for advanced study in princeton. at princeton university press he published “consistency of the axiom of choice and of the generalized continuum-hypothesis with the axioms of set theory” where he introduced the constructible universe, a model of the set theory in which the only sets which exist are those which can be constructed from simpler sets. in such a constructible universe both the axiom of choice and the generalized continuum hypothesis are true, and therefore the model should be consistent. during his many years at princeton, gödel’s interests turned to philosophy and physics. at princeton godel had a legendary friendship with einstein. regarding their walks to and from the institute for advanced studies, “logic will never be the same again” kurt gödel centenary 71 einstein said that “his own work no longer meant much; he came to the institute merely to have the privilege of walking home with gödel”. gödel demonstrated the existence of some paradoxical solutions to einstein’s field equations in general relativity. gödel’s “rotating universes” allow time travel, and caused einstein to have doubts about his own theory. in the early 1970s gödel wrote an ontological proof of god’s existence. due to his psychological disorder, in late 1977 gödel refused to eat, and thus he died of starvation in january 1978. an extensive biography of gödel can be found at the mactutor history of mathematics archive at http://wwwgap.dcs.st-and.ac.uk/ history/mathematicians/ gödel.html, which also provides a list of papers. “kurt gödel collected works” were published in five volumes at oxford university press (s. feferman et al., editors). an international symposium celebrating the 100th birthday of kurt godel was organized between 27 and 29 april 2006 by the kurt gödel society and university of vienna. this symposium commemorated the life, work, and foundational views of kurt gödel, exploring also the current research and ideas in the fields of the logic, mathematics, and computer science. the symposium has attracted important scientific personalities: p.cohen (fields medal), s.feferman (main editor of “collected works”), h.putnam (harvard), h.woodin (berkeley), g.kreisel (frs), d.scott (turing award), a.wigderson (nevanlinna prize), c.papadimitriou (knuth prize), j.d.barrow (kelvin medal), r.penrose (wolf prize). the talks presented various aspects related to logic, mathematics, computer science, artificial intelligence, cosmology, philosophy, and theology. there were a lot of discussions, including difficult questions, debatable answers, and polemics. according to a selection process, two submissions of romanian authors were accepted to be presented at this symposium: g.ciobanu (iasi) with a paper regarding a new characterization of computable real numbers, and l.leustean (darmstadt) with a paper on “proof mining”, a technique of getting new knowledge from proofs in mathematics. the volumes devoted to this symposium will be published in the series collegium logicum of the kurt gödel society, and by cambridge university press. gabriel ciobanu http://thor.info.uaic.ro/ gabriel e-mail: gabriel@iit.tuiasi.ro editor’s note about the author: gabriel ciobanu institute of computer science romanian academy, iaşi born: 5 july 1957, piatra neamţ. dr. gabriel ciobanu has wide ranging interests in computing including distributed systems, theory of programming and computational aspects in biology (molecular interaction, membrane computing). he has edited/authored 6 books and over 100 papers on these topics. he enjoy developing new ideas, both the practical ones and the more abstract ones, drawing on both his computer science and mathematical background. over the years he has received public recognition for his research, including the 2004 “octav mayer” award, and the 2000 “grigore moisil” award of the romanian academy of sciences. he was a visiting academic to edinburgh university, paris xi, tohoku university, and national university of singapore, among others. he is a member of some professional associations and societies (eatcs, eapls, ams, iscb), and he have been in the program committees for many international conferences. international journal of computers, communications & control vol. iii (2008), no. 1, pp. 33-40 learning about learners: system learning in virtual learning environment zhengxin chen editorial note: the program committee of icvl 2007 sent to ijccc nine of the best papers presented at the conference, with the recommendation to be published in an extended form. after a new evaluation, our reviewers decided that five of those papers can be published in ijccc, two of which in this issue. abstract: virtual learning is not just about a set of useful it tools for learning. from an examination on where virtual learning stands in the overall learning spectrum, we point out the important impact of natural computing on virtual learning. we survey and analyze selected literature on important role of natural computing aspects, such as emergence (using swarm intelligence to achieve collective intelligence) and emotion, to virtual learning. in addition, in order to effectively incorporate these aspects into virtual learning, we propose using infrastructural support for virtual learning through system learning: the virtual learning environment not only provides facilities for learners, but also observes the behavior of learners and takes actions, so that its own performance can be improved (i.e., to better serve the learners). in this sense, system learning is concerned with learning about learners. consequently, a virtual learning environment is a true human-machine symbiosis, paired by human learning and system learning. keywords: natural computing, collective intelligence, emergence, emotion, system learning, virtual learning architecture 1 introduction as a software system, a virtual learning environment is intended to offer a virtual environment for learning where the learning process is based on information technology (it). virtual learning environment facilitates computerized learning or computer-enhanced learning (e-learning). many projects in virtual learning have been designed to facilitate teachers in the management of educational courses for their students, especially by helping teachers and learners with course administration. the system can often track the learners’ progress, which can be monitored by both teachers and learners. with advanced learning technology (alt) it supports, virtual learning makes learning as a life-long journey easier to achieve than anytime else in history, and the entire world now becomes an open university. however, although virtual learning has started producing fruitful results, fundamental issues related to virtual learning is seldom examined. related to this is the pulic misconception that virtual learning is just a set of it (information technology) tools or techniques. to address this problem, in this paper we first take a look at the learning spectrum, so that we can have a better understanding about where virtual learning stands in this big picture (section 2). since this examination reveals the importance of natural computing for virtual learning, we provide a brief review on natural learning (section 3). we then examine two important features related to natural computing, namely, emergence and emotion, and review existing work relevant to virtual learning (section 4 and section 5). the introduction of natural computing in virtual learning necessities the concept of system learning; its important role is examined (section 6). we conclude this paper by indicating the need for investigating a virtual learning architecture (or a framework) where human virtual learning is paired with system learning (section 7). copyright © 2006-2008 by ccc publications 34 zhengxin chen 2 the learning spectrum virtual learning is concerned with it assisted human learning and much of research work in virtual learning addresses various technical issues to enhance human learning. however, any discussion concerned with learning using contemporary it techniques is incomplete if we do not take a look on the issue of machine learning (http://robotics.stanford.edu/people/nilsson/mlbook.html), a subfield in ai devoted to developing algorithms for enhanced performance of computers. although virtual learning (for humans) and machine learning are two separate research areas, they are both related to learning with computers. so if we examine them together, we should have a better understanding about where virtual learning stands in the big picture. this broad perspective raises an important issue: virtual learning should not be simply viewed as just a set of it tools (or techniques) to assist learning or education. it reminds us the importance of examining fundamental issues related to learning, such as nature of intelligence, various forms of intelligence, consciousness and thinking, as well as recent research progress related to brain and mind, and even study activities observed in various forms of life not just humans, but animals as well, so long as they demonstrate a kind of learning ability. these studies could shed meaningful insight for developing better virtual learning environments. as noted in [16], in recent years educators have explored links between classroom teaching and emerging theories about how people learn. recommended educational approaches, consist primarily of trying to maintain a relaxed, focused atmosphere that offers options for learning in individually satisfying ways. one thing we must be kept in mind is that the brain is complex and while research has revealed some significant findings, there is no widespread agreement about their applicability to the general population or to education in particular. nevertheless, brain research provides rich possibilities for education [16]. articles in the magazine scientific american minds (http://www.sciammind.com/) also shed interesting lights on brain/mind research and education. the issue of studying various forms of learning activities leads to the next question: is anything setting in between virtual learning and machine learning? although this may be an open question which is subject to debate, we would like to offer a possible answer: learning from nature through natural computing, which is the computational version of the process of extracting ideas from nature to develop "artificial" (computational) systems ("artificial" means human-made). since natural computing is aimed to model the nature or even compute with the nature, it looks for intellectual inspiration from all forms of life (not restricted to human beings) such as flocks or ants. since natural computing sets between the research of "full human" (i.e., virtual learning) and non-human (i.e., machine learning), it fits in the missing link in the learning spectrum. amending the hierarchical diagram provided by [1], we have the learning hierarchy as shown in figure 1. (note: for more details on various forms within distance learning, refer to the original figure in [1]). figure 1: learning spectrum from machine learning to various forms of virtual learning but why should we bother natural computing at all in the context of virtual learning? the answer is learning about learners: system learning in virtual learning environment 35 simple: it may provide new interesting ideas not addressed in traditional learning or education context, so that virtual learning can benefit from that. note that natural computing is not new to virtual learning community: as we are going to see soon, projects using various natural computing techniques have been conducted for virtual learning. yet there is a need to conduct more systematic research on the rich impact of natural computing in virtual learning. such kind of investigation will eventually benefit the study of natural computing as well, because the diverse applications in virtual learning extend the horizon of natural computing. 3 basics of natural computing in order to discuss how natural computing can help, first we have to provide a brief introduction on what natural computing is. there are numerous resources available for natural computing. for beginners, [4] provides a comprehensive coverage on the major fields with natural computing. the brief review in this section is based on that book. the philosophy of natural computing lies in that most of computational approaches natural computing deals with are based on highly simplified versions of the mechanisms and processes present in the corresponding natural phenomena. research work in natural computing can be grouped into three major categories, namely, computing inspired by nature, simulation and emulation of natural phenomena in computers, and computing with natural materials. since the last one does not have direct impact on virtual learning (at least for now), we will not address it here. the first category, computing inspired by nature, refers to making use of nature as inspiration for the development of problem solving techniques. the main idea is to develop computational tools (algorithms) by taking inspiration from nature for the solution of complex problems. the diverse areas (or approaches) under this category include evolutionary computing, neurocomputing, swarm intelligence, etc. swarm intelligence refers to a property of systems of unintelligent agents of limited individual capabilities exhibiting collectively intelligent behavior, and has drawn attention from researchers to find useful applications in virtual learning (as to be briefly described later). the second category, simulation and emulation of natural phenomena in computers, refers to a synthetic process aimed at creating patterns, forms, behaviors, and organisms that (do not necessarily) resemble "life-as-we-know-it." its products can be used to mimic various natural phenomena, thus increasing our understanding of nature and insights about computer models. an interesting area under this line of research is artificial life, which is the study of man-made systems that exhibit behaviors characteristic of natural living systems. it has been hoped that by extending the empirical foundation upon which biology is based beyond the carbon-chain life that has evolved on earth, artificial life can contribute to theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could be. regardless of computing inspired by nature or simulation and emulation of natural phenomena in computers, there are several general concepts underlying various approaches in natural computing, such as agent, parallelism and distributivity, interactivity, adaptation, feedback, self-organization, emergence, etc. in addition, emotion is demonstrated not just in human beings, but many species of animals as well. emotion has also been studied in artificial life. various natural computing concepts have already been used by authors working in virtual learning. much effort has been put on researchers’ "traditional" favorite such as adaptation. yet successful employment of natural computing in virtual learning goes far beyond adaptation. below we examine selected literature involving two crucial elements of natural computing and provide comments on their relevance to virtual learning: emergence and emotion. 36 zhengxin chen 4 exploring emergence for virtual learning using simple terms, emergence can be defined as "a coming into view." however, emergence is definitely not a simple concept, and the computational study on emergence [6] is still in its infancy. as a typical demonstration of computational approach for emergence, swarm intelligence is a property of systems of unintelligent agents of limited individual capabilities exhibiting collectively intelligent behavior. swarm intelligence includes any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insects and other animal societies. swarm intelligence is an emergent property of the swarm system as a result of principles of the five principles: proximity, quality, diversity, stability and adaptability. two main lines of research in swarm intelligence are either based on social insects, or based on the ability of human societies to process knowledge [4]. the ability of ants to find short routes between nests and food sources suggests an approach to cost-effective, flexible and implementable wayfinding support. paths identified by ants are not preplanned, but emerge, spontaneously, as a result of indirect communication between members of an ant colony a form of indirect social navigation. ants deposit a chemical substance called pheromone which can be sensed by other ants, thus achieving a kind of stigmergy, which refers to the process of indirect communication. this property can be very useful for virtual learning. in a virtual learning environment considered by [17], learners’ interactions with learning resources and activities are recorded automatically as they progress through a body of knowledge. the time stamping of these interactions allows learning sequences to be identified which can be processed and aggregated to derive a given "pheromone strength" favoring paths along which more learners have been successful. this information can be fed back to other learners, providing a new source of navigational guidance indicating "good" ways through the body of knowledge a self-organizing, stigmergic approach to wayfinding support. in another experiment by [15], ant colony optimization (aco) heuristics was applied to an e-learning problem: the pedagogic material of an online teaching web site for high school students is modeled as a navigation graph where nodes are exercises or lessons and arcs are hypertext links. the arcs’ valuation, representing the pedagogic structure and conditioning the web site’s presentation, is gradually modified through the release and evaporation of virtual pheromones that reflect the successes and failures of students roaming around the graph. a compromise is expected to emerge between the pedagogic structure as originally dictated by professors, the collective experience of the whole pool of students and the particularities of each individual. collective behavior as demonstrated from ant colonies and simulated in computer programs exemplifies collective intelligence (or symbiotic intelligence), an intelligence that emerges from the collaboration and competition of many individuals – an intelligence that seemingly has a mind of its own. yet emergence is not restricted in collective intelligence. in general, emergence refers to the way complex systems and patterns arise from a multiplicity of relatively simple interactions [4]. two influential monographs on emergence should find profound impact on virtual learning: through a series of narratives to show complex adaptive systems that display emergent behavior governed by small sets of local rules, the discussion in [8] may shed useful intuitive thoughts on infrastructural support to achieve emergence in virtual learning, while [6] provides more technical insights on modeling issues of emergence in a more general, abstract setting, where emergence is explained through a reductionist perspective. we summarize system learning with the following features related to emergence. exploring emergent properties using swarm intelligence and other techniques should be continued and strengthened for research virtual learning. yet, current research related to emergence in virtual learning is largely confined in specific tasks. more systematic studies of the role of emergence in virtual learning are needed, particularly those related to the overall infrastructure of virtual learning. in addition, in the context of learning/education (including virtual learning), there is a need to distinguish emergence as a process (such as emerging ideas) from emergence as a product (such as an emerging pattern) a feature which has not attracted enough attention it deserves. here is a partial list of issues to be studied about emergence related to virtual learning: learning about learners: system learning in virtual learning environment 37 nature of emergence (relevant to learning), such as: what are the relationships and differences between emergence and other features related to learning and discovery? what can be achieved through emergence? in the case of emerging ideas: o specific creative task (such as construction of analogs through emergence for analogical problem solving) what can be achieved through emergence? in the case of emerging "products": o global solution (such as an optimization of student pedagogical path); o solution for individuals (learning by taking advantage of emergence), such as how to come up with creativity thoughts in general. in addition, there is a more general question: is there a need to have a dedicated software component at system level to support emergence in a virtual learning environment? if yes, how to achieve this? 5 exploring emotion in virtual learning recently the importance of emotion in education has drawn attention from researchers. for example, according to [16], educators may find the most useful information in research that focuses less on the physical and biochemical structure of the brain and more on the mind – a complex mix of thoughts, perceptions, feelings, and reasoning. studies that explore the effects of attitudes and emotions on learning indicate that stress and constant fear, at any age, can circumvent the brain’s normal circuits. a person’s physical and emotional well-being is closely linked to the ability to think and to learn effectively. emotionally stressful home or school environments are counterproductive to students’ attempts to learn. while schools cannot control all the influences that impinge on a young person’s sense of safety and well-being, classrooms and schools that build an atmosphere of trust and intellectual safety will enhance learning. letting students talk about their feelings can help them build skills in listening to their classmates’ comments. finding ways to vent emotions productively can help students deal with inevitable instances of anger, fear, hurt, and tension in daily life. in an experimental study, the author of [12] interviewed eleven students studying online. these students identified emotions which were critical to their online learning. in order to better understand where emotion stands in learning and education, it would be beneficial to take a look at the two books on general aspects of emotions with quite different emphases. from a psychological perspective, the experimental research of [9] showed that emotion can occur without cognitive processing in the cortex. in particular, we can learn some general principles of emotions by studying fear. in evolutionary terms, "fearless" animals would have been less likely to survive. the author further demonstrates that fear can be related to learning and fear learning is implicit. although by no means we should endorse any kind of "learning through fear," this example does indicate emotion can have impact on education in a controlled manner, and with more secretes of emotion to be revealed in the future, some of the previously unknown principles involving emotions can be incorporated into virtual learning environment. published one decade later, from a computational perspective, [10] aimed to establish a theory of how emotions get created. according to this theory, each emotional state is a different style of thinking. so there is no general theory of emotions, because the main idea is that each of the major emotions is quite different. for an adult person, the management is able to use these different ways of thinking very quickly as part of ordinary, common-sense thinking. what is the indication of this discussion to learning/education? the notion of emotion as a theory of thinking implies a potential opportunity to incorporate emotion into "mainstream" education theories, including those related to virtual learning. 38 zhengxin chen in a recent comprehensive volume directly address the issue of emotion in education [14], various theoretical perspectives on emotions in education have been examined, include the discussions on control-value theory of achievement emotions, self-regulation and social-constructivist learning, emotions as a main component of attributional theory, implications of goal-theory for achievement-related emotions, macro-cultural psychology, etc. the theoretical work is complemented with sets of studies on students’ emotions in educational context, as well as teachers’ emotions in educational contexts. the comprehensive of research on emotion in education as described in this volume sheds light for future work of dealing with the emotion factor in a virtual learning environment (even the book does not address issues directly related to virtual learning). as the literature surveyed above shows, so far the important issue of emotion for education is still largely discussed at the traditional classroom setting. nevertheless researchers have started addressing this issue in the context related to virtual learning. for example, [3] presents an analysis of the issues pertaining to computational emergence and emotion in (cognitive) agent systems and describes how a developing computational theory of cognition can be used to monitor and manage interactions with and within complex systems; this would harness unwanted and emergent states and behaviors before the computational system becomes dysfunctional. in another work, [13] describes a modular hybrid neural network architecture, called shame, for emotion learning. in addition, computational experiments on emotion also exist. for example, [7] proposes the architecture of learning companion agent with facial expression of emotion. based on architectures referred to as abc and tok, the emotion agent architecture contains five modules to realize the interaction in the world. a particular part of this research is the transition between emotion space in emotion module and facial expression space in facial expression module. although this work is not directly related to virtual learning, it represents an interesting step toward computational emotion, and may shed light to the study of emotion in virtual learning. summarizing the above discussion, we note that current research status shows that emotion for virtual learning is a vast area yet to be systematically explored. the following are some sample issues need to be investigated: from a learning perspective, how many types of emotions can be distinguished? under which conditions certain type of emotions should be controlled and under which conditions educators can take advantage of it? what are basic operations of emotions (such as filter out, enlargement, etc.) related to learning and how to develop computational mechanisms to support them? finally, just like the case of the discussion related to emergence, we may wonder whether there is a need to implement any forms of emotion at the system level in virtual learning. 6 learning about learners: the role of system learning summarizing our discussion in the last two sections, if we agree that natural computing can make significant contributions to virtual learning, and important features such as emergence and emotions should be incorporated into the virtual learning process, then we have to provide infrastructural support in virtual learning environments. (in other words, we would like to have a positive answer for the last question appearing at the end of section 4 and section 5.) this can be achieved through system learning: the virtual learning environment not only provides facilities for learners, but also observes the behavior of learners and takes actions, so that its own performance can be improved (i.e., to better serve the learners). in this sense, system learning is concerned with learning about learners. (note that the term system learning may not necessarily new to virtual learning; but it has a new meaning as it is presented here.) among the basic functionalities of system learning are: trace activities of learners, learning about learners: system learning in virtual learning environment 39 apply various natural computing algorithms to support specific tasks of virtual learning (such as find a global solution or to find a solution for an individual learner), invoke appropriate operators to deal with emotions at various degrees to learners with different profiles, notify teachers to change strategies based on learners’ performance, coordinate all the activities in the virtual learning environment, as well as others. consequently, a virtual learning environment is a true human-machine symbiosis, paired by human learning and system learning. in order to support system learning, a number of issues need to be examined. here we list two of them. first, system learning is not just an abstract concept; it has to be implemented. it consists of selfadjustable software distributed in the entire virtual learning environment. as a second issue, we point out the important role of a system-wide database for storage and retrieval of learners’ behavior, based on which various natural learning algorithms can be applied, so that new solutions can emerge. note that the important role of database management in virtual learning environment [5] is rarely examined in literature, and this should be changed in the future. 7 conclusion in this paper we examined virtual learning in the comprehensive learning spectrum, and emphasized the importance of natural computing for virtual learning. within this context, we also discussed important features of emergence and emotions. in addition, our introduction of natural computing into virtual learning makes the new concept of system learning necessary. this also implies the need for investigating a virtual learning architecture (or at least, a framework, in a sense similar to [11]) where human virtual learning is paired with system learning. significant amount of work is needed for this direction of research. acknowledgement this is the extended version of an earlier paper [2] appeared in international conference of virtual learning (icvl 2007). the author thanks technical program committee (chaired by dr. grigore albeanu) of icvl 2007 for recommendation of publishing this paper in ijccc. bibliography [1] a. anohina, the terminology used in the fields of virtual learning, analysis of the terminology used in the field of virtual learning, educational technology & society, vol. 8, no. 3, pp. 91-102, 2005 (http://www.ifets.info/journals/8_3/9.pdf). [2] z. chen, learning from nature: natural computing meets virtual learning, proc. 2nd int’l conf. virtual learning (icvl 2007), pp. 117-124, 2007. [3] d. n. davis, computational emergence and computational emotion, proc. ieee conf. systems, man and cybernetics (smc), 1999. (http://www2.dcs.hull.ac.uk/neat/dnd/papers/smc99.pdf) [4] l. n. de castro, fundamentals of natural computing: basic concepts, algorithms, and applications, chapman & hall/crc, 2006. [5] a. dong and h. li, ontology-based information integration in virtual learning environment, proc. ieee/wic/acm int’l conf. web intel. (wi’05), 2005. [6] j. h. holland, emergence: from chaos to order, oxford university press, 1998. 40 zhengxin chen [7] c.-c. huang, r. kuo, m. chang, j.-s. heh, foundation analysis of emotion model for designing learning companion agent, proc. ieee int’l conf. advanced learning technologies (icalt), pp. 326 330, 2004. [8] s. johnson, emergence: the connected lives of ants, brains, cities and software, penguin books, 2002. [9] j. ledoux, the emotional brain, simon and schuster, 1996. [10] m. minsky, the emotion machine: commonsense thinking, artificial intelligence, and the future of the human mind, simon and schuster, 2006. [11] a. nguyen, t. nakano, t. suda, learning from nature: network architecture inspired by biology, acm crossroads, 2004 (http://portal.acm.org/citation.cfm?id=1144389.1144391). [12] k. o’regan, emotion and e-learning, journal of asynchronous learning networks, vol. 7, no. 3, 2003. [13] m. poel, r. op den akker, a. nijholt, a. j. van kesteren, learning emotions in virtual environments. proc. european meeting on cybernetics and systems research (emcsr), 2002. [14] p. shutz, r. pekrun, g. d. phye (eds.), emotion in education, academic press, 2007. [15] y. semet, e. lutton, p. collet, ant colony optimisation for e-learning: observing the emergence of pedagogic suggestions, proc. ieee swarm intelligence symposium (sis), pp. 46 52, 2003. [16] southwest educational development laboratory, how can research on the brain inform education? classroom campus, vol. 3, no. 2 (http://www.sedl.org/scimath/compass/v03n02/1.html). [17] b. van den berg, r. van es, r., c. tattersall, j. janssen, j. manderveld, f. brouns, h. kurvers, r. koper, swarm-based sequencing recommendations in e-learning, proc. 5th international conference on intelligent systems design and applications (isda’05), pp. 488-493., 2005 zhengxin chen university of nebraska at omaha department of computer science omaha, ne 68182-0500, usa e-mail: zchen@mail.unomaha.edu received: november 28, 2007 zhengxin chen received phd degree in computer science from louisiana state university in 1988. he is interested in a number of issues related to intelligent systems, database management systems, and data mining. among his publications are three books: computational intelligence for decision support, data mining and uncertain reasoning: an integrated approach and intelligent data warehousing. he also co-edited several books, includingintelligent agents and their applications in systems design and data mining and knowledge management. ijcccv4n1draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 1, pp. 27-40 the impact of java applications at microarchitectural level from branch prediction perspective adrian florea, arpad gellert, lucian vinţan, marius velţan adrian florea, arpad gellert, lucian n. vinţan "lucian blaga" university of sibiu, computer science department emil cioran street, no. 4, sibiu 550025, romania e-mail: {adrian.florea,arpad.gellert,lucian.vintan}@ulbsibiu.ro marius n. velţan sc imc information multimedia communications srl sibiu, product development department cristian street, no. 21, sibiu 550073, romania e-mail: marius.veltan@im-c.de abstract: the portability, the object-oriented and distributed programming models, multithreading support and automatic garbage collection are features that make java very attractive for application developers. the main goal of this paper consists in pointing out the impact of java applications at microarchitectural level from two perspectives: unbiased branches and indirect jumps/calls, such branches limiting the ceiling of dynamic branch prediction and causing significant performance degradation. therefore, accurately predicting this kind of branches remains an open problem. the simulation part of the paper mainly refers to determining the context length influence on the percentage of unbiased branches from java applications, the prediction accuracy and the usage degree obtained using a fast path-based perceptron predictor. we realize a comparison with c/c++ application behavior from unbiased branches perspective. we also analyze some java testing programs, built using design patterns or including inheritance, polymorphism, backtracking and recursivity, in order to determine the features of indirect branches, the arity of each indirect jump and the prediction accuracy using the target cache predictor. keywords: object-oriented programs, branch prediction, indirect jumps/calls, unbiased branches, benchmarking 1 introduction as the internet evolves and becomes increasingly popular, the need for a portable programming language becomes increasingly important. providing a common language interface java technology, that includes java language, java virtual machine (jvm) and java api, allows programs to be used across a wide range of machine platforms from browsers, e-commerce, financial and bioinformatics applications on desktop computing systems to software for real-time embedded systems (intelligent mobile phones, palms, pda, etc.). the portability, the object-oriented and distributed programming models, multithreading support and automatic garbage collection are features that make java very attractive for application developers. in addition to portability, security and ease of application development has made it very popular with the software community. according to sun microsystems, in 2007 there were more than 5 billion java enabled devices (desktops, phones, cards, settop boxes, etc), from among 2.1 billions are phone handsets or pda. also, more than 6 million professional java developers are programming these devices [1]. copyright © 2006-2009 by ccc publications 28 adrian florea, arpad gellert, lucian vinţan, marius velţan besides, the last decade is characterized by the advance of visual or object-oriented applications and the portability trend of many of them, as well as the usage of dynamically-linked libraries. to support polymorphism the object-oriented languages such as java, c#, and c++ include dynamically-dispatched function calls (i.e. virtual functions) whose targets are not known until run-time because they depend on the dynamic type of the object on which the function is called. virtual function calls are implemented using indirect jump/call instructions in the instruction set architecture (isa). from microarchitectural point of view object-oriented programming techniques exercise different aspects of computer architecture to support the object-oriented programming style [2], [3]. the object-oriented languages have significantly more indirect jumps than procedural languages. in addition to virtual function calls, indirect jumps are commonly used in the implementation of programming language constructs such as switch-case statements (with more than five options [3]), jump tables, indirect calls through function pointers, and interface calls [4]. in current pipelined processor designs a particularly difficult challenge consists in target prediction for indirect jumps and calls. because the target of an indirect jump (call) can change with every dynamic instance of that jump, predicting the target of such an instruction is really difficult. such hard-to-predict jumps not only limit processor performance and cause wasted energy consumption but also contribute significantly to the performance difference between procedural and object-oriented languages. simulations made on intel core2 duo processor analyzing suggestive applications such matlab, cygwin, excel, firefox, winamp and internetexplorer show that about 41% of all jump mispredictions are due to indirect jumps [5]. besides indirect jumps/calls, another class of hard-to-predict branches consists in unbiased branches. in two of our previous papers [6], [7] we found a minority of dynamic conditional branches showing a low degree of polarization towards a specific prediction context since they tend to shuffle between taken and not-taken. we called them unbiased branches and we have demonstrated that, irrespective of the prediction information length and type, used in the state of the art branch predictors, these branches are characterized by low prediction accuracies (at average about 70%). we have demonstrated that actual programs have a significant fraction of such difficult to predict branches that severely affect prediction accuracy leading to performance degradation and additional power consumption, which are very important especially in the embedded systems. the main goal of this paper consists in pointing out the impact of java applications at microarchitectural level from two perspectives: unbiased branches and indirect jumps/calls, such branches limiting the ceiling of dynamic branch prediction and causing significant performance degradation. therefore, accurately predicting this kind of branches remains an open problem. following our aim, first we extend our tool advanced branch prediction trace driven simulator (abps) [8] for analyzing (detecting and predicting) unbiased branches from spec jvm98 benchmarks [9], a suite of eight standardized java applications proposed to better understand where performance bottleneck exist in the java virtual machines and what optimizations are possible. we determine the context length influence on the percentage of unbiased branches from java applications, the prediction accuracy and the usage degree obtained using a fast path-based perceptron (fpbp) predictor [10]. also, we realize a comparison with the behavior of c/c++ applications from unbiased branches perspective. the differences between object-oriented and procedural applications from execution viewpoint on ilp processors guided us to analyze some java testing programs built using design patterns. based on these java applications we illustrated that the two computer science components (software and hardware) are just apparently "disjointed". thus, in section 4 we describe six well-known design patterns [11], [12] and applications developed based on them. additionally, we created four simple applications that include inheritance, polymorphism, backtracking and recursivity. these programs were execution-driven simulated on "dynamic simplescalar" environment (dss) [13] in order to determine the features of indirect branches, the arity of each indirect jump and the prediction accuracy using the target cache predictor. the remainder of this paper is organized as follows. in section 2 we illustrated comparatively the the impact of java applications at microarchitectural level from branch prediction perspective 29 c/c++ and java languages, emphasizing especially the differences between them from the execution viewpoint on host architectures. section 3 describes the implemented predictors used for studying unbiased branches and indirect jumps/calls. section 4 includes simulation methodology and presents the benchmarks used for simulation. in section 5 we illustrate the experimental results obtained using our developed simulator. finally, section 6 suggests further work directions and concludes the paper. 2 some differences between c/c++ and java languages c++ programming language was designed and implemented as an extension of c language that supports data abstraction, object-oriented concepts and generic programming, dedicated for desktop programming. unfortunately, it inherited also all problems from c. java was created as a new object-oriented and distributed language, without compatibility constraints, mainly dedicated for small electronic devices that embeds networking capabilities and has cross platform portability and also for internet applications programming. in java were removed all the procedural programming concepts that proved to be dangerous or unsecured. unfortunately, the portability leads to decreasing the execution speed of java programs. thus, it was observed that the interpreted java bytecode is 30 times slower than an optimized c++ code. however, just in time (jit) compilers could produce considerable performance improvements over interpretation (20 times) by removing dispatch overhead and applying some optimizations to the generated code. the classes used by server applications are executed using jit compilers in order to obtain high processing performance. though, the memory required by jit compilers (additional space for the generated native code, for profiling and other data structures) is very expensive especially in the case of embedded systems. for these systems are preferred native java processors that use small memory and have a small power consumption, and last but not least, have small costs, easy to support by any consumer. from this reason, the modern researches try to optimize the java virtual machines. using server applications, when sun jvm was updated from the 3rd version to the 6th version the processing performance was improved with 344%. similarly, using desktop applications, the performance increases but not very spectacular [1]. nowadays java virtual machines use even dynamic metrics that optimize the jvm taking into account not only the host processor but also the applications’ behavior. there are applications that run better if they are written in java than in c++. simulation results on the scimark benchmark have shown that java runs with 4% quicker than c++, and linpack benchmarks are slower with only 2% in java against c++. also, the garbage collector is much quicker than dynamic memory allocation / de-allocation realized with the malloc and free c++ instructions [1]. thus, after releasing the last jvm version (jre 6.0), sun microsystems engineers claim that they destroyed the myth interpreted language means not performing. 3 predictors implemented for studying unbiased branches and indirect jumps/calls for indirect branch prediction we considered a tagged target cache (tc) predictor first introduced by chang [15]. the target cache improves the prediction accuracy for indirect branches by choosing its prediction from the last n targets of the indirect branch that have already been encountered. when an indirect jump is fetched, both the pc and the globalhr (a history register that retains the behavior of last k conditional branches) are used to access the tc for predicting the target address. as the program executes, the tc records the target for each indirect jump target encountered. our proposed scheme uses for set selection the least significant bits of the word obtained by hashing (xor) the indirect jump’s address (pc) and the global history (globalhr). the most significant bits of this hashing form the tag. in the case of a hit in the tc the predicted target consists in the corresponding address belonging to that tc set (field adr from figure 1). in the case of a misprediction, (the tags coincide but the target addresses 30 adrian florea, arpad gellert, lucian vinţan, marius velţan differ) after the indirect branch is resolved, the target cache entry is updated with its real target address. we have implemented and simulated a p-way set associative tc, where p=1, 2, 4 like that presented in figure 1. in the case of a miss in the tc the prediction is considered wrong, it does not provide any value and a new entry updated with the proper tag and target is added to the respective set, according with the implemented lru replacement algorithm. figure 1: the structure of target cache predictor the most accurate single-component branch predictors in the literature are neural branch predictors [10], [16] and [17]. their main advantages consist in the possibility of using longer correlation information at linear cost. the perceptron predictor the simplest neural branch predictor keeps a table of weight vectors (small integers that are learned through the perceptron learning rule) [16]. as in global two-level adaptive branch prediction, a shift register records a global history of conditional branche outcomes, recording true for taken, or false for not taken. to predict a branch outcome, a weight vector is selected by indexing the table with the branch address modulo the number of weight vectors. the dot product of the selected vector and the global history register is computed, where true in the history represents 1 and false represents -1. if the dot product is at least 0, then the branch is predicted taken, otherwise it is predicted not taken. once the perceptron output has been computed, the training algorithm starts: it increments the i-th correlation weight when the branch outcome agrees with the i-th bit from the global branch history shift register and decrements the weight otherwise. unfortunately, the high latency of the perceptron predictor and its impossibility to predict the linearly inseparable branches makes it impractical yet for hardware implementation. in order to reduce the prediction latency, the fast path-based perceptron [10] chooses its weights for generating a prediction according to the current branch’s path, rather than according to the branch’s pc and history register. the prediction latency is hidden due to the speculative calculation of the perceptron’s output. let pc be the current branch address, and pci be the ith most recent branch address in the path history. for the perceptron, each weight is chosen with the same index based on pc. for the fpbp, each weight wi is chosen based on an index derived from pci. this provides path history information that can improve prediction accuracy, and spreading out the weights in different entries also helps to reduce the impact of inter-branch aliasing. to implement the fpbp, the lookup phase is actually pipelined over many stages based on the overall branch path / global history length. undertaken from [18], figure 2 exposes a very intuitive scheme of fast path-based perceptron. for a branch at cycle t, the fpbp starts the prediction at cycle t h using pch. for each cycle after t h, the fpbp computes partial sums of the dot-product of weights vector and global history register. pipeline stage i contains the partial sum for the branch prediction that will be needed in i cycles. at the very end of the pipeline, the critical lookup latency consists of looking up the final weight and performing the final addition. the impact of java applications at microarchitectural level from branch prediction perspective 31 figure 2: the structure of fast path-based perceptron predictor 4 simulation methodology and benchmarking related to our evaluations on procedural programs, we collected results from different versions of spec benchmarks: 3 integer (li, go, cc1) and 4 floating point (applu, apsi, fpppp, hydro) spec’95 benchmarks. from the integer spec2000 suite, we simulated 8 benchmarks (gzip, b2zip, parser, crafty, gap, gcc, twolf and mcf ). we also simulated some spec’95 benchmarks in order to compare their behavior with that of the more recent spec2000. all these benchmarks cover a lot of applications ranging from compression (text/image) to word processing, from compilers and architectures to games enhanced with artificial intelligence, etc. these programs were selected based on their characteristics: a high number of indirect branches and high target entropy. recall that the majority of indirect branches are generated by object oriented programs. in software engineering, after structured programming and object orientation, one of the most important innovation with impact on the art of constructing software systems were design patterns [11]. it is hard to understand and practice effective object orientation without being conscious of design patterns and their suitability for the problem at hand. according to [19] a design pattern represents a formal way of documenting a solution to a design problem in a particular field of expertise. in software engineering, a design pattern is a general reusable solution to a commonly occurring problem in software design. using real-life scenarios helps in understanding programming abstraction to some extent but do not provide the deep insight that one gets by actually seeing these abstractions. a design pattern is not a finished design that can be transformed directly into code. patterns show how to build systems with good object oriented design qualities. most patterns allow some part of a system to vary independently of all other parts. patterns provide a share language that can maximize the value of communication between developers. for indirect branch analysis of java applications we simulated six design pattern applications [11], [12] and also four other simple object-oriented programs: jbytemark that sorts arrays of values through many sorting methods, queens_5 that uses inheritance and polymorphism for solving different problems through recursive backtracking, switch_06 that illustrates the indirect jump generation from switch/case statements and simple_inheritance that uses polymorphism and treats uniformly the heterogenic array of objects. table 1 illustrates the characteristics of the design pattern applications [11], [12] used for indirect branch analysis. the results related to the indirect branch analysis illustrated in chapter v were obtained after simula32 adrian florea, arpad gellert, lucian vinţan, marius velţan table 1: design pattern characteristics design pattern application characteristics strategy ducksimulator it is a behavioral pattern that defines a family of algorithms, encapsulate each one, and make them interchangeable. strategy lets the algorithm vary independently on the clients that are using it. example: adventure game observer weatherstation it is a behavioral pattern that defines a one-tomany dependency between objects so that when one object’s state changes, all its dependents are notified and updated automatically. example: newspaper subscription decorator starbuzzcoffee it is a structural pattern that dynamically attaches additional responsibilities to an object. decorators provide a flexible alternative to sub-classing for extending functionality. example: dressing a character in a social game factory method preparingpizza it is a creational pattern that defines an interface for creating an object, but let subclasses decide which class to instantiate. example: factory methods template method serveingdrinks it is a behavioral pattern that defines the skeleton of an algorithm in an operation, deferring some steps to subclasses. template method lets subclasses redefine certain steps of an algorithm without changing the algorithm’s structure. example: searching in a database state gumball machine test it is a behavioral pattern that allows an object to alter its behavior when its internal state changes. the object will appear to change its class. example: automated teller machine. the impact of java applications at microarchitectural level from branch prediction perspective 33 tion on 2.4 ghz pentium iv microprocessor under microsoft windowsxp operating system, disposing of cygwin emulator. taking into account that the simulation time of over 600.000.000 dynamic instructions from aforementioned java benchmarks vary between 35 minutes (queens_5) and more than 24 hours (jbytemark) we chose to limit the simulation to maximum 10 billion instructions. from the spec’95 suite we simulated 100 millions instructions, from the spec2000 set we simulated 500 millions instructions, whilst all java applications were completely simulated (except jbytemark benchmark), the total number of instructions ranging between 600 millions and 4290 millions instructions, respectively. for indirect jump/call analysis we developed a cycle-accurate execution driven simulator (sim-jindir [12]), derived from the sim-cache simulator of the dynamic simplescalar toolset [13]. we modified it to incorporate the indirect jump predictor proposed in section 3 in order to measure the number of indirect (static / dynamic) branches, the arity of each indirect jump and to predict targets of indirect jumps and calls, respectively. dss represents a standardized software tool used to simulate java programs executed on a java virtual machine (in our case ibm jikes research virtual machine) and was created to extend the simplescalar toolset [20]. jikes rvm is a virtual machine that runs java programs by compiling them to native code at runtime. it comes with two java bytecode to native code compilers. the fast baseline compiler does not perform any optimizations. the optimizing compiler performs a complete set of optimizations most importantly inlining and register allocation. the sim-jindir provides a wider variety of configuration options. we can vary the tc associativity degree, the number of sets of tc, the maximum number of instructions executed, the simulated benchmark, and the platform used for simulation. table 2 illustrates the characteristics of standardized specjvm98 benchmarks [21] used for unbiased branch analyze. abps is a trace driven simulator used to analyze (detect and predict) unbiased branches. it is written in java and includes state of the art branch predictors such as fast path-based perceptron predictor. this request as inputs parameters: the number of entries in prediction table, the global history length, the threshold value used by the learning algorithm, number of bits for storing the weights. abps can detect unbiased branches or predict all branches or only those that are unbiased. the abps simulation results consist in four important metrics. the prediction accuracy is the number of correct predictions divided to total number of dynamic branches. we compute also a confidence metric that represents the total cases when the prediction was correct and the perceptron did not need to be trained (the magnitude of perceptron output was greater than the threshold) divided to the total number of correct predictions. while the first two have impact on processor’s performance, the next two metrics have direct influence on transistors’ budget and integration area (the number of perceptrons used in the prediction process and the saturation degree of perceptrons). the saturation degree represents the percentage of cases when the weights of perceptrons cannot be increased / decreased because they are saturated. if the last two metrics are quite low means that the perceptrons are underused. 5 experimental results tables 3 and 4 illustrate comparatively the percentage of static / dynamic (indirect) branches within the tested programs. figures 3 and 4 illustrate the arity of indirect branches from static and dynamic points of view, respectively. the arity means the number of distinct dynamic targets of the same indirect jump/call. this can be determined either after simulations or knowing profile information (identifying branch class) either estimated through source code level analysis. after this process, monomorphic jumps/calls (having a single target) can be successful predicted by a predictor without history (btb), yet, for polymorphic branch prediction (with two or more distinct targets) additional information are necessary. analyzing tables 3 and 4 and figures 3 and 4, we observe some significant differences related to the behavior of java applications against c/c++ applications at microarchitectural level, from indirect 34 adrian florea, arpad gellert, lucian vinţan, marius velţan table 2: spec jvm98 characteristics benchmark characteristics 227_mtrt it represents a modification of 205_raytrace, a ray tracer that creates a pictorial scene portraying a dinosaur. it uses a multi-threaded driver, where the threads render the scene from an input file. 202_jess this benchmark is an expert system shell written entirely in java. the intent of jess is to give java applets the ability to "reason" by continuously applying a set of rules. the workload used in the benchmark solves a set of puzzles. 201_compress represents java version of the 129.compress benchmark from the spec’95 benchmark suite, but improves upon that benchmark in that it compresses real data from files using a modified lempel-ziv method (lzw). it is highly recursive. 209_db it performs multiple database operations (insertion, removing, finding, sorting) on a 1 mbytes memory resident database. 222_mpegaudio this benchmark is an application that decompresses audio files defined by the iso mpeg layer-3 audio specification. the mp3 encoding technique allows data compression of digital audio signals up to a factor of 12, without losing sound quality as perceived by the human ear. the workload consists of about 4mb of audio data. 228_jack it represents a java parser that is based on the purdue compiler construction tool set, in fact an earlier version of javacc compiler. the workload consists of a file named jack.jack, which contains instructions used for the generation of jack itself. this is fed to jack so that the parser generates itself multiple times (highly recursive). 213_javac it represents the java compiler from the jdk 1.0.2. table 3: statistics about static (indirect) branches from the analyzed benchmarks testing programs (1) average of indirect static branches (2) average of static branches (3) average percentage of indirect static branches (4) = (2)/(3)*100% spec 95 59 4759 2.15% spec 2000 57 8187 0.86% design patterns 6765 16619 40.69% table 4: statistics about dynamic (indirect) branches from the analyzed benchmarks testing programs (1) average of indirect dynamic branches (2) average of dynamic branches (3) average percentage of indirect dynamic branches (4) = (2)/(3)*100% maximum percentage of indirect dynamic branches spec 95 432041 13807188 2.47 5.63% spec 2000 790826 95218337 0.78 2.03% design patterns 17904385 93676540 18.82 20.41% the impact of java applications at microarchitectural level from branch prediction perspective 35 figure 3: the arity of indirect branches from static point of view figure 4: the arity of indirect branches from dynamic point of view 36 adrian florea, arpad gellert, lucian vinţan, marius velţan branches perspective: first, the percentage of dynamic indirect branches from java testing programs (maximum 20.41%) is much higher than that from procedural or object oriented c/ c++ programs (maximum 5.63%, see also [22]). the results are similar with those reported in [23]. according to tao, the performance results are different depending on the jvm mode of execution. in interpreter mode, 19.5% of all dynamic branches are indirect branches and 11.8% of all branches are polymorphic indirect branches. in jit compiler mode, 12.4% of dynamic branches are indirect branches and only 5% are polymorphic branches. second, the significant percentage of duomorphic branches from java programs, recommends using a minimum 2-way associative target cache structure for indirect branch prediction. the very small percentage of dynamic polymorphic indirect branches from our proposed java programs could be due to the fact that these programs do not represent standardized benchmarks but only simple testing programs. however, we consider that there are two major reasons for the high number of indirect branches targets within java applications: 1) as an object-oriented programming language, java promotes a polymorphic programming style in which late binding of subroutine invocations is the main instrument for modular code design. with the help of virtual method table, the implementation chosen for java interpreters and compilers, it executes an indirect branch for every polymorphic call. more than that, in java, instance methods are virtually declared by default. if they are not explicitly declared final, they can be overridden in subclasses. 2) switch/case statements (in the bytecode translation routine of the interpreter) and indirect function calls through pointers (calls to the dynamically shared native interface libraries) from runtime interpretation and jit compilation of bytecodes performed by the java virtual machine are subject to high indirect branch frequency. figure 5: the indirect branch prediction accuracy using a target cache predictor with 512 entries from figure 5 we observe that indirect branch prediction accuracy increases as the associativity degree of tc structure increases. the improvement is significant for a 2-way associative tc against a direct mapped one (20%) whilst the improvements diminish to 2.58% for a 4-way associative tc compared with a 2-way associative one. the results from figure 5 are correlated with those from figure 4 which show a high percentage of duomorphic and an insignificant percentage of polymorphic indirect branches. the improvement of prediction accuracy from 96.11% to 98.59% is due to the miss conflict and capacity miss accesses at a 2-way associative tc (that are solved by increasing the associativity degree), but also to the smaller percentage of polymorphic indirect branches. analyzing procedural programs (spec’95 and spec2000), we have shown in [24] that the best prediction accuracy of indirect branches obtained using a 8-way associative target cache with 128 entries was 88.97%, for target cache capacities greater or equal with 256 entries the prediction accuracy becoming saturated. figure 6 exhibit comparatively the behavior of procedural benchmarks (spec2000) versus objectoriented benchmarks (spec jvm98) from unbiased branches perspectives. repeating the detection methodology for a length-ordered set of contexts used by us in [25] it could be observed how the number of unbiased branches decreases within both procedural and object-oriented applications. on the impact of java applications at microarchitectural level from branch prediction perspective 37 figure 6: reducing the number of unbiased branches by increasing global history length (ghl) the spec2000 benchmarks the percentage of unbiased branches decreases in average from 25.12% to 9.26%. we consider that this value is still too high and further investigations are required. for the spec jvm98 benchmarks the percentage of unbiased branches decreases from 8.87% to 5.80% (almost half compared to the spec2000 integer benchmarks). figure 7: fast path-based perceptron prediction accuracies on spec2000 versus spec jvm98 benchmarks using a table of 1024 entries in figure 7 we considered, comparatively on spec2000 versus spec jvm98 benchmarks, the impact of unbiased branches on a fpbp predictor with a table of 1024 entries considering a global history register of 32. we achieved on the spec2000 suite an average prediction accuracy of 95.21% on all branches but the impact of unbiased branches still remained significant at 74.92%. however, in java benchmarks, the smaller percentage of unbiased branches has a lower impact on prediction accuracy. the prediction accuracy obtained on all branches is very high 98.57% at average and even unbiased branches are predicted more accurately than those from the spec2000 integer benchmarks (where 11 of 12 are procedural applications). based on the number of perceptrons used, we determined the usage degree of perceptron table. this metric will directly affect the prediction accuracy and indirectly the processing performance lost. the reduced usage degree (39.77% in average on the spec2000 benchmarks) proves a high interference degree in the perceptrons table, further diminishing the prediction accuracy, the only advantage being the reduced integration area cost. however, this aspect has been not observed on the spec jvm98 benchmarks where the perceptron table is almost entirely exploited (99.65% in average). 38 adrian florea, arpad gellert, lucian vinţan, marius velţan 6 conclusions and further work java technology from software for embedded systems to commercial and research applications and also, visual, interactive and desktop applications are characterized by high percentage of indirect branches. unfortunately, the prediction accuracy of indirect branches is still very low because many indirect branches have multiple targets that are difficult to predict even with specialized hardware. this paper shows that besides indirect jumps, the unbiased branches represent another class of hard to predict branches in java applications that limits the ceiling of dynamic branch prediction and causes performance degradation and additional power consumption. studying the impact of java applications at microarchitectural level from branch prediction perspectives we concluded: • the percentage of dynamic indirect branches from java testing programs (maximum 20.41%) is much higher than that from procedural or object oriented c/ c++ programs (maximum 5.63%). • the significant percentage of duomorphic indirect branches (>90%) from java programs recommends using a minimum 2-way associative target cache structure for indirect branch prediction. • the prediction accuracy improvement is significant for a 2-way associative tc against a direct mapped one (20%) whilst the improvements diminish to 2.58% for a 4-way associative tc compared with a 2-way associative one. • for java benchmarks the percentage of unbiased branches decreases from 8.87% to 5.80% (almost half compared to the c/c++ integer benchmarks). • for java benchmarks, the smaller percentage of unbiased branches has a lower impact on prediction accuracy. the prediction accuracy obtained on all benchmarks is very high (98.57% in average) and even unbiased branches are predicted more accurately than those from c/c++ integer benchmarks. the lower percentage of unbiased branches occurred in java benchmarks could be determined by the reduced number of conditional branches from object oriented benchmarks compared with procedural applications. it is well known that object-oriented programs contain many methods with few instructions, and fewer conditional branches implicitly. hardware-software interface optimizations are not possible without a deeper understanding process that requires an integrated vision about multiple stages of information processing, coded at different semantic levels. a solution could consist in developing some "semantic predictors", based on high level language (hll) information whose importance related to the generation of indirect jumps (polymorphism, indirect function calls, etc.) is proved by us. this might be a completely new approach in branch prediction domain, where hll semantics are often hidden. in order to efficiently use such information we consider it will be 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[21] bowers k. r., kaeli d., characterizing the spec jvm98 benchmarks on the java virtual machine, technical report, northeastern university, ece department, computer architecture group, 1998, pp. 1-20, boston, massachusetts, usa. [22] florea a., the dynamic values prediction in the next generation microprocessors (in romanian), matrixrom publishing house, 2005, bucharest. [23] tao li, lizy k. john, adapting branch-target buffer to improve the target predictability of java code, acm transactions on architecture and code optimization, volume 2, issue 2, june 2005, pp. 109-130. [24] florea a., vinţan l., advanced techniques for improving indirect branch prediction accuracy, proceedings of 19th european conference on modelling and simulation, june 2005, pp. 750-759, riga, latvia. [25] florea a., radu c., calborean h., crapciu a., gellert a., vinţan l., designing an advanced simulator for unbiased branches prediction, proceedings of 9th international symposium on automatic control and computer science, issn 1843-665x, iasi, 2007. international journal of computers, communications & control vol. i (2006), no. 3, pp. 93-98 65 years from birth of prof. gheorghe s. nadiu (1941-1998) ioan dziţac, loriana andrei abstract: in 1967, gheorghe s. nadiu publishes in “mathematical studies and researches”, the article “on a method for the construction of three valued łukasiewicz algebras” (romania), cited in the book “cylindric algebras” by p. monk, l. henkin, a. tarski. this article attracted the attention of grigore c. moisil, who offers him a scholarship, taking him out of production to make his doctorate at the mathematic institute of the romanian academy. he publishes more than 50 scientific papers in theory of algorithms, logics of mathematics and theory of categories fields. “gheorghe s. nadiu, by introducing the notion of quantified filter in a boolean monadic algebra, showed how one can obtain a trivalent łukasiewicz algebra; also, he obtained an algebraic characterization of a completitude theorem from the intuitionist logic of kripke.” (g. st. andonie, science history in romania, academic publishing house, srr, 1981). phd student gheorghe nadiu (1970) at mathematical institute of the romanian academy prof. univ. dr. gheorghe nadiu (1997) director of mathematics department university of oradea (1990-1994, 1996-1998) 1 biographical dates name and surname: nadiu gheorghe date and place of birth: 1941, september, 12, comlăuş-sântana, arad county, romania date and place of dead: 1998, november 7, oradea, bihor county, romania wife: nadiu doina elena children: 1. loriana; 2. adina fields of scientific research: theory of algorithms, mathematical logic, category theory copyright c© 2006 by ccc publications 94 ioan dziţac, loriana andrei 1956-1959: pupil at "moise nicoară" high school, arad, arad county, romania 1959-1964: student at faculty of mathematics and physics, “babes-bolyai” university of cluj 1964-1969: assistant professor at pedagogical institute of oradea, bihor county, romania 1969-1972: phd student at mathematical institute of the romanian academy (supervisor prof. grigore c. moisil) 1972: he obtain phd in mathematics with the phd thesis: “cercetări asupra logicilor necryssipiene” / “research about necryssipiene logics” (supervisor grigore c. moisil) 1973-1978: lecturer at pedagogical institute of oradea 1980-1990: associate professor at pedagogical institute of oradea 19901998: professor at university of oradea. 2 didactical and managerial activity after graduating the faculty of mathematics-physics at the “babeş-bolyai” university of cluj, in 1964, gheorghe s. nadiu becomes teaching assistant at the pedagogic institute from oradea, job in which he functions until 1969, holding seminars of theory of probabilities, differential equations, theoretical mechanics and in the university year 1968/1969 he teaches the course “fundamentals of mathematics”. during 1969-1972 he is taken out of education after obtaining a scholarship for executing the doctorate in mathematics under the scientific leading of professor grigore c. , moisil, at the mathematic institute of the romanian academy. in 1972, after obtaining the title of doctor in mathematics, (mathematical logics) he comes back to his old workplace where he gets, through contest, a job as lecturer for the disciplines “fundamentals of mathematics” and “mathematical logics” which he teaches until 1978. in 1978 he becomes titular of the courses “superior mathematics” and “computer programming”, and in 1980 he occupies by contest the job as readership, on which he functions until 1990 when he obtains, by contest, the didactic title of university professor. in 1990 the institute from oradea changes into university, and professor nadiu becomes headmaster of the mathematics department of the university of oradea, which he founds starting from a department with 2 personnel and develops until obtaining a department of over 30 personnel who operates in the whole university, and which he leads between 1990-1994 and 1996-1998. between 1990-1998 he teaches the courses “mathematical logics” and “functional analysis” at the science faculty of the university of oradea, at the specializations mathematics-informatics and mathematics-physics. he dies prematurely, at the early age of 57, because of cardio-respiratory death, still in full didactical working potential and creative scientific activity. 3 scientific activity in his first published scientific work [1], written as student, in collaboration with prof. petre brd̆eanu from cluj, he presents an optimal moving regime of the ballistic missile, under the form of periodical functioning of exponential type of the engine, with help of the maximum principle of pontriaghin. the main directions of scientific research of gh. nadiu were directed to the theory of algorithms, logics of mathematics, and theory of categories. in the works [2,4] and [5], from the field of algorithms theory, first, there is given a double recursion theorem, in sense of asser’ recursion theorem, and then a localization procedure of algorithms of nogornâi type through the medium of cernevski type, introduced by the author, as well as the construction of a universal turing machine for the normal class algorithms of a. a. markov. 65 years from birth of prof. gheorghe s. nadiu (1941-1998) 95 in the field of mathematical logic nadiu introduces in [3] an algebraic method of construction of trivalent łukasiewicz algebras, starting from the monadic boolean algebras, by means of quantified filters introduced by him. this result is quoted in the treaty “cilindric algebras” by henkin, monk and tarski [8]. afterwards, the results are taken over in some researches of luiz monteiro from the university bahia blanca from buenos aires, argentina. then, in [7], [9] and [10], there is given an interpolation theorem of craig type for the strict positive logic and also a series of results of relative categorical order at the category of models for elementary logic of moisil. in the doctoral dissertation “research on necryssipic logics” [11] elaborated under the scientific leadership of grigore c. moisil, sustained in 1972, g.s. nadiu introduces the notion of “abstract calculation of superior order” as general developing frame of logic systems and gives then extensions of moisil’s modal logics and of logic of nelson’s and a. a. markov’s false constructible, the results are being found in [16]. in the same [11], gh. s. nadiu treated also s. kripke theorem of completitude for heyting intuitionist logic, from point of view of topological models. after this, he studies the pseudo boolean pre fascicle introduced by him through analogy with the kripke models, for which he gives a series of categorical properties. the results obtained by gh. nadiu in his doctoral dissertation [11] and published in “logique automatique informatique” (1971) are in concordance with the results contained in the work of david p. ellerman [12], which treats a particular case of pseudo boolean pre fascicle with values in the category of relational structures. the studies of formal systems, in categorical sense, lead gh. nadiu to results related by the closed cartesian categories [14] and to the obtaining of an exponentation theorem birkoff type, in such categories as well as also to the development of the skolem pre fascicle theory, introduced by him in [18, 20, 21, 22] which led him to fiber categories. the algebraic study of the polyvalence in sense of moisil’s ideas led him to the introduction of notions of lattice under reziduate skolem [23]. in the ulterior works he developed the correspondence category and of general correspondence, ulterior applied at the monad theory. also, in the sense of fröllich formal group, he gave a characterization of formal commutative moufang curls [15], introduced by s. basarab. after 1980 his researches have been oriented to the study of the influence of mathematic logic in various branches of mathematics: functional analysis, algebra, auto-reglation problems in the theory of abstract automatics, model theory for theories with more variable sorts with application at the normative spaces theory. references [1] p. brădeanu, gh. s. nadiu, asupra unui regim optim de mişcare a rachetelor balistice, an. univ. din timişoara, vol. iii, 1965. [2] gh. s. nadiu, asupra unei teoreme de recursie a algoritmilor normali, studii şi cercetări matematice (mathematical reports), tom. 18, 1966/7, pp. 1071. [3] gh. s. nadiu, on a method for the construction of three-valued łukasiewicz algebras (romanian), studii cerc. mat., 19, 1967, 1063-1070. [4] i. maruşciac, gh. s. nadiu, o interpretare a algoritmilor, schemă-graf în legătură cu maşinile turing şi algoritmi de tip cernevski, studii şi cercetări matematice, tom. 20, 1968/7. 96 ioan dziţac, loriana andrei [5] gh. s. nadiu, the interpolation theorem in strict positive logic, bull. math. soc. sci. math. roum., tom. 13, 1968/2. [6] gh. s. nadiu, o maşină turing universală pentru clasa algoritmilor normali, studii şi cercetări matematice, tom. 21, 1969/1. [7] gh. s. nadiu, sur la logique de heyting, in “logique-informatique-automatique”, ed. of romanian academy, bucureşti, 1971. [8] l. henkin, j.d. monk, a. tarski, cylindric algebras, north-holland, amsterdam 1971. [9] gh. s. nadiu, sur la logique de heyting, abstract of iv congress of logic, bucharest, 1971. [10] gh. s. nadiu, elementary logic and models, bull. math. d soc. sci. math. roum., tom. 15, 1971/1. [11] gh. s. nadiu, cercetări asupra logicilor necryssipiene/research about necryssipiene logics, 1972 (phd thesis, supervisor grigore c. moisil). [12] david p. ellerman, sheaves of structures and generalized ultraproducts, annals of mathematical logic, 7 (dec. 1974), 163-195. [13] gh. s. nadiu, an abstract calculi of superior order, proc. of iv congress of logic„ london-ontario, canada, 1975. [14] gh. s. nadiu, o teoremă de exponenţiere în categorii cateziene, lucr. şt., institul ped. oradea, 1977. [15] gh. s. nadiu, asupra buclelor moufang formele comutative/note sur les boucles moufang formelles commutatives (romanian), stud. cercet. mat. 32, pp. 193-207, 1980. [16] gh. s. nadiu, notă asupra spaţiilor metrice constructive, studii şi cercetări matematice, 1980. [17] gh. s. nadiu, non-clasical logic of superior order, resegna internationale di logica, bologna, bull.math., 1981. [18] gh. s. nadiu, considerations sur les categories cartesienes fermes, bull. math. [19] g. st. andonie, history of sciences in romania. mathematics, mechanics, astronomy, (istoria ştiinţelor în românia. matematica, mecanica, astronomie)(romanian), bucuresti: ed. acad. r.s.r.,1981, pp.123. [20] gh. s. nadiu, category of correspondence and extensions, jounal of pure and appl. algebra. [21] gh. s. nadiu, on boolean formal algebras, bull. math. d soc. sci. math. roum., 1978. [22] gh. s. nadiu, prefascicule peste latici skolem (i), lucrări şt. ale inst. de înv. sup. oradea, 1978, ser. a, pp. 37-63. [23] gh. s. nadiu, asupra laticilor skolem, lucrări şt. ale inst. de înv. sup. oradea, pp. 65-69, 1978. [24] nadiu, gh.s., considerations in category of skolem lattices, (romanian. english summary), bul. stiint. inst. politeh. cluj-napoca, ser. electroteh. energ. inf. 25, 29-36 1982. [25] gh. s. nadiu, logici cu mai multe sorturi de variabile şi spaţii vectoriale, în vol. lucrările celui de al iii-lea simpozion naţional de analiză funcţională, craiova, 6-7 nov. 1981. 65 years from birth of prof. gheorghe s. nadiu (1941-1998) 97 [26] gh. s. nadiu, t pop, automate-tehnologii şi decizii, în vol. “cibernetică aplicată”, bucureşti, ed. acad. r.s.r., 1983. [27] gh. s. nadiu, aspecte booleene în categoria spaţiilor vectoriale normate, în vol. lucrările celui de al iv-lea simpozion naţional de analiză funcţională, craiova 6-7 nov. 1983. [28] gh. s. nadiu, prefascicule peste latici skolem (ii), lucrăi şt. ale inst. de înv. sup. oradea, seria a, 1978. [29] gh. s. nadiu, asupra logicii modale generale a lui moisil, lucrări şt. ale inst. de înv. sup. oradea, seria a, 1978. [30] gh. s. nadiu, asupra ordonărilor rudin-kreisler în clasa ultrafiltrelor, lucrări şt. ale inst. de înv. sup. oradea, seria a, 1978. [31] gh. s. nadiu, contribuţii la teoria relaţiilor (i), lucrări şt. ale inst. de înv. sup. oradea, seria a, 1980. [32] gh. s. nadiu, contribu ctii la teoria relaţiilor (ii). corespondenţe via toposuri., lucrări ştiinţifice, oradea, seria a, 1980. [33] gh. s. nadiu, o clasă specială de l-endometrici probabiliste, lucrări ştiinţifice, oradea, seria a, 1980. [34] gh. s. nadiu, gh. niculae, funcţii parţiale injective şi filtre de compunere, lucrări ştiinţifice, oradea, seria a, 1980. [35] gh. s. nadiu, notă asupra relaţiilor de secvenţă, lucrări ştiinţifice, oradea, seria a, 1979. [36] gh. s. nadiu, gh. niculae, asupra categoriei-algebrelor, lucrări ştiinţifice, oradea, seria a, 19811982. [37] gh. s. nadiu, notă asupra filtrelor într-o algebră łukasievich 0-valentă, lucrări şt., oradea, seria a, 1981-1982. [38] gh. s. nadiu, contribuţii la teoria relaţiilor (iii). interpretări sintactice, lucrări şt., oradea, seria a, 1981-1982. [39] gh. s. nadiu, dinamica deciziilor, rezumat vol. celui de al iv-lea simpozion naţional de cibernetică, 1986. [40] gh. s. nadiu, asupra noţiunii de topologie, simpozion naţional de topologie, cluj napoca, 1986. [41] gh. s. nadiu, produse fibrate în categoria automatelor, comunicare la conferinţa naţională de algebră, timişoara, 1986. [42] nadiu, gh. s., on topologies and overtopologies (english), prepr., “babes-bolyai” univ., fac. math. phys., res. semin. 1988, no.2, pp. 121-126 (1988). [43] gh. s. nadiu, loriana popa, asupra teoriei omotopiei, analele univ. oradea, iii, pp. 12 19, 1993. [44] gh. s. nadiu, notă asupra spaţiilor liniar normate, analele univ. oradea, i, pp. 91 94, 1991. [45] gh. s. nadiu, consideraţii algebrice relaţii la cat. sp. metrice., analele univ. oradea, ii, pp. 99 106, 1992. 98 ioan dziţac, loriana andrei [46] gh. s. nadiu, dinamici. aspecte categoriale, analele univ. oradea, iii, pp. 1 12, 1993. [47] gh. s. nadiu, consideraţii asupra categoriilor carteziene, lucrărrile caim 1996, pp. 201 207. [48] a. georgescu, c.l. bichir, g.v. cîrlig, matematicieni români de pretutindeni, ed. the flower power, pp. 135, 2004. [49] g. georgescu, a. iorgulescu, s. rudeanu, grigore c. moisil (1906 1973) and his school in algebraic logic, int. j. of computers, communications and control, ijccc vol. i (2006), no.1, 81-89. ioan dziţac agora university 8 piata tineretului 410526 oradea romania idzitac@univagora.ro loriana andrei university of oradea 3-5 universitatii st. 410087 romania int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 2, pp. 127-136 application of genetic algorithms for the darptw problem claudio cubillos, enrique urra, nibaldo rodríguez pontificia universidad católica de valparaíso escuela de ingeniería informática av. brasil 2241, valparaíso, chile e-mail: claudio.cubillos@ucv.cl, enrique.urra.c@mail.ucv.cl abstract: on the dial-a-ride with time windows (darptw) customer transportation problem, there is a set of requests from customers to be transported from an origin place to a delivery place through a locations network, under several constraints like the time windows. the problem complexity (np-hard) forces the use of heuristics on its resolution. in this context, the application of genetic algorithms (ga) on darptw was not largely considered, with the exception of a few researches. in this work, under a restrictive scenario, a ga model for the problem was developed based on the adaptation of a generic ga model from literature. our solution applies data pre-processing techniques to reduce the search space to points that are feasible regarding time windows constraints. tests show competitive results on cordeau & laporte benchmark datasets while improving processing times. keywords: dial-a-ride, passenger transportation, darptw, heuristic, scheduling. 1 introduction in the research of transport systems, the dial-a-ride problem (darp) or the customers’ transportation problem is largely known [3]. it consists on searching the optimum way to transport a set of customers which are territorially distributed through a locations network, considering diverse constraints, for example the vehicle capacity and the time windows (tw) which are time intervals where a customer can be picked or delivered on the respective location, in a feasibility context. the problem objective is to optimize the transport system factors (vehicles number, travel costs) and the quality of service for customers (waiting time, travel time). darptw (the time windows problem version) is considered a np-hard problem, especially because of the time-window constraints [10]. for this reason, the problem is usually solved through heuristics to find good solutions, under its diverse variants. in fact, the time windows restrictions make the problem highly non-convex, making it difficult to find feasible solutions. one of the tools considered in this context are the genetic algorithms (gas). after their comparison in the scientific scenario thanks to john holland on the 70’s decade [9], these algorithms have been successfully accepted by their efficiency to solve problems of diverse complexity, and to date there is a large number of proposed ga models that considers the canonical ga problems, particularly the linkage concept [8]. in this work, the application of gas on darptw is extended considering two elements: on the one hand, the implementation of the llga model [8] with and adequate adaptation in the context and on the other hand, the use of data pre-processing techniques (namely precedence table of events and incompatible clients’ list) for aiding the ga to avoid infeasible solutions from the time windows perspective. the paper is structured as follows. section 2 explains the darptw problem for then in section 3 tackling other research in the field. section 4 details the implemented ga and section 5 the experiments and its results. the main conclusion of the work are drawn in section 6. copyright © 2006-2009 by ccc publications 128 claudio cubillos, enrique urra, nibaldo rodríguez 2 the darptw problem darptw is a multiple objective optimization problem, because there are two critical factors to be optimized: on the one hand the total transportation costs and on the other, the quality of service offered to customers (minimizing their insatisfaction with the service). there is a set of transportation requests from customers that are known in advance and do not change during algorithm execution, defining the problem as static. each request defines a time window for the customer delivery and a time window for the customer pickup. the upper bound (latest time) and the lower bound (early time) of the time window are supplied. a solution is considered infeasible when the vehicle arrives outside the time window bounds, defining these as hard time windows. to execute the service, there is a homogeneous vehicles set with the same load capacity that cannot be exceeded. the passengers are picked and delivered by the same vehicle. a vehicle can enter on inactivity times or slacks only without passengers on board. additionally, there is only one depot (single depot), a particular location where vehicles start and end their travels. in this work, maximum route duration is not considered. 3 related work as mentioned previously, there are only few researches where the gas are considered for darptw. there are more works which provide relevant elements in this context on vrp (vehicle routing problem), a generic case of darp. an example is thangiah’s work[11], which describes gideon, a ga based heuristic to solve vrp with time windows. this mechanism uses a cluster first route second strategy, starting with the assignation of customers to vehicles and after improving the best solution by a post-optimizing process. for the system implementation, a ga software called genesis is used, where the individuals are represented by bit strings. the client clusters/sections are obtained from an individual by splitting him on k divisions of b bits. each division is used to compute the size of a sector. the individual quality is obtained through the cost function evaluation when all computed clients are served, regarding their derived sector divisions. in this work, b = 3 is used, bigger values showed less satisfactory results. for the testing, the parameter values for the population size, crossover rate and mutation rate were 1000, 0.5 and 0.001 respectively. a set of 56 instances were tested using the solomon’s benchmark data, widely known in this context. in the results, 41 instances showed improvement in comparison to other heuristics developed by solomon and thompson. regarding darptw, in [1], a cluster first route second strategy is also used. the mathematic model used in their work is a generalization of the one used in this work. this generalization is justified by the use of soft time windows, so the objective function considers additional elements related to quality of service. the individual is based on client clusters. a matrix is used, where the row number equals the available vehicles and the columns equals the clients and depots number. if an element on the matrix equals to 1, then the respective client is assigned to the respective vehicle. additionally, a row (vehicle) represents a specific route. because the absence of standardized benchmark data set in darptw, in contrast to vrp case, a set developed by cordeau & laporte is used [5], that contains 20 random instances. this set considers instances from 24 to 144 customers. the obtained results are compared also with a cordeau & laporte research [4], with similar improvement. finally, there is the cubillos work [6], where the objective is to develop a specific ga model to solve the darptw problem, considering it as a deceptive problem, using the ga to solve the full problem, in contrast to a universal solver ga and other researches where the ga solves only a part of the problem. to accomplish this, an adequate framework was developed, that considers all critical ga elements. at the same time, the considered darptw instance was very specific in contrast to other studies, for example, there were only outbound customers. a bus-passenger representation for each gene on an individual was used. the solution decode is done by an ordered lecture, where the first occurrence of a customer is application of genetic algorithms for the darptw problem 129 always a pickup and the second one is always a delivery, this obligates the representation to consider only two genes per customer. the vehicle associated with the pickup is the one actually considered in the solution and the one of the delivery can be different but not considered. the final results showed an improvement on the solution quality factor, in contrast to the vehicle quantity, when compared to previous research. it is important to highlight that none of the above solutions considers any technique for improving the search over the feasible solution space, especially regarding the time-windows constraints. this is especially true when applying the crossover and mutation operators, reason why the present work improves actual ga solutions by trying to avoid or minimize the infeasibilities and subsequent reparations after the operators through the use of pre-feasibility tables or schemas. 4 implementation in this section, the implemented ga framework and each of its elements will be described in the following. 4.1 preliminary feasibility schemas as stated before, the time window constraints make the problem search space highly non convex, meaning that it is very easy to move from a feasible solution to an unfeasible one when searching through meta-heuristics, being the only solution to roll-back or to repair the unfeasible solution. in the present work an approach has been developed to minimize these problems by reducing a priori the feasible planning combinations considering the time windows restrictions. when processing the dataset of requests, a preliminary precedence table of events is built, where each event (pickup or delivery) is associated a list of other events which need to be inserted before that event on a route (if assigned to the same vehicle) to preserve time windows feasibility. in figure 1, for example, are shown 4 clients (a, b, c and d) with their time windows for pickup (+) and delivery (−). an obvious relation is that the pickup a+ must precede its delivery a−. then, the pickup of c (c+) cannot be before the pickup of a (a+) as it would be impossible for the same vehicle to serve both events within their time window bounds as the last ends before the other stars. in the most general case, an event y must precede an event x when etx + drtxy > lty , where etx corresponds to the early time (time windows lower bound) of event x, drtxy the direct ride time from the location of x to the location of y and lty to the latest time (time windows upper bound) of event y. in this way, it is possible to build a list of precedence events for each event. on the other hand, there is an incompatible clients’ list that defines, on a level of passenger assignation (clusters), which clients cannot be transported by the same vehicle, for the time windows feasibility of a solution. this list can be obtained from the pairs of events that precede each other simultaneously: if an event x is preliminary to y and at the same time y is preliminary to x, then it is impossible for both to be part of the same route and, consequently, its respective clients cannot be transported by the same vehicle. in practical terms, the case involved is when a couple of events are too far the one from the other and their time windows are too close to each other making it impossible for a single vehicle to go from one location to the other within their time windows constraints. 4.2 initial population generation it is based on the client incompatibility previously described. the mechanism is concerned that incompatible clients will not be assigned to the same vehicle, first generating a base feasible solution that 130 claudio cubillos, enrique urra, nibaldo rodríguez figure 1: example of preliminary precedence table of events, where each event has other events that must precede it when they are on the same vehicle contains all conflicting clients on separated vehicles and afterwards generating a final feasible solution as a result of the insertion of the remaining clients over the base feasible solution. the implemented mechanism does not assure the generation of a feasible solution on the first try. in this context, the randomness on the insertion heuristic and the vehicle selection for the clients, facilitates the possibility of restart the process when the incapacity of continue generating a feasible solution is detected. 4.3 genotype and crossover a model like llga [7] has been considered. the incorporation of the locus on the genes allows to order them in different ways representing an identical solution. a gene is composed by the locus, that corresponds to the client number on this implementation, and the vehicle assigned to him. figure 2 shows the clients a, b, c, d and the vehicles v1, v2, v3 to which are assigned. figure 2: crossover operation for the implemented genotype this representation tackles the assignment of clients to vehicles (clustering) while the scheduling itself (route construction) is carried out through a greedy insertion heuristic explained in the next subsection. application of genetic algorithms for the darptw problem 131 on crossover, (see figure 2) a father assumes the donor role, giving an own gene segment of its structure, on a random insertion point of the other father that assumes the recipient role, where the duplicated genes are deleted. in this case, the duplicated clients from the recipient are deleted. this generates a change on the clusters from the chromosome external layer, but also triggers the modification of the routes from the chromosome internal layer. because this process is not exempt from unfeasibility problems, in the basis of the randomness of the internal processes that support the crossover (insertions, eliminations, etc.) and the own crossover factors (insertion point, donated segment), a restart of the whole process is considered, until a feasible crossover has been done. 4.4 route scheduling it regards the greedy insertion heuristic used for route generation, plus feasibility evaluation procedures. this mechanism is based on "madarp" model shown on [6], applying the concept of time windows intersection and the use of pre-calculated data for a direct evaluation. figure 3 (a) shows a portion of a schedule, containing the pickups and deliveries of clients a and b on a first block, then a slack (that is, vehicle idle time without passengers onboard) and a second block serving clients c and d. within the first block, the evaluation of client x is carried out by evaluating all the possible permutations of the new client in the sequence, that is, (n+1)(n+2)/2 with n the number of events already present in the block. figure 3: crossover operation for the implemented genotype in addition, the heuristic also considers the possibility of merging blocks as figure 3 shows. the insertion of client x makes the slack time after a− to disappear, resulting in a bigger block. 132 claudio cubillos, enrique urra, nibaldo rodríguez 4.5 phenotype or evaluation it is worth highlighting that there are two generic elements evaluated on a solution: the transport system efficiency and the quality of service offered to the clients. on the first element, the considered factors are the vehicle travel time, the slacks length and the vehicle quantity. on the second element, the excess ride time and the wait time are considered. a weight is assigned to each of these factors that directly influence the evaluation result. 4.6 selection for the proposed models, a tournament selection is used, from where a population subset is obtained, and an individual from these are selected as a winner. on this operator, the selection pressure can be managed through the tournament size. in this implementation, when a winner is obtained from the tournament, it is not removed from the original population, allowing them be the winner on future tournaments and facilitating the convergence. 4.7 mutation in contrast from other models described in literature, two mutation operators have been developed in this implementation: a cluster mutation and a route mutation. each of these has its own probability, hence an individual can be affected by one, both or none of them on the generation advance. the cluster mutation consists in moving a client from one vehicle to another and the probability is applied to each client in the solution as figure 4 (a) shows. figure 4: crossover operation for the implemented genotype the route mutation consists on interchanging the order of an event’s pair from the route and the probability is applied to each route in the solution (see figure 4 (b)). because this operator is not exempt from unfeasibility problems and the mutation probabilities are small, when all possibilities has been evaluated and a feasible result has not been obtained, the process is discarded for a client or a route according to the respective mutation. 5 experiment settings in literature, a common benchmark dataset used to evaluate heuristics for the darptw problem corresponds to the ones provided by cordeau & laporte [5]. these sets are divided on two subsets: one is used in [3] for an branch-and-cut algorithm, while the other one is used in [4] for a tabu search algorithm and in [1] for a cluster-first route-second ga. these are used on this research both for the ga application of genetic algorithms for the darptw problem 133 calibration stage and final testing stage. for the evaluation, the benchmark data was divided on three groups according to the number of clients: small sets (pr01, pr02, pr11, pr12 and pr17), medium sets (pr03, pr05, pr15 and pr19) and big sets (pr16). on the calibration stage, the following parameters were considered: 3 sets (a small one, a medium one and a big one); population size: 100; maximum generation number: 10000; crossover ratio: 0.35, 0.45 and 0.75; cluster mutation ratio: 0.0025, 0.005 and 0.0075; route mutation ratio: 0.025, 0.05 and 0.075; tournament size: 2. these give a total of 108 possible execution combinations. for each combination, 3 runs were done, generating a total of 324 executions on the calibration stage. the problem parameters used were: route duration factor: 8; slack time factor: 1; vehicle quantity factor: 0; excess ride time factor: 2; wait time factor: 0; ride time factor: 4. finally, the parameters associated to the best results were: crossover ratio: 0.45, cluster mutation ratio: 0.005 and route mutation ratio: 0.075 for the small set; crossover ratio: 0.35, cluster mutation ratio: 0.075 and route mutation ratio: 0.075 for the medium set; and crossover ratio: 0.75, cluster mutation ratio: 0.025 and route mutation ratio: 0.025 for the big set. with these parameters, the final tests were executed. in this stage, a bigger number of runs per test and a bigger generation number were used. for each instance set, 20 runs and 15.000 generations were considered. a total of 200 tests with the fore-mentioned characteristics were carried out. 5.1 obtained results the results were compared with jorgensen et al. [2] research (cluster-first route-second ga) and cordeau & laporte [4] research on tabu search. table 1 shows the results obtained by our work while tables 2 and 3 show the results obtained by the previously mentioned researches. because the compared models do not have the same characteristics, the comparison was done on the basis of time units of two critical factors: on the one hand, the total route duration that is associated with the transport system resources optimization, and on the other hand, the total client travel time that is associated with the offered quality of service. table 1: summary of the results obtained by our llga model it is important to mention that our solution considers two restrictions not covered by both, jorgensen et al. and cordeau & laporte researches. these are the time windows as hard constraints and the incapacity of vehicles to transport clients when they are on slack times. despite this more restrictive scenario our solution performed well compared to them. 134 claudio cubillos, enrique urra, nibaldo rodríguez table 2: summary of the results obtained by cluster firstroute second ga of jorgensenet al. [2] table 3: summary of the results obtained by tabu search of cordeau & laporte [4]. application of genetic algorithms for the darptw problem 135 in terms of solution quality, our llga model clearly performed better than the jorgensen et al. solution. a closer look at the tables shows that for all the evaluated datasets our llga model presented lower average times for vehicles’ route duration, clients’ ride times and also cpu time, being this last one specially important. regarding cordeau & laporte results, based on a tabu search solution, on most cases their solution presented best times for the route duration but not for the ride times or the cpu times. in addition, on three cases (pr02, pr11 and pr12) our llga solution performed better also for the route duration. 5.2 results discussion as exposed in the results section, our llga implementation presents better results for the clients’ travel time with respect the other two studies. this is mainly due to the use of time-windows as hard constraints, making it easier to enforce the optimization on this factor. then, when focusing on the vehicles’ route duration our solution showed better times regarding the cluster-first route-second ga of jorgensen et al. while obtaining worse results when compared to the tabu-based solution. this can be understood as the cost of having good average ride times for clients, as there is a trade-off relation among both variables. the improvement obtained in our solution is mainly explained because of the use of the so-called precedence table of events and the incompatible clients list. both elements provide a way to reduce the search-space when considering the time-windows restriction. in this sense, it is important to remember that the darptw problem behaves as a deceptive problem when solved through gas, mainly because of the difficulty for the ga operators to find the appropriate building blocks for constructing feasible solutions. this fact is especially true when facing the darp with tws, as the time-windows do impose additional restrictions on the clients’ requests that make the region of feasible solutions highly non-convex. this means that it is very easy to move within this region from a feasible to an infeasible solution. furthermore, depending on the tightness or looseness of the time-windows such region may look like a small group of feasible points spread over a big region of infeasible solutions. under such an scenario, what the precedence table does is to make such region more convex by pre-processing which sequences of events (pickup & delivery) are feasible within the "sea" of infeasible sequences due to incompatibilities in their respective time windows, making it impossible for a vehicle to serve both events in that sequence and satisfy their time intervals. a similar situation happens with the incompatible clients’ list. in this case, both events of the client are considered, his pickup and delivery, detecting the cases in which it is not possible for a vehicle to serve both clients and their time-windows constraints no matter the sequence used and no matter which other clients are assigned. this is important for identifying requests that are too close in time (either at pickup or delivery) while being too far geographically, avoiding putting them together. in this way, the initial population and ga operators move more around feasible solutions. this reduction follows a similar principle as in constraint satisfaction problems and their techniques to reduce the domain of variables. from other point of view, it can be seen as a search with memory, as in tabu search. however, in this case the memory is used for making the algorithm to "remember" which portions of sequences are feasible in order to reduce effort instead of remembering the solutions found so far to avoid local optima. another important issue is that the cpu time in all cases is lower, being sometimes even the half of it or even less (e.g. pr5, pr15, pr16 and pr19). it is worth highlighting that the llga tests were done with a 2.66 ghz intel pentium 4 cpu, while the cordeau & laporte tests were done with a 2.0 ghz intel pentium 4 cpu and the jorgensen et al. tests were done with a 2.0 ghz intel celeron cpu. although the hardware configurations are dissimilar, they do not completely justify the time improvement. undoubtedly, the precedence table of events and the incompatible clients’ list have caused this 136 claudio cubillos, enrique urra, nibaldo rodríguez diminishing. 6 conclusions the proposed model has shown interesting results according to the comparisons made, highlighting the times lowering, undoubtedly the weaker ga factor. this work will allow researchers to develop comparisons of highly restrictive models, unlike most works in literature where a bigger number of constraints are relaxed. there are two important elements of the developed model on this work that must be remarked. on the one hand, the use of pre-feasibility schemas has represented an interesting support tool for the ga behavior. although it depends strictly on the problem, the ga behaves better when the search space is bigger and complex, as in many np-hard problems while having a convex feasible region of solutions. bibliography [1] k. bergvinsdottir, the genetic algorithm for solving the dial-a-ride problem, master’s thesis, informatics and mathematical modelling, technical university of denmark, dtu, 2004. [2] r. m. jorgensen, j. larsen, k. b. bergvinsdottir, solving the dial-a-ride problem using genetic algorithms, j. oper. res. soc., vol.58, pp. 1321-1331, 2006. [3] j. cordeau, a branch-and-cut algorithm for the dial-a-ride problem, operations research, vol. 54, pp. 573-586, 2006. [4] j. cordeau, g. laporte, a tabu search heuristic for the static multi-vehicle dial-a-ride problem, transportation research part b, vol. 37, issue 6, pp. 579-594, 2003. [5] j. cordeau, g. laporte, benchmark datasets accessed on march-2007, available online at: http://neumann.hec.ca/chairedistributique/data/darp/. 2007. [6] c. cubillos, madarp: multi-agent framework for transportation systems, thesis for the academic degree of dottore di ricerca in ingegneria informatica e dei sistemi (ciclo xvii), politecnico di torino, 2005. [7] c. cubillos, n. rodriguez, b. crawford, a study on genetic algorithms for the darp problem, springer berlin-heidelberg lncs, vol. 4527, pp. 498-507, 2007. [8] g. harik, d.e. goldberg, learning linkage, foundations of genetic algorithms, vol. 4, pp. 247-262, 1996. [9] j.h. holland, adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence, mit press, isbn 0-262-58111-6, 1998. [10] m.w.p. savelsbergh, the general pickup and delivery problem, transportation science, vol. 29, pp. 17-29, 1995. [11] s. thangiah, vehicle routing with time windows using genetic algorithms, application handbook of genetic algorithms: new frontiers, vol. ii. lance chambers (ed.). crc press, 1995. international journal of computers, communications & control vol. ii (2007), no. 4, pp. 328-339 neural networks-based adaptive state feedback control of robot manipulators ghania debbache, abdelhak bennia, noureddine goléa abstract: this paper proposes an adaptive control suitable for motion control of robot manipulators with structured and unstructured uncertainties. in order to design an adaptive robust controller, with the ability to compensate these uncertainties, we use neural networks (nn) that have the capability to approximate any nonlinear function over a compact space. in the proposed control scheme, we need not derive the linear formulation of robot dynamic equation and tune the parameters. to reduce the nns complexity, we consider the properties of robot dynamics and the decomposition of the uncertainties terms. the proposed controller is robust against uncertainties and external disturbance. the validity of the control scheme is demonstrated by computer simulations on a two-link robot manipulator. keywords: robot manipulator, neural networks, adaptive control, stability. 1 introduction robot manipulators are multivariable nonlinear systems and are frequently subjected to structured and unstructured uncertainties even in a well-structured setting for industrial use. structured uncertainties are mainly caused by imprecision in the manipulator link properties, unknown loads, and so on. unstructured uncertainties are caused by unmodeled dynamics, such as, nonlinear friction, disturbances, and high-frequency dynamics. as a result, it is difficult to obtain an accurate mathematical model so that computed torque controllers [1-6] or other model-based controllers [7-8] can be accurately applied. although adaptive controllers [1-5, 7] can achieve fine control and compensate for partially unknown manipulator dynamics (i.e., structured uncertainties), they often suffer from incapacity to deal with unstructured uncertainties. hence, there is a need for model-free adaptive control strategies. the application of neural networks to robots dynamic control is not new [9-11]. though the proposed methods have been practically successful, it has proved extremely difficult to develop a general analysis and design theory for early nns control systems. during the last few years, a number of papers have been presented to deal with the problem of robot adaptive control [12-14]. the basic idea of these methods is to design the feedback controller based on the computed torque principle, and to use an adaptive nn to approximate the robot nonlinearities needed in the control input design. however, most of the above designs present the drawback that, robot dynamic model is presented as single nonlinearity approximated by a single nn with the robot real and desired positions and velocities as inputs, which results in large nn with lot of parameters to be tuned. in this paper, our goal is to develop a method for designing an adaptive nn control for rigid robot manipulators. a structured or partitioned nn structure, that simplifies the controller design and makes for faster weight tuning online, is designed to ensure the closed loop stability. robust update laws are used to tune the nns parameters, and to ensure their boundedness. lyapunov stability theory is used to drive the stability conditions, and to show the robustness against uncertainties and disturbances. simulation tests for a two-link robot, under uncertainties, disturbance and parameters variations, show the accuracy and the robustness of the proposed adaptive scheme. copyright © 2006-2007 by ccc publications neural networks-based adaptive state feedback control of robot manipulators 329 2 robot control problem the lagrange–euler formulation, the dynamic equation of an n-joint robot arm can be expressed as m(q) .. q + c(q, . q) + g(q) + τc(q, . q) + τd (q, . q) = u (1) where m(q) ∈ rn×n bounded positive definite inertia matrix; c(q, .q) ∈ rn vector representing centrifugal and coriolis effects; g(q) ∈ rn vector representing gravitational torques; τc(q, . q) ∈ rn, τd (q, . q) ∈ rn vectors representing the dynamic effects as nonlinear frictions, small joint and link elasticities, backlash and bounded torque disturbances. here the uncertainties effect is decomposed as continuous part τc(q, . q) and discontinuous part τd (q, . q). u ∈ rn vector of joint torques supplied by the actuators; q ∈ rn vector of joint positions; . q ∈ rn vector of joint velocities and ..q ∈ rn vector of joint accelerations. taking as state vector xt = [ xt1 ... x t n ] with xti = [ qi . qi ] , the robot model (1) can be rewritten as . x = ax + b [ f(q, . q) + g(q)u + d(q, . q) ] (2) where f(q, . q) =   f1 ( q, . q ) ... fn(q, . q)   := −m−1(q) [ c ( q, . q ) + g(q) + τc ( q, . q )] g(q) =   g11 (q) . . . g1n (q) ... . . . ... gn1 (q) . . . gnn (q)   := m−1(q) d(q, . q) =   d1 ( q, . q ) ... dn(q, . q)   := −m−1(q)τd ( q, . q ) and a =diag[a1, .., an], b =diag[b1, .., bn] with ai = [ 0 1 0 0 ] , bi = [ 0 1 ] , i = 1..n the control problem can be stated as: for a given bounded reference trajectories qr, . qr and .. qr ∈ rn design the control input torques u such as the robot’s states follow their references, with all involved signals in closed loop remain bounded. 3 neural networks the general function of one hidden layer feedforward neural network can be described as in (3) as the weighted combination of n activation functions. here the input vector x and ϕi (.) represents the ith activation function (with its parameters vector θi) connected to the output by weight wi. y = n ∑ i=1 ϕi (x, θi) wi (3) the numbers of the input and output layers coincide with the dimension of the input vector and output information number, respectively. since the above neural networks will be trained on line to achieve the 330 ghania debbache, abdelhak bennia, noureddine goléa control task, and in order to reduce computation load, we will assume that the activation functions parameters θi are fixed, i.e., their number and shape is a priori determined. the only adjustable parameters are the wights wi. then, (3) can be rewritten in the compact form y = wt φ (x) (4) where φ t (x) = [ ϕ1 (x) ... ϕn (x) ] and wt = [ w1 ... wn ] . it is known from nns approximation theory [15-19] that the modeling error can be reduced arbitrarily by increasing the number n, i.e., the number of the linear independent activation functions in the network. that is, a smooth function f (x) , x ∈ ωx ⊂ rn can be written as f (x) = w∗t φ (x) + ε (x) (5) where ε (x) is the network inherent approximation error, and w∗ is an optimal weight vector. various well-known results, see e.g. [16-19], for various activation functions ϕi(.), based, e.g. on the stone-weierstrass theorem, say that any sufficiently smooth function can be approximated by a suitably large nn [5-8]. the functional range of nn (4) is said to be dense, if for any f (x) and a constant ε∗ > 0 there exist finite n and w∗ such that (5) holds with |ε (x)| < ε∗. the rang of activation functions include for instance the step, the ramp, the sigmoid and radial basis functions. several algorithms are proposed in the literature to select the structure and parameters for those kind of nns, see e.g. [20-21]. 4 neural state feedback due to approximation property (5), we can assume that the nonlinear terms in (2) can be approximated as fi(q, . q) = θ∗tfi φi(q, . q) + εi(q, . q) gii(q) = θ ∗tgi ψi(q) + εi(q) i = 1..n (6) where θ ∗tfi φi(q, . q) and θ∗tgi ψi(q) are nns of the from (4), and εi(q, . q), εi(q) are the inherent approximation errors due to the finite size of the nns. the optimal weights θ∗fi and θ ∗ gi defined above are quantities required only for analytical purpose. typically θ ∗fi and θ ∗ gi are chosen to minimize εi(q, . q) and εi(q) over the compact regions ω f and ωg respectively, that is θ ∗fi = arg minθ fi { sup q, . q∈ω f ∣∣ fi(q, . q)−θ tfi φi(q, . q) ∣∣ } θ∗gi = arg minθgi { sup q∈ωg ∣∣gii(q)−θ tgi ψi(q) ∣∣ } assumption 1: the neural networks approximation errors are bounded by ∣∣εi(q, . q) ∣∣ ≤ ε0i and |εi(q)|≤ ε0i, i = 1..n, for some constants ε0i and ε0i. assumption 1 results from the universal approximation property of neural networks, that can approximate any well-defined function over a compact space with finite approximation error. using (6) in (2), the robot dynamic can be written as . x = ax + b [ θ∗f φ(q, . q) + θ∗gψ(q)u + h(q)u + ω(q, . q) ] (7) where φ(q, . q) =block-diag [ φ1(q, . q), .., φn(q, . q) ] , ψ(q, . q) =block-diag [ ψ1(q, . q), .., ψn(q, . q) ] , θ∗f =blockneural networks-based adaptive state feedback control of robot manipulators 331 diag [ θ ∗tf1 , .., θ ∗t fn ] , θ∗g =block-diag [ θ∗tg1 , .., θ ∗t gn ] , ω(q, . q) = ε + d(q, . q), with ε t = [ ε1 ... εn ] , and h(q) =   ε1 g12 (q) . . . g1n (q) g21 (q) . . . ... ... . . . g(n−1)n (q) gn1 (q) . . . gn(n−1) (q) εn   based on (7), the control inputs are defined as u = [θgψ(q)] −1 [−θ f φ(q, . q) + .. qr + ke ] (8) where et = [ (qr −q)t ( . qr − . q )t ] is the tracking error vector, θg, θ f are the estimated neural networks parameters, and k =diag[k1, ..., kn] with ki ∈ r2 is pd gain vector, chosen such as the matrix ac = a−bk is hurwitz. then, introducing the control input (8) in (7) yields . e = ace−b [ θ̃ f φ(q, . q) + θ̃gψ(q)u + h(q)u + ω(q, . q) ] (9) where θ̃ f = θ∗f −θ f and θ̃g = θ∗g −θg are the parameters estimation errors. from (9), it can be seen that the tracking error vector is driven by the coupling terms and the finite approximation accuracy effects reflected by h(q) and the uncertainty term ω(q, . q). to design the neural networks parameters update laws and to ensure boundedness of the involved signals in the closed loop robot control, the following assumptions are used: assumption 2: the diagonal elements of g(q) are bounded such as gm ≤diag[g11(q), .., gnn(q)] ≤ gm , the matrix h(q) is bounded by |h(q)| ≤ h0, and the disturbance term ω(q, . q) is bounded by ∣∣ω(q, .q) ∣∣ ≤ ω0. assumption 3: the neural networks parameters are bounded by the constraint sets ω f and ωg such that: ω f = { θ f | ∣∣θ f ∣∣ ≤ fm } and ωg = {θg | gm ≤ |θg| ≤ gm}, respectively, where fm , gm, and gm are some known constants. the first part of assumption 2 follows from the fact that m(q) is bounded positive definite matrix, the second part follows from the boundedness of m(q) and εi(q). finally, the third part follows from boundedness of m(q), τd and εi(q, . q). the bounds used in assumption 3 result from the assumptions 1-2 and are used to ensure the boundedness of the neural networks outputs. in order to constraint the parameters θ f and θg within the sets ω f and ωg, respectively, we use the following parameter projection algorithm [22]: . θ f =    −γ1bt peφt (q, . q) if ∣∣θ f ∣∣ < fm or ( ∣∣θ f ∣∣ = fm and tr [ bt peφt (q, . q)θtf ] ≥ 0) −γ1bt peφt (q, . q) +γ1tr [ bt peφt (q, . q)θ̂tf ] ( 1+|θ f| fm )2 θ f if ∣∣θ f ∣∣ = fm and tr [ bt peφt (q, . q)θtf ] < 0 (10) and . θg =    −γ2bt peut ψt (q) if ∣∣∣θ̂g ∣∣∣ < gm or ( ∣∣∣θ̂g ∣∣∣ = gm and tr [ bt peut ψt (q)θ̂tg ] ≥ 0) −γ2bt peut ψt (q) +γ2tr [ bt peut ψt (q)θtg ] ( 1+|θg| gm )2 θg if |θg| = gm and tr [ bt peut ψt (q)θtg ] ≤ 0 (11) 332 ghania debbache, abdelhak bennia, noureddine goléa where γ1, γ2 > 0 are design parameters, and p = pt > 0 is the solution, for a given q = qt > 0, of the lyapunov equation atc p + pac = −q (12) moreover, in order to guarantee |θg| ≥ gm such that an inverse of θgψ(q) always exists, we use the following law to adjust the parameter θg. 1. whenever any element [θg]i j = gm use [ . θg ] i j = { −γ2 [ bt peut ψt (q) ] i j if [ bt peut ψt (q) ] i j < 0 0 if [ bt peut ψt (q) ] i j ≥ 0 (13) 2. otherwise, use (11). where [a]i j stands for the i jth element of the matrix a. the stability properties of the proposed nns adaptive state feedback are summarized by the following theorem. theorem 1: the robot adaptive control composed by the robot dynamic (2), the control input (8), the update laws (10)-(11) and (13) verifying assumptions 1-3, guarantees the following: 1. ∣∣θ f ∣∣ ≤ fm and gm ≤ |θg| ≤ gm 2. |e| ∈ l∞ 3. |u| ∈ l∞ proof : 1. to prove |θg| ≤ gm , let vg = 12γ2 tr [ θtg θg ] , then . v g = 1γ2 tr [ . θ t g θg ] . if the first line of (11) is true, we have either |θg| < gm or . v g = −tr [( bt peut ψt (q) )t θg ] ≤ 0 when |θg| = gm , that is, we get always |θg| ≤ gm . if the second line of (11) is true, we have |θg| = gm and . v g = tr [ − ( bt peut ψt (q) )t θg + tr [ bt peut ψt (q) θtg ] ( 1 +|θg| gm )2 θtg θg ] = −tr [( bt peut ψt (q) )t θg ] + tr [ bt peut ψt (q) θtg ] ( 1 +|θg| gm )2 tr [ θtg θg ] = −tr [( bt peut ψt (q) )t θg ] + tr [ bt peut ψt (q) θtg ] ( 1 +|θg| gm ) |θg|2 (14) since |θg| = gm, we get . v g = tr [ bt peut ψt (q) θtg ] g2m ≤ 0 (15) that is, |θg| ≤ gm . therefore, we have |θg| ≤ gm , ∀t ≥ 0. from (13) we see that if |θg|i j = gm, then [ . θg ] i j ≥ 0; that is, we have that |θg|i j ≥ gm. using the same analysis, we can prove that ∣∣θ f ∣∣ ≤ fm , ∀t ≥ 0. neural networks-based adaptive state feedback control of robot manipulators 333 2. consider the lyapunov function v = 1 2 et pe + 1 2γ1 tr [ θ̃tf θ̃ f ] + 1 2γ2 tr [ θ̃tg θ̃g ] (16) the differentiation of (16) along (9) yields . v = −1 2 et qe−et pb [ θ̃ f φ(q, . q) + θ̃gψ(q)u + h(q)u + ω(q, . q) ] + 1 γ1 tr [ . θ̃ t f θ̃ f ] + 1 γ2 tr [ . θ̃ t g θ̃g ] (17) which can be arranged as . v = −1 2 et qe−et pb [ h(q)u + ω(q, . q) ] + 1 γ1 tr [( . θ̃ t f −γ1φ(q, . q)et pb ) θ̃ f ] + 1 γ2 tr [( . θ̃ t g −γ2ψ(q)uet pb ) θ̃g ] (18) then, using (10)-(11) and the fact that . θ̃ f = − . θ f ( . θ̃g = − . θg), one can show that the third and fourth terms in (18) are always ≤ 0. therefore, (18) can be written as . v ≤ −1 2 et qe−et pb [ h(q)u + ω(q, . q) ] (19) further, (19) can be upper bounded by . v ≤ −1 2 λmin (q)|e|2 +|e||pb| [ |h(q)||u|+ ∣∣ω(q, .q) ∣∣] (20) then, using (8) and the result in 1, the input torques can be upper bounded by |u| ≤ ∣∣∣[θgψ(q)]−1 ∣∣∣ [∣∣θ f φ(q, . q) ∣∣ + ∣∣..qr ∣∣ +|k||e| ] ≤ 1 gm ( fm + q0 +|k||e|) (21) where q0 is an upper bound on the desired accelerations .. qr. then, using (21) and the assumptions 1-3 in (20), becomes . v ≤ −1 2 λmin (q)|e|2 + κ1 |e|2 + κ2 |e| (22) where κ1 = h0gm |pbk| and κ2 = |pb| ( h0 gm ( fm + q0) + ω0 ) . hence, (22) can be arranged as . v ≤ −1 2 (λmin (q)−2κ1)|e|2 + κ2 |e| (23) if the matrix q is chosen such as λmin (q) > 2κ1, then . v ≤ 0 whenever the tracking error is outside the region given by |e| ≤ 2κ2 (λmin (q)−2κ1) (24) which implies that |e| ∈ l∞. 334 ghania debbache, abdelhak bennia, noureddine goléa 3. using the result (24) in (21) yields |u| ≤ 1 gm ( fm + q0 +|k| 2κ2 (λmin (q)−2κ1) ) (25) this implies that |u| ∈ l∞. remarks: 1. only the diagonal elements of g (q) are estimated and used in the control inputs design. by doing this, we avoid the estimation of the coupling terms (considered here as disturbances) and the need to compute the inverse of the estimation of g (q). 2. although, the control torques (8) are presented in vector form, they can be, in practice, computed independently, since θgψ(q) and k are diagonal matrices and no information is needed from the other input torques. 3. from (25), it can be seen that constraints on the control inputs, i.e., |ui| ≤ ui max can be meet by tuning the the pd gain ki and the desired accelerations magnitudes q0. 5 simulation results to test the proposed adaptive neural control, we consider the two-link manipulator (fig. 1) whose dynamic equations of motion (1) are: m (q) = [ (m1 + m2) l21 m2l1l2 (s1s2 + c1c2) m2l1l2 (s1s2 + c1c2) m2l22 ] c ( q, . q ) = [ 0 −m2l1l2 (c1s2 −s1c2) . q2 −m2l1l2 (c1s2 −s1c2) . q1 0 ] g(q) = [ −g (m1 + m2) s1l1 −gm2l2s2 ] where c1 = cos(q1), c2 = cos(q2), s1 = sin(q1) and s2 = sin(q2). the robot physical parameters are: l1 = l2 = 1m, m1 = m2 = 1kg, and g = 9.81 m/s2. the uncertainties terms in (1) are given by: τc = [ . q1 + sin (3q1) 1.2 . q2 + 0.5 sin (2q2) ] , τd = [ 0.2sign ( . q1 ) 0.1sign ( . q2 ) ] the nns adaptive control design for the two-link robot is as follows: 1. in order to construct the nns approximators, each variable q1, q2 ∈ [−π, π] and . q1, . q2 ∈ [−2π, 2π] range is devised into 3 sub domains, which yields four rbf networks to approximate f1 ( q, . q2 ) , f2 ( q, . q1 ) , g11 (q) and g22 (q), with qt = [ q1 q2 ] , with, respectively, 27, 27, 9 and 9 rbfs. the designed rbf networks take the following compact forms: f̂1 ( q, . q2 ) = θ tf1 φ1 ( q, . q2 ) (26) f̂2 ( q, . q1 ) = θ tf2 φ2 ( q, . q1 ) (27) ĝ11 (q) = θ tg1 ψ1 (q) (28) ĝ22 (q) = θ tg2 ψ2 (q) (29) neural networks-based adaptive state feedback control of robot manipulators 335 m2 l2 q2 m1 q1 l1 figure 1: two link robot where θ f1 , θ f2 ∈ r27×1 and θg1 , θg2 ∈ r9×1 are the nns adjustable parameters. this approach is far from being optimal, but it has the merit to reduce the number of parameters to be learned and thus to reduce the update algorithm complexity and execution time. 2. the control input is designed as in (8) with the adaptive nns defined in (26)-(29). the pd gain is defined as k =diag[k1, k2] with k1 = k2 = [ 16 8 ] . 3. for the choice of q = 100i4 and the solutions of (12) we get p. 4. by analyzing the dynamic of the robot, the following bounds are fixed gm = 2.5, gm = 0.5 and fm = 20. the adjustable parameters are updated using (10)-(11) with γ1 = 0.01 and γ2 = 0.001. in the simulation, the nns parameters are initialized as |θg1 (0)| = |θg2 (0)| = 0.5 and ∣∣θ f1 (0) ∣∣ =∣∣θ f2 (0) ∣∣ = 0. the initial states, in all simulations, are xt (0) = [ 0.5 2 −0.5 1 ] . the first simulation test concerns the regulation of the joint positions under nominal conditions, i.e., no parameters changes and no disturbances. as depicted in figure 2.a, the joint positions exhibit a good transient performance, and no error is remarked in steady state regime. figure 2.b shows the regulation performance under uncertainties effects. from this figure it is seen that these uncertainties affect little the regulation performance and small steady state error is introduced and the nns adaptive control achieves good compensation of the uncertainties effects. in figure 2.c, in addition to the uncertainties effects, a payload change is introduced at t = 15s when m2 passes from 1kg to 3kg. this situation is rapidly taken in account by the nns control and it’s effect is compensated. 336 ghania debbache, abdelhak bennia, noureddine goléa 0 5 10 15 20 0 2 4 x 1 0 5 10 15 20 0 2 4 x 2 0 5 10 15 20 −20 0 20 u 1 t (sec) 0 5 10 15 20 0 2 4 x 3 0 5 10 15 20 0 2 4 x 4 0 5 10 15 20 −20 0 20 u 2 t (sec) a) nominal case 0 5 10 15 20 0 2 4 x 1 0 5 10 15 20 0 2 4 x 2 0 5 10 15 20 −20 0 20 40 u 1 t (sec) 0 5 10 15 20 0 2 4 x 3 0 5 10 15 20 0 2 4 x 4 0 5 10 15 20 −20 0 20 40 u 2 t (sec) b) τc + τd uncertainties effects 0 5 10 15 20 0 2 4 x 1 0 5 10 15 20 0 2 4 x 2 0 5 10 15 20 −20 0 20 40 u 1 t (sec) 0 5 10 15 20 0 2 4 x 3 0 5 10 15 20 0 2 4 x 4 0 5 10 15 20 −20 0 20 40 u 2 t (sec) c) τc + τd + m2 variation effects figure 2: regulation performance 0 10 20 30 40 −2 0 2 x 1 0 10 20 30 40 −5 0 5 x 2 0 10 20 30 40 −20 0 20 u 1 t (sec) 0 10 20 30 40 −2 0 2 x 3 0 10 20 30 40 −5 0 5 x 4 0 10 20 30 40 −20 0 20 u 2 t (sec) a) nominal case 0 10 20 30 40 −2 0 2 x 1 0 10 20 30 40 −5 0 5 x 2 0 10 20 30 40 −20 0 20 u 1 t (sec) 0 10 20 30 40 −2 0 2 x 3 0 10 20 30 40 −5 0 5 x 4 0 10 20 30 40 −20 0 20 u 2 t (sec) b) τc + τd uncertainties effects 0 10 20 30 40 −2 0 2 x 1 0 10 20 30 40 −5 0 5 x 2 0 10 20 30 40 −40 −20 0 20 u 1 t (sec) 0 10 20 30 40 −2 0 2 x 3 0 10 20 30 40 −5 0 5 x 4 0 10 20 30 40 −40 −20 0 20 u 2 t (sec) c) τc + τd + m2 variation effects figure 3: tracking performance the second simulation test concerns the tracking performance for the desired trajectories qr1 = sin(t) + sin(2t) and qr2 = cos(t) + cos(2t). figure 3.a presents the tracking performance in the nominal case. as depicted, the joint positions exhibit a good transient performance, and small error is remarked in steady state regime. figure 3.b shows the tracking performance under uncertainties effects. it is clear that these uncertainties introduce acceptable tracking error, and the nns control inputs compensate the uncertainties with a little effort. in figure 3.c, we add to the uncertainties effects, a payload change is introduced at t = 20s when m2 passes from 1kg to 3kg. this variation affects essentially the developed torques to compensate the additional mass, and small error is remarked. 6 conclusion in this paper, an adaptive nns control scheme for rigid robot control, was proposed. the adaptive capability of handling modeling errors and external disturbances was demonstrated. the error convergence rate with the nns adaptive approach was found to be fast. asymptotic stability of the control system is established using the lyapunov approach. simulation studies for a two-link robot verify the flexibility, adaptation and tracking performance of the proposed approach. the major contributions of the paper are as follows: reduction of the nns complexity and no robustifying control is required to achieve the stability or to enhance the control performance. neural networks-based adaptive state feedback control of robot manipulators 337 bibliography [1] j. y. han, h. hemami, and s. yurkovich, nonlinear adaptive control of a n-link robot with unknown load, int. j. robotics res., vol. 6, no. 3, pp. 71–86, 1987. 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[14] c. torras, natural inspiration for artificial adaptivity: some neurocomputing experiences in robotics, proc. 4th intl. conf. on unconventional computation (uc’05), sevilla, spain, oct. 2005. [15] m. j. d. powell, “radial basis functions for multivariable interpolation: a review,“ in algorithms for approximation of functions and data, j. c. mason, m. g. cox, eds. oxford, u.k.: oxford univ. press, 1987, pp. 143-167. [16] e. j. hartman, j. d. keeler, and j. m. kowalski, “layered neural networks with gaussian hidden units as universal approximations,“ neural comp., vol. 2, no. 2, pp. 210-215, 1990. [17] t. poggio and f. girosi, “networks for approximation and learning,“ proc. ieee, vol. 78, pp. 14811497, sept. 1990. [18] j. park and i. w. sandberg, universal approximation using radial-basis-function networks, neural comp. 3, 246-257, 1991. 338 ghania debbache, abdelhak bennia, noureddine goléa [19] b. liu and j. si, the best approximation to c2 function and its error bounds using regular-center gaussian networks, ieee trans. neural networks, vol. 5, pp.848-857, 1994. [20] r.j. schilling, j.j. jr. carroll, a.f al-ajlouni, approximation of nonlinear systems with radial basis function neural networks, ieee transactions on neural networks, vol. 12, pp. 1-15, 2001. [21] h. guang-bin, p. saratchandran, n. sundararajan, a generalized growing and pruning rbf (ggap-rbf) neural network for function approximation, ieee transactions on neural networks, vol. 16, pp. 5767, 2005 [22] s. s. sastry and m. bosdon, adaptive control: stability, convergence and robustness, printicehall, 1989. ghania debbache electrical engineering institute, oum el-bouaghi university, 04000 oum el-bouaghi, algeria gdebbache@yahoo.fr abdelhak bennia electronic departement, constantine university 25000 constantine, algeria abdelhak.bennia@laposte.net noureddine goléa electrical engineering institute, oum el-bouaghi university, 04000 oum el-bouaghi, algeria n.golea@lycos.com received: march 15, 2007 neural networks-based adaptive state feedback control of robot manipulators 339 ghania debbache was born in constantine, algeria, in 1974. she received the b.sc. and m.sc. degrees in electronics from the constantine university, algeria, in 1997 and 2000, respectively. she is currently pursuing the ph.d. degree on the intelligent control at the university constantine. currently, she is a teaching assistant at the technologic sciences institute of oum el bouaghi university, algeria. abdelhak bennia received his d.e.s. degree in 1983 in physics from the university of constantine, algeria. from 1984 to 1990 he attended the graduate school at virginia tech, majoring in electrical engineering. he received the m.sc. degree in 1986 and the ph.d. degree in 1990. since 1990, he has been with the electronics department of the university of constantine. his current research interests are deconvolution, system identification and neural networks applied to character recognition, control systems, and signal processing. noureddine goléa was born in batna, algeria, in 1967. he received the engineer and master grades from sétif university, algeria, and the doctorat from batna university, algeria, all in industrial control, in 1991, 1994, and 2000, respectively. from 1991 to 1994, he was with electronics institute at sétif university, and from 1994 to 1996, he was with the electronics institute at batna university. currently, he is professor of electrical engineering in the technologic sciences institute at oum el-bouaghi university, algeria. his research interests are nonlinear and adaptive control, and intelligent control applied to motion control. http://horos.rdsor.ro/ijcccv3n4draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 4, pp. 353-365 quality of service scheduling in real-time systems audrey marchand, maryline chetto abstract: in this paper, we deal with dynamic scheduling components integrating new quality of service (qos) functionalities into a linux-based real-time operating system. in our approach, periodic tasks allow occasional deadline violations within given bounds specified according to the skip-over task model. hence, every task has a minimal qos guarantee which is expressed by the ratio of periodic task instances which must complete before their deadline. the work stated here provides two on-line scheduling algorithms, namely rlp and rlp/t, which enhance the existing skip-over algorithms. more specifically, the proposed algorithms aim at improving the actual qos observed for periodic tasks (which is always greater or equal to the qos guarantee). these novel scheduling techniques rely on the edl (earliest deadline as late as possible) scheduling strategy. simulation results show the performance of rlp and rlp/t with respect to basic skip-over algorithms. finally, we present the integration of these qos scheduling services into cleopatre open-source component library, a patch to linux/rtai. keywords: real-time, dynamic scheduling, quality of service, periodic tasks, component-based systems, linux-based systems 1 introduction software environments, and more precisely operating systems have still difficulties to meet the special demands of multimedia applications. in particular, multimedia applications have real-time constraints which are not handled properly by general-purpose operating systems. in order to meet the requirements imposed by multimedia applications on processor scheduling, we have to turn to the temporal stringency of real-time systems. real-time systems are those in which the time at which the results are produced is important. the correctness of the result of a task is not only related to its logic correctness, but also to when the results occur. traditional classification of real-time systems stands for three classes to characterize the real-time requirement of such systems : hard, soft and firm. in hard real-time systems, all instances must be guaranteed to complete within their deadlines. in those critical control applications, missing a deadline may cause catastrophic consequences on the controlled system. for soft systems, it is acceptable to miss some of the deadlines occasionally. it is still valuable for the system to finish the task, even if it is late. in firm systems, tasks are also allowed to miss some of their deadlines, but, there is no associated value if they finish after the deadline. typical illustrating examples of systems with firm real-time requirements are multimedia systems in which it is not necessary to meet all the task deadlines as long as the deadline violations are adequately spaced. a prominent strategy for performing resource management for multimedia systems is qos-driven management, in which quality requirements such as resolution and frame rate are translated into resource requirements such as computation burst frequencies and durations. this resource information is then used for admission testing and resource reservation. the motivation for this translation from application level requirements to resource requirements is to guarantee a given qos. the complexity of both the qos space and the resource space suggests that perfect characterization is hard to achieve, so it would be desirable to have a scheduling policy that would adapt to changes in user qos requirements. such policy should strive to achieve the desired qos, in an environment with variable resources, as well as complex and variable application demands. copyright © 2006-2008 by ccc publications 354 audrey marchand, maryline chetto in this paper, we address the problem of the dynamic scheduling of periodic tasks with firm constraints. the scope of the paper is to maximize the actual qos of periodic tasks by maximizing the number of instances which complete before their deadline. the remainder of this paper is organized in the following manner. next section introduces existing approaches for scheduling firm real-time systems. then we present relevant background material about both the skip-over model and the edl scheduling algorithm. more particularly, we give the definition of rto and bwp scheduling algorithms, which are based on the skip-over model. the functioning and optimality of the edl algorithm is also outlined. further, we describe the proposed algorithms, namely rlp and rlp/t, as an enhancement of the bwp algorithm, based on the edl scheduling mechanism. moreover, we present a model of a real-world problem to show the practical interest of our work. the performance analysis of both rlp and rlp/t, in terms of task completions, is reported after. then, we describe the integration of these qos components into linux/rtai. finally, in section 8, we summarize our contribution. 2 related work there have been some previous approaches to the specification and design of real-time systems that tolerate occasional losses of deadlines. hamdaoui and ramanathan in [7] introduced the concept of (m,k)-firm deadlines to model tasks that have to meet m deadlines every k consecutive invocations. their algorithm uses a distance-based priority (dbp) scheme to increase the priority of a job in danger of missing more than m deadlines over a sliding window of k requests for service. moreover, algorithms such as vds [17] and dwcs [19] are provably superior to dbp in meeting (m,k) service requirements for a number of specific and non-trivial situations. similar to (m,k)-firm scheduling is the work introduced by koren and shasha [8] with the notion of skip factor. if a task has a skip factor of s, it will have one invocation skipped out of s. it is a particular case of the (m,k)-firm model where m = k − 1. they reduce the overload by skipping some task invocations, thus exploiting skips to increase the feasible periodic load. this approach gives a solution to the scheduling problem of overloaded systems, while representing a system quality of service requirement for real-time applications. broadly speaking, the skip-over scheduling algorithms guarantee the timing correctness of the real-time application. one interesting result is that making optimal use of skips is a np-hard problem. there are also examples of (m,k)-hard schedulers [1], but most such approaches require off-line feasibility tests, to ensure predictable service. in [3, 4], caccamo and buttazzo follow this work by scheduling hybrid task sets consisting of skippable periodic and soft aperiodic tasks. they propose and analyze an algorithm, based on a variant of earliest deadline first (edf) scheduling, in order to exploit skips under the total bandwith server (tbs). in previous works [10, 12], we have considered the same approach but using the earliest deadline as late as possible server (edl). these results have led us to propose a raw version of the rlp algorithm (idle time schedule based on red tasks only) [11]. west and poellabauer in [16] proposed a windowed lost rate, that specifies a task can tolerate x deadlines missed over a finite range or window, among consecutive y instances. in [2], bernat et al. introduce a general framework for specifying tolerance of missed deadlines under the definition of weakly hard constraints. 3 theoretical background 3.1 the skip-over model we are here interested in the problem of scheduling periodic tasks which allow occasional deadline violations (i.e., skippable periodic tasks), on a uniprocessor system. we assume that tasks can be preempted and that they do not have precedence constraints. a task ti is characterized by a worst-case quality of service scheduling in real-time systems 355 task t0 t1 t2 t3 t4 ci 3 4 1 7 2 pi 30 20 15 12 10 table 1: a basic periodic task set -6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 0 30 60 0 20 40 60 0 15 30 45 60 0 12 24 36 48 60 0 10 20 30 40 50 60 t0 t1 t2 t3 t4 : processing red task 6 : release time figure 1: rto scheduling algorithm (si = 2) computation time ci, a period pi, a relative deadline equal to its period, and a skip parameter si, which gives the tolerance of this task to missing deadlines. the distance between two consecutive skips must be at least si periods. when si equals to infinity, no skips are allowed and ti is equivalent to a hard periodic task. one can view the skip parameter as a qos metric (the higher si, the better the quality of service). a task ti is divided into instances where each instance occurs during a single period of the task. every instance of a task can be red or blue [8]. a red task instance must complete before its deadline; a blue task instance can be aborted at any time. however, if a blue instance completes successfully, the next task instance is still blue. red tasks only (rto) algorithm the first algorithm proposed by koren and shasha is the red tasks only (rto) algorithm. red instances are scheduled as soon as possible according to earliest deadline first (edf) algorithm, while blue ones are always rejected. deadline ties are broken in favor of the task with the earliest release time. in the deeply red model where all tasks are synchronously activated and the first si − 1 instances of every task ti are red, this algorithm is optimal. rto is illustrated in figure 1 using the task set t = {t0, t1, t2, t3, t4} of five periodic tasks whose parameters are described in table 1. tasks have uniform skip parameter si = 2 and the total processor utilization factor up = ∑ ci pi is equal to 1.15. as we can see, the distance between every two skips is exactly si periods, thus offering only the minimal guaranteed qos level for periodic tasks. blue when possible (bwp) algorithm the second algorithm studied is the blue when possible (bwp) algorithm which is an improvement of the first one. indeed, bwp schedules blue instances whenever their execution does not prevent the red ones from completing within their deadlines. in that sense, it operates in a more flexible way. deadline ties are still broken in favor of the task with the earliest release time. figure 2 shows an illustrative example of bwp scheduling using the task set previously described in table 1. compared with rto, more task instances complete successfully with bwp. we observe that five violations of deadline relative to blue task instances occur at time instants t = 24 (task t3), t = 30 (tasks t2 and t4) and t = 60 (tasks t3 and t4), thus reducing the qos. 356 audrey marchand, maryline chetto -6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 0 30 60 0 20 40 60 0 15 30 45 60 0 12 24 36 48 60 0 10 20 30 40 50 60 t0 t1 t2 t3 t4 : processing red task : processing blue task 6 : release time figure 2: bwp scheduling algorithm (si = 2) 3.2 the edl algorithm the definition of the earliest deadline as late as possible (edl) algorithm makes use of some results presented by chetto and chetto in [5]. under edl, periodic tasks are scheduled as late as possible. an accurate characterization of the idle times during which the processor is not occupied is necessary. the authors introduced an availability function f xy defined with respect to a task set y and a scheduling algorithm x. f xy (t) = 1 if the processor is idle at t, 0 otherwise. so, for any instants t1 and t2, value of ∫t2t1 f x y (t)dt denoted by ω x y (t1,t2) gives the total idle time in [t1,t2]. f edl y can be described by means of the following two vectors: • k, called static deadline vector, represents the times at which idle times occur and is constructed from the distinct deadlines of periodic tasks. • d, called static idle time vector, represents the lengths of the idle times relating to time instants of vector k. the complexity of the edl algorithm is o(kn) where n is the number of periodic tasks, and k is equal to ⌊ r p ⌋, where r is the longest deadline, and p is the shortest period [13]. we also recall the fundamental property relative to the optimality of edl [5]: theorem 1. let x be any preemptive scheduling algorithm and a a set of independent aperiodic tasks. for any instant t, ω x a (0,t) ≤ ωedl a (0,t) (1) we give now an illustrative example of the computation of the idle times performed by edl. consider the periodic task set t = {t1, t2} consisting of two periodic tasks t1(3,10) and t2(3,6). the f edlt computation produced at time zero is described in figure 3. the authors in [5] described how the edl algorithm can be applied, first to the decision problem that arises when a sporadic time critical task occurs and requires to be run at an unpredictable time and secondly, to the scheduling problem that arises in a fault tolerant system using the deadline mechanism [?] for which each task implements primary and backup copies (the processor time reserved for the execution of the backup copies is realized with edl and is reclaimed as soon as the primary task executes successfully). in next sections, we are interested in using edl first to simulate a schedule (rlp implementation) and then to derive a measure required for deciding whether a blue task can be accepted (rlp/t implementation). quality of service scheduling in real-time systems 357 -6 6 6 6 6 6 6 6 6 6 0 10 20 30 0 6 12 18 24 30 k0 k1 k2 k3 k4 k5 k6 t1 t2 f edl t : processing periodic task : processing idle time 6 : release time figure 3: f edl t computation produced at time zero 4 the proposed algorithms 4.1 red tasks as late as possible (rlp) the main drawback of bwp relies on the fact that blue task instances are executed as background tasks. this leads to abort partially or almost completely executed blue task instances, thus wasting processor time. algorithm outline the objective of rlp algorithm is to bring forward the execution of blue task instances so as to minimize the ratio of aborted blue instances, thus enhancing the actual qos (i.e., the total number of task completions) of periodic tasks. from this perspective, rlp scheduling algorithm, which is a dynamic scheduling algorithm, is specified by the following behaviour: 1. if there are no blue task instances in the system, red task instances are scheduled as soon as possible according to the edf (earliest deadline first) algorithm. 2. if blue task instances are present in the system, these ones are scheduled as soon as possible according to the edf algorithm (note that it could be according to any other heuristic), while red task instances are processed as late as possible according to the edl algorithm. deadline ties are always broken in favor of the task with the earliest release time. the main idea of this approach is to take advantage of the slack of red periodic task instances. determination of the latest start time for every red request of the periodic task set requires preliminary construction of the schedule by a variant of the edl algorithm taking skips into account [12]. in the edl schedule established at time τ, we assume that the instance following immediately a blue instance which is part of the current periodic instance set at time τ, is red. indeed, none of the blue task instances is guaranteed to complete within its deadline. moreover, silly-chetto in [13] proved that the online computation of the slack time is required only at time instants corresponding to the arrival of a request while no other is already present on the machine. in our case, the edl sequence is constructed not only when a blue task is released (and no other was already present) but also after a blue task completion if blue tasks remain in the system (the next task instance of the completed blue task has then to be considered as a blue one). note that blue tasks are executed in the idle times computed by edl and are of same importance beside red tasks (contrary to bwp which always assigns higher priority to red tasks). illustrative example consider once again the periodic task set t defined in table 1. the relating rlp scheduling is illustrated in figure 4. in this example, we can see that, thanks to rlp scheduling, the number of 358 audrey marchand, maryline chetto -6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 0 30 60 0 20 40 60 0 15 30 45 60 0 12 24 36 48 60 0 10 20 30 40 50 60 t0 t1 t2 t3 t4 : processing red task : processing blue task 6 : release time figure 4: rlp scheduling algorithm (si = 2) violations of deadline relative to blue task instances has been reduced to three. they occur at time instants t = 40 (task t4), and t = 60 (tasks t3 and t4). observe that t3 first blue task instance which failed to complete within its deadline in the bwp case (see figure 2), has enough time to succeed in the rlp case, since the execution of t1 and t0 first red task instances is postponed. until time t = 10, red task instances are scheduled as soon as possible. from time t = 10 to the end of the hyperperiod (defined as the least common multiple of task periods), red task instances do execute as late as possible in the presence of blue task instances, thus enhancing the actual qos of periodic tasks. 4.2 red tasks as late as possible with blue acceptance test (rlp/t) the main drawback of rlp relies on the fact that this algorithm attempts to execute blue task instances as soon as possible, at the risk of aborting them before their completion, thus generating a processor time wasting. this assessment led us to propose a novel algorithm named rlp/t (red tasks as late as possible with blue acceptance test). algorithm outline red tasks as late as possible with blue acceptance test (rlp/t) algorithm is designed to maximize the actual qos of periodic task sets defined under skip constraints. it acts as follows: red tasks enter straight the system at their arrival time whereas blue tasks integrate the system upon acceptance. once they have been accepted, blue tasks are scheduled as soon as possible together with red tasks. upon acceptance, blue tasks are again of same importance beside red tasks. deadline ties are always broken in favor of the task with the earliest release time. whenever a new blue task enters the system, the idle times are computed using the edl scheduler. in the edl schedule established at time τ, we assume that the instance following immediately a blue instance which is part of the current periodic instance set at time τ, is also blue. indeed, all blue task instances previously accepted at τ are guaranteed by the schedulability test they passed successfully. this one checks whether there are enough idle times to accommodate the new blue task within its deadline, as described in the following section. acceptance test of blue tasks under rlp/t now, we are ready to present the new feasibility test algorithm for the rlp scheduling scheme which, given any occurring blue task b is capable of answering the question "can b be accepted ?". notice that b will be accepted if and only if there exists a valid schedule, i.e., a schedule in which b will execute by its deadline while red periodic tasks and blue tasks previously accepted, will still meet their deadlines. let τ be the current time which coincides with the arrival of a blue task b. upon arrival, task b(r, c, d) quality of service scheduling in real-time systems 359 is characterized by its release time r, its execution time c, and its deadline d, with r + c ≤ d. we assume that the system supports several blue tasks at time τ. each of them has been accepted before τ and has not completed its execution at time τ. let denote by b(τ) = {bi(ci(τ), di), i=1 to blue(τ)} the blue task set supported by the machine at τ. value ci(τ) is called dynamic execution time and represents the remaining execution time of bi at τ. a deadline occurs at di. we assume that b(τ) is ordered such that i < j implies di ≤ d j. the acceptance test of blue tasks within a system involving rlp skippable tasks presented below in theorem 2, is based on the one established by silly-chetto and al. [14] for the acceptance of sporadic requests occurring in a system consisting of basic periodic tasks (i.e., without skips). theorem 2. task b is accepted if and only if, for every task bi ∈ b(τ) ∪{b} such that di ≥ d, we have δi(τ) ≥ 0, with δi(τ) defined as: δi(τ) = ω edl t(τ)(τ, di) − i ∑ j=1 c j(τ) (2) δi(τ) is called slack of task bi at time τ which represents the maximum units of time during which the task could not be served by the processor without missing its deadline. ωedl t(τ) (τ, di) denotes the total units of time that the processor is idle in the time interval [τ, di]. the total computation time required by blue tasks within [τ, di] is given by ∑ i j=1 c j(τ) the procedure that implements the acceptance test calls for the edl algorithm for the computation of the total idle times which will be used to compute the slack of blue tasks. then, this slack is compared to zero. thus, the acceptance test proposed in this paper runs in o(⌊ r p ⌋n + blue(τ)) in the worst-case, where n is the number of periodic tasks, r is the longest deadline, p is the shortest period, and blue(τ) denotes the number of active blue tasks at time τ, whose deadline is greater or equal to the deadline of the occurring task. note that this acceptance test could be implemented in o(n + blue(τ)) by considering and maintaining to update additional data structures using slack tables, as proved in [15]. illustrative example rlp/t scheduling is illustrated in figure 5 with the periodic task set t defined in table 1. it is easy to see that rlp/t improves on both rlp and bwp. only two violations of deadline relative to blue task instances are observed: at time instants t = 40 (task t4) and t = 60 (task t3). the acceptance test contributes to compensate for the time wasted in starting the execution of blue tasks which are not able to complete within their deadline. as we can observe, in the rlp case (see figure 4), t3 blue instance released at time t = 48 is aborted at time t = 60 (2 units of time were indeed wasted). note that the rejection of this blue task instance, performed with rlp/t, contributes to save time used for the successful completion of t4 blue instance released at time t = 50. in section 6, we quantify more precisely the gain of performance of rlp/t upon rlp, bwp and rto. 5 applying theory to real-world problems 5.1 multimedia applications in order to understand the importance of cpu scheduling in multimedia real-time applications, it is useful to place the issue in context. multimedia implies a certain amount of data to be handled within a specified time frame and requires a tremendous amount of resources to accommodate. for multimedia applications to function correctly, there must be a steady stream of data for the output devices to process. for the viewer to perceive continuous media such as movies or music, the output devices have to output 360 audrey marchand, maryline chetto -6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 0 30 60 0 20 40 60 0 15 30 45 60 0 12 24 36 48 60 0 10 20 30 40 50 60 t0 t1 t2 t3 t4 : processing red task : processing blue task 6 : release time figure 5: rlp/t scheduling algorithm (si = 2) new media within strict time constraints (e.g. 30 frames per second for video applications). given this observation, one gets a better understanding of the crucial role of cpu scheduling in such applications. consider a simplified model of a real-time telesurveillance application, as represented in figure 6. the relevant tasks are the acquiring tasks and the display task. video data are first captured and digitized through video capture devices such as video cameras. then, each video capture task “acqi" reads the input video buffer relative to its camera, thus periodically acquiring incoming frames. downstream from the chain, another task named “display" is in charge of continuously consuming frames from an output frame buffer and sending the acquired video frames to a final display device composed of various telesurveillance screens. figure 6: simplified architecture of a real-time telesurveillance application one important problem part of the management of telesurveillance systems is the data refreshing rate on the display device. indeed, by definition, such a system must provide pictures as recent as possible to be useful. if there is no data for the output devices to process, there is buffer underflow. the media application will stall and wait for new data to be provided. naturally, buffer underflow should be avoided whenever possible. scheduling implies multiplexing a resource among several tasks to ensure all throughput requirements are met. in the present case, the problem consists in ensuring an acceptable refreshing rate (i.e. a guaranteed qos level). 5.2 dynamic qos scheduling for multimedia applications, the cpu scheduler determines the resulting quality of service. the more cpu cycles allocated to a task, the more data can be produced, thus providing a better quality output. in the previous example, more cpu cycles produce more frames, thus allowing more frames per second to be displayed on the workstation monitor. however, if several videos are executing at the same time there may not be enough cpu cycles available to produce all the video frames requested. in this quality of service scheduling in real-time systems 361 figure 7: qos of periodic tasks with low and high skip parameters (si = 2 and si = 6) overloaded situation, the quality of service (i.e. frame rate) is reduced to a lower acceptable level, which results for instance to display an image at 15 frames per second instead of 30 frames per second. the resulting video just appears less smooth during the transient overload period. now, let us consider rlp or rlp/t algorithms for scheduling the application described in figure 6. at initialization time, the application specifies a desired average rate of execution by appropriately setting the skip parameter si of each “acqi" task. rlp (or rlp/t) scheduler can be viewed as an edf prioritybased scheduler coupled with a skip-over rate regulator. that ensures every task not to be executed below a specified rate, whatever is the cpu workload. rlp and rlp/t superiority over rto and bwp effectively results in a higher and guaranteed frame rate on the workstation monitors. 6 simulation study in this section, we summarize the results of a simulation study which compares the performance of the different qos scheduling algorithms. the objective is to maximize the actual qos level of periodic tasks, i.e., the ratio of periodic tasks which complete before their deadline. experiments also evaluate the impact of the skip value for each algorithm, namely rto, bwp, rlp and rlp/t. 6.1 simulation context the simulation context includes 50 periodic task sets, each consisting of 10 tasks with a least common multiple equal to 3360. tasks are defined under qos guarantees specified by uniform si. their worstcase execution time is randomly generated and depends on the input setting of the periodic load up. deadlines are equal to the periods and greater than or equal to the computation times. simulations have been processed over 10 hyperperiods. 6.2 varying the periodic load measurements rely on the ratio of periodic tasks which complete before their deadline. the evaluation is done varying the periodic load up. the results obtained for si = 2 (one instance every two can be aborted) and si = 6 (one instance every six can be aborted) are described on figure 7. from the graphs, we can say that bwp, rlp and rlp/t outperform the rto model in which the qos level is still constant whatever is the periodic load applied. for si = 6, the actual qos (which corresponds to the qos guarantee) remains constant at a rate of 5/6=83%. the advantage of rlp over bwp is slight for low skip parameters, and more significant for high skip parameters. we note that the performance of 362 audrey marchand, maryline chetto figure 8: the cleopatre framework bwp and rlp is dramatically worse than the one achieved by rlp/t. this result was expected because both bwp and rlp attempt to schedule blue instances that have not enough time for completing within their deadlines. this wasted time is not saved for executing other blue instances with closer deadline. in contrast, rlp/t finds a way of saving this cpu time by implementing an acceptance test for blue instances. we can observe that this gain of performance is all the more significant as the periodic load up is higher. for instance, in figure 7, for up ≥ 120%, rlp/t enjoys more than factor 14 success rate advantage over bwp. moreover, we observe a very low gradient for the rlp/t curve which is not the case for other models. for up = 150% and si = 2, actual qos levels for rto and rlp/t are respectively equal to 50% and 84%, which figures the great predominance of rlp/t over rto. the variation of the skip parameter value shows that, for wide loads, the actual qos of periodic tasks is all the more improved with rlp/t that the qos constraint is smaller. for instance, for up = 110%, rlp/t applied to periodic tasks with si = 2 will successfully process twice as many periodic instances over bwp, as with periodic tasks with si = 6. as we can see, the major difference in the performance between rlp/t and bwp appears not only for heavy loads but also for small value of si. 7 integration into a linux-based system 7.1 cleopatre library rto, bwp, rlp and rlp/t algorithms have been integrated into a library of free software components called cleopatre (software open components on the shelf for embedded real-time applications) [6]. this library, part of a french national project 1, was designed to provide more efficient and better service to real-time applications. the purpose was to enrich the real-time facilities of real-time linux versions, such as rtlinux [18] or rtai [9]. rtai was the solution adopted for this project because we wanted the cleopatre components to be distributed under the lgpl2 license which is also the one used in the rtai project. the cleopatre library whose framework is shown in figure 8 offers selectable cots (commercialoff-the-shelf) components dedicated to dynamic scheduling, aperiodic task service, resource control access and fault-tolerance. components are totally independent from the kernel and the hardware. reusability of the components with another hardware and os is made possible by just adapting the os abstraction layer in the tcl component. this component hides the specific features of each platform, so that the run-time components can be implemented in a portable fashion and adapted to the target’s processor architecture and board. rto, bwp, rlp and rlp/t can be found in a new shelf called “quality of service"’. final users 1work supported by the french research office, grant number 01 k 0742 2lesser general public license quality of service scheduling in real-time systems 363 can then build their own customized applications through the flexible and easy-to-use interface provided by the cleopatre framework. 8 conclusions this paper pointed out the need of more flexible scheduling solutions for real-time applications dealing with multimedia and active monitoring systems. our main contribution was actually to propose and validate new scheduling algorithms, namely rlp and rlp/t. their purpose is to enhance the qos of periodic tasks that allow skips (i.e the ratio of task instances that do execute within their deadline) while providing a qos guarantee (i.e the ratio of task instances that must complete within their deadline). we considered a real-world problem (i.e. a multimedia application) to bring to light how these algorithms can be implemented in practice in order to provide a better qos. simulation results show that the improvements with both rlp and rlp/t are quite significant compared with basic algorithms. these new qos functionalities are available under linux/rtai. our future work includes extending these qos scheduling algorithms to multiprocessor systems. bibliography [1] g. bernat, a. burns, combining (n/m)-hard feadlines and dual priority schaduling, 18th ieee realtime systems symposium, pp 46-57, 1997. [2] g. bernat, a. burns, a. llamosi, weakly-hard real-time systems, in ieee transactions on computers, vol. 50, no. 4, pp 308-321, 2001. [3] g.-c. buttazzo, m. caccamo, minimizing aperiodic response times in a firm real-time environment, ieee trans. software eng., vol. 25, no. 1, pp 22-32, 1999. [4] m. caccamo, g.-c. buttazzo, exploiting skips in periodic tasks for enhancing aperiodic responsivess, 18th ieee real-time systems symposium, 1997. [5] h. chetto, m. chetto, some results of the earliest deadline scheduling algorithm. in proceedings of the ieee transactions on software engineering, vol. 15, no. 10, pp 1261-1269, 1989. [6] t. garcia, a. marchand, m. silly-chetto, cleopatre: a r&d project for providing new real-time functionalities to linux/rtai. 5th real-time linux workshop, 2003. [7] m. hamdaoui, p. ramanathan, a dynamic priority assignment technique for streams with (m,k)firm deadlines. ieee transactions on computers, vol. 44, no. 4, pp 1443-1451, 1995. [8] g. koren, d. shasha, skip-over algorithms and complexity for overloaded systems that allow skips. 16th ieee real-time systems symposium (rtss’95), pisa, italy, 1995. [9] p. mantegazza, e. bianchi, l. dozio, m. angelo, d. beal, diapm. rtai programming guide 1.0, lineo inc., 2000. [10] a. marchand, m. silly-chetto, qos scheduling components based on firm real-time requirements, acs/ieee international conference on computer systems and applications (aiccsa’05), le caire (egypt), 2005. [11] a. marchand and m. silly-chetto, rlp: enhanced qos support for real-time applications, 11th ieee international conference on embedded and real-time computing systems and applications (rtcsa’05), hong-kong, 2005. 364 audrey marchand, maryline chetto [12] a. marchand, m. silly-chetto, dynamic real-time scheduling of firm periodic tasks with hard and soft aperiodic tasks. journal of real-time systems, vol. 32, no. 1-2, pp 21-47, 2006. [13] m. silly-chetto, the edl server for scheduling periodic and soft aperiodic tasks with resource constraints, journal of real-time systems, vol. 17, pp 1-25, 1999. [14] m. silly-chetto, h. chetto, n. elyounsi, an optimal algorithm for guranteeing sporadic tasks in hard real-time systems. ieee symposium on parallel and distributed processing, pp 578-585, 1990. [15] t. tia, j. liu, j. sun, r. ha, a linear-time optimal acceptance test for scheduling of hard realtime tasks, technical report, department of computer science, university of illinois at urbanachampaign, il, 1994. [16] r. west, c. poellabauer, analysis of a window-constrained scheduler for real-time and best-effort packet streams, 21st ieee real-time systems symposium, orlando, usa, 2000. [17] r. west, y. zhang, k. schwan, c. poellabauer, dynamic window-constrained scheduling of realtime streams in media servers, ieee trans. on computers, vol. 53, pp. 744-759, 2004. [18] v. yodaiken, the rtlinux approach to real-time, fsmlabs inc., 2004. [19] y. zhang, r. west, x. qi, avirtual deadline scheduler for window-constrained service guarantees, tech. rep. 2004-013, boston university, 2004. audrey marchand university of nantes laboratoire d’informatique de nantes atlantique 2, rue de la houssinière bp 92208 44322 nantes cedex 03, france e-mail: audrey.marchand@univ-nantes.fr maryline chetto university of nantes institut de recherche en communications et cybernétique de nantes 1, rue de la noe 44321 nantes cedex 03 france e-mail: maryline.chetto@univ-nantes.fr quality of service scheduling in real-time systems 365 audrey marchand graduated in computer engineering at the ecole polytechnique of the university of nantes (france), in 2002. after getting a master degree in applied computer science at ecole centrale de nantes in 2003, she received the phd degree in october 2006 from the university of nantes. from october 2006 to august 2007, she hold a post-doc researcher position at the polytechnic university of valencia, spain. she is currently an associate professor at the university of nantes, france. her research interests include real-time scheduling theory, os service mechanisms, quality of service guarantees in realtime systems, and linux-based real-time oses and applications. maryline chetto received the degree of docteur de 3ème cycle in control engineering and the degree of habilitée à diriger des recherches in computer science from the university of nantes, france, in 1984 and 1993, respectively. from 1984 to 1985, she held the position of assistant professor of computer science at the university of rennes, while her research was with the institut de recherche en informatique et systèmes aléatoires, rennes. in 1986, she returned to nantes and is currently a professor with the institute of technology of the university of nantes. she is conducting her research at irccyn. her main research interests include scheduling and fault-tolerance technologies for real-time applications. she has published more than 60 journal articles and conference papers in the area of real-time operating systems. she is the leader of a french national r&d project, namely cleopatre, supported by the french government, which aims to provide free open source real-time solutions. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 693-700 meta-rationality in normal form games d. dumitrescu, r.i. lung, t.-d. mihoc dan dumitru dumitrescu, rodica ioana lung, tudor dan mihoc “babes bolyai” university romania, cluj-napoca, st. universitatii 5, e-mail: {ddumitr, mihoct}@cs.ubbcluj.ro, rodica.lung@econ.ubbcluj.ro abstract: a new generative relation for nash equilibrium is proposed. different types of equilibria are considered in order to incorporate players different rationality types for finite non cooperative generalized games with perfect information. proposed equilibria are characterized by use of several generative relations with respect to players rationality. an evolutionary technique for detecting approximations for equilibria is used. numerical experiments show the potential of the method. keywords: non-cooperative games, evolutionary equilibrium detection, generative relations, nash-pareto, meta-strategy. 1 introduction the most common solutions proposed in game theory are the equilibrium concepts. within the present day approaches each equilibrium concept is addressed separately, meaning that in a particular game players interact accordingly to a unique equilibrium concept. this restriction induces unrealistic results. for example, the concept of nash equilibrium, alone, sometimes can lead to deceptive results so we need to cope with more complex situations. in real life players can be more or less cooperative, more or less competitive and more or less rational, therefore agents guided by the different kind of equilibrium concepts should be allowed to interact. we consider a generalized game where players are allowed to have different behaviours according to their rationality type. players can have different behaviours/rationality types resulting in an adequate meta-strategy concept. game equilibria can be characterized using appropriate generative relations [4]. thus nash equilibrium is characterized by the ascendancy relation [6] and pareto equilibrium by the pareto domination. combining the two relations may lead to different types of joined nash–pareto equilibria. we introduce a new generative relation for nash equilibrium and we use it to compose a new joined nash-pareto equilibria. an evolutionary technique for detecting the two joined nash–pareto equilibria for generalized games is used. 2 generalized games in order to cope with different rationality types the concept of generalized game is defined [4]. definition 1. a finite strategic generalized game is defined as a system by g = (n, m, u) where: • n = {1, ..., n}, represents the set of players, n is the number of players; copyright c⃝ 2006-2010 by ccc publications 694 d. dumitrescu, r.i. lung, t.-d. mihoc • for each player i ∈ n, si represents the set of actions available to him, si = {si1, si2, ..., simi }; s = s1 × s2 × ... × sn is the set of all possible situations of the game; • for each player i ∈ n, mi represents the set of available meta-strategies, a meta-strategy is a system (si|ri) where si ∈ si and ri is the ith player rationality type; • m = m1 × m2 × ... × mn is the set of all possible situations of the generalized game and (s1|r1, s2|r2, ..., sn|rn) ∈ m is a meta-strategy profile. • for each player i ∈ n , ui : s → r represents the payoff function. u = {u1, ..., un}. remark 2. in a generalized game the set of all possible meta-strategies represents the metastrategy search space. the rationality type of a player usually represents the player bias towards a certain equilibrium. 3 generative relations for generalized games three generative relations are considered in this section. two of them correspond to pareto and nash equilibria. the third induces a new type of joined nash–pareto equilibrium. 3.1 np–strict pareto domination we consider the np–strict pareto domination in order to be able to combine several concepts of nash and pareto domination. in a finite strategic generalized game consider the set of players pareto biased ip = {j ∈ {1, ..., n}|rj = pareto} and np = cardip. let us consider two meta strategy profiles x and y from m. definition 3. the meta strategy profile x np–strict pareto dominates the meta strategy profile y if the payoff of each pareto biased player from ip using meta strategy x is strictly greater than the payoff associated to the meta strategy y, i.e. ui(x) > ui(y), ∀i ∈ ip. remark 4. the set of non dominated meta strategies with respect to the np–strict pareto domination relation when np = n is a subset of the pareto front. 3.2 nash ascendancy similar to pareto equilibrium a particular relation between strategy profiles can be used in order to describe nash rationality. this relation is called nash-ascendancy (na). a strategy is called nash equilibrium [5] if each player has no incentive to unilaterally deviate i.e. it can not improve the payoff by modifying its strategy while the others do not modify theirs. we denote by (sij, s ∗ −i) the strategy profile obtained from s ∗ by replacing the strategy of player i with sij i.e. (sij, s ∗ −i) = (s ∗ 1, s ∗ 2, ..., s ∗ i−1, sij, s ∗ i+1, ..., s ∗ n). meta-rationality in normal form games 695 definition 5. the strategy profile x nash-ascends the strategy profile y, and we write x <na y if there are less players i that can increase their payoffs by switching their strategy from xi to yi then vice versa. in [6] is introduced an operator k : s × s → n, k(y, x) = card{i ∈ {1, ..., n}|ui(xi, y−i) ≥ ui(y), xi ̸= yi}. k(y, x) denotes the number of players which benefit by switching from y to x. proposition 6. the strategy x nash-ascends y (x is na-preferred to y), and we write x <na y, if the inequality k(x, y) < k(y, x), holds. according to [6] the set of all strategies from s non-dominated by respect of nash ascendancy relation equals the set of nash equilibria. this result proves that the nash ascendancy is the generative relation for the nash equilibrium. 3.3 differential generative relation of nash equilibrium (dgn) a new generative relation for nash equilibrium is proposed. this relation relies on the payoff difference between perturbed and non perturbed strategies. we introduce the measure m(y, x) = ∑ i∈n (ui(xi, y−i) − ui(y)) definition 7. the strategy x dominates y, and we write x <dgn y, if the inequality m(x, y) < m(y, x), holds. 3.4 joint nash–pareto domination let us consider two meta-strategies x = (x1|r1, x2|r2, ..., xn|rn) and y = (y1|r1, y2|r2, ..., yn|rn). let us denote by in the set of nash biased players (n-players) and by ip the set of pareto biased players (p-players). therefore we have in = {i ∈ {1, ..., n}|ri = nash}. we consider the operators kp and kn defined as: kp(x, y) = card{j ∈ ip|uj(x) > uj(y), x ̸= y} and respectively kn(x, y) = card{i ∈ in|ui(yi, x−i) ≥ ui(x), xi ̸= yi}. remark 8. kp(x, y) measures the relative efficiency of the meta strategies x and y with respect to pareto rationality and kn(x, y) measures the relative efficiency of the meta strategies x and y with respect to nash rationality. definition 9. the meta strategy x n–p dominates the meta strategy y if and only if the following statements hold 1. kp(x, y) = np 2. kn(x, y) < kn(y, x) in what follows we consider that efficiency relation induces a new type of equilibrium called joined nash-pareto equilibrium. 696 d. dumitrescu, r.i. lung, t.-d. mihoc 3.5 joint differential nash pareto domination a new domination relation with respect to nash-pareto equilibrium is introduced by using differential generative relation of nash equilibrium. definition 10. the meta strategy x dgn–p dominates the meta strategy y if and only if the following statements hold 1. kp(x, y) = np 2. m(x, y) < m(y, x) 4 detecting joint n–p equilibria in generalized games consider a three player non-cooperative game. let ri be the rationality type of player i. if r1 = r2 = r3 = nash then all players are nash biased and the corresponding solution concept is the nash equilibrium. if r1 = r2 = r3 = pareto then all players are pareto biased and the corresponding equilibria are described by the set of strictly non dominated strategies (pareto front). we also intend to explore the joint cases where one of the players is nash biased and others are pareto and the one where one is pareto and the others are nash biased. in order to detect the joined nash–pareto equilibria of the generalized game an evolutionary approach is used. for a certain equilibrium the corresponding generative relation allows the comparison of two meta-strategies. this comparison may guide the search towards the game equilibrium. let us consider an initial population of meta strategies for the generalized three player game. each member of the population has the form x = (s1|r1, s2|r2, s3|r3). non domination (with respect to a generative relation) is considered for fitness assignment purposes. evolutionary multiobjective optimization algorithms [3] are efficient tools for evolving strategies based on a non domination relation. the state of the art nsga2 [2] has been considered to illustrate how generative relations can be used for evolutionary detection of proposed equilibria. a population of 100 strategies has been evolved using a rank based fitness assignment technique. in all experiments the process converges in less than 30 generations. 5 numerical experiments in order to illustrate the proposed concepts the oligopoly cournot model is considered (see for instance [4]). let q1, q2 and q3 denote the quantities of an homogeneous product produced by three companies respectively. the market clearing price is p(q) = a − q, where q = q1 + q2 + q3, is the aggregate quantity on the market. hence we have p(q) = { a − q, for q < a, 0, for q ≥ a. let us assume that the total cost for the company i of producing quantity qi is ci (qi) = ciqi. therefore, there are no fixed costs and the marginal cost ci is constant, ci < a. suppose that meta-rationality in normal form games 697 the companies choose their quantities simultaneously. the payoff for the company i is its profit, which can be expressed as: πi(qi, qj) = qip(q) − ci(qi) = qi [a − (qi + qj) − ci] . several experiments have been performed for this game by using red technique [4]. the symmetric cournot model with parameters a = 24 and c1 = c2 = c3 = 9 is considered. according to the data from the table 1 in less than 30 generations the algorithm converges to the nash equilibrium point (14.00, 14.00, 14.00) for each relation. we observe that the differential nash domination provides more accurate results than the nash ascendency. we must consider however the particular nature of this cournot game. for other types of games a normalisation of the deviations must be done in order to sum them. figure 1: the payoffs for the nash-nashpareto front detected in less than 30 iterations for the symmetric cournot game with the nash–pareto generative relation figure 2: the payoffs for the nash-nashpareto front detected in less than 30 iterations for the symmetric cournot game with the differential nash–pareto generative relation figure 3: the payoffs for the nash-paretopareto front detected in less than 30 iterations for the symmetric cournot game with the nash–pareto generative relation. figure 4: the payoffs for the nash-paretopareto front detected in less than 30 iterations for the symmetric cournot game with the differential nash–pareto generative relation. the resulting front in the nash-nash-pareto case spreads from the standard nash equilibrium corresponding to the two player–cournot game (25.00, 25.00) to the nash equilibrium corresponding to the three player–cournot game, and from there to the edges of pareto front for 698 d. dumitrescu, r.i. lung, t.-d. mihoc table 1: average payoff and standard deviation of the final populations in 30 runs with 100 meta-strategies after 30 generations for the symmetric cournot model where all three players are nash biased using nash ascendency and differential nash generative relations. n-n-n average payoff st. dev. maximum payoff minimum payoff player p1 p2 p3 p1 p2 p3 p1 p2 p3 p1 p2 p3 nash ascendency relation average 14.05 14.06 14.05 0.03 0.04 0.04 14.85 15.57 15.00 12.25 12.49 12.45 st. dev. 0.02 0.02 0.02 0.08 0.09 0.08 1.39 2.80 1.83 3.25 3.00 3.05 differential nash relation average 14.06 14.06 14.06 0.00 0.00 0.00 14.06 14.06 14.06 14.06 14.06 14.06 st. dev. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 table 2: average payoff and standard deviation of the final populations in 30 runs with 100 meta-strategies after 30 generations for the symmetric cournot model where two player are nash biased and one is pareto for both joint nash–pareto and joint differential nash–pareto generative relations. n-n-p average payoff st. dev. maximum payoff minimum payoff player p1 p2 p3 p1 p2 p3 p1 p2 p3 p1 p2 p3 joint nash–pareto relation average 10.99 11.01 29.80 52.81 53.02 182.28 25.92 25.71 56.24 0.00 0.00 0.49 st. dev. 0.36 0.33 0.78 1.75 2.33 17.62 0.92 0.88 0.00 0.00 0.00 1.67 joint differential nash–pareto relation average 8.50 8.48 35.53 22.76 22.67 165.84 18.47 18.92 56.24 0.00 0.00 7.60 st. dev. 0.35 0.31 0.85 0.25 0.25 0.54 2.88 2.93 0.00 0.00 0.00 5.60 the nash–pareto equilibria (see figure 1). for differential nash-pareto (see figure 2) the front spreads from vicinity of the nash equilibrium for cournot game to the edge of the pareto front corresponding to the pareto player. the numerical results are presented in table 2. as we can see in the figure 3 in the nash-pareto-pareto case for nash–pareto generative relation the result is similar to the pareto front. in the same case for differential nash–pareto generative relation (figure 4) the pareto front is deformed in the nash player’s corresponding edge. the numerical results are presented in table 3. 6 conclusions and future work a new generative relation for nash equilibrium based on differences between perturbations is introduced. generative relations between meta strategies induce corresponding solutions concepts named joined nash–pareto equilibrium, respectively joint differential nash–pareto equilibrium. an evolutionary technique for detecting approximations of the generalized equilibria is used. the ideas are exemplified for cournot games with three players and two types of rationality.results indicate the potential of the proposed technique. future work will address generalized games having other rationality types and other methods meta-rationality in normal form games 699 table 3: average payoff and standard deviation of the final populations in 30 runs with 100 meta-strategies after 30 generations for the symmetric cournot model where one player is nash biased and the other two pareto for both joint nash–pareto and joint differential nash–pareto generative relations. n-p-p average payoff st. dev. maximum payoff minimum payoff player p1 p2 p3 p1 p2 p3 p1 p2 p3 p1 p2 p3 joint nash–pareto relation average 17.74 18.52 18.44 242.42 247.83 247.97 56.23 56.24 56.24 0.00 0.00 0.00 st. dev. 0.40 0.36 0.42 7.36 6.98 6.82 0.04 0.00 0.00 0.00 0.00 0.00 joint differential nash–pareto relation average 15.13 19.83 19.76 154.37 250.62 248.44 48.90 56.24 56.24 0.00 0.00 0.00 st. dev. 0.86 0.77 0.56 0.78 0.29 0.32 3.02 0.00 0.01 0.00 0.00 0.00 of combining them. 7 acknowledgements this research is supported partially by the cncsis grant id508 "new computational paradigms for dynamic complex problems" funded by the mec and from the sectoral operational programme human resources development, contract posdru 6/1.5/s/3 "doctoral studies: through science towards society", babeş bolyai university, cluj napoca, românia. bibliography [1] bade, s., haeringer, g., renou, l.: 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[2] deb, k., agrawal, s., pratab, a., meyarivan, t.: a fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: nsga-ii, marc schoenauer, kalyanmoy deb, günter rudolph, xin yao, evelyne lutton, juan julian merelo, and hans-paul schwefel, editors, proceedings of the parallel problem solving from nature vi conference, paris, france, 2000. springer, lecture notes in computer science, 1917, 849-858. [3] deb, k.: multi-objective optimization using evolutionary algorithms, wiley, 2001. [4] dumitrescu, d., lung, r.i., mihoc, t.d.: evolutionary equilibria detection in noncooperative games, book series: lncs, publisher springer berlin / heidelberg, volume 5484 / 2009, book: applications of evolutionary computing, 2009, 253-262. [5] lung, r. i., muresan, a. s., and filip, d. a.: solving multi-objective optimization problems by means of natural computing with application in finance, in aplimat 2006 (bratislava, february 2006), pp. 445-452. 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[10] osborne, m. j., rubinstein, a.: a course in game theory, mit press, cambridge, ma, 1994 ijcccv4n3draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 3, pp. 273-282 spiking neural p systems with anti-spikes linqiang pan, gheorghe păun linqiang pan department of control science and engineering huazhong university of science and technology wuhan 430074, hubei, china e-mail: lqpan@mail.hust.edu.cn, lqpan@us.es gheorghe păun institute of mathematics of the romanian academy po box 1-764, 014700 bucureşti, romania, and department of computer science and artificial intelligence university of sevilla avda. reina mercedes s/n, 41012 sevilla, spain e-mail: george.paun@imar.ro, gpaun@us.es received: april 20, 2009 accepted: may 20, 2009 abstract: besides usual spikes employed in spiking neural p systems, we consider “anti-spikes", which participate in spiking and forgetting rules, but also annihilate spikes when meeting in the same neuron. this simple extension of spiking neural p systems is shown to considerably simplify the universality proofs in this area: all rules become of the form bc → b′ or bc → λ , where b, b′ are spikes or anti-spikes. therefore, the regular expressions which control the spiking are the simplest possible, identifying only a singleton. a possible variation is not to produce anti-spikes in neurons, but to consider some “inhibitory synapses", which transform the spikes which pass along them into anti-spikes. also in this case, universality is rather easy to obtain, with rules of the above simple forms. keywords: membrane computing, p system, spiking neural p system, computability 1 introduction the spiking neural p systems (in short, sn p systems) were introduced in [4], and then investigated in a large number of papers. we refer to the respective chapter of [7] for general information in this area, and to the membrane computing website from [9] for details. in this note, we consider a variation of sn p systems which was suggested several times, i.e., involving inhibitory impulses/spikes or inhibitory synapses and investigated in a few papers under various interpretations/formalizations – see, e.g., [1], [2], [5], [8]. the definition we take here for such spikes – we call them anti-spikes (somewhat thinking to anti-matter) – considers having, besides usual “positive" spikes denoted by a, objects denoted by ā, which participate in spiking or forgetting rules as usual spikes, but also in implicit rules of the form aā → λ : if an anti-spike meets a spike in a given neuron, then they annihilate each other, and this happens instantaneously (the disappearance of one a and one ā takes no time, it is like applying the rule aā → λ without consuming any time for that). we do not claim having a clear biological counterpart of such issues, we only look for an elegant mathematical definition. this simple extension of sn p systems is proved to entail a surprising simplification of both the proofs and the form of rules necessary for simulating turing machines (actually, the proofs here are based on simulating register machines) by means of sn p systems: all rules have a singleton regular expression, which, moreover, indicates precisely the number of spikes or anti-spikes to consume by the copyright c© 2006-2009 by ccc publications 274 linqiang pan, gheorghe păun rule. (precisely, we have rules of the forms bc → b′ or bc → λ , where b, b′ are spikes or anti-spikes; such rules, having the regular expression e such that l(e) = bc are called pure; formal definitions will be given immediately.) this can be considered as a (surprising) normal form for this case; please compare with the normal forms from [3], especially with the simplifications of regular expressions obtained there. anti-spikes are produced from usual spikes by means of usual spiking rules; in turn, rules consuming anti-spikes can produce spikes or anti-spikes (actually, as we will see below, the latter case can be avoided). a possible variant is to produce always only spikes and to consider synapses which “change the nature" of spikes. also in this case, universality is easily proved, using only pure rules. 2 prerequisites we assume the reader to be familiar with basic elements about sn p systems, e.g., from [7] and [9], and we introduce here only a few notations, as well as the notion of register machines, used later in the proofs of our results. we also assume familiarity with very basic elements of automata and language theory, as available in many monographs. for an alphabet v , v ∗ denotes the set of all finite strings of symbols from v , the empty string is denoted by λ , and the set of all nonempty strings over v is denoted by v +. when v = {a} is a singleton, then we write simply a∗ and a+ instead of {a}∗, {a}+. a regular expression over an alphabet v is defined as follows: (i) λ and each a ∈ v is a regular expression, (ii) if e, e are regular expressions over v , then (e)(e), (e)∪(e), and (e) + are regular expressions over v , and (iii) nothing else is a regular expression over v . with each regular expression e we associate a language l(e), defined in the following way: (i) l(λ ) = {λ } and l(a) = {a}, for all a ∈ v , (ii) l((e) ∪ (e)) = l(e) ∪ l(e), l((e)(e)) = l(e)l(e), and l((e) +) = (l(e)) +, for all regular expressions e, e over v . non-necessary parentheses can be omitted when writing a regular expression, and also (e)+ ∪ {λ } can be written as e∗. the family of turing computable sets of natural numbers is denoted by nre. a register machine is a construct m = (m, h, l, lh, i), where m is the number of registers, h is the set of instruction labels, l is the start label (labeling an add instruction), lh is the halt label (assigned to instruction halt), and i is the set of instructions; each label from h labels only one instruction from i, thus precisely identifying it. the instructions are of the following forms: • li : (add(r), l j, lk) (add 1 to register r and then go to one of the instructions with labels l j, lk), • li : (sub(r), l j, lk) (if register r is non-empty, then subtract 1 from it and go to the instruction with label l j, otherwise go to the instruction with label lk), • lh : halt (the halt instruction). a register machine m computes (generates) a number n in the following way: we start with all registers empty (i.e., storing the number zero), we apply the instruction with label l and we proceed to apply instructions as indicated by the labels (and made possible by the contents of registers); if we reach the halt instruction, then the number n stored at that time in the first register is said to be computed by m. the set of all numbers computed by m is denoted by n(m). it is known that register machines compute all sets of numbers which are turing computable, hence they characterize nre. without loss of generality, we may assume that in the halting configuration, all registers different from the first one are empty, and that the output register is never decremented during the computation, we only add to its contents. we can also use a register machine in the accepting mode: a number is stored in the first register (all other registers are empty); if the computation starting in this configuration eventually halts, then the number is accepted. again, all sets of numbers in nre can be obtained, even using deterministic spiking neural p systems with anti-spikes 275 register machines, i.e., with the add instructions of the form li : (add(r), l j, lk) with l j = lk (in this case, the instruction is written in the form li : (add(r), l j)). again, without loss of generality, we may assume that in the halting configuration all registers are empty. convention: when evaluating or comparing the power of two number generating/accepting devices, number zero is ignored. 3 spiking neural p systems with anti-spikes we recall first the definition of an sn p system in the classic form (without delays, because this feature is not used in our paper) and of the set of numbers generated or accepted by it. an sn p system of degree m ≥  is a construct π = (o, σ, . . . , σm, syn, in, out), where: 1. o = {a} is the singleton alphabet (a is called spike); 2. σ, . . . , σm are neurons, of the form σi = (ni, ri),  ≤ i ≤ m, where: a) ni ≥  is the initial number of spikes contained in σi; b) ri is a finite set of rules of the following two forms: (1) e/ac → a, where e is a regular expression over a and c ≥ ; (2) as → λ , for some s ≥ ; 3. syn ⊆ {, , . . . , m} × {, , . . . , m} with (i, i) /∈ syn for  ≤ i ≤ m (synapses between neurons); 4. in, out ∈ {, , . . . , m} indicate the input and output neurons, respectively. the rules of type (1) are firing (we also say spiking) rules, and they are applied as follows. if the neuron σi contains k spikes, and a k ∈ l(e), k ≥ c, then the rule e/ac → a can be applied. the application of this rule means removing c spikes (thus only k − c remain in σi), the neuron is fired, and it produces a spike which is sent immediately to all neurons σ j such that (i, j) ∈ syn. the rules of type (2) are forgetting rules and they are applied as follows: if the neuron σi contains exactly s spikes, then the rule as → λ from ri can be used, meaning that all s spikes are removed from σi. note that we have not imposed here the restriction that for each rule e/ac → a of type (1) and as → λ of type (2) from ri to have a s /∈ l(e). if a rule e/ac → a of type (1) has e = ac, then we will write it in the simplified form ac → a and we say that it is pure. in each time unit, if a neuron σi can use one of its rules, then a rule from ri must be used. since two firing rules, e/a c → a and e/ac → a, can have l(e)∩l(e) 6= /0, it is possible that two or more rules can be applied in a neuron, and in that case only one of them is chosen non-deterministically. thus, the rules are used in the sequential manner in each neuron, but neurons function in parallel with each other. the configuration of the system is described by the number of spikes present in each neuron. the initial configuration is n, n, . . . , nm. using the rules as described above, one can define transitions among configurations. any sequence of transitions starting in the initial configuration is called a computation. a computation halts if it reaches a configuration where no rule can be used. with any computation (halting 276 linqiang pan, gheorghe păun or not) we associate a spike train, the sequence of zeros and ones describing the behavior of the output neuron: if the output neuron spikes, then we write 1, otherwise we write 0. when using an sn p system in the generative mode, we start from the initial configuration and we define the result of a computation as the number of steps between the first two spikes sent out by the output neuron. we denote by n(π ) the set of numbers computed by π in this way. in the accepting mode, a number n is introduced in the system in the form of a number f (n) of spikes placed in neuron σin, for a well-specified mapping f , and the number n is accepted if and only if the computation halts. we denote by nacc(π ) the set of numbers accepted by π . it is also possible to introduce the number n by means of a spike train entering neuron σin, as the distance between the first two spikes coming to σin. in the generative case, the neuron (with label) in is ignored, in the accepting mode the neuron out is ignored (sometimes below, we identify the neuron σi with its label i, so we say “neuron i" understanding that we speak about “neuron σi"). we can also use an sn p system in the computing mode, introducing a number in neuron in and obtaining a result in (by means of) neuron out, but we do not consider this case here. we denote by nα snp(rulek) the families of all sets nα (π ), α ∈ {, acc}, computed by sn p systems with at most k ≥  rules (spiking or forgetting) in each neuron. let us now pass to the extension mentioned in the introduction. a further object, ā, is added to the alphabet o, and the spiking and forgetting rules are of the forms e/bc → b′, bc → λ , where e is a regular expression over a or over ā, while b, b′ ∈ {a, ā}, and c ≥ . as above, if l(e) = bc, then we write the first rule as bc → b′ and we say that it is pure. note that we have four categories of rules, identified by (b, b′) ∈ {(a, a), (a, ā), (ā, a), (ā, ā)}. the rules are used as in a usual sn p system, with the additional fact that a and ā “cannot stay together", they instantaneously annihilate each other: if in a neuron there are either objects a or objects ā, and further objects of either type (maybe both) arrive from other neurons, such that we end with ar and ās inside, then immediately a rule of the form aā → λ is applied in a maximal manner, so that either ar−s or ās−r remain, provided that r ≥ s or s ≥ r, respectively. we stress the fact that the mutual annihilation of spikes and anti-spikes takes no time and that annihilation has priority over spiking and forgetting rules, so that the neuron always contains either only spikes or anti-spikes. that is why, for instance, the regular expressions of the spiking rules are defined either on a or on ā, but not on both symbols. of course, we can also imagine that the annihilation takes one time unit, when the explicit rule aā → λ is used, but we do not consider this case here (if the rule aā → λ has priority over other rules, then no essential change occurs in the proofs below; the no priority case also remains to be investigated). the computations and the result of computations are defined in the same way as for usual sn p systems – but we consider the restriction that the output neuron produces only spikes, not also antispikes (again, this is a restriction which is only natural/elegant, but not essential). as above, we denote by nα sanp(rulek, f org) the families of all sets nα (π ), α ∈ {, acc}, computed by sn p systems with at most k ≥  rules (spiking or forgetting) in each neuron, using also anti-spikes. when only pure rules are used, we write nα sanp(prulek). 4 universality results we start by considering the generative case, for which we have the next result (universality is known for usual sn p systems, without anti-spikes, but now both the proof is simpler and the used rules are all pure): theorem 1. nre = nsanp(prule). spiking neural p systems with anti-spikes 277 proof. we only have to prove the inclusion nre ⊆ nsanp(prule, f org). let us consider a register machine m = (m, h, l, lh, i) as introduced in section 2. we construct an sn p system π (with o = {a, ā}) which simulates m in the way already standard in the literature when proving that a class of sn p systems is universal. specifically, we construct modules add and sub to simulate the instructions of m, as well as an output module fin which provides the result (in the form of a suitable spike train). each register r of m will have a neuron σr in π , and if the register contains the number n, then the associated neuron will contain n spikes, except for the neuron σ associated with the first register (the neurons associated with registers will either contain occurrences of a, hence ā disappears immediately, or only ā is present, and it is consumed in the next step by a rule ā → a). two spikes are initially placed in the neuron σ associated with the first register, so if the first register contains the number n, then neuron σ will contain n +  spikes. these two spikes are used for outputting the computation result. ¶ µ ³ ´ ¶ µ ³ ´ ' & $ % ' & $ % ' & $ % ¶ µ ³ ´ li a → a a → a a → ā a → a l () i a → a a → λ l j lk ¶ µ ³ ŕ ¶ µ ³ ´ ¶ µ ³ ´a → a a → a l () i l () i l () i l () i ©©©©©©©©¼ ? ? ? j j jĵ hhhhhhhhhj @ @ @@r »»»»»»»»»»»»9 ½ ½ ½½= ppppppppq figure 1: module add, simulating li : (add(r), l j, lk) note that the number of spikes in the neuron σ will not be smaller than two before the simulation reaches the instruction lh and the output module fin is activated, because we assume that the output register is never decremented during the computation. one neuron σli is associated with each label li ∈ h, and some auxiliary neurons σ l ( j) i , j = , , , . . . , will be also considered, thus precisely identified by label li (remember that each li ∈ h is associated with a unique instruction of m). the modules will be given in a graphical form, indicating the synapses and, for each neuron, the associated set of rules. in the initial configuration, all neurons are empty, except for the neurons associated with label l of m and the first register, which contain one spike and two spikes, respectively. in general, when a spike a is sent to a neuron σli , with li ∈ h, then that neuron becomes active and the module associated with the respective instruction of m starts to work, simulating the instruction. the functioning of the module from figure 1, simulating an instruction li : (add(r), l j, lk), is obvious; the non-deterministic choice between instructions l j and lk is done by non-deterministically choosing the rule to apply in neuron σ l () i . the simulation of an instruction li : (sub(r), l j, lk) is also simple – see the module from figure 2. the neuron σli sends a spike to neurons σl() i and σ l () i . in the next step, neuron σ l () i sends an anti-spike to neuron σr, corresponding to register r; at the same time, σ l () i sends a spike to each neuron σ l () i , σ l () i . if register r is non-empty, that is, neuron σr contains at least one a, then ā removes one occurrence of a, 278 linqiang pan, gheorghe păun ¶ µ ³ ´ ¶ µ ³ ´ ' & $ % ' & $ % ¶ µ ³ ´ ¶ µ ³ ´ # " ã ! ¶ µ ³ ´ ¶ µ ³ ´ ¶ µ ³ ´ ¡ ¡¡ª @ @@r hhj¡ ¡ª @ @r j j j jĵ hhhhhhhhhj ¢ ¢ ¢ ¢¢® s s s ssw ©©©©©©©©¼ ­ ­ ­­à ? ? li a → a l () i a → a l () ia → a r ā → a l () i a → a l () i a → a l () i a → λ a → a a → λ l () i a → λ a → λ a → a l j lk figure 2: module sub, simulating li : (sub(r), l j, lk) which corresponds to subtracting one from register r, and no rule is applied in σr. this means σ l () i and σ l () i receive only two spikes, from σ l () i and σ l () i , hence σl j is activated and σlk not. if register r is empty, then the rule ā → a is used in σr, hence σ l () i and σ l () i receive three spikes, and this leads to the activation of σlk , which is the correct continuation also in this case. â á ¿ à ¶ µ ³ ´ ¶ µ ³ ´ ¶ µ ³ ´ ¶ µ ³ ´ ¶ µ ³ ´ ¶ µ ³ ´ ¡ ¡¡ª ? ? ? lh a → a h a → a h a → a out a → a h a → a h a → ā 1 a → a ¶ µ ³ h́ a → a ¶ µ ³ h́ a → a a q q qqs ? ? -6 ¾ 6 q q q q q qk j j j j j j j] figure 3: the fin module note that if there are several sub instructions lt which act on register r, then σr will send one spike to neurons σ l () t and σ l () t while simulating the instruction li : (sub(r), l j, lk), but this spike is immediately removed by the rule a → λ present in all neurons σ l () t , σ l () t . the module fin, which produces a spike train such that the distance between the first two spikes equals the number stored in register 1 of m, is indicated in figure 3. at some step t, the neuron σlh is activated, which means that the register machine m reaches the halt instruction and the system π starts to output the result. suppose the number stored in register 1 of m is n. at step t + , neurons σh , σh spiking neural p systems with anti-spikes 279 and σh contain a spike. neurons σh and σh exchange spikes among them, and thus σh sends a spike to neuron σh continuously until neuron σ spikes and neurons σh , σh , σh are “flooded". at step t + , neuron σout receives a spike, and in the next step σout sends a spike to the environment; at the same time, σ receives an anti-spike that decreases by one the number of spikes from σ. at step t + n + , the neuron σ contains one spikes, and in the next step neuron σ sends a spike to neuron σout . at step t + n + , neuron σout spikes again. the distance between the first two spikes emitted by σout equals n, which is exactly the number stored in register 1 of m. the spike produced by neuron σ “floods" neurons σh , σh , and σh , thus blocking the work of these neurons. after the system sends the second spike out, the whole system halts. from the previous explanations we get the equality n(m) = n(π ) and this concludes the proof. note that in the previous construction there is no rule of the form āc → ā; is it possible to also avoid other types of rules? for instance, the rule ā → a only appears in the neurons associated with registers in module sub. is it possible to remove the ā → a by replacing it with the rules ac → a and a → ā? if the sn p systems are used in the accepting mode, then a further simplification is entailed by the fact that the add instructions are deterministic. such an instruction li : (add(r), l j) can be directly simulated by a simple module as in figure 4. ¶ µ ³ ´ # " ã ! ¶ µ ³ ´ ¢ ¢ ¢® q qqs li a → a rl j figure 4: module add, simulating li : (add(r), l j) together with sub modules, this suffices in the case when the number to accept is introduced as the number of spikes initially present in neuron σ. if this number is introduced in the system as the distance between the first two spikes which enters the input neuron, then a input module is necessary, as used, for instance, in [3]. note that the module input from [3] uses only pure rules (involving only spikes, not also anti-spikes), hence we get a theorem like theorem 1 also for the accepting case, for both ways of providing the input number. it is worth mentioning that in the previous constructions we do not have spiking rules which can be used at the same time with forgetting rules. 5 using inhibitory synapses let us now consider the case when no rule can produce an anti-spike, but there are synapses which transform spikes into anti-spikes. the previous modules add, sub, fin can be modified in such a way to obtain a characterization of nre also in this case. we directly provide these modules, without any explanation about their functioning, in figures 5, 6, and 7; the synapses which change a into ā are marked with a dot. note that this time the non-determinism in the add instruction is simulated by allowing the nondeterministic choice among the spiking rule ā → a and the forgetting rule ā → λ of neuron σ l () i , which is not allowed in the classic definition of sn p systems. removing this feature, without introducing rules which are not pure or other ingredients, such as the delay, remains as an open problem. denoting by nα sanps(prulek) the respective families of sets of numbers (the subscript s in ps indicates the use of inhibitory synapses, in the sense specified above), we conclude having the next result: 280 linqiang pan, gheorghe păun º ¹ · ¸ º ¹ · ¸ # " ã ! # " ã ! ' & $ % º ¹ · ¸ º ¹ · ¸ # " ã ! ¡ ¡¡ª u ppppppqj j jĵ ? ? ? ? z z z zz~ ´ ´ ´ ´ +́ li a → a r l () i ā → a ā → λ l () i a → a l () i a → a a → λ l () i a → a a → λ l j lk figure 5: module add, simulating li : (add(r), l j, lk) ¶ µ ³ ´ ¶ µ ³ ´ ' & $ % ' & $ % º ¹ · ¸ º ¹ · ¸ ' & $ % º ¹ · ¸ ? ½ ½ ½ ½ ½½= q q q qqs a a a au hhhhhhhhj ­ ­ ­ ­à j j j ĵ ©©©©©©©©¼ ¶ ¶ ¶¶/ ? ? li a → a t r ā → a l () i a → a l () i a → a l () i a → λ a → a a → λ l () i a → λ a → λ a → a l j lk figure 6: module sub, simulating li : (sub(r), l j, lk) spiking neural p systems with anti-spikes 281 # " ã ! ¶ µ ³ ´ ¶ µ ³ ´ ² ± °̄ º ¹ · ¸? v lh a → a h a → a h a → a out a → a 1 ā → a ¶ µ ³ h́ a → a ¶ µ ³ h́ a → a ©©©©©©©¼ ¾ ? ? ? figure 7: module fin theorem 2. nre = nsanps(prule). 6 final remarks there are several open problems and research topics suggested by the previous results. some of them were already mentioned, but further questions can be formulated. for instance, can the proofs be improved so that less types of rules are necessary? we have avoided using rules āc → ā, but not the other three types, corresponding to the pairs (a, a), (a, ā), (ā, a). then, following the idea from [6], can we decrease the number of types of neurons, in the sense of having a small number of sets of rules which are used in each neuron (three such sets are found in [6] to be sufficient for universality in the case of usual sn p systems; do the anti-spikes helps also in this respect?). what about cases when the annihilation rule aā → λ takes one time unit or/and it has no priority over other rules? by allowing the output neuron to also produce anti-spikes we can get a spike train over a three letter alphabet: no output, producing spikes, and producing anti-spikes, respectively. this can be an interesting way to produce languages (over three letters or perhaps over two, ignoring the no-output steps). acknowledgements. the work of l. pan was supported by national natural science foundation of china (grant nos. 60674106, 30870826, 60703047, and 60803113), program for new century excellent talents in university (ncet-05-0612), ph.d. programs foundation of ministry of education of china (20060487014), chenguang program of wuhan (200750731262), hust-srf (2007z015a), and natural science foundation of hubei province (2008cdb113 and 2008cdb180). the work of gh. păun was supported by proyecto de excelencia con investigador de reconocida valía, de la junta de andalucía, grant p08 – tic 04200. useful remarks by a. alhazov and m.a. gutiérrez-naranjo are gratefully acknowledged. bibliography [1] a. binder, r. freund, m. oswald, l. vock, extended spiking neural p systems with excitatory and inhibitory astrocytes. submitted, 2007. [2] r. freund, m. oswald, spiking neural p systems with inhibitory axons. arob conf., japan, 2007. [3] o.h. ibarra, a. păun, gh. păun, a. rodriguez-patón, p. sosik, s. woodworth, normal forms for spiking neural p systems. theoretical computer science, vol. 372, pp. 196–217, 2007. [4] m. ionescu, gh. păun, t. yokomori, spiking neural p systems. fundamenta informaticae, vol. 71, pp. 279–308, 2006. 282 linqiang pan, gheorghe păun [5] j.m. mingo, sleep-awake switch with spiking neural p systems: a basic proposal and new issues. proc. 7th brainstorming week on membrane computing, sevilla, 2009, vol. ii, 59–72. [6] l. pan, gh. păun, new normal forms for spiking neural p systems. proc. 7th brainstorming week on membrane computing, sevilla, 2009, vol. ii, 127–138. [7] gh. păun, g. rozenberg, a. salomaa, eds., handbook of membrane computing. oxford university press, 2010 (in press). [8] j. wang, l. pan, excitatory and inhibitory spiking neural p systems. submitted, 2007. [9] the p systems website, http://ppage.psystems.eu. linqiang pan was born in zhejiang, china on november 22, 1972. he got phd at nanjing university in 2000. since 2004, he is a professor at huazhong university of science and technology, china. his main research fields are graph theory and membrane computing. gheorghe păun graduated the faculty of mathematics, university of bucharest, in 1974 and received his ph.d. from the same university in 1977. from 1990 he is a senior researcher at the institute of mathematics of the romanian academy. he (repeatedly) visited numerous universities in europe, asia, and north america. his main research areas are formal language theory and its applications, computational linguistics, dna computing, and membrane computing; this last research area was initiated by him, in 1998, and the respective models are now called p systems, see http://ppage.psystems.eu). he has published a large number of research papers, has lectured at many universities, and gave numerous invited talks at recognized international conferences. he has published eleven monographs in mathematics and computer science, has (co)edited over seventy collective volumes and special issues of journals, and also published many popular science books, books on recreational mathematics (games), and fiction books. he is a member of the editorial board of more than a dozen international journals and was/is involved in the program/steering/organizing committees for many recognized conferences and workshops. in 1997 he was elected a member of the romanian academy and from 2006 he is a member of academia europaea. he also got other honors, in romania or abroad. he is an isi highly cited researcher (see http://isihighlycited.com/). international journal of computers, communications & control vol. i (2006), no. 4, pp. 7-7 preface over the last decades, it has become a strong need for exchange on common computational and algorithmic tools between researchers working in different application backgrounds. under this situation, the first cesa conference (cesa96) was successfully held in lille, france in july 1996. the multiconference on “computational engineering in systems applications” (cesa2006), was co-sponsored by imacs (the international association for mathematics and computers in simulation) and ieee systems man and cybernetics (ieee/smc) society, and has been held on 4-6 october 2006 in beijing, china. it aim was to bring together scholars and practitioners from academia and industries to exchange the latest development in theories, and applications of computational techniques. this conference was co-chaired by professor pierre borne (ecole centrale de lille, france) and professor bo zhang (tsinghua university, china). in addition to the plenary lectures presented by professor james m. tien (rensselaer polytechnic institute, usa), professor tianyou chai (northeastern university, china), professor florin g. filip (vice president of the romanian academy), professor jianwei zhang (university of hamburg, germany) and professor toshio fukuda (nagoya university, japan), 388 communications have been selected and accepted for presentation. the papers presented in this special issue correspond to enlarged and improved papers which have been selected among the best communications presented during the conference. guest editor professor pierre borne chairman of cesa2006 ecole centrale de lille, cité scientifique laboratoire d’automatique, génie informatique et signal bp 48, 59651 villeneuve d’ascq cedex, france e-mail: pierre.borne@ec-lille.fr copyright c© 2006 by ccc publications int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 4, pp. 316-325 a life dedicated to the science, philosophy and romanian society g. tecuci gheorghe tecuci romanian academy george mason university mihai drăgănescu with outstanding contributions in electronics, informatics, and philosophy, and as professor, researcher, and manager, acad. mihai drăgănescu is the most important encyclopedic personality of contemporary romania. educated in the nascent romanian school of electronics, acad. mihai drăgănescu creates a worldclass school of electronic devices and microelectronics. envisioning the evolution of the modern society, becomes initiator and promoter of the informatics revolution in romania, conceptually defining it and coordinating its development. generalizing the concept of information, creates an original philosophy that leads to the development of a new type of science, called structural-phenomenological, with major implications for the understanding of the world and its future. first president of the romanian academy renaissance, he leads both its return to its role and traditional sources, and its renewal and adaptation to the evolution of the civilization. promoter of the scientific and humanistic culture, brings back the deserved recognition to major personalities of the romanian spirituality. mentor and life model, he lightens and encourages many young generations with an extraordinary generosity. any of these achievements would be enough to place acad. mihai drăgănescu among the greatest romanian personalities. their combination, impressive through their diversity and unity, creates the image of a personality of a rare complexity and creativity. 1 founding father of the romanian school of electronic devises and microelectronics acad. mihai drăgănescu was educated as an electronics engineer, and climbed through all the professional levels, from assistant professor to academician. despite the unsupportive research environment of an isolated romania, he succeeded in obtaining truly outstanding results. at the beginning of his scientific career, as a young assistant professor, mihai drăgănescu is among the very few romanian electronics engineers with world-class research results, author of the first romanian phd in the area of copyright c© 2006-2009 by ccc publications a life dedicated to the science, philosophy and romanian society 317 electronic devises, entitled “the capacities of electronic tubes and their dependence of the functioning conditions”. this is followed by other fundamental research results, such as the matz-drăgănescu theory of the transistor at high-level of injection, the study of the inductive effects in semiconductor devices, and the simplification of the dielectric diode theory. they represent the prelude of “electronic processes in semiconductor devices of circuits” [1], one of the first authored books in this domain in the world and an exceptional achievement in the romania of 1962, recognized with the state prize. in 1963 he publishes the first romanian study on microelectronics [2], and is awarded the prize of the ministry of national education for his scientific research. he establishes and heads the research institute for electronic components, in 1969, and teaches the first course on integrated circuits, in 1970. in 1972 he publishes “solid state electronics” [3], the second fundamental book of the romanian school of electronic devices. a unifying understanding of electronics, informatics and philosophy, in a social context, leads prof. drăgănescu to the development of a basic discipline of the science and technology of information, which he calls “functional electronics”. in 1978 he starts teaching this new discipline at the ’politehnica’ university of bucharest. then, in 1991, he publishes the book “functional electronics” [4], followed by several studies, including “microelectronics and functional electronics” [5], “from solid state to quantum and molecular electronics, the deepening of information processing” [6], and “neural engineering and neural electronics facing artificial consciousness” [7]. in an activity spanning almost 50 years, through original research, the publication of world-class foundational books, the development of new courses, the formation and support of a large number of specialists, the guidance of the romanian industry of electronics and computers, including the management of the manufacturing of the integrated circuits, acad. mihai drăgănescu has created the romanian school of electronic devices and microelectronics, significantly influencing the evolution of the romanian society. as an international recognition of his exceptional contributions, acad. mihai drăgănescu is elected fellow ieee in 1994, life fellow ieee in 1997, and receives the millennium iii medal from ieee in 2000. 2 initiator and promoter of the information revolution in romania acad. mihai drăgănescu is credited with envisioning the information revolution, with its conceptual definition, and with the coordination of its development in romania [8]. initiator and leader of the first and only national major technologic program in the area of integrated circuits, computers and informatics, acad. mihai drăgănescu has guided for 20 years, between 1965 and 1985, the economic evolution of romania in these areas, making numerous critical decisions, including the transitioning of romania to the silicon phase in the electronics domain, and the buying of western licenses for the manufacturing of integrated circuits and of 3rd generation computers. in 1971 he is awarded the french order “commandeur de la legion d’honneur” for his contributions to the romanian-french collaboration in informatics. his studies, dating back to 1970, envisioned a future information society (now a reality in the advanced countries), based on an informatics medium aimed at serving each individual, organization, and society as a whole, both in realizing its own functions and in their relationships with each other. moreover, acad. mihai drăgănescu had the vision (now confirmed by the evolution of the internet) that such a medium had to be based on an evolving informatics system, adaptable to the evolution of computers and society. it could not be planned in the smallest details, as required by the political leaders of romania of that time [8]. integrating theoretical research, a global systemic vision, and a creative management, acad. mihai drăgănescu has laid the basis for the development of informatics in romania through several outstanding initiatives. they include the establishment of important institutions, such as the company for the 318 g. tecuci maintenance and repair of computing tools (iiruc, 1968), the institute of computers (itc, 1969), and the institute of electronic components (ice, 1969). an important moment was the establishment, in 1971, of the central institute for informatics which, under his leadership, has quickly become the center of the romanian informatics, both through world-class research in advanced domains (e.g., artificial intelligence, industrial informatics, computer networks) and through the coordination of the development of informatics in the entire country (e.g., the coordination of a network of 40 territorial computer centers, established between 1968 and 1985). at the same time computer centers have been established in universities and research institutes, as well as computer science high schools, and new university degree programs in electronics, automation, computers and informatics. in late 70, acad. mihai drăgănescu proposes a new major national program for the transitioning of romania into the second industrial revolution, based on electronics, automation and informatics [9, 10]. not only that the political leadership of romania rejects this visionary proposal, but the development of informatics is purposely slowed down and, in 1985, even the central institute for informatics is dismantled. “in 1985, a terrible blow was given to the information society in formation” [11]. however, even during this tragic period in his own life and that of the country, acad. mihai drăgănescu formulates the idea of a future knowledge society, idea widely accepted today [12]. in the aftermath of the romanian revolution of 1989, acad. mihai drăgănescu resumes his role of promoter of the information revolution. he establishes the national commission for informatics (1990), the center for new electronics architectures (1991), the section for science and technology of information of the romanian academy (1992), the research institute for artificial intelligence (1994) and the forum for the information society (1997). later on, acad. mihai drăgănescu formulates a new vision for the evolution of the society, defining the era of information, with three stages, the information society, the knowledge society, and the consciousness society [12]. “in essence, the information society is the society based on internet” romania being in a state of underdevelopment of this society [12]. “for romania the development of the information society is essential, but in the current situation this has to take place at the same time with achieving the first objectives of the knowledge society. it is wrong to say: first the information society and then the knowledge society. we should not sentence ourselves to be perpetually delayed” [12]. acad. mihai drăgănescu identifies technological and functional vectors as tools that can transition the information society to the knowledge society: “to take first steps into the knowledge society it is necessary to employ a minimum number of such vectors. the first vector is the development of an ’advanced’ internet, which is a technological vector, then the technology of electronic books (technological vector) and the knowledge management (functional vector with two valences, one for the economic and organizational functioning of a company, multinational corporation or society, and the other one for the moral utilization of knowledge in the global society)” [12]. similar to the national program from 1965-1985, the program for the information and knowledge society [12] is a very significant contribution of acad. mihai drăgănescu, with precise objectives and means, of greatest importance for the progress of the romanian society in the global economy. 3 creator of the structural-phenomenological philosophy of existence (orthophysics) a profound understanding of physics, electronics, informatics and biology, a great analytic, synthetic and unifying spirit, an irresistible philosophical tension, in the context of the romanian spirituality, have led acad. mihai drăgănescu to the creation of an original philosophy of existence, fascinating through its naturalness and generality, a philosophy which, according to the illustrious philosopher constantin noica, “will deeply amaze and impress the unsuspecting thinkers of the xxi century” [13]. acad. mihai drăgănescu realizes that “the ontological model which is at the basis of the structural a life dedicated to the science, philosophy and romanian society 319 science is inadequate, and the structural science itself is limited, it can only be used between certain limits of reality. beyond these limits it is insufficient to explain the reality because it neglects extra ingredients of nature which in cannot recognize due to the character of the methods used. overall, the structural science is incomplete and cannot explain the reality in its entirety” [14]. through fundamental works, such as the depths of the material world [15], orthophysics [16], spirituality, information, matter [17], essays [19], l’universalité ontologique de l’information [20], acad. mihai drăgănescu has developed an original ontological model which, for the first time, explains in a unified way the physical, biological, informational, mental, and psychological processes, based on original fundamental concepts such as informatter, orthoenergy, orthoexistence, orthosense, phenomenological sense, intro-openness, and others. acad. mihai drăgănescu postulates the existence of a tendencial deep reality, with an energetic component (orthoenergy) and an informational one (informatter), outside space and time, from which physical universes are generated, where the non-living matter is a coupling between orthomatter with orthoenergy and informatter, while living organisms contain directly informatter, thus forming rings of existence. the orthophysics philosophy proposes to overcome the paradoxes of the current structural science by developing a new type of science, called structural-phenomenological, for which acad. mihai drăgănescu has formulated several principles, such as the principle of insufficiency of structural knowledge, the principle of existence, of profound matter, of the ontological universality of the information and energy, of the tendencies of becoming, and of the structural-phenomenological modeling [21]. as philosopher constantin noica before him, acad. mihai drăgănescu has made philosophy relying first and foremost on “the pure thought uninfluenced, let flowing in his mind ... feeling than the need of confrontation with the established philosophy and science, but giving science a more prominent role” [19, p.104]. other great thinkers, such as florian nicolau, david bohm and john archibald wheeler, have themselves imagined a more profound stratum of reality than that offered by the structural science, but none has advanced so much in this direction as acad. mihai drăgănescu, who has thus made a major contribution to the human thought. today, an increasing number of scientists in physics, chemistry, and information science adopt points of view that are close to those initially proposed by acad. mihai drăgănescu, confirming the importance and the ramifications of his contributions. a convincing argument for the generality of the orthophysics philosophy of existence is the explanation of the notion of god. the question on the relationship between the orthophysics philosophy and god was raised by constantin noica, in 1987. the answer is given by acad. mihai drăgănescu years later, in 2003, following his interactions with prof. menas kafatos, illustrious american astrophysicist and philosopher who postulated the existence of the consciousness of the universe [22]. following a long investigation in the orthophysics philosophy, and using only the already defined principles, acad. mihai drăgănescu devices a phenomenological-structural model of existence. “which is the relationship between the phenomenological-structural model of the fundamental consciousness of existence and the notion of god from the great and important religions of the world? there is no doubt a very strong relationship, the notion of god in these religions potentially being a certain way of perceiving the fundamental consciousness of existence. if we accept the existence of the fundamental consciousness as being very plausible, then god is also very plausible from the scientific and philosophical point of view, even though he is perceived with different nuances. ... the same way the cosmos is not an organism, the fundamental consciousness does not belong to an organism, yet it exists. the fundamental consciousness of existence is the being by excellence, beyond life and, implicitly, beyond death.” [23] the fruitful collaboration with prof. menas kafatos, with whom acad. mihai drăgănescu has many points of view in common, has led to the development of a new integrated approach to science, which takes into account both the structural and the phenomenological aspects of reality [24, 25]. the publication of a large number of papers, the organization of several conferences on the “structuralphenomenological modeling” at the romanian academy, the emerging of disciples, the collaboration 320 g. tecuci with reputable scientists, the adoption of similar concepts by an increasing number of thinkers, all these confirm noica’s characterization of the orthophysics philosophy [13]. 4 initiator and manager of the romanian academy renaissance next year the romanian academy will celebrate 20 years from its renaissance following the romanian revolution of 1989. in a short period of only four years, between 1990 and 1994, as president of the re-born romanian academy, acad. mihai drăgănescu has initiated and led the return of this primary forum of the romanian spirituality to its traditional role and sources, as well as its renewal and adaptation to the future tendencies of the scientific and humanistic culture, in the national, european and world context. we only need to reflect on the evolution of the romanian society during the never-ending period of transition to understand that acad. mihai drăgănescu was the right person, at the right place and at the right time. as vice prime minister in the first provisional government, acad. mihai drăgănescu sets the renaissance of the romanian academy (which was condemned at physical death by the ceasusescu’s regime) as top priority for the new government which, on january 5th, 1990, promulgates the decreelaw no. 4 on the organization and functioning of the romanian academy, reestablishing its autonomy. one of the first measures the re-born romanian academy under the leadership of acad. mihai drăgănescu was to right the wrongs of history. are recognized as members without interruption of the romanian academy the illustrious representatives of the romanian spirituality, abusively eliminated in 1948 by the communist regime, such as lucian blaga, theodor capidan, dumitru caracostea, ion petrovici, constantin rădulescu-motru, gheorghe i. brătianu, silviu dragomir, dimitrie gusti, alexandru lepădatu, ioan lupaş, simion mehedinţi, petre p. negulescu, nicolae colan, victor slăvescu, constantin brăiloiu, tiberiu brediceanu, onisifor ghibu, pantelimon halipa, dragomir hurmuzescu, grigore t. popa, petru sergescu, emil haţieganu, iuliu hossu, constantin levaditi, iuliu maniu, nicolae bălan, grigore tăuşan, and others [19, p.153]. shining stars who have not received their well-deserved recognition during their lives, are elected posthumously as members of the romanian academy: constantin brâncuşi, mircea eliade, constantin noica, ştefan lupaşcu, mircea florian, nichita stănescu, marin preda, ion barbu, traian lalescu, constantin pârvulescu, călin popovici, ion moraru, ştefan berceanu, virgil madgearu, petre andrei, petru caraman, nicolae c. paulescu, alexandru proca, haralambie vasiliu, ştefan odobleja, bela bartok, herman oberth, eugen lovinescu and theodor aman [19, p.153]. the romanian academy regains its historic role as forum of all romanians through the election of honorary members from bessarabia (grigore vieru, ion druţă, mihai cimpoi, andrei andries, sergiu ion rădăuţan), bucovina (grigore constantin bostan şi alexandrina cernov), and the romanian diaspora (nicolae georgescu-roegen, anghel rugină, radu bălescu, sergiu celibidache, alexandru cioranescu, joseph m. juran, george uscăţescu, ion i. inculeţ, dinu adameşteanu, mihai ion botez, eugenio coşeriu, nicola matteesco-matte, jean negulesco, dinu c. giurescu, iosif antochi, paul stahl, petru dumitriu, mattei dogan, idel ianchelevici, constantin atanasie bona, and other [19, p.154], [26, p. 154-155]. acad. mihai drăgănescu founds, writes its manifesto, and leads “academica”, the romanian academy journal destined to maintain a continuous connection with the romanian intellectuality. well-thinking the role of the romanian nation in the development of the universal scientific and humanistic culture, acad. mihai drăgănescu succeeds in bringing over 50 institutions of fundamental and advanced research back into the romanian academy, and in resurrecting institutions of tradition, successfully defending their activity against powerful opposition from various national and international forums. at the same time, acad. mihai drăgănescu leads the adaptation of the romanian academy to the global tendencies through the founding of the section of arts, architecture and audio-visual, the section of science and technology of information, as well as several new institutes and centers. a life dedicated to the science, philosophy and romanian society 321 another impact on the romanian academy by the first president of its renaissance is his initiation of the election of about 50 of its members. 5 promoter of the romanian scientific and humanistic culture we have already mentioned the major role played by acad. mihai drăgănescu in the development of electronics, informatics and philosophy. but his role as promoter of the romanian scientific and humanistic culture is much wider than his extraordinary achievements in these areas. consider, first of all, his determining role in the reestablishment of the natural orthography of the romanian language, abusively altered in 1953, through political diktat contrary to the national culture. this act, of primary importance for the romanian language, as well as the others already mentioned, shows the extraordinary humanistic and patriotic qualities of acad. mihai drăgănescu. the request by the illustrious linguist, acad. alexandru rosetti, of reestablishing â in the romanian orthography, expressed just before his passing into eternity, was a “testamentary request” for acad. mihai drăgănescu [26, p.206]. as a consequence, on january 31st 1991, he delivers the speech “on several rectifications of the romanian language orthography” at the romanian academy [19, p.162-170]. following a wide public debate, where he confronted the vicious opposition of several linguists and part of the press with numerous scientific arguments, acad. mihai drăgănescu delivers, on february 17th, 1993, the historical speech “the romanian language orthography: with hasdeu, maiorescu, puşcariu and rosetti”, urging “let us have the consciousness of our past, aspirations, and destiny. let us defend our culture, language, and soul.” [19, p.171-194]. the almost unanimous vote (with only one against) of the members of the romanian academy, as well as the support of the saint synod of the romanian orthodox church, the ministry of education, and the ministry of culture, have changed the accusation of imposing “the drăgănescu’s orthography” into a title of glory [26, p. 158]. the exemplary patriotism of acad. mihai drăgănescu is also shown by his relentless work toward the national and international recognition of the personalities of the romanian culture, such as ştefan odobleja. through a 15 years effort, between 1975 and 1990, during which acad. mihai drăgănescu has delivered numerous talks on the contributions of ştefan odobleja, has led the editing of two volumes, romanian precursors of cybernetics [27] and odobleja between ampere and wiener [28], and has edited odobleja’s consonantist psychology in romanian [29], he has succeeded in determining the election of ştefan odobleja, posthumously, as member of the romanian academy, and has increased the prestige of the romanian science [19, p.153]. through his example, acad. mihai drăgănescu has also shown us how to recognize and treasure our professors and mentors. in 1982 he delivers the talk tudor tănăsescu founder of the romanian school of electronics. in 1992 he organizes the conference tudor tănăsescu and the romanian school of electronics at the romanian academy, and leads the editing of the volume tudor tănăsescu founder of the romanian school of electronics [30], solidifying his place in the history of the romanian science and technology. these are only a few examples from acad. mihai drăgănescu’s life-long sustained effort of promoting past and present values of the romanian culture, not only through his own publications and speeches, but also through the organization of conferences and through his support for the publication of books in numerous domains. 6 mentor and life model acad. mihai drăgănescu was always a visionary. he was ahead of his time when he introduced the idea of an information revolution [8], and when he created the orthophysics philosophy [15], and when 322 g. tecuci he defined the concept of functional electronics [4], and when he defined the concept of knowledge society [11], and when he introduced the concept of consciousness society [14]. it is well-known that the younger generation is much better adapted to the new social context created by the computers and the internet than the older ones. in spite of his age, acad. mihai drăgănescu is part of this younger generation. in his seventies, he is the first to publish the first romanian book on the internet [20], as well as the first electronic book [31]. he is the one who, in 2001, organizes the first symposium in romania on the electronic books, as well as the first symposium on the electronic commerce. how could one explain this extraordinary power of assimilation and adaptation that allowed acad. mihai drăgănescu to navigate with such apparent ease the highly dynamic and complex domains of electronics, informatics and philosophy? in his reception speech at the romanian academy, titled “the philosophical tension and the cosmic feeling”, acad. mihai drăgănescu stated: “if the existence forms material world rings, or universes, then we have to accept that our universe has a sense, a tendency, and the good, in its philosophical sense, corresponds to conforming to this tendency by the human and society, which does not mean strict determinism but, to the contrary, advanced knowledge and high creation power. it can be demonstrated that the philosophic good is not only compatible with the social good but also theoretically derives it, as can also be shown based on humanistic considerations” [32, p.27]. the cosmic feeling, advanced by acad. mihai drăgănescu as solution to the philosophical tension for the understanding of the existence, “calls man to a heroic attitude through thought and action in the direction of the fundamental tendency in the universe, that is, toward creation and a spiritual attitude which is also a source for strengthening his will, rationality, and lucidity” [32, p.30]. we see here a philosophical justification of the man’s action in the direction of good, which acad. mihai drăgănescu exemplifies in the most remarkable manner. we think that precisely his deep understanding of the philosophical tension and the cosmic feeling explains his model patriotic vocation for the progress of the romanian society, as well as his capacity of overcoming so many obstacles and injustices. in a long and illustrious career, acad. mihai drăgănescu has lightened and influenced the life of many generations who, as myself, have had the chance of being closer to him, well-thought by him, guided by him, and blessed by him, with unparallel generosity. acad. mihai drăgănescu is a spiritual father and life model for me, the one who has influenced the direction of my life much more than anyone else. 7 final remarks the observer of the activity of acad. mihai drăgănescu is amazed by how a person can excel in domains that are so diverse and difficult. it is the genius of acad. mihai drăgănescu who succeeded to discover and define a line of continuity and complementarity that has led him to the development of a unified encyclopedic work integrating the most dynamic domains of the modern society: electronics, informatics, and the philosophy of science and society. in his words, “the multidisciplinary approach to problems has become common practice. however, this will not be sufficient if we will not find the integrative factors to melt the multidisciplinarity into a unity, be it the case of understanding a complex reality, or that of achieving goals serving the people and society” [33, p.428]. discovering these integrative factors is one of the greatest contributions of acad. mihai drăgănescu. one example: “information technology is a technology of progress in our age, through its two important components: electronics and informatics. there is no boundary between them. the physical seed of the contemporary information society is electronics, or microelectronics to be precise, and the informational seed is provided by the informatics” [4, p.15]. the “physical seed” and the “informational seed” may have inspired acad. mihai drăgănescu in his development of the philosophical conception of deep matter: “under the quantum world there is ... the deep matter governed by two principles: informatter, a matter with informational properties of a life dedicated to the science, philosophy and romanian society 323 phenomenological type (like the mental senses), and energymatter, a matter with energetic properties, which is unstructured but can be structured by the informatter” [33, p.10]. on the other hand, “technology is inherently linked to philosophy because, as instrument of becoming, it has an existential role. however, becoming implies coalescing the social factors with the scientific, technological and economic ones, and with the spiritual-cultural life. none of these factors can be estranged from the others without affecting the reality and endangering the socio-human civilization” [18, p.127]. what ultimately provides this amazing unity in diversity to the work of acad. mihai drăgănescu is his general view of information which encompasses [33, p.428]: “i. philosophy of information; ii. science of information; iii. technology of information; iv. industry of information; v. economy of information; vi. culture-information relationship; vii. information society (including the problems of social intelligence and information democracy); viii. creation (generation) of information.” for acad. mihai drăgănescu, the information “has a multitude of manifestations, it may become thought, consciousness, spirit, computation, poetry, idea, sense. each of these manifestations may be a support for the understanding of matter and life” [18, p.13]. the encyclopedic work of acad. mihai drăgănescu, spanning electronics, philosophy, informatics, and society (see http://www.racai.ro/ dragam/), is an impressive and inspiring example of a life dedicated to the science, philosophy, and romanian society. bibliography [1] m. drăgănescu, electronic processes in semiconductor devices of circuits (in romanian), romanian academy press, bucharest, 1962. [2] m. drăgănescu, on some microelectronics problems (in romanian), automatica şi electronica, 7, 1, 31-40, 1963. [3] m. drăgănescu, solid state electronics (in romanian), editura tehnică, bucharest, 1972. [4] m. drăgănescu, gheorghe ştefan, corneliu burileanu, functional electronics (in romanian), vol. i, editura tehnică, bucharest, 1992. [5] m. drăgănescu, microelectronics and functional electronics (in romanian), in: dan dascalu and gh. brezeanu (eds.), new research in microelectronics, romanian academy press, 9-20, 1994. [6] m. drăgănescu, from solid state to quantum and molecular electronics, the deepening of information processing, invited paper at the international semiconductor conference, sinaia, october 8-11, 1997, published in the volume i of the conference, p.5-21. http://www.racai.ro/md75/ 324 g. tecuci [7] m. drăgănescu, neural engineering and neuroelectronics facing artificial consciousness, communication at the ingimed ii conference, bucharest, dec. 13, 2001; * e-preprint, msreader format, romanian federation of bioengineering. [8] m. drăgănescu, system and civilization, essays (in romanian), editura politică, bucharest, 1976. [9] m. drăgănescu, the second industrial revolution. microelectronics, automatics informatics: determining factors (in romanian), editura tehnică, bucharest, 1980. [10] m. drăgănescu, informatics and society (in romanian), editura politică, bucharest, 1987. [11] m. drăgănescu, the information and knowledge society. the vectors of the knowledge society, study for the romanian academy project "information society knowledge society", bucharest, 9 july 2001, http://www.racai.ro/infosoc-project/ [12] m. drăgănescu broadband and the knowledge society, communication presented at the international conference information society technologies for broadband europe, 9-11 october 2002, marriott grand hotel, bucharest, românia. http://www.racai.ro/ dragam/broadbandinternet-and-the-knowlegde.pdf [13] c. noica, a work for the century (in romanian), contemporanul, 20 june 1986. [14] m. drăgănescu, the consciousness society (in romanian), romanian academy institute for artificial intelligence, bucharest, 2007, isbn 978-973-0-05307-4 [15] m. drăgănescu, the depth of existence, editura politică, bucharest, 1979, http://www.racai.ro/books/doe/ [16] m. drăgănescu, orthophysiscs (in romanian), editura ştiinţifică şi enciclopedică, bucharest, 1985. http://www.racai.ro/ dragam/ortof.html [17] m. drăgănescu, spirituality, information, matter. essays (in romanian), romanian academy press, bucharest, 1988. [18] m. drăgănescu, information of matter (in romanian), romanian academy press, bucharest, 1990. [19] m. drăgănescu, essays (in romanian), romanian academy press, bucharest, 1993. [20] m. drăgănescu, l’universalité ontologique de l’information. préface et notes par yves kodratoff, romanian academy press, bucharest, 1996. html edition by dorin marcu, http://www.racai.ro/books/draganescu/ [21] m. drăgănescu, principes d’une science structurale-phénoménologique, bulletin de la classe des lettres et des sciences morales et politiques, academie royale de belgique, 6e série, tome iv, 7-12, 255-311, 1993. [22] m. kafatos, r. nadeau, the conscious universe, springer verlag, new york, 1990. [23] m. drăgănescu, the fundamental consciousness of existence (in romanian), academica, januarymarch 1998. http://www.racai.ro/md75/ [24] m. drăgănescu, m. kafatos, generalized foundational principles in the philosophy of science, paper presented at the conference on "consciousness in science and philosophy" charleston, illinois, 6-7 nov 1998, published in the noetic journal, vol.2, no.4, oct. 1999, 341-350, republished in the vol. science and the primacy of consciousness, intimation of a 21st century revolution, richard l. amoroso and others (eds), orinda: the noetic press, 2000, chapter 9, 86-98. also * e-copy in msreader format. a life dedicated to the science, philosophy and romanian society 325 [25] m. kafatos, m. drăgănescu, toward an integrative science, noesis, xxvi, 2001. http://www.racai.ro/md75/ [26] m. drăgănescu, caryatids of thought (in romanian), romanian academy press, bucharest, 1996. [27] m. drăgănescu, constantin bălăceanu, victor săhleanu, gheorghe petrovici, gheorghe ştefan and alexandru giuculescu, romanian precursors of cybernetics (in romanian), romanian academy press, 1979. [28] m. drăgănescu, p. golu, c. bălăceanu and a. giuculescu, odobleja between ampere and wiener, romanian academy press, 1981. [29] şt. odobleja, consonantist psychology (in romanian), with an introductory study by mihai drăgănescu and pantelimon golu, editura ştiinţifică şi enciclopedică, bucharest, 1982. [30] m. drăgănescu, a. rusu, şt. iancu (eds), tudor tănăsescu, the founder of the romanian school of electronics (in romanian), editura dorotea, bucharest, 2001. [31] m. drăgănescu, tudor tănăsescu and the romanian school of electronics (in romanian), bucharest, 7 march 2001, * e book, msreader format, edition dragam@racai.ro [32] m. drăgănescu, the philosophical tension and the cosmic feeling. romanian academy reception speech, 6 september 1990. answer by acad. zoe dumitrescu buşulenga. opening remarks by acad. nicolae cajal, romanian academy press, bucharest, 1991. [33] m. drăgănescu, the ring of the material world, editura ştiinţifică şi enciclopedică, bucharest, 1989. gheorghe tecuci is member of the romanian academy, professor of computer science and director of the learning agents center in the volgenau school of information technology and engineering at george mason university, and visiting professor and former chair of artificial intelligence at the us army war college. international journal of computers communications & control issn 1841-9836, 9(6):672-685, december, 2014. auto adaptive identification algorithm based on network traffic flow s. dong, x. zhang, d. zhou shi dong* 1. school of computer science and technology, zhoukou normal university zhoukou, 466001, china 2. school of computer science & technology, huazhong university of science and technology wuhan, 430074, china *corresponding author: njbsok@gmail.com xingang zhang school of computer and information technology, nanyang normal university nanyang, 473061, china zxg@nynu.edu.cn dingding zhou department of laboratory and equipment management, zhoukou normal university zhoukou, 466001, china zdd@zknu.edu.cn abstract: traffic identification is a key task for any internet service provider (isp) or network administrator. machine learning method is an important research method on traffic identification, while impact of the asymmetry router on the traffic identification is considered, so this paper analyzes the impact of asymmetry routing on traffic identification, and proposes an effective method to decrease the impact, and experimental results show the auto adaptive algorithm can improve the traffic identification. keywords: traffic identification, internet service provider (isp), auto adaptive algorithm (aa), asymmetry routing. 1 introduction traffic identification play an important in many fundamental network operations and maintenance activities to detect invade and malicious attacks forbid applications, bill on the content of traffics and ensure quality of service. it increasingly becomes one of the most interesting topics in network science and technology fields, especially in recent years. the current network traffic identification methods roughly five categories: (1) port-based method; (2) based on deep packet inspection (dpi) methods; (3) based on the network flow characteristic; (4) based on host behavior [1]; (5) based on machine learning methods. the machine learning methods are divided into supervised and unsupervised machine learning. these are the more classic identification method; of course, there is also individual qos quality of service features for identification [2]. many share a naive assumption about the internet that traffic on a given link is approximately symmetric, meaning that both directions of a conversation flow across the same physical link. many developers even embed this assumption in their traffic classification tools [3, 4]. in fact, except at network edges, internet traffic is often routed asymmetrically [5], which will impair or invalidate the results of tools and models that assume otherwise. an important cause of this asymmetry is "hot-potato routing" [6], the business practice of configuring traffic crossing one’s network to exit as soon as possible, minimizing resource consumption, and thus cost, of one’s own infrastructure. particularly common in commercial settlement-free peering agreements, hot-potato routing implies that the network on the copyright © 2006-2014 by ccc publications auto adaptive identification algorithm based on network traffic flow 673 receiving side of a packet will bear higher cost per received packet. the underlying assumption is that if both networks in a settlement-free peering agreement follow this practice, it will even out, and both sides will share evenly in carrying traffic exchanged by their customers. another cause of asymmetric traffic is link redundancy, or alternative paths within networks. since routing decisions occur independently for each packet, load-balancing algorithms may cause packets destined to the same endpoint to follow different paths. other traffic engineering techniques, e.g., policy-based spf (shortest path first), may also induce asymmetry in internal routing state of large provider networks, through studying on asymmetric routing, we found it had some impacts on traffic identification, and we propose auto adaptive (aa) method to improve traffic identification. experiments results show that the aa method can achieve better accuracy than others. the paper is structured as follows: section 2 introduces related work of traffic identification; section 3 proposes aa algorithm and evaluation method; in section 4, at last, we list the proportion results which are classified by our identification algorithm, and analyze the impact of ε on traffic identification; section 5 concludes the paper. 2 related work the application identification problem has been changing due the efforts of two factors that are in a continuous competition. on the one hand, the applications, and especially those that do not want to be detected (e.g., p2p applications), in order to use the network resources without control. on the other hand, a group of network operators, investigators and even isps who need to know the traffic characteristics of their networks to manage the resources or even charge the users depending on their consumption. 2.1 research on traffic identification it has become a hot research between domestic and foreign experts who take the traffic identification as research direction, which proceed distinguish, qos, intrusion detection, traffic monitoring, billing and management. from the beginning of the study on port-based method, this method is the use for marking and identifying the traffic type by fixed port which supplied by the iana, the other method is aim at p2p and some certain protocols, which adopt method based on deep packet detection methods, but this method has defect that can’t get some encrypted information and can’t get the new service type. recently traffic identification has new method with a number of new applications come out. with appearance of the new service, the method of machine learning has been applied to the traffic identification. identify fields on the flow, roughly divided into three research directions: one is the feature selection algorithm [7, 8], the other is identification algorithm [1,2,9], another is a category for different types of data sets, for example, all packets can be divided into flows [10–14] that are sampling netflow [15]. complementary information about related work in the field of traffic identification can be found in the survey of traffic identification techniques using machine learning in [16], in the comparison of contemporary classification methods in [13], the survey on internet traffic identification in [17] and the research review on traffic identification in [18]. a critical but constructive analysis of the field of internet traffic identification is proposed in [19], focusing on major obstacles to progress and suggestions for overcoming them. although some articles have been studied on the identification algorithm, but the identification algorithm still exist some problems to be needed to solve, such as the neural network identification algorithm is one point worthy of study. all previous research studies in traffic identification either use insufficient network data, usually non-public, or use very few/meaningless metrics for evaluation, making it impossible to compare results shown in 674 s. dong, x. zhang, d. zhou different papers [17]. in addition to features selection based on flow, especially the impact of the size of packet traffic is always to be concerned. therefore, in this article we propose aa method, and we analyze different feature metric set (bidirection feature or unidirection feature) cause different identification results. 2.2 asymmetry routing for a pair of hosts a and b, if the path from a to b (forward direction) is different from the path from b to a (reverse direction), we say that the pair of paths between a and b exhibit routing asymmetry. this scenario can be very common in the internet core where asymmetric routing is an usual practice [20, 21], this asymmetry in the internet can appear on both as level and router level paths. in fact, the path followed by packets exchanged between end points along one direction can be different from the one followed by packets going in the opposite direction. recent reports suggest that asymmetrical routing might be moving closer to the edge of the internet than one might expect. for example, the analysis presented in [22] argues that this practice is nowadays quite common even in isps directly serving campus-wide networks. 2.3 flow metric definition 1. the definition of flow metric, which is composed with traffic statistical feature such as flow length, flow during etc. these features have high correlation with application type. so considered as flow metric to classify traffic by machine learning. while nowadays there are two kinds of flow metric, one is unidirectional flow metric, and the other is bidirectional flow. unidirectional flow metric uniflow (unidirectional flow)(or one-way) within your network is most likely the result of an incorrect configuration, but may also be symptomatic of a larger problem related to your overall routing architecture. since network communications are bi-directional in nature, unidirectional traffic patterns on your network mean that the traffic flow in one direction is not following the same path as the other. by design, the least cost route to a destination should also be the desired return path. uniclassifier (unidirectional classifier) is classifier which use unidirectional flow metric for training set. where unidirectional flow metric is adopted as table 1 in this paper. bidirectional flow metric biflow(bidirectional flow): a biflow is a flow as defined in the ipfix protocol document [rfc5101], composed of packets sent in both directions between two endpoints. a biflow is composed from two uniflows such that: 1.the value of each non-directional key field of each uniflow (unidirectional flow) is identical to its counterpart in the other, and 2.the value of each directional key field of each uniflow is identical to its reverse direction counterpart in the other. biclassifier(bidirectional classifier) is classifier which use bidirectional flow metric for training set. where bidirectional flow metric is adopted as table 2 in this paper. auto adaptive identification algorithm based on network traffic flow 675 table 1: unidirectional flow feature feature feature description lport low port number hport high port number duration flow duration transproto stream transport protocol used (tcp / udp) tcpflags tcp header flag,transport layer protocol is udp,the feature is 0 pps packets/duration bps bytes/duration mean packets arrived time duration/packets tos tos from netflow mean packet length bytes/packets table 2: bidirectional flow feature feature feature description lport low port number hport high port number duration flow duration transprotocol stream transport protocol used (tcp / udp) tcpflags1 tcp header flag,transport layer protocol is udp,the feature is 0 tcpflags2 tcp header flag,transport layer protocol is udp,the feature is 0 pps packets/duration bps bytes/duration mean packets arrived time duration/packets bidirectional packets ratio forward packets/ backward packets bidirectional bytes ratio forward bytes/ backward bytes bidirectional packet length ratio bidirectional packets length ratio bidirectional packets forward packets + backward packets bidirectional bytes forward bytes + backward bytes tos bidirectional tos or from netflow mean packet length bidirectional bytes/bidirectional packets 676 s. dong, x. zhang, d. zhou 3 methodology 3.1 auto adaptive algorithm (aa) in this paper, we propose an algorithm which can auto adjust the flow metric to adapt the traffic identification. the algorithm is called auto adaptive algorithm(aa). the algorithm’s core thought is that different traffic can select different classifier with different flow metric (unidirectional flow or bidirectional flow). suppose there are n flow samples, each sample has p features, then construct the n*p flow matrix, as follows: a =   x11 x12 · · · x1p ... . . . ... xn1 xn2 · · · xnp   (1) when features number p of the samples are very large which enlarge dimensions of the sample, theoretically, having more features should result in more discriminating power. however, practical experience with machine learning algorithms has shown that this is not always the case. many learning algorithms can be viewed as making an (biased) probability estimate of a set of features with the class label. this is a complex, high dimensional distribution. asymmetric routing existing will impact on the traffic identification. so we can consider to adopt auto adaptive method to do with it. in order to depict the method, we have to introduce the h which represent the threshold. h = bidirection_flow_number total_flow_number (2) definition 2. optimal threshold: which is used to evaluate the traffic accuracy, it is minimum threshold. when the traffic accuracy is maximum. h is optimal threshold ε. according to different h, and select h as optimal threshold to enable to obtain the best traffic results, where h is random variable. when h < ε, it will choose unidirectional flow and generate the unidirectional classifier, conversely, it will choose directional flow and generate the directional classifier. algorithm aa presents the two kinds of flow metric. the sequence of steps that we show in figure 1. the procedure mainly set two kinds of dataset for training and testing data set. with these data, we choose aa algorithm to train and test data. the process of machine learning identification is shown in figure 2: auto adaptive identification algorithm based on network traffic flow 677 1.collecting traffic(input): collecting network data from network traffic 2.selecting traffic features and training data for building traffic classification model(data processing): optimal selecting the known traffic features through the traffic feature selection algorithms. in this paper we only adopt two kinds of feature metric(unidirectional metrics and bidirectional metrics), so extra feature selection method is not added. the traffic classification model is built by training data. 3.classified the traffic by machine learning algorithm (output): using the machine learning identification algorithm to classify network traffic data and generate flow with label. figure 1: traffic identification process of aa method figure 2: process of machine learning, traffic identification 3.2 algorithm evaluation in this paper, we use the routine evaluation standard for verifying the effectiveness of our identification algorithm. the effectiveness of the current flow identification algorithm has the 678 s. dong, x. zhang, d. zhou table 3: noc_set dataset appid application protocal flow number proportion(%) 1 www http 4943 64.6 2 bulk ftp 39 0.5 3 mail imap,pop3,smtp 91 1.19 4 p2p bittorrent,edonkey,gnutella,xunlei 1414 18.5 5 service dns,ntp 433 5.7 6 interactive ssh, cvs, pcanywhere 6 0.08 7 multimedia rtsp,real 20 0.3 8 voice sip,skype 276 3.6 9 others games, attacks 431 5.6 following three concepts evaluation criteria. and the concepts involved are as follows: -tp (true positive): the flows of application a are classified as a correctly, which is a correct result for the identification; -fp (false positive): the flows not in a are misclassified as a. for example, a non-p2p flow is misclassified as a p2p flow. fp will produce false warnings for the identification system; -fn (false negative): the flows in a are misclassified as some other category. for example, a true p2p flow is not identified as p2p. fn will result in identification accuracy loss. the calculating methods are as follows: 1. precision: the percentage of samples classified as a that are really in class a precision = tp tp + fp (3) 2. recall: the percentage of samples in class a that are correctly classified as a recall = tp tp + fn (4) 3. overall accuracy: the percentage of samples that are correctly classified overallaccuracy = ∑n i=1 tpi∑n i=1(tpi + fpi) (5) 4 experiment 4.1 dataset noc_set dataset in order to validate the method and analyze the impact factor,we adopt noc_set as dataset.as shown from table 3. we collected data at southeast university,and the collecting site is a 10g backbone channel on jiangsu province border of cernet. we adopt dpi method to mark flow and generate noc_set dataset,and use ourself l7_filter_modify software to label the flow.l7_filter_modify is developed based on l7filter [23], at last, we generate noc_set dataset. auto adaptive identification algorithm based on network traffic flow 679 lbnl_set dataset table 4: lbnl_set dataset appid category flow number proportion 1 80 15000 47.69% 2 110 1400 4.45% 3 25 1350 4.29% 4 139 3300 10.49% 5 993 400 1.27% 6 443 10000 31.8% this lbnl_set data is randomly sampled in several different periods from one node on the internet. the lbnl traffic traces are collected at the lawrence berkeley national laboratory under the enterprise tracing project [24]. the packet traces are obtained at the two central routers of the lbnl network and they contain more than one hundred hours of traffic generated from several thousand internal hosts. the traffic traces are public, but they are completely anonymized, so ascertaining the "ground truth" on the application behind each recorded flow is not possible. therefore, for this set, we built protocol sets according to the tcp destination port number of each flow, an accepted practice in these cases [25]. we use the traffic traces captured on january 6 and 7, 2005 to obtain the training and the optimization sets. once again we perform the training by using the most frequently used port numbers in the dataset. detail lbnl_set dataset is shown in table 4. caida dataset we built this data set starting from three hour long traces obtained by the cooperative association for internet data analysis (caida) [26], and collect at the ames internet exchange (aix) along an oc48 link on mar 24, 2011. we use flows extracted from the first hour (corresponding to the interval 16:15-17:00 utc) to build the training set the optimization set and from the third hour (18:00-18:10 utc) to buld the evaluation set. as for the previous set, these traces are also anonymized, so port numbers are used as indicators of each protocol. the selection of flows composing the training, optimization and evaluation sets. table 5: caida_set dataset appid category flow number flow(%) packets(%) bytes(%) 1 80 328091 84.69 81.74 81.58 2 110 11539 0.6 0.24 0.25 3 21 28567 3.32 0.03 0.09 4 25 2648 4.57 2.47 2.72 5 4662 2099 0.79 1.34 1.35 680 s. dong, x. zhang, d. zhou 10 15 20 25 30 35 40 45 50 njucernet jsucernet caida−chicago caida−sanjose traffic f s e (% ) flows (a) flows 10 15 20 25 30 35 40 45 50 55 60 njucernet jsucernet caida−chicago caida−sanjose traffic f s e (% ) bytes (b) bytes figure 3: comparison of fses for traffic table 6: the identification overall accuracy rate aa, biclassifier, uniclassifier identification overall accuracy aa 99.6742% biclassifier 88.2% uniclassifier 89.2% 4.2 impact of asymmetry router on traffic identification: in this paper, we adopt experimental data based on the noc-set data set and caida datas set, use matlab tools, weka tools and the corresponding algorithm to identify network traffic data [27]. noc-set data firstly divided into two test data were 20% and 80% of the test data, and we compared our method that is aa with biclassifier and uniclassifier. in order to evaluate and analyze effectiveness of the method about aa. we study traffic identification distribution. in order to analyze asymmetry router, firstly we should remove from the traces any traffic that is inherently asymmetric, such as udp and icmp that do not always expect packet recipients to reply, and which would mislead symmetry comparisons if they appear in different magnitudes across networks. tcp background radiation, such as network scanning and probing, can also be a substantial fraction of total inherently asymmetric flows on some links, although it is usually a much lower proportion of bits. we adopt flow-based symmetry estimator(fse) [28] to evaluate impact degree on traffic, which is a simple method estimate the level of routing symmetry from passively measured flow data. from figure 3 and figure 4 we can see different traffic have different fse, and caida traffic is less. it indicated asymmetry router of caida traffic were more obvious than noc-set. from table 6 we can see that overall accuracy of aa method traffic is better than biclassifier and uniclassifier,we adopt aa method to classify traffic based noc-set data,and select parameter ε=0.5(detailed analysis shown in session f). the data is divided into 9 categories, respectively, www, mail, bulk, service, p2p, interactive, voice, multimedia, others table 6 indicates the aa algorithm achieved better result than biclassifier and uniclassifier method, moreover. p2p can be seen from table 7 and the voice of the precision and the recall has greatly improved. the reason for high accuracy is that the proportion of p2p and voice auto adaptive identification algorithm based on network traffic flow 681 table 7: identification performance for noc_set(precision and recall) category algorithm biclassifier uniclassifier aa precision recall precision recall precosiin recall www 98% 100% 99% 100% 98.5% 99.2% p2p 58% 100% 75% 100% 93.7% 91.2% mail 83% 91.3% 90% 99% 100% 100% service 58.90% 100% 70% 99% 90% 90.4% inter 84.5% 100% 87% 100% 80% 100% multimedia 100% 75% 90% 80% 60% 100% voice 35% 50% 45% 55% 37% 50% others 44% 46% 48% 77% 45% 60% account for set of the total is relatively small,the impact of the identification results reduce to a minimum due to the collection of the specimen caused by imbalance in the ratio.this paper also build noc_set dataset which is constructed by bidirectional flow characteristic. 4.3 comparison of identification algorithm with noc-set dataset experimental data for the noc_set data set (table 3 as fellows) the analysis data are actual measured ip trace [29], while the traffic flow exits about 40% biflow. noc_set dataset is composed by biflow feature.biflow have more information for traffic identification.if use biclassifier to classify the traffic, then the identification result will be improved. in this section, we compare aa algorithm with biclassifier and uniclassifier. traffic identification result is shown in table 7. as shown in table 7, identification result indicates that aa could achieve better accuracy compared with biclassifier and uniclassifier.but observing from inter and service, identification accuracy of aa is lower than the other method. from service to inter types, precision of biclassifier and uniclassifier method is reduced, while the aa is in increments, so that biclassifier and uniclassifier method is easily affected by the number of training samples, while the aa is not vulnerable to the impact of the training sample dataset. among three identification algorithm aa, biclassifier and uniclassifier, the overall accuracy of the aa algorithm is highest. 4.4 comparison of identification algorithm with caida_set dataset the data set used in experimental platform: experimental data for the caida_set data set (table 5 as fellows). the analysis data are actual measured ip trace [29]. the two core links are part of an oc192 tier1 backbone operated by a commercial isp in the u.s. the first link connects chicago and seattle, monitored at an equinix data center in chicago. the other one connects san jose and los angeles, monitored at a datacenter in san jose. on those links, tcp is responsible for about 50% of flows, which was 85% of packets and 93% of bytes on average.udp carried about 45% of flows (13% of packets and 6% of bytes). we adopted port-based method to mark flow and generated caida_set dataset.while the traffic flow exits about 10% biflow. caida_set dataset is composed by uniflow feature. biflow have more information for traffic identification. if use biclassifier to classify the traffic, then the identification result will be improved. in this section, we compare aa algorithm with biclassifier and uniclassifier. traffic identification result is showed in table 8. 682 s. dong, x. zhang, d. zhou table 8: identification performance for caida_set(precision and recall) category algorithm biclassifier uniclassifier aa precision recall precision recall precision recall 80 92% 98% 98% 97% 96.5% 98.2% 110 63% 97% 83% 99% 95.7% 92.2% 21 82% 88.3% 92% 98% 99% 99% 25 60.80% 99% 72% 98% 92% 92.4% 4662 82.4% 99% 89% 98% 82.9% 99.2% overall accuracy 65.72% 94.1342% 95.8921% table 9: identification performance for lbnl_set(precision and recall) category algorithm biclassifier uniclassifier aa precision recall precision recall precision recall 80 96% 98% 97% 93% 96.5% 98.2% 110 78% 90% 85% 90% 92.5% 83.2% 25 88% 82.7% 89% 87% 97% 99% 139 59.80% 98% 78% 92% 93% 91.6% 993 86.5% 99% 79% 99% 87% 99% 443 88.5% 99% 89% 99% 84% 99% overall accuracy 68.83% 93.237% 95.861% as shown in table 8, identification result indicates that aa could achieve better accuracy compared with biclassifier and uniclassifier. according to analysis of 4.4 section on traffic result, we can see caida exists the same phenomena which is unbalance sample data. so that biclassifier and uniclassifier method is easily affected by the number of training samples, while the aa is not vulnerable to the impact of the training sample dataset. among three identification algorithm aa, biclassifier and uniclassifier, the overall accuracy of the aa algorithm is highest. 4.5 comparison of identification algorithm with lbnl_set dataset we obtained lbnl data from the lawrence berkeley national laboratory, and construct the bidirectional and unidirectional flow metric. we respectively train the two metrics and generate biclassifier and uniclassifier. we compute h value the formula 2 in section 3, and adopt aa method to select classifier which is uniclassifier or biclassifier. the experimental results is shown in table 9. from the results we can see uniclassifier and uniclassifier method is affected by unbalance sample data, while aa method can overcome the problem and improve traffic identification results. auto adaptive identification algorithm based on network traffic flow 683 4.6 impact of ε on traffic identification in this paper we propose aa method to auto adaptive select classifier(biclassifier or uniclassifier), while threshold ε is a parameter of aa method.ε decide classifiers which were selected, so it is very important for traffic identification. in this section, we will analyze the impact of ε on traffic identification. detailed experiment method is adopting aa method proposed by varying from ε[0.1,1] based on three dataset(noc_set, caida, lbnl_set). from figure 4 we can 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.8 1 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 ε o ve ra ll a cc u ra cy noc−set caida lbnl figure 4: the identification results with ε see overall accuracy of caida and noc_set have biggest change happened when ε vary from 0.1 to 1. overall accuracy of caida shows an increasing tendency, while noc_set is descending. the possible reasons why is that cernet network contain more symmetry routing, while asymmetry routing is less. collection point of caida data exist more asymmetry routing. thus when threshold ε is very small, more opportunity will be selected by biclassifier. just as mentioned that collection point of noc_set is cernet network containing more symmetry routing, which will have more bidirectional flow metrics, so noc_set showed an descending tendency and when ε =0, overall accuracy is maximum.ε =0.5, overall accuracy of caida and noc_set is equal. lbnl have not obvious asymmetry routing. so overall accuracy is gentle. 5 conclusion in this paper we propose auto adaptive algorithm, and on this basis, the introduction of biclassifier and uniclassifier, and adopt the improved aa method to classify traffic for moore_set as data set, moreover, compare with two other methods which is the biclassifier and uniclassifier method, the results show that, aa method are greatly improved on identification accuracy, to further prove aa method is effective, this paper collect the data in jiangsu provincial network border and organize trace into flow record such as data sets noc_set, the experimental results show that: aa method has high identification accuracy,and we analyze the impact of ε on traffic identification and 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[29] *** ip trace distribution system, http://iptas.edu.cn/src/system.php. international journal of computers, communications & control vol. i (2006), no. 4, pp. 85-91 realization of embedded multimedia system based on dual-core processor omap5910 peng li, yu lu, shen li, hongxing wei abstract: this paper focuses on the realization of a complete embedded system using the dual-core processor omap5910. detailed description of how to compose the hardware system is presented with a description of the software system on our platform. tasks communication between the two cores is realized using the dsp driver. the system bootloader and the dsp bootloader are described in detail. the implementation of the mpeg-4 video decoder has been realized on the presented system. higher speed can be achieved and less power is needed for mpeg-4 video processing on the dual-core platform. this dual-core system can be applied to 3g wireless communication, robot control and vision systems. keywords: embedded system, mpeg-4 decoder, arm, omap5910, multimedia platform, multimedia application, operating system. 1 introduction with the growth of the 2.5/3g wireless markets, full-featured multimedia services are required by the wireless applications. more and more powerful processors are used to achieve the requirement. dual-core architecture processors meet the rapid processing needs of next-generation embedded devices. owing to the urgent need of the dual-core processor application, this paper presents a platform using the dual-core processor omap5910 and applications on this platform. the omap5910 chip is a highly integrated hardware and software platform. it integrates a tms320 c55xdsp core with a ti-enhanced arm925 core on a single chip for the optimal combination of high performance with low power consumption. this unique architecture makes it ideal for audio and video processing in multimedia applications [1], [2]. the remaining part of this paper will be compiled as follows. in section 2, the composing of the hardware system is described and in section 3, the software system architecture is presented. in section 4, a detailed description of the system bootloader and dsp bootloader is given. since video processing is one of the most important applications in multimedia systems, the implementation of the video decoding is described at the end of the paper. 2 hardware system omap5910 contains numerous interfaces for connecting to peripherals or external devices [3]. the complete embedded multimedia system we designed is illustrated as figure 1. as illustrated by figure 1, 32m sdram and 16m nor flash are used to compose the minimum system. the nor flash is used to store the bootloader and the operating system. another 256k sram is applied. a jumper is used to control the boot overlay mode, in which the sram is mapped to bank 0 and the nor flash is mapped to bank 3. besides the minimum system, other devices of the platform are as follows: a camera for video capturing, a tft color lcd for displaying, an audio codec for audio input and output, an ethernet controller for operating system downloading and data transmitting, a ps/2 mouse and a keyboard as human device interfaces. the usb host/function interfaces and sd/mmc card interface are included in omap5910. they are used for the data storage in our system. copyright c© 2006 by ccc publications 86 peng li, yu lu, shen li, hongxing wei figure 1: hardware system due to the integration of the camera interface for cmos sensors on omap5910, we choose the ov7648 for video capturing. the ov7648 is a sensor-on-board camera and lens module designed for mobile applications. the control interface of the sensor is the sccb (omnivision serial camera control bus), a 3-wire serial bus. three mpu i/os are used to realize the sccb protocol. ov7648 output format is ycrcb 4:2:2 or rgb, and the ycrcb format is chosen in the system. the color lcd controller on omap5910 supports a direct connection to the lcd panel and an 18bit tft lcd is chosen for displaying. a high priority, dedicated dma channel has been implemented in the system specifically for the lcd controller. this dedicated dma-lcd channel ensures minimum latency in real-time lcd operations. ethernet makes high-speed data exchanging, a very important function in multimedia systems, available between pc and the platform. it also provides an efficient way for system debugging. here we adopt a fast ethernet controller ax88796. the ax88796 is a high performance and highly integrated local cpu bus ethernet controller with embedded 10/100mbps phy/transceiver and 8k*16 bit sram. it supports both 8 bit and 16 bit local cpu interfaces including mcs-51 series, 80186 series, mc68k series cpu and isa bus. the 80186 16bit mode is chosen to meet the 16bits data bus of omap5910. because omap5910 does not have sufficient general i/os, an avr microcontroller is used to interface the ps/2 mouse and the keyboard. iic bus is used to realize communicate between the avr microcontroller and the omap5910. it is a good solution for the shortage of the gpio in the system. since three mcbsps (multi-channel buffered serial ports) are integrated on the omap5910, an audio codec is connected to one of them directly. tlv320aic23b, a high-performance stereo audio codec with highly integrated analog functionality, is selected to compose our codec system. the tlv320aic23b supports glueless interface to the ti mcbsp. we use the iic bus to implement its control interface. various multimedia applications can be realized using this hardware platform on which we have implemented a mpeg4 video decoder. realization of embedded multimedia system based on dual-core processor omap5910 87 3 software system we choose the das u-boot as the bootloader for the system, and the linux 2.4.21 as the kernel. the software tools we used to develop programs are code composer studio (ccs) and the gnu tool chain. ccs is used to develop dsp programs, while the gnu tool chain is used to compile the bootloader and the kernel. programs on the mpu and the dsp are developed separately. in our system, the arm risc is used for handing control code, such as user interface, os and os application, while the dsp is used for the real-time signal-processing. both the system bootloader and the kernel run on the mpu, and the dsp is registered as a character device in the kernel. applications that run on the mpu can not access or make use of the dsp directly, so they must use the dsp driver to control or communicate with dsp core. the dsp driver in the kernel provides user applications with the capability of controlling and the interfaces to communicate with dsp. to communicate or make use of dsp, applications have to send read, write or i/o control request to kernel, then kernel would deliver such request to dsp driver. subsequently, dsp driver set the corresponding control registers and send the control code or application date to the dsp. there are three ways we used to communicate between the mpu and the dsp. the first one is to use the mpui port. it allows access to the full memory space (16m bytes) of the dsp, and it is the only way for the mpu and the system dma to access the i/o spaces of the dsp. since configuration and data registers for all the peripherals of the dsp reside in the dsp subsystem i/o space, we use this way to set the corresponding peripherals registers in the dsp. the second way is to use the mpu mmu and the dsp mmu, through which shared access to system memory can be realized between the two cores. the mpu mmu performs virtual-to-physical address translations and accesses permission checks for access to the system memory. the dsp mmu is controlled by the mpu. when the dsp mmu is on, the dsp mmu translates addresses from the dsp (virtual address) to addresses mapped by the traffic controller. the dsp mmu is used when the dsp software accesses external memory and we adopt the dsp mmu to map the external memory space of the dsp on the external sdram. we also utilize the mpu mmu to map an appropriate system addressing space on the external sdram. then both the mpu and the dsp can access this shared memory regions. the third way is to use the mailbox-interrupt mechanism. this mechanism provides a very flexible software protocol between the two cores. we use it to create handshaking interrupts which will properly synchronize the mpu and dsp operations. the software architecture is shown in figure 2. figure 2: software architecture when certain user application wants to utilize dsp to accomplish a specific calculating task, it must 88 peng li, yu lu, shen li, hongxing wei send a request to the dsp driver. the dsp driver would write this request code and application data to the mailbox. so the dsp core can get to know what the user application wants and where is the storage of the raw data. then dsp would be interrupted, and both the control code and the application data address could be obtained during this mailbox interrupt. with these data, dsp starts to do the corresponding calculation as the application asks. after finishing the application task, dsp returns a response to the mpu by writing to the mailbox. dsp driver would process this mailbox interrupt on the mpu side, and fetch the result of the application task. 4 system bootloader and dsp bootloader boot process is very important to both the mpu and the dsp. each of the two cores needs their own bootloader. the system (mpu) bootloader is adopted to perform a simple initialization and to load the kernel to the system memory. u-boot is a bootloader for embedded system based on powerpc and arm processors. it is a full featured bootloader that is small and also easy to build and use. one of the most useful features of u-boot is the supporting of the tftp downloading. the u-boot 1.1.1 supports the arm925t architecture and the omap5910 processor. we need to set appropriate configurations and write drivers for various peripherals used during the booting process. the ethernet controller driver enables the tftp boot feature of u-boot and such tftp boot function greatly facilitates the debugging process of the kernel. much higher speed could be achieved when loading kernel to the system ram, compared with the using of serial or parallel port. the loading process can be fully automatic by the u-boot when power on. the mpu is the master in the system and is responsible for setting up and bringing the dsp out of reset. appropriate boot mode of the dsp must be set by mpu before releasing the dsp from reset. when the dsp core is taken out of reset, dsp bootloader will be executed. the bootloader of the dsp resides in the dsp subsystem pdrom. it performs some initialization of the dsp resources before loading code. after the initialization, the bootloader branches to the starting point address specified by the selected boot mode. there are four boot modes: direct boot, external memory boot, dsp idle boot, internal memory boot. the internal memory boot mode is selected in our system, because it is more convenient compared with other modes. to adopt the internal boot mode, we have to load code and data sections into dsp subsystem internal memory. the mpu core or system dma can be used to accomplish this task. we utilize mpu core with the mpu interface (mpui) to perform the loading. mpui allows the mpu to communicate with the dsp and its peripherals. the endian mode should be noticed during the data loading process. the mpu (ti925t) operates in little endian mode while the dsp operates in big endian mode. therefore, data have to be converted into big endian format when loaded into the dsp’s memory. there are swapping buffers between the dsp and the mpui, and the word or byte swapping can be programmed. after loading data into dsp internal memory, the mpu core can call the dsp subsystem out of reset, then the dsp bootloader begins to execute. the dsp bootloader transfers execution to the appropriate byte address, and the loaded application starts running. it should be noticed that the output format of ccs is dsp coff format, and it cannot be used directly in arm side. so we utilize the hex55 utility to convert the coff format file into hex format file. the hex file could be parsed to get the dsp code and its running address. 5 system application mpeg video processing is a very important application for multimedia systems. due to the highspeed requirement of the computation, powerful processors are needed to process these data. our dualcore system provides a better solution for video processing compared with normal risc [6] or dsp realization of embedded multimedia system based on dual-core processor omap5910 89 systems. handling of the control code and the real-time signal processing are implemented by the mpu and the dsp separately. the arm risc is well suited for handling control code, such as user interface, os and os applications. on the other hand, the dsp side is better suited for real-time data processing. appropriate division of the work makes the dual-core system get a better performance. the c55x dsp is especially suited for multimedia signal processing. three hardware accelerators are included along with the c55x dsp core to enhance the multimedia processing capability [4]. they are discrete cosine transform (dct) and its inverse idct accelerator, motion estimation accelerator and pixel interpolation accelerator. dct/idct, motion estimation, and pixel interpolation are common tasks to all industry video-imaging standards. they are also the most time-consuming tasks in video processing. these c55x hardware accelerator modules assist the dsp core in implementing algorithms that are commonly used in video compression applications such as mpeg4 encoders/decoders. these accelerators enable implementation of such algorithms using fewer dsp instruction cycles and dissipating less power than the dsp core is operating alone. figure 3: the decoding procedure on the dsp the hardware accelerators are utilized via functions from the tms320c55x image/video processing library available from texas instruments [5]. imglib is an optimized image/video processing functions library for c programmers using tms320c55x devices. it includes many c-callable, assemblyoptimized, general-purpose image/video processing routines. these routines are typically used in computationally intensive real-time applications where optimal execution speed is critical. by using these routines, we achieve execution speeds considerably faster than equivalent code written in standard ansi c language. the hardware accelerator and the imglib are utilized to implement a mpge-4 video decoder on our multimedia system. the decoding procedure on the dsp core is illustrated as figure 3. the mpeg-4 bit-streams are stored in shared system memory. because the dsp accesses the internal daram and saram at much higher speed, the decoding process is completed in its own local memory. by the help of the tms320c55x image/video processing library, we get the decoded data in the local memory. because the decoded data are in yuv 4:2:0 formats, post-processing is needed to convert the 90 peng li, yu lu, shen li, hongxing wei figure 4: results of comparative benchmarking decoded data into rgb format. having converted the data into16 bits rgb (565) format, we send it to the lcd frame buffer for displaying [7]. compared with a risc processor arm9, the performance of the decoding process is greatly improved. results of comparative benchmarking by pace soft silicon are shown in figure4. these benchmarks demonstrate that multimedia tasks require three times as many cycles and two times as much power to execute on a latest-generation risc processor as they do on a c55x dsp [4]. 6 summary and conclusions this embedded multimedia system is successfully designed and implemented. various multimedia applications can be realized on such platform. the software system presented herein provides a solution for communication between the dual-core. the successful implementation of the mpeg4 decoding procedure on this system proves that the higher speed can be achieved and the less power is needed. with the growth of the 3g wireless markets, dual-core architecture systems will be widely used. the authors believe that the presented platform is suitable for many 3g wireless multimedia applications in the near future. references [1] james song, thomas shepherd, minh chau, a low power open multimedia application platform for 3g wireless, proceedings of soc conference, 2003, ieee international, pp. 377-380, sept.2003. [2] jcheng-nan chiu, chien-tang tseng, and chun-jen tsai, tightly-coupled mpeg-4 video encoder framework on asymmetric dual-core platforms, iscas 2005, ieee international, pp. 2132-2135, may 2005. [3] texas instruments, omap5910 dual-core processor technical reference manual, ti technical document spru602b, jan.2003. [4] texas instruments, tms320c55x hardware extensions for image/video applications programmer’s reference, ti technical document spru098, feb.2002. [5] texas instruments, tms320c55x image/video processing library programmer’s reference preliminary, ti technical document spru037c, jan.2004. realization of embedded multimedia system based on dual-core processor omap5910 91 [6] hae-yong kang, kyung-ah jeong, jung-yang bae, young-su lee, seung-ho lee, mpeg4 avc/h.264 decoder with scalable bus architecture and dual memory controller, circuits and systems, 2004. iscas ’04. proceedings of the 2004 international symposium on, vol. 2, pp.ii 145-8, may. 2004 [7] thanh tran, omap 5910 video encoding and decoding, ti application report spra985, dec. 2003. peng li, yu lu beijing university of aeronautics and astronautics school of automation science and electrical engineering shen li beijing university of aeronautics and astronautics school of software hongxing wei beijing university of aeronautics and astronautics school of mechanical engineering and automation no. 37 xue yuan road, haidian district 100083 beijing p.r. china lastmars@gmail.com received: november 5, 2006 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 775-782 tool support for fuml models c.-l. lazăr, i. lazăr, b. pârv, s. motogna, i.-g. czibula codruţ-lucian lazăr, ioan lazăr, bazil pârv, simona motogna and istván-gergely czibula department of computer science babeş-bolyai university, cluj-napoca, romania romania, 400084 cluj-napoca, 1 m. kogălniceanu e-mail: {clazar, ilazar, bparv, smotogna, istvanc}@cs.ubbcluj.ro abstract: in this paper we present a tool chain that aids in the construction of executable uml models according to the new foundational uml (fuml) standard. these executable models can be constructed and tested in the modeling phase, and code can be generated from them towards different platforms. the fuml standard is currently built and promoted by omg for building executable uml models. the compatibility of the executable models with the fuml standard means that only the uml elements allowed by fuml should be used for the abstract syntax and the extra constraints imposed by the fuml standard should be considered. the tool chain we propose is intregrated with the existing uml tools of eclipse modeling infrastructure. keywords: class diagram, fuml, action language, code generation, eclipse. 1 introduction the executable models are models that can be executed and tested without having to generate code from them and test them in a specific platform. creating executable models in the process of developing an application is considered to be a good approach, because the business model and functionality can be implemented in the modeling phase, while the decisions regarding the implementation in the specific platform can be delayed to the phase for code generation from the model. the executable models have the advantage of not being poluted with code that is not related to the business logic, keeping the model and functionality much more compact and clear. and they also have the advantage of not being tied to a specific platform or technology. the foundational uml (fuml) [1] is a computationally complete and compact subset of uml [2], designed to simplify the creation of executable uml models. the semantics of uml operations can be specified as programs written in fuml. we introduced in a previous paper [3] an action language based on fuml, with a concrete syntax that follows the principles of the structured programming, which is supported by the modern languages like java and c++. in this paper we describe a tool chain that is aimed at building and testing executable uml models, as well as generating code towards different target platforms. the generated code is meant to be complete, with no code placeholders for the developer to fill out. the tools are built on top of the eclipse modeling framework project (emf) and some other projects from eclipse that are part of the eclipse modeling tools distribution. these tools are integrated with the eclipse modeling infrastructure and with each other. the remainder of the paper is organized as follows: section 2 presents the infrastructure needed to build executable models and section 3 presents the research problem. then, section 4 describes the tool chain we propose. section 5 presents the existing work related to ours and section 6 gives the conclusions of this paper. copyright c⃝ 2006-2010 by ccc publications 776 c.-l. lazăr, i. lazăr, b. pârv, s. motogna, i.-g. czibula 2 background the uml class diagrams are widely used to create the structure of a model. they are intuitive and easy to use. however, the uml behavior diagrams (activity diagrams and state machines) are not easy to use for larger models. the fuml standard provides a simplified subset of uml action semantics package (abstract syntax) for creating executable uml models. it also simplifies the context to which the actions may be applied. for instance, the structure of the model will consist of packages, classes, properties, operations and associations, while the interfaces and association classes are not included. however, creating executable fuml models is difficult, because the uml primitives intended for execution are too low level, making the process of creating reasonable sized executable uml models close to impossible. a concrete textual syntax is needed, because it enforces a certain way of constructing models. this means that a lot of elements that need to be created explicitly in the graphical uml activity diagram can be implicitly derived from the syntax and created automatically. omg issued an rfp for a concrete syntax for an action language based on fuml [4]. because there is no standardized action language at this moment, we proposed an action language of our own [3] as part of our framework: comdevalco (framework for software component definition, validation, and composition) [5–7]. the fuml standard also specifies how to create a virtual machine that can execute fuml executable models. to generate code from mof compliant models, omg created mof model to text transformation language [8], which is suitable to generate code from fuml models. 3 research problem the research problem is to investigate the creation of a tool chain that aids in the construction of fuml models that can be created and tested in the modeling phase, and from which code can be generated towards different platforms. the tehniques mentioned below represent the core of a tool chain that can change the development experience for the better, by simplifying the process and allowing the developer to take decisions in the proper stage of development. model creation. to create the structure of the model, the usual uml class diagrams should be used. these class diagrams, however, must restrict the elements that can be used to those included in the fuml standard. to create the part of the model corresponding to the behavior of the operations, an action language based on fuml needs to be used. this is because it is close to impossible to use uml activity diagrams for this task, as the user will need to create, configure and relate too many elements. the class diagram editor needs to be integrated with the action language textual editor, which is used to create the behavior for each operation. the integration refers to the ability to select a class or an operation from the model and, with a simple action (double-click or some key shortcut), the action language editor for the behaviors can be opened and used for the selected element. the action language editor must be able to load the existing behavior under the selected element and display it properly. also, on save action, it should properly update the model under the selected element. an action language that follows the principles of the structured programming is to use by many programmers familiar with structured programming languages. thus, we consider this tool support for fuml models 777 an important factor in choosing the action language, even though the fuml standard allows non-structured control flow. because many programmers are familiar with object-oriented languages like java or c++, having a similar syntax will make the language much easier to be used. also, the effort for learning the new language will be very much reduced. the action language will need to create the abstract representation for the statements and control structures as provided by the programming languages mentioned above, it must support complex expressions and easy access to parameters and variables. model execution. after creating the model, or parts of it, there is a need to simulate the execution from certain starting points. this can be achieved with a virtual machine that knows how to execute fuml models. this means that it can execute the activities built with the action language editor described above. code generation. after creating and testing the model, the only thing left is to generate code to a specific platform. a code generation tool should take into considerations any tags (stereotypes) placed on the model elements, and adjust the code generation process accordingly. if the resulted action language has a well structured abstract representation, it should be possible to apply model transformations and generate code to structured programming languages with little effort. thus, the executable models for which the behavior is created with such an action language can be converted to a multitude of platforms. 4 tool chain in this section we present the tools we use to create fuml based executable models. the tools are integrated in the eclipse modeling infrastructure. the uml class diagram editor and the fuml execution framework exist and they only need to be integrated with the other tools. the fuml based action language editor is a tool proposed by us. the code generation utility exists, but the templates used to generate code are written by us. we use a point-of-sale (pos) example model, presented in fig. 1. the example model consists of a pos class, which contains a list of products. the user can make a new sale (stored in pos as the currentsale) by invoking the makenewsale operation and by adding saleitems to the current sale in the form of product code and quantity. the pos component finds the product associated with the given product code and, if present, passes the product and quantity to the currentsale. the sale creates a new saleitem instance and adds it to its list of sale items. fig. 2 and 3 present the implementation of the operations, as it is written using our action language. fig. 4 shows the abstract representation in fuml of sale.additem’s behavior. figure 1: pos example model class diagram editor 778 c.-l. lazăr, i. lazăr, b. pârv, s. motogna, i.-g. czibula 4.1 uml class diagram editor the uml meta-model is provided by the eclipse uml2 project [9], which is part of the larger model development tools (mdt) project. uml2 is built on top of emf(core) [10] (which is part of the bigger eclipse modeling framework project emf) with some adaptations, as uml has a structure that is not compatible with emf. the uml2 project provides the java classes that correspond to the uml model, so that a uml model can be represented in java. the model can be saved in files with the “uml”extension in xmi format, as it is standardized by omg. the uml2 project comes with a tree-based editor that allows the user to manipulate an “uml” file and build the uml models using a tree. this editor is not quite easy to use, but, for building only the structure of classes of a model, it is enough. the eclipse uml2 tools [11], which is also part of mdt, provides a class diagram editor. this project allows the user to attach uml class diagrams to the uml models and build the uml models using a class diagram editor. the editor is quite mature and easy to use, and most programmers are used to building models using class diagrams, so this editor is the best choice for a tool to build the class structure of a model (fig. 1). because we want to build fuml based models, it is important that only the elements allowed by fuml are used. the interface, for instance, was excluded from fuml, so the user should not include interfaces in the model. to simulate an interface, the programmer can use abstract classes with public abstract methods. a specialized editor for class diagrams that allows only fuml elements to be used, is considered for the future. 4.2 fuml based action language editor we introduced in a previous paper [3] an action language based on fuml, with a concrete syntax similar to the concrete syntax of the modern programming languages like java or c++. this action language follows the principles of the structured programming. we have also built an eclipse textual editor for this action language using the xtext project [12], which is the only remaining project from the bigger textual modeling framework (tmf) project from eclipse. this textual editor can take the textual representation for the functionality of an operation (fig. 2 and 3) and convert it to an activity with all the uml actions and other elements necessary to provide the same functionality in uml (fig. 4). this activity is added to the operation’s classifier (of class type) and set as the behavior of the operation. only the elements allowed by fuml are used to create this uml model of the activity. the textual editor for the action language is integrated with the class diagram editor, so that when the user wants to edit the behavior of an operation, s/he only needs to double-click, or use a context menu item of the operation, or use a key shortcut to open the textual editor. after the behavior is created, the user needs to save it by pressing ctrl+s, at which point the main “uml” model file is updated to contain the model for the operation’s behavior. 4.3 fuml execution framework the fuml standard specifies how a virtual machine for fuml models should work, and there is a reference implementation in progress from modeldriven.org [13]. we managed to integrate this tool in the eclipse workbench, so that we can test our fuml models. we can either pass the activities created with our action language, along with the needed parameters, directly to the execution framework to be executed, or we can write test activities with our action language and execute the tests. tool support for fuml models 779 public makenewsale() { self.currentsale := new sale; } public addsaleitem(code:string, quantity:integer) : boolean { def product:product := null; foreach (prod in self.product) { if (code = prod.code) { product := prod; } } if (product = null) { return false; } else { self.currentsale.additem(product,quantity); return true; } } figure 2: pos::makenewsale and pos::addsaleitem activity concrete syntax 4.4 code generation utility to generate code, we used the acceleo project [14], which is part of the bigger model to text (m2t) project from eclipse. this project implements the mofm2t standard from omg. it allows the user to create templates, which can later be used to generate code from the models. in our case, these templates need to work on the elements included in fuml. generating the structure of classes is straightforward. however, generating code for the behavior of the operations is more complex, because the structure of the elements and the way the actions are connected needs to be considered. because we took the decision to follow the structured programming principles, the uml model resulted for the activities is well structured, so we are able to generate code with ease to languages like java or c++. if we wouldn’t have taken this decision, then the code generation step would have been a real problem, as fuml allows the user to create interactions between actions that might be impossible to represent in the languages mentioned above. also, due to the resemblance in concrete syntax between the action language and the target languages, we are able to generate compact code in the target languages. an important note is that the templates are specifically written for activities constructed with an editor compatible with our action language. this is because the way the elements are structured and the way they interact is very important. if these aspects are not followed, the templates will produce a bad output. for a fuml activity that does not respect these constraints and which might be constructed using a different fuml based action language, a special set of templates need to be written. this is not necessarily an issue, because the action languages can be used in conjunction to construct uml models and because the templates only need to be written once. our action language is a general purpose language and might be a bit hard to specify and build a proper editor for it. but a different action language that is specialized on a more concrete domain could have a simpler syntax and the code generation templates might be easier to be written. fig. 5 shows a snippet from the acceleo templates we used to generate java code. it shows how the statements are iterated, considering the structuredactivitynodes that contain the 780 c.-l. lazăr, i. lazăr, b. pârv, s. motogna, i.-g. czibula 1 public additem(product:product, quantity:integer) { 2 def newitem : saleitem := new saleitem; 3 newitem.product := product; 4 newitem.quantity := quantity; 5 self.item.add(newitem); 6 } figure 3: sale::additem activity concrete syntax ( product : product, quantity : integer ) activity additem product : product quantity : integer main_block [lines 1-6] add_stmt [line 5] <<addstructuralfeaturevalue>> item object value insertat <<valuespecification>> * result <<readself>> self result assign_stmt [line 4] <<addstructuralfeaturevalue>> quantity objectvalue assign_stmt [line 3] <<addstructuralfeaturevalue>> product objectvalue def_stmt [line 2] <<createobject>> saleitem result : saleitem newitem quantity product figure 4: sale::additem activity fuml abstract syntax actions for each statement, and the edges that enforce the sequential flow between these nodes. the operationactivity template selects the only structuredactivitynode it contains, which is the node containing all the statement nodes (main_block node from fig. 4). the activityblock template selects the statement node with no incoming edges, which represents the node corresponding to the first statement (def_stmt [line 2] from fig. 4).the iterateblockstatements template prints the text for the statement node by calling the blockstatement template and, if the node has an outgoing edge to the next statement node, calls recursively the iterateblockstatements for the next statement node. 5 related work only a few frameworks for building executable models exist and some of them are proprietary: mentor graphics’ bridgepoint product with its object action language (oal) [15], kennedy carter’s iuml product with its action specification language (asl) [16] and others. some of these frameworks have their action languages based on the action semantics package from uml, but none is based strictly on fuml, as the standard is still in beta version. tool support for fuml models 781 figure 5: acceleo template snippet for java 6 conclusions and further work in this paper we presented a tool chain that can be used to create, execute and generate code from fuml executable models. the tool chain integrates an uml class diagram editor, a fuml action language editor, a fuml execution framework and a code generation framework. we plan to further work on this tool set, to better integrate the tools, in order to improve the user experience with the tool set as a whole. a specialized editor for class diagrams that allows only fuml elements to be used, is considered as future work. also, we plan to investigate the possibilities of applying transformations to the models created by our action language, and to generate code in other programming languages. acknowledgment this work was supported by the grant id 546, sponsored by nurc romanian national university research council (cncsis). bibliography [1] semantics of a foundational subset for executable uml models, object management group standard, rev. 1.0, beta 2, october 2009. [online]. available: http://www.omg.org/spec/ fuml/1.0/beta2/pdf/ [2] uml superstructure specification, object management group standard, rev. 2.2, february 2009. [online]. available: http://www.omg.org/spec/uml/2.2/superstructure/pdf/ [3] c.-l. lazăr, i. lazăr, b. pârv, s. motogna, and i.-g. czibula, “using a fuml action language to construct uml models,” proceedings of the 11th international symposium on symbolic and numeric algorithms for scientific computing, 2009. [4] concrete syntax for a uml action language, object management group request for proposal, 2008. [online]. available: http://www.omg.org/docs/ad/08-08-01.pdf 782 c.-l. lazăr, i. lazăr, b. pârv, s. motogna, i.-g. czibula [5] b. pârv, s. motogna, i. lazăr, i.-g. czibula, and c.-l. lazăr, “comdevalco a framework for software component definition, validation, and composition,” studia universitatis babeş-bolyai, informatica, vol. lii, no. 2, pp. 59–68, 2007. [6] i. lazăr, b. pârv, s. motogna, i.-g. czibula, and c.-l. lazăr, “an agile mda approach for executable uml structured activities,” studia universitatis babeş-bolyai, informatica, vol. lii, no. 2, pp. 101–114, 2007. [7] c.-l. lazăr and i. lazăr, “on simplifying the construction of executable uml structured activities,” studia universitatis babeş-bolyai, informatica, vol. liii, no. 2, pp. 147–160, 2008. [8] mof model to text transformation language (mofm2t), object management group standard, rev. 1.0, january 2008. [online]. available: http://www.omg.org/spec/mofm2t/ 1.0/pdf/ [9] uml2, the eclipse foundation, 2010. [online]. available: http://www.eclipse.org/ modeling/mdt/?project=uml2 [10] eclipse modeling framework (core), the eclipse foundation, 2010. [online]. available: http://www.eclipse.org/modeling/emf/?project=emf [11] uml2 tools, the eclipse foundation, 2010. 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[online]. available: http: //www.ooatool.com/docs/asl03.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 5, pp. 939-945 on polar, trivially perfect graphs m. talmaciu, e. nechita mihai talmaciu, elena nechita university of bacău, romania e-mail: mtalmaciu@ub.ro, enechita@ub.ro abstract: during the last decades, different types of decompositions have been processed in the field of graph theory. in various problems, for example in the construction of recognition algorithms, frequently appears the so-called weakly decomposition of graphs. polar graphs are a natural extension of some classes of graphs like bipartite graphs, split graphs and complements of bipartite graphs. recognizing a polar graph is known to be np-complete. for this class of graphs, polynomial algorithms for the maximum stable set problem are unknown and algorithms for the dominating set problem are also np-complete. in this paper we characterize the polar graphs using the weakly decomposition, give a polynomial time algorithm for recognizing graphs that are both trivially perfect and polar, and directly calculate the domination number. for the stability number and clique number, we give polynomial time algorithms. keywords: polar graphs, trivial perfect graphs, weakly decomposition, recognition algorithms, optimization algorithms. 1 introduction polar graphs are a natural extension of some classes of graphs like bipartite graphs, split graphs and complements of bipartite graphs. according to ( [3]), a graph g = (v, e) is called polar if the set v of its vertices can be partitioned into (s,q) (s or q possibly empty) such that s induces a complete multipartite graph (that is a join of stable sets) and q is a disjoint union of cliques. in ( [3]) has been proved that the problem of recognizing an arbitrary graph to be polar is np-complete. recently some important result concerning polar graphs have been proven. hereby, in ( [9]) the authors give a characterization through forbidden subgraphs of polar cographs and a polynomial algorithm that finds the largest induced subgraph in a cograph. in ( [8]) is presented a polynomial algorithm to recognize the polar property for triangulated graphs. in ( [7]), a polynomial algorithm for the recognition of graphs that are both polar and permutation is given. in ( [16]) they assert that polynomial algorithms for independent set are unknown and algorithms for domination number are np-complete for split graphs (see ( [2] and [4]). both problems to find independent maximal set of maximum and minimum weight are nphard, in general. in ( [12]) are given polynomial time algorithms that solve the problems formulated above for classes of polar graphs. 2 definition and notation throughout this paper, g = (v, e) is a connected, finite and undirected graph, without loops and multiple edges ( [1]), having v = v(g) as the vertex set and e = e(g) as the set of edges. g is the complement of g. if u ⊆ v, by g(u) or [u]g we denote the subgraph of g induced by u. by g − x we mean the subgraph g(v − x), whenever x ⊆ v, but we simply write g − v, when copyright c⃝ 2006-2010 by ccc publications 940 m. talmaciu, e. nechita x = {v}. if e = xy is an edge of a graph g, then x and y are adjacent, while x and e are incident, as are y and e. if xy ∈ e, we also use x ∼ y, and x ̸∼ y whenever x, y are not adjacent in g. a vertex z ∈ v distinguishes the non-adjacent vertices x, y ∈ v if zx ∈ e and zy ̸∈ e. if a, b ⊂ v are disjoint and ab ∈ e for every a ∈ a and b ∈ b, we say that a, b are totally adjacent and we denote by a ∼ b, while by a ̸∼ b we mean that no edge of g joins some vertex of a to a vertex from b and, in this case, we say that a and b are non-adjacent. the neighbourhood of the vertex v ∈ v is the set ng(v) = {u ∈ v : uv ∈ e}, while ng[v] = ng(v) ∪ {v}; we simply write n(v) and n[v], when g appears clearly from the context. the neighbourhood of the vertex v in the complement of g will be denoted by n(v). if d ⊂ v and every vertex from v − d has at least one neighbour in d, then d is called a dominating set of g. the minimum size of a dominating set is the domination number ν(g). a complete graph is a graph in which every vertex is adjacent to every other. the neighbourhood of s ⊂ v is the set n(s) = ∪v∈sn(v) − s and n[s] = s ∪ n(s). a clique is a subset q of v with the property that g(q) is complete. the clique number of g, denoted by ω(g), is the size of the maximum clique. an independent set or stable set is a set of vertices of which no pair is adjacent. the independence number α(g) of a graph g is the size of a largest independent set of g. by pn, cn, kn we mean a chordless path on n ≥ 3 vertices, a chordless cycle on n ≥ 3 vertices, and a complete graph on n ≥ 1 vertices, respectively. the distance dg(u, v) between two (not necessary distinct) vertices u and v in a graph g is the length of a shortest path between them. a graph is called triangulated if it does not contain chordless cycles having the length greater or equal to four. a graph is called cograph if it does not contain p4. a graph is a split graph if the vertex set can be partitioned into a clique and a stable set. a graph g is trivially perfect ( [10]) if for each induced subgraph h of g, the number of maximal cliques of h is equal to the maximum size of an independent set of h. let n ≥ 1 and π be a permutation over {1, ..., n}. we will denote π equivalently as a permutation sequence (π(1), ..., π(n)). the inversion graph of π has vertex set {1, ..., n} and two vertices u, v are adjacent if (u − v)(π−1(u) − π−1(v)) < 0. a graph is a permutation graph if it is isomorphic to the inversion graph of a permutation sequence. let f denote a family of graphs. a graph g is called f-free if none of its subgraphs is in f. the zykov sum of the graphs g1, g2 is the graph g = g1 + g2 having: v(g) = v(g1) ∪ v(g2), e(g) = e(g1) ∪ e(g2) ∪ {uv : u ∈ v(g1), v ∈ v(g2)}. when searching for recognition algorithms, frequently appears a type of partition for the set of vertices in three classes a, b, c, which we call a weakly decomposition, such that: a induces a connected subgraph, c is totally adjacent to b, while c and a are totally nonadjacent. the structure of the paper is the following. in section 3 we recall the notion of weakly decomposition. in section 4 we present a new characterization of polar, trivially perfect graphs. in section 5 we give a recognition algorithm for this class of graphs. in section 6 we give combinatorial optimization algorithms for polar, trivially perfect graphs. in the last section we have our concluding remarks. 3 preliminary results at first, we recall the notions of weakly component and weakly decomposition. on polar, trivially perfect graphs 941 definition 1. ( [6], [13], [14]) a set a ⊂ v(g) is called a weakly set of the graph g if ng(a) ̸= v(g) − a and g(a) is connected. if a is a weakly set, maximal with respect to set inclusion, then g(a) is called a weakly component. for simplicity, the weakly component g(a) will be denoted with a. definition 2. ( [6], [13], [14]) let g = (v, e) be a connected and non-complete graph. if a is a weakly set, then the partition {a, n(a), v − a ∪ n(a)} is called a weakly decomposition of g with respect to a. below we remind a characterization of the weakly decomposition of a graph. the name of "weakly component" is justified by the following result. theorem 1. ( [5], [13], [14]) every connected and non-complete graph g = (v, e) admits a weakly component a such that g(v − a) = g(n(a)) + g(n(a)). theorem 2. ( [13], [14]) let g = (v, e) be a connected and non-complete graph and a ⊂ v. then a is a weakly component of g if and only if g(a) is connected and n(a) ∼ n(a). the next result, that follows from theorem 1, ensures the existence of a weakly decomposition in a connected and non-complete graph. corollary 1. if g = (v, e) is a connected and non-complete graph, then v admits a weakly decomposition (a, b, c), such that g(a) is a weakly component and g(v − a) = g(b) + g(c). theorem 2 provides an o(n + m) algorithm for building a weakly decomposition for a noncomplete and connected graph. algorithm for the weakly decomposition of a graph ( [13]) input: a connected graph with at least two nonadjacent vertices, g = (v, e). output: a partition v = (a, n, r) such that g(a) is connected, n = n(a), a ̸∼ r = n(a). begin a := any set of vertices such that a ∪ n(a) ̸= v n := n(a) r := v − a ∪ n(a) while (∃n ∈ n, ∃r ∈ r such that nr ̸∈ e ) do begin a := a ∪ {n} n := (n − {n}) ∪ (n(n) ∩ r) r := r − (n(n) ∩ r) end end corollary 2. for g = (v, e) a connected non-complete graph, and (a, n, r) a weakly decomposition with g(a) the weakly component the following relation holds: α(g) = max{α(g(a)) + α(g(r)), α(g(a ∪ n))} . in ( [13]) some applications of weakly decomposition have been depicted. let g = (v, e) be connected, non-complete graph and (a, n, r) a weakly decomposition, with a the weakly component.the following hold: a) g is p4 − free if and only if a ∼ n ∼ r and g(a), g(n) and g(r) are p4 − free; b) g is triangulated if and only if n is a clique and r and g − r are triangulated. each of the results above lead to recognition algorithms for the specified graphs. c) if g is triangulated then α(g) = α(g(a)) + α(g(r)) and this leads to the algorithm that determines α(g). 942 m. talmaciu, e. nechita 4 characterization of polar, trivially perfect graphs in this section, using the weakly decomposition, we present a recognition algorithm for the polar trivially perfect graphs. at first we remind a characterization in terms of forbidden subgraphs of polar cographs and two characterization of trivially perfect graphs. theorem 3. ( [9]) for a cograph g, the following statements are equivalent: a) g is polar; b) neither g nor g contains any one of the graphs h1, h2, h3, h4 as induced subgraphs, where hi = gi ∪ fi (1 ≤ i ≤ 4), and every gi is a p3 and fi, described as sequences of degrees, are: f1 : (4, 3, 3, 3, 3); f2 : (5, 3, 2, 2, 2, 2); f3 : (4, 4, 3, 3, 3, 3); f4 : (5, 5, 3, 3, 3, 3). theorem 4. ( [15]) if g is a connected, non-complete graph and (a, n, r) is a weakly decomposition with g(a) the weakly component, then g is trivially perfect if and only if: i) a ∼ n ∼ r; ii) n is clique; iii) g(a), g(r) are trivially perfect if and only if it contains no vertex subset that induces p4 or c4. theorem 5. ( [12]) a graph is trivially perfect if and only if it contains no vertex subset that induces p4 or c4. theorem 6. let g = (v, e) be a connected, non-complete graph and (a, n, r) a weakly decomposition with g(a) the weakly component. let also g and g be trivially perfect graphs. g is polar if and only if g(a) and g(r) are polar graphs. proof. if g is a polar graph then g(a) and g(r) are polar graphs, as every induced subgraphs of a polar graph is also polar. conversely, suppose that g(a) and g(r) are polar graphs. we show that g is a polar graph. because g is trivially perfect it follows that a ∼ n ∼ r and n is a clique. suppose that x ⊂ v still exists such that g(x) is isomorphic to one of the following four graphs: h1, h2, h3, h4, where hi = gi ∪ fi (1 ≤ i ≤ 4), and every gi is a p3 and every fi, described as a sequence of degrees, is: f1 : (4, 3, 3, 3, 3); f2 : (5, 3, 2, 2, 2, 2); f3 : (4, 4, 3, 3, 3, 3); f4 : (5, 5, 3, 3, 3, 3). because g is trivially perfect it follows that g is {p4, c4}-free. if x is the vertex of degree 4 in f1, z and t are the vertices of degree 4 in f3, u and v are the vertices of degree 5 in f4 then f1 − {x} is isomorphic to c4, f3 − {z, t} is isomorphic to c4, f4 − {u, v} is isomorphic to c4, which is a contradiction. we know that the complement of a polar graph is a polar graph and that g is c4-free, because it is trivially perfect. if y is the vertex of degree 5 in f2 and a is one of the vertices of degree 2 adjacent to the vertex of degree 3 in f2 then f2 −{a, y} is isomorphic to c4, which is a contradiction. 5 recognition of polar trivially perfect graphs theorem 6 leads to the following recognition algorithm. input: g = (v, e) a connected graph satisfying the conditions in theorem 6 output: an answer to the question: is g a polar graph ? begin l = {g} // l is a list of graphs while (l ̸= ∅) begin extract an element h from l find a weakly decomposition (a, n, r) for h if (a ̸∼ n ̸∼ r) then g is not trivially perfect else introduce in l the connected, non-complete components of g(a), g(r) on polar, trivially perfect graphs 943 end return: g is polar end in what follows, we give some remarks on the algorithm. because the operation inside the body of while loop that takes the longest execution time is the weakly decomposition (namely o(n + m)) it follows that the total execution time of the algorithm is o(n(n + m)). 6 combinatorial optimization algorithms for polar, trivially perfect graphs in this section we calculate the domination number, give o(n(n+m)) algorithms to calculate the stability number and clique number. theorem 6 leads to the following result. corollary 3. let g = (v, e) be a connected, non-complete graph and (a, n, r) a weakly decomposition with g(a) the weakly component. if g is trivially perfect and also polar then the following hold: i) α(g) = α(g(a)) + α(g(r)); ii) ω(g) = |n| + max{ω(g(a)), ω(g(r))}; iii) ν(g) = 1. proof. let t ⊂ a ∪ n such that t is stable and |t | = α(g(a ∪ n)). because n is a clique it follows that |t ∩ n| ≤ 1. if t ∩ n = ∅ then t ∪ {r} is a stable set in g(a ∪ r), and if t ∩ n = {n0} then (t − {n0}) ∪ {r} is a stable set in a ∪ r, for every r ∈ r. it follows that in the relation in corollary 2, the maximum is obtained only for the first component. so i) holds. because a ∼ n ∼ r and n is a clique it follows that ω(g) = ω(g(n))+max{ω(g(a)), ω(g(r))}, but ω(g(n)) = |n|. so ii) holds. because a ∼ n ∼ r and n is a clique it follows that a domination set of minimum cardinal is a set determined by any vertex in n. so iii) holds. corollary 3 implies an algorithm for the construction of a stable set of maximum cardinal and of a clique of maximum cardinal in a trivially perfect, polar graph. input: g = (v, e) a connected graph satisfying conditions in corollary 3 output: a stable set s with |s| = α(g) and a clique q with |q| = ω(g) begin s = ∅, q = n l = {g} // l is a list of graphs while (l ̸= ∅) begin extract an element h from l if (h is complete) then return: s = s ∪ {v}, ∀v ∈ v(h) q = q ∪ n else determine a weakly decomposition (a, n, r) for h put [a]h and the connected component of [r]h in l end end 944 m. talmaciu, e. nechita facility location analysis deals with the problem of finding optimal locations for one or more facilities in a given environment ( [11]). a type of problems in facility location analysis concerns the determination of a location that minimizes the maximum distance to any other location in the network. the following centrality indices are defined in ( [11]): the eccentricity of a vertex u is eg(u) = max{d(u, v)|v ∈ v}; the radius is r(g) = min{eg(u)|u ∈ v}; the center of a graph g is c(g) = {u ∈ v|eg(u) = r(g)}. using theorem 6 we obtain the following result. corollary 4. let g = (v, e) be a connected, non-complete graph and (a, n, r) a weakly decomposition with g(a) the weakly component. if g is both trivially perfect and polar the following hold: (i) eg(u) = 2 for u ∈ a ∪ r; (ii) eg(u) = 1 for u ∈ n; (iii) r(g) = 1; (iv) c(g) = n. 7 conclusions and future work using the weakly decomposition we have obtained polynomial time recognition algorithms for polar graphs and directly calculated the domination number, while for the stability number and for density we give polynomial algorithms. future work concerns on other classes of graphs, characterized by forbidden subgraphs. bibliography [1] c. berge, graphs, north-holland, amsterdam, 1985. [2] a. a. bertoss, dominating sets for split and bipartite graphs, inform. process. lett., 19:3740, 1984. [3] z. a. chernyak, a. a. chernyak, about recognizing (α, ω)-classes of polar graphs, discrete mathematics, 62:133-138, 1986. [4] d. g. corneil, y. perl, clustering and domination in perfect graphs, discrete appl. math., 9:27-39, 1984. [5] c. croitoru, e. olaru, m. talmaciu, confidentially connected graphs, proceedings of the international conference "the risk in contemporany economy", in annals of the university "dunarea de jos" of galati, suppliment to tome xviii (xxii), 2000. [6] c. croitoru, m. talmaciu, a new graph search algorithm and some applications, presented at rosycs 2000, univ. "al.i.cuza" iaşi, 2000. [7] t. ekim, p. heggernes, d. meister, polar permutation graphs, report in informatics, report no 385, march 2009. [8] t. ekim, p. hell, j. stacho, d. de werra, polarity of chordal graphs, discrete applied mathematics, volume 156, 2469-2479, 2008. [9] t. ekim, n. v. r. mahadev, d. de werra, polar cographs, discrete applied mathematics, volume 156, 1652-1660, 2008. [10] m. c. golumbic, "trivially perfect graphs", discrete math. 24:105-107, 1978. on polar, trivially perfect graphs 945 [11] d. koschutzki, k. lehman, l. peaters, s. richter, tenfelde-padehl and o. zlotowski, "centrality indices", lecture notes in computer sciences, springer berlin / heidelberg, network analysis, 16-61, 2005. [12] v. v. lozin, r. mosca, polar graphs and maximal independent sets, rutcor research report 4-2004, february, 2004. [13] m. talmaciu, decomposition problems in the graph theory with applications in combinatorial optimization ph. d. thesis, university "al. i. cuza" iasi, romania, 2002. [14] m. talmaciu, e. nechita, recognition algorithm for diamond-free graphs, informatica, vol. 18, no. 3, 457-462, 2007. [15] m. talmaciu, e. nechita, an algorithm for the bisection problem on trivially perfect graphs, bics 2008, amer. inst. physics, volume 1117:60-66, 2008. [16] http://wwwteo.informatik.uni-rostock.de/isgci/classes/gc−314.html. international journal of computers, communications & control vol. i (2006), no. 3, pp. 13-24 a programming perspective of the membrane systems gabriel ciobanu abstract: we present an operational semantics of the membrane systems, using an appropriate notion of configurations and sets of inference rules corresponding to the three stages of an evolution step in membrane systems: maximal parallel rewriting step, parallel communication of objects through membranes, and parallel membrane dissolving. we define various arithmetical operations over multisets in the framework of membrane systems, indicating their complexity and presenting the membrane systems which implement the arithmetic operations. finally we discuss and compare various sequential and parallel software simulators of the membrane systems, emphasizing their specific features. keywords: membrane systems, operational semantics, arithmetical operations over multisets. 1 membrane systems membrane systems represent a computational model inspired by cell compartments and molecular membranes. essentially, such a system is composed of various compartments, each compartment with a different task, and all of them working simultaneously to accomplish a more general task of the whole system. a detailed description of the membrane systems (also called p systems) can be found in [17]. a membrane system consists of a hierarchy of membranes that do not intersect, with a distinguishable membrane, called the skin membrane, surrounding them all. the membranes produce a delimitation between regions. for each membrane there is a unique associated region. regions contain multisets of objects, evolution rules and possibly other membranes. only rules in a region delimited by a membrane act on the objects in that region. the multiset of objects from a region corresponds to the “chemicals swimming in the solution in the cell compartment”, while the rules correspond to the “chemical reactions possible in the same compartment”. graphically, a membrane structure is represented by a venn diagram in which two sets can be either disjoint, or one is a subset of the other. more details (concepts, results) and several variants of membrane systems are presented in [17]. a p system consists of several membranes that do not intersect, and a skin membrane, surrounding them all. the membranes delimit regions, and contain multisets of objects, as well as evolution rules. each membrane has a unique associated region. the space outside the skin membrane is called the outer region (or the environment). because of the one-to-one correspondence between the membranes and the regions, we usually use the word membrane instead of region. only rules in a region delimited by a membrane act on the objects in that region. moreover, the rules must contain target indications, specifying the membrane where objects are sent after applying the rule. the objects can either remain in the same region, or pass through membranes in two directions: they can be sent out of the membrane which delimits a region from outside, or can be sent in one of the membranes which delimit a region from inside, precisely identified by its label. the membranes can also be dissolved. when such an action takes place, all the objects of the dissolved membrane remain free in the membrane placed immediately outside, but the evolution rules of the dissolved membranes are lost. the skin membrane is never dissolved. the application of evolution rules is done in parallel, and it is eventually regulated by priority relationships between rules. a p system of degree m is a structure π = (o, µ, w1, . . . , wm, (r1, ρ1),. . . , (rm, ρm), io), where: (i) o is an alphabet of objects, and µ is a membrane structure; copyright c© 2006 by ccc publications selected from icccc 2006 (invited paper) 14 gabriel ciobanu (ii) wi are the initial multisets over o associated with the regions defined by µ ; (iii) ri are finite sets of evolution rules over o associated with the membranes, of typical form u → v, with u a multiset over o and v a multiset containing paired symbols (messages) of the form (c, here), (c, in j), (c, out) and the dissolving symbol δ ; (iv) ρi is a partial order relation over ri, specifying a priority relation among the rules: (r1, r2) ∈ ρi iff r1 > r2 (i.e., r1 has a higher priority than r2); (v) i0 is either a number between 1 and m specifying the output membrane of π, or it is equal to 0 indicating that the output is the outer region. since the skin is not allowed to be dissolved, we consider that the rules of the skin do not involve δ . these are the general p systems, or transition p systems; many other variants and classes were introduced [17]. the existing results regarding the p systems refer mainly to their computation power and complexity, namely to their characterization of turing computability (universality is obtained even with a small number of membranes, and with rather simple rules), and the polynomial solutions to np-complete problems by using an exponential workspace created in a “biological way" (e.g., membrane division, string replication). other types of formal results are given by normal forms, hierarchies, connections with various formalisms. in this paper we refer to some “programming” aspects of the membrane systems. we first present an operational semantics of the p systems, together with some correctness results. then we define several arithmetical operations in membrane systems using a natural encoding of numbers. finally some software simulators of the membrane systems are presented. 2 structural operational semantics membrane systems provide an abstract model for parallel systems, and a suitable framework for distributed and parallel algorithms [7]. for each abstract model, theory of programming introduces various paradigms and uses different notions of computations. turing machines and register machines are related to imperative programming, and λ -calculus is related to functional programming. it is natural to look at the membrane systems from the point of view of programming theory. this means that we define an abstract syntax, and an operational semantics of the membranes systems. the operational semantics of the membrane systems is given in a big-step style, each step representing the collection of parallel steps due to the maximal parallelism principle. a computation is regarded as a sequence of parallel application of rules in various membranes, followed by a communication step and a dissolving step. the membrane structure and the multisets in π determine a configuration of the system. we can pass from a configuration to another one by using the evolution rules. this is done in parallel: all objects, from all membranes, which can be the subject of local evolution rules, as prescribed by the priority relation, should evolve concurrently. since the right hand side of a rule consists only of messages, an object introduced by a rule cannot evolve at the same step by means of another rule. the use of a rule u → v in a region with a multiset w means to subtract the multiset identified by u from w, and then to add the objects of v according to the form of the rule. if an object appears in v in the form (c, here), then it remains in the same region. if we have (c, in j), then c is introduced in the child membrane with the label j; if a child membrane with the label j does not exist, then the rule cannot be applied. if we have (c, out), then c is introduced in the membrane placed immediately outside the region of the rule u → v. if the special symbol δ appears in v, then the membrane which delimits the region is dissolved; in this way, all the objects in this region become elements of the region placed immediately outside, while the rules of the dissolved membrane are removed. a programming perspective of the membrane systems 15 let o be a finite alphabet of objects organized as a free commutative monoid o∗c , whose elements are called multisets. formally, the set of membranes for a system π, denoted by m(π), and the membrane structure are inductively defined as follows: • if l is a label, and w is a multiset over o ∪ (o∗c ×{here}) ∪ (o+c ×{out}) ∪{δ}, then 〈 l | w 〉 ∈ m(π); 〈l |w〉 is called simple (or elementary) membrane, and it has the structure 〈〉; • if m1, . . . , mn ∈ m(π) with n ≥ 1, the structure of mi is µi for all i ∈ [n], l is a label, w is a multiset over o∪(o∗c ×{here})∪(o+c ×{out})∪(o+c ×{inl(m j )| j ∈ [n]})∪{δ}, then 〈l|w ; m1, . . . , mn〉 ∈ m(π); 〈l |w ; m1, . . . , mn 〉 is called a composite membrane, and it has the structure 〈µ1, . . . , µn〉. a finite set of membranes is usually written as m1, . . . , mn. we denote by m +(π) the set of nonempty finite sets of membranes. the union of two multisets of membranes m+ = m1, . . . , mm and n+ = n1, . . . , nn is written as m+, n+ = m1, . . . , mm, n1, . . . , nn. an element from m+(π) is either a membrane, or a set of sibling membranes. a committed configuration for a membrane system π is a skin membrane which has no messages and no dissolving symbol δ , i.e., the multisets of all regions are elements in o∗c . we denote by c(π) the set of committed configurations for π, and it is a proper subset of m+(π). we have c ∈ c(π) iff c is a skin membrane of π and w(m) is a multiset over o for each membrane m in c. an intermediate configuration is a skin membrane in which we have messages or the dissolving symbol δ . the set of intermediate configurations is denoted by c#(π). we have c ∈ c#(π) iff c is a skin membrane of π such that there is a membrane m in c with w(m) = w′w′′, w′ ∈ (msg(o)∪{δ})+c , and w′′ ∈ o∗c . by msg(o) we denote the set (o∗ ×{here})∪(o+ ×{out})∪(o+ ×{inl(m)}). a configuration is either a committed configuration or an intermediate configuration. each membrane system has an initial committed configuration which is characterized by the initial multiset of objects for each membrane and the initial membrane structure of the system. each p system has an initial configuration which is characterized by the initial multiset of objects for each membrane and the initial membrane structure of the system. for two configurations c1 and c2 of π, we say that there is a transition from c1 to c2, and write c1 ⇒ c2, if the following steps are executed in the given order: 1. maximal parallel rewriting step, consisting of non-deterministically assigning objects to evolution rules in every membrane and executing the rules in a maximal parallel manner; 2. parallel communication of objects through membranes, consisting in sending existing messages; 3. parallel membrane dissolving, consisting in dissolving the membranes containing δ . the last two steps take place only if there are messages or δ symbols resulted from the first step, respectively. if the first step is not possible, consequently neither the other two steps, then we say that the system has reached a halting configuration. an operational semantics of the p systems, considering each of the three steps, is presented in [2]. we mention here the main results. we can pass from a configuration to another one by using the evolution rules. this is done in parallel: all objects from all membranes evolve simultaneously according to the evolution rules and their priority relation. the rules of a membrane are using its current objects as much as this is possible in a parallel and non-deterministic way. however, an object produced by a rule cannot evolve at the same step as source of another rule. the use of a rule u → v in a region with a multiset w has as effect the subtraction of the multiset identified by u from w, followed by the addition of the multiset identified by v. we denote the maximal parallel rewriting on membranes by mpr =⇒ and by mpr=⇒l the maximal parallel rewriting over the multisets of objects of the membrane labelled by l (we omit the label whenever it is clear from the context). the rules defining the maximal parallel rewriting use two predicates regarding mpr-irreducibility and (l, w)-consistency. 16 gabriel ciobanu proposition 1. let π be a membrane system. if c ∈ c(π) and c′ ∈ c#(π) such that c mpr=⇒ c′, then c′ is mpr-irreducible. we denote the parallel communication relation by tar =⇒. the rules defining the parallel communication relation use a predicate expressing tar-irreducibility. proposition 2. let π be a p system. if c ∈ c#(π) with messages and c tar=⇒c′, then c′ is tar-irreducible. we denote the parallel dissolving relation by δ =⇒. the rules defining the parallel dissolving relation use a predicate expressing δ -irreducibility. we note that c ∈ c(π) iff c is tar-irreducible and δ -irreducible. proposition 3. let π be a p system. if c ∈ c#(π) is tar-irreducible and c δ=⇒c′, then c′ is δ -irreducible. according to the standard description in membrane computing, a transition step between two configurations c,c′ ∈ c(π) is given by: c ⇒ c′ iff c and c′ are related by one of the following relations: either c m pr =⇒; tar=⇒ c′, or c mpr=⇒; δ=⇒ c′, or c mpr=⇒; tar=⇒; δ=⇒ c′. the three alternatives in defining c ⇒ c′ are given by the existence of messages and dissolving symbols along the system evolution. starting from a configuration without messages and dissolving symbols, we apply the “mpr” rules and get an intermediate configuration which is mpr-irreducible; if we have messages, then we apply the “tar” rules and get an intermediate configuration which is tarirreducible; if we have dissolving symbols, then we apply the dissolving rules and get a configuration which is δ -irreducible. if the final configuration has no messages or dissolving symbols, then we say that the transition relation ⇒ is well-defined as an evolution between the initial and final configurations. proposition 4. the relation ⇒ is well-defined over the entire set c(π) of configurations. examples of inference trees, as well as the proofs of the results are presented in [2]. operational semantics provides us with a formal way to find out which transitions are possible for the current configuration of a membrane system. given an operational semantics, we can derive easily an interpreter for membrane systems, as well as the basis for the definition of certain equivalences and congruences between membrane systems. moreover, given an operational semantics, we can reason about the rules defining the semantics. a notion of bisimulation can be defined (see [2]), and the bisimulation relation allows to compare the evolution behaviour of two membrane systems. 3 arithmetical operations in membrane systems the problem of number encoding using multisets is interesting and complex. the first paper on the encodings and arithmetical operations in membrane systems is [5]. in [5] we present several combinatorial results and some encodings of numbers using multisets. here we present some arithmetical operations over numbers encoded by a simple and natural encoding (each object of a membrane represents a unit, and we use n objects to represent the number n). we indicate the complexity of some arithmetical operations, and build the membrane systems which implement the arithmetic operations over the encoded numbers. a programming perspective of the membrane systems 17 addition time complexity: o(1) π = (v, µ, w0, (r0, /0), 0), v = {a, b}, µ = [0]0, w0 = a nbm, r0 = {b → a}. addition is trivial; we consider n objects a and m objects b. the rule b → a says that an object b is transformed in one object a. such a rule is applied in parallel as many times as possible. consequently, all objects b are erased. the remaining number of objects a represents the addition n + m. subtraction time complexity: o(1) π = (v, µ, w0, (r0, /0), 0), v = {a, b}, µ = [0]0, w0 = a nbm, r0 = {ab → λ}. subtraction is described in the following way: given n objects a and m objects b, a rule ab → λ says that one object a and one object b are deleted (this is represented by the empty symbol λ ). consequently, all the pairs ab are erased. the remaining number of objects represents the difference between n and m. multiplication without promoters time complexity: o(n·m) the object is a promoter for a rule if the rule can be applied only in the presence of object. figure 1 presents a p system π1 without promoters for multiplication of n (objects a) by m (objects b), the result being the number of objects d in membrane 0. in this p system we use the priority relation between rules; for instance bv → dev has a higher priority than av → u, meaning the second rule is applied only when the first one cannot be applied anymore. initially only the rule au → v can be applied, generating an object v which activates the rule bv → dev m times, and then av → u. now eu → dbu is applied m times, followed by au → v. the procedure is repeated until no object a is present within the membrane. we note that each time when one object a is consumed, then m objects d are generated. π1 = (v, µ, w0, (r0, ρ0), 0), v = {a, b, e, v, u}, µ = [0]0, w0 = a nbmu, r0 = {r1 : au → v, r2 : bv → dev, r3 : av → u, r4 : eu → dbu}, ρ0 = {r2 > r1, r4 > r3}. 18 gabriel ciobanu an bm u eu → dbu bv → dev 0 av → u au → v> > figure 1: multiplier without promoters multiplication with promoters time complexity: o(n) figure 2 presents a p system π2 with promoters for multiplication of n (objects a) by m (objects b), the result being the number of objects d in membrane 0. in this p system we use rules with priority and with promoters. the object a is a promoter in the rule b → bd|a, i.e., this rule can only be applied in the presence of object a. the available m objects b are used in order to apply m times the rule b → bd|a in parallel; based on the priority relation and the availability of a objects (except one a as promoter), the rule au → u is applied in the same time. the priority relation is motivated because the promoter a is a resource for which the rules b → bd|aand au → u are competing. the procedure is repeated until no object a is present within the membrane. we note that each time when one object a is consumed, then m objects d are generated. π2 = (v, µ, w0, (r0, ρ0), 0), v = {a, b, u}, µ = [0]0, w0 = a nbmu, r0 = {r1 : b → bd|a, r2 : au → u}, ρ0 = {r1 > r2}. an bm u 0 au → u>b → bd|a figure 2: multiplier with promoters the membrane systems for multiplication differ from others presented in the literature [17] because they do not have exponential space complexity, and do not require active membranes. as a particular case, it would be quite easy to compute n2 by just placing the same number n of objects a and b. another interesting feature is that the computation may continue after reaching a certain result, and so the system acts as a p transducer [12]. thus if initially there are n (objects a) and m (objects b), the system evolves and produces n·m objects d. afterwards, the user can inject more objects a and the system continues the computation obtaining the same result as if the objects a are present from the beginning. for example, if the user wishes to compute (n + k) ·m, it is enough to inject k objects a at any point of the computation. therefore this example emphasizes the asynchronous feature and a certain degree of reusability and robustness. a programming perspective of the membrane systems 19 division we implement division as repeated subtraction. we compute the quotient and the remainder of n2 (objects a in membrane 1) divided by n1 (objects a in membrane 0) in the same p system evolution. the evolution starts in the outer membrane by applying the rule a → b(v, in1). the (v, in1) notation means that the object v is injected into the child membrane 1. therefore the rule a → b(v, in1) is applied n1times converting the objects a into objects b, and object v is injected in the inner membrane 1. the evolution continues with a subtraction step in the inner membrane, with the rule av → e applied n1 times whenever possible. π = (v, µ, w0, w1, (r0, ρ0), (r1, ρ1), 0), v = {a, b, b′, c, s, u, v}, µ = [0[1]1]0, w0 = a n1 s, w1 = a n2 s, r0 = {a → b(v, in1), b′ → a, r1 : bu → b′|¬v, r2 : u → δ|¬v, r3 : csu → u|v}, ρ0 = {r1 > r2, r2 > r3}, r1 = {r1 : av → e, r2 : v → (v, out), r3 : es → s(u, out)(c, out), r4 : e → (u, out)}, ρ1 = {r1 > r2, r2 > r3, r3 > r4}. 0 1 an2 s a → b(v, in1) an1 bu → b′|¬v b′ → a av → e > v → (v, out) > e → (u, out)>es → s(u, out)(c, out)> t ctu → u|v figure 3: p system for division two cases are distinguished in the inner membrane: • if there are more objects a than objects v, only the rules es → s(u, out)(c, out) and e → (u, out) are applicable. rule es → s(u, out)(c, out) sends out to membrane 0 a single c (restricted by the existence of a single s into this membrane) for each subtraction step. the number of objects c represents the quotient. on the other hand, both rules send out n1 objects u (equal to the number of objects e). the evolution continues in the outer membrane by applying bu → b′|qv of n1times, meaning the objects b are converted into objects b′ by consuming the objects u only in the absence of v (|qv denotes an inhibitor having an effect opposite to that of a promoter). then the rule b′ → a produces the necessary objects a to repeat the entire procedure. 20 gabriel ciobanu • when there are less objects a than objects v in the inner membrane we get a division remainder. after applying the rule av → e, the remaining objects v activate the rule v → (v, out). therefore all these objects v are sent out to the parent membrane 0, and the rules es → s(u, out)(c, out) and e → (u, out) are applied. due to the fact that we have objects v in membrane 0, the rule bu → b′|qv cannot be applied. since n2 is not divisible by n1, the number of the left objects u in membrane 0 represents the remainder of the division. a final cleanup is required in this case, because an object c is sent out even if we have not a ”complete” subtraction step; the rule ctu → u|v removes that extra c from membrane 0 in the presence of v. this rule is applied only once because we have a unique t in this membrane. the natural encoding is easy to understand and work with. however it has the disadvantage that the membranes can contain a very large number of objects when working with very large numbers. we introduce and study the most compact encoding using two object types (binary case) in [5], where we present other p systems implementing the arithmetical operations on numbers encoded using the binary cases of the most compact encoding. we use a web-based simulator available at http://psystems. ieat.ro to implement the arithmetical operations, and test each p system. 4 software implementations several programming paradigms and programming languages have been selected for implementing membrane systems simulators: lisp, haskell, mzscheme (as functional programming languages) prolog, clips (as declarative languages), c, c++, java (as imperative and object-oriented languages). the user interface can be designed separately from the engine performing the evolution, and it is possible to use different programming languages able to communicate with each other. each programming paradigm, each programming language has advantages and disadvantages. transition membrane systems and deterministic membrane systems with active membranes are simulated in prolog [14]; they are used to solve np-complete problems as sat, validity, subset sum, knapsack, and partition problems. sevilla carpets describing the complexity of a membrane system computation [11] are used as a graphical representation for a partition problem in [20]. membrane systems with active membranes, input membrane and external output are simulated in clips and used to solve np-complete problems in [18]. the simulator presented in [18] allows to observe the evolution of the systems with active membranes based on production system techniques. the set of rules and the configurations in each step of the evolution are expressed as facts in a knowledge base. rewriting membrane systems and membrane systems with symport/antiport rules are described as executable specifications in maude in [1]. the advantage of this approach is that it uses the existing tools of maude, and it is used to verify the temporal properties of the membrane systems expressed in linear temporal logic. a more complex simulator (written in visual c++) for membrane systems with active membranes and catalytic membrane systems is presented in [10]. it provides a graphical simulator, interactive definition, visualization of a defined membrane system, a scalable graphical representation of the computation, and step-by-step observations of the membrane system behaviour. the simulation of these membrane systems has to deal with the potential growth of the membrane structure and adapt dynamically the topology of the configurations depending if some membranes are added or deleted. polynomial-time solutions to np-complete problems via membrane systems can be reached trading time by space. this is done by producing (via membrane division) an exponential amount of membranes that can work in parallel. in [10] it is presented a software implementation which provides a graphical simulation for two variants of membrane systems: for the initial version of catalytic hierarchical cell systems, and for membrane a programming perspective of the membrane systems 21 systems with active membranes. its main functions are given by an interactive definition of a membrane system, a visualization of a defined membrane system, a graphical representation of the computation and final result, and saving and (re)loading a defined membrane system. the application is implemented in microsoft visual c++ using mfc classes. for a scalable graphical representation, the microsoft directx technology is used. one of the main features of this technology is that the size of each component of the graphical representation is adjusted according to the number of membranes of the system. the system is presented to the user with a graphical interface where the main screen is divided into two windows: the left window gives a tree representation of the membrane system including objects and membranes. the right window provides a graphical representation of the membrane system given by venn-like diagrams. a menu allows the specification of a membrane system for adding new objects, membranes, rules and priorities. by using the functions start, next and stop, the users can observe the system evolution step-by-step. by simulating parallelism and nondeterminism on a sequential machine one can lose the power and attractiveness of membrane system computing. parallel and cluster implementation for transition membrane systems in c++ and mpi are reported in [8] and [9]. the rules are implemented as threads. at the initialization phase, one thread is created for each rule. rule applications are performed in terms of rounds. to synchronize each thread (rule) within the system, two barriers implemented as mutexes are associated with the thread. at the beginning of each round, the barrier that the rule thread is waiting on is released by the primary controlling thread. after the rule application is done, the thread waits for the second barrier, and the primary thread locks the first barrier. since each rule is modelled as a separate thread, it should have the ability to decide its own applicability in a particular round. generally speaking, a rule can run when no other rule with higher priority is running, and the resources required are available. when more than one rule can be applied in the same conditions, the simulator picks randomly one among the candidates. with respect to the synchronization and communication, for every membrane, the main communication is done by sending and receiving messages to and from its father and children at the end of every round. with respect to the termination, when the system is no longer active, there is no rule in any membrane that is applicable. when this happens, the designated output membrane prints out the result and the whole system halts. in order to detect if the membrane system halts, each membrane must inform the other membranes about its inactivity. it can do so by sending messages to others, and by using a termination detection algorithm [4]. the implementation was designed for a cluster of computers. it is written in c++ and it makes use of message passing interface (mpi) as its communication mechanism. mpi is a standard library developed for writing portable message passing applications, and it is implemented both on shared-memory and on distributed-memory parallel computers. the program was implemented and tested on a linux cluster at the national university of singapore; the cluster consisted of 64 dual processor nodes. the above implementations represent the first generation of membrane systems simulators. the recent developments are related to biological applications, and to a new generation of web-based simulators. webps is an open-source web-enabled simulator for membrane systems [6]. the simulator is based on clips, and it is already available as a web application. as any web application, webps does not require an installation. it can be used from any machine anywhere in the world, without any previous preparation. a simple and easy to use interface allows the user to supply an xml input both as text and as a file. a friendly way of describing membrane systems is given by an interactive javascript-based membrane system designer. the interface provides a high degree of (re)usability during the development and simulation of the membrane systems. the initial screen offers an example, and the user may find useful documentation about the xml schema, the rules, and the query language. the query language helps the user to select the output of the simulation. the simulator is free software, and it offered at http://psystems.ieat.ro under the gnu general public license. this allows anyone to contribute with enhancements and error corrections to the code, and possibly develop new interfaces for the c and clips level apis. these interfaces can be local (graphical or command-line), or yet other 22 gabriel ciobanu web-based ones. in the same paper [6], the authors present an accelerator for parallelization of the existing sequential simulators. this accelerator is used to parallelize an existing clips simulator [18]. the speedup and the efficiency of the resulting parallel implementation are surprisingly close to the ideal ones. 5 conclusion and related work structural operational semantics is an approach originally introduced by plotkin [19] in which the operational semantics of a programming language or a computational model is specified in a logical way, independent of a machine architecture or implementation details, by means of rules that provide an inductive definition based on the elementary structures of the language or model. structural operational semantics is intuitive and flexible, and it becomes more attractive during the years by the developments presented by kahn [15] and milner [16]. configurations are states of transition systems, and computations consists of sequences of transitions between configurations, and terminating (if it terminates) in a final configuration. we present a structural operational semantics of the membrane systems; the inference rules provide a big-step operational semantics due to the parallel nature of the model. a structural operational semantics of the systems emphasizes also the deductive nature of the membrane computing by describing the transition steps by using a set of inference rules. considering a set r of inference rules, we can describe the computation of a membrane system as a deduction tree. in [3] we translate the big-step operational semantics of membrane systems into rewriting logic. by using the rewriting engine maude [13], we obtain an interpreter for membrane systems, and verify various properties of these systems. looking at the membrane systems from the point of view of programming theory, we define an appropriate data representation for p systems, and make the first steps to define an arithmetic unit for these abstract machine inspired by cells. the natural encoding over multisets is very close to biology, and can help to understand some biological mechanisms, improving also some computational models inspired by biology. we have designed and implemented sequential and parallel software simulators; we present some of them, and compare with other software simulators of the p systems. a web-based implementation is presented in [6]. acknowledgements the contributions of this paper were obtained together with my colleagues. many thanks to oana andrei and dorel lucanu for the joint work on the operational semantics of the membrane systems. many thanks to cosmin bonchiş and cornel izbaşa for their contributions to the arithmetical operations over multisets in the framework of membrane systems, and to the software implementation webps. references [1] o. andrei, g. ciobanu, d. lucanu. executable specifications of the p systems. in membrane computing wmc5, lncs vol.3365, springer, 127-146, 2005. [2] o. andrei, g. ciobanu, d. lucanu. structural operational semantics of p systems. proceedings wmc6, lncs vol.3850, springer, 32-49, 2006. [3] o. andrei, g. ciobanu, d. lucanu. operational semantics and rewriting logic in membrane computing. proceedings sos workshop 2005, to appear in entcs. a programming perspective of the membrane systems 23 [4] h. attiya, j. welch, distributed computing: fundamentals, simulations and advanced topics. mcgraw-hill, 2000. [5] c. bonchiş, g. ciobanu, c. izbaşa. encodings and arithmetic operations in membrane computing. in jin-yi cai, s. barry cooper, angsheng li (eds.): theory and applications of models of computation, lncs 3959, springer, 618–627, 2006. [6] c. bonchiş, g.ciobanu, c. izbaşa, d. petcu. a web-based p systems simulator and its parallelization. in c.calude et al. (eds.): unconventional computing, lncs vol.3699, springer, 58-69, 2005. [7] g. ciobanu. distributed algorithms over communicating membrane systems. biosystems vol.70, elsevier, 123-133, 2003. [8] g. ciobanu, r. desai, a. kumar. membrane systems and distributed computing. in proceedings wmc3, lncs vol.2597, springer, 187-202, 2003. [9] g. ciobanu, w. guo. p systems running on a cluster of computers. in procedings 4th wmc, taragona, lncs vol.2933, springer, 123-139, 2004. [10] g. ciobanu, d. paraschiv. p system software simulator. fundamenta informaticae 49, 61-66, 2002. [11] g. ciobanu, gh. păun, gh.ştefănescu. sevilla carpets associated with p systems. report 26/03 rovira i virgili university, tarragona, 135-140, 2003. [12] g. ciobanu, gh. păun, gh. ştefănescu. p transducers. new generation computing 24, 1–28, 2006. [13] m. clavel, f. durán, s. eker, p. lincoln, n. martí-oliet, j. meseguer, j.f. quesada. maude: specification and programming in rewriting logic. theoretical computer science, vol.285, 187-243, 2002. [14] a. cordon-franco, m.a. gutierrez-naranjo, m.j. perez-jimenez, a. riscos-nunez, f. sanchocaparrini. implementing in prolog an effective cellular solution for the knapsack problem. in proceedings 4th wmc, taragona, lncs vol.2933, springer, 140-152, 2004. [15] g. kahn. natural semantics, technical report 601, inria sophia antipolis, 1987. [16] r. milner. operational and algebraic semantics of concurrent processes. in j. van leeuwen (ed.), handbook of theoretical computer science vol.b, 1201-1242, elsevier science, 1990. [17] gh. păun. membrane computing. an introduction. springer, 2002. [18] m.j. perez-jimenez, f.j. romero-campero. a clips simulator for recognizer p systems with active membranes. in proceedings 2nd brainstorming week on membrane computing, university of sevilla tech. rep 01/2004, 387-413, 2004. [19] g. plotkin. structural operational semantics. journal of logic and algebraic programming vol.60, 17-139, 2004. [20] a. riscos-núñez, cellular programming: efficient resolution of numerical np-complete problems. phd thesis, university of seville, 2004. 24 gabriel ciobanu gabriel ciobanu romanian academy institute of computer science address: blvd. carol i nr.8, iaşi e-mail: gabriel@iit.tuiasi.ro int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 2, pp. 193-204 objects detection by singular value decomposition technique in hybrid color space: application to football images m.moussa jlassi, a. douik, h. messaoud mourad moussa jlassi ecole nationale d’ingénieurs de monastir avenue ibn el jazzar 5019 monastir tunisie e-mail: mourad.enim@yahoo.fr ali douik and hassani messaoud ecole nationale d’ingénieurs de monastir avenue ibn el jazzar 5019 monastir tunisie e-mail: ali.douik@enim.rnu.tn e-mail: messaoud.hassani@enim.rnu.tn abstract: in this paper, we present an improvement non-parametric background modeling and foreground segmentation. this method is important; it gives the hand to check many states kept by each background pixel. in other words, generates the historic for each pixel, indeed on certain computer vision applications the background can be dynamic; several intensities were projected on the same pixel. this paper describe a novel approach which integrate both singular value decomposition (svd) of each image to increase the compactness density distribution and hybrid color space suitable to this case constituted by the three relevant chromatics levels deduced by histogram analysis. in fact the proposed technique presents the efficiency of svd and color information to subtract background pixels corresponding to shadows pixels. this method has been applied on colour images issued from soccer video. in the other hand to achieve some statistics information about players ongoing of the match (football, handball, volley ball, rugby...) as well as to refine their strategy coach and leaders need to have a maximum of technical-tactics information. for this reason it is prominent to elaborate an algorithm detecting automatically interests color regions (players) and solve the confusion problem between background and foreground every moment from images sequence. keywords: segmentation, color image, statistic algorithm, histogram analysis, singular value decomposition. 1 introduction image segmentation based on background modeling is a research thematic up until now locates on the head of enormous number of studies. indeed background modeling and image segmentation techniques leads to remedy many problems in a wide spectrum of computer vision applications; k. elgammal et al. [1] present non-parametric model for the images segmentation when the background is dynamic. ying ming et al. [2] propose a statistical algorithm inspired from the idea of elgammal based on cauchy distribution; they proved that the intensity values of background pixels are adapted to cauchy’s distribution. o. javed et al. [3], propose mixture models to handle the backgrounds that exhibit multimodal characteristics, integration of gradient information are suggested as another feature of the multiple models. although gaussian mixture models can converge to any arbitrary distribution provided by enough copyright c⃝ 2006-2010 by ccc publications 194 m.moussa jlassi, a. douik, h. messaoud number of components. r. agarwal et al. [4] present a data-hiding algorithm that exploit the singular value decomposition (svd) representation of the data. they compute the svd of the host image and the watermark and embed the watermark in the singular vectors of the host image. the proposed method leads to an imperceptible scheme for digital images, both in grey scale and color and is quite robust against attacks like noise and jpeg compression. in this paper, we describe an algorithm modeling and subtracting the background pixels based on svd approach which is used in many computer vision problems on one hand, select the appropriate color space among the wide set of color levels commonly used in color image analyze on the other hand. both compactness power of various density distributions and quality of approximated image are increased by singular value decomposition approach. also this technique can be used to answer several purpose for example eliminate noise and highlights [5, 6] issues from the change of illuminations, dynamic background pixels, camera displacement and shadows. finally specify the suitable color space constituted by the three significant color levels, where this segmentation technique will be realized and use a new similarity measure between reference and candidate image that consists to calculate the intersection coefficients of color histogram images. this paper is organized as follows. section 2 presents an overview of statistical algorithms and background model. in section 3 we define the singular value decomposition and we describe the main issues of the svd approach. hybrid color space constituted by significant levels has been explained in section 4. finally, section 5 describes foreground segmentation and experimental results which will evaluate the robustness of this technique. 2 an overview of statistical algorithms and background model 2.1 statistical algorithms a large variety of background subtraction and image segmentation’s algorithms have been developed in last few years ranging from parametric to non-parametric, from pixel to region [8]. statistical algorithms are frequently used in computer vision; m. seki et al. [7] propose background subtraction based on cooccurrence of image variations. oriol pujol et al. [9] propose a new deformable model defined in a statistical framework to segment objects of natural scenes. they perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. linear discriminant analysis allows them to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. instead of using a heuristic potential field, their active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. different tests on synthetic images, natural scene and medical images show the advantages of their statistic deformable model. k. verma et al. [10] propose a new improved mountain clustering technique, which is compared with some of the existing techniques such as k-means, fcm, em and modified mountain clustering. the performance of all these clustering techniques towards color image segmentation is compared in terms of cluster entropy as a measure of information and observed via computational complexity. the cluster entropy is heuristically determined, but is found to be effective in forming correct clusters as verified by visual assessment. a. farhadi et al. [11] present a method for the segmentation of images based on local higher order statistics. the algorithm can be applied for the separation of objects from a texture background and the segmentation of textures. the proposed technique makes no use of a data bank and its complexity is o(χ) where χ is the number of pixels. objects detection by singular value decomposition technique in hybrid color space: application to football images 195 2.2 non-parametric background model because the parametric background model still lacks flexibility when the background pixels are dynamic, a highly flexible non-parametric technique is proposed to estimate background probabilities from many recent samples over time using kernel density estimation. in the non-parametric model all recently observed pixel values x1, x2,.. xn are modeled by probability density functions using a certain kernel estimator function, which is often chosen to be a gaussian. the weighted sum of all these gaussians results in the final probability density function of the pixel value xt : p(xt) = 1 n n∑ i=1 k(xt − xi) (1) the kernel estimator function k is chosen as a normal function n(0,σ) where σ is the kernel function bandwidth. color channels are assumed independent and each channel has its own kernel band width σ 2. these assumptions lead us to the final density estimation which can be written as: pr(xt) = 1 k k∑ i=1 d∏ j=1 1√ 2π σ 2j exp− (xt j − xi j)2 2σ 2j (2) when this probability is higher than a certain threshold which is a global threshold over all the image, the pixel is classified as a foreground pixel. in other applications using kernel density estimation the kernel bandwidth dependent on the number of samples. to estimate the kernel band width σ 2 for the jth color channel for a given pixel we compute the median absolute deviation over the sample for consecutive intensity values of the pixel. that is, the median, m, of |xi − xi+1| for each consecutive pair (xi,xi+1) in the sample, is calculated independently for each color channel. if we assume that the distribution is normal n(µ,σ 2), then the deviation (xi − xi+1) is normal n(0,2σ 2). so the standard deviation of the first distribution can be estimated by σ = m 0.68 √ 2 (3) this method ensures that the local deviation is large when there are many large jumps between consecutive samples and smaller when this is not the case. 3 singular value decomposition (svd) 3.1 introduction the singular value decomposition (svd) is a generalization of the eigen decomposition which can be used to analyze rectangular matrices (the eigen-decomposition is defined only for squared matrix). by analogy with the eigen decomposition, which decomposes an image into two simple matrix, the main idea of the svd is to decompose a rectangular matrix as a product three matrix: two orthogonal and one diagonal. the svd is equivalent to principal component analysis (pca) and is therefore an essential tool for multivariate analysis. 3.2 svd image approximation the svd uses eigen decomposition of a positive semi-definite matrix in order to derive a similar decomposition applicable to all rectangular real number matrix. the main idea is to decompose any matrix into three matrix; two orthonormales and one diagonal. formally, if a is a rectangular matrix, its svd is written by: a = p∆ qt (4) 196 m.moussa jlassi, a. douik, h. messaoud p: the (normalized) eigenvectors of the matrix aat (ppt = i). the columns of p are called the left singular vectors of a. q: the (normalized) eigenvectors of the matrix aat (qqt = i). the columns of q are called the right singular vectors of a. ∆ the diagonal matrix of the singular values, ∆ = λ 1 2 with λ being the diagonal matrix of the eigen values of matrix aat and of the matrix at a, since they are the same. a non-parametric background modeling technique has been applied on soccer video images. the main problem that can appear is the occurring of wrong detection pixels. indeed the detection of moving objects as a shadows pixels allows to an over segmentation which will damage many works where this paper is registered. this algorithm is extremely important because it is a part of player’s classification and tracking [15] on a football scenes. svd representation is very useful in image processing applications in particular for the spectral image compression [16]. the interest of svd in this method is to determine number’s svd of the treated image. besides we will see the prominent contribution using svd approach to restore and eliminate shadows, highlights and noise from camera displacement. in the developed background segmentation method described in this paper, the main objective is summarized by the use of the singular value decomposition. let a is a given image represented by a matrix ap = [ai j], which can be decomposed into a product of three matrix uk sk v tk , where ai j is the appearance frequency of background pixel’s chromaticity and intensity; (p= red, green, blue). d. valentin et al. [17] and h. abdi et al. [18] prove the property’s importance to approximate a given matrix by svd which will be substituted by another having reduced rank and dimension of each initial level from r to k (i.e. suppression of r-k column). the determined singular values for each level was presented in frequency space, their representation prove that for each one correspond a discrete frequency. the noise that can appear in the signal (in frequency space the amplitude of noise is constant) corresponds to a low amplitude, singular value whereas high amplitudes of these represents global signal energy. 3.3 confidence intervals research this section presents the steps which allow to determine the singular values number for each level. – image improvement : in this section, we describe the basic background model and the background subtraction process with singular value decomposition. the latter was used in both restoration, reconstruction of considred image, and increase the compactness distribution of different class, and also provide useful image information. – weights interpretation: to evaluate mathematical contribution of singular value decomposition, a quantification of global signal energy distribution according the weight of each singular value skk was done. figure 1 illustrate the energy distribution e defined by: e = k∑ i=1 a2i (5) the relative energy contained by each singular value k, noted pk is defined by: pk = s2kk e (6) where the energy of the k singular value is equal to s2kk. objects detection by singular value decomposition technique in hybrid color space: application to football images 197 figure 1: evaluation of energy according to singular values decomposition. as it is shown on the figures 2a, 2b and 2c, the choice of the image’s size which will be manipulated, is deduced from the curves representing standard deviation of each colour levels according to the singular value decomposition. in fact a good choice of singular value number leads to reduce both compactness in different distribution and computing time. figure 2: evaluation of standard deviation according to singular values of the red level. 198 m.moussa jlassi, a. douik, h. messaoud figure 3: evaluation of standard deviation according to singular values of the green level. figure 4: evaluation of standard deviation according to singular values of blue level. figures 2a, 2b and 2c denote two zones, the first one defined in the interval [0, (skkl)i where (skkl)i is the singular value limits corresponding to the linear part of the curve i(red, blue, green), on this zone the curve prove a slope, beyond (skkl)i a second zone appears where the standard deviation varied slightly, therefore the optimal singular value (ŝkkl)i must necessarily belong to the first zone of each curve. table 1 illustrates initial and improved standard deviations for three channels (rgb). – choice of the singular values number: the choice of the singular values number, which will be kept, depends on two issues, the first one is the energy curve evaluated by figure 2 and the second is standard deviation curves of each chromatic level shown in figures 2a, 2b and 2c. in fact we specify for each component the limit singular value defined previously. table 2 shows confidence intervals as well as singular value limits and the optimal value. objects detection by singular value decomposition technique in hybrid color space: application to football images 199 table 1: evaluation of improvement parameters. σ µ r 4.2044 119.610 g 4.7227 152.080 b 4.313 88.988 rsvd 3.927 119.07 gsvd 4.620 152.05 bsvd 3 995 88.407 table 2: specification of confidence intervals. (skkl)i (ŝkk)i confidence intervals r 29 19 [0, 29] g 28 13 [0, 28] b 19 13 [0, 13] 4 hybrid color space research by histogram analysis many descriptors can be used in image processing field, c. huang et al. [12] combine texture and color descriptors [19] to form hybrid visual feature index to retrieve natural color images, in fact this method is insensitive to image rotation and translation and then the experimental results show that the method achieves better performance than other recent relevant methods. the rgb space isn’t always the best one [20], indeed, other colorimetric components, deduced from red, green and blue, can be more suitable according to a considered case. michael k. ng et al. [13] consider restoring a single-color image from two degraded frames of the same scene by a rgb sensor and a luminance sensor. the rgb-to-yiq transformation, the classical tikhonov regularization and the neumann boundary condition are used in the restoration process. r. missaoui et al. [14] illustrate the superiority of an efficient content-based image mining and retrieval approach towards similarity analysis and retrieval effectiveness computation both in the use l* c* h* and ciecam02 color spaces. in order to refine the result, a new effectiveness measure are proposed and experimented on an image, this approach consists to convert image from rgb space to other one which are grouped in ψ set and select the suitable system which minimizes the overlapping between different clusters. where ψ = (rrgb, grgb, brgb, rrgb, grgb, brgb, hhsl, shsl, lhsl, xxy z , yxy z , zxy z , llab, alab, blab, yy uv , uy uv , vy uv , ccmy , mcmy , ycmy , i1i1i2i3, i2i1i2i3, i3i1i2i3, aac1c2, c1ac1c2, c2ac1c2, yy iq, iy iq, qy iq, llch, clch, hlch, hhsv , shsv , vhsv , yy cbcr, cby cbcr, cry cbcr, yy pbpr, pby pbpr, pry pbpr, yy dbdr, dby dbdr, dry dbdr), multidimensional space defined by a set of color levels frequently used. a new similarity measures which compare two color histograms and calculates the intersection coefficients, as illustrates in figure 3a and 3b. however histogram analysis and conversion of image in various color space are an important technique to achieve many studies in this field and lead to make decision about discrimination capacity of 200 m.moussa jlassi, a. douik, h. messaoud these levels thus determine a hybrid color space constituted by the three significant components. algorithm 1 calculate similarity criterion between reference, and candidate image. a b figure 5: (a) represents the intersection coefficient (0.041965) between reference level and blab for player a. (b) represents the intersection coefficient (0.019788) between reference level and hhsl for player b. algorithm 1: read original image in gray level. calculate histogram hg. for each level i read the image in this level calculate histogram (hp)i estimate intersection between hg and (hp)i if inter ≈ 0 pi is a suitable plan else choose pi+1 end if end for. this algorithm is compiled for each player with various levels, the intersection coefficients between original (gray level) and converted frame were evaluated and given the following results: – relevant levels for player a: hhsl, vluv, i3 and i2, blab, rrgb and brgb. – relevant levels for player b: hhsl, i3, pry pbpr, jpegcrjpegy cbcr, dry dbdr, vy uv and c2y c1c2. to appreciate and highlight the interest of this technique, we recapitulate results of some favorite levels in tables 3 and 4. this supervised training enables to control statistical parameters, confirm the efficient choice of this method and increase compactness power. table 5 and 6 show the improvements introduced to the distribution of each different class. – for player a: table 5 illustrates mean µ and standard deviation σ before and after segmentation, of original image (gray level) and represented image in each relevant level and the ratio of the standard deviation σ , before and after segmentation, by standard deviation of reference image. σre f be f (before) =15.232, σre f a f t (after) = 8.317. objects detection by singular value decomposition technique in hybrid color space: application to football images 201 table 3: significant levels for player a (pa). level inter 1 hhsl 0 2 vluv 0 3 i3 0.00022401 4 i2 0.00029869 5 blab 0.00171740 6 rrgb 0.03345300 7 brgb 0.04196500 – for player b: similarly for player b and with the same way, segmentation parameters were given in table 6 and we conserve the same notation given for player a. table 4: significant levels for player b (pb). level inter 1 jpegcrjpegy cbcr 0 2 i3 0 3 hhsl 0.00067204 4 dry dbdr 0.00238950 5 pry pbpr 0.00418160 6 vy uv 0.01493400 7 c2y c1c2 0.01978800 this technique contributes to research a suitable hybrid color space that is separate some distributions corresponding for each set pixels class by an iterative selection procedure. this procedure consists to extract the significant level from a set of color components according specified actors present in image. the obtained results show clearly how the use of color improves the segmentation quality. 5 experimental results using the probability pr(xt) calculated in the equation (3), a pixel is considered as a foreground pixel if pr(xt) < th. the threshold th is a global threshold over all the image that can be adjusted to achieve a desired percentage of false positives. practically, this probability can be calculated in a very fast way using pre calculated lookup tables for the kernel function values given the intensity value difference,(pi − pt), and the kernel function bandwidth. the detection of shadows as foreground pixels was a source of confusion with a background. the solution that allows to overcome this problem incorporates both singular value decomposition to increase the compactness power of distributions and color information [21] where pixels intensities will be expressed. indeed chrominance levels were better than these of lightness. they lead to discriminate foreground and their shadows. the work space where this segmentation was carried out is the hrb constituted by hsl and rgb previously determined. 202 m.moussa jlassi, a. douik, h. messaoud table 5: evaluation of parameters in favorite levels before and after segmentation (pa). σbe f rapbe f σa f t rapa f t hhsl 14.353 0.942 2.804 0.099 vluv 3.873 0.254 2.480 0.087 i3 5.477 0.359 4.370 0.154 i2 5.831 0.382 7.424 0.262 blab 6.082 0.399 5.624 0.198 rrgb 10.392 0.682 20.640 0.728 brgb 11.705 0.768 13.935 0.492 table 6: evaluation of parameters in favorite levels before and after segmentation (pb). σbe f rapbe f σa f t rapa f t jpegcr 16.000 1.076 0.747 0.027 i3 5.385 0.362 4.502 0.164 hhsl 16.000 1.076 24.416 0.894 dry dbdr 6.000 0.403 0.811 0.029 pry pbpr 9.0554 0.609 0.765 0.028 vy uv 10.050 0.676 0.762 0.027 c2y c1c2 10.724 0.721 0.760 0.027 objects detection by singular value decomposition technique in hybrid color space: application to football images 203 ! " # $ % & ' ( ) * figure 6: (a, f) original images. (b, g), (c, h) present respectivly binary images before and after treatment. (d, i) segmented image with hrb system. (e, j) output images in rgb system. 6 conclusion in this paper, we present a non-parametric kernel density estimation technique, to model and subtract background which was developed in other color space more suitable instead the rgb space. experimental results has been improved by using an appropriate mathematical approach for objects detection and shadows suppression. this technique incorporates singular value decomposition to increase the compactness power of each distribution, also approximate a new image which will be manipulated on one hand, determine a suitable color space constituted by significant levels among set of levels commonly used in color image analysis based on a novel similarity measures for comparing color histogram of reference and candidate image where we calculate the intersection coefficient which present criterion’s discrimination on other hand. also this technique proves that the gray levels and the rgb space not efficient for all applications. however the deduced color space (hrb) allows to convincing results and confirm the efficiency of this method. bibliography [1] a. elgammal, r. duraiswami, d. harwood, and l. s. davis, background and foreground modeling using nonparametric kernel density estimation for visual surveillance, proc. ieee 90(7), pp. 1151-1163, 2002. 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[21] m. g. linguraru, m. á. g. ballester, n. ayache. deformable atlases for the segmentation of internal brain nuclei in magnetic resonance imaging, international journal of computers, communications and control, vol. ii, no. 1, pp. 26-36, 2007. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 385-397 robust control of particle size distribution in aerosol processes z. xiang zhengrong xiang school of automation nanjing university of science and technology nanjing, 210094, people’s republic of china e-mail: xiangzr@mail.njust.edu.cn abstract: this paper deals with a comprehensive study on robust control of particle size distribution of fractal agglomerate in aerosol processes with simultaneous chemical reaction, nucleation, condensation and coagulation. firstly, a general aerosol process is described by population balance and mass and energy balances, which describes the evolution of particle size distribution, continuous phase species and temperature of the aerosol system, respectively. a lognormal moment approximations of the population balance model is then presented. then, the robust state feedback controller is designed for the aerosol process with some unknown uncertainties, the proposed controller is composed of an nominal control term and a robust control term so that it only ensures the stability of the closed-loop system, but also attenuates the effect of the unknown uncertainties on the system. a high-gain observer is adopted to estimate state variables required in the on-line implementation of the state feedback. finally, the proposed robust controller is applied to an aerosol process for achieving an aerosol size distribution with desired geometric average particle diameter, the simulation results show the robustness properties of the controller with respect to parametric model uncertainty and unmodeled dynamics. keywords: particle size distribution, aerosol process, population balance, nonlinear systems, robust control 1 introduction aerosol process is of interest in many problems of ecology, atmospheric physics, and mechanics of multiphase system[1-3]. the evolution of an aerosol size distribution is modeled by population balance equations, which give rise to nonlinear partial integro-differential equation systems. the nonlinearities usually arise from complex reaction, nucleation, condensation and coagulation rates and their nonlinear dependence on temperature. a variety of solution techniques have been developed to address the complexity at various levels. in most cases, one needs to resort to numerical solutions. one of the standard numerical techniques is to discretize the population balance equation using finite difference/element methods (see[4-8], and the references cited therein), but these methods suffer from extremely large computational requirements which cannot be accommodated by conventional computers. sectional models offer a computationally less demanding solution by approximating the continuous size distribution by a finite number of sections within which the particle size distribution (psd) function is assumed to be constant [9]. on the other hands, under appropriate simplifying assumptions, analytical solutions have been developed to solve the population balance equation [10-13]. recently, the issue of population balance mode-based feedback control of psd has received considerable attention [14-16]. previous work in this area include stability analysis and the application of the conventional control schemes to crystallizers and emulsion polymerization processes [17]. unfortunately, conventional control schemes perform poorly in the face of severe process nonlinearities, and may even copyright c© 2006-2010 by ccc publications 386 z. xiang lead to destabilization of the closed-loop system. these limitations of conventional control schemes have motivated research efforts towards synthesizing nonlinear model-based feedback controllers on spatiallyhomogeneous aerosol processes with application to size distribution control in continuous crystallizers [18,19], and titania aerosol reactor [20,21]. however, most real aerosol systems have uncertainties include unknown or partially known timevarying process parameters, exogenous disturbance, and unmodeled dynamics. it is well known that the presence of uncertain variables and unmodeled dynamics, if not taken into account in the controller design, may be lead to severe deterioration of the nominal closed-loop performance or even to closedloop instability. therefore, it is very important thing to study robust control of nonlinear aerosol systems with uncertainty. this paper focuses on robust control of particle process described by uncertain population balances. the objective is to develop a general method for the synthesis of practically implementable robust nonlinear controller that explicitly handle time-varying uncertain variables (such as unknown process parameters and disturbances) and unmodeled dynamics. the robust nonlinear controller enforces stability in the closed-loop system and attenuation of the effect of uncertain variables on the system, and achieve psd with desired characteristics. to present robust output feedback control techniques that are applicable to a broad range of aerosol systems, we choose to focus on an aerosol process that involves simultaneous chemical reaction, nucleation, condensation and coagulation rather than a specific aerosol. the remainder of this paper is organized as follows: in section 2, a general aerosol process is described by population balance and mass and energy balances, which describes the evolution of particle size distribution, continuous phase species and temperature of the aerosol process, respectively. a lognormal moment approximations of the population balance model is presented. in section 3, the robust state feedback controller is designed for the aerosol system with some unknown uncertainties including unknown or partially known time-varying process parameters, exogenous disturbance, and unmodeled dynamics, the proposed controller is composed of a robust control term and a nominal control term so that it only ensures the stability of the closed-loop system, but also attenuates the effect of the unknown uncertainties on the system. a high-gain observer is adopted to estimate state variables required in the on-line implementation of the state feedback. in section 4, the proposed robust controller is applied to a general aerosol process for achieving an aerosol size distribution with desired geometric average particle diameter, the simulation results show the robustness properties of the controller with respect to parametric model uncertainty and unmodeled dynamics. finally, conclusions are given in section 5. 2 mathematical model of aerosol process 2.1 spatially-homogeneous aerosol process consider a general aerosol process which described by the following nonlinear partial integro-differential equation [22]: ∂ n(v,t) ∂ t + ∂ (g(x̄, v)n) ∂ v − i(v∗)δ (v − v∗) =   ∫ v  β (x̄, v − v̄, v̄)n(v − v̄,t)n(v̄,t)dv̄ − n(v,t) ∫ ∞  β (v, v̄)n(v̄,t)dv̄ (1) where the term n(v,t) represents the psd function at time t , v is the particle volume, i(v∗) is the nucleation rate, g(x̄, v) and β (x̄, v − v̄, v̄) are the diffusional condensation growth function and the brownian coagulation kernel of agglomerates, δ (·) is the standard dirac function. robust control of particle size distribution in aerosol processes 387 a mathematical model which predicts the time evolution of the concentrations of species and temperature of the gas phase has the following form [21]: dx̄ ∂ t = f (x̄) + g(x̄)u(t) + ā ∫ ∞  a(η, v, x̄)dv (2) where x̄(t) is an n-dimensional vector of state variables that depend on time, ā is constant matrix, f (x̄), g(x̄), a(η, v, x̄) are nonlinear vector functions and u(t) is the time-varying manipulated input (e.g. wall temperature). the term ā ∫∞  a(η, v, x̄)dv accounts for mass and heat transfer from the continuous phase to all the particles in the population. the diffusional condensation growth function g(x̄, v) and the brownian coagulation kernel of agglomerates β (x̄, v − v̄, v̄), for the free molecule size regimes, are represented by gf m(x̄, v) = bv /(s − ), b = (π)/vns ( kbt π m )/ , βf m (x̄, v, v̄) = b ( v/d f + v̄/d f ) √  v +  v̄ , b = (/π) /d f −/ (kbt /ρ) / r−/d f (3) and for the near-continuum and the continuum regimes gc(x̄, v) = bv /(s − ), b = (π )/c f vns,c f = λ  ( kbt π m )/ βc (x̄, v, v̄) = b ( v/d f + v̄/d f ) [ c(v) v/d f + c(v̄) v̄/d f ] , b = kbt µ (4) in the above equations, s is the saturation ratio, t is the absolute temperature, d f is the fractal (hausdorff) dimension, c f is the condensable vapor diffusivity, λ is the mean free path of the gas ( λ = vπ mw/2kbt nav, where v and mw are the kinematic viscosity and molecular weight of the fluid, respectively, and nav is the avogadro’s number), µ is the viscosity of the fluid, ns is the monomer concentration at saturation (ns = ps/kbt , where ps is the saturation pressure), m is the monomer mass, v is the monomer volume, ρ is the particle density, r is the particle radius, c(v) =  + bλ /r is the cunningham slip correction factor, and b = .. the nucleation rate i(v∗) is assumed to follow the classical becker-doring theory and is given by the following expression [23]: i = ns ( kbt π m )/ s (  π )/ σ / exp(−k∗ ln s  ) (5) where s is the monomer surface area and k∗ is the number of monomers in the critical size nucleus which is given by: k∗ = π  ( σ ln s ) (6) where σ = γ v/ /kbt and γ is the surface tension. remark 1. it is found experimentally that in many cases of practical interest, the total number of primary particles np in an agglomerate is related to characteristic radius r through a power law expression np ∼ rd f , where the exponent d f is called the fractal dimension. this is usually true in a statistical sense after averaging over many agglomerates with the same np. the value of d f depends on the details of the agglomerate formation process. for compact agglomerates we have d f → , while for chain-like agglomerates we have d f →  . 388 z. xiang remark 2. it is noticed that the collision kernel βf m in (3) is in a form that appears to be rather difficult to expand into a series with a manageable number of terms. the following approximation can be made [11]; (  v +  v̄ )/ = b (  v/ +  v̄/ ) the variables for b depend on the initial geometric standard deviation σ and on the fractal dimension d f . remark 3. the manipulated input u(t) enters system (1)-(2) through (2), this assumption is usually satisfied in most practical applications where the wall temperature is chosen as the manipulated input. 2.2 moment model in this subsection, we present moment model of size distribution of fractal agglomerates in aerosol processes with simultaneous chemical reaction, nucleation, condensation and coagulation. many experimental results and numerical calculation indicate that aerosol psds can be adequately described by unimodal lognormal functions. this result makes it possible to develop a moment model for the aerosol process in terms of the three leading moments of the size distribution. the log-normal size distribution function is written as: n(v,t) =  v n(t)√ π ln σ (t) exp [ − ln{v/vg(t)}  ln σ (t) ] where n(t) is the total number concentration of particles, σ (t) is the geometric standard deviation, and vg(t) is the geometric number mean particle volume. the kth moment of the particle size distribution is written as: mk(t) = ∫ ∞  vkn(v,t)dv where k is an arbitrary real number. according to the properties of a log-normal function, any moment can be written in terms of m, vg and σ as follows mk = mv k− g exp {   (k − ) ln σ } (7) obviously, the three leading moments are sufficient to generate the lognormal psd. using the same derivation procedure as the one in [21], we can easily obtain the following equations, which describe the time evolution of the three leading moments (i.e. the zeroth, first and second moments) of the size distribution for the free molecule size. dm dt = i − bb(m/d f −/m + m/d f m/d f −/ + m/d f m−/) (8) dm dt = iv∗ + b(s − )m/d f −/ (9) dm dt = iv∗ + b(s − )m+/d f +bb(m/d f +/m + m+/d f m/d f +/ + m+/d f m/) (10) similar to the case of the free molecule regime, the dynamics of the zeroth and second moments of the aerosol size distribution in the continuum size regime is described by the following equations: dm dt = i − b[m   + m/d f m−/d f + bλ (   π )/ (mm−/d f + m/d f m−+/d f )] (11) dm dt = iv∗ + b(s − )m/d f (12) robust control of particle size distribution in aerosol processes 389 dm dt = iv∗ + b(s − )m+/d f +b[m   + m+/d f m−/d f + +bλ (   π )/ (mm−/d f + m/d f m+/d f )] (13) table 1 dimensionless variables n = m/ns, aerosol concentration v = m/nsvd, aerosol volume v = m/nsv, second aerosol moment τ = (π m/kbt )//ns, characteristic time for particle growth k = (kbt /µ)nsτ , coagulation coefficient kn = λ /r, monomer knudsen number i ′ = i/(ns/τ), nucleation rate r′r = rr/(ns/τ), reaction rate group v′g = vg/v r ′g = rg/r θ = t/τ the moment equations for the free molecule size and continuum size regimes may be combined to describe the aerosol dynamics over the entire particle size spectrum by using the harmonic average of the dimensionless coagulation rates in the free molecule size and continuum size regimes [23]. using the dimensionless variables listed in table 1, we have to the following equations: zeroth moment dn dθ = i ′ − ξ n (14) where ξ = ξf m ξc ξf m + ξc , ξf m = r ′/ g b [ exp (   ln σ ) +  exp (   ln σ ) + exp (   ln σ )] ξc = k [  + exp (ln σ ) + b ( kn r ′g ) exp (   ln σ ) ( + exp ( ln σ ) ] first moment dv dθ = i ′k∗ + η(s − )n (15) where η = ηf m ηc ηf m + ηc , ηf m = v ′/ g exp ( ln  σ ) ηc = kn  v ′/ g exp (   ln σ ) second moment dv dθ = i ′k∗ + ε(s − )v + ζ v  (16) where ε = εf m εc εf m + εc , ζ = ζf m ζc ζf m + ζc εf m = v ′/ g b exp ( ln  σ ) , εc = kn  v′/g exp (   ln σ ) ζf m = r ′/ g b exp (   ln σ ) [ exp (   ln σ ) +  exp (   ln σ ) + exp (   ln σ )] ζc = k [  + exp (ln σ ) + b ( kn r ′g ) exp ( −   ln σ ) ( + exp (− ln σ ) ] 390 z. xiang the rate of change of s can be obtained from a monomer balance and is given by: ds dθ = r′rnav − i ′k∗ − η(s − )n (17) 2.3 mathematical model of aerosol process we consider a typical aerosol process in a cylindrical volume with diameter dt . under the assumption of lognormal aerosol size distribution, the dimensionless model that describes the evolution of the first three moments of the distribution, along with the saturation ratio, reactant concentrations and fluid temperature, takes the following form: dn dθ = i ′ − ξ n dv dθ = i ′k∗ + η(s − )n dv dθ = i ′k∗ + ε(s − )v + ζ v  ds dθ = cc̄c̄ − i ′k∗ − η(s − )n dc̄ dθ = −ac̄c̄ dc̄ dθ = −ac̄c̄ dt̄ dθ = bc̄c̄t̄ + et̄ (t̄w − t̄ ) (18) where c̄ and c̄ are the dimensionless reactant concentrations, t̄ , t̄w are the dimensionless fluid temperature and the dimensionless temperature of the heat transferring medium at the system boundary, respectively. a, a, b,c, e are dimensionless quantities, their explicit form are given in table 2. table 2 dimensionless variables for the model a = τ kpy/rt a = τ kpy/rt b = τ kp∆ hτ yy/rt cp c = navτ kyy(p/rt)/ns e = u rtτ/dt cpp c̄i = yi/yit̄ t̄ = t /t t̄w = tw/t we formulate the control problem as one of tracking the geometric average particle diameter of the aerosol system, by manipulating the wall temperature, i.e. y(t) = dpg(t), u(t) = t̄w(t) − t̄ws where t̄ws = tws/t = . we write the model (18) in the following form: dx̃ dt = f̃ (x̃) + g̃(x̃)u(t), y = h̃(x̃) (19) where the explicit form of x̃, f̃ (x), g̃(x̃) can be obtained by comparing (18) and (19). robust control of particle size distribution in aerosol processes 391 remark 4. in addition to being highly nonlinear, the real aerosol processes have uncertainties including unknown or partially known time-varying process parameters, exogenous disturbance, and unmodeled dynamics. therefore, the real aerosol systems can be described by the following systems. dx̃ dt = f̃ (x̃) + ∆ f̃ (x̃) + (g̃(x̃) + ∆ g̃(x̃))u(t), y = h̃(x̃) (20) where ∆ f̃ (x̃) and ∆ g̃(x̃) represent uncertainties caused by unknown or partially known time-varying process parameters, exogenous disturbance, and unmodeled dynamics. 3 robust controller design in this section, we will begin with the design of the robust controller. our objective is to design a robust output feedback controller which guarantees boundedness of all variables for the closed-loop system and tracking of a given reference signal yd . by differentiating the output y(t) with respect to t , we obtain the following nonlinear system represented by the differential equation y(r) = f (y, y(),··· , y(r−)) + ∆ f (y, y(),··· , y(r−))+ (g(y, y(),··· , y(r−)) + ∆ g(y, y(),··· , y(r−)))u (21) where r is the relative order (the definition can be found in [24], and omitted here for brevity) of the output y(t) with respect to the manipulated input u(t) . by taking x = y, x = y() up to xr = y(r−), we can represent the augmented system by the state space model ẋ = ax + b(v + d(x)) y = x (22) where x = (x, x,··· , xr)t , v = f (x) + g(x)u, δ(x) = ∆ g(x)/g(x), d(x) = ∆ f (x) + δ(x)(v − f (x)), (a, b) is controllable canonical pairs of the form a =      ···     ···  ··· ··· ··· . . . ...    ···     ···    ∈ rr×r, b =     ...     ∈ rr× assuming the states is available for feedback, taking e = y − yr = x − yr e = ẏ − ẏr = x − ẏr ... er = y(r−) − y (r−) r = xr − y (r−) r and e = (e, e,··· , er)t , we rewrite (22) as ė = ae + b(v + d(e + yd ) − y (r) d ) (23) where yd = (yd , ẏd ,··· , y(r−)d )t . 392 z. xiang obviously, if d(·) =  (i.e. ∆ f (x) = ∆ g(x) =  ), we can use the following nominal state feedback control to achieve our objective. u =  g(x) (−ke + y(r)d − f (x)) (24) where matrix k is chosen such that the matrix am = a − bk is hurwitz, i.e., all its eigenvalues are in the left open plane. however, due to the presence of uncertainties ∆ f (x) and ∆ g(x) , a nominal state feedback control alone cannot ensure the stability of the closed-loop system, it is necessary to add a robust compensator to deal with it. u = −  g(x)( − ∆(x)) bt peµ (x) ‖bt pe‖µ(x) + ε exp(−εt) (25) where ε, ε > , p = pt >  is a solution of the following lyapunov equation p(a − bk) + (a − bk)t p = −q, ∀qt = q >  µ(x) = ∆(x) (∣∣∣y(r)d − ke ∣∣∣ + | f (x)| ) + |∆ f (x)| , ∆(x) = sup x∈rr |δ(x)| the robust state feedback controller we proposed is as follows u = u + u (26) theorem 5. suppose that ∆(x) = sup x∈rr |δ(x)| < , consider system (22) under the robust state feedback controller (26), the closed-loop system have the following properties: (1) the state of the closed-loop system is bounded. (2) the output of the closed-loop system satisfy lim t→∞ |y − yd | = . proof: let ν = −  ( − ∆(x)) bt peµ(x) ‖bt pe‖µ(x) + ε exp(−εt) the closed-loop system under the robust state feedback controller (26) is as follows ė = (a − bk)e + b(v + d(x)) (27) consider the lyapunov function candidate v (e) = et (t)pe(t) the derivative of v (e) along the trajectories of the system is given by v̇ (e) = et p(a − bk)e + et pb(v + d(x)) (28) obviously, we have |d(x)| ≤ |∆ f (x)| + ∆(x)(|v| + | f (x)|) ≤ ∆(x) |v| + |µ(x)| therefore et pb(v + d(x)) ≤ − ( − ∆(x)) ∥∥bt pe ∥∥· |v| +  ∥∥bt pe ∥∥ µ(x) robust control of particle size distribution in aerosol processes 393 ≤ b t peµ(x)ε exp(−εt) ‖bt pe‖µ(x) + ε exp(−εt) ≤ ε exp(−εt) (29) substituting (29) into (28), we have v̇ (e) ≤ −λmin(q)‖e‖ + ε exp(−εt) (30) where λmin(q) denotes the minimum eigenvalue of q . using the lemma in [25], we have lim t→∞ e =  this completes the proof. 2 the on-line implementation of the controller (26) requires that the values of the state variables x are known. unfortunately, x may be not be known in many practical applications. one way to address this problem is to use state observer to estimate x. here we use the high-gain state observer as ˙̂ei = êi+ + αi ε (e − ê),  ≤ i ≤ r −  ˙̂en = αn ε (e − ê) (31) where ê = x̂ − yd , x̂ denote the estimate state of the state x, ε is a small positive parameter, the constants αi >  are chosen such that the roots of sr + αsr− + ···+ αr−s + αr =  have negative real parts. the state feedback controller (26) and the state observer (31) can be combined to yield the following nonlinear robust output feedback controller: ˙̂ei = êi+ + αi ε (e − ê),  ≤ i ≤ r −  ˙̂en = αn ε (e − ê) u =  g(ê + yd ) ( y(r)d − kê − f (ê + yd ) −  ( − ∆(ê + yd )) bt pêµ (ê + yd ) ‖bt pê‖µ(ê + yd ) + ε exp(−εt) ) (32) theorem 6. suppose that ∆(x) = sup x∈rr |δ(x)| < , consider system (22) under the robust state feedback controller (32), the closed-loop system have the following properties: (1) the state of the closed-loop system is bounded. (2) the output of the closed-loop system satisfy lim t→∞ |y − yd | =  . we can prove theorem 2 by following the proof line of theorem 1, the detailed proof is omitted here. 4 simulation study the performance of the nonlinear robust output feedback controller (28) was tested through simulations. the values of the process parameters are shown in table 3. table 3 model parameters for the simulation study 394 z. xiang dt = .m, process diameter p = atm, process pressure t = k, process pressure y = y = ppm, initial mole fraction of reactants u = .jm−s−k−, overall coefficient of heat transfer ∆ hr = .kjml−s−, heat of reaction cp = .jmol−k−, heat capacity of process fluid mw = .×−kgmol−, molecular weight of process fluid k = .mmol−s−, reaction constant µ = .×−kgm−s−, viscosity of process fluid γ = .nm−, surface tension v = .×−m, molecular weight of process fluid r = .jmol−k−, universal gas constant nav = .mol−, avogadro’ constant kb = .×−jk−, boltzmann’s constant several simulations were performed to evaluate the disturbance attenuation and set-point tracking capabilities of the robust nonlinear controller, as well as its robustness with respect to uncertainties in model parameters and unmodeled dynamics. the objective of these simulations is to show that the use of proposed robust control allows producing an aerosol product with a desired geometric average particle diameter (dpg = .µ m) within a given type of batch reactor even in the presence of uncertainty in the process model parameters. figure 1 presents the closed-loop trajectory (dashed line) and closed-loop trajectory (solid line) for dpg under robust nonlinear controller and nonlinear controller, respectively, and the corresponding manipulated input trajectory in the case of 10% error in the value of the rate constant k. it is clear that the robust nonlinear control regulates the output dpg to the desired point value (i.e. dpg = .µ m), and attenuate the effect of timevarying uncertainty on the process. however, nonlinear control cannot guarantee the output dpg to the desired point values in the presence of uncertainty. there is an error between steady-state value and the desired point one. figure 2 presents the closed-loop trajectory (dashed line) and closed-loop profile (solid line) for dpg under robust nonlinear controller and nonlinear controller, and the corresponding manipulated input trajectory in the case of a 8% decrease in the parameters. again, the robust nonlinear controller allows achieving an aerosol product with the desired dpg. also we simulated the robustness properties of the proposed controller with respect to parametric model uncertainty and unmodeled dynamics in the presence of a set-point change, the robust nonlinear controller was found to have very good robustness properties, keeping the output on the set-point. remark 7. it is worth to point out that the proposed approach for the design of robust nonlinear controllers is applicable to most aerosol systems for which the hypothesis of unimodal lognormal aerosol size distribution for long times is valid. 5 conclusions in this paper, we present a comprehensive study on robust control of particle size distribution of fractal agglomerate in aerosol processes with simultaneous chemical reaction, nucleation, condensation and coagulation. initially, a general aerosol process is presented by population balance and mass and energy balances, which describes the evolution of particle size distribution, continuous phase species and temperature of the aerosol system, respectively. a lognormal moment approximations of the population balance model is then presented. then, the robust state feedback controller is designed for the aerosol system with some unknown uncertainties, the proposed controller is composed of a robust control term and an nominal control term so that it only ensures the stability of the closed-loop system, but also attenrobust control of particle size distribution in aerosol processes 395 figure 1: (a) dpg using the robust nonlinear controller and nonlinear controller, (b) the corresponding manipulated input trajectory figure 2: (a) dpg using the robust nonlinear controller and nonlinear controller, (b) the corresponding manipulated input trajectory 396 z. xiang uates the effect of the unknown uncertainties on the system. a high-gain observer is adopted to estimate state variables required in the on-line implementation of the state feedback. finally, the proposed robust controller is applied to an aerosol process for achieving an aerosol size distribution with desired geometric average particle diameter, the simulation results show the robustness properties of the controller with respect to parametric model uncertainty and unmodeled dynamics. acknowledgment this work is supported by the national natural science foundation of china (no. 60974027). bibliography [1] j. m. hidy and 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[22] s. k. friedlander , smoke, dust and haze: fundamentals of aerosol dynamics, new york: oxford university press, 2000. [23] s. e. pratsinis, simultaneous nucleation, condensation, and coagulation in aerosol reactors, journal of colloid interface science, 124, pp. 416-426, 1988. [24] a. isidori, nonlinear control systems: an introduction (second edition). berlin, heidelberg, springer-verlag, 1989. [25] e. yaz, stabilizing compensator design for uncertain nonlinear systems, systems and control letter, 21, pp.11-17, 1993 zhengrong xiang was born in china, in 1969. he obtained his ph.d. degree in control theory and control engineering from the nanjing university of science and technology, china, in 1998. since 2001, he has been an associate professor in the school of automation. he is an ieee member. his main research interests include nonlinear control, robust control, intelligent control, and switched systems. he has published significantly on the subjects with over 80 technical papers in journals and conferences. e-mail: xiangzr@mail.njust.edu.cn. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 417-431 gasant: an ant-inspired least-cost qos multicast routing approach based on genetic and simulated annealing algorithms m. damanafshan, e. khosrowshahi-asl, m. abbaspour morteza damanafshan, ehsan khosrowshahi-asl department of computer and network, institute for research in fundamental sciences (ipm), tehran, iran. e-mail: damanafshan@iranet.ir maghsoud abbaspour department of computer engineering, faculty of electrical and computer engineering, shahid beheshti university, g.c., tehran, iran. e-mail: maghsoud@sbu.ac.ir abstract: computing least-cost multicast routing tree while satisfying qos constraints has become a key issue especially by growing communication networks. to solve this problem, a triplex algorithm called gasant which is based on ant colony optimization (aco), genetic algorithm (ga), and simulated annealing (sa) has been proposed in this paper. through aco, we have both provided improved initial population to feed ga and reduced search process. besides, sa has been deployed to refrain ga from getting stuck into local optimum solutions. simulation results assert that gasant not only has high speed convergence time, but also generates least-cost multicast routing trees of high qos. keywords: multicast routing; quality of service (qos); ant colony optimization (aco); genetic algorithm (ga); simulated annealing (sa) 1 introduction multicasting service is a technique in which the same information is sent concurrenly from a source node to a subset of all possible destinations (multicast group) in a computer network. the current approach to provide such a service is to establish a multicast tree. this tree includes a route node (sender), some internal nodes (intermediate routers) and some leaf nodes (recipients). to carry large numbers of multicast sessions, a network must minimize the sessions’ resource consumption [1]. therefore, it is important for a multicast session to adopt a multicast tree whose network cost is minimal. by network cost we mean the accumulation of the costs of resource usages of all the links constructing the multicast tree. this problem immediately is reduced to finding a steiner tree [2] which is one of the karp’s 21 np-complete problems [3]. the tree cost should be minimized to the most possible extent. this is due to the fact that after the multicast tree is built, all the network traffic flows along the links of the tree, especially in real-time applications which are intrinsically connection-oriented. the less the tree cost, the less the valuable resources are used during the whole connection time. finding minimal multicast tree gets more problematic when some quality-of-service (qos) constraints such as delay (end-to-end delay), and bandwidth constraints are also to be considered at the same time. finding the multicast tree or the steiner tree under any of the aforementioned qos constraints converts the problem into finding a constrained steiner tree (qos multicast routing) problem, which is np-complete itself [4]. several methods [4–9] have applied heuristic to solve qos multicast routing problem. kpp [4], bsma [5], and some others [6–8] are notable heuristic works in computing multicast trees. however, copyright c⃝ 2006-2012 by ccc publications 418 m. damanafshan, e. khosrowshahi-asl, m. abbaspour a comprehensive research in [9] has shown that most of the heuristic algorithms are notorious either for working too slowly or failing in computing of an optimized solution or both. some works [1, 10–13, 15–20] have mainly focused on applying ga to find constrained multicast routing trees. in [1] a bandwidth delay constrained least-cost multicast routing algorithm based on conventional ga has been proposed. it has used tree structure coding for chromosome representation and penalty functions for those candidate solutions that violate predefined thresholds. besides, [10, 11] have solely emphasized on conventional ga. however, all these methods [1, 10, 11] suffer from lack of local search and also problem of premature convergence. also, all these approaches generate their initial population mainly based on a randomized depth-first search algorithm [12, 13].this method suffers from applying uninformed search which most of the time performs worse than a good heuristic based informed search [14]. some others [16, 17] apply shimamoto’s approach [18] for coding routing tables and multicast trees. in this method for each pairs of (source, multicast-destination) several paths are stored, and the final multicast tree is yielded through combining these paths. as the network size grows, maintaining these paths can itself be a problem. the closest work to ours is [20] which presents a method namely ngsa for least-cost qos multicast routing based on both ga and simulated annealing (sa) algorithm. this paper ( [20]) adopts a rather new population initialization method mostly the same way as [12,13] which has two steps: trunk-creating and limb-appending. in trunk creating phase, a path is found from a source node to one of the multicast destinations. then, in the limb-appending phase, other multicast destination nodes are appended to the trunk through randomly discovered paths. [20] also uses sa to escape from premature convergence, one of the eminent shortcomings of ga. however, finding paths in both phases is done through random uninformed selection of neighbors which suffers from deficiencies inherent in uninformed search methods mentioned before. considering the fact that multicast tree creation and maintenance time is crucial and meanwhile ga’s evolution time toward better solution can be unpredictable [21], it is important to improve convergence speed. generating improved initial population [22], and reducing search process are two effective approaches to achieve this goal. [22] has shown that improving generation of initial population and being meticulous about it can significantly improve convergence time. all the aforementioned ga based researches applied random selection approaches in their initial population generation. adopting such a randomized behavior may cause the algorithm to go astray in establishing a multicast tree at least for a while. therefore, ga must compensate its improper selections by making further attempts, and this prolongs the convergence time. therefore, instead of passing the buck to next generations in ga and expecting the next generations to compensate the primitive generations’ probable fault, it is reasonable to make the first decision more scrupulously. this becomes more important when we cope with large networks. reducing search process can also be considered as another method to improve convergence time. actually, reducing search process hinders ga algorithm-at least for a great extent-from blundering and moving back and forth and revisiting the same links for an excessive number of times in hope of finding a solution. applying a randomed paradigm automatically causes ga to undesirably adopt try and error behevior to acheive a satisfactory solution. in this paper, we have proposed and impelmented a new algorithm called gasant which takes the aforementioned two improvements on ga into consideration. to put it in a nutshell, we have both provided improved initial population and reduced search process by deploying ant colony optimization (aco). by improved initial population, we mean that links constructing initial population are more likely to appear in optimal multicast tree. by reducing search process, we mean that for finding a satisfactory solution, gasant visits edges of the network graph for a small number of times rather than excessive number of times. besides, sa has been used to escape from getting stuck into local optimum solutions. aco is based on distributed society of autonomous agents called ants. ants provides a valuable gasant: an ant-inspired least-cost qos multicast routing approach based on genetic and simulated annealing algorithms 419 approach of exploring the network and collecting information about link statuses like link-state protocols, but in an efficient and deliberate way. ants can provide better initial population for ga, since they traverse through network and discover (near) optimal paths among nodes. the produced least-cost multicast tree more likely contains a subset of edges comprising these optimal paths. thus, considering such paths while we want to establish a multicast trees can result in a solution in higher speed. consequently, the search process is reduced. the experiment results proves this claim. also, note that ants are small packets and put low load burden on network nodes; their lightweight approach can be very effective for gathering information for generating qos-aware initial population [23]. the experiments certify this claim, too. the rest of the paper is organized as follows: section 2 explains the problem formulation and modeling. section 3 describes the proposed multicast routing algorithm (gasant) in detail. in section 4, the convergence of gasant is investigated. we evaluate gasant comprehensively in section 5, and finally section 6 concludes the paper. 2 problem formulation and modeling in this paper, the network is expressed as an undirected weighted graph namely g = (v, e), where v is the set of network nodes and e is the set of links connecting network nodes to each other. the link e ∈ e with source node m and destination node n is denoted by (m,n). multicast tree which is denoted as mt (s, m) consists of two main parts: s ∈ v as the source node of multicasting, and m ⊆ v−{s} as the multicast destination nodes. each link e is characterized by a qos 3-tuple (b(e), d(e),c(e)) representing bandwidth, delay and cost associated with it respectively. here, b(e) > 0, c(e) ≥ 0 and d(e) ≥ 0. this paper tries to discover a multicast tree with the minimum cost subject to two qos constraints namely bandwidth, and delay constraints. if p(s,di) is a path in the tree mt starting from the source node s and ending at a multicast destination node di, then bandwidth and delay constraints and cost function are defined as follows: • bandwidth constraint it is required that the minimum value of the link bandwidth in the multicast tree mt , along the path (bpath) originating at the source node s and ending at any multicast destination node di ∈ m be greater than or equal to the predefined required bandwidth ωb. • delay constraint it is required that the end-to-end delay in the multicast tree mt , along the path (dpath) originating at the source node s and ending at any multicast destination node di ∈ m be smaller than or equal to the predefined maximum end-to-end delay ωd . • cost function we define the cost of the (multicast) tree as the sum of the costs of all the links of the tree. formula 1 formalizes it: ctree(mt ) = ∑ e∈mt c(e) (1) c(e) typically represents the cost of the link monetarily or any administratively interested cost. the proposed algorithm of this paper aims at constructing a least-cost multicast tree subject to delay and bandwidth constraints defined above. formalizing this problem as a constrained optimization problem, we have i f ctree(mt ) = ∑ e∈mt c(e) t hen minimize(ctree(mt )) 420 m. damanafshan, e. khosrowshahi-asl, m. abbaspour sub ject to bpath(p(s,di)) ≥ωb and dpath(p(s,di)) ≤ωd 3 the proposed multicast routing algorithm this section explains how gasant utilizes the cooperation between ga and aco to produce multicast trees. as shown in fig. 1, gasant consists of both reactive and proactive components. the proactive component itself has an aco module within. the reactive component is composed of two main modules namely path setup (ps) module and genetic and simulated annealing (gsa) module. figure 1: gasant architecture in this paper, a modified version of antnet [23] has been implemented in aco module. this modified version increases antnet’s performance and adapts it to multicast nature of routing. aco module proactively sends ants through network in order to find feasible paths to different destinations and keep nodes aware of network dynamism. this process continues periodically during the whole network uptime. beside this, the reactive component is responsible for multicast tree construction process. the process is carried out through two modules called ps module and gsa module. when a multicast tree creation request with a certain set of qos criteria is issued by an application, ps module starts to find paths between the multicast source node and each destination node. this process is done through propagating control messages throughout the network. these messages try to discover feasible paths considering the qos metrics by fetching the information provided by aco module stored in intermediate nodes. each of these paths is called trunk which is in fact a simple path that connects the source node to a multicast destination node. the result of ps module is in fact a set of highly crowded paths which were frequently traversed by ants. the discovered paths are fed into the gsa module. the gsa module uses these paths as raw data to construct its initial population. it then tries to find a multicast tree through running crossover and mutation operators in iterations. if a multicast tree with required qos metrics is found, then the mission is complete. otherwise, gasant commences a negotiation with the application that triggered the strict request in order to ask it to relax its qos requirements. in the following sub-sections, we have explained these three modules in more detail. 3.1 aco module at regular intervals, from every network node, aco sends forward ants toward a destination node in order to discover feasible and satisfactory paths. multicast destination nodes have higher chance of being selected as forward ant’s destination nodes. analogously to antnet, ants will travel toward destination node and if successful, then they will return back to source node. while returning back to the source node, ants update routing table of the intermediate nodes en route. this update is based on a reinforcement value r (refer to section 4 of [23] for more detailed information) which is a function of the goodness of the path that the ant has just traversed. in gasant, the value of r is calculated based on ant’s traversed path trip time (t ript ime), bandwidth (bandwidth) and cost (cost) through formula 2: r = c1 × ( t ript imebest t ript ime ) + c2 × ( bandwidth bandwidthbest ) + c3 × ( costbest cost ) (2) gasant: an ant-inspired least-cost qos multicast routing approach based on genetic and simulated annealing algorithms 421 here, t ript imebest is the best trip time experienced by the ants, and bandwidthbest and costbest are the best bandwidth and the best cost discovered while travelling toward same destination over the last observation window (in this paper we considered last 20 samples), respectively. the coefficients c1, c2 and c3 weigh the importance of each term. in our implementation, we have set each of these constants euqally to 0.33. in this way, all three qos parameters are treated equally. ants can easily keep track of bandwidth and cost of paths by saving minimum link bandwidth and summing the cost of individual links they are traversing. 3.2 ps module the multicast route discovery process triggers upon receiving a multicast tree setup request from a source to a number of destinations. the purpose of this reactive process is to find a set of trunks according to qos metrics. this is accomplished by actually sending multicast tree discovery (mtd) messages through network. these messages find links with high pheromones which can provide better trunks. in fact, this procedure mainly uses the information provided by aco module to find the best single paths to destinations. as soon as a multicast tree setup request is received, the source node starts propagating mtd messages to find feasible and shortest paths to multicast destinations. the source node broadcasts a limited number of mtd messages to its connected neighbors. this number is set through branchfactor variable which is the cardinality of a subset of the node’s neighbors. using this variable, the number of packets being broadcast throughout the network can be limited. therefore, the traffic overhead caused by mtd messages can be controlled. this subsetting is carried out through choosing the neighbors which are connected via links with highest pheromones to the source node. each mtd message is composed of seven fields: 1.requesti d, 2.sourcenodei d, 3.cost, 4.bandwidth, 5.delay, 6. path, 7.branchfactor. a mtd message stores the traversed path and its corresponding accumulated cost into its path and cost fields, respectively. the minimum bandwidth and total delay of the path are being kept track in bandwidth and delay fields. each mtd message is associated with a uniquerequesti d field; together with the sourcenodei d field, a network node can uniquely identify a mtd message. these two ids together are considered as the identi f ier(i d) pair. when an intermediate node receives an mtd message, the node inspects the message’s i d pair to check whether the message is a duplicate. in gasant, the intermediate node is allowed to forward at most a limited number of duplicated mtd messages (messages with same id pair) defined by alloweddupnumber. it is critical to somehow limit the number of packets being generated and forwarded through network or the network will get inundated with extraneous traffic. our experiments show that even with the branchfactor of 2 for a network of size 100 nodes, when the number of passing messages is not limited, more than millions of mtd messages of the same id are created and passes the same nodes for more than tens of thousands times. also, our experiments show that values around 50 for the alloweddupnumber for networks with more than 100 nodes can significantly reduce the message overhead while still providing vast amount of available trunks from source to destination nodes. after forwarding alloweddupnumber number of mtd messages of the same identifier pair, any subsequent mtd messages of this identifier pair is discarded by that intermediate node. to keep track of the number of times a particular mtd message has been forwarded, each node maintains a table of counters for each of the mtd messages the node has forwarded. if the counter of the received mtd message does not violate the alloweddupnumber, the node updates some fields of the message before forwarding. the cost field is increased by the cost of the link the message has just traversed. also the current node’s address is appended to the path list. the bandwidth field is set to minimum of the current bandwidth field value and the bandwidth of the traversed link. once the update is complete, like the source node, this node forwards the mtd message to selected branchfactor number of its neighbors. the selection is based on the pheromone level entries of the probabilistic routing table [23]. the higher values have the higher chance of being selected. the value of branchfactor can significantly affect the 422 m. damanafshan, e. khosrowshahi-asl, m. abbaspour amount of messages overhead generated in the network. higher branchfactor values will make messages explore more parts of the network and consequently gather more possible paths to destinations in expense of exponentially increasing message overhead. in this paper, the branchfactor is initially set to a constant large value compared to average degree of the network graph nodes and then it is reduced on next hops. this way most likely all neighbors of the source node will receive the mtd message. as a result, search area is widened and more parts of the network get explored while still having reasonable load. two logarithmic and linear methods have been tested for reducing the branchfactor along the path. in the logarithmic approach, the reduction is fast and branchfactor converges to one just after passing a few hops. when branchfactor value is one, the node forwards one copy of the received mtd message to its best candidate neighbor. in this way, the branchfactor and allowedduplimit together can control the amount of mtd message overhead. a constant branchfactor through the whole network is also investigated. however, our experiments show that the logarithmic approach surpasses the other two approaches in controlling the overhead while still producing nearly the same trunks as the others (constant and linear reduction approaches) produce. when a mtd message reaches its destination node, a reply message containing the same data as the mtd message is generated and is sent back to source node. the reply message traverses the same path, but will be queued in high priority queues. when the first message reaches back to the source node, the source node triggers a timer to collect as many routes as possible from different destinations. as the timeout expires, a set of trunks are extracted from the path field of the collected reply messages. these trunks are then sent to the gsa module. 3.3 gsa module in the following subsections, the specifications and operators of the gsa module are explained in detail. coding the tree structure coding has been chosen in this paper. in this method, every chromosome represents a multicast tree. as a result, the coding space is greatly reduced and coding-decoding operation is omitted and the meaning of genetic operations becomes more visual and the time of conversion between encoding and solution spaces is saved [1]. pruning in this phase, those links with a bandwidth less than the predefined bandwidth threshold are deleted. it is probable that the pruned graph gets decomposed into several smaller connected subgraphs. if all the multicasting nodes including the source node are all in the same subgraph, this means that the pruned network satisfies the bandwidth threshold. otherwise, the source node should start a new round of negotiations with the applicant program in order to reach a mutual satisfactory agreement with more relaxed threshold. initial population formation initial population is performed in two main stages: trunk-selection and limb-appending. trunk-selection: in this stage, one of the trunks created by the aco module is selected randomly. a trunk is a simple path that connects the source node to a multicast destination node (as in section 3). this trunk is supposed as the current multicast tree. gasant: an ant-inspired least-cost qos multicast routing approach based on genetic and simulated annealing algorithms 423 limb-appending: in this stage, a multicast destination node which is not part of the current multicast tree (obtained from previous stage) is selected as the current node. then, one of the links leaving this current node is selected. the more pheromone deposited on the link, the more probable the link is selected. after this, the other end of the link is selected as the current node, and the same action is done about it. this procedure continues until the current node becomes one of the nodes of the current multicast tree. at this time, all of these nodes which in turn were as current nodes and also the current multicast tree nodes together are set to the new current multicast tree. on the condition that the entire multicast destination nodes are in the current multicast tree, the mission is complete; otherwise the whole limb-appending procedure is repeated for the rest of the multicast destination nodes isolated from the current multicast tree. applying selections based on high pheromone values is more efficient than that of [20] which uses a random paradigm. this seems reasonable since the trunk-selection and limb-appending methods use previously gathered global information in making their decisions. the experiments in section 5 also prove this claim. fitness function basically, penalty functions are used to handle the constraints [24]. through these functions the constrained problems are transformed to unconstrained problem. in fact, these functions are used to penalize the individuals based on their constraint violation. the penalty imposed on infeasible individuals can range from completely rejecting the individual to decreasing its fitness based on the degree of violation [16]. in this paper, we have adopted the same fitness function formulae as in [20]. we have utilized penalty function in determining the fitness of each individual. here, the fitness of the multicast tree mt is defined as formula 3: fitness(mt ) = e −penalty(mt ) t (3) where, t is the temperature in which this fitness is calculated and penalty is the amount of penalty that has been considered for mt . the penalty itself is calculated via formula 4: penalty(mt ) = kc × costtree(mt ) + kb × bv (mt ) + kd × dv (mt ) (4) here, costtree(mt ) is calculated via formula 1; bv (mt ), and dv (mt ) are determined via formulae 5 and 6; kc , kb, and kd are the constants to weigh the importance of each of the cost, and the violations occurred from bandwidth and delay constraints, respectively. bv (mt ) = ∑ d j∈mt maximum(ωb − bpath(s,d j),0) (5) dv (mt ) = ∑ d j∈mt maximum(dpath(s,d j) −ωd,0) (6) as it can be understood from all above, the less violation an mt makes (formulae 5,6), the fewer penalties are considered for it (formula 4), and consequently, such an mt is fitter to the delay and bandwidth constraints (formula 3). selection method in this paper, fitness proportionate selection (roulette wheel selection) has been applied. in this method, the probability of selecting a chromosome ci as a parent is defined as follows (formula 7): 424 m. damanafshan, e. khosrowshahi-asl, m. abbaspour s electionprobability= fitness(fitness(ci))∑populations ize j=1 fitness(c j) (7) where fitness(ci) is the fitness of chromosome i. in fact, more elitist chromosomes have higher chances to be selected in comparison with their counterparts. adaptive crossover method crossover is a genetic operator that combines two chromosomes called parents to produce a new chromosome namely offspring. since experiments in [25] indicated that adaptive crossover performed as well or better than a traditional crossover, in this paper, adaptive crossover probability is applied. here, the crossover probability is calculated through formula 8: crossoverprobability=  c1 + c2 × f itmax− f it′ f itmax− f itavg f it′ ≥ f itavg c3 f it′< f itavg (8) here, f itavg is the average of the fitness of the population in the current generation; f itmax is the biggest fitness existent in the current generation; f it′ is the maximum of the fitnesses associated with the two parents to be crossovered; c1. c2, c3 are constants and c3 = c2 + c1. in this paper, for crossovering two parent trees, somewhat the same method as in [12] has been adopted. for more clarity, the crossover and mutation (next section) processes for a hypothetical network has been illustrated in fig. 2. suppose that the network graph is the same as in fig. 2.a in which node 0 (green node) is the source node and the nodes 2 and 4 are the multicast destination nodes (yellow nodes). also, assume that fig. 2.b.1 are the two possible multicast trees. now, for crossovering, first of all, the common edges of the two parent trees (fig. 2.b.1) are extracted and are inserted to the offspring tree as the first set of edges (fig. 2.b.2). the produced offspring is not necessarily a single connected tree and may consist of several disconnected components. in this offspring, it is likely that some of the multicast destination nodes fall into graph components (orphan components) other than the component which the source node is in (main component). therefore, somehow these orphan components must be connected to the main component. to this end, at first, an orphan component is randomly selected. then, it is tried to connect this orphan component to the main component through highly pheromone bridge links (links existent in network graph that can connect two different components to each other). while doing this procedure, these selected bridge links may also connect other orphan components to each other. if there may still be orphan components that are remained disconnected, the same procedure should be performed about them. the produced multicast tree for our example is now the same as fig. 2.b.3. of course, some extra nodes may appear as the leaf nodes of offspring tree. in fact, these nodes are not member of the multicast tree destinations (node 5 in fig. 2.b.3); thus, such nodes and their entering links should be removed after crossover (fig. 2.b.4). adaptive mutation method mutation is a genetic operator that alters one or more gene values in a parent and produces a new offspring. this operator can prevent the population from stagnating at any local optima. in this paper, a simulated annealing technique has been adopted for mutation operator. regarding this, each new offspring produced by the mutation operator is considered as a neighbor of the initial parent. this new offspring replaces its parent according to a probability calculated in formula 9: mutationprobability=  e −( f it− f it′) t f it > f it′ 1 f it ≤ f it′ (9) gasant: an ant-inspired least-cost qos multicast routing approach based on genetic and simulated annealing algorithms 425 figure 2: crossover and mutation operations. (a) the hypothetical network graph. (b.1-4) stages of crossover operator. (c.1-3) stages of mutation operator. here, f it and f it′ are the fitnesses associated with the parent and offspring, respectively, and t is the temperature. as can be inferred from formula 9, if the offspring is better than the parent, it replaces the parent in the next generation, otherwise this replacement is done through an exponential probability. the temperature decreases gradually from a high initial degree. as this decreasing continues, the probability of the replacement of worse offspring decreases accordingly. continuing our example, suppose that we want to apply mutation operator for fig. 2.b.4. mutation for a parent tree (fig. 2.b.4) which leads to finding a neighbor offspring tree is done through the following procedure: a random edge is deleted from the parent tree (edge (0,1) from fig. 2.b.4 is deleted). then, it is tried to reconnect these two subtrees (fig. 2.c.1) with the same procedure explained in section 3.3. this new connected tree (fig. 2.c.2) is considered as the offspring (neighbor) of the parent tree. this offspring replaces its parent according to a probability calculated in formula 9. the same as the crossover, some extra links may appear as the leaf nodes of offspring tree. in fact, these nodes are not member of the multicast tree destinations (node 1 in fig. 2.c.2); thus, as mentioned in crossover, they should not be included in the final multicast tree, and as a result fig. 2.c.3 is the real neighbor of the fig. 2.b.4. 4 analysis of convergence in theorem 2.7 of [26], guoliang et al. has shown that applying ga can finally lead the problem to converge to a global optimized solution. achieving an optimal solution for the aforementioned multicast qos routing can sometimes take up a great deal of time. this is due to the np-completeness property of the problem. nevertheless, most of the times, achieving a solution is possible by well-adjusting various parameters in the proposed algorithm. 5 experiments gasant has been implemented in c++. the aco module of gasant has been implemented by extending and modifying the antnet package provided in [27]. the whole program is simulated in omnet ++ environment on a pentium iv 2.4 ghz cpu, 2 gb ram. to make the results independent of the networks under simulation and also in order to run experiments on a reasonable amount of graphs, we have used random graph generator introduced by waxman [28]. in this method, the network nodes are randomly scattered in a rectangular environment. the probability of existence of an edge between two nodes u, v is calculated via formula (10): p(u, v) =αe −d(u,v) βl (10) 426 m. damanafshan, e. khosrowshahi-asl, m. abbaspour here, d(u, v) is the distance between two nodes u and v; l is the maximum distance between any two nodes in the generated graph. α ∈ (0,1] is responsible for controlling the average degree of the random graph. the greater α results in a denser graph; a graph with more links. in the same way but rather different, β ∈ (0,1] effects the number of longer edges. the larger values of β increase the number of longer edges. also, the randomly generated edges of the graph accepts their cost from [1,100], delay from [0.01,0.1] ms, and bandwidth values from range [10,50] mbps. gasant was run on 20, 40, 60, 80, and 100-node network graphs. also, three different percentages of network nodes were selected to be multicast destinations in the runs: 10%, 20%, and 30%. we call these percentages as multicast percentages, or briefly mp. for the sake of convenience, the delay and bandwidth bounds are supposed to be the same for all multicast destinations. besides, gsa module of gasant iterated for 15 generations, and the population size of each of these generations was 30. all the experiments were run until we reach a confidence interval of less than 5%, using 95% confidence level. before delving into the experiements, we need to define routing request success ratio [29] which is defined as formula 11: s uccessratio = nack/nreq (11) where the nreq is the number of multicast tree requests issued by the application, and nack is the number of these requests that are successfully answered. in the next subsections, we compare gasant and ngsa through a comprehensive set of experiments. 5.1 success ratio analysis in this paper, we have compared gasant to ngsa [20] which is one of the recent works in qos multicast routing literature. table 1 illustrates the success ratio of gasant to ngsa as a function of different parameters. table 1 displays the success ratio of gasant to ngsa as a function of network size and multicast percentage (mp). increasing multicast percentage and network size usually results in higher s uccessratios of gasant to ngsa. this is due to the fact that as network size or multicast percentage increases, applying random paradigm used in trunk creation and limb appending is more likely to adopt improper links toward multicast destination nodes. this figure can be regarded as a scalability performance, too. table 1 and table 1 illustrate the success ratios of gasant to ngsa as functions of minimum possible bandwidth and maximum possible end-to-end delay constrained on the problem, respectively. as it can be inferred from these figures, independent from these constraints, the success ratio of gasant is most often higher than ngsa’s one. table 1: (a) success ratio of gasant to ngsa as a function of network size and multicast percentage. (b) success ratio of gasant to ngsa as a function of minimum possible bandwidth.(c) success ratio of gasant to ngsa as a function of maximum possible end-to-end delay. net. size mp=0.1 mp=0.2 mp=0.3 20 0.83 1 1 40 0.78 1.05 1.2 60 0.83 1.15 1.25 80 0.96 1.05 1.08 100 1 1.25 1.66 min. bw. success r. 10 1.1 15 1.2 20 1.23 25 1.22 30 1.17 35 1.02 40 1.07 max. delay success r. 0.10 1.4 0.20 1.0 0.30 1.5 0.40 1.8 0.50 1.22 0.60 0.81 0.70 1.04 gasant: an ant-inspired least-cost qos multicast routing approach based on genetic and simulated annealing algorithms 427 table 2: (part 1) average time ratio of gasant to ngsa for detecting the first satisfactory multicast tree. (part 2) average cost ratio of gasant to ngsa for the first detected satisfacroty multicast tree. (part 3) average maximum end-to-end delay ratio of gasant to ngsa for the first detected satisfacroty multicast tree. (part 4) average minimum bandwidth ratio of gasant to ngsa for the first detected satisfacroty multicast tree. part 1 2 3 4 mp 0.1 0.2 0.3 0.1 0.2 0.3 0.1 0.2 0.3 0.1 0.2 0.3 network size 20 0.11 0.16 0.49 0.78 0.91 0.89 0.57 0.80 0.85 1.12 0.98 1.04 40 0.14 0.43 0.01 0.80 0.97 0.72 0.67 0.93 0.78 0.99 0.98 1.00 60 0.08 0.01 0.54 0.78 0.80 0.85 0.72 0.81 0.93 1.02 1.07 1.00 80 0.26 0.01 0.13 0.83 0.89 0.82 0.85 0.82 0.97 0.98 1.02 1.01 100 0.74 0.67 0.06 0.87 0.92 0.66 0.96 0.76 0.91 1.08 1.01 0.95 5.2 comparison of execution times, quality of solutions and generations part 1 of the table 2 illustrates the average time ratio of gasant to ngsa for detecting the first satisfactory multicast tree. as can be seen, the average required time for achieving the first solution by gasant is almost less than half of the time required by ngsa. the average cost and average maximum end-to-end delay associated with the first solution discovered by gasant, as shown in parts 2 and 3 of the table 2, are always less than that of ngsa’s. also, part 4 of the table 2 demonstrates that the average minimum existent bandwidth is nearly the same for both multicast trees discovered by gasant and ngsa. part 1 of table table 3 illustrates the average cost ratio of gasant to ngsa for every fifth generation (yellow rows) and the average cost ratio of gasant to ngsa for the least cost multicast tree existent in that generation (white rows) as a function of network size and multicast percentage. as it is obvious, the cost of the trees in each generation associated with gasant is less than that of ngsa’s. also, the least cost trees existent in each generation of gasant has smaller cost in comparison with ngsa’s. as can be inferred from this part of the table, aco module helps gsa start the production of generations with smaller cost trees and this continues along the rest of the generations till end. part 2 of table table 3 illustrates the average maximum end-to-end delay ratio of gasant to ngsa for each generation (yellow rows) and the average maximum end-to-end delay ratio of gasant to ngsa associated with the least cost multicast tree existent in that generation (white rows) as a function of network size and multicast percentage. part 3 of table table 3 illustrates the average minimum bandwidth ratio of gasant to ngsa for each generation (yellow rows) and the average minimum bandwidth ratio of gasant to ngsa associated with the least cost multicast tree existent in that generation (white rows) as a function of network size and multicast percentage. all the parts of the table table 3 illustrate that the qos of multicast trees discovered by gasant is superior to the trees discovered by ngsa by having smaller cost, end-to-end delay and nearly the same bandwidth. it can be inferred from this table that aco module donates a more global view of the network to gasant in comparison with the ngsa which only relies on bare ga and sa. table 3: (part 1)yellow rows: average cost ratio of gasant to ngsa for every fifth generation as a function of network size and multicast percentage. white rows: average cost ratio of the least cost multicast tree for every fifth generation as a function of network size and multicast percentage. (part 2) yellow rows: average maximum end-to-end delay ratio of gasant to ngsa for every fifth generation as a function of network size and multicast percentage. white rows: average maximum end-to-end delay ratio of the least cost multicast tree for every fifth generation as a function of network size and multicast percentage. (part 3) yellow rows: average minimum bandwidth ratio of gasant to ngsa for every fifth generation as a function of network size and multicast percentage. white rows: average minimum bandwidth ratio of the least cost multicast tree for every fifth generation as a function of network size and multicast percentage. 428 m. damanafshan, e. khosrowshahi-asl, m. abbaspour table 3: (part 1) edge hit ratio of gasant to ngsa for detecting the first satisfactory multicast tree. (part 2) average overhead analysis of aco module. part 1 part 2 network size mp=0.1 mp=0.2 mp=0.3 total consumed bandwidth (kbps) 20 0.04 0.12 0.26 0.12 40 0.14 0.28 0.01 0.28 60 0.08 0.16 0.67 0.45 80 0.23 0.24 0.07 0.62 100 0.48 0.33 0.09 0.88 gasant: an ant-inspired least-cost qos multicast routing approach based on genetic and simulated annealing algorithms 429 5.3 analysis of reduction of search process part 1 of table 3 illustrates edge hit frequency ratio of gasant to ngsa during the detection of the first satisfactory multicast tree. edge hit frequency is the number of times that the edge is visited by the multicast tree discovery algorithm during trunk creation or limb appending phases. as can be seen, edge hit ratio of gasant is considerably smaller than ngsa’s. this implies that gasant visits the edges of the network graph sufficiently not excessively and redundantly. this redundancy can be a symptom of high try and error nature of ngsa before achieving a satisfactory multicast tree. whereas, gasant has a global view of the network specific properties. thus, with a smaller effort, it gains a solution even with higher qos. therefore, gasant discovers a satisfactory solution with a smaller search process. gasant owes its success to non-stoping efforts of diligent ants of the proactive aco module. 5.4 overhead analysis of aco module part 2 of table 3 illustates the total bandwidth consumed by control messages of aco module as a function of network size. as can be seen, by increasing network size the amount of overhead increases almost linearly. this implies that the proactive aco module is scalable as the network size grows. 6 conclusion in this paper, we have proposed gasant to solve the np-complete problem of finding a qos constrained least-cost multicast tree for a given communication network. to this end, we have combined aco, ga and sa together to utilize the full advantages of them. here, aco is responsible for both improved initial population which are fed into ga, and also reduced search process. by improved initial population, we mean that links constructing initial population are more likely to appear in optimal multicast tree. by reduced search process, we mean that the ga visits the network edges for a small number of times rather than moving back and forth on the same and previously-visited edges for many times. for fleeing from standing still around a local optimal solution and also extending search space sa has been deployed. experiments show that ants in aco provides a valuable approach of exploring the network and collecting information about states of the links in an efficient and deliberate way. in this way, ga is fed by a better initial population in its first step. also, it considers the links that are highly recommended by ants to be found more likely in the final multicast tree and consequently reaches to a satisfactory solution sooner. the experiments in this paper ensure the aforementioned claims, and prove that gasant outperforms its close counterpart ngsa. bibliography [1] w. zhengying, s. bingxin, z. erdun, bandwidth-delay-constrained least-cost multicast routing based on heuristic genetic algorithm computer communications, volume 24, issues 7-8, april 2001. 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[29] s. kirkpatrick, c. d. gelatt and m. p. vecchi, optimization by simulated annealing, science, new series, vol. 220, no. 4598 (1983) pp. 671-680. ijcccv4n3draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 3, pp. 214-223 p systems with endosomes roberto barbuti, giulio caravagna, andrea maggiolo-schettini, paolo milazzo università di pisa dipartimento di informatica largo pontecorvo 3, 56127 pisa, italy e-mail: {barbuti, caravagn, maggiolo, milazzo}@di.unipi.it received: april 5, 2009 accepted: may 30, 2009 abstract: p systems are computing devices inspired by the structure and the functioning of a living cell. a p system consists of a hierarchy of membranes, each of them containing a multiset of objects, a set of evolution rules, and possibly other membranes. evolution rules are applied to the objects of the same membrane with maximal parallelism. in this paper we present an extension of p systems, called p systems with endosomes (pe systems), in which endosomes can be explicitly modeled. we show that pe systems are universal even if only the simplest form of evolution rules is considered, and we give one application example. keywords: p systems, pe systems, endosomes 1 introduction p systems were introduced by pǎun in [10] as distributed parallel computing devices inspired by the structure and the functioning of a living cell. a p system consists of a hierarchy of membranes, each of them containing a multiset of objects, representing molecules, a set of evolution rules, representing chemical reactions, and possibly other membranes. for each evolution rule there are two multisets of objects, describing the reactants and the products of the chemical reaction. a rule in a membrane can be applied only to objects in the same membrane. some objects produced by the rule remain in the same membrane, where each membrane is identified by its labels, others are sent out of the membrane, others are sent into the inner membranes. evolution rules are applied with maximal parallelism, meaning that it cannot happen that some evolution rule is not applied when the objects needed for its triggering are available. many variants and extensions of p systems exist that include features to increase their expressiveness and that are based on different evolution strategies. among the most common extensions we mention p systems with dissolution rules that allow a membrane to disappear and release in the surrounding membrane all the objects it contains. we mention also p systems with priorities, in which a priority relationship exists among the evolution rules of each membrane and can influence the applicability of such rules, and p systems with promoters and inhibitors, in which the applicability of evolution rules depends on the presence of at least one occurrence and on the absence, respectively, of a specific object. see [11] for the definition of these (and other) variants of p systems and [14] for a complete list of references to the bibliography of p systems. in this paper we present another extension of p systems, called p systems with endosomes (pe systems), with the following features: • objects can be contained inside the regions delimited by the membranes and on the surfaces of the membranes (as in p systems with peripheral proteins [6, 13] and as in membrane systems with surface objects [1, 2]); copyright c© 2006-2009 by ccc publications p systems with endosomes 215 • rules are contained on the surfaces of the membranes (they can rewrite objects outside/on/into the membranes); • endosomes can be explicitly created in order to model a biologically inspired transportation mechanism. the definition of this extension of p systems has a biological inspiration. in fact, the endocytosis of macromolecules is the process by which cells absorb material (molecules such as proteins) from outside the cell by engulfing it with their cell membrane. it is used by all cells because most substances important to them are large polar molecules that cannot pass through the hydrophobic plasma membrane or cell membrane. there exist three kinds of endocytosis: phagocytosis, pinocytosis, and receptor–mediated endocytosis. in particular, phagocytosis (literally, cell–eating) is the process by which cells ingest large objects, such as cells which have undergone apoptosis, bacteria, or viruses. the membrane folds around the object, and the object is sealed off into a large vacuole known as a phagosome. pinocytosis (literally, cell–drinking) is concerned with the uptake of solutes and single molecules such as proteins, and, finally, receptor–mediated endocytosis is a more specific active event where the cytoplasm membrane folds inward to form coated pits. these inward budding vesicles bud to form cytoplasmic vesicles [11]. figure 1 summarizes the kinds of endocytosis. from the point of view of the modeler, these three processes are made possible by vesicles (in fact, this transportation mechanism is known as vesicle–mediated transportation) which, in the most general case, engulf the macromolecules together with molecules from the surface of the membranes (i.e., receptors). this leads to the creation of endosomes containing the engulfed molecules. the endosomes transfer their content inside the cell by possibly interacting with other components. the endosomes could also be degraded by the interaction with the lysosomes. we define an extension of p systems (pe systems) which can explicitly model the creation of endosomes and their interaction inside the cells and, consequently, can easily model these three kinds of endocytosis. this variant of p systems, together with other modeling features such as the modeling of exocytosis (the biologically counterpart of endocytosis), and enriched with channel–mediated communication [3], would provide a powerful and complete modeling language for naturally describing transportation mechanism of molecules inside cells. figure 1: three kind of endocytosis: phagocytosis, pinocytosis and receptor–mediated endocytosis. picture taken from http://cellbiology.med.unsw.edu.au/units/science/lecture0806.htm we show that pe systems are universal even if only the simplest form of evolution rules is considered, namely non–cooperative rules. finally, we give one application example to show that endosomes can ease the description of biological systems when pe systems are used as a modeling formalism. 216 roberto barbuti, giulio caravagna, andrea maggiolo-schettini, paolo milazzo 2 p systems with endosomes in this section we formally define p systems with endosomes (pe systems). we assume the reader to be familiar with the standard definition of p systems [11]. we start by assuming the same membrane structure µ of a p system. as regards objects, similarly to p systems with peripheral proteins [6, 13], we assume that objects can be contained inside a membrane (as in classical p systems) and on the surface of a membrane. in order to qualify in an evolution rule the position of an object with respect to a membrane, we use in to identify the object inside the membrane, out to identify the object outside the membrane and here to identify the object on the surface of the membrane. let t ar be the set of message targets {in, out, here}; given a set of objects v we denote with vtar the corresponding set of messages o × t ar. we can now introduce the evolution rules of pe systems; rules are conceptually divided in evolution rules (in the same sense of p systems) and rules for the creation of endosomes. we recall that, differently from p systems, the rules of pe systems are conceptually associated with the surfaces of the membranes of the system. evolution rules are of the form u → v where u ∈ v +tar, v ∈ v ∗ tar, v + tar = v ∗ tar\{ε} and ε is the empty string. the definition of cooperative and non–cooperative rules are the same as for p systems. notice that this format for evolution rules, which are syntactically different from those of p systems, may seem to be less expressive than the one of p systems, in particular for rule moving objects into specific regions enclosed by membranes (communication rules). in order to show that this is not the case, let us assume an hypothetical membrane structure µ such that (l, l ′) ∈ µ , namely a membrane structure in which l ′ is nested into l. in order to give a rule which moves an object inside membrane l ′ we cannot use the identifier inl ′ in a rule of the surface of the membrane l (as in p systems) because we cannot use the identifier l ′ as subscript to in. however, the same behaviour can be obtained by replacing the rule u → (v, inl ′ ) in the membrane l, as in usual p systems, with the pe system rule (u, out) → (v, in) on the surface of the membrane l ′. the behaviour modeled by this rule, which is in some sense an “attraction” by the nested membrane rather than the “sending” from the top membrane, leads to results analogous to those obtained by p systems, namely to the transportation of the object inside the nested membranes. the rules for creating endosomes are of the form endoe (u, v), where u, v ∈ v ∗ and: • e is a set of evolution rules for the endosome; • u is the multiset of objects that must appear on the surface of the membrane containing the rule; • v is the multiset of objects that must appear outside the membrane containing the rule. note that each endosome has got its own evolution rules in set e. these rules model the behaviour of the endosome. as regards the creation of an endosome, it is necessary that objects in u are present on the surface of the membranes (they can be seen as the receptors) and that objects in v are present outside of the membrane creating the endosome (they can be seen as the molecules to be engulfed). we remark that in our endosome rules, the objects inside and on the surface of the created endosome are explicitely defined. this is different from the approach of [5] in which in the case of pino rules the surface objects are randomly distributed to the two resulting membranes. more formally, the applicability of an endosome rule is possible in the following general case: let ( j, i) ∈ µ and let endoe (u, v) be a rule belonging to the surface of the membrane i, then it can be applied only if u is a submultiset of the objects contained on the surface of the membrane i, and only if v is a submultiset of the objects contained inside the membrane j. the result of the application of such a rule is the creation of an endosome inside membrane i containing u on its surface and containing v inside. the endosome itself behaves like a membrane having on its surface rules e. we can now formally define a pe system as follows. definition 1. a pe system π is a tuple (v, µ, w, . . . , wn, z, . . . , zn, r, . . . , rn) where: • v is an alphabet whose elements are called objects; p systems with endosomes 217 • µ ⊂ n × n is a membrane structure; • wi with  ≤ i ≤ n are strings from v ∗ representing multisets over v associated with the content of membranes , , . . . , n of µ ; • zi with  ≤ i ≤ n are strings from v ∗ representing multisets over v associated with the surfaces of membranes , , . . . , n of µ ; • ri with  ≤ i ≤ n are finite sets of evolution and endosome rules associated with the surfaces of the membranes , , . . . , n of µ . the notions of (successful) computation and of result of computations of pe systems are the same as for standard p systems. 3 universality of pe systems in this section we prove a universality result for pe systems by showing that any matrix grammar with appearance checking can be simulated by a pe system. before giving the result and its proof, we recall from [11] the definition of this variant of matrix grammars and some related notions. 3.1 matrix grammars with appearance checking a (context-free) matrix grammar with appearance checking is a tuple g = (n, t, s, m, f), where n and t are disjoint alphabets of non–terminals and terminals, respectively, s ∈ n is the axiom, m is a finite set of matrices, namely sequences of the form (a → x, . . . , an → xn) of context–free rules over n ∪ t with n ≥ , and f is a set of occurrences of rules in the matrices of m. for a string w, a matrix m : (r, . . . , rn) can be executed by applying its rules to w sequentially in the order in which they appear in m. rules of a matrix occurring in f can be skipped during the execution of the matrix if they cannot be applied, namely if the symbol in their left–hand side is not present in the string. formally, given w, z ∈ (n ∪ t )∗, we write w =⇒ z if there is a matrix (a → x, . . . , an → xn) in m and the strings wi ∈ (n ∪ t ) ∗ with  ≤ i ≤ n +  such that w = w, z = wn+ and, for all  ≤ i ≤ n, either (1) wi = w ′ i aiw ′′ i and wi+ = w ′ i xiw ′′ i , for some w ′ i , w ′′ i ∈ (n ∪ t ) ∗, or (2) wi = wi+, ai does not appear in wi and the rule ai → xi appears in f . we remark that f consists of occurrences of rules in m, that is, if the same rule appears several times in the matrices, it is possible that only some of these occurrences are contained in f . the language generated by a matrix grammar with appearance checking g is defined as l(g) = {w ∈ t ∗ | s =⇒∗ w}, where =⇒∗ w is the reflexive and transitive closure of =⇒. the family of languages of this form is denoted by mat λac, when rules having the empty string λ as right hand side (λ –rules) are allowed, and by matac when such rules are not allowed. moreover, the family of languages generated by matrix grammars without appearance checking (i.e., with f = /0) is denoted by mat λ , when λ –rules are allowed, and by mat , when such rules are not allowed. it is known that (1) mat ⊂ matac ⊂ cs; (2) mat λ ⊂ mat λac = re, where cs and re are the families of languages generated by context–sensitive and arbitrary grammars, respectively. let ac(g) be the cardinality of f in g and let |x| denote the length of the string x. a matrix grammar with appearance checking g = (n, t, s, m, f) is said to be in the strong binary normal form if n = n ∪ n ∪ {s, #}, with these sets mutually disjoint, ac(g) ≤  and the matrices in m are in one of the following forms: 1. (s → x a), with x ∈ n, a ∈ n; 2. (x → y, a → x), with x ,y ∈ n, a ∈ n, x ∈ (n ∪ t )∗, |x| ≤ ; 3. (x → y, a → #), with x ,y ∈ n, a ∈ n; 218 roberto barbuti, giulio caravagna, andrea maggiolo-schettini, paolo milazzo 4. (x → λ , a → x), with x ∈ n, a ∈ n, x ∈ t ∗, |x| ≤ . moreover, there is only one matrix of type 1, and f consists exactly of all rules a → # appearing in matrices of type 3. we remark that # is a trap symbol, namely once introduced it cannot be removed, and a matrix of type 4 is used only once, in the last step of a derivation. for each matrix grammar (with or without appearance checking) there exists an equivalent matrix grammar in the strong binary normal form. consequently, for each language l ∈ re there exists a matrix grammar with appearance checking g satisfying the strong binary normal form and such that l(g) = l. conventions a matrix grammar with appearance checking in (strong) binary normal form is always given as g = (n, t, s, m, f), with n = n ∪ n ∪ {s, #} and with n +  matrices in m, injectively labeled with m, m, . . . , mn. the matrix m : (s → xinit ainit ) is the initial one, with xinit a given symbol from n and ainit a given symbol from n; the next k matrices are without appearance checking rules, mi : (x → α, a → x), with  ≤ i ≤ k, where x ∈ n, α ∈ n ∪ {λ }, a ∈ n, x ∈ (n ∪ t )∗, |x| ≤  (if α = λ , then x ∈ t ∗); the last n − k matrices have rules to be applied in the appearance checking mode, mi : (x → y, a → #), with k +  ≤ i ≤ n, x ,y ∈ n, and a ∈ n. since the grammar is in strong binary normal form, we have (at most) two symbols b() and b() in n such that the rules b ( j) → # appear in matrices mi with k +  ≤ i ≤ n. we remark that in matrix grammars in strong binary normal forms we can assume that all symbols x ∈ n appear as the left-hand side of a rule from a matrix: otherwise, the derivation is blocked after introducing such a symbol, hence we can remove these symbols and the matrices involving them. 3.2 universality we prove that pe systems are universal by showing that the family, denoted pse(ncoo), of sets ps(πe) of results computed by pe systems with at least two membranes and with non–cooperative rules is equivalent to the family, denoted psre, of the images of all the languages in re obtained through the parikh mapping (this is the family of recursively enumerable sets of vectors of natural numbers). as p systems with non-cooperative rules are not universal, our result implies that universality is due to the presence of endosomes. theorem 2. pse(ncoo) = psre. proof. it is enough to show that for a matrix grammar g in strong binary normal form there is a pe system πg such that ps(πg) = ψt (l(g)). we assume that the output of this pe system is given by the objects sent out from the skin membrane. the alphabet of objects v we take into consideration is given by t ∪ n ∪ n ∪ {c} ∪ {ci, di, d ′ i | i = , }. we build πg as a system with a root membrane, labeled , and one child membrane labeled , namely µ = {(, )}. all the objects encoding the grammar will be stored inside membrane  and the matrices will be simulated by membrane . the initial configuration is given by the objects corresponding to xinit and ainit contained in membrane , namely objects of w, and by the token c contained on the surface of membrane , namely z = {c}. differently, w and z are initially empty multisets. this pe system works as follows: it has a cyclic behaviour such that, at the beginning of the cycle, at most one endosome in membrane  can be created and, if possible, all terminal symbols inside membrane  are sent out as output symbols. the created endosome can start a series of steps resulting in the interpretation of the application of a matrix or, differently, it can start a checking phase to model the fact that, if there exist non–terminal symbols which cannot be rewritten by any grammar, then the computation will not halt. in the case in which πg starts the simulation of a matrix of type  or  (a matrix mi with  ≤ i ≤ k), the involved non–terminals are taken by the endosome which contains as rules the ones interpreting the matrix. objects will be sent into membrane  by these rules creating the result of the p systems with endosomes 219 application of the the corresponding matrix to the non–terminals. subsequently, these objects are sent out to membrane  to restart the cyclic behaviour. we recall that during this process no other endosomes can be created, hence no other matrices can be simulated. differently, in the case in which a matrix of type  (a matrix mi with k +  ≤ i ≤ n) is applied, the single non–terminal of n is taken into the endosome. the endosome will work in the same sense of the endosomes interpreting matrices of type  and  even though, at the end of the application of this matrix, instead of restarting with the cyclic behaviour, a checking process is started. this process checks, by creating endosomes, the presence of the proper non–terminal symbol b( j). if this symbol is found, a special endosome is created which will introduce a trap symbol in this pe system so that the computation will not halt. analogously, if the symbol is not found, an endosome will restore the configuration of this pe system so that the cyclic behaviour can start again. we list now the rules of πg. membrane  contains just one single set of rules to create the output of the pe system: 1. {(a, in) → (a, out) | ∀a ∈ t } . all terminal objects in membrane  are sent out as output. the simulation of any matrix is done by the rules of membrane  which are the following: 1. ∀x ∈ n ∪ n. endo(x ,in)→(#,out)(c, x ). if any non–terminal is present in membrane , πg will always be able to create, by using an endosome, a trap symbol inside membrane . this will ensure that, if a derivation of g reaches a deadlock configuration, then πg can always enter an endless configuration. 2. ∀a ∈ n ∪ n ∪ t. (a, in) → (a, out). every terminal and non–terminal present inside membrane  is sent out to membrane . 3. (c, in) → (c, here). object c inside membrane  is restored on the surface of membrane  so that other endosomes can be created. 4. (#, in) → (#, in). the trap symbol lets this computation not to be recognized such. 5. mi : (x → α, a → x),  ≤ i ≤ k. endo(x ,in)→(α,out),(a,in)→(x,out),(c,here)→(c,out)(c, x a). for any rule of type  and , we create an endosome by taking x a from membrane  and c from the surface of membrane  (this locks the creation of other endosomes). the endosome contains rules to rewrite x and a with the result of applying the matrix. object c is not consumed and sent out to membrane  together with α and x. 6. mi : (x → y, a → #), k +  ≤ i ≤ n. endo(x ,in)→(y,out),(c,here)→(ci,out)(c, x ). for any rule with appearance checking, we create an endosome by taking only x from membrane  and c from the surface of membrane  (this locks the creation of other endosomes). the endosome contains rules to rewrite x with y and c with ci. both objects are sent out to membrane . 7. (ci, in) → (ci, here)(di, here). object ci, together with a new object di, is moved on the surface of membrane . 8. endo(b(i),in)→(#,out)(ci, b (i)). this implements the appearance checking feature of grammar g. we create, if possible, an endosome by taking only b(i) from membrane  and ci from the surface of membrane . the endosome creates a trap symbol in membrane ; this will make πg start an endless computation. 9. (di, here) → (d ′i , here). the symbol di is rewritten in the same place as d ′ i . this is done even if also rule 8 can be applied. however, in the case that rule 8 cannot be applied (namely b(i) was not present), this completes the appearance checking operation and lets πg start an operation which will restart its cyclic behaviour. 10. endo(ci,here)→(c,out),(d ′i ,here)→λ (cid ′ i , /0). this endosome lets πg restart its cyclic behaviour. we create an endosome by simply taking both the control symbols only c and d ′ i from the surface of membrane . the endosome destroys d ′i and rewrites ci with c in membrane  (restarting πg will be obtained by applying rule 3). 220 roberto barbuti, giulio caravagna, andrea maggiolo-schettini, paolo milazzo it is clear that these rules, applied in a proper order, provide the correct interpretation of the application of any matrix to the starting symbols of the grammar and, consequently, we get ps(πg) = ψt (l(g)) which concludes the proof. 4 an application: the egf signaling pathway in this section we give an application of pe systems to the description of the initial phases of the egfr signalling cascade. egf egfr eff cell membrane p h o s p h o r y l a t i o n s cbl ub e n d o s o m e lysosome dna rna nucleus figure 2: the egf signaling pathway (picture taken from [3]). in biology, signal transduction refers to any process by which a cell converts one kind of signal or stimulus into another. signals are typically proteins that may be present in the environment of the cell. in order to be able to receive the signal, namely to recognize that the corresponding ligand is available in the environment, a cell exposes some receptors on its external membrane. a receptor is a transmembrane protein that can bind to a signal protein on its extracellular end. when such a binding is established, the intracellular end of the receptor undergoes a conformational change that enables interaction with other proteins inside the cell. this typically causes an ordered sequence of biochemical reactions inside the cell, usually called signalling pathway, that are carried out by enzymes and may produce different effects on the cell behaviour. a complex signal transduction cascade, that modulates cell proliferation, survival, adhesion, migration and differentiation, is based on a family of receptors called epidermal growth factor receptors (egfrs). while egfr signalling is essential for many normal morphogenic processes, the aberrant activity of these receptors has been shown to play a fundamental role in proliferation of tumor cells. epidermal growth factor receptors (egf r) are produced by specific genes in the dna (through the rna) and they are located on the cell surface. receptors are activated by the binding with a specific ligand (epidermal growth factor, egf ) to form a egfr (ligand-receptor) complex (com). upon activation, egfr undergoes a transition from a monomeric form to an active dimeric one (dim). egfr dimerization stimulates its intracellular phosphorylation (dim p) which activates signalling proteins. these activated signalling proteins (effector proteins) initiate several signal transduction cascades, leading to dna synthesis and cell proliferation. after the activation of effector proteins, ligand-receptor dimers are internalized in endosomes. an ubiquitin ligase, known as cbl, binds an ubiquitin protein (u b) to the dimer (ubiquitination). the ubiquitin protein targets the dimers for lysosomal degradation (see figure 2). the pe system modeling the egf is given in figure 3. membrane  models the environment external p systems with endosomes 221 figure 3: a pe systems model of the egf signaling pathway. the rules are represented inside the membranes. to the cell, membrane  represents the cell surface and membrane  is the nucleus. in the external environment egf corresponds to the epidermal growth factor egf which can bind the receptor on the surface of the cell. the receptor is modeled by egf r in membrane 2, which can move on the surface of the membrane. the complex of egf with the receptor is obtained by rewriting egf and egf r with the complex com on the surface of membrane . after the binding of two complexes we can bind them leading to a dimer dim. such a dimer, present on the surface of the membrane, can be phosphorylated by a phosphorus p inside the cell. such phosphorilated dimer dim p could interact with protein shc and start a chain of interactions we do not model here aimed at activating cell proliferation. furthermore, it can be enclosed in an endosome which could either decompose the dim p dimer into its original components (in order to recycle the two egf r proteins) or, if ubiquitine u b is present, degradate the dim p dimer and release the phosporus. the nucleus of the cell (membrane ) is responsible for the production of egf r through the dna and rna (dna and rna). the rna reaches the cell cytoplasm and there it produces egf r which is sent, again, to the cell surface. 5 future work and conclusions in this paper we have presented an extension of p systems, called p systems with endosomes (pe systems), in which endosomes can be explicitly modeled. pe systems uses some ideas taken from other variants of p systems, in particular as regards objects which can be stored on the surface of the membranes we got inspiration by p systems with peripheral proteins [6, 13] and by membrane systems with surface objects [1, 2]. furthermore, as regards other calculi, operations for modeling transportation mechanisms have already been introduced in brane calculi [4] and in p systems with transport and embedded proteins [9]. although similar, pe systems permit to model in a clearer way these mechanisms. an analysis of pe systems and brane calculi [4] (and also some of their variants like projective brane 222 roberto barbuti, giulio caravagna, andrea maggiolo-schettini, paolo milazzo calculi [7]) could be done along the line of the one done in [5, 12] for p systems and brane calculi. as regards expressiveness of this formalism, we have shown that pe systems are universal even if only the simplest form of evolution rules is considered, namely non–cooperative rules. this expressiveness is achieved by the use of endosomes as classical p systems with this kind of rules are shown not to be universal [10]. at the end of the paper we have given an application example describing the modeling of the initial phases of the egfr signalling cascade. bibliography [1] aman, b., ciobanu, g.: membrane systems with surface objects. proceedings of the int. workshop on computing with biomolecules (cbm 2008), vienna, 17–29, 2008. [2] aman, b., ciobanu, g.: mutual mobile membrane systems with objects on surface. proceedings of the seventh brainstorming week on membrane computing (bwmc09), seville, 2009. [3] barbuti, r., maggiolo–schettini, a., milazzo, p. tini, s.: p systems with transport and diffusion membrane channels. int. workshop on concurrency, specification and programming (cs&p’08), gross vaeter, germany, september, 2008. [4] cardelli, l.: brane calculi. interactions of biological membranes. in: danos, v., schachter, v. (eds.), lncs 3082 (2005), pp. 257-280. [5] cardelli, l., pǎun, g.: an universality result for a (mem)brane calculus based on mate/drip operations. internat. j. found. comput. sci. 17(1), pp. 49–68. [6] cavaliere, m., seawards, s.: membrane systems with peripheral proteins: transport and evolution. proc. of the first workshop on membrane computing and biologically inspired process calculi (mecbic 2006), entcs 171 (2007), pp. 37–53. [7] danos, v. pradalier, s.: projective brane calculus. proc. of the fourth conference on computational methods in systems biology (cmsb04), lncs 3082 (2005), pp. 134–148. [8] freund, r., oswald, m.: p systems with activated/prohibited membrane channels. proc. of wmc 2002, lncs 2597 (2003), pp. 261–269. [9] krishna, s.n.: membrane computing with transport and embedded proteins. theoretical computer science 410 (2009), pp. 355–375. [10] pǎun, g.: computing with membranes. journal of computer and system sciences 61 (2000), pp. 108–143 [11] pǎun, g.: membrane computing. an introduction. springer (2002). [12] pǎun, g.: membrane computing and brane calculi. old, new, and future bridges. theoretical computer science 404(1-2), pp. 19–25. [13] pǎun, a., popa, b.: p systems with proteins on membranes. fundamenta informaticae 72(4) (2006), pp. 467 – 483. [14] p systems, web page. http://ppage.psystems.eu/. p systems with endosomes 223 roberto barbuti was born in 1953. he received a laurea degree in computer science in 1977 from the university of pisa. in 1982 he became assistant professor at university of pisa. he took the position of associate professor in 1989, and the position of full professor in 2000. presently, he is working in the research fields of formal methods for systems biology. giulio caravagna was born in 1982. he received a bachelor and a master degree in computer science from the univeristy of pisa in years 2005 and 2007, respectively. at present, he is a phd student in computer science working in the fields of formal methods for systems biology and natural computing. andrea maggiolo schettini was born in 1938. he received a laurea degree in physics from the university of genova and from 1966 to 1968 he was a researcher of the italian national institute for nuclear physics in bologna. after shifting his interest to fundamental studies in computer science, from 1968 to 1981 he was a researcher of the national research council in naples and in pisa. from 1981 to 1983 he has been full professor of computer science at the university of turin and since 1983 he is full professor of computer science at the university of pisa. he has done research in computability theory, semantics of programming languages, specification and verification of concurrent and distributed systems. his present interests include systems biology and natural computing. paolo milazzo was born in 1979. he received a master degree in computer science in 2003 at the university of bologna and a ph.d. in computer science in 2007 at the university of pisa. actually, he is research fellow at the department of computer science of the university of pisa. working on computational systems biology and natural computing with a particular focus on the definition and application of formal methods. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 116-124 e-learning multimedia applications: towards an engineering of content creation maria dolores afonso suarez, cayetano guerra artal, francisco mario tejera hernandez abstract: in the same manner that e-learning applications are becoming increasingly important at the university, there are still some critical questions that should be solved with the objective of making use of the potential offered by current web technologies. the creation of contents that are able of capturing the attention of interest of the students and their disposal in an appropriate way constitute the main purpose of this work. the teaching content engineering expounded shows the different stages that should form part of the process. a development team, composed of different professional profiles, will work together with the lecturers of the subject to which the contents are been created, i.e. multimedia videos and interactive applications. this process should be developed according to a methodology that assure the use of appropriate resources, all that tasks -suitable of beingshould be modularized and factorized. this paper presents the acquired experience in the development and use of multimedia contents for e-learning applications, created for some of the subjects of the degree in computer science engineering. the deliveries of these contents make use of internet and video streaming techniques. the result of the work shows the students satisfaction, including their comments. keywords: teaching content engineering, didactic objectives, active learning, passive learning. 1 introduction during recent years university education has experienced an important progress with respect to the resources used for lecturing. techniques have changed from traditional blackboard methods to the use of projector transparencies (slices) and, later on, the use of video projector and powerpoint to present the content of the lessons. nowadays, the use of these technologies has become more common. as far as content availability for the student is concerned, it has passed from traditional notes taken in classrooms, books and photocopies to digital format of books or notes taken in classrooms, which are available in the subject’s web page. in fact, the universities use to place courses on the web focusing on information delivery rather than learning. the proposal of european higher education area provides the framework to take an important step forward in techniques used in lectures and in the methodology used to create them. for this reason, new methodological approaches are introduced in order to value an active learning [1] as opposed to a traditional passive learning. to value the effort that students should make in order to assimilate knowledge and not only consider the number of hours of attendance to classes. the use of e-learning techniques makes this easier; its wide range of application allows increasing education quality and delivery of information. 2 e-learning in ehea the european higher education area proposes the setting up of a convergence process of educational material in europe. new methodological approaches are introduced in order to value an active learning as opposed to a traditional passive learning. to value the effort that students should make in order to assimilate knowledge and not only consider the number of hours of attendance to classes. all this is made easier by using an e-learning [2] approach, whose extensive work field, in all range of subjects, copyright © 2006-2008 by ccc publications e-learning multimedia applications: towards an engineering of content creation 117 allows the quality of education and its availability to increase [3]. the creation of multimedia contents for e-learning could be developed together with other european universities, and its use could be shared within the same knowledge areas. this allows a new common line of work to be opened. it will be possible to promote european cooperation to guarantee the quality of higher education using comparable methodologies and criteria. 3 teaching content engineering we understand for that concept the whole methodological corpus that allows inserting development elearning technologies in the production of didactic solutions with appropriate invests in production time, resources and person/hour. in this manner, the creation of contents will be developed in a systematic way, planning the process and using adequate techniques. the integration of these techniques and the extent of the teaching material development project require a multidisciplinary team. this multidisciplinary team composed of a group of different professional profiles will carry out different tasks using a wide variety of technologies. these tasks will come together in order to create a production line to maximize results. the improvement of productivity and quality in teaching content creation will constitute the main objective of this engineering [4]. the proposed engineering of teaching content is carried out by means of a methodology, which divides the multimedia production process into phases and assigns tasks to each member of this professional development team. all these different phases include research on suitability of the content for e-learning, a previous analysis to select multimedia techniques to apply, the development of the contents based on the project design and, finally, the maintenance. with respect to the multidisciplinary team, it will be composed of a project manager, programmers, designers, and multimedia experts, as well as the lecturers of each subject. 4 methodology the obtaining of an optimum workflow [5] and the use of resources by the different production tasks is the main purpose of the proposed methodology. for that, we define different production phases. 4.1 suitability of the content for e-learning to create multimedia material for subjects using e-learning techniques means a considerable resource investment. therefore, some factors, which help to make a decision about its profitability, should be taken into consideration. in this first phase a study on the suitability is made in accordance with the factors below: the content validity. the period of time in which teaching contents do not need to be updated. the number of students who attend classroom training. this factor takes into consideration the number of resources used in their learning. the modularity and reusability of independently operable units, which are part of the total structure for creating more contents. 4.2 contents analysis the creation of multimedia contents is carried out through the division of teaching contents in didactic objectives. in this phase the modularization of content takes place. the extraction of the lecturers’ knowledge will be necessary for the selection and proposal of different multimedia didactic elements, 118 maria dolores afonso suarez, cayetano guerra artal, francisco mario tejera hernandez which will constitute part of these multimedia didactic units. it is recommendable to follow a methodology belonging to knowledge engineering, where the knowledge is produced according to the lecturer’s subject specific knowledge, and contains the knowledge and analytical skills of one or more human experts. to achieve this aim, the tasks below are introduced: meetings with the lecturers. in these meetings lecturers expound the teaching objectives of the subject, explain the lesson content and the current way to portray the contents to the students. the selection of lessons for multimedia format. the project manager, together with the lecturers, carries out the selection of lessons for which multimedia teaching content will be created proposal and techniques selection [6]. according to the techniques selected, and the modules repository, the project manager makes a proposal to the lecturers. 4.3 development starting from the analysis of the previous phase, a formal design is carried out, this formal design will identify the activities and work planning that will be done under the supervision of the project manager [7]. therefore, this phase is structured as shown below: formal design includes all the multimedia didactic elements to be used. in accordance with this design, both human and material resources are managed. it is a process of problem solving and planning for reaching the objectives of the project. development of interactive applications will follow a development methodology belonging to software engineering. creation of videos, for which the work will be divided into: script writing, recordings, postproduction and codification [8]. on-line disposition of multimedia contents, becoming, this way, accessible for the students. in the process the creativity of the development team is considered fundamental. 4.4 maintenance once the project is finished, it is difficult to assure that it will work properly unless it is tested. in order to realize a high quality solution, testing throughout each phase of the process is proposed. the project team should be involved in the maintenance phase. they are expected to work on their known issues and prepare for a new release of the created material. in order to detect defects and deficiencies in the multimedia material, some tasks are introduced: interviews with lecturers and students, to know first hand how they feel about this new content to use, in various aspects: accessibility, manage, design. questionnaires, for recover statistics results about all the aspects to be evaluated. comparison of academic results, where an evaluation report will be made, and academic results will be compared with those of other groups of students that have made use of traditional learning methodologies. all these tasks will help to obtain a constructive feedback and to enhance and optimize this multimedia material and its different aspects evaluated. a thorough study of time and staff required for project development will take us to a suitable planning and this to an appropriate economic investment. therefore, cost and resources affect directly in the amortization of the investment made for the creation of contents. e-learning multimedia applications: towards an engineering of content creation 119 5 resources among the means that will be used to carry out this technology we find both human and material resources. the competences of the multidisciplinary team are defined by different profiles: lecturers, whose main tasks include the structure of the subject program, script writing, and the proposal and collective agreement with the project manager on the multimedia didactic units. project manager, who advises lecturers, coordinates efforts of the development team and assigns tasks according to the planning made. designers, which will carry out tasks of graphic design, user interfaces development, and 2d 3d elements creation. analysts/programmers, to analyze and develop software application and programs. multimedia technicians, in charge of recording, editing, and postproduction. regarding to material resources, these are comprised of the multimedia recording studio and the laboratory room for development of software applications and editing of contents. the workstations of the editing room in the laboratory are fully equipped with the necessary computer material for creating and editing audiovisual content. figure 1. figure 1: on the left we can observe the record studio used for the recording of lessons. on the right workstation of the editing room. 6 other methodologies there are different strategies for the creation of teaching content for e-learning. these approaches often use a similar set of stages or phases for content development. among them we find the "waterfall philosophy" characterized by the use of a number of phases strictly ordered in such a way that each phase begins once the previous one is finished; and the "evolutionary philosophy" or "based in prototypes", characterized by considering since the beginning that, although the project start out with a set of requirements, will arise changes in these requirements as the project is developed. however, the methodological proposal made in this work for an e-learning content engineering focuses on lecturing and this means a set of differences, related below: scope. traditional methodologies are directed, generally, towards content development for corporate e-learning. the proposed methodology is focused on content development for lecturing. 120 maria dolores afonso suarez, cayetano guerra artal, francisco mario tejera hernandez contents modularity. in each knowledge area to create independently operable units intended for reuse in the same or a different one is proposed. this means the use of a knowledge area thorough analysis in order to make these operable units relocation possible when creating new teaching programs. extend of involvement. lecturers should be part of the work team that will create multimedia contents for e-learning, should be involved not only in didactic advises tasks, according to the teaching objectives of the subject, but also should take part in the conception of the whole set of tools proposed by the project manager. resources. the wide variety and quantity of multimedia resources used are essential in the proposed methodology for e-learning content creation. multimedia technology and the development of interactive applications provide suitable capabilities to develop quality contents at the university level. 7 lecturing experiences for the evaluation of e-learning techniques in lecture content, some subjects have been selected. specifically, the subjects correspond to bachelor and graduate degrees on "computer science engineering", multimedia and automaton theory and formal languages ii. specially, the subject of multimedia has the particular feature of covering two fundamental aspects in the teaching of computer science knowledge. on the one hand, we find purely theoretical and mathematical content which supports audio compression, image and video technologies. on the other, we find content that makes reference to the use of multimedia tools and web programming languages. in particular, the subject of multimedia has a teaching timetable of 30 theoretical hours (twice a week), using traditional classrooms, and other 30 hours of practical lessons in the laboratory. the prepared material embraces half the timetable in the classroom. the content generated for automaton theory and formal languages ii corresponds to the practical exercises of the subject. as lecturers can now count on this new multimedia teaching material, the methodology, which has been followed till now in multimedia, has changed substantially. firstly, the students still have the same number of learning hours; however, they receive some lessons by means of video in a dedicated server. this means that they receive, weekly, one hour in the classroom and another one through video streaming. this new material allows traditional lessons to be divided into two groups. in this way the lesson in the classroom is repeated and the student attends the most convenient classes. therefore, not only do they not miss the opportunity to ask the lecturer doubts, but also the number of students is reduced, allowing a more personalized treatment. with respect to automaton theory and formal languages ii, the prepared material only provides support to the personal training of the student. the results of this experience have been very positive. students have valuated unanimously this new teaching model as completely recommendable. even, the students have taken part in the improvement of the material contributing with their own ideas, needs, and wishes, like including random questions in order to implement interactivity [9]. from the valuation questionnaire issues below are recovered: multimedia videos are very appreciated. no comments were made about accessibility or reproduction difficulties or even misunderstanding. although students think it is necessary: the inclusion of subject’s content in a pdf format file and an index to facilitate access directly to each unit of the lesson. and just only one student pointed out that the teacher should propose more practical material in the classroom. the valuation questionnaire results are represented in the table 1. as we can observe, percentage for the last one question is quite good. figure 2. with respect to the academic efficiency revealed in the assessments, we should say that the percentage of success in the exams remains constant if we compare with the results of past years where lessons were taught in a traditional way. however, a particular fact, which is known by all lecturers when the exams’ e-learning multimedia applications: towards an engineering of content creation 121 table 1: results from bachelor and graduate degrees on "computer science engineering" figure 2: graphical representation for general assessment. results from bachelor and graduate degrees on "computer science engineering" 122 maria dolores afonso suarez, cayetano guerra artal, francisco mario tejera hernandez dates come close, should be commented. in the previous weeks to exam dates the students come more frequently, and in many cases only on those dates, to the tutorials. it is remarkable that during this teaching experience there have been practically no consultations in tutorials in all the academic year, not even on dates coming up to the exams. 8 future research the acquired experience in the development of multimedia contents for e-learning applications shows that this emergent task should be tackled through the use of a systematic, disciplined, quantifiable approach to the development, manage and maintenance of this teaching content material. in the near future, we will test the process of development of a new project from the university of las palmas de gran canaria. this project involves the creation of multimedia material for subjects of three different areas: the technical area, the health care science area, and the human, society and legal area. the election of the subjects has been made according to the factors expounded in the phase of suitability of the subject. it has been preferential for the technical department to choose subjects of the first courses of various degrees with similar contents, what implies a considerable number of lessons in common. so that, there will be subjects with the 85 or 90 per cent of their content delivered with multimedia material, which is more profitable than having only one or two subjects with the 100 per cent of the content delivered with this new material. the subject is named mathematics in some degrees and calculus in other ones. something similar has happened in the other two knowledge areas: in the health care science area anatomy has been chosen, this is a subject of the first course of "medical degree" with a huge number of students. for the human, society and legal area has been chosen constitutional law, which corresponds to two different degrees: "law" and "economics and business sciences", degrees with a large number of students in the first courses too. in this way, we will delve into each phase of this new engineering focusing on improving the efficiency and cost-effectiveness of the development of multimedia contents for e-learning in these subjects selected. 9 conclusions new information technologies and e-learning will be indispensable tools in lecturing in the near future. the increasing bandwidth available for internet connection and multimedia capacities found in current computers allow them to be used as a completely valid way for teaching. however, the lack of multimedia content production is a very important cause that limits an e-learning widespread. the creation of large-scale teaching content for lecturing needs a production methodology to assure the optimization of resources and, therefore, a reduction in costs. this work intends to contribute with the methodological lines applied in the production of multimedia teaching content for lecturing. new teaching methodologies require new teaching aims; the role of lecturers in the new educational process should be restated in the whole educational process. lecturers should reduce the time dedicated to teaching lessons using traditional blackboard methods. this activity could be mainly, replaced by e-learning techniques. lecturers could dedicate time to activities that offer a better quality in teaching, transmitting motivation about the subject being studied and directing the students in their studies [10]. the future of teaching in the european society should see an improvement in the quality of education, its availability, and a lower cost for students as well as for educative organizations. 10 acknowledgment we wish to thank the programm committee of icvl 2007 that recommended the publication of an extended version of this paper. e-learning multimedia applications: towards an engineering of content creation 123 bibliography [1] a. m. barry, creating and sustaining an effective learning environment!. aishe conference 2006. [2] http://ec.europa.eu/education/programmes/elearning/index_en.html [3] http://ec.europa.eu/education/policies/educ/higher/higher_en.html [4] b. shackelford, project managing e-learning. astd. eeuu, 2002. [5] r. pedrosa and s. petitz, production of interactive contents for training in e-learning environmentsprocedures and methodologies. iadis international conference e-learning 2007. [6] w. horton, k.horton, e-learning tools and technologies, (1st edition), wiley. eeuu, 2003. [7] l. dublin, j. cross, implementing e-learning. astd. eeuu, 2002. [8] j. casteleira and p. leão, production and distribution of contents in digital video of quality for the distance learning. iadis international conference e-learning 2007. [9] r. rheeder, r. diseko and g. lautenbach, the design of interactivity for a web-based learning environment at a higher education institution. iadis international conference e-learning 2007. [10] k. fraser, a blended learning approach to teaching "introduction to multimedia" the e bit!. aishe conference 2006. maria dolores afonso suarez, cayetano guerra artal francisco mario hernandez tejera instituto universitario de sistemas inteligentes y aplicaciones numericas en ingenieria iusiani universidad de las palmas de gran canaria departamento de informatica y sistemas edif. de informatica y matematicas. campus universitario de tafira. 35017. las palmas. spain. e-mail: mafonso@dis.ulpgc.es, cguerra@iusiani.ulpgc.es mhernandez@iusiani.ulpgc.es received: november 30, 2007 124 maria dolores afonso suarez, cayetano guerra artal, francisco mario tejera hernandez maria dolores afonso suarez, graduated at the university of las palmas de gran canaria in computer science. she has worked in private enterprises developing software and as associated professor at the computer science and systems department of the university of las palmas de gran canaria. her main research fields are multimedia and e-learning. cayetano guerra artal, graduated at the university of las palmas de gran canaria in computer science. he has been teaching multimedia for seven years at the computer science and systems department of the university of las palmas de gran canaria. he received his ph.d. in computer vision. his main research fields are computer vision, computer graphics, multimedia and e-learning. francisco mario hernandez tejera graduated in electrical engineering and received his ph.d. in computer science at the university of las palmas of gran canaria. he is assistant professor of computer science and engineering at the computer science and systems department of the university of las palmas de gran canaria. is member of the institute of electrical and electronic engineers ieee , the international association of pattern recognition (iapr), the asociacion española de reconocimiento de formas y analisis de imgenes (aerfai) and the asociacion española para la inteligencia artificial (aepia). his current research interests are in autonomous systems, knowledge-based systems, active vision, visual learning, scene analysis, mobile robotics and interactive robotic systems, that is robots that interact with people. as well as multimedia and e-learning. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 187-195 using fixed priority pre-emptive scheduling in real-time systems d. zmaranda, g. gabor, d.e. popescu, c. vancea, f. vancea doina zmaranda, gianina gabor, daniela elena popescu codruta vancea, florin vancea university of oradea romania, 410087 oradea, 1 universitatii st. e-mail: {zdoina,gianina,depopescu,cvancea,fvancea}@uoradea.ro abstract: for real-time applications, task scheduling is a problem of paramount importance. several scheduling algorithms were proposed in the literature, starting from static scheduling or cyclic executives which provide very deterministic yet inflexible behaviour, to the so called best-effort scheduling, which facilitates maximum run-time flexibility but allows only probabilistic predictions of run-time performance presenting a non-predictable and nondeterministic solution. between these two extremes lies fixed priority scheduling algorithms, such as rate monotonic, that is not so efficient for real-time purposes but exhibits a predictable approach because scheduling is doing offline and guarantees regarding process deadlines could be obtained using appropriate analysis methods. this paper investigates the use of rate monotonic algorithm by making adjustments in order to make it more suitable for real-time applications. the factors that motivate the interest for fixed priority scheduling algorithms such rate monotonic when doing with real-time systems lies in its associated analysis that could be oriented in two directions: schedulability analysis and analysis of process interactions. the analyzing process is carried out using a previously implemented framework that allows modelling, simulation and schedulability analysis for a set of real-time system tasks, and some of the results obtained are presented. keywords: real-time systems, fixed priority preemptive scheduling. 1 introduction real-time systems are often safety critical and require a high quality design in order to obtain and guarantee the requested properties. the design process consists in building models on which the required system properties are assessed; based on this previously developed models an implementation that preserves these properties is further developed. in order to develop a large-scale real-time system we must be able to manage both the logical complexity and timing complexity using a highly disciplined approach [10].the problem of dealing with logical complexity is addressed by the several existing software engineering general methodologies [11] while timing complexity represents an issue that is addressed by specific scheduling algorithms. for real-time systems, two modelling approaches are known in the literature: first of it allows handling of traditional, periodically sampled control systems, and is represented by the so called timed-triggered approach; the second type of model deals with discrete event systems, and it is known as the event-triggered approach. if the main advantage of event-driven approach is flexibility and better resource utilization, the main advantage of time-driven approach is predictability. copyright c⃝ 2006-2011 by ccc publications 188 d. zmaranda, g. gabor, d.e. popescu, c. vancea, f. vancea in the time-triggered approach, all communication and processing activities are initiated at predetermined points in time: there is only one interrupt and that is the periodic clock interrupt, which partitions the continuum time into sequences of equally spaced granules. timed-triggered tasks are characterized by a period and a deadline; also, knowledge about task’s worst case execution time (wcet) is generally assumed. on the other side, event-triggered approach is dictated by the external environment: all communication and processing activities are initiated whenever a significant change of state, i.e., an event other than regular event of a clock tick, is noted. the signalling of significant events is realized by the well-known interrupt mechanism. the event-triggered based systems require a scheduling strategy to achieve the appropriate software task that services the event. in practice, for several real-time applications event-triggered tasks are sporadic, and exhibit a predictable inter-arrival time. thus, this time could be seen as task period, deadline being smaller or equal with this [15]. in practical situations, when dealing with hard real-time issues, mixed systems are often encountered [13]. for these, a common approach is to model the system as a timed-triggered one, and deal with events as periodic tasks with inter-arrival times considered as their period. of course, if the system requires handling of urgent events, that implies a pre-emptive scheduling strategy to be able to meet those deadlines. consequently, when we are modelling such real-time mixed systems, choosing the right scheduling strategy is an important aspect, and several issues has to be considered [17]: if we are considering time-triggered tasks, a static scheduling proves to be efficient both for scheduling and for the communications purposes. it is important into the design phase to correctly divide system functionality into tasks and further, tasks with long periods should be statically divided into subtasks with shorter periods; if sporadic event-triggered tasks occur and they have shorter deadline that execution time of another task, allowing pre-emption is mandatory and therefore leading to a pre-emptive scheduling approach. therefore, using a static and pre-emptive scheduling strategy together with some initial reasonable assumptions when constructing task’s model, could provide a solution for analysing and developing mixed systems [8]. in this case, as the static approach, we consider that fixed priority assignment can be adopted without loosing the benefits of the fully static approach. 2 fixed priority pre-emptive scheduling and real-time systems fixed priority scheduling algorithms exhibit a predictable approach: because scheduling is doing offline, guarantees regarding process deadlines could be obtained using appropriate analysis methods [5]. fixed priority scheduling has been often criticized as being too static by the supporters of best effort scheduling and too dynamic by the supporters of cyclic executives. for building a mixed real-time system from a number of periodic tasks and several sporadic tasks, static priority pre-emptive scheduler implies that at run time the highest priority task is run, this pre-empting other lower priority tasks [6]. from a historical perspective, rate monotonic scheduling algorithm is the most appropriate in this sense, because it is pre-emptive and because it is known to be the optimal in their respective classes [14]. rate monotonic scheduling algorithm is an example of a priority driven algorithm with static priority assignment [2], in the sense that the priorities of all requests are known before their arrival, the priorities for each task being the same and known a-priori (they are determined only by the period of task). therefore, for the time being, rate monotonic is used in most practical applications [3]. the reasons for this choice are the following: easy to implement an to analyze; however, the schedulability assessment given by liu and layland [12] is sufficient (all task sets that pass the using fixed priority pre-emptive scheduling in real-time systems 189 test are guaranteed to be schedulable) but not necessary (a task set that fails to pass the test is not necessarily unschedulable) [12]; more predictable, especially in high overloaded conditions; this prediction is linked to several initial simplifying assumptions and restrictions that rate monotonic has: all tasks are independent and periodic, deadline is equal to their period. the factors that motivated the interest for fixed priority scheduling algorithms such rate monotonic when doing with real-time systems lies in its associated analysis, which could be oriented in two directions [3]: schedulability analysis based on worst case execution times of the processes; one property of the early utilization schedulability analysis is its simplicity both in concept and in computational complexity. this simplicity comes from the initial assumptions that are based on constraints upon characteristics of all processes (all processes are periodic and must have deadline equal to their period) and assumption that process priorities given by their period (according to rate monotonic policy) are correctly assigned, otherwise analysis is not efficient and inclusion of aperiodic processes by making them periodic based on estimated inter-arrival times. rate monotonic priority assignment policy [12] states that process deadlines must be equal with their respective periods di = ti. this assignment could be restrictive, especially for hard real-time sporadic tasks that have deadlines not related to their inter-arrival times, and hence they cannot be modelled as simple periodic tasks with period equal with deadline. for this case the following relation holds: ci ≤ di ≤ ti (1) where: ci represents the computation time for task i di represents task i deadline ti represents task i period a variation of original rate monotonic, called deadline monotonic assign priorities in inverse order to the task deadlines. deadline monotonic algorithm is equivalent with rate monotonic when, for all processes di = ti. deadline monotonic priority assignment is optimal in a similar manner to rate monotonic if there is a feasible priority ordering over a set of processes, a deadline monotonic priority ordering over those processes will be also feasible [4]. both rate monotonic and deadline monotonic approaches assume that all processes have a common release time: if processes are permitted to have arbitrary offsets, then optimality could be affected [8,14]. under these circumstances, neither priority assignment is optimal [9]; but, if controlled offset is allowed this property may be not significant affected. 3 considering the overheads even if in most models overhead induced by scheduling is neglected and considered 0, this is not the case in reality. generally, implementation scheme for fixed priority schedulers implies maintaining at least two queues: a ready queue and a waiting queue. the ready queue contains the tasks that are ready for execution and the waiting queue contains tasks that have already been executed and they are waiting for the next period. the queues are ordered in different ways: the ready queue is ordered based on task priority (the most priority task first for rate monotonic the task with lower period ti is the most priority task) and the waiting queue is ordered based on the starting time of the task ri. if a task from the waiting queue becomes ready for execution, then it is moved to the ready queue according to its priority; if the priority of the first task from the ready queue becomes bigger than the priority of the current task, then a context switch will occur [4]. this leads to two kinds of overheads: context switching overhead being the time needed to pre-empt a task, save its context and load the context of another task and scheduling overhead the time taken to move newly arrived or pre-empted tasks between the two queues. 190 d. zmaranda, g. gabor, d.e. popescu, c. vancea, f. vancea the common ways to consider these times into a system model implies adding the overhead times to the known times according to the following [6]: for context switching times, the simplest way to do it is to increase task computation time ci of all tasks with the double of estimated time needed to do of doing the context switch csw : ci = ci + 2∗csw (2) and for scheduling overhead the same approach could be used, this time being added the scheduling overhead time csch : ci = ci + csch (3) generally, when considering the total of all overheads, an average could be calculated over all tasks, denoted by cov, and could be added to each computation time: ci = ci + cov (4) this approach of measuring the total system overhead, averaging it and including in all computation times is simple to be modelled and used [16]. another way to include overheads into the model could be considered and modelled as additional tasks, but this complicates it too much and sometimes is unnecessarily. when constructing a real-time system model, from the accuracy point of view, it is important to take into consideration these overheads. but, an important aspect is represented also by the possibilities of reducing the overheads impact on the overall system’s performance [20]. because most of these overheads are linked with pre-emption (both context switching and scheduling overheads occur after a task pre-emption), a possibility of doing this could be to reduce the number of pre-emption over system’s task set. generally, one of the weaknesses of rate monotonic algorithm comes from the high level of overhead that results due to high pre-emptions. therefore, reducing unnecessary pre-emptions could have a significant impact on algorithm performance, in the same time by keeping its simplicity [7]. 4 reducing the number of pre-emptions the idea of the proposed method is based on the observations derived from rate monotonic algorithm usage for scheduling real-time tasks (tasks with deadlines), from which a high number of pre-emptions were noticed. as it is well known, every pre-emption induce a run-time overhead and we consider that, by reducing the number of pre-emptions in the resulting scheduling scheme, the overall runtime overhead decreases, and this could result in an increasing efficiency when we speak about real-time applications.in order to avoid pre-emptions, it is important to know when they occur: generally, they take place when a higher priority task is activated during the execution of a lower priority task. lower priority tasks would experience more pre-emption than high priority ones, as they stay longer in the ready queue. to reduce the chance for pre-emption, one possible method implies to reduce the period of time while a task stays into the ready queue. it should be possible to do this, and one method is to delay the activation of the task, by setting ri to a value > 0; of course, this delay should not have implications to the overall schedulability of the task set, and, consequently, must be done under strict control. in order to derive such a method, based on an algorithm that delays start time for a set of tasks, the following task model is considered: every task is denoted by ζi; each task is periodic and the method applies itself only for periodic tasks, the period of task ζi is denoted by ti; ci represents the worst case execution time (wcet) for task ζi; pi represents the priority of task and the priority of each task is fixed; the ratio ci/ti represents the utilization using fixed priority pre-emptive scheduling in real-time systems 191 factor of the task ζi and represents the fraction of processor time that is used by task ζi; the deadline of a task di represents a typical task constraint in real-time systems and represents the time before which the task must complete its execution usually, the deadline of the task is relative, meaning that, from the moment when a task ζi arrives, it should finish within di time units and in particular, when using rate monotonic algorithm, the deadline is considered equal with task period: di = ti; task ready (arrival) time denoted by ri which represents the moment of time when the task ζi is ready for execution. consequently, the algorithm for ready time modification calculates first the maximum delay time for each task, in order not to affect the task deadline. for task ζi that is characterized by a period (and deadline) ti and has a worst case execution time ci, the following relation is used: max_delay(ζi) = ti −ci. (5) let’s denote with s a task set of size n and consider that tasks are ordered by their priority (as they appear in the ready queue): s = {ζ1 ζ2 . . . ζn−1 ζn} (6) where ζ1 has the highest priority and ζn the lower one. the algorithm picks every task from s, in the decreasing order of their priority, and verifies if it is possible to change the ready time ri; in order to reduce the possibility of pre-emption for the higher priority task (ζi), the algorithm starts by delaying it as much as possible [1]. verification is based on the maximum delay that is possible for each task, calculated as presented in (5) and take into consideration the computation time for tasks that were already delayed in order not to compromise the schedulability for the given task. the algorithm tries to delay as much as possible tasks with high priority, given to the tasks with low priority the chance to be less pre-empted. the set of tasks that have been delayed during execution of algorithm are included in delayed (corresponding to the waiting queue) and corresponding delay is calculated (ri). this will be used further into the simulation tool for modifying ready times. consequently, we obtained the following structure of the algorithm: r1 = max_delay(ζ1) = t1 −c1; include (ζ1) in delayed ; for(i = 2; i ≤ n; i ++) { if ((max_delay(ζi)−σj in delayed cj) > ci) { ri = max_delay(ζi)−σj in delayed cj; include(ζj) in delayed; } else { ri = max_delay(ζi); } } 5 case study analysis and results the idea of the above algorithm is illustrated considering the case study shown in figure 1. each task is represented by its computation time c, period t and deadline d, and it is assumed 192 d. zmaranda, g. gabor, d.e. popescu, c. vancea, f. vancea that release time r for all tasks is null. so, we considered a task set consisting of three tasks ζ1, ζ2, and ζ3 with the following characteristics: c1 = 10; t1 = 30; c2 = 30; t2 = 90; c3 = 20; t3 = 120. we simulated the execution of these tasks using the framework developed in [18, 19] and the results obtained are presented in figure 1. the simulation is carried out using a developed framework which allows modelling, simulation and schedulability analysis for a set of real-time systems tasks. the tool has a graphical user interface for introducing tasks parameters, such as: deadline, execution time, priority (as presented in the left panel) permitting that all these parameters to be saved in a (text) file for each given task, in a specific format. also, several scheduling algorithms are implemented into the tool, and the user could choose from a list of implemented algorithms. these algorithms are grouped into two categories: for periodic and non-periodic tasks. for our case study, we consider to work only with rate monotonic. the framework uses a built-in simulator that illustrates a graphical representation of the generated trace of execution for the set of tasks, according to the chosen scheduling algorithm (as presented in the right panel). from these results we noticed that task ζ1 exhibits no pre-emption, ζ2 exhibits maximum one prefigure 1: set of 3 tasks scheduled with rate monotonic without modifying start (ready) times emption and task ζ3 maximum 2 pre-emptions. by applying start times modifications according to the proposed algorithm, the following release times for the considered tasks were calculated: i = 1; r1 = t1 −c1 = 30−10 = 20; s = {ζ1}; i = 2; max_delay(ζ2) = t2 −c2 = 90−30 = 60; 60−20 = 40 ⇒ r2 = 40; s = {ζ1 ζ2}; i = 3; max_delay(ζ3) = t3 −c3 = 120−20 = 100; 100−40 = 60 => r3 = 60; s = {ζ1 ζ2 ζ3}; we modified the release times for our tasks accordingly; consequently, as it is shown into the new simulation presented in figure 2, we observed that the number of pre-emption for task ζ3 is using fixed priority pre-emptive scheduling in real-time systems 193 reduced to maximum one. it is obvious that the number of pre-emptions for task ζ3 is reduced by this modification. figure 2: set of 3 tasks scheduled with rate monotonic with modifying start (ready) times one thing about our resulting model is no concluding is the delay of the least priority task: based on the algorithm idea, it should not be delayed at all, because so it has a chance to run when other tasks have not, due to their delays, and, therefore, the chance for pre-empting it decreases in these conditions. but, in many cases, an overall lower pre-emption rate is achieved. 6 conclusions starting from the premises that rate monotonic algorithm is simpler to implement and exhibits a predictable behaviour resulted from its associated analysis, in this paper, a possible adaptation of rate monotonic algorithm is proposed, in order to overcome some of its disadvantages when using it in real-time applications. one aspect that has impact on the model overall accuracy takes into consideration the overheads implied by the scheduling process by including them into the task’s computation times, and a possible way of considering this is proposed. another aspect, with impact on scheduling overall performance focuses on reducing the number of pre-emptions, accrediting the idea that by doing this, the performance of the algorithm increases. pre-emptions were reduced based on tasks start times modifications, and an algorithm that controls these start time adjustments was proposed. the algorithm was tested using several use cases (one example presented in the paper), pseudo-randomly generated and the same conclusion was reached: by controlling tasks release times according to the proposed algorithm, in most cases the number of pre-emptions decreases. thus, the proposed algorithm together with the implemented tool provides a very powerful analysis framework that can be used in real-time application modelling and further development. bibliography [1] a. aravind and j. chelladurai, activation adjusted scheduling algorithms for real-time systems, advances in systems, computing sciences and software engineering, pp. 425-432, 194 d. zmaranda, g. gabor, d.e. popescu, c. vancea, f. vancea springer 2006. 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[15] i.shin, i. lee, periodic resource model for compositional real-time guarantees , proceedings of 24th ieee real time system symposium, (rtss), pp.2-13, 2003. [16] s. saewong, r. rajkumar, j.p. lohoczky, m.h. klein, analysis of hierarchical fixedpriority scheduling, proceedings of 14th euromicro conference on real time systems, (ecrts), pp. 152-160, 2002. using fixed priority pre-emptive scheduling in real-time systems 195 [17] k. somasundaram; s. radhakrishnan, task resource allocation in grid using swift scheduler, international journal of computer, communication and control, issn 1841-9836, eissn 1841-9844, vol. iv, no.2, pp. 158-166, 2009. [18] d. zmaranda, g. gabor, tool for modeling and simulation of real-time systems behavior, proceedings of the 2nd ieee international workshop on soft computing applications, sofa 2007, gyula, hungary oradea, romania, isbn: 978-1-4244-1608-0, pp. 211-215, 2007. [19] d. zmaranda, c. rusu and m. gligor, a framework for modeling and evaluating timing behaviour for real-time systems, proceedings of the international symposium on systems theory software engineering, sintes vol iii, pp. 514-520, isbn 973-742-148-5, 2005. [20] c. gyorodi, r. gyorodi, m. dersidan, l. bandici, applying a pattern length constraint on the fp-growth algorithm, proceedings of the international workshop on soft computing applications sofa 2009, ieee computational intelligent society, 29 july 1 august 2009, szeged-hungary, arad romania, ieee catalog number cfp0928d-prt, isbn 987-1-42445054-1, pp. 181-185, 2009 international journal of computers, communications & control vol. ii (2007), no. 1, pp. 17-25 the moments in control: a tool for analysis, reduction and design abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou abstract: in this paper we present a new method of model reduction via the moments. the reduction technique is composed of two steps, the first one consists on using the least squares linear optimization algorithm to minimize a cost function representing the norm 2 of the error between different moments of the full order function and the reduced model. this solution represents an initialization of the second step algorithm which is based a non linear programming minimizing a new criterion composed of the cost function of the first step and an equality constraint. keywords: moments, model reduction, optimization. 1 introduction during the last 30 years many design techniques like h∞ [10] was elaborated in order to obtain better performances of controlled plants but once the performances objectives ensured, implementation problems appears because most of industrial applications still use a simple controllers structure like pid (proportional integral derivative) [1][10]; so the aim after the synthesis step is to find a reduced order controller easy to use and to implement, this reduced controller must ensure as possible the same performances of the full order controller[9]. since reduction can not ensure the same performances of the full order controller in all frequencies, it is reasonable to specify a frequency range to proceed to the reduction [3][4]. there is many model reduction techniques like balanced truncation [11] and optimal hankel norm approximation [10]; every method differs from the other one by the importance accorded to d.c gain or to middle and high frequencies. our reduction model method is original, it is based on the notion of moments which is a description of linear time invariant system around a given pulsation [12]. the methodology is composed of two steps, in the first one a cost function representing the norm 2 of the error between different frequency moments of the full order system and the reduced order one is minimized. some parameters are imposed a priori to obtain a linear criterion, so the parameters of the reduced model are computed using least squares algorithm [2][5]. the solution obtained from the step one is used to initialize the non linear programming algorithm of the second step in order to minimize a criterion composed of the cost function of the first step and an equality constraint between the first time moments of the full order system and the reduced model to ensure low frequencies performances [7]. the paper is organized as follows: in section 2, we present definitions and computing methods of time and frequency moments; in section 3, we develop the model reduction technique by presenting the principle, the linear optimization and the non linear optimization and section 4 is devoted to conclusions. notice that the illustrative examples are presented in section 3. 2 the moments let us consider a linear siso system, characterized by its transfer function g (s) analytic in the rhp plan (.i.e re (s) > 0) and let g (t) be its impulse response: g (s) = ∫ ∞ 0 g (t) e−st dt (1) copyright c© 2006-2007 by ccc publications 18 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou the transfer function is given by the following state space (not necessary minimal) realization: g (s) s = [ a b c d ] = c (si −a)−1 b + d (2) where a ∈ rn×n, b ∈ rn×1,c ∈ r1×n and d ∈ r1×1. 2.1 time moments by expanding e−st in taylor series in the vicinity of s = 0, we get: g (s) = ∫ ∞ 0 ∞ ∑ n=0 (−1)n sn t n n! g (t) dt (3) g (s) = ∞ ∑ n=0 (−1)n an (g) sn (4) where: an (g) = ∫ ∞ 0 t n n! g (t) dt (5) an: represents the nth order moment of g (t). remark 1. the time moments an give a description of the system at low frequencies. • a0(g) represents the area or the d.c gain of g(t). • a1(g) defines mean time of g(t). • a2(g) deals with the ’dispersion’ of g(t) around its mean time,...etc [2][5][7] 2.2 frequency moments let consider the variable s = jω . by expanding e−st in taylor series in the vicinity of s0 = jω0, we get: g ( jω) = ∞ ∑ n=0 (−1)n ( jω − jω0)n an,ω0 (g) (6) with: an,ω0 (g) = ∫ ∞ 0 t n n! e−(s−s0)t g (t) dt (7) remark 2. like the time moments, the frequency moments describe the system around ω = ω0: • a0,ω0 represents g ( jω) at ω = ω0. • a0,ω0 − j(ω −ω0)a1,ω0 permits to enlarge the previous approximation around ω = ω0. notice that the moments an,ω0 are complex and if ω0 = 0, we recover the time moments of the system (i.e an,0 = an)[7]. the moments in control: a tool for analysis, reduction and design 19 2.3 computing the moments using state space realization time moments using the following equality: (si −a) ( −a−1 −sa−2 −s2a−3 −···− ) = i ⇒ (si −a)−1 = − ∞ ∑ n=0 ( sna−(n+1) ) (8) an from (2) and (4), we can write: g (s) = −c ( ∞ ∑ n=1 sna−(n+1) ) b + ( −ca−1b + d ) (9) so: a0 (g) = −ca−1b + d and an (g) = (−1)n+1 ca−(n+1)b, (n = 1···∞) (10) frequency moments realizing a variable change µ = jω − jω0, equation (6) becomes: g (µ) = ∞ ∑ n=0 (−1)n (µ)n an,ω0 (g) (11) and (2): g (µ) = c (µ i −(− jω0i + a))−1 b + d (12) so, we get: a0,ω0 (g) = −c (− jω0i + a) −1 b + d (13) an,ω0 (g) = (−1) n+1 c (− jω0i + a)−(n+1) b, (n = 1···∞) (14) 3 model reduction the purpose of model reduction is, starting from a real system, to find a reduced model making it possible as well as possible to approximate it in a given frequency range. 3.1 principle let g (s) be a nominal transfer function of high order: g (s) = b0 + b1s +···+ bmsm a0 + a1s +···+ an−1sn−1 + sn , with m ≤ n (15) and the parameters vectors is: θ t = [a0 a1 ···an−1b0 b1 ···bm] (16) we define a reduced structure: gr (s) = b0r + b1rs +···+ bmrsmr a0r + a1rs +···+ a(n−1)rsnr−1 + snr , with mr ≤ nr (17) 20 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou 3.2 linear optimization let consider the following reduced structure [6]: gr (s) = nr (s) dr (s) (18) in linear optimization, we consider that the reduced denominator dr (s) is fixed a priori and only the numerator parameters have to be optimized. as it is evoked before the reduced model try to ensure: gr (s) = g (s) (19) in a frequency range evidently. equation (19) can be written: b0r + b1rs +···+ bmrsmr dr (s) = g (s) = 1 dr (s) ( mr ∑ k=0 bks k ) (20) using the moments, the previous equation becomes: ( mr ∑ k=0 bks k ) ( ∞ ∑ k=0 (−1)k ak,ω0 sk ) = ( ∞ ∑ k=0 (−1)k a′k,ω0 s k ) (21) where: ak,ω0 represent the n th order frequency moment of 1dr (s) (22) and a ′ k,ω0 represent the n th order frequency moment of g (s) (23) after truncation until nr and by equalizing the terms of the same power in s, the equation (21) can be written as follow:   a0,ω0 0 ··· ··· 0 −a1,ω0 a1,ω0 0 ··· 0 ... ... . . . ··· ... anr,ω0 anr−1,ω0 ··· ··· a0,ω0     b0r b1r ... bmr   =   a ′ 0,ω0 −a′1,ω0 ... a ′ nr,ω0   (24) which can be written as: φnr,mrθ r = γnr (25) let: ε nr = γnr −φnr,mrθ r (26) be the error between the mrth first moments between the real system and the reduced one. notice that the moments can be either temporal or frequency ones it depends on the frequency range chosen. we want to determine θ r which minimize the following quadratic cost: j = ε t ε (27) since the system given by (25) is linear, we can determine θ r using least squares method, so: θ r = [ φtnr,mrφnr,mr ]−1 [ φtnr,mrγnr ] (28) the moments in control: a tool for analysis, reduction and design 21 example 3. let us consider the following transfer function: g (s) = 1.042s7 + 21.77s6 + 206.5s5 + 1049s4 + 2583s3 + 1789s2 + 437.5s + 35 s7 + 22.38s6 + 228.3s5 + 1323s4 + 3832s3 + 6339s2 + 1995s + 157.5 (29) we want to find a two states reduced model which approximates as well as possible g (s) in low, medium and high frequencies. first let choose a reduced denominator, for our case the choice is: dr (s) = (1 + 0.5s) 2 (30) so the number of numerator’s parameters to be computed will not exceed 3 and the number of moments used equals the number of parameters; we choose three pulsations for reduction: ω0 = 0rd/s, ω0 = 0.5rd/s and ω0 = 2rd/s (31) the parameters vector θ tr = [b0r b1r b2r] for the three cases is: θ tr = [0.2222 0.1852 2.9012] , for ω0 = 0rd/s (32) θ tr = [0.1805 0.5030 0.2990] , for ω0 = 0.5rd/s (33) θ tr = [0.2969 0.5255 0.3808] , for ω0 = 2rd/s (34) the frequency response of the real system and reduced model for the three cases is given in (figure 1). −30 −20 −10 0 10 20 30 m a g n itu d e ( d b ) 10 −2 10 −1 10 0 10 1 10 2 −45 0 45 90 135 180 p h a se ( d e g ) bode diagram frequency (rad/sec) −15 −10 −5 0 5 m a g n itu d e ( d b ) 10 −2 10 −1 10 0 10 1 10 2 −30 0 30 60 p h a se ( d e g ) bode diagram frequency (rad/sec) g (s) (−), gr (s) (−.−), ω0 = 0rd/s g (s) (−), gr (s) (−.−), ω0 = 0.5rd/s −15 −10 −5 0 5 m a g n itu d e ( d b ) 10 −2 10 −1 10 0 10 1 10 2 −30 0 30 60 p h a se ( d e g ) bode diagram frequency (rad/sec) g (s) (−), gr (s) (−.−), ω0 = 2rd/s figure 1: frequency response of real system and reduced models 22 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou 3.3 non linear optimization it is clear that the fact of imposing the denominator of the reduced model, limits the optimization’s performances, also if the reduction is needed in high frequencies, it is necessary to ensure at the same time the low frequencies behaviour. in this part, we will optimize both poles and zeros of the reduced model or the parameters of numerator and denominator; a non linear optimization algorithm is used to minimize a quadratic cost between the moments of the real system and the reduced model including an equality constraint between the two first time moments in order to keep the same low frequencies performances. principle let us consider the parameters vector of a reduced model: θ tr = [ a0r a1r ···a(n−1)rb0 b1r ···bmr ] (35) we want to find θ tr which minimizes (27); for that we use marquardt’s algorithm [8] which is a good combination between rapidity and convergence. algorithm principle parameter estimation is performed using an iterative optimization procedure: θ̂ i+1 = θ̂ i −{[j ′′ + λii]−1.j ′}θ̂ =θ̂ i (36) ( ∂ j ∂ θ r ) = j ′ : the gradient vector (37) ( ∂ 2j ∂ θ 2r ) = j ′′ : the hessian matrix (38) λi : coefficient to be adjusted (39) the initialization is given by the vector parameters emerged from the least squares optimization: θ̂0 = θ̂ls (40) computing j ′ and j ′′ the calculation of the gradient and the hessian is crucial for the optimization procedure, we use parametric sensitivity function to calculate them: j ′ ≈ −2 n ∑ n=0 εnθn and j ′′ ≈ 2 n ∑ n=0 θnθtn (41) θ = dan,ω0 (gr) dθ r (42) where: dan,ω0 dθi = (−1)n+1 ( dc dθi a−(n+1)µ b−ca−(n+1)µ da(n+1)µ dθi a−(n+1)µ b + ca −(n+1) µ db dθi ) (43) the moments in control: a tool for analysis, reduction and design 23 where: da(n+1)µ dθi = ( daµ dθi anµ + aµ danµ dθi ) and aµ = (a− jω0i) (44) if we use a control or an observer canonical realization, (43) will be much easier to calculate. for example, for control realization: dan,ω0 dai = (−1)n+1 ( −ca−(n+1)µ da (n+1) µ dai a−(n+1)µ b ) and dan,ω0 dbi = (−1)n+1 ( dc dbi a−(n+1)µ b ) (45) where: ar = [a0r a1r ···anr] and br = [b0r b1r ···bmr] (46) now, we want to find θ r which minimizes (27) and ensures at the same time the following equality: f (θ r) =‖ a0 (g)−a0 (gr) ‖ + ‖ a1 (g)−a1 (gr) ‖= 0 (47) the optimization problem can be reformulated as: min θ r jconst with jconst = j + γ f (θ r) (48) which is equivalent to: γ represents the vector of lagrange multipliers to be estimated. to solve this problem, we can use the algorithm described in (36), by substituting j by jconst , with: j ′ const =   ∂ jconst ∂ θ ∂ jconst ∂ γ   , j ′′ const =   ∂ 2jconst ∂ θ 2 ∂ 2jconst ∂ θ ∂ γ ∂ 2jconst ∂ θ ∂ γ ∂ 2jconst ∂ γ 2   (49) example 4. let us take the same system given in example 2: g (s) = 1.042s7 + 21.77s6 + 206.5s5 + 1049s4 + 2583s3 + 1789s2 + 437.5s + 35 s7 + 22.38s6 + 228.3s5 + 1323s4 + 3832s3 + 6339s2 + 1995s + 157.5 (50) the aim is to find a reduced order model of three states which approximate the real system around ω0 = 11rd/s and have the same low frequencies behaviour; for that we will compare the results obtained from least squares, non linear programming (nlp) and non linear programming with equality constraint. the reduced model have the following structure: gr (s) = b0r + b1rs + b2rs2 a0r + a1rs + s2 (51) of course for least squares optimization, the denominator is fixed a priori: dr (s) = (1 + 0.5s) 2 (52) the three reduced model are: gr (s) = 1.019−1.698s+0.6728s 2 (1+0.5s)2 least squares gr (s) = 14.63+14.19s+1.032s 2 36.96+14.84+s2 non linear programming gr (s) = 0.1351+0.4075s+1.052s 2 0.6078+1.935s+s2 nlp with equality constraint (53) 24 abdelmadjid bentayeb, nezha maamri, jean-claude trigeassou the frequency response of the real system and the reduced models is illustrated by the (figure 2) −15 −10 −5 0 5 m a g n itu d e ( d b ) 10 −2 10 −1 10 0 10 1 10 2 −90 0 90 180 270 360 p h a se ( d e g ) bode diagram frequency (rad/sec) least squares g (s) (−) gr (s) (−.) −15 −10 −5 0 5 m a g n itu d e ( d b ) 10 −2 10 −1 10 0 10 1 10 2 −30 0 30 60 p h a se ( d e g ) bode diagram frequency (rad/sec) nlp g (s) (−) gr (s) (−.) −20 −15 −10 −5 0 5 m a g n itu d e ( d b ) 10 −2 10 −1 10 0 10 1 10 2 −30 0 30 60 p h a se ( d e g ) bode diagram frequency (rad/sec) nlp with constraint g (s) (−) gr (s) (−.) figure 2: frequency response of real system and reduced models remark 5. we saw that the moments may be used for model reduction; we presented three optimization methods: least squares, non linear programming and non linear programming with equality constraint. if the optimization is done in low frequencies,the non linear programming can be used without equality constraint take into account the radius of convergence which allow to ensure the low frequencies behaviour. 4 summary and conclusions in this paper, we presented a new method for model reduction and controller design. the technic is based on the moments tool which is able to give a description of any linear system or linear system with time delay in low frequencies using time moments or around a given frequency using the frequency moments. the optimization procedure is composed of two steps, in the first one, we use least squares algorithm to have reduced model to initialize the non linear programming with equality constraint between the two first time moments between the reduced model and the full order system. for controller design, the aim is to ensure the closed loop performances using a reference model which regroups dominant poles, auxiliary poles and system’s singularities. using youla parametrization, we obtain an ideal controller which will be reduced for implementation using the moments. the moments in control: a tool for analysis, reduction and design 25 references [1] k.astöm and b.wittenmark "computer controlled systems, theory and design", prenctice hall (1984) [2] a.bentayeb, n.maamri and d.mehdi "moments based synthesis approach comparison with h∞ design",ifac decom-tt "istanbul, turkey" (2003). [3] s.boyd and c.barratt "linear controller design-limits of performance". prentice hall (1991). [4] k.v.fernando and h.nicholson "singular perturbational model reduction of balanced systems", ieee transactions on automatic control, ac-27:2:466-468 (1982). [5] n.maamri and j.c.trigeassou "pid design for time delayed systems by the method of moments", european control conference, groningen holland (1993). [6] f.monroux. "méthodologie générale de synthèse de correcteurs par la méthode des moments, approche mixte: fréquentielle et temporelle", thèse de doctorat, université de poitiers, 1999. [7] n.maamri, a.bentayeb and j-c.trigeassou "design and iterative optimization of reduced robust controllers with equality-constraints" rocond-milan (2003). [8] d.w.marquardt "an algorithm for least-squares estimation of non linear parameters" journal of soc. indust. appl. math v(11-2) (1963). [9] m.g.safonov and r.y.chiang "a schur method for balanced truncation model reduction". ieee transactions on automatic control ac-34:729-733, (1989). [10] s.skogested and i.postlethwaite "multivariable feedback control", jw (1996). [11] m.s.tombs and i.postlethwaite "truncated balanced realization of a stable non-minimal statespace system",international journal of control:46:1319-1330. [12] j.c.trigeassou "la méthode des moments en automatique". cifa, lille (2000). a.bentayeb, n.maamri and j-c.trigeassou university of poitiers laboratoire d’automatique et d’informatique industrielle 40 avenue du recteur pineau 86022 poitiers e-mail: abdelmadjid.bentayeb@gmail.com received: november 24, 2006 editor’s note about the author: abdelmadjid bentayeb was born on august 9, 1977 in annaba (algeria). he received the engineer diploma of automatic control in 2000 and the applied studies diploma of advanced control in 2001 at the university of annaba. he received the master degree of automatic and industrial data processing in 2002 and the phd degree of automatic in 2006 with a very honourable distinction at the university of poitiers. currently, he prepares an industrial orientation of his work carried out at university of poitiers. his current research interests include the multivariable robust control systems and model reduction using the method of the moments. international journal of computers, communications & control vol. iii (2008), no. 1, pp. 41-50 composite predictive functional control strategies, application to positioning axes jean-yves dieulot, tarik benhammi, frédéric colas, pierre-jean barre abstract: different predictive control strategies have been validated on a dc motor. icascaded predictive control, which consists of a cascaded loop where the traditional servo algorithms are replaced by pfcs, could enhance the cycle time. predictive functional control alone is simpler to tune and can exhibit comparable performances, except that the controller is more sensitive to nonlinear phenomena such as dry friction, which were not taken into account into the model and generate a static error. keywords: model-based control, dc motor, predictive control. 1 introduction the traditional cascaded-loop servo algorithm of a positioning device consists of a current, a speed and a position loop, for which the individual controllers within each loop use proportional or pi algorithms [2, 4]. this control structure is robust with respect to disturbances and easy to tune, loop after loop, when the inner loops dynamics are much faster than that of the outer loops. this is not always the case and the tracking lag may cumulates through the loops thus achieving poor performances. among various alternative control algorithms, model-based predictive control which embeds both feedforward and a servo algorithms, is known for its tracking abilities due to multiple-step-ahead prediction and robustness with respect to model uncertainties [1]. in this paper, the focus will be on the implementation and fast tuning of the predictive functional control algorithm introduced by richalet (e.g. [7, 8, 9, 10, 12]) which has been shown to be able to control a variety of complicated robot structures (see e.g. [11]). however, the main drawbacks of those predictive control methods are the need for a preliminary black-box model identification which hampers the implementation in real-time industrial world where tuning procedures should be simple and as short as possible. since a global controller may lose some of the advantages of cascaded loops, i.e. rejection of disturbances for each loop, dry friction compensation... . cascaded control structures have been introduced, where both position and speed loop can be replaced by a predictive controller. since the pfc tracking performance is better, the lag in the inner loops should be reduced and better overall performances is assumed [5, 6]. in this paper, a comparison is drawn for different cascaded predictive functional control strategies, which combines the pfc controller with a grey box model which parameters are related to physical characteristics. 2 predictive functional control 2.1 pfc philosophy in this section, the outline of model pfc algorithm, which is fully detailed in [7, 9] is recalled. pfc is based on a discrete linear model, { xm(n + 1) = fm xm(n) + gm u(n) ym(n) = ctm xm(n) (1) xm is the state variable, un is the input, ym the system output, fm ,gm ,cm are constant matrices. the predictive control strategy is summed up in fig. 1. a control sequence is designed to enforce the model copyright © 2006-2008 by ccc publications 42 jean-yves dieulot, tarik benhammi, frédéric colas, pierre-jean barre output to stick to a reference trajectory yr at given coincidence points. over this receding horizon, the reference trajectory which is the path to the future set point, is resetted at every instant and is given by: c(n + i)−yr(n + i) = α i.(c(n)−yp(n)), 0 6 i 6 h (2) where c(n) is the set-point, yp(n) the real process output, α is a parameter α : 0 6 α 6 1, which represents the exponential convergence of the algorithm, and thus fixes the closed-loop behavior. figure 1: principle of pfc control 2.2 computation of control the future controls sequence should optimize the performance index ((4)) which turns to find the minimum in the least-square sense of the tracking error at fixed set-points with respect to the future control sequence: d(n) = nh ∑ j=1 {ŷp(n + h j)−yr(n + h j)}2 (3) where h j, 1 ≤ i ≤ h are the coincidence points. in order to compute easily the control, it will be assumed that the latter is a composition of a priori orthonormal base functions (usually time-dependent polynomials ubk (i) = i k−1 ), u(n + i) = nb ∑ k=1 (µk(n)ubk (i)), i ≥ 0, (4) where ubk (i), 1 ≤ k ≤ nb are the base functions, the optimization problem reducing to find the corresponding weights µ(k). of course, one could apply the whole control sequence, but, in fact, only the current control (5) is used, and the whole algorithm is computed again next step. u(n) = nb ∑ k=1 (µk(n)ubk (0)). (5) for computational purposes, the model output is divided into a free (unforced) response, where the control is set to zero, and a forced response to the control (4). ym(n + i) = yf (n + i) + yu f (n + i), 1 6 i 6 h (6) composite predictive functional control strategies, application to positioning axes 43 where yf (n + i) = nb ∑ k=1 µk(n).ybk (i), 1 6 i 6 h is the forced output, and ybk is the response to the base function ubk , yu f (n + i) = c t m f i m xm(n) is the unforced output. let the predicted future error be approximated by a polynomial function: ê(n + i) = e(n) + de ∑ i=1 em (n) i m (7) where h j, 1 6 j 6 nh is a fixed approximation degree, finally, the controller is obtained by setting ∂ d(n)∂ µ = 0 (see [9], for details). the parameters only depend on the base functions, the convergence exponent and the coincidence points and can be computed off-line: u(n) = k0{c(n)−yp(n)}+ max(dc,de) ∑ m=1 km{cm(n)−em(n)}+ vtx xm(n) (8) where dc is the degree of the polynomial approximation of the set-point and k0 = vt   1−α h1 1−α h2 ... 1−α hnh   , km = v t   hm1 hm2 ... hmnh   , vx =   ctm(f h1 m −i) ctm(f h2 m −i) ctm(f hnh m −i)   t v v = ( nh ∑ j=1 yb(h j)yb(h j)t )−1 [ yb(h1) ... yb(hnh ) ] ub (0) the algorithm consists thus of a feedforward term devoted for tracking error, a disturbance rejection term and of a state-space controller. in the application considered, control tuning will involve a "flat " prediction (ê(n + i) = e(n)) and a parameter α , the parameters will be chosen with a rule of thumb [9] as α = e −3te clrt (9) along with three coincidence points h = ( [ clrt /3 clrt /2 clrt ] /te) (10) where clrt is the closed-loop rise time and te is the sampling period. 3 industrial control and pfc strategies figure 2: full structure of the industrial cascaded-loop control the industrial control consists of nested loops and assumes that the dynamics of the inner loops are the fastest ; the current loop has generally a high dynamics and is thus neglected, the speed loop involves 44 jean-yves dieulot, tarik benhammi, frédéric colas, pierre-jean barre a pi structure and the position loop only a proportional controller, and sometimes a low-pass filter which has been shown to improve the dynamics of axes, particularly in presence of flexibilities. traditionally, the set-point is a bang-bang in acceleration which takes in account limitations in speed and acceleration and is a time-optimal trajectory. as was explained in introduction, this controller has an easy step-bystep tuning procedure, from the fastest to the slowest loop, and saturations, disturbances can be easily handled at every loop level. however, it is known that performances are not as good as those of a global controller, because the tracking lag is cumulating over the successive loops. the speed controller is simply c (s) = kv ( 1 + 1τvs ) where kv, τv are the speed loop proportional and integral gains. as an alternative to this cascaded controller or to a global controller (such as a pfc), a composite cascaded structure where the speed loop pi controller is replaced by a speed loop pfc controller, hence realizing the decoupling of the different dynamics (fig. 3). figure 3: composite cascaded controller the last scheme consists of the introduction of a cascaded pfc control which is the same as the classical industrial controller except that pfc servo algorithms are embedded into both loops (fig. 4). figure 4: cascaded pfc controller 4 modelling and experimentation the experimental set-up is a brushless dc servo-motor (parvex f9m-4-57), equipped with a tacho-generator (f9ts-7-270) and a position coder (resolver), which drives a rotating shaft mounted on bearings, optionnally coupled to an additional flywheel. when neglecting the electrical resistances, the global model of the brushless motor is: jeq dω dt = cm − fvω,cm = kt i (11) where ω is the rotating speed, cm and i are the control torque and current, kt is the torque constant, jeq is the equivalent motor inertia, fv is the viscous damping coefficient. most pfc algorithms require a proper identification and modeling stage; the motor inertia was estimated from the known motor and load moment of inertia which were added to the inertia resulting from the transmission coupling. only the viscous and dry friction coefficients were estimated experimentally from a plot of the motor current versus the shaft rotation speed. characteristics are summed up in table 1. composite predictive functional control strategies, application to positioning axes 45 figure 5: experimental dc motor controller kp (rad.a−1) clrt (s) pfc n/a 0.02 cascaded pfc n/a speed loop 0.01 position loop 0.06 composite pfc 10 0.05 industrial control 10 n/a table 1: motor characteristics 46 jean-yves dieulot, tarik benhammi, frédéric colas, pierre-jean barre nominal torque n.m nominal speed tr.min−1 electric power w equivalent inertia kg.m2 flywheel inertia kg.m2 viscous damping cf. nmsrad−1 0.346 3000 108 1.5.10-4 8.5.10-4 7.8 table 2: control parameters the measure of speed was low-pass filtered with a filter cut-off frequency of 2.10−3 s. measurements recording and real-time control were achieved using a dspace 1103 board with a sampling period of te = 5.10−4 s. the experimental reference position was set to 12 rad, the maximum speed vmax to 150 rads−1, the maximum acceleration to amax = 346 rads−2. the traditional trajectory for positioning axes consists of a time-optimal controller [3] with constrained speed and acceleration. the control gains for each experiment are given in table 2. the integral gain of the pi speed is calculated to compensate the first-order pole, with τv = fv jeq , the speed gain kv is tuned so that the control torque does not exceed the peak torque, which gives kv = 1 rad.s.a 1. pfc algorithms were tuned after performing simulations in a way that the maximum current does not overcome that of the industrial loop. 5 experimental results and discussion figure 6: comparison of control performances fig. 6 shows the basic results that will serve as a comparison of the different control structures: one can see that a global controller such as a pfc or a controller with a fast closed-loop position controller exhibits a small rise time due mainly to the cancellation of the phase lag in the speed loop and in the position loop. the performances of pfc controllers outperform that of the traditional industrial control, for a comparable current maximum amplitude. however, these controllers have two main drawbacks: the pfc has an important static error because the dry friction is not taken into account into the model. this phenomenon does not occur with the cascaded loop because these errors appear firstly in the speed loop, and can then be compensated with the position loop as in traditional control. cascaded pfc seems composite predictive functional control strategies, application to positioning axes 47 to be the most appealing, but one has to remind that the tuning is far more delicate because it involves two pfc loops. one should take an appropriate clrt ratio between the loops (at least 5) in a way that the position loop be truly the fastest. figure 7: drive current control fig. 7 shows that, for cascaded pfc -as for other pfc algorithms , less energy may be needed, but the current signal is less smooth than with an industrial control scheme; clearly, this could excite vibration modes when a very flexible load is carried. most controls are robust when the additional inertia has been removed, only the rise-time is a little higher. the composite controller does not improve the industrial loop by much, except when an external disturbance is applied (e.g. braking with the hand) results are not shown here -, with a better rejection. in this case, classical pfc is the less robust with respect to other controls. figure 8: control performances with no load 48 jean-yves dieulot, tarik benhammi, frédéric colas, pierre-jean barre 6 conclusion composite predictive functional control algorithms a cascaded structure where only the speed loop servo algorithms or both speed and position controllers were replaced by pfcs were validated on a dc servo-motor. the tuning procedure was quite simple and involved the general rigid model which can be found from the physical characteristics of the motor and a single experiment (for the determination of the viscous friction parameter). cascaded predictive control could improve the tracking and was shown to be more robust to load mass variations. classical pfc is easier to tune (only one pfc loop) , the closed-loop performances being nearly the same; however, the controller is more sensitive to unmodelled nonlinear phenomena such as dry friction, which were not taken into account into the model. the resulting static error can be unacceptable and alternative solutions have, in this case, to be proposed. future work will focus on application to flexible manipulators. bibliography [1] clarke, d. w., mohtadi, c.,tuffs, p. s. generalized predictive control. part i: the basic algorithm. part ii: extensions and interpretations, automatica, vol. 23, 1987, pp 137 160. [2] ellis, g., control system design guide (2nd edition), academic press, boston, 2000. [3] erkorkmaz, k., altintas, y., high-speed cnc system design. part iii: high speed tracking and contouring control of feed drives, int. j. machine tools & manuf., vol. 41, 2001, pp. 1637 1658. [4] gross, h., harmann, j., wiegârtner, g., electrical feed drives in automation, mcd corporate publishing, siemens, isbn 3-89578-148-7, 2001. [5] guemghar, k., srinivasan, b., mullhaupt, ph., bonvin, d., analysis of cascade structure with predictive control and feedback linearisation, iee proceedings-control theory and applications, vol. 152, 2005, pp 317 324. [6] hedjar, r., toumi, r., boucher, p., dumur, d., two cascaded nonlinear predictive controls of induction motor, proceedings of 2003 ieee conference on control applications., 2003 [7] richalet, j., industrial applications of model-based predictive control, automatica, vol. 29, 1993, pp 1251 1274. [8] richalet, j., estival, j.l., fiani, p., industrial applications of predictive functional control to metallurgical industries, proc. 4th ieee conf. control applications, 1995. [9] richalet, j., abu, e., arber c., kuntze, h.b., jacubasch, a., schill, w., predictive functional control. application to fast and accurate robot, 10th ifac world congress, munich, germany, 1997 [10] rossiter, j. a., predictive functional control: more than one way to prestabilise, 15th ifac world congress. barcelona, spain, 2002. [11] vivas, a., poignet, p., predictive functional control of a parallel robot, control engineering practice, vol. 13, 2005, 863 874. [12] yim w., singh s. n., nonlinear inverse and predictive end point trajectory control of flexible macromicro manipulators, j. dyn. dyst. meas. control, vol. 119, 1997, pp 412 420. composite predictive functional control strategies, application to positioning axes 49 jean-yves dieulot ensam de lille research technological team cemodyne 8 bd louis xiv, 59000 lille cedex e-mail: jean-yves.dieulot@polytech-lille.fr tarik benhammi ensam de lille research technological team cemodyne 8 bd louis xiv, 59000 lille cedex e-mail: jean-yves.dieulot@polytech-lille.fr frédéric colas ensam de lille research technological team cemodyne 8 bd louis xiv, 59000 lille cedex e-mail: frederic.colas@centraliens-lille.org pierre-jean barre ensam de lille research technological team cemodyne 8 bd louis xiv, 59000 lille cedex e-mail: barre@lille.ensam.fr received: november 6, 2007 50 jean-yves dieulot, tarik benhammi, frédéric colas, pierre-jean barre jean-yves dieulot graduated from french ecole d’ingénieurs institut industriel du nord in 1990, and obtained a phd from the university of lille in 1993. he is currently associate professor at ecole polytechnique universitaire de lille, france, and with lagis (laboratory of automatic control) his main interests are in fuzzy, nonlinear and robot control. tarik benhammi graduated from ecole nationale supérieure des arts et métiers, in meknès, morocco, in 2007. frédéric colas graduated from ecole centrale de lille, france, in 2003. he has obtained a phd from ecole centrale de lille, france, in robotics and automatic control, which was funded by cartesian robot manufacturers. pierre-jean barre is a full professor and head of ecole nationale supérieure des arts et métiers, in lille, since 2004. he has also developed a technological research platform devoted to highspeed flexible robot control and planning, funded by many french and european manufacturers. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 292-300 a novel model for adaptive control systems a state machine approach f. valles-barajas fernando valles-barajas universidad regiomontana information technology department 15 de mayo 567 pte., c.p. 64000 colonia centro, monterrey, nuevo león, méxico e-mail: fernando.valles@acm.org, fernando.valles@ieee.org abstract: in this paper a new model, based on state machines, of adaptive control systems is presented. due to its high level of expressiveness, uml was chosen as the modeling language. in particular the paper presents a model of an indirect adaptive control system. this model can be used to document and to have a better understanding of adaptive control systems. keywords: adaptive control systems, state machines, personal software process (psp), software design 1 introduction adaptive control is one of the research areas of control engineering that deals with time-varying systems [9]. to control a process using this technique, the first step is to obtain a model of the process based on measurements of the input and output of the process (these measurements are stored in a vector called measurement or observation vector φ (k)); this step is called identification and is done by using a parameter adaptation algorithm (paa) like the recursive least squares algorithm. once the parameters of the process are obtained, the controller is designed using these parameters. the personal software process1 is a modern methodology that helps the engineer to ensure quality software products [7]. the design phase of this methodology requires that the software engineer builds four models. every of these models represents a different view of a system; for example there model that represents the states and the transitions between these states of a particular entity using state machines, which represent the internal dynamic view of a system. state machines are used in software engineering to analyze the behavior of complex systems, for example the rational unified process (rup)2 uses state machines to model a use case, an operation or an object [2]. in this paper the concepts of state machines will be applied to model an adaptive control system (acs). there are several notations to represent state machines (see for example [5], [6], [7]), in this paper the notation of the uml will be used. the reason for this, is the maturity of this modeling language and the success shown by uml to model complex systems. motivation and contribution of the paper: 1. the graphical model obtained with the state machine will help the control engineer to have a better understanding of the acs control law. 2. this model could be used by a software engineer as a base to implement the software of the acs. 1 c© software engineering institute 2 c© ibm copyright c© 2006-2010 by ccc publications a novel model for adaptive control systems a state machine approach 293 3. by using this new model, a better understanding by the software engineer of an adaptive control system will be obtained. 4. this model can be used as documentation of the control system. 5. a better communication between the software engineer and the control engineer will be obtained by using the proposed model. conventions used in the paper: in all the paper, for the purpose of clarity, all the parts of the state machines (states, transitions, events, guard conditions and actions) are printed in italic font. the key concepts of the state machines will be written in bold font. related works: in [4] an adaptive strategy based on state machines is presented. the events that are considerd in the strategy are: threshold crossings, commands from the operator of the system and the occurrence of several patterns in the process signals (control error e(k), process input u(k) and process output y(k)) among others. the occurrence of one of these events may provoke that the adaptive strategy changes from one state to another. the states modeled in this strategy are: initial state, open loop state, closed loop state and final state. the open and closed loop states are composite states, which are states composed of other states [12]. in that paper the author shows a successful example of his proposal. the paper of [13] shows an application of state machines in fault-adaptive control systems. as stated in that paper this kind of system can deal with time-varying systems and also with systems that present faults; the performance of the control system must remain unaffected in spite of these two problems. to deal with these problems that paper proposes to add to the control system a supervisory controller module and a reconfiguration management module. the relevant point in the paper is that the supervisor controller module is represented as a finite state machine (fsm). that paper contains an example of the application of the roll control of a simulated airplane. organization of the paper: section 2 presents the necessary concepts of the adaptive control theory to understand the proposed model. in section 3 an explanation of the building blocks for state machines is given. section 4 contains the proposed model for an adaptive control system using state machines. the last section gives some concluding remarks. 2 adaptive control systems fig. 1 shows the configuration of an indirect adaptive control system. in this kind of adaptive control, the model of the process gp(z−) is obtained based on a set of input-output measurements (u(k), y(k)) and then the controller is designed with this model. the parameter adaptation algorithm (paa) block is responsible for obtaining this model. the controller design block designs the controller gc(z−) based on the model obtained by the paa and on the desired performance specified by the operator of the system. the adaptive control system must control the process in spite of the disturbances d(k), d(k), the noise n(k) and the parametric variations of the process. 3 state machines state machines are graphical models that allow the specification of the states of one entity, the legal transitions between these states, the events that can occur in the life of the entity and how the entity manages these events [10]. the transitions from one state to another can be fired by an event. some 294 f. valles-barajas paa gp(z−)gc(z−)σ y(k)u(k)e(k)yre f (k) θ̂paa(k) desired performance controller σ ? d(k) σ ? d(k) supervisor block σ ¾ n(k) --? 6 ¾ ¾ 6 ? u ? θ̂cd(k) design figure 1: indirect adaptive control system equipped with a supervisor block condition (called guard condition) can be specified as a requirement to the occurrence of the transition. to model an entity using a state machine the entity is isolated from the world and the communication with the rest of the world is specified by detecting events and responding to them. in uml state machines are represented using state diagrams [12]. a state machine can also be used to model use cases, which are useful to specify the functional requirements of a system. a state machine that describes the external events and its sequence in a use case is a kind of state diagram of use cases [10]. the external events happen between the actors, which are any external entity that interacts with the system, and the system [8]. events that pass between objects residing in the system are referred to as internal events. some authors like [10] recommend to use state machines to model the external and time events and the responses to them instead of using state machines to model the internal events of objects (instances of a class). a state machine is drawn inside a rectangle with the name label in the top left-hand side (see fig. 2), though the frame may be omitted if the context is clear [1]. if the context is an instance of a class, all the classes related with it are candidate targets for actions and for inclusion in guard conditions [3]. a state is drawn as a rounded rectangle with two compartments: one for the state name and the other for the internal transitions and the internal behavior (see fig. 2). the second compartment may define entry and exit actions, activities, internal transitions and deferred events. there must be only one initial state3, but there may be any number of final states, including no final state at all. initial states are pseudostates, they do not have all the properties of a real state. final states are real states; they posses all the properties of a real state; this means that while the entity is in a final state it can perform some activity and can response to some events. the difference between final states and the rest of the real states is that they do not have transitions out. an initial state is drawn using a solid black circle and a final state is drawn using a shallow black dot (see fig. 2). a state machine can have an exit point (drawn as a circle with a cross, as in fig. 2) that indicates the occurrence of an exception [1]. 3it is important to mention that [1] considers that a state machine may have more than one initial state only if each of these states are labeled with the event that created the entity. a novel model for adaptive control systems a state machine approach 295 state machine name t thw s ¸ state name 1 enter do exit e/defer e[c]/a e[c]/a y ~ initial state final state transition yn nstate name 2 figure 2: a state machine example state machine can model parallel operations; this is important in control systems if we consider the situation when it is necessary that the system responses to commands from the operator and at the same time operates the plant. this section has presented some basic concepts of state machines. the next section contains the model of an adaptive control system based on state machines. also in the next section advanced concepts of state machines will be illustrated using the proposed model. 4 the model this section explains how an adaptive control system can be modeled by a state machine. the explanation is divided in three section, which correspond to every of the building blocks of a state machine: the states, the events and the transitions. the actions that occur while a control system is in some state will be explained in the section of the states and the actions executed when a transition occurs will be explained in the section of the transitions. 4.1 states a state describes the condition of an object [11]. this condition for a control system can be expressed in any of the following ways: • in the terms of the control system attributes: – the values of the attributes that define the control system’s structure. for example the values of the poles of the close loop transfer function (see the polynomial am(z−) in eq. 1) define if the system is in stable or unstable state. hcl (z −) = z−d+bm(z−) am(z−) (1) the control system will be in the stable state if all the roots of the transfer function denominator of eq. 1 are inside the unit circle, as it is stated in the eq.  + amz − + ···+ amnz−n =  ⇒ |z| <  (2) 296 f. valles-barajas otherwise the state of the system will be in the unstable state. – the value of the attributes that define the object’s relationships. in this point it is important to mention that the entire control system is represented with a class called acs. when a fault occurs in the system a fault detection and isolation (fdi) object of the class fdi will be created to manage this fault. a fdi reference variable will be defined inside the acs class, so when the fault occurs an instance of the class fdi will be created inside a method of the class acs. if there is no fault in the system, the reference variable of fdi will have the value of null and the system will be in the no fault state, but when a fault appears the reference variable will point to the fdi object that will handle the fault and the system will be in the fault state. • in terms of a behavior that the object is engaged in: – a period of time during which a control system performs some outgoing activity. for example, one of the activities that a controller must do to achieve robustness against the external agents to the system (disturbances, noise, faults) is to shape the sensitivity functions. this state will be called shaping the sensitivity functions. the output sensitivity function s is the transfer function between the output disturbance d(k) and the plant output y(k); this transfer function is usually named as sensitivity function and is given by: syd (z −) = a(z−)s(z−) a(z−)s(z−) + z−d b(z−)r(z−) = a(z−)s(z−) p(z−) (3) the noise sensitivity function t is the transfer function between the measurement noise n(k) and the plant output y(k) and is given by: syn(z −) = −z−d b(z−)r(z−) a(z−)s(z−) + z−d b(z−)r(z−) = −z−d b(z−)r(z−) p(z−) (4) an equation that relates these two transfer functions is syd (z −) − syn(z −) = s + t =  (5) – a period of time during which an object waits for some event or events to occur. suppose that the acs is equipped with a fault detection and isolation module to manage the faults that can appear in the system. the fdi system can be modeled as an object. one of the states of the fdi is the waiting for a fault state. the response of a control system to an event depends on its state. for example if the system is in closed loop state and an the event change reference yre f (z−) occurs the system will take y(k) close to yre f (k) but if the system is in closed loop state and the same event occurs the system will do nothing. 4.2 events an event is any occurrence that provokes the reaction of an object, this reaction can be the transition from one state to another, the execution of one action or do nothing. it is important to mention that although events starting with uml 2.0 are called triggers; in this paper we will use the term events. there are four types of events: signals events, time events, call events and change events. they will be explained in the following sections. a novel model for adaptive control systems a state machine approach 297 signal events a signal is an asynchronous event; it is not necessary that a sender waits for the answer of a receiver when a sender sends a signal to a receiver. signals can also be triggered by an operation; this kind of signals are called exceptions. signals are a one-way asynchronous communication between active objects. an exception is the most common kind of signals that specifies the abnormal behavior of an operation [8]. exceptions are modeled in uml as stereotyped classifiers. the parameters of the exceptions contain information of the abnormal behavior, and these are modeled as attributes in the exceptions. because exceptions are modeled as classifiers, a hierarchy of exceptions can be drawn. fig. 3 shows some of the exceptions that can be generated in an adaptive control system. these exceptions form a hierarchy. «exception» lack of pec «exception» θ̂ out of v z «exception» p(k) low «send» «send» gc(z−) tune() «exception» exception sethandler() «exception» |am(z−)| ≥  «exception» s bad formed paa updatep() «send» gp(z−) updateθ̂ () figure 3: modeling the exceptions of an adaptive control system the root of this hierarchy is the abstract class “exception” which was specified by typing the name of the class in italic font. the “exception” class has a method called sethanlder(); this method is useful to specify the entity that is going to manage the exception, once it occurs. in this proposal, the supervisor is the entity that handles all the exceptions of an acs. fig. 3 shows some of the exceptions that can be thrown in the methods of the classes paa, gp(z−) and gc(z−). when gc(z−) is tuned, the resulting controller may provoke that the closed loop system changes to the unstable state (the poles of am(z−) are outside the unit circle; see eq. 1). to specify that an operation can trigger an exception the exception can be drawn as a stereotyped class and then a send dependency can be drawn between the operation that can trigger the signal and the signal. see for example that in fig. 3 a send dependency between the operation updateθ̂ () and the signal “θ̂ is out of vz” has been drawn to indicate that the operation updateθ̂ () can trigger the exception “θ̂ is out of vz” (v z is the valid zone which is a zone where the parameters of gp(z−) are allowed. this can be obtained from previous knowledge or by association with the physical parameters of the process). also the resulting controller may be poorly robust; this can be inspected by looking at the sensitive function s (see eq. 3) which is a measure of how the disturbance d(k) affects the output of the process y(k). one of the advantages of the configuration of the fig. 3 is that polymorphism can be used to handle the exceptions; the handler of the exceptions can be specified in such a way that it handles all the exceptions of the acs. also when we specify a transition, we can specify that any of the exceptions of the acs can trigger a transition; in this case the transition is polymorphic and can be triggered by the exception “exception” or by any of its specializations, for example the “lack of pec” exception [3]. 298 f. valles-barajas signals are used to establish a communication between two objects in an asynchronous way. the reception of a signal is an event for the receiver. in this paper d(k), d(k), n(k) and the bursting phenomenon will be modeled as signals as fig. 4 indicates. is is important to mention that because d(k), d(k) and n(k) are unknown, they must be estimated. the entity that generates these signals is the environment and the receiver is the adaptive control system. fig. 4 shows that signals are modeled as stereotyped classifiers and because of this a hierarchy of signals «signal» d(k) «signal» d(k) «signal» n(k) «signal» signal sethandler() «signal» brusting of θ̂ figure 4: modeling of the signals for an adaptive control system can be established. signals can also have parameters in the form of attributes, these are useful to store information related with the signals. when we define signals we are making the opposite procedure for exceptions. when signals are defined the entities that handle the signals are defined and when the exceptions are defined the abnormal behavior that can generate an operation is defined [3]. in the uml, you model the signals that an object may receive by naming them in an extra compartment of the class, as shown in fig. 5 where the signals that the supervisor block can handle are defined. signals do not return values to the caller [8]. supervisor d(k) d(k) n(k) lack of pec θ̂ out of v z bursting of θ̂ p(k) low |am(z−)| ≥  signals figure 5: signals that the supervisor can handle time events a time event evaluates the passage of time as a trigger. it is assumed that the object has some mechanism to monitor the passage of time. time can be specified either in absolute mode (time of day) or relative mode (time elapsed since a given event). time can be considered as an event from the environment. the general form of a time event is: a novel model for adaptive control systems a state machine approach 299 after(exp)[guard condition]/ action. an example of a time event is: after(5 minutes)[|p(k)| low]/reset p(k) where 5 minutes is the expression that is evaluated, [|p(k)| low] is called guard condition and reset p(k) is the action that is executed when the expression and the guard condition are true. call events in the uml, you model the call events that an object may receive as operations on the class of the object. for example, in fig. 3 the operation of the class gc(z−) was modeled as a call event. signal events and call events involve at least two objects: the object that sends the signal or invokes the operation and the object to which the event is directed. change events a change event occurs when a boolean expression becomes true. when an event is declared as a change event, the system always monitors that boolean expression. a change event represents a continuous and potential nonlocal computation (action at a distance, because the value or values tested may be distant). another alternative for a change event is to model this as a guard condition in the transition. the advantage of a guard condition is that it is evaluated only when a transition occurs. the general form for a change event is: when(expression) / operation() where operation() is executed when the expression is true. the persistent excitation condition can be verified by using the following change event: when ( limk→∞  k k∑ k= φ (k)φ t (k) ≤  ) / freezeadaptation() 5 conclusions in this paper a new model for adaptive control systems has been presented. this model is based on state machines and is useful to document and to analyse control systems. the states of an adaptive control system, its transitions and the events that may provoke the transition from one state to another were defined in the proposed model. with this model the software implementation of an adaptive control system, made by a software engineer, will be easier. bibliography [1] s. bennett, j. skelton, and k. lunn. schaum’s outline of uml. mcgraw-hill, usa, 2005. [2] s. bergstrom and l. raberg. adopting the rational unified process: success with the rup. addison wesley, usa, 2004. [3] g. booch, j. rumbaugh, and i. jacobson. the unified modeling language user guide. addisonwesley professional, nd edition, may 19, 2005. 300 f. valles-barajas [4] d. drechsel. an adaptive control system modeled as finite state machine. proceedings of xvi annual convention and exhibition of the ieee in india, pages 124 – 128, 1990. [5] m. fowler. uml distilled: a brief guide to the standard object modeling language. addisonwesley, rd edition, 2003. [6] d. harel. statecharts: a visual formalism for complex systems. science of computer programming, pages 231–274, 1987. [7] w. s. humphrey. pspsm : a self-improvement process for software engineers (sei series in software engineering). addison-wesley professional, usa, 2005. [8] ibm. object oriented analysis and design using uml. usa, 2004. [9] p. ioannou and b. fidan. adaptive control tutorial. society for industrial and applied mathematics, 2006. [10] c. larman. applying uml and patterns : an introduction to object-oriented analysis and design and iterative development. prentice hall, usa, rd edition, 2004. [11] t. pender. uml bible. wiley, 2003. [12] j. rumbaugh, i. jacobson, and g. booch. the unified modeling language reference manual. addison-wesley professional, nd edition, 2004. [13] g. simon, t. kovácsházy, g. péceli, t. szemethy, g. karsai, and a. lédeczi. implementation of reconfiguration management in fault-adaptive control systems. ieee instrumentation and measurement technology conference, 2002. anchorage, ak, usa. fernando valles-barajas obtained a graduate degree in computer science at center for research and graduate programs of la laguna institute of technology (1991). he received an ms in control engineering (1997) and a phd in artificial intelligence (2001) from monterrey institute of technology (itesm) campus monterrey. he was a research assistant at mechatronics department of itesm campus monterrey (1997-2001). he received certification as a psp developer from the software engineering institute of carnegie mellon university (2008). he is member of the ieee and acm. his research interests include topics in software engineering and control engineering. currently, he is full-time professor in the information technology department at universidad regiomontana, monterrey, nuevo león, méxico. he also teaches modules at both bs and ms levels in computer science and software engineering. international journal of computers, communications & control vol. i (2006), no. 2, pp. 33-40 the challenge of designing nervous and endocrine systems in robots felisa m. córdova, lucio r. cañete abstract: we discuss in conceptual terms the feasibility of designing a nervous system and an endocrine system in a robot and to reflect upon the bionic issues associated with such highly complex automatons. the emulation of biological phenomenon in artificial systems, both nervous and endocrine imitation in a mechatronic automaton is an attractive proposition as a mechanism of organic integration. the ability of living organisms to maintain their internal organization in spite of external changes, encourages attempts to imitate such achievements in devices. the complexity of the network sensors-integrators-effectors and the proportion of internal fluids must be increased when seeking the homeostasis of the robot from mechatronic design to bionic design. keywords: bionics, integration, robots. 1 introduction biology has developed in a very accelerated way over the last three decades with the 21st century, widely predicted to be the "biology age" (ovchinnikov, 1987). in this context, a certain interest in emulating biological characteristics in appliances can be seen, for the purpose of recreating the successful behavior of living organisms in artificial systems. sensors such as hair and skin, effectors such as feet and fins and integrators such as artificial brains are some examples of bionic projects (cañete, 2002). however, in order to come close to achieving the behavior exhibited by animals as inspiratory beings, then by necessity (ofek, 2001), it must be recognized the pertinence of nervous system and endocrine system. of course, when the nervous system and the endocrine system work in unison in animals, they could generate synergic effects in the behavior of these alive beings. the use and integration of both systems in a human being can be seen for example when a person is attacked by a swarm of wasps. the electromagnetic receptors (the eyes), acoustics (the hearing) and those related to pain (the skin) receive information which is interpreted as threatening, so the association neurons and effectors not only order the locomotors effectors (the leg’s skeletal muscles) to take flight but also order the glands to release adrenaline and other hormones that designate resources to the task of escaping. no doubt, animal characteristics like these are interesting to emulate in automatons (cañete and córdova, 2003). so, the objects of this work are to conceptually design a nervous system and endocrine system in robots as a biological emulation, and to reflect upon the bionic design of those systems. 2 why a nervous system and a endocrine system? on examining any animal, one observes that the digestive, circulatory, respiratory and excretory systems have specific structures and functions, but none of them can carry out their functions independently of the others; rather they work together in harmony to meet the metabolic needs of the body. any living being, not only an animal, is an organized entity that behaves as a unit and this unity is the result of the participation of a larger coordinated and integrated system. as an automaton, a robot must also possess such a system, and select animal inhalation as a perfect example of how it must fit together with its environments. vertebrates and other multicellular beings whose structures are the result of the evolution (paccault, 1991), the source of the emulation, posses two coordination systems of varying form and complexity copyright c© 2006 by ccc publications 34 felisa m. córdova, lucio r. cañete that are closely related: nervous and endocrine. both serve to make the body a unique active entity, individually regulating the functions of it parts and enabling it to adjust to changes. in biology texts, robots designers are accustomed to seeing both systems compared with each other with the emphasis on the considerable difference that exist between them: in the nervous system the message is an electrochemical disturbance (a nerve impulse) that travels through a nerve fiber, whereas in the endocrine system the message is a chemical substance (a hormone) that travels in fluid, like blood. if a robot had a sort of these system, it behavior in aggressive environments would be better. so, as it shown in figure 1, the immediate task is to emulate the nervous system and the endocrine system from natural stage to artificial stage (animal to robot). figure 1: from a scene of origin towards another one of destiny. considering the contribution of the nervous system and endocrine system to the successful performance of the animal facing its ecosystem, such systems conceptually may be copied in a robot to face its productive environment 3 the nervous system 3.1 the necessity of sensors it is possible to face the design of basic a nervous system that allows to make the sensomotor coordination to guide a load-haul-dump (lhd) vehicle inside a street within a tunnel in an underground mine (córdova, 1996). the vehicle loads mineral from a pit and it dump it in a ore-pass. then, a set of sensors could be in charge to acquire the relevant data from the tunnel. among them, is possible to use some of the following ones: • distance sensors, that can be an adjustment of ultrasonic sensors, or infrared sensors. • laser sensors to measure depth. • tact and of proximity sensors. • vision sensors, that uses tv cameras and processors of stereoscopic images, that can detect a painted line in the ceiling of the gallery or the profile of the tunnel. • angle sensors. • torque and acceleration sensors. • sensors of virtual surroundings, that can detect them holes of ditches and resentments. the challenge of designing nervous and endocrine systems in robots 35 • sensors of the operational conditions of the vehicle, among them sensorial of humidity, temperature, pressure. • sensors of environmental conditions of the tunnel, such as fire detectors. • tags, that allows to detect figures of bar codes and it the location of the vehicle inside the gallery. 3.2 a sensomotor coordination design in this particular design, an array of six located ultrasonic sensors in the flanks of the vehicle is used. the navigation criteria is of homeostatic type, that means that the vehicle maintains a equidistant distance to the walls of the tunnel. in this design, a neuronal network of sensomotor coordination can be used that coordinates the sensors and the actuators. the strategy of sensomotor coordination makes use of a propioceptive network presented in figure 2. figure 2: propioceptive network in the reflective network of senso-motor coordination of figure 3, from the general and previous design of a fuzzy neuron, defined the characteristic of design for each neuron in individual, obtaining itself eight types of neurons. in the case of the sensorial neuron a not-linear excitation (sigmoidal) and with different weight for each sensor from the respective sub-network is required (the agonist-antagonist sub-networks are symmetrical in their construction). modulating neuron m dynamically varies the characteristic of the excitatory neuron e1 and the threshold neuron. this activity of modulation is function of the speed of the vehicle and the ratio of turn rg. the rest of the neurons has excitatory and/or inhibitory activities according to its connectivity in the network. the rank of activity of all the neurons varies between 0 and 1, minimum activity and maximun activity for all the variables of the neurons. the signals of activity are excitation, inhibition, modulation and a threshold of adjustment for each neuron. • excitatory neuron e1: this neuron receives the activity of the distance sensors whose weight decreases and this is modulated as well by the activity of the modulating neuron depending on inverse on the turning ratio and the speed of vehicle v at low speeds and v2 at high speeds (over v2 march). • excitatory neuron e2: this neuron actives the turn actuator, receiving a modulated activity of the previous activity of its sensors, inhibitions of the antagonistic network that are against to him and of its own actuator that restrains it and stabilizes as the networks arrive at an activity balance. 36 felisa m. córdova, lucio r. cañete • inhibitory neuron i1: this neuron allows the incorporation of the activity from one sub-network to the other, and its action is strong in high ranks of internal activity to allow to a fast compensation forehead to strong stimulus of the network that originates it when the networks are very far from the balance. • inhibitory neuron i2: this neuron restrains the activity of the turn actuator when it is in conditions near the balance, or allows a fast turn (depending on the threshold neuron) when a strong sensorial activity of distance exists and the vehicle goes fast. • modulating neuron m: this neuron modulates the activity of sensors of distance in agreement with the speed and the turning ratio of the vehicle, in addition stimulates to the neuron threshold when its own activity is high along with the activity of the distance sensors. • threshold neuron: when this neuron is stimulated by the modulating neuron, it inhibits to inhibitor of the actuator to correct in fast form the direction of the vehicle. this is required in the case that the vehicle has a small turning radius or when the speed is high. • sensor neuron: the activity received by each sensor of the sub-network is heavy in decreasing form while more moved away it is of the advance direction, in addition the sensation one to the activity is not-linear for each sensor. figure 3: sensomotor network the challenge of designing nervous and endocrine systems in robots 37 structurally speaking, nerves can be emulated and in fact they have been, by way of electrical conductors specifically in mechatronic automaton like lhd. 4 the endocrine system 4.1 the necessity of an internal fluid in the case of blood, it has not been emulated in robots and the absence of internal fluid in an automaton was one of the reasons why work was halted on the emulation of other animal features dependent on internal chemical transportation. of course, the existence of fluids in the body of the robot would not only facilitate the emulation of the endocrine systems’ own hormones but also the emulation of antibodies for immunity, of bradicins (when animal tissue is broken, enzymes are released that convert certain plasmatic proteins into a substance called bradicin), for nociception (perception of damage) and of solvents for quimosensitivity (to stimulate the sense of taste, the chemical substances must be dissolved). furthermore, a fluid not only facilitates the movement of intra-corporal messengers but could also act as a lubricant, combustible and a thermo-regulator. a classic mechatronic robot contains in percentage terms of volume approximately one sixth part of fluids which are principally lubricants, and substances for hydraulic devices and combustion. an arthropod on the other hand, contains at least three quarter parts of fluid (cañete and córdova, 2003). therefore, the first challenge in this respect consists of the proportion of internal fluids in the robot. such an increase would lead to an increase in homeostasis which would bring with it an increase in the design complexity as shown in figure 4. figure 4: complexity versus homeostasis. an increase in complexity for endocrine system, comes with increasing the proportion of internal fluids when seeking the homeostasis of the robot from mechatronic design to bionic design 4.2 a simple design when a robot works in changing environments, some parts of the surroundings without its control would become dangerous. in such situations, the robot will have to react in urgent form to fit its internal structures and to assure its viability. one of such urgencies can be the repair or reinforcing a part of the body that is being exposed to an external requesting. in order to face such situation it is possible to be resorted to a endocrine emulation as it is explained next and presented in figure 5. 38 felisa m. córdova, lucio r. cañete the internal part of the sensible zone of the body exposed to harmful emboss and ruptures are designed rough and it is covered with little smooth laminas. the last ones are conductive elements with bad plastic behavior; of such form get fractures and can release them when happens a damage of the outside. the internal part in addition is bathed by a fluid and slightly energized with electrical current. an internal sensor to the circuit of the fluid exists somewhere in addition that is able to capture fragments of little laminas conductors by magnetic action. whenever it captures some piece of laminas, releases a plastic and rough substance in form of grume (small semisolid mass) and it orders to a pump to increase the speed of the fluid circulation. the rough grumes slide by the smooth surface and they are crowded in the rough surface that is being dangerous. finally the grumes reinforce the exposed zone. figure 5: cross section of the bodywork with phenomenon of endocrine emulation. a: an external agent breaks or damages the interior where a fluid circulates, conductive fragments are freed in the open leaving one rough surface. the fragments when traveling by the fluid are captured by a magnetic receiver that releases rough grumes. b: the rough grumes travel by the current and they lodge in the naked zone, cushioning the external action. the effect on the speed flow caused by the grumes is insignificant due to the extension planar of the conduit and to the precise location of the grumes. why not to design a hard bodywork and thus to avoid a complex endocrine system? for the same reason for which the nature through million years of evolution has not done it (david and samadi, 2000). comparatively (massé and thibault, 2000), to maintain a hard bodywork is more expensive than to maintain a light bodywork with repair capacity. 5 organic integration in the robot once the endocrine and nervous systems have been conceived, both systems may be connected to each other and at the same time joined to the functions which they control. it is then that an organization is configured, which for the purpose of the robot being proposed in the current research, would have a internal communication structure as shown in figure 6. in animal kingdom, the levels are recursive and thus subordinates: that is to say the supreme level contains and governs the homeostasis level (nervous and endocrine) which in turn does the same with the sensomotor level. the challenge of designing nervous and endocrine systems in robots 39 figure 6: three levels in organic integration of bionic robot. the nervous and endocrine reaction corresponds at a homeostatic level. such reactions are subordinated to supreme level, but they govern at sensomotor level. adaptive control, as the name implies, is the higher level of coordination and functional integration in a robot. its structure can be a managed by a system that knows the mission the robot and links both information of the inner state of the automata and his surroundings. a proposed future challenge in the present work, is the conceptual design of this adaptive control. 6 conclusions although the robot has neither nerves nor blood where it may send messages in charge of functionally integrating and coordinating all its parts, they can be emulated. there is evidence that the emulation with certain restrictions of both nervous and endocrine system is feasible. however such an emulation would require the convergence of two sciences: biology and robotics. the convergence of biology with its different branches (ecology, genetics, evolution among others) and robotics is no accidental; rather it constitutes an essential issue in as far as abstract terms are concerned, that is: there is little to distinguish automatons and living beings. in reality both beings show behavior that is autopoietic (self produced), telenomic (self initiating) y homeostatic (self regulating with feedback function). put more precisely: many of the features of a living being are desirable in an automaton. aspiring to have such features comes from the observations that man has carried out in the biological world of he himself is a part. the ability of living organisms to maintain their internal organization in spite of external changes, encourages attempts to imitate such achievements in devices, whose internal compositions are quite modest when compared with those of plants and animals. this motivation, which has further increased on the verification of real successes achieved by man more than three decades ago (in sonar, prosthesis and artificial neuronal networks among others) provides a particular incentive to continue with the current research. 40 felisa m. córdova, lucio r. cañete references [1] cañete, l., ecología cognitiva en robots terrenos para el desierto de atacama, tesis doctoral, facultad de ingeniería de la universidad de santiago de chile, 2002. [2] córdova, f., guiado autónomo de equipos cargadores frontales lhd en una mina subterránea, informe proyecto fondef. conicyt. chile, 1996. [3] cañete, l. and córdova, f., ecología cognitiva en robots terrenos, proceedings of the first ieee latin american conference on robotics and automation lcra, santiago de chile, 2003. [4] cañete, l. and córdova, f., a robot with immunity and perception of damage, proceedings of the first ieee latin american conference on robotics and automation lcra, santiago de chile, 2003. [5] david, p. and samadi, s., la théorie de l’évolution, paris: champus université flammarion, 2000. [6] glávic, n. and ferrada, c., biología. tercer año de educación media, santiago: ministerio de educación, 1985. [7] ofek, h., second nature: economics origins of human evolution, cambridge: university press, 2001. [8] ovchinnikov, y., basic tendencies in physico-chemical biology, moscow: mir publisher, 1987. [9] massé, g., and thibault, f., intelligence économique, bruxelles: de boeck université, 2001. [10] paccault, i. (1991). la terre et la vie. paris: larousse. felisa m. córdova, lucio r. cañete universidad de santiago de chile departamento de ingeniería industrial laboratorio de concepción e innovación de productos, lacip santiago de chile e-mail: fcordova@usach.cl int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 362-374 fuzzy filtering of sensors signals in manufacturing systems with time constraints a. mhalla, n. jerbi, s. c. dutilleul, e. craye, m. benrejeb anis m’halla, nabil jerbi, mohamed benrejeb ecole nationale d’ingénieurs de tunis unité de recherche lara-automatique bp 37, le belvédère, 1002 tunis, tunisie e-mail: anis.mhalla@enim.rnu.tn, nabil.jerbi@isetso.rnu.tn, mohamed.benrejeb@enit.rnu.tn anis m’halla, simon collart dutilleul, etienne craye ** ecole centrale de lille laboratoire d’automatique, génie informatique et signal cité scientifique, bp 48, 59651 villeneuve d’ascq, france e-mail: simon.collart_dutilleul@ec-lille.fr, etienne.craye@ec-lille.fr, abstract: the presented work is dedicated to the supervision of manufacturing job-shops with time constraints. such systems have a robustness property towards time disturbances. the main contribution of this paper is a fuzzy filtering approach of sensors signals integrating the robustness values. this new approach integrates a classic filtering mechanism of sensors signals and fuzzy logic techniques. the strengths of these both techniques are taken advantage of the avoidance of control freezing and the capability of fuzzy systems to deal with imprecise information by using fuzzy rules. finally, to demonstrate the effectiveness and accuracy of this new approach, an example is depicted. the results show that the fuzzy approach allows keeping on producing, but in a degraded mode, while providing the guarantees of quality and safety based on expert knowledge integration. keywords: alarm filtering, fuzzy logic, symptoms generation, robustness, time constraints, manufacturing. 1 introduction in general, the detection of failure symptoms related to the process elements requires a development of a system model to be supervised [1]. this model can be either a normal functioning model or dysfunction model. if a model is adopted, two basic mechanisms are used for detection. the first one consists of comparing the evolutions of the observed system with those of the process model, or with those of normal functioning signatures evolving in real time with the system. the second one is based on observing known failures signatures. these signatures describe historical or theoretical failures known from the process or the process elements. without the supervision of a system model, the adopted strategy consists of an exploitation of the information given by the sensors and the detectors at a local level of the process [2]. sometimes, in manufacturing workshops with time constraints, the information given by sensors signals is dubious and the symptoms generated are vague. furthermore, the validation interval associated to each sensor signal is not always exact, this is the case where the temporal windows are badly defined. these reasons bring us to use fuzzy logic which is based on an approximate reasoning able to take into account the uncertainty and the inaccuracy of knowledge. this paper is an extension of jerbi work [3]. in [3] was proposed an integration of the robustness in the filtering mechanism of sensors signals. this mechanism, presented by toguyeni in 1992, aims at generating symptoms for the diagnosis [4]. our main contribution is a fuzzy filtering mechanism of sensors signals. this paper is organised as follows. the first part summarizes the proposed filtering mechanism of sensors signals taking into account the robustness intervals. the second part introduces a new fuzzy filtering approach where copyright c© 2006-2010 by ccc publications fuzzy filtering of sensors signals in manufacturing systems with time constraints 363 fuzzy logic and filtering of sensors signals techniques are integrated. in order to show the effectiveness of this approach, in the third part, an illustrative example is outlined and the results are discussed. finally, conclusions of this work are given. 2 robustness integration in the filtering of sensors signals 2.1 symptoms generation permanently, the state of the process model is updated by the evolutions caused by the control and the sensors signals. these sensors signals are sent by the controlled system in response to a control request. the mechanism developed for the detection of failures symptoms is based on the impact study of sensors signals on the process model, called reference model, and on that of the control. these two models do not make it possible to characterize all failures symptoms of a controlled system, for example, the absence of sensors signals (this can be the case, when a sensor is not functional or the control request was not carried out). in order to take into account these problems, mechanisms of "watchdogs" were integrated in the control and process models. these mechanisms are based on two dates provided by the scheduling task: the beginning date as soon as possible noted ∆ tm/cri and the completion date noted ∆ tm/cri of the control operation [4]. the idea consists of modelling any operation from a temporal approach. at each operation ai is associated a sensor signal cri. to each sensor signal cri is associated a temporal interval [∆ tm/cri, ∆ tm/cri] (figure 1). the report cri is valid only inside this window. ∆ tm/cri and ∆ tm/cri are defined relatively to the beginning of the operation ai (start-event). the filtering principle is to position the temporal window of each sensor signal cri when its start-event was received.two types of symptoms are distinguished: • symptoms type i noted si : this class of symptoms corresponds to awaited sensor signal which is not received at ∆ tm/cri. the detection mechanism of this symptom type corresponds to the traditional mechanism of watchdog, but implemented in a separate way of the control. • symptoms type ii noted si : it is generated by the occurrence of a sensor signal which is not expected. two cases are considered, the first one corresponds to an action but its report occurs before the validation interval. the second one corresponds to the occurrence of a report in absence of any control which can create it. ai (start-event) tm/cri validation interval of cri time 0 tm/cri figure 1: operation associated model [4] 2.2 symptoms generation the robustness of a system can be defined as its ability to preserve the specifications facing some expected or unexpected variations. so, the robustness of a system characterizes its capacity to deal with disturbances [6]. it is interpreted into different specializations. the passive robustness is based upon variations included in validity time intervals.there is no control loop modification to preserve the required specifications. on the other hand, active robustness uses observed time disturbances to modify the control loop in order to satisfy these specifications. therefore, the robustness intervals must be 364 a. mhalla, n. jerbi, s. c. dutilleul, e. craye, m. benrejeb integrated in the filtering mechanism of sensors signals. in [3], a filtering mechanism of sensors signals integrating the robustness values is proposed. this mechanism allows the integration of the robustness results in the symptoms generation and the classification of the various actions reports. this classification is very useful for the supervision because it makes it possible to preserve the production function. this constitutes an enhancement of the filtering mechanism. five time intervals, figure 2, are defined, namely: ii = [∆ tm′′/cri , ∆ tm′/cri [, ii = [∆ tm′/cri , ∆ tm/cri [, ii = [∆ tm/cri , ∆ tm/cri [, ii = [∆ tm/cri , ∆ tm ′/cri [ and ii = [∆ tm ′/cri , ∆ tm ′′/cri [. the margin of passive robustness is available in ii ∪ ii whereas the margin of active robustness is in ii ∪ii. several cases can arise: ai (start-event) time tm/cri tm/cri cri (normal functioning) tm’/critm’’/cri tm’/cri tm’’/cri 0 normal functioning interval passive robustness interval active robustness interval figure 2: robustness integration in the operation associated model [3] • if there are absence of order (not ai) and presence of cri, there are freezing of the control and generation of a symptom si . • if the sensor signal cri arrives in the time interval [, ∆ tm′′/cri [, there are freezing of the control and generation of a symptom si . • if the sensor signal cri arrives in the time interval ii, there are change of the control (active robustness to an advance) and memorizing a symptom si . • if the sensor signal cri arrives in the time interval ii, there is no change of the control (passive robustness to an advance) but only a memorizing of a symptom si . • if the sensor signal cri arrives in the time interval ii, the behavior of the system is normal. • at the instant ∆ tm/cri , there is automatically memorizing of a symptom si . • if the sensor signal cri arrives in the time interval ii, it is a case of passive robustness to a delay. the symptom si is already memorized. • if the sensor signal cri arrives in the time interval ii, a change of the control is necessary (active robustness to a delay). • at the instant ∆ tm ′′/cri , there is freezing of the control. therefore, the robustness intervals are integrated in the filtering mechanism of sensors signals. it makes it possible to continue the production in a degraded mode. however the assumptions formulated in [3] are very restrictive. it is natural to consider different scenarios where the temporal specifications of the fuzzy filtering of sensors signals in manufacturing systems with time constraints 365 process are not fulfilled, nevertheless the production can continue. the next section presents a fuzzy filtering mechanism of sensors signals which introduces a finer classification of abnormal functioning and integrates the vague knowledge of the robustness intervals in the interpretation of sensors signals, coming from the workshop, for the generation of symptoms. the objective is to avoid the freezing of the control when the time disturbance is in the robustness intervals. 3 fuzzy filtering of sensors signals 3.1 introduction fuzzy logic is a mathematical tool that allows us to approach an unknown function by means of linguistic descriptions. nevertheless, the linguistic information is a feature of the human reasoning and not of the mechanical components or programs. in consequence, this tool has achieved widespread applications and success in many areas such as control, supervision, image filtering and communications [6–9]. the essential characteristics of fuzzy logic are as follows [10]: • exact reasoning is viewed as a limiting case of approximate reasoning. • everything is a matter of degree. • any logical system can be fuzzified. • knowledge is interpreted as a collection of elastic or, equivalently, fuzzy constraint on a collection of variables. • inference is viewed as a process of propagation of elastic constraints. fuzzy logic calls upon a base of dubious knowledge, modelled by the sequence of fuzzy rules. this technique seems very promising thanks to its potential of use in dynamic monitoring and supervision, with the possibility of remaining human operator, by taking into account its way of reasoning and offering an interesting traceability. before proceeding, we define some important terms. definition 1. [11]: a fuzzy set f in a universe of discourse u is characterized by a membership function µf : u → [, ] definition 2. [11]: a linguistic variable x in a universe of discourse u is characterized by: t (x) ={ tx, t  x, ..., t k x } and m(x) = { mx , m  x , ..., m k x } where t (x) is the term set of x, that is the set of names of linguistic values of x with each value t ix being a fuzzy number with membership function m i x on u . 3.2 basic structure of a fuzzy system figure 3, shows the basic structure of a conventional fuzzy system. such system can be seen as consisting of four basic building blocks: fuzzification, fuzzy rule set, inference method and defuzzification. let us examine these building blocks in details: • the fuzzification transforms a numerical input variable in a fuzzy set described by linguistic expressions. • fuzzy rules set: the fuzzy if-then rule expresses a fuzzy implication relation between the fuzzy sets of the premise and the fuzzy sets of the conclusion. • the inference makes it possible to implement, on the basis of fuzzy rules, the logical dependence between input variables and output fuzzy variables [12]. 366 a. mhalla, n. jerbi, s. c. dutilleul, e. craye, m. benrejeb fuzzification defuzzification fuzzy rules inference input output x y figure 3: fuzzy set stages • the defuzzification transforms the output fuzzy set in a numerical variable. following the above definitions, the input vector x which includes the input state linguistic variables xi’s, and the output state vector y which includes the output state linguistic variables yi’s, can be defined as: x = [ xi,ui, { txi , t  xi , ..., t k xi } , { mxi , m  xi , ..., m k xi }] i=[,...,n] (1) y = [ yi,ui, { tyi , t  yi , ..., t k yi } , { myi , m  yi , ..., m k yi }] i=[,...,m] (2) the fuzzifier, in figure 3, is a mapping from an observed input space to fuzzy sets in certain input universe of discourse. so, a specific value xi(t) at the time t is mapped to the fuzzy set tx with degree m  x (xi(t)) and to the fuzzy set tx with degree m  x (xi(t)) and so on. fuzzification of the input and output variables if we want to introduce linguistic information, we have to define an interface. this interface is denominated fuzzification, and it translates the sensor measurements into linguistic concepts. to carry out such transformation, the fuzzification resorts to a characteristic function called membership function. the aim of fuzzification is to produce initial membership functions. therefore, the universe of discourse u of the input and output variables are divided into fuzzy subsets. the first step consists of choosing the input and output variables. this choice depends on the parameters available and the type of application [13]. in order to produce initial membership functions, the input and output spaces are divided into fuzzy regions. in our example, we have two inputs and two outputs variables, all membership functions are represented by trapezoidal forms. the sensor signal (cri) and the occurrence of the start-event (ai) are considered as inputs variables. however, the type of symptom (si) and the control decision (cd) are considered as outputs ones. the second step consists of defining the universe of discourse which can take each variable. then, we define the fuzzy sets associated to the inputs and outputs variables and their corresponding membership functions. thus, the universe of discourse is divided into intervals at which a descriptive label is associated. this last choice is based on the experiment of the operator. • fuzzification of sensor signal (cri) we define, figure 4, thirteen time intervals, namely: ii = [, ∆ tm′′/cri [, ii = [∆ tm′′/cri , ∆ tm′′ /cri [, ii = [∆ tm′′ /cri , ∆ tm′/cri [, ii = [∆ tm′/cri , ∆ tm′/cri [, ii = [∆ tm′/cri , ∆ tm/cri [, ii = [∆ tm/cri , ∆ tm/cri [, ii = [∆ tm/cri , ∆ tm/cri [, ii = [∆ tm/cri , ∆ tm/cri [, ii = [∆ tm/cri , ∆ tm ′ /cri [, ii = [∆ tm ′ /cri , ∆ tm ′ /cri [, ii = [∆ tm ′ /cri , ∆ tm ′′/cri [, ii = [∆ tm ′′/cri , ∆ tm ′′ /cri [ and ii = [∆ tm ′′ /cri , +∞[. the full set intervals is summarised in table 1. fuzzy filtering of sensors signals in manufacturing systems with time constraints 367 tm’1/cri µ(cri) tm’’/cri ara pra nf prd ard start-event tm’2/cri tm1/cri tm2/cri tm’1/critm1/cri tm2/cri tm’’/critm’2/cri cri tm’’1/cri npre npre tm’’1/cri figure 4: fuzzy robustness integration in the operation associated model table 1: linguistic variables associated to the input cri tcri linguistic variable tcri cri arrives in the interval ii = [, ∆ tm′′/cri [ tcri cri arrives in the interval ii = [∆ tm′′/cri , ∆ tm′′ /cri [ tcri cri arrives in the interval ii = [∆ tm′′ /cri , ∆ tm′/cri [ tcri cri arrives in the interval ii = [∆ tm′/cri , ∆ tm′/cri [ tcri cri arrives in the interval ii = [∆ tm′/cri , ∆ tm/cri [ tcri cri arrives in the interval ii = [∆ tm/cri , ∆ tm/cri [ tcri cri arrives in the interval ii = [∆ tm/cri , ∆ tm/cri [ tcri cri arrives in the interval ii = [∆ tm/cri , ∆ tm/cri [ tcri cri arrives in the interval ii = [∆ tm/cri , ∆ tm ′ /cri [ tcri cri arrives in the interval ii = [∆ tm ′ /cri , ∆ tm ′ /cri [ tcri cri arrives in the interval ii = [∆ tm ′ /cri , ∆ tm ′′/cri [ tcri cri arrives in the interval ii = [∆ tm ′′/cri , ∆ tm ′′ /cri [ tcri cri arrives in the interval ii = [∆ tm ′′ /cri , +∞[ 368 a. mhalla, n. jerbi, s. c. dutilleul, e. craye, m. benrejeb the margin of active robustness is available in (ii ∪ ii ∪ ii)∪(ii ∪ ii ∪ ii), whereas the margin of passive robustness is in (ii ∪ii ∪ii)∪(ii ∪ii ∪ii). from a functional point of view, there are six intervals of use in which it is possible to prove the validity: intervals of no proof of robustness existance (npre), normal functioning (nf), passive robustness to an advance (pra), passive robustness to a delay (prd), active robustness to an advance (ara) and active robustness to a delay (ard). if the functioning is abnormal, there is duality of advance and delay scenarios. • fuzzification of a start-event (ai) figure 5, shows the different set of the input variable (start-event ai). the full set of linguistic variables associated to each membership is summarised in table 2. µ(ai) ai absence of ai occurrence of ai figure 5: membership functions of ai table 2: linguistic variables associated to the input start event tai linguistic variable tai occurrence of the start-event ai tai absence of the start-event ai • symptoms fuzzification figure 6, shows an uniform distribution of fuzzy logic membership functions associated to the output “ type of symptom ”. similarly, table 3 shows linguistic variables associated to the output “ type of symptom ". µ(si) si generation of si 1 memorizing of si 1 generation of si 2 memorizing of si 2 1 s i m 2 s i m 3 s i m 4 s i m figure 6: membership functions of si fuzzy filtering of sensors signals in manufacturing systems with time constraints 369 table 3: linguistic variables associated to the output “ type of symptom" tsi linguistic variable tsi generation of a symptom s  i tsi memorizing of a symptom s  i tsi generation of a symptom s  i tsi memorizing of a symptom s  i • fuzzification of control decision (cd) the three fuzzy set for the output cd are chosen as indicated in figure 7. hence the three membership functions, uniformly distributed, are denoted m1cd, m 2 cd and m 3 cd. the linguistic variables are summarised in table 4. the integration of the approach generation of symptoms and the classification of the reports of various actions allows a qualitative description of fuzzy variables. these variables have balanced values of truth, pertaining to the interval [0, 1]. no change of control change of control control freezing (cd) cd 1 cdm 2 cdm 3 cdm figure 7: membership functions of cd table 4: linguistic variables associated to the output control decision tcd linguistic variable tcd no change of control tcd change of control tcd control freezing definition of fuzzy rules next we have to evaluate the rules. the rules associate the input variables with the output ones by means of linguistic terms, and according to their physical properties [14]. the rules can present different structures (mimo, miso, siso, ...), although the most common one is the multiple inputs multiple outputs (mimo). we have used this structure to build fuzzy rules, and its arrangement is: r jmimo : if ( x is tx ) and ... and ( xp is txp ) (3) then ( y is ty ) and ... and ( yq is tyq ). 370 a. mhalla, n. jerbi, s. c. dutilleul, e. craye, m. benrejeb being p the number of input variables, q the number of output variables whereas txi and tyi represent their respective fuzzy sets for jth rule. the preconditions of r jmimo, form a fuzzy set (tx1 ×tx2 × ... ×txp ) and the consequent of r j mimo is the union of q independent outputs [11]. so, the rule can be represented by a fuzzy implication: r j mimo : (tx1 ×tx2 ×... ×txp ) → (ty1 + ... + tyq ) (4) where "+" represents the union of independent variables.the fuzzy rules are merely a series of if-then statements. these statements are usually derived by an expert to achieve optimum results. thus, according to (3) we can formulate the rules as following: rule 1: if there are absence of order (not ai) and presence of cri, then there are freezing of the control and generation of a symptom si . rule 2: if the sensor signal cri arrives in the time interval [, ∆ tm′′/cri [ and the start-event ai is occurred, then there are freezing of the control and generation of a symptom si . rule 3: if the sensor signal cri arrives in the time intervals (ii ∪ ii ∪ ii) and the start-event ai is occurred, then there are change of control (ara) and memorizing of a symptom si . rule 4: if the sensor signal cri report arrives in the time intervals (ii ∪ ii ∪ ii) and the start-event ai is occurred, then there are no change of the control (pra) and memorizing of a symptom si . rule 5: if the sensor signal cri arrives in the time intervals (ii ∪ ii ∪ ii) and the start-event ai is occurred, then the behaviour of the system is normal (no change of the control). rule 6: if the sensor signal cri arrives in the time intervals (ii ∪ ii ∪ ii) and the start-event ai is occurred, then it is the case of passive robustness to a delay (no change of the control) and memorizing of a symptom si . rule 7: if the sensor signal cri arrives in the time intervals (ii ∪ii ∪ii) and the start-event ai is occurred, then a change of the control is necessary (ard) and memorizing of a symptom si . rule 8: if the sensor signal cri arrives in the time interval cri in the time interval ii,then there are freezing of the control and memorizing of a symptom si . since the two outputs (symptoms and control decision) of mimo rule are independent, the general rule structure of mimo fuzzy system can be represented as a collection of multiple-input and single-output (miso) fuzzy systems by decomposing the above rules into q (q=2) subrules with as the single consequent of the jth subrule. therefore, the inference engine matches the rule preconditions in the fuzzy rule base with the input state linguistic terms and performs implication. in this subsection, for clarity, we will consider miso system in the following analysis. it is interesting to mention that each fuzzy rule just controls a part of the function to approach. these parts are denominated ”patches”, and they are the result of the localization property of the fuzzy basis functions. nevertheless, from the fuzzy rules base (rule 1, rule 2, ..., rule 8), we need to model numerically the operators and and then. the fuzzy systems define an intermediate stage denominated inference. the inference is the part of the fuzzy systems that carries out an isomorphism between propositional logic and the set and the algebraic theories. however, it is not valid whatever relation between the logical and math operators. in concrete, if they want to be equivalent, their logical and math tables for the crisp values {, } have to be the same. the selected inference method is the mamdani type which is known as the max-min method. defuzzification after inferring all rules, the fuzzy systems need to fusion them.this is the main goal of the defuzzification step, and it constitutes the last part of all fuzzy systems.this fusion is not unique, although the centre of area (coa) defuzzifier is the widespread one, figure 8. in the coa method, the fused fuzzy filtering of sensors signals in manufacturing systems with time constraints 371 measurement output y∗ is obtained as: y∗ = ∑ y∈y µf (y).y ∑ y∈y µf (y) (5) y * g f y o figure 8: centre of area defuzzifier 4 illustrative example to demonstrate the effectiveness and accuracy of the fuzzy filtering approach, an example with two fuzzy rules is outlined. consider the following fuzzy rules base: rule 2: if the sensor signal cri arrives in the time interval [, ∆ tm′′/cri [ and the start-event ai is occurred, then there are freezing of the control and generation of a symptom si . rule 3: if the sensor signal cri arrives in the time intervals (ii ∪ ii ∪ ii) and the start-event ai is occurred, then there are change of control (ara) and memorizing of a symptom si . • each rule use the operator "and" in the premise, since it is an and operation, the minimum criterion is used (mamdani inference method), and the fuzzy outputs corresponding to these rules are represented by figure 9 and figure 10. • next we perform defuzzification to convert our fuzzy outputs to a single number (crisp output), various defuzzification methods were explored to select the best one for this particular application. according to the relation(5),the weighted strengths of each output member function are multiplied by their respective output membership function center points and summed. finally, this area is divided by the sum of the weighted member function strengths and the result is taken as the crisp outputs. in practice, there are two fuzzy outputs to defuzzify (symptoms and control decision). to obtain a numerical output, we can take the coa of each fuzzy output, named gsy and gcd. the measures of the two cao using specific values of sensor signal and start-event are summarized in table 5. table 5, shows the measures obtained by using defuzzification method mentioned above.analysing the data, it is noted that the first and the third cases represent a classic filtering mechanism of sensors signals, integrating the robustness values described in [3]. the second case, using fuzzy filtering approach, gives better results than the two cases previously analysed. these cases reveal that the proposed approach is able to avoid control freezing (the coa gcd belongs to the membership function "change of control") same if the sensor signal arrives in the "no proof of robustness existance (npre)" interval. therefore, the fuzzy filtering approach makes it possible to continue the production in a degraded mode providing the guarantees of quality and safety. consequently, the intelligent fuzzy logic control strategy, based on expert knowledge, provides the avoidance of control freezing if the time disturbance is in the robustness intervals. 372 a. mhalla, n. jerbi, s. c. dutilleul, e. craye, m. benrejeb figure 9: three-dimensional trapezoidal membership function: symptoms= f (cri, ai) figure 10: three-dimensional trapezoidal membership function: control decision= f (cri, ai) table 5: measures of gsy and gcd variables measures first case second case third case cri cri arrives cri arrives cri arrives in the interval ii in the interval ii in the interval ii ai ai is occurred ai is occurred ai is occurred gcd gcd ∈ mcd gcd ∈ mcd gcd ∈ mcd gcd gcd ∈ msi gcd ∈ m  si gcd ∈ m  si 5 conclusion this paper deals with supervision of manufacturing workshops with time constraints. a new approach integrating a classic filtering mechanism of sensors signals and fuzzy logic techniques has been presented. this approach exploits the advantages that both techniques have: the avoidance of control freezing using robustness knowledge and the ability of fuzzy systems to deal with imprecise information by using fuzzy rules. in this new approach, an enhancement technique based on various combinations of fuzzy logic linguistic statements in the form of if-then rules, based on expert knowledge, makes it possible to continue the production in a degraded mode providing the guarantees of quality and safety.the establishment of fuzzy logic is interesting, but it is necessary to call upon the human expertise, in an environment of uncertainty and imprecision, able to formulate and to transmit its knowledge for decision making. fuzzy filtering of sensors signals in manufacturing systems with time constraints 373 the results obtained in the illustrative example show that this fuzzy approach is effective in situations where the sensor measurement is contaminated with different kind of noises. in this case, the temporal windows associates to each sensor signal are badly defined. conventionally, the selection of fuzzy if-then rules often relies on a substantial amount of heuristic observation to express proper strategy’s knowledge. obviously, it is difficult for human experts to examine all the input-output data from a complex system to find the suitable number of rules within the fuzzy systems. for this reason, a fuzzy system with neural network’s learning ability is required. a new approach using neural fuzzy filter (nff), based upon a neural network’s learning ability and fuzzy if-then rule structure can be developed in order to supervise critical time manufacturing job-shops. this fuzzy filtering approach shows how the knowledge of the robustness could make the supervision more efficient, by introducing two events (start-event and sensor signal). a chronicle recognition approach, using the additional information provided by the occurrences of intermediate events, is a challenging technique 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[14] j.l. castro, j.m. benitez, and i. requena, are artificial neural networks black boxes?, ieee transaction on neural networks, vol. 8, no. 5, pp.1156 – 1164, september 1997. anis m’halla was born in mahdia, tunisia in 1980. he obtained the engineer degree in electromechnical engineering from the "ecole nationale d’ingénieurs de sfax (enis) " and obtain the master degree in automatic and industrial maintenance from the "ecole nationale d’ingénieur de monastir" in 2006. he is currently preparing the ph.d. degree in automatic and computer science within the framework of lagis-ec-lille and lara-enit cooperation. his research is related to robustness and supervision of multi-product job-shops with time constraints. nabil jerbi was born in tunis, tunisia, in 1970. he obtained the engineer degree in electrical engineering from the ecole nationale d’ingénieurs de tunis (enit), in 1994. also, he received the aggregation certificate from the ecole supérieure des sciences et techniques de tunis and the master degree in automatic and signal treatment from enit, in 2001 and 2003, respectively. he is currently an assistant at "institut supérieur des sciences appliquées et de technologie de kairouan ". his research interests include robustness and supervision of multi-product job-shops with time constraints. pr. simon collart dutilleul obtained a ph.d. degree in electronics, electrotechnics and automatic control from université de savoie in 1997. he is currently a professor at ecole centrale de lille, and a member of the research team on discrete event systems in the lagis laboratory (laboratory of control engineering, computer science & signal), where he studies specifically time constrained systems. pr. etienne craye was born in roubaix (france) in 1961; he obtained in 1984 the engineer diploma of the "institut industriel du nord" (french "grande ecole") and the same year his master degree in computer sciences. he obtained a ph.d. in automatic control for manufacturing and discrete events systems in 1989 and his "habilitation ŕ diriger des recherches" in 1994. he is now, since 1995, professor at the ecole centrale de lille and in the same time the director of this institution. pr. craye is currently working on monitoring and supervision of fault tolerant systems. specially, reconfiguration and working mode management are today studied by taken into account on one-hand failures and on the other hand the flexibilities of the system architecture. the objective is to be able to go on with the production and not to reconsider the objectives. pr. mohamed benrejeb was born in tunisia in 1950. he obtained the diploma of "ingénieur idn" (french "grande ecole") in 1973, the master degree of automatic control in 1974, the phd in automatic control of the university of lille in 1976 and the dsc of the same university in 1980. full professor at "ecole nationale d’ingénieurs de tunis" since 1985 and at "ecole centrale de lille" since 2003, his research interests are in the area of analysis and synthesis of complex systems based on classical and non conventional approaches. ijcccv11n1.dvi international journal of computers communications & control issn 1841-9836, 11(1):105-112, february 2016. membrane computing and economics: a general view g. păun gheorghe păun institute of mathematics of the romanian academy po box 1-764, 014700 bucureşti, romania gpaun@us.es abstract: three are the points we briefly discuss here: using membrane computing tools for efficient computing/optimization, the possibilities of using “general" membrane computing (p systems using multisets of symbol objects processed by biochemical-like evolution rules) as a framework for modeling economic processes, and the numerical p systems, a class of computing devices explicitly defined with a motivation related to economics. the discussion is rather informal, only pointing out research directions and providing bibliographical information. keywords: membrane computing, p system, economics, numerical p system. 1 a quick glimpse to membrane computing membrane computing is a branch of natural computing aiming to abstract computing models from the structure and the functioning of the living cells – at least this was the initial motivation of the research in this area, [12]. basically, in the compartments of a cell-like membrane structure one places multisets (sets with multiplicities associated with their elements) of objects, represented by symbols of a given alphabet, and evolution rules, in the form of “reactions" transforming certain objects in other objects (multiset rewriting rules, written in the form u → v, where u and v are multisets); the objects can also pass through membranes, from a compartment to another one. starting from an initial configuration (of multisets and membranes) and applying the evolution rules according to specified strategies (semantics), one obtains transitions among configurations, hence computations. in this way we get a computing device, usually called a p system. results are associated with computations, in various ways. most investigated in membrane computing are p systems with the rules applied in the nondeterministically maximally parallel way (a multiset of rules is non-deterministically chosen and applied in each multiset, such that there is no applicable multiset which contains strictly the chosen multiset) and the result associated only with halting computations, those which reach a configuration where no evolution rule can be applied. there are many variations of this basic model. first, one can consider various forms of the rules, as well as ways of controlling their application: priority, promoters, inhibitors, periodical change of rules in time, etc. then, other ways of using the rules can be considered: sequential, limited parallelism, minimal parallelism, asynchronous systems, etc. also the result of a computation can be defined in various ways (internally, externally, in the form of a number or of a string), not necessarily for halting computations. further suggestions come from biology. for instance, instead of multiset rewriting rules, as those corresponding to biochemical reactions, we can consider symport or antiport rules, which move multisets of objects across membranes, [11]. similarly, we can consider rules for handling also the membranes, not only the objects: membranes can be created, dissolved, divided, the relationships between membranes can change in timne (e.g., by exocytosis, phagocytosis, etc.), and so on. copyright © 2006-2016 by ccc publications agora university 106 g. păun then, an extension can be considered from hierarchical arrangements of membranes, like in a cell, described by trees, to a tissue-like arrangement of membranes, described by a general graph. all ingredients mentioned above can be extended to this case. in short, a framework for processing multisets (of symbol objects) in a distributed parallel way. instead of symbol objects, we can also consider more complex objects, strings for instance – and then the evolution rules should be chosen suitably. most of the classes of the p systems suggested above are equivalent as computing power with turing machines, and this happens even for rather restricted (as the number of membranes or the complexity of the evolution rules) systems. furthermore, due to their massive parallelism, and to the possibility of creating an exponential workspace in a linear time, certain classes of p systems can solve computationally hard problems (typically, np-complete problems) in a feasible (polynomial) time. (this has been called in [14] fypercomputing, as an extension from hypercomputing, with “f" coming from “fast".) this is based on a time-space trade-off, with the space created by operations like membrane creation, membrane division, string duplication. depending on the used ingredients, even (polynomial) characterizations of pspace are obtained. two classes of p systems different from the previous ones are the numerical p systems, which will be discussed below, and the spiking neural p systems (in short, sn p systems), [7], inspired from the way the neurons cooperate in a brain, communicating through spikes, electrical impulses of identical shape. the results are the same also for sn p systems: computational universality and the possibility of fypercomputing in the case when neuron division or similar operations are present. another interesting idea leading to fypercomputations in this area is to use (arbitrarily large) pre-computed resources, with a limited amount of information present in the initial configuration. besides these theoretical directions of investigations, concerning the power and the efficiency of p systems, well developed is the application area, naturally, starting with applications in biology and biomedicine. many other applications are already reported, in ecology, linguistics, computer graphics, cryptography, approximate optimization, robot control – as well as to economics. the literature of membrane computing is rather large. details can be found at the domain web site from http://ppage.psystems.eu. an introduction can be found in [13], a comprehensive coverage (at the level of 2009) in [18], while applications can be found in [4], [6], [20]. many collective volumes, phd theses and downloadable papers can be found at the above mentioned web address. 2 using membrane computing tools in economics two ideas mentioned in the previous section suggest already that membrane computing can be useful for economics, especially in the computability, the operation research, and the optimization. the p systems as models will be discussed below, here we have in mind p systems as tools for handling “classic" models. first, we mentioned the many results where np-complete problems are solved in polynomial time by certain classes of p systems. most of the non-trivial practical problems appearing in economics (as well as in other areas) are np-complete. thus, it would be great to solve them in a polynomial time by using a "cellular computer". however, the solutions provided by membrane computing are still in info, theoretical, as there is no implementation of p systems in vitro/vivo, not even in silico (such that the massive parallelism can be effectively used). there are implementations of p systems on parallel hardware, but not at the level of making membrane computing and economics: a general view 107 practical the theoretical fypercomputing. on the other hand, it is highly probably that even a bio-implementation of a p system will not help too much, it will be similar to the case of dna computing, with the 1994 adleman experiment, which remained at the level of a demo, of solving toy problems, without practical consequences. much more practical is the possibility of using the so-called membrane algorithms in solving computationally hard optimization problems. this is a rather developed direction of research in membrane computing, with a large bibliography and very encouraging results. basically, this is nothing else than distributed evolutionary computing, but with the distribution controlled by means of membranes, and with further ingredients from membrane computing used, such as membrane creation, division, dissolution, communication among membranes, neural-like organization of cells, etc. many engineering applications of membrane algorithms were reported – the reader is advised to consult [4], [6], [18], the forthcoming volume [20], as well as the bibliography from the membrane computing web site. 3 p systems as models for economics we pass now to a more particular approach, namely to consider membrane computing as a framework for building models for economic processes. of course, the first question which could be formulated is: why? there are so many (mathematical) frameworks already used in economics, why considering a new, somewhat exotic (for instance, by its biological inspiration), one? the answer is complex. in general, new tools are always good to be checked, maybe they will bring new possibilities to address old problems in a new framework, ideally, to also formulate new questions in the new framework. membrane computing is not only natural in a direct sense, but also sufficiently developed at the theoretical, abstract level that it is just expected to be applicable in many areas, apparently far away from biology. then, the biological metaphor is very general and very useful, as indicated by the many applications of membrane computing in areas rather different from one another. more specifically, there are several features of p systems which make them attractive as models, for economics like for biology, too. they pertain to discrete mathematics, so that they are adequate to situations when we have to deal with small numbers of objects and agents. applying tools of continuous mathematics (especially differential equations) in situations which are clearly of a discrete type leads to wrong results. then, p systems are easily scalable, easily understandable, easily programmable – three appealing properties. the behavior of a (nontrivial) p system is “emergent", impossible to be predicted by simply examining the rules. (the universality also implies, through rice theorem, that no non-trivial question about a p system can be answered algorithmically, hence a computer simulation is needed.) finally, there are many economic issues which can be easily and transparently captured in terms of membrane computing. membranes can describe the organization, the multiset rewriting rules and the symport/antiport rules can describe production and commercial operations. only a hint, recalling some lines from [16]. assume that units of row material a are introduced on a market by a supplier s and are used by producers pi, each having a number of production units bi, to manufacture goods di; these goods are then sold by pi at price pi to retailers rj, each having a number of capacity units cj; in turn, the retailers sell the goods to a general consumer c, which introduces the need for d, denoted by d̄, on the market. for instance, by rules s → san, c → cd̄m, the supplier introduces n units of a and the consumer introduces the need for m copies of d. 108 g. păun producer pi can produce one d by consuming one a and one bi, bia → di, while retailer rj takes one order for d by means of cjd̄ → d̄j. (the good d and the need for d are indexed with the label of the producer – who fix its price – and of the retailer, respectively.) then, the producers and the retailers interact: by did̄j → bicj [pi,j], one copy of good d passes from pi to rj, because rj has an order for this good, and this happens with the probability pi,j, which depends on the previous transactions between producer pi and retailer rj; units of capacities, bi and cj, are free again. we have not yet included money here, but this can be easily done. instead of bia → di, we have to consider the rule biau r i → diu r s, with the meaning that pi has paid r monetary units (denoted by ui when belonging to pi) for a, thus s has gained r copies of us (notation for a monetary unit in possession of s). in turn, instead of did̄j → bicj [pi,j], we have to use did̄jv pi j → bicju pi i [pi,j], with the meaning that pi monetary units have passed from the “account” (= multiset) of rj to that of pi. the retailer will then sell the good at a price (if possible) greater than pi to the general consumer c, and the cycle is repeated. the model can still be enlarged: for instance, the number of a’s and d̄’s introduced on the market can fluctuate, the prices producers and retailers set can vary in time; then, both producers and retailers can make investments (transform money into production/storing units), maybe in a prudent manner (with low probability associated with investment rules and higher probability associated with saving rules, of the form ui → ui, just keeping the money unused), with or without limits on the total number of investments, and so on. it is important to note that coefficients pi,j written in square brackets on the right hand side of the rules are not “pure” probabilities, but they also express the trust of retailers when buying from producers, depending on the history of their collaboration. these coefficients can be changed over time, which adds to the complexity of the model. the reader is refereed to [15], [16], [17] for further details, illustrations, and discussions. several other titles are provided in the bibliography, where economic processes are approached in terms of p systems. after writing such a model, it is necessary to simulate it on a computer, hence a program is needed (ideally, implemented on a parallel support). of course, the model is not used as a computing one, but the evolution itself of the model is of interest (this is similar to the applications of p systems in biology). we are convinced that this direction of research deserves further efforts. 4 numerical p systems this class of p systems was introduced (in paper 16 from the list in the next section) with an explicit economic inspiration. briefly speaking, in the regions of a cell-like membrane structure, we have numerical variables (not “chemicals", as in the p systems discussed above), evolving by means of production-repartition programs. the variables from region i are denoted by membrane computing and economics: a general view 109 x1,i,x2,i, . . . ,xki,i. if the model is used as a computing devices, the variables take integer values, but for applications the values can be real numbers. a production-repartition program (from region i) is of the form fi(x1,i, . . . ,xki,i) −→ c1|v1 + c2|v2 + · · · + cni|vni, where fi(x1,i, . . . ,xki,i) is the “production function” and c1|v1 + c2|v2 + · · · + cni|vni defines the “repartition protocol”. denote ci = ∑ni s=1 cs and denote by xi,j(t) the value of variable xi,j at step t ≥ 0 (t = 0 corresponds to the initial values of variables). at any time t ≥ 0, we compute fi(x1,i(t), . . . ,xki,i(t)). the value q = fi(x1,i(t), . . . ,xki,i(t))/ci represents the “unitary portion” to be distributed to variables v1, . . . ,vni , according to coefficients c1, . . . ,cni in order to obtain the values of these variables at time t+1. specifically, vs will receive q · cs,1 ≤ s ≤ ni. if a variable receives such “contributions” from several neighboring compartments, then they are added in order to produce the next value of the variable. a variable used in a production function is consumed, reset to 0; if a variable does not appear in a production function, then its value remains unchanged – of course, it can change by receiving “production portions” (note that they can also be negative) from its region or from the neighboring regions. in this way, we pass from given values of the variables to next values. the process can be iterated. in this way, we get a computation. the positive values of a specified variable form the set of numbers generated by a numerical p system. again, further ingredients can be added – for instance, a control on the use of a program, such as the “enzymatic control" considered in several papers dealing with robot controllers based on numerical p systems, or restrictions can be considered – particular functions as production functions. as expected, these systems are both powerful (turing complete) and efficient. in the robot control area, numerical p systems were used as devices for computing functions of several real variables, and this seems to be the most plausible idea also for economic applications. anyway, again we expect applications in the two directions mentioned also for usual p systems: as tools for efficient computing and as models. both at the theoretical level and in what concerns the applications, numerical p systems are waiting for further attention, and we believe that the effort will be rewarding. in order to help the reader in this direction, we provide below a list of all titles we know at this moment related to numerical p systems (but further research are in development). 5 a bibliography of numerical p systems 1. o. arsene, c. buiu, n. popescu: snups, a simulator for numerical membrane computing. intern. j. of innovative computing, information and control, 7, 6 (2011), 3509–3522. 2. c. buiu, o. arsene, c. cipu, m. pătraşcu: a software tool for modeling and simulation of numerical p systems. biosystems, 103, 3 (2011), 442-447. 3. c. buiu, c.i. vasile, o. arsene: development of membrane controllers for mobile robots. information sciences, 187 (2012), 22–51. 4. m. garcia-quismondo, l.f. macias-ramos, m.j. pérez-jiménez: implementing enzymatic numerical p systems for ai applications by means of graphic processing units. topics in intelligent engineering and informatics, vol. iv, springer, berlin, 2013, 137–159. 5. a. leporati, a.e. porreca, c. zandron, g. mauri: improved universality results for parallel enzymatic numerical p systems. intern. j. unconventional computing, 9 (2013), 384–404. 110 g. păun 6. a. leporati, g. mauri, a.e. porreca, c. zandron: enzymatic numerical p systems using elementary arithmetic operations. membrane computing. proc. 14th intern. conf., cmc 2013, chişinău, august 2013, lncs 8340, springer, berlin, 2014, 249–264. 7. d. llorente–rivera, m.a. gutiérrez–naranjo: the pole balancing problem with enzymatic numerical p systems. proc. bwmc 2015, sevilla, february 2015, fenix editora, sevilla, 2015 (in press). 8. a.b. pavel: membrane controllers for cognitive robots. master’s thesis, department of automatic control and system engineering, politehnica university of bucharest, romania, february 2011. 9. a.b. pavel: development of robot controllers using membrane computing. phd thesis, department of automatic control and system engineering, politehnica university of bucharest, romania, july 2015. 10. a.b. pavel, o. arsene, c. buiu: enzymatic numerical p systems – a new class of membrane computing systems. the ieee fifth intern. conf. on bio-inspired computing. theory and applications. bic-ta 2010, liverpool, sept. 2010, 1331–1336. 11. a.b. pavel, c. buiu: using enzymatic numerical p systems for modeling mobile robot controllers. natural computing, 11, 3 (2012), 387–393. 12. a.b. pavel, i. dumitrache: hybrid numerical p systems. upb scientific bulletin, series a, submitted 2014. 13. a.b. pavel, c.i. vasile, i. dumitrache: robot localization implemented with enzymatic numerical p systems. proc. conf. living machines 2012, lncs 7375, springer, berlin, 2012, 204–215. 14. gh. păun: some open problems about numerical p systems. proc. 11th brainstorming week on membrane computing, sevilla, 4-8 february 2013, fénix editora, sevilla, 2013, 235–242. 15. gh. păun: some open problems about catalytic, numerical and spiking neural p systems, membrane computing. proc. 14th intern. conf., cmc 2013, chişinău, august 2013, lncs 8340, springer, berlin, 2014, 33–39. 16. gh. păun, r. păun: membrane computing and economics: numerical p systems. fundamenta informaticae, 73 (2006), 213–227. 17. gh. păun, r. păun: membrane computing as a framework for modeling economic processes. seventh intern. symp. symbolic and numeric algorithms for scientfic computing, synasc 2005, ieee press, 2005, 11–18. 18. gh. păun, r. păun: membrane computing and economics. section 23.6 in handbook of membrane computing, oup, 2010, 632–644. 19. c.i. vasile: distributed control for multi-robot systems. phd thesis, department of automatic control and system engineering, politehnica university of bucharest, romania, july 2015. 20. c.i. vasile, a.b. pavel, i. dumitrache: universality of enzymatic numerical p systems. intern. j. computer math., 90, 4 (2013), 869–879. membrane computing and economics: a general view 111 21. c.i. vasile, a.b. pavel, i. dumitrache, j. kelemen: implementing obstacle avoidance and follower behaviors on koala robots using numerical p systems. tenth brainstorming week on membrane computing, sevilla, 2012, vol. ii, 215–227. 22. c.i. vasile, a.b. pavel, i. dumitrache, gh. păun: on the power of enzymatic numerical p systems. acta informatica, 49, 6 (2012), 395–412. 23. x. wang, g. zhang, f. neri, t. jiang, j. zhao, m. gheorghe, f. ipate, r. lefticaru: design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots. integrated computer-sided engineering, to appear. 6 concluding remarks in many circumstances, computer science got very fruitful inspiration from biology, and this became a systematic research direction in the last decades under the mane of natural computing. a comprehensive overview can be found in [19]. membrane computing is part of this endeavor, well developed at the mathematical level, general, versatile, promising at the level of applications in very (apparently) different areas. here, we only mentioned some of the possibilities and the researches reported so far concerning the applications of membrane computing in economics, calling the attention to this research area, with the conviction that it deserves to be explored. bibliography [1] j. bartosik (2004); paun’s systems in modeling of human resource management. second conference on tools and methods of data transformation, wsu, kielce, poland.. [2] j. bartosik (2004); heaps of pieces and paun’s systems. second conference on tools and methods of data transformation, wsu kielce. [3] j. bartosik, w. korczynski (2002); systemy membranowe jako modele hierarchicznych struktur zarzadzania, mat. pokonferencyjne ekonomia, informatyka, zarzadzanie. teoria i praktyka, wydzial zarzadzania agh, tom ii, agh 2002. [4] g. ciobanu, gh. păun, m.j. pérez-jiménez (eds.) (2006); applications of membrane computing. springer, berlin. [5] r. freund, m. oswald, t. schirk (2007); how a membrane agent buys goods in a membrane store. progress in natural science, 17: 442–448. [6] p. frisco, m. gheorghe, m.j. pérez-jiménez (eds.) (2014); applications of membrane computing in systems and synthetic biology, springer, berlin. [7] m. ionescu, gh. păun, t. yokomori (2006); spiking neural p systems. fundamenta informaticae, 71(2-3): 279–308. [8] w. korczynski (2004); on a model of economic systems. second conference on tools and methods of data transformation, wsu kielce. [9] w. korczynski (2005); păun’s systems and accounting. pre-proceedings of sixth workshop on membrane computing wmc6, vienna, austria, 461–464. 112 g. păun [10] m. oswald (2007); independent agents in a globalized world modelled by tissue p systems. artificial life and robotics, 11: 171–174. [11] a. păun, gh. păun (2002); the power of communication: p systems with symport/antiport. new generation computing, 20(3): 295–306. [12] gh. păun (2000); computing with membranes. j. computer and system sciences, 61(1): 108–143, first circulated as tucs report 208, november 1998 (www.tucs.fi). [13] gh. păun (2002); membrane computing. an introduction. springer, berlin. [14] gh. păun (2012); towards "fypercomputations" (in membrane computing). languages alive. essays dedicated to jurgen dassow on the occasion of his 65 birthday (h. bordihn, m. kutrib, b. truthe, eds.), lncs 7300, springer, berlin, 207–221. [15] gh. păun, r. păun (2005); membrane computing as a framework for modeling economic processes. proc. synasc 05, ieee press, 11–18. [16] gh. păun, r. păun (2006); a membrane computing approach to economic modeling: the producer-retailer interactions and investments. analiză şi prospectivă economică, part i: 3(1): 30–37, part ii: 4(2): 47–54. [17] gh. păun, r. păun (2006); membrane computing models for economics. an invitationsurvey. studii şi cercetări de calcul economic şi cibernetică economică, 40(1-2): 5–19. [18] gh. păun, g. rozenberg, a. salomaa, eds.(2010); handbook of membrane computing. oxford university press. [19] g. rozenberg, t. bäck, j.n. kok, eds. (2010); handbook of natural computing. 4 vols., springer, berlin, 2012. [20] g. zhang, m. gheorghe, m.j. pérez-jiménez (2016); real-life applications with membrane computing. springer, berlin (in press). int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 258-265 an authenticated key agreement protocol using isogenies between elliptic curves d. he, j. chen, j. hu debiao he, jianhua chen, jin hu wuhan university school of mathematics and statistics wuhan, hubei, 430072, china e-mail: hedebiao@163.com, chenjh-ecc@163.com, hujin-ecc@163.com abstract: all the current public-key cryptosystems will become insecure when size of a quantum register is sufficient. an authenticated key agreement protocol, which is against the attack of quantum computer, is proposed. the proposed protocol can provide the security properties known session key security, forward security, resistance to key-compromise impersonation attack and to unknown key-share attack, key control. we also prove its security in a widely accepted model. keywords: public-key cryptosystem; quantum computer; isogeny; elliptic curve; key agreement protocol. 1 introduction key agreement is one of the fundamental cryptographic primitive after encryption and digital signature. such protocols allow two or more parties to exchange information among themselves over an adversarially controlled insecure network and agree upon a common session key, which may be used for later secure communication among the parties. thus, secure key agreement protocols serve as basic building block for constructing secure, complex, higher-level protocols. the first modern key agreement protocol is the diffie-hellman protocol given by diffie and hellman in the seminal paper [1] in 1976. its security is based on the difficulty of solving discrete logarithm problems. as the first practical key agreement protocol without authentication, it is not secure to the man-in-the-middle attack. after that, lots of protocols have been published and some of them use certificates generated by thetrusted third parties (public key infrastructures, pkis) to prevent attacks such as the man-in-the-middle attack. most of the systems based on pki are complex and expensive for the cost of the authentication, refresh andrevocation of certificates. security of the known key agreement protocols is based on two general mathematical problems: determination of order and structure of a finite abelian group, and discrete logarithm computation in a cyclic group with computable order. both of the problems can be solved in polynomial time using shor’s algorithm for a quantum computer [2]. thus, most of the current public-key cryptosystems will become insecure when size of a quantum register is sufficient. development of key agreement protocols, which would be strong against a quantum computer, is necessary. the rest of our paper is organized as follows. section 2 describes theoretical background and a public-key encryption technique. section 3 analyses the complicity of the problem of searching for an isogeny between elliptic curves. we give the proposed key agreement protocol in cection4, and analyse the security of the proposed protocol in section 5. we conclude this paper in section 6. copyright c⃝ 2006-2011 by ccc publications an authenticated key agreement protocol using isogenies between elliptic curves 259 2 elliptic curves over fp and isogeny star let e be a elliptic curve, defined on the finite fields fp, and it’s equation is y2 = x3 + ax + b, a, b ∈ fp (1) then the map π : (x, y) → (xp, yp) (2) specifes the frobenius endomorphism of the curve e. a frobenius map satisfies its characteristic equation π2 − tπ + p = 0 (3) where t = p − a − ♯e(fp) is the frobenius trace. through the hasse’s theorem, we know that |t | < 2 √ p). (4) so the discriminant dπ of the frobenius equation (3) satisfies dπ = t 2 − 4p < 0 (5) theorem 1 elliptic curves are isogenous over fp if and only if they have equal number of points. proof. see[3]. theorem 2 let an elliptic curve e(fp) have the frobenius discriminant dπ and (dπl ) be a kronecker symbol for some l-degree isogeny. if (dπ l ) = −1, then there are no l-degree isogenies; if (dπ l ) = 1, then two l-degree isogenies exist; if (dπ l ) = 0, then 2 or l + 1 l-degree isogenies exist and l is called elkies prime number. proof. see[3]. let u = ei(fp) be a set of elliptic curves with equal number of points, so that each element of u is uniquely determined by a j−invariant of an elliptic curve. according to the theorem 1 and the equation (4), we can consider u as a category, and the set of isogenies between elements of u as a set of morphisms of this category. we can compute ♯u = hdπ , where hdπ is the degree of hilbert polynomial[3]. let l is elkies prime number, we can get that there are two isogenous elliptic curves for any elliptic curves of u, from theorem 2. it is practically determined that, when ♯u is prime, all the elements of u form a single isogeny cycle. let l1 ̸= l be one more prime isogeny degree with the property that (dπli ) = 1. in this case, li−degree isogenies form a cycle over u as well. then we can put the l and li degree isogeny cycles over each other. same can be done for other isogeny degrees of such kind. definition 1. a graph, consisted of prime number of elliptic curves, connected by isogenies of degrees satisfying (dπ li ) = 1, is an isogeny star. if an isogeny star is wide enough, we can use it for crypto algorithm constructing. for that purpose, it is necessary to specify a direction on a cycle and route of isogeny stat. the method for direction determination on an isogeny cycle is mentioned in [4], we don’t give the detail here.. it uses impact of frobenius endomorphism on an isogeny kernel. the definition of isogeny stat is following. let s be an isogeny star, l = {li}−a set of elkies isogeny degrees being used and f = {πi}−a set of frobenius eigenvalues, which specify positive direction for every li ∈ l. 260 d. he, j. chen, j. hu definition 2. a set r = ri, where ri is number of steps by the li−isogeny in the direction f = πi, is a route on the isogeny star. we can define composition[3] of routes a = {ai} and b = {bi} as ab = {ai + bi}. routes are commutative: ab = ba. the computation of iosgeny between elliptic curve can be done using the method in [5, 6, 7], we don’t give the detail here. 3 complexity of isogeny search the are several techniques can be used for isogeny search[3]: • brute-force: complexity of these attacks is estimated at isogeny computations. • meet-in-the-middle: complexity of the attack is estimated at isogeny computations. • method described in [8]: complexity of the attack is estimated at isogeny computations. the reason of the problem of searching for an isogeny between elliptic curves can against the attack of quantum computer is following[3]. in order to computer the isogenies between elliptic, we must solve the equation ϕ(x, j) = 0, (6) and the process of computing the isogeny cycle is following e1 → ϕl1(x, je1) = 0 → je2 → e2 → ϕl2(x, je2) = 0 → je3 → ... (7) . to compute a chain of q isogenies, one should consecutively solve these q equations, because of the equation parameter (j−invariant) is changed with every step. so one can’t parallelize and the problem is against the attack of quantum computer. then, we conclude that the complexity of searching for an isogeny between elliptic curves is o( √ n) ≈ o( 4 √ p), and the problem can be against the attack of quantum computer. from the above discussion, the decisional diffie-hellman assumption can be easily extent to the isogenies through the property of isogenies between the elliptic curves. definition 3. the decisional diffie-hellman assumption over isogeny star (ddha-is ): ddha-is is that it is difficult to distinguish the following real diffie-hellman distribution γreal = {r1(einit), r2(einit), r1r2(einit)|r1, r2 ∈ g} (8) and random diffie-hellman distribution γrand = {r1(einit), r2(einit), r3(einit)|r1, r2, r3 ∈ g} (9) . more formally, if we define the advantage function advddh−isg (a) as advddh−isg (a) = |pr[a(x) = 1|x ∈ γreal] − pr[a(y ) = 1|y ∈ γrand]|, (10) we say that the ddha-is holds in set g if advddh−isg (a) is negligible for any probabilistic polynomial time adversary a. an authenticated key agreement protocol using isogenies between elliptic curves 261 4 key agreement protocol based on isogeny in this section we describe the proposed key agreement protocol which is specified by the key generation and the protocol description. 4.1 key generation in this paper we use an elliptic curve e defined over a finite field fp, the parameters is following. 1)fp: the finite field; 2)einit: an initial elliptic curve, its equation is y2 = x3 + ainitx3 + binit, ainit, binit ∈ fpłť 3)d:number of isogeny degrees being used; 4)l = li, 1 ≤ i ≤ d: a set of elkies isogeny degrees being used; 5)f = πi, 1 ≤ i ≤ d: a set of frobenius eigenvalues, which specify the positive direction for every li ∈ l; 6)k:a limit for number of steps by one isogeny degree in a root. for any root {ri}, numbers of steps are selected in −k ≤ ri ≤ k; 7)h:sha-1; 8)select random routes rpriva and rprivb. the value rpriva is a secret key of the user a, and rprivb is the secret key of the user b; 9)compute the curves epuba = rpriva(einit) and epubb = rprivb(einit), which are the public key of a user a and b, respectively; 4.2 our key agreement protocol let e be the elliptic curve, defined on the finite field , with the equation (1), and let ae and be be its parameter and j be its j−invariant. the proposed protocol is following. 1) a generate random route ra and computes ea = ra(einit), ea = h(aea, bea). at last, a sends m1 = {aea, bea, ea} to b. 2) upon receiving m1, b checks whether ea equals h(aea, bea). if not, b stops the session. otherwise, b generate random route rb and computes eb = rb(einit), e′b = rb(ea), e′′b = rprivb(ea), eb = h(aeb , beb , ae′′b ), be′′b ). at last, b sends m2 = {aeb , beb } to a. 3) upon receiving m2, a computes e′a = ra(eb), e ′′ a = ra(epubb), e ′′′ a = ra(epubb) and checks whether the equation eb = h(aeb , beb , ae′′a), be′′a) holds or not. if it does not hold, then a terminates the execution. otherwise, a computes e′a = h(ae′′′a ), be′′′a ) the session key skab = h(ae′ a ) ⊕ be′ a ). at last, a sends m3 = e′a to b. 4) upon receiving m3, b computes e′′′b = rb(epuba) and checks whether the equation e ′ a = h(ae′′′ b ), be′′′ b ) holds or not. if it does not hold, then b terminates the execution. otherwise, computes the session key skba = h(ae′ b ) ⊕ be′ b ). as a result, a and a achieve the same shared secret key: e′a = ra(eb) = ra(rb(einit)) = rb(ra(einit)) = rb(ea) (11) skab = h(ae′ a ) ⊕ bea) = h(ae′a) ⊕ be′a) = skab (12) and authenticate each other. 262 d. he, j. chen, j. hu 5 security analysis 5.1 security model in this work we shall use a modified bellare-rogaway key exchange model [9, 10] to analyse the protocol security. in the model, each party involved in a session is treated as an oracle, and an adversary can access the oracle by issuing some specified queries (defined later). an oracle πsi,j denotes the s−th instance of party i involved with a partner party j in a session. the security of a protocol is defined by a game with two phases. in the first phase, an adversary e is allowed to issue the following queries in any order. 1)send(πsi,j, m). upon receiving the message m, oracle π s i,j executes the protocol and responds with an outgoing message m or a decision to indicate accepting or rejecting the session. if the oracle πsi,j does not exist, it will be created as initiator if m = λ, or as a responder otherwise. 2)reveal(πsi,j). if the oracle has not accepted, it returns ⊥; otherwise, it reveals the session key. 3)corrupt(i). the party responds with its private key. once the adversary decides that the first phase is over, it starts the second phase by choosing a fresh oracle and issuing a query, where the fresh oracle and query are defined as follows. definition 4 (fresh oracle) an oracle πsi,j is fresh if (1)π s i,j has accepted; (2)π s i,j is unopened (not been issued the query); (3) party j ̸= i is not corrupted (not been issued the corrupt query); (4) there is no opened oracle πtj,i, which has had a matching conversation to . the above fresh oracle definition is particularly defined to cover the key-compromise impersonation resilience property since it implies that the user could have been issued a query. 4)test(πsi,j).oracle π s i,j which is fresh, as a challenger, randomly chooses b ∈ {0, 1} and responds with the session key, if b = 0, or a random sample from the distribution of the session key otherwise. after this point the adversary can continue querying the oracles except that it cannot reveal the test oracle πsi,j or its partner π t j,i (if it exists), and it cannot corrupt party j. finally, the adversary outputs a guess b′′ for b. if b′ = b, we say that the adversary wins. the adversary’s advantage is defined as adve(k) = |2pr[b′ = b] − 1|. (13) we use the session id which can be the concatenation of the messages in a session to define matching conversations, i.e., two oracles πsi,j and π t j,i have matching conversations to each other if they have the same session id. a secure authenticated key (ak) agreement protocol is defined as follows. definition 5 protocol π is a secure ak if: 1). in the presence of a benign adversary, which faithfully conveys messages, on πsi,j and π t j,i, both oracles always accept holding the same session key, and this key is distributed uniformly on {0, 1}k; 2). for any polynomial time adversary e, adve(k) is negligible. 5.2 security analysis using the above security definitions, we have the following theorem 1. theorem 3. in the random oracle, if ddha-is is hard, our proposed protocol is a secure ak protocol. proof : the first two conditions follow immediately from the description of our proposed protocol and the assumption that h is random oracle. let’s turn to the second condition. we use the method proposed by pan et al.[10] to analyze the security. consider there exists an adversary e and adve(k) is non-negligible. suppose its an authenticated key agreement protocol using isogenies between elliptic curves 263 running time is t. we will use e to construct an algorithm f which can solve ddha-is. let l, n and qh be the number of sessions related to e, the number of entitys and the queries’ numbers of h(·) made by e. given (e1, e2, e3), where e1 = r1(einit), e2 = r2(einit), e3 = r3(einit). first, f selects randomly i, j, r, s and generates the public/private key pair for every entity. then, f starts e and answers all queries made by e. h query: if the input exists, answers by its corresponding value. otherwise, f picks a random number as the answer to the new query, and adds the input, output pair at the end of the h−string; corrupt query: because f knows all entities’ private keys, f can answers by the corresponding private key; reveal query: if the input is πsi,j or π t j,i, f selects b ′ as its output and halts. suppose the input is πs ′ i′,j or π t′ j,i′ , where (i ′, s′) ̸= (i, t) and (i′, t′) ̸= (i, t). because f knows all entity’s private keys and simulates the run of i′ and j according to the protocol, f can get random numbers selected by i′ and j. so, f knows the session key of πs ′ i′,j or π t′ j,i′ , and answers by this session key. send query: if the input is πsi,j and an empty string, f computes ea = h(ae1, be1) and answers by ae1, be1, ea. if the input is π s i,j and a string that is not none, f computes e′′′a = rpriva(e2) and answers by {e ′ a}. if the input is π t j,i and a string that is not none, computes e′′b = rprivb(e1), eb = h(ae2, be2, ), a ′′ e2 , b′′e2 and answers by ae2, be2, eb. else, answers by random numbers according to the protocol. test query: if the input is not πsi,j, f outputs b ′ ∈ {0, 1}k and halts. else, if ae3 and be3 exist in h−string, let the corresponding value be r; else, f selects r randomly and appends {ae3, be3, r} to the h−string. f answers by r. when e halts, f outputs e’s output b′ and halts. 1)suppose πsi,j, selected by f , is the input of the test oracle, and π s i,j and π t j,i have matching conversations. if e3 is the dh value of e1, e2, r is the session key. else r is a random number. because e3 is the dh value of e1, e2 with the probability 12 and f answers all queries made by e correctly, the probability of the event that distinguishes r and the session key correctly is equivalent to the probability of the event that f decides whether (e1, e2, e3) is a dh triple. 2) suppose πsi,j, selected by f , is the input of the test oracle, or π s i,j and π t j,i have not matching conversations. f outputs correctly with the probability 1 2 . suppose the success probability of e is 1 2 +ϵ, where ϵ is non-negligible. because the first event happens with the probability 1 l2 , the success probability of f in this condition is 1 2l2 + ϵ l2 . the second event happens with the probability 1 − 1 l2 . so the success probability of in this condition is 1 l2 − 1 2l2 . from the above discussion, we know the success probability of f is 1 2 + ϵ l2 . then we can solve the ddhp-is with non-negligible probability. this is a contradiction. so our proposed protocol satisfies the second condition. i.e., the protocol is a secure ak protocol. 5.3 other discussion a number of desirable attributes of key agreement protocols have also been identified [9] and nowadays most protocols are analyzed with such attributes. here, the following six security properties must be considered for the proposed protocol: a known-key security, perfect forward secrecy, a key-compromise impersonation attack, a unknown key-share security, a key-control security. regarding the above mentioned definitions, the following theorems are used to analyze the six security properties of the proposed protocol. our protocol also satisfies the following security notions which are often used to judge the security of key agreement protocols. 264 d. he, j. chen, j. hu known session key security: a protocol is called known session key security, if an adversary, having obtained some previous session keys, cannot get the session keys of the current run of the key agreement protocol. in our scheme, the agreed session key relies on the one-way hash function and session secrets. the output of hash function is distributed uniformly in {0, 1}k, thus one session key which is the output of hash function has no relation with the others. besides, the session key is generated with the session secrets which are computed from the random ephemeral key, thus even one session’s session secrets are revealed, the other session secrets will still remain safe. perfect forward secrecy: a protocol is called perfect forward secrecy, if compromise of the three private keys of the participating entities does not affect the security of the previous session keys. even if an attacker gets the value the secret key rpriva and rprivb in our scheme, he can’t deduce e′a or e ′ b, without the knowledge of the two random numbers ra and rb. therefore, our scheme can provide perfect forward secrecy. no key-compromise impersonation: the compromise of one entity’s static private key does not imply that the private keys of other entities will also be compromised in our protocol. the adversary may impersonate the compromised entity in subsequent protocols, but he cannot impersonate other entities. this property is called no key-compromise impersonation. first, suppose an attacker c obtains the long-term private key rpriva from the compromised user a. in order for the key-compromise impersonation attack to succeed, c must know a’s ephemeral keys . in this case, c would also have to extract from ’s ephemeral public value ra, so as to generate the same session key with a. c, however, will face the problem of searching for an isogeny between elliptic curves. therefore, the proposed protocol is secure against a key-compromise impersonation attack. no unknown key-share: if the adversary convinces a group of entities that they share some session key with the adversary, while in fact they share the key with another entity, we call the protocol as suffering from unknown key-share attack. to implement such an attack on our protocol, the adversary is required to learn the private key of some entity. otherwise, the attack hardly works. hence, we claim that our protocol has the attribute of no unknown key-share. no key control: no key-control security means that neither entity can’t force the session key to a preselected value. from the execution of the proposed protocol, we know that the only possibility of key-control attack may be brought out by the participant of the protocol b. but, for the party b to make the party a generate the session key kb which is preselected value by b, b should solve the equation e′b = rb(ea).this is the problem of searching for an isogeny between elliptic curves. therefore, the proposed protocol provides a no key-control security. 6 conclusion in this paper, we propose a secure and efficient authenticated key agreement, which works on the isogeny star. we prove that our protocol meets the security attributes under the assumption that the problem of searching for an isogeny between elliptic curves is secure. 7 acknowledgments the authors would like to thank the anonymous reviewers for their constructive comments. an authenticated key agreement protocol using isogenies between elliptic curves 265 bibliography [1] w. diffie and m. hellman, new directions in cryptography, ieee trans. info. th, vol. 22, pp.644-654, 1976. [2] boneh d., lipton r. quantum cryptanalysis of hidden linear functions. proceedings of the 15th annual international cryptology conference on advances in cryptology (lncs 963), 1995:424-437. [3] rostovtesv a. and stolbunov a., public-key cryptosystem based on isogenies. cryptology eprint archive, report 2006/145, 2006. http://eprint.iacr.org/. [4] couveignes j. m., dewaghe l., morain f. isogeny cycles and the schoof-elkies-atkin algorithm. ecole polytechnique, france, 1996. [5] elkies n., elliptic and modular curves over finite fields and related computational issues, proceedings of a conference in honor of a.o.l. atkin, ams international press, 1998, pp.2176. [6] muller v., ein algorithmus zur bestimmung der punktanzahl elliptisher kurven uber endlichen korpern der charakteristik groser drei, 1995. http://www.informatik.tudarmstadt.de/ti/forschung/ecc. [7] f.morain, e.schost, fast algorithms for computing isogenies between elliptic curves. http://www.lix.polytechnique.fr/ morain/jcomp.pdf, 2006. [8] s. galbraith. constructing isogenies between elliptic curves over finite fields, journal of computational mathematics, vol. 2, pp.118-138, 1999. [9] s. blake-wilson, d. johnson and a. menezes, key agreement protocols and their security analysis, proceedings of sixth ima international conference on cryptography and coding, cirencester, uk, 1997, pp. 30-45. [10] h. pan, j.-f. li, q.-s. zheng, a provable-security mutual authenticated key agreement protocol for mobile communication, the 4th international conference on wireless communications, networking and mobile computing, 2008, pp.1-4. international journal of computers, communications & control vol. ii (2007), no. 3, pp. 252-268 extraction of critical scenarios in a railway level crossing control system malika medjoudj, pascal yim abstract: this paper deals with the safety of the level crossing control system. we propose one way of the safety evaluation witch consist on the extraction of feared scenarios in the petri net model of the system. we use esa_petrinet tool (extraction scenarios & analyzer by petri net model) that was developed in the aim of extraction of feared scenarios in computer-controlled systems. these scenarios characterize the sequences of actions leading to dangerous situations. the taking into account of the failures, the temporal constraints and partially the continuous dynamic (by temporal abstraction) of the system makes it possible to respect the order of appearance of the events in the generated scenarios. keywords: critical scenarios, hybrid dynamic, level crossing control system, safety, temporal petri net 1 introduction one way to evaluate the safety [2] of complex system such as a level crossing control system (lccs) is the extraction of critical scenarios leading to the feared states. a qualitative analysis method of safety, aiming the extraction of all the critical scenarios from a petri net model [5] of computer-controlled systems was developed by [3]. this approach witch is an extension of a method developed by [6] but which operated only on the discrete aspect of the system, takes into account the continuous aspect of the system and the temporal specifications. this approach based on linear logic [7] determines more precisely the exact conditions of the occurrence of the feared event, i.e what has led the system to leave its normal operation and to evolve into the feared state. the originality of this approach is that the order of occurrence of the events is taken into account, and impossible scenarios with respect to continuous dynamics and temporal specifications of the system are eliminated. the automation of all stages of the process has led to the development of esa_petrinet tool (extraction & scenarios analyser by petrinet model) [4] that has been interfaced with tina tool (time petri net analyzer) [8].we will use in this paper esa_petrinet tool to extract dangerous scenarios from the level crossing benchmark published by [10]. we will present the method of extraction of feared scenarios and the basic of the algorithm in section 2, the level crossing control system in section 3, its petri net modelling in section 4, the use of esa_petrinet tool to generate the critical scenarios in the section 5 and we will end by a conclusion. 2 method of extraction of feared scenarios the application of this method requires the modelling of the system by a time petri net model and identifying the places of nominal behaviour. the appropriate petri net modelling of computer-controlled systems is a predicate transitions differential stochastic petri net (ptds petri net) as they are generally hybrid (discrete and continuous dynamics) and there safety analysis require taking into account failures. a temporal abstraction is necessary to translate this model to a time petri net by associating to the transitions a temporal interval of firing corresponding to the time which the system can spend to reach the state in question. a preliminary analysis will refine fields of variables according to various accessible markings by reasoning on the invariants of places. indeed, the invariants of places determine the possible dynamics, and which other places can be simultaneously marked when a token is present in a given place. copyright © 2006-2007 by ccc publications extraction of critical scenarios in a railway level crossing control system 253 2.1 principal the method of extraction of feared scenarios is made up of two steps [3]: a backward reasoning and a forward reasoning. the backward reasoning takes as an initial marking in the reversed petri net model (the initial petri net in which all the arcs are reversed), the only target state (feared) and seeks exhaustively all the scenarios making it possible to consume the initial marking (feared state since forward reasoning) and reach a final marking composed only of places associated to the normal operation. the forward reasoning takes as an initial state these places of normal operation in the initial petri net model. the objective is to locate the junctions between the feared behaviour and the normal operation of the system as well as the conditions implied in these junctions. thus we have not only the explanation of the dangerous behaviour but also of strategies allowing its avoidance. a significant point of the method is that the context in which occurred the feared event is enriched gradually. the enrichment (of marking) consists on putting tokens in empty places in the petri net model when it is necessary to make evolve the system and generate scenarios. the invariant of places are used as a mechanism of checking the coherence of the enrichment of marking. indeed the new tokens added are removed if they do not respect the dynamics of the system. each scenario is given in form of a partial order between the events necessary to the appearance of the feared event what differs from a failure tree, which gives a whole of static combinations of the partial states necessary for obtaining the feared state. 2.2 dealing with continuous dynamics by temporal abstraction this method takes into account the conditions associated to the firing of certain transitions. these conditions are thresholds involving continuous variables. by temporal approximation of the hybrid dynamics, these thresholds are transformed to durations, which correspond to time that the system puts to reach when the transitions are enabled. from a qualitative point of view, the objective is to determine the firing order of the transition. thus, when we enrich the marking, we can find situation where two transitions t1 and t2 are enabled if only the ordinary petri net is considered, but whose are such as t1 will be always fired before t2 if the temporal abstraction is also considered. in the generation of the scenarios only the firing of t1 will be considered since that of t2 before t1 would be in fact incoherent with the continuous dynamics. this appears in the form of a priority: if t1 and t2 are enabled, only the case of t1, priority, is examined. the taking into account of these precedence relations coming from the continuous dynamics and not specified by the ordinary petri net allows to reduce the number of scenarios generated by eliminating a certain number of incoherent scenarios with respect to continuous dynamics. let us consider an example. in figure 1 we suppose that the differential-algebra system associated to the place p1 guarantees that the variable x is increasing. we associate to the transition t1 the threshold x = v1 and to the transition t2 the threshold x = v2 with v1 < v2. finally, we suppose that when the token arrives in the place p1 we have always x < v1. so, if the place p3 is marked, the transition t1 will be fired before t2 since the threshold associated to t1 is lower than that of t2. in this case we don’t consider the scenario associated to the firing of t2. on the other hand, if t3 is already fired for example if we consider that t1 is a stochastic transition corresponding to a failure (place p3 empty) and if the place p2 is marked, t1 cannot be fired and then t2 will be fired. in the example above, finally only one type of scenarios is examined, those for which the transition t2 is fired after t3. so, there is a precedence relation between the firing of t3, which empties the place p3 and that of t2, however there is no place connecting t3 to t2. this precedence relation is so, a consequence of continuous dynamics and thresholds associated to transitions t1 and t2. we are talking in this case about indirect precedence relation and about indirect causality. the direct precedence relations and causality are those that are highlighted by the only petri net, i.e. by the only discrete aspect. 254 malika medjoudj, pascal yim figure 1: temporal abstraction and priority due to thresholds of transitions 2.3 esa_petrinet tool esa_petrinet tool uses two output files of tina tool as input files. the first file is a textual description of the petri net model of the system and the second contains the invariant of places. the indirect precedence between certain transitions firing resulted by the temporal abstraction of continuous dynamics, is expressed in the algorithm in the form of rules of priority (a certain transition is not fired if another is enabled). the transition time interval of tina tool permits to express this rule of priority. if we take the example of figure 1, if there is an intersection between the time interval associated to transitions t1 and t2, they will have the same priority of firing and the two scenarios will be generated. we note that only one execution of the algorithm generates automatically several scenarios. all the possible and coherent scenarios with respect to the continuous dynamics and the temporal constraints of the system are generated. 3 level crossing control system case study figure 2: railway level crossing 3.1 general description this case study concerns a decentralized radio-based railway level crossing control system taken from a realistic specification of a new radio-based train control system, which has been developed for the german railways. it is presented by [10] and studied by [9] using a transformation of a p-time petri net model of the system to automata in the purpose of avoiding forbidden states. this modelling is of a high level of abstraction and does not take into account the failures of the system. although, simplification has been made in the presentation of this example, it remain especially interesting as it is well known by the railway specialists, takes into account software and hardware specification, hybrid dynamic and temporal constraints. our aim is a whole modelling of the system extraction of critical scenarios in a railway level crossing control system 255 using a petri nets model by taking into accounts the hybrid dynamic, temporal constraints and failures. then, applying the method described above to extract critical scenarios. 3.2 composition of the system and specification the radio-based level crossing control system is used in an intersection area between a single track railway line and a road as illustrated in figure 2a. to avoid collision, trains and road traffic must not enter at the same time this crossing zone called danger zone. the level crossing is controlled by means of signals radio communication between a train-borne control system (on-board system), a level crossing control system and an operation centre which supervises interactions between the two preceding components. it is important to note that transmission times on the network may vary and radio telegrams may be lost. the railway crossing is equipped with half barriers, a red and a yellow road traffic light. road users shall stop at the level crossing if possible when the yellow light is shown and must stop when the red light is shown as the level crossing is closed for road users in this case. the yellow light and the red light never must be shown together and when both are off the danger zone can be crossed by road users. the traffic lights and barriers at the level crossing are controlled by the level crossing control system which will be activated (turn on) with the approach of a train to the level crossing. when the level crossing control system is activated, it carries out a sequence of actions at a specific timing to ensure a safely closing of the crossing and the danger zone to be free of road traffic. first, the yellow light is switched on, then after 3 seconds it is switched off and the red light is switched on. after 9 seconds the barriers are started to be lowered within a maximum time of 6 seconds. if the barriers have been completely lowered within this time, the level crossing control system signals the safe state of the level crossing and the train can cross it. when the train has completely passed the danger zone, the level crossing may be opened for road traffic. in the level crossing opening phase, the barriers are first opened then the red traffic light and the level crossing control system are switched off. the half barriers are used to block the entry lane on either side of the level crossing. as there are no barriers for the exit lanes, imprudent road users may enter the crossing area on the opposite lane if the closure time of the level crossing exceeds 240 seconds. a general view of the normal operating of the level crossing control system is given in figure 2b. the train is equipped on board by a route map which contains the positions of danger points at level crossings and provides information for the train (lineside equipment or signal staff) when or where to send an activation order to the corresponding level crossing control system. the train on-board system sends so a radio message to the level crossing control system in order to close the level crossing in time and let the train pass through without any delay or braking action. it will also set a breaking curve for speed supervision making the train stop at the danger point in a failure situation. the level crossing control system acknowledges receipt of the activation order to the train. after receipt of the acknowledgement the on-board system waits the necessary time for the closing of the level crossing, then sends a status request to the level crossing control system. if the level crossing is in a safe state it will be reported to the train which allows cancelling the breaking curve and safely pass over the level crossing. the vehicle sensor at the rear of the level crossing will be triggered allowing the opening of the level crossing. 3.3 possible failures a main cause of failures is the malfunctioning of sensors or actuators. the main physical structures, communication systems and the control systems themselves may be failed. failure may occur at any time. defective devices will be repaired after some time but will not take place when a train approaching or passing the level crossing in case of non recoverable failure. in this case study, only a limited number of failures are taken into account: 256 malika medjoudj, pascal yim • failure of the yellow or the red traffic light • failure of barriers (actuators) • failure of vehicle sensor • the delay or loss of telegrams on the radio network the traffic lights and the vehicle sensor are constantly supervised and defect is immediately reported to the level crossing control system. failure of the barriers can only be detected by time-out when barriers fail to reach upper or lower end position in time or at all. 3.4 behaviour of the control system under failure the level crossing control system detects the occurrence and repair of failures of traffic lights and vehicle sensor and immediately reports them as an event to the operations centre. train operation is not suspended on the affected track section until repair. when the train sends a status request, if in the sequel it does not receive the status report with the safe state of the level crossing before entering its breaking curve the on-board system will apply the breaks until the status report will be received or the train has come to a stand still. if the status report is received before stand still, breaks are released and train can continue its run. if not a request is prompted on the driver’s display to make sure that the level crossing can be passed safely and to confirm the safe state on the display. if meanwhile the status has been received the message is cancelled from the display, the break are released and the driver does not need to confirm anymore. otherwise the driver has to confirm the safe state of the level crossing in order to release the breaks and continue its run. the train supervises a maximum arrival time of 240 seconds to avoid long waiting times of road users. if the train detects that it cannot arrive at the level crossing within a specified time and still is able to stop before the danger point it cancels the activation order by sending a deactivated order to the level crossing. in this situation the train discards any information received from the level crossing and supervises a breaking curve ending at the danger point. the level crossing will be opened upon receipt of the deactivation order. the driver has to confirm as described above the safe state before passing unclosed level crossing. the level crossing control system will not be activated if the red traffic lights or the vehicle sensor are defective and it will not send an acknowledgment to the train. if the level crossing control system has been activated, a minimum green time is considered since the last deactivation of the level crossing before switching on the yellow light for 3 seconds. if the yellow traffic light becomes defective either before or during the yellow light period, the traffic lights are switched to red and the red light period of 9 seconds is extended correspondingly by the messing time of the yellow light period. if the red traffic light fails after activation of the level crossing control the closing procedure has to be cancelled unless the barriers have yet begun to be lowered. the failure state of the level crossing must be reported if the barriers fail to be completely lowered within a maximum duration of 6 seconds or if in the meantime the red traffic light has become defective. the current status of the level crossing will be reported to the train upon request. if the vehicle sensor becomes defective the level crossing control system can not be deactivated anymore by passing train. consequently the barriers remain lowered and the red traffic light remains switched on. however, the level crossing control system supervises a maximum closure time starting from the red light be switched on. the exceeding of the maximum closure time will be reported to the operation centre by the level crossing control system. the operation centre finds out, whether the train has yet passed the level crossing or not. in the first case, the operations centre sends a deactivation order to the level crossing. otherwise the train is still approaching or just running on the level crossing and the rules for late arrival at the level crossing apply as described above. extraction of critical scenarios in a railway level crossing control system 257 3.5 feared events there are many feared events in the system, but we will interest only to the catastrophic one: the collision, it means the presence of a train and a road user in the danger zone at the same time. 4 modelling petri nets have been used with success as a formal model for traffic signal control [11], urban traffic control [12], and level crossing control system [13] aiming security. this section deals with the modeling of the level crossing control system by a t-time petri net model (temporal intervals associated to transitions). although the appropriate abstraction of certain dynamics of the system is a pt-arc-time petri net (temporal intervals associated to arcs related places to transitions) or a p-time petri net (temporal intervals associated to places) we have chosen the t-time petri net model as the principal of the esa-petrinet tool is based on the priority of firing of conflictive transitions. 4.1 general view in the general view given in figure 3a, msgi represent radio messages. the message msg1 is sent by the train to the lccs to switch on when the on-board system detects the approaching of a level crossing. the message msg2 represents the receipt acknowledgement of the activation order. the message msg3 corresponds to the status request of the level crossing and the message msg4 represents the safe state of the level crossing reported to the train. note that, transmission times on the radio network may vary and messages may be lost as represented in figure 3b. the radio message is represented by the place msgi. the time interval [dmi, dmi] associated to the out put transition of place msgi means that the transmission time may vary between the minimal value dmi and the maximum one dmi. according to the radio message and the crossing state, the train will pass with out braking, with braking, come to a stand still or stop. the dely1 corresponds to the maximum closure time of 240 seconds supervised by the level crossing control system starting from the red lights be switched on. crossing the danger zone by the train and the road user are respectively represented by dz1 and dz2. the message msg7 corresponds to the deactivation order of the opening of the level crossing sent by the train when it detects that it can not arrive at the level crossing within the maximum supervised arrival time of 240 seconds. in this paper, we will interest to the feared scenario corresponding to the presence of a train and a road user in the danger zone at the same time (collision). this is represented by the petri net model of the figure 3c, where the transition e_fail representing the feared event can be fired only when both places dz1 (presence of a train in the danger zone) and dz2 (presence of a road user in the danger zone) are marked. place s_fail represents the feared state (collision). figure 4 represents a general view of different radio messages exchanged between the on-board system and the level crossing control system. to simplify the case study, we have not presented the radio messages exchanged with the operations centre like the failure and repair of different devices. 4.2 petri net model of the level crossing control system a detailed view of the lccs is given in figure 5. note that messages msg1 and msg2 are the same as in figure 3. places lccs_off and lccs_on1 correspond respectively to the deactivation and activation mode of the level crossing control system. transition on1 will be fired after reception of the activation order msg1, to switch to the activated mode if the vehicle sensor (place s3_ok) and the red traffic light (place red_off ) are not defective. the level crossing control system will be deactivated if the place lccs_on2 or lccs_on3 is marked. place lccs_on2 will be marked when the barriers are opened after closure. place 258 malika medjoudj, pascal yim figure 3: general view of the model figure 4: general view of radio messages exchanges lccs_on3 corresponds to the cancelling of the closing procedure if the red traffic light fails after the activation of the level crossing control system. the green time passed since the last deactivation of the level crossing will be described in section 4.7 figure 5: petri net model of the level crossing control system 4.3 petri net model of the yellow light place yell_off1 in figure 6a represents the mode off of the yellow light. it will be switched to the activated mode (place yell_on) when the level crossing is activated (place lccs_on1 marked). after 3 seconds of the activation of the yellow light, it will be deactivated (marking of places yell_off1 and yell_off2). the yellow light can fail in the deactivated mode (yell_ko1) or in the activated mode (yell_ko2). figure 6b represents the petri net model of failure and repair of the devices that may be faired in the system (traffic lights, vehicle sensor and barriers). failure and repair are represented respectively by the stochastic transition faili and repi. while failure may occur at any time, repair will not take place when there is a train approaching or passing the level crossing. this is represented by the minimal value of reparation dri. extraction of critical scenarios in a railway level crossing control system 259 figure 6: petri net model of the yellow light 4.4 petri net model of the red light the model is similar to the yellow light. the red traffic light can be in mode off (place red_off ), mode on (place red_on), fail before activation (place red_ko1) or after activation (place red_ko2). we note three cases of the activation mode of the red light represented in figure 7 according to the time activation of the yellow light. in case (a), the yellow light was activated for 3 seconds before the lights traffic switch to the red. in this case the place yell_off2 is marked and transition on3 can be fired to switch to the activated mode of the red traffic for 9 seconds. this delay is represented by the time interval [9, 9] related to the transition cls1 that corresponds to the order of lowering barriers. the place dely1 is the same as described in figure 4. the red traffic light can be deactivated when the barrier will be completely opened represented by the place br4. place lccs_on2 is the same as described in figure 5. in case of figure 7b, the yellow traffic light becomes defective before the yellow light period (place yell_ko1). in this case the red traffic light period of 9 seconds is extended to 12 seconds to take into account the yellow light period. this is represented by the time interval [12, 12] attached to the transition cls2 that corresponds to the order of lowering barriers. in case (c), the yellow traffic light becomes defective during the yellow light period (place yell_ko2). in this case, the red light period of 9 seconds is extended correspondingly to the missing time of the yellow light period. this is represented by the time interval ]9, 12[ associated to the transition cls3. transitions and places concerning the barriers will be described in section 4.6. figure 7: petri net model of the red light 4.5 petri net model of sensors the system contains three sensors (si): a sensor for the barriers loading, a sensor for the barriers closing and a vehicle sensor. the petri net model of a sensor si is similar to the model given in figure 6b. as described in this figure, a sensor si can be defective and repaired by firing transition repi. 260 malika medjoudj, pascal yim figure 8: petri net model of barriers closing and cancelling of the closing 4.6 petri net model of barriers (actuators) closing to simplify the petri net model, we assume that figure 8a represents the closing of the two half barriers which are actuated by an actuator for opening (place act1_ok). note that places dely2, dely3 and dely4 are the same as in figure 7. the place br1 represents the continuous dynamic of the closing barriers. its temporal abstraction is represented by the temporal interval [1, 6] attached to the transition cls4 as the maximum closure time is 6 seconds and we suppose that the minimum closure duration is 1 second. if the opening actuator fails before the barriers have completely closed (act1_ko marked), the immediate transition fail8 will be fired and the dynamic of place br1 will be interrupted. this corresponds to the blocking of the barriers in opening represented by the marking of the place bck1. if the sensor that detects that the door is closed is defective (marking of place s1_ko), the transition cls4 can not be fired and the level crossing is considered in a failure state. this is represented by the firing of transition fail14 in the temporal interval ]6, 6+]. figure 8b represents the cancelling of the closing procedure if the red traffic light fails after the activation of the level crossing control system before the barriers begun to be lowered. this is represented by firing the immediate transitions fail10, fail11 or fail12 according to the activation mode of the red light represented in figure 7. place lccs_on3 corresponding to the order of deactivation of the level crossing will be marked. opening the petri net model is similar to the petri net model of closing. the dynamic of the barriers opening is determined by the position of the actuator for opening (place act2_ok). the dynamic of the opening is represented by the temporal interval [1, 6]. this dynamic can be interrupted if the actuator fails before the end of the opening procedure. in this case the immediate transition fail9 will be fired and the place bck2 corresponding to blocking on opening will be marked. if the sensor of opening is defected (place s2_ko marked) the transition fail15 will be fired after 6 seconds. we note four cases for barriers opening represented in figure 9. case (a) corresponds to the nominal behaviour. in this case, the vehicle sensor is not defective (place s3_ok) and the train has completely passed the danger zone in time (marking of the place trn12 as it will be detailed in section 4.7). the maximum closure time is represented by the temporal interval [0, 240] associated to the transition opn1. in case (c), the train detects that it can not arrive to the level crossing in time and it sends a deactivation order to open the level crossing. this is represented by the message msg7. in case (b) and (d) the vehicle sensor is defective and the level crossing extraction of critical scenarios in a railway level crossing control system 261 figure 9: petri net model of the barriers opening can not be opened by passing of the train. in this case, the exceeding of the maximum closure time is reported to the operations centre that finds out wither the train has passed the level crossing or is still approaching. accordingly the operations centre sends a deactivation order (b) in the first case and (d) in the second case. 4.7 petri net model of the train nominal operating and late arrival the detailed petri net model of the train is given in figure 10. place trn1 (figure 10a) corresponds to the approaching of the train a level crossing. when the train on-board system detects this approaching, it sends a radio message msg1 to the level crossing control system to switch on by firing the transition tr1. place bc represents the setting of a breaking curve for speed supervision to make the train stop at the potential danger point in a failure situation. after receipt of the acknowledgement (place msg2), the on-board system waits an appropriate time (18 seconds) for the level crossing to be closed and sends the statute request (place msg3) to the level crossing control system. the level crossing is reported to the train to be in a safe state (place msg4) if the barriers are completely lowered (place br2) and the red traffic light is in the activated mode (place red_on). after reception of the safe state of the level crossing, the train cancels the breaking curve (place bc) and passes the level crossing with out braking. this is represented by firing transitions tdz1 and tr6. the continuous dynamic of the train is represented by the temporal interval [dti, dti] attached to transitions tri and tdz1. this means that the tokens has to remain in the input places of this transitions at least dti and at most dti. place trn7 and trn12 represent the train out of the danger zone. transition tr7 can be fired after a green time duration dg to specify a non-finite behaviour of the track. transition tr14 is fired if the train detects that it can not arrive at the 262 malika medjoudj, pascal yim figure 10: petri net model of the train level crossing within the maximum supervised arrival time of 240 seconds (late arrival) and still is able to stop before the danger point. it sends a deactivation order to the level crossing (place msg7) and discards any information received so far from the level crossing and supervises a breaking curve (firing transition tr15 after a green time duration dg). braking and stand still or stop figure 10b represents the case in which the train does not receive the status report with the safe state of the level crossing before entering its breaking curve. note that places trn4, bc, msg4, red_on, br2, trn6, trn12, dz1, msg7 and trn11 are the same as in figure 10a. the temporal interval ]dmi, dmi+] represents the fact that the train does not receive the status report before entering its breaking curve. in this case the on-board system apply the breaks (place trn8) until the status report will be received. the transition tr9 will be fired to release the breaks and continue the run if the status report is received before a stand still (place trn9). place trn10 represents the request prompted on the driver’s display to make sure that the level crossing can be passed safely. transition tr11 is fired if meanwhile the status report has been received. otherwise transition tr12 will be fired to confirm the safe state by sending the message msg5. if the level crossing is in its safe state the transition (place msg6), the transition tr13 will be fired otherwise the train will stop (place trn14). 4.8 petri net model of the road user places road_user and dz2 in figure 11 represent respectively the road user in the entrance of the danger zone and crossing the danger zone. the road user may pass the level crossing only if the red traffic light is not in its activated mode (place red_on is not marked) or the level crossing is still open. it means that road users may pass when the red traffic light is off (place red_off ) or in its defective mode (place red_ko1 or red_ko2) even if the half barriers are lowered as they can pass in the opposite lane or when the half barriers are not yet lowered (place dely2, dely3 or dely4 marked). to simplify, note that we are focussing on the red traffic light as the yellow traffic light is included in this cases: the yellow traffic extraction of critical scenarios in a railway level crossing control system 263 light can be activated when the red traffic light is in its deativated mode or defected before activation (red_ko1). the failure of the yellow light is also taken into account as in this case place dely3 or dely4 will be marked. the transition usr represents the non-finite behaviour of the road users. figure 11: petri net model of the road user 4.9 the whole petri net model of the system places labelled with "n" in figure 12, modelling the whole system, represent normal operating and transitions labelled with "f" will be added to forbidden transitions and can not be fired. this transitions concern repair and non-finite behaviour as repair of defective devices will not take place when there is a train approaching or passing the level crossing and we are interesting in this paper only to one round. we will seek the feared scenarios corresponding to the presence of both train and road user in the danger zone, i.e. all the scenarios which lead to the marking of the place s_fail. 5 extraction of feared scenarios a general view of esa_petrinet and tina tools is given in figure 13. to use esa_petrinet, we first edit the petri net model of the system on the graphic editor of tina tool to generate two input files: a descriptive file of the petri net model and a file containing the invariant of places. generated scenarios can be illustrated in the form of a precedence graph. esa_petrinet generates a total of 196 scenarios (nominal and feared) in which 88 are feared. note that the actual version of esa_petrinet generates non minimal scenarios, so most of the generated scenarios are redundant. this explains the important number of the scenarios generated. note also that this version of esa_petrinet support continuous dynamics and temporal constraints and an important number of incoherent scenarios are yet eliminated. we have chosen these parameters: dmi = 0, dmi = 4, dti = 1, dti = dg = 10, dri= 250 the 88 feared scenarios (collision) correspond to the following situations: 1) crossing of both the road user (tdz4) and the train (tdz1) the danger zone when the red traffic light fails after activation (fail4). in this case, just after the train has received the safe state of the level crossing, the red traffic light fails and the road user passes at the same time the danger zone thinking that the train has already passed. we note three categories of scenarios according to the way of the train crossing. • the train is crossing without braking: sc10, sc13, sc19, sc22, sc16, sc25, sc30, sc33, sc36, sc41, sc46, sc53, sc61, sc65, sc69, sc76, sc80, sc84, sc91, sc95, sc102, sc108, sc112, sc116, sc123, sc127, sc131, sc138, sc142, sc149. these scenarios are represented by sc1a: {tr4, tr5, tdz1, fail4, tdz4, e_fail}. • the train is crossing with braking before stand still: sc50, sc57, sc62, sc66, sc73, sc77, sc81, sc88, sc92, sc99, sc104, sc109, sc113, sc120, sc124, sc128, sc135, sc139, sc146, sc154, sc157, sc162, sc165, sc168, sc173, sc176, sc181. these scenarios are represented by sc1b: {tr8, tr9, tdz1, fail4, tdz4, e_fail}. 264 malika medjoudj, pascal yim trn1 tr1 [0,0] trn2 ko1 lccs_off on1 [0,4] lccs_on1 off1 [0,0] off4 [0,0] tr2 [0,4] msg2 trn3 tr3 [18,18] msg3 on2 [0,0] yell_on yell_off1 lost3 trn4 tr8 tr4[0,4]lost4 trn8 msg6 lost6 tr5 trn5 msg4 ko4 bc tr10 tr12 msg5 trn6 tdz1 dz1 tr14 msg7 lost7 trn7 tr6[1,10] off2 [3,3] yell_off2 cls1 [9,9] br1 fail3rep3 red_ko1 br2 opn1 [0,240] br3 s3_ok on4 [0,0] yell_ko2 fail2 rep2 cls5 [0,4] cls6 [0,4] dely2 cls2 on5 [0,0] cls3 fail11 [0,0] fail10 [0,0] trn12 ko3 ko2 lost2 trn10 tr9[0,4] tr13 [0,4] trn9 tr11 [0,4] dely1 lccs_on3 lccs_on2 fail12 [0,0] cls4 [1,6] msg1 dely3 tdz4 tdz5tdz3 dely4 red_ko2 fail4 rep4 red_on on3 [0,0] tdz2 fail1 yell_ko1 rep1 usr off3 [0,0]red_off dz2 opn2 lost5 ko5 trn13 tr7 tr15 tr16 trn14 ko7 ko6 trn11 lost1 ]9,12[ [12,12] ]4,5] ]4,5] ]4,5] ]4,5] tdz6 tdz7 s1_ok rep5 s3_ko s1_ko rep13 fail13 fail14 ]6,7] fail15 ]6,7] opn3 [0,4] fail8 [0,0] bck1 bck3 act1_ko act1_ok fail6rep6 act2_ok fail7rep7 act2_ko fail9 [0,0] bck2 fail5 opn4 [0,4] bck4 opn5[1,6] s2_ok fail16 s2_ko rep16 br4 e_fail [0,0] s_fail road_user figure 12: whole petri net model of the system extraction of critical scenarios in a railway level crossing control system 265 figure 13: screen shots of tina and esa_petrinet tools • the train is crossing after stand still: sc54, sc71, sc86, sc172, sc180, sc161, sc97, sc118, sc133, sc144. these scenarios are represented by sc1c: {lost4, tr13, tdz1, fail4, tdz4, e_fail}. the precedence graph of these three scenarios is given in figure 14. ii and fi represent respectively initial and final events. figure 14: precedence graph of the scenarios 2) crossing of the road user the danger zone before the barriers to be lowered (tdz5, tdz6 or tdz7) then, crossing of the train (tdz1). in this situation, the road user may be slow down stopped or break down on the danger zone. what often arrives on this zone, in general difficult to cross compared to the normal road as attested by the statistics of this field. we note three categories of scenarios according to the mode of activation of the red traffic light and each category contain deferent scenarios according to the way of train crossing. • in the case of the activation of the red traffic light after the yellow light period (place dely2 marked), we find the scenario sc21a: {tr4, tr5, tdz5, tdz1, e_fail}, sc21b: {tdz5, tr8, tr9, tdz1, e_fail} and sc21c: {lost4, tr13, tdz5, tdz1, e_fail}. the scenario sc21a corresponding to the crossing of the train without braking regroups scenarios sc3, sc5 and sc11. the scenario sc21b corresponding to the crossing of the train with braking before stand still regroups scenarios sc23, sc28, and sc44. the scenario sc21c representing to the crossing of the train after stand still corresponds to the scenario sc37. • in the case of the activation of the red traffic light after the yellow traffic light become defective in its activated mode (place dely4 marked), we find the scenario sc22a: {tr4, tr5, tdz6, tdz1, e_fail}, sc22b: {tdz6, tr8, tr9, tdz1, e_fail} and sc22c: {lost4, tr13, tdz6, tdz1, e_fail}. the scenario 266 malika medjoudj, pascal yim sc22a corresponding to the crossing of the train without braking regroups the scenarios sc1, sc2 and sc7. the scenario sc22b corresponding to the crossing of the train with braking before stand still regroups the scenario sc17, sc20 and sc34.the scenarios sc22c representing the crossing of the train after stand still corresponds to the scenario sc26. • in the case of the activation of the red traffic light when the traffic light become defective in its deactivated mode (place dely3 marked), we find the scenario sc23a: {tr4, tr5, tdz7, dz1, e_fail}, sc23b: {tdz7, tr8, tr9, tdz1, e_fail} and sc23c: {lost4, tr13, tdz7, tdz1, e_fail}. the scenario sc23a corresponding to the crossing of the train without braking regroups the scenarios sc6, sc9 and sc14. the scenario sc23b corresponding to the crossing of the train with braking before stand still regroups the scenario sc31, sc39 and sc51.the scenarios sc23c representing the crossing of the train after stand still corresponds to the scenario sc47. to facilitate the identification of the feared scenarios among the scenarios of normal operating, esa_petrinet tool illustrates them with a different colour. 6 summary and conclusions two objectives have been reached in this paper. the first is a whole modelling of the level crossing by a temporal petri net model. the second is the extraction of the critical scenarios using esa_petrinet tool. the analysis of these scenarios permitted to propose a solution to improve the safety of the level crossing. this simplest solution consists on the importance of adding a sensor to allow the detection of road users by the train in the danger zone. among the perspectives of this work: the quantification of these scenarios by a monte carlo simulation [1] that has been implemented in esa_petrinet, checking different temporal constraints and taking into account the minimality of the scenarios to eliminate the unnecessary events and the redundancies. these analyses can be extended to a level crossing used in the intersection area between a multiple track railway line and a road. bibliography [1] kalos, m.h., whitlock, p.a., mont carlo methods, vol. 1: basics, john wiley and sons, new york, 1986. [2] laprie, j.c., dependability: basic concepts and terminology, vol. 5, springer, 1992. [3] medjoudj, m., khalfaoui, s., demmou, h., valette, r., ”a method for deriving feared scenarios in hybrid systems,” probabilistic safety assessment and management (psam7-esrel04), berlin, germany, 14-18 june 2004. [4] medjoudj, m., demmou, h., valette, r., ”esa_petrinet tool : extraction scenarios & analyzer by petri net model : application to the extraction of feared scenarios in a landing gear system,” european simulation and modeling conference (esm2006), laas, toulouse, france, pp. 375-382, 23-25 october 2006. [5] murata, t., ”petri nets: propreties, analysis and applications,” ieee proc, vol. 77, pp. 541-580, april 1989. [6] demmou, h., khalfaoui, s., riviere, n., valette, r., ”extracting critical scenarios from a petri net model using linear logic,” journal européen des systèmes automatisés (apii-jesa), vol. 36, n7, pp. 987-999, 2002 extraction of critical scenarios in a railway level crossing control system 267 [7] girard, j.y., ”linear logic,” theoretical computer science, vol. 50, pp. 1-102, 1987. [8] berthomieu, b., ribet, p.o., vernadat, f., ”the tool tina construction of abstract state spaces for petri nets and time petri nets,” international journal of production research, vol. 42, n14, pp.27412756, 15 july 2004. [9] collart-dutilleul, s., deffossez, f., bon, p., ”safety requirements and p-time petri nets: a level crossing case study,” imacs-ieee multiconference on computational engineering in systems applications, pp. 1118-1123, oct 2006. [10] jansen, l., schnieder, e., ”traffic control systems case study: problem description and a note on domain-based software specification,” technical rapport, technical university of braunschweig, 2000. [11] list, g.f., cetin, m., ”modeling traffic signal control using petri nets,” ieee trans. on intelligent transportation systems, vol. 5, n3, pp. 177187, 2004. [12] febbraro, a.di., giglio, d., sacco, n., ”urban traffic control structure based on hybrid petri nets,” ieee trans. on intelligent transportation systems vol. 5, n4, pp. 224-237, 2004. [13] padberg, j., gajewsky, m., ”rule-based refinement of petri nets for modeling train control systems,” ifac conference on control systems design (csd2000), elsevier science, pp, 299-304. 2000. malika medjoudj, pascal yim lagis, ecole centrale de lille cité scientifique, bp 48 villeneuve d’ascq, 59651, france e-mail: {malika.medjoudj, pascal.yim}@ec-lille.fr received: june 27, 2007 this work was supported by the pole st2 and the region nord-pas de calais malika medjoudj was born in tizi-ouzou (algeria) on february 21, 1977. she received the engineer diploma degree in electronics (control) and the diploma of higher education applied in technical english from mouloud mammeri university of tiziouzou (algeria) in 2001. she obtained the master in industrial systems from ups-laas-cnrs of toulouse (france) in 2002 and the phd in industrial systems from the same university and laboratory in march 2006. she is actually a post doctorate at the ecole centrale de lille in collaboration with inrets after a scientific stay of six months in the nuclear metrology service of the université libre de bruxelles (fnrs-belgium). her research is related to the reliability of hybrid and dynamic systems (computer-controlled systems), checking of temporal constraints, extended petri nets for safety (transportation systems), feared scenarios and simulation. 268 malika medjoudj, pascal yim pascal yim, married, 3 children, was born in papeete (french polynesia) on november 3, 1963. he is professor at the ecole centrale de lille, a french technical university. his research is based on the cross fertilization of concepts from discrete automatic control, and computer science. he has a particular interest in petri nets, constraint programming and information systems. his main application field is the design and optimisation of transportation systems (mainly railways). he published numerous papers in international journals and conferences. he has also supervised several industrial contracts for various customers: sncf (the french railway company), the fluvial port of lille, the "3 suisses" (a large retail french company) and other companies. professor pascal yim is a member of scientific board of french "competitivity poles" on intelligent transportation systems (i-trans) and distribution (picom). he is also correspondent for the north of france of the european excellence network on railways (eurnex). int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 2, pp. 204-214 ecad system design applications in architecture a.d. styliadis, d.g. konstantinidou, k.a. tyxola abstract: the rapid advances in learning technologies, computer modeling, multimedia and spatial sciences, as well as the availability of many powerful graphics pcs and workstations, make 3-d modeling-based methods for personalized e-learning with ecad (modeling) functionality feasible. personalized ecad learning is a new term in engineering, environment and architecture education, related to the development of learning educational units (3-d learning objects) with re-usable digital architecture functionality, and introduced to literature for the first time within this paper. in particular, for university education courses in ecad, digital architecture, design computing and caad (reagarding spatial information systems, architectures, monuments, cultural heritage sites, etc.), such a e-learning methodolgy must be able to derive spatial, pictorial, geometric, spatial, topological, learning and semantic information from the target object (a 3-d model) or scene (a 3-d landscape environment) or procedure (a 3-d simulation approach to a phenomenon), in such a way that it can be directly used for e-learning purposes regarding the spatial topology, the history, the architecture, the structure and the temporal (time-based) 3-d geometry of the projected object, scene or procedure. this paper is about the system design of such a e-learning method. for this purpose, the requirements, objectives and pedagogical extensions are presented and discussed. finaly, a practical project is used to demonstrate the functionality and the performance of the proposed methodology in architecture. keywords: e-learning documentation, modeling functionality, digital photography, cad, historical living systems 1 introduction the rapid advances in digital imaging sensors and scanners, off-the-shelf haptic devices, computer modeling software and the availability of many powerful graphics pcs and workstations make a method for personalized ecad learning with 3-d modeling or 2-d drafting functionality feasible (kalay, 2006). personalized ecad learning is a new term in informatics, engineering and architecture, related to digital architecture documentation with e-learning functionality, and introduced to literature for the first time within this paper. the proposed term personalized ecad learning is defined as a digital architecture documentation procedure with e-learning functionality based on metric and non-metric (qualitative) data, and spatial and 3-d modeling semantic information (please see next section). in particular, for the historical living systems (i.e. monuments, churches, basilicas, archaeological sites, etc.), such a methodolgy must be able to derive pictorial, geometric, spatial, topological, learning and semantic information from the target architectural object (historical living system), in such a way that it can be directly used for e-learning purposes regarding the history, the architecture, the structure and the temporal (time-based) 3-d geometry of the projected historical living system. improvements and new developments in the fields of sensor technology and computer modeling allow the acquisition of digital images in video-realtime, without developing and digitizing a photographic film (streilein, 1996; kazakeviciute et al., 2005). such a system -which is well described by andre streilein from the eth zurichfor digital photogrammetry and architectural design consists of two sub-systems: a sub-system for the digital photogrammetric station and a sub-system for the caad (streilein, 1996; streilein et al., 1992). in this docopyright © 2006-2008 by ccc publications ecad system design applications in architecture 205 main the aim of a method for e-learning documentation is to make the photogrammetric data acquisition and processing easier and faster, to create a three-dimensional geometric and semantic object description, and to allow caad/multimedia data integration, haptic rendering, íisua1ization and architectural processing in an easy and user-friendly way (vladoiu, 2004; weber, 2004). therefore, such a method must be capable to acquire imagery with sufficient resolution, process the data with a big leíel of automation, and pass the results to a data structure useful for 3-d caad modeling (hirschberg et al., 1996). this can be achieved using solid-state sensors and manual, semiautomatic or automatic measurement techniques. also, for environmental management purposes, the current status of a relative methodology for such a system is described by l. dimen et al. (2005) and a relative method is being deíeloped in a joint project of the atei of thessaloniki in co-operation with the agora university of oradea, romania. with the constant progress of multimedia technology and network bandwidth, the traditional teaching environment that based on text and pictures, will be integrated with media streams, 3-d modeling, intelligent agents, mathematics, virtual reality, haptic rendering and spatial objects (scienses)ů as described by a. styliadis et al. (2006) for the gis case, by engeli et al. (1996) for the intelligent agents, by i. dzitac (2000, 2002) and by i. dzitac et al. (2007) for the mathematics case, and by silva et al. (2002) for the insertion of 3-d architectural objects in photography. for this reason, in this paper, the proposed methodology shows and demonstrates an architecture (3-d model) that can support these new, rich in e-learning functionality, environments. recently, more and more systems come up that use any caad and semantic information available prior the measurement process. such a system is the modelling-and-rendering system developed at the university at berkeley by debevec et al. in 1996. this system uses a rough object description in order to guide a stereo matching technique for the digital reconstruction of the primary object details with relative accuracy better than 10-3. another similar system (a caad system named "naos"), dealing with 3-d geometry (with relative 10-3 accuracy as well) and qualitative information for caad documentation, was developed in 1997 at the aristotle university of thessaloniki, school of surveying engineering and at the atei of thessaloniki, greece (styliadis, 1997). also, very interested is the work at the university of helsinki from h. haggren and s. mattila (1997) dealing for 3-d indoor modeling development based on videography data. in particular, in this work a functional 3-d model of indoor scenes is built first and the measurements of the geometry based on video images are performed thereafter. finally, an interested caad system under development exists at the university of delft (frank van den heuvel, 2003), which makes use of a priori geometric object information in the form of parameterized object models with image lines as the main type of observations. on the other hand, e-learning is a process that needs quite amount of mental and body strength. in order to promote the e-learning efficiency, it is important to improve the learning environment and this is the case of the proposed methodology (e-learning with caad functionality). apart from traditional design, the media stream or the virtual reality, can stimulate learner even more, reinforce the learner’s motivation, attention and mentality. some systems adopt different technology and implemented similar environment also demonstrate satisfactory results. however, they need to spend a lot of money and time to achieve that, such as vrml (virtual reality modeling language) it can establishes a virtual 3-d scene with walk-through functionality in the scene by a simple parameter, but while controlling the behaviors of the 3-d objects that enhance photorealism -such as the materials, the lights, the object scale, etc. a script procedure must be writen in the complicated vrml markup language. evenmore, the vrml modeling relative inaccuracy is greater than 10-3 and so, this is not acceptable for e-learning documentation applications. the proposed method is based on a virtual learning environmental architecture that integrates synchronous, asynchronous and co-operative characteristics. 206 a.d. styliadis, d.g. konstantinidou, k.a. tyxola the paper is structured as follows: in section 2 (personalized ecad learning: term formulation) the new term personalized e-learning is introduced and described. in section 3 (personalized ecad learning: the system design learning requirements) an overview of the proposed personalized e-learning methodology is given. the section 4 (personalized ecad learning: the main streams) presents the main sub-systems outline design of the proposed e-learning methodology. finaly, in section 5 (personalized ecad learning: an application in architecture) a practical 3-d model based application is presented and its e-learning functionality is discussed. 2 personalized ecad learning: term formulation personalized ecad learning is a new term in engineering and digital architecture, related to digital (geometricand semantic-based) learning with re-usable spatial functionality, and introduced to literature for the first time within this paper. the proposed term personalized ecad learning is defined as a digital documentation procedure with e-learning re-usable spatial functionality based on metric and non-metric (qualitative) data, and spatial 3-d modeling semantic information. at the ’heart’ of this e-learning is the 3-d model (vector format) of the object (which could be any architecture, building, monument, etc.) or the process (environmental polution, weather forecast, water flood estimation, etc.) being described (dimen, 2005). the objectives of the personalized ecad learning are: • to facilitate and encourage the collaboration and the critical awareness between the design students, scientists and professionals (architects, designers, engineers and so on). • to design virtual spaces using different representation methods and techniques for haptic rendering regarding architectures, landscapes and urban design. • to communicate through 3-d model-based multimedia data in ways traditional caad, photography and video does not support. • to support access to prior understandings regarding the ’pathology’ (nature) and the characteristics of the described object or process. • to allow ideas exchange and to support design autonomy. • to test the efficiency of the various sub-systems involved in design and construction processes for the architecture, engineering and construction (aec) community. • to promote self-directed reusable learning exercises which lead to a critical awareness of the learning process and the learner’s empirical background. similarly, the pedagogical profits are: • the establishment, through this documentation, of a new reality in education and design practice, whereby the accepted realism and the level of the 3-d modeling accuracy (or inaccuracy), of the object or the process, are not so necessary to communicate performative and reusable educational and design concepts (martin et al., 2004). • the real-time collaborative and reusable interactivity. • the feedback (learning domain 3-d ontologies for semantic caad descriptions). • the ability to develop manual, semi-automatic or automatic reusable caad learning tools that could support architectural, photogrammetric, art, historical or archaeological training and education. ecad system design applications in architecture 207 the pedagogical strategy of the personalized ecad learning is based on encouraging and facilitating the communication and ideas exchange between the personnel involved in the design, implementation and spatial analysis process. finaly, active role, interaction, group work and design autonomy are the characteristics of the proposed personalized ecad learning. 3 personalized ecad learning: the system design learning requirements the structure and the processing steps for the proposed personalized ecad learning method are shown in fig. 1. in this figure, the relation among the 3-d learning objects with modeling functionality and the available learners’ (engineers, cad personnel, architects) profiles are presented in connection with the geometric, topologic and thematic spatial data. figure 1: the proposed ecad system design (personalized ecad learning) on designing the proposed methodology for the e-learning documentation, the statistical analysis results from an e-learning course in caad and the vrlab research project at the atei of thessaloniki are examined (tsakiris et al., 2005). so, according to students suggestion, the functional specifications for a 3-d based e-learning system are defined as follows: • 3-d map video processing • 2-d image processing • archive or historical photography, rich in geometric regularities and properties (i.e. clues, like: planarity, parallelism, orthogonality, symmetry, perpendicularity, topology, etc.), processing (styliadis et al., 2003) • item-by-item 3-d modeling functionality in an e-learning caad environment 208 a.d. styliadis, d.g. konstantinidou, k.a. tyxola • haptic rendering of the resulting virtual representations of both the 3-d maps (models) and 2-d images (drawings) • gui with drag-n-drop functionality • multimedia functionality • learning functionality incorporating historical and semantic data • force-field haptic rendering functionality • virtual reality functionality • notingand shared-board functionality • non-stop study functionality actually, what the learner needs is a synchronism and adaptive e-learning system which can interact in real-time with the teacher in class (wu, 2002). in this domain an asynchronous system can let learner to study in his free time by adapting learning object selection (based on discrete and reusable 3-d modeling items) in intelligent learning systems (karampiperis et al., 2004). also, such as system can let learners discuss with each other through media stream. besides, they also need 3-d virtual environment with haptic rendering functionality, which can increase learner’s interest and attention. 4 personalized ecad learning: the main streams after defining its functional specifications the main sub-systems (streams) of the re-usable e-learning system are defined as follows: • media stream services: this is the server sub-system; for which a number of media stream servers are needed (e.g. a system or central server). these servers can provide data (photography, imagery, history, architecture, modeling) for learners on real-time. these servers can also store material in repository (i.e. material palettes) which then can be searched by researchers or learners (e.g. students in architecture, art, history, etc.). • virtual learning environment for 3-d visualization: this is the client sub-system; which includes a user interface based on 3-d graphics, haptic equipment and virtual reality tools (petrovic, 1996). it is the stage for the learner and it includes virtual and resource classroom, chat room, etc. this e-learning sub-system provides chatting functionality on a learner-to-learner or learner-to-teacher basis. after the learner’s logon to the system, he can control the learning process on focusing: (i) on particular 3-d modeling and rendering methods supporting e-learning functionality, e.g. haptic rendering, phong shading rendering (fig. 2) or phong rendering; and (ii) on particular details of the historical living system using a gui input device like the keyboard, the light-pen or the mouse. evenmore, using this stream the learner can also communicate on-line with other learners (e-students). • web portal: this e-learning stream provides the learner with additional information and operates as an integration platform for the entire e-learning documentation. this stream mainly includes the system’s operation manual, monument’s relevant documents and teaching materials, monument’s architecture, history, archive (historic) photography, digital imagery, etc. ecad system design applications in architecture 209 figure 2: 3-d modeling of aghios achilleios basilica 5 personalized ecad learning: an application in architecture beyond the thirsty coasts of greece lies a lush heartland where a coachload of sunburnt tourists are a rarer sight than a flock of pink-backed pelicans. this is macedonia, a land of legendary battles and untamed landscapes, more familiar for the news headlines it attracts than for its startling natural beauty. nestled, among macedonia’s verdant mountains, are the twin cool lakes of prespa (north-west greece). declared as a national park in 1974; these twin lakes provide a sanctuary for over 280 species of birds and the largest pelican breeding ground in the world. though stranded on the edge of greece, prespa lies at the ’heart’ of the balkans. straddling greece, albania and the former yugoslavian republic of macedonia (fyrom), these lakes are a smooth expanse of serenity caught in the crossfire of shifting borders. locals are an unusual stew of immigrants and refugees from pondos and asia minor, and nomadic vlachs; many still speak their native dialects. but as with most of rural greece, prespa’s population is dwindling. at the turn of the century, there were 12,000 inhabitants and 21 villages. now, only 1,200 locals and 12 villages are left; they survive now, as then, by fishing and farming. until 1969, locals carried special id cards and foreign visitors required a visa. even today, tourism is just a slow trickle in this watery wonderland. the aghios achilleios basilica, in lakes prespes, was chosen to demonstrate the functionality and efficiency of the proposed method. actually, the aghios achilleios basilica is located at the aghios achilleios island on the minor prespes lake. the monument is a three-aisled, wooden-roofed basilica with a narthex and domes over the parabemata (fig. 3). it was founded in ca. 986-990 by tsar samuel of bulgaria. initially, it was the cathedral of samuel’s short lived empire and later, until the middle of the 15th century, was an episcopal church. a tomb covered with a relief tombstone is preserved in the south arm of the cruciform diaconicon; tradition say that the relics of aghios achilleios were kept in this tomb. along the south wall of the south aisle, four other graves are preserved, in which important persons of the church or the local community were buried. the few fragments of the wall paintings belong to two different layers and have been removed from 210 a.d. styliadis, d.g. konstantinidou, k.a. tyxola figure 3: the aghios achilleios basilica at the twin lakes prespes the building. they are now on display in the exhibition of byzantine and post-byzantine art, in the byzantine museum of florina. today, only a part of the super-structure of the building is preserved, especially on the east side. it stands to a privileged and dominating position, nearly 20 metres above the lake of prespes at the isle of aghios achilleios. the monument is about 22 m in length, 16 m in width and 6 m in height. a detailed discussion about the history, architectural design and construction of this basilica is given by prof. emeritus of architecture nikolaos moutsopoulos (1999). the monument has been under restoration since 1987, and the wall masonry will be rebuilt as long as there is available evidence of its construction. the result of the photogrammetric processing was a 3-d geometric and semantic object description, which was passed automatically via java-mdl programming (java and c++ coding) to the microstation masterpiece caad system. this system is able to pre-process the data and store it in data structures adapted to architectural purposes; allowing, as well, data transformation into other representations in an easy way. for the personalized ecad learning, the task of the learner is the creative finding of new modeling solutions (point or parallel perspective) as well as to evaluate the current modeling accuracy, for both the point and the parallel perspective projections, in connection with the imagery processing equipment and technique used. more for 3-d reconstruction from perspective images could be found in (yang et al., 2005). the e-learning documentation is important for documentation and visualization purposes, and for complex simulations, manipulations and analysis of the target architectural object. this could be used in e-learning courses about architecture, archaeology and art history, in preservation of historical monuments and sites, in regional and local planning, as well as in renovations, reconstructions and reverse engineering projects. ecad system design applications in architecture 211 figure 4: a top view of the east part of aghios achilleios basilica (architectural drawing) figure 5: phong haptic rendering: a point-perspective view of the aghios achilleios basilica caad model (haptic representation based on digital low-resolution canon ci-10 imagery) 212 a.d. styliadis, d.g. konstantinidou, k.a. tyxola figure 6: phong haptic rendering: a parallel-perspective view of the aghios achilleios basilica caad model (haptic representation based on digital low-resolution canon ci-10 imagery) figure 4 shows the top view of an architectural drawing regarding the east part of the aghios achilleios basilica, and figures 5 and 6 illustrate two haptic rendering perspective views ïf the photogrammetric generated caad model of the same monument. it is important to note that haptic interaction and rendering is especially important to e-learning students and populations with disabilities, such as the visually impaired, because tactile interpretation is one of the most important modalities they can use to perceive the world and to appreciate the monuments and the cultural heritage. acknowledgments the current paper is supported by the epeaek ii archimedes research project "personalized learning in a reusable way" of the alexander institute of technology & education (atei), department of information technology, thessaloniki, greece. the epeaek project is co-financed by the european union (75%) and the greek ministry for education & religious affairs (25%). bibliography [1] p. debevec, c. j. taylor and j. 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[26] m.-m. vladoiu, "towards building an open digital library for instructional design that facilitates reflective e-instruction", informatics in education, vol. 3, no. 1, pp. 127-140, 2004. [27] w. weber, "selma self-guided learning in teaching mathematics", informatics in education, vol. 3, no. 1, pp. 141-150, 2004. [28] honggang wu, designing a reusable and adaptive e-learning system, m.sc. thesis, university of saskatchewan, saskatoon, 2002. [29] a. y. yang, k. h. huang, s. rao, w. hong and y. ma, "symmetry-based 3-d reconstruction from perspective images", computer vision and image understanding, vol. 99, pp. 210-240, 2005. athanasios d. styliadis, debbie g. konstantinidou, kyriaki a. tyxola the alexander institute of technology and education department of information technology the archimedes 2.2.17 research project p.o. box 141, 574 00 thessaloniki,greece e-mail: styliadis@ath.forthnet.gr received: december 20, 2007 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 4, pp. 401-414 3d virtual spaces supporting engineering learning activities d.m. popovici, j.p. gerval, f. hamza-lup, r. querrec, m. polceanu, n. popovici, r. zăgan dorin-mircea popovici, mihai polceanu, norina popovici, remus zăgan ovidius university of constanta address: 124 b-dul mamaia, 900527, constanta, romania e-mail: dmpopovici@univ-ovidius.ro, polceanum@gmail.com, norinapopovici@yahoo.com, rzagan@univ-ovidius.ro jean-pierre gerval isen 20, rue cuirassé bretagne cs 42807 29228 brest cedex 2 france e-mail: jean-pierre.gerval@isen.fr felix g. hamza-lup armstrong atlantic state university faculty of computer science address: 11935 abercorn st., savannah, ga e-mail: felix@cs.armstrong.edu ronan querrec ecole nationale d’ingénieurs de brest laboratoire d’informatique des systèmes complexes address: 25 rue claude chappe, f-29280 plouzané, france e-mail: querrec@enib.fr abstract: virtual environments constitute the support platform for various teaching and learning activities. instead of a local application for this purpose, this paper explores the effects of using distributed virtual reality environments in the educational process. the architecture of the presented system is based on the recently developed web-based technology called ajax (asynchronous javascript and xml), implemented on a linux operating system configured to run apache with php and mysql support; i.e., lamp architecture, which contributes to the portability and ease of installation of the application. the platform is designed to support the integration of lesson modules such as the engview environment which is discussed in more detail in this contribution. pedagogical, technical, and implementation-related aspects are presented in conjunction with the virtual environment used in the engineering training curriculum. statistical information resulted from the training shows a significant increase in task completion time when the virtual setup is used. keywords: virtual environment, learning, teaching, motivation, virtual training. knowledge acquisition has shifted from an individual to a collective activity. there is a migration of the learning process from one individual to a group of individuals as knowledge becomes a collective activity enhanced by the phenomena of social interaction. the complexity of the information and the way we interact with it makes us active parts in the educational environment. searching, discovering, and testing are the most frequent human activities in such situations. when an interpretive level of comprehension is reached, these activities are mature enough to trigger creational thinking, and constitute the beginning of the applied level of understanding. as complements to learning, virtual training gives constructive feedback to learners by providing them with a hands-on approach to the studied subject. in the following sections, we emphasize the potential of distributed virtual environments to improve the learning process. to prove the point, we try to answer one question: "what do the 3d virtual spaces bring into the learning processes in order to make them effective and evolutionary?" copyright c© 2006-2009 by ccc publications 402 d.m. popovici, j.p. gerval, f. hamza-lup, r. querrec, m. polceanu, n. popovici, r. zăgan 1 introduction a three-dimensional (3d) virtual space is a computer-generated space that is perceived by us via pure virtual reality (vr) technologies and/or mixed reality (mr) technologies [1]. this perception can only be obtained through placing the user in the space, from the user’s interaction with the space. furthermore, this space is not passive since the users interact with each other and/or with other virtual entities, by the means of virtual agents or avatars. virtual objects are subjects in the users’ direct or indirect interactions and may enhance collaboration between users. users’ multimodal communication is realized through exchanging typed or verbal messages, gestures, and facial expressions. in other words, the virtual space must be constructed, first of all, considering the user’s cognitive and empirical attributes. this means that when we create virtual space models, the base criterion should be the accuracy of the human representation of reality which may not necessarily correspond with reality. to this end, the human experience is first constructed by situating the user in the virtual context, then tested through the user’s direct interaction with the environment, and later reconsidered, in a recursive process. how efficient and effective is such experience? a possible answer may be given by evaluating the user experience in the frameworks proposed by burdea [2] and zelter [3]. we do not discuss the imagination aspect of the user experience but leave it private to the user. it may surprise the reader, but we are not trying to obtain an accurate sensorial rendering of the virtual space in order to immerse the user in the environment. instead, by high quality immersion inside this virtual space we mean intense user interactivity with objects or other users within the virtual space. the efficiency and the effectiveness of such experiences are considered acceptable if the user is able to apply the knowledge and skills obtained in the virtual space in similar real-space conditions; i.e., if both the gained knowledge and skills are reusable. applying such virtual spaces in learning and teaching activities provides the conditions for transforming the sometimes passive actors (i.e., students and teachers) into involved, very active actors [4]. with the support of new technologies, we hope to infuse them the sensation that they represent active parts of the learning/teaching process [5]. our goal is to catalyze the creative state-of-mind and self-confidence at an individual level as premises of collaboration among individuals, with personal perspective as the basis for the learning communities. these communities provide the necessary conditions for transforming the users’ interactions, expressed through direct communication and cooperation with other individuals, into long-term social interactions. many educational virtual environments such as "virtual theatre" and "virtual classroom" use various metaphors to facilitate the trainee in learning on an abstract (e.g., math, physics, electronics, and other [6, 7, 8, 9]) or concrete (e.g., gesture or behavior in certain situations [10, 11]) level. few environments take into consideration the trainee’s motivation to learn. driven by this observation, our goal is to obtain a solution designed to serve as a motivational feedback to its users. virtual theatre or narrative-based metaphors have one major advantage as compared to other metaphors; i.e., they challenge and encourage the user to verbalize/render his/her experience in a situational context. such an environment is highly evolutional since every actor comes with his/her own personal experience in a similar situation; this way knowledge is collectively and continuously modeled to better express the social point of view. the more we express the knowledge, the better the result becomes. multimodal environments that combine haptic feedback with 3d visualization and sound rendering [12] prove to be very efficient learning tools, especially for understanding abstract concepts. as students gradually gain confidence in the team they belong to, they become autonomous and willing to acquire new knowledge; thus, they change from being dependent on the team to being independent, and the relationships among individuals become dynamic and friendly. in particular, team-based environments are suitable for interdisciplinary teams. for example, the engview system about which we discuss in this paper was developed by a mixed team of computer scientists, engineers, and managers, as 3d virtual spaces supporting engineering learning activities 403 well as a group of enthusiastic students. engaging such a team, we have attained our main pedagogical objective that is to assure a rapid and successful integration in the professional context for our students. however, some difficulties rise due to factors such as the different levels of knowledge acquired by students during their studies, the student’s level of interest in the information presented and the student’s motivation to learn. learning speed varies from person to person. often, theory is easier to grasp than to translate into practice. or vice-versa, practical skills are quickly acquired, even without any basic understanding of the theory. despite these difficulties, students need to achieve good theoretical and practical skills. at the theoretical knowledge level, the widely used method of multiple choice examinations can be computer-graded or easily marked with a template. however this method does not provide any insight into the trainee’s work methods and adaptability. a much better choice is a written examination. on the other hand, practical examinations are somewhat more probing; however, the trend is to have the candidate demonstrate his/her skills in a simple application where the results can be easily and uniformly graded [13]. because paradigms such as vr and multimodal environments facilitate learning through the construction of concepts relying on the intuition that arises from direct user experience in the virtual environment [14], we decided to complement our teaching/learning process by using these technologies. we do not eliminate multiple choice examinations, but we consider that communication and interaction within a collaborative virtual environment may represent essential motivational dimensions to the trainee. therefore, we consider interaction and communication as being the most important requirements of vr-based training technologies. another important aspect is the reduced accessibility of the real training setups for a group of trainees. by means of switching between training sessions in real environment and virtual replicas, the trainee is able to obtain the confirmation of his/her practical results obtained in the virtual environment. thus, we do not eliminate traditional assessment, but we let the students exercise longer within a virtual setup, without any physical risks and at potentially lower costs. when students reach a certain level of "virtual expertise", they are allowed to prove this expertise in a real environment. 2 engview a training tool for engineers to demonstrate the effectiveness of the educational concepts mentioned above, we implemented a training environment for engineers, called engview [15], that is a supplementary tool in the training process in the domain of non-destructive testing (ndt), as detailed in the next section. because the presented training environment addresses mature users, motivation may not necessarily come from the environment itself, but from the user’s desire to succeed in his/her integration within a professional context. in such a context, social interactions frequently appear in team setups and trigger individual development on both theoretical and practical levels. due to frequent switching between experimentation and theory, it is not surprising that discovery, creation, and innovation are expected side-effects in engineering learning contexts. 2.1 ndt principles the most used formats in the ndt training process are the a-scan, b-scan, and c-scan presentations. these provide different ways of visualizing and evaluating the inspected material region. for our purposes, we have chosen to visualize only the c-scan method. the high-frequency ultrasonic c-scan presentation provides the planar view, depth location, and size of the defects inside the probe; this makes c-scan a valuable tool to monitor the precise location of the defects between certain layers (see figure 1). the plane of the image is parallel to the scan pattern of 404 d.m. popovici, j.p. gerval, f. hamza-lup, r. querrec, m. polceanu, n. popovici, r. zăgan the transducer. c-scan presentations are produced with an automated data acquisition system, such as a computer controlled immersion scanning system. the c-scan method is based on the transmission of a very-high-frequency signal (up to 50 mhz) directed to the sample by the transducer. the sample and the transducer are submerged in a coupling medium (water in our case). the initial signal is partially reflected back to the transducer by the interface’s grains, defects and porosities, or by other substantial differences in acoustic impedance in the sample and the signal of the transducer. if not fully reflected, the signal continues through the sample. in other words, between the initial pulse and the back-wall peaks there is an additional peak caused by the sound wave going from the water into the test material. this additional peak is called the "front wall peak". the ultrasonic tester can be adjusted to ignore the initial-pulse peak, so the first peak it will show will be the front-wall peak. some energy is lost when the ultrasound waves hit the test material, so the front-wall peak is slightly lower than the peak of the initial pulse. in return, the peak amplitudes and the time-of-flight of each returning signal are stored in a computer data file and processed offline to produce maps of the scanned area for the sample placed at a particular depth. figure 1: c-scan principle and samples of scanning figure 1 shows four ultrasonic c-scan images of a silicon solar plate (solar cell). all images were produced using a pulse-echo technique with the transducer scanning from above the sample in an immersion scanning system. for the c-scan image in figures 1.a and 1.c, the gate was set up to capture the amplitude of the sound reflecting from the head surface of the silicon plate. light areas in the images indicate the regions that reflected a greater amount of energy back to the transducer. in the c-scan image in figures 1.b and 1.d, the gate was moved to record the intensity of the sound reflecting from the back surface of the plate. the details on the back surface are clearly visible, but the front surface features are also visible since the sound energy is affected by these features as it travels through the head surface of the silicon plate. 2.2 related work because of the complexity of the real ndt setups, training of experts in nondestructive testing should take place in specially equipped laboratories. the cost of such a training configuration is rather great. this makes its implementation in academic laboratories difficult and, even so, the accessibility of students to the installation is reduced [16]. it also explains the small number of ndt training systems. the virtual nondestructive evaluation (nvde) system proposed in [17] offers a full computerbased replica of a real ndt examination setup. using nvde, the user is able to generate the testing scenario, as trainer, to practice with the virtual setup and to perform assessment sessions to determine the performance level reached by the trainees. 3d virtual spaces supporting engineering learning activities 405 the civa software developed by cea permits the visualization, optimization, and prediction of the performances of several testing techniques. great effort is made in order to optimize the computing time so that the 3d models that are tested can be used in parametric studies, despite the potentially complex configurations. moreover, civa can simulate the ultrasound wave propagation and highlight the defects inside the 3d models [18]. 2.3 the virtual environment in order to solve the problem of time limitation and lack of accessibility for more than one user that the real configuration presents, a virtual implementation of the scanner was developed. all functionalities of the real ndt installation were made accessible through engview’s 3d-immersive simulation software (see figure 2.a). this feature allows any user to practice the scanning procedure without any repercussions in case of faulty maneuvers. this method offers a superior overview and understanding of the device and its mechanism of functioning. more precisely, the user is able to change the viewpoint inside the simulated environment (front/back, left/right, and up/down) and to navigate inside the virtual scanning device for a better view-point. these features allow the user to visualize the surface of the virtual scanned object during the simulation. the user can also move the three crane-like components of the virtual scanning device to virtually scan the simulated 3d probe. the engview setup was used during the second semester of 2007 in training sessions by engineering and physics senior students, organized in eight groups, each containing 25 students. a) b) figure 2: engview : a) practical session, b) theoretical assessment session the ndt curriculum requires one practical evaluation on the basis of six laboratory hours. as mentioned before, the ndt makes no exception in both theoretical and practical evaluation. to this end, the virtual environment contains pedagogical resources that provide users with access to theoretical background and evaluation as well as to practical sessions. students can reproduce different types of realistic experiments using the engview system by preparing the sample, changing the type of transducer, setting the parameters of the moving engines to establish the type of scanning procedure, and to make comparative studies. the students that work on the client machines in the engview system are able to perform the same kind of analysis as in a real system. the engview system can be used either independently not coupled to the real system by installing it on a computer, or directly connected to the scanning device. the former option gives the advantage of 406 d.m. popovici, j.p. gerval, f. hamza-lup, r. querrec, m. polceanu, n. popovici, r. zăgan supporting several students to train simultaneously using their home internet-enabled computer. through the latter option the device is actively controlled, serving as a safe and easy way to perform experiments when accurate data is required. an assessment was organized on the basis of a multiple choice test containing ten pure theoretical and seven practical questions (see figure 2.b) to evaluate the knowledge acquired by the students. the time limit was 30 minutes to answer all questions. the practical evaluation had three steps: the experiment setup/calibration; the experiment itself; and the interpretation of the results. in the real configuration, about 30 minutes are necessary for the experiment per student, without any error recovery, so there is no possibility to try it twice during the exam. in this situation, it often occurs that the student uses the real ndt setup for the first time. 2.4 emulaction a platform for distributing learning/training activities although the solution described above provides the users with a more efficient learning environment, it does not support more users working together at the same time. to overcome this deficiency, we developed a context in which the simulations can take place. this context is constituted by a virtual classroom that holds the fully functional 3d representations of each element from the real educational scenario. organizing the learners in teams in the context offered by the "virtual classroom" metaphor helps to reduce most of the discrepancies between the individual knowledge levels increases communication and competition (in this order). competition becomes cooperation and aids the level of motivation. hence, the complexity that may arise even in the most "simple" subjects is a non-declared motivational factor when introduced gradually. if the students’ needs are satisfied and their expectations are met, they will strive to develop their professional competences. indirectly, students contribute to the development of the learning context (see figure 3). a) b) figure 3: shared training environment (emulaction project): a) users sharing a task, b) virtual office containing theoretical material and assesment tools because the students share the real environment, we want them to share a similar virtual environment also. students naturally start to form small work teams in the virtual setup, based on the real environment configuration. later, these teams may evolve based on the complementary knowledge that the team members possess, in order to assure a higher level of team performance. shared experiences provide different, perhaps even complementary perspectives to the lesson, depending on each individual. combining specific tasks in a distributed platform enables the users to collaborate and focus on the same target, share knowledge and impressions. working in teams can bring great advantages to the 3d virtual spaces supporting engineering learning activities 407 learning experience, as users can communicate and coordinate each other’s actions in real time, while conducting the experiments. figure 4: engview-based shared training session figure 4 shows an example of such a context, where the students that have passed the theoretical assessment have access to the virtual replica of the ndt scanning installation. here they may test different scanning parameters and different probe materials while visualizing the same engview environment. a virtual laptop gradually displays the scanned probe and can receive commands to either start or stop the scan. the scanning device is a fully functional replica of the real equipment, and the cranes from the standard version of engview have been replaced by virtual disks that can be rotated to achieve the desired position of the start and end positions. the visual feedback is coherent with the used scanning parameters and may give hints to the trainee concerning the task currently in progress. 3 implementation-related aspects our educational virtual environments are currently based on the assumption that knowledge and skills acquired in a vr-based environment will be transferred to the real world. the effectiveness of such an environment depends on the user’s capability to apply the knowledge and/or the skills acquired to its real world counterpart. the current learning materials are implemented using the moodle [19] platform for the text and multimedia resources (doc, pdf, ppt, avi, or jpeg files) as well as 3d virtual environments. concerning textual and multimedia support, we explored the moodle facilities to align the pedagogical context with the sharable content object reference model norms [20]. moreover, we manage the users’ access to the corresponding course materials according to their curricula and the course materials. therefore, the tutors have the ability to create, modify, and publish educational materials, such as courses, seminars, homeworks, project subjects, tests, and so on. furthermore, using such a system, the administrator is able to manage the courses, the users, the groups of students, and the students enrolling in each course. our 3d environments are developed using vrml [21] and/or arévi api [22]. the arévi api is open-source, c++ and opengl based, and adaptive to different configurations, ranging from desktops to 3d stereoscopic immersion systems. to put all together, we use a reactive agent-based architecture [23]. this architecture assures the user’s immersion and evolution within the virtual space. to ensure the distributed activities, we have adopted the linux, apache [24], mysql [25], and php [26] based solution. because our educational environment is mostly 3d-oriented, we chose to build it based on the ajax/ajax3d technology [27, 28] and x3d/vrml language [29, 21]. ajax provides optimal update speed between the client and the server by simulating a direct connection, while x3d 408 d.m. popovici, j.p. gerval, f. hamza-lup, r. querrec, m. polceanu, n. popovici, r. zăgan has the advantage of having an accessible structure that can be controlled with the javascript engine through a browser plug-in called fluxplayer [30]. fluxplayer is easily installable on windows (xp and vista) operating systems for firefox [31] and internet explorer browsers. the scene access interface (sai) is achievable also through java, but in this case we considered that having an applet to control the environment was unnecessary. this approach is still at the beginning as more and more gamelike browser-based applications spread over the internet. this launch is facilitated by the increase in processing power of the personal computers, and by the ever-evolving internet browsers that are able to faster process web content. to this end, this architecture can be considered modern and unique in the context of educational purposes. php and mysql are in charge of the user account and database management. the system currently supports two account types: student and teacher, each enabling users to perform certain actions depending on their status. apart from proving a high level of performance, the system is easy to install on any operating system that supports php and mysql. although the update speed is not real-time due to the impossibility of establishing a direct connection with the server through this architecture, this was not a factor of decision because the main objective of this application is only to provide users with a functional collaborative environment in which they can practice. assuming a high number of users connected to the system simultaneously, the application was optimized to cache new events to prevent unnecessary communication with the server that would cause it to slow down. updates are transmitted using the xml format for better information structuring. xml not only brings ease in the use of the received data, but also makes the system adaptable to changes brought to its structure. each client of the application requests updates from the server at customizable time intervals, depending on the connection speed. the virtual classrooms also offer users tools to communicate with each other and submit results for verification, after experiments have been completed. among these tools are the button toolbar, books and files, which are also viewable by all participants when activated (see figures 3 and 4). the environment is designed to be customizable by the teachers that want to hold a course in of different curricula. the teacher account type offers users the freedom to create a personalized classroom, suitable for the course that needs to be held, containing adequate tools and devices for the students to use. this way, the customized classroom is dynamically generated by the application, and becomes ready for the students to join. 4 discussions in order to evaluate the efficiency of the virtual setup, we gathered completion time information from students trained in the classical manner and those who benefited from the virtual practice (see figure 5). we observed that using only the traditional training sessions is neither motivating nor time-efficient. the probability of failure because of poor practical skills and/or errors that may appear during the experiment is too high for the current curricula. on the other hand, by offering students the possibility of practicing in the virtual configuration before the real one, they became more confident in their own potential due to the chance to recover from errors and to experiment with more training situations. in addition, the number of hours dedicated by the faculty’s regulations for training and practice with the scanning device is considerably small. engview makes seminars less expensive by using complex immersive and interactive simulations which are accessible over the internet. moreover, it brings students closer to the practical part of their education and helps them better comprehend each learned concept. in order to evaluate the system’s impact on the user’s learning/training process we have compared the assessment results obtained in classical training context with those obtained after shared 3d-setup was used (see figures 6 and 7). table 1 contains the repartition of the users’ results. for each answer data set we determined the characteristic values as the average, the mode and the 3d virtual spaces supporting engineering learning activities 409 figure 5: comparison of training completion time in real configuration with and without virtual training sessions, respectively variance. the first two values represent the central tendency while the variance represents the dispersion degree around the mean. the mode is the most frequent value that appears in the data set. classic assesment 3d assesment possible theoretical practical theoretical practical values questions questions questions questions 1 2 8 0 2 2 7 7 0 3 3 10 19 10 12 4 17 37 16 23 5 13 46 8 48 6 18 45 22 40 7 29 38 33 72 8 43 0 41 0 9 39 0 47 0 10 22 0 23 0 total 200 200 200 200 mean 7.025 4.965 7.39 5.6 variance 5.134375 2.503775 3.8179 1.93 mode 8 5 9 7 table 1: user results in both classical and 3d training contexts and characteristic values based on these values and the corresponding charts (see figures 6 and 7) we can conclude that the differences indicate a significant overall improvement in the case of using the 3d setup. in order to verify that the improvement brought by the 3d setup is indeed significant, we also applied a statistical t test for mean comparison between the two samples assuming unequal variances. the resulting p-values corresponding with theoretical assessments and practical assessments, i.e. 0.043032 and 0.000014 respectively, are smaller than 0.05; hence the difference between the means of the 2 samples is significant. in other words, since the mean of the 3d setup is obviously higher than the classical approach, we conclude that the 3d method brings significant improvement in the training process. we have also implemented an anonymous questionnaire that focuses on both the engview’s user interface and the environment content. we have opted for a "five-level-choice" questionnaire, with the following grades: "very poor", "poor", "acceptable", "good", and "excellent". 410 d.m. popovici, j.p. gerval, f. hamza-lup, r. querrec, m. polceanu, n. popovici, r. zăgan figure 6: results of the theoretical assessment using classical approach v.s. 3d-based one figure 7: results of the practical assessment using classical approach v.s. 3d-based one question / answer very poor poor acceptable good excelent the interface is self-explanatory? 0% 0% 16.2% 83.5% 0.3% the environment helps me to identify the key concept? 0% 0.28% 15.6% 83.1% 0.02% how natural was the interaction with existing objects? 0% 0.17% 73.2% 12.97% 13.66% how did you find the virtual ndt setup feedback? 0% 0% 1.3% 97.2% 1.5% the reuse of the capabilities in real setup that where obtained in virtual setup 0% 0% 3% 79% 18% table 2: engview evaluation questionary 3d virtual spaces supporting engineering learning activities 411 as the results show (table 2), despite the specificity of the engview environment, what we want to convey to all users of our virtual environments is self-confidence and team-oriented contexts. the virtual space has to motivate users to study the environment by direct and constructive observation of its components, without any temporal or geographical constraints. by simulating real setups into virtual spaces we encourage the users to be active situated actors in self-explanatory pedagogical contexts. 5 conclusions and future work as previously stated at the beginning of this contribution, the subtle goal of this work is to prove whether virtual 3d environments are able to increase the efficiency and of learning processes and their capacity of being evolutionary. first of all, people need feedback in order to comprehend the activities they perform; the lack of feedback is a major issue when dealing with the educational context because materials and equipment are often too expensive to purchase in large amounts. having virtual simulations of the real training material lowers the costs of training, and increases the number of students that are able to be trained using them. secondly, it was proved that collaboration increases the quality of learning, but not all virtual environments support multiple user access. using a distributed platform that can implement various live training sessions makes possible the evolution of teams of students while training. users receive feedback from their own actions as well as from other’s, this way maximizing the intake of information. thirdly, statistics based on the users’ responses show that learning speed is greatly increased when using virtual environments in addition to classical methods. to this end, the point in adopting interactive 3d worlds in the educational context has been proven. one of the central directions of our efforts is to use ontologies in content management and deployment. this may be useful in producing similar pedagogical situations that use different content. this may also allow us to introduce agent-oriented tutors that can evaluate the users’ actions inside the 3d space. 6 acknowledgements our contribution is an extended version of the paper [32] previously published in the proceedings of the 3rd international conference on virtual learning (icvl’08). this work is partially funded by the following projects: intuition (fp6-ist-nmp-1-507248-2), emulaction (fonds francophones des inforoutes ref.no. 14g023), and tomis (pn ii: 11–041/2007, national centre of programs management). bibliography [1] p. milgram, h. takemura, a. utsumi, f. kishino, augmented reality: a class of displays on the reality-virtuality continuum, spie vol. 2351, telemanipulator and telepresence technology, pp. 282-292, 1994. [2] g. burdea, ph. coiffet, virtual reality technology second edition with cd-rom, wiley, new jersey, 2003, isbn 0471360899. [3] d.zelter, autonomy, interaction and presence, presence, vol. 1(1), pp. 127-132, 1990. [4] a. wilson, the promise of situated cognition, in an update on adult learning theory, ed. by s.b.merriam, pp. 71-79, san francisco, jossey-bass, 1993. [5] j. lave, e. wenger, situated learning. legitimate peripheral participation, cambridge, university of cambridge press, 1991. 412 d.m. popovici, j.p. gerval, f. hamza-lup, r. querrec, m. polceanu, n. popovici, r. zăgan [6] g. taxen, a. naeve, cybermath: a shared virtual environment for mathematics exploration, center for user oriented it design, royal institute of technology, technical report cid-129, stockholm, sweden, 2001. [7] b. loftin, m. engelberg, r. benedetti, applying virtual reality in education: a prototypical virtual physics laboratory, in proceedings of ieee symposium on research frontiers in virtual reality, san jose, ca, pp. 67-74, 1993. [8] j.p. gerval, y. le ru, velab: a virtual lab for electronics virtual experiments, advanced technology for learning, volume 3 issue 2, acta press, 2006. [9] n. doukas, a. andreatos, advancing electronic assessment, international journal of computers, communications & control, vol.ii, no.1, pp. 56-65, 2007. [10] w. l. johnson, j. rickel, steve: an animated pedagogical agent for procedural training in virtual environments, sigart bulletin, acm press, vol. 8, number 1-4, pp. 16-12, 1997. [11] j.-m. burkhardt, d. lourdeaux, d. mellet-d’huart: la conception des environnements virtuels d’apprentissage (the design of training virtual environments). in: p. fuchs & g. moreau (eds.): le traité de la réalité virtuelle. paris, presses de l’ecole des mines, 2003. [12] f.g. hamza-lup, i. sopin, haptics and extensible 3d in web-based environments for e-learning and simulation, 4th international conference on web information systems and technologies (webist), may 4-7, funchal, madeira, portugal, 2008. [13] c. roloson, j. zirnhelt, performance based qualification: an ndt service industry perspective, paper no. 744, cd-rom proceedings of the 16th wcndt 2004 world conference on ndt, aug 30 sep 3, montreal, canada, http://www.ndt.net/abstract/wcndt2004/744.htm, 2004. [14] j. bruner, actual minds, possible worlds, harvard university press, cambridge, ma., 1986. [15] cerva, http://www.univ-ovidius.ro/cerva/engview, 2007. [16] p.n. shkatov, d.v. bydanov, s.v. kluev, simulator for education of eddy current testing specialists, proc. of the 17th world conference on nondestructive testing, 25-28 oct 2008, shanghai, china. [17] d.h. harris, j. spanner, virtual nde operator training and qualification, ndt.net october 1999, vol. 4 no. 10. [18] s. lonné, l. de roumilly, l. le ber, s. mahaut, g. cattiaux , experimental validation of civa ultrasonic simulations, international conference on nde in relation to structural integrity for nuclear and pressurised components, 2006. [19] moodle, http://moodle.org, feb., 2009. [20] scorm, sharable content object reference model, http://www.adlnet.gov/scorm/, feb., 2009. [21] vrml, virtual reality modeling language, http://www.web3d.org/x3d/specifications/vrml/, feb., 2009. [22] p. reignier, f. harrouet, s. morvan, j. tisseau, t. duval, arévi: a virtual reality multiagent platform, lectures notes in computer science, volume 1434, issn: 0302-9743, http://www.cerv.fr/fr/activites/arevi.php, 1998. 3d virtual spaces supporting engineering learning activities 413 [23] d.m. popovici, modeling the space in virtual universes, phd thesis, politehnica university of bucharest, 2004. [24] apache, http://www.apache.org, feb., 2009. [25] mysql, http://www.mysql.com, feb., 2009. [26] php, http://www.php.net, feb., 2009. [27] ajax, http://www.ajax.org, feb., 2009. [28] ajax3d, http://www.ajax3d.org/, feb., 2009. [29] x3d, http://www.web3d.org, feb., 2009. [30] fluxplayer, http://sourceforge.net/projects/flux, feb., 2009. [31] firefox, http://www.firefox.com, feb., 2009. [32] d.m. popovici, j.p. gerval, f.g. hamza-lup,n. popovici,m. polceanu,r. zagan: learning distributed activities inside 3d virtual spaces, proceedings of the rd international conference on virtual learning, pp. 289-296, constanta, 2008. dorin-mircea popovici (08/07/1968) obtained his phd in computer science at politehnica university of bucharest, romania (2004). he actively participates in several international and national projects, all oriented on the use of virtual reality in human activities like learning, education and culture heritage. he had written the book "an insight of 3d virtual environments" in romanian, published by ed. muntenia, constanta (2007). he is the founder and the leader of the research team in virtual and augmented reality of the ovidius university of constanta (cerva). (for more information please see http://www.univ-ovidius.ro/cerva) jean pierre gerval (12/12/1957) obtained his phd in automation from the university of valenceinnes in france (1987). he has been project manager at the "institut d’informatique industrielle" in brest, france (1988-2003) and associate professor of computer sciences at the "ecole nationale d’ingénieurs" de brest (1993-2003). he is currently the head of the computer science department at the "institut supérieur de l’electronique et du numérique" in brest. he received the "best software system" award from iasted international conference on computers and advanced technology in education oranjestad aruba august 2005 for the development of the virtual lab for electronics. his research interests include distributed virtual reality and virtual environments especially dedicated to pedagogical applications. dr. hamza-lup (20/03/1976) received a b.sc. in computer science from technical university of cluj-napoca, romania, the ms. and ph.d. in computer science from university of central florida, orlando in 2001 respectively 2004. he is involved with research and development of medical simulation systems and medical training tools that use 3d imaging components and virtual reality paradigms. his research interests are: human computer interaction, distributed systems, virtual reality and simulation/training for medical procedures. he received grants from several organizations including nasa, md anderson cancer center (orlando) and has served as co-pi on several sttr and sbir grants. in 2003 he has received the link foundation fellowship in advanced simulation and training and in 2005 the distinguished service award for mentoring students in science. (for more information please see http://www.cs.armstrong.edu/felix) ronan querrec (18/09/1973) had his phd in 2001 in the science university of brest. he is professor assistant in computer science and works at the cerv. his reasearch work is about virtual environment for training. in this theme, he works on the mascaret project, a virtual environment metamodel. 414 d.m. popovici, j.p. gerval, f. hamza-lup, r. querrec, m. polceanu, n. popovici, r. zăgan mihai polceanu (22/12/1988) is currently a student in computer science at the ovidius university of constanta. as member of the cerva team, he works in the emulaction project as architecture designer and programmer. his domains of interests are: programming, virtual reality, web technologies, cryptography, cryptanalysis and data security. norina popovici (29/04/1970) obtained her phd in economics (2005). her domains of interest are management, human resources management, project management, and usability of web technologies in management and educational systems. remus zăgan (11/06/1967) had his phd in industrial engineering at technical university "gh.asachi", ia¸si in the field of vibration, ultrasound, material characterization, modeling and simulation, he is currently the dean of mechanical industrial and maritime engineering faculty, "ovidius" university of constanta. he had managed 7 national grants, 1 international grant and deposed 1 patent. he has several books in wavelets analysis of noise and vibrations, modeling and simulations of production systems. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 1, pp. 42-51 mining authoritativeness of collaborative innovation partners j. engler, a. kusiak joseph engler adaptive systems rockwell collins, inc. cedar rapids, ia 52498 usa e-mail cjengler1@msn.com andrew kusiak mechanical and industrial engineering 3131 seamans center university of iowa iowa city, ia 52242-1527 usa e-mail: andrew-kusiak@uiowa.edu abstract: the global marketplace over the past decade has called for innovative products and cost reduction. this perplexing duality has led companies to seek external collaborations to effectively deliver innovative products to market. external collaboration often leads to innovation at reduced research and development expenditure. this is especially true of companies which find the most authoritative entity (usually a company or even a person) to work with. authoritativeness accelerates development and research-to-product transformation due to the inherent knowledge of the authoritative entity. this paper offers a novel approach to automatically determine the authoritativeness of entities for collaboration. this approach automatically discovers an authoritative entity in a domain of interest. the methodology presented utilizes web mining, text mining, and generation of an authoritativeness metric. the concepts discussed in the paper are illustrated with a case study of mining the authoritativeness of collaboration partners for microelectromechanical systems (mems). keywords: innovation, web mining, text mining. 1 introduction innovation most often occurs in one of two forms, incremental or radical. radical (discontinuous) innovation assumes focuses on a completely new concept that is radically different from the existing ideas. this type of innovation occurs rarely and is not easily predictable. incremental, or continuous, innovation builds upon previous concepts and therefore it is easier to be quantified. kusiak [1] defined innovation as an iterative process aimed at the creation of new products, processes, knowledge or services by the use of new or even existing technology. this definition summarizes the typology of incremental innovation. innovation has further been quantified into five generational models. the first generational model is linear, where innovation is unidirectionally pushed from the research phase to the commercial application phase [2], [3]. the second model, the pull model, holds the consumer as the main focus of innovation as opposed to the designer [4]. feedback forms the third model and utilizes the consumer’s responses to an initial product/service offering to perform incremental innovation on that product and/or service [5]. the fourth model is known as the strategic model in which innovation lines up directly with the company’s strategy [2]. the model pursued in this paper is the fifth model, also known as the networked model. in this model extraenterprise and cross-discipline organizations form a network to innovate. the term open innovation [6] is often used when describing this model. collaborative networking involves a detection of the optimal sources of collaboration. this can often be viewed as a challenge and often results in the local optimum as the choice of the collaborative source rather than the unseen, and most often unknown, global optima (e.g., the most authoritative person/company). the success of collaborative innovation depends greatly on the quality of the collaboration sources. the probability of innovation success, as measured by the market, can be considered proportional to the quality of the collaborative sources. therefore, it is incumbent upon a company, in pursuit of innovation, to seek out the optimal sources for collaboration. a wealth of information is available upon the world wide web (www) for identifying the optimal sources of collaboration (e.g., white-papers written by authorities of specific domains). copyright c© 2006-2010 by ccc publications mining authoritativeness of collaborative innovation partners 43 various researchers have begun to investigate possible means by which collaboration produces effective results. unfortunately, the literature is lacking solid systematic methodologies by which collaboration authoritativeness may be determined apriori of collaboration inception. chapman et al. [22] proposed a process model for collaborative data mining in an electronic manner but fail to address the need for authoritativeness when determining the collaborative partner selection process. lavraĉ et al. [23] surveyed various methodologies for collaboration but fail to report a systematic methodology of determining the most authoritative entity for collaboration. gajda [24] investigated assessment measures for determining the success of a collaborative partnership on a project. unfortunately, these measures are considered upon the completion of the project rather than to determine the effective partners for collaboration prior to entering in to a collaborative agreement. other researchers have posited criteria and methodologies for collaboration partner selection. geringer [25] proposed task-related selection criteria for international joint ventures but failed to present a systematic methodology for automatically determining the authoritativeness of a collaborative partner. hitt et al. [26] investigated resource-based and organizational learning for collaborative partner selection. again, the methodologies in the literature fall short of the goal of automated systematic determination of authoritativeness for collaboration. this paper presents machine learning algorithms to extract collaborative innovation relationship information from various sources including the www. utilizing machine learning algorithms to discover valuable knowledge from disparate sources has been presented in the literature. chen [27] posited utilizing natural computing techniques, such as swarm intelligence, to foster a collective intelligence in a virtual learning environment. grebla et al. [28] presented a bayesian belief network for mining data from various databases to assist in predicting arteriosclerosis and cardiovascular disease. this paper offers a novel methodology by which the optimal authoritative sources of collaborative partnership may be discovered. through the use of web mining, text mining, and the creation of an authoritativeness matrix, users may determine the optimal authority with which to perform collaborative innovation. of course, optimality may depend upon more than just the most authoritative partner on a given subject. other factors such as the availability of the collaborator or the cultural background of the collaborator (e.g., defense systems collaboration) may be involved. thus, this paper advocates the creation of an authoritativeness matrix as opposed to simply defining global optima for collaboration. the remainder of this paper proceeds as follows. section 2 discusses the focused mining of the world wide web to discover authoritative sources for collaboration. the distillation of these sources to form an authoritativeness matrix is discussed in section 3. an authoritativeness metric is presented in section 4. section 5 discusses clustering of the mined sources of collaboration. section 6 offers a case study for determining the leading authorities on microelectromechanical systems (mems). finally, section 7 offers concluding remarks. 2 focused mining of the web the first major step in forming a collaborative innovation relationship is to seek out and choose partners for the collaboration process. the world wide web (www) presents a proven search space for multiple concepts. a natural inclination is to manually search the internet for such sources of collaboration. some companies hire business development teams to perform this task. manually searching the web is a time intense process that often yields sub-optimal results. sometimes searches can even present misguided or influenced results due to the ability for parties to influence their rank among the various search engines [7]. increasing the difficulty of the manual search, many search engines utilize payer based rankings which facilitate assigning a higher position in the search results. additionally, many websites have multiple internal links thus boosting certain search engine ratings. such forms of ranking manipulation may provide false results and thus the most suitable collaborative candidates could be missed. to overcome the limitations of a manual search of the internet for collaboration resources, a focused web miner is presented. the focused web miner used for this process includes user inputs of a specified phrase which become the search criteria. the focused web miner then proceeds, following standard focused web crawling methodologies as presented by liu [8], to traverse the www in search of white papers, articles, and journal entries related to the search criteria. the presented web miner can easily be extended to handle other sources, e.g., information about companies which have reached phase ii funding from small business innovation research (sbir) programs. the presented version of the focused web miner does not attempt to mine the web blog data or the standard html files. rather, this version of the focused web miner seeks content mimicking academic writings. thus, the focused web miner spends a fair amount of time searching academic web sites, scientific communities, and trade journals. it is from these types of internet resources that the, often academic, writings are extracted. 44 j. engler, a. kusiak the discovery of web pages containing white papers is a significant task involving crawling the internet and classifying the web pages that are examined as a review or non-review page. the standard approach to performing the crawl is to utilize a focused web crawler. a focused web crawler targets a specific corpus of web pages. standard crawling does not consider a specific topic of inquiry; rather its job is to index all pages available on the internet. even with the assistance of algorithms such as pagerank developed by google [17], successful standard crawling requires massive hardware and bandwidth. this drawback prevents most corporations from performing this type of crawl internally. focused crawling requires far less hardware and bandwidth but does require some sophistication of algorithms to weed out the undesirable links as they relate to the given query. the algorithms useful to focused web crawling involve basic classification algorithms. there is a great variety of classification algorithms for determining web pages containing white papers. shih et al. [18] suggested the use of web page content structure as parameters of classification. the authors in [18] indicated that content providers tend to choose urls and page layouts that coherently structure their content. this html structure may be useful in determining the likelihood that a web page contains reviews. kules et al. [19] extended this idea to limit the features used for classification to items such as web page titles, urls and text snippets. jin et al. [20] took a different approach from the previous two and utilized a data-mining algorithm called hidden nad’ve bayes. their methodology considered a large corpus of web pages and calculated the probability that each page fit into a particular category. fortunately for the focused web miner proposed here, document type is often the best indicator of a possible fit. given the query string provided by the user and a set of allowable document types (e.g., pdf files only) the need for classification is reduced greatly. the need for classification increases dramatically when white papers other than standard academic content is searched. in such case the use of a combination of bayesian classification and the web page structure algorithm of [18] is suggested (see [21]). once the focused web miner has discovered a document of the standard academic writing, it attempts to download this document to a central repository that is dedicated to the search criteria. this repository often becomes extremely large, in the range of a terabyte or more, but is central to the process for collaboration resource detection. no partitioning of this repository takes place at the time of download; rather, all documents are placed into the same location. it is upon these documents that the process of determining the most authoritative collaboration partners is performed. while this form of repository may seem excessive, the reader is reminded of the low cost of data storage. additionally, a great deal of information will be gleaned from this repository over time. 3 authoritativeness matrix an authoritativeness matrix is generated from the documents that were obtained from the focused web mining process. utilizing standard text mining techniques the documents are deconstructed to gain the information necessary for the generation of the authoritativeness matrix. a text-mining algorithm extracts from each document the author’s names and the references, other authors, cited by that particular paper. to assist in discovery of the authors who wrote, and are cited in, the papers a list of first names is utilized. the list of first names, freely available on many internet sites, allows for the detection of document patterns within the corpus such that most often names of authors are placed within a given context within the document (e.g., author names at the beginning, authors who are cited at the end). the text mining algorithm utilizes these patterns to classify portions of the document which will have the information extracted from. additional, textmining algorithms may be utilized to detect the sense, positive or negative, in which a citation appears within the document. from the information mined by the text-mining algorithm the authoritativeness matrix may be constructed. the authoritativeness matrix is a two dimensional matrix, or table, made up of columns representing individuals who have been referenced by the papers and rows representing paper authors. the authoritativeness matrix forms a concise but sparsely populated representation of the given, or presented, authorities in the documents. figure 1 presents an example authoritativeness matrix for three documents. the rows represent the authors of the documents while the columns represent authorities who are cited as references in these documents. a cell of the matrix is 1, if the author in the row containing the cell has referenced an authority in the column containing the cell. otherwise the cell is 0. each row represents a single author for a single paper, thus there may exist multiple rows for a single paper. from the example of figure 1 it is easily determined that jr koza is the most authoritative person with which to conduct collaborative innovation for the example domain. this is due to the fact that jr koza is the most cited mining authoritativeness of collaborative innovation partners 45 figure 1: example authoritativeness matrix for three documents author within the tiny corpus of documents for the example. it will be demonstrated later in this section that other factors contribute to this outcome. it should be noted that an author may reference his/her own writings as well as be referenced by others. thus, it is possible for this matrix to hold entities that are in both the rows and columns of the matrix. in fact, it is possible, although highly improbable that the authoritativeness matrix holds exactly the same authors in its rows as it does references in its columns. as will be explained in section 4, there some caution needs to be exercised when an author often references their own work while others do not. this caution is the motivation for the storage of the document year in the authoritativeness matrix as will be explained. the representation of the authorities of the documents discovered by the focused web miner by the authoritativeness matrix ensures ease of storage and traversal. the authoritativeness matrix is compact enough to be stored in main memory especially given the sparseness of the matrix. this allows for efficient processing when determining the true authorities for the collaboration process as presented next. 4 authoritativeness metric to determine which entities in the authoritativeness matrix represent the optimal, or most, authoritative entity, within the search criteria, an authoritativeness metric is used. this metric accounts for the number of documents which were written by the authority, the number of times the authority is referenced in the body of work, discovered by the focused web miner, as well as the average age of the documents written by the authority. additionally, the sense, positive or negative, in which the author is portrayed in the document can be collected. thus, the first step in calculating the authoritativeness metric is to scan the authoritativeness matrix and calculate a number of measures. these measures will be stored in various hash tables for efficient referencing. during the scan of the authoritativeness matrix a hash table representing the authors, or rows, of the matrix is created. each time an author is encountered in the scan of the matrix that author is either added to the hash table as a key value pair of <authorname, 1> or the value of the index of the author in the hash table is incremented. the same action is performed for the columns, or referenced authorities, in the matrix. additionally, a hash table is created for the purpose of obtaining the average age of the documents for each author, or row, in the matrix. the use of the three hash tables makes for an efficient scanning methodology for the authoritativeness matrix since the matrix is scanned only a single time. a forth table may be required to represent sense. the hash table which represents the number of documents written by the authors, or rows, of the authoritativeness matrix is used to obtain the number of out-links of each author. out-links are documents written by the author. similarly, the hash representing the number of times an author is referenced is used to obtain the number of in-links for each authority. in-links are documents written by other authors referencing the given author, thus implicitly conveying authority on, or detracting from in the case of a negative sense, the referenced author. the conveyance of authority on an author by referencing their work is held here in a context similar to that of the pagerank algorithm discussed in [9]. conveyance of authority plays a vital role in the determination of authoritative collaborative sources. thus, the authoritativeness metric proposed weights the in-link measure higher than the out-link measure. additionally, should sense be included, the in-link could have the ability to decrease the author’s authoritativeness. the initial authoritativeness metric is defined in (1). in (1), λ is a user defined parameter which allows for 46 j. engler, a. kusiak changing the weight of the out-links based on the average age of the documents written by author ai. this parameter assists in controlling the conveyance of authority to an author who is a prolific writer but perhaps not often cited by other authors. additionally, the λ parameter allows for decreasing the weight of older documents if only recent documents are desired. if sense is utilized, the in-link, ini, can be a negative number. it should be noted that determination of the final authoritativeness metric measure is an iterative process as will be explained. ai = ln(λ t ′ (outi) + ini) (1) where: outi is the number of out-links; ini is the number of in-links; t ′ is the average age of the documents of author ai in years from the current year; λ is a user parameter in the range of [0, 1]. thus, the initial authoritativeness of an author ai is given by ai. once the initial individual authoritativeness metrics of the entire authoritativeness matrix is calculated, the iterative process of boosting the authoritativeness is performed. similar to the methodology used in the pagerank algorithm [10], it is desirable to instill more authority to an author who is referenced by another author of high authority. thus, if one author is considered a leading authority, deemed so by the authoritativeness metric, and that author references a second author, the second author’s authoritativeness metric measure should be increased. the iterative process of authoritativeness boosting is performed using the average of the in-links pointing to the current author. in-links of high authority contribute to the boosting of the current author’s authoritativeness, while in-links of less authoritative authors are not detrimental to the current author’s authoritativeness. thus the average of the in-links during the authoritativeness boosting process is calculated by including the authoritativeness of the author who referenced the current author as shown in (2). ini = n∑ i= ea n (2) where: ai is the authoritativeness of author ai; n is the number of in-links to ai. at each iteration of the calculation of the final authoritativeness metric the average of the in-links is used to calculate the new authoritativeness metric measure ai for each author ai. equation (3) describes how the new in-link measure is calculated iteratively. in ′ i = n∑ j− { ea − ini if ea > ini  if ea ≤ ini (3) thus, at each iteration of the boosting process, ini of (1) is replaced with ini for the calculation of the authoritativeness of each author. the boosting process is continued for n iterations, as set by the user, or until the order of the authorities remains unchanged which is the preferred method. with the final authoritativeness metric at hand for each author it is easy to determine which author and/or entity is the most authoritative in the subject matter of the search criteria. it is useful to determine the top k authorities in the subject matter to ensure that a good collaborative resource can be found available and willing to collaborate on the innovation at hand. as such it is useful to set a user defined parameter λ which is a threshold below which authoritativeness is discounted. this threshold is utilized in determining the authorities of the clustered documents as explained next. 5 document clustering often the size of the search space or the generality of the search criteria can result in a document set of varied type which is large in size. to ensure that the collaborative partner chosen by the authoritativeness metric is the one mining authoritativeness of collaborative innovation partners 47 that is most appropriate for the specific collaboration it is helpful to cluster the documents into similar categories. once the clustering has been performed, a cluster that is most similar to, or most represents, the specific innovation topic is chosen. from that cluster it is the possible to determine the best collaborative source for the innovation. note, the collaborative authority of a specific cluster may not be the authority whose overall authoritativeness metric is the highest. rather, the cluster authority, or authorities, will be those who are most advantageous for the specific innovation subject. clustering of the documents mined via the focused crawler begins with the generation of a word frequency matrix for the documents. the word frequency matrix represents the counts of each word in the individual documents. each row of the matrix represents a single document; while each column of the matrix represents a single word. there exists columns for every document word, which is not a stop word, thus the matrix can be somewhat sparse. many words, known as stop words, do not assist in properly classifying the documents. stop words are most common in everyday language and thus not specific to the topic. words such as "the", "in" and "here" are removed from the word frequency matrix prior to clustering. further, it is often favorable to generate the root of words as opposed to the actual words for this frequency matrix. thus, words such as "innovation", "innovate", and "innovativeness" would all be placed in the root word frequency cell for the word "innov". figure 2 below represents a partial frequency word matrix. figure 2: word frequency matrix once the word frequency matrix is obtained it is important to reduce the dimension of the matrix to ensure efficient clustering. dimensionality reduction techniques, such as singular value decomposition, that are used for standard data mining are especially helpful here. the word frequency matrix before dimensionality reduction can easily include thousands of words or attributes. rarely are all the attributes of value to the clustering. thus, by performing a dimensionality technique such as singular value decomposition, the attribute set can be reduced down to a size that is more manageable, typically of size 100 or less [11]. once the dimensionality reduction has been performed, the reduced word frequency matrix is clustered with simple k-means clustering algorithm described in [11]. thus, a brief review of the cluster centroids will help to determine which cluster most resembles the subject matter of the specified innovation. the authoritative collaboration partner(s) can easily be determined from those entities that have contributed work to the cluster that most resembles the subject of the innovation. section 4 presented a threshold measure by which authorities could be weeded out of the collaborative search process. following the apriori property discussed in [11] and [12], those authorities that are not authoritative for the entire group should not be considered authoritative for a subsection of that group. therefore, only authorities with the authoritativeness metric higher than the user defined threshold should be sought within the clusters. 6 mems case study this section presents a case study on the discovery of the most authoritative person to perform collaborative innovation with for the domain of microelectromechanical systems (mems). in this study 2403 papers were mined from the internet on the subject of mems simon [13] describes mems as a monolithically integrated device used for microwave applications such as switches, distributed phase shifters and bpsk modulators. other applications for mems have also surfaced. in fact, according to maeda et al. [14] mems is expected to be one of the most promising areas of research and development contributing to future success of electronics businesses. 48 j. engler, a. kusiak after the 2403 papers were mined from the internet using the focused web miner, the author’s names and references were parsed from the documents as described in section 3 above and the authoritativeness matrix was generated. the authoritativeness metric, described in section 4, was applied with λ set to 0.80 to slightly discount the average age of the documents. figure 3 illustrates the top ten authorities after this initial calculation of authoritativeness. figure 3 illustrates the results of running the algorithms presented in this paper prior to the iterative boosting discussed in section 4. thus, the results in figure 3 are more indicative of a rapid manual search. figure 3: the top 10 authors in the non-boosted authoritativeness metric list for mems as seen in figure 3, gm rebeiz is indicated as the leading authority on mems. a quick search of the internet with the name gm rebeiz justifies his rank as the top authority in this non-boosted list. gm rebeiz is a professor at the university of michigan in the college of engineering and leads a team of 8 phd students in a focus on rf-mems [15]. utilizing the authoritativeness matrix, discovered in the mining of the 2403 documents which generated the non-boosted results of figure 3, the iterative boosting of authoritativeness is applied. upon the application of the boosting of the authoritativeness the list changes in order as can be seen in figure 4. boosting has the effect of attributing higher authority to those whose papers have been cited by authors of higher authority. thus, this is the list that a person for collaborative practices in the field of mems should be sought from. figure 4: top 10 authors in the boosted authoritativeness metric list for mems from the boosted authoritativeness it is easy to see that cl goldsmith is the authoritative figure one would wish to collaborate with. in fact, with a quick search of the internet it is found that cl goldsmith is the president of a company called memtronics and received his phd from the university of texas [16]. the list contains other potential candidates who may be sought after should cl goldsmith not be available for collaboration. for this case study, the focused web miner ran for approximately 18 hours to gather the 2403 documents. the parsing of the author’s references and document age took less than 2 minutes. the initial authoritativeness was then calculated from the matrix in approximately 1.5 minutes. the boosting of the authoritativeness took 16 iterations mining authoritativeness of collaborative innovation partners 49 before order no longer changed and took less than 10 minutes to achieve (see figure 5). thus, overall, the process of mining the leading authority in the field of mems, based upon these documents, took less than 18.5 hours and required very little of the user’s time to perform. it is easily seen that this is a marked improvement upon a manual search. figure 5: summary of the algorithm run the case study illustrates the effectiveness of the authoritativeness metric presented in this paper. further, the case study highlights the differences between boosted and non-boosted authoritativeness. from the perspective of a company that is seeking a collaborative partner, the boosted authoritativeness offers a list of highly respected candidates. deriving this list in the short unmanned time frame of 18.5 hours offers companies a great benefit in discovering the most authoritative person(s) to perform collaborative innovation with. the effectiveness of the authoritativeness metric is further explained in a recent scenario that was encountered by an electronics manufacturer who required expertise with legacy 16 bit pcmcia pc cards. due to confidentiality the details of this episode cannot be related although a summary of the scenario can be provided. the electronics manufacturer, a government contractor, was contracted to design a laptop integrated testing device for a piece of electronic equipment for a foreign concern. one of the requirements for this testing device was for it to integrate with the laptop through a legacy 16 bit pcmcia pc card. the contractor lacked the domain knowledge to effectively and rapidly design the testing device with this form of legacy interface. therefore, the author’s were asked to apply the authoritativeness metric to determine which entities to best collaborate with on this issue. the results of the running of the algorithms, described herein, a list of authoritative entities was generated. the second entity on this list was eventually utilized to solve the domain issue. the first entity on the list was not completely suited for the task due to security restriction. the above presented scenario lends additional support towards the effectiveness of the authoritativeness metric. it is shown that the authoritativeness metric is applicable not only to academic, research, and scientific activities but also to integration of various domain expertise in a corporate setting as well. further, it is shown that the list of authoritative entities is crucial for selection of collaborative partners due to various external constraints (e.g., security, geospatial reasons) that cannot be accounted for within the authoritativeness metric. by producing a list of authoritative entities the end user is capable of filtering for these external constraints while still achieving the results of finding the optimal collaborative partner. 7 conclusions and future works open innovation is the means by which companies seek external entities with which to collaborate to form innovation. this paper illustrated that finding the best source of collaboration for a given innovation in a manual fashion is sub-optimal. this paper presented a novel methodology for the automation of collaboration partner detection for the purpose of collaborative innovation. furthermore, a process by which the authoritativeness of the collaborative partner is ensured to be optimal was presented. using data mining, clustering, and analysis of the documents related to the innovation domain increases competitiveness of companies. novel to this paper is the use of boosted authoritativeness. the iterative process of increasing, or decreasing the authoritativeness of possible candidates for collaborative innovation extends the search process, and represents an automated methodology for determining the best candidate entity (company, person) for collaborative innovation. future research should includes increasing the efficiency of document detection during the web mining process as 50 j. engler, a. kusiak well as increasing the rate at which document classification takes place. bibliography [1] a. kusiak, innovation: a data-driven approach, international journal of production economics, 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[28] h. grebla, c. cenan, c., distributed machine learning in a medical domain, international journal of computers, communications & control, 1(s):245-250, 2006. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 3, pp. 281-294 integration of traffic management and traveller information systems: basic principles and case study in intermodal transport system management houcine ezzedine, therese bonte, christophe kolski, christian tahon abstract: as they involve many interacting agents behaving in numerous ways that are extremely difficult to predict, urban transportation systems are complex in nature. the development of intermodal passenger transportation solutions to address the mobility issues constitutes a major thrust area of urban transport policies. but, to offer citizens comprehensive seamless mobility, intermodal transportation system management (itsm) requires the integration of two major components. the traffic regulation support system, to help the operator responsible for the regulation tasks: coordination of timetables, synchronising arrival and departure times between the different transportation modes, and the traveller information system, giving customers access to information and using a comprehensive set of information tools. in this paper, a generic model of a transport management system, integrating these two components is proposed. this generic model is then used to elaborate a traffic regulation system in the case of a bimodal transportation system (tram-bus). the traffic regulation support system, based on the decision model of an operator, and the traveler information system are described. keywords: intermodal transportation systems, traffic regulation, travellers information system, management support system. 1 introduction urban networks were, for years, only designed and built to carry flows of passengers to and from workplaces and their role was limited to their transport function. they did not take into consideration passenger expectations and the integration of the system in its environment. this period is definitely behind us, and a high transport capacity is no longer the sole criteria for assessing urban transportation system efficiency. mobility patterns are hence changing as cities, with their large sprawling populations and are generating greater and more diverse mobility demands. today, due to the growing service-oriented economy, people require a broad and flexible spread of transport services to choose from. citizens are demanding greater mobility more frequent and more widespread and a higher quality mobility. most existing public transport systems were built at a time when the majority of journeys were fixed and routinely made. therefore, they must now fundamentally change their approach. public transport must take an increasingly flexible and competitive approach to meet the needs of today’s travelling public and become more service-oriented. it must increase its quality, punctuality, frequency, attractiveness and comfort because all of which have an impact on its productivity. indeed, urban networks have been transformed from just being a transport system into being an urban achievement around which the city and its mobility system are structured and developed. it is therefore crucial that an optimisation of interchange and transfer points between modes occurs for making them functional and pleasant. as well, there is no interest in setting up an efficient transport system if passengers do not know how to use it. so, another important aspect is the provision of high quality information. these two aspects, minimising waiting times and giving a real-time information to guide the travellers efficiently, are two essential points for the design of an intermodal transportation system. the intermodal transportation systems approach is briefly described in the section ii and a generic model of a traffic management system, integrating a traveller information system and a traffic regulation support system is proposed in the section iii. the section iv shows how this model was used to develop a copyright © 2006-2008 by ccc publications 282 houcine ezzedine, therese bonte, christophe kolski, christian tahon traffic regulation system, based on the decision model of an operator in the case of a bimodal transportation system (tram-bus). the travellers information system used by the operators is also explained. this system provides the operator with the information she/he needs to identify the disturbances and evaluate potential corrective actions to be carried out according to the regulation strategy she/he has selected. this paper is an extended variant of the work [15]. 2 towards intermodal transportation systems intermodality is both a technical term for a specific type of journey including several modes of transport and a policy principle. intermodality describes coordinated interchanges between two or more transportation modes to complete a movement. in this light, intermodal movements involve either the physical transfer of people or individual items from one mode to another, or the transfer of one loaded transport vehicle or container from one mode to another to continue the journey. numerous studies have been carried out on advantages (mainly quality of service) and disadvantages (costs, technical problems) of intermodal transportation systems. these are a number of barriers which prevent the advancement of intermodal passenger transportation (institutional, system integration, interoperability requirements) [20, 10]. nevertheless, the development of intermodal transportation solutions constitutes a main thrust area of urban transport policies and the role of intermodal transportation systems has become increasingly important. the intermodal approach involves looking at how individual modes can be connected and managed as a seamless and sustainable transportation system. that is, the fundamental objective of intermodalism is to integrate all the modes into an optimal, sustainable, and ethical system. such a system should support efficiency, safety, mobility, economic growth, protection of the natural environment. any person departing from any of the available modes of transportation (train, bus, plane, metro, ě) may get on the intermodal system additional services to reach its final destination. the comfort and the attractiveness of passenger transport systems depend largely upon the quality of the transfers at interchanges between public transport modes themselves and with the car. so, a crucial element of a successful intermodal transport system is the coordination of timetables, synchronising arrival and departure times between modes as much as possible to minimise passenger travel and waiting time. here the impressive advances made in both computer technology, telecommunications and decision support tools and methods are playing a crucial role in the promotion of public transport: informing the travellers about existing transport possibilities in order to allow them to define and plan their movement, giving access to information and quality infrastructure to find out what mode of transport is available. in addition, parks and ride also contribute to a successful intermodal transport system, encouraging car drivers to leave their car in a car-park at the outskirts of the city and to travel by metro, tram or a bus on a route which is free of traffic difficulties. the previous description shows that intermodal transport systems are customer-oriented that makes them really attractive. but, to offer citizens comprehensive seamless mobility, they require advanced management systems combining different decision and informational support, especially to help the operator, responsible for the traffic regulation. 3 generic model of a passenger traffic regulation support system the planning process of an urban transport system consists first in calculating a theoretical planning [1] and different timetables describe trips according to lines, frequencies, transport demand and travel times in the transport network. but, random events occur (vehicle’s breakdown, strike, traffic congestion...) that cause disturbances and make the predictive scheduling to be modified. therefore, for reducing the effects of disturbances, regulation tasks must be carried out to adapt the predictive scheduling to the real state of the network. this process consists in creating new schedules resulting from the integration of traffic management and traveller information systems: basic principles and case study in intermodal transport system management 283 decisions the regulator takes and depending on several parameters (the location, the type of disturbance, the time the disturbance occurs, ...). the real-time regulation of an urban collective transport network is a very complex problem, especially in case of simultaneous disturbances, that frequently overload the regulator with numerous information. taking efficient decisions requires: to have a global and significant overview of the network, to access rapidly to the available information related to the disturbances, to analyse the characteristics of the disturbances in order to identify the most appropriate corrective actions, to evaluate and compare the impact of these actions to decide which one must be selected. our purpose here is to assist the regulators (decision-makers) and to propose them effective solutions taking account their preferences and uncertainties related to these preferences. the generic model of a transport regulation support system (trss) is shown in figure 1. the role of each software package as a component of the integrated architecture can be identified as follows: • the exploitation assistance system is the main information system of the trss, interfacing trss with all the network information sources (sensors, gps, phone, radio-locator...). • the decision assistance system (das) is composed of three components: (i) the regulation data base containing all the information required for regulation (location of disturbances, line, bus number, ě),), (ii) the regulation models composed of all regulation methods the regulator can use and/or combine to elaborate a coercive action: algorithms, meatheuristics,.. (iii) the rss monitor which supports data manipulation, allows an interactive navigation through the regulation system, and allows the regulator to manage the regulation process by choosing the regulation models he wants to find out a solution. • the information assistance system: it is used by the operators to send relevant information to the users of the network. • the geographical information system (gis): this module stores cartographic information about the network (streets and transportation networks, measurement locations, contour lines, etc.) and descriptive data (streets names, measurements, statistics, ě).). it can help the regulator by providing a spatial view of the network, locating and displaying the disturbances areas. 4 application the validation of our approach occurred within the framework of a project involving an industrial partner, the transvilles company, as well as several research laboratories (lagis, lamih and inrets). transvilles is the company which actually runs the urban transport network (tramway and bus) in the town of valenciennes. this project is sponsored by the nord/pas-de-calais regional authorities and by the feder (fonds européen de développement régional european fund for regional development). our research work consists in the specification, design and evaluation of (1) a humancomputer supervision interface (referred to later as the information assistance system or ias), and (2) a decision assistance system (das). the ias is intended for human regulators working on passenger information on the transport system in valenciennes. the transport network includes information screens (or display units) intended for the passengers. these screens are found both in the stations and in the vehicles; they show information on the schedules and on the connections. these information are calculated automatically by an exploitation assistance system (eas) in which the position and state of each vehicle are stored. in conclusion, the regulation system is made up of three sub-systems: an information assistance system (ias), an exploitation assistance system (eas), a decision assistance system (das), (see figure 1). 284 houcine ezzedine, therese bonte, christophe kolski, christian tahon figure 1: generic model of an urban transport regulation support system each of the three sub-systems plays a particular role. indeed, the eas has to centralise the information regarding exploitation of vehicles (alerts, vehicles ahead of time, messages, schedules, delays,...), and make it possible to manage these elements. the das is intended to create, evaluate and suggest regulation strategies to the human regulator using the information provided by the eas. in this way, the regulator has less work to do, which should help to improve the quality of regulation and thus the quality of the overall service. the das is not intended to replace the human regulator, but it must provide assistance in decision-making [8]. the ias is intended to present information to the regulators and make it possible to send relevant information to the passengers [3, 4]. a simulation system has been developped to simulate the behaviour of the multimodal network in valenciennes. this simulator is described in the next section. the das and the ias, which are objects of our researches, are more detailed in the two next sections. 5 the simulator the software quest (queuing event simulation tool), developed by delmia, was chosen to build the simulation model. quest is a powerful discrete event simulation tool that allows very complex systems to be modelled. we used it to model the transportation network of our industrial partner. this multimodal network is composed of buses, trams and trains. the simulation system takes both the normal behaviour and the disturbances into account. the topology of the transportation system consists in a road network principally used by buses and a rail network which incorporates both tram and train networks. this type of network is sometimes supported by a road network, in which case bus and tram interaction must be taken into consideration. crossings are modelled to avoid collisions between buses or between buses and trams. intersections between roads or between roads and rail lines are supervised. trams always have priority in road-rail intersections. overtaking is restricted: only stationary buses may be overtaken by moving buses. an overtaking manoeuvre on the rail network can only be accomplished by using a specific procedure, such as following parallel tracks or shunting tracks, which, strictly speaking, is traffic diversion, not overtaking. rolling equipment can be divided into three categories: train, tram or bus. buses can be further divided into standard buses, articulated buses or minibuses. their dimensions, kinematic characteristics and capacities are modelled. macroscopic passenger flow simulation is used to integration of traffic management and traveller information systems: basic principles and case study in intermodal transport system management 285 track passenger movements; the model does not track individual passengers. passenger flow is considered at simple stop areas, at interchanges, and within the vehicles. stop area arrival patterns are modelled using a poisson law. the mean arrival rate per time unit varies according to timeband (period of the day) and type of day (weekday, weekend, school holidays ě).). stop areas are divided into simple stop areas and interchange stop areas. a stop area is simple if passengers can board or disembark without any possiblity of connecting to other lines while an interchange stop area combines multimodal transportation vehicles and may be very complex. in our application, the interchanges are divided into two types: • interchanges between bus and tram: a tram drops passengers off, and when a bus reaches the stop area, it takes passengers on board and leaves, unless the bus driver sees an approaching tram. in this case, the bus waits for the tram. in any case, the tram drops its passengers off, regardless of whether there is an interchange or not, • interchanges between bus and bus: the interchange is organized around an arriving bus, which brings passengers into the interchange, and a waiting bus, which takes waiting passengers out of the interchange. two types of disturbances may occur and disrupt the transportation network: • alarms detected by the vehicle or its driver (failures, emergency calls) or detected by the controller (operational threshold exceeded ě)) • unforeseen events resulting from an accident, traffic jam, passenger behaviour, driver illness ... . in our application, perturbations are generated by the user of the simulation system. vehicle perturbations include delays, early arrivals, unscheduled stops, and network perturbations refers to the unavailability of some part of the network. the regulation action that will compensate for the dysfunction is defined by the user with the help of the decision assistance system and is then simulated. in this instance, quest’s ability to communicate with external softwares via a socket-based mechanism that allows it to send or receive any type of information is clearly an advantage. 6 the decision assistance system 6.1 global modelling approach chosen concerning the decision-making process of the regulator dss design is a very rich research and development field [21]. most of the decision-making systems developed in the field of regulation are based on automatic methods. several authors (see for instance [12]) have contributed to the use of artificial intelligence in the field of transportation system regulation using fuzzy models. balbo [2] adopts a multi-agent approach for offer planning, and laichour [8] uses the same type of approach to regulate transfers. fayech chaar [5] combines a multi-agent approach with a generic algorithm. however, none of these approaches consider the integration of the method in an interactive environment. in parallel, three main modelling approaches have been proposed and used to model a human operator: • the oldest is the “human factor” approach (1940-1955), which models the observable elements of operator behavior during task execution [6]. • then, the “human/automatic” approach (1955-1980), which is based on physics theory, has tried to model the human operator. information theory, the optimal control model and model of the regulation of human activity proposed by [9], are all based on this modelling approach. 286 houcine ezzedine, therese bonte, christophe kolski, christian tahon • the third approach is the “cognitive engineering” approach, which holds that the role of human operators in system operations is supervision and decision-making. thus, problems that this approach seeks to resolve are related to the decision-making involved in system control, management, monitoring and reconfiguration. as we consider that the regulator is part of all supervision and decision-making tasks, our application falls into the “cognitive engineering” approach. our proposed model is inspired by the ones introduced by hoc [11] and [7] (see figure 2). this model integrates the regulator in the decision process and allows conflicts between the user and the system to be avoided. figure 2: the decision model of the regulator 6.2 functional model for the regulation of a transportation system figure 2 shows the functional diagram of the decision model described above. it is composed of five phases: disturbance detection and acquisition, disturbance analysis, solution design, solution evaluation and solution implementation. they are all described below. • phase 1: disturbance detection/acquisition. three detection methods are possible: (1) the automatic vehicle-monitoring system can provide relevant information about disturbances; (2) vehicle drivers can call in by radio; (3) the regulator can anticipate disturbances based on his experience in supervising a transportation network. the third case is the most common. • phase 2: disturbance analysis. descriptive data of a disturbance are numerous. since there are often several simultaneous disturbances, the assistance system provides a disturbance analysis to avoid information overload at the level of the regulator. it provides a synthetic representation of relevant network data for each disturbance, a description of the specific information available (e.g., passenger flow at vehicle stops, number of passengers in the vehicles, characteristics of the line). current disturbances are ranked according to significant criteria defined with the help of the industrial partner. • phase 3: solution design. three methods are proposed to the regulator for choosing or designing a solution. these methods complement approaches mentioned above [2] [5] [8]. they increase interactivity and allow the regulator to adapt to the varying complexity of the disturbances: integration of traffic management and traveller information systems: basic principles and case study in intermodal transport system management 287 – a manual method: all available basic and combined regulation actions are listed in a typology provided by the system and are proposed to the regulator [8]. – a semi-automatic method: the regulator chooses among the different regulation logics. he can focus on respect for punctuality or respect for transfers. the system can then filter acceptable actions and proposes a list of possible actions . – an automatic method: the regulator chooses one of the suitable solutions automatically proposed by the system. • phase 4: solution evaluation. all solutions are evaluated according to indicators computed by the system. these indicators allow the efficiency of the solution to be verified. these indicators are classified according to their importance: (1) “essential” indicators evaluate the service offered to passengers (e.g., regularity, punctuality, respect of transfer times); (2) “important” indicators evaluate the additional consequences of a disturbance (e.g., delays); (3) "secondary" indicators simply provide information to the regulator but have no influence on the decision (e.g., the lines and transfers that are affected by the solution). the regulator chooses the best solution according to these indicators. • phase 5: solution implementation. the chosen solution is implemented by creating a new timetable. 6.3 system components system implementation is based on an architecture including the following modules: • the “acquisition tool” evaluates the complexity of the disturbances and classifies them based on local and network parameters and traffic management criteria (e.g., type of vehicle, geographical location, time, number of passengers in the vehicle and waiting at the next stop). • the “regulation environment” manages three modules according to the method chosen by the regulator: – the “manual tool module” is used if the regulator builds his own solution manually. this module contains a set of mathematical algorithms specifically developed for the project. each algorithm models a specific regulation action such as “u-turn with vehicles and drivers exchange”. these algorithms have been enhanced by a filtering procedure, which enables or disables the algorithms depending on which logic is chosen. the filter is based entirely on specifications defined by the industrial partner transvilles. for example “respect for transfer” logic will disable “u-turn with vehicles and drivers exchange”. – the “case-based reasoning module” uses the traditional cbr approach [19]. its principle is based on the use of previous experience to solve problems. this approach can be used because transvilles has compiled a database of incidents and strategies applied to resolve them. this database is enriched with the activities resulting from manual regulation or those generated automatically by the system. – the “expert system” uses a knowledge base that exploits the expertise of our industrial partner (transvilles). it is used if no solution can be found using the cbr module. 7 the information assistance system 7.1 architecture chosen for the human-computer interface several architecture models of interactive systems have been put forward by researchers over the past twenty years. two main types of architecture can be distinguished: architectures with functional 288 houcine ezzedine, therese bonte, christophe kolski, christian tahon components (language, seeheim and arch) and architectures with structural components (pac, pacamodeus, mvcě);); several of these models have also variations [13]. the classic models distinguish three essential functions (presentation, control and application). some models (such as the seeheim and arch models) consider these three functions as being distinct functional units. other approaches using structural components, and in particular those said to be distributed or agent approaches, suggest grouping the three functions together into one unit, the agent. the agents are then organised in a hierarchical manner according to principles of composition or communication (for example pac, mvc). our approach could be considered as being intermediate as it borrows elements for its principles from both types of model given above: we suggest using a division into three functional components which we have called respectively: interface with the application (connected to the application), dialogue controller, and presentation (this component is directly linked to the user). these three components group together agents [14]. the application agents handle the field concepts and cannot be directly accessed by the user; one of their roles is to ensure the correct functioning of the application and the real time dispatch of the information necessary for the other agents to perform their task. the interactive agents (or interface agents) are in direct contact with the user; they co-ordinate between themselves in order to intercept the user commands and to form a presentation which allows the user to gain an overall understanding of the current state of the application. in this way, a window may be considered as being an interactive agent in its own right; its specification describes its presentation and the services it is to perform. the dialogue control agents provide services for both the application and the user; they are intended to guarantee coherency in the exchanges emanating from the application towards the user, and vice versa. 7.2 agent oriented specification and design of the human-machine interface an agent oriented architecture is used for the human-computer interface. the application agents are intended to manage the passenger information in the vehicles and stations and to calculate the information to be displayed (delays, timetable and route modifications, etc.). according to the traffic context, each agent possesses rules enabling it to act correctly in its environment. concerning the specification of the interface agents, we have identified six types of interface agent responsible for direct interaction with the human regulator.these agents are represented in the form of interactive windows. the regulator can interact with these agents via the various functions possible in the windows, for example: the buttons, the edition zones, the pictures, etc. these agents are: the state of traffic agent, the state of the line agent, the station agent, the vehicle agent, the message agent, the overall view agent. it should be remembered that the aim of the ias is to enable the regulator to visualise, edit, create and transmit information intended for the passengers in the stations and/or vehicles. in order to perform his/her task of regulation correctly, the regulator interacts with these different interface agents. for instance, the state of the traffic interface agent gives a synthetic representation of all the delays concerning mobile units travelling on the network. thus, with the help of the network support system, it ensures the real time surveillance of vehicle delays on the network supervised. the state of the line interface agent is made up of graphic elements such as stations, route sections, vehicles, ě ( (see figure 3a). a click on a vehicle directly displays the view (window) of the vehicle interface agent which will deal with any further interaction with the regulator (see figure 3b). the principle is the same when the operator click on a station (see figure 3c). explanations about the other types of interface agents can be found in [14] or [18]. 8 evaluation we are evaluating and validating the global approach; preliminary results are available in [18]. in parallel with technical tests, evaluation with regulators using the different interactive systems in normal integration of traffic management and traveller information systems: basic principles and case study in intermodal transport system management 289 figure 3: (a) view of the state of the line interface agent, (b) view of a vehicle interface agent, (c) view of a station interface agent 290 houcine ezzedine, therese bonte, christophe kolski, christian tahon and abnormal situations are under development. the evaluation of interactive system consists in ensuring that the users are able to carry out their task by using it; it must therefore meet their needs. the methods for evaluating interactive systems are as varied as they are numerous. in [16, 17] the emphasis is placed on two global evaluation criteria: utility and usability. indeed, evaluation consists in verifying and confirming the interactive system, whatever the domain of application. if it meets advanced criteria, it is then accepted and validated. otherwise it has to be reorganized for a new evaluation. 8.1 data collection using an electronic informer for the evaluation of agent oriented interactive system used in regulation room an “electronic informer” is a software tool which ensures the automatic collection, in a real situation, of users’ actions and their repercussions on the system. the collection of information is done in a discreet and transparent way for the user, who must not at any time feel hampered by the presence of the informer. our evaluation method is divided into two closely dependent phases [15]: the first will be dedicated to the acquisition of the data related to the interaction between the user and the agent-based interactive system; the second phase ensures the classification and the analysis of the recovered data. the parameters to be acquired correspond to the various interactions between the user and the interactive system. they must contribute to the judgement to give on the quality of the system and make it possible to the designers to plan improvements to be brought to it. these parameters allow the identification of the: reactions of the interface further to the actions of the user, reactions of the user further to the actions of the interface, time response of the system, duration of the human tasks, user errors, uses of the help, displayed and not used information, and so on. the acquisition of all these parameters is ensured by the various informer agents. their tasks will consist in perceiving the actions/reactions of the corresponding interface agent and the user, and to record them. indeed, we equipped these agents with a rather simple architecture (see figure 4). this architecture consists of (1) a perception module, ensuring the acquisition of the data provided by the user and the agent presentation, (2) an interpretation figure 4: using the electronic informer for the evaluation of agent oriented interactive system. and classification module ensuring a sorting of the collected data, and (3) a recording module ensuring the data storage in a database. the acquisition of information via the various tools and techniques of evaluation is not the most difficult task during the evaluation phase of a system. indeed, the interpretation of the gathered data as their analysis is a more delicate task. integration of traffic management and traveller information systems: basic principles and case study in intermodal transport system management 291 8.2 analysis of the data collected the second phase is dedicated to the analysis of the recovered data. in order to facilitate the work of the evaluator, we equipped our electronic informer with an analysis data module. the evaluator can thus consult the various data according to the categories already quoted above. with each appearance of a sight (i.e. the visible part of an interface agent), we engage a stopwatch. it stops at the time of its disappearance or its setting behind plane. the output value represents the duration of the visualization of a sight. we attach importance to the duration of appearance of the sights. indeed, it can exist a close connection between the duration of consultation of a sight and the strategies implemented by the users. the interaction of the user with the system is carried out via the interface agents; so an agent informer is assigned to each interface agent. in parallel, the integration of a mouse agent and of a keyboard agent have been done; when collaborating with the other agent informers, the keyboard and mouse agents have to allow the recording of different actions/reactions from the interface (interface agents) and from the user. such principles are more detailed in [18]. 9 summary and conclusions for economical and ecological reasons, the role of intermodal transportation systems has become increasingly important. it is now crucial to provide the human operators in regulation rooms with systems for making easier their management. a generic model of a traffic regulation support system has been proposed. we are validating the global approach during a project concerning the urban transport network in the town of valenciennes, france. the article was focused on the decision assistance system and the information assistance system. for the evaluation, an electronic informer for the evaluation of agent oriented interactive system has been realized; its basic principles have been also explained. the proposed approach and the resulting architecture are promising because they are centred on the human activities in regulation room during the different normal and abnormal real situations. research perspectives concern experimentations in abnormal situations combining different types of dysfunctionnings. 10 acknowledgement the present research work has been partially supported by the “ministère de l’education nationale, de la recherche et de la technologie”, the “région nord pas-de-calais”, the feder (miaou, eucue and sart projects). the authors gratefully acknowledge the support of these institutions. the authors thank also the cesa’2006 staff for recommendation. bibliography [1] m.t. isaai, n.p. cassaigne, predictive and reactive approaches to the train scheduling problem, a knowledge management perspectives, ieee smc, part c, special issue on knowledge management, vol. 31, issue 4, pp 429-437, 2001. [2] f. balbo, s. pinson, dynamic modeling of a disturbance in a multi-agent system for traffic regulation, decision support systems 41, pp. 131-14, 2005. [3] f. cartegnie, h. ezzedine, a. peninou, c. kolski, agent oriented specification of interactive systems: basic principles and industrial case study, c. kolski, j. vanderdonckt (eds.), computer-aided design of user interfaces iii, kluwer academics publishers, dortrecht, pp. 381-388, 2002. 292 houcine ezzedine, therese bonte, christophe kolski, christian tahon [4] h. ezzedine, a. trabelsi, c. kolski, modelling of an interactive system with an agent-based architecture using petri nets, application of the method to the supervision of a transport system, mathematics and computers in simulation, vol. 70, issues 5-6, pp. 358-376, 2006. [5] b. fayech chaar, s. hammadi, régulation spatio-temporelle d’un réseau de transport multimodal, e-sta sciences et technologie de l’automatique, vol 2, 2005. [6] p.m. fitts, j.r. petterson, information capacity of discrete motor responses, j. of experimental psychology, 67, pp. 103-112, 1964. [7] j.m. hoc, supervision et contrôle de processus, la cognition en situation dynamique, grenoble, pug, france, 1996. [8] h. laichour, s.maouche, r. mandiau, traffic control assistance in connection nodes: multi-agent applications in urban transport systems, international scientific journal of computing, vol 1, issue 1, pp. 37-42, 2002. [9] p. millot, v. taborin, a. kamoun, two approaches for man-computer, cooperation in supervisory tasks, 4th ifac congress on analysis design and evaluation of hms, xian, china, 1988. [10] neighbourhood intermodal transfer facilities, l. land, c. foreman, final report, national center for urban transit research, nctr university of south florida, october 2001. [11] j. rasmussen, information processing and human-machine interaction, an approach to cognitive engineering, elsevier science publishing, 1986. [12] s. zidi, s. maouche, s. hammadi, nouvelle approche pour la régulation des réseaux de transport multimodal, 6eme conférence francophone de modélisation et simulation mosim 06, rabat, marocco, 2006. [13] j. coutaz, l. nigay, architecture logicielle des systèmes interactifs, in kolski c. (ed.), analyse et conception de l’ihm, vol. 1, pp. 207-246, paris: hermès, 2001. [14] h. ezzedine, c. kolski, a. peninou, agent oriented design of human-computer interface, application to supervision of an urban transport network, engineering applications of artificial intelligence, 18, pp. 255-270, 2005. [15] h. ezzedine, t. bonte, c. kolski, c. tahon, intermodal transportation system management: towards integration of traffic management system and users information system, f. sun, h. liu (eds.), imacs multiconference on computational engineering in systems applications (cesa’2006, beijing, china, oct. 4-6, 2006), vol. 1, tsinghua university press, pp. 972-979, 2006. [16] j. nielsen, usability engineering, academic press, boston, 1993. [17] b. shneiderman, c. plaisant, designing the user interface: strategies for effective human-computer interaction, 4th edition, reading, ma: addison-wesley, 2004. [18] a. trabelsi, contribution à l’évaluation des systèmes interactifs orientés agents : application à un poste de supervision du transport urbain (in french), ph.d. thesis, university of valenciennes, france, 2006. [19] j. kolodner, case-based reasoning, morgan kaufmann, 1993. [20] m. a. miller, d. loukakos, assessing opportunities for intelligent transportation systems in california’s passenger intermodal operations and services, california path research report, november 1, 2001. integration of traffic management and traveller information systems: basic principles and case study in intermodal transport system management 293 [21] f. g. filip, decision support and control for large-scale complex systems, annual reviews in control, elsevier, vol. 32, no 1, pp. 61-70, 2008. h. ezzedine, t. bonte, c. kolski, c. tahon university of valenciennes laboratoire d’automatique, mécanique et informatique industrielles et humaines lamih umr cnrs 8530 le mont houy f-59313 valenciennes cédex 09, france e-mail: houcine.ezzedine@univ-valenciennes.fr houcine ezzedine has obtained his ph.d in 1985. he is professor in industrial computer science at the university of valenciennes (france) and member of the “human-computer interaction and automated reasoning” research group in the lamih. he is involved in several research networks, projects and associations. he is specialized in human-computer interaction and software engineering for interactive systems. therese bonte has obtained his ph.d at the university of valenciennes in 1985 in simulation for production systems. she is researcher in the production systems research group of the lamih cnrs laboratory at the university of valenciennes. she is involved in several research networks and projects. she is specialized in software engineering, simulation and cad-cam. christophe kolski has obtained his ph.d in 1989. he is professor in computer science at the university of valenciennes (france) and head of the “human-computer interaction and automated reasoning” research group in the lamih. he is involved in several research networks, projects and associations and is a referee for many scientific journals and conferences. he is specialized in human-computer interaction, software engineering for interactive systems and intelligent system design. 294 houcine ezzedine, therese bonte, christophe kolski, christian tahon christian tahon has an estp engineering school degree. he is qualified in applied physics and obtained a ph.d in decision support systems for production system management in 1990 at the university of valenciennes. he is currently a full professor (france) and head of the production systems research group of the lamih cnrs laboratory at the university of valenciennes. he is involved in many research and scientific associations and is a referee for scientific journals. his teaching and research interests focus on the areas of decision-support systems, production control systems design, management of transportation systems and services systems. http://horos.rdsor.ro/ijcccv3n4draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iii (2008), no. 4, pp. 366-373 ant colony solving multiple constraints problem: vehicle route allocation sorin c. negulescu, claudiu v. kifor, constantin oprean abstract: ant colonies are successfully used nowadays as multi-agent systems (mas) to solve difficult optimization problems such as travelling salesman (tsp), quadratic assignment (qap), vehicle routing (vrp), graph coloring and satisfiability problem. the objective of the research presented in this paper is to adapt an improved version of ant colony optimisation (aco) algorithm, mainly: the elitist ant system (eas) algorithm in order to solve the vehicle route allocation problem (vrap). after a brief introduction in the first section about mas and their characteristics, the paper presents the rationale within the second section where aco algorithm and its common extensions are described. in the approach (the third section) are explained the steps that must be followed in order to adapt eas for solving the vrap. the resulted algorithm is illustrated in the fourth section. section five closes the paper presenting the conclusions and intentions. keywords: ant colony optimisation, vehicle route allocation problem, multiagent systems. 1 introduction ant colonies are used nowadays as multi-agent systems to solve different optimization problems from the np-hard class. examples of such problems are traveling salesman (tsp), quadratic assignment (qap), vehicle routing (vrp), graph colouring and satisfiability problem. the multi-agent systems are copying some or all the characteristics of their biological counterparts (depending on how much these characteristics are helpful to the mas solving a particular type of problem) such as [6]: • distributed society of autonomous individuals/agents; • fully distributed control among the agents; • localized communications among the individual; • stochastic agent decisions; • system-level behaviours transcending the behavioural repertoire of the single (minimalist) agent; • simple interaction rules. consequently, the overall very important features of the system are: robustness, adaptability and scalability. the paper presents the technique of solving yet another difficult problem (vrap) which belongs to the np-hard class of problems. related work, regarding the aco and eas algorithms, is described recently (2006-2007) in [1], [2], [3], [9]; to weaken redundancy, here details are skipped over. as a result, the rest of the paper is structured as follows: section 2 expounds the rationale where ant colony optimisation (aco) and its common extensions are explained. section 3 describes the steps that must be followed in order to adapt eas for solving the vrap. the next section concentrates on presenting the resulted algorithm. conclusions and directions of future work (section 5) are closing the paper. copyright © 2006-2008 by ccc publications ant colony solving multiple constraints problem: vehicle route allocation 367 2 rationale and approach the ant colony optimization algorithm (aco), introduced by marco dorigo in 1992 in his phd thesis, is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs and is inspired by the behaviour of ants in finding paths from the colony to food [8]. in all early ant system (as) algorithms, ants are searching for (candidate) solutions based on two main components: pheromone trails and problem-dependent heuristic information. these algorithms have suffered frequent modifications in order to improve their efficiency. thus, the as [5] developed into the elitist ant system (eas) [4], because each ant that finds a better solution has the chance to deposit more pheromone. other systems that emerged from the as are the max-min ant system (mmas) [10] and the ant-q [7] where the deposited pheromone amount is directly proportional to the quality of the found solution. in the rank-based ant system (asrank) all solutions are ranked accordingly to their fitness. the amount of pheromone deposited is then weighted for each solution, such that the more optimal solutions deposit more pheromone than the less optimal solutions. the basic vrap consists in allocating vehicles from a fleet to different routes in such a way that the average distance travelled in a month is (optimally) equal for all vehicles. before designing an aco algorithm some questions regarding important aspects of the problem must be answered first [6]: • the representation of the problem (pheromone model): what are the most effective state features to consider for learning "the good decisions"? • heuristic variables: what are they and what is their relative weight regarding the pheromone in the decisions? • ant-routing table: how pheromone and heuristic variables are combined together to define the goodness of a decision? • stochastic decision policy: how to make good exploration without sacrificing exploitation of promising/good actions? • regarding pheromones: is it useful to put limits in pheromone ranges? how should the pheromone initialization and evaporation be dealt with? what is the best policy for pheromone updating: online or offline? • scheduling of the ants: how and/or how many per iteration? • restarting the algorithm after stagnation: would it be better to restart the algorithm when stagnation is detected? • daemon components: what is the effect of local search? the standard form of the eas algorithm for solving the tsp is presented in figure 1. 368 sorin c. negulescu, claudiu v. kifor, constantin oprean // initialize pheromone trails for (every edge i, j) { τ = τ0 } // choose the starting town for every ant for (k = 1; k ≤ m; k++) { place ant k on a randomly chosen city } // initialize the best tour and length t + = the shortest tour found from the beginning l+ = the length of the best tour // main loop for (t = 1;t ≤ t max;t++) { // compute a tour for every ant for (k = 1; k ≤ m; k++) { build tour tk(t) by applying n −1 times the following step: choose the next node (city) j with the probability pki j(t) = [τi j (t)] α ·[ηi j (t)]β ∑ l∈jk i [τil (t)] α ·[ηil (t)]β , if j ∈ j pki j(t) = 0, if j 6∈ j where i is the current city. } // compute the tour lengths for all ants for (k = 1; k ≤ m; k++) { compute the length lk(t) of the tour tk(t) produced by ant k } // update the best tour if an improved tour is found if (an improved tour is found) { update t + and l+ print t + and l+ } // global update for the pheromone trails for (every edge i, j) { update the pheromone trails by applying the rule: τi j(t) = (1− ρ) · τi j(t) + ∆τi j(t) + e · τ ei j(t), where ∆τi j(t) = ∑ m k=1 ∆τ k i j(t) ∆τ ki j(t) = { q/lk(t),i f (i, j)∈t k(t) 0,otherwise } and τ ei j(t) = { q/l+,i f (i, j)∈t + 0,otherwise } } // calculate the intensity of the pheromone for next iteration for (every edge i, j) { τi j(t + 1) = τi j(t) } } figure 1: elitist ant system pseudocode for solving the travelling salesman problem. ant colony solving multiple constraints problem: vehicle route allocation 369 in this paper is presented a problem that has more constraints than tsp in its basic form: • the number of routes (destinations); • the number of vehicles in the fleet; • the route allocation history (the routes must be allocated in an equally distributed manner for each month turnus); • the average distance travelled by vehicles in a month (this distance must be (optimally) equal for all the vehicles average distance); • the driver’s rest (of two days) between trips (rest). because of these constraints, new variables are necessary to be included in the algorithm for solving vrap. moreover a different approach is necessary regarding the development of the graph used to solve the problem. in figure 2 are depicted the graphs for solving tsp and vrap accordingly. figure 2: comparison between the graphs for solving tsp (a) and vrap (b). if in the case of tsp the nodes in the graph are representing the cities that the salesman must reach, and the arches are standing for the roads (their cost being directly proportional to the distance between cities), in the case of vrap the nodes (seen as pairs node0 and noden) are equivalent to a certain route (e.g. bucharest paris) and the arches are representing the cost of allocating a vehicle to that route. another approach is that the node0 is a unique virtual node regardless of the name of the city that corresponds to the departure station (e.g. node0 can be bucharest, paris, etc.). the cost function must include the following factors: • the vehicle history (the cost is greater if the vehicle departed from node0 and arrived at noden more recently and smaller otherwise); • the average distance ran by a vehicle in a month (the cost is directly proportional with the difference between the average distance and the actual distance). • the rest (of two days) of the drivers (the cost is directly proportional with the difference between the required resting days and the actual resting days). 370 sorin c. negulescu, claudiu v. kifor, constantin oprean 3 the resulted algorithm the algorithm for solving vrap by using the modified elitist ant system is presented in figure 3. // variables used // tmax maximum numbers of iterations // r number of routes // v number of vehicles // h routes history matrix // di j distance between cities i and j // pi j pheromone intensity between nodes i and j // a number of ants used to solve the problem (a is also equal with the number of routes) // tkr(t) the matrix tour of ant k at the iteration t // c the vector containing ant tours costs // t + vector containing the best tour // c+ the cost of the best tour // α ≃ 0.7 how much the ants takes in consideration the history // β ≃ 0.3 how much the ants takes in consideration the pheromone intensity // γ ≃ 0.8 how much the ants takes in consideration the routes distances // ρ ≃ 0.5 percent of the pheromone that evaporates // ε ≃ 0.5 the multiplication value for the pheromone intensity of the elitist ant // p0 minimum pheromone intensity on arches = 10 −6 // initialize pheromone trails for (every edge i, j) { pi j = p0 } // initialize the best tour and best tour cost t + = the best tour found from the beginning c+ = the length of the best tour // main loop for (t = 1;t ≤ t max;t++) { // for every vehicle (every ant colony) for (v = 1; v ≤ v ; v++) { // choose the starting town for every ant for (k = 1; k ≤ a; k++) { place ant k on node zero (start node) } // compute a tour for every ant for (k = 1; k ≤ a; k++) { build tour t r k (t) by applying r −1 times the following step: choose the next node i in witch the ant k will go with the probability pkvi(t) = [hvi(t)] α ·[pvi(t)]β ·[vvi]γ ∑v∈t r k [hvi(t)]α ·[pvi(t)]β ·[vvi]γ , if i 6∈ t r k (t) pkvi(t) = 0, if i ∈ t rk (t), where v is the current vechicle } ant colony solving multiple constraints problem: vehicle route allocation 371 // compute the tour costs for all ants for (k = 1; k ≤ a; k++) { compute the cost ck(t) of the tour t r k (t) produced by ant k } // update the best tour if an improved tour is found if (an improved tour is found) { update t + and c+ print t + and c+ } // global update for the pheromone trails for (every edge i, j) { update the pheromone trails by applying the rule: pi j(t) = (1− ρ) · pi j(t) + ∆pi j(t) + ε · pei j(t) } // calculate the intensity of the pheromone for next iteration for (every edge i, j) { pi j(t + 1) = pi j(t) } } } figure 3: elitist ant system pseudocode for solving the route allocation problem. the α, β and γ parameters are controlling the relative weight of the history, pheromone intensity and the distance in the process of choosing the next node in the itinerary. the formula for computing the pheromone intensity between nodes i and j at the t moment is: pi j(t) = (1− ρ) · pi j(t) + ∆pi j(t) + ε · pei j(t), where • (1 − ρ) · pi j(t) the pheromone quantity that evaporates on the arch between nodes i and j at the moment t; • ∆pi j(t) = ∑ a k=1 ∆p k i j(t) the total pheromone quantity that is deposited on the arch between nodes i and j at the moment t as sum of pheromone quantity deposited by all the ants that have used this arch; • ∆pki j(t) = { 1−h t i k i ,i f (i, j)∈t ik (t) 0,otherwise } the pheromone quantity deposited by ant k on the arch between the nodes i and j if that arch belongs to its itinerary (tki(t)) at the moment t. this quantity is inversely proportional with the history of the vehicle on the route associated to the node that points to that arch; • ∆pei j(t) = { 1−h t e k i ,i f (i, j)∈t +(t) 0,otherwise } the pheromone quantity deposited by the elitist ant k on the arch between nodes i and j if that arch belongs to its itinerary (tki(t) = t +) at the moment t. this quantity is inversely proportional with the history of the vehicle on the route associated to the node that points to that arch. this process repeats until a certain condition is met (number of iterations, time, the finding of an acceptable solution, etc.). 372 sorin c. negulescu, claudiu v. kifor, constantin oprean 4 conclusions and intentions the self-organisation (as in the case of ant colonies) indirectly supports the quality of the allocation service through the lack of the distortions or the deficiencies that may appear in a centralized coordination. the resulting implemented algorithm is robust (both reliable and error tolerant). the optimisation algorithm (elitist ant system) proved to be perfectly adaptable to the vehicle route allocation problem. the ant colony algorithms are showing an obvious potential as problem-solving tool, at least in the context of simple mas, and relevant results can be achieved on affordable hardware configurations. the short-range intentions are: • improving the cost formula; • finishing the research regarding the control of pheromones and search paths; • speeding up the search by fine-tuning the algorithm. the middle-range target is to apply the optimisation in a real-time manner by modifying the algorithm. 5 acknowledgements this research is supported by the ceex 116 / 2006 project: etransmobility agent oriented system for modelling and optimizing the transport of persons; coordinated by "lucian blaga" university of sibiu. bibliography [1] bărbat b.e, moiceanu a., pleşca s., negulescu s.c. 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(2006). user-driven heuristics for nondeterministic problems. studies in informatics and control (special issue dedicated to the 2nd romanianhungarian joint symp. on applied computational intelligence), 15, 3, pp.289-296. [10] stützle t., hoos h.h. (2000). max-min ant system. future generation computer systems, 16 (8), pp.889-914. sorin c. negulescu "lucian blaga" university of sibiu faculty of engineering 4, emil cioran, im 502 550025 sibiu, romania e-mail: sorin_negulescu@yahoo.com claudiu v. kifor "lucian blaga" university of sibiu faculty of engineering 4, emil cioran, im 502 550025 sibiu, romania e-mail: claudiu.kifor@ulbsibiu.ro constantin oprean "lucian blaga" university of sibiu faculty of engineering 4, emil cioran, im 502 550025 sibiu, romania e-mail: constantin.oprean@ulbsibiu.ro int j comput commun, issn 1841-9836 8(3):486-491, june, 2013. a rate based congestion control mechanism using fuzzy controller in manets h. zare, f. adibnia, v. derhami hamideh zare department of computer engineering, yazd branch, islamic azad university, yazd, iran e-mail: khezrabad@imo.org.ir fazlollah adibnia, vali derhami yazd university e-mail: fadib@yazd.ac.ir, vderhami@yazd.ac.ir abstract: the traditional congestion control mechanism tcp, performs very poorly in manets because there are a number of new challenges such as wireless link error, medium contention and frequent route failures in this kind of networks [1]. in this paper, we propose a fuzzy adhoc rate-based congestion control (farcc) to enhance the efficiency of network in manets. in farcc, we use a rate-based transmission scheme using two fuzzy controller of zero order takagi sugeno kang (tsk) model to congestion detection and congestion control. the farcc sender adjusts data rate by receiving a feedback packet from farcc destination. ns2-based simulation results show that farcc outperforms itp and atp to achieve, in terms of throughput and fair resource allocation in adhoc networks under random topology. keywords: rate control, congestion control, fuzzy controller, channel busyness ratio. 1 introduction congestion control is a critical issue to achieve optimal network resource utilization and fairness among end-to-end flows.in manet with shared resources, where multiple senders compete for link bandwidth, it is necessary to adjust the data rate used by each sender in order not to overload the network. packets that arrive into router and cannot be forwarded are dropped, consequently an excessive amount of packets arriving at a network bottleneck leads to many packet drops.these dropped packets might already have travelled a long way in the network.thus consumed significant resources. in recent years, the whole congestion control mechanism designed in manet, specify the number of packets that are droped lead to congestion.these approaches are divided into three categories: window-based, rate-based and a hybrid between a window-based and a rate-based approach. window-based congestion control is not appropriate for manet to provide high throughput, short delay and stable performance with few packet collisions. in this paper, we focus on the problems resulting from the medium contention and propose a novel fuzzy adhoc rate based end-to-end congestion control scheme (farcc).this is unlike tcp, a rate-based congestion control and tuning of rate is operated in destination node. extensive simulations show that our scheme significantly outperforms itp [2] and atp [3] in terms of channel utilization, average delay, and fairness between different flows. 2 fuzzy adhoc rate base congestion control (farcc) in this section, we will elaborate on the specific mechanisms used by farcc. farcc primarily consists of mechanisms at the sender to achieve effective congestion control and reliability. each time the farcc sender transmits a packet, it attaches a congestion header to the packet. the rate-stamp field in header is sender’s current rate. which is filled in by the sender and copyright c⃝ 2006-2013 by ccc publications a rate based congestion control mechanism using fuzzy controller in manets 487 never modified in transit. the br-stamp field is channel busyness ratio that all the intermediate nodes along the path are calculated and may modify it to control the packet sending rate of the sources. the delay-stamp field is the average queuing delay that all the intermediate nodes along the path may modify it. the destination node is similar to a tcp receiver except that when it receives acknowledging a packet, it copies the congestion header from the data packet to its ack. it also acts as a collator of the congestion information provided by the intermediate nodes in the network before the information is sent back to the sender. the destination node provides the reliability, flow control, and collated congestion control information through periodic messages. farcc sender is responsible for the connection management and the startup rate. estimation and controls the sending rate of each flow by the explicit feedback carried in the acks. 2.1 intermediate node delay(q(t)) is queuing delay experienced by per packet traversing the intermediate node. the intermediate nodes maintain an average queuing delay as avg-delay(q(t)) irrespective of the specific flow. it is performed over all the packets traversing through them. average queuing delay is impacted by the contention between different flows traversing the node. this value is maintained using exponential averaging according to (1). for every outgoing packet, an intermediate node updates its avg-delay(q(t)) and delay(q(t)) values. ave − delay(q(t)) = α ∗ ave − delay(q(t)) + (1 − α ) ∗ delay(q(t)) (1) every packet consists of delay-stamp field. it refers to the maximum avg-delay(q(t)) value at the upstream nodes that packet has traversed through. when the packet is dequeued for transmission, the intermediate node updates the delay-stamp field in the packet with avg-delay(q(t)) value if delay-stamp field is smaller than avg-delay(q(t)) value. the intermediate node runs an epoch timer of period e. this period e should be larger than the round trip time of a connection, but at the same time must be small enough to track the dynamics of the path characteristics. e is empirically chosen to be one second in our simulations. in addition, the intermediate node computes channel busyness ratio periodically for the whole channels communicating with it according to [4]. intermediate nodes in the network maintain maximum channel busyness ratio periodically as br(t) values. each packet consists of a br-stamp field that refers to br(t) value at the upstream nodes which packet traversed through. when the packet is dequeued for transmission, the intermediate node updates the br-stamp field in the packet with br(t) value, if it is smaller than br(t) value. 2.2 destination node the destination node provides periodic feedback to sender for the collates congestion information which are provided by the intermediate nodes (through the br-stamp and delay-stamp fields on the packets). when destination node receives a packet, delay-stamp and br-stamp fields in the packet refer to as maximum average queuing delay and maximum channel busyness ratio which are experienced by the packets traversed through intermediate nodes. for every incoming packet belonging to a flow, the destination node performs an exponential averaging of the delaystamp value. in addition it performs periodically an exponential averaging of the br-stamp value which is specified in the packet. the destination nodes in the network maintain avg(br-stamp) and avg(delay-stamp) values. in order to send the feedback periodically, the destination node runs an epoch timer of period t. note that the period t should be larger than the round-trip time of a connection, but at 488 h. zare, f. adibnia, v. derhami the same time must be small enough to track the dynamics of the path characteristics. t is empirically chosen to be one second in simulations. congestion probability estimation using fuzzy logic controller the destination node gets packets and passes them through both the fuzzy logic congesion detection system and fuzzy logic congestion control system. then it calculates appropriate transmission rate and piggybacks new rate on packet to the sender node. the fuzzy logic congestion detection system will calculate the probability of congestion using congestion header of packets. in the following section, we will introduce how use the fuzzy logic controller to estimate congestion probability.the system model of farcc is shown in fig.1. figure 1: system controller to transmission rate all inputs to fuzzy logic congestion probability system are considered as t, where t is the sampling period. the fuzzy congestion probability system consist of three inputs. the input avgdelay(t) is avg(delay-stamp) and the input avg-br(t) is avg(br-stamp) that are both calculated by destination node. the input rate(t) to primary system is current data rate in congestion header of packet. the fuzzy system in farcc is zero order tsk-fuzzy system. the fuzzy rules are determined based on a full combination of the input variables. hence, there will be 36 (3*3*4) fuzzy rules. if rth rule is denoted by rr then the fuzzy rule can be described generally as shown in the following equation: rr: if avg-delay(t) is c and avg-br(t) is d and rate(t) is p then prob(t) is zr. the output of fuzzy model as prob(t) is the probability of congestion, that is a value in the range [0,1] and how input variables are mapped to the probability of congestion detemined by simulation. parameters zr (probability of congestion) are initialized using simulations and gathered experiences. simulation is performed over 200 simulation runs in section 3. the goal of the proposed fuzzy controller is to determine the probability of congestion using current network status and parameters except packet loss rate in the network. congestion control using fuzzy logic controller in farcc approache, there are three congestion levels low, medium and high. the destination node determines network congestion level using secondary fuzzy system at each sampling period according to fig.1. the transmission rate tuned depends on specified congestion level through one of the three phases; multiplicative increase, additive increase and multiplicative decrease. two inputs of fuzzy logic congestion control system are considered at t, where t is the sampling period. the input prob(t) is congestion probability and output of primary fuzzy system. the input rate(t) is current data rate in congestion header of packet. the zero order t-s fuzzy model has been selected for this system. a rate based congestion control mechanism using fuzzy controller in manets 489 plant modeling plant based on one of the congestion levels( low, medium and high) which is specified in output secondary fuzzy system performs one of three phases of multiplicative increase, additive increase and multiplicative decrease on current transmission rate respectively. if specified congestion level is low then it performs multiplicative increase phase according to (2) inorder the improvement of network throughput. [6] new − rate = old − rate ∗ ( th − br avg(br − stamp) ) (2) if congestion level is medium then it performs additive increase phase inorder the increases in transmission rate belong to average queuing delay of path nodes according to (3). [6] new − rate = old − rate + ( 1 avg(delay − stamp) ) (3) if congestion level is high, then probability of congestion event is high. therefore it performs multiplicative decrease and control send rate using average queuing delay and average channel busyness ratio, according to (4). [6] new − rate = old − rate ∗ avg(br − stamp) avg(delay − stamp) ∗ th − br (4) the destination node compares the new transmission rate with the current rate. if difference is greater than e then the new transmission rate is stamped on ack packet and is sent to the sender, since increase in the number of feedbacks lead to an increase in congestion and network load. 2.3 sender node in connection initiation phase, the sender node sends a probe packet to receiver. the probe packet is piggybacked on the next data packet in sequence queued for transmission. when a new path is used, the connection is not aware of the available bandwidth on path. the probe packet perform bandwidth estimation once again allows the connection to operate at the true available bandwidth instead of either over utilizing or under utilizing the resources available along the new path.the sender node calculates time interval packets sending using send rate. 3 simulation results we used the ns2 simulator [7] to evaluate farcc under random topology. the default parameter settings are as follows: the two-ray ground reflection model is used as the radio propagation model, ieee 802.11 dcf as the mac protocol, and aodv as the routing protocol. the parameters in farcc are as follows: the initial values of the transmission rates are 110 kbps; the step sizes are appropriately tuned according to the scale of the network and the number of flows in network. in these topologies, each node is 200 meters away from its closest neighbors. the transmission range and the interference range are 250 m and 550 m, respectively. the data transmission rate is 2mbps. the packets generated are of size 512 bytes in all the simulations. all the simulations are run for 100s and each data point on the figure is averaged over 10 simulation runs. in this scenario the random topologies are generated for the simulations. the mobility model used for topology generation is the random waypoint model. all the simulations are performed 490 h. zare, f. adibnia, v. derhami for a grid consisting of 50 nodes, distributed randomly over the two dimensional grid. the source destination pairs are randomly chosen from the set of 50 nodes in the network. load on the network is 15 flows and ftp is the application that is used for all the flows in the network. figure 2: throughput versus mobility in random topology the first metric is the overall throughput shown in fig.2 we observe with the larger speed of the node mobility, the aggregate throughput achieved by farcc is able to provide an improvement of around 13.8%-48.4% over default atp, and around 0.7%-23.6% over itp. figure 3: fairness index in random topology the result of the fairness index [8] is depicted in fig.3 which shows a remarkable fairness of farcc. the more concurrent flows, the better the improvement of farcc over the congestion control algorithms and the mount of fairness index have an improvement of around 9 percent over atp,and around 20 percent over itp. a rate based congestion control mechanism using fuzzy controller in manets 491 4 conclusion in this paper, we proposed an improved congestion control mechanism called farcc in manets. the proposed scheme uses the fuzzy logic control mechanism to determine an appropriate data rate for sending packets. using the fuzzy logic congestion detection, probability of congestion is estimated. then fuzzy logic congestion control specifies network congestion level based on probability of congestion. the congestion level is used to tune the data rate in plant. farcc uses a feedback scheme to adjust data flow rate. simulation results show that the proposed farcc performs better than itp and atp for aggregate throughput. consequently, our method supports higher fair resource allocation. bibliography [1] zhai, h.; chen, x.; fang, y.; rate-based transport control for mobile ad hoc networks, in proceedings of the ieee wireless communications and networking conference, 4(1): 22642269, 2005. [2] wang1, n.; huang2, y.; liu2, w.; a fuzzy-based transport protocol for mobile ad hoc networks, in proceedings of the ieee international conference on sensor network, 2008. [3] sundaresan, k.; anantharaman, v.; hsieh, h. y.; sivakumar, r.; atp: a reliable transport protocol for ad-hoc networks, in mobihoc 03: proc. of the 4th acm international symposium on mobile ad hoc networking & computing, 64-75, 2003. [4] kumar, s.; raghavan, v.; deng, j.; medium access control protocols for ad hoc wireless networks: a survey, ad hoc networks, 4(3): 326-358, 2004. [5] smitha, k.; anand, h. u.; mallapur, j. d., fuzzy based congestion control in wireless network, international journal of computer science and communication, 2(2):469-473, 2011. [6] zhai, h.; chen, x.; fang, y.; a call admission and rate control scheme for multimedia support over ieee 802.11 wireless lans, in proc. first international conference on quality of service in heterogeneous wired/wireless networks (qshine 04), 2004. [7] the network simulator ns-2 website, available at : http://www.isi.edu/nsnam/ns. [8] wang, x.; kar, k.; cross-layer rate optimization for proportional fairness in multihop wireless networks with random access, ieee journal on selected areas in communications, 24(8):1548-1559, 2006. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 337-348 impact of poor requirement engineering in software outsourcing: a study on software developers’ experience i. perera indika perera department of computer science and engineering, university of moratuwa, sri lanka e-mail: indikaperera@uom.lk abstract: the software requirement engineering (re) is one of the most important and fundamental activities in the software life cycle. with the introduction of different software process paradigms, the requirement engineering appeared in different facets, yet remaining its significance without a doubt. the software development outsourcing is considered as a win-win situation for both developed and developing countries. high numbers of low paid, yet talented workforce in developing countries could be employed for software outsourcing projects with the demanding power of the outsourcer to decide the projects, their scope and priorities with the intention of profit maximization. this study was conducted to analyze the impact of poor requirement engineering in outsourced software projects from the developers’ context (sample size n = 57). it was identified that the present outsourcing scenario has created to have frequent requirement changes, shrunk design and stretched development phases, and frequent deliverables, which have to be accommodated by the software developer with extra effort and commitment beyond the project norms. the results reveal important issues and open policy level discussions while questioning our insights on the outsourcing benefits as a whole. keywords: requirement engineering, software outsourcing, project management, software developer productivity 1 introduction with the development of internet and other communication media, the entire world is becoming a single village with the reach of each other at arms length. many explain this scenario with the combination of other concepts as the globalization. some views globalization with a positive look, where as others have strong critiques on its impact to individual nations and ultimately to the global economy. the development of the software and computing technologies has triggered the globalization and world’s operations to a large scale. in fact, the software outsource process gains its significance in the global economy with more and more projects being outsourced [1]. software applications are complex and intangible products, which are difficult to manage. hence, software lifecycle management becomes one of the key research areas in software engineering. due to the nature of the software, software researchers and practitioners are focused to improve the software processes, which are used to develop software [2]. the underline assumption is that there is a direct correlation between the quality of the process and the quality of the developed software [3]. a software process can be considered as a set of tools, methods, and practices, where we use to produce a software product [4]. requirement engineering is one of the key process phases in the software development process; in most cases, essential enough to determine the success or failure of the project. therefore, having an error free re copyright c⃝ 2006-2011 by ccc publications 338 i. perera activity and specification can be more significant for the project success than any other activity. software requirement errors can cost up to 70-85% of additional cost of reworking in an average software project [5]. this shows the importance of managing the requirement engineering works productively especially in an outsourced project where the cost and profit concerns are high motives [18]. furthermore, it is a known fact that many small to medium scale software companies practice flexible, lightweight, agile like informal processes to suit their development; more or less for the sake of having a process practice to convince the outsourcers and win the projects [6]. however, in practice, these organizations tend to follow ad-hoc set of activities than any established process, which are camouflaged as the agile practice. these informalities make the re phase more complex, resulting the developers’ programming work unnecessarily tedious and unproductive. this research has tried to study on the issues of poor re practices with outsourced software projects and how it affects to junior level software developers’ productivity and work life balance. the rest of the paper presents the research work and outcomes with the following organization. section 2 will discuss the background literature related to this study. section 3 discusses the research problem in brief. thereafter, the section 4 is included with the experiment methodology and section 5, the analysis section explains the analysis done based on the collected results. the conclusion with the possible future directives is included thereafter along with the acknowledgements. the references will compile the paper. 2 background this section contains a comprehensive synopsis of the referred literature for this study in the areas of software requirement engineering and software outsourcing. it is important to mention that, there have very few research works been done to study how the current software outsourcing practices affect to the software developers work. 2.1 software requirements engineering software requirement engineering is a mature and one of the key life cycle activities in any software process paradigm. as nuseibeh and easterbrook explained, software systems’ requirements engineering (re) is the process of discovering that purpose, by identifying stakeholders and their needs, and documenting these in a form that is amenable to analysis, communication, and subsequent implementation [7]. more precisely, zave clearly defines the requirement engineering as; "requirements engineering is the branch of software engineering concerned with the real world goals for, functions of, and constraints on software systems. it is also concerned with the relationship of these factors to precise specifications of software behavior, and to their evolution over time and across software families." [8]. sometimes, it is impossible to have complete and finalized set of requirements at the beginning of a project. this allows requirement changes to happen during the latter stages of the project and create conflicts with the software process been practiced. to overcome such scenarios agile like flexible processes have been introduced and well established in the software industry. agile process welcomes frequent requirement changes even at late stages of the project. with frequent deliverables, agile process measures its progress through the norm of working software [9]. however, the most important point to remember, even if someone practices the agile process is, that still he needs to perform a certain set of requirement engineering activities to ensure smooth and correct process flow [17]. therefore, whatever the process practice used in the development, following proper requirement engineering activities is a must, even though the practices may have different facets. impact of poor requirement engineering in software outsourcing: a study on software developers’ experience 339 2.2 software outsourcing there are number of definitions for the concept outsourcing in general and specifically in the context of software development. rajkumar and mani explain offshore outsourcing as: "when the supplier of software development is from another country than the firm that decides to outsource information systems" [10]. the software development outsourcing has, in the recent past, taken up global dimensions in which companies from the developed countries sub contract software lifecycle function to the developing countries [11]. importantly, outsourcing has become an appealing option to organizations operating within the global economy for a variety of reasons [12]. one of the major and foremost reasons to outsource software development is the lower cost and relative high profit margins for outsourcer [13]. another advantage of software development outsourcing is the opportunity for the outsourcer to focus more on their core businesses and strategic activities [1]. sometimes, this makes the outsourcing company to act solely as an intermediary organization, except for legal and contractual works. furthermore, there are some issues with outsourcing to countries with vast heterogeneous socio-economic and cultural factors. having local representatives may be a key option to overcome cultural and linguistic barriers in offshore outsourcing [14], [15]. however, this can be an extra cost item and will not help developers if the representatives are not technically sound. nevertheless, the outsourcing attracts both developed and developing countries due to their benefits from different perspectives. developing countries trying to secure more and more outsourcing projects, aiming the monetary gains, without conducting grass root level research on how it impacts to their workforce, industry and society at large. 3 research problem the research problem which this study has focused mainly based on the software developers’ experience in an outsourcing environment. there have been number of business and economic forums to attract outsourcing projects for developing countries, but not visible effort been done for assessing the impact of developing an outsourced industry on various contexts, except the monetary gains and employment opportunities. since the majority of studies done to examine how to manage the outsource projects form the view point of the outsourcer, there is a significant vacuum of knowledge to understand the issues faced by the end developers due to the poor requirement engineering. as the outsourcing process consists with two parties mainly; the outsourcer and the service/solution provider, there are different issues for both parties depending upon their context. in many cases, the software developers are employed by the solution providers to engage in outsourced projects. this research primarily focuses on the problems that software developers encounter during their project activities, due to the poor software requirement practices of the projects. it is a known fact that many software outsourced projects get schedule overrun and extended developer time. one research found out that 60-80% of software projects get schedule or effort overrun and the average overrun varies from 30-40% of the initial project scope [16]. why this level of scope variation during the project happens even with mature, experienced staff is a concern need to be examined with extensive studies. there can be other factors, which cause the over time work. furthermore, the software developers’ attitude towards such over work situation need to be assessed. this was a major objective of this study. another questioning area is whether the process of passing requirements to the developer either directly from the main client or from the outsourcer, is effective and efficient enough to facilitate the development process. with the technological advancement and the studies to alleviate geographical barriers, have come up with various methods of communication for outsourcing [15]. in fact there are number of informal communication approaches used in the software outsourcing 340 i. perera and this study has analyzed the developer perception towards each approach and their relative significance to hinder the development activities. identifying key success factors for software process practices may be helpful for the improvement of current processes [19]. this study will also help to provide necessary guidance for possible improvements to the requirement engineering activities in outsourced software projects. 4 research methodology the research methodology was based on a comprehensive survey conducted using the sri lankan outsourcing companies and their employees. since the main objectives of the study and the research problem are generic to any outsourcing company and country as a whole the survey and its findings could easily be generalized with minor alterations. for this study, 57 software developers from 8 small to medium software developing enterprises were used. all these developers are engaged in outsourced projects for more than one year. to avoid any extreme cases in the study and to reduce skill variances, the developers who have industrial experience for less than 3 years were selected. the study was on voluntary basis and to strengthen the trust on the confidentiality of the participants’ responses the study was conducted on individual basis without the intervention of the participants’ employing organization. the study had two major phases for data collection. first all the participants were individually contacted through e-mail and telephone to explain the objective of the study and the confidentiality of their responses. the study was designed flexible enough to conduct by the participants as their own experiment. for the first phase, a worksheet was given to each participant to fill every day for 3 weeks. the worksheet was simple to fill and it has only four fields to be entered, based on the following measurement parameters. n number of requirement changes/emerge for a given day ts start time of the work tf finish time of the work tm -time spent on meetings/discussions to clarify/understand requirements for a given day. these are basic parameters for this study and those were used to analyze the average daily commitment of the developer and as a validating factor for the second phase data. the second phase was based on a questionnaire which was distributed electronically through e-mail and the participants had to fill their one and send back. strong assurances were given on the confidentiality of their responses in order to have more accurate information. the following section describes the collected data and their analysis 5 analysis during the first phase of the study, the above mentioned 4 parameters were recorded and the distribution of average time spent by each developer on meetings/ discussions to clarify requirements was calculated using the following model (1). µmj is the mean time spent for the jth developer, and tmi is the time spent on the i thday up to n , i.e. the number of days he worked during the experiment. µmj = n∑ i=1 tmi n (1) the obtained values are sampled for displaying purpose and shown in the table 1. impact of poor requirement engineering in software outsourcing: a study on software developers’ experience 341 table 1: the distribution of average daily times spent on meetings/discussions for clarifying requirements during the development time spent on meetings for requirements number less than 0.5 hours 5 0.5 1 hours 9 1 1.5 hours 13 1.5 2 hours 14 2 2.5 hours 11 2.5 3 hours 3 3 3.5 hours 1 more than 3.5 hours 1 to obtain the population mean value µm of tm , the following model (2) was used and m represents the number of participants in the sample. µm = m∑ j=1 n∑ i=1 tmij n m (2) the obtained µm value for the entire population is 1.571 hours. the value shows that on average a developer has to spend more than one and half hours of their development time, daily to clarify requirements that they are developing. the main problem with this is that these times are not included in the project schedules and the developers have to get these times from their own expense as the deliverable deadlines are fixed. similarly with the same meaning of the notations as above, the population mean value for n, i.e. µn was calculated with the following model in (3). µm = m∑ j=1 n∑ i=1 nij n m (3) the obtained value for µn was 2.84. this indicates that nearly three requirements alterations happen per day on average during the development phase. there are many reasons for such significant change to occur. the first fact is that everybody wants to have the working software so quickly and success of getting future work depends on the delivery of current work. because of this rapid nature of project development, the outsourcer cuts down the time on requirement engineering, design, and testing phases of the life cycle. however, when the frequent deliverable is done the outsourcer frequently improves his expected functionalities and gives frequent alterations to the planed requirements. moreover, due to the high demanding power, the outsourcer gets what he requests over multiple times of changing the same requirements. finally the work pattern of the participants were analyzed with the model described in (4). µwj = n∑ i=1 {tfi − tsi − k} n (4) 342 i. perera in this model tfi and tsi are the finish time and the start time of a developer on i th day, respectively. k is an average constant to represent the summation of none working times during a given day. it was considered to be 1.5 hours based on preliminary studies. with that model the following sample of work patterns were derived for a given week. in fact there is a reason to analyze this for a week specially. all the developers have in their employment contract that they are supposed to work on weekdays i.e. monday to friday and they have the opportunity of holidays including saturday, sunday and any other declared holidays by the government. however, it was observed that most of the participants have worked in saturdays, special holidays, and in some cases sundays to meet their deadlines. the mean value (µw ) for the population, was table 2: the distribution of average working hours per week average working hours per week number 35 40 hours 2 40 45 hours 7 45 50 hours 16 50 55 hours 21 55 60 hours 10 60 65 hours 1 calculated using the model described in equation (5); hence, µw = 51.76 hours, which indicates an average developer spends nearly 12 hours of additional work than their contractual norm of 40 hours per week. that means they put effort on additional work load on average worth of 29.4% of their monthly salary. in fact when the results finding were informed to the people who participated, they were astonished, and one of their response is worth to mention here. ". . . i can’t believe this. this means up to now i have worked nearly one million rupees work just for free . . . i should have thought of this before . . . " [participant’s response] µw = m∑ j=1 n∑ i=1 {tfi − tsi − k} n m (5) 5.1 questionnaire analysis the questionnaire was consisted with 16 questions. some of the questions were simple yes/no type and some have to be answered with further details. out those 16 questions, 6 were used to condition the participant’s mind to answer correctly, and also used to validate the accuracy of their responses for the main questions. for this analysis, only the relevant questions, i.e. the 10 main questions were used. q1. do you get a sufficient requirement specification for the project? for this question 41 participants said ’no’, 12 said ’yes’ and 4 said don’t know. therefore as a percentage, 71.92% believes that they do not get a sufficient requirement specification for their development. q2. what are the most frequent methods you use to communication on requirements? please rank them the table 3 summarizes the responses for the question 2. only 13 developers are working with proper requirements specification in which only 5 have the opportunity to consider it as the impact of poor requirement engineering in software outsourcing: a study on software developers’ experience 343 table 3: the distribution of average working hours per week method of communication 1st 2nd 3rd 4th total formatted req. spec. as e-mail attachment 5 6 2 0 13 e-mail messages 24 21 8 4 57 instant messaging (im) / chat 19 18 11 9 57 voice conference / telephone 6 10 24 11 51 video conference 3 2 12 19 36 face to face communication 0 0 0 4 4 travel to client places for discussions 0 0 0 1 1 main form of requirement communication. lack of proper requirement specification makes it enormously vulnerable to requirement alternations and scope creep, during the later stages of the project. also only 1 participant has the opportunity to go to client premises and experience their expectations where as only four have the opportunity to meet the clients for discussions. email messages considered as the main form of communication followed by instant messaging or chat. also everybody (all 57) use e-mail and im/chatting with different significance. voice conferencing and video conferencing are used as secondary and tertiary forms of communication when extra clarification is needed on requirements, in generally. q3. what are the reasons you think for requirement issues in your projects? table 4: the distribution of average working hours per week reason number poor technical knowledge at outsourcer 37 optimistic scheduling 29 scope creep and stretching requirements 42 poor communication 17 informal ways of practice 31 management issues 24 practice of "do what they say" 44 the table 4 indicates developers’ responses on what they believe as the causes of requirement issues. there were multiple reasons given and most of the participants have concluded that the current practices in the outsourced industry are mainly responsible for these re issues. as mentioned in the background section, the agile practices has significant impact on this issue as when it is practiced without a proper knowledge on how to use, the agile process becomes a worse practice and creates a chaotic environment. according to other findings of this study it shows that developers have to accommodate such chaos within their activities and meet the deadlines and outcomes as expected. however, these results open a huge necessity for a discussion to improve the outsourcing industry in the developing countries. q4. do you experience spending more time on assigned tasks than they were scheduled? if yes what is the reason for that? as the figure 1 shows, a significant impact to schedule overrun is due to the requirement issues. the other significant factors are technical difficulties, which may be due to lack of training and development opportunities for developers, and the management issues specially having optimistic schedules to delight the outsourcer. the most important fact is that all of the participants ex344 i. perera perience to spend more time than they were scheduled, making them to work more time than the employment norms, without any compensation for the extra effort. figure 1: reasons make the schedule overruns q5. do you experience difficulties for understanding requirements from foreigners? comment on this out of the 57 participants, 49 has responded and 37 had issues with verbal communication due to the accent of the foreigners. 42 of the participants complained about email and instant messages with insufficient information to take a decision; as a result, they have been spending more time and effort to clarify requirements. more importantly, the natural language based e-mails or chat massages, instead of a proper requirement specification make it further difficult to understand what the client is actually expecting. q6. if you are provided with complete and final requirement specification at the beginning of development, will it helps to your work? 23 people said it will help them lot and 9 were not sure about whether it helps them or not. 25 have indicated that it will not help as they will never getting a finalized requirement specification at any time of the project. this shows how much significant the issue of ever changing and poor requirement engineering process in the outsourced industry. q7. are you the direct person to get requirements from client? if not how many to be passed for requirements to reach you? 8 participants have direct access with their clients to get requirements, 17 participants get through one person, 29 participants get through two persons, and 3 participants gets through three persons. the most obvious scenario was to have one person (non-technical) at the out-source end and one person (technical) at the development end to form 2 person layer to process requirements. due to the non-technical managerial person at the client end, the requirements process deters its effectiveness, making developers to work more on requirements fine tuning and error fixing during the development phase. impact of poor requirement engineering in software outsourcing: a study on software developers’ experience 345 q8. how good are you at work and life balancing? the idea of this question is to assess the health and social impact from the outsourcing work to the developers. there was a 5 level lickert scale given to rank them considering their extra activities, exercise time, recreational activities and good life style practices. results are shown in figure 2. 45.61% of the participants are not pay attention as required, for their social, health and other extra activities. when they spend more time on their work they get less time to attend those activities. the impact of this may not visible for short term but there are huge social, economical and health consequences in the long run which may not be easily compensated by their wee bit of extra salary they get compared to other disciplines. the good point is still the majority are happy about their work life balance, but there should be a proper mechanism to improve the situation. q9. are you properly compensated for your effort? figure 2: work life balance of the developers for this question the 21 participants (36.84%) said yes and 26 participants (45.61%) have said no. 3 people said they are not sure about it. 7 participants did not respond to this question. in fact, out of the participants who responded, more than 50% is unhappy about their compensation and believe that they are under paid and over utilized. q10. what is the impact from time zone difference for your work times? this was an open ended question where developers have to express their feeling about the impact from time zone difference. idea of this question was to assess whether their work patterns have been affected from that. in fact, many have responded in similar fashion indicating there is a significant impact from the time zone difference. most of the outsourcing projects are from united states of america and european union member countries, where there is a significant time difference with the sri lankan time, around 11-13 hours and 4-6 hours, respectively. what the participants have indicated was that when the outsourcer wants to change a requirement or impose a new one, the developers have to communicate with them which will mostly the evening or late night in sri lankan local time. in the following day they have to implement those from the morning. therefore, invariably they have to spend at least 11-12 hours per day despite what 346 i. perera was agreed in the contract as 8 hours. and these don’t count for any overtime payment or additional benefit. the above individual analyses of questions provide a clear view on how the poor requirement engineering practices affect to software developers in outsourced projects. the questions were focused into different aspects of significance relevant to the research problem. even though the fact that, responds for these questions covers a larger spectrum of issues related to the problem, there should be further researches with more focus to these significant areas identified in this analysis. 5.2 study limitations in this study, there were some research limitations which are worth to mention. since this study was involved with human activities, this had the experimental limitation of different skill levels between the participants. however this was general scenario of the software industry and has no significant impact for this study’s outcome. the second and the most crucial limitation with this study was the difference between the requirement changes. some requirements were very simple and some were not. though it was really hard to eliminate this, in the general situation every project has both difficult requirement changes and simple changes. therefore, in the large population conditions (n > 30), this behaves with normal distribution and nullifies the impact as the standard error is 0. another limitation with the study was the truncation errors of the collected data. literally, what happened was, the developers were confident on expressing their values with integer figures of hours or in minutes over the decimal or fractional values. for an example they might have said actual time as 7.15 but the time may be 7.11 however, since there is no comparison done based on their given values, the impact for the study outcome was negligible. furthermore, these errors are also normally distributed with the standard error 0 in the large population samples. 6 conclusion despite the outcome of this research, there are some possible future studies relevant to this research, which can be considered as further extensions. this study mainly focused on the requirement engineering issues in the development phase of the outsourced software project life cycle. there are other important life cycle phases in the outsourced projects such as system testing, system design, system implementation, etc. then again, there can be similar future studies to examine the social, economic and health impact from malpractices of software outsource industry in the context to employees, countries, and to industry as in whole. furthermore, impact on software outsourcing due to different cultural characteristics, social and demographic parameters, and geographical and ethnic factors can be another fruitful research area. domain specific examine of the impact of poor requirement engineering on developers is another possible study as a further extension to this study, which will indeed provide an in depth insight for the prevailing issues. due to the limited resources this study was conducted in a limited environment. though the results prove impressive observations, it is expected, and encouraged the other scholars to conduct further researches based on the findings of this study to formulate a global knowledgebase on outsourcing and impact to its stakeholders due to various parameters. for that level of generalization, essentially there should more similar researches have to be conducted within other major countries within the global software outsourcing industry. this study was initiated to examine an unaddressed issue in the outsourcing industry. though the study does not cover each and every issue experienced by the developers, it provides sufficient impact of poor requirement engineering in software outsourcing: a study on software developers’ experience 347 results and analyses to understand the gravity of the issue faced by developers engaged in outsourced industry. it is high time for policy makers to do further research on the identified issues and formulate standardization mechanisms to strengthen outsource industry in many developing countries while facilitating the developer community. in fact, the new computational paradigms such as cloud computing can cause significant impact towards reforming how outsourcing works at present. it is possible to have influential impact on present software outsourcing countries, when emerging solutions offering outsourcing both hardware and software [20]. it is therefore, a must to investigate possible improvements with new technologies to enhance the outsourcing industry, both individual country level and global level. with all this regard it is fair to believe that, this study will create a significant paradigm shift towards rethink of the outsourced software development process. in conclusion, it is evident that this research is one of the significant achievements in the present software engineering field. this study’s outcome will direct the policy implications to benefits the stakeholders of the software outsource industry, thus assist to improve the social, economical and health levels of software developers while avoiding crisis situation within the industry, in the long run. acknowledgement author would like to thank the people who helped for this study in various forms, especially, the developers who participated in this study for their effort and contribution to make this a success. bibliography [1] r. e. ahmed, software maintenance outsourcing: issues and strategies. computers and electrical engineering, vol. 32(6), pp. 449-453, 2006. [2] g.i.u.s. perera, m.s.d. fernando, enhanced agile software development hybrid paradigm with lean practice, in proc. of ieee 2nd iciis conference , peradeniya, pp.239-244, 2007 [3] a. fuggetta, software process: a roadmap, in proc. of the conference on the future of software engineering, icse, limerick, p.25-34, 2000 [4] w.s. humphrey, managing the software process, sei, pearson education, india, pp.03, 2006 [5] k.e. wiegers, software requirements, 2nd ed. redmond, wash, microsoft press, 2003 [6] g.i.u.s. perera, m.s.d. fernando, bridging the gap business and information systems: a roadmap, in proc. of 4th icbm conference, pp. 334-343, 2007 [7] b. nuseibeh, s. easterbrook, "requirement engineering: a roadmap", proc. of the conference on the future of software engineering, 22nd icse, limerick, p. 35-46, 2000 [8] p. zave, classification of research efforts in requirements engineering. acm computing surveys, vol. 29(4), pp. 315-321, 1997 [9] k. beck, et. al., manifesto for agile 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agarwal, tactical approaches for alleviating distance in global software development, ieee software, vol. 18(2), pp. 22-29, 2001 [16] k. molřkken, m. jřrgensen, a review of surveys on software effort estimation. in proc. of the international symposium on empirical software engineering, pp. 223-231, 2003 [17] i. perera, impact of using agile practice for student software projects in computer science education, international journal of education and development using information and communication technology, vol. 5(3), online [http://ijedict.dec.uwi.edu/viewarticle.php?id=755], 2009 [18] g.i.u.s. perera, fernando m.s.d., rapid decision making for post architectural changes in agile development a guide to reduce uncertainty, international journal of information technology and knowledge management, vol. 2(2), in press, 2009 [19] g.i.u.s. perera, "key success factors for e-learning acceptability: a case based analysis on blended learning end-user experience", in proc. of ieee international advance computing conference, iacc’09, pp. 2379-2384, 2009 [20] i. perera, reshaping the computation with clouds: an analysis on opportunities and issues of cloud computing, journal of advances in computational sciences and technology, 2(3), pp.305-324, 2009 ijcccv4n1draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 1, pp. 41-56 flatness-based control and conventional rst polynomial control of a thermal process hajer gharsallaoui, mounir ayadi, mohamed benrejeb, pierre borne hajer gharsallaoui école nationale d’ingénieurs de tunis, laboratoire de recherche en automatique (la.r.a.) b.p. 37, le belvédère, 1002 tunis, tunisia laboratoire d’automatique, génie informatique et signal (lagis), école centrale de lille avenue paul langevin bp 48 59651 villeneuve d’ascq cedex, france e-mail: hajer.gharsallaoui@voila.fr abstract: in this paper, a comparison between conventional rst polynomial control by poles placement and rst flatness-based control is proposed. these approaches were developed, in discrete-time formalism by carrying out a generation of a reference starting from a flat output and by then analyzing the tracking error in closedloop. the case of the thermal system, that we applied an output disturbance, was considered by simulation to study the effectiveness of given flatness-based robust controller with the integration of an anti-windup device, in terms of tracking trajectory and disturbance rejection. keywords: flatness, rst control, tracking trajectory, robustness, anti-windup. 1 introduction the flatness property was introduced by m. fliess, j. lévine, p. martin and p. rouchon in 1992 to propose a new strategy to control continuous nonlinear systems with good performances in term of tracking trajectory. at first, the use of this property consists in the definition of an output trajectory allowing the determination of the variables of the flat system. secondly, it concerns the elaboration of the control in closedloop allowing to obtain a stable system giving place to a tracking of a desired trajectory with an error which tends asymptotically towards zero [1, 2, 5, 18, 19]. several techniques of control resulting from the theory of control were established and applied to real systems, especially the rst polynomial control. the exploitation of robust control with an argued choice for its design satisfying the control objectives in term of the tracking trajectory of reference as well as the regulation in the presence of disturbances, constitute the object of this paper. a comparison between this well-known conventional rst polynomial control and a recent approach of a rst controller design based on the property of flatness of the mono-variable linear and controllable systems are carried out by considering the case of the thermal process control. 2 conventional rst polynomial control 2.1 structure of rst controller by poles placement in the rst control approach it is possible to impose poles in closed-loop and to carry out in a separate way the objectives of tracking and regulation. in a discrete formalism, the blocks of rst controller are given by figure 1, [4, 9, 12]. copyright © 2006-2009 by ccc publications 42 hajer gharsallaoui, mounir ayadi, mohamed benrejeb, pierre borne figure 1: conventional structure rst controller h (q−) = b(q−) a(q−) is the transfer function of the dynamic linear discrete system where the polynomials a and b represent respectively the denominator and the numerator of the transfer function with respective degrees na and nb: a ( q − ) = a + aq − + ... + anaq −na (1) b ( q − ) = b + bq − + ... + bnbq −nb (2) the choice of the polynomials r, s and t allows to solve as well the problem of regulation as well as of tracking. these polynomials are given by: r ( q − ) = r + rq − + ... + rnrq −nr (3) s ( q − ) = s + sq − + ... + snsq −ns (4) t ( q − ) = t +tq − + ... +tnt q −nt (5) where nr = deg(r), ns = deg(s) and nt = deg(t ). the transfer function in closed-loop, in this case, is given by: hbf ( q − ) = t (q−)b(q−) a(q−)s (q−) + b(q−)r(q−) (6) the calculation of the polynomials r and s is carried out starting from the choice of the polynomial p: p(q−) = a(q−)s (q−) + b(q−)r(q−) = p + pq − + ··· + pnpq−np (7) if deg(p) < deg(a) + deg(b), this equation is regular and it can be written in the following matrix form:               a  . . .  b  ···  a a ... ... b b ... ... ... ... ...  ... ... ...  ana ... ... a bnb ... ... b  ... ... a  ... ... b ... ... ... ... ... ... ... ...  ··· ··· ana  ··· ··· bnb               ︸ ︷︷ ︸ m              s ... sns r ... ... rnr              ︸ ︷︷ ︸ x =             p p ... pnp  ...              ︸ ︷︷ ︸ mp (8) flatness-based control and conventional rst polynomial control of a thermal process 43 where m ∈ ℜ(na×nb) × ℜ(na×nb) is called sylvester matrix. the coefficients of r(q−) and s (q−) contained in vector x are given by: x = m−mp (9) for the determination of the coefficients of the polynomial t , in the case of the tracking of a desired trajectory yc k corresponding to a reference model: hm ( q − ) = bm (q −) am (q −) (10) we choose t (q−) = β b(q−), to obtain: hbf ( q − ) = β b ( q − ) (11) hbf ( q − ) = β b(q−)bm (q −) am (q −) (12) with taking β =  b() , a static gain in closed-loop equal to 1 is then imposed. figure 2: detailed structure of rst regulator 2.2 rejection of disturbances let us consider s (q−) the polynomial of the following form: s ( q − ) = (  − q− )m s ′ ( q − ) (13) to reject a step disturbance, we can choose m =  with deg(s′) = deg(s) − m. 2.3 anti-saturation of the control the direct implementation of rst controller often leads to a control signal whose amplitude can be very important. in order to keep this amplitude in an acceptable interval [umin,umax], we add to the structure of control an anti-windup device [4], d is the output disturbance and s∗ (q−) is a polynomial defined from the relation bellow: s ( q − ) = s + q − s ∗ ( q − ) (14) 44 hajer gharsallaoui, mounir ayadi, mohamed benrejeb, pierre borne figure 3: conventional structure of rst controller with anti-windup device 3 design of flatness-based controller 3.1 determination of flat output in this part, let us consider the discrete-time linear system single input single output (siso), described by the following equation: a ( q − ) yk = b ( q − ) uk (15) where yk and uk represent the output and the control signals respectively. the flat output is a variable defined in continuous time; we consider the expressions of the polynomials a(q−) and b(q−) in term of the operator q−, to be able to define thereafter the discrete flat output of the system. the system is considered as linear and controllable, consequently it is flat and its flat output zk is given by [2]: zk = n ( q − ) yk + d ( q − ) uk (16) where n (q−) and d(q−) are polynomials which represent the solutions of the equation of bezout: a ( q − ) d ( q − ) + b ( q − ) n ( q − ) =  (17) the flat output zk on which depend the output yk and the control uk, can be also seen as being the partial state of a linear system [3]: uk = a ( q − ) zk (18) yk = b ( q − ) zk (19) the knowledge of the flat output zk allows to determine the open loop control uk of the system. 3.2 trajectories planning the objective of the trajectories planning [17] is to determine an open-loop control ud (t), carrying out the objective bringing a given system, of a certain initial state in a known final state: u d (t) = b ( z d (t) , ...,zd(α+) (t) ) (20) y d (t) = c ( z d (t) , ...,zd(σ ) (t) ) (21) flatness-based control and conventional rst polynomial control of a thermal process 45 zd is the desired trajectory for the flat output that is sup(α + , σ ) time continuously derivable. if the objective is to reach two equilibrium points of balance: (u(t) ,y(t) ,z(t)) and (u(t f ) ,y(t f ) ,z(t f )), where t and t f are the two times known in advance, it is necessary to solve the problem of trajectory generation offline. a solution for the calculation of zd(t), vector of zd(t) and its successive derivatives, is given by the relation: z d (t) = m (t −t)c + m (t −t)c (22) where m(t) and m(t) are the two following matrices: m (t) =        t ··· tn− (n−)!   ··· tn− (n−)! ... ... ... ...  ···         (23) m (t) =       tn n! tn+ (n+)! ··· tn− (n−)! tn− (n−)! tn n! ··· tn− (n−)! ... ... ... ... t ··· ··· tn n!       (24) the vectors c and c can be defined from 20 and 21 by: c = z d (t) (25) c = m −  (t f −t) ( z d (t f ) − m (t f −t)z d (t) ) (26) in the discrete-time formalism, the real output of system yk to be controlled, follows the desired trajectory yd k expressed by: y d k = b ( q − ) z d k (27) the synoptic diagram of figure 5 summarizes the different steps as well as the necessary tools to generate the desired trajectory yd k in a discrete-time formalism with considering the sampling period te [2, 5, 12]. figure 4: generation of the desired trajectory yd k 3.3 flatness-based rst polynomial controller the realization of flatness rst controller is carried out here by considering the method of direct calculation of the state vector zk = ( zk zk+ ··· zk+n− )t , suggested in [2, 5]. 46 hajer gharsallaoui, mounir ayadi, mohamed benrejeb, pierre borne from the relations (20) and (21), we can determine the representation of state of the system in its controllable form: { zk+ = azk +buk, yk = czk, (28) with: a =            ···   ...  ... ... ... ... ... ...   ···    −a −a ··· −an− −an−         , b =       ...        and c = ( b b ··· bn− ) from this representation, we can write [5]: zk = o − (a,c) ( yk − m(a,b,c)uk ) (29) with the observability matrix is: o(a,c) =      c ca ... can−      (30) and the controllability matrix m(a,b,c) given by: m(a,b,c) =           ··· ···  cb ... ... cab cb ... ... ... ... ...  can−b ··· cab cb          (31) from [5], we obtain the flatness control law by the following relation [12]: uk = k (q)z d k + (a − k)zk (32) where a and k are two constant vectors constituted by the ai and ki coefficients of the a(q −) and k(q) polynomials given by: a = ( a a ... an− ) , k = ( k k ... kn− ) , where k(q) is a polynomial containing the closed-loop poles. the realisable structure of rst controller by flatness with q− operator can be then obtained by: s ( q − ) uk = k (q)z d k + r ( q − ) yk (33) where: r ( q − ) = − (a − k)an−o− (a,c)q (34) s ( q − ) =  + (a − k) ( an−o− (a,c)m(a,b,c) − ( an−b ... b ) ) q ∗ (35) and: q = ( q−(n−) q−(n−) ... q−  )t , q∗ = ( q−(n−) q−(n−) ... q− )t . the dynamics of the closed-loop is defined by the tracking polynomial k (q−) such as: flatness-based control and conventional rst polynomial control of a thermal process 47 a ( q − ) s ( q − ) + b ( q − ) r ( q − ) = k ( q − ) (36) this relation represents the bezout equation already defined in the traditional approach of rst controller. the choice of poles must be well optimized in order to satisfy the desired performances. this step appears the fundamental contribution of the exploitation of the flatness property in the design of such controller. indeed, the choice of the closed-loop poles corresponds to this of the tracking model of a desired trajectory and more precisely, to the poles of the k(q) polynomial. 4 study of the robustness 4.1 disturbances rejection and noises attenuation by using the principle of the internal model, a deterministic disturbance can be rejected by considering in the transfer function of the direct chain an integrator. in fact, in order to obtain a robust controller, the polynomial s (q−) must have a term in ( − q−) which allows the rejection of static disturbance present on the output signal. moreover, to introduce a filtering effect into a certain zone of frequencies, the principle of the blocking of the signal is often used by forcing the system to behave as an open loop system at these frequencies; what is equivalent to impose of the zeros in polynomial r(q−). this led so that polynomial r(q−) contains a term of ( + q−) making it possible to ensure an attenuation of the noises effect in high frequencies on the system input [4, 6]. thus, the following polynomials hs (q −) and hr (q −) are introduced into the model of the system: hs ( q − ) =  − q− (37) hr ( q − ) =  + q− (38) by taking in account of these pre-specified fixed parts, polynomials r̃(q−) and s̃ (q−) of new rst controller can be expressed by: r̃ ( q − ) = hr ( q − ) r ( q − ) (39) s̃ ( q − ) = hs ( q − ) s ( q − ) (40) in addition, the dynamics of the regulation is defined by the choice of the dominant and auxiliary poles of polynomial k (q−) of the closed loop given by the relation: a ( q − ) hs ( q − ) s ( q − ) + b ( q − ) hr ( q − ) r ( q − ) = k ( q − ) (41) in order to take in account the pre-specified parts in the design of the new controller, it is enough to consider the increased model governed by the following transfer function: h̃ ( q − ) = b(q−)hr (q −) a(q−)hs (q −) (42) then, it is a question to redo the calculations of flatness-based rst controller using the method presented previously. 48 hajer gharsallaoui, mounir ayadi, mohamed benrejeb, pierre borne 4.2 analyze of the controller robustness in order to maintain nominal performances in rejection of disturbances and in the presence of modelling errors, the sensitivity functions defined below, are calibrated and recomputed so as to satisfy the performances required. the sensitivity functions are given according to the type of disturbance to consider. by considering the three types of following disturbances: output-input disturbances and noises of measurement, we can thus deduce the output-input functions of sensitivity described in [4, 6]: • output disturbance: represented by the transfer function syd (q −) between the output disturbance d(t) and the system output y(t): syd ( q − ) = a(q−)s̃ (q−) a(q−)r̃(q−) + b(q−)s̃ (q−) = a(q−)s̃ (q−) k (q−) (43) • input disturbance: represented by the transfer function sud (q −) between the output disturbance d(t) and the input u(t): sud ( q − ) = −a(q−)r̃(q−) k (q−) (44) the study of the robustness of the developed rst controller is based on the frequencies analysis of the modules of the various functions of sensitivity. desirable templates for the sensitivity functions will be defined through the constraints of imposed performances and tolerated robustness margins. the purpose is to have the disturbance output sensitivity function inside the upper and lower templates as shown further in figures 14 and 15. also, input sensitivity function must have a decreasing shape meaning that the controller is insensitive to noise. 5 saturation effects the additive static disturbances cause generally the increase in the amplitudes of the control signal applied to the system exceeding certain limit values. this especially presents a problem in the control of process by digital computers having thresholds in tension not to exceed on the level of their inputoutputs. consequently, it is necessary to design a device of anti-saturation according to the technique already developed in [4], where the law of control has the following forms: uk = k (q)z d k − r̃ ( q − ) yk − s̄ ( q − ) uk− (45) with: s̃ ( q − ) =  + q−s̄ ( q − ) (46) in addition, by considering umin and umax respectively the lower and higher limits of the control saturation, we obtain: ūk =    uk i f umin ≤ uk ≤ umax umax i f uk > umax umin i f uk < umin (47) however, it is possible to impose certain dynamics when the system leaves the saturation. this is illustrated in figure 5. the desired dynamics is defined by the polynomial ps (q −) given by equation (48). ps ( q − ) =  − exp ( te τsat ) q − (48) flatness-based control and conventional rst polynomial control of a thermal process 49 where τsat indicates a time-constant of a system of first order. it is it should be noted that outside of saturation, umin < uk < umax, this mechanism of anti-saturation is equivalent to block  s̃ (q−) of rst controller given by figure 5. figure 5: diagram of flatness-based rst controller with taking in consideration of anti-saturation finally, the new control law of the flatness-based polynomial controller in the presence of saturation (47) is given by the expression: ps ( q − ) uk = k (q)z d k − r̃ ( q − ) yk − ( s̃ ( q − ) + exp ( te τsat )) ūk− (49) in the following part, a comparative study between the two laws of rst polynomial controllers, rst flatness-based control and conventional rst polynomial control, is carried out by digital simulation by considering the case of the thermal process control. 6 application to the control of a thermal system 6.1 modelling of the thermal system the thermal process whose simplified diagram is given by figure 6, is constituted of a tube of constant volume v [m] and of a heating resistance rc connected to a direct current power supply u(t) applied to resistance for heating the air entering at a desired temperature by joule effect 6, c [j.m−.◦k−] is the specific heat constant of air, te [ ◦k] the ambient temperature, f j [m .s−] the air rate flow entering according to the aperture of the valve j. the purpose of the control approach is to regulate the temperature ts [ ◦k] of the outgoing air at the constant temperature, given that the air flows into the tube with an initial temperature te [ ◦k] and at the flow rate f j [m .s−]. figure 6: simplified diagram of the thermal process the flow rate signal is assumed piecewise constant and can be vary by changing the throttle position j. the model of the thermal process, dependent, primarily of the valve position, if it is admitted that the temperature of the entering air remains constant, the transfer function of the model is given by [10, 16]: 50 hajer gharsallaoui, mounir ayadi, mohamed benrejeb, pierre borne g(p) = ke−τ p  + tc p (50) where k is the delay, t is the time-constant of the process and p is the laplace operator. for an ambient temperature equal to c, the identified parameters of g(p) are: k = ., τ = .s and tc = .s [10, 11, 16]. the corresponding discrete-time transfer function is then given by: g ( q − ) = .q− + .q−  − .q− (51) te is the sampling period selected equal to .s. 6.2 determination of the desired trajectory let us consider thatb() = 0.7875 , we choose to generate the trajectory expressed in continuous time zd (t) according to the following polynomial form: z d (t) =    yd (t) b() , i f  ≤ t ≤ t poly(t) , i f t ≤ t ≤ t f yd(t f ) b() , i f t ≥ t f (52) by taking the transition times t = s and t f = s, and choosing the polynomial poly(t), like reference trajectory between these two moments: poly(t) = (    ) (m (t −t)c + m (t −t)c) (53) the desired trajectory zd calculated is represented on figure 7. 0 5 10 15 20 25 30 3.5 4 4.5 5 5.5 6 6.5 time(s) d e s ir e d t ra je c to ry z d figure 7: desired trajectory zd(t) 6.3 conventional rst controller by poles placement the tracking polynomial k(q) is obtained by discretizing of the continuous model as third order system formed by the setting in cascade of two subsystems of which one is of second order, characterized flatness-based control and conventional rst polynomial control of a thermal process 51 by ωn = .rad.s − and ξ = ., and the other first order one having a time-constant τ = .s . it becomes then: k (q) = q − .q + .q − . (54) simulations were carried out by considering the following conventional rst controller polynomials: r ( q − ) = . − 3.216q− + 1.015q− (55) s ( q − ) =  + 0.1724q− − 0.5306q− − 0.6419q− (56) t ( q − ) = k (q−) b() = −0.09915 + 0.7326q− − 1.575q−+1.27q− (57) the simulation results show that the response of the thermal process presents an important tracking error. the thermal process presenting one zero unstable it is thus with no minimum of phase that explain the response of the system given in figure 8. 0 5 10 15 20 25 30 −1 0 1 2 3 4 5 6 time(sec) o u tp u t s ig n a ls yd y figure 8: conventional rst control: y the real system output and yd the desired output 0 5 10 15 20 25 30 −3 −2 −1 0 1 2 3 4 5 6 time(sec) c o n tr o l s ig n a l figure 9: conventional rst control: control signal 52 hajer gharsallaoui, mounir ayadi, mohamed benrejeb, pierre borne 0 5 10 15 20 25 30 −0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 time(sec) tr a c k in g e rr o r figure 10: conventional rst control: tracking error 6.4 flatness-based rst controller the tracking polynomial k(q) is selected starting from the discretization of the continuous tracking model considered as third order system. consequently, we deduce the flatness-based rst regulator polynomials designed by flatness are bellow: r ( q − ) = 0.6554 − 0.3274q− (58) s ( q − ) =  + 0.2681q− + 0.2069q− (59) leading to the results of figures 11, 12 and 13. 0 5 10 15 20 25 30 0 1 2 3 4 5 6 time(sec) o u tp u t s ig n a ls yd y figure 11: flatness-based rst control: y the real system output and yd the desired output the results in figures 11, 12 and 13 underline the importance of the implementation of flatness-based controller in term of trajectory tracking. in fact, we notice in addition that the tracking error in the case of first approach rst is significant compared with that obtained by flatness-based controller. thus, the flatness property is very interesting in terms of planning and tracking trajectory exploited for the development of a closed-loop system with high performances. the results of figures 14 and 15 show that the controller obtained is robust considering the sensitivity functions which remain inside the specified templates of robustness [4]. in addition, we underline the flatness-based control and conventional rst polynomial control of a thermal process 53 0 5 10 15 20 25 30 0 1 2 3 4 5 6 time(sec) c o n tr o l s ig n a l figure 12: flatness-based rst control: control signal 0 5 10 15 20 25 30 −0.5 0 0.5 1 1.5 2 2.5 3 time(sec) tr a c k in g e rr o r figure 13: flatness-based rst control: tracking error 0 0.1 0.2 0.3 0.4 0.5 −50 −40 −30 −20 −10 0 10 20 30 40 50 normalized frequency o u tp u t s e n s it iv it y f u n c ti o n |s y p | figure 14: output sensitivity function |syd| case of output disturbance 54 hajer gharsallaoui, mounir ayadi, mohamed benrejeb, pierre borne 0 0.1 0.2 0.3 0.4 0.5 −150 −100 −50 0 50 normalized frequency in p u t s e n s it iv it y f u n c ti o n | s u p | figure 15: input sensitivity function |sud| case of input disturbance robustness of such controller related to the static disturbance and the high frequency noises rejection is clearly guaranteed. moreover, the input sensitivity function presents attenuation in the high frequencies which define an elimination of noises in the input. 7 conclusion in conclusion, the study of the thermal process control shows that flatness-based rst regulator ended in a regulation of the robust and powerful type rst in term of tracking trajectories compared with such obtained by the use of the conventional polynomial rst controller. the design of flatness-based rst controller, depending on the choice of the poles of the tracking dynamic system and on the desired trajectory, allowed the satisfaction of the templates imposed by the typical values of the robustness margins. bibliography [1] m. fliess, j. lévine , ph. martin and p. rouchon, sur les systčmes non linéaires différentiellement plats, compte rendu de l’académie des sciences de paris, série i, 315, pp. 619-624, 1992. [2] m. ayadi, contribution ŕ la commande des systčmes linéaires plats de dimension finie, phd thesis, institut national polytechnique de toulouse, 2002. [3] t. kailath, linear systems, prentice hall, 1980. [4] i. landau , r. lozano and m. m’saad, adaptative control, springer-verlag, london, 1998. [5] f. rotella, f j. carrillo and m. ayadi, polynomial controller design based on flatness, kybernetica special issue on system structure and control, vol. 38, n°5, pp. 571-584. [6] a. oustaloup, la robustesse. analyse et synthčse de commandes robustes, hermčs, 1994. 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[12] k. j. aström and b. wittenlmark, adaptative control, addison wesley, united states, 1989. [13] m. ayadi, n. langlois, m. benrejeb and h. chafouk, flatness-based robust adaptative polynomial controller for a diesel engine model, transaction on systems, signals & devices, vol. 2, n°1, pp. 71-90, 2006. [14] m. fliess, j. lévine, ph. martin and p. rouchon, on differentially flat non linear systems, ifacsymposium nolcos’92, pp. 408-412, bordeaux ,1992. [15] r. marquez, and m. fliess, linear predictive control revisited. a flatness based approach, european control conference, ecc’99, karlshrule, 1999. [16] ph. charbonnaud, j. f. carrilo and s. médar, robust control reconfiguration of a thermal process with multiple operating modes,ieee transaction of control systems technology, vol. 11, n°4, 2003. [17] j.y. dieulot, i. thimoumi, f. colas and r. béarée, numerical aspects and performances of trajectory planning methods of flexible axes, international journal of computers, communications and control, vol.i, n°4, pp. 35-44, 2006. [18] j. lévine, on the necessary and sufficent conditions for differential flatness, th ifac symposium on nonlinear control systems, nolcos’04, pp. 463-468, stuttgart, 2004. [19] h. sira-ramirez and s.k. agrawal, differentially flat systems, marcel dekker, 2004. hajer gharsallaoui was born in 1979 and received the engineer diploma degree in 2004 and the master degree in automatic control in 2005 from the ecole nationale d’ingénieurs de tunis (enit). she is currently preparing the ph.d. degree in automatic control within the framework of lagis-ec-lille and lara-enit cooperation. her research is related to accommodation control and ftc control, diagnostic of flat system, and adaptive control. mounir ayadi graduated from the ecole nationale d’ingénieurs de tunis in 1998 and received his phd degree in automatic control from the institut national polytechnique de toulouse in 2002. he was a post-doctoral fellow at the ecole supérieure d’ingénieurs en génie electrique de rouen in 2003. he is currently maître-assistant at the ecole nationale d’ingénieurs de tunis. his research interests are in the area of control system theory, predictive and adaptive control, and flat systems. mohamed benrejeb was born in tunisia in 1950. he obtained the diploma of "ingénieur idn" (french "grande ecole") in 1973, the master degree of automatic control in 1974, the phd in automatic control of the university of lille in 1976 and the dsc of the same university in 1980. he is currently a full professor at the ecole nationale d’ingénieurs de tunis and an invited professor at the ecole centrale de lille. his research interests are in the area of analysis and synthesis of complex systems based on classical and non conventional approaches. 56 hajer gharsallaoui, mounir ayadi, mohamed benrejeb, pierre borne pierre borne received the master degree of physics in 1967, the masters of electronics, of mechanics and of applied mathematics in 1968. the same year he obtained the diploma of "ingénieur idn" (french "grande ecole"). he obtained the phd in automatic control of the university of lille in 1970 and the dsc of the same university in 1976. he became doctor honoris causa of the moscow institute of electronics and mathematics (russia) in 1999, of the university of waterloo (canada) in 2006 and of the polytechnic university of bucarest (romania). he is author or co-author of about 200 journal articles and book chapters, and of 34 plenary lectures and of more than 250 communications in international conferences. he has been the supervisor of 71 phd thesis and is author of 20 books . he is fellow of ieee and has been president of the ieee/smc society in 2000 and 2001 .he is vice-chair of the ifac tc on large scale complex systems. he is presently professor "de classe exceptionnelle" at the ecole centrale de lille and director of the national french pluriformations group of research in automatic control. int j comput commun, issn 1841-9836 7(5):892-899, december, 2012. mobile network qoe-qos decision making tool for performance optimization in critical web service c. lozano-garzon, c. ariza-porras, s. rivera-diaz, h. riveros-ardila, y. donoso carlos lozano-garzon, christian ariza-porras sebastián rivera-díaz, horacio riveros-ardila, yezid donoso universidad de los andes, bogotá, colombia e-mail: ca.lozano968@uniandes.edu.co cf.ariza975@uniandes.edu.co, s.rivera57@uniandes.edu.co lh.riveros102@uniandes.edu.co, ydonoso@uniandes.edu.co abstract: regardless of the type of service that a company offers the customer satisfaction is a factor for success, if these services are in a highly competitive environment. this situation encourages companies to develop strategies to improve the quality of the experience (qoe) of their users. strategies include improving their processes, or infrastructure for provisioning the services. take these kind of decisions is very difficult because they ignore how the key performance indicators (kpi) services are correlated with the information about user experience. this problem is approached from the perspective of mobile telecom operators, who have addressed this challenge through the quality of service (qos) concept. unfortunately, the qos is only characterized by technical aspects, the user’s criteria are not included. into a highly competitive environment, the user’s loyalty is a key component to be considered in the operator’s development plan. nowadays, the mobile telecom operators focus their efforts to ensure not only the qos but also the qoe. the aim of this paper was the develop a decision making tool that allows the mobile telco operators support their determinations about the maintenance of network infrastructure, as well as the expansion of the same, specifically for their critical web services; based in a correlated information between qos and qoe. this tool was developed on the basis of the pseudo subjective quality assessment (psqa) methodology. keywords: decision making tool, pseudo subjective quality assessment, quality of experience, quality of service, web services. 1 introduction the rapid evolution that has made the telecommunications industry in the world, represented in technological development in networks and the emergence of ip as a fundamental part of both fixed and mobile networks, has led the sector to a converged environment that enables businesses to provide new services. this new environment base its performance in what is now known as next generation mobile networks (ngmn) which is intended to support the growing needs of both technical and quality demands for new services.this environment generates new challenges not only in the design and implementation of the network, also this should allow the provision of services independent of location, time or device from accessing this and besides challenges in the way that operators can implement mechanisms to ensure quality of service (qos) and quality of experience (qoe) under these different technology platforms. the term qos is widely used in the environment for communications networks, it was defined by the itu-t [1] as the collective effect of service performance which determine the satisfaction of a service user. associated with the conceptualization of qos harry in [2] defines three concepts of qos: intrinsic qos perceived qos and evaluated qos, the definite relationship between these three concepts are general qos model proposed by the itu-t. copyright c⃝ 2006-2012 by ccc publications mobile network qoe-qos decision making tool for performance optimization in critical web service 893 stankiewicz, cholda, & jajszczyk in [3] describes the intrinsic qos, it is known as "network performance" by the itu and etsi in recommendation e.800, it covers all the features of service determined by the efficiency of the network. the intrinsic qos is key to the quality perceived and evaluated by the customer. the perceived qos reflects the customer experience in using a particular service through the itu recommendation g.1000 has four important perspectives: the qos required by the client, the qos offered by the provider, the qos achieved by the supplier and the qos perceived by the customer. finally the evaluated qos starts when the customer decides whether to continue using a service or not, this decision depends on the perceived quality, the price of the service, and supplier responses (problems and complaints), the itu defines the guidelines of quality of experience (qoe) in recommendation p.10. even though qos and qoe measurements are quite different, they have a high degree of correlation; nevertheless, some mobile operators have not yet implemented tools for incorporating the "feel" of a user based on the qos parameters measured for a specific service. the mobile phone companies have improved the deployment and delivery of their products influenced by the quality of service (qos), regardless of user perception. however, the mobile telco operators know that user satisfaction is a key success factor for the loyalty and positioning of the company against its competitors. this fact has led these operators to develop strategies for adoption of the perception of its users in their decision processes to the tuning of their infrastructure for the provision of services. this work aims to provide to mobile telecom operators a qoe-qos decision making tool that will allow them to support their determinations about the maintenance of network infrastructure, as well as the expansion of the same, specifically for critical web services. the remainder of this paper is organized as follows. in section 2 shown some works related with the measurement of quality of experience and the correlation of it with quality of service for some specific services. the psqa methodology is presented in section 3. the proposed methodology used to develop the qos-qoe decision making tool is presented in section 4 and the implementation of this methodology is described in section 5. finally, the experimental results of the proposed tool are presented in section 6 and the section 7 presented the conclusions and future work. 2 related works stankiewicz, cholda, and jajszczyk in [3] notes that qoe assessment methods could be classified into qualitative (subjective) and quantitative (objective) methods. qualitative methods are built with the participation of people, a representative sample of the population, whom used a particular service. in these methods the service is assessed in a controlled environment and people fill out a survey with numerical values qualification. quantitative methods provide a qoe assessment based on the measurement of several parameters related with service quality indicators in the signal at the output of the transmission channel. however, the institut national de recherche en informatique et en automatique (inria) in france proposed a hybrid method between subjective and objective assessments of qoe called psqa [4]. other papers examine the relation between qos parameters and user perception. for example the research generated by telenor in [5] about the conceptual difference between qos, considered the quality associated with technical performance parameters; and qoe understood as a measure of user performance, based on objective and subjective measures of the use of information and communications technology (ict) product or service. in [5], [6], and [7] it is evident that between these two concepts clear relationship that allows us to express the quality of the experience in terms of quality of service, which is the starting point for further studies made in order to establish a relationship or a relational model between the two. some of these studies 894 c. lozano-garzon, c. ariza-porras, s. rivera-diaz, h. riveros-ardila, y. donoso have focused on the relationship can be identified for a specific service such as the iptv [8] and [9], transmission media [10] or public internet [11], other studies have sought to establish a generic model of relationship between them as shown in [12], [13] and [14]. as a result of these studies, progress has been made in studies seeking to establish an objective measure based on a subjective view by users. within the work developed in this field we find the following: [15] focused on the development of the concept of the quality of experience focused on the measurement and communication requirements for industrial use, [16] measurement studies quality of experience based on ontologies and [17] which presents a look at the main techniques for measuring the quality of experience focused on the methodologies and tools available free. finally in [18] the authors propose a new methodology called pseudo-subjective quality assesment (psqa) based on random neural networks, to quantify the quality of a video or audio transmission over the internet. they discuss the results concerning psqa-based dynamic quality control and conversational quality assessment. a general system developed to evaluate qoe on ip networks was shown in [19]. their system architecture is designed to be capable of emulating multi agent networks and dynamically changing conditions, in a web browsing qoe experiment. the experiment was conducted on the basis of itu-t recommendation g.1030 , and aimed to update the perceptual model provided in this recommendation to today context. as can be seen both of these studies: relationship between quality of service and quality of experience, as well as metrics for quality of experience; are an important part of an environment using all-ip network. 3 pseudo-subjective quality assessment (psqa) knowing that mobile operators require a method that allows them: correlate qos parameters and subjective perception of the user, it can be used without creating a new testbed, and generating a set of reports that support their decisions about network infrastructure. after making a comparison between the existing metrics the hybrid model (specifically psqa metric) was selected because it takes the best of the subjective and objective models, the results are in terms of mean opinion score (mos), is a not intrusive method, obtains real-time data and its implementation phase is low at the time. this model is divided into three big steps: firstly the application of a subjective evaluation in a controlled environment where the samples are distorted in periods of time; in the second step the samples go through a statistical process where the elements out of range are detected and removed; and finally the results are used to train a statistical learning tool, a random neural network (rnn), that learns the correlation between configurations and mos values defined, related among the parameters that cause distortion and the perceived quality. 4 proposed methodology for development of qoe-qos decision making tool based on the psqa methodology we propose an adaptation of it, in order to develop a making decision tool that allow us to combine the knowledge of end-user experience and technical parameters values for the decision making in mobile operators. it is hoped that this new model assigns to the samples (qos parameters) a qoe value very close to the value that an average human observer would give.(see figure 1). mobile network qoe-qos decision making tool for performance optimization in critical web service 895 figure 1: flow chart of qoe-qos decision making tool 4.1 development of subjective test for assessment web services when we started the development of this work, we didn’t find any documented work related to the existence of a mos test oriented to web services. for this reason, we decided the developing of a subjective test for critical web services based on some mos existing tests. for the development of this test, we considered the following web contexts: a text-only page, a page with images and text, a video and download a file. 4.2 determination of the population sample and statistical analysis of the test based in [20] the number of samples must be calculated so as to ensure a confidence interval of at least 95% and an error no greater than 5%, taking into consideration the following formula: n = (z1− α 2 )2 a2 · ( s mean(x) )2 (1) where n is the number of samples, z1− α 2 is the 1 − α 2 percentile of the standard normal distribution, s is the expected standard deviation, mean(x) is the expected mean value, and a is the relative accuracy. after we collect the samples through the designed test application, the results are passed through a statistical process in order to detect and remove users who present data out of range. 4.3 development of agents to gathering qos parameters the agents are the responsible of making the measurements of the qos parameters defined for the web browsing services. these parameters were selected according to [20]and are: bandwidth, latency, signal strength, trademark of device and the cell where the device is located. another important characteristic of these agents is the need to send the measurements collected to the server in order to use these in a first time like initial information to training the 896 c. lozano-garzon, c. ariza-porras, s. rivera-diaz, h. riveros-ardila, y. donoso neural network and later as the principal source of information to calculate the qoe through the neural network. 4.4 training of the neural network given that the neural network should behave as a classifier, we propose the use of a multilayer perceptron whose input will correspond to the five (5) qos parameters defined above and the output will correspond to one (1) value of qoe (excellent, very good, good, fair, or poor). once the neural network has been trained and validate, it is hoped that the results produced will be very similar to the results of people’s subjective tests. 4.5 storage of information in order to maintain a historical record of both: the values of qos parameters collected and the qoe values calculated; it is necessary to implement a repository of information. this repository will be the primary source of information for the generation of reports that will support decision making. 4.6 development of report manager the report manager should enable operators to generate the required documentation for making decisions regarding the maintenance of network infrastructure, as well as its expansion, specifically for critical web services. some of the basic reports considered are the following: information of qoe by base station or by the device type and the correlation between qos parameters and the estimated qoe. also the operators need that the generator can produce new reports easily according to their requirements. 5 qoe-qos decision making tool implementation as a first step towards the implementation of this tool we designed the software architecture to be used, it is built by six (6) components: the mobile agent, the agent listener, the persistence component, the classifier, the report generator and the presentation component. these components and their connections can be seen in fig. 2. additionally, in order to support connectivity between the agents and the server, we propose a client server network architecture of three layers (see fig. 3). the implementation of the tool was performed according to previously proposed architectures. in the server component is used, among other tools: weka for the classifier, webservices developed to receive data from agents and classified using the trained models and birt as a development tool for reports and report viewer. while the mobile agent components were implemented in java. figure 2: software architecture connection diagram mobile network qoe-qos decision making tool for performance optimization in critical web service 897 figure 3: network infrastructure proposed 6 experimental results as a first step we performed the collection of about 120 subjective tests in 5 different cells of the operator. subsequently we made a statistical analysis on data collected and all data that were outside three times the standard deviation were removed, because they were atypical behaviors that could influence the model training. with the remaining surveys, we conducted the relationship with data taken by the agents and estimated the value of qoe general survey taking an average of the evaluations of the test (text, text and images, video and downloads) and an average compared against the modified test. once the data were purged we proceeded to do the training of the neural network. the classification algorithm used was the multilayer perceptron with 50 nodes in the hidden layer and 5000 epochs (times) as a limit. from 50 additional samples, which are selected to valid the model, after executing the classifier (neural network) had a success rate in the classification of 90% aand the mean absolute error is close to 4.7%. in fig 4 we show the relationship between the calculated qoe and qoe assessment of the user to test video. figure 4: relationship between the qoe assesment calculated and the test user to the video 7 conclusions and future works in conclusion, through the decision making tool developed the mobile telecom operators could estimate the users’ subjective opinion based in network qos parameters. this information allows the generation of specific reports with the aim of supporting decisions oriented to prevent product or service rejections by the users. some of the most important decisions are related with the determinations about: the tuning of network infrastructure, the expansion of this infrastructure, the use of some specific equipment, among others. as future work we plan to work in two actions, the first is related to the exploration of other training algorithms for neural network that allows us to achieve better results, and the second seeks to expand this study to other data transmission services. 898 c. lozano-garzon, c. ariza-porras, s. rivera-diaz, h. riveros-ardila, y. donoso bibliography [1] itu t. , terms and definitions related to quality of service and network performance including dependability, international telecommunication union., recommendation e.800, 1995. 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[20] european telecommunications standards institute, "speech processing, transmission and quality aspects (stq); user related qos parameter definitions and measurements., european telecommunications standards institute, sophia antipolis cedex france, standard etsi eg 202 057-2 v1.3.1, 2009. ijcccv4n3draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 3, pp. 283-290 on parallel graph rewriting systems dragoş sburlan ovidius university of constantza, romania faculty of mathematics and informatics 124 mamaia blvd., 900527 constantza e-mail: dsburlan@univ-ovidius.ro received: april 5, 2009 accepted: may 30, 2009 abstract: in this paper we introduce a new theoretical paradigm, called pgr systems, which can be used to model in a discrete manner some natural phenomena occurring in-vivo/in-vitro environments. pgr systems make use of graphs to describe the spatial structure of space of individuals, while the system dynamics caused by the movement/interaction of individuals is captured by the parallel applications of some graph rewriting rules. in this frame, an illustrative example is studied and based on it, an eloquent comparison between the abstract rewriting machines and pgr systems is done. several further ideas to overcome the global computational effort needed for simulations, but still maintaining the overall ability for modeling are finally proposed. keywords: parallel multiset processing, abstract rewriting systems, p systems 1 introduction nowadays an increasing interest regards the study of the development of biological systems in which more species of individuals interact (usually to perform a certain global task). research ranging from completely different areas like the study of metapopulations (the study of groups of spatially separated populations of the same species that live in fragmented habitats and interact at some level) and hiv infections was done in essentially the same manner. traditionally, such studies were done by employing continuous models where (partial) differential equations were used to capture the dynamics of these systems. currently, the usage of discrete models where the system dynamics is captured from the collective actions of individual entities has been shown to be a promising choice (see [4], [6], [7], [9]). this is based on the fact that living organisms are spatially discrete and the individuals occupy particular localities at a given time. the interactions between individuals are strongly connected with their neighborhood relations. while characterizing these facts a basic issue regards the way the space is represented. simple models that involve no detailed spatial structure are in general analytically easily solvable. however, as the complexity of the reaction-diffusion dynamics grows, the models based on partial differential equations become intractable to be analyzed. on the other hand, integrating within the model a detailed spatial structure (as cellular automata models do, for instance) the setback comes in general from the impossibility to analyze the models except only by performing simulations. although such models have much greater biological reality, they suffer from the difficulty of generalization (hence of finding the exact behavior). this is especially important while formulating some practical testable predictions regarding a given model. p systems are formal computing devices that were initially inspired and abstracted from the cell functioning (see [10]). in general, p systems make use of multisets to represent the computational support. these multisets are placed inside the membranes which in their turn are disposed in some hierarchical tree structure. the (maximal) parallel applications of some multiset rewriting rules (particular to each membrane) were used to process the multisets. although these formal systems were extensively studied with respect to their computational power and efficiency, while representing some biological processes many difficulties arise. representing the data support as multisets essentially simplifies the structure of the environment and of the individuals interaction (the neighboring relations between the individuals are completely ignored), the focus being over the system dynamics. however, in copyright c© 2006-2009 by ccc publications 284 dragoş sburlan this case, two main assumptions are considered: the environment is homogeneous so that the concentration of the individuals do not change with respect to space and the number of individuals of each species in the environment is “adequately" large (hence the concentration of the individuals might be assumed to vary continuously over the time). moreover, the rules that describe the interactions between the individuals are assumed to be executed in a maximal parallel manner and governed by a global clock that marks equal steps. even if all these simplifications are useful while defining a computing formal framework, they are questionable if the aim is to model and simulate actual biological systems. this is way many new features that are meaningful to biologists were added to the original paradigm in order to extend its functionality and versatility for modeling. in order to cope with these issues, probabilistic/stochastic p systems were introduced (see [4], [14], [2]). in general, the main idea was to associate to the rules some weights describing how they should be applied at a given moment. for a particular rule, the weight gives the susceptibility of its execution at certain instant. hence, employing this principle to all interaction rules it sets up more realistic bounds of the nondeterministic application of the rules. the ultimate goal of this approach is to integrate the structural and dynamical characteristics of a real bio-system into the way the rules of the model are selected to be applied and executed step by step (preserving at the same time the unstructured computing support). although this method has in general good simulation time complexity it is inadequate if the interacting species are poorly represented, when there exist many “inactive" individuals (that are not the subject of any rule) with respect to the entire population of individuals, or when the environment is not homogeneous. 2 preliminaries we assume the reader familiar with the basic notions of p systems (one can consult [10] for more details), so that here we only recall some notions regarding the abstract rewriting systems on multisets. arm systems represent a variant of p systems which was proposed in order to perform simulations of some bio-chemical processes. later on, due to its modeling flexibility, it was used to study some symbiotic mechanism of an ecological system and even for proposing a novel theory of evolution. arms is a stochastic model that uses multisets to represent the bio-chemical support. multiset rewriting rules are used to describe the bio-chemical reactions. as opposed to the classical definition of p systems where the rules are applied in a nondeterministic, maximal parallel manner and with competition on the objects composing the multisets, in arms the rules obey the mass action law where the frequency of a reaction follows the concentration of bio-chemicals and a rate constant. consequently, the rules to be applied are chosen probabilistically from the rules set and each probability is given by the ratio of the total number of colliding chemicals of a reaction to the sum of the total number of colliding chemicals of every reaction in the rule; the applications of the rules remain parallel and with competition on the objects. more formally, an arm system is a construct π = (o, w, r) where o is the alphabet of objects, w represents the multiset of objects at the beginning of computation, and r is a set of multiset rewriting rules of type u k → v, where u ∈ o+, v ∈ o∗, and k ∈ r is the rate constant of the rule. for example, in case of a cooperative rule of type ri : aa + bb → cc + dd, where a, b, c, d ∈ in, a, b,c, d ∈ o, and a given multiset of objects m ∈ o∗, the probability of rule execution is defined as prob(ri) = kim a a mb b r , where ki is the rate constant (determined experimentally), ma and mb are the multiplicities of objects a and b in m, and r is a coefficient for normalizing the probabilities (i.e., ∑ i prob(ri) = ). in a straightforward way probabilities can be defined for any type of rules. the system π starts to evolve from the initial configuration (represented by w) by applying the rules in parallel, randomly selecting the rules but according to the probabilities computed as above. π is governed by a universal clock that marks equal time units. a simple example, meaningful for our work, is presented bellow. we ran various tests using an arm system π where o = {a, b,c, d, x , f}, and the set of rules r is given below: the initial configuration of π was w = abcd and in our tests we used several values for ki,  ≤ i ≤ . the system attempts to simulate the behavior of some interacting individuals, represented here as the objects a, b, c, and d, sharing the same environment. in addition, the individuals corresponding to the objects c and d (which are much less than the individuals corresponding to the objects a and b) share a localized patch in the environment. thus, we assumed the environment not to be homogeneous and we aimed to test the arm system ability to simulate such conditions. on parallel graph rewriting systems 285 if at least once the objects c and d interact (i.e., the rule cd k → f is applied) they will produce an object f which will trigger the conversion of all existing objects in the multiset into f (the rules r, r, and r). the rest of the rules (r till r) are used to slow down the rewriting rate of objects a, b, c, d, and x . r : ab k→ x r : f k→ f r : ac k→ x r : a k → a r : bd k → x r : b k→ b r : cd k → f r : c k→ c r : f x k → f f r : d k→ d r : fa k→ f f r : x k → x r : f b k → f f since we have assumed the existence in the environment of a patch of individuals corresponding to objects c and d, then we could make another further assumption. if the patch is "large enough” so that there exists at least two individuals c and d which can interact with each other but which cannot interact initially with the individuals a and b, then there exists a "significant” probability that the rule cd k → f is executed (assuming that k > k and k > k). while using multisets to represent the individuals in the environment we lose the structure, hence when simulating such systems we actually have to relay on the probabilities of the executions of the rules (which in their turn depend on some constants experimentally determined). in figure 1, one can notice the different behaviors of the same system and they are related to the usage of such probabilities. the diagrams shown on the left hand side present a simulation when the rule cd k → f was executed at least once, while the charts on the right hand side present a simulation when the rule cd k → f was not executed at all. although the model considered is very simple a similar situation might happen when representing some complex systems. even more, such situations might emerge during the system evolution and sudden shifts in the behavior might arise from some minor changes in the circumstances (as it is in the presented charts); if this is the case, then it would make almost impossible the precise identification of the rate constants associated to the rules. figure 1: two runs of system π . the results are presented on columns and they show the different behaviors of the same system when some minor changes in the circumstances happen. besides all of these issues, if the number of objects in the model decreases under a certain limit, the usage of probabilities to specify the way the rules are applied becomes inadequate. 286 dragoş sburlan 3 pgr systems aiming to tackle the mentioned issues, in this section we introduce a new model for simulating bio-systems that are composed by interacting individuals of various species in a given environment. denote by c the finite set of species in an environment represented here as a metric space (for simplicity, let r k, k ≥ , be the environment). let v ⊆ l ×c be the finite set of labeled individuals in the environment (l denotes a finite set of labels that uniquely identify the individuals in the environment). in addition, let f : v → rk, k ≥ , be a bijective mapping; for a node v = (n, l) ∈ v , the value h(v) denotes the position of the individual v in the environment. in addition let r ∈ r, r > , be a positive constant. based on the above definitions one can represent the environment and the individuals from within as a graph g = (v, e) where v = v and the set of edges is constructed as follows: for two nodes v, v ∈ v , if h(v) belongs to the open ball centered in h(v) and with radius r (i.e., h(v) ∈ b(h(v), r)), then there exists an edge from v to v. for simplicity we assume that g is connected, that is, for any two nodes m, n ∈ v there exists a sequence m = v, v, . . . , vt = n ∈ v such that h(vi) ∈ b(h(vi−), r), for  ≤ i ≤ t. for example, in figure 2 it is presented a set of 4 individuals which initially lay on an environment represented as r and the way the corresponding labeled graph is constructed. figure 2: the construction of the labeled graph representing the initial computational support motivated by these facts we introduce the following model. a parallel graph rewriting system (in short, a pgr system) is a construct γ = (c, g, r) where: • c = {c, . . . , ck} is a finite set of symbols; • g = (v, e) is the initial global graph – a connected graph such that v ⊆ l ×c is a set of labeled nodes and e ⊆ v ×v is a set of edges between nodes from v; • r is a finite set of graph rewriting rules. a graph rewriting rule r ∈ r is of the following type: r = (g = (v, e), g = (v, e)), where vi ⊆ li ×c, ei ⊆ vi ×vi, i ∈ {, }. the graphs g and g are connected graphs; g represents the neighboring relations between the individuals that are required for an interaction to take place and g represents the output of an actual interaction between the individuals represented in g. in addition we will assume that g and g are not arbitrary graphs, but rather they obey some physical constraints: any node from g and g cannot be the subject of more than a constant tr ∈ in edges – a condition that assumes the nonexistence of more than tr individuals in an open ball of radius r. a graph rewriting rule r = (g, g) ∈ r can be applied on a graph g if g is label isomorphic with one subgraph gs = (vs, es) of g, that is, there exists a bijective mapping h : v → vs such that h((m, c)) = (n, c) and h−((n, c)) = (m, c), where (m, c) ∈ v, (n, c) ∈ vs and such that any two nodes u, v ∈ vs are adjacent in gs if and only if h(u) and h(v) are adjacent in g (see figure 3). in other words, a graph rewriting rule r can be applied on g iff the left-hand side rule’s graph is "contained" in g both as layout and as corresponding node labels (via an edge/label-preserving bijection). on parallel graph rewriting systems 287 figure 3: a graph g = (v, e) denoting the computing support and a graph rewriting rule r = (g, g). the sites where the rule r can be applied in g are explicitly figured. if gs = (vs = {(n, b), (n, a)}, es = {((n, b), (n, a))}) then g is label isomorphic with gs. the neighborhood set of degree k =  of gs is b = {(n, a), (n,c), (n,c), (n, b)}. the following steps are accomplished when a rule r is applied over g: • eliminate gs from g (all the nodes from vs are eliminated from v ; all the edges of the type (v, vs), v ∈ v , vs ∈ vs are deleted from e); • add g to g (some relabeling of the nodes from g is required in order to avoid duplicates of nodes at multiple application of r). all the (relabeled) nodes and edges of g are added to g; • add a set of edges from some nodes of v to some nodes of v \ (vs ∪ v). the edges are established as described below. for the graph gs let us define the neighborhood set of degree k bk = {v ∈ v \ vs | there exists a path of length less or equal with k from v to a node u ∈ vs} as we mentioned above, the output of the application of a rule consists of new individuals that, by hypothesis, at the moment of their apparition it is assumed to belong to the same vicinity. how big is that vicinity and how the new individuals are related to the rest depend on many factors among which we just mention the type of the rule and the environment. consequently, in our framework, the set bk is useful when defining the new neighborhood relations triggered by the application of a rule. by some straightforward physical arguments, the output graph g of the rule r is likely to be "connected" to g via the nodes from bk. however, for simplicity, we will consider the neighborhood set of degree  in our simulations 1. let e = {(n, n) ∈ e | n ∈ b, n ∈ vs}. then, a number equals with card(e) of random edges from the nodes of g to the nodes from b are added to g but such that any node considered is not the subject of more than tr ∈ in + edges. starting from the initial configuration (the initial global graph g), the system evolves according to the rules from r and the current labeled graph in a non-deterministic parallel manner (but not necessarily maximal). the labeled graph of γ at any given moment constitutes the configuration of the system at that moment. for two configurations ga and gb we can define a transition from ga to gb if we can pass from ga to gb by applying rules from r. the problem of determining whether two graphs are isomorphic is referred to as the graph isomorphism problem. although this problem belong to np it is neither known to be solvable in polynomial time nor it is npcomplete. a generalization of this problem (that is used in our formalism) is the subgraph isomorphism problem which is np-complete; hence the known deterministic algorithms for this problem are exponential. 1if the studied individuals are particles that perform the brownian motion, then at each application of a rule a random positive integer k can be generated and correspondingly a new neighborhood set can be defined; hence, the set bk might be also useful to describe the random particle movement in the environment. 288 dragoş sburlan remark 3.1. there is a physical motivation to assume that after applying a rule of the system, the newly produced objects (that correspond to the output nodes of the rule) belong to the same vicinity, hence the right hand side graph of any rule should be complete. remark 3.2. for a given pgr system, as much as the radius r grows (hence the number of edges in the initial global graph is close to n(n−)  where n is the number of the nodes, that is, the initial global graph is "almost” complete) and the degrees of the neighboring sets grow as well, the result of a simulation is as “similar" to the one obtained using parallel multiset rewriting. this is because multisets can be seen in our formalism as complete graphs, hence any individual in the system is in a neighboring relation with any other individual (hence, they can interact if proper rules exist). 4 pgrs simulator and a test case the simulator implements the model introduced in section 3. its main characteristics regard the definition of the rules set by using an xml file, and the possibility to save/load intermediate configurations. the simulator is written in java language hence it benefits of cross-platform compatibility, parallelism, and possibility to distribute the computational effort. the task that has the most computational resource consumption is the subgraph isomorphism problem which is addressed whenever a rule r = (g, g) is selected for application and the set s of all the subgraphs of the global graph that are isomorphic with g has to be determined. even more, whenever a subgraph g ∈ s is selected to be rewritten by r, a run through all the elements of s has to be performed in order to eliminate those subgraphs that have some nodes from g (a task useful when multiple applications of the same rule are performed). considering all these matters for all the rules from the rule set and a relatively small global graph, the overall time complexity for simulating just one computational step is exponential, hence in general unfeasible. nevertheless, if the left hand side graphs of the rules from the rule set are very simple (i.e., less than 4 nodes) and the global graph contains at most hundreds of nodes, the simulation is viable. moreover, taking into account that the problem can be easily parallelized one can divide the problem into smaller instances and distribute them over a network. let us consider the following pgr system γ = (c, g, r) where • c = {a, b,c, d, f, x }, • r = {r, r, r, r, r, r, r, r} is defined as follows: r : (n, a) (n, b) (n, x ) y yy r : (n, a) (n,c) (n, x ) y yy r : (n, b) (n, d) (n, x ) y yy r : (n,c) (n, d) (n, f) y yy r : (n, f) (n, x ) (n, f) (n, f) y yy y r : (n, f) (n, a) (n, f) (n, f) y yy y r : (n, f) (n, b) (n, f) (n, f) y yy y r : (n, f) (n, f) yy in our tests, the initial global graph g was build to obey some properties. first of all, a random graph g ′  was generated and this graph contains 500 nodes labeled only with a and b (for each test case, the apparition of these on parallel graph rewriting systems 289 figure 4: the results of 100 simulations of different gpr systems but having the same properties. the minimal and maximal obtained values are explicitly marked. labels are equally probable) and 2000 edges. a second graph g′′ was generated and this graph contains 10 nodes labeled only with c and d (also in this case, the apparition of these labels are equally probable) and 30 edges. finally, g′ and g ′′  were merged together in order to form g by connecting 10 randomly chosen nodes from g ′  with 10 randomly chosen nodes from g′′ . we ran the simulator for 100 times, considering for each run a new initial global graph generated as above. in figure 4 there are represented the minimal and the maximal values at each step of the simulation for the objects a, b, c, and d. any particular simulation graphic from our test case lay between the boundaries established. 5 conclusions simulations performed using pgr systems in some cases give more accurate answers than arms simulations because they explicitly use the spatial distribution of individuals (hence the neighborhood relations can be extensively expressed). however the price to pay while using pgr systems regards the computational effort which in their case is exponential as time complexity. nevertheless, for some cases when the number of interacting individuals in the environment is small and they are not dense, the pgr systems might be useful for performing simulations. in order to handle these issues, a hybrid system combining features from the arm and pgr systems might be proposed. two directions could be taking into account: • one can use alternatively an arms-type simulation whenever the number of individuals from all the species is large and a pgrs-type simulation whenever the number of individuals from certain species goes below some threshold; in this case the newly obtained system uses in a more careful manner the probabilities for the rules executions. • one can use in parallel an arms-type simulation over a multiset of many individuals and a pgrs-type simulation on relatively small instances of graphs. then one can consider a time sequence and at each moment in the sequence one can merge the arms configuration with the multiset of labels of the nodes from the graph (or one can exchange some data between these simulations). in this way, the newly obtained hybrid systems become more robust against some unexpected changes in the behavior (which might be triggered by some minor changes). acknowledgments the author gratefully acknowledges the support by cncsis-idiei grant, romanian ministry of education, research and innovation, no. 804/2008. bibliography [1] d. besozzi, g. mauri, d. pescini, c. zandron, dynamical probabilistic p systems. int. j. found. comput. sci., 17, 1 (2006), pp. 183–204. [2] d. besozzi, p. cazzaniga, g. mauri, d. pescini, modelling metapopulations with stochastic membrane systems, biosystems, 91 (2008), pp. 499–514. 290 dragoş sburlan [3] b. bollobas, modern graph theory, springer, 1991. [4] m. cavaliere, i.i. ardelean, modeling biological processes by using a probabilistic p system software, natural computing, 2, 2 (2003), pp. 173–197. [5] h. ehrig, introduction to the algebraic theory of graph grammars, lecture notes in computer science 73 (1979), pp. 1–69. [6] p. frisco, the conformon-p system: a molecular and cell biology-inspired computability model, theoretical computer science, 312, 2-3 (2004), pp. 295–319. [7] p. frisco, r.t. gibson, a simulator and an evolution program for conformon-p systems, proc. of the th int. symp. on symbolic and numeric algorithms for scientific computing, 2005, pp. 427–430. [8] d.t. gillespie, exact simulation of coupled chemical reactions, j. physical chemistry, 81 (1977), pp. 2340–2361. [9] v. manca, l. bianco, biological networks in metabolic p systems. biosystems, 91, 3 (2008), pp. 489–498. [10] g. păun, membrane computing. an introduction, springer, berlin, 2002. [11] d. sburlan, parallel graph rewriting systems, proc. of the th bwmc, seville, spain, 2009, in print. [12] y. suzuki, h. tanaka, s. tsumoto, analysis of cycles in symbolic chemical system based on abstract rewriting system on multisets, proceedings of international conference on artificial life 5 (alife 5), 1996, pp. 482–489. [13] y. suzuki, j. takabayashi, h. tanaka, investigation of tritrophic system in ecological systems by using an artificial chemistry, j. artif. life robot., 6 (2002), pp. 129–132. [14] y. suzuki, h. tanaka, modelling p53 signaling pathways by using multiset processing, applications of membrane computing (g. ciobanu, g. păun, m. pérez-jiménez, eds.), springer, berlin, 2006, pp. 203–214. dragoş sburlan graduated the faculty of mathematics and informatics, the master program computational mathematics and modern computer science technologies at the ovidius university of constanta, romania. he defended his european phd in computer science at the university of seville, spain. he followed several research stages (leiden institute of advanced computer science, holland and staki, budapest, hungry) and he is involved in several national and international research projects. currently, he is senior lecturer at the faculty of mathematics and informatics, ovidius university of constantza. his research interests include formal languages, theory of computing, natural computing, and software engineering. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 2, pp. 137-147 presentation of an estimator for the hurst parameter for a selfsimilar process representing the traffic in ieee 802.3 networks ginno millán, gastón lefranc pontificia universidad católica de valparaíso escuela de ingeniería eléctrica avda. brasil #2147. valparaíso chile e-mail: ginno.millan@gmail.com and glefranc@ucv.cl abstract: the hypothesis for the existence of a process with long term memory structure, that represents the independence between the degree of randomness of the traffic generated by the sources and the pattern of traffic stream exhibited by the network is presented, discussed and developed. this methodology is offered as a new and alternative way of approaching the estimation of performance and the design of computer networks ruled by the standard ieee 802.3-2005. keywords: computer networks, ieee 802.3-2005 standard, network traffic, selfsimilar process. 1 introduction the positioning and consolidation of ethernet as a predominant standard in the local and extensive coverage computer network field against some traditional technologies, such as frame relay, dqdb and atm, are facts that are explained by its main characteristics, i.e. compatibility and interoperability among some ethernet equipment from different speeds, high performance, scalability and self-configuration capacity, independence ip addressing and, undoubtedly, the usual scale economy. ethernet, initially at 3 mb/s, has evolved from 10 mb/s to 10 gb/s in the last twenty-two years. the latter does not consider the ieee 802.3ba standard, which specifies the ethernet at 40gb/s and 100 gb/s since 2007. furthermore, it has also evolved from using simple bridges developed for the network bridging with identical physical protocols and average access to n*10 gb/s switches [1], [2] two very interesting, striking and critical aspects are involved in this continuous evolution underwent by the ethernet networks. the first is related to the complete abandonment of the origin of the halfduplex shared system, which has yielded to full-duplex links. on the other hand, the second involves its extension, as ethernet has evolved from lan range distances to wan range coverages [3]. furthermore, although both changes have gradually occurred, they result quite critical from the ethernet point of view, as both represent the disappearance of the mechanism controlling the media access regulated by the csms/cd protocol and also assume a strong and radical change in the transmission media, which tends to the massive and total use of the optical fiber to support the ever demanding application of enormous bandwidths. in general, the local area networks, particularly ethernet, were developed as high capacity interconnected networks; in contrast to wan network technologies that are based on switching and transmission flow generally lower than those available in the lan networks. however, the evolution of the different technologies used in both environments now converges in solutions based in ethernet and its different specifications. therefore, the current ethernet networks are switched and almost totally made from fullduplex links, but they also incorporate the multiplexing according to the ieee 802.1q standard and bear transmission distances similar to those involved in the conventional wan links. this degree of evolution copyright © 2006-2009 by ccc publications 138 ginno millán, gastón lefranc is largely ascribed to great development achieved by the ethernet switches, as there has not only been an increase in the operational transparency and simplicity degree but also a direct effect in the incorporation of additional functionality to the switching, which, from the standard point of view, involves the extension of the format of the original frame and the incorporation of labeling for vlan and the priority establishment for service classes, the size increase of the csma/cd carrier signal, the incorporation of burstiness packets to try to compensate the network velocity loss caused by the extension bits of the carrier and, most of all, in the complete abandonment of the restraint and solution conflict mechanism given by the exponential backward algorithm [4], [5]. on the other hand, the migratory tendency towards ethernet networks without shared media is confirmed through the incorporation of the ieee 802.1x, ieee 802.1w standards (rstp currently included in the ieee 802.1d standard) and the ieee 802.1s standard (mstp; currently included in the ieee 802.1q standard). these establish the dedicated full-duplex links as an essential requirement for the correct operation. it is important to point out these dedicated links are not only necessary to obtain the best benefits from the network, but also to ensure the logical link control (llc), simplifying the protocols and making possible the rapid convergence mechanisms in layer two. one example of the latter is the ieee 802.3e standard (ethernet at 10 gb/s), which does not involve the use of half duplex links in the specifications as the ieee 802.3z (ethernet at 1000 mb/s). the use in the ieee 802.3z is strictly related to the compatibility with the equipment database previously installed and its aim is to work as a platform in the migration or technological transition processes. it is important to remember that the ieee 802.3z is the latest specification in the ieee 802.3 standard giving native support to this device communication method. it seems quite interesting the diffusion and flooding dominated in the traditional ethernet networks as the basic and valid mechanisms to establish the presence or absence of stations; nowadays, however, a minimum diffusion of the frames is sought due to the performance degradation and the exhaustive control of the traffic flow, the same reasons that must be avoided in the wan networks. new technologies are similarly incorporated to the large covering networks, which are typical of the lan environments due to their robustness and great price/assistance relation. these are well established in access and metropolitan environments and, increasingly, in wan environments [6]. all these arguments guarantee a reassessment of the study of the benefits of the csma/cd access control mechanisms, in terms of the impacts of the performance in the current commuted environments, i.e. considering the performance as the useful information quantity that the network is able to transport in relation to the real quantity of transported bits, as well as a characterization of the nature of the traffic under study in terms of a performance pattern capable of describing the temporal evolution as well as the implications on the previously defined performance. the performance parameter is considered as an active form of measuring the benefits from a network, as this is one of the crucial aspects in the global analysis of the communication systems, considering its impacts in the final users. on the other hand, the traffic characterization among the networks is considered, because the performance observed in the performance parameter depends on it, thus becoming a key factor for the characterization. furthermore, a new approach to carry out the modeling processes of the ethernet networks is justified, as in terms of the evolution previously stated, it is inferred that the natural successor of the ieee 802.3u standard (fast ethernet) must be the ieee 802.3z standard (gigabit ethernet), the latter will give way to the access and wan environments, the ieee 802.3ae (ethernet at 10 gb/s) and ieee 802.3ba (ethernet at 40 gb/s and 100 gb/s) standards, respectively. the impacts associated to these technological migrations must be adapted and properly evaluated, sized up and classified in terms of the impacts on the installed equipment databases. this must be previously defined before any adoption. however, the existence of self-similar traffic patterns is accepted on the empirical fact that these are characterized by the constant presence of package traffic bursts through different time scales and that the characteristic property of the self-similar processes is the long range dependence (lrd), which presentation of an estimator for the hurst parameter for a selfsimilar process representing the traffic in ieee 802.3 networks 139 is observed when the trunking level increases [7]. then, the fractal performance of this type of traffic does not coincide with the performance traditionally modeled through the poisson processes, which are characterized by the absence of bursts and a low variability that is reflected in the temporal independence between the samples. in short, these are processes that show a short-term temporal dependence rejecting the relation between temporarily distant processes, i.e. these are null memory processes that, therefore, do not considered as valid the presence of any pattern representing the sent traffic. then, considering the same arguments and the fact the self-similar phenomena show the same aspect or performance when visualized with different enlargement degrees or different scales of a certain dimension, and also that the temporal series showing self-similarity with respect to time are the object of interest in computing networks, a degree of selfsimilarity must be established for such series, which must be expressed using a parameter representing the de-growth speed of the self-correlation function. this responds to the fact that a time series is self-similar when the aggregated series involves the same self-correlation function as the original series. the latter is achieved using the hurst parameter, h, which can be estimated through different methods, being the whittle the one with the highest statistical rigor. the whittle estimator calculation may be carried out from different algorithms, all of which need the underlying stochastic process. in this paper a variation of this estimator is proposed, which allows the obtaining of the self-similarity degree with an acceptable commitment relation between the cost of the computing model (higher inconvenient when obtaining the whittle estimator no matter the chosen method) and the estimation quality. this proposal formally involves the modification of the whittle local estimator, or semi-parametric gaussian estimator, of the memory parameter in the short-term standard process stated in [8]. then, all the advantages of the original technique are expected, which shows all its main attributes as an alternative to the regression technique of the periodogram logarithm shown in [9]. it is expected that under less restrictive suppositions, an asymptotic efficiency profit form is shown. therefore, an analysis of the asymptotic performance of the original semi-parametric gaussian estimator must be carried out on the memory parameter in processes with cyclic or seasonal memory, thus allowing divergences or spectral zeros of asymmetric type. then, modifying the original algorithm, the consistency and asymptotic normality needed for the characterization of the traffic flows will be obtained. 2 problem definition the traffic analysis based on the queuing theory has resulted in great help to the network design and the system analysis when carrying out capacity planning and performance prediction [10]. however, there are some cases in the real world in which all predicted results from a queuing analysis significantly differ from the performance observed in reality [11]. in this sense, it must be remembered the validity of the analysis based on the queuing theory depends on the poisson nature of the data traffic, and when dealing with poisson processes, the representation of the length of each arrival and the time between each frame arrival are represented by independent and exponentially arranged random variables. therefore, these are null memory models. thus, being this the case, of models in those that the probability of an arrival in an instant, is independent of the instants of previous arrivals, property that is not completed in the nets of commutation of packages. however, it is acknowledged the objective of these suppositions correspond to relatively simple models, from an analytical point of view. according to the results obtained in [12], different authors have studied the existence of a temporal dependence and the establishment of the great impact involved in the assistance of a queuing system outstands. therefore, there is plenty of literature on entry traffic models showing more or less complex correlation structures, which are used in cases when the telecomputing system model under study allows an adequate analytical treatment. in any case, all these models, mainly of markovian nature, reject the correlation from a given temporal separation, even when it may be arbitrarily increased at the expense of making the model complicated with additional parameters. 140 ginno millán, gastón lefranc in [13] and [14] is shown that after several measurements on an ethernet network, the traffic shows a self-similar or fractal nature (understanding traffic as the frame quantity in the network by a time unit). this becomes apparent with the existence of a long-term correlation. the self-similar characteristic of the traffic in wan networks is shown in [15] and [16]. furthermore, the fractal nature of the data flow from the protocols involved in the signaling system 7 (ss7) in the common channel signaling networks is shown in [17]. the self-similar nature of the traffic due to the www is shown in [18], using the experimental evidence, the possible causes and origin are shown. the self-similar nature in the traffic of the variable bit rate (vbr) is shown in [19] and then in [20]. in [21] is shown that the probability distribution that is followed by the queuing size of a multiplexor shows a weibull-type asymptotic fall when using certain self-similar process as the entry traffic. then, in [22] it is shown this fall is even slower and hyperbolic when other self-similar processes are used. reference [23] shows the effective band width estimated through markov models, where the queue size distribution show an exponential fall, highly underestimates the loss rate of cells in different orders of magnitudes in atm networks. an outstanding long-term positive correlation in the added traffic is shown in [24], from the analysis of a voice and data multiplexer, where some high delays were obtained, higher than those obtained using the poisson models. in reference [25] the number of entries is shown, counted in adjacent time intervals that result in the superposition of multiple independent and homogeneous voice sources susceptible to be treated through updating process models. this is a complex process that includes strong correlations and involves a significant impact in the assistance of the studied system. the interesting thing about this and other studies is that they highlight the impact the long-term temporal dependence have on the assistance of the communication networks, which is intrinsic to the most diverse traffic types, in contrast to other models that do not include this due to analytical simplicity (renewal processes, for example) or that show a more analytically complex correlation structure (such as the markov or generally self-regressive models). all these are known as short range dependence models (srd). furthermore, the main problem these traditional models involve is that they require a great number of parameters to characterize the strong correlations existing among different traffic types in a network. in this study, it is clearly stated that while the parameter number increases, the analytical complexity also increases (not necessarily in a lineal form) and that there is also a difficulty to give a physical interpretation to all the parameters and to estimate them from empirical data. the self-similarity and fractability characteristics help to describe a phenomenon where certain object property is preserved in relation to the temporal and spatial scalability and in a self-similar and fractal object. thus, the magnified parts are similar to the form of the complete object, where the similarity is measured in some proper form. then, the simpler form of self-similarity is obtained by reconstruction through the iteration of a certain procedure. if this process is indefinitely repeated for each new segment, any portion of the object can be magnified to exactly reproduce a bigger portion, no matter how small the portion. this property is known as “exact self-similarity” [26]. no exact selfsimilar characteristics are intended to be observed in a highly random process such as the one involving the packets arrival to a data network; however, the traffic observed as sample traces from a stochastic process are considered and the similarity is only restricted to certain specific statistics of the temporal series though adjusted in scale. furthermore, the exact self-similarity in abstract mathematical objects and approximate self-similarity for each considered specific execution are also discovered. no exact self-similarity is expected from the network traffic under study; however, a self-similar stochastic behavior is expected and, according to this, the second order statistics may be used to capture the process variability in order to determine the self-similarity rate. in fact, the scale invariance may be defined in terms of the self-correlation function, as the polynomial decrease of this function (in place of the exponential) represents a long-term dependence manifestation similar to the self-similarity, and constitutes the view from which all self-similar presentation of an estimator for the hurst parameter for a selfsimilar process representing the traffic in ieee 802.3 networks 141 processes must be interpreted through the development of this investigation. on the other hand, in relation to the fundamental problem of the self-similar process analysis, or more precisely, to the temporal series showing lrd, i.e. the estimation of the hurst parameter (h), the methods involved in the literature can be classified in the following two groups: 1. geographical methods of lineal regression. used to estimate a statistic t (x) behaving asymptotically for a determined group of x values and, therefore, are based in obtaining the straight line that better adjusts (for that group of x values) to the log(t (x)) against the log(x) using the least square lineal regression, thus obtaining the h parameter value directly from the gradient value of that particular straight line. 2. methods based on maximum likelihood estimators (mle) for h. these help to minimize the differences between the periodogram of the concerning series and its theoretical spectrum. the methods involved in the first group are relatively simple and algorithmically fast to implement. however, their main disadvantage is that an asymptotic performance must be first estimated from a finite sample number, which makes the estimation of the h parameter directly and considerably dependant from the right selection of the x value group. therefore, the graphic representations result crucial for the confirmation of correspondence between the x values and the lineal performance area and the proper adjustment of the straight line for the represented points. furthermore, it is important to point out these methods only allow a precise estimation of h, as the resulting confidence intervals involves a high cost for the computing resource and long processing periods, both as a result of the use of the intensive graphic-type methods. the mle-based methods, on the other hand, though more complex and a higher computing cost, result more flexible and efficient from the statistical inference point of view, as they allow the obtaining of confidence intervals for the h estimated values. therefore, these are the most common methods. this investigation will deal with mle-based methods to solve the confidence intervals directed to obtain the first approximations for an h value, which will be then adjusted using the proposed method. in any case, the methods from the first group will be thoroughly analyzed and the results will be contrasted with those resulting from the mle and the adjustment suggested by the proposed method. then, and as the analysis starts using mle, it is important to stress these are methods designed to minimize the differences between the periodogram of the series and the parametric model suggested for the theoretical spectral density. besides, the exact estimation of the mle is quite expensive form the computing point of view, so likelihood gaussian functions are generally used (gaussian mle). however, even when these kind of functions are considered, the computing costs results quite high, thus the approximations based on gaussian mle are generally used and the most widely used approximation is the whittle approximation; therefore, the suggested method is based on the modification of one of the variables for the result. 3 justification and proposition of an efficient whittle estimator being f(λ, θ) the parametric form of the spectral density of a gaussian stationary process, xt where, θ = (θ1, . . . , θm) is the parameter vector to estimate. then, being i(λ) the periodogram of samples defined by i(λ) = 1 2πn ∣∣∣∣∣ n∑ t=1 xte jtλ ∣∣∣∣∣ 2 (1) the approximated mle o whittle is the vector θ̂ = (θ̂1, . . . , θ̂m) (2) 142 ginno millán, gastón lefranc minimizing the function q(θ)=̂ 1 2π {∫ π −π i(λ) f(λ, θ) dλ + ∫ π −π log[f(λ, θ)]dλ } (3) in practice, the estimation of the whittle estimator is done choosing an adequate scale parameter θ1, complying with f(λ, θ) = θ1f(λ, θ ∗) = θ1f ∗(λ, η) (4) thus annulling the second addend of (3), i.e. ∫ π −π log[f(λ, θ∗)]dλ = ∫ π −π log[f∗(λ, η)]dλ = 0 (5) where η = (θ1, θ2, . . . , θm) y θ∗ = (1, η) in [27] is shown the scale parameter is given by θ1 = σ2ε 2π , being the mean-square prediction medium (mspe). on the other hand, the discreet version of the whittle estimator is suggested in [28], which is approximated to 3 using a riemann addition in the frequency range given by λk = 2πn−1k, with k = 1, 2, . . . n∗ (being n∗ the integer part of (n − 1)/2). then, the function to be minimized is given by the expression q̃(θ1, h) = 4π n { n∗∑ k=1 i(λk) f(λk, θ1, h) + log[f(λk, θ1, h)] } (6) the estimated h parameter is obtained through the selection of the adequate scale parameter, ĥ, a value that minimizes the following expression q̃∗(h) = q̃(1, h) = n∗∑ k=1 i(λk) f(λk, 1, h) = n∗∑ k=1 i(λk) f∗(λk, h) (7) where it is verified that f∗(λ, h) = 1 θ1 f(λ, θ1, h) = 2π σ2ε (8) the following disadvantages of the whittle estimator stand out in its conventional forms, from this point of view: 1. determination of the parametric form of the spectral density. 2. high estimation period due to graphic method use. the central limit theorem for self-similar processes result quite useful from the perspective of the application of the whittle estimator to processes where it is not possible to assure anything in relation to the spectral density, as it is a good approximation for non-gaussian series, which allow the application of aggregated series to any result from the pure self-similar gaussian processes, as shown in [29]. then, it seems quite interesting this theorem allows the supposition that for a temporal series of n size, whose self-correlation shows a lrd hyperbolic fall, if m and n/m are sufficiently big and the variance is finite, then the fgn process is a good approximation for the aggregated sequences of the series, even when it does not represent a gaussian approximation [30]. the latter is the base for the variant of the whittle estimator known as the added whittle estimator, which gives a more robust and less biased form of the whittle estimator when no information on the presentation of an estimator for the hurst parameter for a selfsimilar process representing the traffic in ieee 802.3 networks 143 exact parametric form of the spectral density is available. in other words, a shorter representative series is obtained given by the expression x (m) k = 1 m (k+1)m−1∑ i=km xi 0 ≤ k ≤ [n/m] (9) and then the whittle estimator is used, considering the fractionary gaussian noise (fgn) as the parametric model of its spectral density. however, in spite of the fact of using a shorter series considerably reduces the computing cost, the observed problem is that the variance of the estimator increases and so does the self-similarity degree, thus reducing the pattern representativeness degree. another problem associated to the method is the impossibility to a priori know the value of the appropriate m. however, in this last sense, a method to represent the estimation of the h parameter are shown in [31], which were obtained for different m values, and finding a region where the graphics are approximately plain. the local whittle estimator is shown in [32], which in contrast to the whittle estimator, represents a semi-parametric estimator that only specifies the parametric form of the spectral density for those frequencies close to zero, i.e., f(λ) ∼ g|λ|1−2h (10) when λ → 0 when replacing f(λ, h) given by (10) in (2), and integrating up to the 2πm n frequency, with 1 m + m n → 0 when n → ∞, the following is obtained q(g, h)=̂ 1 m m∑ j=1 [ i(λj) gλ1−2hj + log(gλ1−2hj ) ] (11) when replacing the g constant by its estimation, given by ĝ = 1 m m∑ j=1 i(λj) λ1−2hj (12) the function to be minimized is obtained, i.e., r(h)=̂q(ĝ, h) − 1 (13) r(h)=̂ log   1 m m∑ j=1 i(λj) λ1−2hj   − (2h − 1) 1 m m∑ j=1 log(λj) (14) nevertheless, the problem still remains, as the selection of the m value results critical and from this value the bias and variance depend, and yet again the bias and variance problem shows. however, as long as m increases, the estimated value for h will quickly converge to the real h value but the spectrum form will continuously fall apart from (6) and the srd effects will be even greater, thus increasing the bias. then, as in the previous method, one must choose to represent the estimated value of h against m and find the plain region of the graphic. as it has been observed from the methods about the whittle estimator, all of them require the minimization of an expression, (6) or (14). the most obvious way of carrying out such minimizations is to assess these expressions for a certain number of equidistant h values (q), which will depend on the selected resolution. however, it must be pointed out that as lightly higher number of samples are needed for the algorithm to be immediately translated into a very high computing cost. 144 ginno millán, gastón lefranc then, a reduction through an algorithm is suggested to reduce the computing cost, which is aimed to decrease the number of points to be assessed. therefore, the convex characteristic of the function in all the domain [0.5, 1(( will be considered also that the minimal is then unique. then, a searching method by bi-section used on the function derivative will have to allow the number of evaluated points to be around log 2(q), which will be of advantage in a significant sample saving, and at the same time it does not settle in relation to the bias and variance commitment. therefore, considering the assessment of the derivative in a hi point may be approximated by a difference coefficient for a sufficiently small increase, h, i.e., q′(hi) ≈ q(hi + h) − q(hi) h (15) for h → 0 it is derived the main work hypothesis is related with the establishment of a self-similarity degree based on the whittle estimator; however, due to the complexity involved in the bias and variance commitment behind all models, it is then suggested that in a reduced point spectrum it is possible to give an answer with an acceptable level of commitment. therefore, in order to obtain confidence intervals, and taking advantage of the convexity of the function to minimize in the whole dominium [0.5, 1(, which implies the presence of a unique minimum, it will be considered that when estimating a unique h parameter, if ĥ represents the value minimizing the q(h) function and that h0 represents its real value, then (ĥ − h0) → n(0, σh) (16) where the σh parameter will be defined by: σ2h = 4π n {∫ π −π [ ∂ log[f(λ, h)] ∂h ]2 dλ }−1 h=h0 (17) then, the estimation of the derivative given by (14) may also approximate through a difference coefficient for a sufficiently small increase, h. however, in this case, the chosen value for θ1 different, because a cancellation is produced. then, σ2h = { n∗∑ k=1 [ ∂ log[f(λk, θ1, h)] ∂h ]2}−1 h=ĥ (18) where the [33] it is suggested in the end that σ2h =    n∗∑ k=1 [ log[f(λk, θ1, ĥ + h)] ∂h − log[f(λk, θ1, ĥ)] ∂h ]2  −1 (19) 4 conclusions the hypothesis on the existence of a process with a temporary structure of long-term memory was presented, discussed and developed, which was representative of the independence between the randomness degree of the traffic generated by the sources and the pattern of the traffic flow shown by the network. the latter has been shown to be considered as a new form and alternative to cover the performance and network design estimation topics that are ruled by the ieee 802.3-2005 standard. the traditional models based on the poisson processes, or even more general, based on short-term dependency processes are unable to describe the performance of he current data networks, particularly presentation of an estimator for the hurst parameter for a selfsimilar process representing the traffic in ieee 802.3 networks 145 those related to the switched ethernet networks according to the ieee 802.3-2005 standard. consequently, it is necessary to redefine the study of the load systems, considering self-similar entry processes as a result of the self-similar traffic demand imposing new requirements in the network design, especially in what the buffering strategies are concerned. all methods traditionally used to assess the whittle estimator show the disadvantages of the need to know the parametric form of the spectral density, as well as a high computing cost resulting from the intensive use of graphic methods. it is estimated these problems may be solved as far as it is feasible to introduce an algorithm that reduces the number of points to be assessed. this does not only mean reduced costs in computing processing, but also a new alternative to be considered in the study of the self-similar or fractal traffic consideration on the benefits of a network. a convex function to carry out the minimization of the generalized function of the local whittle estimator is suggested for a delimited domain, which involves the advantage to include a sole minimum that is completely individualizable, and thus using a bi-sectional searching method applied on the derivative of the function, should allow the determination of a point around which all values fluctuate. therefore, this allows the availability of a plain region in which the value of the h parameter is perfectly approximated by a difference coefficient. the latter must be translated into savings in the computing costs and processing time that endorse the new proposed model. the development of the simulations is being tackled through literature search as a first stage, which must clearly include the simulation techniques and statistical analysis for long-term dependency series, as it is considered it is not enough to be able to deduce the results from some aggregations, but there must be a standardization of the procedures to specifically study the long-term dependencies. this area requires urgent attention, as most of the operational costs involved in the computing capacity directly depend on it. bibliography [1] metcalfe, m. and boggs, r. ethernet: distributed packet switching for local computer networks.communications of the acm, vol. 19 n°7, 1976. [2] ibańez, g. contribución al diseńo de redes de campus ethernet autoconfigurables. ph.d. thesis, dept. ing. telemática, universidad carlos iii de madrid, madrid, espańa, 2005. [3] ibańez, g. contribución al diseńo de redes de campus ethernet autoconfigurables. ph.d. thesis, dept. ing. telemática, universidad carlos iii de madrid, madrid, espańa, 2005. [4] garcía, j., ferrando, s., and piattini, m., redes para proceso distribuido, madrid, ra-ma, pp. 127160, 1997. [5] zacker, c., redes. manual de referencia, madrid, mcgraw-hill, pp. 275-341, 2002. [6] halabi, s.,metro ethernet. the definitive guide to enterprise and carrier metro ethernet applications. indianapolis, cisco press, pp. 1, 2003. [7] leland, w., taqqu, m., willinger, w., and wilson, d., on the self-similar nature of ethernet traffic, ieee/acm trans. networking, vol. 2, no. 1, pp. 1-15, 1994. [8] robinson, p., gaussian semiparametric estimation of long-range dependence,annals of statistics, vol. 3, no 1995b, pp. 1630-1661, 1983. [9] geweke, j., and porter-hudak, s., the estimation and application of long memory time series models. j. timer ser. anal. 4, pp. 221-238, 1983. 146 ginno millán, gastón lefranc [10] stallings, w., internet y redes de alta velocidad. rendimiento y calidad de servicio. 2nd ed., madrid, pearson prentice hall, pp. 224-225, 2004. [11] stallings, w., internet y redes de alta velocidad. rendimiento y calidad de servicio. 2nd ed., madrid, pearson prentice hall, pp. 224-225, 2004. [12] kleinrock, l., communication nets, new york, mcgraw-hill, 1972. [13] leland, w., taqqu, m., willinger, w., and wilson, d., on the self-similar nature of ethernet traffic,computer communications review, vol. 23, pp. 183-193, 1993. [14] leland, w., taqqu, m., willinger, w., and wilson, d., on the self-similar nature of ethernet traffic, ieee/acm trans. networking, vol. 2, no. 1, pp. 1-15, 1994. [15] klivansky, s., mukherjee, s., and song, c., factor contributing to self-similarity over nfsnet, georgia institute of technology, 1995. [16] paxon, v., and wilson, d., wide-area traffic: the failure of poisson modeling, ieee/acm trans. networking, vol. 3, no. 1, pp. 266-244, 1995. [17] duffy, d., mcintosh, a., rosenstein, m., and willinger w., statistical analysis of ccsn/ss7 traffic data from working ccs subnetworks, ieee journal on selected areas in communications, vol. 12, pp. 544-551, 1994. [18] crovella, m., and bestavros, a., self-similarity in world wide web traffic: evidence and possible causes, ieee/acm trans. networking, vol. 5, no. 6, pp. 835-846, 1997. [19] garret, m., and willinger, w., analysis, modeling and generation of self-similar vbr video traffic, proc. acm sigcomm’94, pp. 269-280, london, 1994. [20] beran, j., sherman, r., taqqu, m., and willinger, w., long-range dependence in variable-bit-rate video traffic, ieee trans. communications, vol. 24, no. 2, pp.1566-1579, 1995. [21] norros, i., a storage model with self-similar input, ieee tans. queueing systems, vol. 16, pp. 387-396, 1994. [22] likhanov, n., tsybakov, b., and georganas, n., analysis of an atm buffer with self-similar “fractal”) input traffic, proc.ieee infocom’95, pp. 985-992, boston, ma, 1995. [23] elwaid, a., and mitra, d., effective bandwidth of general markovian traffic sources and admission control of high-speed networks, ieee/acm trans. networking, vol. 1, no. 3, pp. 329-343, 1993. [24] sriram, k., and whitt, w., characterizing superposition arrival processes in packet multiplexers for voice and data, ieee journal on selected areas in communications, vol. 4, pp. 833-846, 1986. [25] heffes, h., and lucantoni, d., a markov modulated characterization of packetized voice and data traffic and related statistical multiplexer performance, ieee journal on selected areas in communications, vol. 4, no. 6, pp. 856-868, 1986. [26] bravo, j., and marrone, l., tráfico autosimilar. algoritmo algebraico para asignación dinámica del buffer, revista ingenius, ed. 2, facultad de ingenierías, universidad politécnica salesiana, ecuador, 2007. [27] beran, j., statistics for long-memory processes, in generalized additive models (monographs on statistics and applied probability), new york, chapman & hall, 2000. presentation of an estimator for the hurst parameter for a selfsimilar process representing the traffic in ieee 802.3 networks 147 [28] geweke, j., and porter-hudak, s., the estimation and application of long memory time series models. j. timer ser. anal. 4, pp. 221-238, 1983. [29] taqqu,m., and teverovsky, v., a practical guide to heavy tails: statistical techniques ans applications. http://citeseerx.ist.psu.edu, 1983. [30] taqqu,m., and teverovsky, v., a practical guide to heavy tails: statistical techniques ans application. http://citeseerx.ist.psu.edu, 1983. [31] leland, w., taqqu, m., willinger, w., and wilson, d. (1994), on the self-similar nature of ethernet traffic, ieee/acm trans. networking, vol. 2, no. 1, pp. 1-15. [32] robinson, p., log-periodogram regression of time series with long-range dependence, annals of statistics, no. 23, pp. 1048-1072, 1995. [33] millán, g., and lefranc, g., proposición de un estimador del parámetro de hurst para un proceso autosimilar representativo del grado de aleatoriedad del tráfico registrado en redes ieee 802.32005. xviii congreso de la asociación chilena de control automático (acca), 2007. international journal of computers communications & control issn 1841-9836, 9(6):741-748, december, 2014. parmods: a parallel framework for mods metaheuristics e.d. nino ruiz, s. miranda, c.j. ardila, w. nieto elias d. nino ruiz*, stella miranda, carlos j. ardila, wilson nieto universidad del norte computer science department colombia, barranquilla {enino,stellam,carila,wnieto}@uninorte.edu.co *corresponding author: enino@uninorte.edu.co abstract: in this paper, we propose a novel framework for the parallel solution of combinatorial problems based on mods theory (parmods) this framework makes use of metaheuristics based on the deterministic swapping (mods) theory. these approaches represents the feasible solution space of any combinatorial problem through a deterministic finite automata. some of those methods are the metaheuristic of deterministic swapping (mods), the simulated annealing deterministic swapping (samods), the simulated annealing genetic swapping (sagamods) and the evolutionary deterministic swapping (emods) those approaches have been utilized in different contexts such as data base optimization, operational research [1–3, 8] and multi-objective optimization. the main idea of this framework is to exploit parallel computation in order to obtain a general view of the feasible solution space of any combinatorial optimization problem. this is, all the mods methods are used in a unique general optimization process. in parallel, each instance of mods explores a different region of the solution space. this allows us to explore distant regions of the feasible solution which could not be explored making use of classical (sequential) mods implementations. some experiments are performed making use of well-known tsp instances. partial results shows that parmods provides better solutions than sequential mods based implementations. keywords: mods, combinatorial optimization, parallel framework. 1 introduction combinatorial optimization (co) is a branch of optimization in which problems can be represented (or reduced) to discrete structures. in this ramification, we find many problems related to operational research and networking fields. moreover, since the number of possible solutions in this kind of problems increase exponentially with regard to the input parameters, their numerical solution can be very hard (or impossible) to obtain, for instance solving their mathematical formulations. thus, two important considerations should be taken into in account when we want to solve co problems: the number of solutions to consider is only a subset of the feasible solution space and the solutions should be obtained in a polynomial time. the first item addresses the necessity of having good solutions and the second one, demands the elapsed time to be small for the proposed implementation. however, those features are opposite, this means, when the number of solutions explored from the feasible solution space is small, the solution is obtained in short time but maybe, the approximated optimal solutions are not good enough. on the other hand, exploring more and more the feasible solution space provides better approximations to the optimal solution but, the performance of the method is affected considerably (long elapsed times). thus, we need methods which in a polynomial time consider more and more solutions from the feasible solution space. notice, we are not taking about exhaustive methods such as brute force but, combining information from different metaheuristics in a polynomial time which can be done in parallel. copyright © 2006-2014 by ccc publications 742 e.d. nino ruiz, s. miranda, c.j. ardila, w. nieto this paper is organized as follows: in section 2 the tsp problem is introduced and mods metaheuristics are presented, section 3 describes the proposed implementation and, section 4 and 5 provide the experimental results and conclusions, respectively. 2 preliminaries following the previous section, one of the most widely used co problems is the traveling salesman probem (tsp) its importance is derived owing to its application to different branch and fields from optimization. moreover, some well-known problems such as the vehicle routing problem and the transportation problem are derived from the tsp formulation. in general, this problem is defined as follows: we have a set of n cities c = {c1, c2, . . . , cn}, a matrix of weights w ∈ rn×n whose elements wi,j provides the weight of going from ci to cj, for 1 ≤ i, j ≤ n and, the cost function j(α) = wn,α1 + n−1∑ i=1 wαi,αi+1, (1) which is subjected to • visiting each city from c once. • coming back to the initial city once the path α has been completed. rudely speaking, we want to find the optimal path α∗ which provides the optimal components wα∗i ,α ∗ i+1 , for 1 ≤ i ≤ n−1, from w such that (1) is minimized. note that, the number of possible solutions for the tsp problem increases by n! with regard to the number of cities n. as we mentioned before, the numerical solution of co problems can be an exhaustive work making use of numerical methods and the tsp problem is not the exception. note that, the tsp problem can be seen as a linear programming problem therefore well-known numerical methods based on integer programming and simplex methods could be used in order to solve (1) but, they have been proved to fail when the number of cities is large, as is usual in practice. on the other hand, the optimal solution of the tsp problem can be approximated making use of metaheuristics, we address one set of them in this paper, those are based on the metaheursitic of deterministic swapping (mods) mods is a metaheuristic inspired on the automata theory. its application ranges from the operational research field to the database query optimization area [7]. it is very important to note that, mods methods are not novel methods, in general, they are nothing but classical combinatorial optimization methods represented on deterministic finite automata structures. this improves the manner to design the solution of the problem since the optimial solution space and the transition between solutions (states of the automata) are defined prior any optimization process. this avoids, for instance, to explore unfeasible regions of the solution space. mods considers the next deterministic finite automata (dfa) qmods = {s, σ, δ,s0,j} , (2) where s is the feasible solution space, σ is the input alphabet which is utilized by δ : s → s in order to perturb the solutions, s0 contains the initial solutions and, j is the cost function to be optimized. the s space is unknown since it contains all the possible solutions of the co problem. putting all in the tsp context, s contains all the possible paths, s0 provides the initial path, j is the cost function (1) and σ and δ provides all the possible manners to perturb parmods: a parallel framework for mods metaheuristics 743 a given path, for instance, given the path α′ = [1, 2, 3, 4] and the duple σ1 = (2, 3) ∈ σ then, δ(α′, σ1) = [1, 3, 2, 4]. the mods metaheuristic is defined in algorithm 2. algorithm 2 mods metaheuristic require: σ, δ,s0,j ensure: α+ ≈ α∗ 1: α+ ← s ∈s0 2: for k = 1 → m do 3: σk ← σ ∈ σ 4: α− ← δ(α+, σk) 5: if j(α−) < j(α+) then 6: α+ ← α− 7: end if 8: end for algorithm 3 samods metaheuristic require: σ, δ,s0,j , t0, ρ, l ensure: α+ ≈ α∗ 1: α+ ← s ∈s0 2: for k = 1 → m do 3: for i = 1 → l do 4: σi ← σ ∈ σ 5: α− ← δ(α+, σi) 6: if j(α−) < j(α+) then 7: α+ ← α− 8: else 9: generate (uniformly) η ∈ [0, 1] 10: compute γ = exp ( − j(α+)−j(α−) tk ) (3) 11: if η < γ then 12: α+ ← α− 13: end if 14: end if 15: end for 16: tk+1 ← ρ ·tk 17: end for samods is a simulated-annealing (sa) based mods method which explores the feasible solution space s in a more “generous” manner. it allows bad solution to be accepted in small optimization intervals (usually at the beginning of the iterations) alike mods, samods makes use of the dfa qsamods = {s, σ, δ,s0,j , t0, ρ, l} , (4) where s, σ, δ, s0 and, j remain unchanged, t0 is the initial temperature, ρ is the cooling factor and, l is the number of refinement iterations. note that, mods accepts a new solution 744 e.d. nino ruiz, s. miranda, c.j. ardila, w. nieto only when its optimal value is better than the current value (from the current path) on the other hand, samods makes use of the boltzamm distribution (5) in order to give the chance of a bad solution to be improved. this may provides a better solution than the best solution considered so far. thus, at the beginning of the iterations, the number of solutions accepted as good is large but, this number is decreased when the iterations draws on since the parameter tk is large and then, the condition of line 11 in algorithm 3 is almost never satisfied. following the sa principles, sagamods [5] is defined on the samods method but, when a bad solution is rejected (line 11 in algorithm 3), the solution is improved making use of genetic algorithms. the supporting automata of sagamods method is defined as follows: qsagamods = {s,s0,c(s, r, k), f(s)} s and s0 remain unchanged from the previous methods. in addition, c(s1, s2, k) is the crossover operator where s1 ∈ s and s2 ∈ s are parents solutions. likewise, k provides the cross point. sagamods method is presented in the algorithm 4. algorithm 4 sagamods metaheuristic require: σ, δ,s0,j , t0, ρ, l ensure: α+ ≈ α∗ 1: α+ ← s ∈s0 2: for k = 1 → m do 3: for i = 1 → l do 4: σi ← σ ∈ σ 5: α− ← δ(α+, σi) 6: if j(α−) < j(α+) then 7: α+ ← α− 8: else 9: generate (uniformly distributed) η ∈ [0, 1] 10: compute γ = exp ( − j(α+)−j(α−) tk ) (5) 11: if η < γ then 12: α+ ← α− 13: else 14: generate integer number (uniformly distributed) β ∈ [1, modelsize] 15: call c(α+, α−, β) 16: end if 17: end if 18: end for 19: tk+1 ← ρ ·tk 20: end for emods [6] is an evolutionary mods method which improves the solutions making use of evolutionary techniques (crossover and mutation). a complete taxonomy of sagamods and emods methods can be read in [4, chapter 4]. now we are ready to present our parallel approach of mods based methods. parmods: a parallel framework for mods metaheuristics 745 3 proposed implementation to start, consider an array of processors available at time t: p = [p1, p2, . . . , pn] , (6) where n is the number of processors. for simplicity, we avoid the use of time indexes. moreover, we consider that the metaheuristics mods, samods, sagamods and emods can be run independently at different processors. thus, we want to split the number of available processors per the number of metaheuristics, this is: jobsproc = n 4 . (7) consider the initial solution s ∈ s0, then we denote the next dfas based on the index 1 ≤ i ≤ n: qi =   q1 = qmods = {. . . , s} for i = 1, 5, . . . q2 = qsamods = {. . . , s} for i = 2, 6, . . . q3 = qsagamods = {. . . , s} for i = 3, 7, . . . q4 = qemods = {. . . , s} for i = 4, 8, . . . , (8) and then, we are ready to launch different processes based on the next rule jobi =   p(q1, s) for i = 1, 5, . . . p(q2, s) for i = 2, 6, . . . p(q3, s) for i = 3, 7, . . . p(q4, s) for i = 4, 8, . . . , (9) where the i-th process jobi (p(., .)) is executed in the processor pi of (6), for 1 ≤ i ≤ n. note that, in (8) we choose the automata to be utilized and in (9), we launch the process. for instance, mods metaheuristic is executed on processors 1,4,..., likewise, samods is executed on processors 2,5,... and so on. denote by s1, s2, s3, and s4 the approximated optimal solutions provided by mods, samods, sagamods and emods among processors, respectively, this is s1 = arg min s (i) mods { j ( s (i) mods ) , for i = 1, 4, . . . } s2 = arg min s (i) samods { j ( s (i) mods ) , for i = 2, 5, . . . } s3 = arg min s (i) sagamods { j ( s (i) mods ) , for i = 3, 6, . . . } s4 = arg min s (i) emods { j ( s (i) mods ) , for i = 4, 7, . . . } where, for instance, s(1)1 is the approximated optimal solution of mods from the processor 1. then, we choose the best approximation, s+ = arg min si {j (sk) , for 1 ≤ k ≤ 4} (10) 746 e.d. nino ruiz, s. miranda, c.j. ardila, w. nieto which will serve as the new initial solution in s0. this iterative process is called parmods (parallel mods) and it is summarized in algorithm 5. note that, the required components of this metaheuristic varies from the definitions of the automatas, that is why the common components are shown in the inputs and the optional parameters are expressed by dots. algorithm 5 parmods metaheuristic require: σ, δ,s0,j , . . . ensure: α+ ≈ α∗ 1: α+ ←s0 2: for t = 1 → m do 3: for all i = 1 → n do 4: launch jobi according to (9). 5: end for 6: α− = arg min s (j) ∗mods { j ( s (j) ∗mods ) , for 1 ≤ j ≤ n } 7: if j(α−) < j(α+) then 8: α+ ← α− 9: end if 10: end for notice, the computational cost of the method per iteration will be given by the number of iterations of parmods times the upper bound o (aparmods) = max (o(amods),o(asamods),o(asagamods),o(aemods)) , where the letter a counts for “algorithm". note that, since all the methods are executed in parallel, the computational effort of parmods is provided by the largest upper bound, which, in general, is provided by sagamods. 4 experimental results we study the performance and efficiency of parmods making use of tsp instances from the tsplib. the selected tsp instances are kroa100 and kroa150 which contain 100 and 150 cities, respectively. the solutions obtained by the methods are presented in table 1 and figure 1. metaheuristic processors j kroa100 j kroa150 mods n/a 1.6165 2.6122 samods n/a 0.6583 0.9865 sagamods n/a 0.3739 0.5399 emods n/a 1.5340 2.4663 parmods 4 0.3545 0.3664 parmods 8 0.2827 0.3359 parmods 12 0.2827 0.3359 table 1: cost function values ×105 for different mods implementations. parmods: a parallel framework for mods metaheuristics 747 in figure 1 can be seen how parmods outperforms the other mods implementations (sequential mods, samods, sagamods and emods) in terms of accuracy. moreover, the mods implementations are divided evenly onto the number of processors available (n) for instance, four processors means one instance of mods, samods, sagamods and emods are used at each processor when parmods is executed. notice, parmods do not make use of parallel resources in order to split the domain but to obtain information about the feasible solution space. since parmods spread mods instances among processors, the best solution is used in the next generation of each mods implementation (initial state of each automata). 5 10 15 20 25 iteration mods samods sagamods emods parmods (a) kroa100 n = 4 5 10 15 20 25 iteration mods samods sagamods emods parmods (b) kroa150 n = 4 5 10 15 20 25 iteration mods samods sagamods emods parmods (c) kroa100 n = 8 5 10 15 20 25 iteration mods samods sagamods emods parmods (d) kroa150 n = 8 5 10 15 20 25 iteration mods samods sagamods emods parmods (e) kroa100 n = 12 5 10 15 20 25 iteration mods samods sagamods emods parmods (f) kroa150 n = 12 figure 1: graphical comparison of the cost function values per iteration for the different mods implementations. 5 conclusions we propose a novel parallel method based on mods theor5522894y. the proposed implementation exploits the attractive features of each mods implementation. initial results show that parmods provides better results among the compared methods. moreover, when the number of processors is increased, the results are improved. however, we note that the results obtained for 8 and 12 processors are the same. this motivates to study theoretical bounds regarding the number of processors and 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[6] elias d. nino-ruiz (2012); evolutionary algorithm based on the automata theory for the multi-objective optimization of combinatorial problems. international journal of computers communication & control, 7(5):916–923. [7] miguel rodríguez, daladier jabba, elias d. niño, carlos j. ardila, and yi-cheng tu (2013); automata theory based approach to the join ordering problem in relational database systems. in markus helfert, chiara francalanci, and joaquim filipe, editors, data, pages 257–265. scitepress. [8] li zhengfeng and ye jinfu (2010); study on the evolutionary mechanism from operational research activities to sustainable competitive advantage. in intelligent computation technology and automation (icicta), 2010 international conference on, 3: 580–584. 915filipn6.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):952-953, december, 2015. book review: "biometric and intelligent decision making support" f.g. filip florin gheorghe filip romanian academy, calea victoriei 125, sector 1, bucharest, romania ffilip@acad.ro biometric and intelligent decision making support by artüras kaklauskas intelligent systems reference library (janusz kacprzyk, lakhmi c. jain, series editors) springer, isbn 978-3-319-13659-2, 236 pages http://www.springer.com/us/book/9783319136585 this book is meant to familiarize the reader with the state-of-art of research and application results obtained in the domain of intelligent decision support systems (idss), biometric technologies and their integration. the author of the book is prof. a. kaklauskas, the head of the department of construction economics and real estate management of the vilnius gediminas technical university, faculty of engineering, vilnius, lithuania, a reputed author in the domain of intelligent decision support systems, multi-criteria decision making (mcdm) methods and their applications in the construction, property management and the related fields. in the book, the author makes an exhaustive review of results reported in the domain literature and presents a detailed account of his practical achievements as well as his colleagues’. the book is composed of seven chapters as follows. chapter 1, entitled "introduction to intelligent decision support systems", sets the stage for the following chapters. it introduces the main concepts of the domain and pays a special attention to artificial intelligence (ia)based advanced methods and their integration with data base management systems (dbms) and humancomputer interface (hci). chapter 2, entitled "intelligent decision support systems", reviews basic concepts of several new subclasses of idss, such as recommender, advisory and expert systems, data and text mining. the integration of artificial neural networks is addressed too. a big part of the chapter provides abundant information about specific aspects of biometrics-based dss, such as voice and speech recognition, expression analysis and so on. two very modern concepts and associated technologies, namely ambient intelligence and internet of things, are described in details. several other subclasses of solutions based on various technologies, such as genetic algorithms, fuzzy and rough sets, computer vision, robotic systems and so on, are surveyed in the final part of the text of the chapter. the remaining part of the text contains the presentation of various conceptual and application results obtained by professor kaklauskas and his colleagues. chapter 3 introduces the model of passive house to be used in a qualitative analysis and design of the corresponding intelligent system. chapter 4 contains the results obtained in biometric and self-assessment of the student progress system and describes two relevant case studies. copyright © 2006-2015 by ccc publications book review: "biometric and intelligent decision making support" 953 chapter 5 addresses the subject of analysis of user’s emotions and work productivity by using the state of blood circulatory system and describes a webbased biometric computer mouse advisory system. the content of chapter 4 is complemented by the interesting solutions presented in chapter 6, which addresses student progress assessment by using an intelligent pupil analysis and presents two case studies. chapter 7 addresses also the education domain and presents recommender-system-based solutions meant to increase student productivity. one book addresses two modern research domains: intelligent and integrated decision support systems and biometrics-based human-computer interface. it can be explained, in my view, by the huge effort made by the author and his colleagues to find solutions to problems of the real world. the presentation of the research and application results obtained by the author and his colleagues over the years is a special merit of the book, beside the variety of the topics addressed. consequently, i warmly recommend the book to phd and msc students and instructors, as an up-to-date source of knowledge. i think the book can also be a valuable tool for those consultants who are willing to master new concepts and technologies in order to get a competitive advantage on the market of computer applications. f. g. filip romanian academy bucharest, romania int j comput commun, issn 1841-9836 8(1):161-165, february, 2013. alternative wireless network technology implementation for rural zones f.j. watkins, r.a. hinojosa, a.m. oddershede francisco j. watkins, ricardo a. hinojosa, astrid m. oddershede universidad de santiago departamento de ingenieria electrica santiago, chile e mail francisco.watkins@usach.cl, ricardo.hinojosa@usach.cl, astrid.oddershede@usach.cl abstract: this paper describes a methodology that allows wireless networking allowing interconnection to the internet through a gateway, to interact and obtain products and services delivery. these wireless mesh network(wmn), are based on routers which are programmed to work as nodes of a network. there are certain routers that allow the programming of its firmware to form network nodes. communication is transmitted between nodes in the network and it is possible to cover long distances. the signal of a distant node, hop from node to node till reach the gateway. this generates delays and congestion in the network. a path that contains nodes that make more faster connection to the gateway can be designed as a solution. this is called a backbone, has a different channel frequency of the common nodes. the characteristics of these networks is its fast implementation and low cost. this make them useful for rural areas, for developing countries and remote regions. keywords: wireless mesh network. 1 introduction the methodology can be structured to generate a transmission infrastructure of broadband hybrid wireless mesh of low cost that shall extend to a great extent with nodes placed point to point. this to access products and services delivers the internet. in modern times the internet has been a powerful network of communication and exchange of information with a strong endorsement of communication technology. on the other hand, it can be defined as a powerful instrument of social development that provides information, communication between people and a way of obtaining knowledge. the internet is a valuable tool for business, industry, trade, education, and social development of communities. a computer is physically connected to the internet via a modem or a card nic (net work interface card). the logical connection applies standards called protocols. a protocol is a formal description of a set of rules and conventions that govern so that communicate with devices on the network. the network connection can use multiple protocols this set is the (tcp/ip), transport control protocol, internet packages, to receive or transmit information. the most important is have the connectivity for getting the internet benefits. based on the modern technology support, a communication network can be implemented, hacking the characteristic of its software and implementing some passives devices that modify the hardware characteristics. the network design for a wireless mesh network will depend on the geographic landscape and distances between the points to be connected. a combination of point-topoint long distance links copyright c⃝ 2006-2013 by ccc publications 162 f.j. watkins, r.a. hinojosa, a.m. oddershede (using directional antennas) and local point-to-multipoint links (using omni-directional antennas) between mesh nodes can create a reliable mesh network. this type of wireless communication network has been denoted as wireless mesh network [1]. there are several pilot projects in development at underdevelopment countries, mainly in rural areas. 2 the wireless mesh network (wmn) it is a network structures by several nodes, that form the backbone of the network. the nodes, because of its software, can be configure automatically and re configure to maintain the network connectivity. figure 1: wireless mesh network in figure 1, the wireless nodes are interconnected, and a wireless node is a router with its antenna, the antenna could be omni-directional or directional. a mesh node only communicate with others mesh nodes. a wireless access point is a point which alloys the interaction with the wireless mesh network of any wi-fi device. its is consist of a wireless router and an antenna. in this case, the antenna is a omni-directional antenna. in a mesh wireless network any node can be connected in no structured. a simple wireless network can consist of two wireless routers and its antennas. 3 characteristics and advantages of a wireless mesh network the link between nodes the routers could be configure in different way, generating links that cover big distances or for giving service to several user in a small area. the connection could be by physics means or wireless. alternative wireless network technology implementation for rural zones 163 in mesh wireless network , the unidirectional antennas are used, and in same places its can used wired connection. the large distance s are cover by using static wireless nodes with unidirectional antennas. the mesh wireless network are robust and of simple configuration , because its software will determine the path of the data in real time. the backbone of the network its depend on the site topography. communication of all mesh nodes are based on wi-fi . all the nodes of the mesh wireless network operate at same channel frequency. in a wmn, each node must be communicated with at least with other two nodes in order to maintain a robust mesh connectivity, which is the main features of a wmn. in a wmn, each node have the same name and number. the ip address should be unique to allow to connect to any computer in the network. a computer can connect to the mesh via lan cables connected to the mesh node or a wireless connection to a separate access point connected to a lan or a mesh node. a network device, is connected physically to the network through a modem or a nic(network interface card) and the logic connection is made through protocols. a protocol is a formal description of a set of rules and agreements for defining a communication way between the different devices of the network. the protocols tcp/ip, ( transport and control protocol/ internet packages) and osi (open system interconnection ). the users to manage the mesh thought navigators. a navigators initiate (start up) the connection to a server and could required or received information. this software interpret hipertext language label (html), which is one of language to code of a web page content. in a wmn, a routing protocol, will route de ip traffic between the wireless interfaces of the mesh nodes. manage the routing information and maintains routing tables dynamically. this provide an alternative route when a node fails. the advantages of a wmn, are the following: self-forming. the wireless mesh forms their structure automatically once its nodes have been configured and activated. it is a robust network. fault tolerance because of redundant routes exist in the network. information is not interrupted in the rest of the network when a node fails. low cost of the of the nodes of the wmn, which allows the admission of extra nodes to increment the network, given a low incremental cost of the network. easy deployment of the network. new member of the community, with little training can built their own nodes. 4 the wireless mesh network design wireless mesh networks are no problematic to build when you have a few nodes. in general you must follow the following stages: • map of the network; • place of each node; • network topology and channel allocation; • channel allocation for site users; • plan ip address allocation. 4.1 map of the network the map of the mesh network star with identification of the sites that will receive a mesh node, the coordinates can get with a gps and them plot distribution nodes in a paper. the sites nodes can be linked together using the map. each link is a straight line between to nodes. the length of each link represent the distance between sites. 164 f.j. watkins, r.a. hinojosa, a.m. oddershede 4.2 place of each node for the nodes is a solution to build a backbone reaching the gateway. if we have a complex mesh, several backbones uniformly distributed are needed for the nodes reach the gateway. the backbones are path included in the connection of certain strategic nodes in such way that for any others nodes, the connection with the gateway be expedite (connection with the gateway be through a few nodes). 4.3 network topology and channel allocation nodes in the mesh can communicate to each other, if they have the same frequency channel. when a back bone is incorporated, other channel is needed, which works as an independent network. in this case both network do not have interference problem. 4.4 channel allocation of the backbone if we have a mesh network with a backbone we need two ip ranges . a third range is needed is we adds an access point [2]. 4.5 ip address allocation the ip allocation should assure for each pc and each node a unique address, according to rfc 1918 subnet scheme [3], [4]. for the assignment of the address of the different element of the network, we have the following: backbone node; wireless interface : 10.0.1.x/24 where 1ł x < 255; ethernet interface: 10.3.x.y/24 where 1ł x < 255 where 1ł y < 255; normal mesh node: wireless interface: 10.1.1.a/24 where 1ł a < 255; ethernet interface:10.2.a.b/24 where 1ł a < 255 where 1ł b < 255, pc and laptop connected to a node will be numbered from 100according to the setting; access point : the subnet assigned to a lan or hotspot will be the same as ethernet lan connected to the mesh node. 5 the wireless mesh network 5.1 first steps to build the mesh network the following stages should be cover: • configure all the mesh nodes and wireless access point according to the network document; • attach a paper to the device of the mesh node or wireless access point with the configuration detail; • test the equipment to be sure all is working properly; • connect a pc to a mesh node with a lan cable. the pc will require a ip address. ping other mesh node .if the ping is successful, the mesh node of the pc and the other mesh node are working. if not, check the configuration; • the gateway is the point where the mesh network will be connected to the internet; • installing the mesh nodes from the gateway, in such way you can confirm that the network is still working when a new mesh node be installed; • connect a pc to a mesh node with a lan cable, ping the gateway first, and if that is successful, access to any site on the internet (different web pages) in order to ensure the pc can connect to the internet. alternative wireless network technology implementation for rural zones 165 5.2 the mesh node in order to start with the mesh node you must have the router, the lan cable, and the power supply. to configure the mesh node, the following stage must be cover: • upgrade the firmware for the backbone and normal mesh nodes [5]; • configuration of system settings; • configuration of wireless settings; • configuration of lan settings; • olsr settings [6] (optimized link state routing protocol. (olsr) is developed for mobile ad hoc networks. it operates as a table driven and proactive protocol, thus exchanges topology information with other nodes of the network regularly). 6 conclusions a methodology has been given in order to plan and design a wmn , which can supply available communication systems in a similar way of internet does. but a lower cost of internet. this can be planned specially on rural areas. different countries can access to several routers models, but there are only a few that allow be adapted to this technology. by other hand there are free open software that allows this implementation. this methodology makes it possible to cover a niche of potential users, especially in rural areas and in developing countries where the low density of population, not makes it attractive to commercial isp (internet service provided). bibliography [1] akyildiz i., wang x.,wang w., wireless mesh network: a survey, computer networks 47, 445-487,2005. [2] ding y., xiao l., channel allocation in multi-channel wireless mesh networks [3] bernardos c., calderon m., soto i.,solana a., k weniger k., building an ipbased community wireless mesh network: assessment of pacman as an ip address autoconfiguration protocol, computer networks; 54, 291-303, 2010. [4] avallone s., akyildiz i., a channel assignment algorithm for multi-radio wireless mesh networks, computer communications 31 1343-1353, 2008. [5] hsu ch., .wu j-l, tewang s., chi-yao hong ch y., survivable and delayguaranteed backbone wireless mesh network design, journal parallel distribution computing. 68, 306-320,2008. [6] http://hipercom.inria.fr/olsr/ international journal of computers communications & control issn 1841-9836, 11(6):877-888, december 2016. coverage optimization strategy for wsn based on energy-aware l. zhu, c. fan, h. wu, z. wen li zhu, chunxiao fan*, zhigang wen beijing university of posts and telecommunications, beijing key laboratory of work safety intelligent monitoring, school of electronic engineering, no.10 xitucheng road, beijing, p.r.china, 100876 jolie.zhl@hotmail.com *corresponding author: cxfan@bupt.edu.cn zwen@bupt.edu.cn huarun wu national engineering research center for information technology in agriculture, key laboratory for information technologies in agriculture, ministry of agriculture, room 316, beijing agriculture science and technology building a, no. 11 beijing shuguang garden middle road, haidian district west suburb, beijing,china, 100097 wuhr@nercita.org.cn abstract: in order to optimize the wireless sensor network coverage, this paper designs a coverage optimization strategy for wireless sensor network (eacs) based on energy-aware. under the assumption that the geographic positions of sensor nodes are available, the proposed strategy consists of energy-aware and network coverage adjustment. it is restricted to conditions such as path loss, residual capacity and monitored area and according to awareness ability of sensors, it would adjust the monitored area, repair network hole and kick out the redundant coverage. the purpose is to balance the energy distribution of working nodes, reduce the number of “dead” nodes and balance network energy consumption. as a result, the network lifetime is expanded. simulation results show that: eacs effectively reduces the number of working nodes, improves network coverage, lowers network energy consumption while ensuring the wireless sensor network coverage and connectivity, so as to balance network energy consumption. keywords: wsn, coverage optimization, energy-aware, hole repair, sensing radius. 1 introduction wireless sensor network (wsn) consists of many sensor nodes which draw on self-organization to transmit data between nodes. these sensor nodes are small in size with limited energy and certain awareness, making them widely applied to many fields, such as military, transportation, environment protection, medical care, disaster relief and agriculture. currently, sensor nodes are distributed manually or randomly. manual distribution is executed in certain circumstances requires high on environment. so in most cases, random distribution [1–3] is the primary choice as it is easy to operate and able to reduce the interference on human activities. however, under circumstances of high density and large area, wireless sensor network is weak in expansion, which results in that the transmission signal is easy to be disturbed, the network energy is unevenly distributed and network become unstable. it is urgent to solve problems of increasing the effective coverage in monitored areas as much as possible, lowering network energy consumption, extending network lifetime, and improving network performance under the condition of limited energy and using the least sensor nodes. and network coverage is one of the most important indicators of network performance measurement. copyright © 2006-2016 by ccc publications 878 l. zhu, c. fan, h. wu, z. wen among existing studies, literature [4] proposes a method to calculate redundancy of probability node. with this method, nodes in the network can acquire information about their own, such as redundancy, without knowing the geographic position. it also proposes a node adjustment strategy without knowing node position. but this strategy neglects the awareness redundancy area within two-hop neighbor nodes, making many redundant working nodes exist during node adjustment. literature [5] proposes a node distribution strategy based on wireless beacon selfadaption. this strategy repairs network coverage holes by adding wireless beacon to the network but fails to consider the cost of adding the beacon and the influence on monitored area. literature [6] proposes to use a circle to solve node adjustment coverage. it calculates the angle between the awareness circle and the monitored area to get the minimum number of working nodes and reduces redundant working nodes. but under high coverage, the connectivity is weak. literature [7] acquires information of null nodes and introduces mobile nodes. to be specific, it replaces null nodes with mobile nodes to fill the network holes. the network coverage is increased, but the energy of mobile nodes is limited and there are too many null nodes in the network. literature [8] proposes a coverage strategy based on genetic algorithm. it selects the maximum solution set of coverage nodes. with genetic algorithm, it uses evaluation function to optimize random samples and reach a balance of network coverage. but the algorithm is relatively complicated and requires much calculate. to some extent, it increases network energy consumption. in order to improve effective coverage, this paper proposes a coverage optimization strategy for wsn based on energy aware, establishes a network coverage model. network nodes are distributed randomly; a network coverage model is established the relationship between node residual capacity and sensing range are examined to set reasonable sensing range of nodes, balance network energy consumption, repair network coverage holes, and extend network lifetime. finally, a simulation experiment is conducted to prove the relationship between network energy consumption and coverage of the proposed algorithm under different circumstances. the proposed algorithm aims at balancing network energy consumption, improving network coverage, and minimizing node energy consumption. this paper establishes a non-linear model of coverage optimization strategy for wireless sensor network (eacs) and exercise stricter constraints to turn it to linear-constraints and get the second-best solution. 2 network model and problem description 2.1 analysis model of energy-aware node suppose the wireless sensor network consists of n randomly distributed nodes, during node awareness, the energy of physical signal changes inversely with the distance between the signal and the awareness target. such tendency is mainly due to path attenuation during transmission, select any source node si within the scope of monitoring, when target node sj is anywhere in the plane, the awareness intensity ψi(j) of souse node to target node is expressed as: ψi(j) = { 0 rs < d(i,j) λe−kd(i,j) 0 < d(i,j) ≤ rs(1) (1) where k is the indicator of signal attenuation. rs is the maximum effective sensing radius of the node. d(i,j) is the euclidean distance between node i and j. λ is a constant value. the awareness intensity φ(j) of node j is expressed as: φ(j) = 1 − (1 − ψ1(j))(1 − ψ2(j)) · · ·(1 − ψi(j)) · · ·(1 − ψn(j)) = 1 − i=n∏ i=1 (1 − ψ1(j)) (2) coverage optimization strategy for wsn based on energy-aware 879 when φ(j) > �, target node j is sensed. when φ(j) < �, target node j is not sensed, and at this time, j is the coverage blind-point (ε target node j is not sensed, and at this time, j is the coverage blind-point h0 : g(i) = ϑi i = 1, 2, . . . ,n h1 : g(i) = ϑi + ψi(j) i = 1, 2, . . . ,n (3) where ϑi is the background noise signal following the normal distribution ϑi ∼ n(µ,σ2), and ϑi(j) is useful signal. the signal detected by sensor nodes is g(i). the target that exits is expressed by h0 and the target that does not exist is expressed by h1. 2.2 network model n non-overlapped wireless sensor nodes, s = {si|1 < i < n}, whose sensing radius is rs are randomly distributed in a two-dimensional monitored area a. suppose the network has the following features: (1) the node position is permanent. there is no inherent relationship between the communication radius rc and sensing radius rs. (2) all nodes have isomorphism. the wireless self-organized network is constructed in the monitored area. (3) all nodes adopt the node probability awareness model. (4) the sensing radius of sensor node can be adjusted according to node residual capacity. (5) the initial energy of sensor nodes is w , and they have synchronous clock. (6) sensor nodes use the location technology in literature to acquire their own locations. at the same time nodes can acquire the information of neighbor nodes, such as residual capacity, node position within effective communication distance. 2.3 problem description a number of sensor nodes are distributed randomly within the monitored area a. probabilistic sensing model is deployed, and network nodes are adjusted according to data from 2.2network model; a wireless sensor network is established to balance network energy consumption and network coverage, adjust sensing ranges based on residual capacities, reduce the possibility of "death node" and redundant network coverage, and extend network lifetime. this paper expects to reach a balance between residual capacity of any sensor node and sensing range in the monitored area (where wi is the residual capacity, =i is the sensing range and a is the monitored area), wi < wj && =i < =j (4) a = =1 ⋃ =2 · · · ⋃ =i · · · ⋃ =n = i=n⋃ i=1 =i (5) in the monitored area a, nodes should satisfy (3)(4) to reach the requirement of coverage. this paper also turns energy consumption and coverage balance strategy to a non-linear and 880 l. zhu, c. fan, h. wu, z. wen multi-target optimization problem:   0 < covmax = f( i=n∑ i=1 covi) ≤ a 0 ≤ wmin = ϕ( i=n∑ i=1 ∆wi) ≤ nw0 tmax = max(min(t1,t2 · · ·tn)) ζmin = θ( i=n∑ i=1 ζi) (6) given that path loss, node residual capacity [9] and monitored area are key factors [10–12] to realize network performance optimization and extend network lifetime, the constraint conditions are dealt with according to mathematical programming so that the optimized network has high accuracy. construct the optimized model with maximizing the network lifetime as the target. the optimized model is restricted to constraint conditions. adjust the monitored area, and optimize the network lifetime and the overall network overhead. in the non-linear and multitarget optimization, optimized factors are not in the optimal state, but the network overhead is almost the minimum and the convergence has high accuracy. 3 energy coverage strategy coverage strategy of the wireless sensor network optimize network energy consumption while improving network coverage [13–15]; in addition, they should also balance energy consumption of single node and overall network energy consumption. if nodes with lower energy are assigned the same work load as nodes with higher energy, the former will end in “premature death” and the transmission and reliability of the entire network will be undermined. to reach the balance of two optimization coverage strategies and solve the contradiction between individual nodes and the overall performance, this paper proposes a coverage optimization strategy for wsn based on energy-aware, namely energy-aware coverage strategy (eacs). the strategy is mainly divided into two phases: one is the energy-aware phase in which the sensing field of each node is confirmed by probability according to residual capacity of sensor node in the monitored area. the second is the network coverage adjustment phase where the sensing radius of node is adjusted effectively according to the sensing field of each node and the overall network coverage to lower the redundant coverage, reduce redundant coverage and unnecessary energy consumption, so as to extend network lifetime. 3.1 energy-aware phase in the wireless sensor network, sensor nodes in the monitored area transmit information through awareness coordination. given that sensor nodes have limited energy, this strategy concerns not only about energy consumption of any node in the area, but also the equilibrium of the whole network energy consumption. as time goes by, survived nodes may suffer from breaking the equilibrium of energy consumption due to signal interference, resulting in the change of node residual capacity. considering that node residual capacity is related to node sensing range, as shown in fig.1, reasonable sensing range is set up for each node to balance the network energy consumption and finally, to extend network lifetime. coverage optimization strategy for wsn based on energy-aware 881 in the energy-aware phase, after time t of working, the relationship between the electric quantity wi consumed by node si and the sensing radius ri of sensing field ai is (k is a constant value): wi = kr 2 i (7) for any two neighboring nodes si and sj, their residual capacity is qi and qj respectively. after time t, their electric quantity is consumed simultaneously. the sensing radius between two nodes fits expression (2): ri = d(i,j) · √ qi√ qi + √ qj (8) where d (i, j) is the euclidean distance between nodes. according to expression (8), the sensing radius and residual capacity of si and sj have the following relationship: ri : rj = √ qi : √ qj (9) si sj d(i,j) rs ri d(i,j) rj i(j) 0 sj d(i,j) sensor node figure 1: node sensing range theory 1 the sensing filed of node si is ω, its awareness is radius ri. ω falls in the set γ(γ1,γ2, · · ·γi) and any γk satisfies d(si,γk) < ri. if the probability of x(x ∈ ω) sensed by any node in γ is p, there are pxsensing fields in ω on average that are sensed by γ. suppose the sensed set in the sensing field ω is π{x1,x2 · · ·xn}, and these nodes are independent from each other following even distribution. if k(k < n) nodes in being sensed by γ is called event x, so x follows binomial distribution, namely x ∼ b(n,p). p{x = k} = cknpk(1 −p)n−k where e[x] is the expectation of x. when n → +∞, the average awareness in ω is: lim n→+∞ e[x] n = p so there are psensing fields on average that are sensed by γ in ω. 3.2 network coverage adjustment select any node si in the monitored area, according to the energy-aware strategy, acquire the energy-aware range and node residual capacity of last phase and send the information such as sensing radius and energy consumption to the neighboring node. according to awareness 882 l. zhu, c. fan, h. wu, z. wen intensity, confirm the sensing range and monitored area of each node. when there produce holes in the monitored area, re-distribute the nodes. given that the sensing field may overlap, when no new holes are produced, adjust the sensing radius based on node residual capacity to wipe out redundant monitored area, so as to reduce unnecessary energy consumption. detailed steps are described below: step 1łşdivide the monitored area. for any node in the area, connect it with neighboring nodes and form the minimum triangle network as shown in 2(b). subject each triangle to perpendicular bisector and connect the lines, as shown in 2(c). finally, monitored area a consists of many regional polygons, as shown in 2(d). node distribution the minimum triangle network subject each triangle to perpendicular bisector regional polygon figure 2: distribution of monitored area step 2: detect irreparable holes and nodes filling. firstly, detect holes on the edge of monitored area a. the polygon which cannot be fully covered by the minimum triangle network is called the edge monitoring polygon. take the vertex of the polygon at the edge of the monitored area as the circle center and twice the maximum sensing radius of nodes rmax as the radius to draw a circle. places that cannot be covered by the circle are irreparable holes and need to be filled. new nodes should fill in the middle of the vertex and original nodes. for any side of the triangle in the network < s1,s2 >, if the side is twice more than the maximum sensing radius of nodes, namely l(s1,s2) > 2rmax, it is also considered that there are irreparable holes. thus, we should fill node in the middle of the triangle side. after nodes are filled, repeat step 1 to construct the new triangle network and the monitored polygon. go on to step 3 until no new nodes should be filled. step 3: detect regional holes. confirm the initial sensing radius of nodes according to expression (9). construct the network model with the minimum triangle and detect the triangle network. to be specific, judge whether a minimum triangle s1s2s3 has sensing holes. if one side of the triangle < s1,s2 > is longer than the total sensing radius of two tip nodes, namely l(s1,s2) > r(s1) + r(s2), as shown in 3(a), then 4s1s2s3 has holes. if three sides fit l < ri + rj, in other word, two circles intersect, the premise for no holes is that any circle intersects with the overlapping part of the other two circles. that is to say, the intersection of any two circles should fall within the sensing radius of the third one. otherwise, 4s1s2s3 has holes, coverage optimization strategy for wsn based on energy-aware 883 as shown in 3(b) and 3(c). when all minimum triangles fit this rule, a set with holes is formed. calculate the number of nodes that have appeared, and form the hole-node set {si, . . .,sj}. s1 s3 s2 r1 r2l(s1,s2) s1 s3 s2 r1 r2l(s1,s2) s1 s3 s2 the total sensing radius < the triangle side the intersection circles with hole the intersection circles without hole figure 3: network hole detection step 4: hole repair. start repairing from the nodes that appeared the most times in the statistics in step 2. if two nodes appear the same times, the one with higher residual capacity is adjusted primarily. for example, si is the first one to be filled. increase the transmitting power of si, by adding its sensing radius, until all triangles with si as the tip node do not have holes. repeat step 4 until all holes are filled, as shown in fig.4 (a) wsn with coverage hole (b) wsn without coverage hole figure 4: coverage hole repair step 5: wipe out the redundant holes. get the complementary set of the triangle set with holes in step 2, namely the minimum triangle set without holes. as the initial sensing range of nodes is big, or two neighboring nodes are close to each other, there may be redundant coverage. related nodes with relatively large sensing radius may consume unnecessary energy. thus, it is necessary to adjust the sensing range of nodes in the complementary set. firstly, calculate the number of nodes that appeared and rank them from the most appeared to the least appeared. priority is given to nodes with less residual capacity. reduce the sensing radius of nodes gradually until no new holes appear in the triangle area. repeat step 5 to all nodes in the complementary set. 884 l. zhu, c. fan, h. wu, z. wen 4 analysis of algorithm performance 4.1 analysis of coverage quality conclusion 1 if the awareness intensity probability of j by node s in monitored area a is p{τ < φ(j) < 1}, where ss1,s2 . . .sn is the node set and τ is the threshold of awareness intensity. when and only when the expectation value of network coverage γ in a is bigger than γ0 (where γ0 is the coverage threshold), namely e[γ] > γ0, the coverage in a meets the requirement of the network coverage. coverage connectivity is one of the important indicators to measure the service quality of wireless sensor network. according to the model, the probability of awareness intensity for node q in monitored area a is p{τ < φ(j) < 1}. elements in the node set s form a connected graph g(v,e), where v is the node set, and e is the side set. nodes are independent from each other. the connectivity degree of g is ξ(g) (n is the number of selected nodes). lim n→+∞ p{γ(sn,v, ξ(g)) ≥ k} = 1 when n approaches infinity, the awareness probability of node in the connected graph increases. so does the connectivity degree. according to the definition, the expectation value of network coverage γ is: e[γ] = e[ ∫∫ a cov(i)da/‖a‖] = p{τ < φ(i) < 1} > γ0 select the sub-areas in the monitored area randomly. according to the probability event, when the connectivity degree has a high probability, the node sets in the area are all connectivity set and the network coverage would also meet the expectation. 4.2 analysis of network lifetime conclusion 2 distribute n sensor nodes in monitored area a. the node set is s = {s1,s2. . .si. . .sn} and si and sj are neighboring nodes. the neighboring node set of si is ni. when the network parameters are set the same as that of the network model, the network lifetime is max ti (ti is the lifetime of node, i = 1, 2......n). node si can be any node in the network. its neighboring node set is ni = {sj,sk. . .. . .sm}. select one neighboring node sj. at this moment, the communication distance of two nodes is within the effective sensing range of si. so the energy consumption between nodes is: q = ∑ j∈ni τijηij + ∑ j∈ni νηji (10) where ηij is the information transmitted by si to sj. τij and νji are energy loss factor when node receiving and transmitting information. under normal condition, the lifetime of si is: ti = $i qi (11) where $i is the residual capacity of si. according to expression (10) and (11), the network lifetime tlife is: tlife = max{t1...ti...tn} coverage optimization strategy for wsn based on energy-aware 885 5 simulation experiment analysis through simulation experiment, this paper makes a comparative analysis on algorithm performance. the setting is described as follows: place 120 sensor nodes in the monitored area 100 × 100m2. the sensing radius is 5-20m and the initial electric quantity is 200j. the ratio of energy consumption of working, leisure and dormant state is 24:5:0.02. eacs algorithm is compared with the distributed random algorithm and payy algorithm (proposed approach of yourim y) proposed by literature [5] to assess the performance of eacs algorithm. network coverage, network overhead and network lifetime are important parameters in the comparison. table 1: experiment parameter parameter value m 100m*100m n 120 to 120s w 0.2j node sampling frequency 1hz r 10m initial energy 200j minimum energy limit 0.02j ro 5m rm 20m in the initial state of network operation, the performance parameters of all nodes in the monitored area are set the same, as shown in fig.5(a). after working for some time, there produce difference on nodes. adjust sensing radius of nodes according to their energy consumption. nodes adjust themselves according to residual capacity and relationship with neighboring nodes, as shown in fig.5(b). in the whole operation, due to differences between nodes and the change of sensing range, there may be redundant areas or coverage holes. hole detection is conducted followed by hole repair and redundancy elimination in other to enhance the coverage, as shown in fig.5(c). (a) (b) (c) figure 5: network coverage network coverage ratio refers to the ratio of the effective coverage area by sensor nodes against the overall monitored area. for random algorithm, operate it for 50 times and get the average coverage. fig.1 shows the network coverage under different network scales of different algorithm. from fig.6, it is known that with the increasing of the number of sensor nodes, 886 l. zhu, c. fan, h. wu, z. wen the network coverage also increases. under the same number of working nodes, the coverage of random algorithm is relatively low. when 100 nodes are started, eacs algorithm can reach 90% coverage, 15% higher than random algorithm. eacs algorithm is better that the other two algorithms in terms of coverage, because such algorithm can adjust the sensing radius according to residual capacity and reduces redundant monitored area. when sensor nodes increase in number, the coverage would increase rapidly. figure 6: working nodes and network coverage figure 7: coverage changes over time fig.7 shows network coverage of three algorithms changing over time. in the initial operation of network, the network coverage changes relatively slowly with little divergence. as time goes by, eacs algorithm witnesses the increase of network coverage, though the energy-aware and hole detection require some energy consumption. from fig.7, it can be told that the coverage curve of eacs algorithm drops steadily, reflecting that the network energy consumption is distributed evenly. fig.8 compares nodes location of payy and eacs algorithm under the same initial settings and after the network operates for some time. working nodes of eacs algorithm is more evenly distributed than those of payy algorithm. this is mainly because eacs algorithm adopts energy-aware model. the sensing radius of nodes is adjusted according to real situation of the network, so that the energy can be evenly distributed and the network lifetime can be extended. figure 8: working nodes distribution coverage optimization strategy for wsn based on energy-aware 887 conclusion this paper proposes a coverage optimization strategy for wsn based on energy-aware, namely eacs algorithm. it can reach energy balance of nodes in the wireless sensor network. re-assign tasks of awareness according to working nodes residual capacity, and adjust the sensing radius of nodes according to probability awareness in order to repair the network holes and redundant areas, improve network coverage and reduce redundant coverage and overall network consumption. results show that the method proposed in this paper takes node residual capacity as an important factor of adjusting sensing field to reduce the burden of nodes with little energy and save them from being "null" too early. otherwise, it may affect the connectivity and the network lifetime. at the same time, when the coverage holes are under repair, adjust the redundant coverage so that the balance of coverage reaches a reasonable range and the network coverage satisfies actual need. as a result, the unnecessary network energy consumption can be reduced effectively, a balance between the network coverage and energy consumption can be reached and the network lifetime can be improved. acknowledgments the work presented in this paper was supported by the national natural science foundation of china (grants no. nsfc-61471067) and the national natural science foundation of china (grants no. nsfc-61271257). fund for the doctoral program of higher education of china (grants no.20120005110002).the work presented in this paper was supported by national great science specific project(grants no. 2012zx03005008) bibliography [1] mohamadi h., ismail a.s., salleh s. 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(2011); efficient detection of composite events in wireless sensor networks: design and evaluation, proc. of the ieee symp. on computers and communications, 10(11):821-823. [13] yourim y., yong h.k. (2013); an efficient genetic algorithm for maximum coverage deployment in wireless sensor network, ieee transactions on cybernetics, 45(5): 1473-1483. [14] hossain a., chakrabarti s., biswas p.k. (2012); impact of sensing model on wireless network coverage, iet wireless sensor systems, 2(3): 272-281. [15] mini s., udgata s.k., sabat s.l. (2014); sensor deployment and scheduling for target coverage problem in wireless sensor networks, ieee sensors journal, 14(3): 636-644. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 551-557 a secure and efficient off-line electronic payment system for wireless networks h. oros, c. popescu horea oros, constantin popescu department of mathematics and computer science, university of oradea str. universitatii 1, oradea, romania e-mail: {horos,cpopescu}@uoradea.ro abstract: an electronic cash system allows the exchange of digital coins with value assured by the bank’s signature and with concealed user identity. in an electronic cash system, a user can withdraw coins from the bank and then spends each coin anonymously and unlinkably. in this paper we propose a secure and efficient off-line electronic payment system based on bilinear pairings and group signature schemes. the anonymity of the customer is revocable by a trustee in case of a dispute. because the amount of communication in the payment protocol is about 480 bits, the proposed off-line electronic payment system can be used in wireless networks with limited bandwidth. keywords: electronic payment system, bilinear pairings, group signatures, membership certificate. 1 introduction chaum suggested the first electronic cash system [5] in 1982. in this system the technique of blind signatures was used to guarantee the privacy of users. various extended systems have been proposed, which provide functionalities such as anonymity, double spending prevention, unforgeability, untraceability and efficiency [1], [4], [8]. off-line electronic cash systems were first introduced in [6] and then developed further in [9], [10], [11], [12]. in off-line systems the bank’s involvement in the payment transaction between a customer and a merchant was eliminated. customers withdraw electronic coins from the bank and use them to pay a merchant (a shop). the merchant subsequently deposits the coins back to the bank. in this paper we propose a secure off-line electronic payment system based on bilinear pairings and group signature schemes. in order to construct our electronic cash system, we use the group signature scheme of d. yao and r. tamassia [16] and the blind signature of schnorr [13]. due to the low amount of communication in the payment protocol that is about 480 bits, our off-line electronic payment system can be used in wireless networks with limited bandwidth. the rest of this paper is organized as follows. in the next section we present our off-line electronic cash system. furthermore, we discuss some aspects of security and efficiency in section 3. finally, section 4 concludes the work of this paper. 2 the proposed off-line electronic payment system an e-cash system is a set of parties with their interactions, exchanging money and goods. a typical e-cash system has three parties: • customer: purchases goods or services from the merchant using the e-cash. • merchant: sells goods or services to the customer, and deposits the e-cash to the bank. • bank: issues the e-cash and maintains the bank account for customers and merchants. and there are also three protocols: withdrawal, payment and deposit. a customer withdraws electronic coins from the bank and pays the coins to a merchant. finally, the merchant deposits the paid coins to the bank. our electronic payment system consists of four types of participants: customers, merchants, banks and trusted parties. the customers honestly withdraw money from the bank and pay money to the merchant. the merchants get money from customers and deposit it in the bank. the banks manage customer accounts, issue and redeem money. the bank can legally trace a dishonest customer with the help of the trusted parties. an e-cash system is copyright c⃝ 2006-2010 by ccc publications 552 h. oros, c. popescu anonymous if the bank in collaboration with the merchant cannot trace the coin to the customer. the system is off-line if during payment the merchant does not communicate with the bank. in our off-line electronic cash system, all customers who open a bank account form a group and a trusted party is the group manager. when a customer wants to withdraw an electronic coin from his account, the bank applies a blind signature protocol [13] to this coin and decreases appropriate amount from the customer’s account. everyone including the merchant can verify the validity of the blind signature. the withdrawals are made by the bank by applying the blind signature of schnorr [13] to a coin randomly selected by a customer and the payments are made by the customer by applying the group signature scheme of d. yao and r. tamassia [16] to the random coin. 2.1 system parameters this operation outputs the system parameters and public/private keys of users that will be used in the system. • the group manager chooses a set of public parameters y = (g1,g2,e,p,h,h ′,h ′′), where g1 and g2 are groups of a large prime order q, g1 is a gap group, e : g1 × g1 → g2 is a bilinear map, p is a generator of g1 and h : {0,1}∗ → g1, h ′ : {0,1}∗ → zq and h ′′ : {0,1}∗ × g1 → zq are three collision-resistant hash functions. the group manager chooses his secret key sa ∈ z∗q and computes the public key pa = sap. • the customer chooses a secret su ∈ z∗q as his private key and computes the product pu = sup as its public key. • the bank selects a random secret xb from the interval [1,q−1] and calculates the point pb = xbp. the public key of the bank is pb and the corresponding secret key is xb. the process for selecting the parameters and generating g1,g2,q,e,p is given in [2]. 2.2 the registration protocol we assume that communication between the customer and the group manager is secure, i.e., private and authentic. any customer who wants to withdraw a coin from the bank has to interact with the group manager and obtains two type of certificates from the group manager. one is long-term group membership certificate, which certifies the customer’s public key information. the other is one-time signing permit, which certifies the customer’s one-time signing key information. the latter is used for issuing signatures in the payment protocol. the registration protocol involves the customer and the group manager as follows: 1. a customer obtains a long-term group membership certificate cert from the group manager. the group manager computes cert = sah(in f o||sup), where sa is the private key of the group manager, sup is the customer’s public key and in f o contains information such as group name and membership expiration date. cert is given to the customer. 2. a customer also obtains one-time signing permits from the group manager. the customer randomly chooses a number of secrets x1,...,xl and computes one-time signing secret keys x1p,...,xl p and one-time signing public keys sux1p,...,suxl p. the keys sup and suxip are sent to the group manager, for all i = 1,...,l. the customer also sends cert to the group manager. 3. the group manager first checks if the customer with public key sup is a valid group member. this is done by verifying the following equality: e(cert,p) = e(pa,h(in f o||sup)) where pa is the group manager’s public key and sup is the customer’s public key. the protocol terminates if e(cert,p) ̸= e(pa,h(in f o||sup)). then the group manager tests if e(suxip,p) = e(sup,xip) for all i = 1,...,l. if the test fails, the protocol terminates. otherwise, the group manager computes: si = sah(in f o||suxip) for all i = 1,...,l. si is an one-time signing permit and is given to the customer. the group manager adds the tuple (sup,xip,suxip) to its record for all i = 1,...,l. a secure and efficient off-line electronic payment system for wireless networks 553 2.3 the withdrawal protocol we assume that communication between the customer and the bank is secure, i.e., private and authentic. the withdrawal protocol allows a customer to withdraw e-coins from the bank. after having open a bank account, the customer withdraws an e-coin from his account by using the blind signature. therefore, the bank cannot link the e-coin to the identity of the customer but can debit to the account correctly. the withdrawal protocol involves the customer and the bank in which the customer withdraws an electronic coin from the bank. first, the customer proves his identity to the bank using the elliptic curve version of the signature scheme of shao [14]. then, the bank uses the elliptic curve version of the blind schnorr signature scheme [13] to sign the e-coin. the customer must perform the following protocol with the bank: 1. the customer sets his electronic cash requirement: m = h ′(withdrawal require||id) where id is the identity of the customer. then, the customer chooses a random value ku ∈ [1,q − 1] and signs the message m using the elliptic curve version of the signature scheme of shao [14]: f = h ′(m) (1) r = ku f p (2) h = h ′′(m,r) (3) s = ku − hsu. (4) the customer sends m and its signature (h,s) to the bank. 2. the bank verifies the signature (h,s) of the message m: (a) the bank first computes f = h ′(m), r′ = f (hpu + sp) and h′ = h ′′(m,r′). (b) then, the bank checks that the following equality holds: h = h′. (c) if h ̸= h′ the protocol terminates. 3. then, the bank uses the elliptic curve version of the blind schnorr signature [13] to sign the e-coin: selects k ′ ∈ [1,q −1], computes the point r′′ = k ′p and sends r′′ to the customer. 4. the customer establishes a random coin c, randomly selects α,β ∈ [1,q − 1], computes rb = r′′ + α p + β pb,cb = h ′′(c||α,rb) and blinds the e-coin by computing c′ = cb − β mod q. the customer sends the value c′ to the bank. 5. the bank computes: s′ = k ′ − c′xb mod q and forwards s′ to the customer. 6. the customer computes sb = s′ + α mod q. the pair (cb,sb) is a valid e-coin signature issued by the bank. 7. the customer verifies the blind signature (cb,sb) of the coin c, issued by the bank, by checking that the following equation holds: sbp + cbpb = rb (5) 8. the blind signature of the coin c is the pair (cb,sb). the customer gets the coin c from his account. 2.4 the payment protocol the payment protocol involves the customer and the merchant and should be done through a secure channel (i.e., data privacy and integrity). in the proposed system, during payment the merchant does not communicate with the bank. after withdrawing e-coins, the customer can pay for what the merchant provided. then the merchant verifies the validity of the received e-coins. in order to sign the coin c, the customer uses the protocol of yao and tamassia [16]. the merchant first sends a challenge cm to the customer. then, the customer produces a signature su of the coin c and merges the signature su with his one-time signing permit si associated with the secret suxi. the details are as follows: 554 h. oros, c. popescu 1. the merchant sends challenge cm = h ′(idm||t ) to the customer, where idm is the merchant’s identity and t is the recorded time of the transaction. 2. the customer computes: cu = h ′(c||cm||cb||sb) (6) 3. the customer computes su = suxih(cu). 4. the customer computes the signature s = su + si, where si = sah(in f o||suxip). 5. the customer sends c,cu and the signature s = su + si of the coin c to the merchant. 6. the merchant verifies the signature s of the coin c as follows: (a) computes the hash digest h(cu) and the hash digest h′ = h(in f o||suxip) of one-time signing permit. (b) the signature s is accepted if e(s,p) = e(pa,h ′)e(suxip,h(cu)). (7) if the test fails, the protocol terminates. 2.5 the deposit protocol the deposit protocol permits the merchant to deposit the received e-coins to the bank. when receiving the deposited requirement from the merchant, the bank first verifies the validity of received e-coins and then credits the account of the merchant. in on-line e-cash systems this protocol is part of the payment protocol as executed by the merchant. in our e-cash system, the deposit protocol is executed at a later moment, preferably in batch mode. the bank holds a record of spent cash to prevent double spending of e-cash. the bank cannot link deposited coins to a customer without collaboration from the group manager. the deposit protocol involves the merchant and the bank as follows: 1. the merchant sends c,cm,cu,cb,sb to the bank. 2. the bank verifies the signature as given in the equation (6). 3. after verification succeeds, the bank checks if c obtained from the merchant exists in its database. if the coin c is in the database of the bank, then the bank finds the signature s′ for the deposited coin in its database and sends it to the merchant (detection of double spending). 4. if the merchant receives s′ from the bank, he/she checks whether s′ = s. if s′ = s, then the merchant rejects performing protocol (double spending). otherwise, the merchant sends cu and t to the bank. 5. the bank verifies the validity of the signature s using the equation (7). 6. if the signature s of the coin c is valid, then the bank accepts the coin c. then, the bank will deposit the cash to the merchant’s account and the merchant sends the goods to the customer. the bank stores c and (cu,suxup) in its database. 7. if the bank finds out that c and (cu,suxup) has been stored before but different t and cm, then the coin c has been double spending. the bank performs the tracing protocol and detects the identity of the double spender with the help of the group manager. 2.6 the tracing protocol the bank can legally trace the customer of a paid coin with the help of the group manager. the tracing protocol involves the bank and the group manager. given a signature s and its associated public information pa and suxip, the group manager verifies the signature s. if the signature s is valid, the group manager can identify a customer’s public key sup from suxip value, by consulting the customer group record. the details are as follows: 1. the bank sends cu and the signature s of the coin c to the group manager. 2. the group manager verifies the signature s using the equation (7). a secure and efficient off-line electronic payment system for wireless networks 555 3. the group manager can easily identify the customer from suxip. the group manager can provide a proof that it is indeed the customer’s signature from the following equations: e(suxip,p) = e(sup,xip) (8) 4. the group manager searches through the group customer list to get the identity of the customer and sends it to the bank. similar to what is shown in the group signature scheme of chen et al. [7], the group manager cannot misattribute a signature to frame the customer unless he can compute bp given q, p, ap and dp which satisfies: a ≡ db mod q (9) the authors in [7] define this problem the reversion of computation diffie-hellman problem. they prove that the reversion of computation diffie-hellman problem is equivalent to computational diffie-hellman problem in g1. 3 security and efficiency analysis in this section we discuss some aspects of security and efficiency of our off-line electronic payment system. we prove that our off-line electronic payment system is secure against tracing a honest customer by the bank and the proposed system is secure against forgery of the coin. theorem 1. our off-line electronic payment system is secure against existential forgery of the coin c. proof: long-term membership certificates, one-time signing permits and customer’s signatures using one-time secret signing keys are generated by the sign protocol of the signature scheme of boneh, gentry, lynn and shacham [3]. the authors in [3] shown that their signature scheme is secure against existential forgery attacks. therefore, if an adversary can forge any of these signatures, she can also forge signatures in the signature scheme of boneh et al. [3]. note that a signature computed with one-time secret signing key is in the form of suxih(cu), rather than suh(cu) as in the signature scheme [3]. it can be easily shown that if an adversary can forge a signature in a form of suxih(cu), then she can forge a signature in the form of suh(cu). also, since the blind signature of schnorr is secure against existential forgery, this allows only the legal bank to generate the signature for coin. as the hash function h ′ has the feature of collision free, the customer cannot find a value c′ ̸= c with h ′(c′||cm) = h ′(c||cm). thus, our payment system satisfies unforgeability of the coin. 2 theorem 2. the both valid signatures s and (cb,sb) in our payment system contain a proof of the group membership without revealing the identity of the customer. proof: a valid signature s is obtained from an one-time signing permit of a customer and the customer’s signature using the corresponding one-time signing key. that is s = si +su, where si = sah(in f o||suxip) and su = suxih(cu). because of the definition of signatures [3], a valid signature s implies that si is valid. this proves that the holder of key suxip is a certified customer. a valid s also means that su is valid, therefore su is generated with the secret key suxi. thus, s contains a proof of the customer membership. because the signing key suxi is one-time signing key and xi is chosen randomly by the customer, the identity of the customer is not revealed. also, since cu = h ′(c||cm||cb||sb) and the blind signature (cb,sb) of the coin c can not give any information for the coin c, the bank can not link the blind coin with the identity of the customer. 2 table 1: storage space of the payment systems our system wang lee au canard withdrawal 1120 bits 1824 bits 800 bits 8160 bits 6420 bits payment 480 bits 1282 bits 1304 bits 5188 bits 30740 bits deposit 960 bits 3232 bits 1656 bits 5164 bits 27648 bits 556 h. oros, c. popescu table 2: computation cost of the payment systems our system wang lee au canard withdrawal protocol multi-exp 8 9 15 2156 5 pairing 0 0 0 22 0 payment protocol multi-exp 2 11 9 34 1673 pairing 3 0 0 14 0 deposit protocol multi-exp 0 5 7 10 14 pairing 3 0 0 0 0 next, we evaluate the storage space and computational time of the costly operations. table 1 and table 2 summarize the storage space and computation cost respectively, of different protocols of our e-cash system and the schemes in [1], [4], [9] and [15]. the overall efficiency is improved in our electronic cash system compared to au et al.’s system [1], canard et al.’s system [4], lee et al.’s system [9] and wang et al.’s e-cash system [15] in terms of the storage space and the computation cost. our system has a point p of 160 bits and q of 160 bits. the off-line e-cash system proposed by lee et al. has a point p of 160 bits and 160 bits prime q and the system of wang et al. has 160 bits prime q and 321 bits prime p. spending a coin in [15] requires 11 multi-based exponentiations and a total bandwidth of 1282 bits. the payment protocol in [9] requires 9 multi-based exponentiations and a total bandwidth of 1304 bits. for a moderate value l = 10 and t = 40, the payment protocol in [4] requires 1673 multibased exponentiations and a total bandwidth of 30740 bits. the payment protocol in [1] requires 34 multi-based exponentiations, 14 pairings and a total bandwidth of 5188 bits. in contrast, the payment protocol in our e-cash system requires 2 multi-based exponentiation, 3 pairings and a total bandwidth of 480 bits. 4 conclusions in this paper we presented a secure and efficient off-line electronic payment system based on bilinear pairing and group signature schemes. we used the group signature scheme of yao and tamassia and the blind signature of schnorr. because the amount of communication between customer and merchant is about 480 bits, the proposed off-line payment system can be used in the wireless networks with the limited bandwidth. bibliography [1] m. au, w. susilo, y. mu, practical anonymous divisible e-cash from bounded accumulators, proceedings of financial cryptography and data security, lecture notes in computer science 5143 springer-verlag, pp. 287-301, 2008. [2] d. boneh and m. franklin, identity-based encryption from the weil pairings. advances in cryptology-crypto 2001, lecture notes in computer science 2139, springer-verlag, pp.213-229, 2001. [3] d. boneh, c. gentry, b. lynn, and h. shacham, aggregate and verifiably encrypted signatures from bilinear maps. in advances in cryptology eurocrypt’03, lecture notes in computer science 2656, springer-verlag, pp. 416-432, 2003. [4] s. canard, a. gouget, divisible e-cash systems can be truly anonymous, proceedings of eurocrypt 2007, lecture notes in computer science 4515, springer-verlag, pp. 482-497, 2007. [5] d. chaum, blind signature for untraceable payments. proceddings of eurocrypt’82, plenum press. pp.199203, 1983. [6] d. chaum, a. fiat, m. naor, untraceable electronic cash, proceedings of the crypto’88, pp. 319-327, 1990. [7] x. chen,f. zhang, k. kim, a new id-based group signature scheme from bilinear pairings. journal of electronics, 23, pp. 892-900, 2006. a secure and efficient off-line electronic payment system for wireless networks 557 [8] c. fun, ownership-attached unblinding of blind signatures for untraceable electronic cash, information science, 176(3), pp. 263-284, 2006. [9] m. lee, g. ahn, j. kim, j. park, b. lee, k. kim, h. lee, design and implementation of an efficient fair off-line e-cash system based on elliptic curve discrete logarithm problem, journal of communications and networks, 4(2), pp. 81-89, 2002. [10] t. okamoto, k. ohta, universal electronic cash, proceedings of the 11th annual international cryptology conference on advances in cryptology, pp. 324-337, 1992. [11] t. okamoto, an efficient divisible electronic cash scheme, proceedings of crypto’95, lecture notes in computer science 963, springer-verlag, pp. 438-451, 1995. [12] c. popescu, an electronic cash system based on group blind signatures. informatica, 17(4), pp. 551-564, 2006. [13] c.p. schnorr, efficient signature generation for smart cards, journal of cryptology, 4(1991), pp. 239-252, 1991. [14] zuhua shao, a provably secure short signature scheme based on discrete logarithms, information sciences: an international journal, vol.177(23), pp. 5432-5440, 2007. [15] h. wang, j. cao, y. zhang, a flexible payment scheme and its role-based access control. ieee transactions knowledge data engeneering, 17, pp. 425-436, 2005. [16] d. yao, r. tamassia, cascaded authorization with anonymous-signer aggregate signatures. proceedings of the seventh annual ieee systems, man and cybernetics information asssurance workshop, usa, pp.84-91, 2006. horea oros (b. august 22, 1977) received his phd in computer science (2009) from “babeş bolyai” university of cluj-napoca, romania. since 2001 he is working within the department of mathematics and computer science, faculty of sciences, university of oradea, romania, where currently he is a lecturer. he also is lecturer at agora university of oradea. he co-authored three books in the filed of computer science and published 19 articles in several journals and proceedings of prestigious international conferences. his main research interest is in the field of cryptology and computer security. constantin popescu (b. october 21, 1967) received his phd in computer science (2001) from “babeş bolyai” university of cluj-napoca, romania. since 2005 he is a professor at the department of mathematics and computer science, university of oradea, romania. his research interests include cryptography, network security, group signatures, security protocols and electronic payment systems. he co-authored 7 books in the field of computer science and published 49 articles in several journals and proceedings of prestigious international conferences. he is reviewer for 10 journals and several prestigious international conferences. int j comput commun, issn 1841-9836 8(1):37-41, february, 2013. bio-inspired sensory systems in automata for hazardous environments l. canete lucio canete universidad de santiago de chile av. ecuador 3769, estación central, santiago de chile, chile. e-mail: lucio.canete@usach.cl abstract: every automaton in dynamic and complex environments requires sensory systems with an appropriate level of attention on the hazardous environment. this property in any efficient automaton is analogous to that observed in animal sensory systems. in this context, it is noted that to ensure its viability, the sensory systems of animals must maintain a continuous state of alertness or attention to the environment. however, the state consumes energy so it is impossible to keep a constant level over time. in this regard, biologists have designed models for explaining the variation in the level of surveillance in two vital activities of animals: work and rest. in an alternating pattern between work and rest, the attention level v(t) declines and increases as the animal works and rests respectively along the time. for each of the two states, there is one relation: dv/dt = −α * v while working and dv/dt = β*(1-v) while resting. in this model α is the loss rate of surveillance that depends on the difficulty of the work and β is the recovery rate which depends on the quality of rest. in the case of automata, this phenomenon is analogous to that observed in the animal kingdom. even if the automatic machines have relief structures to monitor their environments, they always require that its sensory system recovers the alertness after being hit by the inexorable entropy. if the task is hard (α is large), the attention level decreases rapidly. once the level has dropped below a threshold of tolerance, it must be recovered. if rest is poor, the automaton will take a lot of time to achieve the desired level. obviously, machines do not rest, but in analogous terms, this phenomenon is emulated in the way of maintenance activities. parameter β represents the quality of these maintenances. this model has been tested with computer simulations to study the performance of automatic machines in hostile environments. after tests, it was possible to quantify α and β for each kind of task-environment and each kind of maintenance. the bio-inspired model showed to have explicative and predictive applications to the conquest of hostile scenarios by means of automata. indeed it is an interesting conceptual tool for increasing the performance of machines. keywords: attention level, model, performance, emulation, automata. 1 introduction in the continuing quest to provide new qualities in automata to improve productivity in crisis scenarios, this work uses the market of ideas in the sense that each field of knowledge is not selfsufficient and therefore must rely on others to import ideas [1]. biology is in this paper the source of ideas. this science shows that over the 3.8 billion years since life is estimated to have begun to appear on earth, evolution has resolved many of nature’s challenges leading to lasting solutions with maximal performance using minimal resources [2]. can biology export ideas toward automation? of course, there are many examples, but the more representative is a robot. this device is a non living thing with some qualities emulated from humans, manufactured to replace them [3]. after all biology and automation have something in common: the study of entities capable of operating by they own. copyright c⃝ 2006-2013 by ccc publications 38 l. canete in the same way, both automaton and living been have in common a vital component to interact with environments: a sensorial system [4]. in this context, the biological studies about the behavior of this system in living things, maybe animals, can be useful for improve the behavior of automata. regarding these assumptions, the present work examines how to model in automaton the level of attention on the hazardous environment. the desired model can contribute to better management of robots and other automata in crisis scenarios, especially where automation has not satisfactory solutions [5]. for reach this goal, this paper identifies a requirement from automation, then search for the right supply in biology and finally applies the idea in a real situation. 2 a requirements from automation for facing complex and dynamic environments, any human organization must maintain a high level of attention to the outside to ensure its viability [6]. this requirement is stronger when the environment is adverse cause of either natural or cultural variables. performing a continuous task, this level decays because the central nervous system of human been cannot sustain a high quality of information processing for a long time [7]. then, the technology allows to replace human been by automata for watching the hazard environments. nevertheless, when automata performance tasks, they reduce their level of attention too. indeed, automata are made of structures which obey of the entropy law [8]. so, in metaphorical sense, automata get tired. this tiredness emerges when their sensory systems show a gradual reduction in performance or vigilance decrement. in fact, optical and mechanical pieces of the sensory system are exposed to environmental aggression such as weathering of shocks. it is possible to know in real time how tired is the automaton? what can do manager whether the know this level ? this kind of question can be answered by biology. 3 a supply of biology any decay of this level may involve a loss of information and thus jeopardize the viability of the automaton. biologists note that subjects that perform a continuous and difficult task (such a working in crisis scenarios) "show a gradual reduction in performance or vigilant decrement" [9]. vigilance implies a general state of alertness that results in enhanced processing of information by brain. performing a difficult task reduces the quality of information processing and this reduction results in decreased performance over time [10]. after perform a task, animals rest and recover the vigilance. this recovery depends on the quality of rest. regarding this phenomena, ecologists assumed that a forager under risk of predation and the same time looking for food, alternates between two short periods of activity and rest [11]. this model explain the variation of attention level v(t): dv/dt = { −α ∗ v while foraging β ∗ (1 − v ) while resting (1) α: rate of vigilante decrement, positive and associated with task difficulty; β: rate of vigilante recovery, positive and associated with quality of rest. bio-inspired sensory systems in automata for hazardous environments 39 figure 1: graphic representation of the biological model 4 the imported model does an automaton get tired? in metaphorical or analogous sense: yes [12]. after any task, the automaton will be deteriorated and the more deteriorated it is, the lower will be the attention level. in management, it is important to know this level because, like persons, it is possible to allocate the work in charge. if the automaton is very tired, its sensory system probably is not in optimal state and this quality of survey will be unsatisfactory, so it is not advisable to apply. how does the automaton recover the satisfactory attention level? stop performing the task and start maintenance, this is the way to recover it. how fast does the attention level decrease and recover. this speed is determined by α and β respectively. how can managers determinate these parameters? each task and each maintenance has an own α and β respectively, both measurable. for getting numerical indicators, the phenomenon was studied in an autonomous vehicle in order to survey (collect field data) environmental variables in patagonia. this rover in figure 2 was programmed to cycle through different sections of equal length, in the same direction at the same speed and other conditions ceteris paribus, differing only in sections of the terrain roughness, measured as the vertical dispersion over an imaginary horizontal line of the surface measured longitudinally every 0.1 m. when the vehicle did the tours, it was assumed that was on stage for the rigorous entropy field and affects the mechanical components including optical. when the automata faced irregular geomorphology and bad weathers, the attention level declined rapidly cause of environmental aggression on mechanical and optical devices; so its performance was unsatisfactory. in friendly environments the attention level declined slowly, requiring small maintenances for retake the level desired. this behavior is shown in figure 3. figure 2 shows how decreased the percentage the achievements (identification of cryptic elements) cause of reduction attention level. after many tests, it was possible to quantify α and β for each kind of task-environment and each kind of maintenance. with this knowledge, managers took new and better decisions. for example set a minimum tolerance vo for attention level. any lower than this, they rejected the work of robots. the bio-inspired model showed to have explicative and predictive applications to the conquest of hostile scenarios by means of automata. indeed it is an interesting conceptual tool for increasing the performance of machines. 40 l. canete figure 2: the rover which "worked" in patagonia figure 3: variation observed of attention level in two kinds of environments in patagonia bio-inspired sensory systems in automata for hazardous environments 41 5 conclusions given the syntax, semantics and praxis of the model, it was possible tested it with computer simulations to study the performance of automatic machines in hostile environments. besides, there were tests in different terrains, most of them in extreme zones of chile to study the quality of the information gathered by surveyor robots. when the automata faced irregular geomorphology and bad weathers, the attention level declined rapidly cause of environmental aggression on mechanical and optical devices; so its performance was unsatisfactory. in friendly environments the attention level declined slowly, requiring small maintenances for retake the level desired. the behavior observed confirmed the hypothesis of this work. this model inspired by biological phenomena has an explanatory utility and a predictive use because it can measure the level of difficulty of tasks and the recovery rate (α and β respectively), to forecast the decay of productive factor of interest. even known values of α and β and several observations regarding the performance, it can be through a statistical procedure to discover the function v = f (α,β), a mission that the author of this trial have begun to develop. the inspiration observed in living phenomena may have more similarities in inert bodies and therefore opens an interesting line of research that contributes to better management of productive factors. bibliography [1] morin e., introduction á la pensée complexe, editions du seuil, 2005. [2] ovchinnikov y., basic tendencies in physico-chemical biology, mir publisher, 1987. [3] siciliano b., sciavicco l., villani l., oriolo g., robotics: modelling, planing and control, springer, 2010. [4] maturana h., varela f., de máquinas y seres vivos, editorial universitaria, 1994. [5] siegwart r., nourbakhsh i., scaramuzza d., autonomous mobile robots, the mit press, 2011. [6] pérez j., design and diagnosis for sustainable organizations: the viable system method, springer, 2012. [7] mason p., medical neurobiology, oxford university press, 2011. [8] atkins p., four laws that drive the universe, oxford university press, 2007. [9] dukas, r., constraints on information processing and their effects on behavior, the university of chicago press,1998. [10] gendron r., staddon j., searching for cryptic prey: the effects of search rate, american naturalist, issn 00030147, 121: 172-186, 1983. [11] parasuram r., mouloua m., interaction of signal discriminability and task type in vigilance decrement, perception, issn 0301-0066, 41: 17-22. [12] gleich p., pade c., petschow c., pissarskoy e., potencials and trends in biomimetics, springer. 2009. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 2, pp. 205-215 on the use of the fuzzyartmap neural network for pattern recognition in statistical process control using a factorial design j. a. vazquez-lopez, i. lopez-juarez, m. peña-cabrera j. a. vazquez-lopez instituto tecnologico de celaya ciatec, a.c. posgrado interinstitucional en ciencia y tecnologia (picyt) industrial engineering department celaya, gto. mexico e-mail: antoniov@itc.mx i. lopez-juarez centro de investigacion y de estudios avanzados del ipn unidad saltillo robotics and advanced manufacturing department carretera saltillo-monterrey km 13. ramos arizpe, coahuila. mexico e-mail: ismael.lopez@cinvestav.edu.mx m. peña-cabrera national autonomous university of mexico engineering computing science and automation department institute of research on applied mathematics and systems mexico, city. mexico e-mail: mario@leibniz.iimas.unam.mx abstract: time-series statistical pattern recognition is of prime importance in statistics, especially in quality control techniques for manufacturing processes. a frequent problem in this application is the complexity when trying to determine the behaviour (pattern) from sample data. there have been identified standard patterns which are commonly present when using the x chart; its detection depends on human judgement supported by norms and graphical criteria. in the last few years, it has been demonstrated that artificial neural networks (ann’s) are useful to predict the type of time-series pattern instead of the use of rules. however, the ann control parameters have to be fixed to values that maximize its performance. this research proposes an experimental design methodology to determine the most appropriate values for the control parameters of the fuzzyartmap ann such as: learning rate (β ) and network vigilance (ρa, ρb, ρab) in order to increment the neural network efficiency during unnatural pattern recognition. keywords: statistical process control, control charts, artificial neural network (ann), fuzzyartmap, and factorial design. 1 introduction to preserve product quality an accurate knowledge of the production process is necessary. this requires the automation of quality control systems and the use of control charts as introduced by dr. walter a shewart to observe the behaviour of the manufacturing process. control charting is the key point in statistical process control (spc) implementation. the correct application of these control charts requires satisfying statistical assumptions such as the independence of the random variable and symmetry in its probability distribution [1]. if these assumptions are met then the use of control charts is correctly applied since the upper and lower limits are established as ±3σ from the global mean of the x random variable. in figure 1, the probability distribution of x is shown under both circumstances with symmetry and without symmetry. copyright c⃝ 2006-2010 by ccc publications 206 j. a. vazquez-lopez, i. lopez-juarez, m. peña-cabrera figure 1: x symmetry importance to establish the control limits the power of an x chart is based on its capacity to differentiate special and natural causes of variation; however, important disadvantages on the use of this form of quality control exist because traditional control chart with control limit can only indicate when to seek a disturbance and not where and what to look for, generating then hurried and sometimes mistaken diagnostics [2]. trying to know where and what has happened in the manufacture process may be possible by using pre-established rules in combination with human judgment. these rules are commonly referred to as: points outside the control limits, run of consecutive points, non-random patterns and points near the control limits [1]. the efficiency of the use of these rules has been investigated and it has been found that is not enough to recognise the type of statistical pattern ([3], [4], [5], [6], [7], [8], [9], [10], [11], [12]), which would give the correct answer to the questions of where and what to look for. this is why researchers suggest the use of the neural networks as an alternative approach to identify variation data in statistical patterns [4]. a neural network is a soft computing system [25], which consists of a number of elements (nodes) strongly interconnected that have the ability to process information as a result of a process of dynamic work of those nodes and connections to external points to the network [13]. neural networks are efficient in recognizing data variation [2], [5], [14], especially in asymmetric probability distributions [2]. among the existing neural networks, the fuzzy artmap network is widely recognized due to its on-line and fast learning capability for pattern recognition tasks [15], [16]. for many processes of manufacture, the parameters can be obtained with neural network, with exception in many complex biotechnological processes. in these cases, [23] proposed the use of grey-box models which combine a priori knowledge expressed in terms of a white-box model, with a black-box model such as neural network, and [24] developed a matlabrtoolbox for the construction of grey-box neural network models. this paper is organised as follows: in the next section an introduction to the art theory is given first followed by the standardisation and coding algorithm for the input data in the neural network using the monte carlo method. section 3 describes the statistical pattern generation. training and testing results are provided in section 4. section 5 describes the factorial design for the selection of the network parameters as well as the experimental results. an analysis for the pattern variation findings is given in section 6 while conclusions and future work are given in section 7. 2 adaptive resonance theory the adaptive resonance theory (art) [17] was developed by stephen grossberg and gail carpenter at boston university to solve the called stability-plasticity dilemma. that is, the system is sensitive to novelty capable of distinguishing between familiar and unfamiliar events (plastic) and still remains stable. different model variations have been developed to date based on the original art-1 algorithm on the use of the fuzzyartmap neural network for pattern recognition in statistical process control using a factorial design 207 for binary input patterns [18], art 2-a for analogue and binary input patterns [19], and art 3 based on chemical transmitters. supervised learning is possible through artmap [20] that uses two art modules and its variants, fuzzy artmap [16], gaussian artmap [21] and art-emap even though there are many other variants adapted for specific applications [22]. in the next section a brief explanation of the mechanics of art-1 and fuzzy artmap is given. 2.1 art-1 the art-1 architecture consists of two parts: attentional subsystem and orienting subsystem as illustrated in figure 2. the attentional subsystem is made up of two layers of nodes f1 and f2. in an art network, information in the form of processing-element output reverberates back and forth between layers. if a stable resonance takes place learning or adaptation can occur. on the other hand, the orienting subsystem is in charge of resetting the attentional subsystem when an unfamiliar event occurs. figure 2: basic art architecture a resonant state can be attained in one of two ways. if the network has learned previously to recognise an input vector, then a resonant state will be achieved quickly when that input vector is presented. during resonance, the adaptation process will reinforce the memory of the stored pattern. if the input vector is not immediately recognised, the network will rapidly search through its stored patterns looking for a match. if no match is found, the network will enter a resonant state whereupon the new pattern will be stored for the first time. thus, the network responds quickly to previously learned data, yet remains able to learn when novel data is presented, hence solving the so-called stability-plasticity dilemma. the activity of a node in the f1 or f2 layer is called short-term memory (stm) whereas the adaptive weights are called long-term memory (ltm). gain controls handle the discrete presentation of the input signals. a vigilance parameter measures how much mismatch is tolerated between the input data and the stored patterns, which can be used to control the category coarseness control of the classifier. 2.2 fuzzy artmap in the fuzzy artmap (fam) network there are two modules arta and artb and an inter-art module map − f ield that controls the learning of an associative map from arta recognition categories to artb categories. this is illustrated in figure 3. the map field module also controls the match tracking of arta vigilance parameter. a mismatch between map field and arta category activated by input ia and artb category activated by input ib 208 j. a. vazquez-lopez, i. lopez-juarez, m. peña-cabrera increases arta vigilance by the minimum amount needed for the system to search for, and if necessary, learn a new arta category whose prediction matches the artb category. the search initiated by the inter-art reset can shift attention to a novel cluster of features that can be incorporated through learning into a new arta recognition category, which can then be linked to a new art prediction via associative learning at the map − f ield. figure 3: fuzzyartmap architecture a vigilance parameter measures the difference allowed between the input data and the stored pattern. therefore this parameter is determinant to affect the selectivity or granularity of the network prediction. for learning, the fuzzyartmap has 4 important factors: vigilance in the input module (ρa), vigilance in the output module (ρb), vigilance in the map field (ρab) and learning rate (β ). these were the considered factors in this research. 2.3 standardization and codification the use of the neural network requires two important mathematical considerations, which are the standardization and the codification of the input data [2]. the training and testing data needs to be preprocessed in these two stages. the standardization means that the data have to be linearly transformed from data with mean (µ ) and standard deviation (σ ) into data with µ = 0 and σ = 1 using equation 1, as a result, the sample data is within the interval (-3.9, +3.9). yt = ( xt − µ σ ) (1) where: xt = sample value at sampling time t. yt = standardized value from xt . µ = process mean. σ = process standard deviation. the xt data are generated by a process simulator of monte carlo, according to equation 2 xt = µ + nt + dt (2) where: µ = process mean. nt = common cause variation at sampling time t. on the use of the fuzzyartmap neural network for pattern recognition in statistical process control using a factorial design 209 dt = special disturbance at time t (dt = 0 when the pattern is natural). shift. dt = ud (3) where: u = parameter to determine the position of shifting (0: before shifting; 1: after shifting). d = displacement of mean in terms of σ . trend slope. dt = st (4) where: s = trend slope in terms of σ . t = sampling time. on the other hand, with the codification of yt the variation interval of [0,1] is obtained, which is a requirement for the neural network operation that reduces the effects of common causes of variation (noise) [2]. the codification of xt considered the interval [-7.625, 7.625], whose range is greater to the expected yt range. 3 pattern data generation a specific value xt of sample data is obtained from the sum of three mathematical considerations: • global and historical effect (µ ). • natural variation effect (nt ). • disturbance variation effect (dt ). mathematically, equation 2 expresses this situation. in terms of industrial quality, these effects can be thought of as the global and historical mean obtained from experience (i), thought of as data variation which is unavoidable and it is always present (ii); and finally, the data variation due to disturbances which is associated to special causes that may cause the process to be out of statistical control (iii). when a sample data has only influence on natural causes of variation, then nt > 0 and dt = 0, and the pattern data will be natural. on the other hand, if dt > 0, then the pattern data will be unnatural, and it means that a cause of special variation has occurred in time t. it must be noticed that 0 < nt < dt for any type of special pattern data. if the dt value is very similar to nt then neural network output can be misleading between a special pattern and a natural one. 3.1 natural pattern the data used for this pattern were generated using the monte carlo simulator using equation 2 with µ = 0 and σ = 1. an example of this type of pattern is shown in figure 4. the graph data comes from a time-series of x that did not consider any trend or shift in the global mean and with data distribution randomly assigned. 3.2 shift pattern data used for either downward shift or upward shift shows two data set separated by an abrupt change as shown in figure 5. this occurs because the reference mean also changes. this can be positive or negative and its magnitude depends on the special variation cause in the manufacturing process. 210 j. a. vazquez-lopez, i. lopez-juarez, m. peña-cabrera figure 4: natural pattern figure 5: shift patterns (a)upwards (b)downwards 3.3 trend pattern the type of pattern can be distinguished at a glance due to its upward or downward trend. in terms of mathematically what happens is that for any xt in the data series (where t is not the last data), there will be points in time t +1,t +2,t +3,...,t + n of higher magnitude (upward trend) or lower magnitude (downward trend). through linear regression is always possible to find out the slope magnitude which is also the magnitude of the effect that caused the special variation. this type of pattern can be observed in figure 6. figure 6: trend patterns (a)upwards (b)downwards 4 training and testing five pattern types with diverse effects of special variation as mentioned above were studied. table 1 shows the corresponding information indicating the used values for dt and the output binary code used during the training and testing phase. the number of patterns for the input vector was 51 for training and 1,350 for testing. 5 factorial design the experimentation required 120 tests based on 8 runs and 3 replicates. the information for the experimental design is shown in table 2. an analysis of variance from multiple experiments revealed the on the use of the fuzzyartmap neural network for pattern recognition in statistical process control using a factorial design 211 table 1: dt values considered for each pattern type and output vector code pattern type dt code natural 0.0 1 0 0 0 0 upward shift +0.5,+1.5,+2.5,+3.5 0 1 0 0 0 downward shift −0.5,−1.5,−2.5,−3.5 0 0 1 0 0 upward trend +0.1,+0.2,+0.3,+0.4 0 0 0 1 0 downward trend −0.1,−0.2,−0.3,−0.4 0 0 0 0 1 significant factors of operation for the neuronal network. the results are given in section 5.1 and in all cases, there were no violations to the normality and independence of the residuals ei, j. table 2: experimental design 5.1 experimental results the results lead to the identification of the factors that most influence the prediction efficiency of the neuronal network (characterization). results also showed the determination of the best combination for the factor levels (relative optimization) that generated higher efficiencies. it was observed that at the level of significance of the experimental test, all the factors influence in the efficiency of the neural network. the relative optimal levels are shown in table 3. the efficiency of the neural network (η ) considering the factors and levels indicated in table 3 are given in table 4 and figure 7. the experimental test validation was carried out with 17,000 data samples created by simulation. 212 j. a. vazquez-lopez, i. lopez-juarez, m. peña-cabrera table 3: ann factors and optimal levels factors optimal levels a (ρa1) 0.8 b (ρab1) 0.2 c (β1) 1.0 d (ρb1,2) 0.8 e (ρab2) 0.2 f (β2) 0.2 g (ρa2) 0.6 table 4: neural network efficiency (η) pattern data η natural 30 % upward shift 93.5 % downward shift 96.4 % upward trend 99.8 % downward trend 96.4 % 6 analysis of pattern variation there is a direct relationship between the value dt assumed by each special pattern data (table 1), the value of the standard deviation (σ ) of the sample data and the efficiency of the neuronal network (η ). table 5, shows this situation. in all cases, while the absolute value of dt increases the corresponding σ and η also increases. a polynomial regression analysis was carried out (σ and dt are the independent variables and η the dependent variable). the coefficient of correlation was high and typically above 89% (see table 6). this fact indicates that as dt approaches nt and σ is low then the neural network may predict a wrong pattern. on the opposite, with higher values of dt and σ the neural network prediction efficiency increases. table 5: relationship between dt , standard deviation (σ ) and the neural network efficiency (η ). pattern type parameters values upward shift η 76.0% 86.8% 92.7% 93.5% downward shift η 42.4% 77.6% 88.2% 96.4% dt ±0.5 ±1.5 ±2.5 ±3.5 σ 1.009 1.237 1.620 2.089 upward trend η 22.0% 25.0% 73.8% 99.8% downward trend η 79.1% 82.8% 95.8% 96.4% dt ±0.1 ±0.2 ±0.3 ±0.4 σ 0.539 0.670 0.862 1.080 on the use of the fuzzyartmap neural network for pattern recognition in statistical process control using a factorial design 213 figure 7: neural network efficiency (η ) vs dt table 6: equations for the multiple linear regressions pattern type multiple regression r2 upward shift efficiency = 107.7+21.4(dt)−42.5(σ) 99.8 downward shift efficiency = 132.2−61.0(dt)−119.7(σ) 99.3 upward trend efficiency = −121.5−294.1(dt)+317.5(σ) 96.0 downward trend efficiency = 77.3−92.3(dt)−15.0(σ) 89.21 7 conclusions this investigation confirms the obtained results from previous studies with respect to the efficiency of the neural network in the recognition of statistical pattern data. it is also demonstrated, that as the effect of the special cause approaches close to zero, the efficiency decreases because the standard deviation of these data is smaller or equal to 1, i.e., the standard deviation of a data set from a natural pattern. another result from this investigation is the definition of the most appropriate parameter values for the fuzzyartmap that facilitated the use of this neuronal network. it is important to mention that in this application the efficiency of the network for the control chart pattern recognition depends greatly on the dt and σ values. future work has been envisaged to look at the dt and σ values in the confusion zone and their relationship with the sampling window size in order to analyse the neural network behaviour in this zone. acknowledgements the authors wish to thank ciatec, a.c., and the posgrado interinstitucional en ciencia y tecnologia (picyt) for the facilities provided to j. a. vazquez-lopez during his doctoral studies. bibliography [1] d.c. montgomery. introduction to statistical quality control. third edition. john wiley & sons, new york, 1991. [2] r.s. guh. real-time pattern recognition in statistical process control: a hybrid neural network/decision tree-based approach. imeche, part b: j. engineering manufacture. vol. 219, no. 3. pp. 283-298, 2005. 214 j. a. vazquez-lopez, i. lopez-juarez, m. peña-cabrera [3] t. l. lucy-bouler. application to forecasting of neural network recognition of shifts and trends in quality control data. proceedings of the world congress on neural networks (wcnn’93). vol. 1, pp. 631-633, uk, 1993. [4] r.s. guh. robustness of the neural network based control chart pattern recognition system to nonnormality. int. j. qual. reliability mgmt. vol. 19(1), pp. 97-112, 2002. [5] c.w. zobel, d.f. cook, q.j. nottingham. an augmented neural network classification approach to detecting mean shifts in correlated manufacturing process parameters. int. journal of production research. vol. 42 (4), pp. 741-758, 2004. [6] f. zorriassantine, j.d.t. tannock. a review of neural networks for statistical process control. journal of intelligent manufacturing. vol. 9, pp. 209-224, 1998. [7] c.s. cheng. a multi-layer neural network model for detecting changes in the process mean. computers ind. engng. vol. 28(1), pp. 51-61, 1995. [8] h.b. hwarng, and c. w. chong. detecting process nonrandomness through a fast and cumulative learning art-based pattern recognizer. int. j. prod. res., vol. 33(7), pp. 1817-1833, 1995. [9] c.s. cheng. a neural network approach for the analysis of control chart patterns. int. j. prod. res., vol. 35(3), pp. 667-697, 1997. [10] e.s. ho and s. i. chang. an integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study. int. j. prod. res., vol. 37(8), pp. 1881-1901, 1999. [11] r.s. guh and j.d.t. tannock. recognition of control chart concurrent patterns using a neural network approach. int. j. prod. res., vol. 37(8), pp. 1743-1765, 1999. [12] m.a. wani, and d.t. pham. efficient control chart pattern recognition through synergistic and distributed artificial neural networks. proc. instn mech. engrs, part b: j. engineering manufacture. vol. 213(b2), pp. 157-169, 1999. [13] d.f cook, c.w. zobel and m. l. wolfe environmental statistical process control using an augmented neural network classification approach. european journal of operational research. vol. 174, pp. 1631-1642, 2006. [14] j.a. swift. development of a knowledge-based expert system for control chart pattem recognition and analysis. doctoral thesis. oklahoma state university, 1987. [15] gerson tontini. pattern identification in statistical process control using fuzzy neural networks. ieee management sciences. department regional university of blumenau (furb), 1996. [16] g.a carpenter, s. grossberg, n. markuzon, j.h. reynolds, and d.b. rosen. fuzzy artmap: a neural network architecture for incremental learning of analog multidimensional maps. ieee transactions on neural networks. vol. 3 (5), pp. 698-713, 1992. [17] g. a. carpenter, s. grossberg. adaptive resonance theory (art). the hanbook of brain theory and neural networks. edited by m. a. arbib, the mit press. pp. 79-82, 1995. [18] g. a. carpenter, s. grossberg. a massively parallel architecture for a self-organizing neural pattern recognition machine. computer vision, graphics, and image processing. academic press, inc. pp. 54-115, 1987. on the use of the fuzzyartmap neural network for pattern recognition in statistical process control using a factorial design 215 [19] g. a. carpenter, s. grossberg, d. b. rosen. art 2-a: an adaptive resonance algorithm for rapid category learning and recognition. neural networks. vol. 4, pp. 493-504, 1991. [20] g. a. carpenter, s. grossberg, j. h. reynolds. artmap: supervised real-time learning and classification of nonstationary data by self-organizing neural network. neural networks. pp. 565-588, 1991. [21] j. r. williamson. gaussian artmap: a neural network for fast incremental learning of noisy multidimensional maps. neural networks. vol. 9, no. 5, pp. 881-897, 1996. [22] g. a. carpenter, w. d. ross. art-emap: a neural network architecture for object recognition by evidence accumulation. ieee trans. on neural networks. vol. 6, no. 4, pp. 805-818, 1995. [23] d. psichogios, l. ungar. a hybrid neural network-first principles approach to process modeling. computers & chemical engineering. vol. 38(10), pp. 1499-1511, 1992. [24] g. acuña, and e. pinto. development of a matlabrtoolbox for the design of grey-box neural models. int. journal of computers, communications & control. vol. i, no. 2, pp. 7-14, 2006. [25] imre j. rudas, jános fodor. intelligent systems. int. journal of computers, communications & control, 3(s):132-138, 2008. jose antonio vazquez-lopez was born in celaya, guanajuato, mexico in 1972. he graduated with a beng and msc in industrial engineering from instituto tecnologico de celaya in 1999 and 2001 respectively. he obtained his phd from the posgrado interinstitucional en ciencia y tecnologia (picyt) in 2009. currently, he is a lecturer at the instituto tecnologico de celaya. his main research interest is in statistical industrial engineering, where he has also served as industrial consultant for several companies. ismael lopez-juarez was born in tequixquiac, mexico in 1968. he obtained a beng from the national autonomous university of mexico (unam) in 1991. he obtained an msc in instrument design and application at the university of manchester in 1996 and a phd in intelligent robotics at the nottingham trent university in 2000, both in th u.k. he was the founder and leader of the mechatronics and intelligent manufacturing systems research group at ciateq, a.c. during 2000-2006. currently, he is a principal researcher at the robotics and advanced manufacturing systems research group at cinvestav-saltillo. dr. lopez-juarez has published over one hundred papers in refereed international journals and conference proceedings in the area of instrumentation, self-adaptive industrial robotics and neural networks. mario peña-cabrera was born in queretaro, mexico in 1954. he graduated with a beng in mechanical and electrical engineering with specialization in control, communications and electronics from the national autonomous university of mexico (unam). he holds a meng in electronics from the same university and a meng from the mcmaster university, canada. he obtained his phd degree in science and technology from picyt, sep-conacyt, mexico. his areas of interest are in robot vision, automation and digital control, areas in which he has been working for over 30 years. currently, he is the head of the computing engineering science and automation department at the institute of research on applied mathematics and systems in the national autonomous university of mexico (unam). int j comput commun, issn 1841-9836 8(1):79-86, february, 2013. reliable critical infrastructure: multiple failures for multicast using multi-objective approach f.a. maldonado-lopez, y. donoso ferney a. maldonado-lopez, yezid donoso universidad de los andes, bogotá, colombia e-mail: fa.maldonado1897@uniandes.edu.co ydonoso@uniandes.edu.co abstract: multicast is the keystone for multimedia internet. multicast is one of the new and most used services in telecommunication networks. however, these networks meet big challenges when facing failures from diverse factors, including natural disasters and bad configurations. networks operators need to establish mechanisms to maintain available multicast services, and plan actions to handle incidents. we study and implement an elitist evolutionary algorithm based on strength pareto evolutionary algorithm spea. our implementation recalculates network routes, even when there are multiple failures. the results indicate that our product finds lower-cost and higheravailability multicast tree to protect multicast services. keywords: resilience, protection, survivability networks, multi-objective evolutionary algorithm. 1 introduction multicast is one of the new and most used services in telecommunication networks. some applications on the internet use a multicast service which is a key factor in multimedia, video conferencing, distributed games, internet television, and telepresence. a multicast service simultaneously sends messages from only one source to a group of destinations creating a multicast tree over a network. networks are exposed to several sources of failure, including misconfiguration or operational errors, natural disasters, attacks, and environmental challenges. additionally, to maintain the services, they must consider unusual but legitimate traffic [1], such as peak traffic on specific hours or dates. due to multicast applications demand important amount of traffic, they need a mechanism to protect them from network failures. the internet, for example, has been designed to survive random failures. if a node is down, the reliable protocol is able to reroute the traffic around and use alternative connections. in networking, resilience and survivability refer to the abilities of the network to overcome failures and maintain its services working. the study of resilience and survivability has become an important aspect of managing infrastructure in multicast networking. in this paper, we formulate a mechanism to protect a multicast service when the network experiences multiple failures. failures in multicast have been widely studied [2] [3]. a common strategy to face this problem is finding a redundant multicast tree (rmt). some authors consider that a rmt could be completely link-disjoint from the original. they propose to find a rmt with the minimal cost. to determine the optimal rmt, we consider the np-hard steiner tree problem [4]. several algorithms have been proposed to deal with this issue, such as topological methods [5], or the nearest participant first (npf) [6]. furthermore, there are network protocols which calculate a multicast tree with the minimal cost, such as mospf, pim-dm, pim-sm, and cbt. however, there are some limitations with existing approaches. first, the algorithms that calculate rmts were designed to optimize only one decision variable; thus, they use only distance to find the minimum cost tree. furthermore, algorithms and protocols previously mentioned were tested with single failures, it means, when only a link disruption occurs. nonetheless, copyright c⃝ 2006-2013 by ccc publications 80 f.a. maldonado-lopez, y. donoso telecommunication operators need to find not only routes with the minimal cost, but also routes with high availability. moreover, operators require techniques to plan and reconfigure the network where a set of links are damaged or eliminated. we propose a mechanism to protect multicast services, with the maximum availability and the minimum cost, considering multiple failures and diverse decision variables. our approach applies an heuristic multi-objective evolutionary algorithm (moea) which is a stochastic search method to estimate the optimal rmt. we implement the strength pareto evolutionary algorithm (spea) which is an elitist evolutionary algorithm that finds pareto-optimal solutions [9]. the used method has polynomial computational complexity o(mn2) where m is the number of arcs and n nodes [10]. result exhibits possible configurations after multiple failure for this problem considered np-hard. 2 multicast protection problem multicast protection problem (mpp) is analysed from the communication survivability and resilience view. multicast services, methods for provisioning, and failures are presented by formal description. a network is depicted as a weighted, directed, connected graph g = (n,a). a set n of n nodes and a set a of arcs. nodes are elements labelled 1 . . .n and an arc is an ordered pair of nodes, an arc between nodes i and j is denoted by (i,j) = {(i,j)|i,j ∈ n}. a network is represented by an n×n adjacency matrix a. the ijth element is 1 if (i,j) ∈ a and 0 otherwise. arc symbolizes a communication link in the network; so, it has assigned two link’s attributes: cost and availability. cost indicates the length of the link and availability is the probability that the link works during a period of time. these attributes are represented by n × n matrices w = {wij} for distance and v = {vij} for availability. table 1 contains used notation to model the problem. n set of nodes labelled {1, 2, . . . , n} s multicast source node s ∈ n a set of arcs {(i, j)|i, j ∈ n} d destinations set {d1, d2, · · · , dl : di ∈ n} a adjacency matrix, n × n t multicast session t = (s, d) w matrix of cost, w(aij) = w(i,j) t multicast session subgraph t = (n, a′)|a′ ⊆ a v matrix of availability, v(aij) = v(i,j) f set of failures {f1, f2, · · · , fk|fi ∈ a} table 1: graph notation definition 1. multicast session is delivery data from source node s to l destinations nodes that belong to set d. according to definition 1, we model a multicast session as a graph t. it is a directed tree with root s and terminal vertices di ∈ d. t is a set of paths from s and di ∈ d. a path p is a walk without repeated nodes, and it is represented as a nodes sequence pj = {s,i1, i2, · · · ,dj} : s,ik,dj ∈ n. a failure is a link disruption; therefore, multiple failures are described as a set of damaged or eliminated links. this set, called vector of failures, is labelled by f where k is the number of edges that have failed. the failures are uncorrelated, and they occur following a uniform random distribution. definition 2. a failure fi is a pair (i,j) ∈ a, where i,j ∈ n. after a failure the graph g changes to g′ = (n′,a′),n′ ⊆ n and a′ ⊆ a then, the multicast protection problem against multiple correlated failures is described as follows. reliable critical infrastructure: multiple failures for multicast using multi-objective approach81 definition 3. given a weighted graph g = (n,a), a multicast demand t , and a set of links that have failed f, the multicast protection problem (mpp) is providing a rmt g′ with maximum availability v(g′) and minimum cost w(g′). mpp is similar to find the optimal tree, which is a classic graph theory problem knowing as steiner tree problem that was proven as np-hard. consequently, mpp is also a np-hard problem. 3 optimization model in order to make feasible the computation, the network is represented by a matrix of adjacency a. we assume that all links on the network are directional. the matrix of adjacency has zero entries on the main diagonal because there are not loops in the vertices. the attribute values of cost and availability are represented into matrices w and v. w(g) is the cost function of graph g which is the sum cost of all arcs in g (1). similarly, availability function v(g) is the conditional probability that all arcs in g are able (2). w : g → ℜ, w(g) = ∑ (i,j) w(i,j) ∀i,j ∈ g (1) v : g → [0,1], v(g) = ∏ (i,j) v(i,j) ∀i,j ∈ g (2) figure 1 shows three trees able to carry data to multiple destinations. figure (1a) is a graph representing a complete network; figure (1b) is a subgraph able to reach both destinations; figure (1c) is another subgraph reaching the same destinations. note that both subgraphs are multicast trees, but these are disjoint trees; it means, one is a multicast protection tree of the other. figure 1: network and multicast protection tree for a demand from node 0 to nodes 4 and 5. 3.1 variable definition we set xi,j in (3) as a binary decision variable that symbols if an edge is used or not by the session t . xi,j corresponds to the input (i,j) value in the matrix of adjacency a. 82 f.a. maldonado-lopez, y. donoso x(i,j) = { 1 when the link is used from node i to node j, 0 in other case. (3) 3.2 objective functions and constraints the expressions (4) and (5) are the objective functions. recall that the goal is to find a set of optimal trees that minimize the total cost and maximize the total availability. min w(g) = min ∑ (i,j) x(i,j)w(i,j) ∀i,j ∈ a (4) max v(g) = max ∏ (i,j) x(i,j)v(i,j) ∀i,j ∈ a (5) in networking, a multicast session is a particular case where only one package is sent from s and multiples copies are delivered to di. multicast session is a variation of the transport problem. in the transport problem an intermediate node ik receives and delivers same amount of goods. in multicast, an intermediate node can replicate packages and send them by several output arcs. then, the multi-objective optimization problem is subject to: x(s,j) = 1 j ∈ a (6) x(j,di) = −1 j ∈ a (7) x(i,j) ≤ ∑ k xj,k|x(i,j) = 1,k ∈ a j ̸= s,j ̸= di (8) x(i,j) ≥ 0 i,j ∈ a (9) constrains (6) and (7), called constrains of offer and demand, are associated to root and destinations nodes. constrain (8) guaranties the replication of package, and the last constrain is a positive flow. 4 case study in this paper we study multiple failures in telecommunications networks and reliability for multicast services. we propose a different mechanism to reconfigure the network. as an example, we propose a case study, generate multiple failures, run our implementation to avoid failed links and generate a new rmt. this work is divided into three stages. first, we implement a mechanism based on spea. secondly, cost239 pan-european network tests the implementation. finally, the simulation is executed, and data is obtained to be analysed. 4.1 creating an evolutionary algorithm genetic algorithm (ga) is a stochastic search method based on chromosomes. each chromosome represents a valid solution to the problem. space of solution ω contains all feasible solutions that satisfy problem constraints. thus, particular solution ωi ∈ ω is a tree represented by a chromosome. ga initiates a population p which is a set of chromosomes randomly generated. after, each chromosome is rated by the objective function or fitness function. then, a pair of solutions, or individuals, are selected and produce an offspring by mixing their genetic information. successor solutions are generated by combining two parent states or modifying a single reliable critical infrastructure: multiple failures for multicast using multi-objective approach83 one, analogue to natural selection. there are two kind of evolutionary algorithms, non-elitist and elitist ones. non-elitist algorithms use whole latter population for the next iteration; also, this procedure allows exploit non-dominated solutions, it means, found optimal pareto p solutions. second, elitist algorithm gives the chance to preserve the best solutions, or elite solutions p , directly to next generation. this kind of problem representation is used to solve complex optimization problems [11]. we use a ge optimization in this paper to solve rmt problem. strength pareto evolutionary algorithm spea zitzler and thiele proposed an evolutionary algorithm called spea [9]. this algorithm maintains elitism by an external population p . this population is a collection of non-dominated solutions ω. let say ω dominates ω, ω ≽ ω, if w(ω) 6 w(ω) and v(ω) > v(ω). the algorithm finds non-dominated solutions and compares them with previous external population until segregate a new external population. this algorithm preserves elitist population p . spea has been widely used to solve network optimization problems [11]. this particular problem was faced from two phases. first, we used a high-level modelling system for mathematical optimization which allows to solve linear, nonlinear, and mixed-integer optimization problems. second, a spea genetic algorithm was implemented. this method demonstrated polynomial computational complexity o(mn2) where m is the number of arcs and n nodes [10] solving a problem considered np-hard [12]. chromosome the chromosome is a tree that can be represented by an adjacency matrix subset of a. however, a matrix representation has difficulties when it is necessary to reconstruct a unique route. consequently, we change the chromosome representation for a list of paths following the original model. the figure 2 depicts the structure representation of a chromosome. path p0 path p1 path pk path p(k-1) d(k -1) d1 dk d0s ... s ... s s . . . figure 2: chromosome as a list of paths. 4.2 spea implementation we design and implement a specific tool to find optimal rmts. initial population p0 is generated by a random tree generator, algorithm 1. random tree generator creates random walks from s node to each destination node di. also, we implement matrix operations and processes for creating multicast demands t , operators for chromosomes, including crossover and mutation. algorithm 2 is a generalized description that we follow to generate a set of solutions. 84 f.a. maldonado-lopez, y. donoso simulation scenario cost239 pan-european network tests the implementation. the network with 28 nodes represents the internet backbone, and connects main cities in europe. after setting topologies, we set the parameters, and generate random link failures in the tree of multicast service. table 2 summarizes simulation parameters. then, our implementation compute rmts and they are stored for analysis. parameter value number of generations 15 initial population size 10 max population size 30 max non-dominant population 20 crossover probability 0.2 mutation probability 0.2 table 2: simmulation paremeters 5 analysis and conclusions after running our implementation, we notice that spea, for reliability in mpp and network design, is a powerful tool. figure (3) is an example using cost239 pan-european network as test topology. first, the implementation find a tree, which in our hypothesis is the original multicast tree (3a). after that, the multiple failures module chooses arcs and delete them, simulating link failures and a reaction against the service. then, our implementation of spea for reliable networks identifies those paths that become unreachable and creates new protection routes. figures (3b) and (3c) show two examples of rmts calculated to reliable network infrastructure. however, cost239 and nsfnet are small network topologies to test real performance of the implementation. for that reason, we also test it with a generated topology. we create a virtual reliable critical infrastructure: multiple failures for multicast using multi-objective approach85 figure 3: multicast network trees over all network including thirty nodes and twenty-nine arcs for each one. results are condensed in figure (4). we can observe that the network is able, high availability and low cost. when it experiments failures, our algorithm recalculate the multicast tree and re-configure the paths. in some cases, the services can be fulfilled without problems; however, services can be degraded or cancel in the worst case. as a result, we can establish a correlation between the topologies, mechanisms to find rmts, and complexity needed to solve alternatives trees. here we can notice that our implementation finds new low-cost high-availability trees, even if the failures increase. we present and investigate a different approach of protection of survivable multicast sessions in networks. results allow to optimize designs in both variables: total cost and availability. we also deploy a spea heuristic algorithm to mpp. we use spea due to its reduced computational time and complexity o(mn2). after using our approach in the pan-european and the nsfnet networks, we find that the performance of the multicast protection schemes is better when the size of the network is bigger and the network experiences uncorrelated multiple failures. moreover, the results show that our implementation is an important tool to support decisions for a network operator with similar conditions to this scenario. also, results display the trade-off between cost and availability in a network and present how multicast sessions can be restored or re-configured using this tool. figure 4: cost and availability for solutions rmts 86 f.a. maldonado-lopez, y. donoso bibliography [1] james p.g. sterbenz, david hutchison, egemen k. çetinkaya, abdul jabbar, justin p. rohrer, marcus schller, paul smith, resilience and survivability in communication networks: strategies, principles, and survey of disciplines, computer networks, 54(8):1245-1265, 2010. 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[12] chern maw-sheng, on the computational complexity of reliability redundancy allocation in a series system, operations research letters. 11:309-315, 1992. international journal of computers, communications & control vol. ii (2007), no. 4, pp. 340-354 bounded controllers for decentralized formation control of mobile robots with limited sensing k.d. do abstract: this paper presents a constructive method to design bounded cooperative controllers that force a group of n mobile robots with limited sensing ranges to stabilize at a desired location, and guarantee no collisions between the robots. the control development is based on new general potential functions, which attain the minimum value when the desired formation is achieved, and are equal to infinity when a collision between any robots occurs. smooth and p times differential jump functions are introduced and embedded into the potential functions to deal with the robot limited sensing ranges. formation tracking is also considered. keywords: formation control, mobile robot, local potential function, nonholonomic mobile robot. 1 introduction formation is an extremely useful tool mimicking from biological systems to man-made teams of vehicles, mobile sensors and embedded robotic systems to perform tasks such as jointly moving in a synchronized manner or deploying over a given region with applications to search, rescue, coverage, surveillance, reconnaissance and cooperative transportation. formation control can be roughly understood as controlling positions of a group of the robots such that they stabilize/track desired locations relative to reference point(s), which can be another robot(s) within the team, and can either be stationary or moving. three popular approaches to formation control are leader-following (e.g. [2], [3]), behavioral (e.g. [4], [5]), and use of virtual structures (e.g. [6], [7]. most research works investigating formation control utilize one or more of these approaches in either a centralized or decentralized manner. centralized control schemes, see e.g. [3], [11], and [10] where actual dynamics of nonholonomic robots were considered in the formation control design, use a single controller that generates collision free trajectories in the workspace. although these guarantee a complete solution, centralized schemes require high computational power and are not robust due to the heavy dependence on a single controller. a nice application of formation control based on potential field method [3] and lyapunov’s direct method [17] to gradient climbing is recently addressed in [18]. however, the final configuration of formation cannot be foretold. on the other hand, decentralized schemes, see e.g. [8], [12], require less computational effort, and are relatively more scalable to the team size. the decentralized approach usually involves a combination of robot based local potential fields (e.g. [3], [15], [16]). the main problem with the decentralized approach, when collision avoidance is taken into account, is that it is extremely difficult to predict and control the critical points of the controlled systems. recently, a method based on a different navigation function from [13] provided a centralized formation stabilization control design strategy is proposed in [11]. this work is extended to a decentralized version in [12]. however, the potential function, which possesses all properties of a navigation function (see [13]), is finite (but its gradient with respect to the system states can be unbounded) when a collision occurs. this complicates analysis of collision avoidance. moreover, the formation is stabilized to any point in workspace instead of being "tied" to a fixed coordinate frame. in [13], [11] and [12], the tuning constants, which are crucial to guarantee that the only desired equilibrium points are asymptotic stable and that the other critical points are unstable, are extremely difficult to obtain. this problem has been removed in [9] where new potential functions were introduced. it is however noted that in [9] each robot requires knowledge of position of all other robots in the group. moreover, the control design methods (e.g. [3], [14], [15], [18]) based on the potential functions that are equal to infinity when a collision occurs exhibit very large control efforts if the robots copyright © 2006-2007 by ccc publications bounded controllers for decentralized formation control of mobile robots with limited sensing 341 are close to each other. hence, a bounded control is called for. in addition, switching control theory [21] is often used to design a decentralized formation control system (e.g. [2], where a case by case basis is proposed), especially when the vehicles have limited sensing ranges and collision avoidance between vehicles must be considered. clearly, it is more desirable if we are able to design a non-switching formation control system that can handle the above decentralized and collision avoidance requirements. in this paper, bounded cooperative controllers are designed for formation stabilization of a group of mobile robots with limited sensing ranges. new general potential functions are constructed to design the controllers that yield (almost) global asymptotic convergence of a group of mobile robots to a desired formation, and guarantee no collisions among the robots. smooth and p times differential jump functions are introduced and embedded into the potential functions to deal with the robot limited sensing ranges. moreover, the controlled system exhibits multiple equilibria due to collision avoidance taken into account. we therefore investigate the behavior of equilibrium points by linearizing the closed loop system around those points, and show that critical points, other than the desired point for an robot, are unstable. the proposed formation stabilization solution is then extended to solve a formation tracking problem. 2 problem statement we consider a group of n mobile robots, of which each has the following dynamics q̇i = ui, i = 1, ..., n (1) where qi ∈ rn and ui ∈ d ⊂ rn are the state and control input of the robot i. we assume that n > 1 and n > 1. here, we treat each robot as an autonomous point. control objective: assume that at the initial time t0 ≥ 0 each robot starts at a different location, and that each robot has a different desired location, i.e. there exist strictly positive constants ε1 and ε2 such that for all (i, j) ∈ {1, 2, ..., n}, i 6= j ‖qi(t0)−q j(t0)‖ ≥ ε1,‖qi f −q j f ‖ ≥ ε2 (2) where qi f , i = 1, .., n, is the desired location of the robot i. moreover, the robot i can only measure its own state and can only detect the other group members if these members are in a sphere, which is centered at the robot and has a radius of ri larger than a strictly positive constant. design the bounded control input ui for each robot i such that each robot asymptotically approaches its desired location while avoids collisions with all other robots in the group, i.e. for all (i, j) ∈ {1, 2, ..., n}, i 6= j,t ≥ t0 ≥ 0 ‖ui(t)‖ ≤ δ , lim t→∞ (qi(t)−qi f ) = 0,‖qi(t)−q j(t)‖ ≥ ε3 (3) where δ is a strictly positive constant and ε3 is a positive constant. 3 preliminaries we present one definition and one lemma to be used in the control design and stability analysis in the next section. 342 k.d. do definition 1. a scalar function h(x, a, b) is called a p times differential jump function if it enjoys the properties: 1) h(x, a, b) = 0 if 0 ≤ x ≤ a, 2) h(x, a, b) = 1 if x ≥ b, 3) 0 < h(x, a, b) < 1 if a < x < b, (4) 4) h(x, a, b) is p times differentiable with respect to x where p is a positive integer, x ∈ r+, and a and b are constants such that 0 ≤ a < b. moreover, if p = ∞ then the function h(x, a, b) is called a smooth jump function. lemma 1. let the scalar function h(x, a, b) be defined as h(x, a, b) = ∫ x a f (τ −a) f (b−τ)dτ∫ b a f (τ −a) f (b−τ)dτ (5) with the function f (y) being defined as follows f (y) = 0 if y ≤ 0, f (y) = g(y) if y > 0 (6) where the function g(y) enjoys the following properties a) g(τ −a)g(b−τ) > 0 a < τ < b, b) g(y) is p times differentiable with respect to y, and lim y→0+ ∂ kg(y) ∂ yk = 0, k = 1, 2, ..., p−1. (7) then the function h(x, a, b) is a p times differentiable jump function. proof. see appendix a. remark 1. several examples of the function g(y) are g(y) = yp, g(y) = tanh(y)p, g(y) = arctan(yp) for any positive integer p, and g(y) = sin(y)p for any even positive integer p. corollary 1. if the function g(y) in (7) is taken as g(y) = exp(−1y ) then the function h(x, a, b) defined in (5) is a smooth jump function. proof. see appendix b. 4 control design to achieve the control objective, we design the control ui for the robot i based on the new potential function ϕ . this potential function must attain its unique minimum value when all robots are at their desired positions, and must equal infinity when there is a collision between any robots. the potential function ϕ should also be chosen such that the gradient based control ui for the robot i can handle the limited sensing range of the robot. as such, we propose the following potential function ϕ = eγ β κ −1 (8) bounded controllers for decentralized formation control of mobile robots with limited sensing 343 where κ is a positive constant, γ and β are the goal and collision avoidance functions. these functions are specified as follows: -the goal function is designed such that it puts penalty on stabilization errors for all robots, and is equal to zero when the robots are at their final positions. a simple choice of this function is γ = 1 2 n ∑ i=1 ‖qi −qi f ‖2. (9) -the collision function β is chosen such that it equals zero when there is a collision between any robots, and equals 1 when the robots are at their desired positions. we choose this function as follows: β = ∏ i, j βi j, i = 1...n −1, j = i + 1, ..., n. (10) the function βi j = β ji is designed as βi j = h(0, b2i j/2,‖qi j‖2/2) (11) where h(0, b2i j/2,‖qi j‖2/2) is a smooth or p > 2 times differentiable jump function presented in the previous section, qi j = qi −q j, bi j is a strictly positive constant such that bi j ≤ min (ri, r j, ε2) with ε2 given in (2). remark 2. thanks to properties of the smooth or p > 2 differentiable jump function (see definition 1), the collision function β equals zero when a collision between any robots occurs, i.e. ‖qi j‖ = 0 for any i 6= j. the function β equals 1 when all robots are at their desired locations, i.e. qi = qi f for i = 1, ..., n. the function β is at least twice differentiable with respect to qi j. hence, the choice of the goal function γ in (9) and the collision function β in (10) with its components given in (11) ensures that the potential function ϕ in (8) attains the (unique) minimum value of zero when all the robots are at their desired positions, and equals infinity whenever a collision between any robots occurs. moreover, the potential function ϕ is at least twice differentiable. the derivative of ϕ along the solutions of (1) satisfies ϕ̇ = eγ γ̇ β κ −eγ κ β κ−1β̇ β 2κ = eγ β κ ( γ̇ −κ n−1 ∑ i=1 n ∑ j=i+1 β̇i j βi j ) = eγ β κ n ∑ i=1 ωti ui (12) where we have used β̇ = β ∑n−1i=1 ∑ n j=i+1 β̇i j βi j , and ωi = qi −qi f −κ ∑ j∈ni β̄ ′i jqi j (13) with β̄ ′i j = β ′i j βi j , β ′i j = ∂ βi j ∂ (‖qi j‖2/2) , and ni the set of all robots, denoted by n, in the group except for the robot i. from (12), a bounded control ui for the robot i is simply designed as follows: ui = −cψ(ωi) (14) where c is a positive constant, and ψ(ωi) denotes a vector of bounded functions of elements of ωi in the sense that ψ(ωi) = [ ψ(ω1i ) ψ(ω 2 i ), ..., ψ(ω l i ), ...., ψ(ω n i ) ]t with ωli the l th element of ωi, i.e. 344 k.d. do ωi = [ω1i ω 2 i ...ω l i ...ω n i ] t . the function ψ(x) is a scalar, differentiable and bounded function, and satisfies 1) |ψ(x)| ≤ m1, 2) ψ(x) = 0 if x = 0, xψ(x) > 0 if x 6= 0, 3) ψ(−x) = −ψ(x), (x−y)[ψ(x)−ψ(y)] ≥ 0, 4) |ψ(x)x | ≤ m2,| ∂ ψ(x) ∂ x | ≤ m3, ∂ ψ(x) ∂ x |x=0 = 1 (15) for all x ∈ r, y ∈ r, where m1, m2, m3 are strictly positive constants. some functions that satisfy the above properties are arctan(x) and tanh(x). indeed, the control ui is bounded, i.e. ‖ui(t)‖ ≤ c √ nm1 := δ ,∀t ≥ t0 ≥ 0. remark 3. when ωi defined in (13) is substituted into (14), the lth element of the control ui can be written as uli = cψ ( −(qli − qli f )− ∑ j∈ni β̄ ′i jqli j ) with qli , q l i f and q l i j being the l th elements of qi, qi f , and qi j. the argument of ψ consists of two parts: −(qli −qli f ) and −∑ j∈ni β̄ ′i jqli j. the first part, −(qli −qli f ), referred to as the attractive force plays the role of forcing the robot to its desired location. the second part, −∑ j∈ni β̄ ′i jqli j, referred to as the repulsive force, takes care of collision avoidance for the robot i with the other robots. moreover, the control ui of the robot i given in (14) depends on only its own state, and the states of other neighbor robots j if these robots are in a sphere, which is centered at the robot and has a radius no greater than ri because outside this sphere β̄ ′i j = 0. now substituting (14) into (12) results in ϕ̇ = −c e γ β κ n ∑ i=1 ωti ψ(ωi). (16) substituting (14) into (1) results in the closed loop system q̇i = −cψ(ωi), i = 1, ..., n. (17) theorem 1. assume that at the initial time t0 ≥ 0 each robot starts at a different location, and that each robot has a different desired location, i.e. the conditions given in (2) hold, the bounded controls given in (14) guarantee that no collisions between any robots can occur, the solutions of the closed loop system (17) exist and the robots asymptotically approach their desired positions (a set of equilibria) defined by qi f , i = 1, ..., n. proof. see appendix c. 5 simulations we carry out a simulation with n = 2, n = 10. the robots are initialized randomly in a circle, which is centered at the origin and has a radius of 1. the desired formation is specified in shape, location and orientation as qi f = r f [sin((i − 1)2π/n); cos((i − 1)2π/n)], i = 1, ..., n with r f = 8, i.e. the desired formation is a polygon whose vertices are uniformly distributed on a circle, which is centered at the origin and has a radius of r f . all robots have the same sensing range: ri = 2. the parameters of the p times differential jump functions are p = 2, bi j = 1, g(y) = yp. the function ψ is taken as arctan. the control gains are chosen as κ = 1, c = 2. simulation results are plotted in fig. 1. it is seen that all robots nicely approach their desired locations. since the robots initialize pretty close to each other, they quickly move away from each other then approach their desired locations, see sub-figure a) of fig. 1, where the trajectory of the robot 1 is plotted in the thick line, and robots 1 and 2 are indicated by 1 and 2. for clarity, only the control input u1 = [u1x u1y]t is plotted in sub-figure b) of fig. 1. noticing that bounded controllers for decentralized formation control of mobile robots with limited sensing 345 the values of the continuous controls u1x and u1y are in the range ±π . sub-figure c) of fig.1 plot the functions β1 j, j = 2, ..., n. it is seen that these functions are always greater than zero and approach 1. sub-figure d) of fig. 1 plots a ’mean-product’ distance, distall = ( ∏(i, j)∈n‖qi j‖ )n(n−1)/2 , see the thick line, and the distances between the robot 1 and other robots in the group. clearly, no collisions between any robots occurred since distall is larger than zero for all simulation time. the bottom figure in fig. 1 plots the functions β1 j, j = 2, ..., n. it is noted that all β1 j equal 1 when ‖q1 j‖ are larger or equal to 1. 10 5 0 5 10 10 5 0 5 10 x y 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 6 4 2 0 2 4 6 time [s] u 1 x , u 1 y 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 5 10 15 20 time[s] d is a ll, | |q 1 j|| 0 0.5 1 1.5 2 2.5 3 0 0.5 1 1.5 ||q 1j || 1 j u 1x u 1y dis all 1 2 a) b) c) d) figure 1: simulation results. 6 extension to formation tracking this section extends the results developed in the previous sections to solve the problem of designing a control input ui for each robot i that forces the group of n mobile robots whose dynamics are given in (1) to track a moving desired formation graph (dfg), in the sense that the dfg is allowed to move on a common desired trajectory qod (s) with s being the common reference trajectory parameter defined in the fixed coordinate system πf , see fig. 2 and fig. 3. we consider the dfg whose center moves along the common reference trajectory qod (s). we assume that qod (s) is regular in the sense that it is single valued and its first derivative exists and is bounded. since the dfg under consideration is only representative, the center does not have to be the "true" center of the dfg but can be any convenient point. when the dfg moves along the trajectory qod (s), the vertex i of dfg generates the reference trajectory qid (s) for the robot i to track. we limit our consideration to twoand three-dimensional (2d and 3d) spaces, which are most common in practice. the control objective is now stated as follows. control objective. assume that at the initial time t0 ≥ 0, for all (i, j) ∈ {1, 2, ...n}, i 6= j, s ∈ r there 346 k.d. do exist strictly positive constants ε1, ε2 and ε3 such that ‖qi(t0)−q j(t0)‖ ≥ ε1, ‖qid (s)−q jd (s)‖ ≥ ε2, ‖∂ qid (s) ∂ s ‖ ≤ ε3 (18) moreover, the robot i can only measure its own state and can only detect the other group members if these members are in a sphere with a radius of ri larger than a strictly positive constant, and centered at the robot i. design the control input ui for each robot i such that lim t→∞ (qi(t)−qid (s)) = 0, ‖qi(t)−q j(t)‖ ≥ ε4 (19) for all (i, j) ∈ {1, 2, ...n}, i 6= j, s ∈r, where ε4 is a positive constant. control design. let us construct qid (s). let the moving coordinate system πm , of which the origin _ o coincides with the center of the desired formation graph, move along qod (s). let _ q id be the coordinate vector of the vertex i of the dfg in the moving coordinate system. we then have _ q id = j(•)(qid (s)−qod (s)) (20) where j(•) whose elements depend on ∂ qod (s)/∂ s and is the rotational (invertible) matrix, which describes the rotation of πm with respect to πf , and is such that δ1 ≤ ‖j(•)‖ ≤ δ2, δ3 ≤ ‖j(•)−1‖ ≤ δ4 with δi, i = 1, .., 4 strictly positive constants. therefore, by specifying _ q id in πm , qid (s) in πf for the robot i can be calculated from (20). similarly, in πm the coordinate vector of each robot i satisfies _ q i= j(•)(qi −qod (s)). (21) from (20) and (21), we can see that the first two conditions in (18) imply the following condition ‖ _ q i (t0)− _ q j (t0)‖ ≥ _ ε 1, ‖ _ q id (s)− _ q jd (s)‖ ≥ _ ε 2 (22) where _ ε 1 and _ ε 2 are some strictly positive constants. moreover, the tracking control goal specified in (19) is achieved by designing the control ui for each robot i such that lim t→∞ ( _ q i (t)− _ q id (s)) = 0, ‖ _ q i (t)− _ q j (t)‖ ≥ _ ε 4 (23) where _ ε 4 is a positive constant, and by letting the dfg move along the common reference trajectory via giving ṡ some desired value. now differentiating both sides of (21) gives _̇ q i = _ ui (24) where _ ui is the new control, and we have chosen the control ui as ui = q̇od (s) + j(•)−1( _ ui −j̇(•)(qi −qod (s))). (25) the problem of designing _ ui for (24) to achieve (23) under (22) is exactly the same as the control objective in section 2. therefore, the control design in section 4 can be used directly to design a bounded control _ ui to achieve the goal (23). after _ ui is designed, the actual tracking control ui is calculated from (25). let us give the expression of the rotational matrix j(•) in 2d and 3d spaces. bounded controllers for decentralized formation control of mobile robots with limited sensing 347 figure 2: formation coordinates in 2d. ( )y j ( )x i ( )z k i y i x i z 1 y 1 x 1 z ( )y n ix iy i z ( )z b ( )x t od x od y od z o o i v 11 13 12 11 12 13, , 21 22 23 , , 31 32 33 , , odq agent i moving frame coordinate system formation graph m figure 3: formation coordinates in 3d. 348 k.d. do two-dimensional space. consider the moving coordinate frame, _ o _ x _ y attached to the dfg, as shown in fig. 2. the origin _ o coincides with the center of the graph, and is on the common reference trajectory qod (s) = [xod (s) yod (s)]t . the _ o _ x and _ o _ y axes are tangential and perpendicular to the reference trajectory qod (s). therefore the angle θ between _ o _ x and ox is calculated as θ = arctan(y ′ d /x ′ d ), where •′ ∆= ∂ •/∂ s. hence the rotational matrix j(•) is given by j(•) = [ cos(θ ) sin(θ ) −sin(θ ) cos(θ ) ] , which is indeed invertible for all θ ∈r, and ‖j(•)‖ = 1. three-dimensional space. consider the moving coordinate frame, _ o _ x _ y _ z , attached to the dfg as shown in fig. 3. the coordinate frame _ o x1y1z1 is parallel to oxy z . the origin _ o coincides with the center of the graph, and is on the reference trajectory qod (s) = [xod (s) yod (s) zod (s)]t . the _ o _ x , _ o _ y and _ o _ z axes coincide with the unit tangent vector~t , the unit principal vector ~n , and the unit binormal vector ~b of the trajectory qod (s) at the point _ o. these unit vectors form a positively oriented triple of vectors called the moving triad, and are given by~t = ~q′ od /‖~q′ od‖, ~n =~t′/‖~t′‖, ~b =~t ×~n, where × stands for the vector cross product operation, (~i, ~j, ~k) are the unit vectors of the oxy z coordinate frame. let (ξi1, ξi2, ξi3), i = 1, 2, 3 be the directional cosines of _ o _ x , _ o _ y and _ o _ z with respect to the fixed axes ox , oy and oz, respectively. this notation means that if we denote (θi1, θi2, θi3), i = 1, 2, 3 as the angles between the axes _ o _ x , _ o _ y and _ o _ z , and the axes ox , oy and oz (see fig. 3 for representation of θ11, θ12 and θ13), we have ξi j = cos(θi j), ∀i, j ∈ {1, 2, 3}. the rotational matrix j(•) is given by j(•) =   ξ11 ξ12 ξ13 ξ21 ξ22 ξ23 ξ31 ξ32 ξ33  . it is shown in [20] that the determinant of j(•) is equal to 1, i.e. j(•) is globally invertible, and ‖j(•)‖ = 1. 7 summary and conclusions this paper has presented a method to design bounded and continuous (even smooth) controllers to force a group of mobile robots with limited sensing ranges to achieve a desired formation while avoiding collisions among themselves. the control development was based on construction of new potential functions, and guaranteed that all critical points, except for the desired points in formation, are unstable points. these potential functions with embedded smooth or p times differential jump functions are attractive parts of the paper since they do not require the use of switching control theory despite the robot limited sensing ranges. these functions can certainly be modified to solve other cooperative control problems such as flocking and consensus of mobile robots. the problem of formation tracking was also addressed. future work is to extend the proposed techniques in this paper and those for centralized formation control of nonholonomic mobile robots in [10] to design a decentralized formation control system for a group of nonholonomic mobile robots. 8 appendix a: proof of lemma 1 we need to verify that the function h(x, a, b) given in (5) satisfy all properties defined in (4). property 1) holds because by (6) for all 0 ≤ x ≤ a, we have ∫ xa f (τ −a) f (b−τ)dτ = 0. property 2) holds since by (6) we have ∫ x a f (τ −a) f (b−τ)dτ = ∫ b a f (τ −a) f (b−τ)dτ for all x ≥ b. to prove property 3), we first note from property a) of the function g(y) given in (7) that ∫ x a f (τ −a) f (b−τ)dτ > 0 for all a < x < b. therefore, 0 < ∫ x a f (τ−a) f (b−τ)dτ∫ b a f (τ−a) f (b−τ)dτ < 1, which means that property 3) of the function h(x, a, b) holds. to prove property 4), we just need to show that f (y) is p−1 times differentiable. we first note that f (y) is bounded controllers for decentralized formation control of mobile robots with limited sensing 349 p times differentiable except at y = 0. hence, we only need to verify that f (k)(0) = ∂ k f (y) ∂ yk ∣∣∣ y=0 = 0 for any positive integer k < p. clearly, limy→0− f (k)(y) = 0 since f (y) = 0, ∀y ≤ 0. on the other hand, since f (y) = g(y), y > 0, from property b) of the function g(y) we have limy→0+ f (k)(y) = limy→0+ g(k)(y) = 0, where g(k) = ∂ k g(y) ∂ yk . since both leftand right-hand limits are equal to 0, we have f (k)(0) = 0. hence property 4) holds. 2 9 appendix b: proof of corollary 1 we first note that property a) of the function g(y) in (7) can be proven without a difficulty. we focus on proof of property b). we note that g(k)(y) = ∂ k g(y) ∂ yk = qk( 1 y )e − 1y where qk( 1y ) is a polynomial function of 1y , and k is any positive integer. we will prove property b) of the function g(y) in (7) by induction. it is clear that limy→0+ g(1)(y) = limy→0+ g(y)−g(0) y−0 = limy→0+ e− 1 y y = limξ→∞ 1 eξ = 0 where ξ = 1y and we have used l’hopital’s rule. this means that property b) of the function g(y) holds for k = 1. assuming that limy→0+ g(k)(y) = 0, we now compute limy→0+ g(k+1)(y) as follows: lim y→0+ g(k+1)(y) = lim y→0+ g(k)(y)−g(k)(0) y−0 = lim y→0+ q̃k( 1 y )e− 1 y = lim ξ→∞ q̃k(ξ ) eξ = 0 (26) where l’hopital’s rule has been used, ξ = 1y , and q̃k(ξ ) = ξ qk(ξ ) is another polynomial of ξ . therefore we have proved that limy→0+ g(k)(y) = 0 for any k, which means property b) of the function g(y) holds for any positive integer p, i.e. p can be equal to infinity. by definition 1, the function h(x, a, b) is a smooth jump function.2 10 appendix c: proof of theorem 1 we prove theorem 1 in two steps. in the first step, we prove that no collisions between the robots can occur and that the robots asymptotically approach their target points or some critical points. in the second step, we investigate stability of the closed loop system (17) at these points, we linearize the closed loop system at these points. we then prove that only desired target points are unique asymptotic stable and that other critical points are unstable. step 1. proof of no collision and existence of solutions. from (16) and properties of the function ψ , see (15), we have ϕ̇ ≤ 0, which implies that ϕ(t) ≤ ϕ(t0),∀t ≥ t0. with definition of the function ϕ in (8) and its components in (9) and (10), we have e0.5 ∑ n i=1 ‖qi(t)−qi f ‖2 ∏ h(0, b2i j/2,‖qi j(t)‖2/2) ≤ e 0.5 ∑ni=1 ‖qi(t0)−qi f ‖2 ∏ h(0, b2i j/2,‖qi j(t0)‖2/2) (27) for all t ≥ t0 ≥ 0, i = 1, ..., n − 1, j = i + 1, ...n. from the first condition in (2) and properties of the jump function h(0, b2i j/2,‖qi j‖2/2), we have ∏ h(0, b2i j/2,‖qi j(t0)‖2/2) is larger than a strictly positive constant. therefore the right hand side of (27) is bounded by a positive constant depending on the initial conditions. boundedness of the right hand side of (27) implies that the left hand side of (27) must be also bounded. as a result, ∏ h(0, b2i j/2,‖qi j(t)‖2/2) must be larger than some positive constant depending 350 k.d. do on the initial conditions for all t ≥ t0 ≥ 0. from properties of h(0, b2i j/2,‖qi j‖2/2), ‖qi j(t)‖ must be larger than some positive constant depending on the initial conditions denoted by ε3, i.e. there are no collisions for all t ≥ t0 ≥ 0, i = 1, ..., n −1, j = i + 1, ...n. boundedness of the left hand side of (27) also implies that of ‖qi j(t)‖ and ‖qi(t)‖ for all t ≥ t0 ≥ 0, i.e. the solutions of the closed loop system (17) exist. +equilibrium points. since we have already proved that there are no collisions between any robots and that the solutions of the closed loop system (17) exist, an application of theorem 8.4 in [19] to (16) yields lim t→∞ ωti (t)ψ(ωi(t)) = 0,∀i = 1, 2, ..., n. (28) thanks to property 2) of the function ψ , see (15), the limit equation (28) implies that lim t→∞ ωi(t) = lim t→∞ [ qi(t)−qi f −κ ∑ j∈ni β̄ ′i j(t)qi j(t) ] = 0 (29) for all i = 1, 2, ..., n. the limit equation (29) implies that the state q(t) = [qt1 (t) q t 2 (t), ..., q t n (t)] t converges to the manifold m of (17) contained in e = {q ∈ rn×n|ω=0} with ω = [ωt1 ωt2 , ..., ωtn ]t , i.e. on the surface where ϕ̇ = 0. this surface is continuous because we have already proved that ‖qi j‖ > 0,∀(i, j) ∈ {1, 2, ..., n}, i 6= j, i.e. β̄ ′i j is continuous. as the time t goes to infinity, it can be verified that one solution of (29) is q f = [qt1 f q t 2 f , ..., q t n f ] t since β ′i j|‖qi j‖=‖qi j f ‖ = 0 ⇒ β̄ ′i j|‖qi j‖=‖qi j f ‖ = 0 (we have chosen bi j ≤ min (ri, r j, ε2), see (11)), and other solutions are denoted by qc = [qt1c qt2c, ..., qtnc]t . it is noted that some elements of qc can be equal to that of q f . however, for simplicity we abuse the notation, i.e. we still denote that vector as qc. indeed, the vector qc is such that ωi|q=qc = [ qi −qi f −κ ∑ j∈ni β̄ ′i jqi j ]∣∣∣∣ q=qc = 0 (30) for all i = 1, ..., n. next, we will show that q f is stable and qc is unstable, by linearizing (17) at these points. +properties of equilibrium points. the closed loop system (17) can be written in a vector form as q̇ = −cψq(q, q f ), and ψq(q, q f ) = [ψt (ω1), ..., ψt (ωi), ..., ψt (ωn )]t . therefore, near an equilibrium point qo, which can be either q f or qc, we have q̇ = −c ∂ ψq(q, q f )/∂ q ∣∣ q=qo (q−qo) (31) where ∂ ψq(q, q f ) ∂ q =   ∆11 ∆12 ··· ··· ∆1n ... . . . ... ... ... ∆i1 ··· ∆ii ··· ∆in ... ... ... . . . ... ∆n1 ··· ··· ··· ∆nn   (32) with ∆i j = ∂ ψ(ωi) ∂ ωi ∂ ωi ∂ qi , (i, j) ∈n, where n denotes the set of all robots in the group. a simple calculation shows that for all i = 1, ..., n, j ∈ni, j 6= i ∂ ωi ∂ qi = ( 1−κ ∑ i∈ni β̄ ′i j ) in −κ ∑ j∈ni β̄ ′′i jqi jq t i j, ∂ ωi ∂ q j = κ β̄ ′i jin×n + κ β̄ ′′ i jqi jq t i j (33) bounded controllers for decentralized formation control of mobile robots with limited sensing 351 where β̄ ′′i j = ∂ β̄ ′i j ∂ (‖qi j‖2/2) . let n ∗ be the set of the robots such that if the robots i and j belong to the set n∗ then ‖qi j‖ < bi j. next we will show that q f is asymptotically stable and that qc is unstable. step 2. behavior of equilibrium points. evaluating (33) at q = q f gives ∂ ψ(ωi) ∂ ωi ∣∣∣∣ q=q f = in, ∂ ωi ∂ qi ∣∣∣∣ q=q f = in, ∂ ωi ∂ q j ∣∣∣∣ q=q f = 0 (34) where we have used β̄ ′i j ∣∣ qi j =qi j f = 0 and β̄ ′′i j ∣∣ qi j =qi j f = 0 since β ′i j ∣∣ qi j =qi j f = 0 and β ′′i j ∣∣ qi j =qi j f = 0 (we have chosen bi j ≤ min (ri, r j, ε2), see (11)). we consider the lyapunov function candidate vf = 0.5‖q−q f ‖2 whose derivative along the solutions of the linearized closed loop system (31) with qo replaced by q f , and using (34) satisfies v̇f = −c ∑ni=1 ‖qi −qi f ‖2 = −2cvf , which implies that q f is asymptotically stable. proof of qc being unstable. now evaluating (33) at q = qc give that for all i = 1, ..., n, i 6= j : ∂ ψ(ωi) ∂ ωi ∣∣∣∣ q=qc = in, ∂ ωi ∂ qi ∣∣∣∣ q=qc = ( 1−κ ∑ j∈ni β̄ ′i jc ) in − κ ∑ j∈ni β̄ ′′i jcqi jcq t i jc, ∂ ωi ∂ q j ∣∣∣∣ q=qc = κ β̄ ′i jc + κ β̄ ′′ i jcqi jcq t i jc (35) where qi jc = qic − q jc, β̄ ′i jc = β̄ ′i j ∣∣ qi j =qi jc and β̄ ′′i jc = β̄ ′′ i j ∣∣ qi j =qi jc . since the related collision avoidance functions βi j, (hence β̄ ′i j and β̄ ′′ i j), are specified in terms of relative distances between robots and it is extremely difficult to obtain qc explicitly by solving (30), it is very difficult to use the lyapunov function candidate vc = 0.5‖q − qc‖ to investigate stability of (31) at qc. therefore, we consider the following lyapunov function candidate v̄c = 0.5‖q̄− q̄c‖2 (36) where q̄ = [qt12, q t 13, ...q t 1n , q t 23, ..., q t 2n , ..., q t n−1,n ] t and q̄c = [qt12c, q t 13c, ...q t 1nc, q t 23c, ..., q t 2nc, ..., q t n−1,nc] t . differentiating both sides of (36) along the solution of (31) with qo replaced by qc gives ˙̄vc = −c ∑ (i, j)∈n\n∗ ‖qi j −qi jc‖2 −c ∑ (i, j)∈n∗ (1−κ nβ̄ ′i jc)× ‖qi j −qi jc‖2 + κ cn ∑ (i, j)∈n∗ β̄ ′′i jc ( qti jc(qi j −qi jc) )2 (37) where i 6= j and (35) has been used. to investigate stability properties of q̄c based on (37), we will use (30). define ωi jc = ωic − ω jc, ∀(i, j) ∈ {1, ..., n}, i 6= j where ωic = ωi|q=qc = 0, see (30). therefore ωi jc = 0. hence ∑(i, j)∈n∗ qti jcωi jc = 0, i 6= j, which by using (30) is expanded to ∑ (i, j)∈n∗ ( qti jc(qi jc −qi j f )−κ nβ̄ ′i jcqti jcqi jc ) = 0 ⇒ ∑ (i, j)∈n∗ (1−κ nβ̄ ′i jc)qti jcqi jc = ∑ (i, j)∈n∗ qti jcqi j f (38) where i 6= j. the sum ∑(i, j)∈n∗ qti jcqi j f is strictly negative since at the point where qi j = qi j f , ∀(i, j) ∈ n∗, i 6= j (the point f in fig. 4) all attractive and repulsive forces are equal to zero while at the point where qi j = qi jc ∀(i, j) ∈ n∗, i 6= j (the point c in fig. 4) the sum of attractive and repulsive forces are equal to zero (but attractive and repulsive forces are nonzero). therefore the point where qi j = 0, ∀(i, j) ∈ n∗, i 6= j (the point o in fig. 4) must locate between the points f and c for all (i, j) ∈ n∗, i 6= j. that is 352 k.d. do 12 q 13 q * 1| | q 23 q * 2| | q * * | | 1,| | q o f c figure 4: illustration of equilibrium points. there exists a strictly positive constant b such that ∑(i, j)∈n∗ qti jcqi j f < −b, which is substituted into (38) to yield ∑ (i, j)∈n∗ (1−κ nβ̄ ′i jc)qti jcqi jc < −b, i 6= j. (39) since qti jcqi jc > 0,∀(i, j) ∈ n∗, i 6= j, there exists a nonempty set n∗∗ ⊂ n∗ such that for all (i, j) ∈ n∗∗, i 6= j, (1 − κ nβ̄ ′i jc) is strictly negative, i.e. there exists a strictly positive constant b∗∗ such that (1−κ nβ̄ ′i jc) < −b∗∗, ∀(i, j) ∈n∗∗, i 6= j. we now write (37) as ˙̄vc = −c [ ∑ (i, j)∈n\n∗ ‖qi j −qi jc‖2 + ∑ (i, j)∈n∗\n∗∗ (1−κ nβ̄ ′i jc)× ‖qi j −qi jc‖2 −κ n ∑ (i, j)∈n∗ β̄ ′′i jc ( qti jc(qi j −qi jc) )2 ] − c ∑ (i, j)∈n∗∗ (1−κ nβ̄ ′i jc)‖qi j −qi jc‖2 (40) where i 6= j. we now define a subspace such that qi j − qi jc = 0, ∀(i, j) ∈ n\n∗∗ and qti jc(qi j − qi jc) = 0, ∀(i, j) ∈n∗, i 6= j. in this subspace, we have v̄c = 0.5 ∑ (i, j)∈n∗∗ ‖qi j −qi jc‖2, (41) ˙̄vc = −c ∑ (i, j)∈n∗∗ (1−κ nβ̄ ′i jc)‖qi j −qi jc‖2 ≥ 2cb∗∗v̄c where we have used (1−κ nβ̄ ′i jc) < −b∗∗, ∀(i, j) ∈n∗∗, i 6= j. clearly (41) implies that ∑ (i, j)∈n∗∗ ‖qi j(t)−qi jc‖ ≥ ∑ (i, j)∈n∗∗ ‖qi j(t0)−qi jc‖ecb ∗∗(t−t0) (42) for all i 6= j, t ≥ t0 ≥ 0. now assume that qc is a stable equilibrium point of the closed loop system (17), i.e. limt→∞ ‖qi(t)− qic‖ = di,∀i ∈ n with di a nonnegative constant. note that n∗∗ ⊂ n, we have limt→∞ ‖qi(t) − qic‖ = di,∀i ∈ n∗∗, which implies that limt→∞ ∑(i, j)∈n∗∗ ‖qi j(t) − qi jc‖ = d∗∗,∀(i, j) ∈ n∗∗, i 6= j with d∗∗ a nonnegative constant, since qi j = qi −q j and qi jc = qic −q jc. this contradicts (42) for the case ∑(i, j)∈n∗∗ ‖qi j(t0)− qi jc‖ 6= 0, since the right hand side of (42) is divergent (so does the left bounded controllers for decentralized formation control of mobile robots with limited sensing 353 hand side). for the case ∑(i, j)∈n∗∗ ‖qi j(t0) − qi jc‖ = 0, there would be no contradiction. however this case is never observed in practice since the ever-present physical noise would cause ‖qi j(t∗)− qi jc‖ for some (i, j) ∈n∗∗, i 6= j to be different from 0 at the time t∗ ≥ t0. we now write (42) as ∑ (i, j)∈n∗∗ ‖qi j(t)−qi jc‖ ≥ ∑ (i, j)∈n∗∗ ‖qi j(t∗)−qi jc‖ecb ∗∗(t−t∗) (43) for all i 6= j, t ≥ t∗ ≥ t0 ≥ 0. since ∑(i, j)∈n∗∗ ‖qi j(t∗)−qi jc‖ 6= 0, the right hand side of (43) is divergent (so does the left hand side). this contradicts limt→∞ ∑(i, j)∈n∗∗ ‖qi j(t)− qi jc‖ = d∗∗,∀(i, j) ∈ n∗∗, i 6= j. therefore qc must be an unstable equilibrium point of the closed loop system (17). proof of theorem 1 is completed. bibliography [1] p. k. c. wang, navigation strategies for multiple autonomous mobile robots moving in formation, j. robot. syst., vol. 8, no. 2, pp. 177-195, 1991. 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[21] d. liberzon, switching in systems and control, birkauser, 2003. k.d. do school of mechanical engineering the university of western australia 35 stirling highway, crawley, wa 6009, australia e-mail: duc@mech.uwa.edu.au received: december 26, 2006 international journal of computers communications & control issn 1841-9836, 9(4):419-429, august, 2014. determining the state of the sensor nodes based on fuzzy theory in wsns m.s. gharajeh mohammad samadi gharajeh department of computer engineering tabriz branch, islamic azad university tabriz, iran mhm.samadi@gmail.com abstract: the low-cost, limited-energy, and large-scale sensor nodes organize wireless sensor networks (wsns). sleep scheduling algorithms are introduced in these networks to reduce the energy consumption of the nodes in order to enhance the network lifetime. in this paper, a novel fuzzy method called fuzzy active sleep (fas) is proposed to activate the appropriate nodes of wsns. it uses the selection probability of nodes based on their remaining energy and number of previous active state. the proposed method focuses on a balanced sleep scheduling in order to belong the network lifetime. simulation results show that the proposed method is more efficient and effective than the compared methods in terms of average network remaining energy, number of nodes still alive, number of active state, and network lifetime. keywords: wireless sensor networks (wsns), fuzzy theory, sleep scheduling, energy consumption, network lifetime. 1 introduction wireless sensor networks (wsns) organize a wireless network, where the sensors called nodes have basically some features such as low cost, limited energy, and large scalability [1]. a very large number of nodes are deployed in the network to sense and to transmit the environmental conditions or occurrence of the physical events. they are used in some applications such as traffic management, close-circuit camera in retail, seismic monitoring, and military usage. organization of the nodes in a hierarchical topology, short length messages, short range of message transfer, and sleep scheduling of the nodes are some of the essential mechanisms to reduce the energy consumption of nodes in order to enhance the network lifetime [2]. the sleep scheduling categorizes the nodes into active or sleep states. a proper sleep scheduling method performs the scheduling operation in a way that maintains the connectivity among nodes and coverage of whole network. a sensor network is connected when each active node can transmit its data via one or multiple hops toward a specified center. meanwhile, the coverage is specified as an area that can be accessed by active nodes. both of the connectivity and coverage are the essential factors to monitor a given area that should be considered by the presented sleep scheduling methods. fuzzy logic is utilized to develop some models such as physical tools, uncertain and complex systems, and non-linear processes. the fuzzy models can be easily understood, and have less external complexity with more useful features [3]. furthermore, fuzzy controllers can take an appropriate decision even by imprecise and incomplete information. the linguistic terms and inexact data can be manipulated as a useful tool to design the uncertain systems. therefore, the sleep scheduling can be designed and implemented by fuzzy decision making to give most advantageous in terms of connectivity, coverage, and network lifetime. the rest of this paper is organized as follows. the related works presented in section 2 discuss about some of the prior sleep scheduling methods. section 3 describes the proposed fuzzy method by addressing the designated fuzzy system. performance evaluation of the simulated methods is explained in section 4. finally, the paper is concluded in section 5. copyright © 2006-2014 by ccc publications 420 m.s. gharajeh 2 related works a sleep scheduling method for stationary nodes of wsns is developed in [4] that utilize a discrete-time markov chain (dtmc) model. it applies an analytical method using slotting the time according to the data unit transmission time in order to discover the trade-offs existing between energy conserving and system throughout metrics including network capacity, energy consumption, and data delivery ratio. besides, the sensor nodes are considered as three operational modes as transmit, receive, and idle to simply adapt them with various traffic conditions. an analytical method for the random scheduling algorithms is provided in [5] to derive the detection probability and detection delay. the simulation results are carried out with discrete event simulation to investigate the impact of number of subsets and number of sensor nodes on coverage intensity, detection probability, and detection delay. a random scheme for wsns is also proposed in [6] that develop an analytical schema to investigate the relation between randomized sleep state and network throughout. a queue model for nodes and an efficiency framework for whole the network are included in the presented framework to derive the throughout, energy consumption, and delay time. another randomized scheduling method is studied in [7] via analysis and simulations in aspects of detection probability, detection delay, and network coverage intensity. furthermore, a problem of prolonging the network lifetime under quality of service (qos) limitation such as bounded detection probability, detection delay, and network coverage intensity is analyzed by authors. an optimal sleep control mechanism is proposed in [8] to prolong the network lifetime with reducing the energy consumption of the nodes. it utilizes the proposed procedure by distance between the sensor nodes and the sink. furthermore, energy of whole the network is balanced through reducing the number of transmissions related to the sensor nodes which are placed more close to the sink. in the method presented in [9], several characteristics of active/sleep model in wsns are investigated. the main mechanism of this method to manage the nodes in an on or off period is that the steady-state probability distribution of number of packets is derived in the reference node. another method is presented in [10] that determine the active and sleep modes of the nodes as randomly or alternatively manner in a stochastic model of wsns. the active mode is categorized as two phases as full active phase and semi-active phase to better manage the energy consumption of the nodes. this method evaluates the energy consumption of the network by developing the important analytical formulae. 3 the proposed method the main objective of the proposed method is to balance the energy consumption of the nodes in order to enhance the network lifetime. they are possible by activating and sleeping the appropriate nodes for a period time based on fuzzy decision making. if the nodes are activated based on a structural scheduling method, their energy are consumed in an equivalent flow so that the energy balance of the network will be considerably enhanced. if various types of the sensors such as temperature, smoke, and light intensity are used in the network, the proposed method will be applied within each category independently. the proposed fuzzy method called fuzzy active sleep (fas) operates based on fuzzy decision making. the whole network is divided to different areas so that only one node is activated in each area for a period time. it is worth noting that the number of divided areas is determined based on network size and number of nodes. a single sink is assumed in the centre point of network that determines the active nodes of the areas. furthermore, it receives the environmental data from nodes, and forwards them to the base station. the reason is that all the nodes cannot directly transmit their data to the base station. an overview of the assumed network is shown in figure 1, so that it is composed determining the state of the sensor nodes based on fuzzy theory in wsns 421 of four areas included by three types of sensors as temperature, smoke, and light intensity. figure 1: an overview of the assumed network there are some input and output variables in the fuzzy systems to make the fuzzy rules. the fuzzy rules are used in a fuzzy system to decide an appropriate action in the uncertain conditions. the input variables of the proposed fuzzy system are determined as follow; the first one is remaining energy of nodes (denoted by rei); the second one is the number of previous active state (denoted by asi); here, ”i” refers to the number of existing nodes in the related area. the output variable is selection priority of the nodes (specified by spi). suppose for this work, rei and spi take on following linguistic values: vl (very low), l (low), m (medium), h (high), vh (very high); and asi take on the following linguistic values: fe (feeble), fw (few), me (medium), ma (many), l (lots). membership graph for the inputs and the output variables are depicted in figure 2. note that the membership functions of asi are defined by triangular [11] method, and membership functions of rei and spi are determined by bell-shaped [11] method. while there are two input variables as each one can accept five linguistic terms, total number of the fuzzy rules is 52 = 25. some of the fuzzy rules used in the proposed fuzzy system are represented in table 1. note that the fuzzy rules are constructed by mamdani-type fuzzy rulebased systems [12]. meanwhile, all the fuzzy rules are aggregated together by or operator to produce the total fuzzy rule. a schematic of the fuzzy rules used in the proposed fuzzy system is shown in figure 3 based on input and output variables. the appropriate node from among the nodes’ groups within each area is selected by sink 422 m.s. gharajeh figure 2: membership graph for the inputs (remaining energy and number of active state) and the output (selection priority) table 1: some of the fuzzy rules used in the proposed fuzzy system input variables output variable rule no. rei asi spi 1 vh fe vh 2 h fw h 3 m me m 4 vh l l 5 h ma l 6 l l l 7 vl fw h 8 l fe h 9 m ma l 10 vh me vl figure 3: a schematic of the fuzzy rules used in the proposed fuzzy system determining the state of the sensor nodes based on fuzzy theory in wsns 423 using the proposed method for a period time as described following. first, remaining energy of the nodes is converted to a fuzzy value by bell-shaped membership function, and the number of active state at nodes is converted by triangular membership function. then, the fuzzy value of each node’s selection priority is determined by the approximate reasoning, total fuzzy rule, and the inputs’ fuzzy values. finally, the output fuzzy value is converted to a crisp value by center-of-gravity [13] which is a defuzzification method as follows spi = n∑ i=1 µsp(xi).xi n∑ i=1 µsp(xi) (1) where n indicates the number of elements in the universe set of selection priority, xi represents each elements of the universe set, and µsp(xi) describes the membership degree of xi in the universe set. the node with highest crisp value is selected as active node, and other nodes are selected as sleep nodes. 4 performance evaluation the simulation processes are carried out in matlab. 40 sensor nodes are randomly deployed in a topographical area of dimension 200 m × 200 m. all nodes have the same initial energy 2j. the proposed method is compared to all active method and random active sleep method called "ras" to evaluate them in terms of average network remaining energy, number of nodes still alive, and number of active state. furthermore, impacts of the different experimental parameters such as interval time between data sense, interval time between sending data, and initial energy of nodes on the network lifetime are evaluated carefully. note that all the nodes are always activated in the all active method, and the active node is selected by a random procedure in the ras. the active nodes transmit their environmental data to the sink in a specific interval time. afterwards, the sink also transmits the aggregated data to the base station in a determined interval time. note that the gathered data are aggregated by the sink as follows dagg = n∑ i=1 di n (2) where n indicates the number of data presented in the sink’s buffer and di refers to each data of the buffer. the simulation will be terminated when the remaining energy of all the nodes is under threshold energy. note that the discrete simulation results are the average value of the results which are independently simulated for 10 times. the transmission and receiving energies are calculated based on the model expressed in [14]. according to this mode, for transmitting an l-bit data packet a long a distance d, the radio spends etx(l, d) = { leelec + lϵfsd 2 , d < d0 leelec + lϵmpd 4, d ≥ d0 (3) where d0 indicates a threshold distance, ϵfsd2 uses the free space (fs) model to calculate the amplifier energy, and ϵmpd4 utilizes the multipath (mp) model to estimate the amplifier energy. meanwhile, the spending radio to receive this data packet is calculates as erx(l) = leelec (4) note that the threshold energy to sense or to receive an l -bit data packet is calculated like erx(l). the simulation parameters and their default values are represented in table 2. 424 m.s. gharajeh table 2: simulation parameters parameter value topographical area (meters) 200 × 200 sink location (meters) (100, 100) location of base station (meters) (500, 500) buffer size of the sink (packet) 10,000 buffer size of the base station (packet) 10,000 number of nodes 40 initial energy of node 2 j interval time between data sense (round) 10 interval time between sending data (round) 5 interval time between active sleep changes (round) 25 interval time between data transmission through the sink (round) 20 eelec 50 nj/bit ϵfs 10 pj/bit/m2̂ ϵmp 0.0013 pj/bit/m4̂ 4.1 an instance of selecting the active node by the proposed fuzzy system as previously described, the active node is independently selected within each area. selection priority is calculated for all the nodes presented in the areas; then, the node with the highest priority is activated for a period time. as represented in table 3, if there are five nodes in a special area, selection priority is calculated based on their remaining energy and number of previous active state. therefore, the node n4 which has the highest priority is selected as the active node for a period time. table 3: an example of determining the selection priority for each node input variables output variable node rei asi spi n1 2 11 44.913 n2 1.2 15 42.027 n3 0.3 5 50.843 n4 0.8 2 54.757 n5 1.6 8 48.058 4.2 simulation results some of the continuous simulation results of the evaluated methods are shown in figure 4 in terms of average network remaining energy and number of nodes still alive. these parameters illustrate the lifetime status of network under the situations represented in table 3. as shown in the results, the network lifetime obtained by the all active method is 2,560 round, by the ras method is 9,125, and by the proposed method is 9,440. as it has been expected, the lifetime in the all active is very low; but it is near to each other in the ras and fas methods. however, the average network energy and number of live nodes in the proposed method is higher than that of ras due to balance the active state of the nodes. average network remaining energy are determining the state of the sensor nodes based on fuzzy theory in wsns 425 calculated in each round as follows remavg = n∑ i=1 reme(i) n (5) where n indicates the number of nodes and reme(i) represents the remaining energy at each node "i". simulation results demonstrate that the average network remaining energy and the number of nodes still alive achieved by the proposed method could be increased by about 700% more than that obtained by the all active method and by about 10% more than that obtained by the ras method. figure 4: lifetime status of the network in a simulation execution based on various methods the number of dead nodes is calculated according to algorithm 1 in each round. if it equals to the number of nodes, the simulation process will be terminated. note that the number of last round is known as network lifetime. number of active state at nodes is one of the most important factors in the sleep scheduling methods. a good method tries to balance this factor in order to enhance the network lifetime. as shown in figure 5, the number of active state in the all active method has a stationary 426 m.s. gharajeh high value due to activate all the nodes in all the active/sleep selection rounds. moreover, this value in the proposed method is more balance than that of the ras method. the reason is that selecting the active and sleep nodes is determined based on remaining energy of nodes and number of previous active state that leads to balance the final number of active state. note that the number of active state at each node increases when it is selected as the active node for a period time. figure 5: number of active state at nodes under various methods the statistical results of the active state at nodes are represented in table 4. the four famous statistical functions including minimum, maximum, mode, and standard deviation are used to determine the detailed information. the minimum and maximum functions specify the range of all the numbers sets. the mode function represents the most frequently occurring, or repetitive, number of active state at all the numbers sets. meanwhile, the standard deviation function specifies a measure of how widely numbers are dispersed from the average number so that it can be calculated as follows stddev = √√√√√ n∑i=1 (xi − x̄)2 n − 1 (6) where n indicates the number of nodes, xi represents the number of active state at node "i", and x̄ specifies the average value of all the numbers. the values calculated by various statistical functions represents that the proposed method is more efficient and balance than both of the other methods. table 4: the statistical values of the active state at nodes under various methods method statistical function all active ras fas minimum 120 26 36 maximum 120 47 45 mode 120 31 37 standard deviation 0 5.44 2.48 determining the state of the sensor nodes based on fuzzy theory in wsns 427 (a) interval time between data sense (b) interval time between sending data (c) initial energy figure 6: network lifetime achieved by different methods under various parameters 428 m.s. gharajeh some parameters such as interval time between data sense, interval time between sending data, and initial energy influences strongly on the network lifetime. when each of these parameters increases, the network lifetime will be considerably enhanced. the interval time between data sense determines a period time to sense the environmental conditions by nodes. meanwhile, the interval time between sending data specifies a period time to transmit the sensed data to the sink. affection of them on the various methods is illustrated in figure 6. as shown in the results, the all active method has the network lifetime very lower than others. besides, the proposed method surpasses the ras method under various parameters changes. the reason is that some of the nodes are activated more than other nodes in the ras method so that the energy consumption of the nodes is unbalanced. the unbalanced energy consumption of nodes causes the network lifetime to be lower than the proposed method. in addition, the energy efficiency of the network can be specified based on total energy consumption of the nodes which is calculated as follows te = n∑ i=1 conse(i) (7) where n indicates the number of nodes and conse(i) represents the energy consumption of each node "i". note that the above formulae can be used to determine the energy consumption of the nodes both in each round and whole the network. 5 conclusions wireless sensor networks (wsns) are composed of some large-scale, low-cost, and limitedenergy sensor nodes. the sleep scheduling methods presented in the wsns cause the network lifetime to be considerably enhanced. in this paper, a novel fuzzy method called fuzzy active sleep (fas) proposed to select the appropriate node in each desired area to be activated for a period time. it selects the active node from among the related nodes based on their remaining energy and number of previous active state. selection procedure of the active nodes is balanced by the proposed method that leads the network lifetime to be enhanced. simulation results represent that the proposed method surpasses the other compared methods in aspects of average network remaining energy, number of nodes still alive, number of active state, and network lifetime. bibliography [1] stankovic, j.a. 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[14] heinzelman, w.b. et al (2002); an application-specific protocol architecture for wireless microsensor networks, ieee t wirel commun, issn 1536-1276, 1(4): 660-670. international journal of computers, communications & control vol. ii (2007), no. 1, pp. 26-36 deformable atlases for the segmentation of internal brain nuclei in magnetic resonance imaging marius george linguraru, miguel ángel gonzález ballester, nicholas ayache abstract: magnetic resonance imaging (mri) is commonly employed for the depiction of soft tissues, most notably the human brain. computer-aided image analysis techniques lead to image enhancement and automatic detection of anatomical structures. however, the information contained in images does not often offer enough contrast to robustly obtain a good detection of all internal brain structures, not least the deep grey matter nuclei. we propose a method that incorporates prior anatomical knowledge in the shape of digital atlases that deform to fit the image data to be analysed. our technique is based on a combination of rigid, affine and non-rigid registration, segmentation of key anatomical landmarks and propagation of the information of the atlas to detect deep grey matter nuclei. the montreal neurological institute (mni) and zubal atlases are employed. results show that detecting important structures such as the ventricles and brain outlines greatly improves the results. our method is fully automatic. keywords: mri, brain, deep grey matter nuclei, atlas, image normalisation, registration, segmentation. 1 introduction the advent of medical imaging modalities such as x-ray, ultrasound, computed tomography (ct) and magnetic resonance imaging (mri) has greatly improved the diagnosis of various human diseases. to date, the most common procedure to analyse imaging data is visual inspection on printed support. in the last decade, computer-aided medical image analysis techniques have been employed to provide a better insight into the acquired image data. [5]. such techniques allow for quantitative, reproducible observation of the patient condition. furthermore, the computing power of modern machines can be used to combine information from several images of the same patient (i.e. image fusion) or add prior information from a database of images. in this paper, we present a fully automated medical image analysis technique aimed at the detection of internal brain structures from mri data. such automated processes allow the study of large image databases and provide consistent measurements over the data. in our case, we employ a priori anatomical knowledge in the form of digital brain atlases. relevant background information about mri and brain anatomy is provided next. section 2 will describe the different components of our image processing framework, which detects and delineates internal brain structures by identifying analogous structures in digital brain atlases. finally, results and conclusions are given. 1.1 magnetic resonance imaging mri has become a leading technique widely used for imaging soft human tissue. its applications are extended over all parts of the human body and it represents the most common visualisation method of human brain. images are generated by measuring the behaviour of soft tissue under a magnetic field. under such conditions, water protons enter a higher energy state when a radio-frequency pulse is applied and this energy is re-emitted when the pulse stops (a property known as resonance) [7]. a coil is used to measure this energy, which is proportional to the quantity of water protons and local biochemical conditions. thus, different tissues give different intensities in the final mr image. from the brain copyright c© 2006-2007 by ccc publications deformable atlases for the segmentation of internal brain nuclei in magnetic resonance imaging27 mri perspective, this quality makes possible the segmentation of the three main tissue classes within the human skull: grey matter (gm), white matter (wm) and cerebrospinal fluid (csf). their accurate segmentation remains a challenging task in the clinical environment. the relative contrast between brain tissues is not a constant in mr imaging. in most medical imaging applications, little can be done about the appearance of anatomically distinct areas relative to their surroundings. in mri, the choice of the strength and timing of the radio-frequency pulses, known as the mri sequence [12], can be employed to highlight some type of tissue or image out another, according to the clinical application. however, the presence of artefacts due to magnetic field inhomogeneity (bias fields) and movement artefacts may hamper the delineation of gm versus wm and csf and make their depiction difficult. there is an entire family of mri sequences that are used in common clinical practice. t1-weighted mri offers the highest contrast between the brain soft tissues. on the contrary, t2-weighted and proton density (pd) images exhibit very low contrast between gm and wm, but high contrast between csf and brain parenchyma. in other mri sequences, like the fluid attenuated inversion recovery (flair) sequence, the csf is eliminated from the image in an adapted t1 or t2 sequence. more about these specific mri sequences and their variations can be found in [2]. multisequence mri analysis combines the different information provided by the employed sequences. combining such knowledge gives substantially more information about brain anatomy and possible occurring changes. mr images depict a 3d volume where the organ or part of the body of interest is embedded. this information can be used to build a 3d representation of the structure of interest. this applies both to 2d sequences, where images are acquired in slices, and to recently developed 3d sequences, where the data are captured in the 3d fourier space, rather than each slice being captured separately in the 2d fourier space [2, 12]. 1.2 deep grey matter nuclei the neurones that build up the human brain are composed of a cellular body and an axon. the latter projects its dendritic connections to other neurones in remote cerebral regions. in essence, grey matter corresponds to the cellular bodies, whereas the axons constitute the white matter. cerebral grey matter is mainly concentrated in the outer surface of the brain (cortex), but several internal gm structures exist, as seen in figures 1 and 2. these are known as deep grey matter nuclei and they play a central role in the intellectual capabilities of the human brain. additionally, deep brain grey matter nuclei are relevant to a set of clinical conditions, such as parkinson’s and creutzfeldt-jakob diseases. however, their detection in mri data sets remains a challenging task, due to their small size, partial volume effects [6], anatomical variability, lack of white matter-grey matter contrast in some sequences and movement artefacts. a methodology for the robust detection of deep brain grey matter nuclei in multi-sequence mri is presented in this paper. 2 method 2.1 spatial normalisation the large variability inherent to human anatomy and the differences in patient positioning across scans leads us to consider spatial normalisation as an approach to put patient images in a standard reference frame. this will allow to localise the areas of interest with the help of an atlas of the brain. furthermore, it will make automatic inter-patient comparisons possible. the identification of brain structures in volumetric images can be automated thanks to the use of digital atlases. these are images that have been segmented and thus contain information about the position and shape of each structure. such atlases can be binary (1 for the location of a structure and 0 28 marius george linguraru, miguel ángel gonzález ballester, nicholas ayache figure 1: deep grey matter internal nuclei as seen in a normal t1 weighted axial mr image with good contrast between wm, gm and csf. the arrows point towards some of these nuclei, namely the caudate nuclei, the thalami and the putamen. figure 2: an annotated map of deep grey matter internal nuclei reproduced from the talairach and tournoux atlas [13]: the caudate nuclei (cn), putamen (pu) and thalami (th). deformable atlases for the segmentation of internal brain nuclei in magnetic resonance imaging29 for "outside") or probabilistic, in which case the values correspond to the probability of a voxel containing the structure of interest. in order to locate such structures in a given patient image, the atlas image is deformed to match the shape of the patient brain. this process is known as registration. depending on the type of geometric deformation allowed, registration can be rigid, affine, parametric (e.g. spline) or free-form (a deformation field specifying the displacement applied to each point). registration to a digital atlas has become a common technique with the introduction of popular statistical algorithms for image processing, such as statistical parametric mapping (spm) [1] or expectation maximization segmentation (ems) [14]. a well-known probabilistic atlas in the scientific community is the mni atlas from the montreal neurological institute at mcgill university [4]. it was built using over 300 mri scans of healthy individuals to compute an average brain mr image, the mni template, which is now the standard template of spm and the international consortium for brain mapping [9]. the averaging is performed for the entire brain, but also on isolated gm, wm and csf, providing a tool for statistical segmentation. for these reasons, we chose the mni template as the basis for image alignment in our approach. figure 3 shows the mni template. figure 3: the mni template. on the left, the probabilistic mni atlas of the brain; on the right, the corresponding gm atlas. please note the arrangement of mr images in radiological convention with an axial, a sagittal and a coronal view. this convention is reflected in figures throughout the paper. we propose the following registration scheme. t1 images have often the highest resolution, hence we register them to the mni template first using an affine transformation. the registration algorithm, previously developed in our group, is described in [11]. it uses a block matching strategy in a twostep iterative method. the standard assumption behind the algorithm is that there is a global intensity relationship between the template image and the one being registered to it. the method proposes several types of correlation measures: linear, functional or statistical. maximising one of these, the correlation coefficient in our case, the transformation between the two images is computed block by block and a displacement field is thus generated. a parametric transformation, either affine or rigid, is then estimated from this deformation field. to further improve robustness, this procedure is repeated at multiple scales. more details can be found in [10]. next, rigid intra-patient registration of all sequences is performed using the same algorithm as above. t2, flair, diffusion-weighted, diffusion tensor or other sequence images can be registered to the t1 image. since this registration is performed on images of the same patient acquired during the same scanning session, rigid registration suffices. by combining these rigid transformations with the affine transformation matching t1 and mni template, we can find correspondences between the atlas and the other sequence images. this is illustrated in figure 5. the final image resolution is that of the mni atlas: 91 109 91 voxels. figure 4 shows an example of spatial normalisation. with all images registered to the atlas, intraand inter-patient analysis becomes simple and statistical algorithms can be applied. 30 marius george linguraru, miguel ángel gonzález ballester, nicholas ayache figure 4: an example of spatial normalisation. the image on top is the subject’s t1 before registration; the image on bottom left shows the subject’s t1 after spatial normalisation and the mni template is presented on the bottom right image. 2.2 a priori anatomical knowledge to be able to segment gm and wm in mri sequences, a good contrast between these types of tissue in t1-weighted images is desired. figure 6 shows a typical t1 with high contrast between brain soft tissues and a common t1 image from our database. under the given circumstances, the segmentation of gm cannot be done directly from the patient images. the mni atlas can provide a probabilistic segmentation of gm, but this is not precise enough for our application. we use instead a segmented anatomical atlas of the brain, the zubal phantom [15], which is introduced next. figure 5: diagram of the spatial normalisation algorithm. intra-patient images are rigidly registered on the corresponding t1. the t1-weighted image is affinely registered to the atlas template. the resulting transformation is used to align all other mr images to the atlas. the zubal atlas offers a precisely labelled segmentation of brain structures from the t1-weighted mr image of one single subject. our interest focuses on the internal nuclei, which are segmented in the phantom. first, the atlas must be aligned to our set of images, which have been previously registered to the mni atlas. thus, we register the zubal phantom to the mni template, again using our block matching algorithm [11], to estimate an affine transformation. however, in order to preserve the correct deformable atlases for the segmentation of internal brain nuclei in magnetic resonance imaging31 figure 6: a typical t1-weighted mr image with good contrast between brain gm, wm and csf (left) versus a t1 image where gm and wm cannot be reliably distinguished from each other. values of the segmentation labels posterior to the application of the transformation, nearest-neighbour interpolation is performed, as opposed to the case of patient image registration, which employed spline interpolation. figure 7 shows the results of registering the zubal phantom to the mni reference without disrupting the zubal labels. 2.3 refined segmentation once the zubal phantom is registered to the working framework, we can easily depict the brain structures that are of interest, namely the deep gm internal nuclei. for the examples in this paper, we will focus on the basal ganglia. hence, we create a mask with the thalamus, putamen and head of the caudate which will be referred as internal nuclei for the rest of this paper from the zubal phantom registered on mni (figure 8). we aim to use this mask for the segmentation of internal nuclei in patient images. although the affine registration gives correct correspondences in a general brain registration framework, the anatomical variability between patients makes the correspondence between the zubal internal nuclei mask and the corresponding internal nuclei in each patient erroneous. a refinement of the registration in the deep gm between the zubal internal nuclei mask and the patient internal nuclei seems necessary to allow us to use the a priori anatomical information resulting from the segmentation of the zubal phantom. the segmentation of internal nuclei in patient images is not an obvious task; this is why we exploit the zubal phantom. nevertheless, there are other important anatomical landmarks in the brain that are easier to identify. we concentrate on the segmentation of ventricles and cortex external boundary. ventricles will give a good approximation of the deformation field around the internal nuclei, whereas the cortex boundary will impose the global spatial correspondence and stabilise the deformation field inside the brain. figure 8 illustrates the segmentation of ventricles, brain contour and internal nuclei from the registered zubal phantom. to obtain similar images of segmented brain margin and ventricles for each patient, we employ morphological opening on patient t2 images. the strong contrast that csf has against the brain in t2weighted images allows us to segment the ventricles, while the cortex boundary can be extracted from either t1 or t2 sequences. we prefer using the t1 sequence, since the t2 image we employ lacks some top and bottom slices. the ventricles being located in the middle of the brain, it is correct to extract them from t2 images, but the cortex would be incomplete. we are now in the possession of two binary maps of ventricles and brain boundaries for each patient: one from the zubal phantom and the other from the patient. non-rigid (free-form) registration is used to align the two images, employing the algorithm developed in our group and described in [3]. this registration method minimises an energy function, which uses measures of intensity similarity, smoothing, noise parameters and correspondence between points. figure 9 shows typical results and figure 10 shows the 3d deformation fields related to the registration in figure 9. the outer margin of the cortex ensures that 32 marius george linguraru, miguel ángel gonzález ballester, nicholas ayache figure 7: the registration of zubal phantom onto the mni template. on the top row, the original zubal phantom is shown; on the bottomleft, we have the registered zubal phantom on the mni template, which is shown in the bottomright image. the deformation fields are spatially sound and do not pull the internal nuclei over their location. having the deformation fields computed, we apply them to the mask of internal nuclei of the zubal phantom, deforming the mask according to the position and size of the ventricles in the patient image. a diagram of the algorithm is shown in figure 11. the deformed mask is used to segment the internal nuclei of the patient, namely the putamen, head of the caudate and thalamus. figure 12 shows an example of registration of internal nuclei in 3d and the internal nuclei segmentation results in a t1-weighted mr image of a patient. in figure 13 we segment the internal nuclei in a patient t2-weighted image. the segmentation can be accurately performed in any mr sequence of the patient, given that multisequence images have been previously registered to the mni atlas. figure 8: the segmentation of the zubal phantom. from left to right: column 1, the zubal phantom registered on mni; column 2, the ventricles segmented from the zubal phantom; column 3, the cortex outer boundary is added to the ventricles; column 4, the internal nuclei segmented from the zubal phantom. the top row shows the axial view, while on bottom we present the coronal view. in this paper, we focused on the segmentation of the basal ganglia to present our algorithm for the segmentation of deep grey matter nuclei. an identical approach can be used for other inner brain deformable atlases for the segmentation of internal brain nuclei in magnetic resonance imaging33 structures to accurately segment them in patient images. each nuclei class has an associated label in the zubal phantom, which facilitates their identification. in figure 14 we illustrate the segmentation of individual types of nuclei, here caudate nuclei, thalami and putamen, using our approach. figure 9: registration of the zubal ventricles and cortex outer boundary on a patient. the patient’s ventricles are larger next to the small ventricles in the zubal phantom, where the subject is young. the algorithm gives robust results, as seen above. from left to right: column 1, the t2-weighted image of the patient; column 2, the ventricles and brain margin of the patient (ventricles segmented from t2 and cortex from t1); column3, the ventricles and brain boundary of zubal phantom; column 4, the ventricles and cortex boundary of the zubal phantom registered on the patient. 3 conclusion a robust automatic technique for the identification of deep brain internal nuclei was presented. the use of key anatomical landmarks such as the ventricles and the outline of the brain imposes anatomical constraints in the deformation fields found by the non-rigid registration algorithm, which otherwise would fail to converge to the correct segmentation. figure 10: deformation fields of the non-rigid registration between the zubal phantom ventricles and those of a patient with very large ventricles. on the left is the x field, the y field is in the middle column and the z field on the right. 34 marius george linguraru, miguel ángel gonzález ballester, nicholas ayache figure 11: diagram of the refined registration of internal nuclei. figure 12: an example of internal nuclei registration and their segmentation in a t1-weighted patient image. on the left, we have the t1 image of the patient; in the middle column, we show the segmentation of internal nuclei according to the binary map before non-rigid deformation with the head of the caudate superposed on the ventricle; on the right, we segment the internal nuclei after non-rigid deformation, showing an accurate segmentation. figure 13: an example of internal nuclei segmentation in a t2-weighted image of the patient. on the left, we have the t2 image of the patient; in the middle column, we show the segmentation of internal nuclei before non-rigid deformation; on the right, we segment correctly the internal nuclei after non-rigid deformation. figure 14: binary maps of deep grey nuclei. from left to right: the caudate nuclei, the putamen and the thalami. from top to bottom: axial and coronal views. these individual masks can be used for the accurate segmentation of each type of nuclei. deformable atlases for the segmentation of internal brain nuclei in magnetic resonance imaging35 acknowledgement the authors would like to thank professor ioana moisil from the “lucian blaga” university of sibiu for her assistance. references [1] j. ashburner, k.j. friston, "voxel-based morphometry the methods", neuroimage, 11:805-821, 2000. 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[13] j. talairach, p. tournoux, "co-planar stereotaxic atlas of the human brain", thieme medical publishers, 1988. 36 marius george linguraru, miguel ángel gonzález ballester, nicholas ayache [14] k. van leemput, f. maes, d. vandermeulen, a. colchester, p. suetens, "automated segmentation of multiple sclerosis lesions by model outlier detection", ieee transactions on medical imaging, 20(8):677-688, 2001. [15] i.g. zubal, c.r. harrell, e.o. smith, z. rattner, g. gindi, p.b. hoffer, "computerized threedimensional segmented human anatomy", medical physics, 21:299-302, 1994. marius george linguraru epidaure/asclepios research group, inria sophia antipolis, france division of engineering and applied sciences harvard university, cambridge ma, usa e-mail: mglin@deas.harvard.edu miguel ángel gonzález ballester university of bern, bern, switzerland mem research center, institute for surgical technology and biomechanics nicholas ayache epidaure/asclepios research group, inria sophia antipolis, france received: november 16, 2006 editor’s note about the author: marius george linguraru joined the diagnostic radiology department at the clinical center at the national institute of health (nih), bethesda, maryland, usa in 2007 as staff scientist. previously, he worked as research fellow in the division of engineering and applied sciences of harvard university in cambridge, massachusetts, usa. he moved to the boston area from the south of france, where he was expert engineer in the epidaure/asclepios research group of the national institute of research in informatics and automatic control (inria) in sophia antipolis, france. he received a phd in information engineering/medical image analysis at the university of oxford, oxford, uk within the medical vision laboratory and was a member of keble college. his previous studies include an ma in british cultural, an msc in parallel and distributed processing systems, studies and a bsc in computer science. all three degrees are from the "lucian blaga" university of sibiu, romania, where he also worked as assistant professor in the department of computer science and automatic control. 7nadaban6.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):834-842, december, 2015. fuzzy continuous mappings in fuzzy normed linear spaces s. nădăban sorin nădăban department of mathematics and computer science aurel vlaicu university of arad, elena drăgoi 2, ro-310330 arad, romania snadaban@gmail.com abstract: in this paper we continue the study of fuzzy continuous mappings in fuzzy normed linear spaces initiated by t. bag and s.k. samanta, as well as by i. sadeqi and f.s. kia, in a more general settings. firstly, we introduce the notion of uniformly fuzzy continuous mapping and we establish the uniform continuity theorem in fuzzy settings. furthermore, the concept of fuzzy lipschitzian mapping is introduced and a fuzzy version for banach’s contraction principle is obtained. finally, a special attention is given to various characterizations of fuzzy continuous linear operators. based on our results, classical principles of functional analysis (such as the uniform boundedness principle, the open mapping theorem and the closed graph theorem) can be extended in a more general fuzzy context. keywords: fuzzy normed linear spaces; fuzzy continuous mapping; fuzzy bounded linear operators. 1 introduction and preliminaries the concept of fuzzy set was introduced by l. zadeh [14] in 1965. if x is a nonempty set, a fuzzy set in x is a function µ from x into the unit interval [0, 1]. the classical union and intersection of ordinary subsets of x can be extended by the following formulas, proposed by l. zadeh ( ∨ i∈i µi ) (x) = sup{µi(x) : i ∈ i} , ( ∧ i∈i µi ) (x) = inf{µi(x) : i ∈ i} . from here to the notion of fuzzy topological space, there was one more step to be taken. thus, in 1968, c.l. chang [4] introduced the notion of fuzzy topological space. the definition is a natural translation to fuzzy sets of the ordinary definition of topological space. indeed, a fuzzy topology is a family t , of fuzzy sets in x, such that t is closed with respect to arbitrary union and finite intersection and every constant function belong to t . one of the important problems concerning the fuzzy topological spaces is to obtain an adequate notion of fuzzy metric space. many authors have investigated this question and several notions of fuzzy metric space have been defined and studied. we just mention the definition given by i. kramosil and j. michálek [9] in 1975. definition 1. the pair (x, m) is said to be a fuzzy metric space if x is an arbitrary set and m is a fuzzy set in x × x × [0,∞) satisfying the following conditions: (m1) m(x, y, 0) = 0, (∀)x, y ∈ x; (m2) (∀)x, y ∈ x, x = y if and only if m(x, y, t) = 1 for all t > 0; (m3) m(x, y, t) = m(y, x, t), (∀)x, y ∈ x, (∀)t > 0; (m4) m(x, z, t + s) ≥ m(x, y, t) ∗ m(y, z, s), (∀)x, y, z ∈ x, (∀)t, s > 0; copyright © 2006-2015 by ccc publications fuzzy continuous mappings in fuzzy normed linear spaces 835 (m5) (∀)x, y ∈ x, m(x, y, ·) : [0,∞) → [0, 1] is left continuous and lim t→∞ m(x, y, t) = 1 . we note that, in previous definition, ∗ denotes a continuous t-norm (see [13]). the basic examples of continuous t-norms are ∧, ·,∗l, which are defined by a ∧ b = min{a, b}, a · b = ab (usual multiplication in [0, 1]) and a ∗l b = max{a + b − 1, 0} (the lukasiewicz t-norm). in studying fuzzy topological linear spaces, a.k. katsaras [8], in 1984, first introduced the notion of fuzzy norm on a linear space. since then many mathematicians have introduced several notions of fuzzy norm from different points of view. thus, c. felbin [6] in 1992 introduced an idea of fuzzy norm on a linear space by assigning a fuzzy real number to each element of linear space. in 1994, s.c. cheng and j.n. mordeson [5] introduced a concept of fuzzy norm on a linear space whose associated metric is kramosil and michálek type. following s.c. cheng and j.n. mordeson, in 2003, t. bag and s.k. samanta [2] proposed another concept of fuzzy norm. in this paper we continue the study of fuzzy continuous mappings in fuzzy normed linear spaces initiated by t. bag and s.k. samanta [3], as well as by i. sadeqi and f.s. kia [12], in a more general settings: definition 2. [10] let x be a vector space over a field k (where k is r or c) and ∗ be a continuous t-norm. a fuzzy set n in x × [0,∞) is called a fuzzy norm on x if it satisfies: (n1) n(x, 0) = 0, (∀)x ∈ x; (n2) [n(x, t) = 1, (∀)t > 0] if and only if x = 0; (n3) n(λx, t) = n ( x, t |λ| ) , (∀)x ∈ x, (∀)t ≥ 0, (∀)λ ∈ k∗; (n4) n(x + y, t + s) ≥ n(x, t) ∗ n(y, s), (∀)x, y ∈ x, (∀)t, s ≥ 0; (n5) (∀)x ∈ x, n(x, ·) is left continuous and lim t→∞ n(x, t) = 1. the triple (x, n,∗) will be called fuzzy normed linear space (briefly fnls). remark 3. a) t. bag and s.k. samanta [2], [3] gave a similar definition for ∗ = ∧, but in order to obtain some important results they assumed that the fuzzy norm also satisfied the following conditions: (n6) n(x, t) > 0, (∀)t > 0 ⇒ x = 0 ; (n7) (∀)x 6= 0, n(x, ·) is a continuous function and strictly increasing on the subset {t : 0 < n(x, t) < 1} of r. the results obtained by t. bag and s.k. samanta [3], as well as by i. sadeqi and f.s. kia [12], can be found in this more general setting. b) i. goleţ [7], c. alegre and s. romaguera [1] also gave this definition in the context of real vector spaces. c) n(x, ·) is nondecreasing, (∀)x ∈ x. example 4. [2] let x be a linear space and || · || be a norm on x. let n(x, t) := { 1 if |x| < t 0 if |x| ≥ t then (x, n,∧) is a fnls. in particular, (c, n,∧) is a fnls. 836 s. nădăban theorem 5. [10] let (x, n,∗) be a fnls. for x ∈ x, r ∈ (0, 1), t > 0 we define the open ball b(x, r, t) := {y ∈ x : n(x − y, t) > r} . then tn := {t ⊂ x : x ∈ t iff (∃)t > 0, r ∈ (0, 1) : b(x, r, t) ⊆ t} is a topology on x. moreover, if the t-norm ∗ satisfies sup x∈(0,1) x ∗ x = 1, then (x,tn) is hausdorff. theorem 6. [10] let (x, n,∗) be a fnls. then (x,tn) is a metrizable topological vector space. definition 7. [2] let (x, n,∗) be a fnls and (xn) be a sequence in x. 1. the sequence (xn) is said to be convergent if (∃)x ∈ x such that lim n→∞ n(xn − x, t) = 1 , (∀)t > 0 . in this case, x is called the limit of the sequence (xn) and we denote lim n→∞ xn = x or xn → x. 2. the sequence (xn) is called cauchy sequence if lim n→∞ n(xn+p − xn, t) = 1 , (∀)t > 0, (∀)p ∈ n ∗ . 3. (x, n,∗) is said to be complete if any cauchy sequence in x is convergent to a point in x. a complete fnls will be called a fuzzy banach space. theorem 8. let (x, n,∗) be a fnls and pα(x) := inf{t > 0 : n(x, t) > α}, α ∈ (0, 1) . then, for x ∈ x, s > 0, α ∈ (0, 1), we have: pα(x) < s if and only if n(x, s) > α . proof: the proof is entirely the same as in [10], where there are considered fnlss of type (x, n,∧). ✷ the structure of the paper is as follows: in section 2, we introduce the notion of uniformly fuzzy continuous mapping and we establish the uniform continuity theorem in fuzzy settings. the concept of fuzzy lipschitzian mapping is introduced and a fuzzy version for banach’s contraction principle is obtained. in section 3, special attention is given to various characterizations of fuzzy continuous linear operators. based on our results, classical principles of functional analysis (such as the uniform boundedness principle, the open mapping theorem and the closed graph theorem) can be extended in a more general fuzzy context. even if the structure of fuzzy f-spaces, recently introduced in [11], is much more complicated than that of fuzzy banach spaces, we intent to study, in a further paper, fuzzy continuous linear operators on fuzzy f-spaces and to prove that the well-known principles of functional analysis are valid in this context too. in the following sections (x, n1,∗1), (y, n2,∗2) will be fnlss with the t-norms ∗1,∗2 which satisfy sup x∈(0,1) x ∗i x = 1, (∀)i = 1, 2. fuzzy continuous mappings in fuzzy normed linear spaces 837 2 fuzzy continuous mappings definition 9. [3] a mapping t : x → y is said to be fuzzy continuous at x0 ∈ x, if (∀)ε > 0, (∀)α ∈ (0, 1), (∃)δ = δ(ε, α, x0) > 0, (∃)β = β(ε, α, x0) ∈ (0, 1) such that (∀)x ∈ x : n1(x − x0, δ) > β we have that n2(t(x) − t(x0), ε) > α . if t is fuzzy continuous at each point of x, then t is called fuzzy continuous on x. theorem 10. [3] a mapping t : x → y is fuzzy continuous at x0 ∈ x, if and only if (∀)(xn) ⊆ x, xn → x0, we have that t(xn) → t(x0). definition 11. a mapping t : x → y is said to be uniformly fuzzy continuous on x, if (∀)ε > 0, (∀)α ∈ (0, 1), (∃)δ = δ(ε, α) > 0, (∃)β = β(ε, α) ∈ (0, 1) such that (∀)x, y ∈ x : n1(x − y, δ) > β we have that n2(t(x) − t(y), ε) > α . remark 12. if t is uniformly fuzzy continuous, then t is fuzzy continuous. theorem 13. (uniform continuity theorem). let (x, n1,∗1) be a compact fnls and (y, n2,∗2) be a fnls. if t : x → y is a fuzzy continuous mapping, then t is uniformly fuzzy continuous. proof: let ε > 0 and α ∈ (0, 1). as sup x∈(0,1) x ∗2 x = 1, then there exists α0 ∈ (0, 1) such that α0 ∗2 α0 > α. as t : x → y is a fuzzy continuous on x, for all x ∈ x, there exist δx = δ ( ε 2 , α0, x ) > 0, βx = β ( ε 2 , α0, x ) ∈ (0, 1) such that (∀)y ∈ x : n1(x − y, δx) > βx ⇒ n2 ( t(x) − t(y), ε 2 ) > α0 . as sup x∈(0,1) x ∗1 x = 1, we can take γx > βx such that γx ∗1 γx > βx. since x is compact and { b ( x, γx, δx 2 )} x∈x is an open covering of x, there exist x1, x2, · · · , xn in x such that x = n ⋃ i=1 b ( xi, γxi, δx i 2 ) . let β = max{γxi} and δ = min { δx i 2 } , for i = 1, 2, · · · , n. let x, y ∈ x arbitrary, such that n1(x − y, δ) > β. as x ∈ x, there exists i ∈ {1, 2, · · · , n} such that x ∈ b ( xi, γxi, δx i 2 ) , namely n1 ( x − xi, δx i 2 ) > γxi . hence n1(x − xi, δxi) ≥ n1 ( x − xi, δxi 2 ) > γxi > βxi . thus n2 ( t(x) − t(xi), ε 2 ) > α0 . we remark that n1(y − xi, δxi) ≥ n1 ( y − x, δxi 2 ) ∗1 n1 ( x − xi, δxi 2 ) ≥ ≥ n1(y − x, δ) ∗1 n1 ( x − xi, δxi 2 ) > β ∗1 γxi ≥ γxi ∗1 γxi > βxi . 838 s. nădăban thus n2 ( t(y) − t(xi), ε 2 ) > α0. in conclusion n2(t(x) − t(y), ε) ≥ n2 ( t(x) − t(xi), ε 2 ) ∗2 n2 ( t(xi) − t(y), ε 2 ) > > α0 ∗2 α0 > α . ✷ definition 14. a mapping t : x → y is said to be fuzzy lipschitzian on x if (∃)l > 0 such that n2(t(x) − t(y), t) ≥ n1 ( x − y, t l ) , (∀)t > 0, (∀)x, y ∈ x . if l < 1 we say that t is a fuzzy contraction. remark 15. it is clear that a fuzzy lipschitzian mapping is necessarily fuzzy continuous. theorem 16. (banach’s contraction principle). let (x, n,∗) be a fuzzy banach space and t : x → x be a fuzzy contraction. then t has a unique fixed point z ∈ x and lim n→∞ t n(x) = z, (∀)x ∈ x . proof: let x ∈ x be arbitrary. then {t n(x)} is a cauchy sequence. indeed, for t > 0 and p ∈ n∗, we have n(t n+p(x) − t n(x), t) ≥ n ( t n+p−1(x) − t n−1(x), t l ) ≥ ≥ ··· ≥ n ( t p(x) − x, t ln ) . as l ∈ (0, 1), we have that lim n→∞ t ln = ∞. thus lim n→∞ n ( t p(x) − x, t ln ) = 1 . hence lim n→∞ n(t n+p(x) − t n(x), t) = 1, namely {t n(x)} is a cauchy sequence. since x is complete, we have that {t n(x)} is a convergent sequence. thus (∃)z ∈ x such that lim n→∞ t n(x) = z. we note that z = lim n→∞ t n+1(x) = lim n→∞ t(t n(x)) = t(z) . now we show the uniqueness. suppose that there exist z, y ∈ x, z 6= y with the property z = t(z), y = t(y). as z 6= y, there exists s > 0 such that n(z − y, s) = a < 1. then, for all n ∈ n, we have a = n(y − z, s) = n(t n(y) − t n(z), s) ≥ n ( y − z, s ln ) → 1 . thus a = 1, which contradicts our assumption. ✷ fuzzy continuous mappings in fuzzy normed linear spaces 839 3 fuzzy continuous linear operators theorem 17. let t : x → y be a linear operator. then t is fuzzy continuous on x, if and only if t is fuzzy continuous at a point x0 ∈ x. proof: ” ⇒ ” it is obvious. ” ⇐ ” let y ∈ y be arbitrary. we will show that t is fuzzy continuous at y. let ε > 0, α ∈ (0, 1). since t is fuzzy continuous at x0 ∈ x, there exist δ > 0, β ∈ (0, 1) such that (∀)x ∈ x : n1(x − x0, δ) > β ⇒ n2(t(x) − t(x0), ǫ) > α . replacing x by x + x0 − y, we obtain that (∀)x ∈ x : n1(x + x0 − y − x0, δ) > β ⇒ n2(t(x + x0 − y) − t(x0), ǫ) > α , namely (∀)x ∈ x : n1(x − y, δ) > β ⇒ n2(t(x) − t(y), ǫ) > α . thus t is fuzzy continuous at y ∈ y . as y is arbitrary, it follows that t is fuzzy continuous on d(t). ✷ corollary 18. let t : x → y be a linear operator. then t is fuzzy continuous on x, if and only if (∀)ε > 0, (∀)α ∈ (0, 1), (∃)δ = δ(ǫ, α) > 0, (∃)β = β(ǫ, α) ∈ (0, 1)such that (∀)x ∈ x : n1(x, δ) > β we have that n2(t(x), ε) > α . theorem 19. a linear operator t : x → y is fuzzy continuous on x, if and only if (∀)α ∈ (0, 1), (∃)β = β(α) ∈ (0, 1), (∃)m = m(α) > 0 such that (∀)t > 0, (∀)x ∈ x : n1(x, t) > β ⇒ n2(t(x), mt) > α . proof: ” ⇐ ” let ε > 0, α ∈ (0, 1) be arbitrary. then there exist β = β(α) ∈ (0, 1), m = m(α) > 0 such that (∀)t > 0, (∀)x ∈ x : n1(x, t) > β ⇒ n2(t(x), mt) > α . in particular, for t = ε m , we obtain n1 ( x, ε m ) > β ⇒ n2(t(x), ε) > α . applying corollary 18, for δ = ε m > 0, we obtain that t is fuzzy continuous on x. ” ⇒ ” we suppose that (∃)α0 ∈ (0, 1) such that (∀)β ∈ (0, 1), (∀)m > 0, (∃)t0 = t0(β, m) > 0, (∃)x0 = x0(β, m) ∈ x, n1(x0, t0) > β and n2(t(x), mt0) ≤ α0 . the set v0 = {y ∈ y : n2(y, t0) > α0} is an open neighborhood of 0y . we will prove that, for all neighborhood u of 0x, we have t(u) 6⊆ v0, which contradicts the fuzzy continuity of t at 0x. as {b(0, β, s)}β∈(0,1),s>0 is a fundamental system of neighborhoods of 0x, it is enough to show that for all β ∈ (0, 1), s > 0 we have t(b(0, β, s)) 6⊆ v0. as m > 0 is arbitrary, we can chose s = t0 m . we note that, for z0 = 1 m x0 ∈ x, we have n1 ( z0, t0 m ) = n1 ( 1 m x0, t0 m ) = n1(x0, t0) > β . 840 s. nădăban hence z0 ∈ b ( 0, β, t0 m ) . we will prove that t(z0) 6∈ v0, namely n2(t(z0), t0) ≤ α0. indeed, n2(t(z0), t0) = n2 ( t ( 1 m x0 ) , t0 ) = n2(t(x0), mt0) ≤ α0 . ✷ corollary 20. a linear functional f : (x, n1,∗) → (c, n,∧) is fuzzy continuous, if and only if (∃)β ∈ (0, 1), (∃)m > 0 such that (∀)t > 0, (∀)x ∈ x, n1(x, t) > β ⇒ |f(x)| < mt . proof: according to the previous theorem f is fuzzy continuous if and only if (∀)α ∈ (0, 1), (∃)β ∈ (0, 1), (∃)m > 0 such that (∀)t > 0, (∀)x ∈ x : n1(x, t) > β ⇒ n(f(x), mt) > α . but n(f(x), mt) > α ⇔ n(f(x), mt) = 1 ⇔ |f(x)| < mt . hence (∃)β ∈ (0, 1), (∃)m > 0 such that (∀)t > 0, (∀)x ∈ x, n1(x, t) > β ⇒ |f(x)| < mt . ✷ corollary 21. let (x, n1,∗1), (y, n2,∗2) be fnlss and pα(x) := inf{t > 0 : n1(x, t) > α}, α ∈ (0, 1) , qα(x) := inf{t > 0 : n2(x, t) > α}, α ∈ (0, 1) . a linear operator t : x → y is fuzzy continuous on x if and only if (∀)α ∈ (0, 1), (∃)β = β(α) ∈ (0, 1), (∃)m = m(α) > 0 such that qα(tx) ≤ mpβ(x) , (∀)x ∈ x . proof: according to the previous theorem, (∀)α ∈ (0, 1), (∃)β = β(α) ∈ (0, 1), (∃)m = m(α) > 0 such that (∀)t > 0, (∀)x ∈ x : n1(x, t) > β ⇒ n2(t(x), mt) > α . thus, for x ∈ x, we have {t > 0 : n1(x, t) > β} ⊆ {t > 0 : n2(tx, mt) > α} . hence inf{t > 0 : n1(x, t) > β} ≥ inf{t > 0 : n2(tx, mt) > α} , namely inf{t > 0 : n1(x, t) > β} ≥ inf { t m > 0 : n2(tx, t) > α } . therefore pβ(x) ≥ 1 m qα(tx),∀)x ∈ x . ✷ fuzzy continuous mappings in fuzzy normed linear spaces 841 corollary 22. a linear functional f : (x, n1,∗) → (c, n,∧) is fuzzy continuous, if and only if (∃)β ∈ (0, 1), (∃)m > 0 such that |f(x)| ≤ mpβ(x), (∀)x ∈ x . remark 23. we note that a subset a of a topological linear space x is said to be bounded if for every neighbourhood v of 0x, there exists a positive number k such that a ⊂ kv . a linear operator t : x → y is said to be bounded if t maps bounded sets into bounded sets. based on this remark the following definitions are natural. definition 24. [12] a subset a of x is called fuzzy bounded, if (∀)α ∈ (0, 1), (∃)tα > 0 such that a ⊂ b(0, α, tα). definition 25. [12] a linear operator t : x → y is said to be fuzzy bounded if t maps fuzzy bounded sets of x into fuzzy bounded sets of y . we must note that the following result was established by i. sadeqi and f.s. kia [12] for fnlss of type (x, n,∧) which satisfy (n7). since the proof is entirely the same as in [12], it is omitted. theorem 26. let t : x → y be a linear operator. the following sentences are equivalent: 1. t is fuzzy continuous; 2. t is topological continuous; 3. t is fuzzy bounded; 4 conclusion as fuzzy continuity and topological continuity are equivalent and since fnlss are metrizable topological linear spaces, all results and theorems in topological linear spaces hold for fnlss. particularly, we can obtain fuzzy versions for the classical principles of functional analysis (such as the uniform boundedness principle, the open mapping theorem and the closed graph theorem). this remark was made by i. sadeqi and f.s. kia [12] for fnlss of type (x, n,∧). based on our results, these principles remain true without assuming (n7) as in [12]. bibliography [1] alegre, c., romaguera, s. 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(1965); fuzzy sets, information and control, 8: 338–353. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 398-409 role-based access control for the large hadron collider at cern i. yastrebov ilia yastrebov 1. european organization for nuclear research switzerland, 1211 geneva 23, and 2. joint institure for nuclear research russia, 141980 dubna, 6 jolio-curie e-mail: ilia.yastrebov@cern.ch abstract: large hadron collider (lhc) is the largest scientific instrument ever created. it was built with the intention of testing the most extreme conditions of the matter. taking into account the significant dangers of lhc operations, european organization for nuclear research (cern) has developed multi-pronged approach for machine safety, including access control system. this system is based on rolebased access control (rbac) concept. it was designed to protect from accidental and unauthorized access to the lhc and injector equipment. this paper introduces the new model of the role-based access control developed at cern and gives detailed mathematical description of it. we propose a new technique called dynamic authorization that allows deploying rbac gradually in the large systems. moreover, we show how the protection for the very large distributed equipment control system may be implemented in efficient way. this paper also describes motivation of the project, requirements and overview of the main components: authentication and authorization. keywords: software development, role-based access control, information security, equipment protection. 1 introduction the large hadron collider was built by the european organization for nuclear research (cern) with the intention of testing various predictions of high-energy physics, including the existence of the hypothesized higgs boson and of the large family of new particles predicted by super-symmetry [1]. the lhc is the world’s largest and highest-energy particle accelerator. it is contained in a circular tunnel with a circumference of 27 kilometers, at a depth ranging from 50 to 175 meters underground [2]. the lhc uses some of the most powerful dipoles and radio-frequency cavities in existence. the size of the tunnel, magnets, cavities and other essential elements of the machine represent the main constraints that determine the design energy of 7tev per proton beam [3]. the energy stored in the lhc magnets and beam is enormous, and the potential for crippling machine damage is a serious concern. that’s why european organization for nuclear research (cern) has developed a multi-pronged approach for machine safety [4]: • hardware protection – lhc beam interlock system – powering interlock system • software interlock system • role-based access control copyright c© 2006-2010 by ccc publications role-based access control for the large hadron collider at cern 399 – prevents unauthorized access to equipment – provide logging to detect errant settings role-based access control (rbac) is an approach to restrict system access to authorized users. within an organization, roles are created for various job functions. the permissions to perform certain operations are assigned to specific roles. members of staff (or other system users) are assigned particular roles, and through those role assignments acquire the permissions to perform particular system functions [5]. rbac is a preventative and therefore inexpensive way to protect the accelerator equipment. it keeps users from making the wrong settings or from logging into the application. other machine protection systems such as interlocks are reactive and once triggered it is expensive to recover operations. rbac is also used to ensure machine stability during a run. once the equipment is fine tuned and beam is in the machine, an error setting can disrupt operations for hours and lose valuable data. it is important to mention that rbac is not a security system against hackers; it is designed only to prevent well meaning people from making the wrong setting, and unauthorized users who have no credentials from running the control applications. the last motivation is that rbac implements logging for each setting protected by access rules. this is crucial during commissioning and debugging. each setting can be traced and bugs in the sequencer or operations can be caught and corrected [6]. as part of the problem solution a new mathematical model of the role-based access control (hereinafter cern-rbac) was proposed. equipment protection subsystem based on this model has been successfully implemented and deployed at cern. the subject of this paper is to describe the new concept of cern-rbac for distributed equipment control system. the paper also demonstrates the practical implementation of the major system components, and presents the results of the comprehensive testing. 2 mathematical model role-based access control, as formalized in 1992 by david ferraiolo and rick kuhn [7], has become the predominant model for advanced access control. in 2000, the ferraiolo-kuhn model was integrated with the framework of sandhu et al. [8] to create a unified model for rbac, published as the nist rbac model [9] and adopted as an ansi/incits standard in 2004. today, most information technology vendors have incorporated rbac into their product lines, and the technology is finding applications in areas ranging from health care to defense, in addition to the mainstream commerce systems for which it was designed [5]. we propose a new model of cern-rbac for the distributed control system. this concept is based on the standard model and preserves the advantages of scalable security administration that rbac-style models offer. moreover it significantly extends standard rbac model according to specific requirements and yet offers the flexibility to specify complex access restrictions based on the dynamic security attributes. the new cern-rbac model is quite general and flexible and could be used in many other areas for equipment access control. below we give a formal mathematical description of the model in terms of sets and relations. u – a set of users, {u, u, . . . , un}. the user is either a human user or a computer program. r – a set of roles, {r, r, . . . , rn}. role is a job function which defines an authority level. p – a set of permissions, {p, p, . . . , pn}. permission (access rule) is an approval of a mode of access to a resource. ua – user assignment: operation which assigns concrete roles to the users. ua ⊆ u ×r (1) pa – permission assignment: r → p – function, defining a set of access rules for each role. this condition must be met at that: ∀p ∈ p,∃r ∈ r : p ∈ pa(r) (2) 400 i. yastrebov figure 1: mathematical model of cern-rbac s – a set of sessions, {s, s, . . . , sn}. session (subject) is mapping of one user to possibly many roles. the double-headed arrow from the session to r in figure 1 indicates that multiple roles are simultaneously activated. the permissions available to the user are the union of permissions from all roles activated in that session. each session is associated with a single user, as indicated by the single-headed arrow from the session to u in figure 1. this association remains constant for the life of a session. user(si) = s → u (3) ar – a set of the subject’s active roles (which can change with time). for each subject, the active role is the one that the subject is currently using. ar(si) = s → r (4) ar(si) ⊆ {r ∈ r | (user(si), r) ∈ ua} (5) sessions are under the control of individual users. as far the model is concerned, a user can create a session and choose to activate some subset of the user’s roles. roles active in a session can be changed at the user’s discretion. the session terminates at the user’s initiative. a system may also terminate a session if it is inactive for too long [8]. cern-rbac model in the previous sections we described the main elements of the rbac model included in the standard [9]. however the standard model is quite general and abstract to protect the equipment against unauthorized access. the standard concept allows defining permissions only for simple objects. without further extension it cannot describe more complex interaction. moreover, the specificity of the equipment requires taking into account its current working mode. that is the authorization algorithm must be dynamic. we propose a new model of the role-based access control for the distributed control system, called cern-rbac. within this model device is a named entity in the control system. device can be controlled via its properties. each property has a name and value. the set of properties specific for the group of homogenous devices forms a device class. the model also defines operations for work with all environments in the system. these are get, set and monitor operations, that allows reading value from device, writing value to device and monitor the device property correspondingly. op – a set of operations, {get, set, monitor}. role-based access control for the large hadron collider at cern 401 pr – a set of device properties, {pr, pr, . . . , prn}. dc – a set of device classes, {dc, dc, . . . , dcn}. dca = dc → pr (6) d – a set of devices (equipments), {d, d, . . . , dn}. now we give a function that for every device from the set d defines associated device class: class(di) = d → dc (7) the properties set for concrete device (apr) are defined as: apr(di) = {pr ∈ pr | (class(di), pr) ∈ dca} (8) a set of transactions, which user can execute working within the distributed control system: t = {(op, pr, d) | o p ∈ op, pr ∈ pr, d ∈ d, pr ∈ apr(d)} (9) this implies that interaction of user and control system comes to execution of operations from the set op for the properties of some devices. in terms of proposed model, permission defines for the given role a set of transactions potentially executable by the user owning that role. function defining a set of transactions for each permission (transaction assignment): t a = p → t (10) the predicate exec(si,t j) is true if subject can execute transaction at the current time, otherwise it is false. subject can execute transaction only if it has active roles: ∀s ∈ s,t ∈ t, (exec(s,t) ⇒ ar(s) 6= ) (11) authorization is the function of specifying access rights to resources or services. in our case subject can execute transaction from the set t only if this transaction is authorized for at least one active role of the subject: ∀s ∈ s,t ∈ t, (exec(s,t) ⇒ t ∈ t a(pa(ar(s)))) (12) this rule ensures that users can execute only transactions for which they are authorized. because the conditional is "only if", this rule allows the possibility that additional restrictions may be placed on transaction execution. that is, the rule does not guarantee a transaction to be executable just because it is in the set of transactions potentially executable by the subject’s active role [10]. dynamic authorization equipment functions in different modes. while a device is working in the test mode it’s often necessary to allow access for wider range of users than during normal operation. in such cases an expansion of the access rules is not always desirable and appropriate. firstly, it may loosen up on the system security, and secondly it requires significant administrative costs. we believe the introduction of different working modes of authorization assists to problem solving. in this case the authorization algorithm will take into account not only access rules, but also the current working mode of the equipment. cern has a lot of exposed equipments due to the size of the lhc, which contains hundreds of thousands different devices with dozens of properties each. thus it takes a lot of time to design access rules for all devices. as far as we cannot force equipment specialists to define these rules in a single day, we have to propose the flexible solution to regulating access to non-protected equipments. this will allow us to deploy the access control system step-by-step, without breaking existing infrastructure. it’s the crucial requirement for the access control system at cern. 402 i. yastrebov dynamic authorization is the algorithm of authorization taking into accounts not only access rules, but also internal state of the authorization subject. moreover this approach defines default privileges for unprotected environment and non-authenticated users. cp a set of checking policies considered by dynamic authorization algorithm, {no-check,lenient,strict}. a checking policy is defined at the level of every device and can be changed at runtime. the function mapping each device from the set d to the associated checking policy: policy(di) = d → cp (13) now let’s introduce the predicate of dynamic authorization as ψ(s, op, pr, d, policy(d)), which will be true if subject can execute operation from op on concrete device property. ∀s ∈ s, op ∈ op, pr ∈ pr, d ∈ d : (exec(s,t) ⇒ ψ(s, op, pr, d, policy(d))) (14) now we define a predicate which is true if property is considered to be protected. protected(op ∈ op, pr ∈ pr, d ∈ d) =  ⇔ (op, pr, d) ∈ t (15) that is property is protected if there is at least one access rule which restricts access for the given operation. below we give a description of the dynamic authorization algorithm for each checking policy. no-check policy grants access for each property without any checking. typically this policy is used at design stage, when the device interface is not fixed and there are no access rules yet. this policy is also used during testing phase if needed to permit equipment access for some additional users for a short period of time. this mode could be useful for system debugging because or for other activity when it’s required to disable cern-rbac authorization checks. ψ(no − check) = { , protected(pr, d) = ; , protected(pr, d) = . (16) lenient checking policy implements relaxed authorization. for the protected properties algorithm gives access only if corresponding access rule permits so. that is in order to deal with protected properties user must be authenticated in the system. for unprotected properties access is not restricted for any users. typically this policy is used at the testing stage, when access rules exist only for the most critical settings of the equipment. some of the devices work in this mode permanently, because sometimes it’s desirable to restrict access only to significant settings while keeping others unprotected. ψ(lenient) = { (op, pr, d) ∈ t a(pa(ar(s))), protected(pr, d) = ; , protected(pr, d) = . (17) strict checking policy implies the most exacting verification. it always requires users to be authenticated in the system; otherwise user’s requests will be blocked. access for the protected properties is granted only if there is an associated access rule. for unprotected properties access is permitted only for reading property value and monitoring operation. setting new value even for unprotected property is forbidden. this checking policy is the strictest in existence at cern. our final goal is to propagate this mode as wide as possible, because it provides best security. all critical equipments are supposed to work in this mode. ψ(strict) =    , s = ; (op, pr, d) ∈ t a(pa(ar(s))), protected(pr, d) = ; , op ∈ {get, monitor}, protected(pr, d) = ; , op = set, protected(pr, d) = . (18) thereby role-based access control concept with dynamic authorization solves the problem of unprotected properties and legacy applications working without authentication. introducing this approach we role-based access control for the large hadron collider at cern 403 get a system that works dynamically and its behavior can be easily changed at runtime. one of the main advantages of this approach is that it allows deploying cern-rbac step-by-step without interruption of the existing software. this requirement is very important for such a huge project like lhc. our final goal is to protect every single device, but it’s impossible to accomplish this job even in one year, taking into account number of environments. we believe that our model of cern-rbac could be a good example of the flexible system and could be useful in many other applications for very large distributed systems. based on the new model of cern-rbac we developed and deployed software products. in the following chapters we overview the main technical requirements of the product, and describe implementation of its main components. 3 system requirements to distinguish authentication from the closely related term authorization, the shorthand notations a1 (authentication) and a2 (authorization) are occasionally used. in the present paper we list only the most important requirements of the authentication and authorization components of the cern-rbac system [11]. both parts are implemented independently, as two different systems, which do not interact in any way other than passing the users’ credentials from a1 to a2. authentication requirements: • encryption: the credentials used to authenticate the user shall be encrypted when sent over the network. • hardware independence: the method of authentication shall be independent of specialized hardware such as a card reader, finger print reader etc. • quick and simple: the method of authentication must be straightforward for the users. • authentication method: authentication shall be done via user name and password from a personal cern account. the software should allow flexibility for future implementations of kerberos and/or x.509 certificates. • authentication by location should be implemented as an additional authentication method. this will allow users to be authenticated without providing credentials from a very limited set of trusted machines located in the cern control centre (ccc). • role activation mechanism should be implemented, which will allow users to pick up active roles from the set of available roles. authorization requirements: • subject of authorization: it shall be possible to restrict access i.e. define access privileges for the following operations on each device property: read, monitor, and write. • permission administration: the permissions shall be defined during the design/deployment phase, and authorized administrators shall be able to edit the permissions at any time. • logging/tracking: cern-rbac shall keep track of all write actions. • performance: authorization shall be fast and shall not hinder the performance of the middleware. 404 i. yastrebov 4 authentication the purpose of the cern-rbac authentication system is to verify the digital identity of a principal (which is either a human user or a program). if the authentication succeeds its result is a digitally signed authentication token that is returned to the application [12]. the program can use the token whenever it needs to interact with various parts to the control system. for example, the token can be provided as one of the arguments in a remote call to set a device. front-ends and the middleware that are receiving such calls will verify the token, thus confirming the identity of the remote party, and can use it as a base for authorization. the cern-rbac authentication token is a short-term uniform substitute of the real credentials. it gets issued by a central service that can reliably verify the user’s identity. various recipients of the tokens can validate them quickly and easily, and use for making authorization decisions [6]. figure 2 demonstrates the authentication process. 1) application sends login request to cern-rbac. 2) cern-rbac checks the user location in the database (for authentication by location). 3) if location is trusted, then the new token immediately created and returned to the user (authentication by location). 4) otherwise cern-rbac sends to the application the login dialog for credentials or certificate. the user types in the name and password or chooses the certificate and sends it to cern-rbac. 5) cern-rbac verifies the credentials with cern account system (nice). 6) if the check was successful cern-rbac retrieves roles from the cern-rbac database, generates a new token and signs it with private key to prevent any modifications. 7) cern-rbac returns to the application a digitally signed token containing the user roles. figure 2: authentication process the a1 server is implemented in java programming language. it receives authentication requests via http from multiple clients, returning back either an authentication token, or an error code. each request from a user contains its credential in some form. all requests are atomic, so no session information is cached by the server. the ssl/tls protocol is generally used over http to protect the communication role-based access control for the large hadron collider at cern 405 between the two parties, and to authenticate the client’s x.509 certificate, if such is provided. the client side is organized as a library implemented both in c++ and java, which can be used by other applications or application frameworks. this library provides a function that should be called in order to obtain the authentication token from the server. java implementation also provides several standard gui components, such as a login dialog, role picker dialog, and others. basically, that authentication client-side library can be used in most application without changes. c++ version of the client library is more lightweight because it does not provide any gui. types of authentication • by the user name and password. the user names and passwords are checked against the central nice account database, via a dedicated web service. no user account information is stored in the cern-rbac own database. • by a x.509 certificate. if the user’s x.509 certificate is available, it can be applied in the standard client authentication mechanism of tls/ssl protocol. then, the certificate information is used to look up the user name in the cern-rbac database. • by the network address (also called authentication by location). certain clients can be authenticated by their ip addresses, using a lookup table in the cern-rbac database. normally, the address authentication is permitted only for a very limited number of machines, such as control room consoles. • by using an existing authentication token. any existing token can be used to request a new one, providing that the original token is not expired, bears valid signature, and was issued to the same location address. the validity time of the new token will not exceed the validity time of the original one. 5 authorization middleware in the control system controls middleware (cmw) is a software infrastructure delivered and managed by cern beams department/controls group. its goal is to provide a generic way of accessing lhc-era accelerator devices [13]. cmw provides access to devices from application programs in a distributed heterogeneous control system. it allows interconnecting applications and devices implemented in java or c++, and running under unix or windows platforms. the cmw design reflects the accelerator device model in which devices, named entities in the control system, can be controlled via properties. each property has a name and a value. the model defines several basic device access methods (get, set, monitor); by invoking these methods, applications can read, write and subscribe to the property values. cmw is based on a client-server model. accelerator devices are implemented in device servers, and client applications access them using the cmw client api. cmw provides location transparent access to devices: servers can run anywhere in the controls network and devices can be moved from server to server without clients becoming aware of it. authorization cern-rbac authorization library is the part of each device server. in most cases, authorization occurs once the application makes a request to get, set, or monitor a property via cmw protocol. this request is made from the application via the cmw client to the cmw server. the token obtained at authentication is passed to the cmw client. there the digital signature is verified, and if valid, the token is sent to the cmw server. if the token is not valid a meaningful exception message is returned to the 406 i. yastrebov sender. the cmw looks up the permission in the access map, and depending on access rights either grants access or blocks the request. figure 3: authorization process authorization process: 1) access map loaded from the local file on device server startup. 2) client application authenticates on the cern-rbac a1 server and obtains a valid token. 3) token is passed to the cmw client and then to the device server via cmw protocol. 4) cmw server receives the accelerator mode from timing source. 5) dynamic authorization algorithm verifies user permissions and either allows to execute operation or thrrows an exception. 6) result of authorization process logged for auditing. 6 access rules decision whether a particular operation is valid or not is dependent on a set of access rules. they are specified by an equipment specialist for every device class, stored and managed centrally in the controls database. every cmw server can read access rules (referred to as access map), relative to device classes it is providing access to, through a tab-separated text file located in the network file system. this file mirrors the access rules located centrally in the database, and has a digitally signed by the server to prevent any modifications. access map is read by cmw server on its start-up. in addition, access map can be reloaded upon a distant call from cmw client. it usually happens when the set of related access rules has been altered by equipment specialist. access rules are parameterized using all factors relative to the authorization process. more specifically, an equipment specialist has to specify the following fields to define an access rule: device class, property, device name, role for which access is permitted, application, location of the remote client, operational mode of the accelerator, and operation on the property (get, set or monitor). apart from specific values, an equipment specialist can put a wildcard ’*’ in any of the fields except device class and operation. this interpreted as all values fit. authorization process is performed for each transaction and therefore should work as fast as possible role-based access control for the large hadron collider at cern 407 figure 4: access rules structure and should not slow down the performance of the system significantly. as an authorization time is a concern for the cmw operation, it was decided to represent the access map in the cmw server in the form of trees, a separate one for every operation type. figure 4 presents the structure of access map tree. nodes placed with the same distance from the tree root group authorization argument of one type. authorization is performed by traversing the tree, appropriate in respect to the operation type, from its root to a leaf, trying at each level to match exact authorization parameter or to find a wildcard ’*’. if this succeeds, the access is granted. such a structuring of the access map allows us to assure authorization with time complexity of o(log n) instructions, where n is the number of access rules [14]. 7 performance and tests an important concern with any design is its performance. if cern-rbac would double the time to make a setting, nobody would use it. the system also had to scale with a large number of access rules. how does a 2000-rule access map on a front-end impacts the search for the right permission? another potential performance hit was logging each request on a protected property, and the 3-tier vs. 2-tier performance. lhc controls can run in 2-tier mode, meaning the application and the client are on one machine and the database and devices are accessed directly on the second machine. this configuration is used when in developer testing. however when the system is in operation the configuration is usually 3-tier. meaning the client is on a middle tier. a common client is shared by several applications and consolidates requests. in 2-tier, a dedicated client ensures that each request is made from the same application. the cernrbac token can be validated once per session and the credentials can be used for each subsequent request. in 3-tier, the requests can come from any application at random times. the simplest design is to verify the token for each request. but how does this affect performance? we ran performance tests to answer these questions on a front-end with a 400 mhz. power pc with linux, and here are the results: • the size of the access map has very little effect on performance due to sophisticated and optimized search algorithms. our test result show a 0.02 ms increase between a 20 rule and 2000 rule access map. 408 i. yastrebov • logging each request also has very little effect on the performance. our tests show the difference between having logging on vs. having it off is 0.003 ms. • 3-tier token verification on every request has a larger impact on performance than the other two concerns. the key size is the most contributing factor. a dsa, 1024 bit key takes 5 ms to validate. a rsa 512 bit key takes 0.150 ms. • for a 2000 rule access map in a 2-tier configuration the average turn around time or a request is 0.7 ms. in a 3-tier configuration it is 2.7 ms. at this time, this is acceptable according to the requirements. 8 conclusions and future works the cern-rbac approach was successfully implemented based on the proposed model. the system successfully passed many centrally organized tests. results of the tests prove that the proposed model and design concepts are valid. we also measured the performance of the implemented system and show that the overhead is acceptable. this allows us to assume that the proposed model of the cern-rbac could be used in many other areas where access control is needed for large distributed systems. currently the rbac system is released in a production version and used by virtually every equipment device at cern. thanks to the algorithm of the dynamic authorization we propagate cern-rbac stepby-step, without interruption of the legacy subsystems. in the future we expect 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(2002) [14] kostro k., gajewski w., gysin s.: role-based authorization in equipment access at cern. proceedings of icalepcs, (2007) ilia yastrebov (b. september 29, 1981) received his m.sc. in information technology (2004). currently he is a phd student in computer science at ulyanovsk state university, russia. he has been working at european organization for nuclear research since 2005 as a software developer for access control. his current research interests include different aspects of access control and distributed systems. he has 12 papers, 4 conferences participation. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 375-384 a swarm intelligence approach to the power dispatch problem d.c. secui, i. felea, s. dzitac, l. popper dinu călin secui, ioan felea, simona dzitac, laurenţiu popper university of oradea, faculty of energy engineering, romania e-mail: csecui@uoradea.ro, ifelea@uoradea.ro, simona.dzitac@gmail.com, director@perfect-service.ro abstract: this paper examines how two techniques of the particle swarm optimization method (pso) can be used to solve the economic power dispatch (epd) problem. the mathematical model of the epd is a nonlinear one, pso algorithms being considered efficient in solving this kind of models. also, pso has been successfully applied in many complex optimization problems in power systems. the pso techniques presented here are applied to three case studies, which analyze power systems having four, six, respectively twenty generating units. keywords: economic dispatch problem, constrained optimization, particle swarm optimization. 1 introduction an important issue in optimizing the power systems is the economic power dispatch. this problem consists in determining the power generated by the plant units of a system in order to minimize the total generation cost of units, taking into account the active power balance and the constraints imposed to the capability of the units. the mathematical optimization model is nonlinear, where both the objective function and the restrictions imposed by equations describing the system functionality are nonlinear. the mathematical model can be solved using conventional optimization techniques such as the lambda iteration method, the gradient method and others [1, 2], if constraints are considered as linear, the objective function is continuous and the domain of the values is convex. some disadvantages that arise in these situations long solving time, objective function discontinuity etc. may be overcome by applying the artificial intelligence techniques. the most common optimization techniques based upon artificial intelligence used for solving economic power dispatch problems are: the genetic algorithm [3–6], the hopfield neural networks [2, 7], the differential algorithm [8], the evolutionary programming [9, 10], fuzzy-optimization [12, 13], tabu search [14], particle swarm optimization [15, 16, 27, 28]. also, the epd can be formulated as a multi-objective optimization problem [11, 13, 17]. twe mention that the particle swarm optimization method was successfully applied to other optimization problems, such as optimal power flow [18–20], reactive power optimization and voltage control [23], power loss reduction in distribution systems [24], network reconfiguration [25], unit commitment problem [26], due to its good convergence, low computational time and good quality solutions. in this paper there are proposed two versions of applying pso method for solving epd, comparing the results obtained for three systems consisting of four, six and respectively, twenty generating units. the paper is structured as follows: section 2 exposes the epd problem, section 3 and 4 describe the proposal for solving this problem using pso algorithm, section 5 presents the results obtained through the application of pso algorithm for three power systems, and section 6 outlines the conclusions drawn. 2 formulation of the economic power dispatch problem we consider a power system containing n generating units, each unit having its own generated power pj, j = 1...n. the total load required in the system is considered to be known and equal to pd. the fuel copyright c© 2006-2010 by ccc publications 376 d.c. secui, i. felea, s. dzitac, l. popper cost (fj(pj)) for each generator j is represented by a quadratic function: fj(pj) = a j ·pj + b j ·pj + c j (1) where: a j, b j and c j are fuel cost coefficients of generator j; pj represent the power of generator j. epd solution consists in determining the pj powers of generating units, so that the total fuel cost of the entire system to be minimal, respecting the restriction of power balance on the overall system and the inequality restrictions for each unit j. the objective function is: min f = n∑ j= fj(pj) (2) the problem constraints are given by relations (3) and (4): fp = n∑ j= pj − pd − ∆ p =  (3) pminj ≤ pj ≤ pmaxj , j = , , ..., n (4) where: pminj and p max j represent the maximum and the minimum operation limits of a generator j; the power loss at the level of the entire system is a quadratic function in relation to variables pj and it is calculated by using constant b coefficient formula: ∆ p = n∑ i= n∑ j= pi ·bi j ·pj + n∑ i= bi ·pi + b (5) where bi j is an element of the loss coefficient matrix, bi is the element i of the loss coefficient vector, and b is the loss coefficient constant. 3 presentation of different particle swarm optimization techniques pso is a heuristic algorithm, used for solving nonlinear and noncontinuous optimization problems, being introduced by kennedy and eberhart [22], in 1995. since then several techniques in applying the pso method have been developed, but in the current paper only two techniques are presented, namely the classical pso (pso classical) and pso with time varying acceleration coefficients (pso accelerated). classical pso: to search for the optimal solution in a space with the dimension n, pso uses a population of np particles. for a given particle i within np population, vector solutions at a certain iteration k are represented by x ki =(x k i, x k i,..., x k i j,..., x k in). in any optimization process, switching from one solution (xki j) to another solution (x k+ i j ) is accomplished by using the velocity of particles, represented by the vector v ki =(v k i, v k i,..., v k i j,..., v k in), according to the relation: x k+i = x k i + v k+ i , i = , , ..., np (6) the updated velocity of the particle in the next iteration (k+1) is given by the relation: x k+i = ω ·v ki + c ·r ·(pbest ki − x ki ) + c ·r ·(gbest k − x ki ) (7) where: v ki , v k+ i represent the velocity vector of particle i at iteration k, respectively k+1; x ki , x k+ i represent the solution vector of particle i at iteration k, respectively k+1; a swarm intelligence approach to the power dispatch problem 377 pbest ki represent the best solution vector of particle i, until iteration k; gbest k represent the vector corresponding to the best solution of the group, until iteration k; c and c are coefficients corresponding to cognitive and social behavior; r and r are random numbers between 0 and 1, and w is the inertia weight factor determined using the relation: ω = ωmax − ωmax − ωmin kmax ·k (8) where ωmax and ωmin represent the initial, respectively final weights, kmax is maximum iteration number and k is current iteration number for the algorithm. pso with time varying acceleration coefficients tries to improve the global search in the early stages of the optimization process and to accelerate the convergence of the particles to the global optimum in the final part of the process. in this case, the calculation of the velocity (v k+i ) and of the solution (x k+ i ) for the next iteration is done with relations (6) and (7), and according to [21], the coefficients c and c are determined by the relations: c = (c f − ci)· k kmax + ci (9) c = (c f − ci)· k kmax + ci (10) where ci, ci, c f and c f are initial and final weights for cognitive and social acceleration coefficients. 4 the methodology based on pso for solving the epd problem the implementation of pso techniques for solving the epd problem involves the following steps: step1. initialization of the parameters and of pso solution. the pso algorithm parameters are set in reference to ωmax, ωmin, kmax, number of particles (np), coefficients c and c (for pso classical), respectively, ci,ci, c f and c f (pso accelerated). initially a population of np particles is randomly formed. each particle defines a possible solution to the problem, which should respect the constraints given by relations (3) and (4). step 2. evaluation of the objective function f and of the auxiliary function f. the problem contains an equality restriction shown by relation (3). thus, the auxiliary function f is formed, using the relation: f = f + α ·f p (11) where α is the penalty factor. for each particle and each iteration the values of function f will be calculated, and by comparing them solutions pbesti and gbest are selected. at the end of the optimization process, functions f and f will have approximately equal values, according to the calculation error admitted by choosing factor α . step 3. update velocity and solution. the minimum (v minj ) and the maximum limits (v max j ) of the velocity for each generating unit j are calculated: v maxj = β ·(pmaxj − pminj ) and v minj = −v maxj (12) where factor β was considered between 0.05 and 0.1. the update of the particle position and velocity is done with relations (6) and (7). for each solution (xi) ) it is verified if the components xi j satisfy the constrain (4). if the constrain is satisfied, then the 378 d.c. secui, i. felea, s. dzitac, l. popper unit pmin[mw] pmax[mw] a[$/mw ] b[$/mw] c[$] 1 30 120 0.00875 18.24 750 2 50 160 0.00754 18.87 680 3 50 200 0.00310 19.05 650 4 100 300 0.00423 17.90 900 table 1: cost coefficients and limits of generated powers for a thermal power plant with four units (cs4) unit pmin[mw] pmax[mw] a[$/mw ] b[$/mw] c[$] 1 100 600 0.001562 7.92 561 2 100 400 0.00194 7.85 310 3 50 200 0.00482 7.97 78 4 140 590 0.00139 7.06 500 5 110 440 0.00184 7.46 295 6 110 440 0.00184 7.46 295 table 2: cost coefficients and limits of generated powers for a thermal power plant with six units (cs6) calculated value for xi j is kept. otherwise xi j is set with the value nearest to the limit of the domain (pmaxj or pminj ). the vectors pbest and gbest are obtained based on the evaluation of the auxiliary function f and on the comparison of the f values calculated in two consecutive steps. if the new value of function f is better than the previous value of f for previous gbest, then gbest is set at the new value. similarly, pbest vector is updated. step 4. stopping process. in this paper the criterion of stopping the calculation process is given through achieving the maximum number of iterations set. 5 numerical examples and simulation results in this section three case studies on how epd solving by applying the two pso techniques (pso classical and pso accelerated) are presented. the objective function is given by the relation (2) and restrictions (3) and (4). all case studies were implemented in mathcad, on a personal computer having a 1.58 ghz processor and 896 mb of ram. 5.1 description of tested systems case study 1 four unit system (cs4). the first system (cs4) is a thermal power plant having four generating units, where the total power losses (∆ p) are considered zero. the data for the four generators (cost coefficients and limits of generated powers) are presented in table 1. the total power demanded in the system is pd=520 mw. case study 2 six unit system (cs6). the second system (cs6) is a thermal power plant with six generating units, where the total (∆ p) is considered zero. the data for the six generators (cost coefficients and limits of generated powers) are presented in table 2 [5]. the total power demanded in the system is pd=1800 mw. case study 3 twenty unit system (cs20). the third system contains twenty units, and the demanded power in the system is pd=2500mw. the data for the generators and the values of the coefficients bi j are available in [9]. a swarm intelligence approach to the power dispatch problem 379 scenarios ci c f best f worst f average f standard deviation f [$/hour] [$/hour] [$/hour] [$/hour] 1 1.5 1.5 12919.76 12920.77 12919.84 0.026 2 1.5 2 12919.76 12920.36 1291981 0.015 3 1.5 2.5 12919.76 12921.01 12919.84 0.035 4 2 1.5 12919.77 12921.00 12919.83 0.028 5 2 2 12919.76 12920.04 12919.79 0.007 6 2 2.5 12919.76 12920.42 12919.80 0.016 7 2.5 1.5 12919.76 12922.35 12919.84 0.052 8 2.5 2 12919.76 12920.17 12919.80 0.009 9 2.5 2.5 12919.76 12920.27 12919.80 0.013 table 3: the influence of the coefficients c1i,c2f upon the results obtained through pso accelerated, for one hundred trials (cs4) 5.2 pso parameters and pso convergence pso parameters involved in the calculation process may affect the algorithm performances and quality of the solutions. for the system having four generators (cs4) the parameters were set to values: wmin = ., wmax = , c = ., c = ., np=6, kmax =  (pso classical) and respectively wmin = ., wmax = , ci = , ci = ., c f = ., c f = , np=6, kmax =  (pso accelerated). in case of pso accelerated algorithm, in order to assess the influence of the coefficients on epd problem solving, 100 distinct trials were performed, noting the best value for f (best f), the worst value for f (worst f), the average value for f (average f) and the standard deviation of cost f. table 3 shows the values best f, worst f, average f and standard deviation f, considering that the values of coefficients (ci, c f ) vary between limits ci, c f ∈ [., .], and the values of coefficient (c f ) and (ci) are constant and equal to c f = ci = .. regarding the four unit system (cs4), in table 3 it can be noticed that coefficient changes do not affect the solutions, but the best results are obtained for scenario 5, where ci = , c f = . for the six generator system (cs6) in both pso classical and pso accelerated algorithm, the coefficients have little influence upon the outcomes, considering that they vary within the limits c1,c2 [1, 4], respective c1i,c2f [1.5, 2.5] and c1f, c2i [0.1, 0.4]. the results are shown in table 4, the parameters being set to values: wmin=0.4, wmax=1, c1=2, c2=2, np=15, kmax=30 (pso classical), and wmin=0.4, wmax=0.9, c1i=2.5, c2i=0.2, c1f=0.4, c2f=1.6, np=15, kmax=30 (pso accelerated). in case of the twenty unit system (cs20), pso classical was applied taking into account the following settings: w1=0.4, w2=1.1, c1=2, c2=2, np=500, kmax=200 and =0.1; for pso accelerated it was considered: w1=0.4, w2=1.1, c1i=2.5, c2f=2, c1f=0.4, c2i=0.2, np=500, kmax=200 and =0.1. the number of particles that constitute the population is another important factor in the pso algorithm. in case of the four unit system, for both methods (pso classical and pso accelerated), the changes in cost f in relation to particles number (np) were graphically represented, considering the number of iterations set at kmax=15 (fig. 1). also, in fig. 2 the variation of cost f in relation to the number of iterations (kmax) is represented, considering the number of particles set to np=10. analyzing the diagram shown in fig. 1 it is found that for both algorithms (pso classical and pso accelerated) the best solution (best f) is obtained considering a population consisting of 5-6 particles. fig. 2 shows that the converging process towards the best value of f is obtained after fifteen iterations for both algorithms, the initial solution being different. in the six unit system, increasing the number of variables involves a higher number of particles (np=15) and a higher number of iterations to (kmax=30) in order to obtain the same solution, presented in table 4. 380 d.c. secui, i. felea, s. dzitac, l. popper figure 1: f convergence with particles number (np)/cs4; figure 2. f convergence with kmax for cs4 algorithms p1 [mw] p2 [mw] p3 [mw] p4 [mw] p5 [mw] p6 [mw] cost f [$/hour] flcga [3] 250.49 215.43 109.92 572.84 325.66 325.66 16585.85 pga [5] 248.14 217.74 75.20 587.80 335.56 335.56 16579.33 aecga [6] 248.07 217.73 75.30 587.70 335.60 335.60 16579.33 ihn [7] 248.08 217.74 75.18 587.90 335.55 335.55 16579.33 pso classical 247.95 218.44 75.16 587.58 335.43 335.44 16579.33 pso accelerated 248.00 217.71 75.16 588.02 335.52 335.59 16579.33 table 4: the comparison of the results obtained for the system with six generating units (cs6) to assess the efficiency of pso algorithms, they are compared to other four algorithms previously presented using the same data, available in [5]. it can be seen that both pso classical, and pso accelerated algorithm reach the same cost f as the algorithms presented in [3], [5], [6] and [7]. the resulting solution and the cost value are comprised in table 4. 5.3 the assessment of the solutions the quality of the solutions was assessed by determining the values best f, worst f, average f and standard deviation f considering 100 trials. ffor the system with four generating units the results are presented in table 5. in order to assess the convergence process and quality of the solutions the average of cost values f (average f fig.3) and its standard deviation (standard deviation f fig.4) for each iteration are determined, considering only one trial. the system considered in fig.3 and fig.4 proves that both algorithms converge quickly toward the best solution for f, the curves presenting a continuous decrease in relation to the number of iterations. the best solution obtained in 100 trials, using algorithms pso classical and pso accelerated, is presented in table 5, together with the results of the gradient method. in case of the six units system (cs6), the values best f, worst f, average f and standard deviation f considering 100 trials are presented in table 6. algorithm robustness was tested starting from different initial solutions, randomly obtained, and retaining the best value of f for a number of algorithm trials within the [1-50] interval. for the system having twenty units (cs20) the following values were obtained, considering 100 trials (table 7). in table 8 is presented the solution for the twenty units system, and the comparison of the results with those obtained in [2]. a swarm intelligence approach to the power dispatch problem 381 figure 3. average variation of f with kmax (cs4); figure 4. variation of standard dev. f with kmax(cs4) unit power output [mw] pso classical pso accelerated gradient method p1 88.554 92.536 92.493 p2 65.340 65.539 65.559 p3 134.662 130.293 130.431 p4 231.444 231.632 231.517 total power [mw] 520.00 520.00 520.00 total generated cost (best f)[$/hour] 12919.96 12919.76 12919.76 cpu time (s) <  <  <  table 5: the best solutions (best f) obtained for the system with four generating units (cs4) algorithm best f worst f average f standard deviation f [$/hour] [$/hour] [$/hour] [$/hour] pso classical 16579.33 16582.64 16579.51 0.0650 pso accelerated 16579.33 16581.93 16579.49 0.0362 table 6: the values best f, worst f, average f and standard deviation f for cs6 algorithm best f worst f average f standard deviation f [$/hour] [$/hour] [$/hour] [$/hour] pso classical 62457.1805 62469.64 62462.45 0.4346 pso accelerated 62456.4380 62466.63 62461.05 0.3340 table 7: the values best f, worst f, average f and standard deviation f for cs20 382 d.c. secui, i. felea, s. dzitac, l. popper the value of cost f obtained in table 7 presents a smaller dispersion for both pso algorithms, showing a high quality of the solutions. the calculation of power losses in the transmission lines was achieved with an error of 0.03 mw. it is also seen that pso accelerated algorithm gets a solution as good as the algorithms presented in [2], but pso classical leads to a farther solution, comparing to the solutions presented in table 8. unit power output [mw] pso pso lambda-iteration hopfield neural accelerated classical method [2] network [2] p1 511.1808 514.2219 512.7805 512.7804 p2 171.9583 163.3928 169.1033 169.1035 p3 125.9410 125.7172 126.8898 126.8897 p4 99.6666 99.4738 102.8657 102.8656 p5 114.9377 112.8359 113.6836 113.6836 p6 75.1378 76.8715 73.5710 73.5709 p7 113.2613 109.2679 115.2878 115.2876 p8 116.2341 116.8953 116.3994 106.3994 p9 101.5174 100.5633 100.4062 100.4063 p10 102.5556 113.8036 106.0267 106.0267 p11 150.6753 154.2216 150.2394 150.2395 p12 292.5836 289.6717 292.7648 292.7647 p13 120.2476 118.0356 119.1154 119.1155 p14 34.9866 38.2194 30.8340 30.8342 p15 117.3186 115.2359 115.8057 115.8056 p16 36.1563 36.4369 36.2545 36.2545 p17 68.7329 69.8707 66.8590 66.8590 p18 83.7616 81.9408 87.9720 87.9720 p19 98.9061 103.8655 100.8033 100.8033 p20 56.4137 51.6055 54.3050 54.3050 total power [mw] 2592.1728 2592.1468 2591.9670 2591.9669 total generation cost [$/hour] 62456.4380 62457.1805 62456.6391 62456.6341 total lines losses [mw] 92.1728 92.1468 91.9670 91.9669 table 8: the best solutions (best f) obtained for the system with twenty generating units (cs20) 6 conclusions in this paper, the economic power dispatch problem is solved using two pso techniques, namely, pso classical and pso accelerated. both techniques are effective in solving this problem, but pso accelerated leads to a better quality of solutions and a lower computing time. for epd problems with small number of variables and linear restrictions, the classical solving techniques (gradient method, lambda iteration method) are also applicable, obtaining the same results as pso. for epd with nonlinear restrictions, pso techniques are more effective, having a better convergence, robustness and stability, indicated by low values of standard deviation. the number of particles and the number of 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pandit, laxmi srivastava, particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch, international journal of electrical power & energy systems, vol. 31 (6), pp: 249-257, 2009 [28] leandro dos santos coelho, chu-sheng lee, solving economic load dispatch problems in power systems using chaotic and gaussian particle swarm optimization approaches, international journal of electrical power & energy systems, vol. 30 (5), pp: 297-307, 2008 international journal of computers communications & control issn 1841-9836, 9(5):593-601, october, 2014. decision model for assessing healthcare ict support implications: user perception a.m. oddershede, f.m. cordova, r.a. carrasco, f.j. watkins astrid m. oddershede, felisa m. córdova university of santiago of chile industrial engineering department,chile astrid.oddershede@usach.cl felisa.cordova@usach.cl rolando carrasco school of ieee newcastle university/ newcastle upon tyne, uk r.carrasco@ncl.ac.uk francisco j. watkins university of santiago of chile, electrical engineering department, chile francisco.watkins@usach.cl abstract: this paper presents a multi-criteria decision model based upon user judgments to assist the evaluation process of an information and communication technology (ict) network system in health care to improve the quality of service (qos). measuring quality in health care services is not an easy task, as there are many competing goals involved, human, economic, communications technology, governmental and others. integrating multiple criteria decision analysis (mcda) methodology with modeling and simulation through optimization network engineering tool (opnet) platform permit to characterize main ict user and identify priority applications to examine network qos requirements and implications. the proposed approach permitted to identify the main users, to elaborate a profile and characterization of the ict support requirements according to their main daily task in answer to a service requirement. the results generate evidence related to the important factors effecting quality in hospital requirement as availability of services and the need for ubiquitous access to integrated information. the stakeholder interface perception and resources for ict network support are investigated through a case study for chilean hospitals. keywords: mcda, decision support, user perception, ict healthcare. 1 introduction information and communications technology appears as an emerging concept in health care undertaking an important role for healthcare-related activities [3]. an information technology system provides stakeholders with several applications to support their duties in clinic care, medical research and administrative issues. these applications rely on the ict network infrastructure and its performance. an ict system should be a facilitator for health care users since they need to access all types of data existing on all types of systems. there is evidence [7], [9] that an ict network system implementation generates an effect on the service and health care providers. in this sense it has become important to consider, [1], [2] evaluation mechanisms for ict healthcare support and applications. the health ict design, implementation and its management has to consider risk for quality and efficiency on patient care and be in agreement with the type of user needs that could be attained through user profiling and requirements analysis. copyright © 2006-2014 by ccc publications 594 a.m. oddershede, f.m. cordova, r.a. carrasco, f.j. watkins the study is concerned with the development of an exploratory assessment model incorporating empirical data collected from the main users of the health network system.the model will be of assistance to find out user perception of quality of the service related to the communications system in a healthcare institution, to identify critical areas for qos of each user type, to provide a decision making tool as a guidance to analyse and evaluate a networked system for health related activities, to compare the different requirements and to enable trade-offs in accordance to the institution necessity. with the purpose to recognize the properties of the system that could subsequently affect the degree of satisfaction with the ict system in unanimity with user activities, a multicriteria approach is utilised, modelling with ahp [13]. the obtained data allows generating user profiles and applications profiles to support the design of models for evaluating ict healthcare network qos through the optimization network engineering tool (opnet) simulation platform to examine the network behaviour and performance. section 2 introduces the proposed evaluation approach. section 3 gives notions of the applied ahp method. following the case study description and its results is presented in section 4 providing information that is not currently available. in section 5, the conclusions are provided. 2 healthcare ict network system evaluation approach the evaluation approach considers integrating user perception modelling through ahp with network modelling and simulation to observe the network and examine performance. qos offered by a particular network could be established by technical parameters that can be measured objectively. however, it is a difficult task to find a set of universal parameters for every type of service because there are many and dissimilar parameters involved in the performance evaluation. the qos technical metrics related to each attribute has to be defined together with the applications profile. these profiles are influenced by healthcare user requests for developing a specific task. the user perception depends upon their needs, their precise applications and their expectations. concerning stakeholder’s perception, ahp modelling plays an important role generating network attributes of the system. it is of great utility to analyze which of the parameters would be relevant when considering the user perception for a determined service. the organization must then define a service level agreement (sla) for their main applications. once healthcare service application and user profiles are characterized it follows to model, design and simulate through optimization network engineering tool to examine the potential network performance. this paper is focused on presenting the perception model related to the communications system and its results for recognizing priority health activities and distinguishing the critical it network support resources that could lead to the improvement of the service. the results will reveal key qos parameters that generate user and activities profiles to support the assessment of communications resources of local area networks (lan) in a hospital. 3 the analytic hierarchy process methodology the ahp involves decision-makers (dm) in breaking down a decision into smaller parts, proceeding from the goal to criteria to sub-criteria down to the alternative courses of action. dms then make pair-wise comparison judgements throughout the hierarchy to arrive at overall priorities for the alternatives [13]. this approach provides the structure and the mathematics to support decision-makers make rational decisions. the basic principles of ahp are: hierarchy representation and decomposition, which is a representation of a complex problem in a multilevel structure whose first level is the goal followed successively by levels of factors, criteria, and sub criteria, and so on down to a bottom level of alternatives. figure 1 shows an illustration of a decision model for assessing healthcare ict support implications: user perception 595 figure 1: generic decomposition of a problem into a hierarchy simple three level hierarchy. the object of a hierarchy is to assess the impact of the elements of a higher level on those of a lower level or alternatively the contribution of elements in the lower level to the importance or fulfillment of the elements in the level above. this type of assessment is usually made by paired comparisons responding to an appropriately posed question eliciting the judgement. the mathematical definition of a hierarchy is given in saaty’s book [13]. setting priorities in a hierarchy requires that we perform measurements throughout the structure. we must then synthesize these measurements to obtain priorities for the bottom level alternatives. the ahp is based on ranking activities in terms of relative ratio scales. in the paired comparison approach of the ahp, one estimates ratios by using a fundamental scale of absolute numbers in comparing two alternatives with respect to an attribute and one uses the smaller value as the unit for that attribute. to estimate the larger one as a multiple of that unit, assign to it an absolute number from a fundamental scale shown in table 1. table 1: saaty’s fundamental scale importance intensity definition 1 equal importance 3 moderate importance 5 strong importance 7 very strong or demonstrated importance 9 extreme importance if activity i has one of the above nonzero numbers reciprocals of above assigned to it when compared with activity j, then j has the reciprocal value when compared with i this process is done for every pair. thus, instead of assigning two numbers wi and wj and forming the ratio wi/wj we assign a single number drawn from the fundamental 1 − 9 scale to represent the ratio (wi/wj) : 1. the absolute number from the scale is an approximation to the ratio wi/wj. the derived scale tells us what the wi and wj are. let w be a matrix (1) 596 a.m. oddershede, f.m. cordova, r.a. carrasco, f.j. watkins whose row elements are ratios of the measurements wi of each of n items with respect to all others. w =   w1/w1 · · · w1/wn w2/w1 · · · w2/wn ... . . . ... wn/w1 · · · wn/wn   (1) a number in the matrix is a dominance judgment. a judgment of 1.0 means that two activities contribute equally to the objective or goal, a judgment of 3.0 means that slightly favour one activity over another or three times as much (if you are dealing with measurable), a judgment of 5.0 means that judgement strongly favour one activity over another, a judgment of 7 means that activity is strongly favoured over another; its dominance is demonstrated in practice and 9.0 means that the evidence favouring one activity over another is of the highest possible order of affirmation. you should group your elements into homogeneous clusters so that it is not necessary to use a number larger than 9. in this way, we can interpret all ratios as absolute numbers or dominance units. the ahp provides guidelines for a test of consistency of judgments to ensure that elements are grouped logically and ranked consistently according to a logical criterion. in general, the ratio should be in the neighborhood of 0.10 [13]. too great a departure from the perfectly consistent value indicates a need to improve the judgments or to restructure the hierarchy. 4 the case study and ahp application the stakeholder interface perception and resources for ict network support are investigated through a case study for chilean hospitals. a pilot study has been carried out collecting data from health institutions in chile (private, public, regional) to examine ict infrastructure, ict network provision and stakeholders perception related to ict network system. an ahp model is constructed to determine user perspective related to the ict support importance in developing their work. for this study, we classified the main ict network system users into three groups: those who develop activities in clinic care, who would make use of ict to deliver a service (physician, nurses, paramedics, etc.); the medical research group, who develop health research, collecting disease statistic and/or investigate new drugs and new devices and a third group integrated by users who perform administrative activities, billing, products distribution, and inventory control or other connected. for this study patients were not considered since from previous work, [10] ict support showed to have a lower impact on patient [1]. figure 2 show the ahp process results. ict support reflects the greatest impact on supplying clinical care service. the next step is concentrated in finding out the relevant ict application for each type of healthcare users that would support to perform a better service. initial data was collected from 480 participants; following an expert team of 36 is grouped comprising representatives from each category from all of the three types of hospitals considered. the ict applications to be supported by a server are: email, web browsing (http 1.1), file transfer, database access, file print, video conferencing, and voice. a new ahp model is developed and processed. the pairwise results from the three group representatives indicated the relative importance of ict system application for performing their decision model for assessing healthcare ict support implications: user perception 597 figure 2: relative ict support importance for user type group table 2: overall relative importance priority for ict applications application priority data base access 51,1 web browsing 16,5 ftp 2,9 e mail 14,5 video and voice 2,9 activities as seen in table (2). though, when analysing separately, the clinical care group revealed a strong tendency, to rely on database applications to have access to patient records throughout email services. the research group indicated a strong interaction with web browsing application and data base application. this result would be in concordance to the nature of their work. while from administration group perspective, the preferences are for data base and file transfer protocol. the activities such as, delivering and obtaining test and exams results, within the institution implies interaction with database application. the applications relative importance according to ict user group for each application is depicted in figure 3. regarding to current ict applications usage in healthcare institutions there is a gap between current usages compared to what users declare important. from data collected and author’s observations the application, e-mail, appears to be mostly employed and there is little usage of the others. figure 4 shows the usage according to different health institutions. in this sense there is work to be done, moreover, the results analysis suggests that health ict network users expectations are that network will help to deliver a service with the required functionality on time and within the budget. consequently, a new ahp model is developed, to bring about information related to the most important network attributes to develop the applications. the essential qos attributes to meet the ict support requirement for each defined activity has to be established. it follows the 598 a.m. oddershede, f.m. cordova, r.a. carrasco, f.j. watkins figure 3: relative ict support importance for user type group figure 4: applications usage in healthcare institutions process to determine the qos attribute relative importance for applications according to user type expectation. this refers to the ict support, user perceives about service satisfaction. for example: success in the connection, accessibility, velocity, etc. ict system users expect that the network will help to deliver a service on time with the required functionality and within the budget. then the hierarchy structure should consider the attributes that would improve/ensure a better performance. the attributes considered are based on standard iso [6] quality software model: functionality, efficiency, reliability, availability, and serviceability. even though, the five attributes are essential, it was possible to detect some differences in relation to the type of user. the two overall most important qos attributes concomitant to the ict applications in performing their health related activities system are availability and reliability. the attribute availability is most important for the group who develop task in clinic care. in effect, clinic care professionals require having information on their patients including those elaborated by others, as, complementary tests results, at the moment and at the place of attendance. availability of decision model for assessing healthcare ict support implications: user perception 599 ict support is critical, when dealing with an emergency situation. figure 5 shows the overall relative importance for attributes that the panel of experts indicated. figure 5: user relative importance for attributes through the ahp it was possible to identify applications priority and to characterize main ict system users. the relative importance of ict network support for each activity to deliver a better service is obtained. at this point, the qos technical metrics that would guarantee a service related to each attribute are defined together with the applications profile. technical requirement demanded for the different activities the group perform is shown in figure 6. this information and data obtained by author’s research and extensive collaboration with the it network and management teams from a range of hospitals which differ in size and category as public, university, private is used for configuring profiles which will be the input for modelling and simulation to examine ict network technical aspects and behaviour through opnet methodology [11]. then the next step is to set up profile applications according to each user type which is based on the ahp results obtained. according to opnet methodology, initially a topology has to be selected from a hospital zone as a first approach to analyze technology infrastructure and network performance. continuing with traffic configuration where every workstation will have a profile application consistent with the users’ main role. once applications and profiles are defined, then different scenarios are characterized for each study case to visualize how sensible network performance is, related to changes. then, simulations are ran increasing the number of ict network user, formulating scenarios, varying the number of users and/or varying the links (ethernet connections) between workstations and switches, to obtain the point to point throughput in bits/secs. and utilization (link usage %). the utilization percentage of link usage is expressed as: point− to−point.throughput(bits/sec)∗100 ethernetconnection(mbps) (2) 600 a.m. oddershede, f.m. cordova, r.a. carrasco, f.j. watkins figure 6: user relative technical requirement demanded 5 conclusions the existing competing objectives for improving quality of service in health institution increase complexity when analysing ict support. the utilisation of a scientific multicriterial decision method ahp was beneficial for identifying high-priority requirements of an ict system in health related activities. the attribute for measuring quality in clinic healthcare system, availability and the need for ubiquitous access to integrated information are considered most important. the combination of fixed and wireless network support can facilitate to obtain the timely information needed. this issue is critical in healthcare institution operation mainly for the clinic care group and work has to be done in this sense. the mcda approach allowed elaborating a profile and characterization of the ict support requirements in healthcare service. the use of ahp modelling and empirical evaluation permits to capture human perspective. this allowed designing profiles characterization that would help to configure network traffic and different scenarios for simulating and evaluating network behaviour. acknowledgements the authors are grateful to the department of industrial engineering and dicyt of the university of santiago of chile for its support. decision model for assessing healthcare ict support implications: user perception 601 bibliography [1] ammenwerth e., nykanen p., rigby m., keizer n. (2013); clinical decision support systems: need for evidence, need for evaluation, institute of health informatics, umit 06. [2] ammenwerth e., graber s., herrmann g., burkle t., konig j. (2003); evaluation of health information systems problems and challenges, international journal of medical informatics, 71: 125-135. [3] bourret c. (2004); data concerns and challenges in health: networks, information systems and electronic records, data science journal, 3:96-113. [4] gao f., ye x. (2002); a hierarchical trade-off assessment model and the systematic evaluation of networked systems, fast abstract issre, copyright chillarege press. [5] heath a., carrasco r.(2001), access techniques for 3g multimedia wireless packet switched networks: simulation using opnettm, iee/ieee/bcs 6th international symposium on communication theory and applications (iscta01), lancaster university, 15-20 jul 2001, 1-29. [6] iso/iec: iso/iec 9126-1(1997); information technology software quality characteristics and metrics part 1: quality characteristics and sub-characteristics. [7] jaspers m.w., smeulers m. vermeulen h., peute l.w. (2011), effects of clinical decisionsupport systems on practitioner performance and patient outcomes: a synthesis of highquality systematic review findings, journal of the american medical informatics association, 18: 327-334. [8] national research council. (2000); networking health: prescriptions for the internet, national academic press isbn-10: 0-309-06843-6. [9] oddershede a. (2009); methodology to evaluate qos of ict networks for the chilean national health service, a thesis submitted to newcastle university for the degree of doctor of philosophy, 2009. [10] oddershede a.m., carrasco r.a (2007); perception of mobile technology provision in health service, chapter book global mobile commerce: strategies, implementation and case studies, edited by dr. wayne huang, dr. y.l. wang and dr. john day, ohio university, usa. [11] opnet: user manual (2004); http://www.opnet.com/university\_program/teaching\ _with\_opnet/textbooks\_and\_materials/materials/opnet\_modeler\_manual.pdf [12] xinjie chang (1999); network simulations with opnet, proc. of the 1999 winter simulation conference, p. a. farrington, h. b. nembhard, d. t. sturrock, and g. w. evans, eds., network technology research centre school of eee nanyang technological university, singapore. [13] saaty, thomas l. (2001); decision making for leaders, vol. ii, ahp series, rws publ., (new ed.), isbn 0 9620317. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 367-374 digital control of a waste water treatment plant r. vilanova, j.d. rojas, v.m. alfaro ramón vilanova, josé david rojas department de telecomunicació i enginyeria de sistemes universitat autònoma de barcelona 08193, bellaterra, spain, e-mail: ramon.vilanova@uab.cat, josedavid.rojas@uab.cat víctor m. alfaro escuela de ingeniería eléctrica universidad de costa rica san josé, 11501-2060 costa rica. e-mail: victor.alfaro@ucr.ac.cr abstract: the activated sludge process (asp) is arguably the most popular bioprocess utilized in the treatment of polluted water. the asp is described by means of a nonlinear model and results on a two-input two-output multivariable system. in this paper a discrete time digital control is proposed where the design of a decentralized controller is faced. local controllers are given the form of a two-degree-of-freedom pi controller tuned using the data-driven virtual-reference feedback tuning approach. keywords: asp, process control, pid, data-driven control, vrft. 1 introduction water pollution represents one of the most serious environmental problems due to the discharge of nutrients into receiving waters. hence, stricter standards for the operation of wastewater treatment plants (wwtps) have been imposed by authorities. in order to meet these standards, improved control of wwtps is needed. wastewater treatment control has begun a gradual progress towards the use of more advanced technology, in the face of more stringent modern water quality standards. several approaches have been reported in the literature that attempt to control the wwtps process. among others, the activated sludge process (asp) is arguably the most popular bioprocess utilized in the treatment of polluted water, using microorganisms present within the treatment plant in the biological oxidation of the wastewater. the simplified but still realistic and highly non-linear four-state multivariable model considered here is the asp as presented in [1]. the asp is described by means of a nonlinear model and results on a two-input two-output (tito) multivariable system. in this paper a discrete time digital control is proposed where the design of a decentralized controller is faced. the paper designs the local controllers as discrete time proportional-integral (pi) controllers. the controllers are synthesized using the virtual reference feedback tuning, which is a model-free based approach where just purely data taken from the system is considered, therefore there is no need for a mathematical model of the system. on the basis of these data the discrete time pi controllers are tuned. 2 the two-degree-of-freedom virtual reference feedback tuning the virtual reference feedback tuning (vrft) is a one-shot data-based method for the design of feedback controllers. the original idea was presented in [2], and then formalized by copyright c⃝ 2006-2011 by ccc publications 368 r. vilanova, j.d. rojas, v.m. alfaro r + + + − cr(θr) p d y cy(θy) figure 1: two degrees of freedom structure lecchini, campi and savaresi (see [3–5]). in this section, an outline of the two-degree-of-freedom case is presented. the design methodology is presented in [5], the control structure is presented in fig. 1. the objective of this method is to minimize the criterion in (1). jmr(θr, θy) = ∥(ψm(z; [θr, θy]) − m(z))wm(z)∥ 2 2 + ∥(ψs(z; θy) − s(z))ws(z)∥ 2 2 (1) with ψm(z; [θr, θy]) = p(z)cr(z; θr) 1 + p(z)cy(z; θy) ψs(z; θy) = 1 1 + p(z)cy(z; θy) (2) and m(z) being the target input-to-output transfer function and s(z) the target sensitivity function. in the vrft framework a plant model is not available, and is not intended to find one. instead, a batch of input/output data is taken from an experiment on the plant (namely input u(t) and output y(t)). so, in order to find the parameters of the controllers (θr and θy) the signals r̄(t), d̄(t) and ȳ(t) are defined. these signals are called “virtual” because they are not really measured, but constructed from the input/output data available and the desired closed-loop relations as follows: • r̄(t) is the virtual reference, so that y(t) = m(z)r̄(t) • d̄(t) is the virtual perturbation, so that y(t) + d̄(t) = s(z)d̄(t) • ȳ(t) is the virtual-perturbed output of the plant, so that ȳ(t) = y(t) + d̄(t) this signals are the ones that would be found if u(t) and y(t) had been measured in closed-loop and if the closed-loop dynamics were given by m and s i.e., if the perfect controllers were set in the loop. on the basis of these “virtual” signals the controller’s parameters are found by minimizing the following alternative identification cost function: jnv r(θr, θy) = 1 n n∑ t=1 [γm(t; [θr, θy])] 2 + 1 n n∑ t=1 [γs(t; [θr, θy])] 2 where γm(t; [θr, θy]) = lm(z)(u(t) − cr(z; θr)r̄(t) + cy(z; θy)y(t)) (3) γs(t; [θr, θy]) = ls(z)(u(t) + cy(z; θy)ȳ(t)) (4) and lm(z) and ls(z) are appropriate filters to be chosen so (3) becomes an approximation to (1). if the controllers are linear in the parameter (cr(z; θr) = βr(z)t θr and cy(z; θy) = βy(z)t θy) the cost criterion (3) becomes a standard quadratic optimization problem. in [5] the authors use the digital control of a waste water treatment plant 369 concept of “ideal controller” to derive the structure of filters lm and ls. the ideal controllers cr0 and cy0 are the ones that, if used in the control loop, would solve (1) exactly, that is cy0 = 1 − s sp cr0 = m sp (5) when comparing (1) and (3) using the parseval theorem the expression of the filters lm and ls that must make the identification problem (3) match the control problem (1) are found to be: |lm|2 = |m|2|s|2|wm|2 1 φu |ls|2 = |s − 1|2|s|2|ws|2 1 φu (6) 3 2-dof pi structure for the vrft in order to apply the vrft framework to the activated sludge process, the structure of the controllers has to be decided before the optimization is carried out. in [6], a decentralized pi structure is used in the same plant with good results for both, reference tracking and disturbance rejection. in this paper, a discretized version of the pi controller is used as the chosen structure for the vrft controllers. using a two-degree-of-freedom pi as in fig. 1, the continuous time version of the controller is: cr(s) = kc ( β + 1 tis ) cy(s) = kc ( 1 + 1 tis ) (7) when applying the bilinear transformation s = 2 ts z−1 z+1 , the controllers are cr(z) = kc ( β + ts 2ti ) + kc ( ts 2ti − β ) z−1 1 − z−1 (8) cy(z) = kc ( 1 + ts 2ti ) + kc ( ts 2ti − 1 ) z−1 1 − z−1 (9) from the vrft point of view, (8) and (9) can be seen simply as linear-in-the-parameters controllers with two parameters as follows: cr(z) = α1 + α2z −1 1 − z−1 cy(z) = γ1 + γ2z −1 1 − z−1 (10) since the continuous time controllers (7) have three adjustable parameters (kc, ti, β), one of the four parameters in its discrete time equivalent (10) should depend of the other three. from (8) to (10) it can be found that α1 + α2 = γ1 + γ2 = kcts ti (11) and we have that γ2 = α1 + α2 − γ1 (12) then the discrete time controllers (10) are now cr(z) = α1 + α2z −1 1 − z−1 cy(z) = γ1 + (α1 + α2 − γ1)z−1 1 − z−1 (13) 370 r. vilanova, j.d. rojas, v.m. alfaro once the parameters of the controllers (13) (α1, α2, γ1) are found, one can recuperate the pi (7) parameters using: kc = γ1 − 1 2 (α1 + α2) (14) ti = ts γ1 − 12 (α1 + α2) α1 + α2 (15) β = α1 − 12 (α1 + α2) γ1 − 12 (α1 + α2) (16) 4 activated sludge process (asp) description the mathematical model considered in this paper is given in [1]. the asp process comprises an aerator tank where microorganisms act on organic matter by biodegradation, and a settler where the solids are separated from the wastewater and recycled to the aerator. the layout is shown in fig. 2. the component balance for the substrate, biomass, recycled biomass and dissolved oxygen provide the following set of non-linear differential equations: dx(t) dt = µ(t)x(t) − d(t)(1 + r)x(t) − rd(t)xr(t) (17) ds(t) dt = − µ(t) y x(t) − d(t)(1 + r)s(t) + d(t)sin (18) ddo(t) dt = − koµ(t) y x(t) − d(t)(1 + r)do(t) + kla(dos − do(t)) + do(t)doin (19) dxr(t) dt = d(t)(1 + r)x(t) − d(t)(β + r)xr(t) (20) µ(t) = µmax s(t) ks + s(t) do(t) kdo + do(t) (21) where x(t) biomass, s(t) substrate, do(t) dissolved oxygen, dos maximum dissolved oxygen, xr(t) recycled biomass, d(t) dilution rate, sin and doin substrate and dissolved oxygen concentrations in the influent, y biomass yield factor, µ biomass growth rate, µmax maximum specific growth rate, ks and kdo saturation constants, kla = αw oxygen mass transfer coefficient, α oxygen transfer rate, w aeration rate, ko model constant, r and β ratio of recycled and waste flow to the influent. the influent concentrations are set to sin = 200 mg/l and doin = 0.5 mg/l. with respect to the control problem definition, the waste water treatment process is considered under the assumption that the dissolved oxygen, do(t), and substrate, x(t), are the controlled outputs of the plant, whereas the dilution rate, d(t), and aeration rate w(t) are the two manipulated variables. the initial conditions and kinetic parameters are taken as in [1] and [6]. 5 discrete-time vrft tuned pi controller applied to the asp using the non-linear model presented in (17) to (21), the data in fig. 3(a) and fig. 3(b) was collected. using only this data without the information of the non-linear model, or any linear approximation, the parameters of the controller are calculated according to section 2 using the pi structure specified in section 3. the vrft controllers are tested against two decentralized digital control of a waste water treatment plant 371 figure 2: activated sludge process layout. taken from [7] 0 50 100 150 200 250 300 0 0.05 0.1 0.15 0.2 time(s) m g /l dilution rate 0 50 100 150 200 250 300 20 40 60 80 time(s) m g /l substrate data (a) substrate-dilution rate loop 0 50 100 150 200 250 300 50 100 150 time(s) m g /l airflow rate 0 50 100 150 200 250 300 2 4 6 8 time(s) m g /l disolve oxygen data (b) disolve oxygen-airflow rate loop figure 3: data used to find the vrft controller pi controllers which parameters are computed using imc [8], based on considering a first-order (fo) controlled-process given by p(s) = kp ts + 1 (22) the identified models obey to k1 = 437.1 and t1 = 2.7h for the first loop and k2 = 0.03 and t2 = 0.51h for the second loop. these values are as in [6]. the controllers were discretized using the bilinear transformation (sampling time of 15min). in the case of the vrft, the controllers are directly found in discrete time. the closed-loop specifications are given in terms of the desired time constants of the controlled system for each loop. the desired time constant of the substratedilution rate loop is approximately t1 = 2.7h while the dissolved oxygen-airflow rate loop constant time is approximately t2 = 0.51h. the closed-loop constant time for each variable is giving in terms of the dimensionless variables τc1 and τc2 via tc1 = τc1t1 and tc2 = τc2t2. if, for example, τc1 < 1, the substrate-dilution rate closed-loop is expected to be faster than in open loop. it is worth to note that for the vrft tuning it is possible to specify a different time constant for the disturbance attenuation transfer function (in fact s(z)). in this case, the vrft tuned pi controllers are found with a time constant for the corresponding s(z) transfer function that is half the one specified for the reference to output relation by using τc (therefore τc/2). if τc1 = τc2 = 1, the resulting controller parameters are kc1 = 0.0023, kc2 = 33.33, ti1 = 2.7h and ti2 = 0.51h for the imc and kc1 = 0.0042, kc2 = 25.41, ti1 = 3.43h, ti2 = 0.42h, β1 = 0.6602 and β2 = 0.74 for the vrft. the response to a change in the set points of both loops is giving in fig. 4. the iae value represents the integrated absolute value of the error. as it can be seen, the responses for the vrft provide smaller iae as well as less demanding control actuation, computed here as the total variation (tv) or the sum of the control movements from 372 r. vilanova, j.d. rojas, v.m. alfaro 0 20 40 60 80 100 120 40 42 44 46 48 50 52 54 time(h) m g /l substrate, c1 = 1 reference imc, iae: 24.284 vrft, iae: 15.6386 0 20 40 60 80 100 120 4 4.5 5 5.5 6 6.5 time(h) m g /l dissolve oxygen, c2 = 1 reference imc, iae: 0.95129 vrft, iae: 0.67543 0 20 40 60 80 100 120 0.08 0.085 0.09 0.095 0.1 0.105 0.11 0.115 time(h) m g /l dilution rate, c1 = 1 imc, tv: 0.03196 vrft, tv: 0.034001 0 20 40 60 80 100 120 10 20 30 40 50 60 70 80 90 100 110 time(h) m g /l airflow rate, c2 = 1 imc, tv: 58.5604 vrft, tv: 45.0193 figure 4: vrft and imc controllers responses to set-points step changes, τc1 = τc2 = 1 one sampling time to the other. in case the closed-loop bandwidth is increased and we set τc1 = τc2 = 0.5 the resulting controller parameters are kc1 = 0.0046, kc2 = 66.67, ti1 = 2.7h and ti2 = 0.51h for the imc and kc1 = 0.0065, kc2 = 33.38, ti1 = 2.68h, ti2 = 0.35h, β1 = 0.7752 and β2 = 1.03 for the vrft. in this case the imc controller presents and undesirable oscillating behavior in the dissolved oxygen loop, as shown in fig. 5. the output of the controllers was saturated to 0, in case it went below this value. also the control effort of the second controller is quite lower in the case of the vrft with a performance nearly 50% better. also a simulation was carried out for a disturbance in the inflow concentration. the results are depicted in fig. 6. again the results are quite similar, and the multivariable characteristic is tackled in a satisfactory way. as it can be seen, this data driven methodology is suitable for the control of the asp process and it allows to skip the modeling step in order to find a good controller. 6 conclusions this paper has presented the application of a purely data based approach for tuning of discrete time pi controllers. the main advantage of the proposed method is that it does not rely on the usual linear model approximation of the system to be controlled. just an experiment that provides input-output data from the system is needed. the performance of the tuning approach has been tested on a non-linear multivariable system and performance compared with that of the well known imc method. as designed performance is more demanding, the resulting control system exhibits better results than its imc counterpart. digital control of a waste water treatment plant 373 0 20 40 60 80 100 120 40 42 44 46 48 50 52 54 time(h) m g /l substrate, c1 = 0.5 reference imc, iae: 12.8966 vrft, iae: 7.9997 0 20 40 60 80 100 120 3 3.5 4 4.5 5 5.5 6 6.5 time(h) m g /l dissolve oxygen, c2 = 0.5 reference imc, iae: 1.0949 vrft, iae: 0.59053 0 20 40 60 80 100 120 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15 time(h) m g /l dilution rate, c1 = 0.5 imc, tv: 0.043395 vrft, tv: 0.04601 0 20 40 60 80 100 120 0 20 40 60 80 100 120 time(h) m g /l airflow rate, c2 = 0.5 imc, tv: 163.5215 vrft, tv: 64.5191 figure 5: vrft and imc controllers responses to set-points step changes, τc1 = τc2 = 0.5 0 20 40 60 80 100 120 40.6 40.8 41 41.2 41.4 41.6 41.8 42 42.2 42.4 42.6 time(h) m g /l substrate (disturbance), c1 = 0.5 imc, iae: 24.7512 vrft, iae: 19.2922 0 20 40 60 80 100 120 6.108 6.11 6.112 6.114 6.116 6.118 6.12 6.122 6.124 time(h) m g /l dissolve oxygen (disturbance), c2 = 0.5 imc, iae: 0.093692 vrft, iae: 0.087208 0 20 40 60 80 100 120 0.072 0.074 0.076 0.078 0.08 0.082 0.084 time(h) m g /l dilution rate (disturbance), c1 = 0.5 imc, tv: 0.043227 vrft, tv: 0.046262 0 20 40 60 80 100 120 89.6 89.8 90 90.2 90.4 90.6 90.8 91 91.2 91.4 91.6 time(h) m g /l airflow rate (disturbance), c2 = 0.5 imc, tv: 25.757 vrft, tv: 24.6659 figure 6: responses changing the specification for the vrft and comparison with the imc controller, τc1 = τc2 = 0.5 374 r. vilanova, j.d. rojas, v.m. alfaro acknowledgment this work has received financial support from the aeci-pci program a/025100/09 and from the spanish cicyt program under grant dpi2007-63356. research work by j.d. rojas has received financial support from the universitat autònoma de barcelona. support from the universidad de costa rica is greatly appreciated. bibliography [1] f. nejjari, a. benhammou, b. dahhou, and g. roux, “non-linear multivariable adaptive control of an activated sludge wastewater treatment process,” int. j. adapt. control signal process., pp. 347–365, 1999. [2] g. guardabassi and s. savaresi, “virtual reference direct design method: an off-line approach to data-based control system design,” automatic control, ieee transactions on, vol. 45, pp. 954–959, may 2000. [3] m. c. campi, a. lecchini, and s. m. savaresi, “virtual reference feedback tuning: a direct method for the design of feedback controllers,” automatica, vol. 38, no. 8, pp. 1337 – 1346, 2002. [4] a. lecchini, m. campi, and s. savaresi, “sensitivity shaping via virtual reference feedback tuning,” in decision and control, 2001. proceedings of the 40th ieee conference on, vol. 1, pp. 750–755 vol.1, 2001. [5] a. lecchini, m. campi, and s. savaressi, “virtual reference feedback tuning for two degree of freedom controllers,” international journal of adaptative control and signal processing, vol. 16, no. 5, pp. 355–371, 2002. [6] r. vilanova, r. katebi, and v. alfaro, “multi-loop pi-based control strategies for the activated sludge process,” in emerging technologies and factory automation, 2009. etfa 2009. ieee international conference on, september 2009. [7] s. caraman, m. sbarciog, and b. marian, “predictive control of awastewater treatment process,” international journal of computers, communications & control, vol. 2, no. 2, pp. 132–142, 2007. [8] m. morari and e. zafirou, robust process control. prentice-hall international, 1989. int j comput commun, issn 1841-9836 9(2):151-159, april, 2014. algorithm of maximizing the set of common solutions for several mcdm problems and it’s application for security personnel scheduling s. dadelo, a. krylovas, n. kosareva, e.k. zavadskas, r. dadeliene stanislav dadelo, aleksandras krylovas, natalja kosareva*, edmundas kazimieras zavadskas vilnius gediminas technical university sauletekio al. 11, lt-10223 vilnius, lithuania e-mail: stanislav.dadelo@vgtu.lt, aleksandras.krylovas@vgtu.lt, natalja.kosareva@vgtu.lt, edmundas.zavadskas@vgtu.lt *corresponding author: natalja.kosareva@vgtu.lt ruta dadeliene lithuanian university of educational sciences studentu st. 39, lt-08106 vilnius, lithuania e-mail: ruta.dadeliene@leu.lt abstract: the article deals with the task of elite selection of private security personnel on the basis of objective and subjective criteria. one of the possible solutions of this multiple criteria decision making (mcdm) problem is creation of heuristics allowing to minimize discrepancy of ranks calculated for the objective and subjective criteria on the basis of the best security staff. the proposed heuristic combines interval points re-selection and random points generation methods. two optimizing algorithms are proposed. it is shown how this method is applied for solving specific task of elite selection from security personnel. keywords: preference feature, multiple criteria decision making, heuristics, optimizing algorithm. 1 introduction eu private security sector employs millions of people and this figure is constantly growing [1]. economic trends provide further development of the private security. it is predicted that over the next 10 years, the u.s. private security sector job demand will increase by 15% and this is much higher growth rate compared to the overall job growth around the u.s. economy [2]. safety business formed specific personnel management challenges. personnel selection and placement to the necessary positions (ranking) is seen as the most important factor affecting the organizations security, stability and development [3] and for private security this process takes on a deeper meaning. personnel selection process focuses on measurement and evaluation of specific potential, skills and personal characteristics of candidates. security personnel evaluation process requires identification of specific characteristics (criteria) for the occupation of the post and their weights determination [4]. it is necessary to develop universal algorithms for personnel selection [5]. developed selection and evaluation systems are mainly focused on companies operating cost reduction, in order to optimize staffing requirements and layout planning [6], but is not enough in the safety field. assessing the activities of private security, their complexity and the peculiarities of the hazard, the following complexity – hazard levels are distinguished: 1) protection of civil objects; 2) protection of critical and strategic objects, collection, and personal protection [7]. evolution of the modern world generated the demand of private security services in military operations. however, armed security services in the "hot" spots of the world providing companies are often confronted with the staff inappropriate behavior in dangerous and emergency situations. assessing the private security business diversity, personnel evaluation and selection procedure copyright © 2006-2014 by ccc publications 152 s. dadelo, a. krylovas, n. kosareva, e.k. zavadskas, r. dadeliene for different tasks becomes necessity. there is a need to look for factors affecting the security staff competence and professional activities as a basis for generation of algorithms for employees evaluation and selection for different tasks procedures [8]. evaluating of personnel engaged in security features in dangerous environment it is necessary to search for specific solutions [3]. critical tasks in hazardous environments, which may result in loss of health and even life, must carry only the elite guards. ryan et al. [9] note that only about 10 – 12 % of the total sample of candidates are able to meet the demands posed by members of special elite forces. dessler [10] states that only 15% of the total sample of individuals can be evaluated in the maximum. recent research reveals the elite diagnostic possibilities according to six sigma principle [11]. private security personnel selection process should point competency assessment in two directions: external (evaluation of subordinate by his immediate superior) and internal (personal competence) [12]. relevance of the problem occurs in the construction of selection algorithms for private security elite, capable of effectively carry out extremely important and dangerous tasks. this scientific problem is not paid enough attention. the aim of this study is construction approval of private security elite selection algorithm. a similar kind of research has not yet been done. an overview of possible mcdm methods for this problem solution is provided by zavadskas and turskis [13]. recently fuzzy mcdm methods are becoming increasingly popular [14]. in this article heuristic methods, based on the function, minimizing non-compliance of ranks, calculated for the best security staff in accordance with objective and subjective criteria are proposed. 2 internal and external evaluation criteria participants. one hundred and eighteen security guards were randomly selected from the company g4s lietuva; twenty two leader managers (experts) of g4s lietuva with not less than 10 years of service at private security structures involving execution and organization of security ranked the competences chosen by authors of the article. security guards internal evaluation (x) (objective testing, measuring, personal competence). selected security guards were tested and evaluated according to 41 criteria. the data received were classified into six groups of competences (variables) regarding the features analysed [12] (table 1): 1. theoretical and practical preparation (x1): knowledge, skills, abilities, practical experience – acquired throughout life; 2. professional activity (x2): carrying out required tasks; 3. mental qualities (x3): individual psychical qualities vital for performance of professional activities; 4. physical development (x4): morphological indications of a body; 5. motor abilities (x5): personal physical conditions allowing carrying out physical tasks at work or home, during leisure, and reflecting the level of physical qualities; 6. fighting efficiency (x6): a set of physical and mental qualities influencing the ability to effectively carry out actions fighting an adversary in direct contact. table1: security guards internal evaluation criteria (objective tests and measurements) algorithm of maximizing the set of common solutions for several mcdm problems and it’s application for security personnel scheduling 153 security guards criteria (competences) x1 x2 x3 x4 x5 x6 max (highest values) 2.582 2.186 2.651 3.388 2.768 1.696 min (lowest values) −1.606 −2.567 −2.518 −1.948 −2.596 −1.709 a1 1.358 −1.503 0.607 0.520 1.381 1.696 a2 0.259 −0.549 −0.424 −1.487 −0.645 0.068 a3 −0.984 0.465 0.101 −0.479 0.690 −0.100 ... ... ... ... ... ... ... a116 0.225 −0.500 −0.837 −0.599 0.749 0.795 a117 0.561 −1.366 0.438 −0.017 0.013 −0.903 a118 −1.512 −1.391 −2.180 −1.666 −1.520 −0.845 security guards external evaluation (y) (evaluation of subordinate by his immediate superior). team leaders evaluated their subordinates based on a. sakalas [15] modified methodology (table 2). they have been assessed on nine criteria that could affect security guards professional competences: 1. specialty knowledge, professionalism (y1): versatility, knowledge about their and related occupations; 2. diligence and positive attitude to work (y2): activeness, responsibility, discipline, zeal, vocation to work; 3. behaviour with colleagues and supervisors (y3): the ability to cooperate and work in a team; 4. reliability at work (y4): ability and willingness to perform tasks independently; 5. quality of work (y5): the ability to avoid mistakes; 6. workload performance (y6): the ability to carry out the maximum amount of work; 7. image (y7): the self-representational skills (exterior, posture, language culture); 8. development rate (y8): the ability to quickly adapt to new requirements and new working conditions; 9. being promissing (y9): potential for career. table 2: security guards external evaluation criteria (evaluation of subordinate by his immediate superior) security guards criteria (competences) y1 y2 y3 y4 y5 y6 y7 y8 y9 max (highest values) 5 5 5 5 5 5 5 5 5 min (lowest values) 0 0 0 0 0 0 0 0 0 a1 3 4 4 4 4 4 4 4 4 a2 4 4 4 4 5 3 3 4 4 a3 3 4 4 4 4 4 4 4 4 ... ... ... ... ... ... ... ... ... ... a116 2 3 3 4 3 3 3 3 2 a117 1 2 2 2 2 3 2 1 1 a118 3 3 4 3 3 3 3 3 2 security staff competency assessment is depicted in figure 1. 3 mathematical model definition. suppose that vector of real components x = (x1,x2, . . . ,xn) ,(∀j)xj > 0, has the meaning of some measurable values and is the set of n criteria values. any function 154 s. dadelo, a. krylovas, n. kosareva, e.k. zavadskas, r. dadeliene figure 1: research progress φ : rn → r will be called criteria convolution, if it nondecreasing for each variable, i. e. (∀j = 1,2, . . .n,∀δ > 0) φ(. . . ,xj + δ, . . .) > φ(. . . ,xj, . . .) . (1) note, that if for the two alternatives x(1) and x(2) the pareto dominance is valid x(1) ≻ x(2) (∀j = 1,2, . . .n : x(1)j > x (2) j and ∃i : x (1) i > x (2) i ), then inequality (1) will be true for the criteria convolution: φ ( x(1) ) > φ ( x(2) ) . suppose, that permutation (j1,j2, . . . ,jn) of natural numbers {1,2, . . . ,n} determine the criterion x components xj preferences xj1 ≻ xj2 ≻ · · · ≻ xjn . for example, permutation (2,3,1) means that the most important influence has the second component of criterion x = (x1,x2,x3), and the least influence has the first component. definition. let’s say that criteria convolution φ has preferences feature (j1,j2, . . . ,jn), if all the inequalities are valid: (∀i < k & δ > 0) φ(. . . ,xi + δ, . . . ,xk, . . .) > φ(. . . ,xi, . . . ,xk + δ, . . .) , (2) when ji > jk. note, that criteria convolutions determined on the basis of weighted averages have preferences feature defined by (2). for example, criteria convolution φ(x) = w1x1 + w2x2 + w3x3 + w4x4 w1 + w2 + w3 + w4 , when w2 > w3 > w1 > w4 > 0 has preferences feature (2,3,1,4). convolution φ(x) = x p1 1 x p2 2 x p3 3 , when p2 > p1 > p3 > 0 has preferences feature (2,1,3). 3.1 formulation of optimization problems suppose that for the certain alternatives criteria-referenced evaluations are known x(k) =( x (k) 1 ,x (k) 2 , . . . ,x (k) n ) , y (k) = ( y (k) 1 ,y (k) 2 , . . . ,y (k) m ) , k = 1,2, . . . ,k and criteria x, y preferences are determined: (j1,j2, . . . ,jn), (i1, i2, . . . , im). suppose that φ and ψ are convolutions of criteria x and y respectively, having corresponding preferences features. for each alternative ( x(k),y (k) ) values of both criteria convolutions algorithm of maximizing the set of common solutions for several mcdm problems and it’s application for security personnel scheduling 155 φ ( x(k) ) and ψ ( y (k) ) are calculated and numbered in ascending order to obtain ranks of alternatives: r(k)x , r (k) y . note that the lower is the rank, the better is alternative. let’s denote akx and bky sets of the best alternatives – those subsets of the set {1,2, . . . ,k} whose items ranks r(k)x < kx and r (k) y < ky respectively. here kx, ky mean the proportion of the best alternatives to be selected (generally 10–15%, i. e. kx,ky ∈ [0.10k,0.15k]). denote s(j1,j2,...,jn) class of convolutions having preferences feature (j1,j2, . . . ,jn). in this class of functions we’ll search for the function φ (analogously ψ). denote ∣∣∣akx ∩ bky∣∣∣ number of elements in intersection of sets akx and bky and formulate the optimization problem: max φ ∈ s(j1,j2,...,jn) ψ ∈ s(i1,i2,...,im) ∣∣∣akx ∩ bky∣∣∣ . (3) notice that if kx = ky = k the problem (3) has trivial solution akx ∩ bky = {1,2, . . . ,k} for any convolutions φ, ψ. decreasing values of parameters kx, ky imply the decreasing number of common elements (intersection) of the sets akx , bky . if this number is less than necessary, we mus increase values of parameters kx, ky. this means that criteria x and y don’t match. choose the numbers k′x 6 kx, k′y 6 ky. the incompatibility of criteria x and y is quantitatively described by number of all elements of the union of sets ak′x and bk′y which don’t belong to the intersection in the right side of formula (3), i. e. the alternatives with high rank according to the one and low rank according to another criterion. so the problem (3) could be supplemented with additional one min φ ∈ s(j1,j2,...,jn) ψ ∈ s(i1,i2,...,im) ∣∣∣(ak′x \ bky) ∪ (bk′y \ akx)∣∣∣ . (4) notice that the problem (4) also has trivial solution ak′x ∪ bk′y = ∅, if k ′ x = k ′ y = 1. in general, this means a certain amount of "fine" for the ignorance of dubious alternatives and numbers k′x 6 kx, k′y 6 ky must to be the larger. 3.2 restrictions and general scheme of the research formulated optimization problems (3) and (4) aren’t easy. construction of the effective algorithms for their solving is interesting, but poorly investigated issue. efficiency of these algorithms should strongly depend on the class of criteria convolutions s(j1,j2,...,jn). in this article we restrict our investigation with the functions from the class of the weighted averages: φ(j1,j2,...,jn) (x1,x2, . . . ,xn) = n∑ i=1 wjixji n∑ i=1 wi . (5) here wj1 > wj2 > · · · > wjn > 0. preferences feature (j1,j2, . . . ,jn) is determined by experts estimates of the form xj1 ≻ xj2 ≻ · · · ≻ xjn . in this article we limit ourselves to calculation of the average estimates for criteria ranks and numbering of xj accordingly. there are many various ranking methods and their comparison will be our further investigation. 156 s. dadelo, a. krylovas, n. kosareva, e.k. zavadskas, r. dadeliene 3.3 solving the personnel selection problem (case study) the particular managerial task is solved in the paper. it requires finding an acceptable alternative sets having a clear and easily verifiable interpretation of the subject matter. therefore, the comparison of solutions received by various methods could be their interesting practical suitability criteria and have a long-term value. task parameters are as follows: the number of alternatives (security personnel compared with each other) – k = 118; the number of vector criterion x components (physical measurements of emploees) n = 6; the number of vector criterion y components (managers assessment) m = 9. criteria x and y components priority was determined from expert estimates. each of 22 experts had to specify priority permutations xj1 ≻ xj2 ≻ · · · ≻ xjn and yi1 ≻ yi2 ≻ · · · ≻ yim . these evaluations are shown in the table 3: table 3: criteria x and y components ranks expert x1 x2 x3 x4 x5 x6 y1 y2 y3 y4 y5 y6 y7 y8 y9 1 6 1 5 3 2 4 7 9 4 8 5 3 6 2 1 2 5 2 3 1 6 4 7 9 5 6 8 2 3 4 1 3 5 1 3 2 6 4 5 9 4 6 8 1 7 3 2 4 6 1 5 3 4 2 5 9 7 8 6 3 4 2 1 5 6 1 5 2 3 4 7 9 6 8 5 3 4 2 1 6 5 1 4 2 6 3 5 9 6 8 7 3 4 2 1 7 4 2 3 1 6 5 4 8 5 9 7 2 6 3 1 8 6 2 5 3 4 1 6 8 5 9 7 3 4 2 1 9 5 4 6 3 2 1 7 9 4 8 6 3 5 1 2 10 5 4 6 1 2 3 6 7 4 9 8 3 5 2 1 11 4 1 3 2 6 5 5 8 6 9 7 2 4 3 1 12 6 2 4 1 5 3 6 9 5 7 8 4 3 2 1 13 6 4 5 1 3 2 5 9 6 8 7 3 4 2 1 14 6 3 5 1 4 2 7 8 6 9 5 4 3 2 1 15 4 1 3 2 6 5 5 9 7 8 6 3 2 4 1 16 4 1 3 2 6 5 7 9 8 6 5 4 2 3 1 17 6 1 4 2 3 5 6 8 7 9 4 5 2 3 1 18 6 3 4 1 5 2 6 9 8 7 4 5 2 3 1 19 4 1 5 2 6 3 4 9 8 6 7 1 5 3 2 20 6 2 4 1 5 3 5 9 6 7 8 3 4 2 1 21 5 2 6 3 4 1 6 8 7 9 5 4 3 2 1 22 4 1 6 3 2 5 6 9 7 8 5 4 2 3 1 σ 114 41 97 42 96 72 127 190 131 172 138 68 84 55 25 ri 5.18 1.86 4.41 1.91 4.36 3.27 5.77 8.64 5.96 7.82 6.27 3.09 3.82 2.50 1.14 rank 1 6 2 5 3 4 5 1 4 2 3 7 6 8 9 prefs x : (1,3,5,6,4,2) y : (2,4,5,3,1,7,6,8,9) component’s x and y ranks preferences were calculated by method of sum (average) of ranks (i. e. the average place of each component was calculated and preferences drawn up. 3.4 optimization algorithm the heuristic is used for solving problem (3). functions φ(x) and ψ(y ) will be selected from the class of functions (5) as follows: φ(x) = α1x1 + α3x3 + α5x5 + α6x6 + α4x4 + α2x2, α1 > α3 > α5 > α6 > α4 > α2 > 0, algorithm of maximizing the set of common solutions for several mcdm problems and it’s application for security personnel scheduling 157 by analogy accordingly to the preference feature given in the last row of table 3: ψ(y ) = β2y2 + β4y4 + · · · + β9y9, β2 > β4 > β5 > · · · > β8 > β9 > 0. weighted coefficients αj and βi are determined so as to satisfy the normalizing condition α1 + · · · + α6 = β1 + · · · + β9 = 1. (6) designate r(k)(α) and r(k)(β),k = 1,2, . . . ,k ranks of numbers { φ ( x(1) ) ,φ ( x(2) ) , . . . ,φ ( x(k) )} and { ψ ( y (1) ) ,ψ ( y (2) ) , . . . ,ψ ( y (k) )} (k = 118). determine function of ranks discrepancies, i. e. sum of squares of highest ranks differences according to criteria x and y : crkx,ky (α,β) = ∑ k ∈ {1, 2, . . . , 118} : r(k) (α) 6 kx & r(k) (β) 6 ky ( r(k) (α) − r(k) (β) )2 (7) and minimize this function min φ ∈ s(j1,j2,...,jn) ψ ∈ s(i1,i2,...,im) crkx,ky (α,β) . (8) here kx and ky are chosen so as ∣∣∣akx ∩ bky∣∣∣ = 12, since the goal is to select the top 12 security guards. minimization problem (8) is solved by re-selecting of values αj, βi. for the search of values α1, α3, α5 and β2, β4, β5 evenly divide intervals: α1 ∈ [ 1 6 ,1 ) , α3 ∈ [ 1 − α1 5 ,min{α1,1 − α1} ) , α5 ∈ [ 1 − α1 − α3 4 ,min{α3,1 − α1 − α3} ) , β2 ∈ [ 1 9 ,1 ) , β4 ∈ [ 1 − β2 8 ,min{β2,1 − β2} ) , β5 ∈ [ 1 − β2 − β4 7 ,min{β4,1 − β2 − β4} ) . values α6, α4, β3, β1, β7, β6, β8 are chosen randomly in the intervals constructed analogously. for example, α4 is uniformly distributed in the interval α4 ∈ [ 1 − α1 − α3 − α5 − α6 2 ,min{α6,1 − α1 − α3 − α5 − α6} ) . the remaining values α2 and β9 are determined from the normalizing condition (6). general scheme of the research is represented in figure 2. 4 results and discussion this heuristic enabled to achieve the criterion (8) value cr12,12 (α,β) = 429 with the following values of weights: α1 α3 α5 α6 α4 α2 0.31 0.19 0.17 0.17 0.12 0.04 β2 β4 β5 β3 β1 β7 β6 β8 β9 0.41 0.24 0.08 0.08 0.06 0.05 0.03 0.03 0.02 158 s. dadelo, a. krylovas, n. kosareva, e.k. zavadskas, r. dadeliene figure 2: research progress top 12 security guards are those belonging to the intersection akx ∩ bky : a21, a34, a36, a47, a56, a76, a77, a81, a91, a102, a106, a112. according to another heuristics we are looking for such convolutions among all φ ∈ s(j1,j2,...,jn) and ψ ∈ s(i1,i2,...,im) which maximize (3) and minimize (4). since there are many solutions of problem (3)–(4), among the entire sample of solutions we are looking for one that minimize the sum (7). let’s choose k′x = k ′ y = 4. then we reached even lower criterion (8) value cr12,12 (α,β) = 406 and the following x and y weights were obtained: α1 α3 α5 α6 α4 α2 0.26 0.18 0.18 0.17 0.13 0.08 β2 β4 β5 β3 β1 β7 β6 β8 β9 0.21 0.12 0.12 0.12 0.10 0.10 0.09 0.07 0.07 top 12 security guards were selected: a21, a34, a36, a47, a56, a76, a77, a81, a91, a102, a106, a111. this solution differs from the first solution by only one employee – in place of a112 is an employee a111. another 4 security guards were distinguished having high rank (> 4) by one and low rank by another criterion: a6, a26, a46, a54. attention is drawn to the fact that future studies are interesting for the aspect of development of general design of the study for the similar problems, since a very different assessments have similar structure. briefly this scheme can be described as follows. the same objects are independently characterized by several vector criteria (in this paper there are two, but the methodology is easily generalized for greater number of vector criteria). vector components significance is not the same and significance hierarchy (j1,j2, . . . ,jn) is known (usually from the expert estialgorithm of maximizing the set of common solutions for several mcdm problems and it’s application for security personnel scheduling 159 mates). the proposed methodology allows the construction of the alternatives evaluation criteria by application of general design of the study and using all the existing information. bibliography [1] van dijk, j. (2008); the world of crime. los angeles, ca: sage publications. [2] bureau of justice statistics. (2008); census of state and federal correctional federal correctional facilities, 2005. washington, dc: office of justice programs, u.s. department of justice. [3] garland b.; hogan n.l.; kelley t.; kim b.; lambert e.g. (2013); to be or not to be committed: the effects of continuance and affective commitment on absenteeism and turnover intent among private prison personnel. journal of applied security research, 8(1). 65-88. [4] dadelo, s.; turskis, z.; zavadskas, e. k.; dadeliene, r. (2012); multiple criteria assessment of elite security personal on the basis of aras and expert methods. journal of economic computation and economic cybernetics studies and research, 46(4). 65–88. [5] brucker p.; qu r.; burke e. (2011); personnel scheduling: models and complexity. european journal of operational research, 210. 467–473. 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[in lithuanian] ijcccv4n1draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 1, pp. 57-64 discussion support system for intra-class discussions and the criteria for group making ikuo kitagaki hiroshima university, research institute for higher education 2-12-1 kagamiyama, higashi-hiroshima, 739-8512, japan e-mail: kitagaki@hiroshima-u.ac.jp abstract: a computerized system has been discussed. it assists group discussion done in a classroom in the way that, first, it presents a topic with the relevant choices, second, each student selects a choice and sends it to the server, third, the server determines the groups according to the choices and other information, forth, it sends to the students’ cell phone the group information with each choice, lastly, students actually make group according to the given information then start to discuss. relating to the system, this paper describes the configuration of the proposed computer system, two aspects of group division(difference in learning and similarity in learning), the algorithm of the group division, and the execution process of actual group discussions, assisted by this system, about specific topics. keywords: group division, discussion support system, algorithm 1 introduction computer-assisted collaborative learning and group learning have become more popular with the development of e-learning systems. the computer system which is characterized by the algorithm for dividing one class of students into several groups for group discussion is discussed (akahori, 1997). this computer system is used for the group division, however, it is not used in the later group discussions because the actual discussions are made as traditional face-to-face communication activities. thus the computer system can be also called the computer-assisted, group discussion support system. concretely speaking, this system divides one class into multiple groups according to students’ answers of a discussion topic by using a specific algorithm, and each student is notified of the names of the members who belong to the same group. the students then form groups according to the notified information, exchange opinions, and discuss the topic to increase understanding. as almost all students have their own cell-phones, the computer server collects information necessary from students and distributes information to students via cell-phones. although the configuration of the above computer system and the algorithm of the group division are presented in this paper, the criteria of the group division has not been discussed in terms of their educational characteristics. this paper deals with not only the mathematical criteria(kitagaki, 2008) but also the educational characteristics of two criteria: similarity in learning and difference in learning both of which are exemplified. this paper also describes the execution process of actual group discussions, assisted by this system, about specific topics. relating the algorithm shown in this paper, we have already discussed how to make group divisions when a test problem and the answers are the topic for discussion(kitagaki et.al., 2007). in this case, i divided a student class based on the students’ answers to the test. that is, values 1 and 0 were assigned to the right and wrong answers of the test respectively, and the class was divided into groups according to these assigned values. on the other hand, there are no concepts of correct/wrong answer in discussion. copyright © 2006-2009 by ccc publications 58 ikuo kitagaki server incl. local pc student attribute list d isc u ssa n t’s c e ll p h o n e topic of discuss. assembl. ans.data group division inform. of group member et.al. regist. thru.web transmit. url brows. web, transmit. answer parameter (4) member, ans.data (1) (2) (3) (5) figure 1: system configuration. thus the previous paper has been extended theoretically to make a revised paper this time. in the case of discussion, there could be the concepts of similar remarks and different remarks. thus it is rational to deal with the choices so that we let them have ’distance’ between 0 and 1. 2 discussion support system discussion support system is implementing the system flow shown in figure 1. 1. to register student attribute list: as the initial process of the proposed system, it is necessary to register student’s name, number, the mail address, sex and so forth. among these, mail address is used for sending relevant url and their names are used for students to know all the group member. these registrations are done on a web page. the url of the page is informed to all students in advance. 2. to determine a topic with its choices for discussion: there could be usually installed many topic for discussion. the teacher selects a topic among them. then the computer sends the url for browsing the topic to all discussants. 3. to gather the answer data as a choice: all students make an access to the url mentioned in (2), then read the topic and the choices. they select a choice among the choices for the given topic then send it to the server. all the answer are gathered and stored in the server. 4. to make groups: the computer server makes the group division according to the student’s answer as transaction data. the basic idea of the group division is explained in the later section. in the actual administration, the following parameters and the necessary information ought to be inputted prior to the group division: the value with which a topic is discussed.: the difference of choices in their contents.: the number of a group constituents when the information of group division is obtained as a processed result, it is possible for a teacher to add, to it, remarks to each group, remarks to each individual and remarks to the all. discussion support system for intra-class discussions and the criteria for group making 59 5. to inform each student of the group member, selected choice, teacher’s remarks: the computer server sends to each student’s mail address the group members, each answer and the remarks above if any. through the process above, each student is informed the name of all member which belong to the same group. then, each group gathers somewhere inside or outside of the classroom and starts to deeply consider the topic by discussion. in the actual classroom it could be that the students don’t know each other, thus, in order to make the students’ group making easier, it is desirable for the server to allocate a group id to each group and send the id with the group information mentioned above. 3 two aspects of criteria for group making a community, in general, is made under some sort of homogeneity: similar thought, similar culture, and so forth, leading to its stable existence. on the other hand, group making for developing creative products which may be called as a small community, often needs heterogeneity such as different idea, different situation, and so forth. thus which is valuable criterion, homogeneity or heterogeneity, will naturally depend upon the objective, characteristics and others of making the relevant human groups. upon the consideration, i here discuss the criteria in the case of educational objectives in a classroom. 1. difference in learning: i here raise the example of regarding difference in learning in a classroom. let us suppose that students make groups and discuss the physical problem/solution in physics. in this case it is natural to think that there could be only one correct answer/solution, which will be proved provided the relevant experiment is done. thus if there happened to be different answers in the group, it could be valuable to makethe difference clear and discuss which idea will be correct, leading to extensive thinking a new idea. as various predictions done by the students may be converged to get a correct answer by observing the experiment for verification, fruitful discussions will be anticipated to proceed. as an another example, a management game in a classroom will be raised where several groups compete with each other for profit in order to survive. one group may be called as a company, which generally consists of different role of constituents. if we think those constituents ought to be selected regarding different ability/characteristics, it is important to set difference in learning as a criterion for making appropriate groups. 2. similarity in learning: if student’s values is important in order to get a solution as learning, there will be a case where similarity is regarded in making students’ groups. let us suppose that students plan to go abroad in a group for a learning objective thus the teacher at first surveys the place which each student wants to visit. if the places proposed in a group locate near to each other in a group, it gets possible for them to visit all the places as one trip. consequently, i can summarize that, in the case of regarding values in order to do the group based determination, the criterion of similarity in learning ought to be used. 4 method of group division in accordance with the previous section, group division using student’s choice of an answer can be made by two kinds of criterion as the followings. 1. difference in answers: groups are made so that choices of each member may be different from those of others as much as possible. 60 ikuo kitagaki 2. similarity in answers group are made so that choices of each member are similar with those of others as much as possible. two criteria are reverse in their evaluation of ’goodness’. thus it is enough only to explain criterion (a). as for the criterion (a), two methods have been proposed(kitagaki, 1996; kitagaki et.al., 1981). the proposed system in this material adopts the simpler method(kitagaki et.al., 1981). the algorithm is outlined below. topic sets: m topic: mi(∈ m) value of the topic: v(mi) group sets: g relevant group: g(∈ g) bigness of group ’g’: | g | discussant(student): x j(∈ g) selected choice: a(mi,x j) difference of choices selected by discussant x j and discussant xk : d{a(mi,x j),a(mi,xk)} goodness of group division using criterion (a): αg goodness of group division using criterion (b): α ′ g goodness per capita of group division: β in the definition above, both of ’value of the topic’ and ”difference of choices selected by discussant x j and discussant xk’ are the value between 0 and 1. the ’bigness of group g” is the number of a group. the number is not always the same for all group. but its algorithm is abbreviated here. all the said variables have to be determined in advance. the ’goodness per capita of group division β ’ is determined as the following. αg = ∑ mi∈m ∑ x j∈g v(mi) ⋃ k {d{a(mi,x j),a(mi,xk)}} (1) β = ∑ g∈g αg/ ∑ g∈g | g | (2) in the equation [2], group division which makes the value maximum is the optimal solution. in order to get the optimum, however, it is necessary to administrate the calculation for all the combination of groups. it is actually difficult to get it because of time for its calculation. thus a simple method is implemented(kitagaki et.al., 1980) as the following. its example deals with the case that thirty discussants are divided into ten groups with three discussants each. as the initial status, i suppose that the computer fix the discussants x, . . . ,x as shown in ’n=1’ figure 2, and define the value of β in eq.[2] as β. then i let it compare cell1 with each cell thereafter one by one. first, let it exchange cell1 x for cell2 x to obtain the pattern as shown in ’n=2’ then get the value as β,. it is clear that β, is same as β in their value. thus there is no reason to exchange thus it ought to be withdrawn. second, it is obvious that the exchange of x and x leads to the same result as above. it is cell1 and cell4 that has actual meaning of exchange because they belong to different groups in the initial pattern. if β is bigger than(or equal to) β,, the computer regards the pattern of ’n=4’ as not better pattern than the one of ’n=1’ then the exchange ought to be withdrawn. on the other hand, if β is smaller than β,, it regards the pattern of ’n=4’ better than the one of ’n=1’ then the exchange ought to be withdrawn. on the other hand, if β is smaller than β,, it regards the pattern of ’n=4’ better than the one of ’n=1’ then the exchange ought to be done to get the new pattern. based upon the new pattern, discussion support system for intra-class discussions and the criteria for group making 61 it searches for a better pattern.(in the new pattern, the content of each cell is re-designated in the way that the content of cell’i’ are written as xi (i=1,30). it means that the new pattern is shown as x1. . . x30 from the leftmost column to the rightmost column.) the search for the better pattern is succeeded in the same way. if the comparison of cell1 and cell30 has been done, cell2 becomes the base of comparison. the exchange of cell2 and cdll3 is done as shown in the pattern of ’n=31’, resulting in meaning nothing, followed by the exchange of cell2 and cell4 in ’n=32’. consequently, the exchange of two cells are done in the following order, and as a result, the number of exchange becomes 870(=29*30) in all.( actually the exchange of two cells in a group ought to be omitted.) cell1 and cell2, cell1 and cell3, cell1 and cell4,cell1 and cell5, ’ś,cell1 and cell29, cell1 and cell30 cell2 and cell3, cell2 and cell4, cell2and cell5, ’ś,cell2 and cell29, cell2 and cell30 cell3 and cell4, cell3 and cell5, ’ś,cell3 and cell29, cell3 and cell30 . . . . . . . . . . . . . . . . . . . . . . . . cell29 and cell30 n=1 cell1 cell2 cell3 cell4 cell5 cell6 … cell28 cell29 cell30 … x1 x2 x3 x4 x5 x6 … x28 x29 x30 n=2 cell1 cell2 cell3 cell4 cell5 cell6 … cell28 cell29 cell30 … x2 x1 x3 x4 x5 x6 … x28 x29 x30 n=4 cell1 cell2 cell3 cell4 cell5 cell6 … cell28 cell29 cell30 … x4 x2 x3 x1 x5 x6 … x28 x29 x30 g1 g2 g10 g1 g2 g10 g1 g2 g10 figure 2: the exchange of two cells(in the case that a classroom consists of thirty students). supposing the number of discussant to be ’n’, the number of the said exchange becomes ’n(n-1)’. for each exchange, the computer gets the value of β , then the optimal group division is obtained. if method (b) shown in the beginning of this section is implemented for group division criterion, we have only to use eq.[3] instead of eq.[1]. α ′ g = ∑ mi∈m ∑ x j∈g v(mi) ⋃ k { − d{a(mi,x j),a(mi,xk)}} (3) 5 the administration of discussion classroom as a topic for the proposed discussion, i raised a topic of career development to which most students might be relevant. the topic relates the consideration on the answer in an interview in job hunting. i administrated the classroom discussion four times. in every administration, the same topic was used. two experimental administrations are discussed below. 62 ikuo kitagaki experimental 1(e1) fifteen hiroshima university students served as subject(twelve undergraduate students and three graduate students). among them, nine students were science in major., jul. 26, 2007. experimental 2(e2) fourteen hiroshima university students served as subject(seven undergraduate students and seven graduate students). among them, nine students were science in major., nov. 11, 2007. table 1: a questionnaire and the answer [6] q: what worker do you want to be? a: as human is not working alone, i want to be a worker regarding communication with others in the actual work where i should say whatever necessary to say and listen to them whenever to do that . # as your remark for the underlined parts, select a choice that is nearest to it. 1. the expression is vague and fuzzy thus have little impact. 2. no fresh awareness as a new comer. can be felt. 3. everybody can say that thus little impact are there. 4. it is the president of a company to say that. thus, if the matter gets worse, it may give them a feeling of impoliteness. in both of e1 and e2, i used the same room and implemented the same topic which consists of two rounds, r1 and r2, that is, two ’question-answer’ set. but the students were different in those two experiments. in either round, the question exemplified in table 1 was used. in its actual administration, i let the students inform others in a group of the choice which each student has selected then discuss what would be a better answer for the given answer. lastly i let them make and write a better answer then offer it as a report with of their consensus. as most students in a group were not acquainted with each other, it was assumed that, when the information of a group member were presented in the step of figure 1(5), it gets difficult for them to make a group. thus id number proper to each group was informed all the classroom, leading to easier making groups. addition to that, as group leaders is necessary in order to facilitate the discussion well, how to determine a leader in a group was also informed them. discussion time was set to nearly fifteen minutes, which was also informed them as a comment. after each administration of r1 and r2, the following questionnaires have been examined to all the students. (influence of selecting a choice on the discussion flow) 1. i don’t think that the information of a choice selection had an influence on the relevant discussion flow. in other words, discussion flow must have been the same as the one without selecting a choice. 2. i think that the information of a choice selection had an influence on the relevant discussion flow to some extent. 3. i think that the information of a choice selection had an influence on the relevant discussion flow to great extent. discussion support system for intra-class discussions and the criteria for group making 63 table 2: answer data processing of ’influence of selecting a choice’ (a)basic statistics number of students x σ condition selecting a choice (%) 1 2 3 (e,r&r) 0.30 0.57 0.13 1.90 0.65 (e,r&r) 0.07 0.46 0.46 2.39 0.61 (e&e,r&r) 0.19 0.52 0.29 2.10 0.69 number of the students is fifteen 15 in e1, and fourteen in e2. x: average, σ : standard deviation (b)z-test(using normalized distribution) m condition 1 2 3 (e1, r1&r2) −.∗ 0.86 .∗ (e2, r1&r2) −.∗ −.∗ .∗ (e1&e2, r1&r2) −.∗ -1.15 .∗ ∗ hypothesis ’x=m’ has been rejected (p < 0.01), z = (x − m) √ n σ in r1 and r2, presented topics were the same type thus those answers were added to have been statistically processed altogether. the result is shown in table 2(a). there is also shown the result in the case that e1 and e2 were combined in their data. from the test result shown in the table, the average choice for the questionnaire is said to be 2(or in-between 2 and 3). it means that their average remark is that selecting a choice has an influence on the discussion flow as their awareness. besides the experiment shown in this material, i have done the questionnaire survey comparing criterion (a) with criterion (b) shown in the rd section supposing the case that groups were divided according the criterion (b). as their awareness, it got obvious that they felt more fruitful in their discussion with variety of choices in a group than that with a similar choices. 6 conclusion this paper discussed a computerized system which assists group discussion done in a classroom. first, configuration of the system was explained. second, two aspects of criteria for making groups, similarity in learning and difference in learning have been discussed. third, a math. model for group division was presented. a trial of administrating a classroom discussion was done. in its administration, a topic and the choices were presented to the students, each student selected a choice and sent it to the server, the server determined the groups, then it sent to the students the group information with each choice. from the administration, it got clear that the discussants have the awareness that letting them know their selected choice each other gave an influence on the further discussion flow. 64 ikuo kitagaki bibliography [1] akahori k. et.al.(1997, in japanese): the skill of university classroom teaching, daiichi-houki, tokyo, 142-145. [2] kitagaki i.,hikita a.,takeya m., fujihara y.(2007): development of an algorithm for groupware modeling for a collaborative learning, int’l journal of computers, communication & control, ii,1, 66-73. [3] kitagaki i.(1996): evaluation of students’ group using fuzzy integral, ieice, j79-d-ii,11, 18881896. [4] kitagaki i., shimizu y. and suetake k.(1980): an instructional method which permits the students to critically discuss their own test answers, japan journal of educational technology, 5,1, 23-33. [5] kitagaki i.: development of group division algorithm and discussion support system for intraclass discussions, proceedings of the rd international conference on virtual learning, pp.101-108, constanta, 2008. [6] nakatani a.(1995, in japanese): expert of interviewing, diamond co., tokyo. ikuo kitagaki was born in toyohashi, japan, in 1947. he has received b.e., m.e. and doctoral degrees from tokyo institute of technology in 1970, 1972 and 1981, respectively. he has worked in tokyo institute of technology, the institute of vocational training, and so forth. during the time, he has been involved in educational technology, literacy in science/technology, fuzzy science, human science, et.al.. he has been a member of the editorial advisory board of the advances in web-based learning(awbl) book series(usa). he has received icvl excellence award “intel® education” from the icvl(the international conference on virtual learning) scientific committee. int j comput commun, issn 1841-9836 7(5):900-906, december, 2012. building a cloud governance bus v.i. munteanu, t.-f. fortiş, a. copie victor ion munteanu, teodor-florin fortiş, adrian copie 1. west university of timişoara romania, timişoara, bvd. v.pârvan 4, and 2. institute e-austria, timişoara romania, timişoara, bvd. v.pârvan 4 e-mail: vmunteanu@info.uvt.ro, fortis@info.uvt.ro adrian.copie@info.uvt.ro abstract: thought still at its first steps, cloud governance lays the foundation upon which business innovations can be built. it fills in the gaps left by cloud providers and allows major players on the it market to be challenged by small and medium-sized enterprises (smes) for their share. at the core of cloud governance, its bus enables interaction and communication between various services and governance components. the cloud governance bus is a step forward for the enterprise service bus (esb) into the cloud environment, addressing data integration and full implementation of enterprise integration patterns. this paper covers current requirements for esb migration to the cloud environment and proposes a cloud governance architecture that meets the given requirements. keywords: cloud governance, cloud management, cloud governance bus, enterprise integration patterns. 1 introduction cloud migration is an ongoing process to which small and medium-sized enterprises (smes) must adhere such that they can benefit of the advantages given by its economic model. this adoption can enable them to challenge large enterprises by creating niche solutions or grouping themselves in order to provide complex applications that are tailored for their customers’ needs. according to [13], cloud computing can be summed up in five core characteristics: on-demand self-service, ubiquitous network access, location independent resource polling, rapid elasticity and pay-per-use. the last one, pay-per-use, is a clear incentive as to why cloud adoption is desired. cloud adoption is also driven by technical characteristics like virtualization, service orientation, link with business models, strong fault tolerance, and loosely coupling, as identified in [10] . the large amount of proprietary technologies used by cloud vendors and the lack of cloud standards has lead to the fragmentation of cloud environments making development hard. by having multiple deployment models (public, community, hybrid and private clouds), the gap is further enlarged because of the different type of policies that need to be implemented for each of them. several solutions that are built on-top of cloud infrastructures (iaas) come in aid by offering flexible cloud-independent development environments and partially handling de facto things like resource provisioning, management and monitoring. unfortunately, these platform-as-a-service (paas) solutions lack the functionality that is required to have a complete cloud management solution and require a set of complementary services, as exposed in [6–8]. furthermore, current cloud applications run in isolation or in a small clusters [19] even though there is a demand for application integration at saas level [4, 5, 19]. this leads to the necessity of a central entity whose purpose is to enable both service and data integration and create a unitary ecosystem where applications can be easily created, managed, discovered and can easily interact one another, the necessity for cloud governance. copyright c⃝ 2006-2012 by ccc publications building a cloud governance bus 901 cloud governance, a step forward for service oriented architecture (soa) governance, is essential for full cloud adoption, and even the lack of a partial solution can lead to serious challenges [14]. while not part of soa governance itself, an enterprise service bus (esb) is a flexible connectivity infrastructure for integrating applications and services [1]. similarly to soa, cloud governance can benefit from the use of such a bus. this paper focuses on defining the requirements for a cloud governance bus while providing partial solutions in the form of already available software. the remainder of the paper is organized as follows. our motivation and related work is covered in section 2. section 3 introduces the mosaic project and its component, the cloud agency (ca). our proposed cloud governance architecture is covered in section 4. the main results are presented in section 5 and conclusions and future work are presented in section 6. 2 motivation and related work 2.1 cloud management and governance cloud management is covered in distributed management task force’s (dmtf) white papers [7,8] which identify concerns and issues related to aspects of cloud service lifecycle, components in the architecture for managing clouds etc. the white papers describe management requirements in close relationship with governance ones. the growing interest in cloud management solutions has lead to an abundance of paas solutions, like mosaic1, openshift2, cloud foundry3, or morfeo 4caast4. however, little interest is payed o cloud governance related concerns like data and security management, logging and audit, event management and others. a clear place for cloud governance in relation to a generic cloud management architecture is specified in [7]. important information related to cloud governance covering service level agreements (slas), security patterns and controls are covered in [6]. several enterprises have taken interest in cloud governance and have integrated it as part of their paas solutions: enstratus5, wso2 stratos6 and fiorano cloud platform7. 2.2 enterprise service bus in the cloud building an enterprise service bus is a challenge for any developer because of the complexity of integrating multiple services in one environment, the use of different technologies etc. there is a high variety available of esbs ranging from commercial ones to open source ones like ibm websphere message broker8, oracle esb9, fuse esb10, mule esb11, petals esb12, jboss esb13, openesb14. in his paper, garcăa-jimăšnez et. al. [9] compares some of the open source esbs. 1http://www.mosaic-project.eu 2https://openshift.redhat.com/app/ 3http://www.cloudfoundry.com/ 4http://4caast.morfeoproject.org/ 5http://www.enstratus.com/ 6http://wso2.com/cloud/stratos/ 7http://www.fiorano.com/products/esb-enterprise-service-bus/fiorano-cloud-platform.php 8http://www-01.ibm.com/software/integration/wbimessagebroker/ 9http://www.oracle.com/technetwork/middleware/service-bus/overview/index.html 10http://fusesource.com/products/enterprise-servicemix/ 11http://www.mulesoft.org/ 12http://petals.ow2.org/ 13http://www.jboss.org/jbossesb 14http://openesb-dev.org/ 902 v.i. munteanu, t.-f. fortiş, a. copie while not originally designed for the cloud, esbs are slowly making their way into cloud environments. some paas providers offer them alongside their products either built in or as a service. some of the commercial esb providing solutions are wso2 statos, fiorano cloud platform, netperspective cloud esb15 and others, while open source ones are mule esb16. 3 mosaic mosaic17 is an fp7-ict project [12], which is developing a platform that promotes an opensource cloud application programming interface (api) and a platform targeted for developing multi-cloud oriented applications. its goal is to provide enough freedom both at resource and programming level such that cloud-based services can be easily developed and deployed. the architecture of the platform [16] is designed around the use of open and standard interfaces. its main goal is to provide a unified cloud programming interface which enables the flexibility needed to build inter-operable applications across different cloud providers [15]. mosaic is comprised of the mosaic api and the cloud agency. the cloud agency [2,17,18] is a multi-agent system that has been designed to handle resource provisioning and monitoring and also to handle reconfiguration of resources. the cloud agency is easily accessible to the mosaic platform through a rest interface. built around a semantic engine, the cloud agency has capabilities that allow dynamic discovery and mapping of cloud providers. the cloud agency works at an iaas level within the mosaic platform. 4 cloud governance architecture the proposed cloud governance architecture (figure 1) is built in close relation with mosaic’s cloud agency and is designed to offer a variety of services which complement it. this architecture closely follows dmtf’s white paper [7] and is built as a multi-agent system. the cloud agency exposes itself within the ecosystem as services. a clear representation of the system is depicted in figure 1, and is composed of four subsystems: service management, security management, audit management and governance management. each of the these subsystems is made of several agents, each agent being able to serve several of them. the service management subsystem is in charge of service lifecycle management (publishing, brokering, instantiation/commissioning, etc.). of the agents which compose it, the service management agent is the most important one as it stores all service related information in the service datastore. security management handles all security for our governance solution. the service management agent is the core of this subsystem as it handles storing, retrieving, generating all the security information within the system. the audit management subsystem covers all governance monitoring, ranging from cloud resource monitoring to service monitoring. it also uses a set of policies in order to notify the system or human administrator of possible errors/faults. governance management manages the system based on setting and policies. it makes sure that all agents are running and that there is a sufficient number so that all systems work properly. several issues have been thought of when designing the system: 15http://www.netspective.com/netspective-cloud-esb-overview 16http://www.mulesoft.org/ 17open source api and platform for multiple clouds building a cloud governance bus 903 • complete integration of cloud management (cloud resource management, scaling, monitoring, reconfiguration); • complete service management and lifecycle related issues (including scaling, monitoring and reconfiguration); • complete security and privacy management; • compliance with business practices and standards. figure 1: cloud governance 5 cloud governance bus the traditional role of an esb in a soa environment is to simplify access by hiding the complexity of the underlying system and providing a generic way for querying, accessing and interacting between services. this is achieved by handling the routing and monitoring of messages between services, handling service deployment and versioning etc. similarly to the esb, a cloud governance bus (cgb) needs to be able to handle messages (queuing, sequencing), security, exceptions, protocol conversion and provide an adequate level of quality of services (qos). unlike traditional esbs, our proposed cgb implements enterprise integration patterns (eip) as well as data integration (extraction, transformation, loading, mapping) which enables easy access to datastores as well as other components like the integrated scala implementation of activemq provided by apache apollo18. the cgb is first and foremost designed to handle the internal communication of our proposed cloud governance solution in a secure manner. having esb-like features is a secondary goal. as soa allows for the development of both tightly coupled and loosely coupled services, having the opportunity to integrate them in our cgb is nice to have, but not a priority. the following list summarizes what cgb features we would like/are a must having: • support both synchronous and asynchronous interaction between services • allow message operations like filtering, routing, translating 18http://activemq.apache.org/apollo/ 904 v.i. munteanu, t.-f. fortiş, a. copie • allow various forms of message routing including, but not limited to, static routing, contentbased routing, rules-based routing, policy-based routing • allow both statically and dynamically bound services • allow any type of data to be handled • handle semantic transformation if required • allow the possibility to define message channels • separate system messages from service messages • allow various ways in which endpoints can be defined in his paper, kiran kanetkar discusses several functions that an esb must handle [11]: routing, transformation, adaptation, messaging, orchestration, uddi registry, security, consumer integration, service integration, metrics and management, and b2b. however, for building our cloud governance system, we only need to handle the most important functions as well as eip. in [3], rob barry identifies several problems an esb has to face when being deployed in a cloud environment. because of the various deployment environments (public, hybrid or private clouds) an esb must adopt specific security policies (encryption) when dealing with the messages or authentication within the system. another issue is the latency that can arise from sending messages between various clouds and the transport protocols the esb needs to know. 5.1 using akka and apache camel in order to address the issues related with building a cloud governance bus, two technologies that can cover them were identified, solutions that are event-driven and enable eip and data integration. one of them, akka19, is an event-driven middleware in scala20. while not a traditional, fipa compliant, multi-agent system, akka can be used successfully for building high performance and reliable distributed applications. akka’s architecture allows easy mapping of agents to its actor system. its event driven system allows building reactive agents, facilitating them with mailboxes, another feature needed for an cgb. akka’s high-performance, self-healing, transparent-distributed system is complemented by features like support for various development libraries that enhance it like rest, comet, spring, guice, lift, apache cametm, persistence and aqmp libraries. akka’s apache cameltm module allows easy integration with it. apache cameltm is a versatile open-source integration framework based on known enterprise integration patterns. it enhances our cgb by enabling the definition of routing and mediation rules in a variety of languages. it can be easily integrated into any kind of transport of messaging model that our cgb employs, and enhances or cloud governance architecture’s ability to communicate with 3rd party applications and partners. 6 conclusions and future work unlike large enterprises which have the resources (financial and otherwise) to build and maintain their own infrastructure, smes find themselves lacking and looking elsewhere for support. that support is found in the cloud, where they can delegate infrastructure management to cloud 19http://akka.io/ 20http://www.scala-lang.org/ building a cloud governance bus 905 providers benefiting from the given pay per use economic model. however they are somewhat limited and need a governance solution that enables them to group and provide complex, targeted services tailored for their customers’ needs. cloud governance is complementary to cloud management through the services it provides. by having a cloud governance bus as the core of our cloud governance architecture, we enable a new approach to business integration and to building a highly complex and business oriented ecosystem. this paper tried to cover requirements for a cloud governance bus from the perspective of our proposed governance architecture. future work will cover patterns for building highly interdependent cloud services by using our bus to route and translate their messages. acknowledgments this work was partially supported by the grant of the european commission fp7-ict-20095-256910 (mosaic), fp7-regpot-ct-2011-284595 (host), and romanian national grant pn-ii-id-pce-2011-3-0260 (amicas). the views expressed in this paper do not necessarily reflect those of the corresponding projects’ consortium members. bibliography [1] wohl associates. soa governance. an ibm white paper. white paper. 2006. available on-line at: http://www-01.ibm.com/software/solutions/soa/amy_wohl_soa_governance_analyst_white_paper.pdf. [2] r. aversa, b. di martino, m. rak, and s. venticinque. cloud agency: a mobile agent based cloud system. in proceedings of the 2010 international conference on complex, intelligent and software intensive systems, cisis ’10, pages 132–137, washington, dc, usa, 2010. ieee computer society. [3] r. barry. esbs in the cloud: tricky in the early going. news. june 2010. available on-line at: http://searchsoa.techtarget.com/news/1514427/esbs-in-the-cloud-tricky-in-the-earlygoing. [4] s. bennett, t. erl, c. gee, r. laird, a. t. manes, r. schneider, l. shuster, a. tost, and c. venable. soa governance: governing shared services on-premise & in the cloud. prentice hall/pearsonptr, 2011. [5] t. cecere. five steps to creating a governance framework for cloud security. cloud computing journal. november 2011. available on-line at: http://cloudcomputing.syscon.com/node/2073041. 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[11] k. kanetkar. a roadmap to building an esb. may 2006. available on-line at: http://www.saterisystems.com/docs/whitepapers/roadmap to building an esb.pdf. [12] mosaic consortium. the mosaic project. 2010. available on-line at: http://mosaiccloud.eu/. [13] a. mulholland, j. pyke, and p. fingar. enterprise cloud computing: a strategy guide for business and technology leaders. meghan-kiffer press, tampa, fl, usa, 2010. [14] p. mynampati. soa governance: examples of service life cycle management processes. november 2008. available on-line at: http://www.ibm.com/developerworks/webservices/library/ws-soa-governance/index.html. [15] d. petcu, c. crăciun, m. neagul, i. lazkanotegi, and m. rak. building an interoperability api for sky computing. in high performance computing and simulation (hpcs), 2011 international conference on, pages 405–411, july 2011. [16] d. petcu, s. panica, and m. neagul. from grid computing towards sky computing. case study for earth observation. proceedings cracow grid workshop 2010, pages 11–20. academic computer center, poland, 2010. [17] s. venticinque, r. aversa, b. di martino, m. rak, and d. petcu. a cloud agency for sla negotiation and management. in proceedings of the 2010 conference on parallel processing, euro-par 2010, pages 587–594, berlin, heidelberg, 2011. springer-verlag. [18] s. venticinque, r. aversa, b. di martino, and d. petcu. agent based cloud provisioning and management design and prototypal implementation. in closer 2010, pages 184–191, 2011. [19] p. wainewright. time to think about cloud governance. august 2011. available on-line at: http://www.zdnet.com/blog/saas/time-to-think-about-cloud-governance/1376. international journal of computers communications & control issn 1841-9836, 12(5), 661-676, october 2017. mining users’ preference similarities in e-commerce systems based on webpage navigation logs p. li, c.x. wu, s.z. zhang, x.w. yu, h.d. zhong ping li college of biological and environmental sciences, zhejiang wanli university no. 8 south qianhu rd., ningbo, zhejiang, 315100, p. r. china, liping_kaixin@163.com chunxue wu school of optical-electrical and computer engineering, university of shanghai for science and technology no. 516 jun gong road, shanghai 200093, p. r. china, tyfond@126.com shaozhong zhang school of electronic and computer science, zhejiang wanli university no. 8 south qianhu rd., ningbo, zhejiang, 315100, p. r. china, dlut_z88@163.com xinwu yu the information center, zhejiang wanli university no. 8 south qianhu rd., ningbo, zhejiang, 315100, p. r. china, herrison@163.com haidong zhong* logistics and e-commerce school, zhejiang wanli university no. 8 south qianhu rd., ningbo, zhejiang, 315100, p. r. china *corresponding author: zhonghaidong@zwu.edu.cn abstract: mining users’ preference patterns in e-commerce systems is a fertile area for a great many application directions, such as shopping intention analysis, prediction and personalized recommendation. the web page navigation logs contain much potentially useful information, and provide opportunities for understanding the correlation between users’ browsing patterns and what they want to buy. in this article, we propose a web browsing history mining based user preference discovery method for e-commerce systems. first of all, a user-browsing-history-hierarchical-presentationgraph to established to model the web browsing histories of an individual in common e-commerce systems, and secondly an interested web page detection algorithm is designed to extract users’ preference. finally, a new method called upsawbh (user preference similarity calculation algorithm based on web browsing history), which measure the level of users’ preference similarity on the basis of their web page click patterns, is put forward. in the proposed upsawbh, we take two factors into account: 1) the number of shared web page click sequence, and 2) the property of the clicked web page that reflects users’ shopping preference in e-commerce systems. we conduct experiments on real dataset, which is extracted from the server of our self-developed e-commerce system. the results indicate a good effectiveness of the proposed approach. keywords: web browsing history mining, e-commerce, preference, recommendation. copyright © 2006-2017 by ccc publications 662 p. li, c.x. wu, s.z. zhang, x.w. yu, h.d. zhong 1 introduction e-commerce (or electronic commerce) usually refers to products or services exchange oriented activities based on the internet technology, which covers online payment, information security, logistics distribution, etc [25,29]. pushed by the widespread availability of the internet, more and more consumers prefer to shift from traditional face-to-face transactions to web-based commercial activities [16]. meanwhile, with the advances of network technology and fast-growing e-commerce systems, such as amazon, stimulated by network technology advances, the rapid growth of networking systems and the boom in netizens, an ever-increasing number of traders and entrepreneurs have participated in e-commerce [19]. nowadays, supply chain management, online transaction processing and many other e-commerce relevant industries have attracted thousands of workers and companies to provide a great many products or services for the online transactions. with online information growing exponentially, it will eventually result in "information overload" and "information loss" in e-commerce systems [11], which seriously hinder the development of e-business or e-commerce industries. since the born of collaborative filtering approach in the 1990s, recommendation systems have become an intensively investigated independent discipline and deemed as an effective means to ease the "information overload" problem. generally, the existing recommendation approach can be divided into three categories [2, 26, 28]: (1) collaborative filtering recommendation, which is based on the idea of similarity of users to make predictions (filtering) about the interests of an individual by calculating preferences from other users (collaborating) who have brought the same items as the target user; (2) content-based recommendation, which is the technology of choosing items as recommended for a target user according to other similar users’ preferred items; (3) hybrid recommendation, which is proposed to combine different recommendation technologies according to different mixed strategies (e.g., weighted, switch, mixed characteristics, combination, series, meta level hybrid, etc.). although, the specific steps of these recommendation methods vary, the fundamental principles of them are similar: finding users with similar preferences with target consumer and using them to make recommendations. however, there are still many challenges, such as data scarcity and cold-start [20, 21], in utilizing these recommendation approaches. searching for candidate users more precisely is deemed as an important solution to improve these challenges. in the majority of the existing researches, insufficient effort has been made to solve the problem that users’ preference is timevarying and can be measured in different granularity. from the view point of srivastava, web usage logs, which is an important part of web data, contain abundant information and can be exploited in many web personalization applications [24]. they provide demographic data (for instance name, age, country, marital status, education, interests, etc.) of each website user, also implicit knowledge about users’ behavior patterns and other preferences. to discover the information explicitly from a novel perspective, a web page navigation logs mining based method is proposed to extract users’ preferences dynamically. the paper directs toward excavating users’ browsing histories in typical e-commerce systems and tries to establish a hierarchical presentation model for the data, which will have wide utilization potentiality in e-commerce system intelligence, monitoring users’ preferences and analyzing on different levels. based on the proposed hierarchical presentation model and interested web page detection algorithm, a user’s shopping preference measure algorithm upsawbh is put forward. mining users’ preference similarities in e-commerce systems based on webpage navigation logs 663 2 related work 2.1 web page navigation logs mining web page navigation logs generally refers to the detailed information that can be gathered from the internet browsing of users, which are lists of links network clients clicked and the elapsed time between them [22,30]. they can be represented as a quintuple weblog={time, remote host, method, page, request status}. the format of the web browsing log may vary slightly in different application servers, but the elements listed in the quintuple are essential. the meaning of each element in the quintuple is explained as follows [32]: "time" denotes the time the server responds to the user’s request and returns the requested resources. in the article, time between two urls is the time interval between the request of a url and the followed url page, which contains of web page load time and page time as shown in fig.1 [1]. "remote host" denotes the logic name or ip address of the network server that a user visits. a proxy server may exists between the user and the web server, so "remote host" may represent the final proxy server that the user has visited. "method" denotes the request method, which include get, post, header, options, put, and so on of the user. among these methods, get and post are the most usually adopted. "page" denotes the requested web page. the “page” can be functionally divided into two types: navigation pages and content pages. navigation pages act as "guiding people" in the internet, while the content page is the place where people usually spend most of their time. "request status" denotes the status code that request the user to return to the server. the status code consists of three digits, which represent the status response of the server to the browser’s request. web page navigation log data contains a lot of valuable information, such as the records of links that a user has visited and the elapsed time between them, the number of clicks on each web page, the complete visit path and the time spent on the web pages. this kind of statistical information can be explored by a variety of methods and used for many scenarios, such as users’ preference prediction and the prefetching of pages to improve users’ browsing experience. a great many scholars have paid the much attention on website access pattern investigation of users by their browsing logs with the help of statistical analysis methods to reduce server-side response time and improve access efficiency of web pages [27]. magdalini eirinaki and michalis vazirgiannisrely on the application of statistical analysis and intelligent data mining methods (for instance, clustering, association rule mining, sequential pattern discovery and classification) to the web log data, resulting in a set of valuable patterns that imply individuals’ access patterns, and the knowledge is then employed to personalize pages for users according to their navigational behavior and profile [9]. based on the theory of probability, borges and levene put forward a data mining method that captures users’ web page access patterns: individuals’ navigation sessions are treated as hypertext probabilistic grammar whose higher probability strings correspond to the interested tails of an individual and the last n visited web pages affect the affect the probability of the following page to be navigated [4]. ezeife and lu proposed a web access pattern tree (waptree) approach to explore frequent visit sequences for users, which can response dynamically without numerous re-constructions of wap-tree during knowledge mining [10]. to overcome the weakness of ineffective content management of websites and the incapability of providing personalized web page services for the users in traditional web usage mining approaches, yaote wanga and anthony j. t. lee introduced the concept of throughout-surfing patterns and present an advanced access pattern mining model [27]. also, they put forward a compact graph 664 p. li, c.x. wu, s.z. zhang, x.w. yu, h.d. zhong model, termed a web page navigation path traversal graph, to store knowledge about the web page access paths of the website users. 2.2 user similarity measurement in website access scenario, a user profile contains static part, which changes seldom (such as demographic information) and dynamic part that changes frequently. the ability to find users with similar preference or distinguish between different individuals is a matter of cardinal significance in various information system applications, especially in e-commerce systems. the process of finding similar users is usually conducted based on users’ profile mining, which focus on knowledge about web page access preferences and characteristics of the users. due to the convenience of collecting users’ web page navigation and other potential valuable information from server-side, users’ profile exploration has attracted much attention of scholars all over the world in recent years. personalized recommendation in e-commerce systems is deemed as one of the most popular applications that based on users’ profile and preference extraction. user similarity measurement is one of the research aspects of users’ preference mining. although, much effort has been paid on this topic, it is still a problem-rich area. the existing researches focus mainly on how to measure user similarity in different circumstance. some investigations follow the perspective of geography to probe users’ similarity. for example, li, zheng, xie, et al. found it important to discover valuable knowledge from large scale spatiotemporal data, and proposed hierarchical-graph-based similarity measurement (hgsm) framework to model an individual’s trajectories. the model considered both the sequence property of people’s movement behaviors and hierarchy attribute of geographic feature, which proved to be an effective way of measuring similarities among users [17]. guy, jacovi, perer, et al. studied nine kind of sources (friending, communities, blogs, forums, et al.) that can be used for users’ similarity measurement in social media applications [13]. their research shows that the aggregation of sources may be valuable to measure the similarity between people. all these approaches are based on the hypothesis that the more two users share the same geographical overlap areas, the more likely they can be similar to each other. however, it may be challenge to evaluate the similarity of two users who are living very close. take two persons, an old man and a young one, living in the same community as an example: they may share the same geographical overlap area (both stay at home) in a period of leisure time (week end or holiday), but it is hard to say that they are similar. while still other works pay much attention to semantic analysis to exploit users’ similarity. ying, lu, lee, et al. argued that geographically close uses’ trajectories may not have to be similar, because the activities implied by nearby landmarks that they passed through may vary and so they put forward a mstps (maximal semantic trajectory pattern similarity) method, which measures the similarity between users based on the calculation of semantic similarity of their trajectories [31]. lee and chung proposed a method to calculate user similarity according to the semantics of frequently visited locations and the user’s potential preference [15]. still others studies focus on mining users’ purchase history or web page access record to measure their similarity. for example, eckhardt a. put forward a collaborative filtering based user preference model to explore users’ similarity [8]; wei, shijun, yunlu, et al. held the opinion that users sharing similar purchase in history are very likely to have similar preference in the future, and constructed a user similarity network to get rid of the negative affect of popular objects or items for personalized recommendation [12]. in this work we focus on web usage logs mining to find users’ preferences, based on the opinion that web page navigation patterns resemble users may have similar interests. we combine the approach of hierarchical-graph-based similarity measurement with web usage mining techniques on web page access records. mining users’ preference similarities in e-commerce systems based on webpage navigation logs 665 2.3 e-commerce recommendation with the boom in e-commerce in recent years, the structure of the e-commerce system has become much more complicated than ever before as it provides a great many customized services for both customers and enterprises. meanwhile, a wide variety of goods is provided by sellers in e-commerce virtual shops, which makes it impossible for a user to view all the products when he/she has something to buy. under this circumstance, the demand for understanding users’ preferences in e-commerce systems and find useful knowledge to make recommendations has greatly increased. by providing precise and useful suggestions to a potential consumer, recommendations in e-commerce systems have made good profits for many popular e-commerce enterprise, such as tabao.com and amazon.com [18], who recommend new products related to the items purchased previously by similar users of the target user. since the collaborative filtering approach first proposed in the mid-1990s, scholars all over the world have devoted their effort on recommender system, and make it a high-profile research area [33]. the interaction between a recommendation system and the user can be divided into explicit approach, using users provided registration information to get a general picture of their static profiles, as well as implicit methods, by exploring the web page access records to infer users’ preferences [14]. the former make recommendations based on the users’ input conditions, while the latter would automatically collect or observe users’ behavior to detect their profile. in e-commerce systems, recommendation seems extremely import because it aim sat customizing/personalizing a given product according to interests of the consumers and help them make decisions. although, recommendation methods (such as collaborative filtering, content-based filtering and rule-based filtering ) may different from one another, the process of e-commerce recommendation generally involves three main steps [3,5–7]: (1) the users’ buying records are collected, processed and analyzed to find their preferences; (2) based on the conclusion of the above first step, commence recommendation for a target user and (3) provide a recommended goods list for the target users to buy. in addition to mining customers’ properties and filtering unnecessary information, an e-commerce recommendation system focuses, as far as possible, on the matter of the ability to suggest items of interest to the user. over the last decade many new algorithms and methods have been put forward to improve recommendation accuracy and efficiency in both practical application and theoretical research. however, it still faces many challenges, such as cold start data sparse. at present, recommender system related research is still a popular issue because it constitutes problem-rich research areas concerning not only about finding accurate recommendation algorithms, but also a great many crucial factors, such as diversity, recommender persistence, robustness, serendipity, privacy etc. in many e-commerce systems, web page browsing pattern mining plays a very important role in generating accurate recommendations. when a user accesses to a web page or a browser-server based e-commerce system, the urls of the pages visited will be stored in the server access log. it plays a crucial role in conveying knowledge of customers’ activities and preferences, which are very useful for personalized recommendation. furthermore, it dynamically reflects their interests in a sense and the similar click sequence of two different users may imply that they have similar preferences at that time, which is very import for real-time recommendations. preference similarity is one of the most useful pieces of knowledge that can be extracted by many kind of data mining models [13]. this knowledge will help in finding groups of visitors with similar preferences and making effective recommendations. in recent years, many scholars have turned 666 p. li, c.x. wu, s.z. zhang, x.w. yu, h.d. zhong their attention to web browsing history mining based e-commerce recommendation, for example, qinbao song and martin shepperd put forward a vector analysis and fuzzy set theory based model to explore similar users, frequently visited web pages and navigation paths and designed a web browsing mining based recommendation model for e-commerce systems [23]. 3 users’ preference similarities exploration in this section, we conduct a detailed introduction to the processes involved in user preferences extraction, which includes web browsing trajectory definition, user browsing history hierarchical presentation and users’ preference similarity measure algorithm. 3.1 preliminary definition 1. web browsing trajectory. a web browsing trajectory (wbtraj) is a clicked url sequence of pages viewed by a user across the entire web visit process: each wbtraj contains a page url (li.url) request time (li.rtime) and leave page time (li.ltime). thus, a web browsing trajectory can be represented as wbtraj = l1 −→ l2 −→ ...ln−1 −→ ln, where li.rtime < li.ltime and li.ltime < li+1.rtime. definition 2. interested web page. generally, an interested web page (iurl) can be represented by a url where a user stays on longer than a certain time interval. therefore, the main factor involved in extraction of the interested web page depends on the time threshold (θt), which implies the time a user stays on a certain web page. formally, a set of interested web pages can be defined as iurl = {li ∈ wbtraj, |li. ltime− li.rtime| >= θt }. figure 1: time of request between two urls figure 2: web browsing trajectory of a general e-commerce system mining users’ preference similarities in e-commerce systems based on webpage navigation logs 667 as demonstrated in figure 2, l1 −→ l2 −→ l3 −→ l4 −→ l5 −→ l6 formulates a general web browsing trajectory in a common e-commerce system, and the interested web page can be extracted according to the web page browsing time threshold. typically, interested web pages may occur in the following conditions: (1) a person enters an e-commerce system, opens a web page and then diverts his (or her) attention away, and (2) a user opens more than one product item web page to compare their characteristics and decide which one to buy, exceeds a time limit at a certain web page. it is impossible to grasp customers’ interested produce web page navigation logs under the former circumstance. we focus on the second case, and put forward an interested-webpage-detection algorithm. detailed process of the algorithm can be described below. algorithm 1 interested webpage detection require: web browsing trajectory wbtraj and a time threshold θt ensure: a set of urls (iurl={l}) of the interested web pages 1: i = 0, iurlcount = |wbtraj| //the number of weburl in wbtraj 2: while i < iurlcount do 3: if |li. ltime− li.rtime| >= θt then iurl.insert(li) 4: end if 5: i + + 6: end while 7: return iurl to capture interested web pages in a server-browser based e-commerce system as accurate as possible, and we need to find a suitable time threshold to detect every stay on a certain web page. a too small time threshold can lead to too many navigated web pages over-detected as iurls. a small time threshold value, for example 1 second, might be more capable of identifying much iurls for an e-commerce system; however, this could cause too many iurls detected, making us get lost and don’t know which is the real web page or produce that users interested. in addition, time interval between the server response and the requested web page shown in the user’s screen may be greater than 1 second (depending on network situation). this is obviously not in accordance with people’s intuitiveness, as the web page has not shown in that short time interval. meanwhile, too large time threshold (θt) value is not appropriate either. it could result in many interested web pages, which indicate users’ real preference, cannot be detected efficiently. 3.2 user browsing history hierarchical presentation from the above section, we can ascertain that the more interested web pages two people share in an e-commerce system, the more likely it is that they may have the same preferences and the similar product purchase inclination. however, it is subjective to measure the similarity of two customers’ preferences directly based on the web pages of interest that they have in common. moreover, it doesn’t make sense to judge users’ preference similarities just by yes or no. therefore, we aim to measure the degree of similarity of two users’ interest quantitatively, and then rank a group of people according to the preference similarities among them. to solve the key point of the issue, we put forward a hierarchical graph to present users’ browsing histories in an e-commerce system, as shown in figure 3. three procedures need to be preformed before building such a graph for an e-commerce system browse path. (1) formulate a set of user click logs according to the time sequence in which he (or she) visits an e-commerce system and form the web browsing trajectory; (2) filter out the common web urls that everyone will click in the e-commerce system, such 668 p. li, c.x. wu, s.z. zhang, x.w. yu, h.d. zhong figure 3: hierarchical structure of the user browsing trajectory as login, logout, product payment or other related urls; (3) construct the hierarchical graph according to the web page properties of the clicked url. we are not concerned with the web page clicked time sequence while creating the graph (in other words, a web page clicked more than one time can be repetitively treated as a multiple node in the graph ). also, a web page clicked many times can be represented as multiple nodes in the hierarchical graph. definition 3. hierarchical graph (hg). hg is a collection of clicked urls in an e-commerce system, with a hierarchical structure hg = {h,l}, where h = {h1,h2, ...,hn−1,hn} represents the collection of layers of the hierarchy graph. l = {lij|0 ≤ i ≤ |h| , 0 ≤ j ≤ |li|}, where lij denotes the jth nodes on the layer hi (hi ∈ h), and li is the set of nodes on layer li 3.3 users’ preference similarity measurement concepts of similar web click sequences definition 4. similar web click sequence (swcs). a similar web click sequence represents for two users (up and uq) who have viewed the same sequence of urls within the same period. formally, a pair of web-click-sequences, webclickseqpi and webclickseq q i , for two users, up and uq, webclickseq p i = [ url p 1(t p 1) ∆t p 1−→ urlp2(t p 2) ∆t p 2−→ urlp3(t p 3)... ∆t p n−1−→ urlpn(t p n) ] webclickseq q i = [ url q 1(t q 1) ∆t q 1−→ urlq2(t q 2) ∆t q 2−→ urlq3(t q 3)... ∆t q n−1−→ urlqn(t q n) ] mining users’ preference similarities in e-commerce systems based on webpage navigation logs 669 where urlj(1 ≤ j ≤ n) are the graph vertices that up and up share on the layer li, tj(1 ≤ j ≤ n) stands for the times the visitor successively stay on the web page urlj and ∆tj (1 ≤ j ≤ n) represents the times interval that the user transfer from urlj to urlj+1. webclickseq p i and webclickseq q i are similar web click sequences only if they meet the following conditions: (1) the two users, up and uq, share the same vertex in one layer of their hgs. formally, ∀1 ≤ j ≤ n, urlpj = url q j. (2) the two users, up and uq, have assemble transition times between the orderly accessed web pages. formally, ∀1 ≤ j ≤ n, 0 ≤ |∆tpj−∆t q j| max(∆t p j ,∆t q j ) ≤ tthreshold, where tthreshold is a predefined time threshold. if the above two conditions hold, a similar web click sequence, simwebclickseqq,pi , included in webclickseqqi and webclickseq q i can be extracted as: simwebclickseq p,q i = 〈url p,q 1 (min(∆t p 1, ∆t q 1) → url p,q 2 (min(∆t p 2, ∆t q 2) → ... → url p,q n (min(∆t p n, ∆t q n))〉 , where min(∆tp1, ∆t q 1) stands for the minimum of the time intervals ∆t p 1 and ∆t q 1. definition 5. n-length similar web click sequence. if there is n nodes in the similar web click sequence simwebclickseqq,pi for two users, up and uq, we call the sequence n-length similar web click sequence. similar web click sequences extracting it can be found in figure 3 that the bottom nodes in the hierarchy graph reveal more specific preferences than those at the top. also, from the top to the bottom, the customers’ shopping intentions increase gradually. therefore, the hierarchical characteristic of this graph is efficient in depicting individuals’ preference similarity. customers who share the same web browsing trajectory on a lower layer are more likely to have similar shopping preferences than those who have web browsing trajectories in common on a higher layer. according to the user browsing history hierarchical presentation model, we propose the following users’ preference similarity (ups) measure approach and define preference similarity between user p and q as: ups(p,q) = sumscorep,q |hgp| + |hgq| (1) where hgp and hgq denote the hierarchical graphs of the user p and q,sumscoreq,q denotes the sum of the score for the same node in each layer, |hgp| is the number of nodes in hgp, |hgq| is the number of nodes in hgq. the sum score (sumscoreq,q) value of two users (p and q) need to be calculated according to the urls they share in each layer. the detailed process of acquiring sumscoreq,q can be described in the following algorithm. obviously, the weighted factor α, in the algorithm, can impact the result of users’ preference similarity to a certain degree. we set f(i) = 1|h|−i+1 (|h| is the total layers of the hierarchical graph) to normalize the preference similarity value to [0,1]. additionally, the greater ups(p,q) value means more preference similarities between users p and q. the process of calculating the total length of the shared trajectory for users p and q in each layer is illustrated in the following algorithm (getsimilarclickseqlength), and the demonstration of similar web page click sequence matching is presented in figure 4. 4 experiment evaluation to examine the effectiveness of the proposed web page navigation logs mining based user preference similarity measure approach, we conduct the experiments on dataset collected from 670 p. li, c.x. wu, s.z. zhang, x.w. yu, h.d. zhong algorithm 2 upsawbh require: hierarchically structured web browsing trajectory hgp, hgq of users p and q ensure: ups(p,q) which shows the preference similarities of users p and q 1: sumscoreq,q = 0, ups(p,q) = 0 2: while hi ∈ h do 3: scoreh = 0// preference similarity on a layer 4: α = f(i) //α is an i-dependent factor 5: while li ∈ l do 6: len = getsimilarseqlength(hgip,hg i q)// get similar web page length 7: scoreh = 2len∗α//get the total length of a similar web page 8: end while 9: sumscoreq,q = sumscoreq,q + scoreh 10: end while 11: ups(p,q) = sumscore p,q |hgp|+|hgq| // calculate ups according to formula (1) 12: return ups(p,q) algorithm 3 getsimilarclickseqlength require: web browsing list set lp,lq ensure: the total length (totallength) of the similar web page click sequence that user p and q share in lp and lq 1: sort lp, lq according to the web click time order, and form the web browsing trajectories trajp and trajq 2: indictor = 0, totallength = 0//variable initialization 3: while urlp in trajp do 4: while indictor < |trajq| do 5: if urlp == trajq[indictor] then totallength + + 6: end if 7: indictor + + 8: end while 9: end while 10: return totallength the server of our self-developed e-commerce system. the dataset covers all the system click logs during a whole month. table 1 depicts the profile of the dataset of the dataset that we used in the experiment, and figure 5 details the click trajectories we extracted. it suggests that most of the system users has trajectory length of 1, which means many users just access a web page of the system and exit within a very short time, because the search engine www.baidu.com is chosen to promote our system, and many link-clicks from the search engine. meanwhile, the numbers of users decrease sharply with the increase of the click trajectory length in our system. all the proposed algorithms and models are implemented and run in a computer with intel (r) core™ i3-2310 cpu @2.10 ghz (4 cpus), 6gb ram, windows 7 ultimate 64-bit using visual programming language c# (in microsoft visual studio 2010 professional). to differentiate the significance of similar web page sequences with various length values on different layers, we set α = 1|h|−i+1 . here α increases in accordance with the layer i in the hierarchical graph, since we intuitively observe that the likelihood of two individuals’ preference similarity rises sharply. there are thousands of combinations of time threshold (θt) with factor (α) in algorithm upsawbh. honestly, it is a great challenge to determine what time interval is proper to detect interested mining users’ preference similarities in e-commerce systems based on webpage navigation logs 671 figure 4: schematic of similar web page click sequence matching table 1: statistical information of the web browsing log data 672 p. li, c.x. wu, s.z. zhang, x.w. yu, h.d. zhong web pages of an individual. therefore, we set the values of θt with reference to commonsense knowledge in real-world web system design. in our experiment, θt is set to 5 seconds, 10 seconds, 15 seconds, 20 seconds, 25 seconds, 30 seconds, 35 seconds, 40 seconds and 45 seconds. the result, as shown in figure 6, reveals that that the number of interested web pages decrease rapidly as the time threshold θt increases. take θt = 5 as an example, nearly 290 interested web pages are detected, but the value drops to less than 80 when θt is set to 10 seconds. figure 5: relationship between trajectory length and number of users figure 6: number of interested web page changing over time threshold we mainly focus on verifying the feasibility and effectiveness of hierarchical graph, interestedwebpage-detection algorithm, web click sequence, in measuring users’ similarity in e-commerce systems. to further explore the accuracy of proposed upsawbh algorithm, six candidate total layer values in the hierarchical graph are tested in the experiment. as we can see in figure 7, with the increase in the total layer in the hierarchical structure of user the browsing trajectory, the accuracy of upsawbh nearly increases linearly, and the calculated preference similarity value (ups) rises simultaneously. intuitively, the more different layer levels in hierarchical graph considered the more similar web click sequences can be checked, that is, both preference similarity and average accuracy are better while 6 total layers are considered. all these prove that our approach has good effectiveness in mining users’ preference similarity knowledge using webpage navigation logs of users in e-commerce systems. mining users’ preference similarities in e-commerce systems based on webpage navigation logs 673 figure 7: the influence of total layer parameter on the accuracy of the upsawbh algorithm 5 conclusion and future work users’ preference similarities mining is an important research area in many information systems, especially in e-commerce application. to some extent alleviate challenges, such as cold start and data sparsity, in e-commerce environment, the article presents a method to explore users ’preference similarities based on web browsing history data mining. in this research, we introduce a web page-navigation-log data extraction based users’ similarity calculation approach which (1) starts from a perspective of geography, treating individuals’ visits of web pages in e-commerce systems as trajectories, (2) devotes to explore the users’ preference based on web page click sequence discovering and (3) tries to check users’ preferences similarity in terms of their web click pattern resemblance in distinct levels. in order to construct a complete a research framework of our approach, a hierarchical graph model is proposed to present the structure of web navigation history and an interested web page detection algorithm is put forward to explore users’ preference patterns. meanwhile, an algorithm, upsawbh, is put forward to figure out the preference similarities among users, and the experimental results on dataset from real-world e-commerce system sever prove good effectiveness of our approach. two traits, the sequence peculiarity of user click and hierarchy feature of webpage browsing levels, have been considered in this similarity measure. one limitation of our method is that it has been tested using only dataset from our selfdeveloped e-commerce system and we just set the value of weighted factor (α), by intuition. in the future, we will pay more attention to collect web page navigation dataset from other b/s based e-commerce systems to compare the result and improve our approach. we plan to focus on the following two areas: (1) further explore the impact of weighted control factor (α) on the result of upsawbh quantitatively, and (2) investigate how to improve the veracity of the proposed user preference similarity computation method to meet the requirement of personalization recommendation. we have just put forward a rough research paradigm to mine users’ preference similarities using web page navigation logs in e-commerce system from another perspective, and still have a long way to go. 674 p. li, c.x. wu, s.z. zhang, x.w. yu, h.d. zhong acknowledgments this work is partly supported by the ministry of education, humanities and social sciences research project (grant no. 14yjc630210), the zhejiang public technology research and application project (grant no. 2015c33065), the zhijiang youth action project: study on mobile e-commerce recommendation ( grant no. g306), the ningbo education science planning key project (grant no. 2017yzd010), the zhejiang business economics association project (grant no. 2016sjyb01), the china national natural science foundation project (grant nos. 61202376, 71071145 and 41201550), the natural science foundation of zhejiang (grant no. ly16g020012), the major research projects of humanities and social sciences in colleges and universities of zhejiang (grant no. 2014gh015), the ningbo huimin project of science and technology (grant no. 2016c51040), the china 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(2014); study on directed trust graph based recommendation for ecommerce system, international journal of computers communications & control, 9(4): 510-523, 2014. ijcccv11n4.dvi international journal of computers communications & control issn 1841-9836, 11(4):538-552, august 2016. detecting topic-oriented overlapping community using hybrid a hypergraph model g.l. shen, x.p. yang, j. sun gui-lan shen*; xiao-ping yang information school, renmin university beijng, china guilan.shen@buu.edu.cn; yang@ruc.edu.cn *corresponding author: guilan.shen@buu.edu.cn jie sun business school, beijing union university beijing,china jie.sun@buu.edu.cn abstract: a large number of emerging information networks brings new challenges to the overlapping community detection. the meaningful community should be topicoriented. however, the topology-based methods only reflect the strength of connection, but ignore the consistency of the topics. this paper explores a topic-oriented overlapping community detection method for information work. the method utilizes a hybrid hypergraph model to combine the node content and structure information naturally. two connections for hyperedge pair, including real connection and virtual connection are defined. a novel hyperedge pair similarity measure is proposed by combining linearly extended common neighbors metric for real connection and incremental fitness for virtual connection. extensive experiments on two real-world datasets validate our proposed method outperforms other baseline algorithms. keywords: information network, overlapping community detection, topic-oriented, hybrid hypergraph model. 1 introduction community is considered to be a fundamental property of complex network. despite the variety of complex networks, community often accounts for the functionality of the system [1]. research of recent years shows that the structure of community is not disjoint. overlapping is an important property of many real-world networks, i.e., they are naturally characterized by multiple community memberships. for example, a person could join in several hobby groups in social networks; one academic paper could cover a number of fields, etc. it is therefore a very essential work to develop approach for efficient overlapping community detection, which will contribute to the links prediction, collaborative recommendation and influence propagation in many application fields. although numerous techniques have been developed for overlapping community detection in recent years, most of them only focus on the structure information for real network. it is well understood, however, that there exist a large quantity of real networks with node content or semantic information, which is referred to as information network, such as www, scientific citation network, and social network. the meaningful detected community of the information network should be topic-oriented, which has two characteristics: the nodes inside one community should have dense connections and consistent or similar topics. communities identified via those topological methods often incorporate different topics since stronger connections represent the interactions that occur across several different topics, which would confuse the meanings of the topic-oriented community [2]. copyright © 2006-2016 by ccc publications detecting topic-oriented overlapping community using hybrid a hypergraph model 539 in this paper, we propose a topic-oriented overlapping community method for information network which combines node content information and link information. firstly, information network is modeled as a hybrid hypergraph composed of hyperedge that features the collection of nodes with common attributes. for different information network, the node attribute could be represented by interest, tab, word or topic. secondly, a hyperedge pair similarity calculation method is proposed, which combines the content information by calculating common neighbors of hyperedge pair and structure information by measuring the link relationships between the nodes involved into two hyperedges. finally, an agglomerative hierarchical clustering algorithm is applied to partition the hybrid hypergraph model into different topic-oriented overlapping communities. compared with the existing methods, our method can identify communities from the perspective of both content and link structure for information network. from this result, we can easily find more meaningful communities, such as topics, research fields and so forth. moreover, with the inherent characteristics of hybrid hypergraph model, overlapping of communities could be identified easily. we proceed to report our work in the rest of the paper as follows. we discuss the related works in section 2. in section 3, we propose our approach for identifying the meaningful overlapping communities based on hybrid hypergraph model for information network. in order to verify our approach, we conducted extensive experiments. the experimental design and results analysis are given in section 4. finally, a conclusion is drawn in section 5. 2 related works overlapping community detection using topology. some methods have been proposed to detect overlapping communities in a network. lfm proposed by lancichinetti et al [3] is a kind of algorithms utilizing local expansion and optimization. this method relies on a local benefit function that characterizes the quality of a densely connected group of nodes. lfm expands a community from a random seed node to form a natural community until the fitness function f(c) = kcin (kcin + k c out) α (1) is locally maximal, where kcin and k c out are the total internal and external degree of the community c, and α is the resolution parameter controlling the size of the communities. after detecting one community, lfm randomly selects another node not assigned to any community to expand another new community. this method obviously can identify the overlapping community, since they allow a single node to be put into different community owing to different optimization process. in real-world network, it’s difficult to decide how many communities that a single node should be put in, however it’s very clear whether the edge incident on the node is in the community or not. now, researcher suggests using links to defining community [4], owing to an edge only is in one community, but the nodes connected by edge could be put into different communities. some methods [5–7] using line graph and link partitioning to detect overlapping community have been proposed. among them, ahn [6] partitions links into clusters via hierarchical clustering of edge similarity. given a pair of links eik and ejk incident on a node k, the edge pair similarity can be computed via the jarracd index defined as, sim(eik,ejk) = |nb+(i) ∩nb+(j)| |nb+(i) ∪nb+(j)| (2) 540 g.l. shen, x.p. yang, j. sun where nb+(i) is the inclusive neighbors of a node i, which the set contains the node itself and its neighbors. with this similarity, single-linkage hierarchical clustering is then used to build a link dendrogram. cutting this dendrogram at a special threshold yields link communities. although the link partitioning method can detect the overlapping communities naturally, there is no guarantee that is provides high quality detection for information network because the method only relies on links of the network while ignores the node content totally. there are many other methods to detect overlapping community. for example, the ones based on subgraphs, such as cpm [8], cpmw [9] etc.al, treat community structure as the composition of adjacent subgraphs, as one node can belong to several subgraphs. however, these methods are usually considered to solve the pattern matching of complex networks rather than finding communities. in addition, the methods extended girvan and newman’s divisive clustering algorithm [10], such as conga [11], conco [12], allow a node to split into multiple copies. despite the use of different techniques, the above methods can always detect overlapping dense connections in network. however, they only focus on the topology information but ignore the content information that contributes to improve the quality of the community [13]. topic-oriented community detection using topology and content. based on the assumption that the content information can improve the quality of the detected community, various approaches have been combined the links and contents for community detection. some approaches have combined content information with structure information for community discovery. one of them is generative probabilistic modeling which considers both contents and links as being dependent on one or more latent variables, and then estimates the conditional distributions to find community assignments. plsa-phits [4], community-user-topic model [15] and pmc [16] are three representatives in this category. other fusing the content and structure methods, such as sa-clustering [17] via augmenting the underlying network to take into account the content information, heuristic algorithm ckc [18] to solve the connected k-center problem, subspace clustering algorithm [19] on graphs with feature vectors. different from those methods using topology, these methods account for the content information of the nodes, so the division results for the network is more cohesive in the topics. however, considering the content of nodes, the complexity of the algorithm is greatly increased which will lead to some new challenges, such as how to deal with the high dimensional sparse for node attributes. furthermore, those methods are not designed for overlapping communities. 3 methodology in this section, we present our method for fusing structure and content via hybrid hypergraph to detect the overlapping communities for information networks. firstly, we present the definitions, and then introduce how to build the hybrid hypergraph model for information network. thirdly, we give the method that how to measure hyperedge pairs similarity. finally, we briefly introduce algorithmic details of hlp (hyper link partition), a novel method extended from link graph partition algorithm. 3.1 definitions link partitioning method is a kind of topology methodology based on classic graph theory. it’s very simple and distinct to model information networks as simple graphs, in which nodes indicate entity object, and links indicate the binary relationships between node pairs. however, real information networks are characterized by node content attribute, hence simple graph is not suitable for representing the content information. as the generalization of simple graph, detecting topic-oriented overlapping community using hybrid a hypergraph model 541 hypergraph can represent the multiple relationships for nodes in finite set, and describe the relationship between general discrete structures, which overcome the defect of the knowledge represented by the simple graph. hypergraph characterized by one hyperedge incident on any number of nodes, is a graph in generalization. the definition of hypergraph is provided as follows. definition 1. a hypergraph [20], h = (v,e) is defined as a set of vertices v = {v1,v2, · · · ,vn}, and a set of hyperedges e = {e1,e2, · · · ,em}, where: (1) e1 6= φ(i = 1,2, · · · ,m) (2) m ⋃ i=1 ei = v according to definition, a hyperedge essentially is the set of vertices which are independent. that is, hypergraph cannot represent the original topology of vertices. thus, we give the definition of hybrid hypergraph. definition 2. a hybrid hypergraph, hh = (v,e,ε,ψ) is defined as a set of vertices v = {v1,v2, · · · ,vn}, a set of hyperedges e = {e1,e2, · · · ,em} and a set of edges ε where: (1) ei 6= ∅ (i = 1,2, · · · ,m) (2) m ⋃ i=1 ei = v (3) ψ(εi) = (vi,vj) (i,j = 1,2, · · · ,n) 3.2 modeling information network as hybrid hypergraph we need to extend a structural graph with tuples describing node attributes. this can be formally expressed as a quad ag = {v,ε,fv ,ψ}, where each node v is associated with a feature vector f(v). fv is the set of features for all nodes, where, f(v) ⊆ fv ,v ∈ v . feature selection is an important issue in system anomaly detection applications [21]. with different node attributes in different information network, the feature vector f(v) can be varied as a topic, a keyword, a place, an author, an activity. the number of features of fv is m, formally, m = ∣ ∣ ∣ ∣ ∣ f(v) ⋃ v∈v ∣ ∣ ∣ ∣ ∣ . so, the question of how to build the information network as the hybrid hypergraph model is simplified as how to map a quad attribute graph ag into a quad hh. here, we take each feature fi as a basic unit to build hyperedge e, when fi ∈ f(v) , the node v ∈ e we use incidence matrix and adjacent matrix to represent the data structures with related to hybrid hypergraph. the nv ×me incidence matrix for a hybrid hypergraph hh, say i, is defined as that ive = { 1, if v ∈ e 0, otherwise (3) the me ×nv transposed matrix for i, say k, is defined as that kev = { 1, if v ∈ e 0, otherwise (4) the nv ×nv adjacent matrix for a hybrid hypergraph hh, say a, is defined as that aij = { 1, if (i,j) ∈ ε 0, otherwise (5) the me ×me similarity matrix for a hybrid hypergraph hh, say sim, is defined as that simij = simlarity(ei,ej) ei,ej ∈ e (6) 542 g.l. shen, x.p. yang, j. sun 3.3 similarity for hyperedge pairs in this section, we present the method of how to calculate the similarity of hyperedge pairs in hh. this technique was originally introduced by link partitioning algorithm and local expansion algorithm for the purposes of identifying the overlapping communities in network. however, link partitioning algorithm and local expansion algorithm both only focus on topological information, but ignore the content information. we argue the hh model for information network can fuse the content information and structure information naturally. according to the definition of hh, a hyperedge can be regarded as a sub-community characterized by a special feature, because the hyperedge is a set of nodes with the same feature. we know clearly that the sub-community represented by the hyperedge is different from the final detected community. the reason is that one node is usually associated with more than one feature, but a hyperedge only reflect one of the features. nevertheless, we can determine whether the hyperedge pairs have the same topic or not by exploring the link relationship between them. if the node only has one feature, moreover, such as topic, our work can be simplified as the work presented by zhao [22]. the relationship between two hyperedges is the link between two node sets which is more complicated, rather than just that between the two nodes in the simple graph. the link type for two hyperedges comes into two kinds: one kind is shared common nodes; the other is that the nodes in one hyperedge are connected with those in the other hyperedge. in order to better illustrate this, we offer two formal definitions as follows. definition 3. given two hyperedge ei and ej, is real connection, where ei ∩ej 6= ∅. for instance, two hyperedges e1, e2 are shared with two common nodes v2, v3 (fig1.a), that is e1 ∩e2 = {v2,v3} 6= ∅, therefore the link type of e1, e2 is real connection. definition 4. given two hyperedge ei and ej, is virtual connection, where (1) ei ∩ej 6= ∅ (2) ∀ε ∈{< vm,vn > |∃vm ∈ ei,∃vn ∈ ej} 6= ∅ or (3) ei ∩ej 6= ∅ (4) ∀ε ∈{< vm,vn > |∃vm ∈ ei −ei ∩ej,∃vn ∈ ej −ei ∩ej} 6= ∅ for instance, as shown in fig1.b e1 ∩ e2 = ∅, and, there are links < v1,v2 >, < v2,v3 > between nodes for e1 and e2, this is one case of virtual connection. another case is also shown in fig1.a, e1 ∩ e2 = {v2,v3} = ∅, considering edge < v4,v6 >, which match the conditions (4) v4 ∈ e1 −e2 ∩e2 and v6 ∈ e2 −e1 ∩e2. therefore, hyperedge e1 and e2 has both real connection and virtual connection. obviously, when measuring the hyperedge similarity, both virtual connection and real connection are meaningful. virtual connection reflects the structure information of the original network essentially, while real connection reflects the number of nodes with common attributes. intuitively, hyperedge similarity is dependent on the tightness of hyperedge pairs. as mentioned above, hyperedge is the group of nodes; therefore, measuring the tightness of hyperedge pair can be converted into how to measure the tightness of two groups of nodes. if there are a lot of links between two sets of nodes, the two sets are strongly tight. for instance, in citation network, if article a1 with keyword k1 cited article a2 with keyword k2, then k1 and k2 have certain correlation or similarity. similarly, the more articles involving the keyword k1 are cited by the articles involving k2 keyword, that is, k1 set has strong tightness with k2 set, which turns out that k1 and k2 have higher correlation. detecting topic-oriented overlapping community using hybrid a hypergraph model 543 (a) real connection (b) virtual connection figure 1: two kinds of link type for hyperedge to better quantify the tightness between the two groups of nodes, we extend the local fitness function in lfm [3]. local fitness function for a given local community s, is formally given as fs = ksin ksin + k s out (7) where ksin and k s out are the total internal and external degree of the community. fitness function fs can measure the internal and external tightness for a local community. in topology detecting community methods, it has shown a good performance, and was expanded or applied by more researchers [23, 24]. inspired by this thought, we propose the fitness function for a hyperedge e to be given as fe = kein kein + k e out (8) where kein and k e out are the total internal and external degree of the hyperedge. the total of k e in and keout is the total degree of all nodes in this hyperedge. similar to fs, fe can measure the internal and external tightness for hyperedge. whether to merge two hyperedge into a larger hyperedge depends on how the changed numbers of virtual connection influence the fitness of the combined hyperedge. we define incremental fitness for combined hyperedge as the similarity for virtual connection of two hyperedges. given two hyperedge ei and ej simvirtual(ei,ej) = ∆fij = fei ∪fei = k eiej in k eiej in + k eiej out (9) where k eiej in is the numbers of virtual connection between ei and ej. the total of k eiej in and k eiej out is the total degree of all nodes in merged hyperedge. for instance, in fig2, considering two hyperedge e1 and e4, k e1e4 in = 4, k e1e4 in + k e1e4 out = 32, so, ∆f14 = fe1 ∪fe4 = 18 . another example in this figure is, ke3e4in = 3 andk e3e4 in + k e3e4 out = 41, so, ∆f34 = fe3 ∪fe4 = 341 . whether to merge two hyperedges into a larger hyperedge depends on the value of incremental fitness function. virtual connection reflects the tightness of hyperedge pair via the structure information, while, real connection reflects the semantic similarity of hyperedge via the content information. in our method, we do not directly calculate the similarity of the features implied by hyperedges, such as that of keyword or topic, instead, we evaluate the similarity via cn metric. cn(common neighbors) [25] is also called structural equivalence, namely, the nodes are similar if they share a lot of common neighbors. cn is one of the most widely used metrics when measuring the similarity in local community detection methods. therefore, we extend the cn metric to make it suitable for measuring the similarity between two hyperedges, or, two groups of nodes. to define clearly the neighbors set for hyperedges, we give the following definitions. 544 g.l. shen, x.p. yang, j. sun figure 2: a sample of hybrid hypergraph model definition 5 (inductive nodes set). given two hyperedge ei, ej in hh, where ei, ej has real connection, the inductive nodes set for ei related to ej, say pj(ei) is formally as pj(ei) = {v|v ∈ ei −ei ∩ej}. in order to illustrate the neighbors for hyperedges, we specially designate those neighbors related to inductive nodes set. definition 6 (extended common neighbors of inductive nodes set). given two hyperedge ei, ej and inductive nodes set pj(ei) in hh, the extended neighbors of pj(ei) including pj(ei), say n+(pj(ei)),is formally as n+(pj(ei)) = {x|d(v,x) ≤ 1,v ∈ pj(ei)} where d(x,y) is the distance of two nodes, formally as follows: d(x,y) ≤ 1 if x ∈ ei,y ∈ ej and ei ∩ej 6= ∅ (10) in fig2, e1 ∩ e2 = {v1}, p2(e1) = {v2}, p1(e2) = {v3,v4}, so we can calculate the extended common neighbors of inductive nodes sets n+(p1(e2)) = {v1,v3,v4,v5,v6,v7,v8,v9,v10}, n+(p2(e1)) = {v1,v2,v9,v10,v11}. based on jaccard index, we propose the method that how to calculate the similarity for hyperedge pair ei, ej with real connection. formally as: simreal(ei,ej) = |n+(pj(ei)) ∩n+(pi(ej))| |n+(pj(ei)) ∪n+(pi(ej))| (11) in fact, this definition is consistent with the similarity for the link pair in ahn[14_6] research. for instance, in fig2, s(e1,e2) = 3 11 . we combine the jarracd index and fitness function to compute the similarity for hyperedge pair ei, ej, formally as: simij = sim(ei,ej) = λsimreal(ei,ej) + (1 −λ)simvirtual(ei,ej) (12) we give the detail of how to compute the similarity for hyperedge pair in algorithm 1. 3.4 stop criterion in our method, we adopt divisive hierarchical clustering to cluster the hyperedges. the results of hierarchical clustering are presented in a dendrogram. one important job in this method is detecting topic-oriented overlapping community using hybrid a hypergraph model 545 algorithm 1: computing the similarity for hyperedge pair input: e1, e2, k output: simvalue j = 0, f = 0 check e1 and e2 is whether real connection or virtual connection if (e1 ∩e2 6= ∅) //real connection for each v ∈ v if (v ∈ e1 & &v ∈ e2) vcomm ← v //get extended neighbors of inductive nodes set niv1 ← veneighbors(k,e1,vcomm) niv2 ← veneighbors(k,e2,vcomm) if niv1 6= ∅ && niv2 6= ∅ internum=intersection(niv1,niv2) unionnum=unionsection(niv1,niv2) jaccard = internum / unionnum //fitness fin=blink(e1,e2) ftotal=totaldegree(e1)+ totaldegree(e2) f=fin/ftotal else fin=blink(e1, e2) ftotal=totaldegree(e1)+ totaldegree(e2) f=fin/ftotal simvalue = λ*jaccard + (1 λ)*f figure 3: algorithm similarity deciding the stop criterion for clustering. we define partition density d, as a function of the dendrogram cut threshold. the maximum of d indicates the discovered hyperedge communities are well structured. for a hh, {hp1,hp2, · · · ,hpk} is a partition of the hyperedges into k clusters. cluster hpk has mk hyperedges, and nk = ∣ ∣ ∣ ∣ ∣ ⋃ ei∈hpk {v ∈ ei} ∣ ∣ ∣ ∣ ∣ nodes. then we define this as normalized form: dk = mhpk −1 mk −1 (13) where, mhpk = nk ∑ i=1 mihpk / n is the mean value for the nodes located in a number of hyperedges. mihpk is the number of hyperedges in which node i is located. the larger dk indicates the probability, at which nodes are clustering into one cluster, the tighter internal connection in the cluster is. the partition density d, is the average of dk. d = k ∑ i=1 di / k (14) the goal of hierarchical clustering is to find k clusters when partition density d is maximal. when d = 1, all clusters are merged into one community. 546 g.l. shen, x.p. yang, j. sun 3.5 hlp algorithm based on the hh model for information network, we apply the previously-defined similarity algorithm to pairs of all hyperedges, and produce a similarity matrix s. in the process of clustering, the clustered hyperedge contain more than one initial hyperedge. for instance, ei′ has n initial hyperedges, ej′ has m initial hyperedges. the jarracd similarity of ei′, ej′ is computed as: simreal(ei′,e′j) = n ∑ t m ∑ c simreal(e c i,e t j) / n∗m (15) we give a simple description of hlp in algorithm 2. we obtain the clusters of merged hyperedges via this algorithm. the nodes located in each clustered hyperedges constitute the communities related to some special topics. algorithm2: clustering for hyperedges input: k output: k′ s ← 0, m = 0, n = 0, k′← k m = lengthofrow(k) n = lengthofcol(k) //initialize the similarity matrix for k for(int i = 0; i < m; i++) for(int j = 0; j < n; j++) s(i,j) = similarity(i,j) s′← s while m > 1 //find the coordinates for the maximal similarity in s′ maxi = maxi(s′) maxj = maxj(s′) k′← clustering(k′(, maxi), k(, maxj)) if density(k′) = maxdensity(k′) return k′ figure 4: algorithm hlp 4 experiments in this section, we present experiments on real datasets to evaluate the performance of our method. we first applied our method to two datasets to choose the optimal value of λ. then we compared the performance of our method with two baseline methods. before going to details, we first describe the datasets and the method to extract the node feature, and introduce the performance metric to be used in our experiments. 4.1 datasets two real datasets used in our experiments are described in the following: cora dataset: the cora dataset [26] consists of the abstracts and references of about 34000 computer science research papers. three subfields of machine learning (ml), programming (pl) and database (db) are used and those articles without references to other articles in the set are removed. the detailed information about each subfield is shown in table1. detecting topic-oriented overlapping community using hybrid a hypergraph model 547 table 1: cora dataset area #of #of #of #of subfields total papers used papers links ml 7 4218 2708 5249 pl 9 4496 3292 7772 db 9 1396 1060 2522 webkb dataset: the webkb dataset [27] consists of about 6000 web pages from computer science departments of four schools. the web pages are classified into seven categories, including, staff(sa), course(cs),department(dp), faculty(fa), student(sd),project(pj),other(ot). we select five categories excluding dp and ot, in that the two categories contain only a few pages. the detailed information about these web pages is shown in table 2. table 2: webkb dataset school #of #of #of #of #of #of #of cs fa sd pj sa total links cn 44 34 18 128 21 245 304 tx 36 46 20 148 2 252 328 wa 77 30 18 123 10 258 446 wi 85 38 25 156 12 316 530 4.2 preprocessing for cora, we treated each subfield as an independent dataset. after stemming and removing stopwords we were left with vocabulary of stemmed unique words for each subfield respectively. all words with document frequency less than 15 were removed. taking db dataset as an example, a vocabulary of size 5911 unique words was reduced to size 1086 words by removing the low document frequency words. the word cloud for preprocessed db dataset is shown in fig.5 figure 5: word cloud for db dataset the same preprocessing is handled by webkb. we extracted words whose document fre548 g.l. shen, x.p. yang, j. sun quency is more than 10. 4.3 evaluation metrics in our paper, we focus on the topic similarity of the detected overlapping communities, therefore when the ground-truth community is known, we utilize two measures of purity and nmi to evaluate the quality of overlapping communities detected by different methods. purity measures the internal topic similarity within the community, and nmi is the most widely used measure to account for overlapping communities. given the ground-truth community structure, g = {g1,g2, · · · ,gs} where gs contains the set of nodes that are in the sth community. the community structure given by the algorithms is represented by c = {c1,c2, · · · ,cs}, where ck contains the set of nodes that are in the kth community. the purity of ci is defined as: purity(ci) = 1 |ci| max j {ci ∩gj} (16) usually, the detected community ci includes nodes that belong to other gj in the groundtruth. for ci, we compute the intersection set with each standard community gj, then take the maximum as the final purity for it. the purity of c is defined as: purity(c) = 1 k k ∑ i=1 purity(ci) (17) the higher the purity, the better the communities are partitioned from the perspective of topics. the mutual information between g and c is defined as mi(g,c) = ∑ x∈g,y∈c p(x,y) log p(x,y) p(x)p(y) (18) the nmi(normalized mutual information) is defined by nmi(g,c) = mi(g,c) max(h(g),h(c)) (19) where h(g) and h(c) are the entropies of the partitions g and c. the higher the nmi, the closer the partition is to the ground truth. 4.4 optimal value of λ as we discussed in section 3, the parameter λ balances the jaccard index and fitness function value when compute the similarity of hyperedge pairs. we perform experiments to study how the λ value affects the purity of detected communities. we set the step 0.1, the result is shown in figure 4. the result shows that λ value is decided by the structure of network and the number of hyperedges. jaccard index and fitness function value both affect the purity of the detected communities. it is proved that the structure and content information both have influence on the topical community detection. however, we observe that the characteristics of information networks detemine the value of parameter λ . different network will lead to different λ . this is detecting topic-oriented overlapping community using hybrid a hypergraph model 549 figure 6: the result of choosing parameter λ because there are noise problems to various degree, including both links and node content in the information network. for webkb dataset,the best performace is achieved when "λ = 0.6", and for cora dataset, the optimal value "λ = 0.5" which are used as default settings for the following experiments. 4.5 results to evaluate the effectiveness of hlp, we compare our method with two baseline methods: one is topology-based method, line graph partition [6], the other is lda to cluster the nodes by using content information only. we use purity and nmi quantifying the performance each algorithm. the details are shown in figure 5, which illustrates hlp achieve the best performance in real information networks. from the results, we also can observe some interesting things. in some datasets, such as db,pl,cn and ws, lda algorithm can achieve better performance than line graph algorithm, in some other datasets, nevertheless, such as ml,ta and wc, the results are opposite. this confirms our assumption again that both node information and link information affect the quality of detected overlapping communities. therefore, we are sure that the combination of node information and link information can improve the quality of overlapping community detection. (a) cora dataset (b) webkb dataset figure 7: the evaluations of community algorithms over two real information networks. 550 g.l. shen, x.p. yang, j. sun conclusion in this paper, we propose a topic-oriented overlapping community detection approach based on hierarchical clustering for hybrid hypergraph model, which can combine the content and structure information of information network naturally. considering the complex of the hybrid hypergraph model, we classify the connections of hyperedges into real connection related to content information, and virtual connection related to structure information. we present incremental fitness to evaluate the tightness for hyperedge pairs in virtual connection. meanwhile, we extend cn metric on hyperedge pairs to conduct the semantic similarity calculation in real connection. in order to balance the influence of two connections, we combine linearly the two measures for similarity of hyperedge pairs. the density function is employed to determine the appropriate number of communities. to evaluate the performance, we conducted experiments on two real datasets. compared with the benchmark, line graph partition algorithm focusing on topological detection, lda focusing on clustering node contents, our approach gained a better performance in information network. furthermore, the overlapping communities detected by our approach were more meaningful since they are topic-oriented. our approach has many potential applications. it can be applied to many kinds of information networks, where nodes contain content. with our method detecting the communities, we are able to improve the efficiency of collaborative scientific research, discover experts for each topic, and analyze topic-oriented influence propagation. future work includes qualifying the weight of each node in the hyperedge to improve the purity of detected communities. we also intend to take the time factor into account, so that we can detect the evolution communities. acknowledgment this paper is supported by natural science foundation of china (no.71572015), scientific research project of beijing union university (no. zk10201506), beijing higher education young elite teacher project (no.yetp1503). bibliography [1] cobanoglu b, zengin a, ekiz h, et al (2014); implementation of devs based distributed network simulator for large-scale networks [j]. international journal of simulation modelling (ijsimm), 13(2): 147-158. 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[accessed: april 9, 2014] int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 4, pp. 415-426 fuzzy logic control system stability analysis based on lyapunov’s direct method r.-e. precup, m.-l. tomescu, şt. preitl radu-emil precup, ştefan preitl "politehnica" university of timişoara department of automation and applied informatics bd. v. parvan 2, ro-300223 timişoara, romania e-mail: {radu.precup, stefan.preitl}@aut.upt.ro marius-lucian tomescu "aurel vlaicu" university of arad computer science faculty complex universitar m, str. elena dragoi 2, ro-310330 arad, romania e-mail: tom uav@yahoo.com abstract: a stability analysis method for nonlinear processes controlled by takagisugeno (t-s) fuzzy logic controllers (flcs) is proposed. the stability analysis of these fuzzy logic control systems is done in terms of lyapunov’s direct method. the stability theorem presented here ensures sufficient conditions for the stability of the fuzzy logic control systems. the theorem enables the formulation of a new stability analysis algorithm that offers sufficient stability conditions for nonlinear processes controlled by a class of t-s flcs. in addition, the paper includes an illustrative example that describes one application of this algorithm in the design of a stable fuzzy logic control system. keywords: fuzzy logic controller, lasalle’s invariance principle, lyapunov function candidate. 1 introduction fuzzy logic controllers have been proposed for a long time and applied successfully in many applications [1, 2, 3, 13, 14, 18]. a comprehensive work on the proof of stability of fuzzy logic control systems represents one of the challenges in fuzzy control [6, 12, 16, 17]. this paper presents a new stability analysis method for fuzzy logic control systems comprising nonlinear processes and t-s flcs. the advantages of this method with respect to the state-of-the-art result from its specific features. first, it is different to lyapunov’s theorem in several important aspects and allows more applications. in particular, it is well-suited to controlling processes where the derivative of the lyapunov function candidate is not negative definite. therefore lyapunov’s direct method can cope with fuzzy control of a wide area of nonlinear dynamic systems. second, the stability of the closed-loop system is guaranteed by the stability in each active region of the fuzzy rules. so making use of the proposed stability analysis approach determines the inserting of new fuzzy rules become very easy because just the fulfillment of one condition in the stability analysis theorem is needed. the paper discusses the following topics. section 2 deals with the description of the accepted class of fuzzy logic control systems. the proposed stability analysis method focused on a stability theorem based on lyapunov’s direct method and the new stability analysis algorithm that guarantees the stability of fuzzy logic control systems are presented in section 3. next, section 4 offers a simple example to validate the theoretical part suggesting ways of applying the proposed algorithm. the conclusions are drawn in section 5. copyright c© 2006-2009 by ccc publications 416 r.-e. precup, m.-l. tomescu, şt. preitl output y(t) x(0) initial conditions u(t) reference input r process ! ! !tuxbxfx "# state variables (x1,x2,…,xn) figure 1: fuzzy logic control system structure. 2 fuzzy logic control systems the structure of a fuzzy logic control system consisting of a process controlled by an flc is presented in figure 1. let x be the universe of discourse and consider a single-input n-th order nonlinear system of the following form representing the state-space equations of the controlled process: ẋ = f (x) + b (x) u, x (t) = x, (1) where: − x ∈ x , x = [x, x, ..., xn]t is the state vector; − f (x) = [ f (x) , f (x) , ..., fn (x)] t , b (x) = [b (x) , b (x) , ..., bn (x)] t are functions describing the dynamics of the process, f , b : d → rn are locally lipschitz maps from a domain d ⊂ rn into rn; − u is the control signal applied to the process input; − the time variable, t, has been omitted to simplify the further formulation; − x (t) is the initial state at time t. the i-th fuzzy (control) rule in the rule base of the t-s flc base is of the form (2): rule i : if x is xi, and x is xi, and ... and xn is xi,n then u = ui (x) , i = , r, r ∈ n∗, (2) where r is the total number of rules, xi,, xi,, .., xi,n are fuzzy sets that describe the linguistics terms (lts) of the input variables xk, k = , n, u = ui (x) is the control signal of rule i, similar to the case of parallel distributed compensation, and the function and is a t-norm. ui can be a single value or a function of the state vector, x. the structure presented in figure 1 cam be viewed as a nonlinear state-feedback control system. however other input variables (to the flc) can be considered as well instead of the state variables xk, k = , n. one simple design of the fuzzy logic control system can be done in terms of parallel distributed compensation. each fuzzy rule generates the firing strength defined in (3): αi (x)= and (µi, (x) , µi, (x) . . . µi,n (xn)) ∈ [, ] ,∀x ∈ x , i = , r. (3) fuzzy logic control system stability analysis based on lyapunov’s direct method 417 it is assumed that for any x ∈ x there exists among all rules at least one αi ∈ (, ] , i = , r. the control signal u is a function of αi and ui. applying the weighted sum defuzzification method the output of the flc is given by u = r∑ i= αiui r∑ i= αi . (4) definition 1. for any input x ∈ x if the firing strength αi (x) corresponding to the fuzzy rule i is zero, that fuzzy rule i, i = , r, is called an inactive fuzzy rule for the input x; otherwise, it is called an active fuzzy rule. it should be noted that with x = x an inactive fuzzy rule will not affect the controller output u (x). hence (4) can be rewritten as follows aiming the consideration of all active fuzzy rules only: u (x) = r∑ i=,αi 6= αi (x) ui (x) r∑ i=,αi 6= αi (x) . (5) definition 2. an active region of the fuzzy rule i is defined as a set x ai = {x ∈ x |αi (x) 6= } , i = , r. (6) 3 stability analysis the stability analysis presented in this paper is based on lasalle’s invariance principle cited and analyzed in [11]. this section is concentrated on the formulation and proof of theorem 1 that ensures sufficient conditions for the stability of nonlinear processes controlled by t-s flcs. the lyapunov function candidate v : rn → r, v (x) = xt px is considered. it is positive and unbounded, where p ∈ rn×n is a positive definite matrix. considering the state trajectories fulfilling (1) in order to obtain the closed-loop system dynamics, it results that v has continuous partial derivatives and the derivatives of v with respect to time expressed in terms of (7): v̇ (x) = ẋt px + xt pẋ = ( f (x) + b (x) u (x))t px+ +xt p ( f (x)+ b (x)u (x)) = f (x) + b (x) u (x) , (7) where: f (x) = f (x)t px + xt p f (x) , b (x) = b (x)t px + xt pb (x) . (8) the following sets are defined to be used in the stability analysis: b = {x ∈ x |b (x) =  } , b+ = {x ∈ x |b (x) >  } , b− = {x ∈ x |b (x) <  } . (9) the main result is given by the following theorem. theorem 3. let the process be described by (1) with x =  ∈ rn an equilibrium point. if there exists a function v : rn→ r, v (x)= xt px, p∈ rn×n, positive definite, unbounded and fulfilling 1, 2 and 3: 1. f (x) ≤ ,∀x ∈ b, 2. ui (x) ≤ − f(x)b(x) for x ∈ x ai ∩b+ and ui (x) ≥ − f(x) b(x) for x ∈ x ai ∩b−, i = , r, 418 r.-e. precup, m.-l. tomescu, şt. preitl 3. the set { x ∈ x ∣∣v̇ (x) =  } contains no state trajectories except the trivial one, x (t) =  for t ≥ , then the closed-loop system composed by the t-s flc and the process (1) will be globally asymptotically stable in the sense of lyapunov at the origin. proof. by the definition of v it results that v () = , v (x) > ,∀x 6=  and v (x) = xt px → ∞ as ‖x‖ → ∞. further on, it will be proved that v̇ is negative semi-definite with respect to time employing (7). an arbitrary initial state vector x ∈ x is accepted. then the following three cases are possible. case 1: b (x) is strictly positive. from the condition 2 of theorem 1 it results that: ui (x) ≤ − f (x) b (x) ⇒ u (x) = r∑ i=,αi 6= αi (x) ui (x) r∑ i=,αi 6= αi (x) ≤ − f(x) b(x) r∑ i=,αi 6= αi (x) r∑ i=,αi 6= αi (x) = − f (x) b (x) ⇒ ⇒ v̇ (x) = f (x) + b (x) u (x) ≤ f (x) + b (x) ( − f (x) b (x) ) = . (10) therefore, ui (x) ≤ − f (x) b (x) ⇒ v̇ (x) ≤ . (11) case 2: b (x) is strictly negative. once more, from the condition 2 of theorem 1 it results that ui (x) ≥ − f (x) b (x) ⇒ u (x) = r∑ i=,αi 6= αi (x) ui (x) r∑ i=,αi 6= αi (x) ≥ − f(x) b(x) · r∑ i=,αi 6= αi (x) r∑ i=,αi 6= αi (x) = − f (x) b (x) ⇒ ⇒ v̇ (x) = f (x) + b (x) u (x)≤ f (x) + b (x) ( − f (x) b (x) ) = . (12) therefore, ui (x) ≥ − f (x) b (x) ⇒ v̇ (x) ≤ . (13) case 3: x ∈ b. in this case using the condition 1 in theorem 1 the result will be f (x) ≤ . hence, v̇ (x) = f (x) + b (x) u (x) = f (x) ≤ . (14) from the above three cases it is obtained that v̇ (x) ≤ ,∀x ∈ x . (15) in conclusion, the derivative with respect to time of the lyapunov function candidate, v̇ , is negative semi-definite. the condition 3 ensures the fulfilment of lasalle’s invariance principle. this justifies the fact that the equilibrium point at the origin is globally asymptotically stable. the proof is now complete. the stability theorem presented here ensures sufficient conditions for the stability of the fuzzy logic control system described in section 2. so it has been proved that if the lyapunov function candidate is negative semi-definite in the active region of each fuzzy rule then, the closed-loop system will be globally asymptotically stable in the sense of lyapunov. fuzzy logic control system stability analysis based on lyapunov’s direct method 419 the conditions 1 and 2 in theorem 1 guarantee that the function v̇ is negative semi-definite in the active region of each fuzzy rule. the condition 3 proves that the set {} is the largest invariance set in{ x ∈ x ∣∣v̇ (x) =  } . by lasalle’s invariance principle it has been guaranteed that the fuzzy logic control system, comprising the nonlinear process described by (1) and the t-s flc, is globally asymptotically stable in the sense of lyapunov at the origin. the stability analysis algorithm ensuring the stability of the class of fuzzy logic control systems considered in section 2 is based on theorem 1. it consists of the following steps: 1. set the lyapunov function candidate v (i.e. set p). 2. check that the set { x ∈ x ∣∣v̇ (x) =  } contains no state trajectories except the trivial one, x (t) =  for t ≥ . 3. determine f (x) , b (x) , b, b−, b+. 4. if f (x) ≤ ,∀x ∈ b then go to step 5. else go to step 1. 5. for each fuzzy control rule i determine ui such that ui (x) ≤ − f(x)b(x) for x ∈ x ai ∩b+ and ui ≥ − f(x) b(x) for x ∈ x ai ∩b−, i = , r. the application of this algorithm will be illustrated in the next section. 4 illustrative example this section is dedicated to the validation of the theoretical results derived in section 3 by the design of a stable fuzzy logic control system with t-s flc controlling a nonlinear process, the inverted pendulum on a cart system. this simple mechanical system is representative to model a class of attitude control problems whose goal is to maintain permanently the desired vertically oriented position. since the inverted pendulum is a nonlinear system, the basic balance equations for the system are derived firstly and put into the standard state-space form. given an inverted pendulum mounted on a cart as shown in figure 2, the first principle nonlinear equations are applied in the sequel. assuming that the rod is massless and that the cart mass and the point mass at the upper end of the inverted pendulum are denoted as m and m, respectively, there is an externally x-directed force on the cart, f(t), and the gravity force acts on the point mass at all times. the coordinate system is defined according to figure 2, where x(t) represents the cart position and θ (t) is the tilt angle referenced to the vertical upward direction. figure 2: variables related to the inverted pendulum on a cart system. the differential equation that describes the behavior of the simplified system, playing the role of controlled process, is usually written as (m + m)·l · θ̈ − (m + m)·l ·g·sin (θ ) = −u, (16) 420 r.-e. precup, m.-l. tomescu, şt. preitl where: m the mass of the cart, m the mass of the pendulum, l the length of pendulum (distance to the center of mass), x the cart position coordinate, θ the pendulum angle with respect to the vertical position, u the control signal, equal to the externally x-directed force, u = f . the state vector consists of the angle, θ , and the angular velocity of the pendulum, θ̇ . therefore, the two state variables are defined as z and z, where z ∈ [−, ], z ∈ [−, ], z (t) = θ (t) and z (t) = θ̇ (t). in order to write equation (19) in terms of state variables, they are substituted resulting in ż = f (z) + b (z) u, (17) where: z = [ z z ] state vector, f (z) = [ z g l sin (z) ] , b (z) = [  −  (m+m)l ] . the goal of fuzzy logic control system design, to be presented as follows, is to ensure the upright stabilization of the pendulum aiming the setpoint value of z, z = . the design starts with setting the fuzzification module of the t-s flc. figures 3 and 4 illustrates the membership functions corresponding to the lts of the linguistic variables z and z. the three lts representing positive, zero and negative values are noted by p, z and n, respectively. figure 3: membership functions of z. figure 4: membership functions of z. fuzzy logic control system stability analysis based on lyapunov’s direct method 421 the inference engine of the flc employs the min and max operators and it is assisted by the complete rule base illustrated in table 1. the weighted sum defuzzification method is used in the t-s flc structure. summarizing, the only parameters to be calculated are the consequents ui in the 9 fuzzy control rules. table 1 fuzzy control rule base rule antecedent consequent z z u 1 p p u 2 n n u 3 p n u 4 n p u 5 p z u 6 n z u 7 z p u 8 z n u 9 z z u the algorithm presented in section 3 will be applied as follows in order to find the values of ui for which the system (19) can be stabilized by the above described t-s flc. step 1: the lyapunov function candidate v (z) = zt pz = z + z   is considered, where p = (     ) . therefore v is positive. if ‖z‖ → ∞ then v (z) → ∞. v (z) > ,∀z 6= . step 2: the derivative is v̇ (z)= z ( z + g l sin (z) −  (m+m)l u ) and v̇ () = . assume that there is a trajectory with z (t) =  and z (t) 6= . then ddt z (t) = g l sin (z (t)) −  (m+m)l u (t) 6= , which means that z (t) can not stay constant. hence, z (t) =  is the only possible state trajectory for which v̇ (z) = . so the set { z ∈ x ∣∣v̇ (z) =  } contains no trajectory of the system except the trivial trajectory z (t) =  for t ≥ . step 3: the expressions of f and b are: f (z) = z ( z + g l sin (z) ) , b (z) = − z (m + m) l . (18) the following elements necessary in theorem 1 obtain the particular values expressed in (23) to (26): b = {(z, )∈ x |z∈ [−, ] } , b+= {(z, z)∈ x |z <  } , b−= {(z, z)∈ x |z >  } , (19) − f (z) b (z) = l (m + m) (zl + g sin (z)) . (20) step 4: if z ∈ b then z =  and f (z) = . step 5: each rule will be analyzed further on. this is not a complex task since only 9 rules are involved. for rule 1: z is p, z is p. so x a = (, ] × (, ], x a ∩ b+ = /0 and x a ∩ b− = (, ] × (, ]. thus, u (z) ≥ − f(z)b(z) = l (m + m) (zl + g sin (z)). it is taken u (z) = l (m + m) (zl + g), and this function fulfills the condition 2 in theorem 1. for rule 2: z is n, z is n. so x a = [−, )× [−, ), x a ∩ b+ = [−, )× [−, ) and x a ∩ b− = /0. thus u ≤ − f(z)b(z) = l (m + m) (zl + g sin (z)). it is taken u (z) = l (m + m) (zl − g), and this function fulfills the condition 2 in theorem 1. 422 r.-e. precup, m.-l. tomescu, şt. preitl for rule 3: z is p, z is n. so x a = (, ]×(−, ], x a ∩b− = /0 and x a ∩b+ = (, ]×(−, ]. thus u (z) ≤ − f(z)b(z) = l (m + m) (zl + g sin (z)). it is taken u (z) = −z, and this function fulfills again the given condition. for rule 4: z is n, z is p. so x a = [−, )×(, ], x a ∩b+ = /0 and x a ∩b− = [−, )×(, ]. thus u (z) ≥ − f(z)b(z) = l (m + m) (zl + g sin (z)). it is taken u (z) = −z, and this function fulfills the given condition. for rule 5: z is p, z is z. so x a = (, ]×(−, ). thus, two possible cases will occur: a) for z ∈ x a ∩b− = (, ]×[, ) ⇒ u (z) ≥ − f(z)b(z) = l (m + m) (zl + g sin (z)) and b) for z ∈ x a ∩b+ = (, ]×(−, ] ⇒ u (z) ≤ − f(z)b(z) = l (m + m) (zl + g sin (z)). in order to satisfy both conditions it is chosen u (z) = l (m + m) (zl + g sin (z)). for rule 6: z is n, z is z. so x a = [−, )×(−, ). the result will be: a) for z ∈ x a ∩b− = [−, )×(, ) ⇒ u (z) ≥ − f(z)b(z) = l (m + m) (zl + g sin (z)) and b) for z ∈ x a ∩b+ = [−, )×(−, ) ⇒ u (z) ≤ − f(z)b(z) = l (m + m) (zl + g sin (z)). in order to satisfy both conditions it is chosen u (z) = l (m + m) (zl + g sin (z)). for rule 7: z is z, z is p. so x a = (−, ) × (, ], x a ∩ b+ = /0 and x a ∩ b− = (−, ) × (, ] . thus u (z) ≥ − f(z)b(z) = l (m + m) (zl + g sin (z)). it is set u (z) = l (m + m) (zl + g) to fulfill the condition 2 in theorem 1. for rule 8: z is z, z is n. so x a = (−, ) × [−, ), x a ∩ b+ = (−, ) × [−, ) and x a ∩ b− = /0. thus u (z) ≤ − f(z)b(z) = l (m + m) (zl + g sin (z)). it is set u (z) = l (m + m) (zl − g) to fulfill the condition 2 in theorem 1. for rule 9: z is z, z is z. so x a = (−, )×(−, ). this will yield: a) for z ∈ x a ∩b+ = (−, )×(−, ) ⇒ u (z) ≤ − f(z)b(z) = l (m + m) (zl + g sin (z)) and b) for z ∈ x a ∩b− = (−, )×(, ) ⇒ u (z) ≥ − f(z)b(z) = l (m + m) (zl + g sin (z)). in order to satisfy both conditions it is set u (z) = l (m + m) (zl + g sin (z)). concluding, from theorem 1 it results that the closed-loop system composed by the nonlinear process modeled in (19) and the t-s flc designed here is globally asymptotically stable in the sense of lyapunov at the origin. considering the values of process parameters m = ., m = ., l = , g = ., the responses of z and z versus time in the closed-loop system are presented in figures 5 to 8 for different initial conditions. 5 summary and conclusions a new approach to the global asymptotic stability analysis of fuzzy logic control systems employing t-s flcs dedicated to a class of nonlinear processes has been introduced. the example proves how the stability analysis algorithm suggested here can be applied to the design of a stable fuzzy logic control system for a nonlinear process. the new stability approach can be applied also in situations when the system has an equilibrium point different to the origin and / or the setpoint is nonzero by an appropriately defined state transformation [15]. fuzzy logic control system stability analysis based on lyapunov’s direct method 423 [s] figure 5: state variables versus time for fuzzy logic control system with t-s flc in the condition z () =  and z () = −. [s] figure 6: state variables versus time for fuzzy logic control system with t-s flc in the condition z () =  and z () = . [s] figure 7: state variables versus time for fuzzy logic control system with t-s flc in the condition z () =  and z () = −. [s] figure 8: state variables versus time for fuzzy logic control system with t-s flc in the condition z () = − and z () = . the stability analysis algorithm suggested in this paper canbe applied also when the rule base (2) of the t-s flc is not complete. however interpolation techniques [10, 19] are needed in the imple424 r.-e. precup, m.-l. tomescu, şt. preitl mentation of the t-s flc. they require the re-assessment of the stability conditoins derived prior to the implementation. further research will be concentrated on new applications of the proposed algorithm to several classes of processes [4, 5, 7, 8, 9, 17, 20]. the complex applications require the computer-aided design of the takagi-sugeno fuzzy logic controllers employing the stability analysis algorithm proposed in this paper to strive for increased generality. acknowledgement the support from the cncsis and cnmp of romania is acknowledged. bibliography [1] m. m. bălaş, v. e. bălaş, world knowledge for control applications by fuzzy-interpolative systems, international journal of computers, communications & control, vol. iii, suppl. issue: proceedings of icccc 2008, pp. 28-32, 2008. [2] a. bazoula, m.s. djouadi, h. maaref, formation control of multi-robots via fuzzy logic technique, international journal of computers, communications & control, vol. iii, suppl. issue: proceedings of icccc 2008, pp. 179-184. 2008. [3] d. bellomo, d. naso, and r. babuška, adaptive fuzzy control of a non-linear servo-drive: theory and experimental results, engineering applications of artificial intelligence , vol. 21, issue 6, pp. 846-857, 2008. [4] s. blažič and i. škrjanc, design and stability analysis of fuzzy model-based predictive control a case study, journal of intelligent and robotic systems, vol. 49, issue 3, pp. 279-292, 2007. [5] s. ertugrul, predictive modeling of human operators using parametric and neuro-fuzzy models by means of computer-based identification experiment, engineering applications of artificial intelligence , vol. 21, issue 2, pp. 259-268, 2008. [6] g. feng, a survey on analysis and design of model-based fuzzy control systems, ieee transactions on fuzzy systems, vol. 14, issue 5, pp. 676-697, 2006. [7] d. hladek, j. vascak, and p. sincak, multi-robot control system for pursuit-evasion problem, journal of electrical engineering, vol. 60, issue 3, pp. 143-148, 2009. [8] l. horváth and i. j. rudas, towards content oriented integration of product and robot system models, international symposium on logistics and industrial informatics, wildau, pp. 7-12, 2007. [9] g.-h. hwang, d.-w. kim, j.-h. lee, and y.-j. an, design of fuzzy power system stabilizer using adaptive evolutionary algorithm, engineering applications of artificial intelligence, vol. 21, issue 1, pp. 86-96, 2008. [10] z. c. johanyák and s. kovács: sparse fuzzy system generation by rule base extension, 11th ieee international conference of intelligent engineering systems, budapest, pp. 99-104, 2007. [11] h. k. khalil, nonlinear systems, third edition, prentice hall, englewood cliffs, nj, 2002. [12] k. michels, f. klawonn, r. kruse, and a. nürnberger, fuzzy control: fundamentals, stability and design of fuzzy controllers, springer verlag, berlin, heidelberg, new york, 2006. fuzzy logic control system stability analysis based on lyapunov’s direct method 425 [13] b. m. mohan and a. sinha, analytical structure and stability analysis of a fuzzy pid controller, applied soft computing, vol. 8, issue 1, pp. 749-758, 2008. [14] a. g. perry, g. feng, y.-f. liu, and p. c. sen, a design method for pi-like fuzzy logic controllers for dc-dc converter, ieee transactions on industrial electronics, vol. 54, issue 5, pp. 2688-2696, 2007. [15] r.-e. precup, s. preitl, and g. faur, pi predictive fuzzy controllers for electrical drive speed control: methods and software for stable development, computers in industry, vol. 52, issue 3, pp. 253-270, 2003. [16] r.-e. precup, s. preitl, i. j. rudas, m. l. tomescu, and j. k. tar, design and experiments for a class of fuzzy controlled servo systems, ieee/asme transactions on mechatronics, vol. 13, issue 1, pp. 22-35, 2008. [17] e. tian and c. peng, delay-dependent stability analysis and synthesis of uncertain t-s fuzzy systems with time-varying delay, fuzzy sets and systems, vol. 157, issue 4, pp. 544-559, 2006. [18] t. vesselenyi, s. dziţac, i. dziţac and m.-j. manolescu, fuzzy and neural controllers for a pneumatic actuator, international journal of computers, communications & control, vol. ii, no. 4, pp. 375-387, 2007. [19] k. w. wong, d. tikk, t. d. gedeon, and l. t. kóczy, fuzzy rule interpolation for multidimensional input spaces with applications: a case study, ieee transactions on fuzzy systems, vol. 13, issue 6, pp. 809-819, 2005. [20] j. yoneyama, robust stability and stabilization for uncertain takagi-sugeno fuzzy time-delay systems, fuzzy sets and systems, vol. 158, issue 2, pp. 115-134, 2007. radu-emil precup was born in lugoj, romania, in 1963. he received the dipl.ing. degree (hons.) in automation and computers from the "traian vuia" polytechnic institute of timişoara, romania, in 1987, the diploma in mathematics from the west university of timişoara in 1993, and the phd degree in automatic systems from the "politehnica" university of timisoara (put), romania, in 1996. from 1987 to 1991, he was with infoservice s.a., timişoara. he is currently with the put, where he became a professor in the department of automation and applied informatics, in 2000, and he is currently a doctoral supervisor of automation and systems engineering. he is also an honorary professor with the budapest tech polytechnical institution, budapest, hungary. he is the author or coauthor of more than 120 papers published in various scientific journals, refereed conference proceedings, and contributions to books. marius l. tomescu was born in arad, romania, in 1967. he received his msc degree in computer science from the west university of timişoara, romania, in 1993, and the phd degree in automation from the "politehnica" university of timişoara, romania, in 2008. from 1993 to 2008, he was with grup service s.r.l., arad. he is with the "aurel vlaicu" university of arad, where he has been a lecturer in the department of computer science since 2002. his research interests include fuzzy control applications and stability analysis of fuzzy control systems. he is the author or coauthor of more than 30 papers published in various scientific journals and refereed conference proceedings. dr. tomescu is a member of the romanian society of control engineering and technical informatics. 426 r.-e. precup, m.-l. tomescu, şt. preitl ştefan preitl was born in timişoara, romania, in 1943. he received the dipl.ing. degree in electrical engineering, and the phd degree in measurement techniques from the "traian vuia" polytechnic institute of timişoara in 1966 and 1983, respectively. from 1967 to 1972, he was with electromotor s.a., timişoara. he is currently with the "politehnica" university of timişoara, timişoara, where he became a professor in the department of automation and applied informatics, in 1992, and he is currently a doctoral supervisor of automation and systems engineering. he is also an honorary professor with the budapest tech polytechnical institution, budapest, hungary. he is the author or coauthor of more than 170 papers published in various scientific journals, refereed conference proceedings, and books in the field of automatic control. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 432-449 performing mapreduce on data centers with hierarchical structures z. ding, d. guo, x. chen, x. luo zeliu ding, deke guo, xueshan luo key lab of information system engineering, school of information systems and management, national university of defense technology, changsha 410073, china e-mail: zeliuding@nudt.edu.cn, guodeke@gmail.com xsluo@nudt.edu.cn xi chen school of computer science, mcgill university, montreal h3a 2a7, canada e-mail: chenxiwarm@gmail.com abstract: data centers are created as distributed information systems for massive data storage and processing. the structure of a data center determines the way that its inner servers, links and switches are interconnected. several hierarchical structures have been proposed to improve the topological performance of data centers. by using recursively defined topologies, these novel structures can well support general applications and services with high scalability and reliability. however, these structures ignore the details of some specific applications running on data centers, such as mapreduce, a well-known distributed data processing application. the communication and control mechanisms for performing mapreduce on the traditional structure cannot be employed on the hierarchical structures. in this paper, we propose a methodology for performing mapreduce on data centers with hierarchical structures. our methodology is based on the distributed hash table (dht), an efficient data retrieval approach on distributed systems. we utilize the advantages of dht, including decentralization, fault tolerance and scalability, to address the main problems that face hierarchical data centers in supporting mapreduce. comprehensive evaluation demonstrates the feasibility and excellent performance of our methodology. keywords: mapreduce; data center; distributed hash table (dht). 1 introduction in the recent years, the data centers have emerged as distributed information systems for massive data storage and processing. a data center provides many online applications [8] [2] and infrastructure services [4] [13] through its large number of servers, which are interconnected via high-speed links and switches. these devices construct the networking infrastructure of a data center, named data center network [9]. the structure of a data center network determines the way these devices are organized. to design suitable structures and make them match well with the data storage and processing applications are fundamental challenges. mapreduce, proposed by google, is a well-known and widely used data processing mechanism on data centers [6]. mapreduce works by separating a complex computation into map tasks and reduce tasks, which are performed in parallel on hundreds or thousands of servers in a data center. mapreduce provides a good control and execution mode for distributed computing and cloud computing [18]. users copyright c⃝ 2006-2012 by ccc publications performing mapreduce on data centers with hierarchical structures 433 without any experience of distributed programming can easily process terabytes of data on data centers with the help of mapreduce. nowadays, increasingly diverse applications and services call for an improvement of data centers in the topological performance, including scalability, reliability, etc. especially, users’ various requirements for processing large amount of data results in an exponentially increasing number of servers. the traditional structure, however, can hardly sustain the incremental expanding of data centers [10]. several novel data center structures, such as dcell [9], ficonn [15], and bcube [11], have been proposed to optimize the topological performance of data centers. these structures are all recursively defined to construct data center networks, interconnecting the servers by a hierarchical way. we represent them as hierarchical structures. these structures mainly focus on the scalability and reliability for data centers. however, the details of some specific applications running on the data centers with these structures are ignored. for example, none of these hierarchical structures treat servers as masters and workers, although this requirement is the basis of many distributed data processing applications, especially mapreduce. the communication and control mechanisms for performing mapreduce on the traditional structure can hardly be operated on these hierarchical structures. to solve this problem, this paper presents a methodology for performing mapreduce on the data centers with hierarchical structures, represented as hierarchical data centers for short. our methodology is based on the distributed hash table (dht) [20] [21], an efficient data retrieval approach on distributed systems. dht works by assigning each server a hash table that records the range of keys handled by all adjacent servers. responsibility for hashing data to keys is distributed among the servers. this approach possesses several advantages. first, dht makes all servers freely communicate with each other without any central coordination. this provides a control mechanism for mapreduce on hierarchical data centers without designating the masters or workers. second, dht ensures that the whole system can tolerate any single node failure. since the information of a server is held by its adjacent servers, a failed server can affect only its neighbors, which causes a minimal amount of disruption [22]. finally, dht can flexibly deal with a large amount of nodes joining or leaving the system. this matches well with the scalability of hierarchical data centers. these advantages bring a feasibility to perform mapreduce on hierarchical data centers. in this paper, we address the main problems that face hierarchical data centers in supporting mapreduce. our methodology utilizes the above advantages of dht to execute the procedure of mapreduce on hierarchical data centers. comprehensive evaluation shows that our methodology is effective and possesses excellent performance. the main contributions of this paper are as follows. • first, we propose the schemes for designating master servers and worker servers, and storing data files on hierarchical data centers, so as to facilitate the execution of mapreduce. • second, we present a specific dht architecture and a corresponding routing scheme for assigning map and reduce tasks and delivering intermediate data on hierarchical data centers. comprehensive evaluation demonstrates that our scheme can evenly distribute the workload and well support throughput-hungry mapreduce applications. • third, we deal with server and switch failures by proposing suitable fault-tolerant approaches for performing mapreduce on hierarchical data centers. experimental results prove that our methodology is a reasonable solution even considering node failures. the remainder of this paper is organized as follows: section 2 introduces the background, related work and our motivation. section 3 proposes the schemes for executing the basic procedure of mapreduce on hierarchical data centers. section 4 presents the dht architecture and routing scheme. section 5 looks into the fault-tolerant routing and issues for performing mapreduce on hierarchical data centers. section 6 evaluates the performance of the proposed methodology. section 7 concludes this paper. 434 z. ding, d. guo, x. chen, x. luo 2 preliminaries 2.1 background with a simple and practical processing procedure, mapreduce provides a standard mechanism for distributed data processing. a basic mapreduce procedure consists of a map phase and a reduce phase [5]. each phase includes multiple parallel map or reduce tasks, respectively. a mapreduce procedure can process terabytes of data through numbers of map and reduce tasks. map tasks are applied for data classification and preparing intermediate data for reduce tasks. by means of predefined map programs, map tasks transform the input data into intermediate data, which are organized as key/value pairs, and then deliver those intermediate data with the same key to corresponding reduce tasks. the keys represent the types of intermediate data. the values represent the content of intermediate data. reduce tasks are responsible for merging those intermediate data and producing output files. after retrieving intermediate data from map tasks, reduce tasks integrate the intermediate values associated with the same key by means of predefined reduce programs, and therefore generate output values. in a data center, mapreduce lets a master server control many worker servers in executing map and reduce tasks [7]. the master assigns each map or reduce task to a worker, and each map task is assigned to the worker that stores the input data for the map task. the workers executing map and reduce tasks are called mappers and reducers, respectively. in the map phase, mappers execute corresponding map tasks simultaneously. when mappers accomplish map tasks, they store derived intermediate data on local disks, and then send the location information of intermediate data to the master. in the reduce phase, the master distributes the location information of intermediate data to reducers. then reducers read corresponding intermediate data from mappers and execute their respective reduce tasks simultaneously. fig.1 shows the basic process of a mapreduce procedure. mapper 0 mapper 1 mapper 2 reducer 0 reducer 1 reducer 2 local write output data 0 output data 1 output data 2 input data 0 input data 1 local read assign map tasks assign reduce tasks input data 2 input data files intermediate data map phase reduce phase output data files key0/value key1/value key2/value key0/value key1/value key2/value master key0/value key1/value key2/value figure 1: the basic process of a mapreduce procedure 2.2 related work many existing data centers adopt the traditional tree structure. namely, all servers are located at leaf nodes. aggregation switches and core switches are placed at inner nodes and root nodes, respectively. the servers are connected by the aggregation switches, which are linked by the core switches. using these expensive and high-speed switches, the servers are fully interconnected. there is a path between each pair of servers without passing through any other server. the traditional tree structure is simple and easy to build, but it does not scale well. expanding such a structure needs to add more inner nodes and root nodes, using more expensive and higher-speed switches. actually, these aggregation switches performing mapreduce on data centers with hierarchical structures 435 and core switches easily lead to bottlenecks. a core switch failure can break down hundreds or even thousands of servers. fat-tree [1] is an improved structure of the traditional tree structure. every inner node in a fat-tree has more than one father node. this improvement increases the number of links between the aggregation switches and core switches. the network connectivity is increased, making fat-tree a relatively reliable structure. however, like the traditional tree structure, it still does not scale well. hierarchical structures constructed recursively are believed to be scalable and reliable. to construct a hierarchical structure, a high level structure utilizes a lower level structure as a unit and connects many such units by means of a given recursive rule. as the level of a structure increases, more and more servers can be added into a hierarchical dcn without destroying the existing structure. dcell [9], ficonn [15], and bcube [11] are three typical hierarchical structures. they use the same smallest recursive unit, in which a switch interconnects several servers. however, they are different in their recursive rules. dcell employs a complete graph as its recursive rule. there is a link between any two units of the same level. as a result, dcell possesses the advantage of the complete graph. in order to obtain high connectivity, each server in dcell should be equipped with multiple network ports. although ficonn and dcell employ similar design principles for constructing high level compound graphs recursively, they have fundamental differences. each server in ficonn is equipped with only two network ports, and the recursive units in ficonn are connected with only a half of the idle network ports in each unit. bcube employs the generalized hypercube as its recursive rule. the neighboring servers, which are connected to the same switch, differ in only one digit in their address arrays. thus, bcube holds the advantages of the generalized hypercube, such as high connectivity and reliability. 2.3 motivation the above hierarchical structures efficiently solve the problems of scalability and reliability in different ways by using recursively defined topologies. nevertheless, they ignore the particular approaches for performing mapreduce on the data centers with these structures. in a hierarchical structure, servers are not fully interconnected, and each server only connects with several adjacent servers. to support mapreduce, the hierarchical structure cannot utilize the approaches proposed by google for performing mapreduce on the traditional tree structure [6]. first, hierarchical structures do not treat servers as masters and workers. in the procedure of google’s mapreduce, the servers controlling the execution of mapreduce procedures are called masters. the servers executing map and reduce tasks are called workers. in the traditional tree structure, servers are partitioned into masters and workers for mapreduce. a master can directly communicate with its workers without intermediate server. in a hierarchical structure, however, the communication among servers is multi-hop. assume the servers are partitioned into masters and workers, there will be lots of control information transmitted through the servers shared by different paths. it is difficult to designate servers as masters or workers in hierarchical data centers. second, hierarchical structures can hardly support the data maintenance mechanism used by the traditional tree structure. in the traditional tree structure, each data file is divided into 64 megabytes blocks, and each block has several copies which are stored on different workers to keep data locality [3]. when a worker updates its data files, it sends related maintaining information to a distributed file system [12], which runs on some particular servers. since the number of servers in a data center can be up to several thousands or even more, if the same method is used on a hierarchical data center whose servers are not fully connected, the amount of maintaining information transmitted on the data center will generate huge traffic load. third, the approaches for transmitting intermediate data on the traditional tree structure may be inefficient on hierarchical structures. in a traditional tree structure, after accomplishing map tasks, mappers store all intermediate data on local disks and send corresponding messages to the master. then the mas436 z. ding, d. guo, x. chen, x. luo ter forwards the information about intermediate data to reducers. after that, reducers send massages to mappers asking for intermediate data. finally, mappers distribute all intermediate data to reducers concurrently. in a hierarchical data center with non-fully connected network, this process becomes much more complex. since mappers are not directly connected with reducers, the intermediate data may be transmitted through several servers shared by different paths. sometimes a mapper can generate megabytes of intermediate data. when mappers are executing map tasks, communication channels might be idle. however, when the map tasks are accomplished, some mappers may have to wait to deliver intermediate data. therefore, delivering all intermediate data concurrently through multi-hop reduces network resource utilization, takes too much bandwidth and may result in network congestion. to perform mapreduce on hierarchical data centers, we would like to propose a methodology for addressing all the above problems with high fault-tolerance. details about the proposed methodology are introduced in the rest of the paper. 3 mapreduce on hierarchical data centers in this section, we study the methodology for performing mapreduce on hierarchical data centers. our methodology is mainly based on the distributed hash table (dht), and can efficiently solve the above problems in terms of storing data files, assigning map and reduce tasks, and delivering intermediate data. 3.1 roles of servers in a data center, servers control and execute mapreduce procedures. in our research, each server in a hierarchical data center can work as a master or a worker. if a server receives a mapreduce request, it will be regarded as a master for the current mapreduce procedure. different from a traditional data center, this master is only responsible for assigning map and reduce tasks to workers. it is not responsible for controlling the transmission of intermediate data. if a server receives a map or reduce task, it will be regarded as a worker for the current mapreduce procedure. moreover, the worker receiving a map task is regarded as a mapper, and the worker receiving a reduce task is regarded as a reducer. a worker executes received tasks according to the rule of fcfs (first come, first served). after accomplishing map tasks, mappers directly send derived intermediate data to reducers without masters’ control. 3.2 scheme for storing data files we would like to design a scheme for storing data files, so that the traffic load due to transmission of the maintaining information can be reduced and the map tasks can be easily assigned. according to the rule of dht [20] [17], the scheme for storing data files on hierarchical data centers can be summarized as three steps. the first step is to define a suitable file key space for all servers, and assign a set of sequential file keys to each server. a file key refers to a fixed-length number or string used for denoting a data file block. the number of file keys assigned to a server depends on the disk capacity of the server. a server with more disk capacity can get more file keys. the second step is to build a file key table on each server, which records the range of file keys of every adjacent server. the third step is to define a suitable function to hash the name of each data file block to a file key, and store the data file block on a server which holds that file key. each server is responsible for maintaining its data file blocks, file keys and the file key table. if a server updates its data file blocks, there is no need to inform other servers. if a server updates its file keys, it sends maintaining information only to its adjacent servers to update their file key tables. each server has only a finite number of adjacent servers. consequently, storing data files according to the performing mapreduce on data centers with hierarchical structures 437 above scheme can reduce the amount of maintaining information transmitted on the whole data center network. 3.3 scheme for assigning map and reduce tasks in a traditional tree structure, a master directly connects with all workers. therefore, a master can easily send a map or reduce task to a worker. in a hierarchical structure, however, a master usually sends a map or reduce task to a worker through a number of other servers. (1) assigning map tasks. based on the scheme for storing data files and the rule of dht [20], we propose the scheme for assigning map tasks on hierarchical data centers as follows. when a server receives a mapreduce request, it first determines the input data file block processed by each map task. this server, namely the master, then hashes the name of the input data file block to a file key for the map task. after that, the master chooses a server from all its adjacent servers to send the map task, and the selected server has the range of file keys closest to the derived file key. this server will further choose another server from its neighbors to forward the map task according to the same rule. this process is iteratively performed until a server, which receives the map task, holds the corresponding file key. that implies the server stores the input data for the map task. here the closest is measured by a function, which is specified according to the definition of file keys. (2) assigning reduce tasks. we propose the scheme for assigning reduce tasks on hierarchical data centers as following steps. similar to the scheme for storing data files, the first step is to define a suitable reduce key space for all servers, and assign a set of sequential reduce keys to each server. a reduce key refers to a fixed-length number or string. the number of reduce keys assigned to a server depends on the computing ability of the server. a server with faster computing capacity can get more reduce keys. the second step is to build a reduce key table on each server, which records the range of reduce keys of every adjacent server. the third step is to define a suitable function used for hashing the keys of intermediate data to reduce keys. finally, when assigning reduce tasks, the master hashes the key of intermediate data processed by each reduce task to a reduce key. the master chooses a server from all adjacent servers to send the reduce task, and the selected server has the range of reduce keys closest to the derived reduce key. this process is iteratively performed until a server, which receives the reduce task, holds the corresponding reduce key. since the master can send map and reduce tasks immediately without searching data files node by node, in a hierarchical data center, assigning map and reduce tasks through dht can reduce the execution time of mapreduce procedures. more than that, since there is no particular path from the master to a worker, the transmission of the map or reduce task cannot be interrupted by node failures. the communication becomes more reliable. 3.4 scheme for delivering intermediate data based on the scheme for assigning reduce tasks, it is easy to delivering intermediate data. our scheme for delivering intermediate data on hierarchical data centers are as follows. when a mapper is executing a map task, it hashes the key of a derived intermediate key/value pair to a reduce key in the same way as that of assigning reduce tasks. then, according to its reduce key table, it directly sends the intermediate key/value pair to an adjacent server, which has the range of reduce keys closest to the reduce key hashed from the intermediate key among all adjacent servers. in a similar way to the scheme for assigning map and reduce tasks, this intermediate key/value pair will be delivered node by node to a server which holds the corresponding reduce key. according to the scheme for assigning reduce tasks, this server has received the reduce task that is responsible for processing the intermediate data. 438 z. ding, d. guo, x. chen, x. luo delivering intermediate data with above scheme can avoid the unnecessary controlling process performed by masters, which is complicated and can delay executing reduce tasks in a non-fully connected data center network. since each intermediate key/value pair is delivered immediately after being generated, this scheme can increase network resource utilization, and facilitate intermediate data transmission. like the scheme for assigning map tasks, delivering intermediate data in this way can avoid the impact of node failures, and therefore can increase the reliability of the transmission. 0 1 2 3 idfilekeyrange reducekeyrange 0(f000, f009) (r000, r009) 2(f020, f029) (r020, r029) idfilekeyrange reducekeyrange 1(f010, f019) (r010, r019) 2(f020, f029) (r020, r029) idfilekeyrange reducekeyrange 0(f000, f009) (r000, r009) 1(f010, f019) (r010, r019) 3(f030, f039) (r030, r039) figure 2: an example of our dht 4 dht architecture and routing scheme this section introduces the specific dht architecture and corresponding routing scheme for executing mapreduce on hierarchical data centers. since the servers in a hierarchical data center are homogeneous and work in the same manner, we assign the file or reduce keys to the servers in the order of their identifiers. the keys held by different servers are logically arranged in a line or a circle, like chord [20]. this dose not means that the longest path to retrieve a key is the whole line. adjacent servers in a hierarchical data center can belong to different recursive units and do not have sequential identifiers. the range of keys held by adjacent servers may not be sequential. only the adjacent servers in the same smallest recursive unit hold sequential identifiers and keys. consequently, for a hierarchical data center, the retrieval efficiency depends on its physical structure instead of the logical form in which the keys are arranged. for ease of maintenance, we integrate the aforementioned file key table and reduce key table into one hash table. this table consists of three attributes, including the range of file keys, the range of reduce keys and the identifier of the adjacent server that holds those file keys and reduce keys. in a hierarchical data center, each server stores such a table for recording the range of file keys and reduce keys of all adjacent servers. in our work, itemi=(filekeyrange, reducekeyrange, i d) denotes a record of the hash table, where filekeyrange, reducekeyrange, and id represent the three attributes, respectively. when a server is added to or removed from the data center, only its adjacent servers need to renew the records of their hash tables, so all other severs will not be affected. fig.2 shows an example of our dht architecture for executing mapreduce on hierarchical data centers. based on the hash table stored on each server, it is easy to implement the routing scheme for assigning map and reduce tasks and sending intermediate data. when a server receives a map or reduce task or an intermediate key/value pair, it first determines whether the corresponding file key or reduce key is in its charge. if the server holds that file key or reduce key, it performs the corresponding task according to reference [6]. otherwise, it forwards the map or reduce task or intermediate data to an adjacent server in the way indicated by algorithm 1. in algorithm 1, t ransob ject denotes a map or reduce task or an intermediate key/value pair, which performing mapreduce on data centers with hierarchical structures 439 needs to be delivered by any server in a hierarchical data center. t ransob ject.key denotes the file key or reduce key derived by corresponding hash functions. function d(, ) is used for calculating the distance between t ransob ject.key and the range of keys held by an adjacent server. algorithm 1 sets a variable, denoted by s erver, for recording the record item of the hash table on current server, which represents the next hop to send t ransob ject. it initializes s erver with the first record item, and then determines the type of t ransob ject. after that, algorithm 1 iterates over the hash table to find a record whose range of file or reduce keys is the closest to t ransob ject.key according to function d(,), and assigns the obtained record item to s erver. finally, it sends t ransob ject to the adjacent server whose identifier is denoted by s erver.id. in this way, t ransob ject will be delivered node by node to the server responsible for executing corresponding map or reduce task. algorithm 1 deliver1 (object t ransob ject) 1: object s erver = item0; 2: if t ransob ject is a map task then 3: for i = 1; i < i − 1; i ++ do 4: if d(t ransob ject.key, itemi.filekeyrange) < d(t ransob ject.key, s erver.filekeyrange) then 5: s erver = itemi; 6: end if 7: end for 8: else 9: for i = 1; i < i − 1; i ++ do 10: if d(t ransob ject.key, itemi.reducekeyrange) < d(t ransob ject.key, s erver.reducekeyrange) then 11: s erver = itemi; 12: end if 13: end for 14: end if 15: send t ransob ject to s erver.i d; 5 fault tolerance a hierarchical data center consists of much more servers, switches and links than a traditional tree structure data center, so it has more tendency to machine or link failures. a link failure leads to the disconnection of the two machines interconnected through the link, and the two machines can be regarded as failed to each other. hence we only focus on server and switch failures in this paper. 5.1 fault-tolerant routing against failures of servers and switches a mapreduce procedure cannot utilize a failed server or switch to assign tasks or transmitting intermediate data. to address this problem, we propose the fault-tolerant routing for mapreduce in hierarchical data centers. in a traditional tree structure data center, a switch failure can break down all the servers connecting to it. since a hierarchical data center employs the redundant structure, a switch failure may not affect the servers connecting to it. however, these servers cannot communicate to each other directly. in this work, we treat a switch failure as several disconnected servers. to ensure that our routing scheme can forward all tasks and intermediate data to available servers, we employ the following approach. each server sends the number of tasks in its service queue as its 440 z. ding, d. guo, x. chen, x. luo state information to all adjacent servers periodically, and therefore each server knows the running state of all its adjacent servers. if a server cannot update its state information to its adjacent servers, it will be regarded as a failed server and its corresponding records in the hash tables of adjacent servers will be denoted as unavailable. based on this failure notification mechanism among adjacent servers, we modify algorithm 1 in order to achieve algorithm 2 as the fault-tolerant routing scheme. algorithm 2 assigns the first available record item to s erver, and iterates over the hash table from that record to update s erver with an available record whose range of file or reduce keys is closer to t ransob ject.key. in such a way, s erver.i d finally gives the identifier of the available next hop to deliver t ransob ject. algorithm 2 deliver2 (object t ransob ject) 1: object s erver = null; int j = 0; 2: for i = 0; i < i − 1; i ++ do 3: if itemi.available == true then 4: s erver = itemi; 5: j = i; 6: break; 7: end if 8: end for 9: if t ransob ject is a map task then 10: for i = j; i < i − 1; i ++ do 11: if itemi.available == true then 12: if d(t ransob ject.key, itemi.filekeyrange) < d(t ransob ject.key, s erver.filekeyrange) then 13: s erver = itemi; 14: end if 15: end if 16: end for 17: else 18: for i = j; i < i − 1; i ++ do 19: if itemi.available == true then 20: if d(t ransob ject.key, itemi.reducekeyrange) < d(t ransob ject.key, s erver.reducekeyrange) then 21: s erver = itemi; 22: end if 23: end if 24: end for 25: end if 26: send t ransob ject to s erver.i d; 5.2 fault-tolerant approaches to address failures of masters and workers in a hierarchical data center, a running mapreduce procedure can be interrupted by a server failure, no matter a master failure or a worker failure. to address this problem, we propose the following approaches. 1) addressing the failure of a master server: • as soon as a master receives a mapreduce request, it sends the request to an adjacent server as a replica. when assigning map and reduce tasks, the master concurrently sends a confirmation performing mapreduce on data centers with hierarchical structures 441 message for each task to that adjacent server. if that server cannot receive any confirmation message of a task within a threshold time, the master will be regarded as failed, and that server will take over the current mapreduce request and reassign the corresponding map or reduce task. 2) addressing the failure of a worker server: • if a worker server receives a map task and its service queue has achieved a predefined threshold length, it will discard the map task and send a message to the master for reassigning the map task to another server which stores a replica of the corresponding input data file. • according to the aforementioned failure notification mechanism, each server keeps the running state of all adjacent servers. if a worker server receives a reduce task and its service queue has achieved a predefined threshold length, it will forward the reduce task to an available adjacent server. moreover, it will forward all the intermediate data to that adjacent server. • when a worker server accepts a map or reduce task, it sends corresponding information of the task to an adjacent server. as soon as the worker server accomplishes that task, it sends a confirmation massage of the task to that adjacent server. if that adjacent server cannot receive the confirmation massage within a threshold time, it will send the task information to the master for reassigning the task to another available server. in this case, the worker server will be regarded as a straggler [16]. 6 evaluation since bcube is a representative hierarchical structure [11], in this section we conduct a comprehensive evaluation based on bcube, to demonstrate that our method is feasible for executing mapreduce on hierarchical data centers. in the following evaluation, we employ equation 1 as the hash function for transferring a task name or an intermediate key into a file key or a reduce key. t ransob ject.key= f (t ransob ject) mod k (1) function f (t ransob ject) calculates the decimal ascii number of t ransob ject. for example, suppose that t ransob ject denotes a character string "abc", then f (t ransob ject) equals 979899. k is a prime number less than the total number of file keys or reduce keys. here the value of t ransob ject.key is an integer, hence the aforementioned function d(, ) can be defined for calculating the absolute value of the difference between t ransob ject.key and the range of keys held by a server. 6.1 load balance according to aforementioned schemes, map tasks are assigned to the servers that hold corresponding data files, so the distribution of input data file blocks determines the load balance of map tasks. since researchers have studied how to allocate date to servers evenly with consistent hashing [14] [23], we assume that all input data file blocks are well distributed in a hierarchical data center. therefore, we can also assume that map tasks can be evenly assigned to different servers. here we mainly study the load balance of reduce tasks, which is much more uncertain than that of map tasks. we perform the simulation for evaluating load balance as follows. we simulate the structure of bcube and corresponding communication among its servers. let n denote the number of servers in a level 0 bcube, and h denote the number of levels. the number of servers in bcube varies from 4 to 625 when n varies from 2 to 5 and h varies from 1 to 3. we assign a unique identifier to each server, and calculate the identifiers of its adjacent servers. in bcube, two servers that connect to the same switch can be regarded as adjacent servers. in reality, different mapreduce applications generate different kinds 442 z. ding, d. guo, x. chen, x. luo of reduce tasks, whose arrival to a data center is a stochastic process. for ease of evaluation, in our simulation, we assume that there is only one reduce task arriving to the bcube data center within a short period of time. the execution time of a reduce task is random and is longer than the arrival period. we consider two cases about the execution time. the first case is that the execution time of a reduce task varies randomly from 10 to 100 times the length of arrival period. the second case is that the execution time of a reduce task varies randomly from 100 to 500 times the length of arrival period. according to hadoop [24], we define that each server can execute two reduce tasks simultaneously. when a server is busy in executing two reduce tasks, it will reject the newly arrived reduce task and foreword it to an adjacent server. if a reduce task is rejected for three times, we regard that this reduce task is dropped. we consider a workload with 2×103 reduce tasks, which are assigned according to the schemes studied in section 3. since a server can only execute limited number of reduce tasks simultaneously, some reduce tasks may get dropped in a data center with skewed load. we repeat the simulation 30 times for each number of servers to calculate the mean percentage of dropped reduce tasks. fig.3 plots the results in the two cases about the execution time. as shown in fig.3, the percentage of dropped reduce tasks decreases rapidly with the increase of the number of servers. in the first case, the percentage of dropped reduce tasks decreases to 0 when the number of servers achieves 64. in the second case, the percentage of dropped reduce tasks decreases to 0 when the number of servers achieves 256. when we further increase the number of arrival reduce tasks to 104, the results of the two cases remain the same. this implies that running mapreduce on such a data center according to our approaches can hardly drop any tasks. 0 50 100 150 200 250 300 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 number of servers p e rc e n ta g e o f d ro p p e d r e d u ce t a sk s execution time: 10 to 100 periods execution time: 100 to 500 periods figure 3: variation of the percentage of dropped reduce tasks along with the number of servers to evaluate the workload of each server, based on the above simulation, we calculate the reduce tasks executed by each server in the two cases. fig.4 plots the variation of the mean percentage of reduce tasks that per server executes along with the number of servers, which varies from 4 to 625. as shown in fig.4, when the number of servers is small, the mean workload of a server in the first case is higher than that in the second case, as the execution time of the first case is much lower than that of the second case. in the first case, the mean workload of a server keeps on a relatively high level until the number of servers achieves 27. in the second case, the mean workload of a server dose not decrease until the number of servers achieves 125. the reason of these variations is, when the number of servers is not enough to sustain continually arrival reduce tasks, all servers work at full capacity and many tasks are dropped. while the number of servers achieves 256, the mean workload of a server decreases to the same low level in both cases. fig.5 shows the percentage of reduce tasks executed by each server when there are 256 servers in all. we can derive from fig.5 that the difference between the workload of any two servers is less than 1.6×10−3, a vary small value. therefore, the workload can be evenly distributed. performing mapreduce on data centers with hierarchical structures 443 4 8 9 16 2527 64 81 125 256 625 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 number of servers p e rc e n ta g e o f r e d u ce t a sk s execution time: 10 to 100 periods execution time: 100 to 500 periods figure 4: variation of the mean percentage of reduce tasks that per server executes along with the number of servers 0 50 100 150 200 250 2 2.5 3 3.5 4 4.5 5 5.5 6 x 10 −3 identifiers of the servers p e rc e n ta g e o f r e d u ce t a sk s figure 5: percentage of reduce tasks executed by each server when there are 256 servers 6.2 data forwarding performance a data center network mainly consists of servers, switches and links. in a hierarchical data center, servers are not only used for executing map and reduce tasks, but also used for forwarding intermediate data. however, since servers are not network devices, they have lower data forwarding capacity than switches and links. in this work, we assume that switches and links can provide sufficient bandwidth and only consider the servers. based on the former simulation, we perform the simulation for evaluating data forwarding performance, including data forwarding throughput and bandwidth between servers, as follows. we assume that our routing scheme held by each server supports receiving and sending only one data packet, namely a key/value pair, within an extremely short period of time. according to literature [11], in a bcube data center, a server forwards at 750mb/s in all-to-all traffic pattern. mtu is usually set to be 1.5kb for a commodity computer. thus, on this condition, we can easily calculate that the short period is 1.6×10−5 seconds. in such a period, a server with a packet to transmit chooses an adjacent server as the destination server, according to our routing scheme. if this destination server is available for receiving a packet, 444 z. ding, d. guo, x. chen, x. luo the former server will forward that packet successfully. then any other packets to the same destination server should wait to be transmitted until the server is available in another period. we vary the number of servers that simultaneously generate packets to calculate the best possible data forwarding performance. this procedure is recursively performed in our simulation so as to evaluate data forwarding performance on a steady working condition. when the number of servers varies from 4 to 625, we repeat the simulation 30 times for each number of servers to obtain the mean and range of the maximum data forwarding throughput, as shown in fig.6. we find that the maximum data forwarding throughput does not closely track the increase of the total number of servers. the throughput of 27 servers is lower than that of 25 servers, and the throughput of 81 servers is lower than that of 64 servers. the reason for that is, throughput depends not only on the number of servers, but also on the number of hops for data forwarding. if the data are forwarded through more hops, the throughput will be lower. for a hierarchical data center, the maximum number of hops is determined by the number of levels, and more levels bring more hops. in our simulation, the bcube data centers with 27 servers and 81 servers have one level more than the bcube data centers with 25 servers and 64 servers, respectively. given a fixed number of servers in a recursively hierarchical data center, it is crucial to make a tradeoff between the number of levels and the number of servers in the smallest recursive unit to achieve desired performance. as shown in fig.6, when the number of servers is larger than 125, data forwarding throughput increases rapidly. therefore, our approaches can well support throughput-hungry mapreduce applications on hierarchical data centers. 4 8 9 16 2527 64 81 125 256 625 0 10 20 30 40 50 60 number of servers m a xi m u m d a ta f o rw a rd in g t h ro u g h p u t (g b /s ) figure 6: variation of the maximum data forwarding throughput along with the number of servers to evaluate the bandwidth between servers, we first keep the number of servers that simultaneously generate packets in a certain value, which ensures that data forwarding throughput remains at the maximum value. on this condition, we then calculate the mean of the maximum bandwidth that each server can achieve for sending data to another server through maximum number of hops. fig.7 illustrates the result when there are 256 servers. we can derive that most values of the bandwidth are larger than 0.3gb/s and less than 0.57gb/s. this variance, which is less than 0.3gb/s, is acceptable for mapreduce applications. in practice, different servers store different types of data, and internet service providers may store popular data on certain servers to save power [19]. hence some of the map or reduce tasks in a mapreduce procedure process and generate more data than other tasks. overall, the result of our simulation implies that the bandwidth can be evenly distributed according to our approaches. performing mapreduce on data centers with hierarchical structures 445 0 50 100 150 200 250 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 identifiers of servers b a n d w id th ( g b /s ) figure 7: mean of the maximum bandwidth that each server can achieve for sending data to another server through maximum number of hops, when there are 256 servers and data forwarding throughput remains at maximum value 6.3 fault tolerance we evaluate the load balance and data forwarding performance under given failure rates of links and nodes to further validate the fault-tolerant capability of our methodology. for a hierarchical data center, let p1 and p2 denote the expected probabilities that a server or a switch fails when they are executing a mapreduce procedure, respectively. here we omit link failures which can be regarded as adjacent nodes failures. then we can calculate the probability that a server keeps on working according to theorem 1. theorem 1. for a hierarchical data center, let p3 denote the probability that a server keeps on working in a mapreduce procedure. let i and j denote the number of servers and switches directly connecting with this server, respectively. p3 is given by p3 = (1 − p1) × (1 − i∑ i=1 ( ( i i ) × pi1)) × (1 − j∑ j=1 ( ( j j ) × p j2)) (2) proof: the probability that this server does not fail equals 1−p1. the probability that i of the i servers fail is ( i i ) ×pi1. then the probability that these i servers keep on working equals 1− ∑i i=1( ( i i ) ×pi1). similarly, the probability that the j switches keep on working equals 1− ∑j j=1( ( j j ) ×p j2). this server can keep on working only if all the i servers and j switches can keep on working and itself does not fail. therefore, the probability that this server keeps on working equals (1−p1)×(1− ∑i i=1( ( i i ) ×pi1))× (1− ∑j j=1( ( j j ) ×p j2)). theorem 1 is proved. based on theorem 1, we modify the aforementioned simulations to evaluate the fault-tolerant load balance and data forwarding performance. in the corresponding period, a server with a reduce task (or a packet) for transmitting chooses an available adjacent server in working order as the destination server, according to the fault-tolerant routing. if there is a third server having a reduce task (or a packet) to the same destination server in the same period, it has to wait until the destination server is available in anther period. in bcube, each server can only directly connect to switches, so p3 equals (1−p1)× (1− ∑j j=1 ( ( j j ) ×p j2)). we obtain different values of p3 by varying p1 and p2. then we calculate the variation of the mean percentage of reduce tasks that per server executes along with the total number of servers, when 446 z. ding, d. guo, x. chen, x. luo p3 equals 0.98, 0.90 and 0.80, respectively. fig.8 illustrates the results in the aforementioned two cases. in the first case, many tasks are dropped when the number of servers is small, so there is no significant difference among the workload of each server for the three values of p3. in the second case, since the workload of each server keeps very low, the difference is not notable when the number of servers is less than 125. while the number of servers are enough to sustain the continually arrival reduce tasks, our method has good fault tolerance, so the difference is also small in both cases. fig.9 plots the percentage of reduce tasks executed by each server when there are 256 servers and p3=0.90. the mean percentage of reduce tasks that per server executes is only 0.5×10−3 lower than that shown in fig.5. moreover, the variance is less than 1.5×10−3. thus, the workload can be evenly distributed under high failure rates of links and nodes. 4 8 9 16 2527 64 81 125 256 625 0 0.01 0.02 0.03 0.04 0.05 0.06 number of servers p e rc e n ta g e o f r e d u ce t a sk s execution time:10 to 100 periods, p3=0.98 execution time:10 to 100 periods, p3=0.90 execution time:10 to 100 periods, p3=0.80 execution time:100 to 500 periods, p3=0.98 execution time:100 to 500 periods, p3=0.90 execution time:100 to 500 periods, p3=0.80 figure 8: variation of the mean percentage of reduce tasks that per server executes along with the number of servers, when p3=0.98, p3=0.90 and p3=0.80 0 50 100 150 200 250 2 2.5 3 3.5 4 4.5 5 5.5 x 10 −3 identifiers of servers p e rc e n ta g e o f r e d cu e t a sk s figure 9: percentage of reduce tasks executed by each server, when there are 256 servers and p3=0.90 due to the failures of links and nodes, we calculate the maximum data forwarding throughput when p3 equals 0.98, 0.90 and 0.80, respectively. for each value of p3, we repeat the modified simulation 30 times to obtain the mean value. fig.10 illustrates the result. when the number of servers is small, the throughput of the network is low, so there is no obvious difference among the throughput for the three values of p3. when the number of servers is larger than 125, the throughput increases rapidly for all the three values of p3. thus, with enough servers, our method can provide satisfactory data forwarding throughput against failures of links and nodes. considering link and node failures, we performing mapreduce on data centers with hierarchical structures 447 4 8 9 16 2527 64 81 125 256 625 0 5 10 15 20 25 30 35 40 45 50 55 number of servers m a xi m u m d a ta f o rw a rd in g t h ro u g h p u t (g b /s ) p3=0.98 p3=0.90 p3=0.80 figure 10: variation of the maximum data forwarding throughput along with the number of servers, when p3=0.98, p3=0.90 and p3=0.80 recalculate the mean of the maximum bandwidth that each server can achieve for sending data to another server through maximum number of hops. fig.11 illustrates the result when there are 256 servers and p3=0.90. although the probability that a server cannot keep on working is 0.10, which is really high in practice, the bandwidth for every server is only 0.05gb/s lower than that shown in fig.7. hence the bandwidth between servers is abundant against failures of links and nodes. moreover, the range of variance, as shown in fig.11, is less than 0.25gb/s. therefore, the bandwidth between servers can be evenly distributed under high failure rates of links and nodes. 0 50 100 150 200 250 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 identifiers of servers b a n d w id th ( g b /s ) figure 11: mean of the maximum bandwidth that each server can achieve for sending data to another server through maximum number of hops, when there are 256 servers and p3=0.90 7 conclusion several hierarchical structures have been proposed to improve the topological properties of data centers. however, the communication and control mechanisms proposed by google for performing mapreduce on the traditional structure can hardly be operated on these hierarchical structures. this paper presents a methodology for performing mapreduce on data centers with hierarchical structures. comprehensive analysis and simulations show that our methodology can evenly distribute the workload 448 z. ding, d. guo, x. chen, x. luo and well support throughput-hungry mapreduce applications. it is also proved that our methodology is competent for mapreduce even under node failures. the mismatch problem between hierarchical data centers and mapreduce is effectively solved in this paper. acknowledgment we would like to thank the anonymous reviewers for their constructive comments. our work is supported in part by the nsf china under grants no.60903206, no.60972166, no.61170284, no.71031007, no.71071160 and no.71171197, the china postdoctoral science foundation under grant no.201104439, and the preliminary research foundation of national university of defense technology under grant no.jc10-05-01. bibliography [1] m. al-fares, a. loukissas, and a. vahdat. a scalable, commodity data center network architecture. proc. acm sigcomm, pp.63-74, aug. 2008. 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[24] http://hadoop.apache.org. international journal of computers communications & control issn 1841-9836, 11(6):845-859, december 2016. stochastic stability analysis of power control in wireless networks via a norm-inequality-based approach r. qian, y. qi rongrong qian, yuan qi* beijing univ. of posts and telecommunications (bupt) beijing, 100876, china rongrongqian@bupt.edu.cn *corresponding author: qiyuan@bupt.edu.cn abstract: owing to the requirements from realistic wireless networks, the stochastic stability analysis for discrete-time power control, which concerns the randomness brought by the fading channels and noise of wireless systems, is of practical significance. by developing a norm-inequality-based framework of analyzing the stochastic stability of linear systems with random parameters, we show that a typical powercontrol law with linear system model is stable in the sense of the pth-moment stability. several conditions of achieving the pth-moment stability for the considered power-control law are obtained, which can easily applied to realistic wireless networks. besides, within this study, the stability analysis of power control for the first time takes into account the effect of multiple-access methods. keywords: wireless networks, power control, stochastic stability. 1 introduction there has been a great deal of research over the past several decades on the power control of wireless networks. these studies span multiple disciplines and include information theory [1,8], communication [2, 6, 7, 9, 13], and control theory [3–5, 21]. it has already been recognized that the stability analysis of power-control laws can efficiently investigate the intrinsic properties of various power-control algorithms [3–5,21]. the power-control laws with two-sided scalable iterative (interference) functions are convergent under the condition that an equilibrium exists [1], and the laws with contractive interference functions guarantee the existence and uniqueness of equilibriums along with linear convergence of iterates [2]. the global asymptotic stability of power control laws involving two-sided scalable interference functions and the exponentially scalability of laws with contractive interference functions are seen even under bounded time-varying delays [3]. a general class of power-control laws whose interference functions are monotonic and scalable are considered in [4, 5]. by employing appropriately constructed lyapunov functions, [5] shows that any bounded power distribution obtained from these laws is uniformly asymptotically stable. further, in [5] lyapunov-razumikhin functions are used to show that, even when the system incorporates time-varying delays, any solution along which the generalized system nonlinearity is bounded must also be uniformly asymptotically stable. in both of above cases the stability is shown to be global. most of current wireless networks are digital communication systems, in which the link gains are random (stochastic) variables fluctuating as wireless channels of the underlying networks are experiencing fading at all time and the noises are also random variables. it is therefore important for the power-control laws to be designed and verified when considering the impact of randomness in discrete-time wireless systems with fading channels and random noise. stochastic power-control algorithm that uses noisy interference estimates (observations) is first studied in [6]. with conventional matched filter receivers, the stochastic power control is shown by [6] to converge to the optimal power vector in the mean square error sense. these copyright © 2006-2016 by ccc publications 846 r. qian, y. qi results are later extended to the cases when a linear receiver or a decision feedback receiver is used [7, 22]. in [9], a stochastic-approximation based power-control algorithm is proposed to handle both measurement errors and randomness in the channel gain matrix, which is proved to converge to the optimal solution in the mean-squared sense. treading the elegant footsteps of recent works [3][5], one can gain a deep insight into the stability theory of typical power-control laws in wireless networks. rather than being concerned with the stability analysis of power control, the studies [6][9] focus on developing extra techniques of reducing the impact of randomness encountered by power control in wireless networks, where these techniques includes matched filter receiver, decision feedback receiver, and stochastic approximation. to the best of our knowledge, it still lacks of systemic study on stability analysis of typical power-control laws taking into account the randomness existed in practical wireless networks while the power control does not use extra randomness reducing techniques proposed by [6][9]. we shall emphasize here that, this kind of study is indispensable, because, on the one hand, it could attentively reveal the inherent attributes of the typical power-control laws when the randomness exists (but no randomness reducing technique is involved) so as to make the stability theory of typical power-control laws complete, on the other hand, extra technique of reducing randomness may not necessarily be available in practical systems due to the objective factors such as realtime processing demands so that engineers have to be aware of the stability of power control in randomness environments without any randomness reducing technique. hence, the aim of our work is to perform this study; specifically, we will develop a framework of stochastic stability analysis for discrete-time power control, which takes the randomness brought by the fading channels and noise of wireless systems into consideration. our main works are: (i) developing a norm-inequality-based framework of analyzing the stochastic stability (to be specific, the pth-moment stability) for linear systems with random parameters so as to investigate the stochastic stability of the power control in consideration of the randomness caused by the fading channels and noise; (ii) clarifying the conditions of achieving the stochastic stability for the considered linear systems and power-control law; and (iii) investigating the effect of multiple-access methods to stochastic stability of power control. 2 notation and preliminaries 2.1 notation throughout, the interval [0 + ∞) is denoted by r+, and the set of positive integers by z+. the non-negative orthant of the n-dimensional real space is represented by rn+ . the vectors are written in bold lower case letters and matrices in bold capital letters, e.g., a and a. the ith component of a vector a is denoted by ai, and the ijth entry of a matrix a is denoted by aij such that aij = [a]ij and a = [aij]. the notation a ≥ 0 means that all of the components of a are greater than or equal to zero. the inequality a ≥ b implies that aij ≥ bij for all components ij. we let (·)t denote the transpose of a vector or a matrix. if a is a vector with components a1,a2, · · · ,an, then its p-norm is defined by ‖a‖p = (∑n i=1 |ai|p )1/p and its euclidean norm by ‖a‖2 = (∑n i=1 |ai|2 )1/2 that is actually p-norm with p = 2. for a square matrix a, the induced norm corresponding to the p-norm of vectors is defined as ‖a‖p = max ‖x‖p 6=0 ‖ax‖p ‖x‖p = max ‖x‖p=1 ‖ax‖p ‖x‖p , (1) where ‖a‖1 = maxj ∑n i=1 |aij| is also known as the maximum column sum matrix norm, and ‖a‖∞ = maxi ∑n j=1 |aij| is the maximum row sum matrix norm. stochastic stability analysis of power control in wireless networks via a norm-inequality-based approach 847 a probability space is a triple (ω,f,p) where ω is a set of "outcomes", f is a set of "events", and p : f → [0 1] is a function that assigns probabilities to events. if x is a random variable on (ω,f,p) then we define the expected value operator to be ex = ∫ xdp . if ex2 < +∞ then the variance of x is defined to be var(x) = e(x−ex)2. we let {x[k],k ∈ z+} denote a stochastic process with random values in a set of vectors, and {x[k],k ∈ z+} denote a stochastic process with random values in a set of matrices, by writing x[k] and x[k] in italic and bold letters. if x is a n ×1 random vector then we define its expected value as x̄ = ex , [ex1 ex2 · · ·exn ]t . analogously, for a n ×n random matrix x, we define its expected value as x̄ = ex , [exij] and its variance as var(x) , [var(xij)]. let lp(ω,f,p) be the set of measurable function f on ω such that ∫ ω |f|pdµ < +∞, we introduce an operator elp as elpf , (e|f|p)1/p . (2) from [15, 2.2.5 example], one shall find that lp(ω,f,p) is a linear space and elp is a semi-norm. 2.2 preliminaries in this part, we collect basic properties and definitions of matrix theory, algebra theory, probability theory, and stochastic stability theory, which will be used in the following analysis. for more details, see, e.g., [10,11,23–25]. basic norm inequalities [23,25]: the p-norm of vectors and the corresponding induced norm of square matrices are nonnegative numbers have the properties that 1. ‖x + y‖p ≤‖x‖p + ‖y‖p and ‖a + b‖p ≤‖a‖p + ‖b‖p; 2. ‖ax‖p ≤‖a‖p‖x‖p, which is derived from the definition of ‖a‖p; 3. ‖ab‖p ≤‖a‖p‖b‖p, since ‖abx‖p ≤‖a‖p‖bx‖p ≤‖a‖p‖b‖p‖x‖p and max ‖x‖p=1 ‖abx‖p = ‖ab‖p. upper bound of induced matrix norm [17]: for any n × n matrix a, the induced norm ‖a‖p has no explicit representation unless p = 1, 2 or ∞. however, one can have the below inequalities [17, (1.8),(1.11)] ‖a‖p ≤ n1−1/p‖a‖1, (3) and ‖a‖p ≤‖a‖1/p1 ‖a‖ 1−1/p ∞ , (4) provide two closed-form upper bounds of ‖a‖p with p other than 1, 2 and ∞. cauchy-schwarz-buniakowsky inequality involving real numbers [25]: let a1,a2, · · · ,an and b1,b2, · · · ,bn be any two arbitrary sets of real numbers, then( n∑ i=1 aibi )2 ≤ ( n∑ i=1 a2i )( n∑ i=1 b2i ) . (5) this inequality can be expressed in the vector form as at b ≤‖a‖2‖b‖2, where a = [a1 a2 · · · an ]t and b = [b1 b2 · · · bn ]t . 848 r. qian, y. qi definition 1 [24]: let (s,s) be a measurable space. a stochastic process {φ[k],k ∈ z+} taking values in s is said to be a markov chain with respect to a filtration fk, if φ[k] ∈fk and for all b ∈ s, p(φ[k + 1] ∈ b|fk) = p(φ[k + 1] ∈ b|φ[k]). in words, given the present, the rest of the past is irrelevant for predicting the value of φ[k + 1]. stability properties of stochastic systems need to be established in the context of stochastic stability, in which a variety of inter-related definitions exist [10, 11]. this study concerns the so-called pth-moment stability which is borrowed from [11] with trivial differences and defined as follows. definition 2 [11]: the pth-moment stability can be stated as, for each initial distribution, there exists limk→+∞e (‖x[k]‖ pp ) < +∞, where p ∈ z+, and it shall hold that (e (‖x[k]‖ pp ))1/p = elp‖x[k]‖p. this study will seek for an analytical framework of deriving the upper bound of e (‖x[k]‖ pp ) so as to prove the pth-moment stability for linear systems with random parameters. 3 system model and problem statement 3.1 system model of power control we consider a wireless network with n wireless nodes, which employs a discrete-time power control algorithm given by x[k + 1] = i(x[k]), where x[k] = [x1[k] x2[k] · · · xn [k]]t and xj[k] ∈ r+ is the transmitted power of node j at the kth iteration, i(x) = [i1(x) i2(x) · · · in (x)]t and ij : rn+ → r+ is the interference function modeling the interference together with noise measured at the receivers for node j that mainly comes from other nodes and local noise source. denote the link gain between the transmitter of node j and the receiver for node i by gij. to perform the study in a systematic fashion, we proceed from a simple but considerably typical law of power control that has a linear system model, helping us avoid any entanglement due to nonlinear effects. this power-control law is given by x[k + 1] = i(x[k]), (6) where i(x[k]) = d[k] (c[k]x[k] + n[k]) = d[k]c[k]x[k] + d[k]n[k], d[k] is a n ×n diagonal matrix whose diagonal elements are { γ1[k] g11[k] , γ2[k] g22[k] , · · · , γn [k] gnn [k] } , in which γj[k] is the target signal-to-interference-and-noise ratio (sinr) of node j at the kth iteration, and gij[k] is the link gain gij at the kth iteration. in this study, we set γj[k] = γj where γj is fixed target sinr value for node j. c[k] = [cij[k]] is a n × n matrix whose entries are either zero or positive depending on whether the entry is diagonal or off-diagonal, i.e., c[k] =   0 g12[k] · · · g1n [k] g21[k] 0 · · · g2n [k] ... ... ... ... gn1[k] gn2[k] · · · 0   . (7) n[k] = [n1[k] n1[k] · · · nn [k]]t denotes the vector of noise power at the receivers for all n nodes. here, note that x[k],n[k] ≥ 0 and d[k],c[k] ≥ 0 because the powers and link gains are all positive values. the model (6) is thought to be typical because it covers the well-known foschini-miljanic algorithm [12] and can be extended (in future) to describe the power-control algorithms of opportunistic communications e.g., the utility-based power control (ubpc) algorithm [13]. stochastic stability analysis of power control in wireless networks via a norm-inequality-based approach 849 in wireless channels, fading is deviation of the attenuation affecting a signal over certain propagation media. the fading can vary with time or geographical position, and is often modeled as a stochastic process. if let g[k] = [gij[k]], now one should bear in mind that, {g[k],k ∈ z+} and {n[k],k ∈ z+} are two stochastic processes when the fading channels and random noise appear in the wireless networks. as a consequences, {d[k],k ∈ z+} and {c[k],k ∈ z+} will also be stochastic processes. to define the randomness behavior of the wireless networks with fading channels and noises, the below assumptions are always employed. assumption 1 (additive white gaussian noise). the noises existed in the wireless networks are i.i.d. additive white gaussian noises with average power δ2n > 0 such that the noise power vector n[k] satisfies e(‖n[k]‖ 11 ) = nδ2n. the noises are independent with the link gains. in words, if nq[k2] has distribution µnq,k2 and gij[k1] has µ g i,j,k1 , then (nq[k2],gij[k1]) has distribution µnq,k2 ×µ g i,j,k1 [24], for 1 ≤ i,j,q ≤ n and k1,k2 ∈ z+. assumption 2 (temporal independency of link gains). the link gains at different iterations are independent. in essence this implies, if gij[k1] has distribution µgi,j,k1 and gpq[k2] has µ g p,q,k2 , then (gij[k1],gpq[k2]) has distribution µgi,j,k1×µ g p,q,k2 [24], for 1 ≤ i,j,p,q ≤ n whenever k1 6= k2. assumption 3 (stationarity). the distributions of gij[k] and nq[k] are unrelated to k, i.e., whatever k is, gij[k] and nq[k] has distribution µgi,j and µ n q , respectively, for 1 ≤ i,j,q ≤ n. this assumption stipulates that {g[k],k ∈ z+}, {d[k],k ∈ z+}, {c[k],k ∈ z+}, and {n[k],k ∈ z+} are stationary stochastic processes which do not change their statistical properties with k. assumption 4 (deployment of multiple access methods). in wireless networks, multiple access methods can suppress the leakage of signal power from one node to the receivers for other nodes under certain level such that gij[k] ≤ βijgjj[k] with constant values βij � 1 for any i 6= j. moreover, gjj[k], 1 ≤ j ≤ n are n stationary and i.i.d. random variables with e(gjj[k]) = µg, e(1/gjj[k]) = µ1/g, e(g2jj[k]) = µg2, and e(1/g 2 jj[k]) = µ1/g2. this assumption holds in case of the power control of wireless networks with multiple access methods, which implies c[k] ≤   0 β12g22[k] · · · β1ngnn [k] β21g11[k] 0 · · · β2ngnn [k] ... ... ... ... βn1g11[k] βn2g22[k] · · · 0   . in summary, the power-control system (6) has a linear system model with random parameters. this study will analyze the pth-moment stability for such a system. 3.2 problem statement in what follows, we begin by considering the linear systems with random parameters as x[k + 1] = a[k]x[k] + b[k]n[k], (8) where {a[k],k ∈ z+} and {b[k],k ∈ z+} be two stationary stochastic processes. {n[k],k ∈ z+} is a stationary stochastic process of additive white gaussian noise, and independent with {a[k],k ∈ z+} and {b[k],k ∈ z+}. clearly, the model of system (8) is a generalization of the model (6), which is not same but closely related to the models appeared in several existed works [10] [14] [16]. main problem of this work. how can we estimate whether the linear system (8) is pth-moment stable or not? 850 r. qian, y. qi theorem. the stochastic process {x[k],k ∈ z+} that corresponds to the state vector x[k] in (8), is a markov chain. proof the conclusion directly follows from the definition of markov chain. 2 the result above is a trivial but fundamental understanding of the system (8). we then turn to taking a closer look at this system. to state the further results, we need to rewrite (8) as x[k + 1] = ( k∏ u=1 a[u] ) x[1] ︸ ︷︷ ︸ non-noise term + k∑ i=1 ( k∏ u=i+1 a[u] ) b[i]n[i] ︸ ︷︷ ︸ noise term . (9) if the noise term does not exist in (9), the furstenberg-kesten theorem [14] and the analytical framework developed by feng et al. [10] would be available to study the stochastic stability properties for the associated system. however now, due to the existence of the noise term, we have to seek for a new proper framework to analyze such a system. in [16], koning provided an analytical framework which is usable to investigate (8), but such a framework can only reflect the firstand second-order statistics of x[k]. in this work, we will develop a norm-inequalitybased framework that is capable of analyzing higher-order as well as the firstand second-order statistics of x[k]. 4 results in this section, the pth-moment stability of the linear system (8) is analyzed through a norminequality-based approach, and then the analysis is applied to the power-control system (6). to attain the main results of this study, we need to derive and use several lemmas; however, we prefer not to introduce them in the main text but, rather, in appendix, so as not to interrupt the presentation. for details, please refer to lemmas a.1 to a.5. 4.1 a norm-inequality-based approach of the pth-moment stability analysis we are now ready to perform the pth-moment stability analysis of the linear system (8) via a norm-inequality-based approach. theorem 1. a sufficient condition for the first-moment stability of the system (8) is limk→+∞ ā k = 0n×n, where 0n×n is the n ×n zero matrix. proof since x[k + 1] ≥ 0, e ( ‖x[k + 1])‖ 11 ) = ‖e(x[k + 1])‖ 11 = ‖x̄[k + 1]‖ 11 = ∥∥∥ākx̄[1] + ∑ki=1 āk−i b̄n̄∥∥∥ 1 1 . if the matrix ā has the property that limk→+∞ āk = 0n×n, i−ā will be nonsingular and its inverse can be expressed by [23, corollary 5.6.16]: (i − ā)−1 = ∑+∞ k=0 ā k, and then we shall have limk→+∞e ( ‖x[k + 1])‖ 11 ) = ∥∥(i − ā)−1b̄n̄∥∥ 1 1 < +∞ as long as limk→+∞ āk = 0n×n holds. 2 the model (8) has an alternative formulation as x[k + 1] = sx[k] + k∑ i=1 skn[i], (10) where, for notational simplicity, sx[k] = (∏k u=1 a[u] ) x[1], skn[i] = (∏k u=i+1 a[u] ) b[i]n[i]. the forthcoming analysis will involve applying the operator elp to p-norm of some random vector x, i.e., substituting f = ‖x‖p into elpf, or to induced matrix norm of some random stochastic stability analysis of power control in wireless networks via a norm-inequality-based approach 851 matrix x, i.e., substituting f = ‖x‖p into elpf. there is one important issue herein that must be mentioned: remark 2: computing the induced matrix norm is a nonlinear optimization problem1, and the induced norm ‖x‖p has no explicit representation unless p = 1, 2 or ∞. if p 6= 1, 2, or ∞, the integral ∫ ω ‖x‖ pp dµ < +∞ might not exist, in which case we can not take elp to ‖x‖p. therefore, in this study when it is needed to apply the operator elp to ‖x‖p, we will seek for an integrable upper-bound ϕ(x) of ‖x‖p and use elpϕ(x) for theoretical analysis. theorem 2. assume that ‖x‖p has an upper-bound ϕ(x) > 0, i.e., ‖x‖p ≤ ϕ(x), where∫ ω |ϕ(x)|pdµ is integrable. a sufficient condition for the pth-moment stability of the system (8) is elpϕ(a) < 1, elpϕ(b) < +∞, and elp‖n‖p < +∞.2 proof by (10), we get elp ‖x[k + 1])‖p = elp ∥∥∥∥∥sx[k] + k∑ i=1 skn[i] ∥∥∥∥∥ p ≤ elp ‖sx[k]‖p + k∑ i=1 elp ∥∥∥skn[i]∥∥∥ p ≤ k∏ u=1 elpϕ(a[u])elp‖x[1]‖p + k∑ i=1 k∏ u=i+1 elpϕ(a[u])elpϕ(b[i])elp‖n[i]‖p, where the first inequality follows from lemma a.4 and the second one is from lemma a.5. under the assumption of stationarity, elpϕ(a[k]), elpϕ(b[k]), and elp‖n[k]‖p shall not change with k such that we can drop k for notational simplicity. thus, elp ‖x[k + 1])‖p ≤ [elpϕ(a)] k elp‖x[1]‖p + k∑ i=1 [elpϕ(a)] k−i elpϕ(b)elp‖n‖p = [elpϕ(a)] k elp‖x[1]‖p + 1 − [elpϕ(a)]k 1 −elpϕ(a) elpϕ(b)elp‖n‖p. if elpϕ(a) < 1, elp ‖b‖p < +∞, and elp‖n‖p < +∞, then lim k→+∞ e ( ‖x[k + 1]‖ pp ) ≤ ( elpϕ(b)elp‖n‖p 1 −elpϕ(a) )p < +∞. therefore, theorem 2 is justified. 2 theorem 2 holds for all p ∈ z+, thus it can reveal any pth-order statistics of x[k]. it’s a progress made by the proposed analytical framework of this study, compared to the framework developed by [16] that can only investigate the firstand second-order statistics of x[k]. since the derivation methods of theorems 1 and 2 are not exactly same, the sufficient condition of the first-moment stability obtained by theorem 1 is not necessarily identical as that by theorem 2 with p = 1. theorem 3. if elpϕ(a) < 1, elp ‖b‖p < +∞, and elp‖n‖p < +∞, there exists α < +∞, such that limk→+∞e ( ‖x[k])‖ pp ) = α. proof let us begin by assuming that k > j > j. we get elp ‖x[j + 1])‖p ≥ elp ∥∥∥∑ji=1 sjn[i]∥∥∥ p , 1an already known approach is to make use of the algorithm of estimating the induced matrix norm as well as the matlab routines provided by higham [17]. 2note that a, b, and n are short for a[k], b[k], and n[k] by dropping the index k, since the statistics of a[k], b[k], and n[k] are irrelevant with k under the stationarity assumption. 852 r. qian, y. qi because sx[j] and s j n[i] (i,j ∈ z+, 1 ≤ i ≤ j) are all positive vectors. by (10), we can have elp ‖x[k + 1])‖p ≤ elp ‖sx[k]‖p + elp ∥∥∥∥∥∥ k∑ i=k−j+1 skn[i] ∥∥∥∥∥∥ p + elp ∥∥∥∥∥ k−j∑ i=1 skn[i] ∥∥∥∥∥ p . (11) due to the stationarity property, it holds true that elp ∥∥∥∑ji=1 sjn[i]∥∥∥ p = elp ∥∥∥∑ki=k−j+1 skn[i]∥∥∥ p . then, with elpϕ(a) < 1, elp ‖b‖p < +∞, and elp‖n‖p < +∞, it implies that elp ‖x[k + 1])‖p −elp ‖x[j + 1])‖p ≤ elp ‖sx[k]‖p + elp ∥∥∥∥∥ k−j∑ i=1 skn[i] ∥∥∥∥∥ p ≤ [elpϕ(a)]k elp‖x[1]‖p + [elpϕ(a)] j 1 −elpϕ(a) elpϕ(b)elp‖n‖p. (12) now we can conclude that, ∀ε > 0, ∃j > 0, such that elp ‖x[k + 1])‖p−elp ‖x[j + 1])‖p < ε as long as k,j ≥ j. it states that elp ‖x[j + 1])‖p has a limit value as k → +∞, and thus finishes the proof. 2 theorem 4. for the system (8), if let ϕ(a) = n1−1/p‖a‖1 or ϕ(a) = ‖a‖ 1/p 1 ‖a‖ 1−1/p ∞ , where a is short for a[k], ∫ ω |ϕ(a)|pdµ would exist which means elpϕ(a) exists. proof both ‖a‖1 and ‖a‖∞ are continues measurable functions. then ∫ ω np−1 ‖a‖ p1 dµ and ∫ ω ‖a‖1 ‖a‖ p−1 ∞ dµ exist. this leads to the results of theorem 4. 2 remark 3: theorem 4 yields two sufficient conditions for the pth-moment stability of the system (8), i.e., elp(n1−1/p‖a‖1) < 1 and elp(‖a‖ 1/p 1 ‖a‖ 1−1/p ∞ ) < 1. although there might exist certain conservation, these two conditions are convenient for practical operations, because both ‖a‖1 and ‖a‖∞ have the explicit representations. remark 4: taniguchi [18] provided stochastic stability theorems of the nonlinear difference equations through using norm inequalities; however, the theorems obtained in [18] can not assist us to achieve the results with practical significance for the system (8). while by employing the norm-inequality-based framework, this study dedicates to derive the results for the system (8). one could also find the moment stability studies attract many interests recently, e.g., the pthmoment exponential ultimate boundedness is investigated for impulsive stochastic differential systems [19], and the pth-moment asymptotic stability is analyzed for stochastic delayed hybrid systems with levy noise [20]. 4.2 the pth-moment stability of power control going back to the power-control system (6), we can obtain many useful results without too much efforts based on the previous analysis. remark 5: by letting a[k] = d[k]c[k] and b[k] = d[k], one can directly apply theorems 1 to 4 to the power-control system (6). one important novelty of this study is not only to assess the stability of power-control system (6) but also to acquire more knowledge of relations between the stochastic stability and power control together with other wireless communication technologies. we will show that the proposed norm-inequality-based approach allows us to recognize the effect of multiple-access methods to the pth-moment stability of power control. the sufficient conditions for the pth-moment stability given by theorems 1, 2, 3, and 4 are only related to a[k] (= d[k]c[k]), while d[k]c[k] is partly determined by the target sinrs stochastic stability analysis of power control in wireless networks via a norm-inequality-based approach 853 and link gains according to (6)-(7). this fact inspires us to investigate the pth-moment stability by thinking over the power control together with the effect of multiple access technique. consider the power-control system (6) with a multiple access method, under assumption 4, we have an upper bound of a[k] as a[k] = d[k]c[k] ≤   0 γ1β12 g22[k] g11[k] · · · γ1β1n gnn [k]g11[k] γ2β21 g11[k] g22[k] 0 · · · γ2β2n gnn [k]g22[k] ... ... ... ... γnβn1 g11[k] gnn [k] γnβn2 g22[k] gnn [k] · · · 0   . in the reminder of this section, the index k will be dropped from a[k], d[k], c[k], and gii[k] such that a, d, c, and gii are used. from above, we get the following results. theorem 5. if the power-control system (6) employs a multiple access method so that assumptions 1 to 4 are satisfied, it will hold that ā = ea ≤ µg ·µ1/g · θγ̄,β, where θγ,β =   0 γ1β12 · · · γ1β1n γ2β21 0 · · · γ2β2n ... ... ... ... γnβn1 γnβn2 · · · 0   . then if the values of γi (1 ≤ i ≤ n) and βij (1 ≤ i,j ≤ n) are properly chosen such that min  maxi n∑ j 6=i γiβij, max j n∑ i 6=j γiβij   < 1µg ·µ1/g , (13) the system will be first-moment stable. proof the upper bound of ā can be obtained by taking expectation to the upper bound of a given above. let ρ(·) denote the spectral radius. using [23, theorem 8.1.22] to show that max  mini n∑ j=1 āij, min j n∑ i=1 āij   ≤ ρ(ā) ≤ min  maxi n∑ j=1 āij, max j n∑ i=1 āij   . i.e., the smallest row sum of a nonnegative matrix is a lower bound on its spectral radius, and the largest row sum is an upper bound. then, by applying āij ≤ µg ·µ1/g ·γiβij, we have ρ ( ā ) ≤ µg ·µ1/g · min  maxi n∑ j 6=i γiβij, max j n∑ i 6=j γiβij   . (14) combing this result with [23, theorem 5.6.12] which says limk→+∞ āk = 0 if and only if ρ(ā) < 1, we see that letting the right-hand side of (14) be less than 1 is a sufficient condition for limk→+∞ āk = 0, which therefore makes the power-control system to be first-moment stable (see theorem 1 for reference). 2 854 r. qian, y. qi (a) 5 10 15 20 25 30 35 40 10 −2 10 0 10 2 10 4 10 6 10 8 10 10 10 12 k e (| |x [k ]| | 1 1 γ=0.1, ρ=0.3 γ=0.2, ρ=0.6 γ=0.3, ρ=0.9 γ=0.4, ρ=1.2 γ=0.5, ρ=1.5 γ=0.6, ρ=1.8 ρ<1 ρ>1 (b) 5 10 15 20 25 30 35 40 10 0 10 5 10 10 10 15 10 20 10 25 k e (| |x [k ]| | 2 2 ) γ=0.1, e l 2 ||a|| 2 =0.28 γ=0.2, e l 2 ||a|| 2 =0.56 γ=0.3, e l 2 ||a|| 2 =0.84 γ=0.4, e l 2 ||a|| 2 =1.12 γ=0.5, e l 2 ||a|| 2 =1.40 γ=0.6, e l 2 ||a|| 2 =1.68 e l 2 ||a|| 2 <1 e l 2 ||a|| 2 >1 figure 1: (a): e(‖x[k]‖ 11 ) versus k, where ρ is short for ρ ( ā ) . the curves with ρ < 1 tend to finite values, while those with ρ > 1 increase ceaselessly; (b): e(‖x[k]‖ 22 ) versus k. the curves with el2 ‖a‖2 < 1 tend to finite values, while those with el2 ‖a‖2 > 1 are progressively growing theorem 6. consider the power-control system (6) with a multiple access method such that assumptions 1 to 4 are satisfied, if the values of γi (1 ≤ i ≤ n) and βij (1 ≤ i,j ≤ n) are properly chosen such that n∑ j=1 n∑ i 6=j γ2j β 2 ij < 1 µg2 ·µ1/g2 , (15) the system will be second-moment stable. proof since ‖a‖2 = √ tr(at a) [23], where tr(·) is the trace operation, by setting ϕ(a) =√ µ1/g2 · tr ( θtγ,βθγ,β ) , it follows that el2ϕ(a) = µg2 · µ1/g2 · tr ( θtγ,βθγ,β ) = µg2 · µ1/g2 ·(∑n j=1 ∑n i 6=j γ 2 j β 2 ij ) . observe that el2ϕ(a) < 1 as long as (15) holds. then, recalling theorem 2 completes the proof. 2 remark 6: the cauchy-schwarz inequality [24] leads to 1 µg·µ1/g < 1 and 1 µ g2 ·µ 1/g2 < 1. so we can have more conservative but simpler conditions than (13) and (15) to achieve the firstand second-moment stability, respectively, which are min { maxi ∑n j 6=i γiβij, maxj ∑n i 6=j γiβij } < 1, and ∑n j=1 ∑n i 6=j γ 2 j β 2 ij < 1. furthermore, let us extend theorems 5 and 6 to a generalized case, i.e., the pth-moment stability with any p ∈ z+. theorem 7. suppose that the power-control system (6) employs a multiple access method so that assumptions 1 to 4 are established, the system will be pth-moment stable if elpϕ(a) < 1 with ϕ(a) = n1−1/p ( maxj ∑n i 6=j γiβij gjj gii ) , or ϕ(a) =  max j n∑ i 6=j γiβij gjj gii  1/p  max i n∑ j 6=i γiβij gjj gii  1−1/p . proof through replacing ‖a‖1 and ‖a‖∞ in theorem 4 with the maximum column sum and maximum row sum of a[k], respectively, theorem 7 can be validated. 2 the importance of theorems 5, 6, 7, and remark 6 lies in that they can guide system designers to assess and select suitable target sinr schemes and multiple access methods for wireless-network systems and also to pick out the proper system parameters for them, from the perspective of power-control stability. stochastic stability analysis of power control in wireless networks via a norm-inequality-based approach 855 table 1: numerical values of examples 2 and 3. γ 73.7 86.0 98.3 110.6 min { maxi ∑n j 6=i γβij, maxj ∑n i 6=j γβij } 0.90 1.05 1.20 1.35 ρ ( ā ) 0.66 0.77 0.87 1.01∑n j=1 ∑n i 6=j γ 2β2ij 0.85 1.16 1.51 1.91 el2 ‖a‖2 0.53 0.72 0.94 1.19(∫ ω ‖a‖ 55 dµ ) 1/5 2.05 2.40 2.70 3.13 el5 ( ‖a‖ 1/51 ‖a‖ 4/5 ∞ ) 2.26 2.64 2.98 3.45 5 numerical examples example 1: we consider the power-control system (8) in main text with i.i.d. rayleigh fading link gains (that is, all gij are rayleigh distributed with unit variance) and fixed target sinrs γ1[k] = γ2[k] = · · · = γn [k] = γ. there are four nodes in the network. we let n[k] be the power vector of gaussian noise with unit variance, and initially set x[1] = [1 0 0 0]t . in case of γ = 0.1, 0.2, · · · , 0.6, figs. 1(a) and 1(b) illustrate how e(‖x[k]‖ 11 ) and e(‖x[k]‖ 22 ) grow with k, where ρ ( ā ) and el2 ‖a‖2 are estimated during the simulations. fig. 1(a) shows that the curves of e(‖x[k]‖ 11 ) with γ = 0.3, 0.6, 0.9 tend to finite values (in other words, the system is first-moment stable), while others increase ceaselessly. this result is in accordance with theorem 1 because limk→+∞ āk = 0 if and only if ρ(ā) < 1 [23, theorem 5.6.12]. from fig. 1(b), it is observed that the curves of e(‖x[k]‖ 22 ) with γ = 0.28, 0.56, 0.84 tend to finite values (or, equivalently, the system is second-moment stable), while others are progressively growing. this observation is a consequence of theorems 2, 3. example 2: we consider the power-control system (8) in main text with multiple access method and fixed target sinrs γ1[k] = γ2[k] = · · · = γn [k] = γ. all gjj = 1 + g′j where g′j is the rayleigh distributed with unit variance. let gij[k] = βijgjj[k] without loss of generality. it can be obtained that µg · µ1/g = 1.20 and µg2 · µ1/g2 = 2.03. there are four nodes in the network which employs a multiple access method such that   0 β12 β13 β14 β21 0 β23 β24 β31 β32 0 β34 β41 β42 β43 0   =   0 1 100 1 200 1 300 1 400 0 1 500 1 600 1 700 1 800 0 1 900 1 1000 1 1100 1 1200 0   . for above, min  maxi n∑ j 6=i βij, max j n∑ i 6=j βij   = 1.22 × 10−2, n∑ j=1 n∑ i 6=j β2ij = 1.565 × 10−4. (16) again, let n[k] be the power vector of gaussian noise with unit variance, and initially set x[1] = [1 0 0 0]t . in case of γ = 73.7, 86.0, 98.3, 110.6, we build table 1 by recalling two previous equalities, i.e., (16), and performing simulations. from table 1, it is clear that min { maxi ∑n j 6=i γβij, maxj ∑n i 6=j γβij } is less than ρ ( ā ) , and ∑n j=1 ∑n i 6=j γ 2β2ij is less than el2 ‖a‖2, as we could expect from the relations between theorems 1, 2, 5, and 6. the numerical results of figs. 2(a) and 2(b) are in agreement with theorems 1, 2, 5, and 6. 856 r. qian, y. qi (a) 0 10 20 30 40 50 60 70 10 2 10 3 10 4 k e (| |x [k ]| | 1 1 γ=73.7 γ=86.0 γ=98.3 γ=110.6 (b) 0 10 20 30 40 50 60 70 10 3 10 4 10 5 10 6 10 7 10 8 k e (| |x [k ]| | 2 2 γ=73.7 γ=86.0 γ=98.3 γ=110.6 figure 2: (a): e(‖x[k]‖ 11 ) versus k; (b): e(‖x[k]‖ 2 2 ) versus k. (a) 0 5 10 15 20 25 30 35 40 0 2 4 6 8 10 12 p ( ∫ ω ||a|| p p dµ)1/p e l p (n1−1/p ||a|| 1 ) e l p (||a|| 1 1/p ||a|| ∞ 1−1/p) (b) 0 5 10 15 20 25 30 35 40 10 10 10 15 10 20 10 25 k e (| |x [k ]| | 5 5 γ=73.7 γ=86.0 γ=98.3 γ=110.6 figure 3: (a): comparison among (∫ ω ‖a‖ pp dµ )1/p , elp(n1−1/p‖a‖1), and elp ( ‖a‖ 1/p1 ‖a‖ 1−1/p ∞ ) for different p. note that, the matlab routines provided by higham [17] which can directly estimate ‖a‖p is used for computing (∫ ω ‖a‖ pp dµ )1/p ; (b): e(‖x[k]‖ 55 ) versus k example 3: the same system model and parameters as example 2 are used. in fig. 3(a), we have compared (∫ ω ‖a‖ pp dµ )1/p with its two upper bounds as given by remark 3, i.e., elp(n1−1/p‖a‖1) and elp ( ‖a‖ 1/p1 ‖a‖ 1−1/p ∞ ) , which can also be refereed to theorem 4. it is seen that, elp ( ‖a‖ 1/p1 ‖a‖ 1−1/p ∞ ) stays quite close to (∫ ω ‖a‖ pp dµ )1/p , however, there is an evident gap between elp(n1−1/p‖a‖1) and (∫ ω ‖a‖ pp dµ )1/p , which is amplified as p increases. therefore, being the upper bound of (∫ ω ‖a‖ pp dµ )1/p , elp ( ‖a‖ 1/p1 ‖a‖ 1−1/p ∞ ) is more tight than elp(n1−1/p‖a‖1). as a consequence, we propose to use elp ( ‖a‖ 1/p1 ‖a‖ 1−1/p ∞ ) when the upper bound of (∫ ω ‖a‖ pp dµ )1/p is needed. for γ = 73.7, 86.0, 98.3, 110.6, fig. 3(b) illustrates how e(‖x[k]‖ 55 ) evolves with k, and table 1 presents the data of (∫ ω ‖a‖ 55 dµ )1/5 and el5 ( ‖a‖ 1/51 ‖a‖ 4/5 ∞ ) . when γ = 73.7, 86.0, e(‖x[k]‖ 55 ) tends to finite values as long as k is sufficiently large. then if γ = 98.3, 110.6, e(‖x[k]‖ 55 ) will be found to grow infinitely. the numerical result is in accordance with theorem 3. stochastic stability analysis of power control in wireless networks via a norm-inequality-based approach 857 conclusion this study develops a norm-inequality-based framework of analyzing the pth-moment stability of linear systems with random parameters, so as to show that a typical power control law with linear system model is stable in the sense of the pth-moment stability. it is the first time to recognize the effect of multiple-access methods to stability analysis of power control. acknowledgment the authors are grateful to the anonymous reviewers for many valuable comments concerning the presentation. this work was supported by the national natural science foundation of china under grant 61501043. we would like to thank dr. miao diao and prof. duan zhisheng for all their help and support while writing this paper. bibliography [1] c.w. sung, k.k. leung (2005); a generalized framework for distributed power control in wireless networks, ieee trans. inf. theory, 51(7): 2625-2635. 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[25] i. s. gradshteyn, i. m. ryzhik (2007); table of integrals, series, and products, 7th edition, academic, 2007. appendix: several lemmas this appendix is devoted to present the lemmas (and their proofs) which are required to derive the main results of our work. lemma a.1: let the nonnegative numbers a1,a2, · · · ,an and the positive p1,p2, · · · ,pn be given. set ∑n j=1 1 pj = 1 then the inequality ∏n j=1 aj ≤ ∑n j=1 1 pj a pj j holds with equality if and only if all ak with pk > 0 are equal. proof: ∏n j=1 aj ≤ ∑n j=1 1 pj a pj j in lemma a.1 is an inequality of the weighted arithmetic mean and geometric mean, which can be proved by using the finite form of jensen’s inequality [24] for the natural logarithm. 2 stochastic stability analysis of power control in wireless networks via a norm-inequality-based approach 859 lemma a.2: let x1,x2, · · · ,xn be n random variables and p1,p2, · · · ,pn be nonnegative numbers numbers. if ∑n j=1 1 pj = 1 and e|xj|pj < +∞ for 1 ≤ j ≤ n, then e (∏n j=1 |xj| ) ≤∏n j=1 (e|xj|pj ) 1 pj , where (e|xj|pj ) 1 pj = elp||xj|| with p = pj. proof: by using lemma a.1, we get ∏n j=1 |xj|∏n j=1(e|xj| pj ) 1 pj = ∏n j=1 |xj| (e|xj|pj ) 1 pj ≤ ∑n j=1 |xj|pj pje|xj|pj . then applying the expectation to above inequality, e( ∏n j=1 |xj|)∏n j=1(e|xj| rj ) 1 pj ≤ ∑n j=1 e|xj|pj pje|xj|pj = ∑n j=1 1 pj = 1. thus, lemma a.2 is verified. 2 lemma a.3: suppose x1,x2, · · · ,xk are random matrices with size s1 ×s2, s2 ×s3, · · · , sk × sk+1, where s1,s2, · · · ,sk,sk+1 are all simply positive integers and the subscripts are labels corresponding to the matrices. if the entries of xk are independent with those of xl for any k 6= l then e (∏k k=1 xk ) = ∏k k=1 e(xk). proof: the product of k matrices can be expressed in the index notation as[ k∏ k=1 xk ] ij = s1∑ i1=1 s2∑ i2=1 · · · sk−1∑ ik−1=1 [x1]ii1 [x2]i1i2 [x3]i2i3 · · · [xn−1]in−2in−1 [xn]in−1j. this implies every entry of the resultant matrix after matrix product is a linear function of the entries of all ak matrices. the independence condition can further yield [24] e  [ k∏ k=1 xk ] ij   = s1∑ i1=1 s2∑ i2=1 · · · sk−1∑ ik−1=1 e([x1]ii1 )e([x2]i1i2 ) · · ·e([xn−1]in−2in−1 )e([xn]in−1j). therefore, lemma a.3 is proved. 2 lemma a.4: suppose that x1,x2, · · · ,xi are random matrices with size s×1, one can have elp ∥∥∥∑ii=1 xi∥∥∥ p ≤ ∑i i=1 elp ‖xi‖p, where p ∈ z+. proof: the norm inequalities and lemma a.2 combine to provide elp ∥∥∥∥∥ i∑ i=1 xi ∥∥∥∥∥ p  p = e  ∥∥∥∥∥ i∑ i=1 xi ∥∥∥∥∥ p p   ≤ e (( i∑ i=1 ‖xi‖p )p) = i∑ i1=1 i∑ i2=1 · · · i∑ ip=1 e ( ‖xi1‖p‖xi2‖r · · · ∥∥xip∥∥p) ≤ i∑ i1=1 · · · i∑ ip=1 ( e ( ‖xi1‖ p p ) · · ·e (∥∥xip∥∥ pp ))1/p = ( i∑ i=1 elp ‖xi‖p )p . this gives the desired result. 2 lemma a.5: let x1,x2, · · · ,xk be random matrices with size s1 ×s2, s2 ×s3, · · · , sk × sk+1 and y be a sk+1×1 random vector. if the entries of xk are independent with those of xl for any k 6= l and y for any 1 ≤ k 6= k, then elp ∥∥∥(∏kk=1 xk)y∥∥∥ p ≤ (∏k k=1 elpϕ(xk) )( elp ‖y‖p ) , where p ∈ z+. proof: the norm inequalities implies ∥∥∥(∏kk=1 xk)y∥∥∥ p ≤ (∏k k=1 ‖xk‖p ) ‖y‖p, and thus we get e (∥∥∥(∏kk=1 xk)y∥∥∥ p p ) ≤ (∏k k=1 e(|ϕ(xk)| p) ) e(‖y‖ pp ). such an inequality directly yields the result. 2 int j comput commun, issn 1841-9836 8(4):594-607, august, 2013. a fuzzy data envelopment analysis approach based on parametric programming s.h. razavi, h. amoozad, e.k. zavadskas, s.s. hashemi seyed hossein razavi hajiagha institute for trade studies and research, no. 240, north kargar st., tehran, iran, s.hossein.r@gmail.com hannan amoozad mahdiraji kashan branch, islamic azad university, kashan, iran, h.amoozad@ut.ac.ir edmundas kazimieras zavadskas* 3*vilnius gediminas technical university, faculty of civil engineering, sauletekio al. 11, lt-10223vilnius, lithuania. edmundas.zavadskas@vgtu.lt *corresponding author shide sadat hashemi kashan branch, islamic azad university, kashan, iran, shide−hashemi@yahoo.com abstract: in this paper, a fuzzy version of original data envelopment models, ccr and bcc, is extended and its solution approach is developed. the basic idea of the proposed method is to transform the original dea model to an equivalent linear parametric programming model, applying the notion of α-cuts. then, a bi-objective model is constructed which its solution has determined the optimal range of decision making units efficiency. the proposed method can be used both for symmetric and asymmetric fuzzy numbers, while the feasibility of its solution for the original problem is guaranteed. the application of the proposed method is examined in two numerical examples and its results are compared with two current models of fuzzy dea. keywords: data envelopment analysis; fuzzy numbers; α-cuts; parametric programming. 1 introduction from the early stages of modernization, the limitation of resources was one of the major challenges in managerial decisions. this limitation motivated the managers to be sensitive about the utilization of acquired resources. the origin of efficiency problem can be referred to this sensitivity. roughly speaking, efficiency tries to answer this question: how well an organization uses its resources to produce the desired outputs? economists suggest the concept of production function as a tool to appraise efficiency and therefore, a majority of methods that are introduced for efficiency appraisal are based on the approximation of this function (see [15] for a definition of production function). based on the concept of production function, the efficiency evaluation methods can be classified into two groups: (1) parametric methods which directly approximate the production function, like stochastic frontier analysis [20], and (2) non-parametric methods which indirectly approximate this function. data envelopment analysis is one of the well known and widely accepted methods in non parametric class [6]. copyright c⃝ 2006-2013 by ccc publications a fuzzy data envelopment analysis approach based on parametric programming 595 farell (1957) in his paper introduced a method of efficiency evaluation which is known as the origin of dea. according to the pareto-koopmans definition, he divided the efficiency of each unit into two technical and assignment components [12]. later, in 1978, charnes, cooper, and rhodes developed the dea method based on the farells model. the first dea model was called ccr model due to its authors [4]. after 1978, the dea method was widely known and accepted as a permanent paradigm in efficiency evaluation. for instance, emrouznejad [11] and cook and seiford [7] surveyed more than thousands of papers and applications of dea in different fields. dea is a set of linear programming based methods for evaluation the efficiency of a group of homogeneous units which use a set of inputs to produce a set of outputs. dea considers the efficiency of each unit as the ratio between its weighted sums of outputs to the weighted sums of inputs. in contrast with the classical methods of constant weights, dea allows each unit to take its variable weights such that its efficiency is maximized, while the efficiency of all units is constrained to be less than one. it can be concluded that the dea weights are closely related to its inputs and outputs data, and a small swing in units data will have a great influence on the dea results. the original dea models were developed based on crisp and deterministic data and no deviation in data were allowed. however, in practical applications, this assumption is violated. scholars proposed some frameworks to deal with data uncertainty and non crispness. some papers examined the efficiency problem under stochastic data [27, 28]. bellman and zadeh [2] in their highly cited paper introduced the concept of decision making in a fuzzy environment. while many inputs and outputs are stated by qualitative or lingual variables, applications of the fuzzy sets theory in dea are proposed in literature. the application of fuzzy sets theory in dea can be traced to sengupta [29]. since that time, there are a continuously increasing interest on fuzzy dea methods and applications. hatamimarbini et al. [14] classified the fuzzy dea method into four primary categories which some instances are just introduced here: (1) the tolerance approach [29], (2) the α-level based approach [19, 26], (3) the fuzzy ranking approach [13], and (4) the possibility approach [21, 22]. also, some approaches like luban [23], wang et al. [31], and zerafat angiz et al. [33] are known as other developments in fuzzy dea. in this paper, a method is proposed to solve the fuzzy dea problems by transformation of fuzzy dea problem to an equivalent interval problem, using the concept of α-cuts. the obtained interval problem is a parametric problem based on α which a solution method is proposed to solve it as a bi-objective problem with the concept of compromise programming. the pareto efficiency of the transformed interval problem is also proved for the fuzzy dea problem. the rest of paper is organized as follows. section 2 consist a brief overview on multiplier and envelopment form of dea ccr and bcc models. the required fuzzy set definitions and operations are overviewed in section 3. the fuzzy dea problem and its solution procedure are described in section 4. two numerical examples are solved in section 5 and the proposed methods solutions are compared with some of the current fuzzy dea methods. finally, the paper is concluded in section 6. 2 data envelopment analysis data envelopment analysis (dea) measures the relative efficiency of a set of congruent decision making units (dmus) that consume multiple inputs to produce multiple outputs. in fact, dea is a multi-factor productivity analysis model for measuring the relative efficiencies of a homogenous set of dmus. the efficiency score in the presence of multiple inputs and outputs is defined as the weighted sum of outputs to the weighted sum of inputs. let, there are n dmus which used m – dimensional input vector xj = ⌊x1j, ..., xmj⌋ to produce an s – dimensional 596 s.h. razavi, h. amoozad, e.k. zavadskas, s.s. hashemi output vector yj = ⌊y1j, ..., ysj⌋. then, the relative efficiency of dmu0, 0 ∈ {1, 2, ..., n} will be as follows: e0 = s∑ r=1 uryr0/ m∑ i=1 vixi0. (1) the basic form of ccr model can be illustrated as follows: max s∑ r=1 uryr0/ m∑ i=1 vixi0 s∑ r=1 uryrj/ m∑ i=1 vixij 6 1, j = 1, 2, ..., n (2) ur > 0, r = 1, 2, ..., s vi > 0, i = 1, 2, ..., m the model (2) is a fractional programming which is converted to a linear programming model by charnes “ cooper [4] variable transformation as follows: max s∑ r=1 uryr0 m∑ i=1 vixi0 = 1, (3) s∑ r=1 uryrj − m∑ i=1 vixij 6 0, j = 1, 2, ..., n ur > 0, r = 1, 2, ..., s vi > 0, i = 1, 2, ..., m model (3) is called the multiplier output oriented ccr model. the ccr models are extended under the constant return to scale assumption. in the case of variable return to scale, the bcc model [1] can be used. the bcc formulation is as follows: max s∑ r=1 uryr0 m∑ i=1 vixi0 − v0 = 1, (4) s∑ r=1 uryrj − m∑ i=1 vixij − v0 6 0, j = 1, 2, ..., n ur > 0, r = 1, 2, ..., s vi > 0, i = 1, 2, ..., m where, is the free in sign return to scale variable. a model is ccr or bcc technically efficient if the objective functions in models (3) or (4) are equal to one and its slack variables are zero. a comprehensive review on different dea models and their economic interpretations are discussed in [8, 25]. a fuzzy data envelopment analysis approach based on parametric programming 597 3 fuzzy sets theory fuzzy sets are introduced by zadeh [32] as a generalization of classic sets. suppose that u is a universe. a fuzzy set ã in u is defined as ã = {( x, µ ã (x) ) |x ∈ u } , where µ ã (x) is called the membership function of ã. if µ ã (x) : u → [0, 1], then ã is called a normal fuzzy set. jain [18] and dubois and prade [10] initially defined the concept of fuzzy numbers. a fuzzy number is a normal and convex fuzzy set ã in universe u. the most common form of fuzzy numbers in practical problems, especially in decision making related problems are trapezoidal and triangular fuzzy numbers. a trapezoidal fuzzy number can be shown as the quadruple ã = (l, m1, m2, r), where l 6 m1 6 m2 6 rare real numbers. a trapezoidal fuzzy number ã is characterized by its membership function as follows: µ ã (x) =   0, x 6 l x − l m1 − l , l 6 x 6 m1 1, m1 6 x 6 m2 . r − x r − m2 , m2 6 x 6 r 0, x > r (5) triangular fuzzy number is a specific form of trapezoidal fuzzy numbers where m1 = m2. a required concept of fuzzy numbers in this paper is the concept of α-cuts. for a fuzzy set ã, its α cut is defined as ã = { x ∈ u|µ ã (x) > α } . the α cuts can be shown as crisp intervals which are called α level interval: ( ã ) α = [( ã )l α , ( ã )u α ] = [ min x { x ∈ u|µ ã (x) > α } , max x { x ∈ u|µ ã (x) > α } ] (6) for a trapezoidal fuzzy number ã = (l, m1, m2, r), its α level interval is determined as follows: ( ã ) α = [m1α + l(1 − α), m2α + r(1 − α)] (7) the arithmetic operations can be defined on fuzzy numbers [34]. an alternative way of fuzzy arithmetic can be defined based on interval arithmetic of α-level intervals. the interval arithmetic is described in moore et al. [24]. if ã and b̃ be two fuzzy numbers with α-level intervals( ã ) α = ⌊( ã )l α , ( ã )u α ⌋ and ( b̃ ) α = ⌊( b̃ )l α , ( b̃ )u α ⌋ , then it follows that [3, 30]: ( ã ) α + ( b̃ ) α = ⌊( ã )l α + ( b̃ )l α , ( ã )u α + ( b̃ )u α ⌋ (8) ( ã ) α − ( b̃ ) α = ⌊( ã )l α − ( b̃ )u α , ( ã )u α − ( b̃ )l α ⌋ (9) ( ã ) α ( b̃ ) α =   min {( ã )l α ( b̃ )l α , ( ã )l α ( b̃ )u α , ( ã )u α ( b̃ )l α , ( ã )u α ( b̃ )u α , } , max {( ã )l α ( b̃ )l α , ( ã )l α ( b̃ )u α , ( ã )u α ( b̃ )l α , ( ã )u α ( b̃ )u α , }   (10) 598 s.h. razavi, h. amoozad, e.k. zavadskas, s.s. hashemi 1/ ( b̃ ) α = ⌊ 1/ ( b̃ )l α , 1/ ( b̃ )u α ⌋ (11) ( ã ) α ÷ ( b̃ ) α = ( ã ) α × 1( b̃ ) α (12) for an interval number ( ã ) α = ⌊( ã )l α , ( ã )u α ⌋ , its center is defined as follows: [( ã ) α ] c = ( ã )l α + ( ã )u α 2 (13) 4 fuzzy data envelopment analysis a fuzzy dea problem can be stated as follows: consider a group of n decision making units. each dmuj, j = 1, 2, ..., n used a set of fuzzy inputs x̃i = (x̃i1, x̃i2, ..., x̃im) to produce a set of fuzzy outputs ỹi = (ỹi1, ỹi2, ..., ỹis),where components of x̃i and ỹi are fuzzy numbers. then, the fuzzy ccr model can be formulated as follows: max s∑ r=1 urỹr0 m∑ i=1 vix̃i0 ∼= 1 (14) s∑ r=1 urỹrj − m∑ i=1 vix̃ij6̃0, j = 1, 2, ..., n ur > 0, r = 1, 2, ..., s vi > 0, i = 1, 2, ..., m where ∼= and 6̃ are fuzzy equality and inequality, means "approximately equal to" and "approximately smaller than". the model in eq. (14) is an input oriented fuzzy ccr model (i f-ccr). in a specific α-level, α ∈ [0, 1], the α-level efficiency of dmu0, eα, can be achieved by solving the following model: eα = max s∑ r=1 ur(ỹr0)α m∑ i=1 vi(x̃i0)α = 1 (15) s∑ r=1 ur(ỹrj)α − m∑ i=1 vi(x̃ij)α 6 0, j = 1, 2, ..., n ur > 0, r = 1, 2, ..., s vi > 0, i = 1, 2, ..., m a fuzzy data envelopment analysis approach based on parametric programming 599 while (ỹrj)α ⊆ ỹrj and (x̃i0)α ⊆ x̃i0, then for each α ∈ [0, 1] the feasible space of model (15) is a subset of feasible space of model (14) and therefore, each feasible solution of (15) is a feasible solution of (14). therefore, it can be stated that: lemma1. if sa is the feasible space of model (15) and s0 is the feasible space of model (14), then ∀α ∈ [0, 1], sα ⊆ s0. substituting the α-level intervals in model (15), the model (16) will be obtained as follows: eα = max s∑ r=1 ur [ (ỹr0) l α, (ỹr0) u α ] m∑ i=1 vi [ (x̃i0) l α, (x̃i0) u α ] = 1 (16) s∑ r=1 ur [ (ỹrj) l α, (ỹrj) u α ] − m∑ i=1 vi [ (x̃ij) l α, (x̃ij) u α ] 6 0, j = 1, 2, ..., n ur > 0, r = 1, 2, ..., s vi > 0, i = 1, 2, ..., m the model (16) is an interval linear programming model and can be solved by interval linear programming techniques. to solve the model (16), first let define some ordering relations between interval numbers. the objective function of model (17) is as the following form: eα = max [ s∑ r=1 ur(ỹr0) l α, s∑ r=1 ur(ỹr0) u α ] (17) according to ishibuichi and tanaka [17] and das et al. [9], the eα will be maximized if and only if its lower bound and center are maximized, i.e.: eα = max   s∑ r=1 ur(ỹr0) l α, s∑ r=1 ur ( (ỹr0) l α + (ỹr0) u α/2 )   (18) the first constraint of model (16) is justified as follows: m∑ i=1 vi [ (x̃i0) l α + (x̃i0) u α 2 ] = 1 (19) because this constraint required that m∑ i=1 vi(x̃i0) l α = 1, and m∑ i=1 vi(x̃i0) u α = 1. adding these equations and dividing by 2, the eq. (19) is obtained. also, the second set of constraints is modified as follows to a linear set of constraints:  s∑ r=1 ur(ỹrj) l α − m∑ i=1 vi(x̃ij) u α, s∑ r=1 ur(ỹrj) u α − m∑ i=1 vi(x̃ij) l α   6 0, j = 1, 2, ..., n (20) 600 s.h. razavi, h. amoozad, e.k. zavadskas, s.s. hashemi the constraints of the form eq. (20) are handled as follows: s∑ r=1 ur(ỹrj) u α − m∑ i=1 vi(x̃ij) l α 6 0, j = 1, 2, ..., n s∑ r=1 ur ( (ỹrj) l α + (ỹrj) u α 2 ) − m∑ i=1 vi ( (x̃ij) l α + (x̃ij) u α 2 ) 6 0, j = 1, 2, ..., n (21) integrating the eqs. (18) – (21), the following multi objective model is constructed as an equivalent model for input oriented fuzzy ccr model (14). eα = max s∑ r=1 ur(ỹr0) l α max s∑ r=1 ur ( (ỹr0) l α + (ỹr0) u α/2 ) subject to m∑ i=1 vi [ (x̃i0) l α + (x̃i0) u α 2 ] = 1 s∑ r=1 ur(ỹrj) u α − m∑ i=1 vi(x̃ij) l α 6 0, j = 1, 2, ..., n (22) s∑ r=1 ur ( (ỹrj) l α + (ỹrj) u α 2 ) − m∑ i=1 vi ( (x̃ij) l α + (x̃ij) u α 2 ) 6 0, j = 1, 2, ..., n ur > 0, r = 1, 2, ..., s vi > 0, i = 1, 2, ..., m the eq. (22) is a bi-objective parametric model. this model is then decomposed to two distinct model to (1) maximize the elα, and (2) maximize the e c α. . if pps is the feasible space of model (22), these two models are formulated as follows: maxelα = s∑ r=1 ur(ỹr0) l α (u, v) ∈ pps minecα = s∑ r=1 ur ( (ỹr0) l α + (ỹr0) u α/2 ) (u, v) ∈ pps (23) the models (22) and (23) are linear programming problems which can be solved easily by available packages. the solutions of these models determine the optimal range of dmus efficiency, i.e. [el∗α , e u∗ α ], which in turn is the efficiency α-cut. in fact, while the outputs of the dmus are determined by fuzzy numbers, their efficiency scores will be also fuzzy numbers with unknown membership function. the result of model (22) for a specific value of α is the efficiency membership function’s α-cut. a fuzzy data envelopment analysis approach based on parametric programming 601 the following lemma shows an important relation between models (21) and (14). lemma2. if (u, v) is a feasible solution of model (21), it will be a feasible solution of the original fuzzy dea model (14). the proof is easily obtained from lemma1. in the case of variable return to scale, the fuzzy bcc model can be formulated as follows: eα = max s∑ r=1 ur(ỹr0) l α max s∑ r=1 ur ( (ỹr0) l α + (ỹr0) u α/2 ) subject to m∑ i=1 vi [ (x̃i0) l α + (x̃i0) u α 2 ] − v0 = 1 s∑ r=1 ur(ỹrj) u α − m∑ i=1 vi(x̃ij) l α − v0 6 0, j = 1, 2, ..., n (24) s∑ r=1 ur ( (ỹrj) l α + (ỹrj) u α 2 ) − m∑ i=1 vi ( (x̃ij) l α + (x̃ij) u α 2 ) − v0 6 0, j = 1, 2, ..., n ur > 0, r = 1, 2, ..., s vi > 0, i = 1, 2, ..., m v0 : unrestricted the model (24) can be solved with a similar approach to model (21). analyzing the efficiency of dmus with the proposed method, for a set of n different values of α, e.g. αi, i = 1, 2, ..., n results in a set of efficiency α-cuts [el∗αi , e u∗ αi ].therefore, it will be necessary to obtain an integrated efficiency score for dmus to rank them. while the membership functions of efficiency are not determined, the conventional methods of fuzzy numbers aggregation which need membership functions cannot be used. here, the chen and klein [5] method is proposed to rank the efficiency scores of dmus based on their α-cuts, like kao and liu [19]. chen and klein [5] introduced the following index for fuzzy numbers: ij = n∑ i=0 (( eu∗αi ) − c ) n∑ i=0 (( eu∗αi ) − c ) − n∑ i=0 (( el∗αi ) − d ), n → ∞ (25) where, c = mini,j {( el∗αi )} and d = maxi,j {( eu∗αi )} . while n is grown, the methods validity is increased, however chen and klein believed that n = 3 or 4 is sufficient. 602 s.h. razavi, h. amoozad, e.k. zavadskas, s.s. hashemi 5 numerical examples in this section, two numerical examples are solved by the proposed method and the results are compared with previously presented methods. example1. consider 4 dmus with inputs and outputs which are presented in table1. table 1: inputs and outputs of 4 dmus dmu input α-cut output α-cut a (11,12,14) [11+α,14-2α] 10 [10,10] b 30 [30,40] (12,13,14,16) [12+α,16-2α] c 40 [40,40] 11 [11,11] d (45,47,52,55) [45+2α,55-3α] (12,15,19,22) [12+3α,22-3α] kao and liu [19] solved this problem in an 11 point scale for α based on bcc model. table 2 shows the kao and liu’s results. considering the dmu d, the model (22) is designed for this dmu as follows. first, two single objective models are solved: eαl (d) : max(12 + 3α)u1 subject to( 100 − α 2 ) v1 + v0 = 1 10u1 − (11 + α)v1 − v0 6 0 (16 − 2α)u1 − 30v1 − v0 6 0 11u1 − 40v1 6 0 (22 − 3α)u1 − (45 + 3α)v1 − v0 6 0 10u1 − ( 25 − α 2 ) v1 − v0 6 0( 28 − α 2 ) u1 − 30v1 − v0 6 0 17u1 2 − ( 100 − α 2 ) v1 − v0 6 0 u1 > 0, v1 > 0, v0 : unrestricted eαl (d) : max 17 2 u1 subject to( 100 − α 2 ) v1 + v0 = 1 10u1 − (11 + α)v1 − v0 6 0 (16 − 2α)u1 − 30v1 − v0 6 0 11u1 − 40v1 − v0 6 0 (22 − 3α)u1 − (45 + 3α)v1 − v0 6 0 10u1 − ( 25 − α 2 ) v1 − v0 6 0( 28 − α 2 ) u1 − 30v1 − v0 6 0 a fuzzy data envelopment analysis approach based on parametric programming 603 17u1 2 − ( 100 − α 2 ) v1 − v0 6 0 u1 > 0, v1 > 0, v0 : unrestrict end these models are designed and solved for different α-levels. the results are shown in table 2. let consider two sets of solutions with more details. considering the dmus a and c, which their outputs are stated by crisp numbers, it is intuitionally more acceptable that their efficiency in an input oriented model and in each specific level of α be a crisp number, as it is in table 2. now consider the dmu a. solving its lower bound model with kao and liu [19], for α = 0, the optimal solution is as follows: table 2: the α-cuts of the efficiency at eleven α-values based on the proposed method and kao and liu [19] method a [ (ea) l α , (ea) u α ] [ (eb) l α , (eb) u α ] [ (ec) l α , (ec) u α ] [ (ed) l α , (ed) u α ] proposed kao and liu proposed kao and liu proposed kao and liu proposed kao and liu 0.0 0.9497 [1.0,1.0] [0.71,0.95] [0.71,1.0] 0.5436 [0.54,0.91] [0.54,1] [0.74,1.0] 0.1 0.9557 [1.0,1.0] [0.73,0.95] [0.73,1.0] 0.5523 [0.55,0.90] [0.56,1] [0.77,1.0] 0.2 0.9615 [1.0,1.0] [0.75,0.95] [0.75,1.0] 0.5612 [0.56,0.88] [0.58,1] [0.80,1.0] 0.3 0.9671 [1.0,1.0] [0.76,0.95] [0.76,1.0] 0.5703 [0.57,0.87] [0.61,1] [0.83,1.0] 0.4 0.9725 [1.0,1.0] [0.78,0.95] [0.78,1.0] 0.5795 [0.58,0.86] [0.63,1] [0.86,1.0] 0.5 0.9776 [1.0,1.0] [0.79,0.95] [0.79,1.0] 0.5890 [0.58,0.85] [0.65,1] [0.89,1.0] 0.6 0.9826 [1.0,1.0] [0.81,0.95] [0.81,1.0] 0.5987 [0.59,0.83] [0.68,1] [0.93,1.0] 0.7 0.9873 [1.0,1.0] [0.83,0.95] [0.83,1.0] 0.6086 [0.60,0.82] [0.70,1] [0.96,1.0] 0.8 0.9917 [1.0,1.0] [0.85,0.95] [0.85,1.0] 0.6187 [0.61,0.81] [0.73,1] [0.99,1.0] 0.9 0.9960 [1.0,1.0] [0.87,0.95] [0.87,1.0] 0.6290 [0.62,0.80] [0.76,1] [1.0,1.0] 1.0 1.0 [1.0,1.0] [0.88,0.95] [0.88,1.0] 0.6395 [0.63,0.79] [0.78,1] [1.0,1.0] ⌊u∗1 = 0.1, v ∗ 1 = 0.07142857, v ∗ 0 = 0.511766⌋ . replacing this solution in the first constraint of model (14), the following equality is obtained: (1.29747587, 1.3689044, 1.51176158) = 1. however, it is clear that 1 ∈ (1.29747587, 1.3689044, 1.51176158) and therefore this constraint is violated. in fact, the kao and liu [19] method overestimated the dmus efficiency. in this case, the proposed methods solution is: ⌊u∗1 = 0.09497207, v ∗ 1 = 0.03351955, v ∗ 0 = 0.5810056⌋ , and the considered constraint is as follows: (0.94972065,0.9832402,1.0502793)=1 which it is clear that 1 ∈ (0.94972065, 0.9832402, 1.0502793). example2. saati-mohtadi et al. [26] applied their model on an example with 10 dmus which used two inputs in order to produce two outputs. data are presented in table 3. table 4 presents the results of efficiency appraisal of dmus based on the proposed method and saati-mohtadi et al. [26] method. applying eq. (25) the following results are obtained: i1 = 0.9407, i2 = 0.9599, i3 = 0.3704, i4 = 0.4629, i5 = 0.5015, i6 = 0.2942, i7 = 0.1409, i8 = 0.1439, i9 = 0.0471, i10 = 0.9243. therefore, the dmus are ranked based on their efficiencies as follows: ẽ2 ≻ ẽ4 ≻ ẽ10 ≻ ẽ5 ≻ ẽ4 ≻ ẽ3 ≻ ẽ6 ≻ ẽ8 ≻ ẽ7 ≻ ẽ9 604 s.h. razavi, h. amoozad, e.k. zavadskas, s.s. hashemi table 3: data for 10 dmus dmus i1 i2 o1 o2 d1 (6.0, 7.0, 8.0) (29.0, 30.0, 32.0) (35.5, 38.0, 41.0) (409.0, 411.0, 416.0) d2 (5.5, 6.0, 6.5) (33.0, 35.0, 36.5) (39.0, 40.0, 43.0) (478.0, 480.0, 484.0) d3 (7.5, 9.0, 10.5) (43.0, 45.0, 48.0) (32.0, 35.0, 38.0) (297.0, 299.0, 301.0) d4 (7.0, 8.0, 10.0) (37.5, 39.0, 42.0) (28.0, 31.0, 31.0) (347.0, 352.0, 360.0) d5 (9.0, 11.0, 12.0) (43.0, 44.0, 45.0) (33.0, 35.0, 38.0) (406.0, 411.0, 415.5) d6 (10.0, 10.0, 14.0) (53.0, 55.0, 57.5) (36.0, 38.0, 40.0) (282.0, 286.0, 289.0) d7 (10.0, 12.0, 14.0) (107.0, 110.0, 113.0) (34.5, 36.0, 38.0) (396.0, 400.0, 405.0) d8 (9.0, 13.0, 16.0) (95.0, 100.0, 101.0) (37.0, 41.0, 46.0) (387.0, 393.0, 402.0) d9 (12.0, 14.0, 15.0) (120.0, 125.0, 131.0) (24.0, 27.0, 28.0) (400.0, 404.0, 406.0) d10 (5.0, 8.0, 10.0) (35.0, 38.0, 39.0) (48.0, 50.0, 51.0) (470.0, 470.0, 470.0) table 4: dmus efficiency scores in six α-values based on the proposed method (p.) and saatimohtadi et al. [26] dmus 0.0 0.2 0.4 0.6 p saati et al. p saati et al. p saati et al. p saati et al. d1 [0.91, 0.94] 1.00 [0.93, 0.96] 1.00 [0.95, 0.97] 1.00 [0.96, 0.98] 1.00 d2 [0.94, 0.95] 1.00 [0.95, 0.96] 1.00 [0.96, 0.97] 1.00 [0.97, 0.98] 1.00 d3 [0.48, 0.57] 0.84 [0.50, 0.57] 0.79 [0.52, 0.58] 0.75 [0.54, 0.58] 0.71 d4 [0.60, 0.62] 0.76 [0.61, 0.63] 0.74 [0.62, 0.63] 0.71 [0.63, 0.64] 0.70 d5 [0.62, 0.64] 0.78 [0.63, 0.65] 0.75 [0.64, 0.65] 0.73 [0.66, 0.66] 0.71 d6 [0.44, 0.49] 0.69 [0.46, 0.50] 0.67 [0.47, 0.50] 0.65 [0.49, 0.51] 0.63 d7 [0.35, 0.35] 0.63 [0.38, 0.39] 0.59 [0.39, 0.40] 0.55 [0.41, 0.42] 0.51 d8 [0.32, 0.33] 0.85 [0.36, 0.37] 0.75 [0.38, 0.40] 0.66 [0.41, 0.4] 0.59 d9 [0.31, 0.31] 0.46 [0.34, 0.34] 0.44 [0.34, 0.34] 0.42 [0.35, 0.35] 0.40 d10 [0.89, 0.93] 1.00 [0.91, 0.94] 1.00 [0.93, 0.96] 1.00 [0.95, 0.97] 1.00 dmus 0.8 1.0 p saati et al. p saati et al. d1 [0.98, 0.99] 1.00 [1.0, 1.0] 1.00 d2 [0.98, 0.99] 1.00 [1.0, 1.0] 1.00 d3 [0.56, 0.58] 0.66 [0.61, 0.61] 0.61 d4 [0.64, 0.65] 0.68 [0.65, 0.65] 0.66 d5 [0.67, 0.67] 0.69 [0.68, 0.68] 0.68 d6 [0.53, 0.54] 0.60 [0.58, 0.58] 0.58 d7 [0.43, 0.44] 0.48 [0.45, 0.45] 0.45 d8 [0.45, 0.46] 0.53 [0.47, 0.47] 0.47 d9 [0.35, 0.35] 0.38 [0.36, 0.36] 0.36 d10 [0.97, 0.98] 1.00 [1.0, 1.0] 1.00 a fuzzy data envelopment analysis approach based on parametric programming 605 now, let compare the results of the proposed method with [26]. table5 presents that d1 attain to full efficiency in all α-levels. suppose that α = 0 . if the problem is solved with saati-mohtadi et al. [26] method, its results will be u∗1 = 0.02439024, u ∗ 2 = 0, v ∗ 1 = 0.1406398, v ∗ 2 = 0.005384859. replacing this solution in the first constraint of model (14), the result will be as follows: (1, 1.146024, 1.297434) which the membership degree of 1 is zero and it violates the ” ∼= ” relation. however, when the model is solved with the proposed method, both in the multipliers in the lower limit model are u∗1 = 0, u ∗ 2 = 0.002235469, v ∗ 1 = 0, v ∗ 2 = 0.03278689 and in the center model, the multipliers are u∗1 = 0.005162761, u ∗ 2 = 0.001776794, v ∗ 1 = 0, v ∗ 2 = 0.03278689, where both cases, the first constraint of model (14) is become as (0.95082, 0.983607, 1.04918) which apparently satisfied the ” ∼= ” relation. both examples show that the feasibility of the proposed method is guaranteed. in fact, in both methods the first constraint is transformed to m∑ i=1 vix̃i0 ≥ 1 which consequently overestimated the efficiency of dmus, while this is prevailed in the proposed method. 6 conclusions in this paper a model is proposed to solve data envelopment analysis models, when the inputs and outputs are determined ambiguously by fuzzy numbers. the method is developed based on the concept of α-cuts which transform the fuzzy problem to an equivalent parametric problem. then, the parametric problem is solved based on different values of α. the proposed method is developed either for constant return to scale ccr model and variable return to scale bcc model. the application of the proposed method is also presented and compared with two existing methods. one of the advantages of the proposed method is that it can be used for symmetric and asymmetric fuzzy numbers. also, the proposed method provides frameworks to analysis the efficiency appraisal problems under ccr and bcc models. the major advantage of the proposed model is that its results guaranteed the feasibility of dea results in original fuzzy model, while other methods don’t have such property in some cases. another feature of the proposed method is that it is a linear programming based parametric method which makes it easy to solve it with the present approaches and applications. bibliography [1] banker r.d., charnes a., cooper w.w., some models for estimating technical and scale inefficiencies in data envelopment analysis, manage. sci. issn 0025-1909, 30 (9): 10781092, 1984. 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[34] zimmermann h.j., fuzzy sets theory and its application, forth ed., kluwer academic publishers, massachusetts, 2001. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 558-566 some aspects about vagueness & imprecision in computer network fault-tree analysis d. e. popescu, m. lonea, d. zmaranda, c. vancea, c. tiurbe daniela elena popescu, doina zmaranda, codruta vancea, cristian tiurbe university of oradea romania, 410087 oradea, 1 universitatii st. e-mail: {depopescu,zdoina,cvancea,ctiurbe}@uoradea.ro madalina lonea "politehnica" university of timisoara romania, timisoara, 2-4 v. parvan blvd. e-mail: madalina_lonea@yahoo.com abstract: based on the available information (eg.multiple functional faults or sensor errors give rise to similar alarm patterns or outcomes), some states in the behaviour of a network can not be distinguished from one another. so, the computer network’s fault tree reliability analysis frequently relies on imprecise or vague input data. the paper will use a dempster-shafer theory to accommodate this vagueness and it will show how imprecision can give rise to false-negative, and false-positive inferences; there will be assigned upper and lower bounds for the probability on elements of the state space. after illustrating the computational simplicity of incorporating the dempster-shafer theory probability assignments, we will apply them for analyzing the reliability of the network of our department. keywords: reliability analysis, networks, dempster-shafer theory, fault tree. 1 introduction the probabilities are no longer appropriate to represent vagueness in risk and reliability analyses; fuzzy set theory was proposed instead to quantify vagueness in this area [1]. development in dst have shown how probability can be adapted to incomplete or vague information, especially information that is based on human judgment or human-machine interaction. false positives and false-negatives are often the end products of vagueness or imprecision. they can arise from imprecision due to noise in monitored data, sensor device failure, or from the ambiguity about the logic rules in the fault tree. so, when the researcher has only imprecise information, he must appeal to fuzzy-set theory techniques, either the common laws or logic his appreciation for the logic relations in fault trees. fuzzy data can be incorporated through dempster shafer theory (dst) [2] [3] [4] [5] in conventional fault-tree analysis yield meaningful results. 2 dempster-shafer theory mass assignments dst generalizes classical probability theory by assigning upper und lower bounds for probabilities, as opposed to point values, to both the elements and the subsets of the state space. for a given state space, ω , mass (probability) is assigned over the set of all possible subsets of ω . because each element of ω is also a subset of ω (comprising 1 element) any classical probability assignment can be represented in dst. just as the probabilities of a distribution sum to 1, so do the masses of a dst-distribution. copyright c⃝ 2006-2010 by ccc publications some aspects about vagueness & imprecision in computer network fault-tree analysis 559 the standard risk analysis possibility set considered is ω ={true, false}. therefore, the power set, 2ω = {φ , t, f, (t, f)} contains an element (t,f) that represents an observation that could be either true or false, but not both. the components of faults trees the initiating events, consequences, and rules are typically given fail or not fail possibilities. in some engineering applications, observation of particular initiating events is imprecise, so an analyst cannot tell whether a given event occurred. by using dst method of assigning probability to the (t,f) event, fault tree analysts can quantitatively depict the imprecision [7]. based on the fact that [sum of all masses] = 1 and based on the fact that in dst m(φ ) = 0, after dst renormalization of the φ , the state space become: t, f, (t, f), and: m (t) + m (f) + m (t,f) = 1 ordinary fault trees are systems of boolean equations with components joined by boolean and & or gates. the boolean and gate for the members of the effective state space is summarized in figure 1 [7], and the boolean or gate is given in figure 2. figure 1: boolean truth table for the and gate ∧ t f (t,f) t t f (t,f) f f f f (t,f) (t,f) f (t,f) figure 2: boolean truth table for the or gate ∨ t f (t,f) t t t t f t f (t,f) (t,f) t (t,f) (t,f) the mass for each entry in the table is obtained by multiply-ing the masses of the edge entries and adding all masses of like entries. for example: let a1,a2,a3 numbers denoting ma over the possibilities t,f,(t,f) where a1+a2+a3=1 let b1,b2,b3 numbers denoting mb over the possibilities t,f,(t,f) where b1+b2+b3=1 for the boolean or gate from table 2 we have: m{a ∨ b} = (a1b1+ a1b2+ a1b3+ a2b1+ a3b1;a2b2;a2b3+ a3b2+ a3b3) (1) = (a1+ a2b1+ a3b1;a2b2;a2b3+ a3b2+ a3b3) thus, for (a ∨ b) the t element has mass assignment (a1 + a2b1 + a3b1), the f element has mass assignment a2b2, the (t,f) element has mass assignment (a2b3+ a3b2+ a3b3). similarly, for the boolean gate from figure 1, we have: ma ∧ b = (a1b1;a1b2+ a2b1+ a2b2+ a2b3+ a3b2;a1b3+ a3b1+ a3b3) (2) = (a1b1;a1b2+ a2+ a3b2;a1b3+ a3b1+ a3b3) 3 fault tree illustration we will apply the above techniques for the fault tree that corresponds to the network monitoring system of our department. our network uses 3 routers and it accesses the internet through the university 560 d. e. popescu, m. lonea, d. zmaranda, c. vancea, c. tiurbe router (figure 3). it comprises the following elements: r1 “hn” router r1’s fault represent the e1 event r2 “cazarma” router r2’s fault represent the e2 event r3 “cnlab” router r3’s fault represent the e3 event r4 “univ.oradea” router r4’s fault represent the e4 event s1 files server s1’s fault represent the e5 event s2 ftp server s2’s fault represent thee6 event s3 database server s3’s fault represent the e7 event s4 domain server s4’s fault represent the e8 event the network’s fault tree representation is shown in figure 4. figure 3: computer science network the top event is labelled t on the tree represented in figure 4. the initiating events ei (i=1, 2, 3, 4, 5, 6, 7, 8) lead to intermediate results (levels), ij (j=1, 2, 3, 4, 5, 6). the fault tree comprises the following boolean equations: t = i1∨ i6 (3) i1 = e3∨ e4 (4) i6 = i4∨ i5 (5) i4 = i2∨ i3 (6) i2 = e5∨ e6 (7) i3 = e7∨ e8 (8) i5 = e1∨ e2 (9) the equal signs in equations (3) ÷ (9) mean that the logical implications works in both directions. to assess the risk of the top event, masses can be assigned to the initiating events ei, and then propagated some aspects about vagueness & imprecision in computer network fault-tree analysis 561 up the tree based on relations (1) and (2). the main problem is the determining of the probabilities of basic initiating events (how are they assigned or determined). figure 4: the fault tree each of (3) ÷ (9) can be interpreted as an "if . . . then . . . " rule of the fault tree and considered as much part of the fault tree as the initiating events. so, let ri be a parameter about the failure rates of rule i = 3, . . . , 9, where the i-th corresponds to equations i [6]. when joined to our existing fault tree, the ri depict a further constraint on the fault tree, through which the initiating events must pass before an intermediate level is reached. the ri can be interpreted in several ways. for example, to model "silent alarms" when the true state of a system is abnormal, the ri could represent incidents in which the alarms are not working properly. if a rule were incorporated in the design of the fault tree such that the rule were true in only 9 out of 10 trials, then coupling ri with a boolean and gate to the existing tree could filter the number of sure fire observations from the rule to 9/10.. joining the ri to the fault tree through an and gate is useful in modelling false negatives. when the ri are joined to the fault tree by and gates and some sensors fail during abnormal conditions, the anticipated consequence might not occur on the fault tree because it has been falsely stopped by the rule parameter. we will focus only on false-negatives, and thus, the ri are joined to the existing fault tree through some boolean and gates. the new fault tree graphically represents the seven equations: t = (i1∨ i6)∧ r3 (10) i1 = (e3∨ e4)∧ r4 (11) i6 = (i4∨ i5)∧ r5 (12) i4 = (i2∨ i3)∧ r6 (13) i2 = (e5∨ e6)∧ r7 (14) i3 = (e7∨ e8)∧ r8 (15) 562 d. e. popescu, m. lonea, d. zmaranda, c. vancea, c. tiurbe i5 = (e1∨ e2)∧ r9 (16) we consider 3 adjustable assignments of mass for the initiating events and rules variables on the fault tree; these are given in table 1. table 1: mass assignment cwev case i case ii case iii e1 (0.9, 0.1, 0) (0.8, 0, 0.2) (0.8, 0, 0.2) e2 (0.9, 0.1, 0) (0.8, 0, 0.2) (0.8, 0, 0.2) e3 (0.9, 0.1, 0) (0.8, 0, 0.2) (0.8, 0, 0.2) e4 (0.9, 0.1, 0) (0.8, 0, 0.2) (0.8, 0, 0.2) e5 (0.8, 0.2, 0) (0.9, 0, 0.1) (0.9, 0, 0.1) e6 (0.8, 0.2, 0) (0.7, 0, 0.3) (0.9, 0, 0.1) e7 (0.8, 0.2, 0) (0.9, 0, 0.1) (0.9, 0, 0.1) e8 (0.8, 0.2, 0) (0.9, 0, 0.1) (0.9, 0, 0.1) r9 (0.8, 0, 0.2) (0.8, 0, 0.2) (0.9, 0.1, 0) r8 (0.7, 0, 0.3) (0.9, 0, 0.1) (0.8, 0.2, 0) r7 (0.9, 0, 0.1) (0.9, 0, 0.1) (0.8, 0.2, 0) r6 (0.9, 0, 0.1) (0.9, 0, 0.1) (0.8, 0.2, 0) r5 (0.9, 0, 0.1) (0.7, 0, 0.3) (0.7, 0.3, 0) r4 (0.8, 0, 0.2) (0.8, 0, 0.2) (0.8, 0.2, 0) 1. the initiating events have the usual probability p assigned to true and the remaining (1-p) is assigned to false. the rule variables have the reliability factor assigned to true and the remaining non-true mass assigned to y (t,f). the rule variables correspond to information that each of the rule has held, eg, in 8 out of 10 trials. in the 2 remaining trials, there are not sufficient informations to validate or invalidate the rule. 2. the reliability factor is assigned to true and the remaining non-true mass on both the rule parameters and the initiating events is assigned to the y (t,f) term. there are no sufficient information to conclude whether the event or rule failed. the remaining mass is assigned to y (t,f) term it is better than assigning the remaining mass to true or false. 3. the evidence is precise on the rule validations but imprecise on the initiating events. there is the situation of a good appreciation of the logic structure of the engineering system but there are problems with the devices. the mass assignments can be obtained from relations (1) and (2) together with the values from figure 1 for our case study we made our calculation in microsoft excel. our first calculation was made based on relations (3) (9) and the results are given in figure 5, and then we made the calculus based on relations (10) (16) and the results are given in figure 6. by analyzing the results from figure 5 and figure 6 we conclude that it is obvious more realistic to compute the probability of the t event with dst than without it. on the other hand, the results of the three cases considered by us are given in figure 7, figure 8 and figure 9. in case i (figure 7) the mass is distributed on all 3 elements. the top event occurs with probability 0,6213 with the remaining non-true mass on the y (t,f) and on the false elements. in case ii (figure 8) the remaining non-true mass on the intermediate and final events of the fault tree is assigned to the false element of the consequences. this is due to the allocation of zero mass to the some aspects about vagueness & imprecision in computer network fault-tree analysis 563 figure 5: calculus without imprecision on rules figure 6: calculus with imprecision on rules 564 d. e. popescu, m. lonea, d. zmaranda, c. vancea, c. tiurbe false elements of the initiating events and rules. the mass of the true element of t is 0,6218, with the remaining 0,3782 mass distributed to the uncommitted term. the case iii (figure 9) reflects the most realistic case with fuzzy input data on the initiating events but clear knowledge of the logic rules. the resulting probability assignments from figure 9 reflect the distribution of all 3 elements. cases i, ii and iii offer interesting comparisons. in case i we have only vague information on the accuracy of the rules, while in case iii we have vague observations of the initiating events. the numerical estimates for the true elements are the same in both cases because both assume the same mass assignments for the true elements of the initiating events and rule parameters. apparently, the effects of the uncertainly on the rules do not begin to offset the certainly on the initiating events until the mass is propagated up through the majority of the boolean gates. figure 7: mass assignment for case i figure 8: mass assignment for case ii 4 conclusions this paper illustrate the advantages of using dst methodology (acting on binary state space with either true or false elements) for representing vagueness and imprecision in reliability analysis of netsome aspects about vagueness & imprecision in computer network fault-tree analysis 565 figure 9: mass assignment for case iii works. we used dst probability assignments for the component of fault trees and a separate parameter on each boolean rule to show how a pattern of false-negative can be observed. so, dst offers a more accurate representation of knowledge. therefore, when there is incomplete knowledge or a limited database upon which to make probability assignments, dst offers a clear advantage over binary (true, false) assignments in representing vagueness. bibliography [1] stephen d.unwin, a fuzzy set theory foundation for vagueness in uncertainly analysis, risk analysism vol.6, num i. 1986, pp.27.34 [2] arthur p. dempster, upper and lower probabilities induced by a multi-valued mapping ann mathematical statistics, vol.38, 1967, pp.325-339 [3] glenn shafer, a mathematical theory of evidence, 1976, princeton university press [4] glen shafer, bayes’s two arguments for the rule conditioning, ann.statistics, vol.10, 1982, pp 10751089 [5] glen shafer, the combination of evidence, int’l j. intelligent systems, vol.i, num.3, 1986, pp.155176 [6] henry prade, a computational approach to approximate and plausible reasoning with applications to expert systems, ieee trans. pattern analysis and machine intelligence, vol.pami-7, 1985, may [7] michael a.s.guth, a probability foundation for vagueness & imprecision in fault tree analysis. ieee trans.on reliability, vol.40, no.5, 1991, dec. daniela elena popescu (b. june 27, 1961) received her phd in computer science (1998) from the "politechnica" university timisoara, romania. since 1990 she is working within the department of computer science, faculty of electrical engineering and information technology, university of oradea, romania, currently position occupied being professor. she is member in the computer architecture and computer testing research group and she has written more then 80 papers in international journals. her current main research field of interest is in computer architecture and 566 d. e. popescu, m. lonea, d. zmaranda, c. vancea, c. tiurbe digital circuits design, computers networks and computational intelligent methods. she is also member in hungarian technical academy. lonea alina-madalina (b. july 16, 1984) is phd student at “politehnica” university of timisoara. she is studying “optimisation in grid systems”. her previous projects are in the following fields: network server management, network systems, web application development, advanced databases, bash scripting and research management skills. doina zmaranda (b. july 14, 1967) received her msc in computer science (1990) and phd in computer science (2001) from the "politechnica" university timisoara, romania. since 1990 she is working within the department of computer science, faculty of electrical engineering and information technology, university of oradea, romania, currently position occupied being professor. her scientific research is focusing on real-time application development and programming. in addition, she also investigates issues related to reliability of complex control systems. she has (co-)authored 5 books and more than 20 papers in international journals in the last five years, participating also within several research projects. codruta vancea (b. august 7, 1967) received her msc in informtics (1990) and phd in mathematics (2003) from the university "babes bolyai" of cluj napoca, romania. since 1991 she is working within the department of electrical engineering, electrical measurement and electric power use, faculty of electrical engineering and information technology, university of oradea, romania. her current position is assistant professor. her scientific research is focusing on modeling of electromagnetic problems and parallel computing. cristian tiurbe (b. october 22, 1979) received his msc in computer science (2003) from the university of oradea, romania. since 2002 he is working within the department of computer science, faculty of electrical engineering and information technology, university of oradea, romania, currently position occupied being assistant. his scientific research is focusing on computer networks security. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 266-277 on the power of small size insertion p systems a. krassovitskiy alexander krassovitskiy university rovira i virgili research group on mathematical linguistics av. catalunya, 35, tarragona 43002, spain e-mail: alexander.krassovitskiy@estudiants.urv.cat abstract: in this article we investigate insertion systems of small size in the framework of p systems. we consider p systems with insertion rules having one symbol context and we show that they have the computational power of context-free matrix grammars. if contexts of length two are permitted, then any recursively enumerable language can be generated. in both cases a squeezing mechanism, an inverse morphism, and a weak coding are applied to the output of the corresponding p systems. we also show that if no membranes are used then corresponding family is equal to the family of context-free languages. keywords: p systems, insertion-deletion systems, context-free languages, matrix grammars, computational completeness 1 introduction the study of insertion-deletion operations on strings has a long history; we just mention [2,5,11,16,20]. insertion-deletion systems motivated from molecular computing have been studied in [1, 3, 10, 19, 21]. with some linguistic motivation they may be found in [9]. in general form, an insertion operation means adding a substring to a given string in a specified (left and right) context, while a deletion operation means removing a substring of a given string from a specified (left and right) context. a finite set of insertion/deletion rules, together with a set of axioms provide a language generating device: starting from the set of initial strings and iterating insertion-deletion operations as defined by the given rules we get a language. the number of axioms, the length of the inserted or deleted strings, as well as the length of the contexts where these operations take place are natural descriptional complexity measures of the insertion-deletion systems. inspired by the structure and the functioning of a living cell, especially by the local information processing, p systems are brought to light few years ago [15]. this is a highly distributed computational model which combines local processing (in membranes) and communication (between them). it is natural to consider insertion and deletion operations in the framework of p systems and it was firstly done by gh. paun in [17]. such a combination permits do define programmed-like insertion-deletion systems, which, as expected, increase their computational power. some combinations of parameters for pure insertion-deletion lead to systems which are not computationally complete [6, 12] or even decidable [22], while in [7, 8] it was shown that p systems framework can easily increase the computational power comparing to ordinary insertiondeletion systems. traditionally, language generating devices having only insertion or only deletion rules were studied. early computational models based only on insertion appear already in [9], and are discussed in [19] and [17] (with membrane tree structure). it was proved that pure insertion systems having one letter context are always context-free. yet, there are insertion systems with two letter context which generate nonsemilinear languages (see theorem 6.5 in [19]). on the copyright c⃝ 2006-2011 by ccc publications on the power of small size insertion p systems 267 other hand, it appears that by using only insertion operations the obtained language classes with contexts greater than one are incomparable with many known language classes. for example, there is a simple linear language {anban | n ≥ 1} which cannot be generated by any insertion system (see theorem 6.6 in [19]). in order to overcome this obstacle one can use some codings to “interpret” the generated strings. the computational power of insertion systems with morphisms and intersection with special languages was investigated in [14] and [18]. in [11] there were used two additional mapping relations: a morphism h and a weak coding φ. the strings of a language are considered being the products h−1◦φ over the generated strings. more precisely, the squeezing mechanism selects only those output strings of the corresponding (p) systems to which h−1 and φ are being applied. as expected, the language generating mechanisms have greater expressivity, and the corresponding language family is larger. it appears that with the help of morphisms and codings one can obtain every re language if insertion rules have sufficiently large context. it is proved in [11] that for every recursively enumerable language l there exists a morphism h, a weak coding φ and a language l′ generated by an insertion system with rules using the length of the contexts at most 7, such that, l = h(φ−1(l′)). the result was improved in [13], showing that rules having at most 5 letter contexts are sufficient to encode every recursively enumerable language. recently, in [4] it was shown that the same result can be obtained with the length of contexts equal to 3. our aim is to reduce the length of the contexts in insertion rules by regulating derivations in terms of membranes. unlike the previous works, our article considers the encoding as a part of insertion p systems. the obtained model is quite powerful and has the power of matrix languages if contexts of length one are used. we also show that if no encoding is used, then the corresponding family is strictly included into the family of matrix languages and is equal to the family of context-free languages if no membranes are used. if an insertion of two symbols in two letters contexts is used, then all recursively enumerable languages can be generated (using of course the inverse morphism, the weak coding, and the squeezing mechanism). 2 prerequisites here we use standard formal language theoretic notions and notations. the reader can consult any of the many monographs in this area (see, e.g., in [20] for the unexplained details). we denote by |w| the length of a word w and by card(a) the cardinality of the set a, while ε denotes the empty string. by cf and re we denote the classes of context-free and recursively enumerable languages, respectively. a language l is context-free if there exists a context-free grammar g such that l(g) = l. a context-free grammar is a construct g = (n, t, s, p), where n and t are disjoint alphabets, s ∈ n, and p is a finite set of context-free rules of the form a −→ v, where a ∈ n and v ∈ (n ∪ t)∗. we say that a context-free grammar g = (n, t, p, s) is in chomsky normal form, if each rule in p has one of the forms a −→ α, or a −→ bc, for a, b, c ∈ n, α ∈ t ∪{ε}. a language l is recursively enumerable if there exists a type 0 grammar g such that l(g) = l. a type 0 grammar is a construct g = (n, t, s, p), where n and t are disjoint alphabets, s ∈ n and p is a finite set of rules of the form u −→ v, where u ∈ (n ∪ t)∗n(n ∪ t)∗ and v ∈ (n ∪ t)∗. we say that a type 0 grammar g = (n, t, p, s) is in penttonen normal form, if each rule in p can be written either as a −→ α, or ab −→ ac, for a, b, c ∈ n, α ∈ (t ∪ n)∗, |α| ≤ 2. it is well known that for every type 0 language there is a modified penttonen normal form where each rule can be written as either: a −→ α, or a −→ ac, or a −→ ca, or ab −→ ac, or ab −→ cb, for a, b, c ∈ n, α ∈ t ∪ {ε}. 268 a. krassovitskiy we also recall the following definition from [17]. a context-free matrix grammar (without appearance checking) is a construct g = (n, t, s, m), where n, t are disjoint alphabets (of nonterminals and terminals, respectively), s ∈ n (axiom), and m is a finite set of matrices, that is sequences of the form (a1 −→ x1, . . . , an −→ xn), n ≥ 1, of context-free rules over n ∪ t . for a string w, a matrix m : (r1, . . . , rn) is executed by applying the productions r1, . . . , rn one after the other, following the order in which they appear in the matrix. formally, we write w =⇒m u if there is a matrix m : (a1 −→ u1, . . . , an −→ un) ∈ m and the strings w1, w2, . . . , wn+1 ∈ (n ∪ t)∗ such that w = w1, wn+1 = u, and for each i = 1, 2, . . . , n we have wi = w′aiw′′ and wi+1 = w ′uiw ′′. if the matrix m is understood, then we write =⇒ instead of =⇒m. as usual, the reflexive and transitive closure of this relation is denoted by =⇒∗. then, the language generated by g is l(g) = {w ∈ t ∗ | s =⇒∗ w}. the family of languages generated by context-free matrix grammars is denoted by mat λ (the superscript indicates that erasing rules are allowed). it is well known fact that every language from mat λ can be generated by a modified binary normal form, (similarly to the binary normal form, see e.g., [17]) having each matrix m of the following form m : (a −→ α, a′ −→ α′), for a, a′ ∈ n, α, α′ ∈ (n ∪ t)∗, max(|α|, |α′|) ≤ 2. an insertion system is a construct γ = (v, a, i), where v is an alphabet, a is a finite language over v , and i is finite set of triples of the form (u, α, v), where u, α, and v are strings from v ∗. the elements of a are called axioms, the triples in i are insertion rules. an insertion rule (u, α, v) ∈ i indicates that the string α can be inserted in between u and v. stated otherwise, (u, α, v) ∈ i corresponds to the rewriting rule uv → uαv. we denote by =⇒ the relation defined by an insertion rule (formally, x =⇒ y iff x = x1uvx2, y = x1uαvx2, for some (u, α, v) ∈ i and x1, x2 ∈ v ∗). we denote by =⇒∗ the reflexive and transitive closure of =⇒ (as usual, =⇒+ is its transitive closure). the language generated by γ is defined by l(γ) = {w ∈ v ∗ | x =⇒∗ w, x ∈ a}. we say that an insertion system (v, a, i) has weight (n, m, m′) if m = max{|u| | (u, α, v) ∈ i}, m′ = max{|v| | (u, α, v) ∈ i}, n = max{|α| | (u, α, v) ∈ i}. we denote by insm,m ′ n the corresponding families of languages generated by insertion systems. an insertion p system is a construct: π = (v, µ, m1, . . . , mk, r1, . . . , rk), where • v is a finite alphabet, • µ is a (cell-like, i.e., hierarchical) membrane structure with k membranes. this structure will be represented by a word containing correctly nested marked parentheses, i.e., by a word from the dyck language. the skin membrane is labeled with “1". • mi, for each 1 ≤ i ≤ k is a finite language associated to the membrane i. • ri, for each 1 ≤ i ≤ k is a set of insertion rules with target indicators associated to membrane i and which have the following form: (u, x, v; tar), where (u, x, v) is an insertion rule, and tar, called the target indicator, is from the set {here, inj, out}, 1 ≤ j ≤ k. on the power of small size insertion p systems 269 a configuration of π is a k-tuple (n1, . . . , nk) of finite languages over v . for two configurations (n1, . . . , nk) and (n ′1, . . . , n ′ k) of π we write (n1, . . . , nk) =⇒ (n ′ 1, . . . , n ′ k) if we can pass from (n1, . . . , nk) to (n ′1, . . . , n ′ k) by applying nondeterministically the insertion rules, to all strings which can be rewritten from the corresponding regions, and following the target indications associated with the rules. more specifically, w′ ∈ n ′j if either w ′ ∈ nj or there is a word w ∈ ni and w =⇒r w′, where r = (u, x, v; tar) ∈ ri. moreover, the membrane labeled by j is immediately outside the membrane labeled by i if tar = out; the membrane labeled by j is immediately below the membrane labeled by i if tar = inj; and i = j if tar = here. no other words are present in n ′j, 1 ≤ j ≤ k. we say that a word w ′ is sent out of the system if there is a configuration (n1, . . . , nk), a word w ∈ n1, and w =⇒r w′ where r = (u, x, v; out) ∈ r1. we use the definition of the language generated by π according to [17]. this language is denoted by l(π) and it is defined as follows: we start from the initial configuration (m1, . . . , mk) of the system and proceed iteratively, by transition steps performed by applying the rules in parallel, to all strings which can be rewritten. all strings over the alphabet v sent out of the system (i.e. sent from the skin membrane) during any step of any computation form the language l(π). insertion tissue p systems are defined in an analogous manner. as the tissue p systems use arbitrary graph structures we write the target indicator in the form tar = goj, j = 1, . . . , k. we remark, that the result of a computation consists of all strings over v which are sent to one selected output cell. the weight of insertion rules (n, m, m′) and the membrane degree k describe the complexity of the system. we denote by lspk(ins m,m′ n )(see [17]) the family of languages l(π) generated by insertion p systems of degree at most k ≥ 1, having weight at most (n, m, m′). if some of the parameters n, m, m′, or k is not specified we write “ * ” instead. we say that a language l′ is from morinsm,m ′ n (from morlspk(ins m,m′ n ), respectively) if there exist a morphism h, a weak coding φ and l ∈ insm,m ′ n (l ∈ lspk(ins m,m′ n )) such that φ(h−1(l)) = l′. for every language l ∈ morlspk(ins m,m′ n ), l = φ(h −1(l(π)), we add h and φ to the system, and we write π in the form π = (v, µ, m1, . . . , mn, r1, . . . , rn, h, φ), hereafter h and φ are naturally extended to strings: h(a1a2 . . . at) = h(a1)h(a2) . . . h(at), and φ(a1a2 . . . at) = φ(a1)φ(a2) . . . φ(at), ai ∈ v. we insert “t′′ before p to denote classes of languages corresponding to the tissue cases (e.g., lstp ). we also write “[t]” (e.g., ls[t]p ) if we do not distinguish between tissue and tree classes. we say that a letter a is marked in a sentential form waw′′ if it is followed by #, i.e., |w′′| > 0, and # is the prefix of w′′. in the following proofs we use a marking technique introduced in [17]. the technique works as follows: in order to simulate a rewriting production a −→ b we add adjacently right from a the word #b specifying that letter a is already rewritten. as soon as the derivation of the simulated sentential form is completed, every nonterminal a is marked and the pair a# is subject to the inverse morphism. 3 main results let us consider insertion systems (without membranes) with one letter context rules, i.e., the family morins1,1∗ . applying the marking technique we get a characterization of context-free languages. theorem 3.1. morins1,1∗ = cf. 270 a. krassovitskiy proof: first we show that cf ⊆ morins1,13 . let g = (v, t, s, p) be a context-free grammar in chomsky normal form. consider the following insertion system π = (t ∪ v ∪ {#}, r, {s}, h, φ), where r = {(a, #γ, α) | α ∈ t ∪ v, a −→ γ ∈ p, γ ∈ (t ∪ v )∗, 1 ≤ |γ| ≤ 2}, the morphism h is defined as follows h(a) = a#, if a ∈ v, and h(a) = a, if a ∈ t, and the weak coding φ is defined as follows φ(a) −→ ε, if a ∈ v, φ(a) −→ a, if a ∈ t. it is clear that l(π) ∈ morins1,13 . we claim that l(π) = l(g). indeed, each rule (a, #γ, α) ∈ r can be applied in the sentential form wa alphaw′ if a is unmarked (not rewritten). hence, the production a −→ γ ∈ p can be simulated by the corresponding derivation of g. hence, by applying the counterpart rules we get equivalent derivations. at the end of the computation every nonterminal is marked, and no rules can be applied any more. (indeed, if the system produces a word having some unmarked nonterminal then h−1 is not defined on this word.) at this point h−1 removes all marks, and φ removes all nonterminal symbols. hence l(π) = l(g). we get cf ⊆ morins1,13 . the equivalence of these two classes follows from theorem 6.4 in [19] stating that ins1,1∗ ⊆ cf and the fact that the family of context-free languages is closed under inverse morphisms and weak codings. � now we consider insertion p systems with the left and right contexts of at most one letter. it is known from theorem 5.5.1 in [17] that lsp2(ins 1,1 2 ) contains non context-free languages. we prove that the more general family lsp∗(ins 1,1 ∗ ) is bounded by the class of languages generated context-free matrix grammars: lemma 3.2. lstp∗(ins 1,1 ∗ ) ⊂ mat λ. proof: the proof uses a similar technique as in [19], theorem 6.4 for context-free grammars. let π = (v, µ, m1, . . . , mn, r1, . . . , rn) be an insertion p system such that l(π) ∈ lstpn(ins 1,1 ∗ ) for some n ≥ 1. consider the matrix grammar g = (d ∪ q ∪ {s}, v, s, p), where q = {qi | i = 1, . . . , n}, d = {da,b | a, b ∈ v ∪ {ε}}, and p is constructed as follows: 1. for every rule (a, b1 . . . bk, c, goj) ∈ ri, a, c ∈ v ∪ {ε}, b1, . . . , bk ∈ v, k > 0 we add to p (qi −→ qj, da,c −→ da,b1db1,b2 . . . dbk−1,bkdbk,c), where a = { a, if a ∈ v, t, ∀t ∈ v ∪ {ε}, if a = ε c = { c, if c ∈ v, t, ∀t ∈ v ∪ {ε}, if c = ε. 2. for every rule (a, ε, c, goj) ∈ ri, a, c ∈ v ∪ {ε}, k > 0 we add to p (qi −→ qj, da,c −→ da,c), where a and c are defined as in the previous case. 3. next, for every w = b1 . . . bk ∈ mi, i = 1, . . . , n, k > 0 we add to p the matrix (s −→ qidε,b1db1,b2 . . . dbk−1,bkdbk,ε). 4. as a special case if ε ∈ mi we add (s −→ qidε,ε) to p. 5. also, for every da,b ∈ d, a, b ∈ v ∪ {ε} we add (da,b −→ a) to p. on the power of small size insertion p systems 271 6. finally, we add (q1 −→ ε) to p (we assume that the first cell is the output cell). the simulation of π by the matrix grammar is straightforward. we store the label of current cell by means of nonterminals from q. every nonterminal da,c ∈ d, a, c ∈ v ∪ {ε} represents a pair of adjacent letters, so we can use them as a context. a rule (a, b1 . . . bk, c, goj) ∈ ri, a, c ∈ v, b1 . . . bk ∈ v k can be simulated by the grammar iff the sentential form contains both qi and da,c. as a result, the label of the current cell is rewritten to qj and da,c is rewritten to the string da,b1db1,b2 . . . dbk−1,bkdbk,c. we note, that in order to simulate rules that have no context we introduce productions with every possible context symbols by writing a ∈ v ∪ {ε} and c ∈ v ∪ {ε}. clearly, since indexed symbols are duplicated for adjacent nonterminals, e.g., dbi−1,bidbi,bi+1 , every nonterminal thus preserves one symbol right and left contexts. the simulation of π by the grammar starts with a nondeterministic choice of the axiom from m1, . . . , mn. then, during the derivation any rule from r1, . . . , rn having the context (a, b) is applied in one to one correspondence with grammar productions having da,b in the left hand side. finally, the string over v is produced by the grammar iff q1 has been deleted from the simulated sentential form. the deletion of q1 specifies that π reached the output cell. so, we obtain l(π) = l(g). hence, lstp∗(ins 1,1 ∗ ) ⊆ mat λ. the strictness of the inclusion follows from the fact there are languages from mat λ which cannot be generated by any insertion p system from lstp∗(ins 1,1 n ), for any n ≥ 1. indeed, consider the context-free language la = {cakcakc | k ≥ 0}. since every context-free language is a matrix language we have la ∈ mat λ. on the other hand, la /∈ lstp∗(ins 1,1 n ), for any n ≥ 1. for the contrary, assume there is such a system π′. we note, that the system cannot delete or rewrite any letter, so every insertion is terminal. as the languages of axioms are finite we need an insertion rule of letter a. consider the final insertion step in a derivation which has at most one step and derives a word w = cakcakc, for some k ≥ n + 1 : w0 =⇒∗ w′ =⇒ w, where w0 is an axiom. since |w0|c ≤ 3, c may be inserted by the last insertion. assume, that |w′|c = 3. in the latter case, let ap be the inserted string, p ≤ n. because, we may insert ap in the distinct positions of w′ we get that either cak−pcak+pc ∈ l(π′) or cak+pcak−pc ∈ l(π′). this is a contradiction. now assume that c is inserted by the last insertion. we note that the insertion of two c is not possible, since k ≥ n + 1. consider three cases: (1) the last applied rule inserts c in the middle, (2) at the end, or (3) at the beginning of w′. (1) let wc = ap ′ cap ′′ be the inserted string, where p′ + p′′ ≤ n − 1. hence, w′ = cak ′+k′′c, where k′ + p′ = k′′ + p′′ = k, and k′ + k′′ = 2k − p′ − p′′ ≥ 2n + 2 − n + 1 ≥ 4. obviously, regardless of the contexts of the last insertion rule there are at least two positions at which wc can be inserted. so, we get a contradiction because either cak ′+p′+1cak ′′+p′′−1c ∈ l(π′), or cak ′+p′−1cak ′′+p′′+1c ∈ l(π′). (2) let aqc be the inserted string, where q ≤ n − 1. the corresponding insertion rule has one of the following forms: (ε, aqc, ε, goj) or (a, aqc, ε, goj), where j is an index of the final membrane. in ether case, aqc may be inserted in w′ before the last letter a. this is a contradiction. the case (3) is a mirror to the case (2) and can be treated similarly. so we proved la /∈ lstp∗(ins 1,1 n ), for any n ≥ 1 and, hence, lstp∗(ins 1,1 ∗ ) ⊂ mat λ. � since trees are special cases of graphs we get the following result corollary 3.3. lsp∗(ins 1,1 ∗ ) ⊂ mat λ. lemma 3.4. mat λ ⊆ morlsp∗(ins 1,1 2 ). 272 a. krassovitskiy proof: we prove the lemma by direct simulation of a context-free matrix grammar g = (n, t, s, p). we assume that g is in the modified binary normal form, i.e., every matrix has the form i : (a −→ bc, a′ −→ b′c′) ∈ p , where a, a′ ∈ n; b, b′, c, c′ ∈ n ∪ t ∪ {ε}, and i = 1, . . . , n. consider a p insertion system π defined as follows: π = (v, [1 [2 [3 [4 ]4 . . . [n+3 ]n+3 ]3 ]2 ]1, {s$}, ∅, . . . , ∅, r1, . . . , rn+3, h, φ), where v = n ∪ t ∪ {ci, c′i | i = 1, . . . , n} ∪ {#, $}. for every matrix i : (a −→ bc, a′ −→ b′c′) we add r.1.1 : (a, #ci, α, in2), to r1; r.2.1 : (ci, bc, α, in3), r.2.2 : (c ′ i, #, α, out) to r2; r.3.1 : (ci, #, α, ini+3), r.3.2 : (c ′ i, b ′c′, α, out) to r3; r.i + 3.1 : (a′, #c′i, α, out), to ri+3 for every α ∈ v \{#}. in addition we add (ε, $, ε, out) to r1. we define the morphism h and the weak coding φ by: h(a) = { a, if a ∈ t, a# if a ∈ v \(t ∪ {#}) φ(a) = { a, if a ∈ t, ε if a ∈ v \t. clearly, l(π) ∈ morlspn+3(ins 1,1 2 ). we claim that l(π) = l(g). to prove this it is enough to prove that w ∈ l(g) iff w′ ∈ l(π) and w′ = φ(h−1(w)). first we show that for every w ∈ l(g) there exists w′ ∈ l(π) and w′ = φ(h−1(w)). consider the simulation of the i-th matrix (a −→ bc, a′ −→ b′c′) ∈ p. it is controlled by letters ci and c′i. first, we insert #ci in the context of unmarked a and send the obtained string to the second membrane. then we use ci as a context to insert adjacently right the word bc. after that, we mark the control letter ci and send sentential form to the (i + 3)-rd membrane. here we choose nondeterministically one letter a′, mark it, write adjacently right the new control letter c′i, and, after that, send the obtained string to the third membrane. (we remark, the third membrane is immediately outside of the i + 3-rd membrane.) it is clear that it is not possible to apply the rule r.i + 3.1 : (a′, #c′i, α; out) in the (i + 3)-rd membrane and to reach the skin membrane if the sentential form does not contain unmarked a′. so, this branch of computation cannot influence the result and may be omitted in the consideration. next, in the third membrane, b′c′ is inserted in the context of unmarked c′i and the form is sent to the second membrane. finally, we mark c′i and send the resulting string back to the skin membrane. at the beginning of the simulation the sentential form in the skin membrane does not contain unmarked ci, c′i. hence, the insertions in the second and third membranes are deterministic. the derivation preserves this property, as after the sentential form is sent back to the skin membrane, introduced ci, and c′i are marked. at the end of the computation we send the resulting form out from the system by the rule (ε, $, ε, out). let w be a string in the skin region which contains some unmarked a and a′. if the letter a precedes a′ then we can write w = w1aα1w2a′α2w3. the simulation of the matrix is the following w1aα1w2a ′α2w3 r.1.1,r.2.1,r.3.1 =⇒ w1a#ci#bcα1w2a′α2w3 r.3+i.1,r.3.2,r.2.2 =⇒ w1a#ci#bcα1w2a ′#c′i#b ′c′α2w3, where w1, w2, w3 ∈ v ∗, α1, α2 ∈ v \{#}. we can write a similar derivation if a′ precedes a. on the power of small size insertion p systems 273 hence, as result of simulation of i-th matrix we get both a and a′ marked and bc, b′c′ inserted in the correct positions. the derivation in π may terminate by the rule (ε, $, ε, out) only in the first membrane. hence it guaranties that the simulation of each matrix has completed. according to the definition of morlsp the string w′ belongs to the language if w′ = φ(h−1(w)), where w is the generated string. we may consider only the final derivations of pi in which each nonterminal is marked. hence, we have l(g) ⊆ φ(h−1(l(π))). the inverse inclusion is obvious since every rule in π has its counterpart in g. moreover the case when the derivation in π is blocked corresponds to the case in which the simulation of a matrix cannot be completed. hence, we get mat λ ⊆ morlsp∗(ins 1,1 2 ). � remark 3.5. one can mention that a similar result can be obtained with a smaller number of membranes at the price of the maximal length of inserted words. precisely, for any context-free matrix grammar g′ there is a p insertion system π′ such that l(π′) ∈ morlspn+1(ins 1,1 3 ) and l(g′) = l(π′), where n is the number of matrices in g′. to prove this we can use the same argument as in the previous theorem and replace rules in r1, . . . , rn+3 by (a, #bc, α, ini+1) to r1, (a′, #b′c′, α, out) to ri+1, for every α ∈ v \{#}, i = 1, . . . , n. since trees are special cases of graphs we get the following result corollary 3.6. mat λ ⊆ morlstp∗(ins 1,1 2 ). taking into account lemma 3.4 and 3.2, and the fact that the class of languages generated by context-free matrix grammars is closed under inverse morphisms and weak codings we get a characterization of mat : theorem 3.7. morls[t]p∗(ins 1,1 ∗ ) = mat λ. now we increase the maximal size of the context of insertion rules to two letters. it is known from [19] that ins2,22 contains non-semilinear languages. by considering these systems with membrane regulation we obtain the following result theorem 3.8. morlsp3(ins 2,2 2 ) = re. proof: we prove the theorem by simulating a type 0 grammar in the modified penttonen normal form. let g = (n, t, s, p) be such a grammar. suppose that rules in p are ordered and n = card(p). now consider the following insertion p system, π = (v, [1 [2 [3 ]3 ]2 ]1, {s$}, ∅, ∅, r1, r2, r3, h, φ), where v = t ∪ n ∪ f ∪ f ∪ {#, #, $}, f = {fa, | a ∈ n}, f = {f a, | a ∈ n}. we include into r1 the following rules: (ab, #c, α, here), if ab −→ ac ∈ p ; (a, #c, bα, here), if ab −→ cb ∈ p ; (a, c, α, here), if a −→ ac ∈ p ; (ε, c, aα, here), if a −→ ca ∈ p ; (a, #δ, α, here), if a −→ δ ∈ p ; ($, ε, ε, out), 274 a. krassovitskiy where α ∈ v \{#}, a, b, c ∈ n, and δ ∈ t ∪ {ε}. it may happen that the pair of letters ab subjected to be rewritten by a production ab −→ ac or ab −→ cb ∈ p is separated by letters that have been marked. we use two additional membranes to transfer a letter over marked ones. in order to transfer a ∈ n we add for each α ∈ v \{#} r.1.1 : (a, #fa, α, in2), to the skin membrane. then we add to the second membrane r.2.1 : (fa, #a, α ′, out), r.2.2 : (fa, #a, α ′, out), r.2.3 : (fax, #fa, #, in3), r.2.4 : (fafb, #fa, #, in3), r.2.5 : (fa#, fa, α, in3), r.2.6 : (fa#, fa, α, in3), r.2.7 : (fa#, fa, #, in3), r.2.8 : (fa#, fa, #, in3), r.2.9 : (fa#, fa, #, in3), r.2.10 : (fa#, fa, #, in3), for every x ∈ f ∪ n, fb ∈ f, α ∈ v \{#, #}, α′ ∈ {ab | a ∈ n ∪ t, b ∈ n ∪ t ∪ {$}} ∪ {$}. finally, we add to the third membrane the rules r.3.1 : (fa, #, α, out), r.3.2 : (fa, #, α ′, out), for every α ∈ v \{#}, α′ ∈ v \{#}. the morphism h and the weak coding φ are defined as h(a) = { a, if a ∈ v \n, a# if a ∈ n. φ(a) = { a, if a ∈ t, ε if a ∈ v \t. we simulate productions in p by marking nonterminals from n and inserting corresponding right hand sides of the productions. this can be done with insertions in the skin membrane by rules of weight (2, 2, 2) since the grammar has such a form that production rewrites/adds at most one letter. the simulation of the transfer is done in the second and third membranes. the idea of the simulation is (1) to mark the nonterminal we want to transfer, (2) jump over the marked letters with the help of one special letter, at the end (3) mark the special letter and insert the original nonterminal. since we use two letter contexts, in one step we can jump only over a single letter. we also need to jump over the marking letter # as well as over the marked nonterminals, and the letters inserted previously. in order to jump over # we introduce one additional marking symbol #. we mark letters from f by #, and all the other letters in v \{#, #} by #, e.g., in a word fa#, letter fa is unmarked. (1) the rule r.1.1 : (a, #fa, α, in2), specifies that every unmarked letter from n may be subjected to the transfer. (2) the rules r.2.3 − r.2.10 in the second membrane specify that fa or fa is copied to the right in such a way that inserted letters would not be marked. in order to do so, the appropriate rule chooses to insert either the overlined copy fa or the simple copy fa. the rules r.2.3, r.2.4 describe jumps over one letter not in {#, #}, and r.2.5−r.2.10 describe jumps over #, #. every rule r.2.3 − r.2.10 sends the sentential form to the third membrane, and the rules r.3.1, r.3.2 in the third membrane send the sentential form back to the second membrane after marking one symbol fa ∈ f or fa ∈ f. on the power of small size insertion p systems 275 (3) the rules r.2.1 and r.2.2 may terminate the transferring procedure and send the sentential form to the first membrane if letter $ or two letters from {ab | a ∈ n ∪ t, b ∈ n ∪ t ∪ {$}} appear in the right context. for example, consider the transfer of a in the string ax#c$ (here, we underline inserting substrings) ax#c$ r.1.1 =⇒ a#fax#c$ r.2.3 =⇒a#fax#fa#c$ r.3.1 =⇒ a#fa#x#fa#c$ r.2.6 =⇒a#fa#x#fa#fac$ r.3.2 =⇒ a#fa#x#fa ##fac$ r.2.1 =⇒a#fa#x#fa ##fa#ac$. the sentential form preserves the following property: (i) the first membrane does not contain unmarked letters from f ∪ f; there is exactly one unmarked letter from f ∪ f in the second membrane; and there are always two unmarked letters from f ∪ f in the third membrane. we mention that property (i) is preserved by every derivation. indeed, we start derivation from the axiom s$ that satisfies the property, then one unmarked symbol is inserted by r.1.1. rules r.2.3 − r.2.12 always add one more unmarked letter, whereas rules r.2.1, r.2.2, r.3.1, r.3.2 always mark one letter from f ∪ f. in order to verify that π generates the same language as g we note that every reachable sentential form in g will be reachable also in π by simulating the same production. we also note that the derivation in π may terminate by the rule ($, ε, ε, out) only in the first membrane. hence, every transfer will be completed. it follows from property (i) that the simulation of the transfer is deterministic in the second membrane. also note, that there is a nondeterministic choice in the third membrane, where the rules r.3.1, r.3.2 may mark one of the two unmarked letters. in the case the rule marks the rightmost letter, the derivation has to “jump" again over the inserted letter. in a special case if r.1.1 starts the transfer of a letter adjacently left from an unmarked one then the rules r.1.1, r.2.1 produce two marked symbols which do not affect the result of the simulation. the output string w is in the language, iff w′ = φ(h−1(w)) is defined. hence, the resulting output of π does not contain unmarked nonterminals. on the other hand every final derivation in π has its counterpart in g. by applying the inverse morphism h−1 we filter out every sentential form with unmarked nonterminals from n. hence, the corresponding derivation in g is completed. finally, the weak coding φ filter away all supplementary letters. hence, we have l(g) = l(π). � 4 conclusions this article investigates the generative power of insertion p systems with encodings. the length of insertion rules and number of membranes are used as parameters of the descriptional complexity of the system. in the article we exploit the fact that a morphism and a weak coding are incorporated into insertion p systems. the obtained family morls[t]p∗(ins 1,1 ∗ ) characterizes the matrix languages. when no membranes are used, the class morins1,1∗ is equal to the family of context-free languages. we proved also the universality result regarding the family morlsp∗(ins 2,2 ∗ ). also, for the family lsp∗(ins 2,2 ∗ ) one can get an analogous computational completeness result by applying right(left) quotient with respect to a regular language. we recall the open problem posed in [4], namely, whether morins2,2∗ is computationally complete. our work gives a partial solution of the problem by using the membrane computing framework. one may see that in order to solve the problem completely (by the technique 276 a. krassovitskiy promoted in the article), it is enough to find a concise way to transfer a letter over a marked context. in our case this can be reduced to the question whether it is possible to compute the membrane regulation in the skin membrane. one may mention that there is a trade-off between the number of membranes and the maximal length of productions. by introducing additional nonterminals and fitting the grammars into the normal forms we decrease the amount of membranes used. on the other hand by raising the number of membranes we can simulate larger production rules. moreover, the descriptional complexity used in the paper may be extended by such internal system parameters as, e.g., the size of the alphabet, the number of rules per membrane, etc. it may be promising to continue the research of the minimal systems regarding these parameters. we are also interested in the computational power of the insertion p systems having only one sided (right or left) contexts. acknowledgments the author acknowledges the support pif program from university rovira i virgili, and project no. mtm2007-63422 from the ministry of science and education of spain. the author sincerely thanks the help of yurii rogozhin and seghey verlan without whose supervision the work would not been done. also the author warmly thanks gheorghe păun and erzsèbet csuhajvarjú who draw attention, during the meeting bwmc-09, to the point of using different normal forms of grammars to simulate p insertion-deletion systems. bibliography [1] m. daley, l. kari, g. gloor, r. siromoney, circular contextual insertions/deletions with applications to biomolecular computation, in: proc. spire’99 , pp. 47-54, 1999. 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[22] s. verlan, on minimal context-free insertion-deletion systems, journal of automata, languages and combinatorics, 12, 1/2, pp. 317–328, 2007. int j comput commun, issn 1841-9836 8(3):460-468, june, 2013. a tight coupling cooperation scheme in wifi/wimax heterogeneous mesh networks w. sun, p. zhang, y. chen, z. qin, d. teng weifeng sun, peng zhang, zhenquan qin school of software dalian university of technology, dalian 116620, china wfsun@dlut.edu.cn, terence_zhang@126.com, qzq@dlut.edu.cn yuanfang chen institut mines-telecom pierre-and-marie-curie university (upmc, paris vi), france yuanfang_chen@ieee.org da teng tencent technology (beijing) co.ltd beijing 100080, china tengda.ustc@gmail.com abstract: a tight coupling cooperation scheme for wifi/wimax networks with qos provisioning has been proposed in this paper. a new wfw (wimax for wifi) module which enables wimax fulfill the procedure of bandwidth request-confirmgrant for wifi was described with a modified mac layer of which msh-dsch (mesh distributed control message) renewed, and thus overhead of interacting of wifi control message was eliminated. the schedule is then evaluated through simulations in two typical transmission scenarios. numerical results show that more effective wifi/wimax heterogonous networks which offer qos guarantees are obtained with the utilization of the scheme. the efficiency of wifi mesh networks increase sharply without obvious decrease of wimax performance. keywords: tight cooperation, wimax for wifi, heterogeneous mesh networks. 1 introduction broadband wireless communication has been a promising area compares to the conventional wireless networks over the last decade [1]. also the technique has diversified different radio access technologies (rats) such as wimax, wifi etc. [2]. heterogeneous networks make an expecting tendency that nodes within which could be embedded with multiple rats. the nodes mentioned above could also work simultaneously in different networks so as to acquire a better qos. in [3], the author explores the resource allocation of nodes in heterogeneous network which could solve some traditional wireless networks’resource allocation problem. author in [4] introduces a call-level quality of service vertical handoff algorithm which could be applied to heterogeneous wireless networks. a further progress could achieved by the use of different rats in a single heterogeneous wireless network. generally speaking, when concerning rat cooperation, two possible classifications are available. a loosely cooperation is often designed by a coordinate mac layer which could effectively allow fast switching between different rats which is transparent to the upper layer [4]. for example, in [6], the author proposes a loose cooperation scheme between wimax and wifi in the airtime-based module which can make the cooperation strategy and control the rats through the collection of each rat’s information. and in [5] a simple integration of wifi/wimax network model was studied, the performance of the heterogeneous network was improved to some degree. tight cooperation, however, could relate rats and mac straightly, so as to make copyright c⃝ 2006-2013 by ccc publications a tight coupling cooperation scheme in wifi/wimax heterogeneous mesh networks 461 more effective ways to take the proper strategies without decreasing the networks performance obviously. in [8], the author introduces a cooperation scheme between wifi and wimax. the scheme could make wifi offload some wimax traffic which is limited and intelligible. further solutions should be given in order to get a better use of the tight cooperation schemes advantages. specifically, a new module and more particularly schemes could be given in order to improve the performance of the networks ulterior. in this paper, we propose a tight coupling cooperation scheme for the wifi/wimax heterogeneous networks. a new wimax for wifi (wfw) module was developed to share the wimax time slot for wifi. in addition, a modified msh-dsch (m-dsch) was designed to consult for both wifi and wimax about bandwidth request information. by making use of different rats, wimax rats could take up the request-confirm-grant procedure for the wifi rats; wifi control message was eliminated. the qos and higher throughput for the whole heterogeneous network could also be guaranteed because of the tightly coupled cooperation. 2 m-dsch message scheduling 2.1 structure of m-dsch the tight coupling cooperation scheme could be fulfilled by the wimax’s replacing for wifi in the bandwidth request-confirm-grant procedure. table 1 illustrates the m-dsch message structure to realize the tightly coupled cooperation scheme. the item marked by a “*” was the original item which also included in the original mshdsch. when we use distributed coordination function (dcf) in the traditional wimax networks. grant/request flag was permanently fixed by a zero. signifying signal contains mshdsch_request_ie(), msh-dsch_grant_ie() simultaneously. the parameters we need to know are the no.request, no.availabilities, and no.grants represents the numbers of the request_ie, avaliable ie, and grant ie respectively. the data structure and the other parameters’ connotation could refer to the ieee std 802.16-2004 [3]. additional items in the m-dsch message are wifi grant/request flag, no.wifi request, no.wifi availabilities, and no.wifi grants. similarly when we use dcf, the wifi grant/request flag was fixed a zero permanently, represents that the signifying signal could also request, confirm and grant for the wifi rats, wifi grant/request flag, no.wifi request, no.wifi availabilities, and no.wifi grants represents the numbers of the request_ie, avaliable_ie, and grant_ie which has the identical data structure of the wimax rats. 2.2 performance analysis of m-dsch analysis of signaling size m-dsch control signal message should transmit trough 7 ofdm symbols without split into segments. the size of the no.grants was 6bits in the original dsch, while it is changed into to 5 bits in the m-dsch. the size of no.wifi grants is fixed in a 5 bits size. in order to meet the demands with the requirement of the signaling transmission delay, the maximum figure of msh-dsch_grant_ie() was decrease from the original 63 to 31. analysis of transmission delay suppose the maximum length of the m-dsch is s_max. when compute with the existing parameters, the result of smax is 1263 bytes. suppose the original dsch message length is 462 w. sun, p. zhang, y. chen, z. qin, d. teng table 1: structure of wimax mesh mdsch message syntax size syntax size msh-dsch_message format() { * if(coordinationflag == 0) * management message type = 41 8 bits * msh-dsch_scheduling_ie() varialbe * coordination flag 1 bit * for(i=0;i < norequests;++i) * grant/request flag 1 bit * msh-dsch_request_ie() 16 bits * sequence counter 6 bits * for(i=0;i < noavailabilities;++i) * no. requests 4 bits * msh-dsch_availability_ie() 32 bits * no. availabilities 4 bits * for(i=0;i < nogrants;++i) * no. grants 5 bits * msh-dsch_grant_ie() 40 bits wifi grant/request flag 1 bit * for(i=0;i < nowifirequests;++i) * reserved 2 bits * msh-dsch_request_ie() 16 bits no. wifi requests 4 bits * for(i=0;i < nowifiavailabilitiess;++i) no. wifi availabilities 4 bits * msh-dsch_availability_ie() 32 bits no. wifi grants 5 bits * for(i=0;i < nowifigrants;++i) reserved 3 bits * msh-dsch_grant_ie() 40 bits } ′max. compute with existing parameters, the size of s′max is 1176 bytes. so far a conclusion could be inferred that m-dsch was 87 bytes more than the dsch. through the equation, the requirement that mdsch should transmit trough 7 ofdm symbols without split into segments could be assured. m-dsch frame design is reasonable. analysis of the wifi and wimax mesh network performance using m-dsch message time slots are divided into frames in the wifi mac layer. in our scheme, the nth frame of wifi starts from the nth data sub-frame of wimax, and finishes at the end of (n + 1)th control sub-frame. suppose the length of the wimax frame is l(s), the percentage of the control sub-frame is r(0 < r < 1), the length of the wifi frame is the same l as wimax. when r = r1(0 < r1 < 1), the length that could be scheduled in each wifi frame ls is described in equation 1: ls = r1l + (1 − r1)l = l (1) then the performance efficiency of the wifi mesh is δ theoretically, then δ = ls/l = 100%. namely wifi mesh network could reach the maximum throughput in theory. the increasing number of the signaling message of m-dsch could affect the performance of wimax mesh network, here is the analysis of the effect. imagine a network with k nodes, ni represents the node i(i ∈ {1, .., k}). m-dsch runs only for wimax. in a sequential ξ scheduler control frames, node ni occupies ni control frames, which means a total ni transmits chances, here comes the equation 2. φ(rl) is transmits chances of control frames which is rl in length. equation 2 based on an assumption that the control frames was saturated, the transmit chances could be utilized completely. k∑ t=1 ni = ξφ(rl) (2) a tight coupling cooperation scheme in wifi/wimax heterogeneous mesh networks 463 suppose ξ series scheduler control frames make the bandwidth request simultaneously, and then the m-dsch messages transmit through node ni could be divided into α m-dsch messages which merely contain the three-way handshake procedure information for wimax, β m-dsch messages which contains the grant, confirm messages for wifi and wimax simultaneously, γ m-dsch messages which merely contain the three-way handshakes procedure information for wifi. because the wimax and the wifi bandwidth arrangement are off interference, then α + β = ni was inferred. if p is the ratio that m-dsch messages which merely contain the three-way handshake procedure information for wifi of the total m-dsch messages in the same nodes, then equation 3 is conducted. p = γ/(α + β + γ) = γ/(ni + γ) (3) when the scheme uses for wimax only, node ni has ni transmit chances. when using for both wimax and wifi in order to fulfill the same requirement, suppose the node ni has n′i transmit chances, n′i could be conducted as equation 4. n′i = ni/(1 − p) (4) then the total transmission chances of all nodes in the network environment are as the following equation 5: k∑ i=1 n′i = k∑ i=1 ni/(i − p) = 1/(1 − p) k∑ i=1 ni = 1/(1 − p)ξφ(rl) = ξφ(rl/(1 − p)) (5) in order to meet the total requirement of all the nodes in our scheme, the percentage of the control sub-frame should increase from r to 1/(1 − p). compared with the total wimax environment, the total decreasing throughput is τ: τ = ((1 − r) − (1 − r/(1 − p)))/(1 − r) = r/(1 − r)(1/(1 − p) − 1) (6) since r is a constant, p and τ are of the positive pertinence, a conclusion could be concluded that when p becomes smaller, the loss of the wimax performance becomes fewer. when p was 0, then r is zero, and wimax performance is lossless. hence, reducing the percentage of the m-dsch messages which service for wifi only was the key point designing the scheme. 3 designing of the tight coulping cooperation system 3.1 dsch handshaking procedure in wimax wimax mac layer is composed by the core disposal wimax mac module (wmm), bandwidth request queue (rq), the availability queue, and the grant/confirm queue. wimax mac layer use a feigned random algorithm to provide the transmission chance for the dsch message, the detail of the algorithm could refer to [3]. the dcf process as the following description: • when receives a bandwidth request from the other nodes, wmn compute the bandwidth grant information (grant) bases on the available information in aq, then insert grant into gcq (grant confirm queue). • when receives a grant in which destination address is the same as the node itself, update aq (availability queue) and generate the confirm information and insert into the gcq. simultaneously inform the node combined with grant information to transmit data in the allocated minislot. if the received destination address if different from the nodes itself, update the aq only. 464 w. sun, p. zhang, y. chen, z. qin, d. teng • when a node receives confirm information, update the aq. • when the nodes get the transmission chances of the dsch, generate the dsch information firstly, wmn fills the parameters’s domain and msh-dsch_schedulin_ie(), rq, aq, gcq fills the msh-dsch_request_ie(), msh-dsch_availability_ie(), mshdsch_grant_ie() respectively. dsch will transmit during the beginning of the transmission chance. the available time slots and the time slot duration information is recorded in the data structure of aq, but the specific design is not given in [3]. in the supposed system, linked list is chosen to achieve the storage function. using the procedures above, wimax mac could fulfill the 3-shook hands procedure of dsch and assure the data sub-frame transmits successfully without any collision. 3.2 implementation of wifi and wimax tight coupling cooperation system design of the system a wfw module is set up between the mac layer of wifi and wimax which is utilized to fulfill the requirement of the system. the module was composed as follows. • information sharing module: wifi and wimax synchronized through the acquired timing information. wifi shares the links id, neighbor mac address, minislot number and other related information with wimax. and wimax shares current frame number, fram start time, fram duration, data subframe start time and minislot length. hence, wimax could take the wifi request-grant-confirm procedure. the data sub-frame of wimax is divided into 256 minislots. supposing each wifi frame has minislots, σ could be computed as the equation 7. σ = floor(l/((1 − r)l/256)) = floor(256/(1 − r)) (7) • wfw processing module: this module is used to receive the mutual require-grant-confirm information between the wimax and wifi mac layer, and then delivers the related information to the other side. when generating the m-dsch, the module is used to fill the related data structure. • wifi request queue (wrq), wifi available queue (waq), and wifi grant/confirm queue (wgcq): wrq is used to store query information generated by wifi mac layer; waq is used to record the location information of the available time slots; wgcq is used to store the grant and confirm information in responding to the neighbors. working procedure of the system the proposed system works as the following procedures: • when a bandwidth request is generated from the wifi mac layer, the message is submitted to the wfw processing module. the wfw module then inserts it into the wrq. • when the wimax mac layer receives wifi grant information, it generates bandwidth grant information named grant based upon the information sharing module and then inserts it into the wgcq. a tight coupling cooperation scheme in wifi/wimax heterogeneous mesh networks 465 • when wimax mac layer receives the wifi grant information, the layer will submit it to the wfw module. if the information’s destination address is exactly the address of the wifi mac layer. then submits the grant to the wifi mac, wifi mac layer will inform the related connection to transmit data in the stipulated minislots. wfw updates the waq and generates the confirm information which is inserted into the wgcq. if the destination address is not the address of the wifi mac layer, update the waq. • when wimax receives the wifi confirm information, it will submit the information to the wfw processing module, then updating the waq through the confirm information. • when wimax gets the transmission change of the m-dsch and generates the m-dsch messages, wimax mac will fill the related domains of the wimax. wfw update the domains of wifi, the msh-dsch_request_ie(), msh-dsch_availability_ie(), mshdsch_grant_ie() was filled by the wrq, waq, wgcq respectively. m-dsch starts transmission from the beginning of the transmission chances. performance analysis of the system the performance analysis of the system is as the theorem 1. theorem 1. the tightly coupling cooperation scheme of the wimax and wifi draws lossless impacts to the original wimax scheme. proof: when wifi mac layer generates the bandwidth request, wfw module will put it into the wrq. imagine the wrq’s length is infinity, then wimax will obtain the m-dsch transmission chances using the feigned random algorithm based on the request, grant, confirm requirements. when generating m-dsch messages, msh-dsch_request_ie() and mshdsch_grant_ie() in the m-dsch contains one or more effective information thus α+β +γ = α = β, then γ = 0 and p = 0. theorem 1 is proved. 2 4 performance evaluation through numerical simulations in this section, we evaluated the performance of the proposed scheme through simulations over ns-2. based on the platform, two typical scenarios are carried out. 4.1 network topology and parameter settings network topology in order to analysis the performance of the proposed scheme, two scenarios are introduced in this paper: • fixed topology of double data flows in a single hop. the network contains 2 fixed nodes; each node is embedded with wifi rats and wimax rats. when we take the noncooperation mode: wifi and wimax works independently, wifi occupancy rate of the channel is larger, the throughput and the network efficiency work nearly the maximum value in theory. while the wimax performance will suffer a sharply decrease compared to the network using the cooperation mode. • random topology of multi-hop and multi flows. several nodes are randomly distributed in the scenario; each node is embedded with wifi rats and wimax rats. in this scenario, the average throughput and the efficiency comparisons between wifi and wimax will help in analyzing the performance gain in our proposed system. 466 w. sun, p. zhang, y. chen, z. qin, d. teng parameter settings in order to measure the performance of wifi and wimax respectively, each node is configured with two constant cbr flows of witch the packet length is 1000 bytes and packet generation interval is 0.005s, each rat serves one cbr flow. the parameters of wifi are set as follows: the signal range of rts/cts and data is 550m, 250m respectively. a 50 length of queue is used with physical layer, of which the highest transmit rate is 1mbps. modified wifi layer does not contain the rts/cts and ack procedure, the other parameters is set upon the [3]. the parameters of wimax are set as follows: the queue length of wimax mac layer is set with a 50, the percentage of the control sub-layer is 30%, the scheduling frames is with a 1 set which means 4 scheduling frames emerges during every two control frames. the protocol of the physical layer is using the ofdm and with a 5mhz bandwidth. the band is set to 3.5ghz; modulation mode of the control sub-frame is set with the ofmd_qpsk_1_2 according to [3], data transmission modulation mode is set with the ofdm_16qam_1_2. 4.2 simulation results performance evaluation in single hop scenario the scenario contains two nodes within each other’s signal coverage. figure 1(a)(b) depicts the average throughput in the single hop scenario, of which the data flow starts at 30s and end at 130s. solid points in the figure 1(a)(b) represent the average throughput in the following 10s from time point it correlates. fig 1(a) shows the wifi performance comparison between cooperation and noncooperation scheme. when using the dcf mode, the average throughput is 0.77mbps. the efficient of the scenario is 0.77mbps/1mbps*100%=77%, which is conform to the analysis in the preceding section. when using the cooperation scheme, the average throughput is 0.96mbps, the network efficient is 0.96mbps/1mbps*100%=96%, the network efficient is approximate to 100%. the 4% loss is due to the inefficient use of the mini-slot during the simulation which could be solved by constraining the length of the data flow. figure 1(b) shows the wimax performance comparison between cooperation and noncooperation scheme. when using the noncooperation scheme, the average throughput of the network is 1.5248mbps; while using the cooperation scheme, the average throughput of the network is 1.5244mbps with a deviation of 0.0026%. hence, the proposed scheme maximizes the throughput and the network efficiency of the wifi mesh, assured a nearly lossless wimax mesh network. performance evaluation in the random topology the scenario contains 10 nodes, distributed randomly in a 1000m*1000m area, with 4 cbr flows transmitting simultaneously. simulation results are showed in the figure 1(c)(d), the data flow similarly starts at 30s and end at 130s. the solid points in the figure are the same as the points defined in the preceding parts. figure 1(c) is the shows the comparison between cooperation and noncooperation scheme. when using the dcf mode the average throughput is 1.91mbps in the noncooperation mode and 2.82mbps in the cooperation mode. the performance of the network increased by (2.82mbps-1.91mbps)/1.91mbs*100%=47.6%. figure 5(d) is the wimax performance comparison between cooperation and noncooperation scheme. when using the noncooperation scheme, the average throughput is 5.184mbps; while using the cooperation scheme, the average throughput is 5.181mbps. the performance of it decreases by a 0.057%. wimax suffer a subtle decrease nearly lossless as well. the result of the simulations indicates that the proposed tight coupling cooperation scheme works in accordance with our anticipation that the scheme could guarantee the wimax rats a tight coupling cooperation scheme in wifi/wimax heterogeneous mesh networks 467 (a) throughput of wifi mesh in single hop scenario (b) throughput of wimax mesh in single hop scenario (c) throughput of wifi mesh in random distributed scenario (d) throughput of wimax mesh in random distributed scenario figure 1: average throughput in random distributed scenario with a lossless performance and the wifi rats performance with an evident promotion. in our network environment, the throughput and network efficient could reach the max value in theory. 5 conclusion the paper is concerned with a tight coupling operation scheme which could support the effective transmission among the wimax rats and wifi rats. a wfw module is employed to do the bandwidth negotiation for wifi. a novel m-dsch was proposed to support the wimax to fulfill the bandwidth request-grant-confirm procedure. numerical results have confirmed that the proposed scheme improve the performance of wifi, and also draws subtle inference to the original wimax performance. increasing performance of the network assures the qos of the heterogeneous network. in the future, we intends to examine the performance of the hybrid network in which the users are equipped with single wifi rats or wimax rats or equipped with the two rats simultaneously. compatibility of the proposed scheme is another direction we will work on. acknowledgement this work is supported by natural science foundation of china under grant no. 61103233, 61202442, 61202443, the fundamental research funds for central universities (dut12jr08). 468 w. sun, p. zhang, y. chen, z. qin, d. teng bibliography [1] weifeng, s. et al. 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(2004); ieee standard for local and metropolitan area networks part 16: air interface for fixed broadband wireless access systems. the institude of electrical and electronic engineers, inc. kak_ijcccv11n5.pdf international journal of computers communications & control issn 1841-9836, 11(5):666-683, october 2016. degree of project utility and investment value assessments a. kaklauskas arturas kaklauskas vilnius gediminas technical university, vilnius, lithuania arturas.kaklauskas@vgtu.lt abstract: this article recommends a new invar method for a multiple criteria analysis (degree of project utility and investment value assessments along with recommendation provisions). its use can be for a sustainable building assessment. the invar method can additionally assist in determining the investment value of a project under deliberation and provide digital recommendations for improving projects. furthermore, the invar method can optimize the selected criterion seeking that the project under deliberation would be equally competitive in the market, as compared to the other projects under comparison. the invar method is additionally able to calculate the value that the project under deliberation should be for this project to become the best among those under deliberation. the case studies presented in this research are for demonstrating this developed method. keywords: copras, duma and invar methods, multiple criteria analysis, investment value, utility degree, recommendations. 1 introduction the increased awareness about building energy consumption and sustainability has resulted in the development of various means for predicting performance and rating sustainability. the building research establishment environmental assessment method (breeam) and leadership in energy and environmental design (leed) are the most commonly used performance rating systems [1]. according to lee [2], statistical analysis reveals a moderate degree of agreement amongst the five schemes (breeam, leed, casbee, beam plus and the chinese esgb) on weights and ranks of weights allocated to five key assessment aspects. ferreira [3] compare the criteria weighting process of four sustainable construction assessment tools (lidera, sb toolpt, code for sustainable homes and leed for homes 2012) and show that the four different weighting sets are robust and generally similar. a discussion on breeam and multiple criteria decision making follows as an example. the hierarchical structures of key criteria and features of breeam offices are by levels of issues, categories and criteria. the top level contains ten distinct issues (the maximum number of obtainable credits appears in parentheses): management (22), health & well-being (14), energy (30), transport (9), water (9), materials (12), waste (7), land use & ecology (12), pollution (13), innovation (10). the second level includes 69 categories and the third level – 114 criteria. expert opinion determines the total number of credits for each category [4]. the use of the breeam credits scoring system is for determining the overall assessment grade, which may be pass (≥ 30%), good (≥ 45%), very good (≥ 55%), excellent (≥ 70%) and outstanding (≥ 85%). no weightings are applied to credits awarded under different categories, as the number of obtainable credits assigned to each category already reflects the weight assigned to a category of assessment relative to other categories (as per [2]). for example, breeam (code for sustainable homes) divides into nine categories, which subdivide into 34 issues (criteria). the award for each issue according to its performance can be a maximum number of credits. then, for each category, the percentage of the total credits awarded for all its issues is determined. that percentage is copyright © 2006-2016 by ccc publications 668 a. kaklauskas multiplied by its weight [5,6]. in the end, the weighted values of all those nine categories are added up to obtain one of the six possible certification classes. thus there is maintenance of the weighting structure with natural adjustments to market needs [3]. multiple criteria decision making (mcdm) comprises a finite set of alternatives, which decision makers must select, evaluate or rank according to the weights of a finite set of criteria. the multiple criteria nature of the problem regarding energy performance assessment of buildings makes the mcdm method ideal for coping with the complexity of the problem [7]. berardi [8] emphasizes sustainability assessments in a built environment using multiple criteria rating systems. other scientists [9–15] have also done multiple criteria and multi-aspect analysis of green buildings. copras method [9,10] was found to be an effective method for the green buildings assessment. copras (complex proportional assessment method) method was developed by e. zavadskas and a. kaklauskas [16]. the copras method consists of five stages. later, this method has been supplemented with a new “method of defining the utility and market value of a property” (duma) developed by kaklauskas [14], see [17]. the degrees of utility of the property considered as well as the market value of a property being valuated is determined in seven duma method stages. the newly developed invar (degree of project utility and investment value assessments along with recommendations) method by kaklauskas integrates the philosophy of copras and duma methods and offers the new opportunities. these new opportunities are as follows: defining the investment value of a project; providing digital tips for improving projects; optimizing a selected criterion; calculating the value of the project, which would permit it to be best among others under deliberation. determining the priorities and utility degree of projects applying stages 1-5 of the invar method are identical to copras method. other invar method 6-11 stages are different from the copras and duma methods. according to the international valuation standards [18], investment value is the value of an asset to the owner or a prospective owner for individual investment or operational objectives. as stated in business dictionary, investment value reflects the value of an asset to its owner, depending on his or her expectations and requirements. schmidt [19] believes that investment value refers to the value to a specific investor, based on requirements of that investor, tax rate, and financing. the invar method for an analysis of sustainable buildings (see case studies) use the same initial data as the breeam method uses. the invar method was applied in research in various eu projects (intellities, idesedu, brita in pubs); the author took part in the research. the results of these projects were discussed in a number of publications by the author in conjunction with colleagues [20–25]. the structure of this paper is as follows: after this introduction, section 2 describes the invar method. section 3 follows with case studies. finally the discussion and conclusions appear in section 4. 2 invar method assessing utility degree and the value of a project under investigation along with the establishment of priorities for this project’s implementation is not especially difficulty. however, this first requires obtaining the numerical values and weights of criteria and applying multiple criteria decision making methods. the presentation of the analysis of projects under comparison is in the form of a grouped decision making matrix, where columns contain n alternative projects under consideration. meanwhile the rows represent all the pertinent quantitative and conceptual information (see table 1) [14]. degree of project utility and investment value assessments 669 table 1: grouped decision making matrix of the multiple criteria analysis of projects under comparison criteria describing the alternatives * projects under comparison w ei g h ts m ea su re m en t u n it s a1 a2 ... aj ... an x1 z1 q1 m1 x11 x12 ... x1j ... x1n x2 z2 q2 m2 x21 x22 ... x2j ... x2n x3 z3 q3 m3 x31 x32 ... x3j ... x3n ... ... ... ... ... ... ... ... ... ... xi zi qi mi xi1 xi2 ... xij ... xin ... ... ... ... ... ... ... ... ... ... xm zm qm mm xm1 xm2 ... xmj ... xmn conceptual information pertinent to projects (i.e., texts, drawings, graphics, video tapes and virtual and augmented realities) * – the sign zi(+(−)) indicates that a greater (lesser) criterion value corresponds to greater (lesser) significance for stakeholders. the invar method [14] assumes direct and proportional dependence of significance and a priority of investigated versions in a system of criteria that adequately describe the alternatives and on the values and weights of those criteria. significance, priority, utility degree and investment value of alternatives, presentation of quantitative recommendations and optimization of different criteria are determined in 11 stages. invar method stages 1-5 are identical as copras method [9,10,14]. stage 1. first, form a weighted, normalized decision making matrix d. the purpose of this stage is to receive dimensionless, weighted values from the comparative indices. upon establishing the dimensionless values of the indices, all criteria, originally having different dimensions, become comparable. the following formula for this purpose is: dij = xij · qi n∑ j=1 xij , i = 1, m; j = 1, n, (1) where xij is the value of the i-th criterion in the j-th alternative of a solution, m – the number of criteria, n – the number of the alternatives compared and qi – the weight of the i-th criterion. the sum of dimensionless, weighted index values dij of each criterion xi is always equal to the weight qi of this criterion: qi = n∑ j=1 dij, i = 1, m; j = 1, n. (2) in other words, the value of the weight qi of the investigated criterion proportionally distributes over all the alternative versions aj according to their values xij. stage 2. the sums of weighted, normalized indices describing the j-th version are calculated. the minimizing of index s −j and maximizing of index s+j describe the versions. the lower value of minimizing indices is better (investment). the greater value of maximizing indices is better (management, health & wellbeing, energy, transport, water, materials, waste, land use & ecology, 670 a. kaklauskas pollution, innovation). the formula for calculating the sums is: s+j = m∑ i=1 d+ij; s−j = m∑ i=1 d −ij, i = 1, m; j = 1,n. (3) in this case, the values s+j (the greater the project "pluses" of this value, the greater the satisfaction of interested parties) and s −j (the lower the project "minuses" of this value, the better the goal attainments by interested parties) express the degree of goals attained by interested parties pertinent to each alternative project. in any case, the sum of the "pluses" s+j and the "minuses" s −j of all alternative projects is always respectively equal to all the sums of the weights of the maximizing and minimizing criteria: s+ = n∑ j=1 s+j = m∑ i=1 n∑ j=1 d+ij, s − = n∑ j=1 s −j = m∑ i=1 n∑ j=1 d −ij, i = 1, m, j = 1, n. (4) this way the calculations performed may be additionally checked. stage 3. thebasis pertinent todetermining the significance (efficiency) of the versionsunder comparison constitutes the descriptions of the features pertinent to positive project "pluses" and to negative project "minuses". the formula for finding the relative significance qj of each project aj is: qj = s+j + s −min · n∑ j=1 s −j s −j · n∑ j=1 s −min s −j , j = 1, n, (5) where s −min is the least value of the s−j. stage 4. determining the priorities of projects pertains to the axiom that the greater the qj the higher the efficiency (priority) of the project. the analysis of the method presented allows stating that it may be easily applied for evaluating projects and selecting the most efficient of them, while fully aware of the physical meaning of the process. moreover, it allows formulating a reduced criterion qj directly proportional to the relative effect of the compared criteria values dij and weights qi on the end result (see table 2). determining the utility degrees of the project under consideration as well as the investment value of a project under valuation occurs in seven stages. stage 5. the formula used for the calculation pertinent to project aj utility degree nj is: nj = (qj ÷ qmax) · 100% (6) here qj and qmax are the significances of the project obtained from equation 5. the utility degree nj of project aj indicates the satisfaction level of the interested parties. the more goals achieved and the more important they are, the higher is the degree of project utility. stage 6. calculating the investment value x1j cycle e of the project under deliberation aj can be by means of e approximation. the problem may be stated as follows: what investment value x1j cycle e of the assessed project aj will make it equally competitive on the market with the projects under comparison (a1 − an) (see table 3)? the measurement of the value x1j cycle e is by price (euro, british pounds, u.s. dollar or others) per square meter. degree of project utility and investment value assessments 671 table 2: alternative results of a multiple criteria analysis criteria describing the alternatives * projects under comparison w ei g h ts m ea su re m en t u n it s a1 a2 ... aj ... an x1 z1 q1 m1 d11 d12 ... d1j ... d1n x2 z2 q2 m2 d21 d22 ... d2j ... d2n x3 z3 q3 m3 d31 d32 ... d3j ... d3n ... ... ... ... ... ... ... ... ... ... xi zi qi mi di1 di2 ... dij ... din ... ... ... ... ... ... ... ... ... ... xm zm qm mm dm1 dm2 ... dmj ... dmn sums of weighted, normalized, maximizing indices (project "pluses”) of the project s+1 s+2 ... s+j ... s+n sums of weighted, normalized, minimizing indices (project "minuses”) of the project s −1 s−2 ... s−j ... s−n significance of the project q1 q2 ... qj ... qn priority of the project p1 p2 ... pj ... pn utility degree of the project (%) n1 n2 ... nj ... nn * – the sign zi(+(−)) indicates that a greater (lesser) criterion value corresponds to greater (lesser) significance for stakeholders. assuming nje > n∑ j=1 nj÷n, then continue increasing the value x1j cycle e of this project aj (see table 3) by 1 unit costs per square meter (e.g., 1 euro/m2) and performing calculations as per stages 1-6 with the gained decision making matrix until arriving at inequality nje < n∑ j=1 nj ÷ n during e approximations. then the final value x1j cycle e (while nje > n∑ j=1 nj ÷ n) equals the investment value: x1j iv = x1j cycle e (7) assuming nje < n∑ j=1 nj ÷ n , then continue reducing the value x1j cycle e of this project aj (see table 3) by 1 unit costs per square meter (e.g., 1 euro/m2) and performing calculations as per stages 1-6 with the gained decision making matrix until arriving at inequality nje > n∑ j=1 nj ÷ n during e approximations. then the final value x1j cycle e (while nje < n∑ j=1 nj ÷ n) equals the investment value (see formula 7). stage 7. performing the optimization of value xij is possible for any criterion during e approximations. it is necessary to determine, what the optimized value xij cycle e should be for alternative aj to be equally competitive in the market with the other alternatives under comparison (a1 − an) (see table 3). the optimization of value xij for any criterion pertinent to the project under deliberation aj may be determined by performing complex analyses of the benefits and drawbacks of these projects. development of a grouped, decision making matrix for the multiple criteria analysis of a project transpires by calculating the optimization of value xij during e approximations of a 672 a. kaklauskas table 3: grouped decision making matrix for the investment value assessment of project aj (optimization of value xij for any criterion) criteria describing the alternatives * project under valuation and projects under comparison w ei g h ts m ea su re m en t u n it s a1 a2 ... aj ... an x1 z1 q1 m1 x11 x12 ... x1j cycle e ... x1n x2 z2 q2 m2 x21 x22 ... x2j ... x2n x3 z3 q3 m3 x31 x32 ... x3j ... x3n ... ... ... ... ... ... ... ... ... ... xi zi qi mi xi1 xi2 ... xij cycle e ... xin ... ... ... ... ... ... ... ... ... ... xm zm qm mm xm1 xm2 ... xmj ... xmn nje n1e n2e ... nje ... nne conceptual information pertinent to projects (i.e., texts, drawings, graphics, video tapes and virtual and augmented realities) * – the sign zi(+(−)) indicates that a greater (lesser) criterion value corresponds to greater (lesser) significance for stakeholders. project under valuation by the block-diagram, as presented in figure 1. use of stages 1-5 and 7 accomplishes a set assessment of all the positive and negative features of a project (criteria, its values and weights). perform calculations by using a grouped decision making matrix (see table 3) and stages 1-5 and 7. the calculation for the corrected optimization of value xij cycle e for any criterion aj is by formula: assuming nje > n∑ j=1 nj ÷ n and xi is xi−, then xij cycle e = xij cycle 0 × (1 + e × r), e = 1, r assuming nje > n∑ j=1 nj ÷ n and xi is, xi+, then xij cycle e = xij cycle 0 × (1 − e × r), e = 1, r (8a) assuming nje < n∑ j=1 nj ÷ n and xi is xi−, then xij cycle e = xij cycle 0 × (1 − e × r), e = 1, r assuming nje < n∑ j=1 nj ÷ n and xi is xi+, then xij cycle e = xij cycle 0 × (1 + e × r), e = 1, r (8b) where e is the number of cycles during which optimization value xij cycle e can be determined by means of e approximation of the project under deliberation aj. meanwhile r is the amount by which the optimization value xij cycle e of the project under deliberation aj increases (decreeses) by means of cycling, to satisfy inequality 9. xi+(xi−) – indicates that a greater (lesser) criterion value corresponds to a greater (lesser) significance for stakeholders. assuming the utility degree nje of the project under deliberation aj is greater than the average utility degree (formula 8a) of the projects under comparison, it means project aj is more beneficial on average than the projects under comparison are. for the project under deliberation degree of project utility and investment value assessments 673 figure 1: block-diagram for a project’s optimization value assessment to be equally competitive on the market with the projects under comparison (a2 − an), reduce (increase) the value xij cycle e of its criterion (see formula 8a) under deliberation by an r amount over e cycles, until satisfying the next inequality: |nje − n∑ j=1 nje ÷ n| < s (9) where s is the accuracy, by percentage, to be achieved by calculating the value xij cycle e of the criterion under deliberation of project aj. for example, given that s = 0.5%, the number of calculation approximations will be lower than it is at s = 0.1%. the decision maker selects the r and s amounts depending on the accuracy needed for the calculations. assuming the utility degree nje of the project under deliberation ax is lower than the utility degree (formula 8b) is on average of the projects under comparison, it means project aj is less beneficial on average than the projects under comparison are. for the project under deliberation to be equally competitive on the market with comparison projects (a1 − an), increase (reduce) the value xij cycle e of its criterion (see formula 8b) under deliberation by an r amount over e cycles, until satisfying inequality 9. assuming inequality 9 is not satisfied, it means the calculation of the value xij cycle e of the criterion under deliberation of the project under valuation aj is not sufficiently accurate, and it is necessary to repeat the approximation cycle. thereby the corrected revision of value xij cycle e of the project under valuation substitutes into a grouped decision making matrix of a project’s multiple criteria analysis. recalculate formulae 1-8 until satisfying inequality 9. there is a determination of the optimization value xij cycle e for any criterion of the project under valuation aj. upon satisfaction of inequality 9, the application of the next, formula 10 is to determine the optimization value xij cycle e for any criterion of project aj: xij opt value = xij cycle e (10) stage 9. presenting indicator xij of the quantitative recommendation iij showing thepercentage of a possible improvement in the value of indicator xij for it to become equal to the best value 674 a. kaklauskas xi max of criterion xi is by the formula (see tables 4 and 8): iij = |xij − xi max|÷ xij × 100% (11) where iij is the quantitative recommendation iij of indicator xij showing the percentage of a possible improvement in the value of indicator xij for it to become equal to the best value xi max of criterion xi. meanwhile xi max is the value of the indicator of the best criterion xi of the variants under comparison. stage 10. indicator xij of quantitative recommendation rij showing the percentage of possible improvement of utility degree nj of alternative aj upon presentation of xij = xi max. in other words, rij shows the percentage of possible improvement in the utility degree nj of alternative aj, assuming the value of indicator xij can be improved up to the best value xi max of the indicator of criterion xi. the calculation is by formula: rij = (qi × xi max) ÷ (s−j + s+j) × 100% (12) where rij is the indicator xij of the quantitative recommendation rij showing the percentage of possible improvement in the utility degree nj of alternative aj, when xij = xi max. the submission of the quantitative recommendations iij and rij of value xij is in a matrix form (see table 4). stage 11. this stage involves calculation by approximation e cycle to determine, what the value x1j cycle e should be for the project under deliberation aj to become the best among those under deliberation. the problem may be stated as follows: what investment value x1j cycle e of the project under valuation aj will make it the best on the market, as per the projects under comparison (a1−an) (see table 3)? the measurement of value x1j cycl e is by price (euro, british pounds, u.s. dollar or others) per square meter. the reduction in the price of this project per 1 square meter unit (e.g., 1 euro/m2) continues until utility degree nj e of the project under deliberation aj equals 100%. 3 case studies: describing the sustainability of buildings assessed by the invar method 3.1 case study 1: calculations of the ikea shopping center utility degree a specific example appears next to demonstrate the invar method more clearly. five buildings for retail operations a1 – a5 are under analysis for this case study. all the data come from the breeam pre-assessment reports and other sources pertinent to ikea shopping center a1 [26,27], orchard park district centre a2 [28], friargate court & retail units a3 [29], dorking store a4 [30] and retail foodstore a5 [31]. table 5 shows this data. table 5 consists of criteria (breeam sections and investment), their values (breeam section scores and prices per square meter) and weights. the sum of the weights of all the breeam criteria (breeam sections) is equal to one, because the calculation of the section score section has assessed the weighting. the weight of the investment criterion is compared to the sum of the weights from all the other criteria (breeam sections). this associates with the requirement that the price of these projects must equal the achieved results. the basis for performing an assessment of the sustainability of retail buildings consists of the 11 invar method stages. these calculations appear in brief below. stage 1: the weighted normalized decision making matrix d is formed (see formula 1, table 5 and 9). the first formula for this purpose is: degree of project utility and investment value assessments 675 table 4: quantitative recommendations submitted in a matrix form criteria describing the alternatives * compared projects w ei g h ts m ea su re m en t u n it s a1 a2 ... aj ... an x1 z1 q1 m1 x11 x12 ... x1j ... x1n possible improvement of the value of indicator x1j for it to become equal to the best value x1 max of criterion x1 % i11 i12 ... i1j ... i1n possible improvement of the utility degree nj of alternative aj upon presentation of x1j = x1 max % r11 r12 ... r1j ... r1n x2 z2 q2 m2 x21 x22 ... x2j ... x2n possible improvement in the value of indicator x2j for it to become equal to the best value x2 max of criterion x2 % i21 i22 ... i2j ... i2n possible improvement of utility degree nj of alternative aj upon presentation of x2j = x2 max % r21 r22 ... r2j ... r2n ... ... ... ... ... ... ... ... ... ... xi zi qi mi xi1 xi2 ... xij ... xin possible improvement in the value of indicator xij for it to be equal to the best value xi max of criterion xi % ii1 ii2 ... iij ... iin possible improvement in utility degree nj of alternative aj upon presentation of xij = xi max % ri1 ri2 ... rij ... rin ... ... ... ... ... ... ... ... ... ... xm zm qm mm xm1 xm2 ... xmj ... xmn possible improvement in the value of indicator xmj for it to be equal to the best value xm max of criterion xm % im1 im2 ... imj ... imn possible improvement of utility degree nj of alternative aj upon presentation of xmj = xm max % rm1 rm2 ... rmj ... rmn d11 = 10 × 1774 ÷ (1774 + 1953.8 + 2370 + 1890 + 2045) = 1.7682 d12 = 1.1 × 1953.8 ÷ (1774 + 1953.8 + 2370 + 1890 + 2045) = 1.9474 d13 = 1.1 × 2370 ÷ (1774 + 1953.8 + 2370 + 1890 + 2045) = 2.3623 the value of weight qi of the investigated criterion distributes proportionally among retail buildings under analysis aj according to their values xij (see table 6). for example: q2 = 0.1068 + 0.2403 + 0.1942 + 0.2403 + 0.2185 = 1.0 q4 = 0.2709 + 0.1996 + 0.0925 + 0.1913 + 0.2457 = 1.0 stage 2: the sums of weighted normalized indices describing the j-th version are calculated. formula 3 calculates the sums: s+1 = 0.1068+ 0.2293 + 0.2709 + 0.2056+ 0.0957 + 0.1186 + 0.13 + 0.1944 + 0.2557+ 0.0 = 1.607 s −1 = 1.7682 etc. in any case, the sums of the “pluses” s+j and “minuses” s−j of all alternative projects are always, respectively, equal to all sums of the weights of maximizing and minimizing criteria (see formula 4): s+ = 1.607 + 1.7515 + 2.2967 + 1.6557 + 2.689 = 10.0 s − = 1.7682 + 1.9474 + 2.3623 + 1.8838 + 2.0383 = 10.0 676 a. kaklauskas table 5: initial data for invar method calculations (see [32]) quantitative and qualitative information pertinent to retail buildings criteria describing the retail buidlings * measurement units weight compared retail buidlings a1 a2 a3 a4 a5 investment euro/m2 10 1774 1953.8 2370 1890 2045 management + points 1 4.8 10.8 8.73 10.8 9.82 health & wellbeing + points 1 10.65 10 7.5 8.3 10 energy + points 1 14.44 10.64 4.93 10.2 13.1 transport + points 1 5.6 4.92 7.11 2.5 7.11 water + points 1 1.98 5.33 4 4.7 4.67 materials + points 1 4.12 5.77 9.62 4.8 10.42 waste + points 1 3.22 4.69 3.75 5.6 7.5 land use & ecology + points 1 7 6 7 7 9 pollution + points 1 5.8 3.08 6.15 3.8 3.85 innovation + points 1 0 0 2 0 2 * – the sign “+/-” indicates that a greater (lesser) criterion value corresponds to greater (lesser) significance for a user (stakeholder). stage 3: formula 5 finds the relative significance qj of each project aj (see table 6): q1 = 1.607 + 1.7682 × (1.7682 + 1.9474 + 2.3623 + 1.8838 + 2.0383) 1.7682 × (1.7682 ÷ 1.7682 + 1.7682 ÷ 1.9474 + 1.7682 ÷ 2.3623+ + 1.7682 ÷ 1.8838 + 1.7682 ÷ 2.0383) = 3.8478 q2 = 1.7515 + 1.7682 × (1.7682 + 1.9474 + 2.3623 + 1.8838 + 2.0383) 1.9474 × (1.7682 ÷ 1.7682 + 1.7682 ÷ 1.9474 + 1.7682 ÷ 2.3623+ + 1.7682 ÷ 1.8838 + 1.7682 ÷ 2.0383) = 3.7861 stage 4: the greater the qj, the higher is the efficiency (priority) of the retail buildings: q5 > q3 > q1 > q2 > q4 (see table 6: 4.6329 > 3.974 > 3.8478 > 3.7861 > 3.759). stage 5: formula 6 is used for calculating utility degree nj: n1 = (3.8478 ÷ 4.6329) × 100% = 83.05% n2 = (3.7861 ÷ 4.6329) × 100% = 81.72% n3 = (3.974 ÷ 4.6329) × 100% = 85.78% n4 = (3.759 ÷ 4.6329) × 100% = 81.14% n5 = (4.6329 ÷ 4.6329) × 100% = 100% the results of a multiple criteria evaluation of the sustainable retail buildings under analysis appear in table 6. table 6 shows that the fiftht version a5 is the best by utility degree equaling n5 = 100%. the third version a3 was second according to priority, and its utility degree was equal to n3 = 85.78%. 3.2 case study 2: calculations of the ikea shopping center investment value the calculations of the investment value of the ikea shopping center under valuation are according to data from table 5 and stages 1-6. construction of the ikea shopping center for furniture and home furnishings was in several stages. first, there was selection of a lot and then, the detailed planning for merging two lots. upon approval of the detailed plan, there were degree of project utility and investment value assessments 677 table 6: invar method calculation results quantitative and qualitative information pertinent to retail buildings criteria describing retail buidlings * measurement units weight retail buidings under comparison a1 a2 a3 a4 a5 investment euro/m2 10 1.7682 1.9474 2.3623 1.8838 2.0383 management + points 1 0.1068 0.2403 0.1942 0.2403 0.2185 health &wellbeing + points 1 0.2293 0.2153 0.1615 031787 0.2153 energy + points 1 0.2709 0.1996 0.0925 0.1913 0.2457 transport + points 1 0.2056 0.1806 0.261 0.0918 0.261 water + points 1 0.0957 0.2577 0.1934 0.2273 0.2258 materials + points 1 0.1186 0.1661 0.277 0.1382 0.3 waste + points 1 0.13 0.1894 0.1515 0.2262 0.3029 land use & ecology + points 1 0.1944 0.1667 0.1944 0.1944 0.25 pollution + points 1 0.2557 0.1358 0.2712 0.1675 0.1698 innovation + points 1 0 0 0.5 0 0.5 sums of weighted, normalized maximizing indices (project “pluses”) of the retail buildings 1.607 1.7515 2.2967 1.6557 2.689 sums of weighted, normalized minimizing (projects “minuses”) indices of the retail buildings 1.7682 1.9474 2.3623 1.8838 2.0383 significance of the retail buildings 3.8478 3.7861 3.974 3.759 4.6329 priority of the retail buildings 3 4 2 5 1 utility degree of the retail buildings (%) 83.05% 81.72% 85.78% 81.14% 100% * – the sign “+/-” indicates that a greater (lesser) criterion value corresponds to greater (lesser) significance for a user (stakeholder). ecological tests conducted on the lot, followed by the design and then the arrangement of the lot. some 2,400 units of garages and their foundations were demolished. the partial use of processed construction materials was for new construction, and the remaining materials, for transferring to other waste handlers. the amount of contaminated soil removed was 1,000 tons (see figure 2). the retail buildings designed a parking lot for 953 automobiles of which 37 are for the disabled and 36 for families with children. the unused areas of the lot have planted greenery. the water supply of the city provides the water for the building. centralized sewage networks of the city handle the captured wastewater from the facilities and rainwater that then flow into appropriate piping. the facility contains an installed, autonomous water heating system using solar energy. air conditioning installations consist of efficient heat pumps and the ventilation – of productive recovery systems. the centralized heating network supplies heat. the design and construction of the building were according to customer specifications and were in consideration of permissible noise level maintenance. the project blueprint stipulates an external enclosure that insulates noise to no less than 32 db. the main indicators of the project are total building area – 25,359 m2, main area – 21,533 m2, building height – 15.84 m, drinking water supply pipeline – 3,300 m, wastewater pipeline – 1,900 m and rainwater pipeline – 2,358 m. air conditioning and ventilation systems are installed in the retail buildings for assuring hygienic stipulations for the facilities and the required, stable air temperature and moisture stipulations for the administrative facilities of the work environment. the lighting for the building divides into zones that are all independently controlled. only certified materials having the least impact on the environment over the life of the building were used for the building’s internal and external systems. the insulation materials used were those having the least impact on the environment but containing the best thermal insulation properties. the investment of the ikea shopping center was 47.2 mln. euro. the aim was to establish, what the investment value x11 cycle e (see the bold-faced numbers 678 a. kaklauskas a b figure 2: ikea shopping center for furniture and home furnishings: a) ikea lot under arrangement and b) operating ikea shopping center in tables 5 and 7) of the investment should be for a1 to be equally competitive in the market against the other retail buildings under comparison (a2 – a5). applications of invar stages 1-6 serve to accomplish a set assessment of the positive and negative features of all these retail buildings. as table 7 shows, the most beneficial retail building during the 124th cycle of approximation (e = 124), according to its designation for use, is a5 (n5 124 = 100%). the second under comparison that is most beneficial is a1 (n1 124 = 86.43%) and the third under comparison – a3 (n3 124 = 85.77%). the calculated utility degrees of the sustainable retail buildings under comparison make it apparent that the cost x11 124 = 1650 (euro/m 2) for ikea shopping center under valuation a1 is still too high. therefore this retail buildings a1 is not equally competitive in the market, as compared to the sustainable retail buildings under comparison, once the assessment of their sets of specific positive and negative features is complete. stage 6 also affirms the same fact: the calculation of the investment value for retail building a1 during the 124th cycle of approximation was not sufficiently accurate (see column 9 in table 7). table 7 shows that inequality (see column 9 in table 7) was unsatisfactory for the first 144 cycles. the determination of the investment value of a1 under valuation with respect to the other retail buildings under comparison appears in the final, 145th approximation cycle – n1 145cycle = 87.04% (n2 145cycle = 81.53%, n3 145cycle = 85.77%, n4 145cycle = 80.91% and n5 145cycle = 100%). in the 144th approximation cycle, the utility degree of project under comparison a1 calculates at n1 = 87.02%. the degrees of utility for the retail buildings under analysis show that a1 under valuation in the 145th approximation cycle is more beneficial than is the second retail building under comparison a2 by 5.51% and more beneficial than retail building under comparison a4 by 6.13%. there was a revision of the investment value x11 in every cycle (from x11 cycle 0 = 1774 euro/m2), each by 1 euro/m2 by size until inequality (see column 9 in table 7) was satisfied (x11 cycle 145 = 1629 euro/m 2). thus investment value x11 cycle e (respectively, 1774, ..., 1629) is checked for accuracy pertinent to retail building a1 by placing them into the bold cell of the decision making matrix (see table 5). all calculations were repeated according to stages 1-6 until inequality (see column 9 in table 7) was satisfied in the 145th cycle. table 7 shows that the calculations of investment value x11 cycle e become more and more accurate with each, next e approximation cycle for retail building a1 under analysis. 3.3 case study 3: provision of recommendations the results of the provision of recommendations by applying stages 1-5, 9 and 10 of the invar method for the retail buildings appear in table 8. initial data for the calculations are presented in table 5. meanwhile, the recommendations for bettering the criteria for these retail buildings under comparison appear in table 8. recommendations arrive in a matrix (see table degree of project utility and investment value assessments 679 table 7: revised changes in value and investment valuedeterminations for ikeashopping center under valuation a1 utility degree change in retail buildings under deliberation by rationalizing the corrected value x11 cycle e of building a1 approximation cycle * utility degree n1e utility degree n2e utility degree n3e utility degree n4e utility degree n5e ** *** 1 2 3 4 5 6 7 8 9 0 1774 83.05% 81.72% 85.78% 81.14% 100% 86.34% |− 4.11%| < 0.02% ... ... ... ... ... ... ... ... ... 124 1650 86.43% 81.56% 85.77% 80.95% 100% 86.94% |− 0.64%| < 0.02% ... ... ... ... ... ... ... ... ... 134 1640 86.72% 81.55% 85.77% 80.93% 100% 87.00% |− 0.34%| < 0.02% ... ... ... ... ... ... ... ... ... 144 1630 87.02% 81.53% 85.77% 80.91% 100% 87.05% |− 0.03%| < 0.02% 145 x1j iv = 1629 87.04% 81.53% 85.77% 80.91% 100% 87.05% |− 0.01 %| < 0.02 % * revised changes in value and investment value x11 cycle e (euro/m 2) of ikea shopping center under valuation a1. ** (n1e + n2e + n3e + n4e + n5e) ÷ 5 *** inequality to determine, whether the calculation of revised value x11 cycle e of ikea shopping center under valuation a1 is sufficiently accurate. 8) by using formulae 10 and 11 during stages 9 and 10. every window in table 8 describing alternative aj consists of three parts: xij – the value of the i-th criterion (xi) in the j-th alternative; quantitative recommendation iij showing the percentage of a possible improvement in the value of indicator xij for it to become equal to the best value xi max of criterion xi (xij = xi max); and quantitative recommendation rij showing the percentage of possible improvement of utility degree nj of alternative aj upon presentation of xij = xi max. if, for example, it would be possible to improve the assessment of the health &wellbeing criterion for building a3 (i33 = 42%) from the x33 = 7.5 value achieved up to the best value for a1 (x34 = 10.65), then the utility degree n3 for building a3 would increase by r33 = 2.1%. analogically, if the assessment of the energy criterion for building a3 (x43 = 5.1) could be improved up to the amount of the best assessment for building a1 (x41 = 14.44), then the effectiveness of the criterion energy for building a3 would increase by i43 = 183.14%, and the utility degree n3 would increase by r43 = 9.1569% (see table 8). 3.4 case study 4: optimization of the value this example, based on stages 1-5 and 7, will determine, what the value x43 cycle e of the breeam energy section (see the number in bold in table 5) must be for project a3 to be equally competitive on the market, as compared to the other retail buildings under comparison (a1, a2, a4, a5) by a set assessment of all their positive and negative features. it is possible to optimize any one of the criteria or their composite parts by the new invar method, which deliberates the sustainability of retail buildings under analysis in an integrated manner by using pre-assessment reports. the optimization of the score of the energy section of breeam, which appears next, will serve as an example (see table 5). the determination of the optimized score x43 cycle e for the project under valuation a3 appears 680 a. kaklauskas table 8: quantitative recommendations submitted in a matrix form quantitative and qualitative information pertinent to alternatives criteria describing the alternatives * measurement units weight alternatives a1 a2 a3 a4 a5 health &wellbeing + points 1 x31 = 10.65 10 x33 = 7.5 8.3 10 (0%) (6.5%) (i33 = 42%) (28.31%) (6.5%) (0%) (0.325%) (r33 = 2.1%) (1.4157%) (0.325%) energy + points 1 x41 = 14.44 10.64 x43 = 5.1 10.2 13.1 (0%) (35.71%) (i43 = 183.14%) (41.57%) (10.23%) (0%) (1.7857%) (r43 = 9.1569%) (2.0784%) (0.5115%) *the sign “+/-” indicates that a greater (lesser) criterion value corresponds to a greater (lesser) significance for a user (stakeholder). in table 9. the formulation of this task is the following: determine, what the optimized score x43 cycle e should be for building under valuation a3 for it to be equally competitive in the market, as compared with the sustainable retail buildings (a1, a2, a4, a5) after a complex assessment of their positive and negative features. the decision making matrix (see table 5), the amalgamated block diagram submitted in figure 1 and the calculations performed by stages 1-5 and 7 serve as the basis for these calculations. the results of the e approximation cycles of these calculations appear in table 9. the aim was to establish, what the score x43 cycle e should be (see the numbers table 9: what score x43 cycle e should be for building a3 to be equally competitive in the market with other retail buildings under comparison (a1, a2, a4, a5) approximation cycle score x43 cycle e utility degree n1e utility degree n2e utility degree n3e utility degree n4e utility degree n5e * ** 0 4.93 83.05% 81.72% 85.78% 81.14% 100% 86.34% |− 0.7%| > 0.1% ... ... ... ... ... ... ... 7 5 83.05% 81.72% 85.81% 81.14% 100% 86.34% |− 0.67%| > 0.1% ... ... ... ... ... ... ... 57 5.5 83.04% 81.72% 86.03% 81.14% 100% 86.39% |− 0.45%| > 0.1% ... ... ... ... ... ... ... 107 6 83.02% 81.72% 86.25% 81.14% 100% 86.43% |− 0.19%| > 0.1% ... ... ... ... ... ... ... 157 6.5 83.01% 81.72% 86.47% 81.14% 100% 86.47% |0%| < 0.1% * (n1e + n2e + n3e + n4e + n5e) ÷ 5 ** inequality 9 to determine, whether the calculation of revised value x43 cycle e of under valuation a3 is sufficiently accurate. in bold in tables 5 and 9) for building a3 to be equally competitive in the market with other retail buildings under comparison (a1, a2, a4, a5). applications of invar stages 1-5 and 7 serve to accomplish a set assessment of the positive and negative features of all these retail buildings. table 9 shows that inequality 9 was unsatisfactory for the first 156 cycles. the score x43 was increased in every cycle (from x43 cycle 0 = 4.93) by an amount of 0.01 until inequality 9 was satisfied (x43 cycle 157 = 6.5). then scores x43 cycle e (respectively, 4.94, ... and 6.5) are checked for accuracy pertinent to building a3 by placing these results into the bold cell of the decision making matrix (see tables 5 and 9). all the calculations were repeated according to formulae stages 1-5 and 7 until inequality 9 was satisfied in the 157th cycle. table 9 shows the degree of project utility and investment value assessments 681 table 10: what should the value x11 cycle e of ikea shopping center be for this project to become the best among those under deliberation? approxımation cycle investment value x11 cycle e (euro/m2) utility degree n1e n2e n3e n4e n5e 0 1774 83.05% 81.72% 85.78% 81.14% 100% 124 1650 86.43% 81.56% 85.77% 80.95% 100% 134 1640 86.72% 81.55% 85.77% 80.93% 100% 174 1600 87.92% 81.49% 85.77% 80.86% 100% 274 1500 91.14% 81.34% 85.77% 80.68% 100% 424 1350 96.73% 81.07% 85.76% 80.37% 100% 474 1300 98.84% 80.97% 85.76% 80.25% 100% 484 1290 99.27% 80.95% 85.76% 80.23% 100% 494 1280 99.72% 80.93% 85.76% 80.20% 100% 499 1275 99.94% 80.92% 85.76% 80.19% 100% 504 1270 100% 80.78% 85.62% 80.04% 99.84% calculations of score x43 cycle e becoming more and more accurate with each, next approximation cycle for building under analysis a3. 3.5 case study 5: what should the value of the ikea shopping center be for this project to be the best among those under deliberation? the calculations in this example are by approximation e cycle to determine, what the value x11 cycle e of ikea shopping center a1 should be for this project to become best among those under deliberation a1-a5. the price of this project continues being reduced by 1 euro/m 2 until n1e becomes equal to 100% (stages 1-5 and 11). table 10 shows that n1e = 100% had not been satisfied over 503 cycles. that is the reason the investment value x11 cycle e of the project under valuation a1, which had been revised 504 times, was entered into the decision making matrix (table 5) for the multiple criteria analysis of retail building. table 10 shows that, in each following approximation cycle, the calculation of the revised investment value x11 cycle e of building under valuation a1 became more and more accurate. all the calculations by stages 1-5 and 11 were repeated, until n1e = 100% was satisfied in the 504th cycle. it can be stated that this project can become the most effective among the projects under comparison, once the value x11 cycle e of the ikea shopping center = 1270 euro/m 2. 4 conclusion this article recommends a new multiple criteria analysis, the invar method (degree of project utility and investment value assessments along with recommendation provisions). invar method stages 1-5 are identical as copras method [9, 10, 14]. it generates conditions to assess management, health & wellbeing, energy, transport, water, materials, waste, land use 682 a. kaklauskas & ecology, pollution, innovation, comfort, quality of life and aesthetics as well as its technical, economic, legal/regulatory, educational, social, cultural, ethical, psychological, emotional, religious and ethnic aspects in conformity with requirements and opportunities for clients, designers, contractors, users and other stakeholders. the systems and the values and weights of the quantitative and qualitative criteria express these requirements. the invar method allows determining the strongest and weakest aspects of each project pertinent to a sustainable building and its constituent parts. performance of the analyses is to learn by what degree one alternative is better than is another. furthermore, this discloses the details, why this is so. the practical case studies presented in this research validate this developed method. an analysis of the results reached by the invar method permits making the following claims: • the invar method can determine the utility degree and investment values of the projects under deliberation. • the invar method can provide digital tips for improving projects. • the invar method can define, what the value of a selected criterion needs to be for the project under deliberation to be equally competitive in the market, as compared with others under comparison after a set assessment of all their positive and negative features. • the invar method can calculate, what the value of the project under deliberation should be for this project to become the best among others under deliberation. acknowledgment the author thanks ms. vijole arbas for her help in translating to english and editing this article. bibliography [1] schwartz, y., raslan, r. 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[32] calculations with invar method. http://iti.vgtu.lt/ilearning/simpletable.aspx? sistemid=675 international journal of computers communications & control issn 1841-9836, 9(6):749-757, december, 2014. an online load balancing algorithm for a hierarchical ring topology c.i. paduraru ciprian i. paduraru computer science department university of bucharest ciprian.paduraru2009@gmail.com abstract: ring networks are an important topic to study because they have certain advantages over their direct network counterparts: easier to manage, better bandwidth, cheaper and wider communication paths. this paper proposes a new online load balancing algorithm for distributed real-time systems having a hierarchical ring as topology. the novelty of the algorithm lies in the goal it tries to achieve and the method used for load balancing. the main goal of the algorithm is to correctly utilize the computing resources in order to satisfy the average response time of clients. the secondary goal is to ensure fairness between the numbers of requests solved per client with respect to the average response time. a request from a client is moving through the network until a node considers that it can solve the request in the promised average time for that client or until it seems like the best opportunity to avoid any additional delays in solving it. a performance analysis and motivation for the proposed algorithm is given with respect to the goals it tries to achieve. the results show that the proposed algorithm satisfies its goals. keywords: ring; hierarchical; distributed; balancing; algorithm; fairness 1 introduction today, the common approach for processing user requests sent to a web or network-based service is to handle them using a distributed architecture of computers. in this context, the performance of the processing system is closely related to user experience and service availability, and can therefore play an important role in the success or failure of the respective service on the market. as sufficient hardware resources for processing a large number of requests are generally expensive, a good algorithm for the distribution of load between the processing units in the distributed system is necessary to save costs in addition to increase client’s satisfaction. this paper presents a load balancing algorithm for hierarchical ring network. a hierarchical ring (figure 1) is an alternative to 2d meshes or tori [5]. hierarchical type was chosen to show the generality of the algorithm. instead, we can have rings combined with other network topologies. in a ring network every node has exactly two neighbors: pi is connected pi+1 to and pi−1. in this paper, if we consider that n is the number of processors in the ring, then we assume that all additions on processors indices are done modulo n. in the hierarchical ring network considered, each sub-network has a leader which is responsible to store information and coordinate some activities. in the continuation, when we refer to a sub-network of a leader node then this includes only the direct nodes under the leader’s level. requests are received by a web service which coincide with the leader node of the network and are considered to have an estimated average time to complete. the goals of the presented algorithm are the most significant for services provided in the present. the main goal is to ensure a certain average response time given for each client, depending on what we call a user license. the user license can be interpreted as a contract between the service provider and the user, where parameters referring to the delivery of the service are specified. these include parameters copyright © 2006-2014 by ccc publications 750 c.i. paduraru l l l figure 1: two levels ring hierarchy. relevant for load distribution, like the average response time of the system for certain types of requests. the secondary goal is to ensure fairness between the numbers of requests solved per client with respect to the average response time. a simulator has been created to demonstrate how the algorithm succeeds to satisfy the desired goals. the rest of the paper is organized as follows: in section 2 there is a discussion about research made on load balancing for ring topologies or other network types but appropriate to our goals. in section 3 the design of the load balancing algorithm is discussed. if first starts with the assumptions made over the proposed algorithm then it describes the main ideas and pseudocode behind the decision making process. section 4 shows the simulation results compared with a general load balancing for a hierarchical ring network . conclusions are given in the last section. 2 related work a description of hierarchical ring networks is given in [5]. they are presented as an interesting alternative to popular direct networks such as 2d meshes or tori. advantages of using them are also described here: simple router designs, wider communications paths and faster networks than their direct network counterparts. however the paper is not dealing with load balancing algorithms. its a study to determine how large hierarchical ring networks can become before their performance deteriorates due to their bisection bandwidth constraints. there are not many papers discussing about real time load balancing for ring networks. the most appropriate paper for our presentation is [1]. in comparison with [1], which is a general load balancer for rings, the new proposed load balancing algorithm has another two goals: satisfy the average response time specified in the owners license type and ensure some fairness between the requests with respect to their specified response times. other papers, like [6], are performing a statical load balancing of requests on a ring network. paper [2] presents a load balancing algorithm for distributed systems having the same two goals. however, the algorithms presented there are inapplicable to the ring networks. it uses the fact that nodes can communicate directly with a master and it would be too much overhead to simulate the same implementation algorithm on a ring. both the requests and results are exchanged directly from master to workers. 3 design of the algorithm 3.1 assumptions it is assumed that when a node finishes a request, the results are sent back to clients directly from that node. the algorithm allows for the system to be heterogeneous, workstations may differ in processing capacity. the processing time of a request is expressed as the ”request’s length” and can be predetermined. we assume that getesttimetocompute(request) returns the an online load balancing algorithm for a hierarchical ring topology 751 estimated processing time of a request on any node and its time complexity is constant. one simple way to do this is to benchmark how fast each node can execute different requests length intervals, then group and store these results in a data structure on the node. it is considered that request processing is workstation independent (all types of requests can be processed by any of the nodes). requests are independent (the order in which requests are processed does not affect the correctness of the result) and indivisible (can only be handled by a single worker at one moment). the communication time is not generally important for the algorithm. the reason is that while a request spends time moving through the network its priority increases. 3.2 high level implementation and the communication protocol the main responsibilities of nodes are to take decisions, solve requests and communicate with neighbors. the communication and request’s solving should run in different threads to avoid communication blocking. requests are received by the leader of the ring and send further until a node can execute it in the required time or when that node is a good opportunity to save additional delay in response time. nodes evaluates if a request can be executed by them or not depending on the time needed to complete all other requests waiting there and having a higher priority than the considered request. also, in the case of requests that are close to their deadline (or already passed deadline), if they have a higher priority than all other requests waiting on a node then we choose that node to minimize the additional delays in the response time. by using the above two conditions there is a possibility that all nodes to decline solving a new request. in this case measures need to taken in order to avoid affecting the performance of the system with the new request running too many times through the ring. the method used is to have a variable on each request that represents a bonus time considered when a node evaluates if it has enough time to execute the request. each time the request goes back through the node that initiated it, the leader of the ring, this variable is incremented by some value determining the nodes to accept it faster. in the continuation of this section these ideas are presented in more details. it starts with the high level operations and messages exchanged between nodes, then it continues to explain the implementation of data structure and decision making in more details using pseudocode and complexity analysis. to send data between nodes, two functions are used: send(data) used to send data to the next node on the same sub-network and sendtosubnetwork(data) which sends a message from a leader to its coordinated sub-network (this helps moving a message from a higher level network to a sub-network). another important function is isleadernode which has two prototypes. the first one doesn’t have any parameters tests if the node is the leader of a sub-ring and the second one with a parameter representing a message tests if the node is a leader and the one who created/added that message in its sub-network. there are two types of messages used in the communication protocol: gather and request. a request message is used for sending requests between nodes and contains the following: data context for request execution, the average response time specified in the owners license, timestamp when created, the time when should ideally finish and the current bonus time. the code below shows the high level implementation of the decision making when a request message is received by a node. a node that is not the leader at the level where a request message is sent can either store the request for later execution or send it further in the same network level. additionally, a leader node has the option to send the request down in its sub-network. users might also want to relax the conditions and not decrease the bandwidth performance with requests that are travelling the ring many times in order to find a node that accepts them 752 c.i. paduraru (bonus_step variable is considered as input given by user). to make this possible, when a leader receives back a request that it previously sent to its sub-network, the bonus time variable on the request will be increased. an interesting property of this communication protocol is that if a request travels again back to the leader node of a sub-network because of the high workload, it can eventually get to another sub-network, if the leader evaluates that it is better to do so. onrequestreceived(request) if (canexecuterequest(request)) addrequest(request); else if (isleadernode(request) and canexecuteonmysubnetwork(request)) { if (already received this request) request.bonustime += bonus_step sendtosubnetwork(request) } else send(request); leader stores info about its sub-nodes pi pi+1 if pi is a not a leader, and can’t execute a request it send it to pi+1 request message contains: (data, id, length class, average time in owner’s license) l l l when a leader node receives a request it evaluates in order the following possibilities: execute itself, send it down in the coordinated sub-network or send to another node on the same level. figure 2: high level decision making for a new request. a gather message is initiated by leaders of each sub-network at fixed time periods and sent only to the nodes on the same level. the role of this message is to have a snapshot of the current load inside nodes. this information will be used to take a decision if a leader node should accept a request or not to be executed in its sub-network. the gather messages are asynchronous between different sub-networks. when a node receives a gather message, it adds its local load information (like how much load is in there) to the message and sends it further. when the message is received by the leader it updates its load information table. figure 3 is representative for this flow. the code below presents the action code of every node and the handler function for receiving gather messages. lasttimegathersent is a variable where we store the timestamp of the last gather message sending occured. t is threshould value set by the user, depending on how often he wants to send the gather message. solve function is supposed to run the effective job on the request. function extractnextrequest selects the task with the highest priority from the local list of tasks. onupdate() if (isleadernode() and (getcurrenttime() lasttimegathersent) > t) an online load balancing algorithm for a hierarchical ring topology 753 l 1 2 3 when a node receives this, it adds the current state to the message and send further leader initiates the update message at this point, the leader will receive a message with the combined states of the nodes at the same level figure 3: an update message in the ring. { lasttimegathersent = getcurrenttime() gather msg sendtosubnetwork(msg) } request = extractnextrequest() if (request != null) solve(request) ongatherreceived(msg) if (isleadernode(msg)) updatelocalstate(msg) else { addlocalstate(msg) send(msg) } 3.3 decision making to execute a request locally on a node (leaf or leader) to make computations easier, the average response times specified in the clients licenses are normalized. if t1, t2, t3, ..., tn are the average response times and tmax = max ti, then tk = tk tmax . requests are stored and evaluated based on their priorities. the priority of a request is defined as the waiting time for the request to be solved divided by the inverse of the normalized average response time specified in the owners license. if we denote withwaitingtime(request) the waiting time of the request to be solved, and owner(request) the index of the owner license then the priority computation can be written as priority(request) = waitingtime(request)1 towner(request) , where waitingtime(request)=currenttime()-request.createtime. priorities of requests waiting on a node are dynamic and could modify in time. this is a key point of the algorithm which gives the fairness between the clients with respect to their average response times. the formula used also helps in the case of requests that travel in the network for a long time. the priority of a request increases with its waiting time regardless of the license’s specifications. if a requests travel a long time then it has a bigger priority and its chances to be added in a node are increased. requests are stored in a node using a linked list. at each query of function canexecuterequest(request) the algorithm iterates over the existing items and sum up the time needed to compute all requests 754 c.i. paduraru that have a priority higher than the new request. using the bonus modifier and comparing the result with the average response time of the request’s owner we can find out if the request can be executed on that node or not. also, in the case of requests that are close to ideal execution deadline or should have been executed until now, we check if their priorities are higher than all other priorities waiting in the node. if this is valid then this node is good fit for the request because it will be the first request selected for execution, thus minimizing the delays in response time. a pseudocode for this is given below. requestslist is storing requests of a node. request.bonus represents the bonus time given by the leader, while request.idealtimeoffinish is the precomputed time when the request should be solved in order to satisfy the owner’s license specfication. t is a threshold value defined by user. it could be either the average time for moving data between consecutive nodes, the average time needed for processing it, or a heuristics combining these. canexecuterequest(request) totalesttime = 0; newesttime = getesttimetocompute(request) / request.bonus bestpriority = null; foreach req in the requestslist { if (priority(req) > priority(request)) totalesttime = totalesttime + getesttimetocompute(req); if (priority(req) > bestpriority) bestpriority = priority(req) } remainingtime = request.idealtimeoffinish (getcurrenttime() + totalesttime) isclosetodeadline = (request.idealtimeoffinish getcurrenttime()) <= t return (remainingtime >= 0 or (isclosetodeadline and bestpriority < priority(request)*request.bonus) 3.4 decision making to execute a request on a sub-network this type of decision is valid only for leader nodes. in order to make this possible the gather messages are sent in the sub-network in order to collect workload information. in an ideal case, a leader would know informations about all waiting requests in its sub-network nodes and run the same canexecuterequest function. but such a message would be too large creating a bandwidth and processing time overhead. a tradeoff solution between performance and quality of the decision result is to gather statistics on how much time the current requests would take to execute on different intervals of priorities. if phigh and plow are estimated bounds of the priorities, then splitting on n equal intervals would result in timesumi storing the sum of times to solve the requests with priorities in interval [ plow + phigh−plow n ∗ i, plow + phigh−plow n ∗ (i + 1)−1 ] . the tradeoff can be adjusted using variable n. the gather message will contain the timesum array. when adding the local state to a gather message, a nodes responsibility is to iterate through all its waiting requests and for each one to add in the corresponding array index (the correct priority interval) the time needed by the node to solve it. the leader will keep the final timesum array and use it for decision making. to find out if a request can be executed by the leaders sub-network we need to sum up the values of all intervals of greater priority than the considered request. then, we divide this sum to the number of nodes in the sub-network to find out the average time needed to finish all higher priority requests. the close to ideal deadline test is used here too, but this time we check if there an online load balancing algorithm for a hierarchical ring topology 755 are any values bigger than zero on intervals with greater priority than the considered request. below is presented the pseudocode for adding a local state to the gather message and the decision making of a leader if it should accept or not a request in its sub-network. getpriorityinterval does simple math to get the interval index from the priority of a request. addlocalstate(message) foreach req in the requestslist message.timesum[getpriorityinterval(req)] += getesttimetocompute(req) canexecuteonmysubnetwork(request) p = priority(request) totaltime = 0; for i = p +1 to n totaltime += timesum[i] averagetotaltime = totaltime / numnodesinsubnetwork remainingtime=(request.idealtimeoffinish (getcurrenttime() + averagetotaltime)) isclosetodeadline = (request.idealtimeoffinish getcurrenttime()) <= t return remainingtime>= 0 or (isclosetodeadline and there is no timesum[k]>0 with k from p+1 to n) the complexities of the operations used here are linear which can be good or bad depending on request’s granularity. if there are generally very small requests to execute then this linear time might affect the global performance. an idea to solve this case would be to use a heap tree data structure (which provides logarithmic time for operations) and to rebuild the tree at different time intervals considering the newest priorities. 4 simulation results to demonstrate that the algorithm satisfies the proposed goals, a simulator in mpi has been created. the nodes are processes on different machines connected in a network. the test implies a total of 64 processes over 8 machines. random requests where continuously generated with a normal distribution in length classes. the estimated times to compute the requests were between [10, 500] milliseconds (depending on the computing power of the nodes). same interval was used for the average response times in the clients licenses. two relevant tests are used to show how the load balancer works. the results are compared to the results of the algorithm in [1]. test 1: check the response times with different workloads. for this test, the simulator created random requests considering the total computing power of the system. table 1 shows a comparison between both algorithms in terms of response time delays, given as a percentage value from the value promised in the owner’s license. final results were obtained by averaging multiple simulation results. in the proposed algorithm the average response time goal is satisfied, with important delays appearing just when the workload was too high. test 2: check the fairness between requests with respect to the average response time when the available hardware resources are not enough for satisfying the requests. the simulator creates random requests to simulate a high workload then checks how many of them were solved per interval of average response time. the initial interval of average response times [10,500] was split in 5 intervals as the table 2 shows. ideally, the number of requests solved per each interval should be inverse proportional to the average value of the interval. 756 c.i. paduraru system workload average delays in response time proposed algorithm average delays in response time algorithm in [1] 20 % 1.23 % 15 % 50 % 1.45 % 26.4 % 100 % 1.72 % 54.8 % 200 % 103.14 % 104.2 % table 1: shows average response times with different system workloads. intervals of average response times average number of requests solved in proposed algorithm average number of requests solved in [1] 10-100 4233 1651 101-200 2397 1675 201-300 1683 1649 301-400 779 1693 401-500 541 1680 table 2: number of requests solved for different average response time intervals. the results show that the second goal is satisfied too in the proposed algorithm, while in the other load balancer there is no fairness between the clients. the proposed algorithm performs much better when comparing the maximum waiting times of requests thanks to the priority formula. because of the overhead needed to satisfy the goals, the proposed algorithm had a throughput with 2.11% smaller than the reference algorithm. with a proper tuning of the bonus_step, the number of intervals for splitting the local state data and the time to initiate a new gather message the algorithm can obtain peak performance with minimizing the overhead. these variables should be tuned considering the granularity of the nodes and the available bandwidth. in the simulation, bonus_step was equal to 2, the number of intervals was 5 and the time to initiate gather messages was 300 milliseconds. as a recommendation, these variables values should actually represent a percentage value of real input data. 5 conclusion this paper presented a load balancing algorithm for distributed real-time systems which have a hierarchical ring topology. the algorithm has two proposed goals: satisfy the average response time if the computing power allows this and keep the fairness between clients with respect to the response times specified in their license. the results presented in section 4 demonstrate that the algorithm satisfies the proposed goals. 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[2] ciprian paduraru, a new online load balancing algorithm in distributed systems, symbolic and numeric algorithms for scientific computing, synasc 14th edition, pages:327-334, 2012. an online load balancing algorithm for a hierarchical ring topology 757 [3] andrew s. tanenbaum, modern operating systems (3rd edition), prentice hall, december 2007. [4] kwang soo cho, un gi joo, heyung sub lee, bong tae kim, and won don lee, efficient load balancing algorithms for a resilient packet ring using artificial bee colony, applications of evolutionary computation, lncs, 6025:61-70, 2010. [5] g. ravindran and m. stumm, hierarchical ring topologies and the effect of their bisection bandwidth constraints, proc. intl. conf.parallel processing, i:51-55, 1995. [6] perry fizzano and clifford stein, scheduling on a ring with unit capacity links, proceedings of the sixth annual acm symposium on parallel algorithms and architectures, pages:210-219, 1994. 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[12] joseph (seffi) naor, adi rosen, gabriel scalosub, online time-constrained scheduling in linear and ring networks, journal of discrete algorithms, 8(4):346-355, 2010. international journal of computers communications & control issn 1841-9836, 9(4):482-496, august, 2014. observer-based non-fragile passive control for uncertain nonlinear sampled-data system with time-delay s.g. wang, y.s. li, y. wu shigang wang school of mechanical & electric engineering heilongjiang university no.74 xuefu road, harbin, 150080, p. r. china wsg6363@gmail.com yingsong li* college of information and communications engineering harbin engineering university no.145 nantong street, harbin, 150001, p. r. china *corresponding author: liyingsong82@gmail.com yi wu school of information science and technology heilongjiang university no.74 xuefu road, harbin, 150080, p. r. china wy51cn@yahoo.com.cn abstract: the problem of observer-based passive control for uncertain nonlinear sampled-data systems with time delay is investigated by using non-fragile passive control. attention is focused on the design of a non-fragile passive observer and a controller which guarantees the passivity of the closed-loop system for all admissible uncertainties. a sufficient condition for passivity and asymptotic stability of the combined system is derived via linear matrix inequality (lmi). finally, a simulation example is presented to show the validity and advantages of the proposed method. keywords: uncertain sampled-data system, time-delay systems, state observer, non-fragile passive control, linear matrix inequality 1 introduction in the past few years, sampled-data systems are widely encountered in the area of control theory and control engineering, such as welding process, aerospace, signal processing, earthquake prediction, due to its character as continuous control plant and discrete controller [1]. since time delay and inherent nonlinearity often occurs and causes serious deterioration of the stability of various engineering system, considerable research has been done and numerous results have been obtained relating to the analysis and synthesis of uncertain nonlinear sampled-data systems with time-delay, see e.g. [2–7]. among these results, non-fragile passive control problem have attracted particular attention. passivity is part of a broader and more general theory of dissipativity and non-fragility is a scheme of solving robustness of controller and observer themselves [8–11], they maintain the system’s internal stability. because non-fragile passive control has attractive features such as fast response, good transient response and insensitivity to variations in system parameters and external disturbance [12–16], which is likely to be an effective technique of control for uncertain nonlinear sampled-data system with time-delay. on the other hand, all above works are based on an implicit assumption that the states are all known. however, this unrealistic assumption is not always verified. and hence, the construction of the unmeasured states through the knowledge of the system’s inputs and outputs still an unavoidable task to solve any desired control problem [17–21]. however, to our knowledge, there copyright © 2006-2014 by ccc publications observer-based non-fragile passive control for uncertain nonlinear sampled-data system with time-delay 483 have been few results in literature of any investigation for observer-based non-fragile passivity uncertain nonlinear sampled-data system with time-delay. the above situation is exactly what concerns and interests us. a novel approach of non-fragile control combined with passive control is proposed for stabilizing a class of uncertain nonlinear systems with time-delay. by utilizing a non-fragile state observer, a novel control law is established such that the resulting closed-loop system is strictly passive. a sufficient condition for the passivity and asymptotic stability of the augmented system is derived via lmi. finally, an example is simulated to illustrated the advantage of the proposed method. 2 problem statement and preliminaries consider the plant of uncertain nonlinear sampled-data system with time-delay described by   ẋ(t) = (a0 + ∆a0)x(t) + (a1 + ∆a1)x(t − τ) + b0u(t) + f(x, u, t) + b1ω(t) y(t) = c1x(t) + h2u(t) z(t) = c2x(t) + h3ω(t) x(t) = x0 , t ∈ [−τ, 0] (1) where x(t) ∈ rn is the state, and u(t) ∈ rm is the control input, y(t) ∈ rp is regulated output, z(t) ∈ rq is measured output, ω(t) ∈ rr is the external disturbance input that belongs to l2[0, ∞], a0, a1, b0, b1, c1, c2, h2, h3 are known real constant matrices of appropriate dimension, ∆a0, ∆a1, are uncertain matrices. f is the uncertain nonlinear function vector, f(0, 0, t0) = 0, and f satisfies the lipschitz condition. assumption 2.1. the continuous plant is time-driven with a constant sampling period h(h > 0). discretizing system (1) in one period, we can obtain the discrete state equation of the plant of sampled-data system   x(k + 1) = (g0 + ∆g0)x(k) + (g1 + ∆g1)x(k − d) + h0u(k) + f̄(xk, xk−d, k) + h1ω(k) y(k) = c1x(k) + h2u(k) z(k) = c2x(k) + h3ω(k) x(k) = x0 , k ≤ 0 (2) where g0 = e a0h , g1 = ∫ h 0 ea0(h−w)dwa1 h0 = ∫ h 0 ea0(h−w)dwb0 , h1 = ∫ h 0 ea0(h−w)dwb1 f̄(x, u, k) = ∫ h 0 ea0wdwf(x, u, t) f̄(x̂, u, k) = ∫ h 0 ea0wdwf(x̂, u, t) consider non-fragile observer described by{ x̂(k + 1) = g0x̂(k) + g1x̂(k − d) + h0u(k) + (l + ∆l)(y(k) − ŷ(k)) + f̄(x̂k, xk−d, k) ŷ(k) = c1x̂(k) + h2u(k) (3) 484 s.g. wang, y.s. li, y. wu where x̂(k) ∈ rn is state of observer, and l is observer gain, ∆g0, ∆g1, are uncertain matrices and ∆l are observer gain perturbation which are assumed to be of the following form: [∆g0 ∆g1 ∆l] = m0f(k)[e0 e1 e2] (4) on the other hand non-fragile controller described by u(k) = (k + ∆k)x̂(k) (5) where k is controller gain, ∆k represents corresponding gain perturbation, and generally, there exist the following two classes of perturbation in ∆k: type1: ∆k is of the additive form: ∆k = m0f(k)e3 (6) type2: ∆k is of the multiplicative form: ∆k = m0f(k)e4k (7) where m0, e0, e1, e2, e3, and e4 are real matrices with appropriate dimension and f(k) ∈ rk×l is an unknown time-varying matrix function satisfying f t(k)f(k) ≤ i assumption 2.2. f̄(xk, xk−d, k), f̄(x̂k, x̂k−d, k) satisfies the quadratic inequality in the domains of continuity, that is f̄t(xk, xk−d, k)f̄(xk, xk−d, k) ≤ δ21xt(k)mt1 m1x(k) + δ 2 d1x t(k − d)mtd1md1x(k − d) (8) let ξt1 = [ et(k) x̂t(k) et(k − d) x̂t(k − d) f̄t(xk, xk−d, k) ] , then (8) can be conveniently written as ξt1 (k)   −δ21mt1 m1 ⋆ ⋆ ⋆ ⋆ −δ21mt1 m1 −δ 2 1m t 1 m1 ⋆ ⋆ ⋆ 0 0 −δ2d1m t d1md1 ⋆ ⋆ 0 0 0 −δ2d1m t d1m ⋆ 0 0 0 −δ2d1m t d1m i   ξ1(k) ≤ 0 (9) in addition, f̄t(x̂k, x̂k−d, k)f̄(x̂k, x̂k−d, k) ≤ δ22x̂t(k)mt2 m2x̂(k) + δ 2 d2x̂ t(k − d)mtd2md2x̂(k − d) (10) let ξt2 = [ et(k) x̂t(k) et(k − d) x̂t(k − d) f̄t(x̂k, x̂k−d, k) ] , then (10) can be conveniently written as ξt2 (k)   0 ⋆ ⋆ ⋆ ⋆ 0 −δ22mt2 m2 ⋆ ⋆ ⋆ 0 0 0 0 0 0 −δ2d2m t d2md2 ⋆ 0 0 0 0 i   ξ2(k) ≤ 0 (11) observer-based non-fragile passive control for uncertain nonlinear sampled-data system with time-delay 485 where δ1, δ2, δd1, δd2 are the bounding parameter, and m1, m2, md1, md2 are constant matrices such that f(0, 0, k) = 0 and x = 0 is an equilibrium of system (2) for dk = 0. the objective of this paper is to design observer-based non-fragile passive controller, substitute non-fragile observer (4) and controller (5) into system (2), and let e(k) = x(k) − x̂(k), then resulting error closed-loop system is obtain by   e(k + 1) = (g0 − lc1 + ∆g0 − ∆lc1)e(k) + ∆g0x̂(k) + (g1 + ∆g1)e(k − d)+ ∆g1x̂(k − d) + h1ω(k) + f̄(x) − f̄(x̂) x̂(k + 1) = (g0 + h0k + h0∆k)x̂(k) + g1x̂(k − d) + (l + ∆l)c1e(k) + f̄(x̂(k)) (12) before proceeding to this main results, the following useful assumption and lemmas are need. assumption 2.3. suppose that the matrix c1 has full row rank (i.e. rank(c1)=p). for convenience of discussion, the singular value decomposition of c1 as follows: c1 = u [ s 0 ] v t where is s ∈ rp×p a diagonal matrix with positive diagonal elements in decreasing order, 0 ∈ rp×(n−p) is a zero matrix, and u ∈ rp×p and v ∈ rn×n are unitary matrices. lemma 1. [22] for a given c1 ∈ rp×n with rank(c1)=p,assume that x ∈ rn×n is a symmetric matrix, then there exists a matrix x̂ ∈ rp×p such that c1x = x̂c1 if and only if x = v [ x̂11 0 0 x̂22 ] v t where x̂11 ∈ rp×p and x̂22 ∈ r(n−p)×(n−p) lemma 2. [23] (schur complement) for a given symmetric matrix s = st = [ s11 s12 st12 s22 ] with s11 ∈ rr×r, the following conditions are equivalent: (1) s < 0 (2) s11 < 0, s22 − st12s −1 11 s12 < 0 (3) s22 < 0, s11 − s12s−122 s t 12 < 0 lemma 3. [24] for given matrices q = qt, h, and e, with appropriate dimensions q + hf(k)e + etf t(k)ht < 0 holds for all f(k) satisfying f t(k)f(k) ≤ i if and only if there exists ε > 0 q + εhht + ε−1ete < 0 definition 4. the systems (2) is called passive if there exists a scalar β ≥ 0 such that ∞∑ k=0 ω(k)z(k) ≥ β , ∀ω ∈ l2[0, ∞] where β is some constant which depends on the initial condition of the system. 486 s.g. wang, y.s. li, y. wu 3 main results theorem 5. for system(2) and observer (3), if there exist two symmetric and positive matrices r̄ ∈ rn×n, p̄ ∈ rn×n, two real matrices y1 ∈ rm×n, y2 ∈ rn×p and three positive constants ε1, ε2, and ε3 such that the following holds: ξ = [ ξ11 ξ t 21 ξ21 ξ22 ] < 0 (13) then there exist two gains k = y1p̄ −1, and l = y2usx̂ −1 11 s −1ut, such that system is asymptotically passive stable. where ξ11 =   w1 − r̄ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 w2 − p̄ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 −q1 ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 0 −q2 ⋆ ⋆ ⋆ ⋆ −c2r̄ −c2p̄ 0 0 −h2 − ht2 ⋆ ⋆ ⋆ 0 0 0 0 0 0 ⋆ ⋆ 0 0 0 0 0 0 0 ⋆ g0r̄ − y2c1 0 g0 0 h1 i −i −r̄   ξ21 =   y2c1 g0p̄ + h0y1 0 g1 0 0 i 0 0 0 0 0 0 0 0 ε1m t 0 e0r̄ − e2c1r̄ e0p̄ e1 e1 0 0 0 0 0 0 0 0 0 0 0 0 e2cr̄ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 e3p̄ 0 0 0 0 0 0 δ1m1r̄ 0 δd1md1 δd1md1+ δd2md2 0 0 0 0   ξ22 =   −p̄ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 −ε1i ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 −ε1i ⋆ ⋆ ⋆ ⋆ ⋆ ε1m t 0 0 0 −ε1i ⋆ ⋆ ⋆ ⋆ 0 0 0 0 −ε1i ⋆ ⋆ ⋆ ε1(h0m0) t 0 0 0 0 −ε1i ⋆ ⋆ 0 0 0 0 0 0 −ε1i ⋆ 0 0 0 0 0 0 0 −ε1i   proof: choose a lyapunov function candidate for the system (12) as follow: observer-based non-fragile passive control for uncertain nonlinear sampled-data system with time-delay 487 v = v1 + v2 + v3 + v4 v1 = e t(k)re(k) v2 = k−1∑ i=k−h et(i)q1e(i) v3 = x̂ t(k)px̂(k) v4 = k−1∑ i=k−h xt(i)q2x(i) where r = rt > 0, q1 = qt1 > 0, q2 = q t 2 > 0, and p = p t > 0. define vector ξ(k) =   e(k) x̂(k) e(k − d) x̂(k − d) ω(k) f̄(xk, xk−d, k) f̄(x̂k, x̂k−d, k)   , θ1 =   (g0 − lc1 + ∆g0 − ∆lc1)t ∆gt0 (g1 + ∆g1)t ∆gt1 ht1 i −i   t θ2 = [ h(l + ∆l)c g0 − h0k 0 g1 0 0 i ] therefore, ∆v = ∆v1 + ∆v2 + ∆v3 + ∆v4 = ξt(k)θt1 rθ1ξ(k) + e t(k)q1e(k) − et(k − d)q1e(k − d)+ ξt(k)θt2 pθ2ξ(k) + x̂ t(k)q2x̂(k) − x̂t(k − d)q2x̂(k − d) = ξt(k)π1ξ(k) (14) on one hand, the sufficient condition of stability ∆v < 0, implies that π1 < 0, that is π1 :=   q1 − r ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 q2 − p ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 −q1 ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 0 −q2 ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 0 0 0 ⋆ ⋆ ⋆ ⋆ 0 0 0 0 0 0 ⋆ ⋆ ⋆ 0 0 0 0 0 0 0 ⋆ ⋆ g0 − lc1+ ∆g0 − ∆lc1 ∆g0 g1 + ∆g1 ∆g1 h1 i −i −r−1 ⋆ (l + ∆l)c g0 − h0k 0 g1 0 0 i 0 −p −1   < 0 (15) 488 s.g. wang, y.s. li, y. wu on the other hand, utilizing (14) with ω(k) ∈ l2[0, +∞] ̸= 0, one is obtained by ∆v − 2zt (k)ω(k) ≤ ξt(k)     q1 − r ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 q2 − p ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 −q1 ⋆ ⋆ ⋆ ⋆ 0 0 0 −q2 ⋆ ⋆ ⋆ 0 0 0 0 0 ⋆ ⋆ 0 0 0 0 0 0 ⋆ 0 0 0 0 0 0 0   + θt1 rθ1 + θ t 2 pθ2 − 2θ t 3 θ4 } ξ(k) = ξt(k)π2ξ(k) (16) if π2 < 0, then ∆v (k) − 2zt(k)ω(k) < 0 and from which it follows that ∞∑ k=0 ω(k)z(k) > 1/2 ∞∑ k=0 ∆v = 1/2[v (0) − v (∞)] (17) due to v (k) > 0 for x ̸= 0 and v (k) = 0 for x = 0, if follows as k → ∞ that system (12) is strictly passive. in virtue of definition 4, the strictly passive condition is guaranteed if π2 < 0 and it can be expressed conveniently as   q1 − r ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 q2 − p ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 −q1 ⋆ ⋆ ⋆ ⋆ 0 0 0 −q2 ⋆ ⋆ ⋆ 0 0 0 0 0 ⋆ ⋆ 0 0 0 0 0 0 ⋆ 0 0 0 0 0 0 0   + θt1 rθ1 + θ t 2 pθ2 − 2θ t 3 θ4 < 0 (18) application of the lemma 2 to (18) puts it into the form:   q1 − r ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 q2 − p ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 −q1 ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 0 −q2 ⋆ ⋆ ⋆ ⋆ ⋆ −c2 −c2 0 0 −h2 − ht2 ⋆ ⋆ ⋆ ⋆ 0 0 0 0 0 0 ⋆ ⋆ ⋆ 0 0 0 0 0 0 0 ⋆ ⋆ g0 − lc1+ ∆g0 − ∆lc1 ∆g0 g1 + ∆g1 ∆g1 h1 i −i −r−1 ⋆ (l + ∆l)c1 g0 − h0(k + ∆k) 0 g1 0 0 i 0 −p −1   < 0 (19) substituting the uncertainty structure into (19) and rearranging, we get observer-based non-fragile passive control for uncertain nonlinear sampled-data system with time-delay 489   q1 − r ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 q2 − p ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 −q1 ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 0 −q2 ⋆ ⋆ ⋆ ⋆ ⋆ −c2 −c2 0 0 −h2 ⋆ ⋆ ⋆ ⋆ 0 0 0 0 0 0 ⋆ ⋆ ⋆ 0 0 0 0 0 0 0 ⋆ ⋆ g0 − lc1 0 g0 0 h1 i −i −r−1 ⋆ lc g0 − h0k 0 g1 0 0 i 0 −p −1   +θ5f(k)θ6 + θ t 6 f t(k)θt5 + θ7f(k)θ8 + θ t 8 f t(k)θt7 + θ9f(k)θ10+ θt10f t(k)θt9 < 0 (20) where θ5 = [ 0 0 0 0 0 0 0 mt0 0 ]t θ6 = [ e0 − e2c1 e0 e1 e1 0 0 0 0 0 ] θ7 = [ 0 0 0 0 0 0 0 0 mt0 ]t θ8 = [ e2c1 0 0 0 0 0 0 0 0 ] θ9 = [ 0 0 0 0 0 0 0 0 (h0m0)t ]t θ10 = [ 0 e3 0 0 0 0 0 0 0 ] then by lemma 3, the inequality (20) holds if and only if for some εi > 0 (i=1,· · · , 3) π3 + ε1θ5θ t 5 + ε −1 1 θ t 6 θ6 + ε2θ7θ t 7 + ε −1 2 θ t 8 θ8 + ε3θ9θ t 9 + ε −1 3 θ t 10θ10 < 0 (21) where π3 :=   q1 − r ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 q2 − p ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 −q1 ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 0 −q2 ⋆ ⋆ ⋆ ⋆ ⋆ −c2 −c2 0 0 −h2 ⋆ ⋆ ⋆ ⋆ 0 0 0 0 0 0 ⋆ ⋆ ⋆ 0 0 0 0 0 0 0 ⋆ ⋆ g0 − lc1 0 g0 0 h1 i −i −r−1 ⋆ lc1 g0 − h0k 0 g1 0 0 i 0 −p −1   on using the lemma 2, it becomes that 490 s.g. wang, y.s. li, y. wu   π3 ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ε1θ t 5 −ε1i ⋆ ⋆ ⋆ ⋆ ⋆ θ6 0 −ε1i ⋆ ⋆ ⋆ ⋆ ε2θ t 7 0 0 −ε2i ⋆ ⋆ ⋆ θ8 0 0 0 −ε2i ⋆ ⋆ ε3θ t 9 0 0 0 0 −ε3i ⋆ θ10 0 0 0 0 0 −ε3i   < 0 (22) thirdly, introduce nonlinearities (8) and (10) into (22), then   π4 ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ε1θ t 5 −ε1i ⋆ ⋆ ⋆ ⋆ ⋆ θ6 0 −ε1i ⋆ ⋆ ⋆ ⋆ ε2θ t 7 0 0 −ε2i ⋆ ⋆ ⋆ θ8 0 0 0 −ε2i ⋆ ⋆ ε3θ t 9 0 0 0 0 −ε3i ⋆ θ10 0 0 0 0 0 −ε3i   < 0 (23) where π4 :=   φ1 ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ −δ21mt1 m1 φ2 ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 φ3 ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 −δ2d1m t d1md1 φ4 ⋆ ⋆ ⋆ ⋆ ⋆ −c2 −c2 0 0 −h2 ⋆ ⋆ ⋆ ⋆ 0 0 0 0 0 0 ⋆ ⋆ ⋆ 0 0 0 0 0 0 0 ⋆ ⋆ g0 − lc1 ∆g0 g0 0 h1 i −i −r−1 ⋆ lc1 g0 − h0k 0 g1 0 0 i 0 −p −1   < 0 φ1 = q1 − r − δ21mt1 m1 φ2 = q2 − p − δ21mt1 m1 − δ 2 2m t 2 m2 φ3 = −q1 − δ2d1m t d1md1 φ4 = −q2 − δ2d1m t d1md1 − δ 2 d2m t d2md2 pre-multiplying and post-multiplying (23) by diag { r−1 p −1 13 columns︷ ︸︸ ︷ i · · · i } , and from lemma 1, the condition c1r̄ = ˆ̄rc1 holds, moreover, setting y2 = l ˆ̄r. in the meanwhile, define p̄ = p −1, r̄ = r−1, y1 = kp̄, w1 = r −1q1r −1, w2 = p −1q2p −1, it is seen that (23)<0 is equivalent to (13), the means that the system (12) is asymptotically passive stable. theorem 6. for system(2) and observer (3), if there exist two symmetric and positive matrices r̄ ∈ rn×n, p̄ ∈ rn×n, two real matrices y3 ∈ rm×n, y4 ∈ rn×p and three positive constants ε4, observer-based non-fragile passive control for uncertain nonlinear sampled-data system with time-delay 491 ε5, and ε6 such that the following holds: ω = [ ω11 ω t 21 ω21 ω22 ] < 0 (24) then there exist two gains k = y3p̄ −1, and l = y4usx̂ −1 11 s −1ut, such that system is asymptotically passive stable. where ω11 =   w3 − r̄ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 w4 − p̄ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 −q1 ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 0 −q2 ⋆ ⋆ ⋆ ⋆ −c2r̄ −c2p̄ 0 0 −h2 − ht2 ⋆ ⋆ ⋆ 0 0 0 0 0 0 ⋆ ⋆ 0 0 0 0 0 0 0 ⋆ g0r̄ − y4c1 0 g0 0 h1 i −i −r̄   ω21 =   y4c1 g0p̄ + h0y3 0 g1 0 0 i 0 0 0 0 0 0 0 0 ε4m t 0 e0r̄ − e2c1r̄ e0p̄ e1 e1 0 0 0 0 0 0 0 0 0 0 0 0 e2cr̄ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 e4y3 0 0 0 0 0 0 δ1m1r̄ 0 δd1md1 δd1md1+ δd2md2 0 0 0 0   ω22 =   −p̄ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 −ε4i ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ 0 0 −ε4i ⋆ ⋆ ⋆ ⋆ ⋆ ε4m t 0 0 0 −ε5i ⋆ ⋆ ⋆ ⋆ 0 0 0 0 −ε5i ⋆ ⋆ ⋆ ε4(h0m0) t 0 0 0 0 −ε6i ⋆ ⋆ 0 0 0 0 0 0 −ε6i ⋆ 0 0 0 0 0 0 0 i   proof: theorem 6 ’s proof is same to theorem 5, so is omitted. 4 numerical example consider the plant of sampled-data system (1) with the parameters: 492 s.g. wang, y.s. li, y. wu a0 =   0 1 0 0 0 1 1 −3 1   , a1 =   0 1 0 1 0 0 1 −3 1   , b0 =   0.1 0.1 0 1.2 0.5 0.1   , b1 =   0.1 0.2 0 1.5 0 0.1   c1 = [ 1.5 0.6 1.0 0.5 0.6 0 ] , c2 = [ 3 4 1 ] , h2 = [ 0.1 0.1 0.1 0.1 ] , h3 = 0.5 by theorem 1 has a solution: p̄ =   3.8697 −2.7891 −0.4465 −2.7891 2.0973 −0.0138 −0.4465 −0.0138 1.3967   , y1 = [ −13.8330 9.8716 2.0314 1.4006 −0.8149 −0.9412 ] r̄ =   0.3864 −0.3364 −0.3658 −0.3364 0.3753 0.2113 −0.3658 0.2113 0.4907   , x̂11 = [ 0.0059 0.0333 0.0333 0.2139 ] , x̂22 = 1.0326 the non-fragile passive observer-based control are given by l = y2usx̂ −1 11 s −1ut =   −6.5298 −9.0418 2.7828 3.9663 8.6606 11.5095   , k = y1p̄ −1 = [ −1.3147 2.9654 1.0635 0.3124 0.0231 −0.5738 ] we present design method of observer-based non-fragile passive controller in this paper, the simulation results are given in figure 1. from figure 1, it can be seen the state estimation x̂(t) has a good trace performance with the external disturbance and input nonlinearity. 5 application to stabilization of an inverted pendulum on a cart an inverted pendulum on a cart [25] is depicted in figure 2 in this model, a pendulum is conjuncted to the topside of a cart by a pivot, which is allowed to swing in the xy−plane. a force u acts on the cart in the x direction, in order to keep the pendulum balance upright. x(t) is the displacement between central mass of cart and the origin 0; θ is the angle of the pendulum from the top vertical. which is described by the following dynamics by applying newtons second law (m + m) ẍ + mlθ̈ cos θ − mlθ̇2 sin θ = u mlẍ cos θ + 4 3 ml2θ̈ − mgl sin θ = 0 observer-based non-fragile passive control for uncertain nonlinear sampled-data system with time-delay 493 1 x 1 x̂ with oberver l 1 x̂ with observer l 2 x 2 x̂ with oberver l 2 x̂ with observer l 3 x 3 x̂ with oberver l 3 x̂ with observer l figure 1: the simulation of non-fragile observer with additional perturbation now, by selecting state variables z = [ z1 z2 ]t = [ θ θ̇ ]t and by linearizing the above model at the equilibrium point z = 0, we obtain the following state-space model: ż(t) = [ 0 1 3(m+m)g l(4m+m) 0 ] z(t) + [ 0 − 3 l(4m+m) ] u(t) (25) here the parameters are selected in table 1, by assuming the sampling time to be ts = 0.1 s, the discretized model for the above pendulum system in (21) is given by x(k + 1) = [ 1.0877 0.1029 1.7794 1.0877 ] x(k) + [ −0.0000 −0.0182 ] u(k) (26) the poles of the system are 0.6598 and 1.5156, thus this discretized system is unstable. it is assumed that a non-fragile control law with additive form is given by u(k) = [ 219.023 49.786 ] x(k) (27) the other non-fragile control law with multiplicative form is given by u(k) = [ 199.755 52.011 ] x(k) (28) table 1. an inverted pendulum parameters 494 s.g. wang, y.s. li, y. wu m y ( )u t xm ( )x t figure 2: a pendulum system perturbation system parameter values mass of the cart m (kg) 8.0 mass of the pendulum (kg) 2.0 half length of the pendulum (m) 0.5 acceleration of gravity (m/s2) 9.8 1 x 2 x (a) regular controller 1 x 2 x (b) non-fragile controller 1 x 2 x (c) non-fragile controller with nonliearity (d) control input figure 3: state response of the pendulum system and control input the simulation results are given in figure 3. in fact, for sampling period ts = 0.1s, lmi (13) remain solvable. state response curve of regular controller is divergent in figure 3(a), however, the curve is convergent for non-fragile controller in figure 3(b). furthermore, it is still convergent, when there exists a nonlinear perturbation in figure 3(c). a corresponding control input is shown in figure 3(d). observer-based non-fragile passive control for uncertain nonlinear sampled-data system with time-delay 495 6 conclusions the problem of observer-based non-fragile passive control of uncertain nonlinear sampleddata system with time-delay has been studied. a lmi based approach to designing state observer and non-fragile controller, which ensure the passivity of the resulting error closed-loop system has been developed. a numerical example has been provided to demonstrate the effectiveness and applicability of the proposed approach. acknowledgment this work is partially supported by 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(2007); new results on stability of discrete-time systems with timevarying state delay, ieee transactions on automatic control, issn 0018-9286, 52(2):328334. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 3, pp. 301-313 srol web-based resources for languages and language technology e-learning s.m. feraru, h.n. teodorescu, m.d. zbancioc silvia monica feraru institute for computer science of the romanian academy, iaşi, romania e-mail: mferaru@etti.tuiasi.ro horia-nicolai teodorescu institute for computer science of the romanian academy, iaşi, and technical university "gheorghe asachi" of iaşi, romania e-mail: hteodor@etti.tuiasi.ro marius dan zbancioc institute for computer science of the romanian academy, iaşi, and technical university "gheorghe asachi" of iaşi, romania e-mail: zmarius@etti.tuiasi.ro abstract: the srol web-based spoken language repository and tool collection includes thousands of voice recordings grouped on sections like "basic sounds of the romanian language", "emotional voices", "specific language processes", "pathological voices", "comparison of natural and synthetic speech", "gnathophonics and gnathosonics". the recordings are annotated and documented according to proprietary methodology and protocols. moreover, we included on the site extended documentation on the romanian language, on speech technology, and on tools, produced by the srol team, for voice analysis. the resources are a part of the clarin european network for language resources. the resources and tools are useful in virtual learning for phonetics of the romanian language, speech technology, and medical subjects related to voice. we report on several applications in language learning and voice technology classes. here, we emphasize the utilization of the srol resources in education for medicine and speech rehabilitation. keywords: spoken language resources, voice education, gnathosony, gnathophony, education, speech rehabilitation. 1 introduction in a world where the web and internet communication is pervasive, the computer is more than a study topic for everyone, it is a ubiquitous tool. computers serve for more than doing computations, they are now one of the most used means of communication and interaction the very basis of any educational system. as a consequence, computer-based education is an obvious choice whenever a distance separates the learner and the learning person. in a general sense, computer-based education and virtual education based on internet is today an undeniable fact of life in every academic campus [28], [29]. while computers and the network are the means, the spoken language represents the prevalent support of communication in the teaching-learning process. hence, the natural need to address e-learning and virtual learning of languages, phonetics, voice pathology, and other aspects related to voice and spoken language. in view of the above, we built during a timeframe of about five years a web site that offers the possibility of teaching and learning various aspects on the romanian language, based on an annotated corpus freely accessible on the internet. the corpus is complemented with in-depth phonetic and linguistic analyses, moreover with specific tools accessible by users from everywhere through the copyright c© 2006-2010 by ccc publications 302 s.m. feraru, h.n. teodorescu, m.d. zbancioc web [16], [17], [18], [19], [25]. this instrument has a high level of dimensionality and aims to cover numerous aspects of the language that are not typical features in language corpora. this makes this "corpus-tool" an unique instrument of its kind existing today in the domain [22]. during the recent years, we developed an emotional speech database which can help in education and re-education of speech, in diagnosis and treatment, and in learning a language aided by computer; examples of related published results are [5], [16], [22]. voice and language e-education is a topic addressed by many research and educational groups. solomon [13] studied the possibilities and issues of learning with and about computers in schools or in other learning environments. the eric education resources page shows the importance of computer assisted education of speech and voice [24]. on the other side, web-based educational resources and training have received attention during the last decade. ake olofsson [10] offers a simple method of compensation for word decoding problems, by using a computer which pronounces the words which can not be read. olofsson developed a program for the ibm-pc/at and a scandinavian multilingual text-to-speech unit that children can use to read a textfile on the monitor and request using a mouse the pronunciation of any word from that text [10]. the computer-assisted learning language software helps the interaction between student and computer by speech, by sound effects, by animation, and by video. on the other hand, the interaction is restricted typically to the mouse and keyboard. an active interaction, through spoken language enhances the educational computer-based tools [1]. in computer-assisted language learning, speech recognition offers the possibilities to have an active participation by oral reading and conversation. the call system reported in [1] includes recordings spelled by native speakers. the user has the possibility to compare the quality of her pronunciation with model recordings. in another direction of research, warschaue [23] observes the uses of online communications for language teaching. he determined that the interest in this domain grows day by day. he proposed a conceptual framework for understanding the role of the interaction assisted by computer [23]. lundberg considers the computer a tool of remediation in the education of students with reading disabilities as dyslexic students which can benefit by computer training in correct reading and spelling the words [9]. a speech database is a collection of files with sounds, structured according to its own purpose. the srol resource (corpus) is located at the address (www.etc.tuiasi.ro/sibm/romanian_spoken_language/ index.htm). the initiator conceived srol as an internet-based "dictionary of sounds and words" for the romanian language supplemented with specific manifestations of voice (including pathologies) and various tools. the srol database includes files with vowels, consonants, diphthongs, sentences with emotional states, linguistic particularities for the romanian language, dialectal voices, and gnathosonic and gnathophonic sounds. it is the first internet based annotated database of emotional speech for the romanian language and contains more than 1500 recordings in different coding formats (.wav, .ogg, .txt, 22 khz sampling rate, 24 bit or 16 bit precision). the phonetic recordings in srol, which refer to an annotated emotional speech corpus (database), are registered to orda. 2 the srol resources and the srol web site the srol corpus evolved from a small research and educational speech database around 1995 (see annex 1). it currently includes several sections, all freely available on the web. the main sections are: i) standard pronunciation of vowels, diphthongs, words and short sentences in romanian; the recordings in this section are appropriate for learning correct pronunciation in romanian, moreover for statistical research on the romanian phonetics; ii) special syntactic constructs (linguistic peculiarities), like double subject and apposition; this section is research-oriented; iii) emotional voices; srol web-based resources for languages and language technology e-learning 303 iv) analytic comparison between the synthetic and natural speech [27]; v) dialectal utterances; vi) a small archive of gnathosonic/gnathophonic sounds (included in the general "archive of sounds"). beyond the main sections, the srol site includes an introductory section on the phonetics of the romanian language, descriptions of the recording protocols and descriptions of the methodology, analysis tools (free software), extended research documentation, a video application, references, and a list of potentially useful links. the srol team developed instruments for signal processing regarding the extraction of patterns from voice signals, and the computing of the fundamental frequency (pitch) traces, respectively the traces of formants f, f, f. the site offers, beside executables programs, descriptions for each of these tools. those descriptions are intended for a "general use", offering elementary explanations and relevant references for a better understanding [4], [22]. in this paper, we provide details about applications of the srol corpus, available to the address http://www.etc.tuiasi.ro/sibm/romanian_spoken_language/index.htm. 3 srol as support for learning the romanian language one of the goals of the srol web site is to provide a free romanian database for students and researchers, for linguists, for teachers, in view of teaching, learning and analysis the romanian language sounds. the database includes the pronunciation corpus and related documentation. the database contains among others, sections with: recordings of syllables and words pronounced in various contexts, like accentuated word, interrogative sentences, exclamations, various emotions conveyed by the speaker, etc. this part of the database is aimed as a source for concatenative synthesizers and as benchmark for the voice recognition systems (isolated words), based on statistical models of language and speech, as [26]; files of sounds, syllables and words pronounced by persons with various pathologies; this section may be useful in medical and phonological researches; files with professional voices ("perfect" pronunciations), as well as non-professional voices, the "voices of the people in the street". for the moment, we concentrate on voices from the iaşi region (east romania) and middle area of moldova. learning and teaching languages require well documented audio-visual tools that exemplify and fully explain spelling for a large variety of voices and contextual and emotional states. while former methods, like tape recordings and audio disks have been helpful, the multimedia internet-based tools offer tremendously increased capabilities. srol represents such a tool for the romanian language. not only it is the first for the romanian language, but its multidimensionality makes it somewhat unique and novel in concept for language learning and teaching in general. as an example of use, consider the case of a foreign student who wants to improve her romanian pronunciation by comparing the prosody of her voice with the prosody of native speakers. the student utters a sentence (from those included in the site), then opens waspt m or another similar tool and displays the energy and fundamental frequency in her voice. she then compares these prosodic features to the ones of native speakers and tries to improve her prosody until she produces correct prosodic patterns. also, the student can compare formant values and try improving the formants of the vowels she pronounces. this instrument is useful for learning to improve speech communication, moreover for humancomputer speech interaction, for security, for medical applications, for video-games and interactive tv, for teachers, in the study of the romanian language, etc. 304 s.m. feraru, h.n. teodorescu, m.d. zbancioc 4 applications in medical education and re-education of speech application fields like language learning, professional voice education, and voice rehabilitation and re-education for medical conditions have different requirements, moreover are based on different methods. on the other side, education in medicine (orl, phoniatrics, dentistry) and in logopedy are other fields of potential applications of speech resources. further, voice analysis for diagnosis is a domain that has seen significant progresses in recent decades. voice education is needed whenever a voice pathology including some neurologic and psychiatric disorders, or pathology of the vocal tract occurs. several groups have addressed the voice re-education topic [9], [10]. 4.1 srol resources for minor voice pathology till now, we included in srol words pronounced by persons with minor pathologies, as trembling voice. we have demonstrated in our research that splitting the signal in frequency bands that correspond to the peaks of the f-f formants and respectively to the peaks of f-f formants helps improving the discrimination process in a significant way. the use of fractal dimensions in assessing the jitter or shimmer in voice produce mixed results [21]. adding other fractal dimension, the rate of recognition of the tremor segments in voice improves, but it still low [21]. the voice pathology section of the database is useful in medical and phonological researches. also for medical education use, the site comprises a gnathosonic and gnathophonic corpus. 4.2 srol resources for gnathosony the gnathosonic analysis refers to the analysis of sounds produced during occlusion, due to the closing of the mandible over the maxillary at some stage in masticatory-like movements. watt (cited in [7], [8], [11], [12]) has initiated the analysis of these sounds with application to diagnosis of the state of the stomato-gnathic apparatus during the 1960s and 1970s. the method has seen some interest, but it is not yet a current method in clinical practice. the shape of the envelope of an occlusal sound is determined by the number of occlusal contacts and by the dynamics of the terminal part of the occlusion, namely by the dynamics of the sliding of the teeth, from the first contact until the equilibrium position in occlusion. a characterization of the waveform should take into account the need to correlate the sound with the medically relevant processes of contact and sliding. a limit in the occlusal sound analysis has been the complexity and the variability of shapes of the sound wave. the envelope of a single contact sound is characterized by the rise and fall times, value of the maximum, duration of the maximum, and total duration. the rise and fall curves follow exponentially laws, whose constants are of interest in the classification of the occlusal dynamics. for gnathosonic purposes, the sound signal s(t) generated by occlusion (teeth impact when closing the mouth like for mastication) and discretized as s[n] is first filtered by an elementary high pass, differential filter, s[n + ] ← s[n + ] − s[n]. then, the signal is filtered with a nonlinear filter introduced in [14]. the filter first extracts the rough envelopes, averages them, applies to them median filters, sums the two resulting envelopes, and then apply to the sum an averaging filter [14]: uin f [n] = mink=−,..,s[n + k], usup[n] = maxk=−,..,s[n + k] vin f [n] =   · ∑ k=− uin f [n + k], vsup[n] =   · ∑ k=− usup[n + k]. the next stage in the filtering is constituted by the median filtering on a moving window, as zin f = mediank=−,.., {vin f [n + k]} , zsup = mediank=−,.., {vsup[n + k]} . srol web-based resources for languages and language technology e-learning 305 and the two envelopes are summed – actually, summed in the sense y[n] = [|zin f | + zsup] /; e[n] =  p +  · ∑ k=−p y[n + k]. we used a window of width 6 (p = ) for the last averaging. the widths of the windows in the above operations depend on the signal sampling frequency used in the recording process. the envelope of the signal is determined by taking the maximal respectively minimal value in a moving window, according to a procedure similar to the one explained for the filtering process. the envelope, e(t), is itself low-pass filtered and then used for determining the occlusal sound parameters. the heuristic procedure applied to determine the duration of the occlusal sounds by forming "binary" impulses during the valid occlusal sound is: if (e[n] > c) and b(e[n − ], ..., e[n + ]) > c then h[n] = ., else h[n] = , where b is a binary function (taking only 0 and 1 values) defined by b = [max (e[n − ], ..., e[n − ]) > c] & [max (e[n + ], ..., e[n + ]) > c] . the constants were chosen semi-empirically, as a function of the amplitude of the signal, c.. ∼ as where as is the average amplitude of the signal after filtering (actually, we used the average amplitude of the sum of the envelopes), and the window width, 14, is determined by tests. we used the values c = ., c = ., c = ., which correspond to the average signal a = ., determined as explained. for a normalized amplitude a, a = , the constants are about c = ., c = ., c = .. the detection procedure can be further improved by reducing the false positives by imposing that the skewness of the impulse is larger than +.; typical values for the skewness are larger than ., showing that the rise of the impulse is significantly faster than the decreasing part. 5 research support in gnathophonics and gnathosonics in previous researches, we identified several ways the pathology of the stomato-gnathic system influences the speech: i) the lack of the frontal dentition, namely of the upper teeth, may dramatically change the spectrum of the fricative consonants. ii) the lack of the upper teeth may significantly modify the spectrum of the dento-alveolar sounds t, d, n, and l. (notice that these sounds are rather alveolar in english, while in some other languages, like spanish and romanian, they may be dental. therefore, the influence of the dentition on phonation is language-dependent.) iii) the limited mobility and the pain in the temporo-mandibular joint (tmj) impedes the production of fast transient vowels, especially in the diphthongs where the second vowel is pronounced with a largely opened mouth, like oa, ea, ua. iv) the uncertainty in uttering due to a forcing in the tmj, or to a poor neuro-muscular control may produce a tremor of the voice (fast amplitude changes, errors in the attacks, i.e. error in transitory regimes etc.). v) the neurological pathology of the buccal cavity may impede on the accuracy of the pronunciation, including deficient starting of the words. vi) defective mobile prostheses may produce extra sounds, especially when the mouth is fast opened for pronunciation, moreover, it may produce clicks before the utterances. 306 s.m. feraru, h.n. teodorescu, m.d. zbancioc vii) prostheses of the upper teeth that do not provide for a physiological "v " shaped space between the teeth impede on the pronunciation of the fricatives, for example f. viii) especially the fricative consonants and the labial vowels are affected by the state of the dental furniture. the s consonant uniformly occupies a large spectrum for a healthy dental apparatus, while it has a multi-band spectrum when the upper front teeth are missing or have deficiencies. the pronunciation of s and v may become close to that of f. for subjects with mobile prostheses, we noticed an uncertainty in the starting of the uttering. the difference ratio in amplitude spectra is a parameter defined as: ∆ s = ∑ k |s( fk) − s( fk)| s( fk) + s( fk) where f [k] is the k-th frequency in the fft (fast fourier transform) power spectrum of the two sounds and s, are the average power spectra of the two sounds. for two similar sounds uttered by the same speaker, a difference larger than 50% means that the sounds are clearly distinguishable, while a difference smaller than 10% means that the sounds are indistinguishable. for example, if the average spectra for two sustained utterances of f and v have a ∆ s index of 40%, they will be distinguished by a listener, while if ∆ s = %, they will be confused. we proposed the sustained consonant differential analysis as a method to further assess the impairment of speech production due to dentition. for this test, two similarly produced sounds are generated in a sustained mode and their spectra contrasted. for example, the sounds f and v are both at least partly fricative (v can be a semi-vowel, only partly fricative) that may be poorly produced due to imperfect dentition or neurological control. we conclude this section by stressing that gnathophonic testing should become a standard test for the dentist in the near future. the knowledge in the field is only emerging today, and fully developed, commercial tools are yet lacking, but the importance of the domain can not be refuted [7], [8], [11], [12]. the proposed tests are non-invasive, objective, and purely instrumental, hence their importance in the evaluation of the health state of the buccal system. these methods can easily be extended to remote, web-based diagnosis. in figure 1, we exemplify a gnathophonic (a) and gnathosonic (b) recording sounds (for the speaker 19743m). in figure 1(a), we exemplified recordings of the romanian words "vata", "fata", "var", intended to obviate similarities and differences in the pronunciations (fourier spectra) of the consonants f and v, in the same context (beginning of the word, same _cv c structure, with the same vowels and consonants, and _ denotes the beginning of the word). this is one of the specific choices of words proposed by the second author to determine when dentition defects produce confusion in the f − v uttered sounds. by analyzing such recordings available at srol, students can learn how to differentiate the normal and pathological states. figure 1: gnathophonic (a) and gnathosonic (b) recording with details, tool goldwavet m srol web-based resources for languages and language technology e-learning 307 6 applications in teaching the voice signal technology classes signal technology classes are taught around the world, especially for the master degrees in computer science and electrical engineering, moreover in some departments of linguistics and in a few medical centers. some universities and education institutions developed their own databases and tools for spech processing. for examples, the center for spoken language understanding (cslu) offers available language database from speech area and hearing science. these resources are important for analyzing the speech, for diagnosing and treating speech and language problems, for training students and so on. the tools and the corpora are distributed to over 2000 sites in 65 countries [2]. in education these tools help students learn about speech, learn a new language, learn through interactive media systems, or to become accustomed to hearing the normal and abnormal voice signal. the second author currently uses the srol corpus in teaching and laboratory activities in the class "speech technology" given for the master degree in "computational linguistics" at the faculty of computer science, "al.i. cuza" university of iaşi. details on the use in voice technology classes of some topics from srol are described in [4]. at the international eurolan 2007 summer school, the second author used the srol site to present "traces of emotion, intentions and meaning in spoken romanian" (http://eurolan.info.uaic.ro/html/profs/hnteodorescu.html). the second author taught the specific methodology aspects, results obtained on the characterization of emotions in speech, possibilities of recognition of emotions and intentions in speech, and the relationship between specific meanings and the prosody in specific constructions in the romanian language. the lesson exemplified applications of analysis of the speech emotional prosody to social, psycho-social, educational, and psycho-medical topics. 7 software tools: pitch (f) extractor the extraction of the fundamental frequency f values combines four different methods: i) autocorrelation method (analysis in time domain) ii) the average magnitude difference function method, amdf (analysis in time domain) iii) the harmonic product spectrum method, hps (based on spectral analysis) iv) the cepstral method (an analysis in que-frequency domain) also applied for the higher formants searching. the autocorrelation method is a classical method for pitch detection in the time domain. the method is based on the quasi-periodicity property of the voice signal and generates a local maximum that corresponds to the signal period. in the case of amdf method, the local minimal values are detected and these values provide the necessary information to compute the fundamental period t, ck =  n · n∑ n= xn ·xn+k, k = ,w dk =  n · n∑ n= (xn − xn+k), k ∈ ,w here, ck is the self-correlation, dk is the difference function coefficient for a delay k, xn is the n-th sample of the signal, n is the number of correlation coeficients, w is the width of the analysis window. the hps method (harmonic product spectrum) is based on the propriety that the spectrum of a periodic signal with fundamental frequency f has maximal spectral values at the multiples of this frequency 2f, 3f, 4f, ... (the harmonics of fundamental). when the signals are rescaled with the factors 1/2, 1/3, 1/4,... after the decimation operation, by the multiplication of the resulted signals (which all have a spectral maximum in fundamental frequency f), the other maximal value from spectrum are strongly attenuated. 308 s.m. feraru, h.n. teodorescu, m.d. zbancioc h kn = h  k·n (decimation) or h k n =  k k−∑ i= hk·n+i the cepstral method relies on the separation of the spectrum of the sound generator, hg (which provide the information regarding the fundamental frequency), from the spectrum of the vocal signal filter, h f (which describe the resonating cavities model). in the cepstral formula, the multiplication operation between the excitatory signal and the transfer function spectrums is transformed using logarithms into an addition operation: h(ω) = f f t (s) = hg(ω)·h f (ω) cepstrum = if f t (log|f f t (s)|) = if f t (log|hg(ω) ·h f (ω)|) cepstrum = if f t (log|hg(ω)|) + if f t (log|h f (ω)|) where fft is the fast fourier transform, and ifft is the inverse fft. the results of the f extraction methods are compared in a decisional block, and a selection algorithm is used if there are significant differences. another algorithm compares a current value with a number of neighboring values in order to select the nearest one, moreover compares the current values with mean values of f. the error correction of the f extractors is performed through three methods: comparing the "neighbors": use the results provided by the same f extractor and if a difference between two consecutive values greater than a specified threshold value (usually 10-20%) is detected, the corresponding samples are considered errors; if the difference in absolute value between the current value of f and the average of fundamental frequency is greater than twice the standard deviation, then we consider those values as erroneous; if the current value of f is below 60% or over 150% of the average values of f, then we consider that the corresponding value is incorrect. the threshold values were empirically determined and the final correction is accomplished by applying all the three correction methods described. the decision block receives the f values provided by the detection methods (amdf differences method, autocorrelation method, hps method, and cepstral method). to achieve the best possible pitch detection, the output values are weighted according on the performance of each f extractor. we assign smaller weights to the methods with a higher probability of providing incorrect outputs. the false detections of the fundamental frequency often consist in selecting the first subarmonic, or the first harmonic of f. when these "false" detection are not repaired by the correction module, we have two options: comparing the outputs of different f detection methods for the same window of analysis; comparing the outputs with a number of previous final results provided by the decision block. 8 discussion our team has a long standing experience with using novel technologies in teaching, lasting for three decades [3], [7], [15], [20]. we applied that experience to the srol e-teaching and e-learning resource. the srol resource is a vast annotated corpus of speech files complemented by tutorials, papers and additional files, moreover with tools for speech processing. if used by an experimented student or teacher, it may become a powerful tool for instruction and learning the romanian language pronunciation, speech technology, and voice pathology and re-education. the srol sound voice resource is useful in many domains, including phonology, applied computer science, and medicine. students and researchers may use this freely accessible site for learning the pronunciation of romanian language, for srol web-based resources for languages and language technology e-learning 309 making comparative study between romanian and other languages, for development of synthetic voice systems, for other linguistic, phonetic, socio-linguistic or medical applications. this database is structured corresponding to precise criteria, documented and annotated according to a well defined methodology. the site has more then 1500 recordings of syllable, word, and sentence with various tonalities and pronounced with various emotional states. the database contains recordings of professional and normal voices, from the north-east region of romania, without dialectal accent. the srol resources have been recognized by several bodies, beyond the scientific publications that included our papers on srol. clarin european network of language resources accepted srol as a member; orda (the romanian office for authorship rights) registered the original recordings, and the srol received a gold medal and media attention at the inventica 2009 fair for inventions and creativity. also, the website of embassy of france in romania briefly described in its bulletin the srol site and its use in education (http://www.bulletins-electroniques.com/actualites/58811.htm). the technical university "gheorghe asachi" of iaşi intends to use srol in helping foreign students enrolled at this university to learn the correct romanian pronunciation. we hope the srol resources will be used in all the universities in romania by foreign students who learn the romanian language, moreover in other academic media and as an online tool by foreign students and teachers. we welcome any request for help and educational advice from all those who wish to use srol and the language-related web resources in virtual e-teaching and for e-learning. 9 conclusions and future work the srol speech annotated corpus constitutes the first extensive educational and research web speech corpus for the romanian language. we believe it also constitutes a speech repository unique in many respects, including the first international language and sound resources for gnathophony and gnathosony, the first resources for comparative study of appositions and double subject constructions, moreover specific features as the rigorous methodology of documenting the records we used. the objectives for the next two years are to increase the speech data base by about 1000 annotated recordings and to significantly extend the medical-oriented section of the resources. also, we intend to add more tools for speech processing, including statistical tools on the grid. acknowledgements the authors have been partly supported by the romanian academy, moreover the second author has been partly supported by a grant of the ministry of education and science of romania, during 20052006. notices 1. a partial version [6] of this paper was presented in the icvl 2009 conference and received the intel special award for education (2009). 2. the authors contributions: the gnathophonic and gnathosonic research was been performed by the second author who also wrote the corresponding section of the paper (sections 2, 4, 5, 6, and 8, and contributed to writing the other sections); 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[10] a. olofsson, synthetic speech and computer aided reading for reading disabled children, journal: reading and writing, vol. 4, no. 2, pp. 165-178, issn: 09224777, 1992 (http://www.springerlink.com/content/j521536n135x2864/). [11] j.f. prinz, computer aided gnathosonic analysis: distinguishing between single and multiple tooth impact sounds, j oral rehabil., vol. 27, no. 8, pp. 682-689, 2000. [12] j.f.prinz, k.w. ng, characterization of sounds emanating from the human temporomandibular joints, arch oral biol. vol. 41, no. 7, pp. 631-639, 1996. [13] c. solomon, computer environments for children a reflection of theories of learning and education, 1988 www.google.com/books?id=eonpz9a81kkc&printsec= frontcover & hl=ro & source=gbs_v2_summary_r& cad=0. [14] h.n. teodorescu, occlusal sound analysis revisted, proc. 3rd int. conf. medsip 2006, advances in medical, signal and information processing, isbn: 0863416586, glasgow, uk, 17-19 july 2006. srol web-based resources for languages and language technology e-learning 311 [15] h.n. teodorescu, computer semiotics: understanding meanings and parallel languages (refereed invited paper) t. yamakawa, g. matsumoto (eds.), proc. int. conf. iizuka’98, world scientific publ., pp. 279-283, 1998. [16] h.n. teodorescu, m. feraru, classification in gnathophonics preliminary results, the second symposium on electrical and electronics engineering, galati university press, pp. 525-530, isbn 1842-8046, 2008. [17] h.n. teodorescu, m. feraru, micro-corpus de sunete gnatosonice si gnatofonice, pistol, cristea, tufis (eds.) resurse lingvistice si instrumente pentru prelucrarea limbii romane, ed. universitatii "al.i. cuza" iaşi, isbn 978-973-703-297-3, pp. 21-30, 2007. [18] h.n. teodorescu, m. feraru, d. trandabat, studies on the prosody of the romanian language: the emotional prosody and the prosody of double-subject sentences, c. burileanu, h-n. teodorescu, (eds.) advances in spoken language technology, the publishing house of the romanian academy, bucharest, romania, isbn 978-973-27-1516-1, pp. 171-182, 2007b. [19] h.n. teodorescu, m. zbancioc, e. mihailescu, speech technology and bio-medical engineering teaching based on the web-a new tool and case study, int. conf. on interactive computed aided learning, villach, austria, 2006. [20] h.n. teodorescu, a. kandel, b. paschall, teaching modern chapters in automata theory and formal languages, (abstract in booklet of the symposium.) symp. 21 century teaching technologies, univ. south florida, tampa, usa 2000. [21] h.n. teodorescu, r. ganea, m. feraru, a. burlui, assement of voice quality based on nonlinear dynamic analysis, proc. of the 15th int. conf. on control syst. & computer sci., bucharest, romania, pp. 536-542, isbn 9738449898, 2005. [22] h.n. teodorescu, d. tandabat, m. feraru, m. zbancioc, r. luca, a corpus of the sounds in the romanian spoken language for language-related education in: c.p. pascual (ed.), revisiting language learning resources, cambridge scholars pub. (csp),uk, ch. 6, isbn 1847181562, pp. 7389, 2007. [23] m. warschaue, computer-mediated collaborative learning: theory and practice, the modern language journal, vol. 81, no. 4, special issue interaction, collaboration, and cooperation learning languages and preparing language teachers (winter, 1997), pp. 470-481, http://www.jstor.org/pss/328890. [24] b.w. wise, r.k. olson, computer speech and the remediation of reading and spelling problems, j. special education technology, vol. 12, no. 3, pp. 207-220, 1994. [25] m. zbancioc, tools for the archive of the romanian language sounds project, 4th european conf. on intelligent systems and technologies, iaşi, romania, isbn 973-730-265-6, 2006. [26] kenko ota, emmanuel dulfos, philippe vanheeghe, masuzo yanagida, bayesian inference for speech density estimation by the dirichlet process mixture, , studies in informatics and control journal, bucharest, romania, issn 1220-1776, vol. 16, no. 3, 2007. [27] florin grigoras, horia-nicolai teodorescu, vasile apopei, nonlinear analysis and synthesis of speech, studies in informatics and control journal, bucharest, romania, issn 1220-1776, vol. 7, no. 1, 1998. 312 s.m. feraru, h.n. teodorescu, m.d. zbancioc [28] tom page, gisli thorsteinsson, andrei niculescu, management of knowledge in a problem based learning environment, studies in informatics and control journal with emphasis on useful applications of advanced technology, bucharest, romania, vol. 18, no. 1, 2009. [29] antonios andreatos, international journal of computers,virtual communities and their importance for informal learning communications and control, international journal of computers, communications and control ijccc, romania, issn 1841-9836, vol. ii, no. 1,pp.39-47, 2007. annex 1. development stages of srol the presently named srol corpus started around 1995 as a small, research and educational database including examples of recordings with vowels and a few typical words in romanian, moreover a few recordings of pathological voices. it was correlated to the class of image and speech processing given by the second author in the "gheorghe asachi" technical university of iaşi, romania. former students (who are now professors in several romanian universities) contributed to that incipient voice database (credit for recordings and other help for that database deserve the now professors radu ciorap and irinel pletea, among others). the database was further developed for educational purposes in relation to the the class of speech technology given by the second author in the faculty of computer science of "al. i. cuza" university in iaşi. the third stage of development started in 2004, when the second author decided to significantly enlarge and move the speech database on the web, partly with the help of two grants that helped forming a team in the institute for computer science of the romanian academy and in the "gheorghe asachi" technical university of iaşi. the first author joined the team, at that time as a fresh ph.d. student. since the second author initiated five years ago the project "the sounds of romanian language" (srol), the team increased to 8 researches. the srol web-based spoken language repository and tool collection as it is today was developed during several years by the collaboration of groups from the institute for computer science of the romanian academy, cerfs excellence center in "gheorghe asachi" technical university of iaşi and by staff of the discipline of language technology, computer science faculty, "al.i. cuza" university. annex 2. typical shapes of gnathosonic signals the sketches below stand for the envelopes of typical gnathosonic signals, corresponding to normal, merged double contact, and isolated double contact signals. the sound is easily categorized by automatic means. figure 2: typical envelopes of occlusal sounds (from [14]) srol web-based resources for languages and language technology e-learning 313 silvia monica feraru (november 21, 1977) received a msc. degree in biomedical engineering (2004) and phd in electronics (2009) from "gheorghe asachi" technical university of iaşi. now she is research assistant at the institute for computer science of the romanian academy, iaşi branch. she received the special awards intel education 2009 at the international conference on virtual learning, icvl 2009. her current research interests include vocal signal processing, cognitive processes, and various aspects of artificial intelligence. she has (co-)authored more than 21 conference, journal or bookchapter papers. horia-nicolai teodorescu (november 14, 1951). ms in electronics, "politehnica" university, bucharest, 1975, ph.d. in applied physics electronics, under the supervision of the late prof. emil luca, at the technical university of iaşi, 1981. currently, he is a professor at the "gheorghe asachi" technical university of iaşi and the director of the institute for computer science of the romanian academy, iaşi. he is a correspondent member of the romanian academy. has authored or co-authored about 300 journal and conference papers, holds 24 national and international patents and has received numerous national and international awards and prizes. he is a senior member, ieee. marius-dan zbancioc (august 15, 1975) teaching assistant at the "gheorghe asachi" technical university of iaşi and researcher at the institute of computer science of the romanian academy, iaşi branch. his current research interests include signal processing, expert systems, fuzzy systems and several aspects of artificial intelligence. he has (co-)authored 3 books and 39 papers. int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 688-695 an electromagnetism-like approach for solving the low autocorrelation binary sequence problem j. kratica jozef kratica 1. mathematical institute, serbian academy of sciences and arts kneza mihaila 36/iii, 11 000 belgrade, serbia e-mail: jkratica@mi.sanu.ac.rs abstract: in this paper an electromagnetism-like approach (em) for solving the low autocorrelation binary sequence problem (labsp) is applied. this problem is a notoriously difficult computational problem and represents a major challenge to all search algorithms. although em has been applied to the topic of optimization in continuous space and a small number of studies on discrete problems, it has potential for solving this type of problems, since movement based on the attraction-repulsion mechanisms combined with the proposed scaling technique directs em to promising search regions. fast implementation of the local search procedure additionally improves the efficiency of the overall em system. keywords: low autocorrelation binary sequence problem, electromagnetism-like metaheuristic, combinatorial optimization. 1 introduction low autocorrelation binary sequence problem (labsp) is a very hard combinatorial optimization problem with quite a simple formulation. the mathematical formulation of labsp is based on a binary sequence s of length n. let s ∈{−1,1}n, i.e. s be represented by (s1, s2, ... , sn), where si ∈{−1,1} for 1 ≤ i ≤ n. each sequence s is associated with the value of its energy function which is defined as follows: e(s) = n−1∑ j=1 c2j (s), where cj(s) = n−j∑ i=1 sisi+j (1) now, the low autocorrelation problem for binary sequences with length n, can be formulated as finding a sequence s of length n whose energy function is as minimal as possible. the second measure of quality of the sequence s is a merit factor f(s) = n2 2e(s) , (2) defined by bernasconi in [2]. mathematically, labsp can be formulated as max s∈{−1,1}n f(s). both formulations are equivalent, and either of them can be used when it is convenient. 2 previous work labsp has been deeply studied since the 1960s by both the communities of physics and artificial intelligence. there are two reasons behind this interest: • it arises in many diverse areas including statistical mechanics and configuration state analysis [2], calibration of surface profile metrology tools [1], satellite and space applications [8], digital signal processing [16], etc.; copyright c⃝ 2006-2012 by ccc publications an electromagnetism-like approach for solving the low autocorrelation binary sequence problem 689 • labsp is also a significant challenge of exact and/or heuristic applications, since it is known that the problem has "bit-flip" neighborhood structure of combinatorial landscapes [5, 6]. with this type of neighborhood, it is extremely steep around the optimum, which is sometimes referred to as ”golf hole” landscapes and it poses a very difficult optimization problem. in that case, small changes in argument values usually cause drastic difference in objective value. for example, alteration of only one bit in binary sequence s can affect the objective value change by several tens of percents. from these reasons the labsp is also listed as a problem 005 in the csplib library. although golay in [9] estimated that lim n→∞ f(s) = 12.32, it is not well enough, because for dimensions between 21 and 60 the merit factor varies from f(s) = 5.627 for n = 23 up to f(s) = 9.85 for n = 27, which is obviously far from the estimated limit 12.32. the state-of-the-art exact method given in [12,13] is based on exhaustive search and it solves problem optimally up to n = 60. the experimental research was carried out for several days on a multiprocessor cluster of 160 cpus. up to now it is the largest dimension with known optimal solution. a hybrid evolutionary approach described in [4] combines the evolutionary search described in [14] and kerninghan-lin heuristic defined in [11]. that evolutionary approach uses a specially defined termination criterion based on statistical analysis of known optimal solutions and their asymptotic behavior. a detailed analysis of different stand-alone local search strategies is given in [7]. that analysis is later used in embedding the best local search strategy within other metaheuristic approaches. the results indicate that pure evolutionary algorithm cannot cope with the complexity of the problem and the assistance of local-search operators it is required to provide optimal or suboptimal results consistently. as a best choice for solving labsp a memetic algorithm endowed with a tabu search local searcher is proposed, and that approach consistently finds optimal sequences in considerably less time than approaches previously reported in the literature. another metaheuristic method for solving labsp, based on the stochastic local search (sls), is presented in [10]. in-depth analysis of labsp fitness landscape and the white-box visualization get insights on how sls can be effective and lead to a slightly better strategy. local search algorithm described in [15], on the other hand, uses a quite different strategy compared to previous local search approaches, which is based on the randomized form of backtracking. in that way, the optimization problem is reduced to a series of constraint satisfaction problems which are to be solved iteratively, with decreasing upper bounds on the given objective function. experimental results indicate that the algorithm is time consuming. for example, the average running time for n = 40 is over 1000 seconds. 3 proposed em method an electromagnetism-like (em) metaheuristic is a powerful algorithm for global optimization that converges rapidly to optimum [3]. the method is also used for combinatorial optimization as a stand-alone approach or as an accompanying algorithm for other methods. em is a population based algorithm that can solve nonlinear optimization problems. in the following text each member pk, k = 1,2, ...,m of the population maintained by the algorithm will be referred to as em point (or solution), and the population itself will be referred as a solution set. the proposed em algorithm for solving labsp is given by the following pseudo code: em points in the first iteration are randomly initialized from [−1,1]n (function random_init()). 690 j. kratica for a given em point pk, sequence s is obtained by rounding, i.e. si = { 1, pki ≥ 0 −1, pki ≤ 0 , for each coordinate i = 1, ...,n. energy e(s) and merit factor f(s) are computed by using (1) and (2). 3.1 local search and scaling this step is used to move the sample points towards the local optima that are near them. points are pushed towards the local valleys using a neighborhood search procedure. the local search method used in this algorithm is simple but effective. regarding the importance of the local search step, it is described in algorithm 2. the proposed local search procedure uses the first improvement strategy, which means that when an improvement is detected, the improvement is immediately applied and local search continues. if for each member of sequence s swap produces energy value greater or equal than the original one, the local search ends with no further improvement. in this implementation, scaling procedure is also applied, which additionally moves points towards solutions obtained by local search. it is considered only with some factor λ ∈ (0,1) to prevent falling into a local optimum and become trapped there. an em point pk is moved by the following formula pk ← λ ·pk′+ (1−λ) ·pk (3) where pk′ denotes sequence s of the k-th em point in the current iteration when the local search procedure finished its work. choosing appropriate value of the scale factor λ is significant for governing the search process. in the extremal case, when λ is close to 1, the search process will likely fall into a local optimum and become trapped. another extremal case, when λ is equal to 0, obviously represents no-scaling situation. experiments showed that λ = 0.1 is a good compromise which yields satisfactory results. 3.2 attraction-repulsion mechanism as can be seen from the literature, the strength of the em algorithm lies in the idea of directing the sample points towards local optima utilizing an attraction-repulsion mechanism. therefore, after applying the local search procedure to each solution in the current population, the solutions must be moved towards promising regions in order to get closer to the optimal solution. an electromagnetism-like approach for solving the low autocorrelation binary sequence problem 691 in this process, each sample point is considered as a charged particle. the charge of each sample point is calculated by the following formula: qi = exp  −n f(pi)−f(pbest)m∑ k=1 f(pk)−f(pbest)   . (4) the amount of charge relates to the value of the objective function (f(pk) = e(s)) at the point, which also determines the magnitude of attraction or repulsion of the point over the sample population. according to the superposition principle of electromagnetism theory, the force exerted on a point via another point is inversely proportional to the distance between the points and directly proportional to the product of their charges. mathematically, the power of attraction or repulsion of charges is calculated as follows: fi = m∑ j=1,j ̸=i f j i , where f j i =   ( qiqj ||pj−pi||2 ) · (pj −pi), f(pj) < f(pi)( qiqj ||pj−pi||2 ) · (pi −pj), f(pj) ≥ f(pi) (5) where ∥pi −pj∥ is the euclidean distance between em points pi and pj. as mentioned before, by using the move() procedure of the electromagnetism approach, current solutions are shifted towards the best ones. all the em points are moved with the exception of the current best solution. detailed explanations about movement are given in algorithm 3. 692 j. kratica as can be seen from algorithm 3, the movement of each em point is in the direction of total force exerted on it by a random step length β. this length is generated from uniform distribution between [0,1]. as can be seen in [3], the candidate solutions have a nonzero probability to move to the unvisited solution along this direction when random step length is selected. moreover, normalizing the total force exerted on each candidate solution implies that infeasible solutions cannot be produced. 4 experimental results in this section, the proposed em solution procedure on labsp is tested for n up to 40 nodes, for which the optimal solutions are known in the literature. each numerical experiment was repeated 20 times and the results are summarized in table 1, which is organized as follows: • the first three columns contain n, optimal solution value (merit factor f(s)) and the em best solution obtained in 20 runs; • the average running time (t) and number of iterations iter used to reach the final em solution for the first time are given in the fourth and fifth columns, while the total running time ttot necessary to finish em is given in the sixth column. • the last two columns (agap and σ) contain information on the average solution quality: agap is a percentage gap defined as agap = 1 20 20∑ i=1 gapi, where gapi = 100∗ embest−emiembest and emi represents the em solution (merit factor f(s)) obtained in the i-th run, while σ is the standard deviation of gapi, i = 1,2, ...,20, obtained by formula σ = √ 1 20 20∑ i=1 (gapi −agap)2. the computational results were performed on an intel 2.5 ghz single processor with 1gb memory, under windows operating system. all em runs were made with the following empirically determined parameters: m = 10, itermax = 100000 and λ = 0.1. these values cause most charges to exhibit convergent behavior with a few individuals diverging, thereby providing a good balance between local and global search. in this case all these values were chosen experimentally as a matter of convenience because they provide good results. an electromagnetism-like approach for solving the low autocorrelation binary sequence problem 693 table 1: computational results n optsol embest t ttot agap σ (sec) (sec) (%) (%) 3 4.500000 opt. 0.0010 1.5503 0.000 0.000 4 4.000000 opt. 0.0010 4.3613 0.000 0.000 5 6.250000 opt. 0.0010 4.8269 0.000 0.000 6 2.571429 opt. 0.0010 11.6996 0.000 0.000 7 8.166667 opt. 0.0010 9.8925 0.000 0.000 8 4.000000 opt. 0.0010 16.4588 0.000 0.000 9 3.375000 opt. 0.0010 17.3339 0.000 0.000 10 3.846154 opt. 0.0010 20.3198 0.000 0.000 11 12.100000 opt. 0.0017 16.8205 18.462 34.585 12 7.200000 opt. 0.0010 17.4573 0.000 0.000 13 14.083333 opt. 0.0031 17.3925 11.429 25.806 14 5.157895 opt. 0.0010 26.4173 0.000 0.000 15 7.500000 opt. 0.0900 22.1761 1.739 7.715 16 5.333333 opt. 0.0010 23.1151 0.000 0.000 17 4.515625 opt. 0.0031 25.3597 0.000 0.000 18 6.480000 opt. 0.0052 28.1081 2.424 7.453 19 6.224138 opt. 0.0249 23.8753 1.212 3.681 20 7.692308 opt. 0.3983 26.3590 8.235 12.230 21 8.480769 opt. 0.0865 26.3949 19.226 12.095 22 6.205128 opt. 0.0266 33.8676 3.404 7.048 23 5.627660 opt. 0.1937 30.9995 2.745 3.847 24 8.000000 opt. 0.2528 32.9262 19.003 13.390 25 8.680556 opt. 0.8880 32.2269 13.759 12.523 26 7.511111 opt. 1.6412 36.4104 4.887 9.350 27 9.851351 opt. 0.9698 35.7136 29.084 18.230 28 7.840000 opt. 1.1606 37.8760 17.338 11.389 29 6.782258 opt. 3.7152 36.8581 6.531 6.318 30 7.627119 opt. 1.7120 42.7611 13.471 10.877 31 7.171642 opt. 2.6340 43.7440 9.258 6.876 32 8.000000 opt. 2.7331 46.6714 15.540 11.667 33 8.507813 opt. 4.4168 49.8097 15.667 9.911 34 8.892308 opt. 6.9574 51.4971 25.079 9.309 35 8.390411 opt. 1.8435 52.5394 19.264 8.260 36 7.902439 opt. 2.4458 55.0151 17.239 8.781 37 7.959302 opt. 6.4090 55.7682 15.507 7.856 38 8.298851 opt. 2.6012 61.6151 20.118 9.361 39 7.681818 opt. 7.8379 62.7660 13.623 8.275 40 7.407407 opt. 9.4135 73.1222 14.369 6.279 694 j. kratica observing the data shown in table 1, it is remarkable that em identifies optimal solutions in all cases. moreover, the em performs very efficiently, since the total running time was less than 74 seconds with one million objective function evaluations. note that, most of this time is spent after the em reach optimal solution merely to satisfy the finishing criterion. also mind that in the case when n = 40, search space is 240 and em searched only 1.17 ·10−7 part of it to reach the optimal solution. 5 conclusions and future works in this article, a hybrid approach combining an electromagnetism-like method (em) with a scaling technique for solving the labsp is proposed. the fast local search procedure and the applied scaling scheme were adapted to facilitate the use of em to boost the performance of the proposed algorithm. to show the efficiency of the proposed hybrid em, a number of experiments was carried out and the results were compared with the optimal solutions taken from the literature. the obtained results clearly indicate that em is a useful tool for solving this problem. as a direction for future studies, it can be interesting to parallelize the em and run it on a powerful multiprocessor computer. another orientation of future research can be incorporation of this method in some exact solution framework. acknowledgments this research was partially supported by serbian ministry of education and science under grants 174010 and 174033. bibliography [1] s.k. barber, p. soldate, e.h. anderson, r. cambie, w.r. mckinney, p.z. takacs, d.l. voronov, v.v. yashchuk, development of pseudorandom binary arrays for calibration of surface profile metrology tools, journal of vacuum science and technology b: microelectronics and nanometer structures, vol.27, no.6, pp.3213–3219, 2009. [2] j. bernasconi, low autocorrelation binary sequences: statistical mechanics and conguration state analysis, journal physique, vol.48, pp.559–567, 1987. [3] s.i. birbil, s.c. fang, an electromagnetism-like mechanism for global optimization, journal of global optimization, vol.25, pp.263–282, 2003. [4] f. brglez, x.y. li, m.f. stallman, b. militzer, reliable cost prediction for finding optimal solutions to labs problem: evolutionary and alternative algorithms, fifth international workshop on frontiers in evolutionary algorithms, cary, nc, usa 2003. [5] a.v. eremeev, c.r. reeves, non-parametric estimation of properties of combinatorial landscapes, lecture notes on computer science, vol.2279, pp.31–40, 2002. [6] f. ferreira, j. fontanari, p. stadler, landscape sstatistics of the low autocorrelated binary string problem, journal of physics a: mathematical and general, vol.33, pp.8635–8647, 2000. [7] j.e. gallardo, c. cotta, a.j. fernandez, finding low autocorrelation binary sequences with memetic algorithms, applied soft computing, vol.9, no.4, pp.1252–1262, 2009. an electromagnetism-like approach for solving the low autocorrelation binary sequence problem 695 [8] r. garello, n. boujnah, y. jia, design of binary sequences and matrices for space applications, proceedings of the 2009 international workshop on satellite and space communications iwssc’09, art. no. 5286416, pp.88–91, 2009. [9] m.j.e. golay, the merit factor of long low autocorrelation binary sequences, ieee transactions on information theory, vol.28, pp.543–549, 1982. [10] s. halim, r.h.c. yap, f. halim, engineering stochastic local search for the low autocorrelation binary sequence problem, lecture notes in computer science, vol.5202, pp.640–645, 2008. [11] b.w. kernighan, s. lin, an efficient heuristic procedure for partitioning graphs, bell system technical journal, pp.291–307, 1970. [12] s. mertens, exhaustive search for low-autocorrelation binary sequences, journal of physics a: mathematical and general, vol.29, pp.473–481, 1996. [13] s. mertens, h. bauke, ground states of the bernasconi model with open boundary conditions, available online at http://odysseus.nat.uni-magdeburg.de/~mertens/ bernasconi/open.dat [14] b. militzer, m. zamparelli, d. beule, evolutionary search for low autocorrelated binary sequences, ieee transactions on evolutionary computation, vol.2, pp.34–39, 1998. [15] s. prestwich, exploiting relaxation in local search for labs, annals of operations research, vol.156, pp.129-141, 2007. [16] a. ukil, low autocorrelation binary sequences: number theory-based analysis for minimum energy level, barker codes, digital signal processing: a review journal, vol.20, no.2, pp.483–495, 2010. int j comput commun, issn 1841-9836 8(1):166-175, february, 2013. issues on applying knowledge-based techniques in real-time control systems d. zmaranda, h. silaghi, g. gabor, c. vancea doina zmaranda, helga silaghi gianina gabor, codruta vancea university of oradea romania, 410087 oradea, 1 universitatii st. e-mail: zdoina@uoradea.ro, hsilaghi@uoradea.ro gianina@uoradea.ro, cvancea@uoradea.ro abstract: at the time being knowledge-based systems are used in almost all life aspects. the main reason for trying to use knowledge-based systems in real-time control is to reduce cognitive load on users (overload), their application proving to be important when conventional techniques have failed or are not sufficiently effective [1]. the development of automated diagnosis techniques and systems can help also to minimize downtime and maintain efficient output. this paper presents some issues of applying knowledge-based systems to real-time control systems. it describes and analyzes the main issues concerning the real-time domain and provides possible solutions, such as a set of requirements that a real-time knowledge-based system must satisfy. the paper proposes a possible architecture for applying knowledge-based techniques in real-time control systems. finally, a way of employing knowledge-based techniques for extending the existing automatic control and monitoring system for the geothermal plant from the university of oradea is presented. keywords: real-time control systems, knowledge-based systems, programmable logic controller 1 introduction real-time systems generally consist of a series of complex, heterogeneous and critical processes. they are also closely coupled systems, consisting of a physical system part and a control computer system part. while the physical part reacts to the control signals from the computer, the control computer part and its software must interact with the dynamic properties of the physical part. real-time systems are also, by definition, reactive systems. to increase their efficiency, different monitoring programs, tools, algorithms, and rules could be utilized. generally, these programs are used for detecting abnormal behaviors, tracing workflow progress, and generating alerts and reports during the different phases of the system. however, using knowledge-based techniques in real-time applications represents a major challenge because of several reasons [10]: problems related to time representation and reasoning about time; problems related to deadline because a knowledge-based system should provide the best solution within a given deadline; problems related to asynchronous evens handling that could lead to interruption of the inference process; problems related to integration of conventional real-time programming and knowledge-based programming. moreover, specific nature of real-time systems that implies interaction with external physical system that also implies specific features when including knowledge-based components [7]: the knowledge-based decision making system is hardly related to the external system; the knowledgebased decision making system should be also a real-time system in order to be sure that the decision making are performed before deadlines; implies both knowledge of control and of the copyright c⃝ 2006-2013 by ccc publications issues on applying knowledge-based techniques in real-time control systems 167 external physical system each of them having a specific form; when dealing with complex realtime systems consisting of several sub-components, the problem of decision making need to be based on distributed knowledge. knowledge-based and real-time control technologies are complementary, rather than competitive technologies. control technologies generally are oriented to quantitative processing while knowledge-based integrates both qualitative and quantitative processing [6]. separating the description (the knowledge) of a process from the control algorithm allows knowledge to be more explicit, visible and analyzable, instead of being hidden inside of the procedural programming code. knowledge-based systems could be used for different purposes in real-time process control, the main domains of their applicability include [5] [9]: fault diagnosis, that implies detection, cause analysis and repetitive problem recognition; complex control schemes; process and control performance monitoring and statistical process control; real time quality management (qm); control system validation. for real-time control systems, an important issue is represented by their capability in fault detection and diagnosis because the availability and productivity can be significantly improved by shortening their downtime. moreover, because personnels ability for observations could be incomplete or wrong and leading to incorrect diagnosis, intelligent system approaches need to be investigated and applied. 2 knowledge-based techniques in real-time applications real-time applications could have different structures, and consequently, different approaches for using knowledge-based techniques can be employed. one typical structure of real-time control systems comes from the requirement of meeting the quest for automation and flexibility in complex manufacturing systems and is based on plc (programmable logic controller) usage. such real-time control systems architectures are widespread because plcs offer an adaptable and modular solution to the control problem. however, there are some shortcomings of this approach that are generated by plcs inflexible programming system that do not support automatic analysis of logic circuits in order to look for a fault. even if in todays modern plcs there are some diagnosis functions available, their usage is limited and need to be extended. consequently, developing a knowledge-based system for diagnosis purposes could represent a solution for implementing automated diagnosis techniques into complex manufacturing real-time systems. in order to create efficient solutions, several specific issues should be considered: knowledge representation and acquisition, real-time reasoning, knowledge validation, integration with real-time software [11] [12]. 2.1 knowledge representation and acquisition the main reason for using real-time knowledge-based systems is to reduce cognitive load on users. therefore, such systems require a knowledge representation that integrates several kind of knowledge taken from several sources: analytical models developed by using differential equations, material or energy balances or overall process behavior kinetics. generally, each object could have a behavior that is represented by a combination of analytic (model-based) and heuristic (rule-based) statements. in plc-based systems automatic monitoring elements available for faults are represented by discrete state signals into the plc memory. these signals are indicating different operation states of the controlled plant and based on their values further diagnosis can be carried out. the values can be obtained by accessing plc memory via a linkage from the computer that has implemented the diagnosis system. the diagnosis system must then use specific reasoning algorithms for searching all possible fault causes under the 168 d. zmaranda, h. silaghi, g. gabor, c. vancea help of relevant knowledge and real-time data. therefore, the knowledge acquisition task is very important, and could be made in two ways: artificial knowledge acquisition, model-based knowledge acquisition [3]. artificial knowledge acquisition is obtained by knowing specific issues about the controlled plant. for example, in each plant there are several alarms that have the purpose of protecting plants equipments or preventing the plant to work in error conditions. these alarms are normally indicated by one or a combination of plc signals. for example: temperature too high, pressure too low or a combination of these. model-based knowledge acquisition is based on knowledge achieved during modeling the system behavior and constructing the plc program, resulting in improvement of knowledge acquisition and diagnosis efficiency. let define s the space that represents the fundamental set for all possible configurations of the control system variables. a specific configuration set s is given as: s = s1,s2, ......sn ∈ s (1) let define the behavior space b that represents the fundamental set of all determinable behavioral attributes: b = b1,b2, ....bm ∈ b (2) every subset f of b that comprise specific required profile for faulty behavior could be expressed by a combination of plc signals associated with correspondent control system variables and their specific state. from formal point of view, these could be represented in the following way [2]: fault− > (plcsignal1,state),(plcsignal2,state)....,(plcsignaln,state) (3) with every fault defined into the faulty behavior profile, all possible sources generating the fault are considered and specified. for example, if for a specific fault occurrence from the fault space f, device mapping dealing and inferring the functional relationship is considered: f : d → crelevant (4) where d represents the device space and crelevant represents relevant cause space. the relationship between behavior space b, configuration space s and required profile for faulty behavior f is presented in figure 1. based on evaluation mapping between a specific behavior and profile of faulty behavior, if a match is found then the corresponding fault could be identified together with all possible causes. 2.2 knowledge validation in traditional real-time control systems, the control problem and its implementation through control algorithms is based on the exact knowledge of the control plant that is determined usually based on the mathematical model of the plant. real-time knowledge-based control systems combine the analytical process model with conventional process control while reasoning about current, past and future situation in order to assess on-going developments and plan appropriate actions. such systems allow the application to be structured into a model that is capable to behave and use its reasoning when taking a decision as human specialists do. generally, a full control strategy requires not only variable (parameters) identification, state estimation and control, but also to check the validity of the data and process models before they are used in estimations. however, there is a relatively high degree of uncertainty concerning the plant starting from the mathematical model itself: there is not apriori knowledge of some parameters (for issues on applying knowledge-based techniques in real-time control systems 169 figure 1: the relationship between behavior space b, configuration space s and required profile for faulty behavior f example, parameters for achieving stability conditions of a feedback control) or, the plant behavior may be not deterministic [4]. there is an important concern when using knowledge-based techniques for real-time control systems: the need for validating systems knowledge, allowing determination if it accurately represents an experts knowledge in the particular domain. in this idea, simulation, when available, could represent a very useful tool that provides a general overview of system dynamics. 2.3 integrating knowledge-based software with real-time software the real-time control software and rule-based software differ in their underlying execution models. procedural software uses generally an imperative model in which software engineer determines the sequence of actions while rule-based systems represent a general control scheme of matching, selecting and execution of rules. consequently, a knowledge-based real-time diagnosis system should be thought as an extension of existing control software that interacts both with knowledge database constructed by using artificial and model-based acquisition and with the real-time data acquired from plc source code execution. a possible structure is presented in figure 2. diagnostic reasoning is based on the knowledge base as well as real-time data from realtime database. the reasoning mechanism is based on logic control of faults. thus, it uses the logical expression for faults presented in (3) where each term represents a possible cause of fault indicated by a specific plc signal. by comparing the fault state with the current state of signals from real-time database in plc, an occurrence of a fault state could be identified. furthermore, by using systems profile mapping to devices and associated causes, as presented in (4), associated devices and causes of the fault could be shown. 2.4 real-time reasoning first attempt for using knowledge-based systems for real-time process control involves using static expert systems that take a snapshot of plant data. static expert systems use pattern matching of a set of facts and rules. with no time constraints, this approach proves to be practical but when time constrains come into picture, this might not be a good idea. the 170 d. zmaranda, h. silaghi, g. gabor, c. vancea figure 2: structure of a diagnostic system for a real-time control system elements that should be considered in this situation are: temporal reasoning and responding within a given response time. in the controlled system, variables such as temperatures and pressures may vary in time; therefore, a time representation together with the possibility of reasoning about time is essential to be included. so, to the elementary entity of knowledge base additional time information should be attached. also, the rules defined into the expert system may be extended with specific, temporal-extended rules, such as: operators always, to formulate the premise of a rule; qualitative statements that refer to the relation of time points or time intervals, without exact time specification: earlier, after and similar; quantitative statements that allow expressing conditions with specification of exact point of time: for example at 12:00:00 p.m. generally, the basic characteristic of a system that guarantees a certain response time is represented by its determinism. knowledge-based systems are by their nature non-deterministic because the time of inference is dependent on the given situation. if imposed real-time deadlines are lower than the maximum searching time that is needed for a certain inference, the response time requirements cannot be met. in order to meet those requirements, three strategies could be applied: implementing algorithms to quantitatively estimate the maximum searching time, reducing the inference searching time or define an embedded diagnosis system approach which will integrate the diagnosis models into the plc control program so that faults could be diagnosed in real-time. the problem of including all diagnosis into the plc has also disadvantages by creating a much more complex control program. integrating inference rules into the control program itself complicate the rules and makes introduction of new rules more difficult. in addition, the process of integration of different knowledge and information of these systems represent a tedious process. a mixed approach could be a possible better solution: only for critical faults, the diagnosis part with corrective actions should be included into the plc and the rest of them should remain in charge of the knowledge-based system. in the next chapter, a way of employing knowledgebased techniques for extending the existing automatic control and monitoring system for the geothermal plant from the university of oradea is presented. issues on applying knowledge-based techniques in real-time control systems 171 3 the automatic control and monitoring system structure the automatic control and monitoring system of the geothermal plant from the university of oradea is an example of a real-time control system that has been developed using a combination between a plc (programmable logic controller) and a pc (for the user interface and supervisory control). from the structural point of view, the controlled plant is composed of 3 parts: the well station, the pump station and the heat station. the system functions in the following way: first, the geothermal water is extracted from the well station using a deep well pump if the necessary flow rate is greater than the artesian one; the water is then stored into a reservoir tank, which acts as an accumulator and also separates the production network from the distribution network. then, from the reservoir tank the water is pumped, through the pump station to the heat station and further, in the heat station, the water is not directly utilized, but through 4 heat exchangers; the water that comes out from these heat exchangers flows into the distribution network and heats the university campus buildings. the structure of the control system consists of a plc for controlling the geothermal heating system based on a control program embedded to the controller, connected with a pc computer and contains the user interface for the operator that is implemented using wonderware intouch software [15]. the intouch display management subsystem handles display call up, real-time display update, data entry and process schematics. it also maintains the plc real-time and historical database that could be used to follow the time evolution of certain parameters or for statistical calculations. the real-time database provided includes maintenance of historical data in addition to current values of process variables. in the current implementation approach, the decision process in order to achieve possible solutions for several faults, such as sensors and other equipments faults implies changing the plant operation into a safety mode and employing the operator for tracing the fault, its effects and related causes. also, plc control program deals with diagnosis to only of few critical faults. based on current situation of the geothermal plant and the current research in the domain, a proposal for extending the existing control system with a knowledge-based system is developed and described further in this paper. 3.1 analysis phase. knowledge acquisition and validation the main issue when constructing a knowledge-based system is the way on which description (knowledge) is built up in accordance with the plant behavior and structure. consequently, the development of a knowledge-based system for the existing plant and control system implies selecting the most important characteristics of the system that will be used in order to construct the knowledge database. moreover, there is the need for validating systems knowledge, that means determination if it accurately represents an experts knowledge in the particular domain. in this idea, the simulator developed for the geothermal plant of the university of oradea proves to be of great help. the simulator, which was previously developed, provides a simplified physical model of the plant dynamics together with plc control, which is formulated into an easier-tooperate computer simulation: the acsl solution to the model equations. key elements of the improved easy-of-operation are the use of general-purpose simulation language acsl (advanced continuous simulation language), and pre-programmed modules of all important plant components, including control elements [16]. in the development phase of control system, the simulator has provided a useful tool for testing the system specifications, including the adopted control strategy; it could be employed also into the process of step-by-step knowledge acquisition and for further validation and updating. based on information gathered from simulation and from the control program development, knowledge acquisition could be achieved. for example, if we refer to possible faults, a structured notation could be used when doing knowledge aquisition, 172 d. zmaranda, h. silaghi, g. gabor, c. vancea based on tables, as shown in table 1. table 1: faults and associated switches in plc (partial) digital inputs fault notation overload protection switch pump p3/p4 p3_ol, p4_ol thermal switch pump p3/p4 p3_td, p4_td overheat in the heat station ts2 local emergency stop switch in the pump station panel es1 3.2 design phase. knowledge representation knowledge acquisition is the most important part when developing a knowledge-based system, but an important point is represented also by the way on which knowledge representation is done. representation is tied to the production rules that should be expressed accordingly to the knowledge system that is used. current knowledge-based industrial systems are generally built within shells, which package a combination of tools. different shells may include different features useful for real-time control applications, such as: hierarchy for objects, associative knowledge, relating objects in the form of connections and relations, rules and associated inference engine, analytic knowledge, such as functions, formulas, and differential equation simulation, real-time features such as time stamping and validity intervals for variables, history-keeping, run-time environment. in this idea, current literature presents several expert systems that could be used for real-time process control systems, the most known being g2 and jess. g2 real-time expert system [13] allows integration of models and rules for combining modelbased and artificial knowledge representation and it is based on an inference engine that can use generic forms of knowledge, interpreted for specific instances in the domain. it is specifically designed for process control and related applications and allows the process engineer to implement and manage the expert system. but, even if g2 is claimed to be real-time, there is no mention of verifiability from temporal point of view. java expert system shell or jess [13] is inspired by the artificial intelligence production rule language clips being a fully developed java api for creating rule-based expert systems. even if it is architecturally inspired by clips, it exhibits a lisp-like syntax. it consists of three components: the rules (knowledge base), the working memory (fact base corresponding to real-time base) and an inference engine (rule engine). jess uses the rete (ree-tee) algorithm to match patterns. rete algorithm is not time predictable but two newer algorithms treat and leaps have introduced some optimization from this point of view; there are as well not time predictable but, with some restrictions, it would allow to be time predictable. consequently, we consider to develop a solution based on jess for our system. thus, a knowledge model can be constructed starting from observing the main problems associated with every specific item (for example, pumps p3/p4 from the pump station) in the operational system and creating the model (table 2) that presents correlation between problems and items that can be solved by expert system. table 2: correlation model slot item problems situation (for pumps p3/p4) engine overheating, vibration, lack of voltage afterwards, for each identified problem, the possible causes are identified and associated, generating a knowledge model (table 3). issues on applying knowledge-based techniques in real-time control systems 173 table 3: knowledge model slot inference level task level cause overheating cooler water missing, too big temperature in pump station vibration bend axle, motor basement snap lack of voltage disruption of wires, general pump station switch off after establishing the knowledge model, the design phase is dedicated for structuring the rules according to requirements of jess inference engine. jess uses the notion of frames that are hierarchical representation that includes several components (slots, facet, datum and how). frame definition is made by using deftemplate, having the following generic form [2]: ((deftemplate <deftemplate-name> [extends <classname>] [<doc-comment>] [(slot <slot-name> [(default | default-dynamic<value>)] [(type <typespec>))]*) for example, the template for pumps p3/p4 will look in the following way: (deftemplate p3/p4 (slot situation (default nf)) (slot cause (default nf) ) , where nf states from not found. jess basis of knowledge composed from rules could be further built based on the previously frame definitions. these slots in the jess rules structure will be unified by using pattern-matching by the inference engine through the rete algorithm [8]. 3.3 overall proposed architecture the proposed modifications of the existing architecture are illustrated in figure 3. it employs as additional element an additional computer on which the jess expert system is running that is connected with the pc on which the user interface was developed and on which plc historical and real-time database resides. historical and real-time databases are, in our situation, created and updated by intouch in real-time, based on plc associated inputs. figure 3: the knowledge system architecture 174 d. zmaranda, h. silaghi, g. gabor, c. vancea in the proposed architecture, the main issue is represented by the way in which collaboration of several components is achieved, because plc real-time database has a specific format for storing, defined by intouch. there is a need of conversion from this format in order to be able to interact with the knowledge base that resides on the expert systems pc computer. a possibility is represented by using an xml file that stores all the tagnames of the faults, generated from plc database. the idea is to use the xml file to feed the knowledge base and to interact as an intermediate level between expert system and plc real-time database. then, this file could be used as input by a jess function in order to check is value; afterwards, for all tagnames that are acted (indicating a fault) rules pattern should be evaluated. consequently, the architecture colaboration of components could be achieved by integration of jess engine with the real-time database through the xml file. there are several issues that are not completely defined in our architecture. for example, the way in which the xml file is generated based on plc real-time database in still in question. also, after completing the implementation, system performance should be evaluated in order to prove that the solution provides satisfactory. 4 conclusions and future works knowledge-based systems are making significant contributions to real-time process control applications. their applications are often in areas which complement traditional process control technology, like, for instance, diagnosis and handling abnormal situations. they integrate knowledge-based techniques with conventional control, having significant benefits in overall quality management. but, a knowledge-based system operating in a real-time situation will typically need to respond to a changing environment involving asynchronous flow of events and dynamically changing requirements with limitations on time, hardware, and other resources. determining how fast this system can respond under all possible situations is a difficult problem that requires using flexible software architecture in order to provide the necessary reasoning on rapidly changing data. in this paper, various issues on applying knowledge-based techniques in real-time control systems have been presented; starting from this foundation, an implementation structure of a knowledge-based system for the existing automatic control and monitoring system for the geothermal plant from the university of oradea was analyzed and an overall architecture is proposed for further implementation. the actually proposed knowledge-based system approach is focused mainly on general architecture and component colaboration. the architecture does not include time-constrains validation, it relies only on the performance of the jess engine; a further solution for creating a specific expert system that includes temporal reasoning will be also investigated in the future. bibliography [1] singh a., verma m., real time expert system its applications, ijcst, issn : 2229-4333 (print), issn : 0976-8491 (online), 1(2):150-153, 2010. [2] farias o., labidi s., neto j. f., albuquerque s., a real time expert system for decision making in rotary railcar dumpers, automation control theory and practice, a d rodi (ed.), isbn: 978-953-307-039-1, intech, available from: http://www.intechopen.com/books/automation-control-theory-and-practice/a-realtime-expert-system-for-decision-making-in-rotary-railcar-dumpers, 2009. issues on applying knowledge-based techniques in real-time control systems 175 [3] el-desouky a. i., arafat h. a., laban s., implementing knowledge-based systems in monitoring real-time systems, geophysical research abstracts, vol. 10, egu2008-a-12044, sref-id: 1607-7962/gra/egu2008-a-12044, egu general assembly, 2008. [4] eremeev a., varshavskiy p., case-based reasoning method for real-time expert diagnostics systems, international journal information theories and applications, 15:119-125, 2008. [5] wai k. s., latif abd., rahman b. a., zaiyadi m. f., aziz a. abd., expert system in real world applications, http://www.generation5.org/content/2005/expertsystem.asp, 2005. [6] bubnicki z., modern control theory, isbn 10 30540-23951-0, isbn 13 978-3-540-23951-2, springer-verlag, berlin, hedelberg, 2005. [7] bubnicki z., kowledge-based and learning control systems, control systems, robotics and automation, ecyclopedia of life-support systems, vol. xvii, 2005. [8] grissa-touzi a., ounally h., boulila a., visual jess: an expandable visual generator of oriented object expert systems, world academy of science, engineering and technology, 2005. [9] alexander a. j., how to use expert systems in real world applications, http://conferences.embarcadero.com/article/32093, 2004. [10] vagin v.n., eremeev a.p., certain basic principles of designing real-time intelligent decision systems, journal of computer and systems sciences international, 40(6):953-961, 2001. [11] laffey t. j., cox p. a., schmidt j. l., kao s. m., read j. y., real-time knowledge based systems, ai magazine, 9(1):27-45, 1988. [12] charpillet f., look on the issue of building real-time knowledge based systems research summary, association for the advancement of artificial intelligence technical report ws97-06, 1997. [13] http://www.gensym.com/ [14] http://herzberg.ca.sandia.gov/jess/ [15] http://global.wonderware.com/en/pages/wonderwareintouchhmi.aspx [16] mga software, advanced continuous simulation language (acsl) reference manual,1995 int j comput commun, issn 1841-9836 8(1):97-104, february, 2013. managing information technology security in the context of cyber crime trends d. neghina, e. scarlat diana-elena neghina institute of doctoral studies ase 11, tache ionescu street, bucharest, romania e-mail: diananeghina@gmail.com emil scarlat academy of economic studies department of economic cybernetics 6, romana square, bucharest, romania e-mail: emil_scarlat@csie.ase.ro abstract: cyber-attacks can significantly hurt an organization’s it environment, leading to serious operational disruptions, from simply damaging the first layers of it security up to identity theft, data leakage and breaking down networks. moreover, the dangers through which current cybercrimes practices affect organizations present a tendency of developing more rapidly that decision makers can assess them and find countermeasures. because cyber threats are somewhat new thus a critical source of risks, within the context of the constantly changing it environments (e.g. cloud services integration) organizations may not effectively implement and manage cyber threat risk assessment processes. this paper highlights the importance of designing effective security strategies and proactively addressing cybercrime issues as key elements within the organizational risk management approaches. malware rises constantly in impact and complexity and has surpassed the traditional security model. one of the main ideas of the study is to present the main areas of risks related to cyber security to which an organization is subject to and provide a baseline of an analysis model that would adequately evaluate input data, rank priorities and represent the results and solutions to decrease these risks. the importance of this study is to increase awareness efforts and to highlight the critical importance of using the full extent of resources provided. each member of an organization has a significant role in decreasing the exposure to the vulnerabilities created by cyber-attacks. keywords: cybercrime, it security, risk assessment, vulnerability management. 1 introduction the dangers through which current cybercrimes practices affect organizations present a tendency of developing more rapidly that decision makers can assess them and find countermeasures, exposing entities to significant risks. vulnerability events are created constantly, through the exposure over shared environments(such as cloud solutions), permanent transfers of critical information between an organizations branches, social platforms, electronic banking solutions, displaying intellectual property, all enabling favorable circumstances for appropriation, disturbances and misuse of critical information and data. the purpose of this paper is to make entities aware of the changes in the last decades of cyber threats within network environments and how to effectively approach these elements, timely finding solutions and actions to be taken. one of the main ideas of the study is to present the principal areas of risk related to cyber security to which an organization is subject to in the current it environments and to determine a specific analysis model that would adequately lead copyright c⃝ 2006-2013 by ccc publications 98 d. neghina, e. scarlat to input data evaluations, rank priorities and represent the results and solutions to mitigate the identified risks. on the current market, the tendency of decision makers is still to take unintended or unexpected risks by following classical patterns of behavior and standard models of security, applied additionally in new environments that are continuously changing, often with the consequence of significantly affecting organizational value. our opinion is that the increasing number of attacks and constantly developing threats are leading to significant gains that are further supporting the constant adaptation of cyber criminals to the classical implementation and management of security tools. we consider that our paper is raising an alarm signal on the lack of perception of the fact that traditional models are starting to be considered as outdated and decision makers must evaluate the need of combining them with additional elements from the operational areas to obtain a better understanding; the real issue is the fact that professional cybercrime tools and methods are so advanced that are continuously increasing the security issue. managements ability of forecasting security breaches that are considered critical for the compliance of a company with its strategies is essential in using corporate information as an asset, as well as a significant competitive advantage. this further sustains the capability of a close collaboration between it resources and operational ones. technical understanding of events and processes must be combined with the business perception of processes and activities for entities to adjust more quickly to challenges and take immediate actions to unexpected events. even though it governance and compliance methods, identity management, applications security and network tools are becoming more and more complex, are processing information from different areas of an organizational environment, cyber criminals are also aware of the changes and are also adjusting their techniques, becoming better at hiding their trace, gaining undetected access and all for extended durations of time; studies present the fact that they are characterized by stealthy and passive methods for attacking a system (schudel, wood) [1]. entities must better determine the key elements that are making them a target of cyber criminals and the way they are perceived by external parties regarding critical information of interest and act on these vulnerabilities first, these being considered as the initial layers of attack. all security analysis and prevention methods should begin from the idea of unauthorized access being gained and core business data being misused, thus leading to appropriate security measures of classification of critical information (high to low risk assessments). as a general overview of it reviews, there are not many entities that are implementing levels of security and classification of data based on values or risk considerations. as a summary conclusion, the actual warning is the tendency of cybercrime attacks of continuously becoming more dangerous and complex, evolving into schemes difficult to anticipate. the attacks are determined to be more destructive, more advanced and more studied (significant resources are invested in these activities, new capabilities are researched based on existing security tools) and have a serious impact over economic and even national security elements. 2 cyber-crime trends analysis the current approaches of cyber criminals may be characterized as proactive. further, attackers actions are dynamic through their frequency and most aggravating, collaborative, in the sense that services emerge over the internet with the scope of committing fraud, theft and exploitation of systems vulnerabilities. managing information technology security in the context of cyber crime trends 99 2.1 brief development of cyber crime as a summary of cyber-attacks tendencies, during the first periods of modern internet and it environments, cyber-attacks were generally performed by employees, within organizational networks, generally due to different dissatisfaction reasons. this type of threat was favored until the 1980 and attackers took advantage of privileged and granted access privileges to it resources; they altered information mainly for financial advantages or simply sabotaged data for revenge at employers. studies revealed that programmers developed their abilities of writing malicious software, including self-replicating programs, to interfere with personal computers. during the 1990s, financial crime over the internet completed through penetration and subversion of computer systems increased significantly. by the late 1990s and in the years following 2000, fraud attacks and identity theft developed. moreover, organized attacks started to be performed more often including groups of cyber criminals and increasing the time of acting out without any detection (kabay) [3]. throughout the 2000s, cyber attackers started to externalize services and offer their unauthorized activities for more complex crimes. there were identified many denial-of-services attacks opposing known websites and malware was designed to record logs of keystrokes and then send this information through secure internet communication channels to cyber criminals. 2.2 trends and security challenges thus, business data, organizational assets are increasingly threatened and traditional it security approaches are offering only the basis of solutions. as a general characteristic, the present it environment can be considered as reactive, without any time allocated for risks assessments; cyber criminals are aware of all these exposures and take advantage of them through the use of end users (social engineering, theft of credentials), phishing attacks, through all sorts of original deceptions, penetration and encryption techniques to make their trace inaccessible. as mentioned previously, security events are continuously increasing, in frequency as well as in impact. however, in the same time quantitative information related to these events are both difficult to obtain as well as hard to place into a meaningful framework (shimeall, williams) [2]. on a general level, organizations should transition from a mainly security based approach to a more risk assessment approach, thus addressing vulnerabilities within the risk management planning and methods. entities should continuously increase their security awareness procedures, apply active monitoring procedures and complete periodical trainings for all operational and technical personnel to achieve an effective cyber security stance. operational and information technology solutions are shifting towards collaboration environments. it environments sustain shared resources and services, including core business applications, centralized architecture and infrastructure elements for which entities must put in place controls to identify and counteract effectively unauthorized actions. thus there must be put in place techniques that set the baseline for risk control and business efficiency, outlining the key issues and introducing risk assessments of specific elements regarding enterprise information protection and personal privacy. however, the vast majority of organizations have restricted abilities to identify and take effective actions against security breaches. researches show that preventive actions are extremely hard to implement. moreover, most of the current analysis tools are based on prior behavior and activities and may not produce significant models to appropriately identify other attacks or to be used for efficient preventive actions (jajodia et al) [4]. vulnerabilities are thus analyzed constantly based on prior security events and not on emerging cyber threats, adding value to the security outline of the organization. 100 d. neghina, e. scarlat however, in practice, defined security assessments performed by organizations are not capable any more to cope with the continuously evolving threats. these are not focused on collecting cyber threat data from a variety of business and operational areas and different sources due to increased time allocation and costs of implied resources. security methods applied are based mainly on high level information and tools and technologies implemented are generally configured based on existing standards, and not customized on the features of the environments that would have the ability to promptly identify, enclose and restrict, evaluate and restore compromised characteristics. with the difficulty of obtaining and updating cyber intelligence information, the implementation of risk management methodologies should be performed. these must be appropriately designed and used to effectively challenge or even block access attempts, security breaches or fraudulent transactions. in the following section we present the approach distinguished through a set of specific tools and techniques that can be easily customized and applied to specific organizations profiles, networks, and significantly improve the already implemented security controls by organizations. 3 cyber threats risk management capability the risks characteristic for an organization and its related industry shape the operational environment, its readiness and effective response to different interactions with internal and external environments. the general characteristic of the current organization is its increasing reliance on technology, information sharing, and connectivity elements. thus dependence leads to risks at all levels. due to the fact that cyber threats are a relatively new and constant source of risk, continuously changing, entities are not as capable at managing cyber threat risk as they are at managing any other operational risk related to business activities. 3.1 the fundamentals of a cyber-threat risk assessment process unless an organization is considerably developed related to cyber threat risk management practices, it cannot have the risk assessment infrastructure and governance elements designed to sustain an adequate security environment. for example, if the basic elements are not defined, such as specific risk definitions and business impact analysis, risk limits of acceptance or specific key performance indicators. if an enterprise cannot sustain at least the above mentioned elements, it is advisable as a starting point the evaluation of the following set of information security practices that significant for an appropriate cyber risk assessment process: 1. existing security controls, implemented by the entity to identify and record known types of cyber-attacks that are characterized by stealth breaches. here are included also the security tools and techniques used to timely identify and contain compromised it resources; 2. available methods of recording security breaches information from multiple sources (internal as well as external). added to this category are the abilities of the entity to implement cyber-crimes risk models in order to collect relevant cyber intelligence information and generate value and actionable data for decision making purposes; 3. exposure of employees to complex social engineering attacks that allows malware to be integrated in the administrative consoles or workstations. here are included the procedures of detecting advanced, persistent threats within the entitys own business environments in the case of identity theft or unauthorized use of authentication elements. managing information technology security in the context of cyber crime trends 101 these are not topics for an elaborate analysis process, but they do represent basic elements of an effective defense mechanism against current cyber-attacks. by applying a more elaborate cyber threat risk assessment framework (presented in figure 1. and further detailed in the following section) an organization can better protect its operational environments as well as gain valuable insights on its vulnerabilities and improvement actions to be taken. figure 1: model score after boosting 3.2 core cyber threats risk management capabilities below we present a new approach of cyber threat risks that, if unaddressed, may lead to the security vulnerabilities exploited firstly by attackers. 1. monitoring and key performance indicators: starting from the basic incident driven communication with management, periodical and formalized communication with decision makers should be implemented. evidence should be kept for ongoing dialogue and critical metrics should be reviewed, analyzed and used to improve already existing security controls. key performance indicators should be designed based on cyber intelligence information that must be logged for predefined periods of time. these must be standardized within the organizational units and have a clear linkage to business value (generally these should be defined in quantitative terms). 2. organizational personnel: employees must be aware of cyber threat risk, recognize attacks as a potential risk area and have basic knowledge of designed security policies, processes, and implemented tools; roles and responsibilities for risk management should be established that would ensure the integration of specific risk assessments into larger security decisions and controls affecting an organization. it personnel must have specialized knowledge about cyber threat risk; moreover, as a required competency security feeds from operational units must be centrally coordinated to manage and 102 d. neghina, e. scarlat keep cyber threat risks within defined acceptance levels. written and approved it security policies, training missions, and communications must be effectively distributed and compliance periodically monitored and reviewed for appropriate enforcement; most of employees should have clearly defined responsibilities for cyber risk management, appropriate to their roles and responsibilities. 3. operational processes: organizational business units are characterized by fragmented processes, which are not always communicating between them, certain manual input and execution of operational activities. defined processes must align with enterprise-wide risk management framework above mentioned and must be monitored by it personnel and executive management at different levels; processes must reach the level of being consistently integrated, automated, and clearly documented related to cyber threats risk assessments. organizations must formally measure and monitor process effectiveness; automation must be viewed as an objective; cyber threat risk management may be organized at a higher level as a self-standing unit and through which all processes are addressed by continuous improvement efforts; the overall objective is to accomplish structured cyber threat risk management programs that are integrated with the already existing it risk management and enterprise risk management agendas. 4. security tools and techniques: technology already installed for security reasons must be enabled to log security events, to centralize them and as a basis must send alerts in case of incidents recorded or exceptions; signature-based controls such as anti-virus and intrusion-detection software must be implemented. for an evolved organizational environment, forensic tools may be used for reacting to exceptional security events. commercially available threat monitoring feeds may be integrated with centralized logging solutions and monitoring software to generate automated alerts. for more complex risk management existing security tools may be automatically enabled to perform advanced correlations related to threat information and to convert obtained data into actionable alerts. these methods applied may be used to automate not just threat monitoring and alerts, but further security events identified, such as malware, or complete forensic analysis, as well as more complex threat assessments. the presented framework of continuously evolving cyber threat risk management capability model may sustain an organization in the process of implementing better security techniques to avoid cyber-attacks of criminals that make it past the already existing access controls mechanisms. for example, a well-developed cyber threat risk management model will include safety elements against unauthorized information distribution, as well as protection opposed to unauthorized information access. effective completion of these features lead to the use of technologies and processes that monitor outbound information traffic for both content and destination in particular. this can be configured as an alert point if data is being transferred to a location outside the normal operational environment, where the organization has not been present before. an accomplished capability will also be able to contain the transfer of information timely, to isolate the elements involved and assess the suspicious communication networks until their authentication credentials are clear; in case of unauthorized actions, procedures are in place for forensic analysis. because cyber-attackers are continuously improving their identity theft techniques, an organization shouldnt assume that each user that authenticates at network level and within the it application systems and is performing the required activities with legitimate credentials is in reality a legitimate user, an employee or an agreed service provider of the entity. managing information technology security in the context of cyber crime trends 103 a complex cyber threat risk management process will be designed to use at least two verification methods, depending on the classification of the information being protected. these activities include the verifications of a persons physical identity through techniques including biometrics such as laptop fingerprint readers, pin code token devices that must be carried by the legitimate users at all times, and finally behavioral programs that track post-login activity against historical patterns defined for a specific user in order to determine the likelihood of that person authenticating of being legitimate. 4 discussions any cyber-attack can hurt an organization in any number of ways, ranging from minor damages to a website informative page to shutting down core networks, committing fraud, and stealing intellectual property. thus organizations should implement actionable, risk-based intelligence processes in order to timely identify unauthorized cyber activity. entities should maximize the use of existing security solutions, logged information and most important, completing cyber threat awareness programs among employees. as a summary process, an organization should effectively identify the cyber-criminal risk, assess and evaluate this risk, integrate it, respond and take isolation actions to the risk, design, implement and test appropriate security controls, further monitor events, as well as assure and escalate future cyber-attacks. as mentioned within prior studies, cyber-crime events will become more precise, more specialized, and for this reason organizations must integrate a dedicated cyber threat analysis tools and it experts. as a general idea, an organization’s security resources will need to focus more on analyzing available internal and external data sources, customize controls and center less on managing and maintaining standard security controls. 5 conclusions entities are responsible for implementing and maintaining an integrated approach between its employees, operational process, and technology resources implemented in order to complete effective risk management procedures. resources must be allocated to gather and process cyber threat analysis information, notifying the results and defining alerts for better security controls and measures to be taken by the operational units. complex cyber risk management processes are repeatable, clearly defined, well-documented, and aligned with an organizations larger it risk management. future work will focus upon cyber intelligence collection methods and processing algorithms, behavioral trends of cyber attackers, which could accommodate customized improvements to the risk management activity of an organization. as the research of cyber security capabilities transforming from raw data to actionable intelligence will provide valuable cyber threat research. hence, such an analysis would support improvements in multiple key threat indicators and metrics related to it security analysis. bibliography [1] gregg schudel, bradley wood, modeling behavior of the cyber-terrorist, in http : //www.dli.gov.in/data/hacking_information/printed20papers /modeling20behavior20of20cyber20terrorist.pdf. [2] tim shimeall, phil williams, models of information security trend analysis, in http : //www.dli.gov.in/data/hacking_information/printed20papers/ models20for20inf20security20trend20analy sis.pdf. 104 d. neghina, e. scarlat [3] m. e. kabay, ma brief history of computer crime, in http://www.mekabay.com/overviews/history.pdf. [4] sushil jajodia, peng liu, vipin swarup, cliff wang, editors, cyber situational awareness: issues and research, in springer international series on advances in information security. [5] sumit ghosh, elliot turrini, editors, cybercrimes: a multidisciplinary analysis, in springer-verlag berlin heidelberg, 2010. [6] martin c. libicki, cyberdeterrance and cyberwar, rand corporation, 2009. [7] jean-marc seigneur, adam slagell, collaborative computer security and trust management, in information science reference (an imprint of igi global), 2010. int j comput commun, issn 1841-9836 7(5):976-989, december, 2012. error correction method in classification by using multiple-criteria and multiple-constraint levels linear programming b. wang, y. shi bo wang school of mathematical sciences, graduate university of the chinese academy of sciences, beijing 100190, china e-mail: wangbo8014@126.com yong shi (corresponding author) 1. research center on fictitious economy & data science, chinese academy of sciences, beijing 100190, china and 2. college of information science & technology, university of nebraska at omaha, omaha, ne 68182, usa e-mail: yshi@gucas.ac.cn abstract: in classification based on multiple-criteria linear programming (mclp), we need to find the optimal solution of the mclp problem as a classifier. according to dual theory, multiple criteria can be switched to multiple constraint levels, and vice versa. a mclp problem can be logically extended into a multiple-criteria and multiple-constraint levels linear programming (mc2lp) problem. in many applications, such as credit card account classification, how to handle two types of error is a key issue. the errors can be caused by a fixed cutoff between a "good" group and a "bad" group. two types of error can be systematically corrected by using the structure of mc2lp, which allows two alterable cutoffs. in order to do so, a penalty (or cost) is imposed to find the potential solution for all possible trade-offs in solving mc2lp problem. some correction strategies can be investigated by the solution procedure. furthermore, a framework of decision supporting system can be illustrated for various real-life applications of the proposed method. keywords: classification, two types of error, multiple-criteria linear programming, multiple-criteria and multiple-constraint levels linear programming, decision supporting system. 1 introduction taking advantage of many optimization based classification algorithms, the transactions data collected by the bank can be analyzed in many ways. as a result, the profit of the new accounts can be predicted, that is, which accounts are inclined to be bankrupt and which ones have good credit. however, for many real-life problems, the accounts cannot be separated by the hyperplane in spite of using some kernel techniques [1]. thus, how to decrease the number of misclassified samples becomes a big issue. in some applications, misclassifications have unequal importance. for example, in credit card account classification, it is essential to classify credit card customers precisely in order to provide effective services while avoiding losses due to bankruptcy from users’ debt. actually, even 0.01 percent increase in early detection of bad accounts can save millions, while losing a good account does not influence much [2], [3]. linear programming (lp) is a useful tool for discriminating analysis of a problem given appropriate groups (e.g., "good" and "bad") [4]. and multiple-criteria linear programming copyright c⃝ 2006-2012 by ccc publications error correction method in classification by using multiple-criteria and multiple-constraint levels linear programming 977 (mclp) has improved the result by minimizing the sum external deviations and maximizing the sum of the internal deviations simultaneously. mostly, the cutoff of mclp is fixed to be a given number (e.g., 1), while this will cause some other problems. for instance, it cannot involve those possible cases that can achieve the ideal cutoff score to be zero. formally, this means that the solutions obtained by linear programming are not invariant under linear transformations of the data [2], [3]. particularly, it is not invariant under vector addition. furthermore, if the classes of the samples exchange, i.e. "good" and "bad" classes swap to each other, the solutions are different. noticing these problems, some researchers made many efforts on this topic. consequently, a new model based on multiple-criteria and multiple-constraint levels linear programming (mc2lp) was posed [5], [6]. however, these methods were domain-driven, which meant they needed some domain knowledge to help finding the best cutoff. nevertheless, our new models are all automatically solving. as a result, it is useful to solve the problems that no domain knowledge is prepared. in particular, it solves classification problem twice. the maximal external deviation is found for the first time, while mc2lp is exploited to search for the optimal hyperplane based on minimizing two types of error for the second time. in addition to this, we also provide another mc2lp based model for the purpose of errors correction. in this model, we fix the cutoff to be 1 whereas we also add two more hyperplanes to detect the misclassified points carefully. accordingly, a subtle discussion is involved regarding the relationship between two types of error and the deviations . in fact, in the statistics theory, two types of error influence on each other oppositely. in other words, reducing of type i error will cause the increasing of type ii error, and vice versa. hence, we focus on different types of error respectively. moreover, some more elaborate introductions are demonstrated in the next sections. 2 multiple-criteria and multiple-constraint levels linear programming for classification 2.1 the mclp model for classification given a set of n variables about the records xt = (x1, x2, ..., xl), and then let xi = (xi1, xi2, ..., xin) t be one sample of data, where i = 1, 2, ..., l and l is the sample size. in linear discriminant analysis, data separation can be achieved by two opposite objectives, that is, minimizing the sum of the deviations (msd) and maximizing the minimum distances (mmd) of observations from the critical value [4]. that is to say, in order to solve classification problem, we need to minimize the overlapping of data, i.e. α, at the same time, to maximize the distances from the well classified points to the hyperplane, i.e. β. however, it is difficult for traditional linear programming to optimize mmd and msd simultaneously. according to the concept of pareto optimality, we can check all the possible trade-offs between the objective functions by using multiple-criteria linear programming algorithm. the mclp model can be described by figure 1: 978 b. wang, y. shi figure 1: mclp model moreover, the first multiple criteria linear programming (mclp) model can be described as follows: min ∑ i αi max ∑ i βi s.t. aix = b + αi − βi, ai ∈ bad, aix = b − αi + βi, ai ∈ good, αi, βi > 0, i = 1, 2, ..., l here, αi is the overlapping and βi is the distance from the training sample xi to the discriminator (w · xi) = b (classification separating hyperplane). 2.2 discussion of the cutoff b a boundary value b (cutoff) is often used to separate two groups, where b is unrestricted. a problem caused by treating b as a variable will meet many cases of no solution. for some applications, the user can choose a fixed value of b (b = 1) to get a solution as the classifier. as a result, efforts to improve the accuracy rate of classification have been greatly confined to the unrestricted characteristics of b (that is, a given b is put into calculation to find coefficients w) according to the user’s experience facing the real time data set [3]. in such procedure, the goal of finding the optimal solution for classification question is replaced by the task of testing boundary b. that is to say, if b is given, we find a classifier using an optimal solution by solving the model above. however, now we will point out the drawbacks of handling model in this way. at first, fixing b = 1 will make the solutions different under vector addition (one kind of linear transformations). besides, the solution will change if we swap the classes of "bad" and "good", which seems to be illogical. thus, we will introduce the examples below to illustrate these two issues in the rest of this part. example 1. in figure 2, there are 20 points (10 points belong to class -1, 10 points belong to class 1). on the left, there are coordinates of the points. on the right, the distribution of the points is displayed. mclp is applied to this data set and the solution is x = (0.58, −0.77). error correction method in classification by using multiple-criteria and multiple-constraint levels linear programming 979 figure 2: data1 figure 3: data2 and then, we transform the data by moving the points along the vector (1, 1), and the new data coordinates and the distribution are shown in figure 3. it is obvious that the samples’ distribution is the same as the former ones’, which means the optimal hyperplane will parallel to the former one. conversely, the result obtained by mclp is x = (0.4, −0.4), which is quite different from the former one. in addition, if the classes are swapped, the result will change into x = (−0.15, 0.77), which should be the same as the first one. for simplicity, in these experiments, the weight between two criterions is always fixed to be (4, 1). the discussion above shows that the result won’t be invariant under linear transformation. this means when the data change, we cannot just keep b to be a fixed number, say 1, and then solve the mclp problem for different data set. moreover, we need to take advantage of mc2lp to trace a better b to decrease the number of errors. 2.3 the mc2lp model for classification according to the discussion above, a non-fixed b is very important to our problem. at the same time, for the simplicity and existence of the solution, b should be fixed in some interval. 980 b. wang, y. shi as a result, for different data, we fix b in different pairs of interval [bl, bu], where bl and bu are two fixed numbers. now our problem is to search the best cutoff between bl and bu at every level of their trade-offs, that is to say, to test every point in the interval [bl, bu]. we keep the multiple-criteria the same as mclp, that is, mmd and msd. and then, the following model is posed [3]: min ∑ i αi max ∑ i βi s.t. aix = [bl, bu] + αi − βi, ai ∈ bad, aix = [bl, bu] − αi + βi, ai ∈ good, αi, βi > 0, i = 1, 2, ..., l where ai, bl and bu are given, and x is unrestricted. in the model, [bl, bu] represents a certain trade-off in the interval. by virtue of the technical of multiple-criteria and multiple-constraint levels linear programming (mc2lp), we can test each trade-off between the multiple-criteria and multiple-constraint levels as follows: min λ1 ∑ i αi − λ2 ∑ i βi s.t. aix = γ1bl + γ2bu + αi − βi, ai ∈ bad, aix = γ1bl + γ2bu − αi + βi, ai ∈ good, αi, βi > 0, i = 1, 2, ..., l here, the parameters of λ×γ are fixed for each programming problem. moreover, the advantage of mc2lp is that it can find the potential solutions for all possible trade-offs in the parameter space systematically [7] [8], where the parameter space is {(λ, γ)|λ1 + λ2 = 1, γ1 + γ2 = 1}. of course, in this model, choosing a suitable pair for the goal problem is a key issue and needs domain knowledge. consequently, a non-parameter choosing mc2lp method should be posed. 3 a new two alterable cutoffs model based on mc2lp 3.1 a framework of the new mc2lp model for the original mclp model, one cutoff is used to predict a new sample’s class, that is to say, there is only one hyperplane. the former mc2lp model points out that we can define two cutoffs instead of the original single cutoff. and then a systematical method can be used to solve this problem. consequently, all potential solutions at each constrain level trade-off can be acquired. however, one problem is how to find the cutoffs, that is, bl and bu. on one hand, we utilize two cutoffs to discover the solution of higher accuracy; on the other hand, we hope the cutoffs can be obtained from the system directly. inspired by the idea above, we address our first mc2lp model, which solves the classification problem twice. for the first step, mclp model is used to find the vector of external deviations α. it is a function of λ. for simplicity, we set b = 1. and then, we fix the parameter of λ to get error correction method in classification by using multiple-criteria and multiple-constraint levels linear programming 981 one potential solution. now a non-parameter vector of external deviations α is acquired. the component (αi > 0) means the corresponding sample in the training set is misclassified. in other words, type i and type ii errors occur. according to the idea of mc2lp, we can detect the result of every single mclp by fixing the parameter of γ at each level in the interval [bl, bu]. now, we find the maximal component of α: αmax = max{αi, 1 ≤ i ≤ l}. (1) indeed, the smaller the weight of external deviations is, the bigger αmax is. the misclassified samples are all projected into the interval [1 − αmax, 1 + αmax] according to the weight vector x obtained from the mclp model. in this way, we define bl and bu as 1 − αmax and 1 + αmax, respectively. it is easy to see, if we want to lessen the number of two types of error, in effect, we just need to inspect the cutoffs by altering the cutoff in the interval [1 − αmax, 1 + αmax]. moreover, for the second step, a new mc2lp classification model can be stated as follows: min λ1 ∑ i αi − λ2 ∑ i βi s.t. aix = [1 − αmax, 1 + αmax] + αi − βi, ai ∈ bad, aix = [1 − αmax, 1 + αmax] − αi + βi, ai ∈ good, αi, βi > 0, i = 1, 2, ..., l where ai, αmax are given, and x is unrestricted, [1 − αmax, 1 + αmax] means a certain trade-off in the interval. at the same time, λ = (λ1, λ2) is the parameter chosen in the first step. claim 2. furthermore, all the notations as [a, b] in the models displayed in this paper mean a certain trade-off in the interval, where a and b are two real numbers. 3.2 discussion of the new mc2lp model the most direct modification of the new mc2lp model is to transfer the single objective function to be a multiple-criteria one. because the vector of external deviations is a function of λ, it is easy to observe that if the weight between external deviations and internal deviations changes, α changes. consequently, αmax alters. and the ideal α is the one that makes αmax not too huge. in other words, we do not hope to check the weight that satisfies λ1 not too small. actually, some papers have proved that only if λ1 > λ2, then α · β = 0, which makes the model meaningful [9]. as a result, we only need to check the parameters of objective functions that make αmax not too big, in short, not too far away from the original one. on the other hand, we expect αmax not too small. that is to say, we hope the model has some generalization. hence, two small positive numbers ϵ1 and ϵ2 are chosen manually. and then, the interval is builded as [[1−αmax −ϵ1, 1−αmax +ϵ1], [1+αmax −ϵ2, 1+αmax +ϵ2]]. this means that the lower and the upper bound of the interval should be trade-off of some intervals, i.e. the multiple-constrained levels are actually multiple-constrained intervals. indeed, checking every trade-off of the intervals is the same as checking every trade-off of 1 − αmax − ϵ1 and 1 + αmax + ϵ2. in this case, we can consider the objective function as a multiple-criteria one. it 982 b. wang, y. shi figure 4: the solutions for mc2lp can be stated as follows: min ∑ i αi max ∑ i βi s.t. aix = [1 − αmax − ϵ1, 1 + αmax + ϵ2] + αi − βi, ai ∈ bad, aix = [1 − αmax − ϵ1, 1 + αmax + ϵ2] − αi + βi, ai ∈ good, αi, βi > 0, i = 1, 2, ..., l (2) where ai, αmax, ϵ1 and ϵ2 are given, and x is unrestricted. here, ϵ1 and ϵ2 are two nonnegative number. example 3. a numeric experiment will be introduced to demonstrate that our new mc2lp model is effective. the data is the same as data1 that has been displayed in figure 2. as we have shown above, the solution for mclp is x = (0.58, −0.77). according to the result, we find there are 6 misclassified points in the data, that is, point 5, 7 for class 1 and point 13, 15, 17, 19 for class -1. moreover, according to the solution, αmax is 1.57. then, we keep ϵ1 = ϵ2 = 0. as a result, [bl, bu] = [−0.57, 2.57] is obtained. next, some trade-offs between multiple-constraint levels have been chosen and the solutions are listed below. for consistency, we keep the weight between the objective function to be (4, 1). the result is laid out in figure 4. the green hyperplane 0.02 ∗ x1 − 0.08 ∗ x2 = 1 is obtained from negative cutoff. from the solutions, we can see that different trade-offs between the constraint levels yield different results, while the solutions will be the same when b is set to be the same sign. that is to say, for all the positive b, the solutions generate the same hyperplane, and then, for all the negative b, the solutions create the same hyperplane. moreover, we want to prove this result as a lemma. error correction method in classification by using multiple-criteria and multiple-constraint levels linear programming 983 lemma 4. for certain trade-off between the objective functions, if b is maintained to be the same sign, then hyperplanes, which are obtained in the mclp model , keep the same. furthermore, different signs result in different hyperplanes. proof: let’s assume that the trade-off between the objective functions is λ = (λ1, λ2) and x1 is the solution obtained by fixing b to be 1. and then, set b1 as an arbitrary positive number. the mclp model can be transformed as follows: min λ1 ∑ i αi − λ2 ∑ i βi s.t. aix = b1 + αi − βi, ai ∈ bad, aix = b1 − αi + βi, ai ∈ good, αi, βi > 0, i = 1, 2, ..., l the problem above is the same as: min λ1 ∑ i αi b1 − λ2 ∑ i βi b1 s.t. ai x b1 = 1 + αi b1 − βi b1 , ai ∈ bad, ai x b1 = 1 − αi b1 + βi b1 , ai ∈ good, αi, βi > 0, i = 1, 2, ..., l and then, we let α′i = αi b1 , β′i = αi b1 , x′ = x b1 . it is obvious that the solution is x′ = x1 b1 and the hyperplane ax′ = b1 is the same as ax1 = 1. similarly, we can prove that when b is a negative number, the solution is the same as the one that is obtained from b = −1. as a result, we just need to compare the solutions (hyperplanes) resulted from b = 1 and b = −1. for this case, it is easy to see that the signs before αi and βi swap when we transform b = −1 into b = 1. if this happens, then the objective function changes into −λ1 ∑ i αi+λ2 ∑ i βi. this means that the solutions will be different. 2 according to the lemma, we have the theorem below: theorem 5. for our mc2lp model (2) above, according to the solutions (hyperplanes), space γ is divided into two non-intersect parts. remark 6. when [1 − αmax, 1 + αmax] is achieved, ϵ1 and ϵ2 are chosen to satisfy that 0 is contained by the interval [1 − αmax − ϵ1, 1 + αmax + ϵ2]. in this case, for any λ, the solutions belong to the trade-offs with same sign will result in the same hyperplane. in other words, there are only two different hyperplanes corresponding to model (2). in short, the flexibility of model (2) is limited. 984 b. wang, y. shi 4 a new model based on correcting of two types of error in many classification models, including original mclp model, two types of error is a big issue. in credit card account classification, to correct two types of error can not only improve the accuracy of classification but also help to find some important accounts. accordingly, many researchers have focused on this topic. based on this consideration, more attention should be paid to the samples that locate between two hyperplanes acquired by the original mclp model, that is, the points in the grey zone [10]. consequently, we define the external deviations and internal deviations related to two different hyperplanes, the left one and the right one, that is, αl, αr, βl and βr. definition 7. the conditions the deviations should satisfy are stated as follows: αli =   0, aix < 1 − αmax and ai ∈ bad; aix − (1 − αmax), aix ≥ 1 − αmax and ai ∈ bad; 0, aix ≥ 1 − αmax and ai ∈ good; (1 − αmax) − aix, aix < 1 − αmax and ai ∈ good. αri =   0, aix < 1 + αmax and ai ∈ bad; aix − (1 + αmax), aix ≥ 1 + αmax and ai ∈ bad; 0, aix ≥ 1 + αmax and ai ∈ good; (1 + αmax) − aix, aix < 1 + αmax and ai ∈ good. βli =   (1 − αmax) − aix, aix < 1 − αmax and ai ∈ bad; 0, aix ≥ 1 − αmax and ai ∈ bad; aix − (1 − αmax), aix ≥ 1 − αmax and ai ∈ good; 0, aix < 1 − αmax and ai ∈ good. βri =   (1 + αmax) − aix, aix < 1 + αmax and ai ∈ bad; 0, aix ≥ 1 + αmax and ai ∈ bad; aix − (1 + αmax), aix ≥ 1 + αmax and ai ∈ good; 0, aix < 1 + αmax and ai ∈ good. figure 5 is a sketch for the model. in the graph, the green and the red lines are the left and right hyperplane, bl and br respectively, which are some trade-offs in two intervals, i.e. [1 − αmax − ϵ2, 1] and [1, 1 + αmax + ϵ1]. and all the deviations are measured according to them in different colors. for instance, if a sample in "good" class is misclassified as "bad" class, it means αri > β l i ≥ 0 and α l i = β r i = 0. and then, if a sample in "bad" class is misclassified as "good" class, it means αli > β r i ≥ 0 and α r i = β l i = 0. thus, for the misclassified ones, αri + α l i − β r i − β l i should be minimized. as a result, a more meticulous model could be stated as follows: min ∑ i (αri + α l i) min ∑ i (αli − β r i ) min ∑ i (αri − β l i) error correction method in classification by using multiple-criteria and multiple-constraint levels linear programming 985 figure 5: mc2lp model max ∑ i (βri + β l i) s.t. aix = 1 + [0, αmax + ϵ1] + α r i − β r i , ai ∈ bad, aix = 1 − [0, αmax + ϵ2] + αli − β l i, ai ∈ bad, aix = 1 + [0, αmax + ϵ1] − αri + β r i , ai ∈ good, aix = 1 − [0, αmax + ϵ2] − αli + β l i, ai ∈ good, αri , α l i, β r i , β l i > 0, i = 1, 2, ..., l. where ai, αmax, ϵ1 > 0, ϵ2 > 0 are given, and x is unrestricted. in figure 5, for each point, there are at most two kinds of deviations nonzero. the objective functions appear to deal with the deviations according to the position shown in figure 5, respectively, whereas they have their own special meaning. that is to say, it measures two types of error in some degree by means of the second and third objective functions. as a result, in this new version of mc2lp, we not only consider the deviations respectively, but also take the relationship of the deviations based on two types of error into account in the objective functions. by virtue of mc2lp method, each trade-off between 1 − αmax − ϵ2 and 1 for the left hyperplane as well as each trade-off between 1 and 1 + αmax + ϵ1 for the right hyperplane can be checked. after obtaining the weight vector x of the hyperplane, ax = 1 is still used to be the classification hyperplane. however, in our new model, we minimize the distance between the left hyperplane and the right one. in other words, we discover the hyperplane that genders the smallest grey area. example 8. we show how the new mc2lp model based on error correction works. the data is the same as data1 that has been displayed in figure 2. two kinds of situations will be considered in this example, that is, the pairs of hyperplanes are ax = 0.57, ax = 2.57 and ax = −6, ax = 10. we choose ϵ1, ϵ2 bigger than 10, so that we can get the pair of hyperplanes ax = −6, ax = 10. from the new model, we obtain two hyperplanes, which are displayed in figure 6. the blue hyperplane is obtained from ax = 0.57, ax = 2.57, while the green one is corresponding to ax = −6, ax = 10. in both experiments, we fix the weight between αli, α r i and β l i, β r i to be (4,1). according to the result, we can see that the point marked is a misclassified sample corresponding to the former pair, while it is classified correctly based on the latter one. 986 b. wang, y. shi figure 6: solutions for new mc2lp model actually, in statistics, type i and type ii errors are two opposite objectives. that is to say, it is very hard to correct both of them at the same time. as a result, we modify the former model into two different models focusing on two types of error respectively as follows: min ∑ i (αri + α l i) min ∑ i (αli − β r i ) max ∑ i (βri + β r i ) s.t. aix = 1 + [0, αmax + ϵ] + α r i − β r i , ai ∈ bad, aix = 1 + α l i − β l i, ai ∈ bad, aix = 1 + [0, αmax + ϵ] − αri + β r i , ai ∈ good, aix = 1 − αli + β l i, ai ∈ good, αri , α l i, β r i , β l i > 0, i = 1, 2, ..., l. (3) where ai, αmax and ϵ > 0 are given, and x is unrestricted. in this model, ∑ i(α r i − β l i) is not contained in the objective functions. this model can deal with type ii error, that is, classifying a "good" point to be a "bad" one. now we provide an example to illustrate the effect of model (3). example 9. the data set is the same as data1 that has been displayed in figure 2. here, the label "-1" is regarded as "good" class, and the label "1" is regarded as "bad" class. two kinds of situations will be considered in this example, that is, the pairs of hyperplanes are ax = 1, ax = 2.57 and ax = 1, ax = 8. the left hyperplane is always kept to be ax = 1, which is also the hyperplane that is used to be the classification hyperplane at last. we set ϵ as 10. it is obvious that more remarkable result will be obtained from bigger ϵ. after fixing the weight of the objective functions as (4,0.1,1), the outcome is shown in figure 7. the blue hyperplane is obtained from ax = 1, ax = 2.57, while the green one is corresponding to ax = 1, ax = 8. two marked points are the ones corrected by model (3) after the right hyperplane moving to the right. error correction method in classification by using multiple-criteria and multiple-constraint levels linear programming 987 figure 7: solutions for new mc2lp model (3) as the result shown above, model (3) can correct type ii error in some degree. we conclude this in the proposition below. proposition 10. model (3) can correct type ii error by moving the right hyperplane to the right based on the concept of multiple-constraint levels. remark 11. the second objective function in model (3) is nonzero for the samples in class "bad" and getting negative when the right hyperplane moving to the right. that is to say, we tolerate some type i errors. at the same time, the first objective function in model (3) renders type ii errors an increasing punishment with moving the right hyperplane to the right. as a result, it can correct type ii error in some degree. similarly to model (3), we pose model (4) which can deal with type i error as follows: min ∑ i (αri + α l i) min ∑ i (αri − β l i) max ∑ i (βri + β r i ) s.t. aix = 1 + α r i − β r i , ai ∈ bad, aix = 1 − [0, αmax + ϵ] + αli − β l i, ai ∈ bad, aix = 1 − αri + β r i , ai ∈ good, aix = 1 − [0, αmax + ϵ] − αli + β l i, ai ∈ good, αri , α l i, β r i , β l i > 0, i = 1, 2, ..., l. (4) where ai, αmax and ϵ > 0 are given, and x is unrestricted. in this model, ∑ i(α l i − β r i ) is not contained in the objective functions. this model focuses on type i error, that is, classifying a "bad" point to be a "good" one. example 12. in order to compare model (3) and model (4), we draw two hyperplanes obtained from these two models in the same graph. figure 8 is the result of using pair ax = 1, ax = 8 for model (3) and pair ax = −0.57, ax = 1 for model (4). 988 b. wang, y. shi figure 8: comparison of model (3) (4) in figure 8, the green hyperplane is obtained from model (3) with less type ii errors, while the blue one is acquired from model (4) with less type i errors. in the last two models introduced above, the first one defines ax = 1 as the left hyperplane, at the same time, ax = 1 + [0, αmax + ϵ] as the right one. oppositely, the second model defines ax = 1 − [0, αmax + ϵ] as the left hyperplane, and then, ax = 1 as the right one. for each trade-off, our model can deal with the misclassifications. in this way, two types of error can be corrected respectively. 5 conclusion this paper focuses on how to correct two types of error. at first, the disadvantage of mclp by fixing the cutoff is addressed. and then, a mc2lp based model is introduced. according to the topic of error correction, we develop mc2lp by measuring the deviations of the misclassified points more sophisticatedly. in this way, we obtain a brand new hyperplane that is different from the one obtained by mclp model. moreover, focusing on correcting two types of error, a result with less errors is gained. acknowledgement this work has been partially supported by grants from national natural science foundation of china (no. 70921061, no. 71110107026) and the cas/safea international partnership program for creative research teams. bibliography [1] zhang z., zhang d., tian y., shi y., kernel-based multiple criteria linear programming classifier, procedia cs 1(1): 2407-2415, 2010. 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[7] shi y., multiple criteria and multiple constraint levels linear programming: concepts, techniques and applications, world scientific, 2001. [8] shi y., he j., wang l., fan w., computer-based algorithms for multiple criteria and multiple constraint level integer linear programming, comput. math. appl. 49(5): 903921, 2005. [9] nakayama h., yun y., generating support vector machines using multiobjective optimization and goal programming, studies in computational intelligence, vol. 16: 173-198, 2006. [10] chen y., zhang l., shi y., post mining of multiple criteria linear programming classification model for actionable knowledge in credit card churning management, icdmw, ieee computer society: 204-211, 2011. international journal of computers communications & control issn 1841-9836, 11(2):224-232, april 2016. a taboo search optimization of the control law of nonlinear systems with bounded uncertainties a. gharbi, m. benrejeb, p. borne amira gharbi*, mohamed benrejeb, pierre borne lara, ecole nationale d’ingénieurs de tunis tunisie, bp 37, le belvédére 1002 tunis and cristal, ecole centrale de lille france, cité scientifique bp 48-59651, villeneuve d’ascq cedex merkarim@gmail.com, mohamed.benrejeb@enit.rnu.tn, pierre.borne@ec-lille.fr *corresponding author: merkarim@gmail.com abstract: the aim of this paper is to propose a method to determine among the eligible controls of a nonlinear system, with bounded perturbations, the one which minimizes the final error. the approach is based on the implementation of aggregation techniques using vector norms in order to determine a comparison system used to calculate an attractor in view of its minimization by implementation of metaheuristics. keywords: attractor, aggregation technique, vector norm, optimization, taboo search. 1 introduction in the presence of uncertainties in modeling, that increase the complexity of the stability study [1], it is not always possible to obtain a control law ensuring the stability of the process with respect to a chosen objective. it is then necessary to estimate the maximum deviation from this target, an operation which can be performed by determining an attractor [2][4] corresponding to the vicinity of the target for which the local stability cannot be guaranteed, [5], [7], [6], [8], [9], [10], [11], [12]. in case of uncertain or poorly defined problems, possibly subject to random perturbations or for which the search for solutions might evolve towards the combinatorial explosion, the exact methods are very unlikely to provide solutions in an acceptable period of time. the method presented in this paper corresponds to a law finding, if we do not obtain the optimal solution of the problem, we obtain at least a good solution in an acceptable run time. the heuristic methods that can be implemented on a computer are referred to metaheuristics. they rely on the following basic principle: the search for optimum is simulating either the behaviour of a biologic system or the evolution of a natural phenomenon, including an intrinsic optimization mechanism. for this reason, a new optimization branch has been developed in the past 20 years, inspired by nature. almost all numerical algorithms designed as metaheuristics are included into this class of optimization techniques [13]. in general, all metaheuristics are using a pseudo-random engine to select some parameters or operations that yield to the estimation of an optimal solution. the procedures to generate pseudo-random (numerical) sequences of optimization are crucial in metaheuristics design. we have two classes of metaheuristic approaches: global approaches and local approaches, such as the taboo search which is one of the easiest to implement. in this paper, the determination of the attractor, when the process is submitted to uncertainties, is achieved by using aggregation techniques and the borne-gentina stability criteria, with the use of vector norms and of comparison systems [14], [15]. in the following section 2, we propose the determination of the control law of a nonlinear process submitted to bounded uncertainties with a view to minimize the effect of these uncertainties. in section 3 we use the taboo search to realize the optimization. an application is presented in section 4 to illustrate the proposed method. copyright © 2006-2016 by ccc publications a taboo search optimization of the control law of nonlinear systems with bounded uncertainties 225 2 attractor determination let us consider the system (s) whose evolution is described by the following state equation ẋ = f(x,.) + g(x,.)u + δ(.) (1) y = h(x) (2) x is the state vector and y is the output, x ∈ rn,y ∈ rm,u ∈ rl δ ∈ rn characterizes the disturbances and/or perturbations acting on the system and u is the control law: u = u(x,θ) (3) where θ ∈ rν is a vector of the adjustable parameters of the control law. a new representation of system (s) characterized by (1) and (3) can be defined by ẋ = a(x,θ, .)x + δ(.) (4) with |δ(.)| ≤ δm (5) a = f(x,.) + g(x,.)u(x,θ) (6) and a comparison system of this system can be determined using the vector norm p(x) defined by p(x) = [|x1| , |x2| , . . . , |xn|]t (7) by noting m(a(x,θ, .)) an overvaluing matrix of a(x,θ, .) related to the vector norm p(x) it comes d dt p(x) ≤ m(a(x,θ, .))p(x) + n(.) (8) let us denote: a(.) = {aij(.)} (9) and m(θ) = {mij(θ)} the matrix such that:{ mii(θ) = maxaii(x,θ, .) ∀i = 1,2, . . .n mij(θ) = max |aij(x,θ, .)| ∀i ̸= j (10) we can define a comparison system by: z ∈ n/ż(t) = m(θ)z(t) + δm (11) if m(θ) is the opposite of an m-matrix, it exists an attractor dθ asymptotically stable such that dθ = { x ∈ rn;p(x) ≤−m−1(θ)δm = pm(θ) } (12) 3 taboo search optimization 3.1 principe of taboo search the metaheuristic described in this section belongs to greedy descent local methods. for this type of methods, the search starts from an admissible solution θi of s. the strategy is then to focus on a local vicinity v (θi), in order to find another solution θj that can improve the criterion current performance. the vicinity v (θi) corresponds to the set of all accessible solutions after applying a single admissible movement, displacement or transition from θi . usually, this set is a hyper-cube or a hyper-sphere including the current solution θi. 226 a. gharbi, m. benrejeb, p. borne 3.2 taboo search method based on the principle of local search for minimizing a criterion, by this method, there is the possibility to jump out from the capturing vicinity and to explore a different zone of the research area. here after, the term of movement stands for any modification allowing the search to focus on vicinity in the neighborhood of the current vicinity. as usual, the search starts from some initial point (solution), θi in the vicinity v (θi) but it is permitted to relocate the exploitation around another point (solution) θj ∈ v (θi), even if this choice degrades the criterion to optimize. this actually is a movement towards another zone of interest. however, in order to avoid infinite search loops, once a solution is focused on, it will never be focused on again in the future iterations. thus, the nt last focused solutions belonging to a taboo list tki become untouchable, "taboo" [16], [17]. starting from the solution θi, a set of possible movements, say mk,j, can be built, during the kth iteration. let δθ ∈ mk,j be such a movement. by convention,θi δθ→θj stands for the transition from solution θi to a new point θj as result of movement δθ. then vk(θi) = { θj ∈ v (θi)/∃ δθ ∈ mkj,θi δθ→θj & θj /∈ tki } (13) the new solution which is the best non taboo one is added to the last taboo list and the oldest one is removed from it. the chosen criterion is for this problem the minimisation of a scalar norm of pm(θ) the optimization of the control law consists to determine the value of θ minimizing a scalar norm of pm . in the following we use the euclidian norm ∥pm∥. the optimisation algorithm corresponds in this paper to the taboo search with nt number of elements of the taboo list and ns the maximum number of iterations without improvement of the solution to stop the research. 4 application to a second order system let us consider the nonlinear system of second order with uncertainties such that ẋ = a(x,t)x + b(x)u(x,θ) + δ(.) (14) y = h(x) (15) with u(θ,x) = −(θ1y + θ2x2) (16) and h(x) = x1 + (1−e−x 2 1)x2 (17) with x(t) ∈ r2,b(.) ∈ r2,a(.) a 2×2 matrix and θ ∈ r2 such that a(x,t) = [ −2 + cost + cosx1 4−e−x 2 1(1 + sinx1) 4 + cosx2 −8 + sinx1 + e−x 2 1 ] (18) b(x) = [ 3 + 0.5 cosx1 2 ] (19) we can write (14) as ẋ = a(x,t,θ)x + δ(.) (20) with a(x,t,θ) = a(x,t)−b(x)[θ1,θ1((1−e−x 2 1)) + θ2] (21) a taboo search optimization of the control law of nonlinear systems with bounded uncertainties 227 it comes a(x,t,θ) = [ a11 a12 a21 a22 ] (22) with a11 = −2 + costcosx1 −θ1(3 + 0.5 cosx1) a12 = 4−ex 2 1(1 + sinx1)− (3 + 0.5 cosx1)(θ1(1−ex 2 1) + θ2) a21 = 4 + cosx2 −2θ1 a22 = −8 + ex 2 1 + sinx1 −2[θ1(1−ex 2 1) + θ2] (23) 4.1 determination of a comparison system for the vector norm p(x) = [|x1| , |x2|]t , we obtain the overvaluation defined by d dt p(x) ≤ m(a(x,θ, .))p(x) + n(.) (24) z ∈ rn/ż(t) = m(.)z(t) + n(.) (25) with m(a(x(t))) = [ a11 |a12| |a21| a21 ] (26) and |n(.)| ≤ δm (27) in our example δ(.) is assumed to be by bounded by δ1 = [ −0.2 0.3 ] ≤ δ(.) ≤ δ2 = [ 0.1 0.5 ] (28) then δm = [ 0.2 0.5 ] (29) and by overvaluation, for the process without feedback, for θ = (θ1,θ2) = (0,0) we obtain the linear comparison system ż = mz + n ż = [ 0 2 5 −6 ] z + [ 0.2 0.5 ] (30) after application of stability conditions we have det(m) < 0 (31) it appears that m is not stable and so is not the opposite of an m-matrix which needs the determination of a suitable feedback optimized in order to limit the influence of the uncertainties. 228 a. gharbi, m. benrejeb, p. borne 4.2 attractor optimization with taboo search for this taboo search we choose nt = ns = 4. starting from the solution θ1 = 2 and θ2 = 0 a set possible movements, can be built, during the k-th iteration. let δθl ∈ mk,j be such a movement, with |δθ1| = 0.2 and |δθ2| = 0.1 . by convention, θi1 δθ1→θj1 , θi2 δθ2→θj2 stands for the transition from solution θli to a new point θlj with l = {1,2} as result of movement δθl . then for θ1 = 2 and θ2 = 0 the overvaluing system for the vector norm p(x) = [|x1| , |x2|]t is defined by (22) with m(x,t,2,0) =   −8 + cost ∣∣∣∣∣ −2−e −x21(−5 + sinx1 − cosx1) −cosx1 ∣∣∣∣∣ |cosx2| −12 + sinx1 + 5e−x 2 1   (32) and n = [ 0.2 0.5 ] (33) then the linear comparison system is the following ż = [ −7 3 1 −7 ] z + [ 0.2 0.5 ] (34) the stability conditions for matrix m can be written{ −7 < 0( −12 ) det(m) > 0 (35) as m is the opposite of m-matrix, we have p(x) ≤−m−1n = [ 0.0630 0.0804 ] = pm(2,0) (36) the strategy is then to focus on a local vicinity v (θi) in order to find the best non taboo solution θi the chosen criterion being the euclidian norm of pm(θ). for this, eight solutions ⋆ will be tested starting from θ = (2,0) θ = (θ1,θ2 + δθ2),θ = (θ1,θ2 − δθ2),θ = (θ1 + δθ1,θ2),θ = (θ1 − δθ1,θ2),θ = (θ1 + δθ1,θ2 + δθ2), θ = (θ1 + δθ1,θ2 − δθ2),θ = (θ1 − δθ1,θ2 + δθ2),θ = (θ1 − δθ1,θ2 − δθ2) 2θ 1θ (2,0) figure 1: the vicinity of θ = (2,0) solution for a taboo search optimization of the control law of nonlinear systems with bounded uncertainties 229 θ = (2,0.1) ⇒ p(x) ≤ pm(2,0.1) = [ 0.0579 0.0775 ] , θ = (2,−0.1) ⇒ p(x) ≤ pm(2,−0.1) = [ 0.0668 0.0787 ] , θ = (2.2,0) ⇒ p(x) ≤ pm(2.2,0) = [ 0.0572 0.0763 ] , θ = (1.8,0) ⇒ p(x) ≤ pm(1.8,0) = [ 0.0727 0.0860 ] , θ = (2.2,0.1) ⇒ p(x) ≤ pm(2.2,0.1) = [ 0.0528 0.0738 ] , θ = (2.2,−0.1) ⇒ p(x) ≤ pm(2.2,−0.1) = [ 0.0621 0.0790 ] , θ = (1.8,0.1) ⇒ p(x) ≤ pm(1.8,0.1) = [ 0.0664 0.0824 ] , θ = (1.8,−0.1) ⇒ p(x) ≤ pm(1.8,−0.1) = [ 0.0796 0.0899 ] , the best non taboo solution minimizing ∥p(x)∥ : pm(2.2,0.1) is obtained for θ = (2.2,0.1), and the solution for θ = (2,0) becomes "taboo". now the strategy is then to focus on a local vicinity of this solution in order to find the best one which does not belong to the taboo list. so, we test other solutions that are neighbouring the current one’s θ = (2.2,0),θ = (2.2,0.2),θ = (2,0.1),θ = (2,0.2), θ = (2.4,0),θ = (2.4,0.1),θ = (2.4,0.2). 2θ 1θ (2,0) : solution taboo (2.2,0.1) θ figure 2: the vicinity of θ = (2.2,0.1) solution θ = (2.4,0) ⇒ p(x) ≤ pm(2.4,0) = [ 0.0523 0.0729 ] , θ = (2.4,0.1) ⇒ p(x) ≤ pm(2.4,0.1) =[ 0.0528 0.0727 ] , θ = (2.4,0.2) ⇒ p(x) ≤ pm(2.4,0.2) = [ 0.0540 0.0690 ] , θ = (2,0.2) ⇒ p(x) ≤ pm(2,0.2) = [ 0.0531 0.0747 ] , θ = (2.2,0.2) ⇒ p(x) ≤ pm(2.2,0.2) = [ 0.0536 0.0719 ] , the best non taboo solution minimizing ∥p(x)∥ : [ 0.0540 0.0690 ]t is obtained for θ = 230 a. gharbi, m. benrejeb, p. borne (2.4,0.2) then the solution θ = (2.2,0.1) becomes "taboo". now we continue the iteration starting from this new solution θ = (2.2,0.2),θ = (2.2,0.3), θ = (2.4,0.1),θ = (2.4,0.3),θ = (2.6,0.1),θ = (2.6,0.2),θ = (2.6,0.3). the best non taboo solution minimizing ∥p(x)∥ : [ 0.0558 0.0673 ]t is obtained for θ = (2.4,0.3), then the solution θ = (2.4,0.2) becomes "taboo". now we will test the solutions in the neighbourhood of θ = (2.4,0.3) the best non taboo solution minimizing ∥p(x)∥ : [ 0.0575 0.0656 ]t is obtained for θ = (2.4,0.4) then the solution θ = (2.4,0.3) becomes "taboo". now we will test the vicinity of this solution the best non taboo solution minimizing ∥p(x)∥ : [ 0.059 0.064 ]t is obtained for θ = (2.4,0.5), then the solution becomes "taboo". 2θ 1θ (2,0) : solution taboo (2.2,0.1) : solution taboo θ (2.4,0.2) : solution taboo (2.4,0.3) : solution taboo (2.4,0.4) : solution taboo (2.4,0.5) figure 3: the vicinity of θ = (2.4,0.5) solution at the next iteration the best non taboo solution minimizing ∥p(x)∥ : [ 0.0605 0.0625 ]t is obtained for θ = (2.4,0.6). for the two following iterations the best non-taboo solutions correspond to pm(2.4,0.7) and pm(2.4,0.8), but ∥pm(2.4,0.4)∥ = ∥pm(2.4,0.5)∥ = ∥pm(2.4,0.6)∥ = ∥pm(2.4,0.7)∥ = ∥pm(2.4,0.8)∥ = 0.870, so as we have had 4 iterations without improvement we can stop the research. the control law defined by θ = (2.4,0.4), corresponds to the best solution. hence the evolution of the state vector, and its evolution of the state vector in the attractor defined in figure 4. 0.022 0.024 0.026 0.028 0.03 0.032 0.034 0.036 0.038 0.04 0.0632 0.0633 0.0634 0.0635 0.0636 0.0637 0.0638 0.0639 0.064 x 2 x1 −0.06 −0.04 −0.02 0 0.02 0.04 0.06 0.08 0. 1 −0.08 −0.06 −0.04 −0.02 0 0.02 0.04 0.06 0.08 0.1 x 2 x1 figure 4: evolution of the state vector in the attractor 5 conclusion the approach proposed here consists, having defined the attractor of the process for a control law depending of parameters to minimize the size of this attractor by implementation of a metaheuristic to determine the optimal values of these parameters. the method presented in this paper is applied, with success, for a second order nonlinear complex system using the concept a taboo search optimization of the control law of nonlinear systems with bounded uncertainties 231 of vector norm for the determination of the comparison system. the minimization of the norm of the vector defining the limits of the attractor is realized by using a taboo search method. bibliography [1] benrejeb, m. ; borne, p. (1978); on an algebraic stability criterion for non-linear processe, interpretation in the frequency domain, measurement and control international symposium meco, athens: 678-682. [2] gharbi, a.; benrejeb, m. ; borne, p. (2013); on nested attractors of complex continuous systems determination, proceedings of the romanian academy, series a, 14(2):259-265. [3] gharbi, a.; benrejeb, m. ; borne, p. (2013); new approach for the control and the determination of attractors for nonlinear systems , 2nd international conference on systems and computer science (icscs), villeneuve d’ascq, france, august 26-27. [4] gharbi, a.; benrejeb, m. ; borne, p. (2014); tracking error estimation of uncertain lur’e postnikov systems, 2nd international conference on control, decision and information technologies (codit’14) metz, france, november 3-5. [5] benrejeb, m. (2010); stability study of two level hierarchical nonlinear systems. large scale complex systems theory and applications ifac symposium, plenerylecture,lille, 9(1), 3041. [6] borne, p.; benrejeb, m. (2008);on the representation and the stability study of large scale systems, international journal of computers communications and control, 3(5): 55-66. [7] borne, p. (1987); nonlinear system stability. vector norm approach, system and control encyclopedia,pergamon press, lille, france, 5:3402-3406. [8] gentina, j.c.; borne, p.; burgat, c.; bernussou, j., : grujic, l.t. (1979). sur la stabilite des systmes de grande dimension. normes vectorielles, 13(1):57-75. [9] gentina, j.c.; borne, p.; laurent, f. (1972a). stabilite des systmes continus non linéaires de grande dimension,rairo, aôut:69-77. [10] gentina, j.c.; borne, p. (1972b), sur une condition d’application du critcre de stabilité linéaire certaines classes de systmes continus non linéaires, cras, paris, t. 275: 401-404. [11] grujic, l.t.; gentina, j.c.; borne, p.; burgat, c.; bernussou, j. (1978). sur la stabilité des systmes de grande dimension. fonctions de lyapunov vectorielles,rairo, 12(4):319-348. [12] grujic, l.t.; gentina, j.c.; borne, p. (1976). general aggregation of large scale systems by vector lyapunov functions and vector norms,inernational.journal.of control, 24(4): 29-550. [13] stefanoiu, d.; borne , p.; popescu, d.; filip, f. g. ; el kamel, a.(2014); optimization in engineering sciences. metaheuristics, stochastic method and decision support, iste, wiley:20-39. [14] siljac, d. d. (1972); stability of large scale systems under structural perturbations.ieeetrans. on syst. manand cyber, 2(5). 232 a. gharbi, m. benrejeb, p. borne [15] borne, p.; richard, j.p.; radhy, n.e. (1996). stability, stabilization, regulation using vector norms, nonlinear systems, 2, stability and stabilization,chapman and hall, chapter 2; 4590. [16] ghédira k., (2007); optimisation combinatoire par métaheuristiques, editions technip, france. [17] ennigron m.; ghédira k (2004); flexible job-shop scheduling with multi-agent system and taboo search,journal européen des systmes automatisés jesa, 38: 7-8. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 509-517 an entropy-based method for attack detection in large scale network t. liu, z. wang, h. wang, k. lu ting liu sklms lab and moe klnnis lab, xi’an jiaotong university xi’an, shaanxi, 710049, p.r.china e-mail: tingliu@mail.xjtu.edu.cn zhiwen wang, haijun wang, ke lu moe klnnis lab, xi’an jiaotong university xi’an, shaanxi, 710049, p.r.china e-mail: wzw@mail.xjtu.edu.cn {hjwang,klu}@sei.xjtu.edu.cn abstract: intrusion detection system (ids) typically generates a huge number of alerts with high false rate, especially in the large scale network, which result in a huge challenge on the efficiency and accuracy of the network attack detection. in this paper, an entropy-based method is proposed to analyze the numerous ids alerts and detect real network attacks. we use shannon entropy to examine the distribution of the source ip address, destination ip address, source threat and destination threat and datagram length of ids alerts; employ renyi cross entropy to fuse the shannon entropy vector to detect network attack. in the experiment, we deploy the snort to monitor part of xi’an jiaotong university (xjtu) campus network including 32 c-class network (more than 4000 users), and gather more than 40,000 alerts per hour on average. the entropy-based method is employed to analyze those alerts and detect network attacks. the experiment result shows that our method can detect 96% attacks with very low false alert rate. keywords: network security, entropy-based, ids, shannon entropy, renyi cross entropy. 1 introduction network attacks are defined as the operations that disrupt, deny, degrade, or destroy information resident in computer networks or the networks themselves. in recent years, more and more network attacks threatened the reliability and qos of internet, compromised the information security and privacy of users. ksn (kaspersky security network) recorded 73 million internet browsers attacks on their users in 2009, and that number skyrocketed to 580,371,937 in 2010 [1]. symantec reported that they recorded 3 billion attacks from their global sensor and client [2]. intrusion detection system (ids) is used to monitor and capture intrusions into computer and network systems which attempt to compromise their security [3]. with the development of networks, a large number of computer intrusions occur every day and idss have become a necessary addition to the security infrastructure of nearly every organization. however, idss still suffer from two problems: 1) large amount of alerts. in fact, more than 1 million alerts are generated by snort each day in our research; 2) high false alerts rate. gina investigated the extent of false alerts problem in snort using the 1999 darpa ids evaluation data, and found that 69% of total generated alerts are considered to be false alerts [4]. these problems result in a huge challenge on the efficiency and accuracy of the network attack detection. several methods have been applied to resolve the problems of large amount of alerts and high false rate. pietraszek used the adaptive alert classifier to reduce false alerts, which is trained with lots of labeled copyright c⃝ 2006-2012 by ccc publications 510 t. liu, z. wang, h. wang, k. lu past alerts [5]. whereas, it is difficult to label large volume alerts generated in large-scale network. in order to reduce the false alarms, mina propose the extend dpca to standardize the observations according to the estimated means [6]. spathoulas and katsikas propose a post-processing filter based on the statistical properties of the input alert set [7]. cisar employ ewma to detect attacks by analyzing the intensity of alerts [3]. in our research, 32 c-class subnets are monitored by snort and more than 1 million alerts are generated every day. therefore, we propose a method to spot anomalies which is more tolerable for the operator rather than reduce false alerts. in information theory, entropy is a measure of the uncertainty associated with a random variable, which is widely used to analyze the data and detect the anomalies in information security. lakhina et al argue that the distributions of packet features (ip addresses and ports) observed in flow traces reveal both the presence and structure of a wide range of anomalies. using entropy as a summarization tool to analyze traffic from two backbone networks, they found that it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods [8]. brauckhoff ind that entropy-based summarizations of packet and flow counts are affected less by sampling than volume-based method in large networks [9]. a. wagner and b plattner applied entropy to detect worm and anomaly in fast ip networks [10]. relative entropy and renyi cross entropy can be used to evaluate the similarity of different distributions. yan et al use a traffic matrix to represent network state, and use renyi cross entropy to analyze matrix traffic and detect anomalies rather than shannon entropy. the results show renyi cross entropy based method can detect ddos attacks at the beginning with higher detection rate and lower false rate than shannon entropy based method [11]. gu et al proposed an approach to detect anomalies in the network traffic using maximum entropy estimation and relative entropy [12]. the packet distribution of the benign traffic was estimated using maximum entropy framework and used as a baseline to detect the anomalies. in this paper, an entropy-based method is proposed to detect network attack. the shannon entropy and renyi cross entropy are employed to analyze the distribution characteristics of alert features and detect network attack. the experimental results under actual network data show that this method can detect network attack quickly and accurately. the rest of the paper is organized as follows: the method is introduced in section 2, and the experimental results are shown in section 3. section 4 is the conclusion and future work. 2 methodology in this paper, snort is used to monitor the network and five statistical features of the snort alert are selected. the shannon entropy is used to analyze the distribution characteristics of alert that reflect the regularity of network status. when the monitored network runs in normal way, the entropy values are relatively smooth. otherwise, the entropy value of one or more features would change. the renyi cross entropy of these features is calculated to measure the network status and detect network attacks. 2.1 snort alert and feature selection each snort alert consists of tens of attributions, such as timestamp, source ip address (sip), source port, destination ip address ( dip), destination port, priority, datagram length and protocol, etc. suppose there are n alerts generated in time interval t. the alerts set in time interval t is denoted as alert(t) = {alert1,alert2, . . . ,alertn}. assuming there are m distinct sip and k distinct dip in alert(t), we can generate the distinct source ip addresses set (sip) and distinct destination ip addresses set (dip): s i p = {sip1, sip2, . . . , sipm}, di p = {dip1,dip2, . . . ,dipk}. an entropy-based method for attack detection in large scale network 511 suppose the number of alerts come from sipi is snumi, and the number of alerts send to dipi is dnumi. the alert number of each source ip (s nu m) and destination ip (dnu m) can be calculated: s nu m = {snum1, snum2, . . . , snumm}, dnu m = {dnum1,dnum2, . . . ,dnumk}. there are 4 default priorities of snort alert: 1, 2, 3 and 4. the threat severity gradually weakens from 1 to 4(high, medium, low, info). in order to strengthen the threat degree of high severity alerts, the threat degree of the alerti is denoted as threati = 5(4−priorityalerti ) in present work. suppose the threat degree sum of all alerts come from sipi is sthreati, and the threat degree sum of all alerts send to dipi is dthreati. the threat degree of each source ip (s t hreat ) and destination ip (dt hreat ) can be calculated: s t hreat = {sthreat1, sthreat2, . . . , sthreatm}, dt hreat = {dthreat1,dthreat2, . . . ,dthreatk}. the datagram length is the size of the packet that breaks the alarm rules of snort. we search the distinct datagram length of all alerts, and generate the datagram length set dgmlen = {dgmlen1,dgmlen2, . . . ,dgmlenx}, where x is the number of the distinct datagram length of all alerts. suppose the number of alerts whose datagram length equal to dgmleni is dgmnumi. the alert number with different datagram length can be calculated: dgmnu m = {dgmnum1,dgmnum2, . . . ,dgmnumx}. above 5 features (s nu m, dnu m,s t hreat, dt hreat, dgmnu m) are selected to evaluate the alerts and detect attacks. 2.2 shannon entropy-based feature analysis shannon entropy is used as measures of information and uncertainty [13]. for a dataset x = {x1, x2, x3, . . . , xn}, each data item x belongs to a class x ∈ cx. the entropy of x relative to cx is defined as h(x) =− n∑ i=1 pi log2 pi (1) where pi is the probability of xi in x. the distribution characteristics of five features are analyzed using shannon entropy. the entropies of s nu m and dnu m in time interval t can be calculated h(s ipt) =− m∑ i=1 (snumi/n) log(snumi/n) (2) h(dipt) =− k∑ i=1 (dnumi/n) log(dnumi/n) (3) the entropy of s t hreat and dt hreat can be calculated: h(s threatt) =− m∑ i=1 threat_o f _sip(i) sum_threat · log ( threat_o f _sip(i) sum_threat ) (4) 512 t. liu, z. wang, h. wang, k. lu h(dthreatt) =− k∑ i=1 threat_o f _dip(i) sum_threat · log ( threat_o f _dip(i) sum_threat ) (5) where threat_o f _sip(i) is the threat sum of the alerts from sipi, threat_o f _dip(i) is the threat sum of the alerts to dipi, and sum_threat is the threat sum of all the alerts in alerts which can be calculated using sum_threat = n∑ i=1 threati (6) the entropy of datagram length is h(dgmlent) =− x∑ i=1 (dgmnumi/n) · log(dgmnumi/n) (7) after calculating the entropies of above features, we can use an entropy vector v (t) = [h(s ipt), h(dipt), h(s threatt), h(dthreatt), h(dgmlent)] to represent the network status of time interval t. 2.3 renyi cross entropy-based attack detection the renyi entropy, a generalization of shannon entropy, is a measure for quantifying the diversity, uncertainty or randomness of a system. the renyi entropy of order α is defined as hα(p) = 1 1 −α log2 ∑ r pαr (8) where 0 <α< 1, p is a discrete stochastic variable, and pr is the distribution function of p [14]. higher values of α, approaching 1, giving a renyi entropy which is increasingly determined by consideration of only the highest probability events. lower values of α, approaching zero, giving a renyi entropy which increasingly weights all possible events more equally, regardless of their probabilities. the special case α→ 1 gives the shannon entropy. the renyi cross entropy of order α is derived as iα(p,q) = 1 1 −α log2 ∑ r pαr qα−1r (9) where p and q are two discrete variables, pr and qr are their distribution functions [14]. if α = 0.5, the renyi cross entropy is symmetric, which means iα(p,q) = iα(q, p). in the rest of the paper, when referring to the cross entropy we mean the symmetric case i0.5(p,q) = 2 log2 ∑ r √ prqr (10) the renyi cross entropy is used to fuse the values of different features. as mentioned above, we use an entropy vector v (t) = [h(s ipt), h(dipt), h(s threatt), h(dthreatt), h(dgmlent)] to represent the network status of time t, thus the network status can be viewed as a time series of entropy vector v (1),v (2), . . . ,v (t). before calculating renyi cross entropy, v (t) is unitized to v̄ (t) = [h̄(s ipt), h̄(dipt), h̄(s threatt), h̄(dthreatt), h̄(dgmlent)] (11) where h̄(s ipt) = h(s ipt)/hsum h̄(s threatt) = h(s threatt)/hsum h̄(dipt) = h(dipt)/hsum (12) h̄(dthreatt) = h(dthreatt)/hsum h̄(dgmlent) = h(dgmlent)/hsum an entropy-based method for attack detection in large scale network 513 and h sum = h(s ipt) + h(dipt) + h(s threatt) + h(dthreatt) + h(dgmlent). to determine if there is any change in the network at time t compare with previous time t−1, we use the following equation to calculate the renyi cross entropy of v̄ (t) and v̄ (t − 1) i0.5(v̄ (t), v̄ (t − 1)) = 2 log2 ∑ r √ pr(t − 1)pr(t) (13) we set η as the threshold of ∣∣∣i0.5(v̄ (t − 1), v̄ (t))∣∣∣ to test whether there is a change. the choice of threshold η is network dependent and it can be set as experience. since our purpose is to detect network attack, it is not enough to compare network status of time t to its previous time t − 1, unless we make sure that no attack occurs in time t − 1. thus, the average of the latest n normalized shannon entropies is employed to replace the t − 1, called v̄ (t,n) v̄ (t,n) = 1 n n∑ i=1 v̄ (t − i) (14) then, we calculate the renyi cross entropy of v̄ (t) and v̄ (t,n), and network attack is detected if its absolute is greater than η. i0.5(v̄ (t,n), v̄ (t)) = 2 log2 ∑ r √ pr(t,n)pr(t) (15) 3 experiment results 3.1 data collection in the research, we have used snort to monitor 32 c-class subnets in the xi’an jiaotong university campus network for two weeks, which include more than 4,000 users. in this paper, we select the alerts gathered in 2010-12-6. there are 862,284 alerts with 65 signatures, which come from 42,473 distinct source ip addresses and send to 11,790 distinct destination ip addresses. figure 1: the statistical results of alerts (2010-12-6). as shown in fig.1, four statistical features of alerts display the trend as the people living customs and habits (the time interval set as 5 seconds). few alerts are generated in the middle night; then, more alerts are detected from 8:00 to 10:00 when students get up successively; the alerts keep the same trend from 10:00 to 23:30; the alerts collapse at last 30 minutes, since network constraint due to the dormitory administrating rules. at the same time, the statistical features change abruptly in some time intervals. in general, these abnormal upheavals are the sign of the faults or network attacks. 514 t. liu, z. wang, h. wang, k. lu we select two alerts sets in different time period as training and test data set: training data set includes 170,516 alerts generated from 10:00 to 14:00. these alerts come from 13,148 ip addresses and send to 7,570 ip addresses. by analyzing these alerts manually, we identify 87 host scan attacks, 5 port scan attacks, 1 dos attack and 1 host intrusion. test data set includes 578,389 alerts generated from 14:00 to 23:30. these alerts come from 29,327 ip addresses and send to 10,590 ip addresses. by analyzing these alerts manually, we identify 203 host scan attacks, 7 port scan attacks, 6 dos attack, 3 host intrusion and 1 worm attack. 3.2 entropy-based attack detection the training data is evaluated by shannon entropy, as shown in fig. 2 (a). we remove the alerts associated to true attacks, which called as attack alert. the remainders are called as flase alert. we re-evaluate the noise alert in the training data set, as shown in fig. 2 (b). the shannon entropies are relatively smooth when no attack occurs; otherwise, one or some of the values would change abruptly. (a) all alerts (b) false alerts figure 2: shannon entropy. although the shannon entropies reflect the regularity of network status, it is difficult to detect attack directly by using five fixed thresholds. because the shannon entropy value varies with the activities of end users even the network runs in normal way. in our experiment, the renyi cross entropy is used to fuse the shannon entropy of five statistical features to detect attack. as shown in fig. 3, we calculate the renyi cross entropy of the alerts in train data set using (13). it is clearly shown that 1) the renyi cross entropy will change sharply when the network are attacked, see fig. 3 (a); 2) the renyi cross entropy will be close to 0 without the large-scale network attacks and failures, see fig. 3 (b). thus, it is easy to detect attack using fixed threshold. in the experiments, when ηdetect = −0.016, 84 attacks can be detected from 94 attacks with 11 false detections. 81 host scan attacks can be detected from 87 host scans. the missed scan attacks last for a relative long time and with small scan density. 1 port scan is detected from 5 port scans. 1 host intrusion and 1 dos attack are detected successfully. according to (14) and (15), the n and η are important for the accuracy of attack detection. in the experiments, we set ηbase = {−0.001,−0.002,−0.003, . . . ,−0.04} and n = {5,10,15, . . . ,200}. for each combination of ηbase and n, the training data is analyzed in the following method. firstly, each v (t) is unitized to v̄ (t) using (11) and (12); secondly, the shannon entropy can be calculated using (14). its unitized form is v̄ (t,n). finally, v̄ (t) is compared with v̄ (t,n) using (15) to calculate renyi cross entropy value. an entropy-based method for attack detection in large scale network 515 (a) all alerts (b) false alerts figure 3: renyi cross entropy. in the experiment, roc (receiver operating characteristic) is used to describe the detection results. roc is a graphical plot of true positive rate and false positive rate [15]. fig. 4(a) shows the roc curve of detection results in training data, where the size of nts n and base threshold ηbase equals (5, 0.005), (50, 0.02) and (100, 0.04) separately. when detection threshold ηdetect comes to 0, almost all the time intervals are detected as network attack. thus, the detection false positive rate and hit rate are both near 100%. a detection result with high hit rate and low false rate is considered to be a good result. in this case, the roc curve is plotted at the top left corner, and the auc value (area under roc curve) has large value. in this paper, we use auc value to evaluate the detection results. the best combination of n and ?base can be obtained using training data. as shown in fig. 4(b), the auc values of all the combinations are calculated, and the highest auc is 0.9962 when n = 95 and ηbase =−0.022. (a) roc (b) auc figure 4: detection result on training data set. 3.3 testing the test data set is analyzed to detect the attacks using entropy-based method. as shown in fig. 5, 211 attacks can be detected from 220 attacks (detection rate is as high as 96%) with 8 false detections. 197 host scan attacks can be detected from 203 host scans. 4 port scans are detected from 7 port scans. 3 host intrusions, 1 worm attack and 6 dos attacks are detected successfully. 516 t. liu, z. wang, h. wang, k. lu figure 5: attack detection results on test data set. 4 conclusion in this paper, a new network attack detection method based on entropy is proposed. the source ip, destination ip, alert treat and alert datagram length are selected from tens of snort alert attributions. the shannon entropy is used to analyze the alerts to measure the regularity of current network status. the renyi cross entropy is employed to fuzz the shannon entropy on different features to detect network attacks. in the experiments, the network traffic of more than 4000 users in 32 c-class network are monitored using snort. 748905 alerts, generated from 10:00 to 23:30 dec. 6 2010, are selected and separated into training data set and test data set. the experiments show that the renyi cross entropy value is near 0 when the network runs in normal, otherwise the value will change abruptly when attack occurs. the attack detection rate of entropy method is as high as 96% with only 8 false alerts. in next step, more alerts from different time segments will be collected to test our method and an attack classification method will be considered. acknowledgment this work was supported by the national natural science foundation (60921003, 60970121, 91018011), national science fund for distinguished young scholars (60825202) and the fundamental research funds for the central universities. bibliography [1] a. gostev, "kaspersky security bulletin. malware evolution 2010," kaspersky, 2011. 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[10] a. wagner and b. plattner, in proceedings of the workshop on enabling technologies: infrastructure for collaborative enterprises, wet ice, linkoeping, sweden, 2005, pp. 172-177. [11] r. yan and q. zheng, "using renyi cross entropy to analyze traffic matrix and detect ddos attacks," information technology journal, vol.8, pp. 1180-1188, 2009. [12] y. gu, a. mccallum and d. towsley, "detecting anomalies in network traffic using maximum entropy estimation," in proc. 2005 proceedings of the 5th acm sigcomm conference on internet measurement, pp. 32. [13] c. e. shannon, "a mathematical theory of communication," sigmobile mob. comput. commun. rev., vol.5, pp. 3-55, 2001. [14] c. e. pfister and w. g. sullivan, "renyi entropy, guesswork moments, and large deviations," ieee transactions on information theory, vol.50, pp. 2794-2800, 2004. [15] a. p. bradley, "the use of the area under the roc curve in the evaluation of machine learning algorithms," pattern recognition, vol.30, pp. 1145-1159, 1997. international journal of computers communications & control issn 1841-9836, 11(6):889-901, december 2016. learning bayesian networks in the space of structures by a hybrid optimization algorithm m. zhu, s. liu, j. jiang mingmin zhu*, sanyang liu school of mathematics and statistics, xidian university 2 south taibai road, xi’an, china, 710071 *corresponding author: zmmzhu2010@126.com liusanyang@126.com jiewei jiang school of computer science and technology, xidian university 2 south taibai road, xi’an, china, 710071 jiangjw924@126.com abstract: bayesian networks (bns) are one of the most widely used class for machine learning and decision making tasks especially in uncertain domains. however, learning bn structure from data is a typical np-hard problem. in this paper, we present a novel hybrid algorithm for bn structure learning, called mmabc. it’s based on a recently introduced meta-heuristic, which has been successfully applied to solve a variety of optimization problems: artificial bee colony (abc). mmabc algorithm consists of three phases: (i) obtain an initial undirected graph by the subroutine mmpc. (ii) generate the initial population of solutions based on the undirected graph and (iii) perform the abc algorithm to orient the edges. we describe all the elements necessary to tackle our learning problem, and experimentally compare the performance of our algorithm with two state-of-the-art algorithms reported in the literature. computational results demonstrate that our algorithm achieves better performance than other two related algorithms. keywords: bayesian network, artificial bee colony, structural learning, metaheuristics, scoring function. 1 introduction bayesian networks (bns), also known as belief networks, are becoming a popular tool for representing uncertainty in artificial intelligence. they have been applied to a wide range of tasks such as natural spoken dialog systems, vision recognition, expert systems, medical diagnosis, and genetic regulatory network inference [1-6]. with a network at hand, probabilistic inference can be performed to predict the outcome of some variables based on the observations of others [7,8]. there has been a lot of work in the last ten years on the learning of bns for both graph structure and probability parameters. however, learning the structure is harder and, arguably, more critical. most of these algorithms can be grouped into two different categories: constraint-based methods [9,10,11] and search-and-score methods [12,13,14]. the first one poses the learning process as a constraint satisfaction problem, and then constructs a network structure by testing the conditional independence relations. the second one poses the learning problem as a structure optimization problem. namely, it uses a score metric to evaluate every candidate network structure, and then, finds a network structure with the best score. though the implement of the former approach is relatively simple, the computation for high-order conditional independence tests is complex and irresponsible. thus, the score-and-search approach gradually becomes a popular approach for learning bns [15,16,17]. copyright © 2006-2016 by ccc publications 890 m. zhu, s. liu, j. jiang recently, a hybrid algorithm that combines the conditional independence methodology with scoring metric optimization, max-min hill climbing (mmhc)[18], has been introduced and proven to be very competitive. in mmhc approach, after reconstructing an undirected graph encoding parent-child relationships by using the max-min parents and children (mmpc) algorithm, a greedy bayesian scoring hill-climbing search is used to orientate the edges. in this paper, the idea is further explored, by using the artificial bee colony (abc)[19,20] algorithm to perform the edge orientation. we call the new hybrid algorithm max-min artificial bee colony (mmabc) algorithm. the abc algorithm can be used to search for the near-optimal graph in the space of directed acyclic graphs (dags). we describe all the elements necessary to tackle our learning problem using this meta-heuristic, and experimentally compare the performance of our abc-based algorithm with other algorithms used in the literature. the remainder of this paper is organized as follows. section 2 describes the concepts and methods related to bns. we introduce the abc algorithm and our main algorithm in section 3. simulation studies are conducted to demonstrate the performance of our algorithm and existing algorithms in section 4. finally, in section 5, we conclude and outline our future work. 2 preliminaries in this section we briefly review some basic concepts related to bns and how to learn them. a bn is a graphical representation of a joint probability distribution that includes two components. one is a directed acyclic graph (dag) g = (v,e), where v = {v1,v2, . . . ,vn}, the set of nodes, represents the random variables, and e is the set of edges, represents direct dependency relationships between variables. the other is a conditional probability table that quantifies the effects (p(vi|pa(vi))) that the parent set of vi have on the variable vi in g, where pa(vi) is the parent set of vi in g, i.e., pa(vi) = {vj|vj → vi ∈ e,vj ∈ v\vi}. if vj is the parent of vi, then vi is the child of vj in g. learning the structure of bn is equivalent to identifying the structure of the dag g that best matches the data set d. robinson [21] showed that the number f(n) of possible structures for bn having n nodes is given by the recurrence formula: f(n) = n∑ i=1 (−1)i+1 n! i!(n− i)! 2 i(n−1)f(n− i). (1) searching for the best structure is difficult because the search space increase exponentially with the number of variables. it has been proven to be an np-hard problem [22]. recently, a new and quite competitive method, the mmhc algorithm [18], that combines both scoring-based and conditional independence test approaches has been introduced. it first learns the undirected graph (i.e., the edges without their orientation) of a bn using a local discovery algorithm called the max-min parents and children (mmpc) algorithm. then, it orients the undirected graph using a greedy hill climbing search. the subroutine mmpc is sound in the sample limit. it attempts to quickly identify the set of parents and children pc(vi) of a variable vi in two phases. in the first phase, variables that are conditionally independent on vi can enter the set of candidate parents and children according to a heuristic function. in each iteration, variable vj enters the candidate set that maximizes the minimum association to vi given the current candidate set. in the second phase, the false-positive variables that possibly entered the candidate set in the first phase are removed. more detailed explanation can be found in [18]. in this paper, we first use the mmpc algorithm to learn the undirected graph of a bn, and then orientate the edges by applying the artificial bee colony (abc) algorithm. in the following, we briefly introduce the abc algorithm, which is a new meta-heuristic approach inspired by the intelligent foraging behavior of honeybee swarm. after that, we give the main algorithm of this paper. learning bayesian networks in the space of structures by a hybrid optimization algorithm 891 3 learning bayesian networks using the abc algorithm 3.1 the artificial bee colony algorithm the abc [20] algorithm is a swarm based meta-heuristic that simulates foraging behavior of honey bees. this algorithm includes three kinds of bees considering the division of labor: employed bees, onlooker bees and scout bees. a bee that is currently exploiting a food source is called an employed bee. a bee waiting in the hive for making decision to choose a food source is named as an onlooker. a bee carrying out a random search for a new food source is called a scout. an employed bee keeps a food source in her mind when she leaves from the hive and she shares the information about her food source with onlookers on dance area. onlookers select a food source by watching the dances of the employed bees and try to improve this source. if a food source is abandoned, its employed bee becomes a scout to explore new food sources randomly. scouts either randomly search the environment in order to find a new food source depending on an internal motivation or based on possible external clues. figure 1: flowchart of the abc algorithm 3v 4v 1v 2v 3v 4v 1v 2v 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 x ! " # " #$ " # " # % & * 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 x ! " # " #$ " # " # % &3v 4v 1v 2v * 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 x ! " # " #$ " # " # % & 3v 4v 1v 2v * 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 x ! " # " #$ " # " # % & figure 2: three edge operators used in the mmabc algorithm in abc algorithm each food source represents a possible solution for the problem and the fitness value of the solution corresponds to the nectar amount of this food source. each food source is exploited by only one employed bee. in other words, the number of employed bees is equal to the number of candidate solutions. similar to the other swarm intelligence based approaches, the abc algorithm is also an iterative process. it repeats a series of steps until a termination criterion is met. we show all these steps and interactions between them as a flowchart on figure 1. 892 m. zhu, s. liu, j. jiang 3.2 a hybrid algorithm for learning bns using abc optimization the above basic abc algorithm, originally designed for the continuous nature of optimization problems, can not directly be used for discrete problems. in this section, we develop a new abc-based algorithm for learning bns that performs the search in the space of directed acyclic graphs. the new algorithm is called mmabc for short. we first use the mmpc algorithm to learn the undirected graph of a bn. based on this undirected graph ug, the initial population for mmabc is produced randomly. more specifically, the edges of the initial graph corresponding to each individual are randomly selected from the edge set of ug, which is the candidate edge set of the optimal bn structure. this greatly reduces the randomness of the initial population and improve the convergence speed. the main components of our mmabc algorithm are described below. a. initialization. let m and n denote the total number of food sources and the number of variables in a bn, respectively. since the structure of a bn is a directed graph g = (v,e), we represent each individual by a binary node-node adjacency matrix x = (xij)n×n, i,j = 1, 2, . . . ,n. entry (i,j) is 1 if there is a directed arc from node i to node j, and 0 otherwise. that is, xij = { 1, if vi → vj ∈ e, 0, if vi → vj /∈ e. (2) initial food sources (solutions) xk = (xij)n×n,k = 1, 2, . . . ,m are generated randomly by selecting edges from an undirected graph ug = (v,e), which is obtained by mmpc. counters which store the numbers of trials of solutions are reset to 0 in this phase. b. employed bees phase. each employed bee is associated with only one food source site. an employed bee generates a neighboring solution by three operators: edge insertion, edge deletion and edge reversal. we show these three operators in figure 2. let xk and xk∗ denote the current solution and the generated one, respectively. the quality of the graph g corresponding to xk∗ is measured by the k2 score function [23] as follows: fk2(g,d) = n∑ i=1 fk2(vi,pa(vi)|dvi,pa(vi)), (3) fk2(vi,pa(vi)|dvi,pa(vi)) = qi∑ j=1 [ log ( (ri − 1)! (nij + ri − 1)! ) + ri∑ k=1 log(nijk!) ] , (4) where d is the domain data set. dvi,pa(vi) are the statistics of the variable vi and pa(vi) in d, i.e., the number of instances in d that match each possible instantiation of vi and pa(vi). nijk is the number of cases in d where vi is in its kth state and its parents are in their jth state. nij = ∑ri k=1 nijk denotes the number of cases in d where pa(vi) is in its jth state and ri denotes the number of states of variable vi, qi is the number of parent configurations of vi. because the k2 metric is decomposable and the graphs corresponding to xk∗ and xk are different from one edge by each one of three operators, we can reuse most of the computations made in the food source xk. in other words, we only need to update the statistics corresponding to the variables whose parent sets have been modified. thus, the time performance can be greatly enhanced. as our problem is a maximization problem and the value of fk2(·) is negative, therefore, the fitness value assigned to the solution xk∗ is determined using the following expression: fitk = 1 1 + abs(fk) , (5) learning bayesian networks in the space of structures by a hybrid optimization algorithm 893 where fk is the k2 value of the graph g corresponding to xk∗. a greedy selection is employed between xk and xk∗ such that the better one is selected depending on fitness values representing the nectar amount of the food sources at xk and xk∗. if xk cannot be improved, its counter is incremented by 1, otherwise, the counter is reset to 0. c. onlooker bees phase. an onlooker bee evaluates the nectar information taken from all employed bees and chooses a food source site with a probability related to its nectar amount. the roulette wheel selection scheme is used here in which each slice is proportional in size to the fitness value as follows: pk = fitk∑m j=1 fitj . (6) by doing so, the onlooker bees are encouraged to visit the food sources that have higher nectar amounts. briefly, food source selection by onlookers is based on the information provided by employed bees. a uniform random number within the range [0, 1] is obtained for each source. if the probability value pk is greater than this random number, then the onlooker bee generates a neighboring food source again by using three operators as in the case of the employed bee. after the source is evaluated, greedy selection is applied. if solution xk cannot be improved, its counter is incremented by 1, otherwise, the counter is reset to 0. this process is repeated until all onlookers are distributed onto food source sites. d. scout bees phase. if the value of the counter is greater than the control parameter “limit”, then the food source associated with this counter is abandoned. assume that the abandoned source is xk, then the scout randomly discovers a new food source xk∗ which can be achieved by randomly generating any directed acyclic graph. this procedure can increase solutions diversity and avoid falling into the local optimum. in our algorithm, there is only a single food source which can be abandoned in each cycle, and only one employed bee can be a scout. e. termination. if a termination is not satisfied, go to employed bees phase; otherwise stop the procedure and output the best food source found so far. as shown in algorithm 1, the mmabc algorithm first constructs the undirected graph ug of the underlying bn structure by the mmpc algorithm, which obtained the parents and children of each variable. it gives the best-so-far undirected graph, from which the initial population can be generated randomly by selecting n− 1 edges from ug (assumed the underlying bn has n variables). figure 3 shows the process of generating initial population by a simple example. consider the second graph in figure 2. assumed that its undirected graph ug obtained by mmpc is shown in figure 3 (a). by randomly selecting 3 directed edges from ug, we can get an initial solution. figure 3(b), figure 3(c) and figure 3(d) show 3 solutions of the initial population generated from figure 3(a). after producing and evaluating the initial population, the main loop of the mmabc algorithm then begins for iter iterations. step 7 and 15 show one of the edge operators can be chosen to obtain the neighbor of the current solution in employed bees phase and onlooker bees phase, respectively. each of the operators can be chosen randomly with equal probability. if there is a cycle in the generated graph, another new graph can be achieved again. 4 experimental results in this section, we present the experimental results carried out with our algorithm and compare mmabc with two hybrid heuristic methods: mmhc [18] and mmaco [24]. in all the 894 m. zhu, s. liu, j. jiang algorithm 1 the mmabc algorithm. 1. obtain the undirected graph u g by mmpc; 2. generate the initial population of solutions xk,k = 1,2,...,m, trk = 0. m is the total number of food sources. trk is the non-improvement number of the solution xk , used for abandonment; 3. evaluate the population; 4. iter=1; 5. repeat 6. for k = 1 to m do 7. produce a new food source xk∗ for the employed bee of the food source xk by using one of the three edge operators and evaluate its quality according to equation (3); 8. apply a greedy selection process between xk∗ and xk and select the better one; 9. if solution xk does not improve trk = trk + 1, otherwise trk = 0; 10. end for 11. calculate the probability value pk by (6), k = 1,2,...,m; 12. t = 0,k = 1; 13. repeat 14. if random < pk then 15. produce a new food source xk∗ for the onlooker bee of the food source xk by using one of the three edge operators and evaluate its quality by (3); 16. apply a greedy selection process between xk∗ and xk and select the better one; 17. if solution xk does not improve trk = trk + 1, otherwise trk = 0; 18. t = t + 1; 19. end if 20. until t = m; 21. if max(trk) > limit then 22. replace xk with a new randomly produced solution; 23. end if 24. memorize the best solution achieved so far; 25. iter=iter+1; 26. until iter=maximum iteration. figure 3: the mmabc algorithm 3v 4v 1v 2v 3v 4v 1v 2v 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 x ! " # " #$ " # " # % & 2 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 x ! " # " #$ " # " # % &3v 4v 1v 2v 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 ug x ! " # " #$ " # " # % & 3v 4v 1v 2v 3 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 x ! " # " #$ " # " # % & (a) ug (b) solution 1 (c) solution 2 (d) solution 3 figure 4: the process of generating initial population learning bayesian networks in the space of structures by a hybrid optimization algorithm 895 cases the scoring metric used to guide the search is the k2 metric, so, the difference among implementations is minimized. all these experiments are based on the bn toolbox written by murphy [25] and the causal explorer system developed by aliferis et al.[26]. the experimental platform is a personal computer with pentium 4, 3.06 ghz cpu, 1.0 gb memory, and windows xp. the standard way of assessing the accuracy of a learning method is to draw samples from a known bn, apply structure learning on the artificial data, and to compare the learned structure with the original one. to benchmark our method, we investigate its performance on artificial data with real-world characteristics focusing on the well-known asia [27], insurance [28], and alarm [29] networks. the first one is a small bn using a fictitious medical example that considers the dependence of the health status of a patient (having tuberculosis, lung cancer, or bronchitis) on risk factors (visit to asia and smoking) and clinical observations (dyspnea and x-ray). it consists of eight nodes with eight edges connecting them. each random variable is discrete in nature and can take two states. the second example is a network for estimating the expected claim costs for a car insurance policyholder. it consists of 27 nodes and 52 edges. the last one is a medical diagnostic system for patient monitoring. it consists of 37 nodes and 46 edges connecting them. the random variables in the alarm network are discrete in nature and can take two, three, or four states. the characteristics of the networks are summarized in table 1. for these three networks, data sets with 200, 500, 1000, 3000, and 5000 samples are drawn. for each of the sample sizes, ten independent data sets are generated by applying gibbs sampling [30] on the benchmark networks, resulting in a set of 50 data sets for each network. 10 replicates are done for each of different network parameters and sample size. we will discuss our experimental results in more detail in the following two subsections. table 1: bayesian networks network variables edges max in/out-degree domain range asia 8 8 2/2 2 insurance 27 52 3/7 2-5 alarm 37 46 4/5 2-4 table 2: the k2 values for the true networks with different samples network n = 200 n = 500 n = 1000 n = 3000 n = 5000 asia -501.79 -1194.9 -2350.1 -6881.9 -11458 insurance -5291.2 -9851.6 -17105 -45406 -73717 alarm -3259.2 -6364.4 -11162 -29894 -48597 4.1 comparison of the solution quality in this subsection, we compare the performance of the different algorithms on all of the data sets. our main objective in these experiments is to determine whether mmabc is more efficient and effective than the other two hybrid algorithms. for mmabc and mmaco, the population size is set to 20, and the maximum number of iterations is set to 100. the significant level used 896 m. zhu, s. liu, j. jiang in these algorithms is set to 0.05. other parameter settings of the mmaco algorithm can be found in [24]. the parameter “limit” for mmabc is set to 6, namely, if the solution associated with an employed bee does not improve for 6 times, then that solution is replaced by some randomly generated solution. in order to provide an objective measure of network structure reconstruction behavior, we compare the underlying dag with the learned dag and record the structural hamming distance (shd), where shd is defined as the total number of operations to verify the learned dag to the underlying dag. the k2 scoring function was also presented to measure the learned network structure, which could be used to compare the quality of the structures obtained by different algorithms. the k2 values for the true network structures with different data sets can be seen in table 2. table 3: the results for three algorithms with different datasets network algorithm n = 200 n = 500 n = 1000 n = 3000 n = 5000 mmabc 3.0 2.1 1.0 0.7 0.3 -500.53±0.7 -1191.6±1.1 -2351.6±3.1 -6877.0±3.5 -11454.0±5.2 asia mmaco 4.8 2.9 1.2 0.6 0.3 -488.8±1.3 -1179.3±3.2 -2346.5±7.1 -6868.0±9.1 -11478.0±11.9 mmhc 6.1 3.3 2.0 1.1 0.9 -507.6±1.1 -1209.2±1.8 -2351.9±3.7 -6896.3±5.7 -11593.0±6.8 mmabc 15.8 11.6 8.7 6.6 4.1 -5292.1±1.2 -9853.8±1.8 -17116.1±2.6 -45408.3±4.7 -73716.9±5.6 insurance mmaco 16.9 12.4 9.4 6.7 5.3 -5295.8±3.1 -9857.6±5.6 -17115.3±6.9 -45412.1±8.2 -73716.7±10.1 mmhc 36.2 27.9 19.3 12.4 8.9 -5361.3±1.5 -9911.5±3.2 -17190.4±3.4 -45815.6±5.3 -73850.3±6.8 mmabc 18.4 11.3 10.0 6.9 3.9 -3198.3±1.2 -6363.5±1.7 -11149.9±2.8 -29883.9±4.1 -48600.9±5.0 alarm mmaco 19.1 12.1 11.2 7.2 4.4 -3072.2±3.1 -6344.3±5.5 -11085.7±6.9 -29891.1±9.2 -48605.9±11.1 mmhc 35.8 27.6 19.2 13.8 9.6 -3085.8±1.4 -6435.1±3.1 -11210.2±3.3 -29849.3±5.3 -48694.0±6.7 table 3 provides the mean and standard deviation of the k2 score and the mean shd values obtained for the learned networks with different data sets. µ ± σ indicates the mean and the standard deviation over the executions carried out. we have carried out 10 executions of each algorithm and for each domain considered. in table 3, the first six rows display the results of three algorithms on asia data sets, and the rest twelve rows is the results on insurance and alarm data sets. for example, figures in the second row are the shd values obtained by the mmabc algorithm on asia data sets. figures in the third row are their corresponding mean and standard deviation of the k2 scores. on the whole, we can see that mmaco algorithm is a little better than mmhc algorithm on the asia network, and our algorithm is best. however, in the case of insurance and alarm networks, the advantages of mmabc and mmaco are more evident, and mmabc performs better than mmaco, which means our algorithm has better performance with more complicated networks. the k2 score of our algorithm is always closer learning bayesian networks in the space of structures by a hybrid optimization algorithm 897 to that of the original network. the reason is that the construction process of a bn for an ant is dependent much too on the pheromone and heuristic information of candidate arcs, which is prone to overfitting. although it can be seen that the k2 score of mmabc and mmaco becomes similar as the sample size increase, mmabc has a lower shd value. we also note that in some cases, the score obtained by mmaco is better than that of mmabc, but mmabc has lower shd values. this indicates that a better score does not necessarily mean a better shd value and vice-versa. it can occur because of small size and because of the parameters given to the scoring function, which have been shown to produce differences in scoring function behavior. it is worth mentioning that the standard deviation of the mmabc is lower than that of the other two algorithms, which means that the mmabc guarantees good quality networks with less fluctuation than other algorithms. 4.2 comparison of the convergence and time performance in order to measure the convergence speed and the runtime complexity to learn the structure, we compared with the typical runs of the mmabc and mmaco algorithms on the same data set by large number of experiments. (a) 10 20 30 40 50 60 70 80 90 1001 -2.380 -2.375 -2.370 -2.365 -2.360 -2.355 -2.350 -2.345 iteration s c o r e x10 3 mmaco mmabc us (b) 10 20 30 40 50 60 70 80 90 1001 1 1.5 2 2.5 3 3.5 4 4.5 iteration s h d mmaco mmabc figure 5: comparison of the solution quality for mmaco and mmabc on asia data sets: (a) scores for asia; (b) shd for asia figure 4 and figure 5 show the curves of the convergence performance on two data sets, asia1000 and alarm-5000, respectively. in these figures, the abscissas depict the number of iterations, and the y-coordinates depict the k2 score or shd value of the learned graph. us specifies the underlying score of the original network. from a general point of view, mmabc achieve better quality networks in the initial stages of the optimization and has a rapidly converging learning curve, which means that mmabc require a significantly lower number of iterations than mmaco to achieve networks of better quality. although we are testing on two very different data sets, we obtain similar observation that as the score is improving over iterations, the shd value is deteriorating. it provides strong evidence that mmabc is performing well against the mmaco algorithm. in figure 6, we test mmabc with 4 different population sizes on asia-1000: 10 (mmabc1), 20 (mmabc2), 30 (mm abc3), 40 (mmabc4). from the curves in figure 6(a) and figure 6(b), it can be found that the population size m can have an effect on the convergence speed of the algorithm. in other words, mmabc takes a comparatively large number of iterations to converge to an acceptable solution with a small number of bees. because a large population 898 m. zhu, s. liu, j. jiang (a) 10 20 30 40 50 60 70 80 90 1001 -4.8635 -4.863 -4.8625 -4.862 -4.8615 -4.861 -4.8605 -4.86 -4.8595 x 10 4 iteration s c o r e mmaco mmabc us (b) 10 20 30 40 50 60 70 80 90 1001 -4.8635 -4.863 -4.8625 -4.862 -4.8615 -4.861 -4.8605 -4.86 -4.8595 x 10 4 iteration s c o r e mmaco mmabc us figure 6: comparison of the solution quality for mmaco and mmabc on alarm data sets: (a) scores for alarm; (b) shd for alarm (a) 10 20 30 40 50 60 70 80 90 1001 -2.380 -2.375 -2.370 -2.365 -2.360 -2.355 -2.350 -2.345 iteration s c o r e x10 3 mmabc1 mmabc2 mmabc3 mmabc4 us (b) 10 20 30 40 50 60 70 80 90 1001 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 iteration s h d mmabc1 mmabc2 mmabc3 mmabc4 figure 7: comparison of the solution quality for different population sizes on asia data sets: (a) scores for asia; (b) shd for asia learning bayesian networks in the space of structures by a hybrid optimization algorithm 899 means that more food sources are employed, there are more different solutions examined than in a smaller population. on the other hand, the time consumed also increased proportionally with increasing population size. it should be noted that tiny changes to the score value can lead to large structural changes as an algorithm converges towards the optimum network. (a) (b) figure 8: comparison of the time performance on two networks: (a) running time for asia data sets; (b) running time for alarm data sets figure 7 gives the average running time to reach the best solution for three algorithms on two networks. based on figure 7, we can see that the proposed mmabc has a considerable better time performance than mmaco, and mmhc has the best time performance. however, from the results of table 3, we can see that mmhc obtains worse k2 score and shd values. moreover, the advantage is very obvious when the data set is large on the same network, namely, the bigger the sample size, the more obvious the improvement. we also noted that the running time of mmabc is affected less by the size of data but more by the problem dimension. it means that mmabc is not sensitive to the increase of the sample capacity and able to handle very large data sets. conclusions in this paper we have designed and implemented a hybrid abc-based algorithm for the bn structure learning problem. ideas from constraint based local learning and score-based abc were combined into effective learning processes. its application to several benchmark networks yielded better results than the previously used mmhc and mmaco algorithms. this paper demonstrates the capability of abc algorithm in learning a probability graphical model. ideas presented in this paper can be applied to many other probability graphical problems also, such as dynamic bn, chain graph, etc. moreover, we have applied the basic three edge operators for determining the neighboring solutions in mmabc. it will be interesting to see whether there exists some alternative approach to serve the same purpose here. we also intend to expand this analysis to larger benchmark networks and make further conclusions on the usefulness of the constraint-based and score-based learning approaches. finally, although we assume in this paper that the data are completely observed, missing data or data with latent variables may arise in practice. generalization of the proposed algorithm to incomplete data is of great research interest. 900 m. zhu, s. liu, j. jiang acknowledgment the authors would like to thank the editor and the anonymous reviewers for their insightful comments and suggestions. this work has been partially supported by the national natural science foundation of china (grant no. 61373174, 11401454), the natural science foundation of shannxi province, china (grant no. 2014jq1031) and the fundamental research funds for the central universities (grant no. jb140711). bibliography [1] z. cai, s. sun, s. si, b. yannou, identifying product failure rate based on a conditional bayesian network classifier, expert systems with applications, 38(5): 5036-5043. 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[30] t. hrycej, gibbs sampling in bns, artificial intelligence, 46(3): 351-363. international journal of computers communications & control issn 1841-9836, 10(4):508-519, august, 2015. a visualization technique for accessing solution pool in interactive methods of multiobjective optimization e. filatovas, d. podkopaev, o. kurasova ernestas filatovas*, olga kurasova vilnius university institute of mathematics and informatics, akademijos str. 4, lt–08663 vilnius, lithuania ernest.filatov@gmail.com, olga.kurasova@mii.vu.lt *corresponding author: ernest.filatov@gmail.com dmitry podkopaev 1. systems research institute polish academy of sciences newelska str. 6, 01-447 warsaw, poland 2. university of jyväskylä departament of mathematical information technology p.o. box 35 (agora), fi-40014 university of jyväskylä, finland dmitry.podkopaev@gmail.com abstract: interactive methods of multiobjective optimization repetitively derive pareto optimal solutions based on decision maker’s preference information and present the obtained solutions for his/her consideration. some interactive methods save the obtained solutions into a solution pool and, at each iteration, allow the decision maker considering any of solutions obtained earlier. this feature contributes to the flexibility of exploring the pareto optimal set and learning about the optimization problem. however, in the case of many objective functions, the accumulation of derived solutions makes accessing the solution pool cognitively difficult for the decision maker. we propose to enhance interactive methods with visualization of the set of solution outcomes using dimensionality reduction and interactive mechanisms for exploration of the solution pool. we describe a proposed visualization technique and demonstrate its usage with an example problem solved using the interactive method nimbus. keywords: multiobjective optimization, interactive methods, pareto front visualization, dimensionality reduction, multidimensional scaling. 1 introduction many real-world optimization problems are multiobjective in their nature. existence of a solution optimizing all objectives simultaneously is unlikely in practice. solving a multiobjective problem is usually understood as finding a pareto optimal solution which is the most preferred for a decision maker (dm) [16]. therefore most of the multiobjective optimization methods rely on preference information obtained from the dm. we concentrate on interactive methods consisting of iterations, where at each iteration the dm provides some preference information and receives solution(s) derived based on this information as feedback [3]. in many interactive methods, a solution obtained at each iteration serves as the starting point for the next iteration. the dm considers this solution and expresses preference information aiming at its improvement. it is useful to allow the dm selecting for consideration the solution obtained not only during the last iteration but any previous iteration. few interactive methods include this possibility (see e.g. [8, 22]) and to our knowledge, none of the papers describe the selection process in detail. the issue to be addressed here is high cognitive load placed on the copyright © 2006-2015 by ccc publications a visualization technique for accessing solution pool in interactive methods of multiobjective optimization 509 dm when dealing with many-objective problem and a large number of accumulated solutions. even though the number of iterations in an interactive solution process can be moderate [7], many interactive methods such as tchebycheff method [33], nimbus [22] and others generate several pareto optimal solutions at each iteration, which makes the solution set grow fast. in this paper, we address the problem of assisting the dm in selecting one among many pareto optimal solutions at each iteration of an interactive method in the case of a large number of objectives. we propose to enhance interactive methods with a technique which visualizes a set of the obtained solution outcomes and provides interactive mechanisms of its exploration. this technique is based on multidimensional scaling method [2] which maps the solution outcomes on a plane while trying to preserve distances between them. despite significant lost of information, such dimensionality reduction creates a holistic view on the outcome set helping the dm to develop a cognitive map. this method has been successfully applied earlier for visualizing solution outcomes in multiobjective evolutionary algorithms [10], but never in interactive methods. its integration into interactive methods requires some modification for handling inclusion of new solutions into the solution pool. this paper is organized as follows. in section 2 we present background information and studies underlying our research, namely basic notions of multiobjective optimization, a general structure of interactive methods and the employed approach to dimensionality reduction. in section 3 we propose a visualization technique which can be integrated in many interactive methods. in order to demonstrate this technique in action, in section 4 we integrate it to the interactive method nimbus, and use the technique for solving an example problem. section 5 presents conclusions. 2 background 2.1 the problem of multiobjective optimization the multiobjective optimization problem is formulated as follows [16]: min x∈s f(x), (1) where x is a decision (variable) vector ; s ⊆ rn is a set of feasible solutions; f(x) = (f1(x),f2(x), . . . ,fk(x))t, fi : rn → r, i ∈ {1,2, . . . ,k} are objective functions, k ≥ 2. for each decision vector x ∈ s, f(x) is called its outcome. a decision vector x∗ is called pareto optimal solution of problem (1) (or pareto optimum for short), if there does not exist another vector x ∈ s such that fi(x) ≤ fi(x∗) for all i ∈{1,2, . . . ,k} and fj(x) < fj(x∗) for at least one j. the set of pareto optimal solutions is called pareto optimal set, and the set of outcomes of all pareto optimal solutions is called pareto front. solving the problem (1) is commonly understood as finding a pareto optimum x∗ whose outcome f(x∗) is the most preferred for the dm (see e.g. [3, 16]). therefore, most of multiobjective optimization methods use preference information provided by the dm. in this paper, we concentrate on interactive methods for they are believed to be most promising methods of multiobjective optimization because of numerous advantages [25]. an important advantage of interactive methods is the possibility for the dm to learn about the problem during the solution process, which makes him/her more confident in the final choice [1]. 2.2 interactive methods in interactive methods [16,18], the dm provides preference information progressively during the solution process and obtains pareto optima derived based on this information as feedback. 510 e. filatovas, d. podkopaev, o. kurasova many interactive methods have the underlying idea of exploring the pareto optimal set by shifting dm’s attention from one solution to another. the notion of current solution is introduced as the pareto optimum considered by the dm in the current iteration. the dm expresses preferences aiming (explicitly or implicitly) at improving the current solution, i.e. finding a more preferred pareto optimum. a pareto optimum derived based on these preferences can be considered as the current solution in the next iteration. this process of producing a sequence of pareto optima can be viewed as moving from one pareto optimum to another, where preference information determines the direction of movement. in the case of a cognitively complex model underlying a multiobjective optimization problem, the dm cannot be sure that his/her preference information always leads to a more preferred solution. besides that, sequential improvement of pareto optimal solutions based on dm’s preferences cannot guarantee the convergence to the most preferred solution among the whole pareto optimal set, by analogy with global optimization problems where a series of local improvements of a feasible solution does not necessary lead to a global optimum. therefore it is reasonable to give the dm possibility of exploring different parts of the pareto optimal set by going back to solutions derived earlier and starting moving from them in different directions. this possibility can be implemented by saving all the obtained pareto optima in the solution pool and enabling the dm in each iteration selecting any element of the pool as the current solution for the next iteration. thus instead of a sequence of pareto optima one can have a tree where each branch relates to such a move back. a general structure of interactive methods including the possibility of moving back is described below step-by-step (algorithm 1). algorithm 1 step 0. initialize. set initial variable values and derive pareto optimal solution(s) using an initial preference model (based on some initial dm’s preference information or a rule of thumb). step 1. update. add the obtained solution(s) into the pool. step 2. select. ask the dm to select a solution from the pool. step 3. preference expression. if the dm regards the selected solution the most preferred, then the method stops with the selected solution as the final one. otherwise set the selected solution as the current solution of the next iteration and ask the dm to express preferences aiming at its improvement. step 4. solution derivation. build the preference model based on dm’s preference information and derive new pareto optimal solution(s) using this model. go to step 1. some authors mention the possibility of selecting the current solution from the solution pool in interactive methods (see e.g. [6, 8, 22, 30]). many interactive methods can be easily enhanced by adding such possibility and thereby be fit into the structure described above. however to our knowledge, no papers present any details about implementing the procedure of solution selection. as the size of the solution pool grows, it becomes cognitively more difficult for the dm to keep and operate with it in his/her mind. indeed, comparing and choosing among a number of objects exceeding the mental capacities obviously leads to mistakes and inconsistencies as illustrated e.g. in [32]. therefore the dm can benefit from a graphical tool visualizing the solution pool and assisting with solution selection. a visualization technique for accessing solution pool in interactive methods of multiobjective optimization 511 2.3 graphical representation of solution outcomes it must be noted that most of the multiobjective optimization methods rely on the assumption that the dm compares different solutions and chooses the most preferred one only based on their outcomes. therefore most of existing visualization techniques (see e.g. [10, 12, 13, 17]) concentrate on presenting the set of the pareto optimal outcomes in the so-called objective space rk. visualizing a set of pareto optimal outcomes is a non-trivial task in the case k > 3. if the solution set is relatively small, then all components of all outcomes can be presented in a diagram such as scatter plot, bar charts, spider-web charts and others (see comprehensive surveys in [9, 17]). for bigger solution sets, such representations enforce the dm to deal with abundance of information. then dimensionality reduction methods can be helpful. as dimensionality reduction involves loss of information, the problem of extracting information which remains can be approached in different ways depending on the aim the visualization serves. for example, in self-organizing maps [36], artificial neural networks are trained to preserve the topological properties of the input space. principal component analysis (pca) [15] tries to preserve variances of data. interactive decision maps [4, 12] are based on two-dimensional projections, they are also used for the visualization of pareto outcomes. multidimensional scaling method (mds) [2, 5] aims at finding points v1, v2, . . . , vm in the low-dimensional space rs, s < k, such that the distances between them are as close as possible to the distances between the original points z1, z2, . . . , zm in the multidimensional space rk. this is achieved by minimizing the stress function emds = ∑ i<j ( d(vi, vj)−d(zi, zj) )2 . (2) here d(·, ·) is the distance between two points in the corresponding space. the procedure of finding points v1, v2, . . . , vm by minimizing (2) is called mapping. we propose to use mds for visualizing the solution pool in interactive methods because it detects the similarity between pareto optimal outcomes, and allows the dm distinguishing areas by how much they have been explored. this helps in focusing the dm’s attention on solutions located in preferred parts of the pareto front. when using mds during an interactive solution process, a problem of mapping new solution outcomes occurs. after adding them to the solution pool and recalculating low-dimensional points by minimizing (2), the points corresponding to already mapped solution outcomes will also change. in order to avoid this changing, so-called relative mapping [27] can be applied, where only coordinates of the newly added points are calculated. the stress function of relative mapping is following: er = m+m̂∑ i,j=m+1,i<j ( d(v̂i, v̂j)−d(ẑi, ẑj) )2 + m+m̂∑ i=m+1 m∑ j=1 ( d(v̂i,vj)−d(ẑi,zj) )2 , (3) where z1,z2, . . . ,zm are the previously mapped multidimensional points and v1, v2, . . . , vm are their corresponding lower-dimensional points which are fixed, m̂ is the number of new points, ẑm+1, ẑm+2, . . . , ẑm+m̂ and v̂m+1, v̂m+2, . . . , v̂m+m̂ are the new points and their corresponding lower-dimensional points, respectively. however, after applying the relative mapping, the value of the stress function emds calculated for all the solution outcomes by formula (2) increases, which indicates deterioration of mapping quality. therefore, after visualizing a large number of the new outcomes using relative mapping, all the mapped points must be recalculated by the mds method again, i.e. remapping must be performed. 512 e. filatovas, d. podkopaev, o. kurasova our previous research was devoted to usage mds for visualizing the pareto front in multiobjective evolutionary algorithms [10]. a similar approach was developed in [34] for reviewing the set of obtained pareto optimal solutions when solving the problem (1). however, the possibility of integrating mds into interactive methods has not been studied before. in the next section, we describe the proposed visualization technique based on mds and its integration into interactive methods. 3 enhancement of interactive methods with visualization the general structure of interactive methods described in subsection 2.2 is modified and presented as a scheme in figure 1. the visualization tool is used in step 2 for assisting the dm in exploring the solution pool and selecting from it a pareto optimal solution to be considered as the most preferred solution or the current solution in the next iteration. in order to enable using the visualization, a visual representation of the solution pool is constructed or updated in step 1. we propose to allow the dm choosing, whether to use the visualization technique or select the solution in a traditional way implemented in the method. thus, adding the visualization in the method does not imply any additional efforts from the dm. if the dm finds the visualization useless, he/she can ignore it which means that its integration into the method does not deteriorate dm’s experience. figure 1: a general enhanced scheme of interactive methods. the proposed visualization technique consists of mapping of solution outcomes on a plane using the mds method and interacting with the dm for exploring the outcome set and selecting a solution. our approach does not depend on the interactive method used, therefore the technique can be implemented for a wide range of interactive methods. we propose to visualize outcomes from the solution pool as a scatter plot of two-dimensional points mapped using the mds method or relative mapping, that can be implemented in visualization tool (see figure 1). the following a visualization technique for accessing solution pool in interactive methods of multiobjective optimization 513 two operations will be used for constructing and updating this visualization: • map all solution outcomes – given outcomes z1, z2, . . . , zm of all pareto optimal solutions from the solution pool, corresponding two-dimensional points v1, v2, . . . , vm are calculated by minimizing the mds stress function (2) as described in subsection 2.3. here m is the number of solution outcomes, zj ∈ rk and vj ∈ r2 for all j = 1, . . . ,m. • map new solution outcomes – given outcomes z1, z2, . . . , zm of pareto optimal solutions from the solution pool mapped earlier, their corresponding two-dimensional points v1, v2, . . . , vm, and the outcomes of newly derived pareto optimal solutions ẑm+1, ẑm+2, . . . , ẑm+m̂, the two-dimensional points v̂m+1, v̂m+2, . . . , v̂m+m̂ corresponding to the new solutions are calculated by minimizing the relative mapping stress function (3) as described in subsection 2.3. in order to evaluate similarities between the obtained solution outcomes the mds method requires several points. therefore the visualization starts being constructed after a predefined number of solutions m has been collected in the solution pool. in the case of a small number of solutions, it can be easier for the dm to select one of them by straightforward comparison of the solution outcomes rather than studying the two-dimensional visualization. therefore we propose to set initial value of m not smaller than 7. once new pareto optimal solutions are derived in step 4 (figure 1) and added to the solution pool, the visual representation is updated in step 1 either by mapping all solutions or only new solutions. in the first case, the locations of the previously mapped solutions will also change which may distort the cognitive map of the solution pool the dm has developed. on the other hand, mapping only new solutions may affect the accuracy of correspondence between distances in the objective space and two-dimensional space. therefore, we propose to use a compromise strategy where new solutions are mapped during most of iterations, but the map of all solutions is recalculated on dm’s demand or after accumulation of a large number of new solutions. when the number of the added solutions l reaches the predefined threshold limit mp, the map of all pareto optimal outcomes is recalculated by applying the mds method again. we propose to set the limit mp = 0.5m, where m is the number of outcomes already mapped by mds. hence, the higher value m is, the less frequent remapping by mds is required. the detailed procedure of constructing the map of the obtained outcomes is presented as algorithm 2. algorithm 2 constructing the map of the solution outcomes input: the solution outcomes z1, . . . , zm, obtained by using an interactive method, where m is the number of outcomes. output: the 2d map (scatter plot). step 1: set the number of solution outcomes mapped by relative mds to l = 0. step 2: the dimensionality of the solution outcomes z1, . . . , zm is reduced to 2 by the mds method, and the two-dimensional points v1, . . . , vm are displayed in a 2d scatter plot and presented to the dm. set the value of threshold limit mp = 0.5m. step 3: when m̂ new solution outcomes ẑm+1, . . . , ẑm+m̂ are obtained by the interactive method, two-dimensional points v̂m+1, . . . , v̂m+m̂ are calculated by the relative mapping, they are added to the 2d scatter plot and we set m = m+m̂, l = l +m̂. if the dm is satisfied with the obtained solution, then go to step 5. step 4: check if l > mp or the dm wants to remap the solution outcomes. if yes then go to step 1, else go to step 3. step 5: stop. 514 e. filatovas, d. podkopaev, o. kurasova the visualization should be accompanied with graphical tools and interactive mechanisms based on graphical user interface, allowing the dm: • to see additional information related to the way solution were obtained, e.g. information about the extent at which solutions satisfy preferences based on which they were derived, relations between solutions related to the sequence in which they were obtained; • to perform operations on visualized outcomes, e.g. hide selected outcomes or outcomes satisfying a given property in order to focus on the most interesting outcomes, recalculate mapping on demand; • to get full information about the solution corresponding to any selected outcome. the implementation of the above enhancements of the visualization might depend on the interactive method used as well as the graphical user interface of the method implementation. 4 example implementation in this section, we demonstrate the proposed visualization technique by solving an example problem using a popular interactive multiobjective optimization method nimbus [16, 19]. it is implemented in the ind-nimbus software platform ( [28, 29]) designed for solving industrial problems, as well as in a web-based application www-nimbus available via the internet [21]. the nimbus method has been applied to a number of engineering problems [11, 22, 24]. in the nimbus method, new pareto optimal solutions are derived by solving scalarized problems based on preference information provided by the dm in the form of a classification. in each iteration of this method, the dm considers the current pareto optimal solution xc ∈ s and corresponding outcome and expresses preferences by classifying the objective functions fi(xc), i = 1,2, . . . ,k into up to the five classes related to five types of desirable or acceptable changes of the function values [22]: i< – to be improved (i.e. decreased) as much as possible; i≤ – to be improved till some desirable aspiration level z̄i < fi(xc); i= – the current function values are acceptable; i≥ – may be impaired (i.e. increased) till some upper bound ai > fi(xc); i⋄ – the function values are allowed to change freely. this classification must be done in such a way that at least one objective function value should be improved, and at least one is allowed to impair. the classification is used to build scalarizing functions for deriving new pareto optimal solutions. in synchronous nimbus [22], four different scalarization functions are used in order to present to the dm diverse solutions related to expressed preferences, thereby up to four different solutions can be obtained according the same preference information. 4.1 numerical example as an example for demonstrating the proposed visualization technique in use, we chose the five-objective river pollution problem which was implemented in ind-nimbus. the problem was originally presented in [26] and modified in [20], where the fifth (nonsmooth) objective function was included. the mathematical formulation of the problem is as follows: a visualization technique for accessing solution pool in interactive methods of multiobjective optimization 515 maximize f1(x) = 4.07 + 2.27x1 maximize f2(x) = 2.60 + 0.03x1 + 0.02x2 + 0.01 1.39−x21 + 0.30 1.39−x22 maximize f3(x) = 8.21− 0.71 1.09−x21 minimize f4(x) = −0.96 + 0.96 1.09−x22 minimize f5(x) = max[|x1 −0.65|, |x2 −0.65|] subject to 0.3 ≤ x1,x2 ≤ 1.0. (4) it describes a (hypothetical) pollution problem of a river, where a fishery and a city are polluting water. the decision variables x1 and x2 represent the proportional amounts of biochemical oxygen demanding material removed from water in two treatment plants located after the fishery and after the city [23]. here f1 and f2 describe the quality of water after the fishery and after the city, respectively; f3 and f4 show the percent return on investment at the fishery and the addition to the tax rate in the city, respectively; and f5 describes the functionality of the treatment plants. hence, the first three objective functions must be maximized, while the fourth and fifth objective functions are to be minimized. the dm’s strategy and solution process when solving the original four-objective river pollution problem [26] using nimbus method was described in [14]. below we describe the process of solving the problem (4) using synchronous nimbus implemented in ind-nimbus, where the dm gets additional support from our visualization technique. when addressing multiobjective optimization problems the dm can have different priorities across objectives in mind. further we assume that the dm has higher priority to improving the quality of the water after the fishery and the city. the solution process described below is divided into several stages according to the dm’s actions related to the usage of the visualization technique: stage 1. when starting solving the problem by using the ind-nimbus, at first a socalled neutral compromise solution is obtained using an initial preference model and presented to the dm. naturally the dm is not satisfied with the obtained solution, therefore he/she tries to improve it by making several different classifications of the objective functions. during the solution process the dm selects the most preferred solution from the obtained four solutions as the current solution and tries to improve it in the next iteration. this process is repetitive, and the solution pool is constantly updated with the new derived solutions. stage 2. after a predefined number of the solutions is collected in the solution pool, the visualization technique is started to be used by the dm (see figure 2a). here the points of the map correspond to the obtained solution outcomes. we omit drawing axes of the two-dimensional coordinate system due to the fact that the coordinates do not have individual meaning, as the only important aspect of mapping is preserving distance between points. the points of the scatter plot are coloured using rdylgn colour scheme, where colour of each mapped point represents its distance to the reference point that express the current dm’s preferences (red colour corresponds to large distance, green – to small distance). the numbers near the points mean the order in which the corresponding solutions were obtained. the presented map of obtained outcomes is constructed by using the mds method, when the 8th solution has been selected as the current one. now the dm can explore the map, see the relations between the outcomes and think about the further directions of the solution process. it can be noticed from the map that the 10th solution is located furthest from the area of the objective space where the dm started the solution process. as the dm wants to explore the objective space in direction further away from first solution, he decides to select the 10th solution as the current one. 516 e. filatovas, d. podkopaev, o. kurasova (a) (b) (c) figure 2: two-dimensional map of the pareto optimal solution outcomes, constructed by the visualization technique. stage 3. the dm tries to improve the 10th solution by using a different classification of objective functions. then the solution pool is updated by new outcomes and they are added to the map by using the relative mapping (see figure 2b). the points mapped using mds have circular shape while the new points are presented as squares. here the graph of the solution process is also displayed (the arrows are drawn between the points corresponding to obtained outcomes which once served as current solutions). the location of new points confirms that the selection of 10th solution as the current solution and provision of the preference information allowed the dm to advance in exploring the objective space, e.g. obtain pareto optimal solutions which demonstrate bigger differences from earlier obtained solutions. afterwards, the dm wants to remap all the obtained outcomes by mds in order to see relations between them more precisely, and the updated map is presented (see figure 2c). after remapping, the places of the points on the map have been changed slightly comparing with the previous ones, which does not affect the cognitive map developed in the dm’s mind. however, after remapping the furthest location of 15th solution outcome from earlier solutions is more expressed. here the arrows are drawn between all the solutions from the solution pool in sequence order in which the solutions were obtained (see figure 2c). next, the dm can select other solution from the solution pool as the current solution for further improvement, change the solution strategy by giving higher priority to other objectives, and search for more preferable solutions in unexplored parts of the decision space with the support of the mds-based visualization technique, or using only the original ind-nimbus user interface. hence, the obtained map helps the dm to keep in mind a holistic image of the current state of the solution pool, as well as the history of the solution process. it must be pointed out that the purpose of this example is not to describe the whole solution process of the problem, but to demonstrate the performance and flexibility of the proposed visualization technique. hence, the three presented stages cover the main aspects of its usage during the solution process. 5 conclusions the core idea of interactive methods is using a pareto optimum derived during each iteration (so-called current solution) as the starting point for the next iteration. the possibility of selecting pareto optima derived in any of previous iterations as the current solution contributes to the flexibility and freedom of pareto optimal set exploration. this aspect of implementing interactive methods has not received any attention in the literature. a visualization technique for accessing solution pool in interactive methods of multiobjective optimization 517 in this paper we have proposed to enhance interactive methods with a technique visualizing the outcomes from the solution pool and assisting the dm in its exploration and solution selection. the resulting visualization is presented in the form of an interactive 2d scatter plot obtained using mds method, which helps the dm to detect similarity between outcomes and focus on more preferred parts of the pareto front. this visualization facilitates the dm in developing a cognitive map of the 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(2012). visualization of pareto solutions by spherical self-organizing map and it’s acceleration on a gpu. journal of software engineering & applications, issn 1945-3116, 5(3): 129–137. international journal of computers communications & control issn 1841-9836, 9(5):602-609, october, 2014. optimal routing strategy based on specifying shortest path f. shao, b. cheng fei shao*, binghua cheng school of computer engineering, jinling institute of technology no. 99, hongjing rd., nanjing, 211169, jiangsu, p.r.china *corresponding author: shaofei@jit.edu.cn cbh@jit.edu.cn abstract: how to enhance the transfer capacity of weighted networks is of great importance. the network transfer capacity, which is often evaluated by the critical packet generation rate, is proved to be inversely proportional to the highest node betweenness. by specifying the shortest path according to the different node characteristics, two different routing strategies are proposed to reduce the high node betweenness for the different node delivery capability schemes. simulations on both computer-generated networks and real world networks show that our routing strategies can improve the network transfer capacity greatly. especially, the greater the new added edge number is, the more efficient our routing strategies are. keywords: complex networks, weighted networks, routing strategy, betweenness. 1 introduction due to the constantly growing significance of large scale communication networks such as the world wide web, the network transfer capacity has attracted an increasing attention. in those previous studies to improve transfer capacity and control traffic congestion on networks, some focus on making appropriate adjustments to the network topology structure [1] [3] while others on finding optimal routing strategies [4] [9]. since the former is too expensive and too difficult to implement in some large-scale networks, most of previous works focused on effective routing strategies. some of them are based on the global information: the shortest path routing strategy [4] which pass through the minimum number of nodes, the efficient path routing strategy [5] whose sum of node degrees is the minimum. some others focus on local topological information since global information is usually unavailable in large-scale networks: the neighbor information [6], the next-nearest-neighbor information [7]. however, those aforementioned studies are mostly focused on the simplest unweighted networks with edges between nodes are represented by binary states according to whether the edges are present or not. in fact, the scientific collaboration networks [10], the cellular metabolism [11], the world-wide airport networks [12] and the internet [13] have been proved to be weighted networks which are specified not only by its topology but also by the weight of the edges. lots of models have been presented to describe weighted network among which the bbv weighted network model, coupled dynamical evolution of topology and weights, is most widely used. in those most widely used traffic models in weighted network, the packets are transferred through the traditional shortest path [4], which is a path with the minimal number of nodes between arbitrary pairs of nodes, or the weighted shortest path [10, 14], where the distance between nodes is just the inverse of the weight of edge linked them. however, it is proved that these two routing strategies are not optimal for weighted networks [15]. since there may be more than one traditional shortest path between some pairs of nodes, we can specify the best one to enhance the network transfer capacity. this paper is organized as follows. in section 2 we describe the model and our routing strategy, followed by the experimental evaluations on the computer generated networks and real world network in section 3. the conclusions are given in section 4. copyright © 2006-2014 by ccc publications optimal routing strategy based on specifying shortest path 603 2 models 2.1 network model the bbv network can be completely described by a weighted adjacency matrix w whose elements wij denote the weight of the edge linking node i and j. the generation of the bbv network is based on two coupled mechanisms: i topological growth. starting from the initial network with n0 nodes which are fully connected by edges with assigned weight w0, one new node is added at every time step. the new added node will be connected to m different nodes with equal weight w0 for every edge and will choose nodes with large strength according to the probability ∏ n→i = si/ ∑ l sl, where si = ∑ j wij is the node strength. ii weight dynamics. the weight of each new added edge is initially set to a given value w0 which is often set to 1 for simplicity. but the adding of edge connecting to node i will result in increasing the weight of the other edges linked to node i which is proportional to the edge weights. if the total increase is δ (we will focus on the simplest form: δi = δ), we can get wij = wij + ∆wij = wij + δ ∗ wij si (1) this will yield the strength increase of node i as: si = si + δ + w0 (2) the degree distribution of bbv network p (k) ∝ k−γk and the strength distribution p (s) ∝ s−γs yield scale-free properties with the same exponent [12], [16][18]: γk = γs = 4γ + 3 2γ + 1 = 2 + 1 2γ + 1 (3) 2.2 traffic model the traffic model can be described as follows: i all nodes can create packets with addresses of destination, receive packets from other nodes, and forward the packets to their destinations. ii at each time step, there are r packets generated in the network, with randomly chosen sources and destinations. once a packet is created, it is placed at the end of the queue if the node already has several packets waiting to be forwarded to their destinations. iii at each time step, the first ci packets at the top of the queue of node i, if it has more than ci packets in its queue, are forwarded one step toward their destinations and placed at the end of the queues of the selected nodes. otherwise, all packets in the queue are forwarded one step. this procedure applies to all nodes at every time step. iv a packet, upon reaching its destination, is removed from the system. 604 f. shao, b. cheng in our model, three node delivery capability schemes are considered: (i) each node has the same packet delivery capability (ci = 1, con stands for this scheme); (ii) the node delivery capacity is considered to be proportional to the node strength si (ci = si/ < s >, str stands for this scheme); (iii) the node delivery capacity is considered to be proportional to the node degree ki (ci = ki/ < k >, deg stands for this scheme). to compare the overall transfer capacity, we normalize the delivery capability to keep the total node delivery capability of the whole network is equal to the node number n in three situations the same. when r is increased from 0 to ∞, two phases will be observed: free flow and congested phase. for r < rc, the numbers of created and forwarded packets are balanced, resulting in a steady free flow of traffic. for r > rc, traffic congestion occurs due to the fact that packet delivery capacity of node is limited. the phase transition from the former to the latter occurred at the critical packet generation rate rc. we focus on the critical value rc which can best reflect the transfer capacity of a network. we utilize the betweenness bi [19] to estimate the traffic passing through a node i under a given routing strategy: bi = ∑ s,t σ (s,i,t) σ (s,t) (4) where σ (s,i,t) is the number of paths under the given routing strategy between nodes s and t that pass through node i and σ (s,t) is the total number of paths under the given routing strategy between s and t and the sum is over all pairs s,t of all distinct nodes. the probability a packet will pass through the node i is bi/ ∑n j=1 bj , and therefore the average number of packets that the node i will receive at each time step is. when the number of incoming packets is equal to or larger than the outgoing packets at the node i,rbi/(n(n−1)) ≥ ci, traffic congestion will occur. so the critical packet generation rate rc is rc = min ( ci ∗n∗ (n−1) bi ) = n∗ (n−1)∗min(ci/bi) (5) 2.3 routing strategy enlightened by the efficient path [5], we also definepi→j as the path between node i and j which pass through the nodes sequence x0 (= i) ,x1,x2, · · · ,xn−1,xn(= j) . however we define f (pi→j,α) = n−1∑ i=0 wαij (6) in our routing strategies, we specify the path between i and j as the one makes f (pi→j,α) minimum under a given tunable parameter α. when α is -1, the specified routing strategy is the same as the weighted shortest path routing strategy [10, 14] (wsh stands for this routing strategy). when α is 0, the specified routing strategy is the same as the traditional dijkstra shortest path routing strategy [4] which pass through the minimum amount of nodes (sht stands for this routing strategy). as we mentioned above, there may be more than one shortest path between some nodes. we calculate the sum of strengths si of all nodes on each shortest path, and select the one with minimum sum as our specified path. sss stands for this routing strategy and ssd for the minimum sum of degrees ki. the definition of our routing strategies (the sss routing strategy and the ssd routing strategy) is shown in tab.1. optimal routing strategy based on specifying shortest path 605 table 1: definition of our routing strategies (sss and ssd) wsh sht sss ssd f (pi→j,α) min ( n−1∑ i=0 w−1ij ) min ( n−1∑ i=0 w0ij ) sht&min ( n−1∑ i=0 si ) sht&min ( n−1∑ i=0 ki ) wsh sht sss ssd 0 10 20 30 40 50 60 70 80 90 r c δ=4,m=4 δ=8,m=4 δ=4,m=8 δ=8,m=8 figure 1: rc of different routing strategies. bbv network with n = 200 and w0 = 1,ci = 1 3 results and discussion to obtain the critical packet generation rate rc in simulations, we use the order parameter[1]: η = lim t→∞ ⟨∆θ⟩ r∆t (7) where ∆θ = θ(t + ∆t)−θ , with ⟨· · ·⟩ indicating average over time windows of width ∆t , and θ(t) is the total number of packets in the network at time t. at the early stage, when r is very small, the generated packets can be delivered, ⟨∆θ⟩ is less than zero and so is η. where η is greater than zero, we can obtain the critical packet generation rate rc. in figure 1, we plot the critical packet generation rate rc of different routing strategies in a bbv network with n = 200 and ω0 = 1. (for every network, 10 instances are generated and for each instance, we run 10 simulations. the results are the average over all the simulations.) fig.1 shows that when each node has the same packet delivery capability, the wsh routing strategy is the most sensible to traffic congestion. the sht routing strategy is better than wsh, and the ssd routing strategy has the maximum transfer capacity. moreover, the greater the new added edge number m is, the more efficient our routing strategies are. take networks with δ = 4,m = 4 and δ = 4,m = 8 for example, the sht, sss and ssd routing strategies can enhance the critical packet generation rate rc 95.72%, 246.53 %, 326.75% than the wsh routing strategy correspondingly in network with δ = 4,m = 4 while 112.95%, 306.81%, 398.81% 606 f. shao, b. cheng wsh sht sss ssd 500 1000 1500 2000 2500 3000 3500 4000 4500 r c δ=4,m=4 δ=8,m=4 δ=4,m=8 δ=8,m=8 wsh sht sss ssd 100 200 300 400 500 600 700 800 900 1000 1100 r c δ=4,m=4 δ=8,m=4 δ=4,m=8 δ=8,m=8 figure 2: rc of different routing strategies. bbv network with n = 200 and w0 = 1 (a) ci = si/ < s > (b) ci = ki/ < k > wsh sht sss ssd 0 10 20 30 40 50 60 r c δ=4,m=4 δ=8,m=4 δ=4,m=8 δ=8,m=8 wsh sht sss ssd 100 200 300 400 500 600 700 r c δ=4,m=4 δ=8,m=4 δ=4,m=8 δ=8,m=8 figure 3: rc of different routing strategies. bbv network with n = 100 and w0 = 1 (a) ci = 1 (b) ci = ki/ < k > in network with δ = 4,m = 8. when the new added edge number m is increased, there are more edges in the network and there might be more traditional dijkstra shortest paths between nodes consequently. that is why our routing strategies are more efficient with larger parameter m. then we turn to the other two schemes: the node delivery capacity is proportional to the node strength si and the node degree ki. simulation results are shown in fig.2(a) and fig.2(b) correspondingly. fig.2(a) presents that when the node delivery capacity is considered to be proportional to the node strength, the sht routing strategy is the most effective while the ssd routing strategy has the largest rc when the node delivery capacity is considered to be proportional to the node degree as shown in fig.2(b). fig.2(a) shows that our routing strategies do not work well in str scheme. in deg scheme, the sht routing strategy is also better than wsh, and the ssd routing strategy still has the maximum transfer capacity. however, the gap among the sht and sss and ssd routing strategies is narrowed. and our routing strategies are more efficient with the greater new added edge number. then we check the impact of the node number n on our routing strategies. we test our routing strategies on bbv weighted networks with n = 100 nodes to achieve the simulation results of con scheme and deg scheme as shown in fig. 3. fig.3(a) and fig.3(b) display the influence of node number our routing strategies. we can optimal routing strategy based on specifying shortest path 607 0 50 100 150 200 250 0 5 10 15 20 25 30 35 s b (s )/ s wsh sht sss ssd 0 5 10 15 20 25 0 20 40 60 80 100 120 k b (k )/ k wsh sht sss ssd figure 4: betweenness per node. bbv network with n = 200, δ = 4,m = 4 and w0 = 1 (a) ci = si/ < s > (b) ci = ki/ < k > discover that the ssd route is still the best way to enhance the critical packet generation rate. and by comparing fig.3(a) with fig.1 and fig.3(b) with fig.2(b), we can discover that the node number n has a little effect on the transfer capacity. to achieve heuristic explanation for the routing strategies corresponding to the highest transfer capacity, we investigate the betweenness distribution on the network as presented in fig.4. the betweenness normalized by the strength of str scheme is shown in fig.4(a) and normalized by the degree of deg scheme shown in fig.4(b). in both figures, the load of the most effective routing strategy is distributed more evenly than the other three. in fig.4(a), the betweenness divide by the strength of the sht routing strategy is relatively flat which means the node with higher strength forward more packets. and in fig.4(b), the node with higher degree forward more packets while using the ssd routing strategy. it is obvious that the traffic load under the sht routing strategy for str scheme and under the ssd routing strategy for deg scheme are distributed evenly to the nodes according to their strength and degree correspondingly. those routing strategies which elongate the average path length lav e unnecessarily may not be efficient for network communications. thus it is of great importance for a routing strategy to maintain the small-world phenomenon, i.e. lav e ∝ lnn. in our routing strategies, all the paths are the shortest path between arbitrary nodes which means the small-world phenomenon is still maintained in our routing strategies. finally, we test our routing strategies for three schemes on real world network. we choose the usair 97 network (network of direct flight connections between us airports for the year 1997, http://vlado.fmf.uni-lj.si/pub/networks/data/) with 332 nodes and 2126 edges. simulation results are shown in tab.2. table 2: rc of different routing strategies of usa airport network wsh sht sss ssd con 3.49 4.86 5.85 5.87 str 2.24 2.26 2.24 2.24 deg 83.69 161.96 169.03 169.90 from table 2 we can discover that in con scheme, the ssd routing strategy has the maximum transfer capacity which is only a bit higher than the sss routing strategy. the wsh 608 f. shao, b. cheng routing strategy has the lowest transfer capacity. it means when each node has the same packet delivery capability, our sss routing strategy and our ssd routing strategy can enhance the transfer capacity in real world network. and in str scheme, the sht routing strategy has the maximum transfer capacity which is also the same as the computer generated weighted network. when it turns to the deg scheme, the sss routing strategy and the ssd routing strategy also achieve better results than the traditional routing strategy. in a word, the sss routing strategy and the ssd routing strategy also works well in the real world network in the con scheme and in the deg scheme. 4 conclusions considering the different node delivery capability, this paper has proposed two novel routing strategies to enhance the network transfer capacity in weighted networks. the characteristic of our strategy is to specify the shortest path according to three kinds of different node delivery capability schemes. the simulation shows that when each node has the same packet delivery capability, we can select the path with the minimal number of nodes and with minimum sum of node degree. and this routing path is also optimal in the scheme which the node delivery capacity is considered to be proportional to the node degree. when the node delivery capacity is considered to be proportional to the node strength, our routing strategies do not work. it is worth mentioning that our routing strategies are more efficient with the more new added edge. at last, we apply our routing strategies on the usair 97 network to show the validity of our routing strategies on real world network. moreover, the above-mentioned research may throw lights on designing better communication protocols. acknowledgment this work was partially supported by the national natural science foundation of china (grants no. 61373136 and 61375121), the natural science foundation of jiangsu province, china (grant no. bk2012082), the research foundation of jinling institute of technology (grant no. jit-b-201406) and sponsored by qing lan project. the author also gratefully acknowledges the helpful comments and suggestions of the reviewers, which have improved the presentation. bibliography [1] arenas a. et al (2001); communication in networks with hierarchical branching, physical review letters, issn 0031-9007. 86(14): 3196-3199. 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(1977); a set of measures of centrality based on betweenness. sociometry, issn 0038-0431, 40(1): 35-41. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 1, pp. 52-70 coordinating aerial robots and unattended ground sensors for intelligent surveillance systems e. pignaton de freitas, t. heimfarth, r. schmidt allgayer, f. rech wagner, t. larsson, c. e. pereira, a. morado ferreira edison pignaton de freitas and tony larsson halmstad university school of information science, computer and electrical engineering halmstad, sweden e-mail: {edison.pignaton, tony.larsson}@hh.se tales heimfarth and flávio rech wagner federal university of rio grande do sul institute of informatics porto alegre, brazil e-mail: {theimfarth, flavio}@inf.ufrgs.br rodrigo schmidt allgayer and carlos eduardo pereira federal university of rio grande do sul electrical engineering department porto alegre, brazil e-mail: {allgayer, cpereira}@ece.ufrgs.br armando morado ferreira military institute of engineering defense engineering graduate program rio de janeiro, brazil e-mail: armando@ime.eb.br abstract: sensor networks are being used to implement different types of sophisticated emerging applications, such as those aimed at supporting ambient intelligence and surveillance systems. this usage is enhanced by employing sensors with different characteristics in terms of sensing, computing and mobility capabilities, working cooperatively in the network. however, the design and deployment of these heterogeneous systems present several issues that have to be handled in order to meet the user expectations. the main problems are related to the nodes‘ interoperability and the overall resource allocation, both inter and intra nodes. the first problem requires a common platform that abstracts the nodes’ heterogeneity and provides a smooth communication, while the second is handled by cooperation mechanisms supported by the platform. moreover, as the nodes are supposed to be heterogeneous, a customizable platform is required to support both resource rich and poorer nodes. this paper analyses surveillance systems based on a heterogeneous sensor network, which is composed by lowend ground sensor nodes and autonomous aerial robots, i.e. unmanned aerial vehicles (uavs), carrying different kinds of sensors. the approach proposed in this work tackles the two above mentioned problems by using a customizable hardware platform and a middleware to support interoperability. experimental results are also provided. keywords: sensor networks, unmanned vehicles systems, wireless communication, heterogeneous platforms copyright c© 2006-2010 by ccc publications coordinating aerial robots and unattended ground sensors for intelligent surveillance systems 53 1 introduction new applications based on the interaction between autonomous robots and static sensor nodes are emerging. part of the growing interest in using this interaction comes from the potential benefits it can provide, such as distributed processing and data gathering enrichment. moreover, the deployment of such systems is becoming a tangible reality due to advances in small and efficient processors, sensor devices, and wireless networking. the integration of static and mobile sensor nodes enhances capabilities of the overall sensor network. it enables new applications in which fixed sensor nodes provide an active response capability, while unmanned mobile nodes acquire a spatial vision of the region, allowing monitoring of larger areas. military and civilian applications, such as borderline patrol, search and rescue, area surveillance, communications relaying, and mapping of hostile territory, can take advantage of this kind of enlarged sensor networks. since these tasks may be repetitive, tedious, and dangerous, they are ideal for autonomous unmanned vehicles [1]. indeed, applications of sensor network can greatly profit of using different kinds of mobile and sophisticated sensors in addition to static ones, which typically are simpler and more resource constrained. the cooperation between these different types of sensors provides advanced functionalities that were not feasible before [2] and thus also opens a vast range of new application scenarios. wireless sensor networks are usually developed to perform specific applications. sensor nodes have usually a small footprint including only the resources that are necessary to meet specific application requirements. however, reconfigurable and customizable architectures are important to make both sensor nodes and sensor networks more flexible. depending on the application, sensor nodes vary from resource-constrained to high performance computing nodes, resulting in a heterogeneous network composed by a variety of sensor node types. the main issues in developing heterogeneous sensor networks are: (i) support for cooperation among heterogeneous nodes; and (ii) customization of sensor nodes. the former is related to concerns such as message exchange synchronization, qos requirements management, task (re-) allocation, and network adaptation. the later is related to the diversity of node platforms, which may be built upon very distinct hardware components controlled by very different pieces of software. considering (i), the use of a middleware is a suitable approach to address the mentioned concerns, since it can integrate the technologies used in different nodes by means of a common communication interface and cooperation support. regarding (ii), customizable architectures can be very useful to build platforms for different sensor network nodes, from the very simple to the more sophisticated ones. this kind of architecture can provide a common base capability for all nodes. however, for nodes that need more advanced capabilities, the required resources can be incorporated. hence, even though all nodes should have the same base capability, some of them could be equipped with additional resources, thus making the sensor network more powerful due to this allowed heterogeneity. this paper presents a flexible and adaptable platform infrastructure intended to support heterogeneous sensor network applications composed by static sensor nodes on the ground and mobile sensors carried by autonomous aerial robotic platforms, such as autonomous uavs. it is based on the proposal of (i) a flexible middleware [3] and, (ii) on a customizable hardware architecture aimed for sensor nodes, called femtonode [4]. the key idea is to use this customizable platform to deploy different kinds of sensor nodes, from very tiny and resource constrained up to more sophisticated ones. both types of nodes run a common middleware in order to provide the desired interoperability that will allow the cooperation among different sensor nodes. nodes may be built upon the femtonode architecture and alternatively upon nodes with other hardware platforms, such as sunspot [5] or similar ones. moreover, the middleware provides services to allow autonomous decision making by the nodes, in order to perform reflection about the runtime conditions and adapt the system according to the current needs. these features allow the mobile nodes to take decisions about their movements and all nodes, i.e. also the fixed nodes, to take decisions about network parameters, such as qos and resource usage. in addition, this paper presents a 54 e. pignaton de freitas, t. heimfarth, r. schmidt allgayer, f. rech wagner, t. larsson, c. e. pereira, a. morado ferreira bio-inspired coordination mechanism that uses artificial pheromones used to provide information about the position of the mobile sensor nodes, facilitating the cooperation among these nodes and the others that make part of the network. the remaining of this paper is organized as follows: section 2 presents related work in the area. in section 3, the application scenario is highlighted, characterizing the network heterogeneity. section 4 presents an overview of the proposed middleware, while section 5 presents details about the key issues addressed by the middleware. the pheromone-based coordination among static and mobile sensors is the focus of section 6. in section 7, the femtonode customizable hardware architecture is described. section 8 presents a case study while section 9 provides results for some of the presented features of both middleware and customizable hardware. finally, section 10 draws concluding remarks and gives directions for future work. 2 related works aware [6] is a middleware whose goal is to provide integration of the information gathered by different types of sensors, including low-end sensor nodes in a wireless sensor network and mobile robots equipped with more sophisticated sensors. our proposal not only addresses heterogeneous sensors and their coordination, but also concerns like qos and runtime reflection in order to cope with changes in the environment and in the network, which is missing in [6]. moreover, in our approach, the autonomous decisions taken by the mobile nodes characterize a higher intelligence degree of our mobile robots if compared with the proposal presented in [6]. batalin et al. [7] propose an approach that uses a sensor network to guide a mobile robot with limited sensor capabilities. this work presents aspects of cooperation among different sensors, but it does not address the same problem as proposed in our work. in that approach, the mobile node has limited sensor capabilities and only gathers data from the sensor network to guide the robot’s movement. in our work, mobile nodes are equipped with sophisticated sensors that can provide data, which may be merged with data that come from the other nodes in order to achieve more refined decisions to guide the movement of the mobile nodes. schmidt et al. [8] present an approach that uses artificial intelligence techniques to configure an underlying middleware. their approach uses the concepts of missions and goals to plan the allocation of tasks in a network of homogeneous nodes. the handling of heterogeneous nodes is one of the differences between our work and their approach. additionally, in that work, the intelligence is outside the middleware and influences it by just sending commands or adjusting its parameters. in our approach, the decision making support is an integral part of the middleware, spreading intelligence over the network. jin et al. [9] provide a very consistent proposal to handle the problem of balance between target search and response by a team of uavs. the work evaluates the tradeoff between search and response within the framework, presenting a predictive algorithm that provides a good balance between these tasks. the first difference between our approach and this work is that we handle only the alarm response, abstracting the concern about the uavs movement planning to perform the search for new targets. this difference is due to the peculiarity of the distinct missions addressed in the current paper and in [9]. we focus on area surveillance, while they focus on target acquisition. in our case, the whole area must be covered, which may not be true in the target acquisition they address. another difference is that we use the uavs in coordination with ground sensor nodes. besides, the assumption of a centralized information base considered in their work is not used in our proposal. their initial centralized off-line task assignment is another premise that is not valid in our work. in [10], an approach using digital pheromones to control a swarm of uavs is presented. the method proposed by the authors uses digital pheromones to bias the movements of individual units within a swarm toward particular areas of interest that are attractive, from the point of view of the mission that the swarm is performing, and away from areas that are dangerous or just unattractive. in the large sense, coordinating aerial robots and unattended ground sensors for intelligent surveillance systems 55 the pheromone-based strategy used in our work has a similar goal, driving the uavs to areas of interest. however, differently from their approach, we use the pheromone traces to localize the uavs when an alarm is issued by a ground sensor node informing an event of interest and then drive the uavs to the location where the event happened. 3 application scenario overview and network heterogeneity in the following, heterogeneity means that nodes in the network may have different sensing capabilities, computation power, and communication abilities. additionally, it means that they run on different hardware and operating systems. therefore, such sensor networks are made up of lowand high-end nodes. moreover, sensor nodes may have fixed positions or be able to move, being carried by mobile aerial robots (uav platforms), which can also vary from very small, as in [11], up to huge aircraft platforms, like globalhawk [12]. low-end sensor nodes are those with constrained capabilities, for instance piezoelectric resistive tilt sensors, with limited processing support and communication resource capabilities. high-end sensor nodes include powerful devices like radar, high definition visible light cameras, or infrared sensors, which are supported by moderate to rich computing and communication resources. mobility, as mentioned, is another important characteristic related to the heterogeneity addressed in this work and requires special attention. sensor nodes can be statically placed on the ground or can move on the ground or fly at some altitude over the target area in which the observed phenomenon is occurring. figure 1 graphically represents the idea of the three heterogeneity dimensions considered in this work, in which each axis represents one of the considered characteristics. figure 1: heterogeneity dimensions. the reason for heterogeneity in the sensor nodes is to support a large range of applications that deal with very dynamic and changing scenarios, which require different types of sensor capabilities. moreover, these different scenarios require also adaptations in the network, in terms of choosing suitable sensors for the tasks at hand as well as feasible qos parameters, among others. in order to illustrate the above idea, suppose that a network has the mission of providing a certain kind of information during a given period of time. the set of sensors selected in the beginning of a mission may not be the most suitable one during the execution of the whole mission. the network must be thus able to choose a better alternative, among the set of all available options, in order to accomplish the mission. for example, an area surveillance system may receive the mission to observe if certain types of vehicles that are not allowed to pass through the surveyed area make any such violation and report if that is the case. to perform this in an efficient way ground sensors are set to alarm in the presence of unauthorized vehicles. suddenly, an alarm is triggered by one of these sensors. then, in order to verify 56 e. pignaton de freitas, t. heimfarth, r. schmidt allgayer, f. rech wagner, t. larsson, c. e. pereira, a. morado ferreira the occurrence, uavs equipped with visible-light cameras are commanded to fly over the area where the ground sensor has issued the alarm. due to a sudden change in the weather (e.g. the area becomes foggy or cloudy), visible-light cameras become useless. however, the mission must still be accomplished. therefore, nodes must be coordinated to accomplish the mission by sending uavs equipped with other kinds of sensors that can provide the required information in bad weather conditions, such as infrared cameras. 4 middleware support overview one of the key ideas of the proposed work is to drive the use of sophisticated sensors carried by uavs using data from low-end nodes. uavs’ equipped with intelligent behavior uses such data to decide the trajectory direction during the system runtime. consequently, the proposed middleware must be able to provide cooperation among the different sensors and still fit in both the low-end and rich sensor nodes. the middleware must thus be lightweight and provide enough customization in order to address the needs of both types of sensor nodes. these goals are achieved by using aspectand component-oriented techniques, as in [13] and [14], and also a mobile multi-agent approach, as discussed in [3]. figure 2 depicts an overview of the middleware layers, whose description is provided in the following. figure 2: overview of the middleware layers. the bottom layer is called infrastructure layer. it is responsible for the interaction with the underlying operating system and also for the management of node resources, such as available communication and sensing capabilities, remaining energy, etc. this layer is also responsible for coordination of necessary resource sharing. the intermediate layer is called common services layer. it provides services that are common to different types of applications, such as qos negotiation, quality of data assurance, and data compression. other concerns also handled within this layer are: deadline expiration alarms; timeouts for data transmissions; number of retries and delivery failure announcements; resource reservation negotiation among applications (based on priorities established by missions and operation conditions); bindings; and synchronous/asynchronous concurrent requests. the top layer is called domain-services layer, whose goal is to support domain specific needs, including data fusion support and specific data semantic support, in order to allow the production of application-related information from raw data processing. fuzzy classifiers, special types of mathematical filters (e.g. kalman filter), and functions that can be reused among different applications in the same domain are found in this layer. ”smile faces” in figure 2 represent autonomous agents that can provide specific services in a certain node at a given moment during system runtime. the domain-services layer hosts a special agent (called coordinating aerial robots and unattended ground sensors for intelligent surveillance systems 57 planning-agent), which performs a special task related to the reasoning about the missions that a node is responsible for performing. concerns that affect elements in more than one middleware layer, such as security and real-time requirements control, are represented as cross-layer features. these crosscutting concerns are addressed by the aspect-oriented approach presented in [13]. a. planning-agent model overview as an important element in the proposed approach, the model of the planning-agent is briefly presented. the model used for the planning-agent is a cognitive one, based on the model of mental attitudes, known as bdi model (belief-desire-intention), presented in [15]. the motivation is that the bdi model appears to suite well to the surveillance problem addressed, as some decisions that need to be taken by the planning-agent require cognitive skills to evaluate if certain actions are adequate to achieve a desired result. these decisions are based on knowledge about conditions that may interfere on the performance of those actions. in the current problem formulation, it is desired to obtain information by means of sensing activities, which are the goals of a sensing mission. this knowledge is the ”belief” that the node has about the relevant conditions, and the intentions are translated into the actions required to retrieve the desired information. as an example, when a uav receives an alarm, it will consult its beliefs in order to decide if it is able to respond to that alarm. if so, it will take the responsibility for the respective alarm and include the incoming information from the alarm into its beliefs, as well as include the accomplishment of the alarm handling into its desires. based on that, the uav will then intend to fly to the place where the alarm was issued in order to execute the respective sensing over the target. b. middleware services usage example taking the application scenario given as example in section 3 and the middleware layers described above, this subsection provides some examples of the utilization of middleware services and of the interaction between nodes. assuming the described example, in the case that alarms are issued by several ground sensor nodes, the domain services layer provides data aggregation, for fusion of data from many sensors in an area, which can provide richer information, such as the direction of a crossing vehicle, or handle problems such as alarm duplicity. the delivery of such information has an expiration time, since after a given time threshold, it is probable that the vehicle may have changed its trajectory, making the previous collected data useless. therefore, the common services layer associates qos with the delivery of messages as well as a guarantee mechanism to assure that an alarm has being correctly delivered and in time to a uav. the infrastructure layer uses these qos parameters to manage resources utilization. in the uavs, the middleware makes the complementary task, providing data fusion of images with matching alarm messages received from low-end nodes, i.e. fusion of position information included in the alarm messages with images that are being taken by the uavs, in the domain services layer. this is the case, for example, when more than one alarm has been issued in a given location. the data fusion helps in distinguishing the source of the alarms. qos verification of incoming messages is performed by the common services layer, which checks if either stored data can be used by the application or a request for fresh data must be sent. this is the case, for example, if the ground sensor network experiences problems and takes a too long time to deliver an alarm message to a uav. it may happen that, when this alarm comes to the uav, the object that triggered the alarm is not anymore at the location where it was detected. at this point, a request for confirmation can be sent by the uav to another uav in the region around the location of that alarm, requiring certain levels of qos. depending on the results from data processing, as described above, the uavs autonomously decide their own placements over the surveillance area, by means of the reasoning mechanisms of their respective planning-agents. additionally, other factors influence this decision, e.g. specific needs of the current situation and also sensible data that must be sent to the base station or to another uav. in this 58 e. pignaton de freitas, t. heimfarth, r. schmidt allgayer, f. rech wagner, t. larsson, c. e. pereira, a. morado ferreira case, data segregation and network utilization control play a crucial role in the efficiency of the system. important data must be prioritized. thus, network resources are allocated to data transmission according to the established priorities. possibly, the specific needs faced by the system in a given situation may require adjustments in the resource usage policy, which influences the services provided by the common services layer. for instance, a change to a ”mandatory priority” may take place in order to assure that data about a detected object or phenomena arrive within a given deadline at the base station via relayed communication through other uavs. in order to consider all the mentioned factors, necessary support for reflection about the network conditions and mission needs must be available. this support is provided by the planning-agent installed in the domain services layer, which will, for instance, analyze the current conditions and requirements and, taking into account the information provided by other sensors, decide the best placement of the uav in order to meet the system needs, thus selecting the next steps to move. 5 addressing key issues of sensor networks with the proposed middleware the proposed middleware uses the publish-subscribe paradigm and is inspired by the data distribution service for real-time systems (dss) specification, from omg [16]. some nodes publish their capabilities and the offered data, while others subscribe to data in which they are interested. although largely being inspired by the omg dss standard, the proposed middleware does not follow the whole specification. as it is intended to fit in both low-end nodes (based on simple and constrained platforms) and more sophisticated ones (carried by the uavs), it must not only be lightweight but also provide capabilities for customizations to cope with the needs of the different sensor nodes. consequently, the middleware uses a minimalist approach, being kept as simple as possible in each node. required features are included by adding components or weaving aspects to handle real-time and other non-functional crosscutting requirements in the minimal middleware. using aspects to tune the middleware is out of the scope of this paper, and for more information readers are referred to [13] and [17]. the following subsections show how the proposed middleware addresses some of the main platform needs in heterogeneous sensor networks, enabling the intelligent behavior of the mobile nodes and providing the required data with real-time guarantees. a. flexibility the middleware provides full communication control, i.e. it does not use underlying mechanisms available in the nodes’ network layer. instead, it provides its own communication control. this means that all parameters related to communication are controlled by the middleware, which uses only basic connectionless communication services offered by the nodes’ network layer. the middleware handles parameters such as number of retries, message priority, memory utilization for buffering, and timing. this provides more flexibility, with direct impact in the reduction of message delivery latency. b. network reflection reflection over the state of the network is a feature that enables the sensor nodes to take decisions regarding their participation in accomplishing a specific mission or sub-mission. in the mobile sensor nodes, this feature is responsible for the decision regarding the movement of the robot platform that carries the sensor, in order to take it to a place or the area related to a mission. the reflection considering the network conditions is performed inside the middleware by the planning-agent, which schedules the activities that should take place in order to accomplish a given mission. c. dynamicity when a node gets into the network, its services are announced using the publish-subscribe paradigm. then, all interested nodes can subscribe for those services. this eliminates the need for a dedicated server coordinating aerial robots and unattended ground sensors for intelligent surveillance systems 59 node that centralizes all available services in the network. additionally, this approach reduces latency in acquiring data because there is no intermediary node between the data producer and consumer. d. minimum message exchange the publish-subscribe paradigm by itself already reduces the number of exchanged messages due to the elimination of intermediate nodes (such as brokers). however, bandwidth for control messages is still required. this bandwidth need can be further reduced with the use of smart techniques such as qos contracts and attaching freshness timestamps to data, thus avoiding unnecessary request for data resend. another important characteristic for minimizing bandwidth requirements is the selection of routing strategies that optimize the use of the communication channel, finding the best path to be followed to arrive at the destination node of a message. an example of such strategy is a routing mechanism based on pheromone traces left by the mobile nodes, which will be explained in the following section and highlighted when presenting the experimental results. e. multicast communication the middleware uses multicast communication to reach selected destination nodes. this type of communication positively influences the latency and throughput, as data is sent at the same time to several nodes without unnecessary broadcast and delays, which would occur in unicast communication. a negative-acknowledgement (nack) strategy (controlled by timeout expiration) is adopted in order to reduce acknowledgement messages in the network. however, very sensible data may require a positive acknowledgement to assure their delivery. hence, positive acknowledgement is also made available as a middleware service and can be used when required. f. network resources usage control in order to improve the overall system performance, the control of the use of communication media and of transmission buffers is crucial. the middleware performs this task by taking into account two factors: (i) the priority associated to each application; and (ii) the resource sharing policy adopted in the system. there are three available resource sharing policies: (a) fair sharing: priorities are not considered and thus all applications have the same right to use the resources in a round-robin scheme, which is organized in an incoming fifo queue; (b) soft priority sorted: the priorities are taken in account, but in a relaxed way. if a higher priority application needs a resource already used by a lower priority one, it must wait until the resource is released. due to its higher priority, it will get access to the resource before other applications, which may be waiting for the resource; (c) mandatory priority: higher priority applications can preempt lower priority ones in order to access the desired resources. priority inversion issues are handled by a priority inheritance mechanism. g. qos control qos control is performed through a contract between the provider and the requester of data. when a node publishes a data service, it informs about the qos (level) offered. nodes interested in the published data service should accept the offered qos and subscribe to the service. however, if a node is interested in the data but does not agree with the offered qos, it has two alternatives: (a) if the application that is requiring the data has a priority lower than any other one using the same service, the requester node looks for another data provider; (b) if its priority is higher than all other applications, the requester node negotiates with the data provider node, in order to obtain the desired qos. this renegotiation occurs in spite of undesired consequences that may affect other lower priority applications, which need to look for another data provider if the qos could not be accepted anymore. h. use of cached values the use of cache in both data providers and requesters may avoid unnecessary data communication. when the measurement device gathers a new value, the data provider publishes the new value, thus updating its subscribers. if the data size is large, requiring many packets to be transmitted, a differential value can be sent instead of the whole data value in order to reduce packets transmission. this option is arranged in advance, at the time when the nodes are negotiating the qos contract. i. data segregation 60 e. pignaton de freitas, t. heimfarth, r. schmidt allgayer, f. rech wagner, t. larsson, c. e. pereira, a. morado ferreira there are two kinds of data exchanged among nodes in the network: control data and application data. control data is small and can usually not experience latency or unexpected delays to achieve their destination. thus, control data are separated from application data by receiving higher priority to be forwarded. moreover, data from different types of applications are handled according to their type and semantics, e.g. the communication of a video stream is handled differently from the communication of character strings. data from a specific application can be handled with higher or lower priority, depending on its semantics. j. synchronous and asynchronous calls the middleware is intended to support both synchronous and asynchronous calls. synchronous calls are time bounded in order to avoid unpredictable waiting periods. the waiting time and number of retries are configurable and negotiated during the qos negotiation. asynchronous calls are also provided in order to support the handling of external unpredictable event. 6 pheromone-based coordination strategy the coordination strategy used in this work to make mobile sensor nodes cooperate with static sensor nodes is based on pheromone traces handed over by the mobile sensors to the static ones. artificial pheromones are usually applied to distributed coordination by means of stigmergy, the indirect communication using environment cues [17]. a pheromone trail is deposited in the environment when the entities are moving. the pheromone provides information to other entities when they pass over it. artificial pheromone also looses its strength along the time, modeling the evaporation of the real pheromones. in the uav research field, pheromones are used to guide the movement of uav swarms, for instance in surveillance and patrolling applications [18] [19]. differently from other existing approaches, in our work pheromones are used to guide the selection and assignment of a suitable uav to handle an alarm issued by a ground sensor node. when an alarm is issued by the detection of a target, the network is responsible for selecting an appropriate uav to respond to the alarm. this is performed by routing a given alarm to the uav that has the strongest pheromone trace over the area. having this information, the uavs will base their movement decisions in a way to respond to the received alarms. this strategy is called here heuristic-p. following the above outlined principles, the uavs that are not engaged in the handling of any target will leave pheromone traces over the area which they cross. this pheromone trace is represented by a piece of information that is taken by the ground sensor nodes that are deployed in the area through which the uavs have passed. when a target is detected by a ground sensor node, an alarm is issued, as already mentioned. the decision about which uav that will handle the potential target indicated by the issued alarm will be taken by the ground sensor nodes, by routing the alarm in the direction that points to the uav which has the strongest pheromone trace over that area of the network. this process just considers the pheromone trace handed over by the uavs to ground sensor nodes. this means that the only parameter taken into account is the time interval since a uav passed by that specific location. heuristic-p is inspired in [20], which presents a pheromone-based strategy to migrate services in a sensor network, in which the pheromone concentration determines the places where the services are required. in heuristic-p, instead of services, alarms are moved through the network following the pheromone concentration. figure 3 presents a scenario that illustrates the strategy. a ground sensor node in the left border of the area detects a target. then it issues an alarm, which is received by its neighbors. however, only those which have pheromone information about a uav stronger than that of the alarm issuer will forward the alarm. this way, the alarm will follow a path to the closest uav, which is represented in the figure by the shaded sensors, until the alarm delivery. figure 4 illustrates the choice of the strongest pheromone trace to be followed by an issued alarm. it is possible to observe that the alarm follows the strongest trace, which corresponds to uav-a, until its delivery to this uav. the arrows illustrated besides each sensor node represent how strong the pheromone coordinating aerial robots and unattended ground sensors for intelligent surveillance systems 61 figure 3: illustrative scenario for the pheromone strategy. of each uav is. as it is possible to see, the pheromone level of uav-a is increasing to the left, while the pheromone level of uav-b is increasing to the right. figure 4: choice of a uav based on the pheromone strategy. when an alarm reaches the uav indicated by the strongest pheromone trace, if this uav is not engaged in the handling of another alarm it sends a confirmation message to the node that had delivered the alarm. if the suggested uav is already engaged in another alarm, the current alarm follows the second strongest pheromone trace to find another uav to engage. when an idle uav detects a new target, it takes the responsibility for handling it. in case that the uav is already busy with another alarm response mission, it relays the incoming alarm that will be routed to another uav, according to the pheromone-based heuristic-p strategy explained above. in order to increase the robustness of the proposal, in case an alarm is issued by a node that has no pheromone trace, a direction is randomly chosen and the alarm is sent in that direction until it finds a pheromone trace. when the trace is found, it follows the trace as explained above. this situation is more likely to occur in the initialization of the system, especially in cases in which the number of uavs deployed in the system is very low with regard to the area under surveillance. when a uav receives an alarm and is not able to perform the task, it may send the alarm back to the network, which will try to find another uav following the traces, or hand it over directly to another uav. this situation may occur when the type of the sensor that the uav carries is not appropriate to handle 62 e. pignaton de freitas, t. heimfarth, r. schmidt allgayer, f. rech wagner, t. larsson, c. e. pereira, a. morado ferreira the event that was detected. 7 femtonode wireless sensor architecture the architecture of a sensor node aims at efficiently supporting specific application needs. it requires a dedicated processing module, including a wireless communication interface, which meets both energy and performance requirements, as well as respects footprint constraints. the fact that application requirements as well as environment and other operational conditions may change during system run time imposes a major challenge [21]. in this context, the use of reconfigurable hardware [22] appears as an interesting alternative. therefore, a customizable sensor node called femtonode is proposed. it contains a customizable asic and a wireless communication interface, which are configured according to application requirements. the nodes use the rt-femtojava processor [23], a stack-based microcontroller that natively executes java byte-codes. it implements an execution engine for java in hardware, through a stack machine that is compatible with the specification of java virtual machine. the customized application code is generated by the sashimi design environment [24]. the code also includes a vhdl description of the processor core and rom (programs) and ram (variables) memories. the sashimi environment has been extended to incorporate an api that supports concurrent tasks, implementing the rtsj standard [25]. as rt-femtojava is customizable, its code can be optimized according to the application requirements, reducing the occupied hardware area and also the energy consumption and dissipation. the customizable hardware architecture of the femtonode allows the use of the sensor node as either a lowor highend node. if the application requires higher performance resources to handle more complex data, such as image processing, additional resources can be included in the femtonode implementation. however, if the application is aimed at processing simple data, such as those from presence sensors, a reduced set of resources is used in the processor. this feature is important for the sensor node, because energy consumption is a great concern in wireless sensor networks, due to the nodes’ limited energy resource. besides, reducing the unused resources during its synthesis the sensor node architecture allows its implementation in reconfigurable circuits with fewer available logical units, which is a feature that provides a larger application portability between different reconfigurable architectures with fewer available resources. in the current implementation, the femtonode includes a wireless transceiver of texas instruments cc2420, which utilizes the ieee802.15.4 standard communication protocol targeted to wireless sensor network applications with a low data rate. a module adapter described in vhdl implements the interface with the wireless transceiver. the module uses data and address buses to communicate with the processor, performing the exchange of data and allowing the transceiver parameters configuration. as the data transfer rate from the wireless transceiver is low, compared to the processor frequency, the wireless communication module implements a buffer to store data, preventing delays while providing the necessary data to the processor. the module uses an interrupt system to inform the processor when a reception occurred. to facilitate the use of the wireless communication module by the application developers, a communication api has been developed. the wireless-api abstracts details of the communication media between the sensor nodes, offering a simplified form for the configuration of the data transfer module. the wireless-api is used by the middleware communication services in the common services layer and also by resource management services in the infrastructure layer, in order to implement end-to-end communication with the desired qos and reliability control. coordinating aerial robots and unattended ground sensors for intelligent surveillance systems 63 8 case study description in order to illustrate the use of the proposed platform infrastructure, including the customizable femtonode and the adaptable middleware, an area surveillance application is studied. in this application, low-end sensors nodes are scattered on the ground along a borderline. in case an unauthorized vehicle crosses the borderline limit, the sensors issue an alarm which will trigger the use of unmanned aerial vehicles (uavs), which are equipped with more sophisticated sensors, such as radars or visible light cameras, in order to perform the recognition of the vehicle. figure 5 presents this scenario. figure 5: area surveillance application scenario. sensor nodes with two different architectures compose the described surveillance system, one based on the femtonode and another one on the sunspot. each of them includes all necessary resources to meet the requirements of their utilization. thus, based on the application specifications, a customization of the femtonode architecture was implemented. the uavs’ architecture is a femtonode with a large set of resources, capable of processing a large amount of data. on the other hand, both femtonodes and sunspots were used to populate the sensor network on the ground. the femtonode architecture used in the ground sensor nodes is simpler and more constrained in terms of available resources, as the sunspot one, and hence only capable of processing simple data, like those produced by piezoelectric sensors or accelerometers, which inform only if an object over a certain weight threshold passed over its sensing area. according to the coordination strategy based on pheromones presented in section 6, the sensor nodes on the ground route the alarms according to the pheromone trace left by the uavs, choosing the strongest trace to follow. when the alarm achieves a node close to the uav, the alarm is delivered. this mechanism addresses several problems related to the communication between nodes, such as controlled delay, delivery assurance, and alarm duplicity handling. 9 results several simulations of the scenario described in section 8 have been performed. they do not cover all aspects mentioned in the above description, but focus on the behavior of the system using the coordination strategy described in section 6. these simulations were conducted using shox [26], a powerful wireless network simulator implemented in java. additional results related to the customizable femtonode working in a heterogeneous network together with sunspot nodes are also presented. these results were gathered from the deployment of a laboratory-size testbed demonstrator, which shows the applicability of the platform. 64 e. pignaton de freitas, t. heimfarth, r. schmidt allgayer, f. rech wagner, t. larsson, c. e. pereira, a. morado ferreira the simulations provided results in an ”ideal-like” operation condition environment for the proposed technique, while the demonstrator was built in order to assess if these results provided by the simulations are possible to be achieved in a real deployment. for more results related to the mechanisms that support the other features mentioned in section 5, interested readers are referred to [27], [28], and [29]. a. simulation results the metrics evaluated in the simulations were: 1) the mean response time to the alarms generated in the system, and 2) the number of alarms lost, due to communication failures. the simulation setup was the following: the surveillance area has dimensions 10 km x 10 km, in which 20,000 ground sensor nodes are randomly deployed with independent uniform probability (homogeneous poisson point process in two dimensions, which generates a geometrical random graph). this distribution gives more than 70% probability that the nodes in the network will form a connected graph [30], for a communication range of 500 meters. six uavs of three different types, equally distributed, patrol the area, having a communication range of 1.5 km and flying at speeds from 100 km/h up to 120 km/h. three different runs were simulated, with one, three, and five targets respectively. the targets can further be of five different types, randomly chosen, with speeds from 50 km/h up to 80 km/h. figure 6 presents the simulation results in terms of the mean time required to respond to the alarms. both raw data from each run (total of 20 runs for each number of targets) and the average value (lines with squared dots) are plotted in the figure. it is possible to observe that, in the worst case, the mean time to find a uav that is idle to engage in the handling of an alarm is around 4 seconds, in the scenario with the maximum number of targets. on the other hand, in the best case, when there is just one target, the time needed to find a uav is in average less than 1 second. an explanation for this behavior is that it is more probable to find an idle uav when the number of targets is smaller. this may happen because, when there are more targets, an alarm message may follow a pheromone trace of a uav that has just engaged in handling a target announced by another alarm, so the alarm must be retransmitted to the network and follow another trace. however, the solution does scale, as the increase in the mean time to find an idle uav is linear with the increase in the number of targets, as can be concluded by taking the average values for all runs for each number of targets. figure 6: alarm response time achieve by the simulation. the second metric evaluates the system efficiency in terms of detecting a target and correctly routing the alarm message to an idle uav. for all simulation runs, no alarm was lost, which means that the system had 100% efficiency for the simulated scenario and correctly found an idle uav at all occasions when an alarm was issued. coordinating aerial robots and unattended ground sensors for intelligent surveillance systems 65 b. demonstrator results the simulations performed in shox showed that the approach proposed in this paper works well in the described scenario. however, wireless communications are very sensible to interferences and unpredictable variations. this means that simulation data, such as communication reachability and delays, are not always confirmed in real deployments. this fact motivated the deployment of a demonstrator to assess the properties of a network used to evaluate part of the system presented in this paper. the deployed demonstrator is composed as a network consisting of sixteen static ground sensor nodes (nine sunspots and the others femtonodes) and one mobile node (femtonode). the ground sensor nodes are equally distributed in a grid in an area of 225 square meters. the mobile femtonode, moved manually, represents a uav that ”flies” over this area leaving pheromones over the ground sensor nodes via a periodic beacon message sent to the network. upon the occurrence of an alarm, the nodes route it in the direction of the nodes with stronger pheromone traces, until it arrives at a node which has communication with the uav. figure 7 presents the demonstrator setup. the radio in the nodes was adjusted to provide a communication range of 5 meters, such that the nodes are capable of communicating only with their immediate vertical and horizontal neighbors, which are 5 meters apart, but not with their diagonal neighbors or any other node in the grid. the mobile node, representing the uav, has the same communication range configuration as the static nodes. figure 7: demonstrator setup. the pheromone traces in the nodes are represented by the numbers in the center of the circles representing the ground sensor nodes in the figure. the smaller the number is, the stronger the pheromone. this translates the idea of the time past since a ground sensor node received the last pheromone beacon from a uav. when a ground sensor node receives this pheromone beacon, it sends this information to its neighbors with a pheromone one point weaker (a number one unit greater than the one representing the node’s pheromone information). this is an indirect beacon that helps the other nodes find the traces to route the alarms. the nodes that receive the indirect beacons do not forward it. the symbol ∞ means that the node has no pheromone trace, i.e. the last beacon (directly from a uav or indirectly from another ground node) was received a long time before, above a tunable threshold. the number representing the pheromone is periodically incremented, indicating that the pheromone trace becomes weaker when time elapses, until disappearing (become ∞). figure 8 presents an example of how an alarm issued by a sensor node (figure 8-a) is routed through the network, following the pheromone traces (figure 8 from a to d), until it is delivered to a uav (figure 8-e). twenty runs were performed. in each of them, an alarm was generated by one of the static nodes, randomly chosen, which had to be routed to the uav according to the pheromone mechanism described in section 6 and implemented as described above. some of the middleware features presented in section 5 were installed in the static nodes, such as message priorization and qos control, in terms of delay to forward an alarm message [31]. in order to stress the network and test these mechanisms, random messages were generated by the static nodes, which competed with the beacon and alarm messages for 66 e. pignaton de freitas, t. heimfarth, r. schmidt allgayer, f. rech wagner, t. larsson, c. e. pereira, a. morado ferreira figure 8: alarm routing and delivery. the utilization of the communication resources. the evaluated parameter with the described testbed was the time to respond to the alarms generated in the system. by obtaining this metric, the delay of one hop communication was calculated and compared with the one achieved in the simulation results described before. figure 9 presents the time taken by the system to deliver the alarm to the uav. the average number of hops to deliver the alarm was 5 hops for the 20 runs of the testbed. taking the average of the time to deliver an alarm, 538.85 ms, and the average number of hops, we get an average delay of 107.77 ms in each hop. considering the simulation results, taking the worst case scenario, the one with 5 targets, in average, the number of hops for an alarm to be delivered was 13.78. taking the average of worst case scenario, 1,821.65 ms to deliver an alarm, we get a 132.14 ms delay for an alarm to be forwarded among the static nodes in each hop. comparing the delays obtained from the simulation runs and from the demonstrator, it is possible to observe that they are very close to each other. the delays obtained with the demonstrator are even better than the ones achieved by simulation, which shows the applicability of the approach described in this paper. figure 9: alarm response time achieved by the demonstrator. coordinating aerial robots and unattended ground sensors for intelligent surveillance systems 67 10 conclusion and future work this paper presented a system solution to provide interoperability and coordination support for heterogeneous sensor networks composed by ground static sensor nodes and mobile sensors carried by autonomous aerial robots. this solution is based on customizable sensor nodes and an adaptive middleware to manage different resources available in each customized sensor node. the femtonode platform provides a support to the development of different types of nodes, customized according to specific requirements. the middleware gives support to the network heterogeneity, enables adaptations to fulfill requirements that may change during the system run-time, and also promotes the necessary coordination among nodes. the coordination and cooperation among distinct nodes in the network allow intelligent behavior of the unmanned vehicles, resulting in autonomous decisions regarding their movement and also how they can complement the work performed by other nodes. this intelligent behavior is based on data exchanged between nodes that then are aggregated and analyzed in order to support the autonomous decisions. simulation and testbed results were provided. these results assessed the suitability of the pheromone coordination mechanism, presenting delay time results for the delivering of alarm messages that drive the movement of the mobile sensor nodes in the system. additional simulations are planned in order to assess the effectiveness of other features of the middleware that were not yet explored, such as the use of cached values and multicast communication. in order to validate the overall proposal, a larger scale demonstrator is also planned to be deployed, with enlarged communication range and real uavs controlled by femtonodes. acknowledgments e. p. freitas thanks the swedish knowledge foundation and the brazilian army for the grant to follow the phd program in embedded real-time systems at halmstad university in cooperation with ufrgs in brazil. bibliography [1] d. a. schoenwald. auvs: in space, air, water, and on the ground. ieee control systems magazine, vol. 20, no. 6, pp. 15-18, 2000. 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[27] e. p. freitas, t. heimfarth, m. a. wehrmeister, f. r. wagner, a. m. ferreira, c. e. pereira, and t. larsson. using link metric to improve communication mechanisms and real-time properties in an adaptive middleware for heterogeneous sensor networks, advances in information security and assurance, lncs, 5576, springer berlin, heidelberg, 422-431, 2009. [28] e. p. freitas, t. heimfarth, f. r. wagner, a. m. ferreira, c. e. pereira, and t. larsson. an agent framework to support sensor networks setup and adaptation, proceedings of international multiconference on computer science and information technology, pp. 533?540, mragowo, poland, 2009. [29] e. p .freitas, t. heimfarth, f. r. wagner, a. m. ferreira, c. e. pereira, and t. larsson. evaluation of coordination strategies for heterogeneous sensor networks aiming at surveillance applications, proceedings of 8th ieee sensors, pp. 591?596, christchurch, new zealand, 2009. [30] c. bettstetter. on the minimum node degree and connectivity of a wireless multihop network, proceedings of the 3rd acm international symposium on mobile ad hoc networking & computing, acm, new york, ny, usa, 2002, pp. 80-91. [31] e. p. freitas, m. a. wehrmeister, c. e. pereira, and t. larsson. real-time support in a reflective middleware for heterogeneous sensor network. in proceedings of the 29th ieee real-time systems symposium: work-in-progress proceedings, barcelona, spain, pp. 57-60, december 2008. edison pignaton de freitas has a position as computer engineer at the brazilian army. he is currently a phd student at halmstad university, sweden, and federal university of rio grande do sul (ufrgs), brazil, in the area of sensor networks, performing his research in the embedded systems groups of both universities and has published several papers in this area. he got his bachelor degree in computer engineering from the military institute of engineering, brazil, in 2003, and his msc degree in computer science from ufrgs, in 2007. in 2001 and 2002 he participated in an interchange program for engineering students between brazil and france, studying at the genie informatique industriel program of the institut national des sciences appliques, in toulouse, and performing an internship at the systems department of the airbus, working in the a380 project. tales heimfarth received his phd degree in computer science from the university of paderborn, germany, in 2007. his phd dissertation was entitled "biologically inspired methods for organizing distributed services on sensor networks". he received a msc degree in computer science in 2002, with the thesis "real-time communication platform over an sci cluster", and a bachelor degree in computer science in 2000, both from ufrgs. since 2008 he holds a post-doc position at ufrgs, performing research in the area of sensor networks. he is author of several papers published in international conferences in the area of sensor networks, especially with focus on basic software and auto-organizing protocols. he participates of the shox project, which is a open-source, event-oriented simulator for ad-hoc networks, led by the university of paderborn. 70 e. pignaton de freitas, t. heimfarth, r. schmidt allgayer, f. rech wagner, t. larsson, c. e. pereira, a. morado ferreira rodrigo schmidt allgayer is currently a phd student at ufrgs, brazil, in the area of wireless sensor networks, embedded systems and reconfigurable architectures, performing his research in the control, automation and robotics group. he got his bachelor degree in electrical engineering in 2005, and his msc degree in electrical engineering in 2009, both from ufrgs. his research interests include wireless sensor networks, reconfigurable architectures, and embedded systems. flávio rech wagner received a bsc degree in electrical engineering (1975) and an msc degree in computer science (1977), both from ufrgs, brazil. he received a phd degree in computer engineering from the university of kaiserslautern, germany, in 1983. in 1992 and 2002 he held post-doc positions at inpg (institut national polytechnique de grenoble), france. he was invited professor at the university of tübingen, germany, in 1994. he is currently professor at ufrgs, position that he holds since 1977, and since 2006 he is the director of the institute of informatics of ufrgs. he has been president of the brazilian computer society, from 1999 to 2003. he is associate editor of the design automation for embedded systems journal and has been member of more than 30 program committees of international conferences, having published 3 books, 7 book chapters, and more than 120 papers in conference proceedings and journals. tony larsson became a member of ieee in 1990. he received a meng degree in 1974, a tech.lic. 1986 in computer systems and a phd degree in computer science in 1989, all at the institute of technology at linköping university, sweden. he worked for ericsson ab from 1974 to 2002, in several different positions, such as engineer, manager, and expert (the latter in system design methods and architecture), in areas such as testing, computer aided design, radio network control, and distributed computer systems platforms for dependable telecommunication applications. he then worked for the swedish defense material administration in the area of network based defense and is since 2003 professor in embedded systems at halmstad university. carlos eduardo pereira received a bsc degree in electric engineering from escola de engenharia (1987), brazil, and an msc degree in computer science from ufrgs in 1990. he received a phd degree in electrical engineering from technische universitat stuttgart, germany, in 1995. in 2000 he held post-doc positions at united technologies research center (utrc). he is a professor in the department of electrical engineering and science computing at ufrgs and since 2009 he is the deputy director of the engineering of ufrgs. he is a senior member of the ieee and ifac technical committee on real-time programming. has experience in electrical engineering, focusing on electronic automation of electric and industrial processes, acting on the following subjects: real-time systems, object-oriented programming, industrial automation, industrial fieldbus and software engineering. armando morado ferreira received bsc (1990) and msc (1997) degrees in mechanic engineering, both from military institute of engineering. he received his phd degree in mechanical engineering from university of delaware (2001), and msc and phd degrees in military sciences from brazilian army’s command and high-staff school (2004 and 2009) lieutenant-colonel military engineer, professor at military institute of engineering and instructor at brazilian army’s command and high-staff school, he has knowledge in mechanical engineering with focus in robotization, concentrating his work in the following themes: trajectory generation, non-linear control and unmanned vehicles control and modelling, besides a great interest in the area of information science and technology management applied to defence. int j comput commun, issn 1841-9836 9(2):172-186, april, 2014. a new information filling technique based on generalized information entropy s. han, l. chen, z. zhang, j.-x. li shan han*, lin chen, zhi zhang, jianxun li department of automation, shanghai jiao tong university, key laboratory of system control and information processing, ministry of education of china, shanghai 200240, china e-mail: hanshan@sjtu.edu.cn, clcyk2002@aliyun.com, mark1896bc@gmail.com, lijx@sjtu.edu.cn *corresponding author: hanshan@sjtu.edu.cn abstract: multi-sensor decision fusion used for discovering important facts hidden in a mass of data has become a widespread topic in recent years, and has been gradually applied in failure analysis, system evaluation and other fields of big data process. the solution to incompleteness is a key problem of decision fusion during the experiment and has been basically solved by proposed technique in this paper. firstly, as a generalization of classical rough set, interval similarity relation is employed to classify not only single-valued data but also interval-valued data in the information systems. then, a new kind of generalized information entropy called "h’-information entropy" is suggested based on interval similarity relation to measure the uncertainty and the classification ability in the information systems. thus, the innovated information filling technique using the properties of h’-information entropy can be applied to replace the missing data by some smaller estimation intervals. finally, the feasibility and advantage of this technique are testified by two actual applications of decision fusion, whose performance is evaluated by the quantification of e-condition entropy. keywords: multi-sensor decision fusion, rough set theory, generalized information entropy, information classification, information filling. 1 introduction decision fusion is the general name of data-based decision making methods. the purposes of these methods are automatically discovering important facts hidden in a mass of data collected from varieties of sensors or other sources, and expressing them by the natural language of decision rules. then, people can effectively use the simplified decision rules to aid decision making in future applications. obviously, this target puts forward higher requirements for both sensors capability and the information processing method. besides basic statistical method, neural networks and bayesian networks, rough set theory as a favorable mathematical tool with high performance of information acquisition and classification takes into account consequently [1] [2]. the theory of rough set was firstly proposed by pawlak in 1982 [3]. it is an extension of classical set theory for the study of information systems characterized by inexact, uncertain and vague, and has been widely used in the fields of knowledge discovery, decision fusion, data mining, pattern recognition, and so on. with the rapid development of rough set, some new frameworks were proposed to extend its application range. the tolerance relation [4], valued tolerance relation [5], similarity relation [6], together with the limited tolerance relation [7], laid a solid foundation for the progress of generalized rough set. until now, the improvements of binary relations are still attracting the scholars attentions [8][11]. as to decision fusion of multi-sensor, data acquired from different sensors is real-valued which characterizes objects of interest. because of the imprecision of acquisition, the fuzziness of cognition and the limitation of knowledge [12] [13], these attributes with real-valued data usually expressed as single-valued information system (sis), interval-valued information system (iis) copyright © 2006-2014 by ccc publications a new information filling technique based on generalized information entropy 173 or the combination of sis and iis cannot be classified by any binary relation mentioned above absolutely. the discretization of a long interval by dividing the range into a certain number of partitioning small intervals and using symbolic values to replace these small intervals before further calculation is the popular handling method [14] [15], but existing dispute about the cut-off points [16]. additionally, the classical rough set may generate an unacceptably large number of classifications from the discretized data resulting in too many classification rules to make final decisions. more important, data missing and uncertain called incompleteness may occur unavoidably because of loss of documents, difficulties of measurement or malfunction of sensors, and increases the difficulties of information processing [17]. the direct information filling methods have not been paid enough attention to, and existed methods [12] [13] are lack of precision to make optimal decisions. thereupon, the researches of solutions to the classifications and the incompleteness in information systems, together with the following calculations of rough set become significant [18]. in this paper, the discussions are paying attention to solve above issues based on rough set theory emphatically. the notion, properties and measurement of generalized rough set are reviewed firstly in section 2. in section 3, a brand new “h’-information entropy" is defined for the measurement of uncertainty and classification ability in the information systems according to the interval similarity relation. in section 4, an innovated information filling technique is proposed by the use of h’-information entropy to solve the incompleteness in both sis and iis, whose performance is evaluated by the quantification of e-condition entropy. in section 5, the advantage and practicability of this new information filling technique are testified by two integrated examples. the missing data in the decision table is filled by smaller estimation intervals and the certain decision rules are extracted by the calculation of rough set finally. 2 the theory of generalized rough set information systems with the form of decision tables provide a convenient basis for the representation of objects in terms of their attributes. but the uncertainty and incoordination take place in most of decision tables. thereupon, rough set as a powerful method is used to deal with these issues. 2.1 basic concept let s = (u,a) be an information system, where u = {u1,u2, . . . ,u|u|} (|u| means the cardinality of u) is a non-empty finite set of objects, denoting the whole research objects of the information system. a = {a1,a2, . . . ,an} is a non-empty finite set of attributes, denoting the whole attributes of the information system. f : u → va is a mapping for a ∈ a ,where va is called the domain of attribute a. this constitutes the basic research contents of rough set. let p ⊆ a be a subset of attributes. t is a binary relation (including but not limited to equivalence relation). sp(u) denotes the object set {v ∈ u|(u,v) ∈ t} , called a class or an information granule. if (ui,uj) ∈ t , then sp (ui) ̸= sp (uj) in general conditions. the whole classes of u are obtained according to t [19]: k(p) = (sp (u1),sp (u2), . . . ,sp (u|u|)) (1) at this time, k(p) is not restricted to “partition", but may be the “covering" of u, namely: ∪sp (ui) = u i = 1,2, . . . , |u| (2) then, a rough set can be described by the definitions of lower and upper approximations. 174 s. han, l. chen, z. zhang, j.-x. li definition 1. let s = (u,a) be an information system, x ⊆ u. with an arbitrary binary equivalence relation on u, the lower and upper approximations of x are [3]: r(x) = ∪{sp (ui)|sp (ui) ⊆ x} i = 1,2, . . . , |u| (3) r(x) = ∪{sp (ui)|sp (ui) ∩ x ̸= ∅} i = 1,2, . . . , |u| (4) the covering-based rough set is the great generalization of classical rough set. so, the application range of rough set is extended significantly. 2.2 the measurement of rough set the uncertainty in rough set refers to the size of classes called information granules generally determined by specified attributes in the information system. it can be measured by information entropy [20]. small information granules imply a precise description of rough set. definition 2. let s = (u,a) be an information system, and p ⊆ a be a subset of attributes.k(p) = (sp (u1),sp (u2), . . . ,sp (u|u|)) are the classes of u created by some binary relation according to p . information entropy of p is defined as [19]: h(p) = − |u|∑ i=1 1 |u| log2 |sp (ui)| |u| (5) this definition of entropy is called “h-information entropy". in other words, h-information entropy quantifies the classification ability of objects set u according to the attributes subset p . the relationship between different classes can be compared by partial relation “ ≼ ”. let p,q ⊆ a be subsets of attributes. if k(p) = (sp (u1),sp (u2), . . . ,sp (u|u|)) and k(q) = (sq(u1),sq(u2), . . . ,sq(u|u|)). the partial relation “ ≼ ” is defined as: k(p) ≼ k(q) ⇔ ∀i = 1,2, . . . , |u|,sp (ui) ⊆ sq(ui) (6) theorem 3. if k(p) ≼ k(q), then h(q) ≤ h(p) [19]. this theorem explains that h-information entropy is monotone increasing with the decrease of elements in each class. the thinner classes are obtained by stricter binary relations. the bigger the h-information entropy is, the smaller the information granules will be. in order to mine the internal relations of subsets p and q, the research of coordination of these two attributes becomes significant. the result embodies the support degree of one subset of attributes to another. it is just the major application of rough set in decision fusion. the condition entropy is introduced to measure this support degree [21]. it is proved that e-condition entropy is suitable for both partition-based and covering-based rough set [19] [22]. definition 4. let p,q ⊆ a be two subsets of attributes. if k(p) = (sp (u1),sp (u2), . . . ,sp (u|u|)) and k(q) = (sq(u1),sq(u2), . . . ,sq(u|u|)), then e-condition entropy is: e(q/p) = |u|∑ i=1 |sp (ui)| − |sp (ui) ∩ sq(ui)| |u|2 (7) in particular, if k(p) ≼ k(q), which means the classes obtained by p are totally included in the classes obtained by q, then e(q/p) = 0 [23]. thus, some attributes “entirely" support the some others. rough set degenerates to the classical set. it is considered that if e-condition entropy is small, the support degree is high. then, the coordination in rough set is better. a new information filling technique based on generalized information entropy 175 3 generalized information entropy in the decision tables, the values of attributes may be some interval ranges instead of simple single values. in order to measure the uncertainty in such kind of system, the new information entropy is defined in this section, called h’ information entropy. 3.1 interval similarity relation the information system with interval-valued data is now called interval-valued information system (iis). definition 5. let s = (u,a) be an iis. if x ∈ u and a ∈ a, then the interval range of f(x,a) is denoted as f(x,a) = [f(x,a)l,f(x,a)u]. as mentioned in introduction, it is clear that the classical binary relations are not suitable for the classifications of iis now. additionally, it is considered that the length of interval implies the uncertainty of cognition. because the intersection degrees among objects are highly different, the negligence will enlarge the possibility of intersection factitiously. thus, the classification ability and the cognition of the system will be reduced. so, the interval similarity relation is employed as the binary relation to classify the objects of iis in this paper. definition 6. let s = (u,a) be an iis. x,y ∈ u and a ∈ a. the interval similarity degree of f(x,a) and f(y,a) is defined as: paxy = |f(x,a) ∩ f(y,a)| |f(x,a) ∪ f(y,a)| (8) where || means the absolute length of interval. in particular, there may exist some single-valued data in iis in practical applications. that is f(x,a) = f(x,a)l = f(x,a)u = constant. in order to generalize the definition of interval similarity degree, if f(y,a) = [f(y,a)l,f(y,a)u] and constant ∈ [f(y,a)l,f(y,a)u], then we define paxy = 1, and vice versa. thus, the definition of similarity degree can be improved. definition 7. let s = (u,a) be an iis. x,y ∈ u and a ∈ a. the interval similarity relation is defined as: s(a) = {(x,y) ∈ u × u|paxy ≥ α,∀a ∈ a} (9) where α is the similarity threshold value. obviously, if f(x,a) and f(y,a) are both single-valued data, then the interval similarity relation will degenerate to simple equivalence relation by this definition. deduction 1. the interval similarity relation satisfies reflexivity and symmetry. it is significant that if an information system is classified by interval similarity relation, the interval length has direct effect on the classification ability. that is interval similarity may affect information entropy of the system. 3.2 definition of generalized information entropy as the improvement, interval similarity degree defined in equation (8) will be added to classical information entropy. a new kind of generalized information entropy is defined to measure the uncertainty and classification ability in the information systems. 176 s. han, l. chen, z. zhang, j.-x. li definition 8. let s = (u,a) be an information system and b ⊆ a be a subset of attributes. k(b) = (sb(u1),sb(u2), . . . ,sb(u|u|)) are the classes of u created by interval similarity relation according to b. pxy is the minimum interval similarity degree between two objects (x and y). pxy = { min{pkxy} pkxy > 0,∀k ∈ b 0 else (10) the generalized information entropy called “h’-information entropy" is defined as: h′(b) = − |u|∑ i=1 1 |u| log2 |u|∑ j=1 puiuj |u| (11) deduction 2. h’-information entropy satisfies non-negativity, symmetry, continuity, monotonicity and extremum property. proof. 1) by the definition of interval similarity degree, it is obvious that 0 ≤ puiuj ≤ 1 . then, 0 < |u|∑ j=1 puiuj ≤ |u| ⇒ 0 < |u|∑ j=1 puiuj |u| ≤ 1 ⇒ − log2 |u|∑ j=1 puiuj |u| ≥ 0 (12) that is h’-information entropy is nonnegative. 2) moreover, because of the symmetry of interval similarity degree (puiuj = pujui), h’information entropy is also symmetric. 3) the numerical ranges of puiuj are continuous and puiuj constitute the whole variables of h’information entropy. the operations of addition, multiplication, and logarithm are continuous totally. hence, h’-information entropy keeps the continuity. 4) let s = (u,a) be an information system and b1,b2 ⊆ a be subsets of attributes. the condition that b1 ⊆ b2 is assumed, then there is k(b2) ≼ k(b1) [24]. because pb1xy = min{pkxy} (pkxy > 0,∀k ∈ b1) and pb2xy = min{pkxy} (pkxy > 0,∀k ∈ b2), with the assumption b1 ⊆ b2 , the consequent that pb1xy ≥ pb2xy is obtained. hence, h′(b1) ≤ h′(b2). as a result, k(b2) ≼ k(b1) ⇒ h′(b1) ≤ h′(b2) (13) it should be noticed that this monotonicity is under the constraint (b1 ⊆ b2) in this paper. if there is no inclusion relation between two subsets of condition attributes, this monotonicity may be broken down. 5) if |u|∑ j=1 puiuj = 1, then h′(b) = log2 |u|. if |u|∑ j=1 puiuj = |u|, then h′(b) = 0. hence, there exist two extreme values. 4 the solution to incompleteness data missing and uncertain called incompleteness occur commonly in data acquisition by multi-sensor. in this section, the research focus will turn to this incomplete information system and try to find the solution to incompleteness. a new information filling technique based on generalized information entropy 177 4.1 the new information filling technique now, a new information filling method is proposed based on the h’-information entropy defined above. theorem 9. in an iis with one attribute, it will assuredly increase the h’-information entropy when a new object of minimal interval is added into the system. proof. let s = (u,a) be an iis. ∀a ∈ a, s = (u,a) is an iis with one attribute. u|u+1| is the newly added object. there are: f(u|u+1|,a) = [f(u|u+1|,a) l,f(u|u+1|,a) u] (14) |f(u|u+1|,a)| = f(u|u+1|,a) u − f(u|u+1|,a) l = δ (15) when u|u+1| is added into original iis, the new h’-information entropy is: h′(a)1 = − |u+1|∑ i=1 1 |u + 1| log2 |u+1|∑ j=1 puiuj |u + 1| = − 1 |u + 1| (log2 |u|∑ j=1 pu1uj + pu1u|u+1| |u + 1| + log2 |u|∑ j=1 pu2uj + pu2u|u+1| |u + 1| + · · · + log2 |u|∑ j=1 pu|u|uj + pu|u|u|u+1| |u + 1| + log2 |u|∑ j=1 pu|u+1|uj + pu|u+1|u|u+1| |u + 1| ) (16) obviously, pu|u+1|u|u+1| = 1. |u|∑ j=1 puiuj (i = 1,2, . . . , |u|) is unchanged since the newly added object does not affect the intersection relations in original system. when δ is a minimal interval, there is δ ≪ |f(ui,a)|(i = 1,2, . . . , |u|). therefore, whether δ has an intersection with other interval or not, it is obtained: puiu|u+1| = pu|u+1|ui = 0 i = 1,2, . . . , |u| (17) as a result: h′(a)1 = − 1 |u + 1| (log2 |u|∑ j=1 pu1uj |u + 1| + log2 |u|∑ j=1 pu2uj |u + 1| + · · · + log2 |u|∑ j=1 pu|u|uj |u + 1| + log2 1 |u + 1| ) def = h′(a)10 (18) let |u|∑ j=1 puiuj = pi for convenience, then: h′(a)10 = − 1 |u + 1| (log2 p1 |u + 1| + log2 p2 |u + 1| + · · · + log2 p|u| |u + 1| + log2 1 |u + 1| ) = − 1 |u + 1| log2 p1p2 · · ·p|u| |u + 1||u+1| = 1 |u + 1| log2 |u + 1||u+1| p1p2 · · ·p|u| (19) 178 s. han, l. chen, z. zhang, j.-x. li the h’-information entropy of original system is: h′(a)0 = − |u|∑ i=1 1 |u| log2 |u|∑ j=1 puiuj |u| = 1 |u| log2 |u||u| p1p2 · · ·p|u| (20) because |u| ≥ 1 and pi ≥ 1(i = 1,2, . . . , |u|), hence, h′(a)10 = 1 |u + 1| log2 |u + 1||u+1| p1p2 · · ·p|u| = log2 |u + 1| − 1 |u + 1| log2(p1p2 · · ·p|u|) > log2 |u| − 1 |u + 1| log2(p1p2 · · ·p|u|) > log2 |u| − 1 |u| log2(p1p2 · · ·p|u|) = h′(a)0 (21) it is clear that, theorem 9 can be expanded to a sis. that is in a sis with one attribute, when a new object of minimal interval is added into the system, it will assuredly increase the h’-information entropy if the interval range of newly added object doesn’t include any existed single-valued data in the attribute. as long as this promise exists, puiu|u+1| = pu|u+1|ui = 0 as well according to equation (8). consequently, the simplifying process from equation (16) to (18) stays the same. in order to make the research more intuitive, the following discussions are based on the assumption that the increase of interval is unidirectional. hence, the lower limit or upper limit is fixed. theorem 10. in an information system with one attribute, the h’-information entropy will be monotone decreasing with the interval increase of newly added object within a limit. proof. followed by previous proof, δ is increased gradually. 1) if the original system is an iis, obviously, only the terms of pi def = puiu|u+1| = pu|u+1|ui = |f(ui,a)∩δ| |f(ui,a)∪δ| are changing during the increase of δ. firstly, h′(a)1 will be monotone decreasing because of the continuous increase of pi. the increase trend of pi will be sustained until the intersection of ui(i = 1,2, . . . , |u|) and u|u+1| is steady, which means the upper /lower limit of δ reaches the upper /lower limit of arbitrary ui (as showed in fig.1). after that, |f(ui,a) ∩ δ| is a constant while |f(ui,a) ∪ δ| is still increased. then, pi will turn to decrease. once this happens to any ui ∈ u, the trend of h′(a)1 may be changed. 2) if the original system is a sis, there should exist growing amount of pi = 1(i = 1,2, . . . , |u|) with the increase of δ. thus, h′(a)1 will be monotone decreasing until the interval range of δ is long enough to cover the largest number of original single-valued data in the domain of the attribute a. theorem 10 shows that the newly added object will have more opportunity to be classified with other objects with interval increase under the interval similarity relation. this phenomenon will cause the weakness of classification ability and the reduction of information entropy. a new information filling technique based on generalized information entropy 179 figure 1: intersection of two intervals. deduction 3. in an information system with one attribute, there is a minimum value of h’information entropy with the interval increase of newly added object. h′(a)1 is continuous according to deduction 2 and it has been proved that h′(a)1 is monotone decreasing within a limit. consequently, there is a minimum value in the limit. deduction 4. in an iis with one attribute, after the upper /lower limit of newly added object reaches the maximal /minimal value in the domain of the attribute a, the h’-information entropy will be monotone increasing with the interval increase. deduction 5. in an sis with one attribute, after the upper /lower limit of newly added object reaches the maximal /minimal value in the domain of the attribute a, the h’-information entropy will be a constant with the interval increase. in an iis, once the upper /lower limit of newly added object reaches the upper /lower limit of interval-valued data of entire original objects, pi will turn to decrease with the increase of δ for all ui ∈ u. then, h′(a)1 is changed to increase assuredly. meanwhile, in a sis, once the upper /lower limit of newly added object reaches the maximal /minimal value of single-valued data of entire original objects, pi will be the number of covered single-valued data and remains unchanged regardless of the interval extension. then, h′(a)1 is a constant assuredly. the deductions above explain that when the interval of the newly added object is increased from zero to the maximal /minimal value in the domain of the attribute a, there exists at least one global minimum value of h’-information entropy. now, the missing data in the information systems can be dealt with above-mentioned results. the new information filling technique follows the assumption that the objects with missing data have no effect with the classifications of information. in other words, regardless of the existing of the existence of these incomplete objects, the information entropy of corresponding condition attribute should be unchanged. the process of the new information filling technique is considered as a new object of minimal interval adds into the complete information system attribute by attribute. then, the h’information entropy is surely increased implying the rapid improvement of classification ability. according to deduction 3, 4 and 5, the trend of h’-information entropy is decreased firstly and increased or flat finally. the filling interval is expected to make the h’-information entropy close to the original one as much as possible before the upper /lower limit reaches the maximal /minimal value in the domain of corresponding attribute. thus, a smaller filling interval is obtained. because of the expanded definition of interval similarity relation, this new information filling technique can be applied for both iis and sis. in general, the process is listed below. 1) calculate the h’-information entropy (h′(a)0) of original information system for each condition attribute without incomplete objects. 2) add a new object with minimal interval (δ = 10−3 is recommended) to one attribute. if 180 s. han, l. chen, z. zhang, j.-x. li the original system is sis, the lower /upper limit of the interval should be set at the left /right side of minimal /maximal value in the domain of corresponding attribute of the objects with the same decision. if the original system is iis, the lower /upper limit of the interval should be known and fixed in advance just like the forms of f(x,a) = [f(x,a)l,∗] or f(x,a) = [∗,f(x,a)u]. thus, the initial position marked as a is established. the h’-information entropy is h′(a)1|start at the beginning. 3) forward /backward increase the length of the new interval by a setting step size until its upper /lower limit reaches the maximal /minimal value in the domain of the attribute. fig.2 is just showing the process of forward increasing for instance. calculate the new h’-information entropy for each step. record the following data: a. mark the position of interval’s upper /lower limit as b when the corresponding new h’information entropy reaches global minimum value h′(a)1|min for the first time. b. the position of maximal /minimal value in the domain of the attribute is marked as c, and the corresponding h’-information entropy is h′(a)1|end. c. mark the position of interval’s upper (lower) limit as d when the corresponding new h’-information entropy is equal to the original h’-information entropy (h′(a)1 = h′(a)0) for the first time. d may not exist in some cases. 4) if h′(a)1|min ≤ h′(a)0 , then the estimation interval is |a − d| (as showed in fig. 2(left)).if h′(a)1|min > h′(a)0 ,then the estimation interval is |a − b| (as showed in fig. 4(right)). thus, there are |a − d| ≤ |a − c| and |a − b| ≤ |a − c|, so the estimation interval is shortened. ( ) ( ( ), ( ),..., ( )) ( ) ( ( ), ( ),..., ( )) ( )' ( ( )', ( )',..., ( )') figure 2: variation curve of h’-information entropy. 4.2 performance analysis in order to check the performance of proposed information filling technique, a deduction is introduced firstly. deduction 6. in an information system, the shortening of estimation intervals will improve the coordination of the system. proof. let s = (u,a) be an information system. a = c ∪d , where c is the set of condition attributes and d is set of the decision attributes. if b ⊆ c , then the classes obtained according to b and d are: k(b) = (sb(u1),sb(u2), . . . ,sb(u|u|)) (22) k(d) = (sd(u1),sd(u2), . . . ,sd(u|u|)) (23) with the shortening of estimation intervals in b, there exists a new classification: k(b)′ = (sb(u1)′,sb(u2)′, . . . ,sb(u|u|)′) (24) a new information filling technique based on generalized information entropy 181 obviously, short estimation interval will decrease the intersection degree and reduce the objects in the classes made by interval similarity relation. that is sb(ui)′ ⊆ sb(ui), |sb(ui)′| ≤ |sb(ui)| (i = 1,2, . . . , |u|). hence, sb(ui)′ ∩sd(ui) ⊆ sb(ui) ∩sd(ui) and |sb(ui)′ ∩sd(ui)| ≤ |sb(ui)∩sd(ui)|. e-condition entropy studied in section 2.2 is used to measure the coordination in rough set. according to its definition, there is: e(d/b) − e(d/b)′ = |u|∑ i=1 |sb(ui)| − |sb(ui) ∩ sd(ui)| |u|2 − |u|∑ i=1 |sb(ui)′| − |sb(ui)′ ∩ sd(ui)| |u|2 = |u|∑ i=1 |sb(ui)| − |sb(ui)′| − (|sb(ui) ∩ sd(ui)| − |sb(ui)′ ∩ sd(ui)|) |u|2 = |u|∑ i=1 |sb(ui)| − |sb(ui)′| − |(sb(ui) − sb(ui)′) ∩ sd(ui)| |u|2 ≥ 0 (25) the equality holds if and only if (sb(ui) − sb(ui)′) ⊆ sd(ui). so, e(d/b) ≥ e(d/b)′. it is mentioned that small e-condition entropy implies great support degree, which means the shortening of estimation intervals improves the coordination between corresponding condition attributes and decision attributes. because of the generality of this result to the whole condition attributes, the coordination of the information system is improved consequently. now, the benefits of smaller estimation intervals obtained in this paper are reflected in two aspects. on the one hand, the coordination between condition attributes and decision attributes is improved according to deduction 6. that is the positive region in the rough set is expanded, while the uncertainty is decreased. the reliability of decision will be increased as a result. on the other hand, the shortened estimation intervals are beneficial for decision making. the new decision rules will be fine and precise with the extending of covering range. 5 experimental studies the new information filling technique can be applied to process the incompleteness in large amounts of data acquired by different sensors for decision fusion in a variety of fields. here, two integrated experiments will be studied below to validate the feasibility and superiority of proposed method. 5.1 single-valued data estimation and filling in the study of single-valued data evaluation and filling, a part of icu data of one patient is acquired by different sensors and listed in table 1. in this decision table, the column "u" refers to the times of examinations. columns "c1" to "c5" denote the data acquired by sensors of temperature, heart rate, systolic blood pressure, spo2 and respiration rate, and compose five condition attributes. the column "d" denotes four degrees of icu decisions and composes the decision attribute. furthermore, the missing data is denoted as "∗" in the table. by applying the new information filling technique, a small interval can be estimated to fill the missing data. figure 3 shows this process of information filling and we can get f(u17,c1) = [36.3,36.4] (as showed in figure 3(left)) and f(u3,c3) = [103.0,115.9] (as showed in figure 3(right)) as a result. then, e-condition entropy discussed before can be used to evaluate the performance of above result. we get e(d/c) = 0 without the objects with missing data, meaning the condition 182 s. han, l. chen, z. zhang, j.-x. li u c1 c2 c3 c4 c5 d 1 37.8 62.0 103.0 94.0 18.0 2 2 37.7 61.0 120.0 97.0 16.0 2 3 37.7 61.0 * 97.0 16.0 2 . . . . . . . . . . . . . . . . . . . . . 14 37.3 62.0 116.0 98.0 18.0 3 15 37.3 62.0 116.0 98.0 18.0 3 16 37.3 62.0 116.0 98.0 18.0 3 17 * 111.0 142.0 95.0 20.0 1 18 36.3 111.0 142.0 95.0 20.0 1 19 36.4 111.0 142.0 95.0 20.0 1 . . . . . . . . . . . . . . . . . . . . . 49 36.7 98.0 133.0 92.0 20.0 4 50 36.7 98.0 133.0 92.0 20.0 4 table 1: incomplete icu data. ] ] ] 730] ] ] ] 5.658] ] ] 26.819] ... ... 0.033] ] 826] ... ... 21 4.997] ] 848] ... ... 5 ] ] 806] ... ... ... ... 31 4.997] 0.038] 4.999] ... ... figure 3: information filling of icu data. attributes entirely support each decision in the original system. then, this index is still 0 when u3 and u17 with filling intervals are added to the system. that is the new information filling technique doesn’t change the coordination between condition attributes and decision attribute in this example and the results make sense. 5.2 interval-valued data estimation and filling in the study of interval-valued data estimation and filling, a part of thruster experimental data is acquired by different sensors. in order to simplify the table, only the objects with missing data denoted as "∗" are listed in table 2. the column "u" refers to the times of experiment. columns "c1" to "c4" denote the data acquired by different pressure and temperature sensors, and compose four condition attributes. these acquired data is formed as interval values including the minimum and maximum from the each sensor without data preprocessing. the column "d" denotes three types of failure simplified as symbolic values and composes the decision attribute. we need to fill the missing data effectively and discover the failure rules hidden in the data as accurately as possible. 1) firstly, the decision table is completed by the method in ref. [13] and showed in table 3. briefly, "∗" is replaced by the minimal /maximal value in the domain of each condition attribute. 2) then, the decision table is completed by the new information filling technique proposed in this paper and showed in table 4. in order to reveal the difference between above two methods, the classifications of condition attributes are made by interval similarity relation. the cardinality of each class obtained by the two methods can be compared in figure 4. a new information filling technique based on generalized information entropy 183 u c1 c2 c3 c4 d 3 [4.344, 4.604] [0.029, 0.037] [4.600, *] [23.865, 26.819] 1 6 [4.283, *] [0.024, 0.033] [4.613, 4.889] [23.872, 25.826] 1 21 [4.801, 4.997] [0.027, 0.035] [*, 4.516] [28.700, 30.848] 2 25 [*, 4.993] [0.023, 0.030] [4.627, 4.904] [28.663, 30.806] 2 28 [4.445, 4.711] [0.025, 0.037] [4.236, 4.490] [28.826, *] 2 43 [2.629, *] [0.024, 0.037] [4.600, 4.876] [30.881, 32.116] 3 46 [2.653, 3.812] [0.025, 0.033] [4.619, 4.896] [30.016, *] 3 55 [1.470, 2.551] [0.027, 0.030] [4.507, *] [30.497, 31.717] 3 . . . . . . . . . . . . . . . . . . table 2: incomplete thruster experimental data. u c1 c2 c3 c4 d 3 [4.344, 4.604] [0.029, 0.037] [4.600, 4.999] [23.865, 26.819] 1 6 [4.283, 4.999] [0.024, 0.033] [4.613, 4.889] [23.872, 25.826] 1 21 [4.801, 4.997] [0.027, 0.035] [4.062, 4.516] [28.700, 30.848] 2 25 [1.015, 4.993] [0.023, 0.030] [4.627, 4.904] [28.663, 30.806] 2 28 [4.445, 4.711] [0.025, 0.037] [4.236, 4.490] [28.826, 32.150] 2 43 [2.629, 4.999] [0.024, 0.037] [4.600, 4.876] [30.881, 32.116] 3 46 [2.653, 3.812] [0.025, 0.033] [4.619, 4.896] [30.016, 32.150] 3 55 [1.470, 2.551] [0.027, 0.030] [4.507, 4.999] [30.497, 31.717] 3 . . . . . . . . . . . . . . . . . . table 3: completed thruster experimental data 1. the histogram indicates that, with the same binary relation, more than 30% classes obtained from the completed decision table by the new information filling technique are “thinner" than before. that is, the new information filling technique can achieve a classification ability no less than the traditional one. the classes are more precise and the uncertainty is lower now. additionally, e-condition entropy is calculated as e(d/c) = 0.0033 in table 4, while this index is 0.0106 in table 3. the result also implies a better coordination obtained by the new information filling technique between condition attributes and decision attributes. then, the reduct of the system is c1,c3,c4 obtained by heuristic algorithm of attribute reduction based on e-condition entropy [22] [25]. the certain decision rules (failure rules) can be achieved explicitly (as showed in table 5) since all the missing data has been filled. at present, the estimation intervals in each attribute are relatively small in this group of certain failure rules. it has a better significance for the practical decision making. the result is agreed with the performance analysis in section 4.2. consequently, the problems of incompleteness in thruster experimental data are solved and the failure rules are achieved favorably. figure 4: cardinality of each class. 184 s. han, l. chen, z. zhang, j.-x. li u c1 c2 c3 c4 d 3 [4.344, 4.604] [0.029, 0.037] [4.600, 4.753] [23.865, 26.819] 1 6 [4.283, 4.601] [0.024, 0.033] [4.613, 4.889] [23.872, 25.826] 1 21 [4.801, 4.997] [0.027, 0.035] [4.235, 4.516] [28.700, 30.848] 2 25 [4.877, 4.993] [0.023, 0.030] [4.627, 4.904] [28.663, 30.806] 2 28 [4.445, 4.711] [0.025, 0.037] [4.236, 4.490] [28.826, 30.471] 2 43 [2.629, 3.810] [0.024, 0.037] [4.600, 4.876] [30.881, 32.116] 3 46 [2.653, 3.812] [0.025, 0.033] [4.619, 4.896] [30.016, 31.240] 3 55 [1.470, 2.551] [0.027, 0.030] [4.507, 4.837] [30.497, 31.717] 3 . . . . . . . . . . . . . . . . . . table 4: completed thruster experimental data 2. rules descriptions r1 if c1 ∈ [4.458, 4.725] ∧ c3 ∈ [4.867, 4.997] ∧ c4 ∈ [23.779, 25.730], then d = 1 r2 if c1 ∈ [4.456, 4.732] ∧ c3 ∈ [4.358, 4.619] ∧ c4 ∈ [23.717, 25.658], then d = 1 . . . . . . r19 if c1 ∈ [4.801, 4.997] ∧ c3 ∈ [4.235, 4.516] ∧ c4 ∈ [28.700, 30.848], then d = 2 r20 if c1 ∈ [4.817, 4.989] ∧ c3 ∈ [4.350, 4.611] ∧ c4 ∈ [29.200, 30.328], then d = 2 . . . . . . r35 if c1 ∈ [2.947, 4.215] ∧ c3 ∈ [4.868, 4.999] ∧ c4 ∈ [30.391, 31.607], then d = 3 r36 if c1 ∈ [3.678, 4.047] ∧ c3 ∈ [4.358, 4.619] ∧ c4 ∈ [29.950, 31.148], then d = 3 . . . . . . table 5: certain decision rules. 6 conclusions the solution to incompleteness in decision fusion is presented with the help of a new information filling technique based on generalized information entropy in this paper. interval similarity relation is employed as the binary relation to classify both single-valued and interval-valued data acquired by different sensors. as the improvement of classical rough set, a new kind of generalized information entropy called h’-information entropy is provided to solve the incompleteness occurring unavoidably in information systems. so, the missing data in the decision tables can 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[25] li, f.; yin, y.-q.(2009); approaches to knowledge reduction of covering decision systems based on information theory. information sciences, issn 0020-0255, 179(11): 1694-1704. int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 218-230 optimization of vertical handoff decision algorithm for wireless networks e. arun, r.s. moni elias arun, r.s. moni department of computer science and engineering, department of electronics and communication engineering, noorul islam university, tamil nadu, india. arunsedly@yahoo.com, r.smoni@yahoo.com abstract: to provide mobile users with seamless access anywhere and anytime there is a strong need forinterworking mechanism between cellular networks and wireless local area networks in the next generation wirelessnetworks. due to the heterogeneous underlying quality of service (qos) support, the admission traffic in these areashas significant impact on overall resource utilization efficiency and qos satisfaction when multiple services areconsidered. this paper addresses a call-level quality of service (cls) vertical handoff algorithm between wlanand cellular networks for seamless ubiquitous access. the cls involves call blocking/dropping probabilities, meandata transfer rate, and number of handoff per call. based on the above proposed admission strategy, the admissionregion of a cell or wlan for the traffic can be derived with the function of new call arrival rate, handoff callarrival rate, and the radius of wlans. the blocking and dropping probabilities are calculated under the guardchanneladmission strategy. the radius of wlan is determined by using simulated annealing (sa) method to minimizethe cost function. moreover, handoff traffic should be differentiated from new traffic in terms of call admission.when a mobile node (mn) moves from an area, with only cellular coverage to an overlaid wlan area, theongoing call of the mn should be handed over to the wlan, and handoff from wlan to cellular network if theyleave the scope. the results based on a detailed performance evaluation study are presented here to demonstrate theefficacy of the proposed algorithm. keywords: heterogeneous wireless networks, vertical handoff, seamless mobility, integration handover. 1 introduction the past decade has witnessed the fast evolution and successful deployment of a number of wireless access networks. the two most promising ones are cellular networks and wireless local area network (wlan). driven by the service anywhere and anytime concept, it is well accepted that fourth-generation (4g) wireless networks will be heterogeneous, integrating different networks to provide seamless internet access for mobile users with multi mode access capability. one major challenge in cellular/wlan internet working is how to take advantage of the wide coverage and almost universal roaming support of cellular networks and the high data rates of wlan. many issues should be carefully addressed to achieve seamless interworking,such as mobility management, resource allocation, call admission control, security and billing.this article focuses on how to properly admit incoming traffic to the cell or wlan and when to take handoff decision. the process of switching connections among networks is called handoff. in each network, an admission control policy either accepts the connection request and accordingly allocates the requested bandwidth or blocks the connection request.higher priority is usually given copyright c⃝ 2006-2012 by ccc publications optimization of vertical handoff decision algorithm for wireless networks 219 to accept the connection requests from handoff users not the new users. the reason is that from the user’s point of view, having a connection abruptly terminated is more annoying than being blocked occasionally on new connection attempts. a service request rejected by its first-choice network can just leave the system or further try to access the other network [1]. there are some related researches on similar problem in two-tier hierarchical cellular networks, in which small size microcells overlay with large macro cells. many proposed admission strategies [1] [2] are based on user mobility and traffic characteristics. the vertical handoff [3] process involves three stages. the first is the network discovery. in this phase, mobile nodes (mn) periodically searches if there are some other different types of wireless networks and take these discovered networks as candidates. the second is the handoff decision phase where mns compare the state of the current network with candidates, and select one as the handoff target from them according to a certain criterion. the last is the handoff implementation phase where mns execute the handoff actions and associate with the newly authenticated network. among these three stages, the handoff decision phase is very important, because it has a direct influence on the network performance and the quality of service of nodes. the objectives of the proposed framework are to maximize network utility through efficient resource allocation, achieve prioritization among different types of connections such as new connections and vertical and horizontal handoff connections, and ensure that the performance of ongoing connections doesn’t deteriorate due to accepting too many connections in a service area. the authors of [4] propose a vertical handoff decision method that simply estimates the service quality for available networks and selects the network with the best quality. in the literature, vertical handoff algorithm is developed in different directions. one of them takes the received signal strength (rss) and some other factors such as bandwidth, delay and distance into consideration to select the best network through a simple comparison [5] [6]. another approach utilizes the artificial intelligent techniques such as neural network, fuzzy logic and machine learning, combining these factors considered to select the best network [7] [8] . the above methods mainly consider the quality of service of nodes after handoff, but do not consider the overall system performance such as resource utilization affected by handoff. the servicedifferentiated admission scheme proposed in [9] applies a different admission strategy for voice service, in which the cellular network is the first choice for voice call and no vertical handoff from the cell to the wlan is executed for ongoing voice calls. to maximize resource utilization, a complex set of admission parameters need to be determined so that the traffic load is properly distributed to the cells and wlans. this research is based on the resource management and the handoff execution of a node is used to make the resources be optimally utilized [10]. by properly setting the effective bandwidth of services, packet-level qos such as packet delay and packet loss can be guaranteed, as long as the allocated bandwidth to traffic is no less than the corresponding effective bandwidth requirements. in the following, the focus is on call-level qos in terms of call blocking/dropping probabilities, mean data transfer time and number of handoffs per call. based on the proposed admission strategy, the admission region of a cell or wlan for the traffic can be derived with the functions of new call arrival rate, handoff call arrival rate and the radius of wlans. in view of that in a period, the call arrival rates are stable, so it can be represented all the probabilities as functions of the radius of wlan. the blocking probabilities and the dropping probabilities are calculated under the guard-channel call admission strategy. the radius of wlans is determined by using simulated annealing method to minimize the cost function. the rest of the paper is organized as follows. section 2 provides cellular/wlan system description. section 3 proposes the optimal admission control for cellular/wlan. section 4 proposes the vertical handoff decision algorithm to minimize the call-level qos. in section 5 performance of the proposed algorithm is discussed. finally the conclusion is stated in section6. 220 e. arun, r.s. moni 2 cellular/ wlan system description consider an integrated cellular/wlan system where one or more wlans may be deployed inside each cell of the cellular system as shown in fig.1. there are two specific coverage areas to be considered: the cellular-only coverage area and the dual cellular/wlan coverage area. in this context, coverage means service availability. a mobile node (mn) can be existing at a given time in the coverage area of a cellular alone. but due to mobility it can move into the regions covered by more than one access networks simultaneously within the coverage area of an umts bs and an ieee802.11 ap. multiple 802.11 wlan coverage areas are usually contained within an umts coverage area. horizontal and vertical handoffs can occur in different coverage areas. in this section it is described a model to formulate a multi-service integrated umts/wlan system. figure 1: integrated heterogeneous network it is assumed that a umts network covers k wlans and all the wlans have no overlapping areas and are directly adjacent between two umts networks. for simplicity, we only draw a single bs and some aps in fig.1, although there are many other cellular networks besides the cellular network. however this has no influence on the design and analysis of our handoff algorithm. let the radii of the umts network and wlan is ru and rw respectively. the number of channels to be cu and cw. wlans are usually deployed in an indoor environment, where user mobility level is very low and may significantly differ from that of other areas. hence homogeneous mobility model may not be applicable and it is necessary to differentiate the user mobility characteristics in the double-coverage area from those in the cellular-only area. in the following analysis, a non-uniform model is used to characterize the user mobility within a cell cluster. let tcores denote the residence time that a user stays within the cellular-only area before moving to neighboring cells with probability pcc or to the overlaying wlan with probability pcw and tdcres the user residence time in the double-coverage area. t co res and t dc res are assumed to be exponentially distributed with parameters ηco and η dc, respectively. as shown in [11], for the mn with mean velocity v and uniformly distributed movement direction over [0 − 2π], the average region boundary cross-over rate ç is given by η = v (l/πs), where l and s are the boundary length and area of the region respectively. to analyze the dwell time, we adapt the third model [12] which is defined as the duration that a node stays in a certain region before it moves out of the boundary of the region, of a node optimization of vertical handoff decision algorithm for wireless networks 221 in a region. the third model assumes the nodes are uniformly distributed throughout the whole area and each mn moves in any direction with equal probability. the area of the region in the heterogeneous wireless networks that is in double-coverage defined as sdc and boundary length ldc. similarly the area of the region that is covered by a wlan is defined as sw and boundary length lw. the area of the region that is covered by cellular-only area is sco and boundary length lco. hence the area sdc equals to the sum of sw and sco. the surface area sco = sdc − ksw (1) where k is the kth wlan. to model the mobility, it is defined inter-boundary time similar to [13, 14], as the time interval between any two consecutive access network boundary crossings by a mobile user. the wider the coverage area or the more stationary users, longer the inter-boundary times. if the inter-boundary time starts at the moment of entering cell i, then it is denoted by tcbi. if an interboundary time starts at the moment of entering wlan k then it is twbk. it is assumed that t c bi and t w bk are exponentially distributed with means 1/ ηci and 1/ η w i respectively. hence it is noted that the arrival rates of handoff calls and new calls follow poison distribution, the dwell time in a certain region follows exponential distribution and the call duration time follows exponential distribution with mean value as 1/η. the channel holding time can be defined as the time that a connected mobile user keeps using basic bandwidth resources in each network. for service s, the channel holding times in cell i and in wlan k are obtained as min(trs , t c bi) and min(trs , t w bi) respectively where ts is the connection time of a service s. since t r s , t c bi and twbiare exponentially distributed, the channel holding times are also exponentially distributed with parameters µcis = ϑs + η c i andµ w ks = ϑs + η w k respectively. with the heterogeneous qos support of the underlying structure, the incoming traffic in the double-coverage area should be properly admitted to the cell and wlan. it is assumed that the calls are uniformly distributed in the cellular region, so the call requests in cellular region can be classified as, (i) new call requests to the wlan with arrival rate λwn which is equal to (s w/su)λn where λn is the new call arrival rate, and su is the surface area of the cell. (ii) new call request to the cellular-only area with arrival rate λcon equaling to (s co/su)λn. (iii) new call arrival rate in double-coverage area λdcn which is equal to (s dc/su)λn. (iv) the arrival rate of handoff calls between neighboring cell λcch . (v) the arrival rate of handoff calls from cellular-only area to overlaying wlan λcwh . (vi) the arrival rate of handoff calls from wlan to the overlaying cell λwch . with the above parameters the average dwell time in kth wlan area ,(aw) is 1/ηwk ,where the ηwk = v l w/πsw (2) the average dwell time in double-coverage area (adc) is 1/ηdc where ηdc = v ldc/πsdc (3) the average dwell time in cellular-only area (aco) is 1/ηco where ηco = v lco/πsco (4) consider an mn is in cellular-only area. when mn is moving out of this region, it may enter into adjacent cell or move into wlan region. hence, the average dwell time in cellular-only region before mn moves into another adjacent cell is 1/ηcch where ηcch = v l dc/π(sdc − ksw) (5) 222 e. arun, r.s. moni the average dwell time in cellular-only region before mn moves into wlan area is 1/ηcwh where ηcwh = v l w/π(sdc − ksw) (6) 3 optimal admission control for cellular/wlan interworking with call admission control the heterogeneous network blocks some new call requests in order to reduce interference on the network so that the outage probability decreases. given the cell bandwidth cc and total offered traffic load, the minimum bandwidth needed to meet the requirements of call blocking and dropping probabilities can be obtained as xn. ncn where xn is the bandwidth requirement of a new call and ncn is the maximum number of new call requests allowed in a cell (ncn ≤ cc/xn). it is noted that in cellular-only region only cellular access is available so in this paper randomized guard channel method [15] is applied to give the new and handoff traffic in this area a priority to access the cell bandwidth over the traffic in the doublecoverage area. because the call blocking and dropping probabilities are very sensitive to the amount of reserved bandwidth , the guard bandwidth for high priority call traffic is randomized instead of an integer number of guard channels. each cellular network has cc channels and each wlan has cw channels and cc − ncn channels in cellular network are reserved only for handoff calls. similarly cw − nwn channels in wlan are reserved only for handoff calls; when the occupied channels are less than ncn in cellular networks, the call admission region of the cell is given in terms of (ncn,g c h,g c nh) vectors, in which gcnh(≤ n c n) is a real number to represent a randomized number of guard channels dedicated to new and handoff calls in cellular-only area and gch is the guard bandwidth reserved only for handoff traffic in this region. when the occupied channels are equal to or more than ncn only the handoff calls could be allowed; and the same to the wlans. within the two-tier overlaying structure, the vertical handoff from the cell to the overlaying wlan is not necessary but optional to maintain an ongoing call. hence the handoff traffic load to the wlan can be controlled by properly adjusting the admission parameters to the wlan, by using a simple guard channel method. due to different quality of service support and resource sharing policy in the underlying networks, the configuration of admission regions of the cell and wlan can have a significant impact on the overall system performance. let preqbn ,p req dh and t req d are the requirements of new call blocking and handoff call dropping probabilities and mean transfer rate respectively. then the admission control problem can be formulated as maxnwn λd (7) subject to : pwbn,p dc bn ≤ p req bn ; p co bn ≤ p req bn ; p c dh ≤ p req bh ,e(td) ≤ t req d . where pcobn and p dc bn are the blocking probabilities of the cell for new calls in the cellular-only area and double-coverage area respectively, pcdh is the handoff dropping probability of the cell, pwbnis the probability that a new call is blocked by the wlan and e(td) is the mean transfer rate time. thus the maximization of λd implies a maximization of the total acceptance traffic load and resource utilization. 3.1 new call blocking and dropping probabilities we use a k+1 dimensional markov chain to analyze the guard channel admission algorithm. let (kwn ,k c nco,k c ndc) denote the state of the new call arrival in a cell cluster, where k w n ,k c nco,k c ndc are the numbers of new calls admitted to the wlan, to the cell from the cellular-only area and optimization of vertical handoff decision algorithm for wireless networks 223 to the cell from double-coverage area respectively. first, the number of new calls in the wlan can be described by a birth-death process with respect to kwn . since both new call duration and user residence time in the double-coverage area are exponentially distributed the channel holding time of new calls in wlans, min(trs , t c bi) is exponential with mean 1/µn + η dc where 1/µn is the mean new call duration. then the steady state probability of k new calls in the wlan is obtained based on m/m/k/k loss system, as πwn = ([(λ dc n + λ cw n )/(µn + η dc)]k/k!)/(σ nwn i=0([(λ dc n + λ cw n )/(µn + η dc)]i)/i! (8) hence the new call blocking probability in the wlan is pwbn = π w n (n w n ) (9) in the following, we derive the state-dependent transition rates, which are given by: i) (kcnco,k c ndc) → (k c nco + 1,k c ndc) and k c nco < n c n. this happens when there is a new call request in sco or a handoff call request comes from adjacent cellular networks. since there is no transition at double-coverage area, it is impossible that a handoff call request comes from one of the wlans, otherwise kcndc should be k c ndc − 1. so the transition rate is λcon + λ cc h (10) ii) (kcnco,k c ndc) → (k c nco + 1,k c ndc) and k c nco ≥ ncn this will happen when there is a handoff request comes from adjacent cellular network. because kcnco ≥ ncn it is impossible that a new request call is admitted, and because there is no change of state for the mn in kcndc it is also impossible that a handoff call request comes from wlan. so the transition rate is λcch . (11) iii) (kcnco,k c ndc) → (k c nco,k c ndc +1) and k c ndc < n w n this will happen when there is a new call request in the kth wlan. since there is no channel released in cellular network, it is impossible a handoff call request comes from cellular network. so the transition rate is λwn . (12) iv) (kcnco,k c ndc) → (k c nco,k c ndc − 1) and k c nco = n c c it shows that the channels in wlan have been used up, the channel released in cellular networks might be driven by three events: call finishes communication; call leaves for neighbor cellular network; call leaves for the k number of used-up-channel wlans. hence the transition rate is kcnco(µn + η cc h ) + (k − k)η co n (13) v) (kcnco,k c ndc) → (k c nco,k c ndc − 1) and k c nco = c c it means that the channels in cellular network have been used up. the channel released in the kth wlan may be driven by two events: call finishes its communication and call leaves for the cellular network. so the transition rate is kcndc(µn + η w) (14) vi) (kcnco,k c ndc) → (k c nco,k c ndc − 1)andk c nco < c c this means that the channels in cellular network have not been used up and the channel released in the kth wlan is only driven by the event that call finishes its communication. it is impossible that a call comes from sw to sco otherwise (kcnco should be k c nco + 1. so the transition rate is 224 e. arun, r.s. moni kcndcµn (15) as given in equation [12] and [13] the state departure rates vary with the number of existing new calls in wlan kwn based on which handoff calls from the cell are admitted or blocked by the wlan. hence the new calls admitted to the cell from cellular-only region and doublecoverage region has different mean channel holding time. therefore, the cell can be viewed as a multiservice loss system [16]. a product-form state distribution exists and is insensitive to service time distributions, provided that the resource sharing among services is under coordinate convex policies. this requires that transitions between states come in pairs. for loss systems with trunk reservation like guard channel method, the insensitivity property and product-form solutions are destroyed due to the one-way transitions at some states. a recursive method is proposed in [17] to approximate the state distribution, which is shown to be accurate for a wide range of traffic intensities and when the service rates do not greatly differ from each other. the blocking probabilities are almost insensitive to service time distributions. hence, we use the recursive approximation in [17] to obtain the steadystate probability of new calls admitted into the cell πcn. thus the blocking probabilities of the cell for new calls in the cellular-only region and double-coverage region are given by pcbnco = g c h − ⌊g c h⌋π c n ⌊n c nco⌋ + ncn∑ i=⌊nnco⌋+1 πcn(i) (16) pcbndc = g c nh − ⌊g c nh⌋π c n ⌊n c ndc⌋ + ncn∑ i=⌊nndc⌋+1 πcn(i) (17) and the dropping probabilities of the cell are given by dcn = π c n(n c n) (18) 3.2 average arrival rates of handoff calls the handoff arrival rates are related to the handoff probabilities. the handoff probability of new calls in the cellular-only area to neighbouring cells is denoted as hccnc is given by p ccφ(−µn) where, φ is the moment generating function. similarly, the handoff probability of new calls in the cellular-only area to the overlaying wlan is denoted as hcwnc is given by h cw nc = p cwφ(−µn) with an exponentially distributed user residence time in the double-coverage area, the handoff probability of new calls from wlan to the overlaying cell is hwcnc = η dc/(ηdc + µn) (19) hence the handoff traffic from the wlan to the overlaying cell has a mean arrival rate λwch is given by λwch = h wc nc (λ dc n + λ cw h )(1 − p w bn) (20) the mean arrival rates of handoff traffic between neighbouring cells is λcch = h cc nc[λ co n (1 − p co bn) + (λ wc h + λ cc h )(1 − p co dh) + λ dcc n (1 − p dc bn)h wc n ] (21) and the mean arrival rates of handoff traffic from the cell to the overlaying wlan is optimization of vertical handoff decision algorithm for wireless networks 225 λcwh = h cw nc [λ co n (1 − p co bn) + (λ wc h + λ cc h )(1 − p co dh) + λ dcc n (1 − p dc bn)h wc n ] (22) thus the new call blocking and dropping probabilities can be obtained recursively from (9),(18),(20),(21) and (22). 4 vertical handoff decision making algorithm from the analysis in section 3, it is learnt that if the radius of wlan is fixed, the drop probability and the block probability will be determined by new call arrival rate and handoff call arrival rate. usually, the arrival rates in a short period are invariable. hence the radius of wlan can be adjusted by regulating the transmission power to optimize these block probabilities and drop probabilities. in order to accomplish an optimization, we formulate a combined cost function g = πcn + β1π c n + β2λ wc h + β3λ cc h + β4λ cw h (23) which is determined by the block probabilities and the drop probabilities in cellular networks and one of the wlan, and is a function of the radius of wlans. when the radius of wlan is determined, all the nodes in the wlan should communicate with wlan. even if the channels in wlan are used up and there are free channels in cellular networks, the call request in wlan should not be admitted by cellular network would be blocked or dropped. the communication node shall handoff to wlan if it enters wlan and handoff to cellular network if it leaves current wlan. in the equation (23), βk , k = 1,2,3,4 denotes the weight of dropping probability in wlan, handoff probability in wlan to cellular network, handoff probability in cellular-only area to adjacent cell and handoff probability in cellular network to wlan respectively. as to the new call originates in aw it is covered by cellular network and couldn’t have been admitted by cellular network. according to the above algorithm it loses the opportunity and this will not be happened much often, so the weight of β1 should be bigger than one. as the mn moving from aco to aw the handoff is not required because it could have been using the original channel to communicate, but handoff is required if mn moves from aw to aco otherwise, it will lose the channel of wlan. so the weight of β4 should be somewhat greater than β2 and β3. because terminating an on-going call is far more annoying than refusing to admit a new call from user’s point of view, β4, β3 and β2 should be much bigger than β1. the objective function and its constraints are, ming = πcn + β1π c n + β2λ wc h + β3λ cc h + β4λ cw h (24) such that rmin ≤ rw ≤ rmax , where rmin and rmax are the minimum radius and the maximum radius of the wlan. it is very difficult to determine the optimal radius of wlan by numerical programming methods. simulated annealing (sa) is a stochastic computational technique derived from statistical mechanics for finding near globally minimum-cost solutions to large optimization problems. finding the global minimum value of an objective function with many degrees of freedom subject to conflict ting constraints is an np-complete problem. therefore, the objective function will tend to have many local minima. a procedure for solving optimization problems of the above nature can be implemented by following the successive decent algorithm by kirkpatric et al [18] which follows the evolution of a solid thermodynamic equilibrium with a decreasing succession of temperature values. the procedure is as follows: step 1: begin minimization. 226 e. arun, r.s. moni select an initial radius ri ∈ e randomly for all wlans; select an initial control parameter t greater than 0; select parameter change counter t = 0; repeat; set repetition counter k=0; repeat. step 2: select a new radius rn in ri − 6,ri + 6 randomly, rn is in [rmin,rmax]; compute s = g(rn) − g(r0). step 3: if s less than 0 then r0 := rn goto step 5. step 4: else if rand(0,1) less than exp(-s/t) then ri := rn. step 5: k := k+1. until k = r(t); t:=t+1. step 6: if t greater than 30, then output r0 as the optimal radius. otherwise k := 0 and; goto step 2. 5 performance analysis in this section, we evaluate the performance of the proposed vertical handoff decision algorithm in terms of call blocking/ dropping probabilities. due to the differentiation of new and handoff traffic in different areas, the analysis is very complex. by applying the call admission control algorithm given in section 3, we can obtain the best configuration for admission parameters to maximize the admissible traffic load with the given cell/ wlan cluster. fig.2 shows the relationship between the total acceptance traffic load λd and the maximum number of new calls arrived in the wlan nwn under blocking probabilities ≤ 0.01, dropping probabilities ≤ 0.001 and mean data transfer ≤ 4s. figure 2: max. acceptable data traffic load versus max. number of data it is observed from fig.2 that the total acceptance traffic load increases with nwn when n w n is relatively small. fig.3 illustrates the call-level quality of service performance with different nwn . it is noted that the simulation results of new call blocking and dropping probabilities are very close to the analytical results. the performance fluctuation of handoff dropping probability is due to the maximum number of calls allowed in the cell and wlan are both integer variables. from fig.4 and.5 it is noted that the block probability and drop probability in cellular network optimization of vertical handoff decision algorithm for wireless networks 227 decreases when the radius of wlan becomes larger. this is because more and more of new call requests are admitted by wlan. figure 3: call level qos performance with different 5 figure 4: block probability in cellular networks fig.6 and fig.7 show that the block probability and drop probability in wlan becomes larger with the radius of wlan. the reason here is, when the radius becomes expanding, more and more new calls can be admitted to the wlan, this will result in increase of block probability and drop probability. fig.8 illustrate the optimal radius of wlan under different new call arrival rate and different handoff call arrival rate. it is observed that when the new call arrival rate is fixed, the radius varies consistent with the handoff call arrival rate. this is because the handoff call requests are only sent to the cellular network, which will increase the drop probability in cellular network, and this leads to maximize the cost function. in order to minimize the cost function, the drop probability must be reduced. this is done by redirecting some of the new call request to wlan. it is also noted that the handoff call arrival rate is fixed; the radius varies inversely with the new call arrival rate. this is because the number of channels in cellular network is more than that in wlan. 228 e. arun, r.s. moni figure 5: drop probability in cellular networks figure 6: block probability in wlan figure 7: drop probability in wlan optimization of vertical handoff decision algorithm for wireless networks 229 figure 8: optimal radius of wlan 6 conclusion when connections need to migrate between heterogeneous networks for performance and high availability reasons, then seamless vertical handoff is necessary the first step. in this paper, we tried to highlight the block probability of new calls and drop probability of handoff calls in heterogeneous networks and their computation and the cost function is proposed which is based on the block probabilities and drop probabilities. the optimal radius of wlan is determined using the simulated annealing method. all the mobile nodes entering the scope of wlan should handoff from cellular network to wlan, and the mn leaving the scope should handoff from wlan to cellular network. our performance results based on detailed simulations illustrate that the proposed algorithm could achieve good effects. acknowledgement. this work was supported by aicte, govt. of india, file no : 8023/bor/rid/rps 66/2009-10. bibliography [1] k.maheswary and a.kumar, performance analysis of microcellization for supporting two mobility classes in cellular wireless networks, ieee trans.veh. technolog, 49(2):321-333, 2000 [2] t.klein and s.j.han,assignment strategies for mobile data users in 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[16] k.w.ross, multiservice loss models for broadband telecommunication networks, newyork: springer-verlag, 1995. [17] p/tran-gia and f.hubner, an analysis of trunk reservation and gradeoff service balancing mechanism in broadband networks, in proc.tc6 task group/wg6.4. int’l. workshop, pp.8397, 1993 [18] koza.j.r, genetic programming ii automatic discovery of reusable programs, the mit press 1994. international journal of computers communications & control issn 1841-9836, 11(2):273-281, april 2016. fuzzy b-metric spaces s. nădăban sorin nădăban department of mathematics and computer science aurel vlaicu university of arad, elena drăgoi 2, ro-310330 arad, romania snadaban@gmail.com abstract: metric spaces and their various generalizations occur frequently in computer science applications. this is the reason why, in this paper, we introduced and studied the concept of fuzzy b-metric space, generalizing, in this way, both the notion of fuzzy metric space introduced by i. kramosil and j. michálek and the concept of b-metric space. on the other hand, we introduced the concept of fuzzy quasi-bmetric space, extending the notion of fuzzy quasi metric space recently introduced by v. gregori and s. romaguera. finally, a decomposition theorem for a fuzzy quasipseudo-b-metric into an ascending family of quasi-pseudo-b-metrics is established. the use of fuzzy b-metric spaces and fuzzy quasi-b-metric spaces in the study of denotational semantics and their applications in control theory will be an important next step. keywords: fuzzy b-metric spaces, fuzzy quasi-b-metric, fuzzy quasi-pseudo-bmetric, b-metric space. 1 introduction and preliminaries the concept of b-metric space was introduced by i.a. bakhtin [5] and exensively used by s. czerwic [10, 11]. definition 1. [10] let x be a nonempty set and k ≥ 1 be a given real number. a function d : x ×x → [0,∞) is a b-metric on x if, for all x,y,z ∈ x, the following conditions hold: (b1) d(x,y) = 0 if and only if x = y; (b2) d(x,y) = d(y,x); (b3) d(x,z) ≤ k[d(x,y) + d(y,z)]. the triple (x,d,k) will be called b-metric space. some examples of b-metric spaces and some fixed point theorems in b-metric spaces can be found in [6–8, 21]. we also note that the class of b-metric spaces is larger than that of metric spaces, since every b-metric is a metric when k = 1. in [22] an example of a b-metric space which is not a metric space, is given. recently, m.a. alghamdi, n. hussain, p. salimi [1] introduced the notion of b-metric-like space, which is an interesting generalization of metric-like space (introduced by a. amini-harandi [2]) and partial metric space (introduced by s.g. matthews [17]). in paper [14], n. hussain and m.h. shah introduced the notion of cone b-metric space, generalizing both notions of b-metric spaces and cone metric spaces. the concept of quasi-b-metric space was introduced by m.h. shah and n. hussain [20] in 2012. in this paper we adopt a slight modification of their definition. definition 2. let x be a nonempty set. a real valued function d : x ×x → [0,∞) is said to be a quasi-b-metric with constant k ≥ 1 if the following conditions are satisfied: copyright © 2006-2016 by ccc publications 274 s. nădăban (qb1) d(x,y) = d(y,x) = 0 if and only if x = y; (qb3) d(x,z) ≤ k[d(x,y) + d(y,z)],(∀)x,y,z ∈ x. the triple (x,d,k) will be called quasi-b-metric space. on the other hand, after l.a. zadeh has introduced in his famous paper [23] the concept of fuzzy set, one of the important problems is to obtain an adequate notion of fuzzy metric space. i. kramosil and j. michálek [16] reformulated successfully the notion of probabilistic metric space, introduced by k. menger in 1942, in fuzzy context. definition 3. [19] a binary operation ∗ : [0,1]× [0,1] → [0,1] is called triangular norm (t-norm) if it satisfies the following condition: 1. a∗ b = b∗a,(∀)a,b ∈ [0,1]; 2. a∗1 = a,(∀)a ∈ [0,1]; 3. (a∗ b)∗ c = a∗ (b∗ c),(∀)a,b,c ∈ [0,1]; 4. if a ≤ c and b ≤ d, with a,b,c,d ∈ [0,1], then a∗ b ≤ c∗d. example 4. three basic examples of continuous t-norms are ∧, ·,∗l, which are defined by a∧b = min{a,b}, a·b = ab (usual multiplication in [0,1]) and a∗lb = max{a+b−1,0} (the lukasiewicz t-norm). definition 5. [16] the triple (x,m,∗) is said to be a fuzzy metric space if x is an arbitrary set, ∗ is a continuous t-norm and m is a fuzzy metric, i.e. a fuzzy set in x ×x × [0,∞) such that for all x,y,z ∈ x we have: (m1) m(x,y,0) = 0; (m2) [m(x,y,t) = 1,(∀)t > 0] if and only if x = y; (m3) m(x,y,t) = m(y,x,t),(∀)t ≥ 0; (m4) m(x,z,t + s) ≥ m(x,y,t)∗m(y,z,s),(∀)t,s ≥ 0; (m5) m(x,y, ·) : [0,∞) → [0,1] is left continuous and lim t→∞ m(x,y,t) = 1. we note that a. george and p. veeramani [12] modified the concept of fuzzy metric space introduced by i. kramosil and j. michálek and defined a hausdorff topology on this fuzzy space. another approach for fuzzy metric spaces was introduced by o. kaleva and s. seikkala in paper [15], by setting the distance between two points to be a non-negative, upper semicontinuous, normal and convex fuzzy number. in recent years, different types of fuzzy generalized metric spaces was considered by different authors in different approaches. thus, v. gregori and s. romaguera introduced in paper [13] the concept of fuzzy quasi-metric space, generalizing in this way the notions of fuzzy metric introduced by i. kramosil and j. michálek and by a. george and p. veeramani to the quasimetric setting. on the other hand, the idea of fuzzy cone metric space has been introduced in [3] and some basic properties and fixed point theorems for different types of contraction mappings have been developed in fuzzy cone metric spaces. in paper [4], t. bag introduced the concept of fuzzy fuzzy b-metric spaces 275 cone b-metric space and some fixed point theorems are established in such spaces for contraction mappings. we must note that bag’s definitions for fuzzy cone metric space and for fuzzy cone b-metric spaces generalized the notion of fuzzy metric introduced by kaleva and seikkala. in this paper we introduced and studied the concept of fuzzy b-metric space, generalizing, in this way, both the notion of fuzzy metric space introduced by i. kramosil and j. michálek and the concept of b-metric space. on the other hand, we introduced the concept of fuzzy quasi-bmetric space, extending the notion of fuzzy quasi-metric space recently introduced by v. gregori and s. romaguera. finally, a decomposition theorem for a fuzzy quasi-pseudo-b-metric into an ascending family of quasi-pseudo-b-metrics is established. 2 fuzzy b-metric spaces definition 6. let x be a nonempty set, let k ≥ 1 be a given real number and ∗ be a continuous t-norm. a fuzzy set m in x × x × [0,∞) is called fuzzy b-metric if, for all x,y,z ∈ x, the following conditions hold: (bm1) m(x,y,0) = 0; (bm2) [m(x,y,t) = 1,(∀)t > 0] if and only if x = y; (bm3) m(x,y,t) = m(y,x,t),(∀)t ≥ 0; (bm4) m(x,z,k(t + s)) ≥ m(x,y,t)∗m(y,z,s),(∀)t,s ≥ 0; (bm5) m(x,y, ·) : [0,∞) → [0,1] is left continuous and lim t→∞ m(x,y,t) = 1. the quadruple (x,m,∗,k) is said to be a fuzzy b-metric space. remark 7. the class of fuzzy b-metric spaces is larger than the class of fuzzy metric spaces, since a fuzzy b-metric space is a fuzzy metric space when k = 1. example 8. let (x,d,k) be a b-metric space. let md : x ×x × [0,∞) → [0,1],md(x,y,t) = { t t+d(x,y) if t > 0 0 if t = 0 . then (x,md,∧,k) is a fuzzy b-metric space. md will be called standard fuzzy b-metric. proof: we check only (bm4), because verifying the other conditions is standard. let x,y,z ∈ x and t,s > 0. without restraining the generality we assume that md(x,y,t) ≤ md(y,z,s). thus tt+d(x,y) ≤ s s+d(y,z) , i.e. td(y,z) ≤ sd(x,y). on the other hand md(x,z,k(t + s)) = k(t + s) k(t + s) + d(x,z) ≥ ≥ k(t + s) k(t + s) + k[d(x,y) + d(y,z)] = t + s t + s + d(x,y) + d(y,z) . we will prove that t + s t + s + d(x,y) + d(y,z) ≥ t t + d(x,y) . hence we will obtain that md(x,z,k(t + s)) ≥ md(x,y,t) = md(x,y,t) ∧md(y,z,s), what had to be verified. we remark that t + s t + s + d(x,y) + d(y,z) ≥ t t + d(x,y) ⇔ 276 s. nădăban t2 + st + td(x,y) + sd(x,y) ≥ t2 + st + td(x,y) + td(y,z) ⇔ sd(x,y) ≥ td(y,z) , which is true. 2 definition 9. let k ≥ 1 be a real given number. a function f : r → r will be called knondecreasing if for t < s we will have that f(t) ≤ f(ks). proposition 10. for all x,y ∈ x the mapping m(x,y, ·) : [0,∞) → [0,1] is k-nondecreasing. proof: for 0 < t < s we have m(x,y,ks) ≥ m(x,x,s− t)∗m(x,y,t) = 1∗m(x,y,t) = m(x,y,t) . 2 theorem 2.1. let (x,m,∗,k) be a fuzzy b-metric space. for x ∈ x,r ∈ (0,1), t > 0 we define the open ball b(x,r,t) := {y ∈ x : m(x,y,t) > 1−r} . then tm := {t ⊂ x : x ∈ t iff (∃)t > 0,r ∈ (0,1) : b(x,r,t) ⊆ t} is a topology on x. proof: it is obvious that ∅ and x belong to tm . let {ti}i∈i ⊆tm and t = ∪ i∈i ti. we will show that t ∈tm . let x ∈ t . then there exists i0 ∈ i such that x ∈ ti0 . as ti0 ∈ tm , there exist t > 0, r ∈ (0,1) such that b(x,r,t) ⊆ ti0 . thus b(x,r,t) ⊆ ∪ i∈i ti = t . let now {ti}ni=1 ⊆ tm and t = n∩ i=1 ti. we will show that t ∈ tm . let x ∈ t . we obtain that x ∈ ti,(∀)i = 1,n. thus (∃)ti > 0,ri ∈ (0,1) : b(x,ri, ti) ⊆ ti,(∀)i = 1,n . let r = min{ri, i = 1,n}, t = min { ti k ,i = 1,n } . we have that b(x,r,t) ⊆ b(x,ri, ti),(∀)i = 1,n. indeed, for y ∈ b(x,r,t), we have m(x,y,t) > 1 − r ≥ 1 − ri,(∀)i = 1,n. as t ≤ tik ,(∀)i = 1,n, we obtain that m(x,y,t) ≤ m(x,y,ti). thus m(x,y,ti) > 1−ri,(∀)i = 1,n. hence y ∈ b(x,ri, ti),(∀)i = 1,n. therefore b(x,r,t) ⊆ ti,(∀)i = 1,n. thus b(x,r,t) ⊆ n∩ i=1 ti = t . 2 remark 11. previous theorem extends to fuzzy b-metric space a similar result obtained by a. george and p. veeramani [12] in the context of fuzzy metric space. the definitions for convergent sequence and cauchy sequence given by a. george and p. veeramani [12] in the context of fuzzy metric space can be translated in the context of fuzzy b-metric space, as follows. definition 12. let (x,m,∗,k) be a fuzzy b-metric space and (xn) be a sequence in x. the sequence (xn) is said to be convergent if there exists x ∈ x such that m(xn,x,t) = 1,(∀)t > 0. in this case, x is called the limit of the sequence (xn) and we note lim n→∞ xn = x, or xn → x. remark 13. let (x,m,∗,k) be a fuzzy b-metric space. a sequence (xn) is convergent to x if and only if (xn) is convergent to x in topology tm . fuzzy b-metric spaces 277 indeed, xn → x in topology tm ⇔ ⇔ (∀)r ∈ (0,1),(∀)t > 0,(∃)n0 ∈ n : xn ∈ b(x,r,t),(∀)n ≥ n0 ⇔ ⇔ (∀)r ∈ (0,1),(∀)t > 0,(∃)n0 ∈ n : m(xn,x,t) > 1−r,(∀)n ≥ n0 ⇔ ⇔ lim n→∞ m(xn,x,t) = 1,(∀)t > 0 . definition 14. let (x,m,∗,k) be a fuzzy b-metric space and (xn) be a sequence in x. the sequence (xn) is said to be a cauchy sequence if (∀)r ∈ (0,1),(∀)t > 0,(∃)n0 ∈ n : m(xn,xm, t) > 1−r,(∀)n,m ≥ n0 . a fuzzy b-metric space in which every cauchy sequence is convergent is called complete fuzzy b-metric space. 3 fuzzy quasi-b-metric spaces definition 15. a fuzzy quasi-b-metric space is a quadruple (x,m,∗,k), where x is a nonempty set, ∗ is a continuous t-norm, k ≥ 1 is a given real number and m is a fuzzy set in x×x×[0,∞) such that for all x,y,z ∈ x we have: (qbm1) m(x,y,0) = 0; (qbm2) [m(x,y,t) = m(y,x,t) = 1,(∀)t > 0] if and only if x = y; (qbm3) m(x,z,k(t + s)) ≥ m(x,y,t)∗m(y,z,s),(∀)t,s ≥ 0; (qbm4) m(x,y, ·) : [0,∞) → [0,1] is left continuous and lim t→∞ m(x,y,t) = 1. remark 16. v. gregori and s. romaguera [13] also gave this definition in the particular case k = 1 and the triple (x,m,∗) is called fuzzy quasi-metric space. proposition 17. if q is a fuzzy quasi-b-metric, then q−1 defined by q−1(x,y,t) = q(y,x,t) is also a fuzzy quasi-b-metric (called the conjugate of q). proof: we have to check only (qbm3). q−1(x,z,k(t + s)) = q(z,x,k(s + t)) ≥ q(z,y,s)∗q(y,x,t) = q−1(x,y,t)∗q−1(y,z,s) . 2 definition 18. [18]. let ∗,◦ be two t-norms. we say that ◦ dominates ∗ and we denote ◦≫∗ if (x1 ◦x2)∗ (y1 ◦y2) ≤ (x1 ∗y1)◦ (x2 ∗y2),(∀)x1,x2,y1,y2 ∈ [0,1]. remark 19. [18]. for any t-norm ∗ we have ∧≫∗. proposition 20. let (x,q,∗,k) be a fuzzy quasi-b-metric space and ◦ be a continuous t-norm such that ◦≫∗. let m be a fuzzy set in x ×x × [0,∞) defined by m(x,y,t) = q(x,y,t)◦q−1(x,y,t) . then (x,m,∗,k) is a fuzzy b-metric space. 278 s. nădăban proof: it is easy to check (bm1)− (bm3) and (bm5). we prove (bm4). m(x,z,k(t + s)) = q(x,z,k(t + s))◦q−1(x,z,k(t + s)) ≥ ≥ [q(x,y,t)∗q(y,z,s)]◦ [q−1(x,y,t)∗q−1(y,z,s)] ≥ ≥ [q(x,y,t)◦q−1(x,y,t)]∗ [q(y,z,s)◦q−1(y,z,s)] = m(x,y,t)∗m(y,z,s) . 2 corollary 21. let (x,q,∗,k) be a fuzzy quasi-b-metric space and m(x,y,t) = min{q(x,y,t),q(y,x,t)} . then (x,m,∗,k) is a fuzzy b-metric space. proof: we apply previous proposition for ◦ = ∧≫∗. 2 example 22. let (x,d,k) be a quasi-b-metric space. let md : x ×x × [0,∞) → [0,1],md(x,y,t) = { t t+d(x,y) if t > 0 0 if t = 0 . then (x,md,∧,k) is a fuzzy quasi-b-metric space. md will be called standard fuzzy quasi-bmetric. proof: the proof is standard. 2 proposition 23. if (x,m,∗,k) is a fuzzy quasi-b-metric space, then the relation ≤m on x defined by x ≤m y if and only if m(x,y,t) = 1,(∀)t > 0 is a partial ordering. proof. it is easy to check. 4 fuzzy quasi-pseudo-b-metric spaces definition 24. let x be a nonempty set. a function d : x ×x → [0,∞) is called quasi-pseudob-metric with constant k ≥ 1 if the following conditions are satisfied: (qpb1) d(x,x) = 0; (qpb3) d(x,z) ≤ k[d(x,y) + d(y,z)],(∀)x,y,z ∈ x. the triple (x,d,k) will be called quasi-pseudo-b-metric space. definition 25. a fuzzy quasi-pseudo-b-metric space is a quadruple (x,m,∗,k), where x is a nonempty set, ∗ is a continuous t-norm, k ≥ 1 is a given real number and m is a fuzzy set in x ×x × [0,∞) such that for all x,y,z ∈ x we have: (qpbm1) m(x,y,0) = 0; (qpbm2) [m(x,x,t) = 1,(∀)t > 0]; (qpbm3) m(x,z,k(t + s)) ≥ m(x,y,t)∗m(y,z,s),(∀)t,s ≥ 0; fuzzy b-metric spaces 279 (qpbm4) m(x,y, ·) : [0,∞) → [0,1] is left continuous and lim t→∞ m(x,y,t) = 1. theorem 4.1. let (x,m,∧,k) be a fuzzy quasi-pseudo-b-metric space and dα(x,y) := inf{t > 0 : m(x,y,t) > α},α ∈ (0,1) . then d = {dα}α∈(0,1) is an ascending family of quasi-pseudo-b-metrics on x. proof: (qp1) dα(x,x) = inf{t > 0 : m(x,x,t) > α} = 0. (qp2) k[dα(x,y) + dα(y,z)] = k[inf{t > 0 : m(x,y,t) > α}+ inf{s > 0 : m(y,z,s) > α}] = = k[inf{t + s > 0 : m(x,y,t) > α,m(y,z,s) > α}] = = inf{k(t + s) > 0 : m(x,y,t)∧m(y,z,s) > α}≥ ≥ inf{k(t + s) > 0 : m(x,z,k(t + s)) > α} = dα(x,z) . it remains to prove that d = {dα}α∈(0,1) is an ascending family. let α1 ≤ α2. then {t > 0 : m(x,y,t) > α2}⊆{t > 0 : m(x,y,t) > α1} . thus inf{t > 0 : m(x,y,t) > α2}≥ inf{t > 0 : m(x,y,t) > α1} , namely dα2(x,y) ≥ dα1(x,y),(∀)(x,y) ∈ x ×x. 2 5 conclusions and further works in this paper we introduce the notions of fuzzy b-metric space and fuzzy quasi-b-metric space. thus, we have built a fertile ground to study, in further papers, some fixed point theorems in these spaces. the first problem is to established fuzzy versions of banach contraction mapping principle in fuzzy b-metric spaces. from here we will obtain a lot of applications both in mathematics as well as in engineering and computer science. the second issue is to study set-valued contractions in fuzzy b-metric spaces and their applications in control theory and convex optimization. a real challenge is to extend the results of c. chifu and g. petruşel [9] in fuzzy b-metric spaces. we intend to obtain some fixed point theorems for multivalued operators in fuzzy b-metric spaces endowed with a graph. this paper may be of interest for researchers working in the following fields belonging to computer science and information technology: (i) integrated solution in computer-based control and communications (ii) computational intelligence methods (iii) advanced decision support systems where fuzzy metric spaces will be applied in dealing with the problems such as: fixed point theorems and their applications in the semantics of programs; distance measurement between programs with important results to measure the complexity of programs and algorithms; color image processing and image denoising; the use of some types of fuzzy metrics in cognitive information, in time series and in bioinformatics; the appplications in neural networks; data mining and web mining applications. 280 s. nădăban bibliography [1] alghamdi, m.a., hussain, n., salimi, p. 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(1965); fuzzy sets, informations and control, 8: 338–353. 8precup.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):843-855, december, 2015. a unified anti-windup technique for fuzzy and sliding mode controllers r.-e. precup, m.l. tomescu, e.m. petriu radu-emil precup* politehnica university of timişoara department of automation and applied informatics bd. v. parvan 2, 300223 timisoara, romania *correspondig author: radu.precup@upt.ro marius l. tomescu aurel vlaicu university of arad faculty of computer science complex universitar m, str. elena dragoi 2, 310330 arad, romania e-mail: tom_uav@yahoo.com emil m. petriu university of ottawa school of electrical engineering and computer science 800 king edward, ottawa, on, k1n 6n5 canada e-mail: petriu@uottawa.ca abstract: this paper proposes the unified treatment of an anti-windup technique for fuzzy and sliding mode controllers. a back-calculation and tracking anti-windup scheme is proposed in order to prevent the zero error integrator wind-up in the structures of state feedback fuzzy controllers and sliding mode controllers. the state feedback sliding mode controllers are based on the state feedback-based computation of the switching variable. an example that copes with the position control of an electro-hydraulic servo-system is presented. the conclusions are pointed out on the basis of digital simulation results for the state feedback fuzzy controller. keywords: anti-windup technique, electro-hydraulic servo-system, fuzzy control, saturation, sliding mode control, digital simulation. 1 introduction there are many situations in industrial control applications when a mismatch between the control signal (controller output) and the input of the process occurs. the saturation of controller output due to the functionality of the controller is such a natural process. the anti-windup techniques correct the controller output in the case of controllers with integral (i) component, but other components of the controllers can cause the saturation. some discussions on the strong or weak impacts of the integrator wind-up and of saturation are presented in [1–5]. the anti-windup is applied to nonlinear systems including sliding mode control systems and fuzzy control systems, with representative examples given in [6–9]. a static anti-windup compensator for linear sliding mode controllers is designed in [10] on the basis of linear matrix inequality (lmi) conditions derived from lyapunov stability and l2-gain performance. the switching function of sliding mode controllers is modified in [11] to reduce the discontinuous component of the control signal during saturation. an adaptive anti-windup pid sliding mode scheme is proposed in [12]. the sliding surface for robust saturated sliding mode control is designed in [13] as a problem of root clustering. a dead-zone technique is employed in [9] in the framework of adaptive sliding mode control combined with fuzzy logic. the necessity of anti-windup measures in fuzzy copyright © 2006-2015 by ccc publications 844 r.-e. precup, m.l. tomescu, e.m. petriu control is pointed out in [14] and focused on mamdani proportional-integral-fuzzy controllers. the fuzzy models of nonlinear models are involved in [15] in the design of piecewise fuzzy antiwindup dynamic output feedback controllers based on piecewise quadratic lyapunov functions. starting with a stabilizing dynamic output feedback fuzzy controller, an anti-windup block is designed in [16] to maximize the size of estimate of the domain of attraction, and sufficient lyapunov-krasovskii stability conditions are derived. this paper suggests the application of a back-calculation and tracking anti-windup scheme to the zero error integrator in the framework of state feedback fuzzy controllers and state feedback sliding mode controllers. the specific feature of state feedback sliding mode controllers is the state feedback-based computation of the switching variable. the paper is supported by our previous results in fuzzy control [17–24], and proposes the unified treatment of anti-windup techniques in fuzzy and sliding mode controllers. this leads to good effects on the overall control system performance. the expression of the parameters of the anti-windup block is given, but these parameters can be optimized in terms of, for example, the optimal tuning of the anti-windup tracking gain carried out in [25]. this paper represents a step forward in the systematic design of fuzzy control systems, pointed out by prof. zadeh in [26] and [27]. the mathematics of fuzzy sets must be incorporated in the structure of fuzzy controllers by appropriate operators and parameters [28–30, 32, 32–34]. the model-based design using fuzzy models is emphasized in [35–45], but neural networks are used in [46–48]. this paper also represents a step forward in the systematic design of sliding mode control systems in the context of other popular techniques [49–53]. the parameters of both fuzzy and sliding mode controllers can be optimally tuned by means of appropriate optimization problems and algorithms [54–58]. the paper is organized as follows: section 2 is dedicated to the modeling of state feedback sliding mode and fuzzy control systems. the proposed back-calculation and tracking anti-windup scheme is presented in section 3. section 4 applies the scheme to the position control of an electro-hydraulic servo-system and simulation results are included. the conclusions are outlined in section 5. 2 models of state feedback sliding mode and fuzzy control systems the unified structure of state feedback sliding mode and fuzzy control systems is presented in figure 1, where: w the reference input, v the disturbance input, u the control signal, y the controlled output, e = w = y the control error, xp the state vector of the process p, supposed to be observable and controllable, xp ∈ rn, zei the zero error integrator (to obtain the zero steady-state value of the control error), xr the nominal integrator output, xrlthe saturated (limited) integrator output, rb the reference block, sb the switching block, g the switching variable, {kr, kw, ti, u0,ktp } the parameters of the sliding mode controller (smc), t stands for matrix transposition, ktp ∈ r 1×n, {−xl, xl} the limits of the saturation element that belongs to zei, xl = const > 0. the structure given in figure 1 is obtained by the appropriate transformation and extension of the sliding mode control scheme presented in [59], the abbreviation fc indicates the fuzzy controller, and the detailed structure of smc is illustrated in the lower part of figure 1. both controllers are presented as nonlinear blocks in figure 1. the p in the smc is described by the nth order state-space model: a unified anti-windup technique for fuzzy and sliding mode controllers 845 figure 1: unified structure of state feedback sliding mode and fuzzy control systems. ẋp = apxp + bpu + bpvv, y = ctp xp, (1) where ap ∈ rn×n, bp ∈ rn×1, bpv ∈ rn×1, ctp ∈ r 1×n. introducing the extended state vector: x = [ xtp xr ] t , (2) the state-space model of cp and zei is: ẋ = ax + bu + bvv + bww, y = ctx, (3) with the matrices: a = [ ap 0 −(1/ti)c t p 0 ] ,b = [ bp 0 ] ,bv = [ bpv 0 ] ,bw = [ 0 1/ti ] ,ct = [ ctp 0 ]. (4) the variable structure control law specific to smc is of relay type: u = u0sgn(g(x)), g(x) = −k t x + kww,k t = [ ktp −kr ], (5) where the matrix kt characterizes the switching hyper-plane and u0 = const > 0 is the absolute value of the control signal. the analysis of the control system in sliding mode is supported by the equivalent control method [60] resulting in the state-space equations of the control system in sliding mode: ẋ = asx + bsvv + b s ww + b s ẇẇ, (6) with the matrices: a s = [ mpap − [kr/(tik t p bp)]bpc t p 0 −(1/ti)c t p 0 ] ,bsv = [ mpbpv 0 ] ,mp = i − [1/(k t p bp)]bpk t p , b s w = [ [kr/(tik t p bp)]bp 1/ti ] ,bsẇ = [ kw/(k t p bp) 0 ] , (7) 846 r.-e. precup, m.l. tomescu, e.m. petriu and i is the nth order identity matrix. equation (6) does not highlight the reaching mode, but its effect can be highlighted by the proper modification of the initial conditions. the sliding mode existence condition involves the equivalent control signal ueq: |ueq| < u0, ueq = [1/(k t p bp)] · [krxr − k t p (apxp + bpvv) + kwẇ]. (8) all above equations specific to smc correspond to the case of zei that operates in its linear operating mode, i.e. the zei is not in saturation: xrl = xr. (9) it is very convenient to use equation (6) in the analysis and design of the smc as it is linear and it characterizes with an acceptable accuracy the behavior of a nonlinear control system (the sliding mode one). similar models for the fc will be presented as follows. the rule base of the continuous-time dynamic takagi-sugeno (t-s) fuzzy model of p consists of nr rules, ri, i = 1...nr. each rule is assigned to the following continuous-time state-space model in its consequent, namely to a local linear model of p: ẋp = apixp + bpiu, y = ctpixp, i = 1...nr, (10) where api ∈ rn×n, bpi ∈ rn×1, ctpi ∈ r 1×n, and the disturbance input is omitted for simplicity. the complete rule base of the continuous-time dynamic t-s fuzzy model of p is: ri : if z1is lt i z1 and z2 is lt i z2 and ... and zm is lt i zm then { ẋp = apixp + bpiu y = ctpixp , i = 1...nr, (11) where z = [ z1 z2 ... zm ]t is the scheduling vector, i.e., the input vector, which contains the measurable variables of p, zk, k = 1...m, and lt izk are the input linguistic terms with the input membership functions µizk(zk). using the sum and prod operators in the inference engine and the weighted average defuzzification method, the firing strengths (of the rules) are: wi(z) = m ∏ k=1 µizk(zk), i = 1...nr, (12) the normalized firing strengths are: hi(z) = wi(z)/[ nr ∑ i=1 wi(z)], i = 1...nr, (13) and the continuous t-s fuzzy model of p is expressed in the state-space form: ẋp = nr ∑ i=1 [hi(z)(apixp + bpiu)], y = nr ∑ i=1 [hi(z)c t pixp]. (14) in parallel distributed compensation (pdc) the structure of the fc model matches the structure of the fuzzy model of p given in (11). considering the absence of the blocks rb and zei, the pdc controller for the system (11) is: a unified anti-windup technique for fuzzy and sliding mode controllers 847 u = − nr ∑ i=1 [hi(z)f t i xp], (15) and the goal of fc design is to obtain the gain matrices fti , i = 1...nr, f t i ∈ r 1×n, of the nonlinear state-feedback control law (15) such that the closed-loop system is stable and eventually robust. as outlined in [61], many design problems derive the least conservative conditions related to the condition: nr ∑ i=1 nr ∑ j=1 [hi(z)hj(z)γij] < 0,γij = γij t . (16) considering the fuzzy control system structure according to figure 1, the control law of the fc given in (15) is modified as follows in the specific case of zei that operates in its linear operating mode: u = kww + krxr − nr ∑ i=1 [hi(z)f t i xp]. (17) using (2), (17) and figure 1 in (14), the state-space equations of the fuzzy control system are: ẋ = asx + bsww, (18) with the matrices: a s =     nr ∑ i=1 [hi(z)api] − [ nr ∑ i=1 [hi(z)bpi] ] · [ nr ∑ i=1 [hi(z)f t i ] ] kr nr ∑ i=1 [hi(z)bpi] −(1/ti) nr ∑ i=1 [hi(z)c t pi] 0     , b s w =   kw nr ∑ i=1 [hi(z)bpi] 1/ti   . (19) the models (6) (of the sliding mode control system) and (18) (of the fuzzy control system) are similar. moreover, the gain matrices fti of fc are similar to the gain matrix k t p of smc (illustrated in figure 1). this justifies the unified treatment of anti-windup techniques for fuzzy and sliding mode controllers. however, sliding mode and fuzzy control systems have different structures; although the fuzzy control system is more complicated, it is not constrained to enter the sliding mode related to the sliding mode existence condition. if zei enters saturation, i.e.: xrl = xr lim, xr lim ∈ {−xl, xl}, (20) the updated expression of the switching variable is: g(xp) = −k t p xp + kww + krxr lim, (21) and the equivalent control method leads to the updated equivalent control signal: ueq = [1/(k t p bp)] · [−k t p (apxp + bpvv) + kwẇ], (22) and to the updated state-space equations of p in sliding mode: ẋp = a s pxp + b s pvv + b s pẇẇ, (23) 848 r.-e. precup, m.l. tomescu, e.m. petriu with the matrices: a s p = mpap,b s pv = mpbpv,b s pẇ = [kw/(k t p bpv)]bp. (24) hence, at least one negative effect on control system behavior can be observed when the saturation of the zei occurs or when the integrator output is maintained in saturation/outside the limits on a too large time interval. this consists in the difficult fulfillment of the sliding mode existence condition due to the different expressions of ueq in (8) and (22). therefore, anti-windup techniques are necessary not just to get zei out of saturation, but just to avoid exaggerate exceeds of limitation. 3 back-calculation and tracking anti-windup scheme the state-space equation of i that belongs to zei is: ẋr = (1/ti)e. (25) equation (25) is also kept in the presence of windup, but with the substitution of e with another i input, el, chosen such that to keep zei in saturation, i.e., (9) is applied, therefore: ẋrl = (1/ti)el. (26) equation (25) holds in the absence of windup, and equation (26) corresponds to the presence of windup. subtracting (26) from (25) results in: el = e − ti(ẋr − ẋrl). (27) the structure of zei with back-calculation and tracking anti-windup scheme is built using (27) and given in figure 2. bct in figure 2 represents the back-calculation and tracking block, with pure derivative character, modeled by the transfer function: hbct (s) = tis. (28) figure 2: unified structure of zei with back-calculation and tracking anti-windup scheme. the proposed anti-windup technique has a shortcoming, namely it does not operate if xr enters saturation, and it is constant, or if the difference (xr − xrl) is constant. therefore, the modified bct block (mbct) is of lead-lag type with the following transfer function: hmbct (s) = (tis + kaw )/(1 + taw s), (29) where the time constant taw (a small value) is necessary in order to make possible the implementation of mbct. the anti-windup tracking gain kaw can take any positive value. however, an as small as possible value of kaw is recommended because: (i) the strong feedback derivative action already determines the integrator to stay very close to the saturation limit, (ii) a large value of kaw could lead to stability problems investigated in [62]. a unified anti-windup technique for fuzzy and sliding mode controllers 849 4 position control application. simulation results an example of nonlinear electro-hydraulic system [63] is taken into consideration in order to illustrate the advantages of the proposed back-calculation and tracking anti-windup scheme in the context of state feedback sliding mode control. the simplified structure of the electrohydraulic system meant for position (y) control is presented in figure 3. the parameters of p are: g0 = 0.0625, ti1 = 0.002s, ti2 = 0.065s, xl = 0.5. figure 3: simplified structure of p. the design of the smc is performed according to [59], but for n = 2, and the following values of controller parameters are obtained: kx1 = 1, kx2 = 0.13, ktp = [ kx1 kx2 ] = [ 1 0.13 ], kw = 0.13, kr = 32.5, ti = 0.1s, u0 = 15. the overall control system is referred to as electrohydraulic servo-system. the designed sliding mode control system was tested with respect to the modifications of w using the nonlinear model of p. figure 4 gives the control system response (the control system output y with continuous line, w with dash dotted line, xrl with continuous line) without limitations imposed to zei. figure 4 shows that y tracks the imposed w. figure 5 gives the figure 4: control system response without limitations. control system response without anti-windup technique. the harmful effect of the limitation is illustrated by the fact that y does not track anymore w, and xr stays during a relatively long time period in limitation within the time interval [1s, 3s]. figure 6 gives the control system response with classical back-calculation and tracking antiwindup scheme using the same w and conditions as in figure 4. the control system performance is improved in comparison with the case without back-calculation and tracking anti-windup scheme. figure 7 gives the control system response that incorporates the proposed modified backcalculation and tracking anti-windup scheme using the same w and conditions as in figure 4, and 850 r.-e. precup, m.l. tomescu, e.m. petriu figure 5: control system response without bct. figure 6: control system response with bct. for kaw = 0.02 and taw = 0.01s. figure 7 shows the control system performance improvement compared to all previous cases. one relatively minor shortcoming concerns the slightly larger effect of the chattering phenomenon. 5 conclusions the paper has proposed an approach to the unified treatment of anti-windup in fuzzy and sliding mode controllers. the unified models of fuzzy control systems and sliding mode control systems have been suggested. an analysis of the possibility to apply the back-calculation and tracking anti-windup scheme to the zero error integrator belonging to a state feedback sliding mode controller has been carried out. a modified back-calculation and tracking anti-windup scheme applicable to these controller structures has been suggested. the future work will be dedicated to the discrete time formulation of the controllers and of the anti-windup schemes and to the stability analysis because the control systems stability can be affected or not by windup. the parameters of the anti-windup schemes will be tuned by a unified anti-windup technique for fuzzy and sliding mode controllers 851 figure 7: control system response with mbct. several optimization algorithms in the context of appropriate optimization problems. acknowledgments this work was supported by a grant of the romanian national authority for scientific research, cncs uefiscdi, project number pn-ii-id-pce-2011-3-0109, by a grant from the partnerships in priority areas pn ii program of the romanian national authority for scientific research ancs, cndi uefiscdi, project number pn-ii-pt-pcca-2011-3.2-0732, by grants from the partnerships in priority areas pn ii program of the romanian ministry of education and research (medc) the executive agency for higher education, research, development and innovation funding (uefiscdi), project numbers pn-ii-pt-pcca-2013-4-0544 and pn-ii-pt-pcca-2013-4-0070, and by a grant from the nserc of canada. bibliography [1] bohn, c.; 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(1986); aspects concerning the algorithmic design of electro-hydraulic servosystems for speed controllers, proceedings of ccsiteh national symposium, timisoara, romania, pp. 143-149 (in romanian). int j comput commun, issn 1841-9836 9(3):276-283, june, 2014. enhancing nodes lifetime optimum protocol for dissemination of information in wsn m. gholami, a. panahi mohammadreza gholami department of electrical engineering, saveh branch, islamic azad university, saveh, iran e-mail: reza5762@ yahoo.com abdorreza panahi* department of mathematics, saveh branch, islamic azad university, saveh, iran *corresponding author: panahi53@ gmail.com abstract: challenging issue in wireless sensor networks (wsns) is assessment of energy and lifetime at different nodes within the networks. various methods may be employed to improve lifetime (reduce energy consumption). one such method involves balancing loads on nodes when data is being transmitted from source to destination nodes. multi-path routing techniques can be used for this purpose. in these techniques no global information is available regard the path, making it difficult to create multipath routes from sources nodes to destination nodes. another problem with these networks is a routing applied to source nodes independently from that applied used for destination nodes. this creates energy loss and reduces lifetime. to overcome this problem, the present paper makes use of clustering by selecting virtual nodes to gather information from sources and sending it to destination nodes. the new protocol for enhancing nods lifetime (penl) is implemented through ns-2. keywords: wireless sensor networks (wsn), optimum routing, dissemination of information, enhancing nodes lifetime. 1 introduction sensor networks have experienced a considerable growth over the recent years [4]. these networks are composed of a large number of nodes of very small sensors used to collect and process environmental information [5]. nodes in sensor networks usually do not have unique addresses, and the information collected through nodes is of greater importance [6]. in addition, the nodes become inaccessible once they are distributed in the environment. they become useless (reach the end of their lifetime) once they consume the available energy [8]. therefore, energy and its optimization is a major challenge is sensor network and received attention from a large body of research over the past years [1, 3]. to this date, sensor networks have found increasing applications in different areas including military, environmental monitoring, medicine, agriculture, and so on. one data-centric method proposed for routing data in sensor networks is directed diffusion [7], in which nodes use only local data in routing packets. in this method, interest packets are disseminated over the network and to all nodes by basic nodes. then the nodes containing information of interest (information sources) receive these packets and direct collected information to the destination node. the present paper attempts to overcome problems (e.g. late aggregation, extra explanatory data, and high levels of power consumption) experienced in previous protocols. figure 1 illustrates the process. copyright © 2006-2014 by ccc publications enhancing nodes lifetime optimum protocol for dissemination of information in wsn277 figure 1: comparison between routing in mech (left one) and penl (right one) the method uses a virtual node in vicinity of source nodes to collect information and send it to destination nodes. this paper presents penl and uses the routing method described above to improve lifetime and reduce overload compared to mech [2], as we shall see in simulation results presented in the final section of this paper. 2 maximum energy cluster head in mech [2], source and destination nodes use characteristics of the graph to determine information that needed to be disseminated and to find a multi-direction efficient path connecting source and destination nodes. to send data, an interest message is disseminated over an area of interest in the network. each node remembers the node through which it has received information and assigns a gradient to that node. the gradient represents both the direction of information flow and the status of query (which can be active or inactive). if the node is able to predict the next path using the gradient, then it delivers the query to an adjacent node related to that query; otherwise, the query will be sent to all adjacent nodes. the sending node will be recognized as a source. when being send to destination, data is stored in intermediate nodes in order to prevent repeated sending. if one node stops working, other nodes will try to locally recover the path. once initial exploratory data are sent, the next data will be sent only through reinforced paths. source nodes alternatively send exploratory data from time to time to update gradients based on dynamic changes in network. properties of mech: • mech uses neighbor-to-neighbor or step-by-step in which each node can interpret data. • information diffusion is a data-centric method and all connections in a wsn use interests to determine named data for dissemination. • nodes are not assigned globally unique addresses and since each node can individually interpret data, it is possible to reduce data load and send data in concise form. 278 m. gholami, a. panahi figure 2: mech protocol a drawback of mech is the increased number of data steps which, in turn, shortens lifetime of nodes and the overall network. npdi, described below, overcomes these issues. 3 the proposed protocol (penl) as seen in c, an appropriate node close to source nodes is selected as virtual node. in d, the virtual node creates a path to destination node. in e, another node adjacent to the first virtual node is selected as the second virtual node. and finally in f, in cases where source nodes do not receive local interest packets for a while, they overlook the virtual node and send collected data directly to the destination node. 3.1 selecting virtual node a major and one of the most difficult steps in penl is selecting virtual node which has to: • be spatially close to sources and have the largest number of nodes adjacent to it in order to be able to collect data as quickly as possible. • maintain a minimum level of energy above some threshold (e) in order to be able to handle a large amount of data. since each node reports its location, it is easy to select a virtual node with the largest number of adjacent nodes close to sources (the goal is to prevent reduction in lifetime as a result of information dissemination). selection of virtual node in this manner meets the above mentioned properties to a large extent. the minimum distance for the virtual node (denoted by d) can be determined based on node density in the network. in simulations through ns-2, d is equal to 3 steps. as seen in fig. 3, vs1 meets the above mentioned conditions and therefore is selected as virtual node. enhancing nodes lifetime optimum protocol for dissemination of information in wsn279 figure 3: flowchart for selecting virtual node 3.2 the shortest path the shortest path problem can be formulated into a linear programming model: min z = ∑ i ∑ i̸=j cijxij st : ∑ k ̸=j xjk − ∑ k ̸=j xkj = 1 if j is source ∑ k ̸=j xjk − ∑ k ̸=j xkj = 0 if j is neither source nor destination ∑ k ̸=j xjk − ∑ k ̸=j xkj = −1 if j is destination (1) the model does not account for the direction of edges (links). each link may transmit data from j to k or in opposite direction. the following graph shows a part of a network where this technique is applied to find the shortest path. here, the goal is to find the shortest path between vs and s. fig. 4 presents a typical graph of a wsn. 280 m. gholami, a. panahi x01 + x02 + x03 − (x10 + x20 + x30) = 1 x10 + x13 + x14 − (x01 + x31 + x41) = 0 x20 + x23 + x25 − (x02 + x32 + x52) = 0 x30 + x31 + x32 + x34 + x35 + x36 − (x03 + x13 + x23 + x43 + x53 + x63) = 0 x41 + x43 + x46 + x48 − (x14 + x34 + x64 + x84) = 0 x52 + x53 + x56 + x57 − (x25 + x35 + x65 + x75) = 0 x63 + x64 + x65 + x67 + x68 + x69 − (x36 + x46 + x56 + x76 + x86 + x96) = 0 x75 + x76 + x79 − (x57 + x67 + x97) = 0 x84 + x86 + x89 − (x48 + x68 + x98) = 0 x96 + x97 + x98 − (x69 + x79 + x89) = −1 a ≤ xij ≤ a + ϵ (i, j = 1, 2, . . . , 9) min z = a01x01 + a02x02 + . . . + a89x89 (2) figure 4: connecting paths in a typical graph 3.3 selecting a new virtual node since virtual node is required to handle a large amount of data, it will be eliminated once its energy is used up. to prevent this, a new virtual node will be selected after a certain period of time is passed. in penl, this time interval is denoted by pe. when pe is reached, the virtual node sends an nr message to its neighbors requesting them for sending an na message in response indicating the remaining amount of energy for the virtual node. the virtual node allows for a delay in responses and then selects an adjacent node with the highest level of energy as the new virtual node. an sn message is sent to this node. the new node will disseminate an interest message to update paths to the new node. after a certain period, the new virtual node floods exploratory data globally over the network to find a path to the destination node. the process is illustrated in fig. 5. 3.4 virtual node expiration in some cases virtual node may become inoperative. this can be caused by different factors such as hardware problems, expiry of working period, used up energy, failure in finding a node with required level of energy, etc. in this case, if virtual node is still working, a message will enhancing nodes lifetime optimum protocol for dissemination of information in wsn281 be sent over the network to request source nodes for overlooking the virtual node and sending individual packets of exploratory data over the network to find a path to the destination node. the process is illustrated in fig. 5. figure 5: selecting a new virtual node using the original virtual node 4 analysis of simulation results this section presents simulation results obtained through ns-2. 4.1 routing overload overloads contain additional bits used to identify and correct errors. this increases the level of disseminated unwanted information and redundant processing at intermediate nodes as well as end stations. fig. 6 shows routing overload. as seen in this figure, routing overhead of penl when the number of sources is greater than 2, is smaller compared to that of mech. figure 6: comparison of routing overload in penl and mech 4.2 packet loss rate fig. 7 compares number of lost data packets for different number of sources when connection is established between source and destination nodes. as seen in this figure, packet loss rate does not change considerably with the increase in the number of sources for penl while increase in 282 m. gholami, a. panahi the number of sources for mech, particularly from 6 to 7 sources, significantly raises packet loss rate. figure 7: a comparison of packet loss rate of nodes in penl and mech 4.3 energy consumption in networks fig. 8 shows overall energy used by all nodes of the network. as seen in this figure, penl is much more energy-efficient compared to mech mainly because of reduction in the number of transmission paths. in this simulation 7 sources were used over a 25 × 15 grid containing 320 nodes. figure 8: a comparison of energy consumption of nodes in penl and mech 5 conclusions in this paper, penl was proposed as a protocol to improve routing in wsns. the protocol is often used to increase efficiency of the previously used protocols in terms of energy consumption by reducing routing overload and balancing loads on nodes. penl outperforms previous protocols such as mech in many aspects including packet loss rate, energy consumption, and routing overload. enhancing nodes lifetime optimum protocol for dissemination of information in wsn283 bibliography [1] allahviranloo, t. et al (2009); a computational method to find an approximate analytical solution for fuzzy differential equations, analele stiintific ale universitatii ovidius costantaseria matematica issn 1224-1784, 17(1): 5-14. 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[7] levis, p. et al (2004); the emergence of networking abstractions and techniques in tinyos, proceedings of the first usenix/acm symposium on networked systems design and implementation. [8] manjeshwar, a.; agrawal, d. (2001); a routing protocol for enhanced efficient in wireless sensor networks, the 15th international parallel and distributed processing symposium, doi: 10.1109/ipdps.2001.925197, 2009-2015. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 204-213 an improved computational model for adaptive communication channel estimation s.a. akinboro, g.a. aderounmu, e.a. olajubu a.o. ajayi, i.k. ogundoyin, o.m olaniyan s.a. akinboro, o.m olaniyan computer science and technology bells university of technology, ota, nigeria e-mail: akinboro2002@yahoo.com g.a. aderounmu, e.a. olajubu, a.o. ajayi, i.k. ogundoyin computer science and engineering obafemi awolowo university, ile-ife, nigeria abstract: channel estimation is an important and necessary function performed by modern wireless receivers. the goal of channel estimation is to measure the effects of the channel on known or partially known transmission. the usual practice in acquiring knowledge about a channel is to model the channel and then acquire the parameters involved in the model. this paper proposes a variable partial update model for adaptive communication channel estimation with a view to improving signal error at the receiver station. the proposed model is composed of finite impulse response transversal adaptive filter and least mean square adaptation algorithm. the performance of the proposed model was compared with the full update model. the evaluation results indicated that the proposed model performed better than the full update model in terms of computational complexity, memory load, and convergence rate. keywords: adaptation algorithm, computational complexity, memory load, convergence rate, partial update 1 introduction channel refers to one way telecommunication link or transmission medium through which information or signal is transmitted from a transmitter to a receiver. it may either be physical or logical depending on the application, for example cables, and radio frequency are physical channels while control and traffic channels within the ratio frequency channel are logical channels. in estimation theory, it is assumed that the desired information is embedded in a noisy signal. noise adds uncertainty and if there is no uncertainty then there would be no need for estimation. the channel deforms the transmitted signals often in unpredictable ways. to retrieve the information that is transmitted, the received signal has to be processed. the retrieval of information about the channel either from the received signal or from the signal sent is known as channel estimation. the major sources of impairment in wireless channels include channel time-variation, inter symbol interference, and co channel interference [6]. in order to deal with these problems, the transmitted signal needs to be processed at the receiver station. a core feature of many modern communication systems is their ability to adapt to the working environment. the technology at the heart of these flexible systems is an adaptive digital filter whose coefficients change in response to external conditions [3]. an adaptive filter has coefficients that are updated by some types of adaptive algorithms to improve or optimize its response to a desired performance. in general, adaptive filters consist of two basic parts: the filter which applies the required processing copyright c⃝ 2006-2011 by ccc publications an improved computational model for adaptive communication channel estimation 205 on the incoming signal to be filtered and an adaptive algorithm which adjusts the coefficient of the filter to improve its performance [1]. many computationally efficient algorithms have been developed for adaptive filtering [2]. they are based on either a statistical approach such as least-mean square (lms) algorithm or a deterministic approach such as recursive least-square (rls) algorithm. the major advantage of the lms algorithm is its computational simplicity. the rls algorithm conversely, offers faster convergence but with a higher degree of computational complexity. increased popularity of mobile phones and other wireless communication products provide the demand for developing appropriate techniques to improve the performance of existing system for reliable transmission of data over wireless communicational system. many applications in wireless communication like channel estimation and echo cancellation require the adaptive filter to have a very large number of co-efficient, and updating the entire coefficient is costly in terms of power, memory and computation, and sometimes impractical for mobile units [2]. in this paper, an improved computational model for adaptive channel estimation is proposed. the adaptive channel estimator is modelled using finite impulse response traversal adaptive filter. the adaptation process of the filtering is performed using variable partial update lms algorithm. the coefficient of the adaptive filter is partially updated to reduce computational cost and memory load, while at the same time updating the step size parameter to enhance the speed and the accuracy of convergence. the outline of this paper is as follows. in section 2, the review of related works is carried out, and in section 3, the proposed model is described together with the adaptation algorithm used. simulation and results are presented in section 4, and section 5 concludes the study. 2 related work several research works have been done in developing computational models and adaptation algorithms for adaptive communication channel estimation but all the models failed to address the issues of convergence rate, memory load and computational complexity efficiently. in particular, no research work has considered the comparison of the full update and the partial update of the adaptive digital filter coefficients using the parameters memory load, convergence rate and computational complexity. in an attempt to reduce computational complexity and improve asymptotic performance, [4] proposed active tap detection lms algorithm. though the research work reduces computational burdens as well as unsatisfactory poor convergence rate asymptotic performance of the adaptive tap in a long channel but storage location is provided for the entire adaptive tap which is quite expensive. also, [2] proposed partial updating of the lms adaptive filter to reduce the cost of power, memory load and computation. sequential partial update lms is employed in their work. they analyzed the alternating odd/even partial update lms algorithm and derived stability bound on step size parameter for wide sense stationary and cyclo-stationary signals based on external properties of the matrix 2-norm but comparing with the proposed model, the memory load and computational complexity is still large. the behavior of three variants of variable step size lms algorithm for training based multi-user detection in a cdma system was studied by [10]. two of the algorithm have smaller computational complexity and memory load but still suffers from the fact that their steady state error and speed of convergence depend on the same parameters (the step size), therefore complementary pair variable step size lms was introduced. although the proposed algorithm has an increased computational complexity and memory load, but it has better speed performance and more simple parameters setup which are very important in practical applications. a number of previous lms algorithms were analyzed by [5]. they pointed out their weaknesses and proposed a variant of modified variable step size lms algorithm which was tested and can ensure convergence in any cases and can provide a higher speed of convergence and a better level of tracking ability. 206 s.a. akinboro, g.a. aderounmu, e.a. olajubu a.o. ajayi, i.k. ogundoyin, o.m olaniyan the normalized lms algorithm is adapted to get higher speed of convergence by adjusting the step size through the power of input signals. however, we can hardly make an accurate estimate of the auto correlation matrix and mean square error practically. they also said that variants of variable step size lms algorithms have been proposed, but in all of them, the step size equation can be written as µ(n + 1) = αµ(n) + γp where p is a function and depends on the different vss-lms algorithm, α and γ are constant, µ is the step size and n is the time index. previous research works focused on improving the effect of channel on transmitted signal through proposition of variants of adaptation algorithm and design of improved adaptive digital filters, this work however, compare the performance of full-update and variable partial update variants of the adaptation algorithm with design of adaptive digital filter using index factors. the three performance metrics considered are convergence rate, memory load and computational complexity. 3 architecture of the proposed model the block diagram for variable partial update lms model is applied in this work, figure 1 depict the model, while the adaptation module of the proposed model is an improvement on [2] which is depicted in figure 2. the difference between the model developed in [2] and this work is the updates of the coefficients. this model uses index factors of three and five for updates while [2] uses alternate even and odd updates of the coefficients the model consists of four major modules vis-ŕ-vis; unknown channel, fir filter, summer ( ∑ ), and adaptation algorithm. the unknown channel is positioned parallel to the fir filter so that the same input signal can be transmitted simultaneously. the estimation is adaptive and parallel, this is because in wireless situation the paths that a signal takes between the transmitter and the receiver may keep changing. the signal transmitted through the unknown channel is the desired training signal d(k), while the signal transmitted through the fir filter output is the estimated training signal y(k). the two signals are transmitted through the summer to give the estimated error signal e(k).the estimated error signal is then used to update the coefficient of the fir filter using the adaptation algorithm called variable step size partial update least mean square algorithm (vpu-lms) [6]. it is assumed that the variable partial update lms filter in fig 2 is a standard transversal fir filter of length l ≥ 5. let (xi,n) be the input sequence and let (wi,n) denote the coefficients of the adaptive filter. wn = [w1, n, w2, nw3, n...wl, n] t xn = [x1, n, x2, nx3, n...xl, n] where the terms define above are for the instant n and t denotes the transpose operator. also, let d(n) denotes the desired response. it represents a known training signal which is transmitted over a noisy channel with unknown fir transfer function. it was assumed that d(n) obeys a fir model given by d(n) = xn + k(n) (1) where k(n) is the autoregressive noise signal that is independent of the input sequence xn. we also assumed that the filter coefficient is a mutually exclusive set. the elements of the set are coefficients with index factors of three and five i.e. filter length that is divisible by three and five. therefore the set s is defined as s = {w3, w5, w6, w9, w10....} the proposed algorithm called variable partial update lms algorithm. the algorithm updates only the coefficient of the adaptive filter with index factors of three and five. it also makes sure that only the active coefficients (i.e. value not equal to zero) are used for the update process. the step size parameter is updated to ensure convergence of the algorithm. the algorithm is described in following steps: an improved computational model for adaptive communication channel estimation 207 figure 1: the block diagram for variable partial update lms model figure 2: transversal fir filter structure for the variable partial update-lms model 208 s.a. akinboro, g.a. aderounmu, e.a. olajubu a.o. ajayi, i.k. ogundoyin, o.m olaniyan • compute the output of the adaptive filter y(n) = wtnjxnj (2) where j ∈ s and t is the transpose operator. • compute the output error e(n) = d(n) − y(n) (3) where d(n) = xn + k(n) is the desired output of the transfer filter • update the coefficient of the adaptive filter using equation(1). wn+1 = { w(n − 1), j + µe(n) ∗ x(n), j if j ∈ si and w¬0, w(n−1),j otherwise. (4) • update the step size of the adaptive filter µ(n + 1) = αµ(n − 1) + µ(n)p(n) (5) µ(n + 1) =   µmax if jµ(n + 1) > µmax, µmin if jµ(n + 1) < µmin, µ(n + 1) otherwise. (6) γ(n) = { βγ(n − 1) if jµ(n + 1) > µmax, γ(n − 1) otherwise. (7) where γ and β are constant values, 0 < γ, β > 1. the algorithm will adjust the parameter γ with the constant β. to ensure convergence the parameter β must satisfy that 0 < β < 1 4 simulation and results to test the performance of the proposed variable partial update lms algorithm, we simulated the discrete signal sequence generated in mat lab environment using pseudo random number generator with zero mean and variance of one, and a noise signal sequence which is obtained by introducing 0.8 noise level to the discrete signal. these signals form the desired signals that were input to the finite impulse response filter. the outputs from the filter form the actual signal which was subtracted from the desired signal to obtain the mean square error. to establish the superiority of the proposed partial update model over the full update model, training was performed using fifty different sets of input data with different value of the step size to obtain the average result of the mean square error (mse) and the efficiency of the two models. table 1 shows the values of the step size and other simulation parameters. the full update lms algorithm and our partial update lms algorithm were simulated using various values of step size. figure 3 shows the comparison of the full update and the partial update model the proposed algorithm exhibit variable update of the step size. in order to test this algorithm for speed and accuracy of convergence, we used the parameters specified in table 2 for the case when step size is less than the minimum step size (0.0036). figure 4 shows a specimen comparison of the proposed partial update model with the equivalent full update model. we illustrate the case when the step size is set to a large value or possibly larger than the maximum step size. table 3 shows the parameters used for the simulation. in a normal situation, when the mean square error is magnified, stability of the filter is affected. this is an improved computational model for adaptive communication channel estimation 209 step µmax µmin γ α β length size value 0.001 1.9 0.001 0.0007 0.2 0.01 50 0.0011 0.0013 0.0017 0.0020 0.0030 table 1: simulation parameters for fixed step size (µ) figure 3: mean square error versus number of iteration for step size of 0.001 step µmax µmin γ α β length size value 0.0036 0.09 0.01 0.0007 0.2 0.01 50 0.0049 0.0052 table 2: simulation parameter for variable step size, for (µ < µmin ) figure 4: mean square error versus number of iteration for (µ < µmin) 210 s.a. akinboro, g.a. aderounmu, e.a. olajubu a.o. ajayi, i.k. ogundoyin, o.m olaniyan shown in figure 5 where the mean square error was magnified and the accuracy of convergence was adversely affected for the full update model. however, the reverse is the case for the partial update model, with the speed and accuracy of convergence still enhanced. figure 5: mean square error versus number of iteration for (µ > µmax) step µmax µmin γ α β length size value 0.25 1.9 0.001 0.0007 0.2 0.01 50 0.30 0.40 table 3: simulation parameter for variable step size for (µ > µmax) the filter length was varied with a constant step size to evaluate the system performance. the filter length is equivalent to the number of coefficients required for the update of the filter. the parameter considered here is the memory load. each of the coefficient requires a unit storage, therefore the lesser the number of coefficient the lesser the memory requirement. for the full update model, all the coefficients are used for the update process therefore the memory load (m) is equivalent to the filter length (l). in the case of partial update model, only the coefficients with index factors of three and five are used for the update. therefore memory load m = l 3 + l 5 . where l 3 and l 5 are the sets of filter length divisible by factors of three and five respectively. simulation was carried out for fifty iterations using the parameters in table 4. the result shown in figure 6 revealed that the performance of the system is below 50% for full update model and up to 86% for the partial update model. 4.1 computational complexity computational complexity refers to the number of hardware resources required to implement the system. the complexity of an algorithm determines the hardware requirement and computational cost. the hardware required to implement the full update and the partial update finite impulse response transversal adaptive filter are the multiplier, summer and the memory. the multiplier is use to multiply the input with the corresponding weight, memory to store the weights, and summer to perform the addition. the computational complexity of our model was estimated by counting the number of hardware resources as described in [12] such as multipliers, an improved computational model for adaptive communication channel estimation 211 filter µmax µmin γ α β step size memory load length full update partial update 20 0.02 0.01 0.0007 0.2 0.01 0.003 20 9 50 50 23 70 70 33 90 90 42 100 100 47 130 130 61 150 150 70 170 170 79 200 200 93 250 250 126 table 4: simulation parameter for system performance evaluation figure 6: system performance versus filter length 212 s.a. akinboro, g.a. aderounmu, e.a. olajubu a.o. ajayi, i.k. ogundoyin, o.m olaniyan summers and memories required for a single iteration for each model as shown in table 5. the evaluation result shown in figure 7 revealed that the computational complexity of the proposed partial update model is considerably lower when compared with the full update model. filter memories summers multipliers memories summers multipliers length (fu) (fu) (fu) (pu) (pu) (pu) 20 20 20 20 9 9 9 50 50 50 50 23 23 23 70 70 70 70 33 33 33 90 90 90 90 42 42 42 100 100 100 100 47 47 47 130 130 130 130 61 61 61 150 150 150 150 70 70 70 170 170 170 170 79 79 79 200 200 200 200 93 93 93 250 250 250 250 126 126 126 table 5: evaluation of computational complexity figure 7: filter lengths versus computational complexity 5 conclusions to achieve the continuous update of the coefficient using adaptive algorithm, an improved computational model was proposed in this research, the novelty of which is the adoption of finite impulse response transversal adaptive filter to filter the noise signal from the transmitted signal. the adaptation process employed the concept of variable partial update least mean square algorithm. in the update process, only the coefficients with the factors of three and five are used. the performance of the proposed model was compared with the full update model using the following parameters convergence rate, memory load, and computational complexity in mat lab environment. the simulation results revealed a better performance of the proposed model over the full update model. the proposed framework will particularly be suitable for wireless communication environment where the characteristics of the channel changes with time. the an improved computational model for adaptive communication channel estimation 213 results obtained in the study will go a long way to reducing the effect of channel time variation, inter-symbol interference and co channel interference on the transmitted signal. bibliography [1] c. s. douglas, and w. pan, exact expectation analysis of the lms adaptive filter, ieee transaction on signal processing, 43(12),2863-2871, 1995. [2] m. godavarti and a. o. hero, analysis of the sequential partial update lms algorithm, proceedings of ieee international conference on acoustics, speech, and signal processing,pp. 3857-3860, 2001. [3] s. mullins and c. heneghan, alternative least mean square adaptive filter architectures for implementation on field programmable gate arrays, department of electronic and electrical engineering, university college dublin available at http://195.134.67.70/eurasip/proceedings/eusipco/2002/articles/paper389.pdf, 2002. [4] s. singh, r.k. bansal, and s. bansal, improved channel estimation with auto-regressive prewhitening techniques for color inputs, international conference on next generation communication system: icongencom-06, pp. 9-14, 2006. [5] y. li and w. xinan, a modified vs lms algorithm, ieee transaction on signal processing, vol. 2, no. 5, 615-618, 2005. [6] s. diggavi, b. chong, and a. paulraj, an interference suppression scheme with joint channel data estimation, ieee journal on communication, vol. 17, no.11,465-469, 1998. [7] j. sanubari, fast convergence lms adaptive filters employing fuzzypartial updates, ieee transaction on signal processing, vol. 4, pp. 1334-1337, 2003. [8] s. jo, j. choi and y. lee, modified leaky lms algorithm for channel estimation in dscdma system, ieee communications letters, vol. 6, no 5, 202-204 2002. [9] m. gadhiok, channel estimation for fast fading environment, ieee transaction on signal processing, vol. 19, pp. 14-22, 2004. [10] k. egiazarian, p. kuosmanen, and r. c. bilcu, variable step size lms adaptive filters for cdma multiuser detection, vol.17 pp. 259-264, 2004. [11] r. bilcu, p. kuosmanen and c. rusu, a novel complementary variable step lms algorithm, ieee transaction on signal processing, vol. 23 pp. 13-19, 2000. [12] v. debrunner and d. zhou, hybrid filtered error lms algorithm: another alternative to filtered-x lms, ieee transactions on circuit and system, vol. 53, no. 3,pp.653-661, 2006. int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 341-352 robust adaptive neural-fuzzy network tracking control for robot manipulator t. ngo, y. wang, t.l. mai, m.h. nguyen, j. chen thanhquyen ngo, yaonan wang, t. long mai, m. hung nguyen, jun chen college of electrical and information engineering hunan university, changsha, hunan province 410082, p.r.china faculty of electrical engineering hcm city university of industry, hcm city, vietnam thanhquyenngo2000@yahoo.com, yaonan@hnu.cn, mailongtk@gmail.com, manhhung@yahoo.com, 2011junchen@gmail.com abstract: this paper presents a robust adaptive neural-fuzzy network control (ranfnc) system for an n-link robot manipulator to achieve the highprecision position tracking. initially, the model dynamic of an n-link robot manipulator is introduced. however, it is difficult to design a conformable model-based control scheme, for instance, external disturbances, friction forces and parameter variations. in order to deal with this problem, the ranfnc system is investigated to the joint position control of an n-link robot manipulator. in this control scheme, a four-layer neural-fuzzy-network (nfn) is used for the main role, and the adaptive tuning laws of network parameters are derived in the sense of a projection algorithm and the lyapunov stability theorem to ensure network convergence as well as stable control performance. the merits of this model-free control scheme are that not only the stable position tracking performance can be guaranteed but also unknown system information and auxiliary control design are required in the control process. the simulation results are provided to verify the effectiveness of the proposed ranfnc methodology. keywords: adaptive control, neural-fuzzy network, robot manipulator. 1 introduction in the past decade, the applications of intelligent control techniques (fuzzy control or neural control) to the motion control of robotic manipulator have received considerable attention [1]-[3]. in general, robotic manipulators have to face various uncertainties in their dynamics, such as friction, and external disturbance. it is difficult to establish exactly mathematical model for the design of a model-based control system. thus, the general claim of these intelligent control approaches is that they can reduce the effects of structured parametric uncertainty and the unstructured disturbance by using their powerful learning ability without a detailed knowledge of the controlled plant in the design processes. recently, the concept of incorporating fuzzy logic into a neural network has grown into a popular research topic [5]-[8]. the integrated neural-fuzzy-network system possesses the merits of both fuzzy system [9] (e.g., humanlike if-then rule thinking and ease of incorporating expert knowledge) and neural network [10] (e.g., learning and optimization abilities and connectionist structure). in this way, one can bring the low-level learning and computational power of neural network into fuzzy systems as well as the high-level humanlike if-then rule thinking and reasoning of fuzzy systems into neural network. copyright c⃝ 2006-2012 by ccc publications 342 t. ngo, y. wang, t.l. mai, m.h. nguyen, j. chen the main of this paper is to design an intelligent control system scheme for the position control of an n-link robot manipulator by using neural-fuzzy-network controller to compensated uncertainness dynamic model and external disturbance via capability self-learning of neural network and human intuitive. this paper is organized as follows: section 2 described a dynamic model of an n-link robot manipulator briefly [11]. section 3 presents a structure of neural-fuzzy-network. the design process of the ranfnc system is investigated to control an n-link robot manipulator for periodic motion in section 4. the design procedures of the proposed ranfnc system are described in detail. the adaptive learning laws in the ranfnc system are designed in the sense of the lyapunov stability theorem [12], [13] so that the network convergence and system tracking stability can be guaranteed in the closed-loop control system. numerical simulation results of a two-link robot manipulator under the possible occurrence of uncertainties are provided to demonstrate the tracking control performance of the proposed ranfnc system in section 5. conclusions are drawn in section 6. 2 system description 2.1 robotic dynamic model in general, the dynamic of an n-link robot manipulator may be expressed in [11] as: m(q)q̈ + c(q, q̇)q̇ + g(q) + f(q̇) + τd = τ (1) where q, q̇, q̈ ∈ ℜn are the joint position, velocity and acceleration vectors, m(q) ∈ ℜn×n denotes the inertia matrix, c(q, q̇) ∈ ℜn×n expresses the matrix of centripetal and coriolis forces, g(q) ∈ ℜn is the gravity vector, and f(q̇) ∈ ℜn is the friction. bounded unknown disturbances are denoted by τd and the control input torque is τ(t) ∈ ℜn. in this paper a robot manipulator is shown in fig.1 which is utilized to verify dynamic properties are given in section 5. y y0 x0 x q1 x1 y1 lc1 m1 lc2 m2 g p x2 y2 lt1 lt2 q2 figure 1: architecture of two-link robot manipulator. 1 1 j 1 z 1 1 b j b z b 1 r j r z r 1 z bz rz k jb w 1 l k l 1 y 2 y n y ik w p l input layer membership layer rule layer output layer figure 2: structure of four-layer nfn given a desired arm trajectory qd(t) ∈ ℜn the tracking error is: e(t) = qd(t) − q(t) (2) and the filtered tracking error (in standard use in robotics) is: r(t) = ė + λe (3) robust adaptive neural-fuzzy network tracking control for robot manipulator 343 where λ = λt > 0, differentiating r(t) and using (1), the arm dynamics may be written in terms of the filtered tracking error as: m(q)ṙ = −c(q, q̇)r − τ + m(q)(q̇d + λė) + c(q, q̇)(q̇ + λe) + f(q̇) + g(q) + τd (4) where the nonlinear robot function is: f(x) = m(q)(q̈d + λė) + c(q, q̇)(q̇ + λe) + g(q) + f(q̇) (5) substituting (5) equation into (4) we have: m(q)ṙ = −c(q, q̇)r − τ + f(x) + τd (6) and, for instance x = [ et ėt qt q̇t q̈t ]t 2.2 defined control law now, we define a control input torque as: τ0 = f̂(x) + kvr (7) with f̂(x) is an estimate of f(x) and a gain matrix kv = ktv the closed-loop system becomes: m(q)ṙ = −(kv + c(q, q̇))r + f̃(x) + τd ≡ −(kv + c(q, q̇))r + ζ0, ζ0 = f̃(x) + τd (8) where functional estimate error is given by: f̃(x) = f(x) − f̂(x) (9) this is a system error wherein filtered tracking error is driven functional estimate error. the control τ0 incorporates a proportional plus derivative (pd) term in kvr = kv(ė + λe). in the remainder of the paper we shall use (8) to focus on selecting nfn weight tuning algorithms that guarantee the stability of the filtered tracking error r(t). then, since (3), with the input considered as r(t) and the output as e(t) describes a stable system, standard techniques [13], [15] guarantee that e(t) exhibits stable behaviour. in fact ∥e∥2 ≤ ∥r∥2/σmin, ∥ë∥2 ≤ ∥r∥2 with σmin(λ) the minimum singular value of λ. generally λ is diagonal, so that σmin(λ) is the smallest element of λ. the following properties of the robot dynamics are required [15]. they hold for all revolute rigid-link manipulator. the inertial matrix m(q) is symmetric and positive definite. it is also bounded as a function of q : m1i ≤ m2i. ṁ(q)−2c(q, q̇) is a skew symmetric matrix, that is yt [ṁ(q)−2c(q, q̇)]y, where y is a n × 1 nonzero vector. the gravity vector g(q) is bounded as a function of q : g(q) ≤ gd. the unknown disturbance satisfies ∥τd∥ ≤ db and this property is new, that is, the dynamic (8) from ζ0(t) to r(t) are a state strict passive system. 3 structure of nfn in the ranfnc scheme, as shown in fig.3, an nfn estimator is designed to tune the nonlinear dynamic function vector, and then, the estimative vector is used to indirectly develop a stable ranfnc law. an nfn controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by using joint position and filtered error vector and information. in this paper, a four-layer nfn 344 t. ngo, y. wang, t.l. mai, m.h. nguyen, j. chen d/dt e + -q iq neural-fuzzy network (nfn) adaptive law proposed robust adaptive neural-fuzzy-network control system i robot manipulator e e )(tq r reference model d q eer + + v k r sgn(•) fk )(ˆ xf f )( xl ŵ d/dt e e )(tq d figure 3: block diagram of ranfnc scheme structure showns in fig.2, which is composed of input, membership, rule, and output layers, is adopted to implement the nfn estimate in ranfnc. the signal propagation and the basic function in each layer of the nfn are introduced as follows. 1. input layer transmits the input linguistic variables zb|1,2,··· ,r to the next layer. 2. membership layer represents the input values with the following gaussian membership functions: µ j b(zb) = exp [ − (zb − m j b) 2 (t j b) 2 ] (10) where exp[·] is the exponential function, mjb and t j b(b = 1,2, · · · ,r;j = 1,2, · · · ,z) are the mean and the standard deviation of the gaussian function in the jth term of the bth input variable zb to the node of membership layer, respectively. it can be referred as the fuzzification procedure. 3. the output of each node in the rule layer is determined by fuzzy and operation. each node in this rule layer is denoted by ∏ , which multiplies the input signals and output the result of the product. the product operation is utilized to determine the firing strength. it can be referred as the fuzzy inference mechanism. the output of this layer is given as: lk = r∏ b=1 wkjbµ j b(zb) (11) where lk|k = 1,2, · · · ,p represents the kth output of the rule layer, wkjb which represents the weights between the membership layer and the rule layer, is assumed to be unity, and p is the number of rules. 4. final layer is the output layer, and nodes in this layer represent the output linguistic variables. each node in the output layer yi(1,2, · · · ,n) is labelled as ∑ , which computes the robust adaptive neural-fuzzy network tracking control for robot manipulator 345 overall output as the summation of all input signals, and be represented as: yi = p∑ k=1 wiklk (12) the output node, together with the links connected to it, acts as a defuzzifier. it can be referred as the normal defuzzification procedure. moreover, it can be rewritten in the following vector form: y = [ y1 y2 . . . yn ]t = wl (13) where w =   w11 w12 · · · w1p w21 w22 · · · w2p ... ... . . . ... wn1 wn2 · · · wnp   = [ w1 w2 · · · wn ] ∈ ℜn×p, l = [ l1 l2 · · · ln ]t ∈ ℜp×1 in the ranfnc scheme, the nfn is used to estimate unmodeled nonlinear function, moreover, the ranfnc law and adaptive tuning algorithms for nfn are introduced from the stability analyses of the closed-loop system by using lyapunov method. the input of the nfn estimator are the elements in the filtered error vector and joint positions signal, the output of the nfn estimator are the nonlinear dynamic function vectors in the local models. based on the powerful approximation ability [4], there exists an optimal nfn estimator to approximate the nonlinear dynamic function in (5) such that f(x) = w∗l(x) + ε(x) (14) with w∗ the ideal weight matrix and the estimative error vector ε(x) ∈ ℜn×1 are assumed to be given by w̃ = arg min ŵ∈mx [ sup x∈mx ∥ f(x) − ŵl(x) ∥ ] , ∥ ε ∥≤ εn (15) in which ∥ · ∥ is the euclidean norm, mx and mw are the predefined compact sets of x and ŵ , and the positive constant εn can be reduced arbitrarily by increasing the number of rules. 4 ranfnc design define the nfn functional estimate by f̂(x) = ŵl(x) (16) with ŵ the current values of the nfn weight as provided by the tuning algorithm. with the ideal weights required in (14) define the weight deviations or weight estimation errors as w̃ = w∗ − ŵ (17) with τ0 defined to be (7), select the control input τ = τ0 − v = ŵl(x) + kvr − v (18) 346 t. ngo, y. wang, t.l. mai, m.h. nguyen, j. chen v = (εn + db)sgn(r) = kfsgn(r) (19) with v(t) a function to be determined to provide robustness in the face of the net reconstruction error ε. then, the closed-loop filtered error dynamics become mṙ = −(kv + c(q, q̇))r + w̃l(x) + (ε + τd) + v = −(kv + c(q, q̇))r + ζ1 (20) theorem 1: consider an n-link robot manipulator represented (1). if the ranfnc law is designed as (18), (19) and the weight update law is designed as (21), then the stability of the proposed ranfnc system can be ensured + )(tq )(tq )(tq e e )(tq d )(tq r )(tq d d/dt reference model d/dt robot manipulator mcg)(tq )(),( )( qgqqqc qekrm dcfl )sgn()sgn( 1 t c ermk fl r k a k r r )sgn(rd/dt error function sgn(•) + + robust feedback linearization control system (c) + )(tq d )(tq e k k pd )(tq )()()( tektekt pd pd control system robot manipulator d/dt e (a) + )(tq computed control system )(tq )(tq e e )(tq d )(tq r )(tq d ct )(),( )( qgqqc ekekqm badct a k bk d/dt reference model d/dt robot manipulator mcg)(tq (b) figure 4: (a) pd control system, (b) computed torque control (ctc) system, (c) robust feedback linearization control (rflc) system robust adaptive neural-fuzzy network tracking control for robot manipulator 347 ˙̂ w = fl(x)rt (21) proof: define a lyapunov function candidate as v (r(t),w̃) = 1 2 rt mr + 1 2 tr(w̃t f−1w̃) (22) where tr(·) is a trace operator. by differentiating (22) with respect to time and using (19), (20), (21), and using properties of the robot dynamics are introduced in section 2, one can obtain. v̇ = 1 2 rt mṙ + 1 2 rt ṁr + tr(w̃t f−1w̃) = −rt kvr + 1 2 rt (ṁ − 2c)r + trw̃(f−1 ˙̃w + lrt ) + rt (ε + τd) − rt (εn + db)sgn(r) = −rt kvr + rt (ε + τd) − rt (εn + db)sgn(r) = −rt kvr + rt (ε + τd) − ∥r∥(εn + db) ≤ −rt kvr ≤ 0 (23) since v̇ (r(t),w̃) ≤ 0, v̇ (r(t),w̃) is a negative semidefinite function, i.e. v (r(t),w̃) ≤ v (r(0),w̃). it implies that r(t) and w̃ is bounded functions. let function h(t) ≡ rt kvr ≤ −v̇ and integrate function h(t) with respect to time τ∫ 0 h(t)dτ ≤ v (r(0),w̃) − v (r(t),w̃) (24) because v (r(0),w̃) is a bounded function, and v (r(t),w̃) is a nonincreasing and bounded function, the following result can be concluded: lim t→∞ τ∫ 0 h(t)dτ < ∞ (25) in addition, ḣ(t) is bounded; thus, by barbalats lemma can be shown that lim t→∞ h(t) = 0. it can imply that r(t) will be converging to zero as time tends to infinite. 5 numerical simulation a two-link robot manipulator as shown in fig.1 is utilized in this paper to verify the effectiveness of the proposed control scheme. the detailed system parameters of this robot manipulator are given as: link mass m1,m2(kg), lengths l1, l2(m), angular positions q1,q2(rad). the parameters for the equation of motion (1) are adopted in [11]. m(q) = [ (m1 + m2l 2 1) m2l1l2(s1s2 + c1c2) m2l1l2(s1s2 + c1c2) m2l 2 2 ] c(q, q̇) = m2l1l2(c1s2 + s1c2) [ 0 −q̇2 −q̇1 0 ] , g(q) = [ −(m1 + m2)l1gs1 −m2l2gs2 ] 348 t. ngo, y. wang, t.l. mai, m.h. nguyen, j. chen where q ∈ ℜ2 and the shorthand notations c1 = cos(q1), c2 = cos(q2), s1 = sin(q1) and s2 = sin(q2) are used. for the convenience of the simulation, the nominal parameters of the robotic system are given as m1 = 4.6(kg), m2 = 2.3(kg), l1 = 0.5(m), l2 = 0.2(m), g = 9.8(m/s2) and the initial conditions q1(0) = 0.5, q2(0) = 0.5, q̇1(0) = 0, q̇2(0) = 0. the desired reference trajectories are qd1(t) = sin(2t), qd2(t) = cos(2t) respectively. 0 2 4 6 8 10 −2 −1 0 1 2 time(s) (a) p os it io n li nk 1 ( ra d) desired position 0 2 4 6 8 10 −2 −1 0 1 2 time(s) (b) p os it io n li nk 2 ( ra d) desired position 0 2 4 6 8 10 −0.6 −0.4 −0.2 0 0.2 time(s) (c) e rr or l in k 1 (r ad ) 0 2 4 6 8 10 −1 −0.5 0 0.5 1 time(s) (d) e rr or l in k 2 (r ad ) 0 2 4 6 8 10 −50 0 50 100 time(s) (e) c on tr ol t or qu e 1 (n m ) 0 2 4 6 8 10 −10 0 10 20 time(s) (f) c on tr ol t or qu e 2 (n m ) figure 5: simulated position responses, tracking errors, and control torques of the ctc control system at joints 1 and 2 the most important parameters that effect the control performance of the robotic system are the external disturbance and the friction term which are denoted tl(t) and f(q̇), in simulation, parameter variation situation and disturbance situation occurring at 5s are considered. the parameter variation situation is that 1(kg) weight is added to the mass of link 2, i.e. m2 = 3.3(kg). the disturbance situation is that external forces are injected into the robotic system, and their shapes are expressed as follows: tl(t) = [ 5 sin(5t) 5 cos(5t) ]t . in addition, friction forces are also considered in this simulation and given as: f(q̇) = [ 2q̇1 + 0.8sgn(q̇1) 4q̇2 + 0.1sgn(q̇2) ]t . to this end, the simulation situations are adopted to demonstrate the robust property of the proposed control scheme. in order to exhibit the superior control performance of the proposed ranfnc scheme, three extra control systems including an rflc system shows in fig. 4(c), a conventional computed torque control (ctc) and a proportional differential (pd) control are examined in the mean time [13]. moreover the conventional ctc system as shown in fig.4(b)can be expressed as τcd = m(q̈d + kaė(t) + kbe(t)) + c(q, q̇) + g(q) (26) robust adaptive neural-fuzzy network tracking control for robot manipulator 349 the pd control system as shown in fig. 4(a) can be expressed as τpd = kαe(t) + kβė(t) (27) the gain in these control system are given as kα = [ 2500 0 0 1000 ] , kβ = [ 20 0 0 25 ] , ka = [ 6 0 0 6 ] , kb = [ 9 0 0 9 ] , kv = [ 5 0 0 5 ] , η = 20, kr = db = 5. (28) the gain matrices of ka, and kb, are determined so that the roots of the characteristic polynomial of kaė + kbe lie strictly in the open left haft of the complex plane, i.e. lim t→∞ e(t) = 0. it means that the ctc system shown in (26) is globally asymptotically stable as the root dynamics (1) without the consideration of system uncertainties. however, the stability of the closed-loop control system may be destroyed if the system dynamics are perturbed by external disturbance. however gains kα and kβ in the pd control are selected according to the ziegler-nichols tuning rule. in addition, the selection of learning rates η is dependent on the significance of tuning objects. 0 2 4 6 8 10 −2 −1 0 1 2 time(s) (a) p os it io n li nk 1 ( ra d) desired position 0 2 4 6 8 10 −2 −1 0 1 2 time(s) (b) p os it io n li nk 2 ( ra d) desired position 0 2 4 6 8 10 −0.5 0 0.5 time(s) (c) e rr or l in k 1 (r ad ) 0 2 4 6 8 10 −0.5 0 0.5 time(s) (d) e rr or l in k 2 (r ad ) 0 2 4 6 8 10 −500 0 500 time(s) (e) c on tr ol t or qu e 1 (n m ) 0 2 4 6 8 10 −100 −50 0 50 100 time(s) (f) c on tr ol t or qu e 2 (n m ) figure 6: simulated position responses, tracking errors, and control torques of the rflc control system at joints 1 and 2 the simulated results of ctc system, the responses of joint position, tracking error and control torque are depicted fig. 5(a-d), and (e-f), respectively. from the simulated results, in interval from beginning to 5s, favourable tracking responses can only be obtained for the nominal situation. however, since the control gains in (28) are determined without considering the joint 350 t. ngo, y. wang, t.l. mai, m.h. nguyen, j. chen friction and external disturbance. so, poor tracking responses after 5s are resulted due to the occurrence of joint friction and external disturbance. in the rflc system are depicted in fig. 6. the joint position responses, tracking error and control torque are depicted in fig. 6(a-d) and (e-f), respectively. the robust control performance of the rflc system is obvious under the occurrence of system uncertainties. however, the undesirable chattering phenomenon in the control torque. 0 2 4 6 8 10 −2 −1 0 1 2 time(s) (a) p os it io n li nk 1 ( ra d) desired position 0 2 4 6 8 10 −2 −1 0 1 2 time(s) (b) p os it io n li nk 2 ( ra d) desired position 0 2 4 6 8 10 −0.5 0 0.5 time(s) (c) e rr or l in k 1 (r ad ) 0 2 4 6 8 10 −0.2 0 0.2 0.4 0.6 time(s) (d) e rr or l in k 2 (r ad ) 0 2 4 6 8 10 −1000 −500 0 500 1000 time(s) (e) c on tr ol t or qu e 1 (n m ) 0 2 4 6 8 10 −400 −200 0 200 400 time(s) (f) c on tr ol t or qu e 2 (n m ) figure 7: simulated position responses, tracking errors, and control torques of the pd control system at joints 1 and 2 the pd control system based on model-free design is offered to apply comparable responses to manifest the performance of the ranfnc system. the simulated responses of joint position, tracking error, and control torque, are represented in fig. 7(a-d) and (e-f). from simulated results see that, the tracking responses are greatly improved and the chattering phenomenon are much reduced. now, the proposed ranfnc system depicted in fig. 3 is applied to control the robot manipulator for comparison. the simulated results of joint position responses, tracking error and control torque are depicted in fig. 8(a-d) and (e-f), respectively. because of, all parameters in the nfn are roughly initialized, the tracking errors are gradually decreased though online training process whether the uncertainties exist or not. furthermore, robust control performance of the ranfnc system, both in the condition of joint friction, parameter variation, and external disturbance are obvious. compared these results with the ctc, rflc and pd control systems, the control torque of proposed ranfnc system is not chattering phenomenon. robust adaptive neural-fuzzy network tracking control for robot manipulator 351 6 conclusions this paper has successfully implemented an ranfnc system to control the joint position of a two-link robot manipulator for achieving desired position control. all the system dynamic could be unknown and no strict constraints. the nfn is used to compensate the uncertainty of the system. all adaptive learning laws in the ranfnc system were derived in the sense of a projection algorithm and lyapunov theorem so that the network convergence and system-tracking stability of the closed-loop control system can be ensured whether or not the uncertainties occur. simulated results of a two link robot manipulator via various existing control frameworks including ctc rflc and pd control were also applied in this paper to compare and display the manipulative performance of the proposed control system. according to the result as depict in figs. 5-8, the desired position tracking response of the ranfnc system can be controlled closely follow specific reference trajectories under wide range of disturbance. the main of the paper is to construct a simpler and more efficient intelligent control system without dynamic knowledge of plant. while ensuring the convergence and tracking stability of the closed-loop system. the proposed ranfnc system can also be applied to other systems, such as mobile robotic, ac servo system and so on. 0 2 4 6 8 10 −2 −1 0 1 2 time(s) (a) p os it io n li nk 1 ( ra d) desired position 0 2 4 6 8 10 −2 −1 0 1 2 time(s) (b) p os it io n li nk 2 ( ra d) desired position 0 2 4 6 8 10 −0.6 −0.4 −0.2 0 0.2 time(s) (c) e rr or l in k 1 (r ad ) 0 2 4 6 8 10 −0.2 0 0.2 0.4 0.6 time(s) (d) e rr or l in k 2 (r ad ) 0 2 4 6 8 10 −50 0 50 100 time(s) (e) c on tr ol t or qu e 1 (n m ) 0 2 4 6 8 10 −100 −50 0 50 100 time(s) (f) c on tr ol t or qu e 2 (n m ) figure 8: simulated position responses, tracking errors, and control torques of the ranfnc control system at joints 1 and 2 7 acknowledgment this work was supported by the national natural science foundation of china (60775047; 60835004), the national high technology research and development program of china (863 352 t. ngo, y. wang, t.l. mai, m.h. nguyen, j. chen program) (2007aa04z244; 2008aa04z214). the authors would like to thank the associate editor and the reviewers for their valuable comments. bibliography [1] jinzhu peng, yaonan wang, wei sun, yan liu, a neural network sliding mode controller with application to robotic manipulator, ieee conf. int. control, 1:2011-2015, 2000 [2] b.k. yoo and w.c ham, adaptive control of robot manipulator using fuzzy compensator, ieee trans. ind. electron, 8(2):186-199, 2000 [3] shuzhi s. ge, adaptive neural network control of robot manipulator in task space, ieee trans. ind. electron, 44(6):746-752, 1997 [4] c.t. lin and c.s. george lee, neural fuzzy systems, englewood cliffs, prentice-hall, 1996 [5] y.q. zhang and a. kandel, compensatory neural-fuzzy systems with fast learning algorithms, ieee trans. neural newt, 9(1):83-105, 1998 [6] vesselenyi t., dzitac s., dzitac i., manolescu m.-j., fuzzy and neural 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1991 [14] h. k. khalil, nonlinear systems. englewood cliffs, englewood cliffs, nj: prentice-hall, 1996 [15] k. liu and f.l. lewis, robust control techniques for general dynamic system, j. intell. robotic syst, 6:33-49, 1992 [16] f.l. lewis, c.t. abdallah, and d.m dawson, control of robot manipulators, new york: macmillan, 1993 int j comput commun, issn 1841-9836 9(2):160-171, april, 2014. a multi-objective optimization algorithm of task scheduling in wsn l. dai, h.k. xu, t. chen, c. qian, l.j. xie liang dai, hongke xu, qian chao school of electronic and control engineering chang’an university xi’an 710064, china e-mail: ldai1981@gmail.com, xuhongke@chd.edu.cn ting chen school of information engineering chang’an university xi’an 710064, china e-mail: tchen@chd.edu.cn lijing xie information and navigation college air force engineering university xi’an 710077, china abstract: sensing tasks should be allocated and processed among sensor nodes in minimum times so that users can draw useful conclusions through analyzing sensed data. furthermore, finishing sensing task faster will benefit energy saving. the above needs form a contrast to the lower efficiency of task-performing caused by the failureprone sensor. to solve this problem, a multi-objective optimization algorithm of task scheduling is proposed for wireless sensor networks (mtwsn). this algorithm tries its best to make less makespan, but meanwhile, it also pay much more attention to the probability of task-performing and the lifetime of network. mtwsn avoids the task assigned to the failure-prone sensor, which effectively reducing the effect of failed nodes on task-performing. simulation results show that the proposed algorithm can trade off these three objectives well. compared with the traditional task scheduling algorithms, simulation experiments obtain better results. keywords: wireless sensor networks (wsn); task scheduling; multi-objective optimization; improved nsga-ii . 1 introduction sensing tasks should be allocated and processed among sensor nodes in minimum times so that users can draw useful conclusions through analyzing sensed data. furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. the primary objective of task scheduling in wireless sensor networks is to find an optimal strategy of splitting the original tasks received by sink into a number of sub-tasks as well as distributing these sub-tasks to the sensors in the right order. the directed acyclic graph [1], independent task sets[2] and divisible load theory [3] are usually used as modeling tools for task scheduling in wireless sensor networks, but these models only take the makespan as the main objective, and assign the task to sensors. however, wireless sensor networks are widely applied to both abominable and military environments. meanwhile, the complexity of networks, the limited energy of sensors, and potential physical or logical faults, bring challenge to task scheduling of wireless sensor networks. wireless sensor network is one kind of the widely used distributed real-time systems. how to assign tasks of system to sensors in unstable and unreliable network environment, and guarantee copyright © 2006-2014 by ccc publications a multi-objective optimization algorithm of task scheduling in wsn 161 their deadlines is one of the key techniques in wireless sensor networks. in wireless sensor network environments, qos guided task scheduling problem is complex and challenging, especially when the tasks have multiple needs. in this paper, a multi-objective optimization algorithm of task scheduling is proposed for wireless sensor networks. it is the first time that the nsga-ii [4] algorithm is used to analyze the task scheduling for wireless sensor networks. based on the characteristics of wireless sensor networks, makespan optimization, the energy-consuming balance optimization and task-performing probability optimization are included. a mathematical model used to optimize the task scheduling problem by nsga-ii was built and the solution was presented, and a detailed process to solute the multi-objective programming model is put forwards. the problem is solved with a multi-objective genetic algorithm (ga) optimization method combined with linear programming (lp) and a group of pareto solutions are provided. 2 related work and motivation wireless sensor networks have restrictions due to energy, memory, and communication ability. we should realize the goal of the improvement and enhancement of sensor networks performance in real time, economy, power aware and harmony. some studies over the past decade have been conducted to reduce the overall energy consumption for task scheduling in wireless sensor networks by using diverse techniques [5-14]. heemin presents an energy-efficient task assignment and migration framework for sensor networks. with the proposed framework, optimal task transformation and assignment is sought so as to minimize given cost function[6]. younis m presented an optimization scheme for task allocation to gateways [7]. the task allocation problem is modeled as a zero-one nonlinear program. he study system partitioning of computation to improve the energy efficiency of a wireless sensor networking application. wang explored system partitioning between the sensor cluster and the base station, employing computation-communication tradeoffs to reduce energy dissipation[8]. also he showed that system partitioning of computation within the cluster can also improve the energy efficiency by using dynamic voltage scaling. tian presented a task mapping and scheduling solution for real-time applications (rt-maps) in wsns[9]. rt-maps incorporates wireless channel modeling, hyper-dag extension, concurrent task mapping, communication and computation scheduling, and dynamic voltage scaling (dvs) methods. tian also presented a task mapping and scheduling solution for energy-constrained applications in wsns, energy-constrained task mapping and scheduling (ecomaps)[10]. ecomaps incorporates channel modeling, concurrent task mapping, communication and computation scheduling, and sensor failure handling algorithm. the performance of ecomaps is evaluated through simulations with randomly generated directed acyclic graphs (dag).yu proposed an energy-balanced allocation of a real-time application onto a single-hop cluster of homogeneous sensor nodes connected with multiple wireless channels[11]. an epoch-based application consisting of a set of communicating tasks is considered. each sensor node is equipped with discrete dynamic voltage scaling (dvs). the time and energy costs of both computation and communication activities are considered. he proposed both an integer linear programming (ilp) formulation and a polynomial time 3-phase heuristic. gu presented an application independent task mapping and scheduling solution in multi-hop video sensor networks (vsns) that provides real-time guarantees to process video feeds[12]. the processed data is smaller in volume which further releases the burden on the end-to-end communication. using a novel multi-hop channel model and a communication scheduling algorithm, computation tasks and associated communication events are scheduled simultaneously with a dynamic critical-path scheduling algorithm. dynamic voltage scaling (dvs) mechanism is implemented to further optimize energy consumption. xie developed a novel task allocation strategy 162 l. dai, h.k. xu, t. chen, c. qian, l.j. xie called beata (balanced energy-aware task allocation) for collaborative applications running on heterogeneous networked embedded systems[13]. the beata algorithm aims at blending an energy-delay efficiency scheme with task allocations, thereby making the best tradeoffs between energy savings and schedule lengths. besides, he introduced the concept of an energy-adaptive window, which is a critical parameter in the beata strategy. by fine-tuning the size of the energy-adaptive window, users can readily customize beata to meet their specific energy-delay trade-off needs imposed by applications. further, he built a mathematical model to approximate energy consumption caused by both computation and communication activities. a task allocation framework for underwater acoustic sensor networks (uw-asns) that participate as a team to accomplish critical missions is developed by kulkarni[14]. the team formed as a result of this task allocation framework is the subset of all deployed auvs that is best suited to accomplish the mission while adhering to the mission constraints. most of the existing task scheduling algorithms is normal ones without consideration of harmony and reliability in our research. this problem will need to be further gone into for improving the wireless sensor networks characteristics on real time, economy, power aware and harmony. the above discussed task scheduling algorithms are mainly for the problem of assignment tasks with the objective of minimizing the makespan and energy consumption, effective algorithms is discussed in these papers, and these algorithms will be able to distribute tasks to nodes with the expected value of the minimum earliest-finish-time and make them executed parallel and efficiently. however, tasks distributed to remote sensors may be unable to complete because of physical failures or attack, and the complexity, dynamics and open deployment of the wireless environment increases the possibility of that happening. existing task scheduling policies are not consider this question, so tasks will be assigned to the sensors with lower reliability to perform, what make measurement tasks automatically cease to be in force due to sensor failure, and then lowered the efficiency and qos of wireless sensor networks. compared with the previous research, the main contributions of this paper are presenting the concept of " task-performing probability ", and proposing a multi-objective optimization task scheduling algorithm for wireless sensor networks considering makespan optimization, the energyconsuming optimization and task-performing probability optimization simultaneously. consideration shall also be given to ensuring a reasonable distribution of tasks to reliable sensors, taking into account the makespan and energy consumption. this algorithm schedules tasks avoiding allocation some tasks to unreliable sensors, thus effectively lowering the influence of sensor failure on task performing. 3 the task scheduling model for wsns in this section, we describe mathematical models which were built to represent a task scheduling framework. 3.1 task model generally, a wireless sensor network consists of a set of heterogeneous sensors in abominable or military environments. sensor nodes always break down due to hardware failure, software error, energy exhaustion and disturb from outer environment, so the precondition of the tasks accomplished successfully is the sensors to provide a stable hardware and software needed to perform the given tasks. each sensor is in a "active" or "inactive" state at a given time. the task is impossibly accomplished on a inactive sensor, and the status information also will be missed or ineffective. a multi-objective optimization algorithm of task scheduling in wsn 163 let λi be the failure probability of sensor si , then the task-performing probability of the tasks accomplished successfully on sensor si is 1 − λi . we assumed that the failure process of each sensor is independent and yields to poisson distribution. we defined the task set as γ = {τ1,τ2, · · ·,τn}. according to the previous assumption, the tasks in the task set is independent each other. 3.2 makespan model suppose that a wireless sensor network consists of m sensors, s = {s1,s2, · · ·,sm}. there are n independent tasks competing the m sensors. we aim to scheduling the n tasks to the m sensors reasonably, to make the minimum makespan of the tasks. and we characterize a n × m matrix x satisfying the scheduling results. when xi,j = 1 (xi,j ∈ x), it meant that we schedule the task τi on sensor sj to process, otherwise, xi,j = 0. the task-processing time of each sensor can be estimated by forecasting techniques and history status based on task type. the task-processing time can be represented by a n × m matrix y , in which the matrix element yi,j represents the estimated task-processing time that task τi runs on sensor sj.the task-processing time of sensor sj is the sum of all the tasks processing time that the tasks run on sj. it can be expressed as follow: tj = n∑ i=1 yi,j (1) then, the makespan that the n tasks scheduled to the m sensors according to the scheduling result x is expressed as: t(γ,s,x) = maxsi∈s[r(sj)] (2) 3.3 energy consumption model because of the limited energy in wireless sensor networks, the research on low power technology and long lifetime is pivotal in the architecture of wireless sensor networks. the energy consumption of wireless sensor networks mainly composed of communication and task-performing energy consumption. communication energy consumption is relevant to the minimum energy-communication for a given standard distance p0 and the distance di,j between sensor si and sensor sj. pi,j = (4π) 2d2i,jβp0/d 2 0gtgrλ 2 (3) where gt and gr are the antenna gains (with respect to an isotropic radiator) of the transmitting and receiving antennas respectively, λ is the wavelength, and β is the energy consumption factor. since (4π)2βp0/gtgrλ2 is constant, one unit of communication energy consumption is indicated as d2i,j/d 2 0. task-performing energy consumption of sensor sj for finishing all its tasks is: ej = cjtj (4) where cj is the task-performing energy consumption of one certain task in one unit time. the relative task-performing energy consumption of sensor sj can be represented as follow: rj = ej/maxsi∈s(ej) (5) 164 l. dai, h.k. xu, t. chen, c. qian, l.j. xie excluding energy consumption, the lifetime of wireless sensor networks is the critical estimated part in task scheduling too. to extend network lifespan, we should balance the energy consumption of every sensor during the period of ordinary operation. we can measure the uniformity of sensors’ residual energy based on entropy theory. the uniformity of sensors’ residual energy after data transmission in t can be represented as follow: hit = − ∑ p(eit) log p(eit) (6) where eit is the residual energy of sensor sj in t, and hit is the value of residual energy entropy in the network. the higher hit, the more residual energy in the network, then the longer lifetime of the network. thus, we define the evaluation function of energy consumption for sensor sj is that: c(si) = −d2i,jrjh/d 2 0 (7) the less c(si) means that the energy consumption of data transmission and executing tasks has less effect on the residual energy in the network. the evaluation function of energy consumption for the network is defined as: c(l,s,x) = m∑ i=1 c(si) (8) the smaller the value of c(l,s,x), the longer network lifetime. 3.4 task-performing probability the concept of task-performing probability comes from the survivability of distributed system. survivability represents that tasks are capable of being performed steadily and regularly in the networks. definition. the task-performing probability means the probability that task could be accomplished successfully on specified sensor. let p(τi,sj) be the task-performing probability that task τi accomplished on sensor sj. in accordance with the illustrations in the previous section, the task-performing probability is exp(−λjt) that sensor be in the normal (active) state within t. because the tasks can be performed normally only when the given sensor is in a "normal" state, so we can get: p(τi,sj) = exp(−λjr(sj)) (9) let p(γ,s,x) is the task-performing probability the n tasks to the m sensors according to the scheduling result x, then we can get: p(γ,s,x) = exp[− i=n∑ i=1 j=m∑ j=1 λjxi,jr(sj)] (10) taking the task-performing probability as goal is to avoid the tasks scheduled to the sensors with low reliability. lowering the influence of sensor failure on task performing, then to maximize the taskperforming probability of task set γ, that is, we will maximize p(γ,s,x) by our task scheduling algorithm to obtain the most suitable x. thus, the quality of service for wireless sensor networks will be improved. a multi-objective optimization algorithm of task scheduling in wsn 165 in eq. 9, let l(τi,sj) = −λjr(sj), we can see that if we want to increase the value of p(τi,sj), then we must lower the l(τi,sj). let l(γ,s,x) = i=n∑ i=1 j=m∑ j=1 λjxi,jr(sj), then we can see that maximizing p(γ,s,x) means to minimize l(γ,s,x). the task scheduling algorithm taking the task-performing probability as goal makes the value of l(γ,s,x) as minimal as possible. 4 optimal task scheduling based on improved nsga-ii 4.1 multi-objective optimization problem in the case of a multi-objective optimization (moo) problem, there is usually no single solution that is optimum with respect to all objectives. there are a set of optimal solutions known as pareto optimal solutions. without additional information, all these solutions are equally satisfactory. the goal of moo is to find as many of these solutions as possible. in the research of pareto front many methods have been proposed. david schaffer first implemented a multi-objective evolutionary algorithm called the vector-evaluated genetic algorithm or vega in 1984. his algorithm started off well but tended to converge to a single solution. to prevent the convergence to a single solution, goldberg and richardson suggested using a nondominated sorting procedure coupled with a niching strategy called sharing. sharing takes into account that individuals in the same niche must share the available resources. this concept is integrated into the pareto genetic algorithm by increasing the cost of chromosomes as a function of their distance from each other. closely grouped chromosomes will find their costs increased more than chromosomes that are spaced far apart. the multi-objective genetic algorithm (moga)[15] starts by finding all non-dominated chromosomes of a population and gives them a rank of one. these chromosomes are removed from the population. next, all the non-dominated chromosomes of this smaller population are found and assigned a rank of two. this process continues until all the chromosomes are assigned a rank. the largest rank will be less than or equal to the size of the population. usually, there are many solutions that have the same rank. the selection procedure uses the chromosome ranking to determine the mating pool. moga also uses niching on the cost to distribute the population over the pareto optimal region [16]. non-dominated sorting genetic algorithm (nsga)[17] ranks chromosomes in the same manner as moga. the nsga algorithm then calculates a unique value. this unique value is related to the distance between each solution and its two closest neighbors. distance may be calculated from the variable values or the associated costs. the resulting values are scaled between 0 and 1 and subtracted from the cost. further information about the evolution of this method can be found in [4,16,17]. as discussed above, the goal of mtwsn is to find the solutions giving the best trade-off between the three conflict objectives, known as pareto optimal. mogas[4,17] are recognized to be well qualified to tackle multi-objective optimization problems. nsga-ii[4] is one of the most popular mogas. some concepts of multi-objective optimization problem are defined as follows. definition 1 (multi-objective optimization problem). given an n-dimensional decision vector x = {x1,x2, · · ·,xn} in the solution space x, find a vector x∗ that maximizes a given set of k objective functions f(x∗) = {f1(x∗),f2(x∗), · · ·,fk(x∗)}. the solution space x is generally restricted by a series of constraints, such as gj(x∗) = bj for j = 1,2, · · ·,m. definition 2 (dominance). a vector u = {u1,u2, · · ·,un} is said to dominate a vector v = {v1,v2, · · ·,vn} if and only if u is partially less than v, i.e., ∀ i = 1,2, · · ·,n, ui ≤ vi ∧ ∃i = 166 l. dai, h.k. xu, t. chen, c. qian, l.j. xie 1,2, · · ·,n, ui ≤ vi. definition 3 (pareto optimal solution). a solution xu ∈ x is said to be pareto optimal if and only if there is no xv ∈ x for which v = f(xv) = {v1,v2, · · ·,vn} dominates u = f(xu) = {u1,u2, · · ·,un}. definition 4 (pareto optimal set and front). let a ⊆ x. the nondominated set regarding a, represented by xp, is defined as xp = {z ∈ a|z is nondominated regardingx}. the corresponding objective function values in the objective space are defined as yp = f(xp) = {f(z)|z ∈ xp}, where xp is called the pareto optimal set and yp is called the cohere pareto optimal front. however, the solutions found by original nsga-ii are likely to be inferior or only comparable to that by classical heuristic search algorithms because of premature convergence. to find perfect solutions, a delete operator for nsga-ii is proposed to enhance the search capability. when selecting the elitist, if neither of the two individuals in a population wins out and their genes are the same, then delete one of them. furthermore, a circulation selection is presented to preserve excellent genes of the parent population. suppose there are k individuals in a population (ind1, ind2, ···, indk) when the crossover operations are carried out. the first time the operation is carried out with (ind1, ind2) as parents, the second time (ind2, ind3) are taken as parents, and so on. similarly, the last child is done by (indk, ind1). by this way, k offspring individuals are generated. the genes of each parent are inherited by two offspring individuals, thus avoiding the loss of excellent solutions. 4.2 an efficient task scheduling algorithm the motivation here is to provide the user with a set of pareto optimal solutions by the nsga-ii algorithm and give it the flexibility to choose the best possible solution from this set, depending on the specific application requirements. now, we can construct the cover set using the optimal pareto solutions generated by the improved nsga-ii algorithm. the chromosomes of a genetic algorithm contain all the building blocks to a solution for the genetic operators and the fitness function. in our implementation, each individual node is represented by a one-bit binary number called gene. this one-bit gene defines the status of the sensors as follows: xi,j = { 1, if task τi runs on sj 0, otherwise (11) we assign each individual with three fitness functions, makespan, energy consumption and task-performing probability. by introducing the non-dominated sorting approach and the crowded distance operator the replacement scheme is executed. first, a combined population rt = pt∪qt is formed with the parent population pt and the offspring population qt, where t is the number of generation. therefore the population rt will be of size 2n. and it is sorted according to the non-domination and crowded comparison. by adding solutions from the first front till the size exceeds n, the new parent population pt+1 is formed. after that, the solutions of the last accepted front are sorted according to the crowed comparison and the first (n − size(pt+1)) points are picked. in this way, the population pt+1 of size n is constructed. subsequently, it is used for the circulated selection, crossover, and mutation to create a new population qt+1 of size n. the recombination operator used in this paper is k-point crossover. after recombination, the mutation operator is applied to complement some genes in the chromosomes of the child randomly. this entire process is repeated until the difference of fitness values among the current pareto optimal set and the previous one is less than a chosen precision (ε). the main procedure of a multi-objective optimization algorithm of task scheduling in wsn 167 algorithm is described as algorithm 1: 5 performance evaluation in this section, we will present the simulation results as the performance evaluation of our proposed task scheduling algorithm. the performance of our proposed algorithm is compared with rt-maps [9], ecomaps [10], and ebta [11] task scheduling algorithms in wireless sensor networks in terms of the makespan, and energy consumption of task scheduling. for our experiments, we use 50 sensor nodes and a sink, which is not power constrained. nodes are distributed in a 100x100 meter square area yielded to random poisson distribution. to evaluate the performances of the task scheduling algorithms properly, experimental parameters of the four algorithms were set as the same ones. the performance of the genetic algorithm is greatly affected by a number of factors, such as the population size, the probability of mutation and crossover, and the method of scheduling. we have run a number of experiments with different values of these parameters to determine the optimal set for our network size. finally, the ga parameters used in our simulations are listed in table 1. although we allow the ga to run for a maximum of 100 generations, we have observed 168 l. dai, h.k. xu, t. chen, c. qian, l.j. xie figure 1: schematic diagram of statistic information for scheduling that the best solution is typically found within 40 generations. table 1: ga parameters used in simulation parameter value population size 200 recombination rate 0.9 mutation rate 0.005 reduction rate 0.5 the simulation results are shown in fig. 1 to fig. 3. figure 1 delineates the statistical information of the proposed task scheduling algorithm. we investigates the space of optimum configuration parameters (makespan, energy consumption and task-performing probability), using unconstrained optimization, to highlight the trade-offs faced by the wsns considered. in order to solve the optimization problem, we chose a pareto-compliant ranking method based on evolutionary techniques, namely the non-dominated sorting genetic algorithm-ii (nsga-ii). fig. 1 shows the result of the joint optimization of the three objective functions. these results show that the higher the balanced energy consumption or the higher the probability of task performing, the shorter the makespan. the important outcome of the results in fig. 1 is that it provides details about the optimal network configuration, since tuning the network with the parameters derived from the execution of the nsga-ii algorithm guarantees that the network performance is not biased toward any one of the performance indicators. the makespan metric is compared between proposed task scheduling algorithms for wireless sensor networks in figure 2. it explains that the makespan for wireless sensor networks is lower for proposed algorithms than rt-maps [9], ecomaps [10], and ebta [11]. this is because that in proposed algorithm the tasks have been finished on minimum makespan as the target function. figure 3 shows the comparative performance in terms of energy consumption of sensor nodes.with the increase of the number of tasks in the network, the energy consumption of task scheduling and data transmission in the algorithms increases. however, the energy consumption in the proposed algorithm is lower than rt-maps [9], ecomaps [10], at fixed number of tasks. a multi-objective optimization algorithm of task scheduling in wsn 169 figure 2: makespan of different scheduling algorithms figure 3: energy-cosumption factor of different scheduling algorithms our proposed task scheduling algorithm results in lowest energy consumption for higher number of tasks. this shows the efficiency of the proposed algorithm. 6 conclusions the optimization of task scheduling is studied to reduce the energy consumption and ensure the effective information acquisition in wireless sensor network. a multi-objective optimization algorithm of task scheduling is proposed for wireless sensor networks in this paper. it is the first time that the nsga-ii algorithm is used to analyze the task scheduling for wireless sensor networks. based on the characteristics of wireless sensor networks, makespan optimization, the energy-consuming balance optimization and task-performing probability optimization were included. a mathematical model used to optimize the task scheduling problem by nsga-ii was built and the solution was presented, and a detailed process to solute the multi-objective programming model was put forwards. 170 l. dai, h.k. xu, t. chen, c. qian, l.j. xie bibliography [1] z. zeng, a. liu, d. li (2008), a highly efficient dag task scheduling algorithm for wireless sensor networks, in proc. of icycs 2008,zhang jia jie , hunan , china, 570-575. 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[11] y. yu, v. k. prasanna (2005), energy-balanced task allocation for collaborative processing in wireless sensor networks. acm/kluwer mobile networks and applications journal, 10(1):115-131. [12] y. gu (2007), real-time multimedia processing in video sensor networks, signal processing : image communication, 22(3):237-251. [13] t. xie, x. qin (2008), an energy-delay tunable task allocation strategy for collaborative applications in networked embedded systems. ieee transactions on computers, 57(3):329 343. [14] i. s. kulkarni, d. pompili (2010), task allocation for networked autonomous underwater vehicles in critical missions. ieee journal on selected areas in communications, 28(5):716727. [15] c. m. fonseca, p. j. fleming (1993), genetic algorithms for multiobjective optimization: formulation, discussion and generalization, in genetic algorithms: proceedings of the fifth international conference, san mateo, ca: morgan kaufmann, 416-423. a multi-objective optimization algorithm of task scheduling in wsn 171 [16] r. l. haupt, s. e. haupt (2004), practical genetic algorithms. john wiley & sons. [17] n. srinivas, k. deb (1995), multiobjective optimization using nondominated sorting in genetic algorithms. journal of evolutionary computation, 2(3):221-248. int j comput commun, issn 1841-9836 8(6):845-853, december, 2013. an improved genetic algorithm for the multi level uncapacitated facility location problem v. korać, j. kratica, a. savić vanja korać*, jozef kratica mathematical institute, serbian academy of sciences and arts kneza mihaila 36/iii, 11 000 belgrade, serbia vanja@mi.sanu.ac.rs, jkratica@mi.sanu.ac.rs *corresponding author: vanja@mi.sanu.ac.rs aleksandar savić university of belgrade, faculty of mathematics studentski trg 16, 11000 belgrade, serbia aleks3rd@gmail.com abstract: in this paper, an improved genetic algorithm (ga) for solving the multilevel uncapacitated facility location problem (mluflp) is presented. first improvement is achieved by better implementation of dynamic programming, which speeds up the running time of the overall ga implementation. second improvement is hybridization of the genetic algorithm with the fast local search procedure designed specially for mluflp. the experiments were carried out on instances proposed in the literature which are modified standard single level facility location problem instances. improved genetic algorithm reaches all known optimal and the best solutions from literature, but in much shorter time. hybridization with local search improves several best-known solutions for large-scale mluflp instances, in cases when the optimal is not known. overall running time of both proposed ga methods is significantly shorter compared to previous ga approach. keywords: evolutionary approach, metaheuristics, discrete location, combinatorial optimization. 1 introduction during the last decades, there was an expansive growth in the ways of solving facility location problems. research has been concentrated mostly on location problems, which require minimization of total travel time, physical distance, or some other related cost. in most cases it is assumed that facilities are large enough to meet any demand. so far, there are several deterministic uncapacitated models proposed in the literature. the multi-level uncapacitated facility location problem (mluflp) is np-hard, since it is a generalization of the uncapacitated facility location problem which is proven to be np-hard in [7]. the multi-level version of uncapacitated facility problem is discussed only in several papers ( [1–5, 8, 9]), compared to several hundreds of papers about the basic version of the problem. moreover, most of the papers that deal with mluflp were of theoretical nature, without experimental results. the only exceptions are papers [5] and [8]. in [5] four methods for solving mluflp are implemented. three algorithms are based on linear programming relaxation of the model, described in section 2, which has an enormous number of variables and moderate number of constrains. consequently, these three algorithms are capable of solving only small size mluflp instances with up to 52 potential facility locations. running times of these methods are greater than 100 seconds. gap values are rather satisfying copyright © 2006-2013 by ccc publications 846 v. korać, j. kratica, a. savić but the corresponding running times are very long even for these small size instances. obviously, all three linear programming based methods are unable to solve larger size problem instances. the fourth method produce very quick results (up to 0.07 seconds), but the gaps are very large up to 732%. from these facts, it can be concluded that none of these algorithms is capable of solving real medium-size and large-scale mluflp instances. the only method capable of reaching optimal solutions in solving small and medium-size mluflp instances was an evolutionary approach presented in [8]. it also gives results on large-scale mluflp instances in a reasonable running time. a binary encoding scheme with appropriate objective function containing dynamic programming approach for solving subproblem wad used, which consisted of finding sequence of located facilities on each level to satisfy clients’ demands. used dynamic programming approach has the polynomial number of states and steps, since the given subproblem is a special case of the shortest path problem. that approach enables the ga to use the standard genetic operators and caching technique, which helps genetic algorithm to reach promising search regions. 2 previous work let us first give a mathematical formulation of the mluflp problem as presented in [5]. the input data to the mluflp consists of a set of facilities f (|f| = m) partitioned into k levels, denoted f1, . . . ,fk, a set of clients d (|d| = n), a fixed cost fi for establishing facility i ∈ f , and a metric that defines transportation costs cij for each i,j ∈ f ∪ d. a feasible solution assigns each client a sequence of k facilities, one from each level fk, . . . ,f1, respectively. a feasible solution is charged the sum of the fixed costs of the facilities used, plus the transportation costs of the clients’ assignments. each client’s transportation cost is the sum of transportation cost from itself to the first facility of its sequence, plus the transportation cost between successive facilities. an optimal solution is a feasible solution with a minimum total cost. the assignment of a client j ∈ d to a valid sequence of facilities can now be represented as the assignment of j to a path p from j to one of the top level facilities f1. the set of all valid sequences of facilities is defined with p = fk × . . . × f1 and the transportation cost of client j’s assignment to sequence p = (ik, . . . , i1) by cpj = cjik + cikik−1 + . . . + ci2i1 . variable yi has value of 1 if facility i is established and 0 otherwise. similarly, variable xpj represents whether or not a client j is assigned to the path p. using the notation mentioned above, the problem can be written as: min ∑ i∈f fiyi + ∑ p∈p ∑ j∈d cpjxpj (1) ∑ p∈p xpj = 1, for each j ∈ d, (2) ∑ p∋i xpj ≤ yi, for each i ∈ f,j ∈ d, (3) xpj ∈ {0,1} , for each p ∈ p,j ∈ d, (4) yi ∈ {0,1} , for eachi ∈ f. (5) an improved genetic algorithm for the multi level uncapacitated facility location problem 847 table 1: fixed costs facilities f1 f2 f3 f4 f5 f6 fixed cost 70 50 30 20 20 40 table 2: distance between facilities on level 1 and level 2 f1 f2 f3 58 23 f4 44 74 f5 67 15 f6 29 38 the objective function (1) minimizes the sum of overall transportation cost and fixed costs for establishing facilities. constraint (2) ensures that every client is assigned to a path while constraint (3) guarantees that any facility on a path used by some client is paid for. constraints (4) and (5) reflect binary nature of variables xpj and yi. the problem can be illustrated with one small example. example 1 an example of the mluflp is shown below. it assumes k = 2 levels of m = 6 facilities: the first level contains 2 potential facilities and the second 4 potential facilities. in this example there are n = 5 clients to be served. the fixed costs of establishing facilities are given in table 1, the distances between facilities of different levels and the distances between clients and facilities on the second level are given in table 2 and table 3, respectively. the total enumeration technique, described in section 4, is used to obtain an optimal solution. established facilities are: f2 on the first and f3, f5 on the second level. the objective function value is 329. the sequences of facilities for each client are shown in table 4. 3 improved ga method proposed genetic algorithm proposed in this paper is an improved version of ga used in [8], so after the short summary of the overall genetic algorithm implementation we only give the detailed description of the improvement parts. the outline of our ga implementation is given below, where npop denotes the overall number of individuals in the population, nelite is a number of elite individuals and ind and objind mark the individual and its value of objective function. input_data(); population_init(); while not stopping_criterion() do for ind:= (nelite + 1) to npop do table 3: distance between clients and facilities f3 f4 f5 f6 client 1 38 13 57 41 client 2 47 52 63 15 client 3 42 48 13 54 client 4 9 54 41 43 client 5 15 18 36 22 848 v. korać, j. kratica, a. savić table 4: sequences of facilities for clients level 2 level 1 client 1 f3 f2 client 2 f3 f2 client 3 f5 f2 client 4 f3 f2 client 5 f3 f2 if (exist_in_cache(ind)) then objind:= get_value_from_cache(ind); else objind:= objective_function(ind); local_search(ind,objind); put_into_the_cache_memory(ind,objind); if (full_cache_memory()) then remove_lru_block_from_cache_memory(); endif endif endfor fitness_function(); selection(); crossover(); mutation(); endwhile output_data(); the encoding of the individuals used in this implementation is binary. the set of facilities f which will be used is naturally represented as the individual represented with a binary string of length m. digit 1 at the i-th place of the string denotes that yi = 1, while 0 shows the opposite (yi = 0). now, for any individual, through its genetic code, string of established facilities is given. objective function value can now be computed using dynamic programming explained in [8] if the array of established facilities has at least one established facility. otherwise, solution is not valid and individual is marked as infeasible. genetic operators are the same as in [8]. we can briefly summarize them • selection operator is fine-grained tournament selection fgts [6], • crossover operator is a standard one-point crossover operator, • mutation operator is a standard simple mutation modified for frozen genes, • initialization of first population is random. the elitist strategy is applied to nelite elite individuals, which are directly passing to the next generation. the genetic operators are applied to the rest of the population (nnnel = npop −nelite non-elite individuals). all duplicates of every individual are eliminated. individuals with the same objective function value, but different genetic codes are limited with the number of their appearance nrv. an improved genetic algorithm for the multi level uncapacitated facility location problem 849 the fitness_function(), the fitness find of individual ind 1,2, ..., npop is computed by scaling values of objective function objind of all individuals into the interval [0,1], so that the best individual indmin has fitness 1 and the worst one indmax has fitness 0. caching technique applied in this improved version of ga is the same as described in [8]. 3.1 improved objective function in [8] for feasible individual ind, the objective_function(ind), is evaluated in four steps. 1. in the first step,the values of variables yi are obtained from the genetic code. let us denote the number of established facilities (number of yi’s with value 1) with m1. 2. in the second step, the array of minimal costs cs is initialized. the array cs carries information about total minimal costs for serving clients and established facilities (except the ones on the first level), regarding the costs for serving facilities on the upper level. the minimal cost values in cs, for the established facilities on the first level are initially set to zero, while the costs for established facilities on remaining levels and clients are set to a large constant inf = 1030. 3. the minimal costs for each client and each facility are calculated by dynamic programming. for each facility in each level (except the first one), the array of total minimal costs (initialized in the second step) is updated. the minimal cost value for each facility is the minimum of the sum of the minimal cost for established facility on the upper level and transportation cost between the two facilities. the same procedure is done for each client: among the established facilities from the last level, the one with the minimal sum of the corresponding minimal cost value and the transportation cost facility-client, is taken. 4. finally, the objective value is computed by adding all transportation costs facility-client and fixed costs for all established facilities. as can we seen from the previous algorithm, some of the operation is unnecessary. for example, all pairs of facilities from the two consecutive levels are taken into consideration where as only pairs of established facilities are what matters. in example 1. there is an optimal solution only for 1 out of 2 facilities established on the first level and only for 2 out 4 facilities established on the second level. therefore, for calculating minimal cost between the first and the second level, previous algorithm performs 2*4=8 operations (one operation per facility pair), while as only 1*2=2 is really needed. a similar situation arises in calculating facility-client costs, which in previous algorithm as 5*4=20 operation, while only 2*5=10 is really needed. in order to correct shortcomings of previous algorithm, the following improvements are proposed. new array of established facility indices is constructed. when calculating minimal costs with dynamic programming approach, instead of using all pairs of facilities on consecutive levels we used only pairs of established facilities obtained from the newly constructed array. similarly, when calculating minimal facility-client costs, , instead of using of all facilities on the last level, only the established once are taken into consideration. therefore, usage of this procedure effectively improves objective function calculation which makes the major part in running time of the overall ga implementation. in presented example we have a significant improvement, but for large scale instances improvement can be even greater. this can be seen on finding objective function for the largest instance mt1_5l_60_120_250_500_1070.2000 best known solutions has 1 established facility on the first 4 levels and 5 established facilities on the 5th level. now, instead of calculating 850 v. korać, j. kratica, a. savić 1. 60*120 pairs of facilities between level 1 and 2, there is only one pair of facilities; 2. 120*250 pairs of facilities between level 2 and 3, there is only one pair of facilities; 3. 250*500 pairs of facilities between level 3 and 4, there is only one pair of facilities; 4. 500*1070 pairs of facilities between level 4 and 5, there are five pairs of facilities; 5. 1070*2000 pairs of facilities-clients we have 5*2000 pairs of facilities-clients. although objective function calculation has spped up factor greater than 200, speed up of overall ga implementation is about 6.5 times. this discrepancy can be explained that the running time of calculating objective function was conducted previously, instead of representing the major part, becomes dominated by running time of other parts of ga implementation. it is obvious that this way of calculating objective function is much faster so its running time is significantly shorter than those of genetic operators (selection, crossover and mutation). 3.2 local search in order to improve the accuracy of the solutions additionally, the proposed ga approach incorporates a local search procedure. it considers the array of facilities and regards which facilities are established and, which are not. in the array facilities which are established they are marked with 1 and those that are not, are marked with 0. local search starts from the first facility in the array and tries to change its status. if there is an improvement in the value of objective function, local search starts from the beginning of the array, otherwise it moves to the next facility. if the facility that is momentarily in consideration is the only established facility on that level, local search moves to the next facility in the array. this procedure is repeating until there are no more facilities in the array. improvement can be determined in two ways, according to the status of the regarded facility. 1) let us first consider the case where the facility is non-established, and its status is changed into being established. then new value of objective function is determined in the following way. all established facilities on the previous levels are unchanged with their fixed transport costs. on the current level costs are added with the fact that running facility is now established. all other transport costs are unchanged. on the next level already found transport costs are compared with transport costs through the new established facilities and changed if the latter is smaller. on the levels following this all transport costs must be evaluated anew. this includes transport costs from the last level of facilities to the clients. 2) in the second case an established facility becomes non-established. transport costs for that level are then reduced for transport cost of that facility. the array is not reduced in length but all paths through that facility are out of consideration. on the next level transport costs are again calculated only for those that were connected through facility considered. on the following levels, transport costs are determined only for those that were on paths leading through the facility considered. local search is applied only on the best individual in population and this only if it stayed unchanged in nrep generations (in this work nrep = 5). on the one individual, local search is applied only once, because of its deterministic nature. if the best individual is replaced with another individual with equal value of an objective function but different genetic code and that individual stays unchanged through nrep generations, local search is again applied. in this paper, local search was not applied in the first 50 generations, whatsoever. an improved genetic algorithm for the multi level uncapacitated facility location problem 851 table 5: ga results on the instances with previously known optimal solution instance name optimal ga improvedga ga + ls solution sol ttot sol ttot sol ttot cap71_2l_6_10.50 1813375.51 opt 0.202 opt 0.195 opt 0.226 cap71_3l_2_5_9.50 4703216.31 opt 0.22 opt 0.195 opt 0.233 cap101_2l_8_17 1581551.39 opt 0.274 opt 0.224 opt 0.269 cap101_3l_3_7_15.50 3227179.81 opt 0.258 opt 0.223 opt 0.265 cap131_2l_13_37.50 1592548.45 opt 0.557 opt 0.358 opt 0.407 cap131_3l_6_14_30.50 3201970.46 opt 0.546 opt 0.355 opt 0.396 cap131_4l_3_7_15_25.50 3630297.67 opt 0.515 opt 0.32 opt 0.352 4 computational results all tests were carried out on an intel 2.5 ghz with 2 gb memory. the algorithms were coded in c programming language. the ga is tested on the same instances as in [8] to show improvement obtained by improved objective function and hybridization with local search. in this paper, only those instances with multiple levels of facilities are taken into consideration, since for the basic problem with only one level several hundreds of papers are available. therefore, instances with multiple levels are the main interest of this research, while results on instances with only one level of facilities are omitted. for small size instances, optimal values are known from the literature which is indicated in table 1. for fair and direct comparison of the results, we leave same ga parameters as in [8]. the finishing criterion of ga is the maximal number of generations ngen = 5000. the algorithm also stops if the best individual or best objective value remains unchanged through nrep = 2000 successive generations. since the results of ga are nondeterministic, the ga was run 20 times on each problem instance. in [8] experimental results are not totally in accordance with the instances. it was possible to repeat testing and about 1/3 of instances values of objective function cannot be validated as presented in that quoted paper. for example, value of objective function, for instance cap131_4l_3_7_15_25.50, is 3630297.67 in the paper and in repeated testing, but for instance capa_3l_15_30_55.100 value function in the paper is 40725103.254, and 25424361.91 in the repeated testing. furthermore, for some instances, results obtained in literature and in repeated testing slightly differ, but that can be caused by slightly different random seed. for example, for instance ms1_4l_64_128_256_552.1000 values of objective function in the literature and in repeated testing are 30936.585 and 31257.27 respectively which means that result in the quoted paper is slightly better. for instance mt1_4l_120_250_520_1110.2000 respective results are 65044.003 and 64995.454 which means that result in repeated testing is slightly better. because of these differences, results of repeated testing will be presented in the whole paper as results obtained by original ga implementation. table 1 presents the ga result on smaller and medium instances. in the first column names of instances are given. the instance’s name carries information about the number of levels, the number of facilities on each level and the number of clients respectively. for example, the instance capb_3l_12_25_63.1000 is created by modifying orlib instance capb, which has 3 levels with 12, 25, 63 facilities, respectively and 1000 clients. the second column contains the optimal solution on the current instance, if it is previously known, otherwise sign as − . the best ga value gabest and running time ttot of the original ga is is given in the following two columns, with marked optimum in cases when ga reached 852 v. korać, j. kratica, a. savić table 6: ga results on the instances with unknown optimal solution instance name ga improved ga ga + ls sol ttot sol ttot sol ttot capa_2l_30_70.1000 14829245.63 32.715 14829245.63 16.589 14829245.63 17.517 capa_3l_15_30_55.1000 25424361.91 17.76 25424361.91 8.679 25424361.91 8.933 capa_4l_6_12_24_58.1000 35421258.15 16.231 35421258.15 7.49 35421258.15 7.636 capb_2l_35_65.1000 14479223.79 27.062 14479223.79 14.118 14479223.79 14.283 capb_3l_12_25_63.1000 25986997.29 28.16 25986997.29 12.147 25986997.29 12.477 capb_4l_6_13_31_50.1000 41787432.24 17.371 41787432.24 8.631 41787432.24 9.083 capc_2l_32_68.1000 14072575.52 29.437 14072575.52 16.218 14072575.52 16.901 capc_3l_13_27_60.1000 26751918.75 26.85 26751918.75 12.932 26751918.75 12.773 capc_4l_4_9_27_60.1000 47109818.66 24.491 47109818.66 11.316 47109818.66 11.184 mq1_2l_100_200.300 8341.287 25.561 8341.287 3.091 8341.287 3.180 mq1_3l_30_80_190.300 12994.871 23.95 12994.871 2.903 12994.871 2.951 mq1_4l_18_39_81_162.300 18048.0305 21.233 18048.0305 3.002 18048.0305 3.000 mq1_4l_20_40_80_160.300 17648.0095 20.759 17648.0095 3.141 17648.0095 3.070 mr1_2l_150_350.500 6733.815 88.939 6733.815 8.979 6733.815 10.277 mr1_2l_160_340.500 6707.505 88.878 6707.505 8.752 6707.505 10.096 mr1_3l_55_120_325.500 10911.319 80.546 10911.319 8.39 10911.319 9.401 mr1_4l_30_65_140_265.500 15237.2605 76.126 15237.2605 7.852 15237.2605 8.297 ms1_2l_320_680.1000 13361.3895 479.724 13361.3895 66.767 13361.3895 98.085 ms1_3l_120_250_630.1000 21923.331 364.135 21881.384 64.965 21881.384 89.360 ms1_4l_64_128_256_552.1000 31257.27 385.73 30902.742 54.715 30902.742 79.729 ms1_5l_25_55_120_250_550.1000 40494.7435 373.781 40249.2415 57.169 40094.335 80.764 mt1_2l_650_1350.2000 27733.057 2410.92 27733.057 457.427 27733.057 685.593 mt1_3l_255_520_1225.2000 46278.719 2398.943 46529.979 426.48 46278.719 622.719 mt1_3l_256_600_1144.2000 46095.09 2403.649 46095.09 406.855 46095.09 585.418 mt1_4l_120_250_520_1110.2000 64995.454 2318.705 64995.454 402.143 64851.16 567.867 mt1_5l_60_120_250_500_1070.2000 83486.9185 2265.78 83363.586 371.867 83363.586 543.857 the optimal solution. in the following two columns are given best values and running times, improved ga and ttot of the improved ga algorithm. finally, in the last two columns best values and running times are given, ga+ls and ttot of the hybridized version of ga algorithm. as it can be seen from table 5, there are three variants of ga reached optimal solutions. as expected, running times of the improved ga has been better than for original ga for all instances, and improvement is up to 40 percent. hybridized ga with ls in some instances also has better running times (4 of 7) than the original ga. it can be noticed that better running times in both variants are accomplished on instances with the greater number of levels and greater number of facilities. in table 6 results on larger instances are given. in this case, optimal solutions are not known so that the column is omitted and all other columns have same meaning as in table 1. from table 6 can be concluded that improvements in both new variants of ga were validated. improved ga has all running times faster than original ga (in some cases like the instances with 500 clients up to 10 times faster)without losing on quality of results. only in one instance, was result was obtained by improved ga, greater than that obtained by original ga (instance mt1_3l_255_520_1225.2000). in all other instances improved ga has better results. hybridization with local search also produced improvements. results obtained from this variant are the best in all instances. this variant of ga produced better results than original ga in 5 instances (mostly in the largest instances), and was better than the improved ga in 3 instances. an interesting fact is that ga with ls has running times shorter almost in all instances (except the smallest instances), and sometimes like in those instances with 300 clients ga with ls run an improved genetic algorithm for the multi level uncapacitated facility location problem 853 up to 8 times faster than the original ga. even in the largest instances hybridized version ran up to 44 times faster and also produced better results. as it could be expected, running times of the hybridized version were always longer than those of improved ga. 5 conclusions and future works this paper presented improved genetic algorithm for solving multi-level uncapacitated facility location problem. improvements are achieved, firstly, in formulating the new version of finding objective function for the problem which resulted in much shorter running times, and secondly, through hybridization with fast and reliable local search procedure, which achieved better results and in almost all cases run faster than original ga approach. these improvements are considerable, since hybrid ga is obtained 5 new best-known solutions and running times are shorter sometimes up to one order of the magnitude compared to original ga version. it can be concluded that both of these versions represent considerable improvements in solving mluflp. future research can be directed to parallelization of presented ga, hybridization with some other heuristics and its application in solving similar facility location problems. bibliography [1] k. aardal, f. chudak, d.b. shmoys, a 3-approximation algorithm for the k-level uncapacitated facility location problem, information processing letters, 72:161–167, 1999. [2] a. ageev, improved approximation algorithms for multilevel facility location problems, operations research letters, 30: 327–332, 2002. [3] a. ageev, y. ye, j. zhang, improved combinatorial approximation algorithms for the k-level facility location problem, siam journal on discrete mathematics, 18: 207–217, 2005. [4] a.f. bumb, w. kern, a simple dual ascent algorithm for the multilevel facility location problem, proceedings of the 4th international workshop on approximation algorithms for combinatorial optimization problems and 5th international workshop on randomization and approximation techniques in computer science: approximation, randomization and combinatorial optimization, 55-62, august 18–20, 2001. [5] n.j. edwards, approximation algorithms for the multi-level facility location problem. ph.d. thesis, cornell university, 2001. [6] v. filipović, j. kratica, d. tošić, d. i. ljubić, fine grained tournament selection for the simple plant location problem. it proceedings of the 5th online world conference on soft computing methods in industrial applications wsc5, 152–158, september 2000. [7] j. krarup, p. m. pruzan, the simple plant location problem: survey and synthesis, european journal of operational research, 12: 36–81, 1983. [8] m. marić, an efficient genetic algorithm for solving the multi-level uncapacitated facility location problem, computing and informatics, 29(2): 183–201, 2010. [9] j. zhang, approximating the two-level facility location problem via a quasi-greedy approach. mathematical programming, 108: 159–176, 2006. int j comput commun, issn 1841-9836 8(5):736-743, october, 2013. feedback linearization with fuzzy compensation for uncertain nonlinear systems m.c. tanaka, j.m.m. fernandes, w.m. bessa marcelo c. tanaka, josiane m.m. fernandes, wallace m. bessa departamento de engenharia mecânica, universidade federal do rio grande do norte campus universitário lagoa nova, natal, rn 59072-970, brazil marcelotanaka.eng.mec@gmail.com, josiane.eng.mec@gmail.com, wmbessa@ct.ufrn.br abstract: this paper presents a nonlinear controller for uncertain single-input–single-output (siso) nonlinear systems. the adopted approach is based on the feedback linearization strategy and enhanced by a fuzzy inference algorithm to cope with modeling inaccuracies and external disturbances that can arise. the boundedness and convergence properties of the tracking error vector are analytically proven. an application of the proposed control scheme to a second-order nonlinear system is also presented. the obtained numerical results demonstrate the improved control system performance. keywords: feedback linearization, fuzzy logic, nonlinear control, van der pol oscillator. 1 introduction due to its simplicity, feedback linearization scheme is commonly applied in industrial control systems, specially in the field of industrial robotics. the main idea behind this control method is the development of a control law that allows the transformation of the original dynamical system into an equivalent but simpler one [11]. although feedback linearization represents a very simple approach, an important handicap is the requirement of a perfectly known dynamical system, in order to ensure the exponential convergence of the tracking error. on this basis, much effort has been made to combine feedback linearization with intelligent algorithms in order to improve the trajectory tracking of uncertain nonlinear systems. the most common strategies are based on artificial neural networks [2,4,9,10,13] or fuzzy logic [1,3,5,6]. a drawback of these approaches is that both neural networks or fuzzy logic are used to model the entire plant, which means that a large computational effort is normally required to characterize system dynamics. considering that the designer of the control system usually has at least some knownledge of the plant to be controlled, a nonlinear controller is proposed in this paper to compensate for the uncertainties of single-input-single-output (siso) nonlinear systems. the adopted approach is based on the feedback linearization method, but enhanced by a fuzzy inference system to cope with modeling imprecisions and external disturbances that can arise. this approach requires a reduced number of fuzzy sets and rules and consequently simplifies the design process. the boundedness and convergence properties of the closed-loop signals are analytically proven and numerical simulations are carried out in order to demonstrate the improved performance of the proposed control scheme. 2 feedback linearization consider a class of nth-order nonlinear systems: copyright c⃝ 2006-2013 by ccc publications feedback linearization with fuzzy compensation for uncertain nonlinear systems 737 x(n) = f(x, t) + b(x, t)u + d (1) where u is the control input, the scalar variable x is the output of interest, x(n) is the n-th time derivative of x, x = [x,ẋ, . . . ,x(n−1)] is the system state vector, f,b : rn → r are both nonlinear functions and d is assumed to represent all uncertainties and unmodeled dynamics regarding system dynamics, as well as any external disturbance that can arise. in respect of the disturbance-like term d, the following assumption will be made: assumption 1. the disturbance d is unknown but continuous and bounded, i. e. |d| ≤ δ. let us now define an appropriate control law based on conventional feedback linearization scheme that ensures the tracking of a desired trajectory xd = [xd, ẋd, . . . ,x (n−1) d ], i. e. the controller should assure that x̃ → 0 as t → ∞, where x̃ = x − xd = [x̃, ˙̃x,. . . , x̃(n−1)] is the related tracking error. on this basis, assuming that the state vector x is available to be measured and system dynamics is perfectly known, i. e. there is no modeling imprecision nor external disturbance (d = 0) and the functions f and b are well known, with |b(x, t)| > 0, the following control law: u = b−1(−f + x(n)d − k0x̃ − k1 ˙̃x − · · · − kn−1x̃ (n−1)) (2) guarantees that x → xd as t → ∞, if the coefficients ki (i = 0,2, . . . ,n− 1) make the polynomial pn + kn−1p n−1 + · · · + k0 a hurwitz polynomial [11]. the convergence of the closed-loop system could be easily established by substituting the control law (2) in the nonlinear system (1). the resulting dynamical system could be rewritten by means of the tracking error: x̃(n) + kn−1x̃ (n−1) + . . . + k1 ˙̃x + k0x̃ = 0 (3) where the related characteristic polynomial is hurwitz. however, since in real-world applications the nonlinear system (1) is often not perfectly known, the control law (2) based on conventional feedback linearization is not sufficient to ensure the exponential convergence of the tracking error to zero. thus, we propose the adoption of fuzzy inference system within the control law, in order to compensate for d and to enhance the feedback linearization controller. 3 fuzzy inference system because of the possibility to express human experience in an algorithmic manner, fuzzy logic has been largely employed in the last decades to both control and identification of dynamical systems. the adopted fuzzy inference system is the zero order tsk (takagi–sugeno–kang), with the rth rule stated in a linguistic manner as follows: if x̃ is x̃r, ˙̃x is ˙̃ xr, · · · , and x̃(n−1) is x̃ (n−1) r , then d̂r = d̂r ; r = 1,2, · · · ,n where x̃r, ˙̃ xr, · · · , and x̃ (n−1) r are fuzzy sets, whose membership functions could be properly chosen, and d̂r is the output value of each one of the n fuzzy rules. considering that each rule defines a numerical value as output d̂r, the final output d̂ an be computed by a weighted average: 738 m.c. tanaka, j.m.m. fernandes, w.m. bessa d̂(x̃) = ∑n r=1 wr · d̂r∑n r=1 wr (4) or, similarly, d̂(x̃) = d̂tψ(x̃) (5) where, d̂ = [d̂1,d̂2, . . . ,d̂n] is the vector containing the attributed values d̂r to each rule r, ψ(x̃) = [ψ1,ψ2, . . . ,ψn] is a vector with components ψr(x̃) = wr/ ∑n r=1 wr and wr is the firing strength of each rule, which can be computed from the membership values with any fuzzy intersection operator (t-norm). 4 fuzzy feedback linearization considering that fuzzy logic can perform universal approximation [7], we propose the adoption of a tsk fuzzy inference system within the feedback linearization controller to compensate for modeling inaccuracies and consequently enhance the trajectory tracking of uncertain nonlinear systems. therefore, the control law with the fuzzy compensation scheme can be stated as follows u = b−1[−f + x(n)d − k0x̃ − k1 ˙̃x − · · · − kn−1x̃ (n−1) − d̂(x̃)] (6) and the related closed-loop system is: x̃(n) + kn−1x̃ (n−1) + . . . + k1 ˙̃x + k0x̃ = d̃ (7) with d̃ = d̂ − d. now, defining ktx̃ = kn−1x̃(n−1) + . . . + k1 ˙̃x + k0x̃, where k = [c0λn,c1λn−1, . . . ,cn−1λ], λ is a strictly positive constant and ci states for binomial coefficients, i. e. ci = ( n i ) = n! (n − i)! i! , i = 0,1, . . . ,n − 1 (8) the convergence of the closed-loop signals to a bounded region is assured. theorem 2. consider the uncertain nonlinear system (1) and assumption 1, then the fuzzy feedback linearization controller defined by (5) and (6) ensures the exponential convergence of the tracking error vector to a closed region ω = {x ∈ rn | |x̃(i)| ≤ ζiλi−nε,i = 0,1, . . . ,n − 1}, with ζi defined by (9). ζi = { 1 for i = 0 1 + ∑i−1 j=0 ( i j ) ζj for i = 1,2, . . . ,n − 1. (9) proof: considering the universal approximation feature of fuzzy logic [7], the output of the adopted inference system (5) can approximate the disturbance d to an arbitrary degree of accuracy, i. e. |d̂(x̃) − d| ≤ ε for an arbitrary ε > 0. thus, from (7) one has |x̃(n) + kn−1x̃(n−1) + . . . + k1 ˙̃x + k0x̃| ≤ ε (10) from (8), inequality (10) may be rewritten as −ε ≤ x̃(n) + cn−1λx̃(n−1) + · · · + c1λn−1 ˙̃x + c0λnx̃ ≤ ε (11) feedback linearization with fuzzy compensation for uncertain nonlinear systems 739 multiplying (11) by eλt yields −εeλt ≤ dn dtn (x̃eλt) ≤ εeλt (12) integrating (12) between 0 and t gives − ε λ eλt + ε λ ≤ dn−1 dtn−1 (x̃eλt) − dn−1 dtn−1 (x̃eλt) ∣∣∣∣ t=0 ≤ ε λ eλt − ε λ (13) or conveniently rewritten as − ε λ eλt − (∣∣∣∣ dn−1dtn−1 (x̃eλt) ∣∣∣∣ t=0 + ε λ ) ≤ dn−1 dtn−1 (x̃eλt) ≤ ε λ eλt + (∣∣∣∣ dn−1dtn−1 (x̃eλt) ∣∣∣∣ t=0 + ε λ ) (14) the same reasoning can be repeatedly applied until the nth integral of (12) is reached: − ε λn eλt − (∣∣∣∣ dn−1dtn−1 (x̃eλt) ∣∣∣∣ t=0 + ε λ ) tn−1 (n − 1)! − · · · + − ( |x̃(0)| + ε λn ) ≤ x̃eλt ≤ ε λn eλt+ + (∣∣∣∣ dn−1dtn−1 (x̃eλt) ∣∣∣∣ t=0 + ε λ ) tn−1 (n − 1)! + · · · + ( |x̃(0)| + ε λn ) (15) furthermore, dividing (15) by eλt, it can be easily verified that, for t → ∞, − ε λn ≤ x̃(t) ≤ ε λn (16) considering the (n − 1)th integral of (12) − ε λn−1 eλt − (∣∣∣∣ dn−1dtn−1 (x̃eλt) ∣∣∣∣ t=0 + ε λ ) tn−2 (n − 2)! − · · · + − (∣∣ ˙̃x(0)∣∣ + ε λn−1 ) ≤ d dt (x̃eλt) ≤ ε λn−1 eλt+ + (∣∣∣∣ dn−1dtn−1 (x̃eλt) ∣∣∣∣ t=0 + ε λ ) tn−2 (n − 2)! + · · · + (∣∣ ˙̃x(0)∣∣ + ε λn−1 ) (17) and noting that d(x̃eλt)/dt = ˙̃xeλt + x̃λeλt, by imposing the bounds (16) to (17) and dividing again by eλt, it follows that, for t → ∞, −2 ε λn−1 ≤ ˙̃x(t) ≤ 2 ε λn−1 (18) now, applying the bounds (16) and (18) to the (n − 2)th integral of (12) and dividing once again by eλt, it follows that, for t → ∞, −6 ε λn−2 ≤ ¨̃x(t) ≤ 6 ε λn−2 (19) the same procedure can be successively repeated until the bounds for x̃(n−1) are achieved: 740 m.c. tanaka, j.m.m. fernandes, w.m. bessa − [ 1 + n−2∑ i=0 ( n − 1 i ) ζi ] ε λ ≤ x̃(n−1) ≤ [ 1 + n−2∑ i=0 ( n − 1 i ) ζi ] ε λ (20) where the coefficients ζi (i = 0,1, . . . ,n − 2) are related to the previously obtained bounds of each x̃(i) and can be summarized as in (9). in this way, by inspection of the integrals of (12), as well as (16), (18), (19), (20) and the other omitted bounds, it follows that the tracking error exponentially converges to the n-dimensional box determined by the limits |x̃(i)| ≤ ζiλi−nε,i = 0,1, . . . ,n − 1, where ζi is defined by (9). 2 corollary 3. it must be noted that the proposed control scheme provides a smaller tracking error when compared with the conventional feedback linearization controller. by setting the output of the fuzzy inference system to zero, d̂(x̃) = 0, theorem 2 implies that the resulting bounds are |x̃(i)| ≤ ζiλ i−nδ,i = 0,1, . . . ,n − 1. considering that ε < δ, from the universal approximation feature of d̂, it can be concluded that the tracking error obtained with the fuzzy feedback linearization controller is smaller than the associated with the conventional scheme. 5 illustrative example in order to illustrate the controller design methodology, consider a controlled van der pol oscillator ẍ − µ(1 − x2)ẋ + x = υ (21) with a dead-zone in the control input defined according to υ =   u + 0.2 if u ≤ −0.2 0 if − 0.2 < u < 0.2 u − 0.2 if u ≥ 0.2 (22) for control purposes, equation (22) can be rewritten as a combination of a linear and a saturation function [8, 12]: υ = u + d(u) (23) where d(u) can be obtained from (22) and (23) as: d(u) =   0.2 if u ≤ −0.2 −u if − 0.2 < u < 0.2 −0.2 if u ≥ 0.2 (24) based on (6) and considering d(u) as uncertainty, a fuzzy feedback linearization controller can be chosen as follows u = x − µ(1 − x2)ẋ + ẍd − 2λ ˙̃x − λ2x̃ − d̂(x̃, ˙̃x) (25) in order to evaluate the performance of the proposed control law (25), a numerical simulation was carried out. the simulation study was performed with an implementation in c, with sampling rates of 500 hz for control system and 1 khz for the van der pol oscillator, and the differential equations were numerically solved using the fourth order runge-kutta method. the chosen parameters for the van der pol oscillator and controller were µ = 1 and λ = 0.8. feedback linearization with fuzzy compensation for uncertain nonlinear systems 741 regarding the fuzzy inference system, the number of fuzzy rules and the type of the membership functions, as well as how they are distributed over the input space, could be heuristically defined to accommodate designer’s experience and experimental knowledge. the fuzzy rule base adopted in this work is presented in table 1, where nb, nm, ns, zo, ps, pm and pb represent, respectively, negative–big, negative–medium, negative–small, zero, positive– small, positive–medium and positive–big. triangular and trapezoidal (at the ends) membership functions are adopted for both x̃r and ˙̃ xr, with the central values defined respectively as cx̃ = {−20; −2; −0.2; 0.0; 0.2; 2; 20}×10−2 and c ˙̃x = {−16; −1.6; −0.16; 0.0; 0.16; 1.6; 16}×10 −2. the chosen fuzzy intersection operator was the minimum t-norm. it should be also emphasized that the input space could be partitioned and represented in many other ways, and that the system designer may test each one of them in order to improve the output value d̂. with respect to the output of each rule, the following values were heuristically adopted for nb to pb: d̂r = {−20 ; −5 ; −2.5 ; 0.0 ; 2.5 ; 5 ; 20}. table 1: adopted fuzzy rule base. x̃ / ˙̃x nb nm ns zo ps pm pb nb pb pb pb pm pm ps zo nm pb pb pm pm ps zo ns ns pb pm pm ps zo ns nm zo pm pm ps zo ns nm nm ps pm ps zo ns nm nm nb pm ps zo ns nm nm nb nb pb zo ns nm nm nb nb nb in this way, considering that the initial state and initial desired state are not equal, x̃(0) = [−2.0,−0.4], figures 1–3 show the obtained results for the tracking of xd = [sint,cost]. -2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50 0 5 10 15 20 25 30 35 40 x t desired obtained (a) tracking performance. -2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50 2.00 0 5 10 15 20 25 30 35 40 u t (b) control action. figure 1: trajectory tracking with xd = [sint,cost]. as observed in figure. 1(a), even in the presence of modeling imprecisions, the proposed control scheme allows the actuated van der pol oscillator to track the desired trajectory. now, in order to demonstrate the improved performance of the fuzzy feedback linearization controller, the tracking error associated with the last simulation is shown in fig. 2. for comparison purposes, the tracking error obtained with conventional feedback linearization is also presented. it can be easily verified that the proposed controller provides a smaller tracking error 742 m.c. tanaka, j.m.m. fernandes, w.m. bessa when compared with the conventional one. -2.00 -1.50 -1.00 -0.50 0.00 0.50 0 5 10 15 20 25 30 35 40 x~ t conventional fuzzy figure 2: tracking error with conventional and fuzzy feedback linearization. the phase portraits of the tracking errors obtained with conventional as well as fuzzy feedback linearization are shown in fig. 3. note that the convergence region related to the proposed control scheme is much smaller than the associated with its uncompesated counterpart, which confirms corollary 3. -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 -2.5 -2 -1.5 -1 -0.5 0 0.5 d x~ / d t x ~ (a) conventional. -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 -2.5 -2 -1.5 -1 -0.5 0 0.5 d x~ / d t x ~ (b) fuzzy. figure 3: phase portrait of the error with conventional and fuzzy feedback linearization. 6 concluding remarks in this paper, a fuzzy feedback linearization controller is developed to deal with uncertain single-input–single-output nonlinear systems. to enhance the tracking performance, the feedback linearization controller is combined with a fuzzy inference system for uncertainty/disturbance compensation. the boundedness and convergence properties of the tracking error vector are analytically proven. to evaluate the control system performance, the proposed scheme is applied to the van der pol oscillator. by means of numerical simulations, the improved performance over the conventional feedback linearization controller is confirmed. acknowledgments the authors would like to acknowledge the support of the brazilian national research council (cnpq), the brazilian coordination for the improvement of higher education personnel (capes), the brazilian national agency of petroleum, natural gas and biofuels (anp) and the german academic exchange service (daad). feedback linearization with fuzzy compensation for uncertain nonlinear systems 743 bibliography [1] boukezzoula, r.; galichet, s.; foulloy, l. (2007); fuzzy feedback linearizing controller and its equivalence with the fuzzy nonlinear internal model control structure, int j appl math comput sci, issn 1641-876x, 17(2):233-248. [2] chen. f.c. (1990); back-propagation neural networks for nonlinear self-tuning adaptive control, ieee control syst mag, issn 0272-1708, 10(3):44-48. [3] couceiro, m.s.; ferreira, n.m.f.; machado, j.a.t. (2012); hybrid adaptive control of a dragonfly model, commun nonlinear sci numer simul, issn 1007-5704, 17(2):893-903. [4] deng, h.; li, h.x.; wu, y.h. (2008); feedback-linearization-based neural adaptive control for unknown nonaffine nonlinear discrete-time systems, ieee trans neural netw, issn 1045-9227, 19(9):1615-1625. [5] hojati, m.; gazor, s. (2002); hybrid adaptive fuzzy identification and control of nonlinear systems, ieee trans fuzzy syst, issn 1063-6706, 10(2):198-210. [6] kang, h.j.; kwon, c.; lee, h.; park, m. (1998); robust stability analysis and design method for the fuzzy feedback linearization regulator, ieee trans fuzzy syst, issn 10636706, 6(4):464-472. [7] kosko, b. (1994); fuzzy systems as universal approximators, ieee trans comput, 43(11):1329-1333. [8] lewis, f.l.; tim, w.k.; wang, l.z.; li, z.x. (1999); deadzone compensation in motion control systems using adaptive fuzzy logic control, ieee trans control syst technol, issn 1063-6536, 7(6):731-742. [9] lu, z.; shieh, l.s.; chen, g.; coleman, n.p. (2006); adaptive feedback linearization control of chaotic systems via recurrent high-order neural networks, inf sci, issn 0020-0255, 176(16):2337-2354. [10] pedro j.o.; dahunsi, o.a. (2011); neural network based feedback linearization control of a servo-hydraulic vehicle suspension system, int j appl math comput sci, issn 1641-876x, 21(1):137-147. [11] slotine, j.j.e.; li, w. (1991); applied nonlinear control, prentice hall. [12] wang, x.s.; su, c.y.; hong, h. (2004); robust adaptive control of a class of nonlinear systems with unknown dead-zone, autom, issn 0005-1098, 40(3):407-413. [13] yeşildirek, a.; lewis, f.l. (1995); feedback linearization using neural networks, autom, issn 0005-1098, 31(11):1659-1664. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 278-285 modelling, implementation and application of a flexible manufacturing cell f. leighton, r. osorio, g. lefranc felipe leigthon universidad de las américas vina del mar chile e-mail: felipe.leigthon@gmail.com román osorio universidad nacional autónoma de méxico instituto iimas e-mail: roman@servidor.unam.mx gastón lefranc pontificia universidad católica de valparaíso escuela de ingeniería eléctrica, valparaiso chile e-mail: glefranc@ucv.cl abstract: this paper presents petri nets model, the implementation and application of a assembly flexible cell. the cell is composed by a robotic manipulator, a computer vision system and a conveyor. the system is applied to assembly several products, showing only two of them. keywords: flexible manufacturing cells, assembly, cim, petri nets. 1 introduction within the area of the manufacturing, cim model (computer integrated manufacturing) permits the automation all the activities in a manufacturing company: the organization, storage (asrs), manufacturing (cam), engineering (cae) and quality control. these activities are supported by local area networks and related database. in cam includes fabrication, assembly and quality control of products, utilizing robotics manipulators, computer vision, etc., to facilitate automation [1]. these industrial systems, allow flexible their production and to automate their processes, without human intervention. assembly systems are widely used in several industries. these systems are composed by robotics manipulators, computer vision and sensors to complete the production task accurately. however, since these systems are composed of many components, it is difficult to deal with unexpected situations. for example, an undetected error may propagate and end up as a detectable failure which may cause the whole line to stop its operation. in this case, it may take considerable time to diagnose the system and identify the main reasons for the failure. there exist approaches to detect and to predict failures, but the usage of these approaches is limited. [2], [3] the ability to produce a variety of products through the combination of modular components is a meaningful benefit of product modularity. there are five different ways that modular products are developed in industry. component-swapping modularity can be achieved when two or more alternative types of components are paired with the same basic product body to create different product variants. this modularity is often associated with the creation of product variety as perceived by the customer. [6] the assembled flexible cell is part of a developed flexible system in the laboratory of robotics, artificial intelligence and advanced automation lab, in the escuela de ingeniería eléctrica de la pontificia universidad católica de valparaíso, chile. fms system includes: an copyright c⃝ 2006-2011 by ccc publications modelling, implementation and application of a flexible manufacturing cell 279 asrs cell (automatic storage retrieval system) [4], with an executive system and database [5], servoing systems [7], [9] and stereo computer vision [8], [10], [11] and flexible manufacturing cell. [12] in this paper is presented an assembly cell modeled by petri nets, the implementation and the application to the assembly of some products, in a manufacturing flexible cell. the assembly flexible cell is composed by a robotic manipulator, a computer vision system and a conveyor. 2 structure of the assembly cell the assembled flexible cell of is constituted by a robotic arm, a system of vision and an system of automated transport. the system robotic manipulator used is ibm 7547 type scara; the computer vision system is an stereo vision (two webcams); and the automated system of transport, is conformed by four conveyor belts that form a "circuit" around the robotic manipulator and the assembled area. it function is to interconnect the different cells from the system, like the asrs (storage), the assembly flexible cell, manufacturing flexible cell and the flexible cell of quality control. this conforms a flexible system of manufacture, as it is seen in the (fig.1). through this system of transport are transferred pallets with different kits to assembly. in (fig.2), is the transfer. figure 1: the flexible manufacturing system figure 2: pallets with kit in the conveyor belt the stereo vision system, (fig.3), determines what pieces are in pallet, and the pallet position of the in the work area to make the assemly. in order to prove the cell a wood figure is assembled (geometric figure), and then making assembly of a electrical interrupt and electrical plug, common in houses and offices. the robotics manipulator takes the pieces inside the pallet and make the assembly programmed, the vision system contributes to the determination of the position and direction of the parts to be assembled. 280 f. leighton, r. osorio, g. lefranc figure 3: the system of vision 3 aplications of flexible assembly cell. the applications of the cell are to assemble geometric figure (fig.4a), to test if the system works. then, making assembly of a electrical interrupt and electrical plug with one, two or three plug per unit (fig.4b). figure 4: a) kit of wood parts. b) kit of plugs. 4 computer vision system. the vision system have to determine the space position of pallets and objects within the space of work of the robotic manipulator. the stereo vision has to be able of visualizing objects of different size, send the order to manipulator to pick up the pallets and put in the working area, recognize the oarts inside the pallets and then send the order to manipulator to assemble the product in other pallet. finally, the vision system verify is the product is well/assembled, giving the orders to manipulator to transport the pallet with assembled product ro the conveyor. the images are captured by two webcam cameras. these images pass through a process of digitalization during which is made a discretización of the space coordinates of the image (x,y) denominated sampling of the image, and a discretización of the amplitude of the intensity of light in each point or píxel of the image, which denominates quantification of the gray level (fig.5). the system make image segmentation, separating the different objects and applying a threshold based on the histogram (fig.6). then, the center of mass or centroide of an object of a binary image is computed (fig.7). the direction of an object determines by the minus existing distance between its center of mass and an element of his contour. therefore, the first step to follow to calculate the direction of an object is to determine which are píxeles that they belong to his contour. for doing this, a gradient of the image is used. so that an object is taken, the effector of the manipulator must be oriented of such way that their direction is perpendicular to the direction of the straight line modelling, implementation and application of a flexible manufacturing cell 281 figure 5: digital image figure 6: segmentation and thresholding figure 7: centroide of the objects figure 8: direction of the object that constitutes the minimum range between the center of mass of the object and its contour. this can be observed in following (fig.8), where the point a corresponds to the center of mass of the rectangle and point b is determined finding the minimum range between the centroide and the contour of the object. the union of these points forms a perpendicular straight line to the direction that must have the effector of the manipulator to pick up the object. 282 f. leighton, r. osorio, g. lefranc 5 petri nets model of the assembly cell. assembly systems has many components and it is difficult to deal with unexpectes situations. error non detected could propagate and to produce failure in the system, causing stop in the operation of the line. it takes time to diagnose, and to identify the reasons of the failure. a petri net model (pn) of the flexible assembly cell permits a simulation of the behaviour of the assembly cell, detecting failure nd an evaluation of the performance. the (fig.9) shows the definitions of places and transitions and the (fig.10), pn model. figure 9: place and transition definitions for pn model definitions: mr robotics manipulator; asrs: automatic storage and retrieval system ct conveyor; rm mobile robot sv3d stereo vision systems; obk 1 cover of the product obj 2 internal cover; obj 3 plug 1 obj 4 plug 2; pe final assembly product 6 evaluations of the flexible assembly cell. for the evaluations of the project different tests were made. one of them is to measure the time taken to make an assembly. table 1 shows the times of joint, like for the evaluations, the iterations, with and without retiring pallet from the circuit of conveyor belts. table 1. time to assembly a product modelling, implementation and application of a flexible manufacturing cell 283 figure 10: flexible assembly cell pn model geometric figures simple plug doble plug triple plug with without with without with without with without retiring retiring retiring retiring retiring retiring retiring retiring 1’4” 1’37” 1’12” 1’34” 1’25” 1’59” 1’45” 2’22” the time of assembled goes from 1 minute 12 seconds to 2’22”, that is to say, the time when the manipulator puts the pallets in the work area, until the manipulator pick up the pallet with the assembled product. a bad position of the pallets in the work area produces that the manipulator do not pick up the pallets or it pick up bad. one of the problem is the illumination affecting the quantification of the objects, like in the determination of the centroide and the direction of each one of the subparts. the stero vision system send to manipulator wrong position. this problem it solves, in part, with the installation of a system of illumination superior to the existing one. a small mobile robots is used to move the pallet to the right place, using two walls to put the pallet in 284 f. leighton, r. osorio, g. lefranc the desired position, the best place for stereo vision system (fig.11). it can see the mobile robot, the walls and the pallet in the workspace. this tobots receives order from the computer cell. figure 11: mobile robot and the two walls. other evaluation is the measurement of the percentage of correct assembly. it is made ten measurement, for different assembled such as for the geometric figure, and each type of plug. each one considers iterations with retiring pallet from the "circuit", and without retiring from them. the results given by table 2, reflecting the percentage of error by amount of products, in other words, if the product to assemble has 3 pieces (geometric figure, simple plug) and fail in one, the result is 33% of error, or, if it has of 5 pieces, and it fails in two, have an error of the 40%, ( triple plug). table 2. percentage of faults to assembly a product geometric figures simple plug doble plug triple plug with without with without with without with without retiring retiring retiring retiring retiring retiring retiring retiring 1 0% 0% 0% 0% 50% 0% 20% 20% 2 33% 0% 33% 0% 0% 25% 0% 20% 3 0% 33% 33% 0% 25% 0% 40% 0% 4 33% 0% 33% 0% 0% 25% 0% 0% 5 0% 0% 0% 0% 25% 0% 60% 0% 6 33% 0% 33% 33% 25% 0% 0% 40% 7 0% 0% 0% 33% 0% 0% 20% 40% 8 33% 33% 33% 0% 25% 50% 0% 60% 9 33% 0% 0% 33% 25% 50% 20% 20% 10 0% 0% 0% 0% 0% 0% 60% 20% 7 conclusions in this paper has been presented a petri nets model, the implementation and the of a assembly flexible cell. the system assemblies several products, only is presented a wood assembly and assembly of different kind of electrical interrupt and electrical plugs. the cell is composed by a robotic manipulator, a computer vision system and a conveyor. different effectors for the robotics manipulator are designed for different kind of product to assembly, considering different factors of the object. the flexible assembly cell is modelled and simulated using petri nets. this model and the simulation permit to evaluate the performance of the cell, and correct it before starting a new assembly. modelling, implementation and application of a flexible manufacturing cell 285 the real cell is tested assembled geometric objects and electrical interrupt and plugs. the time of assembled goes from 1 minute 12 seconds to 2’22”, that is to say, the time when the manipulator puts the pallets in the work area, until the manipulator pick up the pallet with the assembled product. the stereo vision system recognizes the objects whithin the working area, establishing the position of the pallets and the objects in the pallets. problems affects the quatification determination of the centroid and direction of each object, producing error from 33% to 59%. the problem is solved with changing illumination and adding a small mobile robot to move the pallet to the desired position. bibliography [1] lefranc, g., la manufactura integrada por computador: un tutorial, magazine automática e innovación de la asociación chilena de control automático, vol 1, 2, 1993, pg 45. [2] t. c. cao, a.c., sanderson, 1992, sensor-based error recovery for robotic task sequences using fuzzy petri-nets, proceedings of the 1992 ieee international conference on robotics and automation, vol.2, pp.1063-1069. [3] q. jing , w. xisen, p. zhihua, x. youngcheng, 1996, a research on fault diagnostic expert system basedon fuzzy petri nets for fms machining cell, proceedings of ieee international conference on industrial technology, pp. 122-125. [4] andrada i., lefranc g. sistema experimental de un sistema de almacenamiento automatizado. i parte. xv congreso de la asociación chilena de control automático 2002. [5] maizares m., lefranc g., base de datos y programa ejecutivo para un sistema as/sr. ieee latin-american conference on robotics and automation. chile, 2003. [6] lefranc, gastón, simulación de la generación de secuencias de ensamblado en una celda flexible de producción. automática e innovación de la asociación chilena de control automatico. ano 3. volumen 2. n◦7, 1996. [7] lefranc g., cano f., sistema servoing pick and place, congreso latinoamericano de control automático, 2003, guadalajara, méxico. [8] schleyer g., lefranc g., experimental 3-d visual servoing for fms applications. third ifac ieee conference on management of control of production and logistics mcpl’2004. [9] opazo m., lefranc g., visual servoing. fourth ifac ieee conference on management of control of production and logistics, mcpl 2007. [10] schleyer g., lefranc g., tridimensional visual servoing, studies in informatics and control, volume 18 . issue 3 . 2009. [11] schleyer g., lefranc g., color images segmentation using split&merge and region growing techniques in rgb and hsv color spaces, international jornal of computers, communications & control, vol. v issue 1, 2010. [12] leighton f., lefranc g., flexible assembly cell using scara manipulator. third ifac ieee conference on management of control of production and logistics mcpl’2004. international journal of computers communications & control issn 1841-9836, 10(1):62-69, february, 2015. application of the analysis of self-similar teletraffic with long-range dependence (lrd) at the network layer level g. millán, g. lefranc ginno millán universidad católica del norte escuela de ingeniería, larrondo #1281, coquimbo chile gmillan@ucn.cl gastón lefranc* pontificia universidad católica de valparaíso escuela de ingeniería eléctrica, avda. brasil #2147, valparaíso chile *corresponding author: glefranc@ucv.cl abstract: in a previous paper it was proposed, and theoretically confirmed, that analysis of self-similar traffic flows with long-range dependence may be restricted to the network layer. in this paper this novel concept is applied to the study of traffic recorded in an ieee 802.3u network environment with the aim of proving its validity as a simple and efficient tool for high speed computer network traffic flow analysis. keywords: long-range-dependence, network layer, traffic models, self-similar process. 1 introduction it is interesting to reflect on the idea that a purely random process is no more than a theoretical concept, but it is much more interesting to do so considering that no series is yet known whose characteristics correspond exactly to those of such processes. likewise, it is of interest to explain that a given behavioral evolutionary singularity is widely attributable to two stationary stochastic processes without considering their origins, scope and implications. to clarify the above two assertions, [1] made an exhaustive background review that includes research proposals and their results as well as the mathematical foundations underlying all of them. however, a basic idea remains unfinished: if all the arguments given do nothing more than highlight the benefits and advantages of the parsimonious modeling of traffic flow in current high speed network environments, then why is there dissent on its use?, and even more important yet, why do all the results deal with self-similarity as ubiquitous not only on the time scales, a fact that is certainly not put in doubt, but also with respect to the set of circumstances attributable to its origin? with the purpose of answering these questions, that same paper states and then gives the foundation for the validity of the following working hypothesis:"it is completely feasible to restrict the evolution of a statistically self-similar process to a well defined application setting without altering its nature and its more important properties, in that way highlighting the validity of its postulates and giving greater plausibility to its physical interpretation”, clarifying that the plausibility refers to the action of conferring an admissible, and therefore worth considering, character to one or various parameters that compose an analytical model whose interpretations are not only mathematical idealizations, and that the theoretical proof of that hypothesis is based essentially on the proposal of ryu and lowen [2], which consists in making a distinction between the self-similarity observed at the application level and the self-similarity observed at the network level, but with the substantial difference of not considering any particular traffic model as in the case of the authors, in which the proposal is developed to support the analysis of the results obtained from using the fractal point process (fpp) model proposed in [3], to carry copyright © 2006-2015 by ccc publications application of the analysis of self-similar teletraffic with long-range dependence (lrd) at the network layer level 63 out the characterization of traffic flow in high speed networks. it should be specified that the original proposal of ryu and lowen consists in establishing a difference based on the osi level at which is found the source that gives origin to the traffic flows in which self-similarity is seen, and therefore explains its origin. in this way the authors conclude that it is more adequate to refer to application level fractal traffic and network level fractal traffic instead of encompassing under fractal traffic a whole range of dissimilar behaviors that find explanations precisely in the internal processes inherent to each of those levels. it is therefore clear that this division of fractal traffic into those two subcategories addresses effectively the profound differences that exist in the design as well as in the control processes of actual high speed networks at those levels. concretely, the application level self-similar traffic (fractal) has its origin in a source that exhibits self-similarity over a wide range of time and frequency scales without any interactions with the network. in other words, self-similarity is inherent to the source, while network level selfsimilar traffic (fractal), in contrast, exhibits self-similarity over a wide range of time and frequency scales as a result of numerous interactions with the network. an example of application level selfsimilar traffic source is the vbr video sequence of [4], while all the applications based on tcp of [5]-[8] are examples of network level self-similar traffic sources. it should be stated that actually the behavior of an application level traffic source can be affected by the network conditions, depending on the functionalities of the low level protocols used. this effect is insignificant, however, compared to what happens with a network level traffic source, such as an ftp client, under identical conditions, where the ratio of output to input data flow depends directly and critically on the conditions of the network and can largely be considered independent of the size of the files [9]. also, application level self-similar traffic can be managed in the context of the resource assignment admission control subjected to service quality guarantees, since it is independent of the network conditions through which it is sent. this paper presents an experimental application that validates the hypothesis of [1]. considering a real network scenario implemented under ieee standard 802.3u, traffic capture experiments are performed and then analyzed, restricting the results according to the precepts presented above as well as in [1]. this paper is therefore centered on showing the validity of the analysis of restricted self-similar traffic at the network layer level as a simple and efficient tool to study the behavior of traffic flow in present day high speed computer networks. 2 traffic measurements 2.1 description of the network environment figure 1 is a diagram of the topology of the implemented experimental network scenario. it is a lan ieee 802.3u environment that has the following main operational characteristics: • ten workstations that uninterruptedly request an on-demand video service from the video server equipment provided for it. both the client equipment as well as the server make use of the vlc media player application for that purpose. continuous reproduction is achieved by predefining a video program in the server equipment. the ten stations keep an xml file with the page index of the web server. the state of the page index updating is consulted randomly by each of the stations with the purpose of always having the latest version of the file. the network monitoring equipment (tagged "sniffer" in figure 1) makes use of the ethereal application to perform the traffic packet capture. •• the internet access functions for the purpose of updating both the operating systems and the antivirus applications are enabled and automated in all the network’s equipment. 64 g. millán, g. lefranc 2.2 work methodology the procedure to carry out the experiment consists of the following steps: • program the algorithms to estimate the values and study the behavior of the hurst parameter, of the variance-time (v-t), rescaled adjusted range or the r/s statistic, and spectral density analyses. all the programs were developed over matlab because of their availability. verify the correct operation of the programmed algorithms. to perform this operation, use is made of the traffic sample normalized series of [5], bc-paug89.tl, available for downloading in [10], and the values obtained are then compared with those reported in the literature by the authors. capture traffic from the experimental network shown in figure 1. it should be pointed out that the duration of each of the traffic capture periods is governed only by a criterion of availability of storage capacity in the sniffer equipment, trying to capture the largest possible number of samples to face a possible decision scenario based on a figure of merit coming from the bias versus variance relation. •• using the capabilities of the ethereal application the filtering of the captured packets is carried out in such a way as to create time series of data that contain the length of the packets and the arrival times of each of them. these series are then stored in flat text files. • apply the v-t, r/s, and periodgram analyses over the data series previously specified. figure 1: experimental network connections 2.3 simulations table 1 shows the detail of the captured ethernet frames. in that respect the following aspects must be considered: • the temporal resolution of the arrival times that are recorded by the sniffer equipment is set in microseconds. this resolution is delivered as default measurement by the ethereal application. the existence of time fluctuations that are not considered as those due to the latency of the circuits of the equipment’s network card and those due to the code application of the analysis of self-similar teletraffic with long-range dependence (lrd) at the network layer level 65 processing time by the equipment is suggested. however, and in spite of the great impact that both can get to have on the finally recorded times, the option is taken to consider them anomalies belonging to the data processing systems, and they are therefore part of their behaviors. the individual captures present a time resolution of 6 µs, which is the average recorded value. •• the data sets trace-1 to trace-4 contain the data of the time series representative of each capture process. in this respect, each series is composed of an ordered list of pairs of data: the arrival time of the packet, recorded according to the above considerations in floating point format with six positions, and the size of the captured ethernet packet, which records the length of ethernet data. the recorded value does not include the following fields: preamble, address of origin, destination address, length, and crc or verification sequence. it must be recalled that the ethernet protocol forces the frames to have a size with a minimum of 64 bytes and a maximum of 1518 bytes, so the recorded values are within that interval, with the 1518 bytes value as that recorded mostly in all the capture processes. • 99.9% of the ethernet pdus are encapsulated in ip datagrams. • with the purpose of testing the algorithms programmed for each method, whose mathematical expressions are given by (1)-(3), the analysis sequence begins using the series bc-paug89.tl, from figure 2, with the v-t, r/s and periodgram analyses, from left to right, respectively. then, and in the same order, the analyses for each of the four data sets are shown. table 1: qualitative description of the traffic capture sets measurement period data set number of packets november 2010 total: 32 h 2118505 start of trace: first period: trace-1 918896 nov. 29, 06:00 am (06:00 am 12:00 am) end of trace: second period: trace-2 1199609 nov. 30, 08:00 pm (08:00 am 08:00 pm) december 2010 total: 38 h 6096937 start of trace: first period: trace-3 1338789 dec. 1, 08:00 am (08:00 am 08:00 pm) end of trace: second period: trace-4 4758148 dec. 2, 10:00 pm (12:00 am 10:00 pm) var[x(m)] ∼ m−β, 0 < β < 1, h = 1 − β/2 (1) r(n) s(n) = 1 s(n) [max(0,w1, ...,wn) − min(0,w1, ...,wn)], wk = k∑ i=1 xi − kx̄(n) (2) f(λ) ∼ λ1−2h, when λ → ∞ (3) 3 discussion of results table 2 summarizes the results obtained. it shows that the value of h for the bc-paug89.tl data series using variance-time and r/s analyses is correct with respect to the value determined 66 g. millán, g. lefranc by the authors, h = 0.9 using the r/s graphic method. it is also verified that all the values of h found for each of the experimental series (trace-1, trace-2, trace-3, and trace-4) are within the interval of interest 1/2 < h < 1, which certainly implies an asymptotic behavior of the selfcorrelation function given by r(k) ∼ h(2h − 1)k2h−2, when k → ∞ [11], which ensures that these series present a hyperbolic type drop in the tails of their distributions, thereby reinforcing a behavior different from a typically exponential one. clearly, the central limit theorem reinforces the previous condition by considering that the self-covariance function of these processes in the interval 1/2 < h < 1, depends on the value of the h in the approximate form γ(k) ∼ ck2h−2, when k → ∞, with c < 0 [12]. the presence of long-range time dependence is seen from r(k) = rm(k), ∀k ≥ 1, ∀m ≥ 1 [13], a fact that is evidenced from the linear type behavior of the relation subjacent between different levels of aggregation with respect to the variance, which becomes evident from the v-t graph analysis of the figures 3, 4, 5, and 6. figure 2: v-t, r/s, and periodgram for bc-paug89.tl trace. h = 0.9000, h = 0.8991, and h = 0.7681 figure 3: v-t, r/s, and periodgram for trace-1. h = 0.8999, h = 0.8986, and h = 0.7680 figure 4: v-t, r/s, and periodgram for trace-2. h = 0.8999, h = 0.8982, and h = 0.7678 application of the analysis of self-similar teletraffic with long-range dependence (lrd) at the network layer level 67 figure 5: v-t, r/s, and periodgram for trace-3. h = 0.8999, h = 0.8985, and h = 0.7677 figure 6: v-t, r/s, and periodgram for trace-4. h = 0.9000, h = 0.8994, and h = 0.7681 table 2: results obtained for h after applying them to different methods of analysis value of h for the data series measurement period method bc-paug89.tl trace-1 trace-2 trace-3 trace-4 v-t 0.9009 0.8999 0.8999 0.8999 0.9007 r/s 0.8969 0.8996 0.8982 0.8985 0.9004 periodgram 0.6603 0.6680 0.6678 0.6677 0.6681 the self-similar behavior of the different levels of aggregation, as well as its pronounced longrange dependence, are made evident from the linearity shown by the graphs of the size m blocks with respect to the r/s factor. see r/s graph analysis of the figures 3, 4, 5, and 6. the self-similar behavior of the different data series is also shown by the last row of table 2, which considers the spectral estimation based on the periodgram of the series. the noticeable differences between the values of h for each of the series lie in the absence of confidence intervals. the analyses based on the r/s and variance-time graphs show simplified limiting cases that do not consider fine interactions between aggregate components when their differences are small, i.e., when considering, for example, two levels of immediately contiguous aggregation. in this respect, the frequency analysis does consider them, because the spectral handling is, most of the time, sufficiently exact to not omit the bias generated between the compromise of the representation of the singularities of each aggregation level and their general values in terms of the variance. therefore, that is the reason to consider the aggregation of frequencies in relation to the periodgram, instead of the block sizes with respect to the variance. however, for the correct use of this method each result must go together with confidence intervals, which in general it is recommended to obtain from whittle’s maximum likelihood estimator (mle), but this goes beyond the objectives stated for this research, beside the fact that interest is focused on getting 68 g. millán, g. lefranc a general view of the presence or absence of self-similarity characteristics, and not of their exact value, so the values obtained through the variance-time and r/s analyses as upper limits can therefore be considered with minimum error in the final interpretation of the results. it is important to note that, even though no traffic model has been developed and it has only been assumed that the different arrivals are related to an on/off model, as evidenced by the use of the variance-time and r/s statistics in terms of representing aggregations, parsimony is a provable characteristic based on the number of parameters needed to establish self-similarity and long-range dependence. finally, the proposal of restricting the results derived from representing the traffic flows as second order self-similar time series at the network level when necessary, certainly results not only feasible based on the set of arguments presented, but it is also useful for the correct understanding of and search for the origin of the self-similarity (fractality) and the long-range dependence that it may eventually exhibit. in that respect, it is undeniable that it is more adequate to interpret the presence of these singularities as the product of the internal processes subjacent in each osi level in particular, instead of referring to them as intrinsic characteristics of the data flows because of the indetermination to which it leads. therefore, the possibility of restricting a statistically self-similar process to a well defined application environment without altering its nature and its most important properties is true, thereby highlighting the validity of its postulates and adding greater plausibility to its physical interpretation, which is derived precisely from not forcing a reality to fit all the parameters of a given model, but on the contrary, it is taking care directly of a characteristic of the behavior of traffic flows that is exposed through its observation and mathematical interpretation. 4 conclusions a new point of view has been presented with respect to the systematic classification of self-similar processes with long-range dependence, whose purpose, applied to the parsimonious modeling of high speed computer networks, is to propose a common working framework for the interpretation of the results derived from the representation of traffic flows in the form of self-similar second order stationary time series. supported by the use of a set of four high time resolution traffic samples, it is shown that the behavior of traffic flow in a high speed computer network is self-similar and exhibits longrange dependencies over a wide range of time scales, which is evidenced when the dependence that exists among the different aggregation levels with respect to their second order statistics analyzed, and it is confirmed that regardless of the level considered, the trend reflects linearity. this linearity implies a behavior that does not depend on the time scales under consideration, but a behavior inherent to the data flows. the mathematical models of current high speed computer networks must consider selfsimilarity as well as the long-range dependencies. although it is true that the literature often proposes the equivalence of both concepts starting from an indistinct treatment, it happens that these concepts are actually independent, without one necessarily being the consequence of the other, and more profoundly, one not implying the existence of the other; these last two considerations are regardless of the direction in which they are taken. to consider a restriction in the interpretation of the results derived from representing the traffic flows as self-similar stationary time series in relation to the osi observation level results not only feasible, but it must be considered as a useful practice aimed at accuracy in determining the origins of self-similarity and long-term dependence, and for a better understanding of their implications, concretely when dealing with high speed computer networks where the protocol dependencies of the data flows require an adequate interpretation for their practical use in network application of the analysis of self-similar teletraffic with long-range dependence (lrd) at the network layer level 69 engineering. in this respect, it is doubtlessly a better practice to interpret these singularities as a product of the internal processes that underlie each osi level in particular, than to interpret as the indetermination caused by referring to them as intrinsic characteristics of flows without an origin. bibliography [1] millán, g.; kaschel, h.; lefranc, g. (2010); discussion of the analysis of self-similar teletraffic with long-range dependence (lrd) at the network layer level, international journal of computers communications & control , issn 1841-9836, 5(5): 799-812. [2] ryu, b.k.; lowen, s.b. (1997); point process approaches for modeling and analysis for self-similar traffic. part ii: applications, proc. 5th international conference on telecommunications systems, modeling and analysis, nashville, tn. [3] ryu, b.k.; lowen, s.b. (1996); point process approaches for modeling and analysis for self-similar traffic. part i: model construction, proc. ieee infocom ’96, san francisco. [4] garrett, m.w; willinger, w. (1994); analysis, modeling and generation of self-similar vbr video traffic, computer communication review, 24(4):269-280. [5] leland, w.e.; taqqu, m.s.; willinger, w.; wilson, d.v. (1994); on the self-similar nature of ethernet traffic (extended version), ieee/acm trans. netw., 2(1): 1-15, feb. 1994. [6] paxson, v.; floyd, s. (1995); wide-area traffic: the failure of poisson modeling, ieee/acm trans. netw., 3(1):226-244. [7] ryu, b.k. (1996); implications of self-similarity for providing end-to-end qos euarantees in high-speed networks: a framework of application level traffic modeling, lectures notes in computer science (proceedings of international zurich seminar on digital communications (izs ’96)), plattner, b. (ed.), zurich, switzerland: springer-verlag, (1044):65-79. [8] willinger, w.; taqqu, m.s.; sherman, r.; wilson, d.v. (2007); self-similarity through highvariability: statistical analysis of ethernet lan traffic at the source level, ieee/acm trans. netw., 5(1):71-86. [9] aracil, j.; edell, r.; varaiya, p. (1997); a phenomenological approach to internet traffic self-similarity, 35th annual allerton conference on communication, control and computing, urbana-champaign, il. [10] http://ita.ee.lbl.gov/html/contrib/bc.html. [11] park, k.; willinger, w. (2000), self-similar network traffic: an overview, self-similar network traffic and performance evaluation, park, k.; willinger, w. (eds.), new york: john wiley & sons. [12] sheluhin, o.i.; smolski, s.m.; osin, a.v. (2007); self-similar process in telecommunications, chichester, uk: wiley. [13] stallings, w. (2004); redes e internet de alta velocidad. rendimiento y calidad de servicio, 2nd ed., madrid, españa: pearson prentice hall. international journal of computers communications & control issn 1841-9836, 12(1):90-102, february 2017. initial phase proximity for reachback firefly synchronicity in wsns: node clustering m. misbahuddin, r.f. sari misbahuddin misbahuddin* department of electrical engineering, faculty of engineering, universitas indonesia, depok, 16424, indonesia *corresponding author: misbahuddin@ui.ac.id riri fitri sari department of electrical engineering, faculty of engineering, universitas indonesia, depok, 16424, indonesia riri@ui.ac.id abstract: synchronicity is one of the essential basic services to support the main duties of wireless sensor networks (wsns). synchronicity is the ability to arrange simultaneously collective actions in wsns. a high-rate data sampling to analyze the seismic structure and volcanic monitoring is the important applications requiring a synchronicity. however, most of the existing synchronicity algorithm is still executed in a flat network, so that it requires a long time to achieve a synchronous condition. to increase the convergence rate, the synchronicity can be executed concurrently through a clustering scheme approach. in this work, the such scheme is called as the node clustering based on initial phase proximity for reachback firefly synchronicity (ncipp-rfs). the ncipp-rfs solves in three steps: (1) constructing the node clustering, (2) intra-cluster synchronicity, and (3) inter-cluster synchronicity. the ncipp-rfs method is based on the firefly-inspired algorithm. the fireflies are a species in the natural system, which are able to manage their flashing for synchronicity in a distributed manner. the ncipp-rfs was implemented in ns-3 and evaluated and compared with reachback firefly algorithm (rfa). the simulation results show a significant increase in the convergence rate. the ncipp-rfs can reach a convergence time shorter than the rfa. in addition, the ncipp-rfs was compared in the various numbers of clusters, where the least number of clusters can reach the fastest convergence rate. finally, it can also contribute significantly to the increase of the convergence rate if the number of nodes is greater than or equal to 50 nodes. keywords: wireless sensor network, synchronicity, node clustering, phase proximity, firefly-inspired algorithm. 1 introduction wireless sensor networks (wsns) is a set of spatially distributed autonomous sensor nodes that could to interact locally, and some nodes of them interact with a sink node or base station. a sensor node in wsns is a device that has the limitations in the processing unit, communication resources, and sensing capabilities. the wsns has been implemented in various applications for environmental monitoring, condition sensing, and process automation such as battlefield surveillance, habitat monitoring, coordinated target detection and localization, chemical attacked detection, ubiquitous healthcare, and home automation. the duties of the wsns can be executed properly when it is supported by robust basic requirements such as time synchronization, synchronicity, self-configuration, and self-localization. for example, time synchronization is extremely required to ensure the high accuracy measurement for an event-driven measurement application in an area where an event is detected [1]. synchronicity inspired by the biological copyright © 2006-2017 by ccc publications initial phase proximity for reachback firefly synchronicity in wsns: node clustering 91 principle of a firefly is adopted by [2] for handling a dynamic node clustering in data readings. self-configuration characteristic for a routing protocol of wsns developed by [3] aimed at discovery the best route for delivering information with minimum energy consumption. finally, self-localization is a basic feature in wsn for finding out the location of unknown nodes because gps that is usually used to find the location of a device is not suitable for wsns [4]. many phenomena exist in the natural system around us, which have inspired many scientists to solve various problems within the engineering field or the specific problems of wsns. the basic requirements of wsns such as synchronization and synchronicity need a self-organized way. both are a mutually complementing requirement of wsns. the synchronization is an ability to align the time of node’s internal clock referring on the global time to perform a simultaneously collective action. on the other hand, the synchronicity is a way to align the phase of the internal clock of node to conduct a synchronously collaborative action. the application of the synchronicity in wsns is very useful as a simple sensor network coordinator in sampling the high data rates such as seismic analysis of structure [5], and volcanic monitoring [6]. moreover, is also used as a scheduling mechanism of the node duty cycles in order to save energy, so that all nodes in a network can wake up at the same time. there are some challenges of the robust synchronicity requirements, i.e. simple, fast, low energy consumption, self-configuration and high scalability. in this study, we propose a new nodeclustering synchronicity method using a firefly-inspired algorithm to address three synchronicity requirements in simple, fast,and self-configured way. clustering in the wsns is often used in some applications because it is extremely useful for various purposes. the purposes are divided as primary and secondary [7]. the first purpose represents the objectives that are the most substantial in the node clustering such as scaling, faulttolerance, data aggregation, load balancing, network topology stabilization, network lifetime extension. instead, the second purpose points out the objectives that are not highly important, and they are indirectly achieved by clustering node such as increasing connectivity, reducing routing delay, avoiding collision, and utilizing sleeping schemes. therefore, in this research we propose the node clustering model as a new approach to synchronize the node in the network, where the node clustering is an extremely important requirement to relieve the load of the network to reach the synchronicity. our ncipp-rfs approach provides two main contributions that consist of (i) in clustering, there are some node subsets that perform an intra-cluster synchronicity in parallel, continued to an inter-cluster synchronicity. the total of periods required to execute both synchronicity processes is smaller than that of without clustering. this emphasizes that our approach is faster than the non-clustered synchronicity algorithm. (ii) self-configuration is an important problem in the distributed system for executing both its primary duties and basic function as well as for the synchronicity function. in fact, there are many natural phenomena around us in which their population can organize themselves toward a synchronicity state, in which one of them is the firefly. the behavior of firefly flashing has inspired a number of researchers to create the firefly-inspired synchronicity algorithms [8]-[13]. in this research, we utilize the synchronicity algorithm developed by [10] for intra-cluster synchronicity. the remainder of this paper is organized as follows: section 2 presents literature review related with the firefly-inspired synchronicity. section 3 describes the approach used to solve the synchronicity requirements. section 4 presents the simulation results to show the performance evaluation. finally, section 5 concludes this paper and ideas for future work. 92 m. misbahuddin, r.f. sari phase1 1 0 ε f(φ1)+ε f(φ1) ∆φ φ1 φnew figure 1: the state function of m&s model 2 firefly-inspired synchronicity in the natural system, there are several synchronicity phenomena that have been observed by many researchers to understand the mutual interaction of a population toward a self-organized synchronicity without a notion of time. examples of the natural synchronicity include circadian rhythm [14], pacemaker cell of the heart [15], and synchronous flashing of fireflies [16]. fireflies are one of the species around us, which can interact mutually to fire synchronously. this phenomenon is one of the most spectacular self-organized synchronicity, which was imitated as a firefly-inspired synchronicity algorithm. the population of the fireflies can be analogized as a population of the pulse-coupled biological oscillators (pco) that was introduced by mirollow and strogatz [8], which is known as the m&s model. however, this model cannot be implemented directly in a real wsns because it still uses some ideal assumptions that are not in accordance with the realistic wireless communication [17]. the assumptions are: (1) the characteristic of oscillators is identical, (2) the node’s firing event occurs and other nodes respond instantaneously. (3) node’s computations are conducted perfectly and immediately. the m&s model presents a basic concept of the firefly-inspired synchronicity algorithm, which is described through two pulse-coupled oscillators. each oscillator is characterized by a monotonically increasing and concave down function representing a firing function as shown in figure 1. when the oscillator’s phase increase monotonically to reach a threshold, it fires and falls immediately to zero. the mutual interaction occurs between two oscillators when an oscillator fires and sends a firing message to another oscillator that causes another oscillator responds by adjusting its own phase toward firing. as a result, it will jump to a new phase φnew with coupling strength ε. the new phase can be calculated using the following equation [6]: φnew = min(1,f −1(φ) + ε)) (1) where f(φ)=1 b .ln(1+[eb-1].φ), and b is a dissipation parameter. the essential weakness of the m&s model, which violates the practical wsns, is when responding immediately a firing message that is sent by its neighbor nodes without considering an unpredictable delay because of the channel contention prior to message transmission. therefore, the reachback firefly synchronicity (rfa) developed by werner-allen et al.[10] overcomes the realistic wireless communication problems of the pco model. three problems handled by the rfa are related the wireless communication and one problem of the load computation. they are initial phase proximity for reachback firefly synchronicity in wsns: node clustering 93 the timestamping message, the notion of pre-emptive message staggering, reachback response, and simplified firing function. the amount of time a message that was delayed before being broadcasted can be estimated using the low-level timestamping. the oscillator of the pco model that reacts shortly to each firing event can be overcome utilizing the notion of reachback response, that is, all firing events as the phase jumps are recorded and are calculated once at the end of each period that is used to jump at the beginning of the cycle. the wireless contention, in the worst case, can be avoided employing the notion of pre-emptive message staggering. finally, the computation complexity is reduced by simplifying the firing function. the detail description of four ways to be applied in the real wireless network are as follows: 1. a node experiences a delay between when it reaches a firing and when it starts to transmit a message. the delay can be estimated using mac-layer timestamping. the measurement of the mac-delay can be started using a trigger to record it in the header of the outgoing message when the message is transmitted. in the receiver node, the information is used to determine the proper firing time by calculating the difference between the mac-delay and the reception time of the message. 2. in the m&s model, a node responds immediately to each firing message from other nodes. in contrast, the rfa uses the notion of reachback response to record each received firing message and calculates them as the phase jumps once at the end of each period, which is used as a jump at beginning of the next cycle as illustrated in figure 2. this approach will be discussed in more detail in subsection 3.2. 3. in the m&s model, perhaps many nodes transmit the firing message together when they fire simultaneously. this event is the worst case of the csma scheme because they can cause channel collisions. to avoid such a worst case and to control the extent of the message delay, the rfa model introduced a notion of pre-emptive message staggering by adding a random transmission delay to the firing message at the application level of the node. the delay value is assigned to a uniformly random value between 0 up to a constant d before node fires. furthermore, a random waiting time w (where d<w<<t) is added for a node that has fired to receive the delayed messages before processing the firing message in the queue. 4. the m&s model’s firing function can be simplified to make a faster calculation of firing response. therefore, the rfa algorithm reduces the computation complexity by simplifying the firing function. the new phase obtained after receiving a firing message at phase φ1 is φ2 = φ1 + εφ1 or φ2 = (1 + ε)φ1. 3 the proposed method of node clustering based on phase proximity for firefly-inspired synchronicity the challenges of synchronicity in the wireless sensor network are how to increase the convergence rate, and to handle the latency delay in wireless communication. both challenges aim to reduce the power consumption. the model proposed by sun yi et al. [18] is a centralized node clustering mechanism through sorting the phase value of nodes. this model is difficult to be implemented in wsns because they require a node as coordinator to gather the phase value of all nodes. furthermore, the coordinator has to be able to organize the phase order of all nodes in order to construct the node clustering. this research proposes a new decentralized node clustering mechanism based on original phase proximity of nodes where the nodes in the 94 m. misbahuddin, r.f. sari t1 t2 time 1 ∆φ1 ∆φ2 φ1 φ2 φm−1 φm phase t t1 t2 time 1 φ1 φm−1 phase t3 ∆φtot (a) (b) figure 2: phase jump in (a) m&s model, and (b) rfa model φc1 φc2 φc3 cluster 1 cluster 2 cluster 3 figure 3: illustration of the phase cluster for three clusters network construct the clusters through a self-configuration way. this approach is called node clustering based on initial phase proximity for reachback firefly synchronicity (ncipp-rfs). generally, synchronicity steps of the ncipp-rfs are similar to the cfsa method: clusterconstruction, intra-cluster synchronicity, and inter-cluster synchronicity. however, unlike cfsa method, ncipp-rfs uses a new approach in the cluster construction. the clusters are constructed based on the original phase value proximity of nodes that are organized in a selfconfiguration way without assistance a node coordinator. moreover, the inter-cluster synchronicity is added with a late sensitive window (lsw) factor to avoid the overshoot of jump in a group or cluster. 3.1 construction of the clusters based on phase proximity node clustering based on initial phase proximity (ncipp) is a simple algorithm used for clustering in wsns. this algorithm partitions the nodes into k clusters. the node clustering is constructed using the following steps: 1. arbitrarily assign k points as the center clusters, k being the number of clusters desired. the assignment of the center point of the cluster is intended to ensure that each node only initial phase proximity for reachback firefly synchronicity in wsns: node clustering 95 exists in a cluster. next, each node will internally assign its own cluster by calculating a nearest distance to every center point of the cluster (φc1, φc2, φcj, ...,φck). the distance can be computed by the following euclidean equation: dφij = √ φ2i −φc 2 j (2) where dφij is the distance between the ith node’s phase φi and the jth center point of the cluster φcj, and the node’s phase is φi∈[0 1]. each node in the network executes the cluster-initialization function internally to determine its own cluster. the pseudo code is shown in algorithm 1 in which the cluster number can be obtained through the minimum value of the clusterarray. the center points are determined based on the number of clusters k that is desired, where they are assigned through a balanced division of the distribution of phase values (between 0 and 1) in k groups. for illustration, let’s assume that there are three clusters (k = 3) where the center point of the clusters are denoted by φc1 = 0.167, φc2 = 0.5, and φc3 = 0.833 respectively. when the uniformly distributed phase values are mapped in a circle window in which the zero and one phase coincide in a point, the nodes will form three clusters as shown in figure 3. 2. each node provides an internal data structure called neighbornodes to store the source address (srcaddr) of its neighbor nodes in the same cluster and has a cluster number variable (nocluster). 3. the node clustering is executed by two main functions, i.e., the broadcastidcluster and the recieveidcluster. the pseudo codes of the both functions are shown in algorithm 2. the broadcastidcluster is a function to broadcast the source address, and the cluster number of the transmitter node to other nodes in the same cluster. to avoid simultaneous transmission in the periodic broadcast, it is added with a random interval delay and a beacon expire timer in the broadcastidcluster. on the contrary, the receiveidcluster is a function to receive the broadcasting messages. if the sender’s cluster number is equal to the receiver’s cluster number, the receiver node stores the sender’s address and cluster number into its data structure, the neighbornodes. this means that they are in the same cluster. algorithm 1 cluster initialization clusterinitilization() assign the number of cluster k assign the center point of the clusters (φc1, φc2, ...,φck ) for j:k do clusterarray[j] ←dφij nocluster ← index of min(clusterarray) 3.2 intra-cluster synchronicity after all logical clusters based on the initial phase are constructed. every logical cluster can start to perform a synchronicity concurrently. there are two firefly-inspired algorithms that can be used to perform the synchronicity, i.e., m&s and rfa model. the m&s model still uses a simple algorithm without considering the delay effect of real communication in wsns. a firing 96 m. misbahuddin, r.f. sari algorithm 2 node clustering broadcastidcluster(srcaddr, nocluster) send(srcaddr, nocluster) delay(interval+rand()) beaconexpiretimer () receiveidcluster(srcaddr, nocluster) if (noclusterofsender=nocluster) then neighbornodes.srcaddr ← srcaddr numcluster ← numcluster+1 message that is sent by a node is responded instantaneously by its adjacent nodes regardless of its unpredictable delay because of the delay latencies [8]. for illustration, let’s assume that in a cluster, node x receives a firing message from another node y at time t1 or phase φ1. in response, the node x adjusts its oscillator’s phase by increasing slightly to a new phase φ2 that can be computed through the following firing function: φ2 = min(1, (1 + ε)φ1) (3) every time node x receives a firing message from any node in same cluster; it calculates its phase jump using the following equation: ∆φi = min(1, (φ2 −φ1)) = min(1,εφ1) (4) to address the delay latency in a real network, a reachback response mechanism of the rfa model is a proper choice. in this model, the node x does not jump every time it receives a firing message, but it always stores its entire phase jumps until the period end. furthermore, the sum of phase jumps in the previous period will be used to jump at the beginning of next period. figure 2a illustrates the m&s model where the node x receives two firing messages at time t1 and t2 respectively. in contrast, in the rfa model node x will jump at time t3 in the beginning of the next period as shown in figure 2b. the sum of phase jumps can be calculated through the following equation: ∆φtot = n∑ i=1 ∆φi (5) where n is the number of firing events received in a period. 3.3 inter-cluster synchronicity after all clusters achieve the synchronicity condition, every cluster can be considered as a converging node group. it can be assumed as a firing node if its phase comes near to one. in this case, the rfa mechanism can still be used to reach the synchronicity condition for all nodes in the network. the synchronicity condition can be completed through sending the firing message among the node groups. the number of node groups will decrease, which are caused by the merging of a few of the node groups into one group and finally there is only one group as the synchronicity condition of the network. in a period the group gx will receive the firing messages from the firing group gy equal to the number of the gy’s members. the sum of phase jumps of group gx is as follows: ∆φ(tot),gx = ny∑ i=1 ∆φi,gx (6) initial phase proximity for reachback firefly synchronicity in wsns: node clustering 97 where ny is the number of gy’s members. this enlarged the sum of phase jumps of group gx, so that it can cause an overshooting jump in the nodes of the groups gx. to avoid this event, the rfa model is added a late sensitive window (lsw) introduced by [11], where the window inhibits the response of node against the firing messages falling outside of the window. when group gx achieves phase φ = 1 (or t = t), it fires and adjusts its phase based on the sum of phase jump in the previous period and jumps in the beginning of next period. the phase jump of the group gx is calculated by the following equation: ∆φgx =   0 φ ≤ lsw and �φ ≥ 1 εφ φ ≥ lsw and �φ ≤ 1 1 otherwise. (7) 4 simulation results the performance of both rfa and ncipp-rfs method is evaluated using a network simulator ns-3 to obtain the convergence rate. a total of 40 simulations were run for each coupling strength in all scenarios. hence, results presented in figure 4, 5 and 6 for each coupling strength show the average values and the corresponding 95% confidence interval. the network topology uses a mesh type in a variety of sizes of 25, 50, 75, and 100 nodes spread uniformly in a constricted rectangular area sized 1000 x 1000 m2. furthermore, the network is designed to represent a realistic environment to evaluate the convergence rate of the synchronicity. several simulation parameters used to evaluate the convergence rate of the synchronicity are varied in the simulation both rfa and ncipp-rfs model as shown in table 1. the coupling strengths (ε) are varied in the range of 0.01 through 0.1 for cluster 2, 3, 4, and 5. the metric evaluation used to measure the convergence rate is the number of periods required to reach the synchronicity condition. table 1: simulation parameters parameter value oscillating period t (milliseconds) 100 phase of oscillator φ φ ∈ [0,1] phase rate dφ dt = 1 t 0.01 coupling strength � � ∈ [0.01, 0.1] the number of nodes 25, 50, 75, 100 the number of clusters 2, 3, 4, 5 figure 4 shows a set of charts that evaluates the influence of the coupling strength parameter on the number of periods in both the rfa and the ncipp-rfs. it presents some comparison of the convergence rate between the rfa and the ncipp-rfs with various number of clusters. it can be seen that the convergence rate of the rfa is comparable to the ncipp-rfs in 25 nodes. however, at the number of nodes, 50, 75 and 100, the convergence rate of the rfa is much slower than the ncipp-rfs, especially in the lower coupling strength of 0.01 through 0.05. this happens because of the influence of the clustering way in the less the number of nodes to increase the convergence rate does not contribute significantly. actually, the such way is to synchronize some groups of nodes in a network concurrently to reduce the load of the network. if node has not been synchronized in the network, all other nodes must keep the effort to synchronize it. therefore, the clustering method of 25 nodes in the rfa results in the convergence rate similar to the ncipp-rfs. otherwise, in the larger the number of nodes (50, 75, and 100 nodes), the 98 m. misbahuddin, r.f. sari 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 25 nodes coupling strength n u m b e r o f p e ri o d s 0 10 20 30 40 50 rfa ncipp−rfs, 2 clusters ncipp−rfs, 3 clusters ncipp−rfs, 4 clusters ncipp−rfs, 5 clusters 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 50 nodes coupling strength n u m b e r o f p e ri o d s 0 10 20 30 40 50 60 rfa ncipp−rfs, 2 clusters ncipp−rfs, 3 clusters ncipp−rfs, 4 clusters ncipp−rfs, 5 clusters 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 75 nodes coupling strength n u m b e r o f p e ri o d s 0 20 40 60 80 rfa ncipp−rfs, 2 clusters ncipp−rfs, 3 clusters ncipp−rfs, 4 clusters ncipp−rfs, 5 clusters 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 100 nodes coupling strength n u m b e r o f p e ri o d s 0 20 40 60 80 100 rfa ncipp−rfs, 2 clusters ncipp−rfs, 3 clusters ncipp−rfs, 4 clusters ncipp−rfs, 5 clusters figure 4: the comparison of the convergence rate between the rfa and the ncipp-rfs model when the number of clusters is varied for 25, 50, 75, and 100 nodes. initial phase proximity for reachback firefly synchronicity in wsns: node clustering 99 convergence rate of the ncipp-rfs declines along with the increasing number of nodes. the influence of the clustering method in synchronicity that contributes significantly to the increase in the convergence rate is in the larger the number of nodes. finally, the number of nodes which are greater than or equal to 50 nodes generates the best performance of the ncipp-rfs among all scenarios when the number of node is varied. a set of charts presented in figure 5 is to discover the best performance among all scenarios when number of clusters is varied in the ncipp-rfs model. the chart on the top left shows the convergence rate of the ncipp-rfs in the two-clusters scenario. it shows that the number of periods declining rapidly along with the gradual increase of the coupling strength value in all scenarios of the number of nodes. this means that the ncipp-rfs of two clusters will show a better performance. this is in line with the aim of the adjustment of the coupling strength parameter to increase the convergence rate gradually. next, in the chart for the experiment with three clusters, the convergence rate slows down along with the rising number of nodes to 75 nodes, but it can increase the convergence rate in 100 nodes. similarly, the larger the number of clusters (4 and 5) the ncipp-rfs is slower than in the scenario for two clusters. this happens because the increase of the number of clusters causes the increase of the number of periods to reach a convergence in the inter-cluster synchronicity, so that they will slow down the convergence rate of the synchronicity process in the network. therefore, the best performance of the ncipp-rfs among all scenarios of the number of clusters is the scenario of two clusters. an important problem that needs to be understood is the caution of the highly different convergence rate to the others as shown in figure 5. they are 50 nodes with coupling strength of 0.01 in two clusters, 75 nodes with coupling strength of 0.01 in three clusters, 25 nodes with coupling strength of 0.04 in four clusters, and 25 nodes with coupling strength of 0.01 in five clusters. these problems are caused by the process of inter-cluster synchronicity that is hard to reach a synchronicity percentage of 100 percent as shown in figure 6. it can be seen that the graphs express the movement of the synchronicity percentage that is constant in some periods causing a difficulty to rise forward to 100 percent. this problem will slow down the convergence rate of the synchronicity. 0 5 10 15 20 25 30 30 40 50 60 70 80 90 100 periods p e rc e n ta g e o f s y n c h ro n ic it y ( % ) 2 clusters, 25 nodes 3 clusters, 75 nodes 4 clusters, 50 nodes 5 clusters, 25 nodes inter−cluster synchronicity figure 6: inter-cluster synchronicity that is difficult to reach a synchronicity convergence 100 m. misbahuddin, r.f. sari 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 ncipp−rfs, 2 clusters coupling strength n u m b e r o f p e ri o d s 0 5 10 15 20 25 30 35 25 nodes 50 nodes 75 nodes 100 nodes 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 ncipp−rfs, 3 clusters coupling strength n u m b e r o f p e ri o d s 0 10 20 30 40 25 nodes 50 nodes 75 nodes 100 nodes 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 ncipp−rfs, 4 clusters coupling strength n u m b e r o f p e ri o d s 0 10 20 30 40 25 nodes 50 nodes 75 nodes 100 nodes 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 ncipp−rfs, 5 clusters coupling strength n u m b e r o f p e ri o d s 0 10 20 30 40 50 25 nodes 50 nodes 75 nodes 100 nodes figure 5: the comparison of the convergence rate among the number of nodes in the varied number of clusters. initial phase proximity for reachback firefly synchronicity in wsns: node clustering 101 conclusions the clustering scheme approach based on the initial phase proximity is a way to synchronize all nodes in a network executed concurrently. the clusters are constructed through a selfconfigured way. the intra-cluster synchronicity uses the reachback firefly algorithm (rfa) simplifying the firing function and considering the realistic transmission effects. the inter-cluster synchronicity utilizes the rfa with the late sensitive windows (rfa with lsw) model to avoid the overshooting jumps due to the flood of the firing messages from a node group to others. we compare the rfa and the ncipp-rfs clustering scheme using the network simulator (ns-3). the simulation results show that the convergence rate of the ncipp-rfs can reach a synchronicity faster than the rfa. moreover, the ncipp-rfs can contribute significantly to the increase of the convergence rate if the number of nodes is greater than or equal to 50 nodes. the ncipp-rfs was also evaluated and compared in the various number of clusters. the best performance of the ncipp-rfs among all scenarios of the number of clusters is the scenario of the least number of clusters. however, the movement of the synchronicity percentage that is constant in some periods is difficult to rise toward the 100 percent. as future work, we will implement our approach to test it in a data-gathering application referring a temporal and spatial relationships. acknowledgement the authors gratefully acknowledge the ministry of research, technology and higher education of indonesia for supporting this work through dikti’s research grant under grant no. 1173/un2.r12/hkp.05.00/2016 bibliography 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[18] s. yi, j. qing, and z. kai (2012), a clustering scheme for reachback firefly synchronicity in wireless sensor networks, network infrastructure and digital content (ic-nidc), 2012 3rd ieee international conference on, 27-312. int j comput commun, issn 1841-9836 9(2):187-200, april, 2014. vlsi architecture for high performance 3gpp (de)interleaver for turbo codes j.m. mathana, s. badrinarayanan, r. rani hemamalini j. magdalene mathana electronics and communication dept, s.a engineering college, chennai, india e-mail: jm.mathana@gmail.com s. badrinarayanan vinayaka missions university, salem, india e-mail: badri22.jm@gmail.com, r. rani hemamalini elect. and comm. dept, st.peter’s college of engg and tech, chennai, india e-mail: ranihema@yahoo.com abstract: interleaving along with error correction coding is an effective way to deal with different types of error in digital data communication. error burst due to multipath fading and from other sources in a digital channel may be effectively combated by interleaving. normally the interleaver / deinterleaver pair is often designed as reconfigurable architectures able to deal with requirements of large data length variability found in the newest communication standards. in this work reconfigurable interleaver architecture for the turbo decoder in 3rd generation partnership project (3gpp) standard is presented. the interleaver is a key component of radio communication systems. using conventional design methods, it consumes a large part of silicon area in the design of turbo encoder and decoder. the proposed interleaver utilizes the algorithmic level hardware simplifications and generates 100 manage the ow of data streams to achieve very low cost solution. the proposed technique reduces consumption of fpga resources to a large extent compared with existing state-ofthe-art interleaver for turbo codes. the proposed architecture consumes only 4856 logic elements by hardware optimization. keywords: 3gpp, interleaver, reconfigurable, turbo codes. 1 introduction turbo codes [1] have been shown to provide superior error performance amidst hostile transmission media. 3rd generation systems based on the 3gpp air interface standard [2] must support turbo coding at data rates of between 384 kbps to 2mbps, while maintaining low mobile station power. a turbo code is formed from the parallel concatenation of two codes separated by an interleaver [3] .the generic design of a turbo code is depicted in figure 1. the two encoders used are normally identical. the code is in a systematic form, i.e. the input bits also occur in the output. the turbo decoder consists of two elementary decoders in a serial concatenation scheme. since soft decoding performs better than hard decoding, the first decoder provides a weighted soft decision in the form of a posteriori probabilities (apps) to the second decoder. the decoding proceeds in an iterative fashion as illustrated in figure.2 [4]. the interleaver reads the bits in a pseudo-random order. the choice of the interleaver is a crucial part in the turbo code design. the task of the interleaver is to "scramble" bits in a pseudo-random, albeit predetermined fashion. this serves two purposes. firstly, if the input to the second encoder is interleaved, its output is usually quite different from the output of the first encoder. this means copyright © 2006-2014 by ccc publications 188 j.m. mathana, s. badrinarayanan, r. rani hemamalini that even if one of the output code words has low weight, the other usually does not, and there is a smaller chance of producing an output with very low weight. higher weight is beneficial for the performance of the decoder. secondly, since the code is a parallel concatenation of two codes, the divide-and-conquer strategy can be employed for decoding. if the input to the second decoder is scrambled, also its output will be different or "uncorrelated" from the output of the first encoder. this means that the corresponding two decoders will gain more from information exchange [5]. a flexible and reconfigurable interleaver architecture for multimode communication environment is presented in [6]. the presented hardware in [6] enables the mapping of vital types of interleavers including multiple block interleavers and convolutional interleaver onto a single architecture. a parallel interleaver design that support for high throughput is presented in [7].in the proposed work, circuit level vlsi optimization approach is used to reduce the overall computation complexity and provides 100 % interleaved paths. the paper is organized as follows. in section 2, a review of 3gpp algorithm is discussed. section 3 describes the proposed reconfigurable architecture of interleaver/deinterleaver. the controller for the proposed reconfigurable interleaver architecture operated in two phases is explained in section 4. section 5 describes about the address generation and section 6 describes about exception handling. in section 7, simulation and synthesis results are reported and section 8 concludes the paper. 2 interleaver algorithm the 3gpp-defined turbo code interleaver algorithm [8] comprises a series of mathematically complex processes that map an input sequence of length k (40 5114) to a scrambled sequence. the algorithm can be summarized as follows. block size ’k’denotes the number of bits input to turbo code internal interleaver and takes one of the value from 40 to 5114. determine the number of rows ’r’ of the rectangular matrix such that, r= 5; if (40<k<159) r=10; if((160<k<200)or(481<k<530)) r=20 ;if (k= any other value) where rows are numbered form 0 to r-1. determine the prime number ’p’ to be used in intra-row permutations and the number of columns ’c’ of the rectangular matrix as if (481< k <530) then p= 53 and c = p vlsi architecture for high performance 3gpp (de)interleaver for turbo codes 189 else find the minimum prime number p from table 2 in [6] such that k < (rx(p-1)) and determine c such that c = p-1; if k< (r (p-1)) c = p; if r (p-1) < k < r p) c=p+1; if (r p) < k for every prime number p there exists a primitive root v. construct the base sequence s(j) for intra row permutation such that s(j) = (v*s(j-1))mod (p) and s(0) = 1 where j=1,2,...,(p-2). determine the prime integers in the sequence q(i) such that g.c.d(q,p-1) = 1, q(i)> 6 and q(i)> q(i-1) for i=1,2,.....r-1. permute the sequence q(i)to make the sequence r(i) such that r(i) = t(q(i)), i = 0,1,...,r-1. where t(i) is a simple indexing transform. it is defined by the standard and shown in table 1. table 1 list of prime number p and associated primitive root ’v’ perform the intra row permutation if c = p , u(i,j)=s[(j*r(i))mod(p-1)] where u(i,p-1) = 0 if c= p+1,u(i,j)=s[(j*r(i)mod(p-1)] where u(i,p-1) = 0; and u(i,p) = p and if k= r c, then exchange u(r-1,0) with u(r-1,p) if c = p-1, u(i,j)=s[(j*r(i))mod(p-1)]-1 where i = 0,1,...,r-1 and j = 0,1,..,p-2. 190 j.m. mathana, s. badrinarayanan, r. rani hemamalini table 2 inter-row permutation patterns perform the inter row permutation for the rectangular matrix based on the pattern t(i).it is denoted as u(i) u(i) = u(t(i)), where i=0,1,..,r-1. read out the addresses column wise. looking at the interleaver algorithm in 3gpp standard, the computationally intensive functions found are modulo computation, intra-row and inter-row permutations, multipliers, finding least prime integers, and computing greatest common divisor. some of these complex functions are implemented using the support from rom, while the others are simplified to reduce the hardware usage. the interleaving process can be easily separated into two main phases. the two phases are pre-computation phase and execution phase. as different block sizes has different interleaving patterns, thus every time some pre-computation is needed while changing the block size ’k’. the only parameter applied to interleaver is the block size (k) and rest of the parameters is computed by the hardware itself. 2.1 pre-computation phase pre-computation phase involves pre-computing the various parameters required for the interleaving process. it has to be performed each time the change in the block size occurs, that means for a fixed block size k, these operations have to be performed only once. 2.2 execution phase execution phase calculates the intra row permutations ui,j, and the interleaved address i_addr .write or read the data bits ( in/ from a data ram) can be taken place depending upon whether interleaving or deinterleaving is performed of addresses could also be computed and saved in the memory only. for each data block, the execution phase has to be performed. the set of addresses could also be computed and saved in the memory only once.this approach permits to reduce the implementation size of interleaving / deinterleaving algorithm as compared to the memory or look up table (lut) based approach, where the interleaving addresses are saved in memory. thus execution phase performs basic permutations which form the interleaving process. the basic permutations performed in this phase are illustrated in fig.4. 3 proposed architecture of reconfigurable interleaver the main objective of the proposed work is to device an architecture capable of managing every one of the 5074 different block sizes of data ranging from 40 to 5114 defined in the 3gpp vlsi architecture for high performance 3gpp (de)interleaver for turbo codes 191 standard, while maintaining low hardware complexity. the proposed interleaver is said to be reconfigurable, since it adapts or varies its interleaving architecture depending upon the block size ’k’ of the input rectangular matrix. the reconfigurable interleaver accepts only the block size of the rectangular matrix as the input. the rest of the parameters such as the number of rows, columns and the base sequence for permutation (both intra row and inter row permutation) are calculated by the reconfigurable architecture for interleaver address generation. the block diagram of proposed reconfigurable interleaver/deinterleaver is shown in figure 5. it consist of modulo-computation block, multiplication addition and comparison hardware, controller circuit , i/o ram, data ram, exceptional logic block, s(j) ram and luts etc. 3.1 precomputation of parameters the computation of parameters in pre-computation for 3gpp is discussed in detail by blakley [9]. some parameters are computed using look up tables while the others need some close loop or recursive computations. first, the row value ’r’ with logical functions is calculated. parameters like prime number ’p’ and primitive root ’v’ are stored as a pair in a look up table just like in the standard and lut is read via a counter generated by the controller based on equation (1). k ≤ r ∗ (p + 1) → (k − r) ≤ (r ∗ p) (1) in order to perform the operation in equation (1) the architecture given by blakley is used in the proposed work. it is shown in figure 6.the same hardware architecture is used in execution phase also. for computation of ’p’ and ’c’ operations such as the multiplication, addition and comparison are required. it is controlled by the flags generated by the controller. once ’p’ and ’v’ are obtained then numbers of columns have to be determined. the three possible column values are p-1, p or p+1 and always the following condition shown by the equation (5.2) should be satisfied. (r ∗ c) ≥ k (2) 192 j.m. mathana, s. badrinarayanan, r. rani hemamalini vlsi architecture for high performance 3gpp (de)interleaver for turbo codes 193 the computation of intra-row permutation pattern s(j) required modulo computation. modulo function is computed iteratively using the interleaved modulo multiplication algorithm [9].it is an iterative numerical algorithm based on additions, shifts, comparisons and bit retrieval. in order to calculate the s(j) sequence, it is mandatory to perform the modulo operation. a flow diagram depicting this algorithm is shown in figure 7. the hardware for the computation of s (j) is shown in figure 8. this architecture uses two adders, a multiplier and a subtractor to compute the s (j) values. the s array is computed as follows (3) with s (0) = 1. s(j) = (v ∗ s(j − 1))mod(p) (3) where j =1, 2... (p-2) from the above equation it is known that, primitive root ’v’ is required to compute s(j), which is 5 bits. so maximum of 5 iterations are needed to compute one modulo multiplication. with this architecture every s (j) is determined and writes in a ram of 256 x 10 bits. the size of the ram depends upon the value of ’p’. the number of cycles needed to calculate every s (j) depends on v, which can take only six different values 2, 3,5,6,7 and 19. for v = 2 and v = 3 only one iteration is required for v = 5, v = 6 and v =7 two iterations and for v =19 four iterations are required. according to the 3gpp algorithm, the next step is calculation of the prime sequences q (i). it can be noticed that the qi sequences [10] are almost the same in most cases and they only differ by one or two elements from other sequences. based on this observation, sub groups of q (i) sequence are placed into a rom and then choose one according to the ’p’ value [11]. in the proposed work, instead of finding the least prime number sequence q (i), q(i) mod (p-1) is determined. this gives the benefit of computing the ram address recursively and avoiding computation of modulo function. this idea is introduced by shin and park [12] and later used by wang and li [13]. according to the equation (4) q (i) is calculated. 194 j.m. mathana, s. badrinarayanan, r. rani hemamalini vlsi architecture for high performance 3gpp (de)interleaver for turbo codes 195 q(i) = q(i)modp − 1 (4) where i = 0,1,2,3,...p-1 the architecture used to perform q(i) is shown in figure 9. in the proposed work, the architecture of s (j) is used with some alterations to determine q mod (p-1). it is calculated in such a way that the following conditions have to be satisfied. g c d(q, p-1) = 1, q(i) > 6 and q(i) > q(i-1) for i = 1,2,.....r-1. g c d is the greatest common divisor. the selected prime number from the prime number table should be 6 and g c d (q, p-1) = 1.if the above condition is satisfied then the value of i = i+1, j = j+1 and the next prime number is selected. the selected prime number becomes one of the member of q(i) and the value of ’j’ is also incremented by1.if the condition is not satisfied then i=i+1,j=j. 3.2 execution phase computation after completing the pre-computation phase, the controller is set in execution phase and the hardware is configured to perform run time computations for the generation of the interleaved addresses.ram address are computed using the hardware shown in figure 6. the recursive function used to compute the ram address and multiply?add function to compute the final interleaved address are as follows: ramadr(i,j) = [ramadr(i,j − 1) qmod (i)]mod(p − 1) (5) it can be seen that computing the ram address using qmod (p-1) instead of q helps to avoid the full computation of modulo multiplication. after computing the ram address, the final interleaved address is computed by the multiply?add function. the interleaved addresses are obtained by performing the inter row t (i) and intra row ui,j permutations in executing phase, where t(i) is stored in a lut while the ui,j is stored in a ram. with these parameters and the using the equation (6), the interleaving addresses i_addr are finally generated. iaddr ((i × c) + j) = (c × t(i)) + ui,j (6) with i = 0,1,...,r-1; j = 0,1,...,c-1 [c×t(i)] is computed by the architecture shown in figure 6.the result of this computation points to the first element of each of the ’r’ rows in the rectangular matrix, and then by adding ui,j with the same architecture, a displacement along every row is obtained. ui,j is obtained by calculating modulo operation (j × r(i)) mod (p-1).it has the same form as (v × s(j-1))mod p in the pre-computation phase. since the computation of s (j) and ui,j are performed at different phases, the same architecture is used with some modifications. since the same architecture is used in both the phases, hardware complexity is reduced. 3.3 modulo computation one of the important blocks in the proposed reconfigurable architecture is "modulo computation". this block plays a crucial role both in execution phase and in pre-computation phase. figure 10 shows the architecture of modulo computation introduced by rizwan asghar and dake [10]. the main block is depicted in figure 10 by blue colour. it is separated and shown in figure 11a. in pre-computation phase, this architecture works 100% efficiently. but in execution 196 j.m. mathana, s. badrinarayanan, r. rani hemamalini phase, its efficiency is only 88%. the problem will occur when q (i) > 2(p-1)[10]. this problem is nullified by performing subtractions like q(i) (p-1), q(i) 2(p-1), q(i) 3(p-1) and q(i) 4(p-1) instead of q(i) (p-1) [11]. it is illustrated in figure 11b. in the proposed work, this structure is further modified as shown in figure 12. in order to nullify the problem, always q(i) must be less than p. in the proposed work q (i) is compared with p. after comparison the small value is assigned to q (i) and the large value is assigned to p .hence in the proposed work, during the execution phase also modulo computation block works 100% efficiently. there are three multipliers are used.[11] to generate 2(p-1), 3(p-1) and 4(p-1). but the proposed modified architecture does not contain any multipliers; hence hardware complexity is reduced. this architecture performs all modulo operations required by the 3gpp standard. with this modification the obtained hardware architecture works properly in both the pre-computing and executing phase. 4 control finite state machine in order to synchronize every block in the architecture, a "controller" block generates all control signals; its state machine diagram is shown in figure 13. the controller mainly works for the pre-computation phase of 3gpp. it configures different functions like multiply, add, compare and modulo computation hardware to find all the vital parameters r, c, p, v and s (j). usually interleavers in the turbo coded system work with the same block size ’k’ several times before changing it. when an interleaving operation is required the pre-computation phase is performed, where s (j) is calculated and placed in ram. then the execution phase starts where interleaved addresses are calculated continuously meanwhile ’k’ vlsi architecture for high performance 3gpp (de)interleaver for turbo codes 197 does not change. the new pre-computation phase can be initiated by changing the block size ’k’. 5 exception handling in the 3gpp interleaving algorithm, there are some exceptions that are reflected in the architecture. the final address is tagged valid or invalid using the comparator. this is called pruning of the interleaver and is needed for the case when interleaver block size is not exactly equal to r*c. for example, for k = 43 the rectangular matrix size is 5x10, and then there would be seven invalid addresses. because of that, a valid address cannot be generated per clock cycle. interleavers presented by shin and park [12] wang and li[13], [11] and carlos sánchez [14] provides the interleaved addresses with valid or invalid tag. but in the proposed architecture, if the input data stream of any size between 40 and 5114, then the interleaver rearranges it according to the corresponding 3gpp interleaved path and provides the data stream even the exception is present. in order to manage data streams, a data ram is used in the proposed design. when the proposed architecture is operated in interleaver mode, this ram is used to hold data input when invalid addresses are generated and waiting for a valid address in order to write this data to data ram. in deinterleaver mode, when invalid address is read from data ram, the i/o ram ignores this data, waiting for a valid data and then output this data. 6 performance analysis of reconfigurable interleaver the rtl code for the hardware blocks is written in verilog and downloaded it to the fpga cyclone iii from altera. the rtl diagram of reconfigurable interleaver / deinterleaver is shown in figure 14. the number of clock cycles spent for pre-computation through is mentioned in table 3. these cycles mainly depend on the value of p and v for a particular block size. the maximum clock frequency of 69.91 mhz is shown in figure 15.table 4 summarizes comparison of the performance with existing state-of-the-art interleavers. as it can be seen from the table 4, the proposed design occupies about 4586 out of 5136 logic elements. it is worth mentioning that although the proposed design is bigger than others, it includes the hardware for exceptions handling, an i/o ram and a data ram as well as extra hardware to control them. in this way, the proposed architecture is capable of working both as an interleaver and as a de-interleaver.in this proposed interleaver/deinterleaver latency and throughput can 198 j.m. mathana, s. badrinarayanan, r. rani hemamalini vlsi architecture for high performance 3gpp (de)interleaver for turbo codes 199 vary depending on the block size. the maximum block size is k=5114.the proposed architecture requires 5120 clock cycles which represents a realistic insignific ant overhead. 7 conclusions and future work in this paper a fully functional 3gpp turbo code interleaver/ deinterleaver architecture that receives an input data stream of any size established by the 3gpp standard and delivers this stream interleaved or deinterleaved depending on the user requirements is presented. in this proposed design modulo computation is done by computer algorithm to calculate and took advantage of using the same hardware in pre-computation and execution phases by multiplexing it. novel vlsi architectures have been introduced in this design. by adding rams for data handling and achieved a complete architecture that can perform interleaving/deinterleaving operations as required by the 3gpp standard for turbo codes.one of the important parameter is pre-computation cycle cost. if the pre-computation cycle cost is less then, the design supports fast switching among different standards. hence future work is to design reconfigurable interleaver which is suitable for a multimode environment. bibliography [1] 3rd generation partnership project (3gpp) tsg-ran, "multiplexing and channel coding," release 4, version 4.2.0, sept. 2001. [2] c. berrou, a. glavieux, and p. thitimajshima (1993), near-shannon limit errorcorrecting coding and decoding : turbo codes, proceedings of icc, geneva,switzerland, 1064-1070. [3] c. berrou, a. glavieux, and p. thitimajshima (2003), near shannon limit error correcting coding and decoding:turbo codes. proceedings of the ieee international conference on commun, geneva, switzerland, may 2003. 200 j.m. mathana, s. badrinarayanan, r. rani hemamalini [4] m valenti (1999), iterative detection and decoding of wireless communications, ph.d thesis, virginia polytechnic and state university, july 1999. [5] university of south australia, institute for telecommunications research, turbo coding research group, http://www.itr.unisa.edu. [6] rizwan asghar and dake liu (2010), multimode flexinterleaver core for baseband processor platform, journal of computer systems, networks, and communications, 1-16. [7] rizwan asghar and dake liu (2010), towards radix4,parallel interleaver design to support high throughput turbo decoding for reconfigurability, 33rd ieee sarnoff symposium, princeton, new jersey, usa, 1-5. [8] 3rd generation partnership project, technical specification group radio access network; multiplexing and channel coding,release 6, 3gpp ts 25.212 v6.0.0 (2003-12). [9] g.r.blakley (1983), a computer algorithm for calculating the product a*b mod m, ieee trans. on comp, 32(5):497-500. [10] rizwan asghar and dake liu (2008), very low cost configurable hardware interleaver for 3g turbo decoding, 3rd international conference on information and communications technologies theory to applications, ictta 2008, damascus, syria, 1-5. [11] hector borrayo sandoval , r. parra-michel, luis f.gonzález-pérez, fernando landeros printzen claudia feregrino-uribe (2009), design and implementation of a configurable interleaver/deinterleaver for turbo codes in 3gpp standard , in proc. of ieee international conference on reconfigurable computing and fpgas, cancun, mexico, 320325. [12] m. shin and i.-c. park (2003), processor based turbo interleaver for multiple third generation wireless standard, ieee commun. lett., 7(5):210 212. [13] z. wang and q. li (2007), very low complexity hardware interleaver for turbo decoding, ieee trans. on circuits and sys. ii, 54(7):636 640, july 2007. [14] carlos r.sanchez, r. parra-michel and m.e guzmánrenteria (2008), design and implementation of a multi-standard interleaver for 802.11a, 802.11n, 802.16e & dv standards, international conference on reconfigurabl computing and fpgas, reconfig 2008, cancun, mexico, 379 384. [15] p.ampadu and k. kornegay (2003), an efficient hardware interleaver for 3g turbo decoding, proc. of radio and wireless conference, rawcon’03, boston, 199-201. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 418-431 probabilistic proximity-aware resource location in peer-to-peer networks using resource replication m. analoui, m. sharifi, m.h. rezvani morteza analoui, mohsen sharifi, mohammad hossein rezvani iran university of science and technology (iust) 16846-13114, hengam street, resalat square, narmak, tehran, iran email: {analoui,msharifi,rezvani}@iust.ac.ir abstract: nowadays, content distribution has received remarkable attention in distributed computing researches and its applications typically allow personal computers, called peers, to cooperate with each other in order to accomplish distributed operations such as query search and acquiring digital contents. in a very large network, it is impossible to perform a query request by visiting all peers. there are some works that try to find the location of resources probabilistically (i.e. non-deterministically). they all have used inefficient protocols for finding the probable location of peers who manage the resources. this paper presents a more efficient protocol that is proximity-aware in the sense that it is able to cache and replicate the popular queries proportional to distance latency. the protocol dictates that the farther the resources are located from the origin of a query, the more should be the probability of their replication in the caches of intermediate peers. we have validated the proposed distributed caching scheme by running it on a simulated peer-to-peer network using the well-known gnutella system parameters. the simulation results show that the proximity-aware distributed caching can improve the efficiency of peer-to-peer resource location services in terms of the probability of finding objects, overall miss rate of the system, fraction of involved peers in the search process, and the amount of system load. keywords: distributed systems, peer-to-peer network, content distribution, resource location, performance evaluation. 1 introduction 1.1 motivation a peer-to-peer (p2p) system is a distributed system consisting of interconnected peers who are able to selforganize into network topologies with the purpose of sharing resources such as cpu or bandwidth, capable of adapting to dynamic conditions of network, without requiring the support of a global centralized server [1]. the p2p systems are classified as unstructured and structured. in the structured systems such as can [2] the overlay topology is tightly controlled and files are placed at exact locations. these systems provide a distributed routing table, so that queries can be routed to the corresponding peer who manages the desired content. unlike the structured systems, in the unstructured systems such as gnutella [3] and kazaa [4] searching mechanisms are employed to discover the location of the resources. each peer owns a set of resources to be shared with other peers. in general, the shared resource can be any kind of data which make sense, even records stored in a relational database. the most significant searching mechanisms include brute force methods (e.g. flooding the network with propagating queries in a breath-first or depth-first manner until the desired content is discovered) [1], probabilistic searches [5], routing indices [6], randomized gossiping, and so on. most of the current p2p systems such as gnutella and kazaa fall within the category of p2p "content distribution" systems. a typical p2p content distribution system creates a distributed storage medium and allows doing services such as searching and retrieving query messages which are known as "resource location" services. the area of "content distribution systems" has a large overlap with the issue of "resource location services" in the literature. fig. 1 illustrates a possible topology of the unstructured p2p networks. each super-peer is a powerful master node that acts as a local central indexer for files which are shared by its local peers, whereas acts as an ordinary peer for other super-peers. in graph representation, each pair of peers is connected by an edge representing a tcp connection between them. the number of neighbors of a super-peer is called its out-degree. if two nodes are not copyright c⃝ 2006-2010 by ccc publications probabilistic proximity-aware resource location in peer-to-peer networks using resource replication 419 connected by an edge, they could communicate through an indirect path which passes across some other nodes. the length of a path through which two nodes communicate with each other is known as hop-count. upon delivery of a query request message to a super-peer, it looks for matches over its local database. if any matches are found, it will send a single response message back to the node which has requested the query. if no match is found, the super-peer may forward the query request to its neighbor super-peers. figure 1: a typical super-peer network in general, the performance of the p2p systems is strictly evaluated by metrics such as response time, number of hops, aggregated load, throughput, overall miss rate, fraction of participating nodes in the search operation, and so on. to meet these requirements, previous researches resorted to heuristics to locate the resources by incorporating proximity concerns. 1.2 challenges there already exists significant body of researches toward proximity-aware p2p systems. the proximity-aware resource location method in the p2p unstructured systems has been investigated in [7,8]. the proposed method uses flooding mechanism to forward a query request to all neighbors of a peer. it uses the hop-count as the proximity metric. in order to reduce the number of broadcast messages in the network, the header of each query message contains a time-to-live (ttl) field whose value is decremented at each hop. finally, when the ttl reaches zero, the query message is dropped from the network. after locating the resource, a direct connection is established between the originating peer and the destined peers and the file is downloaded. the flooding approach employed in [7] is probabilistic in the sense that each peer replicates the query to its neighbors with a fixed probability. an analytical study on the impact of proximity-aware methodology for the content distribution is presented in [9]. they have evaluated the performance of video streaming p2p network via key scalability metric, namely network load. similar to the previous works, they have used the pair-wise latency of the peers as the proximity criterion. regard to the aforementioned works, in general, there are two strands of work concerning the proximity-aware methodology. first, there are works on content distribution via constructing the p2p topology [9]. second, there are works on resource location services [7, 8]. these works assume a given topology setting such as mesh or tree for the p2p system. it has been shown in [10, 11] that finding an optimal-bandwidth topology for the p2p network is an np-complete problem. so, we shall not try to solve the np problem of topology construction here. instead, we will try to optimize the proximity-aware resource locating problem within the given topology setting in the p2p system. 1.3 contributions in this paper, we are concerned with the design of a resource location service by using scalable proximityaware distributed caching mechanism. we define the resource location service as "given a resource name, find with a proximity probability, the location of peers who manage the resource." 420 m. analoui, m. sharifi, m.h. rezvani we use round-trip time (rtt) latency distance as the criterion for the probabilistic caching of each query. each peer, upon receiving a query, at first searches its local cache. if the query is found, the peer returns it to the original requesting peer along with the reverse path which is traversed by the query. in this order, the so called query is cached in the memory of each intermediate node using replication method based on the proposed proximity-aware distributed caching mechanism. the probability of the resource replication and updating of the caches in each intermediate node is proportional to the latency distance between that node and the location where the resource is found. to the best of our knowledge, there has been no investigation on designing the proximity-aware probabilistic caching in the p2p systems. the rest of the paper is organized as follows. section 2 presents our proposed proximity-aware resource location mechanism along with its specification such as resource replication. section 3 provides an analytical study of the probabilistic search method along with numerical results. section 4 presents the experimental validation of the proposed mechanism. finally, we discuss the related works in section 5, and conclude in section 6. 2 proximity-aware distributed caching each pair of nodes is associated with a latency distance representing the average rtt experienced by communication between them. the latency distance corresponding to a specific pair of nodes may be measured either directly through ping messages, or estimated approximately through a virtual coordinate service [12]. due to large number of nodes in a p2p system, we have adopted the latter approach to measure the latency between each pair of nodes. the virtual coordinate service which has been used in [12] is provided by vivaldi [13], a distributed protocol developed at mit. due to space limitations, we do not explain the details of the virtual coordinate service here. interested readers can refer to [12, 13] for it. as sated above, some works use the hop-count as the criterion for the distance estimation rather than using vivaldi estimation method. every super-peer in our system has a local index table (lit) that points to locally managed resources (such as files, web pages, processes, and devices). each resource has a location-independent globally unique identifier (guid) that can be provided by developers of the p2p network using different means. in a distributed online bookstore application, developers could use isbns as guids [8]. each super-peer has a directory cache (dc) that points to the presumed location of resources managed by other super-peers. an entry in the dc is a pair (id, loc) in which id is the guid of a resource and loc is the network address of a super-peer who might store the resource locally. each peer has a local neighborhood defined as the set of super-peers who have connected to it. table 1 and table 2 provide a high-level description of the proposed proximity-aware distributed caching mechanism. the querysearch (qs) procedure describes the operations in which a source is searching a resource, namely . the string path s1,...,sm is the sequence of super-peers who have received this message so far. this sequence is used as a reverse path to the source. the header of each query message contains a ttl field which is used to control the depth of the broadcast tree. for example, gnutella has been implemented with a ttl parameter equal to 7. the queryfound (qf) procedure indicates that the resource being searched by source has been found at super-peer . in this procedure, the max_latency is the latency distance between the super-peer who manages and the farthest super-peer in the reverse path. figure 2 illustrates a typical unstructured p2p computing system. a given resource is managed by nodes s3,s5,s7,s8,s9,s13, and s18. the resource is saved as a file on disk memory corresponding to clients who are clustered by aforementioned super-peers. inspecting the dc of super-peers s11,s12, and s16 for example, reveals that the resource is located in the super-peer s18. the lits corresponding to the nodes s1,s2,s4,s6,s10,s11,s12,s14,s15,s16, and s17 are empty; indicating that they are not themselves managers of the resource . also, the dcs corresponding to the nodes s1,s10,s11,s12,s14,s15, and s17 are empty; indicating that they do not know the address of the owner of the resource res. each super-peer upon receiving the qs message, at first searches within its lit. if it finds the resource in the lit, it will return a qf message. the qf message is forwarded to the source following the reverse path which has been used by the qs message. it updates the dcs corresponding to each of the intermediate nodes as well. the contribution of our work emerges at this point where the qf message updates the lit in each of the intermediate nodes using replication of resources based on the proposed proximity-aware distributed caching mechanism. the probability of resource replication and updating of the lit corresponding to each intermediate node is proportional to the latency distance between that node and the location where the resource has been found. to this end, each intermediate node r performs the following actions with a probability proportional to the latency distance between itself and the peer who has been found as the manager of the resource: 1) establishing a tcp connection with the super-peer who manages the resource. probabilistic proximity-aware resource location in peer-to-peer networks using resource replication 421 2) downloading the resource object and saving into client c who has enough available space. 3) updating the lit by adding the entry (res,c) to it. table 1: querysearch message received by super-peer r. table 2: queryfound message received by super-peer r. if the super-peer does not find the resource in its lit but finds it in the dc, it will send a qs message to the super-peer who is pointed to by that dc. if this super-peer no longer has the resource, the search process will be continued from that point forward. if a super-peer does not find the resource in its lit or dc, it will forward the request to each super-peer in its neighborhood with a certain probability p which is called the broadcasting probability. this probability could vary with the length of the path that the request traverses. figure 3 illustrates how a qs message would be propagated in the network. in the figure, the maximum number of nodes to be traversed by a qs message is defined to be equal to 3 hops (apart from the source node). similar to gnutella, our system uses a breath-first-search (bfs) mechanism in which the depth of the broadcast tree is limited by the ttl criterion. the difference is that in gnutella every node receiving a query forwards the message to all of its neighbors, while in our proposal, the propagation is performed probabilistically and is done if the query is not found neither in the lit nor in the dc of a node. in fig. 3, the qs message originating from source s1 is probabilistically sent to super-peers s2,s3, and s4 due to search for the resource res. the super-peer s3 finds the resource in its lit, but s2 and s4 do not find such an entry, hence probabilistically forward the message to the nodes registered in their dcs. note that the super-peer s4 does not forward the message to s10 because, for example, in this case the probability of forwarding is randomly selected to be zero. figure 4 illustrates an example of returning qf messages in a reversed path from the location where the resource res is found, to the node who has originated the query request. the qf message is routed to the source (node s1) following the reverse path which is used by the qs message. the qf message updates the corresponding dc of each intermediate node based on the proposed proximity-aware distributed caching mechanism. the probability 422 m. analoui, m. sharifi, m.h. rezvani figure 2: the topology of a typical unstructured p2p system of replication and caching the resource object in the lit of each intermediate node is proportional to the latency distance between that node and the location where the resource is found. the closer is the intermediate node to the discovered resource; the less will be the probability of caching the resource in the node’s lit. this probability is shown by a graphical representation with partially boldfaced circles. in the sequence of nodes which consists of s1,s2,s6, and s13, the node s6 caches the address of the resource res with the least probability; whereas node 1 caches it with the most probability. the probability of caching the resource res by s2 is larger than that of s6 and smaller than that of s1. 3 the analytic study of the proposed mechanism considering that the corresponding super-peer of a query is located at the root of a tree of height d, each super-peer r has kr neighbors, where kr follows a power-law distribution. in such a distribution, the majority of nodes have relatively few local connections to other nodes, but a significant small number of nodes have large wide-ranging sets of connections. the power-law distribution gives small-world networks a high degree of fault tolerance, because random failures are most likely to eliminate nodes from the poorly connected majority [14]. hence, each query originating from a client must visit at most d-1 super-peers. note that the levels of the tree are numbered 1,...,d from the root down. let qi be the probability that a super-peer at level i has a local index for the resource res. let r(i) be the number of super-peers at level i who receive a querysearch message from upstream super-peers, and s(i) be the number of super-peers at level i who may forward the querysearch message to downstream super-peers one level down. so, it must be that r(1)=0 and q1=0. the reason for q1 = 0 lies in the fact that the resource res only may be found in the lit of the super-peers who are located in the levels 2,...,d of the broadcast tree. let us consider j super-peers at level i-1 may not find the resource in their lit and forward query request message one level down. each of j super-peers, say r , located at level i-1 can select at most kr super-peers among its neighbors to send a query request message. let us suppose that each super-peer forwards these query messages independently to the children who are located at level i-1 with probability pi. let m1,...,m j be the number of level i children to receive the query request message from super-peers 1,...,j located at level i-1, respectively. let us assume n super-peers receive these query requests at level i. so, we can define the tuple s as follows s( j,n,k1,...,k j) = { −→m = (m1,...,m j)| j∑ s=1 ms = n & 0 6 ms 6 ks} (1) probabilistic proximity-aware resource location in peer-to-peer networks using resource replication 423 figure 3: forwarding a qs message using maximum hop-count equal to 3 where, k1,...,k j are out-degrees of nodes 1,...,j which are located at level i-1 , respectively. now we are in a position to define conditional probability pr[r(i)=n|s(i-1)=j] . this is the probability of receiving the querysearch messages by n super-peers at level i given that j super-peers at level i-1 may forward the messages one level down. with respect to the above premises we have: pr[r(1) = n|s(i −1) = j] = ∑ ∨ −→m ∈s( j,n,k1,...ks) j∏ s=1 ( ks ms ) × pmsi ·(1− pi) ksms (2) for example let us consider that,j-3, n=5, k1 = 2, k2 = 3 and k3 = 2. three nodes at level i-1 forward the querysearch message to five super-peers at level i. thus, s(3,4,2,3,2)={(0,3,2), (2,1,2),(2,3,0),(2,2,1), (1,2,2),(1,3,1)}. this example shows that eq. (2) does not depend on the order of appearance of values m1,...,m j in m. we can derive the probability pr[r(i)=n] that n super-peers receive a querysearch message at level i as follows pr[r(i) = n] = ni−1∑ j=0 pr[r(i) = n|s(i −1) = j]· pr[s(i −1) = j] (3) where, n=0,..., ∑ j r=1 kr , and ni−1 is the total number of nodes located at level i-1. eq. (3) can be computed recursively by following conditions: pr[r(1)=1]=1, pr[r(1=0)]=0, pr[r(i)=0|s(i-1)=0]=1, and pr[r(i)=n|s(i-1)=0]=0 for n>0. we are now in a position to compute the average number of super-peers, , involved in query search (apart from the source) as follows n = d∑ i=2 ni∑ n=0 n · pr[r(i) = n] (4) where, ni is the total number of nodes located at level i. note that s(i-1) is not necessarily equal to r(i-1). the probability pr[s(i-1)=j] in eq. (3) can be computed by conditioning on r super-peers that receive querysearch messages at level i-1. so, we have: 424 m. analoui, m. sharifi, m.h. rezvani figure 4: forwarding a qs message using maximum hop-count equal to 3 pr[s(i −1) = j] = ni−1∑ r= j pr[s(i −1) = j|r(i −1) = r].pr[r(i −1) = r] (5) the above equation can be simplified as follows pr[s(i −1) = j] = ni−1∑ r= j ( r j ) qr− ji−1(1− qi−1) j.pr[r(i −1) = r] (6) the probability pf that a local index for a resource is found can be computed as pf = 1− d∏ i=2 pr[no local index is f ound at level i] (7) formally, the above equation can be expressed as follows pf = 1− d∏ i=2 ni∑ n=0 (1− qi) n.pr[r(i) = n] (8) now, the probability that an entry for the resource is not found, namely pn f , can be computed as follows pn f (s) = ni∑ n=0 (1− qs) n.pr[r(s) = n] (9) for computing the average number of hops required to find a local index for a resource, namely h, we first define pminf (i) with the assumption that the first level wherein a resource is found at level i: pminf (i) = ∏i−1 s=2 pn f (s)][1− pn f (i)] pf (10) now, the average number of hops can be computed as follows h = d∑ i=2 (i −1)pminf (i) (11) probabilistic proximity-aware resource location in peer-to-peer networks using resource replication 425 as mentioned earlier, eq. (11) is approximately equivalent to mean latency distance. before proceeding, let us define f as the ratio between the average number of super-peers, namely n, involved in the query operation and the total number of super-peers except the originating super-peer: f = n∑d i=2 ni (12) another important factor in our analysis is the load of query requests imposed by other nodes on each superpeer node. assuming that in a deterministic query search case, the overall load on each node is 1byte/sec, then in a probabilistic case, the overall load will be 1byte/sec. the reason is that in the probabilistic search case, each node sees a fraction f of the requests generated by each peer. we anticipate that the idea of caching the local indices, proposed by us, causes the value of f to tend to very low values, thus resulting in a reduction in the processing load of each peer. now, we provide the numerical results of the above analytical model. we assume d=5. for each node j, the out-degree, namely k j , comes from a power-law distribution with α = 1.5. figure 5 shows the variation of pf and f versus p. this figure assumes a fixed message broadcasting probability, i.e., pi = p for i=2,...,d. also the probability of caching in the dcs at each level is assumed to be q2 = 0.5, q3 = 0.25, q4 = 0.1, and q5 = 0.01. as the broadcasting probability p increases, the probability that a directory entry for the resource is found increases and exceeds 0.97 for a value of equal to 0.7. at that point, only 12% of the super-peers would participate in the search (i.e., f= 0.12). figure 6 shows the variation of f versus p f . the assumptions used in this figure are the same as those of fig. 5. it can be seen from fig. 5 that by adjusting the broadcasting probability, one can find the probability that the resource is found. given this, one can tune the fraction of participating nodes from fig. 6. figure 5: probability of finding entry and fraction of participating super-peers vs. different broadcasting probabilities 4 experimental validation we have performed a large number of experiments to validate the effectiveness of our proximity-aware distributed caching scheme. we evaluated the performance of the system with a file-sharing application based on several metrics. these metrics include fraction of involving super-peers in the query search, probability of finding an entry in dcs, overall cache miss ratio, average number of hops to perform query requests, and system load. all of the aforementioned metrics, except system load, are already defined in the previous sections. the load metric is defined as the amount of work an entity must do per unit of time. it is measured in terms of two resource types: incoming bandwidth, and outgoing bandwidth. since the availability of the incoming and the outgoing bandwidths is often asymmetric, we have treated them as separate resources. also, due to heterogeneity of the system, it is useful to study the aggregate load, i.e., the sum of the loads concerning to the all nodes in the system. all of the results are averaged over 10 runs of experiments and have been come up with 95% confidence intervals. we followed the general routine devised in [15] for the efficient design of the p2p network. so, as the first step, we had to 426 m. analoui, m. sharifi, m.h. rezvani figure 6: fraction of super-peers involved vs. probability of finding entry generate an instance topology based on a power-law distribution. we used the plod algorithm presented in [16] to generate the power-law topology for the network. the second step was calculating the expected cost of actions. among three "macro actions", i.e., query, join, and update, which exist in a cost model [15], we have restricted our attention to the query operations. each of these actions is composed of smaller "atomic" actions for which the costs are given in [15]. in terms of bandwidth, the cost of an action is the number of bytes being transferred. we used the specified size of the messages for gnutella protocol in such a way that is defined in [15]. for example, the query messages in gnutella include a 22-byte gnutella header, a 2 byte field for flags, and a null-terminated query string. the total size of a query message, including ethernet and tcp/ip headers, is therefore 82 plus the query string length. some values, such as the size of a metadata record are not specified by the protocol, rather are functions of the type of the data which is being shared. the values which are used from [15] are listed in table 3. table 3: gnutella bandwidth costs for atomic actions [15] atomic action bandwidth cost (bytes) send query 82 + query length recv. query 82 + query length send response 80+28 #addresses+76 #results recv response 80+28 #addresses+76 #results to determine the number of results which are returned to a super-peer r, we have used the query model developed in [17] which is applicable to super-peer file-sharing systems. the number of files in the super-peer’s index depends on the particular generated instance topology i. we have used the so called query model to determine the expected number of returned results, i.e. e[nr|i] . since the cost of the query is a linear function of [nr|i] and also since the load is a linear function of the cost of the queries, we can use these expected values to calculate the expected load of the system [15]. in the third step, we must calculate the system load using the actions. for a given query originating from node s and terminating in node r we can calculate the expected cost, namely csr . then, we need to know the rate at which the query action occurs. the default value for the query rate is 9.26 × 10−3 which is taken from the general statistics provided by [15] (see table 4). the query requests in our experiments have been generated by a workload generator. the parameters of the workload generator can be set up to produce uniform or non-uniform distributions. considering the cost and the rate of each query action, we can now calculate the expected load which is incurred by node r for the given network instance i as follows e[mr|i] = ∑ s∈network e[csr|i].e[fs] (13) where, fs is the number of the queries submitted by the node s in the time unit, and e[fs] is simply the query rate per user. let us define q as the set of all super-peer nodes. then, the expected load of all such nodes, namely mq , is defined as follows probabilistic proximity-aware resource location in peer-to-peer networks using resource replication 427 e[mq|i] = ∑ n∈q e[mn|i] |q| (14) also, the aggregate load is defined as follows e[m|i] = ∑ n∈network e[mn|i] (15) we ran the simulation over several topology instances and averaged e[m|i] over these trials to calculate e[e[m|i]]=e[m] . we came up with 95% confidence intervals for e[m|i]. the settings used in our experiments are listed in table 4. in our experiments, the network size was fixed at 10000 nodes. as mentioned before, the generated network has a power-law topology with the average out-degree of 3.1 and ttl=7. these parameters reflect gnutella topology specifications which have been used by many researchers so far. for each pair of the super-peers (s,r), the latency distance lat(s,r) was generated using a normal distribution with an average µ = 250ms and a variance δ = 0.1 [12]. then, to find the pair-wise latency estimation, namely est(s,r) , we ran the vivaldi method over the generated topology. table 4: experimental settings name default description graph type power-law the type of network, which may be strongly connected or power-law graph size 10000 the number of peers in the network cluster size 10 the number of nodes per cluster avg. out-degree 3.1 the average out-degree of a super-peer ttl 7 the time-to-live of a query message query rate 9.26×10−3 the expected number of queries per user per second in order to be able to compare the results with the previous works, we chose a cache size per super-peer equal to 1% of the total number of the resources managed by the all super-peers. in a distributed system with highly variant reference patterns, it is better to use frequency-based replacement policies. the frequency-based policy takes into account the frequency information which indicates the popularity of an object. the least-frequencyused (lfu) is a typical frequency-based policy which has been proved to be an efficient policy [18]. in lfu, the decision to replace an object from the cache is made by the frequency of the references to that object. all objects in the cache maintain the reference count and the object with the smallest reference count will be replaced. the criterion for replacing an object from the cache is computed as follows costob ject = frequencyob ject × recencyob ject (16) where, frequencyob ject and recencyob ject denote the access frequency and the elapsed time from recent access, respectively. if the cache has enough room, lfu will store the new object in it. otherwise, lfu selects a candidate object which has the lowest costob ject value among all cached objects. then, lfu will replace the candidate object by the new object if the of the new object is higher than that of the candidate object. otherwise, no replacement occurs. figure 7 shows the experimental results concerning the effect of the resource replication on the fraction of participating super-peers, namely f, and the probability of finding objects, namely pf , versus various broadcasting probabilities. it can be seen from the figure that pf attains high values for much smaller values of p. the experimental result in fig. 7 shows a trend similar to what the analytical study provides in fig. 5. by adjusting the broadcasting probability, one can tune the probability of finding the resource. in the case of using resource replication, pf achieves larger values in comparison with the case in which the resource replication is not used. in contrast to pf , the metric f achieves smaller values in the case of using the resource replication in comparison with the case in which the resource replication is not used. its cause lies in the fact that in the case of using the resource replication method, some intermediate nodes replicate the queries in their local disks (cache the queries into their lit); leading to a decrease in the lits miss ratio, thus resulting an increase in the probability of finding the queries. such nodes do not need to propagate the querysearch message to other super-peers anymore. fig. 8 shows the effect of using the resource replication on the cache miss ratio as a function of the broadcasting probability p. in the both cases of fig. 8, the cache miss ratio decreases by an increase in p. its cause lies in the fact that when p increases, more super-peers participate in the search; hence it is more likely that the resource is 428 m. analoui, m. sharifi, m.h. rezvani found by more than one super-peer. so, more dcs of the intermediate nodes in the reverse path to the source will be aware of the resource; leading to a decrease in the dcs miss ratio. the use of the resource replication decreases the miss ratio compared to the case in which the resource replication is not used. however, the amount of the reduction in the miss ratio is not remarkable in both cases for the values of p greater than 0.8. at this point, the use of resource replication method yields a miss ratio of 0.72; giving 20% improvement over the 0.9 miss ratio when no resource replication is used. figure 7: the effect of resource replication on the fraction of participating peers and the probability of finding objects for various broadcasting probabilities figure 8: the effect of resource replication on overall cache miss ratio for various broadcasting probabilities figure 9 shows the average number of the required hops to find the resource, namely h, which is normalized by the total number of super-peers (except the original source). the figure shows the effect of the resource replication method in various broadcasting probabilities. it can be seen in both curves of fig. 9 that the average number of hops initially increases until reaches to a maximum point and then begins to decrease. a higher broadcasting probability means that the super-peers who are located further away from the original source are contacted and the resource tends to be found further away from the original source. as p continues to increase, the increased values of hit ratio concerning to intermediate dcs allow the resource to be found in locations where are closer to the original source; hence decreasing the value of h. it is clear from fig. 9 that the use of resource replication reduces the number of hops needed to find the resource. for example, in a reasonable practical point of broadcasting probability, such as 0.7, it yields a 31% improvement, whereas the hop ratio decreases from 0.08 to 0.055. figure 10 shows the effect of resource replication on the total required bandwidth of the system, i.e. the required incoming and outgoing bandwidth of super-peers, for various broadcasting probabilities. by increasing probabilistic proximity-aware resource location in peer-to-peer networks using resource replication 429 figure 9: the effect of resource replication on hop ratio for various broadcasting probabilities the broadcasting probability, some additional costs are imposed to the system. the most important costs include the cost of sending queries to each super-peer, a startup cost for each super-peer as they process the query, and the overhead of additional packet headers for individual query responses. some of these factors are mentioned in the literature by prior researchers. interested readers can find useful hints in [15]. the upper curve in fig. 10 shows the required bandwidth in the absence of resource replication. in this case, as the broadcasting probability p increases, the required bandwidth of super-peers increases and reaches to 7.7 108 bps for a value of equal to 0.8. from this point forward, the growing of bandwidth occurs more slightly until reaches to 7.9×108 bps at the value of p equal to 1. the lower curve in fig. 10 shows an improvement in the required bandwidth in the presence of resource replication. in this case, the required bandwidth decreases to 6.6×108 bps for a value of p equal to 0.8, resulting in a 14% improvement in comparison with the same point in the upper curve. figure 10: the effect of resource replication on total required bandwidth for various broadcasting probabilities 5 related works unstructured architectures are divided into three sub-classes [1]: 1) hybrid decentralized, 2) purely decentralized, and 3) partially centralized. in a hybrid decentralized p2p system, all peers connect to a central directory server that maintains a table for their ip address, connection bandwidth and other information, and another table that keeps the list of files that each peer holds. upon receiving a request from a peer, the server searches for any matches in its index table and returns a list of peers who hold the matching file. then a direct connection is 430 m. analoui, m. sharifi, m.h. rezvani established between the originating peer and the peers who hold the requested files to download them. although the implementation of hybrid decentralized systems is easy, they suffer from vulnerabilities and attacks as well as scalability problems. napster [19] is an example of such systems. gnutella [3] is a well-known example of purely decentralized system. a significant research toward proximity-aware resource location services in typical gnutella-based unstructured p2p system has been done in [7, 8]. the proximity metric in this works is the ttl of the query messages. forwarding the queries is done with a fixed probability. when a query message is reached to a peer, its ttl is decremented. the forwarding of the query messages will be stopped if its ttl is reached to zero. the analytical study on the impact of the proximity-aware methodology for making the p2p streaming more scalable has been presented in [9]. they have proposed a geometric model which maps the network topology from the space of discrete graph to the continuous geometric domain, meanwhile capturing the power-law property of the internet. they found that, although random peer selection methods can maximally save the server resources, they introduce the maximum load to the network. in systems where availability is not guaranteed, such as gnutella [3], resource location techniques can afford to have loose guarantees [20]. current search techniques in "loosely controlled" p2p systems are rather inefficient because they impose a heavy load on system as well as high response times. the main motivation for many researches in the area of p2p systems was early "loosely controlled" systems such as gnutella, freenet [14], napster [19], and morpheus [21]. other resource location techniques for "loosely guaranteed" systems are mentioned in [20]. in the other proposed techniques which mentioned in [20] each node maintains "hints" as to which nodes contain data that answer certain queries, and route messages via local decisions based on these hints. this idea itself is similar to the philosophy of hints which is used by menasce et al. in [7]. can [2] is an example of systems with "strong guarantee" that employs search techniques. these systems can locate an object by its global identifier within a limited number of hops. it is concluded from the literature that selecting a resource locating methodology depends on the type of system which is planned. a complete literature survey relevant to search techniques is collected in [20] which interested readers can refer to it for more detail. the resource locating techniques for partially centralized p2p networks are addressed in [15, 20]. they also evaluate some proposed query search broadcasting policies using gnutella system and compare their performance with each other. the first query search policy evaluated by yang et al. on gnutella is called iterative deepening in which, a query is sent iteratively to more nodes until the query is answered. the second proposed technique, directed breath first search (dbfs) technique, forwards a limited set of nodes selected to maximize the probability that the query is answered. their experimental analysis shows that if nodes are allowed to answer queries on behalf of other nodes, then the number of nodes that process a query will be reduced without decreasing the number of results. this very nice conclusion has formed our original motivation for designing distributed p2p caching protocol based on super-peers. the last technique which is investigated by them is called local indices technique in which, nodes maintain simple indices over other client’s data. queries are then processed by a smaller set of nodes. this is also quite similar to the concept of the super-peer nodes which we have adopted in our proposal. the performance of hybrid p2p systems such as napster is investigated by yang and garcia-mollina in [17]. morpheus [21] is another hybrid p2p system whose architecture is similar to gnutella. upon joining a new peer to the system, p2p network contacts a centralized server which then directs it to a super-peer. the authors of [17] study the behavior and performance of hybrid p2p systems and develop a probabilistic model to capture the query characteristic of such systems. 6 conclusions in this paper we have targeted the scalable proximity-aware location service for p2p systems. the proposed protocol provides a scalable distributed caching mechanism to find the peers who manage a given resource. the proposed mechanism enhances the mechanisms which have been proposed in previous researches by replicating objects based on latency distance metric, resulting in less aggregate load of the system. the simulation results showed that the use of probabilistic resource discovery service in p2p systems 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[21] http://www.morpheus-os.com. international journal of computers communications & control issn 1841-9836, 11(5):657-666, october 2016. robust face recognition against soft-errors using a cross-layer approach g.-m. jeong, c.-w. park, s.-i. choi, k. lee, n. dutt gu-min jeong, chang-woo park department electrical engineering kookmin university jeongeung-dong, songbuk-gu seoul, 136-702, korea gm1004@kookmin.ac.kr, high1117@nate.com sang-il choi* department applied computer engineering dankook university jukjeon-dong, suji-gu, yongin-si gyeonggi-do, 448-701, korea *corresponding author: choisi@dankook.ac.kr kyoungwoo lee department computer science yonsei university sinchon-dong, seodaemun-gu seoul, 120-749, korea kyoungwoo.lee@gmail.com nikil dutt department computer science university of california, irvine irvine, ca 92697, usa dutt@ics.uci.edu abstract: recently, soft-errors, temporary bit toggles in memory systems, have become increasingly important. although soft-errors are not critical to the stability of recognition systems or multimedia systems, they can significantly degrade the system performance. considering these facts, in this paper, we propose a novel method for robust face recognition against soft-errors using a cross layer approach. to attenuate the effect of soft-errors in the face recognition system, they are detected in the embedded system layer by using a parity bit checker and compensated in the application layer by using a mean face. we present the soft-error detection module for face recognition and the compensation module based on the mean face of the facial images. simulation results show that the proposed system effectively compensates for the performance degradation due to soft errors and improves the performance by 2.11 % in case of the yale database and by 10.43 % in case of the orl database on average as compared to that with the soft-errors induced. keywords: soft-error, face recognition, cross-layer approach, mean face. 1 introduction nowadays, memory errors due to various causes have become a critical threat for the performance and stability of numerous systems. in particular, soft-errors, which are transient bit toggles in memory systems, have become increasingly important factors for the performance copyright © 2006-2016 by ccc publications 658 g.-m. jeong, c.-w. park, s.-i. choi, k. lee, n. dutt degradation of various applications. soft-errors denote the phenomenon that changes the memory bit value temporarily from 1 to 0 and vice versa due to abnormal conditions such as high radiation, high pressure or high temperature [1]. although the soft-errors are not permanent and nondestructive, there have been several reports on the damages due to soft-errors. sun and hewlett packard announced the loss due to soft-errors in their server systems [2,3]. further, soft errors brought a billion-dollar automotive factory to halt every month [4]. until now, there have been several researches on the protection of soft-errors [5–9]. especially, for multimedia systems, cross-layer based approaches have been introduced to compensate for the negative effects of soft-errors. since soft-errors are not critical to the system stability, cooperation across system abstraction layers can help to build a cost-efficient system against soft-errors from a hardware layer to an application layer in mobile embedded systems [10]. in this paper, we propose a robust and cost-effective system for face recognition against soft-errors by using a cross layer approach. first, we analyze the effect of soft-errors for face recognition systems and show that the soft-errors induced in the jpeg image can degrade the performance of the recognition system [11, 12]. next, we propose a cross-layer compensation module consisting of a detector in the hardware layer and a corresponding compensator in the application layer. to attenuate the negative impact of soft-errors in face recognition systems, they are detected in the embedded system layer by using a parity bit checker. when they are detected, the mean face method is used for compensating for the negative impact on the performance at the application layer. the classification experiments are performed for the yale [13] and orl databases [14]. the features for classification are extracted by using the rlda (regularized linear discriminant analysis) method [12]. the experimental results demonstrate the effectiveness of the proposed method. further, the proposed cross-layer based compensation method can improve the system performance by 2.11 % and by 10.43 % on average in case of yale and orl databases, respectively, as compared to the performance with soft-errors in the face recognition system. the remainder of this paper is organized as follows. in section 2, we introduce an exemplary face recognition system for the simulation and the effect of soft-errors in jpeg images. then, in section 2, we describe an analysis of the effect of soft-errors on the face recognition system. in section 4, we present the proposed cross-layer approaches and experimental results. the conclusion follows in section 5. 2 exemplary face recognition system and effect of soft-errors in jpeg images 2.1 exemplary face recognition system figure 1 shows the exemplary face recognition system considered in this study. as shown in figure 1, after a jpeg image is captured using a camera, it is transmitted to the classification part and decoded in the bmp format. then, after preprocessing, it is classified using various classification methods [12,15–17]. for the analysis of the soft-error impacts, we assume that the soft-error occurs inside the jpeg file. hence, we assume that the soft-error can occur in the memory when storing or decoding the jpeg file. 2.2 effect of soft-errors in jpeg file the jpeg file is a compressed image file format. the effects of soft-errors depend on where they occur. for example, the effects are different between the soft-errors in the header information robust face recognition against soft-errors using a cross-layer approach 659 figure 1: an exemplary face recognition system figure 2: effect of soft-errors for the decoding of jpeg files. (a) decoded image without softerrors (b) examples of decoded images with bit-errors 660 g.-m. jeong, c.-w. park, s.-i. choi, k. lee, n. dutt parts and those in the compressed 8×8 blocks. figure 2 shows various examples of decoded images when soft-errors occur in the jpeg file. note that the decoded results may differ according to the implemented decoder. if the exceptions are not handled appropriately, the decoder can even be stopped. when soft-errors occur in the critical data of the header such as errors in the length of the file or the length of the block data, the decoder can lose all its information afterwards. in the case of a soft-error in block data, the block can be decoded inappropriately, causing a significant degradation of the quality. 3 effect of soft-errors on the face recognition system in this section, we discuss the effects of soft-errors on face recognition systems. we build a model of the soft-errors in the face recognition system and analyze the performance degradation for the recognition rate when these errors are induced. 3.1 modeling of soft-errors and system setting to conduct a quantitative analysis of soft-errors, we make the following assumptions of softerrors for a face recognition system: assumptions of the soft-errors • a1) a single-bit soft-error occurs for one facial image, particularly in the test set. • a2) a soft-error occurs only for the block data part. on the basis of a1) and a2), we can conclude that only one block is affected by a single-bit soft-error. further, for the system setting, a soft-error is detected using a parity bit checker in the memory and it is notified to the jpeg decoder. when the jpeg decoder receives the information of the soft-error occurrence, it passes over the decoding of the corresponding block. hence, the corresponding 8×8 block of the soft-error is filled with 0’s instead of the real decoded data. figure 3 shows the overall processing based on the system setting in our study. figure 3: decoding procedure for the jpeg file with a soft error. a) decoding procedure considering a soft-error in the block data. b) decoded image based on the considered system setting 3.2 classification experiment for the soft-errors we apply the proposed method to the yale database in order to observe the effect of soft-error on the recognition rate. the yale database contains 165 gray images of 15 individuals, having robust face recognition against soft-errors using a cross-layer approach 661 different facial expressions, with or without glasses, and under different lighting conditions. each face image is cropped and re-scaled so that the center of each eye is placed at a fixed point in an image of 60×50 pixels. on the basis of assumption a1), we consider that a soft-error occurs in all the images for the test set, while no error occurs for the training set. there exist 42 blocks (size: 8×8) in a 60×50 image as shown in figure 4(a). hence, we conduct 42 times an 11-fold cross validation [17] according to the location of the soft-error, as depicted in figure 4(b). in these 11-fold cross validations, one image from each subject is randomly selected for testing while the remaining images are used for training. in other words, we consider 150 images in the training set and 15 images for the probing. for the selected 15 images for the test, we assume that there occur a soft-error, as shown in figure 4(b). for the classification, the r-lda method [12] is used as a feature extraction method and the one nearest neighbor rule is applied as a classifier with the l2 distance metric. it is noted that the r-lda method is well known but is not necessarily the best one. in table 1, the first and second columns show the recognition rates without a soft-error and with a soft-error, respectively. as summarized in table 1, we can observe the average performance degradation of 2.45 % in terms of the recognition rate in the case with the softerrors as compared to that in the case without the soft-error. as shown in figure 7(a), there is a significant degradation (up to approximately 10 %) according to the block numbers, and we can see that there is a significant performance degradation due to the single-bit soft-errors. figure 4: classification experiment for the soft-errors. a) 42 blocks inside the 60 × 50 image. b) 42 times 11-fold cross validation. 4 cross-layer approach for compensation of soft-errors in the face recognition system 4.1 overall structure of the proposed method considering the performance degradation of the soft-errors, we propose a costand performanceeffective compensation method for the face recognition system. figure 5 shows the overall structure of the proposed compensation method in a cross-layered manner. as shown in figure 5, when the occurrence of the soft-error is notified from the hardware layer to the application layer, the proposed method can compensate the corresponding block for the performance degradation with 662 g.-m. jeong, c.-w. park, s.-i. choi, k. lee, n. dutt the mean value data from the database. by this compensation, we can enhance the performance of the recognition system in a cost-effective manner. figure 5: overall structure of the proposed compensation method in the proposed cross-layered approach, we detect the soft-error by using parity bit checkers cost-effectively rather than by using ecc (error correction codes) modules. indeed, the real compensation for the soft-errors is achieved in the application layer. in the application layer, we should mitigate the impact of soft-errors on the performance in the data block, which is filled with 0’s by skipping the decoding. in the face recognition method, the mean face is effectively used for the illumination compensation and other applications [18,19]. likewise, if we use a mean face for the training data, we can improve the system performance. in this paper, we use the corresponding mean block from the mean face of the training set. figure 6 shows an example of a mean face for the yale database. figure 6: compensation using mean face. a) example of a mean face for the yale database. b) facial images without a soft-error, with a soft-error and with the compensation. 4.2 experimental results and analysis table 1 shows the recognition rate for the cases with a soft-error, without a soft-error, and with the compensation for the soft-error, respectively in the yale database. as shown in table 1, the proposed compensation method can enhance the performance by 2.11 % as compared to that in the case with soft-errors. in the comparison between the cases with no soft-error and compensation, there is a performance difference of only 0.34%. figure 7(a) shows the recognition rate according to the blocks in the yale database. further, table 2 and figure 7(b) show the experimental results for the orl database. the images are resized into 56×46 pixels in this experiment. as shown in table 2 and figure 7(b), robust face recognition against soft-errors using a cross-layer approach 663 figure 7: recognition rate according to the block numbers for the yale and orl databases. a) yale database. b) orl database. table 1: comparison of the recognition rate for the soft error in the yale database(%) no. of features no error soft-error compensation 1 35.15 26.16 29.12 2 54.55 49.62 55.90 3 73.94 68.40 73.61 4 82.42 76.64 80.52 5 83.03 81.52 83.52 6 83.64 83.52 84.49 7 86.67 86.25 87.76 8 89.70 88.86 90.17 9 92.73 91.44 92.81 10 93.33 91.75 92.94 11 93.33 92.44 93.19 12 93.94 92.83 93.67 13 93.94 93.29 93.78 14 93.33 92.73 93.41 average 82.12 79.67 81.78 664 g.-m. jeong, c.-w. park, s.-i. choi, k. lee, n. dutt a soft-error can degrade the performance by approximately 11.48 %. however, the proposed compensation method can achieve a performance improvement of approximately 10.43 % as compared to the case with the soft-error while it incurs a 1.05 % performance degradation as compared to the case without the soft-errors. from these experiments, we can conclude that the proposed method can mitigate the impact of soft-errors on the system performance effectively. table 2: comparison of the recognition rate for the soft error in the orl database(%) no. of features no error soft-error compensation 1 6.75 5.92 7.46 2 13.50 10.86 15.32 3 27.50 17.01 25.58 4 41.75 22.69 38.09 5 48.00 27.58 45.10 6 56.25 33.60 52.59 7 62.75 38.80 59.81 8 69.00 44.29 65.58 9 74.75 49.59 70.98 10 79.00 54.16 76.16 15 87.50 70.49 86.37 20 95.75 82.61 94.73 25 97.25 90.64 96.88 30 98.50 93.14 98.06 35 98.00 94.70 97.90 average 82.62 71.14 81.57 figure 8: recognition rate according to the numbers of feature of for the yale database. the recognition rate versus the numbers of feature of yale database is shown in figure 8 for three cases: non-error, error and compensation. it is obvious that the recognition rates increase with small numbers of feature, from one feature to eight features, and after that, the recognition rates would be almost saturated even if the numbers of feature increase how much. therefore, we will select the minimum numbers of feature that the higher rates can be achieved. table 3: average recognition rates of two selection method rlda and lbp for yale and orl database(%) selection method yale orl no error error compensation no error error compensation rlda 83.11 73.57 82.27 80.04 72.67 78.96 lbp 87 86.79 86.86 89.12 88.58 88.92 we have also applied local binary pattern (lbp) as a feature selection method for both robust face recognition against soft-errors using a cross-layer approach 665 databases by measuring chi-square distances, and the result is shown in table 3. these data in the table show the average recognition rates of forty-two blocks for three cases: non-error, error and compensation. from this table, there is no doubt that the recognition rates obtained from lbp is higher than from rlda. the reason is that lbp uses all blocks as features, whereas rlda only extracts some important features, hence, lbp can achieve higher result. however, lbp’s running-time is also longer than rlda. 5 conclusion in this paper, we have proposed a robust face recognition method for soft-errors by using a cross-layer approach. first, we have analyzed the effect of soft-error on a face recognition system. for the soft-errors in the images of the training set, the performance is degraded. next, we have presented a novel face recognition method for the robust system against soft-errors by using a cross-layer approach. in the proposed method, the soft-errors are detected using a parity bit checker in the hardware layer and compensated in the application layer using a mean face. simulation results have revealed that the proposed system have effectively compensated for the performance degradation due to soft errors. a more systematic compensation method such as the compensation for the occluded images can be considered with the proposed method. it remains as a future work. acknowledgment this research was supported by the basic science research program through the national research foundation of korea (nrf) funded by the ministry of education (nrf-2015r1d1a1a01060917) and the basic science research program (no. 2015r1a1a1a05001065) through the national research foundation of korea (nrf) funded by the ministry of science ict and future planning, and also supported by the human resources program in energy technology of the korea institute of energy technology evaluation and planning (ketep) granted financial resource from the ministry of trade industry and energy, republic of korea (no. 20154030200830). bibliography [1] baumann, robert (2005); soft errors in advanced computer systems, design & test of computers, issn 0740-7475, 22(3): 258-266. 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(2011); shadow compensation using fourier analysis with application to face recognition, ieee signal processing letters, issn 1070-9908, 18(1): 23-26. lang_ijcccv11n5.pdf international journal of computers communications & control issn 1841-9836, 11(5):685-696, october 2016. application of visual servo control in autonomous mobile rescue robots h. lang, m.t. khan, k.-k. tan, c.w. de silva haoxiang lang department of mechanical engineering, university of ontario institute of technology. haoxiang.lang@uoit.ca muhammad tahir khan* institute of mechatronics, university of engineering and technology, peshawar, pakistan. corresponding author: tahir@uetpeshawar.edu.pk kok-kiong tan department of electrical and computer engineering, national university of singapore. address: 4 engineering drive 3 singapore 117576, singapore. eletankk@nus.edu.sg clarence w. de silva department of mechanical engineering, university of british columbia. desilva@mech.ubc.ca abstract: mobile robots that integrate visual servo control for facilitating autonomous grasping and manipulation are the focus of this paper. in view of mobility, they have wider application than traditional fixed-based robots with visual servoing. visual servoing is widely used in mobile robot navigation. however, there are not so many report for applying it to mobile manipulation. in this paper, challenges and limitations of applying visual servoing in mobile manipulation are discussed. next, two classical approaches (image-based visual servoing (ibvs) and position-based visual servoing (pbvs)) are introduced aloing with their advantages and disadvantages. simulations in matlab are carried out using the two methods, there advantages and drawbacks are illustrated and discussed. on this basis, a suggested system in mobile manipulation is proposed including an ibvs with an eye-in-hand camera configuration system. simulations and experimentations are carried with this robot configuration in a search and rescue scenario, which show good performance. keywords: mobile robots, visual servoing, robot control, search and rescue. 1 introduction research and applications of mobile robots have drawn much attention in the robotics community because the emphasis of robotic research appears to have shifted from structured industrial environments to dynamic and partially-known natural environments (e.g., homes, cities, planet surfaces and deep sea). mobile robots can be implemented in homecare, surveillance, exploration, search and rescue tasks where the working environments may be too hazardous for human workers. in such scenarios, robots are expected to have extensive autonomous ability and human capabilities, yet not suitable or impractical for human presence. examples for the necessity of applying autonomous search and rescue robots in practical applications were highlighted in the destruction of the world trade center in 2001 due to terrorist copyright © 2006-2016 by ccc publications 686 h. lang, m.t. khan, k.-k. tan, c.w. de silva attack in new york city and the damage caused by earthquake and tsunami in sendai, japan in 2012. in both incidents, not only did large numbers of victims lose their life, but significant number of rescues scarified their life in rescuing work. they were mainly caused by the search and rescue activities after those incidents because of dangerous environments and lacking of heavy-duty tools. generally speaking, in any incidents, rescuers have about forty-eight hours to save humans in incident sciences. however, most of these time is wasted due to lacking of necessary resources or heavy-duty equipment for accessing damaged or contagious areas that are not suitable for humans. in this scenario, search and rescue robots have the advantages of being deployed instantaneously, and operating in any given working environments without human engagement. an autonomous robot that is able to operate in unknown and dynamic workspace will have significant benefits in the above mentioned two cases in the areas of search and rescue. however, there is no such robot available in the market yet because of existing challenges in developing such systems in terms of both hardware and software. this paper focus on the control software of such system and emphasis on examining two traditional visual servoing methodologies along with their challenges in applying them in autonomous mobile manipulation applications. two traditional methods of visual servo control are presented and examined through simulations; and comparative evaluation of these two methods are discussed, respectively. in the application of visual servoing, a suitable method for autonomous mobile rescue robots is suggested. simulations and experimentations are carried out and demonstrated the effectiveness of the system and the methodologies. 2 two traditional visual servoing methods vision-based mobile manipulation systems typically utilize camera with image processing and computer vision algorithms as its main sensory feedback in the control loop of mobile bases and their manipulators. this approach is called a visual servo control system or visual servoing, which involves many related research areas including image processing, computer vision, dynamic system modeling of robots and nonlinear control theory [1]. vision sensors, usually cameras, are powerful sensors because they mimic human sense of vision and provide non-contact measurement of workspace. furthermore, they can provide various types of sensed data such as pose, distance and objects in camera scenes. because of these advantages of vision sensory, researchers have paid great attention to implement it in practical applications, especially in robotics ares. however, the image capturing hardware and processing software took seconds in producing image and doing analysis that makes real-time control impossible to apply in robotic applications [2]. originally, sanderson and weiss invented a hierarchical dynamic look-an-move structure to overcome the inefficiency of the vision system [3] where vision was utilized to provide control inputs to the joint-level controller. then, the sub-control system utilized the joint feedback to internally stabilize the robot. nowadays, computer vision has benefited from the rapid development of computer technologies and image sensing hardware (ccd and cmos) in terms of the processing time. therefore, it makes possible for so called direct visual servoing where it heavily relies on the speed of vision processing for computing joint inputs [4] in robot control. since 1990s, the term "visual servoing" (more appropriately, visual servo control) has accepted as a generic description of robotic system that has a visual feedback control loop. the subject has been studied in various forms for over twenty years now, with applications ranging from initially simple pick-and-place tasks to complex, real-time and autonomous mobile manipulation applications. visual servo control of mobile robots has drawn more attentions recently. when compared to the traditional fixed-base manipulators, one advantage of mobile robots is their ability to application of visual servo control in autonomous mobile rescue robots 687 navigate in a large workspace because of its mobility. a mobile manipulation system has better maneuverability and larger coverage of the workspace, which make it more widely applicable than a traditional fixed-based manipulator. autonomous navigation control of mobile robots employs the application of visual servo control. there is limited activity in this context possibly because mobile manipulation requires more accurate positioning performance and hence it is rather challenging. some examples of visual servo application in mobile navigation are indicated in [5,6]. the research and physical demonstration of visual servoing in mobile manipulation have been mostly limited two degrees of freedom dof [7] and simplified robot models [8]. 3 developments in visual servoing systems in general, traditional visual servoing can be categorized into image-based and position-based approaches based on how the vision information is utilized [2]. the two traditional approaches can share similar control block diagrams with difference in reference of the control system and the processed information acquired from camera (figure 1). figure 1: block diagram of difference between position-based and image-based visual servoing. the objective of the controller is to minimize either the position error of the image or the position error of the object in the 3d space for two different methods respectively by controlling the motion of the robot joints. the error can be defined as follows: e(t) = s(i(t), c) − sr (1) where e(t) is is the error between desired and measured positions; s is the measurement from the image or pose estimation using computer vision; i(t) is the image data from the camera; c is mathematic model of the camera; and sr is the reference of the control system. s(i(t), c) and sr can be decided in detail depending on the type of visual servoing that is utilized. once they are decided, a velocity controller of robot joints can be designed by ṡ = lξcc (2) where ṡ is the velocity of the target objects (targets in the image from the camera in ibvs approach and 3d pose of the camera through 3d reconstruction in pbvs approach); l is the interaction matrix; and ξcc is the velocity vector of the camera with respect to its own coordinate frame. combining equations (1) and (2), the error dynamic equation can be written below: ė(t) = lξcc (3) where ė(t) is the time variation of the error. the square-of-error norm can be utilized to generate the control law as a candidate lyapunov function: ṡ = l−1(−ke) (4) 688 h. lang, m.t. khan, k.-k. tan, c.w. de silva 3.1 image-based visual servoing approach the objective of image-based visual servoing (ibvs) is to decrease position errors of target objects in images acquired from the camera by moving each joint of the robot. the controller will continuously adjust the speeds of the robot joints based on controller outputs so that the trajectories of the target objects (ui, vi) navigate toward the desired positions (udi, vdi) on camera sciences. the error vector of the target objects in the image plane is given by e = [ u − ud v − vd ] = [ −sx(r − rd) −sy(c − cd) ] (5) where sx and sy represent the physical dimensions of image pixels of the camera sensor; and r, c are pixel coordinates. the velocity of the target objects can be expressed as ė = [ d(u−ud) dt d(v−vd) dt ] = [ u̇ v̇ ] (6) substituting equation (6) into (3), we can acquire the following equation: [ u̇ v̇ ] = lξcc (7) assuming that the error dynamics obeys ė = −ke, a proportional controller based on the lyapunov method can be designed accordingly: ξ c c = l −1(−ke) (8) where k is the proportional gain (a scalar), with k > 0 . therefore, the control law can be obtained by substituting equation (5) into (8), as ξ c c = −kl −1 [ −sx(r − rd) −sy(c − cd) ] (9) in equation (9), r and c represent the pixel coordinate in the captured image by the camera. the desired velocities of the camera in the 3d space can be computed from the image measurements. moreover, the developed controller guarantees asymptotic stability in the closed-loop system. the image jacobian (l) in the model of ibvs [9], which is also named as interaction matrix, describes the motion relationship between the target objects in the image and camera in 3d space in terms of their velocities, respectively. the detailed formulation of the interaction matrix is shown below: [ u̇ v̇ ] = lξcc = [ − λ zc 0 u zc uv λ − λ2+u2 λ v 0 − λ zc v zc λ2+u2 λ − uv λ −u ] ξ c c (10) where λ is focal length; and zc is the distance between the object of interest and the camera coordinate frame in the 3d workspace. the distance must be either measured or approximated for completing the interaction matrix. in many applications, multiple objects of interest are usually measured for the purpose of estimating the distance information if there is no direct way of measuring it. moreover, if it is a typical robotics arm with six degree-of-freedom (dof), at least three objects of interest are required. in practical applications, four objects of interest are usually utilized so that the system can overcome both singularity of the interaction matrix and local minima [1–4]. application of visual servo control in autonomous mobile rescue robots 689 3.2 position based visual servoing in position-based visual servoing (pbvs) approach [10] [11], features of the object of interest are extracted from images from one or more cameras. both camera model and a geometric model of the target object are utilized to mathematically estimate the pose of the camera with respect to the target object in a 3d space. it involves three coordinate frames for designing a control system. they are current frame fc, desired frame fd, and reference frame fr. in the workspace, the reference frame is typically attached to the target object. in the robot workspace, the function of the controller is to calculate and provide outputs for moving the robot with the ultimate goal of reducing the error between the current measured pose and the desired reference pose. the approach is also named 3d visual servoing because the control scheme is defined in the cartesian space. however, the ibvs is usually called 2d visual servoing because it doesn’t need to consider the pose of the robot in its workspace. instead, it only considers target objects in provided image from cameras, which is a 2d workspace. the features in pbvs can be defined below: s = (t, θu) (11) where t is a translation vector and θu is an orientation vector, respectively. the error dynamic equation for the controller can be written by e = (t cr − td, θu) (12) the interaction matrix can be written as: l = [ −i3 [t c r ]x 0 lθu ] (13) where lθu can be described by [18]: lθu = i3 − θ 2 [u]x + ( 1 − sincθ sinc2 θ 2 ) [u]2x (14) where sincx is the sine cardinal, which is defined by xsincx = sin x and sinc0 = 1 . therefore, the velocity control scheme can be generated as follows: vc = −kl −1 e (15) 4 visual servoing simulations figure 2 shows a typical image-based visual servoing system in matlab simulation. in the simulation results, the control system detects the current position of the objects (they are four marks "o" in this case) which locate in the right bottom corner. by comparing the current locations of the objects with the desired locations which are marked by "*", a position error vector will be generated and fed into the image-based visual servoing controller for calculating the velocity commands for each joint of the robot. the robot applied the velocity commands for its joints; and move in the 3d workspace. figure 2(a) shows the trajectory of the objects from the initial locations to the goal locations during the servoing. figure 2(b) shows the convergence of the position error during servoing. since the ibvs scheme decides the 3d motion of the robot based on 2d feedback information, it simplifies the control problem. however, it losses the control of the pose and trajectory of the robot in the 3d workspace. therefore, obstacle 690 h. lang, m.t. khan, k.-k. tan, c.w. de silva (a) (b) figure 2: ibvs in matlab simulation: (a) trajectories of objects of interest in camera scenes; (b) error convergence of the positions of the objects. avoidance and other psychical constraints during the servoing cannot be considered. moreover, if large displace of the camera is required for moving from the current pose to the desired pose, the control system may easily fail because a large change of image scene can be caused by a small displacement of the camera in cartesian space. furthermore, the interaction matrix in the model of the ibvs introduce non-linear and time-varying terms, which are challenges of designing controllers. figure 3 demonstrates a typical position-based visual servoing in matlab simulation. in this given example, the pose of the camera is controlled by eliminating the pose error between the current pose of the camera and the desired pose of the camera in 3d workspace. figure 3(a) demonstrates the initial pose of the camera in the 3d workspace and the desired pose of the camera, respectively. figure 3(b) records the velocity commands that are sent from the controller to each joint of the robot. figure 3(c) shows the trajectory of the camera pose during servoing. comparing the ibvs showed in above section, one of the most significant advantages of this approach is that it controls the pose and trajectory of the camera in the cartesian space directly. obstacles and other physical constraints can be easily considered and integrated into the system during servoing. however, there is a disadvantage in this approach. there is absolutely no control of the image, which will result in losing objects of interest in the camera scene. eventually, it will cause the failure of the whole control system because there will no data and information from the feedback loop in the control system. moreover, 3d models of objects of interest have to be completely available. moreover, 3d reconstruction is a challenge because multiple feature points are required from camera in order to calculate poses, which may not be the case during the servoing. camera calibration result will be another issue for successful pbvs system. 5 application of visual servoing in autonomous mobile robots figure 4(a) shows a robotic search and rescue scenario, in a future city. it presents an emergency situation where a group of general-purpose robots identify injured humans and remove them from the dangerous region. in applications of autonomous search and rescue robots, the workspace is usually large, and sometimes unknown and dynamic; the base frame of the robot has ability to move around in a large area; and the objects of interest are not predefined well, which depend on different applications. therefore, 3d models of target objects are unavailable in most of the time. in this situation, position-based visual servoing will fail and image-based visual servoing has the advantages over the position-based visual serving of reducing the computational burden by avoiding unnecessary image interpretations. also, it eliminates the calibration errors of camera parameters. furthermore, the eye-in-hand camera configuration has more advantages application of visual servo control in autonomous mobile rescue robots 691 (a) (b) (c) figure 3: pbvs simulation in matlab: (a) current and desired pose of the camera cartesian space; (b) controller output; (c) trajectory of the camera pose in cartesian space. 692 h. lang, m.t. khan, k.-k. tan, c.w. de silva (a) (b) figure 4: (a) robot search and rescue scenario; (b) physical configuration of the test bed. than the eye-to-hand one in mobile robot applications. the suggested system for autonomous mobile rescue application is illustrated in figure 4(b). the given system contains a mobile base (pioneer powerbot), a manipulator (robuarm), and different types of sensors (a web camera, laser, sonar, and stereo camera). both two cameras are configured as eye-in-hand configuration. the nonlinear terms in the ibvs model is linearized by updating the position of the feature points in each iteration. the time-varying term (the distance between object and camera) is also updated in each iteration by using the measurement results from the laser distance finder and sonar sensors. therefore, there is no need for distance estimation in the proposed approach. figure 4(b) describes an application of the proposed system where there are general-purpose robots in a future city (e.g., surveillance). if an incident or emergency situation occurs in the city, these robots are able to quickly response and change their roles to involve emergency cases such as search and rescue work. the control law of traditional ibvs is discussed and shown in (9), which describes the relationship between the velocities of the camera and of the feature points in the image. now, the relationship between velocity of the camera with respect to its own coordinate frame and speeds of each joints will be formulated, which is the mathematic model of ibvs of the robot. in order to generate the model, one should first consider the mathematic relationship between the velocity of the end effector with respect to the robot base frame and the velocity of each joint application of visual servo control in autonomous mobile rescue robots 693 (a) (b) (c) (d) figure 5: (a) the performance of the system in terms of the trajectory of the feature point; (b) distance information measured by laser distance finder; (c) error convergence of the system; (d) velocities commands from the controller. as: ξ r t = j1q̇ (16) where j1 is the jacobian matrix, and q̇ is the velocity vector of the robot joints. the velocity of the end effector with respect to its own coordinate frame is derived by ξ t t = g −1 ξ r t (17) where g = [ rrt 03×3 03×3 r r t ] . since the camera is rigidly attached to the end effector, the camera frame and the end effector frame have a constant relationship of homogeneous transformation. therefore, the relationship between camera velocity and the robot end-effector velocity may be written as: ξ t t = j2ξ c c (18) where j2 = [ rtc s(d t c)r t c 03×3 r t c ] . finally, by combining equations 9, 20, 21, 22, the mathematical representation of the relationship between object velocities on camera scenes and joint velocities of the robot can be written as: ṡ = lj−12g −1 j1q̇ (19) 694 h. lang, m.t. khan, k.-k. tan, c.w. de silva denoting the terms lj−12g −1j1 by m , equation (19) can be simplified as: ṡ = mq̇ (20) the square of the error norm can be utilized to derive the control law as a candidate lyapunov function: q̇ = m−1(−ke) (21) figure 6: experimental setup in the laboratory. there are nonlinear and time varying terms in this model which can cause challenges in designing and implementing controller. in order to eliminate the nonlinear and time-varying terms, an adaptive parameter updating mechanism is introduced in the system to linearize and nonlinear terms and update the time varying terms so that the system can be considered a linear system. the time varying term in the model is the distance between camera and objects. a laser distance finder is utilized to update the modeling by using the depth measurement from the laser distance finder. the non-linear terms are about the position of the objects in the image plane in the interaction matrix. to eliminate those nonlinear terms, the position of the objects in the interaction matrix are updated in each iteration of the controller. figure 5 shows the performance of the suggested ibvs system in matlab simulation. the trajectory of the object on images is shown in figure 5(a). the distance measurements between camera and object by the laser distance finder is illustrated in figure 5(b). figure 5(c) shows the performance of the controller, showing the error convergence. figure 5(d) shows the velocity history of each joint of the robot during the servoing. figure 6 shows the experimental setup in our laboratory. the purpose of the mobile robot is to find the object of interest in the workspace, navigate to it and finally approach and grasp the object using image-based visual seroving. in this paper, only the visual servoing aspect is considered. during the final operation, the stereo camera mounted on the mobile base will detect the object of interest in the scene and guide the robot to move toward to the object by using visual servo control. the mobile robot approaches the object until the object is inside the workspace of the manipulator. next, the visual servoing of the manipulator is activated to grasp the object. figure 7 presents an experimental result of the proposed system. figure 7(a) shows the desired camera view, and figure 7(b) shows the current camera view when the robot starts to servo. as discussed in the previous session, the purpose of the ibvs is to eliminate the image error between the current camera view and the desired camera view, by controlling the motion application of visual servo control in autonomous mobile rescue robots 695 (a) (b) (c) (d) (e) figure 7: (a) desired camera view; (b) current camera view; (c) trajectory of the object in in r; (d) trajectory of the object in c; (e) distance between the camera and the target object. 696 h. lang, m.t. khan, k.-k. tan, c.w. de silva of the robot. specifically, the system controls the robot motion by eliminating the position error of the object in the camera view. figure 7(a) gives the trajectory of the object in the camera view in the row (r) axis; and figure 7(b) gives the trajectory of the object in the camera view in the column (c) axis. figure 7(d) gives the distance between the camera and object. 6 conclusions this paper introduced and described two basic methods of vision servoing for mobile robots. the detailed formulation of these two methods were discussed along with their advantages and disadvantages in applying in mobile robots. a suggested method including selection of visual servoing method and hardware configuration was proposed for applications of autonomous mobile manipulation. both simulation and experimentation were carried out with the proposed system. the results showed the effectiveness and good performance of the methodology in a mobile robotic application. bibliography [1] chaumette, f. ; hutchinson, s. (2006); visual servo control part 1: basic approaches, ieee robotics and automation magazine, 13(4): 82-90. [2] agin, g. j. (1979); real time control of a robot with a mobile camera, sri international, menlo park, technical note 179. [3] sanderson, a. c. ; weiss, l. e. (1980), image-based visual servo control using relational graph error signals, in proc. ieee,:1074-1077. [4] hutchinson, s. ; hager, g.d. ; corke, p. i. (1996), a tutorial on visual servo control, ieee transaction on robotics and automation, , 12(5): 651-670. [5] ma, y. ; kosecka, j. ; sastry, s. s. (1999), vision guided navigation for a nonholonomic mobile robot, ieee transactions on robotics and automation, 15(3): 521-536. [6] fang, y.; dixon, w. e. ; dawson, d. m. ; chawda, p. (2005), homography-based visual servo regulation of mobile robots, ieee transactions on systems, man and cybernetics part b, 35(5): 1041-1050. [7] wang, y. ; lang, h. ; de silva, c. w. (2010), a hybrid visual servoing controller for robust manipulation using mobile robots, ieee/asme transactions on mechatronics, 15(5): 757769. [8] gangloff, j. ; de mathelin, m. (2002), visual servoing of a 6-dof manipulator for unknown 3-d profile following, ieee transactions on robotics and automation, 18(4): 511-519. [9] spong, m. w. ; hutchinson s. ; vidyasagar m. (2005); robot modeling and control, wiley. [10] malis, e. ; chaumette, f. ; boudet, s. (1999), 1-1/2-d visual servoing, ieee transactions on robotics and automation, 15(2): 238-250. [11] wilson, w. j. ; hulls, c. c. w. ; bell, g. s. (1996), relative end-effector control using cartesian position-based visual servoing, ieee transactions on robotics and automation, 12(5): 684-696. ijcccv11n1.dvi international journal of computers communications & control issn 1841-9836, 11(1):77-89, february 2016. towards pricing mechanisms for delay tolerant services l. marentes, t. wolf, a. nagurney, y. donoso luis marentes department of systems and computing engineering universidad de los andes, bogota, colombia, south america la.marentes455@uniandes.edu.co *corresponding author tilman wolf department of electrical and computer engineering university of massachusetts, amherst amherst, ma, united states wolf@ecs.umass.edu anna nagurney operations and information management, university of massachusetts, amherst amherst, ma, united states nagurney@isenberg.umass.edu yezid donoso department of systems and computing engineering universidad de los andes, bogota, colombia, south america ydonoso@uniandes.edu.co abstract: one of the applications of delay tolerant networking (dtn) is rural networks. for this application researchers have argued benefits on lowering costs and overcoming challenging conditions under which, for instance, protocols such as tcp/ip cannot work because their underlying requisites are not satisfied. new responses are required in order to understand the true adoption opportunities of this technology. constraints in service level agreements and viable alternative pricing schemes are some of the new issues that arise as a consequence of the particular operation mode. in this paper, we propose a novel model for pricing delay tolerant services, which adjusts prices to demand variability subject to constraints imposed by the dtn operation. with this model we also show how important parameters such as channel rental costs, cycle times of providers, and market sensitivities affect business opportunities of operators. keywords: delay tolerant networks pricing, capacity management, economic models. 1 introduction networks deployed in low income isolated areas operate in challenging environments characterized by discontinuous power supply and long distance wireless link connections. their architectures are adapted to reduce costs, but they are prone to fail due to severe bandwidth restrictions, an operation highly dependent on weather conditions, and low levels of redundancy on key elements. these facts lead to intermittent connectivity between end to end points, which breaks the main assumption for the proper operation of the tcp/ip protocol suite. the research networking community introduced the concept of intermittent connected networks (icn) to cope with this environment. according to [15], icn can be defined as: copyright © 2006-2016 by ccc publications agora university 78 l. marentes, t. wolf, a. nagurney, y. donoso an infrastructure-less wireless network that supports the proper functionality of one or several wireless applications operating in stressful environments, where excessive delays and unguaranteed continuous existence of end-to-end path(s) between any arbitrary source-destination pair, result from highly repetitive link disruptions. the core element is the uncertainty in connectivity partially solved by two mechanisms: storeand-forward and flooding. in the first mechanism, the network’s nodes are capable of storing data during periods of disconnection and forwarding to other nodes closer to the destination when a link is established. in the second mechanism, flooding copes with the routing problem. as nodes cannot maintain origin-destination paths, the network is programmed to send many copies of the same data towards different nodes to increase the probability of reaching the final destination. for isolated networks the first project introducing this concept was [19] with a two-fold objective: to reduce operational cost using offline data mule connections implemented by existing transportation methods such as buses, motorcycles, bicycles, etc., and to provide an infrastructure capable of overcoming interruptions. then the idea was extended to use mechanic backhauls and the delay tolerant network architecture (dtn) proposed by the internet engineering task force (ietf) to handle icns [4]. in recent years prototypes such as [5, 9] using dtn or similar architectures have appeared. potential operators deploying networks in isolated regions would fulfil a mix of delay tolerant and real time services such as video conferencing for medical procedures. we envision that real time services are going to be operated using technologies resulting from projects deploying low-cost infrastructure for cellular networks, see [1,21], or by wireless channels connected to infrastructure provided by states, i.e, the e-mexico system [23], the national broadband plan in india [24], the broadband infraco in south africa [22], to name a few. although most of these proposals cover technical parts of the network, the economics of the services being fulfilled are not considered. therefore, their sustainability is strongly compromised. in economic terms, providers establish service level agreements (slas) to offer alternatives regarding the quality of services and prices that customers are willing to pay. right now, these agreements are based on measures including, but not limited to, packet loss ratio, connection latency and bandwidth. once the agreement is established the provider charges services according to the rules defined in the slas. nonetheless, potential operators in isolated areas cannot directly use these measurements; they are based on the assumption of end to end connectivity which is not guaranteed in the rural environment. so one of the problems faced by these operators is how to establish slas for a diverse set of customers in networks delivering delay tolerant and real time services. in this paper we propose, as a first step, an integrated pricing and resource assignment policy for a delay tolerant service. this proposal is based on two service agreement elements and it optimizes the expected profits from the operator’s point of view. furthermore, using real data from a rural network we test the validity of this model to adjust to variability on traffic demand and study its behavior under different scenarios. the remainder of this paper is organized as follows. section ii provides an overview of previous proposals on internet service usage pricing. section iii introduces a novel pricing model tailored to a delay tolerant service. this model is based on traffic management and average time of delivery. scenario test-bed settings and results are presented in section iii. finally, section iv summarizes and concludes the paper. towards pricing mechanisms for delay tolerant services 79 2 related work from the introduction of the internet, authors like [10] have argued the necessity of creating and using charging models to induce an optimal network resource usage. for internet connectivity in low income regions, which can be isolated, an optimal resource assignment is even more important, howsoever, there is no room for light users to subsidize heavy users, nor enough capacity to deliver non-priority services without affecting high-priority services, the profit enablers for operators [2]. to the best of our knowledge this paper is the first to introduce charging methods for networks using the delay tolerant architecture. despite its novelty, the proposal takes elements of general charging models for the internet and, in particular, it recaptures conclusions to increase operator performance under time-variance consumption patterns, see [11, 16, 20] for an overview of alternative research approaches. hande et al [8] introduce models to understand the relationship between the revenues of a monopolist internet service provider(isp) and the price sensitivity of users under a two part tariff. they conclude that in markets with high price sensitivity, normally found in low income regions, most of the revenues come from the usage based portion of the price. even more, the paper suggests that the loss in revenue can be mitigated if the isp implements charging based on consumption. following these conclusions, the proposed model assumes that the isp collects the usage part of the tariff and it has previous knowledge of the demand function based on the price sensitivity. this paper extends the formulation by including quality attributes on the demand function and costs. these extensions help to provide insights regarding isp profitability and service quality experience. jian l. et al [12] introduce a price scheme to be implemented by a monopolist operator having a set of users, whose utilities not only depend on the traffic quantity, but on the specific access time. they show that the level of revenue extracted by the isp depends on the information available. with perfect information, the isp can extract the maximal revenue. when the isp has only information about the traffic in different time-slots, a more realistic scenario, the loss in revenue compared with the maximal value is in general not bounded. contrary to their proposal, we partially discard the additional utility received by users when access the network in a specific time-slot. the present proposal utilizes a demand changing during the day and forecast prices. we expect that users being charged move to their preferred time-slot as they know pricing information, which somehow follows the results presented by [7]. for us, based on an actual deployment, the authors verify that repeating a game, in which users take into account net prices to decide either to wait or to use the network, helps not only to decrease congestion periods but to increase the network usage. 3 proposed pricing model operators have two modes for connecting to the internet. the first mode is a real time connection with cost and bandwidth capacity. its cost is measured in value per unit of traffic volume and its bandwidth is measured in traffic volume per unit of time. the second mode is a mechanic backhaul connection with a by trip transit time (delivery time), cost, and capacity. operators can divide traffic in both connections. users receive in real time part of the content, for instance the web page’s text, and, after the delivery time, the rest, images and videos. this way of operation is beneficial for users and providers. users consume a reduced service instead of no service and providers increase the use of the network. information is relevant to users during a certain period of time. this is closely related to the value given by users to services, which is partially determined by whether the operator might 80 l. marentes, t. wolf, a. nagurney, y. donoso or not deliver the information within that period. then, the delivery time becomes an intrinsic quality characteristic for delay tolerant services to be modelled as part of the demand. in this proposal the idea of grade of service (gos) was adapted to make quality operational for delay tolerant services. gos is defined, see [6], as a set of traffic engineering variables used to measure the adequacy of a group of resources to fulfil a service under a specified condition. an important concept behind gos is the average behavior, which is used to aggregate the performance of different resources into a unique measure. following this idea, dtn operators are interested in measuring the adequacy of the resources (the two connection modes) for user’s preferences on time of delivery. we propose that operators might use the weighted average time required to get answers to a user’s request as a measure of the service’s quality. our proposed model for pricing delay tolerant services includes price and average delivery time as factors determining aggregated demand. it creates an optimal policy for price and average delivery time searching for the maximum amount of profits. in the following subsections we present the main assumptions: the mathematical representation for the situation being modelled and the development of its solution. 3.1 assumptions demand in this paper demand constructions suggested by authors in [3] are used. they fixed the relationship between demand, price and time by the following linear function. d(p,t) = a−βp p −βt t, (1) where a is the potential market size, βp and βt are the corresponding sensitivity of demand to price and to delivery time. the potential market size is generalized because demand fluctuates within the hours of days and between days. we propose a as a function of time, which is continuous and differentiable. the end form aggregated demand used in this proposal is: d(t,p,tavg) = a(t) −βp p −βt tavg (2) costs the mechanic backhaul connection is charged by a third party. the vendor charges by the distance travelled and not by how much information is being travelled. from the operator’s point of view this charge is a fixed cost by trip. the real time connection is used for fulfil priority and non-priority services –delay tolerant services belong to the second category–, and it has a channel rental cost. constant capacity the model assumes that connections cannot increase capacity dynamically as more demand is requested from users. the mechanic backhaul capacity is determined by the connection bandwidth and the total time that the vehicle is connected to the main gateway. constant consumption requests generated by users in the network are dissimilar in terms of data to be transferred. however, the operator needs to establish prices before observing the actual use of the network. the estimate of the average download and upload consumption is used to charge users. the towards pricing mechanisms for delay tolerant services 81 estimate is an input parameter for the model and it is included in the potential market size function a(t). 3.2 mathematical formulation the operator needs a rule that defines traffic going to be served by the mechanic backhaul connection (f1(t)) or by the real time connection (f2(t)) and the price to charge. see figure 1. each connection has a common set of parameters defined by the tuple (t,c) where t means time of delivery, and c means cost. the real time connection has an instant capacity k2. the mechanic backhaul connection has a by trip capacity capmb. the operator wants to maximize profits while maintaining the service mean delivery time (tavg). the operator cannot differentiate among customers willing to pay more for the time provision. d(t,p,tavg) f1(t) f2(t) (t1,c1,capmb) (t2,c2,k2) gw gw i(t) figure 1: elements in the general model the general model could be stated as: max t1 ∫ 0 p(t)(f1(t) + f2(t)) −c1f1(t) −c2f2(t)dt subject to i′(t) = −f1(t) (3a) i(t1) > 0 (3b) f1(t) + f2(t) = a(t) −βt tavg −βp p(t) (3c) f2(t) 6 k2 (3d) (z(t) −1)f1(t) 6 f2(t) (3e) f1(t) > 0 (3f) f2(t) > 0 (3g) p(t) > 0 (3h) i(0) = capmb (3i) the first constraint indicates that the instantaneous change in the backlog of traffic going to be served by the mechanic backhaul connection is equal to the flow using that channel. the second constraint establishes that the aggregate capacity for services using the mechanic backhaul connection cannot be more than the capacity available. in particular, this condition must be true at the end of the cycle time. the third constraint establishes that demand must go by either of the channels. it constitutes the relationship between channel use and price and service mean delivery time. constraint four 82 l. marentes, t. wolf, a. nagurney, y. donoso indicates that the quantity of flow using the real time connection must be less than or equal to the capacity available. requests made at time t and using the mechanic backhaul must at least have a travel time equal to the vehicle cycle time t1. moreover, when the arrival time coincides with the vehicle being in transit, it has to wait the remaining cycle time and another cycle. therefore, the traffic going by the mechanic backhaul as part of a request made at time t must to wait 2t1 − t. then, the mean delivery time for services being handled at time t is given by: l(t) = (2t1 − t)u1(t) + t2(t)u2(t) u1(t) + u2(t) (4) we can assume that t2(t) = 0 for all t. this measure according to the sla maintained by the operator has to be less than or equal to tavg. defining the decreasing function z(t) = 2t1 − t/tavg the expression is reduced to the following inequality which corresponds to constraint number five. the reader must note that it is valid only for the set {t ≤ t1 : z(t) ≥ 1}. (z(t)−1)f1(t) 6 f2(t) (5) finally, we define b(t) = a(t) −βt tavg as the function representing the potential market at time t after discounting the sensitivity of users for the average time. 3.3 optimal policy in this subsection we develop the optimal policy for problem (3). first, the optimal policy for the formulation without the time average constraint is developed; then as a second step, the policy is extended to include the average time constraint. the optimal policy when providers can break the time average constraint we introduce a new constraint in f1(t) in order to make easier the optimal policy develop. this constraint limits the amount of flow using the mechanic backhaul connection at any time, which is written as f1(t) ≤ k1. multiplier functions θ(t),λ1(t),λ2(t),λ3(t),λ4(t),λ5(t),λ6(t) are respectively associated with (3a), (3c), f1(t) ≤ k1, (3d), (3f), (3g), (3h) to calculate the hamiltonian (h) and lagrangian (lh), which are, see [14]: f(t,i,f1,f2,p) = p(t)(f1(t) + f2(t)) −c1f1(t) −c2f2(t) (6a) g(t,f1) = −f1(t) (6b) h1(t,p,f1,f2) = b(t) −βp p(t) −f1(t) −f2(t) (6c) h2(t,f1) = k1 −f1(t) (6d) h3(t,f2) = k2 −f2(t) (6e) h4(t,f1) = f1(t) (6f) h5(t,f2) = f2(t) (6g) h6(t,p) = p(t) (6h) h(t,i,f1,f2,p,θ) = f(.) + θ(t)g(.) (6i) lh(t,i,f1,f2,p,θ,λ) = f(.) + θ(t)g(.) + λh (6j) from which we obtain the necessary conditions for optimality: towards pricing mechanisms for delay tolerant services 83 lhp = f1(t) + f2(t)−λ1(t)βp + λ6(t) = 0, (7a) lhf1 = p(t) −c1 −θ(t)−λ1(t)−λ2(t) + λ4(t) = 0, (7b) lhf2 = p(t) −c2 −λ1(t) −λ3(t) + λ5(t) = 0, (7c) lhλ1 = b(t) −βp p(t) −f1(t)−f2(t) = 0, (7d) θ′(t) = −hi = 0, i ′(t) = −f1(t), θ(t1) ≥ 0,θ(t1)i(t1) = 0, (7e) λ4(t) ≥ 0,λ4(t)f1(t) = 0, λ5(t) ≥ 0,λ5(t)f2(t) = 0, λ6(t) ≥ 0,λ6(t)p(t) = 0, (7f) λ2(t) ≥ 0,λ2(t)(k1 −f1(t)) = 0, λ3(t) ≥ 0,λ3(t)(k2 −f2(t)) = 0, (7g) from (7) and integrating θ′(t) = 0 we have θ(t) = θ0. without loss of generality we are going to restrict the optimal path to prices greater than zero, so λ6(t) = 0. the reader must to observe: 1. assuming c1 < c2, the unconstrained problem has as optimal price p∗1 (t) = b(t) + (c1 + θ0)βp/2βp . it is important to note that the function f(t) is strictly concave in the parameter p , fp p = −2βp < 0, and it is concave in the parameter f1, therefore, this value is a global optimum. let d1(t) = b(t) − (c1 + θ0)βp/2 the demand corresponding to p∗1 (t). 2. because the mechanic backhaul channel has lower cost (c1 < c2), the optimal solution uses it as much as possible until the point d1(t). indeed, if k1 > 0 ⇒ f1(t) > 0. now, we calculate the optimal paths as functions of the maximal instantaneous capacity for the mechanic backhaul k1. all cases are developed in the following paragraphs. case 1. k1 > d1(t). the constraint on the mechanic backhaul traffic is not binding. from observation two we have: f∗2 (t) = 0,λ ∗ 2(t) = 0,λ ∗ 3(t) = 0, and λ ∗ 4(t) = 0. replacing these results on the set of equations 7, we can calculate the remaining optimal paths as: f∗1 (t) = b(t) − (c1 + θ0)βp/2,λ∗1(t) = b(t) − (c1 + θ0)βp/2βp , and λ∗5(t) = c2 −c1 −θ0. case 2. k1 ≤ d1(t) and f∗2 (t) = 0. from these conditions and observation two we conclude λ∗3(t) = 0 and λ ∗ 4(t) = 0. these let us to find optimal values for the rest of the functions. those values are: p∗2 (t) = b(t) − k1/βp,λ ∗ 1(t) = k1/βp,λ ∗ 2(t) = b(t) − 2k1 − (c1 + θ0)βp/βp,λ ∗ 5(t) = 2k1 + c2βp − b(t)/βp . case 3. k1 ≤ d1(t) and f∗2 (t) > 0. in this case p ∗ 3 (t) = b(t) + c2βp/2βp,f ∗ 1 (t) = k1,f ∗ 2 (t) = b(t) − c2βp − 2k1/2,λ∗1(t) = b(t) − c2βp/2βp,λ ∗ 2(t) = c2 − c1 − θ0,λ ∗ 3(t) = 0,λ ∗ 4(t) = 0,λ ∗ 5(t) = 0. additionally, it is defined d2(t) = b(t) − c2βp/2. case 4. k1 + k2 ≤ d2(t). from observation one, the objective function concavity in p(t) for a given t, and the fact that it is increasing in f1(t) and f2(t) until the point d2(t), it can be concluded as the optimal decision to assign k1 and k2 to f1(t) and f2(t) respectively. with these assignments we have λ∗4(t) = 0,λ ∗ 5(t) = 0. replacing this information on the set of equations, we have p∗4 (t) = b(t) − k1 − k2/βp,λ ∗ 1(t) = k1 + k2/βp,λ ∗ 2(t) = b(t) − 2(k1 + k2) − (c1 + θ0)βp/βp,λ ∗ 3(t) = b(t) − 2(k1 + k2) − c2βp/βp . case 5. k1 = 0,f2(t) > 0,k2 6 d2(t). the objective function in t is increasing in f2(t) as long as k2 6 d2(t). therefore, f∗2 (t) = k2,λ ∗ 5(t) = 0. from k1 = 0, we assign to the multiplier function λ∗4(t) = 0. replacing these results in the set of equations and resolving for the rest of functions, we have: p∗5 (t) = b(t) − k2/βp,λ ∗ 1(t) = k2/βp,λ ∗ 2(t) = b(t) − 2k2 − (c1 + θ0)βp/βp,λ ∗ 3(t) = b(t) − 2k2 − c2βp/βp . case 6. k1 = 0,f2(t) > 0,k2 > d2(t). from these conditions, then λ∗3(t) = 0,λ ∗ 4(t) = 0, and λ∗5(t) = 0. additionally, replacing these conditions, the optimal paths are: p ∗ 6 (t) = b(t) + c2βp/2βp,λ ∗ 1(t) = b(t) − c2βp/2βp,λ ∗ 2(t) = c2 −c1 −θ0. 84 l. marentes, t. wolf, a. nagurney, y. donoso the optimal θ0 value is required for all cases, from sub-equation (3a) it is equal to: (where χ represents the indicator function). i(t1) = ∫ t1 0 −f1(t)dt = ∫ t1 0 −k1χk1≤d1dt + ∫ t1 0 −d1χk1>d1 (8) let ad = ∫ t1 0 b(t)dt the total estimated market during the cycle time. from observation two, we conclude that operators want to send the maximal value by the mechanic backhaul channel. in case of enough mechanic backhaul capacity, i.e, ad − c1βp t1/2 ≤ capmb, it must be assigned θ0 = 0 and k1 = max{b(t) − c1βp/2 : t ∈ [0,t1]}. when ad − c1βp t1/2 > capmb, the constant value θ0 can be obtained observing: 1. it can be verified that profits for cases 1 and 2 are greater or equal than profits for case 3, whenever 0 ≤ θ0 ≤ c2 −c1. 2. the amount of flow not sent because not enough mechanic backhaul capacity is θ0βp t1/2. this issue is the result of using the optimal point b(t) − (c1 + θ0)βp/2 instead of b(t) − c1βp/2. 3. from items 1 and 2, we conclude that the flow potentially lost is constrained in [0, (c2 − c1)βp t1/2]. if ad−c2βp t1 2 ≤ capmb ≤ ad−c1βp t1 2 , then θ0 = ad−2capmb βp t1 −c1 and k1 = max{ b(t)−c1βp 2 : t ∈ [0,t1]}. if capmb < (ad−c2βp t1) 2 , then θ0 = c2 −c1 and k1 = capmb t1 must be assigned. the optimal policy for providers with the time average constraint we use the lagrangian lh defined in the previous section and include the constraint h4 = f2(t) − (z(t) − 1)f1(t). again, a continuous multiplier function λ7(t) is associated with this constraint. the necessary optimality conditions for the modified lagrangian is given by 7 and: λ7(t) ≥ 0, λ7(t)(f2(t) − (z(t)−1)f1(t)) = 0 (9) constraint (3e) is active when f2(t) = (z(t) − 1)f1(t),z(t) ≥ 1 and f2(t) is constrained by k2. then combining constraints (3e) and (3d) we have k2 ≥ (z(t) − 1)f1(t). from this inequality and k1 ≥ f1(t), we conclude that f1(t) is constrained by k2 z(t)−1 as long as this value is less than k1. moreover, this value is the binding constraint for f1(t) between the time 0 and time t1, defined as: t1 = max ( 0,2t1 −tavg ( k2 k1 + 1 )) (10) now, we complete the optimal policy as a function of the instantaneous capacities k1 and k2. if the constraint (3e) is active and t ∈ [0, t1], then the optimal policy is the result of the capacity k2 and the value d3(t) = b(t)z(t)−c1βp −c2(z(t)−1)βp 2z(t) is attained, which is the maximum profit. see table 1, where c1 = c1 + θ0. for t ∈ [t1, t2 = 2t1 − tavg] the optimal policy is governed by values k1 and d3(t), see table 1. finally, for t > t2 the optimal policy follows the results presented in the previous subsection. towards pricing mechanisms for delay tolerant services 85 table 1: optimal assignment policy for t ∈ [0, t2] case t g(t)∗ p (t)∗ f1(t) ∗ f2(t) ∗ d(t) k2z(t) z(t)−1 ≥ d3(t) t ≤ t 1 (z(t)−1)(b(t)z(t)−βp (c1+c2(z(t)−1))) 2z(t)2 b(t) βp − g∗z(t) βp (z(t)−1) g∗ z(t)−1 g ∗ d3(t) k2z(t) z(t)−1 ≤ d3(t) t ≤ t 1 k2 b(t) βp − g∗z(t) βp (z(t)−1) g∗ z(t)−1 g ∗ g ∗z(t) z(t)−1 z(t)k1 ≤ d3(t) t 1 ≤ t ≤ t 2 k1 b(t)−z(t)g∗ βp g ∗ (z(t) − 1)g∗ z(t)g∗ z(t)k1 > d3(t) t 1 ≤ t ≤ t 2 (b(t)z(t)−c 1βp −c2(z(t)−1)βp ) 2z(t)2 b(t)−z(t)g∗ βp g ∗ (z(t) − 1)g∗ d3(t) 4 results and discussion 4.1 scenario and parameter settings overall performance evaluation is done using the software created for network rural planning in [17]. the real data presented in [13] and [18] are used as estimators for investment, maintenance costs, data traffic, and elasticities. the evolving potential market size a(t) and the sensitivity of demand to price βp were calculated for business days and weekends. data used for simulation is summarized in tables 2 and 3. parameters taking values in a range are presented as three arguments: the initial, final, and increment values. with the proposed pricing model, only one delay tolerant service can be handled. authors in [13] report two kinds of traffic that potentially could belong to this category: http and e-mail, with e-mail being more delay tolerant than http. because the same elasticity value is reported for both services, we can consolidate their traffic and manage them as one service. extending the model to more than one service and real time technology requires additional control variables and modifying the inventory constraint. this extension is left for future research. table 2: network general parameters parameter value channel(kbps) 32 annual investment rate(%) 2,14,6 monthly fixed cost (usd) 330 cost by channel (usd) 90,170,10 mechanic backhaul cost (usd) 10,70,15 initial working hour 6 final working hour 19 cycle time (t1) 7 time average (tavg) 7 investment periods 60 table 3: service parameters parameter delay tolerant real time elasticity 1.45 1.337 demand contribution (%) 75 25 βt 0.1,1,0.01 n/a base price (usd/unit) 0.016 0.016,0.05,0.016 86 l. marentes, t. wolf, a. nagurney, y. donoso figure 2: traffic and price behavior 4.2 results the evolution of traffic by connection and price is presented in figure 2. the two figures on the left show the potential market size and the optimal use of the real and mechanic backhaul connections. the two cycles are delimited and for each of them the time interval t ∈ [0, t1] in which constraint 3e is active and determined by k2, and when it is determined by k1, t ∈ [t1, t2]. traffic use is strongly affected by the average time constraint regardless of business days or weekends. as long as the policy has to use the real time connection, it increases prices to cover link cost (see right graph). hence, the optimal demand decreases limiting the network usage. once the time constraint is more relaxed, the mechanic backhaul traffic increases as well as optimal demand. in fact, for the hypothetical scenario of t1 <= t2 the maximum mechanic backhaul connection use is determined by the potential market. the last sub-figure in 2 corresponds to the optimal price. as it can be seen the figure presents jumps which are the result of changing βp every hour within the days. explicitly, we find the size of jumps to be proportional to demand changes between hours. from our tests we inference that maintaining a constant βp eliminates jumps in prices, but under some scenarios decreases profits for operators. these jumps introduce comprehension complexities for users and operators, so we argue that maintaining a constant sensitivity to price for weekdays and weekends is the best decision for the whole system. using mixed technologies, the behavior of prices and demand might indicate a cost of real time channels too high for isolated areas. as this cost decreases, the model starts to decrease prices and the demand is stimulated for both channels. so we can suggest that mixed providers could be profitable in areas where wimax technologies could be installed ( rental costs less that 90 usd dollars for 32 kbps month). second, operators desire to know how traffic and income are affected by different parameters used in the model. the impact of average time, cost of the real time connection, and market’s time sensitivity are presented in figure 3. if the operator only considers the time average parameter, his optimal decision would be to increase it as much as possible. however, this decision is correct as long as users do not have great sensitivity to time, low βt values. if demand is closely related with time, high βt values, the operator must establish an equilibrium between income gained as more traffic uses the mechanic backhaul connection and the corresponding demand lost. the real time connection cost is a critical parameter. when the time average constraint is towards pricing mechanisms for delay tolerant services 87 active, it determines the optimal demand d3, price, and quantity send by the two connection types. in the simulated test-bed, the most extreme case is assumed in which the operator has to use a vsat connection. surprisingly, these results indicate that deployment regions closer to connected internet areas can benefit from mechanic backhaul connections when services require high volume traffic and users are to a certain degree insensible to delay. there exists a correlation between real time costs and the traffic pattern used. it can be seen in figure 3 that a decrease in channel rental costs for business days produces more income than the same increase in weekends. the demand lost during the interval [0, t1] explains this outcome, which is the consequence of the variation in the potential market size throughout time. therefore, we can conclude the need for using continuous models for adapting policies to demand variations. 5.0 5.5 6.0 6.5 7.0 7.5 8.00 50 100 150 200 to ta l d em an d (m by te s) businessdays weekends 5.0 5.5 6.0 6.5 7.0 7.5 8.0 average time tavg (hour) 0 1 2 3 4 to ta l i nc om e( u s d ol la rs ) 80 100 120 140 160 180 200 0 50 100 150 200 250 300 350 businessdays weekends 80 100 120 140 160 180 200 real time cost crt (us dollars) 0 1 2 3 4 5 6 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00 50 100 150 200 businessdays weekends 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 time sensitivity bt 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 figure 3: change in aggregated results due to parameter shifts 5 conclusions we propose a model with price and delay time as demand predictors in which operators compromise themselves to deliver requests to maximize their profits. the results presented suggest that continuous models, like the one proposed, are pertinent to establish prices for delay tolerant services as long as they can handled variability of demand. contrary to our first assumption, we observed that networks markets located close to internet connected points can benefit from these services. although real time channel lease cost continues to be a critical factor determining the demand offered to the market, results suggest that mechanic backhaul connections somehow mitigate the situation. in this paper we do not research, to name a few, the consequences of monopolistic operators’ behavior, the extent to which sustainability of network deployments is reached, and the presence of economies of scale. all these research directions remain open. acknowledgment this research is funded by colciencias grant 567. this support is gratefully appreciated. 88 l. marentes, t. wolf, a. nagurney, y. donoso bibliography [1] anand, a.; pejovic, v.; belding, e.; johnson, d. 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[22] broadband infraco; http://www.infraco.co.za/sitepages/home.aspx [23] e-mexico system; http://www.mexicoconectado.gob.mx/ [24] bharat broadband network limited; http://www.bbnl.nic.in/content/faq/what-isbbnl.php international journal of computers communications & control issn 1841-9836, 9(6):758-767, december, 2014. representing it performance management as metamodel a. pajić, o. pantelić, b. stanojević ana pajić∗, ognjen pantelić department of information systems faculty of organizational sciences, university of belgrade jove ilica 154, 11000 belgrade, serbia ana.pajic@fon.bg.ac.rs, pantelico@fon.bg.ac.rs *corresponding author bogdana stanojević mathematical institute of the serbian academy of sciences and arts kneza mihaila 36, 11000 belgrade, serbia bgdnpop@mi.sanu.ac.rs abstract: many empirical studies have shown that the business value from investment in it projects can be greater than the one being currently achieved. thus it calls for specific focus on it governance in order to reach fusion between business and it goals. good it performance management should enable the business and it executives to understand how it is contributing to the achievement of business goals. the paper addresses the issue of representing it governance best practice frameworks as ontological metamodels. special attention is dedicated to val it framework, which represents a comprehensive framework to maximize business value from it investments. the paper points out the necessity of analyzing, comparing and integrating it governance frameworks in order to complement different knowledge and generate ontological metamodel of it performance management. scope of our work is in the static aspect of the framework and as the metalanguage extended entity/relationship model is used. keywords: it governance, ontology, metamodels, it investment, it performance management. 1 introduction in today’s changing and competitive business environment, organizations are in a constant struggle to obtain a dominant role in the market. information technology (it) has become a key enabler of business process reengineering if an organization is to survive and continue to prosper. they invest substantial financial resources for delivering quality software as their competitive advantage. the increasing use of information technology (it) has resulted in a need for evaluating the productivity impact of it. many empirical studies have shown that the business value from investment in it projects can be greater than the one being currently achieved [8]. more investments do not mean by design achieving desired goals and better business results. it refers to productivity paradox, initially formulated by solow [20] in 1987, who pointed out “you can see the computer age everywhere but in the productivity statistics". thus it calls for specific focus on it governance in order to achieve fusion between business and it. several studies reported rather low success rates in achieving successful business and it alignment on the organization level, as perceived by business and it executives [18]. figures run so low that they are ranked as one of the top concerns for the upper management, in the last few years. one way to reach the strategic alignment and to bridge the gap between business and it is through the way companies govern their it. effective it governance is seen as a critical element to ensure returns on it investment and improved organizational performance [12]. copyright © 2006-2014 by ccc publications representing it performance management as metamodel 759 this can be achieved through creation of organizational structure with clearly defined roles and responsibilities regarding information, business processes, applications and it infrastructure. it can contribute to higher returns on assets with the main goal to provide the support for conducting business in a good manner [19]. strong emphasis is now placed on developing it governance frameworks to help the management to ensure that organizations realize optimal value from it business investments at an affordable cost with a known and acceptable level of risk. itil (information technology infrastructure library) and cobit (control objectives for information and related technologies) are recognized in the business and academic world as the most used and adopted frameworks ( [4], [7]). however, there are a lot of concerns in regard to adaption and integration of different best practice frameworks, which subsequently bring the logical structures and semantic of the frameworks in forefront. achieving it business value and measuring that value are important governance domains. good it performance management should enable the business and it executives to understand how it contributes to the achievement of business goals, in the past and in the future. as brandabur [3] pointed out “the it capability of organization exceeds many concepts like strengths or competitive advantage and had become an absolute necessity". a focus of it performance management should be the removal of non-value adding activities and processes. according to haanappel et al. [9], organizations had very different it performance management approaches and maturity levels. it performance measurement framework needs to be balanced, comprehensive and adopted as the tool for evaluation and assessment of it investments. therefore, the research will address this issue. paper goal is to provide insight into semantically rich structure of best practice frameworks for supporting management and governance of it. special attention is dedicated to val it framework, which represents a comprehensive framework to maximize business value from it investments. moreover, the integration of different it best practice frameworks will result in generating ontological metamodel of it performance management framework. in section 2 we discuss the concept of metamodel and ontology from the academic perspective, and elaborate the it performance management concept. a new ontological metamodel of val it framework, with rich logical structure and semantics of its relationships, is introduced in section 3. an ontological metamodel for monitoring it performance is presented in section 4 to complete the models of it governance best practices. in section 5, we discuss research outcomes and ideas for future work. 2 related works and basic concepts 2.1 metamodels and ontologies models as the main instruments of enterprise architecture (ea) have been very useful in addressing it/business alignment problems [17]. the objective of ea is to enable organization to align business goals and it investment plans. ea models are striving to be executable in order to enable enterprise to adapt it and enterprise models to change situations and to increase business opportunities. models on different levels of abstraction are in existence today. at a higher-level of abstraction “model of model" is called metamodel. metamodels play an important role in ea by ensuring semantic consistency and a common language for the enterprise [6]. atkinson and kühn [2] have come to the conclusion of two dimensions of metamodeling: linguistic and ontological. the difference between them is in the forms of instancing the objects of the metamodel. linguistic metamodeling deals with the definition of the language and relationships between its elements. on the other hand, ontological metamodels are related to the classification of model elements according to their content. they cope with “instance of" relationships between concepts and 760 a. pajić, o. pantelić, b. stanojević their types. ontological metamodels are more oriented to the users, focusing on content instead of forms. keeping in mind a note of karagiannis [13] that the combination of metamodels and ontologies provides a solution of fully semantic integration, we present in this paper a val it framework and it performance management framework as a semantic enrichment metamodels. the scope of our work is in static aspect of the frameworks and as the metalanguage the extended entity/relationship model is used [5]. the data is presented in following types: entity, relationship, attribute and constructor. 2.2 it governance frameworks the problem of implementing the it best practice frameworks is receiving growing attention from scientists. it governance has emerged as an answer to the problem of identifying the business value derived from investments in it. in order to provide the business value, investment in information technology and systems has to be closely aligned to the corporate strategy. “by creating the necessary structures and processes around it investments, management can ensure that only those it projects that are aligned with strategic business objectives are approved, funded, and prioritized" highlighted symons in [21]. webb et al. [22] underlined performance and risk management as essential part of it governance. moreover, their study has proven that companies with good it governance model generate higher results than competitors. nowadays, cobit and itil are the best known and widely accepted best practices. itil represents set of practices focusing on aligning it services with the needs of businesses. it has become the standard for it service management (itsm), providing “a detailed description of a number of important it practices, with comprehensive checklists, tasks, procedures, and responsibilities, which can be tailored to any it organization" [15]. on the other hand, cobit pays more attention on audit and control perspective. in addition it provides maturity models, critical success factors and different metrics [11]. pereira and mira da silva [16] presented in their work the conceptual models of both frameworks, highlighting the fact: “a very complex framework with several dependencies between processes". moreover, the cobit metamodel can be found in the literature [7] and in the following we will focus on it. at the heart of cobit are 34 processes which are grouped into four life cycle domains: plan and organize (po), acquire and implement (ai), deliver and support (ds), monitor and evaluate (me). each of these 34 processes has goals, divided into activity, process and it goals. it is well-structured and therefore applicable for semantic metamodeling. the main contribution of the presented model is reflected in identifying the possibilities of framework improvement. for example, the authors realize that the component’s activity and the control objective are both connected to the process, but not related to one another. they consider that control objectives have significant overlapping with activities and it can be eliminated by dropping the relationship between activity and process. furthermore, new processes can be integrated in the framework, which has substantial impact on the model flexibility. the further subject of this research will be val it framework, developed and maintained by the it governance institute. it sets good practices for the process of value creation. val it framework is primarily designed to complement cobit, but it can be used without a prior implementation of cobit framework. 2.3 it performance management measuring it performance ensures that organizations maximize the business value of their it investments. it performance management can be defined as the area of setting goals, responsibility accounting and monitoring and improving the performance of it [9]. four key areas of representing it performance management as metamodel 761 it performance management are recognized by andra [1]. the two most important areas are it efficiency and effectiveness. according to kifor and tudor [14], it effectiveness and efficiency represents today some of the most important key performance indicators and a permanent concern for every organization. many methods, tools and best practices exist to demonstrate the added business value of it. problems in measuring it performance emerge when traditional financial methods are applied to information systems, which often generate intangible benefits. despite a number of well-established methods available in the market, they are not widely adopted among it and business executives in evaluation of it performance. a limited amount of literature is available on how an organization can apply and improve their it performance management. it is necessary to understand in depth framework’s complex structure and purpose with the aim to adopt framework and using it in practice. 3 the metamodel of val it framework in the age of information and knowledge, it is no longer sufficient to measure only financial performance, but instead it is necessary to determine the value of intangible assets. from an it governance point of view, evaluating the business value of it investments is of high importance in order to control, govern and manage it functions. as a comprehensive framework, val it is dedicated to answer this issue with the primary goal to maximize business value from it investments. according to the itgi, val it “adds best practices for the end, providing the means to unambiguously measure, monitor and optimize the realization of business value from investment in it". it is systematic and comprehensive approach for measuring and delivering value, which represents one of the five focus areas of it governance. val it supports organization by providing clear and consistent policy to improve it investments decisions and returns on investments. the framework covers the value governance processes and management practices, portfolio and investment management, with ongoing measurements. they represent the three main domains. each domain includes a number of processes, key management practices and activities that need to occur in order to select the investments with the highest potential to create value and to manage them. twenty two processes are contained in these domains. each process produces specific outputs and delivers it to other processes as their inputs. therefore, both of them are categorized as results and presented through specialization relationship type. two types of output benefits are distinguished in val it framework. the first are business benefits which influence value directly to. on the contrary, intermediate benefits do not create value despite they might fulfill stakeholders’ needs. thus, we link outputs to the concept stakeholder. furthermore, process is decomposed to activities and for each activity it is indicated who should be responsible, accountable, consulted and informed (raci chart). responsibilities and accountabilities are defined for typical roles. the stated categories might be assigned to one or more of these roles, which might be undertaken by one person or single organization entity in smaller enterprises. categories are identified as entity and its relationship with role and activity entities is treated as higher-level entity, assignment aggregation. the val it management guidelines illustrate possible assignments of responsibilities to different roles. furthermore for each val it process, guidelines include key activities that need to be assumed, activity description, inputs and outputs. besides, val it processes are collection of practices, including activities and procedures. within the processes, a set of key management practices are introduced as main characteristics which lead to success. hence, the process entity is in relationship with management guidelines and management practice entities. it contains management practices and is supported by management guidelines. there are three types of goals and metrics defined for corresponding levels for each of the processes. domain 762 a. pajić, o. pantelić, b. stanojević goals and metrics describe what has to be done in order to deliver optimal value from itenabled business investments. in addition, they were enabled to achieve process goals which are measured by process metrics. the process goals and metrics have been influenced by the process key management practices. activity goals and metrics are established by process goals and they need to occur inside each process. it is important to measure what has actually been achieved, before outcome is met and afterwards. maturity level is assigned to each of three domains, based on particular maturity model. maturity model is given to help enterprise identify its current state and possible future states. the point is to set the priorities for further improvements. the most important component of val it is business case, which is essential for ensuring additional value from business changes. selecting the right investments closely depends on well defined and comprehensive business case. business case includes different business processes and a set of assumptions how value will be created. they guarantee expected outcomes based on the major it input recourses. in addition, business case should be based on key indicators, both financial and non-financial. figure 1 shows our ontological metamodel of val it. domain process activity goal is a domain goal process goal metric is a process metric domain metric activity metric result belongs to (1,3) (1,*) (1,1) is used byis created by (1,*) (0,*) (0,*) (1,1) input output is a (0,1) intermediate benefits business benefits stakeholder beneficial for (0,*) translated in role business case is contained in it resource assumption uses adresses risk identified for (0,*) (0,*) maturity level maturity modelhas (0,*) (1,1) is determined by (1,1)(1,*) (0,*) (1,1) (1,*) (1,1) (0,*) is a activity goal supports (1,*) (1,1) (1,*) (1,*) (1,*) menagement practice menagement guideline is included in is supported by (1,1) (1,*) (1,1) (0,*) verified by indicator (1,*) (1,*) (0,*) is a non-financial financial (1,*) (1,1) is measured by (1,*) (1,1) assignment category (0,*) (1,1) (0,*) (1,*) supports supports (1,*) (1,*) (1,*) (1,*) is defined by (0,1) (0,*) figure 1: ontological metamodel of val it 4 it performance management metamodel although there are a great number of methodologies, from quantitative metric roi, to higher qualitative metric of the it balanced scorecard, there is not the best possible solution or a standard for measuring the value of it. simple roi or other financial metrics are not good enough. therefore, a comprehensive it performance management framework will ensure that organization significantly improve their it investment returns based on projected business value as well as the actual value delivered. representing it performance management as metamodel 763 as we mentioned before, the ontological metamodels represent an adequate tool for the analysis, adaptation and integration of best practices in governance of it. we analyzed the best known and widely accepted it governance frameworks with focus on cobit framework in section 2 of the paper. different kinds of metrics, such as key performance indicators (kpi), key goal indicators (kgi), and critical success factors (csf), are suggested in widely accepted it governance framework cobit in order to monitor the implementation of each process [7]. val it framework complements the earlier established and used cobit, going one step forward in setting good practices for the process of value creation. it includes portfolio and investment management processes with ongoing measurements. therefore, we presented val it framework as ontological metamodel in section 3, with the idea to analyze and compare these two frameworks in order to generate one comprehensive ontological metamodel of it performance management framework. despite different focuses and logical structures, these two frameworks are enough compatible to be compared and integrated in order to generate one comprehensive ontological it performance management metamodel. connections between the components of the frameworks are found after performing in-depth analysis of the metamodels. entity types, such us process, activity, goal, metric and maturity model, have similar meanings and attributes in these frameworks. the study covers only the components in the it performance management domain. furthermore, the new elements are integrated in the metamodel as the improvement of it governance best practice frameworks. the starting point of metamodel is the entity type business case, essential for ensuring additional value from it investments. business case includes different processes and assumptions of how value will be created. for it performance management it is important to evaluate expected outcomes of business case through different milestones. a milestone is a significant event, which belongs to a business case and is used to monitor the progress in achievement of a particular outcome. the relationship between entity types milestone and expected outcome implies that a milestone is used as the checkpoint only for one outcome. reading the relationship the other way around shows that one outcome has been evaluated through one or more milestones. cobit is frequently used as the standard for it governance maturity assessment. in order to apply it as a tool to assess it performance a lot of expert knowledge is needed. there are lots of metrics, but little support for improving decision-making process. combining maturity models elements of both frameworks, we have defined elements maturity model and maturity level for business case. the reason for assessing maturity of business case lies in the fact that it is active during the whole economic life cycle of investments. the following it performance management concepts are defined: critical success factor (csf), key performance indicator (kpi) and key risk indicator (kri). they are defined by management guidelines of cobit and val it frameworks. csfs are elements vital for business case to be successful. each csf refers to the milestone and is supported by kpis and kris. the concept of key risk indicator is introduced and it supports process of risk assessment. relationship between entity types csf and kpi is treated as higher level entity, aggregation with rating criteria as attribute. the same type of relationship is defined among entity types csf and kpi. in addition, business case should be based on kpis, both financial and non-financial. the relationship between employee and kpi implies that an employee is responsible for zero or more key performance indicators. reading the relationship the other way around shows that a kpi can belong to only one employee. kpi owner is responsible for proactive monitoring of results progress and creation of kpi evaluation report, which can lead to actions of improvement. one action can be caused by only one kpi evaluation report according to metamodel. it is important to emphasize the difference between metric and kpi concepts. kpi is a metric, but metric is not necessarily a kpi. the cardinality of relationship defines this limitation. there are three types of goals and metrics defined for corresponding level in business case. process goals and 764 a. pajić, o. pantelić, b. stanojević metrics describe what has to be done in order to deliver optimal value from it-enabled business investments. the activity goals and metrics are established by process goals and they need to occur inside each process. in addition, it goal is introduced with the aim to succeed it and business alignment. both concepts are presented through specialization relationship type. the relationship between entity types goal and metric is treated as higher level entity, aggregation with score as attribute. a metamodel of it performance management framework is presented in figure 2. goals process goal activity goal it goal business case maturity level maturity model support (1,1) (1,*) has has(1,1) (0,*) (1,1) (1,*) metrics process metric expected outcome it metric activity metric measure score milestone critical success factors key risk indicator key performance indicator s financial non financial kpi owner kpi evaluation report action is (0,1) (1,1) realized (0,*) (1,1) check (0,*) (1,1) consist of (0,*) (1,1) support (0,*) (0,*) rating criteria support (0,*) (0,*) rating criteria create (0,*) (1,1) cause (0,*) (1,1) responsible (1,1) (0,*) figure 2: it performance management ontological metamodel 5 conclusion and future works information technologies and systems take a key role within an organization. organizations are more dependent on it than ever before and different challenges in management and governance of it functions have emerged. the goal of achieving a high degree of it/business alignment has been one of the top priorities for it professionals in academic and industry world for several years. therefore, there are a lot of concerns in regard to the it governance frameworks. these frameworks should support the contribution of it to the overall value of the enterprise. there is no single it governance model that fits all businesses. the choice of model depends on multiple factors. many of the existing frameworks are complementary, with strengths in different areas. they overlap each other and an organization will probably use more than one framework to achieve a complete model. effective implementation of the frameworks demands significant changes in the organization and in its processes. it is necessary to understand in depth framework’s complex structure and purpose with the aim to analyze, adapt, compare and representing it performance management as metamodel 765 integrate different frameworks of it best practice. hence, metamodeling can be a good starting point for enterprise specific governance model adaptation and configuration. we used metamodeling as methodology for adaptation and customization of frameworks on a specific it performance management domain. it performance management requires careful preparation and planning, by following set of rules and best practices. it starts by defining what is important to measure and what the goals to be achieved are. afterwards, it is important to continue with monitoring of progress towards defined goals for permanent improvement. in this paper, we analyzed it governance best practice frameworks and their semantic metamodels based on the existing literature. we provided same initial observation on conceptual models and ontologies, underlying the importance of understanding the logical structure of it governance best practice frameworks for planning successful model integration. the approach of creating ontological metamodels supports comparison and integration of different it governance frameworks in order to meet the semantic integration challenges. val it and cobit frameworks are closely related, having the different purposes. val it is addressing strategic and evaluation questions, while cobit is more oriented on it architecture and the delivery of high quality it services. by exhibiting the entity types and associated relationship types of val it framework, we captured points of overlapping among these frameworks and moreover the key areas where they complement each other in order to use strengths of both models. the main contribution of this work is reflecting through the creation of ontology metamodel of it performance management framework. through the model definition the underlying logical and semantically rich structure of the framework was represented. it was examined from different viewpoints, adjusted to conceptual level and improved through the elimination of substantial overlapping, supporting complex structures and relationships amongst entities. our work captured the knowledge of best practice guidance in creating the business value of information technologies investments focusing on their performance. the resulting metamodel is seen as the first step in building specific enterprise governance model and it is essential to complement it with the knowledge of other frameworks. in future studies, the methamodel will be adapted on the processes and structures of an organization. it will be used in developing application for monitoring it performance. it should complete models of it governance best practices to address in a comprehensive way all critical questions regarding the it performance. acknowledgements this research was partially supported by the ministry of education and science, republic of serbia, project number tr36006. bibliography [1] andra, s. 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[22] webb, p. et al (2006); attempting to define it governance: wisdom or folly?, proceeding in 39th annual hawaii international conference on system sciences, hicss, hawaii, usa, pp. 194a. int j comput commun, issn 1841-9836 8(4):635-646, august, 2013. knapsack-model-based schemes and wlb algorithm for the capacity and efficiency management of web cache a.b. xiang, l. xu, b.z. niu anbo xiang development research center of the state council, beijing, 100010, china abxiang@drc.gov.cn liang xu department of logistics management, school of business administration, southwestern university of finance and economics, chengdu, 611130, china arecxuliang1112@gmail.com baozhuang niu* lingnan college, sun yat-sen university, guangzhou, 510275, china niubzh@mail.sysu.edu.cn *corresponding author abstract: web cache refers to the temporary storage of web files/documents. in reality, a set of caches can be grouped into a cluster to improve the server system’s performance. in this paper, to achieve the overall cluster efficiency, we propose a weighted load balancing (wlb) routing algorithm by considering both the cache capability and the content property to determine how to direct an arrival request to the right node. based on knapsack models, we characterize three new placement/replacement schemes for web contents caching and then conduct the comparison based on wlb algorithm. we also compare wlb algorithm with two other widely used algorithms: pure load balancing (plb) algorithm and round-robin (rr) algorithm. extensive simulation results show that the wlb algorithm works well under the examined cluster content placement/replacement schemes. it generally results in shorter response time and higher cache hit ratio, especially when the cache cluster capacity is scarce. keywords: web cache placement/replacement scheme, knapsack model, load balancing and routing algorithm, performance analysis. 1 introduction in recent years, with the rapid growth of the size of contents delivered and the number of internet users, the world wide web (www) is better known as “world wide wait" [1]. although various information technologies are being developed rapidly, which makes the internet becomes faster and faster, according to [2]’s observation, “the trend of increasing traffic on the internet is likely to continue”. it is reported that if a web page can not be loaded within eight seconds, then users are likely to give up or load the link in a second browser [3]. as a result, a loss of revenue will be suffered, which is called the zona effect [1]. for example, the possible revenue loss by increasing a millisecond to execute automated trades, according to [3], can be as high as $100 million. therefore, there exist plenty of incentives to reduce the web response time when the server capacity is constrained (it may cost a lot to enlarge the server capacity.). in practice, an effective approach to alleviate the user-waiting problem and to optimize the web resource utilization is caching, in which the web content is generated once and kept for a period of time for the future use. when a user requests a previously requested content, the content can be accessed directly from the cache. recently, content caching has been a very active research area. generally speaking, a good caching system must address the following issues: copyright c⃝ 2006-2013 by ccc publications 636 a.b. xiang, l. xu, b.z. niu internet client level caching network level caching web site caching lan cache proxy cache browser cache application database database-backed web site web/application server database server reverse proxy cache figure 1: overview of caching (1) what to cache. cacheable elements include dns mapping, connection, and content. (2) where to cache. a cache usually can reside in one of the three locations: (a) client level, at the client browser and lan, (b) network level, at a proxy somewhere in between the client and the web site, or (c) at the web site itself. (3) when and how to cache. when are elements are placed in cache and when are elements are evicted from cache. industrial experts have already found clear answers/rules to address the first two issues [4], however, more analysis and/or solving methods are needed to address the third issue [5]. therefore, in this paper, we mainly focus on the third issue for the web server with content caching cluster at the web site, i.e., reverse proxy cache. reverse proxy cache, which is a web accelerator, can reduce the workload of a busy web application server that provides both static and dynamic contents. the static contents can be cached on the reverse proxy while the web application server is freed up to better handle the dynamic contents. towards the cache placement/replacement schemes, in practice, the least-recently-used (lru) and least-frequently-used (lfu) are commonly used due to their simplicity. however, with no or limited considerations of content properties or request patterns, these schemes could not provide the best performance. many other schemes have been proposed, which take into account the retrieval latency of contents, their sizes, the popularity of references and temporal locality of requested contents, to solve the problem in a systematic manner (e.g., [6]). from a theoretic perspective, the focused problem in this paper is closely related to the knapsack problem that maximizes the benefit under a constraint capacity. different approaches have been proposed based on the knapsack model in caching or similar applications, for example, [7] and [8]. most of these works utilize the cost-benefit optimizing idea of knapsack model to develop cache management schemes. it is worth noting that, unlike hard disk storage space, cache’s internal main memory is still a limited resource that must be managed wisely [9]. accordingly, there is another trend in caching technology which provides larger cache capacity (hereafter, cache capacity refers to its internal main memory) to support more concurrent users: cluster-based design for web application server (e.g., [10], [11], [12]). with this design, a set of caches are grouped into a loosely coupled cluster to solve some common issues such as capacity, availability, and performance. when clustered, cache’s capacity increases linearly and the number of cacheable missed requests sent to the web application server is reduced accordingly. the impact of an individual cache node failure in the cluster on the site availability and performance is also reduced considerably. this web cache cluster architecture is represented in figure 2, in which a load balancer utilizes the inherent locality of the requests and an adaptive scheme to tune the load allocated to each node in the cluster based on that node’s capability. some leading companies also propose similar commercial knapsack-model-based schemes and wlb algorithm for the capacity and efficiency management of web cache 637 smart load balancer cache 1 cache 2 cache m ... backend server figure 2: model of web cache cluster solutions, such as microsoft internet security and acceleration server with cache array routing protocol, ibm web traffic express proxy servers with network dispatcher, oracle9ias web cache cluster, cisco cache/content engine, and so on. in short, our study was motivated by problems arising in practice and trends in cluster caching. most of the previous works required or assumed that all nodes in the cluster are symmetric with an equal chance to serve any incoming request. we would like to relax these assumptions and propose a more general solution. also we want to consider the nonhomogeneity of the content properties together with the request pattern so as to maximize the utilization of the valuable cache capacity. we first study how to efficiently place/replace cache contents by accurately monitoring the properties of contents, i.e., whether to cache a content, and if we do that, which cluster node to place it so that the system performance is optimized? furthermore, based on the prior knowledge about the requests arrival pattern, contents properties, and the cache contents placement/replacement scheme, we develop a routing algorithm to determine how to direct an arrival request by efficiently coordinating the cluster nodes. the remainder of the paper is organized as follows. in the next section, we introduce our web cache cluster model. the content placment/replacement schemes are discussed based on the knapsack model in section 3. in section 4, we propose a weighted load balancing algorithm, by which we can design a smart load-balancer to determine how to direct an arrival request to a cluster node. extensive simulations have been carried out to evaluate the performance of the algorithms. these simulation results are presented and discussed in section 5. section 6 summarizes the paper. 2 model description as a reverse proxy, our cache cluster is dedicated to a single or a set of original web server systems. the model consists of a set of cacheable contents n = {1, 2, ..., n}, and a set of caches m = {1, 2, ..., m}, as illustrated in figure 2. for convenience, we introduce some notation below. more detailed descriptions will be presented when they are used. cj capacity of cache j, j ∈ m, λ total system requests arrival rate, µ0 service rate of back-end server, λ0 requests arrival rate of the back-end server, µj service rate of the cache j, j ∈ m, 638 a.b. xiang, l. xu, b.z. niu λj requests arrival rate of the cache j, we have λ = ∑ j∈m λj, oj set of all contents currently in the cache j, j ∈ m, w forwarding cost per unit of time in the cache cluster, ri retrieval cost of the requested content i from the back-end server, si size of the content i, i ∈ n, pi “profit" from serving the content i, i ∈ n, pn(i) probability that an arriving request is made for the content i, i ∈ n, given that there are n contents in n. we formulate the cache cluster content placement/replacement problem as a multiple-knapsack problem (mkp) with an objective of maximizing the total “profit" by placing the most “valuable" contents in the available caches: maximize n∑ i=1 m∑ j=1 pixij , (1) subject to, n∑ i=1 sixij ≤ cj , j ∈ m, i ∈ n, (2) where xij = { 1, object i is placed in cache j. 0, otherwise. 3 content placement/replacement schemes the cache cluster content placement/replacement scheme specifies the contents in caches to achieve the specific performance objective. at each decision epoch, the following actions are carried out: 1. initiation, preparing the initial cacheable contents, 2. update the cache contents when cache hit ratio is lower than a preset threshold value, 3. or update the cache contents when a hit miss occurs. we can achieve different specific objective by specifying appropriate pi in the equation (1). for example, 1. when pi = pn(i), the objective is to maximize the cache hit ratio, 2. when pi = pn(i)si, the objective is to maximize the byte hit ratio, 3. when pi = pn(i)ri, the objective is to minimize the retrieval cost. the knapsack problem is known to be np-hard. however, there exist fast heuristics with good performance records. in the following subsections, we discuss how different cache management methodologies are applied in different cases. knapsack-model-based schemes and wlb algorithm for the capacity and efficiency management of web cache 639 3.1 as many contents in cache as possible (amap) in this case, a cache cluster is regarded as a pure multiple-knapsack. we follow the principle of placing as many contents in caches as possible to effectively utilize the capacity of the whole cluster. consequently we need to add one more constraint in addition to (2): m∑ j=1 xij ≤ 1, i ∈ n. (3) this constraint guarantees that no content would be cached redundantly. it is a reasonable consideration when the contents space is huge while the cluster capacity is limited. [13] present an approximate dynamic programming (adp) approach for the multidimensional knapsack problem that produces near optimal solutions efficiently. we apply their adaptive fixing heuristic to solve our cluster content placement/replacement problem. our computational evidence suggests that the adp-based heuristic is an attractive methodology that usually generates good quality solutions in reasonable time. 3.2 popular contents replication and hit ratio threshold (threshold value) in this case, we aim at achieving a high availability of the most popular contents on condition that a certain level of hit ratio is guaranteed. some prior knowledge of the web request pattern can facilitate web cache resource planning and cache hierarchy design, and help us to predict the most popular contents. it has been shown that the web page request follows a zipf-like distribution (see [14]). following this result, we rank all the pages in order of their popularity where page i is the i’ th most popular page. suppose that the number of contents in the system is n, the probability that an arriving request is made for page i is approximated by pn(i) = ω iα , (4) where ω = ( n∑ i=1 1 iα )−1 and α is the zipf parameter determined by the system property. if the top k most popular contents can guarantee a certain level of hit ratio, we only place these top k most popular contents in caches. if there are extra free space, we duplicate these contents in caches sequentially until there are no more free space in the cluster. let pth be a threshold value for hit ratio, then k is determined by the formula below: k = arg inf l∈n ∑l i=1 pn(i) ≥ pth. with this replication caching scheme, the more popular contents are cached redundantly and higher hit ratio can be achieved at any single node. at the same time, a high availability of popular contents can still be maintained when any individual node fails. due to the replication of popular contents, the tradeoff is less effective usage of the combined capacity of the whole cluster. 3.3 web contents space partitioning (partition) in a cache cluster, cache members may not be identical and have different capabilities in dealing with the arrival requests. capability (capacity, processing power, bandwidth, etc.) represents a member’s potential contribution to the cluster. in this case, we follow [10] to partition web contents space based on members’ capability, as shown in figure 3. each cache is assigned to a certain part of the web contents. consequently, 640 a.b. xiang, l. xu, b.z. niu cache 1 cache 2 cache 3 cache 4 web objects namespace figure 3: partitioning web objects namespace based on caches’ capability the mkp problem (1) reduces to multiple single-knapsack problems with cache j being in charge of the contents subset nj, nj ⊆ n. a well-known knapsack problem solution method is the greedy heuristic, which selects objects for inclusion in the knapsack using the “density" of object as the criterion to be greedy upon. let dij = pij /sij be the density of content ij, ij ∈ nj. we set xij = 0 for all ij that satisfies sij > cj, and then arrange the remaining contents in decreasing order of density from top to bottom. starting from the top, the greedy heuristic sets xij = 1 as it goes down until the cache capacity cj is reached. at each stage, if the next content cannot be included in the cache because its size exceeds the remaining capacity, xij is set to 0 for that content and the process continues with the contents below it. the process terminates either when the cache capacity is used up (in this case, xij = 0 for all contents below the current one), or when all the contents have been examined in this way. in the simulation section, we will compare the performance of these three schemes: placing as many contents in caches as possible, threshold value for hit ratio and replication for popular contents, and partitioning of web contents based on caches’ capabilities under different parameter settings. 4 weighted load balancing (wlb) routing algorithm cache clustering introduces a new problem: when a browser requests a particular content, to which cache in the cache cluster should the request be directed? how do we match an incoming request with the cache best able to respond? content-aware routing working at application layer certainly can increase the cache hit ratio. but inspecting every incoming http request would increase the system delay, so that the load-balancer may easily become a bottleneck itself and slow down the entire system. thus, the load-balancer should be kept as “light-weight” and simple as possible to avoid introducing new vulnerabilities into the system (system is only as secure as its weakest component). so in designing our load-balancer, we should consider the system response time and the cache hit ration simultaneously. we want it to work at transport layer and focus on forwarding the data at maximal speed without inspecting every incoming http request, while still guarantees an acceptable cache hit performance. 4.1 the dynamic routing model when a new request arrives and is assigned to cache j, cache j will be associated with an expected cost function f(j) for handling the request(s). we want to find a suboptimal and easy knapsack-model-based schemes and wlb algorithm for the capacity and efficiency management of web cache 641 implemented routing strategy to determine which cache to serve the new arrival request so that the cluster-wide total expected cost is minimized. in general, serving a request directly from web cache cluster, i.e., a cache hit, is significantly faster than forwarding the request to the back-end server for generation. in the cache cluster, we also assume that the network bandwidth between peer caches is large and network latency is low, and thus retrieving a cached content from a peer cache is also significantly faster than getting the same content from the back-end server. we denote as w the forwarding cost per unit time in the cache cluster. there are three possible ways for an arriving request to be served when routed to a cache: (1) the cache contains the requested content and serves the request immediately; (2) the cache does not contain the requested content, but another cache in the cluster contains the content and serves the request; (3) the content is not cached and the request is eventually routed to the back-end server. for the cases (1) and (3), the cost associated with routing the request to any cache is the same. thus, the cost difference only lies in the forwarding cost in the cluster when the requested content is not cached in the assigned cache but is cached in the other node(s) in the cluster. from a request’s “viewpoint", the routing decision could be determined by the expected cost of sending the request to a cache. i.e. we should direct each arriving request to the queue with the minimum expected waiting cost. therefore we need to find l = arg min j∈m f(j). such a policy minimizes each arriving request’s individual expected waiting cost as well as the long-run system waiting cost [15]. 4.2 wlb routing algorithm we assume that the request inter-arrival times and service times are all exponential. let λj and µj denote the request arrival rate and the service rate at cache j, respectively. let oj denote the set of all contents currently in the cache j and kj denote the number of outstanding requests waiting at the cache j. p(i /∈ oh, i ∈ m∪ v=1 ov) denotes the probability that the request content i is not in the cache h but is in the cluster. based on the above notation, assumptions and analysis, we need to compare two arbitrary decisions h and l. we have f(h) − f(l) = khw µh − λh n∑ i=1 pn(i)p(i /∈ oh, i ∈ m∪ v=1 ov) − klw µl − λl n∑ i=1 pn(i)p(i /∈ ol, i ∈ m∪ v=1 ov) , where p(i /∈ oh, i ∈ m∪ v=1 ov) = m∑ j=1 ∑ e∈oj pn(e) − ∑ e∈oh pn(e) m∑ j=1 ∑ e∈oj pn(e) . the two terms in the right-hand side of the equation represent the forwarding costs when the requested content is not cached in the assigned cache h (l) but is cached in the other node(s) in the cluster, respectively. the router should direct the arriving request to cache l if f(h) − f(l) > 0, or to cache h otherwise. thus, we have the following heuristic routing policy: rule: when a request arrives, the router directs it to the server l if l = arg min j { kj µj − λj p [ the requested file is in another cache instead of cache j ] } . 642 a.b. xiang, l. xu, b.z. niu the essence of this wlb algorithm is that the queue length kj is weighted by the probability that the cache does not have the target content and the service capability. the router directs each arrival request to the cache with the lightest effective workload. this wlb routing algorithm makes use of the information related to the cache (it’s service capability and workload) and the property of the content (the probability that a request can be satisfied in one cache). moreover, this algorithm can be easily extended to implement web server cluster. since each web server can satisfy all of the requests, it is natural for us to utilize a pure load balancing policy to guarantee the web server cluster’s performance. 5 performance analysis in this section we compare the performances of different caching schemes. with the wlb arrival request routing algorithm, we first compare performance of lru scheme with our proposed three new content placement/replacement schemes based on the knapsack model: 1. placing as many contents in caches as possible (amap scheme, see section 4.1), 2. placing only contents with popularity values higher than a threshold value in caches (threshold value scheme, see section 4.2), 3. partitioning of contents based on caches’ capabilities (partition scheme, see section 4.3). secondly, we compare the performance of the wlb algorithm with the following two routing algorithms under the threshold value content placement/replacement scheme: 1. pure load balancing (plb) algorithm. with this algorithm, a request is directed to the server with the shortest queue length; 2. round-robin (rr) algorithm. a request is directed to the server next to the server which received the previous request. using simulation, the average response time (art), cache hit ratio (chr), and cache cluster hit ratio (cchr), the ratio between the total requests and the requests are not served rightly by the assigned caches but by other cache in the cluster, of these algorithms have been compared over following parameters setting: 1. size rate, the ratio of cache cluster size vs contents total size; 2. ρ, the ratio of request arrival rate vs the cache cluster service rate. this factor indicates the level of system’s workload; 3. cache types, we assumed that the caches can be all identical, or be divided into two groups with different capabilities (in terms of service rate, capacity, etc), or all be different in capability. 5.1 model-driven simulation the primary motivation for performing model-driven simulation is to understand the effect of different schemes on cache cluster content management. in the model-driven simulation experiment, the arrival requests follow a poisson stream with rate λ. the target content of the request has zipf-like frequency distribution with the zipf parameter α. generally, without specifying otherwise, the default settings are λ = 0.3, µ0 = 0.05, µj = 0.045, m = 10 (i.e. there knapsack-model-based schemes and wlb algorithm for the capacity and efficiency management of web cache 643 are 10 caches in the cluster). hence the ρ = 0.6 (this means the system is of moderate level of workload). the size rate equals 0.5, the zipf parameter α = 0.8. we use 103 contents of sizes uniformly distributed between 1 and 1000. the art, chr, and cchr are explored under different parameters combinations. the results are presented in four sets of figures below. we now discuss the numerical results in detail. observation 1: increasing cache cluster’s capacity can improve the system performances. we observe the impact of varying the caches’ size on the system performances. from figure 4 and figure 5, art is decreasing in cache cluster’s size, chr and cchr are increasing in cache cluster’s size. observation 2: the threshold value scheme is more efficient when the cache cluster capacity is scarce. the partition scheme is more efficient when cache cluster capacity is sufficient. we compare the performances of different content placement/replacement schemes under different situations aim to find which scheme is more appropriate for a certain situation. from figure 4(a), it is noticed that when the cache cluster size is small (size rate < 0.6), the threshold value scheme achieves shorter art than other content placement/replacement schemes. observation 3: better performance can be achieved by considering both node’s capability and content’s property. with the threshold value content placement/replacement scheme, we compare the performances of the wlb, plb, and rr routing algorithms under different situations by varying the setting of size rate, ρ, and cache cluster types. the simulation results (figures 5) suggest that the wlb has shorter art, higher chr and cchr than plb and rr algorithm in most of the situations. 0 500 1000 1500 2000 2500 0.01 0.05 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 size rate: cache cluster vs objects a r t amap policy partition policy threshold value policy lru policy (a) average response time vs size rate 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.01 0.05 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 size rate: cache cluster vs objects c h r amap policy partition policy threshold value policy lru policy (b) cache hit ratio vs size rate 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.01 0.05 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 size rate: cache cluster vs objects c c h r amap policy partition policy threshold value policy lru policy (c) cache cluster hit ratio vs size rate figure 4: performances of content placement/replacement schemes with wlb algorithm of all the cases simulated, the average improvement in the art achieved by the wlb is 2.56% compared with the plb algorithm, and 13.96% compared with the rr algorithm. 644 a.b. xiang, l. xu, b.z. niu 0 500 1000 1500 2000 2500 0.01 0.05 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 size rate: caches cluster vs objects a r t weighted load balancing policy pure load balancing policy round robin policy (a) average response time vs size rate 0 0.05 0.1 0.15 0.2 0.25 0. 01 0. 05 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 1 size rate: caches cluster vs objects c h r weighted load balancing policy pure load balancing policy round robin policy (b) cache hit ratio vs size rate 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.01 0.05 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 size rate: caches cluster vs objects c c h r weighted load balancing policy pure load balancing policy round robin policy (c) cache cluster hit ratio vs size rate figure 5: performances of different routing algorithms with the threshold value scheme 5.2 trace-driven simulation we now present results from trace-driven simulations using real web server’s traces, the access log data of a squid proxy server system which was in operation at hkust in september 2012. the trace length is 409623 user accesses. we ran the trace-driven simulation for different values of the cache cluster capacity by varying the size rate. we first compare the performance of the following cache cluster management policies: 1. wlb routing algorithm with threshold value content placement/replacement scheme, 2. plb routing algorithm with lru content placement/replacement scheme, 3. rr routing algorithm with the lru value content placement/replacement scheme, 4. plb routing algorithm with lfu content placement/replacement scheme, 5. rr routing algorithm with the lfu content placement/replacement scheme. from figure 6, we find that wlb algorithm with threshold value content placement/replacement scheme achieves better performance than other combination policies significantly, especially when the size rate is smaller. this observation validates the advantage of our knapsack-based cache cluster management scheme. 6 conclusion in this paper, based on the knapsack model, we first propose three efficient placement/replacement schemes for content caching, and then develop a wlb routing algorithm working at transport layer, which considers both of the node’s capability and content’s property, to determine how to direct an arrival request to the right node by efficiently coordinating the cluster nodes. knapsack-model-based schemes and wlb algorithm for the capacity and efficiency management of web cache 645 0 500 1000 1500 2000 2500 3000 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 size rate: caches cluster vs objects a r t weighted load balancing + threshold value policy pure load balancing + lru policy round robin + lru policy pure load balancing + lfu policy round robin + lfu policy (a) average response time vs size rate 0 0.1 0.2 0.3 0.4 0.5 0.6 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 size rate: caches cluster vs objects c h r weighted load balancing + threshold value policy pure load balancing + lru policy round robin + lru policy pure load balancing + lfu policy round robin + lfu policy (b) cache hit ratio vs size rate 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 size rate: caches cluster vs objects c c h r weighted load balancing+ threshold value policy pure load balancing + lru policy round robin + lru policy pure load balancing + lfu policy round robin + lfu policy (c) cache cluster hit ratio vs size rate figure 6: performances of cache cluster management policies under trace-driven simulation extensive simulation results show that the wlb algorithm with the content replacement scheme leads to satisfactory quality of service (shorter response time and higher cache hit ratio). the simulation results also indicate that content placement/replacement scheme dominates the system performance, and modeling the cache cluster into a multiple knapsack problem is a good way for us to study the content placement/replacement scheme. furthermore, the simulation results point out which content placement/replacement scheme is more appropriate under different situations. in the future study, we plan to integrate the “admission control" and “request priority" mechanisms into our web cache cluster model with the objective to maximize the system reward. we also expect to investigate the distributed data caching infrastructure over internet and the distributed resources optimization problems. moreover, to consider how to organize the cluster (e.g., finding out the optimal number of cache nodes in the cluster) will bring some insight in cache cluster management. acknowledgement this project is supported in part by national natural science foundation of china under grant 71201175 and national natural science foundation of china under grant 71201127 and guangdong natural science foundation under grant s2012040008081 and s2011040001069. bibliography [1] datta, a. et al (2003); world wide wait: a study of internet scalability and cache-based approaches to alleviate it, management science, issn 0025-1909, 49: 1425-1444. 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[15] liu, l. et al (2005); weighted load balancing for web server clusters with caching. working paper. hong kong university of science and technology. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 567-577 a new rymon tree based procedure for mining statistically significant frequent itemsets p. stanišić, s. tomović predrag stanisic, savo tomovic university of montenegro department of mathematics and computer science dzordza vasingtona bb, podgorica, montenegro e-mail: pedjas@ac.me, savica@t-com.me abstract: in this paper we suggest a new method for frequent itemsets mining, which is more efficient than well known apriori algorithm. the method is based on special structure called rymon tree. for its implementation, we suggest modified sort-merge-join algorithm. finally, we explain how support measure, which is used in apriori algorithm, gives statistically significant frequent itemsets. keywords: frequent itemset mining, association analysis, apriori algorithm, rymon tree 1 introduction finding frequent itemsets in databases is fundamental operation behind association rule mining. the problem of mining association rules over transactional databases was introduced in [1]. an example of such rule might be that "85% of customers who bought milk also bought bread". discovering all such rules is important for planning marketing campaigns, designing catalogues, managing prices and stocks, customer relationships management etc. the supermarket is interested in identifying associations between item sets; for example, it may be interested to know how many of customers who bought milk also bought bread. this knowledge is important because if it turns out that many of the customers who bought milk also bought bread, the supermarket will place bread physically close to milk in order to stimulate the sales of bread. of course, such a piece of knowledge is especially interesting when there is a substantial number of customers who buy two items together and when large fraction of those individuals who buy milk also buy bread. for example, the association rule milk ⇒ bread [support=20%, confidence=85%] represents facts: • 20% of all transactions under analysis contain milk and bread; • 85% of the customers who purchased milk also purchased bread. the result of association analysis is strong association rules, which are rules satisfying a minimal support and minimal confidence threshold. the minimal support and the minimal confidence are input parameters for association analysis. the problem of association rules mining can be decomposed into two sub-problems [1]: • discovering frequent itemsets. frequent itemsets have support higher than minimal support; • generating rules. the aim of this step is to derive rules with high confidence (strong rules) from frequent itemsets. for each frequent itemset l all nonempty subsets of l are found; for each a ⊂ l ∧ a ̸= ∅ the rule a ⇒ l − a is generated, if support(l)support(a) > minimal confidence. overall performances of mining association rules are determined by the first step; we do not cosider the second step in this paper. efficient algorithms for solving the second sub-problem are presented in [12]. the paper is organized as follows. section 2 provides formalization of frequent itemsets mining copyright c⃝ 2006-2010 by ccc publications 568 p. stanišić, s. tomović problem. section 3 describes apriori multiple_num algorithm which is a modification of well known apriori algorithm [1]. section 4 presents a new candidate generation procedure which is part of apriori multiple_num. in section 5 we use hypothesis testing to validate generated frequent itemsets. 2 preliminaries suppose that i is a finite set; we refer to the elements of i as items. we primarily use notions from [10]. definition 1. a transaction dataset on i is a function t: {1, ...,n} → p(i), where p(i) is set of all subsets of i. the set t(k) is the kth transaction of t. the numbers 1,...,n are the transaction identifiers (tids). [10] given a transaction data set t on the set i, we would like to determine those subsets of i that occur often enough as values of t. [10] definition 2. let t: 1, ..., n→p(i) be a transaction data set on set of items i, where p(i) is set of all subsets of i. the support count of subset k of set of items i in t is the number suppcountt (k) given by: suppcountt (k) = |{k|1 ≤ k ≤ n ∧ k ⊆ t (k)}|. (1) the support of an item set k (in the following text instead of "item set k" we will use "itemset k") is the number: supportt (k) = suppcountt (k)/n. (2) [10] the following rather straightforward statement is fundamental for the study of frequent itemsets. it is known as apriori principle [1]. proof is presented in order to introduce anti-monotone property. theorem 3. let t: 1, ..., n→ p(i) be a transaction data set on a set of items i, where p(i) is set of all subsets of i. if k and k’ are two itemsets, then k ′ ⊆ k implies supportt (k ′) ≥ supportt (k). [10] proof: the previous theorem states that supportt for an itemset has the anti-monotone property. it means that support for an itemset never exceeds the support for its subsets. for proof, it is sufficient to note that every transaction that contains k also contains k’. the statement from the theorem follows immediately. 2 definition 4. an itemset k is µ -frequent relative to the transaction data set t if supportt (k) ≥ µ . we denote by f µt the collection of all µ -frequent itemsets relative to the transaction data set t and by f µ t,r the collection of µ -frequent itemsets that contain r items for r ≥ 1 (in the following text we will use r-itemset to denote itemset that contains r items). [10] note that f µt = ∪ r≥1 f µ t,r. if it is clear what µ and t are, we can omit them. in this paper we will propose new algorithm for frequent itemsets mining which is based on special structure: rymon tree. the rymon tree was introduced in [8] in order to provide a unified search-based framework for several problems in artificial intelligence; the rymon tree is also useful for data mining algorithms. in definition 5 and 6 we define necessary concepts and in definition 7 we define the rymon tree. definition 5. let s be a set and let d : s → n be an injective function. the number d(x) is the index of x ∈ s. if p ⊆ s, view of p is subset view(d,p) = {s ∈ s|d(s) > maxp∈pd(p)}. a new rymon tree based procedure for mining statistically significant frequent itemsets 569 definition 6. a collection of sets c is hereditary if u ∈ c and w ⊆ u implies w ∈ c. definition 7. let c be a hereditary collection of subsets of a set s. the graph g = (c,e) is a rymon tree for c and the indexing function d if: • the root of the g is /0 • the children of a node p are the sets of the form p ∪{s}, where s ∈ view(d,p) if s = {s1,...,sn} and d(si) = i for 1 ≤ i ≤ n, we will omit the indexing function from the definition of the rymon tree for p(s). let s = {i1,i2,i3,i4} and let c be p(s), which is clearly a hereditary collection of sets. finally, let d be injective mapping: d(ik) = k for 1 ≤ k ≤ 4. the rymon tree for c and d is shown in fig. 1. figure 1: example of rymon tree a key property of a rymon tree is stated next. theorem 8. let g be a rymon tree for a hereditary collection c of subsets of a set s and an indexing function d. every set p of c occurs exactly once in the tree. note that in the rymon tree of a collection p(s), the collection sr, that consists of sets located at distance r from the root, denotes all subsets of the size r of s. 3 apriori multiple_num algorithm apriori multiple_num algorithm generates frequent itemsets starting with frequent 1-itemsets (itemsets consisted of just one item). next, the algorithm iteratively generates frequent itemsets to the maximal length of frequent itemset. each iteration of the algorithm consists of two phases: candidate generation and support counting. in candidate generation phase potentially frequent itemsets or candidate itemsets are generated. the apriori principle [1] is used in this phase. it is based on anti-monotone property of the itemset support (see theorem 3) and provides elimination or pruning of some candidate itemsets without calculating its support. according to the apriori principle, if x is frequent itemset, then all its subsets are also frequent. this fact is used in candidate generation phase in a way that the candidate containing at least one not frequent subset is being pruned immediately (before support counting phase). support counting phase consists of calculating support for all previously generated candidates (which are not pruned according to the apriori principle in the candidate generation phase). calculating candidate support requires one database scan and efficient determination if the candidates are contained in 570 p. stanišić, s. tomović particular transaction t ∈ t. for candidates contained in t ∈ t, it’s support will be incremented. on account of that, the candidates are organized in hash tree. the candidates which have enough support are termed as frequent itemsets. the main difference between iterations in original apriori algorithm [1] and apriori multiple_num algorithm is that iterations in later one are "longer", which is determined by multiple_num parameter. actually, in original apriori algorithm in the iteration k set fk(containing all frequent itemsets with k items) is generated, while apriori multiple_num algorithm in the iteration k generates sets fk+i,0 ≤ i ≤ multiple_num. if kmax < multiple_num is true, where kmax is the maximal length of frequent itemset, apriori multiple_num algorithm terminates in just two iterations, or just two database scans. in addition, all candidate k-itemsets (itemsets containing k items) will be signed as ck, and all frequent k-itemsets as fk. pseudocode for apriori multiple_num algorithm is given bellow. apriori multiple_num algorithm input: t-transactional database; µ -minimal support; output: f-frequent itemsets in t method: 1. f1 = all_large_1itemsets(t, µ) 2. multiple_num = maximal_length_o f _transactions 3. c2 = apriori_gen(f1,f1) 4. for i=3 to multiple_num ci = apriori_gen(ci−1,ci−2) end for 5. for i=2 to multiple_num createcandidatehashtree(ci) end for 6. for each t ∈ t do for i=2 to multiple_num traversehashtree(ci,t) end for end for 7. for i=2 to multiple_num fi = {c ∈ ci|support(c) ≥ µ} end for 8. f = ∪ k fk let us explain the most important steps briefly. generating frequent 1-itemsets is done in the same way as in original apriori algorithm [1]. this step requires one database scan. then, parameter multiple_num is set to the length of the longest transaction from the database t, which ensures that the algorithm will need just one more database scan. steps 3 and 4 are concerned with candidate generation: in step 3 set c2 is generated by calling apriori_gen function, then loop in step 4 generates all other candidates ci,3 ≤ i ≤ multiple_num, by calling apriori_gen function, but with the following difference. according to original apriori algorithm [1] candidate itemsets ck+1 (candidate itemsets containing k+1 items) is formed from the set fk(frequent itemsets containing k items) in iteration k+1. however, we want to generate all itemsets in just one loop in order to reduce number of iterations (database scans) to two, but we do not have the necessary frequent sets. as the solution, arguments are candidate sets ck−1 and ck−2, which is known at this moment. the next section describes modification of apriori_gen function and its fast implementation. the support counting phase comes next. all candidate itemsets ci,2 ≤ i ≤ multiple_num are orgaa new rymon tree based procedure for mining statistically significant frequent itemsets 571 nized in separate hash trees in order to make support counting process efficient. then, we scan database and calculate support for candidates by traversing corresponding hash trees. at the end of support counting phase frequent itemsets fi,1 ≤ i ≤ multiple_num are generated. as we stated earlier, we will not further consider support counting phase. apriori algorithm [1] performs kmax + 1 iterations, where kmax is the maximal length of frequent itemsets, and in each iteration it scans whole database. apriori multiple_num algorithm finishes after 2 iterations and performs 2 database scans. 4 new procedure for candidate generation we assume that any itemset k is kept sorted according to some relation <, where for all x,y ∈ k, x < y means that object x is in front of object y. also, we assume that all transactions in database t and all subsets of k are kept sorted in lexicographic order according to relation <. for candidate generation we suggest an original method by which the set ct,k is calculated by joining cµt,k−1 with c µ t,k−2, for k ≥ 3. candidate k-itemset is created from one candidate (k-1)-itemset and one candidate (k-2)-itemset in the following way. let x = {x1,...,xk−1} ∈ c µ t,k−1 and y = {y1,...,yk−2} ∈ cµt,k−2. itemsets x and y are joined if and only if the following condition is satisfied: xi = yi,(1 ≤ i ≤ k −3)∧ xk−1 < yk−2 (3) producing the candidate k-itemset {x1,...,xk−2,xk−1,yk−2}. we will prove the correctness of the suggested method. in the following text we will denote this method by ct,k = c µ t,k−1 ×c µ t,k−2. let i = i1,...,in be a set of items that contains n elements. denote by gi = (p(i),e) the rymon tree of p(i). the root of the tree is /0. a vertex k = {ip1,...,ipk } with ip1 < ip2 < ... < ipk has n − ipk children k ∪ j, where ipk < j ≤ n. let sr be the collection of itemsets that have r elements. the next theorem suggest a technique for generating sr starting from sr−1 and sr−2. it is a modification of theorem 7.8. from [10]. theorem 9. let g be the ryman tree of p(i), where i = i1,...,in. if w ∈ sr, where r ≥ 3, then there exists a unique pair of distinct sets u ∈ sr−1 and v ∈ sr−2 that has a common immediate ancestor t ∈ sr−3 in g such that u ∩ v ∈ sr−3 and w = u ∪ v . proof: let u and v and p be the three elements of w that have the largest, the second-largest and the thirdlargest subscripts, respectively. consider the sets u = w − {u} and v = w − {v, p}. note that u ∈ sr−1 and v ∈ sr−2. moreover, z = u ∪ v belongs to sr−3 because it consists of the first r-3 elements of w. note that both u and v are descendants of z and that u ∪ v = w (for r=3 we have z = /0). the pair (u,v ) is unique. indeed, suppose that w can be obtained in the same manner from another pair of distinct sets u1 ∈ sr−1 and v1 ∈ sr−2 such that u1 and v1 are immediate descendants of a set z1 ∈ sr−3. the definition of the rymon tree gi implies that u1 = z1 ∪ {im,iq} and v1 = z1 ∪ {iy}, where the letters in z1 are indexed by a number smaller than min{m,q,y}. then, z1 consists of the first r-3 symbols of w, so z1 = z. if m < q < y, then m is the third-highest index of a symbol in w, q is the second-highest index of a symbol in w and y is the highest index of a symbol in w, so u1 = u and v1 = v . 2 the following theorem, together with the obvious fact cµt,k ⊂ f µ t,k for all k, directly proves correctness of our method ct,k = c µ t,k−1 × c µ t,k−2. it is modification of theorem 7.10. from [10]. theorem 10. let t be a transaction data set on a set of items i and let k ∈ n such that k > 2. if w is a µ -frequent itemset and |w | = k, then there exists a µ -frequent itemset z and two itemsets {im,iq} and {iy} such that |z| = k −3, z ⊆ w , w = z ∪ {im,iq,iy} and both z ∪ {im,iq} and z ∪ {iy} are µ -frequent itemsets. 572 p. stanišić, s. tomović proof: if w is an itemset such that |w | = k, than we already know that w is the union of two subsets u and v of i such that |u| = k − 1, |v | = k − 2 and that z = u ∩ v has k-3 elements (it follows from theorem 2). since w is a µ -frequent itemset and z, u, v are subsets of w, it follows that each of these sets is also a µ -frequent itemset (it follows from theorem 1). 2 apriori algorithm [2] generates candidate k-itemset by joining two large (k-1)-itemsets, if and only if they have first (k-2) items in common. because of that, each join operation requires (k-2) equality comparisons. if a candidate k-itemset is generated by the method ct,k = c µ t,k−1 ×c µ t,k−2 for k ≥ 3, it is enough to process (k-3) equality comparisons. the method ct,k = c µ t,k−1 ×c µ t,k−2 can be represented by the following sql query: insert into ct,k select r1.item1,...,r1.itemk−1,r2.itemk−2 from cµt,k−1 as r1,c µ t,k−2 as r2 where r1.item1 = r2.item1 ∧...∧ r1.itemk−3 = r2.itemk−3 ∧ r1.itemk−1 < r2.itemk−2 for the implementation of the join ct,k = c µ t,k−1 × c µ t,k−2 we suggest a modification of sort-mergejoin algorithm (note that cµt,k−1 and c µ t,k−2 are sorted because of the way they are constructed and lexicographic order of itemsets). by the original sort-merge-join algorithm [9], it is possible to compute natural joins and equi-joins. let r(r) and s(s) be the relations and r ∩ s denote their common attributes. the algorithm keeps one pointer on the current position in relation r(r) and another one pointer on the current position in relation s(s). as the algorithm proceeds, the pointers move through the relations. it is supposed that the relations are sorted according to joining attributes, so tuples with the same values on the joining attributes are in consecutive order. thereby, each tuple needs to be read only once, and, as a result, each relation is also read only once. the number of blocks transfers is equal to the sum of the number of blocks in both sets cµt,k−1 and cµt,k−2, nb1 + nb2. the modification of sort-merge-join algorithm we suggest refers to the elimination of restrictions that join must be natural or equi-join. first, we separate the condition (3): xi = yi,1 ≤ i ≤ k −3 (4) xk−1 < yk−2. (5) joining ct,k = c µ t,k−1 ×c µ t,k−2 is calculated according to the condition (4), in other words we compute natural join. for this, the described sort-merge-join algorithm is used, and our modification is: before x = {x1,...,xk−1} and y = {y1,...,yk−2}, for which x ∈ c µ t,k−1 and y ∈ c µ t,k−2 and xi = yi,1 ≤ i ≤ k − 3 is true, are joined, we check if condition (5) is satisfied, and after that we generate candidate k-itemset {x1,...,xk−2,xk−1,yk−2}. the pseudocode of apriori_gen function comes next. function apriori_gen(cµt,k−1,c µ t,k−2) 1. i = 0 2. j = 0 3. while i ≤ |cµt,k−1|∧ j ≤ |c µ t,k−2| iset1 = c µ t,k−1[i ++] s = {iset1} done = false while done = f alse ∧ i ≤ cµt,k−1 a new rymon tree based procedure for mining statistically significant frequent itemsets 573 iset1a = c µ t,k−1[i ++] if iset1a[w] = iset1[w],1 ≤ w ≤ k −2 then s = s ∪ {iset1a} i ++ else done = true end if end while iset2 = c µ t,k−2[ j] while j ≤ |cµt,k−2|∧ iset2[1,...,k −2] ≺ iset1[1,...,k −2] iset2 = c µ t,k−2[ j ++] end while while j ≤ |cµt,k−2|∧ iset1[w] = iset2[w],1 ≤ w ≤ k −2 for each s ∈ s if iset1[k −1] ≺ iset2[k −2] then c = {iset1[1],...,iset1[k −1],iset2[k −2]} if c contains-not-frequent-subset then delete c else ct,k = ct,k ∪ {c} end if end for j++ iset2 = c µ t,k−2[ j] end while end while 5 statistical test for validating frequent itemset frequent itemset mining algorithms have the potential to generate a large number of patterns. for example, even if we assume that no customer has more than five items in his shopping cart and that there are 10000 items, there are ∑5 i=1 ( 10000 5 ) possible contents of this cart, which corresponds to the subsets having no more than five items of a set that has 10,000 items, and this is indeed a large number. as the size and dimensionality of real commercial databases can be very large, we could easily end up with thousands or even millions of patterns, many of which might not be interesting. it is therefore important to establish a set of well-accepted criteria for evaluating the quality of patterns. in apriori multiple_num algorithm support measure is used to determine whether an itemset is frequent: an itemset x is considered frequent in the data set t, if suppt (x) > minsup, where minsup is a user-specified threshold. support measure is kind of objective interestingness measure, which is datadriven and domain-independent approach that uses statistics derived from data for evaluating the quality of association patterns [12]. now, we will explain how statistical hypothesis testing can be applied to validate frequent itemsets generated with support measure. hypothesis testing is a statistical inference procedure to determine whether a hypothesis should be accepted or rejected based on the evidence gathered from data. examples of hypothesis tests include verifying the quality of patterns extracted by many data mining algorithms and validating the significance 574 p. stanišić, s. tomović of the performance difference between two classification models. in hypothesis testing, we are usually presented with two opposite hypothesis, which are known, respectively, as the null hypothesis and the alternative hypothesis. the general procedure for hypothesis testing consists of the following four steps [12]: • formulate the null and alternative hypotheses to be tested. • define a test statistic θ that determines whether the null hypothesis should be accepted or rejected. the probability distribution associated with the test statistic should be known. • compute the value of θ from the observed data. use the knowledge of the probability distribution to determine a quantity known as p-value. • define a significance level a which controls the range of θ values in which the null hypothesis should be rejected. the range of values for θ is known as the rejection region. frequent itemsets mining problem can be formulated into the hypothesis testing framework in the following way. to validate if the itemset x is frequent in the data set t, we need to decide whether to accept the null hypothesis, h0 : suppt (x) = minsup, or the alternative hypothesis h1 : suppt (x) > minsup. if the null hypothesis is rejected, then x is considered as frequent itemset. to perform the test, the probability distribution for suppt (x) must also be known. theorem 11. the measure suppt (x) for the itemset x in transaction data set t has the binomial distribution with mean suppt (x) and variance sup pt (x)∗(1−suppt (x)) n , where n is the number of transactions in t. proof: we will use measure suppcountt (x) and calculate mean and variance for it and later derive mean and variance for the measure suppt (x). the measure suppcountt (x) = xn presents the number of transactions in t that contain itemset x, and suppt (x) = suppcountt (x)/n (definition 2). the measure xn is analogous to determining the number of heads that shows up when tossing n coins. let us calculate e(xn) and d(xn). mean is e(xn) = n ∗ p, where p is the probability of success, which means (in our case) the itemset x appears in one transaction. according to bernoulli low, the following holds: ∀ε > 0,limn→∞p{|xnn − p| ≤ ε} = 1. (6) freely speaking, for large n (we work with large databases so n can be considered large), we can use relative frequency instead of probability. so, we now have: e(xn) = np ≈ n xn n = xn (7) for variance we compute: d(xn) = np(1− p) ≈ n xn n (1− xn n ) = xn(n − xn) n (8) now we will compute e(suppt (x)) and d(suppt (x)). recall that suppt (x) = xn n . we have: e(suppt (x)) = e( xn n ) = 1 n e(xn) = xn n = suppt (x). (9) d(suppt (x)) = d( xn n ) = 1 n2 d(xn) = 1 n2 xn(n − xn) n = 1 n suppt (x)(1− suppt (x)) (10) a new rymon tree based procedure for mining statistically significant frequent itemsets 575 2 the binomial distribution can be further approximated using normal distribution if n is sufficiently large, which is typically the case in association analysis. regarding previous paragraph and theorem 4, under the null hypothesis suppt (x) is assumed to be normally distributed with mean minsup and variance minsup(1−minsup)n . to test whether the null hypothesis should be accepted or rejected, the following statistic can be used: wn = suppt (x)− minsup√ minsup(1−minsup) n . (11) the previous statistic, according to the central limit theorem, has the distribution n(0,1). the statistic essentially measures the difference between the observed support suppt (x) and the minsup threshold in units of standard deviation. let n=10000, suppt (x) = 0.11, minsup=0.1 and α = 0.001. the last parameter is the desired significance level. it controls type 1 error which is rejecting the null hypothesis even though the hypothesis is true. in the apriori algorithm we compare suppt (x) = 0.11 > 0.1 = minsup and we declare x as frequent itemset. is this validation procedure statistically correct? under the hypothesis h1 statistics w10000 is positive and for rejection region we choose r = {(x1,...,x10000)|w10000 > k},k > 0. let us find k. 0.001 = ph0{w10000 > k} ph0{w10000 > k} = 0.499 k = 3.09 now we compute w10000 = 0.11−0.1√ 0.1∗(1−0.1) 10000 = 3.33... we can see that w10000 > k, so we are in rejection region and h1 is accepted, which means the itemset x is considered statistically significant. 6 conclusion in this section we compare the proposed method with original apriori [1] and with apriori multiple which we introduced in [11]. sections 3 and 4 of the paper contain comparison with the original apriori algorithm [1]. the main advantages of the new algorithm are: it finishes in just two database scans and it uses more efficient candidate generation procedure. the algorithm from [11] also finishes in two database scans and it uses similar procedure for candidate generation as the apriori multiple_num algorithm proposed here. but, the apriori multiple_num algorithm is more efficient. the main advantage of the apriori multiple_num algorithm in comparison with algorithm from [11] is in the following. the apriori multiple_num uses rymon tree structure for definition of candidate join procedure as it is explained in section 4. because of that, candidate sets are stored in rymon tree structure before joining instead of storing candidates in array as it is done in [11]. the following experiment confirms that rymon tree based implementation is more efficient. we implemented the apriori multiple_num, the original apriori [1] and the apriori multiple [11] algorithms in c in order to evaluate its performances. experiments are performed on pc with a cpu intel(r) core(tm)2 clock rate of 2.66ghz and with 2gb of ram. also, run time used here means the 576 p. stanišić, s. tomović total execution time, i.e., the period between input and output instead of cpu time measured in the experiments in some literature. in experiments dataset which can be found on www.cs.uregina.ca is used. it contains 10000 binary transactions. the average length of transactions is 8. we did not compare number of i/o operations because the algorithm proposed here finishes in just two database scans, while the original apriori requires at least kmax + 1 scans, where kmax is the length of the longest frequent itemset (as explained in section 3). figure 1 shows that the original apriori algorithm from [1] is outperformed by both the apriori multiple [11] and the apriori multiple_num presented here. also, it can be seen that apriori multiple_num with rymon tree based implementation is significantly better than the algorithm from [11]. figure 2: execution times for different algorithms bibliography [1] agrawal, r., srikant, r., fast algorithms for mining association rules, proceedings of vldb-94, 487-499, santiago, chile (1994) [2] coenen, f.p., leng, p., ahmed, s., t-trees, vertical partitioning and distributed association rule mining, proceedings icdm-2003, 513-516 (2003) [3] coenen, f.p., leng, p., ahmed, s., data structures for association rule mining: t-trees and ptrees, ieee transactions on data and knowledge engineering, vol. 16, no 6, 774-778 (2004) [4] coenen, f.p., leng, p., goulbourne, g., tree structures for mining association rules, journal of data mining and knowledge discovery vol. 8, no. 1, 25-51 (2004) [5] goulbourne, g., coenen, f., leng, p., algorithms for computing association rules using a partialsupport tree, journal of knowledge-based systems vol. 13, 141-149 (1999) [6] grahne, g., zhu, j., efficiently using prefix-trees in mining frequent itemsets, proceedings of the ieee icdm workshop on frequent itemset mining implementations (2003) [7] han, j., pei, j., yu, p.s., mining frequent patterns without candidate generation, proceedings of the acm sigmod conference on management of data, 1-12 (2000) [8] rymon, r., search through systematic set enumeration, proceedings of 3rd international conference on principles of knowledge representation and reasoning, 539-550 (1992) [9] silberschatz, a., korth, h. f., sudarshan, s., database system concepts, mc graw hill, new york (2006) a new rymon tree based procedure for mining statistically significant frequent itemsets 577 [10] simovici, a. d., djeraba, c., mathematical tools for data mining (set theory, partial orders, combinatorics), springer-verlag london limited (2008) [11] stanisic, p., tomovic, s., apriori multiple algorithm for mining association rules, information technology and control vol. 37, no. 4, 311-320 (2008) [12] tan., p.n., steinbach, m., kumar, v., introduction to data mining, addicon wesley (2006). dr. predrag stanisic is a professor in the faculty of science department of mathematics and computer science at university of montenegro. he received his b.sc. degree in mathematics and computer science from university of montenegro in 1996, his m.sc degree in computer science from university of belgrade serbia in 1998 and his ph.d. degree in computer science from moscow state university m.v. lomonosov in 1999. he is currently the dean of faculty of science at university of montenegro and he teaches a wide variety of undergraduate and graduate courses in several computer science disciplines, especially database systems, operating systems and programming. m.sc savo tomovic is a teaching assistant in the faculty of science department of mathematics and computer science at university of montenegro. he received his b.sc. degree in mathematics and computer science from university of montenegro in 2006, his m.sc degree in computer science from university of montenegro in 2007. he is currently a ph.d. student in computer science at university of montenegro. int j comput commun, issn 1841-9836 7(5):990-999, december, 2012. a fault-tolerant scheduling algorithm using hybrid overloading technology for dynamic grouping based multiprocessor systems x.-b. yu, j.-s. zhao, c.-w. zheng, x.-h. hu yu xing-biao 1.institute of software chinese academy of sciences 4# south fourth street, zhongguancun, beijing, p.r. china 2.graduate university of chinese academy of sciences 80# zhongguancun east road, haidian district, beijing, p.r. china e-mail: eliteman5317@hotmail.com zhao jun-suo, zheng chang-wen, hu xiao-hui institute of software chinese academy of sciences 4# south fourth street, zhongguancun, beijing, p.r. china abstract: in order to extend the application area of fault-tolerant scheduling algorithm based on hybrid overloading for multiprocessor and increase the fault-tolerant number of processors, we propose a new fault-tolerant scheduling algorithm, which is based on hybrid overloading and dynamic grouping for multiprocessor by combining logic grouping strategy for processors in primary backup overloading and backup backup overloading.this algorithm presents the formalization of the dynamic grouping for processors in fault-tolerant scheduling based on hybrid overloading and enlarges the task number included in overloading task link. in the process of fault-tolerant scheduling the processors are dynamically divided into some groups based on overloading task link, so as to keep good scheduling success ratio and enhance the fault-tolerant performance of processors. both theoretical analysis and simulation experiment prove this algorithm’s effectiveness respectively. keywords: dynamic grouping; fault-tolerant; overloading; primary backup; backup backup 1 introduction real-time embedded system has been applied in many fields such as military, aeronautics, astronautics and communication, and the relevant research also has made important progress. the result of task scheduling in real-time system lies on not only scheduling correctness but also time restriction. the multiprocessor platform of real-time system takes advantage of the technology of resource redundancy and time redundancy in order to meet the demand of scheduling correctness and system reliability, among them primary-backup overloading(pb) [1, 2] and backup-backup overloading(bb) [2–4]approach is most important one. supposing that there is just a processor fault in same period, the scheduling time of different versions of different tasks is overloaded in scheduling period of same processor for pb overloading , and the scheduling time of backup-backup versions of different tasks is overloaded in scheduling period of same processor for bb overloading. the fault-tolerant scheduling technology of hybrid overloading [2, 6–8] is the combination of pb overloading and bb overloading with better scheduling efficiency and fault-tolerant performance of processor, but still supposing there is only a processor fault in same period. logic grouping strategy [1] for processor based on pb overloading and bb overloading dynamically divides processors into groups in the process of task scheduling so as to tolerate a processor fault in every group, which is more adaptable to apply practically. but this strategy yet supposes the task number of overloading task chain is not more than two tasks, therefore the assigning of grouping size and group number is not enough flexible to be suited for hybrid copyright c⃝ 2006-2012 by ccc publications a fault-tolerant scheduling algorithm using hybrid overloading technology for dynamic grouping based multiprocessor systems 991 overloading. in order to simplify the application of overloading method in scheduling algorithm, some basic application principles of overloading technology were introduced. [2] in this paper dynamic grouping pb-bb algorithm(dg_pb-bb algorithm) combines the advantage of two overloading scheduling technology and logic grouping in the precondition of application principle for overloading technology, not only efficiently improving the efficiency of task scheduling but also enlarging tolerance area of the process. dg_pb-bb algorithm has better application value than no grouping algorithm and logic grouping algorithm. 2 system model system is composed of m same processor nodes based on real time multiprocessor platform with shared memory, centralized dispatcher and processor communication medium without communication cost. processor responsible for scheduling is dispatcher and responsible for execution is executer, which runs in parallel with dispatcher. new real time task is received by dispatcher and centralized dispatched to form assignment queue to execute on each processor. real time tasks to be scheduled are independent aperiodic and nonpreemptalbe scheduling with primary-backup copy technology. each task ti has two versions, namely primary copy(pri) and backup copy(bki) with identical attributes and resource requirements. task sets τ = {ti|i = 1, 2, · · · , n}, ti=(ai,ri ,ci,di), ai is the start time of task ti, ri is the ready time of task ti, ci is the maximal execution time of task ti, di is the deadline time of task ti. processor sets ω = {pi |i = 1, 2, · · · , m}. pri→bki is task chain, if other tasks are overloading scheduled on task ti, then it is overloading task chain. the parameters of system model are defined as follows: definition 1. st(ti) is the start scheduling time of task ti. ft(ti) is the finish scheduling time of task ti. ri≤st(pri)≤ft(pri)≤st(bki)≤ft(bki)≤di. definition 2. proc(pri) is the processor on which the primary task is scheduled, proc(bki) is the processor on which the backup task is scheduled, proc(pri)̸=proc(bki). definition 3. s(ti) is the time interval on which the primary or backup task is scheduled. s(pri)∩s(bki)=ϕ. definition 4. ncascade is the cascade number of overloaded tasks within a processor group and a time slot. groupsize(gi) is the processor number of a processor group gi. ncascade=1 represents the scheduling assignment of no task overloading, ncascade= groupsize(gi) represents the task can not be overloading scheduled.1≤ncascade≤groupsize(gi). definition 5. toverload is the part time of a task overloaded on other tasks. 0≤toverload≤ci. 3 a fault-tolerant scheduling algorithm using hybrid overloading technology for dynamic grouping based multiprocessor systems(dg_pb-bb) this paper states the strategy of dynamic grouping of fault-tolerant processor based on fault-tolerant scheduling technology of hybrid overloading and the finishing of task scheduling with the help of ap(allocation parameter) [5,6]algorithm. in dg_pb-bb algorithm the system can tolerate more than two processor faults at same moment and the tasks of overloading chain can not again be limited as two numbers. 992 x.-b. yu, j.-s. zhao, c.-w. zheng, x.-h. hu 3.1 validity checking 1. backups can be overloaded on any task. primaries can be overloaded only on backups. 2. if a primary pri is overloaded on a backup bkj, then st(pri)≥ft(prj). 3. a overloading task chain should not be looped. a overloading task chain is relating to some processors and the maximal primary number of a overloading task chain is groupsize(gi)-1 in the group gi. 4. no more than one processor is belong to different processor groups. every processor group has at least two processors. 5. the size of every processor group is possibly unequal and dynamically changes in the process of task scheduling. 6. the backups and primaries of same task are scheduled on same processor group. overloading scheduling of task copy can only happen within same processor group. 7. if proc(pri)=proc(prj) within same processor group, then s(bki)∩s(bkj)=ϕ, stating the scheduling time of task bki and bkj can not be overloading. 3.2 scheduling algorithm next specifically describing dg_pb-bb algorithm. in dg_pb-bb algorithm processor grouping is concluded as four conditions, formalizably described as following: 1. if the copy of task ti, pri and bki are not overloaded on other tasks,then the processors occupied by two copies form a new processor group ge. ge=form_group(proc(pri),proc(bki)). 2. if pri is not overloaded on other tasks, but bki is, then the task chain in which task tk overloaded on bki is situated links with the task chain in which pri→bki is situated to extend as a new processor group ge. (proc(prk),proc(bkk))∈group(ge), expand_group(ge,proc(pri)). 3. if bki is not overloaded on other tasks, but pri is, then the task chain in which task tk overloaded on pri is situated links with the task chain in which pri→bki is situated to extend as a new processor group ge. (proc(prk),proc(bkk))∈group(ge), expand_group(ge,proc(bki)). 4. if both of pri and bki is overloaded on other tasks, then the task chain in which task tk overloaded on bki is situated links with the task chain in which task tj overloaded on pri is situated to extend as a new processor group ge. (proc(prj),proc(bkj))∈group(ge), (proc(prk),proc(bkk))∈group(gf ), ge=expand_group(ge,gf ). fig.1 states four conditions of dynamic processor grouping. otc is overloading task chain. otc(a) represents task chain pr1→bk1 and the processors occupied by it form a new processor group, which is the first condition of dynamic processor grouping. within otc(b1), the task chain t4 is situated in links with pr3→bk3 task chain and the processors occupied by it form a fault-tolerant scheduling algorithm using hybrid overloading technology for dynamic grouping based multiprocessor systems 993 figure 1: dynamic processor grouping a new processor group, showing the second condition of dynamic processor grouping. within otc(b2), the task chain t4 is situated in links with task chain pr2→bk2 and the processors occupied by it form a new processor group, expressing the third condition of dynamic processor grouping. within otc(b1), task chain pr3→bk3 links with both task chain t2 and t4, and the processors occupied by it form a new processor group, which is the fourth condition of dynamic processor grouping. dg_pb-bb algorithm is based on ap scheduling strategy of hybrid overloading. new tasks form ready scheduling queue according to fcfs(first come first server) method. bigger the distance of start scheduling time between primaries and backups, smaller the effect of backups to task receiving ratio, and it means start scheduling time of primaries is as early as possible, st(pri)→ri, finish scheduling time of backups is as late as possible, ft(bki)→di. in order to increase task receiving ratio, tasks contained in task sets of hybrid overloading should be as much as possible and scheduling time occupied by them should be as few as possible. in the process of task scheduling the strategy of dynamic processor grouping is adopted to enhance tolerant level of the processor. the principle of processor grouping is the processors occupied by every overloading task chain constitute one group, if new overloading task chain has no relationship with old overloading task chain, then it should add a new processor to schedule new overloading task chain again to form a new processor group. if e=groupsize(ge),then: ap[pri, pj, ft(pri)] = { di−ft(pri) di−ri 1 e ncascade = 1 di−ft(pri)i di−ri ncascade e toverload ci 1 < ncascade ≤ e ap[pri, pj] = max {ap[pri, pj, ft(pri)]} ap[bki, pj, st(bki)] = { st(bki)−ri di−ri 1 e ncascade = 1 st(bki)−ri di−ri ncascade e toverload ci 1 < ncascade ≤ e ap[bki, pj] = max {ap[bki, pj, st(bki)]} ap[pri,pj,ft(pri)] is evaluation factor of scheduling scheme that a new primary pri is assigned to schedule on processor j. similarly,ap[bki,pj,ft(bki)] is evaluation factor of scheduling scheme that a new backup bki is assigned to schedule on processor j. ap is bigger, showing that the scheme of scheduling assignment is more optimal and scheduling efficiency is better. dg_pb-bb algorithm 1. the copy of first task, pri and bki are assigned respectively processor p1 and p2 to form a new group ge. ge is present processor group, g is assigned processor group. schedule(pri)→p1,schedule(bki)→p2,ge=form_group(p1,p2),validity(),g=ge. 2. (1) within assigned processor group g, next task ti finishes scheduling with the adoption 994 x.-b. yu, j.-s. zhao, c.-w. zheng, x.-h. hu of scheduling technology of hybrid overloading and allocation parameter. for task ti, while validity()̸= failed ap(pri,pj1)=next[max(ap(pri,pj))]or ap(bki,pj2)=next[max(ap(bki,pj))], pj ∈ group(g),j = 1, 2, · · · , m,j1 ̸=j2, schedule(pri)→pj1,schedule(bki)→pj2,validity(). (2) if task ti can not be scheduled within assigned processor group, then it should be to add a new processor into assigned processor group g to schedule task ti again and judge whether new and old overloading task chain can be united according to four conditions. otherwise to judge whether task chain pri→bki and task chain included in processor group g are separated each other, and whether processor group ge formed by task chain in which task ti is situated is really contained in group g. if it is true, then group ge should be decomposed, otherwise task ti is dealt with according four conditions. after processors are assigned completely, if new task can not be scheduled, then fault-tolerant scheduling algorithm of dynamic grouping is started again according to principle of processor grouping. if schedule(ti)=failed in g then {for new processor pk, g=pk∪g,go to (1) (constraint pk=pj1∨pk=pj2) if (combine(link(pri → bki),(group(g) → link))=failed) then condition 1,validity() else condition i,validity(),go to (1) until j1∨ j2=m} else {if ((combine(link(pri→bki),(group(g) → link))=failed) and form_group(pri,bki)∈group(g)) then decompose(form_group(pri, bki)) else condition i,validity(),go to (1) until j1∨j2=m} next giving an example of dg_pb-bb algorithm to express the thinking of algorithm.t1=(2, 2,2,7.5),t2=(4,4,2,8),t3=(4.5,4.5,2,9),t4=(5,5,2,10),t5=(5,5,4,13), t6=(6,6,3.5,14.5),m=6. fig.2 describes dg_pbbb algorithm scheduling example of above queue. figure 2: dg_pb-bb algorithm firstly m=2, processor p1 and p2 form group g1.st(pr2)=4 ,m=3 ,proc(bk2)=p3, pr2 and bk1 can be pb overloading ,p3 is added into group g1.st(pr3)=4.5,if m=3, pr3 and bk3 can not be scheduled within group g1, then m=4, bk3 and bk2 are bb overloading, proc(pr3)=p4, p4 is added into group g1. st(pr4)=5,m=4, pr4 and bk4 can be scheduled within group g1,proc(pr4)=p1, a fault-tolerant scheduling algorithm using hybrid overloading technology for dynamic grouping based multiprocessor systems 995 proc(bk4)=p2,and task chain pr4→bk4 is separated from old overloading task chain within group g1 and really included in group g1, so that task chain pr4→bk4 is combined into group g1. st(pr5)=5,m=4,pr5 and bk5 can not be scheduled within group g1, so it is to extend a new processor p5 and rescheduling pr5 and bk5 within processor scheduling sets composed of p5 and group g1, proc(pr5)=p5, proc(bk5)=p4.as a result, task chain pr5→bk5 is separated from old overloading task chain within group g1 and not really included in group g1, therefore it is to extend task chain pr5→bk5 as group g2. st(pr6)=6, task ti can not be scheduled neither in group g1 nor group g2, so that a new processor p6 is added into group g2 and task ti is rescheduled within extended group g2, proc(pr6)=p6, proc(bk6)=p4, bk5 and bk6 is bb overloaded on processor p4. all processors have been assigned completely so that dg_pb-bb algorithm is restarted from processor p1 while there are new tasks arriving into scheduling queue. 3.3 algorithm analysis if task sets showed in fig.2 are scheduled by ap scheduling algorithm of hybrid overloading without grouping(pb-bb_ap algorithm),although it only needs four processors to finish scheduling, but can just tolerate a processor fault and is too strict to apply widely. dg_pb-bb algorithm needs to increase two processors to finish scheduling, but new processor group g2 in system makes fault-tolerant number of processor extend as two processors so as to strengthen the reliability of system. time complexity of pb-bb_ap algorithm is o[n2 · m · (m − 1)], n is average task number of task sets on which a processor has ever scheduled, m is processor number of the system. if in dg_pb-bb algorithm average processor number of group ge is k, then time complexity of dg_pb-bb algorithm is o[n2 · k · (k − 1)].in regard to dg_pb-bb algorithm, comparing with pb-bb_ap algorithm, the algorithm cost decreases and the system reliability increases, but the guarantee ratio decreases, because pb-bb_ap algorithm is an ideal method. 3.4 theory testification the theory testification follows the methodology used in [8] and is based on the following assumptions: • all tasks have unit worst execution time, for example: ci=1. • backup slots are preallocated in the schedule. • fifo scheduling strategy is used. • task deadlines follow uniform distribution [wmin,wmax],called deadline window. if pwin(w) is the probability that an arriving task has a relative deadline w, then pwin (w) = 1/(wmax − wmin + 1), wmin ≤ w ≤ wmax. • task arrivals follow uniform distribution[0,amax], with mean aav=amax/2. if par(k) is the probability of k tasks arriving at a given time, then par (k) = 1/(amax + 1), 0 ≤ k ≤ amax. a simple pre-allocation policy for bb overloading is to reserve a slot for backups every n time slots on each processor. backup slots on the three processors can be staggered. for a task ti, bki is scheduled immediately after pri with probability 0.5 and is scheduled two slots later than pri with probability 0.5. in pb overloading there are three different types of time(0,1 and 2), if (t-1)mod 3=i, any time t has a type of i. at any time t, the number of primaries that can be scheduled to start at that time is s0 if t is of type 0, s1 if t is of type 1, s2 if t is of type 2. 996 x.-b. yu, j.-s. zhao, c.-w. zheng, x.-h. hu using fifo scheduling is equal to maintaining a task queue, to which arriving tasks are appended. given that the number of task that can be scheduled on each time unit is known, then the position of a task in the q indicates its scheduled start time. in bb overloading two tasks can be scheduled on each time (one slot is reserved for backups). if at the beginning of time slot t, a task ti is the qth task in q, then ti is scheduled to execute at time slot t+g bb q . g bb q is the time at which a task, whose position in the q is q (q = 1, 2, · · · , 2wmax), will be executed and is defined as g bb q = ⌊ q 2 ⌋ . in pb overloading s0,s1 and s2 tasks can be scheduled on a given time slot t depending on whether t is of type 0,1,or 2 respectively. the time g p b q is defined as g p b q =(i+j+l), i∑ c=1 s0 + j∑ c=1 s1 + l∑ c=1 s2 ≤ q − 1, |i − j| ≤ 1, |j − l| ≤ 1, |l − i| ≤ 1. where i ≥ j ≥ l if t is of type 0, j ≥ l ≥ i if t is of type 1, and l ≥ i ≥ j if t is of type 2. when a task ti arrives at time t, its schedulability depends on the length of q and on the relative deadline wi of the task. in bb overloading, if ti is appended at position q of q and wi ≥ g bb q , then the primary task pri is guaranteed to execute before t+wi, moreover, if wi ≥ g bb q + 2, then bki is also guaranteed to execute before time t+wi. in pb overloading, if ti is appended at position q of q and wi ≥ g p b q , then the primary task pri is guaranteed to execute before t+wi, moreover, if wi ≥ g p b q +2, then bki is also guaranteed to execute before time t+wi. let pq,k be the probability that one of the k tasks is rejected when the queue size is q, and its value is the probability that the relative deadline of the task is smaller than g∗b +δ,*=pb or bb, δ=1 or 2. g logic b is the time at which a task, whose position in the q is b, will be executed in the pb-bb overloading scheduling strategy of logic grouping, b = q + k/2. showing as t0-t12 in fig.3, processor p1-p12 are divided into 4 groups and every group has 3 processors, adopting with scheduling strategy of bb overloading, processor p13-p24 are also divided into 4 group and every group has 3 processors, adopting with scheduling strategy of pb overloading. above method is described as pb-bb overloading scheduling strategy of logic grouping. g logic b = q+k/2⌊ (i+j+l)(n−n/3)+ i∑ c=1 s0+ j∑ c=1 s1+ l∑ c=1 s2 ⌋, i∑ c=1 s0 + j∑ c=1 s1 + l∑ c=1 s2 ≤ q + k/2 − 1, |i − j| ≤ 1, |j − l| ≤ 1, |l − i| ≤ 1. g dynamic b is the time at which a task, whose position in the q is b, will be executed in the pb-bb overloading scheduling strategy of dynamic grouping, b = q + k/2. different with logic grouping, in dynamic grouping s0=3, s1=4 and s2=2. describe as t12-t24 in fig.3, processor p1p12 is divided into 3 groups and every group has 4 processors, adopting with scheduling strategy of bb overloading, processor p13-p24 are also divided 3 groups and every group has 4 processors, adopting with scheduling strategy of pb overloading, above method is described as pb-bb overloading scheduling strategy of dynamic grouping. g dynamic b = q+k/2⌊ (i+j+l)(n−n/4)+ i∑ c=1 s0+ j∑ c=1 s1+ l∑ c=1 s2 ⌋, i∑ c=1 s0 + j∑ c=1 s1 + l∑ c=1 s2 ≤ q + k/ 2 − 1, |i − j| ≤ 1, |j − l| ≤ 1, |l − i| ≤ 1. obviously, gdynamicb < g logic b , g dynamic b decreases more quickly than g logic b with increasing n, therefore dg_pb-bb algorithm is more efficient than lg_pb-bb algorithm with increasing n. a fault-tolerant scheduling algorithm using hybrid overloading technology for dynamic grouping based multiprocessor systems 997 figure 3: theroy testification of lg_pb-bb and dg_pb-bb 4 simulation experiment supposing lg_pb-bb represents fault-tolerant scheduling algorithm of hybrid overloading based on the strategy of logic grouping. simulation experiment mainly compares with change situation of the guarantee ratio(gr) in variety of fault-tolerant number of processor in circumstance of different task load for dg_pb-bb, lg_pb-bb and pb-bb_ap algorithm respectively. the guarantee ratio=the number of tasks guaranteed/the number of tasks arrived. simulation parameters as following: • the inter-arrival time of tasks follows exponential distribution with mean θ. θ=8. • the inter-arrival time of faults follows exponential distribution with mean λ. λ=200. • the execution time of a task is chosen uniformly [2,8]. • the deadline of a task is chosen uniformly [ri + ci, ri + r ∗ ci], wherer ≥ 1. • processor number m=10, task number n=50. • r(task laxity) represents flexibility time task ti can stay in ready queue in precondition of finishing scheduling before deadline. r=3. • l (task load) is the expected number of task arrivals per mean service time. l =c/θ, c is the mean execution time, θ is the inter-arrival average time of tasks ti. bigger l is, more the average load of processor is and lower the guarantee ratio is. in fig.4 (a),(b) and (c) respectively show the relationship of processor fault and guarantee ratio in dg_pb-bb,lg_pb-bb and pb-bb_ap algorithm for the system of l=0.25,l=0.5 and l=1. experiment results prove gr decreases along with l and processor faults increase. when there is only one processor fault in dg_pb-bb,lg_pb-bb and pb-bb_ap algorithm, the differences of gr for the system of l=0.25,l=0.5 and l=1 are small. when processor fault increases to more than two faults, gr for three kinds of task load in pb-bb_ap algorithm are all low and failure possibility of task scheduling is high. when l=0.25 and l=0.5 in dg_pb-bb algorithm gr enhances significantly comparing with lg_pb-bb algorithm, and for the system of l=1 the difference of gr between two algorithm is small, stating in the system of not full load task dg_pb-bb algorithm can tolerate processor fault better than lg_pb-bb and pbbb_ap algorithm. 998 x.-b. yu, j.-s. zhao, c.-w. zheng, x.-h. hu (a) l=0.25 (b) l=0.5 (c) l=1 fig.4. comparison with dynamic grouping, logic grouping and no group algorithm 5 conclusions this paper improves task number and grouping strategy included in overloading task chain and proposes dg_pb-bb algorithm based on pb-bb_ap algorithm according to logic grouping strategy of pb overloading and bb overloading. simulation experiment shows dg_pb-bb algorithm not only has good guarantee ratio of task scheduling, but also improves fault-tolerant level of processor, with better application valuation. a fault-tolerant scheduling algorithm using hybrid overloading technology for dynamic grouping based multiprocessor systems 999 main creative achievements include 1)introducing the formalization of processor dynamic grouping in fault-tolerant scheduling technology of hybrid overloading, 2)proposing the method of processor dynamic grouping based on overloading task chain,and 3)extending task number included in overloading task chain and increasing fault-tolerant level of processor by the adoption of processor dynamic grouping. bibliography [1] r.al-omari,arun k.somani,g.manimarna,efficient overloading techniques for primarybackup scheduling in real-time systems, j.parallel and distributed computing,64:629648,2004. [2] wei sun,naixue xiong,laurence t.yang,chunming rong, towards free task overloading in passive replication based real-time multiprocessors, 10th ieee international conference on computer and information technology, 1735-1742, 2010. [3] bindu mirle,albert m.k.cheng, simulation fault-tolerant scheduling on real-time multiprocessor systems using primary backup overloading, university of houston, 1-10,2006. [4] r.al-omari,arun k.somani,g.manimarna, an adaptive scheme for fault-tolerant scheduling of soft real-time tasks in multiprocessor systems, j.parallel and distributed computing, 65:595-608, 2005. [5] w.sun,y.zhang,c.yu,x.defago,y.inoguchi, dynamic scheduling real-time task using primary-backup overloading strategy for multiprocessor systems, ieice transactions on information and systems, 796-806, 2008. [6] w.sun,y.zhang,c.yu,x.defago,y.inoguchi,real-time task scheduling using extended overloading technique for multiprocessor system, 11th ieee symposium on distributed simulation and real-time applications, 95-102, 2007. [7] w.sun,y.zhang,c.yu,x.defago,y.inoguchi,hybrid overloading and stochastic analysis for redundant scheduling in real-time multiprocessor systems, 26th ieee international symposium on reliable distributed systems, 265-274, 2007. [8] g. manimaran, c. siva ram murthy,a fault-tolerant dynamic scheduling algorithm for multiprocessor real-time systems and its analysis, ieee trans. parallel distributed system , 9(11):1137-1152, 1998. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 483-489 stream ciphers analysis methods d. bucerzan, m. crăciun, v. chiş, c. raţiu dominic bucerzan, mihaela crăciun, violeta chiş "aurel vlaicu" university of arad faculty of exact sciences department of mathematics-informatics românia, 310330 arad, 2 elena drăgoi e-mail: dominic@bbcomputer.ro, qbt@rdslink.ro, viochis@yahoo.com crina raţiu daramec srl, arad românia, sofronea fn e-mail: ratiu__anina@yahoo.com abstract: the purpose of this paper is to present and to discuss analysis methods applied in symmetric cryptography, especially on stream ciphers. the tests were made on some algorithms and also on the personal symmetric cryptographic algorithm, henkos, based on a pseudorandom number generator. the test confirms that the algorithm appears to be secure and fast. the paper describes first the main parts of the cryptosystem, its implementation and different analysis methods. the code is written in the c/c++ language. the software application and the tests applied were processed on a pc computer. the quality analysis presents the results of many classical statistical tests, comparing some algorithms based especially on pseudo random number generators. the tests use standard sequence of 12.5 mb resulted from some test generators. the main part of the work presents selected results for the most important statistical tests like: fips 1401, fips 1402 , ent tests, diehard battery of tests, nist statistical test suite. the final question is: are these tests enough to certifie the quality of a tested algorithm? keywords: stream cipher, synchronous stream cipher, pseudorandom number generator (prng), performance analysis, statistical tests. 1 introduction stream ciphers are an important class of encryption algorithms. they encrypt individual characters (usually binary digits) of a plaintext message one at a time, using an encryption transformation which varies with time. various design methods where proposed for stream ciphers and the specialists proposed many analysis methods. however, the reality is that in the literature we can find relatively few fullyspecified stream cipher algorithms. one possible explanation can be the fact that many stream ciphers used in practice tend to be proprietary and confidential. a stream cipher generates what is called a keystream (a sequence of bits used as a key). encryption is accomplished by a simple operation combining the keystream with the plaintext, usually with the bitwise xor operation. stream ciphers can be either symmetric-key or public-key. the focus of this chapter is symmetric-key stream ciphers. a stream cipher generates successive elements of the keystream based on an internal state. this state is updated in essentially two ways: if the state changes independently of the plaintext or ciphertext messages, the cipher is classified as a synchronous stream cipher. by contrast, self-synchronizing stream ciphers update their state based on previous ciphertext digits. for the synchronous stream ciphers some properties are mandatory. copyright c⃝ 2006-2010 by ccc publications 484 d. bucerzan, m. crăciun, v. chiş, c. raţiu (i) synchronization requirements. the sender and the receiver must be synchronized – using the same key and operating at the same position (state) within that key. if synchronization is lost due to ciphertext digits being inserted or deleted during transmission, then decryption fails and can only be restored through additional techniques for re-synchronization. (ii) no error propagation. a ciphertext digit that is modified (but not deleted) during transmission does not affect the decryption of other ciphertext digits. (iii) active attacks problem. as a consequence of the synchronization requirement, the insertion, deletion, or replay of ciphertext digits during an attack causes loss of synchronization, and offer the possibility to be detected by the attacker. an active attack offers the possibility to make changes to selected ciphertext digits, and find out what affect these changes have on the plaintext. this conclusion proves that the data origin authentication and data integrity must be assured by additional mechanisms. in a synchronous stream cipher a stream of pseudo-random digits is generated independently of the plaintext and ciphertext messages, and then combined with the plaintext (to encrypt) or the ciphertext (to decrypt). in the most common form, binary digits (bits) are used, and the keystream is combined with the plaintext using the exclusive or operation (xor). this is called a binary additive stream cipher. another approach uses several of the previous n ciphertext digits to compute the keystream. such schemes are known as self-synchronizing stream ciphers or asynchronous stream ciphers. the idea of self-synchronization has the advantage that the receiver will automatically synchronize with the keystream generator after receiving n ciphertext digits, making it easier to recover if digits are dropped or added to the message stream. most stream cipher designs are for synchronous stream ciphers. for further details see [5]. 2 design of a stream cipher the design of a new stream cipher involves some important goals: – to deduce the internal state from the result should be impossible, – there should be no short cycles, – it should be cryptographically secure, – it should be easy to implement, – the code should be optimized for speed, – to create as much confusion and diffusion as possible. i tried to achieve the same goals in designing a new stream cipher named henkos ( see [1], [2]). this cryptosystem is a symmetric synchronous stream cipher encryption system designed for a software implementation. after many efforts, here are the results: – an easy to implement algorithm, – a cryptographically secure algorithm (proven by statistical tests), – a very fast algorithm: a 50 megabytes file is encrypted / decrypted in less then one second, – a fast pseudorandom number generator: a 12,500,000 byte stream needs 0.30 sec for c/c++ code. stream ciphers analysis methods 485 this cryptosystem uses a binary additive stream cipher and two types of keys: – a short-term key named data key (dk) with a fixed length of 1024 bytes that is an input in the keystream generator. this key can be generated with a prng (not necessarily a cryptographic secure prng) or can be an ordinary file, if prng is not available. – a long-term key named master key (mk) with a fixed length, which contains 1024 numbers, used to mix the data key and the internal state of the keystream generator. this key must be generated with a true rng (hardware). – if during the transmission an attacker intercepts the encrypted data, it is not possible to decrypt the ciphertext correctly without having the master key, because there is a very large number of possible combinations of decrypted ciphertext. every attempt to find the master key produces a different plaintext, including the one with the same numbers but the changed order of the numbers in the key affects the decryption process. 2.1 index keys generation in this section of the algorithm the master key mk is transformed into two index keys mks and mkt in two steps. two functions sum and inv are used: the first one is an additive function and the second one produced a sort of symmetrical figures of number transformation. the function sum is sum : {1,2,...,1024}→ {1,2,...,1024}, sum(i;mk) = i∑ j=0 mk( j) modulo 1024 . the function inv is inv : {1,2,...,1024} → {1,2,...,1024}, inv(i) = i∗ modulo 1024, where i∗ is the number obtained by writing the digits of the number i in reverse order. the index keys mks and mkt are: step 1 : mks(i) = sum(i;mk), i ∈ {1,2,...,1024} , (1) step 2 : mkt (i) = inv ( mks(i) ) , i ∈ {1,2,...,1024} . (2) the transformation has two targets: • not to use the original mk key directly in the process, • to create confusion and diffusion for master key. keystream generation transform the dk key to obtain the real k key for encryption using two functions: the first one is the essential function in this algorithm the "switch function" sw and the second function ad is an additive one: (sw) : dk( j)↔ dk(k), where j = mks(i) and k = mkt (i) for i ∈ {1,2,...,1024} , (3) (ad) : dk(i) = dk(i)+ dk(i +1) modulo 256, i ∈ {1,2,...,1023} and dk(1024) = dk(1024)+ dk(1) . (4) these functions create a totally changed image of the data key dk. after these two transformations we obtain dk1; the new key is the input for transformations (3) and (4) and the process will be repeated 64 times: dk → dk1→ dk2→ ...→ dk63→ dk64 . (5) 486 d. bucerzan, m. crăciun, v. chiş, c. raţiu to obtain the keystream bytes k(i) of the final k key, the last operation is: k(i) = ( dk64(i)+ dk64(i +1) ) ⊕ dk64(i) for i ∈ {1,2,...,1023} ; k(1024) = ( dk64(1024)+ dk64(1) ) ⊕ dk64(1024) . (6) the encryption / decryption process will transform a plain text p of 1024 bytes into a cipher text c of 1024 bytes by using the encryption key k and the function ⊕: c(i) = p(i)⊕ k(i) . for every stream of 1024 bytes of plain text, another k key wil be used. the new encryption k key will be obtained by the algorithm with the last values of dk as input and the operations described in (4), (5) and (6) will be effectuated one time. this sequence will run until the plain text is finished for one session. remarks: • the confusion and the diffusion of the bits are given specially from (3) and (4), • the data key for the next session we have generated with the same algorithm. 3 quality analysis the quality of a stream cipher is measured performing statistical tests. these tests fips 140–1, fips 140–2, ent tests, diehard battery, nist statistical test suite (a statistical test suite for testing the pseudo–random number generators used in cryptographic applications) were performed on large ciphertext samples of 12.5 megabytes. 3.1 fips 140–1/fips140–2 test fips statistical tests contain the monobit test, the poker test, the runs test and the long run test. the following tests are based on performing a pass/fail statistical test on 5000 sequences of 2500 bytes each. in my results the well known generators sha–1 and ccg together with the new henkos pass fips 140–1 in proportion of 100%. sha–1 passes fips 140–2 in proportion of 99.6%, ccg in proportion of 99.4% and henkos in proportion of 99.64%. for these statistical tests, even if the generators present good statistical properties this isn’t a guarantee that the algorithm is good for cryptographic purposes. 3.2 diehard statistical tests the next set of tests was designed to identify weaknesses in many common non–cryptographic prng algorithms. these tests analyze a single large file from the output of the generator of 11 megabytes or more (see [3]). the battery of tests include: birthday spacing test, overlapping 5–permutation test, binary rank test 31 × 31, binary rank test 32 × 32, binary rank test 6 × 8, bitstream test, opso, oqso and dna tests, count–the–1’s test on a stream of bytes, parking lot test, minimum distance test, 3dspheres test, etc. majority diehard tests return a p−value, which should be uniform on [0,1) if the input file contains truly independent random bits. those p−values are obtained by p = f(x), where f is the assumed distribution of the sample random variable x . when a bit stream really fails, it get p−values of 0 or 1 (or close to 0 or 1) to six or more places. for sha–1 generator we have 2 p−values very close to 1, and for cubic congruent generator we have 28 p−values near to 1. for henkos we don’t have p−values close to 0 or 1. stream ciphers analysis methods 487 3.3 ent tests ent applies various tests to a sequence of bytes stored in files and reports the results. the program can be used to evaluate pseudorandom number generators for encryption and compression algorithms. it calculates entropy, optimum compression, chi–square distribution, arithmetic mean, monte carlo value for pi and serial correlation coefficient. henkos obtained good results. 3.4 nist statistical test suite the package includes statistical tests for: frequency, block frequency, cumulative sums, runs, long runs, marsaglia’s rank, spectral (based on the discrete fourier transform), nonoverlapping template matching, overlapping template matching’s, maurer’s universal statistical, approximate entropy, random excursions (due to baron and rukhin), lempel–ziv complexity, linear complexity, and serial. the nist framework, like many tests, is based on hypothesis testing. – state your null hypothesis. assume that the binary sequence is random. – compute a sequence test statistically. testing is carried out at the bit level. – compute the p−value which must be less than 0.01, otherwise, failure is declared. the cubic congruential generator fails frequency, cumulative sums, runs, aperiodic template at 11 from 284 templates, approximate entropy test, serial and lempel–ziv test and micali generator has 3 fails at aperiodic template. henkos and bbs pass all this tests. 4 security analisys what is a secure stream cipher? that is a question with no definitive answer, but i can make some assumption on that subject. the entire test package presented here can eventually reveal weaknesses but, even if the ciphers pass with good results, that is not a guarantee of its security. that does not make it fail proof. cryptographers consider that there are two main conditions for the security of a stream cipher with a k−bit key. • the attacker should not be able to predict future keystream generated by the cipher in any conditions: recovering the secret key, recovering the internal state of the cipher at some point, or otherwise. the attacker can obviously test all possible secret keys, so the complexity of a brute force attack (requiring at most 2k executions of the algorithm) gives a performance baseline to which any alleged attack should be compared to. • the attacker should not be able to recover the cipher’s key or internal state from the keystream. cryptographers also demand that the keystream be free of even subtitle biases that would let attackers distinguish a stream from random noise, and free of detectable relationships between keystreams that correspond to related keys or related nonce. this should be true for all keys (there should be no weak keys), and true even if the attacker can know or choose some plaintext or ciphertext. like other attacks in cryptography, stream cipher attacks can be certificational, meaning they aren’t necessarily practical ways to break the cipher but they indicate that the cipher might have weaknesses. securely using a secure synchronous stream cipher requires that one never uses the same keystream twice; that generally means that a different nonce or key must be supplied to each invocation of the cipher. application designers must also recognize that most stream ciphers don’t provide authenticity, only privacy: encrypted messages may still have been modified in transit. 488 d. bucerzan, m. crăciun, v. chiş, c. raţiu stream creaspeed bits attack cipher tion (cycles/ key init. internal best comp. date byte) length vector state known compl.1 a5/1−2 1989 voice 54 114 26? active 240 fish 1993 quite fast huge ? ? k-p a2 211 grain ≤ 2004 fast 80 26 160 key d3 243 hc−256 ≤ 2004 22? 28 28 215 ? ? henkos 2005 7.8 28 −210 28 210 ? 21024 isaac 1996 2.38−4.69 40−28 n/a4 8288 2006 wis5 5×101240 panama 1998 2 28 27? 1216? 2001 hc6 282 rabbit 2003 3.7−9.7 27 26 29 2006 n/a 2006 n/a rc4 1987 impressive 40−28 23 2064 key d 213 −233 seal 1997 very fast ? 25? ? ? ? sober−128 2003 ? ≥ 27 ? ? mes. forge 26 trivium ≤ 2004 22 −23 80 80 288 brute force 2135 table 1: comparison of some well known stream ciphers 5 performances analysis 5.1 testing platform for testing we select only one platform among many other possible platforms, on the criteria of disponibility and reproductibility of measurements. there are also results, reported in papers, that make possible only a relative comparison between the performances of different algorithms. 5.2 performances measurement the performance algorithms are mesuread by a special program, written in visual c and based on reading the processor clock before and after the calls of the main phases implementing functions, using the routines get_start_time and get_stop_time written in asm. the result is calculated as the difference between the two clock readings, minus the additional time consumed with the calls of the clock reading routines. in the henkos case the cpu time for k keys generation is 7.8 cycles/byte, and the encryption process performs 181 megabytes/second. comparing it to well–known algorithms and the algorithms tested in the estream project like cryptmt, dragon or salsa20, which are among the top algorithms in the final list, my results are good enough (for details see [6] and [4]). 5.3 implementation details the performance was measured reading the processor clock cycles using the rdtsc instruction. 5.4 comparison of performances in the real life, in very few cases the authors reveals all the details and the performances of the ciphers. in table 1 there are some results for some well known stream ciphers, [8]. stream ciphers analysis methods 489 6 conclusions there are a lot of stream ciphers used in cryptography because of the speed, but in this case nobody tried a standardisation like in block cipher area. the european union–based nessie project [7], which was aimed at evaluating the security of various cryptographic primitives, did not recommend any stream ciphers in their report. the performances and quality analysis on cryptographic stream ciphers algorithms are an ambitious goal for all the designers of algorithms. in majority of cases there is no proof of the behaviour of the new cipher, but it’s possible to verify the quality by performing statistical tests, and also to measure the performances of implementation and the speed by software means. in the future, new stream ciphers will appear so that new methods for analysis will be a permanent preoccupation for the cryptographic community. bibliography [1] bucerzan d. and gheorghiţă m., henkos – a new stream cipher: performance analysis, wartacrypt ’04 the 4th central european conference on cryptology, bedlewo, poland, july 2004. [2] bucerzan d., a cryptographic algorithm based on a pseudorandom number generator, synasc’08, timişoara, october 2008. [3] marsaglia g., diehard statistical tests, http://stat.fsu.edu/pub/diehard/ [4] matsumoto m., saito m., nishimura t. and hagita m., cryptmt stream cipher version 3, estream project, http://www.ecrypt.eu.org/stream/ [5] schneier b., applied cryptography, j. wiley & sons inc, (second edition), 1996. [6] ***, estream, http://www.ecrypt.eu.org/stream/ [7] ***, nessie european proiect, http://www.cosic.esat.kuleuven.be/nessie/ [8] ***, http://www.answers.com/topic/stream-cipher dominic bucerzan (b. may 17, 1956) received his m. sc. in information technology from "aurel vlaicu" university of arad, romania and a phd in economic cybernetics from the "bucharest academy of economic studies" (2005), with a paper in the field of information security. currently he works as a lecturer in informatics at the department of mathematics-informatics, faculty of exact sciences, "aurel vlaicu" university of arad, românia. his current research interests include aspects of it security and cryptography. he is author or co-author of 4 books and more than 45 papers and participated in 35 conferences and workshops. mihaela crăciun (b. march 10, 1972) received her master of science in information technology from "aurel vlaicu" university of arad, românia. at present she is a candidate for a phd in computer science at "politehnica" university of timişoara, românia. her current research is focused on decision in enterprise analysis. she published articles in her field of interest. 1computational complexity 2known-plaintext attack 3key derivation 4not available 5weak internal state 6hash collisions wang_ijcccv11n5.pdf international journal of computers communications & control issn 1841-9836, 11(5):734-746, october 2016. a momentum theory for hot topic life-cycle: a case study of hot hashtag emerging in twitter l. wang, x. li, l.j. liao, l. liu liu wang, xin li, le-jian liao*, li liu school of computer science beijing institute of technology no. 5 south zhongguancun street beijing, 100081, china wangliu2000@163.com, xinli@bit.edu.cn, liaolj@bit.edu.cn, liuli0407@hotmail.com *corresponding author: liaolj@bit.edu.cn abstract: the existing work on mining of hot topics is mainly based on topic multiplicity and attention from users in unit time. with the advent of social networking, the weight has been put on the hot topics which can effectively describe the importance and hotness of a topic. however, the researches on the influence exerted by the accumulation of attention towards hot topics and the alternation between hot topics and outdated ones are still relatively weak. in this paper, a novel algorithm for calculating the hotness of topics is proposed based on momentum. the number of the participants, but also the long tail effect of the historical accumulation on the topic is taken into consideration. through this algorithm, we can accurately build a model for the hot topics on their emerging growing period and effectively describe the whole life circle of the topic. additionally, the change between hot topics and old ones can be distinguished efficiently. our experiments show that the process of a topic growing into a hot topic can be detected explicitly. potential hot topics can be explored and the overdue ones can be rejected respectively. keywords: hashtag, hot topic, aging theory. 1 introduction in modern times social networking has become an important resource of real-time news updates. as smart phones and other mobile devices spread, there is a growing tendency that people use social networks such as twitter and weibo to obtain the hot issues happening across the world. detecting the hot topics out from the large-scale of information posted online simultaneously is of significant interest for many reasons. for one, it shortens the time for users to obtain the hot topic, which may play an important role in decision-making. also from the hot topics detected, one can easily have an understanding of current social dynamics. most of the social network systems may provide a ranking list of hot topics through the search or post count of keywords. while this approach fails to take the temporal relations of hot topics into account. current hot topic detection solutions are mostly based on the topic multiplicity and attention from users in unit time, whereas they neglect the digestion of the hot topics. also they aim to establish the topics from a clustering of keywords from the content layer, which overlooked the functions of a social network. from the point of view of research methods analysis, physics methods are applied in many fields, such as: newton’s theorem was applied to the [6,18], the gravity was applied to manufacturing modeling [21], the application of the theorem of momentum was used in [26,34], [15] combined with momentum method to launch the stock prediction. they carried out the research of modeling by physics methods. from the social networking features, in addition to the security and network mining research [11,28,31,35], also, many scholars are copyright © 2006-2016 by ccc publications 736 l. wang, x. li, l.j. liao, l. liu still studying hashtags’ other functions [27,40], in [1,37], xiao and aldhelaan were using social network to do the research on hot topic discovery and recommendation, chen [13] combined with evolution model to achieve the topic prediction. the accumulation of topics in social networks and the integration of the topic have strong physics characteristics, so we try to find a way to use physics methods to model the topic in social networks and combine the momentum theorem with hashtags. in this paper, we aim to detect those emerging hot topics according to the momentum theory with the use of hashtags. the momentum of hashtags can reveal the mechanism of dynamics from the temporal characteristics and quantity characteristics and discover the ideal life cycle of topics. this will result in using momentum theory to dig out hot topics more accurately and effectively. in this paper, the proposed algorithm does not need to collect and store a large number of historical data. therefore, it is suitable for data streams real-time calculation. moreover, the nested loops in the algorithm are few and the time complexity is approximate to o(n). due to high computational efficiency, this algorithm is suitable for large data analysis as well. the rest of the paper is organized as follows: in section 2, we give a review of related works. in section 3, we propose the definition of hot topics. section 4 describes the theoretical model of topic hotness. in section 5, we discuss the results of the experiments run. finally, in section 6, we present our conclusions and some future research directions. 2 related work topic detection and tracking (tdt) [2] has long been a foundation of the research related to hot topic detection. however, different from the traditional sources of information such as web pages, texts, etc., information in social networks is often very short and sparse, also spreading rapidly. under the background of the new era of social media, a series of work have been completed towards those characters. you et al. [7] utilized frequent item set mining algorithm sam [8] to find out hot topics denoted by combinations of keywords. thus, detecting the hot topics is comparable to mining frequent patterns from news streams. similar to that, a recent work by kim et al. [22] also took geographic elements into consideration, which provides a simple but useful approach to analyze real-time streaming data and finds geographic communities. according to fp-growth [20], giannella et al. [19] proposed the fp-stream time window and the digestion concept of the support parameters. this new algorithm can let the outdated items expired and accumulate the importance of items based on the timeline as well. however, the algorithm is more complicated. guo et al. [24] improved the tree structure in the frequent pattern stream mining algorithm (fp-stream) [19] and used the new algorithm to detect hot topics from twitter streams, which can lead to time-sensitive results. lee et al. [17] developed an algorithm for ranking topics, using topic energy to represent the significance of a given topic at each time point within a time period. the strategy in determining topic energy value considers factors such as popularity, burstiness and informativeness, which suits the character of information on social network better. in [29], the key entity significance is computed through traditional "tf-idf" evaluation method [33] in the information retrieval literature. then, clustering entities are used to generate significant events. bun et al. [25] computed the value of a term by using tf*idf and clustered terms into a sentence. yang et al. [39] applied the vsm to the task of news tdt and used a time window with a decaying function (tw-df) to model the temporal relations between documents and events. unfortunately however, the strategies above did not consider the temporal relations thoroughly. the newly generated hot topics cannot be distinguished from the outdated ones accurately. chen et al. [12,16] proposed an aging theory to model life cycles of news events. for all user a momentum theory for hot topic life-cycle: a case study of hot hashtag emerging in twitter 737 messages generated from the topic time interval, the aging theory calculates their nutrition and convert the nutrition value to the energy value, and then add into the cumulative energy value of the topic. it also applies energy decaying strategy on the topic. as time goes by, the energy decays gradually. if the increase of the energy is less than the reduction of the energy, the topic will show a trend of attenuation. if the energy value is less than a given threshold, this topic is set to "death" state and is removed from the hot topics list. the aging algorithm defines α as the nutrition transferred factor and β as the nutrition decayed factor, 0 < α < 1, 0 < β < 1, α decides the increase of nutrition from an input news document and β decides the nutrition loss in a period. zheng et al. [41] utilized aging theory on candidate topics discovered by a clustering method in each time slot in bbs. with energy values updated at the end of each time slot according to the three functions of aging theory, hot topics in each time slot can be easily found. chen et al. [14] combined aging theory with a term weighting scheme to extract genuine hot terms. based on the extracted hot terms, key sentences are then identified and grouped into clusters that represent hot topics by using multidimensional sentence vectors. cataldi et al. [10] improved the nutrition formula by using "idf" and formalized the keyword life cycle leveraging a novel aging theory intended to mine terms that frequently occur in the specified time interval which are relatively rare in the past. wang et al. [36] took media focus and user attention into the classical aging theory and proved the importance of these two factors. in comparison with these approaches, our solution extracts hot topics based on hashtags according to the functions of a social network instead of the content aspect. the hot topic emerging theory based on momentum can explicitly distinguish new hot topics from outdated ones. the methods mentioned emphasized topic extraction from a cluster of sentences or the frequency pattern of topics, but ignore the topic life cycle. the aging theory [12,16,41] discovers the life cycle of topic but does not deal with the older topic elimination mechanism very well. chen et al. [23] proposed a similar idea to our model, defining hot velocity and hot acceleration to recognize hot topics but did not reveal the dynamic characteristics. different from that model, we define hot topic emerging theory based on momentum. the utilization of momentum theory can successfully describe the whole life circle of a topic, thus sift out the newly emerged hot topics from the old ones more effectively. 3 definition of hot topic hot topics in real microblogging systems have three characteristics: 1) the number of posts related to this topic would exceed a threshold; 2) the amount of users concerned should be large, especially those key persons; 3) a hot topic would occur at a short time [23]. in [14], the characteristics of hot topics are concluded based on [25] as those appear on many news channels and go through a life cycle of birth, growth, maturity, and death. in this paper, hot topics are defined as the topics which are both influential and latest. four characteristics are summarized as follows: i timeliness. the hot topic refers to the people and things that have happened recently. those topics which have been under discussion for more than seven days or one month cannot be regarded as hot topics. ii development. due to the dynamics while topics are propagated, the influence of public opinion will spread as time goes by. the hot topics diffract backwards. iii accumulation. the more people are involved into the discussion of the topic, the hotter the topic is. however with more and more attention drawn on a certain topic, it becomes less possible to be hot topic again. 738 l. wang, x. li, l.j. liao, l. liu iv digestion. as time goes on, the influence of a topic will unavoidably be watered-down. thus hot topics have the character of obliviousness. 4 kinetic model of topic hotness the topic under a certain time point can be regarded as a set of large number of synonymous terms. in other words, to describe this concept from the perspective of physics, we can assume each term as a particle which owns weight and velocity. besides, the activeness of the particle represents its energy and the activeness of a topic is the reflection of the particles inside the set. as time goes on, the set is dynamically changing with terms increasing, thus the energy of the particles are changing too. in time t − 1, the topic set is represented as topicn(t−1) . in time t, the additional subset topicn∆t will join into topicn(t−1) as the new set topicnt. this process can be regarded as two kinetic physical objects collided and fused into one kinetic physical object with the exchange of energy. in this paper, we adopt conservation of momentum to illustrate this process. in this paper, we propose to use momentum to represent the physical characters in thermodynamics and dynamics of hot topic. negative acceleration is imported to represent the digestion of hotness according to the attenuation of the hot topics while propagated. we use momentum equation to present the active momentum of topics which is aroused by discussion. a topic is consists of many synonymous terms. in social networks, a post can be seen as a term, and similar terms constitute a topic. the topic set is varying all the time and the variation composes a time sequence. for a topic n, the topic set can be expressed as: topicn := {topicn1, topicn2, ..., topicnt} all kinds of topics are formed as the whole topic set: topic := {topic1, topic2, ..., topicn} the current topic set is the combination of the former set and the subset increased during the interval, as: topicnt := topicn(t−1) ∪ topicn∆t topicn∆t := {termn1, termn2, ..., termnm} we can also say that the topic under a specific time spot is the set of all the terms emerged before, as: topicnt := {termn1, termn2, termn3, ..., termnt} 4.1 momentum modelling in this paper, we use the equations below to represent the variation when a topic becomes hotter. the topic in t − 1 can be seen as a physical entity mt−1 with weight and velocity. the increased terms inunder that topic can be regarded as another entity mterm∆t. thus the topic in t as an entity mt is the combination of mterm∆t and mt−1 after a collision with the velocity of vterm∆t. the increased momentum of topic entity mterm∆t is the sum of the momentum of each term added. we can calculate the momentum of each topic as follows: ⇀ mterm∆t = m∑ ∆t=1 (mterm∆t × ⇀vterm∆t) (1) a momentum theory for hot topic life-cycle: a case study of hot hashtag emerging in twitter 739 the momentum of the mt equals to the sum of the momentum of mt−1 and mterm∆t. the total momentum is invariable before and after the collision. ⇀vt−1 + ⇀g∆t represents the final velocity after ∆t. the development and digestion of topic can be expressed clearly by this equation: ⇀ mt = mt−1 × ( ⇀vt−1 + ⇀g∆t) + ⇀ mterm∆t (2) the weight of the topic in time t is the sum of the weight of itself before collision and the weight of the topic added, which shows the accumulation of topic. the more the topic is discussed, the heavier the topic is. mt = mt−1 + m∑ ∆t=1 mterm∆t (3) in time t, the velocity of the topic is the quotient of total momentum and total weight. ⇀vt = ⇀ mt/mt (4) the topic reaches a height in a certain velocity after being represented in physics. in this paper we define this height as the hotness of the topic in that time spot. ⇀ ht = ⇀vt−1 × t (5) the height is not able to keep growing according to the digestion of the topic. under this circumstance we use acceleration of gravity g to lower the height (hotness). the equation is modified as follows: ⇀ ht = ⇀vt−1 × t0 + 1 2 × ⇀g∆t2 (6) 4.2 modelling solution in social networks, hashtag (#) is used extensively to determine the topic, which is convenient to aggregate and classify vast amounts of information and let people who follow a certain topic get the relevant information more easily. twitter showed its significant value in information propagation almost before every emergency and important activity due to the aggregation of using hashtags. we can draw a safe conclusion that hashtag reflects the true tendency of topics and has great potential in the reconstruction and compilation of information. in this paper we use hashtag for identification modeling of topics. a topic under a specific time spot is defined as a set with synonymous hashtags. topicnt := {hashtagn1, hashtagn2, hashtagn3, ..., hashtagnt} at a specified time, more similar hashtags means the topic is more active. in a specific period of time, topic is a sequence of sets. in this section we use m for the weight of hashtag, v for the velocity of hashtag, and m for the momentum of hashtag. the topic hotness modeling algorithm is as follows: 5 experiment analysis and result 5.1 data corpus and parameter settings in this paper we adopt the historical(2009/6/11-2009/12/31) data from twitter as corpus. we extracted 460,496 twitter terms and 22,063 hashtags in total, neglecting those topics which lasted 740 l. wang, x. li, l.j. liao, l. liu algorithm 1 topic hotness modeling input: the set of hashtags which emerged during a certain period of time output: the topics list ranked by hotness in each time spot t 1: for t = t0 do 2: process the data we collected and aggregate into topic set topic. 3: for each topic topicn from topic do 4: obtain the set {termn1, ..., termnt} of the topicnt 5: calculate the hotness of topicnt by ⇀ ht = ⇀vt−1 × t0 + 1 2 × ⇀g∆t2 6: end for 7: output the topic list ordered by ht 8: t = t + 1 9: end for table 1: parameter setting parameter value hashtag initial velocity v 10 hashtag weight m 1 acceleration g -3 interval t (day) 1 less than 3 days or contained less than 2 posts, the coverage can meet the needs of simulation experiments. the main parameters of the computer for this modelling are as follows: cpu is intel(r) core(tm)2 duo, main frequency is 2.0ghz, memory frequency is 777mhz, memory capacity is 1.96gb, running environment is windows xp. the rest parameters are set as the table 1. 5.2 comparison of hot topic detection in this experiment, our momentum theory (m) is compared to two proposed methods. the baseline method (a) [12, 16] is a basic aging method. cataldi et al. [10] improved a method (a-tf) which enhanced the aging method by using an augmented normalized term frequency. as a result, top 3 and top 10 generated hot topics from each method are evaluated using official tdt measures including: precision (p), recall (r) and f1-measure (f1). in table 2, the best score for each item is represented in bold. in the top 3 comparison, our momentum theory achieves both highest precision and recall which results in the best f1 score, while the aging method achieves both reasonable precision and recall. in the top 10 comparison, the precision of our momentum theory is still the highest in the first 10 hot hashtags but loses table 2: comparison of three methods method p r f1 top 3 a 0.67 0.67 0.67 m 1.00 0.67 0.80 a-tf 0.50 0.50 0.50 top 10 a 0.63 0.71 0.67 m 0.88 0.78 0.82 a-tf 0.56 0.83 0.67 a momentum theory for hot topic life-cycle: a case study of hot hashtag emerging in twitter 741 table 3: top 3 hashtags changing circumstances in a week in three methods 2009/7/6 2009/7/7 2009/7/8 2009/7/9 2009/7/10 2009/7/11 m mileycyrus mileycyrus mileycyrus 140army 140army zyngapirates 140army 140army 140army mileycyrus zyngapirates hottest100 140mafia 140mafia gorillapenis gorillapenis urumqi ashes a livestrong moonfruit turnon notagoodlook ff unacceptable iranelection nothingpersonal turnoff iranelection followfriday iranelection 140mafia iranelection iranelection 140mafia iranelection ff a-tf livestrong moonfruit turnon iranelection ff unacceptable iranelection iranelection turnoff 140mafia followfriday iranelection musicmonday livestrong iranelection notagoodlook iranelection ff recall. we believe that is due to the fact that the momentum mechanism likes to remove the old topics. the recall of a-tf is the best, because it improves the aging algorithm which will be sensitive of hot topics and enlarge its coverage, but there is a reduction in the precision. in the f1 item, the momentum theory has maintained the highest standards. in conclusion, the comprehensive performance of momentum theory is better than the other two algorithms. 5.3 comparison of hot topic trending due to space limitations, table 3 represents the top 3 hashtags from 2009/7/6 to 2009/7/11 in the momentum theory (m), the aging algorithm (a) and the improved aging algorithm (atf) . these results show that the topic trending in m is relatively stable. hashtag #mileycyrus, #140army and#zyngapirates are stable up to first place in turn. in the last two algorithms, #iranelection is ranked in the top 3 places. however, according to the historical data checking, this hashtag appeared over 20,000 times a day in june and was already ranked as hottest topic at that time in all three algorithms. clearly, in july #iranelection should not be a hot topic again. hashtag #140mafia’s overall performance is good, the repeating number is higher and the potential of impacting as hottest topic is great, so it is ranked as top 3 in the three algorithms. although it is stable in m, it is unstable in a and a-tf. table 4 presents the top 10 hashtags in two days (2009/7/6-2009/7/7) in three algorithms. hashtag #mileycyrus remains first place in m in two days, ranks 7th in a, falls out of the top 10 in a-tf and only ranks 17th as 0.635 energy value. it is because there are a lot of old hot topics which interfere with these two days of ranking, while these old hashtags already were the hot topics in an earlier time. #140army is in second place in m, but ranks 13th and 11th in a and a-tf separately, falling out of top 10. #flip2009 is captured only by m, while it gets its highest times in 5th july and 6th july. but due to relatively lower times, it is abandoned by two aging algorithms. as we mentioned before, #140mafia is captured in all three methods. #tcot, #spymaster and #tweetmyjobs had more than thousands of times per day from 2009/6/12 and were listed as hot topics in momentum theory at that time. these are not hot news yet, but these hashtags are still ranked as top 10 hashtags in a and a-tf. #mj is a standard hot hashtag which arise sharply in 7th july as 1,077 times occur, however, it is replaced by #tcot in a and a-tf due to the shortcoming of lower discrimination degree of the aging theory. because of the large amount of data and limited space, we take hashtag #mileysycyrus as an example to analyze the trending of different algorithms. figure 1 shows the times, hotness and energy value six months profiles of hashtag #mileysycyrus, which is the hottest topic of 6th july in momentum theory. in figure 1 a, this hashtag began to appear in 12th june. after that, it did not reproduce for about a week. later, it appeared sometime in a lower rate. on 5th july, it shot up suddenly 742 l. wang, x. li, l.j. liao, l. liu !!"#$#%& !!"#'#%& !!"#"#%& !!"#%%#%& !%!#%#%& ! %!!! !!! (!!! )!!! $!!! &!!! !!"#$#%& !!"#'#%& !!"#"#%& !!"#%%#%& !%!#%#%& ! %! ! (! )! !!"#$#%& !!"#'#%& !!"#"#%& !!"#%%#%& !%!#%#%& !*! !*% !* !*( !*) !*$ !*& !!"#$#%& !!"#'#%& !!"#"#%& !!"#%%#%& !%!#%#%& +!*% !*! !*% !* !*( !*) !*$ !*& !*' ! " # ! " # $ % & ' ( ) % * + ) , !$% #&'()* + !$% # ( & ' ( ) % * + ) , #( # #!$% . $ / , 0 #+ # # !$% . $ / , 0 #+,&figure 1: comparison of #mileysycyrus (a:occurrence number of hashtag per day, b:hotness of hashtag per day in momentum theory, c:energy value of hashtag per day in aging method, d:energy value of hashtag per day in a-tf method) a momentum theory for hot topic life-cycle: a case study of hot hashtag emerging in twitter 743 table 4: top 10 hashtags in two days in three methods hashtag m times hashtag a times hashtag a-tf times 7-6 mileycyrus 33.63 187 livestrong 0.56 4026 livestrong 0.67 4026 140army 27.93 781 iranelection 0.55 2640 iranelection 0.66 2640 140mafia 27.68 2316 140mafia 0.53 2316 musicmonday 0.66 2966 moonfruit 26.23 2124 moonfruit 0.51 2124 140mafia 0.66 2316 gorillapenis 24.98 984 musicmonday 0.50 2966 moonfruit 0.66 2124 flip2009 21.19 12 spymaster 0.47 1616 spymaster 0.65 1616 mj 20.86 288 tcot 0.40 1206 tcot 0.65 1206 rw09 20.64 16 gorillapenis 0.39 984 gorillapenis 0.64 984 katemcrae 19.36 10 mileycyrus 0.38 187 militarymon 0.64 933 cmonbrazil 19.11 140 honduras 0.30 676 jobs 0.64 827 7-7 mileycyrus 35.64 18 moonfruit 0.62 5147 moonfruit 0.64 5147 140army 30.88 461 nothingpersonal 0.54 3670 iranelection 0.63 1902 140mafia 29.23 1644 iranelection 0.51 1902 livestrong 0.63 1228 gorillapenis 29.09 559 140mafia 0.49 1644 140mafia 0.63 1644 moonfruit 25.65 5147 livestrong 0.47 1228 musicmonday 0.63 1423 cmonbrazil 20.13 47 musicmonday 0.46 1423 spymaster 0.63 936 urumqi 19.61 153 spymaster 0.40 936 tcot 0.63 839 mj 19.27 1075 tcot 0.36 839 tweetmyjobs 0.63 1013 crocmint 19.27 6 tweetmyjobs 0.34 1013 gorillapenis 0.63 559 xinjiang 19.05 110 threadless 0.30 1192 jobs 0.63 642 and reached 5,302 times, then declined rapidly. although it repeated a few dozens or hundreds times later, but never rose again sharply. in figure 1 b, momentum theory captures the hotness rising rapidly on 5th, 6th, 7th july and declining dramatically quickly. this hotness is higher than other hashtags, so it is ranked in first place. the aging theory also finds this change but does not list this hashtag in a higher position (only the 9th place) due to a relative lower energy value compared with other hashtags. the a-tf method almost gets every higher repeat rate moments, but it cannot distinguish the highest point from all higher points clearly. so, it is not able to choose this hashtag from the data corpus efficiently. as a result, it falls out of the top 10 hashtags. in figure 1, momentum theory presents a higher sensitivity and better ability to discriminate the hottest hashtags from others than the other two algorithms. extrapolating the results from the experiment, we can obtain the hottest topics during the period of time we captured by the algorithm we proposed, and compared with the hot hashtags that are extracted by the rank of occurrence. from the comparison of table 5, we can explicitly observe that the occurrence of the hashtag may mislead to the generation of a hot topic, such as #followfriday, #ff and #1. those topics own little valid information, however are regarded as hot according to the high frequency of occurrence. by the algorithm we proposed, the topics which lack realistic meaning will be eliminated by the momentum equation. the topics which survive will be the active ones with realistic meaning. 6 conclusion and future work in this paper we propose a novel algorithm for hot topic detection based on momentum theory using hashtags for defining a topic. the main contributions of the paper are as follows. 744 l. wang, x. li, l.j. liao, l. liu table 5: hottest hashtags in half a year momentum theory occurrence hashtag hotness hashtag days times 1 tehran 44.73 ff 113 417802 2 nomaschavez 44.19 iranelection 116 368519 3 happybirthdaymikey 43.13 tcot 120 288091 4 nem 43 jobs 115 243586 5 iranelection 41.14 mobsterworld 85 203445 6 zain 38.93 followfriday 113 188664 7 happybirthdaypink 38.2 1 114 166260 8 teenisland 38.15 musicmonday 109 t164399 9 blackbery 37.94 140mafia 105 149078 first we analyze the characteristics of hot topics and conclude into four points:1) timeliness. 2) development. 3) accumulation. 4) digestion. then we build a hot topic detection model with momentum theory. the experiments show that our model can identify those emerging hot topics effectively and accurately. in the future, we hope to filter the posts under each topic to provide the users with a purer source of information without the distraction from irrelevant posts. our algorithm standardizes the topic life cycle into a very stable curve which makes the topic prediction possible. some artificial intelligence techniques have been applied in mathematical modeling [4, 32, 38], and some achievements have been obtained in the prediction of data modeling [3,5,9,30]. we will try to use these techniques to predict the hot topics in the next step. acknowledgment the authors would like to thank the anonymous reviewers for their insightful comments. the authors also thank yanmei zhai and xu han for their help with data preparation and experiments. this work has been partially supported by national program on key basic research project under grant no. 2013cb329605, nsfc under grant no. 61300178 and natural science foundation of beijing under grant no. 4092037. bibliography [1] aldhelaan, m.; alhawasi, h. 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(2009); hot topic detection on bbs using aging theory, web information systems and mining, isbn 978-3-642-05249-1, 5854(14): 129-138. int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 696-708 p2p resource sharing in wired/wireless mixed networks j. liao jianwei liao college of computer and information science southwest university of china 400715, beibei, chongqing, china e-mail: liaojianwei@il.is.s.u-tokyo.ac.jp abstract: this paper presents a new routing protocol called manager-based routing protocol (mbrp) for sharing resources in wired/wireless mixed networks. mbrp specifies a manager node for a designated sub-network (called as a group), in which all nodes have the similar connection properties; then all manager nodes are employed to construct the backbone overlay network with ring topology. the manager nodes act as the proxies between the internal nodes in the group and the external world, that is not only for centralized management of all nodes to a certain extent, but also for avoiding the messages flooding in the whole network. the experimental results show that compared with gnutella2, which uses super-peers to perform similar management work, the proposed mbrp has less lookup overhead including lookup latency and lookup hop count in the most of cases. besides, the experiments also indicate that mbrp has well configurability and good scaling properties. in a word, mbrp has less transmission cost of the shared file data, and the latency for locating the sharing resources can be reduced to a great extent in the wired/wireless mixed networks. keywords: wired/wireless mixed network, resource sharing, manager-based routing protocol, backbone overlay network, peer-to-peer. 1 introduction peer-to-peer technology (p2p) is a widely used network technology, the typical p2p network relies on the computing power and bandwidth of all participant nodes, rather than a few gathered and dedicated servers for central coordination [1, 2]. according to the research and analysis on internet traffic management conducted by ipoque germany, p2p applications dominate internet traffic from 50% to 90%, and the statistics from chinese sources reveal that p2p traffic currently accounts for 70% of china’s total network traffic [3]. this indicates that resource sharing via various p2p techniques contributes to the major part of resource sharing on the internet [4]. in general, p2p systems implement an abstract overlay network, built at application layer on top of the native or physical network topology [2]. from the architecture view [5], p2p systems are generally divided into structured systems, unstructured systems [6] and hybrid systems [7]. a structured p2p system employs a globally consistent protocol to ensure that any node can efficiently find some of the peers that have the desired resources, even though the file is extremely rare. however, since dht-like (distributed hash table, dht) data structure is employed for maintaining the whole structured p2p system, the scalability is a critical problem. chord [8] is a typical structured p2p system. the unstructured p2p system is formed when the overlay links are established arbitrarily. in an unstructured p2p system, if a peer wants to lookup a piece of desired resources in the network, the query has to be flooded through the network to find the peers who have the desired sharing resources as many as possible. however, the unstructured p2p system uses flooding queries to discover the target objects, which may introduce lots of network traffic. bittorrent [10] is a well-known unstructured p2p system. in addition, many structured p2p systems use stronger peers (super-peers or super-nodes [11]) as servers, and the client-peers are connected in a star-like fashion to a single super-peer. this architecture can simplify the copyright c⃝ 2006-2012 by ccc publications p2p resource sharing in wired/wireless mixed networks 697 network architecture, but super-peers hold all routing information, even though a local search is also conducted by a relevant super-peer, thus the super-peers are apt to be overloaded. as examples for hybrid networks can be named modern implementations of gnutella2 [12] and the edonkey network [13]. nowadays, various modern devices can access internet by different resorts, and these devices also want to share resources with others, it is quite clear they can employ the p2p techniques. but different kinds of devices have the different properties and purposes of use, thus the sharing targets are inequality. for instance, widely used handheld devices may share a several megabytes mp3 audio file, and rarely share a size up to several gigabytes video file. but for normal desktops and laptops, the latter sharing is quite common; therefore, treating different kinds of peers as same is not an ideal strategy in the heterogeneous networks. in this paper, we propose, implement and evaluate a new routing protocol called mbrp (manager-based routing protocol) for constructing p2p resource sharing networks, in which there are many disparate devices. as a matter of fact, mbrp has been inspired by the hybrid p2p architecture, but in mbrp, the target sharing resources might be replicated and stored on the manager nodes, and the message forwarding may not conducted by manager nodes. in addition, although the p2p protocols mentioned above are scalable and efficient, they were designed originally for wired networks and are generally not suitable for wireless networks, in which nodes join and depart much more frequently. the main idea of mbrp is to organize the diverse devices into different groups according to their properties such as location, wired or wireless etc., and then appoint a manager node for each group to communicate with other groups. since the devices in the same group have the similar properties, the expected resources are stored and shared in the same group with quite high probability, only the desired resource is not in the group, the inter-group communication is launched. this paper is organized as follows: we present the design and implementation of mbrp in section 2; the evaluation experiments and results are shown in section 3; finally, we make concluding remarks in section 4. 2 the design and implementation of mbrp as various internet-connected devices have the different properties including bandwidth, connection resorts and storage capacity, the proposed mbrp first divides all participant nodes into several groups according to their properties such as wired or wireless connection, then elects a manager node for each group for communicating with the external nodes belonging to other groups. different from traditional hybrid p2p systems, in mbrp, the nodes may communicate with other internal nodes belonging to the same group directly without any intervention from the manager node. besides, the manager nodes might cache the replicas of hot sharing resources to reduce the lookup and transmission overhead for the sharing objects. 2.1 the architecture of mbrp network figure 1 shows the topology of a heterogeneous network built by resorting to mbrp. all nodes are divided into several groups according to their connection property, i.e. wired or wireless access. in each group, there is only one proxy node called manager node, which is responsible for the management of other internal nodes in the same group. for instance, the internal nodes who want to communicate with other nodes belonging to the different groups, are supposed to resort to their manager node. in addition, all manager nodes are connected into a ring, a similar topology to chord [8], but all internal nodes in the same group can connect to each other via different topologies. 698 j. liao figure 1: the topology of wireless/wired mixed network via mbrp 2.2 application routing protocol we assume that mbrp is built on ip-based wide area network, we use a m-bit hash function to calculate the unique m-bit group id. all internal nodes in the same group have their unique group id, which is also used by the manager node as its own private id to communicate with other manager nodes. for the purpose of routing in the whole network, like routing in chord, each manager node holds its predecessor and finger table [14]. tk = bnid + 2 k−1 mod 2m (∀k,where 1 ≤ k ≤ m) (1) equation 1 is employed to calculate the manager node’s successors, where bnid is the manager node id, and m is the number of bits of manager node id, tk is the id of the first successor; then, it calls the find_successor(tk) function, which is shown in figure 2, to calculate the next successor tk′; finally, each manager node has m successors in its finger table when the find_successor function has been called m-1 times. figure 3a shows the finger table of node n5512, in which some example successors of node n5512 are demonstrated. n.find_successor (id) if (id є (n, suceessor)) return successor; else return successor.find_successor (id); figure 2: find manager node’s successor for the nodes who want to publish some sharing files after joining into the group, they are supposed to add the group id as a part of the identification of the sharing files. figure 3b shows that an internal node belonging to group n2124 has published a file named as k4814, but the published name sent to the other manager nodes is n2124#k4814. for other manager nodes, that means the sharing file k4814 is located in the n2124 group. p2p resource sharing in wired/wireless mixed networks 699 figure 3: (a) finger table of n5512 ; (b) routing table and cache table of n5512 creating routing table as shown in figure 3b, for publishing file k4814, the manager node n2124 computes the first successor according to equation 1, therefore, n2124 publishes k4814 to its successor manager node, i.e. n5512 ; then n5512 adds the new entry n2124#k4814 to its own routing table; finally, it makes a replica of k4814 and an associated cache entry in the cache table if the cache strategy is enabled. figure 4: (a) routing table of internal node b; (b) lookup originated from internal node a to b and d; (c) lookup succeed message passed back from d to b and a while an internal node lookups a piece of sharing resource, which is in the different groups, then the lookup process can be taken over by its manager node. otherwise, while the sharing file is in the same group, the desired file data can be transferred within the group directly. thus, each internal node also holds a routing table to show the routines within the group. however, quite different from the manager node, the ids of the internal nodes are their ip addresses; thus, 700 j. liao the routing table is different as well. the routing table of the internal node b (b represents node’s ip address) is shown in the figure 4. bnid#oid stands for the manager node id and the sharing resource id, and the next hop means the next node for getting the sharing resource. for instance, in order to obtain the sharing resource k7189 located in group n5512, internal node b should send the lookup request to the next hop, i.e. the internal node d. the symbol bn in the routing table represents the id of the manager node that in the same group. lookup algorithm the lookup algorithm will be described from 4 parts: sending a request, receiving a request, manager node routing on the backbone overlay network and receiving a lookup hit message. figure 5a shows the algorithm of sending a lookup request. if a node wants to locate a piece of sharing resource labeled as obj_id, then it calls send_lookupreq(obj_id) to send the lookup request. first, the find_cache(obj_id) function is called to make sure whether the sharing object is in its own cache or not; if not,the find_routetable(obj_id) function will be called to obtain the related routing information; while there is no related routing information, it calls get_approximated(obj_id) to obtain the feasible routing information to find the next hop n’. finally, send_req(obj_id, n′) is called to send the lookup request to the next hop n’. figure 4a shows an example of sending a lookup request for the sharing object k9854, since there is no corresponding entry in the route table, the get_approximated(k9854) function is called to obtain the feasible routing entry and the lookup request is forwarded to the selected node d. // send a lookup request to find the obj_id send_lookupreq (obj_id) r = nil; n’= nil; if (find_cache (obj_id) is not nil) r = get_routetable (obj_id); else r = get_approximated (obj_id); n’= r.nexthop; send_req (obj_id, n’); // receive a lookup request from the predecessor recv_lookupreq (obj_id) r = nil; n”= nil; if (find_cache (obj_id) is not nil) send_lookup_hit (src, obj_id, data); return; if (find_routetable (obj_id) is not nil) r = get_routetable (obj_id); else r = get_approximated (obj_id); n” = r.nexthop; send_forward (obj_id, n”); figure 5: (a) sending a lookup request; (b) receiving a lookup request the algorithm of receiving a lookup request from the predecessor is almost same to that of sending a lookup request. it receives a lookup request, and then processes like sending a lookup request, the pseudo-code of the algorithm is shown in figure 5b. while the target sharing object is found, then the send_lookuphit(obj_id, src, data) function will be called to transfer the resource to the request node. figure 4b shows an example of the procedure while the internal node b handles the received lookup request from node a. since there is no corresponding routing entry in node b’s routing table, it calls get_approximated(obj_id) to find the feasible next hop, i.e. node d, and forwards the request to it. the process of handling a received lookup request does not stop until the resource is found or timeout (i.e. maximum hop count exceeded). while the lookup request is not fulfilled within the group, it will be forwarded to the manager node, the core_ring_route(obj_id) function shown in figure 6a, will be called by the group’s manager node to handle the request from other manager nodes. after receiving the p2p resource sharing in wired/wireless mixed networks 701 // routing in backbone overlay network core_ring_route (obj_id) r = nil; n’= nil; b’= nil; if (find_cache (obj_id) is not nil) send_lookup_hit (src, obj_id, data); return; if (find_local (obj_id) is not nil) n’= get_local (obj_id); send_forward (obj_id, n’); else if (find_routetable (obj_id) is not nil) r = get_routetable (obj_id); b’= r.bnid; // the manager id else b’= find_successor (obj_id); send_core_ring_forward (obj_id, b’); // receive a object hit message recv_lookuphit (src, obj_id, data) update_routetable (src, obj_id); if (src is not equal to me) send_lookup_hit _backroute (src, obj_id, data); if (find_caches (obj_id) is nil) append_caches (data); return; figure 6: (a) routing in backbone overlay network; (b) algorithm of receiving a lookup hit lookup request, the manager node checks the target object is in its own group or not. if the object is in its group, then the request is forwarded to the corresponding internal node; otherwise, it forwards the request to other corresponding manager node whose group has the target object or the successor manager node in the finger table. the algorithm of receiving a lookup hit message is shown in figure 6b, while the manager node receives the lookup hit message, it first updates its routing table to label the new routine to the target object; then if the cache mechanism is enabled, it also makes a replica of the target object on its local disk. from the above description, we can see that while the target sharing object is found, then the expected file will be transferred to the request node by the reverse routing path. at the same time, if cache strategy is enabled, one replica of this target object is made and stored in each manager node on the routing path for quick responses to the future lookup requests. we should mention that the number of cached replicas in the manager node is configured and limited, lru is used to evict an existing replica and append the new replica into the local cache. 2.3 dynamic registration since the handheld devices have the roaming property, that means they might change their groups and manager nodes frequently, we have adopted a mechanism like ip mobility support [15] called dynamic handover, to allow the handheld device to register to a new manager node after the roaming. figure 7 illustrates the dynamic registration in following steps: 1. the handheld device issues a registration request with its current ip address, the former ip address and the former manager node id (hashed value from the manager node’s ip address) to the new manager node for registration after it roams to another group. 2. the new manager node creates a new internal node record, and replies the handheld device with message about the registration has been handled. the handheld device updates its manager node and tries to re-build its routing table again. 3. the new manager node notifies the device’s former manager node that the device has joined into its group and the old record should be removed; then the previous manager 702 j. liao previous manager node new manager node mobile client mobile client (1) registration request (2) registration reply (3) deregistration request (4) deregistration reply figure 7: dynamic handover algorithm node deletes the corresponding record, and broadcasts that de-registration message to its all internal nodes to require them to update their routing tables. 4. the previous manager node replies to the new manager node with the message of the de-registration has been handled. 2.4 manager node election the manager node plays a critical role in the mbrp network, while the manager node fails or departs from the group, a new manager node is supposed to be elected. as a matter of fact, the main principle for electing a new manager node is the well relaying property, which means lots of internal nodes choose it as the next hop in their routing table entries; if more than one internal nodes have the same relaying property, then one of them will be selected randomly. on the premise that network is still working when the manager node has exited or failed, the internal node, who first detects the failure or exit of the manager node, broadcasts the election request to other internal nodes. we assume the messages related to election are never lost, then all internal nodes belonging to the same group should take part in the process of election. 1. after receiving the election request, all internal nodes check the possible manager node candidate(s) according to the relaying property, and then reply to the issuer of the election request with the candidate(s). it is possible that the internal nodes may send several candidates who have the same relaying property; 2. the issuer of election request collects all replies from the internal nodes, then determines which node is the unique manager node; 3. the result of election will be broadcast to the corresponding internal nodes; 4. the new created manager node should be insert into the backbone overlay network simply like inserting a node into a ring topology network. in general, the new manager node replaces the failure one in the ring of the backbone overlay network; 5. all nodes in the group (including the manager node), which have a new manager node, remove the records with previous manager node and update the manager node information; p2p resource sharing in wired/wireless mixed networks 703 6. the new manager node broadcasts that the former manager node is not working, to other manager nodes on the backbone network and requires them to update the routing table. at the same time, the new manager node re-builds its routing table and finger table. we should mention that because all nodes in the group should re-build their routing tables, and routines on the backbone overlay network are supposed to be updated as well, the overhead brought by electing a manager node is not trivial. 3 experiments and evaluation the ns2 [16] was employed as our experimental platform while analyzing the performance and overhead on both the mbrp system and its comparison counterpart. much more exactly, the module gnutellasim in ns2 is used for our experiments. the hybrid p2p system gnutella2, which uses super-peers (called hubs) to manage the internal nodes in the same group, has been selected as our comparison counterpart while evaluating the overhead, such as network traffic, in the manager nodes in the mbrp network system. moreover, the manager nodes play significant roles in our proposed mechanism mbrp, so that they are supposed to have enough bandwidth and processing power. because wireless network is connected to the wired network via a gateway (also called access point), in our experiments, we selected such nodes as manager nodes for wireless groups. regarding to wired groups, we simply appointed the fixed servers as manager nodes. thus, all nodes can join the whole system by registering to their own manager nodes. 3.1 overhead on manager node 0 50 100 150 200 250 300 350 400 450 50 60 70 80 90 100 110 120 130 140 a ve ra ge l oo ku p l at en cy ( m s) number of nodes mbrp(1:10) gnutella2(1:10) mbrp(1:1) gnutella2(1:1) mbrp(2:1) gnutella2(2:1) figure 8: the average lookup latency 0 2 4 6 8 10 50 60 70 80 90 100 110 120 130 140 a ve ra ge l oo ku p h op s number of nodes mbrp(1:10) gnutella2(1:10) mbrp(1:1) gnutella2(1:1) mbrp(2:1) gnutella2(2:1) figure 9: the average lookup length we have conducted the comparison experiments between gnutella2 and the proposed mbrp, to show the overhead of introducing the manager nodes in mbrp. for both target systems, the overhead of locating the local objects (belonging to the same group) and locating the external objects (belonging to the different groups) are different, we adopted different internal/external access ratios, i.e. 1:10, 1:1, 2:1, to show the different overhead; each group has 50 internal nodes and each experiments stands 200 seconds. we measured both the average lookup latency and the average lookup hops for gnutella2 and mbrp with different access ratios. figure 8 reports the results of average lookup latency (lower is better), except for the case of internal/external access ratio is 1:10, while no less than half of lookups for sharing resources are hit within the group (i.e. access ratios are 1:1 and 2:1), the lookup latency introduced by mbrp is less than gnutella2, especially while the access 704 j. liao ratio is 2:1, mbrp reduces around 20% lookup latency. that is because all communications within the group should be handled by the super-peers in gnutella2, but the internal nodes may communicate with each other in mbrp. figure 9 presents the results of average lookup length(i.e. hot count, lower is better), the similar tendency to the average lookup latency between gnutella2 and mbrp. in addition, from figures 8 and 9, we can conclude that mbrp needs just a little more time while increasing the total number of nodes, this shows that mbrp has well scalability. 3.2 overhead on backbone overlay network ns2-gnutellasim was also employed to show the traffic on the backbone overlay network with the different network properties. in order to accelerate the access speed to the sharing resources, mbrp applies cache mechanism to store the hot resources in the manager nodes when these resources are transferred via/to them. in this section, we will inspect that different cache strategies bring about what kind of benefits to the lookup latency and negative effect on the traffic overhead in backbone overlay network respectively. the following cache strategies have been taken into consideration: 1. no cache, which represents that the cache mechanism is disabled. 2. unlimited cache, which means the manager nodes can cache unbounded replicas. 3. weighted cache, which indicates that the manager nodes can cache limited resources, while the ceiling is reached, some existing cached resources should be swapped out to make space for the new replicas with lru cache algorithm. because in both of gnutella2 and the mbrp mechanisms, all inter-group messages are handled by the manager nodes or super-peers, the traffic overhead on backbone overlay network of gnutella2 is same to that of mbrp without cache. we do not report the experimental results regarding to gnutella2 in the following experiments. in these experiments, gt-itm [17] is used to construct the network topology, which has 500 manager nodes, the size of the sharing resource is 1024 byte, wired/wireless access ratio is 1:1, and the duration of each experiment is 500 seconds. since p2p is an application level protocol, we only care about application level packets rather than other level packets. average traffic overhead we defined the average traffic overhead as the traffic on backbone overlay network divided by the size of sharing resource. for instance, in order to transfer a sharing file (default size as 1024 byte), which introduces 4096 byte total traffic on backbone overlay network, then the average traffic overhead is 4. figure 10 shows the average traffic overhead by using different cache strategies, the x axis stands for the number of successful lookups, in other words, during 500 seconds for conducting an experiment, how many successful lookups have been completed; the y axis represents the average traffic overhead. without doubt, no cache strategy works worst, meanwhile unlimited cache mechanism works best. figure 11 shows the total traffic in the all manager nodes, which has the similar trend to that of average traffic, in addition, figures 10 and 11 also show that with the increasing the maximum number of caches, the total traffic goes down slowly. p2p resource sharing in wired/wireless mixed networks 705 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 0 2 4 6 8 10 12 14 16 18 20 a ve ra g e t ra ff ic o ve rh e a d number of sucessful lookups (1000) no cache unlimited cache weighted caches (max=30) weighted caches (max=20) weighted caches (max=10) figure 10: average traffic overhead 0 5 10 15 20 25 30 0 2 4 6 8 10 12 14 16 18 20 t o ta l t ra ff ic in m a n a g e r n o d e s (m b yt e s) number of sucessful lookups (1000) no cache unlimited cache weighted caches (max=30) weighted caches (max=20) weighted caches (max=10) figure 11: total traffic in manager nodes 0 10 20 30 40 50 60 70 80 90 0 2 4 6 8 10 12 14 16 18 20 t o ta l f o rw a rd p a ck e ts ( 1 0 0 0 ) number of sucessful lookups (1000) no cache unlimited cache weighted caches (max=30) weighted caches (max=20) weighted caches (max=10) figure 12: total forward packets on backbone overlay network 3.5 4 4.5 5 5.5 0 2 4 6 8 10 12 14 16 18 20 a ve ra g e t ra ff ic o ve rh e a d number of sucessful lookups (1000) wired/wireless nodes access ratio 1:1 wired/wireless nodes access ratio 2:1 wired/wireless nodes access ratio 1:2 figure 13: average traffic overhead with different wired/wireless access ratio total traffic overhead figure 12 shows the total forward packets on the backbone overlay network. while the number of cached resources is increasing, then less packets is forwarded on the backbone overlay network. that is because the more cached replicas, the more lookup hits can be obtained within the group. overhead with different wired/wireless access ratios in the previous experiments, we fixed the wired/wireless access ratio as 1:1. in this section, we will discuss the ratios are 1:1, 2:1 and 1:2 respectively. first, we configured the cache strategy as weighted caches (max=30). then we repeated to measure the average traffic overhead, total traffic in manager nodes and total forward packets on the backbone overlay network. figures 13, 14 and 15 show the relevant results of overhead on backbone overlay network with different wired/wireless access ratios separately. from these figures, we can see that while the ratio is 1:2, the mbrp with cache mechanism can achieve considerable performance improvement because of a major part of lookup hits occurred in the groups. 3.3 discussion from the above experiments, we can see that mbrp has low latency, and the cache mechanism is also suitable for a large amount of accessing to the sharing resources in wireless/wired mixed networks. in addition, mbrp keeps a good scaling property, it employs manager nodes 706 j. liao 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 16 18 20 t o ta l t ra ff ic in m a n a g e r n o d e s (m b yt e s) number of sucessful lookups (1000) wired/wireless nodes access ratio 1:1 wired/wireless nodes access ratio 2:1 wired/wireless nodes access ratio 1:2 figure 14: total traffic in manager nodes with different wired/wireless access ratio 0 10 20 30 40 50 60 0 2 4 6 8 10 12 14 16 18 20 t o ta l f o rw a rd p a ck e ts ( 1 0 0 0 ) number of sucessful lookups (1000) wired/wireless nodes access ratio 1:1 wired/wireless nodes access ratio 2:1 wired/wireless nodes access ratio 1:2 figure 15: total forward packets with different wired/wireless access ratio to construct the backbone overlay network, and then other nodes can register to the manager node to join into the whole network. the manager nodes are responsible for the communication between the different groups, therefore, both traffic overhead and lookup hops do not increase drastically even though lots of the new nodes join into the whole network suddenly. 4 concluding remarks and future work a new routing protocol named as manager-based routing protocol (mbrp) has been proposed, implemented and evaluated in this paper. all nodes in the network have been partitioned into several groups according to their properties, wired or wireless access for instance; then a manager node is elected for each group and in charge of communication between the internal nodes in the group and external nodes belonging to other groups. from our experimental results, compared with gnutella2, which has super-peers for different groups, except the internal/external locating ratio is 1:10, in which mbrp performs a little worse than gnutella2; in other cases, mbrp outperforms than gnutella2. in addition, since the nodes in the same group, which have the similar properties, are mostly like to share the same kind of resources, the most of resource sharing cases may occur within the group. namely, mbrp can work well in the heterogeneous networks, in which internal accesses might more than external ones. furthermore, for responding quickly to the lookup requests for the hot resources, mbrp adopts caching the hot resources in the group while transferring the target objects from external groups. therefore, the future lookups for these cached resources can be fulfilled in the local group. the cache strategy reduces not only the lookup latency and lookup node hops, but also p2p resource sharing in wired/wireless mixed networks 707 the network traffics on the backbone overlay network. consequently, the system performance can be upgraded greatly. however, the current design of mbrp still has it limitations, although we assume that the sharing resources are not modified frequently, modification of the resources really happen, thus we need to consider how to maintain the consistency between the cached copies and the original ones in the near future. in addition, to determine which manager nodes for storing the copies, and make the cache mechanism much more effective is another aspect of our future work. bibliography [1] g. fox, peer-to-peer networks, computing in science and engineering, issn 1521-9615, 3(3):75-77, 2001. 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[17] e. w. zegura, gt-itm: georgia tech internetwork topology models (software), http://www.cc.gatech.edu/fac/ellen.zegura/gt-itm/gt-itm.tar.gz, 1996. international journal of computers communications & control issn 1841-9836, 9(4):497-509, august, 2014. optimized branch and bound for path-wise test data generation y.w. wang, y. xing, y.z. gong, x.z. zhang ya-wen wang (1,2), ying xing* (1,3), yun-zhan gong (1), xu-zhou zhang (1) (1) state key laboratory of networking and switching technology beijing university of posts and telecommunications, beijing, china 10 xitucheng road, beijing, china e-mail: wangyawen@bupt.edu.cn, gongyz@bupt.edu.cn, laomao22311@126.com (2) state key laboratory of computer architecture institute of computing technology, chinese academy of sciences (3) school of electronic and information engineering liaoning technical university *corresponding author: lovelyjamie@yeah.net abstract: the increasing complexity of real-world programs necessitates the automation of software testing. as a basic problem in software testing, the automation of path-wise test data generation is especially important, which is in essence a constraint satisfaction problem solved by search strategies. in this paper, the search algorithm branch and bound is introduced and optimized to tackle the problem of path-wise test data generation. the optimized branching operation is fulfilled by a dynamic variable ordering algorithm with a heuristic rule to break ties. the optimized bounding operation is accomplished by analyzing the results of interval arithmetic. in order to facilitate the search methods, the solution space is represented as state space. experimental results prove the effectiveness of the optimized branching and bounding operations, and show that the proposed method outperformed some other methods used in test data generation. the results also demonstrate that the proposed method is applicable in engineering. keywords: test data generation, constraint satisfaction problem, branch and bound, state space search. 1 introduction with the surge of increasingly complex real-world software, software testing plays a more and more important role in the process of software development [1]. in 2002, national institute of standards and technology (nist) found that over one third of the cost of software failure could be eliminated by an improved testing infrastructure [2]. but manual testing is time-consuming and error-prone, and is even impracticable for real-world programs. so the automation of testing is of crucial concern [3]. furthermore, as a basic problem in software testing, path-wise test data generation (denoted as q) is of particular importance because many problems in software testing can be transformed into q. the methods of solving q can be categorized as dynamic and static. the dynamic methods require the actual execution of the program under test (put), and the metaheuristic (mhs) [4] methods are very popular. recently, the mhs method particle swarm optimization (pso) [5] has become a hot research topic due to its convenient implementation and faster convergence speed. but dynamic methods often consume a large number of iterations, and the definition of objective function is also a big problem. the static methods utilize techniques including symbolic execution [6] and interval arithmetic [7] to analyze the put without executing it. the process of generating test data is definite with relatively less cost. they abstract the constraints to be satisfied, and propagate and solve these constraints to obtain the test data. due to their precision in generating test data and the ability to prove that some paths are infeasible, the static methods copyright © 2006-2014 by ccc publications 498 y.w. wang, y. xing, y.z. gong, x.z. zhang have been widely studied by many researchers. demillo and offutt [8] proposed a fault-based technique that used algebraic constraints to describe test data designed to find particular types of faults. gotlieb et al. [9] introduced static single assignment into a constraint system and solved the system. cadar et al. from stanford university proposed a symbolic execution tool named klee [10] and employed a variety of constraint solving optimizations. in 2013, wang et al. [11] proposed an interval analysis algorithm using forward dataflow analysis. no matter what techniques are adopted, static methods require a strong constraint solver. in this paper, considering the drawbacks of the dynamic methods and the demand for static methods, we propose a new static test data generation method based on code test system (cts)(http://ctstesting.com), which is a practical tool to test codes written in c programming language. our contribution is threefold. first, path-wise test data generation is defined as a constraint satisfaction problem (csp). two techniques (state space search and branch and bound) in artificial intelligence are integrated to tackle the csp. second, the branching operation is optimized with a heuristic variable ordering algorithm. third, the bounding operation is optimized in different stages of the search to reduce the search space greatly. through experimental results, we try to evaluate the performance of our method, especially the optimized branching and bounding operations. we also make comparison experiments to find whether our method outperforms some currently existing test data generation methods in terms of coverage. 2 problem definition and solving strategies 2.1 problem definition a control flow graph (cfg) for a program p is a directed graph g=(n,e,i,o), where n is a set of nodes, e is a set of edges, and i and o are respective unique entry and exit nodes to the graph. each node n∈n is a statement in the program, with each edge e=(nr , nt)∈e representing a transfer of control from node nr to node nt . nodes corresponding to decision statements such as if statements are branching nodes. outgoing edges from these nodes are referred to as branches. a path through a cfg is a sequence p=(n1 , n2 , . . ., nq ), such that for all r, 1≤r<q, (nr , nr+1 )∈e. a path p is regarded as feasible if there exists a program input for which p is traversed, otherwise p is regarded as infeasible. the problem q is in essence a csp [12]. x is a set of variables {x1 , x2 ,. . ., xn}, d={d1 , d2 ,. . ., dn } is a set of domains, and di∈d (i=1,2,. . .,n) is a finite set of possible values for xi . for each path, d is defined based on the variables’ acceptable ranges. one solution to the problem is a set of values to instantiate each variable inside its domain denoted as{x1 7→v1 ,x2 7→v2,. . .,xn 7→vn }. a csp is generally solved by search strategies, among which backtracking algorithms are widely used. in this paper, state space search [13] and the backtracking algorithm branch and bound (bb) [14] are introduced to solve the csp. the process of exploring the solution space is represented as state space search, as introduced in our previous work [15] . this representation will facilitate the implementation of bb. in classical bb search, nodes are always fully expanded, that is, for a given leaf node, all child nodes are immediately added to the so called open list. however, considering that one solution is enough for path-wise test data generation, best-firstsearch is our first choice. to find the best, ordering of variables is required for branching to prune the branches stretching out from unneeded variables. in addition, as the domain of a variable is a finite set of possible values which may be quite large, bounding is necessary to cut the unneeded or infeasible solutions. so this paper proposes best-first-search branch and bound (bfs-bb) to automatically generate the test data, and the branching and bounding operations have both been optimized. optimized branch and bound for path-wise test data generation 499 table 1: some methods used in this paper name operation stage dynamic variable oredering (dvo) branching state space search initial domain reduction (idr) bounding initialization hill climbing (hc) branching state space search 2.2 the search strategies during the search process, variables are divided into three sets: past variables (short for pv, already instantiated), current variable (now being instantiated), and future variables (short for fv, not yet instantiated). bfs-bb includes two stages: initialization and state space search. some methods in bfs-bb are described in table 1. bfs-bb is described by pseudo-codes as follows. the first stage is to perform the initialization operations corresponding to lines 1 to 9. at first, idr (section 3.2.1) is used to partially reduce the input domains of all variables and find infeasible paths on occasion. all the variables in fv are permutated by dvo (section 3.1) to form a queue and its head x1 is determined the first variable to be instantiated. a value v1 is selected from the domain of x1 (d1 ). with all these, the initial state is constructed as (null, x1 , d1 , v1 , active), which is also the current state scur . then the hill climbing (section 3.2.2) process begins for x1 . for brevity, our following explanation refers to the hill climbing process for each xi in fv. in the state space search stage as shown by lines 10 to 21 , hill climbing utilizes interval arithmetic to judge whether vi for xi leads to a conflict or not. in summary, the hill climbing process ends with two possibilities. one is that it finally finds the optima for xi and reaches the peak of the hill, so the type of scur is changed into extensive, indicating that the local search for 500 y.w. wang, y. xing, y.z. gong, x.z. zhang xi ends and dvo for the next variable will begin. the other is that it fails to find the optima for xi and there is no more search space, so the type of scur is changed into inactive, indicating that the local search for xi ends and backtracking is inevitable. bfs-bb ends when the hill climbing processes for all the variables succeed and there is no more variable to be permutated. all the extensive nodes make the solution path. 3 optimized branching and bounding operations 3.1 optimized branching operation this part focuses on the branching operation, which concerns the ordering of variables that determines the next variable to be instantiated. in our method, the next variable to be instantiated is selected to be the one with the minimal remaining domain size (the size of the domain after removing the values judged infeasible), because this can minimize the size of the overall search tree. the technique to break ties works when there are often variables with the same domain size. we use variables’ranks to break ties. in case of a tie, the variable with the higher rank is selected. this method gives substantially better performance than picking one of the tying variables at random. rank is defined as follows. definition 1. assuming that there are k branches along a path, the rank of a branch (nqa , nqa+1 ) (a∈[1, k]) marks its level in the sequence of the branches, denoted as rank(nqa , nqa+1 ). the rank of the first branch is 1, the rank of the second one is 2, and the ranks of those following can be obtained analogously. the variables appearing on a branch enjoy the same rank as the branch. the rank of a variable on a branch where it does not appear is supposed to be infinity. as a variable may appear on more than one branch, it may have different ranks. the rule to break ties according to the ranks of variables is based on the heuristics from interval arithmetic that the earlier a variable appears on a path, the greater influence it has on the result of interval arithmetic along the path. therefore, if the ordering by rank is taken between a variable that appears on the branch (nqa , nqa+1 ) and a variable that does not, then the former has a higher rank. that is because on the branch (nqa , nqa+1 ), the former has rank a while the latter has rank infinity. the comparison between a and infinity determines the ordering. the algorithm is described by pseudo-codes as follows. quicksort is utilized when permutating variables according to remaining domain size and returns qi as the result. if no variables have the same domain size, then dvo returns the head of qi (xi ). but if there are variables whose domain sizes are the same as that of the head of qi , then the ordering by rank is under way, which will terminate as soon as different ranks appear. 3.2 optimized bounding operation this part focuses on the bounding operation, which in fact is the optimization of interval arithmetic. for the purpose of improving efficiency, the optimized bounding operation is taken in both stages of bfs-bb. initial domain reduction the optimized bounding operation taken in the initialization stage is used for initial domain reduction as well as infeasible path detection. following is the introduction to the process that interval arithmetic functions. first we give the definition of branching condition. optimized branch and bound for path-wise test data generation 501 definition 2. let b be the set of boolean values {true, false} and da be the domain of all variables before the ath branch, if there are k branches along the path, the branching condition br(nqa , nqa+1 ): da→b (a∈[1, k])where nqa is a branching node is calculated by formula (1). br(nqa, nqa+1) = { true, da ∩ d̃a≠∅; false, da ∩ d̃a=∅. (1) in formula (1), da satisfies all the a-1 branching conditions ahead and will be used as input for the calculation of the ath branching condition, and d̃awhich is a temporary domain is the result when calculating br (nqa , nqa+1 ) with da and satisfies the ath branching condition. da ∩ d̃a ̸=∅ means that da ∩ d̃a satisfies all the a-1 branching conditions ahead and the ath branching condition, ensuring that interval arithmetic can continue to calculate the remaining branching conditions, while da ∩ d̃a=∅ means that the path has been detected infeasible. in the initialization stage, following algorithm can be quite useful for infeasible path detection as well as initial domain reduction since it is quite clear that d1⊇d2. . .⊇dk⊇ dk+1. if the path is not infeasible, then dk+1 will be input as the domain of all variables for the state space search stage. if it is, because interval arithmetic analyzes the ranges of variables in 502 y.w. wang, y. xing, y.z. gong, x.z. zhang a conservative way, the path to be covered is no doubt infeasible, and it is unnecessary to carry out the following state space search. hill climbing hill climbing is used to judge whether a fixed value vi for the current variable xi makes path p feasible. in other words, a certain vi that makes p feasible is the peak that we are trying to search for xi . initial value is very important for hill climbing. our initial value selection strategy was introduced thoroughly in [16], which is quite effective. this part focuses on the process that interval arithmetic judges whether the value (vi ) assigned to a variable (xi ) leads to a conflict or not. different from the initialization stage, a conflict detected in state space search stage implies that the current vi for xi will not lead the search in the right direction, but another vi may not. so what we are trying to find in this conflict is the information useful for the next search step. to be exact, we seek to find some information to help reduce the domain of the current variable xi (di ). in accordance with the model of state space search, we give the following formula of the objective function f (vi ), which is used to reduce the current domain of di . f(vi) = vi − k∑ a=1 (da ∩ d̃a)(xi) (2) the reduction of di is carried out according to the result of the objective function, a new vi is selected from the reduced domain di , and interval arithmetic will restart to judge whether vi causes a conflict. the above procedure is likened to climbing a hill. f (vi )=0 implies that there is no conflict detected and vi is the value judged to be appropriate for xi . otherwise di will again have to be reduced according to the return value of f (vi ). in the procedure of hill climbing, the absolute value of f (vi ) will approximate more closely to 0, which is the objective or the peak of the hill. the algorithm is shown by following pseudo-codes. it can be seen that f (vi ) provides both the upper and the lower bounds of di for its reduction, so the efficiency of the algorithm is improved greatly. after the conduction of algorithm 4, the hill climbing for a variable(xi ) ends with two possibilities, which can be identified by type of scur : extensive means that the peak is found and dvo for the next variable may continue, while inactive means that no search space is left for xi , the peak is not found, and backtracking is inevitable. 3.3 case study in this part, we give a case study to explain in detail how bfs-bb works. an example with a program test and its corresponding cfg is shown in figure 1. adopting statement coverage, there is only one path to be traversed, namely, path1: 0→1→2→3→4→5→6→7→8→9→10. we choose this example, because the constraints along the path are very strict for two variables. it is very obvious that {x1 7→65,x2 7→35} is only one solution to the corresponding csp. for simplicity, the input domains of both variables are set [1, 100] with the size 100. then idr reduces them to x 1:[31,99],x 2:[1,69] as shown in figure 2, which is used as input for the state space search stage. then dvo works to determine the first variable to be instantiated as table 2 shows, and x 1 is picked out as shown in bold in the last column. the initial value of x 1 is selected from [31, 99] according to the method introduced in [16]. assume that 80 is selected, then after three times of conflict and reduction to d1, 65 is found for x1 at last. f (65)=0 means that 65 is the peak of the hill that corresponding to x1 , which starts from 80. the hill climbing process is shown in table 3. and since there is only one value 35 for x 2, the search succeeds with {x1 7→65,x2 7→35} . optimized branch and bound for path-wise test data generation 503 void test(int x1, int x2){ 1: if(x1>x2) 2: if(x1+x2==100) 3: if(x1>=20) 4: if(x1-x2==30) 5: printf("solved! "); } figure 1: program test and its corresponding cfg 504 y.w. wang, y. xing, y.z. gong, x.z. zhang table 2: dvo process for x 1 and x 2 ordering rule condition of each variable tie encountered? ordering result domain size |d1|=69, |d2|=69 yes x 1→x 2 rank1 rank1(x 1)=1, rank1(x 2)=1 yes rank2 rank1(x 1)=2, rank1(x 2)=2 yes rank2 rank2(x 1)= 3, rank2(x 2)=∞ no 2={ 1:[100,100]-[1,99]=[1,99] ; 2:[100,100]-[2,100]=[0,98]}d x x 1={ 1:[1,100] ; 2:[1,100]}xd x x1-x2=30 4={ 1:[1,98]+[30,30]=[31,128] ; 2:[20,99]-[30,30]=[-10,69]}d x x 5={ 1:[20,99] [31,128] [31,99] ; 2:[1,98] [-10,69] [1,69]}xd x x1>x2 1={ 1:[2,100] ; 2:[1,99]}x xd 2 ={ 1:[1,100] [2,100] [2,100] ; 2:[1,100] [1,99]=[1,99]}xd x 3={ 1:[20,98]}d x 4 ={ 1:[2,99] [20,98] [20,98] ; 2:[1,98]}x xd x1 20 x1+x2=100 3={ 1:[2,100] [1,99] [2,99] ; 2:[1,99] [0,98]=[1,98]}xd x figure 2: the idr process 4 experimental analyses and empirical evaluations to observe the effectiveness of bfs-bb, we carried out a large number of experiments in cts. within the cts framework, the put is automatically analyzed, and its basic information is abstracted to generate its cfg. according to the specified coverage criteria, the paths to be traversed are generated and provided for bfs-bb as input. the experiments were performed in the environment of ms windows 7 with 32-bits, pentium 4 with 2.8 ghz and 2 gb memory. the algorithms were implemented in java and run on the platform of eclipse. 4.1 performance evaluation the number of variables is an important factor that affects the performance of test data generation methods [17]. hence, in this part, experiments were carried out to evaluate the effectiveness of the optimized branching and bounding operations for varying numbers of input variables. testing the optimized branching operation this part presents the comparison between our branching algorithm dvo (a) which is also bfs-bb and the method which orders variables only by remaining domain sizes (b). the other operations (the bounding operations) in the two methods were both accomplished by idr and hc. the comparison was accomplished by repeatedly running the two methods on generated test programs having input variables x1 , x2 ,. . ., xn where n varied from 2 to 50. adopting statement coverage, in each test the program contained n if statements (equivalent to n branching conditions or n expressions along the path) and there was only one path to be traversed of fixed length, which was the one consisting of entirely true branches. in each test, the expression of optimized branch and bound for path-wise test data generation 505 table 3: the hill climbing process for x 1 d1 v1 f (vi) |f (vi)| peak reached? [31, 99] 80 30 30 no [50, 79] 60 -10 10 no [61, 70] 65 0 0 yes the ith (1≤i≤n) if statement was in the form of [a1, a2, . . . , an][x1, x2, . . . , xn]′rel − op const[i] (3) in formula(3), a1, a2 ,. . ., ai were randomly generated numbers either positive or negative, rel -op∈{>,≥,<,≤,=, ̸=}, and const[j ] (j ∈[1, i]) was an array of randomly generated constants within [0, 1000]. the randomly generated aj and const[i] should be selected to make the path feasible. this arrangement constructed the tightest linear relation between the variables. the programs for various values of n ranging from 2 to 50 were each tested 50 times, and the average time required to generate the data for each test was recorded. the results are presented in figure 3. figure 3: test result of dvo in figure 3, (a) shows that a had a better performance than b, but it was not very obvious when the number of variables (expressions) was not very large, because there was no requirement for an optimized ordering algorithm, since remaining domain size was enough to determine the next variable to be instantiated. so the more variables, the better dvo works. for bfs-bb (a), it is clear that the relation between average generation time and the number of variables can be represented as a quadratic curve very well as shown in (b) and the quadratic correlation relationship is significant at 95% confidence level with p-value far less than 0.05. besides, average generation time increases at a uniformly accelerative speed as the increase of the number of variables. the differentiation of average generation time indicates that its increase rate rises by y=9.5294x -120.53 as the number of variables increases. we can roughly draw the conclusion that generation time using dvo is very close for n ranging from 1 to 25, while it begins to increase when n is larger than 25. so dvo will be very useful for puts with more variables, especially the large-scale real-world programs. testing the optimized bounding operation this part presents the comparison between our bounding algorithm hc (a) which is also bfs-bb and the method without hc (b). the other operations (dvo and idr) in the two 506 y.w. wang, y. xing, y.z. gong, x.z. zhang methods were totally the same. the comparison was accomplished by repeatedly running the two methods on generated test programs having input variables x1 , x2 ,. . ., xn where n varied from 1 to 50. adopting statement coverage, in each test the program contained 50 if statements (equivalent to 50 branching conditions or 50 expressions along the path) and there was only one path to be traversed of fixed length, which was the one consisting of entirely true branches. the expression of each if statement was in the form of [a1, a2, . . . , an][x1, x2, . . . , xn]′rel − op const[c] (4) in formula(4), a1, a2 ,. . ., ai were randomly generated numbers either positive or negative, rel -op∈{>,≥,<,≤,=, ̸=}, and const[c] (c∈[1, 50]) was an array of randomly generated constants within [0, 1000]. the randomly generated ai (1≤i≤n) and const[c] should be selected to make the path feasible. this arrangement constructed the tightest linear relation between the variables. in addition, we ensured that there was at least one “=” in each program to test the equation solving capability of the methods. the programs for various values of n ranging from 1 to 50 were each tested 50 times, and the average time required to generate the data for each test was recorded. the comparison result is presented in figure 4. figure 4: test result of hc we exponentiated the axis representing average generation time as shown in (a). it can be seen that the average generation time of a is far less than b. for bfs-bb (a), it is clear that the relation between average generation time and the number of variables can be represented as a quadratic curve very well as shown in (b) and the quadratic correlation relationship is significant at 95% confidence level with p-value far less than 0.05. besides, average generation time increases at a uniformly accelerative speed as the increase of the number of variables. the differentiation of average generation time indicates that its increase rate rises by y=1.06x -8.6817 as the number of variables increases. we can roughly draw the conclusion that generation time using hc is very close for n ranging from 1 to 8, while it begins to increase when n is larger than 8. 4.2 coverage evaluation to evaluate the capability of bfs-bb to generate test data in terms of coverage, we used some real-world programs to compare bfs-bb with both static and dynamic methods adopted in test data generation. comparison with a static method this part presents the results from an empirical comparison of bfs-bb with the static method [11] (denoted as "method 1" to avoid verbose description), which was implemented in cts prior optimized branch and bound for path-wise test data generation 507 table 4: the details of comparison with method 1 project program function ac by method 1 ac by bfs-bb qlib sin.c radian 55% 100% floor.c ceil 66% 100% dell8i-2 asinl.c acosl 55% 100% tanl.c cotl 66% 100% table 5: parameter setting for pso parameter value population size 30 max generations 100 inertia weight w ranging from 0.2 to 1 acceleration constants c1 and c2 c1 =c2 =2 maximum velocityvmax set according to the input space of the tested program to bfs-bb. the test beds were from two engineering projects at http://www.moshier.net/. the comparison adopted statement coverage as the adequacy criterion. for each test bed, the experiments were carried out 100 times, and average coverage (ac) was used for comparison. the details of the comparison are shown in table 4. from table 4, it can be seen that bfs-bb reached higher coverage than method 1 for all the test beds as shown in bold. that is largely due to the optimization methods utilized in bfs-bb. the applicability in engineering remains one of our primary goals in the future. comparison with pso this part presents results from an empirical comparison of bfs-bb with pso, which is mentioned in section 1 as a popular mhs method with relatively fast convergence speed. table 5 is a brief introduction to some parameters used in pso. we used three real-world programs, which are well-known benchmark programs and have been widely adopted by other researchers [18][20]. branch coverage was taken as the adequacy criterion. for each test bed, the experiments were carried out 100 times, and ac was used for comparison. table 6 shows the details of the test beds and the comparison results. obviously bfs-bb achieved 100% coverage as shown in bold on all the three benchmark programs, which are rather simple programs for bfs-bb, and it outperformed the algorithm in comparison. the better performance of bfs-bb is due to two factors. the first is that the initial values of variables are selected by heuristics on the path, so bfs-bb reaches a relatively high coverage for the first round of the search. the second is that the optimized bounding operation is conducted not only in the state space search stage but in the initialization stage as well, which reduces the domains of the variables to ensure a relatively small search space that follows. table 6: the details of comparison with pso program loc branches variables ac by pso ac by bfs-bb triangletype 31 3 5 99.88% 100% cal 53 18 5 96.85% 100% calday 72 11 3 97.35% 100% 508 y.w. wang, y. xing, y.z. gong, x.z. zhang 5 conclusions and future works the increasing demand of testing large-scale real-world programs makes the automation of the testing process necessary. in this paper, path-wise test data generation (q) which is a basic problem in software testing is defined as a constraint satisfaction problem (csp), and the algorithm best-first-search branch and bound (bfs-bb) is presented to solve it, combining two techniques in artificial intelligence which are state space search and branch and bound (bb). the branching and bounding operations in bfs-bb are both optimized. for the branching operation, dynamic variable ordering (dvo) is proposed to permutate variables with a heuristic rule to break ties. the bounding operation is optimized in both stages of bfs-bb. initial domain reduction (idr) functions in the initialization stage to reduce the search space as well as detect infeasible paths. in the state space search stage, the process of determining a fixed value for a specified variable resembles climbing a hill, the peak of which is the value judged by interval arithmetic that does not cause a conflict. to facilitate the search procedure, the solution space is represented as state space. empirical experiments show that the optimized branching operation is especially useful for large-scale programs, while the advantage of the optimized bounding operation hill climbing (hc) is very obvious. the results also show that bfs-bb outperforms some current static and dynamic methods in terms of coverage. our future research will involve how to generate test data to reach high coverage. the effectiveness of the generation approach continues to be our primary work. acknowledgment this work was supported by the national grand fundamental research 863 program of china (no. 2012aa011201), the national natural science foundation of china (no. 61202080), the major program of the national natural science foundation of china (no. 91318301), and the open funding of state key laboratory of computer architecture (no. carch201201). bibliography [1] michael r. lyu; sampath rangarajan; ada p. a. van moorse. 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(2008); observation in using parallel and sequential evolutionary algorithms for automatic software testing, computers & operators research, issn 03050548, 35(10): 3161-3183. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 147-162 analyzing the impact of using interactive animations in teaching r. pinter, d. radosav, s.m. cisar robert pinter subotica tech-college of applied sciences serbia, 24000 subotica, marka oreškovića 16 e-mail: probi@vts.su.ac.rs dragica radosav university of novi sad, technical faculty "mihajlo pupin" zrenjanin serbia, 23000 zrenjanin, djure djakovica bb e-mail: radosav@tfzr.uns.ac.rs sanja maravić čisar subotica tech-college of applied sciences serbia, 24000 subotica, marka oreškovića 16 e-mail: sanjam@vts.su.ac.rs abstract: this study intends to measure the impact of interactive animations on the students’ performance. two courses from subotica tech were included, the subjects “analog and digital electronics” and “microcontrollers”. the experiment lasted over a period of tree years, and it involved the formation of two groups in every academic. both groups’ members participated in traditional frontal teaching, but the experimental group could use interactive flash animations built from selected parts of those courses as supplementary tool. at the end of the semester, the exam marks were analyzed with a two-sample t-test. the results show that learning with properly created interactive animations could have positive effects on most students’ academic performance. keywords: distance education and telelearning, improving classroom teaching, interactive learning environments, simulations, media in education. 1 introduction in the era of modernization in the teaching process, when the use of novel information technologies aims to achieve easier, faster and more efficient knowledge transfer in education, the application of interactive animations has become more and more important. the questions arises as to what the reasons are which have made interactive animations a vital part of modern ecurricula, and whether there is empirical evidence to support claims that using multimedia and interactivity in e-curriculum has positive impact to cognitive development and academic achievement at students. in the first part of this paper, authors analyze characteristics of the interactive animations. the second part presents some research done with interactive animations developed at subotica tech. the e-contents are compiled from selected parts of the course "analog and digital electronics" and "microcontrollers" at subotica tech. the thorough investigation by sekular and blake [1] into how students take in information, how they learn pointed out that the learning process takes place primarily by way of sight, and since it is the most vital of our senses, it is also the most highly-developed one. it enables a person to gather information from one’s surroundings, analyze these and then decide how to process based on the deduced data. in terms of teaching, it is by seeing that students will best grasp a complicated string of steps as it helps transform a vague idea into an image in their brains. copyright c⃝ 2006-2012 by ccc publications 148 r. pinter, d. radosav, s.m. cisar kraidy [2] started that, if the aim is to increase the amount of information to be processed by students within a set time frame, then giving them visual information to work with will help them reach this goal. graphical representations are defined as visual aids that act as supplement to any other textual information and will concentrate learners’ attention [3]. such representations will have maximum effect when accompanying some learning material that is (relatively) new to the learner [4]. this is especially the case with computer animation that is designed to aid long-term learning in the form of focusing learners on certain objects in the beginning. the research of rieber [5] portrayed that abstractions connected with time transitions in a process can be decreased by implementing animations to convey ideas and processes that change over time. dual-coding theory by paivio, [6] [7] offers an explanation as to why graphics are so effective: retaining memory over a long time is made easier if a combination of verbal and visual cues is used. this makes animations a distinctively significant support of visualizing material for long-term memorization. animation and narration further support dual-coding [8]. what makes animations stand out is movement, as opposed to static, still images, and this demonstrates the various relationships within and along a certain process. by goldstein, chance, hoisington and buescher [9] movement will be remembered longer than static images. according to gordin and pea [10] and also brodie, carpenter, earnshaw, gallop, hubbold, mumford, osland, quarendon [11] visualization is a vital part in the acquisition of scientific topics, since important relationships between concepts will be pointed out for learners. it was demonstrated by research results that animations are more effective learning tools that static images, and this was further supported by lesson plans incorporating lectures as well as different learning inputs [12]. based on the dual-coding theory [7] it may be asserted that learning will be the most effective if there are lectures alongside animations, since they together form a base of reference that will help learners fully understand the knowledge that was conveyed through the animations. lectures will cue the students, but actual studying happens through the animations [13]. 2 interactive animations one of the tendencies in education is the continually growing amount of learning content which must be acquired by the student. almost every generation’s curricula are extended by a certain amount of new, updated or revised material. with this swelling of learning contents, another issue arises, namely that the time which is intended for learning these amount of contents is growing ever shorter for each subsequent generation. besides that, students are no longer interested in the foundations of some complex system, and how it is compiled, but rather, they want to know how the system works and how it can be managed. in accordance with these tendencies the educators have been searching for learning tools which can help the students acquire knowledge. as animation are able to unambiguously portray changes over time (temporal changes), they are extremely suitable for using them in process and procedure teaching. animations are applied to show dynamic content, and they reflect alterations in position (translation), as well as form (transformation) which form the basis of learning this kind of topic [14]. unlike static pictures, temporal changes are shown in animations directly (instead of indirectly by some awkward auxiliary markings including arrows and motion lines). the application of animations, as opposed to static graphics, makes these extra markings unnecessary, thus stripping down the displays and making them attractive, lively and easily understandable [15]. furthermore, there is no need for the learner to process these auxiliary markings and what changes they try indicate. interpreting the markings and the inferences may actually surpass the level analyzing the impact of using interactive animations in teaching 149 of graphical skills that the learner possesses. yet with animations, these displays immediately show all information concerning the changes, thus no extra mental depiction is required. learning can be facilitated by animations in two ways. on the one hand, their function is to affect the learner, raise their interest and keep up motivation. the entertainment industry implements this same function in their animations. on the other hand, though, animations also have the function to facilitate comprehension and memorization of a given content. the knowledge-building process is thus supported and this cognitive function is essential to effective learning. superficially, it may seem that animations are the perfect candidates to be applied in presenting dynamic content. nevertheless, there is no unambiguous research evidence supporting this. some researchers have conducted comparisons of how effective static and animated displays are in education by using a number of content domains. although there have been positive results where animations have proven to be rather effective, these results have been countered by other investigations that have found no positive, and even negative effects of using animations. on the whole it is safe to say that animations are not by definition more effective than static graphics. instead, the specific features of certain animations and their method of application is crucial in what kind of effect they will have on knowledge acquisition. 2.1 do animations make learning faster? animations play an important role in computer-based learning environments. so far, however, it has not been sufficiently resolved under which conditions and in which respect animations do actually lead to better learning outcome. well-designed animations are likely to be a real asset to the teacher. they will speed up the learning process and make it easier to grasp and memorize the material. it especially comes in handy when the teacher is trying to explain a difficult subject. the question arises: why is a subject perceived as difficult? it may either be because it requires a certain amount of imagination. for example, in our animations we visualized a clock signals, a values and shapes of the input and output voltage signals, a states and changes of the microcontroller internal registers etc. with the help of computer animations both the teaching and learning process will be made less difficult, it will take less time and it will be livelier. however, what then explains the fact that sometimes animations are not educationally effective as one would expect them to be? a possible answer would be that students are unable to "compute" the information seen in the animation adequately. if a complex subject is to be presented with animation, it may result in an equally complex animation, thus leaving students feeling overwhelmed. this is supported by the role of visual perception and cognition in human information processing. the perceptual and cognitive systems of humans have their limits for information processing. once the presented animation reaches or oversteps the learners’ information processing limits, the learning process may no longer be effective. also negative effects come forward if the new information being presented in animations is faster than the speed of how fast the learner is capable of processing that effectively. replacing current static graphics with animations without careful consideration is not likely to result in improved learning; instead animations should be accompanied by textual explanations, and let the learner have control over the speed of the animation. such user-controllable animations will enable learners to "customize" the animations by varying the playing speed and direction, labels and audio commentary to suit their own personality. the controllable animation can be realized with interactive animation. the interactivity within the animation could mean the own playing speed and walk-through, different amount of auxiliary explanations etc. besides the visualization of the curriculum, this kind of animation offers another advantage: the possibility of modeling and simulating systems. this means that knowledge acquisition can 150 r. pinter, d. radosav, s.m. cisar take place also by changing the model’s parameters, or otherwise experimenting with the system. so, when using interactive simulations besides the previously mentioned advantages, some new ones can be defined: • the model offers the possibility for analyzing and doing experiments with those systems, which cannot be done in real life. • the models enables studying of certain fast occurrences in a much slower mode, or timeconsuming events in a much shorter time span than in reality. • the model makes it possible to focus on the vital characteristics of the learning content being taught. • the model offers the users the freedom of experimentation without any consequences. 2.2 the advantages of flash animations the developing environment provided by the packet adobe flash cs3 (and its prior versions) was used by the authors as the tool of choice for creating these interactive animations. in a simplified form, this software tool is an application for creating vector sketches and animation, with the option of adding this interactive feature. naturally, the flash developing environment offers many more options, but it also includes very straight-forward ways of creating animations. the fact that it is rather easy to create interactive animations is a crucial aspect, as in such a case it is not a pre-requisite for the subject teacher to be highly educated in information technologies. this type of animation can be used for presenting the material in theoretical classes, but also for creating a fully electronic curriculum for consolidating the material previously taught in practices, as well as for independent work outside the classes. practice shows that creating effective interactive animations still requires the close cooperation of the teacher and the expert for flash technologies. successful acceptance of the animations by the students primarily depends on the course teacher. it is their task to determine the following: • goals that are to be achieved with this animation, • the content that is to be shown, • which elements of the learning material are to be represented statically (with an image), and which will take the forms of animation or interactive animation (simulation), • guidelines (design of the outlook, which controls are to be used, the user’s options within the system, etc.) based on which the application will be developed. the task of the "flash expert" is to realize the requirements of the teacher as best as possible. the programmability of the animation thus comes in really handy for the expert. when developing the flash application of the programs that may be used is action script (the current version is 4), an object-oriented programming language. with the help of this language every element of the animation (lines, colors, sound, etc.) can be controlled, calculations can be made using the entered parameters, and finally, the results can be presented, and actually used to draw new objects or their trajectories, as well as communicate with the server, among others. it is safe to say there is no such task in creating an animation that an experienced flash programmer cannot solve. in fact, this is the real advantage of this tool, as it can meet all the requirements irrespective of school age or learning material. besides the listed advantages analyzing the impact of using interactive animations in teaching 151 of a flash animation, it is also rather easy to distribute this application. there are two most commonly used formats for saving this animations: the executive (*.exe) format, which starts in its in-built player; and the standard (*.swf) format for playing in a web browser or in the flashplayer player (it can be downloaded easily from the internet). what is characteristic of these two formats is the small file size, which is a vital factor when distributing the application via the internet. another benefit of the flash animation is that it is a single file, there are no separate sound files, and the images do not comprise a separate module. all this ensures that there is no special installation procedure, only the file to be saved and started, which makes it an accessible program for even the somewhat computer-wary users. besides the so-called technical advantages, with the use of adequate design techniques, the flash-type animation could gain further benefits. one of those benefits is the result of how a flash animation is developed: most often the parts of a flash animation are drawn, and there is little use of images from the real world. the advantage of drawing, i.e. of creating vector objects for animation is that the drawn objects are represented in a simpler form, with less detail than, for example, if they were shown in a bitmap format. this means once the educator has abstracted the material for the students there is yet another simplification of the learning material. but there are other design techniques which could lead to more effective learning process, for example: • using the "inserting and removing fragments" technique. the complexity and information load of the animation interface can be regulated by inserting or removing objects or pieces of information form it. • using the "dimming fragments" technique. with this technique one can differentiate between important parts of the animation and those which serve as additional information. the dimmed elements look like as if they are melting into the background. • using background (blurred) animation to attract and keep user’s attention on the interface. also, in these projects the following design aspects were used: • minimize the number of visual elements, thus making it easy to follow the presented process. • minimal amount of lateral information used solely for presenting the essence as simply as possible. • " data entry by keyboard was not incorporated. the reason for this is that the data entry option does not always mean an advantage in the learning process: they may cause the user to be preoccupied with trying to crash the application by entering invalid formats and values. as a result of these design techniques, the system will show a straight-forward form, using only the vital details, leading directly to a better and easier understanding of the model, and the user cognitive load is kept on adequate (i.e. low) level. are these the only reasons why the animation should be used in teaching? no, they are not. there are problems which occur in educational communication called information barriers, and the flash animation will yield some solutions to this problem. some of these barriers can be classified in the following way: • perceptual barriers – each subject in the communication process feels and interprets events occurring to them differently, depending on their psychological, cultural and social status, 152 r. pinter, d. radosav, s.m. cisar • psychological barriers – the same word or event will have a different meaning for different persons, • social barriers – these barriers become apparent by the different social statuses of the subjects in the educational communication, • cultural barriers – these arise in communication due to the different cultural backgrounds of the subjects participating in the communication process, • semantic barriers – barriers of this type appear when interpreting written contents, speeches, images, and other, thus the way the message is read will change the content itself, • media barriers – this information barrier occurs when the there are different communication media used on educational communication. it is well-known fact that each carrier has their own markings, which may be helpful as well as distracting in communication, • physical barriers informational barriers come up in educational communication when transferring the message, i.e. in the channels of connection. how and where do information barriers occur when there are pcs used in the teaching process? some of possible sources of problems are described below: • experience shows that old programs which exclusively use the keyboard for interaction will be accepted to a lesser extent due to the fact that using the keyboard is more complicated than using the mouse, • programs (simulations) designed using too much detail will be harder to accept because first the users have to make out what is on the screen and only then move on to the explanation of the modeling system, • if there are too many options for simulation set up, result saving, parameter input, etc, where the users might ‘become disoriented’, then, according to murphy ’s law, they probably will. 3 practical applications the following section describes interactive animations which have been successfully in use as an auxiliary teaching tool at subotica tech college of applied sciences [16]. unfortunately, the advantages of the animations as described before are difficult to transfer to paper only with the help of images. the applications have been designed as interactive tutorials for presenting the functioning of some of the basic systems of analogue and digital electronics (figure 1.) and microcontrollers (figure 9. and 10.). for the microcontrollers course two e-contents (interactive flash simulation) were developed. they presents exercises for three out of fourteen lessons, but these three lessons count as "difficult", for example they cover the following themes: using the microcontrollers built in timer/counter in different modes, setting and using interrupts, communication through serial port, controlling analog to digital signal (and vice versa) conversion etc. the e-content for the analogue and digital electronics there are altogether 19 simulations classified into 5 groups/exercises. through these simulations the students can practice approximately about 40% of curriculum’s theory. for example, "exercise 1" contains simulations on the topics: sources of alternating signals, voltage splitter, passive voltage adder, rc low-pass filter, rc high-pass filter. figure 1. shows the screenshot of exercise 3 and the accompanying simulation entitled "pojačavač sa zajedničkim" (common emitter amplifier). the design of the analyzing the impact of using interactive animations in teaching 153 figure 1: representation of the exercise “common emitter amplifier” application shown in this image is followed through in the rest of the simulations, as well: the upper left corner contains the sketch of the system, below are the system parameters which can be altered in the simulation, while the "oscilloscope" is situated in the right side of the screen, showing the change of the signal over time. in this part of the application, by clicking on the link labeled "objašnjenje" (explanation) the theoretical background comes up in text form. below is a detailed description of the content and functions of the elements on the screen: 1. links for transition to the next/other simulation within this exercise. 2. sketch to be simulated. the parameters listed next to the components are changing depending on values of the checkboxes under the sketch. 3. representation of the shape of voltage signal at the input and output. part of the image marked with the arrow 3 shows the shape of output voltage, while the one marked 4 shows the input voltage. these shapes of signals are constantly redrawn. the lighter point on the line shows the current voltage value. the break in the line is the consequence of the change in rc components on the sketch during the simulation. 4. buttons for starting and stopping the simulation. 5. the button for calling up the background explanation for how the sketch functions. 6. the list of equations used for calculating the necessary parameters of the sketch and the results of the calculation/estimation. 7. the return button leading to the introductory page where the exercises can be chosen. 8. values of the sketch components. these parameters can be changed by choosing values from the checkboxes. each change has affects the listing of calculated values based on the new parameters and the change of signal shape at the output (“upper canal of the oscilloscope”). the following image (figure 2) shows the simulation “decade counter” with the help of which students can learn the logic of the synchronous counter. all simulations in this application are entirely controlled by mouse. changing the parameters is done with the help of combo boxes and the predefined values they contain. in this way the 154 r. pinter, d. radosav, s.m. cisar figure 2: representation of the exercise “decade counter” application is protected from irregular data. it is important to mention the following advantages of these simulations: • it is not necessary to really ‘create’ an electric circuit in order to see how it works, • changing the components in the system only takes a few clicks in the checkbox, • it is possible to show the state of important values in continuity, as done by an oscilloscope. the following few paragraphs present some actionscript (version 2) programming code, which shows how one can input data from the combo box, calculate the output voltage, and draw the form of voltage signal like it is done on a real oscilloscope. the combo box is presented as an object on the main animation scene. the next figure shows a combo box, which is used for input of predefined resistor values: figure 3: input option via combo box the following code was attached to the combo box: figure 4: source code for combo box’s onclipevent event when the user selects a value from the "r" combo box’s list, the code is executed . the first line of the code assigns the currently selected item’s label (currently it is a "50k" string) to the ’r1’ variable. the ’r1’ variable is the label in the scheme (see figure 5, dashed line rectangle, right from the r resistor). so changes in the values in the combo box are displayed also on the scheme. the second line of the code assigns the value (numerical value: 50000) associated with the item currently selected ("50k" string) to the "r" variable. the scheme has its own action script code, which uses the "r" variable for calculating the new output value of the voltage. because this code changes several global variables, other movie clips on the scene which also use those variables are affected with it. in this way, for example the changes in the resistor value analyzing the impact of using interactive animations in teaching 155 figure 5: scheme of the rc low–pass filter figure 6: source code attached to the rc low–pass filter schema affects the movie clip which represents the oscilloscope function, and the new form of the output signal is displayed. drawing the form of voltage signal on the oscilloscope is done by moving a special movie clip on the coordinates which are determined in the code above. in the movie clip which presents the current output value one yellow circle changes to a smaller and orange colored circle. this animation of the movie clip with 4 picture out of 10 is presented in figure 7. when drawing the output signal this movie clip is moving on the screen, and with its own figure 7: movie clip of the oscilloscope drawing beam animation the effect preented on the figure 8 is achieved. figure 9 shows one of a series of seven interactive simulations that are part of the e-curriculum which had been developed for the microcontrollers course. the simulations present the i8051 microcontroller’s timer/counter hardware, the setting and use of interrupts, and the application of the special forms of the add and mov instructions. figure 10 presents one of the four interactive simulations created specifically for the microcontrollers course. the simulations refer to the practical use of the i8051 microcontroller. 4 experiments and analysis for the purpose of this study the following research questions were specified: what is the impact of interactivity of the animations on learning? the null hypothesis is defined as follows: interactive animations have no significant positive impacts on studying "microcontroller" and "analog and digital electronics" courses. 156 r. pinter, d. radosav, s.m. cisar figure 8: appearance of the drawing beam in the oscilloscope movie clip figure 9: representation of the exercise “timer0 in mode 1” figure 10: representation of the exercise “microcontroller with a/d” analyzing the impact of using interactive animations in teaching 157 in order to obtain answers to the research questions, the authors compared the final exam score standard deviation at "analog and digital electronics" and "microcontrollers" courses independently, where the animations were used as supplementary tools for learning and practicing after class. 4.1 participants and data collecting method the data acquisition was done at subotica tech college of applied sciences over a threeyear period. it involved the second year students from two undergraduate programs the electrotechnical engineering major (ee) where these two courses were obligatory and the computer science major (cs) where these courses were optional. the number of participants for the first course (analog and digital electronics) over the period of 3 years is 441 students, 56 female (12.7%) and 385 male (87.3%) students. the second course’s participants (microcontrollers) were the same students from ee major, and from the cs major there were some old students and some new ones (those who did not select the first course). the composition of this group was: 464 participants, 58 females (12.5%) and 406 males (87.5%) see table 1. most participants, 98.5%, were between 18 and 20 years old; the remaining percentage is represented by a few students whose age were between 20 and 30. in these 3 years at the beginning of the semesters (the first course was in the fall and the second in the spring semester), the students were divided in two equal-sized groups, the control and the experimental group. the group members were chosen randomly, and only one condition had to be satisfied for the experimental group members: to have possibility of accessing the web application and the simulations from home. if this condition was not satisfied, that student automatically becomes the member of control group. after forming the groups accessing the web application was enabled only for the experimental group. there was no additional motivation for the students. all participants visited face to face (f2f) classes of these two courses, which were taught by the same lecturer presenting identical material. this further strengthens the consistency of comparisons. the web application collected the following data from the users: 1. how many time did he/she logged on to the system to use the e-content, 2. how many time did he/she spent using the particular simulation. students who logged on only few times and spent less time that the authors foresaw are assumed to be not using the system in an adequate mode, and they are not taken as members of the experimental group, so they were transferred to the control group (for details see table 1). ineligibility meant that the number of loggings is less than half of the available exercises, and the time spent in the system is less than 2 minutes per exercise the authors took as null hypothesis that the two groups would have the same mark average at both courses. the alternative hypothesis claims that the control group will achieve better result at both courses. the data was analyzed with one-sided, t-test, assuming that the variances of the two samples are different. because one course was in the fall semester and the second one in the spring semester, the analysis was done twice a year at the end of the semesters and independently for both courses. 158 r. pinter, d. radosav, s.m. cisar courses 2007 school year 2008 school year 2009 school year experiment. group control group experiment. group control group experiment. group control group analog&digital electronics 61 83 72 80 69 76 microelectronics 75 86 74 81 73 75 table 1 –the number of participants in the groups 4.2 student survey at the end of each semester and before the final exam, the control group members were asked to fill out a questionnaire with 5 questions. from the answers (marks from 1 to 5 and comments) the authors received feedback about generally how students were satisfied with simulation, how did it help or not in the learning process and what would they like to see done in a different way. these data were collected in order to perform further improvement of the teaching materials in the way that would lead to a widely accepted e-curriculum. 5 results and discussion the t-test applied to our two sample groups (main and control group) allows us to compare the means of the final exam marks of both groups. the following table presents these values. 2007 microcontrollers course n ss mean meane-meanc df tobs experimental group 75 121.9467 7.973 0.438 159 2.16 controll group 86 139.3953 7.534 2008 microcontrollers course n ss mean meane-meanc df tobs experimental group 71 105.098 7.887 0.420 144 2.06 controll group 75 110.666 7.466 analyzing the impact of using interactive animations in teaching 159 2009 microcontrollers course n ss mean meane-meanc df tobs experimental group 73 99.780 8.054 0.375 146 1.93 controll group 75 104.32 7.680 2007 analog and digital electronics course n ss mean meane-meanc df tobs experimental group 61 57.147 8.540 0.457 142 2.48 controll group 83 124.409 8.084 2008 analog and digital electronics course n ss mean meane-meanc df tobs experimental group 72 110.611 8.139 0.451 150 2.18 controll group 80 135.187 7.687 2009 analog and digital electronics course n ss mean meane-meanc df tobs experimental group 69 104.289 8.232 0.403 143 1.97 controll group 76 110.776 7.829 table 2 – students’ score distribution where the notations in the table are: • n number of participants, • ss sum of squared deviates, • meane/meanc mean for of experimental/controll group, • df degrees of freedom, • tobsobserved values of t-distribution. 6 conclusions and future works the authors compared the observed value of t with the t from the table of critical values that pertain to df > 140, and the results are shown in table 3: 160 r. pinter, d. radosav, s.m. cisar courses significance of the difference between the variances of the two samples m2007 t95%< tobs< t99% 1.98<2.16<2.61 m2008 t95%< tobs< t99% 1.98<2.06<2.61 m2009 tobs< t95% 1.93<1.98 ad2007 t95%< tobs< t99% 1.98<2.48<2.61 ad2008 t95%< tobs< t99% 1.98<2.18<2.61 ad2009 tobs< t99% 1.97<1.98 table 3 – significance differences between two groups from the presented data, the following conclusions can be drawn: • in 4 cases out of 6 we can reject the null hypothesis, and we can say with probability of 95%, that those experimental groups achieved better results on exam than the control groups. • in two cases there are no reasons to reject the null hypothesis. the results show evidence that interactive simulation contents can be very effective tools in the learning process. it can deliver information in a very attractive way, which also can be advantageous in assembling curricula for the students who have different skill levels and learning styles. besides that, it can help learners to understand scientific topics, with presenting important conceptual relationships. it is also important that simulations enable students to become acquainted with the shown system and make changes in the parameters with no additional costs or risks. but only well-designed animations may help to ease and shorten the learning process, and only with them, through play and experimentation can the learning process become more interesting [17] [18]. the students’ answers from the questionnaires show that not every simulation is accepted in the same manner. for example, the third e-content (figure 10) was given lower grades/worse comments than the other two. the reasons for this could be the themes which were presented with the simulation, because it does not contain spectacular and experimenting options. the design/the look of the animation also received worse marks from the students. some future researches should also investigate how effective the interactive animations are when the users have different learning styles various researches focusing on the effectiveness of learning with the help of visualization point out that in order for the animation to be well accepted, by the [19] [20] [21] the following have to be kept in mind : • positive effects in learning can only be achieved in topics that are dynamic in character, • an exaggerated multitude of colors in the animation will have the exact opposite effect, • it is important for the application to contain an optimal amount of information. due to the lack of a standard in creating successful visual applications [22], experiences gained from well-accepted electronic materials may serve as guidelines for defining a methodology, which, if applied in the design of animations and simulations, will lead to greater effect and efficiency in the learning process [23]. however, results also show that there is a tendency of decreasing the difference between those learners who had used the animation and those who had not. is this because there is an increasing number of such and similar e-curricula available to students, and this kind of analyzing the impact of using interactive animations in teaching 161 attractive multimedia presentations are no longer motivate students as they used to before; or was is simply the case of students of the control group getting hold of the animations and using them in their learning process. unfortunately, the questionnaire filled in by the students at the end of the semester failed to provide definitive answers to this question. the questionnaires show that students were on the whole satisfied with the applications. a number of studies indicate that the user’s performance is much better if the teaching methods are matched to the user’s learning style [24]. designing the animation’s interface and contents to match the students’ preferred learning style could lead to a more effective learning process. for example, according to the 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[24] chen s., macredie r.,cognitive styles and hypermedia navigation: development of a learning model, journal of the american society for information science and technology, 53(1):3 15, (2002) [25] richar f., rebeca b.,understanding student differences, journal of engineering education, 94 (1), 57-72 (2005) int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 518-529 an adaptive iterative learning control for robot manipulator in task space t. ngo, y. wang, t.l. mai, j. ge, m.h. nguyen, s.n. wei thanhquyen ngo, m. hung nguyen hcm city university of industry college of electrical engineering hcm city, vietnam e-mail: thanhquyenngo2000@yahoo.com, nmhung289@yahoo.com yaonan wang, ji ge, shuning wei hunan university college of electrical and information engineering changsha, hunan province 410082, p.r.china e-mail: yaonan@hnu.cn, gechunxi@126.com, weishuning@sina.com t. long mai hcm city university of industry college of electronic engineering hcm city, vietnam e-mail: mailongtk@gmail.com abstract: in this paper, adaptive iterative learning control (ailc) of uncertain robot manipulators in task space is considered for trajectory tracking in an iterative operation mode. the control scheme incluces a pd controller with a gain switching technique plus a learning feedforward term, is exploited to predict the desired actuator torque. by using lyapunov method, an adaptive iterative learning control scheme is presented for robotic system with both structured and unstructured uncertainty, and the overall stability of the closed-loop system in the iterative domain is established. the validity of the scheme is confirmed through a numerical simulation. keywords: pd control; learning control; robot manipulator. 1 introduction in general, robotic manipulators have to face various uncertainties in their dynamics, such as friction, and external disturbance. it is difficult to establish exactly mathematical model for the design of a model-based control system. in order to deal with this problem, the branches of current control theories are broad include classical control, robust control, adaptive control, optimal control, nonlinear control, neural network control, fuzzy logic and intelligent control. however, many adaptive control approaches are rejected as being too computationally intensive because of the required of real-time parameter identification and control design owing to its simplicity and robustness to modelling uncertainties, are usually used for repetitive tasks. in this case, the reference trajectory is repeated over a definite operation time. so, iterative learning control (ilc) for robot manipulator has attracted considerable attention in the recent years [1][3], the ideal of learning control is to improve the tracking performance from iteration to iteration for applications of robotics in industry which a single repetitive task [4], [5]. ilc is a relatively recent but well-established area of study in control theory. ilc, which can be categorized as an intelligent control methodology, is an approach for improving the transient performance of system that operates repetitively over a fixed time interval [4]. starting from the classical copyright c⃝ 2006-2012 by ccc publications an adaptive iterative learning control for robot manipulator in task space 519 arimoto-type ilc algorithm, we can develop a pid-like update law can be given in [6]. so far some of robot manipulators control in the published literature [7]-[9] and etc, proposed an adaptive ilc to deal with parameter uncertainties, such as the link length, mass inertia, and friction nonlinearity, with a self-organizing capability. in this paper, a new method is given based on a combination of the advantages of a several control methods into a hybrid one. in particular, it is further extended to the task space or the so-called cartesian space. to apply robot manipulators to a wide class of tasks, it will be necessary to control not only the position of the end-effector, but also the force exerted by the end-effector on the object. by designing the control law in task space, force control can be easily formulated. this paper is organized as follows. section 2 described a dynamic model of an n-link robot manipulator in task space. section 3 presents ailc and its features are discussed. by using lyapunov method to prove the asymptotic convergence of proposed controller. numerical simulation results of a two-link robot manipulator in task space under the possible occurrence of uncertainties are provided to demonstrate the tracking control performance of the proposed ailc system in section 4. conclusions are drawn in section 5. 2 robotic dynamic model in task space in general, the dynamic of an n-link robot manipulator may be expressed in the lagrange form [10] as: d(qi(t))q̈i(t) + c(qi(t), q̇i(t))q̇i(t) + g(qi(t), q̇i(t)) +τa(t) = τ i(t) (1) with t ∈ [0, t f ] denotes the time and i ∈ n denotes the iteration, qi(t) ∈ rn , q̇i(t) ∈ rn, q̈i(t) ∈ rn are the joint position, joint velocity, and joint acceleration variables vector, respectively. d(qi(t)) ∈ rn×n is the inertia matrix, c(qi(t), q̇i(t)) ∈ rn is the coriolis-ccentripetal matrix, is the gravity vector plus friction force vector. bounded unknown disturbances are denoted by τa(t) ∈ rn and the control input torque is denoted by τi(t) ∈ rn. for convenience, a two-link robot manipulator, as shown in fig.1, is utilized to verify dynamic properties are given in section 4. figure 1: architecture of two-link robot manipulator. usually, the manipulator task specification is given relative to the end-effector. thus, it is natural to attempt to derive the control algorithm directly in task space, better than in the joint space. denote the end-effector position and orientation in task space by x ∈ rn . the task space dynamic can be rewritten as follow: dx(q i(t))ẍi(t) + cx(q i(t), q̇i(t))ẋi(t) + gx(q i(t), q̇i(t)) = τa(t) + fx i(t) (2) where dx(q i(t)) = j−t (qi(t))d(qi(t))j−1(qi(t)) 520 t. ngo, y. wang, t.l. mai, j. ge, m.h. nguyen, s.n. wei cx(q i(t), q̇i(t)) = j−t (qi(t))(c(qi(t), q̇i(t)) − d(qi(t))j−1(qi(t))j̇(qi(t)))j−1(qi(t)) gx(q i(t), q̇i(t)) = j−t (qi(t))g(qi(t), q̇i(t)), fix = j −t (qi(t))τi(t) and j(qi(t)) ∈ rn×n is the configuration-dependent jacobian matrix, which is assumed as non-singular in the finite work space ω. the above dynamic equation has the following useful structural properties [11], which can be exploited to facilitate the controller design in the next section. property 1: the inertia matrix dx(qi(t)) is symmetric and positive definite. it is also bounded as a function of q: m1 i ≤ dx ( qi(t) ) ≤ m2 i, where m1,m2 > 0. property 2: ḋx(qi(t)) − 2cx(qi(t), q̇i(t)) is a skew symmetric matrix. therefore, yt [ḋx(qi(t)) − 2cx(qi(t), q̇i(t))]y= 0, where y is a n × 1 nonzero vector. assumption 1: the given desired joint trajectory xd (t) belongs to c2[0, t f ], where c2[0, t f ] denotes 2nd -order continuously differentiable functions on t ∈ [0, t f ]. assumption 2: initial condition xi(0) = xd (0), ẋi(0) = ẋd (0) and for all i ≥ 1. 3 ailc design linearizing the system (2) along the desired trajectory xd(t), ẋd (t), ẍd (t) at the ith iterative, we obtain the following linear time-varying system according [8]: d(t)ëi(t) + [c(t) + c1(t)]ė i(t) + r(t)ei(t) + n(ëi, ėi,ei, t) −τa(t) = s (t) − fi(t) (3) where: d(t) = dx(xd (t)), c(t) = cx(xd (t), ẋd (t)) r(t) = ∂dx ∂x ∣∣∣∣∣ xd (t) ẍd (t) + ∂cx ∂x ∣∣∣∣∣ xd (t),ẋd (t) ẋd (t) + ∂gx ∂x ∣∣∣∣∣ xd (t) s (t) = dx(xd (t))ẍd (t) + cx(xd (t), ẋd(t))xd (t) + gx(xd (t)) +τa(t) e(t) = xd (t) − x(t) the term n(ëi, ėi,ei, t) contains the higher order terms ëi(t), ėi(t) and ei(t) and it can be negligible. the control problem is to find a control law so that the end-effector position x(t) can track specific commands xd (t). we construct controller as follows: fi = fie + h i (4) where the first term fie = k i p(e i(t)) + kid (ė i(t)) is feedback pd control law with the following gain switching rule in [8]: ki+1p = β(i)k i p, k i+1 d = β(i)k i d, β(i + 1) >β(i), i = 0,1,2, ..., n (5) with kip, k i d are the initial proportional and derivative control gain matrices that diagonal positive definite, ki+1p , are the control gains of the ith iterative. β(i) > 1 is the gain switching factor. the gains adaptive law in (5) are used to adjust the pd gains from iterative to iterative. and hi is the initial predicted feedforward control input to be computed at each iterative by a learning rule. according demonstrated in [8], the feedback pd control law with the gain switching factor in (5) plus the feedforward learning control law with the input force profile, the convergence of system (3) is guaranteed. however, in order to, the trajectory tracking convergence fast in some initial iterative, we cannot increase the switching factor arbitrarily large because actuator forces are limited, especially when the system has modelling errors or nonlinearity. hence, according [12] to deal this problem, we propose feedforward control input hi(t) with a learning rule so that hi(t) converges to r(t) for all t ∈ [0, t f ] as follow: hi+1 = hi +αfie (6) an adaptive iterative learning control for robot manipulator in task space 521 figure 2: schematic diagram of the learning control scheme. at the initial stage of learning, the hi(t) are set to zero. α is a positive constant often called a training factor. therefore, for the ith and (i + 1)th iterations, applying the input (4), (6) to system (3), we obtain an error equation as follows: d(t)ëi(t) + [c(t) + c1(t)]ė i(t) + r(t)ei(t) = s (t) − fie − h i (7) d(t)ëi+1(t) + [c(t) + c1(t)]ė i+1(t) + r(t)ei+1(t) = s (t) − fi+1e − h i −αfie (8) to simplicity the proof of stability, let kip = ak i d for the initial iteration, and define the filter errors as follow: x̃i(t) = ėi(t) + aei(t) (9) also, define δx̃i = x̃i+1 − x̃i and δei = ei+1 − ei. then, from (9): δx̃i = δėi + aδei (10) from (5)-(9) and (10), one can obtain the following equation: dδ ˙̃xi + (c + c1 − ad + ki+1d )δx̃ i + (r − a(c + c1 − ad))δei =−(ki+1d + (α− 1)k i d )x̃ i (11) the following theorem can be proved. theorem: suppose robot system (2) satisfies property (1, 2) and assumption (1, 2). with the control input (4), the gain switching rule (5) and learning rule (6). the following should hold for all t ∈ [0, t f ]. xi(t) → xd (t), ẋi(t) → ẋd (t), i →∞ if the controller gains are selected so that the following relationships hold: lp = λmin((2 −α)kid + 2c1 − 2ad) > 0 (12) lr = λmin((2 −α)kid + 2c + 2r/a − 2ċ1/a) > 0 (13) lplr ≥ ∥r/a − (c + c1 − ad)∥2max (14) 522 t. ngo, y. wang, t.l. mai, j. ge, m.h. nguyen, s.n. wei where λmin(a) is the minimum eigenvalue of matrix a, and ∥m∥max = max∥m(t)∥ for t ∈ [0, t f ]. here, ∥m∥ represents the euclidean norm of m. proof: we select a performance index v i(t) as follow: v i = t∫ 0 e−ρτ(x̃i)t qx̃idτ≥ 0 (15) thus, β(i) > 1 according (5) so we have ki+1e > k i d and α is a positive constant, so q = k i+1 d + (α− 1)kid) > 0. from the definition of v j, for the (i + 1)th iteration, one can get: v i+1 = t∫ 0 e−ρτ(x̃i+1)t qx̃i+1dτ (16) let ∆v i = v i+1 − v i then from (15), (16) and (11), we obtain: ∆v i = t∫ 0 e−ρτ((δx̃i)t qδx̃i + 2(δx̃i)t qx̃i)dτ = t∫ 0 e−ρτ((δx̃i)t qδx̃i + 2(δx̃i)t qx̃i)dτ = t∫ 0 e−ρτ(δx̃i)t qδx̃idτ− 2 t∫ 0 e−ρτ(δx̃i)t dδ ˙̃xidτ − 2 t∫ 0 e−ρτ(δx̃i)t ((c + c1 − ad + ki+1d )δx̃ i + (r − a(c + c1 − ad))δei)dτ (17) applying the partial integration, from assumption 2, and property 2, we have: t∫ 0 e−ρτ(δx̃i)t dδ ˙̃xidτ= e−ρτ(δx̃i) t d(δx̃i) ∣∣∣∣t 0 − t∫ 0 (e−ρτ(δx̃i)t d)′δx̃idτ = e−ρτ(δx̃i)t (t)d(t)δx̃i(t) +ρ t∫ 0 e−ρτ(δx̃i)t dδx̃idτ − t∫ 0 e−ρτ(δx̃i)t dδ ˙̃xidτ− 2 t∫ 0 e−ρτ(δx̃i)t cδx̃idτ (18) substituting (18) into (17), and q = ki+1d + (α− 1)k i d yields: ∆v i =−e−ρτ(δx̃i)t (t)d(t)δx̃i(t) −ρ t∫ 0 e−ρτ(δx̃i)t dδx̃idτ − 2 t∫ 0 e−ρτ(δx̃i)t (r − a(c + c1 − ad))δeidτ − t∫ 0 e−ρτ(δx̃i)t (ki+1d − (α− 1)k i d + 2c1 − 2ad)δx̃ idτ (19) an adaptive iterative learning control for robot manipulator in task space 523 from (5), we have: t∫ 0 e−ρτ(δx̃i)t ki+1d δx̃ idτ= β(i + 1) t∫ 0 e−ρτ(δx̃i)t kidδx̃ idτ ≥ t∫ 0 e−ρτ(δx̃i)t kidδx̃ idτ (20) substituting (10) into (19) and noticing (20), we obtain: ∆v i ≤− e−ρτ(δx̃i)t (t)d(t)δx̃i(t) −ρ t∫ 0 e−ρτ(δx̃i)t dδx̃idτ − t∫ 0 e−ρτ(δėi)t ((2 −α)kid + 2c1 − 2ad)δė idτ − 2a t∫ 0 e−ρτ(δei)t ((2 −α)kid + 2c1 − 2ad)δė idτ − 2 t∫ 0 e−ρτ(δėi)t (r − a(c + c1 − ad))δeidτ − a2 t∫ 0 e−ρτ(δei)t ((2 −α)kid + 2c1 − 2ad)δe idτ − 2a t∫ 0 e−ρτ(δei)t (r − a(c + c1 − ad))δeidτ (21) applying the partial integration again gives: t∫ 0 e−ρτ(δei)t ((2 −α)kid + 2c1 − 2ad)δė idτ= e−ρτ(δei) t ((2 −α)kid + 2c1 − 2ad)(δe i) ∣∣∣∣t 0 +ρ t∫ 0 e−ρτ(δei)t ((2 −α)kid + 2c1 − 2ad)δe idτ − t∫ 0 e−ρτ(δėi)t ((2 −α)kid + 2c1 − 2ad)δe idτ + 2 t∫ 0 e−ρτ(δei)t (aḋ − ċ1)δeidτ (22) 524 t. ngo, y. wang, t.l. mai, j. ge, m.h. nguyen, s.n. wei therefore ∆v i ≤− e−ρτ(δx̃i)t dδx̃i −ρ t∫ 0 e−ρτ(δx̃i)t dδx̃idτ − ae−ρτ(δei)t ((2 −α)kid + 2c1 − 2ad)δe i −ρa t∫ 0 e−ρτ(δei)t ((2 −α)kid + 2c1 − 2ad)δe idτ− t∫ 0 e−ρτwdτ ≤− e−ρτ(δx̃i)t dδx̃i − ae−ρτ(δei)t lpδei −ρ t∫ 0 e−ρτ(δx̃i)t dδx̃idτ −ρa t∫ 0 e−ρτ(δei)t lpδe idτ− t∫ 0 e−ρτwdτ (23) where w=(δėi)t ((2 −α)kid + 2c1 − 2ad)δė i + 2a(δėi)t (r/a − (c + c1 − ad))δei + a2(δei)t ((2 −α)kid + 2r/a + 2c − 2ċ1/a)δe i (24) let p = r/a − (c + c1 − ad). then from (12) and (13), we obtain w≥ lp∥δė∥2 + 2aδėt pδe + a2lr∥δe∥2 (25) applying the cauchy-schwartz inequality, we obtain: δėt pδe ≥−∥δė∥∥p∥max ∥δe∥ (26) from (12)-(14) w= lp∥δė∥2 − 2a∥δė∥∥p∥max ∥δe∥+ a2lr∥δe∥2 = lp(∥δė∥− a lp ∥p∥max ∥δe∥)2 + a2(lp − 1 lr ∥p∥2max)∥δe∥ 2 ≥ 0 (27) according property 1 and (27), based on (23), it can be ensured that ∆v i ≤ 0, therefore v i+1 ≤ v i. from the definition, kid is a positive definite matrix. from the definition of v i, v i ≥ 0 and v i is bounded. as a result, yi(t) → ∞ when i → ∞. because ei(t) and ėi(t) are two independent variables, and a is a positive constant. thus, if i → ∞, ei(t) → 0 and ėi(t) → 0 for t ∈ [0, t f ]. finally, the following conclusions hold for t ∈ [0, t f ]. xi(t) → xd (t), ẋi(t) → ẋd (t), i →∞ from the above analysis it can be seen that the adaptive pd control method can guarantee that the tracking errors converge arbitrarily close to zero as the number of iterations increases. the following case studies based on simulation will demonstrate this conclusion. an adaptive iterative learning control for robot manipulator in task space 525 4 numerical simulation a two-link robot manipulator as shown in fig.1 is utilized in this paper to verify the effectiveness of the proposed control scheme. the dynamic equation of the robot manipulator is adopted in [13]. d(q) = m1 + m2 + 2m3 cos(q2) m2 + m3 cos(q2) m2 + m3 cos(q2) m2  c(q, q̇) = −m3q̇2 sin(q2) −m3(q̇1 + q̇2) sin(q2) m3q̇1 sin(q2) 0  g(q) = m4gcos(q1) + m5gcos(q1 + q2) m5gcos(q1 + q2)  and mi are the parameters of interest given by m = p + pl l with m = [ m1 m2 m3 m4 m5 ]t p = [ p1 p2 p3 p4 p5 ]t l = [ l21 l 2 2 l1l2 l1 l2 ]t and pl is the payload, ł1 = 1(m) and ł2 = 1(m) are the lengths of link 1 and link 2, respectively, p is the parameter vector of the robot itself. g = 9.8 m/s2. the jacobian matrix is known as: j(q) = −l1 sin(q1) − l2 sin(q1 + q2) −l2 sin(q1 + q2) l1 cos(q1) + l2 cos(q1 + q2) l2 cos(q1 + q2)  for the convenience of the simulation, the nominal parameters of the robotic system are given as: p = [ 1.66 0.42 0.63 3.75 1.25 ]t kg.m2 the desired reference trajectories in the cartesian space are: xd1 = 1.0 + 0.2 cos(πt), xd2 = 1.0 + 0.2 sin(πt). which represent a circle of radius 0.2m and its centres is located at (x1, x2) = (1.0,1.0)m. the robot is initially rested with its end-effector positioned at the center of the circle. with initial condition are x1(0) = xd1(0) = 1.2, x2(0) = xd2(0) = 1.0, ẋ1(0) = ẋd1(0) = 1.0, ẋ2(0) = ẋd2(0) = 1.2 and t = [0,2]. the most important parameters that effect the control performance of the robotic system are the external disturbance τa(t), the friction term f (q̇), in simulation, payload variation situation and external disturbance situation occurring at fifth the iteration are considered. the payload variation situation is that pl = 0(kg) from first the iteration to fourth the iteration, and then it was put on pl = 3 after the fifth iteration. the disturbance situation is that external forces are injected into the robotic system, and their shapes are expressed as follows: τa (t) = [ 5 sin(5t) 5 sin(5t) ]t (28) in addition, friction forces are also considered in this simulation and given as: f (q̇) = [ 2q̇1 + 0.8sgn(q̇1) 4q̇2 + 0.1sgn(q̇2) ]t (29) 526 t. ngo, y. wang, t.l. mai, j. ge, m.h. nguyen, s.n. wei 0.6 0.8 1 1.2 1.41.4 0.4 0.6 0.8 1 1.2 1.4 position x1 (m) p os it io n x 2 (m ) actual desired 0.6 0.8 1 1.2 1.41.4 0.6 0.8 1 1.2 1.41.4 position x1 (m) p os it io n x 2 (m ) actual desired 0 5 10 15 0 0.2 0.4 0.6 0.8 1 iteration number m ax im um e rr or s (m ) rms of x2 rms of x1 figure 3: reference tracking and actual trajectory for end-effector at different iterative under ailc is adopted in [11]. (a) position tracking of end-effector at first iteration, (b) position tracking of end-effector after fifteenth iteration and (c) profile of rms position errors payload and disturbance variation after the fifth iteration. in order to exhibit the superior control performance of ailc system, two extra control systems including an adaptive switching learning pd control system (asl-pd) is represented in [8], an iterative learning control (ilc) is represented in [12]. in three simulation situations, the pd control gain was set to be the same as [8]: kip = k i d = diag {30,30} , k i+1 p = 2ik i p, k i+1 d = 2ik i d, β= 0.75 the simulated results of ilc system, the responses of end-effector position at first and fifteenth iteration and tracking error from iteration to iteration to be depicted fig. 3(a), (b) and (c) respectively. from the simulated results, we can be seen that the tracking performance was not acceptable because the errors were too large compared to asl-pd and proposed controllers, especially at the fifth iteration poor tracking errors responses are resulted due to the occurrence of payload variation situation and external disturbance. in the asl-pd system are depicted in [8]. the end-effector position responses, tracking error from iteration to iteration to be depicted in fig. 4(a), 4(b), and 4(c), respectively. the end-effector tracking performance is obvious under the occurrence of payload variation and external disturbance. the convergence rate increased greatly compared with the ilc control method. now, the ailc system depicted in fig. 2 is applied to control the robot manipulator for comparison. the simulated results of end-effector position responses and tracking error from iteration to iteration are depicted in fig. 5(a), 5(b) and 5(c), respectively. table 1 shows tracking performance of proposed system from the initial iteration to fifteenth are obvious. therefore, the comparison of their method and our method demonstrated a bit fast convergence rate with the proposed control method. specially, control an adaptive iterative learning control for robot manipulator in task space 527 iterative 1 5 10 15 ilc max |ei1|(m) 0.0822 0.0773 0.0168 0.0140 max |ei2|(m) 0.1998 0.5634 0.0130 0.0105 asl-pd max |ei1|(m) 0.0398 0.0076 0.0012 0.0010 max |ei2|(m) 0.0646 0.0593 0.0008 0.0006 ailc max |ei1|(m) 0.0377 0.0076 0.0011 0.0009 max |ei2|(m) 0.1741 0.0590 0.0008 0.0006 table 1: maximum tracking errors from iteration to iteration. iterative 1 5 10 15 asl-pd max |τi1|(n.m) 90 930 3330 7230 max |τi2|(n.m) 51.45 531.6 1903 3618 ailc max |τi1|(n.m) 82.50 772.5 2647 5647 max |τi2|(n.m) 47.14 441.6 1513 3228 table 2: maximum control forces from iteration to iteration. torques of proposed system is much smaller than the asl-pd controller. detail results are depicted in table 2. 5 conclusions this paper has successfully implemented an ailc scheme to control the position of end-effector in task space for achieving desired position control. all the system dynamics may be unknown. a new control method is a combination of advantages some other method into a hybrid one as explained above. by using lyapunov theorem, the asymptotic convergence of the closed-loop control system can be ensured whether or not the uncertainties occur. simulation results of a two link robot manipulator in task space via various existing control methods including asl-pd and ilc control were also applied in this paper to compare and display the manipulative performance of the proposed control system. according to the result as depict in figs. 3-5 and table 1-2, the desired position tracking and tracking errors response of the ailc scheme decrease with the increase of the iteration number under wide range of payload and external disturbance. the main of the paper is to construct a simple scheme, easy implementation, fast convergence. especially, in this paper is applied not only to control the position of the end-effector, but also the force exerted by the end-effector on the object. by designing the control law in task space, force control can be easily formulated. acknowledgment this work was supported by the national natural science foundation of china (60775047; 60835004), the national high technology research and development program of china (863 program) (2007aa04z244; 2008aa04z214). the authors would like to thank the associate editor and the reviewers for their valuable comments. 528 t. ngo, y. wang, t.l. mai, j. ge, m.h. nguyen, s.n. wei 0.6 0.8 1 1.2 1.41.4 0.6 0.8 1 1.2 1.41.4 position x1 (m) p os it io n x 2 (m ) actual desired 0.6 0.8 1 1.2 1.41.4 0.6 0.8 1 1.2 1.4 position x1 (m) p os it io n x 2 (m ) actual desired 0.6 0.8 1 1.2 1.41.4 0.7 0.8 0.9 1 1.1 1.2 1.3 position x1 (m) p os it io n x 2 (m ) actual desired 0.6 0.8 1 1.2 1.41.4 0.8 1 1.21.2 position x1 (m) p os it io n x 2 (m ) actual desired 0 5 10 15 0 0.2 0.4 0.6 0.8 1 iteration number m ax im um e rr or s (m ) rms of x2 rms of x1 figure 4: reference tracking and actual trajectory for end-effector at different iterative under aslpd is adopted in [6]. (a) position tracking of endeffector at first iteration, (b) position tracking of end-effector after fifteenth iteration and (c) profile of rms position errors payload and disturbance variation after the fifth iteration. 0 5 10 15 0 0.2 0.4 0.6 0.8 1 iteration number m ax im um e rr or s (m ) rms of x2 rms of x1 figure 5: reference tracking and actual trajectory for end-effector at different iterative under ailc controller. (a) position tracking of end-effector at first iteration, (b) position tracking of end-effector after fifteenth iteration and (c) profile of rms position errors payload and disturbance variation after the fifth iteration. an adaptive iterative learning control for robot manipulator in task space 529 bibliography [1] p. bondi, g. cacaline and l. gambardella, on the iterative learning control theory for robot manipulators, ieee j. on robotics and automation, 4:14-22, 1984. 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[9] choi jy, lee js, adaptive iterative learning control of uncertain robotic systems,iee proc contr theory, 147(4):217-223, 2000. [10] b.s. chen, h.j. uang, and c.s. tseng, robust tracking enhancement of robot systems including motor dynamics: a fuzzy-based dynamic game approach,ieee trans. fuzzy syst, 11(4):538552,1998. [11] craig jj. introduction to robotics: mechanics and control. reading, ma: addison-wesley, 1986. [12] t.y kuc, k. nam, j.s. lee, an iterative learning control of robot manipulators, ieee trans robot automat., 7(6): 835-842, 1991. [13] s.s. ge, c.c. hang, adaptive neural network control of robot manipulators in task space,ieee trans. ind. elec., 44(6):746-752, 1997. international journal of computers communications & control issn 1841-9836, 9(5):633-643, october, 2014. a modified ant colony algorithm for traveling salesman problem x. wei, l. han, l. hong xianmin wei*, liqi han, lu hong school of computer engineering, weifang university, weifang 261061, p.r. china weixm@wfu.edu.cn, liqi.han@wfu.edu.cn, sdkfz234@163.com *corresponding author: weixm@wfu.edu.cn abstract: ant colony algorithms such as ant colony optimization (aco) have been effectively applied to solve the traveling salesman problem (tsp). however, traditional aco algorithm has some issues such as long iterative length and prone to local convergence. to this end, we propose we embed aco into cultural algorithm (ca) framework by leveraging the dual inheritance mechanism. best solutions are evolved in both population space and belief space, and the communication between them is achieved by accept and influence operations. besides, we employ multiple population spaces for parallel execution. experiments show that the performance of our proposed algorithm is greatly improved. keywords: traveling salesman problem (tsp), ant colony optimization (aco), cultural algorithm (ca). 1 introduction traveling salesman problem (tsp) is a typical np hard problem [1]. given a number of cities and the distances between them, tsp aims to calculate the shortest tour for the salesman to visit each city exactly once and return to the starting point. solving tsp problem can be beneficial to applications such as deployment of network devices [2], transportation [3] and traffic control [4]. ant colony optimization (aco) algorithm [5] is one possible solution for solving tsp problem. aco simulates the foraging process of ants, where the routes with more pheromones are more likely to be selected. the process of applying aco algorithm for tsp can be described as follows. suppose there are ants and cities. the movement of each ant is to choose the next unvisited city to visit by certain rules, and meanwhile update the amount of pheromones on that route. although the advantages of aco, such as positive feedback, distributed, parallel and selforganization, facilitate the solving of tsp problem, there still remain some issues. for example, at earlier iterations, the amount of pheromones is relative exile, so the accumulation of enough pheromones costs a long time. besides, at later iterations, the positive feedback mechanism greatly increases the possibility of local convergence. to deal with the above long iteration time and local convergence issues, we propose an improved algorithm in this paper. the general idea is to accelerate the evolution and revise the best solution at each iteration. to this end, we leverage the cultural algorithm (ca) [6] framework to embed the traditional aco algorithm. indeed, as indicated in [7, 8], it is feasible and efficient to adapt ca for optimize aco. our experiments prove that the dual inheritance mechanism of ca helps to improve the performance of aco. the remainder of this paper is organized as follows. section 2 reviews related work. the proposed algorithm is discussed in section 3. empirical experiments of evaluating the improved algorithm are conducted in section 4. finally, the paper is concluded in section 5. copyright © 2006-2014 by ccc publications 634 x. wei, l. han, l. hong 2 related work many efforts have been made to solve the problems of traditional aco algorithm. for example, dorigo et al. [9] proposed an ant system with positive feedback, distributed computation, and a constructive greedy heuristic. bullnheimer et al. [10] proposed a new rank based version of ant system. gutjahr et al. [11] proposed a graph based ant system. gambardella et al. [12, 13] introduced q-learning to ant system for solving tsp problem. ciornei et al. [14] developed a hybrid algorithm with ant colony and genetic algorithm, so that the proposed algorithm can achieve faster convergence and better search capability for global best solution. stutzle et al. [15] designed a max-min ant system (mmas) for better exploitation ability and accelerated the accumulating the pheromone of the best solutions. walter and thomas et al. proved the convergence of aco algorithm [16–18]. indeed, aco algorithms have been applied to many problems such as the tsp problem. zhou et al. [19] provided the theoretical convergence analysis on the traditional aco, and proved the feasibility of applying aco algorithm for tsp. zhang et al. [20] proposed an adaptive heterogeneous multiple aco to solve tsp, which used an evolution coefficient to evaluate the solutions obtained by the ant colony and then update the phenomenon. tuba et al. [21] modified aco by improving the pheromone correction mechanism, where the pheromone values for highly undesirable links are significantly lowered by a posteriori heuristic. negulescu et al. [22] presented an adapted aco that incorporates methods and ideas from genetic algorithms (ga) by simulating artificial ants with different behaviors as synthetic genes. besides, aco has been employed in many applications. gajpal et al. [23] applied aco to the route selection of tucks, and the search all the customers during the routing path using a multi-route search principle. hu et al. [24] employed a new method to update the phenomenon to deal with the continuous optimization problems. ghoseiri et al. [25] developed an algorithm to solve the dual-object shortest path problem with a multi-objective aco algorithm. gajpal et al. [26] proposed a modified aco method for a vehicle routing problem. secui et al. [27] applied aco for optimal allocation of capacitor banks in electric power distribution networks. cultural algorithm (ca) was first proposed by reynolds [6]. then, chung et al. [28] developed a new framework to embed the evolutionary programming into the population space to simulate the evolution process. liu et al. [29] combined ca with particle swarm optimization algorithm and applied it to the numerical optimization problem. ma et al. [30] applied ca to solve the tsp problem and utilized the idea from simulated annealing to ensure the accuracy and efficiency. unlike existing work, in this paper we design a modified aco algorithm by leveraging ca framework, that is, to employ a dual inheritance mechanism into the traditional aco algorithm. besides, we apply the modified algorithm on tsp problem. note that, for the consideration of parallelism, we use multiple population spaces. 3 proposed algorithm the disadvantages of the traditional aco algorithm include slow convergence and prone to stagnation. although there are some modifications over the traditional aco version, most of them are built upon the traditional computing structure to determine the internal status of the algorithm and estimate the search capabilities of the ant colony through qualitative analysis. for example, the improvements are made by updating pheromones, changing evaporation coefficient or the amount of released pheromones in order to avoid stagnation. different from existing efforts, we embed the aco algorithm into another framework, cultural algorithm (ca) [6], which could essentially improve the performance. the intuitive is that both algorithms are population based and share information through interactions among the a modified ant colony algorithm for traveling salesman problem 635 population. we perceive that the aco algorithm can benefit from the dual inheritance of ca. besides, the inherent nature of parallelism of aco indicates that parallel algorithm can improve the efficiency due to the mature of hardware and software. therefore, in this section, we propose a parallel aco algorithm based on ca algorithm, and then apply it for solving the traditional tsp problem. figure 1: the framework of culture algorithm ca simulates the evolution process of human society, which regards culture as the information carrier. while the culture is accepted by the whole population of the society, it can be used to direct the behavior of each individual in turn. there are two evolution spaces in ca algorithm: belief space, which is composed of the experience knowledge acquired during the evolution process; and population space, which is composed of the individuals. these two spaces communicate through specific protocols. the framework of ca is illustrated as figure 2. generally, the lower space contributes to the upper space periodically, and the upper space continuously evolves, which in turn influences the lower space. this is called dual inheritance mechanism. as shown in figure 2, the major operations of ca algorithm are accept() and influence(), while other operations are performed inside belief or population space independently. therefore, it is feasible to embed other algorithms into ca framework by adding specific logics into belief and population spaces and implementing accept() and influence() operations between them. the parallel version of ca is illustrated as figure 3, where exist multiple population spaces. each population space performs parallel evolution, and then produces local best individuals for next generation. then accept() operation is performed by all population spaces periodically to update belief space in a synchronous way. on the other hand, belief space conducts evolution as well, and calls influence() operation to direct the evolution process of each population space. 3.1 designing population space let m be the number of ants, n be the number of cities c = {c1,c2, . . . ,cn}, dij(i,j = 1,2, . . . ,n, i ̸= j) be the distance between any two cities, and τij be the amount of pheromones from i to j. at the beginning, τij is initialized as a constant, that is, τij(0) = c , where c is a constant. at t-th iteration, the pheromones on path i,j is notated as τij(t). according to mmas [12] algorithm, τij(t) is restricted between τmin and τmax in order to avoid premature local convergence. 636 x. wei, l. han, l. hong figure 2: the process flow of culture algorithm figure 3: the framework of culture algorithm τmax(t) = 1 2(1−ρ)l(t) + σ l(t) (1) τmin(t) = τmax(t) 20 (2) a modified ant colony algorithm for traveling salesman problem 637 where l(t) is the length of the best solution at iteration t, ρ is the evaporation coefficient of pheromones, and σ is the number of best solutions at iteration t. suppose ηij(t) denotes the heuristic information on path (i,j). the general idea is that ants would select closest path with larger amount of pheromones. therefore, the probability of ant k transferring from city i to j is calculated as: pkij(t) =   [ τij(t) ]α · [ηij(t)]β∑ j∈allowedk [ τij(t) ]α · [ηij(t)]β , j ∈ allowedk, 0, otherwise, (3) where allowedk denotes the available nodes to choose at the next step for ant k, α is the heuristic factor, meaning the importance of path with remaining pheromones, β is the heuristic factor for ηij(t), denoting the affect of heuristic information. after ants iterate all the nodes, remaining pheromones are updated as follows: τij(t + 1) = (1−ρ)τij(t) + m∑ k=1 ∆τkij(t) (4) where 1 − ρ is the residual coefficient of pheromones, and ρ ∈ [0,1). ∆τkij(t) is the amount of pheromones remaining on the path at current iteration for ant k, which can be calculated as: ∆τkij(t) = { q lk , if ant k passes (i,j) at current iteration, 0, otherwise, (5) where q is a constant, and lk is the total length of ant k’s tour. 3.2 designing belief space belief space is responsible for updating knowledge, that is, to further optimize the best solutions provided by the population space, which are transferred through accept() operation. for tsp problem, we employ 3-opt algorithm to evolve belief space. suppose there exist any three nodes i, j, k, and current best solution is c = {cs · · ·cici+1 · · ·cjcj+1 · · ·ckck+1 · · ·ct}. as shown in figure 4, if d(ci,ci+1) + d(cj,cj+1) > d(ci,cj) + d(ci+1,cj+1), then reverse sort is performed on c = {ci+1, . . . ,cj}; if d(cj,cj+1) + d(ck,ck+1) > d(cj,ck) + d(cj+1,ck+1), then reverse sort is performed on c = {cj+1, . . . ,ck}. 3.3 accept operation we accept a fixed ratio of all population spaces as the belief space, e.g. 20%. the accept() operation passes the local best solution of each population space r∗ and the corresponding length of route l∗ to the belief space. when accept() operation is performed, the relative poor individuals are replaced by the current best one of each population space. suppose tcurrent is the current iteration, tend is the predefined maximum iterations, and taccept is the iteration where accept() is called, which can be calculated as: taccept = fix ( c1 + tcurrent tend c2 ) (6) where fix() is a rounding function, and c1, c2 are constants. 638 x. wei, l. han, l. hong figure 4: 3-opt algorithm 3.4 influence operation after further optimization via 3-opt in belief space, influence() operation passes the shortest path and its length to each population space periodically, and updates the global pheromones. suppose the iteration where influence() operation is performed is tinfluence, which can be represented as: tinfluence = fix ( c1 + tend − tcurrent tend c2 ) (7) where fix() is a rounding function, and c1, c2 are constants. the improved algorithm has one belief space and multiple population spaces. each space evolve independently, and the communication between them is achieved by accept() and influence() operations. the process flow of belief space is shown in figure 5. in belief space, the update is achieved by 3-opt algorithm for computing the shortest path. when accept() is called, the solution is updated by the local best from population space. when influence() is called, the global best solution in belief space is transferred to each population space. if the termination condition is satisfied, the algorithm is completed. as shown in figure 6, the update of population space is achieved by aco algorithm. when accept() is called, the local best solution from each population space is updated up to the belief space. when influence() is called, the global best solution in belief space is transferred down to each population space. if the termination condition is satisfied, the algorithm is completed. 4 experiment the environment of our experiments is intel 2.3 ghz, 2gb ram, windows 7 operating system, and matlab 7.0 for programming. the dataset is achieved from tsplib library . the parameters settings are as follows: ρ = 0.5, α = 1, β = 5, q = 100, tend = 200. we use 4 population spaces, and the maximum number of evolution of both belief space and population space is 200. we compare our algorithm with the traditional aco. both algorithms are ran independently for 50 times, and the average execution time is supposed to the performance. as shown in table 1, we evaluate three examples from tsplib, i.e., eil51, berlin52 and st70. we can see that our proposed algorithm outperforms the typical aco method for solving tsp problem. moreover, the best solution provided by our method is much better than the known best. a modified ant colony algorithm for traveling salesman problem 639 figure 5: flow chart of evolution of belief space table 1: results of aco and our algorithm with different datasets example example example example our cities solution aco algorithm eil51 51 426 428.87 412.12 berlin52 52 7542 7542 7088.34 st70 70 675 677.11 652.80 besides, take eil51 as an example, we compare the best tour and convergence speed between our algorithm and traditional aco for tsp in figure 7. the blue line denotes the performance of aco, while the green line is for our algorithm. the result shows that our algorithm can achieve shorter tour with rapid speed of convergence. 640 x. wei, l. han, l. hong figure 6: flow chart of evolution of population space figure 7: evolution of best tour length of eil51 for aco and proposed algorithm a modified ant colony algorithm for traveling salesman problem 641 5 conclusions in this paper, we propose a new ant colony algorithm and apply it to the typical traveling salesman problem. specifically, we embed the traditional aco algorithm into cultural algorithm framework, where the set of ants makes up the population space, and the best solutions found at specific iteration are updated up to the belief space. the evolution of population space is indeed based on aco, and that of the belief space is based on 3-opt algorithm for calculating the shortest path. the communication between population space and belief space is performed by accept and influence operations. moreover, to minimum the execution cost, we design multiple population spaces for parallel execution. the experiments evaluate that our algorithm outperform the traditional aco algorithm. acknowledgments this work was partially supported by shandong natural science foundation (zr2011fl006), shandong science and technology development plan (2011yd01044), shandong spark program (2012xh06005), and weifang municipal science and technology development plan (201301050). 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(2008), research on cultural algorithm for solving routing problem of mobile agent, the journal of china universities of posts and telecommunications, issn:10021310 , 15(4): 121–125. international journal of computers communications & control issn 1841-9836, 9(4):397-407, august, 2014. petri net based modelling of a career syllabus r. carvajal-schiaffino, l. firinguetti-limone rubén carvajal-schiaffino*1 departamento de matemática y ciencia de la computación universidad de santiago de chile av. b. o’higgins 3363, santiago de chile *corresponding author: ruben.carvajal@usach.cl luis firinguetti-limone2 departamento de estadística universidad del bío-bío av. collao 1202 concepción, chile lfiringu@ubiobio.cl abstract: a syllabus is a set of courses, their prerequisites and a set of rules defining the continuance of a student in an academic program. the manual verification of the possible contradictions between prerequisites and the set of rules is a difficult task due to the large number of possible cases. in this article we present the different stages in the verification of a syllabus, its modelling and analysis using petri nets, and suggested ways in which this may be used by the university administration in the decision making process. keywords: decision support systems, system modelling, system verification, educational planning, petri nets. 1 introduction in many countries student retention rates are under scrutiny as part of government funding policy of higher education institutions. as such, retention rates are an increasingly important issue for these institutions ( [2]). it has been found that structural characteristics of higher education institutions, such as enrollment size, selectivity and control, have significant associations with student drop-out (see [5, 15, 16]). in this regard the consistency of a career syllabus may well play a significant role in student persistence/drop-out. we may define a syllabus as a set of courses, their sequence, the institutional rules defining the continuance of a student in an academic program (number of credits, number of times a course can be repeated, student progress schedule, etc.). in particular, student progress schedules, which define minimum requirements for continuance in an academic program, are set up by many institutions of higher education (see for instance [13, 14, 17, 18]) petri net modelling can be an important tool to support decision making at different levels and types of organizations (see for example [4, 6, 10]). in this article we propose a petri net model of a syllabus, to help the decision making process of the university administrators, students and the course lecturers. the administration may use this model to verify the internal consistency of 1partially funded by dicyt-usach under grant cs 060933 2partially funded by the universidad del bío-bío under grant diubb-083308 3/r copyright © 2006-2014 by ccc publications 398 r. carvajal-schiaffino, l. firinguetti-limone a b c d e figure 1: a prerequisites graph an existing and/or under development syllabus; to plan the student career progress; to establish the risk level of a student drop-out. in section 2 we discuss the main characteristics of petri nets, its feasibility to represent the prerequisites and the analysis of the resulting model. in section 3 we demonstrate trough an example the stages in the modelling of a rule added to a petri net representing the prerequisites; an analysis is made of the model obtained and we discuss some related work. finally, in section 4 we conclude, presenting our findings and suggestions for future work. 2 modelling syllabus rules in this article we consider the following rules in a career syllabus: 1.prerequisites between courses. 2.maximum number of times a course can be repeated. 3.maximum duration of stay in the academic program. 4.minimum number of courses to be taken during each academic period. a petri net [7, 8] allows the formal mode description of systems whose dynamics is characterized by concurrency, synchronization, mutual exclusion, conflicts. prerequisites present all these characteristics, except mutual exclusion. for instance, in figure 1 we can find concurrency of courses a, b, c since, as already mentioned, they have to be taken at the beginning of the program. another situation of concurrency may occur between courses b, d if a student passes courses a, c but fails course b. synchronization is present if to take a course it is necessary to pass more than one, as is the case of e since it is necessary to pass b, c. a conflict does not appear explicitly in a prerequisite graph, nevertheless it is present since a course may or may not be passed. petri net based modelling of a career syllabus 399 pass fail course (a) course1 course2 pass1 pass2 fail1 fail2 (b) course1 course2 pass1 pass2 fail1 fail2 (c) figure 2: a course 2.1 modelling prerequisites and maximum number of failures formally, the prerequisites are represented as an acyclic directed graph where each node represents a course and the arcs represent the dependencies (prerequisites) between courses. for example, figure 1 may represent five courses a, b, c, d, e where the first three are the courses to be taken at the beginning of the academic program. the arc joining a and d pinpoints that to take course d it is necessary to pass course a and the arcs joining b, e and c, e indicate that to take course e it is necessary to pass courses b and c. in petri nets terms a course is represented by a place, whereas a place with a token indicates that the course is being taken. the activities that can be performed while taking a course are pass or fail. this is represented in figure 2a. since a course can be taken at most twice, this may be represented as in figure 2b. a token in place course1 pinpoints that a student is taking a course for the first time. firing the transition fail1 indicates that the course was failed, having to be taken again (this is represented by a token in course2). firing the transition fail2 indicates that the student failed course a second time (figure 2c), causing its elimination from the program. on the other hand firing transitions pass1 and pass2 pinpoint that course was passed in the first or second instance respectively. the petri net representing a complete prerequisites graph is shown in figure 3. place enrolled denote the fact that a student is registered in the academic program. places a1, a2, b1, 400 r. carvajal-schiaffino, l. firinguetti-limone 2 a2 d1 d2 t2 t3 t4 t5 t6 t7 c1b1a1 c2b2 2 e1 e2 2 pend enroled t1 t8 t9 t11 t13 t14 t15 t16 t17 t18 t19 t21 t22 t10 t12 t20 t23 pfail t8, t10, t12, t14, t15, t16, t17, t18, t20 t2, t3, t4, t5, t6, t7, from transitions psem to transitions t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12, t13, t20, t21 t14, t15, t16, t17, t18, t19, from transitions t9, t11, t13, t19, t21 figure 3: a net representing the prerequisites graph of figure 1 b2, c1, c2, d1, d2, e1, e2 represent different possibilities of passing or failing the courses. the place pend represents a student having taken all the courses of the program. transition t1 indicates the beginning of an academic period and firing it puts tokens in places a1, b1 and c1. this global state indicates that a student, once enrolled, must take courses a, b, c. firing the transitions t2, t4, t6, t14, t16 respectively represent passing courses a, b, c, d and e the first time these courses are taken. on the other hand, firing the transitions t8, t10, t12, t18, t20 represents passing the same courses but in the second opportunity. first time failures are produced when transitions t3, t5, t7, t15, t17 are fired. second time failures, and elimination from the program, are produced when transitions t9, t11, t13, t19, t21 are fired. since failing a course twice results in the elimination of a student from the program, it is necessary to exclude the possibility of a student taking courses. for this we use place psem, enabling transitions t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12, t13, t14, t15, t16, t17, t18, t19, t20 ,t21. transitions t2, t3, t4, t5, t6, t7, t8, t10, t12, t14, t16, t18, t20 return a token to psem, but firing transitions t9, t11, t13, t19, t21 represent failing a course a second time, not allowing any further courses to be taken. these failure transitions lead to the state pfail from which it is not possible to escape. to verify the properties of boundedness and liveness the transition t22 is fired, putting a token in place enroled. 2.2 counting the number of periods to conclude a program the model discussed on the previous section allows the direct verification of the conditions of elimination from the program by failing a course a second time. this model satisfies three important properties: boundedness, liveness and absence of deadlock, which are verified by computing p-invariants, t-invariants and generating the reachability graph, respectively. petri net based modelling of a career syllabus 401 figure 4: the algorithm. we have carried the automated analysis of the petri net model with the tool ina [9]. the number of academic periods necessary to complete the program is taken as a prerequisite for two reasons: to limit the maximum duration of stay in the program and to establish a minimum number of courses to be taken each academic period. typically, the maximum duration of stay is 1.5 times the duration of the academic program. in our example the duration of the program would be 2 periods and the maximum duration of stay would be 3 periods. a question to be answered is how to compute the number of academic periods (semesters, years, etc.) required to complete the academic program. to answer this question we must take into account that there are many performance cases. these vary from the optimum case: not failing any course, to the worst case: failing each course once. the model reachability graph contains all these cases, but we must consider that each state represents the firing of a transition not allowing the direct representation of the simultaneous passing/failing of several courses. however relevant information to count the number of periods is given only by states 1, 2, 5 and 7. state 1 (enrolled + psem) represents a student enrolling in the program the first time; state 2 (a1 + b1 + c1 + psem) represents a student sitting for the first time and simultaneously courses a, b, c; state 5 (d1 + 2e1 + psem) represents a student having passed all courses in the first period and sitting now courses d, e. the two tokens in place e1 represents the fact that course e has two prerequisites b, c. finally, state 2pend + psem represents a student having taken all program courses. from the previous discussion it may be deduced that, although the reachability graph contains all the states of the model and gives all valid sequences to complete the program (without time limitations) an additional time effort is necessary to recover only the states representing an academic period. in our example the reachability graph contains 96 states, but only 17 of the are relevant for our analysis. a feasible approach to counting academic periods this problem can be solved by computing the model t-semiflows because, conceptually, they represent the sequence of fired transitions (in our case passing or failing sequences) to comeback to the initial state. if we regard as the initial state the enrollment of a student in the program and the possibility of taking courses, then any t-invariant will be a sequence of passing/failing 402 r. carvajal-schiaffino, l. firinguetti-limone t-semiflow number of failures failed courses number of periods t1 + t2 + t4 + t6 + t14 + t16 + t22 0 2 t1 + t3 + t4 + t6 + t8 + t14 + t16 + t22 1 a 3 t1 + t2 + t5 + t6 + t10 + t14 + t16 + t22 1 b 3 t1 + t2 + t4 + t7 + t12 + t14 + t16 + t22 1 c 3 t1 + t2 + t4 + t6 + t15 + t16 + t18 + t22 1 d 3 t1 + t2 + t4 + t6 + t14 + t17 + t20 + t22 1 e 3 t1 + t2 + t5 + t7 + t10 + t12 + t14 + t16 + t22 2 b,c 3 t1 + t2 + t5 + t6 + t10 + t15 + t16 + t18 + t22 2 b,d 3 t1 + t2 + t5 + t6 + t10 + t14 + t17 + t20 + t22 2 b,e 4 t1 + t2 + t4 + t7 + t12 + t15 + t16 + t18 + t22 2 c,d 3 t1 + t2 + t4 + t7 + t12 + t14 + t17 + t20 + t22 2 c,e 4 t1 + t2 + t4 + t6 + t15 + t17 + t18 + t20 + t22 2 d,e 3 t1 + t3 + t5 + t6 + t8 + t10 + t14 + t16 + t22 2 a,b 3 t1 + t3 + t4 + t7 + t8 + t12 + t14 + t16 + t22 2 a,c 3 t1 + t3 + t4 + t6 + t8 + t15 + t16 + t18 + t22 2 a,d 4 t1 + t3 + t4 + t6 + t8 + t14 + t17 + t20 + t22 2 a,e 3 t1 + t2 + t5 + t7 + t10 + t12 + t15 + t16 + t18 + t22 3 b,c,d 3 t1 + t2 + t5 + t7 + t10 + t12 + t14 + t17 + t20 + t22 3 b,c,e 4 t1 + t2 + t5 + t6 + t10 + t15 + t17 + t18 + t20 + t22 3 b,d,e 4 t1 + t2 + t4 + t7 + t12 + t15 + t17 + t18 + t20 + t22 3 c,d,e 4 t1 + t3 + t5 + t7 + t8 + t10 + t12 + t14 + t16 + t22 3 a,b,c 3 t1 + t3 + t5 + t6 + t8 + t10 + t15 + t16 + t18 + t22 3 a,b,d 4 t1 + t3 + t5 + t6 + t8 + t10 + t14 + t17 + t20 + t22 3 a,b,e 4 t1 + t3 + t4 + t7 + t8 + t12 + t15 + t16 + t18 + t22 3 a,c,d 4 t1 + t3 + t4 + t7 + t8 + t12 + t14 + t17 + t20 + t22 3 a,c,e 4 t1 + t3 + t4 + t6 + t8 + t15 + t17 + t18 + t20 + t22 3 a,d,e 4 t1 + t2 + t5 + t7 + t10 + t12 + t15 + t17 + t18 + t20 + t22 4 b,c,d,e 4 t1 + t3 + t5 + t7 + t8 + t10 + t12 + t15 + t16 + t18 + t22 4 a,b,c,d 4 t1 + t3 + t5 + t7 + t8 + t10 + t12 + t14 + t17 + t20 + t22 4 a,b,c,e 4 t1 + t3 + t5 + t6 + t8 + t10 + t15 + t17 + t18 + t20 + t22 4 a,b,d,e 4 t1 + t3 + t4 + t7 + t8 + t12 + t15 + t17 + t18 + t20 + t22 4 a,c,d,e 4 t1 + t3 + t5 + t7 + t8 + t10 + t12 + t15 + t17 + t18 + t20 + t22 5 a,b,c,d,e 4 table 1: t-semiflows obtained from the net in figure 3 a course such that the last transition fired will be t22 which allows to comeback to the initial state. the net possess 33 t-semiflows (table 1), but only 32 of them are valid for computing the number of academic periods. for instance, the firing sequence necessary to take all the courses in the program failing none is t1 + t2 + t4 + t6 + t14 + t16 + t22. with this information it is possible to determine the periods of time taken. for this it is necessary to take into account the places acting as synchronization points, in other words courses with more than one prerequisite. as shown in table 1, from the total of 32 valid sequences to sit courses without failing more than twice, only 15 allow completion of the program without entering in conflict with the maximum time of continuance in the program. note that there are situations where failing two courses (b,e or c,e or a,d) results in the elimination of the program. however, in the case of programs of study with many courses, counting the t-semiflows of the corresponding petri nets is not possible. the reason for this is that the number of t-semiflows is exponential with respect to the number of courses. for example, for a program of study with 16 courses (4 academic periods), the corresponding reachability graph contains 47,935 states and 65,536 valid sequences for sitting the courses failing at most once some of them. to solve the problem of the maximum number of semiflows that can be computed with a tool to analyze petri nets, we propose an algorithm (see fig. 4) that allows the generation of valid sequences to fulfill a program with no more than two failures in each course. this algorithm generates a directed graph in which each vertex contains a set of courses that can be taken in each academic period.the initial vertex of this graph contains the set of courses to be taken at the beginning of the program. then, -taking into account the number of resits and the restrictions asserted by the prerequisite graph-, from each vertex are generated the vertices containing the set of courses to be taken next. figure 5 shows the graph of the syllabus of 5 courses given in figure 1; in this case the first vertex, a1b1c1, represents courses a,b,c which are taken for the first time. the adjacent vertices petri net based modelling of a career syllabus 403 figure 5: graph generated with algorithm of figure 4 using the graph of figure 1 as input represent the courses that are taken in the following period having into account the number of repetitions and restrictions of the prerequisite graph. as can be observed, in this case the number of vertices is 17. a valid sequence of courses taken (having into account repetitions) is obtained if one follows the graph from the initial vertex to vertex end. for instance the sequence a1b1c1, d1e1, end represents passing all courses, failing none. in the opposite case, the sequence a1b1c1, a2b2c2, d1e1, d2e2, end represents taking all courses twice. the number of periods taken is given by the number of vertices visited minus 1. the number of trajectories of all paths in the graph coincides with the number of t-semiflows of the petri net representing the prerequisites graph with the advantage of greater velocity and permitting the analysis of programs with a larger number of courses. 2.3 minimum number of courses to be taken during each academic period a b c d e f g h i j k l m n o p q r s t u v w x y z a1 b1 c1 d1 e1 f1 g1 h1 i1 j1 k1 figure 6: a prerequisites graph for a nine periods program 404 r. carvajal-schiaffino, l. firinguetti-limone period 1 period 2 period 3 period 4 period 5 period 6 2 3 3 3 1 table 2: minimum number of courses passed for the academic program number of courses taken frequency 1 8 2 30 3 72 4 107 5 79 6 24 table 3: frequency of the number of courses taken for the academic program to illustrate the analysis of this rule we use the algorithm of figure 4, with the first four academic periods of the prerequisite graph of figure 6. the reduced graph of this model generates 320 states and 65,536 valid sequences. the minimum number of courses passed per semester is given in table 2. the number of valid courses taken each semester varies from 1 to 6. table 3 shows the distribution of the 320 valid sequences. except for the first, second and sixth period, the minimum number of courses passed per semester is 3. from this, two situations must be analyzed: a) less than 3 courses are taken b) more than 4 courses are taken if less than 3 courses are taken and these are from the last part of the syllabus, no problem will arise, since the student will be taking the courses necessary to complete the program. this will not be the case if the courses are taken before the final part of the program. for a course taken there are 8 possible cases, discarding the 4 cases that represent taking only courses m, n, o, p a second time, there remain four cases to analyze, corresponding to taking courses f, i, j a second time or else p for the first time. path a1b1c1d1 → b2c2d2f1 → f1g1h1k1l1 → f2 a1b1c1d1 → e1f1g1h1k1 → e2g2j1 → i1l1n1o1 → i2 a1b1c1d1 → b2c2d2e1 → f1g1h1i1k1l1 → g2j1m1 → j2 a1b1c1d1 → c2d2e1f1 → e2g1h1j1k1 → h2i1k2l1n1o1 → l2m1n2 → p1 table 4: sequences of courses taken enabling to take only one course when it is feasible to take more than 4 courses in each academic period, there are 79 situations with 5 courses (table 5) and 24 with 6 courses (table 6). the analysis of these cases are useful to limit the number of courses a student can take, since there is a larger number of situations where a student takes courses for a second time. the proposed method allows the analysis of the student progress schedule (that is to say the total number of courses passed up to a certain period) with the valid sequences generated. this analysis allows to detect if failing a course, which is prerequisite of others, produces the elimination of a student because it may not be possible to take anymore courses to comply with the student progress schedule. petri net based modelling of a career syllabus 405 courses taken for the first time total 5 3 4 12 3 19 2 21 1 14 0 10 table 5: distribution of courses taken for the first time in groups of five courses taken for the first time total 6 2 5 3 4 4 3 5 2 5 1 3 0 2 table 6: distribution of courses taken for the first time in groups of six 2.4 decision support of common interest to university administrators, students and course lecturers is to reduce student drop-out. our model may help to plan the courses taken by students to achieve this objective. valid sequences provided by the previously developed algorithm would help academic administrators, students and lecturers in making decisions. in particular, administrators may use this information to detect which students are at risk of dropping-out and suggest which courses are the most convenient to achieve the objective of completing the academic program. on the other hand lecturers, once detected students in risks of drop-out, may advice students and administrators to take some corrective actions to reduce this risk. 2.5 practical evaluation we now evaluate the algorithm used to generate the valid sequences of the possible courses to take with no more than two repetitions. using as entry the graph of prerequisites of figure 6 we have run the algorithm incrementing the number of levels from 4 to 9. the results obtained are shown in figure 7. it can be observed that for the complete syllabus there are 3,838 valid sequences with 93,244,504,769 possible paths. processing time was almost an hour. the experiments were made with a computer with a 2.8 ghz intel processor and 4 gb ram. another experiment made consisted in counting the number of sequences leading to a situation were the number of courses to take is insufficient, (in our case less than 3). we found 70,207,548,816 cases. we also counted the number of situations were the maximum number of semesters was exceeded. we found 18,475,875,736 of these cases. 406 r. carvajal-schiaffino, l. firinguetti-limone levels vertices paths time 4 464 65,537 0.002s 5 1,184 2,097,153 0.126s 6 2,182 67,108,865 2.497s 7 2,862 882,837,761 29.049s 8 3,440 12,598,171,649 433.505s 9 3,838 93,244,504,769 3,463.607s figure 7: experimental evaluation 3 results and discussion the work presented in [3] uses petri nets to assist curricula development, modelling the sequence of courses to be taken by students in an academic program. [11] describes the application of model checking to the automatic planning and synthesis of student careers under a set of requirements and to the automatic verification of the coherence of syllabi subject to a set of rules. for this it is suggested to provide the students with a sort of electronic advisor of studies. however, it is recognized that the verification of the coherence of syllabi with the set of rules is by far the most difficult problem and out of the reach of standard technology of information systems, because it requires a (double) exhaustive search engine. the basic step is to explore all possible course combinations to find a feasible set of courses which satisfies the given set of rules and prerequisites to qualify for the university degree chosen by the student. the authors propose to encode and solve the problems of the of the synthesis of student careers and of the verification of the syllabi with respect to the set of rules as computational tree logic model checking problems. 4 conclusions we have presented a methodology for the analysis and verification of prerequisites of courses of a syllabus modelled with a petri net. also, we have verified that the model is consistent and capable of being generated in automatic mode. besides, we have indicated how this model may be used by administrators and lectures to suggest students corrective actions to reduce drop-out caused by a bad selection of course sequences. the next stage in this research is to enrich the model with the representation of the number credits given to each course. also an advance table will be included which consists in verifying whether student complies with the minimum number of credits required since he or she enrolled in the program. in a subsequent stage we plan to apply the model to a group of students and produce a quantitative analysis using the stochastic extensions of petri nets [1]. bibliography [1] ajmone marsan m., balbo g., conte g., donatelli s., and franceschinis g. (1995); modelling with generalized stochastic petri nets, series in parallel computing, john wiley & sons. [2] doherty w (2006); an analysis of multiple factors affecting retention in web-based community college courses, the internet and higher education, 9:245-255. petri net based modelling of a career syllabus 407 [3] ferraris m., midoro v. and olimpo g. (1984); petri nets as a modelling tool in the development of cal software, computers and education, 8:41-49. [4] jahangirian m., eldabi t., naseer a., stergioulas l., young t. (2010); simulation in manufacturing and business: a review, european journal of operational research, 203:1-13. [5] kim d. b.; the effect of loans on students degree attainment: differences by student and institutional characteristics, harvard educational review, 77(1):64-100. [6] rajabi b. a. and lee s. p. (2009); change management in business process modeling based on object oriented petri net, international journal of human and social sciences, 4:13. [7] reisig w (1986); petri nets. an introduction, eatcs monographs on theoretical computer science, springer. [8] reisig w. and rozenberg g., editors (1998); lectures on petri nets i: basic models, advances in petri nets, lncs 1491. springer. [9] roch s. and starke p. h. (1999); ina: integrate net analizer, humboldt-universität zu berlin. [10] salimifard k., wright m. (2001); petri net-based modelling of workflow systems: an overview, european journal of operational research, 134(3):664-676. [11] sebastiani r., tomasi a. and giunchiglia f. (2001); model checking syllabi and student careers, lecture notes in computer science 2031. springer. [12] silva m., teruel e., and colom j. m (1998); linear algebraic and linear programming techniques for analysis of place/transition net systems. in reisig and rozenberg [8], 308309. [13] stevenson university (may 2011); policies for continuance and progression in the major, www.stevenson.edu/academics/nursing/policies.asp [14] the college of william and mary (may 2011); continuance requirements for fulltime students. www.wm.edu/offices/deanofstudents/policies/academic/contfulltime/index.php [15] titus m. a. (2004); an examination of the influence of institutional context on student persistence at 4-year colleges and universities: a multilevel approach, research in higher education, 45(7):673-699. [16] titus m. a (2006); understanding college degree completion of students with low socioeconomic status: the influence of the institutional financial context, research in higher education, 47(4):371-398. [17] universitat pompeu fabra (may 2011); regulation governing the progression system and the continuance rules for undergraduate courses, www.upf.edu/universitat/en/normativa/upf/normativa/grau/rd1393/permanencia/ [18] wabash college. continuance in college (may 2011); www.wabash.edu/academics/bulletin.cfm?site_code_id=967. ijcccv11n4.dvi international journal of computers communications & control issn 1841-9836, 11(4):507-521, august 2016. an ontology to support semantic management of fmea knowledge z. rehman, c. v. kifor zobia rehman* 1. faculty of engineering and management lucian blaga university of sibiu, romania 2. department of computer science comsats institute of information technology, abbottabad, pakistan *corresponding author: zobia.rehman@gmail.com claudiu v. kifor faculty of engineering and management lucian blaga university of sibiu, romania claudiu.kifor@ulbsibiu.ro abstract: risk mitigation has always been a special concern for organization’s strategic management. various tools and techniques have been developed to manage risk in an effective way. failure mode and effects analysis (fmea) is one of the tools used for effective assessment of risk. it analyzes all potential failure modes, their causes, and effects on a product or process. moreover it recommends actions to mitigate failures in order to enhance product reliability. organizations spend their resources and domain experts make their efforts to complete this analysis. it further helps organizations identify the expected risks and plan strategies in advance to tackle them. but unfortunately the analysis produced after spending a lot of organizational assets and experts’ struggles, is not reusable due to its natural language text based description. information and communication technology experts proposed some solutions but they are associated with some deficiencies. authors in [13] proposed an ontology based solution to extract and reuse fmea knowledge from the textual documents, and this article is the first step towards its implementation. in this article we proposed our ontology for process failure mode and effects analysis (pfmea) for automotive domain, along with its implementation, reasoning, and data retrieval through it. keywords: ontology, fmea, knowledge management, owl, protégé, sparql. 1 introduction since ever we have been managing our knowledge as best as we could. in beginning the acquisition and storage of knowledge were the biggest issues but information technology turned the tables and now just one click on google search button brings us pools of knowledge we desire. ordinary gadgets can store gigabyte to terabytes of information and cloud storage offers access to logical storage of a physical storage spanning over multiple locations. on one hand these advances in technology are making storage and acquisition of knowledge easier and on the other hand it has become awfully essential for organizations to capture, store, apply and share their knowledge in a collective and systematic way, so they could survive and flourish in this knowledge based economy era. thus the knowledge management has become a dire need of organizations and industry. they need to structure their knowledge and transform it into valuable competencies, products, and services for effective and well-timed utilization in order to make fruitful decisions. risk management for an organization is a set of strategic level activities that maximizes the chances of objectives being achieved, by systematically understanding and copyright © 2006-2016 by ccc publications 508 z. rehman, c. v. kifor evaluating the project level risks. it has become a core part of organization’s strategic management. organizations spent the significant share of their investments in handling expected and unexpected risks on a project. risk management is a process of risk identification, risk assessment, risk response and control development [6]. risk assessment is a critical phase after risk identification as outcome of this phase leads to develop appropriate response and control for a risk. there are different tools available for risk assessment, e.g., scenario analysis for event probability and impact, risk assessment matrix, fmea, probability analysis, and semi-quantitative scenario analysis. fmea is a systematic tool to assess the risks associated to a product or process. it highlights all potential failure modes and their impacts on a product in advance so that they could be fixed timely in order to achieve desired goals. in 1960’s for the very first time aerospace industry brought it into use during apollo mission. in 1974, the us navy developed mil-std-1629 about its use. since late 1970’s, it is in use in automotive industry for safety and reliability analysis. nowadays this inductive reasoning tool is extensively being used in almost every engineering sector [14]. it finds all possibilities in which a product or process might go wrong (failure modes), determines the effect(s) of each failure mode along with severity, cause(s) of failure mode along with frequency(occurrence) and also the level of difficulty in order to detect a failure (detection). further it calculates the risk priority number (rpn) by multiplying magnitude values of severity, occurrence, and detection. depending on the value of rpn, recommendations are determined and executed along with newly predicted values of severity, occurrence, detection, and rpn [15]. the knowledge produced during the process of figure 1: conceptual architecture of ontology based knowledge management system for fmea [13] fmea is really valuable. if it is adequately managed and re-utilized, it can reduce cost and effort. although organizations spend huge cost and effort to apply fmea method but knowledge acquired during this analysis is neither reused nor shared. since it is not semantically organized, its interpretation varies from person to person and from situation to situation. it is usually found incomplete but larger in size due to its redundant production and that larger size makes it imprecise as well [9]. artificial intelligence proposed different solutions for this problem, e.g., rule based expert systems, case based reasoning, and knowledge based systems with ontological support. among all these, knowledge based systems with ontological support are found most appropriate as rule based systems are not suitable if domain knowledge is larger enough, because an ontology to support semantic management of fmea knowledge 509 table 1: fmea worksheet [16] process failure mode and effects analysis fmea number: team leader: process responsibility: prepared by: fmea date (orig.): fmea date (rev.): process name potential failure mode effect(s) of failure sev causes occ current controls det rpn recom. action resp.& comp. by action result prevention detection action sev occ det nrpn its coding, verification, validation, maintenance, and inference through it becomes complex and time consuming [2]. in case based reasoning all probable cases are stored in case library with additional overhead of their attributes and references, moreover the information returned by such engines is not well formatted [8]. available ontology based approaches to support fmea also lack some significant aspects, e.g., in (lee, 2001) authors presented fmea only as a conceptual model without any inference and rules. authors in [1] considered the discrepancies left by [7] and proposed a system, based on the combination of knowledge management and quality management concepts but it lacks functional taxonomy. in [10] authors discussed a better ontology based approach for fmea procedure representation in lead free soldering but this proposal is still being worked on. moreover no specific ontology is found to address the fmea knowledge sharing and reuse for automotive domain. to address all these issues authors in [13] presented a conceptual architecture of their proposed system as given in figure 1 and this article in first step towards its implementation. this article is about the ontology we developed for representation and retrieval of pfmea knowledge. knowledge of interest used in this ontology is from automotive domain. automotive engineering (vehicle engineering)deals with the design, manufacturing, and operation of vehicles(automobiles, buses, motorcycles etc.). it assimilates various components from different engineering sectors, e.g., mechanical, electrical, electronics, software, safety and quality engineering. from designing to production there are different kind of activities (belonging to different fields of engineering)are involved. productivity of each activity heavily relies on past experiences, expertise of concerned people, and customer feedback. better management of all this knowledge helps organizations improve time to market and customer satisfaction which not only helps earn better profit but brings sustainability for an organization in the market. in section 2 and 3, tools used to develop and query the ontology, are discussed. section 4 describes the process of ontology development in detail, section 5 illustrates some query examples, and section 6 concludes the discussion with highlights of future work. 2 protégé protégé is an open source software tool by stanford university to develop domain models and knowledge based systems with ontology. it facilitates with both of the foremost means to model an ontology, e.g., frames and owl (web ontology language). it has built-in reasoning support for computing the inferred ontology class hierarchy and ontology consistency checking [13]. we used protégé 5.0 beta version. using it we developed our owl ontology with rdf/xml format. rdf/xml format is a syntax defined by w3c to present rdf (resource description format) graph by defining triples of subject, predicate, and object in xml (extensible markup language) format [12]. we developed ontology in owl, the language beyond rdf schema that allows machines to perform more useful reasoning on its documents, and created its individuals (instances) in rdf. we got reasoning support from protégé’s built-in reasoner hermit version 1.3.8.3. hermit is an efficient reasoner based on “hypertableau”calculus that takes a few seconds to reason complex ontologies that are written in owl. it reads an owl file and determines the consistency of classes and their properties [4] 510 z. rehman, c. v. kifor figure 2: pfmea ontology 3 sparql and jena fuseki server jena is an open source semantic web framework for java. it facilitates with an api (application program interface) for extraction and writing data to/from rdf graphs. the graphs are represented as an abstract model, which can be sourced with data from files, databases, urls or a combination of these. fuseki is an http interface to rdf data that supports sparql (w3c recommended language to query directed labeled rdf graphs) for querying and updating rdf graphs. we used apache jena fuseki server version 1.1.0. it provides rest-style sparql http update, sparql query, and sparql update by using the sparql protocol over http [5]. it can be freely downloaded and runs on port 3030 in web browser. 4 development of fmea ontology according to classic definition by [3] the ontology is an explicit specification of conceptualization. in information science perspective the ontology is a formal representation of the knowledge of a specific domain as a set of concepts within a domain, and the relationship between those concepts. it provides shared vocabulary and common (unambiguous) understanding of a domain and supports reasoning about the concepts. according to w3c definition, ontology is a vocabulary that defines concepts, relationship between concepts, and constraints on their usage in order to define and represent domain of discourse [11]. an ontology consists of different components, e.g., classes, properties (relationship of classes), and individuals (instances of the domain of discourse). in this section all these terms will be discussed in pfmea perspective. in table 1 a pfmea worksheet is shown. using the pfmea attributes given in that worksheet we designed an ontology given in figure 2. this ontology is based on five different classes whereas all these classes are logically subsumed an ontology to support semantic management of fmea knowledge 511 figure 3: pfmea classes and sub-classes in protégé sub-classes of the root concept “thing”. according to iso-15926 “thing”is a top-level ontology (collection of general concepts same in all knowledge domains) that subsumes abstract object and possible individual classes. any immaterial object (which exists only as a concept) can be said an abstract object, e.g., information; and any material object (which exists in time and space) is known as possible individual, e.g., a pen. figure 4: ontograph in protégé rest of the classes, their attributes with data types, and relationships with one another are described following. • fmea class represents the header information of fmea worksheet. its attributes are fmea_no (string), fmea_description (string), process_leader (string), starting_date (datetime), ending_date (datetime), and latest_revision_date (datetime). object 512 z. rehman, c. v. kifor property examine (inverse: isexaminedby) connects fmea class to process class. • process class describes a process or a sub-process under analysis. it has a single attribute process_name (string). object property hassubprocess connects it to itself, whereas the property hasfailuremode (inverse: isfailuremodeof) connects it to class failure_mode. • failure_mode class represents a failure mode, its cause(s) and effect(s). it has three attributes failuremode_description (string), effect_description (string) for sub-concept effect, and cause_description (string) for sub-concept cause. different object properties are part of the concept failure_mode. the hascontrolmethod (inverse: iscontrolmethodof) connects it to class control_method, hasmitigationaction (inverse: ismitigationactionof) connects it to concept mitigation_action, it is related to class rpn through property hasrpn (inverse: isrpnof). as a failure causes another failure and inversely a failure is the effect of another failure, therefore the concept failure_mode is divided into two sub-concepts cause and effect. object properties causes and iscausedby (inverse of one another) make it distinguished if a failure is a cause or an effect. a failure that demonstrates root cause nature is only responsible to bear the relation with concepts control_method, mitigation_action and rpn. • rpn class represents the magnitude impacts of a failure mode, its effect and analysis. its attributes are severity (integer), occurrence (integer), detection (integer), and their product (integer). • control_method class describes the controls for detection and prevention of a failure. it has two attributes control_detection (string) and control_prevention (string). • mitigation_action class describes recommendations and actions taken in order to combat a failure. its attributes are recommended_action (string), responsible_person (string), target_completion_date (datetime), and action_taken (string). object property hasnewrpn (inverse: isnewrpnof) relates it to the concept rpn and on the basis of new rpn value effectiveness of a mitigation action is evaluated. figure 5: object properties in protégé an ontology to support semantic management of fmea knowledge 513 figure 4 represents the ontology graph (ontograph) of proposed ontology as perceived in protégé. in figure 5, protégé is displaying the list of object properties. each object property is a sub-property of topobjectproperty. each property connects two classes, known as domain and range. for example the object property hasfailuremode connects two classes the process and the failure_mode. it is a relationship from process to failure_mode, thus the concept process is domain of the property and concept failure_mode is its range. its inverse property isfailuremodeof would be a relation from concept failure_mode to concept process. figure 6 shows the list of data properties, each data property is sub-property of topdataproperty. data properties are attributes of class which are further used to create variables of instances in order to store some values. each data property has a domain (the class name it belongs to) and a range (the data type, the type of data it allows to be stored). for example action_taken is an attribute of the concept mitigation_action and its assertions can have only string data type. figure 6: pfmea data properties in protégé figure 7 shows an inferred individual of concept cause in protégé. it clearly shows that a failure mode is caused by a cause and it further causes an effect, consequently any cause which causes a failure mode is actually responsible for its effect too. figure 8 shows an instance of concept effect. figure 9 is also about an inferred individual of concept mitigation_action. in figure 10 a complete fmea ontology instance is shown. whereas figure 11 shows a few instances of fmea ontology in rdf form. 5 sparql queries to retrieve fmea information from ontology we used sparql with apache jena fuseki server in order to access information from our ontology. jena fuseki server provides user friendly graphical user interface (gui) to mount ontologies on server and allows retrieving query results in multiple formats. we chose csv (comma-separated values) format as original fmea files are in the same format. query results in csv format can be downloaded and saved for further use and can be easily viewed in any csv viewer. here are some queries used to extract information from ontology. 514 z. rehman, c. v. kifor figure 7: pfmea instances of a sub-class cause in protégé figure 8: pfmea instances of a sub-class effect in protégé an ontology to support semantic management of fmea knowledge 515 figure 9: pfmea instances of a sub-class mitigation_action in protégé figure 10: example of ontology based fmea instances 516 z. rehman, c. v. kifor figure 11: example of an fmea ontology instance in rdf 5.1 prefixes following prefixes are a must for the queries to execute on jena fuseki sever, these are used to declare the ontology being used, the language it is developed in and its syntax and schema. prefix owl: <http://www.w3.org/2002/07/owl#ontology> prefix xsd: <http://www.w3.org/2001/xmlschema#> prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> prefix fmea:<http://www.semanticweb.org/rehman/ontologies/2014/8/untitled-ontology-12#> 5.2 query to display fmea worksheet header information select (str(?fno) as ?fmea_no) (str(?fd) as ?fmea_description) (str(?pl) as ?processleader) (str(?sd) as ?startingdate) (str(?ed) as ?endingdate) (str(?ld) as ?latestrevisiondate) where { ?x fmea:fmea_no ?fno; fmea:fmea_description ?fd; fmea:process_leader ?pl; fmea:starting_date ?sd; fmea:ending_date ?ed. optional { ?x fmea:latestrevision_date ?ld. } } an ontology to support semantic management of fmea knowledge 517 output of this query is shown in figure 12. this query helps know very basic information about an fmea report. for example the description of the process the report is about, its leader,and all important dates about its initiation, completion, and revision. figure 12: fmea worksheet header information in csv viewer 5.3 query to display complete details of fmea process select (str(?pn) as ?process) (str(?fmd) as ?failuremode) (str(?ed) as ?effect) (str(?sv1) as ?sev) (str(?cd) as ?cause) (str(?oc1) as ?occ) (str(?cp) as ?ctrlprevention) (str(?cdt) as ?ctrldetection) (str(?d1) as ?det) (str(?p1) as ?rpn) (str(?ra) as ?recommendedaction)(str(?at) as ?actiontaken) (str(?rp) as ?responsible) (str(?tcd) as ?targetcompletedby) (str(?sv2) as ?psev) (str(?oc2) as ?pocc) (str(?d2) as ?pdet) (str(?p2) as ?prpn) where { ?x fmea:process_name ?pn; fmea:hasfailuremode ?fm. ?fm fmea:failuremode_description ?fmd; fmea:causes ?failureeffect; fmea:iscausedby ?failurecause. ?failureeffect fmea:effect_description ?ed. ?failurecause fmea:cause_description ?cd; fmea:hascontrolmethod ?ctrlmethod; fmea:hasrpn ?rpn1; fmea:hasmitigationaction ?mgt. ?ctrlmethod fmea:control_detection ?cdt; fmea:control_prevention ?cp. ?rpn1 fmea:severity ?sv1; fmea:occurrence ?oc1; fmea:detection ?d1; fmea:product ?p1. ?mgt fmea:recommended_action ?ra; fmea:action_taken ?at. optional { ?mgt fmea:responsible_person ?rp; fmea:target_completiondate ?tcd; fmea:hasnewrpn ?rpn2. ?rpn2 fmea:severity ?sv2; fmea:occurrence ?oc2; 518 z. rehman, c. v. kifor fmea:detection ?d2; fmea:product ?p2. } } result was spanning over large window this is why we split it into three as shown in figure 13, 14, and 15. purpose of this query is to extract all information related to a process. for example its probable failure modes, their causes and effects,magnitude impact of the failure, recommendations and actions taken to reduce impact of failure and the magnitude impact of mitigation made. figure 13: fmea process details in csv viewer figure 14: fmea process details in csv viewer figure 15: fmea process details in csv viewer 5.4 query to display causes and recommendations for each failure mode select (str(?fmd) as ?failuremode) (str(?cd) as ?cause) (str(?ra) as ?recommendedaction) where { ?fm fmea:failuremode_description ?fmd; fmea:iscausedby ?failurecause. ?failurecause fmea:cause_description ?cd; fmea:hasmitigationaction ?mgt. ?mgt fmea:recommended_action ?ra; fmea:action_taken ?at. } an ontology to support semantic management of fmea knowledge 519 output of this query is shown in figure 16. this query helps extract all causes and recommendation for each cause for a specific failure mode. figure 16: causes and recommendations for each failure mode in csv viewer 5.5 query to display causes, effects and recommendations for a specific failure mode select (str(?cd) as ?cause) (str(?at) as ?actiontaken) (str(?sv2) as ?newseverity) (str(?oc2) as ?newoccurrence) (str(?d2) as ?newdetection) (str(?p2) as ?newrpn) where { ?x fmea:hasfailuremode ?fmd. ?fmd fmea:failuremode_description ?fd. filter regex(?fd,"insufficient wax coverage over specified surface") ?fmd fmea:iscausedby ?failurecause. ?failurecause fmea:cause_description ?cd; fmea:hasmitigationaction ?mgt. ?mgt fmea:action_taken ?at. optional { ?mgt fmea:responsible_person ?rp; fmea:target_completiondate ?tcd; fmea:hasnewrpn ?rpn2. ?rpn2 fmea:severity ?sv2; fmea:occurrence ?oc2; fmea:detection ?d2; fmea:product ?p2. } } output of this query is shown in figure 17. this query extracts causes, mitigation actions, and their magnitude impacts for a given failure mode. figure 17: causes and mitigation action(s) for a specific failure mode in csv viewer 520 z. rehman, c. v. kifor 6 conclusion and future work nowadays risk management has become a vital part of an organization’s strategic management. to achieve the organizational objectives it is mandatory to increase the probability of success and decrease the probability of failure for a product or process. it is only possible when an organization knows about all expected risks and has planed policies and actions for its timely avoidance and mitigation. fmea is one of the tools available for risk assessment. because of its effectiveness organizations spend a lot to complete its studies. due to some reasons as mentioned in introduction section, the valuable information produced by fmea is not reusable. to combat this problem authors in [13] proposed a system which would be capable enough to extract information from fmea documents, store it in a knowledge repository, and help retrieving the required information. in this article we presented an important component of that proposed system, the ontology and retrieval of information through it. as we want to develop a system which should be capable of disseminating information in a domain of experts unambiguously, for this we need a common vocabulary, understanding and structure of the specified domain knowledge, machine interpret-able descriptions of concepts and their relations, and a barrier between domain knowledge and operational knowledge; therefore we developed and fmea ontology that qualifies all these specifications. this article not only presents the ontology but also the semantic ways to retrieve information through it. our next step is to use this ontology for auto-population of a knowledge base from csv format fmea worksheets and then we will measure its effectiveness (in terms of completeness and conciseness) by deploying it, so that domain experts could interact with it for required knowledge. bibliography [1] dittmann, l. et al (2004); performing fmea using ontologies, 18th international workshop on qualitative reasoning, 209-216. [2] fernandez, b.i.; saberwal, r. (2010); knowledge management: systems and processes, m.e. sharpe. [3] gruber, t. r. (1993); a translation approach to portable ontology specifications, knowledge acquisition, 5(2): 199-220. [4] horrocks, i. et al (2012); the hermit owl reasoner, emphowl reasoner evaluation workshop, manchester. [5] http://jena.apache.org/documentation/serving_data/ [6] larson, e. w.; gray, c. f. (2011); project management: the managerial process, 5th edition, mcgraw hill. [7] lee, c.f.; (2001); using fmea models and ontologies to build diagnostic models, artificial intelligence for engineering design, analysis and manufacturing, 281-293. [8] mansouri, d.; hamdi-cherif, a. (2011); ontology-oriented case-based reasoning (cbr) approach for training adaptive delivery, 15th wseas int. conf. on computers (cscc’11), 328-333. [9] mikos, w.l. et al (2011); a system for distributed sharing and reuse of design and manufacturing, elsevier journal of manufacturing systems, 133-143. an ontology to support semantic management of fmea knowledge 521 [10] molhanec, m. et al (2010); the ontology based fmea of lead free soldering process, international spring seminar on electronics technology isse, doi: 10.1109/isse.2009.5206998, 1-4. [11] http://www.w3.org/standards/semanticweb/ontology [12] http://www.w3.org/tr/rdf-syntax-grammar/ [13] rehman, z.; kifor, s. (2014); a conceptual architecture of ontology based km system for failure mode and effects analysis, international journal of computers communications & control, 9(4): 463-470. [14] stamatis, d.h. (2003); failure mode and effect analysis: fmea from theory to execution, usa: asq quality press. [15] tay, k. m.; lim, c. p. (2006); fuzzy fmea with a guided rules reduction system for prioritization of failures. international journal of quality & reliability management , 23(8): 1047 1066. [16] wheeler, d. j.; chamber, d. s. (2013); understanding statistical process control wheeler and chambers, 3rd edition, spc press. ijcccv11n1.dvi international journal of computers communications & control issn 1841-9836, 11(1):90-104, february 2016. ann based inverse dynamic model of the 6-pgk parallel robot manipulator l. moldovan, h.-s. grif, a. gligor liviu moldovan*, horatiu-stefan grif, adrian gligor petru maior university of tirgu-mures romania, 540088 tirgu-mures, nicolae iorga, 1 liviu.moldovan@ing.upm.ro, horatiu.grif@ing.upm.ro, adrian.gligor@ing.upm.ro *corresponding author: liviu.moldovan@ing.upm.ro abstract: this paper presents an inverse dynamic model estimation based on an artificial neural network of a complete new parallel robot manipulator prototype 6pgk with six degrees of freedom, built at petru maior university of tirgu-mures. the model estimation of the parallel robot manipulator is performed with a feedforward artificial neural network. in the control engineering domain there are control structures that need the direct or inverse model of the process for ensuring the process control at the imposed performances. usually, the determination of the direct/inverse mathematical model is a difficult or impossible task to be achieved. in these cases different non-parametric or parametric, off-line or on-line identification methods are used. a solution that may support the on-line parametric methods is represented by the feedforward artificial neural networks. by implementing feedforward artificial neural networks as a nonlinear autoregressive model with exogenous inputs, the authors investigate the possibility of choosing the optimum parameters that characterize the neural network so that it approximates as better as possible the model of the 6-pgk prototype robot. finally an innovative algorithm is developed for obtaining the optimal configuration parameters set of the feedforward artificial neural network. the proposed algorithm helps in setting the optimal parameters of the neural network that offer high opportunities to provide satisfactory identification of the robot model. experimental results obtained by a structure derived from the proposed solution demonstrate a good approximation related to the studied system, which is characterized by nonlinearities and high complexity. keywords: 6-dof parallel robot manipulator, inverse dynamics, nonlinear model, unmodeled dynamics, feed-forward artificial neural network. 1 introduction in robotics, the class of parallel robots represent a constructive solution suitable for many industrial, medical or domestic applications, like flight simulators and entertainment rides [1], micromanufacturing [2], tool machine [3], pick and place operation [4], earthquake motion simulator [4], medical haptic devices [6], laparoscopic surgery [7], or even space docking technology [8]. even if parallel robots have a reduced workspace [9], their benefits over their serial counterparts are various: stability and rigidity of contacts during haptic interaction [10], high accuracy, high stiffness, high payload capability, low moving inertia [11], good dexterity, compact size, large power to weight ratio [12]. along with these advantages, there still exist many difficulties in the actual control process. a classic approach in controlling robots consists in the employment of the dynamic model. various methods have been proposed to derive the dynamic model of the parallel manipulator, like the generalized momentum approach [13], principle of virtual work [14] screw theory [15] and hamilton’s principle [16], the recursive matrix method [17] or the lagrangian approach [11], which describes the dynamics of mechanical system from the concepts of work and energy [13]. copyright © 2006-2016 by ccc publications agora university ann based inverse dynamic model of the 6-pgk parallel robot manipulator 91 all these approaches are equivalent as they are describing the same physical system, and lead to equivalent dynamic equations, which present different levels of complexity and associated computational loads. the analytical calculus involved in the dynamic equation is very tedious, thus presenting an elevated risk for errors. furthermore, the duration of numerical computations is getting longer when the number of mechanism’s degrees of freedom is increased. the dynamic model based control arises numerous difficulties because of the time-varying and coupling of the equations. unfortunately, the present-day commercial controllers cannot provide satisfying performance for control. in recent years an important credit in obtaining new enhancements is granted to the support offered by artificial intelligence techniques. the role of artificial intelligence related to the parallel robots aims with predilection the modeling and control domain in terms of kinematics or dynamics. moreover these strategies are designed to improve the classical control strategies, in terms of increasing the current performances, to add new properties such as robustness and improvement of the real-time behavior using fuzzy systems or different types of neural networks (nnet). compared to the mathematical model of the kinematics, neural networks provide significantly better mapping between manipulator’s task space and joint space, as demonstrated by uzunovic et al. [2]. achili et al. in [18] proposed an adaptive force/position controller for parallel robot with constrained motions based on multi-layer perceptron neural network. the solution presents the major advantage of obtaining the control law without the prior knowledge of the inverse dynamic model and being able to take into account the endogenous disturbance (uncertainties and nonlinearities related to the robot dynamics) and to compensate exogenous disturbances. peng et al. [19] design a control system for a parallel mechanism consisting from a combination of linear controllers and two neural networks. the aim of this control system is to compensate the nonlinearities from the model of the system and the dynamic model estimation for improved accuracy of trajectory. le et al. [11] investigate the enhancement of the classical nonlinear pd torque controller by using neural networks for on-line self gain tuning. the proposed solution consists in a self gain tuning control law for minimizing the error in tracking trajectories of the parallel manipulators characterized by simple structure suitable for low computation effort in real-time implementations. yu and weng in [20] propose a robust control structure for parallel manipulators based on h∞ tracking adaptive fuzzy integral sliding mode control scheme mainly able to handle the nonlinear unmodeled dynamics and to compensate external disturbances. all these solutions benefit from the advantages of various artificial intelligence techniques, but underline the difficulty of implementing them, starting from the the structure, the different parameters values or choice of training methods, and finally reaching the problem of required computational effort. recent research in the field of robot control is conducted in new directions based on bioinspired approach that proved their efficiency on other fields of robotics, like the simplified fuzzy controller using neural network based on genetic algorithm learning for mobile robots [21], the communications and social structure of whale pods applied to cooperative robot structures [22] or fast genetic algorithm for robot soccer and planet exploration citelee. we consider that this direction of research may find applicability in the case of parallel robots, for the potential offered in solving many complex problems such as those in the case of parallel robot control discussed in this paper. from the above explored literature we conclude that in the case of parallel robots there is a 92 l. moldovan, h.-s. grif, a. gligor need to find adequate solutions for the real time control which involve increasing the trajectory following performance and low computational effort. the objective of the paper is to build and implement an artificial neural network based on the inverse dynamic model estimation of a complete new parallel robot manipulator 6-pgk type with 6 degrees of freedom built at petru maior university of tirgu-mures. 2 the 6-pgk parallel robot dynamics 2.1 the 6-pgk parallel robot manipulator design the six controlled degrees of freedom (dof) parallel robot configuration is a structure proposed by stewart and hunt and widely referred as stewart platform [23–26]. the parallel kinematic link system proposed by d. stewart is one of a 6-dof robot manipulator used as a flight simulator [25, 26]. the basic geometric structure of the 6-pgk parallel robot manipulator developed at petru maior university consists in a mobile platform (supporting the end-effector) connected to the adjacent links at six distinct points (i=1,2,. . . ,6) at the level iii by cardanic kinematic pairs (figure 1) [27]. six legs connect the platform to the base. each leg has an actuated translational figure 1: the structure of the 6-pgk parallel robot [27] kinematic pair at level i and a spherical kinematic pair at level ii. the translational kinematic pairs form the angles λi with the vertical axis in points bi. the location (position and orientation) of the end-effector and of the manipulated object are expressed in the fixed reference frame oxy z by the generalized coordinates of the manipulated object xp, yp, zp, ψ, φ, θ denoted qpi (i=1,2,. . . ,6), and in the mobile frame oxyz by the xp, yp, zp coordinates. the displacements in the actuated translational kinematic pairs (actuator displacements) are the generalized coordinates of the parallel robot qi (i=1, . . . , 6). ann based inverse dynamic model of the 6-pgk parallel robot manipulator 93 2.2 parallel robot manipulator dynamics modeling the dynamics modeling in case of robot manipulators refers to establishing a mathematical model that relates the time evolution of its end-effector in terms of positions, velocity and acceleration with the forces and moments acting upon it [28]. in order to get the position of the mobile platform with respect to generalized coordinates of the robot, the in-out equations are employed, which solve the positional problem of the robot. they relate the generalized coordinates of the robot to the generalized coordinates of the manipulated object [27]: q2i −2biqi + ci = 0, (i = 1, . . . ,6) (1) where bi, ci are coefficients according to the robot mechanical parameters [27]. determination of the velocity and acceleration of the mobile platform [q̇p] with respect to generalized coordinates of the robot is performed using the kinematics equations: [q̇p] = [j][q̇] (2) obtained by deriving the in-out equations (1) with respect to time, where [j] is the jacobian matrix [27]. the dynamic modeling determines the generalized forces [qj] that are required by the actuators to balance the forces applied to the robot. in the case of the 6-pgk parallel robot, by employing the lagrange method, the dynamic equations are obtained [27]: { 1 2 3 ∑ k=1 3 ∑ l=1 jkl ( ∂ωk ∂q̇pj ω̇l + ω̇k ∂ωl ∂q̇pj ) + m 3 ∑ k=1 q̈pk ∂q̇pk ∂q̇pj + 6 ∑ i=1 miq̈i ∂q̇i ∂q̇pj } +                          1 2 3 ∑ k=1 3 ∑ l=1 ˙jkl ( ∂ωk ∂q̇pj ωl + ωk ∂ωl ∂q̇pj ) − 1 2 3 ∑ k=1 3 ∑ l=1 ∂jkl ∂qpj ωkωl+ 1 2 3 ∑ k=1 3 ∑ l=1 jkl [ d dt ( ∂ωk ∂q̇pj ) ωl + ωk d dt ( ∂ωl ∂q̇pj ) − ∂ωk ∂qpj ωl −ωk ∂ωl ∂qpj ] + 6 ∑ i=1 mi [ q̇i d dt ( ∂q̇i ∂q̇pj ) − q̇i ∂q̇i ∂q̇pj ]                          + { mg ∂zp ∂qpj + 6 ∑ i=1 mig ∂qi ∂qpj cλi −m0g ( xp ∂γ′ ∂qpj + yp ∂γ′′ ∂qpj + zp ∂γ′′′ ∂qpj ) } = qj (j = 1, . . . ,6) (3) where qj are the generalized forces due to the non-conservative forces, mi are masses of the motor links, jkl are the elements of the inertia matrix of the mobile platform of mass m0 and manipulated object of mass mp, their sum is m = m0 + mp, then α′, . . . , γ′′′ are terms of the rotational matrix that relates the coordinates fixed in mobile frame to the base coordinates and ωx, ωy, ωz are the angular velocity terms in oxy z system. the generalized motor forces are deduced from equation [27]: qj = 6 ∑ i=1 lijqmi (4) where li,j are terms of the inverse jacobian matrix. 94 l. moldovan, h.-s. grif, a. gligor the inverse dynamic model is useful for the robotic system control. it consists in determination in real time of the generalized motor forces qmi from equations (4), by solving the positional problem from equation (1), the kinematic problem from equation (2) and the dynamic model from equation (3). parallel robots with a high number of degrees of freedom, in particular six degrees like the 6-pgk parallel robot, are modeled by complex dynamic equations, comprising a large number of parameters. solving them requires crossing a high number of iterations and selection of admissible real solutions. the complexity of the program, the large number of loops, testing all solutions, selecting the nearest current configuration solution, can induce errors in configuration identification. therefore in current industrial exploitation, parallel robots control requires the use of simple and effective control methods. 3 fann based dynamic model of the 6-pgk parallel robot manipulator 3.1 fann structure for robot dynamic modeling for the 6-pgk parallel robot manipulator modeling a feed-forward artificial neural network (fann) with one hidden layer has been designed. the fann is implemented as a nonlinear autoregressive model with exogenous inputs (narx) model. the narx model is based on the linear arx model, which is commonly used in time-series modeling. [30] the numbers of the inputs of the used fann is determined by the numbers of the generalized coordinates of the robot, the number of the generalized forces and the number of theirs considered past values. the numbers of the outputs of the used fann is given by the numbers of the generalized forces from the dynamical model of the robot. by using the compact structure of an artificial neuron presented in [29] we can summarize the mathematical processing that occurs inside of an artificial neuron from the used fann as: a = f ( n ∑ j=1 ωjxj + θ ) (5) where are denoted by: xj (j = 1, . . . ,n) the inputs of the neuron, ωj (j = 1, . . . ,n) the weights of the inputs xj, θ the weight of the offset input, n the numbers of the inputs of the neuron, f is the activation function, a the activation of the neuron. the type of the activation function and the numbers of the inputs corresponding to each neuron depend on the layer to which it belongs. for the neurons from the hidden layer are used two types of the activation function: the hyperbolic tangent function (htf) type and the radial basis function (rbf) type [30]. the numbers of the inputs of the neurons from this layer is given by the maximum lags associated with robot output (nq) and input (nqp) signals described by relation 6(nqp +nq), which also represents the number of the inputs of the fann. the activation function of the neurons from the output layer is the linear type function and the number of the inputs of these neurons equals the number of the neurons from the hidden layer of the fann. for the training of the fann the gradient descent backpropagation algorithm was used [30]. the training of the used fann was achieved by presenting to their inputs and outputs the training set of the n pairs of the vectors: { ( xi(1),aod(1) ) , ( xi(2),aod(2) ) , . . . , ( xi(n),aod(n) ) } where xi(k) is an input training vector (k = 1, . . . ,n) with 6(nqp +nq) elements and a o d(k) is the desired target output vector (with 6 elements) of the fann corresponding to the input training ann based inverse dynamic model of the 6-pgk parallel robot manipulator 95 vector xi(k). 3.2 fann optimal parameters determination analytical determination of the fann parameters is an uncommon task, thus the empirical approach is more often used. the optimum determination however is a more complex task, so in this section a tested methodology for determination of these parameters is proposed. usually, the investigation of a system starts from the searching of the parameters that influence the behavior of the studied system. the same approach was established for the present work. in the case of the proposed structure of neural network the following four parameters were considered: learning rate (γ), the number of the neurons on the hidden layer (nh), the number of the past values of the generalized forces (nq) and the number of the past values of the generalized coordinates (nq). in order to study the influence of these parameters on the neural network behavior for building the fann training data sets the generalized coordinates trajectory of the robot and the corresponding trajectories for generalized motor forces will be used. an adequate solution for the process of searching through the experimental set can be performed by visual analysis of all the available results (large sets of plotted trajectories). but this approach presents a major drawback from time needed for analysis and required storage resources. in order to facilitate and automatize the evaluation process, quality measures can be used. an option is represented by the mean squared error or the mean absolute error. however, both measures present major difficulty in the case of establishing of a threshold value to aid in deciding the quality of the robot model identification. in order to overcome the mentioned drawbacks for the evaluation process the authors propose an automated method and the usage of a quality measure based on signal-to-noise ratio (snr). for the 6-pgk prototype robot the snr is given by the following equation: snri = 10 · log10 n ∑ j=1 ( qmi(j) )2 n ∑ j=1 ( qmi(j) −qmif ann (j) )2 [ db ] , (i = 1, . . . ,6) (6) where qmi represents the generalized motor trajectory forces given by the inverse dynamical model of the robot, qmif ann represents the generalized motor trajectory forces given by the fann and n is the number of the trajectory samples. the proposed snr quality measured was derived from the signal-to-noise ratio used for evaluation of unidimensional electrical signals. the algorithm for fann optimal parameters determination is presented below. general algorithm for choosing the structure of the fann let us consider the following parameters which influence the behavior of the fann: learning rate (γ), the number of the neurons (nh) from hidden layer, the number of the past values for the input (nqp) and output (nq) variables employed to build the robot regressors vectors (input vectors of the fann) used in training and simulation of the fann. these parameters are referred as "fann parameters". the steps of the proposed algorithm for choosing the structure of the fann are: step 0. select (experimentally): the number of the initial sets of values for the fann weights and the corresponding weights values (that are random real numbers in the range [−0.5, 0.5]), having built a collection of initial sets of values for the fann weights, the 96 l. moldovan, h.-s. grif, a. gligor signal-to-noise ratio threshold snrthold, the sets of values for the fann parameters. to each set of values of the fann parameters is attached a set of counter variables of the same size as the size of the value of the considered set. thus for a fann parameter to each value (from the set of values of the considered fann parameter) it corresponds a counter variable. the value of the counter variables is initialized with zero; step 1. set the following values of the fann parameters with start values nh = nhstart, nq = nqstart, nqp = nqpstart. these values are drawn from the sets of the fann parameter values; step 2. keeping constant the values of the fann parameters nh = nhstart, nq = nqstart, nqp = nqpstart establish the optimal learning rate γopt based on the algorithm for evaluation and determination of the optimum value for the fann parameters; step 3. keeping constant the value of the parameters γ=γopt, nq =nqstart, nqp =nqpstart establish the optimal number of hidden layer neurons (nh opt) based on the algorithm for evaluation and determination of the optimum value for the fann parameters; step 4. keeping constant the value of the fann parameters gamma = γopt, nh = nh opt establish optimal values of the number of the past samples for the input and output variables of the robot model (nqopt, nqpopt) based on the algorithm for evaluation and determination of the optimum value for the fann parameters to which the following changes are made corresponding to the steps step 4′′, step 6′′ and step 7 ; step 5. keeping constant the value of the fann parameters gamma = γopt, nh = nh opt and nq = nq opt it is tested the estimation and identification algorithm considering other trajectories for input and output variables of the robot (or model of the robot). algorithm for evaluation and determination of the optimum value for the fann for each value from the set of values corresponding to the fann parameter set to be evaluated, the following steps will be executed separately for the each set of the initial weights: step 1′. initialize the weights of the fann with the corresponding initial selected set of weights; step 2′. run the estimation and identification algorithm calculating the signal-to-noise ratio (snr) based on the relation (6) for each output path (trajectory corresponding to a generalized forces), thus corresponding to each set of initial weights, a set of six values of the noise-to-signal ratio will be obtained; step 3′. compute the average of the signal-to-noise ratio snrmed as mean value of the six signal-to-noise ratio determined in step 2 ; step 4′. compare the value of snrmed with the threshold value snrthold. if the average is greater than the threshold then increment the counter variable corresponding to the current value of the fann parameter and go to step 5′; step 4′′. compare the value of the snrmed with the threshold value snrthold. if the average value is greater than the threshold value, then increment the counter variable corresponding to the current values combination for the two fann parameters; ann based inverse dynamic model of the 6-pgk parallel robot manipulator 97 step 5′. if the sets of initial values for weights are not exhausted select a new set of values for weights and jump to step 1′. if the sets of initial values for weights were exhausted continue with step 6′; step 6′. using the values of the counter variables, the success rates corresponding to each fann parameter value is computed by sri = counteri no.of theinitial weightssets ·100 [%] where i = 1, . . . , no. of the fann parameter values. go to step 7′; step 6′′. using the values of the counter variables, the success rates corresponding to each combination of the fann parameters values are computed and go to step 7′′; step 7′. choose the fann parameter value for which the success rate is the largest. finally this value will be considered as the optimal value for the evaluated fann parameter; step 7′′. choose a pair of values of the two fann parameters for which the success rate is the largest. finally the values of the selected pair will be considered as the optimal for the two parameters of the fann evaluated. 3.3 experimental results and discussions for the implementation and testing of the parallel robot modeling, matlab platform and its neural network toolbox were used. in order to investigate the performance of the fann based modeling the model of the parallel robot based on equations 1 and 2 was implemented. in figure 2 six desired trajectories are presented for the generalized coordinates of the manipulated object (xp, yp, zp, ψ, φ, θ denoted qpi, i = 1, . . . ,6). these trajectories are placed in parallel surfaces having the same 3d spatial shape at different elevation. these trajectories represent the input trajectories for the inverse dynamic model of the 6-pgk parallel robot manipulator. 0 5 10 15 20 -10 -5 0 5 10 t[s] x p [ m m ] generalized coordinate 0 5 10 15 20 0 5 10 15 t[s] ψ [ o ] generalized coordinate 0 5 10 15 20 -10 -5 0 t[s] y p [ m m ] generalized coordinate 0 5 10 15 20 -4 -3.5 -3 -2.5 -2 t[s] φ [o ] generalized coordinate 0 5 10 15 20 280 290 300 310 320 t[s] z p [ m m ] generalized coordinate 0 5 10 15 20 -5 0 5 t[s] θ [o ] generalized coordinate figure 2: the generalized coordinates trajectories of the 6-pgk parallel robot in order to study the influence of the fann parameters on the neural network behavior for building the fann training data sets, one of the spatial generalized coordinates trajectory 98 l. moldovan, h.-s. grif, a. gligor was used, which has the red zp line from figure 2 and the corresponding red trajectories for generalized motor forces from figure 3. in figure 3 six desired trajectories for the generalized motor forces (qmj, j = 1, . . . ,6) are presented. these trajectories represent the output trajectories of the inverse dynamic model of the 6-pgk parallel robot manipulator corresponding to the input trajectories from figure 2. the training data are set up using the trajectories presented in figures 2 and 3, trajectories that consist from 2001 points each. the estimation and identification algorithm is applied in each time step and consists of two phases: the estimation phase based on the curent input vector, the fann computes the curent estimated vector of generalized forces; the identification phase based on curent input/output trainig vectors the fann is trained. an input training vector ( xi(k),k = 0, . . . ,n −1 ) and an output training vector ( aod(k),k = 0, . . . ,n −1 ) , from the training vectors set, are constructed as follow: xi(k) = [ xi1(k −1) . . . x i 6(k −1) . . . x i 6(nqp +nq−1)+1 (k −nq) . . . x i 6(nqp +nq) (k −nq) ] = [ xp(k −1) . . . θ(k −1) . . . xp(k −nqp) . . . θ(k −nqp) . . . qm1(k −1) . . . qm6(k −1) . . . qm1(k −nq) . . . qm6(k −nq) ] (7) aod(k) = [ aod1 a o d2 . . . a o d6 ] = [ qm1(k) qm2(k) . . . qm6(k) ] (8) the order of the vectors in the vectors training set corresponds to the order of the time sample from the time axis. taking into account this observation, the input training vector from (7) and the training output vector from (8) can be expressed in relation with time: xi(t) = [ xi1(kt −t) . . . x i 6(kt −t) . . . x i 6(nqp +nq−1)+1 (kt −nqt) . . . xi6(nqp +nq) (kt −nqt) ] (9) aod(t) = a o d(kt) = [ qm1(kt) qm2(kt) . . . qm6(kt) ] (10) where t is the sampling time. the results obtained by using the above algorithms are presented as follows. the experimental study was started from the following fann parameters values: nh = 5, nq = 1, nqp = 1, 20 initial weights sets values, snrthold = 44.5[db], γ = 0.01,0.05,0.1, . . . ,0.98,1. during the step 2 of the general algorithm the success rates of γ were computed and then synthesized in figure 4. the analysis of figure 4 led to the selecting of the value of 0.25 as the optimal value for the γ parameter. during step 3 the success rates of nh were computed and then synthesized in figure 5. the analysis of figure 5 led to the selecting of the value of 7 as the optimal value for the nh parameter. further, after the evaluation of step 4 the results are synthesized in table 1. the values of the lag for nq are taken successively from 1 to 5. the values for nqp are taken successively from 1 to nq. analyzing the experimental results presented in table 1 were selected as optimal values nqp = 1 and nq = 1. in case of the fann with rbf the proposed algorithm generate the following parameters: γ = 0.25, nh = 6, nq = 1 and nqp = 1. comparing data from table 1 and table 2, figures 4, 5, 6 and 7 shows that fann with rbf offer a better chance of achieving a good estimation even if the learning rate hasn’t a 100% success rate. ann based inverse dynamic model of the 6-pgk parallel robot manipulator 99 0 5 10 15 20 -1000 -500 0 500 1000 t[s] q m 1 [n ] generalized motor force 0 5 10 15 20 -1000 -500 0 500 1000 t[s] q m 4 [n ] generalized motor force 0 5 10 15 20 -1000 -500 0 500 1000 t[s] q m 2 [n ] generalized motor force 0 5 10 15 20 -1000 -500 0 500 1000 t[s] q m 5 [n ] generalized motor force 0 5 10 15 20 -1000 -500 0 500 1000 t[s] q m 3 [n ] generalized motor force 0 5 10 15 20 -1000 -500 0 500 1000 t[s] q m 6 [n ] generalized motor force figure 3: the generalized motor forces of the 6-pgk parallel robot 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 50 60 70 80 90 100 learning rate (γ) s u cc e s ra te o f γ [% ] figure 4: success rates of γ parameter in case of using htf in hidden layer 0 2 4 6 8 10 12 14 16 18 20 0 10 20 30 40 50 60 70 80 90 100 no. of the neurons from hyden layer (n h ) s u cc e s ra te o f n h [ % ] figure 5: success rates of nh parameter in case of using htf in hidden layer (γ = 0.25) 100 l. moldovan, h.-s. grif, a. gligor table 1: the success rates corresponding to ( nq, nqp) (fann: htf, nh = 7, γ = 0.25) success rates [%] nqp 1 2 3 4 5 nq 1 100 2 100 75 3 90 70 35 4 60 55 15 20 5 45 35 20 20 10 table 2: the success rates corresponding to ( nq, nqp) (fann: rbf, nh = 6, γ = 0.25) success rates [%] nqp 1 2 3 4 5 nq 1 100 2 95 85 3 85 85 65 4 75 70 45 40 5 60 65 40 30 15 in order to evaluate the behavior of the built fann, in figure 8 a test motor forces trajectories of the parallel robot and the estimated trajectory generated by the fann are presented comparatively. in figure 9 the estimation error trajectories related to the trajectories from figure 8 is shown. the experimental results demonstrate a good approximation related to the studied system which is characterized by nonlinearities and high complexity. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 50 60 70 80 90 100 learning rate (γ) s u cc e ss r a te o f γ [% ] figure 6: success rates of γ parameter in case of using rbf in hidden layer ann based inverse dynamic model of the 6-pgk parallel robot manipulator 101 0 2 4 6 8 10 12 14 16 18 20 0 10 20 30 40 50 60 70 80 90 100 no. of the neurons from hyden layer (n h ) s u cc e s ra te o f n h [ % ] figure 7: success rates of nh parameter in case of using rbf in hidden layer(γ = 0.25) 0 2 4 6 8 10 12 14 16 18 20 −600 −400 −200 0 200 t[s] q m 1 [n ] robot fann 0 2 4 6 8 10 12 14 16 18 20 −200 0 200 400 600 t[s] q m 4 [n ] robot fann 0 2 4 6 8 10 12 14 16 18 20 −200 0 200 400 600 t[s] q m 2 [n ] robot fann 0 2 4 6 8 10 12 14 16 18 20 −600 −400 −200 0 200 t[s] q m 5 [n ] robot fann 0 2 4 6 8 10 12 14 16 18 20 −600 −400 −200 0 200 t[s] q m 3 [n ] robot fann 0 2 4 6 8 10 12 14 16 18 20 −200 0 200 400 600 t[s] q m 6 [n ] robot fann figure 8: the fann estimated motor forces trajectory and the motor forces trajectory of the 6-pgk parallel robot (fann: rbf, nh = 6, γ = 0.25) 0 2 4 6 8 10 12 14 16 18 20 −100 0 100 200 t[s] q m 1 − q m 1 f a n n [n ] fann: rbf, γ = 0.25, n h =6, n q =1, n qp =1 0 2 4 6 8 10 12 14 16 18 20 −150 −100 −50 0 50 t[s] q m 4 − q m 4 f a n n [n ] fann: rbf, γ = 0.25, n h =6, n q =1, n qp =1 0 2 4 6 8 10 12 14 16 18 20 −200 0 200 400 t[s] q m 2 − q m 2 f a n n [n ] fann: rbf, γ = 0.25, n h =6, n q =1, n qp =1 0 2 4 6 8 10 12 14 16 18 20 −100 0 100 200 t[s] q m 5 − q m 5 f a n n [n ] fann: rbf, γ = 0.25, n h =6, n q =1, n qp =1 0 2 4 6 8 10 12 14 16 18 20 −100 −50 0 50 t[s] q m 3 − q m 3 f a n n [n ] fann: rbf, γ = 0.25, n h =6, n q =1, n qp =1 0 2 4 6 8 10 12 14 16 18 20 −400 −200 0 200 t[s] q m 6 − q m 6 f a n n [n ] fann: rbf, γ = 0.25, n h =6, n q =1, n qp =1 figure 9: estimation error trajectory corresponding to figure 8 102 l. moldovan, h.-s. grif, a. gligor 4 conclusions this paper proposed to build an on-line feedforward artificial neural network with the aim of estimating the inverse dynamic model of the 6-pgk prototype parallel robot. the presented solution was adopted in order to perform an on-line parametric identification of the inverse dynamic model of the robot. the chosen solution was adopted mainly for the use when the robots are characterized by the nonlinearities and high mathematical model complexity. the implementation of the feedforward artificial neural networks allowed to obtain a nonlinear autoregressive model with exogenous input behavior, for which a new method for finding optimum parameters that approximate as better as possible the model of the 6-pgk robot was obtained. in addition, the proposed solution offers, with the best estimation results, the minimal computational load structure of the neural network. even if the fann has a minimal internal structure, a slower training learning method, it offers a robust and efficient estimation 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(2007); neural network toolbox for use with matlab. user’s guide. version 5, 2007, the mathworks, inc. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 214-221 enhanced security protocol in wireless sensor networks t.c. aseri, n. singla trilok c. aseri, neha singla pec university of technology computer science & engineering department house no. 808, pec campus, sector-12, chandigarh-160012 (india) e-mail: a_trilok_chand@yahoo.com, nehasingla1409@gmail.com abstract: the need for security in communications is in fact not new. this need has existed in military communications for thousands of years. in this paper, we focus on network protocols that provide security services. wireless sensor network is an emerging technology that shows applications both for public as well as military purposes. monitoring is one of the main applications. a large amount of redundant data is generated by sensor nodes. this paper compares all the protocols which are designed for security of wireless sensor network on the basis of security services and propose an improved protocol that reduces communication overhead by removing data redundancy from the network. by using the message sequence number we can check whether it is old message or new message. if the message is old then no need to send that message thereby reducing overhead. it also integrates security by data freshness in the protocol. keywords: data freshness, protocol, security, wireless sensor network. 1 introduction sensor networks are typically data driven, i.e., the whole network cooperates in communicating data from sensors (information sources) to information sinks. low-cost, low power, multifunctional sensor nodes that are small in size and communicate untethered in short distances have been developed due to the recent advances in wireless communication. these tiny sensors have the ability of sensing, data processing, and communicating with each other. wireless sensor networks (wsn) which rely on collaborative work of large number of sensors are realized. a wsn is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. in addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. a sensor network normally constitutes a wireless ad-hoc network, meaning that each sensor supports a multi-hop routing algorithm. wireless sensor network is one of the most exciting and challenging research areas. nodes in sensor networks have restricted storage, computational and energy resources; these restrictions place a limit on the types of deployable routing mechanisms. additionally, ad hoc routing protocols for conventional wireless networks support ip style addressing of sources and destinations. they also use intermediate nodes to support end-to-end communication between arbitrary nodes in the network. it is possible for any-to-any communication to be relevant in a sensor network; however this approach may be unsuitable as it could generate unwanted traffic in the network, thus, results the extra usage of already limited node resources. many-to-one communication paradigm is widely used in regards to sensor networks since sensor nodes send copyright c⃝ 2006-2011 by ccc publications enhanced security protocol in wireless sensor networks 215 their data to a common sink node for processing. this many-to-one paradigm also results in non-uniform energy drainage in the network. the applications for wsns are many and varied, but typically involve some kind of monitoring, tracking, and controlling, intelligent buildings, transportation, space exploration, disaster detection. in order to operate these applications successfully, it is necessary to maintain privacy and security of the transmitted data. the rest of the paper is organized as follows: section 2 explains security requirements, section 3 presents a review of the relevant work, section 4 presents the proposed protocol, section 5 shows the results and discussion, and section 6 concludes the paper. 2 security requirements 2.1 confidentiality confidentiality means keeping information secret from unauthorized parties. a sensor network should not leak sensor readings to neighboring networks. the confidentiality objective is required in sensors environment to protect information traveling between the sensor nodes of the network or between the sensors and the base station from disclosure, since an adversary having the appropriate equipment may eavesdrop on the communication. by eavesdropping, the adversary could overhear critical information such as sensing data and routing information. 2.2 authentication in a sensor network, an adversary can easily inject messages, so the receiver needs to make sure that the data used in any decision-making process originates from the correct source. as in conventional systems, authentication techniques verify the identity of the participants in a communication, distinguishing in this way legitimate users from intruders. in the case of sensor networks, it is essential for each sensor node and base station to have the ability to verify that the data received was really sent by a trusted sender and not by an adversary that tricked legitimate nodes into accepting false data. if such a case happens and false data are supplied into the network, then its behavior could not be predicted, and most of the times the mission of wsn will not be accomplished as expected. 2.3 integrity data integrity ensures the receiver that the received data is not altered in transit by an adversary. lack of integrity could result in many problems since the consequences of using inaccurate information could be disastrous, for example, for the healthcare sector where lives are endangered. integrity controls must be implemented to ensure that information is not altered in any unexpected way. 2.4 freshness one of the many attacks launched against sensor networks is the message replay attack where an adversary may capture messages exchanged between nodes and replay them later to cause confusion to the network. data freshness implies that the data is recent, and it ensures that an adversary has not replayed old messages. to achieve freshness, network protocols must be designed in a way to identify duplicate packets and discard them preventing potential mix-up. 216 t.c. aseri, n. singla 2.5 availability availability ensures that services and information can be accessed at the time they are required. in sensor networks there are many risks that could result in loss of availability such as sensor node capturing and denial of service attacks. the availability of a sensor and sensor network may decrease for the following reasons [1]: • additional computation consumes additional energy. if no more energy exists, the data will no longer be available. • additional communication also consumes more energy. besides, as communication power increases so does the chance of a communication conflict or interference. a single point failure exists if we use the central point scheme such as a single sink or gateway. this greatly threatens the availability of the network. 3 related work the various protocols which have been proposed for security in wireless sensor network by various authors are spin, leap, tinysec, zigbee, sm. in spin (sensor protocols for information via negotiation), nodes use three types of messages adv, req and data to communicate. adv is used to advertise new data, req to request for data and data is the actual message itself. the protocol starts when a spin node obtains new data that it is willing to share. it does so by broadcasting an adv message containing meta-data. if a neighbor is interested in the data, it sends an req message for the data and the data is sent to this neighbor node. the neighbor sensor node then repeats this process to its neighbors as a result of which the entire sensor area will get a copy. it consists of two secure building blocks snep (sensor network encryption protocol) and îźtesla (timed efficient stream loss-tolerant authentication). in addition to integrity, snep is used to provide confidentiality through encryption and authentication using a message authentication code (mac). it lowers communication overhead adding only 8 bytes per message [2]. tesla authenticates the initial packet using the digital signature. for an authenticated packet to be sent, the base station computes a mac on the packet with the key that is secret at that point in time. when a node gets a packet, it can confirm that the base station did not yet disclose the corresponding mac key [3]. the goal of leap (localized encryption and authentication protocol) is to satisfy the security properties of authentication and confidentiality in a wireless environment where the intruder may eavesdrop, inject packets, and replay messages [4]. leap, as a key management protocol for sensor networks, is designed to support in-network processing, while restricting the impact of a compromised node to the network. in order to support the in-network processing necessary for most applications of these networks while at the same time providing security properties, such as security and authentication, similar to those of pairwise symmetric keys, leap specifies four types of keys: individual keys, pairwise shared keys, cluster keys and group keys. individual keys are symmetric keys shared between the base station and each of the nodes. for example, a node might use the individual key to notify the base station of a suspicious neighbor. pairwise shared keys are symmetric keys shared between a node and each of its neighbors. while pairwise shared keys are used to establish cluster keys, they prevent passive participation which is desirable for in-network processing. cluster keys are symmetric keys shared between a node and all of its neighbors. these cluster keys can be used for locally broadcast messages such as a routing protocol might use and are also used for updating the group key. the group key, a symmetric key shared between the base station and all of the nodes, allows encrypted and authenticated messages to broadcast through the whole network. enhanced security protocol in wireless sensor networks 217 in the next protocol, tiny sec, the dominant traffic pattern in sensor networks is many-toone, with many sensor nodes communicating sensor readings or network events over a multihop topology to a central base station. however, neighboring nodes in sensor networks often witness the same or correlated environmental events, and if each node sends a packet to the base station in response, precious energy and bandwidth are wasted. to prune these redundant messages to reduce traffic and save energy, sensor networks use in-network processing such as aggregation and duplicate elimination [5, 6]. since in-network processing requires intermediate nodes to access, modify, and suppress the contents of messages, it is unlikely we can use end-to-end security mechanisms between each sensor node and the base station to guarantee the authenticity, integrity, and confidentiality of these messages. with authenticated encryption, tinysec encrypts the data payload and authenticates the packet with a mac [7]. single shared global cryptographic key, link layer encryption and integrity protection cryptography is based on a block cipher. tinysec is a research platform that is easily extensible and has been incorporated into higher level protocols. in zigbee, the concept of a trust center is introduced in the specification. generally the zigbee coordinator performs this duty. this trust center allows other devices to join the network and also distributes the keys. there are three roles played: • trust manager, whereby authentication of devices requesting to join the network is done, • network manager, maintaining and distributing network keys, and • configuration manager, enabling end-to-end security between devices [8]. it operates in both residential mode and commercial mode. the trust center running residential mode is used for low security residential applications. commercial mode is designed for high-security commercial applications. in residential mode, the trust center will allow devices to join the network, but does not establish keys with the network devices. it therefore cannot periodically update keys and allows for the memory cost to be minimal, as it cannot scale with size of the network. in commercial mode, it establishes and maintains keys and freshness counters with every device in the network, allowing centralized control and update of keys. this results in a memory cost that could scale with the size of the network. there are three types of keys employed, the master key, the link key and the network key. master keys are installed first, either in the factory or out of band. they are sent from the trust center and are the basis for long-term security between two devices. the link key is a basis of security between two devices and the network keys are the basis of security across the entire network. link and network keys, which are either installed in the factory or out of band, employ symmetrical keykey exchange (skke) handshake between devices. the key is transported from the trust center for both types of keys. this operation occurs in commercial mode, as residential mode does not allow for authentication. in the latest protocol, sm (security manager), a new method of key agreement has been proposed in [9], whereby, when a new device joins a network, the security manager (sm) gives static domain parameters at the base station such as the order of the curve and the elliptic curve coefficients. after calculating a public key using the base point and a private key, the device sends a public key to the sm. therefore the sm would have the public key list for all the devices in the network. authentication is achieved by using either diffie-hellman or elliptic curve equation. confidentiality is achieved by using message authentication protocol. this shows that sm protocol offers more services than the other existing protocols. 218 t.c. aseri, n. singla 4 proposed protocol a security protocol refers to a set of rules governing the interaction between peer processes to provide a certain type of security service. we propose a new security protocol. security manager (sm) [9] does not guarantee data freshness; and so, we suggest a protocol to make up for the weakness of sm. this provides a solution to maintain data freshness by checking the message sequence number. when the message is sent, it is checked by the message sequence number whether it is already sent or not. if the message is old then no need to send that message. by this way, we can reduce overhead. by reducing the overhead we can make the protocol more efficient. authentication and confidentiality are also provided. authentication is defined to provide assurance about the originator of a message. this prevents an attacker from mimicking the operation of another device in any attempt to compromise the network. confidentiality means keeping information secret from unauthorized parties. the standard solution to keep sensitive data secret is to encrypt the data with a secret key that only the intended receivers possess, hence achieving confidentiality. additionally, this protocol provides freshness through the use of freshness checks. these checks prevent replay attacks, as devices maintain incoming and outgoing messages. whenever a node wants to send message, message sequence number will be checked. one of the many attacks launched against sensor networks is the message replay attack where an adversary may capture messages exchanged between nodes and replay them later to cause confusion to the network. data freshness implies that the data is recent, and it ensures that an adversary has not replayed old messages. to achieve freshness, network protocols must be designed in a way to identify duplicate packets and discard them preventing potential mix-up. this extra feature shows that proposed protocol offers more security services than the existing one. in the proposed protocol, time interval ∆t accounts for request time, response time, delay, as shown in (1) and (2). freshness is computed as the difference between the time a data item is generated and the time it is received at the sink. with data freshness, ∆t = trq + trs + ∆d + t (1) without data freshness, ∆t = trq + trs + ∆d (2) where trq = request transmission, trs = response transmission, ∆d = sum of delays (delays in transmission and propagation), t = time when message sequence number is checked. in case of without data freshness, no message is checked. accuracy is measured as the ratio of total number of messages received at the sink to the total number of messages generated. in case of without data freshness, no message is discarded but in case of with data freshness the message, which is already sent then no need to send again. therefore, the ratio of without data freshness is always one but it is less than one in case of with data freshness. we can calculate the packet ratio by (3) and (4) as follows: without data freshness, packetratio = send/receive = 1 (3) with data freshness, packetratio = send/receive < 1 (4) enhanced security protocol in wireless sensor networks 219 5 results and discussion sensor network is a promising and upcoming technology with usage in important applications. the resource constraint hardware, specialized software, low energy devices and hostile environment makes the security in wireless sensor networks a challenging task as and when compared to the traditional computer networks. energy efficiency can be achieved by reducing the number of packets transmitted. without data freshness, each node will send a packet that will be forwarded to the sink whereas with data freshness no need to send all packets, this reduced number of packets transmitted improve the efficiency. figure 1 shows the average latency. in case of with data freshness, average latency is constant as the number of nodes is increased while it is increased in case of without data freshness. figure 1: number of nodes vs. average latency figure 2 shows the average packet delivery ratio. in case of with data freshness, average packet ratio is 100% because how many packets sent will definitely be received but in case of without data freshness some may already sent. this reduced number of packet transmitted also improves the efficiency. the discussion of the security protocol and authentication mechanism allow for the construction of comparison table as in given table 1, where they can be compared under similar headings. it can be seen from the table that new protocol is better than the existing protocol and offers more security services than the earliest one. 6 conclusion in this paper, firstly we propose a new security protocol for wireless sensor network. secondly, we compared the performances of all the existing protocol with proposed protocol. spin 220 t.c. aseri, n. singla figure 2: number of nodes vs. packet delivery ratio table 1: security architecture comparison protocol / service c f i ava ia a spin yes yes yes no yes no leap yes no no no yes no tinysec yes no no yes yes zigbee yes yes yes no yes yes sm yes no no yes yes our protocol yes yes yes yes c=confidentiality, f=freshness, i=integrity, ava=availability, ia=implicit authentication, a=authentication of user was found to perform better in smaller size networks because of its efficiency and high latency properties. the use of spin in large scale networks could potentially exhaust system resources in a much faster pace. our protocol has one extra feature i.e. freshness. freshness reduces the overhead. this extra feature shows that this is superior to the existing protocols. this also improves the efficiency. bibliography [1] j.p. walters, zh. liang, w. shi, v. chaudhary, security in distributed, grid, and pervasive computing, chapter 17, crc press, 2006. [2] a. perrig, r. szewczk, j.d. tygar, v. wen, d.e. culler, spins: security protocols for sensor networks, wireless networking, vol. 8, no. 5, pp. 521-534, sept 2002. enhanced security protocol in wireless sensor networks 221 [3] a. perrig, r. canneti, j. d. tygar, d. song, the tesla broadcast authentication protocol, cryptobytes, vol. 5, no. 2, pp. 2-13, 2002. [4] d. boyle, t. newe, security protocols for use with wireless sensor networks: a survey of security architectures, proceedings of the 3rd international conference on wireless and mobile communications, guadeloupe, french caribbean, pp. 54, 04-09 march 2007. [5] s. madden, m.j. franklin, j.m. hellerstein, w. hong, tag: a tiny aggregation service for ad-hoc sensor networks, proceedings of the 5th symposium on operating system design and implementation (osdi), boston, massachusetts, usa, pp. 131-146, 09-11 december 2002. [6] s. madden, r. szewczyk, m.j. franklin, d. culler, supporting aggregate queries over adhoc wireless sensor networks, proceedings of the 4th ieee workshop on mobile computing and systems applications (wmcsa), callicoon, ny, usa, pp. 49-58, 20-21 june 2002. [7] c. karlof, n. sastry, d. wagner, tinysec: a link layer security architecture for wireless sensor networks, proceedings of the 2nd acm international conference on embedded networked sensor systems, baltimore, md, usa, pp. 162-175, 03-05 november 2004. [8] zigbee alliance, zigbee security specification overview, [online] available: http://www.zigbee.org/en/events/documents/december2005_open_house_presentations/ zigbee_security_layer_technical_overview.pdf. [9] j. heo, c.s. hong, efficient and authenticated key agreement mechanism in low-rate wpan environment, proceedings of the 1st ieee international symposium on wireless pervasive computing, phuket, thailand, pp. 1-5, 16-18 january 2006. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 53-60 adaptation mechanism based on service-context distance for ubiquitous computing m. cremene, m. riveill, a. rarau, c. miron, b. iulian, v. todica marcel cremene, anca rarau, costin miron, benta iulian, valeriu todica technical university of cluj-napoca romania, cluj-napoca, memorandumului nr. 28, 400114 e-mail(s): cremene@com.utcluj.ro, anca.rarau@cs.utcluj.ro, miron@bel.utcluj.ro, iulian.benta@com.utcluj.ro, todicav23@yahoo.com michel riveill university of nice, sophia-antipolis i3s, route de colles, bp 145 f-06903, sophia antipolis cedex e-mail: riveil@unice.fr abstract: service adaptation is one of the main research subjects in ubiquitous computing. dynamic service adaptation, at runtime, is necessary for services that cannot be stopped (banking, airport, etc.). the classical approaches for dynamic adaptation require predicting all service and context states in order to specify service and context-specific adaptation policies. this prediction may lead to a combinatorial explosion. the aim of this research is to create a service and context-independent adaptation mechanism. our proposal is based on a service-context model that is causally connected with the service and context, in a model@run.time paradigm. a closed-loop control principle is used for the adaptation mechanism. we introduce an equivalent for the error that is expressed by the notion of service-context distance. this distance represents a measure of how adequate is a service to its context. this distance is computed by some generic, reusable components. the adaptation algorithm that minimizes this distance is also service and context-independent. keywords: services, dynamic adaptation, context, autonomic computing, adaptation control 1 introduction background and motivation. service adaptation to the context (physical infrastructure/resources, user needs, and environment) is one of the main research subjects in high interest domains such as ubiquitous/pervasive/mobile computing. adapting a service is to reconfigure that service in order to maximize its qos (quality of service) and also the efficiency of the resource utilization. we use a component-oriented approach for services, thus the reconfiguration concerns the service architecture: parameterize, add/remove, connect/disconnect or migrate components. numerous situations require a dynamic service adaptation [1,2], at runtime, because there are an important number of services that cannot be stopped (modified and recompiled). examples are: banking services, airport services, spatial services, corporate services, groupware services and others. even for services that may be stopped, the human intervention means important time and costs. copyright c⃝ 2006-2012 by ccc publications 54 m. cremene, m. riveill, a. rarau, c. miron, b. iulian, v. todica issues about dynamic adaptation control mechanism. the adaptation mechanism, that encapsulates the system "intelligence", is the most important part of an adaptive system. it main function is to decide when a service should be adapted and how to adapt it. after studying several different approaches for dynamic service adaptation, some of them presented in [3], we observed that the large majority of these solutions are based on the prediction about all possible service and context states. this prediction is an essential condition for specifying adaptation policies. usually these policies are service and context-specific, they are not reusable and they do not evolve as the context evolves, without human intervention. a service can be adapted only inside the limits fixed a priori by these services and context-specific polices. for instance, in the case of "event-condition-action" adaptation mechanism, which is one of the most used, a service cannot be adapted if the events, the conditions and the actions have not been predicted/specified priori by a human expert. but these events, conditions and actions are service and context-specific. this means that the human expert decides a priori when and how to adapt a service. the problem is that, the prediction of all possible service and context states, may lead to a combinatory explosion. for instance, a state machine specification mde (model driven engineering) approach leads to a extremely high number of possible artefacts, as it is shown in [2]. another problem of the classical state machine approach is that it does not deal with adding/removing/changing dynamically new states and transitions. thus, such an approach is impossible to be used in practice for complex services and contexts. objective and approach. the aim of this research is to build a service and contextindependent (generic) adaptation mechanism. our proposal is based on a model@run.time approach. according to this approach, we propose a service-context model describing the service and the context as a whole system. this model is causally connected with the service and the context. the service-context model is represented as a directed graph having as nodes the service components and the context elements. each node has attributes related to the entities (services′ components, context elements) and to the interactions between these entities. a closed-loop control principle is used for the adaptation mechanism, as suggested by the autonomic computing paradigm [4]. we introduce an equivalent for the notion of error that is expressed in our model by the notion of service-context distance. this distance represents a measure of how adequate is a service to its context in terms of user needs satisfaction and resources utilisation. the adaptation algorithm is also service and context-independent. paper outline. this paper is organized as it follows: the next section is an overview about the dynamic service adaptation issues and the models@run.time approach. section three presents the proposed solution that is based on the service-context model, service-context distance and a generic adaptation algorithm. section four present presents the conclusions. 2 dynamic service adaptation in a models@run.time approach 2.1 dynamic service adaptation for defining more precisely what service adaptation means for us, we consider a mixture between the categories defined by the autonomic computing [4] and the adaptation types proposed in [1]: a. self-healing/corrective adaptation. the system automatically detects, diagnoses, and repairs localized software (and hardware) problems. for example, if a system component responds too slow it will be replaced by another component, with the same functions, that responds on time (or the component will be moved on another physical machine). self-healing/corrective adaptation deals usually with failure cases, when a component must be replaced/moved. adaptation mechanism based on service-context distance for ubiquitous computing 55 b. self-adaptation. the adaptation is necessary when the context state changes. we may have two cases of self-adaptation:adaptive adaptation concerns the situations when the context change and the service should change also in order to keep its functions. this type of adaptation is usually transparent to the user.extending adaptation concerns the situations when some new user needs are discovered a posteriori and the service should be extended in order to satisfy these new needs. c. self-optimization/perfective adaptation. the system continuously searches for opportunities to improve its own performance. the performance is maximal when the qos parameter are maximized (for instance the response time) and when the resource utilization is minimal and well balanced. self-optimization/perfective adaptation deals with a service that is working correctly but it may be tuned in order to increase its efficiency/quality. the adaptation is dynamic when the service reconfiguration happens at runtime. in the last years, numerous researchers have concentrated on developing middleware that enables dynamic adaptation. for instance, the wcomp middleware [5, 6] offers a support for composing and adapting services at runtime. the design and the execution phases are simultaneous. an event-based communication model is used. it offers the possibility to use services installed on various devices and also web services from internet. wcomp manages also the dynamic service apparition/extinction. fractal [7] proposes a middleware and a component model enabling hierarchical composition. the middleware offers support for reflection and dynamic reconfiguration. a particularity of fractal is the possibility to share a component between two other composite components. openccm (open corba component model) [8] is the first public available and open source implementation of ccm (corba component model). osgi propose a platform based on dynamic modules/components that may be assembled at runtime, it provides a service-oriented, component-based environment. a service-oriented middleware allowing spontaneous distributed service composition at runtime, based on osgi, is proposed in [9]. facing with a high number of existent middleware, our intention is to propose an independent solution that may be adapted to different middleware. 2.2 the models@run.time approach models@run.time [10] represents a novel approach that aim is to extend the usage of software models (mde model driven engineering) to runtime. the need of such models is motivated by the dynamic adaptation of mission-critical software issues. this is also related with autonomic computing paradigm because the adaptation should be autonomous (without human intervention). this approach is also strongly related with the reflection paradigm and has a similar principle: a model@run.time is an abstraction of a system that is causally connected with that system and may be used for dynamic system adaptation. the difference comparing to reflection is that in model@run.time we look for high-level models while in reflection paradigm the (meta)model is strongly related to the low-level software aspects [10]. such a model@run.time describes aspects as: structure, behaviour and goals of the system, from a problem space perspective. one of the advantages of the model@run.time approach is the possibility to treat independently two main issues: i. propose a model that will be used at runtime for discovering the service-context adequacy problems and for adapting the service by solving these problems. our proposal is based on a service-context model, a service-context distance that reflects the service-context adequacy and an adaptation algorithm that aim is to minimize this distance (and to increase the adequacy). 56 m. cremene, m. riveill, a. rarau, c. miron, b. iulian, v. todica ii. create a middleware that will causally connect the two parts: a) the proposed servicecontext model with b)the adapted system (a modification of one part will be reflected on the other part). if we want to offer a general solution, a part of this middleware will make the adaptation with a specific adaptation support-middleware (see the examples presented in the section 2.1). the second issue is more a technical issue than a scientific one because today many middleware and context monitoring tools offers a large number of solutions that may be reused. that is why; in this paper we are concentrated only on the first issue which is the most important and difficult. 3 the service-context model, the service-context distance and the adaptation algorithm 3.1 service-context model we have introduced the service-context model concept in [3, 11] as a possible approach for autonomic computing. in this paper we re-interpret this model from a models@run.time perspective, we extend it with the service-context distance. service-context graph. the service-context model (figure 1) aim is to describe the service and the context as a whole system that may be analyzed from the service to context adequacy point of view. this model is based on a directed graph composed by the following elements: a. the nodes correspond to service and context entities. the service′ entities (s in fig. 1) are the software components that constitute the service. in order to simplify the model, a complex component is described by decomposing it in a sum of basic components having only three types: source (1 output), sink (1 input) or filter (1 input, 1 output). the context entities are context components: users (u in fig. 1), infrastructure elements (i in fig. 1) such as devices, networks and environment elements (e in fig. 1). b. the vertex corresponds to the relations and interactions existent between these entities. we define three types of vertex corresponding to two types of possible relations/interactions: informational flows interactions. the service components are communicating by exchanging information. also, the user exchange information with the service. this information has some attributes that describes it. resource utilization interactions. the service components consume resources provided by the hardware infrastructure (memory, cpu, bandwidth, etc). environment influence interactions. the environment may influence the user needs (ex. location) or the infrastructure (ex. the rain effect on a radio network). for simplifying the model, we will discuss in this paper only the most important interactions which are the first two: information flows and resource utilization. attributes. the graph is annotated with some attributes. each node has attributes related to the node itself or to the node ports (input and output vertex). these attributes are making a semantic connection between the service nodes and the context nodes. for instance, the ’language’ attribute means the user u language but also the service s language, the ’memory’ attribute means the memory consumed by a component and the memory provided by a device. adaptation mechanism based on service-context distance for ubiquitous computing 57 figure 1: service-context general model a common attribute vocabulary for service and context components should be respected by the component developers/providers. for each attribute a input and output domain are specified. for filter-type components, a transfer function (input-output) should be specified. for instance, a translation component change the language between the input and the output, a compression component change the compression rate, etc. the attributes, their input/output domains and the transfer functions should be specified for each component by the component developer/provider, in a form of a profile (i.e. an xml-based descriptor of the component). this is a necessary condition for enabling the machine to understand what a component does. same thing for the context elements, each one has a profile. composition. one of the most important property of the service-context model is the composition. by composition we understand here to assimilate a graph to a node. this is a necessary condition for describing the service behaviour and properties using only the service’s internal components behaviour and properties. we propose a composition mechanism based on the attributes specified by the profiles. the elementary composition cases are described in figure 2. the composition means to determine the equivalent node for a graph composed by two nodes. c1 c2 c1 c2 figure 2: serial and parallel composition the composition operation depends on the attribute nature. the composition is different for information flows interactions and for resource utilization interactions. these operators are described in the figure 3. 3.2 service-context distance the first idea for describing the service-context adequacy, used in [11], was to use a binary approach: a service is adequate or not to its context. in order to have a more general approach, we introduce in this paper the concept of service-context distance. the distance is minimal when the service to context adequacy is maximal. this distance has two components: the s-u distance measures the distance from the information flow point of view and the s-i distance measures the distance from the resource utilization point of view. 58 m. cremene, m. riveill, a. rarau, c. miron, b. iulian, v. todicainformation flow composition atribute name serial composition parallel composition response_time sum, + max function security_level min function min function language language of the output component intersection type_hmi type_hmi of the output component reunion resource utilization composition atribute name paralel composition serial composition shared composition memory sum, + max function m1 + m2 – mshared cpu_time sum, + max function t1 + t2 – tshared screen_surface reunion max function reunion figure 3: composition operators for different attributes each distance is represented as a vector having as components the distances corresponding to each attribute. for each attribute we define a specific distance that will be computed by some dedicated components/services. in some cases the distance is expressed by a simple formula or set of rules. some examples are described in figure 4.information flow distance evaluation attribute name domain distance response_time 0..inf security_level {none, low, medium, high, very high} d = 0 if security_level(s) >= security_level(u) and ∞ else language {ro, fr, de, es…….} d = 0 if language(s) == language(u) and ∞ else resource utilization distance evaluation attribute name domain distance memory 0…100% d = memory(s) / memory(i) * 100 cpu_time 0..100% d = cpu_time(s) / cpu_time(i) * 100 network_capacity 0..100% d = network_capacity(s) / network_capacity(i) * 100 figure 4: distance expressions for different attributes a more complex distance is the distance between the user requests expressed in natural language and the service features. for such complex distances we cannot present a simple formula but we use dedicated component/services that implement the algorithm for computing the distance. such an algorithm is proposed in [12]. the model may be extended by adding new attributes definitions and new distances measuring components. adaptation mechanism based on service-context distance for ubiquitous computing 59 3.3 adaptation algorithm the adaptation algorithm goal is to minimize the service-context distance. for doing that, the adaptation algorithm dynamically transforms the service-context graph from a less adequate one toward a more adequate one. the primitive operations used by the adaptation algorithm are the following: changing a node parameter, connecting/disconnecting a node, inserting a new node, removing a node, replacing a node. in order to minimize the service-context distance, the adaptation algorithm should find a list of primitive operations that transforms the graph from the current state into the desired state (for the desired state the service-context distance is minimal). several alternative solutions may exists. in order to make a difference between these possible solutions, we associate an adaptation cost to each primitive operation. for instance, the parameterization has a cost equal to 1 while the insertion cost is 5. this is because it is faster and easier to apply a parameterization than an insertion. we have implemented an algorithm, described in [11], that uses one attribute and one adaptation strategy: the insertion of a new component. this algorithm starts from a mismatch between the desired value v2 for an attribute a and the current value v1 and search for a new component that transforms the value of the attribute from v1 to v2. the place where the new component may be inserted is searched based on the syntactic interface compatibility. the user is confirmation is asked before transforming the service. we are working in present on a general adaptation algorithm, able to apply a succession of different primitive operations and deal with several attributes simultaneously. we have done some tests with genetic algorithms which seem promising but are not very fast. 3.4 implementation as a proof of concept, in [11] we have implemented a simple forum service that is adapted dynamically to the user language. the components are developed in java, using the ccm (corba component model) specifications. the adaptation consists in inserting a translation component, at runtime. we have used a mechanism based on isl (interaction specification language) that is used also in wcomp middleware [6]. in present we are developing a general service-context simulator, based on the open source jgraphx api that should allow us to test the adaptation at the model level by simulating various service and context states. 4 conclusions the issue discussed in this paper was to propose a service and context-independent adaptation mechanism for dynamic service adaptation. we have proposed a solution, based on the service-context model and service-context distance concepts, that fits into the very recent model@run.time approach. the generality of the proposed model is given by its following properties: the service attributes/behaviour are determined automatically by composing the attributes/behaviour of its internal components, the service-context distance depends only on the model attributes nature and are not dependent to a particular service/context and the adaptation algorithm is also service/context independent. as future work we intend to generalize the adaptation algorithm. 60 m. cremene, m. riveill, a. rarau, c. miron, b. iulian, v. todica acknowledgments this work was supported by cncsis-uefiscsu, pnii-idei, project number 1062/2007. thanks to members of the rainbow team, i3s laboratory, sophia-antipolis. bibliography [1] a. ketfi, n. belkhatir, and p.-y. cunin, “automatic adaptation of component-based software: issues and experiences,” in pdpta ’02: proceedings of the international conference on parallel and distributed processing techniques and applications, pp. 1365–1371, csrea press, 2002. 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[12] m. cremene, j.-y. tigli, s. lavirotte, f.-c. pop, m. riveill, and g. rey, “service composition based on natural language requests,” in ieee scc, pp. 486–489, 2009. int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 231-240 the effect of heterogeneous traffic distributions on load balancing in mobile communications: an analytical model k.-c. chu, c.-s. wang, w.-w. jiang, n.-c. hsieh kuo-chung chu, wey-wen jiang, nan-chen hsieh department of information management, national taipei university of nursing and health sciences no.365, mingde rd., beitou dist., taipei city 11219, taiwan (r.o.c.) {kcchu, jiang6, nchsie})@ntunhs.edu.tw chun-sheng wang department of information management, jinwen university of science and technology no.99, anzhong rd., xindian dist., new taipei city 23154, taiwan (r.o.c.) seanwang@just.edu.tw abstract: this paper investigates the load balancing problem in an environment of heterogeneous traffic distributions. an analytical model is proposed to determine the effect of heterogeneous traffic distributions on load balancing, in which a generic measure of load balancing level (lbl) that is a function of traffic type coefficient (tyc) and call blocking probability of cells is to analyze the expected level of the load balance. we consider both voice traffic and data traffic to determine which kind of traffic has the greater effect. the performance of cellular systems with sectorization is evaluated; they are normal case (n) of homogeneous distribution and linear case (l) of heterogeneous distribution. the analysis results indicate that the tyc has a significant effect on the accommodation capacity, in which voice calls outperform data calls because the lbl can easily distinguish between normal and linear distributions. load balancing can be achieved more easily for voice only traffic than for data only traffic. sectorization is more effective in achieving load balancing in the scenario of the heavier loads than in the lighter loads. the paper results are useful for network planning to optimize the channel allocation for different traffic type’s distribution. keywords: analytical model, heterogeneous distributions, load balancing, mobile communications, qos. 1 introduction several studies have evaluated the capacity of mobile cellular systems, but most of them (e.g., [1,2]) assume a homogeneous spatial traffic distribution, as it best fits the system’s characteristics to have all signals share all the spectral resources however, homogeneous traffic distribution among base station (bs)/sectors/cells (equal cell loads) is very uncommon in practice. even though sufficient capacity is planned in a cellular system, heterogeneous traffic distribution may occur in other cells, creating a “hot spot" that exceeds the pre-determined capacity and introduces a large blocking probability, as the quality of service (qos) of such cells may be degraded, especially below a pre-defined threshold. this is why load balancing is the most important issue to be discussed before network planning in terms of optimal resource allocation. copyright c⃝ 2006-2012 by ccc publications 232 k.-c. chu, c.-s. wang, w.-w. jiang, n.-c. hsieh in [3], ning et al. discuss load balancing by using a hybrid scheme of channel borrowing scheme and load transfer, it allows borrowing channels from light load cells, and ongoing calls can be transferred from heavy load cells into the overlapping cells they are light load. to improve global resource utilization and reduce regional congestion given heterogeneous arrivals, [4] requires load balancing among multiple cells. however, their works cannot be applied to general system because a lot of issues are different from other systems (wcdma, cdma2000, hspa), e.g. channel definition, interferences, soft handoff. in general, soft handoff enforced by power control has been proposed as a possible solution to local traffic imbalances among cells [5]. actually, power control is one of the most important processes, as interference is the predominant factor that affects the capacity and signal-to-interference ratio (sir). to maximize system capacity, power control can be used efficiently to adapt cell sizes for load balancing; however, the trade-off between coverage and capacity should be carefully considered [6, 7]. an adaptive load-shedding scheme combines the power control and the soft handoff function to force some mobile stations (mss) farthest from the cell to enter forced soft handoff, and transfer their traffic load to neighboring cells that are lightly loaded. in this way, heavily loaded cells dynamically down-size their coverage area in order to handle traffic, while adjacent cells that are less heavily loaded increase their coverage to accommodate the extra traffic. however, in a hot-spot sector, powering up all mss in the sector results in excessive interference with the mss in neighboring cells, so they cannot maintain sufficient sir levels at their sector sites. previous studies also attempt to achieve constant received mean power from each ms within a sector [8–10]. this paper investigates the load balancing problem in an environment of heterogeneous traffic distributions. an analytical model is proposed to determine the effect of heterogeneous traffic distributions on load balancing, in which a generic measure of load balancing level (lbl) that is a function of traffic type coefficient (tyc) and call blocking probability of cells is to analyze the expected level of the load balance. we consider both voice traffic and data traffic to determine which kind of traffic has the greater effect. the performance of cellular systems with sectorization is evaluated; they are normal case (n) of homogeneous distribution and linear case (l) of heterogeneous distribution. the remainder of this paper is organized as follows. in section 2, we discuss the mobile cellular system background. in section 3, we present an analytical model of load balancing, and define sir. section 4 details the numerical results, and section 5 contains some concluding remarks. 2 background of mobile cellular systems 2.1 sectorization generally speaking, a bs configuration is uniformly sectorized in one sector (with omnidirectional antenna, 360◦ per sector), in three sectors (120◦ per sector), and in six sectors (60◦ per sector). the capacity of each sector is calculated subject to the system’s sir requirements. a sector that is lightly loaded usually experiences more interference than a heavily loaded sector, which leads to a higher blocking probability in the lightly loaded sector. denote b as a set of bss, and s as a set of sectors configured in the bs. we further denote the sector s in bs j as sectorjs (∀s ∈ s, j ∈ b) and k as the set of sector configurations. in this paper, the following two probable configurations are given for a bs (|k| = 2): a single sector configuration with an omni-directional antenna, (360◦ per sector), and a three-sector configuration (120◦ per sector); k is assigned as the identification (id) for each configuration. the sector id i identifies the sector in the configuration k in an anti-clockwise direction. table 1 summarizes the sector candidates the effect of heterogeneous traffic distributions on load balancing in mobile communications: an analytical model 233 table 1: sector candidates for the bs the value of s candidate sk,i configuration i.d. (k) sector i.d. (i) s = 1 s(1,1) 1 1 s = 2 s(2,1) 2 1 s = 3 s(2,2) 2 2 s = 4 s(2,3) 2 3 table 2: coverage of candidate sectors candidate sk,i sector i.d.i coverage of sk,i 1 1 (ϕ,ϕ + 360◦) 2 1 (ϕ,ϕ + 120◦) 3 2 (ϕ + 120◦,ϕ + 240◦) 4 3 (ϕ + 240◦,ϕ + 360◦) for each bs for a combination of k and i. let s be the set of sectors; then, each sector sk,i (∀sk,i ∈ s) is defined by the sector configuration (k) and the sector id (i). table 2 details the coverage (in degrees) of each sector, where ϕ is the degree of the baseline. in general, it can be assigned arbitrarily, but in this paper we given −30◦ in our cellular structure example, as shown in figure 1. baseline axis-x (0 ) 30f = r figure 1: the baseline deployed in a cellular structure 2.2 interference between sectors to calculate the interference between sectors, the sector configuration information in table 1 and the sector coverage information in table 2 must be given. without loss of generality, sector sk,i(sk′,i′) is replaced by s(s′); and sector s in bs j is denoted by sectorjs, as shown in figure 2. because of the baseline degree deployed in all cells is the same using ϕ = −30◦, no matter what the bs is configured, the mutual interference between sectors can be well-known. if we define the interference indicator functions ωuljsj′s′ and ω dl jsj′s′ for the respective uplink (ul) and downlink (dl) connections between sectorjs and sectorj′s′, they can be pre-calculated. to pre-calculate the indicator functions, the sector candidates to be configured in the bs must be defined. assuming (xjs,yjs) and (xj′s′,yj′s′) are the respective locations of bs j and bs j′, the vectors −→ a1( −→ a′1) and −→ a2( −→ a′2) covering sectorjs(sectorj′s′) are defined as follows: −→ a1 = [ x1js − xjs,y 1 js − yjs ] , −→ a2 = [ x2js − xjs,y 2 js − yjs ] −→ a′1 = [ x1j′s′ − xj′s′,y 1 j′s′ − yj′s′ ] , −→ a′2 = [ x2j′s′ − xj′s′,y 2 j′s′ − yj′s′ ] 234 k.-c. chu, c.-s. wang, w.-w. jiang, n.-c. hsieh 1, ' 'j s q 2, ' 'j s q 1, js q 2, js q s e c t o r b o u n d a r y a u x i li a r y i n t e r f e r e n c e a x i s -x ( 0 )) js q ( ),tms tx ty ( )' ', 'tms tx ty sector js ' ' sector j s ' 'j s q ( )' ' ' ',j s j sx y ( ),js jsx y ( )1 1' ' ' ',j s j sx y ( )2 2' ' ' ',j s j sx y ( )1 1,js jsx y ( )2 2,js jsx y figure 2: mutual interference between sectors where x1js = rj cos (θ1,js) + xjs,y 1 js = rj sin (θ1,js) + yjs, x2js = rj cos (θ2,js) + xjs,y 2 js = rj sin (θ2,js) + yjs x1j′s′ = rj′ cos (θ1,j′s′) + xj′s′,y 1 j′s′ = rj′ sin (θ1,j′s′) + yj′s′, x2j′s′ = rj′ cos (θ2,j′s′) + xj′s′,y 2 j′s′ = rj′ sin (θ2,j′s′) + yj′s′ then, the dl interference ωdljsj′s′ between sectorjs and sectorj′s′ can be analyzed by an auxiliary vector −→ a3 = [tx′ − xjs,ty′ − yjs], where (tx′, ty′) is the arbitrary position of ms t′ (∀t′ ∈ t and t is the set of mss) serviced by sectorj′s′. furthermore, (θ1,js,θ2,js) and (θ1,j′s′,θ2,j′s′) can also be calculated easily. according to −→ a3, θjs is calculated by θjs = tan−1 ty′−yjs tx′−xjs ,0 ≤ θjs < 360◦. after calculating θjs, the algorithm cal_interf2 is applied to calculate ωdljsj′s′ meanwhile, the ul interference ωuljsj′s′ between sectorjs and sectorj′s′ can be analyzed by an auxiliary vector−→ a′3 = [tx − xj′s′,ty − yj′s′], where (tx,ty) is the arbitrary position of ms t(∀t ∈ t) serviced by sectorjs; θj′s′ is calculated by θj′s′ = tan−1 ty−yj′s′ tx−xj′s′ ,0 ≤ θj′s′ < 360◦. again, applying algorithm cal_interf to the calculation of ul interference ωuljsj′s′. 3 analytical model of load balancing 3.1 sir definition denote zjst as a decision variable, which is 1 if ms t is admitted by sectorjs subject to the sir requirements and 0 otherwise. assuming the power of both the ul and dl are perfectly controlled, the received power in sectorjs from ms t with constant value pulc(t) will be in the same traffic class−c(t) in the ul, and the received power at ms t from sectorjs with constant value pdl c(t) will be in same traffic class−c(t) in the dl. if djt is the distance from ms t to sectorjs, and given an attenuation factor τ = 4 which is the degree to which a beam of radiation has been attenuated, the intra-sector interference on the ul and the dl is given by (1) and (2) respectively, where both αul c(t) and αdl c(t) are activity factors of traffic class−c(t). the inter-sector 2detailed algorithm procedure is omitted due to the length limitation of the paper. a complete version of the procedure is available upon request. the effect of heterogeneous traffic distributions on load balancing in mobile communications: an analytical model 235 interference on the ul and the dl is expressed by (3) and (4) respectively, where both ωulj′s′js and ωdlj′s′js indicate the interference between sectors. iuljst,intra = ∑ t′∈t t′≠t αulc(t′)p ul c(t′)zjst′ (1) idljst,intra = ∑ t′∈t t′̸=t αdlc(t′)p dl c(t′) ( djt′ djt )τ zjst′ (2) iuljst,inter = ∑ j′∈b j′̸=j ∑ s′∈s s′≠s ∑ t′∈t t′̸=t ωulj′s′jsα ul c(t′)p ul c(t′) ( dj′t′ djt′ )τ zj′s′t′ (3) idljst,inter = ∑ j′∈b j′̸=j ∑ s′∈s s′≠s ∑ t′∈t t′̸=t ωdlj′s′jsα dl c(t′)p dl c(t′) ( dj′t′ dj′t )τ zj′s′t′ (4) siruljs,c(t) = wul dul c(t) · pul c(t) + (1 − zjst)v (1 − ρul)iuljst,intra + i ul jst,inter (5) sirdljs,c(t) = wdl ddl c(t) · pdl c(t) + (1 − zjst)v (1 − ρdl)idljst,intra + i dl jst,inter (6) let wul(wdl) be the spectrum allocated to the ul (dl), and dul c(t) (ddl c(t) ) be the information rate in the ul (dl). the sir values sirul js,c(t) and sirdl js,c(t) in the ul and the dl are defined in (5) and (6) respectively, where ρul (ρdl) is the ul (dl) orthogonality factor. equations (5) and (6) give a very large artificial constant value v in the numerator in order to satisfy the sir constraints. this is because the sir value must be larger than a pre-defined threshold, say the bit energy to noise ratio (benr), if ms t is to be admitted by sectorjs(zjst = 1); in other words, the constraint benr 5 sir must be satisfied. for example, in the ul in equation (5), if ms t is to be admitted by sectorjs(zjst = 1), the sir value siruljs,c(t) is calculated by (wul/dul c(t) ) · pul c(t) /((1 − ρul)iuljst,intra + i ul jst,inter) to determine whether the sir constraint can be satisfied. in contrast, if ms t (zjst = 0) is rejected, the sir value is always larger than benr (benr ≪ sir) because the value v is dominant pul c(t) ; thus, sirul js,c(t) is calculated as a very large value. this implies that the constraint benr 5 sir can be ignored, as it is always satisfied. 3.2 the analytical model in this paper, we consider traffic with multiple classes, and use the kaufman model [12] as a performance measure to analyze the blocking probability of each traffic class effectively. assume that m channels are shared by all traffic requirements. then, for each traffic class−c(∀c ∈ c) with distinct channel requirements, the traffic arrival is a stationary poisson process with mean rate λ; and the channel requirement b is an arbitrary discrete random variable (prob{b = bc} = qc,∀c ∈ c). a call request with channel requirement bc has a mean holding time of 1/µc. thus, traffic with channel requirement bc is generated in the poisson arrival process with mean rate λc = λqc and the class−c offered load ac = λc/µc. the blocking probability of traffic class−c is defined in (7) [12], where the distribution of q(·), which is the probability of the total number of channels occupied by the complete sharing policy, satisfies equation (8) [11], and q(x) = 0 for x < 0 and ∑m j=0 q(x) = 1. bc (a,b) = ∑bc−1 i=0 q (|m| − i) ∀c ∈ c (7) 236 k.-c. chu, c.-s. wang, w.-w. jiang, n.-c. hsieh ∑ c∈c acbcq (j − bc) = jq(j) j = 0,1, . . . ,m (8) to deal with variations in the traffic load, we can seek load balancing with an average value of resource utilization [12]. in order to evaluate the experiment results, we define a diversity function for system load balancing, from which the standard deviation (sd) of the call blocking probability among sectors can be derived. the smaller the sd, the better the balancing results will be. let gcjs = ∑ t∈t zjst/µc(t) be the traffic intensity of class−c. then, gjs = ∑ c∈c g c js is the aggregate traffic (in erlangs) in sectorjs, where gjs is equivalent to the traffic load a in (7). we also define mjs = ∑ t∈t zjstm c(t) as the total number of channels allocated in sectorjs, where mjs is equivalent to the required channels b in (7), and mc(t) is the number of channels required for traffic class−c(t). the performance measure bcjs (the call blocking probability of traffic class−c in sectorjs) is expressed by (7), where the sub-script js in bjs indicates bc in sectorjs. if we define llb as the lbl, the load balancing model can be formulated as (9), where sd ( bcjs ) is the sd function of bcjs. the model is calculated subject to sir constraints (5) and (6). llb = ∑ j∈b ∑ s∈s ∑ c∈c kcsd ( bcjs ) ∀j ∈ b,s ∈ s (9) to assess the impact of different traffic types on load balancing, we denote kc as a ratio of traffic class−c, where ∑ c∈c k c = 1. kc is a traffic type coefficient (tyc) used to analyze the expected level of the load balance. given two classes of call requests, e.g., voice and data traffic, if kv = 1 and kd = 0, we only investigate the effect of voice traffic on load balancing; however, if kv = 0 and kd = 1, we only investigate the effect of data traffic on load balancing. 4 numerical results 4.1 parameters two heterogeneous traffic distributions are considered in the structure of a 5 × 5 twodimensional array with hexagonal cells, and their impact on the system’s load balance is compared with that of a homogeneous distribution between sectors, as shown in figure 3. in the figure, each dark cell has an heterogeneous load that is either heavier or lighter than the load of the normal cells (the light cells). it is assumed that the user density in each cell is homogeneous. each cell is configured with 3 sectors (|s| = 3), and assigned a radius rjs = 5.0km. the required benr for voice (v) and data (d) traffic is given by (eb/ntotal) ul v = (eb/ntotal) dl v = 7db and (eb/ntotal) dl v = (eb/ntotal) dl d = 10db respectively [13]. the information rates dulv = d dl v = 9.6bps, d ul d = 19.2bps, d dl d = 38.4bps [14–16], and the activity factors αulv = α dl v = α ul d = α dl d = 0.5 [13, 16, 17] are also given. the number of channels required is mv = 1,md = 4, and the orthogonality factor is ρul = 0.9,ρdl = 0.7 [13]; and the power is perfectly controlled by pulv = 10db, p dl v = 15db, p ul d = 15db, p dl d = 20db. the assigned service rate is φvjs = φ d js = 0.1 [17, 18]. 4.2 traffic models for each sector, call requests for both voice and data calls are generated in the poisson arrival process with λv and λd respectively. the mean call holding time is given as 1/µv = 180(sec), 1/µd = 600 (sec) [18]. denote (eb/ntotal) ul c(t) ≤ sir ul js,c(t) and (eb/ntotal) dl c(t) ≤ sir dl js,c(t) as the qos requirements of the ul and dl respectively. all traffic calculated in gjs must satisfy the qos requirements and the condition zjstdjt ≤ rjsδjst, where δjst is the indicator function if ms t is in the coverage of sectorjs. power is perfectly controlled in both the ul and the the effect of heterogeneous traffic distributions on load balancing in mobile communications: an analytical model 237 22 23 2524 20 14 1513 9 10 191816 17 11 12 21 3 42 87 51 (a) heterogeneous linear model 22 23 2524 20 14 1513 9 10 191816 17 11 12 21 3 42 87 51 (b) heterogeneous hot spot model 22 23 2524 20 14 1513 9 10 191816 17 11 12 21 3 42 87 51 (c) uniform model figure 3: traffic distribution scenarios dl, and soft handoff is not taken into account. the traffic distributions considered in this work are uniform (u), hot spot (h), and linear (l), as shown in figure 3. recall that the cells with heterogeneous loads in figure 3 (a) and figure 3 (b) have either heavier or lighter loads than normal cells in a homogeneous distribution. to evaluate the heterogeneous scenario, we introduce two traffic models. we denote heterogeneous cells with heavier loads as m1 and heterogeneous cells with lighter loads as m2. if normal cells are given traffic arrivals λc for traffic class−c, arrivals in heterogeneous cells are assigned 200% of λc in m1, and 50% of λc in m2. thus, the level of load balance for traffic with multiple classes can be evaluated effectively in a near-realistic environment. 4.3 analysis without loss of generality, the level of load balance is represented in logarithmic form log (flb). if a smaller value of log (flb) is calculated, a better level of load balance will be achieved. in figure 4 (a), no matter what the distribution (linear, hot spot, or uniform) and the offered voice arrivals λv are, log (flb) is a decreasing function of kv. this implies that load balancing can be achieved more easily for voice only traffic than for data only traffic. if only data traffic is considered, given kv = 0 for all distributions, log (flb) is nearly −1.75, whereas log (flb) is nearly −2.4 if only voice traffic is considered (kv = 1). with regard to the effect of traffic intensity, it is easier to achieve load balancing with more offered voice traffic than less offered traffic. for example, in figure 4 (a), given kv = 0.5 with λd = 6, log (flb) calculates (−1.2,−1.6,−1.9) for arrivals (λv =12, 30, 48). again, given λv = 12 in figure 4 (a), log (flb) is in the range −1.15 to −1.4 in figure 4 (a), but it is in the range −1.7 to −1.75 in figure 4(b) with λd = 24. for the traffic models (m1 vs. m2), there is no significant difference in the load balance with λv = 12 and λd = 6 in both figure 4 (a) and figure 5 (a). however, given λd = 6 in figure 5 (a), the load balance level varies in heavily loaded voice traffic (λv = 30,48). in another case, given λd = 24 in figure 5 (b), log (flb) calculates the same results for variations in the load balance. from the analysis, we conclude that the level of load balancing is more stable in m1 than in m2. a better scheme is needed to handle load balancing in cases of heterogeneous cells with light traffic loads. 5 conclusion in this paper, we propose a load balancing model to deal with the ever-increasing number of heterogeneous distributions in mobile wireless communication systems. we have studied the 238 k.-c. chu, c.-s. wang, w.-w. jiang, n.-c. hsieh lo g (f l b ) linear hot spot uniform o ´ d v k 12 v l = 30 v l = 48 v l = (a) λd = 6 lo g (f l b ) linear hot spot uniform o ´ d v k 1212 v ll = 30 v l = 48 v l = 12 (b) λd = 24 figure 4: blf as a function of blc kv with respect to λv, given traffic model m1 and |s| = 3 lo g (f l b ) linear hot spot uniform o ´ d v k 12 v l = 30 v l = 48 v l = (a) λd = 6 lo g (f l b ) linear hot spot uniform o ´ d v k 12 v l = 30 v l = 48 v l = (b) λd = 24 figure 5: blf as a function of blc kv with respect to λv, given traffic model m2 and |s| = 3 effect of heterogeneous traffic distributions on load balancing, as well as the effect of sectorization. the numerical results indicate that the level of load balancing is affected by spatial traffic distributions, especially by lighter loads in heterogeneous cells (the m2 model). in the scenario of heterogeneous cells with heavier loads (the m1 model), the level of load balancing has similar values of log (flb) in three distributions, i.e., the linear, hot spot, and uniform models. sectorization is more effective in achieving load balancing in the scenario of the heavier loads than in the lighter loads. to achieve load balancing as well as capacity maximization in a system with heterogeneous distributions, a hybrid fmda/cdma scheme can be utilized. usually, available wideband spectrum can be divided into a number of subspectra with smaller bandwidths; each of them is further deployed by cdma technique. each subspectrum employs direct sequence spectrum spreading with reduced processing gain, which is transmitted in one and only one subspectrum. the scheme moderately mitigates interference by allocating an appropriate subspectrum in each cell. the results of this work are useful for 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arad, elena dragoi, 2 tom_uav@yahoo.com emil m. petriu university of ottawa school of electrical engineering and computer science 800 king edward, ottawa, on, k1n 6n5 canada petriu@eecs.uottawa.ca abstract: this paper proposes an approach to fuzzy modeling of a nonlinear servo system application represented by an electromagnetic actuated clutch system. the nonlinear model of the process is simplified and linearized around several operating points of the input-output static map of the process. discrete-time takagi-sugeno (t-s) fuzzy models of the processes are derived on the basis of the modal equivalence principle; the rule consequents of these t-s fuzzy models contain the state-space models of the process. three discrete-time t-s fuzzy models are suggested, compared and validated by simulation results. keywords: discrete-time takagi-sugeno fuzzy models, electromagnetic actuated clutch system, linearization, operating points, simulation results. 1 introduction the process taken into consideration and modeled in this paper is an electromagnetic actuated clutch system as a representative nonlinear system application. therefore the derivation of accurate models is a challenging problem. several approaches to fuzzy modeling of nonlinear servo systems are given in the literature. they belong to the general framework of nonlinear process models [1], [2], [3], [4], [5]. a parallel distributed compensation scheme is proposed in [6] with focus on fuzzy reference models; the linear matrix inequalities are formulated and solved in order to linearize the errors between the feedback system and the nonlinear reference model. the nonlinear system behavior is modeled in [7] by the division of the phase plane into sub-regions and a linear model represented either in state-space or arx model form is assigned for each regions; the linear models are next expressed as fuzzy models. a dsp-based fuzzy-linear-model robust tracking control is developed in [8] for a piezoelectric servo system with dominant hysteresis in terms of the weighted combination of n fuzzy linear pulse transfer functions; the fuzzy model is included in a dead-beat control system. an anfis-based neuro-fuzzy model for a low inertia servomotor is suggested in [9], and several comparisons between the performance of the system with the standard motor model and its neuro-fuzzy model are carried out in the framework of copyright c⃝ 2006-2013 by ccc publications 396 c.-a. dragoş, r.-e. precup, m.l. tomescu, s. preitl, e.m. petriu, m.-b. rădac adaptive control. fuzzy feedback linearization and fuzzy sliding mode control applications are given in [10] and [11]. this paper offers discrete-time dynamic takagi-sugeno (t-s) fuzzy model of an electromagnetic actuated clutch system. the computation of the t-s fuzzy models starts with the derivation of the continuous-time models which are obtained on the basis of the local linearization of the process models at five operating points (o.p.s). the local models are next discretized accepting a zero-order hold, and these local models are placed in the rule consequents of the t-s fuzzy model of the process. our approach is advantageous because it is relatively simple and it can be incorporated in many fuzzy control structures [12], [13], [14], [15], [16], [17], [18], [19]. three fuzzy models are offered and compared using simulation results. the paper is organized as follows: section 2 is dedicated to the mathematical modeling of the process, the computation of t-s fuzzy models is synthesized in section 3. simulation results are presented in section 4 to validate the new t-s fuzzy models. the concluding remarks are highlighted in section 5. 2 process modeling the mathematical modeling of the electromagnetic actuator as part of electric drive clutches is based on the schematic structure of a magnetically actuated mass-spring-damper system presented in figure 1 [20]. the state-space model of the nonlinear servo system is: ẋ1 = x2, ẋ2 = − kmx1 − c m x2 + kax 2 3 m(kb+d−x1)2 , ẋ3 = − r(kb+d−x1) 2ka x2x3 + 1 kb+d−x1 x2x3 + [(kb + d − x1)/2ka]v, y = 1000x1, (1) where x1 is the position, i.e., the mass position, x2 is the mass speed, x3 is the current, v is the control signal, y is the measured position (output), k is the stiffness of the spring, c is the coefficient of the damper, r is the electromagnetic coil resistance, and ka, kb are the constants in the relation between the magnetic flux and the current. the numerical values of the process parameters are listed in [21]. figure 1: schematic structure of a magnetically actuated mass-spring-damper system [20]. the linearization of the nonlinear servo system model (1) at five o.p.s aj(x10, x20, x30, x40) (with j-the index of the o.p. j = 1, 5, and 0-the index of the coordinates of the o.p.s, i.e., the state variables) leads to the linearized state-space models: an approach to fuzzy modeling of electromagnetic actuated clutch systems 397 ẋ(t) = ax(t) + b∆v (t), ∆y(t) = ct x(t), x = [ x1 = x x2 = ẋ x3 = i] t , a =   0 1 0 − k m + 2kax 2 30 m(kb+d−x10) 3 − cm 2kax30 m(kb+d−x10) 2 rx30−v0 2ka − x20x30 (kb+d−x10) 2 − x30kb+d−x10 − x20 kb+d−x10 − r(kb+d−x10) 2ka   , b =   00 kb+d−x10 2ka   , ct = [ 1000 0 0 ]. (2) where x(t) is the system state vector, a, b and ct are the linearized system matrices, and t is the continuous time variable. the matrices of the discrete-time systems developed from (2) will be presented in the sequel. 3 approach to takagi-sugeno fuzzy modeling in order to capture both the static nonlinearity and the linear dynamics of the process, the derivation of a discrete-time dynamic t-s fuzzy model of the process is presented as follows. figure 2 illustrates the structure of the t-s fuzzy model identification process. figure 2: structure of the discrete-time dynamic takagi-sugeno fuzzy model identification process. the steps of our modeling approach are: step i. the definition of the membership functions of the input variables x1 and x3. step ii. the choice of the settling time and the discretization of the continuous-time statespace models of the process which result in the discrete-time state-space models with the matrices ad,i, bd,i and cd,i and cd,i, i = 1, 5. step iii. the derivation of the t-s fuzzy model of the process, which has the state variables x1 and x3 as input variables, and the discrete-time state-space models of the process in the rule consequents. the step i starts with the setting of the largest domains of variation of the two state variables used in all electromagnetic actuated clutch system operating regimes: 0 6 x1 6 0.004, 0 6 x3 6 10. (3) 398 c.-a. dragoş, r.-e. precup, m.l. tomescu, s. preitl, e.m. petriu, m.-b. rădac the fuzzification part of the t-s fuzzy model consists of the linguistic terms assigned to the input variables and defined as follows. three cases were considered for the input variable x1 . the first two cases employ five linguistic terms, ltx1,j, j = 1, 5 , with trapezoidal membership functions defined and referred to as ltx1,1, with the universe of discourse [0.0019, 0.0023], ltx1,2, with the universe of discourse [0.0021, 0.0027] , ltx1,3, with the universe of discourse [0.0023, 0.003], ltx1,4, with the universe of discourse [0.0027, 0.0033] and ltx1,5, with the universe of discourse [0.003, 0.004] . the expressions of these trapezoidal membership functions are: µtlx3,j (x) =   0, x < ax1,j 1 + x−bx1,j bx1,j−ax1,j , x ∈ [ax1,j, bx1,j) 1, x ∈ [bx1,j, cx1,j) 1 − x−cx1,j dx1,j−cx1,j , x ∈ [cx1,j, dx1,j) 0, x > dx1,j , ax1,j < bx1,j 6 cx1,j < dx1,j, j = 1.5 (4) the modal values of the membership functions are the parameters ax1,j, j = 1, 5, bx1,j, j = 1, 5, cx1,j, j = 1, 5 and dx1,j, j = 1, 5. the values of these parameters are given in table 1 for the first case and in table 2 for the second case. table 1 parameters of input membership functions in the first case linguistic terms, trapezoidal membership functions ltx1,j, j = {1, 5} ax1,j, j = 1, 5 bx1,j, j = 1, 5 cx1,j, j = 1, 5 dx1,j, j = 1, 5 ltx1,1 0.0019 0.0019 0.0021 0.0023 ltx1,2 0.0019 0.0021 0.0023 0.0027 ltx1,3 0.0021 0.0023 0.0027 0.003 ltx1,4 0.0023 0.0027 0.003 0.00384 ltx1,5 0.003 0.00384 0.004 0.004 table 2 parameters of input membership functions in the second case linguistic terms, trapezoidal membership functions ltx1,j, j = {1, 5} ax1,j, j = 1, 5 bx1,j, j = 1, 5 cx1,j, j = 1, 5 dx1,j, j = 1, 5 ltx1,1 0.0019 0.0019 0.0021 0.0023 ltx1,2 0.0021 0.0023 0.0025 0.0027 ltx1,3 0.0025 0.0027 0.003 0.0033 ltx1,4 0.003 0.0033 0.0035 0.00384 ltx1,5 0.0035 0.00384 0.004 0.004 five linguistic terms, ltx1,j, j = 1, 5, with trapezoidal and triangular membership functions are defined and employed in the third case, and referred to as ltx1,1, with the universe of discourse [0.0019, 0.0023], ltx1,2, with the universe of discourse [0.0021, 0.0027], ltx1,3, with the universe of discourse [0.0023, 0.003], ltx1,4, with the universe of discourse [0.0027, 0.0033], and ltx1,5, with the universe of discourse [0.003, 0.004]. the modal values of the trapezoidal membership functions are the parameters ax1,j, j ∈ {1, 5}, bx1,j, j ∈ {1, 5}, cx1,j, j ∈ {1, 5} and dx1,j, j ∈ {1, 5} given in table 3. an approach to fuzzy modeling of electromagnetic actuated clutch systems 399 table 3 parameters of trapezoidal input membership functions in the third case linguistic terms, trapezoidal membership functions ltx1,j, j = {1, 5} ax1,j, j ∈ {1, 5} bx1,j, j ∈ {1, 5} cx1,j, j ∈ {1, 5} dx1,j, j ∈ {1, 5} ltx1,1 0.0019 0.0019 0.0021 0.0023 ltx1,5 0.0033 0.00384 0.004 0.004 the expressions of the triangular membership functions are: µtlx1,j (x) =   0, x < ax1,j 1 + x−bx1,j bx1,j−ax1,j , x ∈ [ax1,j, bx1,j) 1 − x−bx1,j cx1,j−bx1,j , x ∈ [bx1,j, cx1,j) 0, x > cx1,j , ax1,j < bx1,j < cx1,j, j = 2, 4 (5) where the modal values of the membership functions are the parameters ax1,j, bx1,j, and cx1,j, j = 2, 4 presented in table 4. table 4 modal values of linguistic terms in the third case linguistic terms, trapezoidal membership functions ltx1,j, j = 2, 4 ax1,j bx1,j cx1,j ltx1,1 0.0021 0.0023 0.0027 ltx1,3 0.0023 0.0027 0.003 ltx1,4 0.0027 0.003 0.0033 figure 3 shows the membership functions of x1 in these three cases: the first case in figure 3 (a), the second case in figure 3 (b) and the third case in figure 3 (c). figure 3: membership functions of the input variable x1 in the first case (a), in the second case (b) and in the third case (c). five linguistic terms, ltx3,j, j = 1, 5, are defined for the input variable x3 . the first and the fifth one are modeled by trapezoidal membership functions, and the second, the third and the fourth one are modeled by trapezoidal membership functions. the universes of discourse of the 400 c.-a. dragoş, r.-e. precup, m.l. tomescu, s. preitl, e.m. petriu, m.-b. rădac membership functions of these linguistic terms are: [4, 8] for ltx3,1, [5, 9] for ltx3,2, for [6, 10], ltx3,3, [7, 11] for ltx3,4, and [8, 12] for ltx3,5. the expressions of the trapezoidal membership functions are: µtlx3,j (x) =   0, x < ax3,j 1 + x−bx3,j bx3,j−ax3,j , x ∈ [ax3,j, bx3,j) 1, x ∈ [bx3,j, cx3,j) 1 − x−cx3,j dx3,j−cx3,j , x ∈ [cx3,j, dx3,j) 0, x > dx3,j , ax3,j < bx3,j 6 cx3,j < dx3,j, j ∈ {1, 5} (6) the modal values of the membership functions are the parameters ax3,j, j ∈ {1, 5}, bx3,j, j ∈ {1, 5}, cx3,j, j ∈ {1, 5}, and dx3,j, j ∈ {1, 5}, given in table 5. table 5 parameters of trapezoidal linguistic terms linguistic terms, trapezoidal membership functions ltx3,j, j = {1, 5} ax3,j, j ∈ {1, 5} bx3,j, j ∈ {1, 5} cx3,j, j ∈ {1, 5} dx3,j, j ∈ {1, 5} ltx3,1 4 4 6 8 ltx3,5 8 10 12 12 the expressions of the triangular membership functions are: µtlx1,j (x) =   0, x < ax1,j 1 + x−bx1,j bx1,j−ax1,j , x ∈ [ax1,j, bx1,j) 1 − x−bx1,j cx1,j−bx1,j , x ∈ [bx1,j, cx1,j) 0, x > cx1,j , ax1,j < bx1,j < cx1,j, j = 2, 4, (7) where the modal values of the membership functions are the parameters ax3,j, bx3,j, and cx3,j, j = 2, 4, given in table 6. table 6 modal values of linguistic terms trapezoidal linguistic terms, membership functions ltx3,j, j = 2, 4 ax3,j bx3,j cx3,j ltx3,1 5 7 9 ltx3,3 6 8 10 ltx3,4 7 9 11 figure 4 shows the membership functions of the input x3. the rule consequents of the t-s fuzzy models correspond to the discrete-time state-space models characterized by the matrices ad,i, and bd,i, cd,i, i = 1, 5, detailed in table 7. these models are obtained by discretization of the continuous-time state-space linearized models (1) using the sampling period ts = 0.001 s. an approach to fuzzy modeling of electromagnetic actuated clutch systems 401 figure 4: membership functions of the input variable x3. table 7 numerical values of matrices of discrete-time state-space models o.p.s numerical values of the matrices 1 ad1 =   0.9864 0.0007 0.0000062−24.3234 0.4847 0.011 3.018 −0.1579 0.9816   , bd1 =   0.0000000310.000088 0.0142   , cd1 = [1000 0 0] 2 ad1 =   0.9869 0.0007 0.0000073−23.352 0.4847 0.013 3.4059 −0.1856 0.9811   , bd1 =   0.0000000360.000088 0.0142   , cd1 = [1000 0 0] 3 ad1 =   0.9876 0.0007 0.0000086−22.0872 0.4847 0.0153 3.7379 −0.2153 0.9807   , bd1 =   0.0000000420.00012 0.0139   , cd1 = [1000 0 0] 4 ad1 =   0.9885 0.0007 0.0000101−20.4072 0.4847 0.018 3.9765 −0.2479 0.9801   , bd1 =   0.0000000490.000139 0.0135   , cd1 = [1000 0 0] 5 ad1 =   0.9897 0.0007 0.0000101−18.3864 0.4847 0.0209 4.1934 −0.2814 0.9793   , bd1 =   0.0000000550.000158 0.0132   , cd1 = [1000 0 0] the modal equivalence principle guarantees the equivalence between the fuzzy models and the nonlinear state-space models. that is the reason to express the rule base of the discrete-time dynamic t-s fuzzy models in the following general form: ri : ifx1,kisltx1,jandx3,kisltx3,j then { xk+1 = ad,ixk + bd,iuk yk,m = cd,ixk , i = 1, nr, j = 1, nlt, (8) where k is the index of the current sampling interval, i is the index of the current rule, j is the index of the current linguistic term, nr is the number of rules, nlt is the number of linguistic terms, nr = nlt = 5 in our discrete-time dynamic t-s fuzzy models. the fuzzy controller employs the sum and prod operators and the weighted average defuzzification method. other operators can be used [22], [23], [24], [25], [26], [27]. 402 c.-a. dragoş, r.-e. precup, m.l. tomescu, s. preitl, e.m. petriu, m.-b. rădac 4 experimental results the modeling approach presented in the previous sections is applied and exemplified in this section in order to obtain fuzzy models for the electromagnetic actuated clutch system. three t-s fuzzy models were developed for this nonlinear process. some comparisons were done to illustrate the difference between them. a part of the results is presented as follows. the simulation results include the evolutions of the position versus time (in figure 5), the evolution of the measured position versus time (in figure 6), the evolution of the modeling error versus time (in figure 7), and the evolution of the current versus time (in figure 8). figure 5 and figure 6 present the evolution of both position and measured position y in four cases: nonlinear model (1) of the process, first t-s fuzzy model, second t-s fuzzy model and third t-s fuzzy model. these evolutions point out in all cases an aperiodical evolution with a small overshoot, but the fuzzy modeled responses exhibit a delay of 0.1 s and they exceed the steady-state values of the nonlinear model. the modeling error versus time is highlighted in figure 7 to outline the difference between the nonlinear model and the fuzzy models. figure 5: position of nonlinear model (a), of first t-s fuzzy model (b), of second t-s fuzzy model (c) and of third t-s fuzzy model (d) versus time. figure 8 points out the evolution of the current in the same four cases: nonlinear model (1) of the process, first t-s fuzzy model, second t-s fuzzy model and third t-s fuzzy model. figure 8 illustrates that the current exhibited by the t-s fuzzy models has a delay, but it reaches the steady-state value in approximately 0.5 s and with aperiodical response as that of the model (1). all responses point out a delay of 0.1 s which must be reduced. moreover, the convergence of the modeling error to zero can be achieved by the optimization of the parameters of several parameters of the fuzzy models including input membership functions or parameters in the rule consequents. various optimization algorithms can be implemented in this context [28], [29], [30], [31], [32], [33], [34], [35], [36]. an approach to fuzzy modeling of electromagnetic actuated clutch systems 403 figure 6: measured position of nonlinear model (a), of first t-s fuzzy model (b), of second t-s fuzzy model (c) and of third t-s fuzzy model (d) versus time. figure 7: modeling error of first t-s fuzzy model (a), of second t-s fuzzy model (b) and of third t-s fuzzy model (c) versus time. 5 conclusions the paper has proposed an approach to the fuzzy modeling of an electromagnetic actuated clutch system. this approach is important because it is easily applicable with adequate but not complicated generalizations to a wide category of industrial applications. other similar t-s fuzzy models can be obtained in order to be further used in the t-s fuzzy controller design and tuning. the future work will be dedicated to separating a part of the parameters of the input membership functions. these parameters will be obtained by different optimization algorithms which 404 c.-a. dragoş, r.-e. precup, m.l. tomescu, s. preitl, e.m. petriu, m.-b. rădac figure 8: current of nonlinear model (a), of first t-s fuzzy model (b), of second t-s fuzzy model (c) and of third t-s fuzzy model (d) versus time. will solve the optimization problems with objective functions that depend on the modeling errors. the reduction of the modeling errors will be thus ensured. acknowledgements this work was supported by a grant in the framework of the partnerships in priority areas pn ii program of the romanian national authority for scientific research ancs, cndi uefiscdi, project number pn-ii-pt-pcca-2011-3.2-0732. bibliography [1] škrjanc, i.; blažič, s.; agamennoni o. 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(2012) evolutionary method for designing and learning control structure of a wheelchair, studies in informatics and control, 21(2):155-164. int j comput commun, issn 1841-9836 9(2):209-216, april, 2014. comprehensive energy efficient algorithm for wsn c. tang chengpei tang school of engineering, sun yat-sen university guangzhou 510006 p. r. china e-mail: tchengp@mail.sysu.edu.cn abstract: wireless sensor networks has been widely used. energy problem is one of the important problems influencing the complete application. sensor nodes use batteries as power source and have quite limit lifetime. so, efficiency of energy management becomes a key requirement in wireless sensor network design. based on particle swarm optimization and ant colony optimization, a comprehensive algorithm with weight analysis has been proposed in the paper. in the algorithm, optimization method would be firstly used to determine the nodes number; then, particle swarm optimization would be used to divide the networks into some clusters; finally, ant colony optimization is used to require the best transmission path and select the cluster head. the simulation results show that the new algorithm has higher energy efficiency and balanced energy consumption. it can extend the network lifetime. keywords: wireless sensor network (wsn), particle swarm optimization, ant colony optimization, energy efficiency. 1 introduction wireless sensor networks (wsns) offer a new way of real-time monitor systems that can be used in ample of real life applications, such as temperature, sound, pressure, and so on [1][4]. it is a wireless network and often composed of many nodes or sensors to monitor vast kinds of conditions. generally speaking, wsn consists of vast number of sensors which should be cheaply enough to use in low cost. then, there would be many limitations on the nodes, such as cheap power sources, cheap circuit, and so on. sensors play critical role in the collection of information, and the power source is quite important. usually, the sensor use battery as power source and due to the wireless network, the battery can not be charged or replaced, so, it is quite important to develop proper algorithms to reduce the energy consumption. leach is one of the most popular clustering mechanism in wsn. many communication protocols based on leach for wsn have been developed. the main research fields are energy efficiency, wireless link reliability [5], real-time capabilities [6], or quality-of-service. for the reason of sensor nodes just can use battery without power input. so, energy efficiency research is always a key issue in wsn design with high reliability. many methods and algorithms based on leach have been proposed, such as improved leach [7][10], ndea [11], tb-leach [12], leach-sm [13], energy balanced based routing protocol [14], v-leach [15], tl-leach and dd-leach [16], leach-hpr [17], etc. research of energy efficiency is an endless research, and more and more methods to extend lifetime of nodes or networks would be proposed. in this paper, a comprehensive algorithm based on particle swarm optimization and ant colony optimization with weight analysis has been proposed. the algorithm is mainly used in multi-hop conditions, which can not be set many base stations. in the algorithm, firstly, particle swarm optimization would be used to divide the networks into more than one clusters; secondly, cluster head would be elected with existing energy and distance weighted; thirdly, ant colony optimization would be used to require the best information or data transmission path. the remainder of the paper is organized as follows: leach algorithm would be revisited in section 2. comprehensive leach algorithm and simulation and verification would be expressed in section 3. section 4 would give the conclusion. copyright © 2006-2014 by ccc publications 210 c. tang 2 leach leach protocol is a kind of algorithm. in the network, the nodes work in cluster. in the cluster, a node would be selected as the cluster head [18][20]. the process is organized in periodical manner, and each round would be divided into two steps: cluster building step and stable data communication step. in the first step, close nodes would make a cluster dynamically, and one node would be chosen to be the cluster head, which collects, processes and send information to a sink node. in the second step, each node in the cluster would sent message to the cluster head, and then the head would deal with and send it. in the process, head node would collect and fuse information and send it to sink node, so it would consume more energy than other nodes. leach algorithm could meet the demand that each node in one cluster would have equal possibility to be the head node to balance the exist energy. the election algorithm of head node in leach would be described as following: (1) every node would produce a random number between 0 and 1, and if the number is less than a predefined value t(n), then it would be elected as cluster head and send declaration to other nodes, and if the node has been the cluster node before, and the t(n) would be set to 0. it means that the node cannot be head again. if a node has not been the head node before, the probability of being selected is t(n). t(n) would increase with the number of being head node increases. so the nodes, which have not been head node before, would have a bigger probability. when just one node of not being elected as cluster head node has left, t(n) of the node would be set as 1. that means the node would be cluster head. t(n) =   p 1−p(r mod (1/p)), if(n ∈ g) 0 otherwise (1) where, p means the probability of the number of cluster head in all nodes of the cluster; r is the number of the current round, g is the nodes which have not been the head node before. after the election of the head node, the head node would send information to other nodes. and other nodes would join different clusters dynamically based on the distance and some other indexes. when all the nodes join the clusters, they would send message to the cluster head and the cluster head would produce the time message tdma to inform all the nodes in the clusters. due to keep the nodes in one cluster to join another, the head node would also send cdma code at the same time. each node would send message to the head node in time-interval after they receive the tdma and cdma code. when the transmission is over, cluster head would collect and process the data and results would be sent to sink node and the round would be going into next. 3 c-leach comprehensive leach (c-leach) is aiming at low cost, lifetime extended wsn. 3.1 main idea of c-leach the flowchart of c-leach is shown in figure 1. it includes some main steps: (1) particle swarm optimization would be used to divide the network into more than one clusters with similar number of nodes; (2) cluster head would elected with existing energy and distance weighted; (3) ant colony optimization would be used to require the best information or data transmission path. comprehensive energy efficient algorithm for wsn 211 figure 1: flowchart of c-leach 3.2 the detail steps of algorithm step 1: division of the network into some clusters (1) it is assumed that there are n nodes in the network, and would be going to split into m clusters, this means there are n/m nodes in every cluster. firstly, draw a splitter to make the whole network into two domains with same nodes number, and the split line would be expressed as: u = (x,y,θ).(7) where, (x,y) is location on the splitter of nodes, θ is the angular between the splitter and x-axle. define the function fitness as the following: fitness = (c1 − f1n)2 + (c2 − f2n)2.(8) where, ci(i = 1,2) is the number of nodes in domain i. fi would be determined as the following: fi = mi m ,i = (1,2).(9) where, mi is the expectation of number of cluster nodes in domain i. then, we would complete the first division. the algorithm of cluster division is composed of some steps: (1) all nodes in the network send data (including all kinds of information) to sink node. sink node would split the network into many clusters after received the message and define q particles; (2) set random number to parameters of x,y,θ, and construct the split line. the whole network would be divided into q × 2 clusters. due to the location of nodes in the network are known, ci(i = 1,2) of each node would be determined to calculate the values fitness. (3) compare the value of fitness with minimum fitness value in the last round, the less one would be elected as a general extrum pgd; compare the fitness value of individual node, the least would be elected as individual extrum pid, and update the value of x,y,θ: xxid = xxid + vxid.(10) 212 c. tang xyid = xyid + vyid.(11) xθid = xθid + vθid.(12) where, xxid,xyid represent the location of particle; xθid is the angular of the splitter; vxid,vyid,vθid are the search speed in three dimensions, and they would be determined as following: vxid = ωvxid + c1 × rand()(pid − xxid) + c2 × rand()(pgd − xxid).(13) vyid = ωvyid + c1 × rand()(pid − xyid) + c2 × rand()(pgd − xyid).(14) vθid = ωvθid + c1 × rand()(pid − xθid) + c2 × rand()(pgd − xθid).(15) where, c1 and c2 are the study factor, and c1 = c2 = 2; rand () is random number between 0 and 1; ω is the weighted factor. (4) after the update of x,y,θ, go to step 2 to continue the research process. when the value of fitness is 0 or equal to the maximum times of search, the process would finished. ideally, if the value of fitness is approximately 0, and the whole network would be divided into two clusters with equal nodes. (5) use the method of above to divide the clusters, until get the demanded number of clusters. step 2: search the nearest multi-hop path by aco in this step, first of all is electing cluster head. high existing energy of nodes would be elected as cluster head followed by leach. this search is based on single cluster, so the path in each cluster would be different. and one search path has no influence to that of others. that is to say path search in each cluster is independent. cluster nodes send data or information to sink nodes, if need multi-hop would adopt this algorithm to find the nearest path. according to the definition described before, forward neighbor nodes would be expressed as: nf(i) = {vj|vj ∈ v,di,j ≤ r,αi,j ≤ βi} .(16) each node would have a storage model to reserve the information between self and neighbor nodes, including location, existing energy and neighbor node location and existing energy, and so on. in order to express the pheromone, each node would have a record τij to store the density of pheromone. ant in the network is a data package with memory and storage capability, which just require minimal space. the ant would have characteristics as following: (1) the ant can remember the information of nodes it passed; (2) the ant has capability of recording the nodes it passed with a proper order and forming a path, the ant would return after it get to the cluster node and update the pheromone. (3) ant just can jump to the forward neighbor nodes; (4) ant can read and modify the information it locates. ants in the network would begin with source node, and goes to the cluster head with a way of multi-hop to find a nearest way between source node and cluster head node. ant locates at vi would calculate the jump probability pki,j according to the information of pheromone and existing energy of the neighbor nodes.pki,jrepresents the probability of ant k jump to forward neighbor nodes. it is calculated as following: pki,j =   [τi,j] µ·[ηi,j]λ∑ vh ∈nf (vf ) [τi,h] µ·[ηi,h]λ ,vj ∈ nf(vi) 0 otherwise (17) comprehensive energy efficient algorithm for wsn 213 where, τi,j is value of path li,j from node vi to its neighbor node vj; µ is a factor about distance and pheromone and it means the effect of information accumulated to the travel of ants. bigger the value of µ is, shorter path the ant would choose. λ is a factor of energy, which means the effect of energy in the path selection. if value of λ is bigger, the ant would choose a higher existing energy node to jump. at first time t0, pheromone τi,j(t0) on the path li,j of nearby nodes. it can be calculated as: τi,j(t0) = di,d di,j + dj,d × (1 − di,j∑ vh∈nf (vi) di,h ).(18) ηi,j is a function of energy, which can be calculated as following: ηi,j = ej∑ vh∈nf (vi) eh .(19) where, eh is existing energy of forward neighbor node vh ∈ nf(h) of node i. forward neighbor nodes set limits the jumped node. after the vj is elected to be the next jump node of vi, next jump node of vj would be calculated according to the location of cluster head. following the same method, the ant would jump to the cluster head. in the searching path, if the current sensor j has no forward neighbor nodes, it would be marked as invalid node. the ant would return to node i, and node j with no forward neighbor node would be deleted. then, the ant would select the next jump node again. if all nodes in forward neighbor nodes set are deleted, this means there is no available path between source node and cluster head. if a source node has no available path to the cluster head, it has to transmit the information to its nearest node outside the forward neighbor nodes set, and choose the best path with the method described before. an ant travel from source node to cluster head and return back to the source node with the pheromone information updated is defined to be a process. all ants finish a process, we can say a loop is completed and loop index t would plus 1. in the loop , the pheromone density in the path lij would be adjusted according to equation as following: τi,j(t + 1) = (1 − ρ)τi,j(t) + m∑ k=1 ∆τki,j(t).(20) ∆τki,j(t) =   q lk(t) , if ant k pass 0, otherwise (21) where, m is the number of ants; ∆τki,j(t) is pheromone left on path lij at loop t; l k(t) is the length of ant k moves at loop t; q is a constant about pheromone. ρ is also a constant which describe the volatilization of pheromone. value of ρ is often between 0 and 1 to keep the convergence of the algorithm. in addition of judgment, nearest path of each node would be found. 3.3 simulation and verification the simulation result of c-leach would compared with the result of aco with the whole network as a cluster and all information of each node transfer directly to sink node without to cluster node. same model has been used to verify the advance of two algorithms. figure 2 shows 214 c. tang the energy consumption of the two algorithm in sending 100 data packages. it is easy to see the energy saving with c-leach. figure 3 shows the efficiency of node energy with different algorithm in sending 100 data packages. it can be seen that energy efficiency is higher under c-leach than that under aco algorithm. figure 4 shows the success rate in path search with different algorithm. it can be seen that success rate of less nodes in the network with c-leach is lower than that under aco and when the network with more nodes, the success rate under c-leach is higher. the reason may be the division of the network, and each cluster has less nodes. we can make a decision that the method used in network with more nodes would have a better effect. figure 2: node energy consumption with different algorithm figure 3: efficiency of node energy with different algorithm figure 4: success rate in path search with different algorithm figure 5 shows the lifetime of the network with two different algorithms. from the figure, we can see the lifetime of network under c-leach is longer than that under aco. comprehensive energy efficient algorithm for wsn 215 figure 5: lifetime of network 4 conclusion the paper proposed a comprehensive method to save energy of wsn. it uses pso dividing the whole network into clusters and then aco has been used to find the nearest path to transfer the data package. results of the simulation show that, c-leach is an effective method used in the wsn with multi-hop conditions, especially used in vast area with more nodes. the method can extend the lifetime of the network and increase the energy efficiency. bibliography [1] hart, j. k., martinez, k. 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[20] yang yong-jian, jia bing, wang jie (2013). an improved algorithm for leach protocol in wireless sensor network. journal of beijing university of posts and telecommunications, 36(1): 105-109. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 1, pp. 91-105 design of protocols for task administration in collaborative production systems h. s. ko, s. y. nof hoo sang ko school of industrial engineering, purdue university 315 n grant st, west lafayette, in 47907, usa e-mail: ko@purdue.edu shimon y. nof school of industrial engineering, purdue university 315 n grant st, west lafayette, in 47907, usa e-mail: nof@purdue.edu abstract: customer-focused and concurrent engineering service systems process tasks more effectively as a result of the power of collaboration among multiple participants. in such environments, however, complex situations might arise that require decisions beyond simple coordination.task administration protocols (taps) are designed as a control mechanism to manage complex situations in collaborative task environments. this article presents the design of taps for collaborative production systems in which tasks are performed by the collaboration of multiple agents. three component protocols are found to constitute taps and are triggered at appropriate stages in task administration: 1) task requirement analysis protocol, 2) shared resource allocation protocol, and 3) synchronization & time-out protocol. a case study with taps metrics for task allocation in a collaborative production system is investigated to compare performance under taps, and under a non-tap coordination protocol (which is considered to be simpler). in terms of task allocation ratio, the case study indicates that performance under taps is significantly better (up to 10.6%) than under the non-tap coordination protocol, especially under medium or high load conditions. the advantage of taps can be explained by their design with relatively higher level of collaborative intelligence, addressing more complex control logic compared with non-tap coordination protocols. keywords: rules, figures, citation of papers, citation of books, examples. 1 introduction in highly distributed networked systems, tasks arrive at agents in the system, and then are processed by the collaboration of agents that have the skills and capabilities to process the entire task or parts of it. the achievement of goals depends on how effectively each individual agent coordinates tasks with others to solve the given problems in a collaborative manner. there have been numerous research studies regarding collaborative problem solving in a decentralized way by multiple agents, e.g., optimization by collaborative swarm intelligence [22], and collaborative negotiation in global supply networks [29]. as systems and systems-of-systems become more complex, especially when attempting to improve performance via collaboration, the dynamic complexity of interactions among agents requires a higher level of collaborative intelligence. thus, there should be an effective control mechanism to rationalize, coordinate, and harmonize tasks by exchanging information and decisions among collaborating participants. copyright c© 2006-2010 by ccc publications 92 h. s. ko, s. y. nof coordination protocols have been developed to achieve effective control by managing a given, known type of dependence among tasks, e.g., producer/consumer relationships, as defined in coordination theory [1, 2]. application of coordination protocols in collaborative service systems includes multi-robot systems [3, 30], supply chain management [4, 29], and agent-based manufacturing systems [10, 28], to name a few. the operation of these systems is usually represented by collaborative work in multi-agent systems [6, 23], and various coordination protocols have been developed to provide the agents in the systems with the rules and the interaction procedures on how to cooperate and coordinate effectively. many coordination protocols have been developed based on the framework of contract net protocol (cnp) [7], a market-based approach in which tasks are announced to agents and allocated to the agent that provides the best bid. such decentralized approaches have been developed to achieve effective resource allocation in distributed systems and have shown good performance in terms of communication and computational efficiency, scalability, and flexibility [5, 8, 9], compared to centralized approaches [2, 7]. a critical limitation of coordination protocols, however, is that there exist events that cannot be handled by merely coordination protocols. these situations include: 1. the priority of tasks must be dynamically changed; and 2. there are complicated situations requiring decisions beyond simple coordination. for example, consider tasks that are close to their deadline and need to be handled quickly with high priority over other, less urgent tasks. resources need to be allocated for the urgent tasks first in order to meet the deadline. later, if a more urgent task, e.g., resource failure requiring repair, or an emergency task, is generated, the previously scheduled tasks may be preempted by the new one so that it will not cause a significant damage to the system. coordination protocols, however, do not handle such situations since they only allow fixed task allocation, i.e., the winner to whom the task was allocated is deemed to commit itself to the awarded task until it is completed. in order to overcome such limitations of coordination protocols, the protocols need to be able to indentify repeatedly the current state of the system and take proper actions to deal with complicated conditions. such protocols, which assume the responsibility of making decisions actively and triggering timely actions so that the overall system’s performance can be further improved, are defined as task administration protocols (taps) [11, 20]. the purpose of this article is to design taps and analyze taps’ advantage over non-tap coordination protocols. this is more important when more distributed and decentralized networks of activities require collaboration, which increases the complexity of the problem. in this article, the design of taps for effective task allocation and administration is developed by addressing three basic elements in task administration: 1) task, 2) resource, and 3) time. three main reasons require the development of taps with abilities beyond conventional coordination protocols, as follows. (1) dynamic tasks re-prioritizing when tasks enter the system, they need to be analyzed and sorted based on their priority, so that important or urgent tasks can be handled first. priority of tasks is an essential consideration in task administration. with coordination protocols, priority is evaluated by managerial decision or prior assignment based on task type [1], or by pricing based on market-based approach [8, 9]. tasks with higher priority receive more monetary value, so the priority is reflected in the level of funding. the limitation in the coordination protocols is that the priority of a task is predetermined and fixed by its price and deadline in the task description at the moment the task arrives. while this approach may be adequate in some cases, in real situations, however, the priority may change due to emergent events. thus, dynamic change of priority along time should be considered. (2) resource availability appropriate resources need to be allocated by considering the task requirements, the status of resources and their schedules. a bidding procedure can be processed to obtain good resource allocation solutions using the cnp framework (e.g., [8, 10]). design of protocols for task administration in collaborative production systems 93 (3) time-out conditions a resource serving a task needs to be monitored. if a task occupies (engages) a resource excessively for certain reasons, e.g., resource failure, or abnormal tasks, the task access to the resource should be timed-out so that the overall performance of all tasks’ services does not seriously degrade by the given task over-occupying the resource. in order to prevent such wasteful situations, a time-out protocol with an appropriate threshold needs to be used. in several studies and applications, time-out protocols have been developed and proved to be effective [12, 14]. in order to deal with the three basic elements of administration, taps’ design model requires three components as developed in this study. the rest of the article is organized as follows. section 2 describes the definitions and structures of taps with the three component-protocols. each of the components is described in detail in section 3. in order to illustrate the advantage of taps over simple coordination protocols, a case study of applying taps for a collaborative production system is presented in section 4. finally, section 5 concludes the article. 2 task administration protocols (taps) in a coordination network an active middleware model of coordination network [15, 16] is used to define the model of taps. a coordination network (co-net) is a network of autonomous agents that enables collaboration among the agents. each agent exchanges information and execution to achieve its objectives while it tries to maximize the system (or system-of-systems) goals. the co-net is defined as: co-net =< π, τ, α, σ > (1) where π : a set of agents in co-net; π = {a1, a2,..., a|π|};τ a set of tasks to be processed in co-net; τ = {t1, t2,..., t|τ|};α : a set of activities performed by π to fulfill τ ; α = {activity, activity,..., activity|α|}; and σ : a set of control mechanisms used by π . coordination is defined as the process of managing dependencies between activities [1]. in the conet, dependence (δ ) is a relationship between τ , π , and α , and defined as follows: {ti ×π(ti)×α(ti)|ti ∈ τ} → δ ∈ 4 (2) where π (ti): a set of agents who can process ti; α (ti): a set of activities or sub-tasks required to process ti; 4: a set of dependence types. a particular dependence δ can be decided by analyzing tasks and evaluating certain factors in the system, e.g., task priority and time-out threshold. in the co-net, coordination protocols (cp) are designed to manage a particular dependence (δ ) between tasks and resources. a coordination protocol is defined as follows: definition 1. pj = {δ , i, r, pa, s} ∈ cp (3) where pj: a particular coordination protocol for handling ti (j=1,..., j; j is the number of cp); i: a set of initiators which initiate coordination activities; i ⊂ π ; r: a set of responders which respond to initiators’ requests; r ⊂ π and i ∩r = /0; pa: a set of parameters of the coordination protocol; s: a set of decision logic in each transition stage of the protocol; and cp is a set of coordination protocols. taps are defined as a set of protocols which assume the responsibility of making decisions actively and triggering timely actions so that these decisions and triggers can improve the coordinated performance [11, 20]. when a task (ti) or, in a broader sense, an event (e) occurs in the system, tap mechanism should identify the dependencies between tasks and activate the appropriate protocol (pj) between agents with proper parameter values (pa) for the protocol. in the co-net, therefore, a tap is defined as 94 h. s. ko, s. y. nof follows: definition 2. t ap = {σ ∪pj|σ (e|δ ⊂ 4) → pj} (4) where σ is a mechanism to select an appropriate protocol to handle the upcoming events; e is an event (e ∈ e; e: a set of events), e.g., arrival of a task, failure in processing a task, etc. in other words, the tap is a control mechanism to handle an event by analyzing dependence between tasks and involved initiators and responders, as well as the parameters and decision logic required to deal with the event. according to the type of event and systems of interest, different task administration schemes need to be applied. theorem 1.(tap-set). cp is a subset of but not identical to tap. proof. according to definition 1, cp is a set of pj’s which handles tasks under certain dependence δ ⊂ 4. by definition 2, tap includes pj’s as its elements but also contains the mechanisms (σ ) to dynamically activate appropriate pj to handle the upcoming events e based on the current dependence δ ⊂4. moreover, cp cannot handle all types of events in task administration shown in table 1, unlike tap. theorem 2. (tap-performance). better tap yields better performance than cp. proof. let θ be a performance metric. since cp can handle only predefined δ , even though the dependence changes from δt to δt+ along time t, pt (δt+) = pt+ (δt+), where pt is a protocol activated at time t. since tap can trigger a more appropriate protocol and its parameters under the current dependence and event, one can find pt+ such that θ (pt (δt+)) < θ (pt+ (δt+)). therefore, the overall performance ∑ t θ (p t (δt )) under tap (pt variable) will be better than under cp (pt fixed) along time. this will be illustrated in a case study in section 4. there are three general elements in task administration: task, resource, and time. the general administration elements, their properties and protocol solutions, which has been found from existing literatures, are presented in table 1. taps are composed of three component-protocols, each of which will be activated by the tap mechanism under certain dependence and deals with the events corresponding to an administration element. when a task arrives at the system and is inserted to task queue, the task requirement analysis protocol (trap) is activated to analyze the task and assign its priority and dependence to other tasks and resources. upon arrival at task queue, a task must be assigned to the best resource, which is decided by the shared resource allocation protocol (srap), based on the current status of resources. while processed, a task may occupy a resource excessively, preventing other tasks from being processed by the resource. if the time taken in the resource is beyond a certain threshold, the task needs to be returned to task queue so that other tasks can be processed at the resource. sometimes, a task cannot be performed by its due date under the current schedule. in this case, the task may need to seek a resource which can complete the task by preempting the other task currently being processed at the resource. these procedures are controlled by the synchronization & time-out protocol (stop). each of the three protocols is explained in the subsequent sections. the theorems presented above are illustrated with three examples in table 2. in the examples, both taps and cps are used to control multi-agent interactions in certain distributed production systems, but taps are more intelligent because of inclusion of one or more tap components which are missing in cps. in all the three examples, the performance under taps is better than under cps as they are designed to dynamically handle complex tasks. even though the taps include all three components, there could be a difference in performance between different tap designs, as in the first example [21]. the three component protocols are explained in the next section. design of protocols for task administration in collaborative production systems 95 table 1: administration elements, their related events and dependence, and tap solutions and examples admin. elements task (τ) (resource (r) time (t)) events (e) task arrival resource allocation excessive process time, urgent tasks dependence 1. prioritize tasks 1. optimal/effective 1. synchronization analyses (δ ) 2. identify task allocation 2. uncertainty in requirement/dependence (δ ) 2.decentralized processes & products decision making protocols task requirement shared resource synchronization & pj analysis protocol allocation protocol time-out protocol (trap) (srap) (stop) examples [ref’s] pricing of tasks [8-10, market-based resource time-out protocol 17-18]; priority assignment allocation [8-10,16-18] [12-14] protocol [13,21] table 2: illustration of tap-set and tap-performance example tap tap cp performance results [re f ′s] component metrics (θ ) testlan protocol 1) tap1: adaptable trap, testlan protocol waiting time, θ (tap1) [] 2) tap2: nonsrap, w/ fcfs flowtime > θ (tap2) 2) tap2: nonstop > θ (cp) adaptable [] time-out protocol stop non-timeout flow time, θ (tap) > protocol service time θ (cp) viability-based resource profit, number of [] resource allocation srap allocation protocol un-allocated θ (tap) > protocol w/o viability tasks θ (cp) 96 h. s. ko, s. y. nof 3 components of task administration protocols 3.1 task requirement analysis protocol (trap) a key objective in task administration is to allocate incoming tasks to appropriate resources continuously. when this allocation is needed, trap, which is a component-protocol of taps, is activated to handle the event. this protocol finds the dependence between tasks by their priority relationships. whenever a new task ti arrives, a task agent (ta) calculates the priority of ti and previous tasks still in the system at time t by using a dynamic task priority evaluation function (pf(ti, t)). if the priority of ti is less than the priority of previous tasks still in the system, it is just added to the end of the task queue as in first in, first out regime. otherwise, the tasks in the queue need to be sorted by their current relative priority. from time to time, the task currently being processed may need to be preempted by an urgent task with relatively higher priority. each task has different requirements, such as the type of task, quantity (volume), deadline, estimated cost, etc. in general, a task can be defined as follows: ti =< typei, gtyi, ddi, vi, pri(t) > (5) where typei is the class of ti that requires a certain skill of a resource agent (ra); qtyi is the task amount involved in ti that engages the capacity of a ra; ddi is the latest time by which ti must be served by an ra; vi is the estimated value per unit of ti; and pri(t) is the priority of ti at time t. pri(t) represents the relative importance of ti and the task that has higher priority should be served first. pri(t) is dynamically evaluated by using the priority evaluation function pf(ti, t). priority evaluation function needs to be of different form to correspond with specific applications. in its generic form of supplydemand networks, the priority evaluation function is defined as: p f (ti,t) = wsi + w gtyi(vi − ci)∑ j gty j(v j − c j) + w{ − ddi − t∑ j(dd j − t) } + w( − gtyt gtyo ) + w ∑ k ( − pek) (6) where wn: weight of each factor (0 ≤ wn ≤ 1; ∑ wn = 1; n = 1, 2,..., 5); j: index of a task previously assigned to the agent; 0: index of the current task; si = 1 if typei = type, 0 otherwise; ci: estimated unit cost to perform ti by a server; qtyt: remaining quantity of task t at t; k: index of tasks that will be decommitted due to ti (pri(t) > prk(t)); pek: penalty of decommitment of tk; 0 ≤ pek ≤ 1. the implication of the five factors is as follows: 1) if ti is the same type of task as the current task, its priority is high since setup is minimized or not needed. in some case, however, the opposite is required. 2) the relatively larger ti’s quantity and profit (value) are, the higher its priority. 3) a task relatively closer to its deadline has higher priority. 4) if the current task is not finished yet but almost is close to being done, it has a relatively higher priority. 5) after its priority re-assignment,timay cause some tasks, which have already been assigned to resources, to be decommitted if they have a lower priority. the penalty due to the decommitment needs to be considered for fair evaluation of ti’s priority. to sum up, trap is triggered upon arrival of a new task at a ta, which receives tasks and identifies task type and analyzes task requirements, i.e., due date, sub-tasks and required resources. ta assigns a priority value to each of the tasks based on the priority evaluation function. tasks will be sorted and re-sorted in task queue by their relative priority. after sorting its queue, ta announces the tasks to the ras who are capable of processing the tasks, and a resource will be allocated to each task one by one design of protocols for task administration in collaborative production systems 97 by srap, which is explained in the next section. the overall procedure in trap can be summarized as follows: 1)task agent (ta) receives a task ti. 2)calculate priority of tasks by eq. (6). 3)sort the tasks in task queue by pri(t). 4)activate srap. 3.2 shared resource allocation protocol (srap) after tasks are analyzed and their priorities are assigned, srap is activated to find the best resources for the tasks. each of the resources in the system is managed by a resource agent (ra). ta announces the first task (with the highest priority) in task queue to ras which are capable of processing the task. each ra calculates the bid based on expected waiting time in the queue for the resource. ta collects the bids and selects the ra with the best bid (the lowest cost). the steps in srap are as follows: 1) ta announces ti to rar where r is the index of ra which is capable of processing ti. 2) rar sorts its queue including ti by the priority of the tasks in the queue. 3) rar calculates the bid for ti by using the following equation: bri = cr ∑mr k= µptk + csr (7) where bri is the bid by rar for ti; cr is the cost of rar per unit time; k is the index of tasks in the queue of rar (k = 1,..., mr);µptk is mean processing time of tk; and csr is setup cost of rar, which is added only if the next task is of different class from the previous task. 4) rar submits bri for ti to ta. 5) ta selects rar∗ where r∗ = arg minr bri . 6) ta assigns ti to rar∗. ti enters the queue of rar∗ with expected cost br∗i . the policy applied in the above protocol pursues earliest completion time, which is reasonable when the effective cost of tasks is higher compared to the effective (variable) cost of resources. if the effective cost of resources is higher, relatively high utilization of resources may be preferred. in this case, a different policy, i.e., minimizing the total idle time of required resources, needs to be applied. 3.3 synchronization and time-out protocol (stop) from time to time, the current task being served by a resource needs to be timed-out and return to task queue of ta. this situation is triggered and handled by stop, which is activated in the following cases. a. excessive resource occupation since a task with faults needs to be reworked by the resource, it may occupy the resource excessively even while idling it, and cause other tasks to be delayed. stop checks if the current task uses the resource more than certain time-out threshold, which is calculated as follows [13]: toi = µpti + σpti (8) where toi is the time-out threshold for ti, and σ pti is standard deviation of processing time for ti. (two standard deviations are assumed for simplicity here, but another coefficient can be used.) even if the task has been occupying the resource beyond the time-out threshold, the task remains at the resource’s service in the following cases: 1) no other task is waiting in the queue. 98 h. s. ko, s. y. nof 2) the current task ti may be late if timed-out. if ddi t < epti, where t is the current time and epti is the extra processing time needed for ti, ti remains in the resource. b. preemption by an urgent task even though the current task ti has been served by the resource for less than the time-out threshold, a task tk in the queue may be late if it is not served until the current process is completed. this problem may be solved by preempting ti and serving the urgent task tk first. the protocol logic can be summarized as follows: 1) check stk = ddk t µpti of the tk waiting in the queue of the resource, where stk is the slack time for tk, i.e., the remaining time until ddk. 2) if stk < 0 and ddi > epti, stop ti and process tk. 3) once tk is completed, resume ti. to sum up, taps are composed of three inter-related protocols: trap, srap, and stop. upon arrival of tasks, trap is activated to analyze the task requirements and assign their priority. next, srap is activated to select the best resources based on their current workload. during processing tasks, stop is activated to monitor if the current task in service needs to be timed-out because of its excessive use of the service or preempted by another urgent tasks. the overall protocol logic is shown in figure 1. the following case study illustrates their application. figure 1: overall logic of task administration protocols with three component-protocols 4 a case study: collaborative production systems in order to analyze the advantages of taps over non-tap coordination protocols, this section illustrates a case study of taps application for task administration in a collaborative production system. a collaborative production system is defined as a distributed network of production resources in which redesign of protocols for task administration in collaborative production systems 99 sources can communicate and coordinate with each other to process tasks in the system in an effective and efficient manner, based on collaborative resource sharing. a good example of collaborative production systems is testlan (testing local area network), which is a local area network integrating distributed test operations in manufacturing facilities [13]. testlan was developed to increase throughput and reduce the waiting time for testing by integration and communication of distributed testing servers and clients. in testlan, products are tested by shared resources, and those with faults are reworked and retested until all the faults are eliminated. efficient testing resource allocation is difficult since the testing process is highly variable due to design changes, new quality control requirements, and occasionally faulty manufacturing processes. in collaborative production systems like testlan, priority of tasks can be evaluated by their slack time. in testlan, assume only the third term in eq. (6), regarding due date of the task, is effective in priority evaluation, and for simplicity assume every task is of the same importance and the objective is to complete as many tasks as possible in time. hence, priority of a task in testlan is evaluated by a time-based cost of each task as follows: sti = ddi − e[tti] (9) where tti is testing time of ti. e[tti] can be calculated by using the following equation [13]: e[tti] = e[pti + rni∑ r= rtir] = µpti + µrni · µrti (10) where pti is processing time of ti; rni is number of reworks for ti; rtir is rework time for r-th faults occurring in ti; and µpti , µrni , and µrti are mean of processing time, number of rework, and rework time for the tasks of the same class as ti, respectively. in order to reflect the rework in testlan, eq. (7) and (8) need to be modified as follows: bir = cr · ∑mr k= (µptk + µrnk · µrtk ) + csr (11) toi = µpti + σpti + µrni (µrti + σrti ) (12) taps for the testlan function as follows. trap assigns a higher priority to a task with a lower slack time. tasks are sorted in task queue by their priority and a resource who submits the best bid will be allocated to each task one by one by srap. in srap, each testing resource agent can sort the tasks in its own queue plus the new task to find the best bid. during processing the current task by each resource, time-out conditions are checked by stop and the previously assigned tasks may be preempted if any of the two time-out preemption conditions are met. for this case study, two taps are developed: tap1 and tap2. both tap1 and tap2 are composed of the three component-protocols. tap1 considers, however, only time-out condition b in section 3.3 above, while tap2 considers both time-out conditions a and b. a coordination protocol, cp, which is considered to be a subset of tap, is used to compare performance under the various protocols. the logic of cp is similar to the one of srap, except the procedure of resorting tasks in resources’ queue in srap is not included, as typical in traditional coordination protocols. a simulation analysis is designed with two classes of tasks with different levels of urgency. tasks are generated randomly and processed by three servers. the simulation parameters are shown in table 3. performance under the three protocols is measured by 1) task allocation ratio, tar, and 2) weighted tar, war, defined as follows: t ar =  − t |τ| (13) 100 h. s. ko, s. y. nof table 3: simulation parameters parameter value number of servers 3 task class normal: ddi = 600 sec, pti = 60 sec (80%) urgent: ddi = 180 sec, pti = 60 sec (20%) interarrival time low: exp(µ = 25 sec); medium: exp(µ =20sec); high: exp(µ = 15 sec) where ? is the mean of interarrival time weight of pf(ti, t) w = 1 simulation length te = 8 hrs rework rate 5% warm-up time t = 10 min replication rep = 10 treatment tap1 = trap, srap, stop1; tap2 = trap, srap, stop2; cp srap performance measures tar and war table 4: java classes in tie/tap simulator sim supports the entire simulation, e.g., simulation initialization, termination, simulation clock, data collection model simulation modeling and execution, e.g., testlan model and parameters source random task generation ta task agent which collects and announce tasks ra resource agent which bids for and processes tasks task task definition and requirements trap task requirement analysis protocol srap shared resource allocation protocol stop synchronization & time-out protocol war = |τ|∑ i= r∑ r= pri ·t ri |τ|∑ i= pri (14) where t = |τ| − ∑|τ| i= ∑r r= |t r i |;, r is the index of ra, r = , ..., r; t r i = 0 when ti is neither assigned to rar nor completed within ddi; 1 otherwise. while tar does not consider the priority of tasks, war is used to measure the performance with consideration of task priority, i.e., assign more weights to relatively higher priority tasks when evaluating tar. the simulation is developed with a protocol evaluation tool, called tie/tap, which is implemented with java, so as to compare the performance under three protocols: 1) tap1, 2) tap2, and 3) cp. the java classes in tie/tap and their descriptions are listed in table 4. tar results (figure 2 and table 5) are shown under low operational load (µ = 25; system capacity is sufficient to process all the tasks), medium (µ = 20; the number of tasks is close to the system capacity), and high (µ = 15; the number of tasks is beyond the system capacity). war results (figure 3 and table 5) are shown for the same load conditions. the observed results were tested for statistical difference by t-test as shown in tables 6 and 7. in every case, it is found that tap2 performs significantly better design of protocols for task administration in collaborative production systems 101 table 5: simulation results protocol task allocation ratio weighted task allocation ratio low/medium/high[%] low/medium/high[%] tap1 100/95.7/72.8 100/90.5/52.1 tap2 100/97.4/74.6 100/94.2/54.0 cp 98.8/92.8/73.0 98.1/83.6/49.9 than tap1 and than cp. except for one case (tar under high load condition), tap1 is also significantly better than cp. under the low load condition, all three protocols studied perform similarly (tar = 100% for tap1 and tap2, and 98.8% for cp). under the medium load, the difference between the protocols increases. tap2 yields the best tar (97.4%), tap1 – 95.7%, cp – 92.8%. under the high load, tap2 is also best (tar = 74.6%), cp – 73.0%, tap1 – 72.8%. under every condition, tap2 performs better than tap1 and cp. tap1 performs better than cp under the low and the medium load, but slightly worse under the high load. this situation results from the fact that tap1 (and tap2) considers priority of tasks by trap, tries to allocate the tasks with higher priority first, and even preempts the previously assigned tasks by stop for the sake of the urgent tasks. thus the tar, which does not consider the priority of tasks, can be reduced under tap1 (and tap2) due to the failed tasks with low priority. when war is considered (table 5 and figure 3), however, the performance of tap1 is significantly better than cp under the high load. tap2 is again the best (war = 54.0%), and tap1 (war = 52.1%) also performs significantly better than cp (war = 49.9%). under the medium load, war is 94.2%, 90.5%, and 83.6% for tap2, tap1, and cp, respectively. therefore, when war is used to measure performance with consideration of task priority, the difference between taps and cp becomes larger under all conditions. this difference is expected from the fact that when tasks’ priority changes dynamically, cp are not designed to track and respond to those changes. figure 2: tar under (a) low (µ = 25), (b) medium (µ = 20), and (c) high (µ = 15) load condition 102 h. s. ko, s. y. nof table 6: t-test for war of tap2 and cp treatment mean std. dev. t t−α,v µ =  µ =  µ =  µ =  µ =  µ =  α = ., v =  tap2 0.942 0.540 0.0198 0.0127 11.444 7.3414 1.734 cp 0.836 0.499 0.0192 0.0112 table 7: t-test for war of tap1 and tap2 treatment mean std. dev. t t−α,v µ =  µ =  µ =  µ =  µ =  µ =  α = ., v =  tap1 0.905 0.521 0.0219 0.0101 3.774 3.641 1.734 tap2 0.942 0.540 0.0198 0.0127 figure 3: war under (a) low (µ = 25), (b) medium (µ = 20), and (c) high (µ = 15) load condition in the studied case, the illustrated observations imply: (1) systems operating under taps perform better than under non-tap cp, since taps can effectively handle the complicated situations that cp cannot (θ (cp) < θ (tap); see table 6); and (2) even among taps, there can be better designed taps depending on the applications and logic applied (θ (tap1) < θ (tap2); see table 7). 5 conclusions the design of taps for task administration in a collaborative production system is investigated in this article. taps are designed as a control mechanism that can manage complicated situations in the collaborative task workflow environment. in order to overcome certain limitation of coordination protocols, taps are designed with three component-protocols, i.e., trap, srap, and stop, each of which deals with inter-related aspects of task administration, including task, resource, and time. a case study of design of protocols for task administration in collaborative production systems 103 applying taps for testlan, as an example of collaborative production system, is developed to illustrate design of taps and show the advantage of taps over non-tap cp. the simulation, implemented based on the tie/tap java-based simulator, show that taps perform significantly better than other non-taps, in particular under medium or high load conditions (up to 10.6% in terms of war). this advantage results from the fact that the three protocol combinations in taps dynamically interact to consider the dynamic priority of tasks and the current situations and conditions in tasks and resources. thus, taps can address a higher level of collaborative intelligence compared to non-tap cp. finally, the results imply that (1) taps are better than non-tap cp under certain conditions; and 2) there can be better design of taps even among taps to increase system performance. the logic in the protocols is designed to fit the given case study. although the general structure of protocols can be followed in order to handle the complicated situations in collaborative tasks/resources networks, for better effectiveness the protocol logic needs to be context-specific. different applications require different decision policies, heuristics and logic, and they should be reflected in the protocol logic, as illustrated in the case study. in order to obtain better performance, the logic and the parameters in the protocol should be carefully selected and modified. for example, the system performance in the case study can be significantly affected by parameters such as time-out threshold. this recommendation is one of the challenging issues in protocol design, and some research has tried to address this issue by serviceoriented protocol adaptation [21]. further research is currently ongoing to develop and improve tap logic with protocol adaptation for other applications, e.g., taps for collaborative intelligence in research activities [19], in which taps should be developed to manage collaborative workflow between distributed research groups and enhance their collaboration. other similar, potential areas of taps application include collaborative intelligence in e-learning environment [24], knowledge management in enterprise portal [25], and e-service collaboration protocols [26]. bibliography [1] t.w. malone, k. crowston, the interdisciplinary study of coordination, acm computing surveys, 26(1):87-119, 1994. 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[26] g. kramler, e. kapsammer, w. retschitzegger, g. kappel, towards using uml 2 for modelling web service collaboration protocols, interoperability of enterprise software and applications, d. konstantas et al (eds.), springer, 2006. [27] f.g. filip, g. neagu, d.a. donciulescu, job shop scheduling optimization in real-time production control, computers in industry, 4(4):395-403, 1983. [28] j.m. frayret, s. d’amours, b. montreuil, coordination and control in distributed and agent-based manufacturing systems, production planning and control, 15(1)42-54, 2004. [29] j. jiao, x. you, a. kumar, an agent-based framework for collaborative negotiation in the global manufacturing supply chain network, robotics and computer-integrated manufacturing, 22(3):239-55, 2006. [30] l. iocchi, d. nardi, m. piaggio, a. sgorbissa, distributed coordination in heterogeneous multirobot systems, autonomous robots, 15(2):155-168, 2003. hoo sang ko is a senior researcher of prism center and a doctoral candidate in the school of industrial engineering at purdue university. he received his b.s. in 1999 and m.s. in 2003 in mechanical engineering at seoul national university in south korea. after graduation, he worked as an r&d engineer at digital appliance business of samsung electronics. his research interests include design of collaboration support systems, task administration protocols for control of collaborative e-work, and facility sensor networks. shimon y. nof is professor of industrial engineering and director of the nsf-industry supported prism center (production, robotics and integration software for manufacturing & management) at purdue university. the prism center was established in 1991, and its motto is "knowledge through information, wisdom through collaboration." in 2001, pgrn, prism global research network was established with affiliate labs and centers throughout the world. professor nof was awarded an honorary doctorate in engineering from the university of sibiu, romania in 2007, when sibiu served as the european capital of culture. 9tang6.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):856-864, december, 2015. an improved attribute reduction algorithm based on granular computing x. tang, l. shu xiao tang* 1. university of electronic science and technology of china, school of mathematical sciences, chengdu, 611731, china 2. college of mathematics and software science, sichuan normal university, chengdu, 610066, china *corresponding author: 80651177@163.com lan shu university of electronic science and technology of china, school of mathematical sciences, chengdu, 611731, china lanshu@uestc.edu.cn abstract: granular computing is a new intelligent computing method based on problem solving, information processing and pattern classification. granular computing based attribute reduction method is an important application of granular computing. these algorithms are mostly based on reduction core. however, some information systems may have no reduction core, especially in the actual application data. for this case, those algorithms are powerless. in this paper, an improved reduction algorithm based on granular computing is proposed. the algorithm is validated by the experimental result. keywords: attribute reduction, granular computing, rough set, attribute significance. 1 introduction granular computing is a method for analysis of multi-layer granular structure based on problem solving, pattern classification and information processing. it’s also a newly cross discipline among rough set theory, fuzzy set theory, data mining and artificial intelligence. with less than 20 years’ development, granular computing has already made remarkable achievements and great contribution to the field of computer science [1, 2]. through rapid development of society and continuous progress in science and technology, a variety of data is increasing gradually, and then we entered the so-called "big data time". the main goal of data mining is to find potential, desired and useful knowledge from those big data. rough set theory is an efficient mathematical tool to deal with imprecise, incomplete and inconsistent data. it has already made great strides in its theory and has been widely used in practical application. attribute reduction is the main content of rough set theory. the core task of attribute reduction is that dimensionality and storage space may be reduced under the condition of maintaining classification capacity, so as to improve the efficiency of system classification [3, 4]. therefore, it is not only the hot spot of intelligence computing, but also the important task of information processing. in 1979, professor l.a. zadeh discussed the theory of fuzzy information granulation in his paper "fuzzy sets and information granularity", and first proposed the concept of information granulation. then, professor j.r. hobss of stanford university introduced granularity theory copyright © 2006-2015 by ccc publications an improved attribute reduction algorithm based on granular computing 857 in his paper "granularity" published on international joint conference on artificial intelligence held in los angeles [5]. the granularity theory is presented firstly. the idea of granularity theory is that the bigger, whole, unresolved questions can be broken into several smaller ones by granulating, and these small questions can be combined into the bigger, whole questions. in 1990, the chinese scholar zhang bo and zhang ling proposed the theory of quotient space based on problem solving [6]. they thought that human beings can analyze the same problem from different granulation, and make an easy conversion in different knowledge granularity. if people can formalize the analysis process to make the computer possess the ability, it will greatly improve the development of artificial intelligence. furthermore, professor l.a. zadeh raised the theory of computing with words in his paper "fuzzy logic=computing with words", and thus the fuzzy granularity theory was born [7]. this theory is to do fuzzy reasoning and judgments by using natural language, so as to realize the fuzzy intelligent control. in the same year, when professor l.y. lin visited in professor zadeh’s key laboratory of uc-berkeley university, he presented the subject "granular computing" and got approval from zadeh, it marked the birth of granular computing. professor miao, et al. [8] gave the definition of knowledge granularity and knowledge discernibility in fuzzy set theory model, and pointed out the relationship between knowledge granularity and knowledge discernibility: the smaller knowledge granularity is, the stronger knowledge distinguishable ability is; on the contrary, the bigger knowledge granularity is, the weaker knowledge distinguishable ability is. reference [9] defined the concept of the difference of granularity and granularity entropy on the basis of fuzzy set’s algebraic method and information theory approach, and proposes attribute reduction algorithm based on granular computing. reference [10] presented attribute reduction algorithm based on granular computing, using the equivalent relation in rough set to construct granule, and attribute significance is regarded as heuristic information. reference [11] put forward attribute reduction method based on model of granular computing in information systems. reference [12] proposed an improvement of attribute reduction algorithm based on granular computing. this algorithm is to get attribute core using discernibility matrix, and then make attribute reduction based on attribute significance as heuristic information. reference [13] proposed an incomplete order decision table reduction algorithm based on granular computing. these reduction methods based on granular computing are mainly first to calculate reduction core of system, then get reduction based on core. however, in practical application, some information systems may have no reduction core. in this case, this paper proposes an improved reduction algorithm based on attribute significance of granular computing, and numerical experiments show the effectiveness of the algorithm. 2 basic concepts of rough sets 2.1 rough sets let a quadruple s = (u, a, v, f) be an information systems (is), in which u = {x1, x2, · · · , xn} is a non-empty finite set called the domain of discourse; a = {a1, a2, · · · , am} is a non-empty and finite set of attributes; v is a set of attribute values domain, v = ∪ a∈a va; f : u × a → v is a mapping, each attribute of the object in the domain of discourse by the mapping has a corresponding information value, i.e. ∀a ∈ a, x ∈ u, f(x, a) ∈ va. if the attributes set a is composed of condition attributes set c and decision attributes set d, the quadruple s = (u, a, v, f) is also called decision information system (dis). the information system, also known as knowledge representation system, is the main expression of knowledge of rough sets. it is simply expressed in (u, a). if p is a subset of attributes set a , each subset p ⊆ a determines a binary indistin858 x. tang, l. shu guishable relation ind(p), ind(p) = {(u, v) ∈ u × u|∀a ∈ p, a(u) = a(v)}. a set x ⊆ u represent a concept and partition included by ind(p) is called a knowledge base and denoted by u/ind(p). in particular, the partition u/ind(p) = {y1, y2, · · · , yk} is the knowledge base of decision classes. a knowledge base (u, r) is also called an approximation space, where u is the domain of discourse and r is an equivalence relation on u. let x ⊆ uandr ⊆ a, the sets r(x) = {x ∈ u| [x]r ⊆ x} and r̄(x) = {x ∈ u| [x]r∩x 6= φ} are respectively called lower approximation set and upper approximation set. where [x]r refers to an equivalence class of ind(p) = ∩ind(r) determined by element x. if r(x) = r̄(x), then x is called a definable set on u; if r(x) 6= r̄(x), then x is called a rough set on u. if ind(r) 6= ind(r−|a|), then a is indispensable in the set r, otherwise a is dispensable. if every a ∈ r is indispensable, then r is called independent. let q ⊆ p , that is, q is a subset of p , if q is independent and ind(q) = ind(p), then q is a reduction of p , denoted as q = red(p). the union set of indispensable attribute in the set a is called a core set, denoted as core(p), core(p) = ∩red(p). 2.2 knowledge granulation and partition definition 1[8]. let (u, r) be an approximation space, p ∈ r is an equivalence relation on u, called knowledge. the approximation space is also called knowledge base. the equivalence class [x]p = {x ∈ u, (xi, xj) ∈ p} is called knowledge granule. the quotient set u/p = {[x ]p |x ∈ u} is called a p − granularity partition. the granularity of knowledge is defined as gd(p), gd(p) = |p | |u × u| = |p| |u| 2 (1) where |p | denotes the cardinality of the set p ⊆ u × u. the granularity of knowledge p can express its distinguishable ability. for ∀u, v ∈ u, if (u, v) ∈ p , then they belong to the same equivalence class, i.e. they are indistinguishable. the knowledge p ’s discernibility could be defined as dis(p), dis(p) = 1 − gd(p). in general, the greater the granularity is, the weaker the distinguishable ability will be, vice versa. theorem 1[8]. let p ∈ r be a knowledge of knowledge base k = (u, r), if u/p = {x1, x2, · · · , xn}, then gd(p) = ( n ∑ i=1 |xi| 2 )/ |u| 2 . (2) property 1. let p, q ∈ r be an equivalence relations on u, u/p = {x1, x2, · · · , xn}, u/q = {y1, y2, · · · , yn}, if p = q, then gd(p) = gd(q) and dis(p) = dis(q); if p ≺ q, gd(p) < gd(q) and dis(q) < dis(p). proof. (a) if p = q, then m = n, xi = yi, so gd(p) = gd(q), dis(q) = dis(p). (b) if p ≺ q, then |p | < |q|, so gd(p) < gd(q). since dis(p) = 1 − gd(p), we could observe that dis(q) < dis(p). property 2. let p ∈ r be an equivalence relation on u, u/p = {x1, x2, · · · , xn}, if the equivalence relation p divides from knowledge granules in u/r, then gd(p) ≤ gd(r), dis(r) ≤ dis(p). proof. we suppose that the knowledge granule xi from u/r is divided into two knowledge granules xi1 and xi2, that is xi = xi1 ∪xi2 and xi1 ∩xi2 = ∅, u/p = {x1, x2, · · · , xi−1, xi1, xi2, xi+1, · · · , xn}, so gd(r) = ( n ∑ j=1 |xj| 2 )/ |u| 2 an improved attribute reduction algorithm based on granular computing 859 = ( i−1 ∑ j=1 |xj| 2 )/ |u| 2 + |xi| 2 / |u| 2 + ( n ∑ j=i+1 |xj| 2 )/ |u| 2 = ( i−1 ∑ j=1 |xj| 2 )/ |u| 2 + [|xi1| + |xi2|] 2 / |u| 2 + ( n ∑ j=i+1 |xj| 2 )/ |u| 2 ≥ ( i−1 ∑ j=1 |xj| 2 )/ |u| 2 + [ |xi1| 2 + |xi2| 2 ]/ |u| 2 + ( n ∑ j=i+1 |xj| 2 )/ |u| 2 = gd(p), dis(r) = 1 − gd(r) ≤ 1 − gd(p) = dis(p). property 3. let (u, r) be a knowledge base and p ∈ r be an equivalence relation on u, u/r = {x1, x2, · · · , xn}, q is the union of knowledge granules in u/r, then gd(r) ≤ gd(q), dis(q) ≤ dis(r). proof. we suppose that the knowledge granule xk is the union of xi and xi + 1, then u/q = {x1, x2, · · · , xi−1, xk, xi+2, · · · , xn}, so gd(r) = ( n ∑ j=1 |xj| 2 )/ |u| 2 = ( i−1 ∑ j=1 |xj| 2 )/ |u| 2 + |xi| 2 / |u| 2 + |xi+1| 2 / |u| 2 + ( n ∑ j=i+2 |xj| 2 )/ |u| 2 ≤ ( i−1 ∑ j=1 |xj| 2 )/ |u| 2 + [|xi| + |xi+1|] 2 / |u| 2 + ( n ∑ j=i+2 |xj| 2 )/ |u| 2 = ( i−1 ∑ j=1 |xj| 2 )/ |u| 2 + |xk| 2 / |u| 2 + ( n ∑ j=i+2 |xj| 2 )/ |u| 2 = gd(q), dis(q) ≤ dis(r). property 4. let s = (u, a, v, f) be an information system,p, q ⊆ a, (1) if p ⇒ q, then gd(p) ≤ gd(q); (2) if p ⇔ q, then gd(p)=gd(q). proof. (1) if p ⇒ q, then ind(p) ⊆ ind(q), this is |ind(p)| ≤ |ind(q)|. on the other hand, gd(p) = gd(ind(p)) = |ind(p)| / |u| 2, and gd(q) = gd(ind(q)) = |ind(q)| / |u| 2, so gd(p) ≤ gd(q). (2) if p ⇔ q, then p ⇒ q and q ⇒ p . by (1), we could see that gd(p) ≤ gd(q) and gd(q) ≤ gd(p), sogd(p)=gd(q). property 5. let s = (u, a, v, f) be an information system,p, q ⊆ a, (1) if p ⇒ q, then dis(p) ≥ dis(q); (2) if p ⇔ q, then dis(p)=dis(q). proof. it follows immediately from definition 1 and property 4. deduction 1. let s = (u, a, v, f) be an information system, if p ⊆ q ⊆ a, then gd(q) ≤ gd(p) and dis(q) ≥ dis(p). remark. deduction 1 illustrates that for the subset of a, when the attribute number increased, the knowledge granularity is reduced, thus, the discernibility is increased. 3 attribute reduction algorithm based on attribute significance definition 2[8]. let s = (u, a, v, f) be an information system, the attribute significance 860 x. tang, l. shu could be defined as siga−{a}(a), siga−{a}(a) = gd(a −{a}) − gd(a). (3) remark. in an information system s = (u, a, v, f), the attribute significance of each attribute a ∈ a could be measured by knowledge granularity. definition 3[8]. let s = (u, a, v, f) be an information system, c is a subset of a, c ⊆ a, for ∀a ∈ a − c, the attribute significance of attribute a relative to attribute set c could be defined as sigc(a), sigc(a) = gd(c) − gd(c ∪{a}). (4) remark. definition 3 illustrates that the attribute significance of attribute a relative to attribute set c could be measured by change of the knowledge granularity. when a attribute is added to attribute set c, c’s knowledge granularity may change. if c’s knowledge granularity change, then attribute a is indispensable. definition 4[8]. let s = (u, a, v, f) be an information system, a ∈ a, if gd(a − {a}) = gd(a), then attribute a is dispensable, otherwise, attribute a is indispensable. if every a ∈ a is indispensable, then a is called independent. definition 5[8]. let s = (u, a, v, f) be an information system, p ⊆ a, if p is independent and gd(p) = gd(a), then p is a reduction of a, denoted as red(a). the union set of indispensable attribute in the set a is called a core set, denoted as core(p), core(p) = ∩red(p). property 6. attribute a is indispensable, if and only if siga−{a}(a) > 0. (5) proof. ⇒ if attribute a is indispensable, then gd(a − {a}) 6= gd(a). as we know that gd(a −{a}) ≥ gd(a), so siga−{a}(a) = gd(a −{a}) − gd(a) > 0. ⇐ obviously. property 7. core(a) = ∪ { a ∈ a|siga−{a}(a) > 0 } . proof: it follows immediately from definition 3 and property 6. remark. the attribute significance from the perspective of knowledge granularity provides a method of attribute reduction: we could judge the significance of attribute a by discussing whether gd(a − {a}) is equal to gd(a). if gd(a − {a}) = gd(a)łźthen a is dispensable, otherwise a is indispensable. thus we could obtain the reduction core core(a). next calculate the significance of the rest attribute relative to core(a). if gd(core(a) ∪ a) = gd(a), then the set core(a) ∪ a is the reduction of the information system, where a = { a ∈ a − core(a )|max sigcore(a)(a) } . algorithm 1: input: an information system s = (u, a, v, f), where u = {x1, x2, · · · , xn}, a = {a1, a2, · · · , am}. output: red(a) and core(a) // the sets of reductions and core. step 1: for i = 1, i ≤ n, + + i; j = 1, j ≤ m, + + j begin, calculate gd(a)// the knowledge granularity of attribute set a. step 2: calculate siga−{a}(a) // the significance of attribute a ∈ a. step 3: calculate core(a), core(a) = { a ∈ a|siga−{a}(a) > 0 } . step 4: if gd(core(a)) = gd(a), output red(a) = core(a), end. if gd(core(a)) > gd(a), turn next. step 5: calculate max b∈b sigcore(a)(b ), b = a − core(a) // the significance of attribute b ∈ b = a − core(a) for core(a). an improved attribute reduction algorithm based on granular computing 861 step 6: if gd(core(a) ∪ a′) = gd(a), output red(a) = core(a) ∪ b and core(a), end. if gd(core(a) ∪ b) > gd(a), repeat step 5, step 7: calculate max c∈c sigcore(a)∪b(b ) ∪ (core(a) ∪ b) // the significance of attribute c ∈ c = a − core(a) ∪ b for core(a) ∪ b, step 8: for j = 1 to |c|, repeat step 7 until the knowledge granularity is equal to gd(a) step 9: output red(a) = core(a) ∪ b ∪ c ∪·· · and core(a). example 1. let s = (u, a, v, f) be an information system (table 1). there are 6 objects and 4 attributes, where u = {x1, x2, x3, x4, x5}, a = {a1, a2, a3, a4}. calculate the reduction of the system. table1. an information system u a1 a2 a3 a4 x1 1 0 2 2 x2 0 1 1 1 x3 2 0 0 1 x4 1 1 0 2 x5 2 2 0 0 x6 2 1 1 1 it is easy to calculate that u/a = {x1, x2, x3, x4, x5, x6}, gd(a) = ( n ∑ i=1 |xi| 2 )/ |u| 2 = 6 36 = 1 6 , u/a −{a1} = {x1,{x2, x6}, x3, x4, x5}, gd(a −{a1}) = 8 36 , siga−{a1}(a1) = gd(a −{a1}) − gd(a) = 8 36 − 6 36 = 2 36 , u/a −{a2} = {x1, x2, x3, x4, x5, x6}, gd(a −{a2}) = 6 36 , siga−{a2}(a2) = gd(a −{a2}) − gd(a) = 6 36 − 6 36 = 0, u/a −{a3} = {x1, x2, x3, x4, x5, x6}, gd(a −{a3}) = 6 36 , siga−{a3}(a3) = gd(a −{a3}) − gd(a) = 6 36 − 6 36 = 0, u/a −{a4} = {x1, x2, x3, x4, x5, x6}, gd(a −{a4}) = 6 36 , siga−{a4}(a4) = gd(a −{a4}) − gd(a) = 6 36 − 6 36 = 0. based on the result above, we can see that the core of the system is core(a) = { a ∈ a|siga−{a}(a) > 0 } = {a1}, gd(core(a)) = 14 36 > 6 36 = gd(a). let a set b = a − core(a) = {a2, a3, a4}, calculate the significance of the rest attribute relative to core(a): sigcore(a)(a2) = gd(core(a)) − gd(core(a) ∪ a2) = 14 36 − 6 36 = 8 36 , sigcore(a)(a3) = gd(core(a)) − gd(core(a) ∪ a3) = 14 36 − 8 36 = 6 36 , sigcore(a)(a4) = gd(core(a)) − gd(core(a) ∪ a4) = 14 36 − 10 36 = 4 36 . since max b∈b sigcore(a)(b ) = 8 36 , u/core(a) ∪ a2 = {x1, x2, x3, x4, x5, x6}, gd(core(a) ∪ a2) = gd(a), we can see that core(a)∪a2 = {a1, a2} is the reduction, that is red(a) = {a1, a2}, core(a) = {a1}. 4 an improved reduction algorithm the reduction of an information system is not the only, some may have more than one reductions. but the reduction results may not be able to get reduction core, especially in the actual application data. for this case, algorithm 1 is powerless. now we will improve the algorithm, which can deal with the system with reduction core and no reduction core. 862 x. tang, l. shu in an information system s = (u, a, v, f), if siga−{a}(a) = 0, then then a is dispensable. for ∀a ∈ a, if siga−{a}(a) = 0, then the system has no reduction core. definition 6. let s = (u, a, v, f) be an information system, p is a subset of a, p ⊆ a, the attribute significance of p relative to a could be defined as siga−p (p), siga−{p}(p) = gd(a − p) − gd(a). (6) particularly, if p = a, sig∅(p) is represented as sig(p), and sig(p) = sig∅(p) = gd(∅) − gd(a) = 1 − gd(p) = dis(p). where gd(∅) = 1 (since u/ind(∅) = {u}). algorithm 2: input: an information system s = (u, a, v, f), where u = {x1, x2, · · · , xn}, a = {a1, a2, · · · , am}. output: red(a) and core(a) // the sets of reductions and core. step 1: for i = 1, i ≤ n, ++i; j = 1, j ≤ m, ++j begin, calculate gd(a).// the knowledge granularity of attribute set a step 2: calculate siga−{a}(a). // the significance of attribute a ∈ a if siga−{a}(a) 6= 0 // the system has reduction core, turn step 8; if siga−{a}(a) = 0 // the system has no reduction core, next; step 3: calculate siga−{ai,aj}(ai, aj), 1 ≤ i 6= j ≤ m // the significance of the combination of any two attributes in a, step 4: for i = 1, i ≤ n, + + i; j = 1, j ≤ m, + + j, find out red′(a) = { (ai, aj)| max 1≤i 6=j≤m siga−{ai,aj}(ai, aj) } . // the suboptimal reduction of the system step 5: if gd(red′(a)) = gd(a), then output the reduction red(a) = red′(a), end. if gd(red′(a)) > gd(a), turn next; step 6: calculate max b∈b sigred′(a)(b ), b = a − red′(a). // the significance of attribute b ∈ b = a − red′(a) for red′(a) step 7: if gd(red′(a) ∪ b) = gd(a), then output red(a) = red′(a) ∪ b, end. if gd(red′(a)∪b) > gd(a), repeat step 6 until gd(red′(a)∪b) = gd(a), output red(a), end. step 8: calculate core(a) = { a ∈ a|siga−{a}(a) > 0 } . step 9: if gd(core(a)) = gd(a), output red(a) = core(a), end. if gd(core(a)) > gd(a), turn next; step10: calculate max b∈b sigcore(a)(b ), b = a − core(a) // the significance of attribute b ∈ b = a − core(a) for core(a) step11: if gd(core(a) ∪ b) = gd(a), output red(a) = core(a) ∪ b and core(a), end. if gd(core(a) ∪ b) > gd(a), repeat step 10. step12: calculate max c∈c sigcore(a)∪b(b ) ∪ (core(a) ∪ b) // the significance of attribute c ∈ c = a − core(a) ∪ b for core(a) ∪ b. step13: output red(a) = core(a) ∪ b ∪ c ∪·· · and core(a), end. example 2. let s = (u, a, v, f) be an information system (table 2). there are 5 objects and 4 attributes, where u = {x1, x2, x3, x4, x5}, a = {a1, a2, a3, a4}. calculate the reduction of the system. an improved attribute reduction algorithm based on granular computing 863 table2. an information system u a1 a2 a3 a4 x1 1 0 2 1 x2 2 1 2 1 x3 0 1 2 0 x4 2 0 1 1 x5 1 2 2 0 it is easy to calculate that u/a = {x1, x2, x3, x4, x5}, gd(a) = ( n ∑ i=1 |xi| 2 )/ |u| 2 = 5 25 = 1 5 , u/a −{a1} = {x1, x2, x3, x4, x5}, gd(a −{a1}) = 1 5 , siga−{a1}(a1) = gd(a −{a1}) − gd(a) = 0; u/a −{a2} = {x1, x2, x3, x4, x5}, gd(a −{a2}) = 1 5 , siga−{a2}(a2) = gd(a −{a2}) − gd(a) = 0; u/a −{a3} = {x1, x2, x3, x4, x5}, gd(a −{a3}) = 1 5 , siga−{a3}(a3) = gd(a −{a3}) − gd(a) = 0; u/a −{a4} = {x1, x2, x3, x4, x5}, gd(a −{a4}) = 1 5 , siga−{a4}(a4) = gd(a −{a4}) − gd(a) = 0. based on the result above, we can see that the system has no core. next we calculate the significance of attribute combination siga−{ai,aj}(ai, aj), 1 ≤ i 6= j ≤ 4: gd(a −{a1, a2}) = 9 25 , siga−{a1,a2}(a1, a2) = gd(a −{a1, a2}) − gd(a) = 4 25 ; gd(a −{a1, a3}) = 7 25 , siga−{a1,a3}(a1, a3) = gd(a −{a1, a3}) − gd(a) = 2 25 ; gd(a −{a1, a4}) = 7 25 , siga−{a1,a4}(a1, a4) = gd(a −{a1, a4}) − gd(a) = 2 25 ; gd(a −{a2, a3}) = 5 25 , siga−{a2,a3}(a2, a3) = gd(a −{a2, a3}) − gd(a) = 0; gd(a −{a2, a4}) = 7 25 , siga−{a2,a4}(a2, a4) = gd(a −{a2, a4}) − gd(a) = 2 25 ; gd(a −{a3, a4}) = 5 25 , siga−{a3,a4}(a3, a4) = gd(a −{a3, a4}) − gd(a) = 0. we can see that gd(a1, a2) = gd(a), so red(a) = {a1, a2} is the reduction of the system. 5 results and discussion the idea of algorithm 1 is listed as follow: at first, work out the reduction core core(a) by finding out the set { a ∈ a|siga−{a}(a) > 0 } . then calculate the significance of the rest attribute relative to core(a). if gd(core(a) ∪ a) = gd(a), then the set core(a) ∪ a is the reduction of the information system. the time complexity of the algorithm is t ≈ o(|c|3 ·|u|2). the precondition of algorithm 1 is working out reduction core. however, some information systems may have no reduction core. in this case, algorithm 1 is powerless. in this paper, an improved algorithm is proposed. in an information system with no reduction core, the suboptimal reduction red′(a) replaces the reduction core. the improved algorithm does not increase in time complexity. it can deal with the system with reduction core and no reduction core. acknowledgment we would like to thank all the referees for their valuable comments in improving this paper. this work is supported by the foundation of sichuan normal university (grant no. 13kyl15). 864 x. tang, l. shu bibliography [1] zhang w.x.; wu w.z.; liang j.y.(2001); the theory and method of rough set, beijing: science press, china. 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[13] shi j.l.; du g.y.; xiong d.l.(2012); an incomplete order decision table reduction algorithm based on granular computing. computer applications and software, 29(10): 113-116. int j comput commun, issn 1841-9836 9(3):348-355, june, 2014. backstepping-based robust control for wmr with a boundary in prior for the uncertain rolling resistance m. yue, s. wang, x.l. yang ming yue*, shuang wang, xiaoli yang school of automotive engineering dalian university of technology, dalian 116024, china *corresponding author: yueming@dlut.edu.cn abstract: in this study, we focus on the trajectory tracking control problem of a wheeled mobile robot (wmr) in an uncertain dynamic environment. concerning the fact that the upper boundary may be usually achieved in prior according to the physical properties of the terrain, this crucial message is utilized to construct the controllers. firstly, a dynamic model for wmr including the rolling resistance is presented, whose state variables are longitudinal and rotational velocities, as well as the rotational angle of the mobile platform. secondly, with the aid of backstepping technique, the robust controllers based on the upper boundary are proposed and the globally asymptotic stability of the closed-loop system is proven by the lyapunov theory in the following. lastly, a saturation function is applied to replace the signum function, by which the inherent chattering can be suppressed greatly. numerical simulation results demonstrate that the proposed controllers with upper bound in prior possess robustness characteristics which yields potentially valuable applications for the mobile robot, especially in the unstructured environment. keywords: wheeled mobile robot (wmr), boundary estimation, uncertain rolling resistance. 1 introduction in practice, the physical properties of the wheeled mobile robot (wmr) within an unstructured environment are seldom considered in lots of trajectory tracking control issues. among these physical properties, the rolling resistance acting on the robot wheels is such a crucial physical behavior that can effect the control performances greatly. particulary, in some rough terrain, such as loose soil and sand conditions, it is difficult to obtain high-performance of trajectory tracking without considering the influences of the rolling resistance. therefore, when the wmr explores in an outdoor environment and even planetary surface, the rolling resistance may not be neglected for designing the system controllers [1]. many efforts are devoted to the rolling resistance acting on the wmr’s wheels recently. most references (see [2,3]) had investigated the interactions of soil and wheels and then some new mechanical structures had also been reported to enhance the contact between the wheels and terrain. however, there are only a few investigations to deal with rolling resistance problem by control methods. after analysing of the terrain properties, we notice that the boundary (especially for the upper bound) of off-road rolling resistance can be calculated in advance according to the geologic parameters when the terrain of the exploration is determined. for practical implementation, the rolling resistance appears drastically changing, which is similar to the disturbances to the mobile benchmark, but with the aid of the boundary message in prior, the robust controller for wmr can be designed to make it possible to against the uncertain dynamics directly. it should be mentioned that, compared with modifying the system mechanical structure, the adopted control approach yields much more flexibility for the mobile robot. certainly, we are also aware of adaptive scheme to update the upper bound, but the control algorithm with adaptive scheme copyright © 2006-2014 by ccc publications backstepping-based robust control for wmr with a boundary in prior for the uncertain rolling resistance 349 may make the regulation process more elastic and cause further time-delay usually [4, 5]. to overcome this drawback, a powerful robust control approach is determine to employ to deal with the uncertain problem of the unavoidable rolling resistance. on another hand, if the rolling resistance is concerned, the dynamics of the wmr must be considered. for the past decades, most efforts have been done to the wmr with nonholonomic constraint on kinematic model, all of which the system velocities were assumed to be the control inputs [6, 7]. but unfortunately, there are only a few researches address the dynamic behaviors of nonholonomic system, where control inputs are transformed to be the system actuators, i.e., in most cases the driving torques of motors [8–10]. since the rolling resistance is described in torque form when it takes place in practice, the dynamic properties must be considered when designing the system controllers to overcome this kind of uncertain dynamics. with a widely survey of the current dynamic models for the wmr, we observe that a dynamic model proposed by watanabe et.al [11] is so succinct and effective to describe the relationship between the rolling resistance and robot posture on the level of dynamics. then, we utilize this dynamic model to establish the control system in this investigation. particularly, it should be noted that the rolling resistance is somewhat difference from the external disturbances and unmodeled dynamics, because the rolling resistance lies in the movement process around a constant value; while the external disturbances and unmodeled dynamics possess white noise property which is around zero. the objective of this study is to address the rolling resistance behaviors of the wmr in detail, and derives the dynamic model to discuss the influence of rolling resistance. based on addressed dynamic model, a simple and effective approach to stabilize the trajectory tracking system via backstepping techniques is developed. in order to illustrate the efficacy of the control approach, numerical simulations for a practical wmr have been performed. 2 wmr dynamics the typical wmr can be described in fig.1. it is assumed that the mobile robot is driven by two independent wheels as well as a passive auxiliary wheel is adopted to support the workbench. to begin with, some notations is introduced to help the controlling system design, as follows: l is the distance between the driving wheels and the symmetry axis; d is the distance between point c(x, y) and the mass center of the robot p(xc, yc), which is assumed to be on the symmetry axis; r is the radius of the driving wheels; mp and mw are the masses of vehicle body and wheel; ic is the inertia moment of vehicle body w.r.t. vertical axis through c; iw and im are the inertia moments for the wheel w.r.t. wheel axis and diameter, respectively. according to the parallel axis theorem, the equivalent mass m = mp + 2mw and rotation inertia i = 2im + 2mwl2 + mcd2 + ic can be introduced for simply. the robot position can be described by the coordinates (x, y), which is the midpoint c of the axis of two robot wheels, and the orientation angle ϕ, which is heading angle of body coordinate with respect to fixed frame (see fig.1). with the hypothesis of pure rolling and non slipping condition, the nonholonomic constraint, i.e., ẋ sin ϕ − ẏ cos ϕ = 0, hold throughout the movements. let v and w be represent the linear and angular velocities of the mobile robot, and τr and τl express the driving torque of the right and left wheels, respectively. through the analysis of the forces acting on the mobile robot, we can obtain that iϕ̈ = τr r l − τl r l (1) mv̇ = τr r + τl r (2) 350 m. yue, s. wang, x.l. yang x y o d auxiliary wheel symmetry axis figure 1: a wheeled mobile robot τi r θ figure 2: rolling resistance generation while rolling on the ground, especially operating on the soft soil or sandy terrain, the robot will suffer unavoidable rolling resistance, as shown in fig.2. let the notation τ̃d represent this rolling resistance, and the dynamic behaviors of the wheel can be governed by iwθ̈i + cθ̇i = kui − τi − τ̃di (3) where τi is the driving input of the wheel (i expresses r or l for left or right wheels, the same hereinafter); c is the viscous friction coefficient and θ is the rolling angle of the wheel; k is driving gain between the motor’s voltage and its output torque; ui is the excited voltage signals for the motor installed in the relative wheel. let vr, vl denote the right and left linear velocity of the wheel center, and we can further have vr = rθ̇r = v + lϕ̇ and vl = rθ̇l = v − lϕ̇. then, the relationship between linear velocity of the robot’s platform and angular velocity of the robot wheels can be formulated by r(θ̇r + θ̇l) = 2v and r(θ̇r − θ̇l) = 2lϕ̇. with the analysis, we can ultimately obtain the dynamics described by the linear velocity v and orientation angle ϕ as follows: v̇ = − 2cv mr2 + 2iw + kr mr2 + 2iw (ur + ul) − r mr2 + 2iw (τ̃dr + τ̃dl) (4) ϕ̈ = − 2cl2 ir2 + 2iwl2 ϕ̇ + krl ir2 + 2iwl2 (τ̃dr − τ̃dl) (5) if the system state variables are chosen as x = [v ϕ ϕ̇]t , the driving input is selected as u = [ur ul] t , the disturbance vector is defined as τ̃d = [τ̃dr τ̃dl]t , and the output variables are y = [v ϕ]t , a new dynamic model for control system design can be rewritten by:{ ẋ = ax + bu + dτ̃d y = cx (6) with a =   a1 0 0 0 0 1 0 0 a2   , b =   b1 b1 0 0 b2 −b2   , d =   d1 d1 0 0 d2 −d2   , c = [ 1 0 0 0 1 0 ] , where a1 = − 2c mr2 + 2iw , a2 = − 2cl2 ir2 + 2iwl2 , b1 = kr mr2 + 2iw , b2 = krl ir2 + 2iwl2 , d1 = − r mr2 + 2iw , d2 = − rl ir2 + 2iwl2 . backstepping-based robust control for wmr with a boundary in prior for the uncertain rolling resistance 351 3 control system design 3.1 system decoupling noticing that the system (6) is a coupled system, for the convenience of designing the system controller, the proposed dynamic model should be decoupled in the first place. to achieve this objective, a new control input vector should be introduced as follow:[ ur ul ] = [ 1 −1 0 1 ] [ u1 u2 ] (7) with these modified inputs u1 and u2, the system (6) is transformed into two independent subsystems: v̇ = a1v + b1u1 + τ̃dv (8) ẇ = a2w + b2u1 − 2b2u2 + τ̃dw (9) where τ̃dv = d1(τ̃dr + τ̃dl) and τ̃dw = d2(τ̃dr + τ̃dl), which represent disturbances for the two independent subsystems. supposing the robot moves on a special terrain ground, although the rolling resistance is in a changeable state, the maximum amplitude for τ̃dr and τ̃dl could be estimated in advance from the terrain property. let the notation τ̄dr and τ̄dl represent the upper bound values of τ̃dr and τ̃dl respectively, and then it can be achieved that τ̄dv = max |d1(τ̃dr + τ̃dl)| (10) τ̄dw = max |d2(τ̃dr + τ̃dl)| (11) 3.2 control scheme design considering the decoupled form of the system (8) and (9), a backstepping technique can be applied to derive the robot controller due to the high dimension feature of the system. the process of control design procedures may be divided into two steps which is given as bellows. step 1. linear velocity control after the trajectory tracking system has been decoupled, the control algorithms for linear and angular velocities can be derived stage by stage. here, the control input u1 is used to control the linear velocity, such that the robot can track an desired trajectory from an arbitrary original point with the required performances. suppose that the desired linear velocity vd, and the tracking error may be expressed by ve = vd − v. then, a theorem can be given as follow: theorem 1. for the linear velocity system (4) with the bounded linear velocity, the tracking error ve will globally converge to zero, i.e., lim t→∞ ve(t) = 0, if the control law is given by u1 = 1 b1 [v̇d − a1v + c1ve + sgn(ve)τ̄dv] (12) where c1 is a positive constant and sgn(·) is a signum function. proof: consider a candidate lyapunov function as v1 = 1 2 v2e (13) 352 m. yue, s. wang, x.l. yang differentiating v1 with respect to time yields v̇1 = vev̇e = ve(v̇d − a1v − b1u1 − τ̃dv) (14) substituting (12) into (14), it can be ultimately obtained v̇1 = −c1v2e ≤ 0. obviously, by lyapunov stability theorem, the propose theorem 1 is proved. 2 step 2. angular velocity control let ϕd represents the desired orientation angle of the robot to be tracked. then, the tracking error of the orientation angle can be defined as ϕe = ϕd −ϕ and ϕ̇e = ϕ̇d −ϕ̇. in terms of angular velocity tracking control, it is to design the control input u2 such that the ϕe and ϕ̇e converge to zero as t → ∞. in order to realize this purpose, two virtual control inputs are introduced, which are defined as z1 = ϕe and z2 = ϕ̇e + c2ϕe where c2 is an arbitrary positive constant. similar to the linear velocity control, based on the above definitions, a theorem could be given as theorem 2. for the angular velocity system (5) with the bounded angular velocity, the tracking error ϕe and ϕ̇e will both globally converge to zero simultaneously , i.e., lim t→∞ ||ϕe ϕ̇e|| = 0, if the control law is given by u2 = 1 2b2 [a2w + b2u1 − ϕ̈d − c2z1 + c3z2 + sgn(z2)τ̄dw] (15) where c3 is a positive constant and sgn(·) is a signum function. proof: consider a candidate lyapunov function as v2 = 1 2 z21 (16) differentiating v2 with respect to time achieves v̇2 = z1ż1 = z1(ϕ̇ − ϕ̇d) = z1z2 − c2z21 (17) notice that only lyapunov function v2 can not guarantee the global stability of the system. in order to stabilize the system, another virtual input z2 is designed such that the entire tracking errors converge to zero. hence, another candidate lyapunov function constructed by z2 is established for this purpose. the new candidate lyapunov function is put forward as v = 1 2 z21 + 1 2 z22 (18) differentiating v with respect to time obtains v̇ = v̇2 + z2ż2 = z1z2 − c2z21 + z2(a2w + b2u1 − 2b2u2 + c2ż1 − ϕ̈d) (19) substituting (12) and (15) into (19), one can obtain that v̇ = −c2z21 − c3z 2 2 ≤ 0. obviously, according to lyapunov stability theorem, the theorem is proved. 2 since the systems (8) and (9) are decoupled, the proposed controller (12) and (15) may guarantee the stabilization of the linear and angular velocity subsystems overall. to sum up, we can summarize the investigation results and ultimately give a theorem as follow: theorem 3. for the trajectory tracking system (4) and (5), the tracking errors ve, ϕe and ϕ̇e will all globally converge to zero, i.e., lim t→∞ ||ve ϕe ϕ̇e|| = 0, with the controller (12) and (15). backstepping-based robust control for wmr with a boundary in prior for the uncertain rolling resistance 353 the proof can be easily achieved according to the above analysis. from the controller designing process, it can be observed that the candidate lyapunov functions are established step by step, which make the control design process be simple and convenience. this is a backstepping approach which is usually applied for controller design of a sophisticated nonlinear systems. meanwhile, it should be emphasized that there are inherent chattering caused by signum function in the proposed controllers. to overcome this drawback, one can replace signum function sgn(·) by saturation functions sat(·) to suppress this behaviors, one of which can be given by: sat(χ) = χ |χ| + ϵ (20) where ϵ is a positive constant and χ represents arbitrary variable. 4 simulation results in this section we perform numerical simulations to verify the effectiveness of the proposed controllers. the simulated parameters of the mechanical structure of the wmr are supposed to be mc = 14kg, mw = 3kg, ic = 0.9kgm2, im = 0.05kgm2, iw = 0.015kpgm2, l = 0.3m, d = 0.05m, r = 0.1m, k = 0.95, and c = 0.01. from the required performance of the trajectory tracking, the control parameters as selected as follows: c1 = c2 = c3 = 10 and ϵ = 0.002. suppose the mobile robot starts from initial point [x y ϕ]t = [0 0 0]t ; meanwhile, the desired trajectory of linear and angular velocity are hypothesized to be vd = 1m/s, wd = sin t with the initial state vector x0 = [v0 ϕ0 w0]t = [0.5 0.2 0]t . moreover, the rolling resistance acting on the left and right wheels are supposed to be a worse situation in simulation than that in practice to verify the effectiveness of the control algorithms. so a synthesis signal consisted of two sinusoid signals with different frequency is introduced to imitate the rolling resistance. here, assume that the rolling resistances are same on the left and right wheels, i.e., τ̃dr = τ̃dl = sin wdt +sin 2wdt where wd denotes the resistance frequency. according to practical experiment, the τ̄dv and τ̄dw can be set as the value of 0.75nm and 3nm respectively in this simulation circumstance. in addition, the saturation should be considered for actual driving motor, so a maximum value 10nm is introduced to restrict the control outputs, i.e., |τr| ≤ 10nm and |τl| ≤ 10nm. 0 2 4 6 8 10 12 14 16 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 x /m y / m figure 3: actual trajectory on the plane υ e / m /s with resistant consideration without resistant estimation -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0 2 4 6 8 10 12 14 16 18 20 t /s figure 4: tracking error for υe under the proposed simulation circumstance, the actual tracking process for the desired trajectory is plotted in fig.3. it indicates the proposed control method can make the mobile robot to track the desired trajectory with satisfactory tracking performances. besides, we simulate two cases, that is, with and without estimation, to illustrate the signification of control with and 354 m. yue, s. wang, x.l. yang without upper bound information. as shown in fig.4, the tracking error relative to the linear velocity without boundary is described as the dashed line, as well as the case with boundary is plotted by the dashed line. the results state the tracking errors become much smaller (reduced to 20% of the amplitude) when τ̄dv and τ̄dw are adopted. particularly, owing to the sat(·), the inherent chattering is also suppressed. lower chattering may be achieved with smaller ϵ, but it will cause more violent changes for the control inputs. furthermore, to exhibit the better tracking performance with the upper bound, the tracking errors of ϕ̇e and ϕe are also addressed in fig.5 and fig.6, respectively. it is observed that the amplitudes of ϕe and we are reduced to 21% and 18% nearly compared with the case without upper bound estimation, which yields that the derived controller can provide better performances for tracking system and the robustness is enhanced in the following. φ e / r a d 0 2 4 6 8 10 12 14 16 18 20 -0.05 0 0.05 0.1 0.15 0.2 t /s with resistant consideration without resistant estimation figure 5: tracking errors for ϕe 0 2 4 6 8 10 12 14 16 18 20 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 t /s ω e / r a d /s with resistant consideration without resistant estimation figure 6: tracking errors for ωe 5 conclusions the trajectory tracking issues were discussed in this study, and the main contribution can be summarized as follows: 1) a decoupled dynamic model was used to describe the complicated nonlinear behaviors of wmr where the rolling resistances are concerned. the decoupled model consisted of two parts: a first-order model for linear velocity and a second-order model for orientation angle of the robot, which not only kinematic postures but also dynamics behaviors were all included; 2) based on the fact that the upper bound of rolling resistance which can be achieved in prior, a robust controller was developed to obtain the better tracking performances. the backstepping methodology was employed to derive the system controller considering the higher dimensional property of the tracking system; and 3) a saturation function was introduced to alleviate the inherent chattering caused by signum function. such treatment was simply but effective for a practical wmr system. acknowledgement this research was supported by grants from the national natural science foundation of china (no. 61175101), and the fundamental research funds for the central universities (no. dut12lk45). backstepping-based robust control for wmr with a boundary in prior for the uncertain rolling resistance 355 bibliography [1] l. ding, h. b. gao, z. q. deng, k. nagatani, and k. yoshida, experimental study and analysis on driving wheels performance for planetary exploration rovers moving in deformable soil, journal of terramechanics, 48(1): 27-45, 2011. 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[11] k. watanabe, j. tang, m. nakamura, s. koga and t. fukuda, fuzzy-gaussian neural network and its application to mobile robot control, ieee transactions on control systems technology, 4(2): 193-199, 1996. international journal of computers communications & control issn 1841-9836, 10(4):463-470, august, 2015. alleviation of binding update re-registration handoff latency at home agent failure in mipv6 network a. avelin diana, k. sundarakantham, s. mercy shalinie a. avelin diana*, k. sundarakantham, s. mercy shalinie department of computer science and engineering thiagarajar college of engineering, madurai dianaavelin@gmail.com, kskcse@tce.edu, shalinie@tce.edu *corresponding author: dianaavelin@gmail.com abstract: home agent (ha) is an indispensable entity for binding connectivity to route packets between mobile node (mn) and correspondent node (cn). mipv6 allows the deployment of redundant has to overcome ha failure. different approaches resolve this issue to recuperate binding association information. this paper compares the effect of handoff latency in various methods and proposes a reliable ha delivery (rhad) mechanism to mitigate the binding update (bu) registration latency in ha at the time of active ha failure. we use bgp domain in network architecture and apply ibgp protocol to transmit packets between edge router (er) and ha. both the theoretical evaluation and simulation results reveal that rhad effectively reduces bu re-registration handoff latency and increases packet delivery ratio. keywords: mipv6, home agent, binding update, ibgp, vharp, edge router. 1 introduction mipv6 networks are experiencing a rapid growth due to scarcity of ipv4 addresses and tremendous growth in portable communication devices, ubiquitous computing and mobile users. mipv6 network is a vital for evolving next generation internetworking systems. mipv6 supports the mobility of mobile node (mn) to route packets transparently within the network [1]. mn ascertains its identity location with the router advertisement (ra) broadcasted from the router. ha is a router that maintains the mobility binding table to correlate the home address (hoa) with the care-of-address (coa) of mn. binding update list of mn maintains its binding information with ha and cn. the binding information lifetime is 420 seconds [2]. similarly, cn contains binding cache (bc) to store the mns information and communicate with it directly by forming a tunnel via route optimization (ro) technique [1]. the ro is enhanced to tackle security, signaling overhead and handoff latency [3]. ha is responsible for the binding association of mn and cn. mn discovers ha with the secured dynamic home agent address discovery mechanism (dhaad) [4]. ha maintains the ha list which includes the link address of all the has within the network along with its preference value. mn selects the ha based on the higher preference value [5]. icmp dhaad message contains the preference value which gets decremented by one as it propagates along each router. ha list between has are sustained by different protocols like harp [6], vharp [7], ha-to-ha protocol [8]. has experience single point of failure and it results in bottleneck within the network [9]. a single ha failure causes loss of mns binding association information. to overcome such problems, mipv6 allows the deployment of multiple has with the redundant ha to backup the bindings at the time of failure [10]. this paper evaluates the bu registration handoff latency of various approaches during ha failure and proposes a rhad mechanism to mitigate the registration delay of mn with the new active ha. this method reduces the bu re-registration handoff latency and improves quality of service (qos) in mipv6 network. copyright © 2006-2015 by ccc publications 464 a. avelin diana, k. sundarakantham, s. mercy shalinie this paper is organized as follows: section 2 discusses the related work in this area. section 3 proposes the network architecture of rhad mechanism with the theoretical evaluation. section 4 provides the simulated results and finally we conclude this paper in section 5. 2 related work ha failure results in service connectivity breakdown. redundant ha can avail uninterrupted service by the capture of bc contents from active ha. the standby ha detects the active ha failure with the periodic hello messages [6]. different solutions have been proposed to solve the ha failure issue. secondary has from various home link takeover the service of primary ha and the registration delay is high in this approach [11]. in [12], the redundant has are in the same home link and backup the data of failed primary ha. redundant ha of [13] and [14] follow the same approach as said in [12]and additionally it considers load balancing issues. primary and secondary has are synchronized with the transport layer connections and the registration delay is less in [13]. efficient fault tolerant protocol provides a stable storage method in which the failure of a mobility agent is recovered by another mobility agent using check pointing and receiver based pessimistic message logging approach [15]. the mobility agent of this approach stores all the mns binding. in quorum based mechanism, mobility binding of mn is stored in the backup quorum of network segment [16]. here, every ha finds a new ha as its standby ha. all the has of virtual home agent method share one global address and only one ha is set active [17]. redundant ha set is a collection of active and standby has in which the failure recovery is maintained by havirtual switch (ha-v) and ha hard switch (ha-h). in ha-v the mn executes ike exchange on standby ha with home address (hoa) assignment but in ha-h it exchanges without hoa. at the time of active ha failure, standby ha sends home agent switch message to all the registered mns to redirect the bu messages [7]. in vharp, the virtual home agent address is activated by standby ha and every state of mn is to be synchronized. the backup has are considered from different home links in vhaha [18]. the home agents are arranged 2 in a chain and backup its binding to the adjacent home agent in [19].in mobile ipv6 the message exchange is twice than that of harp and vharp since themn does not involve in ha failure detection and recovery [10]. in all the above approaches, the mn should update its registration to the redundant ha at the time of active ha failure. our rhad technique reduces the registration delay since the mn is not essential for this registration process. the connectivity between mn to a router in the network is wireless, so the packet delivery ratio is also improved by rhad. 3 rhad mechanism 3.1 rhad network architecture our network entities include mn, ha, access router, access point, cn and edge router (er) as in figure 1. mn selects the nearest ha to reduce the probability of ha handoff failure. we create bgp domain and use ibgp to transfer the packets between mn and ha. it is noticed that the packets are never propagated to exterior network. er is a boundary router that scrutinizes the bu message from mn and records it in router list. when the bu packet is routed to ha, first transmission occurs via er that is nearer to mn. the stored bu information contains hoa, coa and lifetime. it remains in the router list until the bu lifetime becomes zero. the bu is transmitted to active ha through edge router and access router. the active ha selects a standby ha from redundant ha set based on the highest preference value and back up its data. the alleviation of binding update re-registration handoff latency at home agent failure in mipv6 network 465 figure 1: rhad network redundant ha is provided with virtual hoa and it should be noticed that the binding should be synchronized between active and standby ha. in our approach we combine vharp and bgp protocol to mitigate the failure recovery handoff latency. vharp is transparent to mn and it maintains the ha list within the network. at the time of active ha failure, the edge router transmits bu information to the redundant ha. 3.2 theoretical evaluation the bu re-registration handoff ow using rhad approach is illustrated in figure 2. the total message ow handoff time includes processing time and transmission time. message retransmission is unnecessary in wired medium. wireless link is least stable since message loss can occur at any instance and therefore message retransmission becomes indispensable. the transmission of bu message between mn and edge router is wireless. the additional signal processing time is evaluated below. t1bu = ∞∑ i=n tbu (nf) ·prob(nf failure and one success) (1) nf represents the number of link failures. if the ba is not received after sending bu request we assume that message is lost and hence retransmitted. if nf failure occurs then toutand message retransmission takes nf times. usually, tout is observed as 2 ms [13]. tbu(nf) = pw + nf · (tout + pwl) (2) from (1) and (2), t1bu = σ ∞ n {pwl + nf · (tout + pwl)} ·prob(nf failure and one success) (3) σ∞n nf ·prob(nf failure and one success) is obtained from infinite geometric progression. σ∞n nf ·prob(nf failure and one success) = r r −1 (4) 466 a. avelin diana, k. sundarakantham, s. mercy shalinie here, r represents the link probability failure and it has the value 0.5 [13]. t1bu = pwl + (tout + pwl) 0.5 1−0.5 (5) t1bu = 2pwl + tout (6) similarly, the ba received from edge router to mn is obtained as t2ba = 2pwl + tout (7) figure 2: bu recovery registration handoff therefore total time for the message transmission in wireless medium is twl = t 1 bu + t 2 ba (8) twl = 2 (2pwl + tout) (9) the message transmission in wired medium includes the packet propagation between edge router and standby ha. the binding synchronization between active and standby home agent is achieved through vharp protocol. the total message transmission time in wired medium is twi = ter−ar + tar−aha + tbsync + tar−er + taha−ar (10) twi = 2ter−ar + 2tar−aha + tbsync (11) at the time of active ha failure, the total transmission time in harp is obtained as tharp = 2twl + twi + ter−ar + tar−sha + tar−er + tsha−ar (12) tharp = 2 (twl + ter−ar + tar−sha) + twi (13) alleviation of binding update re-registration handoff latency at home agent failure in mipv6 network 467 edge router contains the information of mns bu and hence in rhad method the standby virtual ha acquires the bu directly from edge router and sends ba after successful delivery. therefore the transmission between edge router and mn is not necessary. trhad = 2 (ter−ar + tar−sha) + twi + twl (14) we have the fixed processing time to configure coa and to update bu message at edge router, active ha and standby ha. the processing time is same for both harp and rhad method. ptrhad = ptcoa + 4 ptbu = 5 pt (15) the total handoff failure is obtained by summing up the transmission and processing time. thoharp = tharp + ptharp (16) thoharp = 2 (twl + ter−ar + tar−sha) + twi + 5pt (17) thoharp = trhad + ptrhad (18) thorhad = 2 (ter−ar + tar−sha) + twi + twl + 5pt (19) from (7) and (8), it is clear that the bu re-registration handoff in rhad is fast and reliable. 4 simulation results the system parameter for the simulation of rhad network is based on j. mc nair et al. [20] as depicted in table 1. in this experiment we analyse the bu re-registration latency of different approach. figure 3 depicts the number of recovery messages exchanged during bu registration at the new active ha. the number of recovery messages is high in mipv6 and other methods when compared to our rhad approach. table 1: system parameter system parameter value wired link message propagation time 0.5 ms wireless link message propagation time 2 ms link failure probability 0.5 bit rate of wired link 155 mbps bit rate of wireless link 144 kbps message processing time 0.5 ms after the recovery of new ha, the mn sends bu to confirm its binding registration with it. the bu re-registration latency over time is described in figure 5. since this process is not initiated by mn in rhad, the registration latency is alleviated within 35 ms for the simulation time of about 300 sec as shown in figure. the fraction of packets that suffers due to different delays in various approaches at the time of re-registration is shown in figure 6. packet delivery ratio is evaluated as the number of packets received by the new active ha to the total number of packets sent by the mobile node. the packet delivery ratio of diverse 468 a. avelin diana, k. sundarakantham, s. mercy shalinie figure 3: no of recovery messages of different approach figure 4: active ha packet delivery ratio figure 5: bu recovery registration latency alleviation of binding update re-registration handoff latency at home agent failure in mipv6 network 469 figure 6: suffered packets at delay approaches is illustrated in figure 4. for 175 nodes, the percentage of packet delivery ratio in rhad is 81% whereas for vharp, harp and mipv6, it is 61%, 53% and 34% respectively. the successful packet delivery is comparatively large in our technique. 5 conclusion rhad network is simulated and its handoff latency is examined. comparatively this method mitigates the registration latency of bu after the ha failure. our approach outperforms all the other approaches with the enlarged packet delivery ratio at ha. currently we are analysing the ha fault tolerant mechanism and failure recovery. in future work we would like to include an efficient ha fault tolerant method to recuperate the registered bu without any loss of service connectivity. bibliography [1] johnson, d.; perkins, c.; arkko, t. (2004); mobility support in ipv6, ietf rfc 3775, https://www.ietf.org/rfc/rfc3775.txt. [2] http://www.kame.net [3] arkko, j.; vogt, c.; haddad, w.(2007); enhanced route optimization for mobile ipv6, network working group, rfc 4866, https://www.rfc-editor.org/rfc/rfc4866.txt. [4] qian sun et al (2004); security issues in dynamic home agent address discovery, draft-sunmipv6-dhaadsecurity-00.txt. [5] http://www.cisco.com/en/us/docs/ios/ipv6/command/reference/ipv6_07.html [6] wakikawa, r. (2009); home agent reliability protocol, mext working group, draft-ietfmip6-hareliability-05.txt. [7] wakikawa, r.(2011); home agent reliability protocol, mext working group, draft-ietfmip6-hareliability-09.txt. 470 a. avelin diana, k. sundarakantham, s. mercy shalinie [8] devarapalli, v.; wakikawa, r.; thubert, p. (2006); local ha to ha protocol, draftdevearapalli-mip6-nemo-local-haha-01.txt. [9] ng, c.; thubert, p.; watari, m.; zhao, f. 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(2011); efficient fault-tolerant protocol for mobility agentsin mobile ip, international parallel and distributed processing symposium, doi:10.1109/ipdps.2001.925103, 1273-1280. [16] chen, y.s.; chen, c.h.; fang, h.y. (2008); an efficient quorum-based fault-tolerant approach for mobility agents in wireless mobile networks, ieee international conference on sensor networks, ubiquitous and trustworthy computing (sutc), 373-378. [17] faizan, j.; rewini, h.e. et al (2006); introducing reliability and load balancing in mobile ipv6 based networks, journal of wireless communications and mobile computing, 6:1-19. [18] rathi, s.; thanushkodi, k. (2006); design and performance evaluation of aneffcient home agent reliability protocol, international journal of recent trends in engineering, 2(1):2632. [19] yujun zhang; hanwen zhang (2011); a mobile agent fault-tolerant method based on the ring detection backup chain for mobile ipv6 networks, ieee int. conf. on communications, doi: 10.1109/icc.2011.5962677, 1-5. [20] mcnair, j.; akyildiz, i.f.; bender, m.d. (2001); handoffs for real-time traffc in mobile ip version 6 networks, ieee globecom, doi:10.1109/glocom.2001.966325, 6: 34633467. int j comput commun, issn 1841-9836 7(5):916-923, december, 2012. evolutionary algorithm based on the automata theory for the multi-objective optimization of combinatorial problems e. niño-ruiz elias d. niño-ruiz universidad del norte km5 via puerto colombia barranquilla, colombia web: http://combinatorialoptimization.blogspot.com/ e-mail: enino@uninorte.edu.co abstract: this paper states a novel, evolutionary metaheuristic based on the automata theory (emods) for the multiobjective optimization of combinatorial problems. the proposed algorithm uses the natural selection theory in order to explore the feasible solutions space of a combinatorial problem. due to this, local optimums are often avoided. also, emods exploits the optimization process from the metaheuristic of deterministic swapping to avoid finding unfeasible solutions. the proposed algorithm was tested using well known multi-objective tsp instances from the tsplib. its results were compared against others automata theory inspired algorithms using metrics from the specialized literature. in every case, the emods results on the metrics were always better and in some of those cases, the distance from the true solutions was 0.89%. keywords: combinatorial optimization, multi-objective optimization, automata theory, metaheuristic of swapping. 1 introduction as well known, combinatorial optimization is a branch of the optimization. its domain is optimization problems where the set of feasible solutions is discrete or can be reduced to a discrete one, and the goal is to find the best possible solution [8]. in this field it is possible to find a large number of problems denominated np-hard, that is mean that the problem does not have a solution in polynomial time. one of the most classical problems in the combinatorial optimization field is the traveling salesman problem (tsp), it has been analyzed for years [6] either in a mono or multi-objective manner. formally, tsp is defined as follows: min n∑ i=1 n∑ j=1 cij · xij, (1) subject to: n∑ j=1 xij = 1, ∀i = 1, . . . , n, (2a) n∑ j=1 xij = 1, ∀j = 1, . . . , n, (2b) ∑ i∈κ ∑ j∈κ xij ≤ |κ| − 1, ∀κ ⊂ {1, . . . , n} , (2c) xij = 0, 1∀i, j, (2d) copyright c⃝ 2006-2012 by ccc publications evolutionary algorithm based on the automata theory for the multi-objective optimization of combinatorial problems 917 where cij is the cost of the path xij and κ is any nonempty proper subset of the cities 1, . . . , m. (1) is the objective function. the goal is the optimization of the overall cost of the tour. (2a), (2b) and (2d) fulfill the constrain of visiting each city only once. lastly, equation (2c) set the subsets of solutions, avoiding cycles in the tour. tsp has an important impact on different sciences and fields, for instance in operations research and theoretical computer science. most problems related to those fields, are based in the tsp definition. for instance, the hard scheduling optimization [5] had been derived from tsp. although several algorithms have been proposed for the solution of tsp, there is not one that optimal solves it. for this reason, this paper discuss novel metaheuristics based on the automata theory in order to approach the solution of the multi-objective traveling salesman problem. this paper is structured as follows: in section 2 important definitions about the multiobjective combinatorial optimization and the metaheuristics based on the automata theory are given, section 3 discusses an evolutionary metaheuritic based on the automata theory for the multi-objective optimization of combinatorial problems, lastly, in section 4 and 5 experimental results are given for each algorithm in order to estimate their performance using multi-objective metrics from the specialized literature. 2 preliminaries 2.1 multi-objective optimization the multi-objective optimization consists in two or more objectives functions to optimize and a set of constraints. mathematically, the multi-objective optimization model is defined as follows: optimize f(x) = {f1(x), f2(x), . . . , fn(x)} , (3) subject to: h(x) = 0, (4a) g(x) ≤ 0, (4b) xl ≤ x ≤ xu, (4c) where f(x) is the set of objective functions, h(x) and g(x) are the constraints of the problem. lastly, xl and xu are the bounds for the set of variables x. 2.2 metaheuristic of deterministic swapping (mods) metaheuristic of deterministic swapping (mods) [4] is a local search strategy that explores the feasible solution space of combinatorial problems based on a data structure named multi objective deterministic finite automata (mdfa) [3]. a mdfa is a deterministic finite automata that allows the representation of the feasible solution space of combinatorial problems. formally, a mdfa is defined as follows: m = (q, σ, δ, q0, f(x)), (5) where q represents all the set of states of the automata (feasible solution space), σ is the input alphabet that is used for δ (transition function) to explore the feasible solution space of a 918 e. niño-ruiz combinatorial problem, q0 contains the initial set of states (initial solutions) and f(x) are the objectives to optimize. mods explores the feasible solution space represented through a mdfa using a search direction given by an elitist set of solutions (q∗). the elitist solution are states that, when were visited, their solution dominated at least one solution in qϕ. qϕ contains all the states with non-dominated solutions. lastly, the template algorithm of mods is defined as follows: 1. create the initial set of solutions q0 using a heuristic relative to the problem to solve. 2. set qϕ as q0 and q∗ as ϕ. 3. select a random state q ∈ qϕ or q ∈ q∗ 4. explore the neighborhood of q using δ and σ. add to qϕ the solutions found that are not dominated by elements of qf . in addition, add to q∗ those solutions found that dominated at least one element from qϕ. 5. check stop condition, go to 3. 2.3 simulated annealing metaheuristic of deterministic swapping (samods) simulated annealing & metaheuristic of deterministic swapping [2] (samods) is a hybrid local search strategy based on the mods theory and simulated annealing algorithm for the multi-objective optimization of combinatorial problems. its main propose consists in optimizing a combinatorial problem using a search direction and an angle improvement. samods is based in the next automata: m = (q, q0, p(q), f(x), a(n)), (6) alike mods, q0 is the set of initial solutions, q is the feasible solution space, f(x) are the functions of the combinatorial problem, p(q) is the permutation function (p(q) : q → q) and a(n) is the weighted function (a(n) : n → ℜn). n represents the number of objective for the combinatorial problem. samods exploits the search directions given by mods and it proposed an angle direction given by the function a(n). due to this, samods template is defined as follows: 1. setting sets. set q0 as the set of initial solutions. set qϕ and q∗ as q0. 2. settings parameters. set t as the initial temperature, n as the number of objectives of the problem and ρ as the cooler factor. 3. setting angle. if t is equal to 0 then got to 8, else set ti+1 = ρ × ti, randomly select s ∈ qϕ, set w = a(n) = {w1, w2, · · · , wn} and go to 4. 4. perturbing solutions. set s′ = p(s), add to qϕ and q∗ according to the next rules: qϕ = qϕ ∪ { s′ } ⇔ (̸ ∃r ∈ qϕ)(r is better than s′), (7a) q∗ = q∗ ∪ { s′ } ⇔ (∃r ∈ q∗)(s′ is better than r), (7b) then, if qϕ has at least one element that dominated to s′ go to step 5, otherwise go to 7. evolutionary algorithm based on the automata theory for the multi-objective optimization of combinatorial problems 919 5. guess with dominated solutions. randomly generated a number n ∈ [0, 1]. set z as follows: z = e(−(γ/ti)), (8) where ti is the temperature value in moment i and γ is defined as follows: γ = n∑ i=1 wi · fi(sx) − n∑ i=1 wi · fi(s′x), (9) where sx is the vector x of solution s, s′x is the vector x of solution s′, wi is the weight assigned to the function i and n is the number of objectives of the problem. if n < z then set s as s′ and go to 4 else go to 6. 6. change the search direction. randomly select a solution s ∈ q∗ and go to 4. 7. removing dominated solutions. remove the dominated solutions for each set (q∗ and qϕ). go to 3. 8. finishing. qϕ has the non-dominated solutions. 2.4 genetic simulated annealing metaheuristic of deterministic swapping (sagamods) simulated annealing, genetic algorithm & metaheuristic of deterministic swapping [2] (sagamods) is a hybrid search strategy based on the automata theory, simulated annealing and genetics algorithms. sagamods is an extension of the samods theory. it comes up as result of the next question: could samods quickly avoid local optimums? although, samods avoids local optimums guessing, it can take a lot of time accepting dominated solutions for finding non-dominated. thus, the answer to this question is based on the evolutionary theory. sagamods proposes crossover step before samods template is executed. due to this, sagamods supports to samods for exploring distant regions of the solution space. formally, sagamods is based on the next automata: m = (q, qs, c(q, r, k), f(x)), (10) where q is the feasible solutions space, qs is the initial solutions and f(x) are the objectives of the problem. c(q, r, k) is defined as follows: c(q, r, k) : q → q, (11) where q, r ∈ q and k ∈ n. q and r are named parents solutions and k is the cross point. lastly, sagamods template is defined as follows: 1. setting parameters. set qs as the solution set, x as the number of solutions to cross for each iteration. 2. set qc (crossover set) as selection of x solutions in qs, qm (mutation set) as ϕ and k as a random value. 3. crossover. for each si, si+1 ∈ qc/1 ≤ i < ∥qc∥ : qm = qm ∪ {c(si, si+1, k)} 4. mutation. set q0 as qm . execute samods as a local search strategy. 5. check stop conditions. go to 2. 920 e. niño-ruiz 3 evolutionary metaheuristic of deterministic swapping (emods) evolutionary metaheuristic of deterministic swapping (emods), is a novel framework that allows the multiobjective optimization of combinatorial problems. its framework is based on mods template therefore its steps are the same: create initial solutions, improve the solutions (optional) and execute the core algorithm. unlike samods and sagamods, emods avoids the slowly convergence of simulated annealing’s method. emods explores different regions from the feasible solution space and search for non-dominated solution using tabu search. the core algorithm is defined as follows: 1. set θ as the maximum number of iterations, β as the maximum number of state selected in each iteration, ρ as the maximum number of perturbations by state and qϕ as q0. 2. randomly select a state q ∈ qϕ or q ∈ q∗. 3. mutation tabu search set n as the new solutions found as result of perturbing q. add to qϕ and q∗ according to the next equations: (qϕ = qϕ ∪ {q}) ⇐⇒ (̸ ∃r ∈ qϕ/r is better than q) (12a) (q∗ = q∗ ∪ {q}) ⇐⇒ (∃r ∈ qϕ/q is better than r) (12b) and then, the states with dominated solutions for each set are removed. 4. crossover. randomly, select states from qϕ and q∗. generate a random point of cross. 5. check stop condition, go to 3. step 2 and 3 support the algorithm in removing dominated solutions from the set of solutions qϕ as can be seen in figure 3. however, one of the most important steps in the emods algorithm is 4 where new solutions are found after the crossover step. 4 experimental analysis 4.1 experimental settings the algorithms were tested using well-known instances from the multi-objective tsp taken from tsplib [1]. the test of the algorithms was conducted using a dual core computer with 2 gb ram. the optimal solutions were constructed based on the best non-dominated solutions of all algorithms in comparison for each instance used. the instances were constructed using the combination of the mono-objective instances kroa100, krob100, kroc100, krod100 and kroe100. for instance, kroab100 is a bi-objective instance whose matrices of distance are given by the instance kroa100 and krob100. we full combine the instances (kroab100, kroac100, . . ., kroabcde100) and then we run the experiments. the metrics used for the measurement of the different algorithms are described below, most of them use two pareto fronts. the first one is pftrue and it refers to the real optimal solutions of a combinatorial problem. the second is pfknow and it represents the optimal solutions found by an algorithm. in all the cases ∥ · ∥ represents the number of elements. gndv = ∥pfknow∥ , (13) regndv = ∥{y|y ∈ pfknow ∧ y ∈ pftrue}∥ , (14) evolutionary algorithm based on the automata theory for the multi-objective optimization of combinatorial problems 921 where generation of non-dominated vectors (gndv) and real generation of non-dominated vectors (regndv) measure the number of solutions and the number of true solutions found by an algorithm respectively. on the other measures the number of true solutions generated. on the other hand, generational distance (gd) and inverse generational distance (igd) measure the distance between fpknow and fptrue: gd = ( 1 ∥pfknow∥ ) ·  ∥pfknow∥∑ i=1 di  (1/p), igd = ( 1 ∥pftrue∥ ) ·  ∥pfknow∥∑ i=1 di  , (15) where di is the smallest euclidean distance between the solution i of fpknow and the solutions of fptrue and p is the dimension of the combinatorial problem. for the measurement of the range variance of neighboring solutions in pfknow the spacing (s) is proposed: s = ( 1 ∥pfknow∥ − 1 )2 ·  ∥pfknow∥∑ i=1 ( d − di )2(1/p) (16) where di is the smallest euclidean distance between the solution i and the rest of solutions in pfknow. d = 1∥pftrue∥ ∑∥pftrue∥ i=1 di. the error rate (ε) depicts the error rate respect to the precision of the solutions as follows: ε = (∣∣∣∣∥pftrue∥ − ∥regndv ∥∥pftrue∥ ∣∣∣∣ ) · 100% (17) 4.2 experimental results the average of the metrics applied to each algorithm are shown in table 1. furthermore, a graphical comparison for tri-objectives instances is shown in figure 1. figure 1: graphical comparison between mods, samods, sagamods and emods for triobjective tsp instances. 922 e. niño-ruiz table 1: average performance for the algorithms in comparison using multi-objective instances of tsp with multi-objective optimization metrics. inst ance algorit hm gndv regndv ( regndv gndv ) % s gd igd ε bi-objective tsp mods 262.7 0 0% 0.0286 21.6672 2329.4338 100% samods 6487.2 1425.7 22.03% 0.0016 0.2936 265.8974 89.47% sagamods 6554.5 1581.8 23.97% 0.0015 0.3062 286.547 88.3% emods 19758.6 10671.8 54.89% 0.0003 0.0492 75.2773 22.23% tri-objective tsp mods 1992.5 63.9 3.21% 0.1508 0.302 3206.7459 99.91% samods 12444.2 269.3 2.16% 0.0727 0.0434 2321.5258 99.6% sagamods 12332.5 271.1 2.2% 0.0743 0.0437 2312.3389 99.6% emods 68969.1 67097 97.3% 0.0468 0.0011 6.3914 0.89% quad-objective tsp mods 5364.8 3273.2 60.99% 0.3468 0.0252 5810.4824 94.31% samods 27639.6 11594.2 41.94% 0.2325 0.0043 3397.7495 79.87% sagamods 35649.6 14754.8 41.4% 0.2231 0.0032 3013.1894 74.39% emods 200420.6 27991.6 13.97% 0.176 0.0005 1891.9864 51.43% quint-objective tsp mods 7517 7517 100% 0.5728 0.0125 15705.6864 98.41% samods 26140 26140 100% 0.4101 0.0033 10801.6382 94.46% sagamods 26611 26611 100% 0.4097 0.0033 10544.8901 94.36% emods 411822 411822 100% 0.3136 0.0001 950.4252 12.77% 5 conclusion samods, sagamods and emods are algorithms based on the automata theory for the multi-objective optimization of combinatorial problems. all of them are derived from the mods metaheuristic, which is inspired in the theory of deterministic finite swapping. samods is a simulated annealing inspired algorithm. it uses a search direction in order to optimize a set of solution (pareto front) through a linear combination of the objective functions. on the other hand, sagamods, in addition to the advantages of samods, is an evolutionary inspired algorithm. it implements a crossover step for exploring far regions of a solution space. due to this, sagamods tries to avoid local optimums owing to it takes a general look of the solution space. lastly, in order to avoid slow convergence, emods is proposed. unlike samods and sagamods, emods does not explore the neighborhood of a solution using simulated annealing, this step is done using tabu search. thus, emods gets optimal solution faster than sagamods and samods. lastly, the algorithms were tested using well known instances from tsplib and metrics from the specialized literature. the results shows that for instances of two, three and four objectives, the proposed algorithm has the best performance as the metrics values corroborate. for the last instance worked, quint-objective, the behavior of mods, samods and sagamods tend to be the same, them have similar error rate but, emods has a the best performance. in all the cases, emods shows the best performance. however, for the last test, all the algorithms have different solutions sets of non-dominated solutions, and those form the optimal solution set. acknowledgment first of all, i want to thank to god for being with me in my entire life, he made this possible. secondly, i want to thank to my parents elias niño and arely ruiz and my sister carmen niño for their enormous love and support. finally, and not less important, to thank to my beautiful wife maria padron and our baby maria gabriela for being my inspiration. bibliography [1] university of heidelberg. tsplib office research group discrete optimization university of heidelberg. http://comopt.ifi.uni-heidelberg.de/software/tsplib95/. evolutionary algorithm based on the automata theory for the multi-objective optimization of combinatorial problems 923 [2] elias d. niño. samods and sagamods: novel algorithms based on the automata theory for the multi-objective optimization of combinatorial problems. int. j. of artificial intelligence special issue of ijai on metaheuristics in artificial intelligence, accepted, 2012. [3] elias d. niño, carlos ardila, yezid donoso, and daladier jabba. a novel algorithm based on deterministic finite automaton for solving the mono-objective symmetric traveling salesman problem. int. j. of artificial intelligence, 5(a10):101-108, 2010. [4] elias d. niño, carlos ardila, yezid donoso, daladier jabba, and agustin barrios. mods: a novel metaheuristic of deterministic swapping for the multi objective optimization of combinatorials problems. computer technology and application, 2(4):280-292, 2011. [5] elias d. niño, carlos ardila, adolfo perez, and yezid donoso. a genetic algorithm for multiobjective hard scheduling optimization. int j comput commun, 5(5):825-836, 2010. [6] j.g. sauer and l. coelho. discrete differential evolution with local search to solve the traveling salesman problem: fundamentals and case studies. in cybernetic intelligent systems, 2008. cis 2008. 7th ieee international conference on, pages 1-6, 2008. [7] yang xiawen and shi yu. a real-coded quantum clone multi-objective evolutionary algorithm. in consumer electronics, communications and networks (cecnet), 2011 international conference on, 4683-4687, 2011. [8] qin yong-fa and zhao ming-yang. research on a new multiobjective combinatorial optimization algorithm. in robotics and biomimetics, 2004. robio 2004. ieee international conference on, 187-191, 2004. int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 353-364 advance and immediate request admission: a preemptable service definition for bandwidth brokers i.t. okumus, f.u. dizdar ibrahim taner okumus k.maras sutcu imam university, computer engineering department avsar kampusu, 46000, k.maras, turkey okumus@mu.edu.tr ferhat umut dizdar mugla university, graduate school of science kotekli kampusu, 48000, mugla, turkey abstract: differentiated services architecture lacks control level functionalities and bandwidth brokers are proposed to fill that gap. in order to provide proper control level functionalities, bandwidth brokers need to provide services for both advance requests and immediate requests. there is a tradeoff between preemption of immediate flows and utilization of links. it is important for a resource manager to provide the promised qos level to a flow without any preemption. in this study, we solve the preemption and the experienced qos problem by defining a preemptable service and explain how this service works and also show the performance and scalability characteristics of resource manager with the addition of a preemptable service. keywords: admission control, ir flows, ar flows,preemptable forwarding service, quality of services. 1 introduction differentiated services (diffserv) architecture [1] is the de facto standard to provide qos in ip networks. however diffserv only specifies the data level functionalities. control level functionalities are left out from the architectural definition.there are various options to provide control level functionalities of diffserv. one of the solutions is named bandwidth broker (bb) [2], which is essentially an intra-domain resource manager (idrm) [3] in a diffserv domain. this manager has admission control, authentication, authorization, inter-bb communication and resource management duties in a diffserv network. in the internet, different applications generate traffic flows. depending on the application, different flows have different qos needs. diffserv architecture defines qos classes to address the needs of different flows [4] [5]. again depending on the application, actual traffic generation time will be different. some applications generate traffic right away and ask for resources to be used immediately. some applications make advance requests for a traffic that will be generated in the future. in order to provide qos for all kinds of applications, idrm needs to provide service for both advance requests (ar) and immediate requests (ir). in this study, to provide proper qos for both type of requests we propose a new service called preemptable forwarding (pf). we present the details of the architecture with the new pf service and analyze the performance and scalability characteristics of the approach. rest of the paper is organized as follows. in the following section, we summarize the previous work in this subject. section 3 gives the details of the idrm and also the definition of a pf service. section 4 provides the analysis results. comparison of the results with earlier work is given in section 5. we give the concluding remarks in section 6. copyright c⃝ 2006-2012 by ccc publications 354 i.t. okumus, f.u. dizdar 2 related work one of the earlier works in the area of resource planning in advance request agents is provided by schelen and pink [6]. in this study, authors tried to reduce the preemption ratio of the ir flows. authors experimented with different look-ahead times to analyze its effect on preemption rate of ir flows. the look-ahead time is kept constant in time (clat). authors show that when look-ahead time increases, the preemption rate decreases but utilization also decreases. lin et.al. [7] proposed to change the look-ahead time depending on the application. in this scheme, authors use prediction methods to calculate the holding time of an ir flow and use this time as the look-ahead time. this scheme requires sensitive prediction techniques which brings high cost to the computing entity. ahmad et.al. [8] proposes to use dynamic look-ahead time to solve the problem. authors argue that look-ahead time is dependent on resource scarcity, ir arrival and release rate, and bandwidth release per ir call. authors calculate the lat dynamically (dlat) by considering these parameters at the time of the calculation. when there is a need for preemption, ir calls that are accepted most recent time are preempted to reduce wasted throughput. however size of these flows is also important. preempting a recently accepted giant flow will cost more to the network than preempting an old small flow. also when the ar limit is low and the lat is high, dlat model exhibits comparable results with clat model. degermark et.al. [9] used a model where all flows declare their duration, and admission is based on measurements and future predictions of the traffic load. in this scheme none of the flows are preempted. ar flows are expected to provide the duration of the flow. however assuming all ir calls to provide their duration is not realistic in today’s networks. greenberg, srikant, and whitt [10] proposed a probabilistic approach for ar admission control. in this method, an ar call is admitted based on the call interruption probability. if the probability is below a threshold, request is admitted. this method assumes all ar calls are made for far ahead in time. this scheme allows occasional service disruptions to achieve higher utilization. srikant and whitt [11] used clt approximation to calculate interrupt probability for resource sharing between ar and ir flows. this method allows multi-class resource scheduling. karsten et.al. [12] proposed a policy-based service specification for advance resource reservations. in the study, an ir request is assumed to be nonpreemptable for certain amount of time and preemptable after that duration. during the admission process, ir requests provide the nonpreemptable duration. also it is possible for a flow to request nonpreemptable for the whole lifetime. however, it is not realistic for a flow to require certain qos for a limited time and not need that qos after that. applications require certain qos for the whole lifetime. if all ir requests are nonpreemptable, then the network utilization is low. this study does not provide any evaluation result for the suggested scheme. in order to provide an acceptable service for both ar and ir calls it is important to have both flows accepted in the network and also to have a sensitive preemption scheme to reduce preemption rate of ir flows. when providing qos, if an idrm accepts a flow into the network, whether it is ar or ir, flow expects to get the promised qos through the duration of the flow. unaware preemption should not be an option in any case. none of the proposed solutions consider user satisfaction and experienced-qos in their study. in the next section we provide the solution we propose for this problem. advance and immediate request admission: a preemptable service definition for bandwidth brokers 355 3 proposed model: preemptable forwarding in our work, we used idrm architecture as a resource manager [3]. idrm is mainly a specialized bandwidth broker. idrm is responsible for admission control and intra-domain resource management along with other tasks. these two tasks are the important ones for our study. in order to better manage intra-domain resources, idrm knows intra-as topology. idrm keeps track of the available capacity of individual links in the network. idrm also keeps track of the currently reserved resources. in this context, to support advanced requests (ar), idrm needs to keep records of start and end times and required capacity of advanced reservation flows. in bandwidth database, idrm keeps current load of the links and differentiates immediate and advance reserved shares of the bandwidth (table 1). table 1: a sample database state linkid utilizedbw reservedbw 40 5 7 41 5 5 table 2: time slot table for reservation states linkid 40 time slot 1 2 3 4 5 6 7 8 9 reserved bw 1 2 4 2 0 1 5 4 0 idrm also keeps reservation database for ar requests. for the admission of advance requests, idrm needs to know the available capacity and the reserved capacity of a future time interval. since time is an analog entity, it is impossible to keep track of the link states in every point in time. usual way to make time more manageable is to quantify it. we call these quantified time intervals time slots. idrm keeps track of reserved capacity of every time slot through the future. to prevent the scalability problems, the number of time slots into the future needs to be finite. the actual amount of a time slot can be determined according to the network’s need. it can be seconds, minutes, hours. a sample state of reservation database is shown in table 2. 3.1 admission control admission control methods for ir, ar and pf requests are different. the goal is to reduce the preemption rate of ir flows and to increase the overall throughput as much as possible. in order to achieve this goal, we need to keep a balance between ir and ar flows. if we only accept ir flows, admission control decision is simply based on current available capacity on the path of the flow. determination of this capacity can be parameter-based or measurement-based which is out of scope of this paper. if we only accept ar flows, then the admission decision is based on the available capacity from the start time to the end time of the request. when we have both ir and ar together, we need to use a mixture of these two admission control methods. we define lc as the total link capacity, irlc as the link capacity share of ir flows, ciri as the capacity of the ith active ir flow,cari as the capacity of the ith active ar flow, cir(t0) as the total ir capacity used by ir flows at time t0, car(t0) as the total ar capacity used by ar flows at time t0. cir(t0), and car(t0) is calculated as follows: cir(t0) = n1∑ i=0 ciri, i = 0,1, ...,n1 (1) car(t0) = n2∑ i=0 cari, i = 0,1, ...,n2 (2) residual capacity at time t0for ar and ir share is calculated as follows: 356 i.t. okumus, f.u. dizdar irlcr(t0) = irlc − cir(t0) (3) arlcr(t0) = arlc − car(t0) (4) admission control of ir flows considering the preemption and throughput tradeoff, there can be two different admission control approaches for ir admission. in first case, to increase the throughput, ir flows can be allowed to overflow into the ar share of the link capacity. however some ir flows can be preempted if necessary. in the second case, to prevent preemption, ir flows can be limited into their own share of the capacity and not allowed to use the excess ar capacity. admission of the first case is based on checking the available capacity on the whole link. if there is enough available capacity at the time of the request, then the ir flow is accepted: c < irlcr(t0) + arlcr(t0) (5) admission of the second case is based on checking available capacity only on the ir share. if there is enough capacity ir flow is accepted: c < irlcr(t0) (6) admission control of ar flows admission control criterion for ar flows is different. ar flows always have priority over ir flows. admission control decision does not take active ir flow capacity into account. decision is based on the ar reservations in the duration of the requested flow. if ar flow starts at t1 and ends at t2, condition to accept the flow is: arlc > c + max[car(τ)],τ = [t1, t2] (7) where arlc stands for ar link capacity share, max[car(τ)] shows the maximum reserved ar capacity in the interval τ. if this condition is satisfied, ar flow is accepted. at time t1 ar flow will start. it is possible that in between the time of accepting the ar flow and the actual ar flow start time, some ir flows could be accepted. during interval τ, some of the ir flows can be preempted to provide capacity to the pre-accepted ar flows. preemptable forwarding service this requirement led us to define a new service type. as we will show in our analysis results, in order to get the most benefit from the network while having both ir and ar flows together, there will be a need to preempt ir flows. instead of selecting a flow without the consent of the owner, we define a new service called preemptable forwarding (pf). in this service type, some ir calls accept the probability of preemption beforehand during the admission control process. from hereon, we refer this flow type as pfir. for pfir flows, there is a chance that accepted flow will never be preempted. in this case the experienced qos will be the same as other ir and ar flows. however if there is a need for preemption, network will select one of the pfir flows to preempt. none of the regular ir flows will be preempted. since the user is expecting this preemption and has consented to preemption beforehand, there will not be any user dissatisfaction in terms of the experienced qos. this service type is most suitable in cases where there is a limit on both ir and ar flows. bandwidth capacity is divided between ir & ar flows and none of these flows can overflow advance and immediate request admission: a preemptable service definition for bandwidth brokers 357 to other flow’s share. if there is no capacity available in ir share for a new ir request, pf admission is used.pf flows will be using the excess capacity in the ar share of the link capacity. the amount that can be used by pf flows can be determined beforehand and can be static or dynamic. admission control of pfir flows the admission control decision for pfir depends on the ir capacity, ar capacity and pf share. if available capacity on the ir share is not enough to accept an ir request, user is prompted to retry for pfir. pfir request is accepted only if the residual ir capacity and residual pf share are in total bigger than the requested capacity. residual pf capacity pflcr is dependent on the look-ahead time (lat), ar reservations in that time interval and the active pfir flows. residual ar capacity (arlcr) is defined as the capacity available on the arlc and is defined as: arlcr = arlc − max[car(lat)] (8) at time t0, total pfir capacity in use is calculated as: pfir(t0) = n3∑ i=0 pfiri, i = 0,1, ...,n3 (9) residual pf capacity at time t0 is then defined as: pflcr(t0) = { pflc − pfir(t0) arlcr ≥ pflc arlcr − pfir(t0) arlcr < pflc (10) pf admission is based on the residual ir and residual pf capacities: if (c < irlcr + pflcr) then accept else reject in order to clarify the admission of pfir flows, lets take a look at the admission process. an ir request will arrive at the bb via a signaling protocol such as sibbs [14]. bb will determine the type of the request ( ir or ar). if the request is ir, bb performs ir admission procedure. if ir capacity is not enough to accept the request, bb sends back a negative message with pf admission option. this message suggests a pf request and also contains an indicator about the preemption possibility. we define pfrate(t0) as the pf share usage rate at time t0: pfrate(t0) = 1 − pflcr(t0) pflc ,0 ≤ pfrate(t0) ≤ 1 (11) average pf rate is calculated using moving average to take into account the previous trends on the pf usage rate. average pf rate takes values between 0 and 1. 0 indicates that no pf flow is active and pf capacity is available. 1 indicates pf capacity is fully used. values of variables a and b can be set based on the network needs. in our study, we used values a=0.2 and b=0.8. average pf rate is calculated as follows: avgpfrate(t0) = a ∗ pfrate(t0 − 1) + b ∗ pfrate(t0),0 ≤ a,b ≤ 1,a + b = 1 (12) average pf rate shows the possibility of preemption of a pfir flow in lat time window. based on this indicator, user can predict preemption possibility and decide whether or not to use the 358 i.t. okumus, f.u. dizdar pf service. after receiving the negative response and average pf rate, request can be submitted as a pf request. after pfir flow is admitted there is a possibility that some ar flows can request a reservation and reclaim the bandwidth currently used by pfir flows. then some pfir flows will be preempted. in this case we propose to preempt latest pfir flow. a detailed loss-benefit analysis needs to be made on the preemption choices to develop a method to choose the flow to be preempted that will have least harm on the network benefit. however, this study is out of scope of this paper. 4 simulation results our analysis mainly covers the effects of the admission control methods on throughput and preemption rate. along with these, to measure the scalability we define benefit and processing load as other measures. 1 unit benefit is defined as the amount of income network gets from a 1mbps of flow in 1 sec. if a 100mbps link is fully utilized for 20 seconds, benefit is 100*20=2000 units. processing load is the amount of work required to process an incoming request. in order to test this, we calculate the number of accesses to databases by idrm to produce a response to a request. if an idrm accesses bandwidth table 2 times and time slot table 4 times that requests’ processing load is 6 units. figure 1: simulation topology idrm is implemented on ns2 simulation software [15]. we used the topology shown in figure 1 in our simulation. in this topology, there are 10 source nodes and 10 destination nodes. all the link capacities are 100mbps. the link between idrm+router and router is the main bottleneck link. we assume that all flows are unidirectional from sources to destinations. flows and reservation requests are entered into the domain from edge routers. edge routers forward reservation requests to idrm. idrm applies admission control methods and sends back an accept/reject answer to the source. the incoming reservation requests follow poisson distribution with a mean arrival of 10 requests per time slot. time slots are used as the time measure and on average 10 request is produced in a single time slot by all the traffic sources. ir & ar limit of these requests are varied depending on the scenario. every request asks for 1mbps capacity from the network and flows last for 20 time slots. in all the simulation scenarios, there is a warmup time. because of the warmup time, first 100 time slots is excluded from evaluation. all results indicate the steady state case. advance and immediate request admission: a preemptable service definition for bandwidth brokers 359 in order to determine the network behavior and use it as a benchmark for other results, we first tested the network with only ir and with only ar flows. 4.1 ir-only and ar-only flows in ir-only scenario, sources only make ir requests from the network. there is no limit on the ir flows. these flows can consume the whole link capacity. during the simulation, links are saturated and the network behavior under full capacity is observed. in this case, 29.24% of ir are rejected because of unavailable capacity. maximum benefit is 20000 units and achieved benefit for this scenario is 19627 units. average utilization is calculated as 98.13%. average process-time is 34.17 units and total process time is 70577 units. in the ar-only case, sources make only ar requests from the network. no limit is imposed on ar flows in terms of the bandwidth use. in this scenario, 38.51% of ar requests are rejected. the difference between ir-only and ar-only case is that, ar-only case works with time slots. that means flows can start only at the beginning of a time slot. however, ir flows can start anytime. this is one of the reasons for high rejection ratio of ar flows. total network utilization in this scenario is on average 90.73%. network gained 18146 units of benefit out of 20000 maximum benefit. total process-time for ar-only case is 166327 units. comparing the two cases, ar flows achieve lower network utilization because of the scheduling problems and the slot timing. ar flows achieve lower benefits and also higher process times compared to ir flows. 4.2 ir & ar together, no limit on ar in this scenario, ir and ar flows appear together in the network. 70% of the requests are ir requests and 30% of the requests are ar requests. since the holding time for flows is 20 time slots and each flow consumes 1mbps of bandwidth, resources are saturated in 10 time slots. results show that network utilization is 96.62% on average. in case of congestion, ir flows are preempted. 44.2% of the total accepted flows are ir flows and 55.8% are ar flows. 67.9% of ir flows and 0.35% of ar flows are rejected. during the simulation 4.66% of accepted ir flows are preempted. in this ir&ar-no-limit case, when both flows are active in the network, utilization is lower compared to the ir-only case, but it is higher than the ar-only case. 4.3 ir & ar, ar limited in this scenario, our goal is to analyze the effect of limiting the ar flows on network utilization. to determine this effect we set the limit of ar flows to 50% in the first simulation and reduced it by 10% for each successive runs. ar flows can use the limited capacity reserved for them. if there is not enough capacity, then ar flows are rejected. however, ir flows do not have any limit. if there is available capacity in the ir part, flow is accepted immediately. ir flows are preempted in the future when an ar flow asks for the capacity that is being overused by ir flows. figure 2 shows ar share and accept/reject ratios for both ir and ar flows. figure 3 shows ir and ar throughput and total throughput for ar-limited scenario. as it can be seen from the figure, as the ar percentage drops, total throughput increases. network utilization changes from 97.23% for 50% ar share to 98.26% for 10% ar share. figure 4 shows the ir drop rate. ir flows are preempted as ar flows move in for their share of the capacity. figure clearly shows that ir drop rate increases as the ar share increases in the network. 360 i.t. okumus, f.u. dizdar figure 2: accept/reject rates of ir & ar flows for different limits on ar flows figure 3: ir, ar bandwidth shares and total throughput for ar limited case. figure 4: ir drop rate for ar limited case table 3 shows processing time shares of ir and ar flows for different limits on ar flows. results show that as the ar limit decreases, total processing time also decreases. this is in accordance with the fact that admittance of ar flows is more costly than admittance of ir flows. table 3: process times for ar limited case limit iravg irtotal aravg artotal total 50% 23 159756 99 279883 439639 40% 25 174704 83 236285 410989 30% 27 190244 66 187944 378188 20% 29 206265 49 140279 346544 10% 32 222212 28 80077 302289 table 4: process times for both limited case limit(%) iravg irtotal aravg artotal total 50-50% 21 146917 99 279883 426800 60-40% 23 165047 83 236285 401332 70-30% 26 183177 66 187944 371121 80-20% 29 201307 49 140279 341586 90-10% 31 219347 28 80077 299424 4.4 ir & ar, both limited as we can see from the ar-limited scenario, some ir flows are preempted, which is not desired in a qos environment. to prevent that, one option is to limit both flows. in this scenario we divide the total capacity between these two flows. we do not allow any of the flow types to use the capacity from the other flow types’ share. as in previous case, highest capacity reserved for ar flow is 50%. this limit is reduced by 10% for each simulation run and ir limit is increased by 10% to analyze the effect of the amount of share each flow type gets from the network. we start with 50-50 (ir-ar), then change to 60-40, 70-30 and so on for each successive runs. figure 5 shows accept/reject ratios for both limited case. compared to previous scenarios, ir accept-rate is reduced. the reason is that after using the capacity reserved for ir flows, all ir requests are rejected. advance and immediate request admission: a preemptable service definition for bandwidth brokers 361 figure 5: accept/reject rates of ir & ar flows for different limits on both flows figure 6: ir, ar shares and throughput for both limited case figure 6 shows ir-ar shares and total throughput for both-limited case. in this scenario, throughput is decreased significantly compared to the base cases and to the ar-limited case. for 50-50 case, throughput is 93.18% and for 90-10 case throughput is 97.06%. table 4 shows process times for both limited case. compared to the ar-limited case, ar process times are same; however ir process time is decreased because less ir flows are accepted into the network. in this scenario, no ir flows are dropped. since both flows have their own share and they are bound to it, preemption is not necessary. however, this is achieved by sacrificing from total throughput. this result also confirms that there is a clear tradeoff between throughput and the preemption of ir flows. since the main goal of the network provider is to get the maximum benefit from the network, which can be achieved by maximum utilization of the network capacity, we need to find a balance between the preemption and the total throughput. if preemption is inevitable, then the question is which flows should be preempted. in our study we prefer to define a new service called preemptable forwarding (pf). ir flows that are accepted as pf flows will be preempted in case of an overuse. 4.5 preemptable forwarding service with lookahead time in our scheme, in order to balance the throughput and preemption we select to limit the capacities of both ir and ar flows. as we showed in section 4.4, this scheme has low throughput but no ir drops. in order to increase the throughput we allow certain percentage of ar capacity to be used by pf flows if necessary. we analyzed this scenario on the same topology with the same parameters. in the analysis, we have two parameters to consider. first one is the lat. how lat affects the throughput? second parameter is the pf percentage in the ar region. we analyzed the effect of pf percentage on the throughput. in the simulations we set the ir-ar ratio to 50-50 and changed the pf share from 1% to 15% of the total link capacity and also changed lat from 1 time slot to 15 time slots. figure 7 shows ir lost benefits because of preemption for different pf shares. for low lat values, ir drop rate is higher for high pf shares. as lat value increases, lost benefit for all pf shares converge to zero. figure 8 shows the total throughput of the network for different lat values and pf shares. this graph shows that total achieved throughput is higher for lower lat values and higher pf shares. again these results show the clear tradeoff between ir drops and the total throughput. table 5 gives the utilization values for different pf share and lat values. table 6 shows 362 i.t. okumus, f.u. dizdar figure 7: ir lost benefits figure 8: total throughput lost ir benefits for different lat values and pf shares. table 5: throughput values for different lat values and pf shares pf(%) 1 3 5 10 15 lat utilization (%) 1 93.94 95.07 95.78 96 96.04 2 93.88 94.88 95.44 95.61 95.57 3 93.825 94.80 95.24 95.27 95.23 4 93.77 94.57 94.88 94.81 94.85 5 93.69 94.37 94.56 94.57 94.52 8 93.50 93.88 94.01 94 94.01 10 93.42 93.62 93.63 93.64 93.66 15 93.33 93.46 93.50 93.53 93.5 table 6: lost ir benefits for different lat values and pf shares pf(%) 1 3 5 10 15 lat lost ir benefits 1 13 38 62 40 41 2 8 32 41 20 5 3 7 27 30 12 0 4 2 14 11 0 0 5 0 2 0 0 0 8 0 0 0 0 0 10 0 0 0 0 0 15 0 0 0 0 0 results on table 5 and 6 suggest that for 50-50 ir-ar share, best option is to set pf share at 15% and lat value at 3 time slots. these values give the highest throughput on the network without any preemption. highest utilization that can be achieved in this scheme is 96.035%. this value is considerably higher than the 50-50 ir-ar both limited case, which has 93.18% utilization. processing time for a pf request consist of two components. a pf request is originally an ir request. when there is lack of resources on ir share, request is rejected for resubmission as a pfir request. pfir admission is similar to an ar admission. residual ar capacity is calculated and decision is made depending on that value. so the process is a two pass process. first pass results in rejection of an ir request. second pass is pf admission. in terms of the processing time, admission of a pfir request is more costly than both admission of an ir request and also admission of an ar request for the same lat value. regular ar admission has a constant lat value. pfir processing time increases with the increase of lat. table 7 shows average processing times of pfir requests for different lat values. table 7: average processing times for different lat values lat 1 2 3 4 5 8 10 15 avg. process time 82 84 85 87 90 97 103 110 advance and immediate request admission: a preemptable service definition for bandwidth brokers 363 5 comparison of the results ahmad et.al. [8] proposed a dynamic lookahead time (dlat) solution for reduce preemption rate at the cost of lower throughput. this study provides results for dlat and compared the results with the study conducted by schelen and pink [6]. schelen and pink used constant lookahead time (clat) in their study. we will also compare our findings with dlat and clat models. in our pf model, we used 50%ir and 50%ar share. we compare these results with the same ar limit case in schelen and ahmad study. when ar limit is set to 50%, highest normalized throughput achieved by dlat model is 0.84 with dlat c=1.0 and highest throughput achieved by clat is 0.85 with clat 30. these lat values corresponds to lat value 2 in our study. with lat=2, highest utilization in our case is 95.6% (pf %10) and lowest utilization is 93.8% (pf %1). this shows that our scheme performs better than both schemes in terms of the network utilization. in terms of the preemption rate, our results show the number of preempted flows, while dlat and clat models show preemption probability. we will compare the preemption trends with those studies. our results indicates that it is possible to prevent preemption by selecting appropriate lat and pf share values. also while longer lat values result in lower preemption, higher lat values result in higher preemption. dlat and clat models also show the similar trends. longer dlat and clat values result in lower preemption probability. as a summary, dlat model achieves lowest throughput than clat and our pf model. pf model performs best in terms of the total throughput. preventing preemption is necessary in order not to disrupt the qos of the accepted flows. dlat model provides this with the cost of throughput. our model suggest a pf scheme where user pre-agrees a preemption when and if necessary. also pf scheme can provide low preemption ratios by selecting lat and pf% values properly. our proposed scheme results in higher utilization and also in higher user satisfaction in terms of the perceived qos. 6 conclusion in this study we analyzed the admission control method of an idrm that supports both ir and ar flow types.the main tradeoff in this environment is between preemption of ir flows and the total throughput of the network. as the throughput increases, ir preemption also increases. if we decrease ir drop rate, throughput decreases. another issue is user satisfaction due to ir drops and also the scheme to select which ir flows to drop in case of a capacity problem. in order to increase the perceived qos on the user side, instead of selecting an ir flows among the ones that the network accepted and promised to provide a certain qos without any interruption, we propose to employ a new qos class called preemptable forwarding (pf). this flow type will be accepted to the network with the condition that the flow will be preempted and not get the promised qos in case of congestion. users will be accepting the service with the possibility of a preemption or not get any qos at all. we analyzed the effects of the pf share from the total capacity on the ir preemption and the total throughput. as the pf rate increases, total throughput also increases. however, increased pf also causes high ir drop rate. when we employ lookahead time (lat) before accepting the pf requests, behavior changes. with high lat values, ir drop rate is reduced. however, this causes the network utilization to decrease. results show that employing a resource manager that uses pf service in admission control of the flows can increase the total throughput and also the user satisfaction in a qos network. 364 i.t. okumus, f.u. dizdar bibliography [1] blake, s. et.al., an architecture for differentiated services, rfc 2475, 1998 [2] nichols k. ,jacobson v. ,zhang l. , a two-bit differentiated services architecture for the internet, rfc 2638, 1999 [3] mantar h.a., okumus ý.t., hwang j., chapin s.j., a scalable intra-domain resource management architecture for diffserv networks, 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analysis for fmri using connex array a. ţugui andrei ţugui politehnica university of bucureşti romania, 061071 bucureşti, splaiul independenţei, 313 andrei.tugui@yahoo.com abstract: in the last decades, magnetic resonance imaging gained lot of popularity, and also functional magnetic resonance imaging (fmri), due to the fact that mri is a harmless and efficient technique for human cerebral activity studies; fmri aims to determine and to locate different brain activities when the subject is doing a predetermined task. in addition, using fmri analysis, nowadays we can make prediction on several diseases. this paper’s purpose is to describe the general linear model for fmri statistical analysis algorithm, for a 64 x 64 x 22 voxels dataset on a revolutionary parallel computing machine, connex array. we make a comparison to other computing machines used in the same purpose, in terms of algorithm time execution (statistical analysis speed). we will show that by taking advantage on its specific parallel computation each step in glm analysis, connex array is able to answer successfully to computational challenge launched by fmri computation: the speed-up. keywords: connex array, functional magnetic resonance imaging, image reconstruction, parallel algorithms, parallel processing. 1 introduction nowadays, neurological activity can be studied using several investigation techniques, each of them having its own advantage and disadvantage, by studying human brain from several perspectives. although other techniques like eeg (electroencephalography) or meg (magneto encephalography) have a satisfactory temporal resolution (milliseconds), when it comes to spatial resolution, pet (positron emission tomography) and fmri are much more indicated to use [1] [4]. those investigation techniques pick information from the blood flow changes. as for fmri statistical analysis on which we will focus in this paper, far away from the biological and functional characteristics, from computational point of vue it is very time consuming. processing dataset is basically mri images acquired voxel by voxel; once the blood flow changes, mri signal strength changes also, this way one can analyze these changes using the statistical data analysis. typically, input data acquired one time can be in range of 100 000 voxels, but the investigation’s main problem remains the fact that this data is repeatedly acquired by 100 up to 2000 times [5] (usually because of head moving, to compensate discrete acquisition in time, slice by slice, or because most of the time we acquire a lot of noise which must be filtered). a typical fmri computational chart is presented in fig. 1. usually, fmri dataset acquired during one observation consist of 8 volumes 64 x 64 x 22 voxels each, having a 3.75 x 3.75 x 3.75 mm spatial resolution. if we represent this data as simple precision floats, the data requires about 29 mb memory space. obviously, we deal to a high enough spatial and temporal memory space, in fact the acquisition process itself is not time-consuming (usually 1 volume/s), but the processing data is, which bottlenecks a lot nowaday’s computers tasks, especially when large amount of data must be computed in real time [6][7]. estimated, today we can process a volume dataset using a dedicated processor during about 5 s, but if we use a gpu, the processing time decreases to just 0.5 s! so, this is the high importance that we assign to processing speed in fmri computation. copyright © 2006-2014 by ccc publications glm analysis for fmri using connex array 769 figure 1: fmri pipline in addition, many times fmri data reconstruction and analysis are made in real time, like bci (brain computer interface) —a cooperation method between the pc and the subject aiming to solve a given task. as one can see in fig.1, fmri data analysis requires two steps: • preprocessing input dataset • statistical dataset analysis in the following, we will present a typically 3d fmri volume computation using input data from [8], meaning first the preprocessing step and then the statistical data analysis using the glm (general linear model), on a revolutionary parallel computing machine, connex array. a full description of this parallel machine’s architecture and processing manner is given in papers [9][10]. we introduce here only the data vector definition, being in fact the cell that connex operates, a new n-length data type containing fmri samples, modeling the vectors from connex array [10]. once we understand the vectorial architecture, we will describe much better this machine’s parallelism. thereby, if we have a c language instruction to be executed as the following one: for( ind = 1;ind <= sizeof_vector;ind ++ ) vect3[ind] = vect1[ind] + vect2[ind] then connex will sum in one step vect1 and vect2 in the sum vector vect3, whereas a sequential processor would normally do the addition element by element in sizeof_vector steps. here, sizeof_vector is a constant holding the vector’s length we operate with, by default. thereby, by typical operators’ overloading, using a special c++ library named cvector [11], connex can sum, multiply, decrease or shift vectors of data. all cycling instructions like for are executed using a new cvector instruction like the following: where (vect1 %2 == 0) {vect1 = 0} elsewhere {vect1 = 1} ; 2 fmri data preprocessing preprocessing usually requires signal filtering. the acquired signal is exposed to a lot of perturbation as head moving (physiological noise), which can be eliminated using motion cor770 a. ţugui rection filtering step, or even the acquisition process itself, which is done in discrete steps (once a section) can generate noise: this noise is filtered using the slice time correction filtering step. we show this discrete time slice acquisition of one voxel in figure 2. figure 2: fmri slice time acquisition 3 slice time correction during this preprocessing step, slice time correction is made using sinc interpolation. from computational point of view, this step involves: • one 1d fft (one fast fourier transform) • one point-wise multiplication • one 1d ifft (inverse fast fourier transform) loading dataset into connex’s memory was made thereby in 64 vectors, 64 floating point elements each (one slice at the time), this way: vector<float>x[64]; x[0] = [-0.00416487...0.09823550 ]; x[63] = [-0.00687657...0.09823550]; 1dfft computation took 6 steps using cooley-tuckey algorithm [12], each step the output vector is loaded with the old sample and summed with the new resulted sample, like this: x[0]+ = (ci ∗x[0] + x12) (1) where ci is the coefficient vector at current step i and x12 is a temporary vector obtained from successive input vector shifts, like: i = 0; temp1 = shiftleft(x[0],32); where (index <32) { x12 = temp1; glm analysis for fmri using connex array 771 } temp1 = shiftright(x[0],32); i += 32; where (index >= i && index <(i+32)) { x12 = temp1; } [13] for sinc function computation we used this formula to multiply each pixel: sinc[(π/tr)(r − itr)] (2) where tr = repetition time for each pulse sequence, resulting a coefficient vector c[index] like this: where (x[index] == 0) {c[index] = 1;} elsewhere { c[index]=(sin(3.14*(1-index*2)/2))/ (3.14*(1index*2)/2);} for the reverse fourier transform computation, the algorithm is similar to 1dfft algorithm. it worth nothing to see that for this preprocessing step, the total computation time used by spm (statistical parametric mapping software) is about 32 s [14], but the same algorithm implementation on connex array took only about 100 ms. it worth nothing to say that the same algorithm implementation using fixed-point representation is much faster [15]. 4 image registration this preprocessing step filters the noise generated by head movement. it involves the alignment of all voxels to a reference voxel (still state). for fmri, it is sufficient one resolution range with 3 iterations per volume. in each iteration, three quadrature filters are applied on the x, y and z directions, the filters having each 7 x 7 x 7 voxels, complex elements and not cartesian separable. from the filter response we compute three phase shifts, three gradient shifts and the statistical certainty. then it results an equation system by adding all voxels and all filters to find the optimization parameter vector. from the optimization parameter vector we compute then a movement vector for each voxel and we apply trilinear interpolation using a 3d texture to rotate and translate the volume. paper [16] describes the complete image registration algorithm. head movement field is modeled using a 12—length parameter vector p, like: p = [p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12] t (3) p vector is computed by solving the 12–length linear equation system with: p = a−1h (4) in this paper we used fft convolution to compute head movement, although there is also the possibility to use spatial convolution in this purpose. it is worth nothing to follow the inverse matrix computation with connex, which reveals the parallelism used once the data is loaded into memory. one may consult papers [10] and [13] for further information on connex architecture and its vectorial computation. we mention that image registration is the most time consuming preprocessing step (about 95% from total time computation time used for a single volume), but using connex’s computational power, we showed once again (see table 1) 772 a. ţugui that time bottleneck can be eliminated using connex array. if we compare image registration algorithm implementation on connex array to the same algorithm implemented on an usual pc using matlab testing environment, in matlab we solve this problem during about 14 s, whereas connex uses only half of this time (table 1). a closer look on matrix computation with connex, intense used in this paper, can be found in reference [17]. 5 glm smoothing algorithm smoothing dataset is an additional filter step applied before statistical fmri analysis. typically, this filter is done for each voxel, except for those voxels near the edge of dataset, and it involves one 3d convolution. basically, this is done by using a gaussian lowpass cartesian separable filter, so the smoothing algorithm becomes in fact a 1d convolution applied in all the three directions (x, y, z). for fmri smoothing, we use typically 9 x 9 x 9 filters. thus, for an entire 1d slice we define: • the input vector: – vector<float >x[64]; • the output vector: – vector<float >y[64]; • the impulse response vector: – vector<float >h; by applying smoothing to each line (finally to the entire slice and then to the entire volume), the output vector is processed by multiplying the input vector with the impulse response vector: y = xh (5) 6 detrending the final step in preprocessing fmri data is a special filtering step called detrending. thus, papers [18] and [19] show how exactly detrending is eliminating the drifts caused by physiological noise and scanner imperfections. typically, detrending is applied to each slice of the voxel and it searches for the best linearity fit between the slices on one hand, and a particular polynomial on the other hand, fit which is eliminated by the filtering process (polynomial detrending). this filtering procedure uses in fact linear regression, an algorithm similar to statistical glm analysis, which we will describe in the following. 7 statistical fmri analysis using glm model after preprocessing, fmri dataset is subjected to a statistical analysis which can be made using two approaches [20]: • general linear model (glm) • canonical correlation analysis (cca) glm analysis for fmri using connex array 773 in this paper we approached general linear model (glm) analysis on each slice (the whole volume), where observation matrix is computed like this: y = xβ + ϵ (6) where: • y = the observations (all samples from the slice) • β = optimization parameters • x = design matrix given by stimulus paradigm • ϵ = the errors can not be explained by the model by minimizing ∥ ϵ ∥ 2 it can be shown that the best parameters [14] are given by: β̂ = (xt x)−1xt y (7) the term (xt x)−1xt (we note it with c) is slice independent and can be precomputed and loaded into memory. the only thing we must compute for each voxel in order to find the estimated parameters is the product between the constant c and the current slice y. if we define the contrast as a column vector (e.g. [1 0]t ), the test value t [14] is given by: t = ct β̂√ var(ϵ̂)ct (xt x)−1c (8) the matrix product is computed fast, as the error and its variance. then we load again the slice time-series into memory to compute the mean error: ϵ̄ = 1 n n∑ t=1 (y (t)−x(t)β̂) (9) n = number of time-points and x(t) = design matrix values corresponding to time-point t. the third memory load is made this time to compute error variance an then the test value t. the term ct (xt x)−1c is a scalar in fact, which can be precomputed. on connex, parameter matrix is loaded into 64 vectors 64-length pixels. after the data load into the memory and the matrix computation, we compute matrix product β̂ = ct y for each pixel this way: bij = addall(y [i]∗ct [j]), where (index == i) {b[i] = bij; } addall adds all products from elements located at the same positions: mi = addall(e[i]) where (index == i) { m =mi; } finally, the test value t is computed with: t[0] = b[0]∗ (ct [0]∗ √ (variance)) where variance is the statistical variance (the difference between the mean’s square and the mean, computed prior). 774 a. ţugui 8 conclusions if on a sequential processor like spm, glm analysis would take about 33 s, glm algorithm implementation on connex array requires only 1.7 ms (see table 1). we demonstrate in this paper that the speed-up in fmri reconstruction is a challenge that connex array can easily overcome. table 1: fmri glm data analysis on different processors glm step spm matlab openmp matlab cuda connex array slice time correction 32 s 280 ms 235 ms 7.8 ms 100.4 ms motion compensation 28 s 13.7 s 4.6 s 650 ms 7.857 s smoothing 32 s 1.5 s 195 ms 10.4 ms 0.05 ms detrending 8.6 ms 4.6 ms 0.37 ms 1.73 ms glm 33 s 16.6 ms 5.8 ms 0.38 ms 1.7 ms total time for glm 125 s 15.51 s 5.04 s 0.43 s 7.96 s bibliography [1] tong, s.; alessio, a.m. (2010); noise properties in psf—based fully—3d pet image reconstruction: an experimental evaluation, physics in medicine and biology, 55: 1453—1473. 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(2011); handbook of functional mri data analysis, cambridge university press, new york, usa. international journal of computers communications & control issn 1841-9836, 10(1):70-77, february, 2015. fuzzy euclidean normed spaces for data mining applications s. nădăban sorin nădăban* aurel vlaicu university of arad romania, 310330 arad, elena dragoi, 2 *corresponding author: snadaban@gmail.com abstract: the aim of this paper is to introduce some special fuzzy norms on kn and to obtain, in this way, fuzzy euclidean normed spaces. in order to introduce this concept we have proved that the cartesian product of a finite family of fuzzy normed linear spaces is a fuzzy normed linear space. thus any fuzzy norm on k generates a fuzzy norm on kn. finally, we prove that each fuzzy euclidean normed space is complete. fuzzy euclidean normed spaces can be proven to be a suitable tool for data mining. the method is based on embedding the data in fuzzy euclidean normed spaces and to carry out data analysis in these spaces. keywords: fuzzy norm, fuzzy euclidean normed spaces, data mining. 1 introduction data mining and information retrieval are two important components of the same problem: discovering new and relevant information and knowledge, through investigation of a large amount of data, through extracting the information and knowledge out of a very large databases or data warehouse. in information retrieval the user knows what he is looking for, but sometimes it is very difficult to express this thing. the use of fuzzy sets in representing the knowledge proves to be successful on many occasions, allowing the user to express his expectations in a language close to the natural one. on the other hand, many times the matching between the requests of the user and the existing data in the databases is only an approximate one, thus, the use of fuzzy sets and the degrees of membership proves to be not only useful but also necessary. in data mining, the user looks for new knowledge. the aim is to divide the data into homogeneous categories, in data classes. the use of the of fuzzy sets brings about flexibility both in representing knowledge and in interpreting the results as well. the measures of similarity are the most used, at all levels in the data mining and information retrieval. the notion of similarity, or more general of the measures of comparison is the central point for all applications in the real world. the measure of similarity aims at quantifying the degree to which two objects are similar or dissimilar, offering a numeric value for this comparison. machine learning techniques use similarity measures. machine learning represents an important method of extracting the knowledge out of very large databases. a study concerning fuzzy learning methods was realized by e. hüllermeier [4]. in the last twenty years, the world wide web has become a major source of data and information for all domains. web mining is the process of discovering useful knowledge and information through investigating the web structure and its content. different web mining tasks and advanced artificial intelligence methods for information retrieval and web mining are discussed by i. dzitac & i. moisil [3]. clustering and classification are both important tasks in data mining. since clustering means the grouping of similar objects, we need some suitable measures on data sets. in order to determinate the similarity or dissimilarity between any pair of objects, the most used measures are distance measures. copyright © 2006-2015 by ccc publications fuzzy euclidean normed spaces for data mining applications 71 if each data point is view as a n-dimensional vector x = (x1,x2, · · · ,xn) ∈ rn, where each component xi is the value of an attribute of the data, the distance between the two data instances can be calculated using euclidean distance, manhattan distance, minkowski distance, max distance, etc. what will we do if the distance between the vectors x and y can not be precisely measured and thus we are not able to assign it, with certainty, the value t ∈ r+. there are probably different approaches enabling to handle somehow this situation. one of them, fuzzy approach, consists in using on rn some fuzzy metrics, i.e. mappings m : rn × rn × [0,∞) → [0,1], where m(x,y,t) = α indicates the truth value of the statement "the distance between x and y is smaller than the real number t" and which belongs to [0,1]. it will be better that such fuzzy metrics to come from fuzzy norms on rn, namely m(x,y,t) = n(x − y,t), where n : rn × [0,∞) → [0,1]. the aim of this paper is to introduce some special fuzzy norms on kn and to obtain, in this way, fuzzy euclidean normed spaces. in order to introduce this concept we have proved that the cartesian product of a finite family of fuzzy normed linear spaces is a fuzzy normed linear space. thus any fuzzy norm on k generates a fuzzy norm on kn. finally, we prove that each fuzzy euclidean normed space is complete. fuzzy euclidean normed spaces can be proven to be a suitable tool for data mining. the method is based on embedding the data in fuzzy euclidean normed spaces and to carry out data analysis in these spaces. in studying fuzzy topological vector spaces, a.k. katsaras [5] first introduced the notion of fuzzy norm on a linear space. since then many mathematicians have introduced several notions of fuzzy norm from different points of view. our definition looks similar, but it is more general, to the definitions introduced, almost in the same time, by t. bag & s.k. samanta (see [1], [2]) and r. saadati & s.m. vaezpour (see [7]). in 2006, r. saadati & j.h. park introduced the notion of intuitionistic fuzzy euclidean normed space (see [8], [9]). 2 preliminaries definition 1. [10] a binary operation ∗ : [0,1] × [0,1] → [0,1] is called triangular norm (t-norm) if it satisfies the following conditions: 1. a ∗ b = b ∗ a,(∀)a,b ∈ [0,1]; 2. a ∗ 1 = a,(∀)a ∈ [0,1]; 3. (a ∗ b) ∗ c = a ∗ (b ∗ c),(∀)a,b,c ∈ [0,1]; 4. if a ≤ c and b ≤ d, with a,b,c,d ∈ [0,1], then a ∗ b ≤ c ∗ d. example 2. three basic examples of continuous t-norms are ∧, ·,∗l, which are defined by a∧b = min{a,b}, a·b = ab (usual multiplication in [0,1]) and a∗lb = max{a+b−1,0} (the lukasiewicz t-norm). remark 3. ∗ ≤ ∧, i.e. ∧ is stronger that any other t-norms. indeed, a ∗ b ≤ a ∗ 1 = a, a ∗ b ≤ 1 ∗ b = b. thus a ∗ b ≤ a ∧ b. definition 4. let ∗,∗′ be two t-norms. we say that ∗′ dominates ∗ and we denote ∗′ ≫ ∗ if (x1 ∗ ′x2) ∗ (y1 ∗ ′y2) ≤ (x1 ∗ y1) ∗ ′(x2 ∗ y2),(∀)x1,x2,y1,y2 ∈ [0,1]. proposition 5. for any t-norm ∗ we have ∧ ≫ ∗. 72 s. nădăban proof: let x1,x2,y1,y2 ∈ [0,1]. case 1. x1 ≤ x2,y1 ≤ y2. then x1 ∗ y1 ≤ x2 ∗ y2. thus (x1 ∗ y1) ∧ (x2 ∗ y2) = x1 ∗ y1. on the other hand (x1 ∧ x2) ∗ (y1 ∧ y2) = x1 ∗ y1. therefore (x1 ∧ x2) ∗ (y1 ∧ y2) = (x1 ∗ y1) ∧ (x2 ∗ y2). case 2. x1 ≤ x2,y2 ≤ y1. as x1 ≤ x2, we have x1 ∗ y2 ≤ x2 ∗ y2. as y2 ≤ y1, we have x1 ∗ y2 ≤ x1 ∗ y1. thus x1 ∗ y2 ≤ (x1 ∗ y1) ∧ (x2 ∗ y2). hence (x1 ∧ x2) ∗ (y1 ∧ y2) = x1 ∗ y2 ≤ (x1 ∗ y1) ∧ (x2 ∗ y2). case 3. x2 ≤ x1,y1 ≤ y2 and case 4. x2 ≤ x1,y2 ≤ y1 are similar to previous cases. 2 definition 6. [6] let x be a vector space over a field k (where k is r or c) and ∗ be a continuous t-norm. a fuzzy set n in x × [0,∞) is called a fuzzy norm on x if it satisfies: (n1) n(x,0) = 0,(∀)x ∈ x; (n2) [n(x,t) = 1,(∀)t > 0] if and only if x = 0; (n3) n(λx,t) = n ( x, t|λ| ) ,(∀)x ∈ x,(∀)t ≥ 0,(∀)λ ∈ k∗; (n4) n(x + y,t + s) ≥ n(x,t) ∗ n(y,s),(∀)x,y ∈ x,(∀)t,s ≥ 0; (n5) (∀)x ∈ x, n(x, ·) is left continuous and lim t→∞ n(x,t) = 1. the triple (x,n,∗) will be called fuzzy normed linear space (briefly fnl-space). remark 7. a) t. bag and s.k. samanta [1], [2] gave a similar definition for ∗ = ∧, but in order to obtain some important results they assume that the fuzzy norm satisfies also the following conditions: (n6) n(x,t) > 0,(∀)t > 0 ⇒ x = 0 ; (n7) (∀)x ̸= 0,n(x, ·) is a continuous function and strictly increasing on the subset {t : 0 < n(x,t) < 1} of r. the results obtained by t. bag and s.k. samanta can be found in this more general settings. b) r. saadati and s.m. vaezpour [7] suppose that 1. n(x,t) > 0,(∀)t > 0; 2. n(x, ·) is a continuous function, (∀)x ̸= 0. remark 8. n(x, ·) is nondecreasing, (∀)x ∈ x. theorem 2.1. [6] let (x,n,∗) be a fnl-space. for x ∈ x,r ∈ (0,1), t > 0 we define the open ball b(x,r,t) := {y ∈ x : n(x − y,t) > 1 − r} . then tn := {t ⊂ x : x ∈ t iff (∃)t > 0,r ∈ (0,1) : b(x,r,t) ⊆ t} is a topology on x. moreover, if the t-norm ∗ satisfies sup x∈(0,1) x ∗ x = 1, then (x,tn) is hausdorff. theorem 2.2. [6] let (x,n,∧) be a fnl-space. let pα(x) := inf{t > 0 : n(x,t) > α},α ∈ (0,1) . then p = {pα}α∈(0,1) is an ascending family of semi-norms on x. moreover, for x ∈ x,s > 0,α ∈ (0,1) we have: pα(x) < s if and only if n(x,s) > α. fuzzy euclidean normed spaces for data mining applications 73 3 convergence in fnl-spaces definition 9. [2] let (x,n,∗) be a fnl-space and (xn) be a sequence in x. the sequence (xn) is said to be convergent if (∃)x ∈ x such that lim n→∞ n(xn − x,t) = 1 , (∀)t > 0 . in this case, x is called the limit of the sequence (xn) and we denote lim n→∞ xn = x or xn → x. definition 10. [2] let (x,n,∗) be a fnl-space and (xn) be a sequence in x. the sequence (xn) is called cauchy sequence if lim n→∞ n(xn+p − xn, t) = 1 , (∀)t > 0,(∀)p ∈ n∗ . remark 11. if (x,n,∗) is a fnl-space, then every convergent sequence is cauchy sequence. definition 12. [2] let (x,n,∗) be a fnl-space. (x,n,∗) is said to be complete if any cauchy sequence in x is convergent to a point in x. a complete fnl-space will be called fuzzy banach space. definition 13. let (x,n,∗) be a fnl-space, α ∈ (0,1) and (xn) be a sequence in x. the sequence (xn) is said to be α-convergent if exists x ∈ x such that (∀)t > 0,(∃)n0 ∈ n : n(xn − x,t) > α,(∀)n ≥ n0 . in this case, x is called the α-limit of the sequence (xn) and we denote xn α→ x. theorem 3.1. let (x,n,∗) be a fnl-space and (xn) be a sequence in x. the following sentences are equivalent: 1. (xn) is convergent to x; 2. (xn) is convergent to x in topology tn; 3. (xn) is α-convergent to x, (∀)α ∈ (0,1); 4. lim n→∞ pα(xn − x) = 0,(∀)α ∈ (0,1). proof: (2) ⇔ (1) xn → x in the topology tm ⇔ (∀)r ∈ (0,1),(∀)t > 0,(∃)n0 ∈ n : xn ∈ b(x,r,t),(∀)n ≥ n0 ⇔ (∀)r ∈ (0,1),(∀)t > 0,(∃)n0 ∈ n : n(xn − x,t) > 1 − r,(∀)n ≥ n0 ⇔ lim n→∞ n(xn − x,t) = 1 , (∀)t > 0 . (1) ⇔ (3) it is obvious. (4) ⇔ (3) lim n→∞ pα(xn − x) = 0 ⇔ (∀)t > 0,(∃)n0 ∈ n : pα(xn − x) < t,(∀)n ≥ n0 ⇔ (∀)t > 0,(∃)n0 ∈ n : n(xn − x,t) > α,(∀)n ≥ n0 ⇔ xn α→ x . 2 theorem 3.2. let (x,n,∗) be a fnl-space and (xn) be a sequence in x. then (xn) is a cauchy sequence if and only if lim n→∞ pα(xn+p − xn) = 0,(∀)α ∈ (0,1),(∀)p ≥ 1. 74 s. nădăban proof: lim n→∞ pα(xn+p − xn) = 0,(∀)α ∈ (0,1),(∀)p ≥ 1 ⇔ (∀)t > 0,(∃)n0 ∈ n : pα(xn+p − xn) < t,(∀)n ≥ n0,(∀)α ∈ (0,1),(∀)p ≥ 1 ⇔ (∀)t > 0,(∃)n0 ∈ n : n(xn+p − xn, t) > α,(∀)n ≥ n0,(∀)α ∈ (0,1),(∀)p ≥ 1 ⇔ (∀)t > 0, lim n→∞ n(xn+p − xn, t) ≥ α,(∀)α ∈ (0,1),(∀)p ≥ 1 ⇔ (∀)t > 0, lim n→∞ n(xn+p − xn, t) = 1,(∀)p ≥ 1 ⇔ (xn) is a cauchy sequence . 2 definition 14. let (x,n,∗), (x,n′,∗′) be two fnl-space. the fuzzy norms n and n′ are said to be equivalent if for any sequence (xn) in x, we have xn → x in (x,n,∗) if and only if xn → x in (x,n′,∗′). 4 fuzzy euclidean normed spaces in this section we will denote by k the field of real numbers r or the field of complex numbers c. theorem 4.1. let (x1,n1,∗),(x2,n2,∗), · · · ,(xn,nn,∗) be fnl-spaces. let ∗′ be a continuous t-norm such that ∗′ ≫ ∗. let n : x1 × x2 × · · ·xn × [0,∞) → [0,1], n(x1,x2, · · · ,xn, t) = n1(x1, t) ∗ ′n2(x2, t) ∗ ′ · · · ∗ ′nn(xn, t) . then (x1 × x2 × · · ·xn,n,∗) is a fnl-space. proof: (n1) n(x1,x2, · · · ,xn,0) = n1(x1,0) ∗ ′n2(x2,0) ∗ ′ · · · ∗ ′nn(xn,0) = 0 . (n2) n(0,0, · · · ,0, t) = n1(0, t) ∗ ′n2(0, t) ∗ ′ · · · ∗ ′nn(0, t) = 1 . conversely, if n(x1,x2, · · · ,xn, t) = 1,(∀)t > 0, we obtain that n1(x1, t) ∗ ′n2(x2, t) ∗ ′ · · · ∗ ′nn(xn, t) = 1,(∀)t > 0. as ∗′ ≤ ∧, we have 1 ≤ min{n1(x1, t),n2(x2, t), · · · ,nn(xn, t)},(∀)t > 0. thus n1(x1, t) = 1,n2(x2, t) = 1, · · · ,nn(xn, t) = 1,(∀)t > 0. hence x1 = x2 = · · · = xn = 0. (n3) for λ ̸= 0, we have n(λx1,λx2, · · · ,λxn, t) = n1(λx1, t) ∗ ′n2(λx2, t) ∗ ′ · · · ∗ ′nn(λxn, t) = = n1 ( x1, t |λ| ) ∗ ′n2 ( x2, t |λ| ) ∗ ′ · · ·nn ( xn, t |λ| ) = n ( x1,x2, · · · ,xn, t |λ| ) . (n4) n(x1+y1,x2+y2, · · · ,xn+yn, t+s) = n1(x1+y1, t+s)∗′n2(x2+y2, t+s)∗′ · · ·∗′nn(xn+yn, t+s) ≥ ≥ (n1(x1, t) ∗ n1(y1,s)) ∗ ′(n2(x2, t) ∗ n2(y2,s)) ∗ ′ · · · ∗ ′(nn(xn, t) ∗ nn(yn,s)) ≥ ≥ (n1(x1, t) ∗ ′n2(x2, t) ∗ ′ · · · ∗ ′nn(xn, t)) ∗ (n1(y1,s) ∗ ′n2(y2,s) ∗ ′ · · · ∗ ′nn(yn,s)) = = n(x1,x2, · · · ,xn, t) ∗ n(y1,y2, · · · ,yn,s). (n5) let x = (x1,x2, · · · ,xn) ∈ x1 × x2 × · · ·xn. as n1(x1, ·),n2(x2, ·), · · · ,nn(xn, ·) are left continuous and ∗′ is a continuous t-norm, we obtain that n(x, ·) is left continuous. it is obvious that lim t→∞ n(x,t) = 1. 2 fuzzy euclidean normed spaces for data mining applications 75 proposition 15. if (x1 × x2 × · · ·xn,n,∗) is a fnl-space, then (x1,n1,∗), (x2,n2,∗), · · · , (xn,nn,∗) are fnl-spaces, where n1(x1, t) = n((x1,0, · · · ,0), t),n2(x2, t) = n((0,x2, · · · ,0), t), · · · , nn(xn, t) = n((0,0, · · · ,xn), t). proof: we will prove that n1 is a fuzzy norm. similarly it can be shown that n2, · · · ,nn are fuzzy norm. (n1) n1(x1,0) = n((x1,0, · · · ,0),0) = 0; (n2) n1(x1, t) = 1,(∀)t > 0 ⇔ n((x1,0, · · · ,0), t) = 1,(∀)t > 0 ⇔ (x1,0, · · · ,0) = 0 ⇔ x1 = 0; (n3) n1(λx1, t) = n((λx1,0, · · · ,0), t) = n(λ(x1,0, · · · ,0), t) = = n ( (x1,0, · · · ,0), t |λ| ) = n1 ( x1, t |λ| ) ; (n4) n1(x1 + y1, t + s) = n((x1 + y1,0, · · · ,0), t + s) = n((x1,0, · · · ,0) + (y1,0, · · · ,0), t + s) ≥ ≥ n((x1,0, · · · ,0), t) ∗ n((y1,0, · · · ,0),s) = n1(x1, t) ∗ n1(y1,s) ; (n5) it is obvious. 2 example 16. let n : k × [0,∞) → [0,1], defined by n(x,t) := { e− |x| t , if t > 0 0, if t = 0 . then (k,n,∧) is a fnl-space. proof: (n1) it is obvious. (n2) n(x,t) = 1,(∀)t > 0 ⇔ e− |x| t = 1,(∀)t > 0 ⇔ − |x| t = 0,(∀)t > 0 ⇔ x = 0. (n3) let x ∈ r, t > 0,λ ∈ r∗. then n(λx,t) = e− |λx| t = e − |x| t/|λ| = n ( x, t |λ| ) . (n4) fix x,y ∈ r, t,s > 0. we assume, without restricting the generality, that e− |x| t ≤ e− |y| s . thus − |x| t ≤ − |y| s , i.e. |x|s ≥ |y|t. we will show that e− |x+y| t+s ≥ e− |x| t , namely − |x+y| t+s ≥ − |x| t , i.e. |x + y|t ≤ |x|(t + s). but |x + y|t ≤ (|x| + |y|)t = |x|t + |y|t ≤ |x|t + |x|s = |x|(t + s) . therefore n(x + y,t + s) = e − |x+y| t+s ≥ min { e− |x| t ,e− |y| s } = n(x,t) ∧ n(y,s) . (n5) it is obvious. 2 lemma 17. let (k,n,∗) be a fnl-space. then there exists α ∈ (0,1) such that pα(1) ̸= 0. proof: pα(1) = inf{t > 0 : n(1, t) > α}. we suppose that pα(1) = 0,(∀)α ∈ (0,1). then n(1, t) > α,(∀)α ∈ (0,1),(∀)t > 0. thus n(1, t) = 1,(∀)t > 0. therefore 1 = 0, contradiction. 2 proposition 18. a sequence (xn) is convergent in a fnl-space (k,n,∗) if and only if (xn) is convergent in (k, | · |). 76 s. nădăban proof: a sequence (xn) is convergent to x in (k,n,∗) ⇔ lim n→∞ pα(xn − x) = 0,(∀)α ∈ (0,1) ⇔ lim n→∞ |xn − x|pα(1) = 0,(∀)α ∈ (0,1) ⇔ lim n→∞ |xn − x| = 0 ⇔ (xn) is convergent in (k, | · |) 2 corollary 19. any two fuzzy norm on k are equivalent. definition 20. the triplet (kn,n,∗) is called fuzzy euclidean normed space (briefly fen-space) if ∗ is a continuous t-norm and n : kn × [0,∞) → [0,1] is a fuzzy norm defined by n(x1,x2, · · · ,xn, t) = n1(x1, t) ∧ n2(x2, t) ∧ · · · ∧ nn(xn, t) , where n1,n2, · · · ,nn are fuzzy norms on k ((n4) is satisfied with the t-norm ∗, for all fuzzy norms n1,n2, · · · ,nn). remark 21. theorem 4.1 and the fact that ∧ ≫ ∗ assure the accuracy of the previous definition, meaning that n is fuzzy norm on kn indeed. proposition 22. a sequence (xk) is convergent in a fen-space (kn,n,∗) if and only if (xk) is convergent in (kn, || · ||), where || · || denotes the euclidean norm on kn. proof: (xk) is convergent to x in (kn,n,∗) ⇔ lim k→∞ n(xk − x,t) = 1,(∀)t > 0 ⇔ lim k→∞ n1(x 1 k − x 1, t) ∧ n2(x2k − x 2, t) ∧ · · · ∧ nn(xnk − x n, t) = 1,(∀)t > 0 ⇔ lim k→∞ ni(x i k − x i, t) = 1,(∀)t > 0,(∀)i = 1,n ⇔ |xik − x i| → 0,(∀)i = 1,n ⇔ ||xk − x|| → 0 . 2 theorem 4.2. any fen-space (kn,n,∗) is complete. proof: let (xk) be a cauchy sequence in (kn,n,∗). then pα,n(xk+p − xk) = inf{t > 0 : n(xk+p − xk, t) > α} = = inf{t > 0 : n1(x1k+p − x 1 k, t) ∧ n2(x 2 k+p − x 2 k, t) ∧ · · · ∧ nn(x n k+p − x n k, t) > α} = = inf{t > 0 : n1(x1k+p − x 1 k, t) > α,n2(x 2 k+p − x 2 k, t) > α, · · · ,nn(x n k+p − x n k, t) > α} ≥ ≥ inf{t > 0 : ni(xik+p − x i k, t) > α},(∀)i = 1,n . thus pα,n(xk+p − xk) ≥ pα,ni(x i k+p − x i k) = |x i k+p − x i k|pα,ni(1),(∀)i = 1,n . by lemma 4.4, applied to fuzzy norm ni, we obtain that there exists αi ∈ (0,1) such that pαi,ni(1) ̸= 0. therefore pαi,n(xk+p − xk) ≥ |x i k+p − x i k|pαi,ni(1),(∀)i = 1,n . as (xk) is a cauchy sequence in (kn,n,∗), we obtain that (xik) is a cauchy sequence in (k, | · |),(∀)i = 1,n. thus (xik) is convergent to x i,(∀)i = 1,n. therefore (xk) is convergent to x = (x1,x2, · · · ,xn) in (kn, || · ||) and previous proposition implies that (xk) is convergent to x in (kn,n,∗). 2 5 conclusion in this paper some special fuzzy norms on kn is given in order to obtain, in this way, fuzzy euclidean normed spaces. these spaces can be proven to be a suitable tool for data mining. the method is based on embedding the data in fuzzy euclidean normed spaces and to carry out data analysis in these spaces. fuzzy euclidean normed spaces for data mining applications 77 bibliography [1] bag, t., samanta, s.k. (2003); finite dimensional fuzzy normed linear spaces, journal of fuzzy mathematics, 11(3): 687–705. [2] bag, t., samanta, s.k. (2005); fuzzy bounded linear operators, fuzzy sets and systems, 151: 513–547. [3] dzitac, i., moisil, i. (2008); advanced ai techniques for web mining, proceedings of the 10th wseas international conference on mathematical methods, computational techniques, non-linear systems, intelligent systems, 343–346. [4] hüllermeier, e. (2005); fuzzy methods in machine learning and data mining: status and prospects. fuzzy sets and systems, 156(3): 387–406. [5] katsaras, a.k. (1984); fuzzy topological vector spaces ii, fuzzy sets and systems, 12: 143–154. [6] nădăban, s., dzitac, i. (2014); atomic decompositions of fuzzy normed linear spaces for wavelet applications, informatica, (in press). [7] saadati, r., vaezpour, s.m. (2005); some results on fuzzy banach spaces, j. appl. math. & computing, 17(1-2): 475–484. [8] saadati, r., park, j.h. (2006); intuitionistic fuzzy euclidean normed spaces, communications in mathematical analysis, 1(2): 85–90. [9] saadati, r., park, j.h. (2006); on the intuitionistic fuzzy topological spaces, chaos, solitons and fractals, 27: 331–344. [10] schweizer, b., sklar, a. (1960); statistical metric spaces, pacific j. math., 10: 314–334. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 286-296 a hybrid artificial bee colony algorithm for flexible job shop scheduling problems j. li, q. pan, s. xie, s. wang jun-qing li, sheng-xian xie school of computer, liaocheng university, liaocheng liaocheng, 252059, pr china e-mail: lijunqing.cn@gmail.com, xsx@lcu.edu.cn quan-ke pan 1. school of computer, liaocheng university, liaocheng liaocheng, 252059, pr china 2. state key lab. of digital manufacturing equipment and technology, huazhong university of science and technology wuhan, 430074, pr china e-mail: qkpan@gmail.com song wang department of economic and management shandong university of science and technology huangdao, 266510, pr china abstract: in this paper, we propose a hybrid pareto-based artificial bee colony (habc) algorithm for solving the multi-objective flexible job shop scheduling problem. in the hybrid algorithm, each food sources is represented by two vectors, i.e., the machine assignment vector and the operation scheduling vector. the artificial bee is divided into three groups, namely, employed bees, onlookers, and scouts bees. furthermore, an external pareto archive set is introduced to record non-dominated solutions found so far. to balance the exploration and exploitation capability of the algorithm, the scout bees in the hybrid algorithm are divided into two parts. the scout bees in one part perform randomly search in the predefined region while each scout bee in another part randomly select one non-dominated solution from the pareto archive set. experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm. keywords: flexbile job shop scheduling problem, artificial bee colony, multiobjective optimization, hybrid algorithm 1 introduction the flexible job shop scheduling problem (fjsp), as a branch of the classical job shop scheduling problem (jsp), has been studied in very recent years. brandimarte (1993) [1] is among the first author to solve the fjsp instances with tabu search (ts) algorithm. in very recent years, some meta-heuristic algorithms, such as ts algorithm [2] [3], particle swarm optimization (pso) [4] [5], ant colony optimization (aco) [6], and genetic algorithm (ga) [7] [8], have been used in solving the single-objective fjsps. although the single-objective fjsp has been widely investigated, the research on the multi-objective fjsp is still considered relative limited. kacem et al. (2002a, 2002b ) [9] [10] proposed an effective evolutionary algorithm. xia and wu copyright c⃝ 2006-2011 by ccc publications a hybrid artificial bee colony algorithm for flexible job shop scheduling problems 287 (2005) [11] studied the problem with the hybrid algorithm of the pso and the simulated annealing (sa). zhang et al. (2009) [12] introduced a hybrid algorithm combining pso algorithm with ts algorithm. ho et al. (2008) [13]studied a hybrid evolution algorithm combined with a guided local search and an external pareto archive set. in this paper, we propose a hybrid algorithm combining an external pareto archive set and the artificial bee colony (abc) optimizer to solve the multi-objective fjsp. the rest of this paper is organized as follows: in section 2, we briefly describe the problem formulation. then, the artificial bee colony (abc) algorithm is introduced in section 3. the elements and framework of the hybrid algorithm are presented in section 4 while section 5 shows the experimental results and comparisons with other algorithms in the literature to demonstrate the superiority of the habc performance. finally, the last section presents conclusion of our work. 2 problem formulation the fjsp considers n jobs to be processed on m machines. there are some assumptions and constrains in the fjsp considered in this study as follows: 1) each job has predefined number of operations and a known determined sequence among these operations; 2) each machine and each operation is ready at zero time; 3) each machine can only process one operation at a time, and each job must be processed on one machine at a given time; 4) each machine can process a new operation only after completing the predecessor operation; 5) each operation can be operated on a given candidate machine set instead of only one machine like in jsp; 6) given an operation oij and the selected machine mk, the processing time pijk is also fixed. let ci be the completion date of job ji. wk is the workload of machine mk, which is the total processing time of operations that are operated on machine mk. pijk be the processing time of oi,j on machine mk. three objectives are considered in this study, namely [13]: 1) minimization of maximum completion time (makespan): f1 = max{ci | i = 1, . . . , n} (1) 2) minimization of total workload f2 = ∑ pi,j,k (2) 3) minimization of critical machine workload: f3 = max{wk | k = 1, . . . , m} (3) 3 artificial bee colony algorithm very recently, by simulating the behavior of honey bee swarm intelligence, an efficient bee colony (abc) algorithm is proposed by karaboga ( [14] [17]). due to its simplicity and ease of implementation, the abc algorithm has gained more and more attention and has been used to solve many practical engineering problems. in the basic abc algorithm ( [14] [17]), there are two components: the foraging artificial bees and the food source. the position of a food source represents a possible solution to the optimization problem and the nectar amount of a food source corresponds to the quality or fitness of the associated solution. the artificial bee is divided into three groups, namely, employed bees, onlookers, and scouts bees. the employed bee is the one who is currently performing exploitation on a food source. a bee that is waiting in the hive for making decision to choose a food source is called an onlooker. the scout bee is 288 j. li, q. pan, s. xie, s. wang the one who perform exploration procedure and random exploitation search to find a new food source. the main steps of the algorithm are given as follows ( [14] [18]). step1. produce initial population; step2. while stop criteria is not satisfied, perform steps 3 to steps 6. step3. send the employed bees onto their food sources. step4. send the onlooker bees onto the food sources depending on their nectar amounts. step5. send the scout bees to search possible new food sources. step6. memorize the best food source found so far. 4 the hybrid algorithm habc the basic abc algorithm was originally designed for continuous function optimization. in order to make it applicable for solving the problem considered, a novel hybrid version of the abc algorithm, named habc, is proposed in this section. 4.1 solution representation the solution of the problem is represented with two vectors [19]: the machine assignment vector and the operation scheduling vector. the first part places the assigned machine number for each operation at the corresponding position, while the second part puts the same number symbol for each operation of a job and interpret them according to the occurrence in the operation scheduling vector. 4.2 employed bee phase the employed bee is to perform the local search around a given food source. therefore, the employed bee takes the exploitation search of the algorithm. in order to generate good quality and diversity neighboring solutions, two types of local search operators are applied for the employed bees in this study, which are shown as follows. (1)local search operator in machine assignment component the local search operator in machine assignment component is very simple and easy to be implemented. the perturbation is obtained by following steps. step1. select a position in the machine assignment component, randomly or using some priority rules. step2. assign a suitable machine different with the old one for the operation in the corresponding position. step3. replace the machine number in the selected position and produce the new machine assignment component for the solution. (2)local search operator in operation scheduling component the local search in the operation scheduling component is just like the perturbation in solving the jsp, where insert and swap operations are commonly used in the literature [20-22]. the insert operator is to remove a number symbol for an operation in the permutation π from its original position j and insert it into another position k such that (k ̸= j). the swap operator is to interchange two job symbols of π in the different positions. after performing the above two local search approaches, the employed bee obtains a new neighboring food source around the old one. then the new food source will be evaluated and a hybrid artificial bee colony algorithm for flexible job shop scheduling problems 289 compared with the old one. the better food source will be kept in the population as in the basic abc algorithm which performs a greedy selection procedure. 4.3 onlooker bee phase in the classical abc algorithm, each onlooker bee selects a food source based on the percent of the nectar amount of each food source among the total nectar amounts. however, the above approach consumes large computational time to compute the nectar amount of each food source. for this reason, we propose a tournament selection with the size of 3 in the habc algorithm. in the tournament selection, three food sources are picked randomly from the population, and then the food source with highest nectar amount will be selected by the onlooker bee. after selecting the food source, each onlooker bee performs local search for the selected food source and produce a new neighboring food source. the better food source between the old one and the new neighboring one will be memorized in the population. 4.4 scout bee phase a scout bee performs randomly search in the basic abc algorithm. this will increase the population diversity and avoid local minima, whereas this will also decrease the search efficacy. since the food sources memorized in the pareto archive set often carry better information than others, and the search space around these non-dominated solutions could be the most promising region. therefore, in the habc algorithm, the scout bees are first divided into two parts. one half of the scout bees randomly select a solution from the external pareto archive set and perform several insert and swap operators to the selected solution, while the other half scout bees perform randomly search in the predefined search scope. in the hybrid algorithm, at least 5% − 10% of the population is scout bees. 4.5 multi-objective optimizer the pareto archive set as to provide a set of solutions with good diversity, a pareto archive set (as) was introduced in this study, which is used to maintain a limited number of non-dominated solutions found so far. during the optimization process, the archive set is iteratively updated with adding some nondominated solutions and removing some dominated solutions to get closer to the pareto-optimal front. once a new non-dominated solution is found, it will be added to as and any solution which is dominated by the added one will be removed from as. in case as becomes overfull, its member which is in the crowded domain is eliminated to maintain the diversity of the pareto archive set. the storage structure of as to reduce the computational time complexity consumed on the update process of the archive set, the members of the as firstly sequence in an ascending order according to their first objective function value (pan, 2009) [21]. non-dominated sorting algorithm for the population, we should sequence each solution according to a certain criteria. for multi-objective optimization problems, we can not use one objective function value to determine the solution quality. in this study, a non-dominated sort algorithm (deb et al., 2002 ) [23]was 290 j. li, q. pan, s. xie, s. wang introduced to divide the population solutions into several levels according to their dominated solutions number. 4.6 the framework of habc the details steps of the proposed habc algorithm are as follows: step1 initialization phase; step 1.1 set the system parameters; step 1.2 produce the initial population. step2 apply the pareto non-dominated sorting function on the population, and then update the external pareto archive set by using the solutions in the first pareto level front. step3 if the stopping criterion is satisfied, output the non-dominates solutions in the external pareto archive set; otherwise, perform steps 4-7. step4 employed bee phase. step 4.1 put each employed bee on each solution in the population. step 4.2 for each employed bee, perform local search on the assigned solution and generate a new neighboring solution. step 4.3 evaluate the new neighboring solution and record the better solution among the new solution and the old one as the current solution and put it into the population. if the two solutions are non-dominated with each other, randomly select one as the current solution. step 4.4 if a solution has not been improved through limit cycles, then the corresponding employed bee becomes a scout bee and perform step 6. step 4.5 evaluate each solution corresponding to each employed bee, apply the pareto non-dominated sorting algorithm on the new population and update the external pareto archive set using the solutions in the first pareto level. step5 onlooker bee phase. step 5.1 for each onlooker bee, randomly selects three solutions from the population and selects the best one as the food source. if the three solutions cannot dominate each other, then randomly select a non-dominate solution. step 5.2 for each onlooker bee, performs local search for the selected food source and carries out greedy selection procedure to record the better solution in the population. step 5.3 evaluate each solution corresponding to each onlooker bee, apply the pareto non-dominated sorting algorithm on the new population and update the external pareto archive set using the solutions in the first pareto level. step6 scout bee phase. step 6.1 divide the scout bees into two parts with the same number of bees. step 6.2 the scout bees in the first part randomly select a food source and perform local search operator in the predefined region. after generating a new solution, performs greedy selection procedure. step 6.3 each scout bee in the second part randomly select a non-dominate solution in the external pareto archive set and perform several local search for the selected solution.. after generating a new solution, performs greedy selection procedure. a hybrid artificial bee colony algorithm for flexible job shop scheduling problems 291 step 6.4 evaluate each solution corresponding to each scout bee, apply the pareto nondominated sorting algorithm on the new population and update the external pareto archive set using the solutions in the first pareto level. step7 go to step 3. 5 experiment results this section describes the computational experiments to evaluate the performance of the proposed algorithm. the test samples come from kacem instances set [9]. the current instantiation was implemented in c++ on a pentium iv 1.8ghz with 512m memory. 5.1 setting parameters each instance can be characterized by the following parameters: number of jobs (n), number of machines (m), and the number of operations (op_num). followings are the detail parameters value: the size of the population is equal to the number of employed bee and the number of onlooker, which is set to 5n; the maximum cycle of the algorithm is set to 10 × n × m; the limit number of cycles through which no improvement occurs on the food source, then the employed bee becomes a scout bee; the limit number is set to n× m 2 ; the percent of scout bee is set to a random number between 0.05 and 0.1. 5.2 results comparisons the five test instances come from kacem [9] [10], which range from 4 jobs×5 machines to 15 jobs ×10 machines. two tests are performed for comparison, i.e. the instances with single objective and the problems with three objectives. several recently published algorithms are compared with the proposed habc algorithm, such as the al+cga proposed by kacem et al. (2002b) [10] ,the genace approach employed by ho (2004) [24], the pso+sa developed by xia and wu (2005) [11], the ant systems & local search optimization method (hereafter called aco+ls) presented by liouane et al. (2006) [6], and the pso+ts introduced by zhang (2009) [12]. the signle objective five kacem instances for solving the five instances with single objective to minimize the makespan criterion, the experimental results and comparisons are given in table 1. it can bee seen from table 1 that the habc algorithm can obtain the best results for all the kacem instances. the proposed algorithm outperforms the al+cga in 4 out of 5 instances, while outperforms the genace method in 2 out of 4 cases. for comparison with the very recently published algorithms, the habc algorithm obtained a superior result in solving the largest problem than the aco+ls proposed by liouane (2006). particle swarm optimization (pso) is an efficient swarm intelligent algorithm and the experimental results obtained by pso+sa and pso+ts are considered as the competitive results for the fjsp. table 1 shows that the habc algorithm outperforms the pso+sa algorithm in 2 out of 3 problems. the habc algorithm can obtain the same experimental results with the pso+ts in very short computational times. for example, in solving the largest problem 15 × 10, our algorithm consumes just about 50 seconds to reach the best result so far. 292 j. li, q. pan, s. xie, s. wang table 1: comparison of the five instances with single objective (makespan) problem set al+cga genace aco+ls pso+sa pso+ts habc 4 × 5 16 11 11 11 8 × 8 15 15 14 14 10 × 7 15 12 11 11 10 × 10 7 7 7 7 7 7 15 × 10 23 12 12 12 11 11 "-" denotes not given by the author the multi objective five instances table 2 shows the comparison of the results on the five multi-objective fjsp instances. the three objectives are considered simultaneously, i.e. minimization of the makespan (denoted by f1),the total workload (denoted by f2), and the maximal workload (denoted by f3). it can bee seen from table 2 that the habc algorithm is competitive to other algorithms. the experimental results of the proposed algorithm dominate the results of the al+cga for solving the four instances. for comparison with the very recently published algorithms, the habc can either obtain more non-dominated solutions or obtain superior result than pso+ts and pso+sa algorithms. for example, our algorithm obtain three non-dominated solutions in solving the 8 × 8 instance,while the pso+sa and pso+ts can only obtain two results. in addition, our algorithm obtains all these results in a run while the other algorithms can obtain only one result in a run. in other words, the other algorithm should run several times to obtain different results for an instance. therefore, the external pareto archive can enhance the population diversity of our algorithm. fig. 1 shows the gantt chart of the resulted solution of 15 × 10 instance, due to our proposed algorithm. to make a further comparison with the aco+ls proposed by liouane (2006), we also test the problem listed in the paper [6]. the problem is given in table 3. the comparison of the experimental results from aco+ls and our algorithm are given in table 4 and the two solutions obtained by the habc algorithm are given in table 5 and table 6, respectively. it can be seen from table 4 that the habc algorithm obtained two non-dominated solutions for the example benchmark while the ts method in literature [6]can obtained only one solution. furthermore, the resulted solutions obtained by our algorithm dominated all the results by the aco method. in addition, our algorithm obtained the two non-dominated solutions consuming just only 0.01 seconds for the example benchmark. therefore, table 4 concludes that the proposed algorithm is efficient in solving the example problem especially when compared with the aco method. 6 conclusion in this paper, we have proposed an efficient algorithm for solving multi-objective fjsps. instead of applying the basic abc algorithm, we developed a hybrid abc method. to memory the non-dominated solutions found so far and increase the population diversity, we presented an external pareto archive set. a fast pareto update function is also introduced in the algorithm to enhance the computational capability. in the hybrid algorithm, the balance of the capability of exploration and exploitation is considered. experimental results on several well-known benchmarks show that our algorithm is competitive to other recently published algorithms for solving the fjsps. the future work is to improve the neighborhood structure of the problem considered a hybrid artificial bee colony algorithm for flexible job shop scheduling problems 293 table 2: comparison of the five instances with three objectives al+cga pso+sa pso+ts habc f1 16 11 11 12 13 f2 34 32 32 32 334 × 5 f3 10 10 10 8 7 f1 15 16 15 16 14 15 14 15 16 f2 79 75 75 73 77 75 77 75 738 × 8 f3 13 13 12 13 12 12 12 12 13 f1 11 12 f2 61 6010 × 7 f3 11 12 f1 7 7 7 8 7 8 f2 45 44 43 41 42 4310 × 10 f3 5 6 6 7 6 5 f1 23 24 12 11 12 11 f2 95 91 91 93 91 9315 × 10 f3 11 11 11 11 11 11 figure 1: the gantt chart of the solution 2 (f1=11, f2=93, f3=11) for the instance 15 × 10 294 j. li, q. pan, s. xie, s. wang table 3: example benchmark 3 jobs-6 machines from [6] m1 m2 m3 m4 m5 m6 o11 10 7 6 13 5 1 o12 4 5 8 12 7 11 o13 9 5 6 12 6 17 j1 o14 7 8 4 10 15 3 o21 15 12 8 6 10 19 o22 9 5 7 13 14 7 j2 o23 14 13 14 20 8 17 o31 7 16 5 11 17 9 o32 9 16 8 11 6 3 j2 o33 6 14 8 18 21 14 table 4: comparison of the example benchmark f1 f2 f3 lower bound value 18 45 8 avgtime(s) 19 51 13 19 50 13 19 48 14aco+ls 19 47 14 ts 18 45 12 18 45 12habc 19 46 10 0.01 "-" denotes not given by the author table 5: solution 1 for the example benchmark (f1=18, f2=45, f3=12) o1 o2 o3 o4 j1 m6:[0,1] m1:[1,5] m2:[5,10] m6:[10,13] j2 m4:[0,6] m5:[6,10] m5:[10,18] *** j3 m3:[0,5] m6:[5,8] m1:[8,14] *** table 6: solution 2 for the example benchmark (f1=19, f2=46, f3=10) o1 o2 o3 o4 j1 m6:[0,1] m1:[1,5] m2:[11,16] m6:[16,19] j2 m4:[0,6] m5:[6,11] m5:[11,19] *** j3 m3:[0,5] m6:[5,8] m1:[8,14] *** a hybrid artificial bee colony algorithm for flexible job shop scheduling problems 295 and enhance the convergence ability of the algorithm. 7 acknowledgments this work supported by national science foundation of china under grants 60874075, science research and development of provincial department of public education of shandong under grant j08lj20, j09lg29, j08lj59, soft science foundation of shandong under grant 2009rkb125. bibliography [1] p. brandimarte. routing and scheduling in a flexible job shop by tabu search.annals of operations research , vol 22, pp. 158-183, 1993. [2] m. mastrolilli and l. m. gambardella. effective neighborhood functions for the flexible job shop problem.journal of scheduling , vol. 3(1), pp. 3-20, 2000. [3] j. q. li, q. k. pan, p. n. suganthan and t. j. chua. a hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem. international journal of advanced manufacturing technology. doi: 10.1007/s00170-010-2743-y. [4] l. gao, c. y. peng, c. zhou and p. g. li. solving flexible job shop scheduling problem using general particle swarm optimization. in proceedings of the 36th cie conference on computers & industrial engineering, pp. 3018-3027, 2006. [5] j. q. li, q. k. pan and s. x. xie. a hybrid variable neighborhood search algorithm for solving multi-objective flexible job shop problems. comsis computer science of software information and system. doi: 10.2298/csis090608017l. 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[24] n. b. ho and j. c. tay. genace: an efficient cultural algorithm for solving the flexible job-shop problem. in proceedings of the ieee congress on evolutionary computation (cec2004) piscataway, pp.1759-1766, 2004. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 578-585 an ontology to model e-portfolio and social relationship in web 2.0 informal learning environments d. taibi, m. gentile, g. fulantelli, m. allegra davide taibi, manuel gentile, giovanni fulantelli, mario allegra italian national research council institute for educational technologies via ugo la malfa 153 90146 palermo, italy e-mail: {davide.taibi,manuel.gentile,giovanni.fulantelli,mario.allegra}@itd.cnr.it abstract: web 2.0 applications and the increasingly use of social networks have been creating new informal learning opportunities. students interact and collaborate using new learning environments which are structurally different from traditional e-learning environments. in these informal unstructured learning contexts the boundaries between the learning contexts and social spheres disappear, and the definition of the students competences appears more and more important. in this paper we propose a semantic web approach in order to create the basis for a software platform to model learner profiles. in particular we propose to extend the foaf ontology, used to describe people and their personal relationships, with an ontology related to the ims learning portfolio used to model students’ competencies. this ontology could be a fundamental layer for a new web 2.0 learning environment in which students’ informal learning activities carried out in social networks can be managed and evaluated. keywords: semantic web, e-portfolio, social networks, informal learning. 1 informal learning and social communities the significant changes in society that castells in [1] sums up in what he calls “the rise of the network society” also have considerable implications in the definition of learning activities. the information society dramatically increases the opportunities for knowledge acquisition. beyond the structured training activities designed by specialists in the education field, we have to consider the large number of educational opportunities related to everyday activities that define the so-called “informal learning” [2]. in this perspective, the concept of networked learning is drastically changing. the informal learning opportunities created by information technologies, such as web 2.0 applications and social networks, allow users to interact and collaborate in new ways thus leading to the definition of new learning environments; these are structurally different from traditional e-learning environments, since the boundaries between the learning contexts and other social spaces tend to disappear. in these unstructured learning contexts, the definition of the skills acquired by the users is a central objective. consequently, the use of software environments that model learner profiles and can deal with them in a semantic way appears increasingly important. in [3] the authors argue that learning is related to the activities, the environmental and cultural contexts in which it is developed and therefore social interaction is a critical factor. from this point of view learning can be described as a process: students are involved in a community of practice that represents knowledge and behavior in which students play a more active role in the cultural sphere. the concept of situated learning comes from vygotsky’s social development theory, which affirms that social interaction has a fundamental role in the knowledge development process [4]. this theory argues that situated learning is generally unintentional and for this reason learning is more effective if the student is a member of a community of practice he has chosen to join rather than copyright c⃝ 2006-2010 by ccc publications an ontology to model e-portfolio and social relationship in web 2.0 informal learning environments 579 being assigned to a group by external actors such as teachers. the social aspect in learning activities is extremely important and leads to a further consideration. for example, tinto claims that participation in a collaborative learning group allows students to develop a supporting network, that helps students to maintain relations with a wider social community [5]. a peer-to-peer community promotes participation in learning activities. moreover, the communities of learners provide students with the opportunity to satisfy simultaneously both social and academic requirements. these unstructured learning contexts give rise to the need to measure and asses the acquired knowledge; the traditional competence based certification systems are not designed for this type of environment and for this reason are less suitable in this kind of educational context. the semantic web provides a technological substrate which can overcome the limits of current web technologies, setting the base for creating ontological systems in order to model competences in informal educational contexts that are being developed in web 2.0 environments. in this paper we consider the problems connected to the description of competences in informal learning environments within social networks mediated by technologies. in particular, we propose the integration of the foaf (friend of a friend) ontology, which is used to model people and their personal contacts, with semantic ontology related to student e-portfolios used to model their competences. the use of ontologies and the surrounding semantic web technologies allow us to create relationships between the students ongoing educational experiences and the evolution of their social network. for this to happen, we integrate foaf ontology with the ims learning portfolio model in order to support the creation of a new web 2.0 learning environment based on social networks and competences. 2 social semantic web at present a huge amount of shared contents such as bookmarks, images, videos and photos are being created within so called web 2.0 applications, very popular in social and personal spheres as well as in professional and organizational ones. they possess common features like the creation and sharing of contents (images, photos, papers), discussions (comments) and connections between users (group of friends, private messages, and so on). this scenario raises new considerations related to the sharing of social contributions between software applications and the interoperability of social networks. due to the heterogeneity of the nature of social contribution, sharing, searching, connecting and retrieving these kinds of contents has become more complex. the semantic web technologies provide standards and models which are useful for creating a network of data, with unified models which can represent data from different sources appropriately. the unification of semantic web technologies and social paradigms gives rise to "social semantic information spaces" in which information is socially created and managed, as well as being interconnected and available in a machine understandable format, promoting new methodologies to discover information present on the web [6]. moreover, the semantic web offers a generic infrastructure to interchange, integrate and reuse structural data, in order to overcome the limits of web 2.0 platforms. currently, in fact, web 2.0 applications have search mechanisms based mainly on tags and few keywords. adding semantics to the web would enable this kind of problem to be solved, by providing easier search mechanisms, supporting the reuse of contents and creating more connections between different types of contents. moreover, the use of ontologies is useful to structure and elaborate information. ontologies represents entity-relationship models related to a specific knowledge or practice domain. a typical web ontology contains the definition of classes, objects and their relationships, and a set of deduction rules that give inferential power about the concepts. through ontologies the semantic web provides the basis for enriching the resources description with a well defined meaning and in a comprehensible format which can be elaborated by software applications. the strict relationship between documents produced in web 2.0 environments and the specific social network [7] bring us to consider the information objects as the result of the activities of the network; 580 d. taibi, m. gentile, g. fulantelli, m. allegra consequently, we should also represent social relationships in a well structured way, using approaches based on the semantic web concepts. foaf, the acronym of friend of a friend, uses semantic web technologies, in particular the resource description framework (rdf) and the ontology web language (owl), to define a machine-readable ontology describing people, their activities and their relations to other people and objects. foaf is useful for describing social networks and their relationships. 3 learner model in social semantic networks educational activities adopt web 2.0 technologies and social networks more and more frequently. the interaction paradigms at the basis of web 2.0 technologies and social networks are different from those adopted by conventional e-learning tools. foaf is considered the most common vocabulary for constructing social networks, it has been very successful in the applications that use semantic web technologies, and it is useful to model learner in social networks [8]. each student can be described through an foaf file that can be extended and modified at any time. this is useful, for example, for publishing data regarding students using the uri that represent them. to facilitate the creation of the profiles it is possible to use an interface based on foaf-a-matic; in this way, it will be possible to describe social links in the community of practice. defining ontologies it is possible to use a inferential engine like jena and the sparql language to work with data and generate new knowledge about the domain. as reported by [9] there are several advantages in using the foaf approach to model student profiles: • the use of rdf facilitates extensibility and interoperability • the presence of different extensions of the foaf vocabulary, makes foaf very flexible • the creation of foaf files is simplified by the use of foaf-a-matic • foaf simplifies the identification of people with common interests, which is essential for creating communities of practice to use foaf in a learning context, it is necessary to extend this model to include specific characteristics related to learning. regarding this aspect we should take into consideration elements related to: the extension of the foaf vocabulary to include specific information regarding students activities; the consideration of privacy problems in sharing personal information; the evaluation of the ties strength between students belonging to the group. in conclusion, using foaf as a basis for learning models makes it possible to exploit the benefits of the numerous existing tools, and also to use the extension of the model to define specific aspects and personal and group relationships, which are indispensable for creating and supporting social learning networks. foaf is used successfully to describe a student profile, in particular the profile can be extended to bring together information coming from other models containing student data, like, for example the competences described following the ims e-portfolio standard. the model of students competences plays a key role in making the use of social networks to better support learning activities. 4 e-portfolio and ontologies in social learning environments an e-portfolio is defined by the educause nlii (national learning infrastructure initiative) as "a collection of authentic and diverse evidence, drawn from a larger archive, that represents what a person an ontology to model e-portfolio and social relationship in web 2.0 informal learning environments 581 or organization has learned over time, on which the person or organization has reflected, designed for presentation to one or more audiences for a particular rhetorical purpose." as sustained by [10], an e-portfolio can be used to developmental, presentation, assessment purposes, and it can contain different information related to personal and professional achievements, competences, digital works. this relevant information about students can be stored and maintained by different institutions in different sites, so the management can be improved by the use of web-based e-portfolios. a key concept in this scenario is the interoperability between different institutional systems which requires a unified model describing students e-portfolios. the pedagogical objectives of e-portfolios are various: they allow students to describe their learning path, increase awareness of their strengths and weaknesses, take responsibility and increase their autonomy and have a unified way of presenting their competences. at present, the lack of common standards to describe e-portfolio information means that most eportfolio systems are using different proprietary formats to store this type of information, and moreover, they don’t provide features for importing and exporting e-portfolio information from other systems. in this scenario the interoperability between e-portfolio systems is hindered, and for example, it is difficult to integrate the e-portfolio information coming from a university system and from an enterprise. for these reasons it is desirable to use a common standard in order to unify the description processes of competences in lifelong learning. there are two main standards for describing student learning experiences. the ieee learner model working group has defined the public and private information for learners [11] as a standard for a student model, with the aim of gathering information related to competences, personal data, learning style, and so on. this standard considers six types of data related to personal, relations, security, preference, performance and portfolio information; in addition, it is possible to extend and integrate the standard in order to enrich the student description. in 2005 the ims consortium released the ims eportfolio practice and implementation guide [12]. this specification uses the xml language to define the characteristics of an e-portfolio. xml is at the basis of the semantic web layer cake, so this specification constitutes the first step towards a semantic description of student competences. the use of specific ontologies can enrich this description by considering also the relationships between the concepts that are at the basis of e-portfolio systems. for example, figure 1 shows the e-portfolio activity concept, its properties and its relations reported in our ontology. an e-portfolio can bring together different kinds of information such as: digital and non digital works; activities in which the student has participated, is participating, or plans to participate; competences and skills of the student; students achievements, whether or not certificated; student’s preferences; student’s goals and plans; student’s interests and values; any notes, reflections or assessments relevant to any other part; the results of any test or examination taken by the student; contextual information to help the interpretation of any results. 5 semantic framework for e-portfolio management many educational approaches are based on groupwork, since peer learning promotes cognitive processes. there are many different kinds of collaborative work that allow students to learn in different modalities, such as group discussions, group problem solving and group study. the form of collaboration differs according to the duration, the complexity and the level of collaboration. social interactions can help students to share their experiences and to work collaboratively on relevant topics. in this sense social networks occupy a key role in the learning dynamics. the number of informal learning activities which take place in technology supported social networks is constantly increasing. collaborative group activities are frequently used by teachers in the educational curriculum. in 582 d. taibi, m. gentile, g. fulantelli, m. allegra figure 1: the activity concept of the e-portfolio ontology these activities it is necessary to create well balanced groups with the aim of maximizing the attainment of the learning objectives. to ensure the success of a learning activity, teachers must consider the constraints that can affect the entire group or an individual performance, such as previous experiences by students in similar educational contexts, cultural background or interests and competences. the importance of a system based on competences in informal learning environments such as those developed using social software is undeniable. for example, there are clear benefits in involving members with different levels of experience within the group in order to improve the dynamics of collaborative work in problem solving activities. from this point of view it is increasingly important to have software applications that can store data related to the user profiles and process these data semantically. the approach proposed in this paper consists in integrating and extending foaf ontology, used for modeling contacts and personal relationship, with semantic data related to students competences. enriching the description of social networks using semantics can provide precious support for a more effective use of the network for educational purposes. social learning experiences must consider the competences and the e-portfolio of the participants, so web semantic technologies are an essential substrate for merging models related to the social network description with models used to define and structure competences. in particular, the use of ontologies and semantic web technologies makes it possible to relate the evolution of educational activities experienced by the students with their relationships. the description of a students social network using foaf, integrated with the definition of competences by means of the ims model, is the basis for the creation of a competence based ontological system for virtual learning environments using social networks and web 2.0 technologies. the result is a learning environment which is no longer based on the transmission of information from teacher to student but rather is focused on the ability of the students to play an active role in their learning activities. an ontology to model e-portfolio and social relationship in web 2.0 informal learning environments 583 figure 2: an example of the relation between foaf and e-portfolio in the proposed ontology the figure 2 shows an example of how our ontology describes the relation between a concept of the e-portfolio and a concept in foaf. as an example, we consider how our approach can be used to increase learning effectivness in informal learning activities that take place within an on-line social network environment. using the ontology proposed in our work, it is possibile to describe both social relationships between students and the eportfolio for each student. by social relationships, we mean the friendship links explicitly declared by students within the on-line social network software, while the e-portfolio allows us to describe the competences acquired by students, their learning goals and so on. all this information can be available in a software platform that uses our ontology to: • create a sub-group from a student’s friendship group, which includes the friends that have common learning interests and objectives • suggest new friends to a student by selecting the people from within the on-line social network who have specific competences in their portfolios that can help the student to achieve his learning objectives in a social network it is important not only to be connected to other people but to be connected to the right people, depending on your goals. this is true not only for business or work experiences but it is also important for informal learning activities that take place in an on-line social network. the results of learning activities are highly influenced by the group in which students participate. several studies have been conducted analizyng the impact of positive interdependence on the effectiveness of cooperation. as stated by johnson and johnson one of the essential elements for efficacy in cooperation is positive interdependence [13]. the authors affirm that positive interdependence is structured in three categories: outcome, boundary and means. our approach provides helpful conditions to support the first two categories of positive interdependence. in particular, the outcome categories include goals and rewards; in our case we create a sub-group of friends in which learning objectives and competences coincide, thus facilitating a structuring positive outcome interdependence in order to increase achievement and productivity [14], [15], [16]. johnson and johnson also state that “the boundary category includes: outside enemy (or negative interdependence with another group), identity (which binds members together as an entity), and environmental (such as a specific work area) interdependence” [13]. from this point of view, creating the sub-group from the existing friendship network increases some factors of boundary interdependence. in conclusion, our ontological approach is useful for supporting the creation of learning groups, promoting positive interdependence that has a positive influence to produce higher achievement and 584 d. taibi, m. gentile, g. fulantelli, m. allegra productivity more then the membership in and of itself [17] and the interpersonal interaction itself [18], [19]. 6 conclusions collaborative group activities are frequently used by teachers in the educational curriculum. in these activities it is necessary to create well balanced groups with the aim of maximizing the attainment of the learning objectives. to ensure the success of a learning activity, teachers must consider the constraints that can affect the entire group or an individual performance, such as previous experiences by students in similar educational contexts, cultural background or interests. the greater the number of constraints to consider, the more complex becomes the management of the learning experience. semantic web technologies offer the substrate needed to overcome the problems of social network with large groups of students. the versatility of these technologies means that they can be successfully applied for describing social networks and competences in learning experiences. an interesting approach for creating an ontological system based on semantic web technologies that makes it possible to define a social network considering the competences of participants, the quality of the group and its robustness, is based on the use of an ontology as a result of an extension of the foaf vocabulary, to create a semantic data base including specific references to educational paths. in particular, the approach proposed in this work is based on the creation of a specifically designed ontology that extends foaf ontology, in order to describe the domain of competences as defined by the ims e-portoflio standard. bibliography [1] m. castells, the information age, economy, society and culture volume i: the rise of network society, blackwell, oxford 1996. [2] a. andreatos, virtual communities and their importance for informal learning international journal of computers, communications & control, 2(1):39-47, 2007. [3] j. lave and e. wenger, situated learning: legitimate peripheral participation, cambridge university press, 1991. [4] l.s. vigotsky, mind in society. harvard university press. cambridge, 1978. [5] v. tinto, classrooms as communities: exploring the educational character of student persistence, journal of higher education, 68(6):599-622, 1997. [6] j.g. breslin, social semantic information spaces. in s.r. kruk and b. mcdaniel, semantic digital libraries. springer, 2008. [7] j. jung, j. euzenat, towards semantic social networks. in proceedings of the 4th european semantic web conference, innsbruck, austria, pp. 267-280, 2007. [8] a. ounnas, i. liccardi, h.c. davis, d.e. millard and s.a. white towards a semantic modeling of learners for social networks. in proceedings of the international workshop on applications of semantic web technologies for e-learning (sw-el) at the ah2006 conference, dublin, ireland, 2006. an ontology to model e-portfolio and social relationship in web 2.0 informal learning environments 585 [9] i. liccardi, a. ounnas, r. pau, e. massey, p. kinnunen, s. lewthwaite, m. midy, c. sarkar, the role of social networks in students’ learning experiences. sigcse bulletin 39(4):224-237, 2007. [10] r. mason, c. pegler and m. weller, e-portfolios: an assessment tool for online courses. british journal of educational technology, 35,6, pp. 717-727, 2004. [11] ieee p1484.2.1/d8 : draft standard for learning technology public and private information (papi) for learners (papi learner) core features sponsored by the learning technology standards committee of the ieee computer society, 2001. [12] ims eportfolio practice and implementation guide, ims global learning consortium, 2005. [13] d. w. johnson, r. t. johnson. an educational psychology success story: social interdependence theory and cooperative learning. educational researcher, 38(5), 365-379, 2009. [14] m. jensen, cooperative quizzes in the anatomy and physiology laboratory: a description and evaluation. advances in physiology education, 16(1), s48-s54, (1996). [15] m. jensen, d.w.johnson, r. johnson, impact of positive interdependence during electronic quizzes on discourse and achievement. journal of educational research, 95, 161-166, (2002). [16] t. matsui, t. kakuyama, m. onglatco, effects of goals and feedback on performance in groups. journal of applied psychology, 72, 407-415, (1987). [17] n. hwong, a. caswell, d.w. johnson, r. johnson, effects of cooperative and individualistic learning on prospective elementary teachers’ music achievement and attitudes. journal of social psychology, 133, 53-64, (1993). [18] d. mesch, m. lew, d.w. johnson, r. johnson, isolated teenagers, cooperative learning and the training of social skills. journal of psychology, 120, 323-334, (1986). [19] d. mesch, d.w. johnson, r. johnson, impact of positive interdependence and academic group contingencies on achievement. journal of social psychology, 128, 345-352, (1988). int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 530-539 brain tumor segmentation on mri brain images with fuzzy clustering and gvf snake model a. rajendran, r. dhanasekaran arthanari rajendran professor and hod, department of electronics and communication engineering, sriguru institute of technology, coimbatore,tamilnadu, india e-mail:rajendranav@gmail.com raghavan dhanasekaran professor, director-research, syed ammal engineering college, ramanathapuram, tamilnadu, india e-mail:rdhanashekar@yahoo.com abstract: deformable or snake models are extensively used for medical image segmentation, particularly to locate tumor boundaries in brain tumor mri images. problems associated with initialization and poor convergence to boundary concavities, however, has limited their usefulness. as result of that they tend to be attracted towards wrong image features. in this paper, we propose a method that combine region based fuzzy clustering called enhanced possibilistic fuzzy c-means (epfcm) and gradient vector flow (gvf) snake model for segmenting tumor region on mri images. region based fuzzy clustering is used for initial segmentation of tumor then result of this is used to provide initial contour for gvf snake model, which then determines the final contour for exact tumor boundary for final segmentation. the evaluation result with tumor mri images shows that our method is more accurate and robust for brain tumor segmentation. keywords: deformable model; fcm; segmentation; mri image; gvf 1 introduction the accurate and automatic segmentation of brain tumor on mri image is of great interest for assessing tumor growth and treatment responses, enhancing computer-assisted surgery, planning radiation therapy, and constructing tumor growth models. this is very difficult task in existing methods. the existing methods are divided into region-based and contour-based methods. region-based methods [1-9] seek out clusters of pixels that share some measure of similarity. these methods reduce operator interaction by automating some aspects of applying the low level operations, such as threshold selection, histogram analysis, classification, etc. they can be supervised or non-supervised. in general these methods take advantage of only local information for each pixel and do not include shape and boundary information. contour-based methods [10-14] rely on the evolution of a curve, based on internal forces and external forces, such as image gradient, to delineate the boundary of brain structure or pathology. these methods can also be supervised or non-supervised. in general these methods suffer from the problem of determining the initial contour and leakage in imprecise edges. in this paper we propose a method that is a combination of region-based fuzzy clustering method called enhanced possibilistic fuzzy c-means (epfcm) and gradient vector flow (gvf) snake model to remove the problems using the capabilities of each one. for example a region-based method can solve the problem of the initialization of a contour-based method (gvf snake model) and a contour-based method is able to improve the quality of region-based segmentation at the boundary of objects. copyright c⃝ 2006-2012 by ccc publications brain tumor segmentation on mri brain images with fuzzy clustering and gvf snake model531 so the proposed method has two main phases for tumor segmentation on mri brain images namely, initial segmentation which is done by a region-based method and final segmentation that is performed by a boundary-based gvf snake model. we discuss these approaches in the following section. 2 region-based enhanced possibilistic fuzzy c-means (epfcm) in this proposed enhanced possibilistic fuzzy c-means (epfcm) method, distance metric di j in pfcm [15] is modified in such a way that it includes membership, typicality and both local, nonlocal spatial neighbourhood information to overcome the noise effect in mri brain medical images. this modified distance metric is incorporated into objective function of pfcm. then resultant algorithm is called enhanced possibilistic fuzzy c-means (epfcm) is obtained for enhanced segmentation results. therefore objective function of our proposed epfcm is defined as follows, jm (u,v,t ; x) = c∑ i=1 n∑ j=1 ( aµmi j + bt η i j ) d2i j + c∑ i=1 γi n∑ j=1 ( 1 − ti j )η (1) where, the modified distance metric is given by d2 ( x j, vi ) & = &d2i j = ( 1 −λ j ) d2l ( x j, vi ) +λ jd 2 nl ( x j, vi ) (2) c∑ i=1 µi j& = &1 ∀ j,0 ≤ µi j, ti j ≤ 1 and a > 0,b > 0,m > 1,η > 1 (3) the membership function: µi j =  c∑ i=1 ( di j dk j ) 2 m−1  −1 (4) typicality: ti j = 1 1 + ( b γi d2i j ) 1 η−1 (5) cluster centre: vi = n∑ j=1 ( aµmi j + bt η i j ) x j n∑ j=1 ( aµmi j + bt η i j ) (6) in the following equation is suggested to compute γi: γi = k n∑ j=1 µmi j d 2 i j n∑ j=1 µmi j , k > 1 (7) 2.1 importance of modified distance metric term ( di j ) the modified distance metric or dissimilarity measure is rewritten from equation (2) as follows, d2 ( x j, vi ) & = &d2i j = ( 1 −λ j ) d2l ( x j, vi ) +λ jd 2 nl ( x j, vi ) (8) 532 a. rajendran, r. dhanasekaran where, dl is the distance metric influenced by local spatial information. this added local spatial neighborhood term is similar to the one which is used in [16] to incorporate the neighborhood effects in the classic fcm.the local spatial constraint is evaluated by the feature difference between neighboring pixels in the image. dnl is the distance measurement influenced by nonlocal spatial information. this added non local term is obtained from the non local means (nl-means) algorithm [17] for image denoising. the nonlocal constraint determined by all points whose neighborhood configurations look like the neighborhood of the pixel of interest. λ j is the weighting factor controlling the tradeoff between local and nonlocal spatial information. it varies from zero to one. 2.2 importance of local distance metric (dl) let n j denote a chosen local neighborhood configuration of fixed size with respect to a center pixel x j. if the value of a pixel xk in n j is close to the center pixel, then x j should be influenced greatly by it, otherwise, its influence to x j should be small. according to the above description, the distance measurement influenced by local information dl is given by, d2l ( x j, vi ) = ∑ xk∈n j ωl ( xk, x j ) d2 (xk, vi) ∑ xk∈n j ωl ( xk, x j ) (9) where d2 (xk, vi) = ∥xk − vi∥2 is the euclidean distance metric measure the similarity between pixel pixel xk and cluster centroid vi,ωl(xk, x j) is the weight of each pixel xk in n j and is given by ωl ( xk, x j ) = e |xk − x j|2 σ2 (10) where, σ2 is the variance of ni. it specifies the steepness of the sigmoid curve. 2.3 importance of non local distance metric (dnl) the distance measurement influenced by non-local information dnl is computed as a weighted average of all the pixels in the image i, xk ∈ i d2nl ( x j, vi ) = ∑ xk∈i ωnl ( xk, x j ) d2 (xk, vi) (11) where the family of weight ωnl ( xk, x j ) ; xk ∈ i depends on the similarity between the pixel xk and x j, and satisfies the usual conditions 0 ≤ωnl ( xk, x j ) ≤ 1 and ∑ ωnl ( xk, x j ) = 1. the similarity between two pixels xk and x j depends on the similarity of the intensity gray level vector v(nk) and v(n j), where nk denotes a square neighborhood of fixed size and centered at a pixel xk. this similarity is measured as a decreasing function of the weighted euclidean distance ∥∥∥∥v(nk) − v(n j)∥∥∥∥2 2,a , where a > 0 is the standard deviation of the gaussian kernel. the pixels with a similar gray level neighborhood to v(n j) have larger weights in the average. these weights are defined as ωnl ( xk, x j ) = 1 q ( x j ) s (xk, x j) (12) brain tumor segmentation on mri brain images with fuzzy clustering and gvf snake model533 where, s ( xk, x j ) is the exponential form of the similarity and q ( x j ) is the normalizing constant. these terms are defined as, s ( xk, x j ) = e ∥v(nk )−v(n j)∥22,a h2 (13) q ( x j ) = ∑ xk∈i e ∥v(nk )−v(n j)∥22,a h2 (14) the parameter h acts as a degree of filtering. it controls the decay of the exponential function and therefore the decay of the weights as a function of the euclidean distance. 2.4 importance of trade-off parameter (λ) for computational purpose, the search of the similar neighborhood configuration always be restricted in a larger "search window" denoted by ωi. let x j be the pixel under consideration. for each pixel xk in the search window of size s × s , calculate its exponential similarity to x j using equation (13). the tradeoff parameter of x j is then defined as λ j = 1 m m∑ i=1 s i ( xk, x j ) (15) where s i represents the ith exponential similarity term in the search window and choose m = s − 1.the parameter λ j decides the trade-off between local and non local spatial information. 3 algorithm for proposed epfcm method finally the algorithm for carrying out our proposed epfcm for tumor segmentation of mri brain images can now be stated from the following steps 1. select the number of clusters ′c′ and fuzziness factor ′m′ 2. select initial class centre prototypes v= {vi} ; i = 1,2 · · ·c, randomly and ∈,a very small number 3. select the neighbourhood size and search window size 4. calculate modified distance measurement d2i j using the equation (2) 5. update membership function µi j using d2i j 6. update γi; i = 1,2 · · ·c, using equation (7) 7. update typicality using the equation (5) 8. update cluster centre using equation (6) 9. repeat steps 4 to 8 until termination. the termination criterion is as follows, ∥vt+1 − vt∥ ≤∈ where′t′ is the iteration steps, ∥.∥ is the euclidean distance norm. we applied this proposed algorithm to segment tumor on mri images. in this case, we segmented the brain image into five classes: namely, csf (cerebrospinal fluid), wm (white matter), gm (gray matter), tumor and background .due to some classification errors, there are undesired additional pixels in the tumor class. to remove these misclassified components, several binary morphological operations 534 a. rajendran, r. dhanasekaran are applied to the tumor class after users defined segmentation classes are obtained (number of clusters). an opening operation is first used to disconnect the components. then we select the largest connected component, which proved to always correspond to the tumor, even if it has a small size. here the elementary neighborhood of the morphological operations corresponds to 6-connectivity. the result of this algorithm gives segmented tumor class as shown in figure 1(c) .this output is the initial contour for the gvf snake model. 4 boundary-based gvf snake model the traditional deformable active contour model [18-19] is a curve x(s ) = [ x(s),y(s) ] , s ∈ [0,1],that move within the image to minimize the energy function. the curve dynamically changes the shape of an initial contour in response to internal and external forces. the internal forces provide the smoothness of the contour. while the external forces push the curve move toward the desired features, such as boundaries. the object contour will be got when the energy function is minimized. the energy is defined as: e = 1∫ 0 1 2 [ α|x′(s )|2 +β|x′′(s )|2 ] + eext (x(s )) d s (16) where, x′(s ) and x′′(s ) are first and second derivatives of x(s ) with respect to s. the parameter α controls the tension of the curve and β controls its rigidity. eext is the external energy which is calculated from the image data. to minimize the energy function, the snake must satisfy the euler equation αx′′(s ) −βx′′′′(s ) −∇eext = 0 (17) then the snake is made dynamic by treating as the function of time t, as follows: xt (s, t) =αx ′′ (s, t) −βx′′′′(s ) −∇eext (18) when the solution x(s, t) stabilizes, the term xt(s, t) is zero. then we get the solution of equation (18).the typical external energies include: eext (x,y) = −|∇i (x,y) |2 (19) eext (x,y) = −|∇ [ gσ (x,y) ∗ i (x,y) ] |2 (20) eext (x,y) = i (x,y) (21) eext (x,y) = gσ (x,y) ∗ i (x,y) (22) where, gσ(x,y) is a 2-d gaussian function with standard deviation σ and mean is zero.∇ denotes the gradient operator ∗ denotes linear convolution. these external forces have a short capture range and poor convergence to boundary concavities. to overcome these problems, gradient vector flow snake was proposed by xu and prince [18], which uses the force balance condition as a starting point of snake. it defined a new static external force field called gvf field fext = v (x,y) = [u(x,y), v(x,y)] (23) where, u and v are the grey changes on x-axis and y-axis of the image respectively .fext can be got by minimizing the following energy function: ∈= ∫ ∫ µ ( u2x + u 2 y + v 2 x + v 2 y ) + |∇ f |2|v−∇ f |2d xdy (24) brain tumor segmentation on mri brain images with fuzzy clustering and gvf snake model535 where, ux, uy, vx, vy are derivative of x-axis and y-axis respectively. f (x,y) is the edge map (using canny edge detector) which is derived from image i(x,y). µ is a regularization parameter governing the tradeoff between the first term and the second term in the formula. it should be set according to the noise of the image. the calculus of variations and numerical implementation discussed in [18] is used to obtain the solution of equation. this deformable contour is first initialized by the tumor class output of epfcm method, which then moves towards the final tumor boundary. 5 results and discussion initially tumor mri brain image is segmented for tumor class using epfcm method, which then initial contour for gvf snake. then the contour attracted towards final tumor boundary by edge map derived from the image using canny edge detector. we set parameter h = 500, search window size is 7 × 7,neighborhood window size is 3 × 3,m = 2,a = 5,b = 3 and η = 2 for epfcm method to have proper segmentation result. we set α and β value between 0.1 to 0.2 and µ value between 0.2 to 0.3 for gvf snake model to have final tumor boundary.the application of our combined method to 10 contrasts enhanced t1-weighted images and 5 flair images shows better tumor segmentation. the results of four cases are as shown in figure 1. figure 1: (a)first column: first two images; original ce-t1w enhanced tumors; third image; original ce-t1w ring enhanced tumor; fourth image; original non enhanced tumor flair image (b) second column: manual segmentation result (c) third column: epfcm result showing tumor class after morphological operations (d) fourth column: segmentation of tumor class using combined approach (epfcm and gvf snake model) (e) fifth column: final boundary detection (blue curve) shows tumor region using gvf snake model. the evaluation of segmentation performance is also carried out quantitatively by employing four volume metrics namely, the similarity index(s), false positive volume function (fpvf), false negative volume function (fnvf) and jaccard index in our experiment. for a given image, suppose that ai and bi represent the sets of pixels belong to class i in manual and in automatic segmentation, respectively. |ai| denotes the number of pixels in ai. |bi| denotes the number of pixels in bi. the similarity index is an intuitive and clear index to consider the matching pixel between ai and bi, 536 a. rajendran, r. dhanasekaran and defined as s = 2|ai ∩ bi| |ai|+ |bi| (25) similarity index s > 70% indicates an excellent similarity [20]. the false positive volume function (fpvf) represents the error due to the misclassification in class i and the false negative volume function (fnvf) represents the error due to the loss of desired pixels of class i, they are defined as follows, fpvf = |bi| − |ai ∩ bi| |ai| (26) fnvf = |ai| − |ai ∩ bi| |ai| (27) higher value of s, and lower value of fpvf, fnvf gives better segmentation result. the jaccard index between two volumes is represented as follows, ji (a, m) = |ai ∩ bi| |ai ∪ bi| × 100 (28) table 1: evaluation of the segmentation results of enhanced tumors and nonenhanced tumor by combined approach (epfcm and gvf model) on a few ce-t1w and flair images.(fet denotes the full enhanced tumor, ret the ring-enhanced tumor,net the enhanced tumor mri modality type type of tumor volume metric functions (%) ce-t1w & flair fet, ret & net s fpvf fnvf j ce-t1w fet1 98.8 0.4 0.2 88.2 ce-t1w fet2 96.3 0.7 0.4 84.5 ce-t1w fet3 92.6 1.2 0.7 86.7 ce-t1w fet4 95.7 0.6 0.6 89.5 ce-t1w fet5 93.2 0.4 0.5 87.8 ce-t1w ret1 95.8 0.1 0.2 80.2 ce-t1w ret2 92.3 1.3 0.7 78.6 ce-t1w ret3 97.6 0.2 0.3 76.2 ce-t1w ret4 91.8 2 1.2 75.5 ce-t1w ret5 96.3 0.1 0.3 77.3 flair net1 98.8 0.9 0.6 76.9 flair net2 91.5 2.1 1.8 83.2 flair net3 98.6 2.9 3 80.1 flair net4 94.4 1.8 2.2 85.2 flair net5 95.3 1.2 2.1 81.6 average 95.3 1.06 0.98 82.1 the result of four volume metrics for our method applied to 15 tumor cases is as shown in table 1and plotted in figure 2. from this table, we can see that an average similarity metrics and jaccard index of our method is 95.3% and 82.1% that is, the overlap degree between our segmentation result and the manual segmentation is higher. the average fpvf and fnvf values are equal to 1.06% and 0.98%. it shows misclassification and loss of desired tumor pixels are reduced in great degree. these average values are obtained from 15 tumor cases as shown in figure 3.to compare the results with other methods, there is no a good standard, however in comparison with works such as in [1,2,7] shows that our method has a better tumor segmentation performance. brain tumor segmentation on mri brain images with fuzzy clustering and gvf snake model537 figure 2: graph of the quantitative comparison results of three volume metrics for 15 mri brain tumor images. figure 3: graph of the average value of the three volume metrics obtained from 15 mri brain tumor images. 6 conclusions we have presented in this paper a tumor segmentation method which combines both region based fuzzy clustering method called epfcm and boundary based method called gvf snake model .we verified our method with brain tumour mri images. the obtained results are quantitatively verified with other existing methods and they show that our combined approach provides better result. 538 a. rajendran, r. dhanasekaran bibliography [1] m. prastawa, e. bullitt, s. ho, g. gerig, a brain tumor segmentation framework based on outlier detection, medical image analysis, 2004, 18 (3), 217-231. 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[20] zijdenbos, a. p., dawant, b. m., margolin, r. a., and palmer, a. c. morphometric analysis of white matter lesions in mr images: method and validation, ieee transactions on medical imaging, 1994;13(4):716-724. int j comput commun, issn 1841-9836 8(1):42-49, february, 2013. datastores in cloud governance a. copie, t.-f. fortiş, v.i. munteanu adrian copie, teodor-florin fortiş, victor ion munteanu 1. west university of timişoara romania, timişoara, bvd. v.pârvan 4, and 2. institute e-austria, timişoara romania, timişoara, bvd. v.pârvan 4 adrian.copie@info.uvt.ro,fortis@info.uvt.ro vmunteanu@info.uvt.ro abstract: the small and medium enterprises benefit now, due to the scale adoption of cloud computing, from an emerging market where they can associate and collaborate to form virtual enterprises or virtual clusters, aiming to compete with the large enterprises and provide tailored it solutions for their customers. however the lack of standardization for the cloud services and technologies leads to a myriad of different components that cannot be easily set to work together in the absence of a real cloud governance solution. cloud governance acts like a catalyst to allow small and medium enterprises to easily manage and optimize their services infrastructure, and to facilitate collaboration in a clustered or virtual-enterprise environment. we have proposed a cloud governance architecture based on mosaic’s multi-agent cloud management solution. the cloud governance solution relies on various datastores that are responsible with maintaining and managing a set of crucial data that are used during the cloud governance process . our paper is focused to analyze and emphasize the requirements that must be fulfilled by different database systems in order to have a reliable storage system and also to suggest a concrete solution. keywords: cloud computing, cloud governance, datastores, databases 1 introduction in the last years, cloud computing becomes a common paradigm, due to the straightforward way of resource provisioning, dynamically scalability, service orientation and its simple pay-asyou-go financial model. along with these characteristics, in [1] are identified others like strong fault tolerance, loosely coupling, virtualization, ease of use and the link with the business model which lead to the development of new business models [2], [3]. the selected architectural model allows smes to collaborate and associate in virtual enterprises or virtual clusters and expose services in direct competition with the large enterprises. however, there are still issues to overcome in order to benefit of this kind of collaboration due to the diversity of services offered by the cloud providers and the lack of standardization. efforts have been made in this direction, different solutions that aim to abstract the characteristics of some existing cloud providers have been released: mosaic1, cloudfoundry2, morfeo 4caast3, activestate stackato4, openshift5, reservoir6, sla@soi7 and many others . even if they are a step forward in the process of the cloud services standardization, [4], [5] and [6] suggest 1http://www.mosaic-cloud.eu 2http://cloudfoundry.org 3http://4caast.morfeo-project.org 4http://www.activestate.com/stackato 5http://openshift.redhat.com/app/ 6http://www.reservoir-fp7.eu/ 7http://sla-at-soi.eu/ copyright c⃝ 2006-2013 by ccc publications datastores in cloud governance 43 complementary services related to the cloud management which assures that the resources in the cloud are used optimally and properly interact with the users and other services. taking into account the growth rate of the cloud services, [7], [8], [9] reveal the need a better integration and a demand for complementary mechanism for cloud management: cloud governance. this is an evolutionary step in the service oriented architecture (soa) which makes possible the awareness of the existence of the cloud services for the potential consumers. due to its similarities, the awareness mechanism rely on different specialized datastores which hold critical information and which have various requirements in order to correctly function and offer reliable data. however, according to [11] there must be a clear separation between the management and governance processes, because they describe different activities, have distinct goals and involves different organizational structures. 2 the mosaic project ’open source api and platform for multiple clouds’ (mosaic) is a fp7-ict project that aims to provide an open-source platform and an api which abstracts the particularities of various cloud providers and encourages the applications development based on the cloud-programming paradigm. its main components are the cloud agency which is an embedded cloud management solution based on agents that is designed to negotiate cloud resources and provide them to the second component called mosaic platform which consumes them by offering a cloud-oriented application development framework. 3 cloud governance our proposed cloud governance architecture is based on variuos proposals, including distributed management task force’s (dmtf) white papers [6], [10] and is built around the mosaic’s cloud agency. the cloud agency is a core cloud management component in the mosaic platform that exposes its functionality as a service that is consumed inside our cloud governance component (figure 1). figure 1: cloud governance the architecture reveals several distinct components like the cloud management solution based on mosaic’s cloud agency, the cloud governance bus, the cloud governance functional modules and the datastores used to persist various data during the governance process. the cloud agency’s main role is to assure the management of the resources but also to perform 44 a. copie, t.-f. fortiş, v.i. munteanu table 1: general requirements for the datastores in cloud governance crt.no. requirement s1 cost optimisation by trying to find solutions in order to minimize the costs with the storage and virtual machines in the cloud through intense multiplexing s2 high performance in terms of throughput, small latency, scalability independent of the data size and the dynamic of the workload s3 security (confidentiality, integrity, privacy) s4 high availability d1 simple internal api in order to expose a relative small number of methods to be used by the governance agents various slas monitoring activities. the messages and data exchange between the cloud services is made through the cloud governance bus. however the governance process is realized using four specialized agencies namely service management, security management, audit management and governance management. the service management takes care about the lifecycle of the services registered in the cloud governance environment. security management handles the identities of the service consumers and provides the security tokens used to access the requested services. the audit management agency processes the audit information obtained from the cloud management component and in the same time allows the access to a wide range of logs generated by the interacting components. finally, the governance management coordinates the entire governance activity based on a set of rules, policies and settings. 4 datastores in cloud governance every cloud governance agency handles specific data, being in direct relation with a dedicated datastore called namely service datastore, security datastore, audit datastore and governance datastore. every datastore is in charge with keeping crucial information related to the functionality of the entire system but also very sensitive from the privacy and confidentiality point of view, such as credentials, contracts, partners, policies, etc. in the same time the data storage system must be very responsive in what concerns the time took to perform various operations over the data set and also must offer a good performance in terms of bandwidth, to accommodate with the processed data flow. this is why the data storage systems must fulfil some general requirements, as revealed in table 1. the general requirements for the data storage systems have been divided in two categories: requirements related directly to the intrinsic characteristics of the storage like cost optimization, high performance, high availability, high security and also requirements related to the development phase, like a very simple and intuitive api. these requirements apply to all the datastores inside the governance environment and, from case to case, there are other specific requirements that will be exposed in the appropriate section. 4.1 governance datastore the cloud governance implies the use of various policies related to the way in which the cloud services interact with the consumers, together with different types of constraints that limit the datastores in cloud governance 45 access to the underlying resources, or even functionality of the governance process building blocks. there are several types of policies that are administered by the cloud governance component like service level agreements (slas) and service level objectives (slos) and which represent the cornerstone of the cloud governance process [4], [6]. the relevant constraints involved in the decision taking process are deployment constraints, data residency constraints, auditability constraints and security constraints. after two partners, namely the provider and the consumer, agree about a set of constraints, they will further govern the interaction between the entities involved in the contract. the governance management agency coordinates the entire activity performed by the cloud governance component based on different policies, constraints and system settings that are stored in the governance datastore like access policies, virtual machine settings, security credentials, interaction policies, etc. every cloud service provider has its own policies and constraints that will be published and used during the service lifecycle. some of the policies and constraints could be editable by the service consumers in order to customize them, like access control lists, some of them are not, like quality of services, all of them being part of the request and offer operations. the information represented by the policies and constraints is usually structured, being related to the guaranteed functionally parameters of the services, security policies, configuration parameters of the virtual machines on which the agents are executed and many more. the data persisted in the governance datastore is critical to the whole governance process, therefore requirements of the storage system are also in terms of highly availability and reliability. some of the data is very sensitive so a level of encryption must be added. in the same time, this datastore must maintain relations with other storage systems which recommends a flexible graph database to hold the information related to governance. this choice will assure high performances in terms of records processing, allowing to be installed either in the private cloud on commodity servers or in the public cloud through virtual machines. 4.2 services datastore this is the most complex datastore, part of the cloud governance process. it could be seen like a service itself offering essential interfaces for the cloud services that want to register in the cloud governance component and then to be discovered and used, together with other additional features related to offers, contracts, audits and billing. figure 2 depicts the schematic of the service datastores. figure 2: services datastore and their internal relationships the service datastores, through their specific mechanisms and information that compound 46 a. copie, t.-f. fortiş, v.i. munteanu them, facilitates the services publishing and discovery processes. like in the case of soa governance, cloud governance relies on a service repository model in which the services that want to offer their functionality must register and must offer methods to be discovered by the consumers. this datastore is also tied with global catalogues that maintain information about the system’s customers and the partners. the character of the information contained in the service datastore is extremely heterogeneous, holding many kinds of specific information, grouped in several sub-components: • service descriptors. the services functional and non functional requirements are described through service descriptors and they are persisted inside the descriptors component. this is usually a structured information, the service descriptors contains basically information of the same type and must be highly query-able in order to offer flexible information about the contained services. • semantic definitions. besides the syntactic description stored inside the descriptors component, the cloud services are described and defined from their semantic point of view through semantic descriptors and stored in the semantic definition sub-component. • offers. the relation between provider and consumer usually benefit from a resource model that describes what the service can offer in terms of functionality. service templates are used to describe in a generic form what a provider can offer, but when the template contains information about a specific provider, the template becomes an offer and it is persisted for future use in the offers sub-component. because the information is based on generic templates, it could be structured so an appropriate storage system could be used. • contracts. when a consumer looks for a specific service and discover it in the services datastore, it consults its offer, agrees with the slas and if it decides to use that services the offer becomes a contract that is also stored inside the contracts sub-component. for a specific service, more than one contract could exist. • instance sets. after a cloud service is contracted, in order to be effectively used it is instantiated and an additional information called service instance is generated. this information is an aggregate of trading, billing and deploying information which is stored in a separate sub-component of services datastore called instance sets. • running instances. the information about the running instances of the cloud services is very much the same as the one contained in the service instances and is persisted in the running instances sub-component • billing. for every contract, billing information is generated in accordance with the parameters agreed during the contracting phase and this information is located in the billing sub-component. taking into account the format of the data persisted in the individual database components in the service datastores, together with the general requirements already exposed, the choices in terms of database types are • newsql database for the service descriptors database • rdf datastore for the semantic definitions database in order to benefit from the specialized sparql query language • graph database for the offers, contacts, instance sets, running instances and billing databases datastores in cloud governance 47 4.3 security datastore the cloud governance involves distinct entities, components, agents running in behalf of various parties, therefore the security is one of the most important concerns in order to assure a safe and trustful environment. inside the cloud governance system we distinguish two security planes: the security of the cloud services, usually assured by the service providers and the intrinsic security of the cloud governance component. the access to the cloud services must be done in a secure way, only authorized consumers having the rights to use the provided functionalities, sometimes in a granular way through various access rights and security policies. the security management agent is responsible for the credentials management for the authentication and authorization process together with providing the security tokens used to access different cloud resources after the successful authentication process. all the credentials and security policies governing the cloud services in the system are stored in the security datastore. in most cases, the services are offered on a pay-as-you-go schema, being accessed by providing credentials in the form of user name and password, or in terms of security keys. this special kind of data is kept inside the security datastore. to add an extra security layer over the persisted data, the information contained in the identities sub-component must be encrypted and the passwords hashed. the security policies used to consume the cloud services are related to the consumer identities and establish what kind of actions are allowed over a given resource by an authenticated consumer. the appropriate database type to hold the security information is newsql, allowing to be implemented either in the private or public cloud. 4.4 audit datastore the services involved into a complex cloud system that requires governance are provided on a paid scheme, and must respect an sla previously negotiated and agreed between the cloud service provider and the consumer. usually the service is monitored only on the provider side, such that the client is not aware when the cloud service does not respect the parameters that are stipulated in the contract. through the accepted sla, different metrics are established in order to facilitate the service control and monitoring mechanisms. the mosaic’s cloud agency component provides the required functionality that allows the service monitoring on the consumer side and to permanently compare the measured parameters with the contractual ones that is leveraged by the audit management agent which obtains all the necessary information about a specific service. along with the performance measurements, billing information is generated based on the time spent for a cloud service to perform specific tasks together with audit information , triggered when the cloud service is accessed by various entities. the audit mechanism is working based on various policies established at the service level. all these data together with the audit policies is stored in the audit datastore component. usually the data contained in the audit datastore is not structured, but has a large volume depending on the number of the performance parameters monitored and the sampling frequency. because many decisions in the process of governance are taken based on the data analysis, it is recommended that the storage system that host this data to offer high availability, high writability and scaling, which is better achieved by a key-value datastore. 48 a. copie, t.-f. fortiş, v.i. munteanu 5 conclusions and future works cloud computing adoption embraced by the small and medium enterprises makes possible the access on new markets where they can associate in virtual clusters or virtual enterprises in order to be able to compete with the large enterprises, by offering complex solutions, tailored on the customers specific needs. we have proposed a cloud governance architecture based on mosaic’s cloud management components that aims to manage and govern the services infrastructure. this government solution relies on a set of datastores that maintains and manage various data produced and consumed during the services lifecycle. our paper focused in expanding the information contained in the most important datastores in cloud governance, determining the requirements for every database that form the datastores and suggests the appropriate database types. this paper is a base for a future work consisting in the concrete implementation of a cloud governance component. acknowledgments this work was partially supported by the grant of the european commission fp7-ict-20095-256910 (mosaic), the views expressed in this paper do not necessarily reflect those of the corresponding projects consortium members. bibliography [1] c. gong, j. liu, q. zhang, h. chen, and z. gong, the characteristics of cloud computing, in w.-c. lee and x. yuan (eds.), icpp workshops, ieee computer society, pp. 275-279, 2010. 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(2012, march) cobit 5 introduction. [online]. available: http://www.isaca.org/cobit/documents/cobit5-introduction.ppt int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 163-172 load-aware and position-aided routing in satellite ip networks l. wang, l. liu, x. hu wang lu 1. graduate university of chinese academy of sciences beijing, china, 100049, and 2. institute of software, chinese academy of sciences beijing, china, 100190 e-mail: wlu810@gmail.com liu lixiang, hu xiaohui institute of software, chinese academy of sciences beijing, china, 100190 abstract: satellite communication is regarded as a promising way to achieve seamless global coverage with an array of advantages over the traditional terrestrial network. however, the non-uniform distribution of users and the highly topological dynamics have been a matter of facts. the traditional load balancing technique is able to guarantee a better traffic distribution around the inter satellite links with heavy loads after considering the non-uniform distribution of users, but it transmits load information passively and fails to provide a fair traffic distribution over the entire constellation. given the highly topological dynamics, traditional routing protocols may experience the convergence processes frequently or use a large amount of storage to keep the snapshots. this paper proposes a novel routing protocol named lpr for the satellite ip networks, which transmits the bottleneck link load information to upstream satellites in a hop-by-hop manner actively, to guarantee a better distribution of traffic among satellites. in response, upstream satellites select less congested paths and communicate a portion of data according to the priority of different classes of traffic. in addition, lpr applies the geographical position when selecting the routing path to eliminate the convergence process which may cause the routing tables invalid. finally, various aspects of performance of lpr have compared with other mechanisms. our evaluations show that a routing protocol with load-aware and position-aided leads to higher throughput and better traffic distribution compared to the traditional routing protocol and load balancing technique. keywords: satellite networks, load balancing, position, routing. 1 introduction satellite networks exhibit unique features and offer an array of advantages over traditional terrestrial networks. they are able to provide coverage to extensive geographic areas and to interconnect among remote terrestrial networks. a satellite communicates with other satellites via inter-satellite links(isls). the links between satellites in the same plane are called intraplane isls. the links between satellites in different planes are called inter-plane isls. satellites communicate with the ground stations over user data links(udl). leo satellites provide short round-trip delays and have been the focus of several researches for a period of time [1], [2]. given recent advancements in satellite communications, researchers find that a combination of different layers of satellites would yield a better performance than copyright c⃝ 2006-2012 by ccc publications 164 l. wang, l. liu, x. hu these layers individually. researchers have proposed severals routing protocols for multi-layered satellite networks, such as mlsr [3], sgrp [4], sos [5] et. al. mlsr is a link state routing protocol for multi-layered satellite networks. sgrp is a multi-layered satellite network routing protocol based on snapshots. in these multi-layered satellite network routing protocols, leo satellites transmit data, and meo/geo satellites are managers and compute the routing paths. another type of satellite network routing protocol is based on geographical location, such as lpdb [6]. lpdb utilizes satellites’ inherent broadcast capabilities, and predict transmission direction according to the geographical location. the majority of the routing protocols have convergence processes to collect information and update routing tables when the topology changes, however satellites move rapidly relative to the surface of the earth and to the ground stations(leo satellites speed at over 2500km/hour [7]) and a lot of packets may be lost during the convergence process, due to routing tables becoming invalid. furthermore, satellites’ rapid movements cause frequent convergence processes, which increases the algorithms’ overhead. when satellites use routing protocols based on snapshots, it takes a large amount of storage. if satellites’ links interrupted, routing tables of the current snapshot and the following snapshots would be invalid, and in turn it takes a long interval for the central control nodes, such as ground stations, geo/meo, to calculate new routing tables and update routing tables for the satellites. in addition, an important missing point in the previous designs consists in their focus on searching for the paths without any consideration of the total traffic distribution over the entire constellation, which leads to unfair distribution of traffic. recently, researchers propose some load balance mechanisms, such as cemr [8], elb [9] et. al. cemr is based on a cost metric that involves both propagation and queuing delays, however it does not reflect the congestion state of the next hop, nor does it estimate the queuing delay a packet may experience there. elb is an explicit load balancing mechanism that classifies users into three classes. neighboring satellites exchange load information and reduce data forwarding rates when the queue occupancies exceed a pre-determined threshold. however, elb only exchanges information among neighboring satellites actively and it takes a long interval to notify sources of the congestion state. furthermore, although elb applies a routing metric that instantly reflects both the one-way propagation delay and the instant queuing delay to avoid loops, the traffic forwarding may still cause loops. the objective of this paper is to design a novel satellite network routing protocol named lpr (load-aware and position-aided routing), which is able to guarantee fair distribution of traffic among the entire constellation and eliminate the convergence process. lpr calculates every link’s load information and transmits it to the upstream satellites actively. lpr calculates the next hop set based on geographical position, and the next hop set includes a main path and several alternative paths. lpr transmits traffic on the main path when the load on the main path is low, and begins to communicate a portion of traffic on the alternative paths when the load on the main path becomes high. the main path is the routing path with the shortest distance. 2 dissemination and estimation of load information 2.1 load transmission in traditional load balancing technique, the load information is exchanged among neighboring satellites. it is able to achieve better traffic distribution than the routing protocols which do not take traffic distribution into account, but the isls are expected to be heavily loaded around the overload region and others remain underutilized, and in turn it fails to achieve fair traffic load-aware and position-aided routing in satellite ip networks 165 distribution over the entire constellation, as in figure 1. in lpr, each satellite calculates the load information and compares its own link load with the received link load , and updates the link load, if the local link load is larger than the received link load. then satellites transmit the load information in a hop-by-hop manner. in other words, each satellite transmits the bottleneck link load information to upstream satellites actively. the upstream satellites receive the bottleneck link load, and begin to detour traffic when the bottleneck link load is larger than the threshold, resulting that all upstream satellites on the transmission path are able to communicate a portion of data when there is a link congested. in turn, the traffic is distributed fairly over the entire constellation and satellites are able to return back to free state quickly, as in figure 2. in addition to the fair traffic distribution, lpr is able to disseminate the load information instantly to all upstream satellites. in traditional load balancing technique, it takes a relatively long interval to transmit the overload information to upstream satellites, for the traditional load balancing technique only transmits load information actively to neighboring satellites, and load information is transmitted passively to the upstream satellites. let i and o denote the total input and output traffic at a given satellite. a satellite is considered to be overloaded if the load exceeds the threshold. let tn denote the time for satellite n becomes overloaded when i is larger than o. let dn denote the transmission delay between satellite n and satellite n + 1. in traditional load balancing technique, it takes ∑j−1 n=i (tn + dn) for satellite i to know the overload sate originated from satellite j. but in lpr, it only takes ∑j−1 n=i dn for satellite i to know the load information of satellite j, due to the active transmission of load information in a hop-by-hop manner. figure 1: traditional load balancing figure 2: lpr’s load balancing 166 l. wang, l. liu, x. hu 2.2 load estimation and setting of thresholds in lpr, satellites keep a virtual queue for every link and estimate link load rl over an appropriate interval time tr, and tr is estimated according to rtt in one hop. rl is computed similar to [10]: rl = λl + kq · ql γl · cl · tr (1) here the definitions of λl and ql are different from [10]. λl is the total amount of input data in a virtual queue for one link during tr, and ql is the persistent queue length of a virtual queue for one link during tr . kq controls how fast the persistent queue drains, γl is the target utilization that sets close to 1, and cl is the link capacity. the input traffic λl and the persistent queue length ql are measured using bytes [10]. lpr sets kq to 0.8 to make full use of the limited memory space. the persistent queue length ql is measured by using a low-pass filter that samples the instantaneous queue size, q(t), every tr/20 . 2.3 setting of detouring ratio the objective of setting the detouring ratio is to allow the satellites’ overloaded links to return back to light load state and keep this state for at least a period of θ. let it denote the total rate of traffic coming from terminals to the bottleneck link within the coverage area of a satellite, in denote the total rate of traffic coming from neighboring satellites to the bottleneck link, o denote the total rate of output traffic of the bottleneck link, and ld denote the isl delay. a satellite receives link load rl(t) from neighboring satellites at time (t + ld). rl(t) is the neighboring satellite’s link load at time t, and the satellite’s link load at time (t + ld) is rl(t + ld) : rl(t + ld) = γl + kq · (ql + (it + in − o) · id) γl · cl · tr (2) in order to ensure a prompt recovery and a residual time in the light load for at least θ, the new rate of traffic coming from neighboring satellites inewn should satisfy: rl(t + id + θ) = γl + kq · (ql + (it + in − o) · id + (it + inewn − o) · θ) γl · cl · tr < threshold (3) threshold is the link load’s threshold. if the link load is larger than threshold, the link is overloaded and satellites begin to detour a portion of traffic according to the detouring ratio on the alternative paths. the traffic detouring ratio φ can be computed as: φ = 1 − min(max(0, inewn in ),1) (4) 3 load-aware and position-aided routing 3.1 path selectioin the key technologies required to support positioning over satellite systems have been developed, such as gps, galileo and glonass. with these advancements, satellites are able to get the high precision orbit determination. in lpr, path selection is performed by using the position. lpr calculates the routing path for every arriving packet, instead of maintaining routing tables to avoid the convergence process, however, it takes a large amount of computing resources. so load-aware and position-aided routing in satellite ip networks 167 satellites cache the routing results until the topology state changes, to reduce the computation overhead. step 1: predicting transmission direction-the polar constellation networks have orbital seams, and lpr assumes that there are no cross-seam links, for the regular hand over as satellites pass. satellites would transmit receiving packets cross seam, if the destination and current satellites located on different side of the orbital seam. the orbital seams are located between the fixed planes of ascending and descending satellites in the same layer and in turn lpr is able to utilize the longitude difference between the orbital plane of current satellites and the orbital seam to judge whether packets should cross the seams or not. assuming that satellite a’s geographical position is (longa, latitudea).the points of the intersections of the orbital seams and the equator are left(longleft_seam,0) and right (longright_seam,0). the point of the intersection of the plane of satellite a and the equator is a′(plong,0): plong = longa + arcsin(tan(latitudea)/tan(inclination)) (5) where inclination is the constellation’s inclination angle, and the constellation is composed of satellites in one layer. let left_seam_diff denote the longitude difference between a′ and left and right_seam_diff denote the longitude difference between a′ and right. left_seam_diff = plong − longleft_seam (6) right_seam_diff = longright_seam − plong (7) left_seam_diff and right_seam_diff are fixed in the same layer. assuming that the point of intersection of the destination and the equator is (longdest,0). if the difference between plong and longdest is less than left_seam_diff or right_seam_diff, packets will not be transmitted across the orbital seams and a’s neighboring satellites set nei(a) includes all satellites having isls with a, otherwise packets will be transmitted across the orbital seams and nei(a) only includes satellites which are able to transmit packets across the seams by means of passing through the poles or another satellites layer. after estimating neighboring satellites set nei(a), satellite a calculates the distance from x , where x ∈ nei(a) , to the destination to get next hop set n(x) : n(x) = {x|d(x,dest) < d(a,dest),x ∈ nei(a)} (8) in multi-layered satellite networks, all satellites are projected to the surface of the earth when calculating the distance. if several satellites in different layers are projected in one point on the surface of the earth, the satellite in the same layer with the sender has the highest priority. step 2: selecting next hop-after predicting transmission direction, satellite a will choose next hop from n(x) depending on the load information collecting from the transmission paths. lpr transmits traffic on the main path when the load is low, and begin to detour traffic when the load of the main path becomes high. lpr classifies traffic into three types, similar to [11], to guarantee the qos requirements. class a is delay-sensitive traffic, class b is through-put sensitive traffic, and class c is best effort traffic. when the load increases, lpr starts detouring traffic of class c. if the requested detouring ratio of traffic is larger than the traffic percentage of class c, the traffic of class b is detoured as well. if the requested detouring ratio of traffic φ is larger than the traffic percentage of class c and class b, the traffic of a begins to detour. let a, b and c denote the predicted traffic ratio of class a, b and c, the traffic detouring ratio is shown in table 1. 3.2 loop avoidance in lpr, forwarding traffic depending on the geographical position and the load information may cause loops. to cope with this issue, lpr adds the routing path into the packet header 168 l. wang, l. liu, x. hu table 1: traffic detouring ratio class a class b class c φ < c 0 0 φ c c ≤ φ < (b + c) 0 φ−c b 1 φ ≥ (b + c) φ−b−c a 1 1 after selecting the next hop, to form the traversed link set ls. consider satellite a sending the set of traversed link ls to satellite b, and satellite b will not select links in the ls. however it would take a lot of bandwidth to transmit the traversed link set information. lpr proposes a mechanism to reduce this overhead at the expense of local mapping. considering that satellite a associates a traversed link set ls to labells, and sends the traversed link set ls and the mapping label labells to satellite b. satellite b will establish a mapping labells → ls and send an acknowledgement to satellite a. after satellite a receives the acknowledgement of the mapping from satellite b, satellite a only includes labells rather than the entire traversed link set ls in the packet header. labels exchange among neighboring satellites do not have global meaning. furthermore, a time-to-live value t is associated with a mapping at the receiver and the mapping is deleted, if no packet with the label associated with this mapping is received for t . then the receiver notify the sender of deleting the timeout mapping labells → ls. considering that satellite b finds that labells → ls is timeout, satellite b will delete the mapping and notify satellite a that the mapping is timeout. satellite a will delete the mapping after receiving the notification. 4 simulation setup in this section, we describe the simulation topology and parameters that have been used to compare the performance of lpr, dijkstra, elb on dijkstra and snapshot [12]. we have used omnet++ simulator [13] and developed a constellation of iridium satellites and the main parameters are shown in table 2. table 2: main parameters of satellite topology parameters values satellite number 66 orbit altitude 780km inclination angle 84.4degrees isls 2 intra-sls, 2inter-sls polar area border 70degrees plane number 6 seam links no constellation type polar it is assumed that each satellite maintains four isls with its neighboring satellites. uplinks, downlinks and isls are each given a capacity equal to 25mbps. the average packet size is set to 1kb. lengths of drop-tail based buffers equal to 200 kb. simulations are all run for 2 hours, for load-aware and position-aided routing in satellite ip networks 169 leo satellite constellation operates at the altitude of 780 km with the system period of about 100 minutes. in the simulation, we consider 100 data flows. the source and destination end-terminals are dispersed all over the world and the distribution of end-terminals is shown in table 3. the traffic distribution is identical to [14]. the sources send data at constant rates from 0.8 mbps to 1.5 mbps. the traffic percentages of delay-sensitive traffic, throughput-sensitive traffic and best effort traffic are set to 20%,30% and 50%. table 3: distribution of end users north america south america europe africa asia oceania end users number 35 10 25 5 20 5 5 simulation results in this section, we will show the performance results of lpr in the presence of different traffic classes in terms of average transmission delay and average throughput, and will compare the load balancing index among lpr, elb on dijkstra, snapshot and dijkstra. 5.1 multiple traffic classes the performance of lpr with multiple traffic classes is compared with dijkstra algorithm. figure 3 shows the average packet transmission delay. the delay of packets belonging to the delay-sensitive traffic is smaller compared to other types of traffic this is because the delaysensitive data is always sent via the paths with the shortest distance. when the traffic load gets higher (the individual transmission rate is larger than 1.3mbps), dijkstra exhibits the minimum transmission delay, but at the expense of significant packet drops, as shown in figure 4. lpr’s transmission delay increases higher, due to the traffic detouring which increases the communication delay. 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 0.1 0.12 0.14 0.16 0.18 0.2 0.22 individual data transmission rate(mbps) a ve ra g e p a ck e t t ra sm is si o n d e la y( s) dijkstra lpr delay−sensitive lpr throughput−sensitive lpr best effort figure 3: packets transmission delay as shown in figure 5, the average throughput of dijkstra is always lower than other schemes, due to the overflow of the drop-tail buffers, as shown in figure 4. throughput of the delaysensitive traffic outperforms the other two types of traffic, because of the detouring priority. 170 l. wang, l. liu, x. hu 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 individual data transmission rate(mbps) p a ck e t d ro p r a te (% ) lpr dijkstra figure 4: packets drop rate 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 individual data transmission rate(mbps) a ve ra g e d a ta t h ro u g h p u t dijkstra lpr delay−sensitive lpr throughput−sensitive lpr best effort figure 5: throughput these results demonstrate that lpr is able to guarantee qos requirements by the effective load balance mechanism and a routing method without the convergence process. 5.2 load balancing index to evaluate the traffic distribution of lpr, this paper compares lpr with dijkstra, snapshot and elb with the following traffic distribution index: f = ( ∑n i=1xi) 2 n · ∑n i=1x 2 i (9) where n is the total number of isls and udls, xi denotes the actual number of packets that traversed the ith link. the index ranges from zero to one and high value of this traffic distribution index represents good distribution of traffic over the entire constellation. in the simulation, lpr’s threshold ranges from 0.85 to 0.95 and the interval is 0.5, and lpr’s distribution index is the average of the results of these different thresholds, in order to evaluate the performance of lpr under different thresholds. in dijkstra scheme, the traffic distribution index is low (figure 6), for packets always traverse through paths with the smallest number of hops without taking the traffic distribution into account. lpr scheme always try to distribute the traffic on the transmission paths when the link load-aware and position-aided routing in satellite ip networks 171 is overloaded, so the traffic distribution is higher than the plots of dijkstra and snapshot. elb on dijkstra is higher than the plots of dijkstra, snapshot, for elb searches for less congested paths, after receiving a busy signal advertisement from neighboring satellites. however, in lpr scheme, not only neighboring satellites are able to communicate a portion of data via less congested paths, but also the upstream satellites. furthermore the load estimation of lpr is more accurate than elb, in that lpr’s granularity of load estimation is the link, but elb is the node. in turn, the distribution index of lpr is much higher than elb on dijkstra. 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 40 50 60 70 80 individual data transmission rate(mbps) t ra ff ic d is tr ib u tio n i n d e x( % ) lpr elb on dijkstra snapshot dijkstra figure 6: traditional load balancing 6 conclusions in this paper, we propose a load-aware and position-aided routing protocol named lpr, which transmits load information in a hop-by-hop manner and enables all the upstream satellites to know the load information instantly and to communicate a portion of traffic on the less congested paths when the load becomes high. furthermore, lpr utilizes the geographical positions of satellites and select the routing path to eliminate the convergence process. finally, we evaluate the performance of lpr in the iridium constellation. the simulation results indicates that lpr is capable of guaranteeing a good distribution of traffic among satellites and supporting different qos requirements. as future work, we intend to improve the ability of providing different qos by differentiating traffic, and analyze lpr’s impacts on 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[14] m. mohorcic, m. werner, a. svigelj, et. al., adaptive routing for packet-oriented intersatellite link networks: performance in various traffic scenarios, ieee transaction on wireless communications, 1(4):808-818,2002. ijcccv11n4.dvi international journal of computers communications & control issn 1841-9836, 11(4):580-593, august 2016. a forward-connection topology evolution model in wireless sensor networks c. zhang, c. li, n. ning changlun zhang*, nan ning science school beijing university of civil engineering and architecture beijing, china zclun@bucea.edu.cn, ningnan@stu.bucea.edu.cn *corresponding author: zclun@bucea.edu.cn chao li beijing key laboratory of communication and information systems beijing jiaotong university beijing, china lichao21261@163.com abstract: the stability and reliability of the topology structure play an important role in the efficiency of the data collecting for wireless sensor networks. in this paper, a topology evolution model is proposed. the model considers the directionality of the data flow, and adopts the forward connectionism to ensure the neighbor nodes of each node. furthermore, the model considers the balanced energy overhead in each communication path, adopts the energy balanced mechanism to compute the connection probability to the neighbor nodes. meanwhile, the process of topology evolution is distributed and the communication radiuses of all sensor nodes are limited. a theoretical analysis exhibits that the model has power-law distribution of node degrees. simulation shows that the proposed topology evolution model make energy overhead more balanced, and prolongs the lifetime of the network. keywords: wireless sensor networks; topology evolution; energy balanced mechanism; power-law distribution 1 introduction wireless sensor networks (wsns) are a kind of wireless networks which are constructed by plenty of sensor nodes. wsns can gather the data from its monitoring physical or environment conditions (e.g. the temperature, the sound etc.) and send their data to the destination (base station) directly or via multi-hop [1,2]. wsns cover a wide range of applications, since it is easily deployed and self-organized, such as environmental monitoring, military target tracking, natural disaster relief and health monitoring, and so on [3,4]. on the other hand, wsns are weakness in processing capability and storage capacity. especially, when the wsns are deployed in a harsh environment that people hardly reach, the nodes are difficultly recharged and replaced, and it leads to the limited energy sensor nodes. therefore, establishing a model which can prolong the lifetime of wsns efficiently is a very important issue. the study of complex networks has become a common focus of many branches of science since the end of last century [5, 6]. the complex networks study the characteristics of the networks, which can describe many systems in nature, such as the cooperative networks, social networks and so on. most complex networks are scale-free networks, which are robust against random removal or failures of nodes. recently, complex network is used to study connectivity, fault tolerant and topology evolution of the wireless sensor networks. copyright © 2006-2016 by ccc publications a forward-connection topology evolution model in wireless sensor networks 581 in this paper, we proposed a forward-connection topology evolution model for wireless sensor networks. different from existing schemes, in the proposed model, a node sends the data to those nodes that are nearer to the base station than itself. the remainder of this paper is organized as follows. in section 2, the related work is summarized. in section 3 and 4, an algorithm of forward-connection topology evolution model is proposed and analyzed. in section 5, the simulation to present the features of the networks generated by the proposed algorithms is provided. finally, the conclusion of this paper is given. 2 related works the topology evolution models can be divided into two types roughly: the cluster-based and the non-cluster-based. in the non-cluster-based topology evolution models, all the nodes transit the data by a chain, a tree and so on to the base station. these models are used to some small wsns or the single type nodes. in 2002, s. lindsey et al. [7] proposed pegasis (power-efficient gathering in sensor information systems) model, an optimal chain-based protocol, which can reduce the energy consumption. but if a middle node in the chain is drained, all the nodes behind the drained node can′t transmit the data to the base station. in 2003, h. o. tan et al. [8] improved the pegasis model, and proposed pedap algorithm. the algorithm is near to optimal minimum spanning tree based routing schemes. in 2003, xiang y l et al. [9] considered how the transmission range is related with the number of nodes in a fixed area such that the resulted network can sustain k fault nodes with high probability. then they presented a localized method to control the network topology. in 2005, thallner et al. [10] presented an improvement of topology control algorithm for dynamic networks and low power devices, which provided an improvement of topology control algorithm for dynamic networks and low power devices. in 2006, abhishek et al. [11] proposed an approximation algorithm, which used additional relay nodes to construct a fault-tolerant backbone network. on the other hand, the cluster-based topology evolution models divide the wsns into the inner-cluster layer and inter-cluster layer. these two layers usually possess the self-similarity. these models are used to some large or mixed wsns. in 2000,w. r. heinzelman et al. [12] presented an leach protocol which is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated. but the leach protocol is not fit for the huge wsns and the wsns which have the unbalanced energy for each node. in 2003, s. bandyopadhyayet al. [13] proposed a distributed, randomized clustering algorithm to organize the sensors into clusters in a wireless sensor network. they then extended this algorithm to generate a hierarchy of cluster heads and observe that the energy savings increase with the number of levels in the hierarchy. in 2004, ossama et al. [14] proposed heed protocol, which selected cluster heads periodically according to the node residual energy. the heed protocol can prolong the lifetime of wsn. recently, the evolution models which combine the complex networks come up. in 2009, li j c et al. [15] proposed an evolving model among the cluster heads of wsn. the model is based on the random walker theory, and exhibits a power-law distribution. in 2009, zhu et al. [16] presented two self-organized energy-efficient models for wsns. the first model constructed evolving network considering the connectivity and remaining energy of each node. the second model considered the energy consumption balance of the whole network. in 2011, xiao g q et al. [17] proposed a topology evolution tebas, which introduces fitness and local world and more suitable for wsns than erw (topology evolution by random walker). 582 c. zhang, c. li, n. ning in 2012, ya q w et al. [18] studied the influence of node failure on the performance of wsn, and different immunization strategies were given. in 2012, xiao j l et al. [19] considered the energy-aware mechanism, and proposed a new topological evolving model based on the complex network theory. they found that node energy distribution had the weak effect on the degree distribution. in the practical applications, a node sends the data to the others that are nearer to the base station than itself. this kind of connection reduces the energy consumption and the delay of transmission. based on this connection, a forward-connection topology evolution model is proposed, which considers both the forward-connection mechanism and the energy-balanced mechanism. analysis and simulation show that the network exhibits well robustness, a powerlaw degree distribution and a long lifetime. 3 forward-connection topology evolution model the model uses the cluster network which contains three kinds of nodes: base station, cluster heads and cluster nodes. the network is divided into two layers: inner-cluster and inter-cluster. in the inner-cluster, data are sent to the cluster head, and the cluster head verifies the data integrity to restrict the range of compromised node. in the inter-cluster, data are sent to the base station, and the integrity is verified at the base station. furthermore, a mechanism is proposed to locate the compromised node. sppda model can be divided into initialization, key distribution, inner-data aggregation and inter-data aggregation. in this section, a forward-connection mechanism is considered. meanwhile, the energy of each node in a path should be kept balance. it means that there is no node which energy is less than others. then, an energy balanced mechanism is considered, which prolong the lifetime of the path. in wsns, most energy of sensor nodes was used in data transmission. so, it ′s assumed that the higher energy a node has, the greater connection probability the node holds in this model. besides, referred to the evolution principles of complex networks, preferential attachment of the nodes is present in the processes of the topology evolution. meanwhile, considering the relationship between the distance and the energy consumption, a threshold of communication radius should be set, which decides the communication probability between two nodes. the probability equals zero when the distance of two nodes is larger than the threshold. and the closer of two heads are, the larger probability connects to these heads is. here, dj,max is used to represented the communication radius of node j, and dmax is the communication radius of the node whose energy is emax,then dj,max = ej emax dmax . since each node in the network are homogenous, the initial node actual does not exist in the theory of distributed mechanism, which is considered in this paper. all nodes will send a message to its surrounding node when the topology evolution begins. according to the time of index, the node sends agreement to the earliest request, and refuses to the rest. the messages that nodes sent are direction in the evolution. then a directed network is considered, where the direction shows the direction of data flow. 3.1 forward-connection mechanism in forward-connection mechanism, the forward neighbors of each node are confirmed. the mechanism is divided into two steps. in the first step, the neighbors of sending node are confirmed. in the second step, if the base station is the neighbor of sending node, the sending node has no forward neighbors, because the sending node transmits the data to the base station directly. if the base station is not the neighbor of sending node, the neighbor nodes whose distance a forward-connection topology evolution model in wireless sensor networks 583 to the base station is smaller than the sending node are selected. these neighbor nodes are the forward neighbors of sending node. as shown in figure 1, point o is the base station. point i is the sending node. line oa, oi and ob is the distance between sending node and the base station. lind ia and ib is the communication radius of the sending node. point p is a neighbor of sending node. then, a conclusion to confirm the forward neighbors is given as follows. conclusion: if ∠pio is less than 60 degree, p is the forward neighbor of sending node. figure 1: forward-connection mechanism proof: the forward neighbors of sending node exist. so, we have oa > ai, then ∠oia > ∠aoi. on the other hand, oa = oi, so ∠oia = ∠iao.in △ aio, ∠aio +∠iao +∠aoi = 180◦. so, 180◦ = ∠aio + ∠iao + ∠aoi < ∠aio + ∠iao + ∠aio = 3∠aio. that is ∠aio > 60◦. in the same way, we have ∠oib > 60◦. ✷ according to the conclusion, the forward neighbors of each node can be confirmed easily. 3.2 energy-balanced path mechanism in energy-balanced mechanism, the neighbors of sending node in two hops are considered. in this paper, an energy-balanced element is used to judge the level of the energy-balanced. and a threshold śĺ is used to distinguish the high energy nodes and the low energy nodes. if the energy of a forward neighbor ǫ is larger than the energy of sending node, this forward neighbor is a high energy node. node i is one of the forward neighbors of sending node. n is the number of the forward neighbors of nodei, and nǫ is the number of the high energy nodes of node i. thus, the energy-balanced element of node i can be counted as δi = nǫ n . 3.3 model of evolution network in this model, a network is modeled as a directed graph g(v, e), where nodes are represented as the set of vertices v and the links as the set of edges e. the number of sensor nodes is defined as |v | = n. in the topology evolution networks, there are two rules of the distributed and local-world topology evolution model which is r-1 (growth) and r-2 (preferential attachment) is given as follows: r-1:after the node i is selected, this node sends the message to other nodes in its local-world, which means the circle area is within the maximum communication radius dj,max of the head i. the nodes send agreement to the earliest request, and refuses to the rest. the evolution ends when each node succeed to connect other nodes actively. 584 c. zhang, c. li, n. ning r-2: until the each node in its local-world returns the agreement to the request node, the request node connects m edges to these nodes with the probability πi→j . if the number of the nodes is less than m, it connects all of the nodes. πi→j represents the probability of a head j connected to head i. the probability πi→j depends on the connectivity, the distance of two nodes and the threshold of node i. the form of πi→j is πi= ejδjkj∑ l∈λi elδlkl ,where ∧ i is the local-world of node i. 3.4 model of evolution network in a network, the energy consumptions in each node are different according to the different distances and the amount of data. the route is constructed by the initial energy. then, the energy is consumed when the nodes send the data to the base station. and the remaining energy in each node is changed. so, the local-reconstruction is needed when the network runs for a while. in this section, two local-reconstruction mechanisms are provided which are initiative localreconstruction mechanism and passive local-reconstruction mechanism. the initiative localreconstruction mechanism is a centralized reconstruction, and the local-reconstruction require is sent by the node that has a lower energy. the passive local-reconstruction mechanism is a distributed reconstruction. and the local-reconstruction require is sent by the node that whose parent node has a lower energy. these two mechanisms are proposed as follows: the initiative local-reconstruction figure 2: local-reconstruction mechanisms in initiative local-reconstruction, when a node that has a lower energy exists in the network, this node determines the nodes that need to change the routes in its neighbors. then, this node sends the local-reconstruction requests to these nodes. the nodes that receive the requests delete the link from the lower energy node. finally, these nodes connect to other nodes with the evolution model above. figure2(b) shows the initiative local-reconstruction. algorithm1 shows the algorithm of initiative local-reconstruction. ni is the lower energy node,nfi is the parent node of node ni, nsi is the set of son nodes with node ni. the initiative local-reconstruction in passive local-reconstruction, each node determines whether it needs reconstruction according to the rate between the node and its parent node. a node need, it sends the localreconstruction requests to its parent when necessary. then, the parent node deletes the link between them after receiving the requests. lastly, this node connects to other nodes with the a forward-connection topology evolution model in wireless sensor networks 585 figure 3: algorithm of initiative local-reconstruction figure 4: algorithm of passive local-reconstruction 586 c. zhang, c. li, n. ning evolution model above. algorithm 2 shows the passive local-reconstruction.figure.2(c) shows the algorithm of initiative local-reconstruction. ni is the lower energy node, nfi is the parent node of node ni, nsi is the set of son nodes with node ni. 4 analysis degree distribution is an important and useful feature for a given complex networks. the degree distribution of this network is analyzed by the mean field theory. we assumed that n is the total number of the nodes in the wsns, and these nodes are uniformly distributed in the wsns. some important parameters are defined in table 1. table 1: definition of important parameters parameters definition n number of nodes in a network dmax the maximum communication radius0 m numbers of new edges which a new node connect at every time step ki number of links connected to node i dmax 60 600 150 200 e remaining energy of a node l number of nodes in every new comer′s local-area ti time of node i newly introduced into the network the evolution process described in this paper adopts the distributed mechanism, which can describe the real evolution processing more efficiently. actually,the nodes can not begin to send the message at the very same time/concurrently, so it can be approximately regarded the process as follows. r-1: starting with one node and m edges, at each step, a new node with m edges was added. the node can connect with all the nodes surrounding. r-2: when a new node comes into the network, it will choose some nodes in its local-world to connect with the probability ∏ i→j. during each unit of time, m new edges are formed. so we get ∂ki ∂t ≈ mli ∏ j→i n = mliejδjki nσl∈λλjelδlkl (1) here, li is the number of nodes which can connect to node i. according to the mean field theory [20]: ∑ l∈λj elδlkl = ljēδ̄〈k〉 (2) similarly, lj is the number of nodes in the new comer ′s (node j ) local-world. ē is the mean value of the local-world energy, and 〈k〉 is the average degree of local-world. in a large scale network, the average degree can be calculated as < k >= 1 t + 1 = εjm (3) a forward-connection topology evolution model in wireless sensor networks 587 where l is the number of edges connected to the node that has joined. it′s not a fixed number while it satisfied the equation:0 ≤ l ≤ (t + 1)m , εjm is the mean degree of the network after node j joined. the ǫj increases when time t increases constantly. so it′s simply defined as ǫj = t n . combine three equations above, we get ∂ki ∂t = liδiei liēδ̄ ki t (4) we set liδiei liēδ̄ = g (5) then we have ∂ki ∂t = g ki t (6) solving this differential equation, we get ki(t) = ct g . since ki(ti) = mi, thus we have ki(t) = mi t g i tg (7) the probability that a node has connectivity ki(t) smaller than k is p(ki(t) < k) = p( mi t g i tg < k) = 1 −p(ti < ( mi k ) 1 g t) (8) assume that we add the nodes to the network at equal time intervals, the probability density at the time is f(ti) = 1 t+1 , therefore, we get p(ki(t) < kin) = 1 −p(ti < ( mi kin ) 1 g t) = 1− t t + 1 ( mi kin ) 1 g (9) the probability density function of the degree of a node with remaining energy e is p(kin) = ∂p(ki(t) < kin) ∂kin = tm 1 g i t + 1 k −(1+ 1 g ) in (10) here,g = lje j δj ljēδ̄ therefore, the distribution has a power-law form with degree exponent γ = −(1 + 1 g ) in the network. thus, we can organize wsns with scale-free feature in this energy-aware algorithm. this algorithm can not only make the network evolution in energy-efficient, but also improve the network reliance against random errors, which is an inherent advantage of most scale-free networks. especially, when the distributions of energy and nodes are uniform distribution, it is got that g ≈ (1 −d(i,j)/dj,max) , which is obvious that γ < −2 , and if dmax is big enough, the degree exponent γ →−2. 588 c. zhang, c. li, n. ning 5 simulations in the evolution of the network, we assume that the cluster heads have been selected, and the network has 1000 cluster heads. these cluster heads are randomly deployed over a 600 meters ×600 meters. the network parameters are listed in table 2. in the simulation of lifetime and network-efficiency, we compare three kinds of aggregation tree: the tag aggregation tree [21] (tag aggregation tree), the tag aggregation tree with forwardconnection evolution model and the fctag tree with reconstruction. 5.1 the degree distribution table 2: main settings in simulation parameters setting area 600×600 n 1000 m 2 6 15 30 e random in [0.8 1] dmax 60 600 150 200 in the analysis, there are two parameters influence the degree distribution: dmax and m. the following simulation shows the influence of dmax and m on the evolving network. the influence of dmax in this section, the influence of dmax is discussed. four different value of dmax are listed in table 2 (m=3). as shown in figure 5, when dmax is small,f < 1, so the exponent γ < −2. when dmax gets larger, f closes to 1, then the exponent γ closes to -2. besides, figure 1 also shows that when the dmax increases constantly, the maximum in-degree get larger. this is because the larger the communication radius is, the more likely be connected by other nodes. the influence of m in this section, the influence of m is discussed with different m (dmax = 120). as shown in figure.6, when m is small, the process of preferential attachment works well, and a node is connected randomly, the network exhibits a power-law degree distribution. when m gets larger, the process of preferential attachment is limited. it leads to the reduction of the randomness of the connection. so the degree distribution can′t obey the power-law form well. especially, when m is larger than the neighbors, the node will connect all nodes surrounding, which is independent of the preferential attachment. 5.2 lifetime in the simulation of lifetime, we assume that all sensor nodes have an initial energy which is 0.5j. the data packet size is 1000 bits. there is not an exact definition in lifetime for a wsn. some papers use the round when the first drained node appears. some papers use the round when a certain ratio of drained nodes appears. some papers use the round when the network canążt cover the monitor area. in this paper, we just run the network for a certain round (here a forward-connection topology evolution model in wireless sensor networks 589 figure 5: the influence of dmax figure 6: the influence of m 590 c. zhang, c. li, n. ning uses the 1500 round), and we observe the changing rule in different aggregation tree. we deem an aggregation tree has a longer lifetime if the drained nodes increase slower. nodes consume energy both in sending and receiving data according to [22]. in this paper, we use the model that the pass loss exponent is 2. the model is as follows: a k-bit data packet is transmitted and the energy consumption of sending node is given by et = ε1×k+ε2×d2×k, d is the distance between the two sensor nodes, and ε1 = 50njbit ,ε2 = 100pj bit·m2 .a k-bit data packet is transmitted, and the energy consumption of receiving node is given by eγ = ε1 ×k without local-reconstruction as shown in figure 7, the dotted line is the lifetime of tag aggregation tree. the solid line is fctag aggregation tree. obviously, in the dotted line, the first drained node appears at the 592th round. in the solid line, it appears at 737th round. this shows that the fctag aggregation tree consumes the energy more balance than the tag aggregation tree. another key point appears at about the 1280th round. two lines intersect. and after this point, the number of drained nodes in dotted line is larger than that in solid line. it means that, after some round, the energy consumption in our model is more unbalance than the pure tag aggregation tree. the point of intersection appears normal and reasonable. there are two reasons for the point. firstly, the fctag aggregation tree is constructed according to the energy and the position of nodes at that time. with the network running, the energy decreases which make the fctag aggregation tree is no longer suit for the network now. secondly, the idea of fctag aggregation tree is that, when the nodes transmit the data, the nodes that have large energy use more energy, and the nodes that have low energy use less energy. so the nodes that have low energy can run more round, but it makes the nodes that have large energy run less round. in fact, the fctag aggregation tree has large total energy consumption in one round than tag. in the first reason, a local-reconstruction mechanism can solve it well. but the second reason is because of the idea of our model. finally, the point of intersection is difficult to remove, but it can be delayed. local-reconstruction in this part, we simulated the lifetime with tag aggregation tree and fcrtag aggregation tree. in the figure 8, the dotted line is the lifetime of tag aggregation tree, the solid line is fcrtag aggregation tree. it shows that when a local-reconstruction mechanism is considered in the fctag aggregation tree. the lifetime of the network is better. the drained node appears in 737th round too. and obviously, there is no intersected point before the 1500th round. but in fact, these two lines are getting more and more near when the round increasing. so it can be inferred that there is an intersected point after 1500th round. a forward-connection topology evolution model in wireless sensor networks 591 figure 7: the lifetime without local-reconstruction figure 8: the lifetime with local-reconstruction 592 c. zhang, c. li, n. ning conclusions and future work in this paper, we present a new secure privacy-preserving data aggregation model, which adopts a mixed data aggregation structure of tree and cluster. the proposed model verifies the data integrity both at the cluster nodes and the base station. meanwhile, the model gives a mechanism to locate the compromised nodes. finally, the detail analysis shows that this model is robust to many attacks, and has lower communication overhead. acknowledgment this work is supported by beijing natural science foundation under grant 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[22] m. hussaini, h. bello-salau, a. f. salami, f. anwar, a. h. abdalla (2012); enhanced clustering routing protocol for power-efficient gathering in wireless sensor network, international journal of communication networks and information security, 18-28. pin_ijcccv11n5.pdf international journal of computers communications & control issn 1841-9836, 11(5):697-708, october 2016. nvp: a network virtualization proxy for software defined networking b. pinheiro, e. cerqueira, a. abelem billy pinheiro*, eduardo cerqueira, antonio abelem federal university of para brazil, para, belem *corresponding author: billy@ufpa.br cerqueira@ufpa.br, abelem@ufpa.br abstract: the combination of network function virtualization (nfv) and software defined networking (sdn) can improve the control and utilization of network resources. however, this issue still requires proper solutions to virtualize large-scale networks, which would allow the use of sdn and virtualization in real environments. thus, this paper proposes a virtualization architecture for sdn that relies on a proxybased approach. the nvp (network virtualization proxy) is a virtualization proxy that intercepts messages exchanged between controllers and switches sdn enabling network virtualization. an implementation of the proposal was developed as a proof of concept and load testing was performed showing that the solution can provide network virtualization in a scalable manner, using less than 2.5 mb of memory to manage 100 switches performing simultaneous requests, whereas flowvisor requires more then 200 mb. keywords: sdn, virtualization, nvp, flowvisor. 1 introduction the internet was originally designed to provide network services to a closed community, and has today become an undeniable success worldwide, with users of various types of services everywhere on the planet. specific adaptations have historically been proposed and implemented at the emergence of new demands. this approach, despite having attended to momentary needs, has generated increasing complexity and cost of maintaining the internet. moreover, the higher the number of these adjustments, the greater is the complexity of the resulting architecture, making it even more difficult to overcome future challenges, in a situation commonly referenced as internet "ossification", increasingly resistant to structural changes [1]. in this context, software defined networking (sdn) is today one of the most relevant solutions for future internet environment, and the openflow protocol implementation is the bestknown method [2]. along with sdn, the field of network function virtualization (nfv) has grown so as to allow resources to be shared and dynamic topologies to be created [3]. the main concept for working with network virtualization is the division into slices, to optimize the use of their resources (nodes or links) and to separate it into different logical instances. for example, this approach has been used in the fibre project, to create a common space between brazil and the eu for future experimental internet research into network infrastructure and distributed applications with more then 40 openflow nodes [4]. the partitioning of the network enables the actions taken in one of the network slices, to not interfere with others, even if they are sharing the same physical infrastructure. in traditional architectures, the network is sliced through vlan’s technique, but, with the diversity of network models, the structure of vlans makes the experiments with others protocols rather difficult to manage [1]. copyright © 2006-2016 by ccc publications 698 b. pinheiro, e. cerqueira, a. abelem currently, flowvisor [5] is the leading virtualization solution available for openflow networks. it acts as a layer of virtualization located between openflow switches and the network controller allowing resources to be shared, but maintaining the isolation of a virtual network from others. a major problem with this solution is the scalability since the recommended requirements for operating the flowvisor are 4 processor cores and 4 gb java heap. depending on the size and use of the network, these requirements can increase [6]. this article proposes the network virtualization proxy (nvp), a new model of network virtualization that improves the use of computational resources in the virtualization process, through an innovative architecture for greater scalability. to prove the effectiveness of the proposal, an implementation has been developed, and load tests were performed comparing nvp with flowvisor. this article is structured as follows: section 2 presents sdn virtualization related works. in section 3, the nvp is detailed. in section 4, the tests performed and their results are discussed. finally, section 5 presents the conclusions and future works. 2 related work flowvisor is a well-known networks virtualization solution for sdn openflow environment [5]. it is proposed as a special controller that acts as a transparent proxy, located between the openflow switch and the network controller. by using flowvisor, it is possible divide the switches and have their resources controlled by more than one controller at the same time, thus enabling the creation of virtual networks with logical isolation between them. the flowvisor adopts an hierarchical architecture that allows multiple flowvisor networks to be interlinked making virtualization of an already virtualized resource possible. in order to function seamlessly, it acts as a controller for the switches and as an openflow switch for the controllers. thus, it must store the information of the switches and controllers to enable the routing of messages between them. however, its architecture generates an overhead, because the same entity responsible for managing the virtualized network’s information is responsible for forwarding the generated messages, including the packet_in. these are packets coming through the network and sent to the controller for review, which can consume a lot of resources to be forwarded between flowvisors to reach the controllers destination. the prefix-based layer 2 network virtualization (l2pnv) [7] is an extension of flowvisor, with the main objective of providing a level 2 virtualization engine without using vlan, instead basing the virtual networks created in media access control (mac) on the address of origin and destination. however, it was necessary to modify flowvisor, the switch firmware used to support a modified version of openflow and the host since it was necessary to enable mac masking for these elements. despite being discussed in the article that a test environment was created using the proposed solution and the necessary changes in the above-mentioned elements fit the demands of the proposal, no results related to scalability of the solution or comparison with other existing solutions were presented. in fact, no evaluation was submitted. aiming to overcome flowvisor’s major limitation, the fact that virtual topologies that may be created with flowvisor are restricted to a subset of the physical topology, the advisor [8] was proposed. this solution leverages the vlan tag to differentiate between virtual links and the virtual network, making it possible to create virtual links by joining different physical links. however, the solution that the authors validate necessitates the manual configuration of network devices and the sending of dpctl commands. furthermore, the solution limits the use of the vlan header, leaving the less transparent solution for the user. in addition, tests have shown an increase in terms of latency when compared it to flowvisor. again, load tests with a large network, such as the fibre network, showing the scalability of the solution were not performed. nvp: a network virtualization proxy for software defined networking 699 to solve the problem of virtual links, vertigo [9] was proposed. an extension of flowvisor, it basically adds a layer of intelligence which enables the creation of virtual topologies over the physical topology. in this case, it creates virtual links interconnecting physical links. moreover, it may provide a complete virtual structure, since it is possible to virtualize the network nodes together with the links. the proposal was implemented in the testbed ofelia, where it was possible to prove that the solution can, in fact, provide virtual links. it is possible to verify the data taken from the article itself, whereby the solution shows an increase of 44.70 % in overhead when compared with flowvisor, a fact that makes the scalability of the proposal questionable. the flowvisorqos proposed [10] solves the problem of ensuring bandwidth usage per slice in flowvisor. thus, it is possible to define minimum requirements for quality of service (qos) that will be respected in the switches, ensuring that each flow does not exceed the specified limits. for the implementation of the solution, changes in the firmware of the switches were performed. however, despite achieving the solution’s objectives, tests to show the solution behavior in a large-scale environment were not performed. openvirtex [11] builds on the design of flowvisor, and functions as an openflow controller proxy , but, differently of flowvisor, openvirtex provides virtual links and slices isolation. the hardware requirements for both flowvisor and openvirtex are the same and they requires 4 core processors and 2gb ram [12]. the flow-n [13] is, to our knowledge, the first work on network virtualization that addresses scalability problems. it was proposed as an extension of the nox controller. it enables virtualization-based containers and uses relational databases to map between the physical and virtual network topologies. the scalability tests presented in the study show an almost stable latency with 100 virtual networks created, while flowvisor starts with a low value but increases to almost equalize the value of flow-n. unfortunately, only one chart is presented, and the proposal can not be evaluated further while details about the operation of the solution are not provided. as discussed in the papers presented in this section, the virtualization solution for sdn must provide isolation, virtual links, and be prepared to support a large quantity of equipment, because all other services will be running virtualized over the physical equipments table 1 summarizes the main features of the proposed virtualization in this section. it was concluded that none of the protocols can fully meet all the requirements cited above in order to provide a reliable and scalable communication that might require an sdn for the virtualization. table 1: virtualization solutions comparison proposal scalability virtual links transparent isolation flowvisor no no yes partial l2pnv no no yes partial advisor no yes no partial vertigo no yes yes partial flowvisorqos no no partial yes openvirtex no yes yes yes flow-n partial yes yes partial finally, is important to emphasize that, despite the clear importance of scalability for virtualization solutions in the sdn environment, only one proposal attempts to address this issue. however, it does not provide the information necessary for a more satisfactory evaluation, such as the comparative numbers of supported switches, number of controllers, and number of virtual networks. 700 b. pinheiro, e. cerqueira, a. abelem 3 network virtualization proxy (nvp) as presented in section 2, the fundamental requirements for virtualization in sdn are virtual links to enable a greater variety of topologies and scalability, aiming to cover a larger number of devices in the physical substrate. such features expected for this technology could actually be used as a basis for production networks. a key feature of the flowvisor architecture is to enable its use hierarchically. this feature brings high flexibility regarding different possible topological configurations. however, this feature imposes an important limitation for network scalability, since it introduces more elements between the controller and the switch, as the hierarchical level increases. the nvp enables virtualization scalability in an efficient way. thus, the main contributions of our proposal are the following: • allow resources of openflow switches to be shared between different controllers; • increase the network scalability, allowing the virtualization layer to support a high number of switches and controllers without generating a big network overhead; • provide a global network abstraction, in which all available resources are not separated hierarchically, i.e., increasing the number of network devices does not increase the number of elements between switches and controllers; • enable a formal modeling of virtualized resources. in order to better structure the display and use of nvp, an cim extension (common information model) was developed to allow modeling of sdn using uml (unified modeling language) [14]. 3.1 nvp architecture the nvp is composed of two elements: the proxy flow switch (fps) and the flow proxy manager (fpm). its design was inspired in openflow itself, in which the complexity of control is the removal of switches and passed to an external control entity. in this case, the fps acts as a forwarder and fpm concentrates all of the system virtualization’s information and intelligence. its architecture is shown in figure 1. figure 1: nvp architecture flow proxy switch (fps) it is the entity responsible for forwarding the information between switches and controllers. it will exchange messages with openflow switches and controllers and pass them on to the fpm. nvp: a network virtualization proxy for software defined networking 701 after the network start up, fps use is dedicated to consulting its routing table and deciding to which controller/switch the messages should be forwarded. it is formed of the components: fakecontroller, fakeswitch, proxytable, and network virtualization proxy protocol (nvpp). • fakecontroller: this is an entity that acts as an openflow controller, passing the idea on to the openflow switch that is connected to the network controller. it is responsible for ensuring the transparency from the switches side and use nvpp to send the information obtained from the switches to the fpm; • fakeswitch: like fakecontroller, it is responsible for ensuring transparency from the side of the controllers and providing information to those obtained about the resources that each controller will access (with the fpm through nvpp). it is an entity that acts as an openflow controller, communicating the idea to the openflow switch that is connected to the network controller; • network virtualization proxy protocol (nvpp): it is a binary protocol, similar to openflow, developed to enable communication between fpss and fpm. because of the need to maintain fps as simple as possible, we chose to develop a binary protocol that can meet the need for communication between entities, generating as little computational overhead possible. the data types used in the nvp are similar to those used in openflow protocol, and its use follows the same logic defined in the openflow protocol specifications. [15]; • proxy table: it is a table that stores which resources of a given switch can be used by a particular controller. it is composed of the fields ctr _addr and ctr _port, which uniquely identify the network controller, the field datapath_id which identifies to which switch this flow is related, and the fields in_port and dl_vlan that identify the port and the vlan associated with the virtual network. table 2: proxy table ctr_addr ctr_port datapath_id in_port dl_vlan 192.168.1.2 6633 2 1 2 192.168.1.1 6633 2 1 1 in the example of table 2, an action of the flow type_mod arrival of a controller with ip 192.168.1.2 and running on port 6633 will be forwarded exclusively to the switch with the datapath_id 2 on port 1 and vlan 2. if one of the fields differ, the packet is discarded. flow proxy manager (fpm) it is the element that performs network virtualization’s effective control. it will contain a database with information about the switches, controllers, and the network slices. all the slices changes must pass through it, and it should embed the rules in fps. a fpm will be used in each domainthere may be several fps associated with itallowing the network to be scalable, since the actual load generated in the network through the packet_in, and these will be handled only by the pfss. its architecture follows the pattern model-view-controlller (mvc) and is described next. • storage: it is the component responsible for storing the switches’ information, controllers, and the network slices. the data stored here will be used by the slice policy to feed the fps; 702 b. pinheiro, e. cerqueira, a. abelem • slice policy: responsible for the slices creation, allowing the creation of policies and associating resources to controllers required by the administrator. it should use the information from switches and policies present in the storage and create slices in flowtable; • virtual link: this element is responsible for mapping the physical links and enabling the creation of virtual links by setting up forwarding rules in the switches. thus it is possible to interconnect continuous physical links and provide the abstraction of a virtual link directly connecting two ports of the switches; • text configurator: it is the nvp configuration interface. it is similar to the configuration file used by flowvisor to facilitate the adoption of nvp and uses json (javascript object notation) syntax; • cim-sdn model: this is an xmi (xml metadata interchange) file that models the entire network to be virtualized and the virtual networks themselves. it uses the cimsdn [14], which is a cim extension that allows the representation of sdn elements in a uml diagram, ensuring the consistency the network. it also maintains an updated documentation for easy understanding. the nvp has its architecture divided into two main elements: the fps and the fpm, aiming to provide, at the same time, scalability and a global network view. scalability is the result of multiple fpss that can be used in the network in order to provide the best use of the resources of the switches and controllers. the global view is available at fpm, which contains all the network information and can provide easy integration to other networks through interconnection with other fpms from other networks. 3.2 applying the nvp typically, nvp is located between the switches and the network controller. it is possible to see in figure 2 as the components of nvp relate with other network components. one fps can suit various switches and controllers, and, to enable network scalability, the number of fpss can be increased to suit more switches and controllers. the fpm is unique to the network and is responsible for providing a global network view. in figure 3, the fps and the mpf activity diagrams are presented. in the case of fps, actions are started by the controllers and switches. the information of switches and controllers is collected, and subsequently, proxytable is used to determine the forwarding of packets. in fpm, the administrator starts activities, providing the cim-sdn model as input to generate the creation of slices. it is worth noting that a packet is never forwarded to the mpf; only the fps handles packets and does it using only proxytable as a decision resource. the use of nvp is similar to flowvisor plus the cim-sdn. however, it is possible to directly use the configuration file, a fact that enables an easy integration with other tools that already have flowvisor as the nfv. 4 proposal evaluation an implementation to prove the concept of nvp was developed and its performance was compared with flowvisor. the metrics used in the evaluation were the number of requests and cpu loads and memory usage. in order to ensure the most efficient proposal implementation, the development of the nvp prototype was performed in c++ using the boost::asio 1. in this version, fakecontroller 1http://www.boost.org/library nvp: a network virtualization proxy for software defined networking 703 figure 2: applying the nvp figure 3: nvp activity diagrams 704 b. pinheiro, e. cerqueira, a. abelem and fakeswitch components were implemented in their entirety, as well as a short version of proxytable. the only external library used was a file defining the types of data available in openflow itself, to enable communication between the switches and controllers. the current implementation of fps allows it to act as a proxy virtualization, although it may not include all elements of the proposal, since the fpm has not been fully developed. 4.1 test methodology the methodology for evaluation and validation of nvp is based on the application execution in a real environment, but, with tools that emulate openflow switches and allow the creation of a large number of elements for the experiment, the use of real openflow switches is not necessary [16]. the tool used to emulate openflow switches was cbench, which is part of the framework oflops 2. using the tool, it is possible to define the number of switches to be emulated and indicate to which network controller it must connect. the controller used in the experiments was the pox, having switches emulated by cbench as clients. the experiments were performed on the same machine so that network traffic would not influence the response time of the components. the machine is a cpu intel core 2 quad 2.5ghz with 4gb of ram and uses debian wheezy. figure 4: experiments topology the experiments used the pox controller 3 to handle the requests from the switches created with the cbench following this topology in figure 4. three experiments were performed, the first with the nvp between the switches and the controller, the second with flowvisor taking the place of nvp, and the last experiment without any virtualization tool. ten replicates were performed for each experiment using the number 1, 5, 10, 20, 40 and 100 switches to generate requests simultaneously. 4.2 results analysis the goal of these experiments was to validate the nvp, demonstrating load tests with it to verify its behavior and compare it with the flowvisor. the pox is presented, not for the 2http://archive.openflow.org/wk/index.php/oflops 3http://www.noxrepo.org/pox/about-pox/ nvp: a network virtualization proxy for software defined networking 705 purpose of direct comparison, because it perform different tasks on the network, but to demonstrate the extent to which nvp, flowvisor and openvirtex introduce overhead when acting as virtualization proxies. figure 5: reply per milisecond x number of switches figure 5 shows the number of responses that the controller can produce per millisecond. the direct comparison with flowvisor shows a huge difference between the number of requests answered for each virtualization proxy. for one switch, the nvp is 53.68% better than flowvisor, and, when the number of switches achieves 100, the nvp results are 89.83% better, showing a great increase in performance. when the nvp is compared with openvirtex their results are getting almost the same according to the increase of the number of switches, but for a small number of equipment the openvirtex has a better result. also, it is possible to see, when compared with pox, that the nvp shows fewer responses, as expected, since the message must be sent to the pox anyway. however, as the number of switches increases, it is possible to notice a reduction in the difference in the environment both with and without nvp. for one switch, the performance loss is 44.90 %, but it will decrease to 22.53 % with five switches until arriving at 1.10 % for 100 switches, showing a better performance when the number of switches is elevated. figure 6 shows the cpu load percentage of nvp, flowvisor and openvirtex during the experiments. it is possible to observe that, even with requests for 100 switches at the same time, the cpu usage by nvp was less than 12% while flowvisor gets, for the same number of switches, 107% of cpu, i.e., more than one cpu core is needed to handle the requests. the openvirtex shows a better result than flowvisor, but uses more than 3 times the amount of memory when compared with nvp. 706 b. pinheiro, e. cerqueira, a. abelem figure 6: cpu load time(%) x number of switches figure 7: memory usage (mb) x number of switches nvp: a network virtualization proxy for software defined networking 707 figure 7 presents the memory consumption of nvp and flowvisor during the experiments. the values presented are rss (resident set size), which is the physical memory the process currently uses. again, even with 100 switches, total memory usage did not exceed 2.5 mb, showing that the solution exhibits a good memory usage. nvp would therefore be more attractive to clients, as opposed to flowvisor, which uses more then 200 mb to handle the 100 switch requests. the memory used by openvirtex stays almost constant in 150 mb, overcoming in 60 times the memory used by nvp. 5 conclusions and future work this article proposes a framework for sdn virtualization, named network virtualization proxy (nvp). the purpose of nvp is to provide a mechanism for scalable virtualization with isolation between virtual networks by creating and enabling the creation of virtual links. the innovative aspect of this work is that the proposed solution combines scalability and global network view. the results presented in the experiments to allow for a validation of the proposal for the use of machine resources that will host the virtualization solution. thus, nvp has demonstrated that it can act as a virtualization proxy without using many host resources, specifically, consuming, in our experiments, fewer than 2.5 mb of memory to manage connections originating from 100 different outfits. also, all the comparisons with flowvisor show a better results for the nvp proposal. as future work, we intend to verify the individual limits of fps and fpm, as well as examine a more detailed comparison with other virtualization proposals. 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[16] jarschel, m. et al.(2011); modeling and performance evaluation of an openflow architecture, proceedings of the 23rd international teletraffic congress. itc’11, 1-7. int j comput commun, issn 1841-9836 9(3):292-304, june, 2014. formal specification and verification of mobile agent systems l. kahloul, m. grira laid kahloul* linfi laboratory, computer science department, university of biskra, biskra, 07000, algeria *corresponding author: kahloul2006@yahoo.fr messaouda grira computer science department, university of biskra biskra, 07000, algeria abstract: mobile agent systems offer efficiency and flexibility as a design paradigm. these two characteristics allow to these systems to be an adequate solution for many problems. these systems are used in many critical domains. this expansion, in use, obliges designers to insure the reliability and correctness of such systems. formal methods can be used to verify the correctness of these systems. this paper presents a formal specification and verification of mobile agent systems using the high order π-calculus. the verification exploits the two tools uppaal and spin. keywords: mobile agent, formal verification, π-calculus, promela, spin, uppaal. 1 introduction formal methods are methods with mathematical background. unlike informal methods, descriptions using formal methods are more precise, understandable, and unambiguous. there are many formal languages and methods, among these languages we recall: tl (temporal logics) [2], processes algebras (ccs [1], π-calculus [4], hoπ-calculus [19]) and state transition systems [3, 4]. to be formal, a language expressing a specification must comprise three components: a syntax defining the rules for forming expressions, a semantics with rules for the interpretation of formed sentences and a proof theory governing rules for inferring useful information from the specification [3]. π-calculus is a mathematical model of processes whose interconnections change as they interact. the basic computational step is the transfer of the communication link between two processes; the recipient can then use this link for further interaction with other parties. this makes the calculus suitable for modelling systems where the accessible resources vary over time [1]. higher-order-π-calculus (hoπ) treats another kind of mobility, where processes (called agents) themselves move. the π-calculus and its extensions are processes-algebras that focus on mobility. in these calculi, processes communicate using channels. a process sends a channel’s name in the monadic π-calculus, tuples of channels names in the polyadic π-calculus, and tuples of processes and channels names in higher-order-π-calculus (hoπ) [4]. in software engineering, formal specification is the expression in a formal language, and at an abstract level, a set of proprieties; that a system is designed to satisfy [3]. it is recognised that good specifications should be adequate, internally consistent, unambiguous, complete, minimal and satisfied by lower-level ones [2]. system verification is a domain appeared after the crisis witness of enterprises specialised in the development of systems, where the verification is a viable solution to these problems. during this period, verification tools were designed to verify systems. some of the most important verification tools are: copyright © 2006-2014 by ccc publications formal specification and verification of mobile agent systems 293 1. uppaal: is the acronym of the university of uppsala (sweden) and aalborg (denmark). it is an educational tool for formal specifications and verification of systems [5]; 2. spin: is the acronym of simple promela interpreter. it is a tool for verifying the logical consistency of concurrent systems, specifically of data communication protocol. the verified systems must be described in the promela [6] modelling language; 3. lotos: is the acronym of (language of temporal ordering specifications). lotos has been applied to describe complex systems, formally. a number of tools have been developed for lotos, covering user needs in the areas of simulation, compilation, test generation, and formal verification [7]. the formal specification and verification of mobile agent systems contribute to the best formalisation of these systems. specification can be used to analyse some known mas (mobile agent system) properties, which are: safety, accessibility, boundedness, and liveness. the objective of our work is to specify and verify an example of a mas. this system manages service locations in particular networks. this system is called service location protocol (slp) [26]. to present this work, we organize this paper as follows: in the next section, we present briefly mobile agent systems, the section three will present hoπ-calculus, promela and uppaal languages. section four will present the modelling and verification using spin and uppaal tools. before concluding this paper, we will show some related works in section five. finally, a conclusion will summarize this paper and will discuss possible prospects. 2 mobile agent systems (mas) 2.1 agent concept agent’s term comes from two distinct domains: the distributed systems domain and the multiagent systems domain. these last belongs in the base to intelligence artificial domain, where the programmers tent to imitate humane intelligence [8]. russel and norvig define the agent term as entity that broach its environment and interact on it [9]. 2.2 mobile agents mobile agents have been introduced initially in 1994 with the telescript environment [16] that permitted to processes to choose themselves to move on the sites of a network in order to work locally onto resources. a mobile agent [12, 13, 15] is a process that can move from a site to another site in order to achieve a task. in general, the mobility is provided using some primitive like: move(site) that permits the agent to move toward the site designated by the parameter. a mobile agent is composed of his corresponding code, as well as of a context including some data. this context can evolve under execution, for example while collecting some data when an agent achieves a research of information on a set of servers. the code and the agent’s context are displaced with the agent when this one visits different servers. in general, a mobile agent system provides the primitive of communication allowing to the agents to interact between them, but also to the agents to interact with the services that they visit. these primitives of communications take the form of sending messages or calling procedures or methods [17]. 2.3 mobility mechanisms mobile agents can move between network hosts, transporting their code, data and state information to continue their execution on different environments. in literature [5], we can find 294 l. kahloul, m. grira many mechanisms. in the case of remote execution, the agent is sent before it starts to be executed. when it arrives on the destination, it is executed until it finishes. in this case the agent is transferred once. when it is executing it can use the same remote execution mechanism to start the execution of other agents. in the remote execution the destination of the agent is determined by the execution starter. the mobile agent can do a weak migration by sending its data with its code. usually, the implementations of this scheme allow choosing which part of data will be transferred to the new location of the agent. in this case, the agent programmer might design some mechanism based on the value of agent’s data to resume the execution from some point. strong migration is the highest degree of mobility. using this scheme not only agent code and data is sent, but also the state of execution. when the agent arrives to the destination, it is fully restored and its execution is resumed from the same execution point it was just before migration. strong migration turns to be complex, since it involves low level internal mechanisms for execution restoring that must be standard to provide migration transparency in heterogeneous environments. 2.4 applications and limits of mobile agents mobile agents [1] are software abstractions that can migrate across the network representing users in various tasks. this is a contentious topic [14, 18] that attracts some researchers. mobile agents provide a very appealing, intuitive, and apparently simple abstraction. the authors in [1] give some applications of mobile agents: 1. distributed research of information; 2. active documents: active e-mail, web page (hypertext); 3. advanced telecommunications services: video-conferencing, mobile users (with the potential disconnections); 4. monitoring and remote configuration of devices: industrial processes, network management; 5. management and cooperation in the work-flow: the work-flow defines activities, sites, relationships, time for their implementation, to achieve an industrial product. mobile agents are responsible for conveying information between co-workers in a work-flow; 6. active networks: flexible and dynamic networks according to application needs. two approaches are proposed: (i) programmable switches: dynamically extend the networks. this approach is based on code on demand paradigm; and (ii) capsule approach: attach codes to the transferred packets. the node that receives the packet performs the associated code to treat the data in the packet; 7. e-commerce: an agent looks in a market for catalogues, and then it returns to the laptop of a customer with the best rates available; 8. applications deployment and maintenance of components in distributed environments. 9. parallel processing: the agents dispatch several computing units to make parallel certain tasks. 3 formal languages for mas we are interested to present two formal languages in this section hoπ-calculus, promela language and two tools which are spin and uppaal. formal specification and verification of mobile agent systems 295 3.1 hoπ-calculus in this higher order paradigm, mobility is achieved by allowing agent to be passed as values in a communication. hoπ-calculus [19] is an extension of the first order π-calculus introduced by d.sangiorgi [4]. this calculus enriches the π-calculus with an explicit higher order communication. in the hoπ-calculus, not only names, but also agents of arbitrarily high order, can be transmitted. the syntax of the hoπ-calculus is an extension of the syntax of the first order π-calculus. let be {a, b, ..., x, y, ...} a set of names and {p, q, ...} a set of processes [19]. processes of hoπ-calculus are defined by the following grammar: p ::= x̃(k).p |x(u).p |p ||q|τ.p|υxp|p + q|[x == y]p |!p |0 where: 1. the output prefix x̃(k).p can send the name k via the name x and continue as p ; 2. the input prefix x(u).p can receive any name via x and continue as p with the received name substituted for all free occurrences of u in p ; 3. the parallel composition p ||q represents the combined behaviour of p and q executed in parallel, where p and q can proceed independently and interact via shared names; 4. the silent prefix τ.p represents an agent that can evolve to p without interaction with the environment; 5. the restriction υxp behaves as p but the scope of the name x is restricted to p . p can not interact with other process through x; 6. the sum p + q represents an agent that can enact either p or q; 7. the match [x == y]p can evolve as p if x and y are the same name, and will do nothing otherwise; 8. the replication !p can be seen as an infinite composition !p = p |p | or, equivalently, an agent satisfying the equation !p = p |!p =!p |p ; 9. the empty agent 0 cannot perform any action. 3.2 promela promela [6] is the acronym of process (or protocol) meta language; it is the modelling language for the spin (simple promela interpreter) [27]. promela programs consist of processes, message channels, and variables. processes are global objects that represent the concurrent entities of the distributed system. message channels and variables can be declared either globally or locally in a process. it supports rendezvous and asynchronous communication between processes via channels. processes specify behaviour, while channels and global variables define the environment in which the processes run. 3.3 uppaal uppaal [6] is a tool to model, to validate and to verify real time systems; it is appropriated for the systems that can be modelled by timed automata or linear hybrid automata. the model-checker uppaal is based on the theory of timed automata [29,30] and its modelling language offers additional features such as bounded integer variables and urgency (priority between the same actions defined in two automata). the query language of uppaal, used to specify properties to be checked, is a subset of ctl (computation tree logic) [31]. 296 l. kahloul, m. grira 4 specification and verification of the slp protocol 4.1 the slp protocol the service location protocol is an ietf standard track protocol [26]. the ietf is the internet engineering task force committee that is part of the iab (internet activities board) and determines internet standards. slp provides a framework to allow networking applications to discover the existence, location, and configuration of networked services in enterprise networks. slp can eliminate the need for the user to know the technical features of network hosts. using the slp, the user needs only to know the description of the service he is interested in. based on this description, slp is then able to return the url of the desired service. slp is a language independent protocol. thus the protocol specification can be implemented in any language. the slp infrastructure (figure 1) consists of four types of agents [26]: 1. user agent (ua), which is a software entity that is looking for the location of one or more services; 2. service agent (sa), which is a software entity that provides the location of one or more services; 3. directory agent (da), which is a software entity that acts as a centralised repository for service location information; 4. memory directory agent (mda), which is a software entity that memorizes the service information. figure 1: the slp infrastructure in next section, we present a formal specification of slp in hoπ-calculus. this specification will be transformed firstly into the promela language, then into the uppaal modelling language. the section six will present the verification phase. we achieve the verification using the two tools: spin and uppaal, then we discuss a comparison between the results obtained from the two tools. 5 slp specification in this section, we will specify the slp protocol by three formal specification languages: the hoπ-calculus, promela, and uppaal modeling language. the formal specification of the slp formal specification and verification of mobile agent systems 297 system using the hoπ-calculus is given as the following: system = (reqst, reply, save, nosave, connect, ack, askinfo, sendinfo)| ua(reqst, reply)|sa(connect, save, nosave, ack)| da(reqst, ack, connect, save, nosave, askinfo, sendinfo)| mda(askinfo, sendinfo) in this specification, the system is composed of one user agent (ua), one directory agent (da), one memory directory agent (mda), and one services agent (sa). the mobility is described through the parameters of the channel. for each new service registered by the sa within the da, there are mda belonging to this specific service. in our case the service will be service. the parameters for each agent are used for the communication between agents. in the ua specification, we use request and reply as two channels for the communication between the ua and da. the term service specifies the service that will be asked. in this specification, the ua sends a request to ask a service, and receives a reply that contains the service information. the user agent (ua) specification is as follows: ua(reqst, reply) = υ(service) reqst(service, reply).reply(service, info) the sa can register a service within the da using the channel save and also remove a registered service within the da using the channel nosave, and it receives an ack as an ok to save or nosave. the service agent (sa) specification is as follows: sa(connect, save, nosave, ack) = υ(service) connect(adrs).save(service).ack| sa(connect, save, nosave, ack) +connect(adrs).nosave(service).ack| sa(connect, save, nosave, ack) the da can communicate with ua and sa, it generates the service location. this da saves the service information if it receives a save(service), and does not save it otherwise. if the da receives a reqst(service), it asks the memory directory agent (mda) for the service information via askinfo(service), and it waits the reply (sendinfo(service)) from this agent, before sending this service information to the ua via ˜reply(info, service). the specification of the da is as follows: da(reqst, ack, connect, save, nosave, saveservice, nosaveservice, askinfo, sendinfo) = connect(adrs)|save(service).ack.damem(askinfo, sendinfo)| saveservice(service)| reqst(service).askinfo(service)| sendinfo(service, info).reply(service, info)| da(reqst, ack, connect, save, nosave) +connect(adrs)|nosave(service).ack| damem(askinfo, sendinfo).nosaveservice(service)| da(reqst, ack, connect, save, nosave) the mda is the memory directory agent; it saves information about services. it communicates only with the da. the specification of the mda is as follows: mda(askinfo, sendinfo, saveservice, nosaveservice) = saveservice(service)|askinfo(service).sendinfo(service)|mda(askinfo, sendinfo) +nosaveservice(service).mda(askinfo, sendinfo) 298 l. kahloul, m. grira formal methods may be used to specify the system behaviour and to verify that the designed and implemented system satisfies the expected properties. the higher-order π-calculus is able to describe the mobile systems behaviour and, because it possesses formal semantics, it is capable of verification as well. to insure verification of the hoπ-calculus, one is obliged to use some automated tools. we have found two tools that can be used: spin and uppaal. to realise verification using this two tools, we were obliged to apply some transformation on the former specification. the two next sections will present the specification in spin and in uppaal. 5.1 modelling slp using promela to allow the verification of the slp protocol, we propose (firstly) to use the spin tool. the spin tool uses promela as a specification language. requirements and properties are specified as ltl (linear temporal logic) [2]. these ltl formulae can be entered as assertions in the promela specification, and so be used in simulation, or they can be verified using the modelchecker of spin. the following paragraphs present the promela specification of the slp protocol. the messages declarations are formalised as follows: #define msgtype 10 mtype = service, name, ok, info the channels between the entities (agents) are formalised as follows: chan reqst = [1] of mtype, byte chan reply = [1] of mtype chan save = [1] of mtype chan nosave = [1] of mtype chan ack = [1] of mtype chan connect = [1] of byte chan askinfo = [1] of mtype chan sendinfo = [1] of mtype chan saveservice = [1] of mtype chan wait = [1] of byte boolfree = true, souscribe = false; byteadrs; we declare four processes. the first one is a process named user agent, specified as follows: active proctype ua(){ xsreqst; xrreply; again1 : reqst!service, reply; if :: reply?service → free = false; wait!msgtype(10) fi; goto again1} the second one is a process named service agent, specified as follows: formal specification and verification of mobile agent systems 299 active proctype sa(){ xs save, nosave; xr ack; again2; connect!adrs; if :: wait?(10) → free = false fi if :: free == true → save!service :: else → nosave!service fi; ack?ok; goto again2} the third one is a process named directory agent, specified as follows: active proctype da(){ xs ack, ask_info, reply; xr save, nosave, send_info, reqst; if :: connect?adrs → subscribe = true fi assert((free == true) & &(subscribe == true)); if :: save?service → ack!ok; save_service!service :: nosave?service → ack!ok :: reqst?service → ask_info!service fi; if send_info?service → reply!service, info fi} the four one is a process named memory directory agents and it is specified as follows: active proctype mda(){ xs send_info; xr ask_info, save_service; if :: save_service?service; :: ask_info?service → send_info!service fi} 5.2 modelling slp by uppaal figure 2 presents the graphic modelling of the whole system specification as a timed automaton. the agents sa, da, and mda are presented respectively in the figures: 3, 4, and 5. 6 slp verification this section is dedicated to present the verification phase in this work. we consider three important properties (i.e. requirements), that we will verify: • safety: every agent sa which registers a service within da is recognised by the da and the service becomes available; 300 l. kahloul, m. grira figure 2: diagram of the system figure 3: service agent diagram • reachability: if ua asks for the service “service” and if sa subscribes this service at the da, then ua obtains the service; • bounded liveness: during the registration period within the da, a service must be accessible. 6.1 slp verification by the spin tool we use the spin tool to verify the safety, reachability and liveness properties. this tool allows verifying properties during the simulation using the assert function (assertion): assert((free == true) & &(subscribe == true)), or using the following never claims: never{ to_init : if :: (!(ask) & &(frr) & &(subscribe)) → goto accept_all :: (!ask) → goto to_s3 fi; to_s3 if ((free) & &(subscribe) → goto accept_all (1) → goto to_s3 fi; accept_all skip} formal specification and verification of mobile agent systems 301 figure 4: directory agent diagram figure 5: memory directory agent diagram 6.2 slp verification by the uppaal tool we use the spin tool to verify the safety and liveness properties using the ctl (computational tree logic) [31] as following: e<>directory.receive_reqst_service: this expression allows us to verify the reachability of some sate in the directory agent behaviour. a[]notdeadlock: this expression allows us to verify that the system has no deadlock. 7 related works mobile agent paradigm attracts researchers in the domain of software engineering and artificial intelligence. many interesting researches on formal specification and verification of mobile code system can be found. in the present work, we have presented firstly a specification of the service location protocol system in the hoπ-calculus. this specification was then verified using the two tools: spin and uppaal. this verification validates the safety propriety. in the literature, we found some similar works that tended to specify and verify formally mobile code systems using other specification languages and other verification tools, as examples: • in [1], the slp is specified in hopi-calculus and verified by uppaal tool. in this work, the hopi-calculus specification is then translated to an automaton model, where the mobile aspect of data is not respected. in our work, we have used a specification with only four agents. another difference is that we have used the spin tool and promela. • in [3], a formal specification and verification of secure routing protocols for mobile ad hoc networks (manets) is presented. in this work, authors used the promela as specification languages and spin as verification tool. 302 l. kahloul, m. grira • formal specification of standards for distance victor routing protocol is presented in [10]. this project presents how to use an interactive, hol (high order logic), together with a model checker, spin, to prove key proprieties of distance victor routing protocols. the formal verification techniques in this project are suited to routing protocol generally. 8 conclusion a mobile agent is an entity that can change its location during its execution. it can migrate from one host to another, in a network. the use of mobile agents knows an expansion in several domains. this inclusion of mobile agents in critical domains obliges the designer and developer to make attention during the elaboration of these agents. the reliability and the correctness of these programs (mobile agents) are important. formal methods can help the designer to specify systems then to prove the correctness of these systems. some of these methods are: π-calculus, hoπ-calculus. in this paper, we have presented the use of the hoπ-calculus and promela language as specification languages for mobile agent systems. we have presented a specification of the slp protocol (services location protocol), and we have done verification using the two tools: uppaal and spin. the use of formal methods meets some difficulties in software development. these difficulties are argued by the lack in experiments of these methods and the requirement of some mathematical background by the developer. in this work and some other previous works [32–34], we have tried to show the power of formal methods to insure the reliability of a mobile agent systems. in our future work, we will be interested to apply formal methods on more examples of mobile agent systems. these experiments will establish an approach on the use of formal methods, in software engineering process. bibliography [1] milner, r. 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(2010). modeling reconfirgurable systems using flexible petri nets, 4th ieee international symposium on theoretical aspects of software engineering, august 24 27, 2010, taipei, taiwan. international journal of computers communications & control issn 1841-9836, 11(6):776-788, december 2016. lossless compression of data tables in mobile devices using co-clustering b. han, b. li bo han*, bolang li international school of software, wuhan university 37 luoyu road, wuhan, hubei, china, 430079 *corresponding author: bhan@whu.edu.cn abstract: data tables have been widely used for storage of a collection of related records in a structured format in many mobile applications. the lossless compression of data tables not only brings benefits for storage, but also reduces network transmission latencies and energy costs in batteries. in this paper, we propose a novel lossless compression approach by combining co-clustering and information coding theory. it reorders table columns and rows simultaneously for shaping homogeneous blocks and further optimizes alignment within a block to expose redundancy, such that standard lossless encoders can significantly improve compression ratios. we tested the approach on a synthetic dataset and ten uci real-life datasets by using a standard compressor 7z. the extensive experimental results suggest that compared with the direct table compression without co-clustering and within-block alignment, our approach can boost compression rates at least 21% and up to 68%. the results also show that the compression time cost of the co-clustering approach is linearly proportional to a data table size. in addition, since the inverse transform of co-clustering is just exchange of rows and columns according to recorded indexes, the decompression procedure runs very fast and the decompression time cost is similar to the counterpart without using co-clustering. thereby, our approach is suitable for lossless compression of data tables in mobile devices with constrained resources. keywords: data tables, lossless compression, co-clustering, redundancy. 1 introduction internet of things and mobile internet churn out huge volume of real-time data in mobile computing devices, such as smartphones, tablets and e-readers. by using ubiquitous mobile sensors, these digital devices constantly collect environment information or persons’ behavior information. the information is generally recorded in a structured data table. for example, in a table of a healthcare application, rows represent the records in a sequential sampling time series and columns show the values of multiple measured parameters, such as heart rates. by cumulating records with the time, multi-columns mobile data tables can grow sharply in size, varying from few hundred kilobytes to few hundred megabytes dependent upon different applications. in the internet context and big data era, these sensor data will generally be sent to cloud servers for further processing in a time frequency. for a mobile device, a larger size of a data table not only takes more storage space, but its transportation to cloud servers takes more network transmission latencies and more energy costs in batteries. therefore, lossless compression of data tables is critical to optimize the usage of system resources and thus play an important role in optimizing system performance. traditional lossless compression methods view a dataset as a large sequence of strings and employ the occurrence frequency of duplicated symbols to compress them in variable length codes in a dictionary, such as huffman, lempel-ziv, run length encoding and other compression encoders [1]. these methods do not account for dependency patterns in a table and therefore they cannot compress a table with the size less than the limit given by the shannon entropy [2–4]. copyright © 2006-2016 by ccc publications lossless compression of data tables in mobile devices using co-clustering 777 recently, some researchers studied data dependency and exploited the resulted redundancy for compression. mielikainen et al. applied spatial dependency to propose an adaptive prediction length in a clustered differential pulse code modulation method for lossless compression of hyperspectral data [5]. venugopal et al. applied hadamard transformation to eliminate the correlation inside local blocks in medical data [6]. patauner et al. combined vector quantization, delta calculation and a huffman coding algorithm together to reduce the correlation among a sequence of data records acquired from pulse digitizing electronics and then apply lossless compression [7]. kolo et al. proposed an adaptive lossless data compression algorithm for wireless sensor networks [8]. the network data sequence is partitioned into consecutive blocks, and the optimal compression scheme is applied for each block by dynamically analyzing data dependency in a block. they further improved their approach and proposed a fast lossless adaptive compression scheme with low memory requirements for wireless sensor networks [9]. the approach can generate its coding tables on the fly and compress data blocks very fast. buchsbaum et al. proposed a novel dynamic programming algorithm to discover column dependencies in a table by a one-time, offline learning procedure from a small number of training examples [10]. by exploiting the dependencies, they contiguously partitioned table columns into disjoint groups and found that compressing each group of columns separately can significantly improve compression rates. in their further research, buchsbaum et al. applied a tsp (traveling salesman problem) tour method to reorder table columns prior to partitioning for further improving compression rates [11]. it provided a unified theory of entropy-like functions to explain both contiguous partitioning and column rearrangement. however, the time cost of the algorithm is expensive and is not linearly proportional to a data table size. yang et al. studied a transform compression approach for a boolean matrix [12]. it firstly located the largest columnwise-constant submatrix, and next rearranged columns such that the columnwise-constant is moved to the left-upper corner of the matrix. following the procedure, the approach recursively applied transformation on the rest of the matrix until the partition resulted in a matrix smaller than a user-defined threshold. however, its running-time cost is also very expensive and the choice of the user-defined threshold is a tricky problem and some improper choices can negatively affect the compressibility. in a word, the above methods exploited data dependencies and correlations among columns in a table (or called a matrix in mathematics). however, rows in a table also can show dependencies and redundancies. a distinct characteristic of tables is that reordering of columns and rows will not lead to information loss. therefore, we aim to apply an approach to group dependent or similar columns and rows together, such that the redundancy in grouped homogeneous blocks can be exposed and then we apply compression on these blocks. co-clustering is an approach simultaneously clustering of similar rows and columns for revealing hidden structures of a data matrix [13,14]. it provides great potential for compression. firstly, co-clustering reorders and groups rows and columns into similar or homogeneous rectangular regions, such that redundant information gets exposed and can be removed by a statistical encoder (such as a huffman compressor). next, the inverse transform of co-clustering is very simple and fast. this property will be desirable for uncompressing data. in addition, the time complexity of the algorithm is o((m+n)kl) [15], where m is the number of rows, n is the number of columns, k is the number of row-clusters and l is the number of column-clusters. since k and l are much smaller than m and n, the algorithm can run in linear time with the size of a data table. in another word, the algorithm is scalable to big size of data tables. co-clustering has been widely studied for information clustering, pattern structure discovery, et al [16–19]. in this paper, we propose a novel lossless compression approach for data tables. unlike traditional compression methods by exploiting data dependencies in a single view of either columns or rows, the proposed approach can shape homogeneous rectangular blocks by reordering columns and rows simultaneously via co-clustering. to the best of our knowledge, the proposed ap778 b. han, b. li proach is the first algorithm that integrates co-clustering and information coding theory for the purpose of lossless compression of data tables. it not only constructs homogeneous blocks by co-clustering, but also optimizes columns/rows alignment in a block to further expose block redundancy, such that the downstream compression with a standard lossless encoder becomes more efficient. we compare empirically the performance of a standard compressor 7z before and after the application of co-clustering and within-block alignment on a synthetic dataset and several public datasets with properties similar to tables in mobile systems, such as information collected from wearable sensors, clinical care, customer reviews, et al. the extensive experimental results suggest that the proposed approach can effectively improve compression ratios for data tables. in addition, the decompression procedure runs very fast and thus it is very suitable for lossless compression of data tables in mobile devices with constrained resources. 2 methodology in this paper, we propose a novel data table compression approach consisting of three steps: 1. reorder table columns and rows by co-clustering; 2. refine table columns and rows alignment to further expose redundancy; 3. compress the resorted data table by a standard compressor. 2.1 reorder table columns and rows by co-clustering given a data table t with m rows and n columns, co-clustering transforms t into another table t ′ with k row-clusters and l column-clusters (here k and l are smaller than m and n respectively), where each element bij(i ∈ [1,k],j ∈ [1, l]) in t ′ corresponds to a homogeneous two-dimensional block after reordering columns and rows. the adjacent elements in the block have the same or similar values and they provide potentials to boost compression rates. the co-clustering transform can be illustrated in figure 1 as below, t −→ t ′  a11 a12 . . . a1n a21 a22 . . . a2n . . . . . . . . . . . . am1 am2 . . . amn   →  b11 . . . b1l. . . . . . . . . bk1 . . . bkl   figure 1: co-clustering of a data table in a statistical way, the original table t can be viewed as a joint probability distribution between two discrete random variables denoting rows and columns respectively. let r and c be such two discrete random variables that take values in the set {r1, . . . ,rm} and {c1, . . . ,cn} respectively. co-clustering aims to simultaneously quantize r into k disjoint clusters, and c into l disjoint clusters. in other words, co-clustering will generate mappings mrow and mcol as below, mrow : r = {r1,r2, . . . ,rm}→ r̂ = {r̂1, r̂2, . . . , r̂k} mcol : c = {c1,c2, . . . ,cn}→ ĉ = {ĉ1, ĉ2, . . . , ĉl} the mappings also can be represented in functional forms:r̂ = mrow(r) and ĉ = mcol(c). for obtaining homogeneous blocks in those clusters and further be applied compression, an optimal co-clustering minimizes the loss in mutual information, that is, minimize i(r; c) − i(r̂; ĉ) by subject to the given k and l; (1) here, the mutual information i(r; c) measures the amount of information random variable r contains about c. lossless compression of data tables in mobile devices using co-clustering 779 to facilitate the search for the optimal co-clustering, the above objective function can be expressed as the following "distance" of p(r,c) to an approximation q(r,c), i(r; c) − i(r̂; ĉ) = kl(p(r,c)||q(r,c)) (2) here, kl(·||·) denotes the kullback-leibler divergence, q(r,c) is a distribution of the form q(r,c) = p(r̂, ĉ)p(r|r̂)p(c|ĉ),where r ∈ r̂,c ∈ ĉ (3) by following kullback-leibler divergence, the following equations (4) and (5) can be derived, kl(p(r,c,r̂,ĉ)||q(r,c,r̂,ĉ)) = ∑ r̂ ∑ r:mrow(r)=r̂ p(r)kl(p(c|r)||q(c|r̂)) (4) kl(p(r,c,r̂,ĉ)||q(r,c,r̂,ĉ)) = ∑ ĉ ∑ c:mcol(c)=ĉ p(c)kl(p(r|c)||q(r|ĉ)) (5) here, the distribution q(c|r̂) can be defined as "row-clustering prototype". similarly, the distribution q(r|ĉ) can be defined as "column-clustering prototype". in this way, the above equations show that the objective function in (1) can solely be expressed in terms of row-clustering or column-clustering. with this intuition, the co-clustering algorithm is listed in figure 2. in the first step, co-clustering algorithm starts with an initial mapping function m(0)row and m (0) col , and then computes the approximation distributions q (0), including the initial row-cluster prototype q(0)(c|r̂). for every column c, the row-cluster is computed as, q(t)(c|r̂) = q(t)(c|ĉ)q(t)(ĉ|r̂) where, ĉ = mcol(c). (6) from line 6 to 16, the algorithm keeps iterative computing of row-clusters and column-clusters until a desired convergence condition is satisfied. specifically, from line 7 to 9, we re-assign each row r into a row-cluster by using the mapping function m(t+1)row (r). in line 10, the algorithm recomputes the required marginal of q(t+1) based on the updated row-clustering results. it also recomputes the column-cluster prototype by (7). for every row r, we have, q(t+1)(r|ĉ) = q(t+1)(r|r̂)q(t+1)(r̂|ĉ) where, r̂ = mcol(r). (7) from line 11 to 13, we re-assign each column c into a column-cluster by using the mapping function m(t+2)col (c), while keeping the row-cluster fixed. in line 14, the algorithm recomputes marginals of q(t+2) and the row-cluster prototype q(t+2)(c|r̂) by (6). in line 15, we let the iterative variable t = t + 2. the processes from line 6 to 16 are repeated and the row/column clusters are updated until the change of objective function is very small. actually, the co-clustering algorithm monotonically decreases the objective function given in (2) [15,16] . it grantees an optimal co-clustering results will be achieved by the iterative procedure in the algorithm. by co-clustering, similar columns/rows are grouped into one cluster. it facilitates coding and provides compression potentials by using a standard compressor. 780 b. han, b. li algorithm co-clustering of a data table input: table t with a joint probability distribution p(r, c) the number of row-clusters k, the number of column-clusters l output: the cluster mapping functions mrow and mcol 1: //initialization: 2: let iterative variable t = 0 3: start with some initial mapping functions m (0) row and m (0) col 4: compute q(0)(r|r̂) , q(0)(c|ĉ) , q(0)(r̂, ĉ) and q(0)(c|r̂)(1 ≤ r̂ ≤ k) using (6) 5: //compute new row-cluster index and column-cluster index by iterative rounds: 6: repeat do 7: for each row r do 8: m (t+1) row (r) = argminr̂kl(p(c|r)||q(t)(c|r̂)), m(t+1)col = m (t) col 9: end for 10: compute q(t+1)(r|r̂), q(t+1)(c|ĉ), q(t+1)(r̂, ĉ) and q(t+1)(r|ĉ) (1 ≤ ĉ ≤ l) using (7) 11: for each column c do 12: m (t+2) col (c) = argminĉkl(p(r|c)||q(t+1)(r|ĉ)), m (t+2) row = m (t+1) row 13: end for 14: compute q(t+2)(r|r̂), q(t+2)(c|ĉ), q(t+2)(r̂, ĉ) and q(t+2)(c|r̂) (1 ≤ r̂ ≤ k) using (6) 15: t = t + 2 16: until convergence of the change in objective function 17: (that is, |kl(p(r, c)||q(t)(r, c)) − kl(p(r, c)||q(t+2)(r, c))| < µ, µ is a small threshold) figure 2: co-clustering algorithm 2.2 refine table columns and rows by alignment to further expose redundancy next, a series of refinements are applied on a co-clustered table t’ to further expose information redundancy. for each column, we compute the minimum value col_min and their standard derivation value col_std. if col_min/col_std is larger than a threshold α, it shows all elements in the column have similar values. thereby, by deducting col_min from all column elements, we can only code their differences more efficiently in a smaller range of numbers. in a column-cluster, we sort columns by their column mean values such that neighborhood columns show the maximum similarity. this will help to expose the redundancy among columns in a cluster. in addition, we compute the standard derivation value for each column to obtain the homogenous status in each column. next, in a row-cluster, rows are sorted by the most similar columns according to the minimum standard derivation. the above transformation helps form the homogenous blocks. their same or similar values will facilitate a statistical encoder (such as the run-length coding) in a standard compressor to improve compression effects. furthermore, for the same reason, if the number of rows is larger than the number of columns (i.e.m > n), we transpose the matrix, such that more number of similar neighbor elements are listed in a row and more redundancy can be exposed. 2.3 compress the resorted data table by a standard compressor after the above transformation, we use a statistical encoder for data compression. 7-zip is a popular and fast file archiver with a high compression ratio [20]. we select it since it supports several different data compression file formats and encryption algorithms, such as .7z format with lzma algorithm and .gzip format with deflate algorithm [21]. lzma is a variation of lz77 lossless compression of data tables in mobile devices using co-clustering 781 algorithm compressing a data table input: a table t with m rows and n columns the number of row clusters k, the number of column clusters l a threshold α, a standard compressor sc output: an index r′ recording the original order of rows an index c′ recording the original order of columns a mean vector v ′, a compressed file f ′ 1: //co-clustering: 2: generate a measure matrix w with m rows and n columns, let all entries in w are 1s 3: setup co-clustering parameters (options) according to guidance in [15] 4: let [r, c] = co − clusting(t, w, k, l, options) 5: t ′ = reorder(t, r, c); //get a reordered data table 6: // refine table columns and rows in a co-clustering block: 7: //if a column has little variance, each element is deducted from the column minimum value 8: for each column i do 9: col min = min(ith column of t ′); col std =standard derivation(ith column of t ′) 10: if col min/col std > α then deduct each element in the ith column of t ′ by col min 11: record col min in v ′ 12: end for 13: //for those columns in a column cluster,sort them by their mean 14: for each column-clustering i do 15: temp = t ′(:, c == i) //find columns in column cluster i 16: col mean = mean(temp), temp = sort(t ′, col mean) 17: add sorted column-cluster temp into new matrix sortt 18: record the updated column index in c′ 19: end for 20: //for those rows in a row cluster, sort them by the column with the minimum std value 21: [order index] = sort(std(sortt)) 22: for each row-clustering i do 23: temp = sortt(r == i, :) //find those rows in row cluster i 24: //sort rows by the column with the minimum std value 25: temp = sort(temp(:, order index(1))) 26: //use the reordered rows in row cluster i to form a matrix 27: add sorted row-cluster temp into new matrix sortt’ 28: record the updated row index in r′ 29: end for 30: //compress the resorted table by a standard compressor: 31: f ′ = compress(sc, sortt ′) figure 3: compression algorithm using co-clustering algorithm and it uses entropy coding with a markov chain based range coder and binary trees. deflate is a standard algorithm based on lz77 and huffman coding. in the above two steps, our approach applies co-clustering to expose redundancy in a data table and the redundancy can be removed by any of these encoding algorithms. thereby, we use 7-zip to test our approach. 782 b. han, b. li 2.4 complete compression and decompression algorithm the details of the compression algorithms are shown in figure 3. accordingly, the decompression algorithm consists of two steps: decompression and reorder back to the original table. its details are shown in figure 4, algorithm decompressing a file to its original data table input: a compressed file f ′, a mean vector v ′ an index r′ recording the original order of rows an index c′ recording the original order of columns a standard compressor sc output: the original table t with m rows and n columns 1: // decompression: 2: dt = decompress(sc, f ′) 3: //reorder back to the original table: 4: reorder rows in dt according to r′, reorder columns in dt according to c′ 5: for a column with a col min value in v ′, add each element in the column with col min figure 4: decompression algorithm using co-clustering 3 experimental results to test the effectiveness of our approach, we designed two experiments. one is the compression on a synthetic dataset. we would like to show how co-clustering can help to reorder and group columns and rows in a data table such that redundancy is exposed. since compression ratio results are dependent upon different data tables used in mobile applications, we perform the other experiment which is compressing public benchmark datasets. these benchmark datasets are selected from uci real-life datasets. they cover many typical topics in mobile applications, such as wearable sensors, clinical care, customer reviews, and they have the similar table structures and contents as data in mobile systems. we aim to compare the compression performance before and after applying co-clustering transformation for the benchmark datasets. the coclustering software we used is co_cluster (version 1.1) [15]. the compression software used is 7-zip. all experiments were performed on a machine with an intel core i7 2.30ghz processor and 8gb main memory. for comparing the compression effectiveness, we define a measure called improved compression rate (icr) based on compression rate (cr) as below, cr = original file size compressed file size (8) icr = cr with co− clustering −cr without co− clustering cr without co− clustering (9) another two measures are compression time and decompression time. 3.1 experiment on a synthetic dataset we firstly generate a data table with 10000 rows and 30 columns. it includes 2 row-clusters and 3 column-clusters. thereby, the data table is designed with six blocks. two of them are homogenous (entries values all are 1). the other four blocks contain entries with random values lossless compression of data tables in mobile devices using co-clustering 783 ranging in [0, 1]. by random permutation on rows and columns, the data table mixes six blocks together. the image of the designed data table is shown in figure 5(a) and its original text file size is 946kb. in compression algorithm, the parameter m is set to 10000, n is 30, k is 2, l is 3, and options vector is set to default values.α is configured with 10. figure 5(b) shows the reordered data table images after co-clustering. it illustrates clearly that the two homogenous blocks (or equivalently say, redundancy) are discovered by reordering columns and rows. the homogenous blocks are helpful boost the compression effects by a statistical encoder. in addition, in this case m > n, so we transpose the transformed table such that the number of consecutive entries with the same or similar values in a row increases for improving the performance of an encoder. by comparing figure 5(a) with figure 5(b), we see that the redundancy information has been well exposed by co-clustering. it has been proved by final compression results. in 7-zip, by setting compression format as 7z, compression level as maximum and compression algorithm as lzma, other parameters as default values, the compressed file sizes before and after co-clustering are 268kb and 216kb respectively. (a) 5 10 15 20 25 30 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 (b) 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 5 10 15 20 25 30 figure 5: synthetic data matrix image: (a) before co-clustering, (b) after co-clustering and transpose by (8), cr with co-clustering and without co-clustering is 4.38 and 3.53 respectively. consequently, icr for the synthetic dataset is 24%, a significant improvement on the original compression rate. the compression procedure without co-clustering takes 0.55 seconds, and the counterpart compression procedure with co-clustering takes 4.77 seconds. for decompression, they both take 0.21 seconds. 3.2 experiments on ten uci datasets we further test our approach on ten real-life benchmark datasets in multiple fields, such as wearable sensors, clinics, ecommerce, et al. they are from the discretized version of uci machine learning repository [22] and their structures and contents are very similar to data tables in mobile applications. the list of the ten datasets is described in table 1. for each dataset, we list the number of rows and number of columns in a table, its original file size and its brief description. obviously, the ten datasets differ in table rows and columns, from 700 rows to 1000000 rows and from 6 columns to 10000 columns. subsequently, their file sizes vary from hundreds of kb to tens of mb. the diversified tables are good to test the general compression and decompression performance of our approach. 784 b. han, b. li table 1: dataset description dataset rows*columns original size brief description nursery 12960*8 288kb applications for schools waveform 5000*21 304kb attributes of waves chesskrvk 28056*6 463kb chess positions gait freeze 151987*11 6018kb data from wearable acceleration sensors connect4 67557*42 8775kb 8-ply positions in a game diabetes130 101766*48 21482kb 10 years of clinical care data at 130 us hospitals arcene_test 700*10000 18560kb biomedical features poker_hand 1000000*11 22978kb a hand of playing cards opportunity 51116*250 39022kb dataset for human activity recognition amazon 1500*10000 29396kb customer reviews in a commercial website in co-clustering settings, m and n are set to the original dimension information of a data table. for simplicity, k is fixed to 4. if a table contains at least 10 columns, l is set to 4, otherwise, l is set to 2. α is configured with 10. table 2 compares the compression performance by using lzma algorithm in 7z without co-clustering or with co-clustering. in 7z, the compression level is set to maximum. other parameters in 7z are configured with default values. the 3rd and 4th columns in table 2 (column header "size by 7z" and "cr by 7z") show the compressed file sizes and compression rates by using 7z without co-clustering. and the 5th and 6th columns (column header "size by 7zco" and "cr by 7zco") give the compressed file sizes and compression rates by using co-clustering. from the 7th column, we see clearly that co-clustering can significantly improve the compression rates with icr ∈ [21%, 59%]. the 8th and 9th columns compare the compression time difference between 7z and 7z with co-clustering. it suggests co-clustering takes more time when a table size increases. the 10th and 11th columns show decompression times between 7z and 7z with co-clustering are similar with each other. for testing the compression performance by using other algorithms in 7z, table 3 list the compression results by using gzip algorithm. the compression level is also set to maximum and other parameters are configured with default values. compared with the results in table 2, the dataset compression rates by gzip are mostly lower than those by lzma. however, our co-clustering approach still can improve the compression rates by the range from 23% to 68%. #rows × #columns ×106 0 5 10 15 ic r 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 compression with 7z+lzma+co-clustering compression with 7z+gzip+co-clustering figure 6: icrs obtained using co-clustering with lzma and gzip lossless compression of data tables in mobile devices using co-clustering 785 table 2: file compression comparison by using lzma algorithm in 7z dataset original size (kb) size by 7z (kb) cr by 7z size by 7zco (kb) cr by 7zco icr ct by 7z (s) ct by 7zco (s) dt by 7z (s) dt by 7zco (s) nursery 288 35 8 22 13 59% 1.88 5.66 0.20 0.20 waveform 304 30 10 22 14 36% 1.84 3.74 0.23 0.25 chesskrvk 463 78 6 52 9 50% 3.00 7.13 0.32 0.30 gait freeze 6018 1167 5.16 792 7.60 47% 67.64 149.13 0.42 0.41 connect4 8775 385 23 275 32 40% 46.73 98.70 0.44 0.40 diabetes130 21482 2177 9.84 1554 13.79 40% 187.69 427.70 3.33 2.85 arcene_test 18560 5572 3.33 4598 4.04 21% 260.46 539.58 3.81 4.09 poker 22978 4794 4.79 3749 6.13 28% 444.35 855.87 3.63 3.98 opportunity 39022 8403 4.64 5573 7.00 51% 522.43 1026.00 4.01 4.56 amazon 29396 1691 17.38 1220 24.10 39% 170.13 437.01 3.96 4.24 table 3: file compression comparison by using gzip algorithm in 7z dataset original size (kb) size by 7z (kb) cr by 7z size by 7zco (kb) cr by 7zco icr ct by 7z (s) ct by 7zco (s) dt by 7z (s) dt by 7zco (s) nursery 288 37 8 24 12 54% 2.00 4.02 0.22 0.23 waveform 304 34 9 24 13 42% 2.06 3.88 0.24 0.24 chesskrvk 463 84 6 54 9 56% 2.78 6.24 0.28 0.26 gait freeze 6018 1339 4.49 1025 5.87 31% 80.24 178.79 1.78 1.61 connect4 8775 499 18 297 30 68% 50.01 106.13 2.23 1.74 diabetes130 21482 2615 8.21 1680 12.79 56% 237.95 490.94 6.71 6.69 arcene_test 18560 5878 3.16 4792 3.87 23% 307.20 643.88 6.80 6.23 poker 22978 5342 4.30 4056 5.67 32% 433.88 986.79 7.58 8.27 opportunity 39022 10256 3.80 6617 5.90 55% 524.55 1124.00 11.51 12.34 amazon 29396 1908 15.41 1550 18.97 23% 194.45 497.12 8.87 7.42 figure 6 illustrates that with the increased products of number of rows and columns in the ten tables, the icrs are waved around 40%. it shows our algorithms keep stable of improved compression rates without much affected by table properties. figure 7 visualizes the compression times by using two different compression algorithms in 7z with co-clustering or without co-clustering. it shows the two algorithms lzma and gzip have no much difference from compression time view. except for the last dataset "amazon", we see that with the product of rows number and columns number increasing, the corresponding compression times increase linearly, without regarding to using co-clustering algorithm. it confirms that the time cost of co-clustering algorithm is just linearly related to the product of rows number and columns number in a table. thereby, our approach is faster than other approaches where time costs are nonlinearly proportional to a data table size. the last dataset digitalizes customer reviews in amazon website. by digitalization representation of text reviews, the table is a high dimensional sparse matrix and it contains a lot of consecutive 0s. it leads to a sharp decrease on compression time though it contains a huge number of columns. 786 b. han, b. li #rows × #columns ×106 0 5 10 15 c o m p re ss io n t im e ( s) 0 200 400 600 800 1000 1200 1400 1600 compression time by 7z+lzma compression time by 7z+lzma with co-clustering compression time by 7z+gzip compression time by 7z+gzip with co-clustering figure 7: compression time comparisons #rows × #columns ×106 0 5 10 15 d e co m p re ss io n t im e ( s) 0 2 4 6 8 10 12 14 16 decompression time by 7z+lzma decompression time by 7z+lzma with co-clustering decompression time by 7z+gzip decompression time by 7z+gzip with co-clustering figure 8: decompression time comparisons figure 8 compares the decompression time costs with different compression settings. it shows lzma algorithm runs consistently faster than gzip on decompression for the ten datasets. the co-clustering approaches, either using lzma or gzip, take similar time on decompression than those counterparts without using co-clustering. one reason is that the compressed file after co-clustering has a smaller size than the packaged file without co-clustering. in addition, the inverse transform of co-clustering is just reordering rows and columns from recorded indexes in a compression procedure. thereby, it is very simple and fast in table transformation. by integrating the two factors, we see in figure 8 that our co-clustering compression approach takes very similar time costs on decompression as original 7z approach. it is favorable for compressed data tables in a mobile device, which will mostly be sent to a cloud server and be decompressed. conclusions data tables are widely used in many mobile applications. lossless compression of a data table can not only reduce storage sizes and network transmission latencies, but also save energy in batteries. in this paper, we propose a novel co-clustering compression approach consisting of lossless compression of data tables in mobile devices using co-clustering 787 three steps: reordering and grouping columns and rows by co-clustering; refine table rows and columns to further expose redundancy; data compression by using a standard compressor. we tested the approach on a synthetic dataset and ten uci real-life datasets. the experimental results suggest that our approach can significantly improve compression rates at least 21% and up to 68%. the approach is robust both to lzma and gzip encoders. in addition, the time cost of our approach is linearly proportional to the product of number of columns and rows in a data table. furthermore, since the inverse transform of co-clustering is just exchange of rows and columns according to records, the decompress procedure is fast and takes similar time on decompression as the original compressor. therefore, our approach is suitable for compressing a data table in a mobile device where the table requires a limited storage size and reduced communication latency with constrained energy cost. in next step, we would like to test our co-clustering approach on lossy compression of multimedia files in mobile devices, such as images and videos. we are also interested in incorporating other dimension reduction techniques, such as principle component analysis and independent component analysis, to further improve the effectiveness of multimedia compression in the mobile systems with constrained resources. acknowledgment this work was supported by the national natural science foundation of china under grants 61272272 and u1531122, and by the natural science foundation of hubei province under grant 2015cfa058. bibliography [1] rao s., bhat p. 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[20] http://www.7-zip.org [21] http://en.wikipedia.org/wiki/7z [22] f. coenen, the lucs-kdd discretised/normalized arm and carm data library, in http://www.csc.liv.ac.uk/~frans/kdd/software/lucs-kdd-dn/datasets/datasets.html int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 606-616 the role of visual rhetoric in semantic multimedia: strategies for decision making in times of crisis a.m.p. brasoveanu, i. dzitac adrian m.p. brasoveanu modul university vienna, department of new media technology, austria, 1 am kahlenberg, 1190 vienna, e-mail: adrian.brasoveanu@modul.ac.at ioan dzitac 1. aurel vlaicu university of arad, romania, 310330 arad, 2 elena dragoi, and 2. agora university romania, 410526 oradea, 8 piata tineretului e-mail: rector@univagora.ro abstract: as semantic multimedia is approaching mainstream, even the great improvements that can be seen in its classic schools, like the data mining inspired information retrieval based on metadata analysis, or computer vision, might not be enough. we identify a new group that gains traction in the semantic multimedia community and which uses as starting point developments from psychology and visual communication. for the purposes of this article we restrict our domain to visual rhetoric as we consider it to yield the biggest potential for future developments. living in times when the periods between crises seem to be shorter and shorter, we look at how developments in semantic multimedia can be used for predicting and overcoming crises. we analyze at least 2 aspects related to this: using information visualization to understand the evolution of crises and creating multi-layered semantic multimedia technologies that can easily be adapted to use a variety of sources and solve problems from different domains. in both cases we show how techniques inspired by visual rhetoric (information linking, framing, composition) in conjunction with named entity recognition offer a lot of benefits. the section related to multi-layered semantic multimedia technologies also draws on the lessons learned while designing a prototype application aimed at improving tourism decision making process. the article ends with a discussion on evaluation methods for multi-layered semantic technologies applications. we look at how to evaluate them on both levels: mechanisms (information linking versus raw named entity recognition when generating visuals, for example), and decision making strategies (do such systems actually solve real problems related to crises, create jobs or at least can they be repurposed to solve other problems than the one with which we have started?). keywords: semantic multimedia, visual rhetoric, text to visual matching, interactive documentary, crisis strategies, multimedia storytelling "the progress of civilization can be read in the invention of visual artifacts, from writing to mathematics, to maps, to printing, to diagrams, to visual computing." stuart card et. al. readings in information visualization 1 introduction in today’s fast web there is a need for elegant mechanisms that can help us understand how to process, store, retrieve and present the huge amount of information contained in multimedia files. semantic multimedia [23] [24], the branch of semantic web focused on the analysis of multimedia documents, traditionally employed methods like metadata analysis, feature extraction copyright c⃝ 2006-2012 by ccc publications the role of visual rhetoric in semantic multimedia: strategies for decision making in times of crisis 607 or multimodal analysis, and as a result, the core researchers were split in two large groups: a group that had its roots in text processing and data mining, and a group with roots in computer vision [21] [23] [24]. of course at times one will need to apply both methods to get meaningful information from a multimedia system. today, new groups are slowly emerging, driven by the advances from fields like visual communication, psychology or biology. the group at which we adhere is still a small one and tries to tie the ideas from the modern visual rhetoric to information visualization, multimedia processing and generation, interactive documentaries and other semantic multimedia areas. visual rhetoric [11] is one of the new disciplines taken into account by the semantic multimedia researchers. we consider this cross-pollination natural since both fields deal with the interpretation of visual media (paintings, photographs, movies, games, etc.). the question we would like to address in this paper is how can we design semantic technologies that take into account the findings from visual rhetoric? the rest of the paper is organized in 4 sections. section 2 starts with a discussion about the various schools and definitions of visual rhetoric; and identifies several ideas that have a potential for growth in the field of semantic multimedia. it also contains a review of the related work. section 3 continues with a short analysis of the role that visual methods have in decision making with a special focus on crisis economics. section 4 presents some ideas about prevention of crises and a case study built around a prototype application. we conclude our paper with a discussion on the various evaluation methods that can be used in order to assess the success of our enterprise, both on the level of the mechanisms described (comparisons between different methods for generating multimedia content) and on the level of decision making strategies. 2 the role of visual rhetoric in semantic multimedia and related work images, video files or graphics of any kind (paintings, infographics, interactive visualizations) always tell more than we would like to admit. since the biggest processing engine we have is our brain we should pay more attention to how we process and present any information using multimedia channels. visual rhetoric is one of the modern disciplines that can help us do precisely this, if we take the time to study it and apply it to our representation and interactivity problems [11]. while visual rhetoric is not new, its theoretical treatment and multimedia applications are. the seeds of this discipline can be found in the articles about art theory, film art and iconology, published in the german space since the third decade of the 20th century. probably the most famous exponents of that period were rudolf arnheim [2] and ernst gombrich [9]. the term visual rhetoric was rarely used at the time, but most of the elements discussed in their essays (from equilibrium to lighting or color, but also space or dynamics) are included in modern treatments of visual rhetorics. the influence of this movement goes well beyond visual communication, and their ideas can be found almost everywhere from architecture to game design. this wave was mostly focused on issues of representation and composition in art. during the ’60s and ’70s there was a french wave of visual rhetoric, which was inspired from the film criticism of cahiers du cinema, literary criticism and philosophy, its most famous exponents being roland barthes [3] and jacques bertin [4]. cinema, photography and charts were the focus of the essays published during this wave. today, the dominant current in visual rhetoric theory is anglo-saxon, gunther kress [17] or charles hill [11] being some of its most respected proponents. eric kandel [16], belongs probably to both the german and anglo-saxon wave (he left vienna when he was 9 years old in 1938, but he always tried to keep contact with fellow austrians like ernst kris or ernst gombrich who were 608 a.m.p. brasoveanu, i. dzitac influential in establishing the grounds on which a scientific theory of visual rhetoric will someday be formed). the current wave is one of solid grounding, multimodality, mathematics, computer science and semiotics being used to connect the dots between the various long running threads. complex questions are asked (questions like: what is the role of music in a certain scene from a movie? how can certain elements be used in the same scene to enhance its meaning?). by doing this, the current wave starts to deconstruct the authorial intentions in a scientific manner. while there is no single all-encompassing definition of visual rhetoric to this day, there is a consensus regarding the fact that you can create a visual rhetoric space for any discipline. some of the fields where visual rhetoric can and should be used are investigated in [11] together with the possible definitions of visual rhetoric as seen from those fields of study. basically all definitions agree on one aspect: visual rhetoric is a form of communication that uses images to construct meaning or arguments. by extension, visual literacy defines the way we respond to images and it implies that we are already well trained in how to read images. the beginnings of visual rhetoric were controversial (as proved by the ernst haeckel biological images forgery case [8]), but its continuous improvement, especially during the last two decades, has led to its acceptance as one of the leading areas of research in visual communication. it is enough to look up the list of publications from the premier venues for semantic web, multimedia or information visualization publications (journal of web semantics, acm multimedia, ieee multimedia, ieee tvvg, acm tomccap) during the last years (2008 2012) to discover that some of the articles that received a lot of attention (best paper awards, quotations, discussions in other papers, even sequels) apply ideas inspired by visual rhetoric like: visual query suggestion [30], narrative visualization [20], affective image classification [18], framing effects [13] and color naming models and their applications [10]. hullman and diakopoulos [13] apply their ideas on visualization rhetoric to a class of visualizations identified by segel and heer [20] as narrative visualizations. narrative visualizations do not just visually present some numbers, but also draw attention to the story behind those numbers, and in doing so they need to deploy an entire arsenal of techniques like provenance rhetoric, mapping rhetoric (visual metaphors, contrast, etc), linguistic or procedural rhetorics [13]. the paper about color naming models [10] is important mainly for library builders (especially javascript libraries), while [18] uses features generally used in psychology and biology to create an affective image classification. some of the metrics used in [18] are color (name, contrast, features), texture (wavelet, tamura, etc.), composition (depth of field, rule of thirds) or content (human faces, skin). an interesting problem in our view is that of using images to illustrate arguments. an existing approach towards this problem involves finding the entities from texts (or even a search box) and associating them with images from medical literature (using caption processing, image processing, and topic discovery), and can be found in the structured literature image finder [1]. the training data used manually annotated images from different subcellular locations. slif, developed at cmu, is basically a restricted search engine, one that has as domain medical literature. for medical texts software like this is extremely valuable, as it is often the case that an image presents an entire story. identifying images that best represent entities is a challenging task by itself, but identifying images that would best illustrate a point of view is an even more daunting task. it is however the task that anyone involved in visual rhetoric would like to solve, especially people involved in advertising. bocconi, hardman and nack [5] were able to generate matter of opinion documentaries by "framing" into a larger conversation clips from interviews that captured people’s reactions to the september 11 terrorist act. after proving how to build the "framing" mechanisms using semantic graphs, they conclude that the role of visuals in providing support for the subject matter in documentaries needs to be further developed. we used this assumption as a the role of visual rhetoric in semantic multimedia: strategies for decision making in times of crisis 609 starting point for our investigations and discovered that there are several theories in social semiotics [11] [17] [27] dedicated to supporting verbal meaning with visual artifacts, and we started to build a framework around these theories. what we noticed by reviewing the literature is that while these articles are hardly related when it comes to subject (apparently there aren’t many connections between visual query suggestion, information visualization or color naming models), and some of them do not even mention the term visual rhetoric (except for [13] which refers to "visualization rhetoric"), almost all of them quote some of the pioneers of the field (rudolf arnheim [2], johanes itten [14], roland barthes [3], or jacques bertin [4], for example), and some were influenced by the research group led by alberto del bimbo [7]. this suggests that a third group is gaining lots of traction in the area of semantic multimedia, and that this group embraces theories from art, psychology or biology. the surveyed papers are built upon the philosophy of the early pioneers of the field, while we base our work on theories developed in the last 15 years. 3 visual rhetoric for decision making in times of crisis mainstream economists almost always fail to predict crises, and the example of the current crisis (started in 2008 with the collapse of the american housing market, and at the time of writing 2012 still unfinished) is one of the best. there a few economists who are said to have predicted this crisis (nouriel roubini, peter schiller, nassim taleb and others) [34], but there are hardly any graphics that prove their theories. the most interesting theories present dragon-kings (significant or meaningful outliers), black swans (in essence events that are almost impossible to predict) and other models that would lead to accurate predictions. dragon-kings models proposed by eth’s didier sornette [15] [22] [31] do not involve events that can’t be predicted, as black swan events suppose. sornette’s group anticipated some short bubbles and published the results sometimes few days before the actual events occurred [31]. other economists just improved som models and run them against datasets related to the current crisis [19]. while they did not predict a new crisis or the length of the current one with a great accuracy, these models are still useful and can help us understand what happens in today’s high frequency trading markets. we think that the worst part when it comes to crisis prediction or visualization is that even some of the best visualizations (take the visualizations from one of the top contests, for example [33]) do not present us with the visual cues (dragon-kings, black swans) that would make us easily understand what happened during the last years. just plotting some data without highlighting the events that suggest bubbles or other crisis scenarios is not going to help us too much. we have to spend more time thinking about the results of the visualizations and how to present those results in such a way that they are easy to understand. this means more time spent tackling the problems related to manipulation and bias, since graphics can in the same time improve our understanding of current or past events, but they can also be used to manipulate the public opinion. in today’s connected world, where most of the governments put their data online (including financial data), we think that this dual nature of visualization rhetoric is something we must carefully address. [13] shows how to "frame" narrative visualizations to present different points of view related to the same event (poll predictions). another method to insert more information into visualizations would be to combine various visual metaphors as demonstrated in [12]. we think there are multiple reasons why current visualizations do not take into account such phenomena. first, many visualizations are not done by interdisciplinary teams, so if the researchers have not heard about such events (dragon kings, black swans, etc) they will not be represented in the 610 a.m.p. brasoveanu, i. dzitac end product (this assumption does not apply to bigger outlets with traditions in visualization like the new york times or guardian). second, most of the visualization rhetoric used today still comes from several sources (usually jacques bertin [4], william cleveland [6], edward tufte [25] [26], leland willkinson [29], the last one being more recent than the rest) mostly focused on visual presentation of data, but not on framing and the consequences of framing and story selection. third, there is no single, uniform, easy to understand and use visual rhetoric for any type of decision making. this means that all visualization designers need to master visual literacy. for today’s visualization designers there is an easy path towards mastery of both visual rhetoric and visual literacy: they can start learning online (outlets like: [36][41]) and then go on and read the masters (classics: [4], [6], [25], [26], [29], or modern: [42][44]). just by choosing a story and applying some creative layering techniques over a plot we will not be able to predict and prevent a crisis. if we want to be able to do that, we will have to rely on other mechanisms like job creation, for example. or we will have to create technologies that work on multiple levels and are easy to adapt for solving different problems. prevention is not going to be of much help as long as the visualizations we use do not show us the dragon-kings or black swans. 4 multi-layered strategies for overcoming crisis semantic technologies to increase profits a good case study for our ideas would involve a situation in which we can produce multimedia content in order to leverage some of the advantages that interactive visual environments have over traditional media. tourism industry, entertainment, or politics could be considered some of the premier venues where image is everything and such applications would help a lot. take for example this scenario: you have several videos in which speakers tell stories about what they like in a city. if you want to create a short movie from this clips, you will likely want to convince those who watch it that the city being spoken about is really beautiful. this means you will have to replace in many places the images of the speakers with those of the objects or concepts being spoken about. using visual rhetoric is just one of the best ways to automate this process. our approach towards the use of visual rhetoric in semantic multimedia is based on some of the more recent findings, like [11] and [17]. the main goal of our project is to understand how to use visual rhetoric in multimedia environments. some secondary goals for the project are to use textual to visual matching methods and visual content generation methods. most of the work presented in section 2 uses visual rhetoric only for interpretation/explanation purposes (this visualization presents the evolution of a player during the last season, for example), whereas our goals are more inclined towards the possible applications of visual rhetoric in the space of multimedia content generation (automated movie generations, automated footprint generation, automated summary generations). the case study involves a prototype web application called interview explorer and used to generate visuals for a series of interviews with people (mostly students) regarding their lives in a foreign city (vienna, the capital of austria). we selected vienna as the city that must be explored, not only because we spend a lot of time in the city, but also because: • it is the city where the whole movement with integration between science and art started around 1900, according to the nobel prize laureate eric kandel [16]; • it is a city with lots of historical sites which are well represented on any media property, from twitter to wikipedia (belvedere or stephansdom, for example); • it is a big city with enough green spaces (donauinsel, vienna woods, etc.); the role of visual rhetoric in semantic multimedia: strategies for decision making in times of crisis 611 figure 1: the components of the prototype • the landscape changes quite fast because there are lots of new construction sites; • while it does not have so many iconic images like london, paris or new york, it consistently ranks higher on mercer’s quality of life tops for few years now [36] (which can make for interesting life questions which should theoretically be harder to illustrate through images). the generated short documentaries (webisodes) can be used in effective marketing campaigns for attracting students or tourists, for example, but they can also be used as research tools in economy or social sciences in order to understand the needs and the habits of the population from a certain area. the interviews contain questions related to the parts of vienna that attract the students (buildings, parks, danube easier to illustrate if we use an automated approach based on named entity recognition), but also questions about how well they integrated into the new environment (work, friends, social life harder to illustrate even if you use a manual approach, and it gets even worse with automated approaches). the application can function in 2 modes: search (where we can just see the proposed visuals and some widgets with additional information about the entities mentioned) and explore (where we can see proposals on how to illustrate episodes with the related identified visuals). for additional information we integrated widgets that display data from wikipedia or twitter, or the maps from google, for example. integrating facebook widgets was problematic, as some of the people interviewed felt that it would be a serious breach of their privacy. they were basically not aware that all you need to find a person on facebook is a name. people that had twitter accounts were more likely to agree to display their information on the twitter widget, because they see this as free publicity. currently, all the videos, images and interviews used in the prototype are from the personal collection of the author. a long term goal of the project would be to use any free images or videos available on the web that are related to the topic discussed. the front end of the prototype was programmed in javascript, while the back end uses several other programming languages. the communication between different components is done using the json format. since this is a prototype, and not a commercial application, we are not able to provide metrics regarding its profitability. the expression from the title of this section should be interpreted as: since semantic technologies are now cheaper than they used to be, they can easily be used to 612 a.m.p. brasoveanu, i. dzitac figure 2: interview explorer prototype first iteration figure 3: visual suggestions tab from the explore mode. this simple visualization offers only several suggestions for replacing the frames of each video block. the role of visual rhetoric in semantic multimedia: strategies for decision making in times of crisis 613 create applications that provide lots of features at an accessible price. maybe the most interesting aspect related to the development of such applications is the fact that you can create a stack of technologies to solve a specific problem (replace the image of the person that is interviewed with images about the things he is talking about) and easily repurpose them to solve other problems (like use the generated documentaries for marketing purposes, or add an interactive visualization layer on top and provide data that can be useful for government to understand foreign citizens living in austria, for example). seen from this perspective, the fact that semantic web is now becoming mainstream should help a lot of companies create the tools that will help them easily navigate through the bad periods they are now confronting. 5 discussion and future work by looking at the work presented in section 2, it can be easily seen that there is a new current in semantic multimedia. this current might not yet be on par with information retrieval or computer vision, but it is certainly developing into something powerful and useful in the same time. it might not be a fully formed school like the ones we mentioned, but given the large number of papers in top venues we think that this school will be important in the next couple of years. transforming visual rhetoric into science is not something that will happen overnight. it takes time to build the mathematical models and the simulations that are associated with scientific processes, and also needed in order to reproduce the results. as we have seen in section 3, many crisis prediction models should also include a visual component (svm visualization, dragon-king, black swan or something else). we think that visualizations should take this finding into account and such patterns should be discovered as soon as possible. it is hard for us to understand why big companies invest millions or billions of dollars or euros into visualization systems for real-time stock trading, but when it comes to presenting their findings to the public they almost never show the patterns that could lead to crises. before fractal analysis we could argue that there were no good mathematical models to predict crises [15] [22] [31], but now since we have such models we should use them. the prototype from section 4 is going through new iterations. future work will involve trying to replace images of speakers with images of the concepts being spoken about. we will also add new layers to our architecture: a visualization layer, a sentiment analysis layer, and so on. we are currently undertaking an evaluation of the system on components level. it is currently the only method to evaluate it since our system uses images and videos that are not from standardized datasets like trecvid [35]. we also do user evaluations against each component, because it is important to know what the users feel about the end result. first reactions of the interviewed persons were that the system looks good and it is useful. they would even see themselves using it, if it would be open to public, since they consider it has the potential to be great in the space of personalized entertainment. we will perform a more detailed survey to assess the strength and weaknesses of our approach in the eyes of the users. since today cinema viewers are accustomed to seeing complex narratives, we know that their expectations are high. for example, the ending from one of last year’s most critically acclaimed movies: tinker, tailor, soldier, spy mixes scenes that happened during at least 4 periods of time (the distance between each being several years) in only 5 minutes (the focus is on the relationship between two characters, and the ascension of the third in light of the recent events), but still manages to keep us involved and provide a satisfactory and artsy conclusion in the same time. we are aware that reaching such an artistic mastery requires a long commitment from our side, but our first target is not tinker, tailor, soldier, spy, when it comes to generating an engaging narrative (as that movie is also a dramatic adaptation of a novel), but rather the autobiography of nicolae ceausescu, a documentary which manages to 614 a.m.p. brasoveanu, i. dzitac tell the dictator’s story without any background narration. from our point of view reaching the artistic and information complexity level of this documentary (editing, scoring, narrative comprehension, etc.) will take several years, but the first steps toward this goal have already been made. assessing the success of our prototype system on the level of decision making strategies represents a complex process which is also likely to take several years. it involves developing future versions and showing the end product to potential customers in order to draft a commercial version in one day. a commercial version might easily function on multiple levels as we suggested. for example, if we would implement it for a touristic city portal, the system could generate both the ads that could be served to visitors (ads for ski during winter, and for hiking during summers), but also the presentations for various locations from the city and webisodes that show different aspects of living in that community. combining such systems with 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[44] http://twitter.com/nytgraphics/, last accessed: 12.07.2012. int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 709-720 distributed collaborative processing under communication delay over wireless sensor and actuator networks l. mo, b. xu lei mo, bugong xu key laboratory of autonomous systems and network control, ministry of education college of automation science and engineering south china university of technology guangzhou 510640, china e-mail: mo.lei@mail.scut.edu.cn, aubgxu@scut.edu.cn abstract: in wireless sensor and actuator networks (wsans), the sensor nodes are involved in gathering information about the physical phenomenon, while the actuator nodes take decisions and then perform appropriate actions upon the environment. the collaborative operation of sensor and actuator nodes brings significant advantages over wsns, including improved accuracy and timely actions upon the sensed phenomena. however, unreliable wireless communication and finding a proper control strategy cause challenges in designing such network control system. in order to accomplish effective sensing and acting tasks, efficient coordination mechanisms among different nodes are required. in this paper, the coordination and communication problems in wsans are studied. first, we formulate the mathematical models for the wsans system. then, a predictor-controller algorithm based on distributed estimation is adopted to mitigate the effects of network-induced delay. finally, we apply a collaborative processing mechanism to meet the desired system requirements and improve the overall control performance. this approach will group the sensor and actuator nodes to work in parallel so as to reduce the computation complexity and enhance the system reacting time. simulation results demonstrate the effectiveness of our proposed method. keywords: wireless sensor and actuator networks, distributed estimation, collaborative processing. 1 introduction wireless sensor and actuator networks(wsans) comprise groups of sensor and actuator nodes that are connected with wireless medium. it is an important extension of wireless sensor networks (wsns), allowing actuator nodes within the network to make autonomous decisions and then perform appropriate actions in response to the sensor nodes measurements [1]. thus, the novel network architecture performs not only ’read’ operations, but also ’write’ operations, which brings about unique and new challenges that need to be addressed [2]. in order to satisfy the requirements introduced by the coexistence of sensor and actuator nodes, multiple coordination levels among nodes are required to implement, which can be defined as: sensor-sensor(ss), sensor-actuator(s-a) and actuator-actuator(a-a). the s-s coordination is similar to the scheme already used in wireless sensor networks applications. thus, in this paper we mainly focus on the s-a and a-a coordination. due to the unreliable wireless communications, system noise and time-delay are the common phenomenons which will influence the overall system performance. to this end, it is quite necessary for the nodes to perform the estimation and compensation algorithms of the required information [3]. a queuing strategy is introduced both in controllers and actuator nodes in [4], and the time delay between controllers and actuator nodes is compensated by multi-step control increment given by the algorithm of general predictive control. the work given in [5] copyright c⃝ 2006-2012 by ccc publications 710 l. mo, b. xu presents a real-time architecture for automated wsans, the delay bound of s-a communication is maintained by the distributed mechanisms for s-a event reporting and self-aware coordination. in [6] a general reliability-centric framework for event reporting in wsans is proposed, which contains an fault-tolerant event data aggregation algorithm and a delay-aware data transmission protocol. in [7], the authors study the model-based predictive networked control systems that compensate random delays and data loss of the communication, and use a predictive control scheme to avoid performance loss. our work is motivated by the above studies. the key difference is that we focus on the s-a delay, not the uniform network nodes delay. moreover, we apply a predictor-controller algorithm based on the state estimation to mitigate the detrimental effects of the communication delay. in this context, the model of the wsans system needs to be analyzed in detail. finding a proper control strategy is still the core in designing the a-a coordination [8], this process involves which actuator node should be scheduled to execute a specific task and how to adjust its actuation to meet the desired system requirements. according to the way data is routed among different actuator nodes, control strategies can be categorized into the distributed control (dc) and centralized control (cc) scheme [9]. in the dc scheme, the control decision of a signal actuator node relies on the local information received from its neighbor nodes rather than global information [10]. then it can achieve a superior performance in modularity, integrated diagnostics, quick and easy maintenance and low cost. in [11] a framework of optimizing a collaborative sensing and actuation system is built for environment control, the sensor is set in the actuator node and the control objection is to balance the energy saving against the spatial smoothness of the control signals. in [12], the authors propose two control schemes in wsans for building-environment control systems, a cc scheme in which control decisions are made based on global information, and a dc scheme that enables distributed actuator nodes to make decisions locally. in [8] a new distributed estimation and collaborative control scheme is proposed for industrial control systems with wsans, which can achieve robust control against inaccurate system parameters. in this paper, we focus on the problem of utilize distributed sensor measurements to design control strategies in order to elicit a desired response from the monitored environment. our methodology incorporates a dynamic clustering schedule into the collaborative estimation and control framework, which can minimize the control error and improve the control quality. the remainder of this paper is organized as follows: section 2 models the wsans system. while section 3 provides a delay compensation algorithm. then a distributed collaborative proceeding method is designed in section 4. at last, the results of simulations conducted to explore the performance of proposed algorithms are demonstrated in section 5. 2 system models we consider the wsans system that are employed to the industrial instrumentation and control applications. the control objective is to adjust the system variables to meet our requirements. a set of static sensor and actuator nodes that are spread throughout the region of interest (roi) to detect and track events and take necessary actions. let x denote the system variable of our concern, such as temperature, brightness, humidity, sound, pressure, vibrations, etc. in different parts of the field. let sa represent the set of actuator nodes, with na = |sa|. let ss represent the set of sensor nodes, with ns = |ss|. we make the following assumption of our network: (1) sensor node is the time-driven device, input reception or output transmission is controlled by a sample time, while the actuator node is the event-driven device depends on the control techniques used; (2) sensor and actuator nodes are aware of their geographical position; (3) the network is synchronized by means of one of the existing synchronization protocols [13]; distributed collaborative processing under communication delay over wireless sensor and actuator networks 711 (4) the randomly varying delays between s-a are bounded; (5) the system is observable and controllable. let si denote the ith sensor node, each node has the following model: zi(k) = hixi(k) + νi(k), i = 1, ...,ns (1) where hi and νi(k) are the observation item and measurement noise, respectively. assume that νi(k) is a zero-mean gaussian white noise with e{νi(k)} = 0, e{νi(k)νtj (l)} = ri(k)δklδij, where δkl = 1 if k = l, and δkl = 0, otherwise. then the matrix form of eq.(1) is: z(k) = hx(k) + ν(k) (2) where z(k) = [z1(k), ...,zns(k)] t , h = diag[h1, ...,hns], x(k) = [x1(k), ...,xns(k)] t and ν(k) = [ν1(k), ...,νns(k)] t . let aj denote the jth actuator node, fj denote its output which influences its ambient plant state, uj denote the control signal that is used to adjust aj’s actuation. the change of each actuator node’s actuation is assumed linearly proportional to the control signal received by this node, which is modeled as: fj(k) = guj(k),j = 1, ...,na (3) where g is the transfer function of aj. here, we consider a scenario with homogenous actuator nodes. then the matrix form of eq.(3) is: f(k) = gu(k) (4) where f(k) = [f1(k), ...,fna(k)] t , g = diag[g1, ...,gna] and u(k) = [u1(k), ...,una(k)] t . here, we used two sets to indicate the interaction between the sensor and actuator nodes: the associated sensor nodes of ai,∀i ∈{1, ...,na}: sai = {sj|dij ̸= 0,j = 1, ...,ms} (5) and the influenced actuator nodes of sj,∀j ∈{1, ...,ns}: ssj = {ai|dij ̸= 0, i = 1, ...,ma} (6) where the parameter dij represents the relation between the ith and the jth nodes: dij = { 1, influenced 0, isolated (7) eq.(5) and eq.(6) show that the sensor nodes in set sai will transmit the sensing data to ai, while the actuator nodes in set ssj will influence the plant state monitored by sj. in wsans, the sensor and actuator nodes are usually linked with wireless medium, since the actuator nodes are connected with each other directly and much more powerful than the ordinary sensor nodes, communication delay between s-a become a general problem of such network control system [14]. the s-a delays do not only degrade the system performance, but can also destabilize the system [15]. the delay system at sample step k has the following dynamics: za(k) = z(k −∆k) (8) the finite non-negative integers ∆k represent the s-a delays at the kth step, za(k) is the sensing data received by the actuator nodes with communication delays. 712 l. mo, b. xu in the course of the practice, the variation of plant state xi at time t is caused by the output transferred form the actuator nodes and its ambient environment [12], under that assumption we have: dxi dt = ∑ 1≤k≤ns,k ̸=i αki(xk −xi) + ∑ 1≤l≤na βli(fl −xi) (9) where αki and βli are coefficients relating to the state-transfer efficiency. so, the plant state equation can be written in the matrix form as follows: dx dt = φx(t) + ψf(t) (10) where φ ∈ rns×ns , ψ ∈ rna×na , we assume that f is constant within each step, i.e., f(t) = f(k), t ∈ [kt,(k + 1)t). since φ and ψ are coefficients or constants, then the dynamic system can be modeled as: x(k + 1) = ax(k) + bf(k) (11) where a = eφt and b = φ−1(eφt −1)ψ. 3 delay compensation algorithm the main idea of the delay compensation algorithm is to utilize an observer to estimate the plant states and a multi-step predictor to compute predictive control inputs based on the past sensor measurements. the block diagram of the delay compensation algorithm is shown in figure 1. k b a h a a a b b b ( 1)x k p k p ! ! ! ( 1 )x k p k p ! " ! ' ( )u k p ' ( 1)u k p ! ( 2 )x k p k p ! " ! ' ( 2)u k p ! ( 3 )x k p k p ! " ! ( )x k k p ! ' ( 1)u k ++ + + + + + + + observer predictor l l l ( )z k p figure 1: block diagram of the delay compensation algorithm. in order to keep the track of past measurements, received sensing data have to be stored in a p length fifo (first-in-first-out), denoted as q, and p is the upper bound of ∆k. thus, the s-a delay is transformed to a constant delay, which is much easier to control than the random delay systems [15]. the delay compensation algorithm is delineated below: observer model: x̂(k−p+1 | k−p) = ax̂(k−p | k−p−1)+bu ′(k−p)+k(k−p)(z(k−p)−hx̂(k−p | k−p−1)) (12) predictor model: x̂(k | k −p) = ax̂(k −1 | k −p) + bu ′(k −1) (13) distributed collaborative processing under communication delay over wireless sensor and actuator networks 713 control law: u ′(k) = l(k)x̂(k | k −p) (14) in [16], the authors have proved that the resulting closed-loop equations can be expressed as:[ x(k + 1) ê(k −p + 1) ] = [ a + bl(k) ∗ 0 a−k(k −p)h ] [ x(k) ê(k −p) ] (15) where: ê(k) = x(k)− x̂(k | k −1) (16) if k and l are constant, then eq.(15) determines the stability of the delay compensator due to the separation of the controller and observer. since the performance of the observer and predictor are highly dependent on the model certainty, then the dynamic model of the plant has to be very precise. 4 distributed collaborative processing 4.1 dynamic clustering schedule in order to maximize the network lifetime and data throughput, and provide load balancing and fault tolerance [17], the clustering schedule should be established. in this paper, based on the characteristics of the current events, an event-triggered dynamic clustering schedule is designed for wsans. if there is no event occurs, the nodes follow a static sleep schedule. when an event occurs, the sensor nodes whose sensing range cover it will be activated, and transmit the sensing data to each coordinator, then the coordinator organizes its neighbor nodes into a working cluster to take a proper action till the error signal becomes zero. during this process, the coordinator will act as the cluster head and the neighbor nodes will be selected as the cluster members. then the control decision is made by the cluster head according to the fusion data aggregate from the associated sensor node and cluster members. for sensor node’s coordinator is the nearest actuator node, since the closer the actuator node to the sensor node is, the earlier the actuator node is informed, thus the quicker the actuator node reacts and the earlier action to be initiated. here, neighbor nodes can be defined as the actuator nodes which are within the communication range of coordinator and the associated sensor nodes are activated. so, the energy constrained sensor node does not need to transmit its readings to multiple actuator nodes. instead, the coordinator will receive this message and relay it to its neighbor nodes to come up with an appropriate actuation. the process of dynamic clustering schedule is shown in figure 2. here, we assume that the data route from source sensor node to terminal actuator node is in one hop, |ssj| = 1,j = 1, ..,ns and |sai| = 1, i = 1, ..,na. 4.2 collaborative processing algorithm consider the control objective which is to meet the set points x∗ = [x∗1, ...,x ∗ ns ]t . in order to balance the control requirements against the spatial smoothness of the control signals, we define the control objective of ai as: ji(k + 1) = α 2 ∑ j∈nai,j ̸=i (ei(k + 1)−ej(k + 1))2 + 1−α 2 e2i (k + 1) (17) where nai is the neighbor nodes set of ai, ei(k + 1) = xi(k + 1)−x∗i = aiix̂i(k) + biigiui(k)−x ∗ i and ej(k + 1) = xj(k + 1)−x∗j = ajjx̂j(k) + bjjgjuj(k)−x ∗ j,(j ∈ nai,j ̸= i). 714 l. mo, b. xu actuator1 actuator2 actuator3 sensor area1 sensor area2 sensor area3 sensor2 sensor1 sensor3 event area dormant sensor nodes active sensor nodes dormant actuator nodes active actuator nodes figure 2: dynamic clustering schedule. in order to minimize ji(k + 1), the gradient descending method can be used. the partial derivative of ji(k + 1) with respect to ui(k) is calculated as: ∂ji(k + 1) ∂ui(k) = [α ∑ j∈nai,j ̸=i (ei(k)−ej(k)) + (1−α)ei(k)]biigi (18) for each ai, i ∈ (1, ...,na), its control law ui(k) is updated by: ui(k + 1) = ui(k) + ∆ui = ui(k)−ε ∂ji(k + 1) ∂ui(k) (19) where ε is a positive step size called the learning step length. if ε is small, the convergence speed of the objective function ji will be slow. if ε is too large, it often leads to unstable. so it is important how to choose the proper ε. in order to investigate the stability of eq.(19), we rewrite ∆ui as: ∆ui = −ε ∂ji(k + 1) ∂ui(k) = −ε ∂ji(k + 1) ∂ei(k + 1) ∂ei(k + 1) ∂ui(k) = −εbiigi ∂ji(k + 1) ∂ei(k + 1) = −λi ∂ji(k + 1) ∂ei(k + 1) (20) we define the learning error as: ∆ei = −λi ∂ji(k + 1) ∂ei(k + 1) (21) where ∂ji(k + 1) ∂ei(k + 1) = α ∑ j∈nai,j ̸=i (ei(k + 1)−ej(k + 1)) + (1−α)ei(k + 1) (22) let the array [∂j1(k + 1)/∂e1(k + 1), ...,∂jna(k + 1)/∂ena(k + 1)] t to be zero, then it can be represented as: de(k + 1) = 0 (23) here, d is a na × na positive definite matrix, e(k + 1) = [e1(k + 1), ...,ena(k + 1)]t and the elements of d satisfy the following equation: |dii|− na∑ j=1,j ̸=i |dij| = 1−α (24) distributed collaborative processing under communication delay over wireless sensor and actuator networks 715 we define the residual error as: r(k) = e(k) − e∗, where e∗ is the solution of de∗ = 0. from eq.(21), we have: r(k) = r(k −1)−λ(de(k −1)) = r(k −1)−λ(de(k −1)−de∗) = (i −λd)r(k −1) = ∏k i=1 (i −λd)r(0) = y ∏k i=1 (i −λλ)y t r(0) = y ∏k i=1 (i −ελ′)y t r(0) (25) where d = y λy t , λ = diag(η1, ...,ηna), λ = diag(λ1, ...,λna), and η1, ...,ηna are the eigenvalues of d, so we can get: λ′ = bgλ = diag(b11g1η1, ...,bnanagnaηna) = diag(σ1, ...,σna) (26) if we select 0 < ε < 2/max(σi),1 ≤ i ≤ na, then r(k) −→ 0 as k −→∞. eq.(19) is a completely distributed collaborative processing method, there has no need a sink to help in the coordination of the sensor and actuator nodes. instead, each actuator node combines itself and neighbor nodes’ messages to access the control law and pursuit the optimal solutions step by step. 5 numerical examples let’s consider a simple humility, ventilation, air conditioning (hvac) control system for temperature control with two sensor nodes (ns = 2) and two actuator nodes (na = 2). the control arm is to meet the set points x∗ = [16(◦c),18(◦c)]. the system parameters are: a = [ 0.9 0 0 0.9 ] ,b = [ 0.57 0 0 0.68 ] ,h = [ 1 0 0 1 ] ,k = [ −0.68 0 0 −0.57 ] ,l = [ 0.5 0 0 0.5 ] where k and l satisfy the stability condition according to eq.(15). the effectively actuation of actuator nodes are highly depend on the precision of the sensing data. the longer time delay between sensing and acting is, the bigger estimation error introduced. the increasing control decision error does not only degrade the system performance, but also can destabilize the system, just as shown in figure 3(a) and 3(c). figure 3(b) and 3(d) clearly show that the compensated system are less oscillatory than those of the uncompensated system. the predictor-controller compensation algorithm provides a valid way to estimate the sensing data with latency, reduce the estimation bias and enhance the precision of feedback control. 0 10 20 30 40 50 −10 0 10 20 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 50 100 150 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (a) ε = 1.63, α = 0.5, ∆k = 1, p = 0 0 10 20 30 40 50 2 3 4 5 6 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 1 2 3 4 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (b) ε = 1.63, α = 0.5, ∆k = 1, p = 1 716 l. mo, b. xu 0 10 20 30 40 50 −500 0 500 1000 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 1 2 3 x 10 5 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (c) ε = 1.63, α = 0.5, ∆k = 2, p = 0 0 10 20 30 40 50 0 5 10 15 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 10 20 30 40 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (d) ε = 1.63, α = 0.5, ∆k = 2, p = 2 figure 3: dynamic responses of uncompensated (∆k ̸= 0,p = 0) and compensated system (p = ∆k ̸= 0). in eq.(17), α is a collaborative factor between 0 and 1. when α = 0, the neighbor nodes’ messages are not taken into consideration, but if we select α ̸= 0, the collaborative processing among different nodes are introduced. moreover, α can also performs as a smooth factor, it will reduce the control overshooting and stabilize the system from oscillating. the performance of compensated system with and without collaborative processing method are shown in figure 4. it is obviously seen that the proposed method can greatly improve the system performance, which can smooth the actuator control signal and accelerate the system convergence speed. 0 10 20 30 40 50 0 2 4 6 8 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 2 4 6 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (a) ε = 2.17, α = 0, ∆k = 0, p = 0 0 10 20 30 40 50 2 3 4 5 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 0.5 1 1.5 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (b) ε = 2.17, α = 0.5, ∆k = 0, p = 0 distributed collaborative processing under communication delay over wireless sensor and actuator networks 717 0 10 20 30 40 50 −5 0 5 10 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 5 10 15 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (c) ε = 1.74, α = 0, ∆k = 1, p = 1 0 10 20 30 40 50 2 3 4 5 6 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 2 4 6 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (d) ε = 1.74, α = 0.5, ∆k = 1, p = 1 0 10 20 30 40 50 −10 0 10 20 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 50 100 150 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (e) ε = 1.96, α = 0, ∆k = 2, p = 2 0 10 20 30 40 50 0 5 10 15 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 10 20 30 40 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (f) ε = 1.96, α = 0.5, ∆k = 2, p = 2 figure 4: dynamic responses of real-time (∆k = 0) and compensated system (p = ∆k ̸= 0) under different α. 0 10 20 30 40 50 2 2.5 3 3.5 4 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 0.2 0.4 0.6 0.8 1 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (a) ε = 0.54, α = 0.5, ∆k = 0, p = 0 0 10 20 30 40 50 2 3 4 5 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 0.2 0.4 0.6 0.8 1 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (b) ε = 1.63, α = 0.5, ∆k = 0, p = 0 718 l. mo, b. xu 0 10 20 30 40 50 0 2 4 6 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 1 2 3 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (c) ε = 2.24, α = 0.5, ∆k = 0, p = 0 0 10 20 30 40 50 −5 0 5 10 k c o n tr o l s ig n a ls u1 u2 0 10 20 30 40 50 0 5 10 15 20 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 (d) ε = 2.72, α = 0.5, ∆k = 0, p = 0 figure 5: dynamic responses of real-time system (∆k = 0) under different ε. 0 10 20 30 40 50 0 2 4 6 8 10 12 14 16 18 20 k o b je ct iv e f u n ct io n s ε=0.54 ε=1.63 ε=2.11 ε=2.29 (a) α = 0.5, ∆k = 1, p = 1 0 10 20 30 40 50 0 5 10 15 20 25 30 35 40 k o b je ct iv e f u n ct io n s ε=0.54 ε=1.63 ε=2.34 ε=2.50 (b) α = 0.5, ∆k = 2, p = 2 figure 6: dynamic responses of compensated system (p = ∆k ̸= 0) under different ε. 0 20 40 60 80 100 0 10 20 30 40 k c o n tr o l s ig n a ls u1 u2 0 20 40 60 80 100 0 20 40 60 80 100 k o b je ct iv e f u n ct io n s j1 j2 j=j1+j2 ε = 1.63, α = 0.5, ∆k = 10, p = 10 figure 7: dynamic responses of system with multi-step delay compensation. distributed collaborative processing under communication delay over wireless sensor and actuator networks 719 the learning step length ε plays an important role in the gradient descending method. figure 5 compares the responses of real-time system under different ε (ε = 0.54,1.63,2.24,2.72). we could observe that the bigger ε is, the faster convergence speed of j can be achieved. but if ε exceeds the critical value εc = 2.24, i.e., ε = 2.72, the system become unstable. figure 6 shows the dynamic responses of compensated system with collaborative processing method under different ε. ∆k between s-a are set at one-step and two-step, respectively. we can get that εc = 2.11 and εc = 2.34 are the critical step lengths, and the stable control can be achieved within those values. comparing figure 6(a) and 6(b), we could see that, the bound of j is influenced by the delay step ∆k. the variance of j increased as the longer s-a latency. figure 7 shows that when the system suffers a multi-step delay, such as ∆k = 10, the proposed compensation and control scheme is also useful. j will tend to be zero eventually and control signals both converge to their stable states. 6 conclusions in this paper, we focus on the communication and control problems in wsans. we argue that the system performance is closely related to the communication delay and control strategy. in order to mitigate the detrimental effects of the s-a latency, a delay compensation algorithm based on the state estimation is applied to this system. then, a distributed collaborative processing method is proposed to control actuator option in a coordinate way to accomplish the desired tasks. we formulate it as an optimization problem and utilize gradient descending algorithm to calculate the optimal control law for actuator nodes. on this basis, we discuss the control strategy parameters that relate to the system performance and provide a guide line how to choose properly. in our framework, the proposed collaborative processing method does no need a central sever and make an optimum usage of the available resources, which can be easily applied in the industrial automation systems. acknowledgments this work was supported by the national natural science foundation of china under grant 61174070, the specialized research fund for the doctoral program under grant 20110172110033. bibliography [1] i. f. akyildiz, i. h. kasimoglu, wireless sensor and actor networks: research challenges. ad hoc networks, vol.2, no.4, pp.351-367, 2004. 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[17] b. mclaughlan, k. akkaya, coverage-based clustering of wireless sensor and actor networks. ieee international conference on pervasive services, istanbul, turkey, 45-54, 2007. int j comput commun, issn 1841-9836 8(5):744-753, october, 2013. performance analysis of epidemic routing in dtn with overlapping communities and selfish nodes y. wu, s. deng, h. huang, y. deng yahui wu, su deng, hongbin huang science and technology on information systems engineering laboratory national university of defense technology changsha, 410073, china wuyahui@nudt.edu.cn, yahui_wu@163.com, dzyxxxb@163.com yiqi deng university college london department of computer science zndxxb@sina.com abstract: routing algorithms in delay tolerant networks (dtn) adopt the storecarry-forward way, and this needs the nodes to work in a cooperative way. however, nodes may not be willing to help others in many applications and this behavior can be seen as individual selfish. on the other hand, nodes often can be divided into different communities, and nodes in the same community often have some social ties. due to these social ties, nodes are more willing to help the one in the same community. this behavior can be seen as social selfish. note that some nodes may belong to more than one community in the real world, and this phenomenon makes the network have overlapping communities. this paper proposed a theoretical model to describe the performance of epidemic routing (er) in such network. simulation results show the accuracy of our model. numerical results show that the selfish nature can make the performance of the routing policy be worse, but those nodes belonging to multi-communities can decrease the impact of the selfish nature in certain degree. keywords: delay tolerant networks (dtn), selfish nodes, overlapping communities, epidemic routing, performance analysis. 1 introduction at present, there has been a growing interest to study the communication policy for challenged networking applications, such as deep-space exploration [1], vehicular networks [2], mobile social networks [3], etc. in these new environments, the end-to-end connectivity cannot be assumed because the network is quite sparse or nodes are moving fast. that is, a complete path from source to destination does not exist or such a path is highly unstable and may change or break soon after it has been discovered. these networks belong to the general category of delay tolerant networks (dtn) [4]. in traditional mobile ad hoc networks (manet), nodes communicate with each other based on the assumption that there exists at least one fully connected path between communication nodes. therefore, routing policies in manet cannot be used directly in dtn. in order to overcome the network partitions, nodes of dtn communicate through a store-carry-forward mode. due to the node mobility, different links come up and down. if the sequence of connectivity graphs over a time interval is overlapped, then an end-to-end path might exist, so the message should be forwarded over the existing link, stored and carried at the next hop until the next link comes up [5]. many routing policies have been proposed in dtn. according to the number of replicas, these policies can be divided into two classes: that is, single-copy and multi-copy. in the first class, nodes keep only one copy of the message and attempt to forward that copy towards the node which has higher probability to meet the destination, such as the works in [6], [7], etc. therefore, copyright c⃝ 2006-2013 by ccc publications performance analysis of epidemic routing in dtn with overlapping communities and selfish nodes 745 how to select the proper relay nodes to carry the copy is critical. in the multi-copy methods, one message may have many replicas and they are transited at the same time to increase the successful ratio [8][9]. the core is to select proper relay nodes to store or forward these copies. therefore, routing policies in both classes depend on the help of other nodes. however, nodes may not be willing to help others due to the constraint of buffer space or power resources [10]. this behavior can be seen as individual selfish [11]. hui et al. studied the its impact in mobile social network, and they found that mobile social network is robust to the individual selfish due to the multiple paths [11]. then, it was studied further in [12]. there are also some incentive methods to make nodes be cooperative[13]-[14]. on the other hand, nodes can be divided into different communities according to their interesting, citation relation, etc. obviously, nodes in the same community often have some social ties, and they are more willing to help each other. this behavior can be seen as social selfish. li et al. proposed this behavior for the first time [15]. its impact on the routing performance of er was explored in [16], and then they studied the impact of both individual selfish and social selfish on multicasting application [17]. however, above works failed to consider the fact that some nodes may belong to more than one community which is common in the real world [18]. in this paper, we studied the routing performance of er in dtn with overlapping communities and selfish nodes by the markov process for the first time. at present, many researchers are interesting in er algorithm [19]. for example, the performance of er based on the sparsely exponential graph was studied in [20], and the problem was explored again with heterogeneous nodes [21]. the performance of two-hop relay routing (a special case of er) under limited packet lifetime was studied in [22]. the authors in work [23] studied the routing performance with contention. in addition, some works begin to study how to decrease the energy consumption of er. authors in [24] proposed the optimal probabilistic forwarding policy under a fluid model approximation, and they proved that the optimal policy is the threshold form. then, they addressed the problem of online estimation of optimal policies in [25], and explored the problem with heterogeneous nodes in [26]. the optimal forwarding problem with multiple destinations was proposed in [27]. li et al. designed an optimal relaying scheme for dtn, which considers nodes’ heterogeneous contact rates and delivery costs when selecting relays to minimize the delivery cost while satisfying the required message delivery probability [28]. the optimal control problem with dead nodes was proposed in [29]. however, to our best knowledge, none of the works considered the problem as ours. 2 network model the set of nodes in the network is denoted by v. besides the source s and destination d, every node belongs to at least one of the two communities, which are denoted by c 1 and c 2, respectively. it is easy to see that the source and destination may belong to any class. for simplicity, we assume that d belongs to c 1 and s belongs to c 2. in fact, our work can be extended to other cases easily. nodes other than the destination can be seen as relay nodes. the number of relay nodes in the first class is m and the second class has n nodes. due to the overlap of the communities, there are o<= min {m, n -1} nodes belonging to both classes. therefore, there are totally v =m +n +1-o nodes. the link exists between two nodes only when they come into the transmission range of each other, which means a contact, so the mobility of the users is critical. in this paper, we assume that the occurrence of contacts between two nodes follows a poisson distribution, which is found in many well-known mobility models, such as random waypoint and random direction [30]. this assumption also has been checked by certain real motion traces [31]. therefore, we can assume that the inter-meeting time between two contacts follows an exponential distribution 746 y. wu, s. deng, h. huang, y. deng with parameter λ . nodes may not be willing to help others due to the individual selfish nature. in this paper, we assume that nodes in c 1 help the one in the same class with probability p1, and nodes in c 2 help the one in the same class with probability p2. on the other hand, nodes is social selfish, so we assume that nodes in c 1 help nodes belonging to c 2 with probability p12, and nodes in c 2 help nodes belonging to c 1 with probability p21. in fact, many papers used this mode to denote the selfish nature of nodes [15], [16] [17], etc. because nodes are more willing to help the one in the same community, we have p1 > p12 and p2 > p21. in addition, for any two nodes i and j which belong to c 1 and c 2 at the same time, we assume that they communicate with each other with probability p >= max {p1, p2}. this assumption is based on the observation that nodes having more common hobbies often have much closer relationship. therefore, they are more willing to help others. for simplicity, we assume p=max{p1, p2} in this paper. that is, if p1 > p2, nodes i and j communicate with probability p1, or with probability p2. 3 data dissemination process and performance analysis now, we begin to explore the data dissemination process based on the er algorithm. first, we give a new classification of the nodes. in particular, nodes only belonging to c 1 are denoted by c 11, and nodes just belonging to c 2 are denoted by c 22. nodes belonging to both c 1 and c 2 are denoted by c 12. therefore, class c 11 has m-o relay nodes, class c 22 has n-o relay nodes, and class c 12 has o relay nodes. a snapshot of the network can be seen in figure 1. figure 1: a snapshot of the network the dashed lines in figure 1 mean that the link between the nodes is opportunistic. the caption on the line denotes the forwarding probability. specially, it denotes the forwarding probability from the starting point to the end point. on the other hand, we have p1m=max{p1, p21} and p2m=max{p2, p12}. that is, for any node i in c 11 and j in c 12, because j takes i as a friend, it forwards toward i with probability p1. however, node j also belongs to c 2, if nodes in c 2 are altruism, node j may forward toward i with probability p21 which is bigger then p1. therefore, j should forward to i with probability p1m. we can get the meaning of p2m according to above analysis easily. 3.1 date dissemination model let x (t) denote the number of relay nodes in class c 11 which is carrying data at time t (not including d), y (t) denote the number of nodes carrying data in c 22 (including s ), and z (t) denote the corresponding number in c 12. therefore, the state of the network at time t can be denoted as (x (t), y (t), z (t)), and there are totally (m-o+1)( n-o+1)(o+1) transient states. when the destination gets data, the transmission stops, and this state can be seen as the absorption state which is denoted by dst. from state (x (t), y (t),z (t)), the network may change to one of the following four states through one-step transition, that is, s 1=(x (t)+1, y (t),z (t)), s 2=(x (t), y (t)+1,z (t)), s 3=(x (t), performance analysis of epidemic routing in dtn with overlapping communities and selfish nodes 747 y (t),z (t)+1) and dst. state s 1 means that one node in c 11 received data. obviously, one premise condition of this transition is x (t)< m-o, which means that at least one relay node in c 11 does not receive data at time t. the node in c 11 which just received data can get the data from nodes in any class. for simplicity, if the node received data from node j, we say that the transition is triggered by j. obviously, node j may be any node in the network which is carrying data. if j is in class c 11, one node in c 11 without data must encounter with node j, and node j is also willing to forward data to it. because there are x (t) nodes in the class c 11 carrying data at time t, so there are m-ox (t) nodes without data in c 11. obviously, node j may be any node in the x (t) nodes, and the new node which just received data may be any one in the m-ox (t) nodes. in addition, nodes encounter with each other according to the exponential distribution with parameter λ , combining the selfish behavior, we know that the transition rate is λ x (t)(m-ox (t))p1. if the transition is triggered by nodes in c 22, nodes without data in c 11 must encounter with one node which has received data before in c 22. because there are y (t) nodes in the class c 22 which is carrying data at time t, and nodes in c 22 forward to nodes in c 11 with probability p21, we can know that the transition rate is λ y (t)(m-ox (t))p21. by the same method, we know that if the data comes from c 12, the transition rate is λ z (t)(m-ox (t))p1m. now, we can get the total transition rate from state (x (t), y (t),z (t)) to s 1 through one-step transition which is shown as follows, (x(t),y (t),z(t)) → s1,rateλ(m − o − x(t))(x(t)p1 + y (t)p21 + z(t)p1m) (1) similarly, we can get the transition rate from state (x (t), y (t),z (t)) to s 2 and s 3 through one-step transition, which is shown as follows, (x(t),y (t),z(t)) → s2, rateλ(n − o − y (t))(x(t)p12 + y (t)p2 + z(t)p2m), (x(t),y (t),z(t)) → s3, rateλ(o − z(t))(x(t)p1 + y (t)p2 + z(t)p) (2) if the network comes into dst from state (x (t), y (t),z (t)), the destination d must receive data. according to above analysis and the forwarding probability in figure 1, we can get, (x(t),y (t),z(t)) → dst, rateλ(x(t)p1 + y (t)p21 + z(t)p1m) (3) let q denote the generate matrix which is defined as follows, q = ( t r 0 0 ) (4) the elements in the matrix are different. t is a sub-matrix and it denotes the rate of the transition from one transient state to another. so the number of the rows and columns of the matrix is both (m-o+1)(n-o+1)(o+1). r is a column vector with (m-o+1)(n-o+1)(o+1) elements and it denotes the rate of the transition from one transient state to the absorbing state dst. the left 0 is a row vector with (m-o+1)(n-o+1)(o+1) elements and it denotes the rate of the transition from dst to any transient state. the right 0 is a vector with only one element and it denotes the rate of the transition from dst to dst. according to equations (1), (2) and (3), we can get every element of q. for example, from state (x, y, z ), we have,  t(x + 1,y,z|x,y,z) = λ(m − o − x)(xp1 + yp21 + zp1m), t(x,y + 1,z|x,y,z) = λ(n − o − y)(xp12 + yp2 + zp2m), t(x,y,z + 1|x,y,z) = λ(o − z)(xp1 + yp2 + zp), r(dst|x,y,z) = λ(xp1 + yp21 + zp1m), r(others|x,y,z) = 0 (5) symbol others may be any state other than (x +1, y, z ), (x, y+1, z ), (x, y, z +1) and dst. 748 y. wu, s. deng, h. huang, y. deng 3.2 performance analysis first, we define the one-step transition probability matrix p which can be got from the generator matrix q easily. for example, the transition probability from state i to j is p(j|i), which is an element of p. each row of q represents the transition rate from one state to others. therefore, the sum of all elements in one row denotes the rate of leaving the current state. for example, given state ss, the rate of leaving this state denoted by speed(ss ) can be shown as, speed(ss) = ∑ i∈sspace q(i|ss) (6) symbol sspace represents the set of all valid states and q(i|ss ) is one element in q which represents the transition rate from ss to i. now, we can get the probability of the transition. p(i|ss) = q(i|ss)/speed(ss), i ∈ sspace (7) let dt (k ) denote the average delivery delay till d received data, starting from state k = (x, y, z ). obviously, we have dt (dst)=0. similarly, let st (k ) denote the residence time in state k and we also have st (dst)=0. for any transient state k, we have speed (k )>0 and st (k )=1/speed (k ).by conditioning on the one-hop transition out of the current state, we have dt(k) = ∑ j∈sspace−{k} p(j|k)dt(j) + st(k) = ∑ j∈sspace p(j|k)dt(j) + st(k) − p(k|k)dt(k) = ∑ j∈sspace p(j|k)dt(j) + st(k) (8) define dt as a column vector of the average delivery delay starting from any valid transient state, and st also a column vector of the residence time. then, we can obtain, dt = p ∗ dt + st ⇒ dt = (i − p)−1st (9) because only the source has data at the beginning, we know that the initial state is initialstate=(0, 1, 0). therefore, the average delivery delay is dt(initialstate). now, we begin to compute the average energy cost using similar method. here, we only consider the energy cost in the transition process. as descried in [24], [25] and [26], the energy cost is proportional to the number of transmissions which contain both the forwarding and receiving process. in this paper, we use the number of transmissions to denote the energy cost simply. let er(k ) denote the average energy cost till d received data, starting from state k = (x, y, z )), obviously er(dst)=0. according to above analysis, from state k, the network may come into any state of the following four states: k 1=(x +1, y, z ), k 2=(x, y+1, z ), k 3=(x, y, z+1) and dst. obviously, if the network changes into one of them, one node must forward data and the other one must receive data. that is, if the network changes state, there is one transmission. therefore, we can obtain, er(k) = p(k1|k)(1 + er(k1)) + p(k2|k)(1 + er(k2)) + p(k3|k)(1 + er(k3)) + p(dst|k)(1 + er(dst)) = ∑ j∈sspace p(j|k)(1 + er(j)) = ∑ j∈sspace p(j|k) + ∑ j∈sspace p(j|k)er(j) = 1 + ∑ j∈sspace p(j|k)er(j) (10) define er as the corresponding column vector. equation (10) can be changed to the following equation. er = p ∗ er + e ⇒ er = (i − p)−1e (11) symbol e is a column vector and every element in it equals to 1. therefore, the average energy cost starting from state initialstate=(0, 1, 0) is er(initialstate). performance analysis of epidemic routing in dtn with overlapping communities and selfish nodes 749 10 20 30 40 50 60 70 80 90 100 200 400 600 800 1000 1200 1400 1600 number of relay nodes a ve ra g e d e liv e ry d e la y (s ) theoretical, rwp simulation, rwp (a) rwp mobility mode 10 20 30 40 50 60 70 80 90 100 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 x 10 5 number of relay nodes a ve ra g e d e liv e ry d e la y (s ) theoretical, poisson contact model simulation, poisson contact model (b) poisson contact model figure 2: theoretical and simulation result comparison of the average delivery delay 4 simulation and numerical results 4.1 simulation results in this section, we will check the accuracy of our theoretical model, and we run several simulations using the opportunistic network environment (one) simulator [32]. the simulation is based on both synthetic mobility model and real-world-based scenarios. the synthetic model is the famous random waypoint (rwp) mobility model. in this model, the simulation terrain is 1000mąá1000m, and the speed varies from 0.5 to 1.25m/s. the transmission range is 2m. for the real-world-based scenario, we use the poisson contact model. specially, we have λ =3.71ąá10-6s−1. as shown in [17] and [33], this value is obtained from the vehicle model, which is based on real motion traces from about 2100 operational taxis for about one month in shanghai city collected by gps. authors of [34] proposed a least-fitting method to identify the exponential parameter and find that above value is well proper. for the theoretical model related parameters, we set p1=0.5, p12=0.2, p2=0.8 and p21=0.1. as described above, there are totalnum=m+n-o relay nodes in the network. without loss of generality, we set m=n and o=0.2totalnum. through let the number of relay nodes increase from 10 to 100, we get the results in figure 2. from the result we can see that the average deviation between the theoretical results and the simulation is very small. for example, the deviation is about 2.8% for the rwp mobility model and 4.6% for the poisson contact model. this demonstrates the accuracy of our theoretical model. then, we will use the theoretical results obtained by our model to evaluate the performance in different cases. 4.2 performance analysis with numerical results first, we will explore the impact of the overlap between communities. here, we increase the value of o continuously till reaching to totalnum. let the ratio o/totalnum increase from 0.1 to 1. other settings are the same as that in rwp model in the simulation. the numerical results are shown in figure 3 when the number of relay nodes equals to 20, 40 and 80, respectively. figure 3 shows that if there are more nodes belonging to both communities at the same time, the average delivery delay will be smaller. for example, when n =20, the average delivery delay is reduced by about 79.2% when the value of o/totalnum increases from 0 to 1. however, the value of o/totalnum has little influence on the average energy cost. then, we will explore the impact of the overlap when the selfish level is different. first, we explore the case with different social selfish level, so we can assume that nodes in the same community are altruism for each other, that is p1=p2=1. the total number of relay nodes 750 y. wu, s. deng, h. huang, y. deng 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 100 200 300 400 500 600 700 800 900 1000 value of o/totalnum a ve ra g e d e liv e ry d e la y (s ) n=20 n=40 n=80 (a) average delivery delay 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 10 15 20 25 30 35 40 45 50 55 value of o/totalnum a ve ra g e e n e rg y co st n=20 n=40 n=80 (b) average energy cost figure 3: impact of the overlap phenomena with different number of relay nodes 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 180 200 220 240 260 280 300 320 340 360 value of o/totalnum a ve ra g e d e liv e ry d e la y (s ) p 12 =p 21 =0.1 p 12 =p 21 =0.5 p 12 =p 21 =0.8 p 12 =p 21 =1 (a) average delivery delay 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 20 20.5 21 21.5 22 22.5 23 23.5 24 24.5 25 value of o/totalnum a ve ra g e e n e rg y co st p 12 =p 21 =0.1 p 12 =p 21 =0.5 p 12 =p 21 =0.8 p 12 =p 21 =1 (b) average energy cost figure 4: impact of the overlap phenomena with different level of social selfish totalnum is 40, and we have m=n. other settings are also the same as that in rwp model. we set p12= p21, and let their value equal to 0.1, 0.5, 0.8 and 1, respectively. through letting o/totalnum increase from 0.1 to 1, we can get figure 4. from figure 4 we can see that with fixed social selfish level, the average delivery delay is decreasing with the increasing of the nodes in c 12 when p12= p21<1. when p12= p21=1, every node is altruism, so the overlap between communities cannot have any impact. this result also shows that the smaller of p12 (p12= p21), the bigger of the decreasing ratio will be. in addition, the difference of the delivery delay with different social selfish level is decreasing with o/totalnum, and they have the same value when o/totalnum=1. figure 4(b) is a surprising result, and it shows that the average energy cost is not monotonous with o/totalnum. this demonstrates the complex correlation between the overlap and the social selfish behavior. when the cooperative level is small, for example when p12= p21=0.1, the average energy cost is decreasing with o/totalnum. this is because that with the increasing of o/totalnum, the average delivery delay decreasing rapidly (see figure 4(a)), data has less time to spread further. however, when nodes are more cooperative, the average energy cost is first increasing, and then begins to decrease with o/totalnum. in this case, though the average delivery delay still decreases with o/totalnum, the increasing degree of the data spreading speed is much bigger. therefore, the energy cost increases in some degree, but the impact of the increasing of the data spreading speed becomes smaller when o/totalnum is big enough. in fact, the fluctuation of the average energy cost is very small under different value of o/totalnum. therefore, if there are more nodes belonging to more community, the network can get better performance without much increasing of the energy cost. now, we want to explore the results with different individual selfish level when the social performance analysis of epidemic routing in dtn with overlapping communities and selfish nodes 751 (a) average delivery delay 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 22 22.5 23 23.5 24 24.5 25 value of o/totalnum a ve ra g e e n e rg y co st p 1 =p 2 =0.2 p 1 =p 2 =0.5 p 1 =p 2 =0.8 p 1 =p 2 =1 (b) average energy cost figure 5: impact of the overlap phenomena with different level of individual selfish selfish level is fixed. here, we assume that nodes in one community helps the one in other community with probability 0.1. other settings are the same as that in figure 4. we give the numerical results when p12= p21=0.2, 0.5, 0.8 and 1, respectively. let o/totalnum increase from 0.1 to 1, we can get figure 5. the result also shows that the average delivery delay decreases with o/totalnum. the average energy cost has similar changing rule as that in figure 4(b). this further demonstrates that the overlap is good for the network. 5 conclusions this paper explored the performance of er algorithm in dtn which has overlapping communities and selfish nodes, 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[33] s. j. u. traffic information grid team, grid computing center, shanghai taxi trace data. http://wirelesslab.sjtu.edu.cn/ [34] h. zhu, l. fu, g. xue, y. zhu, m. li and l. ni, recognizing exponential inter-contact time in vanets, in proc. ieee infocom,2010. international journal of computers communications & control issn 1841-9836, 12(2):183-200, april 2017. model of network topic detection based on web usage behaviour mode analysis and mining technology m. chen mo chen 1. business college of beijing union university a3, yanjingdongli, chaoyang district, beijing, 100025, p.r. china. mo.chen@buu.edu.cn 2. school of information, renmin university of china. no.59, zhongguancun street, haidian district, beijing, 100872, p.r. china. chenmoky@sohu.com abstract: this research has caught researchers’ wide attention for detecting network topic exactly with the arrival of big data era characterized by semi-structured or unstructured text. this paper proposes a model of network topic detection based on web usage behaviour mode analysis and mining technology taking web news as object of research. the author elaborates main function and method proposed in this model, which include the analysis module of web news instance clicking mode, the analysis module of web news instance retrieval mode, the analysis module of web news instance seed and the analysis module of similar web news instance supporting topics. based on these functions and methods, the author elaborates main algorithm proposed in this model, which include the mining algorithm of web news seed instances and the mining algorithm of similar web news instances supporting topics. these functional algorithms have been applied in processing module of model, and focus on how to detect network topic efficiently from a large number of web usage behaviour towards to web news instances, in order to explore a research method for network topic detection. the process of experimental analysis includes three steps, firstly, the author analyses the precision of topic detection under different method, secondly, the author completes the impact analysis of web news topic detection quality from the number of web news instances concerned and seed threshold, finally, the author completes the quality impact analysis of web news instances mined supporting topic from the number of web news instances concerned and probability threshold. the results of experimental analysis show the feasibility, validity and superiority of model design and play an important role in constructing topic-focused web news corpus so as to provide a real-time data source for topic evolution tracking. keywords: web usage behaviour, network topic detection, clicking mode analysis, retrieval mode analysis. 1 introduction with the arrival of big data era, the field of information technology and internet has developed a challenging stage so far. according to survey of tecktarget that is a global leading professional it network media [15] [2] [22], it has shown that the number of enterprises’ data has broken through pb level with development of network, social media, business and other fields. based on data existed and existing, people should think how to analyse complicated network data showing a tendency of explosive growth [4] [5], which have been concerned and are characterized by semi-structured or unstructured text, nevertheless, in whole process of cognizing network data, detecting topic exactly and effectively is the important and critical application direction. in a mass of network data, the number of web news released has reached eb level with events that continue to take place in social [6] [7], which shows the 4v features of big data, it is volume, variety, velocity and value [15] [9]. based on these features above, web news should reflect copyright © 2006-2017 by ccc publications 184 m. chen high currency and reliability, on the basis of which, the topic contained in web news should be condensed quickly and its path of evolution should also be tracked nearly in real time. however, how to detect network topic efficiently from a large number of web usage behaviour towards to web news instances, it has become an urgent problem solved to construct a topic-focused web news corpus so as to provide real-time data source for topic evolution tracking. this paper proposes a model of network topic detection mainly containing four processing modules based on web usage behaviour mode analysis and mining technology taking web news as object of research. the author elaborates function, method and technology on every processing module of the model in detail, which have been used or completed, and focuses on how to detect network topic efficiently from massive web usage behaviour towards to web news instances. this process of research does key contribution for exploring a method for network topic detection, this experimental analysis results show the feasibility, validity and superiority of model design and implement. 2 related works in recent several years, some scholars have conducted some research about network topic detection method using different theory and technology. for example, yang et all. survey research on the method of topic link detection based on improved information bottleneck theory [10], in this paper, a method of representing text is proposed, which can divide text into several sections of sub-topic features based on the regular pattern of semantic distribution and improve information bottleneck theory, then, the text represented by the attributes is utilized to do topic link detection, the experimental results have shown that this method has a fast convergent rate, and can improve the performance of topic link detection system. suhara, yoshihiko and others survey research on the method of information detection based on sentence-level topic [11], in this paper, the text sentence-level diversity features based on the probabilistic topic model is proposed, an information content classifier is also constructed combining features proposed, the experimental results show that this method outperforms the conventional methods. pang, jb and others survey research on the method of unsupervised web topic detection using a ranked clustering-like pattern across similarity cascades [12], in this paper, a method using a clusteringlike pattern across similarity cascades is investigated from the perspective of similarity diffusion, a topic-restricted similarity diffusion process is also proposed to identify real topic from a large number of candidates efficiently, the experimental results demonstrate that this approach outperforms the state-of-the-art methods on several public data sets, those works are related to author’s research direction of network topic detection and application. in recent several years, some scholars have also conducted certain research about method and technology of web usage behaviour analysis and mining. for example, dziczkowski, grzegorz and others survey research on the opinion mining approach for web user identification and clients’ behaviour analysis [13], in this paper, an approach based on statistical analysis of natural language is proposed, three different methods are used for classifying opinions from clients’ data, two new methods are introduced based on linguistic knowledge, in order to assign a mark dependent upon the client’s emotions and opinions described in comments, the effect of experiments demonstrates that the system developed can carry out an evaluation and rating of opinions. karakostas, bill and others survey research on the mapreduce architecture for web site user behaviour monitoring in real time [14], in this paper, a mapreduce style architecture is proposed, where the processing of event series from the web users is performed by a number of cascading mappers, reducers, local to the event origin, the experimental results show that this architecture is capable to carry out time series analysis in real time for very large web data sets based on the actual events instead of resorting to sampling or other extrapolation techniques. zhang, model of network topic detection based on web usage behaviour mode analysis and mining technology 185 yh and others survey research on the new replacement algorithm of web search based on user behaviour [15], this paper analyses the search ranking based on the user behaviour investigated mass distribution of information on the website, then proposes a replacement algorithm for web search, the simulation experimental results show that this approach under the search algorithm can reduce the execution time of retrieval effectively, and the optimal parameter selection for this blocking organization can be discussed continuously, those works are related to author’s research direction of web usage behaviour application for analysis and mining. based on the analysis of the related research on network topic detection method and web usage behaviour application technology, experts and scholars have studied on two directions, but the research of constructing a network topic detection model based on web usage behaviour mode analysis and mining technology taking web news as an object of analysis according to its attention and usage trait is missing. therefore, this paper proposes a model of network topic detection based on web usage behaviour mode analysis and mining technology mainly, in order to explore how to detect network topic accurately. 3 problem definition and notations with rapid development of information and network technology, there are many types of network information, such as short text of micro blog, short, moderate or long text of web news, long text of document and so on, while the biggest difference is structure of text content among them. this paper selects web news as the object of research in view of ensuring high adaptability that the model of network topic detection based on web usage behaviour mode analysis and mining technology should have, in order to achieve the ideal effect of topic detection in the aspects of analysis precision and so on, this research provides scientific method for constructing and validating model of network topic detection. 3.1 usage feature analysis users can search and browse web news from different dimensions, granularities and frequencies, which have been extracted and analyzed, these processes have been elaborated in previous journal article published by authors [16], [17], [18], [19]. in the process of searching and browsing web news, the user usage behaviour can be recorded, which not only explains web news features used by users, but also contains the concerning topics hidden in web news instances. therefore, based on the analysis of web news usage features, it is conducive to discover knowledge hidden in massive web news, detect topic that the users are concerned about, track a series of events occurring in topic, and comb out process of event evolution. from the perspective of global usage, every web news instance concerned by users can be seen as a node in the range of web news websites with authority, and the node set of some relevant instances supporting social events can be considered as a topic, each topic can also trigger a series of events, therefore, when users directly conceren a series of topics reflected by the social event, not only browse multi web news instances content that support the topic, but also browse a series of events triggered by the topic [21]. from the perspective of local usage, when users search for web news, in addition to input keywords that are related to the social events reported by web news, but also input semantic keywords that may appear in web news title or content, core event, core event occurring time, core event occurring location, subjective or objective object of the core event, and relation event information triggered by the core event [21]. therefore, in the process of topic detection towards to web news, if the usage features of web news can be considered, then it can be mined for social events reported by web news, topics 186 m. chen concerned by social events, web news instances supporting topics, which will provide a web news topic corpus with high quality for web news topic evolution analysis. 3.2 topic detection norm analysis based on the analysis towards to the usage features of web news, if logical relationship need to be mined from behaviour data among the social events reported by web news, the topics concerned in social events, web news instances supporting topics, then the data and norms adopted should be specified in the process of topic detection [22], [23], which include user behaviour records, web news clicking frequency based on s-u, web news clicking frequency corresponding to url, web news clicking rate corresponding to url and so on. in the application process of web news topic detection for social events, users can input the interesting keywords searched, when clicking submit request, web news page will show multitudinous title, releasing time, releasing source and content link of web news instances, and when users click on the contents link of the web news instance, the application platform will record user names, the search information submitted, the behaviour clicking on web news instances, the usage time and other data items, in which the search information submitted is expressed in english, but processed in this paper by the way of chinese. based on the web news usage behavior above, (s,u) of s − u information can express retrieval keywords and web news instances url contained in behavior synchronously, fq(s,u) can express web news instances clicking frequency based on s −u, which explains the number of (s,u) appearance in a certain time period, fqi(u) can express web news clicking frequency corresponding to url, which explains the number of the web news instance appearance in the i particle size of a certain time period, fq(u) can express web news instance clicking frequency in a certain time period as shown in formula 1, rti(u) can express web news clicking rate corresponding to url as shown in formula 2. fq(u) = fq1(u) + . . . + fqi(u) + . . . + fqn(u) (1) rti(u) = fqi(u) fq(u) (2) based on the web news usage behavior above, it can be converted into a graph g = (s,u,e), in which s can express the set of retrieval keywords submitted, u can express the set of web news instances url clicked, e can express the set of edges between s and u, the edge (s,u) can express behavior of clicking the web news instance after submitting search request for users, whose weight value is fq(s,u) corresponding to it. s(u) can express the set of s, which directly connect with u in g, u(s) can express the set of u, which directly connect with s in g, d(s) can express the degree of node in search request, which is the number of web news instance nodes that is connected to retrieval requirement, d(u) can express the degree of the web news instance node, which is the number of retrieval requirement nodes that is connected to the web news instance as shown in figure 1, in which the s set is expressed in english, but processed in this paper by the way of chinese. 3.3 problem notations in this section, the author provides notations used in model and algorithms based on practical value and application direction of web news topic detection. let newsset be a set of web news instances, which is a data source using web news for user and contains a large number of instances in web news websites with authority. let userbehavior be a set of records using web news model of network topic detection based on web usage behaviour mode analysis and mining technology 187 figure 1: the behavior transition diagram based on web news usage for users behavior for users, which is also a data source of web news topic detection, the specific notations are as follows. definition 1. given a set of newsset, it can denote using newsset = {ns1, . . . ,nsi, . . . ,nsk}, the range of i is between one and k.nsi.url stores address of the web news instance, nsi.title stores title of the web news instance, nsi.pubtime stores releasing time of the web news instance, nsi.pubsource stores releasing source of the web news instance, nsi.content stores content of the web news instance, nsi.keyword stores keywords of the web news instance, the extraction process of these information has been elaborated in previous articles published by authors [16], [17]. definition 2. given a set of userbehavior, it can denote using userbehavior = {ub1, . . . ,ubi, . . . ,ubn}, the range of i is between one and n.ubi.username stores user names using web news, ubi.searchword stores keyword retrieving web news, ubi.url stores url of the web news instances clicked by users, ubi.systemtime stores system time using web news for users. definition 3. based on the definition and notations above, the problem that the web news topic detection model needs to solve is to detect topic set contained in massive web news instances from massive web news usage behavior, and mine set of web news instances that can support relevant topic, this result can denote using topicurl = {tu1, . . . , tui, . . . , tuk}, the range of i is between one and k.tui =< topic,topicurl >, tui.topic can express topic description detected, tui.topicurl can express the set of web news instances url mined, which can support relevant topic detected. 188 m. chen 4 the design of network topic detection model in the era background of big data development, it has become an important research direction to detect network topic exactly in web text mining field through the process of defining detection targets, extracting valuable network information, analysing web user usage behaviour, mining potential topics and applying topics detected and so on. based on this process, the model of network topic detection based on web usage behaviour mode analysis and mining technology taking web news as object of research is divided into four modules, which include the analysis module of web news instance clicking mode, the analysis module of web news instance retrieval mode, the analysis module of web news instance seed and the analysis module of similar web news instance supporting topics as showed in figure 2. figure 2: the model of network topic detection 4.1 the analysis module of web news instance clicking mode the inputting content of this module is set of records using web news behaviour for users, the outputting content of this module is analysis results of web news instance clicking mode, the main function of this module is to analyse the outbreak and concern mode of web news instances according to records using web news behaviour for users, and infer clicking mode of web news instance. based on analysis of web news usage behaviour, in a certain period of time, the web news instance mays show a clear outbreak mode regarded as a kind of sensor for social event topic aggregation in records of user’s web news usage behaviour, which indicates whether the web news instance is able to describe relevant topic reflected in social events. therefore, in this module, firstly, administrator should use the outbreak mode of the web news instance to measure whether it is able to describe corresponding topic reflected in social events, whose value is expressed with br(u) that is a conjecture to sharpness of clicking rate changes, and its range is between 0 and 1 as shown in formula 3, this formula cites characteristic of entropy. br(u) = 1 − (−(rt1(u)lognrt1(u) + . . . + rti(u)lognrti(u) + . . . + rtn(u)lognrtn(u))) (3) model of network topic detection based on web usage behaviour mode analysis and mining technology 189 in the above formula, n represents the number of continuous granularity components clicked for web news instances, the granularity can be set at hour or day for unexpected events in society, while the granularity can be set at week or month for normal events in society, if the fluctuations of clicking rate is not strong in the granularity setting for the web news instance, then br(u) value is smaller, if the fluctuations of clicking rate is strong in the granularity setting for the web news instance, then br(u) value is bigger. although using formula 3 can explain outbreak mode of the web news instance, but its value cannot be absolutely certain that the key information of the web news instance can describe topic in social events, because the br(u) value may be one, while the web news instance is only once clicked by users. therefore, in this module, secondly, administrator should use the concern mode of the web news instance to measure whether it is able to describe corresponding topic reflected in social events again, whose value is expressed with cr(u), and its range is between 0 and 1 as shown in formula 4. cr(u) = log(fq(u)) −minui∈u (log(fq(ui))) maxui∈u (log(fq(ui))) −minui∈u (log(fq(ui))) (4) because the process clicked has a characteristic of power law distribution for web news instances, so in the above formula, the clicking frequency of the web news instance is transformed to logarithm, if the web news instance can be concerned by more users, then cr(u) value is larger, whereas, cr(u) value is smaller. based on the above measurement of outbreak and concern mode for web news instances, in this module, finally, administrator should use formula 5 to infer results of the web news instance clicking mode. clickmode(u) = br(u)cr(u) (5) 4.2 the analysis module of web news instance retrieval mode the inputting content of this module is set of records using web news behaviour for users, the outputting content of this module is analysis results of web news instance retrieval mode, the main function of this module is to analyse the degree distribution and similar mode of web news instances according to records using web news behaviour for users, and infer the retrieval mode of web news instance. based on analysis of behaviour diagram g using web news for users, it can be found that the degree of web news instances shows a characteristic of power law distribution, so in this module, firstly, administrator should use the degree distribution mode of web news instance to measure whether it is able to describe corresponding topic reflected in social events and express it using dr(u) as shown in formula 6. dr(u) = log(d(u)) −minui∈u (log(d(ui))) maxui∈u (log(d(ui))) −minui∈u (log(d(ui))) (6) although using formula 6 can explain degree distribution mode of the web news instance, but its value cannot be absolutely certain that the key information of the web news instance can describe topic in social event, because the reason of generating web news instance degree is that users search it using keywords. therefore, in this module, secondly, administrator should use the similar mode of web news instance to measure whether it is able to describe corresponding topic reflected in social events again, whose value is expressed with ss(u), in order to solve problem of existing sparse records in user clicking behaviour as shown in formula 7. 190 m. chen ss(u) = 2 n(n + 1) sum( sum(sik(u)sjk(u))k∈dataitem√ sum((sik(u)) 2)k∈dataitem √ sum((sjk(u)) 2)k∈dataitem )ni<=j (7) based on the above measurement of degree distribution and similar mode for web news instances, in this module, finally, administrator should use formula 8 to infer results of the web news instance retrieval mode. searchmode(u) = dr(u)ss(u) (8) 4.3 the analysis module of web news instance seed the inputting content of this module is analysis results of web news instance clicking and retrieval mode, the outputting content of this module is sets of topic contained in massive web news instances and web news seed instances, the main function of this module is to infer set of web news seed instances according to analysis results of web news instance clicking and retrieval mode, and describe corresponding topic referring to web news key information researched in previous job. in this process, firstly, administrator should use formula 9 to mine set of web news seed instances, its weight value is more than or equal to seed threshold, secondly, based on releasing time of web news, the web news seed instances should be sorted in set, finally, the corresponding topic should be described using key information of the web news seed instance, in following experiment, the optimal value of seed threshold will be analysed. seedurl(u) = clickmode(u)searchmode(u) (9) 4.4 the analysis module of similar web news instance supporting topics the inputting content of this module is set of web news seed instances and records using web news behaviour for users, the outputting content of this module is set of similar web news instances supporting topics, the main function of this module continues to mine set of similar web news instances with topic described according to sets of web news seed instances, the corresponding topics described, the records using web news behaviour for users that have been analysed above. in this process, administrator should take web news seed instances as core, and use the probability of first transfer from the web news instance to itself as possibility that whether it is able to support topic described. if the web news seed instance is set su, then the variable tu indicates that whether the web news instance is able to support topic described by su, the variable ts indicates that whether the retrieval keyword is able to support topic described by su. if the key information of the web news instance can support topic described by su, then tu = 1, otherwise tu = 0, if the retrieval keyword can support topic described by su, then ts = 1, otherwise ts = 0. for each web news seed instance mined, in initial state, tsu is set one, then p(tsu = 1) = 1, and the probability is set zero for any other web news instances supporting topic described by su, in this way, p(ts = 1) can be used to calculate the probability arriving su to itself, which is directly linked to the search keyword with it. p(ts = 1) = sum(ψsup(tu = 1))u:(s,u)∈e (10) ψsu = fq(s,u) sum(fq(s,ui))(s,ui)∈e (11) model of network topic detection based on web usage behaviour mode analysis and mining technology 191 in formula 11, ψsu expresses transfer probability form the search keyword to the web news instance, based on this probability, the value of p(tu = 1) can be calculated by using the following formula for all other web news instances that are directly connected to it. p(tu = 1) = sum(ψusp(ts = 1))s:(s,u)∈e (12) ψus = fq(s,u) sum(fq(si,u))(si,u)∈e (13) in formula 13, ψus expresses transfer probability form the web news instance to the search keyword, when p(tu = 1) is greater than or equal to probability threshold, then the web news instance can be found to support topic described by su, in order to mine set of similar web news instance that can support topics, in the following experiment, the optimal value of probability threshold will be analysed. 5 the design of network topic detection algorithm based on model design of network topic detection above, in this section, the author designs the mining algorithms of web news seed instances and similar web news instances supporting topics, in order to make sure that the topic detection has a high accuracy for web news, in following experiments, the precision of algorithms will be analysed, the optimal value of parameters will be determined. 5.1 the mining algorithm of web news seed instances the key information of web news have been expressed using web news information extraction and analysis method researched in previous job [16], [17], [18], but what topics the users concern are still unknown in the face of massive web news released based on social events. therefore, this algorithm mainly uses set of records using web news behaviour for users and results of web news information extraction and analysis, and through analysing web news instance clicking and retrieval mode to mine set of topic contained in massive web news instances. 5.2 the precision analysis of topic detection under different method this experiment compares precision of web news topic detection under web news instance clicking mode analysis method, web news instance retrieval mode analysis method and the method proposed in this paper. as shown in figure 3, the precision represents quality of web news topic detection using three methods, the red column expresses precision change situation of web news topic detection using web news instance clicking mode analysis method that is called dclickmode method in this chart, from its trend, it can be known that the quality of web news topic detection is not high only through a single analysis for web news instance clicking mode with increasing number of web news instances concerned, although the precision has a certain improvement, but the maximum can only float on the 62.8% the blue column expresses precision change situation of web news topic detection using web news instance retrieval mode analysis method that is called dsearchmode method in this chart, from its trend, it can be known that the quality of web news topic detection is not also high comparing with dclickmode method only through a single analysis for web news instance retrieval mode with increasing number of web news instances concerned, although the precision has also a certain improvement, but the maximum can also only float on the 63%, the green column expresses precision change situation of web news topic detection using method proposed by this paper in this chart, from its trend, it can 192 m. chen be known that the quality of web news topic detection has been significantly improved, because of integrating two analysis methods of web news instance clicking and retrieval mode, while the quantity of web news instances concerned is less, although the difference of precision is not big comparing with other two methods, the distance of precision is constantly widening among other two methods with increasing number of web news instances concerned, the maximum can float on the 75.2%, this experiment shows that the quality of web news topic detection using method proposed in this paper is higher than dclickmode and dsearchmode method. figure 3: the quality of web news topic detection under different methods 5.3 the impact analysis of web news topic detection quality from the number of web news instances concerned and seed threshold under the increasing number of web news instances concerned, this experiment analyses precision change situation of web news topic detection through adjusting seed threshold. as shown in figure 4, the precision represents quality of web news topic detection with increasing number of web news instances concerned in y axis through adjusting seed threshold in x axis, when the threshold value is certain, from this graph, it can be known that the quality of web news topic detection can increase slowly until a relatively stable trend with increasing number of web news instances concerned, the main reason is that when the number of web news instances concerned is less, the data and its relationship among them are relatively simple in the process of analysing clicking and retrieval mode, when the number of web news instances concerned is increasing, the link relationship will exist among data in the process of analysing clicking and retrieval mode, which is conducive to topic detection, so that the precision of web news topic detection is gradually increasing until more stable, when the number of web news instances concerned is certain, from this graph, it can be known that the quality of web news topic detection shows a trend of increasing firstly and then decreasing with increasing of threshold value, the main reason is that when the threshold value is less, a part of inaccurate or approximate accuracy web news topic is likely to be detected as accurate web news topic, when threshold value increases to a certain stable interval, only a small number of approximate accurate web news topic are likely to be detected as accurate web news topic, when threshold value is increased to a certain value, a part of accurate web news topic may not be detected, this experiment shows that when the number of web news instances concerned is 160, and seed threshold is 0.75, the quality of web news topic detection can reach the highest value, which is close to 78.5%. 5.4 the mining algorithm of similar web news instances supporting topics although the topics concerned by users have been detected using the mining algorithm of web news seed instances, but what web news instances supporting these topics the users concern model of network topic detection based on web usage behaviour mode analysis and mining technology 193 algorithm 1 miningseedtopic input: userbehavior, newsset, threshold, initialtime, t; output: topicurl miningseedtopic(userbehavior ub, newsset ns, systemdata s); begin userrecord u[]=extractrecord(ub); double br,cr,cm,dr,ss,sm; groupuserrecord<u> gur[]; topicurl tu=new topicurl(); gur=groupbyurl(u); for i do=0 to gur.size()-1 br=calculatebr(gur[i]); cr=calculatecr(gur[i],u); cm=br*cr; dr=calculatedr(gur[i],u); ss=calculatess(gur[i]); sm=dr*ss; if c thenm*sm>=s.getthreshold() tu.add(ns.getft(gur[i].url),gur[i].url); end if if g thenetcurrenttime()-s.getinitialtime()>=s.gett() resort(tu); readjust(s.getthreshold()); end if end for end are still unknown in addition to web news seed instances. therefore, this algorithm mainly uses sets of records using web news behaviour for users and web news seed instances analysed to mine set of similar web news instance which can support topics. 6 the experimental analysis and results in this section, the author carries out experimental analysis and shows experimental results in order to validate feasibility, validity and superiority of model proposed in this paper, in this process, the author adopts experimental environment towards to event of german a320 plane crash shown as follows. the processor is dual core, the memory is 32g, the language of computer programming design is java, its version is java se development kit 8, the platform of experimental design and implementation is myeclipse 2015, the platform of experimental data storage and management is microsoft sql server 2016. 6.1 the quality impact analysis of web news instances mined supporting topic from the number of web news instances concerned and probability threshold under the increasing number of web news instances concerned, this experiment analyses precision change situation of web news instances mined supporting topic through adjusting probability threshold. as shown in figure 5, the precision represents quality of web news instances 194 m. chen algorithm 2 miningsimilartopicurl input: topicurl, userbehavior, threshold, initialtime, t. output: topicurl miningsimilarurl(topicurl tu, userbehavior ub, systemdata s) begin double pts,ptu; for i do=0 to tu.size()-1 searchword sw1,sw2; webnewsurl wnu1,wnu2; sw1=isexist(tu[i].get("topicurl"),ub); while s dow1!=null for j do=0 to sw1.size()-1 pts=calculate(sw1.get(j).position,tu[i].get("topicurl"),ub); if p thents>=s.getthreshold() sw2.add(sw1.get(j)); end if ub.set(sw1.get(j).position,pts); end for for j do=0 to sw2.size()-1 wnu1=isexist(sw2.get(j).position,ub); if ( thenwnu1=isequal(wnu1,wnu2))!=null for k do=0 to wnu1.size()-1 ptu=calculate(wnu1.get(k).position,sw2.get(j).position,ub); if p thentu>=s.getthreshold() wnu2.add(wnu1.get(k)); end if ub.set(wnu1.get(k).position,ptu); end for end if end for sw1=isexist(wnu2,sw2,ub); end while tu[i].set(wnu2); end for if g thenetcurrenttime()-s.getinitialtime()>=s.gett() resort(tu); readjust(s.getthreshold()); end if end model of network topic detection based on web usage behaviour mode analysis and mining technology 195 figure 4: the changing trend of precision with number of web news instances concerned and seed threshold mined supporting topic with increasing number of web news instances concerned in y axis through adjusting probability threshold in x axis, when the threshold value is certain, from this graph, it can be known that the quality of web news instances mined supporting topic can increase slowly until a relatively stable trend with increasing number of web news instances concerned, the main reason is that when the number of web news instances concerned is less, the data and its relationship among them are relatively simple in the process of analysing similar web news instances supporting topic, when the number of web news instances concerned is increasing, the link relationship will exist among data in the process of analysing similar web news instances supporting topic, which is conducive to instance mine, so that the precision of web news instances mined supporting topic is gradually increasing until more stable, when the number of web news instances concerned is certain, from this graph, it can be known that the quality of web news instances mined supporting topic shows a trend of increasing firstly and then decreasing with increasing of threshold value, the main reason is that when the threshold value is less, a part of inaccurate or approximate accuracy web news instances are likely to be mined, when threshold value increases to a certain stable interval, only a small number of approximate accurate web news instances are likely to be mined, when threshold value is increased to a certain value, a part of accurate web news instances may not be mined, this experiment shows that when the number of web news instances concerned is 140, and probability threshold is 0.7, the quality of web news instances supporting topic can reach the highest value, which is close to 75.7%. 6.2 the process analysis of detecting web news topic the author illustrates effectiveness of web news topic detection method implemented in this paper, as shown in figure 6, in this experimental webpage, firstly, users can choose the social event occurred that is german a320 plane crash, secondly, users can choose releasing time of web news reporting the social event chose, thirdly, users can choose place, object or core event related 196 m. chen figure 5: the changing trend of precision with number of web news instances concerned and probability threshold to the social event chose, whose selection range is imported from result of web news extraction and analysis, fourthly, users can input keywords in textfield component again, finally, when users click web news search button, the experimental platform will display webpage as shown in figure 7. in this webpage, the users can browse retrieval results of web news instances according to retrieval condition chose or inputted that include title, releasing time, leading content and keyword of the web news instances. when users are concerned about the web news instance, and then click its title link, the experimental platform will display corresponding webpage that is linked source url, at the same time, it will also record the current user name, the search keywords submitted, url of clicking the web news instance, usage time and other information in order to detect web news topic. as shown in figure 8, in this experimental webpage, firstly, users can choose the social event occurred that is german a320 plane crash, secondly, users can choose the number of topic detected that need to be displayed, finally, when users click submit button, the experimental platform will display top n topics description that have been detected, these topics are sorted in accordance with releasing time of corresponding web news seed instance, in addition, it will also show set of web news instances supporting every topic and category information of each topic, web news instances are also sorted in accordance with its releasing time in set, when users are concerned about the web news instance supporting topic, then click on its title link, the experimental platform will show corresponding web news instance browsing webpage. as shown in figure 9, in this experimental webpage, firstly, users can choose the social event occurred that is german a320 plane crash, secondly, users can choose the number of topic detected that need to be displayed, finally, when users click submit button, the experimental platform will show top n topics description detected by the way of time axis, these topics are sorted in accordance with releasing time of corresponding web news seed instance, in addition, it will also show set of web news instances supporting every topic, web news instance are also sorted in accordance with its releasing time, when users are concerned about the web news instance model of network topic detection based on web usage behaviour mode analysis and mining technology 197 supporting topic, then click on its title link, the experimental platform will show corresponding web news instance browsing webpage. figure 6: the searching webpage of web news figure 7: the clicking webpage of the web news instance figure 8: the webpage of web news topic detection conclusion this paper completes a research on model of network topic detection based on web usage behaviour mode analysis and mining technology, which takes web news as research object, takes web usage behaviour application technology as research core and executes process of defining detection targets, extracting valuable network information, analysing web user usage behaviour, mining potential topics and applying topics detected from point of innovation. this result is important and valuable for researchers in the same or related field. in the process of model research, design and implement, this paper proposes the mining algorithm of web news seed instances and similar web news instances supporting topics in order to eliminate shortcomings existing in previous traditional method. 198 m. chen figure 9: the browsing webpage of web news topics the experimental analysis and results of model do key contributions for feasibility, validity and superiority of network topic detection request, improve efficiency of understanding network information for users, enhance availability of websites, build scientifically and improve service functions of websites, and improve business operational efficiency and clicking rate of websites. in a word, the process of research, design and implement model of network topic detection has certain practical application value, which establishes real and exact foundation of corpus for continuative research and application on web text mining direction. acknowledgement this work was supported by the national natural science foundation of china under grant nos.71572015, the national natural science foundation of china under grant nos.71271209, the project of philosophy and social sciences planning in beijing under grant 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[23] lu ran, xue suzhi, ren yuanyuan, zhu zhenfang, a modified approach of hot topics found on micro-blog, lecture notes in electrical engineering, 269(1): 603-605. international journal of computers communications & control issn 1841-9836, 11(3):381-393, june 2016. increasing face recognition rate b. lagerwall, s. viriri brett lagerwall serestina viriri* school of mathematics, statistics and computer science university of kwazulu-natal, westville campus durban, south africa *corresponding author: viriris@ukzn.ac.za abstract: this paper describes and discusses a set of algorithms which can improve face recognition rates. these algorithms include adaptive k-nearest neighbour, adaptive weighted average, reverse weighted average and exponential weighted average. essentially, the algorithms are extensions to the basic classification algorithm used in most face recognition research. whereas the basic classification algorithm selects the subject with the shortest associated distance, the algorithms presented in this paper manipulate and extract information from the set of distances between a test image and the training image set in order to obtain more accurate classifications. the base system to which the algorithms are applied uses the eigenfaces technique for recognition with an adapted viola and jones algorithm for face extraction. most of the algorithms proposed show a consistent improvement over the baseline test. keywords: face recognition, eigenfaces, classification algorithms, weighting algorithms. 1 introduction the process of obtaining the identity of a person from an image can be successfully performed by only looking at his/her face. this typically involves a face detection stage and a face recognition stage (taking detected faces and classifying them using an already existing database of faces). the full potential of face recognition applications has not been realized, since most suffer from an inability to handle light and pose variations [1]. yet, face recognition remains an important topic in computer vision because of the large number of real-world scenarios it can be applied to. senior and bolle [1] state that the three main application domains for face recognition are access control, identification systems and surveillance. it is evident that face recognition systems falling into these three domains have in fact become a part of our every day life. example usage cases of the three domains are: access control systems in security environments (e.g. banking), identification systems in websites such as facebook and surveillance systems which compare faces against a threat list in sporting events. moreover, humans are becoming increasingly reliant on face recognition systems to achieve day to day tasks. hence, researchers continue to focus on methods of improving the current recognition rates. the rest of the paper is structured as follows: section 2 explores the state-of-the-art of face recognition, section 3 gives a brief overview of preprocessing, section 4 describes an overall face recognition system, section 5 explores the novel classification techniques, section 6 presents experimental results and discussion; and section 7 draws the conclusions and future work. 2 related work lin [2] shows that face recognition is generally broken down into two modules. these are: copyright © 2006-2016 by ccc publications 382 b. lagerwall, s. viriri • a feature extractor which transforms the pixels of a face into a useful vector representation. • a pattern recognition module which searches the database to find the best match for the inputted face. however, it must be noted that the generic face recognition framework shown by lin [2] is only concerned with classifying a face. a large number of algorithms will pre-process the face images before any classification occurs. one of the earliest techniques used in face recognition was performed by turk and pentland [3]. they used principal component analysis (pca) to encode facial images in what they call an information theory approach. basically, all of the faces in a face database are manipulated to form a set of eigenvectors (eigenfaces in face recognition literature [4]). each of the original faces can be reconstructed via linear combinations of the eigenvectors. for classification, turk and pentland [3] reconstruct the input image by applying weights to each of the eigenvectors. the vector of weights for the input image is compared against the respective weight vectors for each of the images in the database. the subject in the input image is classified to be the subject in the database with the closest match. moon and phillips [4] attempt to improve on the eigenfaces technique by introducing various ideas and optimizations. most notably, they try using different nearest-neighbour distance classifiers. however, recognition rates for frontal, upright facial images (used in this study) do not seem to dramatically increase from the baseline pca algorithm developed by turk and pentland [3]. another face recognition system using an information theory approach was created by liu and weschler [5]. their system uses independent component analysis (ica) instead of pca, since ica provides a more powerful data representation. improvements range between 0 and 4 percent depending on the number of features used [5]. face recognition algorithms which do not use the information theory (or encoding of information) approach are less common. however, bronstein et al. [6] attempted to use a 3d model of a face in order to overcome weaknesses suffered by 2d systems (head orientation and facial expressions). other common methods of performing face recognition involve using physiological biometrics. zhang et. al. [15] compared and contrasted the sparse representation [18] and collaborative representation techniques ( [16], [20]) for face recognition. the sparse representation based classification first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. while the collaborative representation technique is based on the regularized least square. the experimental results show that both collaborative representation and sparse representation achieved accuracy rate of 93.7% on ar database. furthermore, collaborative representation has significantly less complexity than sparse representation based classification. hao et. al. ( [17], [21]) proposed a novel method, called heteroscedastic sparse representation based classification which addresses the complexity problem of sparse representation based classification. in the presence of noises, the sparse representation based classification model exists heteroscedasticity, which makes residual estimation inefficient. therefore, heteroscedastic correction must be carried out for homoscedasticity by weighting various residuals with heteroscedastic estimation. the experimental results show that heteroscedastic sparse representation based classification has significantly less complexity than sparse representation based classification, while it is more robust. robust face recognition via sparse representation [18] considered the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. they cast the recognition problem as one of classifying among multiple increasing face recognition rate 383 linear regression models, and argue that new theory from sparse signal representation offers the key to addressing this problem. this new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. furthermore, wagner et. al. [19] proposed a conceptually simple face recognition system that achieves a high degree of robustness and stability to illumination variation, image misalignment, and partial occlusion. the system uses tools from sparse representation [18] to align a test face image to a set of frontal training images. they demonstrated how to capture a set of training images with enough illumination variation that they span test images taken under uncontrolled illumination. their system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training. many of the face recognition methods reviewed used different nearest-neighbour distance classifiers to calculate the distance between a test image and a training image. however, once the distances were calculated, the test image with the smallest distance was always selected to be the best match. this paper takes the classification process one step further by analyzing the results which can be achieved after post-processing the set of calculated distances. 3 preprocessing before performing face recognition, a region of interest (roi) within the image must be extracted. a modified version of the viola and jones [7] face detection algorithm was chosen for this purpose. the key points and modifications are mentioned below. 3.1 classifier a drawback to building a classifier is the sheer amount of training data required (5000 facial images and 10000 non-facial images in viola and jones [7]). thus, an already available classifier is used: the haarcascade_frontalface_default classifier provided with opencv [8]. 3.2 passing the classifier the classifier being used consists of a number of stages and each stage consists of a number of features. at a particular stage, a sub-window can only pass that stage if the sum of the values generated by testing various features (specified in the haarcascade_frontalface_default classifier) is above a certain threshold. for the sub-window to be considered as a face, it must pass all 25 stages. 3.3 merging detections viola and jones [7] said that they merged all overlapping detections. however, if two faces are close together this could accidentally be counted as a single detection. instead, a merging algorithm partially based on work done by rowley et al. [?] was constructed. for each pair of rectangles i and j, the rectangles can be said to be detecting the same face if both (1) and (2) hold. euclideandistance(ci,cj) ≤ t × width(i) (1) euclideandistance(ci,cj) ≤ t × width(j) (2) where ci is the centre of rectangle i, cj is the centre of rectangle j and t is a threshold chosen to be 0.2 [9]. 384 b. lagerwall, s. viriri (a) ghost face 1 (b) ghost face 5 (c) ghost face 50 figure 1: showing example ghost faces. 4 face recognition the eigenfaces technique is used for face recognition. the key advantages of this algorithm are its quick classification of the probe/test image and its ease of implementation. 4.1 training recognition must be performed against a base set of known images. this set is typically called the training set. on each image in the training set, illumination normalization is performed using: imagei = imagei max (3) where imagei is the ith pixel of the image and max is the largest value of any of the pixels in the image. then, the basic steps of the eigenfaces algorithm from turk and pentland [3] are performed on the training set, involving: • mapping the pixel intensities of an image onto a face vector. all of the face vectors from the training set are then placed into a matrix. • performing various matrix operations, including eigenvalue decomposition, to obtain eigenfaces or ghost faces (see figure 1). • calculating a weight matrix, where each weight represents the amount to which an eigenface counts towards making up an image. 4.2 classification when a testing image is inputted, the algorithm must be able to classify the face in the image as one of the subjects in the database. again, the steps described in turk and pentland [3] are performed on the testing image, eventually resulting in a matrix of weights specifying the contribution of each of the eigenfaces in making up the test image. these weights are then compared to the weights of the training images/database images. a distance classifier (euclidean distance in this research) is used to determine the distance between the test image and each of the training images. typically, the person in the test image is classified to be the person in the training set who has the smallest distance between their training image and the testing image. increasing face recognition rate 385 5 novel classification algorithm – introduction in traditional face recognition algorithms, a subject in a test image is classified as the subject in an image in the database against which it has the shortest distance. more specifically, if all of the distances calculated between the n training images in the database and the test image are sorted and thus ranked from smallest to biggest, the subject in a test image is classified as the subject in a training image that corresponds to the first distance (or shortest distance) in the sorted list. if a database contains only one image of each subject, then taking the top match is the only logical classification. however, if a database contains multiple images per subject, then a number of other classification possibilities occur. described below are some of the classification algorithms proposed which can improve the recognition rates. many of the algorithms described rely on the same mathematical foundations. thus, let us define the following: n number of images m number of subjects k a percentage of the images in the sorted list k′ a percentage of the images of a subject sx the set of training images of subject x sxk′ the set containing the top k′ percent of training images of subject x |sxk| the number of images of subject x in the top k percent ij the distance between the jth training image and the testing image wj the weight applied to the jth training image note that where applicable, the algorithms will use a k percent of images, rather than k images. this is because using a percentage allows for better scaling when subjects in the same database have a different number of images. novel classification algorithm – algorithm description 5.1 top match the test image is classified as the top (or first) image in the sorted list. this algorithm is used as the baseline for comparisons. 5.2 top-k average the top k percent of images from the sorted list are chosen. within the chosen images, the average distance from the images of each subject to the test image is calculated. the subject with the smallest average is selected. more specifically, the subject with the smallest value defined by (4) is chosen. subjectavgkx = ∑nk j=1 ij |sxk| ij ∈ g (4) where g is defined by: 386 b. lagerwall, s. viriri g = {ip|p ∈ sx} (5) 5.3 adaptive k-nearest neighbour the k-nearest neighbour algorithm is commonly used to solve pattern recognition problems. however, its performance suffers when subjects have differing numbers of training images. the adaptive k-nearest neighbour attempts to rectify this. the top k percent of images from the sorted list are chosen. the total number of images of each subject within the top k percent of images is obtained. this total within k for each subject is then divided by the total number of images of the subject in the database – thus forming a ratio of the subject which falls within the k images. the subject with the highest ratio defined by (6) is chosen. subjectratiox = |sxk| |sx| (6) 5.4 k′ subject average the top k′ percent of images of each subject are chosen. the average of the distances pertaining to the top k′ percent of images of a subject is calculated. the subject with the lowest average is selected. it must be re-emphasized that in this algorithm, the k′ percent is used for each subject, rather than the whole sorted list. the subject with the lowest average defined by (7) is chosen. subjectavgk′x = ∑n j=1 ij |sx|k′ ij ∈ h (7) where h is defined by: h = {ip|p ∈ sxk′} (8) 5.5 average the average of all the distances for each subject is calculated. the subject which has the lowest average distance is then selected. note that this algorithm is equivalent to k′ subject average with k′ = 100. the subject with the smallest value defined by (9) is chosen. subjectavgx = ∑n j=1 ij |sx| ij ∈ g (9) 5.6 weighted average the average distance for each subject is calculated. however, the images belonging to a subject are given a weighting. the image with the largest distance is given a weighting of 1; the image with the second largest distance is given a weighting of 2, etc. finally, if there are |sx| images of a particular subject, then the image with the smallest distance is given a weighting of |sx|. the weighted sum can then be calculated using (10): weightedsumx = n∑ j=1 ijwj ij ∈ g (10) increasing face recognition rate 387 table 1: algorithm 1: weighted average 1: sort(images) 2: for i = 0 to n do 3: add the weight of image i to the appropriate list in an array of lists arr, where each subject has its own list 4: end for 5: for i = 0 to m do 6: w ⇐ arr[i].size() 7: for j = 0 to arr[i].size() do 8: wsum ⇐ wsum + arr[i].get(j) ∗ w 9: w ⇐ w − 1 10: end for 11: total[i] ⇐ wsum/arr[i].size() 12: end for 13: lowestname ⇐ 0 14: wavg ⇐ total[0] 15: for i = 1 to m do 16: if total[i] < wavg then 17: lowestname ⇐ i 18: wavg ⇐ total[i] 19: end if 20: end for 21: return lowestname when calculating the average, instead of dividing by the total number of images belonging to the subject, it is divided by the sum of the weights pertaining to the subject. the total weight can be calculated using (11): totweightx = |sx|(|sx| + 1) 2 (11) finally, the weighted average for a particular subject can be calculated using (12): weightedaveragex = weightedsumx totweightx (12) the subject with the lowest weighted average is then selected. see algorithm 1 for detailed pseudocode of this algorithm. other weighting algorithms will follow similar pseudocode, but have a different weighting mechanism. 5.7 reverse weighted average this algorithm is the same as the weighted average algorithm except that the larger weights are applied to the images with larger distances. this algorithm attempts to find out if the less closely matched images are in fact better at distinguishing subjects. 5.8 exponential weighted average an average of the distances for each subject is calculated. however, each image belonging to the subject is given a weighting. the image of a subject which has the smallest distance is given an arbitrary weighting of 10.0. subsequent images of the same subject are given weights 388 b. lagerwall, s. viriri exponentially less by dividing the previous weight by the |sx|th root of 10.0. so, if image j has a weight of wj, then the weight of image j + 1 can be calculated with (13): wj+1 = wj |sx|√ 10.0 (13) to find the exponential weighted average, the weighted sum must be divided by the sum of the weights (total weight). the weighted sum and total weight are computed using (10) and (14) respectively. exptotweightx = |sx|∑ j=1 10 j |sx| (14) finally, the exponential weighted average for a particular subject is calculated using (15): expweightedaveragex = weightedsumx exptotweightx (15) the subject with the lowest exponential weighted average is then selected. 6 results and discussion in order to test the validity of results, three publicly available test databases were obtained, namely caltech faces, the georgia face database and the orl database of faces. the feret database ( [10], [11]) is perhaps the most commonly used face database to benchmark the eigenfaces technique. however, the feret database does not contain multiple images of each subject and hence is not useful for testing the novel classification algorithms proposed here. 6.1 testing procedure a standard test procedure was used on each database. this involved the following: 1. running the viola and jones [7] algorithm on each image in the database to extract faces. 2. manually discarding all subjects from the database which do not have a minimum of 10 separate face detections from the viola and jones algorithm. this results in a reduction of the total number of images. 3. setting aside a number of random images of each subject for testing. in this research, 5 per subject were used. 4. performing training on the remaining images. 5. performing the test procedure using the test images and the trained database. 6. running 100 repetitions of steps 3-5 and taking the average as the final result. 7. repeating steps 3-6 for each different classification algorithm. increasing face recognition rate 389 table 2: caltech faces recognition rates classification algorithm rate (%) top match (baseline, (literature)) 87.71 top-k average (k=5) 73.52 adaptive k-nearest neighbour (k=5) 77.39 k′ subject average (k=40) 90.14 k′ subject average (k=60) 92.04 k′ subject average (k=80) 91.05 average 92.63 weighted average 94.54 reverse weighted average 72.97 exponential weighted average 93.75 table 3: georgia face database recognition rates classification algorithm rate (%) top match (baseline, (literature)) 58.26 top-k average (k=5) 51.46 adaptive k-nearest neighbour (k=5) 62.13 k′ subject average (k=40) 65.68 k′ subject average (k=60) 69.13 k′ subject average (k=80) 70.32 average 71.67 weighted average 69.87 reverse weighted average 64.67 exponential weighted average 69.01 6.2 caltech faces the caltech face database, used by kevenaar et al. [12], was obtained for testing. it contains 450 images of 27 subjects. the database is considered to be relatively easy since the faces are always upright, are uniform in presentation and never suffer from occlusion. since the database does not provide a consistent number of images per subject, it was deemed necessary to ignore step 2 from the generic testing procedure. thus, after performing face detection, the database was reduced to 435 images of 25 subjects with between 4 and 26 images per subject. table 2 shows that the weighted average algorithm improves on the baseline results by close to 7% (from 87.71% to 94.54%). this worked out to an extra 1441 correct recognitions over the 22400 image test. the results also show that the images with a smaller distance to the test image contribute more towards identifying the subject – since both weighted average and exponential weighted average perform better than average. the opposite is not true, with the reverse weighted average (giving higher weightings to less closely matched images) does not perform well. finally, it was envisaged that top-k average and adapted k-nearest neighbour would be used with small k values, but they did not improve on the baseline. using higher k values would only result in the algorithms degenerating towards the average classification algorithm. 6.3 georgia face database to determine whether the novel classification algorithms could raise recognition rates in suboptimal conditions, a more difficult database was required. thus, the georgia face database, 390 b. lagerwall, s. viriri table 4: orl database of faces recognition rates classification algorithm rate (%) top match (baseline, (literature)) 83.82 top-k average (k=5) 77.46 adaptive k-nearest neighbour (k=5) 82.44 k′ subject average (k=40) 87.08 k′ subject average (k=60) 88.62 k′ subject average (k=80) 88.79 average 88.24 weighted average 89.27 reverse weighted average 81.27 exponential weighted average 89.04 used by chen et al. [13], was obtained. the database consists of 50 subjects and a total of 750 images, but this was reduced to 30 subjects and 355 images after steps 1 and 2 (pre-processing) in the testing procedure. the database is considered difficult because many of the faces are tilted, have different facial expressions and were taken under different lighting conditions. further to that, the pictures were taken in separate sessions – thus, some subjects exhibited physical changes (e.g. grew a moustache). although the overall results from testing are worse than the caltech faces, table 3 shows that the novel classification algorithms are still effective. in fact the best algorithm (average) raises the recognition rate from 58.26% to 71.67% – an improvement of over 13%. it is interesting to note that the average algorithm outperforms both the weighted average and exponential weighted average. this is probably because the distance between the better images and the test image is relatively large (since the database is extremely difficult) and thus the distribution of distances for a particular subject is more even. with an even distribution of distance, weighting becomes superfluous. 6.4 orl database of faces the final test for the novel classification algorithms was to see how well they scaled. more specifically, would they still show improvements over the baseline if the number of subjects was increased? to test this, a third database was required. the orl database of faces, created by samaria and harter [14], was obtained. the database consists of 40 subjects with 10 images per subject and all of these images remained intact after steps 1 and 2 of the testing procedure. table 4 shows that using k′ subject average, average, weighted average or exponential weighted average as a classification algorithm will give better results than the baseline algorithm. in fact, weighted average and exponential weighted average both show improvements of over 5% – even with the large number of subjects being used. 6.5 algorithm complexity tables 5, 6 and 7 shows the timing increase per classification which occurs when using a novel classification algorithm versus using the baseline algorithm (top match). the increase in classification time is minimal for all of the algorithms running on any of the databases and thus confirms that the algorithms are feasible for use in real-time applications such as streaming video at 25 fps. in fact, the roughly 2ms worst case increase would only become a factor if classifying thousands of faces (e.g. at a sports stadium). these results were generated using an intel core increasing face recognition rate 391 table 5: caltech faces timing increase in ms classification algorithm time top-k average (k=5) 0.58 adaptive k-nearest neighbour (k=5) 0.98 k′ subject average (k=40) 1.39 k′ subject average (k=60) 1.98 k′ subject average (k=80) 1.56 average 1.99 weighted average 1.32 reverse weighted average 1.85 exponential weighted average 1.33 table 6: georgia face database timing increase in ms classification algorithm time top-k average (k=5) 1.07 adaptive k-nearest neighbour (k=5) 0.74 k′ subject average (k=40) 0.98 k′ subject average (k=60) 0.69 k′ subject average (k=80) 1.32 average 1.48 weighted average 1.52 reverse weighted average 1.23 exponential weighted average 1.26 table 7: orl database of faces timing increase in ms classification algorithm time top-k average (k=5) 0.27 adaptive k-nearest neighbour (k=5) 0.79 k′ subject average (k=40) 0.86 k′ subject average (k=60) 0.84 k′ subject average (k=80) 0.95 average 0.87 weighted average 0.88 reverse weighted average 0.94 exponential weighted average 0.86 2 duo e8400 (3.0ghz) with 2gib of ram running ubuntu linux 10.10 64-bit. 7 conclusions and future works methods for increasing face recognition rates have been investigated with the primary focal area being the classification algorithm. a number of the new classification algorithms show improvements of the literature results termed as the baseline results. it can be concluded that the best performing classification algorithm is weighted average with improvements over the baseline of 6.83%, 11.61% and 5.45% in the three test databases. importantly, the algorithms are computationally efficient making them feasible for use in real-time applications. 392 b. lagerwall, s. viriri so far, the novel classification algorithms have only been applied to the eigenfaces technique. future research will involve applying the classification algorithms to other face recognition techniques to see if similar improvements are obtained. bibliography [1] a.w. senior, r.m. bolle (2002); face recognition and its applications: biometric solutions for authentication in an e-world, kluwer academic publishers, 2002. 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[21] b. mikhail, p. niyogi (2003); laplacian eigenmaps for dimensionality reduction and data representation, neural computation, 1373-1396. ijcccv11n1.dvi international journal of computers communications & control issn 1841-9836, 11(1):113-125, february 2016. a preflow approach to maximum flows in parametric networks applied to assessing the legal information l. sângeorzan, m. parpalea, m.m. parpalea, a. repanovici, r. matefi, i. nicolae livia sângeorzan* transilvania university of brasov faculty of mathematics and computer science 25, eroilor, blvd, 500030, brasov, romania *corresponding author:sangeorzan@unitbv.ro mircea parpalea national college andrei şaguna of brasov 1, şaguna, street, 590000, brasov, romania parpalea@gmail.com mihaela marinela parpalea transilvania university of brasov faculty of letters 25, eroilor, blvd, 500030, brasov, romania mihaela.parpalea@unitbv.ro angela repanovici transilvania university of brasov faculty of product design and environment 25, eroilor, blvd, 500030, brasov, romania repa@unitbv.ro roxana matefi, ioana nicolae transilvania university of brasov faculty of law 25, eroilor, blvd, 500030, brasov, romania roxana.matefi@unitbv.ro, ioana.nicolae@unitbv.ro abstract: the present paper proposes a fractal-like approach to the parametric flow problem, derived from the rules and recursion of generative linguistics. in the same manner in which any sentence can be analysed in terms of "theme" of the sentence (that which is spoken about in the sentence) and "rheme" of the sentence (that which is said about the theme in the sentence), in the proposed parametric preflow-push algorithm, a "partitioning push" (a non-cancelling push of flow in the parametric residual network) might leave the node unbalanced for only a subinterval of the range of parameter values. this will lead to separating the problem for the two disjoints subintervals, which allows the algorithm to continue after the same rules, independently, on each of the partitioned subintervals. the algorithm runs as the template-like structure of a dialogue act which reveals a design where information about the items (part-of-speech) is a two sections vector with one segment for each of the used part of speech categories. the article also proposes a possible application of the algorithm in assessing legal information and in semantic evaluation of legislation. keywords: parametric preflow algorithm, partitioning technique, generative linguistics, legal information. copyright © 2006-2016 by ccc publications agora university 114 l. sângeorzan, m. parpalea, m.m. parpalea, a. repanovici, r. matefi, i. nicolae 1 introduction computing maximum flows in various networks is an important problem not only because the results are applied directly to traffic analysis of communication networks, but also because some efficient algorithms are often employed as sub-problems in other general network problems. consequently, fundamental algorithms for computing maximum network flows were designed and efficient algorithms exist [1] to solve different instances [7] of the problem. a natural generalization of this problem can be obtained by considering that the capacities of some arcs are functions of a real parameter. the obtained parametric maximum flow problem is to compute all maximum flows for every possible value of the parameter. for the maximum flow problem in parametric networks with zero lower bounds and linear capacity functions of a single parameter, hamacher and foulds [3] investigated a "horizontal" approach, based on augmenting directed paths algorithm. in each of its iterations, the algorithm computes an improvement of the flow defined on the whole interval of the parameter. for the same problem, ruhe [8,9] proposed a "piece-by-piece", "vertical" approach. the main idea of his algorithm is to consider that the parametric maximum flow is known up to a parameter value λk and to compute a flow augmentation that assures the optimality of the flow for parameter values λ ≥ λk as well as the parameter maximum value λk+1 up to which the computed flow remains a parametric maximum one. each of the algorithm’s iterations represents a non-parametric maximum flow sub-problem which can be solved via one of the classic approaches for the maximum flow problem. partitioning technique in networks has been, in the latest years, a more and more active research topic in both engineering and theoretical research. the reason why the problem under consideration is of genuine practical and theoretical interest lies in that graph partitioning applications are described on a wide variety of subjects, such as: data distribution in parallelcomputing, vlsi circuit design, image processing, computer vision, route planning, air traffic control, mobile networks, social networks, etc. [2]. unfortunately, graph partitioning is an nphard problem, and therefore all known algorithms for generating partitions merely return approximations to the optimal solution. partitioning algorithms for the parametric maximum flow problem can be developed starting from any of the three classic approaches for the maximum flow problem: flow augmenting directed paths algorithms, preflow algorithms or min-max algorithm. the approach based on flow augmenting directed paths [4], makes use of the concept of shortest conditional augmenting directed path. in order to avoid working with piecewise linear functions, the approach uses a series of parametric residual networks defined for successive subintervals of the parameter values where the parametric residual capacities of all arcs remain linear functions. the approach based on preflow algorithms will further be presented in detail in this paper while the parametric min-max approach [6] is not itself an algorithm but rather a method of obtaining a parametric minimum flow using any algorithm for obtaining a maximum parametric flow and vice versa. the idea of the partitioning approach based on preflows, which is used in this paper, derives from the rules and recursion of generative linguistics [10]. the concept which has proved to be the most useful in the description of german word order has become known under the name of functional sentence perspective (fsp). its main idea is that information is not transmitted in random order but the speaker seeks to give his information to his interlocutor in portions, normally starting from what he assumes is common to both and proceeding to what he regards as important new information [5]. in the same manner in which any sentence can be analysed in terms of "theme" of the sentence (that which is spoken about in the sentence) and "rheme" of the sentence (that which is said about the theme in the sentence), the proposed algorithm for the parametric maximum flow problem uses a fractal-like approach. in the proposed parametric a preflow approach to maximum flows in parametric networks applied to assessing the legal information 115 preflow-push algorithm, a ’partitioning push’ (a non-cancelling push of flow in the parametric residual network) might leave the node unbalanced for only a subinterval of the range of the values of the parameter. like in all fractal approaches a partitioning push is followed by separating the problem into disjoints subintervals, allowing the algorithm to continue after the same rules independently on each of the partitioned subintervals. making use of the partitioning approach, the present paper proposes an original algorithm for computing the maximum flow in parametric networks with linear upper bound (capacity) functions. although both the algorithm presented in [10] and the one that is proposed in this article deals with the same kind of approach of the parametric flow, they are totally different algorithms and they fundamentally differ because of their purpose: the first computes the minimum flow in networks with constant capacities and linear lower bound functions while the latter computes the maximum flow in networks without lower bounds and with linear capacity functions. further on, this paper is organized as follows. section 2 presents the parametric maximum flow problem and the basic parametric network flow terminology and results which are used in the rest of the paper. most of the definitions in this section are straightforward modifications of those in [4] and [6], adapted for the maximum flow problem in parametric networks with zero lower bounds. more specialized terminology and further details on the notions, definitions and main results within this section can be found in [1], [4] and [6]. in section 3 we present our algorithm for solving the parametric maximum flow problem, called "highest label partitioning push (hlpp) algorithm". in the same section there are also presented the corresponding theorems of correctness and of complexity of the algorithm. using a multithread based parallel implementation, the complexity of the algorithm is linearly dependent on the number of breakpoints. section 4 deals with a possible application of the proposed algorithm to assessing the legal information. finally, section 4 presents some conclusions regarding to our contribution to the topic presented in the article. in the presentation to follow, some familiarity with flow algorithms is assumed and many details are omitted, since they are straightforward modifications of known results. 2 flows in parametric networks the parametric maximum flow problem is an extension of the classical maximum flow problem [1] in which the capacities of certain arcs are functions of a parameter λ. the parametric maximum flow problem is to compute all maximum flows for every possible value of the parameter. for the case of linear capacity functions, the maximum flow value function in a parametric network is a continuous piecewise linear function of the parameter. each linear segment of the maximum flow value function between the two breakpoints λk and λk+1 corresponds to a cut that remains a minimum cut for any λk < λ ≤ λk+1. the approach presented in this article refers to the maximum flow problem in a network with linear capacity functions of a single parameter. 2.1 terminology and preliminaries let g = (n,a,u,s,t) be a capacitated network with n = |n| and m = |a|, n = {. . . , i, . . .} being the set of nodes i and a = {. . . ,a, . . .} being the set of arcs a, so that for every arc in a holds that a = (i,j) with i,j ∈ n. the capacity (upper bound) function is a nonnegative function u(a) associated with each arc a ∈ a. the network has two special nodes: a source node s and a sink node t. a flow is a function f : a → r+ satisfying the next conditions: ∑ j|(i,j)∈a f(i,j) − ∑ j|(j,i)∈a f(j,i) =      v, i = s 0, i 6= s,t −v, i = t (1) 116 l. sângeorzan, m. parpalea, m.m. parpalea, a. repanovici, r. matefi, i. nicolae for some v ≥ 0, where v is referred to as the value of the flow f. any flow on a directed network satisfying the flow bound constraints: 0 ≤ f(i,j) ≤ u(i,j), ∀(i,j) ∈ a (2) for every arc (i,j) ∈ a is referred to as a feasible flow. a cut is a partition of the node set n into two subsets s and t = n −s, denoted by [s,t ]. a cut is nontrivial if both s and t are nonempty. an arc (i,j) ∈ a with i ∈ s and j ∈ t is referred to as a forward arc of the cut while an arc (i,j) ∈ a with i ∈ t and j ∈ s as a backward arc of the cut. let (s,t) denote the set of forward arcs in the cut and let (t,s) denote the set of backward arcs. a cut [s,t ] is an s− t cut if s ∈ s and t ∈ t . the maximum flow problem is to determine a flow f̃ for which v is maximized 2.2 the parametric maximum flow the parametric flow problem consists in generalising the classic problem of flows in networks by transforming the upper bounds of some arcs (i,j) ∈ a of the network g = (n,a,u,s,t) in linear functions of a real parameter. definition 1. [4] a directed network g = (n,a,u,s,t) for which the upper bounds u of some arcs (i,j) ∈ a are functions of a real parameter λ is referred to as a parametric network and is denoted by ḡ = (n,a,ū,s,t). for a parametric network ḡ, the parametric upper bound (capacity) function ū : a× [0,λ] → r+ associates to each arc (i,j) ∈ a, for each of the parameter values λ ∈ [0,λ], the real number ū(i,j;λ), referred to as the parametric upper bound of arc (i,j): ū(i,j;λ) = u0(i,j) + λ ·u(i,j), (i,j) ∈ a, (3) where u : a → r is a real valued function associating to each arc (i,j) ∈ a the real number u(i,j) referred to as the parametric part of the upper bound of the arc (i,j). the nonnegative value u0(i,j) is the upper bound of the arc (i,j) for λ = 0, i.e. : ū(i,j; 0) = u0(i,j) with 0 ≤ u0(i,j). for the problem to be correctly formulated, the upper bound function of every arc (i,j) ∈ a must respect the condition 0 ≤ ū(i,j;λ) for the entire interval of the parameter values, i.e. ∀(i,j) ∈ a and ∀λ ∈ [0,λ]. it follows that the parametric part of the upper bounds u(i,j) must satisfy the constraint: u(i,j) ≥−u0(i,j)/λ, ∀(i,j) ∈ a. the parametric flow value function v̄ : n × [0,λ] → r associates to each of the nodes i ∈ n a real number v̄(i;λ) referred to as the value of node i for each of the parameter λ values. definition 2. [4] a feasible flow in the parametric network ḡ = (n,a,ū,s,t) is called a parametric flow, f̄ : a× [0,λ] → r+ satisfying the following constraints: ∑ j|(i,j)∈a f̄(i,j;λ) − ∑ j|(j,i)∈a f̄(j,i;λ) = v̄(i;λ), ∀i ∈ n, ∀λ ∈ [0,λ], (4) 0 ≤ f̄(i,j;λ) ≤ ū(i,j;λ), ∀(i,j) ∈ a, ∀λ ∈ [0,λ], (5) where ∑ i∈n v̄(i;λ) = 0, ∀λ ∈ [0,λ]. the parametric maximum flow (pmf) problem is to compute all maximum flows for every possible value of λ in [0,λ] : maximize v̄(λ) for all λ ∈ [0,λ], (6) a preflow approach to maximum flows in parametric networks applied to assessing the legal information 117 ∑ j|(i,j)∈a f̄(i,j;λ) − ∑ j|(j,i)∈a f̄(j,i;λ) =      v̄(λ), i = s 0, i 6= s,t −v̄(λ), i = t (7) 0 ≤ f̄(i,j;λ) ≤ ū(i,j;λ), ∀(i,j) ∈ a. (8) this problem looks like a classic maximum flow problem with the decisive difference that the variables f̄(i,j;λ) of this problem are piecewise linear functions instead of real numbers and that the upper bounds ū(i,j;λ) are linear functions instead of constants. definition 3. [6] a parametric s−t cut partitioning denoted by [sk;jk], k = 0, . . . ,k, is defined as a finite set of cuts [sk,tk], k = 0, . . . ,k, together with a partitioning of the interval [0,λ] of the parameter in disjoints subintervals jk, k = 0, . . . ,k, so that j0 ∪·· ·∪jk = [0,λ]. definition 4. [4] for the parametric maximum flow problem, the capacity ˜̄c[sk;jk] of a parametric s− t cut partitioning is a linear function on every subinterval jk, k = 0, . . . ,k, defined as: ˜̄c[sk;jk] = ∑ (i,j)∈(sk,tk) ū(i,j;λ), k = 0, . . . ,k. (9) definition 5. [4] a parametric s− t cut partitioning [sk;jk] with the subintervals jk assuring that every cut is a minimum cut [s̃k, t̃k] within the subinterval [λk,λk+1] is referred to as a parametric minimum s− t cut and is denoted by [s̃k;jk], k = 0, . . . ,k. theorem 6. (parametric max-flow min-cut theorem [6]) if there is a feasible flow in the parametric network ḡ, the value function ˜̄v of the parametric maximum flow ˜̄f from a source s to a sink t equals the capacity ˜̄c of the parametric minimum s− t cut [s̃k;jk], k = 0, . . . ,k. let f̄ = (. . . f̄(i,j;λ), . . .)(i,j)∈a be a vector of feasible flow functions defined on the interval [0,λ]. supposing that an arc (i,j) ∈ a carries a flow f̄(i,j;λ), the existing flow can be increased either by sending the additional flow (pushing) ū(i,j;λ) − f̄(i,j;λ) from node i to node j over the arc (i,j) or by cancelling the flow f̄(j,i;λ) from node j to node i over the arc (j,i) which is equivalent to pulling the flow from node i to node j over the arc (j,i). these flows are computed as differences between piecewise linear functions of λ. definition 7. [4] for the parametric maximum flow problem, the parametric residual capacity ˜̄r(i,j;λ) of any of the arcs (i,j) ∈ a, with respect to a given parametric flow f̄, represents the maximum additional flow that can be sent from node i to node j over the arcs (i,j) and (j,i) and is given by: ˜̄r(i,j;λ) = ū(i,j;λ) − f̄(i,j;λ) + f̄(j,i;λ). (10) definition 8. [4] the subintervals ĩ(i,j) ⊆ [0,λ] where an augmentation of the flow f̄(i,j;λ) is possible along the arc (i,j) are defined as follows: ĩ(i,j) = {λ|˜̄r(i,j;λ) > 0}, (i,j) ∈ a. (11) definition 9. [4] given a feasible flow f̄ in the parametric network ḡ, the network denoted by ˜̄g(f̄) = (n,ã(f̄)), with ã(f̄) = {(i,j)|(i,j) ∈ a, ĩ(i,j) 6= ∅} being the set consisting only of arcs with positive parametric residual capacities, is referred to as the parametric residual network with respect to the given flow f̄ for the parametric maximum flow problem. if an arc (i,j) ∈ a does not belong to ˜̄g(f̄) then ĩ(i,j) := ∅ is set. 118 l. sângeorzan, m. parpalea, m.m. parpalea, a. repanovici, r. matefi, i. nicolae definition 10. the parametric excess of a node i ∈ n is defined as: ẽ(i;λ) = ∑ j|(j,i)∈a f̄(j,i;λ) − ∑ j|(i,j)∈a f̄(i,j;λ). (12) definition 11. [4] the subintervals ĩ(i) ⊆ [0,λ] where the excess of node i is positive are defined as follows: ĩ(i) = {λ|ẽ(i;λ) > 0}, i ∈ n −{s,t}. (13) in the residual network ˜̄g(f̄) the distance function d̃ : n → ℵ is a function from the set of nodes to the nonnegative integers. a distance function is said to be valid if it satisfies the following conditions: d̃(t) = 0 and d̃(i) ≤ d̃(j) + 1, ∀(i,j) ∈ ã. definition 12. [4] an arc (i,j) ∈ ã in the parametric residual network ˜̄g(f̄) is referred to as conditionally admissible if both d̃(i) = d̃(j)+1 and ĩ(i,j)∩ ĩ(i) 6= ∅; otherwise it is conditionally inadmissible. 3 partitioning push algorithm 3.1 highest-label partitioning-push algorithm the highest-label partitioning-push (hlpp) algorithm maintains a set l of active nodes organised as a priority queue. in the initialisation step of the algorithm, all the nodes i ∈ n with (s,i) ∈ ã will gain a positive excess, becoming thus active nodes, by setting the flow to the upper bound value f̄(s,i;λ) := ū(s,i;λ) for every arc (s,i). consequently, they are added to the priority queue l and then removed one by one, in the descending order of their priorities d̃(i). for an active node i ∈ ˜̄g(f̄), if there exists an conditionally admissible arc (i,j), the flow will be pushed over this arc and node j will be added to the priority queue l with the priority d̃(j); otherwise node i will be relabelled so that at least one conditionally admissible arc to be created and node i is added to l with its new priority d̃(i). the algorithm terminates when the queue of active nodes is empty. a push of flow from node i to node j is referred to as a cancelling push if it deletes the arc (i,j) from the residual network; otherwise it is a non-cancelling push. algorithm 1 highest-label partitioning-push (hlpp) algorithm 1: procedure hlpp 2: j ← [0,λ] l ←∅ f̄ ← 0 compute d̃(·) in ˜̄g(f̄) 3: for all (s,i) ∈ ã do 4: f̄(s,i;λ) ← ū(s,i;λ) ẽ(i;λ) ← ū(s,i;λ) 5: if ẽ(i;λ) > 0 and i 6= t then 6: add i with priority d̃(i) to l 7: end if 8: end for 9: d̃(s) ← n 10: pp(l j) 11: end procedure for any node i ∈ ˜̄g(f̄), the expressions: active node and balanced node holds only for subintervals of the parameter values. while both the parametric residual capacity ˜̄r(i,j;λ) of any a preflow approach to maximum flows in parametric networks applied to assessing the legal information 119 algorithm 2 partitioning push (pp) procedure 12: procedure pp(l jp) 13: if l 6= ∅and jp 6= ∅ then 14: remove the first node i from l 15: if ∄ an admissible arc (i,j) then 16: d̃(i) ← min{d̃(j)|(i,j) ∈ ã}+ 1 17: add i with priority d̃(i) to l 18: pp(l jp) 19: else 20: select an admissible arc (i,j) 21: push g̃(i,j;λ) ← min{ẽ(i;λ), ˜̄r(i,j;λ)} over (i,j) 22: if j /∈ l and j 6= s and j 6= t then add j to l 23: end if 24: jp1 ←{λ|ẽ(i;λ) ≤ ˜̄r(i,j;λ)} 25: jp2 ← jp −jp1 26: lp1 ← l lp2 ← l 27: add i with priority d̃(i) to lp2 28: do in parallel 29: pp(lp1jp1 ) 30: pp(lp2jp2 ) 31: end do 32: end if 33: end if 34: end procedure arc (i,j) ∈ ã and the parametric excess ẽ(i;λ) of any node i ∈ n −{s,t} are linear functions of the parameter values, cancelling or non-cancelling pushes are defined only for certain subintervals of the parameter values. a non-cancelling push of flow from a node i ∈ n −{s,t} along an arc (i,j) ∈ ã in a subinterval jp = (λp,λp+1] ⊆ [0,λ] which leaves the node i unbalanced is referred to as a partitioning push. whenever the algorithm performs a partitioning push in ˜̄gp(f̄), i.e. the network ˜̄g(f̄) defined for the subinterval jp, a new partitioning of the interval jp in the two subintervals jp1 and jp2, with jp1 ∪jp2 = jp and jp1 ∩jp2 = ∅ will take place. let jp1 be the subinterval within which the partitioning push balances the node i, i.e. jp1 = {λ|ẽ(i;λ) ≤ ˜̄r(i,j;λ)}. if jp2 6= ∅ then, as on every subinterval jp both ˜̄r(i,j;λ) and ẽ(i;λ) are linear functions of λ, the partitioning push generates two parametric residual networks: ˜̄gp1(f̄) for λ ∈ jp1 and ˜̄gp2(f̄) for λ ∈ jp2, so that node i is balanced in ˜̄gp1(f̄) and active in ˜̄gp2(f̄) while arc (i,j) will not belong to ˜̄gp2(f̄), since ˜̄r(i,j;λ) = 0 after the partitioning push. the algorithm will then continue separately in each of the parametric residual networks and for each of the two subintervals. under these observations, the push/relabel procedure from the non-parametric highest label preflow algorithm is replaced with a recursive call of a partitioning push(l,j) procedure. theorem 13. (theorem of correctness) highest-label partitioning-push algorithm computes correctly a maximum flow in the parametric network ḡ = (n,a,ū,s,t). proof: the proof of the theorem follows from the correctness of the general hl preflow algorithm for each of the subintervals of the parameter values. when the algorithm terminates, let sp be the set of all nodes which are reachable from the source node within the subinterval jp. for the resulting cuts [sp,tp] and the intervals jp = (λp,λp+1], p = 1, . . . ,k, the following observations 120 l. sângeorzan, m. parpalea, m.m. parpalea, a. repanovici, r. matefi, i. nicolae hold: (i ) if i ∈ sp, j ∈ tp and (i,j) ∈ a then jp ∩ ĩ(i,j) = ∅ for otherwise node j could be reached from s in ˜̄g(f̄). hence, by the definition of ĩ(i,j), ˜̄f(i,j;λ) = ū(i,j;λ), ∀λ ∈ jp; (ii ) if i ∈ tp, j ∈ sp and (i,j) ∈ a then jp ∩ ĩ(j,i) = ∅ for otherwise node i could be reached from s in ˜̄g(f̄). hence, ˜̄f(i,j;λ) = 0, ∀λ ∈ jp. summarizing, ∑ (i,j)∈(sp,tp) ˜̄f(i,j;λ) = ∑ (i,j)∈(sp,tp) ū(i,j;λ) and ∑ (i,j)∈(tp,sp) ˜̄f(i,j;λ) = 0, thus the obtained flow is a maximum parametric flow which equals the capacity of the minimum s− t parametric cut. ✷ 3.2 complexity issues a breakpoint is a place where the slope of the piecewise linear maximum flow value function is changing. in the worst case the number of breakpoints may be exponential in the size of the problem. the example originates from the pathological graph of zadeh [8]. the highestlabel partitioning-push algorithm overcomes this inconvenient by using the multi-thread parallel implementation of a non-parametric algorithm [12]. the main idea of this implementation is to assign a processor to each newly generated subinterval jp which will carry out the problem forward from the current configuration of the problem. for each of the newly generated subintervals, a copy of the current distance labels values is generated so that they can be independently modified in the further parallel evolution of the algorithm. theorem 14. (theorem of complexity) the parallel implementation of the highest-label partitioningpush algorithm solves the parametric maximum flow problem in o(n2m1/2 + kn) time. proof: the complexity of the non-parametric hl preflow algorithm [1] is o(n2m1/2). the hl partitioning-push algorithm generates new copies of distance label values (one for each of the two new threads which will further run in parallel) every time a breakpoint occurs, i.e. copying distance labels takes o(kn) time where k is the number of breakpoints. thus, the total complexity of the algorithm is o(n2m1/2 + kn). ✷ 4 application to assessing the legal information to write an appropriate, objective and specific rule for every imaginable situation is an impossible thing to do. "given the numberless potential variations, foreseeable and unforeseeable, in motives and circumstances, there can, probably, be no end to the possible specific scenarios and thus no limit on the number of rules that would result from trying to write an appropriate one for each possible, distinct fact situation." [11] the solution of many complex decision problems, such as assessing legal information, involves combinatorial optimization, i.e. obtaining the optimal solution among a finite set of alternatives. such optimization problems are notoriously difficult to solve. one of the primary reasons is that in most applications the number of alternatives is extremely large and only a fraction of them can be considered within a reasonable amount of time. as a result, heuristic algorithms, such as evolutionary algorithms are often applied in combinatorial optimization but their major problem consists in their high complexity. in our opinion, the proposed algorithm can successfully be used in assessing legal information, in their semantic evaluation or in generating files or legal information recording models and subsequently transmitting them to the members of the information society. a preflow approach to maximum flows in parametric networks applied to assessing the legal information 121 4.1 concepts association network regarding the law as a mathematical phenomenon, the legislation represents a logical structure. any law can be considered as a collection of fractal scenarios, made up of legal stipulations and exceptions. in order to enhance assessing features, large collections of legal information must be organized in hierarchical structures based on key concepts. generally, there are two approaches to hierarchical structures generation: the query-independent approach, offering a consistent view of the whole corpus, and the query-dependent approach which allows concepts to be organized differently depending on the query. the approach presented in our paper is a query-dependent one, based on statistical co-occurrence in determining the relationship between concepts. the algorithm builds a hierarchical structure where, for a certain topic, each of the main concepts is related to distinct sub-concepts with different degrees of association values, described by the parameter values. the first stage in generating a concept hierarchy consists in extracting a set of main concepts, related to the topic, and computing the degree of co-occurrences in relation with other subconcepts. once the set of main concepts is extracted and their degrees of co-occurrences are calculated, the concepts association network, covering all concepts, is constructed. the network is built as a directed graph with weighted arcs, where each of the concepts represents a node and the source node represents the topic of the query-dependent structure. the directed graph contains an arc between two concepts only if those concepts co-occur in at least one legal stipulation. definition 15. the degree of co-occurrence is computed as the number of legal articles (stipulation) that contain (refer to) both concepts. definition 16. the strength of a concept, representing that concept’s importance, is the sum of all its co-occurrences. the capacities of the arcs (s,i) are set to u(s,i) := strength(i) while all the arcs (j,t) have no upper value limit. definition 17. the parametric upper bound ū(i,j;λ) of an arc (i,j) is computed as: ū(i,j;λ) = strength(i) externaldegree(i) + λ · co−occurrence(i,j). (14) finally, based on the computed parametric maximum flow in the concepts association network, the sets of concepts defined by the parametric cut partitioning group the concepts in classes which are ordered in hierarchies. as long as the sink node is reached in the concepts association network, any of the directed paths starting from the source node represents a legal demarch. in the case that for various reasons the sink node can not be reached, the meaning of this fact is that some legal provisions are contradictory, inconsistent or ambiguous. 4.2 theoretical legal aspects let us analyze, for example, the general provision of the romanian constitution, namely article 21, first alignment "any person can appeal to the court for protecting its legitimate interests and liberties" and the second alignment "no law can restrict the exercise of these rights". among the main concepts with which the lawmaker operates, we can name: the free access to justice, rights, liberties, legitimate interests. these concepts are in close connection to other sub concepts which are not currently found in laws. thus, according to article 192 of the civil procedure code, "in order to protect one’s legitimate interests and rights, any person can 122 l. sângeorzan, m. parpalea, m.m. parpalea, a. repanovici, r. matefi, i. nicolae appeal to justice by approaching the competent court of law". however, there are some exceptions to the general rule stated above, namely article 193, the first alignment of the civil procedure code, according to which "approaching the court is an action which can occur only after a preliminary procedure is completed if the law is clear on that matter. the proof of completing this procedure will be attached to the summon", as well as the second alignment of the same law, according to which "failure to complete the preliminary procedure can only be invoked by the defendant in his response". to complete these provisions, we have the first alignment of article 7 of law no 554/2004 regarding the administrative procedures which state the following "before addressing the competent legal court, the person who claims that it has suffered an injustice through an unilateral administrative act must first ask the public authority, within 30 days, to revoke that certain document or some parts of it (...)". 4.3 legal case study first court solution to demonstrate the theoretical aspects discussed above, we will use the following example: by their petition addressed to the galati county appeal court, the plaintiffs r.i., b.s., g.m., s.c., g.v., s.g., r.v., s.c., a.c.e., g.c., s.a., b.i., e.a., g.g., g.g versus the romanian government, the galati county pension institution and the romanian council for fighting discrimination have asked the annulment of the government’s decision no 737/2010 regarding the method used for recalculation the amount of retirement money for the categories stated in article 1, letters c to h of law no 119/2010 regarding the establishing of some measures for maintaining the amount of retirement money, published in the official bulletin no 528 of june 29th, 2010, which the plaintiff was receiving at the date the document whose annulment is asked came into force. by the same petition, they asked, according to the provisions of article 15 of law no 554/2001 the suspension of the government’s decision no 737/2010 until this matter is resolved. by its response, the romanian government claimed the petition was inadmissible as it did not complete the preliminary procedure, according to article 7 of law no 554/2004. it further asked for the petition to be dismissed as it is not insubstantial. the romanian ministry for work, family and social protection intervened on behalf of the defendant the romanian government. by its decision no 52 of february 22nd, 2011, the galati county appeal court, dismissed the plaintiffs petition as inadmissible and allowed the ministry to intervene on behalf of the romanian government. in order to reach this decision, the court stated that the provisions of article 7, the first alignment claim that the legal proceedings can only occur after the administrative authorities are given the opportunity to revoke their document or amend it. in the matter at hand, although it was claimed that the government’s decision no 737/2001 regarding the method used for recalculating the amount of retirement money for the categories stated in article 1, letters c to h of law no 119/2010 regarding the establishing of some measures for maintaining the amount of retirement money, should be annulled, there was no proof that a preliminary proceeding existed, as this was a legal condition for the admissibility of the petition, according to article 109, second alignment of the civil procedure code. appeal court solution the plaintiffs appealed this decision, by criticizing it for being illegal and unfounded and claiming that, in the matter at hand, the preliminary procedure can be completed at any time and this condition was met. the high court of justice, by analyzing the works and documents of this case, ruled that the first court was correct to dismiss the plaintiff’s petition as inadmissible, a preflow approach to maximum flows in parametric networks applied to assessing the legal information 123 as it does not respect the provisions of the first alignment of article 7 of law no 554/2004. according to these provisions "before addressing the court, the person who claims one of its rights was disrespected must first ask the public authority who created the document, within 30 days of that date, to revoked that document. the petition can also be addressed to the superior hierarchical organ, if such an organ exists". respecting the terms and conditions stated by law for the preliminary procedure is a special demand of the law and the disrespecting of this demand causes the plaintiffs inability to appeal the courts. in the matter at hand, the plaintiffs asked for the annulment of government’s decision no 737/2010 without proving they have completed the preliminary proceedings. this is similar to not respecting the legal obligation enforced by the first alignment of article 7 of law no 554/2004. indeed, according to the provisions of article 7, the first alignment of law no 554/2004, "in case of an administrative act, the preliminary procedure can be completed at any time", as the government’s decision no 737/2010 is considered to be an administrative act with power of the law. however, these legal provisions must be interpreted in close connection with those of article 11 of the administrative proceedings law which describes the term to formulate such a demand, as well as with the provisions of article 7, the first alignment of the same law. the phrase "at any time" allows for the possibility of formulating such a petition and for completing the preliminary proceedings without respecting the term stated in article 11 of law no 554/2010, except for "ordinances of their dispositions which are considered to be unconstitutional, as well as administrative documents with power of the law which are considered to be illegal", as this course of action is regulated by the fourth alignment of this article. the preliminary procedure regulated by article 7, alignments (1) and (11) of law no 554/2004, is a condition for the admissibility of the petition, according to article 109, second alignment of the civil procedure code, as it is previous to appealing the court. considering all aspects mentioned above and seeing that there are no reasons to annul the first court’s decision, the high court will dismiss the appeal as unfounded [13]. figure 1: concepts association network for the presented legal example. as can be easily seen in the concepts association network presented above in fig.1, by following the directed path which does not include the preliminary procedure attached to a summon, the sink node can never be reached, revealing the fact that some legal provisions are contradictory, inconsistent or ambiguous. further developing the above presented model, based on an appropriate corpus (database) of legal stipulations which is generated by our algorithm in the way it has previously been presented above already, a computer application can be developed so that to any online transmitted legal question, a solicitor could receive the adequate legal information. 124 l. sângeorzan, m. parpalea, m.m. parpalea, a. repanovici, r. matefi, i. nicolae 5 conclusions the maximum flow problem in parametric networks turns out to be an important scenario in practice since the complexity of its solving algorithm was reduced to a linear dependency of the number of breakpoints. the present article presents the state-of-the-art of the approaches for solving the parametric maximum flow problem and presents an original parametric preflow algorithm, based on network partitioning technique. after presenting the basic parametric network flow terminology adapted for the parametric network with linear capacity functions and zero lower bounds, the proposed highest label partitioning push (hlpp) algorithm is described in details, being accompanied by the corresponding theorems of correctness and of complexity of the algorithm. in one of its final sections, the article also proposes a way of implementation of our algorithm in the legislation domain. the definitions (12-14) and interpretations contained in this section are also original contributions of the authors. moreover, the given example shows the way the proposed algorithm progressively generates hierarchical structures of legal articles (stipulations), gathered according to their relevance in explaining a general legal topic (or legal problem). using suitable file structures for legal information recording, these sets of legal stipulations can be transmitted to any user (or solicitor). bibliography [1] ahuja, r., magnanti, t. and orlin, j.(1993); network flows. theory, algorithms and applications, prentice hall, inc., englewood cliffs, new jersey, 1993. [2] bichot, c-e., siarry, p. (2011), graph partitioning: optimisation and applications, iste wiley. [3] hamacher, h.w. and foulds, l.r. (1989); algorithms for flows with parametric capacities, zor methods and models of operations research, 33:21–37. [4] parpalea, m., ciurea, e.(2013); partitioning algorithm for the parametric maximum flow, applied mathematics (special issue on computer mathematics), 4(10a):3–10. [5] parpalea, m.m. (2009);german word order, bulletin of the transilvania university of brasov, series iv, 2(51): 175–182. [6] parpalea, m. (2010); min-max algorithm for the parametric flow problem, bulletin of the transilvania university of brasov, series iii: mathematics, informatics, physics, 3(52):191– 198. [7] parvan, m., ghionea, f., flaut, c. (2008); a mathematical model of the relevant request determination in the transport problem, mathematics and computer in business and economics, proc.of the 9th wseas intl. conf. on mathematics and computer in business and economics (mcbe’08), bucureşti, 24-26 iunie 2008, 105–111. [8] ruhe, g. (1988); complexity results for multicriterial and parametric network flows using a pathological graph of zadeh, zeitschrift fur oper. res., 32: 9–27. [9] ruhe, g. (1985); characterization of all optimal solutions and parametric maximal flows in networks, optimization, 16(1): 51–61. [10] sângeorzan, l., parpalea, m., et al. (2010); partitioning preflow-pull algorithm for the parametric net-work flow problem a linguistic rule-based constraints optimisation a preflow approach to maximum flows in parametric networks applied to assessing the legal information 125 approach, recent advances in neural networks, fuzzy systems and evolutionary computing, proc.of the 11th wseas international conference on neural networks, iasi, romania, 111–116. [11] stumpff, a.m. (2013); the law is a fractal: the attempt to anticipate everything, loyola university chicago law journal, 44. [12] yong, c., chang-le, lu (2006); using multi-thread technology realize most short-path parallel algorithm, world academy of science, engineering and technology, 15: 11–13. [13] high court’s decision no 656/2012, given in a public hearing on february 9th, 2012, in the file no 1259/44/2010, http://www.iccj.ro/cautare.php?id=68146, accessed september 11th, 2014, at 13,40. international journal of computers communications & control issn 1841-9836, 11(2):233-247, april 2016. content based model transformations: solutions to existing issues with application in information security j. janulevičius, s. ramanauskaitė, n. goranin, a. čenys justinas janulevičius*, simona ramanauskaitė, nikolaj goranin, antanas čenys vilnius gediminas technical university, lithuania, lt-10223 vilnius, sauletekio al. 11 justinas.janulevicius@vgtu.lt, simona.ramanauskaite@vgtu.lt, nikolaj.goranin@vgtu.lt, antanas.cenys@vgtu.lt *corresponding author: justinas.janulevicius@vgtu.lt abstract: model-driven engineering uses models in various stages of the software engineering. to reduce the cost of modelling and production, models are reused by transforming. therefore the accuracy of model transformations plays a key role in ensuring the quality of the process. however, problems exist when trying to transform a very abstract and content dependent model. this paper describes the issues arising from such transformations. solutions to solve problems in content based model transformation are proposed as well. the usage of proposed solutions allowing realization of semi-automatic transformations was integrated into a tool, designed for opc/xml drawing file transformations to cysemol models. the accuracy of transformations in this tool has been analyzed and presented in this paper to acquire data on the proposed solutions influence to the accuracy in content based model transformation. keywords: cyber security modeling language; model transformation; model driven engineering. 1 introduction model-driven engineering (mde) [1] uses models as a reference in various phases of software engineering. the model is created in the early stages and reused later for a number of purposes. since most of the processes and aspects can be formalized and represented as a model they are commonly used for their commodity. to obtain a certain output from different type of models is vital for mde and a variety of solutions has been proposed by the research community, spanning from experimental approaches [2] to frameworks [3]. model transformation is a very actual problem in practice as well as research as new types of models appear and more accuracy is needed. the aim of this paper is to simplify transformation of abstract, content based model transformations. content based models have very abstract structure. it can be a benefit as it increases the meta-model adaptation area, but one of the main drawbacks is that model transformations have to be done in content rather than structure level. two main problems with content based model transformations are presented in this paper along with the solutions. to analyze the effectiveness proposed solutions, they are integrated into a tool for opc/xml drawing file to cysemol model transformation. the accuracy results of the transformation are presented in this paper as well. 2 related works numerous research approaches have been carried out on model transformations, as it is a very useful process that not only leads to automation of processes [1], ease of migrating data [2] and copyright © 2006-2016 by ccc publications 234 j. janulevičius, s. ramanauskaitė, n. goranin, a. čenys at the same time liberating the systems from legacy components [3], but also, most importantly, from the economic point of view reducing costs by reusing the existing data [4]. methodologies have been developed to manage the correctness of data, stored as model attributes in the process of transformation. of which, the triple graph grammar case offers a methodology for attribute handling for bidirectional model transformations [5]. dedicated model transformations for information security modeling is a relatively new yet very important area for research. model-driven security is a growing trend with an expanding list of tools and methodologies for the subject [6]. approaches, such as secureuml model transformation semantics and analysis [7] as well as transformations between secureuml and umlsec [8] exist. however, new information security assessment tools require a more flexible approach with an ability to acquire data from less formalized model structures as information security modeling typically involves representing the analyzed infrastructure in a formal way. architectural modeling languages are typically used in this case. they include sysml [9], business process modeling notation (bpmn) [10] that enable representation of information system architecture and system environment through diagrams that can be used for various forms of analysis, one of which is security. some of them also offer extensions for industrial control system security analysis [11]. however, the aforementioned modeling languages do not offer the reasoning process. some solutions that offer modeling capabilities along with the reasoning based on the systemized expert knowledge base exist. one of them is openmads [12], the other is cyber security modeling language (cysemol) [13]. 2.1 model to model transformations model transformation enables information reuse preserving consistency between the two models [14]. in this case preservation of relationship between the source and target models as well as heterogeneity of the transformed data comes as a challenge [15]. model transformation is facing two issues: impedance mismatch and heterogeneity [16]. heterogeneity forces to deal with different data models and encodings of values. impedance mismatches are caused by the difference between logical schemas required by the applications and the ones exposed by data sources. these issues support the idea that data consistency between the models by adjusting the level of abstraction is the main task in order to avoid data loss along the transformation process [17]. model transformation patterns are obtained by using the formal concept analysis [18], where relations and element meta-classes of target and source models are linked together based on model classification group links that have similarities between them. 2.2 content dependent model transformations some languages are equipped with an abstract meta-model. the content of the model is provided in text based form as the label or property value of an element (see fig. 1). this type of meta-model is very common in general purpose systems. the abstract meta-model allows presentation of wider, not predefined content. according to taxonomy of model transformations [19] this type of transformation is considered to be exogenous, vertical transformation. typically it is used as synthesis of a higher-level, more abstract, specification into a lower-level, more specific one. transformation of such model is very content dependent. therefore the definition of transformation rules is time consuming due to these reasons: • every model component and property label has to be listed in order to write a transformation rule. as labels are human generated, the list is infinite or very long as all components and properties can have multiple synonyms. content based model transformations: solutions to existing issues with application in information security 235 figure 1: example of general purpose source meta-model • all component synonyms have to be taken into the account for the transformation rules. therefore multiple rules are required for one target concept or rule. these reasons cause higher resource consumption compared to discrete formal models. there is also a level of uncertainty, as some of the synonyms or concepts can be missed out of the model transformation rules and the process will not be able to transform the elements into the target model. an example of a content dependent source model definition is presented in fig. 2. figure 2: example of content dependent source model definition in ecore file an example of transformation rules of such model in atl is presented in fig. 3. the provided example in figure 3 only has five synonyms, however, the list of synonyms increases by taking different languages, dialects and situations into account. therefore, it would be difficult to modify the list of synonyms if the rule is hardcoded into the source code of software product. a solution for easy synonym integration is a valuable improvement. the source model element type identification in content dependent models complicates when abstract element does not have a name nor a description. in such situation the information is not enough. therefore element identification can be performed according to the structure of the element. however this task in content dependent models is complicated as well as there is no predefined specific element structure for different content source elements. therefore, to identify the type of source model, rules can to be used to check if the containing attributes match the ones expected in the target model (see. fig. 4). since the source model is abstract, the transformation is facing some complications as well: • the attribute set for each element has to be defined individually as there is no list of attribute labels and values in the meta-model. the complications are amplified if the attribute labels are hardcoded in the software source code. 236 j. janulevičius, s. ramanauskaitė, n. goranin, a. čenys r u l e element2network { from // d e f i n e which component t o t a k e from t h e s o u r c e s : a b s t r a c t ! element i n in ( s . l a b e l = ’ network ’ o r s . l a b e l = ’ net ’ o r s . l a b e l = ’ i n t e r n e t ’ o r s . l a b e l = ’lan’ o r s . l a b e l = ’wan’ ) t o // d e f i n e how t h e s o u r c e e l e m e n t have t o be t r a n s f o r m e d n1 : cysemol ! networkzone ( // c r e a t i n g networkzone e l e m e n t i d <− s . id , // with a p p r o p r i a t e p r o p e r t i e s name <− s . l a b e l , o r i g i n a l c o n n e c t i o n <− s . c o n n e c t e d . id , i n t e r f a c e <− n2 ) , // c r e a t i n g n e t w o r k i n t e r f a c e e l e m e n t t o c o n n e c t networkzone n2 : cysemol ! n e t w o r k i n t e r f a c e ( network <− n1 ) } figure 3: example of content dependent element transformation rule in atl for element, associated to networkzone in target model r u l e element2computer { from // s e a r c h e s f o r e l e m e n t s with needed a t t r i b u t e s s : a b s t r a c t ! element i n in ( s . a t t r i b u t e−>c o l l e c t ( l | l . l a b e l )−> i n c l u d e s a l l ( s e t { ’ cpu ’ , ’ ram ’ , ’ hdd ’ } ) ) t o n1 : cysemol ! operatingsystem ( name <− ’ computer with ’+ s . l a b e l ) } figure 4: example of content dependent element transformation according to the obtained parameters rule in atl content based model transformations: solutions to existing issues with application in information security 237 • a decision has to be made on which of the attributes provide a better definition of the element, at the same time which ones are unimportant and may be discarded. in case too many attributes are compared in the transformation rule, a missed attribute in the source model would make the rule worthless. on the contrary, if not enough attributes are be used in the transformation rule, element can be inconclusively (multiple possibilities) identified. • attribute labels and values are content based. therefore multiple labels and values can be linked to the same content. knowing all possible values is nearly impossible and it increases the complexity of transformation rules. • source element identification according to its structure element label, attribute labels and attribute values can be the crucial element. there is no unified methodology for measurement of the significance of the element identity from list of possible cases. all these reasons make the source model element difficult to identify using only static rules. 3 assumptions for model to model transformation improvement existing model transformation methodologies seem to have drawbacks when dealing with specific situations or have to be applied in dynamic situations [20]. therefore new solutions are proposed to improve the process and provide an alternative method that improves the efficiency and accuracy of the transformations. in this chapter ideas on how current situation in specific situations can be improved using advanced techniques, such as grammar-based model transformations [21] and model transformation by-example [22–25] element identification are presented. 3.1 dictionary based element identification a context analysis is a compex task as some words can have different meaning, synonyms for most of words exists etc. one of ways to implement context analysis is synonym based analysis. this aproach is used in web serach engine optimization [26] and user review analysis [27] during the last two years and shows promissing results. therefore dictionary based element identification approach on model to model transformation is proposed for simplification of the transformation of content dependent models. the main idea is to use a synonym database for each target metamodel element. this is done by providing additional dictionary meta-model (see fig. 5) and input of synonyms for each of the target model elements. figure 5: dictionary meta-model for element identification the condition for element identification in source model is simplified and achieved using only one condition rather than a list of conditions. an example of synonym search in dictionary model and its usage are provided in fig. 6 and fig. 7. 238 j. janulevičius, s. ramanauskaitė, n. goranin, a. čenys h e l p e r d e f : getbysyninym ( sn : s t r i n g ) : s t r i n g = i f ( // l o o k i n g f o r a synonym i n t h e d i c t i o n a r y model d i c t i o n a r y ! synonym . a l l i n s t a n c e s ()−> s e l e c t ( e | e . v a l u e . tolowercase ( ) . s t a r t s w i t h ( sn . tolowercase ( ) ) ) ) . isempty ( ) then // i f t h e r e i s no synonym − we t a k e t h e same v a l u e ’<<’+sn+’>>’ e l s e // i f t h e r e i s a synonym − we t a k e t h e word d i s t i o n a r y ! synonym . a l l i n s t a n c e s ()−> s e l e c t ( e | e . v a l u e . tolowercase ( ) = sn . tolowercase ())−> c o l l e c t ( e | e ) . f i r s t ( ) . word . v a l u e e n d i f ; figure 6: example code for search of an element name by comparing it to existing synonyms . . . from s : a b s t r a c t ! element i n in ( // h e l p e r u s a g e t o g e t synonyms from t h e d i c t i o n a r y t h i s m o d u l e . getbysynonym ( s . l a b e l ) = ’ network ’ ) . . . figure 7: simplified situation of fig. 6 used to identify element type of source model the proposed solution is more flexible as the list of synonyms for target model elements can be provided as input file and modified at any given moment. these changes do not require source code to be changed, so the dictionary file can vary depending on the target metamodel, language and other factors. 3.2 example based element identification example based model transformation is well known strategy for transform one model to another and other tasks. this technology is used for images and videos color transformation [28], semantic data analysis from a give string [29] etc. therefore we propose to use example based model transformation in order to simplify the transformation of content dependent models where elements are defined by structure only. in this case a database of target meta-model element examples is used. for each target meta-model element one example is stored in the database of source meta-model. to simplify the atl code a new meta-model was created as a copy of source meta-model (see fig. 8). when example target elements of source meta-model are presented, each source element is compared to the one stored in the example database to find the most similar element based on its structure. similarity estimation q is calculated using as follows: q = m s + m e (1) in (1) m is the number of matched labels between source and example elements; s is the number of attributes in the source element; e is the number of attributes in the example element. content based model transformations: solutions to existing issues with application in information security 239 figure 8: modified source meta-model structure for target element descriptionl sum of these two proportions takes both attribute redundancy and shortage into account. the implementation of this method is presented in fig. 9. there is some space for the improvement of this example by optimizing the code, adding the comparison and attribute labels. example based element identification should be executed after dictionary based element and attribute transformation as the labels and values of source model are content dependent and dictionary usage leads to formalization. 4 case analysis: opc /xml drawing file transformation to cysemol opc is a container file standardized format [30]. an opc format for storing graphical notation has an extension of .vsdx. the structure of the opc/xml drawing file is presented in fig. 10. the information about the element layout of the pages are stored in separate xml format files in sub-directory "visio/pages" (marked red in fig. 10). in this case object and relationship information is extracted from files stored in this directory. for this model transformation specific tags of the xml files are used. they are: • shapes describes a shape array; • shape describes a shape and its’ identification number, name, type and master template; • cell it is a versatile tag, containing information about name and value of many properties of cells under shape and section tags; • text gives text output, most commonly an object of instance, visible graphically; • section contains attribute information under it; • row stores attribute information; • connects describes array of connections; • connect defines a connector between instances, specifying sheets, cells and parts connected. 240 j. janulevičius, s. ramanauskaitė, n. goranin, a. čenys h e l p e r d e f : c a l c u l a t e v a l u e ( a : i n t e g e r , b : i n t e g e r , c : i n t e g e r ) : // c a l c u l a t e s t h e s i m i l a r i t y v a l u e q i n t e g e r = ( (m/a + m/b ) ∗ 1 0 0 ) . f l o o r ( ) ; h e l p e r d e f : c a l c u l a t e s i m i l a r i t y ( a : a b s t r a c t ! element , b : a b s t r a c t 2 ! elementd ) : i n t e g e r = t h i s m o d u l e . c a l c u l a t e v a l u e ( // c a l c u l a t e a v a l u e ( number o f a t t r i b u t e s i n s o u r c e ) a . a t t r i b u t e−>c o l l e c t ( l | l . l a b e l )−> s i z e ( ) , // c a l c u l a t e b v a l u e ( number o f a t t r i b u t e s i n t a r g e t ) b . a t t r i b u t e−>c o l l e c t ( l | l . l a b e l )−> s i z e ( ) , // c a l c u l a t e m v a l u e ( number o f maching a t t r i b u t e s ) ( ( a . a t t r i b u t e−>c o l l e c t ( l | l . l a b e l ) . a s s e t ( ) . i n t e r s e c t i o n ( b . a t t r i b u t e−>c o l l e c t ( e | e . l a b e l ) . a s s e t ()))−> s i z e ( ) ) ) ; h e l p e r c o n t e x t a b s t r a c t ! element d e f : g e t b y s t r u c t u r e ( ) : s t r i n g = l e t sk : s t r i n g = s e l f . getbyexample2 ( ) i n // s k i p p i n g f i r s t l e t t e r s , which i n d i c a t e s s i m i l a r i t y // a s t h e most s i m i l a r e l e m e n t l a b e l i s p r e s e n t e d a t t h e end sk . s u b s t r i n g ( 5 , sk−>s i z e ( ) ) ; h e l p e r c o n t e x t a b s t r a c t ! element d e f : getbyexample2 ( ) : s t r i n g = l e t elem : sequence ( a b s t r a c t 2 ! elementd ) = a b s t r a c t 2 ! elementd . a l l i n s t a n c e s ()−> a s s e q u e n c e ( ) i n elem−>i t e r a t e ( p ; l a b e l : s t r i n g = ’ 0 0 0 ’ | // l o o k i n g f o r t h e maximum q v a l u e i f t h i s m o d u l e . c a l c u l a t e s i m i l a r i t y ( s e l f , p ) > l a b e l . s u b s t r i n g ( 1 , 3 ) . t o i n t e g e r ( ) then l e t numb : i n t e g e r = // r e t u r n i n g 3 d i g i t v a l u e and l a b e l o f t h e e l e m e n t t h i s m o d u l e . c a l c u l a t e s i m i l a r i t y ( s e l f , p ) i n ( ’000 ’+numb ) . s u b s t r i n g ( ( ’ 0 0 0 ’ +numb)−> s i z e ()−2 , ( ’000 ’+numb)−> s i z e ( ) ) + ’ ’+p . l a b e l e l s e l a b e l // o t h e r w i s e r e t u r n i n g t h e same v a l u e e n d i f ) ; figure 9: example code for search of element name by comparing it to example structure content based model transformations: solutions to existing issues with application in information security 241 figure 10: the structure of an opc /xml drawing file the proposed transformations were implemented as a tool to convert .vsdx file data into cysemol meta-model. the proposed transformation methods use synonym database for target meta-model. the current version is constructed for english language only. it stores over 6000 synonyms for most common cysemol classes and attributes. an additional integrated database for connection comparison is built as well. this database has up to 1000 possible connections between cysemol elements and serves as an alternative to the example based content dependent model transformation. the ideas of model transformations based on triple graph grammars are integrated [31] as well as class identification using missing elements based on the target model connection example database. this allowed a more accurate class mapping. 4.1 transformation accuracy estimation experiment an experiment has been carried out to estimate the accuracy of the proposed model transformation methods. this experiment includes estimation of the results provided by a group of 48 informatics engineering senior year students. they were assigned to draw two diagrams in microsoft visio 2013 tool: one to present basic smes local network and one basic web server diagram. the diagram type, diagram elements, description, and detailing level were entirely a matter of choice. the only constraint was to use english language exclusively. the experiment resulted in 86 different diagrams. the most common examples are presented in fig. 11 and fig. 12. all provided diagrams were transformed to a cysemol model. the transformed models were analyzed and compared to expert prepared cysemol model in the eaat tool. the eaat tool allows graphical representation of cybersecurity area as well conforms to the model requirements for cysemol. automated formal comparison as the results were not compliant to any formalization. therefore multiple output results were generated. this fact required to analyze every situation individually by experts. during the experiment most cysemol models had more elements in comparison to the source model file data. this is due to some additional elements had to be added as interfaces (see fig. 13 and fig. 14 as results of fig. 11 and fig. 12 in cysemol). 4.2 results of transformation accuracy estimation experiment the network and web server diagrams use different microsoft visio diagram templates and elements, therefore they are analyzed separately. diagram description level categorization was to the following categories: no diagram element descriptions; defined diagram element name; defined associated diagram element properties. these categories are used for assessment of usefulness of diagram name and property descriptions. 242 j. janulevičius, s. ramanauskaitė, n. goranin, a. čenys figure 11: typical result diagram for basic smes network figure 12: typical result diagram for basic web server figure 13: typical result diagram for basic smes network in cysemol content based model transformations: solutions to existing issues with application in information security 243 figure 14: typical result diagram for basic web server the summary of analyzed file data and transformation accuracy for different diagram type and description level is presented in tables 1. the table 1 shows the number of property level detailed smes network opc/xml files are bigger comparing to web server diagrams as well as property level detailed diagrams usually have less components, comparing to less detailed microsoft visio diagrams. the most important the generated cysemol model has a bigger number of elements, comparing to the source files as existing cysemol meta-model requires more elements to evaluate the risk. transformation accuracy analysis shows that the proposed transformation method is capable of generating cysemol models from more abstract opc/xml drawing files 94% of generated cysemol model elements are correctly identified; 88% of cysemol objects are transformed to template level (with defined default values); 87% of connections between cysemol objects are added as expected (see table 1). as seen in table 1 the accuracy is dependent on used diagram content and detailing level. opc/xml drawing files have a predefined attribute list, however not all diagram elements are covered. therefore some elements cannot be detailed by defining their attribute values. moreover, model transformation might fail due to incorrect diagram element description, using modified terms. this requires maintenance of synonym database, keeping it up to date with human language and technology improvement changes. 244 j. janulevičius, s. ramanauskaitė, n. goranin, a. čenys table 1: summary of opc/xml files and cysemol model data and transformation accuracy sme networm situation web server situation total components and links with no descriptions component name added with no properties component name and properties added components and links with no descriptions component name added with no properties component name and properties added number of files 6 18 22 21 15 4 86 number of .vsdx elements 42 121 145 169 127 22 626 number of cysemol elements 130 349 435 569 412 105 1976 objects 62 184 231 268 193 50 988 connections 68 165 204 301 219 58 1015 correctly identified element % 95% 100% 100% 96% 100% 100% 98% correctly identified connection % 81% 98% 100% 64% 98% 97% 87% total 88% 99% 100% 79% 99% 98% 94% content based model transformations: solutions to existing issues with application in information security 245 5 conclusions the proposed model transformation methods offer a semi-automatic abstract relationshipbased model transformation into more detailed, domain specific template-based model. as this is a content dependent situation detailed knowledge databases are required to extract knowledge and identify model elements according to text based names and descriptions. textual dictionary based analysis is used for element identification, however further reasoning is required for definition of the source model element relation to destination metamodel. element identification in source model is one of the most important steps in model to model transformation. the 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[31] f. hermann, h. ehrig, f. orejas and u. golas (2010), formal analysis of functional behaviour for model transformations based on triple graph grammars, graph transformations, berlin, springer, 155-170. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 432-446 how to write a good paper in computer science and how will it be measured by isi web of knowledge r. andonie, i. dzitac răzvan andonie department of computer science central washington university, usa and department of electronics and computers transylvania university of braşov, romania e-mail: andonie@cwu.edu ioan dzitac department of mathematics-informatics aurel vlaicu university of arad 310330 arad, romania and cercetare dezvoltare agora piatţa tineretului, 8, oradea, romania email: ioan.dzitac@uav.ro abstract: the academic world has come to place enormous weight on bibliometric measures to assess the value of scientific publications. our paper has two major goals. first, we discuss the limits of numerical assessment tools as applied to computer science publications. second, we give guidelines on how to write a good paper, where to submit the manuscript, and how to deal with the reviewing process. we report our experience as editors of international journal of computers communications & control (ijccc). we analyze two important aspects of publishing: plagiarism and peer reviewing. as an example, we discuss the promotion assessment criteria used in the romanian academic system. we express openly our concerns about how our work is evaluated, especially by the existent bibliometric products. our conclusion is that we should combine bibliometric measures with human interpretation. keywords: scientific publication, publication assessment, plagiarism, reviewing, bibliometric indices. 1 introduction faculty work generally falls into three categories: research, teaching, and service. assessment protocols have considered, to a varied extent, scholarly activities performed in each of these areas. faculty assessment is conducted for purposes of reappointment, promotion, the awarding of tenure, and professional development. during the last decades, a societal focus on the work of university faculty as a measure of return on the public’s investment in higher education stimulated a reevaluation of how faculty performance ought to be measured and assessed. the development of workable assessment systems is difficult largely due to the fact that the value of assessment is often controversial: • assessment methods are defined differently from discipline to discipline. • assessment methods depend on the communication of standards upon which judgments of quality will be based and acceptable mechanisms for documenting faculty work. • as members of a profession, faculty reserve the right to be the sole judges of the quality of the work performance of those claiming membership among their ranks. at different levels, non-faculty administrators are also involved in the assessment process of faculty. there are cases when non-faculty are judging the scientific activity of faculty solely based on criteria like number of publications and impact factors, without having the expertise to pertinently judge these publications. this creates a possible conflict and, in many cases, we can observe tensions in the faculty-administrator relationship [1]. the conflict starts from a communication failure between the two groups. basically, copyright c⃝ 2006-2010 by ccc publications how to write a good paper in computer science and how will it be measured by isi web of knowledge 433 faculty and administrators, share the same goals. beside the psychological aspect (faculty do not like to be judged by non-academics), another cause of tension is the difficult question “how do we measure academic performance?” research performance is typically measured in terms of productivity, relying largely on the use of quantitative measures such as the number of publications, value of grants, or other creative works produced over a specified period of time. many universities use indexing systems, like thompson scientific, as a main assessment tool for publications. but how much can we trust such a numerical criterion? is it enough to count the number of citations of your paper to judge its value? our paper has two major goals. first, we focus on assessment techniques for scientific publications. we discuss the limits of numerical assessment tools. we particularly analyze the specific aspects of computer science (cs) publications knowing that cross-disciplinary comparisons should be generally avoided. second, we give guidelines on how to write a good paper, where to submit the manuscript, and how to deal with the reviewing process. we report our experience as editors of ijccc. from this perspective, we also analyze two important aspects of publishing: plagiarism and peer reviewing. we illustrate with the promotion assessment criteria used in the romanian academic system. finally, we discuss the “publish or perish practice” from the perspective of the current publication assessment techniques. 2 assessment of cs scientific publications books, which some disciplines do not consider important scientific contributions, can be a primary vehicle in cs. we discuss here only dissemination of scientific results by conference proceedings and journals and we start with an important statement: the order in which a cs publication lists authors is generally not significant. in the absence of specific indications, it should not serve as a factor in researcher evaluation. in the cs publication culture, prestigious conferences are a favorite tool for presenting original research, unlike disciplines where the prestige goes to journals and conferences are for raw initial results. acceptance rates at very selective cs conferences are between 13% and 20%. can we tell from the acceptance rate alone how good a conference is? the answer is negative. for example, the international joint conference on neural networks (ijcnn) is a much better conference than shown by its 2008 acceptance rate, which was 58%. as a regular reviewer of ijcnn, one of the authors of this paper (r.a.) considers that about 80% of the submitted papers are at least acceptable. we cannot tell how selective a conference (or a journal) is only by the percentage of papers it accepts because far fewer bad papers are submitted to the best conferences and journals. cs journals have their role, often to publish deeper versions of papers already presented at conferences. while many researchers use this opportunity, others have a successful career based largely on conference papers. there is an increasing tendency to numerically measure the quality of a paper. the starting point would be data from citation databases, such as institute for scientific information’s web of science, that can be analyzed to determine the popularity and impact of specific articles, authors, and publications. in isi web of science citation metrics information is available only when records on the publication list have been added from the web of science. usually metrics are “sum of the times cited”, “average citations per item” and “h-index”. according to hirsch [2], h is the number of articles greater than h that have at least h citations. an h-index of 20 means that there are 20 items that have 20 citations or more. the accuracy of these metrics largely depend on the accuracy of the isi database. the journal citation reports (jcr), published annually by thomson reuters, provides quantitative tools for ranking journals in accordance to statistical information from citation data. ranking is performed, for instance, by the impact factor, which is a measure of the frequency with which the “average 434 r. andonie, i. dzitac article” in a journal has been cited in a particular year or period. the annual jcr impact factor is a ratio between citations and recent citable items published. only citations from papers indexed by isi web of science are considered. thus, the impact factor of a journal is calculated by dividing the number of current year citations to the source items published in that journal during the previous two years. the impact factor jcr(j,y ) of journal j in year y , is c(y ;y −2,y −1)/p(y −2,y −1), where p(y − 2,y − 1) is the number of articles published in journal j in the previous two years (y − 1 and y − 2), and c(y ;y − 2,y − 1) is the number of citations in year y of papers published during the previous two years in journal j. publication quality is just one aspect of research quality, impact is one aspect of publication quality, and the number of citations is one aspect of impact. citation counts rely on databases such as isi, citeseer, acm digital library, scopus, and google scholar. they, too, have limitations. an issue of concern to computer scientists is the tendency to use publication databases that do not adequately cover cs, such as thomson scientific’s isi web of science. the principal problem is what isi counts [3]. the results make niklaus wirth, turing award winner, appear for minor papers from indexed publications, not his seminal 1970 pascal report. as another example, knuth’s milestone book series does not even figure. other evidences of isi’s shortcomings for cs are [3]: • isi’s internal coverage (i.e., percentage of citations of a publication in the same database) is over 80% for physics or chemistry, but only 38% for cs. therefore, we should not make crossdisciplinary comparisons based on number of citations. • isi does not index many top conferences (for instance, the international conference on software engineering (icse), the top conference in the field) but indexes sigplan notices, an unrefereed publication. • isi’s “highly cited researchers” list includes many prestigious computer scientists but leaves out such iconic names as wirth, parnas, knuth and all the ten 2000-2006 turing award winners except one. • any evaluation criterion, especially quantitative, must be based on clear, published criteria. the methods by which isi selects documents and citations are not published or subject to debate. the problem for computer scientists is that assessment relies on often inappropriate and occasionally outlandish criteria. evaluation criteria, like isi’s impact factor or conference acceptance rates are flawed. assessment criteria must themselves undergo assessment and revision. we should at least try to base it on metrics acceptable to the profession [3]: “publication counts only assess activity. giving them any other value encourages “write-only” journals, speakers-only conferences, and stakhanovist research profiles favoring quantity over quality.” 3 where to publish your work: conference vs journal the first thing to start your research is to know what the major journals and conferences in that field are. the rule of thumb is to read “good” papers and submit your papers to “good places”. how to recognize a good journal or conference? it is quite easy if you already went through the reviewing process of that publication: a good journal/conference tends to have rigorous review process. if you are a graduate student, work with your mentors to understand what constitutes good versus bad conference/journal. how to write a good paper in computer science and how will it be measured by isi web of knowledge 435 when ranking conferences, you should look at the following factors: acceptance rate, review process, program committee, who the publisher of the proceedings is, and which database is indexing the published proceedings. this is an example of a good conference (see t. n. vijaykumar [14]): the acm/ieee international symposium on computer architecture (isca) is the top forum for architecture and has been so since 1975. isca papers are 10-12 pages in length with detailed results, and go through around 5-6 double-blind reviews by the top experts on the topic. the acceptance rate is 15-20%, decided by a national science foundation (nsf) panel-style, 20-person program committee. isca takes only 30-35 papers a year (there are no short papers, no posters). for ranking journals, we have to look at the jcr impact factor, publishing house, and editors. the isi ranking system is based on the jcr impact factor (see fig. 1). figure 1: isi journal ranking: ijccc has impact factor 0.373. for example, let us compute the ijccc jcr 2009 impact factor. we have: 34 items published in ijccc (in 4 regular issues) in 2007; 35+ 89 = 124 items published in ijccc (in 4 regular issues + 1 supplementary issue) in 2008. the total number of articles published in 2007 and 2008 in ijccc is p(2007,2008) = 34 + 124 = 158. in 2009, there are c(2009;2007,2008) = 22 + 37 = 59 citations to items published in 2007 and 2008. hence, jcr(ijccc,2009) = c(2009;2007,2008)/p(2007,2008) = 59/158 = 0.373. ijccc is a new journal, founded in 2006. authors use different journal title abbreviations, and this makes journal identification by isi problematic. in addition to this, since the supplementary 2008 issue contains the icccc 2008 proceedings, many citations appear as “proceedings of icccc 2008”, without mentioning the journal. these are two reasons why the isi web of science database contains incorrect ijccc entries, which influences the impact factor of our journal. we recognize here the traditional “garbage in garbage out” problem. a solution would be to use for each journal its unique issn. this is certainly not very easy, because all indexing systems have to change, and authors may have to include the issn’s in their list of publications. however, we think that the effort is worth, since this would make bibliometric indicators more precise. 436 r. andonie, i. dzitac here are examples of important journals and conferences, for different cs domains: • database: ieee trans on knowledge and data engineering, acm trans on database systems, int’l conf on vldb. • software engineering: ieee trans on software engineering, acm trans on software eng. and methodology, ieee int’l conf on software engineering. • computer networks: ieee/acm trans on networking, ieee infocom, acm mobicom. • parallel/distributed systems: ieee trans on parallel and distributed systems, acm trans on computer systems, icdcs, ipdps. • neural networks: ieee trans. on neural networks, neural computation, nips, ijcnn, icann, iwann, esann. should we submit to a journal or conference? in the cs context, this question deserves a discussion. 3.1 why prefer a conference according to patterson et al. [4], in cs, conference publication is preferred to journal publication, at least for experimentalists. this was the recommendation (a memo) of the computer research association (cra) in 1999. the cra memo asserts that conference publication is superior to journal publication in computer science. according to the memo, the typical conference submission receives four to five evaluations, whereas the typical journal submission receives only two to three evaluations. computing researchers are right to view conferences as an important archival venue and use acceptance rate as an indicator of future impact. papers in highly selective conferences, with acceptance rates of 30% or less, should continue to be treated as first-class research contributions with impact comparable to, or better than, journal papers [5]. this distinguishes computer science from other academic fields where only journal publication carries real weight. there are two main reasons to publish in the proceedings of selective conferences: • conferences are more timely than journals. • conferences have higher standards of novelty. journals often only require 20-30% of the material to be new, compared to an earlier conference version. conference selectivity serves two purposes: pick the best submitted papers and signal prospective authors and readers about conference quality. is there a connection between conference acceptance rate and impact factor, where impact is measured by the number of citations received? the answer is positive, up to some threshold. adopting the right selectivity level helps attract better submissions and more citations. chen and konstan [5] found, with respect to acm-wide data, that acceptance rates of 15-20% seem optimal for generating the highest number of future citations for both the proceedings as a whole and the top papers submitted. conferences rejecting 85% or more of their submissions risk discouraging overall submissions and inadvertently filtering out high-impact research. 3.2 why prefer a journal many universities evaluate faculty on the basis of journal publications because, in most scientific fields, journals have higher standards than conferences. journals may have longer page limits and journal reviews tend to be more detailed. many times, conference committees enlist inexperienced graduate students as reviewers of papers in order to meet the quota for reviews. because conference papers are how to write a good paper in computer science and how will it be measured by isi web of knowledge 437 limited in length, and because a large number of papers must be reviewed within a short time, the quality of reviews of conference papers is generally low. in contrast, for journals, because there are usually no page limits, authors can explain their ideas completely. editors can choose qualified reviewers carefully. reviewers can take adequate time to write thorough reviews. by polishing a manuscript for journal publication, the author minimizes the number of errors and improves the clarity of the exposition. thus, journal papers are more likely to be correct and readable than conference papers. journals are more widely distributed through libraries than conference proceedings, which go out of print quickly. in all disciplines, the criteria for quality include innovation, thoroughness, and clarity, appraised through rigorous peer review. across disciplines, there are common standards for the evaluation and documentation of publicly presented scholarly work [6]. according to some authors, computer science is not sufficiently different from other engineering disciplines to warrant evaluation on completely different grounds. the evaluation of the scholarship of academic computer scientists should continue to emphasize publications in rigorously refereed, archival scientific journals. the “conferences vs journal” debate is far from over and was recently relaunched in communications of the acm. studying the metadata of the acm digital library, chen and konstan [5] found that papers in low-acceptance-rate conferences have higher impact than in high-acceptance-rate conferences within acm. highly selective conferences those that accept 30% or less of submissions are cited at a rate comparable to or greater than acm jounals. according to vardi [7], unlike every other academic field, computer science uses conferences rather than journals as the main publication venue. this has led to a great growth in the number of low level conferences. some call such conferences “refereed conferences” but we all know this is just an attempt to mollify promotion and tenure committees. the reviewing process performed by program committees is done under extreme time and workload pressures, and it does not rise to the level of careful refereeing. only a small fraction of conference papers are followed up by journal papers. 4 how to write a good paper and how to deal with the editor ask two questions before starting: i) what is new in your work?, and ii) what are you going to write? emphasize on the originality and significance of your work. organize your thinking and decide the structure (outlines) of your paper. stick on your central points throughout the whole paper and remove all unnecessary discussions. there are many good papers on “how to write a good paper”, and one of the best known was authored by robert day [8]. one could find there some general guidelines which always help: • start writing the day you start the research and maintain a good bibliographic database (use bibtex and latex). • think about where to submit early. • don’t try and prove you are smart and avoid the kitchen sink syndrome. • start from an outline. • work towards making your paper a pleasure for the reviewer to read. • obey the guide to authors. the structure of a cs paper is not different than the structure of any scientific publication: title, abstract, introduction, background, related work, system model & problem statement, methods / solutions, simulations / experiments, conclusion, acknowledgment, and references. almost everybody knows this. however, there are some simple rules of thumb which can make life easier. 438 r. andonie, i. dzitac according to the “hourglass model” [9], a paper should start from general, and go through particular back to general (fig. 2). figure 2: hourglass model (swales [9]). 1. choose a right title. the title should be very specific, not too broad. the title should be substantially different from others. avoid general titles, e.g., “research on data mining”, “contributions to information theory”, “some research on job assignment in cluster computing”, or “a new framework for distributed computing”. 2. write a concise abstract. an abstract should tell: • motivation: why do we care about the problem and the results? • problem statement: what problem is the paper trying to solve and what is the scope of the work? • approach: what was done to solve the problem? • results: what is the answer to the problem? • conclusions: what implications does the answer imply? a good hint is pack each of these part into one sentence. 3. organization of your paper. • plan your sections and subsections. use a top-down writing method. use a sentence to represent the points (paragraphs) in each subsections. • writing details: expand a sentence in the sketch into a paragraph. • keep a logical flow from section to section, paragraph to paragraph, and sentence to sentence. 4. introduction: the most difficult part. this is why one of the authors of the present paper (r. a.) prefers to write the introduction in the final stage and, whenever he writes a paper with students, he prefers to write this section entirely himself. an introduction should: • establish a territory: – bring out the importance of the subject – make general statements about the subject – present an overview on current research on the subject how to write a good paper in computer science and how will it be measured by isi web of knowledge 439 • establish a niche: – oppose an existing assumption – reveal a research gap – formulate a research question – continue a tradition, or propose a completely new approach • occupy the niche: – sketch the intent of the own work – outline important characteristics and results of your own work – give a brief outlook on the structure of the paper 5. related work and list of references. use a proper selection of references. show your knowledge in the related area. give credit to other researchers (reviewers are usually chosen from the references). cite good quality work, particularly when citing your own work, and up to date work. related work should be organized to serve your topic. emphasize on the significance and originality of your work. 6. your conclusions. a research paper should be circular in arguments, i.e., the conclusion should return to the opening, and examine the original purpose in the light of the research presented. assuming that you have decided where to submit, and your paper is ready. what is the next step after writing a nice letter to the editor (if it is a journal) with your manuscript submitted electronically? most probably, your paper will be rejected, or conditionally accepted after a major review. it is almost impossible to have your paper accepted without any modification suggested ( except if your name is donald knuth or david patterson!). even an acceptance “with minor modifications” is rare. the best scientists get rejected and/or have to make major revisions. it is unreasonable to get defensive, unless it is really called for. you should address every aspect of the reviewers concerns. make it obvious to the reviewer through the summary of changes and the revised manuscript itself of the changes you have made. do not add new science unless it is called for. a good referee report is immensely valuable, even if it tears your paper apart. remember, each report was prepared without charge by someone whose time you could not buy. all the errors found are things you can correct before publication. appreciate referee reports, and make use of them. an author who feels insulted and ignores referee reports wastes an invaluable resource and the referees’ time. finally, we have to remember what you put in the literature is your scientific legacy after all else is gone. 5 plagiarism and innovation since ijccc is a young journal, it is reasonable to believe that our review process is less professional than for a top ranked journal like ieee transactions, and our journal attracts many authors who are in their early research stages. such authors are usually under terrible pressure to get something published or not to finish their phd, or not to be promoted (and possibly lose their jobs). the matter becomes serious for them, and some authors try everything to save their career, including plagiarism. after receiving your paper, editors and reviewers have to deal with a very unpleasant task of which authors are probably not aware: they have to detect possible plagiarism. only after your paper passes this preliminary screening it is considered for the true review. as ijccc editors we have to reject about 8% because of detected plagiarism. authors of these papers are blacklisted and not considered for future publication. we do not have any statistics about plagiarism frequencies at other publications, but this would certainly be of interest. we may even imagine a “plagiarism world map”! therefore, we consider important to discuss plagiarism here. 440 r. andonie, i. dzitac the rules of what constitutes plagiarism and how it should be dealt with are not always clear. according to the ieee institute print edition, there are five level of plagiarism: “1. uncredited verbatim copying of a full paper. results in a violation notice in the later article’s bibliographic record and a suspension of the offender’s ieee publication privileges for up to five years. 2. uncredited verbatim copying of a large portion (up to half) of a paper. results in a violation notice in the later article’s bibliographic record and a suspension of publication privileges for up to five years. 3. uncredited verbatim copying of individual elements such as sentences, paragraphs, or illustrations. may result in a violation notice in the later article’s bibliographic record. in addition, a written apology must be submitted to the original creator to avoid suspension of publication privileges for up to three years. 4. uncredited improper paraphrasing of pages or paragraphs (by changing a few words or phrases or rearranging the original sentence order). calls for a written apology to avoid suspension of publication privileges and a possible violation notice in the later article’s bibliographic record. 5. credited verbatim copying of a major portion of a paper without clear delineation of who did or wrote what. requires a written apology, and to avoid suspension, the document must be corrected.” the guidelines also make recommendations for dealing with repeated offenses. according to the ijccc author guidelines, submissions to ijccc must represent original material: “papers are accepted for review with the understanding that the same work has been neither submitted to, nor published in, another journal or conference. if it is determined that a paper has already appeared in anything more than a conference proceeding, or appears in or will appear in any other publication before the editorial process at ijccc is completed, the paper will be automatically rejected. papers previously published in conference proceedings, digests, preprints, or records are eligible for consideration provided that the papers have undergone substantial revision, and that the author informs the ijccc editor at the time of submission. concurrent submission to ijccc and other publications is viewed as a serious breach of ethics and, if detected, will result in immediate rejection of the submission. if any portion of your submission has previously appeared in or will appear in a conference proceeding, you should notify us at the time of submitting, make sure that the submission references the conference publication, and supply a copy of the conference version(s) to our office. please also provide a brief description of the differences between the submitted manuscript and the preliminary version(s). editors and reviewers are required to check the submitted manuscript to determine whether a sufficient amount of new material has been added to warrant publication in ijccc. if you have used your own previously published material as a basis for a new submission, then you are required to cite the previous work(s) and clearly indicate how the new submission offers substantively novel or different contributions beyond those of the previously published work(s). any manuscript not meeting these criteria will be rejected. copies of any previously published work affiliated with the new submission must also be included as supportive documentation upon submission.” whereas plagiarism can more or less measured and there are even software tools available which can help for this, the hardest part to judge as a reviewer is the level of innovation: how much innovation is enough to accept a paper? according to patterson et al. [4]: how to write a good paper in computer science and how will it be measured by isi web of knowledge 441 “when one discovers a fact about nature, it is a contribution per se, no matter how small. since anyone can create something new (in a synthetic field), that alone does not establish a contribution. rather, one must show that the creation is better. accordingly, research in computer science and engineering is largely devoted to establishing the “better” property.” the degree of innovation required depends on the policy of the publication and how selective the conference/journal is. for example, let us illustrate with a good journal. the ieee transactions on neural networks is ranked 13th overall in terms of impact factor (2.62 ) among all electrical and electronic engineering journals (206 journals), according to the latest journal citation report (see [10]). the average time between submission and publication (in print) is 18.8 months, which implies that the average time between final acceptance of a paper and publication is approximately 8 months. the conditions to have a paper accepted for ieee transactions on neural networks are posted in the authors guidelines: • full papers are characterized by novel contributions of archival nature in developing theories and/or innovative applications of neural networks and learning systems. the contribution should not be of incremental nature, but must present a well-founded and conclusive treatment of a problem. well organized survey of literature on topics of current interest may also be considered. • brief papers report sufficiently interesting new theories and/or developments on previously published work in neural networks and related areas. the contribution should be conclusive and useful. it is important to read very carefully these guidelines before submitting a paper. words like “incremental” research are important and should be understood clearly. editors, like accountants, are serious people and they do not play with words. according to qiu [11]: “one-third of more than 6,000 surveyed across six top chinese institutions admitted to plagiarism, falsification or fabrication. many blamed the culture of jigong jinli seeking quick success and short-term gain as the top reason for such practices.” “most academic evaluation in china from staff employment and job promotion to funding allocation is carried out by bureaucrats who are not experts in the field in question. when that happens, counting the number of publications, rather than assessing the quality of research, becomes the norm of evaluation.” “to critics such as rao yi, dean of the life-science school at peking university in beijing, the lack of severe sanctions for fraudsters, even in high-profile cases, also contributes to rampant academic fraud.” we discover the same situation in india [12]: “the resulting push to publish, combined with ignorance about what exactly constitutes plagiarism and research misconduct, has led to a rise in such incidents in the last eight to 10 years.” “meanwhile, the lack of both federal and institutional mechanisms that could detect and punish instances of misconduct have compounded the problem, say some scientists.” actually, plagiarism appears in all countries, but it is more visible in countries: i) with high level of corruption, where plagiarism is not punished, ii) where only the number of papers is the measure of success, and iii) where plagiarism is not considered a major offense. as editors and reviewers, we spend sometimes more time with detecting plagiarism than with judging the novelty of a paper. 442 r. andonie, i. dzitac 6 the task of the reviewer there is an endless stream of research papers submitted to conferences, journals, and other periodicals. many such publications use impartial, external experts to evaluate papers. this approach is called peer review, and the reviewers are called referees. refereeing is a public service, one of the professional obligations of a computer science and engineering professional. unfortunately, referees typically learn to produce referee reports without any formal instruction; they learn by practice [13]. the quality of a publication is also determined by the quality of the reviews. good publications attract the best reviewers and keep this way, in a positive feedback, a high publication standard. for an acceptance rate of 33%, it is fair to ask each published author to provide at least nine good reviews for submitted papers, assuming that each submitted paper has three reviewers. beside detecting plagiarism, editors have to face another administrative problem: they have to find good reviewers. since ijccc is less prestigious than an ieee or acm journal, it is perhaps less attractive for a good computer scientist to collaborate with us. as ijccc editors, we have difficulties in motivating and recruiting good reviewers. the name of the editor can help. in our case, many of our international professional friends have accepted to write reviews simply because of our personal relationship. one rule we try to apply is to let all authors from romania be reviewed by non-romanian residents. the goal is to make our review process unbiased. the most reliable reviewers are experts in their postdoc stage. senior computer scientists are less willing to meet the review deadlines. our review process is blind, but not double-blind: reviewers do know the author’s name. the simple blind review process is possibly more biased, but it has a big advantage: plagiarism is easier to detect. reading a paper as a referee is closer to what a teacher or professor does when grading a paper than what a scientist or engineer does when reading a published work. as a referee you must read the paper carefully and with an open mind, checking and evaluating the material with no presumption as to its quality or accuracy. if you want to be taken seriously as a referee, you must have a middle-of-the-road. a referee who always says “yes” or always says “no” is not helpful. don’t waste that effort on a detailed critique of a badly flawed paper that can never be made publishable. finding one or more fatal and uncorrectable flaws excuses the referee from checking all subsequent details. your report should not be insulting. don’t refer to the author as “idiot” nor to the paper as “trash”. your review should be directed at the paper, not the author. in all cases, the evaluation should be objective and fair. the more psychologically acceptable the review, the more useful it will be. after comparing the paper to an appropriate standard (not your own standards, which may be high or low), you should be able to put it into one of these categories: (1) major results; very significant (fewer than 1 percent of all papers). (2) good, solid, interesting work; a definite contribution (fewer than 10 percent). (3) minor, but positive, contribution to knowledge (perhaps 10-30 percent). (4) elegant and technically correct but useless. this category includes sophisticated analyses of flying pigs. (5) neither elegant nor useful, but not actually wrong. (6) wrong and misleading. (7) so badly written that technical evaluation is impossible. but what are the standards of a journal or conference? you should compare the paper with the average paper in that specific journal or conference, not with the best or worst. of course, in some cases the average is too low and needs to be raised by critical refereeing. as a reviewer, you should be alert to the author who tries to publish the same work in all its various combinations, permutations, and subsets, and to the author who adds the “least publishable unit” of new material to each paper. how to write a good paper in computer science and how will it be measured by isi web of knowledge 443 7 case study the ijccc reviewing process from the many hundreds of emails received by us from authors, we have selected some representative ones. an “excuse letter” for detected plagiarism: “respected sir, i am mrs. j. from ***. this paper was originally prepared in 2008 during my course work period as a conceptual paper. by the time i was not aware of the act of plagiarism. and this paper was submitted only by me without the knowledge of my supervisor. but, later in the middle of 2009 i came to know that preparing an article in this manner is avoided. regarding this i sent a mail to the journal office stated that when can it be published. but i came to know that this paper has been sent for review process and you got the result as such. so, i request you to forgive me for my activity which i have been done unknowingly and now i know to prepare the articles which exhibit only my own findings and i am sure that hereafter this type of work will not be done by me.once again i apologize for my action and sorry for the inconvenience.” here are four hilarious submission letters, with their typo and language errors: “dear sir / madam this is two paper when you received my email just reply me. **notes : which time i can recieved the final result. thank you very much” “hi,dear professor i have send a new paper for your’s journal . . . , msc. faculty member and head research group” “dear sir pl find my paper attched to this mail id. . . . ” “knowing the importance of your journal, we want to submit to you the advances of our research in the area in order to share them with your readers. hoping to hear from you soon” certainly, a nice submission letter is not a sufficient condition for a manuscript to be accepted. but can we expect a good paper from an author who does not know how to write a simple letter? here is a nice professional submission letter: “dear dr. . . . , please find attached our paper entitled . . . . this is joint work of . . . . i will serve as corresponding author. please accept it as a candidate for the publication in ijccc. this manuscript is the authors’ original work and has not been published nor has it been submitted simultaneously elsewhere. all authors have checked the manuscript and have agreed to the submission. thank you for your consideration. best regards,” finally, here is the first part of a good summary of changes document addressed to us after a major review: 444 r. andonie, i. dzitac “dear editors: i am the author of the paper entitled . . . . i revised my paper according to your suggestions and here is the explanation of the changes: 1. section 1, paragraph 5, the first sentence is changed from “a fuzzy qos routing protocol proposed to...” to “we present a fuzzy controller based qos routing algorithm...”. 2. in abstract, “ns2” is explained as network simulation version 2 explicitly. . . . 11. in simulation section, the nodes are assigned classes randomly and we removed the class distribution item in table 2 accordingly.” 8 case study promotion requirements in romania in an effort to uniformly regulate promotion requirements in romanian universities, the romanian ministry for education [15] asks for a minimum number of published papers indexed by the isi web of science citation system or other major citation indexing service. under this relatively flexible umbrella, for each disciplines there are specific standards, in an attempt to automatize academic ranking. the ranking procedures are many times ambiguous and contradictory because of the possible exempts. exempts are frequently modified, in accordance to the acting minister of education. for instance, one isi indexed paper may be replaced by several papers indexed by other citation indexing services. everybody is asking for “isi papers”. each year thomson reuters evaluates approximately 2,000 journals for possible coverage in web of science. isi web of science covers over 10,000 of the highest impact journals worldwide and over 110,000 conference proceedings. these are defined as isi indexed papers. for cs, isi indexed papers are the papers indexed by science citation index expanded. according to the present promotion regulations of the romanian ministry for education, the required isi indexed papers can be journal or conference papers. among the many good publications covered by isi web of science there also journals and proceedings of questionable quality. most promotion standards, including the basic criteria of the romanian ministry for education, consider the number of isi indexed papers, but not other publication assessment indicators, like impact factor and h-index. this stakhanovist criterion favors quantity over quality. physicists are sophisticated and they use more assessment indicators [16]: number of authors, number of citations, and impact factor. it is not easy to be a physicist in romania, especially when you have to prepare your promotion portfolio. but, after all, let us mention that the author of the h-index is jorge e. hirsch, a physicist! one may think that replacing the publication counter by the impact factor of the journal, or by the number of citations of the paper, would be sufficient to accurately quantify scholarship. at thomson reuters’ web site [17], we find the following warning: “the impact factor should be used with informed peer review. in the case of academic evaluation for tenure it is sometimes inappropriate to use the impact of the source journal to estimate the expected frequency of a recently published article.” using excessively the isi indexing scheme to evaluate cs papers has additional drawbacks: • as we have mentioned before, this creates from the very beginning a handicap for computer scientists since isi does do not adequately cover cs. • another weakness of the isi indexing scheme in cs is its poor coverage of high impact conferences, knowing that computer science uses conferences rather than journals as the main publication venue. • a third weakness is the temptation to perform cross-disciplinary comparison. observation: one of the promotion requirements of the romanian ministry for education is the publication of books as “first author”. as we have mentioned before, the order in which a cs publication lists authors is generally not significant. for articles, these requirements do not refer to the order of authors. how to write a good paper in computer science and how will it be measured by isi web of knowledge 445 9 conclusions: the current publication and review model is killing research how efficient are bibliometric measures, like impact factor and h-index? the uk government is considering using bibliometrics in its research excellence framework, a process which will assess the quality of the research output of uk universities and on the basis of the assessment results, allocate research funding. the bibliometric indicators of research quality were tested during 2009-09 [18]. the bibliometrics pilot exercise was conducted with 22 higher education institutions and covered 35 units of assessment from the 2008 research assessment exercise. both thomson reuters web of science and elsevier’s scopus databases were used. the pilot exercise showed that citation information is not sufficiently robust to be used formulaically or as a primary indicator of quality; but there is considerable scope for it to inform and enhance the process of expert review. according to [19], german universities distribute money to researchers by a formula that includes the thomson impact factor. each point of impact factor is worth about 1000 euros. in pakistan, researchers receive bonuses of up to us$20,000 a year depending on the sum of the impact factors of the journals in which they publish. and the critique addressed to the thomson impact factor, which is embedded in a commercial product, continues [19]: “to an extent that no one could have anticipated, the academic world has come to place enormous weight on a single measure that is calculated privately by a corporation with no accountability, a measure that was never meant to carry such a load. yes, some of us benefit from this flawed system-in addition to other rewards that come from publishing in high-impact journals, we collect nice cash bonuses. but none of this changes the fact that evaluating research by a single number is embarrassing reductionism, as if we were talking about figure skating rather than science.” definitely, we have to express openly our concerns about how our work is evaluated, especially by commercial bibliometric products. not only that these products are expensive, but their misuse reduces us to figures in different statistics and rankings. while numeric criteria trigger strong reactions, peer review is strongly dependent on evaluators’ choice and availability (the most competent are often the busiest), can be biased, and does not scale up. the solution is in combining techniques, subject to human interpretation. for instance, extract, first, a citation record for the individual candidate via one of the free internet search engines (e.g., google scholar). second, ask for evaluations concerning the significance of a candidate’s work from carefully selected (i.e., impartial and highly qualified) scientific peers. the pressure to publish is too large for most to ignore. grants don’t get funded unless we splatter our names across journals and conferences the world over. grad students don’t graduate. assistants and adjuncts don’t get tenure. your cv is fewer than 5 pages? you must be stupid. join more vacuous clubs, dues-hungry societies, and enter more regional poster conferences. too much time is spent writing papers rather than developing research. too much time is spent calculating impact factors and finding out who is indexing what. evaluation criteria, like isi’s impact factor or conference acceptance rates are flawed. the reviewing process is inherently flawed and may kill good papers. it is hard to find good reviewer, willing to do this voluntary work. what is the solution? one option would be to slow down. without the pressure to publish a number of isi indexed papers each year, regardless where and how important they are, we might get thorough, lengthy, reproducible publications. is this not what publishing is about? what do we gain from publishing incremental research papers? there are more people writing papers than people who have time to verify their results. 446 r. andonie, i. dzitac bibliography [1] m. del favero and n. j. bray, “herding cats and big dogs: tensions in the faculty-administrator relationship,” in higher education: handbook of theory and research, j. c. smart, ed. springer, 2010, vol. 25, pp. 477–541. isbn 978-90-481-8597-9 (print), 978-90-481-8598-6 (electronic), doi: 10.1007/978-90-481-8598-6-13. 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[7] m. y. vardi, “conferences vs. journals in computing research,” commun. acm, vol. 52, no. 5, pp. 5–5, 2009, issn: 0001-0782. [8] r. a. day, how to write & publish a scientific paper. oryx press, 1998. [9] j. swales, genre analysis: english in academic and research settings. cambridge university press, 1990, isbn-10: 0521338131; isbn-13: 978-0521338134. [10] m. m. polycarpou, “editorial: a new era for the ieee transactions on neural networks,” neural networks, ieee transactions on, vol. 19, no. 1, pp. 1–2, january 2008, issn 1045-9227. [11] j. qiu, “publish or perish in china,” nature, vol. 463, pp. 142–143, 2010, issn: 0028-0836; eissn : 1476-4687 [12] s. neelakantan, “in india, plagiarism is on the rise,” globalpost, pp. 142–143, october, 18th, 2009. [13] a. j. smith, “the task of the referee,” ieee computer, vol. 23, pp. 65–71, 1990. [14] [online]. available: http://cobweb.ecn.purdue.edu/~vijay/papers/ acceptance.html [15] [online]. available: www.edu.ro/ [16] [online]. available: www.fizica.unibuc.ro/fizica/ [17] [online]. available: thomsonreuters.com/products_services/science/free/ essays/impact_factor/ [18] [online]. available: www.hefce.ac.uk/pubs/hefce/2009/09_39/ [19] a. wilcox, “rise and fall of the thomson impact factor,” epidemiology, vol. 19, pp. 373–374, 2008, issn: 1044-3983. online issn: 1531-5487. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 490-505 implementation of the timetable problem using self-assembly of dna tiles z. cheng, z. chen, y. huang, x. zhang, j. xu zhen cheng college of computer science and technology zhejiang university of technology 288 liuhe road, hangzhou, p.r. china email: chengzhen0716@163.com zhihua chen, yufang huang, xuncai zhang department of control science and engineering huazhong university of science and technology 1037 luoyu road, wuhan, p.r.china jin xu school of electronics engineering and computer science peking university no.5 yiheyuan road haidian district, beijing, p.r.china e-mail: jxu@pku.edu.cn abstract: dna self-assembly is a promising paradigm for nanotechnology. recently, many researches demonstrate that computation by self-assembly of dna tiles may be scalable. in this paper, we show how the tile self-assembly process can be used for implementing the timetable problem. first the timetable problem can be converted into the graph edge coloring problem with some constraints, then we give the tile self-assembly model by constructing three small systems including nondeterministic assigning system, copy system and detection system to perform the graph edge coloring problem, thus the algorithm is proposed which can be successfully solved the timetable problem with the computation time complexity of θ (mn), parallely and at very low cost. keywords: timetable, self-assembly, graph edge coloring, dna tiles 1 introduction since adleman [1] demonstrated the use of recombinant dna techniques for solving a small combinational search problem, the field of dna-based computing has experienced a flowering growth and leaves us with a rich legacy. dna computing [2, 3] potentially provides a degree of parallelism and high density storage far beyond that of conventional silicon-based computers. dna tile self-assembly is an important method of molecular computation and it is also a crucial process by which objects autonomously assemble into complexes [4]. this phenomenon is common in nature and yet is poorly understood from mathematical and programming perspectives. it is believed that self-assembly technology will ultimately permit the precise fabrication of complex nanostructures. the dna nanotechnology was initiated by seeman [5] who proposed self-assembled nanostructures made of dna molecules, and the key of this technology is immobilization of holliday junction (crossover) to make well-defined dna structures. seeman [6] also utilized one of such structures called dx (double crossover) tile to realize a patterned lattice made of these tiles, which is used to construct not only simple pattern such as periodic stripes or barcodes, but also the complex algorithmic pattern. winfree [7] proposed 2d self-assembly process and showed that computation by self-assembly is turing-universal. eng [8] demonstrated that self-assembly of linear, hairpin, and branched dna molecules can generate copyright c⃝ 2006-2010 by ccc publications implementation of the timetable problem using self-assembly of dna tiles 491 regular, bilinear, and context-free languages, respectively. researchers have used dna tile algorithmic self-assembly to create crystals with patterns of binary counters [9, 10] and sierpinski triangles [11], which can be used to implement arbitrary circuit [12]. but those crystals are deterministic, generating nondeterministic crystals may hold the power to solve complex problems quickly. because of the complex and special structure of dna tiles, tile self-assembly is theoretically an efficient method of executing parallel computation where information is encoded in dna tiles and a large number of tiles can be self-assembled via sticky-end associations. mao et al. [13] experimentally implemented the first algorithmic dna tile self-assembly which performed a logical computation (cumulative xor), however that study only executed two computations on fixed inputs. for the application in arithmetic, brun [14] proposed and studied theoretically the systems that computed the sums and products of two numbers using the dna tile self-assembly model, which enough revealed that dna tile selfassembly had the basic computational ability; for the complex application in combinational problems, tile self-assembly has been proposed as a way to cope with huge combinational np-complete problems, such as solving the satisfiability problem [15] by using 2d dna self-assembly tiles, nondeterministically factoring numbers [16], deciding a system of subset sum problem [17]. but generally, the scale is limited to only moderate size problem at best, which further explores the power of computing using dna tile self-assembly. furthermore, this model can also be used in the cryptography. xor computation on pairs of bits can be used for executing a one-time pad cryptosystem that provides theoretically unbreakable security [18]. it is well known that timetable problems [19] are very difficult and time consuming to solve, especially when dealing with large instances. the timetable problem is a combinatorial problem [20] consisting in finding an assignment of a fixed number of teachers to a fixed number of hours in a week, in such a way that a large number of given constraints are satisfied. and it is also known in general to be np-complete [21]. for the most important, the timetable problems are subject to many strict constraints that are usually divided into two categories: ‘‘hard" and ‘‘soft" [22]. hard constraints are rigidly enforced and have to be satisfied for the timetable problem. soft constraints are those that are desirable but not absolutely essential. so it is difficult to generate a satisfactory solution within a short time. in order to avoid the disadvantage of their exponential computation complexity, here we mainly focus on the timetable problem based on dna tile self-assembly, which is a kind of better technique and the model can successfully perform the problem with the operation time complexity of θ (mn), parallely and at very low cost. the rest of this paper is structured as follows: section 2 describes the mechanism of self-assembly based on the dna tiles in detail. section 3 shows the process of performing the timetable problem by self-assembling. the conclusion will summarize the contribution of our work. 2 algorithmic dna tile self-assembly algorithmic dna self-assembly is both a form of nanotechnology and a model of dna computing. as a nanotechnology, the aim of algorithmic dna self-assembly is to design tiles with carefully choosing glue types on their sides. two tiles are said to be of different types if their sides have different glue types. useful tile types are nontrivial to design but relatively easy to duplicate in large quantity. a key design challenge for algorithmic dna tile self-assembly is to use only a small number of different tile types to assemble a target nanostructure to complete the corresponding computation. 2.1 models for algorithmic dna tile self-assembly the tile assembly model extends the theory of wang tilings [23] of the plane by adding a natural mechanism for growth. as a computational model, algorithmic dna self-assembly encodes the input of a computational problem into dna patterns and then manipulates these patterns to produce new dna 492 z. cheng, z. chen, y. huang, x. zhang, j. xu patterns that encode the desired output of the computational problem. informally, the model consists of a set of four sided wang tiles whose sides are each associated with a type of glue. the bonding strength between any two glues is determined by a glue function. a special tile in the tile set is denoted as the seed tile. assembly takes place by starting with the seed tile and attaching copies of tiles from the tile set one by one to the growing seed configuration whenever the total strength of attraction from the glue function meets or exceeds a fixed parameter called the temperature. generally, the tile set and the seed configuration should be constructed before the biological operations together with the suitable temperature. in addition, the tile assembly model [24] is a formal model of crystal growth. it was designed to model self-assembly of molecules such as dna. rothemund and winfree [25] defined the abstract tile assembly model, which provides a rigorous framework for analyzing algorithmic self-assembly. here, we mainly use the abstract tile assembly model to solve the timetable problem. intuitively, the model has tiles or squares that stick or don’t stick together based on various binding domain on their four sides. figure 1 gives the structures of dna tiles, mainly including the tao and tae tiles. figure 1(a) describes the structure of tao tile. figure 1(b) shows the three tao tiles joining diagonally. the tae tiles and the corresponding abstract tiles can be seen in (c), (d) and (e). figure 1(f) gives the structures which are assembled to form a compact lattice. (a) (b) (c) (e) (f) (d) figure 1: dna tiles. (a)structure of tao tile. (b)three tao tiles join diagonally. (c)structure of tae tile. (d)tx tile. (e)two tae tiles join. (f)the two types of tiles can assemble to form a compact lattice structure. 2.2 computation by dna tile self-assembly computation by self-assembly is the spontaneous self-ordering of substructures into superstructures driven by annealing of watson-crick base-pairing dna sequences. computation by dna tile selfassembly entails the building up of superstructures from starting units such that the assembly process implementation of the timetable problem using self-assembly of dna tiles 493 itself performs the actual computation. dna tile self-assembly is also a highly parallel process, where many copies of different molecules bind simultaneously to form intermediate complexes. one might be seeking to construct many copies of the same complexes at the same time, as in the assembly of periodic 1d or 2d arrays; alternatively, one might wish to assemble in parallel different molecules, as in dna-based computation, where different assemblies are sought to test out the combinatorics of the problem. a sequential or deterministic process of dna tile self-assembly has three highly parallel instruction steps [4]. the first one is molecular recognition: elementary molecules selectively bind to others. the second is growth: elementary molecules or intermediate assemblies are the building blocks that bind to each other following a sequential or hierarchical assembly. the cooperativity and non-linear behavior often characterize this process. the third way is termination: a built-in halting feature is required to specify the completion of the assembly. in practice, their growth is interrupted by physical and/or environmental constraints. dna tile self-assembly is a time-dependent process and because of this, temporal information and kinetic control may play a role in the process before thermodynamic stability is reached. 3 implementing the timetable problem based on dna tile self-assembly in this section, we first give the definition of the timetable problem, then we mainly show the algorithm for solving the timetable problem based on dna tile self-assembly, and concretely introduce the process how they can perform this problem. finally, examples of success and failure in the tile attachments are given to demonstrate the reasonability and validity of the algorithm. 3.1 the timetable problem the typical timetable problem consists in assigning a set of activities/actions/events (e.g. work shifts, duties, classes) to a set of resources (e.g. physicians, teachers, rooms) and time periods, fulfilling a set of constraints of various types. constraints stem from both nature of timetable problems and specificity of the institution involved. in other words, timetable or planning is a process of putting in a sequence or partial order a set of events to satisfy temporal and resource constraints required to achieve a certain goal, and is sometimes confused with scheduling, which is the process of assigning events to resources over time to fulfill certain performance constraints. however, many scientists consider scheduling as a special case of timetable and vice versa [26]. in this paper, we solve a special kind of timetable problem which is the coursetable problem [27]. the problem consists in scheduling courses for a set of courses in a university, taught by available teachers in a given period composing a number of weeks, and in available classrooms. although the constraints of timetable problem vary from case to case, one can classify all constraints into hard constraints and soft constraints. hard constraints must be strictly satisfied because any timetable that violates just one will become useless. a timetable that violates some soft constraints can still be usable although it may cause some inconvenience to the users. it is often very difficult to satisfy all the soft constraints in a real life. some concrete definitions of hard constraints and soft constraints in a coursetable problem will be given as follows [28]. some examples of hard constraints are: hc0 a teacher can only teach in a single place at a time. hc1 a teacher can only give one course at a time. hc2 a room can only host one course at a time. hc3 a student can only attend one course at a time. hc4 room capacities must be respected. hc5 no more than a teacher is scheduled to teach in a room each time. hc6 each subject is scheduled in a proper room (for example, a laboratory needs a proper equipment). 494 z. cheng, z. chen, y. huang, x. zhang, j. xu hc7 every teacher must have scheduled all his hours. hc8 every student must have scheduled all his hours. we denote the fact that some conditions can be deduced from other constraints. for example, hc0, hc2→ hc1. some examples of soft constraints are: sc0 the courses should be scheduled in the morning and the seminaries and laboratories in the afternoon. sc1some courses are scheduled with a prior consideration. sc2 as much as possible the preferences of the teachers and the ones of the students should be respected. 3.2 solving the timetable problem based on dna tile self-assembly here, we mainly introduce the algorithm for implementing the timetable problem based on dna tile self-assembly. first, the timetable problem can be converted into the graph edge coloring problem, then the tile self-assembly model is used to solve the graph edge coloring problem with some constraints including mainly constructing three small systems which are nondeterministic assigning system, copy system and detection system, thus the timetable problem can be successfully carried out. examples can be given to indicate how the tile self-assembly model performs in this problem. the graph edge coloring problem let g be an undirected graph where v is the set of vertices and e is the set of edges. mathematically, an assignment of colors to the edges of a graph g(one color to each edge so that adjacent edges are assigned different colors) is called a coloring of g. edges with a same color define a color class. a k-coloring of g is proper if incident edges have different colors; that is, if each color class is a matching, otherwise conflicts happen. a coloring with at least one conflict is called an infeasible coloring. a graph is k-edge-colorable if it has a proper k-edge-coloring. for the given coloring of a graph g, a set consisting of all those edges assigned the same color is referred to as a color class. in this study, the timetable problem can be converted into the graph edge coloring problem. first, a complete bipartite graph, denoted as km,n, is a graph consisting of two sets of vertices, one with m vertices and the other with n vertices. there is exactly one edge from each vertex in the one set to each vertex in the other set. there are no edges between vertices within a set. then we give the bipartite graph from the arrangement matrix of the timetable problem. second, the hard and soft constraints can be considered as the constraints of the graph edge coloring problem. according to the graph theory, the feasible solutions of the edge colorings are the arrangements of courses in the timetable problem. here, we mainly propose non-deterministic algorithm to solve the graph edge coloring problem by using the massive parallelism possible in dna tile selfassembly, thus the timetable problem with some given constrains can be successfully solved. in the process of implementing the tile self-assembly systems, many assemblies happen in parallel by creating billions of billions of copies of the participating dna tiles, so this is simulated by an exponential number of dna assemblies which can be converted into the space occupied by the dna molecules, thus we expect that the procedure will run in parallel on all possible colorings. in this case, there are many possible valid tilings, any or all of which may be produced. when tiles are implemented by real molecules, one would expect a set of tiles to nondeterministically generate a combinatorial library of input assemblies, and then a deterministic set of rule tiles could evaluate each input assembly to determine whether it represents the desired answer. implementation of the timetable problem using self-assembly of dna tiles 495 the nondeterministic assigning system non-determinism implies that at some steps the algorithm makes a non-deterministic choice. of course, there are many differences between the deterministic computation and nondeterministic computation. in terms of deterministic computation, it can be defined as a tile system to produce a unique final seed configuration if for all sequences of tile attachments, all possible final configurations are identical. comparing to the deterministic computation, the nondeterministic computation is a system in which different sequences of tile attachments can attach different tiles in the same position. intuitively, a system nondeterministically computes a function if at least one of the possible sequences of tile attachments produces a final configuration, which contains the computation results. furthermore, in many implementations of the tile assembly model that would simulate all the nondeterministic executions at once, it is useful to be able to identify which executions succeed and fail in a way that allows selecting only the successful ones. the nondeterministic assigning system can give a color set to the edges of the graph. first, the edges of the given graph can be labeled as ‘‘e1,e2,··· ,em", the vertices can be noted as xi(1 ≤ i ≤ n). here, m, n is the number of edges and vertices of the graph respectively. each edge in the graph can be nondeterministeically obtained one color. the same edge connecting different vertices should share the same color. if there is only one edge which is adjacent to the vertex, the information about the vertex and the edge needn’t be arranged on the rightmost column in the seed configuration, but should be assigned with one color at the bottom of the seed configuration of the nondeterministic assigning system. c sc(e1)c(e2)... b r x1e1rx1e2b ## # x1e1x2e2 ...r xnemr ## c sc(e1)c(e2)c(em) ... # x1e1x2e2 ... xnem c xiejc ## xiej xiej #= xiej xiej == xiej c(em) xnem (a) (b) (c) figure 2: the framework of the nondeterministic assigning system. (a) the basic tile types of this system. (b) the seed configuration of the system. (c) an example of the nondeterministic assigning system. the color set of the edges can be nonderministically generated by the tile self-assembly configuration. here, suppose the edge e j is on the vertex xi which is labeled as xie j. c(e j) is denoted as the edge e j with the color ‘‘c". the same edge on the remainder vertices can pass the information from the bottom to the upper in the tile and can obtain the same color. the basic tile types of this system can be shown in figure 2(a). figure 2(b) shows the seed configuration of the system. figure 2(c) is an example of the nondeterministic assigning system which can assign a color set to the edges. the copy system the copy system mainly carries out three functions by designing three basic tile types which can be shown as follows in figure 3. here, xie j denotes the edge e j is adjacent to the vertex xi(1 ≤ i ≤ n). ‘‘c" is the color of the edge e j on the vertex xi. 496 z. cheng, z. chen, y. huang, x. zhang, j. xu the first function is to pass the color of the edge given by the nondeterministic assigning system to the same edge on different vertices, so it can make sure that the same edge adjoining different vertices has the same color. the tile type is labeled with blue. if the edge is adjacent to different vertices, ‘‘xie jc∗" and xie j should be passed to the left and the upper of the tile respectively. otherwise, the color ‘‘c" can be passed to the edge xie j. at the same time, if the input at the bottom of the tile includes the information of the colors, the outputs are only needed to copy the information of the inputs from left to right, and synchronously from bottom to upper in the tile. the second is to copy the possible colorings generated by the nondeterministic assigning system from the bottom of the seed configuration to the uppermost of the self-assembly complexes. after the edges sharing the common vertex have been checked the colorings by the detection system, there would be a condition that the edges adjoining different vertices (k ̸= i) and with different colors (c1 ̸= c2) will meet together, but they have no need to be checked the coloring and also should be passed to the left tiles which is shown in the second tile type with the color turquoise. the third is to copy the information with the edges which are adjacent to the vertices on the rightmost column to the left tiles, so the detection system can check up whether colorings of the edges sharing the common vertex are feasible, and synchronously, they also should be passed to the upper in the tile. here, xie j is the edge which has at least two adjacent edges sharing a common vertex. once a vertex has l adjacent edges, (l −1) edges with the labels smaller then should be arranged on the rightmost column in the seed configuration of the problem, but all the edges should be at the bottom of the seed configuration. the tile type can be shown as follows with the color rosiness. xiejc x i e j x i e j xkelc xkelc xiejc2 xiejc2 x k e l c 1 k i xiejc xiejc xiejc xiejc xiejc x i e j c * xiej xiejc x i e j c * x i e j c * x i e j c * x i e j c * xiej x k e l c * xiej x k e l c * k i figure 3: the basic tile types of the copy system the detection system the key to the detection system is to make one system implement the checking operations. if the adjacent edges sharing the common vertex have different colors, the feasible coloring of the edges for the vertex has been completed with the symbol ‘‘ok". once the edges sharing one common vertex have at least two same colors, the self-assembly complexes will stop to grow with the information ‘‘no tile can match" and the coloring is not feasible. here, the coloring of the adjacent edges for each vertex should be satisfied with the same constraints. for the timetable problem, the hard and soft constraints can be described by the constraints of the edges coloring for the corresponding graph. when the edges which are adjacent to the vertices on the rightmost column and the coloring of each edge from the copy system are passed to the detection system, it can check up whether the coloring is feasible or not. if the comparison result at this step is ‘‘ok", it should be passed to the left tiles to continuously make the next color checking until the edges don’t share the same vertex, then it doesn’t check the coloring with the left tiles any more, and synchronously the colors of the edges at the bottom of the seed configuration in the nondeterministic assigning system should be passed to the higher layers. implementation of the timetable problem using self-assembly of dna tiles 497 here, ek and e j(k ̸= j) are the adjacent edges on the common vertex xi, and they have different colors, so the comparison result is ‘‘ok". for the second tile type, the edge e j which is adjacent to the vertex labeled as xi meets the color ‘‘c", then it can pass ‘‘xie jc" to the right in the tile, and the value of the tile is the color ‘‘c" of the edge e j. if the result is ‘‘no tile can match", the self-assembly complexes can’t grow any more and the input colors of the edges are not the feasible solutions of the graph edge coloring problem. the formula of the detection system can be described as follows: ok c x i e j xiejc xiejc x i e j c xiejc1 x i e k c 2 xiejc1 x i e k c 2 k j figure 4: the basic tile types of the detection system here, this system will use the l-configuration to encode inputs, and produce its output on the top row of an almost complete rectangle. therefore, systems could chain results together. the input structure encodes the edges colorings of the graph on the bottom row and encodes the edges and their adjacent vertices on the rightmost column. the output tiles needs three different kinds of tiles as follows in figure 5. the pink tile shows the edge e j with the color ‘‘c" adjoining the vertex xi which is the feasible coloring for the graph. only if all the edges adjoining different vertices have feasible colorings, the result is ‘‘success", and the feasible solution can’t be obtained otherwise. c ** success * xiejc * figure 5: the output tiles of the timetable problem here, we design the algorithm to implement the timetable problem as following steps: step 1: convert the timetable problem into the graph edge coloring. step 2: generate all possible input combinational colors of the edges to the given graph for the timetable problem with some constraints. step 3: according to the rules for dealing with the constraints using the massive parallelism of dna self-assembly to check up whether all the possible inputs are the feasible colorings for the edges in the graph. in this process, the copy system can pass the color of each edge in the graph from the bottom of the seed configuration to the higher layers, and synchronously copy the information of edges which are adjacent to the vertices, then the detection system can judge whether the colorings of the edges sharing the common vertices are feasible or not. step 4: reject all infeasible solutions according to constraints of the edge coloring and reserve all feasible solutions, therefore we can obtain feasible solutions of the graph edge coloring problem which are also the solutions of the timetable problem. step 5: reading the operation result is done by the reporter strand method. under certain biological operations, we can obtain all the result strands which run through all the results of the feasible colors of the edges for the given graph. each report strand records the result of one feasible input. the strands can be amplified by polymerase chain reaction using the primers to ligate each end of the long reporter strand. then through gel electrophoresis and dna sequencing, we can read out the result strands of 498 z. cheng, z. chen, y. huang, x. zhang, j. xu different lengths representing the information with the feasible coloring results. finally, we can easily get the feasible solutions of the timetable problem. the actual implementation detail is not discussed here since they fall outside of the scope of this paper. however, we believe that we make no arbitrary hypotheses. in fact, our work is based on the achievements that come with dna tiling computation in general. examples of the timetable problem here, we take an example of timetable problem to verify the validity of our method. suppose there are three teachers x1, x2, x3, and four classes y1, y2, y3, y4. the arrangement matrix of the courses is shown as follows: y1 y2 y3 y4 p = x1 x2 x3   1 0 1 00 1 1 0 0 1 1 1   the timetable problem has the hard constraints are: hc0 a teacher can only teach in a single place at a time. hc1 a teacher can only give one course at a time. hc2 a room can only host one course at a time. hc3 a student can only attend one course at a time. hc4 no more than a teacher is scheduled to teach in a room each time. hc5 the teacher x3 should give a course to the class y2, which is arranged in the second period in the morning time. hc6 there are enough rooms for the courses where the students attend. some soft constraints are: sc0 some courses are scheduled with a prior consideration. sc1 as much as possible the preferences of the teachers and the ones of the students should be respected. sc2 if possible, the order of the courses classes taken yj are more earlier than yj+1. first, we should convert the timetable problem into the graph edge coloring problem with some constraints. the bipartite graph from the arrangement matrix of the timetable problem can be shown in figure 6. all the edges of the graph are ‘‘e1,e2,e3,e4, e5,e6,e7" and the vertices are ‘‘x1, x2, x3, y1, y2, y3, y4". x1 x2 x3 y2 y3 y4y1 e 1 e 2 e 3 e 4 e5 e 6 e 7 figure 6: the bipartite graph from the arrangement matrix of the timetable problem second, according to the method introduced above, we need construct the basic tile types in each of the three small systems and they are the same as the tiles described above and the seed configuration, which can be shown in figure 7. when all the tiles and the seed configuration are prepared, we put them implementation of the timetable problem using self-assembly of dna tiles 499 together into the reaction buffer. according to the dna tiles prepared and the mechanism of algorithmic dna tile self-assembly through watson-crick base pairing, the self-assemble process starts at the same time with the connector tiles, so the final stage can be seen in figure 8. we also can see that the process of the three small systems performing. the nondeterministic assigning system can give a color set ‘‘rbrybry" to all the edges of the graph ‘‘e1,e2,e3,e4,e5,e6,e7" which are adjacent to the vertices ‘‘x1, x2, x3, y1, y2, y3, y4" respectively. all the edges on different vertices should be arranged at the bottom of the seed configuration no matter whether the vertices adjoin only one edge or not. at the same time, the edges ‘‘x1e1,x3e5,y2e3,y3e2,y3e4" are on the rightmost column of the seed configuration. the copy system can pass the colors of the edges from the bottom of the seed configuration to the upper layers, and pass the edges and their adjacent vertices on the rightmost column to the left tiles, so that the detection system can check whether the colorings of the edges sharing one common vertex are feasible. the vertex x1 which has two adjacent edges e1 and e2 with different colors ‘‘r" and ‘‘b" respectively is checked up the feasibility of the colorings by the detection system and the result is ‘‘ok". for the vertex x3, it has three adjacent edges e5, e6 and e7 which are at the bottom of the seed configuration. one of the two edges e5, e6 with the smaller subscripts than e7 are on the rightmost of the column. the detection system only need verify the colors of e5 and e6, e5 and e7, e6 and e7, and the three comparison results are all ‘‘ok". the method of checking the colorings of other vertices is also the same. finally, to output the computation result, we would implement a modification of the standard sequencereading operation that uses a combination of pcr and gel electrophoresis. on adding these tiles, and allowing them to anneal, then we get the final tile assembly. on adding ligase to seal the bonds, we will have a single strand of dna passing through the tiles in the final output layer, which encodes the colorings of the edges. this single strand begins with the unique nucleotide sequence labeled ‘‘success". therefore, the feasible assignment of the edge colorings can be obtained if and only if the symbol ‘‘success" appears in the result dna strand. through using the operations, we can extract the strands of different lengths representing the output tiles in the result strands. in this example, we can obtain the feasible solution of the ‘‘rbrybry". the color sets ‘‘r", ‘‘b" and ‘‘y" have the corresponding relationship with the edge sets ‘‘e1,e3,e6" , ‘‘e2,e5" and ‘‘e4,e7" which are also ‘‘x1y1, x2y2, x3y3", ‘‘x1y3, x3y2" and ‘‘x2y3, x3y4". thus the feasible solution of the timetable problem which is also the arrangement of the courses can be described as: ‘‘x1y1, x2y2, x3y3" are arranged in the first period, ‘‘x1y3, x3y2" and ‘‘x2y3, x3y4" in the second and third period respectively which are satisfied with the constraints in the problem. for the nondeterministic algorithm, we give the same example to show the failure in attaching tiles in figure 9 and don’t get the right results. if the nondeterministic assigning system gives a color set ‘‘rbrybby" to the edges ‘‘e1,e2,e3,e4,e5,e6,e7" respectively, there will be some conflicts in the process of the growth for the assembly complexes. when the detection system checks up the coloring of the vertex x3, the colorings of the edges e5 and e6 are the same which are both ‘‘b", so the conflict generates and the result is ‘‘no tile can match", thus the self-assembly complexes can’t grow any more, therefore, the coloring of the edges assigned is the infeasible solution of the problem. it means that ‘‘x1y1, x2y2", ‘‘x1y3, x3y2,x3y3" and ‘‘x2y3, x3y4" are not the feasible arrangements of the courses, here there is a conflict that the teacher x3 can’t give a course in two different classes y2 and y3 at the same period. complexity analysis the complexity of the design is considered in terms of computation time, computation space and the number of distinct tiles required. generally, suppose there are m teachers, and n classes for the timetable problem. it is obvious from the given examples that the upper bound of the computation time t is t = m(n − 1)+ n(m −1)+ mn +4+ mn + mn +2 = θ (mn). the upper bound of the computation space s taken for each assembly is the area of the assemble 500 z. cheng, z. chen, y. huang, x. zhang, j. xu x1 x2 x3 x3 y2 y3 y3 x 1 e 1 x 2 e 3 x 3 e 5 x 3 e 6 y 2 e 3 y 3 e 2 y 3 e 4 * c sc(e3) c(e1)c(e2)c(e4)l c(e7) c(e5)c(e6) x1e1x1e2x2e3x2e4x3e5x3e6x3e7y2e3y2e5y3e2y3e4y3e6 # y2e3y2e5y3e2y3e4y3e6 figure 7: the seed configuration of the timetable problem in the example implementation of the timetable problem using self-assembly of dna tiles 501 x1 x2 x3 x3 y2 y3 y3 ok rl x 1 e 1 ok rl x 2 e 3 l ok bok l rok l l l x 3 e 5 x 3 e 6 x 1 e 1 r y 2 e 3 y 3 e 2 y 3 e 4 y 3 e 4 y 3 e 4 y 3 e 4 y 3 e 4 ok r ok bok rok x 1 e 1 r x 2 e 3 x2e3rx2e4y x 2 e 3 r x 2 e 3 x 3 e 5 x 3 e 5 x 3 e 5 x 3 e 5 x3e5b x3e5b x 3 e 5 b x3e6r x3e6r x 3 e 5 b x3e7y x3e7y x 2 e 3 r x 3 e 6 x 3 e 6 x 3 e 6 x 3 e 6 x3e5b x3e5b x 3 e 6 x3e6r x 3 e 6 r x3e7y x3e7y x 3 e 6 r x 3 e 5 b x2e4y y 2 e 3 y 2 e 3 y 2 e 3 y 2 e 3 y 2 e 3 y 2 e 3 y 2 e 3 y2e3r y2e3r y2e3r y2e3r y2e3r y 2 e 3 r y2e5b y2e5b y2e5b y2e5b y2e5b y 2 e 3 r y3e2b y3e2b y3e2b y3e2b y3e2b y 3 e 2 y 3 e 2 y 3 e 2 y 3 e 2 y 3 e 2 y 3 e 4 y 3 e 4 y 3 e 4 y 3 e 4 y 3 e 2 y 3 e 2 y 3 e 2 y 3 e 2 y 3 e 2 b y3e4r y3e4r y3e4r y3e4r y3e4r y 3 e 2 b y 3 e 2 b y3e4r y 3 e 4 y 3 e 4 y 3 e 4 r y3e6r y3e6r y3e6r y3e6r y3e6r y3e6r y 3 e 4 r x3e6r x1e1rx1e2b x2e3r y3e2b x2e3rx2e4yx3e5bx3e6rx3e7yy2e3ry2e5b x1e1rx1e2b x2e3rx2e4yx3e5bx3e6rx3e7y x1e1rx1e2b x2e3rx2e4y x1e1rx1e2b x2e3rx2e4y x1e1rx1e2b x1e1rx1e2b b ry rsuccess r byb rr br y3e6r x2e3rx2e4yx3e5bx3e6rx3e7yy2e3ry2e5by3e2by3e4r x1e1rx1e2b ************* cb ry rl r by sc(e3) c(e1)c(e2)c(e4)l c(e7) c(e5)c(e6) x1e1x1e2x2e3x2e4x3e5x3e6x3e7y2e3y2e5y3e2y3e4y3e6 ######## l l y2e3y2e5y3e2y3e4y3e6 x1e1rx1e2bx2e3rx2e4yx3e5bx3e6rx3e7y x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x1e2b x1e1r x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * y2e3y2e5y3e2y3e4y3e6 l l l y2e5y3e6 x1e1rx1e2bx2e3rx2e4yx3e5bx3e6rx3e7y x1e2b x1e1r y2e5y3e4 y3e6 x1e1rx1e2bx2e3rx2e4yx3e5bx3e6rx3e7yy3e2by3e4r y2e3y2e5 x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 4 y * x 2 e 4 y * x 2 e 4 y * x 2 e 4 y * x 2 e 4 y * x3e5b x 3 e 5 b * x 3 e 5 b * x 3 e 5 b * x 3 e 5 b * x2e4y x2e4yx3e6rx3e7y x 1 e 1 r * x2e3rx3e5bx3e6rx3e7y y2e3r y2e3r y3e4r l l x1e2b x1e1rx3e5b x2e4yx3e6rx3e7yy2e3ry3e4r x1e1rx1e2bx2e3rx2e4yx3e5bx3e6rx3e7yy3e2by3e4r y2e3ry2e5b x2e3r x 3 e 6 r * x 3 e 6 r * x 3 e 6 r * x 3 e 7 y * x 3 e 7 y * x 3 e 7 y * x2e3r x1e2b x1e1rx3e5b x2e4yx3e6rx3e7yy2e3ry3e4r x2e3r y3e6r y3e6r y2e3y2e5y3e2y3e4y3e6 === x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 4 y * x 2 e 4 y * x 2 e 4 y * x 2 e 4 y * == x 3 e 5 b * x 3 e 5 b * x 3 e 5 b * x 3 e 5 b * x 3 e 6 r * x 3 e 6 r * x 3 e 6 r * x 3 e 6 r * x 3 e 7 y * x 3 e 7 y * x 3 e 7 y * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * y3e6 y3e6 y3e2b y3e2b y3e2b y3e2b y2e5b y2e5b figure 8: the final stage of the successful example for the problem 502 z. cheng, z. chen, y. huang, x. zhang, j. xu x1 x2 x3 x3 y2 y3 y3 ok rl x 1 e 1 ok rl x 2 e 3 b x 3 e 5 x 3 e 6 x 1 e 1 r y 2 e 3 y 3 e 2 y 3 e 4 y 3 e 4 y 3 e 4 y 3 e 4 y 3 e 4 x 1 e 1 r x 2 e 3 x2e3rx2e4y x 2 e 3 r x 2 e 3 x 3 e 5 x 3 e 5 x 3 e 5 x 3 e 5 x3e5b x3e5b x 3 e 5 b x3e6b x3e6b x3e7y x3e7y x 2 e 3 r x 3 e 6 x 3 e 6 x 3 e 6 x 3 e 6 x3e5b x3e5b x 3 e 6 x2e4y y 2 e 3 y 2 e 3 y 2 e 3 y 2 e 3 y 2 e 3 y2e3r y2e3r y2e5b y2e5b y3e2b y3e2b y 3 e 2 y 3 e 2 y 3 e 2 y 3 e 2 y 3 e 2 y 3 e 4 y3e4r y3e4r y3e6b y3e6b x1e1rx1e2b x2e3r x2e3rx2e4yx3e5b x1e1rx1e2b x2e3rx2e4yx3e5b x1e1rx1e2b x2e3rx2e4y x1e1rx1e2b x2e3rx2e4y x1e1rx1e2b x1e1rx1e2b b ry rb x2e3rx2e4yx3e5b x1e1rx1e2b ****** cb ry rl b by sc(e3) c(e1)c(e2)c(e4)l c(e7) c(e5)c(e6) x1e1x1e2x2e3x2e4x3e5x3e6x3e7y2e3y2e5y3e2y3e4y3e6 ######## l l y2e3y2e5y3e2y3e4y3e6 x1e1rx1e2bx2e3rx2e4yx3e5bx3e6bx3e7y x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x1e2b x1e1r x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * y2e3y2e5y3e2y3e4y3e6 l l l y2e5y3e6 x1e1rx1e2bx2e3rx2e4yx3e5bx3e6bx3e7y x1e2b x1e1r y2e5y3e4 y3e6 x1e1rx1e2bx2e3rx2e4yx3e5bx3e6bx3e7yy3e2by3e4r y2e3y2e5 x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 4 y * x 2 e 4 y * x 2 e 4 y * x 2 e 4 y * x 2 e 4 y * x3e5b x 3 e 5 b * x 3 e 5 b * x 3 e 5 b * x 3 e 5 b * x2e4y x2e4yx3e6bx3e7y x 1 e 1 r * x2e3rx3e5bx3e6bx3e7y y2e3r y2e3r y3e4r l l x1e2b x1e1rx3e5b x2e4yx3e6bx3e7yy2e3ry3e4r x1e1rx1e2bx2e3rx2e4yx3e5bx3e6bx3e7yy3e2by3e4r y2e3ry2e5b x2e3r x 3 e 6 b * x 3 e 6 b * x 3 e 6 b * x 3 e 7 y * x 3 e 7 y * x 3 e 7 y * x2e3r x1e2b x1e1rx3e5b x2e4yx3e6bx3e7yy2e3ry3e4r x2e3r y3e6b y3e6b y2e3y2e5y3e2y3e4y3e6 === x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 3 r * x 2 e 4 y * x 2 e 4 y * x 2 e 4 y * x 2 e 4 y * == x 3 e 5 b * x 3 e 5 b * x 3 e 5 b * x 3 e 5 b * x 3 e 6 b * x 3 e 6 b * x 3 e 6 b * x 3 e 6 r * x 3 e 7 y * x 3 e 7 y * x 3 e 7 y * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 2 b * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * x 1 e 1 r * y3e6 y3e6 y3e2b y3e2b y3e2b y3e2b y2e5b y2e5b no tile can match figure 9: the failure of the example for the problem because of infeasible coloring sets implementation of the timetable problem using self-assembly of dna tiles 503 complexes represented by s = [m(n−1)+n(m−1)+mn+4]∗[mn+mn+4] = θ (m2n2) which is upperbounded polynomially to the number of variables. finally, suppose the graph g which is converted from the given timetable is k-edge-colorable. the upper bound of tiles needed contain the following tiles: boundary tiles. these tile types include the input boundary tiles and computation boundary tiles. according to the number of the edges in the graph, there are 2mn tiles for the input tiles at the bottom of the seed configuration, and [m(n − 1) + n(m − 1) + 3] on the rightmost column. the computation boundary tiles contain (kmn + km +3) types. so the upper bound of the boundary tiles is [2mn + m(n − 1)+ n(m −1)+ kmn + km +6]. computation tiles. for the assigning operations, there must be (kmn+mn) tiles with the upper bound, and they are shown in figure 2. for the copy system which can be seen in figure 3, the upper bound of the tile types is [km(n − 1) + kn(m − 1) + kmn + kmn + kmn]. for the detection system in figure 4, there must be [km(n − 1) + kn(m − 1) + kmn] tiles with the upper bound. thus the upper bound of the computation tiles is [9kmn + mn − k(m + n)]. output tiles. finally, there must be some tiles to output the results. the upper bound is (2kmn +2) and the tiles are shown in figure 5. summing up all the tile types, because the value of k can be determined for the given timetable problem, so we can have the upper bound of the total number of tiles: [2mn + m(n − 1) + n(m − 1) + kmn + km +6]+[9kmn + mn − k(m + n)]+(2kmn +2)= θ (mn). 4 summary and conclusions dna tile self-assembly is looked forward to many applications in different fields. in this paper, we show how the dna self-assembly process can be used for solving the timetable problem. the advantage of our method is that once the initial strands are constructed, each operation can compute fast parallelly through the process of dna self-assembly without any participation of manpower, thus the algorithm is proposed which can be successfully implemented the timetable problem with the operation time complexity of θ (mn), parallely and at very low cost. a limitation of the algorithm, which is common for most dna computations, comes from the fact that the exponential dimension of the problem has been pushed into the physical space (volume) occupied by the dna molecules. this will eventually become a restrictive factor. the input size and thus the dna volume can’t grow forever. this implies an upper bound to the size of instances that can be solved in practice. while dna tile self-assembly suffers from high error rates, the possible sources of errors are, either an error in constructing the tiles, or an erroneous binding of tiles, methods of error control and error correction may be used to decrease the error rates in the computation of dna tile self-assembly model. many experimental results in dna tile self-assembly have not appealed to the advantages of crystal growth; however, these early works on the fundamentals of self-assembly and the physical experimental evidence of actual dna tile crystals suggest a bright future for dna tile self-assembly. the field of nanotechnology holds tremendous promise, but many technical hurdles will have to be overcome before algorithmic dna tile self-assembly can be developed into a practical commercial technology. if the molecules and supramolecules can be controlled at will, then it may be possible to achieve vastly better performance for computers and memories. so we can see that the dna tile self-assembly model has various applications in many fields and it also might open up a host of other applications in materials science, medicine, biology and other ways. 504 z. cheng, z. chen, y. huang, x. zhang, j. xu acknowledgments the work was supported by the national natural science foundation of china (grant nos. 60674106, 30870826, 60703047, 60533010 and 60803113), 863 program of china (2006aa01z104), and program for new century excellent talents in 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[28] a.r. mushi, mathematical programming for mulations for the examinations timetable problem: the case of the university of dar es salaam. african journal of science and technology (ajst) science and engineering series, vol. 5, pp. 34-40, 2004. international journal of computers communications & control issn 1841-9836, 10(4):500-507, august, 2015. an energy-efficient and routing approach for position estimation using kalman filter techniques in mobile wsns y. donoso, g. a. montoya, f. solano yezid donoso*, germán a. montoya system and computing engineering department universidad de los andes bogotá, colombia ydonoso@uniandes.edu.co, ga.montoya44@uniandes.edu.co *corresponding author: ydonoso@uniandes.edu.co fernando solano warsaw university of technology warsaw, poland fs@tele.pw.edu.pl abstract: mobile wireless sensor networks is being an attractive field due to its applicability to an increasingly amount of mobile scenarios such as wild monitoring, disaster prevention, object guidance and health monitoring. in addition, since the sensors have limited batteries, data routing has to be planned strategically in order to extend the battery lifetime as much as possible [1] [2]. in this paper, we assume gps free sensor devices, where considering a predictive technique to estimate the sensor position in a circular trajectory scenario can be useful to know when the sensor will be as close as possible to a sink, and then, help us to reduce the energy consumption by the fact of transmitting data at a short distance respect to the sink. in this paper, we propose an predictive algorithm based on kalman filter techniques to estimate the proper time at which the sensor is close as much as possible to a sink, in order to reduce the energy consumption in the sensor. specifically, we propose the usage of two kalman filters. one kalman filter is used for estimating the received signal strength indicator (rssi) level based on several control packets received at the sensor device. this rssi estimation indicates the distance from the mobile sensor device to the sink at a given time. the second kalman filter, based on the outputs from the first kalman filter, estimates the angular velocity and the angle of the mobile sensor device at a given time. once this information is processed, it is possible to estimate the mobile sensor position in a circular trajectory in order to determine how much close is the mobile sensor device respect to the sink. in addition, the communication channel noise may affect the packet content, generating non-accurate information measurements at the receptor. for this reason, our proposal is evaluated under different noise channel levels and compared against a traditional technique. our predictive routing algorithm shows better results in terms of distance accuracy to the sink and energy consumption in noisy communication channels. keywords: mwsn, position estimator, double kalman filter. 1 introduction the advances of wireless sensor networks (wsn) have allowed attaching the sensors to an entity such as an object, animal or human, to monitor a physical variable presented in its environment. however, the sensors are equipped with limited batteries whereby it is required to implement energy efficient routing techniques to extend the lifetime of the sensors. in addition, the sensors mobility can be disadvantageous for those ones that are distant from a sink, since copyright © 2006-2015 by ccc publications an energy-efficient and routing approach for position estimation using kalman filter techniques in mobile wsns 501 this indicates employing more power transmission to set a communication link, which represents more energy consumption [1] [2]. given the scenario described above, a possible solution would consist to implement energy efficient routing techniques considering the sensor position to know accurately when the sensor is near to a sink. some of these solutions propose the usage of sensors equipped with gps devices, called gps non-free approaches. however, these gps non-free solutions have in most of cases drawbacks such as high implementation costs, delays for acquiring position information and non-accurate position information [3]. in addition, these types of solutions require an extra chip for the gps [4], whereby more energy consumption is experimented. in this sense, gps free solutions can be a viable option to solve the problem described above through using predictive techniques to estimate when it is pertinent to send data packets a sink [3]. however, the prediction processes must take into account external factors such as the inherent noise introduced by the wireless channel in the environment. for this purpose, one of the most well-known and often-used tool is the kalman filter. it is basically a predictorcorrector type estimator that minimizes the estimated error covariance when some presumed conditions are accomplished. the kalman filter has been the subject of extensive research and application, for instance in the area of autonomous or assisted navigation [8]. thanks to the advances of digital computing, currently the filter is practical in many applications, and due to its simplicity and robustness it can be used in sensor devices since they have severe computation and memory constraints [2], [8]. in this paper, we propose an energy efficient routing algorithm based on kalman filtering techniques to predict a future state of the system in order to determine the accurate time to send data packets from a sensor to a sink in despite of the wireless channel noise. specifically, we propose the usage of two kalman filters. the first kalman filter is employed for estimating the rssi level based on several control packets received at the sensor device. this rssi estimation indicates the distance from the mobile sensor device to the sink at a given time. the second kalman filter, based on the outputs from the first kalman filter, estimates the angular velocity and the angle of the mobile sensor device at a given time. once this information is processed, it is possible to estimate the mobile sensor position in a circular trajectory in order to determine how much close is the mobile sensor device respect to the sink. the remainder of the paper is organized as follows: section ii shows in detail the problem description as well as the theoretical basis and the algorithm of our solution is shown in section iii. in section iv, we present results for the predictive approach and the classical solution showing the advantages and disadvantages of them. finally, in section v, we show conclusions and present future directions of our work. 2 problem description the scenario to be analysed is shown in the figure 1, which it is compound of at least two sinks, s1 and s2, and one sensor (target) t attached to an entity (human, animal or object) describing a circular trajectory. for illustrative reasons, our application case will be an athlete monitoring scenario, in which it is assumed a sensor will be attached to an athlete for sending and collection of physiological data. the scenario characteristics is described in detail below. in the circular trajectory, the target will have an angular velocity wk which it is assumed to be constant. however, the angular velocity and the circular trajectory are assumed no ideal such as in real life application scenarios, whereby we assume these variations follow a gaussian distribution with mean µ = 0 and with a standard deviation σ defined in detail later. this means the target angular velocity will increase or decrease according to a gaussian distribution along the circular trajectory, and likewise, the radius r of the circular trajectory will have a little 502 y. donoso, g. a. montoya, f. solano figure 1: application scenario. variation in time following also a gaussian distribution. in addition, it is assumed there are two sinks located close to the road, (s1 and s2) with a fixed d distance between them. further, we assume there is always a noisy wireless link communication between sinks and the target t. due to the target is compound of a wireless sensor with a limited battery, it must be used efficiently in order to extend its lifetime for future usage. the target has to collect relevant information which must be sent to any sink each certain time in order to use efficiently the sensor buffer, which also is limited. for our case, we assume the best time to send data is when the target is as close as possible to a sink. due to, for our case, it is not possible to know at any time the accurate position of the target, knowing the distance between the target and a sink could be a encouraging start to determine the target position. the distance can be inferred by extracting the rssi value of a packet, however, the noise can disturb our measurements, and then, affecting our position estimation. as a result, our problem consists how to send the data collected by the target in an efficient energy way without knowledge of the target position in the circular trajectory, taking into account the negative noise effects caused by the wireless channel to estimate distances. given the problem above, a classical/intuitive solution, proposed by us, would be the target receiving a packet at each time with its correspondent rssi value to establish how close it is from a sink. in a similar way, our novel proposal also uses the rssi attribute, but more times than the classical solution, employing an algorithm which use the kalman filter basis. 3 proposed solution taking into account the figure 1, our proposed solution would estimate when the target is close to a sink based on d1, d2 and d distances, the angular velocity wk and the angle variation θk. once these variables are calculated, it is possible to estimate when the target will be as close as possible to a sink. the d1 and d2 distances can be inferred through the rssi measured when the sinks and the target interchange control messages. however, a rssi value can be unstable since it is affected by the wireless channel, in other words, by the noise introduced by the channel itself. in this sense, it could be not enough to estimate the distance based only on a unique rssi value, because it could be a wrong estimation. for this reason, in order to do a suitable estimation of distances, it is required to collect more than one rssi values, which, in turn, will provide proper estimated values of wk and θk variables. the quality of these variables is very important since it will allow to estimate properly the moment in which the target should send its data to a sink. as a result, an energy-efficient and routing approach for position estimation using kalman filter techniques in mobile wsns 503 the more rssi values are sampled, the higher quality of wk and θk is obtained. however, the above will produce more energy consumption because more control packets are transmitted, and then, it has to be considered at setting parameters for the simulation. in addition, it is necessary to determine how many control packets should be sent in order to obtain suitable distance estimations and, at the same time, saving energy consumption to extend the lifetime of the sensor, and avoiding the noise negative effects. in terms of energy consumption, it is necessary to consider consumption by transmission and reception in the sensor. for transmission, the energy consumption is defined as (eelec + eamp)∗k ∗dn, while for reception, it corresponds to eelec ∗k. eelec is the energy consumption for codification, modulation and filtering, and eamp corresponds to energy consumption for the transmitterpoweramplifier [5]. additionally, the following parameters values are assumed: eelec is 50nj/bit, eamp is 100pj/bit/m2, n is assumed to be 2 and d corresponds to the distance to send a k-bit packet [6]. in addition to determine the number of packets for distance estimations, it is necessary to define a ∆t at which the samples will be taken. if ∆t is too high, that is, the frequency to take samples is too low, is highly probable that the closest distance to a sink generated by the solution will be too high respect to the theoretical best distance to a sink. on the contrary, if ∆t is too low, the solution will be quite better than the previous, but at the expense of a lot of energy consumption. 3.1 kalman filter applied to our proposal the kalman filter is a recursive algorithm, which is compound of two phases, as shown in the figure 2, that are performed iteratively: a prediction phase and a correction phase. for space reasons, the kalman filter basis are omitted, but if you are interested in the basis details, you can refer to [7] [8]. figure 2: kalman filter algorithm phases. we propose to perform two kalman filters as it is shown in the figure 3. one kalman filter for rssi and distances (d1 and d2), and the other one for θk and wk estimations. for the first one, since we are assuming the rssi and distances estimated should converge to a constant value, the matrix a, indicated in figure 2 expressions (i), (iii) and (iv), must be equal to "1" according to the kalman filter theoretical basis. in addition, the observation matrix h also has 504 y. donoso, g. a. montoya, f. solano figure 3: kalman filter block diagram implementation. a "1" value, because only one variable is observed. for the second kalman filter, the expression (i) in the figure 2 can be defined as follows, where a matrix can be inferred: xk = [ θk wk ] = [ 1 ∆t 0 1 ][ θk−1 wk−1 ] + wk−1 (1) in the expression above, notice that the angular velocity is indicated in uppercase and the process noise in lowercase. the a matrix expressed in (1) indicates the transition model state which, basically, defines the circular trajectory of the target. notice that ∆t indicates the time in which we want generate the next kth state of the system. the ∆t should not be selected arbitrary since a too high value of ∆t will generate excellent results but at the expense of a high energy consumption, and a too low value of ∆t will not afford accurate target position. in the algorithm1 is presented the main aspects of our solution. in addition, a block diagram in the figure 3 is shown to complement the pseudocode explanation. in line 2, the target is moved with an angular velocity ws, an angle displacement θs during a lapse of ∆ts for the movement simulation of the sensor, which is indicated with the index s. in line 3, ∆tk indicates the lapse in which the samples for kalman algorithm will be taken. in 6, paqtx indicates the number of packets with rssi values in order to do a proper estimation using a kalman algorithm, that is, to generate the values indicated in line 8 and 9. in 10, it is predicted the rssi for s1 and s2 according to the previous corrected values using a kalman filter. in 11, the estimated distances to s1 and s2 are predicted based on the estimated rssi previously obtained. in 13, a kalman filter is applied over d̂1 − k and d̂2 − k to obtain the corrected values d̂1k and d̂2k. once the distances has been calculated in 13, in line 14 the cosine law is applied to calculate θ̄k based on the values of d̂1k, d̂2k, r and d (figure 1). due to θ̄k and ∆tk is possible to obtain w̄k based on the expression (1). notice that θ̄k and ∆tk correspond to the observed values zk indicated in the expression (ii) of the figure 2. in line 15 and 16, based mainly on observed values θ̄k and w̄k; the transition model state matrix a described in the expression (1); and through the kalman filter theory is possible to predict and correct the values of θ and w , that is, θ̂−k and ŵ−k , and θ̂k and ŵk respectively. once these values are calculated the (x,y) coordinates can be found and determine how close is the target from the sinks. an energy-efficient and routing approach for position estimation using kalman filter techniques in mobile wsns 505 algorithm 1 solution pseudocode 1: while batsensor > 0 do 2: move ws, θs 3: set ∆tk 4: if ∆tk is triggered then 5: calculate d1 and d2 6: set n = paqtx 7: for i = 1 to n do 8: generate rssi1, rssi2 + white noise 9: send rssi packets to the sinks 10: predict ̂rssi1−k , ̂rssi2−k 11: predict d̂1−k , d̂2 − k 12: end for 13: correct d̂1k, d̂2k 14: calculate θk, wk 15: predict θ̂−k , ŵ − k 16: predict θ̂k, ŵk 17: end if 18: end while 4 results before to show the results, it is necessary to present the following figures, which represent some performance aspects of the traditional and our proposed solution. figure 4: performance results for traditional and our proposed solution. the figures a.1) and a.2) show the prediction based on kalman for a noise of 0.01 and 0.1, respectively, and for 20 packets to determine a distance at a given time. in the figure a.1), as the noise is lower than a.2), the prediction is more accurate than a.2). in the figures b.1) and b.2) is presented the delta angular velocity for a noise of 0.01 and 0.1, respectively, in order to determine the target position in a given time. in b.2), as the noise is quite high, the prediction is more variable than b.1). as a result, in b.1) the target position is more accurate than b.2). 506 y. donoso, g. a. montoya, f. solano for c) and d) figures, the main simulation parameters were: ∆tk = 700ms, paqtx = 200, datasizefortransmission equal to 500kb, perimeter = 400m and targetspeed = 5.52m/s. in c) it is presented the energy consumption for both traditional and our proposed solution according to different noise values. for 0.1, 0.2, and 0.3 noise values, the traditional has a lesser energy consumption than our proposed solution. however, for values higher than 0.3, our proposed solution presents a lesser energy consumption because the traditional solution behaviour is affected by the high noise level. the performance is affected due to sending extra data transmission caused by the high noise present in the communication channel. in d), the distance from the target and the sink 2 is calculated at the moment of data transmission for both traditional and our proposed solution. from 0.1 to 0.3 the performance for both are quite similar. however, for values higher than 0.3 our algorithm outperforms classic solution since it less prone to disturbances caused by noisy wireless channels. 5 conclusions in this paper, we propose a predictive algorithm based on kalman filter techniques to estimate the proper time at which the sensor is close as much as possible to a sink, in order to reduce the energy consumption in the sensor. we propose an energy efficient routing algorithm based on kalman filtering techniques to predict a future state of the system in order to determine the accurate time to send data information from a sensor to a sink. the theoretical basis and operation of our algorithm is explained in detail, and its performance is evaluated for an athlete monitoring scenario for illustrative reasons. the main conclusion of our proposed solution is about its performance in noisy communication channels. it means that the energy consumption of our algorithm is lesser than the traditional method, presenting less impact for noisy communication channels. acknowledgement this research paper was made possible through the help and support from the european union project goldfish at framework programme 7. bibliography [1] i. f. akyildiz and m. c. vuran (2010), wireless sensor networks, john wiley & sons. [2] j. zheng and a. jamalipour (2009); wireless sensor networks: a networking perspective, wiley-ieee press, isbn: 978-0-470-16763-2. [3] d. gavalas, c. konstantopoulos, b. mamalis and g. pantziou (2010); mobility prediction in mobile ad hoc networks, book chapter for next generation mobile networks and ubiquitous computing by s. pierre, igi global, 226-240. [4] b. buchli, f. sutton and j. beutel (2012); gps-equipped wireless sensor network node for high-accuracy positioning applications, wireless sensor networks lecture notes in computer science, springer, 7158: 179-195. [5] s. li, x. ma, x. wang, m. tan (2011); energy-efficient multipath routing in wireless sensor network considering wireless interference, journal of control theory and applications, 9(1): 127-132. an energy-efficient and routing approach for position estimation using kalman filter techniques in mobile wsns 507 [6] p. s. boluk, s. baydere, a. e. harmanci (2011); robust image transmission over wireless sensor networks, mobile networks and applications, 16(2): 149-170. [7] p. s. maybeck (1979); stochastic models, estimation and control, mathematics in science and engineering, vol.141. [8] g. welch and g. bishop (1995); an introduction to the kalman filter. technical report. university of north carolina at chapel hill, chapel hill, nc, usa. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 222-226 perturbation in population of pulse-coupled oscillators leads to emergence of structure a. bartha, d. dumitrescu attila bartha babes-bolyai university of cluj-napoca romania, 400084 cluj-napoca, 1 mihail cogalniceanu e-mail: abartha@cs.ubbcluj.ro d. dumitrescu babes-bolyai university of cluj-napoca romania, 400084 cluj-napoca, 1 mihail cogalniceanu e-mail: ddumitr@cs.ubbcluj.ro abstract: a new synchronization model based on pulse-coupled oscillators is proposed. a population of coupled oscillators is represented as a cellular automaton. each cell periodically enters a firing state. firing of a cell is sensed by other cells in a neighborhood of radius r. as a result the sensing cell may change its firing rate. the interaction strength between a firing and a sensing cell decreases with the squared distance between the two cells. for most starting conditions waves of synchronized firing cells emerge. simulations indicate that for certain parameter values the emergence of synchronization waves occurs only if there is dispersion in the intrinsic firing frequencies of the cells. emergence of synchronization waves is an important feature of the model. keywords: cellular automaton, synchronization, emergence, pulse-coupled oscillators 1 introduction many physical and biological systems can be described by mathematical models of coupled oscillators. examples include earthquake formation [1], synchronously firing neurons [2], synchronous flashing of fireflies [3] and heart pacemaker cells [4]. pulse-coupled oscillators are a special case when the interaction between oscillators is pulsatile. each oscillator can enter a firing state when emits a pulse of physical signal. this signal is received by other oscillators in the population and as a result they may change their own oscillation frequency. a model of pulse-coupled oscillators in the area of cardiology has been first described by peskin [4]. the model has proved itself to be applicable in many other areas and was extensively studied [5, 6]. many variations of the model have been considered, including different coupling mechanisms and topolgies. a population of pulse-coupled oscillators on a 2d grid is considered. changes in each oscillator occur at discrete time moments. oscillators (grid cells) interact only if the distance between them is less than a given radius. the interaction strength decreses with the squared distance between the interacting cells. in many models of pulse-coupled oscillators a total synchronization of the population is eventually observed. however, for the proposed model, extensive simulations indicate that a total synchronization of the population does not occur. instead, waves of synchronously firing oscillators emerge (see section 3). this is an interesting feature of the model that indicate a good potential for applications. copyright c⃝ 2006-2011 by ccc publications perturbation in population of pulse-coupled oscillators leads to emergence of structure 223 2 proposed oscillator population model 2.1 grid model a population of pulse-coupled oscillators arranged on a 2-dimensional grid is considered. the oscillator’s states are synchronously updated at regular intervals, called generations. the structure of each oscillator consist of a classical integrate-and-fire mechanism combined with a separate mechanism for sensing and integrating the signal emitted by neigbouring oscillators. a classical integrate-and fire oscillator consists of a unit which integrates an input signal until the output value of the integrator reaches a certain threshold. then the oscillator enters firing state, the integrator discharges and the process repeats by itself. the time elapsed between two consecutive firing states is called the firing period of the oscillator. the discharge time is usually considered negligable compared to the firing period. the firing period of the oscillator can be changed in at least two ways: by changing the slope of the integrator or by changing the firing threshold. in the proposed model the second approach is considered. the integrator has a constant slope (which is a parameter of the oscillator). it generates a signal which increases proportionally with time (the number of generations in our discrete model). a separate mechanism in each oscillator is responsible for sensing the signals emitted by other oscillators. the energy of the detected signals are integrated by a leaky integrator, i.e. the accumulated energy of the integrator decays exponentially in time. an oscillator meats the firing condition if the output value of the integrate-and-fire mechanism summed with the weighted value of the sensing integrator is greater than one. this is equivalent to a classic integrate-and-fire oscillator with a non-constant firing threshold: at each moment the firing threshold is one minus the (weighted) value of the sensing integrator. a particular feature of the model is that the intensity of the signal emitted by a firing oscillator decreases with the squared distance. this models the behaviour of a physical signal (like light or sound) propagating in the three-dimensional space. the proposed model limits the interaction distance between two cells to a certain value called the interaction radius. this corresponds to a sensitivity threshold of a cell. 2.2 oscillator state and update rules the internal state of an oscillator is represented by a set of three variables (p, s, f) with the following meaning : p, called potential, represents the current value of the constant-slope integrator; s, called signal, represents the current value of the sensing integrator; f, called firing state, is a boolean value which is true when the oscillator is in firing state in the given generation. at each generation the state variables of each oscillator are updated according to the following rules: the potential p is incremented with a constant value e. the value of e is an oscillator-specific parameter and determines the slope of the integrator. p <= p + e (1) the signal s is calculated by summing the intensities of the signals emitted by all oscillators in the interaction radius which are in firing state, then decrementing s by an amount proportional to the value of s in the previous generation: 224 a. bartha, d. dumitrescu s <= ∑ k 1 d2k (2) where dk is the distance between the oscillator and a neighbouring oscillator k in the interaction domain dk <= r which is in firing state and d is an oscillator-specific decay parameter. the firing state f of an oscillator is determined by comparing the weighted summ of the potential p and signal s with a constant threshold: f <= true if p + ks >= 1 f <= false otherwise where k is an oscillator-specific coupling parameter. a cell is in firing state for only one generation. in the next generation returns to normal state and the potential p is reset to zero: p <= 0 if f = true in the previous generation the model implies that if the oscillator has no firing neighbors inside the interaction radius r than it will fire periodically with a firing rate determined by the parameter e. the potential is incremented in each generation with e and when it becomes grater than 1, the oscillator enters firing state for one generation. on the next generation the potential is reset to zero and the process repeats by itself. however, if there are firing oscillators in the neigbourhood of the oscillator then the ’radiated energy’ of the firing increases the signal value of the oscillator. this value, multiplied by the coupling parameter k is added to the potential and together determine the firing condition of the oscillator. thus, if many oscillators fire in the neigbourhood of the oscillator, the oscillator will fire more frequently. 3 experimental results the proposed automaton model is simulated for different values of the parameters e, d and k. different interaction radii r are also considered. the potential p of each oscillator is initialized to a random value uniformly distributed in the interval [0, 1). the starting value of the signal s is set to zero for each oscillator. in each experiment the decay and the coupling parameters d and k are set to the same value for each cell. in some experiments the parameter e which determines the intrinsic firing frequency of the cell is chosen to have the same value. in other experiments the e value is peturbed by a small random noise, so that each cell is initalized with different values of e. in experiments where the parameter is the same for all cells, the evolution of the cell array displays a random pattern of firing cells. this pattern is maintained along more than 100,000 generations. however, if the value of the parameter e is perturbed with a small noise, in a few hundred generations clusters of firing cells emerge. waves of synchronously firing cells emerge tipically after 600-1000 generations. figure 1 depicts a typical evolution of the automaton on a grid of 150 x 150 cells. perturbation in population of pulse-coupled oscillators leads to emergence of structure 225 parameter values are e = 0.05, d = 0.7, k = 0.1 and r = 30. the initial value of parameter e for each cell is perturbed by a random value in the range [0, 0.005] around the central value of 0.05. in the diagrams of figure 1 the potential values of the cells are displayed in red color with intensity proportional to the potential. cells in the firing state are indicated in a bright color. figure 1: a typical evolution of the oscillator population. parameter setting: e = 0.05, d = 0.7, k = 0.2, r = 30. the variance of the e parameter was set to 0.005. the sequence of images represents 16 consecutive states of the oscillator array for generations 2400 to 2415. the emergence of a synchronized front of firing oscillators can be observed in this sequence. this front travels through the oscillator array and disappears at the array boundary. after 16 generations the oscillator array returns to a similar state and the process repeats itself. 4 conclusions a new model of pulse-coupled oscillators is proposed and investigated. a population of pulsecoupled oscillators on a 2d grid is considered. changes in each oscillator occur at discrete time 226 a. bartha, d. dumitrescu intervals. the interaction strength decreases with the squared distance between interacting cells. the interaction distance is limited by a maximum radius. a total synchronization of the population is eventually observed in many models of pulse-coupled oscillators. simulations indicate that total synchronization of the population does not occur in the propsed model. instead some synchronization patterns emerge. these emergent patterns are observed as waves of synchronously firing oscillators. the condition of emergence is the existence of a small perturbation in the intrinsic frequencies of individual oscillators. if all oscillators have the same intrinsic frequency then the synchronization patterns do not emerge. the proposed model describes a new autoorganization paradigm conditioned by the presence of a noise mandatory in inducing an emergent synchronization process. therefore the model provides a way of understanding self-organization in some natural or artificial systems. acknowledgment this work was supported by cncsis uefiscsu, project number pnii idei 508/2007. investing in people! phd scholarship, project co-financed by the european social fund, sectoral operational programme human resources development 2007-2013 babeş-bolyai university, cluj-napoca, romania bibliography [1] herz, a. v. m., hopeld, j. j.: earthquake cycles and neural reverberations: collective oscillations in systems with pulse-coupled threshold elements. phys. rev. lett, 75(6), 12221225 (aug. 1995) [2] izhikevich, e. m.: weakly pulse-coupled oscillators, fm interactions, synchronization, and oscillatory associative memory. ieee trans. on neural networks, 10(3), 508-526 (may, 1999) [3] buck, j., buck, e., case j., hanson, f.: control of repteroptyx cribellata. j. comp. physiology a, 144(3), 630-633 (sep, 1981) [4] peskin c.: mathematical aspects of heart physiology. courant institute of mathematical sciences, new york university (1975) [5] mirollo, r.e., strogatz, s.h.: synchronization of pulse-coupled biological oscillators, siam journal on applied mathematics, 50, 1645-1662 (1990) [6] strogatz, s.h.: from kuramoto to crawford: exploring the onset of synchronization in populations of coupled oscillators. physica d 143, 1-20 (sep, 2000) int j comput commun, issn 1841-9836 9(1):48-55, february, 2014. modeling and simulation of genetic fuzzy controller for l-type zcs quasi-resonant converter m. ranjani, p. murugesan mani ranjani* department of electrical and electronics engineering sathyabama university, chennai, 600119 tamilnadu, india *corresponding author: meranjanisasi@gmail.com palzha murugesan department of electrical and electronics engineering s.a.engineering college, chennai, 600077 tamilnadu, india murugu1942@yahoo.co.uk abstract: a new method of speed control of dc drives using series quasi-resonant (qr) zero current switching (zcs) dc to dc converters is proposed. it employs a fuzzy logic controller (flc) in feedback loop conventionally but due to the advent of new intelligent techniques the flc are optimized by genetic algorithm (ga). in this paper the ga optimization technique is applied for speed control of series zcs-qrc fed drive. the main objective of this work is to obtain reduced transient response, reduced switching stresses and switching losses which in turn enhances the efficiency and commutation capability of motor. keywords: fuzzy logic controller (flc), zero current switching quasi-resonant converter (zcs-qrc), genetic algorithm (ga), integral absolute error (iae), direct current (dc) 1 introduction the dc motor drives have the advantage of high controllability and are used in many applications such as robotic manipulators, position control, steel mining, and paper and textile industries. in some industrial applications, the dynamic response of drives is bounded by certain limitations such as transient time and steady state error. in addition when they are fed from pwm converter [1], they suffer from high switching losses, reduced reliability, electromagnetic inference and acoustic noise. to overcome the above difficulties quasi-resonant converters [2][3] are used which can be either zero current (zc) or zero voltage switching (zv) .in order to improve the speed response and regulation of the converter fed drives it is necessary to have closed loop control. conventionally closed loop response employs pi controller but the performance of the drive is sensitive to load disturbances and parameter variations. the advent of flc has been suggested as an alternative approach to conventional control techniques for complex control system like non linear system. the design of flc does not require the exact mathematical model of the system and can compensate the parameter variation due to load disturbances ( [4][5]). unfortunately a good performance cannot be obtained for incorrect membership functions, fuzzy rules and scaling factors. this necessitates the optimization technique of flc by genetic algorithm [6][8] to achieve optimal solutions of membership function, fuzzy rules and scaling factors. in this paper speed control of series zcs-qrc fed dc drive is composed of two steps. the first step is conventional control of dc drive by flc. the second step is optimization of flc by ga. copyright © 2006-2014 by ccc publications modeling and simulation of genetic fuzzy controller for l-type zcs quasi-resonant converter 49 2 analysis of series fm-zcs-qrc fed dc drive the qrc with zcs topology is considered for the present work.fig.1. shows the circuit diagram of the qrc fed motor.the waveform for the model is shown in fig.2. figure 1: zcs-half-wave series quasi-resonant converter figure 2: waveforms for half wave series zcs-qrc to analyze its behavior, the following assumptions are made: • armature inductance is much larger than resonant inductance. • the dc motor is treated as a constant current sink. • semiconductor switches are ideal. • reactive elements of the tank circuit are ideal. a switching cycle can be divided into four stages.. suppose that before mosfet turns on, diode carries the steady-state output current ia and capacitor voltage vcr is clamped at zero. at time t0, mosfet turns on, starting a switching cycle. mode 1 : linear stage [ t0 , t1 ] input current ilr rises linearly and its waveform is governed by the state equation: lr(dilr/dt) = e (1) the duration of this stage td1 (= t1 t0 ) can be solved with boundary conditions of ilr (0) = 0 and ilr (td1)= ia , thus td1 = (lria)/e (2) 50 m. ranjani, p. murugesan mode 2: resonant stage [ t1 , t2] at time t1, the input current rises to the level of ia , freewheeling diode is commutation off, and the difference between the input current and the output current ilr(t) ia flows into cr, voltage vcr rises in a sinusoidal fashion.the state equations are cr(dv cr/dt) = ilr(t)ia (3) lr(dilr/dt) = ev cr(t) (4) with initial conditions vcr(0) =0 , ilr(0)= ia and therefore, ilr(t) = ia + (e/z0)sint (5) v cr(t) = e(1− cost) (6) mode 3 : recovering stage [ t2 , t3] since mosfet is off at time t2, capacitor begins to discharge through the output loop and vcr drops linearly to zero at time t3 . the state equation during this interval is cr(dv cr/dt) = −ia (7) the duration of this stage td3 ( = t3 t2) can be solved with the initial condition vcr (0) = vcr td3 = crv cr/ia (8) td3 = cre(1− cos)/ia (9) mode 4: freewheeling stage [ t3 , t4 ] after t3, output current flows through diode. the duration of this stage is td4 ( t4 t3), td4 = ts− td1− td2− td3 (10) where ts is the period of the switching cycle. after an interval of toff, during which it is zero and vcr = 0, the gate drive to the mosfet is again applied at t4 to turn it on, and the operation during the next cycle is similar to that of the preceding cycle. by controlling the dead time ( t4t3), the average value of the armature voltage and hence the speed of the dc motor can be controlled. • characteristic impedance zn = (l1/c1) (11) • resonant angular frequency ω = 1/(l1c1) (12) • resonant frequency fn = ω/2π (13) 3 fuzzy logic controller fuzzy logic control is derived from fuzzy set theory introduced by zadeh in 1965. in fuzzy set theory, the transition between membership and non-membership can be gradual. therefore, boundaries of fuzzy sets can be vague and ambiguous, making it useful for approximate systems. the fuzzy logic controller employed to control the speed of zcs-qrc fed dc drive is as shown in fig.3.the flc is an attractive choice when precise mathematical formulations are not possible. other advantages of flc are it can work with less precise inputs, it does not need fast processors, modeling and simulation of genetic fuzzy controller for l-type zcs quasi-resonant converter 51 figure 3: fuzzy controlled series zcs qrc fed dc drive table 1: rule table with 25 rulres it needs less data storage in the form of membership functions and rules than conventional look up table for non-linear controllers; and it is more robust than other non-linear controllers, parallel with the z-network output terminals. the simplest form of membership function is triangular membership function and it is used here as the reference. as sugeno type of implication is considered, the singleton membership function is used for the output variable namely the change in duty cycle. the spread of membership functions for the inputs (error and change in error) and output (pulses) are shown in fig.4.respectively. figure 4: triangular membership functions for flc the rules for the designed fuzzy controller are given in the tables 1 .table 1 uses five linguistic variables for error and change in error with 25 rules. the five sets used for fuzzy variables ’error’ and change in error are negative big (nb), negative small (ns), zero (z), positive big (pb), and positive small (ps). from the rule table, the rules are manipulated as if error is nb and change in error is nb, then output is nb. 4 ga optimized flc genetic algorithms [9][10], which are adopted from the principle of biological evolution, are efficient search techniques that manipulate the coding representing a parameter set to reach a 52 m. ranjani, p. murugesan near optimal solution. hence by strengthening fuzzy logic controllers with genetic algorithms the searching and attainment of optimal fuzzy logic rules and high-performance membership functions will be easier and faster. gas is used regularly to solve difficult search, optimization and machine-learning problems that have previously resisted automated solutions. they can be used to solve difficult problems quickly and reliably. these algorithms are easy to interface with existing simulations and models, and they are easy to hybridize. gas includes three major operators: selection, crossover, and mutation, in addition to four control parameters: population size, selection crossover and mutation rate. this paper is concerned primarily with the selection and mutation operators. there are three main stages of a genetic algorithm; these are known as reproduction, crossover and mutation. the flow chart of a genetic algorithm is shown in figure 5. figure 5: flowchart for genetic algorithm the steps involved in genetic algorithm are described below. • start: generate random population of n chromosomes (suitable solutions for the problem). • fitness: evaluate the fitness f(x) of each chromosome x in the population. • new population: create a new population by repeating following steps until the new population is complete. – selection: select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected). – crossover: with a crossover probability, cross over the parents to form new offspring (children). if no crossover was performed, offspring is the exact copy of parents. – mutation: with a mutation probability, mutate new offspring at each locus (position in chromosome). modeling and simulation of genetic fuzzy controller for l-type zcs quasi-resonant converter 53 – accepting: place new offspring in the new population. • replace: use new generated population for a further run of the algorithm • test: if the end condition is satisfied, stop, and return the best solution in current population. • loop: go to step 2. the objective functions iae (integral absolute error). the main objective of controller is to minimize the error signal or in other words we can say that minimization of performance indices. iae = ∫ t 0 |e(t)dt| (14) the fitness value of the chromosome is the inverse of the performance indices. the fitness value is used to select the best solution in the population to the parent and to the offspring that will comprise the next generations. the fitter the parent greater is the probability of selection. this emulates the evolutionary process of survival of the fittest. parents are selected using roulette wheel selection method. fitness function is reciprocal of performance indices. in this paper we have taken the discrete form of itae. itae is treated as performance indices and fitness function denoted by j can be described as j = 1/(100 + n∑ k=1 |ωref −ωm|) (15) the membership functions obtained for error, change in error and pulses for ga optimized flc are shown in fig.6. the simulation structure of optimized fuzzy speed controller with the iae is shown in fig.7 & fig.8 respectively. figure 6: ga optimized membership functions figure 7: simulated structure of ga based flc of series zcs-qrc fed dc drive 54 m. ranjani, p. murugesan figure 8: simulated structure of optimized fuzzy speed controller 5 simulation results the closed loop operations of series fm-zcs-qrc fed dc drive has been simulated. controlling the freewheeling period using the controller regulates the speed of the drive. the speed variations for sudden load disturbances are shown for increased load torque and decreased load torque in fig.9 and fig.10 respectively. it can be said that the speed remains constant even for various disturbances of load torque. figure 9: speed response for increase in load torque figure 10: speed response for decrease in load torque 6 conclusion we design the speed controller of the series fm-zcs-qrc in two steps. in step one the conventional flc is designed and simulated and second step optimization of membership function ,fuzzy rules and scaling factors of flc by genetic algorithm is considered. the simulation results show that the optimal fuzzy logic controller is functioning better than a conventional flc in terms of the rise and settling time. hence it can be concluded that the ga optimized flc implemented in zcs-qrc fed drive enhances the drive robustness by reducing the transient time and steady state error and superior than the conventional fuzzy controller. modeling and simulation of genetic fuzzy controller for l-type zcs quasi-resonant converter 55 bibliography [1] ned mohan et.al. (1995); power electronics conveiters, application and design,john wiley and sons. [2] liu.k.h.; lee.f.c. f.c. lee.; zerovoltage switching technique in dc/dc converters; ieee trans. power electron, 5(3):293-304, 1990. [3] rama reddy et al, a step down frequency modulated zero current switching qrc fed dc drive,etep joumal, ref.no.et-1088, 1995. [4] abraham kandel; gideon langholz; fuzzy control systems, crc press, 1994. [5] kung.y.s.; liaw.c.m.; a fuzzy controller improving a linear model following controller for motor drives; ieee transactions on fuzzy systems, 2(3):194-201, 1994. [6] reznik.l; evolution of fuzzy controller design; ieee transactions on fuzzy systems, 503-508, 1997. [7] hu.b.g.; gosine.r.g.; theoretic and genetic design of a three-rule fuzzy pi controller; ieee transactions on fuzzy systems, 489-496, 1997. [8] tan. g.v.; hu.x.; more on designing fuzzy controllers using genetic algorithms: guided constrained optimization;ieee transactions on fuzzy systems, 497-502, 1997. [9] mohammadian.m.; stonier. r.j.; generating fuzzy rules by genetic algorithms; proceedings of 3rd ieee international workshop on robot and human communication, 362-367, 1994. [10] arulselvi.s.; uma govindarajan.; real time implementation of modified fuzzy logic controller for a non linear quasi resonant dc-dc converte ; iete journal of research, 53(5):401-416, 2007. int j comput commun, issn 1841-9836 9(1):8-15, february, 2014. handwritten documents text line segmentation based on information energy c.a. boiangiu, m.c. tanase, r. ioanitescu costin-anton boiangiu*, radu ioanitescu "politehnica" university of bucharest romania, 060042 bucharest *corresponding author: icostin.boiangiu@cs.pub.ro mihai cristian tanase virtualmetrix design romania, 060104 bucharest mihaicristian.tanase@gmail.com abstract: the first step in the text recognition process is represented by the text line segmentation procedures. only after text lines are correctly identified can the process proceed to the recognition of individual characters. this paper proposes a line segmentation algorithm based on the computation of an information content level, called energy, for each pixel of the image and using it to execute the seam carving procedure. the algorithm proposes the identification of text lines which follow the text more accurately with the expected downside of the computational overhead. keywords: text line segmentation, text recognition, information energy, ocr. 1 introduction the identification of the boundaries of the lines of text represents an essential step in many algorithms like the ones for document structure extraction or text recognition. the research in this field has focused mostly on the development of such algorithms for printed documents. this limitation of the domain of application reduces the complexity of the problem as the printed documents are perfectly formed and the main problem that would need to be solved is the skew angle introduced in the process of printing or scanning, angle which is assumed to be the same for the entire document,. with such documents, the problem is reduced to the identification of the skew angle which is assumed to be constant for the entire page because the text lines are parallel with each other. such methods are presented in [1][3]. however, when dealing with handwritten documents, the assumptions made by such algorithms do not hold anymore. there is no constant skew angle, the lines are not parallel and even the size and format similarity between the same characters found on different areas of the page cannot be assumed. even worse, the separation between lines cannot be assumed as for printed documents because, in handwritten tex, the characters often overlap the line bellow as they are more compactly spaced on the vertical. algorithms trying to address these increased complexities have also been attempted in [4] [13]. the pixel of a given document stored as a digital image have different levels of information. a white pixel in a big white region or one that is found between two lines of text contains very little information while a character defining pixel would contain a lot of information. this paper proposes the association of an energy level for each pixel of the input image which tries to estimate the importance or the amount of information provided by that pixel. although the concept can be applied to general images, printed or handwritten text, we will apply this concept for handwritten documents. the information level is then used by the seam carving algorithm for the segmentation of the text lines. copyright © 2006-2014 by ccc publications handwritten documents text line segmentation based on information energy 9 2 related work the algorithms that try to locate the text lines in a document are divided mainly by the information they take from the input document. as the input documents are the results of a digitization process, they are acquired, generally through scanning, as grayscale images. this grayscale representation is converted into binary or black and white for algorithms which are designed to work with this type of documents only. the binary representation conversion is done using a previously defined threshold level. all pixels that have gray levels above the threshold are converted to black, the rest are converted to white. a too high threshold will results in an image containing too little information for text recognition to be possible and a too low threshold will result in too many artifacts, again making the text recognition process impossible. based on the observation that the body of the lines of text contains gray pixels and because the pixels that make the characters being of a darker shade of gray, some algorithms compute projection profiles representing the sum of all the pixels values in a given direction. the method works well for printed documents but fails to produce good results when applied to handwritten documents. to address these problems, different approaches were used: identify the local skew of the handwritten text, calculate the accumulated space between characters, try to fill the space between characters or use attraction from the text pixels and repulsion from the previously detected line trying to estimate the text line boundaries closer. 3 information energy the different pixels from an image carry different information content. a document can be viewed as a group of low information pixels representing the space between lines of text and respectively a group of high information pixels representing the actual lines of text. each pixel in the energy map has a value associated with it that represents the amount of information that the given pixel stores in the image. if a high energy pixel is removed from the image, the resulting image has a significant drop in detail, whereas removing a low energy pixel results in a negligible information loss. the information energy concept can be understood by trying to eliminate a continuous band of pixels from an image representing handwritten text. this concept is illustrated in 1. original image removing low information pixels removing high information pixels figure 1: information energy exemplification 10 c.a. boiangiu, m.c. tanase, r. ioanitescu a band with the same amount of pixels has been removed from a text document. it can be seen that when removing high information pixels there is not enough remaining information to detect the content, while when removing low information pixels, there is hardly any information loss and the content can be usually detected entirely. an important observation is that for handwritten documents and in general text documents, the variation of pixel values represent information in itself. it can be seen that large continuous areas with similar pixel values constitute low energy areas, in particular spaces between lines of text, while high variation pixel values constitute high energy area. computing the energy map of the entire document will thus provide information of the location of the high energy areas which represent the lines of text. 4 accounting for the text direction the algorithm begins with the calculation of the information energy for each pixel. similarly to [4] and [11], for each pixel, the energy value is calculated using the next formula: e(i,j) = 2∗e(i,j) + min ( d( neighborsnumber 2 + k)∗e(i + direction,j + k) ) (1) where: • k fulfills the following condition: −neighborsnumber 2 < k < neighborsnumber 2 • direction represents the direction of processing the energy map +1 representing left to right processing and -1 the opposite direction. using a neighborsnumber value of 3 and because k is a natural number since we work with discrete pixel positions, we get only the immediate neighbors. during the calculation the direction coefficient has been kept constant. as shown in images from the chapter “test and results” section, for documents with horizontal lines or very low skew angles we obtained good results. however, for larger skew angles the results quality decreased with wrong text line segmentation and as a result we considered accounting for the text direction during processing. to accurately follow the text, the direction of the text lines should be taken into consideration at each pixel. for each pixel, the direction coefficient that accounts for the direction of the text line when that given pixel is reached, will be calculated. the calculated information energy map is presented in 2 as a result from interpolating the two processing directions. computation algorithm input: image to be processed, processing window (height*width), skewing angle for every pixel in the image to be processed: 1. for every processing window skewing angle i. sum up the pixels in the skewed window ii. if the sum is less than the current minimum then a) update the current minimum b) save the window variation 2. with the minimum variation saved in step 1 ii: i. update the direction coefficient for the minimum variation ii. rescale the minimum window variation good results were obtained experimentally with processing window sizes that were at least the width and height of the average character in the processed text and for values of a few order handwritten documents text line segmentation based on information energy 11 original image computed information energy map figure 2: the information energy map of an image of magnitude larger. if the processing windows has a smaller size then the algorithm attempts to segment the characters specific structures as separate lines of text leading to erroneous results. because the size of the processing text is usually roughly known and since the results are good with larger sizes for the processing window, this aspect does not represent a limitation. similarly, the minimum and maximum variation angles can be set to 20 degrees. these variation angles represent the maximum skewing angles and combined cover a 40 degrees area that is sufficient for most documents. the size of the processing window is fixed upfront, the calculations at each step are fixed. the total number of operations depends on the direction coefficient and height of the initial image since the algorithm is repeated for every pixel. for each pixel and for each variation of the skewing angle, the information energy level is computed. line identification algorithm input: computed information energy for each pixel in the image to be processing, neighborsnumber output: the number and layout of the identified lines 1. for each line (1..h) 2. for each column (1..w) 3. select the minimum cost pixel on the right not found further away than neighborsnumber i. if the selected pixel is not included in any line include it ii. else move to the next line the algorithm looks through all the information energy levels to locate the minimal values. the values with the minimum energy level represent the blank pixel regions that separate the lines of text. 5 tests and results to test the algorithm, a number of images of handwritten documents have been used. the test data files consisted of about 500 different types of documents representing old letters, library index files, patents, receipts and various printed documents. the documents showed pronounced skew angles and their layout was not trivial. the database was considered to be relevant, although the number of documents is not large, because of their variety which allowed the testing of the algorithm on a wide set of conditions. different methods for the computation of the information energy are used as examples to show how the algorithm depends on this type of variation and to show the general application 12 c.a. boiangiu, m.c. tanase, r. ioanitescu of the concept. the results of the algorithm are discussed in the conclusions section and future possible solutions are presented with an explanation of the associated computational costs. original image line segmentation result figure 3: gaussian first derivative energy computation test 1 original image line segmentation result figure 4: gaussian first derivative energy computation test 2 original image line segmentation result figure 5: magnitude of the gradient energy computation test 1 handwritten documents text line segmentation based on information energy 13 original image line segmentation result figure 6: magnitude of the gradient energy computation test 2 original image line segmentation result figure 7: inverse distance transform energy computation 1 original image line segmentation result figure 8: inverse distance transform energy computation test 2 14 c.a. boiangiu, m.c. tanase, r. ioanitescu 6 conclusions and future works this paper describes a text line segmentation algorithm that shows good results even for handwritten documents. the algorithm is based on the concept of information energy which is used to estimate the text lines in the processed document. experimental results showed that using constant direction coefficients when calculating the information energy levels produces bad results for inputs that show high levels of skew. the algorithm addresses this problem by updating the direction coefficients with a window processing algorithm. these direction coefficients account for the local text direction and for variations of the skew angles that are not uncommon in handwritten documents. by using direction coefficients, pixels from the same line have higher probability of selection when calculating the minimum values in the information energy map. the algorithm that computes the direction coefficient has a large computation complexity. since the computation time depends on the size of the processing window, one solution is to use a smaller size. experimental testing showed that at minimum, the height and width of the average character should be used for the size of this window. alternatively the skew angle could be assumed to be smaller eliminating parts of the computation cases. another change could be the limitation of the possible line curvature angle for the detected document lines to a lower interval. by constraining the line curvature angle. this limitation would also address cases in which a detected line would follow the space between two words and reach the previous or the next line which would represent a wrong line detection. a possible future direction is the evaluation of the robustness of the algorithm on larger images datasets and with a variation of the document types which would allow to more extensively evaluate the accuracy of the handwritten text segmentation algorithm. the work presented in this paper is a building block of a much bigger project: a complete, modular, fully automatic content conversion system developed for educational purposes. in the near future, with the completion of the system and the running in automatic batch processing of large image databases of all kind of skewed documents (containing handwriting or not) the algorithm will be fully evaluated in order to assess its real potential as a preprocessing phase for ocr applied on handwritten documents. acknowledgement the work presented in this paper was funded by the sectorial operational programme human resources development 2007-2013 of the romanian ministry of labour, family and social protection through the financial agreement posdru/89/1.5/s/62557. bibliography [1] dos santos, r.p. et al, text line segmentation based on morphology and histogram projection, document analysis and recognition (icdar), 651655, 2009. [2] saha, s. et al, a hough transform based technique for text segmentation, journal of computing, 2(2):135-140, 2010. [3] arivazhagan, m. et al, a statistical approach to line segmentation in handwritten documents, proceedings of spie, 2007. [4] strand, l. et al, minimal cost-path for path-based distances, image and signal processing and analysis, 379-384, 2007. handwritten documents text line segmentation based on information energy 15 [5] avidan, s. et al, seam carving for content-aware image resizing, acm siggraph, article 10, 2007. [6] saabni, s. et al, language-independent text lines extraction using seam carving, document analysis and recognition (icdar), pp. 563-568, 2001. [7] papavassiliou, v. et al , handwritten document image segmentation into text lines and words, pattern recognition, 43(1):369-377, 2010.. [8] du, x. et al, text line segmentation in handwritten documents using mumford-shah model, pattern recognition, 42(12):3136-3145, 2009. [9] t ripathy, n.; pal, u., handwriting segmentation of unconstrained oriya text, frontiers in handwriting recognition, 306-311. 2004. [10] kennard, d.j., barrett, w.a., separating lines of text in free-form handwritten historical documents, document image analysis for libraries, 12-23, 2006. [11] asi, a. et al, text line segmentation for gray scale historical document images, proceedings of the 2011 workshop on historical document imaging and processing, pp. 120-126, 2011. [12] bar-yosef, i., input sensitive thresholding for ancient hebrew manuscript, pattern recognition letters, 26(8):1168-1173, 2005. [13] bar-yosef, i. et al, line segmentation for degraded handwritten historical documents, document analysis and recognition, 1161-1165, 2009. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 586-591 cryptanalysis on two certificateless signature schemes f. zhang, s. li, s. miao, y. mu, w. susilo, x. huang futai zhang, songqin miao 1. school of computer science and technology nanjing normal university, nanjing 210046, p.r. china, and 2. jiangsu engineering research center on information security and privacy protection technology nanjing 210046, p.r. china e-mail: zhangfutai@njnu.edu.cn, miaosongqin@163.com sujuan li 1. school of computer science and technology nanjing normal university, nanjing 210046, p.r. china, and 2. nanjing university of technology nanjing 210037, p.r. china e-mail: lisujuan1978@126.com yi mu, willy susilo, xinyi huang centre for computer and information security research school of computer science and software engineering university of wollongong nsw 2522, australia e-mail: ymu@uow.edu.au, wsusilo@uow.edu.au, xyhuang81@gmail.com abstract: certificateless cryptography has attracted a lot of attention from the research community, due to its applicability in information security. in this paper, we analyze two recently proposed certificateless signature schemes and point out their security flaws. in particular, we demonstrate universal forgeries against these schemes with known message attacks. keywords: certificateless cryptography, certificateless signature, public key replacement, universal forgery. 1 introduction certificateless cryptography [1] is a new paradigm that not only removes the inherent key escrow problem of identity based public cryptography [2] (id-pkc for short), but also eliminates the cumbersome certificate management in traditional pki. in cl-pkc, the actual private key of a user is comprised of two secrets: a secret value and a partial private key. the user generates a secret value by himself, while the partial private key is generated by a third party called key generating center (kgc), who makes use of a system wide master key and the user’s identity information. in this way, the key escrow problem in identity-based public key cryptosystems is removed. a user’s public key is derived from his/her actual private key, identity and system parameters. it could be available to other entities by transmitting along with signatures or by placing in a public directory. unlike the traditional pki, there is no certificate in certificateless public key cryptography to ensure the authenticity of the entity’s public key. a number of certificateless signature schemes [3–14] have been proposed. some of them are analysed under reasonable security models with elaborate security proofs [8, 11, 13, 14], while some others are subsequently broken due to flawed security proof or unreasonable model [3, 6–8, 12]. copyright c⃝ 2006-2010 by ccc publications cryptanalysis on two certificateless signature schemes 587 recently two certificateless signature schemes were proposed in [4] and [5] respectively. they were claimed to provide high efficiency and provable security. in this short note, unfortunately, we show that these two schemes [4, 5] are insecure even in a very weak security model. namely these two schemes are suffering from universal forgeries under known message attacks. 2 review of the original schemes we omit the preliminaries, basic notions, and security models about certificateless signature schemes. please refer to [1, 8, 11, 13, 14] for details. the two original schemes [4, 5] are based on bilinear maps. they were both called mccls scheme. to distinguish them, we call the one in [4] as mccls1, and the other one in [5] as mccls2. 2.1 description of mccls1 we first describe mccls1. it consists of the following five algorithms. • setup. on input a security parameter, it generates a list of system parameters { p, g1, g2, ê, p, ppub, h1, h2} and a system master private key s ∈ z∗p, where p is a large prime, g1,g2 are groups of order p with an admissible bilinear map ê : g1 × g1 → g2, h1 : {0,1}∗ → g1 and h2 : {0,1}∗ → z∗p are cryptographical hash functions, p is a generator of g1, and ppub = sp. • extract partial private key. on input a user identity id, it computes qid = h1(id), and outputs did = sqid as the user’s partial private key. • generate key pair. a user with identity id selects a random x ∈ z∗p as its secret value sid, and publish its public key pid = xppub. • cl-sign. given a user’s private keys (did,sid) and a message m, the user randomly picks an element r ∈ z∗p, computes s = sid −1did, r = (r − sid)p, v = h2(m,r,pid)r, and outputs σ = (s,r,v ) as his/her signature on message m under the public key pid. • cl-verify. given a signature (s,r,v ) on a message m of a user id with public key pid, a verifier computes h = h2(m,r,pid) and checks whether (ppub,v p − hr,h−1s,qid) is a valid diffiehellman tuple, namely whether the equation ê(v p − hr,h−1s) = ê(ppub,qid) holds. 2.2 description of mccls2 the first three algorithms of mccls2 in [5] are exactly the same as those of mccls1 in [4]. there are slight differences in the cl-sign and cl-verify algorithms. we just depict the differences here. • cl-sign. given a user’s private keys (did,sid) and a message m, the user randomly picks an element r ∈ z∗p, computes s = sid −1did, r = (r − sid)p, v = h2(m,r,pid)rp and outputs σ = (s,r,v ) as his/her signature on message m under public key pid. • cl-verify. given a signature (s,r,v ) on a message m of a user id with public key pid, a verifier computes h = h2(m,r,pid) and checks whether (ppub,v −hr,h−1s,qid) is a valid diffie-hellman tuple, namely whether the equation ê(v − hr,h−1s) = ê(ppub,qid) holds. 588 f. zhang, s. li, s. miao, y. mu, w. susilo, x. huang 3 universal forgery as we can see, in the mccls schemes, a signature on a message m of a user id with public key pid consists of three components s, r and v . note that for a user id with public key pid, s remains unchanged for all messages, r and v are irrelevant to the partial private key did. here we give two kinds of universal forgery under known message attacks. 3.1 attacks against mccls1 1. universal forgery by replacing public key the scheme mccls1 cannot resist public key replacement attacks of a type i adversary a. for the definition of type i and type ii adversaries, please refer to [1, 4, 5, 8, 11, 13, 14]. let σ = (s,r,v ) be id’s valid signature on a message m, where s = sid −1did, r = (r − sid)p, v = h2(m,r,pid)r, and r ∈r z∗p. given r and v , the random number r can be easily derived as r = v h2(m,r,pid)−1. and then sidp is known as sidp = rp−r. now a is able to forge a user id’s valid signature on any message m as follows: (a) choose a random c ∈ z∗p and let r ′ = cr ∈ z∗p; (b) replace id’s public key as p′id = cpid (the new secret value corresponding to the public key p′id is s ′ id = csid ); (c) compute s′ = c−1s,r′ = cr,v ′ = h2(m,r′,p′id)r ′; (d) set σ ′ = (s′,r′,v ′) as id’s signature on message m under the public key p′id. we can see that (ppub,v ′p − h2(m,r′,p′id)r ′,h2(m,r′,p′id) −1s′,qid) is a valid diffie-hellman tuple since ê(v ′p − h2(m,r ′,p′id)r ′,h2(m,r ′,p′id) −1s′) = ê((h2(m,r ′,p′id)crp)− h2(m,r ′,p′id)(crp − csidp)),h2(m,r ′,p′id) −1c−1s) = ê(h2(m,r ′,p′id)csidp,h2(m,r ′,p′id) −1c−1s) = ê(sidp,s) = ê(p,did) = ê(ppub,qid) 2. universal forgery without replacing public key from id’s valid signature σ = (s,r,v ) on a message m, the adversary can get r = v h2(m,r,pid) −1,sidp = rp − r. with these he can forage a signature σ ′ = (s′,r′,v ′) on any message m without replacing id’s public key as follows: pick r ′ ∈r z∗p, and compute s′ = s, r′ = r ′p − sidp, v ′ = h2(m,r′,pid)r ′. the verification will always output “accept" since (ppub,v ′p − h2(m,r ′,pid)r ′,h2(m,r ′,pid) −1s′,qid) cryptanalysis on two certificateless signature schemes 589 is really a valid diffie-hellman tuple. the reason is ê(v ′p − h2(m,r ′,pid)r ′,h2(m,r ′,pid) −1s′) = ê(h2(m,r ′,pid)r ′p − h2(m,r ′,pid)(r ′p − sidp),h2(m,r ′,pid) −1s) = ê(h2(m,r ′,pid)sidp,h2(m,r ′,pid) −1s) = ê(sidp,s) = ê(ppub,qid) 3.2 attacks against mccls2 1. universal forgery by replacing public key let σ = (s,r,v ) be id’s valid signature on a message m. it is obvious that rp = h2(m,p,pid)−1v , sidp = r p − r. a type i adversary a may forge id’s valid signature on any message m as follows: (a) choose a random c ∈ z∗p and let r ′ = cr ∈ z∗p. (b) replace id’s public key as p′id = cpid (this implies the new secret value corresponding to the new public key p′id is s ′ id = csid ). (c) compute s′ = c−1s,r′ = (r ′ − s′id)p = cr,v ′ = h2(m,r′,p′id)r ′p = ch2(m,r′,p′id)rp. (d) set σ ′ = (s′,r′,v ′) as id’s signature on message m using public key p′id. we can see (ppub,v ′ − h2(m,r′,p′id)r ′,h2(m,r′,p′id) −1s′,qid) is a valid diffie-hellman tuple since ê(v ′ − h2(m,r ′,p′id)r ′,h2(m,r ′,p′id) −1s′) = ê(h2(m,r ′,p′id)crp − h2(m,r ′,p′id)(crp − csidp),h2(m,r ′,p′id) −1c−1s) = ê(h2(m,r ′,p′id)csidp,h2(m,r ′,p′id) −1c−1s) = ê(sidp,s) = ê(p,did) = ê(ppub,qid) 2. universal forgery without replacing public key the adversary can get rp = h2(m,r,pid)−1v , sidp = rp − r = h2(m,r,pid)−1v − r, from id’s valid signature σ = (s,r,v ) on a message m. then it (may be type i or type ii) can forge a signature σ ′ = (s′,r′,v ′) on any message m without replacing id’s public key as follows: pick r ′ ∈r z∗p, and compute s′ = s, r′ = r ′p − sidp, v ′ = h2(m,r′,pid)r ′p. the verification will always output “accept" since (ppub,v ′ − h2(m,r ′,pid)r ′,h2(m,r ′,pid) −1s′,qid) 590 f. zhang, s. li, s. miao, y. mu, w. susilo, x. huang is really a valid diffie-hellman tuple. this is because ê(v ′ − h2(m,r ′,pid)r ′,h2(m,r ′,pid) −1s′) = ê(h2(m,r ′,pid)r ′p − h2(m,r ′,pid)(r ′p − sidp),h2(m,r ′,pid) −1s) = ê(h2(m,r ′,pid)sidp,h2(m,r ′,pid) −1s) = ê(sidp,s) = ê(ppub,qid) from these attacks, one can see mccls1 and mccls2 are insecure even in the weakest security model. 4 conclusion recently, two certificateless signature schemes mccls1 and mccls2 were proposed for mobile wireless cyber-physical systems. they only require two scalar multiplications in signing phase and two scalar multiplications and one pairing in verification phase. so they are efficient with respect to computational cost. although the authors claimed and proved that mccls1 and mccls2 were secure, as we have shown in this paper they are in fact insecure. universal forgeries against those two schemes have been presented under known message attacks. 5 acknowledgment this research is supported by the natural science foundation of china under grant number 60673070 and academic discipline fund of njut. bibliography [1] s. al-riyami, k. paterson. certificateless public key cryptography. proceedings of asiacrypt 2003, lecture notes in computer science 2894, springer-verlag, 452-473, 2003. [2] a. shamir. identity based cryptosystems and signature schemes. proceedings of crypto’84, 47-53, 1984. [3] x. huang, w. susilo, y. mu, f. zhang. on the security of a certificateless signature scheme. proceedings of acisp 2005, 13-25, 2005. [4] z. xu, x. liu, g. zhang, w. he, g. dai, w. shu. a certificateless signature scheme for mobilewireless cyber-physical systems. the 28th international conference on distributed computing systems workshops, 489-494, 2009. [5] z. xu, x. liu, g. zhang, w. he. mccls: certificateless signature scheme for emergency mobile wireless cyber-physical systems. international journal of computers, communications & control (ijccc), 3(4): 395-411, 2008. [6] w. yap, s. heng, b. goi1. an efficient certificateless signature scheme. proceedings of euc workshops 2006, lecture notes in computer science 4097, springer-verlag, 322-331, 2006. [7] j. park. an attack on the certificateless signature scheme from euc workshops 2006. cryptology eprint archive, report 442, 2006. cryptanalysis on two certificateless signature schemes 591 [8] z. zhang, d. feng. key replacement attack on a certificateless signature scheme. cryptology eprint archive, report 453, 2006. [9] k. choi, j. park, j. hwang, d. lee. efficient certificateless signature schemes. proceedings of acns 2007, lecture notes in computer science 4521, springer-verlag, 443-458, 2007. [10] r. castro, r. dahab. two notes on the security of certificateless signatures. proceedings of provsec 2007, lecture notes in computer science 4784, springer-verlag, 85-102, 2007. [11] z. zhang, d. wong, j. xu, d. feng. certificateless public-key signature: security model and efficient construction. proceedings of acns 2006, lecture notes in computer science 3989, springerverlag, 293-308, 2006. [12] b. hu, d. wong, z. zhang, x. deng. key replacement attack against a generic construction of certificateless signature. proceedings of acisp 2006, lecture notes in computer science 4058, springer-verlag, 235-346, 2006. [13] x. huang, y. mu, w. susilo, d. wong, w. wu. certificateless signature revisited. proceedings of acisp 2007, lecture notes in computer science 4586, springer-verlag, 308-322, 2007. [14] l. zhang, f. zhang, f. zhang. new efficient certificateless signature scheme. proceedings of euc workshops 2007, lecture notes in computer science 4809, springer-verlag, 692-703, 2007. futai zhang (b. augest 28, 1965) received his m.sc. in mathematics (1990) from shaanxi normal university, china, and phd in cryptology (2001) from xidian university, china. now he is a professor at nanjing normal university, china. his current research interest is public key cryptography. sujuan li is now a phd candidate in nanjing normal university, china. her current research interests include information security and cryptography. songqin miao is now a m.sc candidate in nanjing normal university, china. her main research interest is cryptography. yi mu received his phd from the australian national university in 1994. he is an associate professor at the school of computer science and software engineering at the university of wollongong. he is the co-director of centre for computer and information security research (ccisr) at the university of wollongong. his current research interests include network security, electronic payment, cryptography, access control, and computer security. willy susilo received a ph.d. in computer science from university of wollongong, australia. he is a professor at the school of computer science and software engineering at the university of wollongong. he is the co-director of centre for computer and information security research (ccisr) at the university of wollongong.his current research interests include information security and cryptography. xinyi huang received his ph.d. in computer science from university of wollongong, australia in 2009. his current research mainly focuses on cryptography and its applications. int j comput commun, issn 1841-9836 9(1):79-84, february, 2014. efficiency of a combined protection method against correlation power analysis side-attacks on microsystems h.-n. teodorescu, e.-f. iftene horia-nicolai teodorescu*, emanuel-florin iftene 1. "gheorghe asachi" technical university of iasi romania, iasi, 8 bd. carol i, and 2. institute of computer science of the romanian academy, romania, iasi *corresponding author:hteodor@etti.tuiasi.ro abstract: we analyze the efficiency of the masking of instruction patterns using a chaotic driven clock and power supply, in front of a side attack intruding the power supply of a microsystem. the differential analysis is supposedly conducted by correlation power analysis. we demonstrate that the use of a chaotically-driven masking based on relatively simple circuits may be a significant candidate for the protection of embedded systems. keywords: physical security, protection, hardware, side attack, chaos, control signal, security evaluation. 1 introduction with a field less than 20 year old (the first paper, by paul c. kocher [1], was published in 1998, with the first significant expansion published in 2000, [2]), the protection against hardwarelevel attacks of the information in microsystems, including embedded systems is fast developing, due to the huge interest of the banks, security companies, card manufacturers, and military, moreover due to the interest in power minimization [3]. citing [4], ‘side channel analysis is a [· · · ] form of attack [· · · ] that uses information that leaks, unintentionally, from the real-world implementations of cryptographic hardware.’ side-channel attacks (sca) extract and decode the executed instructions and the manipulated data in microsystems, bypassing the cryptographic protections [1], [2]. the basic methods of attack were named simple power analysis (spa), respectively differential power analysis (dpa), depending on the details of the attack. for various approaches of dpa, see [5]. while the literature includes numerous papers on the attacks and on the sibling topic of power analysis for software optimization [3], understandably fewer papers present hardware methods of mitigating these attacks. several manufacturers include various solutions against side attacks. for example, newell and juliano [4] cite freescale inc., who uses ‘patented dpa functions, licensed from cryptography research.’ other manufacturers, as maxim inc. and infineon also use various protection means, but details on them are not public. for example, the 32 risc ‘deepcover secure microcontroller’ max32590 released in 2013 by maxim includes on the chip, according to the manufacturer datasheet, a ‘tamper detection controller’ that ‘monitors voltage, frequency, temperature, die shield, and external sensors’, erasing essential information when any type of suspicious external activity is detected. in [6], [7], and [8] we introduced the masking of the instructions using a chaotically-driven clock and power supply. however, a detailed analysis of the masking efficiency under dpa has not been performed for that method. in this paper, we provide results of the analysis of differential power attacks, when the chaotic masking as above is used to protect the system. the protection method proposed in [6], [7], and [8] and further analyzed here is, at the hardware level, more of a proof of concept of the capabilities of the method, not a blueprint solution ready to put into silicon. copyright © 2006-2014 by ccc publications 80 h.-n. teodorescu, e.-f. iftene figure 1: block diagram for the hardware for protection against sca. (a) diagram with analog control of the voltage. (b) both the clock and the voltage control are digital signals. (c) pulse forming circuit. (d) controlled voltage regulator. (e) example of noisy control pulse. (f) waveforms from the chaotic circuit and the corresponding pulses generated for control 2 the proposed protection method beyond software protection methods, such as specific algorithms, hardware protection methods play an essential role today in data and code security, as proved by several specific chips produced by companies as the ones quoted in section 1. we present in this paper an operation principle demonstrator of the protection method. we assume that only the external power supply is available to the intruder for monitoring with a series resistor. while the proposed method is similar to the typical injection of (pseudo-)random pulses on the power supply line, in this type of protection the random signal produced by the chaotic circuit is used to drive a controlled voltage regulator (cvr), which modifies the voltage that powers the microcontroller, moreover is used to generate the clock signal of the system. the protection circuits include a chaotic signal generator, a pulse shaper, and the cvr, as shown in fig. 1 (a-c). the cvr designed and used in this research is shown in fig. 1 (d) and an example of control pulse in fig. 1 (e). the circuits where described in [9] and [8]. more than one level of voltage jump can be produced with such a scheme, provided that multiple loops with different zener diodes are used in parallel on the lower branch of the circuit in fig. 1 (d). the random character of the pulses produced by the pulse shaper refers to the variation of their duration, especially on the long run, due to the change of operation condition of the chaotic circuit (changes in the ambient temperature, fluctuations of the power supply of the chaotic circuit.) for recordings spaced in time by about 10 minutes, under apparently unchanged laboratory conditions, variations of the number of samples per pulse were of more than 20 % for measurements performed during the same day. on the other hand, changes from one pulse to the other were less than 0.5 %. the slow change of the pulse duration due to the change of the chaotic regime produced by ambient factors is beneficial for the protection because it makes difficult the learning of the patterns of the instruction, as they continuously and unpredictably change. notice in fig. 1 (f) that the control pulses remain noisy with an amplitude of the noise of about 0.5 v (peak amplitude more than 1 v). this high frequency noise makes the masking efficiency of a combined protection method against correlation power analysis side-attacks on microsystems 81 process more effective, therefore we have not tried to reduce the noise. the evaluation of the randomicity of the clock signal, as produced by the pulse shaper of the chaotic clock generator proposed in [8] was performed by determining the fluctuations of the width of the pulses. for this purpose, the time between two successive up-down impulse edges was determined for all pulses during a long period of time. the analysis showed that the width of the pulses varies by about 0.1 to 2% over short periods (less than 1 ms), but with almost 40% over longer periods (minutes to hours). the presented circuits serve only to illustrate the operation principle and the feasibility. these circuits are not designs for on-chip implementations. on the other hand, the hardware-level protection discussed in this paper assumes that the protection circuits are built on the same chip as the protected microsystem (soc system on chip technology), or at least that they are on a chip included in the same package (multi-chip technology). 3 analysis of the strength of the protection method various attack methods and related countermeasures were presented in the literature, see [5], [10], [11], [12]. attacks based on correlation analysis are among the most common. the intercorrelation function for two sequences, {x(n)} and {y(n)}, is defined as cx,y(τ) = 1n ∑n−1 n=0 x[n]y [n+τ], where τ is the delay. let vk = (sn,sn+1, ,sn+t ) be the expected (average) vector standing for the pattern of an unperturbed (unmasked) instruction k. let x = (xm,xm+1, ,xm+t ) be the vector of an instantiation of a masked, unknown instruction. the duration (number of samples) is taken the same as for vk, when the clock can be determined independently and the number of clock periods for an instruction is known. the purpose of the attack is to identify the instruction from its signature, x. several approaches for the attack are possible, among others the determination of the distance between x and all the patterns of the instructions, vk, k = 1 · · ·n, the computation of the inter-correlations between all vk and x, or determining the distances between the fourier transforms of the unknown, masked sequence x, f(x), and the fourier transform of the sequence of the instructions, f(vk). some authors, e.g. [5], consider the correlation power analysis (cpa) a distinct, more advanced method than dpa. when using correlation functions, attackers may try to determine the instruction in various ways, depending on the information they can acquire about the microsystem. when the attackers are able to determine the patterns of the unmasked instructions, they could proceed as follows. the attackers may compute in the first place the intercorrelation functions between the ‘clean’ patterns of instructions and segments of the waveform that correspond to one machine cycle (m.c.), assuming the instructions take one m.c. the attackers may reason that the true instruction is there where the intercorrelation is the greatest. denote the ‘clean’ pattern of the instruction #k by x0k . we denote an instance of the masked instruction #j by xmj . the correlation between them is denoted by cx0k,xmj (t). in the simplest (ideal) case, maxtcx0 k ,xm k (t) ≫ maxtcx0 k ,xmj (t),j ̸= k. then, the instructions are easily identifiable. if, instead, there is some index j such that maxtcx0 k ,xm k (t) < maxtcx0 k ,xmj (t),j ̸= k, confusion appears between the instructions #k and #j. in the next section, we demonstrate that a key sub-set of the instruction set of the microcontrollers in the 16fxxx series is securely masked by the method we proposed in [7], [8] against cpa analysis. for this purpose, we compute the correlation functions between the waveforms produced by various instructions when they are masked, respectively unmasked. 82 h.-n. teodorescu, e.-f. iftene figure 2: examples of intercorrelations with masked instructions 4 results and assessment of the robustness against cpa-sca the main results refer to the efficiency of the masking as determined by the lack of correlation between the unmasked pattern of the instruction and the masked ones. the results are summarized in tables i and ii. table i shows that the maximal values of the self-correlations of unmasked instruction waveforms is (almost) 1 for all the instructions, as expected, while the maximal values of the intercorrelations between unmasked instructions is less than 0.8. this allows the easy discovery of the unknown instruction by performing the correlation of its waveform with the waveforms of the other instructions. in our case, as table ii shows, seven out of eight instructions have maximal values of intercorelations with other instructions than with themselves. for example, the instruction movlw, when masked, better intercorrelates with addwf, andwf, movf, rrf, and btfss than with itself (larger correlation values, see table ii). table i. maximal values of the inter-correlation functions for eight instructions; unmasked operation, at 4 mhz clock addwf#2 andwf#7 movf#4 rrf#3 btfsc#4 btfss#3 andlw#2 movlw#7 addwf#2 1 0.73801 0.73584 0.60686 0.39528 0.75491 0.66487 0.78685 andwf#7 0.73801 1 0.85923 0.60065 0.4209 0.77955 0.73043 0.8248 movf#4 0.73584 0.85923 1 0.5919 0.39232 0.76756 0.67754 0.80839 rrf#3 0.60686 0.60065 0.5919 1 0.81175 0.57169 0.5507 0.57 btfsc#4 0.39528 0.4209 0.39232 0.81175 1 0.40628 0.46722 0.38796 btfss#3 0.75491 0.77955 0.76756 0.57169 0.40628 1 0.85267 0.73405 andlw#2 0.66487 0.73043 0.67754 0.5507 0.46722 0.85267 1 0.68391 movlw#7 0.78685 0.8248 0.80839 0.57 0.38796 0.73405 0.68391 1 table ii. maximal values of the inter-correlation functions between eight instructions, when one instruction is unmasked (first column in the table) and the other one is masked (first row). addwf#3 andwf#7 movf#3 rrf#6 btfsc#6 btfss#5 andlw#4 movlw#4 addwf#2 0.4508 0.30583 0.33292 0.27566 0.37544 0.37642 0.30343 0.44512 andwf#7 0.34256 0.36752 0.28884 0.41852 0.39059 0.30863 0.48923 movf#4 0.36011 0.29453 0.38931 0.40252 0.32866 0.45862 rrf#3 0.23007 0.47391 0.29884 0.27176 0.51101 btfsc#4 0.39219 0.22641 0.20155 0.40093 btfss#3 0.3588 0.28241 0.45588 andlw#2 0.21974 0.33482 movlw#7 0.44189 notice that the first table is symmetrical with respect to the main diagonal (hermitian). tables i and ii should be considered from the point of view of the identification of the instruction based on correlation functions. each element of the tables (matrices) is the maximal value of the correlation function for specified execution instances of a first and second instructions. the instruction is identified when the correlation is 1, in table i. the attacker is supposed here efficiency of a combined protection method against correlation power analysis side-attacks on microsystems 83 to have access to the true waveform of the instruction and to be able to directly or indirectly determine the clock frequency of the attacked system. assume that the attackers have acquired the waveforms of the non-masked instructions. the attackers can determine the true clock frequency of a system by running in a loop the correlation between an interpolated, respectively extrapolated version of the clean waveforms with the unknown, masked waveforms. for some value(s) of the interpolation, the correlation function exhibits the maximal highest value and a strong periodicity, due to the machine cycles in the waveform. in that case of interpolation, the time alignment between the known ‘clean’ waveform and the given waveform with unknown clock is the best and, therefore, the unknown clock period is found. we assume that the attackers have performed this determination. next, the attackers can perform with a known interpolation factor all the intercorrelations, to extract the information on the instructions in the attacked program. figure 2 shows examples of self-correlations of unmasked and masked instructions and correlations between masked and unmasked instructions. as expected, in all cases the correlations exhibit the periods of the clock and of the machine cycles, but not the instruction patterns. notice in table ii that values of selfcorrelation for a specified instruction that are lower than values of the correlation of the same instruction with others means that the criterion of maximal value of correlation will not work for the discovery of the instruction, based on correlations. 5 discussion and conclusions this paper synthesized partial and preliminary results reported in [6], [7], [8] and presented a thorough analysis of the masking efficiency under cpa attacks against a microsystem protected with the masking method proposed. the method is based on the randomization of both the clock and the supply voltage. the randomization uses an approach based on a simple chaotic system and the related circuitry. the proposed protection can be effective only when the attacker has no access to the chaotic circuit, or to the controlled voltage regulator. these circuits should be included in the same package as the microsystem. moreover, the electromagnetic radiation (emr) from the cvr should not be easy measured, because it reveals to the attacker the control of the voltage (that is, the chaotic circuit output). with the chaotic signal known, the attacker would be able to demodulate the masked signal and the masking one. in addition to limiting the direct and indirect (emr mediated) access to the chaotic signal, the protection must insure that the modulating signal and the protected one have similar characteristics, for example, similar amplitudes and heavily overlapping spectra. only with all these conditions satisfied, could the protection be effective. we reported only on an idea demonstration, not on an effective circuit. therefore, neither the condition on the amplitude of the swings of the vcr, nor the overlapping spectra condition is satisfied. concluding, we presented a method for instruction masking against cpa and showed that the method proves highly effective even with simple circuits for protection. the core of the method is the use of a chaotic circuit to alter at the same time the clock frequency and the supply voltage of the protected microsystem. the method is appropriate for integration either on the chip of the microsystem or in a multi-chip package. acknowledgment. this research was not supported by any institution; most of the conceptual work was benevolently performed by hnt. while there is a partial overlapping of this research with the project ‘cognitive systems’ of the romanian academy, there was no support from that project. authors’ contributions. hnt proposed the protection method in fig. 1, the schemes of the circuits for chaotic signal generation and power supply control, determined the tests and participated 84 h.-n. teodorescu, e.-f. iftene in the tests and experiments, performed part of the data processing, derived conclusions and wrote the paper. efi built all the circuits based on the design and schemes provided by hnt, wrote the test programs based indicated by hnt, and made most of the experiments. both authors discussed the paper and agreed with its final form. conflicts of interest. the authors declare no conflict of interest. bibliography [1] p. kocher, j. jaffe, b. jun, (1998), introduction to differential power analysis and related attacks, cryptography research inc, www.cryptography.com/public/pdf/dpatechinfo.pdf. accessed jan. 2012. [2] p. kocher, j. jaffe, b. jun, (2000), differential power analysis, cryptography research inc, www.cryptography.com/public/pdf/dpa.pdf. accessed jan. 2012. [3] v. tiwari, s. malik, a. wolfe, m. t.-c.lee, instruction level power analysis and optimization of software, j. vlsi signal processing, 13(2-3):223-238, aug 1996. [4] r. newell, f. juliano, protecting sensitive networked embedded systems from aggressive intrusion. edn, electronic design news magazine, may 5, 2013. www.edn.com/pdf/viewpdf?contentitemid=4413418 [5] t.-h. le, m. berthier, mutual information analysis under the view of higher-order statistics. in: echizen, i., kunihiro, n., sasaki, r., (eds.), advances in information and computer security, lncs, springer, berlin heidelberg, 6434: 285-300. 2010. [6] h.-n. l. teodorescu, e.-f. iftene, analysis of the code masking efficiency of chaotic clocks in microcontroller applications, 3rd int. symposium on electrical and electronics engineering (iseee2010), sep 16-18, galati, 261-266, 2010. [7] e.-f. iftene, h.-n. l. teodorescu, masking the instructions of a microcontroller using a ‘chaotic’ power supply, bull. polytechnic inst. iasi, e&e, lix (lxiii), 1:21-28, 2013. [8] e.-f. iftene, h.-n. l. teodorescu, protecting the code against side attacks using chaotically controlled clock and supply, proc. ecai 2013 5th int. conf. electronics, computers and a.i., ieee conf. #20924, 27-29 june 2013, pitesti, romania, 79-82, 2013. [9] h.-n.l. teodorescu, v. p. cojocaru, complex signal generators based on capacitors and on piezoelectric loads. in: c. h. skiadas, i. dimotikalis and c. skiadas (eds), chaos theory: modeling, simulation and applications. world scientific publishing co., 423-430, 2011. [10] e. brier, c. clavier, f. olivier, correlation power analysis with a leakage model. in m. joye and j.j. quisquater (eds.), cryptographic hardware embedded system, ches 2004, vol. 3156, lncs, pp. 16-29, springer-verlag, 2004. [11] y. zhang, a. juels, m.k. reiter, t. ristenpart, cross-vm side channels and their use to extract private keys. acm, 2012. available at http://dx.doi.org/10.1145/2382196.2382230, 2012. [12] r.e. atani, s. mirzakuchaki, s.e. atani, w. meier, on dpa-resistive implementation of fsr-based stream ciphers using sabl logic styles, int j comput commun, issn 18419836, 3 (4):324-335, 2008. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 297-304 the communication in distributed client server systems used for management of flexible manufacturing systems v. lupu, d.e. tiliute valeriu lupu, doru e. tiliute “stefan cel mare” university of suceava department of informatics, faculty of economics and public administration e-mail: valeriul@seap.usv.ro, dtiliute@seap.usv.ro abstract: labour productivity growth is a necessary condition for social and economic progress, in general, and to overcome the economic crisis facing most of the world, in special. applying innovative solutions, based on the itc, is one of the straight ways for achieving that objective, both important and necessary. this paper presents a software solution applicable to industrial production based on numerically controlled machines. it involves a distributed client server communication system, combined with mlp neural networks for the recognition of the 2d industrial objects, viewed from any angle. the information on prismatic and rotational parts to be processed by numerically controlled machine, are stored on a database server together with the corresponding processing programs. the client applications run on the numerically controlled machines and on the robots serving groups of machines. while the machines are fixed, the robots are mobile and can move from a machine to another. as a novelty of the proposed solution, in some well defined situations, the clients are allowed to change messages among them, in order to avoid the server overload. the neural networks are used to help robots to recognize the parts before and during manipulation. keywords: client, server, web, java, image. 1 introduction in domain the flexible production system consists in a number of fixed numerically controlled machines which process the parts, several mobile robots [1] whose main task is to load/unload the machines and storing parts in technologic stocks. the production system is driven by a complex distributed informatics system containing two servers, an application server and a database server, and several clients. the application server provides those applications required by the coordination of the robots and controlling the processing [2] stage of each part on each machine. it also has the communication protocols with the clients and with database server. two video cams equip each robot and machine in order to ensure a 3d vision of parts to be processed or handled. the position of each robot is monitored continuously by the application server using an appropriate number of video cams fixed on the production hall ceiling. an overview of the communication system is shown in figure 1. for each part, the following geometrical features are stored and available in database server: aria, perimeter, scale factor, minimum radius, maximum radius, average radius, standard radius deflection, minimum embedded rectangle dimensions, maximum inertia momentum, minimum inertia momentum, elongation factor, average of the gray levels, pentland ratio, rmin(x)/rmax(x) ratio, aelipsa(x)/belipsa(x) ratio, form characterization using momentum, etc.). copyright c⃝ 2006-2011 by ccc publications 298 v. lupu, d.e. tiliute functional features achieving an accurate enough description of the image objects using analytically developments easy to utilize. this category comprise fourier coefficients of the inherent contour curve function, inertia momentum of different orders calculated on contour curve and the invariant computed using inertia momentum. topological features conveying objects proprieties non depending on the distortions who aren’t affecting the surface. these features are free from distances. we specify some of them: c the connected components number; h the cavities number; e = c-h euler’s number. figure 1: an example of distributed client-server system also, the relations among different areas are forming a good descriptor. these relations can be represented as a graph of connected components. the features mentioned above are needed in order to recognize the parts about to be processed using numerically controlled machine. images of the parts are captured by two video cameras placed on each robot in perpendicular planes, resulting in a 3d view [3]. for recognition it is used a neuronal architecture of multilayer perceptron type. the solution presented in this paper employs neural networks for image processing and for the recognition of the model-based industrial objects [5]. in this order, a software application was created, giving to the robots the ability of recognizing the parts to be processed on the numericalcontrol machines . the software provides also a human interface that was used to verify correct functionality of neural network pattern recognition and communications, as described bellow. the neural network [4] training is ensured by a software component that implements the wellknown backpropagation algorithm. for the classification of the vectors bearing the characteristics of the shapes in the input space, the model of the multilayer perceptron was used. the vectors of the training set present themselves to the network with the class they belong to. in other words, the training inputs are organized as association lists in which the associated vectors correspond to a classification of the key vectors. consequently, an element of the list is a pair key vector corresponding class. if sufficient objects have been memorized from each class, an internal representation can be made of each class by means of the connection weights of the network. the model of the multilayer perceptron used in this software solves the problem of the shape the communication in distributed client server systems used for management of flexible manufacturing systems 299 classification. the network inputs are the vectors that are to be classified, while its outputs are the corresponding classes. 2 the client-side program the "client" program contains three classes: "client", "network" and "writer". initially, the program imports three packages: "java.io.*" (used for input/output actions; io = input / output), "java.awt.*" (used for graphical objects; awt = abstract window toolkit) and "sun.net.*" (used to establish a connection between client and server) [6], [7]. in order to run the client program, the user will type the following command: java client < iporaddress >< clientname >; where: → java the name of the application that launch the classes; → client the name of the class that will be executed; → ip or address ip address or server name; → clientname the name of the client who wants to connect to the server. the following sections explain the three classes of the client application. the "client" class. the "client" class extends the frame class, which has a method called add. using this method we can insert in the middle of the frame an object of type network. the class contains two methods: main and client. in the main method it is verified if the number of arguments is smaller than two. the arguments meaning is: args[0] the ip or the server name; args[1] the name of the client who wants to connect to the server. this function is the first executed when the client class starts. with this method, an instance of the client class is created, having as parameters the server ip address and the client’s name. the client method receives as parameters the name or the ip address of the server (string host) and the user name (string username). inside the frame we insert a new object, which is actually a new instance of the network class, having as parameters the same values received by the function. then, the frame size is set to (500,500). the method show() displays the frame on the main screen. the "network" class. this class extends the panel class. the meaning of the variables is the following: • public static boolean afis if it is true, then the messages that are received from server are shown in the green box from the client’s main window; • networkclient network_client using this method, we can declare a client. we use the specifications from the sun.net.* package; • datainputstream net_input using this variable, the client can receive messages from server; • printstream net_output using this variable, the client can send messages to the server; • static int modif=0 if it is 1, then the image received from server is shown in the client’s main window. this image is called "imagine.gif". if it is 0, then this image isn’t shown in the window. • string username user name; • boolean connected=false if it is true, this means that the client is connected to the server; • writer w using this variable, we can create a new instance of the writer class, which handles with the read of characters or strings received from server; 300 v. lupu, d.e. tiliute • string typed_line="" the message that’s typed by client. bellow are the methods of this class: the class constructor. the constructor receives two parameters, which represents the name or the ip address of the server and the user name. each time a client tries to connect to the server, the connect method is called. if the connection succeeded, the connected will be set on true and also a new instance of the writer class will be created. if the connection doesn’t succeed, an error message is returned to the client (it may also be viewed on the human interface client) and the execution of the application stops. the connect method. this method tries to connect to the server on a specific port (we chose port no. 1111 but it can be any unassociated port). the connection is achieved by creating a new instance of the class networkclient, which can be found in the sun.net.* package. the arguments of this constructor are the name or ip address of the server and the port number. after establishing the connection the net_input and net_output variables are initialized with instances of the classes that correspond to their definition. the serveroutput variable is declared inside the networkclient class and it is initialized when the constructor of the networkclient class is called. the read_net_input method. this method reads a line from server and returns the string received or null in case that an input/output error occurs. it also returns null if there is nothing available from the server. the write_net_output method. this method writes a string to the server. the close_server method. this method tries to close the communication between client and server. the keydown method. this method is executed when a key is pressed in the client’s window (in human interface client version). also, it calls the repaint method in order to make a quick repaint of the client’s window. the paint method. this method paints the client’s frame. if writing messages from server is allowed, a welcome message is displayed: "hello, <username> !", then two colored boxes are drawn, one red and the other one green, under the welcome message. each box displays different messages; the red one displays the messages sent by client and in the green one are displayed the messages received from server. if the modif variable is set, then the image "imagine.gif" will be shown. this image is received from the server. the "writer" class. this class handles with the reading of data from server. this method extends the thread class, which is specific for repeated actions. if the keyword "recv" is received from server, the variable primesc becomes true. the string specifies that a data transaction between server and client is about to begin. the transmission consists in the content of a ".gif" image, whose size is specified by "size nnnn" string, which precedes the actual content. data are received until the "stop" string is detected. in this case the modif variable from network class will become 1 and the repaint method from the same class will be called. 3 the server-side program the server application contains two classes [6], [7]. the server class extends the networkserver class, which is a part of "sun.net" package. in this class we consider that the maximum number of users is 1,000. the meaning of the variables from the server class is given bellow: string user[] this array will contains the name of the users that are currently connected to server; the communication in distributed client server systems used for management of flexible manufacturing systems 301 string sir[] in this array will be stored the last message from each user; datainputstream net_input[] by using this array, the server can receive data from clients; printstream net_output[] by using this array the server can send information and data to cliens; reader r[] in this vector will be stored the addresses of the "reader" class instances; static int client_counter = 0 the current number of connected clients. the main method creates a new instance of the server class. the constructor of this class initializes the arrays presented above. when idle, the server console displays waiting for users... the most important method in this class is servicerequest, which is a function of the networkserver class. when an event occurs at the server, this function is automatically called and creates an instance of the reader class. this thing happens each time when a client connects to the server. the method read_net_input reads data from clients. the method write_net_output writes data to clients. then the server is started, by calling the "startserver(1111)" instruction. this instruction starts the server, specifying that the port on which the server will receive message is 1111. this port number can be changed, but if is changed in the server class it also has to be changed in client class. if the server was started successfully, a message is printed on the server console: waiting for users ... if it’s not possible to open the server on the port 1111, then an error message will be shown and the execution of this application will be canceled. the most important method in this class is servicerequest, which is a function of the networkserver class. when an event occurs at the server, this function is automatically called. in this function it is created an instance of the reader class. this thing happens each time when a client is connecting to the server. the method read_net_input reads data from clients. the method write_net_output writes data to clients. the reader class this class extends the thread class. the most important method of this class is run. this method is called each time when an event occurs (connection of a client, the send of a message from a client, etc.). when the end of sentence is detected (point, !, ?), it checks if the message represents the standard syntax for sending data (images). this syntax is: send < imagename > to < user > where imagename is the name of the image stored on the server and the user is the name of the user that has to receive the image. examples: send image1 to machine1. send image2 to machine2. the syntax is "case-insensitive". the application server has the possibility to search the image in database. using the methods of the package "java.sql", queries may be sent to database and the results are fetched. snapshots of server and clients with human interface are depicted in figure 2. samples of pictures of parts to be processed are given in figure 3. 302 v. lupu, d.e. tiliute 4 case study mlp type neural network for recognizing objects from any angle the network has three layers. at the input, each neuron is connected to the outputs of all the neurons from the previous layer, its output being connected to the inputs of all the neurons of the following layer. within the same layer, the neurons are not connected between them. the dimension of the input layer is given by the dimension of the five vectors describing each shape (sphericity, pentland’s ratio, radii ratio, ratio between the ellipses on the two axes, shape characterisation by means of moments). the dimension of the output layer is equal to the number of classes into which the input data will be separated, that is four (corresponding to the classes: disk, square, triangle, cross). figure 2: a) windows screen the activation function used for the output of the neurons is: f(a) = 1/(1 + exp(−a)). as a result, the outputs of the network (y) will be in the domain [0, 1]4. the representative of the class where the data set presented at the input is placed will have the output 1, while the others, 0. for other types of pieces, the theoretical values cannot be determined directly, requiring measurements and experiments specific to each class. the values obtained, following the implementation of the parameters calculation are given in table 2 (through medium values): table 2 medium values of the shapes’ parameters the communication in distributed client server systems used for management of flexible manufacturing systems 303 figure 3: b) picture 1 c) picture 2 c(x) p(x) rmin(x)/ aeclipse(x)/ f(x) rmax(x) beclipse(x) 1.014 1.120 0.921 1.042 0.040 0.841 0.531 0.673 2.512 0.125 0.691 0.512 0.481 3.824 0.230 0.265 0.401 0.213 14.412 0.612 where: 1. sphericity c(x) = 4 π a(x) / (p(x))2. 2. pentland’s ratio p(x) = 4 a(x) / (πfmd(x))2. 3. radii ratio rmax(x)=maxdi / i=1..n; rmin(x)=min di / i=1..n. 4. ratio between the ellipses on the two axes. aelipsa(x) = ˝ ( p(x) / +((p(x)/)2 4 a(x)/)1/2); belipsa(x) = ˝ ( p(x) / ((p(x)/)2 4 a(x)/)1/2). 5. shape characterisation by means of moments f(x) = √ 1/n ∑n 1 (di − m1)2 1/n ∑n 1 di structure of the file data.txt n patterns number of input vectors for the network training x1, x2, x3, x4, . . . , xnpyi (i=1, nc, where nc = number of classes) the components of the input vector (shapes’ parameters), where np = number of shapes’ parameters. the parameters were introduced as follows (according to the table corresponding to each shape): 304 v. lupu, d.e. tiliute class 1-square 0.867 0.660 2.145 0.124 0.583 0.583 2.302 0.164 0.738 0.738 1.968 0.102 2-disk 1.018 0.867 1.000 1.000 1.021 0.857 1.000 0.039 0.994 0.867 1.168 0.051 3-cross 0.160 0.063 22.924 0.732 0.292 0.250 11.615 0.462 0.169 0.143 21.596 0.647 4-triangle 0.670 0.462 3.696 0.229 0.712 0.423 3.313 0.231 0.708 0.514 3.347 0.211 5 conclusions the functionality of the proposed system was proved by a dedicated application software that responds to the following main requirements: • provides the recognition of the 2d and 3d industrial objects viewed from any angle; • provides border touching, partial overlapping and clipping of the objects in the image; • it is noise tolerant. the comparative study of the existent solutions, in correlation with the above-mentioned requirements, leads to the conclusion that 3d object-centered models are suitable in order to ensure the deduction of the model views from any angle. bibliography [1] c.v. kifor, c. oprean, d.d.m. banciu intelligent systems for assisting decisions in advanced product and process planning and design, studies in informatics and control, sic vol.18, no.3, 2009 [2] mircea ivanescu, mihaela cecilia florescu, nirvana popescu the control of the hyper redundant manipulators by frequency criteria, studies in informatics and control, sic vol.18, no.3, 2009 [3] lupu valeriu contributions to the management of the flexible manufacturing systems (phd thesis), 2004 [4] mohammad reza soltanpour, seyed ehsan shafie design and stability analysis of a robust impendance control system for a robot manipulator, studies in informatics and control, sic vol.19, no.1, 2010 [5] o.khatib, k.yokoi, k.chang, d.ruspini, r.holmberg, a.casal coordination and descentralized cooperation of multiple mobile manipulators, journal of robotic systems, volume 13, no.11, 1996, issn: 0741 2223 [6] d.logofatu algoritmi fundamentali in java. aplicatii, isbn: 978 973 46 0815 7, editura polirom, 2007 [7] java on-line documentation (http://java.sun.com) int j comput commun, issn 1841-9836 7(5):816-823, december, 2012. disaster prevention integrated into commonly used web rendered systems with gis capabilities m. cioca, s.c. buraga, c. cioranu marius cioca lucian blaga university of sibiu e-mail: marius.cioca@ulbsibiu.ro sabin-corneliu buraga a. i cuza university of iasi e-mail: busaco@infoiasi.ro cosmin cioranu uefiscdi e-mail: cosmin.cioranu@gmail.com abstract: the end of the 20th century brought a remarkable increase in the field of positioning techniques and communications, making them visible and available to the public, which led to an unprecedented interconnectivity. at the same time, disasters are part of our life. regardless of their nature, measures can be taken, in order to prevent and mitigate their effects, by anticipative preparation or by avoiding the calamity area (if possible). to this end, this paper presents an integrated system, composed of a software component, a hardware component, and a decision-making human element, all having the declared role of diminishing or eliminating human and material losses. keywords: : dss, disaster, open layers, apache. 1 introduction the end of the 20th century brought a remarkable increase in the field of positioning techniques and communications, making them visible and available to the public. among them, one of the most spectacular which revolutionized the way in which we relate to our planet was in fact the introduction of gis (geographical information system) positioning technologies on the consumer level, which were actively supported by communities of developers and which transposed this knowledge into a format that the ordinary user can access, starting with the mobile ones and ending with the old and well-known pc. (e.g. google earth) [16]. fire, floods, earthquakes, storms, disasters caused by human negligence can occur and can give rise to irremediable damages, but in some cases, material and human losses can be avoided or, in the worst case, diminished through a set of measures that can be assisted or implemented by means of an information system with spatial capabilities, that collects data from different sources in order to develop a prevention system for such events, that finally has an dissemination role, that can act through deployment of resources, so that the event or the events concerned affect the normal course of life as little as possible [18]. 2 decision support technologies and tools the complexity of sustainable development requires rational decision-making, and decisionmaking becomes increasingly difficult especially regarding environmental issues. the progress in decision-making theory and decision support systems have determined the emergence of methods copyright c⃝ 2006-2012 by ccc publications disaster prevention integrated into commonly used web rendered systems with gis capabilities 817 and tools that can assist the decision-maker in making the most adequate decisions. however, in helping decision-making on complex issues, these tools are not easily developed and built [1], [7], [8], [10], [11] and [12] . the use of general accepted techniques and methods in dsss may improve the identification and prevention of complex risk situations. these imply making strategic decisions supported by expert boards and groups. their activity is often burdened with physical, time and cognitive barriers. moreover, these methods and techniques used by dsss represent only general approaching instructions on specific decision-making situations. in practice, in order to be used efficiently, they need reinterpretations, refinements, adjustments and completions. 3 technical approach to this end, this paper presents an integrated system (fig.1), informationally and spatially scalable from a community to a certain area, that stratifies both decisional and informational passive and active elements, in order to prevent human losses and/or danger situations, generated by natural or human calamities (floods, radiations, earthquakes etc.). for harmonization, these elements have been grouped into passive elements, meaning those elements that provide information related to environment, active elements, which in this case, are human decisionmakers, that have as primary attributes the position in latitude and longitude system, as well as their type. among the active elements of the system, we mention a list of spatially-localized decisional personnel, attached to each regional entity (village, commune, county), that can be alerted when the system detects a case of disaster, through specific algorithms. one of the main elements, of a great importance, are the specific approved sensors, both fixed and mobile, placed on the entire area of focus, that measure events like humidity level, landslides, earthquakes, radiations etc. these are equipped with point-to-point communication equipment like gsm, radio or other methods that ensure information transmission to the decisional cluster. another passive element that brings a better accuracy to the system are the meteorologic maps, but which imply a high data volume, meaning that these should be served to the system in a semi-processed manner. the major problem of this system was the link between these elements, their harmonization being accomplished by integrating these data into a decision-making neural network, which concentrates the spatial information by a number of minimization operations, so that the result can be transposed into a gis capable system and that generates a well-determined spatial answer. in such an approach, methodologies for this kind of wireless sensor-systems have to be used [4], security protocols for wireless sensor networks [2] have to be considered, as well as efficient algorithms, specific to this kind of mobile sensor networks [3]. 4 computer system implementation from the informational standpoint, the system is based on a web-type, client-server architecture, one of the most frequently employed in application development on one hand and sensor and meta-data set on the other hand. this approach ensures the separation of the function logic model of the application into smaller functional units, the usage being split into two major components: client and server. on the client-side, the users, both on administration level and also as common users, integrate browsing technologies, being compatible with 99,3% [17] of all users, using technologies such as web2.0, html, css, javascript, openlayers. at server level, apache, php, python, inkscape, imagemagick, gdal, mysql are among the technologies employed [14]. additionally, besides these technologies, the data server receives inputs on other communication channels such as gsm or radio. the architecture used and shown in figure 2 is 818 m. cioca, s.c. buraga, c. cioranu figure 1: integrated and scalable spatial information system easy to approach. likewise, the data central system can send alerts to some decision-making entities, firefighters, the police, responsible with disaster prevention and management etc. this architecture is one of the most versatile and can be accessed from: smartphones, desktop pc, laptops, tablets etc. figure 2: general system architecture the presented system has a temporal role, giving the possibility to be loaded with statistical data. once mature, it can play a crucial role in further cases. from a functional standpoint, the system is divided into several distinct components: a. the transfer component, communication; b. the alert component; c. collection hardware components passive elements, i.e. sensors, that collect states of the environment, processing systems etc.; d. software components, i.e. the implementation of a mathematical model and a logic for the analysis of the risk of the various inputs from the external environment, all marked and positioned, using a gis system; e. finally, human components active, decision-making elements, which will be covered in this study only as constitutive element. from the communication standpoint, two ways are used, namely: a. gsm serial communication, usually for timely communications, status data or alert information, characterized by small data volume; b. or other tcp/ip wireless channels, for viewing and creating a graphical information map. the advantage of gsm communications consists in a broader coverage area, but with a lower bandwidth, usually serial communications. they usually meet the need of speed and coverage for stationary sensors, that send the useful data in burst mode. the second is the wireless channel, which provides a wider bandwidth, but which is limited to a shorter distance. in terms of alert, disaster prevention integrated into commonly used web rendered systems with gis capabilities 819 this system integrates information capabilities of the decision-making elements, by using the following channels: a. gsm channels, by sms [5]; b. ordinary channels, mail, newsletters, form-boards etc. industrial gsm modems ensure communication with the gsm network, but for a higher number of sms messages, one can choose the sms bulk service of a local mobile network provider, which is a service provided by local mobile network operators, by which they ensure a way of interconnection of the local system with the mobile network operator. the disadvantage of such a system is the fact that this connection can be interrupted due to the way of implementation (cables), but can provide a greater number of alert messages even greater than a gsm modem (which is, a mobile phone with advanced features of interconnectivity and interoperability with a computer) [6]. in terms of hardware component, the architecture is composed of sensors and/or other data sources, either automatic or human, which are afterwards integrated into the software architectural level. the internal structure of the software component is divided into the following (figure 3), in a top-dow approach: figure 3: the internal structure of the software component a. the mathematical model; b. the logic analysis model; c. the interaction model. from the functional standpoint, this component uses accumulated data and by specific algorithms, it generates visually usable data, which can also be used in the analysis process. the mathematical model has the following formal structure: s = rxr (1) where s geographical space, georeferenced wgs84 (as a rule) [16], but it can be transformed by using other utilities (e.g. gdal) in any format compatible with openlayers [14]. pi(α, β) ∈ s (2) the sensor coordinates, where α, β, geographic coordinates vpi(α,β) ∈ [vpmin, vpmax] (3) where vpmin, vpmax are the normal values. above these values, maximum or minimum, a danger or disaster impact is to be generated. therefore, attached to each point (sensor) defined as such, the notion of impact is a specific function, defined as follows: ipi = 0 (4) if vpi(α,β) ∈ [vpmin, vpmax]. ipi = ψ(vpi(α,β)) (5) if vpi(α,β) ̸∈ [vpmin, vpmax]. and 820 m. cioca, s.c. buraga, c. cioranu ψ ∈ rxr (6) in order to establish the implementation of thei ψ, function, the following criteria will be taken into account: risk level: disaster, high risk, medium risk, low risk and minor risk. event type with human implications: earthquake, floods and fire. taking into account the above-mentioned relations, we can define the disaster area, passing to an iteration through all the points where information can be collected, generating a layer shown in figure 4, which overlapping with the already existing layers, generates figure 1. figure 4: the layer generated on the basis of the information received from the collection points in its turn, the logic model integrates analysis and decision-making components and prepares the needed elements for decision. at this level, one must specify the mathematical model that underlies the entire software component and has as a result a georeferenced layer, which includes the information of each important element that provides information about the environment. the human element plays two roles, one is to be informed, namely the decision-making element, and the second is to inform oneself, in order to avoid a calamity area. 5 technologies client [19], [14] client requires a minimum number of software applications, which as we can see, are relatively inexpensive: a. web browser (firefox, internet explorer >5.5, nescape) to access the system residing on the server; b. access to the server that hosts the system (intranet, internet) to ensure the interconnectivity for this station. any communication media can usually be employed, from wire to wireless technologies; c. the required hardware is restricted to the above-mentioned software requirements; d. for interface software, open layer is used (in order to render the data). server [14] the server software requirements are the following: a. operation system: linux based -provides very good stability in terms of security and performance; b. programming language: php 5.x; c. sgbd: mysql 5.x; d. web server web : apache 2.x; e. gdal and imagemagick for data display. 6 the experimental section. results the methodology for impact assessment is: a. establishing the area center or sensor coordinates; b. establishing the limits affected on the 4 major directions n, s, e, w; c. establishing the negative impact mitigation rates tn , ts, te, tv in order to calculate distance d between two coordinate points p1(lat1, long1) and p2(lat2, long2) ) the haversine formula was used: [15], [20] disaster prevention integrated into commonly used web rendered systems with gis capabilities 821 d = r ∗ 2 ∗ arcsin (√ sin2 (∆lat 2 ) + cos(lat1) ∗ cos(lat2) ∗ sin2 (∆long 2 ) ) (7) where r is the earth radius, which varies from the equator to the poles between 6378.14 and 6356.78, where the accuracy of the above-mentioned formula derives from, an error of about 0.5%. stage 1 collecting the field data following the format below (in order to calculate distance d1). location : p1 (45.017,27.525) => haversine [13] => p1(450 1′ 1′′, 270 31′30′′) affected area o maximum coordinates: (n (45.097, 27.557), e (45.056, 27.613), s(45.012, 27.552), v(45.053, 27.482); o meters , from the point of origin [16]: n 5088m, with an impact lasting for up to 8 days, e 4661m, 6 days, s 4449m, 5 days, v 5792m, 8 days impact parameters are indicated at each point. collecting the field data following the format below (in order to calculate the distance d2). location: p2(45.391,27.76) => haversine [13] => p2(450 23′ 28′′, 270 45′ 36′′) affected area o coordinates: n(45.415, 27.757), e(45.395,27.783), s(45.375,27.757), v(45.395,27.783) o meters, from the point of origin: n 2178m, with an impact up to 6 days, e1897m, 6 days, s 1992m, 8 days, v 2136m, 9 days figure 5: disaster positioning on the map stage 2 stage 2 involves the development of the graphic management functions. the progression calculation function for the affected area d(t) = tmax − t tmax dmax (8) where t ∈ [0, tmax], d ∈ [0, dmax] d(t) ∈ [0, dmax] (9) the impact function, calculates the impact level using the maximum distance, being applied on the 4 directions n, e, s, w l(t, d) = tmax − t tmax d dmax (10) where t ∈ [0, tmax], d ∈ [0, dmax] l(t, d) ∈ [0, 1] (11) 822 m. cioca, s.c. buraga, c. cioranu where 0 means there is no impact and 1, maximum impact. figure 6 results using the relations [2], [4] figure 6: disaster size at moment t0 =0 the following figure represented in a logarithmic scale disaster cases, using the n direction. figure 7: represented in a logarithmic scale where: blue is space variation/day affected on direction n; red is intensity variation of the space affected /day on direction n on zero-day of disaster the area was about 5000m and the eighth day this impact falls to 0. on zero-day of the impact the disaster was labeled 1 then as time passed by, on the eighth day, it fell to 0. the same calculation is used for the impact area p1 for the eastern element. 7 conclusions and future research in conclusion, we can say that, in order to develop high-quality and complex dsss, able to cover as much as possible in case of disasters, on the one hand, a multidisciplinary approach is required, an approach which should bring together specialists in various fields, such as: environment, gis, geographers, mathematicians, computer scientists, organizations in charge with such situations etc.; and on the other hand, a global approach which should reunite institutions and people from different countries, as such phenomena are not restricted to the borders of one specific country and they may affect large areas, encompassing people and goods from different states and at the same time, the transfer of know-how between partners/researchers from different countries brings benefits to such catastrophic situations, that might end up with material damages or worse, with the loss of human lives. bibliography [1] anica-popa i., cucui g., a framework for enhancing competitive intelligence capabilities using decision support system based on web mining techniques, int j comput commun, issn 1841-9836, 4(4):326-334, 2009. disaster prevention integrated into commonly used web rendered systems with gis capabilities 823 [2] aseri t.c., singla n., enhanced security protocol in wireless sensor networks, int j comput commun, issn 1841-9836, 6(2):214-221, 2011. [3] ban d. , yang w., jiang j., wen j., dou w., energy-efficient algorithms for k-barrier coverage in mobile sensor networks, int j comput commun, issn 1841-9836, 5(5):616624, 2010. [4] chen j.i.-z. , chung y.-n., a data fusion methodology for wireless sensor systems, int j comput commun, issn 1841-9836, 7(1):39-52, 2012. [5] cioca, m., cioca, l.i. decision support systems used in disaster management, in decision support systems, chiang s. jao (ed.), isbn: 978-953-7619-64-0, 2010. [6] cioca, m., cioca, l.i., buraga, s.c., spatial (elements) decision support system used in disaster management, digital ecosystems and technologies conference, ieee-dest, isbn: 1-4244-0467-3, ieee catalog number: 07ex1418c, pp. 607-612, 2007. [7] filip, f.g. decision support and control for large-scale complex systems, annual reviews in control (elsevier),issn: 1367-5788, 32(1): 61-70, 2008. [8] filip, f.g. sisteme suport pentru decizii, isbn 978-973-31-2308-8, editura tehnica, bucuresti, 2007. [9] peng, y., gang kou, shi, y., and chen, z., a descriptive framework for the field of data mining and knowledge discover, int. j. of information technology & decision making, 7(4):639-682, 2008. [10] peng, y., gang kou, wang, g., wu, w., and shi, y., ensemble of software defect predictors: an ahp-based evaluation method, doi: 10.1142/s0219622011004282, int. j. of information technology & decision making, 10(1):187-206, 2011. [11] power, d. j. decision support systems: concepts and resources for managers, quorum books, westport, connecticut, 2002. [12] suduc-ana maria, bizoi, m., filip, f.g., user awareness about information systems usability, studies in informatics and control, 19(2):145-152, 2010. [13] http://andrew.hedges.name/experiments/convert_lat_long/ [14] http://asrc.ro/imeteosat_beta (accessed on 07.04.2012) [15] http://en.wikipedia.org/wiki/haversine_formula [16] http://support.google.com/earth/bin/answer.py?hl=ro&answer=148110 (accesed on 2012) [17] http://www.articlesnatch.com/article/certification-authorities-with-browser-ubiquityof-99-3-are-best-in-industry-/253347 [18] http://www.cimec.ro/resurse/patrimoniu/dezastre.htm (accessed on 5.04.2012) [19] http://www.developer.com/java/web/print.php/10935_3528381_2 (accessed on 3.04.2012) [20] http://www.movable-type.co.uk/scripts/gis-faq-5.1.html int j comput commun, issn 1841-9836 7(5):924-932, december, 2012. decision support for healthcare ict network system appraisal a.m. oddershede, l.e. quezada, f.m. cordova, r.a. carrasco astrid m. oddershede, luis e. quezada, felisa m. cordova university of santiago of chile, industrial engineering department av. ecuador 3769, santiago, chile e-mail: astrid.oddershede@usach.cl, luis.quezada@usach.cl, felisa.cordova@usach.cl rolando a. carrasco newcastle university, uk, school of ieee newcastle upon tyne, uk e-mail: r.carrasco@ncl.ac.uk abstract: a framework to support the appraisal process to improve the quality of service (qos) of an information and communication technology (ict) network system in health care service is presented. most of health-related activities stand to benefit from ict endorsement; however, technical problems may appear, as an inadequate physical infrastructure, insufficient access by the user to the hardware/software communication infrastructure and qos issues. the aim is to develop a prototype assessment model based on data collected from the main users of a health network system an evaluation process is carried out to analyze and assess the support of qos of ict, its infrastructure and user interface perception of the qos offered through case study for hospitals in chile. performance has been evaluated by simulation and modelling network architecture. the optimization network engineering tool (opnet) simulation platform is used to examine the network behaviour and performance to ensure consistency and reliability for thousands of staff across the hospital network. keywords: ict, healthcare, opnet, mcdm. 1 introduction nowadays, healthcare institutions are universally urged to improve quality of their services and hospital units are concentrated on how to develop better services, to allocate methods, to provide resources to satisfy professional aspirations and to comply with citizen necessities. most of the actions oriented to improve the operation and the quality of healthcare service depends, to a great extent, on the level of information available and the communications system. a poor ict network system implementation may generate a negative effect on the service, patients and health care providers. furthermore, the ict system from a health centre may be endowed by attributes that not often matches with the user’s requirement s (quality level, performance, cost and others). the ict system may be inappropriate for satisfying user requirements or may be inefficient in doing it. ict system should be a facilitator for health care users since they need to access to all types of data existing on all types of systems. the challenge of providing quality of service (qos) in a health environment is further complicated by the extremely variable qos needs of individual health organizations over time. the kinds of information exchanges in which an organization engages typically vary considerably in the course of a day, from simple exchanges of information regarding a patient’s coverage by a health plan, through transfers of medical records with affiliated organizations, to the exchange copyright c⃝ 2006-2012 by ccc publications decision support for healthcare ict network system appraisal 925 of large medical images for interpretation and diagnosis. for example, the bandwidth needs of a small medical clinic could, accordingly, vary enormously during the course of a day, ranging from near nothing one minute to several megabits per second the next. finding ways to satisfy such variable demand for bandwidth economically represents a significant challenge. the agents (patient, clinical doctors, hospital staff, government, etc) involved should attend contradictory petitions: on one hand, to respond the increasing demand and, by another, to attend to the budgetary restrictions. the answer to this complexity is to improve qos and the efficiency [1] [2] . user needs and expectations are indispensable to count on better information that lead to an improvement in operation services, welfare and clinical information. reorienting the health care system towards the needs of the client, medicine based on the evidence, clinical management and the continuous improvement of the processes (qos), total quality, contemplating the human factor as the main assets of the business) are key elements in future. the hospital management challenge is based on the achievement of the articulation and the convergence of values among the agents, the staff members and the patients; the information technology and communications systems needed are not just mere data agents, instead, they are of information, knowledge and intellectual capital [3] some general questions to be answered are: what are user expectations about the ict needs? is the current ict infrastructure appropriate? what are the main qos parameters? how can be evaluated the ict network for healthcare service? what technical capabilities do health applications demand of the internet? how do these capabilities differ from those needed by applications in other sectors, such as banking, defense, and entertainment?. to deal with this complexity, a two stages methodology involving user perception is proposed. the first stage corresponds to the identification of the user types, the activities they are involved in, the ict network system requirements, applications and priorities attributes. this is achieved through multicriteria decision making approach (mcdm) [4], using the analytical hierarchy process (ahp) in author previous work [8] obtaining key qos parameters. this paper focuses on the second stage, which is the development of a model for evaluating ict healthcare network quality of service (qos) integrating user perception. this stage is concerned with modelling and simulation to examine the network performance on applications. the qos technical metrics related to each attribute has to be defined together with the applications profile. qos offered by a particular network could be established by technical parameters that can be measured objectively. however, user perception depends upon their needs, their precise applications and their expectations. it is a difficult task to find a set of universal parameters for every type of service because there are many and dissimilar parameters involved in the performance evaluation. a typical public hospital zone is considered as a case study to analyze technology infrastructure and network performance. further, profile applications must be set up according to the main role health care users perform. a new model based on the results obtained on the first stage is designed and opnet simulation platform is used to examine the network behaviour and performance .the model incorporates communications resources of lan architecture from typical chilean hospitals where. opnet model has demonstrated to give a good representation to real world to analyze traffic flows, and network performance providing a tool to demonstrate different type of networks and protocols. this pilot case study revealed that the model shows to be useful by evaluating quality, user pertinent criteria and to connect higher objectives with lower performance metrics and conclude that through the proposed assessment model is possible to detect whether there is connection between human perception of qos and the technical metrics associated to the ict network. this analysis helps decision makers, network planners and operations engineers to 926 a.m. oddershede, l.e. quezada, f.m. cordova, r.a. carrasco manage complex and constantly changing networks, using predictive planning for reacting to significant network issues using real-time network visualization and troubleshooting. this is critical in a healthcare institution. section 2 provides a description of the conceptual model, in section 3 the case study description is presented .the simulation results generate new information, and section 5 provides the conclusions. 2 conceptual model for healthcare service ict network a conceptual model is proposed for health ict network system evaluation connecting qos user perception (qualitative) with qos technical aspects, (parameters, and network performance). modeling and simulation provides a good tool, to plan network infrastructure and manage application performance using predictive planning. the model scheme is depicted through figure 1 illustrating the diverse factors involved in the evaluation of qos for a particular service starting from the end user. initially, the main users, the type of user and its applications must be categorized , then the ict support needs to deliver a better service, the attributes indispensable to meet the requirement in concordance to their expectation, are to be characterized , next , technical metrics should be defined and analyzed to check the performance on an application [5]. the perceived qos cluster includes the parameters related to each service the user perceive and determine the satisfaction of the service received. for example: success in the connection, accessibility, velocity, etc. the technical ict parameters refers to the basic metrics that would guarantee a service, i.e., end to end delay, packet loss and should be detected by the network operators in charge. qos offered by a particular network could be established by technical parameters that could be measured objectively. however the user perception depends upon their needs, their directly application and their expectations. it is a difficult task to find a set of universal parameters for every type of service for the reason that there are many and dissimilar parameters involved in the performance evaluation. then, it is useful to analyze which of the parameters is relevant when considering the user perception for a determined service. the organization must then define a service level agreement (sla) for their main applications. figure 1: proposed conceptual model for health ict network evaluation decision support for healthcare ict network system appraisal 927 for the case in study, we suggest modeling and simulation using optimization network engineering tool (opnet) simulation platform, (www.opnet.com). [6], [7] to examine the network behavior and performance when it is not possible to compute through the operators the basic metrics. opnet supports simulation technologies and it is well suited to examining network behavior. the opnet models execute the protocol in much the same way as a production environment. the modeling it is based on cisco network which is 99% accurate and good representation of real world. [8] it is possible to study protocol behavior under different network conditions and application performance. 3 hospital ict network scenarios simulations with opnet a pilot case study is pursued following the methodology proposed, where the results can be used for analyzing the network performance en ict healthcare system, when it is not possible to measure directly, the performance metrics, and parameters. according to opnet methodology, the initial phase for analyzing the effect of qos on an ict network and/or on application performance is to select a network topology, followed by traffic configuration for each application before running simulations. then qos has to be configured defining slas to analyze the results. 3.1 hospital zone case study the authors have collected information from a public community hospital/medical centre/clinic hospital dr. luis tisne brousse in santiago chile. a local ethernet network from a zone of the medical centre is considered as an initial pilot study case. in view of the fact that the purpose is to examine the backbone utilization, the study zone is represented as a partial network topology along with the methodology. hospital zone description the backbone giga bit ethernet in study is located at a building next to the hospital where they perform administrative functions to assist the hospital and some health service functions. a diagram showing the zone in study is given in figure 2. the section in study is a two floor building, plus a basement named: "zócalo". within the first floor, there are rooms used for providing diagnosis for minor ailments and medical consultation. this first floor includes 3 switches: • ws2950g-24, switch, located at the electric room, with five workstations, • ws2950-24, switch located at the medicine/physiatrist room, with six work stations, • ws2950g-12 located at the x ray room, with 3 workstations. • the basement floor includes a ws2950g-24, switch with 24 ports and there are 6 workstations connected, • the first floor switches are connected to the second floor to a ws2950g-24 switch located at the director office by fibber optical cable. the switches are connected to and between each other by optic fiber, except for the x ray room switch which is a cable utp cat. 5e. 928 a.m. oddershede, l.e. quezada, f.m. cordova, r.a. carrasco figure 2: hospital zone diagram 3.2 opnet network modeling for public hospital zone case study the authors have designed network modeling and implementation for the public hospital stated in conformity with the diagram described on figure 4. the partial network topology is represented following opnet methodology in figure 3. the partial network characterizes the initial situation and first scenario to be explored. a total of 30 workstations are initially linked to the switches. the different services the users deliver are expected to be supported by the network through different applications that will help to accomplish their assignment. the applications regard as sustained by the server are based on the results obtained on the previous study. 3.3 health ict network applications communications networks enable applications to exchange data. popular applications that use data networks include virtual terminal services, file transfer utilities, database transactions, and e-mail. each of these applications generates its own sort of traffic: virtual terminals slowly generate many small packets, while file transfer utilities send long streams of large packets. each type of traffic causes and experiences a different set of problems in the underlying network, so you may want to accurately model the traffic patterns generated by a variety of applications. opnet uses a generic network application model to generate typical application traffic patterns. this is the applications model, also called the standard network application model.. depending on their underlying networks, application architectures may differ [7]. the applications are modeled explicitly, and end-to-end delays or response times are studied in detail. the factors that contribute to application response time include,: decision support for healthcare ict network system appraisal 929 figure 3: partial network topology • delays due to contention on servers (server processing time),or/and • delays due to contention on the client (client processing time), • delays due to contention with "other" traffic at the various intermediate devices (queuing delays), • delays due to contention with traffic of the same application type from other users at the intermediate devices (queuing delays), • network delays (transmission and propagation), • delays due to protocol effects (tcp retransmissions, windowing etc.) in the course of the investigation and survey developed on a previous work [9], [10], the findings revealed that the relative usage of ict applications in health care differs to some extent depending on the institution. indicating that, so far, the real application that has more usage is email and there is very little usage of the others. web browsing is used mainly on research activities. however, through the multicriterial analysis by the use of ahp, to find out the importance of ict provision in quality of service in healthcare institutions developed in[10], one of the important new information, conclusions and contribution are the relative importance users assigned to the applications in performing a health care service. user assigned the highest priority in importance to data base access and the least relative importance to e-mail. this shows the existence of a gap between what healthcare participants actively use and what they consider important for the development of their daily work as a service for the patients. then, considering these results the applications to be supported by the server for every scenario are: email, web browsing (http 1.1), file transfer, database access, file print, video conferencing, and voice. 930 a.m. oddershede, l.e. quezada, f.m. cordova, r.a. carrasco 3.4 applications configuration a profile is applied to each workstation, server, or lan. it specifies the applications used by a particular group of users. an application may be any of the common applications, email, file transfer, etc., that may be defined. the next step is to set up profile applications according to each user type. every workstation will have a profile application consistent with the users’ main role. after the participants have studied the nature of the system, then profiles are designed with respect to the functions a user makes use of in/at a health service. this profile name is assigned for classification. these applications profiles were defined using the results from the analytic hierarchy process. once applications and profiles are defined, it is possible to characterize different scenarios for each study case intended for visualizing how sensible is a networks performance with respect to changes. 4 simulation results different scenarios for the study case are characterized to determine the sensitivity of main key parameters (as throughput and utilization). initially, the applications described are modeled explicitly and all background traffic is disabled. simulations are ran for each scenario, increasing the number of users and their respective applications. through figure 4 it is possible to visualize overlaid run results for delay average (in ethernet. delay (secs) for four scenarios. as the number of users is increased, the average delay time increases at different rates, until it tends to be stabilized. figure 4: [delay] average [in ethernet. delay (sec)] for four overlaid scenarios. the simulations scenarios are run allowing the same probability distribution for the start time. the difference in average delay for the scenarios is due to contention among the network decision support for healthcare ict network system appraisal 931 users on all the intermediate devices and links. figure 5 gives a picture of the delay variation in seconds as the number of users is augmented. figure 5: ethernet average delay vs number of users. after running simulations for different scenarios and for all the applications considered, increasing number of users, incorporating new number of applications and devices, varying traffic and others we could observe that for this hospital zone ict network infrastructure there are no problems concerning channel utilization. ethernet delay showed to be low with a high throughput. then, the network prepared to support a greater number of users, more and new applications, as voice, images, video and others. 5 conclusions through the case studied, we observe for data transmission that there is no connection with human perception. even though from human perspective showed discontent about the access and ubiquity. qos parameters are good. however, users complain about the straightforward access, new applications and number of computers. in this respect, it is possible to conclude that if the delay is low and throughput is high, there should not be any availability problem, but one of the reasons could be caused of unbalanced resources distribution, some resource policy distribution, or other. from the various simulations results we conclude that the model is able to detect whether there is connection between human perception of qos and the technical metrics associated to the ict network. from the technology aspects perspective there is a big potential forthcoming for gaining benefit from of ict support in healthcare service that has to be analyzed also from financial perspective to optimise the available resources. an increase in the number of workstations, increasing the number of new applications, preparing and training people in informatics and technology is a tactic that contributes and leads to increase the usage of technology in the healthcare sector, mainly in rural communities. bibliography [1] elske ammenwertha,*, stefan gräberb, gabriele herrmannc, thomas bürkled, jochem königb, evaluation of health information systems-problems and challenges , int journal of medical informatics 71, 125-135, 2010. 932 a.m. oddershede, l.e. quezada, f.m. cordova, r.a. carrasco [2] e. babulak, quality of service provision assessment in the healthcare information and telecommunications infrastructures, international journal of medical informatics, 75(3):246-252, 2006. [3] networking health: prescriptions for the internet, computer science and telecommunications board, national academic press, isbn-10: 0-309-06843 , 2000. [4] triantaphyllou, e.. " multi-criteria decision making methods: a comparative study" (applied optimization, volume 44) nov 2000 [5] sun, l., and ousmanou, k., articulation of information requirements for personalized knowledge construction, journal of requirements engineering, 11(4):279293, 2006. [6] optimized network engineering tool (opnet). www.opnet.com, opnet: user’s manual, http://www.opnet.com/university_program/teaching_with_opnet /textbooks_and_materials/materials/opnet_modeler_manual.pdf, 2004. [7] opnet documentation v.11.o.a, opnet technologies, inc., bethesda, md, 2004. [8] heath a., carrasco r.( 2001), access techniques for 3g multimedia wireless packet switched networks: simulation using opnet, iee/ieee/bcs 6th international symposium on communication theory and applications (iscta01), lancaster university, 15-20 jul 2001. [9] oddershede, a.m, carrasco, r.a e. barham. multicriteria decision model for assessing health service information technology network support using ahp, iberoamerican journal of computing, computacion y sistemas, issn 1405-5546, 12 (2): 173-182, 2008, [10] oddershede, a.m, carrasco, r.a, information and communications technology significance in health care: user perception, mediterranean journal of electronics and communications, 2(2): 82-89, 2006. int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 365-376 fuzzy control design for a class of nonlinear network control system: helicopter case study p.q. reyes, j.o. arjona, e.m. monroy, h.b. pérez, a.d. chavesti p. quiñones-reyes instituto tecnológico de jiquilpan carr. nac., s/n km. 202, cp 59510, jiquilpan, michoacán, méxico. j. ortega-arjona, e. méndez-monroy, h. benítez-pérez, a. durán-chavesti universidad nacional autónoma de méxico apdo. postal 20-726, admón. 20, del. a. obregón, méxico d. f., cp. 01000. polbond@hotmail.com, jloa@ciencias.unam.mx, {hector,chavesti}@uxdea4.iimas.unam.mx abstract: this paper presents a fuzzy control approach to a helicopter mimo nonlinear system, implemented on a networked control system, as case study. for this, a hardware-in-the-loop implementation is developed using several multi-channel a/d cards, integrated to a computer network system. variant time delays are considered over ethernet and canbus networks. fuzzy logic is used to deal with the complexity of the integrated computer network as well as with the dynamics of the system. two fuzzy logic control systems are coupled for both signals of the helicopter case study: yaw and pitch. both these tend to concentrate around desired references, considering variant time delays. keywords: fuzzy control, networked control systems. 1 introduction reconfiguration is a transition that modifies the structure of a system so it changes its representation of states. here, it is used as a feasible approach for fault isolation, and also, it is a response to time delay modification. in control systems, several modelling strategies for managing time delay within control laws have been studied by different research groups. nilsson [16] proposes the use of a time delay scheme integrated to a reconfigurable control strategy, based on a stochastic methodology. jiang [12] describe how time delays are used as uncertainties, which modify pole placement of a robust control law. izadi present an interesting case of fault tolerant control approach related to time delay coupling. blanke [7] study reconfigurable control from the point of view of structural modification, establishing a logical relation between dynamic variables and the respective faults. thompson [17] and benítez-pérez [4] consider that reconfigurable control strategies perform a combined modification of system structure and dynamic response, and thus, this approach has the advantage of bounded modifications over system response. recent approximations are presented by dai, which allow a reasonable but static approximation for time variable strategy. also, kim [20] have followed a maximum allowable time delay (madb), where complex task behaviour is permitted as long as madb is preserved [22] [23]. copyright c⃝ 2006-2012 by ccc publications 366 p.q. reyes, j.o. arjona, e.m. monroy, h.b. pérez, a.d. chavesti the approach here makes use of a case study that takes time delays due to communication as deterministic measured variables. for this, a fuzzy control law [1] is used, where time delays result from the deterministic reconfiguration of communications due to a scheduling algorithm. fuzzy control is used for managing extended horizons from system inputs and outputs, to determine several scenarios modified by time delays. recent results encourage this approximation, as shown in benítez-perez [5] [6]. for experimental purposes, the following considerations are taken: 1. time delays are bounded. 2. the combination tends to be globally stable. the objective of this paper is to present the design of a fuzzy control strategy developed from the time delay knowledge, as well as computer network behaviour considering communication amongst nodes for a helicopter case study. the novelty is to propose a fuzzy control [24] [25] for network control system (ncs) based on the defined communication network and variant time delays. present approach makes use of time delays due to communication as deterministic measured variables, defined by previous knowledge of computer network. in here, control law views time delays as a result of bounded communications based upon scheduling algorithm. a basic consideration for this approach is that time delays are bounded to madb. the main reason for these is the behavior of computer network as well as node processing time. however, in order to integrate this variable as global time delay (∆t∗), it is necessary to consider its nominal value. this is presented as a percentage: 0% refers to the current time delay, and 100% represents extreme time delays. hence, this value is any value produced as time delay less than or equal to total sampling period. having defined global time delays as its nominal value, the fuzzy control structure is proposed as shown in figure 1. fuzzy control is chosen here for implementing a gain-scheduler controller, on contrary of smith’s predictor [18], since it has a smooth transition between scenarios. furthermore, the chosen operating points are the reference elements of proposed fuzzy control. thus, any degradation from time delays would degrade the control law, however keeping a stable response from the plant. time delay degradation is bounded from communication protocol, as explained by [13]. figure 1: fuzzy control structure. fuzzy control design for a class of nonlinear network control system: helicopter case study367 notice that the current approach follows a mamdani strategy [18] rather than a takagi sugeno (tks) [1] for the implementation of the fuzzy control. the development of a tks strategy is considered as a future work, aiming for integrating time delays into a subsequent part of the fuzzy control rules. the fuzzy control law for a fault free scenario is presented in figure 2, based on [18]. membership functions are gaussian distributions, where e has six membership functions (pb, pm, ps, ns, nm, nb), and de has 6 membership functions (pb, pm, ps, ns, nm, nb). the output variable has eight membership functions (pb, pm, ps, pz, nz, ns, nm, nb). an additional variable, named current nominal time delay (cntd), has three membership functions (n, z, p). figure 2: classical structure for fuzzy control law notice that this implementation is common for fuzzy control design, however, here fuzzy control design focuses on the strategy of the control law due to the communication in the computer network, as well as the consequent time delays. these time delays are measurable and bounded, according to previous knowledge of computer network, based on a finite state machine that selects when valid strategies take place. here, the scheduling approximation is the inherent edf algorithm [14] with certain lost data. the aim is to study how this transition is carried out when using a fuzzy mamdani approach [1], based on a ncs. particularly, for the actual ncs, the communication network strongly affects the dynamics of the system, expressed as a time variance that exposes a nonlinear behaviour. such nonlinearity is addressed by incorporating time delays. from real-time system theory, it is known that time delays are bounded even in the case of causal modifications due to external effects [15]. using this representation, time delays are counted using simple addition, as described as follows. 2 case study and experimental setup the case study here is a helicopter mimo system, integrated to a computer network as shown in figure 3 [21]. it is integrated by three a/d cards: an ad512 card is the interface of joystick, acting as moving reference; an ad612 card is the interface of actuators yaw and pitch; and a q4 card is used for sampling the information from two encoders which sense yaw and pitch information from the movement. two networks are used for this case study: an ethernet network at 10/100mhz, and a can network at 1mbit/s. for experimental purposes, the controller node works as well as gateway for both databuses. 368 p.q. reyes, j.o. arjona, e.m. monroy, h.b. pérez, a.d. chavesti figure 3: helicopter mimo system for the case study while the description of the helicopter is beyond the scope of this paper, a brief description is provided here. however, further information can be found in the original quanser [21] documentation. the quanser 2 dof helicopter consists of a helicopter model mounted on a fixed base with two propellers that are driven by dc motors. the front propeller controls the elevation of the helicopter nose about the pitch axis and the back propeller controls the side-to-side motions of the helicopter about the yaw axis. the pitch and yaw angles are measured using high-resolution encoders. the two degrees of freedom helicopter pivots about the pitch axis by angle θ and about the yaw axis by angle ψ.the pitch is defined positive when the nose of the helicopter goes up and the yaw is defined positive for a clockwise rotation. table 1 lists the various lengths, masses, and moment of inertias associated with this helicopter model. variable description value unit beq,p equivalent viscous damping about pitch axis. 0.800 n/v beq,y equivalent viscous damping about yaw axis. 0.318 n/v j eq,p total moment of inertia about pitch pivot. 0.0384 kg.m2 j eq,y total moment of inertia about yaw pivot. 0.0384 kg.m2 k pp thrust torque constant acting on pitch axis from pitch motor/propeller. 0.204 n.m/v k py thrust torque constant acting on pitch axis from yaw motor/propeller. 0.0068 n.m/v k yp thrust torque constant acting on yaw axis from pitch motor/propeller. 0.0219 n.m/v k yy thrust torque constant acting on yaw axis from yaw motor/propeller. 0.072 n.m/v lcm center-of-mass length along helicopter body from pitch axis. 0.186 cm mheli total moving mass of the helicopter. 1.3872 kg table 1: helicopter specifications and model parameters. fuzzy control design for a class of nonlinear network control system: helicopter case study369 the linear state-space model of the helicopter is given by equation 1. notice that the nonlinear equations of motion are considered linear about the quiescent point (θ0=0, ψ0=0, θ.0=0, ψ.0=0). substituting the state x = [θ, ψ, θ., ψ.] and solving for x.: x. =   0 0 1 0 0 0 0 0 0 1 0 0 0 0 − bp jeq,p+mhelil 2 cm 0 0 0 0 0 0 − by jeq,y+mhelil 2 cm 0 0 1 0 0 0 0 0 0 1 0 0 0 0   x +   0 0 0 0 kpp jeq,p+mhelil 2 cm kpy jeq,p+mhelil 2 cm kyp jeq,y+mhelil 2 cm kyy jeq,y+mhelil 2 cm 0 0 0 0   u (1) y =   1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1   x in order to establish an experimental setup, several conditions nave to be taken into account. for example, the communication network frequencies for ethernet and canbus are set 10/100mhz and 1mbit/s, respectively. moreover, the derivative boundary for pitch is -0.3 to 0.3. the rest of the boundaries are shown table 2. maximum value minimum value standard deviations derivative value -0.3 0.3 error value -0.25 0.25 control value 0 25 derivative center -0.3 -0.2 0 0.2 0.3 0.12 error centers -0.25 -0.05 0 0.05 0.25 0.1 names of current membership functions cmp cp cm cg cmg table 2: signal characteristics of pitch. table 3 shows the related consequent matrix for pitch signal. this is coupled following control law for yaw signal, where time delays are affecting both control laws. error-derv mp p m g mg mp cp cp cp cm cm p cp cp cm cm cm m cp cm cm cm cg g cm cm cm cg cg mg cm cm cg cg cg table 3: membership relationships amongst consequent parts for pitch signal. 370 p.q. reyes, j.o. arjona, e.m. monroy, h.b. pérez, a.d. chavesti table 4 shows the main characteristics of control approach for the yaw signal. notice that the numerical values tend to be different to those related to pitch control law. this is basically due to dynamic conditions of yaw signal. maximum value minimum value standard deviations derivative value -0.2 0.2 error value -0.5 0.5 control value -20 15 derivative center -0.2 -0.1 0 0.1 0.2 0.08 error centers -0.25 -0.125 0 0.125 0.25 0.25 names of current membership functions pmp pp pm pg pmg table 4: signal characteristics of yaw. table 5 shows the matrix for yaw signal. this is considerable different from pitch due to dynamic response of the plant. errorderv mp p m g mg mp pmg pmg pmg pmg pmg p pmg pmg pg pg pg m pm pm pm pm pm g pp pp pp pp pp mg pp pp pp pp pmp table 5: membership relationships amongst consequent parts for yaw signal. now for both signals, the mamdani integration is defined in eqn 2: u = ∑r j=1 αjβj∑r j=1 αj r = mxn αj = γmγn j = 1...mxn m = 1...m n = 1...n γi = exp ( − ( x−ci σi )2) (2) where: • ci are the centers of the gaussians • σi are the standard deviations of the gaussians. • m is the total number of gaussians per error • n is the total number of gaussians per error derivatives, and fuzzy control design for a class of nonlinear network control system: helicopter case study371 • βj is the group control centers figure 4 presents the control surface for pitch signal. this surface tends to have smooth transitions between rules and consequent parts. it has a local valley due to delay interaction and error. this response is reflected on error and derivative conversion. figure 4: control surface for pitch signal. figure 5 shows the control surface for the yaw signal. this tends to be more accurate in terms of formal fuzzy transitions, where only one small local valley exists. figure 5: control signal for yaw signal. in both resulting surfaces, these tend to be smaller at negative error and derivative error values. on the contrary biggest values are related to both positive values (error and derivative respectively). 3 experimental results current time delays are bounded to normal representation as shown in fig. 6. where the delays are mainly considered as a result of communication media following fig. 3. the current experimental results show how the helicopter mimo case study adequately performs during time variant conditions. considering an experimental execution of 50 seconds, several results show that, for instance, current error of yaw signal (figure 7.a) presents a bounded response from 5 seconds onwards, while its derivative (figure 7.b) tends to be almost zero, even in the case of change perturbation around 5 seconds. 372 p.q. reyes, j.o. arjona, e.m. monroy, h.b. pérez, a.d. chavesti figure 6: communication time delays and the related estimation error. figure 7: error response for yaw signal. fuzzy control design for a class of nonlinear network control system: helicopter case study373 figure 8 shows the control response for the yaw signal. it is a bounded response, where several glitches are spotted. these result from loosing data from the ethernet network. such a behaviour is not the actual delay from computer network, but it is an inherent feature of the ethernet computer network. figure 8: control response for yaw signal. figure 9 presents a not-smooth transition between error and its derivative, due to local valley shown at figure 4. although system response is feasible for the current combination, it tends to have drastic, undesirable jumps. figure 9: error vs derivative of error response. regarding the pitch signal error, the response tends to be smooth and bounded. there is an inherent oscillation at error response (figure 10.a), which results of slight movements from the joystick. the related derivative (figure 10.b) reflects the bounded responses with some glitching. figure 11 shows the control response, presenting a similar behaviour, where glitches are present. these glitches result from loosing data in the communication network. 374 p.q. reyes, j.o. arjona, e.m. monroy, h.b. pérez, a.d. chavesti figure 10: error response for pitch signal. figure 11: control response for pitch signal. figure 12 shows a common response between error and derivative of error as convergence signal. figure 12: error vs derivative of error response for pitch signal. fuzzy control design for a class of nonlinear network control system: helicopter case study375 4 concluding remarks this paper presents an approach for the integration of two fuzzy logic controllers, in order to perform control under time variant conditions. these two techniques are applied in parallel. although there is no formal verification for this sequence, it has been adopted since mamdami approximation provides stable conditions for control reconfiguration. moreover, the use of a real-time testbed to approve or disapprove changes on the behaviour of a computer network allows bounding time delays during a specific time frame. this local time delay allows the design of a control law, capable to cope with new conditions. hardware-in-the-loop implementation is feasible, since complex integration through computer network as well as real-time operating systems are integrated through bounded time delays acknowledgements the authors would like to thank the financial support of disca-iimas-unam, and unampapiit (in103310-3) , picco 10-53, mexico in connection with this work. bibliography [1] j. abonyi, fuzzy model identification for control, birkhďż˝user, 2003 [2] l. almeida, p. pedreiras and j. a.-fonseca, the ftt-can protocol: why and how, ieee transactions on industrial electronics, 49(6):1189-1201, 2002 [3] h. benítez-pérez and f. garcía-nocetti, switching fuzzy logic control for a reconfigurable system considering communication time delays, proceedings, cdrom, european control conference, ecc 03 september, 2003 [4] h. benítez-pérez and 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6(1):101-112, 2011 [24] negoita c.v., remembering the beginnings, int j comput commun, issn 1841-9836, 6(3):458-461, 2011 [25] nyirenda c.n., dong f., hirota k., distance based triggering and dynamic sampling rate estimation for fuzzy systems in communication networks, int j comput commun, issn 1841-9836, 6(3):462-472, 2011 int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 540-549 neural network model predictive control of nonlinear systems using genetic algorithms v. ranković, j. radulović, n. grujović, d. divac vesna ranković, jasna radulović, nenad grujović faculty of mechanical engineering, university of kragujevac department for applied mechanics and automatic control serbia, 34000 kragujevac; sestre janjić 6 e-mail: vesnar@kg.ac.rs, jasna@kg.ac.rs, gruja@kg.ac.rs dejan divac institute for development of water resources "jaroslav černi" serbia, 11000 belgrade jaroslava černog st., 11226 beli potok e-mail: ddivac@eunet.rs abstract: in this paper the synthesis of the predictive controller for control of the nonlinear object is considered. it is supposed that the object model is not known. the method is based on a digital recurrent network (drn) model of the system to be controlled, which is used for predicting the future behavior of the output variables. the cost function which minimizes the difference between the future object outputs and the desired values of the outputs is formulated. the function ga of the matlab’s genetic algorithm optimization toolbox is used for obtaining the optimum values of the control signals. controller synthesis is illustrated for plants often referred to in the literature. results of simulations show effectiveness of the proposed control system. keywords: model predictive control, nonlinear system, identification, digital recurrent network, genetic algorithm. 1 introduction the predictive controllers are based on the mathematical model of the object, which is being controlled. nonlinear system identification and prediction is a complex task. all the processes in nature are nonlinear. in large number of processes, the nonlinearities are not prominent, so their behavior can be described by the linear model. in the linear systems theory there exist a large number of methods that can be applied for obtaining the linear model of processes. the nonlinear model must be chosen when the nonlinearity is strongly exhibited. in the identification process, the parameters of the mathematical model are being determined as such that the difference between the system response and its mathematical model is as least as possible, both in the transient regime and in stationary state. the general model of linear processes is arx (auto regressive exogenous), while for the nonlinear ones it is narx (nonlinear auto regressive exogenous). the narx model structure enables application of the neural networks, the fuzzy systems and the neuro-fuzzy systems for approximation of the nonlinear function. neural networks have been applied to the identification of nonlinear dynamical systems. the most of the works are based on multilayer feedforward neural networks with backpropagation learning algorithm. however, the conventional back-propagation algorithm has the problems of local minima and slow rate of convergence. a novel multilayer discrete-time neural network is presented for the identification of nonlinear dynamical systems, [1]. in [2] a new scheme for on-line states and parameters estimation of a large class of nonlinear systems using radial basis function neural network has been designed. a new approach to control nonlinear discrete dynamic systems, which relies on the identification of a copyright c⃝ 2006-2012 by ccc publications neural network model predictive control of nonlinear systems using genetic algorithms 541 discrete model of the system by a feedforward neural network with one hidden layer, is presented in [3]. nonlinear system identification via discrete-time recurrent single layer and multilayer neural networks are studied in [4]. in [5] an identification method for nonlinear models in the form of fuzzy-neural networks is introduced. the fuzzy-neural networks combine fuzzy if-then rules with neural networks. the adaptive time delay neural network is used for the identification of nonlinear systems [6], and four architectures are proposed for identifying different classes of nonlinear systems. the identification of nonlinear systems by feedforward neural networks, radial basis function neural networks, runge-kutta neural networks and adaptive neuro-fuzzy inference systems is investigated in [7]. result of simulation indicates that adaptive neuro fuzzy inference systems are a good candidate for identification purposes. however, neural networks are the simplest approaches in the sense of computational complexity. in [8] nonlinear system identification via feedforward neural network and digital recurrent network is studied. model predictive control (mpc) is applied to a large number of nonlinear industrial process, [9,10,11,12]. the methodology to design and implement neural predictive controllers for nonlinear system has been developed in [13,14,15]. in [13] feedforward neural networks to estimate the nonlinear process are applied. also, for the minimization of the cost function, the matlab′s optimal toolbox functions fminunc and fmincon were used. the design methodology for predictive control of industrial processes via recurrent fuzzy neural networks is presented in [14]. in [15] the multilayer perceptron is used to identification of the nonlinear object and genetic algorithm is applied to solve the multi-criteria optimization problem. in this paper the control of the nonlinear object is studied and it is identified by the drn. recurrent networks are more powerful than nonrecurrent networks and have important uses in control and signal processing applications, [16]. they have been shown to be more efficient than feedforward neural networks in terms of the number of neurons required to model a dynamic system [17,18]. models with recurrent networks are shown to have the capability of capturing various plant nonlinearities, [19,20]. the major objective of the study presented in this paper is to take advantages of the recurrent neural networks for modelling and genetic algorithms for optimization. the proposed method formulates a dynamic nonlinear optimization problem, where the cost function consists of two terms: the differences between the drn model predictions and the desired output trajectory over a prediction horizon and the control energy over a control horizon. for the solution of nonlinear optimization problem, a genetic algorithm is used, which can approximate the optimum solution very fast, compared to conventional optimization techniques. in this paper, the genetic algorithm has been successfully used in combinations with digital recurrent network. in the second section the identification of the nonlinear object by application of the drn is explained. in section three the principle of work of the predictive controllers is analyzed. in this paper for obtaining the optimum values of the control signals, the genetic algorithm (ga) was used. results of simulations are given in section four, while section five presents the concluding remarks. 2 neural network for identification of nonlinear dynamic different methods have been developed in the literature for nonlinear system identification. these methods use a parameterized model. the parameters are updated to minimize an output identification error. a wide class of nonlinear dynamic systems with an input and an output can be described by the model: ym(k) = fm(φ(k),θ), (1) where ym(k) is the output of the model, φ(k) is the regression vector and θ is the parameter vector. depending on the choice of the regressors in φ(k), different models can be derived: 542 v. ranković, j. radulović, n. grujović, d. divac • nfir (nonlinear finite impulse response) model φ(k) = (u(k − 1),u(k − 2), . . . ,u(k − nu)), where nu denotes the maximum lag of the input. • narx (nonlinear autoregressive with exogenous inputs) model φ(k) = (u(k − 1),u(k − 2), . . . ,u(k − nu),y(k − 1),y(k − 2), . . . ,y(k − ny)), where ny denotes the maximum lag of the output. • narmax (nonlinear autoregressive moving average with exogenous inputs) model φ(k) = (u(k − 1),u(k − 2), . . . ,u(k − nu),y(k − 1),y(k − 2), . . . ,y(k − ny), e(k − 1),e(k − 2), . . . ,e(k − ne)) where e(k) is the prediction error and ne is the maximum lag of the error. • noe (nonlinear output error) model φ(k) = (u(k − 1),u(k − 2), . . . ,u(k − nu),ym(k − 1),ym(k − 2), . . . ,ym(k − ny)). • nbj (nonlinear box-jenkins) model uses all four regressor types. the narx and noe are the most important representations of nonlinear systems. the block scheme of the drn model which corresponded to the noe model is shown in figure 1. figure 1: the block scheme of the neural network model figure 2 is an example of a drn. the output of the network is feedback to its input. the output of the network is a function not only of the weights, biases, and network input, but also of the outputs of the network at previous points in time. in [16] dynamic backpropagation algorithm is used to adapt weights and biases. drn network is composed of a nonlinear hidden layer and a linear output layer. the inputs u(k − 1),u(k − 2), . . . ,u(k − nu) are multiplied by weights ωyi j outputs ym(k − 1), ym(k − 2), . . . ,ym(k − ny) are neural network model predictive control of nonlinear systems using genetic algorithms 543 multiplied by weights ωyi j and summed at each hidden node. then the summed signal at a node activates a nonlinear function. the hidden neurons activation function is the hyperbolic tangent sigmoid function. in figure 2, ωi represents the weight that connects the node i in the hidden layer and the output node; bi represents the biased weight for i-th hidden neuron and is a biased weight for the output neuron. the output of the network is: ym(k) = nh∑ i=1 ωiνi + b (2) figure 2: digital recurrent network where nh is the number of hidden nodes and: νi = eni − e−ni eni + e−ni (3) ni = nu∑ i=1 u(k − j)ωui j + ny∑ j=1 ym(k − j)ωyi j + bi (4) this error is used to adjust the weights and biases in the network via the minimization of the following function: ε= 1 2 [y(k) −ym(k)]2 (5) using the gradient decent, the weight and bias updating rules can be described as: ωui j (k + 1) =ωui j (k) −η ∂ε ∂ωui j (6) ωyi j (k + 1) =ωyi j (k) −η ∂ε ∂ωyi j (7) bi(k + 1) = bi(k) −η ∂ε ∂bi (8) 544 v. ranković, j. radulović, n. grujović, d. divac b(k + 1) = b(k) −η ∂ε ∂b (9) where: ∂ε ∂ωui j = ∂eε ∂ym ∂ym ∂ωui j ; ∂ε ∂ωyi j = ∂eε ∂ym ∂ym ∂ωyi j ; ∂ε ∂bi = ∂eε ∂ym ∂ym ∂bi ; ∂ε ∂b = ∂eε ∂ym ∂ym ∂b ; where the superscript e indicates an explicit derivative, not accounting for indirect effects through time. the terms ∂ym ∂ωui j , ∂ym ∂ωyi j , ∂ym ∂bi and ∂ym ∂b must be propagated forward through time, [16]. 3 model predictive control here the principle of operation of the predictive controllers will be briefly presented. let us suppose that the mathematical model of the process is known. based on the model, it is possible to determine the future outputs from the object y(k + 1), l = 1,2, . . . , nh , where nh is the prediction horizon. the future outputs depend on the object current states and future control signals, u(k+ l), l = 1,2, . . . , nc, where nc is the control horizon and nc ≤ nh . the predictive controllers compute potential future control signals such that the future outputs will be as close as possible to the desired values r(k + l), l = 1,2, . . . , nh . figure 3 shows the basic concept of the model predictive control. figure 3: basic concept of a model predictive control method there are several types of the predictive controllers. in this work the gpc (general predictive control) controller is used, where the cost function is calculated as: j(k) = nh∑ l=1 [ r(k + l) −ym(k + l) ]2 +α nc∑ l=1 ∆u2(k + l − 1), (10) where: ∆u2(k + l − 1) = u(k + l − 1) − u(k + l − 2), and α is the weight factor of the control signal. neural network model predictive control of nonlinear systems using genetic algorithms 545 real processes are subject to constraints. we consider constraints which limit the range of the control signal, the gradient of the control signal and the future model predictions: umin ≤ u(k + l) ≤ umax | u(k + l) − u(k + l − 1) |≤∆umax ymin ≤ ym(k + l) ≤ ymax. model output ym(k + l) is calculated based on (2): ym(k + l) = nh∑ i=1 ωi · 1 1 + e−( ∑nu j=1 u(k+l− j)ωui j+ ∑ny j=1 u(k+l− j)ωyi j+bi) + b. (11) taking into account the term α ∑nc m=1 ∆u 2(k + l − 1) in the cost function (10) prevents the control signals to be too big such that the executive organs would not be able to realize. the control signals in the k-th step: [u(k),u(k + 1), . . . ,u(k + nc − 1)]t , is possible to solve both numerically and analytically. analytically it is solved in such a manner that from the system of algebraic equations: ∂j ∂u(k) = 0, ∂j ∂u(k + 1) = 0, . . . , ∂j ∂u(k + nc − 1) = 0 one obtains [u(k),u(k + 1), . . . ,u(k + nc − 1)]t . for analytical solution, it is necessary for the model to be linear. the neural network model is nonlinear, its linearization should be performed first, or apply the numerical methods for solving the optimization problem. in this work for obtaining the optimum values of the control signals, the genetic algorithm is used (the function ga of the matlab’s genetic algorithm toolbox). the genetic algorithms represent the global optimization technique. in the k-th step nc −1 control signals are obtained. the first calculated signal is being sent to the controller output. the ga used in this study is simple genetic algorithm. the elements of the populations are encoded into bit-strings. the chromosome selection for reproduction is performed using the roulette selection method. the multi-point crossover operator was used. the uniform mutation operator is applied in this study. 4 simulation results example 1 for the simulation example 1, we consider the nonlinear plant, which is described by the following nonlinear difference equation: y(k) = 0.5y(k − 1) + u(k − 1) ( 1 + 0.2y2(k − 1) ) + u3(k − 1), (12) where y is the output of the plant and u is the plant input. we assume that structure of the model is known, nu = 1,ny = 1. the inputs and output of the neural network model are u(k − 1),ym(k − 1) and ym(k) respectively. the data set are obtained by applying random input signal uniformly distributed in the interval [-0.5 0.5]. the plant output is bounded within region [-2.2 2.2]. in this example, 4500 training patterns are generated to train the drn and 1500 to test the obtained drn model. 546 v. ranković, j. radulović, n. grujović, d. divac selection of an appropriate number of neurons in the hidden layer is very important. the optimal network size was selected from the one which resulted in maximum correlation coefficient for the training and test sets, table 1. based on table 1, it was concluded that the optimal number of hidden neurons is 12. table 1.correlation coefficient for the training and test sets drn-structure 2-10-1 2-12-1 2-15-1 2-17-1 training 0.9878 0.9987 0.9856 0.9879 test 0.9845 0.9921 0.9795 0.9812 since the object model is formed, it is necessary to define the cost function parameters, (10). these parameters are selected by the trial and error method. by increasing the variance of the control signal is decreasing, but simultaneously the difference between the set and real value of the object output is increasing. in the considered example, the satisfactory results are obtained for the following values of parameters: nh = 3, nc = 3,α= 0.05. the optimal predictive controller in the k-th step computes the control signals: [u(k),u(k + 1)]t . in figure 4, the reference signal is shown. the difference between the reference signal and the object output is presented in figure 5. from the figure 5, it is obvious that the tracking error is small. the obtained solution is good for practical realization. example 2 the plant is given by: y(k) = 0.35  y(k − 1)y(k − 2) [ y(k − 1) + 2.5 ] 1 +y2(k − 1) +y2(k − 2) + u(k − 1)  , (13) where y is the output of the plant and u is the plant input. the dynamical system used in the simulation example is given in [21]. it is assumed that structure of the model is known, nu = 1,ny = 2. the inputs and output of the neural network are u(k − 1),ym(k − 1),ym(k − 2) and ym(k), respectively. the input-output patterns are generated randomly. the data set included 4000 data samples. in the training process of the drn, 3000 samples were used. the drn model was tested using 1000 selected data. in this example, it is found that the optimal number of hidden neurons is 15 (table 2). table 2.correlation coefficient for the training and test sets drn-structure 3-10-1 3-12-1 3-15-1 3-17-1 training 0.9811 0.9899 0.9945 0.9921 test 0.9799 0.9831 0.9897 0.9895 the control and predictive horizons are chosen as respectively, nh = 3, and nc = 3. the choice of these values determines the complexity of the optimization problem. the values of nh and nc may not be too large, because the computation time would become also too large. these values should provide a good compromise between performance and computational load. it is found that the weight factor of the control signal α= 0.045 by the trial and error method. the optimal predictive controller in the k-th step computes the control signals: [u(k),u(k + 1)]t . the reference signal is shown in figure 6. the difference between the reference signal and the object output is presented in figure 7. from the figure 7, it is obvious that the control is quite effective. the obtained solution is good for practical realization. neural network model predictive control of nonlinear systems using genetic algorithms 547 figure 4: the reference signal figure 5: difference between the reference signal and the object output figure 6: the reference signal 548 v. ranković, j. radulović, n. grujović, d. divac figure 7: difference between the reference signal and the object output 5 conclusions in this paper the synthesis of the predictive controller for control of the nonlinear object is considered. the object is modeled by the digital recurrent network. in the designing of neural network model, the problem is how to determine an optimal architecture of network. the determination of the values of nu and ny is an open question. large time lags result in better prediction of the nn. however, large nu and ny also result in large number of parameters (weights and biases) that need to be adapted. the simulation results, given in section 4, show that the predictive controllers can successfully be applied for control of the prominently nonlinear object. the optimum values of the control signals are obtained by the genetic algorithm which represents the global optimization technique. the given trajectory tracking error is small. the proposed structure can be applied in control of the linear objects that are modeled by the neural network. recurrent networks are more powerful than nonrecurrent networks and have important uses in control and signal processing applications. simulation results on the examples selected from literature provide good results and that encourages our future plan for real time control system implementation. acknowledgement parts of this research were supported by the ministry of sciences, technologies and development of republic of serbia. bibliography [1] 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[18] d.r. hush, b.g. horne, progress in supervised neural networks, ieee signal processing magazine, 10(1):8-39, 1993. [19] k.l. funahashi, y. k.l. funahashi, y. nakamura, approximation of dynamical systems by continuous time recurrent neural networks, neural networks, 6(6):183-192, 1993. [20] l. jin, p. nikiforuk, m. gupta, approximation of discrete-time state-space trajectories using dynamic recurrent neural networks, ieee transactions on automatic control, 40(7):1266-1270, 1995. [21] p.s. sastry, g. santharam, k.p. unnikrishnan, memory neuron networks for identification and control of dynamical systems, ieee transactions on neural net-works, 5(2):306-319, 1994. international journal of computers communications & control issn 1841-9836, 11(3):441-449, june 2016. a fuzzy logic software tool and a new scale for the assessment of creativity i. susnea, g. vasiliu ioan susnea*, grigore vasiliu dunarea de jos university of galati romania, 800008 galati, domneasca, 47 ioan.susnea@ugal.ro; grigore.vasiliu@ugal.ro *corresponding author: ioan.susnea@ugal.ro abstract: it is difficult to measure something we cannot clearly define. no wonder that, for the over 100 definitions of the creativity proposed in the literature, there are almost as many scales and assessment tools. most of these instruments have been designed for research purposes, and are difficult to apply and score, especially in the educational environment. not to mention that they are expensive. the research described in this paper is aimed to develop a free, fast, and easy to use software tool for the assessment of creativity in the educational context. to this purpose, we have designed a new scale with 20 items, based on a novel approach focusing on detecting the factors known to block the creativity, like stereotypical thinking, and social conformity. the user input is collected through a web based interface, and the actual interpretation of the results is automated by means of a fuzzy logic algorithm. the proposed solution is interesting because it can be easily integrated in almost any e-learning platform, or used as a stand-alone tool for tracing the evolution of the students involved in courses for the development of creative thinking skills, and also for possible other applications. keywords: assessment of creativity, e-learning, fuzzy logic 1 introduction the world of the 21’st century is very different from what it used to be just a couple of decades ago. new professions emerge overnight (think of android developer, market research data mining specialist, or cloud services engineer ), while others quickly fade out (postal services, newspaper delivery, travel agents, word processors, and many others). to help students succeed in this world, the educational system should create new skills and should be able to assess these new skills ( [5]). one of the fundamental skills required in our rapidly changing society is creative thinking ( [27], [11], [31]). this is the reason why the modern school has been intensely criticized ( [23], [22]) for it is unable to foster the creativity of the students, or even worse for killing their innate creativity. by reviewing the vast literature dedicated to creativity, it appears that the real reason why we don’t have serious initiatives to foster creativity in school is that we still don’t fully understand this construct ( [20]). there are currently over 100 definitions of the creativity ( [33], [1]), many explanatory theories ( [29], [12], [26]), an ocean of literature about creativity, but very few initiatives explicitly aimed to develop educational content for the education for creativity ( [28]). the lack of consensus of the researchers concerning the many facets of the creativity is also manifest in the field of formal education: though most teachers declare that they value and encourage the creativity of the students, most of them can barely recognize it and are totally unprepared to stimulate it ( [7]). an additional obstacle is the lack of easy to use assessment tools to trace the evolution of the students involved in creativity courses, and to demonstrate the efficiency of the specific educational content. copyright © 2006-2016 by ccc publications 442 i. susnea, g. vasiliu the existing assessment tools are hard, if not impossible, to apply in the educational environment, either because they are too complex and difficult to score, like the famous ttct ( [32]), or simply because they are not free (e.g. rcab runco creativity assessment battery, www.creativitytestingservices.com) . considering the fact that, the first creativity lesson in any course is that “there are no right and wrong answers”, it results that the moodle-style evaluation tests, wherein the students must select one or more “correct”answers from a predefined list are totally useless. for these reasons, the main objective of the research described here is to develop a new creativity assessment tool that is free, easy to use, and score, and compatible with most existing e-learning platforms. unlike the vast majority of the existing psychometric approaches, which treat the tendency towards social conformity as a bias, in our study we assumed that the social conformity is a clue indicating a type of “stereotypical thinking”that blocks creative thinking itself, not the process of measuring it. in other words, in our approach, the social conformity is treated as a signal, not as noise. the automatic scoring is performed by means of a fuzzy logic algorithm starting from two subscales, each having 10 items, focused on different “dimensions”of the creativity: one subscale assessing the ideational behavior, the other aimed to measure the stereotypical thinking and social conformity. beyond the present introduction, this work is structured as follows: section 2 is a brief review of the related work aimed to clearly define the context of this study. section 3 contains the description of the proposed solution, and section 4 is reserved for discussion and conclusions. 2 related work given the polymorphic nature of the creativity, it’s no wonder that the assessment instruments are equally diverse as the definitions of creativity. for this reason, it is convenient to present the assessment tools from the perspective of the 4 p’s (person, product, process, and press/place) commonly used to illustrate the definitions of the creativity. other, more comprehensive reviews of the state of the art in the field of creativity assessment are available in ( [24], [13], [4]). there are many tests that focus on traits or behaviors specific to creative persons, which is understandable considering that most researchers of the creativity are psychologists. among this type of tests, it is worth to mention: the kirton adaptor-innovator inventory ( [16]), scab the scale for creative attributes and behaviors ( [14]) and ribs runco ideational behavior scale ( [25]). in another approach, the tests focus on creative behaviors from the past. examples of products based on this idea are spca the state of past creative achievements ( [6]) and the creative achievement questionnaire ( [8]). the tests that evaluate the creativity by analyzing the creative products are, in most cases, based on the measure of the divergent thinking by means of open ended prompts. these are by far the most frequently used, which determined kaufman ( [13]) to ironically note: “one of the great ironies of the study of creativity is that so much energy and effort have been focused on a single class of assessments: measures of divergent thinking. in other words, there’s not much divergence in the history of creativity assessment. ” it is not unlikely that this preference for the divergent thinking tests is connected with the popularity of ttct (the torrance test of creative thinking, [32]). however, beyond the a fuzzy logic software tool and a new scale for the assessment of creativity 443 incontestable success, the divergent thinking measures have their critics ( [21], [15]), who note that applying and scoring these tests are cumbersome and their predictive value is questionable. in the same class of product oriented tests it is worth to mention cat the consensual assessment techniques ( [2]), which is based on a methodology of evaluating the creative products by independent experts. the creativity assessment tools based on the analysis of the processes leading to creative outcomes are far less common. one example is cpac cognitive processes associated with creativity ( [19]). in what concerns the assessment of the influence of the environment on creativity, this is a difficult and less studied problem. one notable example in this direction is the keys test ( [3]). there are also complex tests containing specific subscales for multiple p’s of the creativity. for example, csq-r creativity styles questionnaire revised. ( [17]) contains 78 items organized in the following subscales: belief in unconscious processes (person), superstition (person), final product orientation (product), use of techniques (process), use of other people (process), and use of senses (process), and environmental control (press). in what concerns the way to collect the user’s responses, the most popular solution is the likert scale, but there are also tests that use the simplified dialogue based on binary responses yes/no, or true/false. from the perspective of the software implementation, the problem of creating the gui (graphic user interface) to collect the user’s responses is quite simple. however, things become considerably more complex when it comes to automate the interpretation of multiple subscales addressing distinct “dimensions”of the creativity. see next section for details on how we solved the problem of computing a global creativity quotient cq. we will conclude this brief presentation of the state of the art in the field of the creativity assessment by citing again the opinion of kaufman: “creativity assessment is a work in progress we know far less about creativity and its measurement than we would like to know but that is not to say that we know nothing at all. ”( [13]). 3 description of the proposed solution probably the most common fallacy about creativity is to confound it with the divergent thinking. in fact, divergent thinking is just one of the many facets of the construct called creativity (see figure 1). when dealing with such complex concepts, the evaluations based on a single dimension of the creativity be it divergent thinking or any other have, inevitably, a limited reliability, and in the same time, addressing multiple dimensions leads to large and cumbersome scales (e.g. csq-r, described in [17] with 78 items). one possible approach to facilitate the understanding of complex intellectual constructs is to consider their opposite, or their associated “negative space”(see figure 2 for a graphical metaphor that illustrates this idea) so, what is the opposite of creative thinking? one possible answer is “thinking inside a box”, a style of thinking heavily biased by stereotypes, prejudices, illicit generalizations, superficiality and conformism. starting from this idea we have developed a scale that attempts to indirectly assess the individual creativity by considering the factors that indicate stereotypical thinking. the proposed scale, called iacest (indirect assessment of creativity through the estimation of stereotypical thinking) contains two subscales as shown in tables 1 and 2. see [19] and [10] for details on how the items of similar scales are formulated. note: in order to prevent the attempts to learn by heart the “right”answers, a third set of items containing 5 additional filler statements has been included in the online implementation 444 i. susnea, g. vasiliu divergent thinkingconvergent thinking s u b je c ti v it y o b je c ti v it y h o l is t ic d e t a il o r ie n t e d v is u a l v e r b a l a s s o c ia t iv e l o g ic a l r ig h t b r a in le ft b r a in c r e a t iv it y l a c k o f c r e a t iv it y figure 1: the multiple facets of creativity non-a a figure 2: the apple logo redesigned by jonathan mak a metaphor based on the negative space table 1: subscale 1. creative personality and thinking style items. item statements 1 an image is worth a thousand words. 2 people say i am a bit lazy and scatterbrained. 3 i have a great sense of humor, and i always see the funny side of life. 4 sometimes i get obsessed with a problem, and i keep trying until i find a solution. 5 a bit of adrenaline is always welcome. life is boring without it. 6 i am very curious. 7 people think that i am good at finding solutions to common problems. 8 i enjoy trying to find new solutions to problems. 9 i have lots of ideas in every domain. 10 one plus one does not always equal two of the scale. (the answers to these items are simply ignored in the evaluation.) for the same reason, the items are presented in random order each time the test starts. the graphic user interface implementing the five point likert scale for collecting the user’s responses is presented in figure 3. each answer is scored with a numeric value between 0 (totally disagree) and 4 (definitely agree). for each subscale we compute a total score: < ss1 = 10∑ i=1 ai >;< ss2 = 20∑ i=11 ai > (1) a fuzzy logic software tool and a new scale for the assessment of creativity 445 table 2: subscale 2. items for detecting stereotypical thinking and other blocking factors for creativity. item statements 1 i always play by the rules. 2 my parents were very strict with me. 3 if anything can go wrong, it will. 4 i am very disciplined and diligent. 5 sometimes i use oracles when i need to make difficult decisions. 6 i know exactly what i will do next summer. 7 i always trust reputable scientists. 8 i like to solve the problems one by one. 9 i like to quote the opinions of wiser people. 10 i feel very embarrassed if i fail. figure 3: a snaposhot of the gui of the application obviously ss1,ss2 ∈ [0,40]. while a high score for the first subscale indicates a high creativity, the second subscale is aimed to detect social conformity tendencies, and stereotypical thinking. a high score for the second subscale is likely to indicate the existence of important blocking factors for the subject’s creativity. since the two subscales address distinct factors of the creativity, it is not possible to compute the final creativity quotient cq by simply adding or substracting the scores of the subscales. assuming that the domain od variation for cq is the interval [0,100], one possible way to compute this quotient is: cq = 50 ∗ (1 + tanh(k ∗ (ss1 − ss2))) (2) where k is a scaling factor, empirically set to the value k = 0.07. though this heuristic method of computing cq spreads the user responses reasonably well over the interval [0,100] when using exactly two subscales that reflect opposite influences, we preferred to use a fuzzy inference algorithm to compute cq. this solution is proven effective in 446 i. susnea, g. vasiliu many other difficult problems (see for example [9]), provides superior flexibility, and the resulting code is largely reusable in other applications. for this experimental version, and considering the limitations of the php language required by the dedicated web based application, we chose the simplest implementation with three fuzzy domains for ss1 and ss2 and linear membership functions, as shown in figure 4. 10 20 30 40 1 low medium high x m l m mm h m ss ,ss 1 2 fuzzy domains and membership functions figure 4: fuzzy domains and membership functions for ss1, ss2 with these assumptions, the knowledge base for the actual interpretation of the scores ss1 and ss2 for computing cq is described by the set of rules presented in table 3: table 3: the fuzzy rule base ss1 ss2 cq low low low low medium low low high low medium low medium medium medium medium medium high low high low high high medium high high high medium each line of table 3 should be read as an if/then statement of the following type: if (ss1 is low) and (ss2 is low) then cq is low the truth values µl,µm,µh of the statements (ss1 is low), (ss1 is medium), (ss1 is high), (ss2 is low), (ss2 is medium), (ss2 is high), for the particular values of ss1, ss2 derived from the user’s responses are determined using the equations of the membership functions. and the truth value of the entire statement for the rule i is: zi = min(µ1,µ2) (3) assuming that the output domain is cq ∈ [0,100], we can choose constant values for the output fuzzy domains (“singletons”, si) e.g. slow = 10,smedium = 50, shigh = 100. with these notations, the final “crisp”value of cq is the center of gravity of the entire knowledge base: cq = ∑9 i=1 zi ∗ si∑ 9 i=1zi (4) a fuzzy logic software tool and a new scale for the assessment of creativity 447 for details on the tehory behind the above implementation, see [30]. figure 5 is a snapshot of the screen presenting the results of the test and the computed value of cq. the actual implementation contains additional software modules for user authentication, and report generation. the results of the tests are stored in a database. a beta version of the web application can be tested at http://dev.ugal.ro/creativity/ figure 5: a snaposhot of the final screen presenting the result of the test 4 discussion and conclusions the validation of the proposed scale is in progress. a simple pretest for internal consistency has been conducted with n=30 undergraduate students of the faculty of automation, computers, electrical and electronics engineering resulting in statistically acceptable values of the cronbach quotient α = 0.73 for subscale 1, and α = 0.78 for subscale 2. though this research has been conducted in the context of an educational project partly funded by eacea the education, audiovisual and culture executive agency of the european commission (namely tecrinoteaching creativity in engineering, 538710-llp-1-2013-1-cyleonardo-lmp), for unknown reasons, the eacea representatives stubbornly denied all the requests for permission to disseminate the results of this work through scientific publications, and to allocate funds, within the same budget, to deepen this study. due to lack of funding, our work in this direction is much slower than we hoped. therefore, for this moment, we must align to the expectations formulated by miller in ( [18]): “it should also be noted that validation of any instrument is an ongoing procedure.... once a measure has been adequately developed, it is the responsibility of all researchers in the field to further the generation of evidence for its validity.” obviously, further validation studies using a larger sample are definitely required. the preliminary results are promising: the proposed tool is free, simple, easy to use, easy to integrate in almost any e-learning platform, and serves the purpose of assessing the evolution of the students enrolled in creativity training courses. and the idea of using a fuzzy algorithm for automated scoring of psychometric scales may have other interesting applications. 448 i. susnea, g. vasiliu acknowledgment the authors gratefully acknowledge the contribution of dr. mihai vlase, who wrote the code for the software implementation of the instrument described in this paper. bibliography [1] aleinikov, a., kackmeister, s., and koenig, r. 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(1996); creativity, creative thinking, and critical thinking: in search of definitions. sarasota, fl: center for creative learning. international journal of computers communications & control issn 1841-9836, 9(6):776-785, december, 2014. dissonance engineering: a new challenge to analyse risky knowledge when using a system f. vanderhaegen frédéric vanderhaegen 1 univ lille nord de france, f-59000 lille, france 2 uvhc, lamih, f-59313 valenciennes, france 3 cnrs, umr 8201, f-59313 valenciennes, france frederic.vanderhaegen@univ-valenciennes.fr abstract: the use of information systems such as on-board automated systems for cars presents sometimes operational risks that were not taken into account with classical risk analysis methods. this paper proposes a new challenge to assess risks by implementing an automated tool based on the dissonance engineering principle. it consists in analysing knowledge in term of dissonances. a dissonance is defined as a knowledge that sounds wrong, or in other words that may present conflicts. the paper focuses on two kinds of dissonances: erroneous affordances when events can be related to erroneous actions and contradictory knowledge when the application of knowledge relates to opposite actions. the proposed automated tool analyses the knowledge base content in order to detect possible dangerous affordances or contradictory knowledge. an example of application is given by using a limited number of simple rules related to the use of an automated speed control (asc) system for car driving. keywords: dissonance engineering, erroneous affordance, contradictory knowledge, risk analysis, car driving system. 1 introduction is there something wrong when engineers or researchers design walking robots directly with two legs without copying the learning process of the human walking that begins initially with the legs and the hands, then with the use of supports and finally with both legs after the complete control of the equilibrium? to control such a process, undesirable events such as lack of knowledge to control equilibrium or breakdown of equilibrium or fatal fall should be studied in order to design algorithms or other devices that are able to prevent the walking robots from a loss of their equilibrium. classical risk analysis focuses on the identification and the control of such undesirable events and aims at providing the human-machine systems with barriers in order to protect them from the occurrence or the impact of these events [1]. despite these barriers, accidents remain and retrospective analyses can help the designers to identify what was wrong. safety based analysis can apply different methods. the rams based methods (reliability, availability, maintainability and safety based analyses) treat about technical failures. the methods from cindynics treat about organizational dangers human reliability or human error based analyses focus on the success or the failure of human behaviours respectively. resilience or vulnerability based methods consider the analysis of the success or the failure of the control of the system stability respectively. this paper proposes on a new way to analyse risks: the use of the dissonance concept to assess conflicts between knowledge. the dissonance engineering is the engineering science that treats on dissonance [2]. a cognitive dissonance is defined as an incoherency between individual cognitions [3]. cindynics dissonance is a collective or an organizational dissonance related to incoherency between persons or between groups of people [4]. the occurrence of these dissonances can relate to individual copyright © 2006-2014 by ccc publications dissonance engineering: a new challenge to analyse risky knowledge when using a system 777 or collective knowledge when something sounds wrong, i.e. will be, is, maybe or was wrong. a dissonant cognition is linked with contradictory information and a dissonance may produce discomfort due to the occurrence of conflicting cognition or knowledge that controls or affects behaviors, attitudes, ideas, beliefs, viewpoints, competences, etc. dissonant knowledge of a person or of several persons can explain such conflicts. nevertheless, dissonances can also be due to the occurrence of important or difficult decisions involving the evaluation of several possible alternatives [5], to divergent viewpoints on human behaviours [1], to the occurrence of failed competitive or cooperative activities [6], [7], to organisational changes that produce incompatible information [8], [9]. then, the updating or the refining of a given knowledge due to new feedback from field is required but this can also generate dissonances [2]. the more difficult the learning process is to face a dissonance, the less acceptable this dissonance is. therefore, human operators aim at reducing any occurrence or the impact of a dissonance because it produces discomfort. this activity leads to maintain a stable state of knowledge without producing any effort to change it [3]. despite this reduction, a breakdown of this stability is sometimes useful in order to facilitate the learning process and refine, verify or confirm knowledge [10]. such knowledge reinforcement improves the learning abilities. finally, dissonance can also be seen as a feedback of a decision: dissonance occurs after a decision and this requires a modification of knowledge [9]. therefore, a discomfort can be a dissonance or can be due to the production of a dissonance, and the detection or the treatment of a dissonance can also produce discomfort. discomfort can also occur if this dissonance is over the control of the human operators or because the treatment of a detected dissonance increases the human workload or the human error for instance [11], [12]. such an activity involves a minimum learning process in order to improve the human knowledge and to control such a discomfort. there are then positive and negative feedbacks from the dissonance management. negative feedbacks relate to discomfort and positive ones to the learning aspect for instance. different structures to share tasks between human and machine such as those developed on [13], [14] can be applied for dissonance management and a learning process is usually required to facilitate the control of the knowledge content. several models exist for self-learning, autolearning, co-learning or cooperative learning [6]. main of them is based on the reinforcement principle by taking into account previous knowledge and integrating new or future knowledge, in order to create, modify or delete data from the knowledge base and to make it more coherent [15], [16], [17], [18]. this paper proposes an original knowledge analysis based tool to support the control of dissonances into human knowledge. it focuses on two kinds of dissonances: erroneous affordances and contradictory knowledge. this knowledge analysis is based on the knowledge modelling presented on [19]. the concept of affordances was firstly presented by gibson [20]. affordances can be defined as invariant relationships between direct perception and possible opportunities for action. the concept is used for different research applications related to human-machine system knowledge management [21]. for instance, the perception of a chair relates directly to the action of sitting. regarding other human experiences, a chair can also be related to the actions of climbing when a chair is used as a ladder, or for moving or transporting when the action concerns disabled persons. the paper focuses on particular affordances: erroneous affordances when links between occurred or desired events and actions can be erroneous. the last section of this paper gives a practical example of application related to the use of an automated speed control (asc). 778 f. vanderhaegen 2 the knowledge analysis based tool to control dissonances the proposed tool analyses the content of a given knowledge base. two modules are integrated: the module that detects possible erroneous affordances and the module that detects possible contradictory knowledge, figure 1. the knowledge modelling and reinforcement to create, modify or delete some knowledge are allocated to the users. an interface gives the results of these detections that are validated by the users to reinforce the knowledge modelling and then the knowledge content. the knowledge analysis is based on the knowledge modelling presented on [19]. therefore, the set of knowledge k contains a list of rules r(i) with a condition of activation noted condit(r(i)) and a conclusion noted conclu(r(i)): r(i) ∈ k → r(i) = (condit(r(i)) → conclu(r(i))) (1) if condit(r(i)) is to be achieved or realized then conclu(r(i)) has to be realized. for instance, condit(r(i)) can relate to a goal to be achieved or realized and conclu(r(i)) can correspond to the actions to be done on the process by using specific supports of the system or its environment to achieve this goal. these actions can be realized by the users of the system or by an automated module implemented into this system. some rules are then the procedures to apply in order to achieve a goal and other rules concern the different steps required to achieve a goal. the affordance detection and the contradictory knowledge detection use the function k_analysis to identify the rules that can generate possible erroneous affordances or that can present incoherency. the k_analysis function is defined as follows: k_analysis : k → k r → r+ = k_analysis(r), ∀r(i) ∈ r, condit(r(i))∩be ̸= {∅}, r+ ←∪r(i) (2) figure 1: the automated tool modules. the set k is the set of all the possible rules. for a given rule base noted r of k containing a limited number of rules, the k_analysis gives a reduced rule base noted r+ of k. r+ contains the rules related to the inputs noted be. be contains the events that occur or the goals to be achieved. when the condition condit(r(i)) of a rule occurs on be entirely or partially, then this rule is integrated into r+. the k_affordance function aims at identifying possible new rules combining the condition dissonance engineering: a new challenge to analyse risky knowledge when using a system 779 and the conclusion of existing rules. it is defined as follows: k_affordance : k → k r → ra = k_affordance(r), ∀r(i) ∈ r,∀r(j) ∈ r, i ̸= j, condit(r(i)) ⊂ condit(r(j)), ra = ∪((condit(r(i)), conclu(r(j))), (condit(r(j)), conclu(r(i)))) (3) the result of this function is a new rule base noted ra of k that combines conditions and conclusions of some rules of r. this function proposes new rules based on the affordance application concept in order to list possible new rules taking into account possible relationships between the condition of a given rule with the conclusion of another one. if a condition of a rule is included into to the condition of another one, then both rules can be used to create new rules. this process is limited to the rules identified by the k_analysis function. then: ra = k_affordance(k_analysis(r)) (4) the k_contradictory function aims at listing the contradictory rules, i.e. rules that present opposite behaviours. it is defined as follows: k_contradictory : k → k r → rc = k_contradictory(r), ∀r(i) ∈ r,∀r(j) ∈ r, i ̸= j, (conclu(r(i)) ⊂¬conclu(r(j))) or (¬(conclu(r(i))) ⊂ conclu(r(j))), rc = ∪(r(i), (r(j)) (5) when opposite conclusions appear on r, i.e. when both conclu(r(i)) and ¬(conclu(r(i))) exist, then a possible incoherency occurs. the result of this function is a new rule base noted rc that contains possible conflicts between rules of r. this process is limited to the rules identified by the k_analysis function. then: rc = k_contradictory(k_analysis(r)) (6) this formalism was applied to car driving domain by integrating into the initial rule base the rules related to the use of a cruise/speed control (asc) system. if the asc system is activated and if an initial setpoint value is given by the car driver, the asc has in charge the regulation of the car speed by maintaining this setpoint speed. the "+" and the "-" buttons are used for giving the initial setpoint speed or to modify this setpoint, figure 2. the "+" button aims at increasing the setpoint value whereas the "-" button at decreasing it. several dissonances can generate a possible evolution of the car driver knowledge. the next two sections presents some examples of the detection of possible erroneous affordances and contradictory rules linked to the use of such a system. the wording of the rule condition and conclusion are voluntarily simple in order to illustrate the feasibility of such a knowledge analysis to detect dissonances. perspectives will consist in using more complex rules with a numerical model integrating for instance belief on rules, belief on condition occurrence or belief on conclusion occurrence. 780 f. vanderhaegen figure 2: the "+" and "-" buttons of an asc system of a car. 3 example of possible erroneous affordance detection suppose that a knowledge modeling process produced a knowledge base containing these rules: • r(1): (the use of the asc system → turn the activation button on "on") • r(2): (the deactivation of the asc system→ brake with the braking pedal) • r(3): (the increasing of the car speed setpoint → push the "+" button) • r(4): (the decreasing of the car speed setpoint → push the "-" button) • r(5): (the increasing of the car speed → push the gas pedal) • r(6): (the decreasing of the car speed → release the gas pedal) for instance, whatever the context of the asc system use, if be contains initially increasing of the car speed, ra will list several dissonances to be tested or validated by the car driver. it will contain two possible new rules: • (the increasing of the car speed setpoint → push the gas pedal) • (the increasing of the car speed → push the "+" button) if be contains initially "decreasing of the car speed", ra will then contain other new possible dissonances: • (the decreasing of the car speed setpoint → release the gas pedal) • (the decreasing of the car speed → push the "-" button) 10 subjects who usually used an asc system were invited to evaluate the proposed erroneous affordances, table 1. the outputs of the automated systems were presented to these subjects who have to make comments about them. they have to give their own point of view about the dissonances. all of them considered that the rules related to the management of the car speed setpoint value are erroneous affordances. however, 8 of them do not consider the other rules as problems and decided to integrate the proposed rules into the knowledge base content. therefore, these subjects consider that they can manage the car speed without taking into account the management of dissonance engineering: a new challenge to analyse risky knowledge when using a system 781 table 1: subjective evaluation of the proposed erroneous affordances. proposed erroneous affordance real erroneous affordance? consequences (the increasing of the car speed setpoint → push the gas pedal) yes (10 upon 10 subjects) no modification of the knowledge base (the increasing of the car speed → push the "+" button) no (8 upon 10 subjects) creation of this rule into the knowledge base (the decreasing of the car speed setpoint → release the gas pedal) yes (10 upon 10 subjects) no modification of the knowledge base (the decreasing of the car speed → push the "-" button) no (8 upon 10 subjects) creation of this rule into the knowledge base the pedals anymore. this reduces their workload. the 2 subjects who did not accept these new rules consider these rules as dangerous because this can lead to adapt a possible body position that can generate problems in case of emergency stop for instance. figure 3 gives an example of such a body position into the car when managing the car speed only with the "+" and "-" buttons of the asc system: the legs are crossed because the car speed is managed by a finger that activates the "+" or ""-" buttons, and the position of the legs can therefore become an obstacle or a discomfort in case of emergency stop that may require a quick press on the brake pedal! figure 3: example of a possible dangerous body position when applying the proposed rules for regulating the car speed. 4 example of possible contradictory knowledge detection suppose that, for another use context, the knowledge modelling process produced another knowledge base combining rules related to the aquaplaning control and some rules related to the acs control: 782 f. vanderhaegen • r(1): (the use of the asc system → turn the activation button "on") • r(2): (the deactivation of the asc system → brake) • r(3): (the reduction of the current car speed that becomes under the setpoint managed by the asc → accelerate automatically to reach the setpoint value) • r(4): (the increasing of the current car speed that becomes over the setpoint managed by the asc → decelerate automatically to reach the setpoint value) • r(5): (the control of an aquaplaning → ¬ (brake)) • r(6): (the control of an aquaplaning → ¬ (accelerate)) knowing that the asc is activated, the car speed setpoint is high and the current car speed is equal to the required setpoint, the occurrence of an aquaplaning may reduce the current car speed due to the natural braking and friction related to the water level on the road. suppose that in this case, the initial be content is (control of aquaplaning, deactivation of the acs, reduction of the current car speed). the contradictory knowledge module identifies some couples of possible dissonant rules. rc will contains this list of couples of rules: • ((the deactivation of the asc system → brake), (the control of an aquaplaning →¬(brake))) • ((the control of an aquaplaning → ¬(accelerate)), (the reduction of the current car speed that becomes under the setpoint managed by the asc → accelerate automatically to reach the setpoint value)) 10 subjects were invited to assess these contradictory knowledge proposals. they use an acs system and are aware about the behaviour to follow and about the car behaviour when an aquaplaning occurs. all of them are agree with the contradictory rules proposed by the automated system, table 2. table 2: subjective evaluation of the proposed contradictory knowledge. proposed contradictory knowledge real contradictory knowledge? consequences ((the deactivation of the asc system → brake), (the control of an aquaplaning → ¬(brake))) yes (10 upon 10 subjects) modification of the current rules or creation of new rules ((the control of an aquaplaning → ¬(accelerate)), (the reduction of the current car speed that becomes under the setpoint managed by the asc→ accelerate automatically to reach the setpoint value)) yes (10 upon 10 subjects) modification of the current rules or creation of new rules the contradictory actions (brake, ¬(brake)) and (accelerate, ¬(accelerate)) are then solved by reinforcing the knowledge in different possible ways such as the modification of some current rules or the creation of new rules. they recognize that in emergency situations, it is more natural to use the braking pedal for stopping the car or deactivating a system instead of using the clutch pedal or the "off" button of the asc system as it is noted on the asc user manual. some user manual recommends using the system in particular conditions. for instance, it indicates not to use the system when it is raining. however, water can sometimes occur under bridges for instance even if it is not raining and this can generate aquaplaning. dissonance engineering: a new challenge to analyse risky knowledge when using a system 783 5 conclusion this paper is an original contribution on risk analysis based on dissonance engineering. it proposes a knowledge analysis based tool composed by two main modules: an erroneous affordances detection module, and a contradictory knowledge detection module. the knowledge analysis consists in identifying possible dissonances into a knowledge base composed by rules. rules contain conditions of activation and conclusions when they can be activated. the conditions relate to occurred or desired events and the conclusions to the associated actions to be achieved. the erroneous affordances module treats particular dissonances that the users may create. they are new rules for which desired or occurred events may be related to wrong actions. these possible erroneous relations between events and actions are obtained by using the existing rules and initial events. the contradictory knowledge module manages the rules for which the activation presents opposite actions regarding initial inputs. a practical example is then proposed to study the feasibility of use of such a knowledge analysis based tool. it relates to the use of an automated speed control systems dedicated to car driving. rules associated to its use and other rules applied for controlling events such as an aquaplaning were proposed. erroneous affordances and contradictory knowledge are then given by the proposed automated modules. 10 subjects were invited to make comments about these outputs from the proposed tool. among the erroneous affordances proposed by the system, there is a rule that was not considered as wrong and that was accepted by 8 subjects upon 10. it concerns the use of the "+" and "-" buttons of the asc in order to increase or decrease respectively the current car speed on demand instead of using these buttons to control a stable car speed setpoint. the acceleration and deceleration on road or motorway can then be done with such new procedures without involving the legs anymore. the 2 last subjects that agree with the system consider this new rule as dangerous because problems may occur if an emergency stop is required for instance. problems can be related to the position of the legs or to the body into the car due to the new function allocated to the "+" and "-" buttons for controlling the car speed. regarding the contradictory knowledge, all the subjects were agree with the proposals they consider as very dangerous. indeed, they considered that, in emergency case, people may activate the braking pedal instead of the "off" button of the asc system or the clutch pedal to deactivate the asc system. then, the rules associated to the control of aquaplaning that required no action on the braking pedal and no action on the speed control are contradictory with rules related to the use or behavioural model of the asc system. this simple example has shown the interest of such a new approach to analysis risks involving rule based knowledge. future work research will then focus on more complex applications implementing numerical models of knowledge and will integrate criteria such as uncertainties, beliefs or preferences on knowledge [22], [23]. future applications will connect automated reinforced learning systems to assist the knowledge reinforcement process. 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[23] f. vanderhaegen, p. polet, s. zieba (2009), a reinforced iterative formalism to learn from human errors and uncertainty engineering applications of artificial intelligence , 22(4-5): 654-659. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 61-72 a new approach to nonlinear tracking control based on fuzzy approximation z. du, t.-c. lin, v.e. balas zhenbin du yantai university, yantai, shandong 264005, china e-mail: zhenbindu@yahoo.com.cn tsung-chih lin feng-chia university taichung, 40724, taiwan e-mail: tclin@fcu.edu.tw valentina e. balas aurel vlaicu university b-dul revolutiei 77 310130 arad, romania e-mail: balas@drbalas.ro abstract: the problem of tracking control is addressed for a class of nonlinear systems with uncertainties. the original nonlinear systems are approximated by a fuzzy t-s model based on which a state-feedback controller is constructed by using the linear matrix inequalities. the approximating error is eliminated by an adaptive compensator based on fuzzy logic systems. the effectiveness of the proposed control scheme is demonstrated by a simulation example. the main advantage is that the designer makes milder constraint assumption for the approximation error and the uncertainties in nonlinear systems. keywords: fuzzy t-s model; fuzzy logic systems; nonlinear systems; uncertainties; tracking control. 1 introduction the problem of controller design for the nonlinear systems with uncertainties is a challenging work. one of effective tools used to solve the problem is fuzzy method. there are two frequently used fuzzy models: fuzzy t-s model [1-13] and fuzzy logic systems [16-17]. fuzzy t-s model is usually used to approximate nonlinear systems, and it has been widely applied to analyze the stability of nonlinear systems [1-4]. the contributions of these works are very important, but these works could be further improved by a simpler and more practical control scheme. the approximating error is neglected in [1-4], which impacts the stability of the system. therefore, the designed controller can’t always guarantee the stability of the original system. to overcome the effect of the approximating error, some relaxed stability methods are developed in [5-8]. these methods improve the approximation accuracy for nonlinear system. however, the original nonlinear system is still neglected in [5-8]. in order to further relax the effect of the approximating error, it is assumed to satisfy the matching condition in [9-11] and have a upper bound in [1213]. however, the matching condition and the upper bound are not easy to be found in practice, which adds some difficulties to the controller design. the uncertainties in nonlinear systems are assumed to satisfy the constraint of the upper bound in [14-15]. however, the upper bound may be too large or doesn’t exist. on the other hand, fuzzy logic systems have been proved to have copyright c⃝ 2006-2012 by ccc publications 62 z. du, t.-c. lin, v.e. balas the universal approximation property. by constructing a set of fuzzy "if-then" rules, fuzzy logic system is used to model uncertain nonlinear systems [16-17]. there exist some conservatism of pure fuzzy t-s model in dealing with the approximating error. the pure adaptive fuzzy control also has some shortages of excessively depending on the chosen membership functions. therefore, it is more interesting to combine both fuzzy models to overcome their shortages each other. based on the above discussions, fuzzy t-s model and fuzzy logic systems are combined to design a new tracking-control scheme for a class of nonlinear systems with uncertainties in this paper. a fuzzy t-s model is used to approximate the nonlinear system based on which a statefeedback controller is constructed by use of the linear matrix inequalities. the approximating error and the uncertain nonlinear parts are eliminated by a compensator based on fuzzy logic systems. the main advantages are summarized as follows: firstly, fuzzy t-s model and fuzzy logic systems are combined to develop a controller. compared with the existing works based on fuzzy t-s model [1-13], the proposed method in this paper makes milder constraint assumptions for the approximating error. secondly, the dimension of the matrix inequalities is reduced, thus, the difficulty of solving the matrix equalities is relaxed. thirdly, the existing works based on fuzzy logic systems [16-17] excessively depend on the chosen membership functions which are improved in the proposed method of this paper. finally, the developed controller makes full use of the advantages of two fuzzy models. as a result, it is more convenient to implement the controller in practice. the rest of the paper is organized as follows. section 2 provides preliminaries and the formulation of the problem. section3 develops a procedure of the controller design. section 4 presents a simulation example of 2-link manipulator to illustrate the effectiveness of the proposed method. these are followed by conclusions in section 5. 2 problem formulation consider the following nonlinear systems with uncertainties ẋ1 = x2, ... ẋ(β1−1) = xβ1, ẋβ1 = f1(x,u) + f̃1(x,u) + d1, ẋ(β1+1) = x(β1+2), ... ẋn = fm(x,u) + f̃m(x,u) + dm, (1) where x,u are the system state vector, control input vector, respectively; x = [x1, ...,x (β1−1) 1 , ...,x(n−βm+1), ...,x (βm−1) (n−βm+1) ]t ∈ rn , β1 + β2 + ... + βm = n, u = [u1, ...,um] t ∈ rm, fi(i = 1, ...,m) are known smooth nonlinear functions, f̃i(i = 1, ...,m) are unknown uncertain nonlinearities of the system, and di(i=1,2,...,m) denote the external disturbances. remark 1: there are many practical physical systems which can be described by the model (1), for example, the mass-spring-damper [18], the rotated inverted pendulum [19] and the n-link manipulator [20]. a reference model is as follows: ẋr(t) = arxr(t) + r(t), (2) a new approach to nonlinear tracking control based on fuzzy approximation 63 where xr(t) is a reference state, r(t) is a bounded reference input, and ar is an asymptotically stable matrix. control objective: design a controller to guarantee that the nonlinear system (1) is stable and the state can track the reference state xr(t). the known part of the system (1) can be approximated by a fuzzy t-s model composed of l rules. for convenience of research, fuzzy t-s model includes the external disturbance d. the ith rule of the fuzzy model is as follows: if z1(t) is f i1 and,...,and zs(t) is f i s, then ẋ(t) = aix(t) + biu(t) + d, i = 1,2, ...,l, (3) where z1(t), ...,zs(t) are the premise variables, f ij (j=1,2,...,s) are the fuzzy sets, l is the number of if-then rules, ai and bi are some constant matrices with compatible dimensions, bi = [0, ...,bti1, ...,0, ...,b t im] t ∈ rn×m with bi1 ∈ rm, ...,bim ∈ rm, and d = [0, ...,d1, ...,0, ...,dm]t . the final output of the fuzzy system is inferred as follows: ẋ(t) = l∑ i=1 µiaix(t) + l∑ i=1 µibiu(t) + d, (4) where µi = νi(z(t)) / l∑ i=1 νi(z(t)), νi(z(t)) = s∏ j=1 f ij (zj(t)), (5) and f ij (zj(t)) is the grade of membership of zj(t) in f i j . therefore, the approximating error for the nonlinear system (1) and the uncertainties of the nonlinear system (1) can be expressed as b∆(x), where b = diag[b1, ...,bm],bi = [0, ...,0,1]t ∈ rβi and ∆(x) = [∆1, ...,∆m]t . therefore, the nonlinear system (1) could be rearranged as ẋ(t) = l∑ i=1 µiaix(t) + l∑ i=1 µibiu(t) + b∆(x) + d. (6) 3 design of controller and stability analysis 3.1 design of controller the controller is chosen as u(t) = ul(t) − uf(t), (7) where ul(t) denotes the state-feedback controller based on fuzzy t-s model, uf(t) is the adaptive compensator based on fuzzy logic systems. the state-feedback controller ul(t) based on fuzzy t-s model is designed as ul(t) = l∑ i=1 µiki(x(t) − xr(t)), (8) where ul(t) is used to stabilize the linear part of the system (1), and ki(i=1,2,...,l) are matrices with proper dimensions and satisfy a t ij + paij + 1 ρ2 pp + q < 0, i,j = 1,2, ...,l, (9) 64 z. du, t.-c. lin, v.e. balas where aij = [ ai + bikj −bikj 0 ar ] , q = diag{2q,2q}, p and q are some symmetric and positive definite matrices, and ρ is a positive constant. the adaptive compensator based on fuzzy logic systems is given by uf(t) = { e−1û(x|θ), if e is nonsingular et (i + eet )−1û(x|θ) if e is singular, (10) which is used to compensate the approximating error and the uncertainties. in(10), ei = [b t i1, ...,b t im] t ∈ rm×m,e = l∑ i=1 µiei, and û(x|θ) is constructed by fuzzy logic systems. the updating law of θ is as follows: θ̇ = η1ψ t (x)b t px, (11) where η1 is a positive constant, ψ(x) is a fuzzy basis-function matrix, and the definition of ψ(x) is given in (16). 3.2 stability analysis note that l∑ i=1 µibiuf(t) − b∆(x,x(t − τ)) = b(euf(t) − ∆(x)) (12) = { b(û(x|θ) − ∆(x)) b(û(x|θ) − (i + eet )−1û(x|θ) − ∆(x)) ∆b(û(x|θ) − ∆(x)). (13) substituting (7) into (6) yields ẋ(t) = l∑ i=1 µiaix(t) + l∑ i=1 l∑ j=1 µiµjbikj(x(t) − xr(t)) − b(û(x|θ) − ∆(x)) + d. (14) denote x̃(t) = [xt (t),xtr (t)] t , and b = [bt 0]t . from (2) and (14), a new extended closed-loop system is as follows: ˙̃x(t) = l∑ i=1 l∑ j=1 µiµjaijx̃(t) + b(−(û(x|θ) − ∆(x))) + d′, (15) where d′ = [dt ,rt (t)]t . when fuzzy logic systems û(x|θ) eliminate ∆(x), then the closedloop system (15) is stable. thus, fuzzy logic systems are constructed to approximate the vector function ∆(x) as follows: ∆̂(x|θ) = ψ(x)θ, (16) where a new approach to nonlinear tracking control based on fuzzy approximation 65 ψ(x) = diag[ξt1 (x), ...,ξ t m(x)],θ = [θ t 1 ,θ t 2 , ...,θ t m] t in which θi(i=1,2,...,m) are the column vectors, and the weight θ is an adaptive parameter. define the optimal parameter estimation θ∗ as follows: θ∗∆ arg min θ∈ω [sup x∈u ||∆̂(x|θ) − ∆(x)||], (17) where u = {x ∈ rn},ω = {θ ∈ rpm×1}. u,ω denote the sets of suitable bounds on x, θ, respectively. then the estimation error for the vector function ∆(x) can be expressed as ∆̂(x|θ) − ∆(x) = ψ(x)θ̃ + w, (18) where w = [w1, ...,wm]t is a residual term, θ̃ = θ − θ∗ = [(θ1 − θ∗1) t , ...,(θm − θ∗m)t ]t . denote w′ = [wt ,rt (t)]t, and w = [0, ...,d1 − w1, ...,0, ...,dm − wm]t . substituting (18) into (15), (15) is rearranged as ˙̃x(t) = l∑ i=1 l∑ j=1 µiµjaijx̃(t) + b(−ψ(x)θ̃) + w′. (19) theorem 1. for the nonlinear system (1), if the controller is chosen as (7) composed of the fuzzy state-feedback controller (8) and the adaptive compensator(10), and the updating law for the weight is chosen as (11), then the closed-loop system (15) is uniformly ultimately bounded (uub) and the following tracking performance is achieved as ∫ t 0 (x(t) − xr(t))t q(x(t) − xr(t))dt ≤ x̃t (0)px̃(0) + 1 η1 θ̃t (0)θ̃(0) + ρ2 ∫ t 0 (w′t w′)dt, (20) where ρ > 0, p, q are some symmetric and positive definite matrices. proof. consider the following functional v = 1 2 x̃t px̃ + 1 2η1 θ̃t θ̃ (21) whose derivative can be computed as follows: v̇ = 1 2 ˙̃xt (t)px̃(t) + 1 2 x̃t (t)p ˙̃x(t) + 1 η1 θ̃t ˙̃ θ = v̇1 + v̇2, (22) where v̇1 = ( l∑ i=1 l∑ j=1 µiµjaijx̃(t)) t px̃(t)+x̃t (t)p( l∑ i=1 l∑ j=1 µiµjaijx̃(t))+ 1 2 w′t px(t)+ 1 2 xt (t)pw′ (23) v2 = [x̃ t pb(−(ψ(x)θ̃) + 1 η1 θ̃t ˙̃ θ. (24) v̇1 ≤ 1 2 l∑ i=1 l∑ j=1 µiµjx̃ t (t)(a t ijp + paij + 1 ρ2 pp)x̃(t) + 1 2 ρ2w′t w′ (25) substituting (9) into (25) yields 66 z. du, t.-c. lin, v.e. balas v̇1 ≤ − 1 2 x̃t (t)qx̃(t) + 1 2 ρ2w′t w′. (26) from (11),(24) v2 = [x̃ tpb(−(ψ(x)θ̃) + 1 η1 θ̃t ˙̃ θ] = 0. (27) thus v̇ = v̇1 + v̇2 ≤ − 1 2 x̃t (t)qx̃(t) + 1 2 ρ2w′t w′. (28) when ||e|| > ρ λmin(q) ||w||, v̇ < 0.thus, the closed-loop system (15) is uub. note that ∫ t 0 (x(t) − xr(t))t q(x(t) − xr(t))dt = ∫ t 0 [xt (t) xtr (t)] [ q −q −q q ] [xt (t) xtr (t)] t dt ≤ ∫ t 0 [xt (t) xtr (t)]diag{2q,2q}[x t (t) xtr (t)] t dt = ∫ t 0 x̃t (t)qx̃(t)dt (29) integrating the above inequality (28) from t=0 to t yields (20). by schur complements, the inequalities (9) are transformed into the linear matrix inequalities. therefore, the common solution p and kj(j=1,2,...,l) are required to be found. p is chosen as the form p = diag{p1,p2}, where p1,p2 are some symmetric and positive definite matrices. the inequalities (9) are equivalent to the following matrix inequalities   s11 −p1bikj 0 −(bikj)t p1 s22 p2 0 p2 −ρ2i   < 0, i,j = 1,2, ...,l, (30) where s11 = p1(ai +bikj)+(ai +bikj)t p1 + 1ρ2 p1p1 +2q, and s22 = p2ar +a t r p2 +2q. the matrix inequalities (30) imply s11 < 0. denote w=p −1 1 and yj = kjw . s11 < 0 is equivalent to the linear matrix inequalities [ s w w −(2q)−1 ] < 0, i,j = 1,2, ...,l, (31) where s = aiw + wati + biyj + (biyj) t + (ρ2)−1i. p1 and kj(j = 1,2, ...,l) are obtained by (31).and then, substituting p1 and kj(j = 1,2, ...,l) into (30), p2 is obtained. remark 2: if a controlled system is fourth-order, the dimension of the matrix inequalities (30) is 12. by use of the method in [12], the dimension of the matrix inequalities (58) is 20. by use of the method in [9], the dimension of matrix inequalities in theorem 1 is no less than 20. thus, the dimension of matrix inequalities is reduced. a new approach to nonlinear tracking control based on fuzzy approximation 67 4 simulation example consider a 2-link manipulator system in [20] m(q)q̈(t) + c(q, q̇)q̇(t) + g(q) = τ(t). (32) consider the existence of the uncertainties and external disturbances in system (32). thus, the plant is modified as follows: q̈(t) + c(q, q̇)q̇(t) + g(q) = b(q)u(t) + r∑ i=1 ξi(t)q(t) + d′, (33) where c(q, q̇) = h−1(q)c′(q, q̇),g(q) = h−1(q)g′(q),b(q) = h−1(q),d′ = h−1(q)d,q = [q1,q2] t and u(t) = [u1,u2]t = τ(t). ξi(t)(i = 1,2, ...,r) are uncertain and bounded. d is random noise with zero mean and variance 0.05, and d is bounded. the reference model is as follows: ẋr(t) = arxr(t) + r(t), where ar = diag{ar1,ar2},ar1 = ar2 = [ 0 1 −6 −5 ] ,r(t) = [0,r1(t),0,r2(t)] t . denote x1 = q1,x2 = q̇1,x3 = q2,x4 = q̇2. fuzzy t-s model is used to approximate the nonlinear system at x1 = −π2 ,0, π 2 and x3 = −π2 ,0, π 2 . the membership functions are adopted as triangle type. fuzzy t-s model with nine rules in the form (3) is given, where a1 =   0 1 0 0 5.927 −0.001 −0.315 −0.0000084 0 0 0 1 −6.859 0.002 3.155 0.0000062   ,a2 =   0 1 0 0 3.0428 −0.0011 −0.1791 −0.0002 0 0 0 1 −3.5436 0.0313 2.5611 0.0000114   , a3 =   0 1 0 0 6.2728 0.003 0.4339 −0.0001 0 0 0 1 −9.1041 0.0158 −1.0574 −0.000032   ,a4 =   0 1 0 0 6.5434 0.0017 1.2427 −0.0002 0 0 0 1 −3.1873 0.0306 −5.1911 −0.000018   , a5 =   0 1 0 0 11.1336 0 −1.8145 0 0 0 0 1 −9.0918 0 9.1638 0   ,a6 =   0 1 0 0 6.1702 −0.001 1.687 −0.0002 0 0 0 1 −2.3559 0.0314 4.5298 −0.000011   , a7 =   0 1 0 0 6.1206 0.0041 0.6205 0.0001 0 0 0 1 8.8794 0.0193 −1.0119 0.000044   ,a8 =   0 1 0 0 3.6421 −0.0018 0.0721 0.0002 0 0 0 1 2.429 −0.0305 2.9832 −0.000019   , 68 z. du, t.-c. lin, v.e. balas a9 =   0 1 0 0 6.2933 −0.0009 0.2188 −0.000012 0 0 0 1 −7.4649 0.0024 3.2693 −0.0000092   ,b1 = [ 0 1 0 −1 0 −1 0 2 ]t ,b2 = [ 0 0.5 0 0 0 0 0 1 ]t b3 = [ 0 1 0 1 0 1 0 2 ]t ,b4 = [ 0 0.5 0 0 0 0 0 1 ]t b5 = [ 0 1 0 −1 0 −1 0 2 ]t ,b6 = [ 0 0.5 0 0 0 0 0 1 ]t b7 = [ 0 1 0 1 0 1 0 2 ]t ,b8 = [ 0 0.5 0 0 0 0 0 1 ]t b9 = [ 0 1 0 −1 0 −1 0 2 ]t . using the lmi box in matlab, kj(j = 1,2, ...,l) are obtained k1 = [ −75.5707 −41.2895 −19.7728 −8.9886 4.5163 −0.5944 −49.7511 −24.5727 ] ,k2 = [ −76.4364 −41.3132 −13.7595 −5.9976 7.1834 1.0290 −49.0587 −24.2578 ] , k3 = [ −75.4503 −41.2461 −19.8614 −9.0371 4.3907 −0.6474 −49.6965 −24.54837 ] ,k4 = [ −76.4364 −41.3132 −13.7595 −5.9976 7.1834 1.0290 −49.0587 −24.2578 ] , k5 = [ −76.4364 −41.3132 −13.7595 −5.9976 7.1834 1.0290 −49.0587 −24.2578 ] ,k6 = [ −76.4364 −41.3132 −13.7595 −5.9976 7.1834 1.0290 −49.0587 −24.2578 ] , k7 = [ −76.4364 −41.9511 −6.2666 −2.2808 11.1565 3.4005 −48.7116 −24.1209 ] ,k8 = [ −76.4364 −41.3132 −13.7595 −5.9976 7.1834 1.0290 −49.0587 −24.2578 ] , k9 = [ lcl − 76.7763 −42.0400 −6.0715 −2.1821 −11.3875 3.4986 −48.8204 −24.1694 ] , then, the controller is given by u(t) = ul(t) − uf(t), where ul(t) = 9∑ i=1 µiki(x(t) − xr(t)) and uf = ( 9∑ i=1 µiei) −1û(x|θ) with the updating law (11). in (11), the symmetric and positive definite matrix p =   0.0070 0.0035 −0.0004 −0.0002 0.0035 0.0021 0.0004 0.0001 −0.0004 0.0004 0.0073 0.0032 −0.0002 0.0001 0.0032 0.0017   . a new approach to nonlinear tracking control based on fuzzy approximation 69 seven fuzzy rules are defined in adaptive fuzzy logic systems. r(j): if x1 is f j 1 ,..., x4 is f j 4 , then y is g j (j=1,2,...,7), µf 1i (xi) = 1 1 + exp[5(xi + 0.8)] (i = 1,2, ...,4), µf 1i (xi) = exp[−(xi + 0.6)2](i = 1,2, ...,4), µf 3i (xi) = exp[−(xi + 0.4)2](i = 1,2, ...,4), µf 4i (xi) = exp[−(xi) 2](i = 1,2, ...,4), µf 5i (xi) = exp[−(xi − 0.4)2](i = 1,2, ...,4), µf 6i (xi) = exp[−(xi − 0.6) 2](i = 1,2, ...,4), µf 7i (xi) = 1 1 + exp[5(xi − 0.8)] (i = 1,2, ...,4). denote s1 = 7∑ j=1 4∑ i=1 µ f j i (xi), then ξ(x) = [ 4∏ i=1 µf 1i (xi)/s1, ..., 4∏ i=1 µf 7i (xi)/s1 ] = [ξ1, ...,ξ7],ψ(x) = diag[ξ t (x),ξt (x)]. the initial condition is set to be (x1(0),x2(0),x3(0),x4(0),xr1(0),xr2(0),xr3(0),xr4(0)) = (0.4,0,−0.4,0,0,0,0,0). choose r1(t) = r2(t) = 4sin(t),r = 2,ξ1(t) = 1 + 20 sin(t),ξ2(t)=2(1-exp(-t))/(1+exp(-t)) and the parameter η1 = 20. simulation results are shown in fig.1fig.3. figure 1: state responses of x1,x2,x3 and x4 (dotted line), xr1,xr2,xr3 and xr4 (solid line) by only using the fuzzy controller based on t-s model [20], simulation result is shown in fig.4 and the tracking performance comparison between proposed method and approach in [20] is given in table 1. 70 z. du, t.-c. lin, v.e. balas figure 2: the control input u1 figure 3: the control input u2 figure 4: state responses of x1,x2,x3 and x4 (dotted line), xr1,xr2,xr3 and xr4 (solid line) a new approach to nonlinear tracking control based on fuzzy approximation 71 table 1: the tracking performance comparison between proposed method and approach in [20]. ei = ∫ t 0 (xi − xri)2dt e1 e2 e3 e4 the proposed method 0.0677 0.1338 0.0587 0.2177 the method in [20] 0.1380 0.1760 0.1244 0.3343 5 conclusion the developed controller makes full use of the advantages of two fuzzy models. theory analysis 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[20] c. s. tseng, b. s. chen, h.j. uang. fuzzy tracking control design for nonlinear dynamic systems via t-s fuzzy model. ieee transactions on fuzzy systems,2001,9(3): 381-392. international journal of computers communications & control issn 1841-9836, 10(4):480-491, august, 2015. development of a fuzzy logic system to identify the risk of projects financed from structural funds m.i. boloş, d.c. sabău-popa, p. filip, a. manolescu marcel ioan boloş*, diana-claudia sabău popa departament of finance-accounting, faculty of economic sciences, university of oradea romania, 410087 oradea, universitatii st. 1 marcel_bolos@yahoo.com, dpopa@uoradea.ro *corresponding author: marcel_bolos@yahoo.com petru filip 1. dimitrie cantemir christian university, romania, 040042 bucharest, splaiul unirii, 176 2. agora university of oradea, romania, 410526 oradea, piata tineretului, 8 3. university of oradea romania, 410610 oradea, university street, 1 pfilip@uoradea.ro adriana manolescu departament of social sciences agora university of oradea romania, 410526 oradea, piata tineretului, 8 adrianamanolescu@univagora.ro abstract: the fuzzy logic system developed in this research paper seeks to identify the financial risk of projects financed from structural funds when changes occur in project values, in the duration of the projects and in the implementation durations. those two factors are known to influence the financial risk. the fuzzy system was simulated using matlab and the results showed its operation and the conclusion that the financial risk of the project is dependent on the developments values and on the implementation duration. the developed and tested fuzzy logic system provides information on financial risk intensity organized into three categories: small, medium and large and on the inflection point of transition from low risk to high risk. this is considered an early warning system for the management staff with responsibilities in structural funds. keywords: fuzzy logic system (fls), artificial intelligence, financial risk, structural funds, centroid method. 1 introduction the fuzzy logic systems (fls) are used as a tool for decisions making, for the projects financed from structural funds, for the early identification of risks that affect the performance of the allocation of funds for eu member countries, through various financial instruments known as operational programs. the risk of the projects financed from structural funds has various forms, but the most important remains the financial aspect, that generates losses for the budget of the member states. although there are now used statistical methods for risk measurement, the most common being the standard deviation (σ2), it should be noted that they have a major drawback since they reflect the risk at the project level. moreover, the classical statistical indicators provide insight into project financial risk without taking into account the influence factors or copyright © 2006-2015 by ccc publications development of a fuzzy logic system to identify the risk of projects financed from structural funds 481 correlations that exist between the various projects [3]. fls have the advantage of being able to identify the financial risk for the entire portfolio of projects contained by the operational program and to contribute to the decision of management to avoid or eliminate the financial risk. the fls input variables of this model are set according to the project particularities, on which ultimately depend the financial risk of the project, or project value (vp) and the duration of their implementation (di). the output variable is the financial risk of the project (rf). the assessment of the financial risk of the project (as an output variable of fls) was structured by verbal expressions (specific to fuzzy logic): high financial risk, medium financial risk and low financial risk, depending on the seriousness of the risk but also to highlight the intensity of the losses from the budget allocated to eu member states as a result of the manifestation of the financial risk event [6]. the fls developed to identify the financial risk of the projects financed from structural funds becomes a novelty in literature but also a management tool for decision makers. with the help of fls they can measure the financial risk for all the projects entering in the structure of the operational program. based on these ways of measuring the financial risk, identified using fls, corrective action can be taken for the efficient and fair presentation of structural funds for member states of the eu budget. 2 the concept of financial risk of projects financed from structural funds the financial risk of the projects financed from structural funds is a fairly new concept both in literature and in practice. in essence the financial risk of the projects should answer the following question: "what is the financial size of the potential loss that a member state is expected to suffer, due to the implementation of projects financed from structural funds?" the project financial risk depends on a number of factors determined by the main project implementation cycle. in this category are included factors that are measured through the implementation period, resulting the physical progress of projects or the requests repayment duration. in practice, the most important factor that influences the financial risk of the project remains the physical progress [11]. the physical progress, although it seems a technical term, is most often defined as the ratio between the duration of implementation of a project under implementation cycle (dic ) and the duration of implementation actually achieved (dr ) according to a relation of the form: pf = dr dic ×100 (1) the achieved physical progress can record higher values than those set, for a project suitable for the implementation cycle, situation in which the financial risk of the project is small or can record lower values, where the financial risk of the project increases. the financial dimension of a project risk therefore depends on the value of the project (vp), on the physical progress established under implementation cycle (pfc) and on the actual physical progress achieved (pr) after a relationship of the form: rf = vp(pfc −pfr) (2) or rf = vp × ( dct dip − dr dip ) (3) the higher is the difference between the physical progress of the project under implementation cycle (pfc) and the actual physical progress achieved (pfr), the higher is the risk of losing a larger 482 m.i. boloş, d.c. sabău-popa, p. filip, a. manolescu amount of the budget allocated to a project. in practice, the most common form of financial risk measurement remain in the project’s value (vp) and the deviation of the achieved implementation duration of the project dr towards the set one based on implementation cycle (dci), after a relationship of the form: pf = vp × dci −dr dci ×100 (4) there are projects with high financial risk, for which the difference between the two periods (according to implementation cycle and the one actually achieved) is far from average, or projects with low financial risk for which the same difference is close to average. the average difference between the two periods is determined for all projects that are part of the operational program. to appreciate the intensity of the financial risk of a project it can be used the standard deviation or variance [13], characterizing the removal from the average financial risk of a project after a relationship of the form: rf = √ 1 n n∑ i=1 rfi −rf (5) depending on the value of this statistical indicator, the intensity of the financial risk is estimated. the higher is the percentage value of this indicator, the higher is the financial risk of lossing the budget allocated to projects. the indicator, thus, doesn’t provide a complete picture of risk, as it is determined based on historical values recorded in previous periods of time [2]. in fls , the financial risk of the projects will be divided into three categories (large, medium and small), considered as an output of the system. in essence, the financial risk of a project, regardless its quantification and intensity, depends on a number of factors that will transform the input for fls, as follows [10]: 1. the value of projects, that influences the size of the financial risk: the rule is simple: the higher is the project value and the deviation from the implementation period, the higher is the financial risk 2. the project implementation duration, which is determined according to the intensity of risk: the implementation period is known in the literature as the duration necessary to implement a project, so that it can be achieved its project objectives and outcome indicators. the further the implementation period of this project removes from the one set according to the implementation period, the higher is the risk that a part of the budget will be lost.the project value and its implementation duration, as influencing factors underlying the financial risk will be considered for fls as input variables. 3 the development of the fls to identify financial risk of a project each fuzzy logic system supposes four distinct phases [14] as follows: setting the input variables and their associated fuzzy sets, the fuzzy rule base identification, the establishment of fuzzy inference operators and defuzzification. in the first stage of development of the fuzzy logic system were established the input variables mentioned above, namely: the project value (pv) and the duration of implementation (di), while the output variable (result) is the financial risk of the project. the input variables are structured according to the size of their impact on financial risk. the project value (vp) is divided into three categories, namely: high value development of a fuzzy logic system to identify the risk of projects financed from structural funds 483 projects (hvp), the average project value v pm, and the small projects value v pm. at the same time, the duration of project implementation is structured according to the exceeding of the project implementation duration to that established in the implementation cycle as follows: high exceeding (between 180 days and 360 days) (dim), average exceeding (between 90 days and 180 days) dim and low exceeding dim under 90 days. the financial risk of the project, as the output variable of the system, is determined by the fuzzy base rules, the fuzzy inference operators as well as by the expert assessments, that are divided into three categories namely: high financial risk (rfm), average financial risk rfm and low financial risk rfm. in order to completely define the fuzzy set, for the input variables were established the following membership functions [3]: 1. the trapezoidal membership function, for the value of projects (vp) defined according to the value types of projects 2. the triangular membership function, for the exceeding duration of the project implementation (di) which was also structured in: high, medium and low exceeding; figure 1: the trapezoidal membership function for the input variables the fuzzy sets allow the partial membership of elements, the fuzzy membership degree being able to take any value from 0 (not belonging) to 1 (full membership). for example, the trapezoidal membership’s function for v p ∈ [v pn−2,v pn+2], and for project with medium values will be expressed as: µv pm(v p) =   0 if v pn−2 < v p and v p > v pn+2 v p−v pn−2 v pn−1−v pn−2 if v pn−2 ≤ v p ≤ v pn−1 1 if v pn−1 ≤ v p ≤ v pn+1 v pn+2−v p v pn+2−v pn+1 if v pn+2 ≤ v p ≤ v pn+1 similarly, the trapezoidal membership function for the interval v p ∈ [v pn−k,v pn−1], which corresponds to the values of small projects, which will be expressed as follows: 484 m.i. boloş, d.c. sabău-popa, p. filip, a. manolescu µv pm(v p) =   0 if v p < v pn−k and v p > v pn−1 v pn−1−v p v pn−1−v pn−2 if v pn−2 ≤ v p ≤ v pn−1 1 if v pn−k ≤ v p ≤ v pn−2 the triangular membership function for the input variable, the exceeding of the duration of implementation (di) of the project is represented in figure no.2. the triangular membership function can be expressed, for example, for the low exceeding of the duration of implementation by the relationship, (di ∈ [din−k,din−1]): µdim(di) =   0 if din−k < di and di > din−1 di−din−k din−2−din−k if din−k ≤ di ≤ din−2 din−1−di din−1−din−2 if din−2 ≤ di ≤ din−1 similarly, the triangular membership function for the input variable, the duration of the implementation of the project on the interval [din−2,din+2]], can be expressed as: µdim(di) =   0 if din−2 < di and di > din+2 di−din−2 ḋin−2−din−2 if din−2 ≤ di ≤ ḋin−2 din+2−di din+2−ḋin−2 if ḋin−2 ≤ di ≤ din+2 figure 2: the triangular membership function for input variable of the system the duration of implementation (di) for the fls were identified the input variables of the system and their membership functions according to fuzzy rules, to which a fuzzy set is completely determined by the set of ordered pairs [7, 8]: a = {(x,µa(x))/x ∈ x} (6) in the second stage of the fls are set the fuzzy rules base. the specific of these rules is that based on two conditions, namely "if" and ‘"then", are established with the help of experts the development of a fuzzy logic system to identify the risk of projects financed from structural funds 485 influence factors of the financial risk. the number of fuzzy rules base will be equal to 32 = 9 and the financial risk will be divided into three risk classes. the fuzzy rules base for financial risk related to projects financed from structural funds will have the following form: rule 1: if the project value is large (vpm) and the exceeding of the duration of the implementation is high (dim), then the project financial risk is high (rfm); rule 2: if the project value is average v pm and the exceeding the implementation is high (dim), the financial risk is high (rfm); rule 3: if the project value is small v pm and the exceeding of the duration of the implementation is high (dim), then the financial risk is medium rfm; rule 4: if the project value is large (vpm) and the exceeding of the duration of the implementation is average dim, then the financial risk is high (rfm); rule 5: if the project value is average v pm and the exceeding of the duration of the implementation is average dim, then the financial risk is medium rfm; rule 6: if the project value is small v pm and the exceeding of the duration of the implementation is average dim, then the financial risk is small rfm; rule 7: if the project value is large (vpm) and the exceeding of the duration of the implementation is small dim, then the financial risk is medium rfm; rule 8: if the project value is average v pm and the exceeding of the duration of the implementation is small dim, then the financial risk is medium rfm; rule 9: if the project value is small v pm and and the exceeding of the duration of the implementation is small dim, then the financial risk of the project is small rfm; the fuzzy rules base for fls that targets the projects financial risk, aim to capture the best way in which financial risk occurs when there is a change in the value of projects and concomitant a change in terms of exceeding the project implementation duration. depending on the intensity of these changes, the risk of project budget loss may be, as mentioned above: large, medium or small. on the third stage were applied the fuzzy inference operators on the rules basis generated in the second stage [4]. as shown, the fuzzy rules base is connected by "and" which means that the operator inference for the rules base is minimum. for each of the previous defined fuzzy rules, is established the degree of membership of the output variable (rf). therefore will result: for rule 1: µrfm,1 = min[µv pm(v p),µdim(di)] for rule 2: µrfm,2 = min[µv pm(v p),µdim(di)] for rule 3: µrfm,3 = min[µv pm(v p),µdim(di)] for rule 4: µrfm,4 = min[µv pm(v p),µdim(di)] for rule 5: µrfm,5 = min[µv pm(v p),µdim(di)] for rule 6: µrfm,6 = min[µv pm(v p),µdim(di)] for rule 7: µrfm,7 = min[µv pm(v p),µdim(di)] for rule 8: µrfm,8 = min[µv pm(v p),µdim(di)] for rule 9: µrfm,9 = min[µv pm(v p),µdim(di)] from the rules analysis is shown that the affiliation of the system financial risk in a fuzzy set can be from one or more fuzzy rules which are likely to result in different degrees of belonging to the same fuzzy set. but it takes a single degree of belonging and in order to establish it, is applied a fuzzy controller max corresponding to the reunion of the fuzzy sets. under these conditions will result [9]: µrfm = max[µrfm,1,µrfm,2,µrfm,4] µrfm = max[µrfm,5,µrfm,7,µrfm,8] µrfm = max[µrfm,3,µrfm,6,µrfm,9] in this stage are obtained the solutions of the fuzzy rules, without a certain amount of input variables in the system (v pi,dii) to be determined the intensity of the financial risk by applying all rules of financial risk in the fuzzy base. it is therefore necessary to identify these solutions at 486 m.i. boloş, d.c. sabău-popa, p. filip, a. manolescu the last stage of fls, namely defuzzification. in the last stage of defuzzification is extracted a deterministic scalar value, from the fuzzy information which is associated to the output variable, the essence of which is to provide more explicit the best value of the output variable [1]. each of the result in the third stage of the fls will be used to determine the surface area (si) bounded by the parallel to the horizontal axis, taken through the point that determines the size of the output variable, the horizontal axis (ox) and the graphic of the function associated to output variables. for a given value of the project (v pi) and some exceeding of the project implementation duration (dpi ) would result that from the original surface (si) only a certain percentage (p) is the result that will be taken into account to determine the final amount of financial risk [5]. the conversion of the fuzzy result in a real number value is done by determining the center of gravity of the surface obtained by aggregating the proportion (p) of the initial areas for each graphical input variables as follows: z = ∑9 i=1 µv p,di(v pi,dpi)×v pi/dpi∑9 i=1 µv p,di(v pi,dii) (7) the result (s) of equation 7 undergo a conversion from area into a real value through the centroid method, which consists in determining the numerical value (z) through which the perpendicular traced to the horizontal axis divides the s area into two equal parts by a relationship of the form: s = 9∪ i=1 %psi (8) the obtained numerical z value represents the size of the financial risk of a project that has a certain value and a certain level recorded for the (exceeding) implementation duration of the project. the higher this value is, the higher is the probability of losing a part of the project budget. 4 the fls simulation for the financial risk of projects financed from structural funds the developed fuzzy logic system was simulated using matlab programming language, taking into account the following assumptions, namely: 1. the input variable the value of projects was divided into three classes: small projects (between 0 and 350 million um), average project (between 250 million and 750 million u.m.) and large projects ( between 650 million um and 1,000 mil); 2. the input variable the exceeding of the project implementation duration was also divided into three classes, namely: low exceeding (between 0 and 90 days), average exceeding (between 60 to 180 days) and high exceeding (between 150 days and 270 days). in the first fls stage, were established the input variables and their membership functions. thus, for the input variable, the project value (pv), was stated the following fuzzy set (trapezoidal membership function) as depicted in figure 3. for the input variable, the exceeding of the project implementation duration (di), resulted the following fuzzy sets (using the trapezoidal membership function) as depicted in figure 7. for the output variable, the financial risk of the projects, were established three risk classes, using the triangular membership function as follows: low financial risk for values between 0 and development of a fuzzy logic system to identify the risk of projects financed from structural funds 487 figure 3: the fuzzy set for the input variable the project value figure 4: the fuzzy set for the input variable the implementation duration 488 m.i. boloş, d.c. sabău-popa, p. filip, a. manolescu 3, average financial risk for values between 2 and 6 and greater financial risk for values between 5 and 10. the resulted fuzzy set for the output variable is represented in figure 5. figure 5: the fuzzy set for the output variable the financial risk of the projects after continuing the simulation of fls, for the financial risk of the projects, were established 9 fuzzy rules base according to the developed and were introduced in the program shown in figure 6. figure 6: the fuzzy rules base for the financial risk in matlab subsequently were applied the inference operators on fuzzy rules base and the results were presented in figure 7. the obtained results are for a project with a value of 500 million u.m., 135-days exceeding duration of the project implementation and a financial risk value of 4.33. the fls simulation for the financial risk was further carried out for different values of input variables in order development of a fuzzy logic system to identify the risk of projects financed from structural funds 489 figure 7: results obtained by applying the inference operators on fuzzy rules base to identify the developments that the financial risk has when changes occur in values and in exceeding duration of the project implementation. the results were obtained in figure 8. figure 8: the evolution of financial risk for projects based on input variables the simulation results are: 1. for project values between 0 and 600 million and an exceeding duration of the project implementation of 55 days, the financial risk tends to zero; 2. for project values between 600 million and 800 million and an exceeding duration of the project implementation between 55 and 100 days, the financial risk is increased from 0 to about 4.5 (and is considered a medium risk); 3. for project values between 800 million and 1,000 million and an exceeding duration of the project implementation between 100 and 270 days, the financial risk is increased from 4.5 to the maximum 9 (being considered a high risk); 490 m.i. boloş, d.c. sabău-popa, p. filip, a. manolescu 5 conclusion the computational intelligence is an area of great interest for both specialists in computer science and finance. this is because often are abandoned the statistical concepts and methods that didn’t characterize the phenomena and economic processes and are used specific computational intelligence methods that adapt quite well the dynamics of the phenomena studied. the fuzzy logic system developed in this paper, as part of computational intelligence, studies the behavior of financial risk of the projects financed from structural funds when there is a change in the value of projects or in the durations of implementation. this financial risk was defined as the risk of losing a part of the project budget, when there are exceedings in the duration of implementation, based on their implementation cycles. the rules of the fuzzy base were defined according to the impact that the system input variables have on financial risk. the fuzzy inference operators were applied on the basis rules to determine the membership in fuzzy output variable. with the help of defuzzification was ensured the convertion of the fuzzy values in numeric values to determine the size of financial risk for different values of the input variables. following the simulation for the output variable (the financial risk of projects), were reached the following conclusions: 1. there are situations where the financial risk is zero, or almost zero, for different values of input variables in the system; 2. the financial risk of projects increases as changes occur in the value of projects and in the duration of implementation. this increase in financial risk value becomes in time proportional to changes in the input variables in the system; 3. the financial risk is maximum, when the input variables in the system approach the maximum values that they may register; the developed fuzzy logic system is a management tool for decision making in the structural funds, under which can be taken measures to avoid or reduce the financial risk, especially for the early identification of the emerging financial risk in the portfolio of projects falling within the structure of the operational program. this early identification of financial risk can be very useful in structural funds management, for taking the necessary 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[14] l.a. zadeh (1996); fuzzy logic computing with words, ieee trans. fuzzy systems, 4(2):103-111. int j comput commun, issn 1841-9836 8(3):354-365, june, 2013. breast cancer diagnosis based on spiculation feature and neural network techniques v. bălănică, i. dumitrache, l. preziosi victor bălănică, ioan dumitrache university politechnica of bucharest romania, 060042 bucharest, splaiul independentei, 313 vicord20011@gmail.com, idumitrache@ics.pub.ro luigi preziosi university of politechnics torino italy, 10129 torino, corso duca degli abruzzi, 24 luigi.preziosi@polito.it abstract: the degree of spiculation of the tumor edge is a particularly relevant indicator of malignancy in the analysis of breast tumoral masses. this paper introduces four new methods for extracting the spiculation feature of a detected breast lesion on mammography by segmenting the contour of the lesion in a number of regions which are separately analysed, determining a characterizing spiculation feature set. in order to differentiate between benign and malignant tumors based on the extracted spiculation sets, an intelligent neural network is first trained on a number of 96 cases of known breast cancer malignancy and then tested for diagnosing and classifying breast cancer tumors. the input of the neural network is thus the extracted spiculation feature set and the output is represented by the histopatological diagnostic given by doctors. finally, the performance of the introduced methods is analysed depending on the number of regions in which the contour is segmented and the performance-related conclusions are stated for each of the methods. the highlight of this paper is the division of the tumour contour in regions and the assessment of a spiculation indicator for each region, resulting a set of spiculation indicators that characterise the tumour and by training a neural network can be used in classifying breast tumours with high performance. keywords: breast cancer, spiculation feature extraction, neural network, diagnosis. 1 introduction in the detection process of suspicious breast cancer lesions, mammography screening is usually followed by an analysis of diagnostic mammography, i.e. a more detailed analysis of the artifacts visible on the mammographic imaging results, such as: breast tissue density, tumoral masses, (macroand micro-) calcifications, architectural distortion, etc, [1]. among them, the detected microcalcifications and the identified masses (also known as tumours) are considered the most relevant signs of malignancy that need to be investigated in detail. on one hand, the microcalcifications are tiny mineral deposits within the breast, representing an early indicator of the presence of a tumor. on the other hand, the tumoral mass is a tissue composed of an abnormal growth of cells (normal or neoplastic, cancerous, cells) with or without integrated calcifications that are also being detected on the mammographic results. when analyzing any pathology of breast cancer, the location, the size, the shape of the mass are usually assessed and the mass density and the tumoral margins are moreover evaluated. while the benign masses have usually smooth edges, are well circumscribed, compact and approximately circular or elliptical (see figure 1), malignant lesions usually have vague edges and irregular form, presenting spiculations (i.e. a radial pattern of spiculs), [2]. thus, in analyzing the tumoral mass, in addition to the lesion size, the degree of spiculation of the tumor edge is a particularly relevant indicator of malignancy (differences visible in figure 1). copyright c⃝ 2006-2013 by ccc publications breast cancer diagnosis based on spiculation feature and neural network techniques 355 figure 1: benign and malignant tumors extracted from available mammographies the evaluation of the spiculations of masses visible on the mammographic results implies form recognition techniques that extract numerical information usable in the process of differentiating between benign and malignant tumours. these techniques are continuously being developed and improved in order to make possible a more refined characterization of the particular morfological features and a higher classification performance. [3] computes a spiculation index that can classify tumours with 93% accuracy; [4] proposes three methods for morfological feature extraction and reaches 93% accuracy when classifying breast lesions; [5] explores the classification power of 6 contour algorithms on 349 masses using three popular classifiers (bayesian classifier, fisher linear discriminant analysis and support vector machine) and shows that a big variation (14%) of the quality of the segmentation method influences only 4% of the classification performance given by the contour features. besides the current trend in technology and computer automation, the bioengineering interdisciplinary developments of the last years allowed the implementation of digital cad (computer aided diagnosis) aid tools to assist novice radiologists in making diagnostic and recommended treatments decisions, herewith providing a second opinion on the decision and playing the role of the second pairs of eyes in the analysis, certifying or not the quality and/or the choice. although the computational cad techniques used in determining a medical prognostic do not always provide the desired quality, their implementation using intelligent techniques (neural networks, fuzzy techniques, genetic algorithms and their hybrid variants) that are based on human experience and provide learning and adaptation abilities, demonstrates high performance in these kinds of tasks. in this paper, we are trying to define new methods for extracting the spiculation feature set of a breast lesion identified on a mammographic result by analyzing the lesion contour on a number of contour regions. the methods have been tested by using a neural architecture integrated into a cad that provides good classification performance when trained on the extracted spiculation feature sets of 96 breast cancer cases with known malignancy diagnostic. thus, this neural cad is able to distinguish, diagnose and classify breast cancer tumors in benign or malignant, highlighting the benefit and usefulness of both the methods and their neural cad application in national breast cancer screening programs. 2 materials and methods in terms of imaging, the starry distortions visible on mammography are caused by the intrusion of cancer in surrounding tissue (invasion occurs in malignant tumors), so that the contour/outline form of the tumor is generally correlated to the degree of malignancy. the contour is thus an extremely valuable information in the differentiation between benign and malignant tumors. as stated above benign tumors usually have smooth, circumscribed, macrolobulate and well defined contours (figure 1), while malignant tumors are characterized by vague, irregular, 356 v. bălănică, i. dumitrache, l. preziosi microlobulate and spiculate contours (figure 1). based on these observations, researchers have defined certain objective and quantitative measures/indicators to characterize the contours in order to improve the imaging classification capability of the detected tumors. in terms of performance, among the most important and most frequent contour indicators found in the literature we count: the degree of compactness, the spiculation index, the fractional concavity, the fourier factor and the fractal dimension, [6]. in our paper, the neural networks are used for classifying the tumours based on the extracted spiculation feature set of a breast lesion identified on a mammographic result. the neural networks (nns) models are simplified structural and functional nervous systems, formed by a number of processing elements, the neurons, which are bound by weighted connections, similar to the neural synapses, [7]. the neural network architecture mainly used in the medical field is known as the multi-layer perceptron (or mlp), [8], figure 2, which consists of neurons usually organized in three feed-forward interconnected layers (i.e. all neurons in one layer are fully connected to all neurons in the next layer, but there are no feedbacks to previous layers). a perceptron has the ability to learn, to self-organize and to generalize (i.e. it can have same output for similar sets of inputs). the most commonly algorithm used for training the neural network is the back-propagation algorithm, which calculates the error gradient of the network and adjusts each connection weight by minimizing the mean square difference and achieving convergence to a local minimum, [9]. figure 2 shows a graphical representation of such a network, where, in the case of a medical system, the input layer neurons often corresponds to the clinical symptoms, the hidden layer simulates the medical inference process done by the doctor and the neurons from the output layer correspond to the medical diagnosis. figure 2: graphical representation of a mpl neural network with one hidden layer the focus in this paper is not to develop a novel mpl, but rather to apply it in the clinical relevant problem of breast cancer. because many clinical scenarios can not be explained based on only one parameter, but as interaction of various clinical-pathological factors, [10], the inputs of the mpl may include any set of mammographic descriptors of the calcifications (distribution, number, description), of the detected masses (size, shape, density, margins, location) or of any other associated findings (asymmetries, distorsions), being able to include also inputs related to the patient history. the ouputs of the mpl are usually numbers between zero and one that may correpond to the prediction of the biopsy outcome (benign or malignant) as in [11], the stage of development for the analyzed lesion (in situ or invasive) or it may be correlated to the survival chance/rate of the patient (under 1 year, under 2 years, under 3 years, under 5 years, more than 5 years) as in [12]. breast cancer diagnosis based on spiculation feature and neural network techniques 357 3 new methods for lesion spiculation feature extraction the mammographic lesions segmentation methods proposed below assumes that the spicules are visible on the mammography in the form of tumor infiltration or branches, usually narrow, in normal biological tissue. therefore, the analysis of tumor contour on neighborhoods (or regions) of the contour and the measuring of the curvature change in each region offers an useful set of valuable information in determining the degree of spiculation in the analysed regions of the lesion contour and allowing subsequent cad classification of tumors in benign or malignant. for each of the neighborhoods of the lesion contour four spiculation indicators are computed, namely: a. a maximum level difference for every region of the contour, relative to the center of gravity of the lesion, according to the following figure (figure 3): figure 3: calculation of the maximum level difference of for the regions of the tumoral contour b. the total area of triangles in a neighborhood, calculated by adding the triangle areas formed by each three consecutive points along the contour, according to the following figure (figure 4): figure 4: calculation of the total area of the triangles formed along the contour for a contour region c. the total angle in a neighborhood, calculated by adding all the angles formed by every three consecutive points along the contour, according to the following figure (figure 5): 358 v. bălănică, i. dumitrache, l. preziosi figure 5: calculation of total angle summed for the angles formed along a contour region d. the total quadratic curvature for each region, calculated by adding the curvatures of the circles passing through each three consecutive points along the contour, according to the following figure (figure 6): figure 6: calculation of the total quadratic curvature, by adding the curvatures of the circles passing through each three consecutive points of the analysed region of the contour the main steps for the defined a, b, c and d methods are described as following: i. the starting point of the method is an image of a detected mass (figure 7 a), ii. calculate and plot the center of the mass based on the pixels that form the lesion (computing center of mass for lines and for columns) (figure 7 b), iii. calculate and plot the marginal pixels of the tumor (figure 7 b), iv. calculate and plot the distances between the center of mass and the pixels describing the edge of the tumor (figure 7 c), v. the distance vector is ascendently ordered according to the angle formed by these distances lines with one reference line, vi. depending on a relevant, experimentally determined, number of neighborhoods, x, for each angle z = 360/x are done a number of operations specific for each algorithm. for each angle z, for algorithm: breast cancer diagnosis based on spiculation feature and neural network techniques 359 a. is computed the maximum difference of the distances (= center of gravity marginal pixels) that fall within the z angle, thus identifying the characteristic vector of spiculation for the tumor. table 1 shows an example of this vector for a malignant and a benign lesion). b. is computed the total area of triangles in the neighborhood described by the z angle, by adding the areas of the triangles formed by every three consecutive points along the contour, according to the formula (1), [13]. table 1 shows an example of this vector for a malignant and a benign lesion). ∑ iϵz ai, ai = 1 2 [(xi − xi+2)(yi+1 − yi) − (xi − xi+)(yi+2 − yi)] (1) c. is computed the total angle for the neighborhood described by the z angle, by summing of all angles formed by every three consecutive points along the contour, according to the formula (2), [13]. table 2 shows an example of this vector for a malignant and a benign lesion). ∑ iϵz ui, ui = arcsin[2·ai/ √ (xi − xi+1)2 + (yi − yi+1)· √ (xi+1 − xi+2)2 + (yi+1 − yi+2)] (2) d. is computed the total quadratic curvature for the neighborhood described by the z angle, by adding quadratic curvatures of the circles passing through each three consecutive points along the contour, according to the formulas (3), (4), (5), [13]. table 2 shows an example of this vector for a malignant and a benign lesion). ∑ iϵz ci, ci = 1 r2i , ri = √ d2 + e2 4a2 − f a , where (3) a = ∣∣∣∣∣∣∣ xi yi 1 xi+1 yi+1 1 xi+2 yi+2 1 ∣∣∣∣∣∣∣ , d = ∣∣∣∣∣∣∣ x2i + y 2 i yi 1 x2i+1 + y 2 i+1 yi+1 1 x2i+2 + y 2 i+2 yi+2 1 ∣∣∣∣∣∣∣ , (4) e = ∣∣∣∣∣∣∣ x2i + y 2 i xi 1 x2i+1 + y 2 i+1 xi+1 1 x2i+2 + y 2 i+2 xi+2 1 ∣∣∣∣∣∣∣ , f = ∣∣∣∣∣∣∣ x2i + y 2 i xi yi x2i+1 + y 2 i+1 xi+1 yi+1 x2i+2 + y 2 i+2 xi+2 yi+2 ∣∣∣∣∣∣∣ (5) the spiculation features set computed with any of the four methods shows, after tests, a very good performance in terms of differentiating benign and malignant tumors and can be used to compute an objective measure/degree of spiculation for the examined lesions. moreover, these methods are suitable for training a neural classifier of malignant or benign patterns of spicules that is able to make assessments in the presence of a new set of extracted mammographic spiculation features. as observed, depending on the analyzed lesion, the nature of these sets of indicators show a large variation of values in each neighborhood which makes them very suitable to being analyzed with classification modules such as neural networks in order to determine patterns and nonlinear correlations. the following practical approach shows the great classification potential of these indicators, given the fact that the trained neural modules reached maximum performance after a very short training time. 360 v. bălănică, i. dumitrache, l. preziosi figure 7: lesion segmentation algorithm for computing the spiculation degree: a) a tumor identified on mammography, b) identification of the center of the mass and of the marginal pixels, c) calculating and sorting the angular distances from the center of the mass to the marginal pixels table 1: the characteristical spiculation vectors for a malignant lesion and for a benign one for method a and method b however, the efficient utilization of these methods may vary depending on the size and the quality of the imaging results (processed or unprocessed, noise filtered or unfiltered regions of interest, the quality of image binarization, etc.) and in what concerns the choice of the number of neighborhoods for the segmentation of the lesion contour. in this respect, one can successfully use a genetic algorithm to determine the optimal number of neighborhoods to achieve the maximum classification performance. 4 neural networks trained for breast cancer diagnosis the data sets obtained by applying the above methods can be used to train a neural intelligent module for the classification of the detected lesions on imaging results and determine the nonlinear correlations and the useful predictions. thus, a feedforward neural network with one hidden layer, configurable in terms of the learning rate, of learning momentum, of the training epochs and of the minimum desired error, was trained using the backpropagation learning algorithm based on the extracted imaging spiculation feature sets determined with the above defined a, b, c and d methods (the feature sets will be noted as seta, setb, setc and setd). the breast cancer diagnosis based on spiculation feature and neural network techniques 361 table 2: the characteristical spiculation vectors for a malignant lesion and for a benign one for method c and method d general training and testing algorithm of the neural module is given below: i. having a selection of mammographies for which the diagnostic output is known, i.e. the diagnosis (benign or malignant), ii. the spiculation set for each of the mammographies is extracted with one of the a, b, c, or d methods described above, for a number of neighborhoods, iii. both the input (the spiculation data set) and also the output (the diagnostic) are normalized, iv. based on the normalized data, the neural network is being trained and validated for the predicton of the output (the diagnostic), v. the predicted output is compared with the actual output (truth values of malignancy) by calculating performance measures like: specificity (i.e. number of true negatives divided by the sum of true negatives and false positives), sensitivity (i.e. number of true positives divided by the sum of true positives and false negatives), accuracy (i.e. sum of true positives and true negatives divided by sum of true positives, false positives, true negatives and false negatives) and the matthews correlation coefficient (mcc explained below). the training, the testing and the validation of the neural modules are based on the information of 96 mammographic cases acquired from the department of medical physics, university of freestate, bloemfontain, south africa, of which 48 cases are benign and 48 are malignant, [14]. the practical experience has focused on the analysis of several selections of features in order to determine the training and classification performances of the neural networks. rapid convergence of the training error and the classification and prediction power of the neural network indicate the correlation strength/weight between inputs and outputs, directly justifying the existence or the absence of non-linear correlations between them, in this case evaluated by matthews correlation coefficient (mcc) belonging to the interval [-1, +1], +1 representing a perfect correlation, 0 an arbitrary correlation and -1 inverse correlation. 362 v. bălănică, i. dumitrache, l. preziosi because the quality of the spiculation characteristics introduced in the previous section may vary depending on the number of neighborhoods set for segmentation of the lesions contour, the tests include also the variation of this value. simultaneously, the regions of interest (roi) processed and used in evaluating the imaging characteristics have the dimension of 335x436 pixels, an increase of the size being possible to the expense of the time needed for extracting data and for running the tests. on average, 5 to 8 neural networks were trained for the same set of features in order to observe and determine the recorded performance variation. the performed tests (see table 3) are not exhaustive, the results being obtained based on the currently available data sets. 5 discussion of results the performance results presented in table 3 give a clear indication about the high potential of the introduced spiculation-feature extraction methods in what concerns the differentiation between benign and malignant tumors when using a neural classifier. however, as shown in table 4, the developed methods differ in terms of power and speed of convergence of the classifications and provide a different classification stability when changing the number of analyzed lesion contour neighborhoods. thus, the method that calculates the maximum level difference on each region of the contour offers the optimal method for extracting the spiculation feature and the algorithm that calculates the total quadratic curvature for each region offers the most reliable measure of the lesion malignancy regardless of the neighborhoods number. 6 conclusions this paper introduces four new methods for the assessment of the tumor contour on a number of boundary neighborhoods. the maximum level difference is calculated for every region of the analyzed contour, relative to the center of gravity of the lesion. the total area of triangles in a neighborhood is calculated by adding the areas of each triangle formed by three consecutive points along the contour. the total angle for a neighborhood is calculated by adding up all the angles formed by every three consecutive points along the contour. the total quadratic curvature for each region is calculated by adding the curvatures of the circles passing through each three consecutive points along the contour. the spiculation feature extraction algoritms are tested on a selection of mammographies with known diagnostic by using a neural network that is trained on the extracted feature sets. following the performance tests, the spiculation feature sets computed with all of the four methods show a high potential in the differentiation of benign and malignant tumors and can be used to compute an objective measure/degree of spicularity for the analysed lesion. moreover, these methods are suitable for training a neural classifier of malignant or benign patterns of spiculs that is able to make assessments in the presence of a new set of extracted mammographic spiculation features. however, as shown in the pages of the paper, the developed methods differ in terms of power and speed of convergence of the classifications and provide a different classification stability when changing the number of neighborhoods the lesion contour is segmented and analyzed. these cad extraction methods show high reliability and prove high implementation potential being currently used in an automated decision support systems for breast cancer designed for national mammography screening programs in romania and south africa. breast cancer diagnosis based on spiculation feature and neural network techniques 363 id no. of neighborhoods inputs of the neural network outputs of the neural network malignity classification performances mcc correlation 1 5 seta =5 inputs diagnostic sensitivity: [ 97 100 %] specificity: [ 100 98.1 %] accuracy: [ 98.9 %] for 50-200 training iterations 0.97 2 5 setb =5 inputs diagnostic sensitivity: [ 86.3 100 %] specificity: [ 96.2 83 %] accuracy: [ 91.7 90.7 %] for 100-300 training iterations 0.83 3 5 setc =5 inputs diagnostic sensitivity: [ 90.9 93.1 %] specificity: [ 90.5 98.1 %] accuracy: [ 90.7 95.8 %] for 100-300 training iterations 0.86 4 5 setd =5 inputs diagnostic sensitivity: [97.7 100 %] specificity: [ 98.1 98.1 %] accuracy: [ 97.9 98.9 %] for 50-100 training iterations 0.98 5 5 the best sets: seta,setb =10 inputs diagnostic sensitivity: [ 100 %] specificity: [ 100 %] accuracy: [ 100 %] for 20-40 training iterations 1 6 5 seta,setb, setc,setd =20 inputs diagnostic sensitivity: [87.7 91.6 %] specificity: [ 97.9 100 %] accuracy: [ 92.7 95.8 %] for 100-300 training iterations 0.90 7 20 seta =20 inputs diagnostic sensitivity: [ 100 %] specificity: [ 100 %] accuracy: [ 100 %] for 20-30 training iterations 1 8 20 setb =20 inputs diagnostic sensitivity: [ 100 %] specificity: [ 100 %] accuracy: [ 100 %] for 50-150 training iterations 1 9 20 setc =20 inputs diagnostic sensitivity: [ 100 %] specificity: [ 100 %] accuracy: [ 100 %] for 4-10 training iterations 1 10 20 setd =20 inputs diagnostic sensitivity: [ 100 %] specificity: [ 100 %] accuracy: [ 100 %] for 150-200 training iterations 1 11 20 the best sets: seta,setc =40 inputs diagnostic sensitivity: [ 100 %] specificity: [ 100 %] accuracy: [ 100 %] for 3 training iterations 1 12 20 seta,setb, setc,setd =80 inputs diagnostic sensitivity: [ 100 %] specificity: [ 100 %] accuracy: [ 100 %] for 3-4 training iterations 1 table 3: the trained neural networks and the achieved performances 364 v. bălănică, i. dumitrache, l. preziosi set id description of the spiculations feature set observed features description seta spiculation set for the a algorithm: the maximum level difference for every region of the contour average convergence and avarage stability regardless of the neighborhoods number, the classification performance converges with average speed, and the obtain results vary very little. setb spiculation set for the b algorithm: the total area of triangles in a neighborhood slow convergence and low stability regardless of the neighborhoods number, the classification performance converges with low speed. for a small number of regions, the results are much weaker compared with those obtained for a higher number of regions. setc spiculation set for the c algorithm: the total angle in a neighborhood fast convergence and low stability regardless of the number of neighborhood, the classification performance converges with high speed. for a small number of regions, the results are poor compared to those obtained for a larger number of regions. setd spiculation set for the d algorithm: the total quadratic curvature for a region slow convergence but very high stability regardless of neighborhoods number, the classification performance converges with low speed, but the results are always powerful and do not vary. table 4: the efficiency of the spiculation feature extraction methods for a lesion breast cancer diagnosis based on spiculation feature and neural network techniques 365 bibliography [1] sultana, alina (2010); on improving image-based diagnosis using digital image processing, faculty of electronics, telecommunications and information technology, bucharest. 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(2009); integration of image and patient data, software and international coding systems for use in a mammography research project, world academy of science, engineering and technology, 58:1002-1005. qu_ijcccv11n5.pdf international journal of computers communications & control issn 1841-9836, 11(5):708-719, october 2016. fuzzy h2 guaranteed cost sampled-data control of nonlinear time-varying delay systems z.-f. qu, z.-b. du zifang qu shandong institute of business and technology yantai, shandong, 264005, china quzifang@163.com zhenbin du* yantai university yantai, shandong, 264005, china *corresponding author: zhenbindu@126.com abstract: we present and study a delay-dependent fuzzy h2 guaranteed cost sampled-data control problem for nonlinear time-varying delay systems, which is formed by fuzzy takagi-sugeno (t-s) system and a sampled-data fuzzy controller connected in a closed loop. applying the input delay approach and stability theorem of lyapunov-krasovskii functional with leibniz-newton formula, the h2 guaranteed cost control performance is achieved in the sense that the closed-loop system is asymptotically stable. a new sufficient condition for the existence of fuzzy sampled-data controller is given in terms of linear matrix inequalities (lmis). truck-trailer system is given to illustrate the effectiveness and feasibility of h2 guaranteed cost sampleddata control design. keywords: fuzzy t-s system; sampled-data; nonlinear systems; time-varying delay; h2 guaranteed cost control 1 introduction fuzzy takagi–sugeno(t-s)models [1] are used to describe nonlinear systems by a set of if– then rules which gives a local linear representation. since the work of tanaka and sugeno [2] on stability analysis and stabilization being published, many efforts have been made in developing systematic theory for such systems. because of the fast development of the digital circuit technology, using computers to design controller to reduce the implementation cost and time is more and more popular. the system of control is a sampled-data system. in sampling period,its control signals are constant. the overall control system becomes a sampled-data system, where the control signals are kept constant during the sampling period. it’s a popular trend to study the analysis and synthesis of fuzzy sampleddata systems in many papers, see, for instance, [3–12] and the references therein. of these works, stability analysis [3], stabilization [11], h∞ control [4,6,7,9], h2 gc control [8,10], fault-tolerant control [12] and tracking control [5] are researched, respectively. stability and robust stability theory was adopted in sampled-data time-delay systems [3,4, 7, 9]. in industrial systems and information networks, it’s popular to use time-delay systems. so, we should study time-delay systems and design some controllers for them. there have two ways for the stability analysis and synthesis of time-delay fuzzy t–s systems, i.e. delayindependent and delay-dependent approaches. with no respective of the size of the delay, we use delay-independent approach to assure stable conditions. the delay-dependent approach, contrast with the delay-independent approach, is complex in design procedure. so, it always have more conservative results. the delay-dependent approach supplies an upper bound of the time-delay. copyright © 2006-2016 by ccc publications 710 z.-f. qu, z.-b. du it deals with the size of the time-delay, as a consequence, it usually provides less conservative results. among these works [3, 4, 7, 9], [7] is delay-independent and [3, 4, 9] are delay-dependent, where time delay is assumed to be constant. however, in practical engineering systems, the occurrence of time delay phenomena is often time-varying. thus, fuzzy sampled-data control for time-varying delay systems is more appealing. in fuzzy sampled-datacontrol, there is no report abouth2 guaranteed cost controlproblem for the nonlinear time-varying delay systems. in this paper, we consider the delay-dependent sampled-data h2 guaranteed costperformance problem of the nonlinear time-varying delay system represented by a fuzzy t-s model. a lyapunov-krasovskii functional with leibniz-newton formula is employed to obtain new sufficient conditions in terms of linear matrix inequalities (lmis) to the fuzzy h2 guaranteed cost control performance. based on the stability condition, the guaranteed cost control is minimized for the closed-loop system. we use truck-trailer system to prove the effectiveness and the feasibility of the proposed method. the main contributions and advantages of the present paper are summarized as follows: (i) the h2 design via fuzzy sampled-data control for nonlinear systems with time-varying delay is first obtained. (ii) fuzzy sampled-data control algorithm is less conservative. comparing with the existing works, the dimension of the lmis in this paper is simplified, which adds the existence of feedback gains and lowers the implementation time. experimental results illustrate that the fuzzy sampled-data controller has a larger sampling interval. notations: throughout this paper, if not explicitly stated, we assumed that matrices have compatible dimensions. the notation p>0(< 0) is used to denote a positive (negative) definite matrix. the transpose of a matrix p is denoted by p t . the symbol ∗ stands for the transposed element in symmetric positions. 2 problem formulation consider the following nonlinear time-varying delay system: ẋ(t) = f(x(t), x(t − d(t)), u(t)), (1) where x(t) ∈ rn is the state vector, u(t) ∈ rm is the control input vector, f is a nonlinear function, and d(t) is time-varying delay. the following fuzzy t-s model with time-varying delay described by if–then rules is used to represent nonlinear time-varying delay system: if ξ1(t) is mi1 and · · · and ξp(t) is mip, then ẋ(t) = aix(t) + aidx(t − d(t)) + biu(t), i = 1, · · · , l, (2) where ai, bi and aid are constant matrices of appropriate dimensions. l is the number of if– then rules, mij are fuzzy sets and ξ1, . . . , ξp are premise variables, ξ(t) = [ξ1 . . . ξp] t , and ξ(t) is assumed to be given or a measurable function vector. we consider the following two cases for the time-varying delay. case 1: d(t) is a differentiable function satisfying for all t ≥ 0: 0 ≤ d(t) ≤ dm and ḋ(t) ≤ dd, where dm and dd are constants. fuzzy h2 guaranteed cost sampled-data control of nonlinear time-varying delay systems 711 case 2: d(t) is a continuous function satisfying for all t ≥ 0: 0 ≤ d(t) ≤ dm, where dm is a constant. by fuzzy blending, the overall fuzzy model is inferred as follows: ẋ(t) = l ∑ i=1 λi(ξ(t))[aix(t) + aidx(t − d(t)) + biu(t)], (3) where λi(ξ(t)) = βi(ξ(t)) ∑ l i=1 βi(ξ(t)) , βi(ξ(t)) = p ∏ j=1 mij(ξj(t)) and mij(.) is the grade of the membership function of mij. βi(ξ(t)) ≥ 0, i = 1, 2, . . . , l, l ∑ i=1 βi(ξ(t)) > 0 for any ξ(t), λi(ξ(t)) ≥ 0, i = 1, 2, . . . , l, l ∑ i=1 λi(ξ(t)) = 1. we design the following fuzzy sampled-data controller for (3): if ξ1(tk) is mj1 and · · · and ξp(tk) is mjp, then u(t) = kjx(tk), j = 1, 2, . . . , l, where kj is the sate feedback gain, the time tk is the sampling instant satisfying 0 < t1 < t2 < · · · < tk < · · · , and sampling interval is a constant, i.e. tk+1 − tk = hk = h. the overall fuzzy sampled-data controller is as follows: u(t) = l ∑ j=1 λj(ξ(tk))kjx(tk). (4) by using input delay approach, (4) is equivalent to (5) u(t) = l ∑ j=1 λj(ξ(tk))kjx(t − τ(t)). (5) the closed-loop system (3) with (5) is given by ẋ(t) = l ∑ i=1 l ∑ j=1 λi(ξ(t))λj(ξ(tk))[aix(t) + aidx(t − d(t)) + bikjx(t − τ(t))]. (6) the following h2 guaranteed costcontrol performance j = ∫ ∞ 0 (xt (t)qx(t) + ut (t)ru(t))dt. (7) must be minimized, where the weighting positive-definite matrices q and r are specified beforehand according to the design purpose. determine a sampled-data state feedback controller such that the closed-loop system (6) is asymptotically stable and the upper bound of h2 guaranteed cost function is minimized. lemma 2.1 (gu et al. [13]). for any positive definite symmetric constant matrix m ∈ rn×n, scalars r1, r2 satisfying r1 ≤ r2, if ̟ : [r1, r2] → r nis a vector function such that the integrations concerned are well defined, then ( ∫ r2 r1 ̟(s)ds )t m ( ∫ r2 r1 ̟(s)ds ) ≤ (r2 − r1) ∫ r2 r1 ̟t (s)m̟(s)ds. (8) 712 z.-f. qu, z.-b. du remark 1: the premise variables ξ 1 , . . . , ξ p can be function of measurable state variables x(t) and x(t − d), or combination of measurable state variables. the limitation of design of fuzzy t–s approach is that some state variables must be measurable to construct fuzzy controller. remark 2: it should be noted that the control signal u(t) holds constant during the period of tk ≤ t ≤ tk+1. 3 fuzzy h2 guaranteed cost sampled-data control in this section, we present a h2 guaranteed cost sampled-data control scheme of the fuzzy system and minimization of the upper bound of (7). here, we give some sufficient conditions for the stability of the closed-loop system (6) in terms of lmis. theorem 1. suppose that, under case 1, for given matrices q > 0, r > 0, scalars h > 0, dm > 0, dd > 0, µ > 0,there exist matrices p > 0, r1 > 0, r2 > 0, r3 > 0, such that the following lmis hold for all i, j = 1, 2, · · ·, l, σij =             σij11 σij12 σij13 0 σij15 σij16 ∗ σij22 0 0 0 0 ∗ ∗ σij33 σij34 σij35 0 ∗ ∗ ∗ σij44 0 0 ∗ ∗ ∗ ∗ σij55 σij56 ∗ ∗ ∗ ∗ ∗ σij66             < 0, (9) where σij11 = aip + pa t i + r1 − r2 − r3, σij12 = p, σij13 = bikj + r2, σij15 = µpa t i , σij16 = aidp + r3, σij22 = −q −1, σij33 = −r2, σij34 = k t j , σij35 = µk t j b t i , σij44 = −r −1, σij55 = −2µp + h 2r2 + d 2 mr3, σij56 = aidp, σij66 = −(1 − dd)r1 − r3. then there exists a sampled-data controller (4) with kj = kjp −1 (j = 1, 2, · · ·, l) such that h2 guaranteed cost control performance (7) is minimized in the sense that the closed-loop system (6) is asymptotically stable. proof. choose the lyapunov-krasovskii functional: v (xt) = v1(x) + v2(xt) + v3(xt) + v4(xt), (10) where v1(x) = x t (t)px(t), v2(xt) = ∫ t t−d(t) xt (s)r1x(s)ds, v3(xt) = h ∫ 0 −h ∫ t t+θ ẋt (s)r2ẋ(s)dsdθ, v4(xt) = dm ∫ 0 −dm ∫ t t+θ ẋt (s)r3ẋ(s)dsdθ and p > 0, r1 > 0, r2 > 0,r3 > 0. the derivative of v along the trajectories of the system (6) is computed as follows: fuzzy h2 guaranteed cost sampled-data control of nonlinear time-varying delay systems 713 v̇1(x) = ẋ t (t)px(t) + xt (t)pẋ(t) = l ∑ i=1 l ∑ j=1 λi(ξ(t))λj(ξ(tk))[x t (t)ati px(t) + x t (t − d(t))atidpx(t) +xt (t − τ(t))ktj b t i px(t) + x t (t)paix(t) + xt (t)paidx(t − d(t)) +xt (t)pbikjx(t − τ(t)). (11) v̇2(xt) = x t (t)r1x(t) − (1 − ḋ(t))x t (t − d(t))r1x(t − d(t)) ≤ xt (t)r1x(t) − (1 − dd)x t (t − d(t))r1x(t − d(t)). (12) by using lemma 2.1, we have −h ∫ t t−h ẋt (s)r2ẋ(s)ds ≤ −τ(t) ∫ t t−τ(t) ẋt (s)r2ẋ(s)ds ≤ − ( ∫ t t−τ(t) ẋ(s)ds )t r2 ( ∫ t t−τ(t) ẋ(s)ds ) . (13) leibniz-newton formula is ∫ t t−h ẋ(s)ds = x(t) − x(t − h). (14) applying (13) and leibniz-newton formula, we have v̇3(xt) = h 2ẋt (t)r2ẋ(t) − h ∫ t t−h ẋt (s)r2ẋ(s)ds ≤ h2ẋ(t)t r2ẋ(t) − (x(t) − x(t − τ(t))) t r2(x(t) − x(t − τ(t))) t = h2ẋt (t)r2ẋ(t) − x t (t)r2x(t) + x t (t − τ(t))r2x(t) + x t (t)r2x(t − τ(t)) −xt (t − τ(t))r2x(t − τ(t)). (15) similarly, by lemma 2.1 and leibniz-newton formula, we have v̇4(xt) ≤ d 2 mẋ t (t)r3ẋ(t) − x t (t)r3x(t) + x t (t − d(t))r3x(t) +xt (t)r3x(t − d(t)) − x t (t − d(t))r3x(t − d(t)). (16) from (6), for a given µ > 0, 0 = −2µẋt (t)pẋ(t) + µẋt (t)p{ l ∑ i=1 l ∑ j=1 λi(ξ(t))λj(ξ(tk))[aix(t) + aidx(t − d(t)) +bikjx(t − τ(t))]} + µ{ l ∑ i=1 l ∑ j=1 λi(ξ(t))λj(ξ(tk))[aix(t) + aidx(t − d(t)) +bikjx(t − τ(t))]} t pẋ(t) = −2µẋt (t)pẋ(t) + l ∑ i=1 l ∑ j=1 λi(ξ(t))λj(ξ(tk))[µẋ t (t)paix(t) +µẋt (t)paidx(t − d(t)) + uẋ t (t)pbikjx(t − τ(t)) + uxt (t)ait pẋ(t) +µxt (t − d(t))atidpẋ(t) + µx t (t − τ(t))ktj b t i pẋ(t)]. (17) 714 z.-f. qu, z.-b. du from (11-12) and (15-17), we obtain v̇ (xt) + x t (t)qx(t) + ut (t)ru(t) ≤ l ∑ i=1 l ∑ j=1 λi(ξ(t))λj(ξ(tk))x̃ t (t)s′ijx̃(t) (18) where x̃(t) = [ xt (t) xt (t − τ(t)) ẋt (t) xt (t − d(t) ] t , s′ij =       s′ij11 s′ij12 s′ij13 s′ij14 ∗ s′ij22 s′ij23 0 ∗ ∗ s′ij33 s′ij34 ∗ ∗ ∗ s′ij44       (19) with s′ij11 = a t i p + pai + r1 − r2 − r3 + q, s′ij12 = pbikj + r2, s′ij13 = µa t i p, s′ij14 = paid+r3, s′ij22 = −r2 + k t j rkj, s′ij23 = µk t j b t i p, s′ij33 = −2µp + h 2r2 + d 2 mr3, s′ij34 = paid, s′ij44 = −(1 − dd)r1 − r3. pre-andpost-multiplying thematrix s′ij in (19)by diag [ p−1 p−1 p−1 p−1 ] withp = p−1,r1 = p −1r1p −1,r2 = p −1r2p −1,r3 = p −1r3p −1,q̄ = p−1qp−1,k̄j = kjp −1 (j = 1, 2, · · ·, l), we have sij =        ∑ ij11 +q̄ ∑ ij13 ∑ ij15 ∑ ij16 ∗ ∑ ij33 +k̄ t j rk̄j ∑ ij35 0 ∗ ∗ ∑ ij55 ∑ ij56 ∗ ∗ ∗ ∑ ij66        . (20) if (9) is satisfied, then σij < 0 is equivalent to sij < 0 in (20) by using the schur complement. and, sij < 0 in (20) is equivalent to s′ij < 0 in (19). thus, v̇ (xt) + x t (t)qx(t) + ut (t)ru(t) < 0. (21) integrating both sides of (21) from t= 0 tot = ∞, we obtain v (xt(∞)) − v (xt(0)) + ∫ ∞ 0 (xt (t)qx(t) + ut (t)ru(t))dt < 0. (22) thus, we have j < v (xt(0)) = x t (0)px(0). (23) now, we provide a stability condition for the fuzzy t–s system (6) under case 2. theorem 2. suppose that, under case 2, for given matrices q > 0, r > 0, scalars h > 0, dm > 0 , µ > 0, there exist matrices p > 0, r1 > 0, r2 > 0such that the following lmis hold fuzzy h2 guaranteed cost sampled-data control of nonlinear time-varying delay systems 715 for all i, j = 1, 2, · · ·, l σ̄ij =             σ̄ij11 σ̄ij12 σ̄ij13 0 σ̄ij15 σ̄ij16 ∗ σ̄ij22 0 0 0 0 ∗ ∗ σ̄ij33 σ̄ij34 σ̄ij35 0 ∗ ∗ ∗ σ̄ij44 0 0 ∗ ∗ ∗ ∗ σ̄ij55 σ̄ij56 ∗ ∗ ∗ ∗ ∗ σ̄ij66             < 0, (24) where σ̄ij11 = aip + pa t i − r1 − r2, σ̄ij12 = p, σ̄ij13 = bikj + r1, σ̄ij15 = µpa t i , σ̄ij16 = aidp + r2, σ̄ij22 = −q −1, σ̄ij33 = −r1, σ̄ij34 = k t j , σ̄ij35 = µk t j b t i , σ̄ij44 = −r −1, σ̄ij55 = −2µp + h 2r1 + d 2 mr2, σ̄ij56 = aidp, σ̄ij66 = −r2. then there exists a sampled-data controller (4) with kj = kjp −1 (j = 1, 2, · · ·, l) such that h2 guaranteed cost control performance (7) is minimized in the sense that the closed-loop system (6) is asymptotically stable. proof. choose the following lyapunov-krasovskii functional: v (xt) = v1(x) + v2(xt) + v3(xt), (25) where v1(x) = x t (t)px(t), v2(xt) = h ∫ 0 −h ∫ t t+θ ẋt (s)r1ẋ(s)dsdθ, v3(xt) = dm ∫ 0 −dm ∫ t t+θ ẋt (s)r2ẋ(s)dsdθ and p > 0, r1 > 0, r2 > 0 are to be determined. then following the similar line in theorem 1, we can obtain theorem 2. if there is no time delay, then we have the following corollary 1. corollary 1. suppose that, for given matrices q > 0, r > 0, scalars h > 0, µ > 0, there exist matrices p > 0, r1 > 0, such that the following lmis hold for all i, j = 1, 2, · · ·, l ¯̄σij =          ¯̄σij11 ¯̄σij12 ¯̄σij13 0 ¯̄σij15 ∗ ¯̄σij22 0 0 0 ∗ ∗ ¯̄σij33 ¯̄σij34 ¯̄σij35 ∗ ∗ ∗ ¯̄σij44 0 ∗ ∗ ∗ ∗ ¯̄σij55          < 0, (26) where ¯̄σij11 = aip + pa t i − r1, ¯̄σij12 = p, ¯̄σij13 = bikj + r1, ¯̄σij15 = µpa t i , ¯̄σij22 = −q −1, ¯̄σij33 = −r1, ¯̄σij34 = k t j , ¯̄σij35 = µk t j b t i , ¯̄σij44 = −r −1, ¯̄σij55 = −2µp + h 2r1. then there exists a sampled-data controller (4) with kj = kjp −1 (j = 1, 2, · · ·, l) such that h2 guaranteed cost control performance (7) is minimized in the sense that the closed-loop system (6) is asymptotically stable. 716 z.-f. qu, z.-b. du in the following, we give the design procedure of fuzzy sampled-data controller. the h2 guaranteed cost sampled-data fuzzy control problem can be formulated as the following optimization problem: min p̄ trace(j) s.t.(9)and [ j xt (0) ∗ p̄ ] > 0. (27) design procedure: the delay-dependenth2 guaranteed cost sampled-datacontrol for fuzzy time-varying delay system is summarized as follows. step 1: select membership functions and fuzzy rules in (1). step 2: give the upper bound of sampling interval h > 0 and a scalar µ > 0. step 3: solve the lmis (27) to obtain kj(j = 1, 2, · · ·, l) and p .thus,kj = kjp −1 (j = 1, 2, · · ·, l) can also be obtained. step 4: increaseh, and repeat step 3 until kj(j = 1, 2, · · ·, l) and p can not be found. step5: confirmfuzzy h2 guaranteedcostsampled-datacontrol and stability of the closed-loop system, substitute p ,kj(j = 1, 2, · · ·, l), µ and h into (19) and verify sij < 0. step 6: construct the fuzzy sampled-data controller (4). 4 simulation example to test the effectiveness and feasibility of the proposed method, we consider the following truck-trailer system [14] ẋ1(t) = −a vt lt0 x1(t) − (1 − a) vt lt0 x1(t − td) + vt lt0 u(t) ẋ2(t) = a vt lt0 x1(t) + (1 − a) vt lt0 x1(t − td) ẋ3(t) = vt lt0 sin(x2(t) + a(vt/2l)x1(t) + (1 − a)(vt/2l)x1(t − td)), (28) where l = 2.8 l = 5.5, v = −1.0, a = 0.7, t̄ = 2.0, t0 = 0.5. x1(t) ∈ [−π/2, π/2], ẋ1(t) ∈ [−3, 3], x2(t) ∈ [−π/2, π/2], ẋ2(t) ∈ [−2, 2].x(t) = [x1(t) x2(t) x3(t)] t , [x1(0) x2(0) x3(0)] = [ 1.5 −2 5 ] . the nonlinear truck-trailer system is modeled by two-rule fuzzy t-s system. rule 1: if θ(t) = x2(t) + a(vt/2l)x1(t) + (1 − a)(vt/2l)x1(t − td) is about 0, thenẋ(t) = a1x(t) + ad1x(t − τd) + b1u(t). (29) rule 2: if θ(t) = x2(t) + a(vt/2l)x1(t) + (1 − a)(vt/2l)x1(t − td) is about π or −π, thenẋ(t) = a2x(t) + ad2x(t − τd) + b2u(t). (30) where a1 =     −a vt lt0 0 0 a vt lt0 0 0 a v 2t 2 2lt0 vt t0 0     , ad1 =     −(1 − a) vt lt0 0 0 (1 − a) vt lt0 0 0 (1 − a) v 2t 2 2lt0 0 0     , b1 =     vt lt0 0 0     , fuzzy h2 guaranteed cost sampled-data control of nonlinear time-varying delay systems 717 a2 =     −a vt lt0 0 0 a vt lt0 0 0 adv 2t 2 2lt0 dvt t0 0     , ad2 =     −(1 − a) vt lt0 0 0 (1 − a) vt lt0 0 0 (1 − a)dv 2t 2 2lt0 0 0     , b2 =     vt lt0 0 0     , and d = 10t0/π. the membership functions are defined as λ1(θ(t)) = ( 1 − 1 1 + exp(−3(θ(t) − 0.5π)) ) × ( 1 1 + exp(−3(θ(t) + 0.5π)) ) , λ2(θ(t)) = 1 − λ1(θ(t)). a two-rule sampled-data fuzzy controller is employed to stabilize the truck trailer system. the sampled-data fuzzy controller is designed as follows: u(t) = 2 ∑ j=1 λj(θ(tk))kjx(tk). first, we assume that time delay d(t) = 0. applying various methods of [8] ( h2 control), [10] ( h2 control) and corollary 1, the dimensions of the lmis are given in table 1. it is seen from table 1 that the dimension of the lmis is greatly simplified in the proposed method of this paper. table 1: the comparison for the dimensions of lmis (corolarry 1) method [8] [10] corollary 1 dimension 25 28 13 next, we assume that the delay is time-invariant, i.e. dd = 0. by using various the methods of [3](h2 control) and theorem2, the dimensions of the lmis are given in table 2. it is seen from table 2 that the dimension of the lmis is simplified in the proposed method of this paper, which adds the existence of feedback gains and lowers the implementation time. table 2: the comparison for the dimensions of lmis (theorem 2) method [3] theorem 2 dimension 20 16 by using various methods of [3] and theorem 2, the maximum allowable upper bounds of sampling interval are given in table 3, which show that theorem 2 of this paper can get a larger sampling interval. this implies that the proposed method achieves a better performance. table 3: the maximum allowable upper bounds of sampling interval method [3] theorem 2 hmax(td = 0.5) 0.374 0.562 hmax(td = 1) 0.315 0.471 hmax(td = 2) 0.251 0.283 finally, we consider the control design for time-varying delay td = 1 + sin t. the maximum allowable upper bound of sampling interval that is obtained by theorem 1 is 0.295.when the design parameters are given by µ = 1 , dm = 2 , dd = 1 with the sampling interval h = 0.295,theorem 1 gives the fuzzy state feedback control gains k1 = [1.0319 -0.1019 0.0009] , k2 = [1.0319 -0.1019 0.0009] . 718 z.-f. qu, z.-b. du figure 1: state response x1 figure 2: state response x2 figure 3: state response x3 figure 4: state response x4 fuzzy h2 guaranteed cost sampled-data control of nonlinear time-varying delay systems 719 when time-varying delay td is 1.5 + 1.5 sin t, theorem 1 gives the maximum allowable upper bound of sampling interval 0.172.with the design parameters h = 0.172 , µ = 1.5 , dm = 3 , dd = 1.5 , q = diag{ 1 10 0.1 }× 10 −6,r = 10−5, theorem 1 gives the fuzzy state feedback control gains k1 = [0.9656 -0.0657 0.0006] , k2 = [0.9656 -0.0657 0.0006] . the sampled-data fuzzy controller with the above control gains is applied to the truck trailer system, the results on the state responsesx1, x2, x3 and control lawuare shown in figures 14. simulation results illustrate the fuzzy h2 guaranteed cost sampled-data control design is effective and feasible. figs. 1-3 show the system stability, and fig.4 shows the sampled-data control signal for the system (28). 5 conclusion this work considers the fuzzyh2 gc sampled-data control problem for nonlinear systems with time-varying delay. it should be pointed that this problem is more complicated and harder to deal with due to the coexistence of feedback delay and sampled-data control. a new sufficient condition for the existence of fuzzy sampled-data controller is given in terms of lmis. to better demonstrate our results, a truck-trailer system with sampled-data control is given. simulation results show the effectiveness and feasibility of sampled-data control design. furthermore, this method could be extended to h∞ control. acknowledgment this work was supported by the national natural science foundation of china(61203320) and shandong natural science foundation (zr2014fl023). bibliography [1] t. takagi; m. sugeno. 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(2001); stability analysis and synthesis of nonlinear time-delay systems via linear takagi-sugeno fuzzy models, fuzzy sets and systems, issn 0165-0114, 124(2):213-229. international journal of computers communications & control issn 1841-9836, 10(1):89-99, february, 2015. an approximate algorithm combining p systems and active evolutionary algorithms for traveling salesman problems x. song, j. wang xiaoxiao song* school of electrical and information engineering xihua university, chengdu, sichuan, p.r. china, 610039 *corresponding author: sxx_pippen@163.com jun wang school of electrical and information engineering xihua university, chengdu, sichuan, p.r. china, 610039 745257101@qq.com abstract: an approximate algorithm combining p systems and active evolutionary algorithms (aeaps) to solve traveling salesman problems (tsps) is proposed in this paper. the novel algorithm uses the same membrane structure, subalgorithms and transporting mechanisms as nishida’s algorithm, but adopts two classes of active evolution operators and a good initial solution generating method. computer experiments show that the aeaps produces better solutions than nishida’s shrink membrane algorithm and similar solutions with an approximate optimization algorithm integrating p systems and ant colony optimization techniques (acops) in solving tsps. but the necessary number of iterations using aeaps is less than both of them. keywords: p systems, active evolutionary algorithms, traveling salesman problems. 1 introduction membrane computing, a milestone in natural computing, was introduced by gheorghe pǎun [1] in 1998. this computational model, which was inspired by the structure and the behavior of living cells, was proposed. in the following more than ten years, a sizeable group of researchers were seduced by membrane computing. membrane algorithm is one of the research hotspots after nishida [2–4] first proposed this concept by combining p systems and meta-heuristic search methodologies. huang [5, 6] and cheng [7] combined genetic algorithm and differential evolution with membrane systems to solve some singleand multi-objective optimization problems. quantum-inspired evolutionary algorithm based on p systems was proposed to solve some classical theoretical [8,9] and practical problems [10–17]. also some novel membrane algorithms based on particle swarm optimization [18] and artificial fish swarm algorithm [19] were proposed. evolutionary algorithm is based on survival of the fittest. creatures do not have ability to decide their mutation directions and choose advantageous gene to their offspring. but based on the researches in biology, this stochastic evolution theory could not explain some problems in creature’s adaptability. the modern biology research shows that the evolution process is not completely stochastic [20–22]. so called stress-induced mutation mechanisms were proposed. specifically, when creatures are maladapted to their environment, that is, when they are stressed, stress-induced mutation mechanisms produce mutations [23–26]. these facts have been considered in evolutionary algorithm for solving optimization problems [27]. in this paper an approximate algorithm combining p systems and active evolutionary algorithms (aeaps) is proposed in order to solve traveling salesman problems (tsps) in the special case of complete graphs with euclidean distance. it follows the nested membrane structure adopted by nishida [2], and adopts genetic algorithm (ga), tabu search and active evolutionary algorithms (aea) as the subalgorithms. experiment results are compared with nishida’s copyright © 2006-2015 by ccc publications 90 x. song, j. wang figure 1: the membrane structure of a cell-like p system algorithm and an approximate optimization algorithm integrating p systems and ant colony optimization techniques (acops) [28, 29]. in section 2, a brief introduction of p systems and nishida’s membrane algorithm for tsps is given. details of aea designed for tsps and aeaps is discussed in section 3. the experiment results and analysis are mentioned in section 4. conclusions are drawn in section 5. 2 p systems and membrane algorithm 2.1 p systems p systems could be divided into three groups: cell-like p systems, tissue-like p systems and neural-like p systems [30]. the structure of cell-like p systems is the basic structure of other p systems. the membrane structure of a cell-like p system is shown in fig. 1. the outermost membrane is the skin membrane. outside of the skin membrane is the environment. usually, there are some other membranes inside the skin membrane. we call the spaces between membranes regions. the region just inside the skin membrane is the outermost region, and the region in an elementary membrane is an elementary region. in membrane computing, regions contain multisets of objects and sets of evolution rules. a cell-like p system is formally defined as follows [1, 31]: π = [v,t,µ,w1, . . . ,wm,r1, . . . ,rm, i0] . (1) where: (i) v is an alphabet; its elements are called objects; (ii) t ⊆ v is the output alphabet; (iii) µ is a membrane structure consisting of m membranes; m is called the degree of π; (iv) wi, 1 ≤ i ≤ m, is a string representing the initial multiset over v associated with region i, 1 ≤ i ≤ m; (v) ri, 1 ≤ i ≤ m, is a finite set of evolution rules associated with region i, 1 ≤ i ≤ m; (vi) i0 is a number between 1 and m which specifies the output membrane of π. the rules of ri, 1 ≤ i ≤ m, have the form a → v, where a ∈ v and v ∈ (v ×{here,out,in})∗. the multiset v consists of pairs (b,t), b ∈ v and t ∈ {here,out,in}. here means when the rule is used in one region, b will stay in the region; out means that b exits the region and in means that b will be communicated to one of the membranes contained in the current region. an approximate algorithm combining p systems and active evolutionary algorithms for traveling salesman problems 91 figure 2: membrane structure of membrane algorithm 2.2 the membrane algorithm for tsps membrane algorithm is designed with the hierarchical or network structure of membranes and rules of p systems, and the concepts and principles of meta-heuristic search methodologies. it is a new kind of parallel-distributed framework for solving optimization problems. nishida first proposed a membrane algorithm using cell-like p systems (nested membrane structure) [2] to solve tsps. nishida also proposed some improved membrane algorithms based on tissue-like p systems, such as compound membrane algorithm [3] and shrink membrane algorithm [4]. all the basic concepts of improved algorithms are based on the membrane algorithm. for example, compound membrane algorithm has two phases. the first phase is using membrane algorithm generating good initial solutions for phase 2; and the second phase is also similar to membrane algorithm but using good initial solutions. the shrink membrane algorithm incorporates dynamic membrane structure into compound membrane algorithm. the membrane algorithm with nested membrane structure is a special case of multi-deme evolutionary algorithm [32]. in this paper, we only research membrane algorithm with this structure. in nishida’s membrane algorithm, nested membrane structure, rules in membrane separated regions and transporting mechanisms through membrane from p systems are adopted. the structure of the membrane algorithm is shown in fig. 2. in solving tsps, the membrane algorithm can be described as follows: 1. generate one initial solution in region 0 and two initial solutions in all regions from 1 to m−1 respectively; 2. in one iteration, the solution in region 0 is updated by tabu search and the solutions in regions from 1 to m − 1 are updated by genetic algorithm, simultaneously; 3. regions from 1 to m−2 send the best solution to adjacent inner region, and the worst solution to adjacent outer respectively. region 0 sends the worst solution to region 1 and region m − 1 sends the best solution to region m − 2. 4. erases solutions but the best two in regions from 1 to m − 1 and the best one in region 0. 5. jump to step 2 if the termination condition is not satisfied; otherwise the output of the algorithm is the solution in region 0. 3 aeaps for tsps in active evolution organisms adapt their behaviour to changing environment. in tsps, the “environment" means the structure characteristics of a solution, like without crossings in its path of traveling. if a solution is maladapted to the “environment", some active mutation mechanisms should be considered to improve it. in this section we propose two classes of active evolution conditions and show how to deal with these conditions; then we give a simple method of obtaining good initial solutions; finally, every step of aeaps is described. 92 x. song, j. wang figure 3: triangle inequality for (g,w) 3.1 1st class active evolution condition tsp is one of the well-known combinatorial optimization problems. the tsp problem is about finding the hamilton cycle. i.e., the optimum shortest path of a given weighted undirected and connected graph (g,w) with n nodes and where w is a distance metric. this distance is symmetric, which means w(i,j) = w(j,i). in the two dimensional space, the distance between vertex i and vertex j is w(i,j) = √ (xi − xj)2 + (yi − yj)2. (2) where i and j have the coordinates (xi,yi) and (xj,yj), respectively. the value of one solution is v = n−1∑ i=1 w(i, i + 1) + w(n,1). (3) since w is a distance, then it satisfies the triangle inequality, i.e., w(i,j) ≤ w(i,k) + w(j,k), for any vertices i ̸= j, and k ̸= i,j (see fig. 3). it has been shown that 2-opt iterative improvement method [10, 33, 35] leads to optimum solutions for the tsp without any crossings. this means that any optimal path contains edges of the graph that do not intersect each other. if we consider four nodes. the total graph associated with these four nodes shows convex and concave quadrilaterals. when the convex quadrilateral is considered, any two edges are disjoint (they do not intersect each other). this is no longer true for the concave quadrilateral. in fig. 4, we select a as the starting node, and there can be 6 possible solutions. solutions abcda and adcba have no crossings, and solutions abdca, acbda, acdba and adbca have crossings. one can easily show that the solutions without crossings are better than those with crossings, by referring to the triangle inequality. for example, the path abdca, has two edges, (a,b) and (d,c), overlapping the path abcda ((d,c) = (c,d)). for the path abdca, we have w(b,d) = w(b,o) + w(o,d) and w(c,a) = w(c,o) + w(o,a). according to the triangle inequality, we have w(b,d) + w(c,a) = w(b,o) + w(o,d) + w(c,o) + w(o,a) = w(b,o) + w(c,o) + w(o,d) + w(o,a) ≥ w(b,c) + w(a,d) (4) so we found solution abcda without crossings is better than solution abdca with crossings. with this method, other solutions with crossings can also be transformed into a better ones without crossings. one can conclude stating that for the tsp problem, with euclidian distance, for any solution with crossings there is always a better one without crossings. in our aeaps method we use a specific stress-induced mutation to the current solution. this mutation operator is different from the usual one theory of evolution. this is an active evolution an approximate algorithm combining p systems and active evolutionary algorithms for traveling salesman problems 93 figure 4: example with four nodes figure 5: a 1st class active evolution condition applied operator. if in the current path there is an edge from i to i + 1 that crosses other existing edge in the path, then i is called a 1st class active evolution node. the method of revising the path according to a 1st class active evolution node is the following: 1. find the nearest n1 nodes to i and denote by a the set consisting of these nodes; 2. select one node from the set a and name it j; 3. find out node j + 1 which is the next node after j in the solution; 4. swap the nodes i and j + 1 in the solution; 5. if the value v of the new solution is less than the old one, keep the new one, otherwise keep the old one. an illustration of the method is provided in fig. 5. 3.2 2nd class active evolution condition if the distance from i to the next node, i + 1, is larger than some value d, i is called a 2nd class active evolution node. in this case insert a new node between i and i + 1. also computedi = e × total_distancei/(n − 1), where e is a parameter, total_distancei is the total distance between i and the other nodes and n is the number of nodes. in aeaps we consider an approach similar to 2.5-opt iterative improvement scheme [36] for dealing with 2nd class active evolution nodes. this method is described below, where i is a 2nd class active evolution node and i + 1 is the next node after i in the current solution: 1. find the nearest n2 nodes to i and put them all into a set a; similarly build the set b for i + 1; 2. select one element from a ∩ b and name it j; 3. eliminate j from the solution and insert it between i and i + 1; 4. if the value of the new solution is less than the old one, keep the new one, otherwise keep the old one. an illustration of the use a 2nd class active evolution node is shown in fig. 6. 3.3 initial solutions nishida proposed a membrane algorithm, called compound membrane algorithm, which has two phases. the function of the first phase is producing good solutions which are used as initial 94 x. song, j. wang figure 6: a 2nd class active evolution condition applied solutions for phase two. the better initial solutions can improve the final output solution. as nishida said the computation time of compound membrane algorithm is quite prohibitive. we propose a simpler and faster method for generating good initial solutions. the method is as follows: 1. select one node randomly as the starting node, and name it current node; 2. find the nearest neighbour of the current node which is not selected, and name it current node; 3. repeat step 2 until all nodes are selected; a good initial solution has been then found. if we repeat the above steps for 2 × m − 1 times, we get enough initial solutions for each region of the p system. we have one solution in region 0 and two solutions in each of the regions 1 to m − 1. 3.4 aeaps algorithm aeaps uses the basic idea of the membrane algorithm proposed by nishida; a nested membrane structure with m regions is considered. we still use tabu search in region 0 and genetic algorithms in regions 1 to m−1 as sub-algorithms. finally, the same communication mechanisms between adjacent regions are used; the best and worst solutions are sent to adjacent inner and outer regions, respectively. unlike the nishida’s algorithm, aeaps adds two classes of active evolution operators in every region and use a new initial solution generation method. the overall membrane algorithm can be described as follows: 1. generate initial solutions by using the method mentioned in section 3.3, one for region 0 and two for each of the regions from 1 to m − 1; 2. modify solutions simultaneously in each of the regions 1 to m−1 by using genetic algorithms, simultaneously; 3. find out all 1st class active evolution nodes in every solution and revise them; then find out all 2nd class active evolution nodes and also revise them all; 4. use tabu search in region 0; 5. use the communication mechanisms between adjacent regions (as proposed by nishida); 6. remove all solutions but the best one from region 0 and best two in each of the regions 1 to m − 1; 7. jump to step 2 if the number of iterations is not satisfied; otherwise the output of the algorithm is the solution in region 0. 4 experiments and results we have tested the searching efficiency of aeaps on two benchmark problems, eil51, with 51 nodes, and kroa100, with 100 nodes, from tsplib [37], running 10 times each. the parameters in experiments are chosen as follows: m = 50, e = 0.1, n1 = 25, n2 = 35. the number of iterations is 300. table 1 shows the results. a comparison of simulated annealing (sa), shrink an approximate algorithm combining p systems and active evolutionary algorithms for traveling salesman problems 95 figure 7: curves of solving kroa100 problem by aeaps membrane algorithm (sma) and aeaps is shown in table 2. we have implemented aeaps in c and tested the algorithm on a microsoft visual c++ 6.0 platform with windows 7 and using a computer with 2.4ghz cpu and 2g ram. table 1. results of aeaps for eil51 and kroa100 1 2 3 4 5 6 7 8 9 10 eil51 431 429 428 428 426 430 429 428 429 427 kroa100 21282 21282 21320 21282 21389 21282 21373 21379 21282 21282 table 2. a comparison of sa, sma and aeaps sa sma aeaps best average worst best average worst best average worst eil51 430 438 445 429 430 433 426 429 431 kroa100 21369 21763 22564 21299 21504 21750 21282 21315 21389 results of sa and sma from [4] are shown in the tables. from table 1 and 2, one can see that aeaps gets better results than sa and sma for both eil51 and kroa100. fig. 7 shows the curves of average values for all the solutions and the average value of the solution in region 0 for the kroa100 problem solved by aeaps. for initial solutions which are generated by the method in section 3.3, the average value of the initial solutions in aeaps is much smaller than those using the membrane algorithm [4]. compared to nishida’s algorithm for solving the same problem by the 50 membranes, aeaps converges to remarkably fast to good solutions, in approximately 50 steps. zhang [28, 29] proposed acops for tsp, which uses a smaller number of function evaluations to achieve better solutions. experimental comparisons between nishida’s algorithm, acops and aeaps are listed in table 3 and table 4. results of nishida’s algorithm and acops are from [28]. the results of nishida’s algorithm were calculated by using 50 membranes and 10000 iterations. in acops the number of function evaluations (nofe) is the 96 x. song, j. wang stopping criterion. an equivalent number of iterations was obtained by using the product of average iterations for elementary membranes (gmin+gmax)/2 and the number of communications 2∗(gmin+gmax)∗nofe/(n +m). in solving ulysses22, eil51, eil76, eil101 and kroa100 problems, the parameters of aeaps have the values: m = 50, e = 0.1, n1 = 25, n2 = 35, and the maximal number of iterations is 300. in solving ch150, gr202 and tsp225 problems, the parameters of aeaps are: m = 50, e = 0.1, n1 = 40, n2 = 50, and the maximal number of iterations is 500. the results of the aeaps are obtained from 10 independent runs. table 3. number of iterations in nishida’s algorithm, acops and aeaps with 8 tsps nishida′s algorithm acops aeaps ulysses22 1.0e+5 7.7e+2 3.0e+2 eil51 1.0e+5 7.3e+2 3.0e+2 eil76 1.0e+5 7.5e+2 3.0e+2 eil101 1.0e+5 7.6e+2 3.0e+2 kroa100 1.0e+5 9.6e+2 3.0e+2 ch150 1.0e+5 7.8e+2 5.0e+2 gr202 1.0e+5 6.8e+2 5.0e+2 tsp225 1.0e+5 3.1e+2 5.0e+2 table 4. results of nishida’s algorithm, acops and aeaps with 8 tsps nishida′s algorithm acops aeaps best average worst best average worst best average worst ulysses22 75.31 75.31 75.31 75.31 75.32 75.53 75.31 75.31 75.31 eil51 429 434 444 429 431 434 426 429 431 eil76 556 564 575 546 551 558 543 545 547 eil101 669 684 693 641 647 655 631 638 643 kroa100 21651 22590 24531 21285 21320 21427 21282 21315 21389 ch150 7073 7320 7633 6534 6560 6584 6549 6554 6565 gr202 509.7 520.1 528.4 489.2 492.7 497.1 491.3 496.1 498.9 tsp225 4073.1 4153.6 4238.9 3899.6 3938.2 4048.2 3938.7 3992.3 4034.1 as compared with nishida’s algorithm, aeaps uses much smaller number of iterations to achieve better solutions. as compared with acops, aeaps uses smaller number of iterations only except for the tsp225 and gets better results for the first 5 tsps, similar results for ch150 problem and slightly worse results for the last 2 tsps. 5 conclusions this work is the first attempt to discuss the role of active evolutionary operators in membrane algorithms. we present an approximate algorithm combining nested membrane structure, rules within regions and communication mechanisms of the p systems, and two classes of active an approximate algorithm combining p systems and active evolutionary algorithms for traveling salesman problems 97 evolution operators and a good initial solution generating method. aeaps is used to solve euclidian tsps, well-known np-hard problems. the experiment results show that aeaps performs better than sa and nishida’s membrane algorithm and similar with acops, which requires a smaller number of iterations. in order to improve the performance of aeaps, especially in solving large scale tsps, our future studies will focus on other membrane structure options and communication mechanisms. acknowledgments. this work is supported by the national natural science foundation of china (grant no. 61170030), the chunhui plan of ministry of education of china (grant no. z2012025), the open research fund of key laboratory of xihua university (grant no. szjj2012-002), the research projects of eduction department of sichuan province (grant no. 13zb0017) and the key scientific research foundation of xihua university (grant no. z1120943). the authors also gratefully acknowledge helpful comments and suggestions made by reviewers, which have significantly improved the presentation. bibliography [1] păun, g. 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[37] http://www.iwr.uni-heidelberg.de/groups/comopt/software/tsplib95/ int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 241-251 network element scheduling for achieving energy-aware data center networks w. fang, x. liang, y. sun, a.v. vasilakos weiwei fang, xiangmin liang, yantao sun school of computer and information technology beijing jiaotong university, beijing 100044, china {wwfang, 11125116, ytsun}@bjtu.edu.cn athanasios v. vasilakos department of computer and telecommunications engineering university of western macedonia kozani gr 50100, greece vasilako@ath.forthnet.gr abstract: the goal of data center network is to interconnect a massive number of servers so as to provide reliable and scalable computing and storage infrastructure for cloud-based internet services and data-intensive scientific applications. recent studies reveal that the network elements consume 10∼20% of the overall power in a data center, which has introduced a challenge to reducing network energy cost without adversely affecting network performance. considering unique features of traffic patterns and network topologies in data centers, this paper proposes a novel network element scheduling scheme (ness) to reduce data center energy consumption from the networking perspective. the core idea is to turn on only a minimal subset of network elements to satisfy routing requirements, and put to sleep or shut down the rest unneeded ones for energy saving. in ness, the logical network architecture formed by the active elements not only achieves the basic purpose for server interconnections in data centers, but also can support multi-path routing between pairs of hot servers for load balancing. simulation experiments are performed in representative data center network topologies, and the results demonstrate the effectiveness of ness in energy conserving on network elements in data centers. keywords: data center networks, green computing, energy aware, steiner tree. 1 introduction in recent years, many large data centers are built around the world to provide highly reliable and scalable infrastructure for cloud services and scientific computations. as the actual tendency is to exponentially increase the number of demanded servers, a natural consequence is that the power consumption becomes a critical concern for data center operators. for example, it has been reported that data center power usage in u.s. doubled between 2000 and 2006 to nearly 61 billion kilowatt-hours, representing 1.5% of all u.s. electricity consumption [1]. researchers are now seeking to find effective solutions to make data centers reduce power consumption while keep the desired service performance. most of the recent research has focused on reducing the two major components of data center power usage: servers and cooling [2]. however, the underlying network infrastructure, namely routers, switches, high-speed links, still lacks effective energy management solutions. the networking part of data center has been found to consume 10∼20% of its total power consumption [2], and thus should not be neglected. copyright c⃝ 2006-2012 by ccc publications 242 w. fang, x. liang, y. sun, a.v. vasilakos fortunately, there is an opportunity for substantial reductions in the energy consumption of data center networks due to two factors. on one hand, the high network capacity of data center networks is specially provisioned for worst-case or busy-hour load, and far from being exceeded by traffic load most of time. moreover, data center traffic varies considerably over time exhibiting temporal patterns (e.g., daily, weekly, monthly and yearly [3]), and over location exhibiting spatial patterns (e.g., hot/non-hot server racks [4]). in the data center with rich link connectivity, a great number of network elements may consequently work in idle state. on the other hand, today’s network elements are not energy proportional, since fixed overheads such as fans, switch chips and transceivers waste power at low loads. it has been found that the energy consumption of common networking devices at the idle state still accounts for more than 85% of that at the working state [5]. the implication of these factors is that significant amount of power energy is wasted on idle elements in the data center network. to address the challenges stated above, this paper proposes a network element scheduling scheme (ness) that acts as a network-wide energy optimizer for data center networks. it selects a subset of network elements that must stay active to meet traffic routing requirements, and then puts as many unneeded routers, switches and links as possible into dormant mode. considering unique features of network topologies and traffic patterns in data centers, ness achieves this goal in two steps. firstly, ness models and solves the basic problem of network element scheduling for guaranteeing server interconnection and traffic routing by using the steiner tree framework. on the basis of the initial selection result, ness then additionally activates as few unselected network elements as possible to support different degrees of multi-path routing between server pairs. network elements not involved in the routing service are finally powered off or put into sleep state. we have conducted extensive simulations in typical architecture models of data center network to illuminate the effectiveness and performance of the proposed scheme. the rest of this paper is organized as follows. section 2 presents related works. section 3 describes the ness scheme in detail. section 4 explores on experiment results, and finally the paper is concluded in section 5. 2 related works the issue considered in this paper involves two network-related research directions, data center networking and green networking. the rest of this section presents the state-of-art related to these two directions. the research on data center networking mainly focuses on how to implement a network infrastructure that achieves the following goals [2] [6]: (1) it must be scalable to an increasing number of servers; (2) it must be fault tolerant against various types of hardware failures; (3) it must be able to provide high network capacity; (4) it must achieve high utilization and be cost efficient. due to the limitations of the conventional tree-based architecture [7], a number of novel network architectures for data center networks have been proposed recently, which can be roughly divided into two categories. one is the switch-centric architecture, which organizes switches into structures rather than trees and puts interconnection intelligence on switches, such as fat-tree [8], vl2 [7] and portland [9]. the other is the server-centric architecture, which puts interconnection intelligence on servers and uses switches only as cross-bars, such as bcube [10], ficonn [11] and dcell [6]. accordingly, each of the architecture proposals has its own solution for node addressing and traffic routing [6] [8]. furthermore, multi-path routing [6] [8] [12] [13] has been exploited for load balancing in data center networks. other complementary research for data center networking has focused on tcp incast problem [14], traffic-aware virtual machine migration [15], switch design [16] [17] or cost efficiency [2] [3], etc. reduction of unnecessary energy consumption, referred to as "green networking", has become network element scheduling for achieving energy-aware data center networks 243 a major concern in wired/wireless networking, because of the potential economical benefits and the expected environmental impact. [18] is a pioneer work on this topic, and the authors of [18] suggested putting network elements to sleep for saving energy in local area network in a later paper [19]. additionally, link rate adaptation is also employed to reduce energy consumption in ethernet [20]. based on these techniques, the ieee 802.3az energy efficient ethernet task force proposes the low power idle solution to reduce power consumption of ethernet devices [21]. elastictree [3] is a pioneer work that optimizes the energy consumption of data center networks by turning off unnecessary links and switches during off-peak hours. it models the problem based on the multi-commodity flow model, in which some parameters, e.g., server traffic demand, are difficult to be accurately obtained in practice. besides, elastictree focuses on only tree-based topologies such as fattree. another similar work [22] models the same problem as a 0-1 knapsack model, and proposes a heuristic based solution. however, it doesn’t support multi-path routing between server pairs for load balancing. vmflow [23] is a recent work on how to migrate virtual machines among data center servers to minimize the amount of serving network elements while satisfying a large fraction of the network traffic demands. 3 ness: network element scheduling scheme 3.1 research motivations a data center network is typically provisioned for peak traffic load, and run well below capacity most of the time. from previous researches, we can discover the two important characteristics (i.e., temporal and spatial) of data center traffics, both of which can help us to define the design goals. on the one hand, network traffic and its temporal dynamics implicitly reflect the behavioural pattern of end users for whom the data center provide services [3]. for example, traffic may vary daily (e.g., more email exchanging during the day), weekly (e.g., more enterprise data processing on weekdays), monthly (e.g., more multimedia file sharing on holidays), and yearly (e.g., more e-shopping and e-payment in december). rare events like cable breaks or breaking news may hit the peak capacity, but most of the time data center traffic follows the temporal pattern and actually can be satisfied by a subset of active network elements [3]. on the other hand, physical servers in the data center are commonly organized into racks and richly-interconnected by a number of links, switches and routers. the typical upper-layer applications usually generate a traffic demand with only a few of server racks being hot (i.e., sending or receiving a large volume of traffic) [4]. moreover, the hot racks generally exchange much of their data with only a few other racks. such a spatial pattern of data center traffic is determined by the role and tasks of servers in the current application. to avoid network congestion in data centers [4], it is necessary to further provide different degrees of multipath routing for the flows from/to hot servers. motivated by the analysis above, we propose ness, a network element scheduling scheme that acts as a network-wide energy optimizer for data center networks. the following subsections will present the key issues on its design and implementation in detail. 3.2 design details in data center networks, the physical servers are interconnected by a number of high-speed links, switches and even routers. the data center network topology can be modelled as a simple undirected weighted graph g(v, e) with equal edge weights [15], where v is the set of vertices and e ⊆ v × v is the set of edges. there are two types of vertices in v : the servers and the 244 w. fang, x. liang, y. sun, a.v. vasilakos networking devices (i.e., switches and routers). the sets of them can be denoted by vs and vd respectively. therefore, v = vs ∪ vd. the edge e ∈ e represents a communication link between a server and a networking device, or between a pair of networking devices. as an illustration, we show four typical data center topologies in figure 1, namely 2n-tree [3], vl2 [15], fat-tree and bcube. it must be noted that in the first three architectures vs ∩ vd = ∅, while in bcube vs ⊂ vd. (1) 2n-tree (3) fat-tree (2) vl2 (4) bcube networking device physical server figure 1: illustration of state-of-the-art data center topologies. before problem formulation, we first define the following notation: |x| denotes the cardinality of set x, fi,j denotes the flow from server i to server j, pi,j denotes the path having i and j as its ends, and li,j denotes the physically upper bound for total routing paths. now, the network element scheduling problem can be formally formulated as follows: minimize: |v ′d| (1) subject to: g′(v ′,e′) ⊆ g(v,e) (2) v ′ = v ′d ∪ v ′ s (3) v ′d ⊆ vd (4) v ′s = {i ∈ vs | ∑ ∀j∈vs (fi,j + fj,i) > 0} (5) network element scheduling for achieving energy-aware data center networks 245 |{pi,j | i,j ∈ v ′s,∀k ∈ pi,j,k ∈ v ′ d}| ∝ ∑ (fi,j + fj,i) (6) 1 ≤ |{pi,j | i,j ∈ v ′s,∀k ∈ pi,j,k ∈ v ′ d}| ≤ li,j (7) in the above formulation, the term of the objective represents the set of active networking devices for interconnections of servers having incoming and/or outgoing traffic. formula (5)∼(7) guarantee different degrees of multipath routing between pairs of servers can be provided according to traffic requirements. moreover, formula (2) implies that unneeded idle links can actually be turned off to further reduce energy consumption [3] [5] since e′ ⊆ e. such a scheduling problem can be easily proved to be np-complete by restricting that only one path is established between any server pair (i,j). in the equal-edge-weight graph g(v,e), the objective of minimizing |v ′d| is equal to minimizing |e ′|, and thereby is equal to determining the minimal-weight connected sub-graph g′(v ′,e′) spanning v ′s . this is identical to the sterner tree problem in the equal-edge-weight graph, which is a np-complete problem [24]. therefore, the problem above is np-complete. because by now no polynomial-time algorithm is available to obtain the optimal solution of a np-complete problem, we propose ness, a scheme that solves the scheduling problem modelled above heuristically in two steps. in the first step, ness solves the steiner tree problem on graph g(v,e) with the terminal node set v ′s . a lot of heuristic algorithms can be used to obtain the results, among which mph (minimum path heuristic) is one of the best-known solution [24] [25]. the output vertex set v ′d1 is a subset of vd, representing the minimal number of networking devices that can guarantee the basic purpose for server interconnection and traffic routing. in the second step, ness selects from vd\v ′d1 as few vertices as possible to construct multiple path between some vertices in v ′s that represents the hot servers. to achieve this goal, ness integrates the well-known ecmp (equal-cost multiple-path) routing mechanism [12] for discovering multiple paths connecting hot servers, and then chooses the paths containing more vertices already in the set v ′d1. denoting the set of newly selected vertices in this step as v ′ d2, we finally have v ′d = v ′ d1 ∪ v ′ d2. to illustrate the approach stated above, we take an example in the fat-tree topology shown in figure 2. assume v ′s = {v21,v25,v27,v28}, and v21,v28 are hot servers requiring two routing paths to be established between them. in the first step, ness executes the mph algorithm to solve the steiner tree problem, and obtains v ′d1 = {v1,v5,v7,v13,v15,v16}. in the second step, ness can discover totally four equal-cost shortest paths through a basic version of ecmp [8] as follows: path1 : v21 → v13 → v5 → v1 → v7 → v16 → v28 path2 : v21 → v13 → v5 → v2 → v7 → v16 → v28 path3 : v21 → v13 → v6 → v3 → v8 → v16 → v28 path4 : v21 → v13 → v6 → v4 → v8 → v16 → v28 then we can choose path1 and path2 for v21,v28 since path2 requires to additionally activate only v2, while path3 and path4 requires to activate v6,v3,v8 and v6,v4,v8 respectively. therefore, v ′d2 = {v2}. 3.3 implementation issues as a scheduling software, ness consists of four logical modules, i.e., network analyzer, network scheduler, power controller and route controller. the network analyzer obtains network topology and traffic requirement by performing statistics and analysis to the collected running data of the data center network. the role of network scheduler is to find the minimum network subset that can satisfy current traffic according to the approach stated in section 3.2. with 246 w. fang, x. liang, y. sun, a.v. vasilakos v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v13 v14 v15 v16 v17 v18 v11 v19 v12 v20 v21 v22 v23 v24 v25 v26 v27 v28 v29 v30 v31 v32 v33 v34 v35 v36 figure 2: an fat-tree example for illustrating network element scheduling in ness. the input of topology and traffic conditions from the analyzer, the scheduler outputs the set of active elements to power controller and the set of flow routes to route controller. the power controller toggles the power states of different types of network elements (i.e., links, switches and routers), while the route controller checks routing paths for traffic flows and pushes routes into the network. figure 3: system diagram of ness. similar to elastictree [3], ness can be implemented as a nox application [26] to run atop a network of openflow switches [27]. openflow is an open standard for commercial switches and routers to enable controlling the forwarding plane by a software running on a separate server, and nox is an open-source openflow controller that is designed to provide a simplified platform for writing network control software in c++ or python. the route controller in ness can be implemented with nox. besides, we can leverage the existing mechanisms such as snmp set operations and command line interface to support the power control features of ness. moreover, the network analyzer can collect the state records of traffic and topology through snmp get operations and passive packet tracing [28]. 4 experimental evaluation in this section, we evaluate the performance of ness experimentally, using a custom simulator developed in c++ with the boost graph library [29]. this simulator supports constructing the four types of data center network architectures described in section 3.2 with a number of 48port gigabit ethernet switches [8] [27]. by using this simulator, we have created data center network element scheduling for achieving energy-aware data center networks 247 communication scenarios with different network sizes (e.g., a network of s =2304/4608/13824 end servers), different traffic conditions (e.g., a network of servers with h=1%/10% hot ones [4] in which each hot server randomly selects about 10% other ones under different racks as its communication counterparts) and different routing requirements (e.g., a pair of hot servers may require 2∼6 equal-cost paths for traffic multiplexing). for a specific scenario, the simulation has been carried out independently for multiple times by network reconstruction, and the results are averaged over these runs. figure 4 shows the percentage of the dormant network elements in 2n-tree, vl2, fat-tree and bcube respectively. 0 10 20 30 40 50 p er ce nt ag e( % ) (a) 2n-tree 0 5 10 15 20 25 p er ce nt ag e( % ) (b) vl2 switch link 0 20 40 60 80 100 p er ce nt ag e( % ) (c) fat-tree s = 2304 h = 1% s = 2304 h = 10% s = 4608 h = 1% s = 4608 h = 10% s = 13824 h = 1% s = 13824 h = 10% 0 20 40 60 80 100 p er ce nt ag e( % ) (d) bcube figure 4: the percentage of dormant network elements in six different networking scenarios. we have several observations concerning the simulation results: (1) ness effectively reduces the total amount of active switches as well as active links to different extents in typical communication scenarios under the four data center network architectures. (2) all else being equal, the communication scenario with more hot servers generally will have greater demand for network resources, i.e., more network elements have to be kept active by ness for fulfil routing requirements. (3) for 2n-tree, we can find that its dormant percentages of network elements remain essentially flat under the same condition of hot rate h. that’s because in 2n-tree both the total 248 w. fang, x. liang, y. sun, a.v. vasilakos amount of network elements (i.e., switches or links) and the amount of the ones selected by ness are approximately in direct proportion to the network size. in general, our simulation results on 2n-tree (figure 4 (a)) indicate that we can roughly predict the energy saving for applying ness to the large-scale data center network built in this architecture through small-scale experiments. (4) for vl2, we can find that its dormant percentages of both switch and link are the lowest among the four architectures. an examination of simulation traces reveals that the total amount of switches remains the same (i.e., 336) for all the six simulation scenarios, while most of these switches (i.e., 288) and the links from them to the servers have to be kept active. therefore, the optimization space for ness is quite limited in vl2 architecture, especially when the hot rate h is relatively high. from the results in figure 4 (b), the scenario with fewer hot servers has higher dormant percentage of switches, especially when the number of hot servers is less than 230 (i.e., 10% of 2304). moreover, the dormant percentage of links decreases accordingly along with the increment of network size. (5) for fat-tree, we can find that that it has the relatively highest dormant percentage of links among the four architectures. besides, the dormant percentage of switches is also relatively high (i.e., about 50%) for each scenario. an examination of simulation traces reveals that fat-tree has the most switches (i.e., 2880) and links (i.e., 50000∼70000) among the four architectures. while such redundancy of available network elements makes the fat-tree topology attractive for fault-tolerance, it also brings about considerable energy wastage due to element idle in most cases [3] [22]. moreover, as that of vl2, all scenarios have the same amount of switches and the one with fewer hot servers will have higher dormant percentage of switches. in all, the simulation results in figure 4 (c) indicate that there is a tradeoff between fault tolerance and energy consumption for fat-tree. (6) for bcube, the simulation results are significantly different from those of switch-centric architectures, i.e., the dormant percentages increase sharply when s grows lager than 2304. that’s because: when s =2304, a 2-level bcube1 built with totally 96 switches (48 for level 0 and 48 for level 1) is enough. however, a 3-level bcube2 has to be constructed with 2304 switches for level 2 when s>2304. all these level 2 switches only interconnects two bcube1 when s =4608 and six bcube1 when s =13824. actually, the utilization of these switches are quite low in result of high redundancy, and thus most of them can be put into dormant state. moreover, it can also be found that the increment of network size under the same condition of bcube level, e.g., from 4608 to 13824, will lead to higher element utilization and lower dormant percentages. in all, the simulation results in figure 4 (d) indicate that for each bcube level the dormant percentages of network elements will decrease from high to low along with the increment of network size. accordingly, table 1 specifically illustrates to how much extent power energy can be saved by applying ness to data center networks. the power consumption parameters of network elements are obtained from a previous work [5]. for the three switch-centric architectures above, pv l2<p2n−tree<pfat−tree. for the sever-centric architecture bcube, the scenario with a smaller network size at a bcube level (e.g., s =4608 at bcube2) will have higher reduction percentage of power consumption due to high redundancy of network elements. 5 conclusion in this paper, we introduce the energy cost problem in today’s data centers. based on detailed analysis on traffic patterns and network topologies, we propose a novel scheme (ness) to reduce data center energy consumption from the networking perspective. extensive simulation results demonstrate that it is possible to switch off idle networking elements, so that the total network power consumption can be reduced without adversely affecting network performance. the work presented in this paper is somewhat preliminary, but shows that energy-aware networking in the network element scheduling for achieving energy-aware data center networks 249 table 1: power consumption reduction percentage p. s 2304 4608 13824 h 1% 10% 1% 10% 1% 10% 2n-tree 43.99% 25.44% 43.74% 22.50% 43.68% 24.78% vl2 10.66% 6.26% 8.59% 5.12% 4.63% 3.43% fat-tree 66.08% 57.59% 62.87% 55.69% 55.56% 52.18% bcube 37.40% 23.34% 80.87% 78.97% 76.09% 38.69% data center is promising [30] [31]. in the future, we plan to implement ness on our prototype system of data center network, which is still under development now. acknowledgements this work was partially supported by the china postdoctoral science foundation (20110490282), the fundamental research funds for the central universities of china (2011jbm225) and the intel china research institute bjtu collaborative research program (k09l00030). bibliography [1] u.s. enviromental 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[29] the boost graph library, http://www.boost.org/doc/libs/release/libs/graph. [30] d. abts, m.r. marty, p.m. welles, p. klausler, h liu, energy proportional datacenter networks. proceedings of the 37th acm/ieee international symposium on computer architecture (isca), 338-347, 2010 [31] k. chen, c.c. hu, x. zhang, k. zheng, y. chen, a.v. vasilakos, survey on routing in data centers: insights and future directions. ieee network, 25(4), 6-10, 2011 910teon6.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):865-872, december, 2015. a retrospective assessment of fuzzy logic applications in voice communications and speech analytics h.-n.l. teodorescu horia-nicolai l. teodorescu 1. romanian academy iasi branch romania, iasi, carol i, 8 2. gheorghe asachi technical university of iasi romania, iasi, str. d. mangeron, 67 hteodor@etti.tuiasi.ro abstract: voice and speech communication is a major topic covering simultaneously ’communication’, ’control’ (because it often involves control in the coding algorithms), and ’computing’ from speech analysis and recognition, to speech analytics and to speech coding over communication channels. while fuzzy logic was specifically conceived to deal with language and reasoning, it has yet a limited use in the referred field. we discuss some of the main current applications from the perspective of half a century since fuzzy logic inception. keywords: fuzzy logic, fuzzy system, speech, communication, vad, speech segmentation, speech coding, speech analytics. 1 introduction at 50 years since the advent of fuzzy logic, 40 years since by lotfi a. zadeh introduced the concept of linguistic variable [50] and at more than 60 years since the mathematician grigore c. moisil argued that a new logic must be invented for describing human language and reasoning, it is compelling to ask: how much fuzzy logic did contributed to our understanding and technical use of language and speech in communications? as fuzzy logic (fl) was specifically conceived to model the human language and logic vagueness, one could expect that it played a central part in improving voice communications and speech recognition. yet, the current situation does not seem to fully support this expectation – at least not at the level fl gained popularity in (fuzzy) control theory. in the review [44], not a single mention to fuzzy logic is made in connection to voice communication, showing no penetration in the mainstream of communication applications after almost 30 years since the first paper on fl. the situation has not much improved. the recent review [11] deplores the fact that there are very few if any papers using fl in the major conferences devoted to speech. major journals publish only seldom papers on fl in speech applications and voice communications. as a matter of example, there is a single paper in this journal to refer to the control and optimization of voice communications, rather indirectly, namely [16]. that paper does not use fl-based methods. there is also another paper referring to fuzzy control for data and indirectly, but not directly to voice communication, [48]. sparsely, there are however papers on the topic in major journals, see for example the approach in [14] on a fuzzy traffic controller for atm networks. an hypothesis for explaining this astonishing, general situation of the low number of papers on voice communication and speech is that something is still missing to allow for the expected eruption of fl applications in the field. we critically review the state of the art and search for answers for the current state of affairs. in this paper we assess the use of fl in four narrow sub-domains: voice activity detection (vad), speech segmentation, and speech coding, which are strongly related, on one side, respectively speech analytics, on the other side. copyright © 2006-2015 by ccc publications 866 h.-n.l. teodorescu 2 some applications of fl to voice communications and speech coding 2.1 fuzzy vads and fl in speech segmentation voice communication in telephony systems and over internet is done in digital form, by packets of data with speech coded using pcm or other coding techniques. it is a matter of minimizing the transmission effort and thus optimizing transmission capacity and minimizing energy to send only useful packets. speech is full of pauses that may include ambient noise. sending over the networks pause (noise) packets is useless. therefore, detection of speech and noise (pause) segments, and next coding and sending only the speech segments may significantly improve communication efficiency and channel useful capacity. the so-called voice-activity detectors (vads) are meant to separate useful and useless segments before transmission and are included in virtually all communication equipment. the main difficulty in building high quality vads is to differentiate between consonants and noise, because some of the consonants, especially the fricative ones are noise-like. both the frequency spectra and the amplitude of the fricatives as /s/, /f/ are close to white noise of low amplitude, as one encounters in offices. that makes difficult the task of the voice activity detectors. compounded with that is the variability of the noise, which is typically nonstationary and may be white, pink, impulsive or a mixture of them, with variable amplitudes. discerning between noise and unvoiced consonants is a matter of classification, possibly solved with fuzzy voice activity detection (fvad) algorithms as in [3], [4], [5], [6], [7], [8], [12]. the va detection needs several preliminary stages. in one approach, one detects and separates the periodical and a-periodical segments (pap analysis) in the speech [3], [4], [5] using a linear predictor (lp). lps approximate the sampled speech signal sn as a linear combinations of the previous samples, according to san = ∑m k=0 aksn−k + ∑q j=1 bjs a n−j, where s a denotes the approximated samples and ak, bj are the lp coefficients. lps are able to model well periodic signals, with low error en = sn−san, while they are inefficient for a-periodic signals (large prediction errors). signal parameters as the energy and the number of zero-crossings (nzc) are also used for supplementing the lp pap analysis, where nzc is computed as the number of times, in a specified length segment of signal (signal window), successive samples satisfy sn−1sn ≤ 0. alternatively, one may use only amplitude, spectral properties such as the ratio of powers in the low and high frequency bands and nzc, possibly supplemented with the values of the self-correlation function, or properties of the cepstrum or of the mel-spectrum (mel-frequency cepstral coefficients mfcc) etc., to discriminate between voiced, unvoiced and noise segments. in vad, as well as in speech segmentation and speech and emotion recognition, the decision is made based on the original parameters, such as lpc coefficients, energy, and nzc, or based on a set of derived, fused parameters – the representation space. in the second case, several parameters in the primary parameter space are processed together and a new representation (representation space) is derived, for example, the coherence between the periodic part of the signal and the noise (remaining part), as in [3], or the fuzzy information space representation as in [42]. vads are included in communication standards, but none of the standards refers to fl and fvads. yet, [3] found that employing a decision based on fl rules applied to the ’coherence measure between the noisy speech and its prediction residue’, the performances of the fvad performs ’globally better than g.729b and presents moderate improvement when compared to umts 3g ts 26.094 vad.’ they used the coherence function computed on every frame k, c(f, k), c2(f, k) = s 2 sn (f,k) ss(f,k)×sn(f,k) , where f is the frequency, s denotes spectra in the frame for the noise n and signal s, and ssn stands for the inter-signal spectral density [3], [4]. after defining a set of frequency bands, bi, the coherence function on each band is computed as a retrospective assessment of fuzzy logic applications in voice communications and speech analytics 867 cbi (k) = ∑ f∈bi |c(f, k)| and these values are fuzzified according to three membership functions [3]. a fuzzy decision is optimized for determining the type of signal segment and thus the va. beretelli et al. [6], [7], [8] tested another approach, using the same parameters employed by the itu-t g.729 vad standard, namely the energy differences between successive speech frames, for full-band ∆et and low-frequency band ∆el, the difference of the nzcs, ∆zc, and the spectral distortion ∆s between successive frames. in their vad algorithm, the decision is made based on a set of simple fuzzy rules given in [7], such as ’if (∆s is medium or low ) then (voice is active)’ and ’if (∆el is low) and (∆s is very low) and (∆zc is high) then (voice is active)’ (rules form [12]). further improving the system, these authors considered multi-channel (two or several microphone) systems and took into account the delays between the signals. using the output of the basic fvad and the delays as inputs to a fuzzy network, after training the complex fvad, and thus obtained better performing vads than the simpler fvad and than the g.729 standard vad. further refinements to increase the robustness in noise are given in [6], [7], [8], [12]. these authors report in [9] an improvement, compared with the vad g.729, of more than 80% improvement in false activity detection. a close topic is that of acoustic event detectors; we notice the interesting approach in [45], where information fusion for classification of non-speech sounds is performed by a skilled use of fuzzy integrals. similarly, fl-based techniques applied to speech segmentation have been proposed by many authors, but the penetration of these techniques in the mainstream of speech segmentation is still limited. speech segmentation may regard several levels, from voice activity to vowel (voiced sound)-consonant phoneme boundaries, to phonemic and syllabic unit segmentation, under various conditions of noise. lin et al. [35] improved the noisy speech segmentation using neural fuzzy networks based on so-called ’adaptive time-frequency (atf) and refined timefrequency (rtf) parameters’. hsieh et al. [24] presented a neuro-fuzzy segmentation method specific for the mandarin language, while [47] combined a context-dependent phonetic hmm recognizer with a fuzzy logic post-correction system that takes into account the conditions specific for each phonetic boundary for improving the precision of phoneme boundary determination. they report remarkable improvements, from errors of 400% for a basic hmm segmenter, compared to the durations determined by human operators, to a few percents after the corrections made by the fuzzy rules block. there are three main classes of techniques for speech coding [44]: the waveform (direct) coding, coding in the model space, typically named parametric coding, and hybrid coding that is mixing the first two techniques. speech coding is based on a compromise between speech perceived quality and the used bandwidth, and thus cost. the best quality is obtained by waveform coding methods, which are also the most costly in terms of transmitted bandwidth. parametric (model) coding achieves low bandwidths, but the quality is poor-to-good at best. today speech coding, as in mpeg and telephony, is based on detailed psychoacoustic models derived from celp. in brief, the low delay (ld) celp coder standardized by ccitt as g.728 uses a 50th order linear predictor (lp) excited by (i.e, having as input) predefined signals. the set of excitation signals is predefined and indexed on a ’codebook’ (memory). after the lpc coefficients are determined on a speech frame, one searches the type of excitation and the best value of its amplitude (codebook gain) that produces at the lpc synthesizer output a signal that is the closest to the original speech frame (minimal error). the ’codebook’ waveform index and the code of the best matching amplitude, together with the lpc coefficients code the speech frame. a perceptual filter is also used to improve the perceptual quality of the decoded speech signal. while the lp is computed with performant algorithms, the best choice of excitation and of its gain as available in the codebooks are time consuming. sheikhan et al. [43] proposed a fuzzy adaptive resonance theory mapping (artmap) for achieving fast codebook index selection. however, these authors have not justified their choice (the artmap) in terms of the required 868 h.-n.l. teodorescu computation power and time (complexity) and one could suspect that a simpler nn could have performed more efficiently in this application. 2.2 fl in speech analytics a surprising low development already in 2006 a gartner report [18] found that audio search and speech analytics is one of the new technologies companies are adopting. beyond marketing and services, speech analytics are used in various applications as security [49], learning and teaching [19]. carlsson [10] argues that analytics and fl could be profitably combined in management. there are multiple reasons to believe that fl may play an essential role both in interpreting the text and uncovering emotional states in speech; see for example the comparison of methods for emotion detection in [1], the example of method in [2] and the recent excellent paper [32]. however, many approaches applying fl to emotional speech are somewhat mechanistic, with no direct relevance for psychological, neurologic, and phonetic processes. there is virtually no fl or analytics-related study on the influence of the emotions on the articulatory processes (changes in vocal fold vibration and non-vocal fold vibration frequencies, degree of creakiness, changes in the articulation place and other elements of interest in articulatory phonetics). there are exceptions from the mechanistic approach to assessing the speaker state; such exceptions deserve recognition, e.g., [21], [22], [23], who study correlations between qualitative representations of emotions such as valence, activation, and dominance and the acousto-physical parameters (acoustic features). on the other hand, one has to recognize the market value of the mechanistic approaches in analytics and other applications: they aim to produce real-life applications such as synthesizing emotional speech for the games and movies industry [40], monitoring the state of drivers [26], [27], [28], call center control and crowd/social state monitoring and control. there was little research on differentiating simulated emotions with variable degrees of likeness to the true ones. some studies addressed simulated emotions by non-actors, aiming to voice communication enhancement and education; e.g., [19], [38] found significant differences in emotion detection when comparing acted emotions by layman (corpus described in [19]) and actors. genuine emotions, as studied in several other researches, were found more challenging to determine than the acted ones. we expect that fl can help represent the degree of likeness by actors and laymen, moreover help build emotion simulation detectors. because fl found reputed applications in classification, e.g., the knn method and neurofuzzy classifiers [51] moreover because the analytics extensively use concepts easier interpretable by people than by current machines, one could expect that fl is largely present in analytics modules, including speech analytics. while this is not true, fuzzy ontologies are quite popular and at least some analytics have fl-based modules, see sap-hana [?] which considers fuzzy search as one of the ’few important techniques being used in text analysis’; namely fuzzy search stands for ’finding strings that match a pattern approximately’. although this use relates to language, the technique simply applies fl in defining a fuzzy distance over the set of strings. the search is based on a minimal matching value and the respective command is like contains (<string-tolook-for>, fuzzy (0.x)), with 0.x the minimal accepted similarity [53]. note that this analytics provides the function score() that determines the degree of similarity for every string in a specified set and the given string, but this is simplistic and far from what would may be expected as level of use of fl in understanding people’s language communication. an interesting direction was opened in [39], who proposed the use of prosodic features to prioritize the call servicing. while not using fl, the approach in [39] is an example of applications where fl looks promising for speech analytics. similarly, there is an interesting paper, [13], thoroughly analyzing the possibilities of mining fuzzy association rules in texts; that track could be followed and applied on step further to finding fuzzy associations between textual information a retrospective assessment of fuzzy logic applications in voice communications and speech analytics 869 and prosody and emotions in speech. another remarkable approach to analytics based on fl but not related to speech is constituted by a series of papers [30], [31] that apply fuzzy data analysis and inductive fuzzy classification using a normalization of the likelihood ratio to metadata and for knowledge discovery. surprisingly, there are few reports on research on fl applied to speech analytics related to emotions. maybe this is due to the fact that sentiment analysis on texts have developed earlier and that it is considered sufficient for deriving the mood of the speaker. 3 discussion and conclusions while the contributions of fl to speech technology, specifically to vad, speech segmentation, and coding cannot be disregarded, these contributions seem to be less significant than one may expect from applying fl to speech. few researches compare the results and the advantages or disadvantages of the fl approach to non-fuzzy approaches, or even try to justify the fl-based approach. while some good results obtained using fl-approaches are expected based on the known power of universal approximation (and thus nonlinear classification) of flss, the capabilities of others, including their generalization power are less clear. a more systematic research program for employing fl in speech analysis is needed to overcome the current limits. an explanation for this state of affairs could be that fl requires extensive computations, while systems as cellular phones and even pcs are restricted in computation power. however, the recent processors have tremendously increased in computation power, favoring a larger use of fl. thus, one can look forward with the hope that fl will achieve more in this field, in the near future. acknowledgments. this work was supported in part (section on ’analytics’) by the sps nato program under grant g4877 /sfp 984877. bibliography [1] amir n., kerret o., karlinski d.(2001); 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[53] http://saphanatutorial.com/sap-hana-fuzzy-search/ int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 173-183 small universal tissue p systems with symport/antiport rules x. zhang, b. luo, l. pan xingyi zhang, bin luo key lab of intelligent computing and signal processing of ministry of education school of computer science and technology anhui university, hefei 230039, china e-mail: xyzhanghust@gmail.com,luobinahu@yahoo.com.cn linqiang pan key laboratory of image processing and intelligent control department of control science and engineering huazhong university of science and technology wuhan 430074, china e-mail: lqpan@mail.hust.edu.cn (corresponding author) abstract: in this note, we consider the problem of looking for small universal one-symbol tissue p systems with symport/antiport rules. it is proved that six cells suffice to generate any recursively enumerable set of natural numbers by such a onesymbol tissue p system with symport/antiport rules, under the restriction that only one channel is allowed between two cells or between a cell and the environment. as for the case of allowing two channels between a cell and the environment, it is shown that the computational completeness can be obtained by one-symbol tissue p systems with symport/antiport rules having at most five cells. these results partially answer an open problem formulated by artiom alhazov, rudolf freund and marion oswald. keywords: membrane computing, tissue p system, symport/antiport rule, universality. 1 introduction membrane computing is one of the recent branches of natural computing, which was initiated by gh. păun in 1998 [7]. the aim of membrane computing is to abstract novel computing ideas or models from the structure and the functioning of a living cell, as well as from the organization of cells in tissues, organs, and other higher order structures. the obtained models, called p systems, provide distributed parallel and non-deterministic computing models. the field of membrane computing has rapidly developed (already in 2003, isi considered membrane computing as a “fast emerging research area in computer science”, see http://esi-topics.com). please refer to the handbook of membrane computing [9] for general information in this area, and to the membrane computing web site [11] for the up-to-date information. tissue p systems form a class of p systems, which were introduced in [6]. tissue p systems were inspired by intercellular communication and cooperation between cells. briefly, a tissue p system consists of a set of membranes (abstracted from cells) placed in the nodes of a graph. the net of membranes deals with symbols and communicates symbols along channels specified in advance. the communication among cells is based on symport/antiport rules [8]. symport rules move objects across a membrane in one direction, whereas antiport rules move objects across a membrane in opposite directions. between two cells or between a cell and the environment, it is copyright c⃝ 2006-2012 by ccc publications 174 x. zhang, b. luo, l. pan possible that there exists only one channel or more than one channel [2]. a tissue p system works in a synchronized mode (a global clock is assumed, marking the time for the whole system). in each time unit, if there are rules that can be applied in each channel, then one of the rules must be applied. at most one rule is applied in each channel, non-deterministically chosen among the rules that can be applied in the channel. so, the use of rules is sequential at the level of each channel, but it is parallel at the level of the system. in this note, we shall consider the following two restrictive versions of tissue p systems: (1) only one channel is allowed between two cells or between a cell and the environment, (2) two channels are allowed between a cell and the environment. table 1: the computational completeness results for tissue p systems with only one channel between two cells, or between a cell and the environment; where ? indicates an open problem, † indicates that the result is obtained in this note, the other results are from [1]. symbols/cells 1 2 3 4 5 6 7 1 nreg ? ? ? ? nre† nre 2 nreg ? ? nre nre nre nre 3 nreg ? nre nre nre nre nre 4 nreg nre nre nre nre nre nre one of the central problems about tissue p systems is to investigate their computational power. this topic has been widely investigated for tissue p systems of various forms (e.g., see [4, 6, 10]). for the special case when tissue p systems use only a (very) small number of symbols and cells, please refer to [1, 3]. there seems to be a trade-off between the number of cells and the number of symbols needed for the computational power of tissue p systems. if only one channel is allowed between two cells or between a cell and the environment, it was shown that any recursively enumerable set of natural numbers can be generated by a tissue p system with at most seven cells and only one symbol [3]. the number of cells can decrease when two symbols are used; specifically, a tissue p system with two symbols and at most four cells can generate any recursively enumerable set of natural numbers [3]. the known results about the computational power of tissue p systems with only one channel between two cells or between a cell and the environment are listed in table 1. if two channels are allowed between a cell and the environment, then one cell is enough to obtain computational completeness for tissue p systems with at most five symbols [1]. the number of symbols for computational completeness can decrease when the number of cells increases: for the case of tissue p systems with two channels between a cell and the environment, the computational completeness can be obtained by tissue p systems with two cells and three symbols, or three cells and two symbols. the known results about the computational power of tissue p systems with two channels between a cell and the environment are listed in table 2. table 2: the computational completeness results for tissue p systems with two channels between a cell and the environment; where ? indicates an open problem, † indicates that the result is obtained in this note, the other results are from [1]. symbols/cells 1 2 3 4 5 6 1 nfin ? ? ? nre† nre 2 ? ? nre nre nre nre 3 ? nre nre nre nre nre 4 ? nre nre nre nre nre 5 nre nre nre nre nre nre small universal tissue p systems with symport/antiport rules 175 in this note, we continue to investigate the computational power of tissue p systems with small numbers of symbols and cells. specifically, we look for universal one-symbol tissue p systems with symport/antiport rules having a small number of cells. it is proved that six cells suffice to generate any recursively enumerable set of natural numbers for such a one-symbol tissue p system, under the restriction that only one channel is allowed between two cells or between a cell and the environment. the idea used in the proof of the result can also be extended to the case of allowing two channels between a cell and the environment, then one cell can be saved. the results obtained in this note partially answer open problems formulated in [1] (see tables 1 and 2). this note is organized as follows. in section 2, the formal language theory preliminaries are recalled, including the formal definition of register machine. the formal definition of tissue p systems is introduced in section 3. two universal one-symbol tissue p systems are presented in section 4, with an overview of the computation. conclusions and comments are presented in section 5. 2 formal language theory preliminaries for an alphabet v , v ∗ denotes the set of all finite strings over v , with the empty string denoted by λ. the set of all nonempty strings over v is denoted by v +. a register machine is a construct m = (m,h,l0, lh,i), where m is the number of registers (each holds a natural number), h is the set of instruction labels, l0 is the start label (labeling an add instruction), lh is the halt label (assigned to instruction halt), and i is the set of instructions. each label from h labels only one instruction from i, thus precisely identifying it. the instructions are of the following forms: • li : (add(r), lj, lk) (add 1 to register r and then go to one of the instructions with labels lj, lk), • li : (sub(r), lj, lk) (if register r is non-zero, then subtract 1 from it, and go to the instruction with label lj; otherwise, go to the instruction with label lk), • lh : halt (the halt instruction). a register machine m computes (generates) a number n in the following way. the register machine starts with all registers empty (i.e., storing the number zero). it applies the instruction with label l0 and proceeds to apply instructions as indicated by labels (and, in the case of sub instructions, by the content of registers). if the register machine reaches the halt instruction, then the number n stored at that time in the first register is said to be computed by m. it is known that register machines compute all sets of numbers which are turing computable, hence they characterize nre [5] (nre is the family of length sets of recursively enumerable languages; that is, those recognized by turing machines). especially, it is known that three registers are enough to generate any recursively enumerable set of natural numbers. without loss of generality, it can be assumed that l0 labels an add instruction and that in the halting configuration all registers different from the first one are empty, and that the output register is never decremented during the computation (its content is only added to). without loss of generality, it can be assumed that in each add instruction li : (add(r), lj, lk) and in each sub instruction li : (sub(r), lj, lk) the labels li, lj, lk are mutually distinct (for a short proof, see [2]). 176 x. zhang, b. luo, l. pan 3 tissue p systems with symport/antiport rules tissue p systems were introduced in [6], and tissue-like p systems with channel states were introduced in [2]. in this note, the following type of systems is considered, omitting the channel states. a tissue p system (of degree m ≥ 1) with symport/antiport rules is a construct π = (o,t,e,w1, . . . ,wm,ch,(r(i,j))(i,j)∈ch), where: • o is the alphabet of objects; • t ⊆ o is the alphabet of terminal objects; • e ⊆ o is the set of objects present in the environment in arbitrarily copies each; • w1, . . . ,wm are strings over o, representing the multisets of objects placed in the cells of the system at the beginning of the computation (it is assumed that the system contains m cells, labelled with 1,2, . . . ,m); • ch ⊆ {(i,j) | i,j ∈ {0,1,2, . . . ,m},(i,j) ̸= (0,0)} is the set of links (channels) between cells (they were also called synapses in [2]; 0 indicates the environment); • r(i,j) is a finite set of antiport rules of the form x/y, for some x,y ∈ o∗, associated with the channel (i,j) ∈ ch. an antiport rule of the form x/y ∈ r(i,j) for the ordered pair (i,j) of cells means moving the objects specified by x from cell i (from the environment, if i = 0) to cell j, and at the same time moving the objects specified by y from cell j to cell i. the rules with one of x,y being empty are, in fact, symport rules, but we do not always explicitly consider this distinction here, as it is not relevant for what follows. note that the objects from e are never exhausted, irrespective of how many copies of each of them are brought into the system, arbitrarily many copies remain available in the environment. a configuration of a tissue p system is described by the multisets of objects over o associated with the cells of the system. the tuple (w1,w2, . . . ,wm) is the initial configuration. a halting configuration is a configuration such that there is no rule that can be applied. the computation starts from the initial configuration; in each time unit, a rule is used on each channel for which a rule can be used (if no rule is applicable for a channel, then no object passes over it). therefore, the use of rules is sequential at the level of each channel, but it is parallel at the level of the system: all channels which can use a rule must do it (the system is synchronously evolving). the computation is successful if and only if it halts (reaching a configuration where no rule can be applied). the result of a halting computation is encoded by the multiset of objects over t appearing in a cell specified in advance in the halting configuration. in this note, we deal with a restricted version of the systems introduced above. only channels (i,j) with i ̸= j are allowed. if only one channel is allowed between two cells or between a cell and the environment, then for any i,j only one of (i,j) and (j,i) is allowed. if two channels are allowed between two cells or between a cell and the environment, then both (i,j) and (j,i) are allowed. furthermore, only one-symbol tissue p systems are considered, hence we assume o = t = e = {a}. then, for simplicity, a one-symbol tissue p system is written as π = (w1, . . . ,wm,ch,(r(i,j))(i,j)∈ch); the output cell is that with label 1. we also write x/y for an antiport rule ax/ay and x for multiset ax. small universal tissue p systems with symport/antiport rules 177 4 universality results in [3], it was shown that one-symbol tissue p systems with symport/antiport rules having seven cells are turing universal when only one channel is allowed between two cells or between a cell and the environment. this result is improved by showing that six cells are enough for turing universality. theorem 1. for any recursively enumerable set l of natural numbers, a one-symbol tissue p system with symport/antiport rules having at most 6 cells can be constructed to generate l, under the restriction that only one channel is allowed between two cells or between a cell and the environment. proof: let us consider a register machine m = (m,h,l0, lh,i). as stated in section 2, a register machine with three registers can generate any recursively enumerable set l of natural numbers, where the instructions that act on the first register are add instructions. this means that m = 3. in what follows, we shall construct a tissue p system π with only one symbol a to simulate the register machine m. the system π consists of six cells labeled by 1, . . . ,6. the cell with label 1 represents register 1 (the output register); the cells with labels 2 and 3 represent registers 2 and 3, respectively; the cells with labels 4 and 5 are program cells (the cell with label 4 controls the simulation of the instructions that act on registers 1 and 2, the cell with label 5 controls the simulation of the instructions that act on register 3); the cell with label 6 is used as a trap that, whenever started, leads to a non-halting computation by using the antiport rule 2/2 in channel (6,0). the number stored in each register r (r = 1,2,3) is represented by the number of copies of symbol a in the cell with label r in the following way. if register 1 contains number n (n ≥ 0), then the cell with label 1 has n + 5 copies of symbol a; if register 2 (resp. 3) contains number n (n ≥ 0), then the cell with label 2 (resp. 3) has 2n + 2 copies of symbol a. without loss of generality, we assume that li = 2i + 1, 0 ≤ i ≤ t, t ≥ 0, are the labels of instructions of register machine m, and lt is the label of halting instruction halt. we define the function c: c(0) = 12, c(i + 1) = i∑ j=0 c(j) + c(0), for i ≥ 0. it is easy to check that the function c has the following properties. – for any i < k,j < k, i ̸= j, we have c(k) > c(i) + c(j) + 12. – for any i, 0 ≤ i ≤ t, we have c(li) > 12. – for any i ̸= j, 0 ≤ i,j ≤ t, we have c(li) ̸= c(lj). – for any i < j, 0 ≤ i,j ≤ t, the value c(li + 1) is between c(li) and c(lj). in the initial configuration of π, the cell with label 1 contains five copies of symbol a, and the cells with labels 2 and 3 contain two copies of symbol a, which represents the fact that registers 1, 2 and 3 initially have number 0; the cell with label 4 contains c(l0) copies of symbol a; the cells with labels 5 and 6 contain 0 copy of symbol a. in general, when the cell with label 4 contains c(li) copies of symbol a, system π starts to simulate the instruction li. formally, the tissue p system π is a construct of the form π = (5,2,2,c(l0),0,0,ch,(r(i,j))(i,j)∈ch), where: • ch = {(4, i) | i ∈ {0,1,2,5,6}} ∪ {(5,0),(5,3),(5,6),(6,0)}; 178 x. zhang, b. luo, l. pan • the sets of rules r(i,j), (i,j) ∈ ch, are as follows: r(4,0) = {(c(li) − 6)/(c(lj) − 5),(c(li) − 6)/(c(lk) − 5) | li : (add(1), lj, lk) ∈ r} ∪{(c(li) − 3)/(c(lj) − 1),(c(li) − 3)/(c(lk) − 1) | li : (add(2), lj, lk) ∈ r} ∪{(c(li) − 1)/(c(li + 1) − 3),(c(li + 1))/(c(lj)), (c(li + 1) − 2)/c(lk) | li : (sub(2), lj, lk) ∈ r}, r(4,1) = {6/5},r(4,2) = {3/1,1/3},r(4,6) = {2/0},r(5,3) = {3/1,1/3}, r(4,5) = {c(li)/0,0/c(lj),0/c(lk) | li : (add(3), lj, lk)} ∪{c(li)/0,0/c(lj),0/c(lk) | li : (sub(3), lj, lk)}, r(5,0) = {(c(li) − 3)/(c(lj) − 1),(c(li) − 3)/(c(lk) − 1) | li : (add(3), lj, lk) ∈ r} ∪{(c(li) − 1)/(c(li + 1) − 3),(c(li + 1))/(c(lj)), (c(li + 1) − 2)/c(lk) | li : (sub(3), lj, lk)}, r(5,6) = {2/0}, r(6,0) = {2/2}. in order to show that tissue p system π can correctly simulate register machine m, we only need to show how the following six kinds of rules of register machine m are simulated by tissue p system π. (1) simulating add instructions that act on register 1. let li : (add(1), lj, lk) be an add instruction that acts on register 1, and the cell with label 4 contains c(li) copies of symbol a (in the initial configuration, the cell with label 4 contains c(l0) copies of symbol a). the simulation uses the rules: 6/5 ∈ r(4,1), (c(li) − 6)/(c(lk) − 5) ∈ r(4,0), (c(li) − 6)/(c(lj) − 5) ∈ r(4,0). the simulation takes one step. at this step, six copies of symbol a from the cell with label 4 are sent to the cell with label 1 by the rule 6/5 ∈ r(4,1) (the number n stored in register 1 is encoded by n + 5 copies of symbol a in the cell with label 1, so the cell with label 1 contains at least five copies of symbol a, and thus the rule 6/5 ∈ r(4,1) can be applied), exchanging with five copies of symbol a. the number of copies of symbol a in the cell with label 1 increases by one, which simulates that the number stored in register 1 is increased by one. at the same time, the other copies of symbol a from the cell with label 4 can be used by the rule (c(li) − 6)/(c(lj) − 5) ∈ r(4,0) or (c(li) − 6)/(c(lk) − 5) ∈ r(4,0), non-deterministically chosen. note that the cell with label 4 also gets five copies of symbol a at this step by the rule 6/5 ∈ r(4,1). the cell with label 4 accumulates c(lj) or c(lk) copies of symbol a in total after this step. in this way, system π starts to simulate instruction lj or lk. all objects in cell 4 are simultaneously used by communications along the channels (4,0) and (4,1). if the system does not use at the same time the rule 6/5 ∈ r(4,1) and one of the rules (c(li) − 6)/(c(lj) − 5) ∈ r(4,0) or (c(li) − 6)/(c(lk) − 5) ∈ r(4,0), then the system will enter an infinite loop and then the computation gives no result. for instance, the rule (c(li) − 6)/(c(lj) − 5) ∈ r(4,0) or (c(li) − 6)/(c(lk) − 5) ∈ r(4,0) consumes c(li) − 6 copies of symbol a in the cell with label 4, and there are 6 copies of symbol a that are subject to other possible rules. because for 0 ≤ i′,j′,k′ ≤ t, the encoding c(li′) of each instruction li′ of register machine m is larger than 6, these 6 copies of symbol a cannot be used by the rules (c(li′) − 6)/(c(lj′) − 5), (c(li′) − 6)/(c(lk′) − 5), (c(li′) − 3)/(c(lj′) − 1), (c(li′) − 3)/(c(lk′) − 1), (c(li′) − 1)/(c(li′ + 1) − 3), c(li′ + 1)/c(lj′), (c(li′ + 1) − 2)/c(lk′), c(lt)/0 from r(4,0) and the rule c(li′)/0 from r(4,5). these 6 copies of symbol a can be used by the rule 3/1 or 1/3 from r(4,2), but not both, since at most one rule is allowed to be used in each channel at one step. so there are at least 3 copies of symbol a that are subject to other possible small universal tissue p systems with symport/antiport rules 179 rules. by the maximal parallel manner of application of rules, the rule 2/0 ∈ r(4,6) must be applied at this step, hence two copies of symbol a are sent to the cell with label 6. conversely, the rule 6/5 ∈ r(4,1) consumes 6 copies of symbol a in the cell with label 4, and there are c(li) − 6 copies of symbol a that are subject to other possible rules. for 0 ≤ i, i′, i′′,j′,k′ ≤ t, i′ ̸= i′′, i′ < i and i′′ < i, these c(li) − 6 copies of symbol a can be used by the rule c(li′′)/0 from r(4,5), and one of the rules (c(li′) − 6)/(c(lj′) − 5), (c(li′) − 6)/(c(lk′) − 5), (c(li′) − 3)/(c(lj′) − 1), (c(li′) − 3)/(c(lk′) − 1), (c(li′) − 1)/(c(li′ + 1) − 3), c(li′ + 1)/c(lj′), (c(li′ + 1) − 2)/c(lk′) from r(4,0). by the first property of the function c, c(i) > c(i′) +c(i′′) + 12, there are at least 6 copies of symbol a that are subject to other rules at this moment. these 6 copies of symbol a can be used by the rule 3/1 or 1/3 from r(4,2), but not both. therefore, there are at least 3 copies of symbol a that are subject to the rule 2/0 ∈ r(4,6); the rule 2/0 ∈ r(4,6) must be applied, and two copies of symbol a are sent to the cell with label 6. in each of these two cases, the cell with label 6 receives two copies of symbol a, and the rule (2,2) ∈ r(6,0) would be applied forever. consequently, the rule 6/5 ∈ r(4,1) and one of the rules (c(li) − 6)/(c(lj) − 5) ∈ r(4,0) or (c(li) − 6)/(c(lk) − 5) ∈ r(4,0) must be used, hence the instruction li : (add(1), lj, lk) is correctly simulated by system π (otherwise, the computation cannot halt). (2) simulating add instructions that act on register 2. let li : (add(2), lj, lk) be an add instruction that acts on register 2, and the cell with label 4 contains c(li) copies of symbol a. the following rules are used to simulate the instruction li : (add(2), lj, lk): 3/1 ∈ r(4,2), (c(li) − 3)/(c(lj) − 1) ∈ r(4,0), (c(li) − 3)/(c(lk) − 1) ∈ r(4,0). the simulation takes one step. the content of the cell with label 4 is split into two parts, with one part containing c(li) − 3 copies of symbol a and the other part containing 3 copies of symbol a. the c(li) − 3 copies of symbol a in the cell with label 4 are exchanged with c(lj) − 1 copies of symbol a from the environment by the rule (c(li) − 3)/(c(lj) − 1) ∈ r(4,0), or exchanged with c(lk) − 1 copies of symbol a by the rule (c(li) − 3)/(c(lk) − 1) ∈ r(4,0), non-deterministically chosen. the 3 copies of symbol a in the cell with label 4 are exchanged with one copy of symbol a from the cell with label 2 by the rule 3/1 ∈ r(4,2). in this way, the number of copies of symbol a in the cell with label 2 increases by two, which simulates that the number stored in register 2 is increased by one. the number of copies of symbol a in the cell with label 4 becomes c(lj) or c(lk), which means that system π starts to simulate instruction lj or lk. similar to the case of simulating add instructions that act on register 1, if system π does not use the above rules, then the rule 2/2 ∈ r(6,0) can be applied and would be applied forever. the computation is not a successful one and gives no result. therefore, the add instruction li : (add(2), lj, lk) is correctly simulated by system π. (3) simulating sub instructions that act on register 2. let li : (sub(2), lj, lk) be an sub instruction that acts on register 2, and the cell with label 4 contains c(li) copies of symbol a. the following rules are used to simulate li : (sub(2), lj, lk): (c(li) − 1)/(c(li + 1) − 3) ∈ r(4,0), 1/3 ∈ r(4,2), c(li + 1)/c(lj) ∈ r(4,0), (c(li + 1) − 2)/c(lk) ∈ r(4,0). the simulation takes two steps. note that one of the main issues in the process of simulating li : (sub(2), lj, lk) is to check whether the number stored in register 2 is non-zero. the number n in register 2 is represented by 2n+2 copies of symbol a in the cell with label 2, so the system should check whether the cell with label 2 contains more than two copies of symbol a. the checking is done as follows. at the first step, the c(li) copies of symbol a are split into two parts, with one 180 x. zhang, b. luo, l. pan part containing c(li) − 1 copies of symbol a and the other part containing one copy of symbol a. the c(li) − 1 copies of symbol a can be used by the rule (c(li) − 1)/(c(li + 1) − 3) ∈ r(4,0), exchanging with c(li + 1) − 3 copies of symbol a. for the remaining copy of symbol a, there are two cases. if the number stored in register 2 is non-zero and thus the cell with label 2 contains at least 4 copies of symbol a, then the only rule which can be used is 1/3 ∈ r(4,2). if the number stored in register 2 is zero and thus the cell with label 2 contains only 2 copies of symbol a, then no rule can be used and the symbol stays in the cell with label 4. in the case of the number stored in register 2 being non-zero, the number of copies of symbol a in the cell with label 2 decreases by two (this simulates that the number stored in register 2 is subtracted by one), and the number of copies of symbol a in the cell with label 4 becomes c(li + 1). for the case of the number stored in register 2 being zero, the number of copies of symbol a in the cell with label 2 keeps unchanged (this simulates that the number stored in register 2 is still zero), and the cell with label 4 accumulates c(li + 1) − 2 copies of symbol a. at the second step, by using the rules c(li + 1)/c(lj) ∈ r(4,0) or c(li + 1) − 2/c(lk) ∈ r(4,0), the cell with label 4 eventually obtains c(lj) or c(lk) copies of symbol a, respectively. note that system π should simulate li : (sub(2), lj, lk) by using the above rules; otherwise, the rule 2/0 ∈ r(4,6) must be used, which actives a trap in the sense of the rule 2/2 ∈ r(6,0) being used forever. (4) simulating add instructions that act on register 3. let li : (add(3), lj, lk) be an add instruction that acts on register 3, and the cell with label 4 contains c(li) copies of symbol a. the simulation uses the rules: c(li)/0 ∈ r(4,5), (c(li) − 3)/(c(lj) − 1) ∈ r(5,0), (c(li) − 3)/(c(lk) − 1) ∈ r(5,0), 3/1 ∈ (5,3), 0/c(lj) ∈ r(4,5), 0/c(lk) ∈ r(4,5). the simulations of instructions that act on register 3 are controlled by the cell with label 5. the simulation of the instruction li : (add(3), lj, lk) takes three steps. at the first step, the c(li) copies of symbol a in the cell with label 4 are sent to cell with label 5 by the rule c(li)/0 ∈ r(4,5). in the cell with label 5, the c(li) copies of symbol a are split into two parts at the second step, with one part containing c(li) − 3 copies of symbol a and the other part containing 3 copies of symbol a. the c(li) − 3 copies of symbol a are exchanged with c(lj) − 1 copies of symbol a from the environment by the rule (c(li) − 3)/(c(lj) − 1) ∈ r(5,0) or exchanged with c(lk) − 1 copies of symbol a from the environment by the rule (c(li)−3)/(c(lk)−1) ∈ r(5,0), non-deterministically chosen. the 3 copies of symbol a in cell with label 5 are exchanged with one copy of symbol a from the cell with label 3. in this way, the number of copies of symbol a in the cell with label 3 increase by two, which simulates that the number stored in register 3 is increased by one. the cell with label 5 accumulates c(lj) or c(lk) copies of symbol a, these copies of symbol a are sent to the cell with label 4 by the rules 0/c(lj) ∈ r(4,5) or 0/c(lk) ∈ r(4,5) at the third step. in this way, the system can continue to simulate the next instruction lj or lk. note that system π should simulate li : (add(3), lj, lk) by using the above rules; otherwise, the rule 2/0 ∈ r(5,6) must be used, which actives a trap in the sense of the rule 2/2 ∈ r(6,0) being used forever. therefore, the add instruction li : (add(3), lj, lk) is correctly simulated by system π. (5) simulating sub instructions that act on register 3. let li : (sub(3), lj, lk) be an sub instruction that acts on register 3, and the cell with label 4 contains c(li) copies of symbol a. the following rules are used to simulate the instruction li : (sub(3), lj, lk): c(li)/0 ∈ r(4,5), (c(li) − 1)/(c(li + 1) − 3) ∈ r(5,0), c(li + 1)/c(lj) ∈ r(5,0), 1/3 ∈ r(5,3), (c(li + 1) − 2)/c(lk) ∈ r(5,0), 0/c(lj) ∈ r(4,5), 0/c(lk) ∈ r(4,5). the simulation takes four steps. similar to the simulation of add instructions that act on register 3, the c(li) copies of symbol a in the cell with label 4 are first sent to the cell with label small universal tissue p systems with symport/antiport rules 181 5 and then the cell with label 5 takes care of the simulation of sub instructions that act on register 3. at the second step, system π starts to check whether the number stored in register 3 is non-zero. note that the number n in register 3 is represented by 2n + 2 copies of symbol a, thus the system should check whether the cell with label 3 contains more than two copies of symbol a. the checking is done as follows. the c(li) copies of symbol a in the cell with label 5 are split into two parts, with one part containing one copy of symbol a and the other one containing c(li) − 1 copies of symbol a. the c(li) − 1 copies of symbol a are exchanged with c(li + 1) − 3 copies of symbol a from the environment by the rule (c(li) − 1)/(c(li + 1) − 3) ∈ r(5,0). for the one copy of symbol a, there are two cases. if the cell with label 3 contains at least 3 copies of symbol a (this corresponds to the fact that the number stored in register 3 is non-zero), then the only rule which can be applied is 1/3 ∈ r(5,3) and thus the number stored in register 3 is decreased by one. if the number of copies of symbol a in the cell with label 3 is less than 3 (this corresponds to the fact that the number stored in register 3 is zero), then no rule can be applied and thus the number in register 3 keeps unchanged; furthermore, the one copy of symbol a stays in the cell with label 5. in this way, the cell with label 5 accumulates either c(li + 1) copies of symbol a or c(li + 1) − 2 copies of symbol a. by using the rules c(li + 1)/c(lj) ∈ r(5,0) or (c(li + 1) − 2)/c(lk) ∈ r(5,0), the c(li + 1) copies of symbol a are exchanged with c(lj) copies of symbol a or the c(li + 1) − 2 copies of symbol a are exchanged with c(lk) copies of symbol a at the third step. at the fourth step, the c(lj) or c(lk) copies of symbol a in the cell with label 5 are sent to the cell with label 4 by the rules 0/c(lj) ∈ r(4,5) or 0/(lk) ∈ r(4,5), and system π starts to simulate the next instruction lj or lk. note that the rules 2/0 ∈ r(5,6) and 2/2 ∈ r(6,0) guarantee that system π should use the above rules to simulate li : (sub(3), lj, lk); otherwise, system π would enter into a computation that cannot halt. (6) simulating the halting instruction. the halting instruction lh is simulated by the rule c(lt)/0 from r(4,0). when the register machine m reaches the halting instruction lh, the cell with label 4 accumulates c(lt) copies of symbol a. at that moment, the rule c(lt)/0 ∈ r(4,0) can be used, by which all the copies of symbol a in the cell with label 4 are moved into the environment, and thus the computation halts. from the above explanation, we can find that the register machine m can be correctly simulated by tissue p system π. from the formal definition of tissue p system π, we see that system π has at most one channel between two cells or between a cell and the environment. therefore, theorem 1 holds. 2 corollary 2. for any recursively enumerable set l of natural numbers, a one-symbol tissue p system with symport/antiport rules having at most 5 cells can be constructed to generate l, when two channels are allowed between a cell and the environment. proof: let us first recall that the cell with label 6 in system π constructed in the proof of theorem 1 is used as a trap. if two channels are allowed between a cell and the environment, then the “trap” cell can be saved by introducing two antiport rules 2/2c(lt) ∈ r(4,0) and 2/2c(lt) ∈ r(5,0), where the function c is defined as in the proof of theorem 1. these two rules are used as a “trap” in the sense that, once one of the rules is used, then the rule will be used forever. note that the number 2c(lt) is so large that even in the case of all possible rules which can be used being used, there remain enough copies of symbol a to repeat the “trap” rule 2/2c(lt) ∈ r(4,0) or 2/2c(lt) ∈ r(5,0). in this way, the computation enters an infinite loop. the changes in the construction from the proof of theorem 1 are left to the reader. 2 182 x. zhang, b. luo, l. pan 5 conclusions and comments in this note, a one-symbol universal tissue p system with symport/antiport rules having six cells is obtained, under the restriction that one channel is allowed between two cells or between a cell and the environment. as a corollary of the above result, a one-symbol universal tissue p system with symport/antiport rules having five cells is also constructed, under the restriction that two channel are allowed between a cell and the environment. these results partially answer open problems formulated in [1] (see tables 1 and 2). some improvement of the number of cells used in the universal tissue p systems given in this work may be still possible (thus answering more open problems in tables 1 and 2). a natural idea is to consider removing the trap cell, just as done in the case of allowing two channels between the cell and the environment. in the universal tissue p systems given in this work, two cells have been used to control the simulation of the sub instructions of register machine: the cell with label 4 is responsible for the simulation of the sub instructions that act on register 2; the cell with label 5 takes care of the simulation of the sub instructions that act on register 3. we conjecture that one cell is enough to take care of the simulation of the sub instructions that act on registers 2 and 3. acknowledgements this work was supported by the national natural science foundation of china (grant nos. 61033003, 30870826, 61003131 and 61003038), the fundamental research funds for the central universities (2010zd001), ph.d. programs foundation of ministry of education of china (20100142110072), the opening foundation of key laboratory of university of science and technology of china for high-performance computing and applications (grant no. nhpcc-kf1102), and scientific research foundation for doctor of anhui university (grant no. 02203104). bibliography [1] a. alhazov, r. freund, m. oswald, tissue p systems with symport/antiport rules and small numbers of symbols and cells, lecture notes in computer science, 3572:100–111, 2005. [2] r. freund, gh. păun, m.j. pérez-jiménez, tissue-like p systems with channel states, theoretical computer science, 296:295–326, 2003. [3] r. freund, m. oswald, tissue p systems with symport/antiport rules of one symbol are computational complete, in: m.a. gutiérrez-naranjo, gh. păun, m.j. pérez-jiménez (eds.), proceedings of the european science foundation pesc exploratory workshop cellular computing (complexity aspects), sevilla, pp.178–187, 2005. [4] s.n. krishna, k. lakshmanan, r. rama, tissue p systems with contextual and rewriting rules, lecture notes in computer science, 2597:339–351, 2003. [5] m. minsky (eds.), computation: finite and infinite machines, prentice hall, 1967. [6] c. martín vide, j. pazos, gh. păun, a. rodríguez patón, tissue p systems, theoretical computer science, 296:295–326, 2003. [7] gh. păun, computing with membranes, journal of computer and system sciences, 61(1):108–143, 2000. small universal tissue p systems with symport/antiport rules 183 [8] a. păun, gh. păun, the power of communication: p systems with symport/antiport, new generation computing, 20(3):295–305, 2002. [9] gh. păun, g. rozenberg, a. salomaa (eds.), handbook of membrane computing, oxford university press, 2010. [10] y. rogozhin, s. verlan, on the rule complexity of universal tissue p systems, lecture notes in computer science, 3850:356–362, 2006. [11] the p systems web page: http://ppage.psystems.eu international journal of computers communications & control issn 1841-9836, 10(2):211-221, april, 2015. modelling and analysis of mobile computing systems: an extended petri nets formalism l. kahloul, a. chaoui, k. djouani laid kahloul linfi laboratory, computer science department, university of biskra biskra, 07000, algeria. e-mail: kahloul2006@yahoo.fr allaoua chaoui misc laboratory, computer science department, university of constantine constantine, 25000, algeria. e-mail: a_cahoui2001@yahoo.fr karim djouani lissi laboratory, paris est university, paris, france f’sati at tut, pretoria south africa. e-mail: djouani@univ-paris12.fr abstract: in its basic version, petri nets are defined as fixed graphs, where the behaviour of the system is modelled as the marking of the graph which changes over time. this constraint makes the petri nets a poor tool to deal with reconfigurable systems as mobile computing systems, where the structure of the system can change as its behaviour, during time. many extended petri nets were proposed to deal with this weakness. the aim of this work is to present a new extension of petri nets, where the structure of the graph can be highly flexible. this flexibility gives a rich model with complex behaviours, not allowed in previous extensions. the second aim is to prove that even these behaviours are so complex; they can be translated into other low level models (as coloured petri nets [21]) and so be analysed. this translation exploits dynamic petri nets [11] as an intermediary representation between our model and coloured petri nets. keywords: petri nets , coloured petri nets, dynamic petri nets, mobile computing system. 1 introduction the development of computer science technologies and increasing user requirements are the major drivers of the birth of sophisticated solutions. mobility with its soft (code mobility) and hard (device mobility) aspects is one of these solutions. when some disaster menaces a critical system during its execution, it seems a good idea to transfer this system and to save its state to another, more secure site, where it can continue its execution. by soft mobility, we mean a system where code can migrate from one site to another site. many reasons can cause such migration and many methods and techniques can be used. on the other hand, travelling users who request some computing services need also some specific mobile devices. in this last case, we talk about hard mobility. applications using code mobility are increasing. code mobility touches critical domains (military, spacial, medicine). such domains require that used applications insure a set of properties. safety, liveness, no deadlock, fault tolerance, and security are example of such required properties. using formal methods, one can develop systems and proof (or verify) presence or absence for specific properties, in these systems. formal methods are languages, tools, and approaches allowing specification and verification of systems. formal languages are based on a well-defined syntax and a formal semantics. their formal semantics allow developers to copyright © 2006-2015 by ccc publications 212 l. kahloul, a. chaoui, k. djouani verify specification written in such languages. for some languages, automatic tools are proposed to verify the specifications. using formal methods in code mobility is not recent. most currently methods can be considered as derived from processes-algebra [6] or state-transition systems. as a state-transition model, petri nets [18] was proposed to model concurrent and parallel systems. this formalism has a graphic representation and a formal background. using places, transitions and connecting arcs, this formalism can specify states, actions and transitions between states through which a system evolves. using petri nets, one can analyse behavioural or structural properties of a system. to model mobility with petri nets, the most important contribution can be found in high level petri nets. many extensions have been proposed to adapt petri nets to mobile systems: mobile nets and dynamic petri nets [11], nested petri nets [13], hyper-petrinets [14], mobile synchronous petri net [15]... the first idea that has motivated our work was mobile code systems. in these systems, type of resources and their bindings play a central role in the migration procedure. resources decide also the success or failure of the process. proposed formal methods founded in the literature do not deal with these aspects and their problems. in our first work, we have proposed a naive version of ”labelled reconfigurable nets” [23] extended to ”coloured reconfigurable nets” in [24]. our objective was to propose a graphical tool to model mobile code systems in an easy and intuitive way. in these works, we were interested to provide formalisms that model mobility, explicitly (the mobility is modelled through the reconfiguration of the net’s structure when some transitions are fired). when trying to offer this quality in a model, we have to deal with the problem of interpreting this reconfiguration formally. in [23, 24], we have introduced specific labelled transitions: ”reconfigure-transitions”, which reconfigure the structure of the net when they are fired. the first drawback of this solution is that we must provide a specific treatment of these transitions when the model is analysed. in [25], we have proposed an interpretation of reconfigurable labelled nets into a high order maude (reconfigurable maude). the idea was to extend maude [21] with some “reconfigure rewriting rules”. these rules can represent the reconfigure-transitions. reconfigurable maude can be used to simulate reconfigurable labelled nets. in the current work, we will present another version that we call extended “labelled reconfigurable nets”, where the label of a reconfigure-transition is defined as a tuple. this tuple contains a set of values which belong to different types. three specific types are defined: p (for places), t (for transitions), and a (for arcs). these types will contain signed (negative as well as positive) objects (places, transitions, and arcs). the presence of a positive object (resp. negative object) in a label of a reconfigure-transition can be the cause to add (resp. delete) this object to (resp. from) the structure of this net. using these labels, the structure of the net can be expanded, reduced, or destroyed. our second contribution is the proposition of a method to analyse this model, using dynamic nets [11]. dynamic nets can be translated into cpn [21], and can be analysed using cpn-tool [26]. we propose the encoding of our model into dynamic nets. this encoding will be used to analyse this model. this encoding has been proved. the encoding and its proof are not presented in this paper. this paper is organized as follows: the section two presents the formal definition of extended labelled reconfigurable nets (elrn), its semantics, and an example of modelling. section three discusses the analysis issue that we have developed for the analysis of elrn using a translation of the model into dynamic nets [11]. section four presents a comparison between our work and other similar works, and finally, section five will conclude this paper. 2 extended labelled reconfigurable nets extended labelled reconfigurable nets (elrn) are an extension of coloured petri nets [21]. in elrn, the set of transitions is divided into two subsets: ordinary transitions (ot ) and modelling and analysis of mobile computing systems: an extended petri nets formalism 213 reconfigure-transitions (rt ). a reconfigure-transition has a label (a tuple of values). the firing of an ordinary transition will change the marking of the net in an ordinary manner, as in cpn [21]. the firing of a reconfigure-transition changes the marking of the net as well as the structure of the net. a reconfigure-transition changes the structure of the net, by adding or deleting a node (places, transitions, arcs). to add a place, a reconfigure-transition must have a label which contains: (the name of the place, its initial marking, its input (resp. output) transitions with their incoming (resp. outgoing) expressions). to add a transition, a reconfigure-transition must have a label which contains: (the name of the transition, its guard, its input (resp. output) places with their incoming (resp. outgoing) expressions). finally, to add an arc, a reconfiguretransition must have a label which contains: (the name of the arc, its labelling expression, and its input/output nodes). names of nodes (places, transitions, arcs) can be preceded by a negative sign. the presence of negative node in a label causes its elimination from the net (iff it existed in the original net), once the reconfigure-transition is fired. in the following subsection, we present the formal definition of this model, its semantics, and a modelling example. 2.1 formal definition let name be a set of names. let p , t , and a be three finite and disjoint subsets of the set name. an extended labelled reconfigurable nets np,t,a is a 10-tuple (σ, p ′, t ′, a′, c, g, e, i, l), where: • σ: a set of types (colours). we denote by σ∗ the set of all multi-sets of the set σ; • p ′: a set of places; p ′ ⊆ p . • t ′: a set of transitions; p ′ ⊆ p . t ′ = ot ∪ rt (ot for ordinary transitions, and rt for reconfigure-transitions). • a′: a set of arcs. a′ ⊆ (t ′ × p ′) ∪ (p ′ × t ′). for a place p in p ′ and a transition t in t ′, we can have an arc a in a′ written a = (p, t) (resp. (t, p)), if it connects p to t (resp. if it connects t to p). we write (p, .) (rep (t, .)) to denote the set of arcs that start from p (resp. to denote the set of arcs that start from t). • c: a colour function associated with each place. c : p ′ → σ. for each place p, c associates a unique colour (type) c(p); • g: a guard function associated with each transition. g : t ′ → exp. where exp is the set of all boolean expressions that can be constructed using constants and variables defined in types σ; • e: an expression function that associates to each arc a in a′ an expression e(a). the expression e(a) is a multi-set of c(p) where a ∈ (p, .). • i: is an initial state of the net. i=< m0, s0 >, where m0 is the initial marking of places p ’. m0 : p ′ → σ∗. s0 is the initial structure of the net. we take s0 = p ′ ∪ t ′ ∪ a′. • l: a labelling function which associates to each transition rt in rt a label. we denote by (p, exp)∗ (resp. (t, exp)∗) the set of couples composed of a place in p and an expression (resp. composed of a transition in t and an expression). we denote by p − the set of names defined in p preceded by a negative sign (idem for t − and a−). we denote by ϵ the empty untyped element. a label is a tuple of values. three kinds of labels can be used:(i) (p ∪ p −, σ∗ ∪ {ϵ}, (t, exp)∗ ∪ {ϵ}, (t, exp)∗ ∪ {ϵ}) labels a transition which adds a place 214 l. kahloul, a. chaoui, k. djouani to the net, (ii) (t ∪ t −, exp ∪ {ϵ}, (p, exp)∗ ∪ {ϵ}, (p, exp)∗ ∪ {ϵ}) labels a transition which adds a place to the net, and (iii) (a∪a−, σ∗ ∪{ϵ}, name∪{ϵ}, name∪{ϵ}) labels a transition which adds an arc to the net. so, we have: l : rt → (p ∪ p − × σ∗ ∪ {ϵ} × (t, exp)∗ ∪ {ϵ} × (t, exp)∗ ∪ {ϵ}) ∪ (t ∪ t − × exp ∪ {ϵ} × (p, exp)∗ ∪ {ϵ} × (p, exp)∗ ∪ {ϵ}) ∪ (a ∪ a− × σ∗ ∪ {ϵ} × name ∪ {ϵ} × name ∪ {ϵ}). 2.2 semantics let n be an extended labelled reconfigurable nets, and t a transition in t. as in cpn (coloured petri nets) [21], we denote by ◦t the set of input places for the transition t, and by t◦ the set of output places for the transition t. let i0 =< m0, s0 > be the current state of n. firing t changes i0 towards i1 =< m1, s1 >. we denote this as: < m0, s0 >→t< m1, s1 >. in case of t in ot , we have: s1 = s0. preconditions to fire t a binding β is a function that assigns some values to some variables. we denote by e(p, t)[β] a binding in which every variable in e is assigned to some values depending on β. now, the transition t can be fired iff there is a binding β on the variables of e(p, t) such that m0(p) ≤ e(p, t)[β], for each p ∈◦ t, and g(t) is true. post-conditions of firing t after the firing of t, n will transit from its current state i0 to another state i1 =< m1, s1 >. for each p in ◦t, we will have: m1(p) = m0(p) − e(p, t)[β]. for each p ∈ t◦, we will have: m1(p) = m0(p) + e(t, p)[β]. if t ∈ ot then s1 = s0. if t ∈ rt then: s0 (which is p ′0 ∪ t ′0 ∪ a′0) will be updated to s1 = p ′1 ∪ t ′1 ∪ a′1. three cases are possible: • t changes p ′: – by adding a place p: the label of t must be: (p, mp, {(in_t1, in_e1), ..., (in_tn, in_en)}, {(out_t1, out_e1), ..., (out_tl, out_el)}); where: mp is the initial marking of p, and (in_ti, in_ei) (resp. (out_tj, out_ej)) an input (resp. an output) transition with its incoming (resp. outgoing) expressions. so, p ′1 = p ′0∪{p}, t ′1 = t ′0, and a′1 = a′0. – or deleting the place p: the label must be (−p, ϵ, ϵ, ϵ). so, p ′1 = p ′0 \{p}, t ′1 = t ′0, and a′1 = a′0. • t changes t ′: – by adding the transition at: the label of t must be: (at, gat, {(in_p1, in_e1), ..., (in_pn, in_en)}, {(out_p1, out_e1), ..., (out_pl, out_el)}); where: gt is a guard, and (in_pi, in_ei) (resp. (out_pj, out_ej)) an input (resp. an output) places with its incoming (resp. outgoing) expressions. so, p ′1 = p ′0, t ′1 = t ′0 ∪ {at}, and a′1 = a′0. – or deleting the transition dt: the label of t must be: (−dt, ϵ, ϵ, ϵ). so, p ′1 = p ′0, t ′1 = t ′0 \ {dt}, and a′1 = a′0. • or t changes a′: modelling and analysis of mobile computing systems: an extended petri nets formalism 215 – by adding the arc a = (p, t′): the label must be (a, ea, p, t′); where: ea is a labelling expression, p the name of a place, and t′ the name of a transition. so, p ′1 = p ′0, t ′1 = t ′0, and a′1 = a′0 ∪ {a}. – by adding the arc a = (t′, p): (a, ea, t′, p); where: ea is a labelling expression, p the name of a place, and t′ the name of a transition. so, p ′1 = p ′0, t ′1 = t ′0, and a′1 = a′0 ∪ {a}. – or deleting the arc a: the label of t must be (−a, ϵ, ϵ, ϵ). so, p ′1 = p ′0, t ′1 = t ′0, and a′1 = a′0 \ {a}. 2.3 a modelling example in the example of fig 1, we make an explicit subdivision of the net into a set of sub-blocs. each sub-bloc can represent an agent or a site where many agents reside. each sub-bloc has a title presented on the top of this sub-bloc. we use a specific graphical representation for the reconfigure-transitions, to distinguish them from the ordinary transitions. in fig 1, we have three agents. agent1 and agent3 are immobile agents which existed on two different sites (s1 and s2). agent2 is a mobile agent, which is located initially on the site s1. on the site s1, agent2 communicates with agent1 through the communication place c1. agent2 receives an information (of a some type that we denote: information) from c1, then it moves towards the site s2, where agent3 is located. on the site s2, agent2 passes the information received from agent1 to the agent3, through the place c2. to do the transfer of this information, a new arc (t22, c2) must be added to the model, and the arc (c1, t21) must be deleted from the model. to do this reconfiguration in the model, we use the two reconfigure-transitions: rt1 and rt2, with two labels: l1 =< −(c1, t21) > and l2 =< (t22, c2), t22, c2, x >. x is the variable which transfers the information to agent3. fig 2 shows the system after the movement of the agent2, where the system is reconfigured. the initial marking of the places is {m0(p11) =< inf >, m0(p21) =< • >}, where inf is a data of the type information and < • > represents the constant black-token of the type black-token (a type which contains only one value which is < • >). the variable x is also of type information. the non-labelled arcs are implicitly labelled < • >. 3 on the analysis of elrn our aim is to offer a way to analyse elrn models. we have proposed that the analysis of elrn can be done through the analysis of some equivalent models in cpn (coloured petri nets [21]) or pn (petri nets [18]). petri nets and coloured petri nets have been studied for many years and have many automatic verification-tools [22]. to profit from these tools, we must show that there is some correct transformation (an unfolding) from the elrn formalism (as high level nets) towards cpn (and pn). the transformation of elrn directly into cpn or pn is a hard task; so we propose to prove that the extended labelled reconfigurable nets (elrn) models can be encoded into dynamic nets (dn) [11]. we use the dn as an intermediary pass between elrn and cpn. at this stage, the obtained specification can be transformed into cpn (coloured petri net model) model and so, be analysed using cpn-tool for example. the unfolding of elrn models into dn is developed and has been proved. 216 l. kahloul, a. chaoui, k. djouani figure 1: example of an elrn. figure 2: example of an elrn (after reconfiguration). modelling and analysis of mobile computing systems: an extended petri nets formalism 217 4 related works research on extending petri nets (to model systems, with dynamic structure) has provided some remarkable results. we can distinguish between extensions that model mobility in an implicit way (no modification in the structure of the net), or in an explicit way (the reconfiguration, of the net structure, models mobility). the most important propositions are dedicated to mobile systems and mobile agents. in prn (predicate/transition nets) [16], mobile agents are modelled through tokens. these agents are transferred by transition firing from an environment to another. in this work, the structure of the net does not change. the agents are represented as token, so this abstraction does not allow representing some complex behaviour of this kind of agents. in [15], authors proposed mspn (mobile synchronous petri net) as formalism to model mobile systems and security aspects. they have introduced the notions of nets (an entity) and disjoint locations to explicit mobility. a system is composed of set of localities that can contain nets. to explicit mobility, specific transitions are introduced. two kinds of specific transitions were proposed: new and go. firing a go transition moves the net from its locality towards another locality. the destination locality is given through a token in an input place of the go transition. in this work, mobility is not also explicit. mobility is implicitly modelled by the activation of some nets and the deactivation of other nets, using tokens. migration of an agent is modelled by the deactivation of the net modelling this agent in a locality and the activation of the net that represents this same agent in the destination locality. so, this is a kind of simulation of mobility. in nested nets [21], tokens can be petri nets themselves. this model allows some transition when they are fired to create new nets in the output places. nested nets are hierarchic nets where we have different levels of details. places can contain nets, and these nets can contain also nets as tokens in their places etcetera. so all nets created when a transition is fired are contained in places. so the created nets are not in the same level with the first net. this formalism is proposed to adaptive work-flow systems. self modifying nets (smn) [18] is an extension of petri nets. in this formalism, edges can be labelled by names of places. if the name p is used as the weight of an arc, then this means that the number of tokens to be moved through this arc is equal to the current marking of the place p. so, in self modifying nets, the weights of arcs are dynamic. these weights depend on the current marking of the net. even smn offers more computational power than pn; mobility was not the objective of this extension. though, this formalism was the basis for other more important formalisms like "reconfigurable nets" [10]. in "reconfigurable net" [10], the structure of the net is not explicitly changed. no places or transitions are added in runtime. the key difference with coloured petri nets is that firing transition can change names of output places. names of places can figure as weight of output arcs. this formalism is proposed to model nets with fixed components but where connectivity can be changed over time. in object petri nets [21], and elementary object nets (eon) [12], tokens can be petri nets themselves. in an eon, we distinguish between system nets and object nets. object nets play the role of object tokens that can appear in places of a system net. here, a two-level system modelling technique is introduced. the system net which represents the external level and the object net which represents the internal level can have some synchronous transitions. in this case, these transitions must be fired simultaneously in the two levels. object nets used as tokens in a system net can also interact. eon formalism was proposed to model some kind of systems like: work-flow, flexible manufacturing, and mobile agents. based on the eon formalism, other proposals were done: nested nets [13], petri hypernets [14], nets within nets [19], etc in nested nets [21], firing some transitions creates new nets (called token nets) in their output places. nested nets are also hierarchic nets, where we have different levels of details. places can contain nets, and these nets can contain also nets as tokens in their places. this formalism was proposed to adaptive work-flow systems. adaptive means an ability to modify processes in a structured 218 l. kahloul, a. chaoui, k. djouani way, for example by replacing a sub-process or extending it. petri hypernets [14] are proposed to model mobile agents. mobile agents are modelled as nets. these mobile agents are manipulated by other agents (modelled as nets) who can be also mobile. we call open net a net used to model a mobile agent. this open net plays the role of a token in another net; this last one is called hyper-marking net. as a difference with valk’s proposal [12, 20], the inter-level synchronization in hyper-nets is achieved solely by means of exchanging messages. in [17], pepa nets are proposed, where mobile code is modelled by expressions of the stochastic process algebra pepa which play the role of tokens in (stochastic) petri nets. the petri net of a pepa net models the architecture of the net, which is a static one. mobile petri nets (mpn) [11] extend coloured petri nets to model mobility. mpn is inspired by join-calculus [4]. the output places of transition are dynamic. the input expression of a transition defines the set of its output places. in all these formalisms, the structure of the net is not changed and mobility is modelled implicitly through the net’s dynamic. in these models, an important work is required from the modeller to model mobility implicitly. mpn are extended to dynamic petri net (dpn) [11]. mobility in dpn is modelled explicitly, by adding subnets when transitions are fired. however, the dynamic petri nets formalism implies some constraints: (i) no transition without input places, (ii) added nets, to the original net, must not modify the input of an existing transition in the original net, (iii) we can not add a connection between two disconnected existing nodes, (iv) and we cannot delete nodes (place, transition or connection). in this paper, we have proposed an extension for petri nets that can be used to model mobility (and in general, reconfigurable systems): extended labelled reconfigurable nets. extended labelled reconfigurable nets is more flexible and more expressive and does not imply constraints on the dynamic of the structure. we consider that extended labelled reconfigurable nets can be used by reconfigurable systems developers with more flexibility than other formalisms. this is due to the feature that it models mobility explicitly through mobility of nodes in the labelled reconfigurable net. developers can encode mobile aspects of their system directly and explicitly in the earn formalism. the power of petri nets resides in its verification methods. when extending petri nets, we reach some formalism with a high expressiveness, but the analysis becomes more complex or even impossible. developers of new formalisms must propose analysis techniques. mostly, they are proposing some translation (or encoding) of their formalisms into some well-known formalism or approach in modelling domain. such translation allows the analysis of the new formalisms models using techniques of well-known formalisms. the most famous encoding can be found in the unfolding of petri nets into automaton to apply model-checking, and then the unfolding of cpn [21] (coloured petri nets) into pn [18] (petri nets) to analyse some properties that are not analysed on the cpn directly. we can find other works, in literature. in [22], author authors studied equivalence between the join calculus [4] and different kinds of high level nets. they proved the equivalence between reconfigurable nets (rn) [10] (an extension version of pn) and the join calculus. this equivalence allows to interpret rn into join calculus and to verify those using join-calculus tools. in [19], petri nets are translated into linear logic programming. this translation can be used to analyse petri nets using prolog model-checker. authors of [20] encoded synchronous mobile nets (smn) [15] into rewriting logic [22]. this encoding allows the use of maude [21] to verify smns specifications. in this paper, we have discussed an encoding of elrn behaviours into dynamic nets [11]. this encoding was proved to be correct. the advantage of such encoding resides in the possibility to encode dynamic nets into cpn (coloured petri nets). so, elrn can be translated into cpn. once translated into cpn, elrn nets can be analysed using cpn verification tools. modelling and analysis of mobile computing systems: an extended petri nets formalism 219 5 conclusion mobile systems are systems with a dynamic structure. their structure changes as they are executed. this class of systems can be found in many domains of our life. mobile robots used to explore hostile environment, mobile agents used in the internet or in distributed systems, mobile nodes in a mobile wireless networks ... all these systems can be considered as reconfigurable systems. the use of these systems is in expansion for many reasons: their efficiency, their abstractions for the designer, their flexibility ...these characteristics make these systems in the kernel of many critical systems: aeronautics, military, medicine, commerce... the design of these systems becomes a critical activity. their reliability and their correctness are crucial. to ensure the correctness of these systems, formal methods seem to be an adequate solution. using formal methods, the designer specifies the system in a formal language. a formal language has a well defined syntax, and formal semantics which allows the verification of properties of the designed system. we found in the literature, many formal methods. classical formal methods (proposed for classical systems) are well defined and are mature. however, these classical formal methods have not the expressiveness to specify reconfigurable systems. the use of the classical methods makes the designer’s task a hard task. extended versions are proposed to deal with the idea of reconfigurable systems. in the literature, we can find two principal classes: processes algebra based methods, and state-transition based methods. state-transition based methods can be found in extensions of petri nets model. petri nets are an elegant model for concurrency. with its graphical representation and its formal background, it was used to specify and verify concurrent multi-processes systems. the classical model has not the power of expressiveness to deal with current aspects such as mobility. to take benefits from the power of the model in mobility domains, several works have been proposed. these works try to extend petri nets with the same ability to specify mobility (and more generally: reconfigurability). in this paper, we have presented the extended labelled reconfigurable nets formalism. the formal definition of this formalism, its semantics and a modelling example are presented. the encoding of this formalism into another formalism dynamic nets [11] was proved using and offers a method to do the analysis of this model. as perspectives of the current work, we propose the below axes as open domains: • the experimentation of elrn in the modelling of mobile systems: mobile agents systems, mobile networks, ... this modelling work can prove the power of our formalism and shows its shortcomings and so allow us to introduce necessary adaptations; • the work on automatic verification: the translation of dynamic nets into coloured petri nets is presented in [11]. we are working on the development of a tool-kit to implement this translation. the encoding presented in section three is formal and proved to be correct; so it is possible to think of an implementation of this last encoding also; • in the current time, complexity and decidability issues are not yet studied. these aspects 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[26] http://wiki.daimi.au.dk/cpntools/cpntools.wiki. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 506-516 cereal grain classification by optimal features and intelligent classifiers a. douik, m. abdellaoui ali douik, mehrez abdellaoui ecole nationale d’ingénieurs de monastir (enim) département de génie electrique laboratoire atsi rue ibn el jazzar, 5019 monastir tunisie e-mail: {ali.douik,mehrez.abdellaoui}@enim.rnu.tn abstract: the present paper focused on the classification of cereal grains using different classifiers combined to morphological, colour and wavelet features. the grain types used in this study were hard wheat, tender wheat and barley. different types of features (morphological, colour and wavelet) were extracted from colour images using different approaches. they were applied to different classification methods. keywords: morphological, colour, wavelet transform, neural networks, statistical classifier, fuzzy logic. 1 introduction the past few years was marked by the development of researches that contribute to reach an automatic classification of cereal grains which is perceived as a possible solution to prevent human errors in the quality evaluation process. computer vision system which is a promising technology in the quality control can replace the human operator. after hours of working the operator may loose concentration which in turn will affect the evaluation process. so a computer vision system proved to be more efficient at the level of precision and rapidity. but, the natural diversity in appearance of various cereal grains varieties makes classification by computer vision a complex work to achieve. many researches were carried out to classify cereal grains. characterization models were based on morphological features ( [1–9]), colour features ( [10–13]) or textural features ( [14]). other researchers ( [15–18]) have tried to combine these features for the sake of improving the efficiency of classification. recently, wavelet technique was integrated in cereal grains characterization ( [19, 20]). this technique, developed by mallat [21], is used in textural image analysis to make object classification more precise. the present paper is divided into four main parts. the first one will deal with the cereal image acquisition system, the second part will be devoted to present the classification features with its morphological, colour and wavelet components, the third section will focus on the different methods used in the classification process and the last one will compare the different methods accompanied with their performance evaluation. 2 cereal image acquisition system 2.1 image acquisition device a high resolution colour camera (vivitar) with a usb 2.0 cable was used to acquire grain images. the acquired images were of 3.1 mega pixel resolution. light sources were placed symmetrically over and under a glass plate over which the grains are spread out. all the samples were taken at constant camera settings, i.e., exposure time, saturation and gamma. the images obtained were pre-processed to eliminate background pixels using image subtraction. indeed, the active image containing grain sample is compared to image containing background. the image we got contains the grains and a uniform background (black). this step of pre-processing makes the gains segmentation easier and more efficient. copyright c⃝ 2006-2010 by ccc publications cereal grain classification by optimal features and intelligent classifiers 507 table 1: freeman code features and their abbreviations region direct1 direct2 direct3 direct4 direct5 direct6 direct7 direct8 region1 v z11 v z12 v z13 v z14 v z15 v z16 v z17 v z18 region2 v z21 v z22 v z23 v z24 v z25 v z26 v z27 v z28 region3 v z31 v z32 v z33 v z34 v z35 v z36 v z37 v z38 region4 v z41 v z42 v z43 v z44 v z45 v z46 v z47 v z48 2.2 image database samples a database of images was created from various samples of several cereal varieties obtained from different sources and for different crop years from laboratories of the tunisian cereal office. tunisian hard wheat (hw), tunisian tender wheat (tw) and tunisian barley (b) are the main classes of the samples considered. 3 classification features for each grain type, 152 parameters are extracted from the colour images of the database (122 morphological, 18 colour, and 12 wavelet features). 3.1 morphological features after isolating the grain, the region of interest was selected around the boundary of the edge. the morphological features were obtained from the binary images containing only pixels of the grain edge. we can classify these features as follows: • grain size measurements: length (l), width (l), width by lengh ratio (r1), area (s), perimeter (p), area by perimeter ratio (r2), angles (gra,pta) and radius of curvature (rr,rl) of the two extremities, likelihood between the grain and the nearest ellipsis for the grain (e), mean (sx,sy) and standard deviation (σx,σy) of horizontal and vertical symmetry. • freeman code features: after dividing the grain image in four regions as shown in the figure (1.a). we perform for every region the freeman code ( [22]); it’s the oldest contour descriptor and the most used today; it’s mainly based on the position of the pixels set that are the nearest neighbours (nn-set) of the actual pixel. in fact, every region is coded starting from a given origin and according to the directions of the nearest neighbour that are represented in 8-connexity (coded on 3 bits) as demonstrated in figure (1.b). the features extracted from the freeman code are 32; eight for every region. these features are summed up in table 1. figure 1: freeman code extraction, (a) dividing image in four regions to compute the freeman code, (b) direction codes • fourier transform features: the fourier transform is an important image processing tool which is used to decompose an image into its sine and cosine components ( [23]). the application of 508 a. douik, m. abdellaoui table 2: list of colour parameters and their abbreviations colour mean mean square variance standard kurtosis skewness components value value deviation red rm1 rm2 rv rsd rm3 rm4 green gm1 gm2 gv gsd gm3 gm4 blue bm1 bm2 bv bsd bm3 bm4 this transform on the contour pixels creates a set of complex coefficients that represents the shape of the contour. from these coefficients we extract the morphological descriptors using different signatures (1). a(u) = 1 n n−1∑ k=0 s(k)ex p [ − j2puk n ] (1) where: u ∈ [0,n −1](n : numbero f pointsincontour) s(k) : the signature chosen. a(u) : harmonic descriptors. the signatures used are complex, radial distance and polar. from each signature, we selected the first 25 harmonic coefficients that can be added to the set of the morphological features. the three signatures used are invariant by translation and consequently their fourier descriptors (fd), but it was proved that they are sensitive to rotation. invariance by rotation is then realized by ignoring the fd phase and by considering only modules of these fourier descriptors. for the complex signature all descriptors except the first (dc component) are needed to index the form. the dc component describes only the contour position, and it is useless with the form description. the descriptors standardization consists in dividing their modules by the one of second descriptor. the vector which indexes the form is given by the (2). f = [ |f d2| |f d1| , |f d3| |f d1| ,..., |f dn−1| |f d1| ] (2) the radial distance function and the polar coordinates are real. they have n/2 different frequencies for that half of the fd is necessary to index the form. the invariant vector (3) is obtained by dividing the module of the n/2 first descriptors by the module of the first descriptor. f = [ |f d1| |f d0| , |f d2| |f d0| ,..., ∣∣f dn/2∣∣ |f d0| ] (3) 3.2 colour features for each colour image that contains an isolated grain, we perform statistical parameters on values of pixels belonging to the grain. color parameters included: mean value, mean square value, variance, standard deviation, kurtosis and skewness of the red, green and blue primaries. in table 2 we present these parameters and their abbreviations. cereal grain classification by optimal features and intelligent classifiers 509 table 3: list of wavelet parameters and their abbreviations matrix type average value variance standard deviation matrix of approximation image mvap vap sdap matrix of horizontal details mv hd v hd sdhd matrix of vertical details mv v d v v d sdv d matrix of diagonal details mv dd v dd sddd 3.3 wavelet features the wavelet analysis of an image is a multi resolution analysis which is defined by linear operators allowing analyzing a signal on various frequencies. indeed, the signal is projected on a scale function that gives a representation of the original signal at higher scale. this projection causes a back zoom of the original signal, where the approximation is performed. in order to rebuild the signal, starting from approximation coefficients, we must also project the original signal on a wavelet to recover information lost during the first projection. the second projection contains the details of the original signal. the details of wavelet features have been reported earlier in [20]. table 3 resumes the chosen features. they were statistically tested to extract the best parameters leading to an optimal classification. the tests done on the 12 parameters proved that only two parameters are judged like not-significant (adh and add). thus; the number of parameters which is going to be retained for the characterization phase is 10: sdap, aap, vap, sddh, vdh, sddv, adv, vdv, sddd and vdd. 4 classification methods starting from the classification features extracted, we developed many methods using different approaches. the first approach is a statistical classification method that uses only morphological and colour features. the second approach is a classification using a fuzzy logic based method. the third is a combination between the first and the second. the last approach is an artificial neural network classification method that exploits all features leading to the best classification result. in what follows, we present these different approaches and their contribution to the classification of cereal grains. 4.1 statistical classification method from the set of samples, we achieved statistics related to morphological and color features extracted from color images of grains. from these statistics we obtained a distribution curve of every feature. this method operates directly on the distributions intervals of the morphological and color parameters. the classification is made by successive tests on parameters according to their ranks. conceived algorithm has been tested on images containing a mixture of grains collected from treated samples. to classify the grain types using a statistical method we considered the morphological and color features in this approach. classification results for the grain types using this method are illustrated in figure 2. we notice that the recognition rate for tw is weak while working with morphological or colour features (morph. 56%; color 51%). for hw, colour features gave an optimal recognition rate exceeding 99,4%, but does not exceed 67% when working with morphological features. for barley grains, due to their form that is different from other types of grains, the morphological features gave us a good classification result reaching 98,7%. the global recognition rate for the statistical classification method 510 a. douik, m. abdellaoui table 4: test of best parameters parameters barley hard wheat tender wheat total lsb 82,43% 77,02% 90,28% 80,94% e 77,82% 20,75% 90,91% 46,28% gra 72,80% 70,57% 67,13% 68,62% rm2 64,02% 50,52% 39,18% 50,25% is limited to 76%. this is explained by the overlapping that exists between the distribution curves of grain classes when working with all the morphological and colour features. figure 2: results of the statistical classification method when applied to morphological and colour features 4.2 fuzzy logic based classification method due to the overlaps of distribution curves of grains types we implement a classification method based on fuzzy logic techniques to improve the recognition rate issued from the statistical classification method. classification using the fuzzy logic is made according to the following steps: � classes’s definition. � generation of the membership functions for every parameter. � development of inference rules. � decision making. it results three classes corresponding to the different grain types considered. membership functions are deduced from the distribution curves of the different parameters of every grain type. the membership functions were conceived by normalization of the curves and then by a gaussian approach for every curve. the number of rules depends on the number of parameters considered. the chosen norm is the max-prod. then, the rules form is: "if (condition1) and (condition2) then (decision)". the choice of entries is based on a test of identification parameters. table 4 illustrates the test of the four best parameters for the classification from the set of morphological and colour features associated to the fuzzy logic method. from the possible combinations of the four parameters we select the best ones according to its recognition rates. the combinations selected are illustrated (lsb and gra : 83,42%; lsb, gra and rm2 : 72,71%; lsb, gra, e and rm2 : 68,23%). from this test we chose the parameters lsb and gra since cereal grain classification by optimal features and intelligent classifiers 511 when combining them it gives the best recognition rate. the result of this method using the combination lsb and gra is shown in figure 3. figure 3: results for fuzzy logic based classification method concerning the hard wheat and tender wheat grains, this method gives us a best recognition rate than the statistical one. on the other hand for the barley grains, the first method is more reliable. so that, we opt to use a method which combines the two previous methods and gives t best recognition rate. 4.3 statistical and fuzzy logic combined classification method it consists in making a decision about the grain type by the fuzzy logic method in the cases where the statistical method cannot make a decision. the fuzzy logic is used in the combined method in the cases of overlaps of all morphological and colour parameters. the improvement concerns the hard wheat and tender wheat grains only; the barley grains possess an optimal recognition rate. the results of this method are illustrated in figure 4. figure 4: results for statistical and fuzzy logic combined classification method 512 a. douik, m. abdellaoui table 5: number of neurons in the hidden layer morphological features colour features wavelet features sm fc ft amf number of features 15 32 25 122 18 10 number of neurons 3 7 5 5 10 3 table 6: classification results for ann classifier rates (%) morphological features colour features wavelet features grain type cr rjr rcr cr rjr rcr cr rjr rcr b 1,1 0 98,9 2,8 0 97,2 2,5 0,7 96,8 hw 1,6 0,5 97,9 7,6 0,7 91,7 1,7 1 97,3 tw 7,7 2,8 89,5 3,5 1,3 95,2 0 0 100 mean rates 3,5 1,1 95,4 4,6 0,7 94,7 1,4 0,6 98 4.4 artificial neural network classification method (ann) training phase and network architecture the network architecture is a multi-layer neural network mlp. the training is done using the function "trainlm" from the matlab neural network toolbox. activation functions are hyperbolic tangent and the linear matlab functions "tansig" and "purelin". during the training phase, we varied the neurons number in the hidden layer and we determine the training error. we chose 40000 as training iterations number since this value leads to a minimum training error. the number of neurons in the hidden layer depends of the type of features considered as entries of the network in the table 5 we illustrate the variation of the number of neurons in the hidden layer when using different types of features (for the morphological features sm means size measurments, fc : freeman code, ft : fourier transform and amf : all morphological features) . classification results for this test we used 3000 grains (1000 grains of each class), 600 grains for characterization and 400 grains for validation. the training of each class is done using 1800 grains, 600 grains will be used to learn the true membership and the others 1200 will be used to learn the system the false membership to the class. this technique seems to be very original and will make it possible to enlarge the classification space, to refine space collates and to reduce the conflict rate between various classes. thus this test will determine the conflict rate (cr), the rejection rate (rjr) and recognition rate (rcr). table 6 represents the results obtained during the first test. 5 evaluation and discussion figure 5 shows the classification recognition rates of the four developed methods. the ann classifier lead to the best recognition rates for barley (98,9%) using morphological features and tender wheat (100%) using wavelet features whereas the statistical and fuzzy logic combined classifier was the best for hard wheat classification (98,7%). these two methods gave better results than the first and second one. the tables 7, 8, 9 and 10 present the confusion matrixes of the four developed classifiers. when we cereal grain classification by optimal features and intelligent classifiers 513 figure 5: comparison of the classification methods based on average recognition rates table 7: confusion matrix (%) for the statistical method b tw hw b 91,4 1,5 7,1 tw 5,2 53,5 41,3 hw 2,9 13,9 83,2 observe these matrixes, we note that the major confusions are between tender wheat and hard wheat in the statistical classification method (41,3% for hw and 13,9 tw), fuzzy logic classification based method (12% for hw and 15,7% for tw) and statistical and fuzzy logic combined classification method (5% for hw and 0,8% for tw) this is due to the similarities that exists in the morphology and the texture of these two cereal grain classes. this problem is resolved using the ann classification method (0% for hw and 1,6% for tw). barley grains are more confused with hard wheat (sta: 7,1% ; fuzzy: 9,6% ; sta+fuzzy: 10,5% and ann : 0,6%) than with tender wheat (sta: 1,5% ; fuzzy: 0,9% ; sta+fuzzy: 1,1% and ann : 0,5%) this is due to the size that is larger than tender wheat and colour features. to evaluate the time performance for each classification method we count the time in seconds that takes every algorithm to classify grains in a sample of 300 grains containing 100 grain of each type. the algorithms are developed on a toshiba satellite (intel core 2 / 1,6 ghz) laptop under windows vista environment. table 11 presents the cost time for each method. table 8: confusion matrix (%) for the fuzzy logic based method b tw hw b 89,5 0,9 9,6 tw 0,5 87,5 12,0 hw 4,1 15,7 80,2 514 a. douik, m. abdellaoui table 9: confusion matrix (%) for the statistical and fuzzy logic cobined method b tw hw b 88,4 1,1 10,5 tw 0,6 94,4 5,0 hw 0,5 0,8 98,7 table 10: confusion matrix (%) for the ann method b tw hw b 98,9 0,5 0,6 tw 0 100 0 hw 0,5 1,6 97,9 table 11: time performance of the different methods method time(s) sta 72 fuzzy 43 sta+fuzzy 84 ann 177 we have noticed that the fuzzy logic based classification method appears to run about 60% faster than the second fastest (statistical classification method). the method leading to best recognition results is 4 times slower than the fastest methods. the statistical and fuzzy logic combined classification method can be considered as the most performing method as it have a good recognition rate (94%) and take 50% less time than the method leading to the optimal recognition rate. while the reported execution times depend on the implementation language, we note that we have used matlab 2007. 6 conclusion as dealt above, the classification of different grain types was successfully achieved using different parameters based on different types of features (morphological, colour and wavelet). these parameters were tested on different classification methods; the statistical classification method gave an average recognition rate of 76%. the second method based on fuzzy logic techniques gave an average recognition rate of 85,73%. the hybrid method, which is a combination of the two fore mentioned methods gave an average recognition rate of 93,83%. finally, the ann classification method was tested on all features and gave the best recognition rate reaching 98%. bibliography [1] m. abdellaoui, a. douik, m. annabi, détérmination des critéres de forme et de couleur pour la classification des grains de céréales, proc. nouvelles tendances 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[20] a. douik, m. abdellaoui, cereal varieties classification using wavelet techniques combined to multi-layer neural networks, proc. 16th mediterranean conference on control and automation, (ajaccio, france, 2008) pp1822-1827. 516 a. douik, m. abdellaoui [21] s. g. mallat, a theory for multiresolution signal decomposition: the wavelet representation, ieee transactions on pattern analysis and machine intelligence, 11(7) (1989) 674-693. [22] h. freeman, on the encoding of arbitrary geometric configurations, ieee trans on electr. comput. 10 (1961) 260-268. [23] d. zhang, g. lu, a comparative study on shape retrieval using fourier descriptors with different shape signatures, proc. ieee international conference on multimedia and expo, (2001), pp. 11391142. ali douik was born in tunis, tunisia. he received the master degree from the “ecole normale supérieure de l’enseignement technique de tunis”, in 1990 and the ph.d. degree in automatic from the “ecole supérieure des sciences et techniques de tunis, tunisia”, in 1996. in 2010, he received the ability degree from the “university of monastir, tunisia”. he is presently “maitre assistant” in the “ecole nationale d’ingénieurs de monastir”. his research is related to automatic control and image processing. mehrez abdellaoui was born in tunis in 1979. he received his electrical engineering diploma from electrical engineering department in enim-monastir in 2003 and the master degree in automatics from the enim-monastir in 2005. he is currently a phd student in the electrical engineering department at the enim-monastir. his research interests include image processing and video analysis. int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 721-732 specification and validation of a formative index to evaluate the ergonomic quality of an ar-based educational platform c. pribeanu costin pribeanu national institute for research and development in informatics ici bucharest romania, 011455 bucuresti, bd. maresal averescu, 8-10 e-mail: pribeanu@ici.ro abstract: the ergonomic quality of educational systems is a key feature influencing both the usefulness and motivation for the learner. desktop augmented reality (ar) systems are featuring specific interaction techniques that may create additional usability issues affecting the perceived ease of use. measuring key usability aspects and understanding the causal relationships between them is a challenge that requires formative measurement models specification and validation. in this paper we present an evaluation instrument based on two main formative indexes that are capturing specific usability measures for two ar-based applications. the formative indexes are forming a second order formative construct that acts as predictor for both the general ease of use and ease of learning how to operate with the application. keywords: formative measurement model, formative index, augmented reality, usability, ergonomic quality. 1 introduction educational systems based on desktop ar technologies are creating an appealing user experience for the learner by integrating real life objects into computer environments. touching and holding real objects is increasing the students’ motivation to learn and could better support active and collaborative learning [18], [22]. as ar technologies become more wide-spread, there is an increasing interest in their ergonomic quality. designing for usability is not easy in emerging technologies, like ar systems, which are featuring novel interaction techniques [5], [15]. the iso standard 9126-1 defined usability as the capability of a software system to be easy to understand, easy to learn how to operate with, easy to operate with, and attractive, when used under specified conditions [19]. by ergonomic quality we refer to the first three usability aspects: ease of understanding, ease of learning how to operate, and ease of operating with a software system. how to measure and improve the usability of interactive systems is a key research topic in hci. a research challenge is to better understand the relationships between different usability measures as well as between usability and other factors of interest [17]. in a previous work we developed a measurement model that was grounded in the technology acceptance models (tam) theory [9] in order to explain the causal relations between various factors influencing the intention to use of an ar-based educational platform [3], [4]. although the structural model was useful to test some typical tam hypotheses the variance explained was small and several items targeting specific usability aspects were eliminated in order to achieve the unidimensionality required by a reflective measurement model. moreover, reflective measurement assumes the same antecedents for reflective indicators (as manifest variables) so causal relations are estimated at construct level [8], [24]. these shortcomings suggest looking for an alternative modeling approach. in this paper we present a measurement model for the evaluation of the ergonomic quality of applications developed onto an ar-based educational platform. the augmented reality teaching platform (artp) was developed in the framework of the arise (augmented reality for copyright c⃝ 2006-2012 by ccc publications 722 c. pribeanu school environments) european project. two ar applications implementing learning scenarios for biology and chemistry were developed and tested onto artp. the measurement model consists in two sets of formative indicators that are measuring two dimensions of the ergonomic quality of desktop ar applications: the quality of visual and auditory perception and the ease to operate and collaborate in a constrained space. the two indexes are forming a second order formative construct. in order to achieve identification requirements we used as outcome variables a reflective construct measuring the perceived ease of learning how to use artp and a general reflective item measuring the overall ease of use. the formative measurement model was estimated on the biology scenario data. then we cross validated the models on the chemistry scenario data. the rest of this paper is organized as follows. in the following section we describe the formative measurement models and discuss some methodological aspects related to the specification, identification and validity. in section 3 we present and discuss the estimation results with the biology scenario data. in section 4 we present the results of a confirmatory assessment of the formative measurement model using the chemistry scenario data and we comparatively discuss the results for each scenario. the paper ends with conclusion and future research directions. 2 the formative measurement model 2.1 reflective vs. formative measurement models a measurement model describes the relationships between a construct (latent variable) and its measures (indicators, items) while a structural model describes the relationships between different constructs [12], [13]. the causal relation between a construct and its measures could be from construct to measures (reflective model) or from measures to construct (formative model). there are distinct characteristics of each measurement model that were systematically presented and discussed in detail in [6], [10], [12], [20]. in the reflective measurement model the indicators are manifest variables of the latent variable. a change in the constructs is reflected in simultaneous changes in all indicators. as such, the items are interchangeable and elimination of one of them doesn’t change the construct domain. measures should be positively correlated and the measurement model should have convergent and discriminate validity. in the formative measurement model the measures are defining the conceptual meaning of the construct. indicators are not interchangeable since each is capturing a distinct cause. since the measures are defining the construct, a census of indicators is recommended [6]. there are no assumptions on unidimensionality and correlations between indicators. however, collinearity should be avoided. indicators don’t have an error term and items are intercorrelated. although there is an error terms at construct level this is not a measuring error but a disturbance accounting for other causes not specified by the model [11]. the nomological net of formative indicators could differ as this is a distinct feature of the formative measurement [20]. a formative measurement model taken in isolation is under identified and cannot be estimated. jarvis et al. and diamantopoulos et al. recommend achieving identification based on a 2+ rule: specifying effects (outcomes) of the formative constructs on at least two other variables that are reflectively measured [12], [20]. the outcome variables could be: two reflective indicators (mimic model), two reflective constructs, or a reflective construct and a reflective variable. the selection of the outcome variables is just as important as is the selection of indicators [11], [14]. according to wilcox et al., the selected effect variables are determining the empirical meaning of the formative construct and the set of indicators [26]. the proper specification of the measurement model is a precondition before analyzing and specification and validation of a formative index to evaluate the ergonomic quality of an ar-based educational platform 723 assigning a meaning to the structural model [1]. according to jarvis et al., there are many studies in literature that are based on inappropriate specification of the measurement models [20]. in recent years, there is an ongoing debate regarding the formative versus reflective specification of various constructs and the appropriateness of measurement scales that are frequently used in different domains [12]. taking the appropriate measurement perspective is not a simple issue. as pointed out by jarvis and colleagues, based on the analysis of 178 papers published in four top journals in marketing research, there are about 29% cases (reported at 1192 constructs) of misspecification [20]. moreover, the authors themselves experienced difficulties in classifying 14% of constructs featuring both reflective and formative characteristics. wilcox and colleagues argued that a construct is not inherently formative or reflective so the researcher has a choice to take a perspective or another [26]. in this respect, the specification of alternative models is useful since is providing with more insights into the field of study. 2.2 experiment, samples and data analysis artp is a "seated" ar environment: users are looking to a see-through screen where virtual images are superimposed over the perceived image of a real object placed on the table [27]. two ar-based applications were developed onto this platform (see figure 1). figure 1: students testing the artp learning scenarios: biology (left) and chemistry (right) the first application implemented a biology learning scenario for secondary schools. the implemented paradigm was "3d process visualization of hidden processes" and was targeted at enhancing the students’ understanding and motivation to learn the human digestive system. the real object is a flat torso of the human body. a pointing device having a colored ball on the end of a stick and a remote controller wii nintendo as handler has been used as interaction tool that serves for three types of interaction: pointing on a real object, selection of a virtual object and selection of a menu item. the second application implemented a chemistry scenario. the implemented paradigm was "building with guidance" and was targeted at enhancing the students’ understanding and motivation to learn the periodic table of chemical elements, the structure of atoms / molecules, and the chemical reactions. the real objects were the periodic table of chemical elements and four sets of colored balls symbolizing atoms. the remote controller wii nintendo has only been used as interaction tool for confirming a selection. the test was conducted in 2008, on the ici’s platform which is equipped with 4 artp modules. a total number of 139 students (13-14 years old), from which 65 boys and 74 girls tested the platform. all were 8th grade students enrolled in 3 general schools in bucharest. none of them was familiar with the ar technology. the students came in groups of 7-8, accompanied 724 c. pribeanu by a teacher. each student tested the platform twice: once for the biology scenario and second time for the chemistry scenario. each scenario consists of a demo lesson and a number of exercises. after testing, the students were asked to answer a usability questionnaire by rating the items on a 5-point likert scale (1-strongly disagree, 2-disagree, 3-neutral, 4-agree, and 5-strongly agree). the questionnaire has 28 closed items and 2 open questions, asking users to describe the most 3 positive and most 3 negative aspects. the first 24 closed items are targeting various dimensions of the artp such as ergonomics and usability (items 1-14), perceived utility (items 15-17), perceived enjoyment (items 18-21) and intention to use (items 22-24). the last four items were to assess how the students overall perceived the platform as being easy to use, useful for learning, enjoyable to learn with, and exciting. in order to estimate the new measurement model we used the biology scenario data. we analyzed the initial sample of 139 observations for normality (skewness and kurtosis), univariate and multivariate outliers. we transformed the data (square root extraction) and we repeated the analysis and successively removed 9 observations. the final sample has 130 observations that present moderate deviations from normality. in order to cross validate the model on another sample, we used the chemistry scenario data. we performed the same data analysis procedure on the initial sample and successively removed 11 observations. the final sample has 128 observations with moderate deviations from normality. 2.3 model specification and identification according to our knowledge, there are few approaches to formative index construction for the usability and / or ease of use [21]. although the perceived ease of use and perceived usability are frequently used in information systems research, in almost all studies they are specified as reflectively measured constructs. as such, their indicators have a limited contribution (as manifest variables) to explain the effect of usability problems. since the objective of this study is to analyze the relationships between different aspects related to the ergonomic quality of the artp, 15 items in the usability questionnaire are of interest, from which 11 are formative measures and 4 are reflective measures. the 15 items (presented in annex 1) are grouped into four constructs and a single item measure: • the quality of visual and auditory perception (erg-p): clear observation and superposition, easy to read the information on the screen, and easy to understand the vocal explanations. • the ease of interaction and collaboration and collaboration (erg-o): comfortable work place, easy to select a menu item with the remote control, easy to correct errors, and easy to collaborate with colleagues. • the ease of adjusting the devices and accessories (erg-a), i.e. the see-through screen, stereo glasses and head phones. • the ease of learning (peol): easy to understand, easy to learn and easy to remember how to use artp. • the general item measuring the overall ease of use (peou1). the first three constructs are composite indexes measuring distinct usability aspects that are specific to an ar-based learning application. as such, the indicators are not interchangeable and elimination of any of them will alter the conceptual domain of the construct. for example, if we analyze the three items measuring the quality of the visual perception, each is targeting a specification and validation of a formative index to evaluate the ergonomic quality of an ar-based educational platform 725 different usability aspect. the clarity of observation through the screen is a hardware issue while the clarity of superposition between the augmentation and the real object is a software issue. reading the information on the screen relates to augmentation, messages to the user and menu items. note that apart from the specific ar devices and accessories there are also several usability aspects which are specific to a given application. for example, in the biology scenario the user selects an organ by pointing on flat torso of the digestive system which is a real object shared by to students staying face-to-face. in the chemistry scenario, the students create a molecule by bringing together several colored balls symbolizing atoms. in this respect, the interaction with the remote control, the correction of mistakes (selection errors) and the collaboration between students depend on the real objects registered with the application. therefore a formative model is an appropriate measurement perspective. the ergonomic quality of artp is a multidimensional construct conceptualized as a composite of formative indexes. each dimension is a formative index measuring a set of specific usability aspects. each index is assumed to have a significant positive influence on two general usability aspects: the perceived ease of learning how to use artp (the construct peol) and on the overall ease of use (the general item peou1). 2.4 validity of the formative indexes according to recent studies, there are several criteria to assess the validity of formative indexes [6], [10], [12], [14]: adequate coverage of the construct’s domain, absence of multicollinearity, indicator validity, significant γ-coefficients, complete mediation of effects, significant influence (β-coefficients) on outcome variables, and acceptable fit with the data. although a census of indicators is ideal to cover the scope of a formative index, this is rarely possible. in our model, each index is addressing a distinct aspect of the ergonomic quality of artp. since formative indicators are also capturing critical usability aspects as indicated in previous studies (e.g. [23]) the coverage of the domain is acceptable. the collinearity of formative indicators was analyzed with the vif (variation inflation factor) statistic for each index. vif values were in the range 1.183-1.946 for the biology scenario, respectively 1.085-1.715 for the chemistry scenario bellow the 3.3 cut-off value [12]. the general item peou1 is an overall measure of the ergonomic quality of artp which qualify it for using as criterion validity. an analysis using pearson’s rho indicated that there are significant positive linear relationships between peou1 and the formative indicators of erg-p and erg-o but no significant correlations with the formative indicators of erg-a. nevertheless, in both samples erg-a indicators are positively correlated with the formative item ergo1. this suggests that erg-a is not a distinct dimension of the ergonomic quality of artp but only an antecedent of a formative indicator measuring the comfort with the workplace. a regression analysis on the biology data sample showed that erga1 and erga2 are two antecedents of ergo1 (standardized coefficients βerga1 =0.191, sig=0.046 and βerga2=0.185, sig=0.039). the regression analysis on the chemistry data sample confirmed this finding (βerga1=0.156, sig=0.083 and βerga2=0.255, sig=0.005). in order to estimate the formative indexes we used a mimic model and a structural model presented in figure 2. the models were estimated using amos 17.0 [2]. each index has n formative indicators, more specifically n=4 for erg-p and erg-o, and n=3 for erg. there are four outcome variables in the mimic model. three of these reflective indicators are further grouped in the structural model that features 2 outcome variables: the general item peou1 (overall ease of use) and the reflective construct peol (ease of learning how to operate). all outcome variables are closely related to the focal construct as they measure general aspects 726 c. pribeanu figure 2: estimation of formative indexes with mimic (left) and structural models (right) of the perceived ergonomic quality. there are three general hypotheses assessed with these models: 1. there is a significant contribution of the formative indicators to the composite index (xi→η, i=1...n). 2. there is a significant positive influence of the composite index on the perceived ease of learning how to use artp (η→peol1, η→peol2, η→peol3 in the mimic model, respectively η→peol in the structural model). 3. there is a significant positive influence of the composite index on the overall ease of use (η→peou1). since the structural model includes a reflectively measured construct, the internal consistency and convergent validity should be assessed. the scale reliability and unidimensionality were analyzed with spss 16.0 and amos 17.0. the consistency of scale (cronbach’s alpha) was 0.701 for the biology scenario and 0.704 for the chemistry scenario which is acceptable. convergent validity was assessed by examining the standardized factor loadings, composite reliability, and average variance extracted for peol in each scenario [16]. almost all factor loadings are over the minimum recommended level of 0.60. the composite reliability was 0.711 for the biology scenario and 0.704 for the chemistry scenario, above the minimum recommended value of 0.70 in each scenario. the average variance extracted was 0.456 for the biology scenario and 0.438 for the chemistry scenario. overall, peol construct has an acceptable convergent validity. 3 estimation results on the biology scenario data 3.1 first order formative indexes the results of mimic and structural model estimations for erg-p and erg-o are presented in table 1. all γ-coefficients are significant at p<0.05 level thus supporting the first hypothesis. there are small differences between the magnitudes of γ-coefficients in the two models. the variance of the error term associated with the formative index is small in each model, so the formative index is sound and each formative item has a distinct contribution to the explained variance [11]. fit indices are acceptable, over the recommended values [16]: χ2=1.115, df=13, χ2/df=1.624, gfi=0.962, cfi=0.974, srmr=0.036 (erg-p, structural model), and χ2=22.963, df=13, χ2/df=1.766, gfi=0.960, cfi=958, srmr=0.042 (erg-o, structural model). in both models all β-coefficients are significant (p<0.001), which supports the last two hypotheses. the influence of formative indexes is stronger on the perceived ease of learning how to specification and validation of a formative index to evaluate the ergonomic quality of an ar-based educational platform 727 table 1: estimation results for erg-p and ergo biology scenario erg-p mimic model structural model erg-o mimic model structural model γ/β sig.(p) γ/β sig.(p) γ/β sig.(p) γ/β sig.(p) contribution contribution ergp1 .33 <.001 .36 <.001 ergo1 .22 0.018 .27 0.006 ergp2 .31 0.001 .30 0.002 ergo2 .22 0.017 .21 0.030 ergp3 .20 0.010 .21 0.010 ergo3 .29 0.003 .30 0.003 ergp4 .27 0.002 .29 0.002 ergo4 .33 <.001 .33 0.001 effect variables effect variables peou1 .63 <.001 .63 <.001 peou1 .62 <.001 .66 <.001 peol .91 <.001 peol .87 <.001 peol1 .64 <.001 peol1 .63 <.001 peol2 .73 <.001 peol2 .71 <.001 peol3 .61 <.001 peol3 .63 <.001 variance expl. variance expl. erg-o 71% 78% erg-o 54% 62% peol 83% peol 75% use artp than on the general ease of use. this means that once the user understands and learns how to use the system he finds it easy to use. the highest contributions to erg-p have the first two items (clarity of observation through the see-through screen and accuracy of superposition). the most important contribution to erg-o has the last item related to the ease of collaboration with colleagues. the ease of correcting the mistakes proved also to be an important measure for the biology scenario. 3.2 second order formative index erg-p and erg-o are two distinct dimensions of the ergonomic quality of artp that are forming a second order formative construct (erg). we used the scores of the first order constructs (the predicted values of the multiple regression) as formative indicators in the second order construct. similar approaches are described in [7], [8]. the estimation results are presented in table 2. table 2: estimation results for second order construct biology scenario erg mimic model structural model γ/β sig.(p) γ/β sig.(p) contribution erg-p .65 <.001 .68 <.001 erg-o .27 0.006 .30 0.004 effect variables peou1 .63 <.001 .63 <.001 peol .90 <.001 peol1 .64 <.001 peol2 .71 <.001 peol3 .61 <.001 variance expl. erg 75% 84% peol 81% the γ-coefficients are significant in each model. the contribution of the first dimension is much higher showing that the quality of visual perception is a critical requirement for the desktop ar systems. the analysis of modification indices showed that the formative index is completely mediating the effects of its items. both β-coefficients are significant (p < 0.001), which supports the last two hypotheses. the variance of the error term associated with the formative index is 0.009 (medium effect). the magnitude of the error term is suggesting some other aspects not covered by the indicators. 728 c. pribeanu fit indices are acceptable, over the recommended values: χ2=11.759, df=7, χ2/df=1.679, gfi=0.972, cfi=0.984, srmr=0.032 (structural model). 4 cross validation of the formative indexes on the chemistry scenario 4.1 first order formative indexes the results of estimation are presented in table 3. almost all γ-coefficients are significant at p<0.05 level thus supporting the first hypothesis. there is only one exception: ergp4 in the mimic model, where the γ-coefficient is signifficant at p<0.10 level. there are relatively small differences between the contributions of each item in each model. table 3: estimation results for erg-p and erg-o chemistry scenario erg-p mimic model structural model erg-o mimic model structural model γ/β sig.(p) γ/β sig.(p) γ/β sig.(p) γ/β sig.(p) contribution contribution ergp1 .23 0.016 .29 0.010 ergo1 .25 0.009 .24 0.018 ergp2 .28 0.009 .31 0.012 ergo2 .27 0.003 .30 0.002 ergp3 .24 0.010 .32 0.004 ergo3 .22 0.021 .24 0.016 ergp4 .20 0.053 .24 0.047 ergo4 .36 <.001 .38 <.001 effect variables effect variables peou1 .55 <.001 .61 <.001 peou1 .48 <.001 .49 <.001 peol .75 <.001 peol .93 <.001 peol1 .70 <.001 peol1 .71 <.001 peol2 .67 <.001 peol2 .70 <.001 peol3 .52 <.001 peol3 .55 <.001 variance expl. variance expl. erg-o 47% 67% erg-o 49% 55% peol 56% peol 86% in both models β-coefficients are significant (p<0.001), which supports the last two hypotheses. the variance of the error term associated with the formative index is 0.022 (0.008) for erg-p and 0.021 (0.016) for erg-o. since the magnitude of the error term is small and all indicator coefficients are significant, the formative index is sound and each formative item has a distinct contribution to the explained variance. fit indices are acceptable, over the recommended values [16]: χ2=15.154, df=13, χ2/df=1.624, gfi=0.973, cfi=0.990, srmr=0.038 (erg-p, structural model), and χ2=22.392, df=13, χ2/df=1.722, gfi=0.958, cfi=0.947, srmr=0.048 (erg-o, structural model). the influence of formative indexes is stronger on the perceived ease of learning how to use artp than on the general ease of use. the highest contributions to erg-p have the items ergp2 (accuracy of superposition) and ergp3 (understanding the vocal explanation). the contribution of ergp3 shows the importance of vocal explanations for students. the most important contribution to erg-o has the last item related to the ease of collaboration with colleagues. the ease of selecting a menu item proved also to be an important measure for the chemistry scenario. 4.2 second order formative index the results of structural model estimation are presented in table 4. both γ-coefficients are significant. the contribution of each dimension is similar for the chemistry scenario. the analysis of modification indices showed that the index is completely mediating the effects of its items. specification and validation of a formative index to evaluate the ergonomic quality of an ar-based educational platform 729 table 4: estimation results for second order construct chemistry scenario erg mimic model structural model γ/β sig.(p) γ/β sig.(p) contribution erg-p .45 <.001 .55 <.001 erg-o .47 <.001 .49 0.004 effect variables peou1 .53 <.001 .55 <.001 peol .83 <.001 peol1 .70 <.001 peol2 .68 <.001 peol3 .53 <.001 variance expl. erg 63% 80% peol 69% both β-coefficients are significant (p<0.001), which supports the hypotheses. the variance of the error term associated with the formative index is 0.016 (0.222) which means a medium to large effect. the magnitude of the error term is suggesting some other aspects not covered by the indicators. fit indices are acceptable, over the recommended values: χ2=14.932, df=7, χ2/df=2.133, gfi=0.964, cfi=0.960, srmr=0.049 (structural model). 4.3 comparison of results and discussion the estimation of formative indexes on the chemistry scenario data cross validated the measurement model and enables a comparison between the two implemented scenarios. the variances explained by the structural models for the formative indexes are higher for the biology scenario than for the chemistry scenario. the variance explained by the model for the second order construct is slightly higher for the biology scenario. the contribution of erg-p to the super ordinate index is higher than the contribution of erg-o in both scenarios but the relative importance is much higher for the biology scenario. the variance explained by the model for the outcome variable peol is also higher for the biology scenario (81% vs. 69%). as regarding the erg-p index, the comparison reveals that understanding of vocal explanations (ergp3) is the most important item for the chemistry scenario and the less important for the biology scenario. this is explained by the fact that the chemistry demo lesson and exercises were more difficult for students so a clear understanding of the lesson and how to perform the exercises was critical. the accuracy of superposition between the projection and the real object (ergp2) has a higher importance for biology. as regarding erg-o, the comparison reveals that the ease of collaboration with colleagues (ergo4) is the most important item for both scenarios. selecting a menu item (ergo2) was easy for the biology scenario (lowest γ-coefficient) and difficult the chemistry scenario. this is explained by the fact that the students had to use both hands to manipulate the colored balls (symbolizing atoms) so handling also the remote control became more difficult. correcting the mistakes (ergo3) was more difficult for the biology scenario because of frequent selection errors when students tried to select a small organ. in both scenarios, erg-a had a significant positive influence on the formative indicator ergo1, showing that the ease to adjust the see-through screen and stereo glasses is influencing the comfort on the work place. 730 c. pribeanu 5 conclusion and future work the main contribution of this study is a measurement model for the perceived ease of use of the artp featuring a second order formative index with two dimensions: the quality of visual and auditory perception and the ease of interaction and collaboration. these indexes are antecedents of a reflective construct measuring the perceived ease of learning how to use artp. the latter could be then integrated in structural models that are based solely on reflective scales. there are several strengths and limitations of this study. an outcome of this research is the integration of almost all items related to the perceived ease of use that were eliminated in a previous work [4] for unidimensionality and convergent validity reasons. the new measurement model includes 12 of 15 items related to the ergonomic quality. as such, it provides a wider perspective on the ergonomic quality and enables the analysis of specific usability aspects. second, the estimation of a formative measurement model provides a more detailed information (at indicator level) shedding light on usability aspects that are critical for artp and a given learning scenario. third, the formative indexes were specified and validated with a structural model that addressed all general aspects related to the perceived ergonomic quality: ease of understanding, ease of use and ease of operating with a software system. since all variables are strongly related to the focal construct the structural model is well supporting an external validity. up to now, there is no similar model developed for the ergonomic quality of a software system. fourth, the model was estimated and cross validated on two different samples which enables a comparison between scenarios and makes it possible to further integrate and discuss in more detail the answers at open questions (qualitative data). as regarding the limitations, the sample used in this study was collected from only 6 classes (3 romanian schools), having a limited representativeness. second, both samples are small, at limit for sem (structural modeling equation) requirements. third, the convergent validity of the relatively measured construct is at limit (acceptable for an exploratory study). fourth, the breadth of formative indicators is inherently limited since the evaluation questionnaire was indented to capture the main usability aspects. fifth, there are inherent limitations since the methodology regarding formative indexes estimation and validation is not mature yet. the usability questionnaire used to collect the data was conceptualized in 2007 while the main recommendations for formative indexes development have been published only in 2008. based on this work we intend to develop a new evaluation questionnaire having both formative and reflective items. the questionnaire will be used for the evaluation of a new version of the chemistry application which is currently under development. acknowledgements this work was supported by the research projects tehsin 503/2009 and arise fp6-027039. specification and validation of a formative index to evaluate the ergonomic quality 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[27] wind, j., riege, k., bogen m. spinnstubež: a seated augmented reality display system, virtual environments: proc. ipt-egve eg/acm symposium, 17-23, 2007. annex 1 constructs and items erg mm items variables quality of f ergp1 observing through the screen is clear of visual and f ergp2 the superposition between projection and the real object is clear auditory perception f ergp3 understanding the vocal explanations is easy (erg-p) f ergp4 reading the information on the screen is easy ease of f ergo1 the work place is comfortable interaction and f ergo2 selecting a menu item is easy collaboration f ergo3 correcting the mistakes is easy (erg-o) f ergo4 collaborating with colleagues is easy ease of f erga1 adjusting the "see-through" screen is easy adjusting devices f erga2 adjusting the stereo glasses is easy (erg-a) f erga3 adjusting the head phones is easy perceived ease r peol1 understanding how to operate with artp is easy of learning to operate r peol2 learning how to operate with artp is easy (peol) r peol3 remembering how to operate with artp is easy *** general item r peou1 overall, i find the system easy to use note: mm(measurement model): f (formative) / r (reflective) international journal of computers communications & control issn 1841-9836, 10(5):686-701, october, 2015. adaptive probe-based congestion-aware handover procedure using sip protocol r. libnik, a. svigelj rok libnik telekom slovenije, d.d., cigaletova 15, 1000 ljubljana, slovenia rok.libnik@telekom.si ales svigelj* 1. department of communication systems, jozef stefan institute jamova cesta 39, 1000 ljubljana, slovenia ales.svigelj@ijs.si 2. jozef stefan international postgraduate school jamova cesta 39, 1000 ljubljana, slovenia *corresponding author: ales.svigelj@ijs.si abstract: wireless technologies have evolved very rapidly in recent years. in the future, operators will need to enable users to use communication services independently of access technologies, so they will have to support seamless handovers in heterogeneous networks. in this paper we present a novel adaptive congestion aware session initiation protocol (sip) based procedure for handover in heterogeneous networks. in the proposed algorithm the handover decision is based in addition to signal strength, also on target network congestion status, which is tested during the conversation. as sip protocol was used, the proposed procedure is independent of access technologies. for performance evaluation of proposed procedure we developed a purpose built simulation model. the results show that the use of the proposed adaptive procedure significantly improves the qoe of voip users, compared to reference scenario, in which only signal strength was used as the trigger for handover decision. keywords: session initiation protocol (sip), seamless handover, heterogeneous networks, performance evaluation, congestion awareness. 1 introduction fixed, nomadic and mobile telecommunications networks, which provide voice and data services, are nowadays converging toward a seamless heterogeneous telecommunication network. due to many different wireless access technologies, comprising licenced (e.g. gsm/umts, hspa, wimax, and lte) and unlicensed (e.g. wlan) access, there is a need for a uniform access to converged services. such services should be independent of the access technologies, providing seamless connectivity and sufficient quality of experience (qoe). in the future networks the wireless access networks will play the key role, as their inherent characteristics of the limited radio bandwidth and channel properties, are of the paramount importance for the provision of appropriate transmission rates and quality of service (qos). in addition, wlan is increasing its popularity, in particular in home/business environment (limited coverage areas). thus, to enable mobile users to communicate using a variety of different access technologies, terminals have to support several network interfaces. terminal manufacturers are already producing multi mode terminals and the number of interfaces is bound to increase with technology limits. operators, on the other hand are starting to offer fixed mobile converged services. with increasing demand from the users to communicate independent of access technologies, operators will need to offer seamless handover between heterogeneous access copyright © 2006-2015 by ccc publications adaptive probe-based congestion-aware handover procedure using sip protocol 687 networks (i.e. vertical handover). in homogeneous networks handover techniques (i.e. horizontal handover) are well studied in the literature and already integrated in mobile networks. the horizontal handover is usually triggered by received signal strength (rss) only. in the future, this will not be sufficient and other parameters should be taken into account (network congestion status in our case) in handover decision. thus, new mechanisms need to be developed. the main contribution of this paper is an advanced adaptive sip based procedure for congestion aware handover in heterogeneous networks, together with its performance evaluation in simulation environment. the remainder of the paper is organized as follows. in the following subsections the related research work on mobility management techniques and support for handovers when using the sip protocol is described. in section 2 the novel adaptive congestion aware sip based procedure for handover in heterogeneous networks is presented, while its performance evaluation is presented in section 3. the paper ends with conclusions in section 4. 1.1 mobility management mobility management techniques are defined as techniques that support user movement within and between different networks. the handover process can be in general divided into three phases: (i) handover information gathering phase, (ii) handover decision phase and (iii) handover execution phase [1]. in the first phase (i.e. handover information gathering phase) a mobile node collects not only network information, but also information about the other components of the system such as network properties, mobile devices, access points, and user preferences [1]. the information/parameters typically collected/measured are the following [1] [2] [3] [4] [5]: availability of neighbouring network received signal strength (rss), signal noise ratio (snr), carrier to interference ratio (cir), signal to interference ratio (sir), bit error ratio (ber), delay, jitter throughput, economic price of the usage of the network. the mobile device’s state by gathering information about battery status, resources, speed, and service class. user preferences information such as budget and services required, preferred network operator. context information. as seen, some parameters (snr, delay, jitter, bandwidth, and power consumption) are network/hardware related and cannot be influenced by the user, while others (price, preferred network operator) represent parameters that can be selected/set by the user. gathered parameters can be grouped also according to the origin of the parameters. they can be provided by the network (i.e. service independent) or can be provided by application/service (i.e. service dependent). in the second phase (i.e. handover decision phase) the decision for handover is made, based on criteria function, taking into account different information/parameters, which were gathered during the first phase. the second phase is one of the most critical processes during the handover, as in this phase, the decision about time (if and when) and to which network (selecting the best network fulfilling requirements) the handover is made is taken. in a homogeneous network environment the decision about time usually depends on rss values, while the selection of the network is not an issue since the same networking technology (horizontal handover) is used. in heterogeneous networks the selection of the appropriate network is quite complex, as 688 r. libnik, a. svigelj many parameters/information obtained from the different information sources (i.e. network, mobile devices, and user preferences) must be evaluated in order to make the best decisions. in this paper we are focusing on vertical handover solutions based on the combination of different parameters. selection of appropriate parameters and handling of appropriate trigger algorithm in handover decision phase is of a paramount importance, as wrong handover decision can lead into unsatisfied users. this is in particular important when performing handover using real time applications such as voice over ip (voip), where the assurance of appropriate level of quality of experience (qoe) in the target access network, represents the main challenge. operator’s backbones usually do not present a bottleneck as they are well maintained and controlled in order to provide an adequate level of qos. in the last (third) phase (i.e. handover execution), traffic flow is handed over to the target network. this means that all the traffic is sent using the new connection, while the connection with the old network is terminated. this phase should also guarantee a smooth session transition process. in this paper we focused in particular on the second phase (i.e. handover decision), proposing new procedure that improves the handover decision and consequently the user experience when performing handover using voip applications in heterogeneous networks. handover in heterogeneous network can be performed using different protocols and this has been the subject of several studies [1] [5] [6]. at the network layer, mobile ip (mip), defined in [8], has been most frequently selected [8] [9] [10] [11] as the protocol for handover. with the modifications presented in [12] it can provide greater support for real time services on a mobile ipv4 network, by minimizing the period of time when an mobile node (mn) is unable to send or receive ipv4 packets due to delay in the mobile ipv4 registration process. in [13] authors proposed an enhancement of mobile ip (mip) called mip with home agent handover (hh-mip) to enjoy most of the advantages of route optimization mip (romip) but with only a small increase of signalling overhead. the most widely used protocols at the transport layer are tcp and udp. both have some limitations for mobility support. however, a new solution called mobile sctp (msctp) has been developed to enable ip addresses to be added, deleted and changed during active sctp association [14] [15]. for mobility management at the application layer, sip is usually selected as the most favoured protocol [9] [10] [11] [12] [16] [17] [18]. sip runs on top of several different transport protocols and is today’s most widely used protocol for ip telephony penetrated in both terrestrial and satellite networks [19]. the advantage of using the sip protocol for handover execution is, that sip is an application layer protocol, and thus its use does not have a great impact on the network changes needed. the transport independence of sip means that it does not require great network involvement in handover execution. however, the application usually needs to be improved / customized to support handover and only sip based application can perform handover. application level solutions based on tunnelling [20], using sip only for signalling can overcome this problem. the biggest advantage of using sip is its wide adoption in real operator environments, since almost all operators that are offering voip services are using sip for signalling. in addition, sip is used in many operator environments and has been selected as the primary signalling protocol in ims (ip multimedia subsystem) networks. in this paper we focus on solutions that can be deployed easily in a real operator environment. thus, we decided to focus on the use of sip for mobility management, which is shortly described in the next subsection. 1.2 sip mobility sip protocol [21] [22] is an application layer signalling protocol for establishing, modifying, and terminating internet multimedia sessions. these sessions include internet telephone calls, adaptive probe-based congestion-aware handover procedure using sip protocol 689 multimedia distribution, and multimedia conferences. sip invitations used to create sessions carry session descriptions that allow participants to agree on a set of compatible media types. when using sip protocol for ip telephony in operator’s environment, the regulatory and security issues are forcing operators to provide additional functionalities that can affect the architecture of the ip telephony solution [23]. usually, the session border controller (sbc) is added to their network [24], which can lead to a conflict with sip architectural principles. sip based sbcs typically handle both signalling and media, resulting in call flow to be changed in a way that all rtp streams are routed via sbc (see also figure 4). with minor modifications, sip can support four types of mobility. terminal mobility enables devices to move between subnets and be accessible to other hosts and to continue any ongoing session when they move. session mobility enables users to maintain a session while moving from one terminal to another. personal mobility allows users to use the same set of services, even when changing devices or network attachment points. service mobility enables users to be identified by the same logical address, even if the user is at different terminals. in this paper we are focusing on terminal mobility, for which two types of mobility management have been defined: pre-call mobility and mid call mobility. sip protocol supports several applications; however, in this paper we have selected ip telephony as a typical representative of real time application. in the pre-call mobility scenario mn gets a new ip address prior to the call, thus this operation does not affect the quality of ip telephony service and will therefore not be discussed further. in the mid call mobility scenario first a call is established between the corresponding node (cn) and the mn that is in the home network. when mn moves to the target network, it gets new ip address and sends sip re invite message to cn and informs it about the location change. the new rtp session is then established. the limitation of this approach is that the sip server is not informed about the location change. some solutions have been presented in the literature in which mn informs the sip server about the location change after sending the sip re invite message [13]. however, in a real operator environment, information about location change needs to be sent to the sip server prior to starting a new sip session between mn and cn. this should be done, for example, to support proper charging, since prices can differ between networks. to overcome the limitation of mid call mobility in [25] we have proposed sip enhanced mid call scenario (semcs) in semcs scenario, a call is established between the cn and the mn that is in the home network after moving to target network, the mn sends the sip re-invite message to the sip server to inform it about the location change. the sip server then forwards the sip re invite message to the cn . after the acknowledgement the new rtp session is established using (new) ip address of the target network. in [25] we proposed message exchange via sip server and we focused on handover execution based on snr ratio only. that procedure was upgraded in [26], where we are presenting a novel procedure for handover decision based on congestion detection (cahp-c). in this paper, we are extending the cahp-c procedure described in [26] with an adaptive procedure for congestion aware handover. 2 cahp-a adaptive procedure for congestion aware handover as stated in the introduction in today’s mobile/wireless homogenous networks the trigger for handover is usually based on snr only. this is sufficient as the whole network uses the same access technology and is under control of one operator offering service. in the future heterogeneous networks, the target access network could be covered by different operator or it is 690 r. libnik, a. svigelj not even operator’s network. the later is usually the case with open wlan network in a hotel or in a congress centre, where there are several access points (ap) connected to single xdsl connection. on one hand the users can have good signal coverage and very fast (e.g. 52 mbit/s) connection between mn and ap, while on the other hand, when traffic from all aps is gathered on single xdsl line bandwidth becomes a bottleneck. thus, the snr level is not sufficient parameter for handover decision and should not be performed only when snr level exceeds the threshold, but should be done based also on other parameters (e.g. congestion status) [26]. this is especially important when providing real time applications (ip telephony in our case), where ability to provide sufficient qoe for the user is the most important and where performing handover to congested network could lead to degraded service level. our solution is described in [26], where we defined basic congestion aware handover procedure (cahp-c), which enables efficient handover performance, taking into account also the congestion status of the target network. it should be noted, that receiving signal power measurement remains the prerequisite for handover (i.e. handover cannot take place to a network lacking or with limited signal coverage). in our study two groups of networks with different characteristics were defined. networks in the first group are reliable and expensive (e.g. umts, hspa and lte), while networks in the second one are cheaper or even free of charge and unreliable (e.g. wlan). in order to present our solution more clearly we selected one representative from each group. the hspa was selected from the first group of networks and wlan from the second group. those two will be used in the rest of this paper. please note, that our approach can be used between any two networks (e.g. also between different wlan networks). we assumed that congestion (i.e. qoe degradation) can happen in the wlan network only. we are not focusing on "who" or "what" is causing the additional delay (e.g. mac, tcp, routing, low bandwidth on the access link), but only on the fact that if there is a delay, which is not acceptable for ip telephony, we do not use the corresponding access network. as the proposed cahp-a procedure is an extension to cahp-c procedure we provide short description of cahp-c emphasizing only the main characteristics, as partially depicted in figure 1, while in depth description of cahp-c can be found in [26]. 2.1 cahp-c the cahp-c procedure is used only if snr exceeds the predefined threshold [26]. when snr is below threshold, the proposed procedure is not used. in order to detect possible congestion in the wlan network before the handover is executed, we proposed pre-probe algorithm. the congestion is detected with delay testing. in order to monitor congestion (i.e. round trip delay measurement) we defined new sip message named sip pre_probe , which is sent before the sip re invite message. as characteristics of the wlan access network can also change during the call, we defined another algorithm named mid-probe algorithm. another sip message called sip mid_probe was defined, which is used to check the congestion status of the wlan access network when in use. after receiving the responses the mn calculates the average delay dpre (pre-probe algorithm) or dmid (mid-probe algorithm) from mn to sbc. we also defined two parameters tpre and tmid. when user is trying to handover to wlan network, the parameter tpre defines the period, when sip pre_probe messages are sent again if measured delay is above predefined threshold td (i.e. 200ms in our case) [26]. when user is already using wlan network the parameter tmid defines the period when sip mid_probe messages are sent again if the measured delay is below td [26]. those two parameters are the most important in cahp-c procedure and need to be set carefully as they affect the level of signalling overhead and the speed of detecting possible congestion. the main limitation of cahp-c procedure was that adaptive probe-based congestion-aware handover procedure using sip protocol 691 tpre and tmid parameters are constant, which means that they do not change if the network characteristics are improved. thus, we are proposing cahp-a procedure in which tpre and tmid parameters change adaptively according to current network characteristics (see also figure 1). the cahp-a procedure is presented in the next chapter. 2.2 cahp-a the sip pre_probe and sip mid_probe messages present additional traffic in the network and can be seen as signalling overhead. as the size of the messages is the same as in rtp traffic, the use of newly defined messages does not have significant effect on backbone traffic. however, such increase of signalling affects the sbc, which needs to handle additional messages. in this paper we are extending the cahp-c procedure with adaptive calculation of tpre and tmid, as depicted in figure 1 (bolded blocks). in order to keep the signalling overhead as low as possible we propose that parameters tpre and tmid change adaptively according to characteristics of network (i.e. measured delay) as the networks differ by the ability to provide sufficient qoe. some of them get congested likely (e.g. in congress centre) while the other used by a single user (e.g. at home) are not. with the pre probe algorithm wlan network is tested prior handover. in the case that calculated delay dpre is above threshold td the parameter tpre change according to difference between dpre and td. after handover, the wlan network is tested with mid probe algorithm. in case that calculated delay dmid is below threshold î¤d the parameter tmid should change according to difference between dmid and td. the calculations happen after the delay measurements (see bolded block in figure 1). in order to achieve such dependency we defined equations (1) and (2) which are used for calculation of tpre and tmid respectively: tpre =   n/a; dpre ≤ dmax tmax · ( dpre−dmin dmax−dmin −1 ) ; dmax < dpre < 2 ·dmax −dmin tmax; dpre ≥ 2 ·dmax −dmin (1) tmid =   tmax; dmid ≤ dmin tmax · ( 1− dmid−dmin dmax−dmin )α ; dmin < dmid < dmax n/a; dmid ≥ dmax (2) where tmax represent maximum possible time set for tpre or tmid, dmin minimum (i.e. delay of non congested network) and dmax maximum delay that does not have effect on qoe ( td in proposed algorithm). as seen the values for tpre and tmid are function of α and measured dpre or tmid. by selecting different values for α we can define different curves that define tpre and tmid. it is worth noting, that different α can be set for calculating tpre and tmid. some possibilities are presented in figure 2 and figure 3, where parameter α was set to 1/16, 1/8, 1/4, 1/2, 1, 2, 4, 8, and 16. when tpre and tmid are low more sip probe messages are sent as they are more frequent and the possible degradation of service is detected faster. in such a manner we managed to maintain qoe of the user. when fewer messages are sent ( tpre and tmid are high) the degradation of service may not be detected fast enough and degradation of qoe can be expected. however, more frequent sending of sip pre_probe and sip mid_probe messages tpre and tmid are high) increases signalling overhead. from figure 2 and figure 3 it can be seen that level of overhead changes also with parameter α. thus, the α can be seen as the parameter that defines signalling overhead (i.e. the bigger the α, the bigger the signalling overhead). when α is approaching 0 692 r. libnik, a. svigelj m id -p ro b e a lg o rith m call via hspa established measure snr of wlan snr > tsnr sending sip pre_probe via wlan calculate dpre dpre < td adaptive calculation of tpre wait tpre perform handover to wlan sending sip mid_probe via wlan calculate dmid dmid > td adaptive calculation of tmid wait tmid perform handover to hspa true true true false false false p re -p ro b e a lg o rith m mn sbc cnsip server sip invite rtp sip 200 ok sip pre_probe (via wlan) de2e_pre > τd s/n > τs/n . . . sip 200 ok . . . sip 200 ok . . . sip 200 ok . . . sip 200 ok sip re-invite rtp sip 200 ok sip mid_probe sip mid_probe . . . sip 200 ok . . . sip 200 ok sip mid_probe sip mid_probe . . . sip 200 ok . . . sip 200 ok sip re-invite rtp sip 200 ok hspa wlan hspa sip pre_probe (via wlan) sip pre_probe (via wlan) sip pre_probe (via wlan) de2e_pre < τd de2e_mid < τd de2e_mid > τd τinter τinter τpre τmid figure 1: flow diagram of cahp-a procedure and message exchange. adaptive probe-based congestion-aware handover procedure using sip protocol 693 figure 2: dependency between sip dpre and sip tpre for different values of α the values for tpre and tmid are approaching tmax and when α is approaching infinity the values the values for tpre and tmid are approaching 0 s. in the proposed handover cahp-a procedure two assumptions were made: (i) the procedure starts when mn is connected to both current and target networks and (ii) mn is capable of sending sip messages via wlan interface while, for rtp the hspa network is still used. both functionalities were necessary to perform seamless handover from hspa to wlan network. in the case that mn would not be capable of connecting to two different networks at the same time, first hspa connection should be terminated and only then the establishment of wlan connection would start. by second functionality the mn sends newly defined and standard signalling sip messages via wlan when still connected to hspa. if mn would not have such capabilities, again hspa connection should be terminated first and sip messages could be sent only when wlan connection would be established. without those functionalities the connection would be lost several times which would affect qoe of the user. the sip pre_probe and sip mid_probe messages are sent to sbc, through which rtp packets are also sent. thus, the use of the proposed probes in the target networks can provide the real status of the ability of the network to provide an adequate level of qoe for ip telephony. as sip protocol was used for sending the newly proposed messages, this approach is completely independent of the lower layers (i.e. transport, network and link). furthermore, it can be used independently of the protocol used in lower layers and easily integrated in the operator’s environment. it means that the role of lower layers protocols stays the same (i.e. providing ip connectivity) and that they do not need to be modified. the application uses theirs functionalities (e.g. measurement of signal, establish physical connection, ip connection). our proposal is also easily scalable, as operators add additional sbcs to their network, when the number of users and traffic flows increases and existing sbcs can not serve all the signalling and rtp traffic load. 3 performance evaluation of the cahp-a procedure for performance evaluation of the proposed handover procedure presented in section 2 we developed a simulation model of a telecommunication system, which is discussed in [26]. the simulation model comprises two networks, wlan and hspa. two hosts, mn and cn, that are using ip telephony as an application, were also defined. the g.711 codec was used for voip. 694 r. libnik, a. svigelj figure 3: dependency between dmid and tmid for different values of α silence suppression was not used, resulting in a constant rtp traffic stream of 100 packets/s. the network architecture of the simulation model, resembling real operator environment, is presented in figure 4. the following assumptions were made in the simulation scenario: hspa network is always available; wlan network has limited coverage, but its access link between router 1 and ip network representing fixed operator, could become congested, as it aggregates traffic from all wlan access points (ap); the usage of wlan network is prioritized by the user, which means that mn will always try to perform a handover when this network is available; mn is a dual mode handset capable of sending rtp packets and sip invite messages at the same time via different interfaces. the first two assumptions were made just for simulation scenario in order to validate proposed procedure in environment of two networks, where one is reliable and the second is unreliable. we focused only on access link traffic load, as this is usually critical part of connection from mn to sbc. as described in chapter 2, in proposed procedure the congestion will be detected by measuring delays from mn to sbc. the simulation model of the communication system was developed using the discrete event, object-oriented modelling simulation tool opnet modeler [26]. it has an open source code of commonly used protocols, which is very convenient for performance evaluation of user developed/enhanced mobility management mechanisms [28]. it enables network modelling and simulation for designing new protocols and technologies, together with performance evaluation of existing and newly developed optimized protocols and applications. opnet supports sip telephony but it does not support handover on the application layer, thus some pre-defined process models that incorporate sip procedures were customized [25]. beside modification of process’s functionalities, we also define the newly proposed sip pre_probe and sip mid_probe messages, which are used for congestion testing of the wlan access network. figure 4 shows the simulation network configuration. the mn is a dual mode terminal, capable of connecting to wlan and to hspa network. both networks are connected via ip networks to the sbc. the cn is an ip phone connected to sbc. to increase traffic in the wlan network access link, other clients were added (represented by laptops in figure 4), which generated additional udp traffic in the lan network. adaptive probe-based congestion-aware handover procedure using sip protocol 695 bts ip network (fixed operator) ap1 mn cn sip server router 1 router 4 router 2 wlan network coverage hsdpa network coverage mn sbc lan router 3 ip network (mobile operator) rtp stream path: hspa wlan figure 4: network architecture of the simulation model. 3.1 simulation scenario the proposed cahp-a procedure was evaluated in simulation model. in order to get statistically representative results, one long call that lasted 28,800 s (i.e. 8 hours) was simulated. however, the results can be easily applied to the scenario with several users that are making calls with the total sum of conversation time 28,800 s. in defined network architecture wlan network access link was randomly congested with additional udp traffic, which was generated by additional voip clients. the duration of each "congestion" was distributed exponentially with mean value of 20 s. the time between two congestions was also distributed exponentially with mean value of 10 s. such distribution enabled us to test the proposed procedure several times in different traffic conditions. the user movements were defined by changing snr ratio. the snr values were measured in real environment and imported in the simulation model. table 1: simulation scenarios scenario tmid(s) tpre(s) α r_1 n/a n/a n/a r_2 0 0 n/a s_1 adaptive adaptive 1/16 s_2 adaptive adaptive 1/8 s_3 adaptive adaptive 1/4 s_4 adaptive adaptive 1/2 s_5 adaptive adaptive 1 s_6 adaptive adaptive 2 s_7 adaptive adaptive 4 s_8 adaptive adaptive 8 s_9 adaptive adaptive 16 in order to evaluate the proposed procedure, we prepared eleven scenarios. two first scenarios were defined to get reference results for two opposite situations. in the first reference scenario (r_1) the proposed procedure was not used and handover was made based on snr only. in the second scenario (r_2) parameters tpre and tmid were set to 0 s, which gave us maximal sending frequency of newly defined sip messages sip pre_probe and sip mid_probe. we 696 r. libnik, a. svigelj added additional 9 scenarios (s_1 s_9) with cahp-a procedure in which parameters α was set to 1/16, 1/8, 1/4, 1/2, 1, 2, 4, 8 and 16. all simulation scenarios are summarised in table 1. 3.2 simulation results in the simulation several results were collected. from the user’s point of view the end to end delay should not be significantly affected by performing handovers. we measured end to end delay of ip telephony for each packet. this enabled us to get the cumulative simulation time with end to end delay above 200 ms, which will be presented in results. the cumulative time when hspa interface was used will be also presented as this affects the cost of communication, which is also very important to the user, as he wants to minimise the usage of expensive network (i.e. hspa), while still having the appropriate qoe. the signalling increase caused by additional newly proposed messages during handovers was also tracked. if users perform large number of handovers the probe messages will increase signalling traffic and traffic load of the sbc, which is important from the operator’s point of view. thus, the number of overhead messages will be also presented in the results. the end to end delay distribution for simulation scenarios is presented in figure 5. for the sake of clarity only delays above 200 ms are presented, as those delays significantly affect qoe of the user. delays above 400 ms occur in r_1 only, in which cumulative time, with end to end delay above 200 ms, presents 48.7% of all communication time. that means that in such conversation the qoe of the user is highly degraded. it can be seen that in all other scenarios the cumulative conversation time above 200 ms is much smaller (see percentages above histograms in figure 5), resulting in qoe to be highly improved compared to r_1. the best results are measured in scenarios from s_7 to s_9. it can be seen that results in s_7 to s_9 are even better than in r_2, where tpre and tmid were set to 0 s as in r_2 the signalling overhead itself was causing additional congestion and deteriorate the results. figure 5: end to end delay distribution the biggest share of cumulative simulation time above 200 ms is measured in r_1, thus we adaptive probe-based congestion-aware handover procedure using sip protocol 697 took this scenario as a reference for normalizing other scenarios as presented in (1): nstati = stati statr1 ·100% (3) where nstatî¤i normalized simulation time above threshold (200 ms) of scenario i, staî¤i simulation time above threshold for scenario i, staî¤tr1 simulation time with delay above 200 ms for scenario r_1. normalized values are presented in table 2. it can be seen that the nstaî¤i is decreasing with increase of parameter α in scenarios s_1-s_9. the best results are achieved in s_7, where α was set to 4. cumulative conversation time when hspa interface is used is also presented in table 2. in r_1 the hspa was used only for 16.4% of simulation time, as handovers were performed based on snr only. in all other scenarios (r_2 and s_1 s_9) the measured hspa usage is between 52.0% and 59.6%, which give us only 7.6 percentage points of difference. table 2: normalized cumulative simulation time above 200 ms scenario nstaî¤ti cum. conv. time on hspa r_1 100.0% 16.4% r_2 13.1% 59.6% s_1 23.1% 52.0% s_2 21.9% 52.6% s_3 19.7% 55.8% s_4 17.6% 55.0% s_5 14.7% 57.4% s_6 13.9% 53.7% s_7 12.3% 58.1% s_8 12.6% 59.3% s_9 12.5% 57.7% normalized signalling overhead caused by newly proposed sippre_probe and sip mid_probe messages is presented in figure 6. maximum number of signalling overhead messages is, as expected, measured in r_2 ( tpre = tmid = 0 s) in which about 273 thousand overhead messages were sent. this scenario was taken as a reference for normalizing other scenarios as presented in (2): nsoi = nsmi nsmr2 ·100% (4) where nsoi presents normalized signalling overhead for scenario i, nsmi number of signalling messages of scenario i, nsmr2 number of signaling messages of scenario r_2. from figure 6 it can be seen that the signalling overhead is highly decreased in scenarios form s_1 to s_9 (where cahp-a was used), with lowest overhead in s_1 (only 4.4 % of r_2 signalling traffic). the results show that parameter α defines signalling overhead (i.e. the bigger the α, the bigger the signalling overhead) proofing our theoretical analysis in chapter 2. as in the reference scenario r_1 cahp-a procedure was not used, no messages were sent, thus we get result of 0.0 698 r. libnik, a. svigelj figure 6: normalized signalling overhead 3.3 discussion among presented results we are looking for optimal values for parameter α (defining tpre and tmid). as the results presenting simulation time when hspa was used (table 2) are not very dependent on tpre and tmid (7.6 percentage points of difference among scenarios), these results are excluded from further analysis. from the results of end to end delay distribution (figure 5) and normalized signalling overhead (figure 7) it can be seen, that high signalling overhead results in low end to end delay (i.e. good qoe) and vice versa. thus, compromise will be needed between user’s and operator’s objectives. users want qoe to be as good as possible. to assure this, the end to end delay should be sufficiently low during voip session also when using unreliable network (i.e. wlan network in or case). in all scenarios we measured end to end delay between 250 and 300 ms, thus end to end delay of 300 ms presents upper limit for sufficient qoe. from results (figure 5) it can be seen that scenarios r_2 and s_6 s_9 meet that condition as no packet appeared with delay above 300 ms. from the operators point of view the signalling overhead needs to be low in order to save sbc resources. the limit for signalling overhead is harder to set, thus we defined two conditions. our requirement is that signalling overhead is lowered for at least (a) 70% and (b) 80% compared to r_2. results presented in figure 6 show that scenarios s_1 to s_8 all meet condition (a) and scenarios s_1 to s_6 meet condition (b). as seen, scenario s_6 appears in all groups. based on defined conditions the optimal setting of parameter α is 2. by using those setting, cumulative time with measured end-to end delay above 200 was decreased for 86% (from 48.7% to 6.8%) compared to reference scenario r_1, while signalling overhead was lowered for 85% compared to reference scenario r_2. the comparison of the results for optimal setting (α = 2) with the results obtained for different constant values of tpre and tmid described in [26] shows, that when using chap-a, the endto-end delay is significantly decreased, in particular for the delays higher than 250 ms, while the signalling overhead stays almost the same. adaptive probe-based congestion-aware handover procedure using sip protocol 699 4 conclusions in this paper we presented a novel adaptive sip based procedure for congestion aware handover in heterogeneous networks. with newly proposed pre-probe and mid-probe algorithms the handover decision is (in addition to snr) based also on target network congestion status. in order to analyze and evaluate the proposed procedure several scenarios were prepared. the results show that using the proposed adaptive cahp-a procedure, the qoe of voip users was significantly improved, compared to reference scenario, in which only signal strength was used as the decision for handover. this is achieved by eliminating handovers to unreliable highly congested target network, which could cause degradation of service. with the cahp-a procedure the unreliable network is tested also after handover. in the case of the detection of congestion the handover back to reliable network is triggered, which prevents the qoe degradation. in the proposed procedure sip protocol was used for sending the proposed probe messages. as it runs on the application layer, our solution is completely independent of the underlying access technologies and thus applicable easily to next generation wireless systems. furthermore, it is also independent of the lower layer protocols used. another advantage of using sip protocol for messages is that sip usage is increasing in operators environments. in this paper we have focused only on measurement of end-to-end delay based on transmission of the proposed sippre_probe and sipmid_probe messages. in order to further improve the handover decision algorithm, we will in our further work evaluate more parameters (e.g. user profiles, other network parameters, context awareness) to make even more efficient handover decision. in addition, the historic information about particular network can also be used in order to make the handover decision even more efficient, in particular where there are more than two wlan networks available. bibliography 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[28] r. libnik, g. kandus, a. svigelj (2011); simulation model for performance evaluation of advancde sip based mobility management techniques, international journal of communications, 5(1):26-35. ijcccv10n1dz.pdf international journal of computers communications & control issn 1841-9836, 10(1):8-21, february, 2015. method for visual detection of similarities in medical streaming data j. bernatavičienė, g. dzemyda, g. bazilevičius, v. medvedev, v. marcinkevičius, p. treigys jolita bernatavičienė*, gintautas dzemyda, gediminas bazilevičius, viktor medvedev, virginijus marcinkevičius, and povilas treigys vilnius university institute of mathematics and informatics lithuania, lt-08663 vilnius, akademijos, 4 jolita.bernataviciene@mii.vu.lt, gintautas.dzemyda@mii.vu.lt, gediminas.bazilevicius@mii.vu.lt, viktor.medvedev@mii.vu.lt, virginijus.marcinkevicius@mii.vu.lt, povilas.treigys@mii.vu.lt *corresponding author: jolita.bernataviciene@mii.vu.lt abstract: the analysis of medical streaming data is quite difficult when the problem is to estimate health-state situations in real time streaming data in accordance with the previously detected and estimated streaming data of various patients. this paper deals with the multivariate time series analysis seeking to compare the current situation (sample) with that in chronologically collected historical data and to find the subsequences of the multivariate time series most similar to the sample. a visual method for finding the best subsequences matching to the sample is proposed. using this method, an investigator can consider the results of comparison of the sample and some subsequence of the series from the standpoint of several measures that may be supplementary to one another or may be contradictory among themselves. the advantage of the visual analysis of the data, presented on the plane, is that we can see not only the subsequence best matching to the sample (such a subsequence can be found in an automatic way), but also we can see the distribution of subsequences that are similar to the sample in accordance with different similarity measures. it allows us to evaluate differences among the subsequences and among the measures. keywords: streaming data, similarity measures, multivariate time series, visualization, multidimensional scaling. 1 introduction time series data are widely available in different fields including medicine, finance, and science. a time series is a collection of chronologically performed observations of the values of a feature that characterizes the behaviour of a particular object. there are many topics in time series data mining, i.e., similarity search, clustering, classification, anomaly detection, motif discovery, etc. the similarity problem can be defined as a comparison of two time series to determine whether they are similar or not. usually, the choice of a similarity measure can affect the result of data mining tasks. by a similarity measure we mean a method, which compares two time series and returns the value of their similarity. if the object is characterized by several features, we have a multivariate time series (mts) [1]. in this paper, we investigate the similarity search in multivariate physiological time series. a physiological time series is a series of some medical observations over a period of time. such a type of data can be collected using devices (or sensors) that collect personal medical features, such as heart rate, blood pressure, etc. an example of such data can be the intensive care multivariate online-monitoring time series [2]. a sensor is an instrument that detects or measures a physical or environmental characteristic or state, transmits and/or records the reading in some form (e.g., copyright © 2006-2015 by ccc publications method for visual detection of similarities in medical streaming data 9 figure 1: example of the multivariate time series a visual display, audio signal, digital transmission, etc.). a sensor converts the physical quantity to electric output. for example, a pressure sensor converts pressure to electric output. remote monitoring of health parameters such as the pulse rate, oxygen level in blood or blood pressure can be very helpful for early detection of diseases, resulting in reduction of treatment time. most methods, used to analyse medical data, focus primarily on analysing the univariate time series. however, because of parameter dependences and variation over time, examination of all medical data together in a multivariate time series can provide more information about the data and patient, to make a better diagnosis and treat the patient [3]. therefore, this paper deals with the multivariate time series analysis with a view to compare the current situation with that of in chronologically collected historical data, and to find subsequences of the multivariate time series most similar to the sample, corresponding, e.g. to the current situation. an example of mts of four features (heart rate hr, non-invasive systolic arterial blood pressure sys, non-invasive diastolic arterial blood pressure dias, temperature temp) is presented in figure 1. let us have a multivariate time series of n features and ta observations: xa =     xa 11 · · · xa 1ta ... . . . ... xan1 · · · x a nta     . denote the sample of n features and tb observations as xb =     xb 11 · · · xb 1tb ... . . . ... xbn1 · · · x b ntb     . here ta > tb. in fact, xa and xb are matrices. as a result, we need to find the optimal place of xb on xa. the place is defined by some time moment t∗ : 1 ≤ t∗ ≤ ta − tb + 1. our procedure analyses the multivariate time series xa by using the moving time window the width of which is adapted to the current situation xb (width is equal to tb) and comparing the content of this window with the sample, in the sense of several similarity measures, at the same time. visual method of finding the best subsequences matching to the sample is proposed in this paper. as it is indicated in [4], the goal of visual analytics research is to turn the information overload into an opportunity, i.e. decision-makers should be enabled to examine massive, multidimensional, multisource, time varying information stream to make effective decisions in time critical situations. 10 j. bernatavičienė, g. dzemyda, g. bazilevičius, v. medvedev, v. marcinkevičius, p. treigys there are attempts to apply visual analysis for the streaming data. the example is the visual content analysis of real-time data streams project [5] at the pacific northwest national laboratory. its goal was to allow users to quickly grasp dynamic data in forms that are intuitive and natural without requiring intensive training in the use of specific visualization or analysis tools and methods. the project has prototyped five different visualization prototypes that represent and convey dynamic data through human-recognizable contexts and paradigms such as hierarchies, relationships, time and geography. in this paper, we suggest using the specific visualization tools and methods (multidimensional data visualization [6]) that are effective and also do not require intensive training of the users. moreover, we show a possibility of making the decision, based on five criteria of similarity of the sample with the subsequence of real-time data stream by representing the similarity as a point on a plane. dimensionality reduction and visual analysis of multidimensional data [6] have been applied when comparing the best found subsequences in xa. the proposed method is described in section 2. similarity measures for multivariate time series and comparative analysis of the measures are presented in section 3. multidimensional data visualization is reviewed in section 4, where the emphasis is put on the multidimensional scaling. comparative analysis of similarity measures for multivariate time series is presented in section 5. an example, illustrating the proposed method, is presented in section 6. 2 visual method for finding the best subsequences matching to the sample in our method, the multivariate time series xa are analysed by using the moving time window. the width of this window is adapted to the current situation (sample) xb and is equal to tb. the content of this window is compared with the sample, in the sense of several similarity measures at the same time. it includes the dimensionality reduction procedure that allows us to observe multidimensional data visually. the visual method for finding the best subsequences, matching to the sample, can be generalized as follows: 1. let us have: a multivariate time series xa of n features and ta observations; sample xb of n features and tb observations; m similarity measures si, i = 1, . . . , m. 2. the sample xb is compared with all subsequences of xa by using m similarity measures si, i = 1, . . . , m. the subsequences are obtained by moving the time window in the xa from beginning to end. the content of such a window is a matrix of n rows. denote it by xc. the width of the window (the number of columns of xc) is adapted to the current situation (sample) xb (its width is equal to tb). for each measure, k subsequences are chosen most similar to the sample. therefore, the total number of subsequences for a further analysis is equal to km. 3. each comparison of the sample with a subsequence, chosen in the way defined in the step above, produces a m-dimensional point sq = (sq1, sq2, . . . , sqm), where, in our case, q = 1, . . . , km. let us derive two additional points: s0 = (s01, s02, . . . , s0m) is the array of values of all similarity measures, computed for the subsequence, that is ideally coincident with the sample (the array of the best values of m similarity measures); sc = (sc1, sc2, . . . , scm) is the weight center of sq = (sq1, sq2, . . . , sqm), q = 1, . . . , km. therefore, the total number of m-dimensional points for discovering the most similar subsequences to the sample is equal to km+2. afterwards, the normalization of the components method for visual detection of similarities in medical streaming data 11 of these points is performed by z-score. denote the obtained matrix of normalized points by z. it consists of km + 2 rows and m columns. 4. the points from matrix z are mapped on the plane using the multidimensional scaling [6] (or are other algorithm of nonlinear projection of multidimensional points on the plane). denote the resulting matrix by y , that contains km + 2 rows, corresponding to different comparisons of the sample with other subsequences, and 2 columns. each row is coordinates of the point on the plane. 5. the investigator analyses the information presented graphically, where all m-dimensional points are represented as the points on a plane, and makes decisions. in general, the most similar subsequence to the sample can be the subsequence, the corresponding point of which on a plane is closest to the projection of s0 on the plane. however, more subsequences may be considered as similar to the sample. the points on the plane, corresponding to such subsequences, must be closer to the projection of s0 on a plane than to the projection of sc. these rules can be checked automatically in the program realization of this method, however participation of the investigator is valuable, because it gives a possibility to him to cognize the data deeper. the advantage of this method is that the investigator can consider the results of comparison from the standpoint of several measures that may be supplementary to one another or contradictory among themselves. therefore, the similarity of subsequences with the sample will be evaluated from different standpoints. the method is universal, because different sets of similarity measures can be chosen, depending on the problem, but the scheme of decision remains the same. moreover, the involvement of the dimensionality reduction and visual analysis of multidimensional data in the proposed method renders the opportunity to the investigator to participate in the final decision, when comparing the best found subsequences of the multivariate time series with the sample. however, the decision on their similarity can also be made automatically. 3 similarity measures for multivariate time series to detect events in real multivariate time series, it is necessary to compare time series using the appropriate similarity measure [7]. different techniques and similarity measures are introduced and used for comparison of multivariate time series of different nature [8], [9]. multivariate time series can be reduced to univariate time series and their similarity can be measured, using a univariate time series approach [10]. that may lead to a great loss of information, therefore, we concentrate on the multivariate time series approach here. five similarity measures si, i = 1, . . . , 5, used in this paper for multivariate time series, are presented below. let us compare two multivariate time series: xa =     xa 11 · · · xa 1ta ... . . . ... xan1 · · · x a nta     and xb =     xb 11 · · · xb 1tb ... . . . ... xbn1 · · · x b ntb     . the frobenius norm is often used in the matrix analysis [11]. this similarity measure is based on the euclidean distance. the frobenius norm of a matrix xb is defined by the formula: ∥ ∥ ∥ x b ∥ ∥ ∥ f = √ √ √ √ n ∑ p=1 tb ∑ q=1 (xbpq) 2 = √ tr((xb)′xb), (1) 12 j. bernatavičienė, g. dzemyda, g. bazilevičius, v. medvedev, v. marcinkevičius, p. treigys where tr is the sum of elements on the diagonal of the square matrix. the frobenius norm is used to compare the similarity of two matrices. the similarity of xb and xc is defined by the formula frob = ∥ ∥xb − xc ∥ ∥ f . the best possible value of the frobenius norm is 0. the correlation coefficient between two matrices of the same size (matrix correlation coefficient) can also be used as a similarity measure [12]: r = ∑n p=1 ∑tb q=1 (x b pq − x̄ b)(xcpq − x̄ c) √ ∑n p=1 ∑tb q=1 (x b pq − x̄ b)2 ∑n p=1 ∑tb q=1 (x c pq − x̄ c)2 , (2) where x̄b and x̄c are the means of xb and xc, respectively. this measure is the pearson correlation coefficient adapted to matrices and calculated using the matlab corr2 function [12]. the best possible value of the matrix correlation coefficient is 1.the correlation coefficient between two matrices has found wide applications in the image analysis, molecular biology, etc. the third similarity measure for multivariate time series is the principal component analysis (pca) similarity factor [9], [13]. pca is a well-known and wide used technique for dimensionality reduction of data. it is a linear transformation that projects the original data to a new coordinate system with the minimal loss of information. in multivariate cases, the information is the structure of the original data, i.e. the correlation between the features and alteration of the correlation structure among them. to create a projection, pca selects coordinate axes of the new coordinate system one by one according to the greatest variance of any projection. the pca similarity factor is defined by the following formula: spca(x b , x c) = tr(l′mm ′l), (3) where l and m are matrices that contain the first l principal components of xb and xc, respectively. it means that the principal components are computed by the standard algorithm using the matrices (xb)′xb and (xc)′xc, and then l principal components with the highest eigenvalues are selected. the best possible value of the pca similarity factor is l. in our experiments l = 1. dynamic time warping (dtw) [14] is the most widely used technique for comparison of time series data, where extensive a priori knowledge is not available. the euclidean distance reflects the similarity in time, while the dynamic time warping (dtw) reflects the similarity in shape. dtw searches for the best alignment between two time series, attempting to minimize the distance between them. the advantage of dtw is that it can handle unequal series and distortions. multidimensional dynamic time warping (mdtw) is presented in [15]. some distance matrix is defined: {d(p, q) = ∑n k=1 (x b kp − xc kq ) 2 , p, q = 1, . . . , tb}. then the matrix d of cumulative distances is calculated as in the traditional dtw algorithm [15]: d(p, q) =                          d(1, 1), if p = 1, q = 1, d(p, q) + d(p − 1, q), if p = 2, . . . , tb, q = 1, d(p, q) + d(p, q − 1), if p = 1, q = 2, . . . ., tb, d(p, q) + min        d(p − 1, q) d(p, q − 1), d(p − 1, q − 1) in other cases. (4) (p, q) defines the pair of the pth observation in xb and the qth observation in xc. finally, the minimal path and the distance along the minimal path are obtained using matrix d. the path must start at the beginning of each time series at (1, 1) and finish at the end of both time series method for visual detection of similarities in medical streaming data 13 at (tb, tb). see [13] for details. the best possible value of mdtw is 0. on the other hand, dtw can lead us to unintuitive alignments, where a single point on one time series maps onto a large subsection of another time series [16], [17]. also, dtw can fail to find the obvious and natural alignments in two time series because of a single feature (i.e. peak, valley, infection point, plateau, etc.). one of the causes is due to the great difference between the lengths of the compared series. in this paper, the fifth similarity measure for multivariate time series is eros (extended frobenius norm) [9]. eros is based on the principal component analysis and computes the similarity between two mts items by measuring how close the corresponding principal components are using the eigenvalues as weights. in our case, xb and xc are two multivariate time series items of n features and tb observations. v b = [vb1, . . . , v b n] and v c = [vc 1 , . . . , vcn] are two right eigenvector matrices obtained by applying a singular value decomposition (svd) to the covariance matrices mb and mc of features in xb and xc respectively. the eros similarity of xb and xc is defined as follows: eros(xb, xc, w) = n ∑ i=1 wi| 〈 v b i , v c i 〉 |, (5) where 〈 vbi , v c i 〉 is the inner product of vbi , and v c i , w is a weight vector, based on eigenvalues of the mts data set (see more in detail in [9]), ∑n i=1 wi = 1. 4 multidimensional data visualization the method, proposed in section 2 for finding the best subsequences matching to the sample, is based on the visual presentation and analysis of multidimensional points the coordinates of which are the values of similarity measures, computed for a pair of subsequences. the visualization technology is introduced below. for an effective data analysis, it is important to include a human into the data exploration process and combine the flexibility, creativity, and general knowledge of the human with the enormous storage capacity and computational power of today’s computer. visual data mining aims at integrating the human in the data analysis process, applying the human’s perceptual abilities to the analysis of large data sets, available in today’s computer systems. visualization finds a wide application in the medical data analysis, too [18], [19]. the goal of the projection method is to represent the input data items in a lower-dimensional space so that certain properties of the structure of the data set were preserved as faithfully as possible. the projection can be used to visualize a data set, if rather a small output dimensionality is chosen. one of these methods is the principal component analysis (pca). the well-known principal component analysis [6] can be used to display the data as a linear projection on a subspace of the original data space such that best preserves the variance in the data. pca cannot embrace nonlinear structures, consisting of arbitrarily shaped clusters or curved manifolds, since it describes the data in terms of a linear subspace. therefore, several methods have been proposed for reproducing nonlinear higher-dimensional structures on a lower-dimensional display: multidimensional scaling and its modifications [6], [20], [21], [22], isomap [23], locally linear embedding [24], etc. various neural network approaches are used for this aims as well (see e.g. [25], [26], [27]). multidimensional scaling (mds) is a group of methods that project multidimensional data to a low (usually two) dimensional space and preserve the interpoint distances among data as much, as possible. let us have the m-dimensional points sq = (sq1, sq2, . . . , sqm), q = 1, . . . , t, (sq ∈ 14 j. bernatavičienė, g. dzemyda, g. bazilevičius, v. medvedev, v. marcinkevičius, p. treigys rm). the pending problem is to get the projection of these points onto the plane r2. twodimensional points y1, y2, . . . , yt ∈ r2 correspond to them. here yq = (yq1, yq2), q = 1, . . . , t. denote the distance between the points sq and sp by d∗qp, and the distance between the corresponding points yq and yp on the projected space by dqp. in our case, the initial dimensionality is m, and the resulting one is 2 (2-d). naturally, 1-d and 3-d projections could be considered, too. however, in the 1-d case, we lose knowledge that can be obtained from 2-d or 3-d views. advantages of 3-d can be achieved when special means to present such data on the screen are applied. therefore, 2-d projections of the multidimensional data are commonly used. there exists a multitude of variants of mds with slightly different so-called stress functions. in our experiments, the raw stress is minimized emds = ∑t q<p (d ∗ qp − dqp) 2, seeking to find the optimal coordinates of points y1, y2, . . . , yt. 5 comparative analysis of similarity measures for multivariate time series the data from physionet/computing in cardiology challenge are used for the experimental analysis (http://www.physionet.org/challenge/2012/ ). the records were collected in the intensive care unit. in the experiments we used a set xa, containing multivariate time series of 50 patients of the same age, i.e. if to follow the notation of section 1, we have 50 different multivariate time series xai , i = 1, . . . , 50, each consisting of 48 observations (columns) of n = 4 features (rows: non-invasive diastolic arterial blood pressure, non-invasive systolic arterial blood pressure, heart rate, temperature). xa = {xai , i = 1, . . . , 50}. in general, the sample xb (the current situation) does not belong to xa. however, seeking for more precise conclusions in this research, we have chosen xb in xa at random of the length tb = 8. moreover, we have chosen xb such that its contents belongs to the same patient, i.e. xb does not consist of the parts of different patients’ records. the goal is to go through time windows xc (of the size of xb) in xa and compare them with xb. for each similarity measure the optimal place of xb on xa has been found. by the optimal place of xb, in accordance with the chosen similarity measure, we assume xc such that produces the best value of this similarity measure when comparing xb and xc. then the values of remaining measures were computed for the same xc. due to the specific character of data (50 patients), the place of xc on xa may be denoted as follows: i[tstart; tend], where i is the order number of a patient, i = 1, . . . , 50, tstart and tend are start and end positions of xc on xa. the illustration results are presented in table 1. let us choose the sample at random. in our case, xb = 1[23−30]. it was compared with all the other available subsequences xc of the analysed data set xa. considering that the sample taken for the analysis is selected from xa and striving for the independence of investigation results on this case, we choose xc only such that has no more than 5 common observations with xb. five subsequences xc 6= xb, that are most similar to the sample, were selected according to each similarity measure. therefore, we get five collections of five most similar subsequences. the total number of the selected subsequences is 25 and some of them coincident according to different measures. for each collection, the values of all similarity measures are computed and presented in table 1. the best values obtained for all similarity measures are marked in bold. table 1 shows that different measures often mark different (not the same) subsequences as similar. all the measures acquire their best values in the case of identical coincidence of xb and xc. thus, a further task is to summarize the obtained results and to develop a method for selecting of the most similar subsequence xc to the sample xb. the analysis of subsequences, found by method for visual detection of similarities in medical streaming data 15 table 1: most similar subsequences according to each similarity measure; sample xb = 1[23−30] no. xc similarity measures r eros frob mdtw spca ideal coincidence 0 1[23-30] 1 1 0 0 1 r 1 1[16-23] 0.9639 0.1745 4.4224 16.9434 0.7175 2 48[37-44] 0.9575 0.1302 2.5551 19.2153 0.6874 3 1[17-24] 0.9504 0.6067 3.9967 17.0556 0.8480 4 42[16-23] 0.9455 0.3440 3.8527 30.8482 0.1680 5 1[19-26] 0.9430 0.2969 3.1215 18.8935 0.8474 eros 6 10[30-37] 0.0013 0.9423 6.5985 59.1889 0.9847 7 25[31-38] -0.3214 0.9099 7.1453 62.7647 0.6705 8 49[35-42] -0.2209 0.9064 8.0460 41.6136 0.5461 9 17[9-16] 0.6266 0.8979 4.6676 39.8056 0.4329 10 40[16-23] 0.1164 0.8948 7.7252 55.2800 0.5772 frob 11 48[37-44] 0.9575 0.1302 2.5551 19.2153 0.6874 12 48[36-43] 0.9366 0.4445 2.8003 18.6496 0.9789 13 1[36-43] 0.8931 -0.2531 2.9396 31.2548 0.9206 14 48[35-42] 0.9169 -0.2479 3.0346 23.2913 0.9148 15 48[38-45] 0.8943 0.1853 3.0886 25.6391 0.5152 mdtw 16 34[39-46] -0.0158 0.4709 7.2486 8.8982 0.3182 17 22[14-21] 0.1724 0.6900 6.5888 9.6141 0.5765 18 29[4-11] 0.5061 -0.0177 4.9202 9.6744 0.6974 19 12[12-19] 0.6511 -0.3166 4.8690 10.1095 0.9553 20 15[3-10] 0.5351 0.2744 7.4371 11.0668 0.2524 spca 21 10[32-39] -0.1145 0.0688 7.5452 96.4585 0.9920 22 16[33-40] -0.0749 0.2350 9.2904 67.8260 0.9896 23 10[33-40] -0.0695 0.0074 7.6335 93.8404 0.9896 24 10[34-41] 0.0036 -0.4202 7.5849 70.1514 0.9892 25 4[5-12] 0.4171 0.1522 6.9828 57.2766 0.9890 different measures shows that matrix correlation coefficient and frobenius norm measures try to find the most similar subsequence according to the values of the subsequence elements. mdtw tends to compare the data change dynamics: the scales of features of the subsequences can be different, but mdtw can indicate these sequences as similar. spca and eros measures do not also depend on the scale and are less sensitive to abrupt signal changes. it is very important in the medical data analysis. the correlation analysis (see table 2) has the depicted a strong inverse correlation between the frobenius norm and the matrix correlation coefficient. for the investigation, 50 randomly selected samples xbi , i = 1, . . . , 50 consisting of tb = 8 successive observations were selected; from each xai one subsequece x b i was selected as a sample. for each selected sample and for each similarity measure 25 most similar subsequences were identified in the whole xa. just like above, we choose xc only such that has no more than 5 common observations with xb. the total number of comparisons of xb and xc is 1250. according to these data, the correlation matrix of similarity measures has been computed. it is presented in table 2. as the results shows, there is a very strong inverse correlation (-0.7355) between the frobenius norm frob and the matrix correlation coefficient r. because of the strong inverse correlation between the frobenius norm and the matrix corre16 j. bernatavičienė, g. dzemyda, g. bazilevičius, v. medvedev, v. marcinkevičius, p. treigys table 2: correlation matrix of similarity measures r eros frob mdtw spca r 1.0000 -0.1917 -0.7355 -0.2127 -0.1652 eros -0.1917 1.0000 0.1819 0.1731 -0.1748 frob -0.7355 0.1819 1.0000 0.1071 0.1055 mdtw -0.2127 0.1731 0.1071 1.0000 0.0454 spca -0.1652 -0.1748 0.1055 0.0454 1.0000 lation coefficient, the frobenius norm was casted away. it has been done with a view to reduce the general impact of the correlated parameters on the investigation results. in such a way, outcasting of the frobenius norm measure evens the impact of the rest measures. for the second investigation 50 randomly selected samples xbi , i = 1, . . . , 50 consisting of tb = 8 successive observations have been selected. one sample xbi is selected from each x a i . for each selected sample and for each similarity measure (4 similarity measures are investigated now), 20 most similar subsequences were identified in the whole xa. just like previously, we choose xc only such that has no more than 5 common observations with xb. the total number of comparisons of xb and xc is 1000. according to the obtained data, for each sample xbi and for four measures, a 4-dimensional point is constructed: s i q = (s i q1, s i q2, s i q3, s i q4), q = 1, . . . , 20. coordinates of the point are the values of different measures. for example, the ideal subsequence match is assumed as s0 = (1, 1, 0, 1). further, the euclidean distance is calculated between the ideal match s0 and all the rest s i q of similar subsequences. then the subsequences are sorted according to the shortest distance and the first 5 subsequences are treated as similar (from the total 5x50 subsequences). table 3 summarizes the investigation results, i.e. shows the most often found similar subsequences. table 3: most often found similar subsequences according to each similarity measure frequencies r eros mdtw spca total 250 93 32 91 34 percentage from 250 subsequences 31 11 30 11 as can be seen from table 3, the matrix correlation coefficient and mdtw measures are the best. in addition, the frobenius norm measure can be considered together with these two measures because of its high correlation with the matrix correlation coefficient measure. moreover, a high correlation of the frobenius norm with the matrix correlation coefficient does not mean that they yield very similar results. the experiment below illustrates this fact. ten most similar subsequences were found for the chosen sample, using these two similarity measures for multivariate time series. the obtained subsequences are presented in table 4 in decreasing order of their goodness, depending on the similarity measure. coincident subsequences are presented in different tint of grey colour. we see three coincident subsequences only with quite different order numbers. method for visual detection of similarities in medical streaming data 17 table 4: comparison of the frobenius norm with the matrix correlation coefficient 6 experimental illustration of the visual method for finding the best subsequences, matching to the sample the performance of the proposed method is illustrated by the example. like in section 5, the data from physionet/computing in cardiology challenge (http://www.physionet.org/challenge) are used for the experimental analysis. 1. sample xb = 1[23 − 30] has been chosen. 2. the sample is compared with all the subsequences of xa by using 5 similarity measures, given in section 3. in accordance with each measure, 10 subsequences, most similar to the sample, are chosen. therefore, the total number of subsequences for a further analysis is equal to 50. 3. each comparison of a sample to a subsequence, chosen in the way defined in the step above, produces the 5-dimensional point sq = (sq1, sq2, . . . , sq5), where q = 1, . . . , 50. let us derive two additional points: • s0 = (s01, s02, . . . , s05) is the array of values of all the similarity measures computed for the subsequence that is ideally coincident with the sample (the array of the best values of m similarity measures); in our case, s0 = (1, 1, 0, 0, 1); • sc = (sc1, sc2, . . . , scm) is the weight center of sq = (sq1, sq2, . . . , sq5), q = 1, . . . , 50; in our case, sc = (0.39298, 0.31968, 5.98348, 35.26159, 0.715413). therefore, the total number of 5-dimensional points for discovering the most similar subsequences to the sample is equal to 52. afterwards, the normalization of the components of these points is performed by z-score. the obtained matrix z of normalized points consists of 52 rows and 5 columns. 4. the points from matrix z are mapped on the plane using the multidimensional scaling. the resulting matrix is y , the rows of which correspond to different comparisons of the sample with other subsequences and are the coordinates of points on the plane. 5. figure 2 presents graphically all 52 5-dimensional points from y on a plane for decision making. in general, the subsequence best matching to the sample can be the subsequence the corresponding point of which on a plane is closest to the projection of s0 on a plane. 18 j. bernatavičienė, g. dzemyda, g. bazilevičius, v. medvedev, v. marcinkevičius, p. treigys however, more subsequences are similar to the sample. the points on the plane, corresponding to such subsequences, must be closer to the projection of s0 on the plane than to the projection of sc. if we draw a circle with the center on the projection of s0 on the plane and the radius that is equal to the distance between the projections of s0 and sc , we can visually detect subsequances most similar to the sample. in figure 2, the colour of the point corresponds to the similarity measure according to which the subsequence falls among the most similar ones: • red points matrix correlation coefficient, • green points eros, • yellow points frobenius norm, • blue points mdtw, • magenta points spca. (a) (b) figure 2: results for the visual analysis: a) sample 1[23-30]; b) sample 5[15-22] figure 3: two subsequences most similar to the sample 1[23-30] in figure 2a, two best similarity measures, in accordance with which the subsequence falls among the most similar ones, are the matrix correlation coefficient and frobenius norm measures. in figure 2b, these two measures are exceptional again, however mdtw and eros have influenced the appearance of subsequences in the most similar subsequence list. a relatively small number of the most similar subsequences is produced by spca, only. therefore, all the measures are important and can be successfully used jointly . finally, the sample and two found most similar subsequences are presented, as an example, in figure 3. method for visual detection of similarities in medical streaming data 19 7 conclusions this paper deals with the multivariate time series analysis seeking to compare the current situation (sample) with that in chronologically collected historical data and to find subsequences of the multivariate time series most similar to the sample. the visual method for finding the best subsequences matching to the sample has been proposed. using this method, the investigator can consider the results of comparison of the sample and some subsequence of the series from the standpoint of several measures that can be supplementary to one another or contradictory among themselves. the analysis of medical streaming data is quite a difficult problem. the data are very specific to an individual patient. it may cause the problem of reliability of the decision if the problem is to estimate the health-state situations in real time streaming data in accordance with the previously detected and estimated streaming data of various patients. the usage of a larger number of similarity measures (not a single fixed measure) can increase the efficiency of decisions on the health state of the patient based on the streaming data of other patients. the advantage of the visual analysis of the data, presented on the plane, is that we can see not only the subsequence best matching to the sample (such a subsequence can be found in the automatic way), but also we can see the distribution of subsequences that are similar to the sample in accordance with different similarity measures. it allows us to evaluate differences among the subsequences and among the measures. five similarity measures were integrated in this research. note that the correlation coefficient between two matrices is quite effective and easily interpreted among other measures, that are usually used for multivariate streaming data analysis. however, the best efficiency of applications of this measure is achieved when combining it with other measures. this method is universal and can be used in the analysis of streaming data of various nature (not only medical data). it is necessary to select the proper set of similarity measures depending on the problem solved only. bibliography [1] batal i., sacchi l., bellazzi r., hauskrecht m. 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(2011); efficient data projection for visual analysis of large data sets using neural networks, informatica, issn 08684952, 22(4): 507–520. international journal of computers communications & control issn 1841-9836, 10(4):539-550, august, 2015. understanding social characteristic from spatial proximity in mobile social network d. hu, b. huang, l. tu, s. chen duan hu,benxiong huang, lai tu, shu chen* department of electronics and information engineering huazhong university of science and technology 1037 luoyu road, wuhan, china huduan1985@gmail.com, huangbx@hust.edu.cn, laitu@hust.edu.cn, remark.chen@gmail.com *corresponding author: remark.chen@gmail.com abstract: over the past decades, cities as gathering places of millions of people rapidly evolved in all aspects of population, society, and environments. as one recent trend, location-based social networking applications on mobile devices are becoming increasingly popular. such mobile devices also become data repositories of massive human activities. compared with sensing applications in traditional sensor network, social sensing application in mobile social network, as in which all individuals are regarded as numerous sensors, would result in the fusion of mobile, social and sensor data. in particular, it has been observed that the fusion of these data can be a very powerful tool for series mining purposes. a clear knowledge about the interaction between individual mobility and social networks is essential for improving the existing individual activity model in this paper. we first propose a new measurement called geographic community for clustering spatial proximity in mobile social networks. a novel approach for detecting these geographic communities in mobile social networks has been proposed. through developing a spatial proximity matrix, an improved symmetric nonnegative matrix factorization method (snmf) is used to detect geographic communities in mobile social networks. by a real dataset containing thousands of mobile phone users in a provincial capital of china, the correlation between geographic community and common social properties of users have been tested. while exploring shared individual movement patterns, we propose a hybrid approach that utilizes spatial proximity and social proximity of individuals for mining network structure in mobile social networks. several experimental results have been shown to verify the feasibility of this proposed hybrid approach based on the mit dataset. keywords: mobile social network; geographic community; community structure; measurement; 1 introduction recent years, with the increasing popularity of mobile terminal devices , the emerging massive urban sensors such as gps, mobile phone and online user-generated social media enable us understand the intrinsic nature of human mobility such as high predictability in daily routine and the social demographic characteristics of the individuals. given the importance of human mobility, e.g. human movement could be responsible for the geographical spread of human infectious diseases [2], many researchers have focused on capturing the statistical characteristics of individual human trajectories [3, 4], understanding the role of geographic distance and social interaction in human spatial redistribution on geographic scales[5-7]. early empirical studies on albatrosses, monkeys and marine predators [8] suggested that animal trajectory can be approximated by a levy fight [9]. given that money is carried by individuals, brockmann [1] et al. illustrated individual geographic trajectory using half a million dollar bills, suggesting that copyright © 2006-2015 by ccc publications 540 d. hu, b. huang, l. tu, s. chen human trajectories could be best modeled as a continuous-time random walk (ctrw model) with fat-tailed displacements and waiting-time distributions. however, several types of empirical data [10]on human mobility, captured by mobile phone traces, show that the predictions of the ctrw models are systematically in conflict with the empirical results. due to the fundamental importance of statistical properties of human travel, in recent years, the increasing pervasiveness of location-acquisition technologies such as 3g network and gps is conducive to the collection of large spatial-temporal datasets and opening up new opportunities for discovering some different knowledge about movement behaviors. indeed, the availability of these collected large-scale call data such as mobile phone records and global-positioning-system data, has offered researchers from various disciplines access to detailed patterns of human behavior, and greatly enhanced our understanding of human mobility [3]. recently, distinguished from previous studies, a new understanding [4] about building individual predictive model for future locations suggests that at a global scale human mobility exhibits structural patterns subject to geographic and social constraints. researchers find that humans experience a combination of periodic movements limited by geographical factors and seemingly random jumps correlated with their social networks [4]. in this case, individual movements can be divided to the two parts: short-range travel driven by periodic movements and long-range travel driven by social interaction. meanwhile, some studies [4, 5] found that individual (mobile devices) mobility patterns can shape and impact the inherent properties of social network. similarity of individual mobility patterns, which is called "spatial proximity", is expected to exert a significant impact on the inner structure of such social networks, from the maintenance of long-lasting friendships to the formation of new links. it seems that social links are often driven by spatial proximity, from joband family-imposed programs to joint involvement in various social activities [11]. on the contrary, the degree of similarity in periodic movements and social proximity of individuals can positively affect the level of spatial proximity since the similarity of both types of individual movements can be identified by spatial proximity. based on collecting large-scale call data that are recorded, spatial proximity between any two mobile phone users has been used to predict the potential call in cell phone social networks [5]. the booming development of mobile social network gives rise to several amazing sub-issues, such as community detecting, link prediction, social-aware recommendation. this was explored through evaluating social property of individuals and mining network structure evolution using general social network analysis. however, given those fascinating features of spatial proximity for forming social interaction among individuals in mobile social network that has been revealed, it is just natural to consider the social influence of spatial proximity that can be used to improve current researches on those amazing sub-issues. therefore, the aim of this paper is to investigate the social features of spatial proximity and further mine social network structure evolution to explore some potential possibility for optimizing user experience of mobile social networking service and social recommendation in mobile social network. although our present work is only confined to analyzing and applying the significant correlation between spatial proximity and social properties of individuals in mobile social network by several well-known data mining methods based on two real datasets, our experimental results also deepen our understanding about the social influence of spatial proximity in mobile social network. the contributions of this paper are as follows: we propose a new measurement dimension called geographic community for clustering spatial proximity in mobile social network. a novel approach for detecting these geographic communities in mobile social network has been proposed. through developing a spatial proximity matrix, an improved symmetric nonnegative matrix factorization method (snmf)[13] is used to detect geographic communities in mobile social network. understanding social characteristic from spatial proximity in mobile social network 541 we suppose that geographic community can provide a bridge between spatial proximity and social properties of individuals in mobile social network. by a real dataset containing thousands of mobile phone users in a provincial capital of china, the correlation between geographic community and common social properties of users has been shown. by mining shared individual movement patterns, we propose a hybrid approach that utilizes spatial proximity and social proximity of individuals for mining network structure in mobile social network. we first propose two new measurement matrixes. combing these matrixes with the known adjacency matrix in social network, the joint nonnegative matrix factorization (jnmf) method has been used to mine network structure evolution. finally, a collaborative matrix has been obtained to analyze the existing community structure and potential links in mobile social network. the rest of this paper is organized as follows: section 2 describes a well-known index of spatial proximity. the two measurement matrixes for evaluating spatial proximity and social ties in such mobile social networks will also be formed. section 3 presents the definition of our new measurement dimension "geographic community", and the strong correlation between geographic community and the similarity of several key indexes from the socio-economic background of those individuals based on thousands of cell phone call records and corresponding user information. section 4 first provides a hybrid approach to mine network structure in mobile social network, and then show several experimental results to verify the feasibility of this proposed hybrid approach based on the mit dataset. section 5 provides related work and finally comes to the conclusion. 2 preliminaries in this section, we first choose spatial cosine similarity to represent the level of spatial proximity of any two individuals. the measurement matrixes that describe such spatial proximity of corresponding individuals have been presented. furthermore, measurement matrixes that utilize the relationship between locations and users for evaluating spatial proximity and social ties in such mobile social networks have also been formed. spatial cosine similarity[5]:scos(x,y) the cosine similarity of user x and y’s trajectories, describing how similar their visitation frequencies are, is represented by the cosine of the angle between the two vectors of number of visits at each location for x and y and, scos(x,y) = ∑ l=loc pv (x,l)×pv (y,l) ||pv (x,l)||× ||pv (y,l)|| . (1) where pv (x,l) = n(x)∑ i=1 δ(l,li(x))/n(x) is the probability that user x visits location l, loc is the set of all locations (cell phone towers and etc.). spatial proximity matrix in order to reflect the spatial proximity between any two individuals in mobile social networks, we propose an approximate overlay g = (v,e,w), then the adjacency matrix of g can be written as: 542 d. hu, b. huang, l. tu, s. chen g =   s11 · · · s1n ... . . . ... sn1 · · · snn   (2) where sijis spatial cosine similarity scos(i,j), i ,j is each individual respectively. specially, when i = j ,scos(i,j) = 1. note that scos(i,j) = scos(j,i), g is a symmetric nonnegative matrix. the two measurement matrixes: user-location matrix and location-user matrix in previous studies, for evaluating the similarity in mobility patterns of two individuals, several indexes of spatial proximity have been proposed by the references [5, 14], spatial proximity can be defined by one key index of them: the cosine similarity of user x and y’s and s trajectories, describing how similar their visitation frequencies are, is represented by the cosine of the angle between the two vectors of number of visits at each location for x and y: scos(x,y) = ∑ l=loc pv (x,l)×pv (y,l) ||pv (x,l)||× ||pv (y,l)|| . (3) where pv (x,l) = n(x)∑ i=1 δ(l,li(x))/n(x) is the probability that user x visits location l, loc is the set of all locations (cell phone towers and etc.). as mentioned above, since the internal relationship between individuals and their usual travel locations can form spatial proximity, which has a significant impact on network structure of mobile social network, we develop two measurement matrixes that can reflect such internal relationship to evaluate network structure. given n individuals (or mobile personal devices) in a mobile social network and the number of all locations, we propose a user-location matrix u. (user-location matrix) u =   pv11 · · · pv1m ... . . . ... pvn1 · · · pvnm   (4) where pvxl = pv (x,l),x ≤ n,l ≤ m (location-user matrix) l =   lv11 · · · lv1n ... . . . ... lvm1 · · · lvmn   (5) where lvxl = n(l)∑ i=1 δ(l,li(x))/n(x),x ≤ n,l ≤ m is the probability that user x visits location l, loc is the set of all locations (cell phone towers and etc.). understanding social characteristic from spatial proximity in mobile social network 543 3 geographic community and its correlation with the social properties of individuals in this section, we first propose a new measurement dimension for clustering those individuals with close spatial proximity in mobile social network. based on snmf method, an approach for detecting these geographic communities has been proposed. several numerical experiments have been shown to verify the feasibility of this measurement approach. geographic community given n individuals (mobile personal devices) in a mobile social network, the similar degree of geographic trajectories of two individuals can be measured by the spatial proximity index. in this case, we can develop an approximate overlay which indicates the spatial proximity between two individuals (nodes) in the mobile social network. suppose g = (v,e,w) is an undirected weighted graph with n nodes and m links representing this approximate overlay. it is quite different from general social graphs in that these links between any two nodes are the spatial proximity index. the weight w ∈ [0,1] of the edge e denotes the similar degree of two individuals which can be calculated by spatial cosine similarity. c = c1,c2, . . . ,ck denotes a collection of disjoint geographic communities, where ci ∈ c is a geographic community of g. using general definition of community structure in social network, the geographic community has several meanings: the nodes in geographic community are those individuals which have higher similar degree, that is, those individuals had met each other frequently in the past long period of time, unlike those individuals between different geographic communities. the strong correlation between this geographic similarity and their proximity in social network indicates that more social connections among the nodes will occur in geographic community in the future. therefore, geographic community can be a measurement dimension that estimates the interaction between social community and spatial proximity. in fact, we consider geographic community as an indicator which may be used for evaluating the evolution of social community structure in the future. detecting geographic community using symmetric nonnegative matrix factorization (snmf) we consider symmetric nmf as our tool to find the geographic community because of its powerful interpretability and close relationship with clustering methods. it has been verified that snmf has outstanding performance in detecting communities in undirected unweight complex networks. given that our approximate overlay is an undirected weighted virtual network, we further apply snmf method to achieve our purpose. if g is symmetric, then s is symmetric. we can then absorb s into x. i.e. x̂ = xs1/2. then our task is to solve the following problem: min x≥0 ∥g− x̂x̂t∥2f (6) according to [15],x̂ can be obtained by the following multiplicative update rule x̂ik ← x̂ik [ 1 2 + (gx̂)ik (2x̂x̂t x̂)ik ] (7) 544 d. hu, b. huang, l. tu, s. chen figure 1: the reported friendship network in reality mining datasets after iteration, the obtained x̂∗ is just the scale partition matrix of the network g of size n × k, whose i-th row corresponds to the cluster (geographic community) membership of the i-th unit. we can further normalize x̂ to make ∑ j x̂ij = 1 so that x̂ij corresponds to the posterior probability that the i-th unit belongs to the k-th geographic community. we choose reality mining data set [6] provided by the mit media lab to test our proposed approach. the reality mining dataset contains communication, proximity, location, and activity information from 94 students or teachers at mit during the 2004-2005 academic years. in particular, the dataset includes call logs, bluetooth devices in proximity, cell tower ids, application usage, and so on. in this dataset, a mobile social network which contains 94 nodes and several million links can be modeled as fig.1, where the links representing real social friendship which can be evaluated by the number of calls, messages and so on. to design this numerical experiment, we propose those cell tower id lists of each individual communication record representing those locations l that can be used to calculate the probability pv (x,l).we firstly obtain the spatial proximity matrix of this real mobile social network, then an approximate overlay network can be inferred in fig.2. as is shown in fig.2, this network does not have obvious community structure. we consider the reasons as follows: every two individuals have their spatial similarity. in the overlay network, the weights of links are inferred by corresponding spatial proximity. in this case, it is essential to properly evaluate the impact of the weight value on network structure. the binarization method will lose much useful information. furthermore, these individuals have more overlaps in geographic community than general communities. therefore, we choose to delete those weight links which have lower weights (i.e., the links that weight w ≤ 0.2 will be deleted). we use snmf method for detecting geographic community on this overlay, the symmetric nonnegative matrix g is a n × n(n = 94) spatial proximity matrix. we assume the number of main geographic communities k = 2, the k × n matrix will be calculated by (7). the final iteration result of x is shown in fig. 2. in fig. 3, the interval [0,1] located in the y-axis represents the community id = 1, the interval [1,2] located in the y-axis represents the community id = 2, the sequence of nodes i(i = 1,2, . . . ,94) is located in the x-axis, and the gray level is obtained by corresponding element value of x , where the higher the gray level of the block (i,id(i))tend to be, the more understanding social characteristic from spatial proximity in mobile social network 545 figure 2: the spatial proximity overlay of reality mining datasets figure 3: the results obtained by applying our proposed method 546 d. hu, b. huang, l. tu, s. chen likely that unit i belongs to the geographic community id. the relevance test between geographic community and the social properties of individuals we use a dataset that captures for two-month period the time-resolved trajectories of ten thousand anonymized mobile phone users in an anonymous provincial capital in china to uncover the relevance between geographic community and the social properties of individuals. given the multiplicity of the social properties of individuals and this dataset mainly contains the cell phone charging records of all users in an anonymous telecommunications company, the similarity of the cell phone charging of these users depends on the similarity of some social properties such as the economic ladder to some extent. therefore, we choose the type of plan the user used and the interval of user charging per month as the two parameters of the economic ladder and present the value of the economic ladder of them. three geographic communities have been obtained from ten thousand users by the above approach based on this dataset. through clustering these economic ladder values in each geographic community, we found that the economic ladder of individuals is highly consistent in each geographic community. in this test, our parameters are set as follows: table 1: the initial situation of ten thousand mobile phone the number of individuals economic ladder matching results in general 10000 0(budget)(arpu < 100) 5613(56.13%) 1(ordinary)(100 < arpu < 200) 3052(30.52%) 2(luxury)(arpu > 200) 1335(13.35%) finally, our test truly shows the higher consistency in table2, given the number of geographic communities nc = 3, and we consider the posterior probability of nodes x̂ic > 0.4, c ∈ 0,1,2 means node i belong to c,then we obtained following numerical results: table 2: the matching results between the geographic communities and economic ladder the label of geographic communities matching results 0(number : 5767) 63.4% 28.85% 7.6% 1(number : 4978) 56.3% 40.1% 3.57% 2(number : 3244) 44.1% 35.2% 20.6% given that periodic movements can explain about 50-70% of the geographic trajectories of individuals, and the repetitive trajectories that are formed by periodic movement mainly depend on the role of daily routine and geographic features of human movement, we consider the similarity of the geographic trajectories between individuals is largely attributable to the overlapping functional areas for these individuals, for example, identical residential area, the same lunch venue and similar workplace. furthermore, these overlapping functional areas just depend on the social properties of individuals, such as economic ladder, vocation, and so on. therefore, the spatial proximity of individuals not only suggests the proximity of social relationship of individuals in mobile social network, but also implies to some extent the similarity of the social properties of individuals. furthermore, we suggest that the similarity of the social properties of users also indicates the likeliness for these users to interact with each other. understanding social characteristic from spatial proximity in mobile social network 547 4 a hybrid approach to mine network structure in mobile social network since sna techniques have gained extensive development in recent years, many studies on detecting existing community structure in various social networks can be found in an excellent survey [16]. furthermore, a large amount of studies on predicting potential links have been presented for link prediction in complex networks [17]. mining network structure, which provides us with meaningful insights into the internal structure of corresponding real social network and the possibilities to optimize the performance of social recommendation and link prediction, mainly contains two parts, i.e. detecting existing network structure and tracking the trend of network structure evolution. in general, mining network structure in mobile social networks has been investigated as a typical example of dynamic social networks by many researchers [6, 18]. spatial proximity and social proximity of individuals can be used for mining network structure in mobile social network. especially, existing community structure and potential social relationship in a real mobile social network can be obtained and analyzed by our hybrid approach. we first propose two new measurement matrixes which provide a bridge between spatial proximity and the social properties of individuals in mobile social networks. combing these matrixes and the known adjacency matrix in social network, the joint nonnegative matrix factorization (jnmf) method [13] is used to mine network structure evolution. finally, a collaborative matrix is obtained to analyze the existing community structure and potential links in mobile social network. based on a real dataset, several experimental results have been shown to verify the feasibility of this proposed hybrid approach. mining community structure in mobile social network using joint nonnegative matrix factorization (jnmf) method in previous studies, jnmf was usually used to detect community structure or hidden links in complex networks. in fact, it has been verified that jnmf has outstanding performance in such aspects in complex networks. in this section, we choose jnmf method as our tool to find community. based on the mit dataset, this social graph is an undirected weight network. given its adjacency matrix of friend graph d, we further apply jnmf method to achieve our purpose. the problem we aim to solve is min x≥0 l(x,u,d,l) s.t. x ∈ rn×m+ where l(x,u,d,l)def = ∥u −x∥2 + α∥d−xxt∥2 + β∥l−xt x∥2, and α > 0,β > 0 are constants to tradeoff the importance between different terms. given n individuals (or mobile personal devices) in a mobile social network, and the number of all locations m , and d represents the adjacency matrix of friend graph , u and l represent user-location matrix (4)and locationuser matrix (5), respectively . according to [15], x̂ can be solved by the following multiplicative update rule. x̂ij ← x̂ij [ [u + 2αdx + xl̂]ij 2(α + β)[xxt x]ij ]1 4 (8) where d̂ = 2βd −1 after iteration, the obtained x̂∗ is just the scale partition matrix of the network g of size n×m , and then we obtain the hybrid matrix c = xt x. let this matrix is the initial adjacent matrix, we further use the above approach in section 3 for detecting community structure based on snmf. 548 d. hu, b. huang, l. tu, s. chen figure 4: two main communities in the community structure of this friendship network figure 5: the hybrid communities structure in this mit dataset in our experiment, we also use reality mining data set [6] provided by the mit media lab to test our proposed approach. from this real friendship network, two main communities can be inferred by real social connections as shown in fig.2, which shows the first-year business school students and individuals working together in the same building respectively. moreover, we can simply detect these social communities by newman method. however, the dynamic evolution of the social ties among these individuals cannot be timely detected. this leaves an important question that to what extent individual mobility patterns impacts the social network. therefore, in this section, the communities of the same dataset will be detected by our proposed method. during our implementation process, we suppose spatial proximity effect on social community structure is limited, and present two weight parameters. as is shown in fig.5, the difference in gray-level between two communities labels for nodes illustrates the segmentation map presented in fig.4. meanwhile, some nodes (ex:i = 2,33,76) that are not covered by this segmentation map shows the potential possibility for joining the two communities together. in fig. 5, the interval [0,1] located in the y-axis represents the community id = 1,and the interval [1,2] located in the y-axis represents the community id = 2, the sequence of nodes i(i = 1,294) is located in the x-axis, and the gray level is obtained by corresponding element understanding social characteristic from spatial proximity in mobile social network 549 value of x , where the higher the gray level of the block (i,id(i)) tend to black, the more likely that the unit i belongs to the geographic community id . 5 results and discussion since recent research progress on the interaction between human mobility and social ties has provided us with valuable information that spatial proximity can be used to predict future social links, in this paper, we first proposed a new measurement dimension (geographic community) for evaluating the spatial proximity influence on the evolution of social community structure in mobile social networks. the correlation between geographic community and common social properties of users has been analyzed based on a real dataset. we found the spatial proximity of individuals not only suggests the proximity of social relationship of individuals in mobile social networks [5], but also implies to some extent the similarity of the social properties of individuals. then a hybrid approach that utilizes spatial proximity and social homophily of individuals for mining network structure in mobile social network has been proposed. however, due to the inner ambiguity of individuals spatial proximity, the direct applicability of spatial proximity in mobile social network still leaves much room to be studied. the results presented in this paper will lead us to 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(2011); adaptive algorithms for detecting community structure in dynamic social networks, proc.ieee infocom, shanghai,china, 2282-2290. int j comput commun, issn 1841-9836 9(3):261-275, june, 2014. advantages of using an ontological model of the state development funds s. arsovski, b. markoski, p. pecev, n. petrovački, d. lacmanović saša arsovski guarantee fund of autonomous province of vojvodina serbia, 21000 novi sad, hajduk veljkova 11 e-mail: garfondapv@neobee.net branko markoski*, predrag pecev, dejan lacmanović university of novi sad technical faculty "mihajlo pupin" zrenjanin serbia, 23000 zrenjanin, djure dakovica, bbi e-mail: redrag.pecev@tfzr.rs, dlacman@tfzr.uns.ac.rs *corresponding author: markoni@uns.ac.rs neboša petrovački university of novi sad faculty of technical sciences computing and control department serbia, 21000 novi sad,trg dositeja obradovića 6 e-mail: petrovackin@uns.ac.rs abstract: ontologies generated from the workflow of administrative procedures, can provide significant improvements and reduce the time of modeling, testing and integration in the process of building an information system of the public administration. in this paper we has analyzed the ontology annotation aimed to provide the groundwork for automatic generation administrative act. the proposed semantic representation of the administrative procedures enables use of the document templates, which are the framework for the automatic generation of an administrative act. this would be a result of the administrative procedures execution. the proposed approach is verified by a case study of building an ontological model of the administrative procedures for the guarantee fund of autonomous province of vojvodina (apv). keywords: e-government, ontology, services, information system. 1 introduction state development funds are an interventionist mechanism by which the state affects the development of small and medium enterprises. these are institutions set up by authorities of different levels (state, provincial, local government) and whose activity is aimed at stimulating the development of small and medium-sized enterprises while reducing risk and transaction costs related to the implementation of stimulating instruments (e.g. loans) of small and medium enterprises. the following development funds are established in ap vojvodina: vojvodina development fund, agricultural development fund, capital investment fund and the guarantee fund of apv. there are two scenarios of use of ontologies in the development of information systems, depending on the type of information system that is being modeled [1]: traditional information systems: semantic content described in the ontology is transformed into a standard component of the information system ontology-driven information systems: the ontology is an individual component of the information system. copyright © 2006-2014 by ccc publications 262 s. arsovski, b. markoski, p. pecev, n. petrovački, d. lacmanović the ontological concept, which defines the administrative procedures and tasks, will be described and analyzed in detail. the course of generating ontology will be resting on the role of defined entity domains and the activities performed by those entities which are observed during the process of the state administration. the model should meet the following conditions: 1. to describe structural aspect of public administration, administrative units (funds) and their hierarchical relationship. 2. to provide an explicit representation of knowledge of administrative processes in guarantee fund of apv. 3. to provide ontological representation of the participants and documents in the development funds. 4. to describe electronic services profiles those are providing the groundwork for automatic generation administrative act. our main intention is to reduce development time and effort in order to meet the demands of the information system based on knowledge of the administrative processes and documents. the core idea in our method is ontology annotations with user interface components in order to automatic generate user interface and modeling ontology of the electronic services that will provide generation of the final administrative act. main advantages of the model which will be described hereinafter, is a semantic representation of the administrative procedures business logic which, in the use of traditional techniques for developing information systems, are firmly integrated into the user interface code. [4] [8] the paper is organized as follows: the second section provides an overview of the related work. the third section presents an ontological model of the guarantee fund of apv. the fourth section shows the detailed administrative tasks semantic representation and shows practical use of designed ontology. in the final, fifth section, concluding remarks and directions for further research are given. 2 related work this section will present research results related to two aspects of the problem investigated in this paper. the first aspect relates to the use of ontologies in modeling business processes of public administration, and the second aspect relates to the use of ontologies in the generation of the administrative act. a ontology is an information structure, which helps to acquire knowledge, share it, and check consistency within knowledge. authors in [19] conclude that the ontology provides a better communication, reusability and organization of knowledge by decreasing language ambiguity and structuring data. author [18] proposed a formal definition of ontology as a 5-tuple (n, r, d, f, t) where each element is defined as follows: n, a set of nodes. r,n is a set of relation types. d, is a set of description logic sentences. each sentence can use the elements in n and two variables subject and object. indicating respectively the first and third element in 5-tuple in t. f is a function that maps each element from r onto one element in d. t is a set of relations which is defined as a set of 3-tuples where for each element consists of (s, r, o) where: s is the subject, an element of n, r is the relation, an element of r and o is the object, an element of n. the authors in [2], propose a model of the system in which the procedures performed by each administrative unit in the decision-making process and the creation of administrative documents are described. the main component of the proposed system is ontology of the public administration. the proposed ontology comprises following aspects of the observed domain. structural aspects of public administration represent administrative units and their hierarchical relationships. textual aspect of ontology describes the documents that appear as a product of advantages of using an ontological model of the state development funds 263 the administrative unit described in the structural aspects of ontology. procedural aspects of public administration ontology given in [2] are represented as an extension (specialization) to owl-s. service aspect is also represented by owl-s. an ontology modeled in this way allows hierarchical control of the administrative units, control of administrative acts anticipated by legislation, communication between administrative units involved in the creation of administrative documents and calling of the procedures that trigger electronic services. the authors [5], [6], define three main reasons for the use of ontologies in generating user interfaces: 1. improved visualization of ui; 2. improvement of interactions between the system and users, 3.improvement of the development process of user interface. in [3] the authors propose the creation of an ontology, which represents different aspects of service including administrative documents and legislation. this ontological model is focused on describing the electronic services of public administration. basic concepts of the proposed ontology are services, service users, organization, administrative, service implementation, legislation, form, document, event. the ontological model created in this paper relies on the results of [2] and [3]. in a study of applying ontologies in the development of e-government the authors presented a method of modeling ontologies in the domain of e-government [15]. according to the authors of [9], each public service of egovernment is semantically modeled and contains references that point to the required input data. predefined values of input data or preconditions can be expressed with semantic rules. in this way, this allows automatic creation of (web) forms and interactive validation of input data. [13] according to the authors, the development of new applications and projects begins with modeling the ontology. when modeling the ontology, the authors used two types of classes and subclasses, by introducing the following assumptions: each class that contains a subclass is seen as an abstract class; each class that does not contain a subclass is considered a realistic class (the basis for the creation of form); electronic services accept only the instances of realistic classes as input data. the presented rules and ways of modeling ontologies, according to the authors of [9], allow the unambiguous identification of e-government services. the authors of [10] proposed the use of algorithm for the direct transformation from owl to relational data structures. the authors of [11] also dealt with these problems. the basic idea is to transform the created ontology with the help of the transformation tools in the dll script and thus preserve all relations, constraints, and information about the domain. dll script is used to generate a relational database. authors of [14] proposes a feasible implementation of a multi-agent environment which makes use of ontologies and ontology mapping to achieve semantic interoperability. authors use an ontology model to facilitate semantic interoperability in a simulated multi-agent environment. the authors in [16] exploits different semantic web technologies and builds a prototype of semantic web mash up functionality based on combination of rdf/owl with sqwrl. the main scope is to improve decision-making processes. sugumaran and storey present a heuristics-based method for developing and creating ontologies [17]. they identify all the basic terms; this is done by using use cases and then revising synonyms and related terms manually or by an online thesaurus. in the next step they identify the relationships among these terms. they define three types of relationships: generalization, synonyms and associations. generalization corresponds to an is-a-relationship. this paper will present the methodology of ontology construction and annotation of state development funds. 3 creating an ontology this section presents a method aimed at building an ontological model of state development funds. the basic administrative activities that are common to all development funds in apv 264 s. arsovski, b. markoski, p. pecev, n. petrovački, d. lacmanović are as follows: registration of participants of the competition, analysis of the submitted documentation, risk assessment of funds placement, the decision on funds placement, and type of placement. it is noticeable that the administrative activities are almost identical for all state development funds, regardless of the type of fund and type of placement. the authors in [12] propose the following four-level typology of administrative activities of public administration: identification-identification of types of services offered to end users: 1. specifications specifying administrative procedures and documents for the identified type of service; 2. interaction specifying a communication protocol for the identified type of service (for example, signing a contract); 3. transaction the realization of services (for example, issuing guarantees for loan). this typology can be applied to state development funds. an important role in the development of ontology represents the conceptualization and organization of knowledge. the task of conceptualization is to transform informal knowledge into an ontological concept with the help of professionals in their field of ontology modeling. figure 1 shows a simplified model of the activities of state development funds. [7] figure 1: a simplified model of the activities of state development funds ontological descriptions of the administrative activities are generally applicable to all government development funds. below is a detailed description of the ontological model of the guarantee fund of apv (gfapv om), which defines the administrative procedures and tasks. 3.1 ontological model of the gfapv the public administration aspects given in [2] were taken as the basis for the creation of concepts in om gfapv. ontology of public administration in [2] consists of two parts. in the first part of the ontology, two aspects are defined: 1. a structural aspect of public administration in which administrative units and their hierarchical relationship are described where hierarchical relationships between the administrative units were presented with properties (belongs to and supervised by). 2. the textual aspect of ontology describes the documents that appear as a product of the administrative units described in the structural aspect of the ontology. the textual aspect is presented in the ontology with four main classes as follows: 1. administrative documents -presented as a product of administrative units; 2. civil documents -all types of documents that people create and fill out in the course of communication with public administration; 3. the legal texts -the laws and secondary legislation; advantages of using an ontological model of the state development funds 265 4. court decisions -decisions related to administrative actions and decisions of the supreme court. in the second part of the ontology given in [2], both the procedural and service aspects are presented. the procedural aspect of public administration ontology is presented as an extension (specialization) of owl-s. the basic concepts of procedural aspects are tasks, procedures and full procedures. tasks are atomic actions that cannot be further broken down and are executed by an administrative unit. each task has input data that needs to be filled by the system administrator or worker. the result of executing the task is an administrative act. a procedure contains at least one informative task (i.e. it tries to find or notify some information from/to another unit) and only one executive task (i.e. it produces a single act). thus, the procedures are composed of one or more tasks. each task is executed within a framework of procedure. full procedure represents a number of procedures intertwined. a full procedure may reflect to the provision of a service to one or several entities (property providedto). procedures may be sequential or in an acyclic graph. in this ontology, the control constructs of owl-s are adopted. when defining the ontological model of om gfapv, the following elements of the domain were analyzed: 1. participants. this aspect corresponds to the structural aspect of the model [2]. it represents internal funds structure and its position within the public administration, as well as actors who perform tasks defined in the business process and external participants to the process. 2. documents. this part, which corresponds to the text aspect of the model [2], represents administrative acts appearing during the execution of tasks within business processes. 3. the business process logic, which corresponds to the procedural aspect of the model [2], is defined by business rules and operating procedures of the business system (state credit guarantee funds). 4. electronic services are the services invoked in order to execute procedures implementing business process logic. in the case of om gfapv, administrative procedures are defined as ontology concepts. communication between administrative procedures and services is presented as atomic process. in the case of om gfapv each service belongs to only one procedure that is defined in the ontology. services are described by service profile properties. the generalization (superclass-of) and specialization (subclass-of) of ontological concepts is represented by the taxonomy of the main concepts of the ontology of the guarantee fund of ap vojvodina. 3.2 the taxonomy of the concept participants by this concept, all participants in procedures are represented. the concept participants is shown in figure 2. figure 2: participants-ontological concept three subclasses of the participants class are defined in this way: financial-institutions, publicadministration, clients. 266 s. arsovski, b. markoski, p. pecev, n. petrovački, d. lacmanović the financial-institutions concept this ontological concept describes financial institutions that cooperate with the guarantee fund as well as external participants in carrying out work procedures of the fund. two groups of financial institutions were identified: 1. state institutions ( the national bank ), 2. the independent financial institutions (commercial banks, credit bureau association of serbian banks). the properties of these concepts are subclassof and haveservice. the property subclass of describes the structural position of the concept, while the property haveservice indicates the existence of external services that are invoked during the execution of procedures. the public-administration concept the classes described by the concept public-administration represent the organizational structure of state administration in ap vojvodina and the place and role of the guarantee fund of apv in this organizational structure. the fund concept the classes identified in the taxonomy of the fund are, as follows: managing board, fund director, professional service, administrative office, commission for the issuance of guarantees. the clients concept the classes described by the concept clients represent participants who are allowed to apply to fund open competition. the classes identified in the taxonomy are: legal entity and natural person 3.3 the documents concept the concept documents is created by analyzing all of the documents identified in the guarantee fund of apv. figure 3 shows the taxonomy of the documents concept. figure 3: taxonomy of the documents ontological concept three groups of documents were identified: administrative acts, planning acts, and general acts. advantages of using an ontological model of the state development funds 267 the administrative-act concept as defined above, administrative documents are the product of administrative tasks within business procedures. semantic descriptions of documents that have been identified as a result of the execution of administrative tasks are given below. if we look at the documents as a product of administrative tasks in the case of the guarantee fund of apv, the following documents that appear as a result of the execution of administrative tasks in the business process of issuing a guarantee can be identified: the application document to open competition; the proposals document to the commission for the issuance of guarantees; the decision proposal document for the board; the decision document of the board; the contract document to issue guarantees. the document of guarantees administrative-act concept is a subclass of the documents concept. in addition, it is associated with the property producedby that describes it in terms of administrative procedure which produces the document of administrative-act type, and the property fillby which determines the entity from the participants taxonomy who fills out a pre-defined form of the document of administrative-act type. the planning-act concept documents belonging to this group are created each year. this group includes the following documents: work program, and open competition text. apart from their property subclass of which defines hierarchical relations among documents, they are associated with properties that describe them in terms of creation and approval: createdby defines the entity from the participants taxonomy that created the document. acceptedby defines the entity from the participants taxonomy that approved the document. the general-acts concept the documents that determine the legal framework of the fund belong to this group. in the case of the guarantee fund of apv, these are the following documents: the establishment decision, the statute, the fund business rules, and the fund code of conduct. apart from their property subclass of which defines hierarchical relations among documents, they are associated with properties that describe them in terms of creation and approval: createdby defines the entity from the participants taxonomy that created the document. acceptedby defines the entity from the participants taxonomy that approved the document. 3.4 procedures concept the procedures concept represents taxonomy of the administrative procedures of the fund. this taxonomy is created based on operational procedures for issuing guarantees and procedures relating to the creation of planning acts of the fund. in addition, the taxonomy contains procedures related to utilization of the documents that define legal and regulative framework of funds within the state administration. the taxonomy is shown on figure 4. the taxonomy of the procedures concept consists of following classes: planning-procedures, operative-procedures. 268 s. arsovski, b. markoski, p. pecev, n. petrovački, d. lacmanović figure 4: the procedures taxonomy planning-procedures concept this concept describes the procedures aimed at planning annual activities of the fund. the outputs of these procedures are various documents that contain activities to be performed in a calendar year and related financial resources. the following procedures have been identified: creating an annual work plan document, and creating an open competition document. the properties of classes describe these procedures: subclass of identifies the structural position of the concept within the procedures taxonomy. haveoutputdoc -identifies the document that results from performing general administrative procedures. performedby -identifies the perpetrators of the procedure. generalactsreferenced identifies all general-act concepts, which are legal/regulative basis for the created planning document. operative-procedures concept basis for creating the operative-procedures ontological concept are: task, procedure, and full procedure as defined in [2]. each task is executed within a procedure. each task has input data that an administrative worker or system should fill out and the result of the execution of the task is an administrative act. the procedure contains only one task with the corresponding input and output data, which is filled out by an administrative worker or system. full-procedure is composed of one or more procedures, while operativeprocedure is composed of one or more tasks. in the case of the guarantee fund of apv, the full-procedure is the procedure of issuing the guarantee. analyzing operative-procedures concept the following classes are identified: application-processing, committee-preparation, committee-decision, board-decision, contract-generation, and guarantee-generation. figure 5. depicts application-processing class, and properties and relations of the operative-procedures concept. services that call the specified operative-procedures during their execution are presented by their profiles (pservice-a-p, etc.). communication between these procedures and the service can be defined as an atomic process since the action of the service can be performed in a single interaction with the service. advantages of using an ontological model of the state development funds 269 figure 5: properties and relations of the operative-procedures concept 3.5 the services concept classes of internal and external services that are invoked for executing the work procedures of issuing guarantees are described in this taxonomy. the following classes are defined: internalservices and externalservices. internal services are the services that are provided by the fund, while external services are those that are provided by third parties like banks, other administrative bodies, etc. both have same properties subclass of and availableon. the property subclass identifies the structural position of the concept within the services taxonomy, while the property availableon indicates service provider uri. members of the class internalservices (service-a-p, service-c-p, etc.) are presented by the presentedby property. this property points to the service profile that presents the service and the operational procedure that uses this electronic service. 4 the semantics of the administrative task as described above, in our model, an operative-procedure is composed of one or more tasks. we call these tasks administrative tasks. figure 6 shows the ontological representation of an administrative task. figure 6: ontological representation of an administrative task each class and subclass of the ontology within the concept of the operative-procedures has input data. properties dataproperties describe input data of each operative-procedures class. an administrative worker or service fills input data into pre-defined document template(s) corresponding to an administrative task. document template is an active document that contains a code aimed at invoking a service presented by internalservices profile. it serves as a starting point for a new document creation. individuals of each operative-procedures class represent corresponding service and corresponding document template that are filled with input data during the execution of an administrative task. result of an administrative task execution is a 270 s. arsovski, b. markoski, p. pecev, n. petrovački, d. lacmanović custom administrative act output document (one or more in general, because the ontology allows it). output documents of an administrative task have their corresponding object properties. these properties describe all relations between an administrative task and concepts documents (producedby, fillby) and participants (performedby). figure 7 shows the ontological model of the administrative task aimed at processing applications on open competition. figure 7: ontological model of the administrative task semantics of an administrative task can be described as follows: the procedure applicationprocessing is executed by the administrative-office. the document application-document-toopen-competition is the result of the execution of application-processing procedure. the procedure for application processing has a unique document template txtapplication. the service profile pservice-a-p describes the electronic service invoked by the procedure applicationprocessing. 4.1 application of the ontology of administrative task the annotation of the ontology with elements of user interface is based on the rule that administrative tasks have input data. as shown in figure 7, input data is a series of variables that are filled with the resulting document template of the administrative task. the names of the input data are represented with dataproperties of the observed task. the descriptions of input data are defined in dataproperties isdefinedby property in the following form: <control>component_type</control>...(type of the ui /database component ) <data>data_type</data>.(data type (string), (integer)) <order>x</order>.(component index order on the form) <label>component_label</label>.(label of the component) <id>unique_name</id>(unique name of the defined component) individuals of each class denote the template of the document that is filled with input data the feature data-properties-assertion references a document that represents the template. data-properties-id defines the order of execution of the administrative procedures and/or tasks. an algorithm for a direct transformation of the semantic description of the administrative task in the user interface components is shown in figure 8. advantages of using an ontological model of the state development funds 271 figure 8: transformation ontology into user interface module for semantic content transformation: the requirement set in the introductory section of this paper is that the created ontology should enable automatic generation of information system components. the basis of this procedure is the transformation of the created administrative task ontology represented in owl format in two xml documents witch enable creation of the user interface. the module for transformation of semantic content represents the application that loads the created ontology and executes sparql queries. parsing the query results generates two xml documents (ontoclass, ontoform). administrative tasks sorted in the order of execution within the administrative process are presented by a tree view component. for each administrative task, it is necessary to load the descriptions of the components of the information system from the property dataproperty isdefinedby. the first xml document is a representation of classes and subclasses of the ontological concept procedure that represents an ontological description of the administrative business processes. ontoclass.xml is an xml document, which represents a workflow of the defined administrative procedures. the second xml document represents components that are extracted from the semantic description of the administrative task. as shown in figure 8, our model proposes the application of ontologies for the representation of administrative processes defined in workflows and extraction of information system components from the generated ontology. figure 9 shows the user interface of the module for the transformation of semantic content. the module for semantic content transformation provides the following functions: selection and loading of the desired ontology; entering the names of basic classes of the ontology that describe the tasks of the business process; generating the first xml document; entering the name of an administrative procedure for which it is necessary to generate user interface components; generating the second xml document; generating user interface for the selected administrative task. as described, annotating administrative task ontology with user interface components, enable automatic creation of the user interface for each procedure defined in the ontology. depending on the application, the appropriate xsl file provides mechanisms of transformation and formatting ontoform.xml and ontoclass.xml documents into user interface components. ontological representation of the administrative task contains the resulting document template of a selected task. listing 1 displays the sparql queries by which we extract the name of the resulting document template of the chosen procedure. 272 s. arsovski, b. markoski, p. pecev, n. petrovački, d. lacmanović figure 9: the module for semantic content transformation listing 1. query code for the template extraction prefix xsd: <http://www.w3.org/2001/xmlschema#> prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> prefix owl: <http://www.w3.org/2002/07/owl#> prefix gf: <http://localhost/gfontologies/2011/8/19/ontology1316412802796.owl#> prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> select ?x ?y where { ?x rdf:type gf:adm_task_1 ?x gf:doctxt ?y } 4.2 using document templates each administrative task has exactly two ontological individuals: a corresponding service and a corresponding document template to be filled with input data during the execution of the administrative task thus generating the final document of this administrative task. it and legal experts are responsible for creating document templates and corresponding electronic services based on the semantic annotation of the administrative task. an example of executing application-processing procedure, which illustrates the flexibility of the administrative task representation achieved by the proposed administrative task semantic description and use of document templates, is shown in sequel: when a user select desired administrative task (on the generated user interface applicationprocessing figure 10) and fill out all the required fields, corresponding service generate document template shown on figure 10. document template acquires application data from the generated user interface and enable generation of the final administrative act. advantages of using an ontological model of the state development funds 273 figure 10: functionality of the created document template 5 conclusion egovernment solutions represent a major challenge in redefining the role of public administration agencies and organizations. creating and using ontologies of knowledge of administrative processes and modeling and controling systems that would speed up and automate the work of state administration, constitute prerequisites for technical and organizational interoperability of different government agencies. the proposed method reduces development time and effort in order to meet the demands of the information system based on knowledge of the administrative processes and documents. in this paper, we presented a methodology for ontology creation and annotation utilized to describe knowledge of the administrative task. in this paper, we have proposed an ontological model of state development funds. the proposed model enables document templates creation, which are the framework for the automatic generation of an administrative act as a result of the administrative procedures execution. the proposed approach is verified by a case study of building an ontological model for the guarantee fund of apv. when defining the ontological model of om gfapv, the following elements of the domain were analyzed: 1 participants, represents internal funds structure and its position within the public administration, as well as actors who perform tasks defined in the business process and external participants to the process; 2 documents, represents administrative acts appearing during the execution of tasks within business processes; 3 the business process logic, is defined by business rules and operating procedures of the business system (state credit guarantee funds); 4 electronic services are services that are invoked in order to execute procedures implementing business process logic. the generalization (superclass-of) and specialization (subclass-of) of ontological concepts are represented by the taxonomy of the main concepts of the ontology of the guarantee fund of ap vojvodina. an atomic process presents communication between administrative procedures and services. in the case of om gfapv, each service belongs to only one procedure that is defined in the ontology. services are described by service profile properties. following the proposed ontology, semantics of an administrative task is described in details. finally, an example illustrating usage of the proposed ontology for implementation of application-processing procedure is presented. further research should focus on the development of ontologies of administrative 274 s. arsovski, b. markoski, p. pecev, n. petrovački, d. lacmanović processes within the domain of state bodies. such ontologies could serve for fast and efficient creation of interoperable egovernment applications. acknowledgement this work was partially supported by the serbian ministry of education and sciences (grant no: 171039). bibliography [1] guarino, n. 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(2013). security ontology for adaptive mapping of security standards, int j comput commun, issn 1841-9836, 8(6):878-890. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 447-457 decentralized controller design for forbidden states avoidance in timed discrete event systems a. aybar aydın aybar anadolu university, dept. of electrical and electronics engineering 26555, eskişehir, turkey. e-mail: aaybar@anadolu.edu.tr abstract: a decentralized controller design approach is developed for the timed discrete event systems which are modelled by timed automata in this work. an approach, called augmentation, is presented to obtain the new modelling method such that each unit delay of any event represents a pair of new state and event. the augmented automata model, obtained by using this approach, is considered to design a decentralized controller. this controller design approach is developed such that the local controller is designed for each subautomaton, obtained by using overlapping decompositions and expansions and these controllers are then combined to obtain a decentralized controller for the given timed automaton. the designed decentralized controller guarantees the unreachability of a forbidden state in the considered automaton. keywords: discrete event systems, automata, time delays, decentralized controller. 1 introduction although automata and petri nets are known as common modelling methods for discrete event systems (see, [1]– [4]), these models were first presented without time notation. since there exist time delays in the dynamic systems, time notation is a necessity for the modelling methods of the discrete event systems [5,6]. time notation was used for automata (see, [7]). in this timed automata model, a class of finite state automata was extended with a set of clocks. the clocks were chosen as real values and timed event, denoted by a pair of an event and its occurence time, were used to determine the reachability of any state. afterwards, the timed automata model was used by many works (for example, [8–10]). moreover, the basic supervisory controller approaches were presented for these timed systems, modelled by timed automata (for example, [11–13]). it is known that the computational complexity of a supervisory controller design depends on the number of states and clocks for the timed automata model [10]. moreover, the computational complexity increases exponentially with the number of states of the untimed automata model [14] (also see, [15]). thus, a controller design for timed automata (especially, large scale automata have more number of states and events), can be more complex. an approach, called augmentation, is first introduced for timed automata in order to decrease the computational complexity, depending on the time and/or clock, of a controller design. this approach, based on [16, 17], is described such that each unit delay of any event represents a pair of new state and event, and then these pairs are added to the original automaton. a new modelling model is introduced such that the augmented automaton is obtained by adding the pairs of events and states, corresponding to unit time delays, to the original automaton in this work. in [16, 17], the strecthing approach was developed for timed petri nets. in this developed approach, each delay, assigned to a transition, denotes a pair of new place and transition. using the similarity between automata and petri nets, we first develop the augmentation approach in this work. although any event of automata can be related to any transition of petri net, there exist some differences between these models (for example, a marking vector of petri net is corresponding to a state of automaton). copyright c⃝ 2006-2010 by ccc publications 448 a. aybar the augmented automaton is used to design a decentralized controller in this work. an algebraic approach, which gives the state space representation for automata, is also developed to determine the state vectors. our aim is a decentralized controller design which prevents the occurence of the forbidden states. to facilitate the controller design, we use the approach of overlapping decompositions. the overlapping decompositions approach was first introduced by [18] for the case of continuous-state systems (systems described by differential or difference equations with continuous state variables). this approach was then used for discrete event systems by ( [4, 14, 19, 20]). 2 preliminaries 2.1 mathematical model the timed automata model is represented by a(q,σ,c,q0,d). here, q is the set of states, σ is the set of events, c : q × q → σ ∪{0} is the connection matrix, q0 is the initial state at the initial time, and d is set of the time delays of the events such that de ∈ r+ is the time delay of the event e ∈ σ , where r+ is set of nonnegative real numbers. the connection matrix is given as c(qi,q j) := { e, if qi is obtained when event e occurs at state q j 0, otherwise , for qi,q j ∈ q c(qi,q j) = 0 denotes no connection between two states qi and q j. c(qi,q j) = e denotes a connection between these states via e. it is assumed that the connection of between two states is done by only one event. in this work, the vector-matrix form is used to determine new state. the state vector at time τ is denoted by s(τ) s(τ) := { λq(q), if τ = γ (q), for any q ∈ q z, otherwise here, γ (q) denotes the obtained time of state q (it is assumed that each event occurs immediately as it becames possible), λq : q → {0,1}|q|, λq(q) := { 1, if q = [q] j 0, otherwise , j ∈ {1,..., |q|} where, [q] j denotes the jth element of q, and |q| indicates the number of the elements of q, τ denotes the global time, z, which is zeros vector, denotes that the occurence of the event e has not finished at τ or the considered event can not occured at the given state. the state equation is given as follows: s(τ) = (c∨ s(τe))∧o(e,τe), e ∈ σ (1) it is assumed that the initial state s(τ0) = λq̄(q0) and there exists an event e such that τe = γ (q) for q ∈ q (there is one exception such that if there exists deadlock in the considered automaton, no event can occur at deadlock state), where τe denotes the occurence time of the event e. note that, ∨ and ∧ are used respectively. here, • the event function is defined as o(e,τe) := e ⊙ ϕ(τ − τe − de), where, ϕ : r+ → {0,1}; ϕ(x) ={ 1, if x ≥ 0 0, otherwise . • the operation ⊙ on the set σ̈ := σ ∪{0,1} is defined as 0⊙0 = 0, ek ⊙0 = 0, 0⊙ek = 0, 0⊙1 = 0, 1⊙0 = 0, 1⊙1 = 0, ek ⊙1 = ek, 1⊙ ek = ek, ek ⊙ ek = 0, ek ⊙ el = 0, el ⊙ ek = 0. decentralized controller design for forbidden states avoidance in timed discrete event systems 449 • the operation ∨ on the matrices h ∈ σ̈ |q|×|q| and r ∈ σ̈ |q|, f = h ∨ r, is defined as f(i) =∑|q| k=1 h(i,k)⊙ r(k), i ∈ {1,..., |q|}. • the operation ⊗ on the set σ̈ is defined as 0 ⊗ 0 = 0, ei ⊗ 0 = 0, 0 ⊗ ei = 0, 0 ⊗ 1 = 0, 1 ⊗ 0 = 0, 1 ⊗ 1 = 1, ei ⊗ 1 = ei, 1 ⊗ ei = ei, ei ⊗ ei = 1, ei ⊗ e j = 0, e j ⊗ ei = 0. • for f ∈ σ̈ |q| and e ∈ σ , f ∧ e = [f(1)⊗ e ... f(|q|)⊗ e]t . in the given example automaton, shown in fig. 1a, the set of states is q = {q0,q1,q2,q3,q4}, the set of events is σ = {e1,e2,e3,e4,e5,e6,e7}, and the initial state is q0. the set of time delays, assigned to the events, is given as de1 = de2 = de6 = 2 sec., de3 = 3 sec., de4 = de5 = de7 = 1 sec. let the occurence time of e3 be τe3 = 5 sec. and s(5) = λq(q2) = [0 1 0 0 0] t . the state vector is obtained as s(τ) = (c∨ s(5))∧o(e3,5) =     0 0 0 e4 0 e1 0 0 0 0 0 e3 0 e5 0 0 e2 0 0 e6 e7 0 0 0 0  ∨   0 1 0 0 0    ∧(e3 ⊙ ϕ(τ −5−3)) =   0 0 e3 ⊙1 0 0  ∧(e3 ⊙ ϕ(τ −5−3)) = { [0 0 1 0 0]t , if τ ≥ 8 [0 0 0 0 0]t , if 5 < τ < 8 q2 is obtained when the occurence of event e3 is completed (q2 is not yet obtained in time interval between 5 sec. and 8 sec.). in this work, a new model is introduced in next section and used to design a decentralized controller. 2.2 overlapping decompositions and expansions overlapping decompositions and expansions [21] have been widely used to design decentralized controllers for continuous-state systems. these concepts have also been used to design supervisory controllers for discrete event systems modeled by petri nets [19] and by automata [14]. to our best knowledge, overlapping decompositions and expansions of discrete event systems modelled by automata or formal languages have been first introduced in [14]. in the given approach, overlapping subautomata of an automaton are first identified by examining the topological structure of the given automaton. these subautomata are identified such that the only interconnection between the subautomata are through the overlapping part, i.e., no event should connect two states in different subautomata, unless one of these states is in the overlapping part of the two subautomata. as an example, the automaton (fig. 1a) can be decomposed into two subautomata as shown in fig. 1b-1c ( [14]). after an overlapping decomposition of the original automaton is obtained, the expansions of the automaton is explained as follows [14]: i) a state or an event in the overlapping part of n subautomata is repeated n times and each repeated state/event is assigned to a different subautomaton. ii) two events are introduced between any two repeated states, such that when such an event occurs the state changes from one repeated state to the other. note that, a delay of each new event is assigned to the biggest common divisor of time delays of the original automaton in this work. iii) if the initial state is in the overlapping part of the original automaton, then the initial state of the expanded automaton can be chosen as any one of the repeated states of the original initial state. 450 a. aybar otherwise, the initial state of the expanded automaton is chosen as the initial state of the original automaton. 4 7 6 4 7 6 subautomaton 1 subautomaton 2 4 7 6 a a a b b b 12 2 21 1 21 2 12 1 figure 1. (a) example automaton (b) overlappingly decomposed automaton (c) expanded automaton as a result of this procedure, an expanded automaton, which consists of α disjoint subautomata, is obtained from an original automaton which was decomposed into α overlapping subautomata. the set of states of the expanded automaton is given by q̃ := ∪αi=1qi, where qi is the set of states of the ith subautomaton. the set of events of the expanded automaton is given by σ̃ := σ̆ ∪ σ̀ . here, σ̆ = ∪αi=1σi, where σi is the set of events of the ith subautomaton and σ̀ is the set of additional events introduced between the repeated states. as an example, the states q0, q3, and the event e4 are repeated, and new events e112, e 1 21, e 2 12 and e 2 21 are added to the repeated states in the expanded automaton in fig. 1a. then, the time delay of these events is determined as one second. 3 new model for timed automata although the usage of time in the mathematical model is a necessity for the real world system, the computational complexity of time delay systems increases because of the defined all processes and functions need more memories and time. we introduce the augmentation approach. using this approach, a new model is obtained for timed automata and called as the augmented automata, where each event has only unit time delay. the augmentation approach is defined such that time delays are represented by new states and events in this work. the augmented automaton, āt (q̄,σ̄,c̄,q0), is introduced, where, c̄ : q̄ × q̄ → σ̄ , q̄ := q ∪( ∪ e∈σ ∆s(e)), and σ̄ := σ ∪( ∪ e∈σ ∆e(e)) are given following items. • the time delays of the events are scaled such that dse := de/λ , for e ∈ σ and de ∈ d, where λ indicates the biggest common divisor of time delays. note that, the set of the scaled time delays of the events is denoted by ds. it is assumed that dse ≥ 1, for all e ∈ σ in this work. • for the event e ∈ σ such that c(qi,q j) = e and dse = 1, the input connection from e to the state is hold such as c̄(qi,q j) = c(qi,q j) = e for qi,q j ∈ q. note that, if c(qa,qb) = 0, then c̄(qa,qb) = 0. • for the event e∗ ∈ σ , and dse∗ > 1, δe∗ := d s e∗ −1 numbers new events and states are defined such as f e ∗ 1 , f e∗ 2 , ... , f e∗ δe∗ , and pe ∗ 1 , p e∗ 2 , ... , p e∗ δe∗ . the sets are constructed by using these events and states as ∆e(e∗) and ∆s(e∗), respectively. • the pairs are constructed by using the new events and states for any event e∗ ∈ σ , dse∗ > 1, such that ( f e ∗ i , p e∗ i ) for i ∈ {1,2,...,δe}. for c̄(qk,qn) = e ∗, the connections are described such as from qn to f e ∗ 1 , from f e∗ 1 to p e∗ 1 , from p e∗ 1 to f e∗ 2 , ... from f e∗ δe∗ to qk. hence, the new connection matrix is constructed for the new automaton model such as c̄(pe ∗ 1 ,qn) = f e∗ 1 , c̄(p e∗ 2 , p e∗ 1 ) = f e∗ 2 , ...., c̄(qk, pe ∗ δe∗ ) = ee ∗ δe∗ . decentralized controller design for forbidden states avoidance in timed discrete event systems 451 as a result of the above procedure, we obtain the augmented automaton which has more events and states but each event has only unit time delay. we introduce the algebraic approach for the augmented automaton. let s̄n be denote the present state vector and s̄n+1 be denote the next state vector (for n ∈ {0,1,2,...}, s̄0 = λq̄(q0) denotes the initial state vector). the state equation is defined as follows: s̄n+1 = (c̄∨ s̄n)∧ ē, ē ∈ σ̄. (2) it is possible to obtain pek as the current state. it shows that the occurence of the event e has not finished yet and also the duration time is determined as k ∗ λ + τe for the event e. compared to (1), the evaluation of the above equation (2) is much simpler, since it does not require the time notation and the event function o. qq q q p e e e e e1 2 4 5 1 4 1 23 f e1 11 pe3 1 pe3 2 f e3 1 f e3 2 pe2 1 f e2 1 e 7 e 6 f e6 1 pe6 1 q0 e 3 figure 2. augmented automaton for example, we obtain the augmented automaton (fig. 2.) for the given timed automata (fig. 1a). the set of states is q̄ = {q0,q1,q2,q3,q4}∪{pe11 , pe21 , p e3 1 , p e3 2 , pe61 }, where, ∆s(e1) = {p e1 1 }, ∆s(e2) = {p e2 1 }, ∆s(e3) = {p e3 1 , p e3 2 }, and ∆s(e6) = {pe61 }, the set of events is σ̄ = {e1,e2,e3,e4,e5,e6}∪{ f e11 , f e21 , f e3 1 , f e3 2 , f e61 }, where ∆e(e1) = { f e11 }, ∆e(e2) = { f e2 1 }, ∆e(e3) = { f e3 1 , f e3 2 }, and ∆e(e6) = { f e61 }, and the connection matrix is given as c̄ =   0 0 0 e4 0 0 0 0 0 0 0 0 0 0 0 f e11 0 0 0 0 0 0 0 e5 0 0 0 0 f e3 2 0 0 0 0 0 0 0 f e21 0 0 f e6 1 e7 0 0 0 0 0 0 0 0 0 e1 0 0 0 0 0 0 0 0 0 0 e2 0 0 0 0 0 0 0 0 0 e3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 f e3 1 0 0 0 0 0 0 e6 0 0 0 0 0   4 decentralized controller design a decentralized controller for the forbidden states avoidance is developed for the considered automaton in this section. 4.1 centralized control the centralized controller guarantees the unreachability of a forbidden state for the original automaton (f denotes the set of the forbidden states). now, we consider a centralized controller design for the original augmented automaton (oaa). 452 a. aybar in the oaa, the set of forbidden states is taken as f̄ = f. it is possible that there exists a state which only leads to any element of the set f̄. thus, the set f̄ is extented by these sets and a new set, denoted by ḡ, is obtained by the following algorithm. this algorithm, called as f s, requests the set of the forbidden states and the definition. note that, this algorithm also finds the deadlock states, in which no event can occur, and adds these deadlock states to the set ḡ. in this work, each element of ḡ is called as forbidden state. a controller for the oaa is defined as k̄(s̄n,ē) = k̄(λq̄(q),ē) = { 0, if s̄n+1 ∈ ḡv 1, otherwise , ē ∈ σ̄ (3) where, s̄n = λq̄(q), s̄n+1 = (c̄ ∨ s̄n) ∧ ē and ḡv := ∪ q∈ḡ λq̄(q) denotes the set of the state vectors, corresponding to states of ḡ. note that, if q f is a forbidden state, q f ∈ ḡ, then λq̄(q f ) is called as forbidden state vector, λq̄(q f ) ∈ ḡv. once k̄(s̄n,ē) = 0 denotes disabling event ē ∈ σ̄ , k̄(s̄n,ē) = 1 denotes enabling event ē. then, this controller guarantees the unreachability of an element of ḡ. the oaa with the controller can be also called as controlled automaton, denoted by ākt (q̄,σ̄,c̄,q0,k̄). the controlled state equation, which is obtained by adding this controller to the equation (2), is given as follows: s̄n+1 = (c̄∨ s̄n)∧(ē ⊗ k̄(s̄n,ē)), ē ∈ σ̄ (4) thus, any element of of ḡ does not occur in this controlled automaton. algorithm to construct the set ḡ ḡ = fs(āt ,f̄) ḡ = f̄ do loop construction f̂ = /0 for i = 1 to |q̄| if [q̄]i /∈ ḡ then cnt = 0 for j = 1 to |q̄| if c̄( j,i) = 0 or [q̄] j ∈ ḡ then cnt = cnt +1 if cnt = |q̄| then f̂ ← f̂ ∪̂ {[q̄] j} end end end end end if f̂ = /0 then exit loop construction end ḡ← ḡ ∪̂ f̂ loop construction here, both ∪ and ∪̂ are used to denote the set union. l ∪̂ m is used, rather than l ∪ m, whenever it is known apriori that l ∩ m = /0. to evaluate n = l ∪ m, the set c is first initialized as l; for each element (from first to last), m, of m, it is then checked whether m ∈ l. if m /∈ l, then m is added to set n. to evaluate n = l ∪̂ m, on the other hand, elements of l and of m are simply appended to form n. decentralized controller design for forbidden states avoidance in timed discrete event systems 453 4.2 decentralized control now, we first consider to design a controller for each disjoint subautomaton. then, a controller of the expanded augmented automaton (eaa) is obtained by using these controllers of subautomata. finally, a decentralized controller is designed by using the controller of the eaa for the oaa. it is known that the augmented subautomata are easily obtained by using overlapping decompositions and expansions. let āit (q̄i,σ̄i,c̄i,qi0) be denote the i th subautomaton. now, some definitions and notation are given such that the eaa is denoted by ãt (q̃,σ̃,c̃,q̃0), where, q̃ := ∪αi=1q̄i, σ̃ := ∪αi=1σ̄i ∪ σ̀ , and the connection matrix can be easily determined by using new sets of states and events. ψq̄ : q̄ → q̃, ψq̄(q) denotes the set of states in the eaa which corresponds to the state q in the oaa and ψς̄ : σ̄ → σ̃ , ψς̄ (e) denotes the set of events in the eaa which corresponds to the event e in the oaa. also, we define ψ −1σ̄ : σ̃ → σ̄ and ψ −1q̄ : q̃ → q̄ such that e = ψ −1σ̄ (ẽ) ⇐⇒ ẽ ∈ ψς̄ (e) and q = ψ −1 q̄ (q̃) ⇐⇒ q̃ ∈ ψq̄(q). the set of the forbidden states for the ith augmented subautomaton is obtained as f̄i := f̃∩ q̄i, where f̃ = ∪ q̄∈f̄ ψq̄(q̄). for the i th subautomaton, ḡi and ḡiv are obtained by using the algorithm fs. note that, this algorithm needs the definition of the ith subautomaton, and the set f̄i. it is possible to design a controller, k̄i for āit , if the initial state of this subautomaton is not a forbidden state (qi0 /∈ ḡi). since this repeated state is used for the interconnection between the subautomata (see, section 2.2), it is assumed that any repeated state in the ith subautomaton is not element of ḡi for all i ∈ {1,...,α} (q̃ /∈ ḡi for q̃ ∈ q̃0i which denotes the set of repeated states in the i th subautomaton). the controller for the eaa is designed by using local controllers, k̄i for all i ∈ {1,...,α}, where α denotes the number of subautomata, k̃(λq̃(q̃),ẽ) = { k̄i(λq̄i(q̃),ẽ), if ẽ ∈ σ̄i 1, otherwise , q̃ ∈ q̃ (5) note that, g̃ := ∪ i∈{1,...,α} ḡi. consequently, the controlled state equation is obtained by adding this controller to the state equation, for s̃n = λq̃(q̃), s̃n+1 = (c̃∨ s̃n)∧(ẽ ⊗ k̃(s̃n,ẽ)), ẽ ∈ σ̃ theorem 1: k̃ avoids the existence of the elements of g̃ in ãkt . proof: let q̃ ∈ q̃ and ẽ ∈ σ̃ . this state is also an element of any subautomaton, q̃ ∈ q̄k, for k ∈ {1,...,α}. i) if there is no relation between q̃ and ẽ, then k̃(λq̃(q̃),ẽ) = 1 because of its definition (see, the equation (5)). in this case, ẽ is not occured at q̃ and also this value of k̃ does not affect the controlled state equation because of the definition of operation ⊗. ii) if ẽ ∈ σ̀ , then k̃(λq̃(q̃),ẽ) = 1. in this case, the next state is also repeated state and not a forbidden state (q̃o /∈ ḡ j for q̃o ∈ q̃0j , ∀ j ∈ {1,...,α}). iii) if ẽ ∈ σ̄k, then k̃(λq̃(q̃),ẽ) = k̄k(λq̄k (q̃),ẽ). let the next state, q̃ +, be obtained by using ẽ from q̃. if q̃+ ∈ ḡk , then k̄k(λq̄k (q̃),ẽ) = 0 and q̃ + ∈ g̃ because of definition of g̃ [k̃(λq̃(q̃),ẽ) = 0]. otherwise, k̄k(λq̄k (q̃),ẽ) = 1 and q̃ + /∈ g̃ [k̃(λq̃(q̃),ẽ) = 1]. note that, k̃(λq̃(q̃),ẽ) = 1 if there is no relation between q̃ and ẽ. 2 we now obtain a controller, k̄, for the oaa, by using the controller, k̃, for the eaa as follows: k̄(λq̄(q̄),ē) = π q̃∈ψq̄(q̄) π ẽ∈ψς̄ (ē) k̃(λ̃ (q̃),ẽ), q̄ ∈ q̄, ē ∈ σ̄ (6) 454 a. aybar furthermore, the controlled state equation is given as s̄n+1 = (c̄ ∨ s̄n) ∧ (ē ⊗ k̄(s̄n,ē)), ē ∈ σ̄ , where, s̄n = λq̄(q̄), in the oaa. theorem 2: k̄ guarantees the unreachability of a forbidden state in ākt . proof: it is known that the sets ḡ j for all j ∈ {1,...,α} and g̃ are determined. any repeated event in the eaa is only connected to the repeated states because of overlapping decomposition approach. i) if q̄‡ ∈ q̄, ψq̄(q̄ ‡) = {q̄‡} and ē∗ ∈ σ̄ , ψς̄ (ē ∗) = {ē∗a,ē ∗ b,...,ē ∗ x}, then k̄(λq̄(q̄ ‡),ē∗) = k̃(λq̃(q̄ ‡),ē∗a) . k̃(λq̃(q̄ ‡),ē∗b). ... k̃(λq̃(q̄ ‡),ē∗x). since any event, in the overlapping part, is only connected to the states in the overlapping part, k̄(λq̄(q̄ ‡),ē∗) = 1. ii) if q̄‡ ∈ q̄, ψq̄(q̄ ‡) = {q̄‡} and ē∗ ∈ σ̄ , ψς̄ (ē ∗) = {ē∗}, then q̄‡ and ē∗ are elements of subautomata. note that, k̄(λq̄(q̄ ‡),ē∗) = k̃(λq̃(q̄ ‡),ē∗) = 0 if q̄‡ and ē∗ are not in same automaton. if there is a relation between q̄‡ and ē∗ in the jth subautomaton, then k̄(λq̄(q̄ ‡),ē∗) = k̃(λq̃(q̄ ‡),ē∗) = k̄ j(λq̄ j (q̄ ‡),ē∗). in this case, if q̄u, which is obtained by using ē∗ from q̄‡, is an element of ḡ j, then k̄ j(λq̄ j (q̄ ‡),ē∗) = 0. otherwise, k̄ j(λq̄ j (q̄ ‡),ē∗) = 1. iii) if q̄‡ ∈ q̄, ψq̄(q̄ ‡) = {q̄‡a,q̄ ‡ b,...,q̄ ‡ y} and ē ∗ ∈ σ̄ , ψς̄ (ē ∗) = {ē∗}, then k̄(λq̄(q̄ ‡),ē∗) = k̃(λq̃(q̄ ‡ a),ē ∗) . k̃(λq̃(q̄ ‡ b),ē ∗). .. .k̃(λq̃(q̄ ‡ y),ē ∗). if ē∗ is not connected to any element of ψq̄(q̄ ‡), then k̄(λq̄(q̄ ‡),ē∗) = 1 . 1 ... .1 = 1. let q̄‡l and ē ∗ in the lth subautomaton (the elements of ψq̄(q̄ ‡) \ {q̄‡l } are in the other subautomata, k̃(λq̃( ˙̄q),ē ∗) = 1, for ˙̄q ∈ ψq̄(q̄ ‡) \ {q̄‡l }). in this case, k̄(λq̄(q̄ ‡),ē∗) = 1 .... 1 .k̄l(λq̄l (q̄ ‡ l ),ē ∗) .1 ...1 = k̄l(λq̄l (q̄ ‡ l ),ē ∗) is obtained. here, if ^̄q, which is obtained by using ē∗ from q̄‡l , is an element of g̃ then k̄l(λq̄l (q̄ ‡ l ),ē ∗) = 0 [k̄(λq̄(q̄ ‡),ē∗) = 0]. otherwise, k̄l(λq̄l (q̄ ‡ l ),ē ∗) = 1 [k̄(λq̄(q̄ ‡),ē∗) = 1]. iv) if q̄‡ ∈ q̄, ψq̄(q̄ ‡) = {q̄‡a,q̄ ‡ b,...,q̄ ‡ y} and ē ∗ ∈ σ̄ , ψς̄ (ē ∗) = {ē∗a,ē ∗ b,...,ē ∗ x}, then k̄(λq̄(q̄ ‡),ē∗) = 1 .... 1 = 1 since any repeated state is not element of g̃. note that, each element of ḡ is also an element of g̃ because of overlapping decompositions and expansions approach and q̄ ∈ ḡ ⇒ q̄ ∈ q j, for j ∈ {1,...,α}. thus, q̄ is an element of ḡ j and also q̄ ∈ g̃. this decentralized controller prevents the forbidden states in ākt . 2 we can obtain a decentralized controller for the original automaton as follows: k(λq(q),e) = k̄(λq̄(q),e), q ∈ q, e ∈ σ (7) this controller is added to the state equation (1) and s(τ) = (c∨s(τe))∧(o(e,τe)⊗k(s(τe),e)), for e ∈ σ is obtained. it is known that, although the forbidden states are elements of q, the new states may be elements of ḡ. the controller of the oaa only disables the elements of σ because of the connection of the pairs new states and events (see, section 4). therefore, the occurence of any event, which is disabled at any state by the controller (6), is disabled for the original automaton by the controller (7). decentralized controller design for forbidden states avoidance in timed discrete event systems 455 5 example q 4 e 7 e 6 f e6 1 pe6 1 qq q p e e e e e1 2 4b 5 1 1 2 3b f e1 11 pe3 1 pe3 2 f e3 1 f e3 2 pe2 1 f e2 1 q0b e 3 q e 4a 3a q0a 12 2 21 1 21 2 12 1 figure 3. expanded automaton in this section, we design a decentralized controller, which guarantees the forbidden states avoidance, for the given timed automaton (fig. 1a). the augmented automaton for this automaton is obtained as fig. 2. the eaa, shown in fig. 3, is obtained by using overlapping decompositions and expansions. for the original timed automaton, the set of forbidden states is given as f = {q2} and f̄ = f. then, f̃ = ∪ q̄∈f̄ ψq̄(q̄) = {q2}. now, we consider two subautomata to design a decentralized controller. in the first subautomaton, ā1t (q̄1,σ̄1,c̄1,q10), the set of states is q̄1 = {q0a,q3a,q4, p e6 1 }, the set of events is σ̄1 = {e4,e6a,e7, f e61 }, and the initial state is q10 = q0a. in the second subautomaton, ā2t (q̄2,σ̄2,c̄2,q20), the set of states is q̄2 = {q0b,q2,q3b, p e1 1 , p e2 1 , p e3 1 , p e3 2 }, the set of events is σ̄2 = {e1,e2,e3,e4b, f e11 , f e2 1 , f e3 1 , f e3 2 }, and the initial state is q20 = q0b. the connection matrices are c̄1 =   0 e4a 0 0 0 0 0 f e61 e7 0 0 0 0 0 e6 0   and c̄2 =   0 0 0 e4b 0 0 0 0 0 0 0 0 f e11 0 0 0 0 0 0 e5 0 0 0 f e3 2 0 0 0 0 0 f e21 0 0 e1 0 0 0 0 0 0 0 0 e2 0 0 0 0 0 0 0 e3 0 0 0 0 0 0 0 0 0 0 0 0 f e3 1 0   . for each subautomaton, f̄i = f̃ ∩ q̄i for i ∈ {1,2} is obtained such as f̄1 = /0 and f̄2 = {q2}. in the subautomata, the set ḡ1 = /0 is obtained by using the algorithm fs (note that, ḡ1v = /0). thus, k̄1(λq̄1(q0a),ē +) = k̄1([1 0 0 0]t ,ē+) = 1 for all ē+ ∈ σ̄1 and k̄1(λq̄1(q̄ ∗),ēx) = 1 for all q̄∗ ∈ q̄1 and ēx ∈ σ̄1. the set ḡ2 = {q2, p e3 2 , p e3 1 } is obtained by using the algorithm fs (note that, ḡ2v = {[0 1 0 0 0 0 0 0] t ,[ 0 0 0 0 0 0 0 1]t , [ 0 0 0 0 0 0 1 0]t }). thus, k̄2(λq̄2(q1),e5) = 0, k̄2(λq̄2(q3a),e3) = 0 and k̄2(λq̄2(q̄ x),ē∗) = 1 for all q̄x ∈ q̄2 \{q1,q3a} and ē∗ ∈ σ̄2. using the equation (5), the controller is obtained for the eaa as k̃(λq̃(q1),e5) = k̄2(λq̄2(q1),e5) = 0, k̃(λq̃(q3b),e3) = k̄2(λq̄2(q3b),e3) = 0, and k̃(λq̃(q̃ ‡),ẽc) = 1, ∀q̃‡ ∈ q̃ \{q1,q3a}, ∀ẽc ∈ σ̃. it is known that s̃n = λq̃(q1) = [0 1 0 0 0 0 0 0;0 0] t , and λq̄2(q1) = [0 1 0 0 0 0 0 0] t . finally, a decentralized controller, which avoids the forbidden states, is designed by using (6). this controller is given as k̄(λq̄(q1),e5) = k̃(λq̃(q1),e5) = k̄2(λq̄2(q1),e5) = 0, k̄(λq̄(q3),e3) = k̃(λq̃(q3a),e3) . k̃(λq̃(q3b),e3) = k̄1(λq̄1(q3a),e3) . k̄2(λq̄2(q3b),e3) = 1 . 0 = 0, and k̄(λq̄(q d),ew) = 1, ∀qd ∈ q̄\{q1,q3}, ∀ew ∈ σ̄. the final result is given such that the occurence of e5 is disabled at the state q1 and the occurence of e3 is 456 a. aybar disabled at the state q3. thus, decentralized controller avoids state q2. for the original timed automaton, the controller is obtained as k(λq(q3),e3) = 0, k(λq(q1),e5) = 0, and k(λq(qx),ez) = 1, ∀qx ∈ q \{q1,q3}, ∀ez ∈ σ . now, let us compare the results of centralized and decentralized controllers for the given automaton. both of these controllers disables the occurence of e5 the state q1 and the occurence of e3 at the state q3. the most advantage is that the size of the connection matrix for each subautomaton is smaller then the size of the connection matrix of the oaa. 6 conclusion a decentralized controller approach using overlapping decompositions for the timed discrete event systems. an approach, called augmentation, is presented to obtain the new modelling method such that each unit delay of any event represents a pair of new state and event. the augmented automaton is constructed by adding the pairs of events and states to the original automaton in this work. the decentralized controller design approach is presented to prevent the occurence of the forbidden states. the augmented automaton is first decomposed overlappingly and expanded to obtain subautomata. then, a controller is designed for each disjoint subautomaton. these local controllers are then combined to obtain a controller for the augmented automaton. moreover, the state space representation is used for timed and untimed automata by the given algebraic approach. since the clock or timer does not used to analyse for the augmented automaton, the first advantage is that the computational complexity does not depend on clock for the timed automata. for the construction of the augmented autonmaton, the new states and events are added to the original automaton, and then the size of the connection matrix of the original automaton is smaller than the size of the connection matrix of the augmented automaton. although this seems to be a disadvantage, the connection matrices of the augmented subautomata are only used to design the decentralized controller (i.e., the connection matrix of the augmented automaton is not used for the decentralized controller design approach). the size of the connection matrix of each subautomaton is an advantage for the decentralized approach (the number of states and events of subautomata is less than original automaton, [14, 15]). although the effort needed to obtain a useful the overlapping decomposition, this can be not comparable to the controller design since the decomposition may, in most cases, be easily made. further research can also be undertaken to use this approach to design decentralized controllers for various objectives (for example, a controller can be designed such that this controller leads the given discrete 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[21] m. ikeda and d. d. šiljak, “overlapping decentralized control with input, state, and output inclusion,” control theory and advanced technology, vol. 2, pp. 155–172, 1986. 3ashraf.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):789-802, december, 2015. group decision making with incomplete interval-valued fuzzy preference relations based on the minimum operator s. ashraf, a. rehman, e.e. kerre samina ashraf*, atiq ur rehman department of mathematics comsats institute of information technology lahore, pakistan saminaa561@gmail.com, atiq.math@gmail.com *corresponding author: saminaa561@gmail.com etienne e. kerre department of applied mathematics, computer science, b-900 gent, belgium etienne.kerre@ugent.be abstract: this paper presents a new method to estimate the unknown values in incomplete interval-valued fuzzy preference relations (ivfprs). the method is based on the min-consistency and is used to develop the algorithm for group decision making (gdm) dealing with incomplete ivfprs. keywords: fuzzy preference relation, interval-valued fuzzy preference relation; incomplete interval-valued fuzzy preference relation; min-consistency; group decision making. 1 introduction and preliminaries gdm is a situation faced when a number of experts work together to find the best alternative(s) from a set of feasible alternatives. each expert may have exclusive inspirations or objectives and a different decision procedure, but has a common interest in approaching to select the “best” option(s). preference relation is the most common representation format used in gdm because it is a valuable tool in modeling decision processes, when we have to combine experts’ preferences into group preferences [6, 14, 15]. in a preference relation an expert assigns a numerical value to every pair of alternatives that reflects some degree of preference of the first alternative over the second alternative. mainly two types of preference relations have been used to develop the decision models; multiplicative preference relations (mprs) [2,14], and fuzzy preference relations (fprs) [6, 16]. the popular preference relations, which are being used to express an expert’s preferences over alternatives, are fprs. in a decision making procedure, an expert mostly needs to compare a finite set of alternatives xi (i = 1, 2, ..., n) and construct an fpr [6, 13, 16, 17]. however, an expert may have imprecise information for the preference degrees of one alternative over another and it may not always be possible to estimate his/her preference by means of an exact numerical value. in such a situation, an expert constructs an ivfpr. in 2004, z. s. xu defined the notion of compatibility degree of two ivfprs and showed the compatible connection among individual and collective ivfprs [18]. in 2005, f. herrera et al. established an aggregation process for combining ivfprs with other forms of information as; numerical preference relation (npr) and linguistic preference relation (lpr) [7]. in 2007, y. jiang proposed a technique to measure the similarity degree of two ivfprs and used the error-propagation rule to find the priority vector of the accumulated ivfprs [8]. in 2008, z. s. xu and chen developed some linear programming models to derive the priority weights from several ivfprs [20]. copyright © 2006-2015 by ccc publications 790 s. ashraf, a. rehman, e.e. kerre all the above researches focused on the ivfprs with complete information. however, in dm problems such situations are unavoidable in which an expert does not have comprehensive information of the problem because of time constraint, lack of knowledge and the expert’s limited expertise within the problem domain [1, 3, 5, 10, 19, 22, 24, 33]. consequently, the expert may not be able to give his/her opinion about specific traits of the problem, and hence an incomplete preference relation would be constructed. in literature, researches based on incomplete fprs have been given, but there are only few researches in gdm related to incomplete ivfprs [23]. in this paper, a new technique for gdm by using incomplete ivfprs is developed. obviously, the consistent information is more applicable or important than the information having ambiguities, consistency is linked with definite transitivity properties. several properties have been endorsed to model transitivity of fprs, one of these properties is the max-min transitivity. in this paper, a procedure, based on min-transitivity property is proposed to determine unknown interval-valued preferences of one alternative over others and further, it is extended to develop an algorithm for gdm to select the best alternative. definition 1.1. [4] an interval-valued fuzzy set a on a universe x is defined as: a = {(a, [x−, x+])|a ∈ x, [x−, x+] ∈ l([0, 1])} where l([0, 1]) = {[x−, x+]|[x−, x+] ⊆ [0, 1] with x− ≤ x+}. arithmetic operations can be performed on closed intervals . the following formulae can be used for all p, q ∈ l([0, 1]) (p = [p−, p+] and q = [q−, q+]) [12]: • p + q = [p− + q−, p+ + q+], • p − q = [p− − q+, p+ − q−], • p · q = [min(p−q−, p−q+, p+q−, p+q+), max(p−q−, p−q+, p+q−, p+q+)], • p/q = [p−, p+] · [ 1 q+ , 1 q− ] if 0 /∈ [q−, q+]. definition 1.2. [4] let x be a universe and a and b two interval-valued fuzzy sets. the inclusion of a into b is defined as: a ⊆ b if and only if a(a) ⊆ b(a) for all a ∈ x and the equality between a and b is defined as: a = b if and only if a(a) = b(a) for all a ∈ x . definition 1.3. [9] a triangular norm (t-norm) t is an increasing, associative, commutative and [0, 1] × [0, 1] → [0, 1] mapping satisfying: t(1, x) = x for all x ∈ [0, 1]. the t-norm to be used in this paper is t(x, y) = min(x, y). the concept of a t-norm on [0, 1] can be extended to subintervals of [0, 1]. definition 1.4. an extended t-norm, te, is an increasing, commutative, associative and l([0, 1]) × l([0, 1]) → l([0, 1]) mapping that satisfies: te([1, 1], [x −, x+]) = [x−, x+] for all [x−, x+] ∈ l([0, 1]). let t be a triangular norm. the mapping te defined as: te([a −, a+], [b−, b+]) = [t(a−, b−), t(a+, b+)] for [a−, a+], [b−, b+] ∈ l([0, 1]), is an extended t-norm on (l([0, 1]),⊆), where ⊆ represents the crisp set inclusion. the extended interval t-norm corresponding to the minimum-operator can be computed by: tmin([a −, a+], [b−, b+]) = [min(a−, b−), min(a+, b+)]. (1) group decision making with incomplete interval-valued fuzzy preference relations based on the minimum operator 791 definition 1.5. [15] a fuzzy preference relation r over a finite set x of alternatives, x = {x1, x2, x3, ..., xn}, is a fuzzy set on the product set x×x, i.e., it is characterized by a membership function µr : x × x → [0, 1]. according to definition 1.5, a fuzzy preference relation r on x can be conveniently expressed by an n × n matrix r = (rij)n×n, where rij denotes the degree of preference of alternative xi over the alternative xj with rij ∈ [0, 1], rii = 0.5, rij +rji = 1 (additive reciprocity) for 1 ≤ i ≤ n and 1 ≤ j ≤ n. if rij = 0.5, then there is no difference between the alternatives xi and xj. if rij > 0.5, then alternative xi is preferred over the alternative xj. if rij = 1, then the alternative xi is definitely preferred over the alternative xj. definition 1.6. [18] let r = (rij)n×n be a fuzzy preference relation over the set of alternatives x = {x1, x2, x3, ..., xn} where rij = [r − ij , r+ ij ], 0 ≤ r− ij ≤ r+ ij ≤ 1, rij = [1, 1]−rji and rii = [0.5, 0.5] for all i, j ∈ n, then r is called an interval-valued fuzzy preference relation. definition 1.7. an ivfpr r is said to be min-consistent, if for all i, j and k belonging to {1, 2, 3, ..., n} it holds: rik ≥ tmin(rij, rjk) (min -transitivity). definition 1.8. an ivfpr relation r = (rij)n×n is said to be incomplete if it contains at least one unknown preference value rij for which the expert has no idea about the degree of preference of alternative xi over the alternative xj. 2 method to repair an incomplete ivfpr this section presents a new technique to estimate missing values in an incomplete ivfpr. further, the algorithm is used to construct a min-consistent matrix. in order to determine unknown values in an incomplete ivfpr r = (rij)n×n, the pairs of alternatives for known and unknown preference values are represented by the following sets: kp = {(i, j)|rij is known}, (2) up = {(i, j)|rij is unknown}, (3) where the preference value of alternative xi over xj belongs to the family of closed subintervals of [0, 1] (i.e., rij ∈ l([0, 1])). since rij = [1, 1] − rji, rii = [0.5, 0.5] for 1 ≤ i ≤ n and 1 ≤ j ≤ n, therefore, the min-transitivity of definition 1.7 can be written as: rik ≥ tmin(rij, rjk); rik ≥ tmin(1 − rji, rjk); rik ≥ tmin(rij, 1 − rkj). (4) hence, the following sets can be defined to determine the unknown preference value rik of alternative xi over alternative xk: s1ik = {j|(i, j) ∈ kp , (j, k) ∈ kp and (i, k) ∈ up}, (5) s2ik = {j|(j, i) ∈ kp , (j, k) ∈ kp and (i, k) ∈ up}, (6) s3ik = {j|(i, j) ∈ kp , (k, j) ∈ kp and (i, k) ∈ up}, (7) for i = {1, 2, 3, ..., n}, j = {1, 2, 3, ..., n} and k = {1, 2, 3, ..., n}. based on (5),(6) and (7), we can determine the unknown preference value rik for xi over xk as follows: rik = r1 ik + r2 ik + r3 ik 3 , (8) 792 s. ashraf, a. rehman, e.e. kerre where r1ik =    1 |s1 ik | ∑ j∈s1 ik tmin(rij, rjk), if |s 1 ik | 6= 0 [0.5, 0.5], otherwise (9) r2ik =    1 |s2 ik | ∑ j∈s2 ik tmin([1, 1] − rji, rjk), if |s 2 ik | 6= 0 [0.5, 0.5], otherwise (10) r3ik =    1 |s3 ik | ∑ j∈s3 ik tmin(rij, [1, 1] − rkj), if |s 3 ik | 6= 0 [0.5, 0.5], otherwise (11) where |s1 ik |, |s2 ik | and |s3 ik | are the cardinalities of the sets s1 ik , s2 ik and s3 ik respectively. k ′ p = kp ∪{(i, k)}, (12) u ′ p = up −{(i, k)}. (13) to achieve min-consistency of the ivfpr r, following scaling conditions will be used: (i) if r− ij + r+ ji < 1 and r+ ij + r− ji < 1, then rij = [ r− ij + 1 − (r− ij + r+ ji ) 2 , r+ ij + 1 − (r+ ij + r− ji ) 2 ] (14) and rji = [ r− ji + 1 − (r+ ij + r− ji ) 2 , r+ ji + 1 − (r− ij + r+ ji ) 2 ] . (15) (ii) if r− ij + r+ ji < 1 and r+ ij + r− ji > 1, then rij = [ r−ij + 1 − (r− ij + r+ ji ) 2 , r+ij − r+ ij + r− ji − 1 2 ] (16) and rji = [ r−ji − r+ij + r − ji − 1 2 , r+ji + 1 − (r−ij + r + ji) 2 ] . (17) (iii) if r− ij + r+ ji > 1 and r+ ij + r− ji < 1, then rij = [ r− ij − r− ij + r+ ji − 1 2 , r+ ij + 1 − (r+ ij + r− ji ) 2 ] (18) and rji = [ r− ji + 1 − (r+ ij + r− ji ) 2 , r+ ji − r− ij + r+ ji − 1 2 ] . (19) (iv) if r− ij + r+ ji > 1 and r+ ij + r− ji > 1, then rij = [ r−ij − r− ij + r+ ji − 1 2 , r+ij − r+ ij + r− ji − 1 2 ] (20) group decision making with incomplete interval-valued fuzzy preference relations based on the minimum operator 793 and rji = [ r− ji − r+ ij + r− ji − 1 2 , r+ ji − r− ij + r+ ji − 1 2 ] . (21) example 2.1. let r = (rij)4×4 be an incomplete ivfpr for the alternatives x1, x2, x3 and x4, given as follows: r =   [0.5, 0.5] r12 [0.4, 0.6] [0.3, 0.7] r21 [0.5, 0.5] [0.7, 0.8] r24 [0.4, 0.6] [0.2, 0.3] [0.5, 0.5] [0.3, 0.4] [0.3, 0.7] r42 [0.6, 0.7] [0.5, 0.5]   where r12, r21, r24 and r42 are unknown preference values. now applying (2)-(13) to estimate the unknown preference values for the alternative xi over xk, 1 ≤ i ≤ 4 and 1 ≤ k ≤ 4, we obtain: kp = {(1, 1), (1, 3), (1, 4), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3), (3, 4), (4, 1), (4, 3), (4, 4)}, up = {(1, 2), (2, 1), (2, 4), (4, 2)}. s112 = {3}, s 2 12 = {3}, s 3 12 = {3}, r112 = tmin(r13, r32) = tmin([0.4, 0.6], [0.2, 0.3]) = [0.2, 0.3], r212 = tmin([1, 1] − r31, r32) = tmin([0.4, 0.6], [0.2, 0.3]) = [0.2, 0.3], r312 = tmin(r13, [1, 1] − r23) = tmin([0.4, 0.6], [0.2, 0.3]) = [0.2, 0.3], r12 = 1 3 (r112 + r 2 12 + r 3 12) = [0.2, 0.3]. k ′ p = {(1, 1), (1, 2), (1, 3), (1, 4), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3), (3, 4), (4, 1), (4, 3), (4, 4)}, u ′ p = up −{(1, 2)} = {(2, 1), (2, 4), (4, 2)}. s121 = {3}, s 2 21 = {1, 3}, s 3 21 = {2, 3}, r121 = tmin(r23, r31) = tmin([0.7, 0.8], [0.4, 0.6]) = [0.4, 0.6], r221 = 1 2 [tmin([1, 1] − r12, r11) + tmin([1, 1] − r32, r31)] = 1 2 [tmin([0.7, 0.8], [0.5, 0.5]) + tmin([0.7, 0.8], [0.4, 0.6])] = 1 2 [[0.5, 0.5] + [0.4, 0.6]] = [0.45, 0.55], 794 s. ashraf, a. rehman, e.e. kerre r321 = 1 2 [tmin(r22, [1, 1] − r12) + tmin(r23, [1, 1] − r13)] = 1 2 [tmin([0.5, 0.5], [0.7, 0.8]) + tmin([0.7, 0.8], [0.4, 0.6]] = 1 2 [[0.5, 0.5] + [0.4, 0.6]] = [0.45, 0.55], r21 = 1 3 (r121 + r 2 21 + r 3 21) = [0.433, 0.567]. k ′′ p = {(1, 1), (1, 2), (1, 3), (1, 4), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3), (3, 4), (4, 1), (4, 3), (4, 4)}, u ′′ p = u ′ −{(2, 1)} = {(2, 4), (4, 2)}. s124 = {1, 3}, s 2 24 = {1, 3}, s 3 24 = {1, 3}, r124 = 1 2 [tmin(r21, r14) + tmin(r23, r34)] = 1 2 [tmin([0.433, 0.567], [0.3, 0.7]) + tmin([0.7, 0.8], [0.3, 0.4])] = 1 2 [[0.3, 0.567] + [0.3, 0.4]] = [0.3, 0.484], r224 = 1 2 [tmin([1, 1] − r12, r14) + tmin([1, 1] − r32, r34)] = 1 2 [tmin([0.7, 0.8], [0.3, 0.7]) + tmin([0.7, 0.8], [0.3, 0.4])] = 1 2 [[0.3, 0.7] + [0.3, 0.4]] = [0.3, 0.55], r324 = 1 2 [tmin(r21, [1, 1] − r41) + tmin(r23, [1, 1] − r43)] = 1 2 [tmin([0.433, 0.567], [0.3, 0.7]) + tmin([0.7, 0.8], [0.3, 0.4])] = 1 2 [[0.3, 0.567] + [0.3, 0.4]] = [0.3, 0.484], r24 = 1 3 (r124 + r 2 24 + r 3 24) = [0.3, 0.506]. k ′′′ p = {(1, 1), (1, 2), (1, 3), (1, 4), (2, 1), (2, 2), (2, 3), (2, 4), (3, 1), (3, 2), (3, 3), (3, 4), (4, 1), (4, 3), (4, 4)}, u ′′′ p = u ′′ −{(2, 4)} = {(4, 2)}. s142 = {1, 3}, s 2 42 = {1, 2, 3}, s 3 42 = {1, 3, 4}, r142 = 1 2 [tmin(r41, r12) + tmin(r43, r32)] = 1 2 [tmin([0.3, 0.7], [0.2, 0.3]) + tmin([0.6, 0.7], [0.2, 0.3])] = 1 2 [[0.2, 0.3] + [0.2, 0.3]] = [0.2, 0.3], r242 = 1 3 [tmin([1, 1] − r14, r12) + tmin([1, 1] − r24, r22) + tmin([1, 1] − r34, r32)] = 1 3 [[0.2, 0.3] + [0.5, 0.494] + [0.2, 0.3]] = [0.298, 0.367], group decision making with incomplete interval-valued fuzzy preference relations based on the minimum operator 795 r342 = 1 3 [tmin(r41, [1, 1] − r21) + tmin(r43, [1, 1] − r23) + tmin(r44, [1, 1] − r24)] = 1 3 [[0.3, 0.567] + [0.2, 0.3] + [0.494, 0.5]] = [0.331, 0.456], r42 = 1 3 (r142 + r 2 42 + r 3 42) = [0.276, 0.374]. k ′′′′ p = {(1, 1), (1, 2), (1, 3), (1, 4), (2, 1), (2, 2), (2, 3), (2, 4), (3, 1), (3, 2), (3, 3), (3, 4), (4, 1), (4, 2), (4, 3), (4, 4)}, u ′′′′ p = φ. hence, the complete ivfpr is r =   [0.5, 0.5] [0.2, 0.3] [0.4, 0.6] [0.3, 0.7] [0.433, 0.567] [0.5, 0.5] [0.7, 0.8] [0.3, 0.506] [0.4, 0.6] [0.2, 0.3] [0.5, 0.5] [0.3, 0.4] [0.3, 0.7] [0.276, 0.374] [0.6, 0.7] [0.5, 0.5]   (22) by applying scaling condition on (18), r becomes a min-consistent ivfpr r̃ as follows: r̃ =   [0.5, 0.5] [0.316, 0.434] [0.4, 0.6] [0.3, 0.7] [0.566, 0.684] [0.5, 0.5] [0.7, 0.8] [0.463, 0.615] [0.4, 0.6] [0.2, 0.3] [0.5, 0.5] [0.3, 0.4] [0.3, 0.7] [0.385, 0.537] [0.6, 0.7] [0.5, 0.5]   . 3 a new algorithm to choose the best alternative in gdm with incomplete ivfprs. in this section, a new algorithm is presented for gdm with incomplete ivfprs by using min-consistency. an explanatory example is given to validate the anticipated technique. for ease, the structure of the determination process is also shown in figure1. suppose that there are n alternatives x1, x2, ..., xn and m experts e1, e2, ..., em. let r q be the ivfpr for the expert eq shown as follows: r q = ( r q ij ) n×n =   [0.5, 0.5] r q 12 . . r q 1n r q 21 [0.5, 0.5] . . r q 2n . . . . . . . . r q n1 r q n2 . . [0.5, 0.5]   , (23) where r q ij ∈ l([0, 1]) is the preference value given by expert eq for alternative xi over xj, r q ij = [1, 1] − r q ji , r q ii = [0.5, 0.5], 1 ≤ i ≤ n, 1 ≤ j ≤ n and 1 ≤ q ≤ m. the proposed gdm technique is given as follows: step 1: determine the sets k q p and u q p of pairs of alternatives for known and unknown preference values respectively, shown as follows: k q p = {(i, j)|r q ij is known}, (24) u q p = {(i, j)|r q ij is unknown}, (25) 796 s. ashraf, a. rehman, e.e. kerre where 1 ≤ i ≤ n, 1 ≤ j ≤ n and 1 ≤ q ≤ m. step 2: if u = φ, then skip step 2, otherwise construct the sets s q1 ik , s q2 ik and s q3 ik based on the sets k q p and u q p . the constructed sets are used to estimate the unknown preference values r q ik for the alternative xi over xk by expert eq as follows: rik = r q1 ik + r q2 ik + r q3 ik 3 , (26) s q1 ik = {j|(i, j) ∈ k q p , (j, k) ∈ k q p and (i, k) ∈ u q p }, (27) s q2 ik = {j|(j, i) ∈ k q p , (j, k) ∈ k q p and (i, k) ∈ u q p }, (28) s q3 ik = {j|(i, j) ∈ k q p , (k, j) ∈ k q p and (i, k) ∈ u q p }, (29) r q1 ik =    1 |s q1 ik | ∑ j∈s q1 ik tmin(r q ij , r q jk ), if |s q1 ik | 6= 0 [0.5, 0.5], otherwise (30) r q2 ik =    1 |s q2 ik | ∑ j∈s q2 ik tmin([1, 1] − r q ij, r q jk ), if |s q2 ik | 6= 0 [0.5, 0.5], otherwise (31) r q3 ik =    1 |s q3 ik | ∑ j∈s q3 ik tmin(r q ij , [1, 1] − r q jk ), if |s q3 ik | 6= 0 [0.5, 0.5], otherwise (32) where |s q1 ik |, |s q2 ik | and |s q3 ik | are the cardinalities of the sets s q1 ik , s q2 ik and s q3 ik respectively. k ′q p = k q p ∪{(i, k)}, (33) u ′q p = u q p −{(i, k)}. (34) step 3: to satisfy min-consistency of the complete interval-valued fuzzy preference relation r q = ( r q ij ) n×n , the following scaling conditions are used: (i) if r q− ij + r q+ ji < 1 and r q+ ij + r q− ji < 1, then r q ij = [ r q− ij + 1 − (r q− ij + r q+ ji ) 2 , r q+ ij + 1 − (r q+ ij + r q− ji ) 2 ] (35) and r q ji = [ r q− ji + 1 − (r q+ ij + r q− ji ) 2 , r q+ ji + 1 − (r q− ij + r q+ ji ) 2 ] . (36) (ii) if r q− ij + r q+ ji < 1 and r q+ ij + r q− ji > 1, then r q ij = [ r q− ij + 1 − (r q− ij + r q+ ji ) 2 , r q+ ij − r q+ ij + r q− ji − 1 2 ] (37) and r q ji = [ r q− ji − r q+ ij + r q− ji − 1 2 , r q+ ji + 1 − (r q− ij + r q+ ji ) 2 ] . (38) group decision making with incomplete interval-valued fuzzy preference relations based on the minimum operator 797 (iii) if r q− ij + r q+ ji > 1 and r q+ ij + r q− ji < 1, then r q ij = [ r q− ij − r q− ij + r q+ ji − 1 2 , r q+ ij + 1 − (r q+ ij + r q− ji ) 2 ] (39) and r q ji = [ r q− ji + 1 − (r q+ ij + r q− ji ) 2 , r q+ ji − r q− ij + r q+ ji − 1 2 ] . (40) (iv) if r q− ij + r q+ ji > 1 and r q+ ij + r q− ji > 1, then r q ij = [ r q− ij − r q− ij + r q+ ji − 1 2 , r q+ ij − r q+ ij + r q− ji − 1 2 ] (41) and r q ji = [ r q− ji − r q+ ij + r q− ji − 1 2 , r q+ ji − r q− ij + r q+ ji − 1 2 ] . (42) a min-consistent matrix r̃ q = ( r̃ q ij ) n×n is obtained under these conditions. step 4: determine the collective matrix r c against all experts, shown as follows: r c = ( rcij ) n×n = 1 m ( r̃ 1 ij + r̃ 2 ij + r̃ 3 ij... + r̃ m ij ) n×n , (43) where 1 ≤ i ≤ n, 1 ≤ j ≤ n. step 5: calculate the average degree ai of alternative xi over all other alternatives by using interval normalizing method: ai = n∑ j=1 rcij n∑ i=1 n∑ j=1 rc ij , i = 1, 2, 3, ..., n. (44) step 6: [21] calculate the possibility degree dij = d(ai ≥ aj) by using the formula: d(ai ≥ aj) = min { max ( a+ i − a− j a+ i − a− j + a+ j − a− i , 0 ) , 1 } (45) and construct the complementry matrix d = (dij)n×n, where dij ≥ 0, dij + dji = 1, dii = 0, i, j = 1, 2, 3, ..., n. step 7: [33] calculate the ranking value rv (xi) of alternative xi by using formula: rv (xi) = 2 n2 n∑ j=1 dij, (46) where 1 ≤ i ≤ n and n∑ i=1 rv (xi) = 1. 798 s. ashraf, a. rehman, e.e. kerre figure 1: resolution process for gdm with ivfprs. example 3.1. a firm produces solar water refiners. in its production process, the company has to buy solar panels in different sizes and voltages from different suppliers. presently, japan solar company has four potential suppliers in four different countries, namely, korea, china, italy and turkey, signified as xi(i = 1, 2, 3, 4), respectively. a committee consisting of three experts eq(q = 1, 2, 3) from different departments has been formed to assess the four suppliers xi(i = 1, 2, 3, 4). suppose that the experts eq(q = 1, 2, 3) provide their assessments in the form of following incomplete ivfprs: r 1 =   [0.5, 0.5] r1 12 [0.6, 0.8] r1 14 r1 21 [0.5, 0.5] r1 23 [0.3, 0.7] [0.2, 0.4] r1 32 [0.5, 0.5] [0.6, 0.9] r1 41 [0.3, 0.7] [0.1, 0.4] [0.5, 0.5]   , r 2 =   [0.5, 0.5] r12 [0.4, 0.6] [0.3, 0.7] r21 [0.5, 0.5] [0.7, 0.8] r24 [0.4, 0.6] [0.2, 0.3] [0.5, 0.5] [0.3, 0.4] [0.3, 0.7] r42 [0.6, 0.7] [0.5, 0.5]   , and r 3 =   [0.5, 0.5] r3 12 [0.7, 0.8] r3 14 [0.4, 0.6] [0.5, 0.5] r3 23 [0.5, 0.7] [0.2, 0.3] r3 32 [0.5, 0.5] r3 34 r3 41 [0.3, 0.5] r3 43 [0.5, 0.5]   . step 1: for the fuzzy preference relation r 1 , the sets of pairs of alternatives for known and unknown preference values are determined as follows: k1p = {(1, 1), (1, 3), (2, 2), (2, 4), (3, 1), (3, 3), (3, 4), (4, 2), (4, 3), (4, 4)}, u1p = {(1, 2), (1, 4), (2, 1), (2, 3), (3, 2), (4, 1)}. group decision making with incomplete interval-valued fuzzy preference relations based on the minimum operator 799 step 2: s1112 = φ, s 12 12 = φ, s 13 12 = φ, r1112 = [0.5, 0.5], r 12 12 = [0.5, 0.5], r 13 12 = [0.5, 0.5], r112 = 1 3 (r1112 + r 12 12 + r 13 12) = [0.5, 0.5]. k1 ′ p = {(1, 1), (1, 2), (1, 3), (2, 2), (2, 4), (3, 1), (3, 3), (3, 4), (4, 2), (4, 3), (4, 4)}, u1 ′ p = u 1 p −{(1, 2)} = {(1, 4), (2, 1), (2, 3), (3, 2), (4, 1)}. s1114 = {2, 3}, s 12 14 = {3}, s 13 14 = {2, 3}, r1114 = 1 2 [tmin(r 1 12, r 1 24) + tmin(r 1 13, r 1 34)] = 1 2 [[0.3, 0.5] + [0.6, 0.8]] = [0.45, 0.65], r1214 = tmin([1, 1] − r 1 13, r 1 34) = tmin([0.2, 0, 4], [0.6, 0.9]) = [0.2, 0.4], r1314 = 1 2 [tmin(r 1 12, [1, 1] − r 1 42) + tmin(r 1 13, [1, 1] − r 1 43)] = 1 2 [tmin([0.5, 0.5], [0.3, 0.7]) + tmin([0.6, 0.8], [0.6, 0.9])] = 1 2 [[0.3, 0.5] + [0.6, 0.8]] = [0.45, 0.65], r114 = 1 3 (r1114 + r 12 14 + r 13 14) = [0.3667, 0.5667]. k1 ′′ p = {(1, 1), (1, 2), (1, 3), (1, 4), (2, 2), (2, 4), (3, 1), (3, 3), (3, 4), (4, 2), (4, 3), (4, 4)}, u1 ′′ p = u 1 ′ p −{(1, 4)} = {(2, 1), (2, 3), (3, 2), (4, 1)}. hence, continuing as above the fuzzy preference relation r 1 against expert e1 is obtained as follows: r 1 =   [0.5, 0.5] [0.5, 0.5] [0.6, 0.8] [0.3667, .5667] [0.4667, 0.5222] [0.5, 0.5] [0.2889, 0.4574] [0.3, 0.7] [0.2, 0.4] [0.3055, 0.5389] [0.5, 0.5] [0.6, 0.9] [0.2519, 0.4673] [0.3, 0.7] [0.1, 0.4] [0.5, 0.5]   . step 3: min-consistency preference relation r̃ 1 based on r 1 is obtained as follows: r̃ 1 =   [0.5, 0.5] [0.4889, 0.5166] [0.6, 0.8] [0.4497, .6574] [0.4834, 0.5111] [0.5, 0.5] [0.375, 0.576] [0.3, 0.7] [0.2, 0.4] [0.424, 0.625] [0.5, 0.5] [0.6, 0.9] [0.3426, 0.5503] [0.3, 0.7] [0.1, 0.4] [0.5, 0.5]   . likewise, min-consistency preference relations r̃ 2 and r̃ 3 against the experts e2 and e3 respec800 s. ashraf, a. rehman, e.e. kerre tively, given as below: r̃ 2 =   [0.5, 0.5] [0.33, 0.46] [0.4, 0.6] [0.3, 0.7] [0.54, 0.67] [0.5, 0.5] [0.7, 0.8] [0.46, 0.61] [0.4, 0.6] [0.2, 0.3] [0.5, 0.5] [0.3, 0.4] [0.3, 0.7] [0.39, 0.54] [0.6, 0.7] [0.5, 0.5]   , r̃ 3 =   [0.5, 0.5] [0.4166, 0.55] [0.7, 0.8] [0.4722, 0.599] [0.45, 0.5834] [0.5, 0.5] [0.5333, 0.6592] [0.5, 0.7] [0.2, 0.3] [0.3408, 0.4667] [0.5, 0.5] [0.384, 0.4994] [0.401, 0.5278] [0.3, 0.5] [0.5006, 0.616] [0.5, 0.5]   . step 4: the collective matrix against all the experts is shown as follows: r c =   [0.5, 0.5] [0.4118, 0.5089] [0.5667, 0.7333] [0.4073, 0.6521] [0.4911, 0.5882] [0.5, 0.5] [0.5361, 0.6784] [0.42, 0.67] [0.2667, 0.4333] [0.3216, 0.4639] [0.5, 0.5] [0.428, 0.5998] [0.3479, 0.5927] [0.33, 0.58] [0.4002, 0.572] [0.5, 0.5]   step 5: the average degree ai, i = 1, 2, 3, 4, of each alternative is derived by using interval normalizing method given as: a1 = 4∑ j=1 rc 1j 4∑ i=1 4∑ j=1 rcij = [1.8858, 2.3943] [6.9274, 9.0726] = [0.2078, 0.3456]; a2 = 4∑ j=1 rc 2j n∑ i=1 n∑ j=1 rcij = [1.9472, 2.4366] [6.9274, 9.0726] = [0.2146, 0.3517]; a3 = 4∑ j=1 rc 3j n∑ i=1 n∑ j=1 rc ij = [1.5163, 1.997] [6.9274, 9.0726] = [0.1671, 0.2883]; a4 = 4∑ j=1 rc 4j n∑ i=1 n∑ j=1 rcij = [1.5781, 2.2447] [6.9274, 9.0726] = [0.1739, 0.3240]. step 6: by using eq. (45), the complementry matrix d = (dij)4×4 is obtained as follows: d = (dij)4×4 =   0.5 0.4765 0.6892 0.5964 0.5235 0.5 0.7147 0.619 0.3108 0.2853 0.5 0.4217 0.4036 0.381 0.5783 0.5   group decision making with incomplete interval-valued fuzzy preference relations based on the minimum operator 801 step 7: the ranking value rv(xi) of alternative xi, 1 ≤ i ≤ 4, is obtained as follows: rv(x1) = 2 42 4∑ j=1 d1j = 0.2827625; rv(x2) = 2 42 4∑ j=1 d2j = 0.29465; rv(x3) = 2 42 4∑ j=1 d3j = 0.189725; rv(x4) = 2 42 4∑ j=1 d4j = 0.2328625; where 4∑ i=1 rv(xi) = 1. thus, the final ranking of the alternatives is derived as follows: x2 > x1 > x4 > x3. therefore, x2 is the best alternative. the numerical examples show the way to apply the proposed technique to construct the complete ivfpr based on min-consistency. in general, the proposed approach is quite easy for use in estimating unknown preference values. 4 conclusion in this paper the extended minimum t-norm has been used successfully to determine the missing values in incomplete ivfpr and further extends to construct the min-consistent matrix. numerical studies show that the proposed technique can handle all type of incomplete ivfpr. consequently, another algorithm is established to deal with gdm problems with incomplete ivfprs. this process involves two stages, the estimation of unknown interval-valued preference values and the choice of the best alternative(s). bibliography [1] s. alonso, f. chiclana, f. herrera, e. herrera-viedma, j. alcala-fdez, c. porcel (2008); a consistency-based procedure to estimate missing pairwise preference values, international journal of intelligent systems, 23 (1): 155–175. 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[24] y. j. xu, r. patnayakuni, h. m. wang (2013); logarithmic least squares method to priority for group decision making with incomplete fuzzy preference relations, applied mathematical modelling, 37 (4): 2139–2152. international journal of computers communications & control issn 1841-9836, 11(3):315-330, june 2016. biometrics systems and technologies: a survey i. buciu, a. gacsadi ioan buciu*, alexandru gacsadi department of electronics and telecommunications faculty of electrical engineering and information technology university of oradea, 410087, romania *corresponding author: ibuciu@uoradea.ro abstract: in a nutshell, a biometric security system requires a user to provide some biometric features which are then verified against some stored biometric templates. nowadays, the traditional password based authentication method tends to be replaced by advanced biometrics technologies. biometric based authentication is becoming increasingly appealing and common for most of the human-computer interaction devices. to give only one recent example, microsoft augmented its brand new windows 10 os version with the capability of supporting face recognition when the user login in. this chapter does not intend to cover a comprehensive and detailed list of biometric techniques. the chapter rather aims at briefly discussing biometric related items, including principles, definitions, biometric modalities and technologies along with their advantages, disadvantages or limitations, and biometric standards, targeting unfamiliar readers. it also mentions the attributes of a biometric system as well as attacks on biometrics. important reference sources are pointed out so that the interested reader may gain deeper in-depth knowledge by consulting them. keywords: biometric modalities, biometric attacks, biometric standards. 1 biometrics introduction let us begin with a simple scenario. let us assume a user wants to remotely access a tele-presence group with secured and restricted credentials. the access is done by launching a verification application requiring a password and user id. unfortunately, for some reasons, the user does not remember the password. in this case, the application or system provides a link for forgot password. the user needs then to type his e-mail address to which a new temporary password is sent by the verification system. the user enters his e-mail account, copies the temporary password and the system asks the user to change again the temporary password with a new more meaningful password. moreover, the brand new password must be re-typed to avoid any typo mistake. the whole process may take several minutes and requires the use of the keyboard for several times, not mentioning memorizing or writing down the brand new password. the action may become very frustrating and inconvenient for the user who might become angry soon. let us now consider there is another application which can get the user access easily and very fast without requiring any typing and with a minimum user interaction or effort. such applications already exist and they are based on user’s biometric features. a biometric feature can be defined as a physiological (face, fingerprints, iris, etc.) or behavioural (gait, voice, signature, etc.) attribute of a human being that can discriminate one individual from another. nowadays, the great interest for biometric recognition systems can be justified due to increased demand for security. the goal of a biometric based recognition system is either automatic identification or verification of identities, given input data comprising images, speech or videos. unlike the traditional ways, such as password, biometric traits have some advantages: they cannot be stolen (although spoof attacks may exist to tamper the biometric system), lost or forget. however, to be reliable, biometric traits should be unique and persistent copyright © 2006-2016 by ccc publications 316 i. buciu, a. gacsadi over time. some other criteria should be met such as user convenience and acceptability (mainly due to privacy reasons). biometric recognition is usually performed by extracting a biometric template in query from the input device and compare it against some enrolled biometric templates. the comparison is processed using of the two modes: a) verification (or authentication) and b) identification (or recognition). verification is an one-to-one process where the query face is compared against the user claiming his genuine identity to verify his claimed id. the output is binary, either accept or reject, based on a matching procedure. we should note here that a biometric authentication technology may be used in conjunction with traditional authentication methods such as password, passports, pin, smart cards, access tokens, etc, employed as second factor authentication. identification is one-to-many process where the query is compared against each enrolled biometric template (multiple templates) from the database to search for the identity of the query. identification is a bit more complex than verification as the system serves as both identifier and authenticator. a biometric based recognition system needs an enrollment procedure which allows the registration of persons in a biometric database that may be later used for identification or verification. the acquired initial data may undergo some pre-processing steps depending on the biometric modality. for instance, in the case of images, histogram equalization may help when the image suffers from illumination imbalance. for audio data, voice separation from the background may be also a pre-processing step. biometric features are constructed by feature extraction step resulting a biometric template, further stored in the database. after a person is enrolled, the person’s biometrics are scanned and matched against the enrolled biometric templates. matching is a complex pattern recognition problem between the enrolled samples and the test one. a matching score is computed to reflect the similarity between two biometric templates. overall, the person’s recognition process is challenging because the representation of the same biometric is basically taken either by different sensors or, more often, at two or multiple different points in time, so that the acquisition conditions between the enroll and test samples may greatly vary due to various factors: noise, change in illumination, partial occlusion, different resolution, etc. this issue translates in a matching score lower than its optimum value. a threshold level is next setup for a final decision. a matching score higher than the threshold would give a match and consequently an accept, while a lower score would lead to rejection. the associated risks for any biometric system are false accept (when an unauthorized person is wrongly accepted), represented by false acceptance rate (far) and false reject (when an authorized person is incorrectly denied for access), represented by false rejection rate (frr). an ideal biometric system should have both far = frr = 0. in real life, no such biometric system or technology exists. while connected to the threshold level, far and frr are inversely proportional. more precisely, a low threshold level would decrease frr and increase far. this situation is preferred for applications where the level of security is not critical. however, for applications demanding high security level, the threshold is set to a very high value to favour low far in detriment of high (possible disturbing) frr. one such applications is the authorized and secure remote access for telepresence requiring very strict authorization. when frr equals far we have equal error rate (eer), a measure that is often reported when the performance of a biometric system or technology is addressed. biometric systems or technologies can be categorized based on several classifications: a) physiological versus behavioural; b) cooperative versus non-cooperative; c) mono-modal versus multimodal biometric systems; d) contact versus touchless versus “at distance” (or remote) technology, e) server based versus mobile based biometric technology; f) human versus no human monitoring for data acquisition. the appropriateness of each classification is highly depending on the application type. for instance, a biometric based surveillance technology may operate with full a) category (face as physiological and gait as behavioural) using non-cooperative user interaction, remote sensors and server based processing (involving large video data), while the biometrics systems and technologies: a survey 317 contact based biometric type is fully absent. similarly, a biometric authorization based access for telepresence application might also require either contact or contactless sensing technology, but no human monitoring is typically involved at the sensory remote spot. 2 physiological versus behavioural biometrics physiological biometrics addresses direct measurement from parts of the human body, while behavioural biometrics relates to measurements derived from human actions. in terms of acquisition, behavioural biometrics need measurements acquired over a certain period of time which is an important factor. 2.1 physiological biometrics the majority of commercial biometrics technologies involve physiological measurements which are considered to remain steady over relatively large time interval. such measurements may include the following modalities: • face recognition • facial thermography • fingerprint recognition • hand geometry based recognition • ear geometry based recognition • iris recognition • retina recognition • vascular pattern recognition the above biometric modalities have, more or less, reached maturity. however, the performance of such technologies are greatly dependent of application class. if, for a controlled acquisition environment (high image resolution, good quality light, occlusion free and steady images a process preferable under external human monitoring) a face recognition technology may easily acquire a very high matching score for an genuine individual, the matching score may significantly degrade for different environmental conditions in the absence of a watching guard compensating the ideal conditions. this case is specific for surveillance applications or, more generally, when the remote biometric sensor is separated by the processing module location. moreover, in the absence of any preventive or detective control, an attacker can get access by attacking the system with stolen biometric data (a printed photo of the genuine’s face, fingerprint, video record, etc.). the scenario is closely related to network authentication such as telepresence, automated teller machine access, internet banking, mobile based biometrics, cloud technology. for a centralized biometric network, authentication the user who needs to be authenticated is physically present at the same site with the sensor while the recognition process takes place on a general purpose computer or server, located some distance away from the sensor. the remote access is granted or rejected based on server decision which communicates with the sensor. 318 i. buciu, a. gacsadi face recognition face identification and verification dates back to 60’s when the computer vision community has started to address the problem. while the face recognition technology is somehow inferior as performance compared to other biometric modalities (such as iris recognition for example), it is more accepted due to its major advantage: it is the only physiological biometric that can be reliable measured at distance and, moreover, the authentication of the users can happen without their explicit interaction with the sensor or their knowledge. the performance of face recognition systems can vary considerable depending upon the context and various factors. the modifications of facial features are caused by both long-term and short-term changes. long-terms changes refer to aging where prominent and wrinkles may appear upon the face and permanently change the facial texture. in this case, periodic enrollment is necessary to update the biometric template. short term changes may refer to weight loss or gain. other factors affecting the system’s accuracy are partial occlusions (growing beard or moustache, glasses, hat, scarf) or various environment conditions (distance from camera, varying lighting conditions, noise, motion blur, etc). another factor is given by face position. while the enrollment is usually taken in frontal pose, the matching process may suffer from non-frontal pose acquisition, where pose estimation might be needed. the face recognition systems and technologies are based either on 2d or 3d representation of the face appearance. the 2d based face biometrics systems are pose variant and rely on the information conveyed in the gray level structure of the facial image (2d face texture), while the 3d approaches are pose invariant and involve the volumetric structure of the face along with its depth map. the 3d image acquisition technologies come with different cost and approaches. the most cost-effective solution called stereo acquisition is to use several calibrated 2d cameras to acquire images simultaneously followed by 3d reconstruction. while the acquisition is fast, this approach is highly light sensitive. changes in illumination can lead to image artifacts compromising the performance of the 3d face recognition system. an alternative is to project a structured light pattern on the facial surface during acquisition. a third solution relies on active sensing where a laser beam is scanning the face surface generating a reflected facial pattern. once the facial data are acquired, either as 2d or 3d, an automatic landmarking process is necessary to detect facial interest points such as eyes, eyebrows, mouth contour, nose, chin, etc. these landmarks are further used for face registration so that facial features (landmarks) are localized at the same position (geometrical coordinates) across multiple face images. the process continues with feature extraction and matching. for feature extraction, various techniques were proposed, including subspace methods (principal component analysis, linear discriminant analysis, independent component analysis), filtering approaches (different implementation of gabor wavelets) or statistical approaches (including vacuous versions of local binary patterns). for the last step, matching, methods starting from distance based classifiers (euclidean distance, cosine similarity measure) up to complex classifiers (neural networks or support vector machines) were implemented. the literature presents an abundant source of methods and it is impossible to provide references for all representative proposed techniques. the reader my consult the most recent edited or authored face recognition books such as [1], [2], [3], [4], [5], [6], where all related aspects and techniques are presented in great details. facial thermography a very challenging issue for conventional face recognition systems is when they are operating under low illumination environment. off-the-shell methods to cope with this issue exist, but their outcome is typically very noisy and the overall performance of this biometric system is slightly improved. one solution is to shift from visible spectrum to infrared (ir) or near infrared biometrics systems and technologies: a survey 319 (nir) range that requires dedicated sensors. for long-wave ir (8 14 µm) the human body is a light source emitting heat due to the blood flow under the skin and the resulting thermal patterns can be collected in total darkness [7], [8], [9]. while an external light is not necessary, these sensors are completely useless when the system operates in daylight, as usually happens. this is because the body thermal patterns strongly interferes with the surrounding temperature coming from artificial light or sunlight. alternatively, less heat sensitive near infrared spectrum nir (0.8 2.5 µm) can also be used. for a reliable functioning, the nir sensor need an ambient light. studies have reported, however, modest performances of somewhere between 84% and 93% [10], [11]. this is due to the fact that many facial texture patterns that are clearly distinctive in the visible spectrum are absent in the ir or even nir facial imagery, thus lowering the discriminant capability of facial thermography based biometrics systems. facial thermography is affected by medical conditions such as, for example, fever. fingerprint recognition the fingerprint recognition is perhaps the most used biometric having applications in both law enforcement and computer systems, having a mature and widely accepted technology. the technology is implemented on various platforms and devices, including laptops, mobile phones or personal digital assistants. conventional fingerprint systems belong to touch-based sensing technology and require touching or rolling a finger onto a rigid surface with a live-scan device. one disadvantage is the usability aspect as the system depends on the finger placement, finger scarfs or skin conditions (dirt, sweat, moisture). to make them robust against skin conditions, touchless fingerprint technologies are emerging. two classes exist: reflection based touchless finger imaging (rtfi) and transmission-based touchless finger imaging (ttfi). in the case of rtfi there are 2 light sources illuminating the fingerprint [12]. the finger must absorb only a small portion of the incident light, while the majority of light is reflected back to the optical detector. the device must be designed to allow different light quantity absorbed by the fingerprint’s valleys compared to the light absorbed by the ridges to assure a good contrast. the other parameters such as the depth-of-focus and field-of-view of the camera, the irradiation and the frequency of the light sources are also crucial for reliable performance when the skin condition changes from dry to wet. the light sources has to be placed as close as possible to detector to minimize the shadowing effect and the emitted light should be in the blue spectrum (around 500 nm wavelength). the design is very complex and acquiring an optimum image contrast is not an easy task, leading to expensive devices compared to the touch-based fingerprint devices. the other approach, ttfi is similar to the optical coherence tomography principle where the light is transmitting through the finger [13] and the light that is back-scattered from the skin tissue is captured. the finger is placed between the light source and detector so that the light illuminates the nails side of the finger while the opposite finger part is oriented towards the detector. a red light with wavelength of 660 nm is used here because this wavelength corresponds to the maximum transmittance ratio to the skin tissue. the light penetrate the finger and then collected by the detector. apart from high cost for the touchless fingerprint recognition technology, the curvature of the finger represents another limitation of this technology resulting in a relatively low interest for commercial adoption. the latter issue can be solved by employing a multi-vision system consisting of several cameras located on a semicircle and pointing out to the center of the finger. such device may contain five cameras along with a set of green led arrays to illuminate the finger [14]. once the finger is in the correct position, each led array is set to a specific light intensity and the five cameras start capturing a picture of the finger simultaneously. using five cameras a enlarged (180 degree) field-of-view is obtained and the views are combined together to form a 3d finger reconstruction. 320 i. buciu, a. gacsadi hand geometry based recognition hand geometry based recognition systems consider the measurement of length, width, thickness, and surface area of the fingers and hand [15]. this biometric offers low security level because it is not scalable, i.e. those measurements do not tend to be unique for large-scale identification systems [16]. one important inconvenient is that such biometric systems require complex hardware to capture the hand image and may not be appropriate for computer-based login [17]. iris recognition iris recognition is the most reliable type of biometric identification. it is considered to be the ideal biometric in terms of uniqueness and stability (its features do not vary over time) leading to massive deployment for large-scale systems that proved to be very effective [18]. the iris is the colored portion of the eye surrounding the pupil and the biometric system searches for its specific intricate patterns composed of many furrows and ridges. the basic steps are: image acquisition, iris localization using landmark features and segmentation, biometric template generation and biometric template matching. the acquisition factors are resolution, signal/noise ratio, contrast and illumination wavelength. once the iris is segmented, it may suffer a pseudo-polar coordinate transformation operation to take into account variations in pupil size. to capture the iris image, the conventional iris recognition technology requires a very short focal length, increasing the intrusiveness of this approach. however, iris recognition systems where the iris image is captured at longer distance exist nowadays. while for short focal length the image resolution is not an issue, this becomes very challenging with increasing distance, leading to significant drop in accuracy. iris biometric technologies operating at long distance (beyond 1 m) are developed by various vendors. for instance, aoptix has implemented a system able to operate within a range of 1.5 2.5 m [19]. they have also developed a system having onboard coaxial imaging and adaptive optics to facilitate the capture of iris image at up to 18 m. another vendor, honeywell [20] has designed a combined face and iris recognition system that is capable of acquiring face and iris images at distances from 1 m and beyond 4 m. carnegie mellon university’s cylab biometrics center have been developing an iris recognition solution for the past several years that can successfully identify subjects from up to 12 meters away [21]. however, regardless of technology, one important aspect with the iris recognition systems is that the approach is not applicable when the user has contact lens. retina recognition in order to obtain retinal images, an infrared camera is used to capture the unique pattern of veins located at the back of the eye. similarly to iris recognition, this modality also suffers from the problem of user inconvenience, which is even more inconvenient as the user is asked to carefully present their eyes to the camera at very close proximity. another inconvenient is that this approach requires complex and expensive dedicated hardware, making such solution to be limited to applications with very high security demands [22]. on a positive side, unlike face, iris or fingerprint biometrics, retina based patterns are very difficult to spoof. vascular pattern recognition this approach uses the subcutaneous vascular network on the back of the hand for specific individual patterns which are distinctive even for identical twins. a near-infrared light is emitted toward the back or palm of the hand. the reflected light prints an image on the sensor, image representing the encoded absorbance of blood vessels [23]. due to the hemoglobin presented biometrics systems and technologies: a survey 321 in the blood, the veins appear as dark areas pattern and clearly delimited. the image is next digitized and specific features including vessel branching points, thickness, or branching angles are extracted as distinct features. 2.2 behavioural biometrics the behavioural biometrics typically measures the behaviour of the user over time. this biometric type usually does not explicitly ask the user to be cooperative and thus, it is more transparent, user-friendly, less intrusive and more convenient than their physiological counterparts. on the downside, the behavioural biometrics suffers from low level of uniqueness and permanence, compared to the physical biometrics. moreover, their accuracy for authentication is lower and are rather more suitable for verification. the approaches can be split into the following classes: • gait recognition • keystroke analysis based authentication • mouse dynamics • speaker recognition gait recognition analyzing the way an individual walks is the key issue for the gait recognition systems. the main advantage is the fact that this approach has no physical contact being ideal for acquiring data at long distance with low resolution. the fine details are not crucial here, rather the movement time patterns are considered. such systems are influenced by external factors such as footwear walking surface or clothing. the gait recognition systems can be classified either as model based or appearance based [24]. model based approaches fit a model representing time pattern of the human anatomy against video data then extracting and analyzing its parameters. appearance model based approaches analyze the silhouette shape and motion of an individual and the way this vary in time. the images are recorded while the individual walks in a plane normal to the camera view. the performance of these biometric systems are highly dependent on the camera viewpoint. a change in the walking direction can negatively influence their performance. by using geometric cues, some systems improve viewpoint invariance when dealing with 2d imagery. another alternative is to use 3d human figure capturing to have viewpoint independence. both 2d and 3d approaches are described in great details in [24]. keystroke analysis based authentication keystroke analysis based authentication is defined as the process of recognizing an individual from his typing characteristics. the verification can be performed either static (text-dependent) or dynamic (text-independent) mode. the characteristics are typically composed of the time between successive keystrokes, more precisely the inter-stroke latency, time durations between the keystrokes, dwell times (i.e. the time a key is pressed down), overall typing speed, frequency of errors (use of backspace), use of numpad, etc. for a large scale application, these characteristics are not unique amongst too many users. therefore this analysis cannot be reliable used as recognition feature (although some report indicate this could also be possible [25]), but they can be suitable for verification systems. aside from relative low accuracy, the enrollment procedure is the major drawback of such behavioural biometric systems. to generate representative biometric 322 i. buciu, a. gacsadi templates the user might be asked to repeat the enroll procedure by providing a username, password or a specific text for a large number of times. an interesting application is presented in [26] where the keystroke dynamics based authentication has been analyzed in the context of collaborative systems. mouse dynamics a behavioural profile can be also constructed using mouse actions performed by an user. mouse derived features are easy to handle without user’s knowledge. the mouse authentication involves registration phase and login phase. a template is built using the mouse features captured at the time of registration. the same template is compared with login details which are captured by the mouse task. in case of laptop, touchpad helps to extract the mouse features. the mouse’s sensitivity affects the performance. the mouse features include general movement, drag and drop, stillness, point and click (single or double) actions, [27], [28]. speaker recognition speaker recognition is the most researched behavioural biometric. although the voice production considers the physical aspects of the mouth, nose and throat, this biometric is considered as behavioural type because the pronunciation and the manner of speech is intrinsically behavioural. the specific voice features refer to various analysis such as amplitude spectrum, localization of spectral peaks related to the vocal tract shape or pitch striations related to the user’s glottal source. similar to keystroke analysis, the speaker recognition can be performed either in static (text-dependent) or dynamic mode (text-independent) mode [29]. in the text-dependent mode the use is asked by the biometric system to pronounce a particular phrase, while, in the case of the text-independent mode the user is free to speak any phrase. in the latter case the verification accuracy usually improves as the text length increases. in between, a pseudo-dynamic mode exists where the user is requested to say two numbers randomly previously enrolled in a database. as principle, the normalized amplitude of the input signal is decomposed into several band-pass frequency channels with the purpose of feature extraction. the type of the extracted feature may vary. typical features are the fourier transform of the voice signal for each channel, along with some extra information consisting in pith, tone, cadence or shape of the larynx. amongst behavioral biometric systems, the speaker recognition is the most accurate behavioral approach. nevertheless, the voice might be perturbed by various factors such as illness, emotional or mental state or even age, conducting to inaccurate results. 3 mobile and web-based biometrics technology according to acuity market intelligence, the mobile biometric market will technically explode from $1.6 billion in 2014 to $34.6 billion in 2020 [30]. this prediction is foreseen for all biometric sensors (modalities) embedded in smart mobile devices (smart phones, tablets, and intelligent wearables. another mobile biometrics considered also refer to biometric applications offered by vendors or mobile service providers, including retailers, payment procedures or banks. a third identified biometric sector is given by payment or non-payment transactions using secure web (cloud)-based services augmented with biometric authentication option. the report claimed that 100% of smart mobile devices will include embedded biometric sensors as a standard feature by 2020. according to the report, each year, more than 800 billion transactions requiring different level of biometric authentication will be processed, while more than 5.5 billion biometric applications are foreseen to be downloaded. biometrics systems and technologies: a survey 323 the aforementioned statistics come with no surprise. millions of internet users experience a malware or hacker attack each year. the situation is even more critical with the use of mobile access where losses to fraud or identity theft are measured in the hundred of billions of dollars. thus, implementing more authentication modalities by replacing the conventional ways with biometrics for mobile devices seems to be a reliable solution. mobile biometrics solutions are implemented by device manufacturers as well as independent vendors as third-parties which offer software solutions. intel security division has developed a biometric authentication application relying either on face or fingerprint named truekeytm [31], application available for various platforms either server-based or mobile. another company, sensory, released an authentication application named trulysecuretm that combines voice and vision (face-based) authentication for mobile phones, tablets, and pcs [32]. fingerprints, face, iris palm print or voice biometric authentication and verification solutions are developed by neurotechnology [33]. mobile technology also incorporates various biometric modalities. devices with built-in fingerprint sensors exist on the market. one example is touchid fingerprint technology developed for iphone 5s by apple, that incorporates a fingerprint module. samsung also came up with fingerprint solution for its samsung’s galaxy tab s model as well as samsung galaxy s6 model. vision based authentication solutions are allowed by all smartphones which integrate high resolution cameras into their hardware, facilitating third-parties to easily develop such software based authentication options. other mobile or web-based biometric technology vendors include applied recognition with ver-id for various applications [34]. notable, a facial biometric application has been recently released by isityou augmented with a unique anti-spoofing mechanism [35]. to address the interoperability among authentication devices a fast identity online (fido) alliance was formed in 2012 [36], including powerful partners such as google, paypal, miscrofsot, mastercard, github or dropbox. the alliance works towards creating secure interfaces between fido-enabled biometric devices and cloud-based website by developing dedicated plugins. fido is very active in defining web-based related biometric standards and a great impact is foreseen for macroeconomic application. recently (november 2015), fido submitted to the world wide web consortium three technical specifications with the purpose of defining a web-based api to be integrated into all web browsers and platform to facilitate a strong and secure authentication option. 4 biometrics attack similar to hacking a conventional authentication modality (password, token, etc), efforts have been made to hack or break a biometric authentication systems. a potential attack against a biometric system is possible for any component of the system. the network distributed (webbased) systems are more vulnerable to the attacks compared to the stand-alone biometric systems. this is due to the fact that, for a stand-alone biometric system, all processes are performed into a single processing unit. on the contrary, for physically disparate biometric systems, the attack may also occur in the transmission path, or any server performing the authentication. the most common attack is the one against the sensor. when the samples acquisition process is fully automated (i.e. no watching guard exists to monitor the acquisition process) an impostor can easily bypass the system by simply presenting a copy of biometric data of a legitimate user in front of the sensor. the attempt of breaking the biometric system using such method is named spoofing attack. to date, there is no commercial biometric technology that is robust against such attacks. the copy may come in various formats, depending on the biometric modality. in the case of facial biometric, the impostor may present a still image, video sequence playback, or even a 3d silica or rubber mask of the genuine user. a demonstration carried out by the security 324 i. buciu, a. gacsadi and vulnerability research team of the university of hanoi drawn attention regarding this issue by bringing evidence on how easy is to bypass the biometric systems namely lenovo’s veriface iii, asus’ smartlogon v1.0.0005, or toshibas face recognition version 2.0.2.32 each set to its highest security level, using fake facial images of the valid user and thus gaining illegitimate access to the laptops [37]. this vulnerability was also tested in the spoofing challenge competition organized as special session at icb 2013 [38]. these examples clearly point out the weaknesses of such systems and emphasize the necessity of incorporating reliable anti-spoofing mechanisms into the fa systems. hence, not surprisingly, many research works were devoted to find robust solutions for detecting spoofing attacks. within the same framework, a competition on counter measures to 2d facial spoofing attacks was also settled at icb 2013 where anti-spoofing methods were evaluated [39]. the spoofing attack issue for various biometrics (face, iris, fingerprint, gait, etc) is a theme for the fp7 funded project tabula rasa, where the topic was intensively and specifically addressed and analyzed [40]. various solutions have been proposed to detect spoofing attacks. the spoof detection approaches may fall into four categories: a) challenge response based methods requiring user interaction, b) behavioral involuntary movements detection for parts of the face and head, c) data driven characterization, and d) presence of special anti-spoofing devices. google proposed a blinking based antispoofing mechanism [41]. the fingerprint modality can also be easily spoofed, as fingerprints are left behind on many objects the user touches. an impostor can use the same approach deployed by law enforcement agencies for lifting the fingerprints. once lifted, a duplicate can be easily constructed from silicon or gelatin material. by so doing, in 2002, matsumoto successfully fooled eleven different commercial fingerprint readers, with both optical and capacitive sensors, and some with live finger detection option, with a rate of success of 80% [42]. matsumoto created a copy of a live finger as well as an artificial finger using a latent fingerprint left on a glass also accepted as genuine. eleven years later (i.e. 2013), a biometrics hacking team of the chaos computer club (ccc) has successfully bypassed the biometric security of apple’s touchid implemented on iphone 5s and samsung galaxy s5 [43]. regarding samsung’s s5 biometric authentication system, the team claimed that not only it was possible to spoof the fingerprint authentication system, even after the device has been turned off, but the system also allows for seemingly unlimited authentication attempts without ever requiring a password, which was unacceptable. two more years later, the vulnerability remains as it was reported at the blackhat hacking conference in las vegas by zhang et al. [44]. similar to facial spoofing challenge competition, an iris and fingerprint based live detection competition is open [45]. the most recent report (2015) confirms the issue is still not solved, although some improvements exist [46]. spoofing a real iris with a good quality image is also possible. gupta et al. [47] used a commercial sdk, verieye [48] and successfully spoofed the system with printed images of iris. finally, voice impersonation can be applied to trick both automated and human verification for voice authentication systems [49]. a legitime user’s voice can be recorded in various ways, including close proximity between the attacker and user, throughout a spam call or searching for audio-video recordings over the internet. with the help of a voice morphing program, the attacker may synthesize the user’s voice by using just a few samples. the cloned voice “borrowed” the features of the authentic voice and the authors successfully fooled the speaker authentication system that was based on the bob spear speaker verification system [50]. their findings were alarming, i.e. the system was able to reject only 20 % of fake voices. 5 attributes of biometric technology the biometric systems and technologies is expected to posses several characteristics to be practically usable, as follows: biometrics systems and technologies: a survey 325 • universality. this is the ability for a specific biometric system to be applied to a whole population of users. this is directly connected to failure to enroll (fte) condition that refers to the case when a part of the population may not be enrolled for whatever reason. on particular reason is when the individual does not have the required biometric, leading to failure to enroll (fta) error. a person suffering from mutism can not be enrolled with a speaker recognition technology; a fingerprint system can not be used for persons with missing fingers, etc. • uniqueness. the ability to successfully discriminate people. the biometric features must be as distinct as possible from one individual to another. the biometric features must convey large differences between individuals (large inter-class variability) while having small difference between samples taken from the same individual (small intra-class variability). • permanence. the ability of biometric features not to change over time. some features do not change (iris, fingerprint patterns, vascular system, etc.) while others do (facial features). for time varying features a periodic biometric update is required. • collectibility. the ability of the system to perform the acquisition for any occasion (regardless of environment change, such as change in illumination, etc). there are cases where the acquisition process can not be performed for the same individual previously enrolled. for example, if the person suffers some skin condition destroying the epidermis or gets a serious scar on his finger, the fingerprint biometric authentication system will more likely output a false reject due to significant difference between the enroll and test biometric features. similar scenario is possible for people suffering from cataract when tested with an iris authentication system, or people undergoing facial plastic surgery recovering from accidents or facial injury, when tested against a facial biometric system. • simplicity. recording and transmission should be easy to use and not error-prone. • cost-efficiency. the whole process should be cost-efficient. • acceptability. the degree to which a biometric technology is found acceptable by the society. typically, gross invasive biometric technologies (such as retina based systems) are tend to be less acceptable than those using non-invasive approaches (such as vision based or touchless sensors). another aspect to be considered is the access to privacy. • scalability. this attribute refers to the ability of the system to accommodate a large number of enrollment individuals while providing a reasonable accuracy. the degree of scalability is application dependent. for instance, when using a biometric-based lock option for a mobile device with the intent to lock some specific applications inside the device, only one (device’s owner) or a few individuals (perhaps family’s members) would enroll. the matching is then either one-to-one or one-to-few type and the scalability is not an issue. on the contrary, for a network distributed system (such as bank application or decision systems) the number of enrolled individuals might easily reach millions and the system should cope with this overwhelming data. for such large-scale biometric application its performance (accuracy, frr and far) is more critical. more exactly, one false rejection a month might be acceptable, but hundreds false rejection a day would be disastrous. the same rationale applies to far. just to give a simple example, let us suppose a biometric system is 99.9% accurate. that is, if someone is an attacker, there is a 1-in-1000 chance that the biometric system fails to detect the attacker (outputs a false accept), while the same chance would be for a legitimate user to be denied as false attacker. 326 i. buciu, a. gacsadi • resilience. the ability of the system to handle exceptions. an example would be an individual whose biometric features might not be easily acquired. if a user has a broken arm, he may need human intervention to use a hand or fingerprint based biometric system. • circumventable. the ability of the system to detect attacks. an important role has the sensor that should be tamper-proof. 6 biometric standards as noted throughout the chapter, a network distributed biometric technology involves several components, including the sensor, the communication channel, the web-based decisional server, components that rely on different hardware architecture. not only the hardware is different but also the integrated software is different for each hardware configuration. moreover, a fully operational system working on a specific operating system is not compatible with another operating system. another representative example is provided by the multi-modal biometric systems relying on two or multiple biometric modalities (face, fingerprint, voice, as an example) that yield individual scores which are finally fused to output a single matching score. without a common format that has to be shared among these modalities, the multi-modal biometric system can not operate. finding mechanisms for each component of the biometric system to communicate has leaded to standardization. there are several working groups concerning biometric standards. at international level, the international standard organisation (iso) and international electrotechnical commission (iec) play a significant role. iso and iec have established a joint technical committee 1 jtc 1/sc 37 [51] to ensure a high priority, focused, and comprehensive approach worldwide for the rapid development and approval of formal international biometric standards. there are several aspects to consider, including data interchange formats, data structure standard and technical interface standards. • data interchange format represents the lowest level of interoperability between systems using the same modality and addresses the actual representation of the biometric data itself. the number and type of features can vary considerable depending upon the matching algorithm. there is a need to format these features so that the other components of the system may properly interpret the transmitted information. • once the data is formatted it needs to be transmitted. data structure standard addresses this by providing the necessary wrapper around the biometric data within the so called common biometric exchange file format to facilitate interoperability between different systems or system components, forward compatibility for technology improvement, and software hardware integration. data interchange format standards provide the mechanism for extraction, matching and decision modules of the biometric system. the main component of this standard is the biometric information record composed of three parts. the first part named the standard biometric header contains information to an application regarding the format of and other properties of the next part named the biometric data block that contains the biometric data conforming to a defined format. the third part named the security block provides information related to the encryption protocol and the integrity of the biometric information record. • technical interface standards provide an application programming interface (api) by defining the format for the biometric information record so that components can understand and interpret records. a representative standard is bioapi [52] that defines biometrics systems and technologies: a survey 327 a framework for installing the components, making them compliant with plug-and-play concept. bioapi tries to hide as much as of unique attributes of individual biometric technologies, vendor implementations, products and devices. a biometric service provider could then plug the components throughout a service provider interface. an application can use biometric services using two fundamental ways: either through primitive functions or through abstract functions. primitive functions are the most basic functions and relates to bioapi−capture, bioapi−process, bioapi−v erifymatch and bioapi−identifymatch. the abstract functions are defined by bioapi−enroll, bioapi−v erify and bioapi−identify. the standardization of biometric technology leaded to proper interoperability between and within biometric systems, ensuring a cost-effective technology implementation. 7 conclusions this paper only briefly touches the main issues of biometric systems and technologies, pointing out their differences, modalities, open problems and standardization. their performance greatly vary upon the operating and external conditions as no universal biometric technology exists. the best performance is obtained where the technology is designed for strict controlled conditions and where data acquisition is accomplished under human supervision. not mentioning a possible accuracy drop, when no human guard is present, a 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[51] http://www.iso.org/iso/home/store/catalogue_tc/catalogue_tc_browse.htm? commid=313770&published=on [52] http://www.iso.org/iso/catalogue_detail.htm?csnumber=33922 int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 617-631 nondeterministic algorithm for breaking diffie-hellman key exchange using self-assembly of dna tiles z. cheng zheng cheng zhejiang university of technology 288 liuhe road, hangzhou, p.r. china chengzhenhust@gmail.com abstract: the computation based on dna tile self-assembly has been demonstrated to be scalable, which is consider as a promising technique for computation. in this work, i first show how the tile self-assembly process can be used for computing the modular multiplication by mainly constructing three small systems including addition system, subtraction system and comparing system which can also be parallely implemented the discrete logarithm problem in the finite field gf(p). then the nondeterministic algorithm is successfully performed to break diffie-hellman key exchange with the computation time complexity of θ(p), and the probability of finding the successful solutions among many parallel executions is proved to be arbitrarily close to 1. keywords: modular multiplication; discrete logarithm; nondeterministic; diffiehellman; key exchange; self-assembly; dna tiles 1 introduction nature is a rich source for computing devices design. in recent years, computing models and algorithms inspired by biological systems are deeply investigated, e.g., membrane computing inspired by cells and dna computing inspired by dna molecules. most of such computing models inspired by cells (or dna molecules inside cells) are proved to be universal and computationally efficient [1, 2]. this work focuses on computing systems based on dna molecules, especially the self-assembly of dna tiles. since adleman demonstrated that the dna recombination techniques can be used to solve combinational search problem [3], the field of dna-based computing has developed very fast. dna computing potentially provides a degree of parallelism and high density storage far beyond that of conventional silicon-based computers [4]. dna tile self-assembly is a crucial process, by which the small objects can autonomously assemble into big complexes [5]. seeman proposed dna nanotechnology to make self-assembled nanostructures from dna molecules [6]. based on this landmark work, winfree utilized a kind of dna nanostructure called dx (double crossover) tile to realize a patterned lattice and the complex algorithmic pattern [7]. winfree et al. demonstrated that two dimensional dna selfassembly can be capable of turing-universal computation [8]. winfree and eng proved that self-assembly of linear, hairpin and branched dna molecules can be generated regular, bilinear and context-free languages respectively [9]. dna tile algorithmic self-assembly can also be used to create crystals with patterns of binary counters [10, 11] and sierpinski triangles [12] to implement arbitrary circuit [13]. however, those crystals are deterministic. mao experimentally implemented the first algorithmic dna tile self-assembly [14], where a logical computation (cumulative xor) is performed. brun proposed the application of dna tile self-assembly in arithmetic [15]. dna self-assembly is also used to cope with combinational np-complete problems, such as solving the satisfiability problem [16] by using 2d dna self-assembly tiles, nondeterministically factoring numbers [17], deciding a system of subset sum problem [18]. dna self-assembly has potential applications in the cryptography. xor computation on pairs of bits copyright c⃝ 2006-2012 by ccc publications 618 z. cheng can be used to execute a one-time pad cryptosystem, which provides theoretically unbreakable security [19]. to provide modern security features, the algorithms for public-key cryptosystems such as rsa [20], diffie-hellman key agreement [21], the digital signature algorithm [22] and systems based on elliptic curve cryptography [23] are widely used. all these algorithms have one thing in common: they all need operate the modular multiplication and exponent multiplication [24, 25]. in 1976, diffie and hellman [26] proposed the public-key distribution scheme based on the discrete logarithm problem in a finite field gf(p). chen [27] gave deterministic algorithm to break diffie-hellman key exchange and constructed the basic tiles in which the complex modular multiplication operation in decimal is contained, so it can’t be easily executed based on the experiment of simple binary arithmetic and logical operations. here i mainly propose the nondeterministic algorithm to solve this problem by the addition, subtraction and comparing operations for binary numbers, which can be performed easily in experiments. the computation time complexity of our algorithm is θ(p), and the probability of finding the successful solutions among the many parallel executions is proved to be arbitrarily close to 1. the rest of this paper is structured as follows: section 2 will describe the tile self-assembly model. section 3 gives the method of computing modular multiplication using dna tile selfassembly. section 4 shows the process of breaking diffie-hellman key exchange based on modular multiplication by self-assembling. the conclusion will summarize the contribution of our work. 2 algorithmic dna tile self-assembly the abstract tile assembly model [28] is a formal model of crystal growth which is designed to model self-assembly of molecules such as dna. rothemund and winfree [29] defined the abstract tile assembly model, which provides a rigorous framework for analyzing algorithmic self-assembly. the tile assembly model extends the theory of wang tilings [30] of the plane by adding a natural mechanism for growth. for the tile self-assembly model, the assembly complexes take place by starting with the seed tile denoted as the basic tile type set, which can be produced the seed configuration s. for each tile, it can be represented by the binding domains ∑ which is a 4-tuple {σn,σe,σs,σw}∈ ∑4. here, n,e,s,w is labeled as the direction of north, east, south and west respectively. the set of directions is a set of four functions from positions to positions denoted as d = {n,e,s,w}, i.e. z2 to z2 such that all positions (x,y), n(x,y) = (x,y+1), e(x,y) = (x+1,y), s(x,y) = (x,y−1), w(x,y) = (x− 1,y). for a tile t, for d ∈ d, bdd(t) is used to denoted as the binding domain of tile t on d′s side. given a tile system s = {t,g,τ} which is designed preparedly, here the parameter t is a function z × z → t , which is a configuration of the tile self-assembly model. g is a strength function ∑ × ∑ → r, which denotes the strength of the binding domains, and τ ∈ n is the temperature. when the growth of process terminates, these can be produced a unique final configuration s based on the seed configuration s. s is also a function z2 → γ, here γ is a set of tiles which can be used to design the configuration of the tile self-assembly model. here, i mainly use the abstract tile assembly model to break diffie-hellman key exchange which is shown in figure 1. intuitively, the model has tiles or squares that stick or don’t stick together based on various binding domain including σn,σe,σs,σw on their four sides. nondeterministic algorithm for breaking diffie-hellman key exchange using self-assembly of dna tiles 619 n s w e figure 1: the structure of basic tile unit. 3 the algorithm for computing modular multiplication based on dna tile self-assembly various techniques for speeding up modular multiplication have been reported in literature. among them, two major approaches stand out: one is based on the interleaved modular multiplication algorithm where the multiplier is processed from the most significant position [31]. the other one is based on the montgomery algorithm where the multiplier is processed from the least significant position [32]. the key that enables the linking of these two approaches is a new representation of residue classes modulo. the two methods account for most of the complexity in terms of time and resources needed. so this gives reason to search for dedicated solutions which compute the modular multiplications efficiently with minimum resources. here, i use the method of interleaved modular multiplication based on dna tile self-assembly. it takes advantage of these techniques and the ones that may eventually be devised to further boost speed. for the integers x,y,m with 0 ≤ x,y < m, in order to get the result of x∗y mod m, i use the algorithm which gives a pseudo code implementation of interleaved modular multiplication as follows: (1)p = 0; (2) for (i = n−1; i ≥ 0; i = i−1 ){ (3) p = 2∗p ; (4) p = p + xi ∗y ; (5) if (p ≥ m) p = p −m; (6) if (p ≥ m) p = p −m;} here, n is the number of bits of x. xi is denoted as the i-th bit of x. the idea of interleaved modular multiplication is very simple: the first operand is multiplied with the second operand bitwise and added to the intermediate result. the intermediate result is reduced with respect to the modulus. for this purpose at most two subtractions per iteration are required. the process is to interleave multiplication and reduction such that the intermediate results are kept as short as possible. for every xi(0 ≤ i ≤ n − 2), there is a left shift, a partial product computation, an addition, and at most two subtractions. the partial product computation and left shift operations are easily performed by using an array of and gates and wiring respectively. in each iteration of the loop, the estimation of the previous iteration can be added to the intermediate result. in this section, i will introduce the algorithm for performing modular multiplication through constructing three small systems which are addition system, subtraction system and comparing system. examples can be given to indicate how the tile assembly model performs the computations. 3.1 addition system this system will do addition operations between two positive integers. when the comparison result at the previous step is “<” which means the value of p is smaller than the modulus m, then 620 z. cheng this system will make addition operations between two integers p and m. if xi = 0(0 ≤ i ≤ n−2), the result is the value of p which should be shifted one bit from right to left, intuitively the sum is the value of 2 ∗p . if xi = 1(0 ≤ i ≤ n− 2), the addition between p and y is done, here p should be shifted one bit from right to left and y does not need to shift one bit. theorem 3.1. let the addition system be denoted as ∑ +={ab, abcd, a′bcd, a*bcd, a*bc, a*b, a, a*, #, a*bcd′, a′bc; a, b, c, d ∈{0,1}}, and t+ be the set of tiles as described in fig.2(c). let g+ = 1, τ+ = 2, and s+ be a seed configuration. let np and ny be the sizes of p and y in bits respectively. let n = max(np , ny ). if np < n, the number needs to be padded to be n bits long with extra 0 in the p ’s high bit, and if ny < n, the number needs to be padded to be n bits long with extra 0 in the y ’s high bit. then, there exists some (x0,y0) ∈ z2, such that s+(x0+1,y0−1) = s0, s+(x0−n,y0−1) = e0, s+(x0+1,y0−0) = add; for all i ∈{0,1, · · · ,n}, bdn(s+(x0 − (i−1)),y0 −1) = xipimiyi, for all other positions (x,y),(x,y) /∈ s+. then the tile system s+ produces a unique final configuration based on s+ and can compute the sum of two input numbers by using θ(1) distinct tiles in linear assembly time. proof. consider this tile system ∑ +. let γ+ be composed of the tiles {0∗,#, #,null}, {a<, null,null,null} with the label s0, {#,null,null,null} denoted as the tile e0, {a<, a,∗,null} represented as the tile add, and the basic tile set t+, here a ∈ {0,1}. let the seed configuration s+ : z2 → γ+ be such that  s+(1,−1) = s0; s+(−n,−1) = e0, and s+(1,0) = add; ∀i ∈{0,1, · · · ,n}, s+(−i,−1) = xipimiyi; for all other (x,y) ∈ z2, s+(x,y) = empty. it is clear that there is only a single position where a tile may attach to s+. and after that tile attaches there will only be a single position where a tile may attach. by induction, because ∀t ∈ t+, the triplet < bds(t),bde(t) > is unique, and because τ+ = 2, it follows that this tile system s+ produces a unique final configuration on s+. fig.2(a) gives a sample seed configuration for adding two n-bit input numbers. fig.2(b) shows the final configuration of the example for adding two integers p = (001110)2 and y = (001101)2 with the result (011011)2. the set of tiles which is described in fig.2(c) shows the functions of the addition system has three functions. the addition system has three functions. first, the value of p , m and y are arranged in the seed configuration from the lowest bit to the highest bit, and the value of x is set from the highest to the lowest bit. here, a, b, c, d, e, f, g, k ∈ {0,1}. the initial value of p is set as 0. of course, the highest bit of x denoted as xn−1 is 1, so the first addition operation only needs to pass the value of y to p for the corresponding bits. the tile types with the red color can be seen in fig.2(c). the value “a0cd” at the bottom of the tiles are denoted as x, p , m, y respectively. the number 1 as the input, and output of the tile is the value of xn−1 which is assigned to 1. the numbers of bits of p and m are more than x and y , so “bc” in the first tile of the tile types are represented as the higher bits of p and m respectively. more importantly, i use “a′′′ to label the highest bit of x which should be passed to the lowest bit, then xi = 0 or 1 (0 ≤ i ≤ n− 2) can be passed to the addition system to determine whether the value of y should be added with p together. “e′′′ is the value passed from the highest bit, so the label of “a′′ will be passed to the addition system at next step. second, for the next addition, if xi = 0(0 ≤ i ≤ n − 2), the lowest bit of p is 0, and the value of p should be shifted one bit from right to left, then the result of the addition operation is 2 ∗p . at the same time, xi = 0(0 ≤ i ≤ n− 2) should be passed to the higher bit of y , and the bits of y and m are passed to the upper layer. so here, there is no carry bit for the addition at this step. if xi = 1(0 ≤ i ≤ n− 2), the lowest bit of p is the corresponding lowest bit of y , nondeterministic algorithm for breaking diffie-hellman key exchange using self-assembly of dna tiles 621 s0 add e0 x0p0m0y0 * x1p1m1y1 x1p1m1y1 x0p0m0y0 ... xn-1pn-1mn-1yn-1 xn-2pn-2mn-2yn-2 xn-1pn-1mn-1yn-1 xn-2pn-2mn-2yn-2# a< a e0 s0 add 101101001101110100100000 101101001101110100100000# 111101001001110101100000# 0 * 0 1 1 0 * 1 1 1 * 1 1 1 * 1 0 * 0 0 * 1< * bc fc e * f g * b abcd aecd f* g 1 k * b 1 abcd adcd 0 * b 1 10* bc bc 0 * 0 0 e 0 b a ' 0 b a'bcdabcd aecda'ecd 0 e f' a'bcdabcd abcda'ecd f* g 1 f* g 1 h ' k * 1 h * b 1 a ' 11 bc bc 11 a0cd adcd 1 e ' 1 a0cd a'dcd a'0cd 1 a ' # adcd 0 b 0 0 0 * 0 abcd00 a0cd00 0 e 0 b abcd aecd 0 e 0 b bc ec (a) (b) (c) figure 2: (a) a sample seed configuration for adding two n-bit input numbers. (b) the final configuration of the example for adding two integers p = (001110)2 and y = (001101)2 with the result (011011)2. (c) the basic tile types of the addition system. then the addition is done between p and y , here p also should be shifted one bit from right to left. so if the carry bit from the lower bit is “f*”, and the shifted bit from p is “g”, thus the addition result “e′′ and the carry bit “k*” can be generated at this step, synchronously the bit of p and y labeled as “b” and 1 respectively should be passed to the higher bit. the tile types with lilac are shown this function of the addition system. third, this system can determine whether and what the bit of x is passed to the next addition system. if the lowest bit of x has been completed the addition operation, then it only should be passed to the final result. otherwise, the value of xi should be passed to the lower bit xi−1 which will be done the next round addition. so if the input on the right of the tiles includes the label “f′′′, it can determine the value of xi−1 denoted as “a′′ will be passed to the next addition system. 3.2 subtraction system in this section, i will describe a system that can make subtraction between two positive integers. when the comparison result at the previous step is “>” or “=” which means the value of p is bigger than or equal to the modulus m, then this system will make the subtraction between the two integers p and m. theorem 3.2. let the subtraction system ∑ −={ab, abcd, a*, *; a, b, c, d ∈ {0,1} }, and t− be the set of tiles as described in fig.3(c). let g− = 1, τ− = 2, and s− be a seed configuration. let np and nm be the sizes of p and m in bits respectively. let n = max(np , nm). if np < n, the number needs to be padded to be n bits long with extra 0 in the p ’s high bit, and if nm < n, the number needs to be padded to be n bits long with extra 0 in the m’s high bit. then, there exists some (x0,y0) ∈ z2, such that s−(x0 + 1,y0 − 1) = s0, s−(x0 − n,y0 − 1) = e0, 622 z. cheng s−(x0 +1,y0−0) = sub; for all i ∈{0,1, · · · ,n}, bdn(s−(x0−(i−1)),y0−1) = xipimiyi, for all other positions (x,y),(x,y) /∈ s−. then the tile system s− produces a unique final configuration based on s− and can compute the difference of two input numbers with θ(1) distinct tiles in linear time. proof. consider the tile system ∑ −. let γ− contain the tiles {0*, #, #, null}, {> ,null,null,null} denoted as s0, {#,null,null,null} represented as the tile e0, {∗,∗,>,null}} labeled as the tile sub, and the basic tile set t−. let the seed configuration s− : z2 → γ− be such that  s−(1,−1) = s0; s−(−n,−1) = e0, and s−(1,0) = sub; ∀i ∈{0,1, · · · ,n}, s−(−i,−1) = xipimiyi; for all other (x,y) ∈ z2, s−(x,y) = empty. it is obvious that there is only a single position where a tile may attach to s−. and after that tile attaches there will only be a single position where a tile may attach. by induction, because ∀t ∈ t−, the triplet < bds(t),bde(t) > is unique, and because τ− = 2, it follows that this tile system s− produces a unique final configuration on s−. e0 1 * 0 * 0 * 0 * s0 sub * 10110100100111010111# 10110100100111010111# 11110000100111010001# > * e0 x0p0m0y0 # x1p1m1y1 x1p1m1y1 x0p0m0y0 ... xn-1pn-1mn-1yn-1 xn-2pn-2mn-2yn-2 xn-1pn-1mn-1yn-1 xn-2pn-2mn-2yn-2 s0 sub * > * g * * abcd aecd f* g * abcd aecd f* g * a' bcd a' ecd f* g * bc ec (a) (b) (c) 0 * figure 3: (a) a sample seed configuration for subtraction operation for two n-bit input numbers. (b) the final configuration for the example of subtracting two integers p = (11010)2 and m = (10001)2 with the result (01001)2. (c) the basic tile types of this system. here, the value of “a, b, c, d” at the bottom of the tile is denoted as x, p , m, y respectively, and a, b, c, d, e, f, g ∈{0,1}. the subtraction operation begins from the lowest bit to the highest bit with the label “*”. “a′” is the value of x with the label which is passed to the addition system. the representation “f*” is the borrow bit from the lower bit and “g*” is the borrow bit which is generated at this step. the value of “e” is the result which can be computed by the sum of “b” and “c” minus “f”. fig.3(a) is a sample seed configuration for subtraction operation for two n-bit input numbers. fig.3(b) shows the final configuration for the example of subtracting two integers p = (11010)2 and m = (10001)2 with the result (01001)2, and (c) gives the basic tile types of the subtraction system. 3.3 comparing system this system is to check the relationship for given input string of binary bits which are represented as the addition or subtraction result p at each step and the modulus m respectively. nondeterministic algorithm for breaking diffie-hellman key exchange using self-assembly of dna tiles 623 at the same time, the relationship can determine the next operation should be made addition or subtraction. the system compares two input numbers bit-by-bit from the highest bit to the lowest bit until the relationship which is less than (<), greater than (>) or equal (=) is determined. here, i will describe the comparing system which uses θ(1) distinct tiles in linear time to compare the size of two input numbers. theorem 3.3. let the comparing system ∑ r={ab, abcd, a′bcd, >,<,=, a>, a<, a=, #; a, b, c, d∈ {0,1}}, and t be the set of tiles as described in fig.4(c). let gr = 1, τr = 2, and sr be a seed configuration. let np and nm be the sizes of p and m in bits respectively. let n = max(np , nm). if np < n, the number needs to be padded to be n bits long with extra 0 in the p ’s high bit, and if nm < n, the number needs to be padded to be n bits long with extra 0 in the m’s high bit. then, there exists some (x0,y0) ∈ z2, such that sr(x0 −n,y0 − 1) = s0, sr(x0 + 1,y0 − 1) = e0, sr(x0 − n,y0 − 0) = com; for all i ∈ {0,1, · · · ,n}, bdn(sr(x0 − (i − 1)),y0 − 1) = xipimiyi, for all other positions (x,y),(x,y) /∈ sr. then the tile system sr produces a unique final configuration based on sr and can give the relationship of two input numbers. proof. the proof is referred to theorem 3.1 and theorem 3.2, so i don’t restate it here. this system mainly makes the comparison between two non-negative integers which are used in the binary form. first, for two given integers, the numbers of bits are the same. second, the comparison begins from the most significant bit to the rightmost bit. when the (i + 1)-th bit of the addition or subtraction result p is smaller then the modulus m, then the tile is labeled as “<”, which also should be considered with the result of the i-th bit which is “<”, “>” or “=”, then the result which is “<” can be obtained and should be passed to the (i − 1)-th bit. on the contrary, the final result is “>” also should be passed to the (i − 1)-th bit no matter what the relationship of the i-th bit is. if the (i + 1)-th bit of the addition or subtraction result p is equal to the modulus m, then the tile is labeled as “=”, which also should be considered with the result of the i-th bit which are “<”, “>” or “=”, then the result which is “<”, “>” or “=” respectively can be obtained and should be passed to the (i−1)-th bit. furthermore, if the comparison result of the two integers is “<”, the next operation would turn to the addition computation with xi = 0 or 1. if the comparison result is “>” or “=”, the next operation would turn to the subtraction computation between p and m. when the comparison result is “<” with the label of the position for the lowest bit of x, the modular multiplication is completed at this step. fig.4(a) is a sample seed configuration for two n-bit input numbers. fig.4(b) shows the final configuration for the example of comparing two integers p = (1010)2 and m = (0101)2 with the result “>”. the tiles used in this system are as follows in fig.4(c). in each tile, the value of “a, b, c, d” is denoted as x, p , m, y respectively. 3.4 an example now i take an example to verify the validity of the algorithm based on dna tile self-assembly model introduced above. here, for the integers x,y,m with 0 ≤ x,y < m, i suppose x = 11, y = 13, m = 17. the modular multiplication x∗y mod m is computed as the following steps: first, x,y,m can be represented as x = 11 = (1011)2, y = 13 = (1101)2, m = 17 = (10001)2 respectively. the initial value of p is set as 0 and arranged from the lowest bit to the highest bit together with y,m. on the contrary, the bits of x are from the highest bit to the lowest bit. second, according to the method introduced above, i need construct the basic tile types in each of the three small systems and they are the same as the tiles described above. when all the tiles and the seed configuration are prepared, they are put together into the reaction buffer. 624 z. cheng s0 com e0 # x0p0m0y0 # # s0 com e0 >>># > 1111010010111101 101101001101# 101101001101# > * x1p1m1y1 x1p1m1y1 x0p0m0y0 ... xn-1pn-1mn-1yn-1 xn-2pn-2mn-2yn-2 xn-1pn-1mn-1yn-1 xn-2pn-2mn-2yn-2 * 1011 1011 =# 00 00 == aa aa <= 01 01 >= 10 10 << a'bcd a'bcd <= a'bcd a'bcd b<c >= a'bcd a'bcd b>c << abcd abcd <= abcd abcd b<c >= abcd abcd b>c a '<< a'bcd a'bcd a '<= a'bcd a'bcd >> abcd abcd >> a'bcd a'bcd a << a < abcda'bcd abcda'bcd a < a== a = abcda'bcd abcda'bcd a = b<c (a) (b) (c) figure 4: (a) a sample seed configuration for two n-bit input numbers. (b) the final configuration for the example of comparing two integers p = (1010)2 and m = (0101)2 with the result “>”. (c) the basic tile types of the comparing system. according to the mechanism of algorithmic dna tile self-assembly through watson-crick base pairing, the self-assemble process starts at the same time with the connector tiles, so the final stage can be seen in fig.5. the process of the three small systems performing is shown. here, because the highest bit of x which is denoted as x3 = 1, the value of p is equal to y , so the comparing system can check that p is smaller than m. the next addition operation begins with x2 = 0, so the value of p at the previous step shifted one bit from right to left can be assigned to p at this step, which can be represented as p = (011010)2, then the comparing system determines it is more than m, so the next operation is the subtraction operation between p and m, and the subtraction result should be assigned to p which is (001001)2. when x1 = 0, the addition system can make the sum between y and p , here p should be shifted one bit from right to left. so the addition result also should be assigned to p which is (011111)2, and it is bigger than m by the comparing system, then the subtraction result is (001110)2. the computation stops until x0 attaches to the addition system with the result of p which is smaller than m. the final result of p is (000111)2 which can be obtained by repeated computations using the addition, subtraction and comparing system. 4 the nondeterministic algorithm for breaking diffie-hellman key exchange using self-assembly of dna tiles on basis of the algorithm for computing modular multiplication, i will first give the method of performing the discrete logarithm problem in the finite field gf(p), then the nondeterministic algorithm for breaking diffie-hellman key exchange is successfully proposed. nondeterministic algorithm for breaking diffie-hellman key exchange using self-assembly of dna tiles 625 e0 1'011 1 1 ' 1 ' 1 ' 1 ' 11 com 0 1 0 1 0 ' 0 0 0 1 0 0 0 * 0 <<<<=# com 1 * 0 * 0 * 0 * 0 * 0 * com 0 * 1 1 0 * 1 < 1 <<<=# com 0 * 0 * 0 * 0 * 0 * 0 * com 1 < 1 < 1 <<=# >>>==# com 0 * 0 * 0 * 0 * 1 * 0 * s0 add sub add sub add sub >>>==# >>>>># # 0 0 < * 1 1 < * 1 < > * > > com >>>==# > 0000100110010100# 11110'000110111010100# 11110'100100111010100# 101101001'00111011100# 101101001'00111011100# 111100001'00111010100# 111100001'00111010100# 1111010011011'1011100# 1111010011011'1011100# 1011010011011'1010100# 1011010011011'1010100# 1111000010011'1010110# 1011000010011'1011100# com 1111 1 '< 1 '< 1 '< <=# sub re 1 '< 010011011'0010100# 1111010011011'0010100# 1011000010011'1011100# 1 * 1 * 1 * 0 * 0 * * 1111000010011'1010110# 0 * 0 * 0 1 0 * 0 1 1 ' 0 * 1 0 * 0 0 * 0 1 1 0 * 1 1 1 * 1 1 1 * 1 0 * 0 0 * 0< 1< 1< > > > > * * * * * * * * figure 5: an example of computing modular multiplication 1011∗1101 mod (10001). 626 z. cheng 4.1 the nondeterministic algorithm for solving the discrete logarithm problem a group g is cyclic if there is an element α ∈ g, such that for each b ∈ g, there is an integer i with b = αi. such that an element α is called a generator of g. now given a cyclic group g of order p, a primitive-root g and the element y of the group, the problem is to find an integer x such that y ≡ gx(mod p) with 1 ≤ x ≤ p − 1, and this problem is called discrete logarithm problem in the finite field gf(p). considering the equation y = gx(mod p), for given y, g and p, it is very difficult to compute the value of x. so here i solve the discrete logarithm problem by implementing the algorithm for computing the modular multiplication based on dna tile self-assembly model. in the process of implementing the tile self-assembly systems, many assemblies happen in parallel by creating billions of billions of copies of the participating dna tiles, so this is simulated by an exponential number of dna assemblies which can be converted into the space occupied by the dna molecules, thus i expect that the procedure of computing y will run in parallel on all possible value of x under the condition 1 ≤ x ≤ p−1. then i can compare the computation result with the given value of y, finally the result of x can be read by the biological operations described in section 3. so first, i give the algorithm for computing modular exponentiation based on the modular multiplication introduced above. there are two differences between the two computations. firstly, the modular exponentiation is actually a series of modular multiplications, so in the process of designing the seed configuration, it only needs to give different labels of the lowest bit of x which can be used to distinguish the value of x. here, i consider one of the value of x as y . secondly, given the value of i, after the computation of gi(mod p) is completed, there are some tiles that can convert the result of gi(mod p) into the new value of x, and the value of y doesn’t need to change, then the assembly complexes can be used to implement the computation of gi+1(mod p). therefore i don’t give too much explanation for the process of computing modular exponentiation. on this basis, i use the nondeterministic algorithm to solve the discrete logarithm problem in the finite field gf(p). it can nondeterministically guess the value of x so that a parallel implementation can be executed. i need to construct different labels to denote the value of x, which can determine the computation of modular exponentiation. here, an example in gf(11) is taken. suppose the equation y = 7x(mod 11), for given y = 2, i can get the value of x which is 3 as following steps and the final stage can be shown in fig.6. step 1: the binary forms of g,p is obtained respectively, here i consider y = x. the initial value of the integer p is set as 0 and arranged from the lowest bit to the highest bit together with y,p. on the contrary, the bits of x are from the highest bit to the lowest bit. then the basic tile types and the seed configuration are constructed and put together into the reaction buffer. according to the nondeterministic algorithm, there are many choices at the first position of the seed configuration, it can nondeterministically guess the value of x under the condition 1 ≤ x ≤ p− 1. in this example, the tile containing the label “′′” which is denoted as the value of x = 3 attaches the seed configuration. so the modular exponentiation y = 73(mod 11) can be parallely performed by the three small systems including addition system, subtraction system and comparing system, the assembly complexes can grow, therefore i can obtain the solutions of the problem. step 2: once the self-assembly has occurred, it is necessary to extract the answer. an estimate of the length of the reporter strands can be obtained by annealing the reporter strands with the component strands, so i can extract the result strands of different lengths representing the output tiles which run through the value of y. nondeterministic algorithm for breaking diffie-hellman key exchange using self-assembly of dna tiles 627 e0 1011 com com com com com com s0 add sub add sub sub re 10111001010000# 11111''1111101010000# 11111''1111101010000# 111110111''101011000# 111110111''101011000# 101111111''001110000# 101111111''001110000# 111111111''001111000# 111111111''001111000# 101110110''001011000# 101110110''001011000# 111110111''101010000# com 1111 com com add sub > 0'0011101010000# 11110'0011101010000# 101101011'101110000# 111101011'101110000# 111101011'001010000# com add 111100011'101110000# 101100011'101010000# sub 111100011'1010100 re 101101011'0010100 101101011'0010100 com 00# 00# 00# * * * * * * 1< 1< > * > * 0< 1< * > 1011'00011'001010000# 111110111''101010000# 101100011'001010000# 1 > * & & 1 1 '' 1 '' 1 '' 111 # 1 <<<==# 1 < 1 * 1 1 * 0 0 * 0 0 * 1 0 * 1 1 1 * 1 1 1 '' >>>>=# > 0 * 1 * 1 * 1 * 0 * 0 * * 1 = 1 ====# 1 < 0 * 1 0 * 0 0 * 0 * 0 1 10* 1 1 1 * 0 1 >>>>=# > 0 * 0 * 0 * 0 * 0 * 0 * * >>>>=# > 1 * 1 * 1 * 1 * 0 * 0 * * 1 < 1 <<==# 1 < c *101000 11&& && 1 1 1 ' 1111 11 0 <<<==# 0 < 0 1 0 0 0 ' 0 1 0 0 0 00 0 >>===# > 1 * 1 * 1 * 0 * 0 * 0 * * <<<==# 1< 0 * 0 1 * 0 1 * 1 0 * 0 * 0 * 1 >>>==# > 1 * 1 * 0 * 0 * * 0 * 0 * <<<= <# = figure 6: an example of computing modular exponentiation y = 73(mod 11). 628 z. cheng step 3: by comparing the computation result of y for every feasible value of x with given y = 2, so i can obtain the solution of the discrete logarithm problem. 4.2 the nondeterministic algorithm for breaking diffie-hellman key exchange diffie-hellman method is used for symmetric key exchange between entities. the steps of the algorithm are as follows: (1) key generation (a) generate a large random prime integer p and a generator α of the multiplicative group up. the set un is the multiplicative group of zn with order φ(n), which is defined as follows: un = {[a] ∈ zn : gcd(a,n) = 1} (b) select two random integers a and b, 1 ≤ a, b ≤ p− 1 for entity a and b, then compute x = αa(mod p) and y = αb(mod p). (c) broadcast the public key of a and b where a′s public key is (α,x) and b′s public key is (α,y). (2) a computes the symmetric key ka = ya(mod p) and b computes the symmetric key kb = x b(mod p). a and b will use the diffie-hellman method to exchange symmetric keys. in order to get the symmetric key k which is equal to ka or kb between the users a and b, i can give the nondeterministic algorithm for breaking the diffie-hellman key exchange as follows: first, i can get value of the secret key of user a denoted as a according to the public key (α,x) and the primer p. as a matter of fact, this process can be performed by the method of solving the discrete logarithm problem in the finite field gf(p) which is introduced above. i can design the all needed tiles in the computation, including the basic types of tiles, boundary tiles and the seed configuration by the binary form of the integer x, α and p. when all kinds of the tiles and the seed configuration are prepared, i puts them together into the reaction buffer, there are many choices at the first position of the seed configuration, it can nondeterministic guess the value of a under the condition 1 ≤ a ≤ p − 1. so the modular exponentiation αa(mod p) can be parallely performed by the three small systems, the assembly complexes can grow, therefore i can obtain the solutions of the problem. then i can read the result of the process of the dna tile growth using a combinational of pcr and gel electrophoresis. second, i can make the modular multiplication k = ya(mod p) together with the value of a obtained at the first step and the public key (α,y) of the user b. for example, suppose the multiplicative group u11 and the generator α = 7. the user a selects one random integers a as his own secret key, at the same time, the users a and b broadcast their public key x = 2 and y = 3 respectively. considering the equation x = 7a(mod 11), i can get the solution of the discrete logarithm problem in the finite field gf(11) which is the secret key of the user a by the method described above, and the result of a is equal to 3. so the common key can be computed as k = ya(mod p) = 33(mod11) = 5. 4.3 complexity and probability analysis considering the nondeterministic algorithm for breaking diffie-hellman key exchange based on self-assembly of dna tiles, the complexity of this algorithm can be computed in terms of computation time and the number of distinct tiles required. generally, for the equation y = gx(mod p), here, 1 ≤ x ≤ p − 1, suppose the number of the bits of g be k, and for the given problem, the value of k is a constant. for a round modular multiplication, there is one addition operation and at most two subtraction operations followed by the comparison operation respectively, so the upper bound of the computation time t which is the number of assembled steps is x(6k + 2) + 2 = θ(p). nondeterministic algorithm for breaking diffie-hellman key exchange using self-assembly of dna tiles 629 finally, these distinct tile types include the input boundary tiles and computation boundary tiles. as the basic tiles described in section 3, the tile types are all θ(1), so i can obtain the basic tile types are θ(1) for a round modular multiplication, then i also consider the labels to distinguish the number of the rounds which can be determined by the size of x(1 ≤ x ≤ p− 1), therefore the tile types needed for this algorithm is θ(p). now, i give the probability analysis for this algorithm. theorem 4.1. let each tile that may attach to a configuration at a certain position attach there with a uniform probability distribution. for all integers x(1 ≤ x ≤ p− 1), np is denoted as the number of the bits of the primer p, for given integers g and y, then the probability of assembling a particular final configuration which attaches the value of x such that y = gx( mod p) is at least ( 1 p−1) np . proof. now i calculate the probabilities of each tile attaching in its proper position and then multiply those probabilities together to get the overall probability of a final configuration. on one hand, there are (p−1) possible tiles that may attach to the positions which represent the bits of the primer p in the seed configuration, and only one is correct, so the probability of it attaching is pi = 1p−1(1 ≤ i ≤ np). on the other hand, for the next two positions, there is only one tile that may attach, so the probability of the last two tiles are both pnp+1 = pnp+2 = 1. the overall probability of a specific final configuration can be computed as the following formulas:∏np+2 i=1 pi = ( 1 p−1) np this is the probability of a nondeterministic assembly successfully identifying the solution. i use the nondeterministic computation to perform the function y = gx(mod p), and construct three small systems to define the whole tile systems that use θ(p) input tile types, and assemble in θ(p) steps with probability of each assembly finding the solution at least ( 1 p−1) np. therefore a parallel implementation of this tile system such as the execution of the dna tile self-assembly model in [12], with (p − 1)np seeds has at least a (1 − e−1) chance of finding the secret key of the user a or b, and one with 100(p − 1)np seeds has at least a (1 − e−100) chance. the probability of finding the successful solutions among the many parallel executions can be proved to be arbitrarily close to 1. 5 summary and conclusions dna tile self-assembly is looked forward to many applications in different fields. in this paper, i show how the dna self-assembly process can be used for breaking diffie-hellman key exchange based on the discrete logarithm problem in the finite field gf(p) by computing the modular multiplication. the advantage of our method is that once the initial strands are constructed, each operation can compute fast parallelly through the process of dna self-assembly without any participation of manpower, thus the algorithm is proposed which can be successfully solved the modular multiplication, and then can be used to break diffie-hellman key exchange with the computation time complexity of θ(p), and the probability of finding the successful solutions among the many parallel executions is proved to be arbitrarily close to 1. acknowledgments this work was supported by the research project of department of education of zhejiang province (y201120124) and the national natural science foundation of china (grant nos. 60703047, 60803113, 60903105). 630 z. cheng bibliography [1] l. pan and c. martin-vide, solving multidimensional 0−1 knapsack problem by p systems with input and active membranes, journal of parallel and distributed computing, vol. 65, pp. 1578-1584, 2005. 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[32] p.l. montgomery, modular multiplication without trial division, mathematics of computation, vol. 44, pp. 519-521, 1985. ijcccv7n5.pdf int j comput commun, issn 1841-9836 7(5):796-797, december, 2012. editorial challenge: from a quarterly to a bimonthly journal f.g. filip, i. dzitac florin gheorghe filip editor in chief of ijccc, romanian academy, bucharest, romania ffilip@acad.ro ioan dzitac associate editor in chief of ijccc, aurel vlaicu university of arad, romania & agora university, oradea, romania rector@univagora.ro abstract: starting with issue 4 of volume 7(2012) international journal of computers communications & control (int j comput commun, ijccc) [4] is a member of, and subscribes to the principles of, the committee on publication ethics (cope) [2]. beginning with issue 1 of volume 8(2013) ijccc will be published as a bimonthly journal (6 issues/year) [5]. keywords: ethics, bimonthly journal, impact factor. 1 why a bimonthly journal? in the period 2006-2012 the international journal of computers, communications & control, issn 1841-9836, has been published as a quarterly journal (4issues/year), with a supplementary issue every even year (5 issues/year) and has been covered in sci expanded [3] starting with the supplementary issue s of volume 1(2006) and has been indexed in journal citation reports/science edition (jcr), with 3-years journal impact factor 0.373 (in jcr2009), 0.650 (in jcr2010) and 0.438 (in jcr2011). this decreasing of the last impact factor can be explained by increasing of the number of published papers in issue 5 (december, 2010). consequently many papers can be cited only in 2012, but no more in 2011, the year considered for journal impact factor calculation in jcr2011. the correction of this anomalous fluctuation of the impact factor might be solved by a change of publishing policy of ijccc, from a irregular frequency of publication (4-5 issues/year) to a bimonthly publication (6 issues/year) with a regular frequency. this new policy is sustainable because in the last two years we have received over 1,200 manuscripts. publication frequency starting with issue 1 of volume 8(2013): ijccc will be published as a bimonthly journal (6 issues/ year), with a regular schedule such as: issue 1 (february); issue 2 (april); issue 3 (june); issue 4 (august); issue 5 (october); issue 6 (december). 2 focus and scope. topics ijccc is directed to the international communities of scientific researchers in computer and control from the universities, research units and industry. to differentiate from other similar journals, the editorial policy of ijccc encourages the submission of scientific papers that focus on the integration of the 3 "c" (computing, communication, control). in particular the following topics are expected to be addressed by authors: 1. integrated solutions in computer-based control and communications; 2. computational intelligence methods (with particular emphasis on fuzzy logic-based methods, ann, evolutionary computing, collective/swarm intelligence); copyright c© 2006-2012 by ccc publications editorial challenge: from a quarterly to a bimonthly journal 797 3. advanced decision support systems (with particular emphasis on the usage of combined solvers and/or web technologies). 3 author guidelines length of a manuscript the maximum number of pages of one article is 16. the publishing of a 6 page article is free of charge. for each supplementary page there is a fee of 50 eur/page that must be paid after receiving the acceptance for publication. technical instructions for authors the papers must be written in english. the first page of the paper must contain title of the paper, name of author(s), an abstract of about 300 words and 3-5 keywords. the name, affiliation (institution and department), regular mailing address and email of the author(s) should be filled as in [1]. manuscripts must be accompanied by a signed copyright transfer form. the copyright transfer form is available at website. initial submission (manuscript for review: the manuscript can be write in latex by the ijccc template or in ms word format with the following specifications: paper a4, font tnr 12p, single column. the manuscript must be uploaded online in journal management and publishing system of ijccc (open source software: open journal systems developed by the public knowledge project) [5]. final submission (accepted paper for publication: checklist of documents which must be send by e-mail: completed copyright transfer form; source files (one latex file for the text, eps files for figures they must reside in a separate folder); final pdf file (for reference). peer review process the submissions will be revised independently by minimum two reviewers and will be accepted for publication only after end of the editorial workflow. evaluation period and rejection rate information: • evaluation period after paper submission: up to 6 months; • publication time is pending on the number of papers received. we aim at a time not longer than one year; • mean acceptance rate: of approximately 20%. bibliography [1] andonie r., dzitac i., how to write a good paper in computer science and how will it be measured by isi web of knowledge, int j comput commun, issn 1841-9836, 5(4):432-446. [2] committee on publication ethics (cope) (http://publicationethics.org). [3] http://ip-science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?pc=d&issn=1841-9836. [4] http://www.journal.univagora.ro (ijccc archive for 2006-2012). [5] http://univagora.ro/jour/index.php/ijccc/ (new ijccc website). int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 227-235 a time-bound ticket-based mutual authentication scheme for cloud computing z. hao, s. zhong, n. yu zhuo hao 1. university of science and technology of china department of electronic engineering and information science hefei, anhui 230027, p.r.china, and 2. state university of new york at buffalo department of computer science and engineering 201 bell hall, amherst, ny 14260, usa e-mail: hzhuo@mail.ustc.edu.cn sheng zhong state university of new york at buffalo department of computer science and engineering 201 bell hall, amherst, ny 14260, usa e-mail: szhong@buffalo.edu nenghai yu university of science and technology of china department of electronic engineering and information science hefei, anhui 230027, p.r.china e-mail: ynh@ustc.edu.cn abstract: cloud computing is becoming popular quickly. in cloud computing, people store their important data in the cloud, which makes it important to ensure the data integrity and availability. remote data integrity checking enables the client to perform data integrity verification without access to the complete file. this service brings convenience to clients, but degrades the server’s performance severely. proper schemes must be designed to reduce the performance degradation. in this paper, a time-bound ticket-based mutual authentication scheme is proposed for solving this problem. the proposed authentication scheme achieves mutual authentication between the server and the client. the use of timebound tickets reduces the server’s processing overhead efficiently. the correspondence relationship between the digital ticket and the client’s smart card prevents user masquerade attack effectively. by security analysis, we show that the proposed scheme is resistant to masquerade attack, replay attack and password guessing attack. by performance analysis, we show that the proposed scheme has good efficiency. the proposed scheme is very suitable for cloud computing. keywords: cloud computing, mutual authentication, digital ticket, masquerade attack. 1 introduction cloud computing [1, 2] has become a very popular technology. by using cloud computing, both the data storage and the computation resources are moved from personal computers into the cloud. recently many companies provide the cloud storage services, including amazon simple copyright c⃝ 2006-2011 by ccc publications 228 z. hao, s. zhong, n. yu storage service (s3) [3], microsoft skydrive [4], nirvanix cloudnas [5], etc. as people store their important data in the cloud without keeping a local copy, it is important for the cloud clients to be able to verify the data integrity and availability. remote data integrity checking protocols [6–10] are proposed to ensure the data integrity in the cloud. ateniese et al. [6] propose the technology of provable data possession to enable the client to verify the data integrity without retrieving it from the server. juels and kaliski [7] propose the scheme called proofs of retrievability to enable the server to produce a concise proof about the data availability. other schemes [8–10] have also been proposed with increased scalability and efficiency. however, as the clients of cloud services are numerous and are also increasing very quickly, these technologies bring a lot of extra computation and storage overhead to the server. mechanisms should be designed to reduce these overhead as much as possible. one possible solution is to limit the client’s data verification frequency. in this method, the server releases a certain number of digital tickets to the client at a constant frequency, e.g., 12 tickets per year. the client uses one ticket for one time of data verification, and after that, the used ticket becomes invalid and cannot be reused. if the client needs more tickets, she must purchase them from the server. on the other hand, the client’s identity should be properly authenticated to the server before she uses these services. recently lin and chang [11] propose a countable and time-bound password-based user authentication scheme, which is a possible method for solving this problem. in the lin-chang scheme, the tickets {tk1, tk2, ..., tkt} are generated by the server in a way that the authentication value in tkj is one of the square roots of the value in tkj+1. the lin-chang scheme is secure against masquerading attack and replay attack, and also achieves good efficiency at the client side. however, the lin-chang scheme has several disadvantages, which make it not suitable in this environment. firstly, the ticket in lin-chang scheme is not associated with the client’s identity, which allows anyone who obtains the ticket to be able to use it. secondly, the client’s tickets can only be used sequentially. whenever tkj expires, the tickets tk1, tk2, ..., tkj−1 cannot be used anymore. thirdly, the lin-chang scheme involves high-cost modular exponentiation operations at the server side, which bring large amount of overhead as the quantity of cloud clients increases quickly. finally, the lin-chang scheme does not achieve mutual authentication. in this paper, we propose a new ticket-based mutual authentication scheme which has improvements in these aspects. firstly, we use smart card in the mutual authentication scheme. each client has her own unique smart card. the client’s tickets are associated with her smart card, so that even when her tickets are lost, they cannot be used by other clients. secondly, in the proposed scheme, tickets are relatively independent of one another, so that the tickets need not be used sequentially. when one ticket expires, all other tickets that do not expire can still be used. thirdly, the proposed scheme involves only lightweight exclusive-or and hash computations, which makes it very efficient at both the client side and the server side. finally, the proposed scheme achieves mutual authentication, so that both the server and the client authenticates each other when performing the data verification. by security analysis, we show that the proposed scheme is secure against lost smart card attack, lost ticket attack, masquerade attack, replay attack, etc. by performance analysis, we show that the proposed scheme achieves good performance at both the server side and the client side. the proposed scheme is suitable for cloud computing. the rest of this paper is organized as follows. section 2 presents the system model and introduces common notations used throughout this paper. in section 3, the proposed timebound ticket-based mutual authentication scheme is presented. in section 4, we show that the proposed scheme is secure against adversary’s attacks. in section 5, we show that the proposed scheme is cost-efficient. finally, we conclude in section 6. a time-bound ticket-based mutual authentication scheme for cloud computing 229 2 system model and common notations in the proposed scheme, there are two different entities: the cloud server and the client. the cloud server provides data storage services to a lot of clients, and also provides data integrity verification services to the clients. furthermore, the cloud server is in charge of the client registration, client authentication and digital ticket management. clients put their data at the server and retrieves the data on demand. each client has a unique identification and a password with which she can prove her identity to the server. in addition, similar to the lin-chang scheme [11], a bulletin board is maintained by the server for preventing repeated usage of one ticket. for convenience of description, we denote the cloud server by s and the clients by {ui, i = 1, 2, 3, ...}. denote the identification and password of ui by idi and pwi. we use a cryptographic hash function h() and a keyed hash function hk(), with k as a cryptographic key. s has two long-term secret keys, denoted by k1 and k2. 3 the proposed mutual authentication scheme in this section, we present the proposed time-bound ticket-based mutual authentication scheme. the proposed scheme consists of 4 phases: registration phase, verification request phase, mutual authentication phase and password change phase. 3.1 registration phase when the client ui initially registers herself to s, the registration phase is invoked. the registration phase consists of the following steps: 1. ui selects her own idi, her password pwi and a random number b. then ui computes ipbi = h(idi||h(pwi ⊕b)) and sends {idi, ipbi, t} to the server, in which t is the number of digital tickets ui needs. at the same time ui pays the corresponding ticket fee to s. the ticket fee can be payed in the form of either real or virtual currency, which depends on the policy of s. 2. after s receives the message and ticket fee from ui, s generates t tickets for ui. denote the jth ticket of ui by t (j) i . denote by tid (j) i and v p (j) i the ticket id and valid period of t (j) i respectively. specifically, s generates {(tid(j)i , v p (j) i ), j = 1, 2, ..., t} and computes as follows: wi = ipbi ⊕ h(idi, k1), α (j) i = hk2(idi ∥ tid (j) i ∥ v p (j) i ), β (j) i = α (j) i ⊕ ipbi. t (j) i has two parts: t (j) i = (t (j)1 i , t (j)2 i ), in which t (j)1 i = (tid (j) i , v p (j) i ), t (j)2 i = β (j) i . s also computes zi = hk2(idi) ⊕ ipbi, which is used during the password change phase. 3. s writes idi, t, wi, zi and t (j) i , j = 1, 2, ..., t into a smart card and sends it to ui. 4. when ui receives her smart card, she writes b into the card. 230 z. hao, s. zhong, n. yu 3.2 verification request phase as the client receives t tickets, she can use these tickets to perform data verification at most t times. the description below assumes that this is the kth verification request that ui invokes. 1. ui inserts her smart card into a card reader and enters idi and pwi. 2. the smart card generates a nonce ru according to system time, and computes ipbi = h(idi||h(pwi ⊕ b)), hi = wi ⊕ ipbi ; c1 = ru ⊕ hi, c2 = h(ru) ⊕ t (k)2 i ⊕ ipbi. then ui’s smart card sends {idi, t (k)1 i , c1, c2} to s. 3.3 mutual authentication phase when s receives the verification request message from ui, it executes the following steps: 1. s checks the validity of idi and rejects the service request if idi is invalid. 2. s checks whether the ticket id tid(k)i is on the bulletin board. if it is on the bulletin board, then s rejects ui’s service request and terminates the process. 3. s checks whether the current date is in the range of v p (k)i or not. if not, then s rejects ui’s service request and terminates the process. 4. s computes d0 = h(idi, k1), d1 = c1 ⊕ d0 and d2 = h(d1) ⊕ c2. 5. s computes hk2(idi ∥ tid (k) i ∥ v p (k) i ) and checks whether it is equal to d2. if they are not equal, then s rejects ui’s service request and terminates the process. otherwise, s authenticates ui successfully. 6. s generates a random nonce rs, computes c3 = d0 ⊕ rs and c4 = h(ru, rs) and sends {c3, c4} to ui. s also computes ks = h(d0, ru||rs), which will be used as a subsequent session key. 7. ui’s smart card computes d3 = c3 ⊕ hi. then it compares h(ru, d3) with c4. if they are equal, ui authenticates s successfully. then the smart card computes kc = h(hi, ru||rs) and uses k to communicate with s in the subsequent data verification process. note that kc = h(hi, ru||rs) = h(h(idi, k1), ru||rs) = ks. when the kth data verification is finished, ui deletes the ticket t (k) i from its smart card, and s publishes tid (k) i to its bulletin board. finally the smart card deletes the nonce ru , and s deletes rs from its memory, to prevent replay attack. 8. after the mutual authentication, the data verification is performed. the data verification schemes [6–10] are independent of the proposed authentication scheme, so we don’t describe the details. the keys kc and ks can be used for secret communication. 3.4 password change phase this phase is invoked when ui needs to change her password pwi to a new one. the password change phase consists of the following steps: 1. ui inserts her smart card into a card reader, and enters idi and pwi. 2. the smart card generates a nonce ru according to system time, and computes ipbi = h(idi||h(pwi ⊕ b)), c1 = ru ⊕ wi ⊕ ipbi, c2 = h(ru) ⊕ zi ⊕ ipbi. then ui’s smart card sends {update, idi, c1, c2} to s, in which update is the message type indicating this is a password change request message. a time-bound ticket-based mutual authentication scheme for cloud computing 231 3. when s receives this message, it checks the validity of idi. if idi is invalid, it rejects the service request. 4. s computes d1 = c1⊕h(idi, k1) and d2 = h(d1)⊕c2. after that s checks whether d2 is equal to hk2(idi). if they are not equal, s rejects the password change request. otherwise, s authenticates ui successfully and accepts the password change request. 5. s generates a random nonce rs, computes c3 = h(idi, k1)⊕rs and c4 = h(ru, rs) and sends {c3, c4} to ui. 6. the smart card computes d3 = c3 ⊕ wi ⊕ ipbi. then it compares h(ru, d3) with c4. if they are equal, the smart card successfully authenticates s and prompts ui to enter a new password. 7. ui enters a new password pw newi . then the smart card computes ipb new i = h(idi||h(pw new i ⊕ b)). after that the smart card computes w newi = wi⊕ipbi⊕ipb new i , which yields h(idi, k1)⊕ ipbnewi , and computes z new i = zi ⊕ ipbi ⊕ ipb new i , which yields hk2(idi) ⊕ ipb new i . the smart card also updates t (j)2i to t (j)2 i ⊕ ipbi ⊕ ipb new i for all remaining tickets, which yields α (j) i ⊕ ipb new i . 4 security analysis of the proposed scheme in this section, we present security analysis of the proposed scheme. in section 4.1, we show that the server’s secret key cannot be obtained by an adversary who monitors the communication. in sections 4.2-4.5, we show that the proposed scheme is resistant to lost smart card attack, masquerade attack, replay attack and valid period extending attack. 4.1 secrecy of the server’s secret key in the proposed scheme, communications between ui and s are through a common channel. so we assume the adversary can eavesdrop this channel and get any messages transmitted between ui and s. during the kth verification request and mutual authentication phase, the adversary can get messages {idi, t (k)1 i , c1, c2, c3, c4}, in which idi and t (k)1 i have no relationship with k1 or k2, and c1, c2, c3, c4 are as follows: c1 = ru ⊕ wi ⊕ ipbi = ru ⊕ h(idi, k1) c2 = h(ru) ⊕ t (k)2 i ⊕ ipbi = h(ru) ⊕ hk2(idi ∥ tid (k) i ∥ v p (k) i ) c3 = h(idi, k1) ⊕ rs c4 = h(ru, rs) as ru and rs are both random nonces generated by ui and s, the adversary cannot guess their values. in addition, due to the one-wayness of hash function, the adversary cannot get ru or rs from h(ru, rs). so the adversary cannot get the value of h(idi, k1) or hk2(idi ∥ tid (k) i ∥ v p (k) i ). as a result, the adversary cannot get any information on s’s secret keys by eavesdropping channels. 4.2 resistance to attacks based on lost smart card a user’s smart card may get lost due to an accident or the user’s carelessness. in this section, we show that when an adversary gets a lost smart card, he cannot carry out attacks to the proposed scheme. we first show that the proposed scheme is resistant to offline password guessing attack. after that, we show that the lost tickets cannot be used by the adversary. 232 z. hao, s. zhong, n. yu resistance to offline password guessing attack when an adversary gets the lost smart card of ui, he can extract the stored data from it by monitoring the power consumption [12] or analyzing the leaked information [13]. the values stored in ui’s smart card are idi, t, wi, zi, b, t (j) i , j = 1, 2, ..., t. the adversary can pick up a password candidate pw ′. then he can computes ipb′i = h(idi||h(pw ′i ⊕ b)). from wi = ipbi ⊕ h(idi, k1), zi = hk2(idi) ⊕ ipbi and β (j) i = α (j) i ⊕ ipbi = hk2(idi ∥ tid (j) i ∥ v p (j) i ) ⊕ ipbi he can further compute h ′(idi, k1) = wi ⊕ ipb′i, h ′ k2 (idi) = zi ⊕ ipb′i and h ′ k2 (idi ∥ tid (j) i ∥ v p (j) i ) = β (j) i ⊕ ipb ′ i. however, as k1 and k2 are s’s secret keys, the adversary cannot get them. so the adversary cannot test whether pw ′ is equal to pwi from these values. from the above analysis, we can see that the proposed scheme is resistant to offline password guessing attack. resistance to attacks based on lost tickets when an adversary gets the lost smart card of ui, he can extract the stored tickets t (j) i , j = 1, 2, ..., t from it. however, as the adversary cannot get pwi from offline guessing attack (refer to 4.2), he cannot compute ipbi = h(idi||h(pwi ⊕ b)). as a result, the adversary cannot make a valid verification request message {c1, c2}, because both the computations of c1 and c2 require the knowledge of ipbi. from the above analysis, we can see that the proposed scheme is secure when an adversary gets a lost ticket. 4.3 resistance to masquerade attack suppose the adversary gets a set of history messages during the past channel eavesdropping. in order to masquerade as ui, the adversary has to forge a valid message {id∗i , t (k)∗1 i , c ∗ 1, c ∗ 2} which can pass s’s authentication process. it’s easy to see that the computation of c∗1 = ru ⊕ h(idi, k1) requires the adversary to get knowledge of h(idi, k1). from 4.1 we know that the adversary cannot get h(idi, k1), so he cannot forge a valid verification request message either. on the other hand, if the adversary wants to masquerade as the server, he must be able to compute a valid message {c3, c4}, in which c3 = h(idi, k1)⊕rs and c4 = h(ru, rs). because the computation of c3 requires the knowledge of h(idi, k1), which the adversary does not have, the adversary cannot masquerade as the server. from the above analysis, we can see that the proposed scheme is resistant to masquerade attack. 4.4 resistance to replay attack in the proposed scheme, the unique ticket id and the nonces are used for preventing replay attack. assume that the adversary gets a valid message {idi, t (k)1 i , c1, c2} by eavesdropping communications between ui and s. then he tries to resend the message to s after a period of time when the mutual authentication process finishes, with the expectation of obtaining data verification service. however, when s receives this message, it will find that the ticket id tid(k)i is a time-bound ticket-based mutual authentication scheme for cloud computing 233 already on the bulletin board. so s will reject the adversary’s service request and terminate the process. on the other hand, if the adversary gets a message from s to ui: {c3 = h(idi, k1)⊕rs, c4 = h(ru, rs)}. the adversary may try to resend it to ui after a period of time when the mutual authentication process is finished. however, when ui’s smart card receives {c3, c4}, the nonce ru has already been deleted from its storage. so this message will be discarded immediately. from the above analysis, we can see that the proposed scheme is resistant to replay attack. 4.5 resistance to valid period extending attack whenever ui wants to use a ticket whose valid period does not include the current date, the protocol can ensure that the ticket cannot be used even if ui tries to modify the ticket’s begin date or expiration date. assume ui wants to use ticket t (k) i by changing the ticket’s v p (k) i to ˆv p (k) i , so that the current date is included in ˆv p (k) i . denote the modified ticket by t̂ (k) i . when ui sends the message {idi, t̂ (k)1 i , c1, c2} to s, s computes d1 = c1 ⊕ h(idi, k1) and d2 = h(d1) ⊕ c2. then s computes hk2(idi ∥ tid (k) i ∥ ˆv p (k) i ) and compares it with d2. since d2 = h(d1) ⊕ c2 = h(c1 ⊕h(idi, k1))⊕c2 = α (k) i = hk2(idi ∥ tid (k) i ∥ v p (k) i ) ̸= hk2(idi ∥ tid (k) i ∥ ˆv p (k) i ), s will reject ui’s verification request and terminate the process immediately. from the above analysis, we can see that the server can limit the data verification frequency by specifying a valid period for each ticket. for example, the typical coverage of the valid period can be one week, one month, one year, etc. the proposed scheme can prevent the client from using a ticket whose valid period does not include the current date. 5 performance analysis in this section we present performance analysis of the proposed scheme. the main operations include the hash computation and the exclusive-or operations, which are summarized in table 1. we use the hash-based message authentication code (hmac) [14] as the keyed hash, which incurs 2 exclusive-or and 2 common hash operations. table 1: performance analysis of the proposed scheme operation verification request phase mutual authentication phase client xor 5 1 hash 3 2 keyed hash 0 0 server xor 0 3 hash 0 4 keyed hash 0 1 compared with [11], which uses expensive operations like modular exponentiations, our scheme is much more efficient. according to the crypto++ benchmarks1 [15], the sha-256 hash algorithm [16] can achieve a throughput of 111mib/sec under intel core2 1.83ghz processor. in the proposed scheme, the length of a message to be hashed does not exceed 1kb, so the average time for one hash computation is about 0.009ms. if the lin-chang scheme uses 1the crypto++ 5.6.0 benchmark is evaluated in intel core2 1.83 ghz processor under windows vista in 32-bit mode 234 z. hao, s. zhong, n. yu the primes of length 1024bits, then one time of modular exponentiation will cost about 1.46ms under the same platform [15]. our scheme is at least 40 times more efficient than the linchang scheme [11] in case of the server’s computation time. this is a tremendous performance improvement to the cloud servers. performance benefit by limiting verification frequency the cloud server can also benefit its performance by limiting the client’s data verification frequency. this is achieved by controlling the valid period of the digital ticket. from the performance evaluation of [6] we know that for a 30mb file, the computation time of the data integrity verification is approximately 1 second2 at the server side. assume the cloud storage system has 100,000 clients, and each client performs data integrity verification once a week. then the average computation time of the cloud server during one day is approxiately: 100, 000 7 · 1s ≈ 3.97 hours. by using the proposed scheme, the server can limit the data verification frequency to once per month, or even once per quarter, so that the average computation time of the cloud server is reduced to 56 minutes per day and 18.5 minutes per day respectively. 6 conclusions in this paper, we propose a time-bound ticket-based mutual authentication scheme. in the proposed scheme, the digital tickets are associated with the client’s smart card, which effectively prevents the ticket from being used by other clients. by designing a mutual authentication based on the client’s smart card, both the server and the client are assured of each other’s identity. the proposed authentication scheme can efficiently decrease the server’s processing overhead by limiting the data verification frequency. by security analysis and performance analysis, the proposed scheme is shown to be both secure and efficient. it is very suitable for providing mutual authentication in cloud computing. acknowledgment this work was supported by nsf cns-0845149, nsf ccf-0915374 and knowledge innovation program of chinese academy of sciences (no. yyyj-1013). bibliography [1] b. hayes, “cloud computing,” commun. acm, vol. 51, no. 7, pp. 9–11, 2008. 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[12] p. c. kocher, j. jaffe, and b. jun, “differential power analysis,” in crypto ’99: proceedings of the 19th annual international cryptology conference on advances in cryptology, (london, uk), pp. 388–397, springer-verlag, 1999. [13] t. messerges, e. dabbish, and r. sloan, “examining smart-card security under the threat of power analysis attacks,” ieee transactions on computers, vol. 51, no. 5, pp. 541–552, 2002. [14] h. krawczyk, m. bellare, and r. canetti, “hmac: keyed-hashing for message authentication,” rfc2104, february 1997. [15] “crypto++ 5.6.0 benchmarks,” http://www.cryptopp.com/benchmarks.html. [16] “secure hash standard,” federal information processing standards publication 180-2, august 2002. ijcccv11n1.dvi international journal of computers communications & control issn 1841-9836, 11(1):126-141, february 2016. computing sorted subsets for data processing in communicating software/hardware control systems v. sklyarov, i. skliarova, a. rjabov, a. sudnitson v. sklyarov*, i. skliarova university of aveiro, dept. of electronics, telecommunications and informatics/ieeta campus universitário de santiago, 3810-193, aveiro, portugal *corresponding author: skl@ua.pt a. rjabov, a. sudnitson tallinn university of technology, dept. of computer engineering akadeemia tee 15a, 12618, tallinn, estonia abstract: computing and filtering sorted subsets are frequently required in statistical data manipulation and control applications. the main objective is to extract subsets from large data sets in accordance with some criteria, for example, with the maximum and/or the minimum values in the entire set or within the predefined constraints. the paper suggests a new computation method enabling the indicated above problem to be solved in all programmable systems-on-chip from the xilinx zynq family that combine a dual-core cortex-a9 processing unit and programmable logic linked by high-performance interfaces. the method involves highly parallel sorting networks and run-time filtering. the computations are done in communicating software, running in the processing unit, and hardware, implemented in the programmable logic. practical applications of the proposed technique are also shown. the results of implementation and experiments clearly demonstrate significant speed-up of the developed software/hardware system comparing to alternative software implementations. keywords: computing sorted subsets, communicating hardware/software systems, filtering, sorting networks, control applications. 1 introduction many electronic, environmental, medical, and biological control applications need to process data streams produced by sensors and measure external parameters within given upper and lower bounds (thresholds) [1]. let us consider some examples. applying the technique [2] in real-time applications requires knowledge acquisition from controlled systems (e.g. plant). for example, signals from sensors may be filtered and analyzed to prevent error conditions (see [2] for additional details). to provide more exact and reliable conclusion, combination of different values need to be extracted, ordered, and analyzed. similar tasks appear in monitoring thermal radiation from volcanic products [3], filtering and integration of information from a variety of different sources in medical applications [4] and in other practical applications described in [5]. since many control systems are real-time, performance is important and hardware accelerators may provide significant assistance for software. a similar data processing is applicable to data mining algorithms, such as [6]. let us consider control systems that collect, filter and analyze data produced by some measurements. we will describe below such computations that permit: • the maximum and/or minimum sorted subsets to be extracted (the maximum/minimum sorted subset of size lmax/lmin contains lmax/lmin data items with maximum/minimum values from a given set); • the maximum and/or minimum sorted subsets to be found within the given upper bu and lower bl bounds. copyright © 2006-2016 by ccc publications agora university computing sorted subsets for data processing in communicating software/hardware control systems 127 o p ti o n a l d ig it a l fi lt e r d a ta p ro c e s si n g m e a s u re d s e t o f d a ta c o m p u te d ( e x tr a c te d ) su b se t o f d a ta f il te ri n g d a ta t h a t fa ll w it h in g iv e n b o u n d s figure 1: general architecture of data processing the problem can be solved as it is shown in fig. 1. there are two blocks in fig. 1. measured data items are handled in such a way that the maximum and/or minimum subsets with lmax and/or lmin items are extracted by the data processing block. input data may optionally be filtered allowing only items (such as d) that fall within pre-given constraints (e.g. bl ≤ d ≤ bu or bl < d < bu) to be processed. the paper suggests a method and high-performance implementation of architecture in fig. 1 in all programmable systems-on-chip (apsoc) from the xilinx zynq-7000 family [7] that are recently developed field-configurable devices integrating the most advanced programmable logic (pl) and a widely used processing system (ps) based on the dual-core arm® cortex™ mpcore™. the available interfaces between the ps and pl are supported by ready-to-use intellectual property (ip) cores. these, combined with numerous architectural and technological advances, have enabled apsocs to open a new era in the development of highly optimized computational systems for a vast variety of practical applications, including high-performance computing, data, signal and image processing, control, and many others. the main target of apsocs is integration in the developed systems of software and hardware components assuming that such integration enables characteristics (most often performance) of the system to be improved. the complexity of hardware only solutions is frequently limited by the available resources in the pl. software/hardware solutions can be very complex and they are appropriate for control applications, such as that are described, for example, in [2,4]. the most close related work can be found in [5,8] where the importance of the considered problem is underlined, but the methods that allow the problem to be solved are different and the proposed below methods permit better results to be achieved. the remainder of the paper is organized in eight sections. section 2 presents the proposed software/hardware architecture. section 3 describes a novel method allowing the maximum and minimum sorted subsets for a given set of data items to be computed. section 4 suggests a run-time filtering method. section 5 is dedicated to on-chip communication mechanisms linking software and hardware components. section 6 shows how large subsets (for which hardware resources are not sufficient) can be computed and discusses additional capabilities such as extracting only the maximum or only the minimum subsets. section 7 demonstrates potential practical application (from the areas of control and data mining). implementations in zynq microchips and the results of thorough evaluation and comparison of software only and software/hardware solutions with explicit indication of the achieved acceleration are discussed in section 8. section 9 concludes the paper. 128 v. sklyarov, i. skliarova, a. rjabov, a. sudnitson 2 software/hardware architecture fig. 2 presents the proposed software/hardware architecture. e x te rn a l d d r m e m o ry on-chip memory (ocm) ps pl zynq device high-performance interfaces 1. acquiring data and saving them in ocm or external memory. 2. requesting to extract subsets in the pl. 3. collecting extracted subsets in ocm or external memory. 1. processing requests from the ps in highly parallel circuits. 2. optional interaction with analog and digital sensors, acquisition of data and transmitting them to the ps. figure 2: the proposed software/hardware architecture the ps collects data, that may be acquired from different sources (such as from a host pc or from sensors connected to a zynq device), and stores them in on-chip or external memory. the pl processes requests from the ps, that is, reads data from memories, and rapidly extracts the maximum and/or minimum subsets. both parts, that are the ps and pl, may function in parallel and any request can be seen as a macroinstruction executed in the pl concurrently with other potential instructions in the ps. it is shown in [9] that for transferring a small number of data items between the ps and the pl on-chip general-purpose ports (gpp) can be used more efficiently than other available interfaces. thus, requests from the ps to the pl are formed through gpp where the ps is the master and the pl is a slave. it is also shown in [7, 9] that large volumes of data can be more efficiently transferred from/to memories to/from the pl through high performance (hp) interfaces: highperformance advanced extensible interface (axi hp) and axi accelerator coherency port (axi acp). in all our designs memories are slaves and either the pl or the processor in the ps is the master. to increase performance, data from memories may be requested to be cacheable. 3 computing sorted subsets let set s containing n m-bit data items be given. the maximum subset contains lmax largest items in s and the minimum subset contains lmin smallest items in s (lmax ≤ n and lmin ≤ n). we mainly consider such tasks for which lmax < < n and lmin < < n which are more common for practical applications. since n may have very large value (millions of items) it cannot be processed completely in hardware due to the unavailability of sufficient resources. it is shown in [10, 11] that even for relatively complex field-programmable gate arrays (fpgas) the size computing sorted subsets for data processing in communicating software/hardware control systems 129 n is limited. for example, for even-odd merge and bitonic merge networks [12] n cannot exceed a few hundreds of 32-bit items even for very advanced fpgas (such as the largest devices from the xilinx virtex-7 family). in zynq devices implementing circuits from [12] the maximum value of n does not exceed 128 32-bit items. iterative even-odd transition networks from [11] permit significantly larger number of items (exceeding thousands of 32-bit items) to be processed and they will be used for computing sorted subsets in hardware. however, in practical cases the given sets anyhow cannot be entirely processed and computing the maximum and/or minimum sorted subsets needs to be done sequentially, nevertheless handling many items in parallel. fig. 3 depicts the proposed architecture that enables the considered problem to be solved. sorting network lmax m-bit data items for maximum subset lmin m-bit data items for minimum subset in p u t d a ta (k m -b it d a ta i te m s ) m m m m m m maximum subset minimum subset lmax m-bit data items (from k m-bit data items) for initialization at the beginning lmin m-bit data items (from k m-bit data items) for initialization at the beginning lmax×m lmin×m feedback copying figure 3: computing the maximum and the minimum sorted subsets address counter (count) in c r e m e n t register address k m -b it i n p u t re g is te rs r0 rk-1 k m -b it d a ta i te m s to s o rt in g n e tw o rk (s e e f ig . 3 ) data item ik from hp port the maximum item from the minimum subset m u x up to the last after the last figure 4: processing the last (possibly incomplete) subset let us divide the given set s into q = ⌈n/k⌉ subsets, all of which contain exactly k m-bit 130 v. sklyarov, i. skliarova, a. rjabov, a. sudnitson items except the last one, which may have less than k m-bit items. computing subsets is done incrementally in q steps (we assume below that k ≤ n). at the first step, the first k m-bit data items are sorted in the network [11] which processes lmax+k+lmin data items but comparators linking the upper part (handling lmax m-bit data items) and the lower part (handling lmin m-bit data items) are deactivated (i.e. the links with the upper and bottom parts are broken). so, sorting is done only in the middle part handling k m-bit items. as soon as the sorting is completed, the maximum subset is copied to the upper part of the network and the minimum subset is copied to the lower part of the network (see fig. 3). from the second step, all the comparators are properly linked, i.e. the network from [11] handles lmax+k+lmin items, but the feedback copying (see the first step and fig. 3) is disabled. now for each new k m-bit items the maximum and the minimum sorted subsets are appropriately corrected, i.e. new items may be appended. at the last step, the number of incoming items may be less than k. fig. 4 explains how the maximum and minimum subsets are corrected for the last possibly incomplete subset of items. there is an additional mux in fig. 4, which supplies data items from a hp port (linking the pl with memory) until the received item is not the last. as soon as the last item is read from memory, the next items (until k) are taken as the maximum value from the minimum subset (see the lower subset in fig. 3). clearly, such an item cannot be moved again to the minimum subset and the last sorting step is executed similarly to the previous steps. let us look at the example shown in fig. 5 for which: n = 21, k = 8, lmax = lmin = 4, and s = 26,37,11,19,3,7,99,56,29,37,22,99,1,55,39,47,12,45,83,5,18. the set s is divided into the following three subsets: a = 26,37,11,19,3,7,99,56, b = 29,37,22,99,1,55,39,47, and c = 12,45,83,5,18. note that the last subset c contains only 5 elements and is incomplete. symbol u in fig. 5 indicates undefined value. the iterative sorting network is exactly the same as in [11]. any comparator is shown in knuth notation [13] and it converts two-item inputs in two-item outputs in such a way that the upper value is greater than or equal to the lower value. the maximum number of iterations for sorting is k/2 [14] and this number is almost always smaller because the method [11] terminates subsequent iterations as soon as all items are sorted. there are 3 steps in fig. 5. at the first step, k (k=8) items are sorted and copied to the maximum and minimum subsets. two comparators are disabled in accordance with the explanations given above (breaking links of the middle section in the sorted network with the upper and the lower sections). at the second step, all the network comparators are enabled and lmax+k+lmin items are sorted by the iterative network with feedback register (fr). all necessary details can be found in [11]. it is easy to show that the maximum number of iterations is ⌈(max(lmax,lmin)+k)/2⌉ and much like the previous case this number is almost always smaller [11]. at the last (third) step, the incomplete subset c is extended to k items by copying the maximum value (11) from the minimum subset 11,7,3,1 to the positions of missing data (see fig. 5). after sorting lmax+k+lmin items at the step 3 the final result is produced. computing sorted subsets for data processing in communicating software/hardware control systems 131 0 7 1 2 3 4 5 6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 f e e d b a c k r e g is te r (f r ) 8 15 9 10 11 12 13 14 l m a x l m in k load sort copy load sort load sort u u u u 26 37 11 19 3 7 99 56 u u u u a u u u u 99 56 37 26 19 11 7 3 u u u u 99 56 37 26 99 56 37 26 19 11 7 3 19 11 7 3 99 56 37 26 29 37 22 99 1 55 39 47 19 11 7 3 b 99 99 56 55 47 39 37 37 29 26 22 19 11 7 3 1 c o m p a ra to r is d is a b le d ( i. e . it i s b ro k e n ) comparator may be either enabled or disabled 99 99 56 55 12 45 83 5 18 11 11 11 11 7 3 1 c 99 99 83 56 55 45 18 12 11 11 11 11 7 5 3 1 in c o m p le te t h e m a x im u m v a lu e m a x im u m s u b se t m in im u m s u b se t step 1 step 2 step 3iterative sorting network figure 5: an example (computing subsets) 4 filtering let bu and bl be predefined upper (bu) and lower (bl) bounds for the given set s. we would like to use the circuit in fig. 3 only for such data items d that fall within the bounds bu and bl, i.e. bl ≤ d ≤ bu (or, possibly, bl < d < bu). fig. 6 depicts the proposed architecture that enables data items to be filtered at run-time (i.e. during the data exchange between the ps and pl). there is an additional block on the upper input of the mux (see also fig. 4), which takes a data item ik from a hp port and executes the operation indicated on the right-hand part of fig. 6. if the counter is incremented, then a new register is chosen to store ik. otherwise, the signal we (write enable) is passive and a new item with a value that is out of the bounds bu and bl is not recorded in the registers. (count = count+1, we) when b l £ ik £ bu else null; if input value ik is within the constraints b u and b l then increment the address counter and allow writing data (activate the signal we – write enable). otherwise deactivate the signal we and do not increment the address counter. address counter (count) in c r e m e n t register address k m -b it i n p u t re g is te rs r0 rk-1 k m -b it d a ta i te m s to s o rt in g n e tw o rk (s e e f ig . 3 ) the maximum item from the minimum subset m u x b u and/or b l ikik we up to the last after the last figure 6: digital filter 132 v. sklyarov, i. skliarova, a. rjabov, a. sudnitson let us look at the same example in fig. 5 for which we choose bu = 90 and bl = 10 (see fig. 7). at the first step incoming data items have preliminary been filtered, the values 99, 7, and 3 have been removed (because they are either greater than bu = 90 or less than bl = 10), and the subset a with 8 items is built from 11 first elements of the set s. at the second (last) step, the values 99, 1, and 5 have been removed, and the subset b = 55,39,47,12,45,83,18 is built from the remaining allowed elements of the set s. since there are 7 items in b and k = 8, this subset is incomplete. as can be seen from fig. 7, two steps are sufficient to extract the maximum and the minimum subsets from the filtered set s. similarly, filtering and computing sorted subsets can be done for very large data sets. 0 7 1 2 3 4 5 6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 f e e d b a c k r e g is te r (f r ) 8 15 9 10 11 12 13 14 l m a x l m in k load sort copy load sort u u u u 26 37 11 19 56 29 37 22 u u u u a u u u u 56 37 37 29 26 22 19 11 u u u u 56 37 37 29 56 37 37 29 26 22 19 11 26 22 19 11 56 37 37 29 55 39 47 12 45 83 18 26 26 22 19 11 b 83 56 55 47 45 39 37 37 29 26 26 22 19 18 12 11 t h e m a x im u m v a lu e m a x im u m s u b se t m in im u m s u b se t step 1 step 2iterative sorting network in c o m p le te figure 7: an example (filtering and computing subsets) clearly, the described above operations can be done in software. for example, c function qsort permits large data sets to be sorted. after that extracting the maximum and minimum subsets may easily be done. filtering can be provided much like it is shown in fig. 6 eliminating items that do not fall within the predefined constraints. however, for many practical applications performance of the described above operations is important. to evaluate software/hardware solutions three different components need to be taken into account (see fig. 8): 1) software part; 2) hardware part; and 3) the circuits that provide for data exchange between software and hardware. numerous experiments were done in [15] to compare such solutions with software only systems. one example in [15] enables sorting blocks of data composed of 320 32-bit items in the pl that are further merged in the ps (see fig. 8). from 512,288 to 4,194,304 of 32-bit data items were randomly generated in the ps (i.e. the size of data varies from 2mb to 16mb) and then sorted in software with the aid of the function qsort and in the software/hardware system (see fig. 8). the actual performance improvement was by a factor of about 2.5. it was shown in [15] that hardware circuits in the pl are significantly faster than software in the ps. in this paper we evaluate and compare software/hardware and software only solutions taking into account all the computing sorted subsets for data processing in communicating software/hardware control systems 133 involved communication overheads that were measured in [15]. we will mainly use axi acp [7] which provides one of the fastest interfaces for exchange of large data sets between the ps and pl [7, 9, 15]. the number of data items transferred from the ps/memory to the pl is the same as in [15]. however, the number of data items transferred from the pl to the ps/memory is significantly smaller enabling much better acceleration to be achieved. software part hardware part circuits that provide support for data exchange software/hardware merging blocks sorting blocks transferring blocks between software and hardware ps pl figure 8: an example of a software/hardware system 5 communication of software and hardware fig. 9 shows how communication is organized between software and hardware. it is done similarly to [8,15], but the proposed in this paper processing is different. the developed hardware in the pl is divided in two parts: application-specific (that is filtering and computing subsets) and communication-specific processing. the latter is studied in [15] and provides support for data exchange with storage of the pl that is either block ram or registers built from flip-flops of configurable logic blocks. application-specific processing (filtering and computing subsets) start k b u communication-specific processing pl ps these operations are controlled by software data transfer from/to selected memories (ddr, ocm, or cache) gpp burst mode memorythe ps software gpp axi acp interrupt from hardware using software only to solve exactly the same problem without hardware circuits b l … figure 9: communication between software and hardware components 134 v. sklyarov, i. skliarova, a. rjabov, a. sudnitson data are transmitted in blocks of 32/64-bit items (i.e. either m=32 or m=64) and the fastest burst mode is applied. input data items ik (k=0,1,. . . ,k-1) are processed by the described above circuits (see fig. 3, 4, 6). note that gpp do not allow burst mode to be applied but are very appropriate for transferring small number of signals that may be used for control in the pl and for some additional details. in our system they are: 1) start requiring data processing in the pl to be initiated; 2) k (see fig. 3); 3) bu and bl (see fig. 6); 4) additional signals, namely source address, destination address and sizes of data to be transferred from the ps to the pl and vice versa. fig. 10 demonstrates a component diagram for reading the initial large volume data from the ps and for transferring the results (i.e. the computed maximum and minimum subsets) from the pl to the ps. we found that in such interactions between the ps and pl the best way is to use hp ports to read data from the ps (memories) and transfer them to the pl and to write data from the pl to the ps (memories). since memory controllers belong to the ps we can talk about data transfers between the ps and pl. exchange of data in both directions is done in burst mode supported by a burst reader and a burst writer described in [8, 15]. both processing (th) and communication (tc) times are measured and taken into account. init read proc write done reading data r e a d in g i s c o m p le te d p ro c e ss in g i s c o m p le te d writing the computed subset processing is completed begin processing in hardware computing sorted subsets burst reader burst writer vhdl components top module in te rr u p t fr o m th e p l t o t h e p s time (th+tc) measured time (th) measured x il in x i p c o re c o m p o n e n t pl application-specific processing figure 10: operations in hardware components 6 computing large subsets and additional capabilities for some practical applications the maximum and/or minimum subsets may be large and the available hardware resources become insufficient to implement the circuits in fig. 3. computing sorted subsets for data processing in communicating software/hardware control systems 135 the arising problem can be solved using the following technique. let lmax and lmin be constraints for the upper and bottom parts of the sorting network in fig. 3, i.e. circuits with larger values (than lmax and lmin) cannot be implemented due to the lack of hardware resources or for some other reasons. let the parameters for the maximum and minimum subsets be greater than lmax and lmin, i.e. lmax > lmax and lmin > lmin. in such case the maximum and minimum subsets can be computed incrementally [8] as follows: 1. at the first iteration the maximum subset containing lmax items and the minimum subset containing lmin items are computed. the subsets are transferred to the ps (to memories). the ps removes the minimum value from the maximum subset and the maximum value from the minimum subset. such correction avoids loss of repeated items at subsequent steps. indeed, the minimum value from the maximum subset (the maximum value from the minimum subset) can appear for subsets to be subsequently constructed in point 3 below and they will be lost because of filtering (see point 3). 2. the minimum value from the corrected in the ps maximum subset is assigned to bu. the maximum value from the corrected in the ps minimum subset is assigned to bl. the values bu and bl are supplied to the pl through gpp. 3. the same data items (from memory), as in point 1 above, are preliminary filtered (see fig. 6) in such a way that only items that are less than bu and greater than bl are allowed to be processed, i.e. computing sorted subsets can be done only for the filtered data items. thus, the second part of the maximum and minimum subsets will be computed and appended (in the ps) to the previously computed subsets (such as subsets from point 1). note, that the method for processing incomplete subsets (see fig. 4) may need to be applied for the last iteration. 4. the points 2 and 3 above are repeated until the maximum subset with lmax items and the minimum subset with lmin items are computed. note, that if the number of repeated items is greater than or equal to lmax/lmin, then the method above may generate infinite loops [8]. this situation can easily be recognized. indeed, if after corrections in point 1 above any new subset becomes empty then an infinite loop will be created. in such case we can use another method based on software/hardware sorters from [9]. in section 8 we will present the results of experiments for such sorters. for some practical cases only the maximum or the minimum subsets need to be extracted. this task can be solved easier than in fig. 3 with the aid of the circuit shown in fig. 11 (for computing only the maximum subset). sorting network in p u t d a ta (k m -b it d a ta i te m s ) m bit m bit m m maximum subset lmax this part of the network register is filled with the smallest values at the beginning figure 11: computing the maximum subset for a given set at initialization stage lmax m-bit words of the fr (see fig. 5) are filled in with the smallest 136 v. sklyarov, i. skliarova, a. rjabov, a. sudnitson possible value (such as zero or the minimal value for m-bit data items). after that the processing is executed as before (see section 3) and finally the maximum subset will be computed. for computing the minimum subsets the bottom part of fig. 3 is filled in with the largest possible value (such as the maximum value for m-bit data items). 7 practical applications let us consider practical applications from the scope of control. we have already mentioned in section 1 that applying the technique [2] in real-time systems requires knowledge acquisition from controlled devices. the data may be compared with the previously collected data that are kept in databases for similar control scenarios. the results of comparison can be analyzed and used to modify the algorithms allowing control operations to be optimized, undesirable (or error prone) situations to be avoided, etc. let us look at fig. 12 where software collects important data from a controlled system, such as changes in temperature, deviation of positions, offsets, etc. the collected data are optionally filtered and their subsets (maximum, minimum, or both) are computed (see the bottom part of fig. 12). data from previous scenarios for analogous conditions are extracted from the database and they are also optionally filtered and similar subsets are computed (see the upper part of fig. 12). data base select a set of parameters optional filtering computing subsets analysis and comparison modifications of control algorithms controlled system collecting data optional filtering computing subsets controller software hardware hardware software software figure 12: an example of control application data from the controlled system (see the bottom part of fig. 12) and from the database (see the upper part of fig. 12) are analyzed. for example, average maximum values are checked. the results of analysis may be used to modify control algorithms much like it is done in [9, 16]. for example, modules of controllers from [9] can be replaced to optimize execution of relevant operations. another group of potential applications is from the scope of statistical data manipulation such as data mining. to describe one of the problems from this area informally let us consider an example [6] with analogy to a shopping card. a basket is the set of items purchased at one time. a frequent item is an item that often occurs in a database. a frequent set of items often occur together in the same basket. a researcher can request a particular support value and find the items which occur together in a basket either a maximum or a minimum number of times computing sorted subsets for data processing in communicating software/hardware control systems 137 within the database [6]. similar problems appear to determine frequent inquiries at the internet, customer transactions, credit card purchases, etc. producing very large volumes of data in the span of a day [6]. computing sets of the most frequent or the less frequent items in large data sets permits the relevant data mining algorithms to be simplified and accelerated. sorting of subsets is involved in many known algorithms from this area e.g. [17–19] and the results of the paper may provide a valuable assistance. 8 implementation, experiments, and comparisons much like [8] we have used a multi-level computing system [9]. initial data are either generated randomly in software of the ps with the aid of c language rand or prepared in the host pc. in the last case data may be generated by some functions or copied from available benchmarks. computing subsets in software/hardware systems is mainly done in zynq apsoc xc7z020 housed on zedboard [20] with the aid of the described above software/hardware architectures (see fig. 2-4, 6, 9, 10). computing subsets in software only sorters is completely done in software of the ps calling c language qsort function which sorts data and after that the maximum and/or the minimum subsets are extracted. the results are verified in software running either in the ps or in the host pc. functions for verification of the results are given in [9]. verification time is not taken into account in the measurements below. synthesis and implementation of hardware modules were done in xilinx vivado 2015.2. standalone software applications were created in c language and uploaded to the ps memory from the xilinx software development kit (sdk) using methods described in [9]. interactions with apsoc are done through the sdk console window. for all the experiments 64-bit axi acp port was used for transferring blocks between the pl and memories. the size of each block for burst mode is chosen to be 128 of 64-bit items. two memories were tested: the ocm (for smaller number of data items) and external (onboard) ddr. the ocm is faster because it provides for 64-bit data transfers [7] but the size of this memory is limited to 256 kb. the available on zedboard 4 gb ddr supports 32-bit data transfers. the measurements were based on time units (returned by the function xtime_gettime [21]) for lmax = lmin = 128, m=32, and k = 256 (see fig. 3). the following operations have been executed: a) copying data to the selected memory in the ps; b) providing the necessary initialization for the function xtime_gettime (i.e. the consumed time will be measured from this point); c) making the request, i.e. setting (through gpp) source address, destination address, the size of data to be copied, and start processing in the pl (optionally some other data, such as bu and bl for filtering, may be provided); d) copying data from the ps to pl and executing all the required operations in the pl; e) copying the computed subsets from the pl to ps; f) generating a hardware interrupt that is handled in the ps as a completion of the request (thus, the consumed time is measured at this point in the ps). each unit returned by the function xtime_gettime corresponds to 2 clock cycles of the ps [21]. the ps clock frequency is 666 mhz. thus, any unit corresponds to approximately 3 ns. the pl clock frequency was set to 100 mhz. fig. 13 shows the time consumed for computing the maximum and minimum subsets for data sets with different sizes in kb (from 2 to 128). since m=32 the number of processed words (n) is equal to the indicated size divided by 4. fig. 14 shows the acceleration of the software/hardware system comparing to the software only system. note that fig. 13, 14 give diagrams for the ocm. if ddr memory is used then communication overheads are slightly increased but acceleration in the software/hardware system comparing to the software only system is again significant. 138 v. sklyarov, i. skliarova, a. rjabov, a. sudnitson let us compare the results with [5, 8]. the number of data items in the proposed solutions is larger than in [5] and can easily be additionally increased. for similar data sets the achieved acceleration is better than in [8] thanks to additional optimization of the proposed circuits. we also implemented and tested the proposed circuits in a more advanced prototyping board zc706 [22] with zynq microchip xc7z045. data were taken from ddr memory and the maximum and minimum subsets were extracted with k data items where k varied from 256 to 1,024 (as before m = 32, n is equal to 256 kb). the consumed time varies from 1,850 µs for lmin = lmax = 256 to 7,200 µs for lmin = lmax = 1,024. thus, the proposed solutions can be used for solving significantly more complicated problems that cannot be solved, in particular, with the aid of the methods [5]. if only the maximum or only the minimum subsets have to be computed the acceleration is slightly increased (although it is almost the same) and the occupied hardware resources are reduced. the proposed filtering (see fig. 6) does not consume any additional time because it is combined with data transfers. so, we can say that the time is included in communication overheads and the latter were taken into account in all measurements. it should be noted that filtering is not described in [5, 8]. 1 10 100 1 000 10 000 100 000 2 4 8 16 32 64 128 software/hardware software only time in ms size of data in kb figure 13: computing time in software only and software/hardware systems if the size of the requested subsets is increased in such a way that all data need to be read from memory several times then the results are the same as in [8] (see comments in [8] for additional details). we found that parallel circuits that enable the maximum and minimum subsets to be excomputing sorted subsets for data processing in communicating software/hardware control systems 139 25 35 45 55 65 75 85 95 105 2 4 8 16 32 64 128 acceleration of software/hardware system comparing to software only system acceleration size of data in kb example: this point indicates acceleration by a factor of 75 of the proposed software/ hardware solution comparing to the software only solution figure 14: acceleration of software/hardware system comparing to software only system tracted in the zedboard [20] can be built up to k = 256. in this case additional hardware resources that enable data exchange between the ps (memories) and pl are available. similar circuits for the zc706 can be built up to k = 1,024. note that if a block of data needs to be sorted in hardware then the number of processed data may be greater because in our case two blocks (each of which possesses k items) have to be handled in parallel and in case of data sorting [9] it is sufficient to handle just one block of data. additional optimizations such as partial merging in hardware circuits permit the size k to be additionally increased. however, the processing time will also be increased. 9 conclusion the paper suggests methods for computing the maximum and minimum subsets that are extracted from large data sets in communicating software/hardware systems, namely in devices from the xilinx zynq family, which combine a high-performance processing system with advanced programmable logic. the extracted subsets may be filtered and this feature is useful for control applications. the proposed solutions are highly parallel permitting capabilities of programmable logic to be used very efficiently. all the proposed methods were implemented in commercial microchips, tested, evaluated, and compared with alternatives. the results of experiments have shown significant speed-up of the proposed software/hardware systems comparing to software only systems and to competitive hardware/software implementations. in particular, the size of 140 v. sklyarov, i. skliarova, a. rjabov, a. sudnitson subsets was increased and additional tasks important for control applications were discussed and solved. practical applications of the proposed technique for control applications and statistical data manipulation were also given. acknowledgment this research was supported by eu through european regional development funds, the institutional research funding iut 19-1 of the estonian ministry of education and research, esf grant 9251, and portuguese national funds through fct foundation for science and technology, in the context of the project pest-oe/eei/ui0127/2014. bibliography [1] sklyarov, v.; skliarova, i. (2013); digital hamming weight and distance analyzers for binary vectors and matrices, int. journal of innovative computing, information and control, 9(12): 4825-4849. [2] zmaranda, d.; silaghi, h.; gabor, g.; vancea, c. (2012); issues on applying knowledgebased techniques in real-time control systems, international journal of computers communications & control, 8(1): 166-175. [3] field, l.; barnie, t.; blundy, j.; brooker, r. a.; keir, d.; lewi, e.; saunders, k. (2012); integrated field, satellite and petrological observations of the november 2010 eruption of erta ale, bulletin of volcanology, 74(10): 2251-2271. [4] zhang, w.; thurow, k.; stoll, r. (2014); a knowledge-based telemonitoring platform for application in remote healthcare, international journal of computers communications & control, 9(5): 644-654. [5] farmahini-farahani, a.; duwe, h. j.; schulte, m. j.; compton, k. 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[10] mueller, r.; teubner, j.; alonso, g.; (2012); sorting networks on fpgas, the vldb journal—the international journal on very large data bases, 21(1), 1-23. computing sorted subsets for data processing in communicating software/hardware control systems 141 [11] sklyarov, v.; skliarova, i. (2014); high-performance implementation of regular and easily scalable sorting networks on an fpga, microprocessors and microsystems, 38(5): 470-484. [12] baddar, s. w. a. h.; batcher, k. e. (2012); designing sorting networks: a new paradigm, springer science & business media. [13] knuth, d. e. (2011); the art of computer programming: sorting and searching (vol. 3), addison-wesley. [14] kipfer, p.; & westermann, r. (2005); improved gpu sorting, gpu gems 2: programming techniques for high-performance graphics and general-purpose computation, edited by m. pharr, 733-746, available at http://http.developer.nvidia.com/gpugems2/gpugems2_chapter46.html. [15] silva, j.; sklyarov, v.; skliarova, i. (2015). comparison of on-chip communications in zynq-7000 all programmable systems-on-chip, embedded systems letters, ieee, 7(1): 3134. [16] sklyarov, v.; skliarova, i.; barkalov, a.; titarenko, l. (2014); synthesis and optimization of fpga-based systems, springer. [17] sun, s. (2011); analysis and acceleration of data mining algorithms on high performance reconfigurable computing platforms, ph.d. thesis, iowa state university, available at: http://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1421&context=etd. [18] wu, x.; kumar, v.; quinlan, j. r. et al. (2008); top 10 algorithms in data mining, knowledge and information systems, 14(1): 1-37. [19] firdhous, m. (2012); automating legal research through data mining, journal of advanced computer science and applications, 1(6): 1-8. [20] avnet, inc. (2014); zedboard (zynqtm evaluation and development) hardware user’s guide, version 2.2, available at: http://www.zedboard.org/sites/default/files/documentations/zedboard_hw_ug_v2_2.pdf. [21] xilinx, inc.; os and libraries document collection ug647, available at: http://www.xilinx.com/support/documentation/sw_manuals/xilinx2014_2/oslib_rm.pdf. [22] xilinx, inc.; zc706, all programmable soc evaluation kit, ug961, available at: http://www.xilinx.com/support/documentation/boards_and_kits/zc706/2014_3/ug961zc706-gsg.pdf. int j comput commun, issn 1841-9836 9(3):340-347, june, 2014. an efficient solution for the vrp by using a hybrid elite ant system m. yousefikhoshbakht, f. didehvar, f. rahmati majid yousefikhoshbakht, farzad didehvar*, farhad rahmati department of mathematics and computer science amirkabir university of technology no.424, hafez avenue, tehran 15914, iran e-mail: khoshbakht@aut.ac.ir, frahmati@aut.ac.ir *corresponding author: didehvar@aut.ac.ir abstract: the vehicle routing problem (vrp) is a well-known np-hard problem in operation research which has drawn enormous interest from many researchers during the last decades because of its vital role in planning of distribution systems and logistics. this article presents a modified version of the elite ant system (eas) algorithm called heas for solving the vrp. the new version mixed with insert and swap algorithms utilizes an effective criterion for escaping from the local optimum points. in contrast to the classical eas, the proposed algorithm uses only a global updating which will increase pheromone on the edges of the best (i.e. the shortest) route and will at the same time decrease the amount of pheromone on the edges of the worst (i.e. the longest) route. the proposed algorithm was tested using fourteen instances available from the literature and their results were compared with other well-known meta-heuristic algorithms. results show that the suggested approach is quite effective as it provides solutions which are competitive with the best known algorithms in the literature. keywords: vehicle routing problem (vrp), elite ant system, , global and local updating, np-hard problems. 1 introduction the vehicle routing problem (vrp) is one of the most important combinational optimization problems that was first defined by dantzig and ramser more than 50 years ago [1]. the vrp involves designing a set of vehicle routes each of which starts and ends at a depot. these routes are used for a fleet of vehicles which provide services for a set of customers with known demands. each customer is visited by exactly one vehicle only once and the total demand of a route does not exceed the capacity of the vehicle type assigned to it. the objective is to minimize the total distance traveled by all the vehicles. to make vrp models more realistic and applicable, there are various forms of the vrp obtained by adding constraints to the basic model. examples of such extensions are vrp with pickup and delivery (if the vehicles need to pick up and delivery) [2], vrp with time windows (if the services have time constraint) [3], heterogeneous fleet ovrp (if the capacity of vehicles in ovrp are different) [4], vrp with backhauls (if the customers with delivery demand have to be visited before by the customers with a pickup demand) [5] and generalized vrp (if the customers are partitioned into clusters with given demands such that exactly one customer from each cluster should be visited) [6]. the vrp solution methods fall into three main categories: exact methods, heuristic and metaheuristic algorithms. exact approaches for solving the vrp are successfully used only for relatively small problem sizes but they can guarantee optimality based on different techniques. these techniques use algorithms that generate both a lower and an upper bound on the true minimum value of the problem instance. if the upper and lower bound coincide, a proof of optimality is achieved. branch-and-bound [7] and branch-and-cut [8] are two of exact methods which have copyright © 2006-2014 by ccc publications an efficient solution for the vrp by using a hybrid elite ant system 341 been proposed for vrp by researchers. since this problem is known to be np-hard (non-deterministic polynomial-time hard) problem [1], exact algorithms are not often suitable for real instances because of the computational time required to obtain an optimal solution. therefore, in the past forty years, researchers have developed the heuristic algorithms using a permissible solution instead of the optimal solution. there are many celebrated algorithms in this class such as savings heuristic of clarke and wright [9] that gains a single route instead of two routes according to the savings obtained by this merger. the sweep algorithm is another famous construction heuristic that is proposed by gillett and miller [10]. a new kind of algorithm which basically tries to combine basic heuristic methods in higher level frameworks aimed at efficiently exploring a search space is meta-heuristics. since the metaheuristic approaches are very efficient for escaping from local optimum, they are one of the best group algorithms for solving combinatorial optimization problems. some of the most popular metaheuristics applied to the vrp are simulated annealing (sa) [11], genetic algorithm (ga) [12], tabu search (ts) [11], computational intelligence approach [13], large neighborhood search [3], ant colony optimization (aco) [14], hybrid ant colony optimization [15] and particle swarm optimization [16]. the vrp is intrinsically a multiple objective optimization problem (mop) in nature that has received much attention because of its practical application in industrial and service problems [17]. on the other hand, there are few research studies which have used the aco in order to solve vrp. furthermore, the elite ant system (eas) is one of the most important and powerful versions of aco that nowadays is applied on a lot of combinatorial optimization problems [18]. therefore, this paper proposes a hybrid eas (heas) mixed with insert and swap algorithms for solving the vrp. the remaining parts of the paper are organized as follows. section 2 describes the eas, and the proposed algorithm. section 3 describes computational experiments carried out to investigate the performance of the proposed algorithm. finally, section 4 presents the results of the conclusions and future works. 2 our algorithm in this section, first, the eas are presented and then the proposed algorithm will be analyzed in more detail. 2.1 elite ant system the first modification to ant system (as) which was conducted by dorigo and his colleagues in 1996 was using elite strategy in eas [19]. the decision for choosing the unvisited ni node by ant k located in node i is made based on formula (1) where τij indicates the amount of pheromone on (i, j) edge while ηij shows inverse distance between i and j. however, both are powered by α and β which can be changed by the user. therefore, their relative importance can be altered. p kij =   τij αηij β∑ j∈ni τijαηijβ if j ∈ ni 0 if j /∈ ni (1) in the eas, in addition to depositing pheromone on all local edges, in each iteration pheromone is released on the edges of the best path. imagine t gb is the best path gained after the algorithm is completed, tk is total length of edges traversed by ant k and ρ is the rate of the pheromone evaporation in order to prevent rapid convergence of ants to a sub-optimal path. 342 m. yousefikhoshbakht, f. didehvar, f. rahmati while pheromone is being updated, the edges marched by the ant which have constructed the t gb path absorb an additional amount of pheromone which is equal to e/lgb(t). the formula for pheromone updating can be written in (2). in this way, the edges of the shortest path up to the current iteration become more attractive and are updated based on the value of the best lgb(t) tour. as it is shown, 1/lk(t) is the formula for the local trail updating. while traversing between nodes i and j, ants release pheromone on the respective edge which is equal to the inverse of the cost of the tour lk(t) taken by ants. τij(t + 1) = (1 − ρ).τij(t) + m∑ k=1 1/lk(t) + e/lgb(t) (2) 2.2 the proposed algorithm in eas, local pheromone release is not a good guide for finding the best route because there might be some edges which belong to no best route in none of the iterations but pheromone is still deposited on them in each iteration. therefore, local pheromone release must be abandoned so that the ants resort to global pheromone release in finding new solutions. this attracts the attention of ants to the edges which belong to the best route ever found. furthermore, the accuracy of solutions in eas is low at the beginning but it increases with the iterations of the algorithm and pheromone release. therefore, constant e coefficient cannot be a suitable formula for encouraging the best path found ever because it is not important when and with what accuracy the best solution was found. to improve the mentioned shortcomings, the heas has made some changes to the eas as follows: route construction in the original eas, an ant, say k, moves from the present node i to the next node j according to the state transition rule given by formula (3). here we define µij as the savings of combining two nodes on one tour as opposed to serving them on two different tours. the savings of combining any two customers i and j are computed as µij = di0 + d0j − dij where dij denotes the distance between nodes i and j, and node 0 is the depot. furthermore, λ like α and β is control parameter. p kij =   τij αηij βµλij∑ j∈ni τijαηijβµ λ ij if j ∈ ni 0 if j /∈ ni (3) pheromone updating in eas, the pheromone of all edges belonging to the routes obtained by ants will be updated. the pheromone updating includes local and global updating rules. the pheromone updating formula was meant to simulate the change in the amount of pheromone due to both the addition of new pheromone deposited by ants on the visited edges and the pheromone evaporation. in heas, not only does the pheromone release increases pheromone on the edges of the best solution in each iteration but also the pheromone release reduction is used in order to distract ants from the edges of the worst solution. the idea of the elitist strategy in the context of the heas is to give extra emphasis to the best and the worst paths found so far after every iteration. the value of coefficient e in pheromone increase and pheromone decrease which is shown as -e is a function. it was found out that using polynomial k2 can generate better solutions for the algorithm. in k2, k is an integer number whose value is 1 at the beginning and increases one unit. moreover, this polynomial is considered an appropriate function since it is not only an ascending function an efficient solution for the vrp by using a hybrid elite ant system 343 but also has variable slope as well. in other words, the function increases and the best routes are encouraged and strengthened compared with the previous paths. it should be noted that the low value of the function at the beginning causes the pheromone released on the best route to have less impact on the route selection in the following iterations and ants demonstrate a kind of forgetting in their search. in other words, this helps the ants to forget the weak solutions found at the start of the algorithm. however, as the algorithm is iterated and the accuracy of the solutions increases, the value of the polynomial rises rapidly and the edges belonging to the best solution absorb more pheromone and the edges of the worst solution lose more pheromone. finally, in order to prevent the edges of the worst solution from gaining a negative value, a minimum value which is equal to the half of the initial pheromone is considered for the edges so that when the amount of pheromone on the edges falls below this minimum, it is replaced with the minimum value. local search a local search approach starts with an initial solution and searches within neighborhoods for better solutions. abundant literature on aco indicates that a promising approach for obtaining high-quality solutions can only be obtained through coupling aco with a local search algorithm. therefore, in the heas, after the ants have constructed their solutions and before the pheromone is locally updated, each ant’s solution is improved by applying a local search. because local search is a time-consuming procedure, only a local search is applied to the iteration’s best solution. the idea here is that better solutions may have better chance to find a global optimum. in the proposed algorithm, at first a local search based on insert move and then the swap move is applied to the ant. in insert algorithm a customer is moved to another route. however, in swap algorithm a customer in a certain route is swapped with another customer from a different route. it should be noted that the new solution will be accepted only if first, vrp constraints are not violated especially about each vehicle’s capacity and second, a novel solution will gain a better value for a problem than the previous solution. 3 results the heas was coded in matlab 7. all the experiments were implemented on a pc with pentium 4 at 2.4ghz and 2gb ram and windows xp home basic operating system. because the proposed heas approach is a meta-heuristic algorithm, the results are reported for ten independent runs and in each run the algorithm was iterated n times. furthermore, the pack of optional parameters obtained through several tests is α = 1, β = 4, λ = 3, ρ = 0.2. the performance of the proposed algorithm was tested on a set of 14 benchmark instances designed by christofides et al. which have been widely used as benchmarks in order to compare the ability of proposed heas to the results of six meta-heuristic algorithms including sa and ts [11], genetic algorithm (ga) [12], scatter search algorithm combined by aco (ss-aco) [14], particle swarm intelligent (pso) [16] and genetic algorithm combined with particle swarm intelligent (gapso) [20]. the information of the 14 instances is shown in table 1. in this table, n, m, heas, mheas, bks, and pd are the number of customers, the number of vehicles of best known solution (bks), the best solutions found by heas algorithm, the used vehicles of heas, the best known solution, and the percentage deviation of heas compared to the best know solutions (bkss) respectively. the pd is computed by formula (4) where c(s∗∗) is the best solution found by our algorithm for a given instance, and c(s∗) is the overall bks for the same 344 m. yousefikhoshbakht, f. didehvar, f. rahmati instance on the web. a zero pd indicates that the bks is found by the algorithm. pd = c(s∗) − c(s∗∗) c(s∗) × 100 (4) table 1: computational results for standard vrp problems instance n m sa ts ga ss-aco pso gapso heas mheas pd bks c1 50 5 528 524 524.61 524.61 524.61 524.61 524.61 5 0 524.61 c2 75 10 838 844 849.77 835.26 844.42 835.26 847.14 10 -1.42 835.26 c3 100 8 829 835 840.72 830.14 829.40 826.14 712.36 8 13.77 826.14 c4 150 12 1058 1052 1055.85 1038.20 1048.89 1028.42 1066.89 12 -3.74 1028.42 c5 199 17 1376 1354 1378.73 1307.18 1323.89 1294.21 1311.35 17 -1.54 1291.45 c6 50 6 555 555 560.29 559.12 555.43 555.43 555.43 6 0 555.43 c7 75 11 909 913 914.13 912.68 917.68 909.68 909.68 11 0 909.68 c8 100 9 866 866 872.82 869.34 867.01 865.94 865.94 9 0 865.94 c9 150 14 1164 1188 1193.05 1179.4 1181.14 1163.41 1162.89 14 -0.03 1162.55 c10 199 18 1418 1422 1483.06 1410.26 1428.46 1397.51 1404.75 18 -0.64 1395.85 c11 120 7 1176 1042 1060.24 1044.12 1051.87 1042.11 1042.11 7 0 1042.11 c12 100 10 826 819 877.8 824.31 819.56 819.56 840.64 10 -2.57 819.56 c13 120 11 1545 1547 1562.25 1556.52 1546.20 1544.57 1545.93 11 -0.31 1541.14 c14 100 11 890 866 872.34 870.26 866.37 866.37 866.37 11 0 866.37 as can be seen from this table, the proposed algorithm finds the optimal solution for 6 out of 14 problems that are published in the literature. the results indicate that heas is a competitive approach compared to the bkss. furthermore, one new best known solution of the benchmark problem including c3 is also improved by the proposed method. for instances c4 and c12, the gap is about as high as 3%. however, in most of the instances, the proposed algorithm finds nearly the bkss and for overall the average difference is 0.25%. as the results in table 1 indicate, the ga has not been able to find the best solutions in thirteen of the fourteen examples. therefore, it is considered to be the weakest algorithm among the seven presented algorithms. however, ss-aco has been able to find better solutions than the ga and has come up with the best solutions in 12 examples. among remaining 5 algorithms, in 11 examples sa has not been almost capable of finding the bks. pso has failed in improving the solutions in 10 examples and has come up with solutions similar to the ones found by sa. hence, it can be concluded that ts is more efficient than ga, ss-aco, pso and sa in finding better solutions. from the comparison between gapso and heas, it can be seen that gapso in four examples has been able to find solutions with a gap of less than 1 percent. despite being able to find the best solution ever found for ten examples, it has failed to achieve these results in the remaining four examples. however, heas has found better solutions than gapso for the two examples. a simple criterion to measure the efficiency and the quality of an algorithm is to compute the average of solutions on specific benchmark instances. in figure 1, the average of each algorithm’s solution is reported. from this table we conclude that the heas method has the best average with 975.44 and has been able to escape local optimum points. the algorithms in terms of their performance from the worst to the best can be listed as: ga, sa, ts, pso, ss-aco, gapso and heas. in addition, in order to demonstrate the efficiency of the proposed algorithm, two of the solutions found for the examples in table 1 are presented in figure 2. it should be noted an efficient solution for the vrp by using a hybrid elite ant system 345 figure 1: comparison of mean for 14 instances between meta-heuristic algorithms in table 1 that in 1 out of 2 examples presented in this figure, the heas has been able to improve the best solution ever found. figure 2: some of the solutions to the vrp found by the proposed algorithm 4 conclusion and future works in this paper, a modified version of eas which employs several effective modifications was presented. the modifications improved the performance of the classic eas algorithm in escaping from local optimum points and finding better solutions in comparison with the other metaheuristic algorithms. it seems that combining the proposed algorithm with other metaheuristic algorithms like tabu search and making use of strong local algorithms like lin-kernigan algorithm can bring better results for the heas. furthermore, heas can be used for other versions of vrp like ovrp and heterogeneous fixed fleet ovrp. future projects will focus on working on such ideas and making them operational. 346 m. yousefikhoshbakht, f. didehvar, f. rahmati bibliography [1] 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(2012). solving the vehicle routing problem by a hybrid meta-heuristic algorithm, journal of industrial engineering international, 8(12):1-9. [2] yousefikhoshbakht, m., didehvar, f. and rahmati, f. (2014). a combination of modified tabu search and elite ant system to solve the vehicle routing problem with simultaneous pickup and delivery, journal of industrial and production engineering, 31(2): 65-75. [3] hong, l. (2012). an improved lns algorithm for real-time vehicle routing problem with time windows, computers and operations research, 39(2):151-163. [4] yousefikhoshbakht, m., f. didehvar, and f. rahmati, (2013). solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm, international journal of production research, http://dx.doi.org/10.1080/00207543.2013.855337. [5] toth, p. and vigo, d. (1997). an exact algorithm for the vehicle routing problem with backhauls, transportation science, 31:372-385. [6] pop p.c., sitar c.p., zelina i., lupse v. and chira c. (2011). ,heuristic algorithms for solving the generalized vehicle routing problem, international journal of computers communications & control, issn 1841-9836, 6(1): 158-165. [7] valle, c. a., martinez, l. c. and cunha, a. s., mateus, g. r. (2011). heuristic and exact algorithms for a min-max selective vehicle routing problem, computers and operations research, 38(7):1054-1065. [8] brad, j., kontoravdis, g. and yu, g. (2002). a branch-and-cut procedure for the vehicle routing problem with time windows. transportation science, 36(2):250-269. [9] clarke, g. and wright, j. w. (1964). scheduling of vehicles from a central depot to a number of delivery points. operations research, 12: 568-581. [10] gillett, b. e. and miller, l. r. (1974). a heuristic algorithm for the vehicle dispatch problem. operations research, 22: 340-349. [11] osman, i. (1993). metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem, operations research, 41: 421-451. [12] baker, b., and ayechew, m. (2003). a genetic algorithm for the vehicle routing problem, computers and operations research, 30: 787-800. [13] hirota, k., dong, f. and chen, k. (2006), a computational intelligence approach to vrsdp (vehicle routing, scheduling, and dispatching problems), international journal of computers communications & control, issn 1841-9836, suppl. issue, 1(s): 53-60, [14] zhang, x. and tang, l. (2009). a new hybrid ant colony optimization algorithm for the vehicle routing problem, pattern recognition letters, 30(152): 848-855. [15] negulescu s.c., kifor c.v. and oprean c. (2008). ant colony solving multiple constraints problem: vehicle route allocation , international journal of computers communications & control, issn 1841-9836, 3(4):366-373. an efficient solution for the vrp by using a hybrid elite ant system 347 [16] ai, j., kachitvichyanukul, v. (2009). particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem, computers and industrial engineering, 56:380-387. [17] pintea c.-m., chira c., dumitrescu d. and pop p.c. (2011). sensitive ants in solving the generalized vehicle routing problem, international journal of computers communications & control, issn 1841-9836, 6(4):734-741. [18] yousefikhoshbakht, m., didehvar, f. and rahmati, f. (2013). modification of the ant colony optimization for solving the multiple traveling salesman problem, romanian journal of information science and technology, 16(1):65-80. [19] yousefikhoshbakht, m. and sedighpour, m. (2012). a combination of sweep algorithm and elite ant colony optimization for solving the multiple traveling salesman problem, proceedings of the romanian academy, a, 13(4):295-302. [20] marinakis, y. and marinaki, m. (2010). a hybrid genetic-particle swarm optimization algorithm for the vehicle routing problem, expert systems with applications, 37(33):1146-1455. int j comput commun, issn 1841-9836 7(5):824-831, december, 2012. data dimensionality reduction for data mining: a combined filter-wrapper framework m. danubianu, s.g. pentiuc, d.m. danubianu mirela danubianu, stefan gheorghe pentiuc dragos mircea danubianu "stefan cel mare" university of suceava romania, 720229 suceava, 1 universitatii e-mail: mdanub@eed.usv.ro, pentiuc@eed.usv.ro dragosdanubianu@yahoo.com abstract: knowledge discovery in databases aims to extract new, interesting and potential useful patterns from large amounts of data. it is a complex process whose central point is data mining, which effectively builds models from data. data type, quality and dimensionality are some factors which affect performance of data mining task. since the high dimensionality of data can cause some troubles, as data overload, a possible solution could be its reduction. sampling and filtering reduce the number of cases in a dataset, whereas features reduction can be achieved by feature selection. this paper aims to present a combined method for feature selection, where a filter based on correlation is applied on whole features set to find the relevant ones, and then, on these features a wrapper is applied in order to find the best features subset for a specified predictor. it is also presented a case study for a data set provided by terapers a personalized speech therapy system. keywords: data mining, feature selection, filters, wrappers. 1 introduction as an efficient way to find new and useful knowledge in data, knowledge discovery in databases (kdd) process, and implicitly data mining as its main step, have been the subject of extensive research. the main issue relates to model building process performances, and these performances are affected by factors such as type, quality and dimensionality of available data. as most data mining techniques may not be effective for high-dimensionality data, the solution consists in its reduction. in order to reduce the number of cases, one can use sampling or filtering, whereas feature reduction may be achieved by feature selection or feature composition. feature selection aims to identify and to remove as many irrelevant and redundant features as possible with respect to the task to be executed, and it can be made using two approaches: filters and wrappers. filters do not consider the effect of selected features on the performance of the whole process of knowledge discovery, since the used feature selection criterion does not require a predictor evaluation for reduced data sets. wrappers take into account the feed-back related to the performance of the selected set of features in the kdd process. so, it is used as criteria for feature selection the predictor performance. wrappers often give better results than filters, because feature selection is optimized for the specific learning algorithm used, but if the computational complexity and execution time are considered, they are too expensive for large dimensional datasets since each selected feature set must be evaluated with the predictive algorithm used. in these circumstances we aim to study if an apriori filtering of features based on their relevance related to the class may improve a closed loop feature selection process. section 2 presents some theoretically aspects related to data mining and the influence of data dimensionality on its performances. there are also enumerated some data dimensionality reduction methods. section 3 refers some aspects regarding feature selection whereas section 4 provides a comparison copyright c⃝ 2006-2012 by ccc publications data dimensionality reduction for data mining: a combined filter-wrapper framework 825 between filters and wrappers. section 5 presents a framework, which proposes a combination filter-wrapper and section 6 offers some experimental results obtained by applying the proposed method over a dataset collected by terapers a computer-based speech therapy developed within the center for computer research in the stefan cel mare university of suceava, and used by the therapists from regional speech therapy center of suceava from march 2008. 2 data mining and data dimensionality defined as the process of exploring and analyzing large volumes of data, in order to find new relationships within data or new patterns, data mining is a step in knowledge discovery in database (kdd). its task is to analyze large volumes of data in order to extract previously unknown, interesting and potential useful patterns, and its performances are affected by factors such as: type, quality and dimensionality of data. hypothetically, having more data, results are more precise, but practical experience with data mining algorithms has shown that this is not always true. on the one hand, the high dimensionality of data can cause data overload, and on the other hand if there are a lot of features, it is possible that the number of cases in data set to be insufficient for data mining operations. [1] this make some data mining algorithms non applicable. the solution for these problems is the reduction of data dimensions. the size of a data set is determined both by the number of cases and by the number of features considered for each case. in order to reduce number of cases one can use sampling or filtering. feature reduction may be achieved either by feature selection or by feature composition. these methods should produce fewer features, so the algorithms can learn faster. sometimes, even the accuracy of built models could be improved. [2] methods used for feature selection, can be classified as: filters or open loop methods, and wrappers or closed loop methods. 3 feature selection feature selection aims to identify and to remove as much irrelevant and redundant features as possible with respect to the task to be executed. it has the potential to be a fully automatic process, and brings some benefits for data mining, such as: an improved predictive accuracy, more compact and easily understood learned knowledge and reduced execution time for algorithms. feature selection methods are divided in two broad categories, filters and wrappers, and within these categories algorithms can be further individualized by the nature of their evaluation function and by the means the space of feature subsets is explored. typically, feature selection algorithms perform a search through the space of feature subsets, and must solve four problems which affect such search: to select a point in the feature subsets space from which to start the search. a first choice, called forward selection, supposes to begin with no features and successively add attributes, whereas a second one, backward selection, begins with all features and successively remove them; even heuristic search strategies not guarantee finding the optimal subset, such strategies can give good results, and are more feasible than exhaustive search strategies which are prohibitive just for a small initial number of features; the most important factor which makes difference among feature selection algorithms is evaluation strategy. there are feature selection methods which operate independent of any learning algorithm, and irrelevant features are filtered before learning begins, based on general characteristics of the data to evaluate. other methods use an induction algorithm combined with a statistical re-sample technique to estimate the final accuracy of feature subsets; 826 m. danubianu, s.g. pentiuc, d.m. danubianu each feature selection process must solve the problem regarding stop searching through the space of feature subsets. one might stop adding or removing features when none of the alternative improves upon the gain of current feature subset, or one might continue to alter the feature subset as long as the gain does not degrade. 4 filters vs. wrappers the earliest and simplest approaches to feature selection were filters, called also open loop feature selection methods. based on selecting features through class separability criteria, filters do not consider the effect of selected features on the performance of the whole process of knowledge discovery, as is presented in figure 1(a). they provide usually a ranked list of features that are ordered according a specific evaluation criterion such as: accuracy and consistency of data, information content or statistical dependencies between features. [3] they give also information about the relevance of a feature compared with the relevance of other features, and do not tell to the analyst what is the desirable minimum set of the features. [4] figure 1: open loop feature selection method (a), and closed loop feature selection method (b) wrappers, known also as closed loop feature selection methods take into account the feedback related to the performance of the selected set of features for the complete kdd process. they use the prediction performance as selection criteria, and evaluate the quality of selected features by comparing the performances for prediction algorithms applied on the reduced set of features and on the original one. figure 1(b) presents a closed loop feature selection method. [2] regarding the final predictive accuracy of a learning algorithm, wrappers often give better results than filters, because feature selection is optimized for the specific learning algorithm used. but if the computational complexity and execution time are considered, wrappers are too expensive for large dimensional datasets, since each selected feature set must be evaluated with the predictive algorithm used. additional, since the feature selection is closely coupled with a learning algorithm, wrappers are less general than filters and they must be run every time when one switch from one learning algorithm to another. 5 a combined approach filter-wrapper for feature selection studying the advantages and limitations for the two general feature selection methods we can conclude that, if improved performance for a specific learning algorithm is required, a filter can provide a reduced initial feature subset for a wrapper which contains only relevant features, as shown in figure 2. this approach could produce shorter and faster search for the wrapper. data dimensionality reduction for data mining: a combined filter-wrapper framework 827 figure 2: a framework which combines feature relevance analysis with closed loop feature selection since practice has demonstrated that irrelevant input features lead to great computational cost for data mining process and may cause overfitting, more feature selection researches have focused on extraction of relevant features from the whole data set in order to apply data mining algorithms upon these data. [5] [6] but how can we establish if a feature is relevant or not? in [7] is stated that features are relevant if their values vary systematically with category membership. that means that a feature is relevant if it is correlated with the class. formally this was defined in [2] as follows: definition 1. a feature fi is relevant iff there exists fi and c for which p(fi = fi) > 0 such that p(c = c|fi = fi) ̸= p(c = c) (1) relevance is usually defined in terms of correlation or mutual information. in order to define mutual information for two features we start from the concept of entropy, as a measure of uncertainty of a random variable. for a variable x the entropy is defined as: e(x) = −σp(xi)log2(p(xi)) (2) the entropy of a variable x after observing values of another variable y is defined as: e(x|y ) = −σp(yi)σp(xi, yi)log2(p(xi|yi)) (3) where p(xi) is the prior probability for all values of x, and p(xi|yi) is the posterior probabilities of x given the value of y. the value by which the entropy of x decreases, estimates additional information about x provided by y. it is called information gain [8] and is calculated using the following expression: i(x, y ) = e(x) − e(x|y ) (4) we take into account that for discrete random variable, the joint probability mass function is: p(xi|yj) = p(xi, yj)/p(yj) (5) 828 m. danubianu, s.g. pentiuc, d.m. danubianu and the marginal probability function, p( x) is: p(xi) = σp(xi, yj) = σp(xi|yj)p(yi) (6) where p(x,y) is joint probability distribution function of x and y, and p(xi) and p(yj) are the marginal probability distribution functions of x and y respectively. finally, for two discrete random variables x and y , information gain is formally defined as: i(x, y ) = ∑ j ∑ i p(xi, yj)log p(xi, yj) ((p(xi)p(yj)) (7) according to this expression, one says that a feature y is more correlated to feature x than feature z if: i(x, y ) > i(z, y ) (8) it can be observed that information gain favors features with more values, so it should be normalized. in order to compensate its bias and to restrict its values to range [0,1] it is preferable to be used symmetrical uncertainty, defined as: su(x, y ) = 2 i(x, y ) e(x) + e(y ) (9) a value of 1 for symmetrical uncertainty means that knowing the values of either feature completely predicts the value of the other whereas a value of 0 implies that x and y are independent. starting from these considerations, in the proposed framework, first a relevance analysis is made using the symmetrical uncertainty su(fi, c) between each feature fi and the class c. [9] based on this analysis one removes the irrelevant features, and one obtains a features subset containing only the relevant features. then, on this dataset one applies closed loop feature selection methods, using as search strategy both forward and backward selection, and using a decision tree as predictor, both for feature selection and for the performance evaluation. 6 experimental results we have applied the framework described above for a real dataset collected by terapers system. this is a system which aims to assist the personalized therapy of dyslalia (an articulation speech disorder) and to track how the patients respond to various personalized therapy programs. implemented in march 2008, the system is currently used by the therapists from regional speech therapy center of suceava. an important aspect of assisted therapy refers its adaptation according to individual patients’ characteristics and evolution, for which therapist must perform complex examination of children, materialized in recording of relevant data relating to personal and family anamnesis. these collected data may provide information relative to various causes that may negatively influence the normal development of the language. further, one provide to the personalized therapy programs data such as number of sessions/week, exercises for each phase of therapy and the changes of the original program according to the patient evolution. the tracking of child progress materializes data which indicate the moment of assessing the child and his status at that time. all these data are stored in a relational database, composed of 60 tables. data stored in the terapers database is the set of raw data that can be the subject of data mining process. it might be useful, because as it was shown in [10] one can use classification in order to places the people with different speech impairments in predefined classes (if attribute data dimensionality reduction for data mining: a combined filter-wrapper framework 829 diagnosis contain the class label one can predict a diagnosis based on information contained in various predictor variables), one can use clustering to group people with speech disorders on the basis of similarity of different features and to help therapists to understand who are they patients, or one can use association rules to determine why a specific therapy program has been successful on a segment of patients with speech disorders and on the other was ineffective. for our experiments one considers a data set consisting of 72 features with numeric and descriptive values and 312 cases. these are anamnesis data or data derived from complex examination on which one intend to build a classification model to predict, in order to suggest to therapist the diagnosis for future cases. on this data set one have applied the feature selection method described above. shown in figure 3, such experiment is designed and implemented in weka. [11] figure 3: weka knowledge flow for the proposed framework one has considered the attribute diagnosis as class label, and we have built a feature selection process in two steps.in the first stage one have applied a unsupervised filter against the whole set of features. based on values for symmetrical uncertainty su(fi, c) there were retained 52 relevant features. in the second stage over this feature subset one have applied a wrapper which uses as estimation predictor a decision tree classifier (j48). for this wrapper one applies alternatively the two search strategies forward and backward search. to analyze the influence of data reduction we need to know what we gain or what we lose so, we must compare computing times and the accuracy for the model built for reduced data sets obtained using the described approaches. figure 4 presents the performances of the classifiers built on feature subset produced by wrapper using a backward selection strategy and on features obtained from the same wrapper which use, this time, a forward selection strategy. as one can see in figure 4 for the subset obtained by backward selection, the predictions’ performance, measured in correctly classified instances, are better than those for the feature subset obtained by forward selection. in figure 5 a comparison of the execution time for forward and backward selection in both cases, with filter before wrapper, and without filter before wrapper is made. one see that for backward selection, which provides better performance for prediction, the process which considers as input for wrapper the data subset provided by filter is three times faster than the other process. 830 m. danubianu, s.g. pentiuc, d.m. danubianu figure 4: performances of classifier built on feature subsets obtained by proposed method figure 5: performances of classifier built on feature subsets obtained by proposed method 7 conclusions and future works as a possibility to reduce the number of feature considered by data mining algorithms, in order to make them more efficient, this paper presents a method which uses a combination filterwrapper. we have used a correlation based filter on the whole set of features, then on relevant subset of features we have applied a wrapper which uses a decision tree classifier for prediction. as a case study we have applied this method on data collected by terapers a system which aims to assist speech therapists on personalized therapy of dyslalia. the process was designed and implemented in weka. we have compared the performances obtained both for feature selection by the described method, and for feature selection using only the same wrapper as in first case. we have achieved clearly superior performances for execution time, when we have used for feature selection the combined approach and backward selection as search strategy for wrapper. the positive results obtained for the considered data encourage us to continue our work. we will try to improve these execution times by parallelization of feature selection operations. acknowledgments this paper was supported by the project "progress and development through post-doctoral research and innovation in engineering and applied sciences pride contract no. posdru/89/1.5/ s/57083", project co-funded from european social fund through sectorial operational program human resources 2007-2013. bibliography [1] danubianu m., pentiuc s.g., tobolcea i., schipor o.a., advanced information technology support of improved personalized therapy of speech disorders, int j comput commun, issn 1841-9836, 5(5): 684-692, 2010. data dimensionality reduction for data mining: a combined filter-wrapper framework 831 [2] kohavi r., john g., wrappers for feature subset selection, artificial intelligence, special issue on relevance, 97(1-2):273-324, 1997. [3] hall, m., correlation-based feature selection for discrete and numeric class machine learning, proc. of international conference on machine learning, 359-365, morgan kaufmann, 2000. [4] douik a., abdellaoui m., cereal grain classification by optimal features and intelligent classifiers, int j comput commun, issn 1841-9836, 5(4):506-516, 2010. [5] peng h. long f., ding c., feature selection based on mutual information: criteria of maxdependency, max-relevance and min-redundancy, ieee transaction on pattern analysis and machine intelligence, 27(8):1226 1238, 2005. [6] john g.h., kohavi r., pfleger p., irrelevant features and the subset selection problem, machine learning: proceedings of the eleventh international conference, 121-129, morgan kaufman, 1994. [7] gennari j.h., langley p., fisher d., models of incremental concept formation, artificial intelligence, (40):11-16, 1989. [8] quinlan j.r., c4.5: programs for machine learning, morgan kaufman, 1993. [9] yu, l., liu, h., efficient feature selection via analysis of relevance and redundancy, journal of machine learning research, 5:1205-1224, 2005 . [10] danubianu m., pentiuc st. gh., socaciu t., towards the optimized personalized therapy of speech disorders by data mining techniques, the fourth international multi conference on computing in the global information technology iccgi 2009, vol: cd, 23-29 august, cannes la bocca, france, 2009. [11] hall, m., frank, e., holmes, g., pfahringer, b., reutemann, p., witten, i., the weka data mining software: an update, sigkdd explorations, 11(1):10-18, 2009. int j comput commun, issn 1841-9836 9(2):131-138, april, 2014. input projection algorithms influence in prediction and optimization of qos accuracy r.d. albu, i. dzitac, f. popentiu-vladicescu, i.m. naghiu răzvan-daniel albu*, florin popentiu-vlădicescu**, iuliana maria naghiu 1. university of oradea, universitatii st., 1, 410610 oradea, romania *corresponding author: ralbu@uoradea.ro **2. academy of scientist in romania, 54, splaiul independentei, 0590094, bucharest e-mail: popentiu@imm.dtu.dk. ioan dzitac 1. aurel vlaicu university of arad elena dragoi st., 2, 310330 arad, romania ioan.dzitac@uav.ro 2. agora university of oradea piata tineretului 8, 410526 oradea, romania e-mail: idzitac@univagora.ro abstract: regardless of new achievements in the research of prediction models, qos is still a great issue for high quality web services and remains one of the key subjects that need to be studied. we believe that qos should not only be measured, but have to be predicted in development and implementation phases. in this paper we assess how different input projection algorithms influence the prediction accuracy of a multi-layer perceptron (mlp) trained with large datasets of web services qos values. keywords: quality of service (qos), adaptive models, web services, large/big data. 1 introduction the major web services qos requirements are: availability, accessibility, integrity, performance, regulatory reliability and security. models of prediction and/or optimization of qos in web services are presented in many actual works: [19], [14], [16], [15], [20], [21], etc. this research work continues the investigation on web services qos criteria prediction and completes the results obtained in [1][5], works (co)authored by first author of this paper. we build in this work an adaptive model that offers good prediction results of qos in web services. since more data may lead to more accurate analyses and more precise analyses may lead to more confident decision making, the development of accurate and adaptive prediction models is both challenging and essential. enhanced conclusions can cause superior operational efficiencies, cost reductions and lower risks. scott zucker, vice president of business services at family dollar, said: "small data is gone. data is just going to get bigger and bigger and bigger, and people just have to think differently about how they manage it" [8]. large data is an expression used to define the exponential growth of data availability and size. several recent technology improvements, that allow organizations to make the most of big data analytics, are: affordable bigger storage, faster and parallel processors, open source platforms, clustering, virtualization, grid computing, increased throughput and last, but not least, cloud computing. copyright © 2006-2014 by ccc publications 132 r.d. albu, i. dzitac, f. popentiu-vladicescu, i.m. naghiu another new term for the quantity of information generated by business, government, and science is: data deluge [10]. for instance, in 2010, the large hadron collider (lhc) facility at cern delivered 13 petabytes of data. concurrently to this important growth, data are also becoming strongly interconnected. for example, facebook is approximately fully connected. presently, social networks interconnect people or groups who share similar interests, but soon, we expect they will also link software modules such as: web-based services, or workflows. in recent happenings interrelated with the 2013 boston marathon violence, social networks of marathon competitors and high-performance computational systems were combined to group and analyze huge collections of photos and videos, finally leading to the identification of the terrorists. big data is typically characterized by the "three v’s": volume, velocity, and variety. in terms of volume, at the end of 2011, facebook had 721 million individuals and 68.7 billion friendship edges [7]. in terms of velocity, twitter generates 7 tbytes of data daily, while facebook produces 10 tbytes. on 11 november 2012, a sales event at taobao, the largest online shopping marketplace in china, generated 100 million transactions and reached a peak transaction rate of 205,000 per minute [9]. in terms of variety, data today come from various sources, ranging from surveillance videos, to satellite images, to mobile tweets, to sensors and meters in the power grid [18]. a key difference between big data and large data is the rate at which the data can be collected and made accessible for analysis. large data can also be handled by the traditional reporting and analysis tools. big data is generally used to describe the massive amount of unstructured data, which costs a lot of time and money for analysis. however, large data may not have such special meaning, they just refer to the volume. we don’t think there is any value in defining a threshold for what constitutes "big data." and what means "large data". therefore, a flexible definition we use is "big data is data that’s an order of magnitude bigger than you’re used to". the big data analysis affects the qos because, if implemented efficiently, the big data infrastructure will permit carriers to preserve constantly optimized services and to set apart in the marketplace by offering qos reports, diagnostic tools and other decision-support front-ends. cloud computing is a technology that exploded in the recent years and seems to be the ideal way to provide big and large data for mainstream uses, because it offers scale-out and ondemand computing resources in a pay-per-use style. for instance, netflix stores movies and tv shows, while dropbox saves clients’ documents, both in amazon’s simple storage service. in recent years, scale-out data stores, usually mentioned as nosql systems, were quickly gaining admiration as a possible solution for applications scaled at internet level. these stores consist of technologies like: amazon’s dynamodb, google’s bigtable and yahoo’s pnuts. to address the "big and large data" challenge, nosql supporters limit acid restraints, deliver completely scalable solutions and then gradually add back the relational database management system (rdbms) features like index or transaction support. a newly appeared paradigm named stream computing facilitates continuous queries over streaming data like social media feeds. social networking on the cloud could empower sharing based on the social relationship between users. this would possibly make available technologies like volunteer computing. this is a distributed computing model in which associated users donate computing resources to a project. two examples of volunteer computing are the following projects: storage@home14 and boinc15 [18]. in these scenarios, the resources are owned by individuals and they are shared in return for access to other resources. this could hypothetically transform the cloud’s economics and raises doubts about the reliability and qos warranties [11] [13]. 2 input projection and optimization algorithms input projection is the procedure that further reduces input dimensions by automatically mapping multiple pieces of information to single inputs. the main goal of input optimization input projection algorithms influence in prediction and optimization of qos accuracy 133 is to make the network learn faster in the same time offering the same prediction performances or better. input optimization automatically determines the most informative inputs through genetic algorithms, greedy search, back-elimination and other techniques [6]. the four input projection algorithms we have investigated in this study are: pca (principal component analysis), mds (multi-dimensional scaling), som (self organized map) and lle (locally linear embed). next, we will briefly describe each input projection algorithm. principal component analysis (pca) also named karhunen-loeve transform of singular value decomposition (svd) finds an orthogonal set of directions in the input space and delivers a method of finding the projections into these directions in a well-ordered style. the first principal component is the one that has the largest projection (the shadow of our data cluster in each direction). the orthogonal directions are named the eigenvectors of the correlation matrix of the input vector, and the projections the equivalent eigenvalues. because pca orders the projections, we can decrease the dimensionality by truncating the projections to a given order. the reconstruction error is equal to the sum of the projections left out. the features in the linear projection space become the eigenvalues. pca networks are typically utilized for data compression, offering the best m linear features, but they can also be used for data reduction in conjunction with multilayer perceptron classifiers [17]. the som algorithm is as follows [12]: a. initialize the weights with small different random values for symmetry breaking; b. for each input data find the winning pe using a minimum distance rule; ī(x) = argjmin ∥ x̄(n) − wj ∥ (1) c. for the winning pe, update its weights and those in its neighborhood by: wj(n + 1) = wj + η(n)[x(n) − wj(n)] (2) where η(n) is the step size. in the beginning, the step size should be large, but decrease progressively to zero, according to: η(n) = 1 aη + bηn (3) where aη and bη are problem dependent constants. the purpose of these adaptive constants is to guarantee, in the early stages of learning, malleability and the formation of local neighborhoods as well as, in the later stages of learning, constancy and adjustment of the map. these problems are very difficult to study theoretically, so heuristics have to be involved in the designation of these values. multi-dimensional scaling (mds) is a set of related statistical techniques used in information visualization for exploring similarities or dissimilarities in data. the mds algorithm starts with a matrix of item-item similarities and then assigns a location to each item in a lower dimensional space say m, such that the original similarities or dissimilarities are represented by the relative position in the lower dimensional space. the implemented algorithm measures the similarity or distance between points by correlation coefficient and applies simulated annealing to find the mapping from n dimensional space to m dimensional space, where n>=m, such that the distances between points in the m dimensional space approximate the correlation coefficients between points in the n dimensional space [12]. locally linear embedding (lle) is an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embedding of high dimensional data. lle attempts to discover nonlinear structure in high dimensional data by exploiting the local symmetries of linear reconstructions. the lle algorithm includes following three major steps [12]: 134 r.d. albu, i. dzitac, f. popentiu-vladicescu, i.m. naghiu 1. based on desired number of inputs say m, get the neighbors of each point in the sense euclidean distance. 2. compute the weights wij that best reconstruct each data point xi with dimension n from its k neighbors, minimizing the cost defined as: ζ(w) = n∑ i=1 |xi − k∑ j=1 wijxj| (4) the solution can be reached by first solving the linear system of equations involving local covariance matrix and then rescaling the weights so that they sum to one. 3. compute the projected inputs yi with dimension m that is best reconstructed by the weights wij minimizing the quadratic form defined as: ζ(w) = n∑ i=1 |yi − k∑ j=1 wijyj| (5) this is found by extracting the appropriate bottom m eigenvectors of a matrix derived from the cost function. 3 experiments and results the adaptive models compared in this study have a name that respects the following syntax: topology_name number_of_hidden_layers learning_rule input projection_algorithm for example, the adaptive model named: mlp-2-cg-som is a multi-layer perceptron with two hidden layers, trained using conjugate gradient learning rule and self organizing map input projection algorithm. in this study, in order to train the mlp, we made use of two large datasets: rtmatrix and fpmatrix. each matrix has a size of 339 lines x 5825 columns. rtmatrix consists of web services response time values while fpmatrix stores similar web services throughput values. in our previous research works [1][5] we concluded that mlp-2-m and mlp-2-cg offered the most accurate prediction results for rtmatrix and fpmatrix, respectively. fpmatrix and rtmatrix were built by z. zheng, y. zhang and m.r. lyu in [20] and [21]. to implement, train and test the adaptive models we utilized neuro solutions 6.21 development environment. as we can see in figure 1, the main difference of this neuro solution implementation of mlp is the presence of inputprojectionaxon. the inputprojectionaxon will apply either linear or non-linear transformation to convert n input data set into m input data set, where n>=m. the inputprojectionaxon inspector allows us to select a different input projection algorithm and to set its parameters [12]. in this study, we have tested pca in conjunction with mlp-2-m, but it works only on a subset of rtmatrix/fpmatrix since the separability of the classes is not always guaranteed. another problem with linear pca networks is outlying data points. outliers will distort the estimation of the eigenvectors and create skewed data projections. rtmatrix and fpmatrix, having a huge number of values, have also a lot of outliers. nonlinear networks are better able to handle this case. the importance of pca analysis is that the number of inputs for the mlp classifier can be reduced a lot, which definitely influences the number of necessary training samples and the training time. and this was achieved when we made use of just a subset of rtmatrix/fpmatrix. input projection algorithms influence in prediction and optimization of qos accuracy 135 figure 1: mlp-2-m with inputprojectionaxon som are an alternative to the pca concept, and in our experiments, it works on both the entire rtmatrix/fpmatrix and a subset of them. by "works" we mean neuro solutions offer a results report. the parameters of som utilized in our simulation are presented in figure 2. figure 2: som parameters output dimension is used to specify the desired number of inputs as a percentage of number of inputs in the original data set. initial sd of gaussian is used to set the initial standard deviation for the gaussian function that is used to define the neighborhood function. initial learning rate is used to specify the starting learning rate and it is gradually reduced until the final learning rate is 0.01. square radius is used to define the neighbours. the shape of the neighborhood is defined as a square. units/dimension is used to define the smoothness of the projected space or grids. maximum epochs is used to specify the maximum number of epochs before termination of the computation. mds algorithm applied on the entire rtmatrix/fpmatrix determined neuro solutions to block indefinitely in a "not responding" state, but when we selected just a subset of them, it worked and offered some results. lle offers the best results, in comparison with the other four input projection algorithms, since it works on both fpmatrix and rtmatrix and provides the lowest prediction error. in neuro solutions, the best input optimization algorithm is performed by the geneticcontrol component. this component implements a genetic algorithm to optimize the inputs. genetic algorithms are search procedures based upon the principles of evolution witnessed in nature that combine selection, crossover, and mutation operators. they search for an optimal solution until 136 r.d. albu, i. dzitac, f. popentiu-vladicescu, i.m. naghiu a termination criterion is met. in neuro solutions the criteria used to evaluate the fitness of each potential solution is the lowest cost attained during the training run. the solution to a problem is called a chromosome and consists of a collection of genes, which are simply the inputs to be optimized. the genetic algorithm produces an initial population, evaluates this population by training a neural network for each chromosome and then evolves the population through multiple generations in the search for the best inputs. in figure 3 is presented the compared results between mlp-2-m with no input optimization or projection, mlp-2-m with only genetic optimization and mlp-2-m with both genetic and lle input projection. figure 3: effect of input optimization and projection on prediction accuracy the comparison was performed on the entire rtmatrix and the results show that mlp2-m-lle is the most accurate adaptive model for web services response time prediction. as we can observe, the dataset was divided in three subsets: training (50%), cross-validation (30%) and testing (20%). the training data set is obviously used for training, the cross-validation data set tests the model in the training phase and determines when the training is stopped, while the testing data set is utilized to investigate the prediction accuracy of the model when it receives new samples at the input. having the experience with the mlp trained on rtmatrix, the subject of fpmatrix investigation can be reduced at the comparison between mlp-2-cg-som and mlp-2-cg-lle, both having as input optimization a genetic algorithm. we tested all input projection algorithms on fpmatrix, each one separately and the only two that could find a solution were lle and som. consequently, we have labeled mds and pca input projection algorithms as not suitable for our prediction problem. the literature also recommends them for other types of problems like classification or clustering. the results of the comparison between lle and som, when training a mlp with two hidden layers, conjugate gradient learning rule and genetic input optimization, are shown in figure 4. the results reported in figure 4 show that lle is again the best input projection algorithm, since it offers the best prediction accuracy when training the mlp-2-cg on both rtmatrix and fpmatrix. neuro solutions offers a fifth input projection algorithm, k-means clustering, but in our researches we have not used it, since it is not appropriate for prediction problems. input projection algorithms influence in prediction and optimization of qos accuracy 137 figure 4: a comparison between mlp-2-cg-som and mlp-2-cg-lle 4 conclusions and future works qos is still essential for high quality web services and remains one of the subjects that raise researchers’ interest. more and more authors believe that large data may be as significant to society as the internet. social networks can play an important role in large and big data analytics and the technology trends indicate that they soon will interconnect not just people, but software modules like web services. consequently, in this research work we have studied four input projections algorithms, in order to determine which one increases the prediction accuracy of a multi-layer perceptron (mlp) with two hidden layers, trained with web services large data. the result reports, for both fpmatrix and rtmatrix, show the locally linear embed (lle) as the most accurate input projection algorithm. concluding, mlp with two hidden layer, having as input projection algorithm lle and a genetic algorithm for input optimization, can provide more accurate prediction results, when it is trained with large datasets of web services qos criteria values. in future works we will investigate different prediction adaptive models in order to improve web services qos criteria prediction accuracy. bibliography [1] r.-d. albu (2013), contributions regarding 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[18] wei tan, m. brian blake, iman saleh, schahram dustdar (2013), social-network-sourced big data analytics, web-scale workflow, internet computing, ieee, 17(5):62-69. [19] liang-jie zhang, jia zhang, hong cai (2007), services computing, tsinghua university press, springer. [20] z. zheng, y. zhang, m.r. lyu (2010), distributed qos evaluation for real-world web services, proc. of the 8th int. conference on web services (icws2010), miami, florida, usa, 83-90. [21] z. zheng, y. zhang, m.r. lyu (2011), exploring latent features for memory-based qos prediction in cloud computing, proc. of the 30th ieee symposium on reliable distributed systems (srds 2011), madrid, spain, 1-7. int j comput commun, issn 1841-9836 8(4):560-570, august, 2013. a model for collaborative filtering recommendation in e-commerce environment y. jing, h. liu yuanchun jing & honglu liu school of traffic and transportation beijing jiaotong university po box 100044, beijing, china 12121021@bjtu.edu.cn, hlliu@bjtu.edu.cn abstract: in modern business environment, product life cycle gets shorter and the customer’s buying preference changes over time. time plays a more and more important role in collaborative filtering. however, there is a gap in one class collaborative filtering (occf). on the basis of collecting different real-time information, this paper proposes an optimization model for e-retailers. through comparing different methods with different weights, results show that real-time dependent in occf performs better in improving the quality of recommendation. the model is effective in cross-selling e-commerce, personalized, targeted recommendation sales. keywords: integration of real-time information, one class collaborative filtering, e-commerce. 1 introduction with the rapid development of internet and the increase of the amount of data on the internet, retail businesses face many challenges, such as assessment of customer interests to products, as well as an accurate assessment of products at the market’s perspective in modern life. these challenges in the transaction process, often translate into how to find the best match between the customer and the product. in recent years, many companies began to get involved in ecommerce mode, making great progress. according to the “2012 china online retail market data monitoring report” released by the third-party e-commerce research institutes, china electronic commerce research center (100ec.cn): china online retail market transactions reached 1.3205 trillion rmb, attaining a year-on-year growth of 64.7% by december, 2012; this report also predicts: online retail market turnover of 2013 is expected to reach 1.8155 trillion rmb. as of 2012, china’s online retail market transactions have been accounted for 6.3% of the same period’s total retail sales of social consumer goods, while the proportion data was only 4.4% in 2011, which means that e-commerce has begun to change the retail landscape. despite a huge opportunity of e-commerce development, retailers have to solve complex problems which at a higher level. i.e., in recent years, because of the increase of customers and product, the retailers need to develop a systematic recommendation list. the list may be a list of items for the target customer, or a customer list for the target item. collaborative filtering (cf) is the method that an associated list generated by the information from customer clearly expressed their interests or extracted from the potential customer behavior information. one of the most common research problems is the netflix movie recommendation in this area. through huge amount of datasets, like purchases transaction datasets, news recommendation records of recommended sites, the problem to be solved can be converted to one class collaborative filtering (occf) problems. one class collaborative filtering is a problem with fewer researches, the effectiveness based on one class collaborative filtering depends on the consideration and processing of non-labeled or missing dataset. i.e., its basic idea is to construct a negative dataset (negative sample). as for the one class collaborative filtering problems which have weight copyright c⃝ 2006-2013 by ccc publications a model for collaborative filtering recommendation in e-commerce environment 561 distribution application for the matrix factorization technique, recent studies are often based on frequency information, such as customer number, product number, product popularity. in spite of extensive literature researches we found little study in which similar real-time information is combined, undeniable, real-time plays an important role in the occf. therefore, in this paper, we built a recommended model which can make full use of the real-time information about product and customer in the market. through the integration of real-time information including: the information of product launched into the market which is related to product life cycle, the information about product itself, and customer recently visiting information related to the customer relationship management, this model can be divided into two aspects: real-time customer-oriented weight and real-time product-oriented weight, to improve the quality of one class collaborative filtering (occf) for electronic retailers and meet their demand of the online recommendation. the rest of this paper is organized as follows: section 2 contents relevant concepts; in section 3, we proposed a real-time dependent model, added the weight to the model and compared seven different methods with different weight; section 4, we use the movielens dataset to test the model and get results; in section 5, conclusion and the future work. 2 relevant concepts nowadays, customers are presented with a majority of options for products and services. on the other hand, a vendor in the e-retailer can reach lots of customers what means that there is no limiting factor between the e-retailers and global customers. so, in today’s e-retails’ competition, the winner may be the one, which can offer a highly level of personalization to an individual customer. for the personalization needs recommendation from the e-retailers, a good recommendation system is the key factor for them to solve the problem. the recommendation algorithm most widely used in the recommendation system is collaborative filtering, namely, cf. 2.1 collaborative filtering (cf) the kernel of collaborative filtering problem is analysis of user interests, and finding some similar (interest) users for the specified user in the user group. after combining these similar users’ evaluations to products and developing a recommendation system with this information, the e-commerce site can make prediction to the specified user preference. the earliest defined “collaborative filtering” are goldberg and his partners [1]. they developed the first recommendation system, tapestry, mainly for coordination and filtering newsgroups streaming media files. another system to study news item filtration, grouplens, developed by resnick, etc., similar to tapestry. grouplens is mainly for scoring, to test the level of users’ interests on the news item. based on a similar approach, grouplens project team then got down to movielens project, from the filtration problem of news items transferred to movie recommendation problem. in recent years, collaborative filtering algorithm has been widely studied at home and abroad. such as, li g. and li l. (2012) [2], shani g., d. heckerman, and r. i. brafman (2005) [3], banati h. and mehta s. (2010) [4]. on the basis of different technologies, cf system is mainly divided into two categories, one is based on storage, and the other is model. pearson correlation approximation method can be applied to the collaborative filtering based on storage technology, this kind of cf can also make use of the item-to-item’s top-n recommendation method, which is widely used in e-commerce, like www.amazon.com, www.jd.com, etc. that recommended for users of all kinds of goods they may like, such as books, audio-visual products, electrical appliances, clothing. 562 y. jing, h. liu model-based cf approach makes up for many deficiencies of the one based on the storage. this method usually takes steps: firstly, find the clear or potentially preferences complex patterns between users and items. then, collect historical data, and provide recommendation model suggests, such as bayesian model, relies on the network based on the model, clustering model, and the model based on the mdp. 2.2 one class collaborative filtering (occf) according to different data, collaborative filtering processes can be divided into two categories: one category is the clear preference data processing, for example the score; the other is implicit data’s processing, for instance page hits or not. the later always exists in the real world application environments widely, such as whether the user bought a product, whether the user clicked a web page or not. because of no requirement that provide a clear score, the users, obtain this data much easier than the ratings data. besides that, in the implicit datasets, only active factors can be clearly distinguished, while the negative cases are uncertain. so this kind of problem is called one class collaborative filtering (occf) problem. one class collaborative filtering’s task is to rank recommendation sets according to the user’s preference levels by analyzing the implicit information with the particular user’s favorite. despite easy acquisition for such data, it is difficult to explain. for example, as for the data of a user clicks the website, among all data, the data which consists of the user clicks the pages can be interpreted as active factors; the rest of the data is the mix of negative data and missing data. how to explain such combined data, and how to process this data after explanation, are the main difficulties of one class collaborative filtering’s current studies. compare to the cf, the researches about one class collaborative filtering are fewer. we learned something from those few literatures, for example, yang s. and xue w. [4]. as for one class collaborative filtering problems, due to the active dataset rarely (sparsity), while the other two types of datasets, negative and missing datasets, are very confusing, it has many difficulties to future research. negative and unobserved data processing is the key issue of one class collaborative filtering. for this issue, a large quantity of research focuses on the technology of weight distribution, because the weighted low rank approximation can improve the quality of unobserved data recommendations. in this aspect, the typical studies contain: pan, etc. proposed a matrix factorization model based on different weight distribution of potential factors [5]. sindhwani used the formula simplifies weight distribution scheme, proposed another optimization variables, providing an unobserved data processing measure [6]. 2.3 real-time research concerning cf problems, most of models regard users’ behaviors as static, for example, a customer who buys item a will buy item b. however, product has its product life cycle, and the customer’s preferences will change over time, therefore, to regard these problems as static has certain deficiency. in 2009, koren proposed a real-time changing model which is based on the evaluation of netflix recommendation in the report of improving the performance [7]. recent some related studies began to consider the real-time information, such as, the research of lu, agarwal, and dhillon (2009) [8]; xiang l. and yang q. (2009) [9]; xiong l., x. chen, t. huang, j. schneider, j. g. carbonell (2010) [10]. but their studies focus on the cf, few mentioned the occf. even few reports establish the problem that real-time information applied to the system of occf. in one class collaborative filtering field, previous studies have only considered frequency-based information, there is few research which designs real-time information to expand collaborative a model for collaborative filtering recommendation in e-commerce environment 563 filtering problem. so, this paper, on the basis of exploration the information of product launched into market and the customers’ recent access information, considered the product life cycle (plc) and customer relationship management (crm) real-time information our model shows that predictions accuracy can be improved by occf recommendation model of e-commerce based on real-time information. real-time information integration has significant significance for business processes such as cross-selling, up-selling and others’ accurate recommendations. 3 real-time dependent model 3.1 the notation and formula to facilitate the study and discuss, firstly, we presume some items as follows. x: binary representation of actual transaction matrix, 1 means purchase; 0 is no purchase u: user feature matrix with latent features of customers p : product feature matrix with latent features of products y : recommendation resultant matrix w : weight matrix: ∈ [0, 1] we are given that there are m customers, n products, then, x matrix has m rows and n columns. if i−th row and j−th column in the x matrix has a value of 1, it means that customer i purchased a product j. if it is 0, it means that this particular customer-product has no purchase record. as for the actual transaction, x matrix is very large, and there is a certain degree of sparsity, x manifests itself in a display that less 1 more 0. in the matrix, r is the rank; it needs to analyze the number of potential characteristics. presume u is on behalf of user feature matrix, so, u = [u1, u2, . . . , um] t is a m×r matrix. i−th in the matrix u means a customer, ui means customer i in r-dimensional customer feature space. in the same way, supposes that p is in the name of product feature matrix, thus, p = [p1, p2, . . . , pn] is a r×n matrix. j−th in the matrix p stands for a product, pj means product j in r-dimensional product feature space. typically, there is m, n ≫ r and m × n ≫ (m + n) × r. joint matrix is expressed by uti pj which represent the joint relationship between customers and products. let’s assume y = up, therefore, the problem can be converted into the following optimization problem: arg min u>0,p>0 ∑ l (x, y ) (1) in the formula, l is the square error function or other loss functions: square error: l (x, y ) = ∥x − y ∥2 = m,n∑ i=1,j=1 (xi,j − y i,j)2 (2) relative entropy loss: l (x, y ) = d (x∥ y ) = m,n∑ i=1,j=1 ( xi,j log xi,j yi,j − xi,j + y i,j ) (3) in order to prevent over-fitting, parameter λ is introduced in the formula (1), to revise the optimization model: arg min u>0,p>0 λ ( ∥u∥2f + ∥p∥ 2 f ) + ∑ l (x, y ) (4) among it, ∥u∥2f and ∥p∥ 2 f are the u and p matrices’ f norm. 564 y. jing, h. liu collaborative filtering system is primarily intended to provide customers with similar product recommendations. the above-mentioned optimal formula major considers the situation that just value of 1. thus, in terms of the one class collaborative filtering, this formula is not perfect. in one class collaborative filtering, describing the potential customers’ preferences to a variety of products or different importance degrees of different data sets through simply the weights setting. therefore, adds the weight to original optimization model: arg min u>0,p>0 λ ( ∥u∥2f + ∥p∥ 2 f ) + ∑ wl (x, y ) (5) srebro and jakkola applied the low-rank approximation based on weights in collaborative filtering model with two extremes weights [11]: active factors has a weight of 1, other factors have weight of 0. supposes x1, x1 = {(i, j) : xi,j = 1} which contains (i, j) pairs corresponding all 1of a matrix x. x0, x0 = {(i, j) : xi,j = 0} containing (i, j) pairs corresponding all 0 (no purchase) in x matrix. according to the above weight setting scheme, the formula can be further amended to this: arg min u>0,p>0 λ ( ∥u∥2f + ∥p∥ 2 f ) + ∑ i,j∈x1 wi,jl ( xi,j, u t i pj ) (6) among it, wi,j = [ 1 ∀ (i, j) ∈ x1 0 ∀ (i, j) ∈ x0 ] however, the model ignores the non-buyers groups. in order to comply with the requirement of one class collaborative filtering that settings different weight for different types of factors, the above formula (6) further revised as follows: arg min u>0,p>0 λ ( ∥u∥2f + ∥p∥ 2 f ) + ∑ i,j∈x0 wi,jl ( 0, uti pj ) + ∑ i,j∈x1 wi,jl ( 1, uti pj ) (7) the formula covering all types of customers by setting the weighting values wi,j: good customers, potential customers and non-customers. after setting the corresponding weights, the equation (7) can be rewritten as the following equation, similar to the model provided by sindhwani: arg min u>0,p>0 λ ( ∥u∥2f + ∥p∥ 2 f ) + ∥ω ⊗ (x − up)∥2f (8) ωi,j = √ wi,j, ⊗ means for computing according to element, it is to make the two different corresponding elements within matrices multiplied. finally, according to the steps described by lee and seung [12], final optimization scheme is made by multiplication replaced: p = p ⊗ ut (ω ⊗ x) ut (ω ⊗ (up)) + λp (9) u = u ⊗ (ω ⊗ x) p t (ω ⊗ (up)) p t + λu (10) a model for collaborative filtering recommendation in e-commerce environment 565 3.2 weight setting scheme time weight applied to the collaborative filtering is more and more popular, we can find it from the previous studies. such as, the research of lan w. and zhengjun z. (2007) [13]; huaizheny., xiaoqi c. and meilian. l. (2009) [14]; donghui l., dewei p. and hui, zh. (2012) [15]. in our research, we compared the real-time weight with basic methods. 1) basic methods the basic method used by initializing values and other relevant information can be summarized as follows: (1) 0 weight: weight matrix is a determine matrix similar to the transaction matrix, 1 is the weight of all transactions entered, 0 is the weight of all non-transaction entries. (2) full weight: whole weight matrix is 1. error function in the formula not only needs to calculate the maximum error of transaction entry, but also need to calculate the maximum error of non-transactional input. (3) uniform weight: unlike the two extremes of weight setting, the method is to set a smaller weight δ ∈ (0, 1) for all non-transaction input. (4) the customer oriented weight: the weight of non-transaction setting is proportional to customer transactions. associated customers weight is calculated as follows: δi,j ∝ ∑n j xi,j[ max (∑n j xi,j )] δ ∈ (0, 1) , if a customer has many products’ historical purchase records, namely, a lot has been tagged data, that non-trading or unlabeled data, is likely to be treated as a customer non-purchases situation. (5) the product oriented weight: similar to the method (4), weight setting in proportion to the number of traded products. calculated as follows: δi,j ∝ { 1, ∑m i xi,j [max ( ∑m i xi,j)] } after obtained the results, using subtraction allows δ ∈ (0, 1). the main principle is that if the transactions number of a product is small, and most of situations that the non-trading or missing data will be treated as the case of non-purchases. 2) real-time weight (1) real-time customer-oriented weight: in addition to transaction data set, it also involves the customer’s recent visiting. record the history recently visiting vector as ζcr, which stands for that customers’ recently visited records. ζcr, it is the vector that customer recently visited distance; it can be expressed as follows: ∆cr = τc − ζcr among it, τc is the current or assessment phase. δcri ∝ [ 1 − ∆cri max (∆cr) ] so, δ ∈ (0, 1) 566 y. jing, h. liu in the formula, δcri is customer i recently visited value based on the real time and the weight value. the weight of each customer in real time is δ, δ ∈ (0, 1). this means that if a customer has recently visited the e-commerce site, then compared to those who have not recently visited the site of all customers, he is more likely to become frequent customer. (2) real-time product-oriented weight: reference different time information that products launched into market, set up a vector ζpl. ζpl it is the vector which stands for the time information that products launched into market. it can be expressed as: ∆pl = τc − ζpl. in the formula, τc is the current or assessment phase. δplj ∝ [ ∆plj max (∆pl) ] so, δ ∈ (0, 1) δplj , it is the weight setting that product j based on the real time launched into the market. if a product exists long time in the market, it will attract many customers. because of innovation emerging, imperfect goods will have a longer product life cycle. in any retail establishment, the records that products launched into the market exist in the internal management organization, they are the information which can be collected. in summary, different methods of setting the weights can be shown in the following table: table 1: different weight-setting methods weight(wi,j) methods transaction(1) no-transaction(0) 0 weight 1 0 full weight 1 1 uniform weight 1 δ, δ ∈ (0, 1) the customer oriented weight 1 ∝ ∑ j xi,j the product oriented weight 1 ∝ ( m − ∑ j xi,j ) real-time customer-oriented weight 1 ∝ δcri real-time product-oriented weight 1 ∝ δplj 4 experiment and results the algorithm of occf based on real-time information model can be showed: input: customer-product matrix x, the rank r. output: the approximation matrix y of x. 1) initialize p with a random number less than 1; 2) repeatedly using the formula (9), (10). update the u and p until the auc value calculated has convergence; a model for collaborative filtering recommendation in e-commerce environment 567 3) y = up return y ; 4) using matrix y make a recommend list. this paper selected the area under the roc curve (auc) as a quality measure to compare measuring recommendation quality of different methods. auc = 1 |u| ∑ u 1 |e (u)| ∑ (i,j)∈e(u) ρ (x̂ui > x̂uj) ρ, is an indicator function, it can be showed like: ρ (α) = [ 1 if α is true 0 else ] e (u), is the target pair of evaluation. e (u) = {(i, j)| (u, j) ∈ stest ∧ (u, j) /∈ (stest ∨ strain)} for all evaluations, we use the frequently-used datasets, the movielens dataset that grouplens research group provided. the dataset contains 943 users and 1682 films; each user has at least 20 film scores. thus, the total of assessment records is 100,000. despite huge number, this data set is very sparse, sparsity is 6.305%, which is only 6.305% of the items are rated. the simulation environment includes: the pc with windows 7, intel core duo processor and 4g ram. when we make the r = 3, figure 1 is the typical results for all data sets, with each method running. 0 0.2 0.4 0.6 0.8 1 1.2 0 full uniform the customer oriented the product oriented real-!me customeroriented real-!me productoriented figure 1: an auc comparison that all methods in a single run. obviously 0 weight is the worst, its’ the roc curve almost shows the diagonal. in all other ways, the degree of improvement in performance is similar, we can learn from the shape of the roc. in the new methods, the real-time product-oriented weight method’s roc curve is the best of all. compare with other methods, the real-time customer-oriented weight method has some performance improvement. then we repeated 10 times by changing the rank and got the average value of auc. like table 2 and fig 2 shows: from the figure and table, we found that 0 weight methods performed badly, while the realtime customer oriented method and real-time product oriented method had good effect for sake of the recommend. 568 y. jing, h. liu table 2: the average auc of different rank and methods rank 1 3 6 9 12 15 methods 0 0.51 0.501 0.505 0.497 0.494 0.49 full 0.71 0.699 0.697 0.693 0.689 0.687 uniform 0.699 0.712 0.71 0.708 0.705 0.702 the customer oriented 0.692 0.701 0.698 0.696 0.692 0.693 the product oriented 0.692 0.702 0.699 0.698 0.695 0.694 real-time customer-oriented 0.697 0.718 0.714 0.71 0.709 0.704 real-time product-oriented 0.779 0.78 0.778 0.78 0.781 0.779 0 full uniform the customer oriented the p roduct oriented real-time customeroriented real-time p roductoriented figure 2: the average auc of different ranks and methods. 5 conclusion on the basis of previous studies, this paper presents an integrated approach, through different weights to the one class collaborative filtering problem obtained the use of real-time information. the integration of real-time information including: the information of product launched into the market which is related to product life cycle, the information about product itself, and customer recently visiting information related to the customer relationship management. but this datasets used aren’t from e-commerce websites, and it can’t completely replace business process and actions. so how to apply the model to e-commerce needs to practice in the future. the next work is to test this model by changing the number of customers, products and transactions. now real-time information in the field of collaborative filtering experiments is limited, confined to movie ratings recommendations problem. one class collaborative filtering method which is based on e-commerce transaction record set, involved the customer’s history access to sites and product information that launched into market, and got a good result in this paper. this solution to one class collaborative filtering problems will benefit the field of cross-selling e-commerce, personalized, targeted recommendations sales. a model for collaborative filtering recommendation in e-commerce environment 569 bibliography [1] goldberg d., d. nichols, b. m. oki, and terry d., using collaborative filtering to weave an information tapestry, communications of acm, 35 (12): 61–70,1992. 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[18] kabir golam and hasin m. ahsan akhtar, evaluation of customer oriented success factors in mobile commerce using fuzzy ahp, journal of industrial engineering and management, 4(2): 361-386, 2011. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 90-100 quantized feedback control for networked control systems under communication constraints q.q. liu, g.h. yang qing-quan liu school of information science and engineering shenyang ligong university no. 6, nan ping zhong road, hun nan xin district, shenyang, 110159, china e-mail: lqqneu@163.com guang-hong yang college of information science and engineering northeastern university shenyang, 110004, china abstract: this paper investigates the feedback stabilization problem for networked control systems (ncss) with unbound process noise, where sensors and controllers are connected via noiseless digital channels carrying a finite number of bits per unit time. a sufficient condition for stabilization of ncss, which relies on a variable-rate digital link used to transmit state measurements, is derived. a lower bound of data rates, above which there exists a quantization, coding and control scheme to guarantee both stabilization and a prescribed control performance of the unstable discrete-time plant, is presented. an illustrative example is given to demonstrate the effectiveness of the proposed method. keywords: networked control systems (ncss), quantized feedback control, communication constraints, feedback stabilization. 1 introduction as is well known, in recent emerging applications (e.g., industrial automation, sensor networks, vehicle systems, aerospace industry and etc), the main aim is to control one or more dynamical systems employing multiple sensors and actuators transmitting and receiving information via a digital communication network. however, the limitations in the communication links often affect control performances significantly. our focus in this paper is on control under communication constraints. the research on the interplay among coding, estimation, and control was initiated by [1]. a high-water mark in the study of quantized feedback using data-rate limited feedback channels is known as the datarate theorem that states the larger the magnitude of the unstable poles, the larger the required data rate through the feedback loop. the intuitively appealing result was proved (see [2][5]), indicating that it quantifies a fundamental relationship between unstable physical systems and the rate at which information must be processed in order to stably control them. when the feedback channel capacity is near the data-rate limit, control designs typically exhibit chaotic instabilities. this result was generalized to different notions of stabilization and system models, and was also extended to multi-dimensional systems (see [6][8]). the research on gaussian linear systems was addressed in [9][11]. information theory was employed in control systems as a powerful conceptual aid, which extended existing fundamental limitations of feedback systems, and was used to derive necessary and sufficient conditions for robust stabilization of uncertain copyright c⃝ 2006-2012 by ccc publications quantized feedback control for networked control systems under communication constraints 91 linear systems, markov jump linear systems and unstructured uncertain systems (see [12][16]). the decentralized control schemes were addressed in [17]. the result on continuous-time linear gaussian systems was derived in [18]. the result on time-varying communication channel was derived in [19]. control under communication constraints inevitably suffers signal transmission delay, date packet dropout and measurement quantization which might be potential sources of instability and poor performance of control systems (see [22] and [23]). the survey papers [20] and [21] gave a historical and technical account of the various formulations. the data-rate inequality is now well known and provides good intuition into the connection between achievable bit-rate and stability of linear time-invariant systems. it states that for a linear time-invariant system which is open-loop unstable, a controller can be designed to stabilize it if and only if the data rate r around the closed feedback loop satisfies the data rate inequality: r > log | det(a)| (bits/sample) where a denotes the system matrix composed by only unstable modes. however, as the data rate r is reduced to the critical value log | det(a)|, the plant states must always become unbounded. in engineering systems, it is of importance to present a lower bound of data rates above which there exists a quantization, coding and control scheme to guarantee both the stabilization and control performances of plants. in this paper, we address quantized feedback control problem for stochastic linear systems with limited data rates. the aim is to present a lower bound of data rates above which there exists a coder-controller to guarantee both the stabilization and a prescribed control performance. the following notation is adopted throughout this paper. • upper case variables, like x, represent random vectors. • we write log2(·) simply as log(·). • let p(x) denote the probability density function of x and p(x|y ) denote the conditional probability density function of x given y . • ∥·∥ represents either the euclidean norm on a real vector space or the matrix norm induced by it. • ex[·] denotes expectation on x. the remainder of this paper is organized as follows: section ii introduces problem formulation. section iii deals with quantized feedback control problem for ncss with limited data rates. the results of numerical simulation are presented in section iv. conclusions are stated in section v. 2 problem formulation 2.1 preliminaries we start this section by summarizing the main definitions of information theory and adopt [24], as a primary reference. the definitions listed in this section hold under general assumptions. • let x and y denote two random variables. the differential entropy h(x) is defined as h(x) := ex[log 1 p(x) ]. the conditional differential entropy of x given y is defined as h(x|y) := ex,y[log 1 p(x|y) ]. 92 q.q. liu, g.h. yang • the mutual information between x and y is defined as i(x;y) := h(x) − h(x|y) = ex,y[log p(x|y) p(x) ]. • the information rate distortion function between x and y is defined as r(d) := inf p(y|x)∈ξ {i(x;y)} with ξ = {p(y|x) : ex,y[d(x,y)] ≤ d}. therein, d(x,y) denotes a distortion function or distortion measure, which is a mapping d : x × y → r+ and d is a given constant. • over communication channels, there are two different methods to define data rates. one is the code element rate rc(t) that denotes the number of code element transmitted in unit time, and the other is the information transmission rate re(t) that denotes the amount of information transmitted in unit time. re(t) may or may not be time-varying. we write re(t) simply as r(t). therefore r(t) is given by r(t) = 1 h i(x;y)(bits/s) where h is the transmission time for i(x;y). in ncss, after completing sample quantization procedure in state observation, we generally encode state values and transmit the information of plant states over digital channels. therein, quantization precision and sampling period are determined by conditions for the stabilization of control systems and demands for control performances. based on quantization precision and sampling period given, we may calculate the amount of information to be transmitted, which determine the value of the data rate r(t). it means that the data rate r(t) must satisfy requirements of the stabilization of control systems and control performances. example 1. assume p(x) = 1√ 2πσ21 e −x2 2σ2 1 and p(x|y) = 1√ 2πσ22 e −x2 2σ2 2 . let h = 1s. then, we obtain h(x) = 1 2 log 2πeσ21 (bits), h(x|y) = 1 2 log 2πeσ22 (bits), i(x;y) = 1 2 log σ21 σ22 (bits). then, we quantity, encode the value x and obtain the estimate y of x. here, d = σ22. thus, we have r(d) = { 1 2 log σ21 σ22 (bits), 0 ≤ σ22 ≤ σ 2 1, 0 (bit), σ22 > σ 2 1. finally, we transmit y, and the information transmission rate r(t) must satisfy the following inequality: r(t) ≥ 1 h r(d) = { 1 2 log σ21 σ22 (bits/s), 0 ≤ σ22 ≤ σ 2 1, 0 (bit/s), σ22 > σ 2 1. quantized feedback control for networked control systems under communication constraints 93 2.2 system model consider a stochastic linear system described by x(t + 1) = ax(t) + bu(t) + fw(t), y (t) = cx(t) (1) where x(t) ∈ rn is the state process, u(t) ∈ rm is the control input, y (t) ∈ rp is the observation output, and w(t) ∈ rl is the process disturbance. a, b, c and f are known constant matrices with appropriate dimensions (see fig. l). figure 1: a networked control system with a communication channel. the following is assumed to hold. a0. without loss of generality, suppose that the pair (a,b) is controllable, and the pair (a,c) is observable. a1. the matrix a is uniquely composed by unstable modes (having magnitude greater than or equal to unity). a2. the initial condition x(0) and disturbance w(0), · · · ,w(t) are mutually independent random variables with zero mean, satisfying e∥x(0)∥22 < φ0 < ∞ and e∥w(t)∥ 2 2 < φw < ∞, respectively. a3. the sensors and the controller are geographically separated and connected by errorless digital channels. 2.3 problem statement we consider the system (1) under assumptions a0 −a3 above. we quantify the performance of a coder-controller as a prescribed control performance by the asymptotic state norm j := lim t→∞ e∥x(t)∥2. (2) here we are concerned with how small the plant states can be made as t → ∞. if the system (1) is controllable in the unstable modes, then there exists a matrix k such that all eigenvalues of a − bk have magnitude smaller than unity. in this case it seems logical to try to implement a quantized state feedback control law of the form u(t) = −kx̂(t) where x̂(t) denotes the estimate of x(t). 94 q.q. liu, g.h. yang the main problem is to construct an encoder-decoder pair and to design a controller which guarantee both the stabilization and the control performance (2) of the system (1) in the mean square sense lim t→∞ supe∥x(t)∥2 < ∞ (3) using the finite data rate provided by the digital feedback link. 3 feedback control under data-rate constraints this section deals with the quantized feedback stabilization problem for the system (1). the purpose here is to present a quantization, coding and control scheme to stabilize the closed-loop system (1) with the performance (2) in the mean square sense (3). assume that the encoder has access to the full state vector for practical plants. first we give a preliminary lemma. lemma 1[24]. let x ∈ r denote a random variable and x̂ denote an estimate of x. the expected distortion constraint is defined as d ∈ r+. for a given d ≥ ex(x − x̂)2, there must exist a quantization and coding scheme if r(t) satisfies r(t) > r(d) ≥ 1 2 log σ2(x) d (bits/sample) where σ2(x) = ex(x − ex)2. proof. see [24]. remark 1. • r(d) denotes the lower bound of the amount of information which is needed to reconstruct the initial condition to some distortion fidelity which satisfies conditions for the stabilization. it means that more information available at the decoder will lead to better control performance. • since lemma 1 gives a lower bound of data rate for all quantization and coding scheme, for a given d, x̂t can be obtained by many quantization and coding schemes. however, r(d) can not approach the fundamental lower limit if the scheme used is not optimal. the following theorem is our main result in this paper. theorem 1. consider the fully-observed system (1). under assumptions a0 −a3, given a state feedback control law of the form u(t) = −kx̂(t), satisfying that all eigenvalues of a-bk have magnitudes smaller than 1, ∀ε ∈ (0,1), the system (1) is mean-square stabilizable with j = lim t→∞ e∥x(t)∥2 < 1 1 − ε ∥f∥2φw if the data rate r(t) satisfies the following inequality: r(t) > 1 2 log | det[at a−(a−bk)t (a−bk)]| | det[εi−(a−bk)t (a−bk)]| (bits/sample). proof: consider the closed-loop system x(t + 1) = ax(t) − bkx̂(t) + fw(t) which we can also write as x(t + 1) = a(x(t) − x̂(t)) + (a − bk)x̂(t) + fw(t). quantized feedback control for networked control systems under communication constraints 95 then, we have e∥x(t + 1)∥2 =trace[ex(t + 1)xt (t + 1)] =trace[ae(x(t) − x̂(t))(x(t) − x̂(t))t at ]+trace[few(t)wt (t)ft ]) +trace[(a − bk)ex̂(t)x̂t (t)(a − bk)t ] + 2trace[(a − bk)ex̂(t)wt (t)ft ] +2trace[ae(x(t) − x̂(t))wt (t)ft ] + 2trace[ae(x(t) − x̂(t))x̂t (t)(a − bk)t ]. (4) notice that x(t) and w(t) are mutually independent. this implies ex̂(t)wt (t) = 0, e(x(t) − x̂(t))wt (t) = 0 (5) following from assumption a2. furthermore, e[x(t) − x̂(t)]x̂t (t) = ev (t)x̂t (t) = 0 where v (t) denotes the quantization error with zero mean. substituting it and (5) into (4), we obtain e∥x(t + 1)∥2 =traceaς x(t)|x̂(t)a t +trace(a − bk)σ x̂(t) (a − bk)t + e∥fw(t)∥2 where we define σ x(t)|x̂(t) := e(x(t) − x̂(t))(x(t) − x̂(t)) t and σ x̂(t) := ex̂(t)x̂t (t). if assume that e∥x(t)∥2 >traceaς x(t)|x̂(t)a t +trace(a − bk)σ x̂(t) (a − bk)t which is equivalent to εe∥x(t)∥2 =trace[aς x(t)|x̂(t)a t ]+trace[(a − bk)σ x̂(t) (a − bk)t ] (6) with ε ∈ (0,1), we see that e∥x(t + 1)∥2 = εe∥x(t)∥2 + e∥fw(t)∥2. then, we obtain e∥x(t)∥2 = εte∥x(0)∥2 + ∑t−1 i=0 ε t−i−1e∥fw(i)∥2 < εtφ0 + 1−εt 1−ε ∥f∥ 2φw following from assumption a2. thus, j = limt→∞ e∥x(t)∥2 < 11−ε∥f∥ 2φw . it means that if we can design a quantization and coding scheme such that (6) holds, the system (1) is mean square stabilizable. next, we further argue that there exists a lower bound of the data rate r(t) based on the quantization and coding scheme to stabilize the system (1) with j = limt→∞ e∥x(t)∥2 < 11−ε∥f∥ 2φw . observe that εe∥x(t)∥2 = εtraceσx(t) 96 q.q. liu, g.h. yang and σ x̂(t) = ex̂(t)x̂t (t) − ex(t)xt (t) + ex(t)xt (t) = σx(t) − e(x(t)xt (t) − x̂(t)x̂t (t)) = σx(t) − e(x(t) − x̂(t))(x(t) − x̂(t))t = σx(t) − σx(t)|x̂(t) where we define σx(t) := ex(t)xt (t). substituting the equations above into (6), we have εtraceσx(t) =trace[aσx(t)|x̂(t)a t ]+trace[(a − bk)σ x̂(t) (a − bk)t ] =trace[at aς x(t)|x̂(t)]+trace[(a − bk) t (a − bk)σ x̂(t) ] =trace[at aς x(t)|x̂(t)]+trace[(a − bk) t (a − bk)(σx(t) − σx(t)|x̂(t))] =trace[(at a − (a − bk)t (a − bk))σ x(t)|x̂(t)] +trace[(a − bk)t (a − bk)σx(t)] which we can also write as trace(θ1σx(t)) =trace(θ2σx(t)|x̂(t)) (7) where we define θ1 := εi − (a − bk)t (a − bk) and θ2 := at a − (a − bk)t (a − bk). notice that there exist unitary matrices p , q, h that diagonalize the symmetric matrices θ1, θ2, σx(t) and σx(t)|x̂(t), respectively. namely, θ1 = p t θ̄1p , θ2 = qt θ̄2q, σx(t) = ht σx̄(t)h and σx(t)|x̂(t) = h t σ x̄(t)|x̃(t)h where we define x̄(t) := hx(t), x̃(t) := hx̂(t), θ̄1 :=diag[α1, · · · ,αn] and θ̄2 :=diag[β1, · · · ,βn]. then, (7) is equivalent to the following equations: trace(pt θ1σx(t)h) =trace(qt θ2σx(t)|x̂(t)h). since tracemn=tracenm with matrices m ∈ rn×m and n ∈ rm×n holds , thus the equality above is equivalent to trace(σx(t)hpt θ1) =trace(σx(t)|x̂(t)hq t θ2). then, we have trace(ht σx(t)hpt θ1p) =trace(ht σx(t)|x̂(t)hq t θ2q). namely trace(σx̄(t)θ̄1) =trace(σx̄(t)|x̃(t)θ̄2). thus, we obtain trace(θ̄1σx̄(t)) =trace(θ̄2σx̄(t)|x̃(t)) which we can also write as σni=1αiex̄ 2 i (t) = σ n i=1βie(x̄i(t) − x̃i(t)) 2 (8) where define x̄(t) := [x̄1(t), · · · , x̄n(t)]t and x̃(t) := [x̃1(t), · · · , x̃n(t)]t . (8) can be viewed as a quantization and coding criterion. then, based on the criterion (8), we can design a quantization and coding scheme to stabilize the system (1) in the mean square sense (3). however, the analyses on the quantization and coding scheme rely heavily on the assumption of the boundability of the initial condition x(0) and the disturbance input w(t). similar to quantized feedback control for networked control systems under communication constraints 97 shannon source coding, there exists a lower bound of the data rate r(t), satisfying the criterion (8). we quantize each x̄i(t) and obtain the estimate x̃i(t) of x̄i(t)(i = 1, · · · ,n), and encode it into a symbol sti which is transmitted over an errorless digital channel. then, the symbol st=[st1 · · · stn] is decoded and converted into a control input ut = −bkx̂t = −bkht x̃t. namely, we get x̂(t) = ht x̃(t). notice that σ2(x̄) ≤ ex̄2i (t). define d := [ d1 · · · dn]. thus, if let di ≤ αiβi σ 2(x̄i(t)), the criterion (8) must be satisfied. following from lemma 3.1, the following inequality can be obtained: ri(t) > ri(di) ≥ 1 2 log σ2(x̄i(t)) di ≥ 1 2 log σ2(x̄i(t)) αi βi σ2(x̄i(t)) ≥ 1 2 log βi αi (bits/sample). thus, it follows that r(t) = ∑n i=1 ri(t) > 1 2 log ∏n i=1 βi αi = 1 2 log | det(θ̄2)| | det(θ̄1)| = 1 2 log | det(θ2)| | det(θ1)| = 1 2 log | det(at a−(a−bk)t (a−bk))| | det(εi−(a−bk)t (a−bk))| (bits/sample). thus, if r(t) satisfies the inequality above, there exists a coder-controller to stabilize the system (1) with j = lim t→∞ e∥x(t)∥2 < 1 1 − ε ∥f∥2φw . 2 remark 2. • theorem 1 states how fundamental tradeoffs between the data rate and the prescribed control performance. clearly, the better performance can be achieve when the greater data rate is employed. • observe that corresponding to the classical situation without communication constraints, the performance becomes j = lim t→∞ e∥x(t)∥2 → ∥f∥2φw as the limit r(t) → ∞. 4 numerical example to illustrate the effectiveness of communication constraints for stabilization of stochastic linear systems, we present an open loop unstable system as follows, x(t + 1) =   1.61 0.52 0.25 0.52 1.61 0.36 −0.21 0.61 4.31  x(t) +   1 1 2  u(t) + 0.3w(t). 98 q.q. liu, g.h. yang 0 1 2 3 4 5 −200 −150 −100 −50 0 50 100 150 200 time (s) x1 x2 x3 figure 2: the system state responses with limited data rates. let ε = 0.4, x(0)=[50 -20 -50]t , and φw = 10. here we give a controller gain k = [−0.6738 − 0.0681 2.9360]. a simulation is shown in fig.2. remark 3. the obtained state responses are shown in fig.2. it states that the encodingcontrol scheme based on theorem 1 can stabilize the system above. 5 conclusion this paper addressed the feedback control problem for stochastic linear systems with limited data rates. the approach taken here was based on the hypothesis that sensors and controllers are connected by a rate-limited, time-varying communication channel. based on the presented lower bound of data rates, there exists a coder-controller to guarantee both stabilization and a prescribed control performance. the simulation results have illustrated the effectiveness of the quantization, coding and control scheme. acknowledgment this work was supported in part by the funds for creative research groups of china (no. 60821063), national 973 program of china (grant no. 2009cb320604), the funds of national science of china (grant no. 60974043), the 111 project (b08015), and the funds of doctoral program of ministry of education, china (20100042110027). bibliography [1] w. s. wong and r. w. brockett, systems with finite communication bandwidth constraints ii: stabilization with limited information feedback, ieee trans. automat. control, 44(5):1049-1053, may 1999. 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[24] t. cover and j. thomas, elements of information theory. new york: wiley, 2006. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 8-20 genetic algorithm for fuzzy neural networks using locally crossover d. arotaritei dragos arotaritei “gr. t popa” university of medicine and pharmacy romania, 700115 iasi e-mail: dragos_aro@yahoo.com abstract: fuzzy feed-forward (ffnr) and fuzzy recurrent networks (frnn) proved to be solutions for "real world problems". in the most cases, the learning algorithms are based on gradient techniques adapted for fuzzy logic with heuristic rules in the case of fuzzy numbers. in this paper we propose a learning mechanism based on genetic algorithms (ga) with locally crossover that can be applied to various topologies of fuzzy neural networks with fuzzy numbers. the mechanism is applied to ffnr and frnn with l-r fuzzy numbers as inputs, outputs and weights and fuzzy arithmetic as forward signal propagation. the α-cuts and fuzzy biases are also taken into account. the effectiveness of the proposed method is proven in two applications: the mapping a vector of triangular fuzzy numbers into another vector of triangular fuzzy numbers for ffnr and the dynamic capture of fuzzy sinusoidal oscillations for frnn. keywords: rules, figures, citation of papers, citation of books, examples. 1 introduction fuzzy neural networks are usually based on neural network architecture [1] with fuzzification of inputs, outputs, weights, or rules that are applied using fuzzy systems [2]. ga is one of the alternatives to gradient techniques (see [3], [4] and [5]) that can be used to develop effective end efficient learning algorithm for fuzzy neural networks. in [6], the authors proposed a learning algorithm for ffnn with fuzzy inputs and weights called "fuzzy delta rule". this algorithm is derived by replacing fuzzy differentiation with real differentiation, this fact being point out by the authors. architecture of multilayer feedforward algebraic neural network for fuzzy input vectors, fuzzy outputs and fuzzy weights is proposed in (see [7] and [8]). the input-output relations in this fuzzy neural network, using max/min operators, are defined by the extension principle of zadeh. in these papers, the authors used only symmetric triangular fuzzy numbers (see [7] and [8]). a cost function for the level sets (α-cuts) of fuzzy outputs and fuzzy target is defined. the fuzzy output from each unit in the fuzzy neural network is computed for level sets of fuzzy inputs, fuzzy weights and fuzzy biases. the learning algorithm, based on the cost function, uses a gradient descent method. a heuristics method, in order to respect the shape of triangular fuzzy weights, during the learning stage is proposed (see [7] and [8]). the same architecture but with non-symmetric and trapezoidal fuzzy numbers has been used in [9]. the authors proposed and "trail-and-error" algorithm using adaptive updated based on gradient techniques developed in the frame of fuzzy arithmetic [9]. the α-cuts and fuzzy biases are also taken also into account [9]. a learning algorithm to fully connected fuzzy recurrent neural network (fcfrnn) has been developed in [10]. the algorithm is developed for non-symmetric triangular fuzzy numbers and gradient techniques developed in the frame of fuzzy arithmetic. the symmetric fuzzy numbers and crisp numbers are considered particularly cases [10]. copyright c⃝ 2006-2011 by ccc publications genetic algorithm for fuzzy neural networks using locally crossover 9 few papers refer to ga for adjusting fuzzy weights. in [11] the authors present a ga mechanism for adaptation of fuzzy weighs as lr-fuzzy numbers for ffnr. the single chromosome includes all the fuzzy weights in binary format. the single point crossover (split point sp) is applied to entire chromosome that represent the coded binary value of al the triangular fuzzy weights expressed as l-r fuzzy number with n parameters [11]. a structure of fuzzy recurrent neural network with ga learning algorithm is proposed in [12]. all the fuzzy weights are coded in a genome, a single string of bits that represent l-r fuzzy numbers. this string represents an individual,a potential solution of the problem. the connections in the recurrent structure follow a limited connection that is the structure is not fully recurrent structure in the sense of [14]. in both papers (see [11] and [12]) the fitness is the performance index and the objective is to minimize this error. this paper presents a ga algorithm based on learning mechanism that adjusts the fuzzy weights represented as l-r fuzzy numbers. the fuzzy neural networks computing operations are described in the fuzzy arithmetic framework. the proposed method is applied to two structures: feedforward structure and fully recurrent structure. the proposed method proved to be better than the existing algorithms in term of number of generations until the optimal solution has been reached. 2 the fuzzy arithmetic framework and the fuzzy neuron in this paper we used the input-output relations fuzzy neural network,using max-min operators, defined by the extension principle of zadeh, and applied in previously papers (see [9] and [10]). all the nodes have a semilinear (sigmoidal) transfer function. we denote a triangular fuzzy number as follow: x̃ = (xl, xc, xr) (1) a non-standard algebraic operation between two tfn,ã and b̃ is defined, as in (see [9] and [10]), by: c̃ = ã♢̃b̃ = x̃ = (cl, cc, cr) = (min(p), ac♢bc, max(p)) (2) p = al♢bl, al♢br, ar♢bl, ar♢br (3) the h-level set of a fuzzy number x̃ is defined, as: [x̃h] = {x|µx ≥ h, h ∈ r} for 0 < h ≤ 1 (4) in the equations (2)-(3), ♢ ∈ {+,−, ·, /} are the operations in classic arithmetic, and ♢̃ ∈ {+̃,−̃, ·̃, /̃} are the corresponding modified operations in fuzzy arithmetic, defined according to [7]. the division is restricted to division by a non-zero numbers, that is {bl, bc, br} ≠ 0 similar relations can be used to get the α-cuts. we consider a fuzzy neuron (fn) that perform input-output operations. the fn get an input vector fuzzy signal x̃ = (x̃1, x̃2, ..., x̃n} nd transfer it after multiplication with fuzzy weights w̃ = (w̃1, w̃2, ..., w̃n} by activation function to output. the total input of the fuzzy neuron is given by (see [9] and [10]): s̃ = w̃1̃·x̃1+̃w̃2̃·x̃2...+̃w̃n ·̃x̃n = fuzz∑ i=1,n w̃ĩ·x̃i+̃b̃ (5) 10 d. arotaritei ỹ = f(s̃) (6) in the equations above the sum is in the fuzzy arithmetic framework (2)-(3) and b̃ is the fuzzy bias. the output is computed by (5) and the extension principle of zadeh [13]. we used sigmoidal function: f(x) = 1 1 + e−x (7) due to monotonic increasing property of the sigmoidal function f, the tfn shape is preserved for the output of the neurons. in section 3 and 4 we will apply these assertions to two types of neural networks architectures: feedforward neural networks [1] and fully connected recurrent neural networks [14]. 3 feed-forward fuzzy neural networks there are three basically types of fuzzy neural networks depending on the type of fuzzification of inputs, outputs and weights (including biases): fuzzy weights and crisp inputs, crisp weights and fuzzy inputs and fuzzy weights and fuzzy inputs [11]. in what follows we consider the most complete fuzzification of neural networks: fuzzy inputs, fuzzy weights and fuzzy outputs. figure 1: a three layered ffnn in fig. 1 we showed an example of ffnn, a three layer ffnn. ffnn propagate the signal layer by layer until the signal reach the output layer. for each k layer we have: s̃kj = fuzz∑ i=1,nk w̃kjĩ·x̃i+̃b̃i (8) f(x) = f(s̃ j k) (9) in the equations above, j is the fuzzy neuron from k layer and i-th is the fuzzy neuron from k-1 layer. the training of ffnn is to adapt the network in order to mapping the known inputs to target outputs. the objective of the learning algorithm is to minimize the error measure between the desired outputs and the real outputs. the learning algorithm must adjust the fuzzy weights based of error measure in order to achieve this objective. genetic algorithm for fuzzy neural networks using locally crossover 11 figure 2: fully connected frnn 4 fuzzy recurrent neural networks let the frnn network have n units and m inputs. each bias allocation will be seeing as an input line whose value is always 1̃ = (1, 1, 1, ). let the frnn network have n units and m inputs. each bias allocation will be seeing as an input line whose value is always 1̃ = (1, 1, 1, ). we denote by (i, u, t) the set of indices for input units, output units and target units. lets x̃(t), ỹ(t) and d̃(t) denote the inputs, the outputs and the targets (if exists) of the units in rnn at time t, respectively. we denote, similar to [14], a generalized z̃(t) as follow: z̃k =   x̃k(t) if k ∈ i d̃k(t) if k ∈ t(t) ỹk(t) if k ∈ u −t(t) (10) the basic algebraic fuzzy neuron is based on the operations defined in (2)-(5), where the sum above denoted by ∑fuzz is the sense of the algebraic sum (4) and b̃k is the bias of the k-th unit. s̃k(t) = fuzz∑ r∈u∪i w̃kr·̃z̃r(t) = w̃k1̃·z̃1+̃...+̃w̃k,m+n·̃z̃m+n(t)+̃b̃ (11) ỹk(t + 1) = f(s̃k(t)) = f(s l k , s c k , s r k ) (12) the missing connection in the rafnn architecture is simply represented by a zero weight value (fuzzy number zero). 5 ga with locally crossover (galc) ga are optimization techniques based on principles of mechanism of natural evolution (see [3] and [4]). ga are working on possible space of solutions (usually codified by chromosomes) in order to find the best candidate suitable for a particularly problem. the fitness is a measure of performance of the individual in order to achieve to desired objectives. the objective function can be the maximization of fitness or minimization of fitness. two problems arise from ga usage for solving the optimization problems: the choice of the fitness function and the codification the values of parameters that must optimized into an 12 d. arotaritei individual chromosome. the range of values that are part of this codification play an important role related to both convergence speed of ga and the accuracy of the results. in our approach we will use the minimization of objective function that is a measure of output errors related to fuzzy numbers. this function is usually named the fitness function. the general fitness function can be defined based distance among desired fuzzy numbers and actual fuzzy numbers at outputs [11]. however, in our application we will use a more practical measure based on hamming distance. by this objective function we estimate more intuitive the difference between desired and real outputs. dfitness = min k∑ i=1 (|ỹdi − ỹi|) (13) ga uses binary encoded in order to represent genes or chromosomes. each binary value (0 or 1) is named allele [3]. the value of solutions is mapped as binary string in the process of encoding using linear of nonlinear functions. we used the representation of tfn as lr-type fuzzy numbers with n parameters [13]. each parameter is coded as binary string of a specified length. ã = (m, dl, dr) (14) in the case of no α-cuts, each fuzzy weight is represented by three chromosomes corresponding to central value wkjic , and l, r values that represents the left and the right spread are denoted by dwkjil and dw k jir (fig. 3). the values are coded into binary string in a predefined range. figure 3: chromosomes of one individual in the case of ffnn, w̃kji is fuzzy connection weight between the j-th neuron from k layer with i-th neuron from k-1 layer. each individual has three chromosomes that represent the strings for the three types of values. we must remark that in tfn the order must be preserved: wkji −dw k jil ≤ w k ji ≤ w k ji + dw k jir, dw k jil ≥ 0, dw k jir ≥ 0 (15) the selection process has two stages. the first stage is selection of two parents for crossover using a known schema: tournament, roulette or stochastic sampling. after this global selection, we perform a locally selection inside of the chromosome. this selection is inspired by the fact observed in crisp feedforward neural networks that not all the weights have the same contribution to forward propagation of the signal. usually, the weights from k layer has a different contribution than the weights from k+1 layer. conversely, it is natural to suppose that some parameters give a more contribution to best solution than others, so a fine locally adjustment can improve the global solution. genetic algorithm for fuzzy neural networks using locally crossover 13 the locally selection selects the weights, the locations in the chromosome where the corresponded weight is coded in binary string for locally crossover (fig 4). figure 4: selection of genes (weights) for locally crossover for c value the same selected location are taken into account in one step for all three values, as in (13). the representation is given in fig. 5. figure 5: selection of genes (weights) for locally crossover for dl, dr values the crossover operation pick the selected genes and mate them in order to produce two offspring genes. the split point (sp) is chosen randomly, the same for both parents. the example from fig. 6 illustrate the locally crossover. the same sp is applied to corresponding gene that code the left and right spread of the selected weights. figure 6: locally crossover between selected genes mutation is made globally al level of entire chromosomes. the mutation pick and bit (allele) with a probability pm and flip this value to 1 if the value is 0 and to 0 if the value is 1. mutation 14 d. arotaritei implements a random search at global level. by pm , the level of search can be modified, the greater is pm , the level of search in the solution space is greater. figure 7: global mutation the galc algorithm is based on the practical following observation. due to nonlinear aspect of the transfer function of the neuron, the changes in some weights can have a greater contribution to result than the changes in the other weights. also, the impact of this changes for ffnn can decrease or increase in the forward process (fig. 8). galc propose to use this observation in order to "encourage" the more contributing weights to be selected in order to do crossover and evolve faster meanwhile the rest of weights will evolve slowly, based only on mutations. for the same dx (dx1 = dx2 ), we can have dy2 > dy1 in same cases with one or two magnitude order. figure 8: the influence of function of activation over quantitative adaptation of the weight let denote by nn the total number of neurons in the ffnn. the proposed algorithm based on basic ga procedures is summarized below. step 1: linear mapping of the solution space (fuzzy weights)into chromosomes (binary string) that represent individuals of the population p. step 2: initialize the population p with random solutions (uniform distribution). the population has n individuals that represents 3*n set of chromosomes for tfn with no fuzzy bias and 3*n + 3*nn set of chromosomes for tfn with fuzzy bias. step 3: evaluate the fitness of the population p. step 4: evaluate the stop criteria. if the stop criteria is fulfilled, go to step 10. step 5: store the best individual of the current generation and the best individual of all the generations. step 6: generate a new population using selection operator according to selection schema based on fitness values of the chromosomes. step 7: random selection of genes for locally crossover. the selected genes are a percent pr from total number of the genes that represent a chromosome. genetic algorithm for fuzzy neural networks using locally crossover 15 step 8: locally crossover for selected genes and creation of new generation of the population. the corresponding genes are mated with probability pc and the results offspring replace the parents in the new population. step 9: global mutation over chromosomes with random probability pm . step 10: map the best individual of all the generations into solution, the fuzzy weights of the ffnn. the stop criterion can have different forms. one of the most common is a predefined number of generations. another one can be the stop of the process, before a maximum number of generations if no significant improvement has been made in the best individual. in our application we used the first criterion. the galc algorithm can be easily extended to α-cuts. for each h-level we must allocate 2 chromosomes that correspond to dl and dr values al level h (fig. 9). the central value is unchanged from one level to next level. we start with the base level (h=0) where we adapt the l, c and r values using l-r type representation (12). next we consider the c value and we must adapt the left and right spread at the level h=1,2,3,... n. figure 9: chromosomes of one individual for h-level because of max/min operators and multiplication operation, the shape of membership for fuzzy weights (and biases) is a curved triangle. restrictions must be made in order to avoid non desired occurrence in the weights adaptation process. figure 10: a non-permitted occurrence in the weights adaptation process the restrictions are: dw k,h jil ≤ dw k,h−1 jil , dw k,h jir ≤ dw k,h−1 jir (16) these restrictions are included in the algorithm in the following way. if the after the crossover, the children accomplish the rules (15) the local crossover operation is validated and the children are selected in the next generation depending on the fitting. if the children doesn’t accomplish the rules (15) the local crossover operation is canceled and the selected weights remain unchanged. 16 d. arotaritei 6 applications 6.1 a mapping of non-symmetric triangular fuzzy numbers with ffnn we apply the proposed method to approximate realisation of non-linear mapping of fuzzy numbers. the tfn from input space have the bases inside the interval [0, 1] and are mapping to tfn which have the bases in [0,+1]. the ffnn has 3 inputs, 6 neurons in the hidden layer and 3 neurons in the output layer. the ffnns fuzzy error can be defined as a distance between fuzzy desired output and the fuzzy real output [15]. because we are interested in accuracy of outputs for all the points that represents the fuzzy number, we express the error measure: e l,c,r i = ∣∣∣dl,c,ri −yl,c,ri ∣∣∣ (17) jtotal(t) = ∑ q=l,c,r;i=1...n e q i (18) the inputs and the target (desired outputs are): x̃ = (0.2,0.1,0.1),(0.3,0.1,0.2),(0.6,0.2,0.1) d̃ = (0.4,0.2,0.2),(0.8,0.2,0.1) we generate an initial population of random binary strings that represent the chromosomes. the population is set to p=100 individuals, pc = 0.7, pm = 0.3, gen = 1000 (the maximum number of generations). we set pr = 20%, that is from 6×3 + 6× 2 = 30 weights, and at each generation we select random a number of 0.2*30 = 6 weights. figure 11: the error evolution (ffnn example) the best result is obtained in gen= 68, when j = 0.0927 (fig. 11). the best fitness individual has the lowest value of fitness (we used the best individual minimum fitness approach). the desired and the real output values are: d̃ = (0.4,0.2,0.2),(0.8,0.2,0.1) x̃ = (0.4055,0.1945,0.2176),(0.7714,0.1636,0.1119) genetic algorithm for fuzzy neural networks using locally crossover 17 we made experiments with α-cuts, also. in our experiment we used three α-cuts, at levels h=0, h= 0.33, and h = 0.66. the experimental results for one of the fuzzy weights (k=2, j =1, i=2) and the levels of the α-cuts mentioned above is showed in fig. 12. figure 12: the fuzzy weight w̃212 with three α-cuts) 6.2 frnn learning of defined dynamic the algorithm designed to train an arbitrary network dynamics can be tested using an interesting class of behaviors, the oscillations [14]. a pair of logistic units (fig. 13) was used to learn a trajectory of a tfn, composed by three sine waves with the same frequencies and the same phase but different range corresponding to l, c and r values of the tfn (fig. 14). figure 13: fuzzy sine wave network. the overall error measure at each sample time t is given by: j(t) = ∑ k∈u [[∣∣elk (t)∣∣ + ∣∣erk (t)∣∣] + ∣∣eck (t)∣∣] (19) j fuzz total (t0, t1) = { d l,c,r k (t)−y l,c,r k (t) if k ∈ t(t) 0 otherwise (20) the network performance measure over trajectory is defined by: j fuzz total (t0, t1) = t1∑ t=t0+1 j(t) (21) 18 d. arotaritei figure 14: the training sine waves (targets). in our particular case we consider a complete trajectory a sinus period (2π). the best individual is obtained after 176 generation after 10 starting new generations and epochs. usually, the stable solution is obtained in less than 500 generation with uniform random distribution of initial population made from 100 individuals. table 1: rafnn parameter for sine waves l c r w̃11 0.3026 0.3687 0.4169 w̃12 -0.4405 -0.2509 -0.0286 w̃21 -0.2506 -0.0076 -0.0035 w̃22 0.2214 0.4054 0.5207 b̃1 -0.3521 -0.3028 -0.2499 b̃2 -0.1499 -0.0085 0.0772 stable, the sine like oscillation are obtained for sine frequencies above 20 network ticks per cycle with the 20,000 ticks or less. the output of the unit 1 in the absence of a teacher imposed to unit 2, after stable oscillation had been established is shown in fig. 15 as we are the expectations, taking into account the results from [14], the frequency of the free-running logistic is around 8% lower than the trained frequency. the amplitude is smaller and it is diminishing in time. this example proved that the frnn is able to learn a dynamics that is the sinusoidal waves. the output of the unit 1 has at each step a triangular fuzzy number, and values yl, yc and yr have a sinusoidal shape. it is clear from results that the output error is important, but out target was to learn predefined dynamic using minimization of error. the algorithm stopped when the performance not improve during the last s generations (in our case, s=3). 7 conclusions we proposed to use a novel method and a new learning algorithm, galc for fuzzy neural networks with feedforward and fully recurrent architecture. the method and a learning algorithm proved by theoretical and experimental approach to be effective with acceptable stable solution. genetic algorithm for fuzzy neural networks using locally crossover 19 figure 15: the output of the unit 1. the solution is usually obtained in a shorter time (in terms of number of generation) than the actually ga based learning algorithms. moreover, the algorithm is effective also for fully fuzzy recurrent neural networks as they has been defined in section 4. different from (see [11] and [12]) we proposed a complete fuzzification of the neural network in the sense that the sum of the fuzzy weighted inputs are also in the fuzzy arithmetic framework (see [11] and [12]). the proposed method offer a possibility to work with long strings. it is know that in the case of very long chromosome, the global crossover can slow considerably the algorithm convergence. that is a very large number of generation is required in order to achieve the global solutions. the proposed method that start with a larger number of individual in the initial population and selective locally adaptation could improve the performance of convergence by pressure of these parameters that has a greater importance in the best solution. bibliography [1] s. haykin, neural networks: a comprehensive foundation, prentice hall, 1998. [2] d.j. dubois, h. prade, fuzzy sets and systems: theory and applications, academic press, 1980. [3] d. e. goldberg, genetic algorithms in search, optimization, and machine learning, addison-wesley professional, 1989. [4] j. r .koza, genetic programming: on the programming of computers by means of natural selection, the mit press, 1992. [5] o. cordon, v. herrera, m. lozano, on the combination of fuzzy logic and evolutionary computation: a short review and bibliography, in: w. pedrycz (ed.), evolutionary computation, kluwer academic publishers, dordrecht, pp.33-56, 1997. [6] y. hayashi et. al., fuzzy control rules in convex optimization, fuzzy neural networks with fuzzy signals and weights ijcnn’92, vol. 2, pp. 165-195, 1992. 20 d. arotaritei [7] h. ishibuchi, k. kwon, h.tanaka, a learning algorithm on fuzzy neural networks with triangular fuzzy weights, fuzzy sets and systems, vol. 72, no. 3, pp. 257-264, 1995. [8] h. ishibuchi, r. fujioka, h.tanaka, an architecture of neural networks for input vectors of fuzzy numbers, proc. fuzz-ieee ’92, san diego, usa, pp. 643-650, 1992. [9] h.n. teodorescu, d. arotaritei, e. lopez gonzales, a general trail-and-error algorithm for algebraic fuzzy neural networks, proceedings of the fourth european congress on intelligent techniques and soft computing, aachen, germany, september 2-5, vol. 1, pp. 8-12, 1996. [10] d. arotaritei, recurrent algebraic fuzzy neural networks based on fuzzy numbers, joint 9th ifsa world congress and 20th nafips international conference, vol. 5, pp. 2676 2680, 2001. [11] r.a. aliev, b. fazlollahi, r.m. vahidov, genetic algorithm-based learning of fuzzy neural network, part 1: feed-forward fuzzy neural networks, fuzzy sets and systems, vol. 118, issue 2, pp. 351-358, 2001. [12] r. a. aliev, r. r. aliev, b. g. guirimov, k. uyar, recurrent fuzzy neural network based system for battery charging, lecture notes in computer science, vol. 4492, pp. 307-316, 2007. [13] l.a. zadeh, fuzzy sets, inform. control 8, pp. 338-353, 1965. [14] r.j. williams, d. zipser, a learning algorithm for continually running fully recurrent neural networks, neural computation, vol. 1, issue 2, pp. 270-280, 1989. [15] c. chakraborty, d. chakraborty, a theoretical development on a fuzzy distance measure for fuzzy numbers, mathematical and computer modelling, nr. 43, pp. 254-261, 2006. international journal of computers communications & control issn 1841-9836, 9(5):531-538, october, 2014. estimation of the technical state of automotive disc brakes using fuzzy logic m. baban, c.f. baban, c. bungau, g. dragomir, r.m. pancu marius baban, calin florin baban*, constantin bungau, george dragomir, rares mihai pancu university of oradea, romania, oradea, universitatii st., 1 mbaban@uoradea.ro, bungau@uoradea.ro, georgedragomir@yahoo.com, pancurares@yahoo.com *corresponding author: cbaban@uoradea.ro abstract: according to existing studies the phenomena that occur in the exploitation of the braking system are very complex and an analytical mathematical modeling of braking process it is difficult to be developed [1]. since these phenomena are also characterized by some uncertainties, a fuzzy logic approach has been employed in this research for the estimation of technical state of the disc brakes. their technical state was expressed through the thickness variation, which was used as the output linguistic variable. the vibrations and temperature of the disc brakes were used as the input linguistic variables. the fuzzy decision system for the estimation of technical state of the disc brakes has been implemented with the fuzzy logic toolbox t m of the matlab r software, which can be employed to determine if the thickness of the disc brakes becomes smaller than the limit value prescribed by the manufacturer. keywords: disc brake, temperature, vibration, thickness variation, fuzzy logic approach. 1 introduction throughout the exploitation of the disc brake its thickness is modified in the contact interface with the friction material of the brake pads, due to the effect of wear. if the thickness of the disc brake becomes smaller than a certain limit value prescribed by the manufacturer, the probability of the deformation of the disc brake or even its damage is increasing at the high intensity or long duration of braking. this may have serious implications for the road accidents. the replacement of discs and brake pads depends on the number of traveled kilometers and it is usually done preventively, when periodic revisions are carried out. such replacement can occur even if maximum wear is not reached, which leads to higher exploitation costs due to unused of disc brakes for their entire lifetime. on the other hand, there may be situations where disc brakes reach maximum wear before a periodic revision is scheduled, because of some exploitation conditions that require a more intense use of the braking system of the vehicle. due to the complexity of the phenomena that occur in the exploitation of the braking system, phenomena that are influenced by different factors [7] and are characterized by some uncertainties, an analytical mathematical modeling of braking process it is difficult to be developed [1]. during the braking process, vibrations are produced within the contact area between the discs and brake pads and their spectrum depends on factors such as the degree of wear and temperature. studies on vibrations in the car braking system are presented in ( [13], [15], [16]), while investigations about the temperature of disc brakes are shown in ( [3], [8], [23]). the employement of fuzzy logic in automotive engineering has been presented in different studies. von altrock [20] emphasized the use of fuzzy logic for the abs systems, control of the engine and control of automatic transmission. the control of the braking system, the suspension control and the vehicle dynamics control are presented among the relevant research areas of fuzzy control in automotive field ( [9]). the use of fuzzy logic for the abs systems has been shown copyright © 2006-2014 by ccc publications 532 m. baban, c.f. baban, c. bungau, g. dragomir, r.m. pancu in other studies ( [5], [11], [21], [22]). fuzzy logic was also used to improve the handling and stability of vehicles ( [4], [18]), to control the speed of an automotive engine ( [19]) or to control a suspension system of automotive ( [6]). while fuzzy logic in automotive field has been used lately, its employement for the estimation of technical state of the disc brakesis is still a challenging approach and this area of research is less developed. within this framework, the fuzzy logic ( [12], [14], [17]) has been proposed for the estimation of technical state of the disc brakes, so they can be replaced before the maximum limit of wear is reached and without depending on the planned periodic revisions. the research begins with the description of the experimental stand developed for the estimation of the technical condition of disc brakes. a fuzzy logic decision approach for the estimation of technical state of the disc brakes is shown in the next section. the implementation of the fuzzy logic decision approach for the estimation of technical state of the disc brakes with the fuzzy logic toolbox tm of the matlab r software is also depicted. at the end, conclusions and recommendations for future research are presented. 2 experimental method for the estimation of the technical condition of disc brakes, the stand depicted fig. 1 has been developed. the stand is based on a powertrain that consists of the following main elements: a) four cylinders diesel engine with the following characteristics: maximum power 43.5 kw at 4600 rot/min, maximum torque 110 nm at 2000 rot/min; b) gearbox 5-speed manual transmission; c) macpherson front suspension; d) hydraulic brake system composed of: a master brake cylinder, operated by a screw to ensure precise movement of the piston within its interior; -a brake pipe and a brake caliper; -a full disc brake with the diameter of 247 mm; -brake pads. the flir sc 640 infrared camera was used to monitor the temperature of braking disc. the vibration monitoring was performed with a system composed of pc-topmessage device (made by delphin technology ag)-accelerometer sensor. the fluid pressure in the hydraulic circuit was monitored through a manometer with glycerin (scale 0 ... 6 mpa). the speed rotation of the disc brake (expressed in km/h) was monitored by a speedometer. the contact force between the brake pads and disc was kept constant by maintaining the pressure of the brake pipe fluid at 1 mpa. the disc rotation speed was also maintained constant at a value corresponding to vehicle speeds of 30 km/h. the first speed of the gearbox was used to provide an engine speed in the stable operation area, without trepidation that could affect the vibrations measurement. due to the dilatation of the material of the disk brake as a result of increasing its temperature during the braking process, we proceeded to fine tune of the position of the piston in the brake master cylinder using a screw-nut mechanism to maintain constant the pressure of the brake fluid. the experiments were conducted using two disc brakes, corresponding to a new disc (thickness = 10 mm) and to a disc with the thickness at the lower limit prescribed by the manufacturer (thickness = 8.5 mm). estimation of the technical state of automotive disc brakes using fuzzy logic 533 speedometer manometer accelerometer sensor figure 1: the experimental stand for the estimation of the technical condition of disc brakes 3 results a lowest value of the vibration amplitude equal to 1.46 mm/s rms was measured for a new disc brake (thickness=10 mm) at a temperature of 50.9 0c. a highest value of the vibration amplitude equal to 2.4 mm/s rms was measured for a disc brake with the thickness of 8.5 mm at a temperature of 154.2 0c. the fuzzy logic decision approach described in [2, p.22-23] was employed for the estimation of technical state of the disc brakes, as follows: 1) the t=temperature [0c] and avib=vibration’s amplitude [mm/s] rms were used as the input linguistic variables: lvi = {t,avib} (1) considering the measured value of the t and avib, their domain values have been defined as follows: t : dt = [50.9,154.2] avib : davib = [1.46,2.4] (2) 2) for the t and avib variables the linguistic terms have been established as: t : ltt =   tverysmall tsmall tmedium tbig tverybig   (3) 534 m. baban, c.f. baban, c. bungau, g. dragomir, r.m. pancu and avib : ltavib =   av verysmall av small av medium av big av verybig   (4) among the membership functions, the triangular functions are extensively employed and they have been proposed as the membership functions for both t and avib variables. according with the definition of [10, p.25], their expressions are: mftk (x,atk,btk,ctk) =   0, x ≤ atk x−at k bt k −at k , atk ≤ x ≤ btk ct k −x ct k −bt k , btk ≤ x ≤ ctk 0, ctk ≤ x ,k = 1...5 (5) and mfavibj (x,aavibj,bavibj,cavibj ) =   0, x ≤ aavibj x−aavibj bavibj −aavibj , aavibj ≤ x ≤ bavibj cavibj −x cavibj −bavibj , bavibj ≤ x ≤ cavibj 0, cavibj ≤ x ,j = 1...5 (6) where: atk < btk < ctk are the parameters of each triangular membership function mf t k in the expression (5), k=1...5; aavibj < bavibj < cavibj represent the parameters of each triangular membership function mfavibj in the expression (6), j=1...5. 3)the tbd=the thickness of the brake disc [mm] was employed as the output variable: lvo = {tbd} (7) for the tbd variable, the domain value has been defined as: tbd : dtbd = [8.5,10] (8) 4) for the tbd variable the linguistic terms have been established as: tbd : lttbd =   tbdverysmall tbdsmall tbdmedium tbdbig tbdverybig   (9) estimation of the technical state of automotive disc brakes using fuzzy logic 535 the triangular function have also been proposed as the membership function for tbd variable and its expression is [10, p.25]: mftbdr (x,atbdr,btbdr,ctbdr) =   0, x ≤ atbdr x−at bdr bt bdr −at bdr , atbdr ≤ x ≤ btbdr ct bdr −x ct bdr −bt bdr , btbdr ≤ x ≤ ctbdr 0, ctbdr ≤ x ,r = 1...5 (10) where atbdr < btbdr < ctbdr represent the parameters of each triangular membership function mftbdr in the expression (10), r=1...5. 5) the fuzzy rules base is: rule1 : {if (av verysmall) and (tverysmall) then (tbdverybig)} rule2 : {if (av verysmall) and (tsmall) then (tbdverybig)} rule3 : {if (av small) and (tverysmall) then (tbdverybig)} ... rule9 : {if (av mediu) and (tmedium) then (tbdmedium)} ... rule23 : {if (av verybig) and (tbig) then (tbdverysmall)} rule24 : {if (av big) and (tverybig) then (tbdverysmall)} rule25 : {if (av verybig) and (tverybig) then (tbdverysmall)} (11) 6) the centroid method [17, p. 98] has been used to obtain the the defuzzified value tbd of the thickness of the disc brake. the fuzzy logic decision approach for the estimation of technical state of the disc brakes has been implemented with the fuzzy logic toolbox tm of the matlab r software (fig. 2). the inference rules are shown in figure 3. figure 4 depicts the dependence of the tbd=f(t, avib). considering t=116 0c and avib=2.16 mm/s rms we obtain tbd=8.9 mm, so that thickness of the disc brake is within prescribed limits [8.5,10]. 536 m. baban, c.f. baban, c. bungau, g. dragomir, r.m. pancu figure 2: the fuzzy decision system technical-state-disc-brakes.fis figure 3: the inference rules of the fuzzy decision system technical-state-disc-brakes.fis figure 4: the dependence tbd=f(t, avib) estimation of the technical state of automotive disc brakes using fuzzy logic 537 4 conclusions the phenomena that occur in the exploitation of the braking system are very complex and they are characterized by some uncertainties. therefore, a fuzzy logic approach has been employed in this research for the estimation of technical state of the disc brakes, expressed through their thickness variation. a stand has been developed to establish the domain values of the temperature and vibration amplitude of the disc brakes, which were used as the input linguistic variables. the thickness of the disc brakes was employed as the output linguistic variable. the fuzzy logic toolbox tm of the matlab r software was used to develop the fuzzy decision system, which can be applied to establish if the thickness of the disc brakes is within prescribed limits by the manufacturer. in our research both the contact force between the brake pads and disc, and the disc rotation speed were maintained constant. future studies are needed to investigate the development of a fuzzy decision system when these two characteristics are also variable. the use of fuzzy logic approach in joining with other artificial intelligence methods, including neural networks may also represent important area for future research. bibliography [1] aleksendric, d.; barton, d. c. 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(2010); temperature and thermal stresses in a pad/disc during braking, applied thermal engineering, 30: 354-359. int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 377-387 a 2-level metaheuristic for the set covering problem c. valenzuela, b. crawford, r. soto, e. monfroy, f. paredes claudio valenzuela, broderick crawford pontificia universidad católica de valparaíso valparaíso, chile {claudio.valenzuela, broderick.crawford}@ucv.cl ricardo soto 1. pontificia universidad católica de valparaíso valparaíso, chile, and 2. universidad autónoma de chile ricardo.soto@ucv.cl eric monfroy universidad técnica federico santa maría valparaíso, chile eric.monfroy@inf.utfsm.cl fernando paredes escuela de ingeniería industrial universidad diego portales santiago, chile fernando.paredes@udp.cl abstract: metaheuristics are solution methods which combine local improvement procedures and higher level strategies for solving combinatorial and nonlinear optimization problems. in general, metaheuristics require an important amount of effort focused on parameter setting to improve its performance. in this work a 2-level metaheuristic approach is proposed so that scatter search and ant colony optimization act as “low level" metaheuristics, whose parameters are set by a “higher level" genetic algorithm during execution, seeking to improve the performance and to reduce the maintenance. the set covering problem is taken as reference since is one of the most important optimization problems, serving as basis for facility location problems, airline crew scheduling, nurse scheduling, and resource allocation. keywords: metaheuristics, genetic algorithm, scatter search, ant colony optimization, set covering problem. 1 introduction the set covering problem (scp) is a classical problem in computer science and one of the most important discrete optimization problems since it can model conveniently real world problems. some of these problems — which can be modeled as a set covering problem — include facility location, airline crew scheduling, nurse scheduling, resource allocation, assembly line balancing, vehicle routing, among others [6]. there are several studies which have implemented a scp solution using metaheuristics [1,2,12]. depending on the algorithm that has been used, the quality of the solution wanted and the complexity of the scp chosen, it is defined the amount of customization efforts required. conveniently, this work proposes transferring part of this customization effort to another metaheuristic (a “high level" metaheuristic) which can handle copyright c⃝ 2006-2012 by ccc publications 378 c. valenzuela, b. crawford, r. soto, e. monfroy, f. paredes the task of parameters adjustment for a low level metaheuristic. this approach is considered as a multilevel metaheuristic since there are two metaheuristics covering tasks of parameter setting, for the former, and problem solving, for the latter [9]. the main design of the implementation proposed considers a genetic algorithm (ga) [10] at online (control) and offline (tuning) parameter setting for a low level metaheuristic (ant colony optimization (aco) or scatter search (ss)) using a reactive search approach and an automatic parameter tuning approach. in reactive search, feedback mechanisms are able to modify the search parameters according to the efficiency of the search process, i.e. the balance between intensification and diversification can be automated by exploiting the recent past of the search process through dedicated learning techniques [13]. the automatic parameter tuning is carried by an external algorithm which searches for the best parameters in the parameter space in order to tune the solver automatically. ant colony optimization and scatter search techniques [11] have shown interesting results at solving scp [6] and similar problems [5]. for the purpose of this work, the former is selected by its constructional approach for generating solutions, plus its stochastic-based operators. the latter is considered as an evolutionary (population-based) algorithm which uses, essentially, deterministic operators. both of them provide good reference metaheuristics in terms of their foundations, their problem solving approaches, their design maturity, and in terms of how different one is from the other, making them highly suitable to the development of this work. 2 set covering problem a general mathematical model of the set covering problem can be formulated as follows: (1) minimizez = n∑ j=1 cjxj j = {1,2,3, ...,n} subject to: (2) n∑ j=1 aijxj ≥ 1 i = {1,2,3, ...,m} (3) xj = {0,1} equation (1) is the objective function of set covering problem, where cj is the cost of jcolumn, and xj is decision variable. equation (2) is a constraint to ensure that each row is covered by at least one column where aij is a constraint coefficient matrix of size m x n whose elements comprise of either “1” or “0”. finally, equation (3) is the integrality constraint in which the value xj can be “1” if column j is activated (selected) or “0” otherwise. different solving methods have been proposed in the literature for the scp. there exist examples using exact methods [8], linear programing and heuristic methods [7], and metaheuristic methods [2]. has being pointed out, that one of the most relevant applications of scp is given by crew scheduling problems in mass transportation companies where a given set of trips has to be covered by a minimum-cost set of pairings, a pairing being a sequence of trips that can be performed by a single crew. 3 multilevel metaheuristics and parameter setting metaheuristics, in their original definition, are solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable a 2-level metaheuristic for the set covering problem 379 of escaping from local optima and performing a robust search of a solution space [3]. over time, these methods have also come to include any procedures that employ strategies for overcoming the trap of local optimality in complex solution spaces, especially those procedures that utilize one or more neighborhood structures as a means of defining admissible moves to transition from one solution to another, or to build or destroy solutions in constructive and destructive processes. a number of the tools and mechanisms that have emerged from the creation of metaheuristic methods have proved to be remarkably effective, so much that metaheuristics have moved into the spotlight in recent years as the preferred line of attack for solving many types of complex problems, particularly those of a combinatorial nature. multilevel metaheuristics can be considered as two or more metaheuristics where a higher level metaheuristic controls de parameters of a lower level one, which is at charge of dealing more directly to the problem. therefore, parameter setting is a key factor in the design of metaheuristics and multilevel metaheuristics [4], since they improve their solving performance by modifying and adjusting themselves to the problem at hand, either by self-adaptation or supervised adaptation. 3.1 parameter setting many parameters have to be set for any metaheuristic. parameter setting may allow a larger flexibility and robustness, but requires a careful initialization. those parameters may have a great influence on the efficiency and effectiveness of the search. it is not obvious to define a priori which parameter setting should be used. the optimal values for the parameters depend mainly on the problem and even the instance to deal with and on the search time that the user wants to spend in solving the problem. a universally optimal parameter values set for a given metaheuristic does not exist. 3.2 tuning before solving also know as “offline parameter initialization”. as previously mentioned, metaheuristics have a major drawback; they need some parameter tuning that is not easy to perform in a thorough manner. those parameters are not only numerical values but may also involve the use of search components [15]. usually, metaheuristic designers tune one parameter at a time, and its optimal value is determined empirically. in this case, no interaction between parameters is studied. this sequential optimization strategy (i.e., one-by-one parameter) do not guarantee to find the optimal setting even if an exact optimization setting is performed. main flavors of “tuning before solving” techniques include: parameter tuning on preliminary experiments, empirical manual tuning and automatic parameter tuning by an external algorithm. 3.3 control during solving also know as “online parameter initialization”. the drawback of the offline parameter setting approaches is their high computational cost, particularly if this approach is used for each input instance of the problem. indeed, the optimal values of the parameters depend on the problem at hand and even on the various instances to solve. then, to improve the effectiveness and the robustness of offline approaches, they must be applied to any instance (or class of instances) of a given problem. the control of the solverős behavior during the run can be achieved by either modifying its components and/or its parameters. this corresponds, for instance, to an online adjustment of the parameters or heuristics. such control can be achieved by means of supervised control schemes or by self adaptive rules. of course, such approaches often rely on a learning process that tries to benefit from previously encountered problems along the search or even 380 c. valenzuela, b. crawford, r. soto, e. monfroy, f. paredes during the solving of other problems. at this section are considered the approaches that change the parameters during the search with respect to the current state and other parameters. of course, these parameters have a direct influence on the heuristics functions, but also these latter functions stay the same during the solving process. some remarkable approaches are introduced as follows: hyperheuristics and reactive search [16]. 4 implementation details 4.1 ga-ss design the design proposed for the multilevel implementation is based on documented standards proposed for each metaheuristic. both metaheuristics are looking to be as close as they can to its origins. obviously, a multilevel implementation with adaptive parameter control approach forces some changes to the “high leveló metaheuristic, in this case, ga. this case will be similar to both combinations: ga-ss and ga-aco. hence, the design of ga seems particularly faithful to its basic design. [ size of p , bestset , diverseset , enhancetrials , maxsol ] figure 1: ga chromosome representation for ss. the chromosome representation shows that the first gene, for this case the “size of p” is the number of initial solutions for ss, will take values between 1 and n/2 where n number of variables. same way for the second gene “bestset”, which is the size of the best solutions reference set. similarly, “diverseset” is the size of the most diverse solutions set. “enhancetrials”, represents the number e of trials for the improvement method to try to enhance a solution, where e = {1...n}. finally, “maxsol ” is the limit of solutions generated by a call to the scatter search algorithm. 4.2 ga components the main components of ga components are: initialization function: initializes the values of genes within the variables bounds. it also initializes (to zero) all fitness values for each member of the population. it takes upper and lower bounds of each variable from user defined parameters. it randomly generates values between these bounds for each gene of each genotype in the population. evaluation function: the upper level metaheuristic uses two criterions; (1) the same evaluation function of the lower level metaheuristic, this is the corresponding objective function of scp, and (2) a objective function of scp penalized by the processing effort. “keep the best” function: this function keeps track of the best member of the population. elitist function: the best member of the previous generation is stored. selection function: standard proportional selection for maximization/minimization problems incorporating elitist model — makes sure that the best member survives. crossover selection: selects two parents that take part in the crossover. this work implements a single point crossover. mutation: random uniform mutation. a variable selected for mutation is replaced by a random value between lower and upper bounds of this variable. a 2-level metaheuristic for the set covering problem 381 4.3 ss components a description of ss components included in ga-ss design: diversification generation method: this method generates diverse binary initial solutions, it starts with an all-zero arbitrary solution, and then begins adding “1” in every position with a jump of k bits, where k = {1...n} and n < (number_of_vars) − 1. in example, for k = 1 it will generate a solution vector [1,0,1,0,1,0,1,0...]. should be noticed that this method always starts placing a bit “1” at the first position. together with the previously generated solution, the method generates the complement of that solution, which will be [0,1,0,1,0,1,0,1,0,1...]. the quantity of solutions generated by this method is a parameter controlled by the ga. improvement method: transforms a trial solution into a enhanced trial solution. if the trial solution is not feasible, should be fixed until it turns feasible. the input solutions are not required to be feasible. if the input trial solution is not improved as a result of the application of this method, the “enhanced” solution is considered to be the same as the input solution. the limit of enhancement trials (not the fix trials) is controlled by the ga. the duties of fixing and enhancing are improved since a vector of ratios is calculated (the length of the vector is the number of variables, or columns). this vector is then sorted from min to max ratio. each element represents a variable at the objective function. then, when trying to fix a solution should be followed in-order, so the first item will represent the column which covers more rows of the incidence matrix. if this position is “0” at the solution, should be turned to “1", because the method is wanting to add “1” for covering rows and turn the solution to feasibility. this process continues until the solution is feasible. when trying to enhance a solution, the vector of ratios should be accessed in post-order, trying to turn as many “1" to “0" as it can while the solution keeps feasible. the method will try to enhanced until the limit l is reached, where l is a parameter controlled by the ga. reference set update method: builds and maintain two reference sets, consisting of the b “best” solutions and the d “most diverse” solutions found (where the value of b and d is set by the ga), organized to provide efficient accessing by other parts of the solution procedure. the criteria for adding the “best” solutions to its set is the cost at the objective function, also, the criteria for adding solutions at the “most diverse” set is the hamming distance to all the solutions at the “best set” and the “diverse set”. subset generation method: operates on the reference set, to produce a subset of its solutions as a basis for creating combined solutions. the most common subset generation method will be used, which generates all pairs of reference solutions (i.e., all subsets of size 2) from the “best set" and the “diverse set" together. solution combination method: transforms a given subset of solutions produced by the subset generation method into one combined solution. the combination method is based in the cost at objective function/ rows covered ratio, which is calculated per column. when combining two solutions, the following procedure is used: if (sol_1[x] = sol_2[x]) then newsol[x] := sol_1[x] elseif (ratio[x] < median) then newsol[x] := 1 else newsol[x] := 0 where x is the position of the bit being evaluated with x = {1...maxcolumns}, and the median is the median of the vector of ratios for a given scp instance. 382 c. valenzuela, b. crawford, r. soto, e. monfroy, f. paredes 4.4 ga-aco design for this implementation, the same structure of ga components presented at 4.2 is used. added to this, the implementation of aco for scp is very straightforward. the components used by aco are: pheromone. denoted by t = τi, the matrix of pheromones will be used to consolidate information obtained by each ant, i.e. the amount of pheromone stored in each column i. τi(t) specifies the intensity of the pheromone at column i in time t, and updates in a local way (according to the path of the ants), and in a global way (the pheromone evaporates in every matrix column). the matrix is initialized with a value t0 which will be 10−6 for this implementation. transition. for apply a transition between two columns, this should be done according to the list of candidates. the list of candidates of a column contains the c more attracting columns, and they are sorted in a sequential manner. the transition is carried according to: if exists at least one column j ∈ candidate list, then, choose the next column j ∈ jki , between the c columns in the candidate list according: j = { maxu ∈ jki [τu(t)][ηu] β if q ≤ q0 j if q > q0 where ηu represents the heuristic information, and j is chosen with the probability: pkj (t) = [τj(t)][ηj] β∑ l∈nk[τl(t)][ηl] β where nk is a possible neighbor of ant k. after each selection of a column j, occurs a local modification of the level of pheromone of that column, given by the equation: τj(t) = (1 − ρ)τj(t) + ρτ0 this evaporation is done so the visited column will not be interesting for the following ants, stimulating in this way the exploration of solutions. at the equation above, ρτ0 is a stabilization factor of the pheromone modification, used with the intention of not having less attractive columns so quickly, allowing the exploration of a number even bigger of new solutions. when every ant ended an iteration (a solution has been found), the pheromone level of the best solution found is updated globally, loading pheromone to each column of the solution according to: τj(t) = (1 − ρ)τj(t) + ρ△τj(t) where △τj(t) is the variation of pheromone left on column j by the best ant. this variation is calculated as the frequency of column j in the routing of each ant, i.e. the number of times where column j is in the solutions found by the ants. to operate, the following parameters controlled by the ga will be defined: number of ants, ρ (the evaporation factor), β (the importance in the choice of the next column, length of the list (which will be used to limit the number of columns that can be visited next), q0 (parameter which indicates if the exploration is supported at the moment of next column election) and maxiter (max number of iterations). a 2-level metaheuristic for the set covering problem 383 4.5 penalizing function an aid to the parameter setting function is needed so the genetic algorithm can handle a trade-off between keep improving a solution — ergo more resources applied — or to quit a solution and try another search. a penalizing function is a very good solution to this problem. the direct comparison is between a parameter tuning version and a parameter control one. as a penalizing function we propose: fitness(sol) = objfuncv alue(sol) + timetaken(sol) ∗ fct where sol is a solution and objfuncv alue is the value — or cost — of the solution generated evaluated at the scp objective function of the corresponding benchmark. also, timetaken is the amount of time taken to generate that solution. fct is a correction factor which makes it possible to compare the time with the cost. 4.6 parameter control considerations designs introduced in sections 4.1 and 4.4 were directly coded into the parameter tuning versions. to obtain the counterparts to the parameter control versions, some changes were introduced between transitions of the metaheuristics. ga was modified to enabling an intermediate results memory taking feedback of the performance of the lower level metaheuristic. 5 experimental results 5.1 performance measurement the performance measures evaluated will are: • best solution found (value at scp’s objective function) • time taken to find the best solution (best represented by “calls to objective function made before best solution is found”) • average fitness (penalized) and its standard deviation the quality of the solutions is taken measuring its closeness to the optimal value of a certain instance (or-library scp file). the average fitness is also an aid to evaluating quality in combination with the standard deviation. the computational effort is measured by: the number of calls to objective function needed to generate the best solution. 5.2 reference benchmarks each implementation — i.e. ga-ss and ga-aco — are tested using the benchmarks provided by [17] which are widely used by operation and optimization researchers. the files used are set covering problem instances denoted by series scp4x, scp5x and scp6x. to obtain more experimental results — and to compare the performance of each implementation — such benchmarks will be tested against both versions of parameter setting configurations. the benchmarks presented are minimization problems, where the idea is to find the lowest cost at the objective function. each benchmark is based on a matrix of 200 rows and 1000 columns, which makes them a fairly large set of problems. 384 c. valenzuela, b. crawford, r. soto, e. monfroy, f. paredes 5.3 environment all the algorithms required for testing will be coded under standard c language and compiled with gnu gcc. the xcode 3.2 ide was selected for coding and depuration task, and as reference computer an intel core 2 duo cpu with 4gb of ddr3 ram was used. to notice, testing implementation code does not allow the use of multiples cores as is the case of current cpu. 5.4 robustness based on ga’s parameters the first table of this section shows the results for ga-ss with parameter tuning. these results are important since accomplish two objectives: to allow to evaluate the measures at this offline parameter setting configuration and to represent the execution of several single ss metaheuristic executions. as was explained before, a parameter tuning configuration runs a whole instance of ss metaheuristic with parameters defined — by ga — before its execution. when the ss execution finish, the best result is given as feedback to ga, which might continue its process of experimenting with other ss parameters. hence, the results obtained consider a neutral parameter configuration — a random one — representing a human trial-and-error testing, where best results might be as good as the ones with ga-ss, but surely requiring more human work in terms of defining those parameters and to experiment with them. pxover pmut bestfound callsof average fit-stdev 0.25 0.25 1009 5778578 3017 433 0.5 0.5 1009 4698886 2915 675 0.75 0.75 1007 5750974 3104 571 0.25 0.75 1008 4545205 2803 458 0.75 0.25 1009 4698886 2644 578 table 1: different ga-ss parameters configurations using parameter tuning. benchmark: scp41. first results (table 1) show a considerable variability. the overall look represent what a human can expect when experimenting — no good luck considerations. as will be seen in the next tables, pxover and pmut tend to lose its impact, being this instance the most unexpected. pxover pmut bestfound callsof average fit-stdev 0.25 0.25 509 3446206 2235 1075 0.5 0.5 509 3462702 2329 1078 0.75 0.75 509 3481599 2002 954 0.25 0.75 509 3502315 1735 797 0.75 0.25 509 3308283 1980 890 table 2: different ga-ss parameters configurations using parameter control. benchmark: scp41. at the second table (table 2) best results found show no variation, mainly due to ss improvement function. the other measures show a low standard deviation specially in the overall calls to objective function. as the average solution results are the ones with more variation, there is logic in thinking that there was a good level of exploration between solution spaces. a 2-level metaheuristic for the set covering problem 385 in the next table (table 3) the performance with aco is evaluated, using parameter tuning. aside of the better results obtained with aco — and its best resources management — the same fashion as in ga-ss is seen: very low variation at each measure. pxover pmut bestfound callsof average fit-stdev 0.25 0.25 449 1477894 781 236 0.5 0.5 434 1095816 768 240 0.75 0.75 449 1659306 896 274 0.25 0.75 449 1599972 862 260 0.75 0.25 449 1435961 762 220 table 3: different ga-aco parameters configurations using parameter tuning. benchmark: scp41. using an online parameter setting criteria (table 4) shows no more variation at the results; actually, a very low variation is obtained. is clearer that the results under this configuration are the best of all. this will be reviewed in following experiments show down. pxover pmut bestfound callsof average fit-stdev 0.25 0.25 436 673804 442 7 0.5 0.5 436 691756 442 8 0.75 0.75 436 743647 444 8 0.25 0.75 436 671342 444 8 0.75 0.25 436 703318 441 7 table 4: different ga-aco parameters configurations using parameter control. benchmark: scp41. 5.5 convergence to the best solution this experiments were selected between the most interesting results: which presented a noticeable convergence through time. at first look, the temptation is to compare the obvious; ga-aco performed better than its counterparts. after a careful review, the comparison is unfair fundamentally because aco uses a more “intelligent” way of building solutions whereby ss is more blind. a preliminary conclusion is that the constructive process of aco is better than the evolutive of ss. anyway, the purpose of this graphics is to allow to observe how each version converges through time to a better solution. the plot is based on benchmark scp48 and the clearest idea is that parameter control allows to get better results in lesser time, which is mainly due to the intensification of the harvest of certain solution space where “best looking” results are found, and to the saved time when quitting to explore “bad looking” results. also, a common item in each plot is that ga-ss (tuning) tent to obtain a better result than ga-ss (control) when a great quantity of solutions are searched. 5.6 wide comparison of best results found following table introduces the best results found through the various instances executed. faster convergence of parameter control versions has been considered in previous section — which is not represented in this table. same results obtained by both versions of aco hide evidently the efficiency factor. every instance executed to obtain this results was using pxov er = 386 c. valenzuela, b. crawford, r. soto, e. monfroy, f. paredes figure 2: convergence analysis to a better solution. benchmark: scp48 0.5 and pmut = 0.5 which seems a fair tradeoff between crossover and mutation at ga. under this circumstances, another criteria of robustness is handled: to perform well in a variety of instances. the worse performer through all experiments was ga-ss with parameter tuning, but, even with this issue, it was not an extremely bad perform. scp41 scp42 scp48 scp61 scp62 scp63 optimum 429 512 492 138 146 145 ga-ss-offline 1007 981 561 154 155 166 ga-ss-online 509 603 642 164 165 176 ga-acs-offline 434 529 497 142 154 148 ga-acs-online 434 529 497 142 154 148 table 5: table of best results. 6 conclusions a 2-level metaheuristic has been tested on different scp benchmarks showing to be very effective. one of the main goals of a multilevel approach is to provide an unattended solving method, for quickly producing solutions of a good quality for different instances. the overall work was extended on several relevant issues and quality measures: quality of solutions, robustness in terms of the instances and insensitivity to small deviation at parameters (ga parameters) solving large-scale problems, easiness of implementation, easiness to combine with other algorithms, automatic setting of parameters. all of them provided a fair approach to the core problem, and many of them have already shown lights of been covered, producing a renovated energy for keep working and admiring the synergy produced. benchmarks have shown interesting results in terms of robustness of every approach being the parameter control the most robust in terms of a good performance in several instances using same parameters and in terms of stable results when small deviations are made to parameters. also, parameter control shown to converge faster to better results than parameter tuning, but, when long running times are taken, both seem to obtain equal results. considering the particular performance of a constructive approach and an evolutive one for the scp, it seems to be more effective to “construct” intelligently a solution rather than “blindly” get one and then evolve it. an interesting fact is that ss with parameter control might get a performance closer to aco. the overall implementation of a multilevel metaheuristic is pretty straightforward, no big obstacles were found. also, the implementation of the ga using real numbers was a direct representation of what parameters a human user might a 2-level metaheuristic for the set covering problem 387 choose to operate, therefore achieving an implementation which 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mode control x.z. zhang, y.n. wang, x.f. yuan xizheng zhang hunan university p.r.china, 410082 changsha, yuelushan, and hunan institute of engineering p.r.china, 411104 xiangtan, 88 fuxing east road e-mail: z_x_z2000@163.com yaonan wang, xiaofang yuan hunan university p.r.china, 410082 changsha, yuelushan e-mail: yaonan@hnu.cn, yuanxiaof@21cn.com abstract: this paper presents the fuzzy design of sliding mode control (smc) for nonlinear systems with state delay, which can be represented by a takagi-sugeno (ts) model with uncertainties. there exist the parameter uncertainties in both the state and input matrices, as well as the unmatched external disturbance. the key feature of this work is the integration of smc method with h∞ technique such that the robust asymptotically stability with a prescribed disturbance attenuation level γ can be achieved. a sufficient condition for the existence of the desired smc is obtained by solving a set of linear matrix inequalities (lmis). the reachability of the specified switching surface is proven. simulation results show the validity of the proposed method. keywords: sliding mode control, t-s fuzzy model, time-delayed system, h∞ control. 1 introduction recently, the dynamic t-s fuzzy model has become a popular tool and has been employed in most model-based fuzzy analysis approaches [1]. moreover, the ordinary t-s fuzzy model has been further extended to deal with nonlinear uncertain systems with time-delays [2]. the stability analysis and stabilization controller design for fuzzy time-delayed systems have attracted much attention over the past few decades due to their extensive applications in mechanical systems, economics, and other areas. a large number of results on this topic have been reported in the literature, see, e.g. [3–5]. note that the uncertainties may exist in the real systems, or come from the fuzzy modeling procedure. hence, the robust stabilization problems have recently been investigated in [6] for nonlinear uncertain fuzzy systems. in practice, the inevitable uncertainties may enter a nonlinear system in a much more complex way. the uncertainty may include modeling error, parameter perturbations, fuzzy approximation errors, and external disturbances. in such circumstances, especially in the existence of external disturbances, the above established methods to control fuzzy time-delay systems could not work well any more. however, it is well known that the sliding mode control (smc) is a reasonable approach to take effect if the lumped uncertainties are known to be bounded by smooth functions. in a more detail, the smc system could drive the trajectories onto the so-called switching surface in a finite time and maintain on it thereafter, and on the switching surface the system is insensitive to internal parameter perturbations and external disturbances [7]. smc approach has been successfully adopted in the control of time-delay systems these years. quite recently, smc approach has been also applied to solve the stabilization and tracking problems for fuzzy systems with matched uncertainties [8]. however, the sliding motion cannot be detached copyright c⃝ 2006-2010 by ccc publications h∞ robust t-s fuzzy design for uncertain nonlinear systems with state delays based on sliding mode control 593 from the effect of unmatched parameter uncertainties, especially, unmatched external disturbances [9]. this means that the unmatched external disturbances make the design of smc complex and challenging. on the other hand, the h∞ control, in the past decades, has been widely employed to deal with the uncertain systems with external disturbance [10, 11]. the goal of this problem is to design a controller to stabilize a given system while satisfying a prescribed level of disturbance attenuation. the h∞ control for uncertain time-delayed systems has been considered by some researchers [12, 13]. in [14], peng and yue investigated the h∞ controller design for uncertain t-s fuzzy systems with time-varying interval delay by using a new lyapunov-krasovskii functionals and an innovative integral inequality. the output feedback controller in [15] was designed for uncertain fuzzy systems such that the closed-loop systems are robustly asymptotically stable and satisfy a prescribed h∞ performance. lin et al. [16] further presented the mixed h2/h∞filter design for nonlinear discrete-time systems with state-dependent noise. motivated by the above discussion, it is certain that the integration of the smc method with h∞ technique would have a great potential in extending the smc to the systems with unmatched uncertainties and obtain a better dynamic performance. therefore, in this paper, by utilizing the h∞ technique to attenuate the effect of unmatched external disturbance, we proposes a novel smc controller that can ensure the robust stability with a prescribed disturbance attenuation level γ for the fuzzy time-delayed system, irrespective of parameter uncertainties and unmatched external disturbance. the controller design method is presented in terms of lmis. the notations used in this paper are quite standard: ℜn denotes the n-dimensional real euclidean space; iii is the identity matrix with appropriate dimensions; www < 0 (www > 0) means that www is symmetric and negative (positive) definite; l2[0,∞]denotes the space of square-integrable vector functions over [0,∞]; the superscript "t " represents the transpose of a matrix, and the notation "∗" is used as an ellipsis for terms that are induced by symmetry; ∥·∥ denotes the spectral norm; matrices, if they are not explicitly stated, are assumed to have compatible dimensions. 2 problem formulation as stated in introduction, t-s fuzzy models can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input-output submodels. hence, in this work, a class of nonlinear time-delay systems is represented by a t-s model. as in [2], the t-s fuzzy time-delay system with uncertainties is described by fuzzy if-then rules, which locally represent linear input-output relations of nonlinear systems. the i-th rule of the fuzzy model is formulated in the following equation: plant rule i: if θ1 is η i1 and θ2 is η i 2 ··· and θp is η p 1 , then  ẋxx(t) = [(aaai + ∆ aaai(t))xxx +(aaadi + ∆ aaadi(t))xxx(t − τ)]+(bbbi + ∆ bbbi)uuu(t)+ bbbwiwww(t) zzz(t) = cccixxx(t) xxx(t) = φ(t),t ∈ [−τ(t),0],i = 1,2,.,r (1) where η ij is the fuzzy set, θθθ = [θ1(t),θ2(t),··· ,θp(t)] t is the premise variable vector, r is the number of rules of this t-s fuzzy. xxx(t) ∈ ℜn is the state vector, uuu(t) ∈ ℜm is the control input vector, zzz(t) ∈ ℜl is the controlled output, wwwi(t) ∈ ℜp denotes the unknown external disturbances or modeling error. aaai, aaadi, bbbi, bbbwi and ccci are known real constant matrices with appropriate dimensions. ∆ aaai(t), ∆ aaadi(t), ∆ bbbi are unknown time-varying matrices representing parameter uncertainties. τ is the time-varying delay for the state vector satisfying 0 < τ(t) < d < ∞, ˙τ(t) < h < 1, where d and h are known real constant scalars. φ(t) is a continuous vector-valued initial function. the overall fuzzy model achieved by fuzzy blending of each plant rule is represented as follows: ẋxx(t) = r∑ i=1 hi(θθθ )[(aaai + ∆ aaai)xxx(t)+(aaadi + ∆ aaadi)xxx(t − τ)+(bbbi + ∆ bbbi)uuu(t)+ bbbwiwww(t)] (2) 594 x.z. zhang, y.n. wang, x.f. yuan where hi(θθθ ) = αi(θθθ )∑r j=1 αi(θθθ ) ,αi(θθθ ) = ∏p j=1 η i j(θθθ ), in which η i j(θθθ ) is the membership grade of θ j in η i j. according to the theory of fuzzy sets, we have θθθ ≥ 0 and ∑r i=1 θθθ ≥ 0. therefore, it implies that hi(θθθ ) ≥ 0 and ∑r i=1 hi(θθθ ) = 1. in this work, the following assumptions are introduced. (assumption.1) the time-varying uncertainties ∆ aaai and ∆ aaadi are assumed to be norm-bounded, that is, [∆ aaai,∆ aaadi] = hhh ifff i(t)[eee1i,eee2i] (3) where hhh i,eee1i, eee2i are known constant matrices, and fff i(t) is an unknown matrix function with lebesguemeasurable elements and satisfies fff ti (t)fff i(t) ≤ iii,∀t. (assumption.2) it is assumed that the matrices bbbi satisfy bbb1 = bbb1 = ··· = bbbr = bbb. moreover, the pair (aaai,bbb) is controllable and the input matrix bbb has full-column rank m and m < n. (assumption.3) the uncertainty matrix ∆ bbbi is assumed to be matched, i.e., there exists a matrix δδδ i(t) ∈ ℜm×m such that ∆ bbbi = bbbiδδδ i(t) with ∥δδδ i(t)∥ ≤ ρb < 1, where ρb is a positive constant. (assumption.4) the upper bound for wwwi(t) is known. it is noted that there exists parameter uncertainties in both the state and control input matrices and unmatched external disturbance wwwi(t) in the systems under consideration. remark 1: assumptions 1 4 are standard assumptions in the study of variable structure control. before proceeding, some standard concepts and lemma are given as follows, which are useful for the development of our result. definition 1. the uncertain fuzzy time-delayed systems in (2) is said to be robustly asymptotically stable if the system with uuu(t) = 0 and wwwi(t) = 0 is asymptotically stable for all admissible parameter uncertainties. definition 2. given a scalar γ > 0, the unforced fuzzy system in (2) with uuu(t) = 0 is said to be robustly stable with disturbance attenuation γ if it is robustly stable and and under zero initial condition, ∥zzz(t)∥e2 ≤ γ∥www(t)∥2 for all non-zero and all admissible uncertainties, where ∥zzz(t)∥e2 = √∫t 0 |zzz(t)|2dt (4) (lemma.1 choi [10]): let eee, hhh, and fff(ttt) be real matrices of appropriate dimensions with fff(ttt) satisfying fff ti (t)fff i(t) ≤ iii. then, we have (i) for any scalar ε ≤ 0, eeefff(t)hhh + hhh t fff t (t)eee t ≤ ε−1eeeeee t + ε hhh t hhh (ii) for any matrix p > 0, −2eee t hhh ≤ eee t pppeee + hhh t p−1hhh. 3 controller design the objective of this work is to design a smc law such that the desired control performance for the resulting closed-loop system is obtained despite of parameter uncertainties and unmatched external disturbance. in this section, a smc law is first synthesized such that the closed-loop systems are robustly asymptotically stable with disturbance attenuation γ . it is further proven that the reachability of the specified switching (sliding) surface s(t) = 0 can be ensured by the proposed smc law. thus, it is concluded that the synthesized smc law can guarantee the state trajectories of uncertain systems (2) to be driven onto the sliding surface, and asymptotically tend to zero along the specified sliding surface. 3.1 sliding mode controller design essentially, a smc design is composed of two phases: hyperplane design and controller design. there are various methods for designing hyperplane, however in this paper the switching surface is h∞ robust t-s fuzzy design for uncertain nonlinear systems with state delays based on sliding mode control 595 defined as s(t) = r∑ i=1 hi(θθθ (t))gggixxx(t) (5) where gggi ∈ ℜm×n is designed so that gggibbbi is not singular. furthermore, we design the vsc control law as follows  uuu(t) = uuus(t)+ uuur(t) uuus(t) = − r∑ i=1 kkkixxx(t) uuur(t) = − r∑ i=1 hi(θθθ (t))gggi [ aaaixxx(t)+ aaadixxx(t − τ(t)) ] − r∑ i=1 hi(θθθ (t))ρi(xxx,t)sgn(s(t)) (6) where kkki ∈ ℜm×n is chosen such that (aaai − bbbikkki) is hurwitz, sgn()is a sign function and ρi(xxx,t)is a positive scalar function given as ρi(xxx,t) ≥ 2 1− ρ2b {[ ∥φ(aaai − bbbikkki)∥+∥φ hhh ieee1i∥+ ρb∥kkki∥+(1+ ρb)∥gggaaai∥ ] ∥xxx(t)∥ + [ ∥φ aaadi∥+∥φ hhh ieee2i∥+(1+ ρb)∥gggaaadi∥ ] ∥xxx(t − τ)∥ +∥s∥∥φ bbbwi∥∥www∥+ β } (7) with φ = (gggibbbi)−1gggi and β > 0 is a small known scalar. thus, substituting (6) into (2), we obtain the closed-loop system as follows ẋxx(t) = r∑ i=1 hi(θθθ ) {[ aaai − bbbikkki + ∆ aaai(t)− ∆ bbbi(t)kkki]xxx(t)+ [ aaadi + ∆ aaadi(t) ] xxx(t − τ(t)) + [ bbbi + ∆ bbbi(t) ] uuur(t)+ bbbwiwww(t) } (8) the above expression eq.(8) is the sliding-mode dynamics of the fuzzy uncertain system (2) in the specified sliding surface s(t) = 0. 3.2 stability of the sliding mode motion in this subsection, we analyze the dynamic performance of the closed-loop system described by (8), and derives some sufficient conditions for the asymptotically stability of the sliding dynamics via lmi method. the following theorem shows that system (2) in the defined switching surface is robustly stabilizable with disturbance attenuation level γ . theorem 3. consider the fuzzy uncertain systems (2) with assumptions 1 4, with the prescribed switching function, if there exist matrices ppp > 0, qqq > 0, and positive scalars ε1, ε2 and ε3 such that the lmi shown in (11) holds, with θ1 = ppp(aaai − bbbikkki)+(aaai − bbbikkki)t ppp + qqq + ε1eee t1ieee1i + ε3ρ 2 bkkk t i kkki +ccc t i ccci (9) θ2 = −(1− h)qqq + ε2eee t2ieee2i (10) for i = 1,2,··· ,r, then, by choosing gggi = bbbti ppp, the sliding-mode dynamics (8) is robust asymptotically stable with disturbance attenuation γ . 596 x.z. zhang, y.n. wang, x.f. yuan   θ1 ∗ ∗ ∗ ∗ ∗ aaatdippp θ2 ∗ ∗ ∗ ∗ bbbtwippp 0 −γ 2iii ∗ ∗ ∗ hhh ti ppp 0 0 ε1iii ∗ ∗ hhh ti ppp 0 0 0 ε2iii ∗ θ1 0 0 0 0 ε3iii   < 0 (11) proof: to analyze the stability of the sliding-mode dynamics (8), we consider the fuzzy uncertain system (2) with www(t) = 0 and choose the following lyapunov functional candidate v (xxx,t) = xxxt (t)pppxxx(t)+ ∫t t−τ xxx(m)t pppxxx(m)dm (12) by differentiating the given lyapunov function, we obtain the differential along the trajectories as v̇ = r∑ i=1 hi(θθθ ) { xxxt (t) [ ppp(aaai − bbbikkki)+(aaai − bbbikkki) t ppp + qqq +2ppp(∆ aaai + ∆ bbbikkki) ] xxx(t) +2xxxt (t)ppp(aaadi + ∆ aaadi)pppxxx(t − τ)−2st [iii + δ (t)]{ρisgn(s))+ bbbti ppp[aaaixxx + aaadixxx(t − τ)] }} −(1− τ̇)xxxt (t − τ)qqqxxx(t − τ) (13) . noting the definition of switching function s(t) and the control law (6), we have v̇ = r∑ i=1 hi(θθθ ) { xxxt (t) [ ppp(aaai − bbbikkki)+(aaai − bbbikkki) t ppp + qqq ] xxx(t) +2xxxt (t)ppp(aaadi + ∆ aaadi)pppxxx(t − τ)+2xxxt (t)ppp(∆ aaai − ∆ bbbikkki)pppxxx(t) −2st (t)[iii + δ (t)]bbbippp [ aaaixxx + aaadixxx(t − τ) ] −2st (t)[iii + δ (t)]ρisgn(s) } −(1− τ̇)xxxt (t − τ)qqqxxx(t − τ) (14) . by lemma 1, we obtain that for εi > 0, the following inequalities hold. 2xxxt ppp∆ aaaixxx(t) ≤ ε−11 xxx t (t)ppphhh ihhh t i pppxxx(t)+ ε1xxx t (t)eee t1ieee1ixxx(t) (15) 2xxxt (t)ppp∆ aaadixxx(t − τ) ≤ ε−12 xxx t (t)ppphhh ihhh t i pppxxx(t)+ ε2xxx t (t − τ)eee t2ieee2ixxx(t − τ) (16) 2xxxt (t)ppp∆ bbbikkkixxx(t) ≤ ε−13 xxx t (t)pppbbbibbb t i pppxxx(t)+ ε3ρ 2 bxxx t (t)kkkti kkkixxx(t) (17) −2st [iii + δ (t)]ρisgn(s) ≤ −2ρi∥s∥+ ρi[st δ δ t s + st s]∥s∥−1 ≤ ρi(ρ2b −1)∥s∥ (18) noting that (3) and ∑r i=1 hi(θθθ (ttt)) = 1, and substituting the above inequalities into (14) results in v̇ ≤ r∑ i=1 hi(θθθ ) [ xxxt (t) xxxt (t − τ) ] × π × [ xxx(t) xxx(t − τ) ] (19) where π = ( ξ1 pppaaadi aaatdippp ξ2 ) , with h∞ robust t-s fuzzy design for uncertain nonlinear systems with state delays based on sliding mode control 597 ξ1 = ppp(aaai − bbbikkki)+(aaai − bbbikkki)t ppp + qqq + ε−11 ppphhh ihhh t i ppp + ε1eee t 1ieee1i +ε−12 ppphhh ihhh t i ppp + ε −1 3 pppbbbibbb t i ppp + ε3ρ 2 bkkk t i kkki (20) ξ2 = −(1− h)qqq + ε2eee t2ieee2i (21) in the following, it will be shown that the lmi (11) implies π < 0. by schur’s complement, π < 0 is equivalent to the lmi shown in (24), with ξ3 = ppp(aaai − bbbikkki)+(aaai − bbbikkki)t ppp + qqq + ε−11 ppphhh ihhh t i ppp + ε1eee t 1ieee1i + ε3ρ 2 bkkk t i kkki (22) ξ4 = ξ2 (23)   θ3 ∗ ∗ ∗ ∗ aaatdippp θ4 ∗ ∗ ∗ hhh ti ppp 0 −ε1iii ∗ ∗ hhh ti ppp 0 0 −ε2iii ∗ bbbti ppp 0 0 0 −ε3iii   < 0 (24) it is shown that the lmi (11) implies the above matrix inequality (24). together with (19) implies that for all [ xxxt (t) xxxt (t − τ) ] ̸= 0, we have v̇ (xxx(t),t) ≤ 0 (25) this means that the closed-loop fuzzy system (8) with www(t) = 0 is robustly asymptotically stable. next, we shall show that the fuzzy uncertain system (2) satisfies ∥zzz(t)∥e2 ≤ γ∥www(t)∥2 (26) for all non-zero www(t) ∈ l2[0,∞]. to this end, we assume zero initial condition, that is, with xxx(t) = 0 for all t ∈ [−d,0]. then, we can rewritten the lyapunov function candidate as follows: v̇ = r∑ i=1 hi(θθθ ) { xxxt (t) [ ppp(aaai − bbbikkki)+(aaai − bbbikkki) t ppp + qqq ] xxx(t) +2xxxt (t)ppp(aaadi + ∆ aaadi)pppxxx(t − τ)+2xxxt (t)ppp(∆ aaai − ∆ bbbikkki)pppxxx(t) +2xxxt (t)pppbbbwiwww(t)−2s t (t)[iii + δ (t)]bbbti ppp [ aaaixxx(t)+ aaadixxx(t − τ) ] −2st (t)[iii + δ (t)]ρisgn(s) } −(1− h)xxxt (t − τ)qqqxxx(t − τ) (27) . now, set j(t) = ∫t 0 [zzzt (m)zzz(m)− γ2wwwt (m)www(m)]dm (28) with t > 0. it is easy to show that j(t) = ∫t 0 [zzzt (m)zzz(m)− γ2wwwt (m)www(m)+v̇ (xxx,t)]dm −v (xxx,t) ≤ ∫t 0 [zzzt (m)zzz(m)− γ2wwwt (m)www(m)+v̇ (xxx,t)]dm (29) 598 x.z. zhang, y.n. wang, x.f. yuan hence, noting (15)-(19), it follows from (29) that j(t) ≤ ∫t 0 [ xxxt (m) xxxt (m − τ) wwwt (m) ] × ω × [ xxx(m) xxx(m − τ) www(m) ] dm (30) with ω =   θ1 pppaaadi 0∗ θ2 0 ∗ ∗ −γ2iii  , where θ1 and θ2 are given as in (9) and (10). by schur’s complement, it can be shown that ω < 0 is ensured by lmi (11). this together with (30) implies that j(t) < 0 for all t > 0. hence, we obtain (26) from (30). 2 remark 2: it is noted that the condition in theorem 1 is delay independent, which might be conservative when the time delay is known and small. hence, it would be appropriate to extend the current study to delay-dependent issues in future research. 3.3 reachability of the sliding-mode as the last step of design procedure, we will further prove that the vsc controller in (6) ensures the reachability of the specified switching surface. it is known from [17] that the solution of the system (2) is given by j(t) = xxx(t) = φ(0)+ ∫t 0 r∑ i=1 hi(θθθ ) [ (aaai + ∆ aaai)xxx +(aaadi + ∆ aaadi)xxx(m − τ) +(bbbi + ∆ bbbi)uuu(m)+ bbbwiwww ] dm (31) hence, the switching function s(t) can be expressed as s(t) = r∑ i=1 hi(θθθ )bbbti pppφ(0)+ ∫t 0 r∑ i=1 hi(θθθ )bbbti ppp [ (aaai + ∆ aaai)xxx(m)+(aaadi + ∆ aaadi)xxx(m − τ) +(bbbi + ∆ bbbi)uuu(m)+ bbbwiwww ] dm. (32) this means that s(t) varies finitely. that is, it is rational to take the time derivation of s(t). hence, we have ṡ(t) = r∑ i=1 hi(θθθ )bbbti ppp [ (aaai + ∆ aaai)xxx(t)+(aaadi + ∆ aaadi)xxx(t − τ)+(bbbi + ∆ bbbi)uuu(t)+ bbbwiwww ] (33) and then, the reachability of the specified sliding surface s(t) = 0 can be obtained in the following theorem. theorem 4. for the uncertain fuzzy time-delay systems (2) with the given switching function (5) where gggi = bbb t i ppp and ppp,qqq, εi(i = 1,2,3) is the solution of lmis (11). then, it can be shown that the state trajectories of the system (2) will be driven onto the switching surface s(t) = 0 for all www(t) ∈ l2[0,∞] by the above vsc law (6). proof: for purpose of design integrity, a simple stability analysis based on lyapunov direct method is carried out. define the lyapunov function v (t) = 1 2 st (gggibbbi) −1s (34) h∞ robust t-s fuzzy design for uncertain nonlinear systems with state delays based on sliding mode control 599 noting that (6), the expressions of ρ and ṡ, thus, we have v̇vv (t) ≤ st (t) r∑ i=1 hi(θθθ )(gggibbbi)−1gggi { (aaai + ∆ aaai)xxx(t)+(aaadi + ∆ aaadi)xxx(t − d) +st (t)[iii + δ (t)]uuu(t)+ st (t)(gggibbbi)−1gggibbbwiwww(t)} ≤ st (t) r∑ i=1 hi(θθθ )(gggibbbi)−1gggi { (aaai − bbbikkki + ∆ aaai)xxx(t)− st (t)δ (t)kkkixxx(t) +st (gggibbbi) −1gggi(aaadi + ∆ aaadi)xxx(t − d)− st [iii + δ (t)]gggi[aaaixxx(t)+ aaadixxx(t − d)] −st (t)[iii + δ (t)]ρi(xxx,t)sgn(s(t))+ st (t)(gggibbbi)−1gggibbbwiwww(t) } (35) by (18), we have v̇vv (t) ≤ ∥s(t)∥ r∑ i=1 hi(θθθ ) {[ ∥φ(aaai − bbbikkki)∥+∥φ hhh i∥∥eee1i∥+ ρb∥kkki∥ ] ∥xxx(t)∥ + [ ∥φ aaadi∥+∥φ hhh i∥∥eee2i∥ ] ∥xxx(t − d)∥ +(1+ ρb) [ ∥bbbti pppaaai)∥∥xxx(t)∥+∥bbb t i pppaaadi∥∥xxx(t − d)∥ ] +∥φ bbbwi∥∥www(t)∥−0.5ρ(xxx,t)(1− ρ2b) } (36) then, it follows from (36) that for s(t) ̸= 0 v̇ (t) ≤ −β ∥s(t)∥ < 0 (37) which implies that the reachability of the specified switching surface is guaranteed, and the trajectories of the fuzzy uncertain system (2) are globally driven onto the specified switching surface s(t) = 0 for all www(t) ∈ l2[0,∞]. moreover, it is seen that the existence domain of the sliding mode is the whole switching surface. 2 remark 3: in fact, the design strategy of the sliding-mode controller (6) accords with the so-called parallel distributed compensation (pdc) scheme [3, 5, 6, 10, 12]. this idea is that the overall controller is a fuzzy blending of each individual controller for each local linear model. the pdc method has been widely utilized in fuzzy control, and is proven to be a very appealing approach. 4 simulation studies in this section, a simple design example is used to illustrate the approach proposed in this paper. consider a t-s fuzzy uncertain stated-delay system with the following model plant rule i: if x2(t) is ηii, then{ ẋxx(t) = [(aaai + ∆ aaai)xxx +(aaadi + ∆ aaadi)xxx(t − τ)]+(bbbi + ∆ bbbi)uuu(t)+ bbbwiwww(t) zzz(t) = cccixxx(t) where i = 1,2. the model parameters are given as aaa1 = [ 0.1 0 0 −2 ] ,aaa2 = [ −0.3 0 1 −3 ] ,aaad1 =[ 0.1 0.1 0 0.1 ] ,aaad2 = [ 0.1 0 0 0.2 ] ,bbb1 = bbb2 = [ 1 1 ] ,bbbw1 = [ 2 0 ] „bbbw2 = [ 1 0 ] ,ccc1 = ccc2 = [ 2 0 0 1.5 ] . the uncertainties are set to be ∆ aaa1 = [ 0 0.08sin t 0 0.06sin t ] , ∆ aaa2 = [ 0.06sin t 0 0.02sin t 0.01sin t ] , ∆ aaad1 = [ 0 0.06sin t 0 0.06sin t ] , 600 x.z. zhang, y.n. wang, x.f. yuan ∆ aaad2 = [ 0.01cos t 0 0 0.06sin t ] , ∆ bbb1 = [ 0.1cos t 0.1cos t ] , ∆ bbb2 = [ 0.1sin t 0.1sin t ] , and the time-varying delay τ(t) = 0.5+0.5sin t with d = 1 and h = 0.5. when choosing the matrix function as fi(t) = 0.02sin t, one can easily obtain the real constant matrices hhh i, eee1i and eee2i from assumption 1. it is also obviously that ρb = 0.1 with δ (t) ≤ ρb. the membership functions are selected as η11 = sin2(x2) and η22 = cos2(x2). let the initial state xxx = [0.9,0.9]t ,t ∈ [−1,0]. the problem at hand is to design a sliding mode controller such that the sliding motion in the specified switching surface is robustly stable, and the state trajectories can be driven onto the switching surface. to this end, we select the attenuation level γ = 0.5 and the matrices as follows: kkk1 = [1.5,2.3],kkk2 = [0.2,3.4]. by solving lmis (11), we obtain:ppp = [ 1.5757 −0.0144 −0.0144 1.6211 ] ,qqq = [ 0.0729 0.127 0.127 0.5216 ] . hence, the switching surface can be obtained as s = [0.6405,0.6223]xxx(t). it following from theorem 2 that the desired vsc law can be obtained. the simulation results are given in figures 1-3. since it is well known that the chattering phenomenon is undesirable as it may incite high-frequency un-modeled dynamics and even leads to the instability of controlled system, we replace sgn(·) by s/(∥s∥+ ε)(ε is the thickness of boundary layer) in the previous vsc law so as to prevent the control signals from chattering. however, it should also be pointed out that such an approach may lead to delay or make the controller less robust. recently, to avoid chattering the use of high order and adaptive sliding mode is receiving more attentions; see, e.g., [10] for more details. it is seen that the reachability of the sliding motion can be guaranteed. furthermore, the simulation results also show that our present design effectively attenuates the effect of both parameter uncertainties and external disturbances. figure 1: trajectories of state x1, x2 5 conclusions this paper has firstly generalized the t-s model to represent a class of nonlinear uncertain systems. then, a novel robust vsc method integrated with h∞ technique, has been proposed for the fuzzy timedelayed system with parameters uncertainties and unmatched external disturbances. moreover, by means of lmis, a sufficient condition for the robustly stability of sliding motion with h∞ disturbance attenuation level γ has been derived. it has been shown that both the switching surface and the vsc controller have been obtained by means of the feasibility of lmis. h∞ robust t-s fuzzy design for uncertain nonlinear systems with state delays based on sliding mode control 601 figure 2: switching surface s(t) figure 3: control effort uuu(t) 602 x.z. zhang, y.n. wang, x.f. yuan bibliography [1] t. takagi, m. sugeno, fuzzy identification of systems and its applications to modeling and control, ieee trans. syst., man, cybern., 15(1):116-132, 1985. 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[17] p. gahinet, a. nemirovski, a. j. laub and m. chilali, lmi control toolbox, natick, ma: the mathworks, 1995. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 305-316 fuzzy based packet dropping scheme in wireless cellular networks j.d. mallapur, s.s. manvi, d.h. rao jayashree d. mallapur department of electronics and communication engineering basaveshwar engineering college bagalkot, india e-mail: bdmallapur@yahoo.co.in sunilkumar s. manvi department of electronics and communication engineering reva institute of technology and management bangalore, india e-mail: sunil.manvi@revainstitution.org d.h. rao jain college of engineering belgaum, india e-mail: dr.raodh@gmail.com abstract: wireless multimedia networks are becoming very popular owing to the user demands for multimedia services. packet dropping in the event of buffer congestion is one of the important issue in wireless multimedia networks. a packet dropping scheme has to be flexible and adaptive such that acceptable quality of an application is maintained. the paper presents a fuzzy based packet dropping scheme for wireless multimedia networks. a buffer manager placed at the base station performs packet dropping depending upon the traffic conditions and type of an application. packet dropping is performed by computing dropping factor by considering packet priority, queue length and adaptive queue length threshold. the adaptive queue length threshold is used to dynamically adjust the dropping factor. the queue length threshold is varied by using two fuzzy input parameters, channel condition and rate of flow of an application. the scheme has been extensively simulated to test the performance in terms of acceptance and dropping probability of real-time handoff and new calls. keywords: buffer, multimedia, fuzzy logic, packet drop. 1 introduction the use of wireless connectivity has been steadily growing over the recent years. new generation of mobile networks are opting for multimedia applications, such as video conferencing, e-commerce, e-learning, e-gaming, etc. these applications need good end-to-end quality of service (qos) control even under the conditions of network overload. the qos parameters are bandwidth, buffers, delays, jitters, packet loss, etc. the bandwidth and buffer management have become critical issues because of their scarcity. whenever sufficient bandwidth is not available, buffers are employed to avoid packet losses. due to excess traffic, buffers may become full and cause buffer congestion. the buffer congestion leads to dropping of packets randomly irrespective of application importance (real time or non real time). there is a need for employing flexible and copyright c⃝ 2006-2011 by ccc publications 306 j.d. mallapur, s.s. manvi, d.h. rao adaptable scheme for packet dropping, which may not deteriorate qos of a real time application in wireless multimedia networks. as per the literature survey, it is observed that most of the buffer management schemes in wireless networks, employ queue length as a threshold for dropping. parameters such as round trip time and application priority are also used to make packet dropping decisions. apart from these parameters, channel conditions and flow rates may also affect the dropping decisions. the rigid packet dropping schemes probably may not give flexibility to the buffer manager, hence soft packet dropping schemes by using fuzzy logic may be employed for wireless multimedia networks. fuzzy system’s characteristics are based on the concept of fuzzy partitioning of the information. the decision-making ability of the fuzzy model depends on the existence of rule base and fuzzy reasoning mechanism. the fuzzy logic based solutions are better for uncertain inputs which minimize the random outputs. some of the works done in the area of packet dropping in wireless networks are as follows. an algorithm called red (random early detection), which drops the packets of all flows with the same probability depending on queue length is given in [1]. flows with reduced transmission rates and shorter packet length are given unfair share in red. the work presented in [2] proposes a packet dropping scheme that drops the packet based on the sharing index computed using differentiated services. in [3], an adaptive fuzzy based control algorithm which computes the packet dropping probability according to pre-configured fuzzy logic using only queue length as input variable is presented. a fair packet dropping algorithm is presented in [4] depending on channel condition and fairness so as to achieve trade off between throughput and fair service. an adaptive link layer retransmission and active drop mechanism based on fuzzy logic considering queue length as fuzzy parameter is discussed in [5]. a scheme for handoff calls is presented in [6] by using user population, used bandwidth and received power level as fuzzy parameters for handoff decision at the base station. the scheme given in [7] is based on the fuzzy timer which changes the transmission rate to avoid congestion. a fuzzy green controller is presented in [8] which is expected to act as congestion controller in the routers, for both conditions such as with background load and without back ground load. a congestion index has been proposed for intelligent packet dropping scheme by using fuzzy logic in [9]. in [10], average queue length is replaced by the product of delay and retransmission times of rts (request to send) as congestion indicator to calculate dropping probability. the work presented in [11] is a new scheduling algorithm, named as delay-sensitive dynamic fair queuing (dsdfq) algorithm which is designed to meet delay requirements of multimedia applications as well as maintain high network efficiency to adapt to load fluctuations of different traffic classes and varying wireless channel conditions caused by user mobility, fading and shadowing. in [12], estimation of channel capacity in rayleigh fading environment and the gaussian noise environment is presented. development of a systematic methodology of fuzzy logic modeling with three distinct features such as reasoning formation, fuzzy clustering and membership assignment for input and output is presented in [13]. a simple channel predictor is presented in [14] that responds to impairments in the channel at the packet transmission time scale and makes a decision on packet transmission. the work given in [15] presents multihop relaying technology utilization for mitigating unfairness in qos, which comes about due to the location-dependent signal quality and multihop system. the work presented in [16] depicts that cooperative relaying can improve both capacity and fairness in cellular network. the work proposed in [17] is an adaptive multirate auto rate fallback (amarf) algorithm, in which each data rate is assigned a unique success threshold. this criterion is used to judge when to switch a rate to the next higher one, and the success thresholds can be adjusted dynamically in an adaptive manner according to the running condifuzzy based packet dropping scheme in wireless cellular networks 307 tions such as packet length and channel condition parameters. in wireless networks, the available bandwidth undergoes fast time-scale variations due to channel fading and error from physical obstacles [18]. in [20], an adaptive qos handoff priority scheme which reduces the probability of handoff call failures in a mobile multimedia network with a micro/picocellular architecture is presented. the scheme given in [21] supports voice and data calls with some grade of service guarantee in mobile multiservice networks. the variations in the channel condition and data rate have motivated us to use fuzzy controller for developing a packet dropping scheme. the proposed scheme provides soft packet dropping, which minimizes the bursty losses and also provides better qos for wireless multimedia network applications. rest of the paper is organized as follows. section 2 presents the proposed fuzzy based packet dropping scheme. sections 3 and 4 present simulation model and results, respectively. finally, section 5 concludes the work with some remarks. 2 proposed work the proposed scheme is located at the base station. the components of the scheme are as shown in figure 1. it consists of a knowledge base, buffer manager, and a fuzzy controller. knowledge base comprises of status information of base station and the running applications. the status information of the base station include queue length, queue length threshold, maximum bandwidth, bandwidth available, maximum buffers, buffers available, channel condition, aggregated flow rate, aggregated departure rate, correction factor, etc. the information details about the running applications include application id, priority, type (handoff/new), rate of flow at previous and current time instants, packet service time limit, packet departure time, buffer required, sustainable dropping threshold, dropping factor, etc. queue length threshold and dropping factor unit decision taking unit fuzzy controller correction factor (cf) buffer manager accept/reject computation request base ledge know− figure 1: fuzzy based packet dropping scheme buffer manager comprises of computational and decision taking units. computational unit computes queue length, queue length threshold and dropping factor for an application packet request by using the data such as correction factor, priority, etc., available in the database. the dropping factor is made available to decision taking unit to decide either to drop or accept the packet. the buffer manager employs fuzzy controller to tune the queue length threshold. buffer manager updates the knowledge base as and when required, either periodically or on arrival of application request. fuzzy controller uses channel condition and packet flow rate of an application as inputs and provides correction factor as output, and updates the knowledge base. the correction factor is used to dynamically change the value of queue length threshold. the knowledge base is used for providing data for fuzzy controller to find out the correction factor. 308 j.d. mallapur, s.s. manvi, d.h. rao the computation of queue length, queue length threshold and dropping factor are discussed in the following sections. 2.1 computation of queue length threshold and dropping factor the following assumptions have been made to compute the queue length threshold and dropping factor. (1) handoff calls and new calls both arrive at base station with different priorities, (2) packet dropping is considered in case of non-availability of buffers, and (3) priority is assigned depending upon the request time and current time within the different type of calls, i.e., high priority is assigned for handoff real-time calls. the computation of queue length, queue length threshold and dropping factor is divided into three phases: (1) queue length computation, (2) correction factor computation, and (3) priority an dropping factor computation. the notations used in each of the phase are as follows. q(ti) queue length in the buffer at time ti, r(ti) aggregated flow rate (sum of flow rate of all applications) at time ti, x(ti) aggregated departure (sum of departure rate of all applications) rate of packets at time ti, qth(ti) queue length threshold in buffer at time ti, cf correction factor, chc(ti) channel condition at time ti, df dropping factor for a packet of an application, bwmax maximum bandwidth in the base station area, bumax maximum buffer at the base station, and p priority of a packet. computed queue length in phase-i will be used in phase-ii and phase-iii for computing queue length threshold and dropping factor. computation of queue length in the buffer is performed using two factors: aggregated flow rate and departure rate from the buffer. the queue length is given by formula 1. q(ti) = q(ti−1) + (δr(ti) ∗ (ti − ti−1)) − (x(ti) ∗ (ti − ti−1)) (1) δr(t) = r(ti) − r(ti−1) (2) where δr(ti) is the change of aggregated flow rate at time instant ti. in phase-ii, correction factor (cf) is computed to tune the queue length threshold. the new queue length threshold computed in phase ii is used by phase iii for calculating the dropping factor. the correction factor is a value that depends upon channel conditions as given in formula 3. the value of cf is varied between 0 to 1.0 depending on fuzzy parameter chc. the variables ’h’ and ’z’ lie in the range 0 to 1 as defined by the system administrator. cf =   0 if 0 < chc < h; µ(rf) × µ(chc) if h ≤ chc < z; 1.0 if z ≤ chc < 1.0 (3) the correction factor depends upon the two fuzzy parameters, rate of flow (µ(rf)) and channel condition (µ(chc)) for one of cases. where µ(rf) and µ(chc) are membership function for flow rate and channel condition, respectively. before providing computation of new queue length threshold, let us first describe initial queue length threshold computation. initial queue length threshold value is computed as given in formula 4 by using the bumax and threshold value setter f (varied in the range 0.7 to 0.9). the new queue length threshold is computed periodically as given in formula 5. qth(tinitial) = f ∗ bumax. (4) qth(ti) = (qth(ti−1)) − (cf ∗ (qth(ti−1) − qth(ti−2))). (5) fuzzy based packet dropping scheme in wireless cellular networks 309 in phase-iii, priority of the arriving packet and dropping factor are computed. the priority of the packet is used for computing dropping factor. the priority (p) of a packet from a connection of a call is computed (see formula 6) based on arrival time of a packet during a refreshing interval w, where, refinit is the starting time of interval w and pktarr is packet arrival time. to provide higher priority to handoff packets, p′ is recomputed using formula 7, where x is a value chosen between the range 1 to 3 (3 means 30 percent) or as desired by the administrator. the dropping factor of a packet is represented by formula 8 by considering n applications existing at the base station, where bw denotes bandwidth used by an application. the computed value for dropping factor is communicated to buffer manager for taking the final decision to either drop or accept the packet. the accepted packet is placed into buffer. the fuzzy controller used in computation of correction factor is discussed. p = 1.0 − (pktarr − refinit)/w. (6) p′ = { p for − handoff − packets p/x for − new − packets (7) df =   1 if q(ti) > qth(ti); 1 − max(q(ti)/qth(ti), p′) if ∑n k=1 bw[k] > bwmax; 0 ∑n k=1 bw[k] < bwmax (8) 2.2 fuzzy controller the fuzzy controller used in computation of correction factor in phase ii is as shown in figure 2. the fuzzy inputs are flow rate and channel condition. the output parameter is a correction factor. fuzzy controller has four components: fuzzification, inference, defuzzification and rule base. in the fuzzification step, fuzzy input parameter values (called crisp input) are converted into linguistic values (such as low, high or medium), each of which is represented by a fuzzy set. in the inference step, a set of rules called rule-base, which emulates the decision-making process of a human expert is applied to the linguistic values of the inputs to infer the output sets which represents the actual control signal for the process. we refer the reader to [22] for more complete background information on the fuzzy control. inference defuzzifierfuzzifier rule base rate of flow condition channel correction factor figure 2: fuzzy controller fuzzification fuzzy based packet dropping scheme considers two parameters for fuzzification: channel condition (chc) and the rate of flow of each application (rf ). the output of linguistic parameter is the correction factor which is used for tuning of the queue length threshold. membership 310 j.d. mallapur, s.s. manvi, d.h. rao functions g(rf ), g(chc) and g(cf ) for each of the considered fuzzy parameters are depicted in figure 3, along with the linguistic values. each of the fuzzy parameter is represented by triangular membership function since it represents minimum and maximum boundary conditions. the membership to each of fuzzy variables is assigned using intuition method. 0 1 1 rate of flow channel condition 0 1 0 correction factor low medium high worst good best rf0 rf1 rf2 rf3 rf4 cf0 cf1 cf2 cf3 cf4 medium highlow chc0 chc1 chc2 chc3 chc4 figure 3: membership function for input and output fuzzy parameters • rate of flow each application has different flow rate based on the requirements. the rate of flow is represented by linguistic values low (rf0 to rf2 ), medium (rf1 to rf3 ) and high (rf2 to rf4 ). • channel condition it is different at a given instant of time. three linguistic values of channel conditions are represented by worst (chc0 to chc2 ), good (chc1 to chc3 ) and best (chc2 to chc4 ). • correction factor the correction factor is used as tuning parameter for queue length threshold. the linguistic values for correction factor are low (cf0 to cf2 ), medium (cf1 to cf3 ) and high (cf2 to cf4 ). the fuzzy packet dropping scheme forms a fuzzy set of dimension g(rf) ∗ g(chc). the values of fuzzy variables assigned depends on the network administrator, i.e., he/she can assign the different values at different instant of time depending upon the network conditions. inference and defuzzification mamdani controller is used for reasoning. the two fuzzy inputs are rf and chc with each input having three linguistic values. total number of rules will be 9. the fuzzy rule base is as shown in figure 4. if the condition is true, we call the rule as being active. each rule i is written as follows. for example, rule i: if rfi is low and chci is good, then cfi = low. to decide an appropriate output membership function, the strength of each rule is considered. for this fuzzy based packet dropping scheme in wireless cellular networks 311 reason, the output membership function is a complicated function and center of area method [22] is used for defuzzification. this method finds the center point of the fuzzy output membership function, which is used as output value. the defuzzified output parameter provides flexibility to the network administrator to perform soft packet dropping. rate of flow channel condition correction factor low medium high low low medium medium high high worst worst worst good good good best best best high high high high medium medium medium low low figure 4: fuzzy rule base table for correction factor 2.3 algorithms this section presents algorithmic description of the proposed work. algorithm 1: packet dropping decision in buffer manager nomenclature: n= number of running applications, bumax=maximum buffer size at the base station, i=ith running application, bureq= buffer required, rf =rate of flow, chc = channel condition, qti = queue length in the buffer at ith time instant, qthi = queue length threshold in the buffer at ith time instant, cf = correction factor, and df = dropping factor. 1. receive buffer request bureq from new/handoff call to buffer manager; 2. if (buffers are available) then allocate and go to step 8; else execute the following steps; 3. initialize the computational unit with queue length and queue length threshold; 4. check for channel conditions and set the cf according to formula 3; call algorithm 2 in case of chc lying between h and z. 5. compute queue length threshold as given in the phase ii (formula 5); 6. compute df as given in the phase iii (formula 8); 7. decision unit in buffer manager compares the dropping factor with predefined dropping threshold; if the dropping factor is above the threshold, then the packet is dropped, else accepted; 8. stop. 312 j.d. mallapur, s.s. manvi, d.h. rao algorithm 2: computation of correction factor 1. initialize fuzzy controller with aggregated rate of flow and channel condition; 2. find the membership function of rate of flow and channel condition; 3. compute cf by using rule base; 4. return defuzzified cf; 5. stop. 3 simulation the simulation of proposed work is carried out using c programming language in different scenarios on a pentium iv machine. simulation uses the network model, channel model, fuzzy model and traffic model. models are presented as follows. network modela cell covers an area of x1 ∗ y 1 sq. kms. base station is located at some median point of the area. there are n number of mobile nodes arrive at the base station, which may include n1 handoff nodes and n2 new nodes that are generated with probability p and (1 − p), respectively. mobile nodes move in random directions. maximum bandwidth required in a cell is assumed to be bwmax mbps. maximum buffer size at the base station of the cell is assumed to be bumax gbytes. channel model the wireless fading due to physical environment are often characterized to envelop fading of the carrier signal. rayleigh fading has been used to model channel characteristics, where signal strength is varied by considering the mean and variance of signal strength. each node is modeled to receive data if signal strength of the data is within the acceptable value. fuzzy model fuzzy parameters considered are: rate of flow rf0, rf1, rf2, rf3 and rf4; channel condition chc0, chc1, chc2, chc3 and chc4; correction factor cf0, cf1, cf2, cf3 and cf4. the triangular membership is assigned to the input and output parameters because it is easy to compute and strictly specify the lower and upper limits of the fuzzy parameters. the intuition method of fuzzification is used to fuzzify the input parameters such as flow rate and channel condition. to decide an appropriate output crisp value for cf, we consider the center of gravity method. traffic model poisson distribution is used to model the generation of data packets with arrival rate λ in interval t. 3.1 simulation inputs following inputs are considered for simulation. x1 = 5 km., y1 = 10 km., n=100, p = random number between 0 and 1, n1= n*p, n2= (n-n1). mobile nodes can move in any of eight directions: n, s, e, w, ne, nw, se, sw. bwmax=20 mbps. bumax=100 mbytes. queue length threshold initialized with f= 0.9. queue length (qti) ranges from 10-90 mbytes. the channel condition parameters such as µ set between 0.2 to 1.0, σ set between 0.3 to 0.5. the fuzzy input rate of flow data is randomly distributed between [rf0, rf2]=[500kbps,1mbps], [rf1, f3]= [750kbps,1.5mbps ], [rf2, rf4]=[1mbps-2mbps]. channel condition limits are z=0.9, h =0.1. channel condition fuzzy values are: [chc0, chc2]=[0.1,0.4], [chc1, chc3]=[0.2,0.6], [chc2, chc4]= [0.4,0.9]. the fuzzy output parameter correction factor is distributed between [cf0, cf2]= [0.0,0.4], [cf1, cf3]= [0.2,0.6], [cf2, cf4]= [0.4,0.8]. the value of λ is positive real number between 0 to 10 and t is time interval set to 10. simulation procedure is as follows: generate a wireless network and traffic across the network, apply the proposed model and find the dropping factor, and compute the performance of the system. the performance parameters measured in simulation are as follows. acceptance of new fuzzy based packet dropping scheme in wireless cellular networks 313 calls: it is defined as the ratio of acceptance of new calls to total number of new calls arrived. acceptance of handoff calls : it is defined as the ratio of acceptance of handoff calls to total number of handoff calls arrived. dropping of new calls : it is defined as the ratio of new calls dropped to total number of new calls arrived. dropping of handoff calls: it is defined as the ratio of handoff calls dropped to total number of handoff calls arrived. variations in queue length threshold: it is defined as change in the queue length threshold. number handoff calls corrected: it is defined as number of handoff calls got corrected to total number of handoff calls arrived. number of new calls corrected: it is defined as number of new calls got corrected to total number of new calls arrived. 4 results figure 5 depicts the rise in acceptance of new calls upto certain limit with the variations in channel conditions. as the variance (sigma) of channel reduces, acceptance reduces. in a similar way, as the mean (mu) of channel condition reduces, acceptance reduces. this is because, the scheme attempts to get broader range of channel conditions for computing dropping factor. the packet drops for new calls change with respective change in the mean and variance of the channel conditions as observed in figure 6. 0 10 20 30 40 50 60 70 5 10 15 20 25 30 35 40 45 50 a c c e p te n c e ( % ) number of new calls arrived acceptence(%) vs. number of new calls arrived mu=1.0,sig=0.5 mu=1.0,sig=0.3 mu=0.5,sig=0.5 mu=0.5,sig=0.3 mu=0.2,sig=0.5 mu=0.2,sig=0.3 figure 5: acceptance of new calls (%) .vs. new call arrivals 0 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 d ro p o f n e w c a ll s (% ) number of new calls arrived drop of new calls (%) vs. number of new calls arrived mu=1.0,sig=0.5 mu=1.0,sig=0.3 mu=0.5,sig=0.5 mu=0.5,sig=0.3 mu=0.2,sig=0.5 mu=0.2,sig=0.3 figure 6: dropping of new calls (%) .vs. new calls arrival more number of new calls get corrected as the number of calls increase as depicted in figure 7. this happens because of scarcity of buffers at the base station. the variations in acceptance for handoff calls is shown in figure 8. the handoff calls acceptance is more than new calls for the same mean and variance of the channel condition (see figures 5 and 8). this indicates that scheme gives higher priority to handoff and real-time calls. figure 9 shows the dropping percentage of handoff calls with respect to the handoff calls arrived. it is seen that less number of packets from handoff calls are dropped compared to new call packets (see figures 6 and 9). the corrected handoff calls increase with increase in the handoff call arrivals as observed in figure 10. the queue length threshold tuning with respect to channel conditions is shown in figure 11. it shows that as the channel condition becomes better, the tuning value increases to avoid buffer overflows. the queue length threshold variations with respect to time is shown in figure 12. this indicates the tuned value of queue length threshold at different instants of time. 314 j.d. mallapur, s.s. manvi, d.h. rao 0 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 n u m b e r o f c a ll s g o t c o rr e c te d number of calls number of calls got corrected vs. number of calls mu=1.0,sig=0.5 mu=0.5,sig=0.5 mu=0.2,sig=0.5 figure 7: corrected new calls (%) .vs. new calls arrival 0 10 20 30 40 50 60 70 5 10 15 20 25 30 35 40 45 50 a c c e p te n c e (% ) number of handoff calls arrived acceptence(%) vs. number of handoff calls arrived mu=1.0,sig=0.5 mu=1.0,sig=0.3 mu=0.5,sig=0.5 mu=0.5,sig=0.3 mu=0.2,sig=0.5 mu=0.2,sig=0.3 figure 8: acceptance of handoff calls (%) .vs. handoff calls arrival 0 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 d ro p o f h a n d o ff c a ll s (% ) number of handoff calls arrived drop of handoff calls (%) vs. number of handoff calls arrived mu=1.0,sig=0.5 mu=1.0,sig=0.3 mu=0.5,sig=0.5 mu=0.5,sig=0.3 mu=0.2,sig=0.5 mu=0.2,sig=0.3 figure 9: dropping of handoff calls (%) .vs. handoff calls arrival 0 5 10 15 20 25 30 35 40 45 5 10 15 20 25 30 35 40 45 50 n u m b e r o f h a n d o ff c a ll s g o t c o rr e c te d number of handoff calls arrived number of handoff calls got corrected vs. number of handoff calls arrived mu=1.0,sig=0.5 mu=0.5,sig=0.5 mu=0.2,sig=0.5 figure 10: number of handoff calls got corrected (%) .vs. total handoff calls arrived 5 conclusions the main objective of the proposed work is to use the base station buffers efficiently and decrease the packet dropping especially for handoff and real-time calls during good channel conditions and high flow rate causing the buffer overflow. extensive simulation results reveal that proposed scheme features very low call dropping, better handoff and new calls acceptance with different channel conditions. this is achieved because of tuning the queue length threshold by correction factor. the correction factor is derived by two fuzzy parameters such as channel condition and flow rate. the scheme can be extended for different fuzzy parameters such as device type, user priority, sustainable jitters, etc. fuzzy based packet dropping scheme in wireless cellular networks 315 2 4 6 8 10 12 14 16 18 20 22 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 v a ri a ti o n s i n q u e u e l e n g th t h re s h o ld ( % ) channel condition variations in queue length threshold (%) vs. channel condition mu=1.0,sig=0.5 figure 11: queue threshold (%) .vs. channel condition 0 2 4 6 8 10 12 14 10 20 30 40 50 60 70 80 90 100 v a ri a ti o n s i n q u e u e l e n g th t h re s h o ld ( % ) time variations in queue length threshold (%) vs. time mu=1.0,sig=0.5 figure 12: queue length threshold (%) .vs. time bibliography [1] s. floyd and v. jacobson, random early detection gateways for congestion avoidance. ieee/acm transactions on networking, vol. 1, pp. 397-413, aug., 1993. 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[21] k. m. passino and s.yurkovich, fuzzy control, addission wesley, 1998. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 517-524 e-learning & environmental policy: the case of a politico-administrative gis n.d. hasanagas, a.d. styliadis, e.i. papadopoulou, l.a. sechidis nikolaos d. hasanagas, athanasios d. styliadis, lazaros a. sechidis kavala institute of technology, department of landscape architecture city of drama, greece, gr 66100 email: styliadis@ath.forthnet.gr, nikolaos.hasanagas@gmail.com, lazikas@photo.topo.auth.gr eleni i. papadopoulou aristotle university of thessaloniki, faculty of agricultural science department of agricultural economics, city of thessaloniki, gr 54124 e-mail epapa@agro.auth.gr abstract: is an effective knowledge exchange and cooperation between academic community and practitioners possible? implementation of e-learning in specialized policy fields pertains to the most challenging priorities of icts and software engineering. in multidisciplinary academic areas which combine environmental policy studies with positivist subjects (like environmental issues, forest policy, rural development, landscape architecture etc), the using of e-learning system in analyzing policy issues steadily gains in importance and is a method which connects the academic community and the researchers with the practitioners and field experts. such initiatives incorporate a number of politometricsrelevant algorithms embedded in a context of political geography (i.e. visualized hierarchies in different regionrelated policy issues). this is the case addressed in this paper. the gis learning management system introduced in this paper is based on certain criteria concerning organizational models and region-specific politico-administrative hierarchies. scenarios of politico-administrative metadata achieving optimal power synergy are extracted through a sequencing technique, combining vector-algebra software and statistics and can be used for both teaching and research purposes. keywords: e-learning, gis, politometrics, forest policy, environmental issues, rural development policy, socio-informatics. 1 introduction although the political structures of the "western" and "civilized" world are considered to be standardized in the framework of a single "cosmopolite" value system, the power structures in environmental policy issues are quite different between regions. not in every region of europe the state actors necessarily concentrate the same degree of power. sometimes private enterprises, environmental or economic groups are the leading actors. an actor (e.g. environmental or landowner interest group) should also be adjusted to the particular condition of regional policy networks in order to succeed. socio-informatics software like visone (network analysis) is based on elaborated vector-algebra algorithms [3] which are aiming at quantifying and visualizing the intangible political relations [1] and informal dynamics of environmental policy and in general of rural development policy. the application of such a computer-aided "political geometry" methodology with region-specific cases is the "cornerstone" of structuring a gis functional for politometrics and thus of implementing a gis learning managament system (glms) [4] in the context of a post-modern political geography depicting regional-specific (in)formal hierarchies (archimedes findings). the implementation of e-learning in specialized policy fields pertains to the most challenging priorities of icts and software engineering. in multidisciplinary academic areas which combine environmental policy studies with positivist subjects (like landscape architecture, rural sociology and economics, forest science etc), the using of e-learning system in analyzing policy issues steadily gains in importance (archimedes findings). moreover, in cross-sectoral policy networks such as those which are developed on forest policy issues and are discussed in this paper, it is impossible to separate policy sectors; environmental issues, rural development policy and forest policy copyright c⃝ 2006-2010 by ccc publications 518 n.d. hasanagas, a.d. styliadis, e.i. papadopoulou, l.a. sechidis are an inseparable part of what is called "integrated rural development". such networks involve not only forest owner and industrial interests but also other groups (e.g. agricultural museums, environmental ngos, agrarian associations etc). not only the classical rural territory but also urban-related interests are involved. thereby, a new urban-countryside relation is developed. thus, a policy-relevant filtering of learning objects is necessary in order to enable the exchange of knowledge between academic community (or researchers) and practitioners/ field experts. the e-learning system suggested in this paper is expected to be appropriate for achieving not only an effective over-bridging between academic community and practitioners in forest policy and in the wider environmental and rural development issues but also an effective organisation and coordination of means of forest and rural resource management, an acceptable evaluation of forest resources and acceptable procedures of estimating or accounting the economic, material and non-material values of forest, frictionless goal-setting and decision-making involving private and public actors, and a method of examining issues of ambiguity and law-making concerning forest and wider natural resources. as long as this e-learning is implemented among target groups from different policy sectors related to forestry (e.g. spatial planning, agriculture, tourism, water management etc), a minimization of conflicts is feasible [6]. this holistic approach of policy-making is enabled through the complete analysis of policy networks, which is an operational form of system theory [8, 10]. in other words, a new systemic analysis of classical forest policy is the basis of this e-learning system. an adaptive process that selects learning objects (region-specific policy structures and actors) from a digital repository and sequences them in a way which is appropriate for the targeted gis learning community or individuals [2, 6, 11, 13, 16, 17], is also necessary for reducing computational time and gaining in objectivity and acceptance of politico-administrative conclusions. such a method is also required as the rural development issues are characterized by complex and unpredictable informal procedures and there are no clear and common indicators for evaluating rural development policy in europe (rudi findings). nevertheless, a qualitative and participative evaluation is necessary on the part of learners (students specialized in forest policy, rural sociology and economics, development and spatial engineering, lobbyists of interest groups etc). many types of intelligent learning systems are available but without gis functionality. in the glms proposed in this paper, five key components could be identified, which are common in most gis systems: region-specific data acquisition, algebraic and statistical analysis, processing data of actors and regional networks, construction of political geography database, and calculating/ visualizing formal and informal hierarchies. figure 1 depicts the interactions between these five gis components [4, 18]. figure 1: the main components of an intelligent glms. the selection of learning content (in this case, the region-specific formal and informal hierarchies) is based on policy-research criteria depending on complex and heterogeneous cognitive styles [3, 8, 15] which may be characterized as "region-" and "administrative-based" (archimedes findings). in this way, a wide range of learner expectations is satisfied [9, 10, 12]. particularly, the administrative-based elements (organisational theories) are much more subjective than the regional ones. therefore, cooperation between multidisciplinary academic and field experts (e.g. environmental, forest and agricultural scientists, public administrators, lobbyists, sociologists, informatics experts etc) is necessary in order to achieve acceptance and integrated analysis of real cases. such a criteria set constitutes a decision support system (dss) for learners [4] and teaching staff which enables both of them to reduce hypothetical options and produce original and accurate research results and constitutes a basic component of an intelligent glms [16, 19]. although many digital dss types have been proposed, these are applicable only to examination of human-building interaction and perceptual relations [10, 17, 18, 22, 23, 25] and they are not combined with glms. other dss types which are combined with glms [16,24] are strictly related to e-learning & environmental policy: the case of a politico-administrative gis 519 spatial elements and not to region-specific politico-administrative issues (archimedes findings). in this paper, the development of a politico-administrative gis is addressed. a regionand administrative-based filtering process of learning objects is discussed. politico-administrative metadata are proposed which can be used for learning object filtering on the basis of the open gis consortium guidelines (standards) concerning gis functionality [4, 20]. 2 digitalizing the "political geometry" in a region-specific gis the formal and informal hierarchy shaped in every region surveyed is composed of three power dimensions, as shown in formula (1) [21]: politico − administrative power = trust + incentives +u niqueness (1) trust is used for leading even when surveillance is infeasible, provision of incentives is for assuring commitment, and uniqueness is useful for exerting institutional pressure. according to the rudi findings, informal hierarchies are more decisive for the policy output than the formal ones because of the lack of detailed criteria of decision-making and evaluation in rural development policy. trust is a relational value based on expertise, experience and personality and is accumulated through successive transfer of reputation. if e.g. the forestry commission trusts the royal scottish forestry society, which trusts the national trust of scotland and the last two actors trust the friends of the loch lomond, then the last one proves to be the most trustworthy as it is able to gain the trust of all previous actors (also of the forestry commission indirectly). for the reputation of the actor a, it is not merely important how many actors trust a, but also how much reputation these actors gain from other actors etc. these actors can be ordered on the vertical and horizontal axis of a matrix. thereby the network can be algebraically processed. formula (2) which is known as katz-status formula is applied for calculating the power status of an actor in a network: t = (i − ac)−1 − i (2) where t is a matrix including the status values of all actors as elements, c is the matrix presenting the real network of trust, and a is a dumping factor. the same formula is applied in the case of the provision of incentives and uniqueness dependence relations. this algorithm is used by visone software. visone layers vertically the actors (learning objects) according to their power status measured in % (figure 2). the horizontal order has no politico-administrative meaning. it is obvious that in the simple polygon form, the policy networks are not disclosing any hierarchies developed in their regions. when they are layered, they acquire a pyramid-shape form. the sharper the pyramid (vertical length in relation to horizontal length), the higher the oligarchy, as defined in formula (3) [14, 21]: oligarchy = status max − status min status average (3) the sharpest pyramid is this of uk1 issue network (oligarchy=2,40), while the "pyramid" of greek network does not seem to be a pyramid at all, as the oligarchy is quite low (1,48). in figure 2, the power status of each actor can be examined by the learners in relation to its orientation (use or conservation of natural resources) and its legal character (private or state actor). in this way, learning effects and original conclusions with academic and practical value can be made by the learners through the interpretation of this digital visualization of region-specific hierarchies with abstract but applicable politico-administrative metadata [1, 13, 20]. an output of such a gis produced by the comparative analysis of these digital pyramids of (in)formal regional hierarchies is concisely presented in figure 3: the power status can be examined by the learners again in relation to the legal character and to the orientation among various regions. thereby, policy-relevant conclusions can be made regarding the winning possibility, considering these determinants (legal character and orientation). e.g. in the greek and spanish regions, the private actors are much more powerful than the state ones, with noticeable difference in comparison with the european average. the inverse hierarchy can be recognized in the case of the uk1 and uk2 networks (scotland). the conservation-oriented actors (e.g. environmental ngos and agencies) are more powerful than the use-oriented ones in denmark. this is a case subversive to the average power relation. the science-oriented actors (universities, research institutes) are more powerful than the other actors in the network of finland and in one region-specific network in spain. these results can be further interpreted by using qualitative information about the content of the policy issues (rudi and archimedes findings). 520 n.d. hasanagas, a.d. styliadis, e.i. papadopoulou, l.a. sechidis figure 2: region-specific networks of politico-administrative power. a further elaboration of dss [2, 5, 16] for the diagnosis of favorable (or unfavorable) participation in a regionspecific network [9, 19] is possible by applying stepwise regression to this political-geographic database: not every actor can participate in every regional network with equal chance of developing power. according to the new-institutionalist approach the power (p) achieved by an actor does not depends only on the organizational (o) features of this actor but also on the regional network (n) conditions in which this actor is involved, as shown in the figure 4. which combinations of actor and regional network features lead to the optimal power? one can deduce hypotheses on the o-factors and induce the n-factors through stepwise regression [21]. in figure 5, the procedure of the stepwise regression works as a filtering process, reduces the power-ineffective combinations and produces ideal types of politico-administrative metadata (actorand regional network-related power determinants). an ideal type, for instance, is the following one: an actor (e.g. an environmental ngo) with multidisciplinary team (0,284*multidis), which is not radical (-0,261*radicali), and has no state representatives at its board (-0,203*statech), can develop optimal power in a network which is composed of only a few actors (-0,427*actors), provides many opportunities of lobbying (0,394*potlobb), is characterized by low relative importance of state (-0,296*relimpst), and involves only a few policy sectors (-0,243*intersec). comparing these actorand network specific factors with gis outputs such as these which are described in figure 3, it is concluded, for instance, that this type of actor described in figure 5, has optimal chance to develop power in the regional networks of bavaria. o-factors were selected by using specific organisational theories (i.e. contingency model which is expressed by the absence of state representative at the board and the using of alternative expertise for surviving in heterogeneous regionalpolitical environments). the regional n-factors were inductively selected by the stepwise regression. the combinations of oand n-factors can also be characterized as regional-specific critical scenario analysis [2,5,7,21]. according to rudi results, the deviation between formal and informal hierarchies and the differences between regional networks can be attributed to the inflexible bureaucracy, the complexity, the centralisation and to the lack of formal and clear criteria of decision-making and evaluation in greece and in other european countries. furthermore, the challenges posed by the requirement of harmonizing social, economic and environmental standards e-learning & environmental policy: the case of a politico-administrative gis 521 figure 3: politometrics-embedded gis. figure 4: optimal power synergy through politico-administrative meta-data: actor-related and regionspecific power determinants. 522 n.d. hasanagas, a.d. styliadis, e.i. papadopoulou, l.a. sechidis figure 5: stepwise regression on politometrical gis data. e-learning & environmental policy: the case of a politico-administrative gis 523 complicate the policy-making. 3 summary and conclusions the main goal of vector-algebra software (i.e. visone) is the digitalization and visualization of the formal and informal politico-administrative hierarchies of region-specific issue networks. the goal of the stepwise regression as a filtering process is the reduction of the searching space. gis learning object repositories often contain numerous possible combinations of regional hierarchy features and actor-related characteristics. without visual complete network analysis and statistical techniques like the stepwise regression, the examination of and familiarization with all possible learning objects would be characterized by conceptual complexity and time-consumption. this would be discouraging for the learners, especially if they were practitioners (e.g. lobbyists of environmental and industrial groups, or employees of forest services and agricultural directorates) and not only normal students. the method presented in this paper is a system for filtering learning objects which is based on knowledge domains [7, 11, 19] (i.e. organisational theories and practical experience) and seems to be appropriate for student and adult education as well. acknowledgements the research initiative proposed by this paper has been supported by the eu-funded "archimedes" research project (department of landscape architecture, kavala institute of technology, drama, greece), by the eufunded research project "rudi: rural development impactsassessing the impact of rural development policies, incl. leader" consortium no: 213034, 7th framework programme for research and technological development, (department of agricultural economics, faculty of agricultural science, aristotle university of thessaloniki, greece), and by the institute of forest policy and nature conservation of goettingen university (germany). bibliography [1] m. a. rajan, m. girish chandra, l.c. reddy, p. hiremath, concepts of graph theory relevant to ad-hoc networks. int. j. of 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[24] a.d. styliadis, i.i. akbaylar, d.a. papadopoulou, n.d. hasanagas, s.a. roussa, l.a. sexidis, metadatabased heritage sites modeling with e-learning functionality. journal of cultural heritage, 10, 296-312, 2009. [25] a.d. styliadis, d.g. konstantinidou, k.a. tyxola, ecad system design applications in architecture. int. j. of computers, communications & control, 3(2), 204-214, 2008. nikolaos d. hasanagas born in 1974. assistant professor in environment-related subjects at the kavala institute of technology, drama, greece. bsc and msc eq. in environmental sciences (aristotle univ. of thessaloniki, greece), ba and ma eq. in social sciences, phd in environmental policy analysis (goettingen univ., germany). athanasios d. styliadis born in 1956. professor of digital architecture and design computing at the department of landscape architecture at the kavala institute of technology, drama, greece. diploma in surveying engineering, msc in computer science (dundee univ., scotland), phd in caad and gis (aristotle univ. of thessaloniki, greece). eleni i. papadopoulou born in 1957. assistant professor in rural policy at the faculty of agricultural science at the aristotle university of thessaloniki, greece. bsc in agriculture engineering, msc in agricultural economics (univ. of reading, uk), phd (aristotle university of thessaloniki). lazaros a. sechidis born in 1968. assistant professor in geodesy at the department of landscape architecture at the kavala institute of technology, drama, greece. dipl. in surveying engineering, phd in photogrammetry (aristotle univ. of thessaloniki). international journal of computers communications & control issn 1841-9836, 11(3):405-413, june 2016. influence of the qos measures for voip traffic in a congested network r.l. luca, p. ciotirnae, f. popescu robert luca, petrica ciotirnae*, florin popescu military technical academy romania, 050141 bucuresti, bdul george cosbuc, 39-49 robert.lucian.luca@gmail.com, ciotirnae@mta.ro, fdpopy@yahoo.com *corresponding author: ciotirnae@mta.ro abstract: the paper revolves around the subject regarding quality of service (qos) in a telecommunication network. the chosen scenario is based on the transmission of data and voice packets using a wan connection, which has a limited bandwidth and emphasize the need of implementing qos mechanisms in order to fulfill the quality requirements of the traffic, especially for voip. this topology will outline the impact and importance of the qos implementation, illustrated by the desired quality resulted through voip traffic simultaneously with maintaining the data conectivity using a lower bandwidth for applications which require a smaller amount of qos properties, such as ftp. keywords: quality of service (qos), voip, weighted fair queuing (wfq), low latency queuing (llq), class-based weighted fair queue (cbwfq). 1 introduction this paper emphasize a method to implement mechanisms that provide quality of service (qos) to voip services. the qos problem is a subject that will be studied continuously, according to other papers which studied this matter [1], [2]. there are a few challenges that concern the voip implementation: finding solutions to transmit a real-time service using a data network, which offers no guarantees regarding the delivery of the service, by using the best effort mode. this type of technology is successful only if the features that inffluence the quality of service in data networks are being decreased: latency, jitter and packet loss (see [3][8]). moreover, qos represents one of the 4 characteristics which a network has to provide in order to fulfill the clients expectations, beside scalability, security and fault tolerance [9]. 2 general description of the used mechanisms in order to implement the qos mechanisms, it is neccessary to use specific mechanisms depending on the type of network and service used. some of them are described in the paragraphs below. the weighted fair queuing (wfq) [10] mechanism creates dinamically queues based on traffic flows. a packet can be asigned to a certain type of flow depending on the value obtained through applying a hash function on the source ip address and destination address, source port and destination port or tos value. this type of flow will have his own queue. the maximum number of queues that can be recorded on the interface is 4096, which is much higher than other similar mechanisms (ex: priority queue which allows the network administrator to configure four separate buffers with different priorities: high, normal, medium and low. packets in buffer with higher priority are always processed before packets in lower priority buffer; custom queue allows users to define up to 16 queues. each queue is processed at a time and can be transmitted according to the copyright © 2006-2016 by ccc publications 406 r.l. luca, p. ciotirnae, f. popescu parameter "weight" (set by user) a certain number of packets or bytes). in wfq, a flow exists as long as there are packets in that stream waiting to be transmitted. in other words, when the queue allocated to that stream is empty, it is removed . for this reason the number of queues in the system is changing rapidly . the mechanism which allocates bandwidth among different streams is based on a factor (cost, weight), which depends on the precedence field for the given buffer. when there is a free space in the hardware buffer, the wfq mechanism takes a packet from the software buffers and places it in the hardware buffer. it will be chosen the packet with the minimum "sequence number" (sn) among all queues. sn value is assigned to a packet before it is placed in the queue of the associated stream and also before taking a decision on its eventual disposal (the wfq mechanism uses a modified "tail-drop" algorithm which takes into account the sn value before discarding packets). the formula used to calculate the sn of a packet: sn = snant + (w × dim) (1) where: snant represents the sn of the previous packet, w represents the cost (weight)and has the formula: w = 32348 ipp + 1 (2) (ipp represents the value of the ip precedence field) and dim represents the packet dimension. from the expression (1) we can have some conclusions: • the calculation of the sn takes account of packet size, being smaller for smaller packets; • packets arriving in queues that already have a large number of packets will get a higher sn whereas the calculation of the sn takes account of the previous packet sn ; • it can be seen that the sn varies inversely with the value of the precedence field. the higher the priority of a flow, the lower the cost value "w" and hence the sn. in fig. 1 it can be seen how the packets are scheduled for transmission in two different queues that have the same cost w. if the a flow from fig. 1 would have a bigger cost than the cost of stream b it would be possible that sn a1 be greater than sn b1 and the packet "b1" may be transmitted before the transmission of packet "a1", situation which is graphically depicted in fig. 2. wfq algorithm automatically creates eight queues with smaller weights (high priority) for management traffic generated by the router (messages from routing algorithms, control traffic of osi layer 2, etc.). wfq works well for networks where delay sensitive traffic requires less bandwidth than the average of other flows. the method has the advantage that it does not require manual configuration of classes and can thus be used as the default method when traffic characteristics are unpredictable and classification is difficult [12]. disadvantages of this method: no guarantees for the delivery of a specific type of traffic and the fact that this algorithm doesn’t have a high priority queue that would provide minimum values for delay and jitter for voip traffic. another mechanism that can be used for providing qos measures is class-based weighted fair queue (cbwfq) [10], [11]. this congestion management mechanism permits the classification of packets into classes with costs (weights) according with the bandwidth specified by the administrator for each class. cbwfq band division is performed inversely to the cost assigned influence of the qos measures for voip traffic in a congested network 407 figure 1: programming packet transmission according to the sn (same cost w) [14] figure 2: programming packet transmission (different cost, w(a) > w(b)) 408 r.l. luca, p. ciotirnae, f. popescu to the flow. the method in which bandwidth is shared between the streams is described by the following equation : share(k) = w(1) + w(2) + ... + w(k) + ... + w(n) w(k) (3) equation (3) outlines the fact that the flows which have a smaller "w" cost get more bandwidth. unclassified traffic (which does not fall within any user-defined class) is sent to a special class (default) using wfq mechanism and the weights defined by it according to the equation (2). the cost of user-defined classes can be determined from the following formulas based on how the bandwidth command is applied to a certain class (as the number of bits or as a percentage of the total bandwidth) according to the following equations: w(k) = ct bwtot bw(k) (4) ,where ct depends on the number of queues in the system (inversely to the number), bwtot represents the available bandwidth on an interface and bw(k) is the bandwidth configured for a specific class as the number of bits. w(k) = ct × 100 bwpercentage(k) (5) bwpercentage(k) represents the bandwidth configured as a percentage from the total bandwidth. each manually configured class represents a separate queue managed by a fifo algorithm that receives a percentage of the total bandwidth depending on how the packets are chosen for transmission to the hardware buffer. after configuration, the cbwfq algorithm works similar to wfq, which means that it is based on the sn value defined by the same formula as in wfq, equation (2). it can be demonstrated that any user-defined class dominates the dynamically determined flows (wfq) almost all the time (except when certain classes are configured with a very low percentage 1.5%, which is unlikely) [13]. same as wfq, a predefined set of queues is allocated for the cbwfq mechanism called "link queues" that the system uses for transmission of network management traffic. these buffers are set at a fixed cost w = 1024, a cost which is much better compared to the cost of any dynamic flow and about the same level as the traffic classified by the user. due to the fact that this type of traffic is intermittent, the bandwidth distribution is not affected. moreover, there is a rule that user-defined traffic classes should not reserve more than 75% of the available bandwidth on an interface, such that these special buffers do not remain without bandwidth [15]. low latency queuing mechanism (llq) or "cbwfq with priority queue" [11] complements cbwfq mechanism and allows you to specify a traffic class with high priority and therefore minimal latency. the delay sensitive traffic can be transmitted before other types of traffic by using a special buffer with the highest priority (cost w = 0). in fig. 3 it can be seen the essential difference between llq and cbwfq mechanism, namely the existence of the priority buffer. this is a buffer with the minimum cost (0) which means total priority. in other words, the traffic that will be selected to use this buffer will always be sent before other types of traffic. for this class it applies a "policing" mechanism (when the configured rate is exceeded, packets are automatically discarded) to avoid the situation in which traffic with priority monopolizes all available bandwidth on an interface and starves other traffic classes that have been configured for guaranteed bandwidth. due to the fact that in the case of voip, traffic packets are sent at influence of the qos measures for voip traffic in a congested network 409 regular intervals, the transmission rate is relatively constant and for that reason voip is the best choice for the priority buffer. due to the advantages of minimum values for delay and jitter,llq is one of the most recommended method of "queuing" in literature and represents the version tested in the practical application presented below. on the low-capacity links, llq can be used with other qos mechanisms such as header compression and fragmentation and interleaving (to avoid the situation in which voice packets must wait for the transmission of large data packets, which would lead to significant delays and jitter). figure 3: llq mechanism operation [16] 3 experimental results for laboratory testing the following scheme was used for the interconnection of the equipments (fig. 4): figure 4: logical scheme of the equipments interconnection the aim of the laboratory tests is to highlight and compare the statistical values obtained for voice traffic in voip format in two distinct situations. the first case refers to a congested 410 r.l. luca, p. ciotirnae, f. popescu network in which it does not take into account the priority of traffic classes and the second situation where specific qos measures are applied. functional laboratoty platform description: • voip service is provided by the cme server (call manager express) on router 1 for the wireless network 2.0.0.0/24 and cme server on router 2 for the network 3.0.0.0/24. between the two servers there is a trunk for data link; • computer at ip 1.0.0.2 will play the role of a ftp client that will receive multiple streams of data from the laptop with the address 3.0.0.2 that will be the ftp server. between router 1 and router 2 it will be generated so much traffic as possible so that congestion occurs. • in addition, on the link between router 1 and router 2 the rate is limited at 500 kbps. this limitation has been achieved using an additional router (router 3) configured in such a way that it receives all the traffic transmitted between router1 and router 2. • traffic received by the router 3, regardless of its source or nature is selected by access lists and configured on router 3 as a single class of traffic. for this class it applies a shaping policy at 500 kbps (limiting the transfer rate at a given value while maintaining the packets in a buffer when the limit is exceeded). to analyze the quality of voice traffic was used an mechanism available on cisco equipments called "cisco ip sla." this application may generate packets similar with voip packets with predetermined characteristics depending on the type of the codec used. router 4 is used as the source and the probe and switch 1 as the device addressed. generated packets go through the network to the switch 1 and then return to the probe device to calculate the value for delay, jitter and number of packets lost. shortly after the initiation of data traffic congestion occurs in the network and the quality of voice traffic will be affected by high latency, variations in delay and packet loss. a range of objective assessments can be achieved by using a voip traffic generator (router 4) whose statistics are taken, analyzed and displayed in charts by a program called prtg network monitor. this application identifies network devices by ip and assignes them "sensors" which return various statistics such as cpu load, system temperature, check the ping response time etc. such a sensor that receives and interprets voip statistics is called cisco ip sla voip and can be associated with a router / switch cisco that operate as transmitter of ip sla test packets (probe). so, before this program can collect statistics from a voip device it must be configured to generate test traffic. ip sla voip udp jitter mechanism can generate packets similar to voip packets with characteristics that are preset depending on the type of codec used, e.g. g.711, g.729. after the values for delay , jitter and packet loss have been collected an approximate value for the r factor is calculated which is an objective measure of voice quality (e-model), which in turn is converted into an equivalent value called mos (mos-cqe that conversational quality estimated). therefore, ip sla generates voip traffic statistics: delay, jitter, packet loss, mos, and these statistics are retrieved and displayed in intuitive charts by the dedicated sensor in prtg. thus, the degradation of voice quality that comes with the installation of congestion can be confirmed by objective means (up to 1.2 seconds delay, packet loss up to 50% and jitter up to 20 ms.), shown in fig. 5 and fig. 6: router 1 and router 2 are connected by a link that is limited in terms of available bandwidth. when the traffic rate tends to exceed that available bandwidth, every packet will be dropped regardless of the class to which it belongs. to avoid this, it is applied a policy to all the traffic influence of the qos measures for voip traffic in a congested network 411 figure 5: mos, rtt (average values) and packet loss statistics for a congested network without qos measures (for voip sla traffic) leaving the interface (higher level policy), so the transfer rate is less than the maximum available bandwidth. in this way, it will move the congestion from the router 3 on the output interfaces of the border routers (router1 and router2) where the quality requirements of traffic can be managed (in a lower-level policy). in this application it has been studied low latency queuing algorithm performance. figure 6: highlighting the increse of jitter values during congestion so there will be an aggregate traffic consisting mainly of voip traffic, ftp traffic and management traffic (arp, dhcp) that will have a maximum output rate of 480 kbps (limitation imposed by the higher-level policy). from this rate a part will be allocated for voip traffic(with priority, 190 kbps), another part will be guaranteed for ftp traffic (150 kbps) and the remainder will be allocated to other types of traffic. this last category will share the remaining bandwidth with a wfq policy type. llq algorithm must be carefully implemented because a poor setting may have unintended consequences (packet loss) on delay sensitive traffic (voip class in this case) due to the "policing" rule applying to it. if llq is implemented correctly, the traffic that is found in the priority buffer will never exceed the configured rate. when hierarchical qos policy is applied to the output interface of the two border routers it will be seen an immediate increase in mos values and also the call quality (subjective) is much better in the absence of packet losses and delays as it can be seen in fig. 7. in fig. 7 it may be noted that packet losses have decreased from high levels of 40 − 50% to 0 and the delays have also decreased to values of a few ms. 412 r.l. luca, p. ciotirnae, f. popescu figure 7: mos, icpif, jitter, rtt statistics (average) and packet loss for voip sla traffic with qos measures applied in fig. 8 it can be seen a comparison between the results obtained for delay and jitter when the mechanism used is cbwfq and llq mechanism respectively. although apparently the differences between the two are small and don’t affect speech quality in the lab scenario, in a real situation where there can be multiple nodes and multiple paths between source and destination the values for delay and jitter in the case of cbwfq can grow significantly and can sum up and degrade the quality of voice traffic. hence the importance of using a high priority buffer for voice traffic. figure 8: highlighting the differences for jitter and rtt between cbwfq and llq 4 conclusions using appropriate qos mechanisms it can be achieved the desired quality for voip traffic while maintaining the operation at lower rates for other types of traffic. thus, by applying the appropriate qos measures we have achieved a considerable improvement of mos index from values of about 1.5 to ideal levels (about 4) which indicates a very good quality for voip traffic. the increase in mos is due to improvements in the values of delays (from hundreds, thousands of milliseconds to a few milliseconds); lost packets (at levels of about 40 − 50% to 0%) and jitter (from 15msec to 1 msec). llq is a highly effective mechanism to meet qos requirements because it allows control of service quality based on traffic classes that can be defined with precision. also, analyzing the mechanism behind cbwfq (llq) it can be concluded that none of the classes is not restricted to the configuration set up if there is enough bandwidth available. influence of the qos measures for voip traffic in a congested network 413 we also demonstrated the necessity of using a high priority buffer for voip traffic through a comparative analysis of the results obtained in the two cases llq and cbwfq which showed that values for delay and jitter can be minimized by using low latency queuing. bibliography [1] p. sharma (2014); challenges of quality of services in mobile ad hoc networks, international journal of advance research in computer science and management studies, 2(3): 342-347. [2] r.d. albu, i. dzitac, f. popentiu-vladicescu, i.m. naghiu (2010); input projection algorithms influence in prediction and optimization of qos accuracy, international journal of computers communications & control, 9(2): 132-139. [3] v. fineberg (2002); a practical architecture for implementing end-to-end qos in an ip network, ieee communications magazine, 40(1): 122-130. [4] w. c. hardy (2003); voip service quality: measuring and evaluating packet-switched voice, mcgraw-hill, 2003. [5] t. wallingford (2005); switching to voip, o’reilly, 2005. [6] o. hersent, j.p. petit, d. gurle (2005); beyond voip protocols: understanding voice technology and networking techniques for ip telephony, john wiley and sons, 2005. [7] j. davidson, j. peters (2000); voice over ip fundamentals, cisco press, 2000. [8] cisco systems, inc.(2001); cisco ip telephony qos design guide, www.cisco.com. [9] m. a. dye, r. mcdonald, a.w. rufi (2008); network fundamentals ccna exploration companion guide, 2008 cisco press. [10] cisco systems, inc (2014); qos: congestion management configuration guide, cisco ios xe release 3s, www.cisco.com. [11] t. szigeti, r. barton, c. hattingh, k. briley jr. (2014); end-to-end qos network design: quality of service for rich-media and cloud networks, second edition, cisco press, 2014. [12] http://mynetworkingwiki.com/index.php/weighted_fair_queuing [13] http://myway2ccie.blogspot.ro/2009/04/qos-weighted-fair-queuing-and-class.html [14] http://www.perihel.at/2/rno/03-qos-queuing-methods.pdf [15] http://blog.ine.com/2008/08/17/insights-on-cbwfq/?s=wfq [16] http://josephmlod.files.wordpress.com/2010/12/llq-pq.jpg int j comput commun, issn 1841-9836 7(5):933-944, december, 2012. mining association rules from empirical data in the domain of education d. radosav, e. brtka, v. brtka dragica radosav, eleonora brtka, vladimir brtka university of novi sad technical faculty "mihajlo pupin" serbia, 23000 zrenjanin, djure djakovica bb e-mail: radosav@tfzr.uns.ac.rs, brtka@sbb.rs, vbrtka@tfzr.uns.ac.rs abstract: the data mining techniques and their applications are widely recognized as powerful tools in various domains. in the domain of education there is a variety of data of various types that are collected. the important question is: is it possible to process collected data with the data mining technique and what are main advantages of data mining and e-learning interaction? if an e-learning system accumulates a huge volume of data, then it is possible to deploy techniques and tools from the domain of data mining in order to gain valuable information. the research presented in this paper is conducted on real-life data that originates from the balkan region. the software system weka is used to generate association rules. the main result of this research is the assessment of the parameters that are associated with the opinion that computer skills will be helpful in the future, from the students point of view. this result is very important because it gives the exact insight to computer technology usage in the balkans schools. furthermore, some advantages of the usage of data mining techniques in the domain of education are determined. keywords: association rules, education, data mining. 1 introduction in the past few years e-learning techniques have significantly improved as the result of progress and increased use of the internet. the "desktop" e-learning systems, in many cases, have been replaced by systems that operate using the internet. some of the web-based systems allow the determination of preferences for each participant in the process and adjustment of activities in accordance with the profile of participants [1]. recently, techniques from the domain of data mining have been incorporated into systems for e-learning. the changes that are constantly taking place in terms of rapid technical and technological developments affect society as a whole. the educational system is experiencing changes in terms of modernization and globalization. however, the educational system that is "inert" is not suitable for rapid change and modernization. the educational processes in serbia and some other countries in the region of western balkans are changing so that the "reproduction" style of learning is replaced with the style that prefers "understanding" of the learning content and usage of the acquired knowledge. the theories of learning that are used are no longer associative and behavioral, but have become constructive and cognitive. students are required to improve the style of self-training and their skills. in these processes the information capacity is an important factor in development, especially the internet and the resources that are available through world wide web. the usage of the internet by some course management system (cms) is not unusual occurrence in serbia, but cannot be said that such systems are widely present. efforts that have been invested in the integration of cms and data mining (dm) systems are evident. this integration often means adding dm modules to the existing cms [1, 2], but copyright c⃝ 2006-2012 by ccc publications 934 d. radosav, e. brtka, v. brtka it is possible to approach to cms and dm system integration through serial connection [3, 4]. serial connection, in this case, means collecting data with cms, and then processing collected data by dm system. the results of dm analysis are fed back to cms in order to improve their effectiveness. however, this paper lists the basic dm techniques and some tools that allow the application of these techniques in domain of education, but cms and their application is not the topic of this paper, although the importance of cms is evident. this paper deals with some special dm techniques when applied to data collected in the domain of education. the application of dm techniques results in some rules or patterns that can be used as feedback to cms. rather than investigation of the connection between cms and dm system, this paper deals with dm techniques when applied to data in the domain of education and gives some conclusions and remarks about the importance of inferred knowledge. special contribution is the analysis of the results of dm technique when applied to real-life data collected from the region of serbia and bosnia and herzegovina. the paper is organized as follows: section two gives the short description of various dm techniques used in the domain of education. one of dm techniques is chosen to be used for the analysis of the real-life data. it is explained why this dm technique is the most suitable in this particular case. section three contains data description, as well as the methodology description. section four lists the results obtained by application of the dm technique, as well as the interpretation of these results. finally, section five contains conclusions and remarks about applicability of dm techniques in the domain of education. 2 previous work and dm techniques the well-known cms that are in use are: blackboard, webct, angel and moodle. in previous papers dm techniques are used as a functional element (module) of cms [2, 5]. the dm module can be an integral part of cms but, in some cases, can be used separately. the field of data mining, like statistics, concerns itself with "learning from data" or "turning data into information" [6]. according to [5,7], dm can be defined as the intersection of the domain of statistics, computer science, artificial intelligence, machine learning, database management and data visualization. data mining is the process of identifying valid, novel, potentially useful, and ultimately comprehensible and understandable patterns or models. the basic techniques of data mining are [7]: • classification examining the feature of a newly presented object and assigning it to a predefined set of classes. • affinity grouping or association rules determining which things go together, also known as dependency modeling. • clustering segmenting a population into a number of subgroups or clusters. description and visualization exploratory or visual data mining. 2.1 the application of the dm in practice, there are a lot of general and specific data mining tools [2]. the commercial mining tools are: dbminer [8], spss clementine: [9] and db2 intelligent miner [10], etc. some public domain mining tools are: weka [11] and keel [12]. there are also specific educational data mining tools such as: mining tool [13] for association and pattern mining, multistar [14] mining association rules from empirical data in the domain of education 935 for association and classification, kaon [15] for clustering and text mining and ciecof [16] for association rule mining. the application of these systems in the field of education includes the selection of suitable dm techniques. it is not unusual that multiple dm techniques are applied to the same data sample. as in [2, 7] the main steps of application of dm techniques are: 1. collection of the data. the cms system is used and the collected data are stored in database. this step can be executed by a questionnaire or some other data collection technique instead of cms usage. 2. preprocessing the data. the data is "cleaned" and transformed into an appropriate format to be mined. 3. application of suitable dm technique. the dm technique is applied to build the model that discovers new rules, patterns and knowledge. to execute this step, either a general or a specific data mining tool, or a commercial or a free data mining tool can be used. 4. the interpretation, evaluation and deployment of the results. in particular, it is necessary to apply and elaborate in detail each of these steps depending on the data to be analyzed. some of the systems for data mining that are used to analyze data from different domains are: • rosetta system, developed by researchers from the university of warsaw and the university of trondheim [17, 18]. rosetta is capable of synthesis of the if ... then rules by usage of the rough sets theory. this system is based on classification, reduction of data and decision rules synthesis. • weka system was developed by researchers from the university of waikato, new zealand [19]. rosetta system allows data to be loaded from ms excel table; the format of the loaded data can also be csv (comma separated values). rosetta system performs the extraction of the if...then rules. the data can be collected by various methods; the format of the collected data does not have to be specially adapted to dm techniques implemented in rosetta. on the other hand, the weka gui chooser provides a starting point for launching wekas main gui applications and supporting tools. the weka system can be used to start the particular dm applications: • explorer an environment for exploring data with weka. • experimenter an environment for performing experiments and conducting statistical tests between learning schemes. • knowledgeflow this application supports essentially the same functions as the explorer but with a drag-and-drop interface. one advantage is that it supports incremental learning. • simplecli provides a simple command-line interface that allows direct execution of weka commands for operating systems that do not provide their own command line interface. system weka allows (see figure 1): 936 d. radosav, e. brtka, v. brtka figure 1: the main menu of weka system • classification (classify) a classifier is a mapping from a (discrete or continuous) feature space x to a discrete set of labels y [20]. classification or discriminant analysis predicts class labels. this is supervised classification which provides a collection of labeled preclassified patterns; the problem being to label a newly encountered, still unlabeled, pattern. in e-learning, classification has been used for: discovering potential student groups with similar characteristics and reactions to a specific pedagogical strategy [21]; predicting students performance and their final grade [22]; detecting students misuse or students playing around [23]; predicting the students performance, as well as assessing the relevance of the attributes involved [24]; grouping students as hint-driven or failure-driven and finding students common misconceptions [25]; identifying learners with little motivation and finding remedial actions in order to lower drop-out rates [26]; for predicting course success [27]. • clustering (cluster) clustering is a process of grouping objects into classes of similar objects [28]. it is an unsupervised classification or partitioning of patterns into groups or subsets (clusters) based on their locality and connectivity within an n-dimensional space. in e-learning, clustering has been used for: finding clusters of students with similar learning characteristics, and for promoting group-based collaborative learning, as well as for providing incremental learner diagnosis [29]; grouping students and personalized itineraries for courses based on learning objects [30]; grouping students in order to give them differentiated guiding according to their skills and other characteristics [31]; grouping tests and questions into related groups based on the data in the score matrix [32]. • association rule mining (associate) association rule mining discovers relationships among attributes in databases, producing if-then statements concerning attribute-values [33]. an association rule expresses a close correlation between items (attribute-value) in a database with values of support and confidence. the confidence of the rule is the percentage of transactions that contains the consequence in transactions that contain the mining association rules from empirical data in the domain of education 937 antecedent. the support of the rule is the percentage of transactions that contains both antecedent and consequence in all transactions in the database. association rule mining has been applied to web-based educational systems for: building recommender agents that could recommend on-line learning activities or shortcuts [34]; diagnosing student learning problems and offering students advice [35]; guiding the learners activities automatically and recommending learning materials [36]; determining which learning materials are the most suitable to be recommended to the user [37]; identifying attributes characterizing patterns of performance disparity between various groups of students [38]; discovering interesting relationships from students usage information in order to provide feedback to the author of the course [39]; finding out relationships in learners behavior patterns [40]; finding students mistakes that often accompany each other [41]; guiding the search for the best fitting transfer models of student learning [42]; and optimizing the content of the e-learning portal by determining what most interests the user [43]. • selecting attributes (select attributes) attribute selection involves searching through all possible combinations of attributes in the data to find which subset of attributes works best for prediction. to do this, two objects must be set up: an attribute evaluator and a search method. the evaluator determines what method is used to assign a value to each subset of attributes. the search method determines what style of search is performed. • visualization (visualize) information visualization [44] is a branch of computer graphics and user interface which is concerned with the presentation of interactive or animated digital images so that users can understand data. these techniques facilitate analysis of large amounts of information by representing the data in some visual display. weka visualization section allows visualizing 2d plots of the current relation. rosetta system and weka are particularly suitable for data analysis in the field of education because they offer a selection of dm techniques and are relatively easy to use. 3 methodology the data sample, in form of ms excel document, consists of a total of 256 instances (students). it is important to mention that the data are not collected with the aim to be analyzed by dm techniques. the survey was conducted on students from the territory of the republic of serbia and the territory of bosnia and herzegovina. the computer technology that is used in this region is mostly out of date but is sufficient for elementary usage in education. description of the data, presented in table 1, shows the names of attributes and their possible values. attribute names and associated values comply with the form of survey. various techniques can be used on this small data set: first of all, there are statistical techniques, for example students distribution that is used when estimating the mean of a normally distributed population when the data set is small; then there are techniques for inferring decision rules (based on pawlaks rough sets theory, decision trees, etc.), even neural networks can be trained on small data set [45]. in addition, the data sample which is described in table 1 can be analyzed by various dm techniques or dm systems. the association rule mining is adopted as the most suitable dm technique. as defined by agrawal et al. [33] the problem of association rule mining is defined as: let u = {u1, u2, ..., um} be a discrete universe, a finite set of objects. let a = {a1, a2, ..., an} be a finite set of attributes with binary values. each object of universe u is described by attributes ai, i = 1, 2, ..., n thus generating a data set. an associative rule is defined as an implication of the form x ⇒ y where x, y ∈ a and x ∩ y ̸= ⊘. the set of attributes x is 938 d. radosav, e. brtka, v. brtka table 1: description of used data, attribute names and their possible values name value, number of objects and distribution description of the attribute a1 1. yes, 210 , 82.03125% does the student 2. no, 46, 17.96875% has a computer at home a2 1. do not know, 31, 12.109375% what type of computer 2. other, 7, 2.734375% do the student have 3. pii, 7, 2.734376% 4. piii, 60, 23.4375% 5. piv, 144, 56.25% 6. laptop, 7, 2.734376% a3 1. yes, 123, 48.046875% does the student 2. no, 133, 51.953125% use the internet a4 1. 1 hour per day, 98, 38.28125% how many hours per day 2. 2 hours per day, 85, 33.203125% does the student use 3. 3 hours a day, 23, 8.984375% the computer 4. 4 hours a day, 24, 9.375% 5. 5 hours a day, 26, 10.15625% a5 1. 0 hours a day, 67, 26.171875% how many hours 2. 1 hour per day, 74, 28.90625% per day 3. 2 hours per day, 64, 25.0% the student 4. 3 hours a day, 18, 7.03125% use the internet 5. 4 hours a day, 12, 4.6875% 6. 5 hours a day, 21, 8.203125% a6 1. yes, 89, 34.765625% does the student use 2. no, 167, 65.234375% e-mail a7 1. web sites on serbian, 142, 55.46875% what web sites does 2. web sites on other lenguages, 114, 44.53125% the student visit most frequently a8 1. educational, 27, 10.546875% what kind of web sites 2. entertainment, 116, 45.3125% is the most visited 3. other, 113, 44.140625% by student a9 1. yes, 185, 72.265625% does the student use 2. no, 71, 27.734375% his/her home computer for learning a10 1. educational, 42, 16.40625% what type of media does 2. film, 18, 7.03125% the student use most 3. music, 130, 50.78125% frequently at home 4. other, 66, 25.78225% a11 1. yes, 159, 62.109375% does the student want 2. no, 5, 1.953125% more educational 3. do not know, 92, 35.9375% computer software to be used in school in order to improve teaching a12 1. yes, 192, 75.0% does the student want to 2. no, 30, 11.71875% review learning materials 3. do not know, 34, 13.28125% used in school, by distant learning system at home a13 1. games, 78, 30.46875% what type of software is 2. educational, 11, 4.296875% most frequently used by 3. games, educational, 167, 65.234375% student a14 1. yes, 221, 86.328125% does the student like the 2. no, 35, 13.671875% subject of informatics a15 1. yes, 219, 85.546875% does the student believe 2. no, 37, 14.453125% that he/she gained enough knowledge to work independently on a computer a16 1. independently, 82, 32.03125% from who or where has 2. from others, 36, 14.0625% the student learned most 3. at school, 138, 53.90625% about computer usage a17 1. yes, 235, 91.796875% does the student think 2. no, 21, 8.203125% that his/her computer skills will help him/her in the future mining association rules from empirical data in the domain of education 939 table 2: simple example of data set object the student has the student uses the student thinks that a computer at home the internet his/her computer skills will help him/her in the future (a1) (a2) (a3) 1. yes yes no 2. no yes yes 3. no no no 4. yes yes yes 5. no yes no called antecedent (left-hand-side or lhs) of the rule; the set of attributes y is called consequent (right-hand-side or rhs) of the rule. there are many rules of the form x ⇒ y , but to select interesting rules from the set of all possible rules, various measures of significance can be used; the best-known are minimum thresholds on support and confidence. the support supp(x) is defined as the proportion of objects in the data set which contains the attributes from x. the confidence of a rule is defined as: conf(x ⇒ y ) = supp(x ∪ y ) supp(x) the previous concepts are explained in next simple example. for a data set given in table 2 it is possible to infer some association rules, as well as the confidence and support parameters. for x = {a1, a2, a3} supp(x) = 1 5 = 0.2 because there is one object (number four) for which there is a "yes" value for every attribute. for example, the confidence of the rule x ⇒ y , where x = {a1, a2} and y = {a3} is: conf(x ⇒ y ) = supp(x ∪ y ) supp(x) = 0.2 0.4 = 0.5 the previous rule x ⇒ y is interpreted as follows: the student who has a computer at home and uses the internet is associated with the opinion that his/her computer skills will help him/her in the future. there are many algorithms for association rule computation, but so called apriori algorithm [46] is the best-known algorithm to mine association rules. it is based on breadth-first search strategy [47]. in the data set described by table 2 attribute selection is more complicated by the fact that some attribute values are not binary so this involves more extensive search. the association rule generation is chosen to be performed due to multiple reasons: 1. this is an exact method and therefore excludes any subjective influence while analyzing data set. 2. the result is presented in a readable and easy-to-understand form. 3. it is expected that number of generated association rules would not be high (data set contains 256 instances) due to computation of confidence for each rule and selection of rules with the highest confidence. 4. it is expected that association rules have a great value when inferred from data set in education domain because association rules can be treated as a hypothesis. 940 d. radosav, e. brtka, v. brtka the experiment was conducted on data set by software system weka in order to generate association rules. after loading, the data are ready for pre-processing and application of dm techniques. weka system requires that the attributes with numerical values do not participate in the association rule mining, so they are ignored. association rules generated by weka system (apriori algorithm is used) are shown in table 3. table 3: association rules generated by apriori algorithm rule if then rule confidence 1 a11=yes, 159 a17=yes, 154 0.97 2 a9=yes and a14=yes, 159 a17=yes, 154 0.97 3 a13=games, educational, 167 a17=yes, 159 0.95 4 a9=yes and a15=yes, 166 a17=yes, 158 0.95 5 a12=yes and a14=yes, 169 a17=yes, 160 0.95 6 a9=yes, 185 a17=yes, 174 0.94 7 a14=yes and a15=yes, 195 a17=yes, 183 0.94 8 a12=yes, 192 a17=yes, 180 0.94 9 a6=no, 167 a17=yes, 156 0.93 10 a14=yes, 221 a17=yes, 206 0.93 there are 10 rules generated. the if part of every rule is followed by support measure, as is the case with the then part of each rule. the confidence for each rule is given in the separate column. 4 results the analysis of generated association rules provides insight into the dependence of the monitored parameters. each rule is accompanied by a factor of confidence that takes a value in the range [0, 1]. ten association rules have been generated, see table 3. by association rule 1 attribute a11 (does the student want more educational computer software to be used in school in order to improve learning) is associated with attribute a17 (does the student think that his/her computer skills will help him/her in the future). the factor of confidence for this rule is 0.97, that rule is guaranteed to a great extent. if the value of attribute a11 is "yes" then, by this association rule, the value of attribute a17 is also "yes". a possible conclusion which can be drawn is that most of the students think that usage of computer technology in school generates skills and knowledge that could be used in the future. this students opinion is a good indicator of the importance of the usage of computer technology and educational software in teaching process. it is evident that technology usage in future is closely related to computer technology software and methods which are used at present. other association rules can be interpreted in analogous way. the rule 2 can be interpreted as follows. two facts: the fact that student uses home computer for learning and the fact that student likes the subjects of computer science, are associated with opinion that computer skills will be helpful in the future. rule number 3 associates the type of software that is most frequently used by student (games, educational software) with the opinion that computer skills will be helpful in the future. the opinion that computer skills will be helpful in the future is mostly associated with: believing that student gained enough knowledge to work independently on a computer, the usage of the computer for learning and aspiration to use distant learning system at home to review learning materials previously used in school. attribute a14 (the student likes the subject of computer science) is most frequently associated with opinion that computer skills will be helpful in the future. mining association rules from empirical data in the domain of education 941 however, rule number nine associate the students that are not satisfied with the usage of e-mail with the opinion that computer skills will be helpful in the future. in fact, this may be in accordance with rule number eight: the usage of distant learning system at home to review learning materials previously used in school is associated with the opinion that computer skills will be helpful in the future. so, in students opinion, distant learning system makes e-mail service obsolete in a way. 5 conclusions and future works the application of association rule mining allows automatic generation of hypotheses and factors of confidence that are related to them. considering rule number one (see table 3) the hypothesis is: if a student wants more educational computer software to be used in school in order to improve learning then the student thinks that his/her computer skills will help him/her in the future. other rules may also be interpreted as a hypothesis. obviously the factor of confidence has a great impact on confirmation of hypotheses. at the technical faculty "mihajlo pupin" in zrenjanin, serbia, extensive research is underway, investigating the possibilities of applying dm techniques to data from the domain of education, extracted from a survey conducted in the wider balkan region. the fact that it is not obligatory that the data are collected with the aim to be analyzed by dm techniques, offers an excellent chance to assess real possibilities in the actual practice. in future, this leads to identification of the advantages of dm techniques over standard statistical techniques. so far, there have been identified the following advantages of application of the dm techniques to data from the domain of education: • it is possible to extract the "special" cases of the statistical rarity that are often discarded as "noise". the discovery of such "isolated" cases is conducted by automated if ... then rule synthesis. as in [48] the association data mining based on a uniform support misses some patterns of low support. although it is possible to exploit support constrains, which specifies some minimum supports, we opted to use automated if ... then rules synthesis by rosetta system or similar. • clustering technique allows the exact definition of average student and special groups (clusters) of students, so that further action of e-learning can be implemented while respecting the characteristics of each cluster. • analysis of generated association rules provides insight into the dependence of the monitored parameters. • ranking of attributes (parameters) in order of importance of influence on a selected attribute allows the rejection of the parameters of lesser importance. • data visualization provides a figurative description of the data, so we can actually see patterns which may exist. the usage of weka system, or a similar 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[48] m. pater, d.e. popescu, multi-level database mining using afopt data structure and adaptive support constrains, int j comput commun, issn 1841-9836, 3(s):437-441, 2008. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 458-468 genetic algorithm based feature selection in a recognition scheme using adaptive neuro fuzzy techniques m. bhattacharya, a. das mahua bhattacharya indian institute of information technology & management morena link road, gwalior-474003, india e-mail: mb@iiitm.ac.in arpita das institute of radio physics & electronics university of calcutta 92, a.p.c. road, kolkata-700009 e-mail: arpita.rpe@caluniv.ac.in abstract: the problem of feature selection consists of finding a significant feature subset of input training as well as test patterns that enable to describe all information required to classify a particular pattern. in present paper we focus in this particular problem which plays a key role in machine learning problems. in fact, before building a model for feature selection, our goal is to identify and to reject the features that degrade the classification performance of a classifier. this is especially true when the available input feature space is very large, and need exists to develop an efficient searching algorithm to combine these features spaces to a few significant one which are capable to represent that particular class. presently, authors have described two approaches for combining the large feature spaces to efficient numbers using genetic algorithm and fuzzy clustering techniques. finally the classification of patterns has been achieved using adaptive neuro-fuzzy techniques. the aim of entire work is to implement the recognition scheme for classification of tumor lesions appearing in human brain as space occupying lesions identified by ct and mr images. a part of the work has been presented in this paper. the proposed model indicates a promising direction for adaptation in a changing environment. keywords: adaptive neurofuzzy, genetic algorithm, feature selection, pattern recognition. 1 introduction the boundary detection based on fourier descriptors introduces a large number of feature vectors in a pattern recognition scheme. to classify different boundaries, any standard classifier needs large number of inputs and to train the classifier large number of training cycles and huge memory are also required. a complicated structure of the classifier invites the problem of over learning, and which may cause for misclassification [2]. therefore need exists for significant feature selection for efficient pattern recognition scheme. among many existing methods for solving feature selection problem (fsp), pruning methods for neural network [7],[8], classification trees [9] fuzzy clustering [10] may be referred. ga is an efficient search algorithm based on the mechanics of natural selection and natural genetics [1]. it combines survival of the fittest among string structures with a structured yet randomized information exchange to form a search algorithm with some of the innovative flair of human search. since genetic algorithm is invented to simulate evolutionary processes observed in nature the goal of survival or optimization in a changing environment could be achieved [3]. however, ga [1],[4],[5],[6],[11] differs copyright c⃝ 2006-2010 by ccc publications genetic algorithm based feature selection in a recognition scheme using adaptive neuro fuzzy techniques 459 from other searching algorithm in that sense, it does not deal with the neighborhood of a single current solution. ga use a collection (or population) of parameters, from which using selective crossover and mutation strategies, better solutions may come out. in present paper, the network architecture used for final classification is anfis adaptive neuro fuzzy inference system. anfis [13],[14] architecture for sugeno fuzzy model is an innovative soft computing expert system that removes the limitations of conventional neural networks [12],[13],[15]. the proposed method of feature selection has been compared with fuzzy clustering theory where ga based feature selection shows the improvement over fuzzy clustering due to natural selection mechanisms. proposed fsp methodology combined with anfis classifier is an intelligent, expert system that gives the user accurate detection even in presence of additive noise. the objective of entire work is to identify the different space occupying lesions appearing in human brain as tumor / cancer lesions in different grades of benignancy / malignancy using boundary as feature. presently a part of the work has been presented considering few pattern boundaries in order to develop an accurate classification technique using ga based feature selection. 2 proposed methodology in the proposed method the significant boundary of roi is extracted and ga has been applied to reduce the feature vector size. these reduced and significant features are then fed to anfis sugeno fuzzy network for classification. a comparative study has been conducted for efficient feature selection using both ga and fcm and finally to classify patterns using anfis. this study effectively gives the superior results for ga based feature selection. the method is summarized in figure-1. figure 1: proposed technique 2.1 boundary extraction using fourier descriptors feature selection is the choice of descriptors in a particular application. the boundary of pattern to be analyzed has been detected by implementing canny edge detector and fourier descriptors of the edges then used as shape information. a figure with k-points digital boundary in the x-y plane as, x(k) = xk, 460 m. bhattacharya, a. das y(k) = yk can be represented as s(k) = [x(k)y(k)] for k = 0,1,2,...,k −1. (1) each co-ordinate pair can be treated as a complex number so that, s(k) = x(k)+ j ∗ y(k) for k = 0,1,2,...,k −1. (2) the discrete fourier transform (dft) of s(k) is given below a(u) = 1 k ∗ k−1∑ k=0 s(k)∗ e− j2(π)uk k fork = 0,1,2,...,k −1. (3) the complex coefficient a(u) is called the fourier descriptor of the edge points. let us suppose that instead of all fourier coefficients, only the first ’p’ coefficients are used. this is equivalent to set a(u) = 0 for u > (p − 1). the overall global shape of the images has been identified (it can be shown that if p ≈ u/3, approximate boundary detection would be possible). thus a few fourier descriptors can be used to capture the gross essence of a boundary. this property is valuable, because these coefficients carry shape information and can be used as the basis for differentiating between distinct boundary shapes. 2.2 genetic algorithm for feature selection ga manipulates chromosomes, which are the encoded string set of parameters of a target system to be optimized. presently different boundaries extracted from ct and mr images of section of human brain having space occupying lesions are recognized on the basis of fourier descriptors and which play the role of payoff values (objective function) associated with individual strings. in ga, a new set of offspring has been created in every generation on the basis of the fittest of old generation. ga efficiently exploits the historical information to speculate on new search points with expected improved performance. it is the best learned from the careful study of biological example that, where robust performance is desired, nature does it better which is the secret of adaptation and survival. ga uses three operators: selection (or reproduction), crossover and mutation to achieve the goal of evolution [1],[3]. 2.3 fuzzy c-means clustering algorithm for feature selection in the proposed method, fuzzy c-means clustering algorithm used for reduction of input feature vector sizes without loss of accuracy level of detection. algorithm 1. let x = {x1,x2,...,xn} be a set of given data. a fuzzy c-partition of x is a family of fuzzy subsets of x, denotes by p = {a1,a2,...,ac}, which satisfies c∑ i=1 ai(xk) = 1 for all k ∈ nn (4) and i = 1 0 < n∑ k=1 ai(xk) < n for all i ∈ nc (5) where c is a positive integer given a set of data x = {x1,x2,...,xn}, where xk, in general is a vector, for all k ∈ nn , the problem fuzzy clustering is to find a fuzzy pseudo partition and the associated cluster centers by which the structure of the data is represented as best as possible. to solve the problem of fuzzy genetic algorithm based feature selection in a recognition scheme using adaptive neuro fuzzy techniques 461 clustering, we need to formulate a performance index. usually, the performance index is based upon cluster centers, v1,v2,...,vc associated with the partition are calculated by the formula. vi = ∑n k=1[ai(xk)] mxk∑n k=1[ai(xk)] m (6) for all i ∈ nc, where m > 1 is a real number that governs the influence of membership grades. observe that the vector vi calculated by above equation is viewed as the cluster center of the fuzzy class a i, is actually weighted average of data in ai. the performance index of a fuzzy pseudo partition p, jm(p), is defined in terms of the cluster centers by the formula jm(p) = n∑ k=1 c∑ i=1 [ai(xk)] m∥xk − vi∥2 (7) where ∥xk − vi∥2 represents the distance between xk and vi . clearly, the smaller the value of jm(p), the better the fuzzy pseudo partition p. thus, the goal of fuzzy c-means clustering method is to find a fuzzy pseudo partition p that minimizes the performance index jm(p). 2.4 classification of features using anfis model a generalized anfis model based on sugeno fuzzy architecture is utilized for classification of significant features. the numbers of input nodes are equal to the reduced input feature space sizes. the number of membership functions in each of the input node is continually adjusted to achieve the optimum classification results. to adapt the model with ever-changing environments, hybrid-learning rule is used. figure 2: the anfis model for final classification. figure-2 illustrates the reasoning mechanism of the sugeno fuzzy anfis architecture for boundary detection and texture analysis of masses respectively, where nodes of the same layer have similar functions as described below. 462 m. bhattacharya, a. das layer 2. every node i in this layer is an adaptive node with a node function o1i = µai(x) for i = 1,2. and o1i = µbi(y) where x (or y) is the input to node i and ai (or bi) is a linguistic label (such as large or small) associated with this node. in other words o1i, i is the membership grade of fuzzy set a(a1,a2) or b(b1,b2). here the membership function for a can be any appropriate parameterized membership function, such as generalized bell function: µa(x) = 1 1+ | (x−ci) ai |2b (8) where ai,bi,ci is the parameter set. as the values of these parameters change, the bell-shaped function varies accordingly. parameters of this layer are referred to as premise parameters. layer 3. every node in this layer is a fixed node labeled ∏ , whose output is the product of all the incoming signals: o2,i = wi = µai(x)µbi(x) for i = 1,2. (9) in general, any t-norm operator that performs fuzzy and can be used as the node function in this layer. layer 4. every node in this layer is a fixed node labeled n. the ith node calculates the ratio of the rule’s firing strength to the sum of all rule’s firing strengths: o3,i = wi = wi (w1 + w2) for i = 1,2. (10) for convenience, outputs of this layer are called normalized firing strengths. layer 5. every node i in this layer is an adaptive node with a node function o4,i = wi fi = wi(pix + qiy + ri) (11) where wi is a normalized firing strength from layer 3 and pi,qi,ri is the parameter set of this node. parameters of this layer are referred to as consequent parameters. layer 6. the single node in this layer is fixed node labeled ∑ , which computes the overall output as the summation of all incoming signals: overall output = o5,i = ∑ i wi fi∑ i wi (12) thus anfis architecture is functionally equivalent to a sugeno fuzzy model. hybrid leaning rule combines steepest decent method and least-squares estimator for fast identification of parameters in anfis model. for hybrid learning to be applied in a batch mode, each epoch is composed of a forward pass and a backward pass. in the forward pass, after an input vector is presented, node outputs go forward until layer 4 and consequent parameters are identified by the least squares method. in the backward pass, the error signals propagate backward and the premise parameters are updated by gradient decent. the hybrid approach converges much faster since it reduces the search space dimensions of the original pure back propagation. 2.5 decision making logic the anfis model is trained with targets for each of the output classes, which are well separated, and then membership functions are generated for detecting the possible range of output values. each membership function corresponds to each of the output class; the overlapping regions between two or more classes give the possibility of existing of the particular pattern in all of the overlapped classes. but highest membership grade determines that the particular image pattern belongs to the corresponding genetic algorithm based feature selection in a recognition scheme using adaptive neuro fuzzy techniques 463 class. thus to construct a boundary region for a particular class, we design a decision rule using fuzzy if-then conditions that states: 2.5 <= output value <= 7.5, test image belongs to class-a; 7.5 <= output value <= 12.5, test image belongs to class-b; 12.5 <= output value <= 17.5, test image belongs to class-c. the decision making membership function through the range of all possible output values is given in figure-3. each membership function is a generalized bell shaped curve, which corresponds to each output classes; the overlapped region between two or more classes gives the possibility of existing of the particular pattern in all of the overlapped classes. figure 3: output decision making membership function 3 experimental results the experiment has been conducted with three distinct boundary shapes extracted from ct and mr images for section of human brain having space occupying lesions shown in figure 4 belonging to classa, class-b & class-c. two membership functions are chosen for each input terminal of the network, to obtain the best possible classification result. the superiority of ga is investigated over the conventional fcm clustering technique to classify the noisy images. 3.1 choice of string length in ga based feature subset selection problem in genetic algorithm a particular string of length l contains 2l search points. as a result, a population of size n contains some where between 2l to n∗2l search points, depending upon the population diversity. now among these large numbers of search points, only a few are processed in a useful manner. the reproduction, crossover and mutation operators determine the exponential growth or decay of important search points from generation to generation. it has been observed that ga with samples containing less number of bit strings which are shifted towards the enumerative search. with string length 20, 464 m. bhattacharya, a. das figure 4: boundary features of tumors in human brain there are a least 220 = 1.04 ∗ 106 search points in the search space. thus ga converges rapidly with the samples containing large string length. but too much increase of string length is not profitable for computational enumeration. figure 5 shows an optimum sting length which is 20 and is acceptable for efficient feature subset selection. the variations of average and maximum values of objective function and the corresponding population size for each generation with different string lengths are shown below in figure 5,6,7 respectively. figure 5: variations of average value with different string length it is also viewed from above results that ga with samples containing less number of bit string, shifted towards the enumerative search or random walk. but with string length 24 or more, there are at least 224 = 1.68 ∗ 107 search points in the search space and random walk or enumeration would not be profitable. thus ga converges rapidly with the samples containing large number of bit string. genetic algorithm based feature selection in a recognition scheme using adaptive neuro fuzzy techniques 465 figure 6: variations of maximum value with different string length figure 7: variations of population size with different string length. 466 m. bhattacharya, a. das table 1: recognition of distinct noise free test images using ga and anfis model classification rate tested image training value tested value decision boundary-1 5.0000 4.9496 belongs to class-a. boundary-2 10.000 9.8162 belongs to class-b. boundary-3 15.000 14.8400 belongs to class-c. table 2: ga based classification rates noise training value tested value classification decision 0.002 5.0000 5.5309 class-a correct 0.004 5.0000 4.5801 class-a correct 0.006 5.0000 5.9943 class-a correct 0.008 5.0000 6.6275 class-a correct 0.010 5.0000 5.0767 class-a correct 0.015 5.0000 3.0135 class-a correct 0.020 5.0000 8.6202 class-b misclassification 3.2 results of experiment to recognize the distinct noise free test images using ga based feature subset selection & anfis model anfis sugeno fuzzy model is implemented to recognize the image boundary-2. the network with three significant input features and two optimum membership functions on each would result in 23 = 8 fuzzy if-then rules and thus the input space is partitioned with 8 grids. 3.3 comparative study of ga & fcm based feature subset selection (fss) mode in presence of noise in the proposed model, the inputs of anfis network are ga based feature subset. this reduced feature subset helps to form a simple anfis classifier. table-2 and table-3 compare the classification rates of ga and fcm based fss model respectively for image boundary-1 in presence of gaussian noise. table 3: fcm based classification rates noise training value tested value classification decision 0.002 20.0000 39.6124 class-a correct 0.004 20.0000 83.5869 class-c incorrect 0.006 20.0000 33.3989 class-a correct 0.008 20.0000 96.9512 class-c incorrect 0.010 20.0000 34.6393 class-a correct 0.015 20.0000 92.7572 class-c incorrect 0.020 20.0000 91.0327 class-c incorrect genetic algorithm based feature selection in a recognition scheme using adaptive neuro fuzzy techniques 467 4 discussions authors have presented a pattern recognition scheme by efficiently selecting the significant features and finally using adaptive neuro-fuzzy techniques for design of classifier. for efficient feature selection, two approaches like genetic algorithm and fuzzy clustering techniques have been implemented. finally the classification of patterns has been achieved using adaptive neuro-fuzzy techniques. the aim of entire work is to implement the recognition scheme for classification of tumor lesions appearing in human brain as space occupying lesions identified by ct and mr images. the comparative study of ga and fcm based feature subset selection (fss) reveals that there is a large possibility of misclassification if fcm is used for significant fss in presence of noise. ga based fss is resistant from noise up to a certain level and classification rate is improved for ga based fss model. this is because, fcm has partitioned the large number shape descriptors such that the degree of association is strong for the descriptors within the same cluster and weak for the descriptors in different clusters. genetic algorithm (ga) searched the significant shape descriptors by applying the beauty of natural argument. using three operators like reproduction, crossover and mutation, ga is capable to select significant feature subset. bibliography [1] i. d. e. goldberg, genetic algorithms in search, optimization and machine learning. reading, ma: addison-wesley, 1989. [2] b. k. fukunaga and r. r. hayes, "effects of sample size in classifier design," ieee trans. pattern anal. mach. intell., vol. 11, pp. 873-885, aug. 1989. [3] d’haeseleer, p. "context preserving crossover in genetic programming"’ proc. of the 1994 ieee world congress on computational intelligence, vol. 1, pages 256-261, orlando, fl, usa. ieee press, 1994. [4] [4]. burke, e., gustafson, s., and kendall, g. diversity in genetic programming: an analysis of measures and correlation with fitness. ieee transactions on evolutionary computation, 8(1): pp. 47-62, 2004. [5] j. yang and v. hanovar, "feature subset selection using genetic algorithm", journal of ieee intelligent systems, vol. 13, pp. 44-49, 1998. [6] s. s. sanz, g.c .valls, f. p. cruz, j. s. sanchis, c. b. calzn, "enhancing genetic feature selection through restricted search and walsh analysis", ieee trans. on systems, man, and cybernetics, vol. 34, no. 4, november 2004. [7] p. leray and p. gallinari, "feature selection with neural networks," behaviormetrika, vol. 26, jan. 1999. [8] b. hassibi and d. g. stork, "second order derivatives for network pruning: optimal brain surgeon," in advances in neural information processing systems, s. j. hanson, j. d. cowan, and c. l. giles, eds. san mateo, ca: morgan kaufmann, 1993, vol. 5, pp. 164-171. [9] l. breiman, j. friedman, r. olshen, and c. stone, classification and regression trees, 3rd ed. london, u.k.: chapman & hall, 1984. [10] t. e. campos, i. bloch, and r. m. cesar jr., "feature selection based on fuzzy distances between clusters: first results on simulated data," lecture notes in computer science, vol. 20, no.13, pp. 186, 2001. 468 m. bhattacharya, a. das [11] e. nabil; a. badr; i. farag; "an immuno-genetic hybrid algorithm", international journal of computers, communications & control, vol. iv, no. 4, issn 1841 9836; e-issn 1841-9844, 2009. [12] adlassnig, k. p., "fuzzy neural network learning model for image recognition." integrated computer-aided engineering, pp. 43-55, 1982. [13] kim, j.s. and h. s. cho, "a fuzzy logic and neural network approach to boundary detection for noisy images." fuzzy sets and systems, pp. 141-159, 1994. [14] jang, j.-s.r., c.-t. sun, e. mizutani, "neuro-fuzzy and soft computing, a computational approach to learning and machine intelligent" pearson education. [15] c. muńoz, f. vargas, j. bustos, m. curilem, s. salvo ; h. miranda; "fuzzy logic in genetic regulatory network models", international journal of computers, communications & control, vol. iv, no. 4, issn 1841 9836; e-issn 1841 9844, 2009. mahua bhattacharya, an associate professor of indian institute of information technology & management, gwalior, india is working in the area of medical image analysis more than a decade in various fields of bio medical applications like multimodal medical image fusion and registration, mammographic image analysis, classification of tumor / cancer lesion in cns, computational techniques for study of neurodegeneracy in brain, study of bone degeneracy and erosion. she had her b.tech and m.tech degree and from the institute of radio physics and electronics, university of calcutta. she worked as a research scientist at indian statistical institute, calcutta from 1995 till 2000 calcutta and got her ph.d degree in the area of multimodal medical image processing and analysis used knowledge based approach in 2001 she was recipient of frank george award for the paper cybernetic approach to medical technology : application to cancer screening and other diagnostics’ wosc the world organization of systems & cybernetics, uk. she has published more than 70 papers in international journals and conference proceedings and as book chapters. arpita das is an assistant professor of institute of radio physics & electronics, university of calcutta, india. she received her b.tech. and m.tech. degree in radio physics and electronics, university of calcutta, in 2004 and 2006, respectively. presently she is pursuing her ph.d. on ’some studies on medical image processing methods and their implementation’. she was a senior research fellow under csir. her research interests include image processing, pattern recognition, soft computing approaches for biomedical applications int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 252-263 prioritization of traffic for resource constrained delay tolerant networks g. fathima, r.s.d. wahidabanu g. fathima adhiyamaan college of engg hosur. tamilnadu fathima_ace@yahoo.com r.s.d. wahidabanu govt. college of engg salem. tamilnadu drwahidabanu@gmail.com abstract: in networks with common shared wireless medium, the available bandwidth is always valuable and often scarce resource. in addition to it, memory available at nodes (eg., sensor nodes) might be limited relative to the amount of information that needs to be stored locally. as delay tolerant networks (dtns) rely on node mobility for data dissemination, the high node mobility limits the duration of contact. besides the issue of contact opportunities between nodes, the bandwidth, available storage at peering nodes and contact duration also affect data forwarding. these factors also influence the mechanisms such as buffer replacement and scheduling policies. so there are secondary problems that routing strategies may need to take care of such as to deal with limited resources like buffer, bandwidth and power. furthermore, despite inherent delay tolerance of most dtn driving applications, there can be situations where some messages may be more important than the others and expected to get delivered earlier. so considering the network limitations and application requirements, the problem of choosing the messages to be transmitted when a contact opportunity arises and the messages to be dropped when buffer full is formulated. a buffer management policy to address these issues is proposed and analysed in this paper. additionally the buffer utilization of various dtn routing protocols and the impact of buffer size on the performance of dtn are studied. keywords: delay tolerant networks, buffer management, prioritization of messages, delivery ratio and delivery delay. 1 introduction communication networks whether they are wired or wireless operate with the assumption of existence of end-to-end path always between source and destination. however, emerging applications such as earth-quake monitoring, habitat monitoring, vehicular ad hoc networks (vanets) [17] and military ad hoc networks, will likely to change the typical conditions under which networks operate. in fact in such scenarios, networks pose new challenges like frequent disconnections, limited bandwidth, long delays and high bit error rates. the critical fact is that in such situations, the transmission control protocol (tcp) often does not work [26]. to enable those applications to operate under such challenging conditions, a new network paradigm called delay tolerant networking has been proposed by researchers [16]. networked environments which operate under such intermittent connectivity are referred as delay/disruption-tolerant copyright c⃝ 2006-2012 by ccc publications prioritization of traffic for resource constrained delay tolerant networks 253 networks (dtns). delay-tolerant networking architecture given by [25] is designed to provide support for message based asynchronous communication over network prone to frequent disconnection, long and variable delays. the details of delay tolerant networking architecture are available in [11]. from the literature survey [9], [13], [15], [22], [24] it is understood that a large amount of research has been performed in developing efficient routing algorithms for dtns. dtns operate with the principle of store, carry and forward. according to this principle, a node may store a message in its buffer and carry it along for long period of time until it can forward it further. in addition, to achieve high delivery probability, messages are replicated multiple times. most of the routing protocols in dtn assume that the buffer available as infinite, which is not the case in reality. therefore the buffers in the node will run out of capacity at certain point of time due to long term storage and extensive message replication. further, transmission takes place when nodes come into communication range of each other. the node has to decide which of the messages to be transmitted among the messages those are available in the buffer. it is a challenging problem because of the resource constraints like limited bandwidth and limited period of contact due to node mobility. in this paper, a prioritized buffer management approach is proposed which takes care of both: which messages to be transmitted when a new contact arises and which messages to be dropped when buffer is full. the opportunistic network environment (the one) simulator [12], [14] which is designed specifically for dtn environment is used for evaluation of the proposed approach. the result of evaluation is compared with other dropping policies. the remainder of this paper is organized as follows: section 2 gives overview of various dtn routing protocols and their buffer utilization. additionally, the impact of buffer size on performance of dtn is analysed. the buffer management and related work is discussed in section 3. section 4 discusses about the proposed policy. the simulation setup and the results are discussed in section 5. section 6 concludes the paper. 2 routing protocols of dtn and their buffer utilization 2.1 overview of routing protocols of dtn extensive study of different routing mechanism is essential to understand the design of dtns. the details of various routing protocols of dtn are available in [9], [13], [15]. these protocols differ in the knowledge that they use in making routing decisions and the number of replication they make. examples of some of the dtn protocols are direct delivery, first contact, epidemic [4], [5], spray and wait [3], [21], prophet [2], and maxprop [10] routing. recently, the work in [28] categorized the routing solutions for dtn based on the approach they follow and the information known, as deterministic or scheduled, enforced and opportunistic routing. deterministic routing approaches are used when knowledge of contact information are known ahead of time. in enforced routing scheme, special-purpose mobile devices like message ferries proposed in [29] and data mules proposed in [27] are employed. the opportunistic routing approach relies on arrival of contact opportunities. it is used where there is no knowledge about connectivity or mobility is known a priori and no network infrastructure exists to provide connectivity. in this paper the proposed buffer management policy is used with opportunistic routing approach. 2.2 buffer utilization of dtn routing protocols in this section, the buffer utilization of various routing protocols at different transmission ranges are observed. buffer utilization is defined as the ratio of total size of the buffer occupied 254 g. fathima, r.s.d. wahidabanu and the total size of the buffer. it is represented mathematically as buffer utilization = total size of buffer occupied total size of the buffer the one simulator is used to perform the experimentation. a new simulation environment is created that combines movement modelling, routing simulation, visualization and reporting in a single framework. they are loaded dynamically based on the given configuration files. the figure 1 depicts the percentage of buffer utilization by various dtn routing protocols. it is observed from the results as shown in figure 1 that, irrespective of transmission ranges, the buffer utilization is identical and also less for direct delivery routing protocol as expected. this is due to the fact that the messages are delivered directly to the destination. the buffer utilization is at the maximum in the transmission range of 250 m for multi-copy routing protocols like epidemic and prophet routing as they do replication based on node encounters and they have comparatively more neighbours in this range. due to the individuality of spray and wait routing, it has less buffer usage compared to that of epidemic and prophet routing. the utilization of buffer by maxprop routing though varies with the transmission ranges still, it is noticeably less. it is due to the fact that it uses acknowledgement and removes the stale data from the buffer. it is also evident from the result in figure 1. in the transmission range of 50 m and below, the possible contact opportunities are less. therefore the requirement of buffer and their utilization is also less for all routing strategies. figure 1: buffer utilisation 2.3 impact of buffer size it is essential to understand the impact of buffer size on performance as this resource is limited in reality (example, sensor nodes). therefore the impact of buffer size is analyzed in this section. it is examined by varying the buffer size in terms of percentage of total number of messages generated. both epidemic and spray and wait routing are chosen for evaluation. both the protocols operate on the assumption of infinite buffer and are respectively of type uncontrolled and controlled flooding which requires huge buffer space. the trade-off between the delivery probability and the buffer size is explored at heavy and light traffic load and the results are shown in figure 2 and 3 respectively. the traffic load is varied by changing the message generation intervals. it is derived from the results that a buffer size of 5-25 % of the generated messages is sufficient to achieve high delivery ratio with reasonable latency. prioritization of traffic for resource constrained delay tolerant networks 255 figure 2: delivery probability vs. buffer size (at heavy traffic load) figure 3: delivery probability vs. buffer size (at light traffic load) 3 buffer management and related work this section reviews existing literature on buffer management in dtn. the single-copy protocols like direct delivery and first contact routing and multi-copy protocols like epidemic and spray and wait routing transmit messages in fcfs order, that is the messages are transmitted in the order in which they were stored in the buffer. prophet routing makes forwarding decision based on delivery predictability of the destination of the message. it needs history of past encounters for calculation of delivery predictability. maxprop routing [10] assigns priority to messages based on hop count and delivery likelihood. it needs to maintain history of data to estimate delivery likelihood. rapid protocol [7] explicitly calculates per-packet utility value for administrator-specified routing metric and forwards the message with highest utility value first. in prioritized epidemic routing [19], transmission and dropping is done based on the priority assigned to each bundle. the priority is based on hop count the bundle has traversed thus far. the optimal policy in [18], [23] deduces the optimal function for the metrics like delivery ratio and delivery delay independently and the message with smallest utility value is dropped when the buffer is full. the authors in [20] had given a framework for devising optimal routing and scheduling algorithm. it is a centralized algorithm. irrespective of the routing protocols, several dropping and scheduling policies were proposed in the literature [1]. a different combination of queuing and forwarding strategies had been proposed in [6]. but those policies can be used only with prophet routing. it is observed from the study that so far no policies had considered the importance of messages or the requirement of the application. 256 g. fathima, r.s.d. wahidabanu 4 proposed prioritized policy in addition to network and individual node features and capabilities, application specific requirement must be taken into account when developing dtn routing mechanism. more specifically, some applications that use delay tolerant networking service require preferential delivery of certain messages. for example, field agents wish to communicate their findings, regarding environment hazards to other field agents which are more important than the regular findings. however there is no existing solution which can be applied to variety of dtn applications given their requirements. under such conditions, a forwarding policy is required to serve different types of traffic differently. consequently it would be necessary to introduce traffic differentiation mechanism and ensure that they get best possible service according to their requirement. therefore the goal is to determine the policy which maximizes the delivery probability or equivalently minimizes the delivery latency of high priority messages. a buffer management policy to address these issues is proposed and analyzed in detail in this section. the proposed approach attempts to differentiate traffic based on class-of-service (cos) and schedules according to their class. further the messages are ordered according to their deadline within the class. the assumptions regarding system model is discussed in the following section. 4.1 system model it is assumed that the network is partially connected with low node density and the node meetings are short lived. when two nodes meet, transmission between them succeeds instantaneously. the messages are stored in the buffer until contact opportunity arises or until storage is full. the bundle protocol specified in [8] is used for transfer of messages in dtn. the bundle processing control flags bit in the dtn bundle protocol is used to differentiate the traffic through class-of-service (cos) field. the class-of-service is identified in this work by the size of the messages. the priority class is based on the concept proposed by dtn architecture. it is assumed that there are three priority classes of traffic: high, medium and low. the messages are transmitted as a whole from node to node. in this proposed approach, the available buffer is logically divided into three queues to hold the incoming messages. separate queue is maintained for each priority class as shown in figure 4. the size of the queue is represented in terms of number of messages. their sizes are defined at the beginning. they can be varied as the correlation of traffic changes. initially the buffer is divided equally among all queues. it is assumed that each node has a buffer b of size b = n(b) which is logically divided into three queues: b1, b2, b3 to accommodate high, medium and low priority bundles respectively such that b = b1 ∪ b2 ∪ b3. the size of b1, b2, b3 is b1, b2, b3 respectively such that b = b1+b2+b3. a minimum size qmin is specified to reserve space for medium and low priority queues, to avoid the complete negligence of medium and low priority traffic. it is an algorithmic parameter which is set dynamically according to the requirement of the application. 4.2 proposed approach the proposed approach comprises (i)bundle classifier which classifies the bundles based on the traffic class as soon as they arrive and stores them in appropriate queue, (ii)bundle scheduler which schedules the bundles based on the priority from high priority to low priority, (iii) bundle dropper which drops the message according to the policy. each bundle si in the buffer has a set of information stored with it such as source id, traffic class and time-to-live. several criteria can be used to categorize the bundles such as source id, destination id, size and the ttl of the bundle. in this paper, it is assumed that emergency prioritization of traffic for resource constrained delay tolerant networks 257 high priority medium priority low priority incoming messages figure 4: maintaining priority queue messages are small and have short deadline which is quiet a natural phenomenon. with this assumption, the bundles are classified as high priority when the size of the bundle is between 1 and 10kb. the bundles are classified as medium priority when the size of the bundle is between 11kb and 100kb. the bundles are classified as low priority when the size of the bundle is between 101kb and 1000kb. the bundle classifier is a function of newly arrived bundle snew such as f(snew) =   (s11,s12, ...s1b1) ∪ (snew) if snew is of high priority (s21,s22, ...s2b2) ∪ (snew) if snew is of medium priority (s31,s32, ...s3b3) ∪ (snew) if snew is of low priority where (s11,s12, ...,s1b1) are the messages in high priority queue, (s21,s22, ...,s2b2) are the messages in medium priority queue, (s31,s32, ...,s3b3) are the messages in low priority queue. the bundle classify procedure is shown in algorithm 1. algorithm.1 bundle classify procedure bundle_classify() receive bundle; if buffer full call bundle_drop procedure; else check for class-of-service of the bundle; if received bundle is expedited message, store it in high priority queue; else if received bundle is normal message, store it in medium priority queue; else if received bundle is bulk message, store it in low priority queue; bundle scheduler is invoked when contact opportunity arises. the number of messages that can be transmitted is limited by the bandwidth and the duration of the contact between the nodes. in such cases only few messages are transmitted and the order in which the messages 258 g. fathima, r.s.d. wahidabanu are transmitted is significant. bundle scheduler transmits the bundles from high priority to low priority. the procedure of bundle scheduler is shown in algorithm 2. the selection of the bundle to be transmitted st is represented mathematically as follows: st =   si|si ∈ b1 if high priority queue is not empty si|si ∈ b2 if medium priority queue is not empty si|si ∈ b3 if low priority queue is not empty the messages whose destination encountered are the first to be transmitted irrespective of the scheduling policy adopted and the same may be deleted from the buffer. nodes do not delete messages that are forwarded to other nodes (other than destination) as long as there is sufficient space available in the buffer. algorithm.2 bundle schedule procedure bundle_schedule() while high priority queue is not empty, transmit bundle from high priority queue; while medium priority queue is not empty, transmit bundle from medium priority queue; while low priority queue is not empty, transmit bundle from low priority queue; once the buffer is full, the bundle dropper is invoked. as the ttl of the bundle expires, it is dropped automatically. the low and medium priority bundles are dropped to give room for high priority bundles. it is also taken care that a node should not drop its own bundle (source) to give room for newly arrived bundles. the idea of giving priority to source bundles has been proposed in [19], and was shown to improve the average delivery ratio. so the same idea is followed here. the bundle drop procedure is shown in algorithm 3. there are three possible classes of bundles that arrive like high priority, medium priority and low priority. bundle dropping is a function which selects the bundle sd to be dropped based on the priority of the bundle that arrive. the identification of the bundle to be dropped is mathematically represented as follows: case 1: when high priority bundle arrives sd =   si|si ∈ b3 iflmin > qmin si|si ∈ b2 ifmmin > qmin si|si ∈ b1 otherwise case 2: when medium priority bundle arrives sd = { si|si ∈ b3 iflmin > qmin si|si ∈ b2 otherwise case 3: when low priority bundle arrives sd = si|si ∈ b3 prioritization of traffic for resource constrained delay tolerant networks 259 algorithm.3 bundle drop procedure bundle_drop( ) let lmin and mmin be the currently occupied space by low priority and medium priority bundles respectively. let qmin be the threshold value which controls the minimum space for low and medium priority bundles. case 1: high priority bundle arrives if lmin > qmin drop bundle from low priority and the room is added to high priority queue; else if mmin > qmin drop bundle from medium priority and the room is added to high priority queue; else the remaining time of existing bundles of high priority queue is compared with newly arrived bundle. the bundle with least remaining time is dropped; case 2: medium priority bundle arrives if lmin > qmin drop bundle from low priority and the room is added to medium priority queue; else the remaining time of existing bundles of medium priority queue is compared with newly arrived bundle. the bundle with least remaining time is dropped; case 3: low priority bundle arrives the remaining time of existing bundles of low priority queue is compared with newly arrived bundle.the bundle with least remaining time is dropped; by providing differentiated service based on the class, the best effort service has been enhanced. moreover the proposed policy takes a state less approach that minimizes the need for nodes in the network to remember anything about flows. it is more practical to implement. the messages are marked in a way that describes the service level that they should receive. these markings are used to provide appropriate service without the need to remember extensive state information for every flow. the metrics used to evaluate the performance of dtn are the delivery ratio and average delivery delay. both the metrics are defined below. delivery ratio = number of messages delivered total number of messages sent by the sender delivery delay = average(time taken by all the messages to reach from source to destination) when compared to other approaches of [18], [19], [20], [23], the proposed approach has the credit of no requirement to maintain state information. moreover it has less overhead than other approaches as there is no exchange of control traffic before bundle exchange. the proposed approach avoids the complete negligence of medium and low priority messages by reserving minimum space for them. 5 simulation results and analysis to evaluate the performance of proposed policy, experimentation is done using the one simulator. the simulation environment consists of sparsely distributed mobile nodes. they are capable of communicating when they are in the communication range of one another. the parameter of the nodes like buffer size, transmit range, transmit speed, number of nodes are set as mentioned in the table 1. further, qmin is an algorithm parameter which is set as 10% of the messages generated. the environment where nodes move randomly is considered. so mobility model is set as random waypoint model, in which nodes move independently to a randomly chosen destination. priority based scheduling is not a basic primitive of dtn routing. therefore 260 g. fathima, r.s.d. wahidabanu it has to be incorporated with any of the routing algorithms. epidemic routing is chosen as baseline for evaluation due to its simplicity and recognition as unbeatable routing protocol from the point of reliable delivery. furthermore it is suitable for opportunistic environment as it does not rely on previous contact information. however the proposed policy can be incorporated with any of the multi-copy routing protocols. table.1 simulation parameters parameters values number of nodes 50 transmit range (m) 250 transmit speed (mbps) 2 node speed (km/hr) 10 50 ttl of message (min) 60 buffer size (mb) 1500 message size 10 kb 1 mb number of messages /min 4 20 movement model random waypoint model simulation time (min) 160 the performance of epidemic routing under different buffer management policies are compared in terms of metrics like delivery probability and average delivery latency. figure 5 and 6 depict the behavior of the proposed policy versus other dropping policies like drop front (df), drop old (do) and drop random (dr) with respect to delivery probability and average delivery latency respectively at various traffic loads. the traffic load is increased by increasing the number of messages generated per interval. it is observed from the result that as and when the traffic load increases, the delivery probability decreases. it also shows that incorporating the prioritized policy does not degrade the performance when compared to other policies. from the literatures [6], [15] it is noticed that df policy results in highest delivery ratio and do figure 5: delivery ratio as a function of load results in least average delivery latency. the simulation results shown in figure 5 and 6 support it. the rationale behind this result is that messages nearing the deadline may get automatically removed from the buffer on the expiry of their deadline. so forcing such messages to drop will not decrease the delivery ratio. furthermore, high priority messages with earliest deadline are forwarded first. so they have more chances of reaching the destination quickly than other messages before missing their deadline. as the size of high priority message is less compared to other priority class, the number of messages that are transmitted per contact duration is also more. this results in good delivery ratio. prioritization of traffic for resource constrained delay tolerant networks 261 figure 6: delivery latency as a function of load 6 conclusion the paper studies the buffer utilization of different routing protocols and the impact of buffer size on performance. the work targets on application that requires preferential delivery in opportunistic dtn environment. the prioritized approach presented in this paper differentiates the traffic based on class-of-service and does scheduling and dropping based on their priorities. thereby it ensures the delivery of high priority messages first with least latency satisfying the application requirement. the proposed policy is more suitable and advantageous in strict resource constrained environment with emergency applications. the service required can be specified by the application. so it can be used in vehicular networks where accident notification messages are more important than other messages. in order to avoid starvation of low priority messages, weighted fair queuing can be used which is carried as future work. bibliography [1] james. a. davis, andrew h. fagg, and brian n. levine, n., 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networks, vol. 16, no. 8, 2010 [29] zhao, w., ammar, m. and zegura, e., a message ferrying approach for data delivery in sparse mobile ad hoc networks, in proceedings of the 5th acm international symposium on mobile ad hoc networking and computing, mobihoc, pp. 187-198, 2004 int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 733-743 structural regular multiple criteria linear programming for classification problem z. qi, y. shi zhiquan qi research center on fictitious economy & data science, chinese academy of sciences, beijing 100190, china e-mail: qizhiquan@gucas.ac.cn yong shi 1. research center on fictitious economy & data science, chinese academy of sciences, beijing 100190, china and 2.college of information science & technology, university of nebraska at omaha omaha, ne 68182, usa e-mail: yshi@gucas.ac.cn abstract: classification problem has attracted an increasing amount of interest. various classifiers have been proposed in the last decade, such as anns, lda, and svm. regular multiple criteria linear programming (rmclp) is an effective classification method, which was proposed by shi and his colleagues and have been applied to handle different real-life data mining problems. in this paper, inspired by the application potential of rmclp, we propose a novel structural rmclp (called srmclp) method for classification problem. unlike rmclp, srmclp is sensitive to the structure of the data distribution and can construct more reasonable classifiers by exploiting these prior data distribution information within classes. the corresponding optimization problem of srmclp can be solved by a standard quadratic programming. the effectiveness of the proposed method is demonstrated via experiments on synthetic and available benchmark datasets. keywords: classification, rmclp, structural information of data, svm 1 introduction for the last decade, the researchers have extensively developed various optimization techniques to deal with the classification problem in data mining or machine learning. support vector machine (svm) ( [1,2]) is one of the most popular methods. however, applying optimization techniques to solve classification has seventy years history. linear discriminant analysis(lda) ( [3]) was first proposed in 1936. mangasarian ( [4]) has proposed a large margin classifier based on linear programming in 1960’s. from 1980’s to 1990’s, glover proposed a number of linear programming models to solve discriminant problems with a small sample size of data ( [5,6]). recently, shi and his colleagues( [7]) extend glover’s method into classification via multiple criteria linear programming (mclp), and then various improved algorithms were proposed one after the other ( [8, 9]). these mathematical programming approaches to classification have been applied to handle many real world data mining problems, such as credit card portfolio management ( [11, 12]), bioinformatics ( [13]), firm bankruptcy ( [14]), and etc. recently, how to apply the structural information of data to build a good classifier is a new research focus. many new large margin classifiers based on structural information have been proposed. exploiting clustering algorithms to extract the structural information embedded with classes is one popular strategy [15–17]. the structured large margin machine (slmm) [15] is a representative work based on the strategy. firstly, slmm explores the structural information within classes by ward’s agglomerative hierarchical clustering method on input copyright c⃝ 2006-2012 by ccc publications 734 z. qi, y. shi data [18], and then introduces the related structure information into the constraints. finally, slmm can be solved by a sequential second order cone programming (socp). experimentally, slmm is superior to support vector machine minimax probability machine (mpm) [19] and maximin margin machine(m4) [20]. however, as we all know, solving the involved socp problem is more difficult than the quadratic programming problem (qpp) as in svm, so slmm has more higher computational complexity than traditional svm. consequently, a novel structural support vector machine (srsvm) was proposed by xue et. al [17]. unlike slmm, srsvm exploits the classical framework of svm rather than as constraints in slmm and the corresponding optimization problem can still be solved by the qpp. srsvm has been shown to be theoretically and empirically better in generalization than svm and slmm. in this paper, inspired by the success of srsvm and the application potential of rmclp, we propose a novel structural rmclp (called srmclp) method for classification problem. unlike rmclp, srmclp is sensitive to the structure of the data distribution and can construct more reasonable classifiers by exploiting these prior data distribution information within classes. the remaining parts of the paper are organized as follows. section 2 introduces the basic notions and formulation of mclp; section 3 describes in detail our proposed algorithms; all experimental results are shown in section 4; conclusions are given in the last section. 2 background we give a brief introduction of mclp in the following. for classification about the training data t = {(x1,y1), · · · ,(xl,yl)}∈ (ℜn ×y)l, (1) where xi ∈ ℜn,yi ∈ y = {1,−1}, i = 1, · · · , l, data separation can be achieved by two opposite objectives. the first objective separates the observations by minimizing the sum of the deviations (msd) among the observations. the second maximizes the minimum distances (mmd) of observations from the critical value [6]. the overlapping of data u should be minimized while the distance v has to be maximized. however, it is difficult for traditional linear programming to optimize mmd and msd simultaneously. according to the concept of pareto optimality, we can seek the best trade-off of the two measurements [11, 12]. so mclp model can be described as follows: min u et u & max v et v, (2) s.t. (w ·xi) + (ui −vi) = b, for {i|yi = 1}, (3) (w ·xi)− (ui −vi) = b, for {i|yi = −1}, (4) u,v ≥ 0, (5) where e ∈ rl is a vector whose all elements are 1, w and b are unrestricted, ui is the overlapping and vi the distance from the training sample xi to the discriminator (w ·xi) = b (classification separating hyperplane). by introducing penalty parameter c,d > 0, mclp has the following version min u,v cet u−det v, (6) s.t. (w ·xi) + (ui −vi) = b, for {i|yi = 1}, (7) (w ·xi)− (ui −vi) = b, for {i|yi = −1}, (8) u,v ≥ 0, (9) structural regular multiple criteria linear programming for classification problem 735 figure 1: geometric meaning of mclp. the geometric meaning of the model is shown in figure 1. a lot of empirical studies have shown that mclp is a powerful tool for classification. however, we cannot ensure this model always has a solution under different kinds of training samples. to ensure the existence of solution, recently, shi et al proposed a rmclp model by adding two regularized items 1 2 wt hw and 1 2 ut qu on mclp as follows (more theoretical explanation of this model can be found in [8]): min z 1 2 wt hw + 1 2 ut qu + det u− cet v, (10) s.t. (w ·xi) + (ui −vi) = b, for {i|yi = 1}, (11) (w ·xi)− (ui −vi) = b, for {i|yi = −1}, (12) u,v ≥ 0, (13) where z = (wt ,ut ,vt ,b)t ∈ rn+l+l+1, h ∈ rn×n, q ∈ rl×l are symmetric positive definite matrices. obviously, the regularized mclp is a convex quadratic programming. compared with traditional svm , we can find that the rmclp model is similar to the support vector machine model in terms of the formation by considering the minimization of overlapping of the data. however, rmclp tries to measure all possible distances v from the training samples xi to separating hyperplane, while svm fixes the distance as 1 (through bounding planes (w · x) = b ± 1) from the support vectors. although the interpretation can vary, rmclp addresses more control parameters than the svm, which may provide more flexibility for better separation of data under the framework of the mathematical programming. in addition, different with svm, rmclp considers all the samples to solve classification problem. these make rmclp have stronger insensitivity to outliers. 3 structural regular multiple criteria linear programming for classification problem 3.1 extracting structural information within classes following the strategy of the slmm and srsvm, srmclp also has two steps. the first step is to extract structural information within classes by some clustering method; the second step is the model learning. in order to compare the main different of the second step between srmclp and the other two methods, here we adopt the same clustering method: ward’s linkage clustering(wil) [15–18], which is one of the hierarchical clustering analysis. a main advantage of wil is that clusters derived from this method are compact and spherical, which provides a 736 z. qi, y. shi meaningful basis for the computation of covariance matrices [15]. concretely, if s and t are two clusters with means µs and µt , the ward’s linkage w(s,t) between clusters s and t can be computed as [15] w(s,t) = |s| · |t| · ∥µs −µt∥ |s|+ |t| . (14) initially, each sample is considered as a cluster. the ward’s linkage of two samples xi and xj is w(xi,xj) = ∥xi −xj∥2/2. when two clusters are being merged to a new cluster a′, the linkage w(a′,c) can be conveniently derived from w(a,c),w(b,c) and w(a,b) by [15] w(a′,c) = (|a|+ |c|)w(a,c) + (|b|+ |c|)w(b,c)−|c|w(a,b) |a|+ |b|+ |c| . (15) during the hierarchical clustering, the ward’s linkage between clusters to be merged increases as the number of clusters decreases [15]. a relation curve between the merge distance and the number of clusters can be drawn to represent this process. the optimal number of clusters can be determined by finding the knee point. furthermore, the wil can also be extended to the kernel space. more details of wil can be found in [15]. 3.2 model learning we obtained two groups of p and n clusters in class cp and cn by the first step, i.e., p = p1 ∪ · · ·pi ∪ · · ·pcp ,n = n1 ∪ · · ·nj ∪ · · ·ncn . consider the optimization(10), choosing h,q to be identity matrix and introducing 1 2 b2 and 1 2 w⊤σw into the object function, srmclp can be be formulated as: min z 1 2 ∥w∥2 + 1 2 c1w ⊤σw + 1 2 ∥u∥2 + 1 2 b2 + c2e t u− c3et v, s.t. (w ·xi) + (ui −vi) = b, for {i|yi = 1}, (w ·xi)− (ui −vi) = b, for {i|yi = −1}, u,v ≥ 0, (16) where z = (wt ,ut ,vt ,b)t ∈ rn+l+l+1, c1,c2,c3 ≥ 0 are the pre-specified penalty factors, σ = σ+ + σ−, where σ+ = σp1 + · · · ,+σpcp , σ− = σn1 · · · ,+σncn , σpi and σnj are respectively the covariance matrices corresponding to the i th and j th clusters in the two classes, i = 1, · · · ,cp , j = 1, · · · ,cn . obviously, the regularized srmclp is a convex quadratic programming. by introducing its lagrange function l(w,u,v,b,α,β,η) = 1 2 ∥w∥2 + 1 2 c1w ⊤σw + 1 2 ∥u∥2 + 1 2 b2 + c2e t u− c3et v+ (17) l∑ i=1 αi(yi((w ·xi)− b) + ui −vi)− l∑ i=1 βiui − l∑ i=1 ηivi, (18) where αi,βi,ηi ∈ r are the lagrange multipliers, therefore the dual problem of (39) can be formulated as max w,u,v,b,α,β,η l(u,v,w,b,α,β,η), s.t.∇w,u,v,bl(u,v,w,b,α,β,η) = 0, βi,ηi ≥ 0, i = 1, · · · , l. (19) structural regular multiple criteria linear programming for classification problem 737 from (19) we get ∇wl = (i + c1σ)w + l∑ i=1 yiαixi = 0, (20) ∇uil = ui + c2 + αi −βi = 0, i = 1, · · · , l, (21) ∇vil = −c3 −αi −ηi = 0, i = 1, · · · , l, (22) ∇bl = b− l∑ i=1 yiαi = 0, (23) where i is an identity matrix. substituting the above equations into problem (19), the dual problem can be expressed as max α,u,b − 1 2 l∑ i=1 l∑ j=1 yiyjαiαj[x ⊤ i (2i + c1σ)xj]− 1 2 l∑ i=1 u2i , (24) s.t. l∑ i=1 yiαi = 0, i = 1, · · · , l, (25) −c2 −ui ≤ αi ≤−c3, i = 1, · · · , l. (26) solving the convex quadratic programming problem , we can obtain its solution. w = −(i + c1σ)−1 l∑ i=1 yiαixi, (27) b = l∑ i=1 yiαi. (28) (29) so the decision function can be formulated as follows f(x) = − l∑ i=1 yiαix ⊤ i (i + c1σ) −1x + b. (30) applying the kernel trick, we also extend the linear srmclp to the nonlinear case. introduce the kernel function k(x,x′) = (φ(x) · φ(x′)), where φ(·) is a mapping from the input space rn to hilbert space h φ : rn →h, x → φ(x). (31) then the optimization problem of srmclp in the kernel space can be described as: max α,u,b − 1 2 l∑ i=1 l∑ j=1 yiyjαiαj[φ(xi) ⊤(2i + c1σ φ)φ(xj)]− 1 2 l∑ i=1 u2i , (32) s.t. l∑ i=1 yiαi = 0, i = 1, · · · , l, (33) −c2 −ui ≤ αi ≤−c3, i = 1, · · · , l. (34) 738 z. qi, y. shi 2φ(xi) ⊤iφ(xj) = k(xi,xj), so we only need to consider how to compute the kernel matrix c1φ(xi) ⊤σφφ(xj). suppose tpi is a matrix corresponding to the cluster pi, tpi ∈ ℜ pi×n, in which the k-th row is x⊤k . opi is a mean matrix of cluster pi, opi ∈ℜ pi×n. each row of opi is the same, i.e. µpi = 1 pi ∑ xk∈pi xk. (35) the related covariance matrix for cluster pi can be expressed as σφpi = 1 pi (φ(tpi)−φ(opi)) ⊤(φ(tpi)−φ(opi)). (36) so we obtain φ(xi) ⊤ σφ+φ(xj) =( 1 √ pi (φ(tpi)−φ(opi))φ(xi)) ⊤ ( 1 √ pi (φ(tpi)−φ(opi))φ(xj)) =( 1 √ pi (k(tpi,xi)−k(opi,xi)) ⊤ ( 1 √ pi (k(tpi,xj)−k(opi,xj)). (37) similarly, φ(m)⊤σφ−φ(m) of fφ can computed φ(xi) ⊤ σφ−φ(xj) =( 1 √ pi (k(tni,xi)−k(oni,xi)) ⊤ ( 1 √ pi (k(tni,xj)−k(oni,xj)), (38) where tni is a matrix of cluster ni, oni is a mean matrix of cluster ni. srmclp has a similar structure of rmclp, we can easily proof that rmclp are the special case of srmclp. suppose the variance-covariance matrix of each cluster is σpi = σnj = i, i = 1, · · ·cp , j = 1, · · · ,cn . for an example of linear srmclp, the primal optimization problem (39) of srmclp becomes min z 1 2 ∥w∥2 + c1 cp + cn 2 ∥w∥2 + 1 2 ∥u∥2 + 1 2 b2 + c2e t u− c3et v, s.t. (w ·xi) + (ui −vi) = b, for {i|yi = 1}, (w ·xi)− (ui −vi) = b, for {i|yi = −1}, u,v ≥ 0, (39) it is not difficult to see that the optimization problem (10) is equivalent to one of the primal problem of rmclp. 4 experiments we compare the srmclp against rmclp and srsvm [16, 17] on various data sets in this section. the testing accuracies of all experiments are computed using standard 10-fold cross validation. c1,c2,c3 and rbf kernel parameter σ are all selected from the set {2i|i = −7, · · · ,7} structural regular multiple criteria linear programming for classification problem 739 by 10-fold cross validation on the tuning set comprising of random 10% of the training data. once the parameters are selected, the tuning set is returned to the training set to learn the final decision function. the “quadprog" function is used to solve the qp problems in srmclp, rmclp and srsvm. the “1 vs r" method [2] is used to solve the multi-class classification. all algorithms are implemented by using matlab 2010. the experiment environment: intel core i7-2600 cpu, 4 gb memory. 4.1 toy data in the subsection, we use a 2-d toy data to show the intuitive performance of srmclp. the 2-d toy data is the synthetic xor dataset [17], which is a typical linearly nonseparable problem in classification and randomly generated under two gaussian distributions in each class. in practise, samples in each class are designed to two clusters p1,p2 and n1,n2(the number of samples in each cluster is equal), and each gaussian distribution contains 100 samples. we respectively use 10%,20%,30%,50% of data in each cluster as the training set, and others for testing. the comparative results of srmclp and srsvm are shown in figure 2. in the xor dataset, the positive class and negative class have both the horizontal distribution and the vertical distribution. how to fully exploit these prior knowledge will be a very difficult task. from figure 2, we can find that srmclp’s discriminant boundaries basically enclose those of rmclp, which means that srmclp has better generalization performance than rmclp. figure 3, we can also find that the accuracy’s different of these methods decreases with the increase of training samples. those show that srmclp can fully exploit these prior structural information to design a more reasonable classifier. 4.2 uci datasets in this subsection, we perform these methods on the uci datasets [21]. for each dataset, we randomly select the same number of data from different classes to compose a dataset. 50% percent of each extracted dataset are for training, 50% for testing. the results are shown in the table 1. from the table 1, we can draw the conclusion as follows: 1) srmclp and srsvm have the better predictive ability than rmclp in all cases. this shows that these priori structural information embedded in classes has a great help to improve the classification performance of the classifier. 2) srmclp is superior to srsvm in most cases. this shows srmclp is a strong competitive method. 5 conclusion in this paper, we proposed a novel structural rmclp (called srmclp) method for classification problem. unlike rmclp, srmclp is sensitive to the structure of the data distribution and can construct more reasonable classifiers by exploiting these prior data distribution information within classes. the corresponding optimization problem of srmclp can be solved by a standard quadratic programming. the effectiveness of the proposed method is demonstrated via experiments on synthetic and available benchmark datasets and applications on the decision supporting system. in the future work, we will apply srmclp into other actual classification problems such as stock forecast, credit card analysis to further test its effectiveness. 740 z. qi, y. shi −10 −5 0 5 10 15 −4 −2 0 2 4 6 8 10 12 14 16 −10 −5 0 5 10 15 −4 −2 0 2 4 6 8 10 12 14 16 −10 −5 0 5 10 15 20 −5 0 5 10 15 −10 −5 0 5 10 15 20 −5 0 5 10 15 −10 −5 0 5 10 15 20 0 5 10 15 20 −10 −5 0 5 10 15 20 0 5 10 15 20 −10 −5 0 5 10 15 20 0 5 10 15 20 −10 −5 0 5 10 15 20 0 5 10 15 20 figure 2: the performance of srmclp and rmclp in the case of rbf case. the first column and second column are the results on the training set and testing set. each row is the result on 10%,20%,30% and 50% training sets, respectively. the magenta dotted curve and red solid curve denote the hyperplanes of srmclp and rmclp, respectively. structural regular multiple criteria linear programming for classification problem 741 40 60 80 100 120 140 160 180 200 0.91 0.915 0.92 0.925 0.93 0.935 0.94 0.945 0.95 0.955 0.96 size of training data a cc u ra cy srmclp rmclp figure 3: accuracy of srmclp and rmclp on the xor dataset table 1: the testing accuracy and training times on uci datasets datasets srmclp srsvm rmclp accuracy accuracy accuracy hepatitis 79.91±2.55 79.83±1.27 77.82±4.22 (155×19) australian 68.18±2.12 69.32±2.31 67.73±1.56 (690×14) bupa liver 68.12±1.34 68.96±1.09 67.61±2.71 (345×6) cmc 65.71±2.54 65.27±2.35 64.53±3.62 (844×9) credit 76.36±2.18 76.11±2.61 75.82±2.87 (690×19) diabetis 63.98±1.90 64.19±2.43 62.44±3.47 (768×8) flare-solar 59.11 ±2.98 58.43±2.77 57.96±2.51 (1066×9) german 63.91±1.93 63.84±1.88 62.55±2.86 (1000×20) heart-statlog 76.13 ±2.50 76.04±2.47 76.21±2.83 (270×14) image 83.18 ±2.57 83.44±1.40 82.64±2.88 (2310×18) ionosphere 76.93 ±2.61 76.55±2.63 76.43± 3.26 (351×34) 742 z. qi, y. shi 6 acknowledgment this work has been partially supported by grants from national natural science foundation of china( no.70921061, no.10601064), the cas/safea international partnership program for creative research teams, major international(ragional) joint research project(no.71110107026), the president fund of gucas. bibliography [1] vapnik v.n. the nature of statistical learning theory. 2nd ed. new york: springer, 2000. 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[21] murphy p.m. and aha d.w. uci machine learning repository, 1992. international journal of computers communications & control issn 1841-9836, 9(6):786-799, december, 2014. application of fuzzy reasoning spiking neural p systems to fault diagnosis t. wang, g. zhang, h. rong, m.j. pérez-jiménez tao wang, gexiang zhang*, haina rong school of electrical engineering, southwest jiaotong university chengdu, 610031, china wangtaocdu@gmail.com, zhgxdylan@126.com ronghaina@126.com *corresponding author: wangtaocdu@gmail.com mario j. pérez-jiménez research group on natural computing department of computer science and artificial intelligence university of sevilla, sevilla, 41012, spain marper@us.es abstract: this paper discusses the application of fuzzy reasoning spiking neural p systems with trapezoidal fuzzy numbers (tfrsn p systems) to fault diagnosis of power systems, where a matrix-based fuzzy reasoning algorithm based on the dynamic firing mechanism of neurons is used to develop the inference ability of tfrsn p systems from classical reasoning to fuzzy reasoning. some case studies show the effectiveness of the presented method. we also briefly draw comparisons between the presented method and several main fault diagnosis approaches from the perspectives of knowledge representation and inference process. keywords: fuzzy reasoning spiking neural p system with trapezoidal fuzzy number, fuzzy reasoning, fault diagnosis, trapezoidal fuzzy number, linguistic term. 1 introduction membrane computing, introduced by gh. păun in [1], is an attractive research field of computer science aiming at abstracting computing models, called membrane systems or p systems, from the structures and functioning of living cells, as well as from the way the cells are organized in tissues or higher order structures. in recent years, much attention is paid to the spiking neural p systems (sn p systems, for short), an important class of p systems introduced in [2] and investigated in a series of papers (see [3][12]), which can be described as a directed graph. an sn p system is a kind of distributed and parallel computing model inspired by the neurophysiological behavior of neurons sending electrical impulses (spikes) along axons from presynaptic neurons to postsynaptic neurons. the features of sn p systems, such as inherent parallelism, understandability, dynamics, synchronization/asychronization, non-linearity and nondeterminism [3], [4], are suitable for solving various engineering problems. until now, only a few investigations have focused on the use of sn p systems to solve engineering problems. in [3], a fuzzy reasoning spiking neural p system with real numbers (rfrsn p system) was presented to perform diagnosis knowledge representation and reasoning. in [13], an rfrsn p system was used for fault diagnosis of power systems and three examples were used to verify its effectiveness. the studies in [3, 13], dealing with the fault diagnosis problem, used certainty factors and truth degree values, which are described by real numbers obtained from the frequency of occurrences in historical data, it is known how difficult it is to obtain and process real-time statistical data from power network data and the knowledge of dispatchers and experts in electrical power systems as they usually contain linguistic terms with copyright © 2006-2014 by ccc publications application of fuzzy reasoning spiking neural p systems to fault diagnosis 787 some degree of uncertainty. so, using various models, including rfrsn p systems to solve fault diagnosis problems with a certain degree of uncertainty, represents a way to tackle this difficult problem. this paper discusses the extended version of rfrsn p systems, i.e., fuzzy reasoning spiking neural p systems with trapezoidal fuzzy numbers (tfrsn p systems), and its application to fault diagnosis of power systems. to adapt tfrsn p systems to solve fault diagnosis problems, a matrix-based fuzzy reasoning algorithm (mbfra) is used inspired by the dynamic firing mechanism of neurons. given initial pulse values of all input neurons of a tfrsn p system, mbfra can perform fuzzy inference to obtain the pulse values contained in other neurons and export reasoning results represented by trapezoidal fuzzy numbers. to make mbfra suitable for multiple faults diagnosis of power systems, a defuzzification method is applied for processing the reasoning results in order to obtain crisp numbers corresponding to them. some case studies show the effectiveness of the presented method. we also briefly draw comparisons between tfrsn p systems and several other fault diagnosis approaches. the remainder of this paper is organized as follows. section 2 introduces concepts and notations used in this work. section 3 provides the definition of tfrsn p systems and mbfra. section 4 discusses the application of tfrsn p systems to fault diagnosis of power systems. discussions on several fault diagnosis methods are made in section 5. conclusions are finally drawn in section 6. 2 preliminaries a trapezoidal fuzzy number can be characterized as a 4-tuple of real numbers t̃f = (a, b, c, d), a < b < c < d, shown in fig. 1, where a and d represents the left hand and right hand width of the trapezoidal distribution, (b, c) denotes the interval in which the membership value is equal to 1 and h t̃f (x) represents the membership function of t̃f defined as follows [14]: h t̃f (x) =   0, x ≤ a x−a b−a , a < x ≤ b 1, b < x ≤ c d−x d−c , c < x ≤ d 0, x > d (1) let ã and b̃ be two trapezoidal fuzzy numbers, ã = (a1, b1, c1, d1) and b̃ = (a2, b2, c2, d2). the arithmetic operations of ã and b̃ are defined as follows [15]: 1. addition ⊕: ã⊕ b̃ = (a1, b1, c1, d1)⊕ (a2, b2, c2, d2)=(a1 + a2, b1 + b2, c1 + c2, d1 + d2); 2. subtraction ⊖: ã⊖ b̃ = (a1, b1, c1, d1)⊖ (a2, b2, c2, d2)=(a1 −a2, b1 − b2, c1 − c2, d1 −d2); 3. multiplication ⊗: ã⊗b̃ = (a1, b1, c1, d1)⊗(a2, b2, c2, d2)=(a1×a2, b1×b2, c1×c2, d1×d2); 4. division ⊘: ã⊘ b̃ = (a1, b1, c1, d1)⊘ (a2, b2, c2, d2)=(a1/a2, b1/b2, c1/c2, d1/d2). we define four logic operations, where ã and b̃ are trapezoidal fuzzy numbers, and a, b are real numbers: 1. minimum operator ∧: a∧ b = min(a, b); 2. maximum operator ∨: a∨ b = max(a, b); 788 t. wang, g. zhang, h. rong, m.j. pérez-jiménez figure 1: a trapezoidal fuzzy number. 3. and ∧○: ã ∧○ b̃ = (a1, b1, c1, d1) ∧○ (a2, b2, c2, d2) =((a1∧a2), (b1∧b2), (c1∧c2), (d1∧d2)); 4. or ∨○: ã ∨○ b̃ = (a1, b1, c1, d1) ∨○ (a2, b2, c2, d2)=((a1 ∨a2), (b1 ∨ b2), (c1 ∨c2), (d1 ∨d2)). we define a scalar multiplication operation as follows: scalar multiplication: x · ã = x · (a1, b1, c1, d1) = (x ·a1, x · b1, x · c1, x ·d1), where a is a trapezoidal fuzzy number and x is a real number: the defuzzification method in [16] is chosen to obtain a crisp number tf associated with a trapezoidal fuzzy number t̃f , it is shown in (2), where e and g are the extreme values of the whole fuzzy set range. in this study, e and g are equal to 0 and 1, respectively. tf = (d−e) + (c−e) ((d−e) + (c−e))− ((a−g) + (b−g)) (2) 3 tfrsn p systems a tfrsn p system of m ≥ 1 is a construct π = (o, σ1, . . . , σm, syn, in, out), where: (1) o = {a} is a singleton alphabet (a is called spike); (2) σ1, . . . , σm are neurons, of the form σi = (θi, ci, ri), 1 ≤ i ≤ m, where: (a) θi is a trapezoidal fuzzy number in [0,1] representing the potential value of spikes (i.e. value of electrical impulses) contained in neuron σi; (b) ci is a trapezoidal fuzzy number in [0,1] representing the fuzzy truth value corresponding to neuron σi; (c) ri represents a firing (spiking) rule contained in neuron σi with the form e/aθ → aβ, where e is the firing condition and its form will be specified below, θ and β are trapezoidal fuzzy numbers in [0,1]. (3) syn ⊆{1, 2, . . . , m}×{1, 2, . . . , m} with i ̸= j for all (i, j) ∈ syn, 1 ≤ i, j ≤ m, is a directed graph of synapses between the linked neurons; (4) in, out ⊆ {1, 2, . . . , m} indicate the input neuron set and the output neuron set of π, respectively. application of fuzzy reasoning spiking neural p systems to fault diagnosis 789 in tfrsn p systems, the definition of neurons and pulse values can be extended. specifically, in tfrsn p systems, the neurons are extended to four types, i.e., proposition neurons and three kinds of rule neurons: general, and and or, and the pulse value contained in each neuron is no longer the number of spikes represented by a real value, but a trapezoidal fuzzy number in [0, 1], which can be interpreted as the potential value of spikes contained in neuron σi. it is worth pointing out that the number of spikes in each neuron is determined by the problem to be solved and the pulse value contained in each neuron is different. if neuron σi contains no spike, then θi = 0; otherwise, if neuron σi contains only one spike, then θi equals to the pulse value of this spike; in any other case, θi equals to the result of a operation on all pulse values received from its presynaptic neurons. for different types of neurons, the operations for pulse values are different. for proposition neurons and and rule neurons, they use operation ∧○ to handle all the pulse values received from their presynaptic neurons while or rule neurons use operation ∨○, where symbols ∧○ and ∨○ represent the and and or operators of trapezoidal fuzzy numbers, respectively. the firing condition e = as means that the spiking rule, e/aθ → aβ, contained in neuron σi, can be applied if and only if neuron σi contains at least s spikes, otherwise, the firing rule cannot be applied. more details about tfrsn p systems can be found to the preliminary work [9]. fuzzy production rules consist of five types: type 1 : ri(ci) : pj(θj) → pk(θk); θk = θj ⊗ ci. type 2 : ri(ci) : p1(θ1) ∧○ . . . ∧○pk−1(θk−1) → pk(θk); θk = (θ1 ∧○ . . . ∧○θk−1)⊗ ci. type 3 : ri(ci) : p1(θ1) → p2(θ2) ∧○ . . . ∧○pk(θk); θ2 = . . . θk = θ1 ⊗ ci. type 4 : ri(ci) : p1(θ1) ∨○ . . . ∨○pk−1(θk−1) → pk(θk); θk = (θ1 ∨○ . . . ∨○θk−1)⊗ ci. type 5 : ri(ci) : p1(θ1) → p2(θ2) ∨○ . . . ∨○pk(θk). where ri represents the ith fuzzy production rule; ci is the certainty factor of rule ri; pi is a proposition appearing in the antecedent or consequence part of a rule, 1 ≤ i ≤ k (k is the number of propositions in a rule-based system); pj in type 1 represents the jth proposition, 1 ≤ j ≤ k−1; θi represents the fuzzy truth value corresponding to the ith proposition [15]. ci and θi are trapezoidal fuzzy numbers defined in the universe of discourse [0, 1]. the causality between a fault on a faulty section in a power system and the status information about protective relays and circuit breakers (cbs) of this section can be described by the aforementioned fuzzy production rules. a simplified transmission network shown in fig. 2 is used to illustrate the notations in a fuzzy production rule. according to the protection principle, if there is fault on transmission l, then its main protective relays, i.e., mlr1 and mlr2 and their corresponding cbs, i.e., cb1 and cb2, will operate to protect l, which can be backward described by a fuzzy production rule: r(1, 1, 1, 1) : mlr1 operates (0.975, 0.98, 1, 1) ∧○ mlr2 operates (0.975, 0.98, 1, 1) ∧○ cb1 trips (0.975, 0.98, 1, 1) ∧○ cb2 trips (0.975, 0.98, 1, 1) → l has a fault. the certainty factor of this rule is (1, 1, 1, 1) which represents the contribution of this rule to the final diagnosis result. in this rule, there are four propositions mlr1 operates, mlr2 operates, cb1 trips and cb2 trips, and they have an equal fuzzy truth value (0.975, 0.98, 1, 1) representing the contributions of the propositions to the result l has a fault. because rules type 5 are unsuitable for diagnosis, they are not further described in section 3. tfrsn p system models for rules type 1 to type 4 are shown in fig. 3. to adapt tfrsn p systems to solve fault diagnosis problems, we describe mbfra in the following description. given initial truth values of propositions corresponding to all input neurons in an tfrsn p system, mbfra can perform fuzzy reasoning to obtain the fuzzy truth values of other neurons with unknown pulse values and output reasoning results. let us assume that the tfrsn p system contains l proposition neurons and n rule neurons, each of which may be general, and or or rule neurons, m = l + n, where m is the number of all the neurons in this system. 790 t. wang, g. zhang, h. rong, m.j. pérez-jiménez figure 2: a simplified transmission network. figure 3: tfrsn p system models for fuzzy production rules. (a) type 1 ; (b) type 2 ; (c) type 3 ; (d) type 4. in order to clearly present the reasoning algorithm, we first introduce some parameter vectors and matrices as follows. 1) θ = (θ1, θ2, . . . , θl)t is a fuzzy truth value vector of the l proposition neurons, where θi represents the pulse value contained in the ith proposition neuron, 1 ≤ i ≤ l, and is expressed by a trapezoidal fuzzy number in [0, 1]. if there is not any spike contained in a proposition neuron, its pulse value is “unknown” or (0, 0, 0, 0). 2) δ = (δ1, δ2, . . . , δn)t is a fuzzy truth value vector of the rule neurons, where δj represents the pulse value contained in the jth rule neuron, 1 ≤ j ≤ n, and it is expressed by a trapezoidal fuzzy number [0, 1]. if there is not any spike contained in a rule neuron, its pulse value is “unknown” or (0, 0, 0, 0). 3) c = diag(c1, c2, . . . , cn) is a diagonal matrix, where cj is the certainty factor of the jth fuzzy production rule, 1 ≤ j ≤ n, and it is expressed by a trapezoidal fuzzy number. 4) d1 = (dij)l×n is a synaptic matrix representing the direct connection between proposition neurons and general rule neurons. if there is a directed arc (synapse) from the proposition neuron σi to the general rule neuron σj, then dij = 1, otherwise, dij = 0. application of fuzzy reasoning spiking neural p systems to fault diagnosis 791 5) d2 = (dij)l×n is a synaptic matrix representing the direct connection between proposition neurons and and rule neurons. if there is a directed arc (synapse) from the proposition neuron σi to the and rule neuron σj, then dij = 1, otherwise, dij = 0. 6) d3 = (dij)l×n is a synaptic matrix representing the direct connection between proposition neurons and or rule neurons. if there is a directed arc (synapse) from the proposition neuron σi to the or rule neuron σj, then dij = 1, otherwise, dij = 0. 7) e = (eji)n×l is a synaptic matrix representing the direct connection between rule neurons and proposition rule neurons. if there is a directed arc (synapse) from the rule neuron σj to the proposition neuron σi, then eji = 1, otherwise, eji = 0. subsequently, we introduce some multiplication operations as follows. 1) ◦○: c ◦○ δ = (c1 ⊗ δ1, c2 ⊗ δ2, . . . , cn ⊗ δn)t ; dt ◦○ θ = (d̄1, d̄2, . . . , d̄n)t , where d̄j = d1jθ1 +d2jθ2 + . . . +dljθl, j = 1, 2, . . . , n. 2) ⊙: dt ⊙ θ = (d̄1, d̄2, . . . , d̄n)t , where d̄j = d1jθ1 ∧○ d2jθ2 ∧○ . . . ∧○ dljθl, j = 1, 2, . . . , n. 3) ∗○: et ∗○ δ = (ē1, ē2, . . . , ēl)t , where ēi = e1iδ1 ∨○ e2iδ2 ∨○ . . . ∨○eniδn, i = 1, 2, . . . , l. next, we list the pseudocode of mbfra. algorithm mbfra require: d1, d2,d3, e, c, θ0, δ0 1: set the termination condition 0 = (unknown, unknown, . . . , unknown)tn 2: let t = 0, where t represents the reasoning step 3: while δt ̸= 0 do 4: for each input neuron (t = 0) or each proposition neuron (t > 0) do 5: if the firing condition e = as is satisfied then 6: the neuron fires and computes the fuzzy truth value vector δt+1 via δt+1=(dt1 ◦○ θt)⊕ (dt2 ⊙θt)⊕ (d t 3 ∗○ θt) 7: if there is a postsynaptic rule neuron then 8: the neuron transmits a spike to the next rule neuron 9: else 10: just accumulate the value in the neuron 11: end if 12: end if 13: end for 14: for each rule neuron do 15: if the firing condition e = as is satisfied then 16: the rule neuron fires and computes the fuzzy truth value vector θt+1 via θt+1= et ∗○ (c ◦○ δt+1) and transmits a spike to the next proposition neuron 17: end if 18: t = t + 1 19: end for 20: end while ensure: θt, which represents the final states of pulse values contained in proposition neurons. 4 application examples and results in this section, a power system with 14-buses, chosen from [17] and as shown in fig. 4, is applied as an example to describe how to use tfrsn p systems with mbfra to solve a fault diagnosis problem. the system contains 34 system sections, including 14 buses and 20 transmission lines. the buses are marked as bpq and the transmission lines are represented 792 t. wang, g. zhang, h. rong, m.j. pérez-jiménez figure 4: the power system with 14 buses. as lpquv, where 0 ≤ p, q, u, v ≤ 9. the protection system of the 14-bus system contains 174 protective devices consisting of 40 circuit breakers (cbs), 40 main transmission line relays, 40 first backup transmission line relays, 40 second backup transmission line relays and 14 bus relays. a local part, which is composed of a transmission line l1314, its adjoining two buses, b13 and b14, and its adjoining three transmission lines, l1213, l0613 and l0914, of the protection system is given to describe its structure and symbols of protection devices. the local system is shown in fig. 5. the operational rules of the protective devices are descried as follows [17]. the main transmission line relay mlr1314 protects the entire line l1314 and it will operate to trip its associated circuit breaker (cb), i.e., cb1314, to clear a fault on the line l1314. the bus relay br13 protects the bus b13 and it will operate to trip the three cbs, i.e., cb1312, cb1306 and cb1314, if there is a fault on the bus b13. the first backup transmission line relay blr1314 is a local backup of the relay mlr1314 and has the same protection zone as mlr1314. the relay blr1314 will operate to trip cb1314 to clear a fault if the fault clearance by the relay mlr1314 fails. secondary backup transmission line relays slr1213 and slr0613 are the remote backups of the relays mlr1314 and blr1314. they will operate to trip their corresponding cbs, i.e., cb1213 and cb0613, respectively, to clear a fault if the fault clearance by both mlr1314 and blr1314 fails. the relays slr1213 and slr0613 are also two remote backups of the relay br13 and they will operate to trip cbs, i.e., cb1213 and cb0613, respectively, to clear a fault if the fault clearance by the relay br13 fails. the functions of the four relays, mlr1413, blr1413, slr0914 and br14, and three cbs, cb1413, cb1409 and cb0914, in the process of protecting the line l1314 and the bus b14 are similar and the protection systems for other sections in this 14-bus power system have the same protection rules, so it is not necessary to repeatedly describe application of fuzzy reasoning spiking neural p systems to fault diagnosis 793 figure 5: a local part of the protection system of the 14-bus power system. their operation rules. the protection rules described above show that when a fault occurs on a certain section of a power system, protection devices will reach certain statuses to protect the section. meanwhile, the relay trip signals and cbs status signals, used as inputs of fault diagnosis models of sections, can be obtained from remote terminal units (rtus) of supervisory control and data acquisition (scada) systems. the diagnostic strategy in this study is to build one tfrsn p system diagnosis model for each candidate fault section of a power system and each model performs mbfra by using scada data, i.e., relay trip signals and cbs status signals, to get a trapezoidal fuzzy number which represents the fault confidence level of this section. in a single fault case, the section with the highest fault confidence level is the faulty section. in multiple faults cases, several sections with fault confidence levels which are greater than a threshold, which is set as real number 0.5 in this study, are regarded as faulty sections. thus, to obtain real numbers for easily comparing the fault confidence levels with the threshold, a defuzzification method shown in (2) is used to process the reasoning results represented by trapezoidal fuzzy numbers. in addition, the fault confidence levels of faulty sections in multiple faults cases are ranked from high to low to help operators to decide a repair order of the sections. fig. 6 and fig. 7 show the tfrsn p system diagnosis models for l1314 and b13, respectively. it is worth noting that there are several assistant arcs (synapsises) with different arrow endings in the figures. for illustration purposes, we take arcs, from σ2 to σ25 and from σ2 to σ26, as examples. the meanings of the two arcs are that if cb1314 opens, the operation of its corresponding second backup protective devices, including relays (slr0613 and slr1213) and cbs (cb0613 and cb1213), is invalid and then the values of these relays and cbs are set as (0, 0, 0, 0); otherwise, the operation of the second backup protective devices is valid. in what follows we take transmission line l1314 as an example to show the fuzzy reasoning process of mbfra based on tfrsn p systems. case 1: a single fault. transmission line l1314 has a fault. operated relays: mlr1314, mlr1413, blr1314. tripped cbs: cb1314, cb1413. a tfrsn p system for l1314 is π1 and its corresponding tfrsn p system diagnosis model is shown in fig. 6. π1 = (o, σ1, σ2, . . . , σ36, syn, in, out) 794 t. wang, g. zhang, h. rong, m.j. pérez-jiménez figure 6: fault diagnosis model of transmission line l1314 based on a tfrsn p system. figure 7: fault diagnosis model of bus b13 based on a tfrsn p system. application of fuzzy reasoning spiking neural p systems to fault diagnosis 795 table 1: linguistic terms and their corresponding trapezoidal fuzzy numbers linguistic terms trapezoidal fuzzy numbers absolutely-false (af) (0, 0, 0, 0) very-low (vl) (0, 0, 0.02, 0.07) low (l) (0.04, 0.1, 0.18, 0.23) medium-low (ml) (0.17, 0.22, 0.36, 0.42) medium (m) (0.32, 0.41, 0.58, 0.65) medium-high (mh) (0.58, 0.63, 0.80, 0.86) high (h) (0.72, 0.78, 0.92, 0.97) very-high (vh) (0.975, 0.98, 1, 1) absolutely-high (ah) (1, 1, 1, 1) where 1) o = {a} is the singleton alphabet (a is called spike). 2) σ1, . . . , σ22 are proposition neurons corresponding to the propositions with fuzzy truth values θ1, . . . , θ22; that is, l = 22. 3) σ23, . . . , σ36 are rule neurons, where σ23, . . . , σ29 are and rule neurons, σ30 and σ36 are or rule neurons and σ31, . . . , σ35 are general rule neurons; that is, n = 14. 4) syn = {(1, 23) , (2, 23), (2, 24), (2, 25), (2, 26), (3, 24), (4, 25), (5, 25), (6, 26), (7, 26), (8, 27), (9, 27), (9, 28), (9, 29), (10, 28),(11, 29),(12, 29),(13, 30),(14, 30),(15, 31), (16, 32), (17, 33), (18, 34), (19, 35), (20, 36), (21, 36), (23, 13), (24, 14), (25, 15), (26, 16), (27, 17),(28, 18),(29, 19),(30, 20), (31, 20), (32, 20), (33, 21), (34, 21), (35, 21), (36, 22)}. 5) in = {σ1, σ2, . . . , σ12}, out = {σ22}. the knowledge of dispatchers in power systems may contain linguistic terms and the statuses of devices may have a certain degree of uncertainty. table 1 shows an example of linguistic terms and their corresponding trapezoidal fuzzy numbers. in the tfrsn p system π1, input neurons σ1, . . . , σ12 are assigned as the empirical values v h, v h, h, af, af, af, af, v h, v h, l, af, af , respectively. certainty factors corresponding to rule neurons σ23, . . . , σ36 are given values v h, v h, v h, h, v h, v h, h, v h, v h, v h, v h, v h, v h, v h, respectively. according to table 1, we obtain the trapezoidal fuzzy numbers θ0 and δ0. in order to succinctly describe the matrices, let us denote or = (x1, . . . , xr)t , where xi = (0, 0, 0, 0), 1 ≤ i ≤ r. θ0 =   (0.975, 0.98, 1, 1) (0.975, 0.98, 1, 1) (0.72, 0.78, 0.92, 0.97) o4 (0.975, 0.98, 1, 1) (0.975, 0.98, 1, 1) (0.04, 0.1, 0.18, 0.23) o12   22×1 , δ0 = [ o ] 14×1 when t = 0, we get the results 796 t. wang, g. zhang, h. rong, m.j. pérez-jiménez δ1 =   (0.9506, 0.9604, 1, 1) (0.702, 0.7644, 0.92, 0.97) o2 (0.9506, 0.9604, 1, 1) (0.39, 0.098, 0.18, 0.23) o8   14×1 , θ1 =   o12 (0.9268, 0.9412, 1, 1) (0.6845, 0.7491, 0.92, 0.97) o2 (0.9268, 0.9412, 1, 1) (0.2808, 0.0764, 0.1656, 0.2231) o4   22×1 when t = 1, we obtain the results δ2 =   o7 (0.9268, 0.9412, 1, 1) o2 (0.9268, 0.9412, 1, 1) (0.2808, 0.0764, 0.1656, 0.2231) o2   14×1 , θ2 =   o19 (0.9268, 0.9412, 1, 1) (0.9268, 0.9412, 1, 1) o1   22×1 when t = 2, we have the results δ3 = [ o13 (0.9268, 0.9412, 1, 1) ] 14×1 , θ3 = [ o21 (0.9036, 0.9224, 1, 1) ] 22×1 when t = 3, we get the results δ4 = [ o ] 14×1 . thus, the termination condition is satisfied and the reasoning process ends. we obtain the reasoning results, i.e., the fuzzy truth value (0.9036,0.9224,1,1) of the output neuron σ22. the transmission line l1314 is a faulty section with a confidence level (0.9036,0.9224,1,1). tfrsn p systems and mbfra are also suitable for multiple faults diagnosis problems in power systems. in what follows we take an example of the power system in fig. 4 to show the effectiveness of the method in diagnosing multiple faults. case 2: multiple faults. transmission line l1314 and bus b13 have faults. operated relays: mlr1314, mlr1413, slr0613, slr1213. tripped cbs: cb1314, cb1413, cb0613, cb1213. according to the scada data, four candidate fault sections, i.e., l1314, b13, l0613 and l1213, are selected. the tfrsn p systems of the four sections are established to perform mbfra, respectively. after the fuzzy reasoning, fault confidence levels, (0.9036, 0.9224, 1, 1), (0.6673, 0.7341, 0.92, 0.97), (0.2165, 0.299, 0.623, 0.7849) and (0.2165, 0.299, 0.623, 0.7849), represented by trapezoidal fuzzy numbers of sections l1314, b13, l0613 and l1213 are obtained. according to (2), we obtain their corresponding real numbers, i.e., 0.92, 0.7595, 0.475 and 0.475. thus, there are two faulty sections, i.e., l1314 and b13. the results are summarized in table 2. the logic analysis about case 2 is described as follows. in this case, information of protective relay br13 is not observed and cb1312 and cb1306 fail to trip. for transmission line l1314, its main transmission relays, mlr1314 and mlr1413, operate to trip their corresponding cbs, application of fuzzy reasoning spiking neural p systems to fault diagnosis 797 table 2: relay trip signals and cbs status signals observed, and diagnosis results candidate confidence corresponding ranking fault fault section level real number section l1314 (0.9036,0.9224,1,1) 0.92 1 yes b13 (0.6673,0.7341,0.92,0.97) 0.7595 2 yes l0613 (0.2165,0.299,0.623,0.7849) 0.475 no l1213 (0.2165,0.299,0.623,0.7849) 0.475 no cb1314 and cb1413, to clear a fault. so it is a faulty section. although main protective relay br13 of bus b13 fails to clear a fault, its remote backup protective relays, slr0613 and slr1213, operate to trip their corresponding cbs, cb0613 and cb1213, to clear this fault. so b13 is a faulty section. for transmission lines l0613 and l1213, only their single-ended remote backup protective relays slr0613 and slr1213 operate to trip their corresponding cbs, cb0613 and cb1213, respectively. actually, slr0613 and slr1213 and their cbs act as remote backup protections of b13. so, l0613 and l1213 are not faulty sections. therefore, according to the logic analysis and table 2, we can know that the presented method can obtain correct results in multiple fault situations. 5 discussions a tfrsn p system is a novel graphical model for representing fuzzy knowledge and information. this study employs it to diagnose the faults of power systems. the fault diagnosis ability of a method is usually associated with the knowledge availability and the reasoning process. thus, in what follows, we make a comparison between tfrsn p systems and several fault diagnosis approaches regarding the aspects of knowledge representation and inference process. (1) expert systems (ess). both es and the fault diagnosis method based on tfrsn p systems (fdm-tfrsnp) can make full use of experts’ knowledge. the differences are: an es needs long response time and the maintenance of its knowledge base is difficult [18]; fdmtfrsnp possesses parallel reasoning ability and adopt graphical knowledge representation and reasoning, which can avoid the main limitation of es. (2) fuzzy set theory (fst). fst is an effective way to represent uncertain information but the definition of membership function is a hard job [18]. the fst-based method and fdm-tfrsnp both possess the ability to deal with uncertain information of protective devices. in addition, linguistic terms used in both methods make them closer to the human thinking compared with the methods using crisp numbers. the main differences between them are that fdm-tfrsnp has a fast reasoning speed and the matrix reasoning process is easier to describe diagnostic process as well as its programming. (3) artificial neural networks (anns). anns can be regarded as opaque black boxes and can be easily used. the main problems of anns lie in the difficult acquisition of a complete sample set and a tedious training process needing extra time consumption. in addition, premature convergence is also a problem. fdm-tfrsnp neither needs a training process with a set of comprehensive training data nor has a premature convergence problem [13]. besides, fdmtfrsnp can intuitively represent the relationships between faults and operations of protection devices. this feature is very helpful for operators to analyze and summarize failure processes. (4) fuzzy petri nets (fpns). both fpns and tfrsn p systems have graphical knowledge representation and parallel computing ability. however, the mechanism of tfrsn p systems is originated from neurophysiological behavior of neurons or/and living cells. thus, the working principle of different types of neurons or/and cells may provide new inspirations for extending sn 798 t. wang, g. zhang, h. rong, m.j. pérez-jiménez p systems (or tfrsn p systems), which can increase the ways of knowledge representation and reasoning to solve new problems in power systems [4]. in addition, tfrsn p systems with trapezoidal fuzzy numbers have three rule neurons types, i.e., general, and and or, and one proposition neuron type, while fpns only contain same places and transition types. thus, different types of neurons make fdm-tfrsnp have better flexibility and trapezoidal fuzzy numbers (linguistic terms) make tfrsn p systems more understandable to operators of power systems. 6 conclusions in this study, tfrsn p systems and a matrix-based fuzzy reasoning algorithm, mbfra, for fault diagnosis are discussed to extend the application area of sn p systems in fault diagnosis of power systems. mbfra is based on the dynamic firing mechanism of neurons. given initial pulse values of all input neurons of a tfrsn p system, mbfra can obtain the pulse values of other neurons by performing fuzzy reasoning. to make mbfra suitable for fault diagnosis in power systems, a defuzzification method is employed to treat reasoning results represented by trapezoidal fuzzy numbers. application examples show that tfrsn p systems with mbfra is effective in diagnosing faulty sections of power systems. besides, a comparison between tfrsn p systems and different fault diagnosis approaches is made. the aim of this study is to construct a tfrsn p system diagnosis model for each candidate fault section. the scale of each diagnosis model depends on protective devices connections of the candidate fault section rather than the scale of power systems. thus, the presented method can be used for large-scale power systems. this study focuses on the effectiveness and correctness of the fault diagnosis method and the results of application examples are obtained by manual computation. to test the speed, convergence and accuracy of mbfra and to explore automatical generation of tfrsn p systems in diagnosing faulty sections in power systems, our future work will simulate them on matlab, p-lingua or mecosim [19][21]. moreover, how to verify and realize the parallelism of tfrsn p systems and mbfra on hardware such as fpga and cuda is also our further task. acknowledgment this work is supported by the national natural science foundation of china (61170016, 61373047, 61170030), the program for new century excellent talents in university (ncet-110715) and swjtu supported project (swjtu12cx008). bibliography [1] păun, g. 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[21] research group on natural computing, university of seville: the mecosim website. http://www.p-lingua.org/mecosim. international journal of computers communications & control issn 1841-9836, 10(1):100-111, february, 2015. routing optimization for delay tolerant networks in rural applications using a distributed algorithm c. velásquez-villada, f. solano, y. donoso carlos velásquez-villada universidad de los andes bogotá d.c., colombia, south america c.velasquez@ieee.org fernando solano warsaw university of technology poland, nowowiejska 15/19, warsaw fs@tele.pw.edu.pl yezid donoso* universidad de los andes bogotá d.c., colombia, south america *corresponding author: ydonoso@uniandes.edu.co abstract: internet access can improve people’s life quality by helping them to reduce and overcome the poverty and educational gaps. however, most rural communities in the world, specially in underdeveloped countries, do not have access to the internet. delay/disruption tolerant networking (dtn) is a recent low-cost technology now being used to provide connectivity to rural towns were some transportation means periodically arrive. dtns can be implemented to connect communities to internet, since this technology takes advantage of the existing people’s transportation infrastructure using it to move packets and messages to and from internet. this paper proposes a dtn mathematical optimization model that maximizes the availability probabilities of the paths from sources to destinations. we also present an opportunistic forwarding algorithm that takes into account the availability probability of a node’s neighbors to decide if a node should forward a message or store the message until a node with a higher availability probability contacts it. this algorithm was tested in five different scenarios and in all of them it found a path to the destination. keywords: disruption-tolerant, delay-tolerant, availability probability, opportunistic forwarding, ict for rural development, rural telecommunications. 1 introduction most rural communities in the world have scarce or non-existent internet availability [1]. according to the itu [1], only 38.8% of the world’s population have access to internet, either fixed or mobile. most of these connections are concentrated in the main cities, leaving most of the world’s surface with almost no internet availability. several governments are increasing the availability of internet in their territories, showing an increase of 145.57% from 2006 to 2013, as can be seen in the internet penetration figures from itu [1]. the initiatives to bring internet to more people include optical fiber deployments, satellite connectivity solutions, electronic devices for people in remote towns and training for them to use these new devices and connections. even with these efforts, many people will still be left behind without constant internet connectivity or without connectivity at all. the delay/disruption tolerant networking (dtn) architecture proposed in [2] is a new technology that can connect users in rural and disconnected places. this dtn technology can give disconnected users access to information that can help them overcome the digital divide and become net citizens taking full advantage of the information, copyright © 2006-2015 by ccc publications routing optimization for delay tolerant networks in rural applications using a distributed algorithm 101 education and opportunities that internet can provide. benefits of this connectivity include access to learning materials like wikipedia, distance learning courses or even offline versions of massive online open courses (moocs) that gives them the same education contents that their urban counterparts receive; thus, helping them to reduce the poverty gap [1, 3, 4]. there are also benefits when the local infrastructure is created in the communities where people can create contents and share them with others. local news can be reported on a local website, people can share their music, documents, videos or libraries. in some communities, people have created local websites for their businesses and even local radio stations over the net. these applications depend on a strong deployment of a local infrastructure and the presence of people willing to train and educate locals. even without the community network, the opportunities that can arise from a connection point and information distribution in an asynchronous way can bring many benefits for the inhabitants around these deployments. applications that show the benefits of giving ict to people in remote communities and accompanying these technologies with the respective knowledge, training and following can be found in many fields: healthcare [5–7], banking opportunities [8, 9], agriculture development [10–12], businesses creation and growing [8, 13], politics accountability and participation [14], and education [15]. although there are many studies about dtn architectures and technologies (as can be seen in section 2), research and developments in application software and specially in routing protocols are still missing. most current applications and protocols fail when implemented in a dtn environment, due to long delays or frequent disruptions. the focus of this work is on developing a routing algorithm that can be used in a real application to bring internet connectivity to rural communities around the world. this paper introduces a mathematical model to represent the best path choices in a delay/disruption tolerant network designed for rural connectivity scenarios. this model is built based on previous models developed by the authors [28–30]. the model uses the availability probabilities of the network’s links and their changes through time to calculate the best paths from the information sources to their destination. the model gives, as a result, the optimal decisions that the nodes can make to send their information to the destination (when should a node send a message to a neighbor and to which neighbor and when should this node store the message in its buffer to send it later instead). this paper also introduces a distributed heuristic algorithm to make forwarding decisions. this algorithm is implemented at each node in the simulation, and it decides whether to forward or store the messages based on the availability probability of a node’s neighbors (if this availability probability is good enough will be decided via a variable parameter that will be used and tested in the simulation). this paper extends paper [31]. the key additions of this journal version are as follows. first, section 2 includes and describes more related works, the dtn architecture, and dtn implementations for rural solutions. secondly, this paper contains an extended explanation of the mathematical model presented in section 3 and of the heuristic algorithm from section 4. finally, this paper contains additional results for the simulated scenarios. this paper is organized as follows: previous and related works are summarized in section 2; section 3 introduces the mathematical model used to calculate the optimal forwarding decisions; section 4 describes the distributed heuristic algorithm implemented for the simulation; section 5 shows and discusses optimization and simulation results; finally, conclusions and possible directions for future research are summarized at the end of the document. 2 related work and dtn architecture delay/disruption tolerant networks (dtn) are a relatively new kind of network that work in challenging environments [2, 16–23]. these networks have been developed with new protocols 102 c. velásquez-villada, f. solano, y. donoso tailored to their needs, which can make them incompatible between different applications and even with the internet. each one of these networks works within its own boundaries with known and relatively homogeneous delays and error rates, and some have trusted boundary devices to translate inner messages to different protocols for external communications. the initial application for the dtn, where the name appeared, was the interplanetary internet project [17], an idea from nasa to interconnect devices in space. it was motivated from the pathfinder mission to mars (1997), but it was tested in the spirit and opportunity missions in mars (2004) due to a failure in the communication solution implemented in one of these rovers. after this experience, nasa started working and researching for an interplanetary internet with the idea of connecting most of the devices in the space to provide a reliable store and forward network for all current and future missions. they also imagined a future where different spatial agencies could use these protocols and extend the reach of this interplanetary network (ipn) for further spatial exploration. earthly dtn protocols face other challenges, mainly high rates of disruptions or failures. they must enable interconnectivity between different networking technologies in order to allow these technologies–used for wireless sensor networks, unmanned vehicular networks, battlefield monitoring and smart infrastructure–to communicate through the internet and with each other. protocols must account for variable delays, variable error rates, network disruptions and attacks. the architecture for dtn proposed in [2] tries to change or at least relax some assumptions built into the internet architecture. these assumptions are that there are end-to-end paths between source and destination; that retransmissions based on feedback from data receivers are effective for errors correction; that end-to-end loss is relatively small; that all network nodes support the tcp/ip protocols; that applications does not need to be aware of the communications performance; that security can be achieved through mechanisms on end nodes; that packet switching is the most appropriate abstraction for interoperability and performance; and, that a single route is sufficient for acceptable performance. the dtn architecture relaxes most of these assumptions using variable-length messages; a naming syntax that supports a wide range of naming and addressing; store-and-forward over multiple paths; security mechanisms against unauthorized use; classes of service; delivery options; and a way to express the useful lifetime of data. the dtn architecture [2] uses a bundle layer [21] that serves as middleware between the application layer and lower layers. this bundle layer shows a transparent interface for applications that can go through the internet or dtn. however, applications for dtn must follow some design principles: they should minimize the number of round-trip exchanges, cope with restarts after failure while network transactions remain pending, and inform the network of the useful life and relative importance of data to be delivered. the bundle layer provides unacknowledged, prioritized (but not guaranteed) unicast message delivery. it also provides two options for enhancing delivery reliability: end-to-end acknowledgments and custody transfer. applications can use these end-to-end acknowledgments for reliability. the custody transfer option allows a node to give the responsibility for reliable transfer of messages to other node, a “custodian”. nodes in the dtn can be custodians or can choose not to be based on available resources and network congestion. a custodian-to-custodian acknowledgement mechanism is implemented in the bundle layer. the bundle protocol defines a convergence layer where underlying protocols will function to provide successful end-to-end communications. these convergence layers accomplish communications between nodes and can comprise a whole network stack with transport and network protocols that can run on top of existing networking technologies. many protocols can exist in these convergence layers (the protocols themselves are called convergence layers), but they must provide at least two services to the bundle protocol agent: “sending a bundle to all bundle routing optimization for delay tolerant networks in rural applications using a distributed algorithm 103 nodes in the minimum reception group of the endpoint identified by a specified endpoint id that are reachable via the convergence layer protocol; and delivering to the bundle protocol agent a bundle that was sent by a remote bundle node via the convergence layer protocol.” [21]. demmer [24] presents a full implementation of the previous architecture with storage and routing developments and an available simulation environment. daknet [25] is a project developed by mit researchers that tackles the problem of internet connectivity in rural or remote communities and is the closest one to the idea presented here. daknet aims to provide connectivity to remote villages were some wireless networking devices have been installed (kiosks) in strategic places and in buses or public service vehicles (mobile access points, maps) that eventually will go to this and other villages and bigger towns, delivering messages between these disconnected networks and also accessing internet (for non-real time access) at some moment and delivering and retrieving requested information. authors in [20], [26] and [27] present developments on dtns for underdeveloped regions concluding that providing full reliability and good strategies for data forwarding are difficult objectives in these scenarios. 3 optimization problem topology of dtns is usually unreliable and constantly changes due to their nodes movement. however, we can use these same node movements to transmit messages in the network and to internet. in this section, we present the problem of finding the best path in the network from one or more sources to a destination (it can be a node connected to internet) based on the availability probabilities of the links between the nodes. the mathematical model takes into account that the availability probability of a link can change from one instant of time to another. the model has complete information over the network and can decide whether to send a message over a link or wait (storing the message in an internal buffer) until there is a link with a better availability probability. thus, the network model presented in this paper maximizes the availability probability of the paths from sources to destinations, see fig. 1. the model assumes that every node has storage capabilities, that it can serve as a relay for messages from other nodes and that it can send a message at any moment of time. the links between the nodes are given as are given their availability probabilities that serve to model the movement of the nodes, these probabilities are assumed to be independent and following an uniform distribution for testing purposes. table 1: mathematical model parameters parameter definition p set of candidate paths t set of discrete time intervals cij capacity of edge (i,j) ∈ e. cii storage capacity of node i aij(t) availability probability of edge (i,j) ∈ e at time t it is assumed that the availability probability of the edge (i,j) is the same as the one for the edge (j,i) t discrete time interval, where t ∈ t δt time interval duration the model starts with a network represented by a graph g = (n,e), where n is the nodes set and e is the edges set. the edges set has the connections between the nodes. the graph g is extended in a new graph g′ = (n′,e′) that contains a copy of the original graph for every change 104 c. velásquez-villada, f. solano, y. donoso figure 1: network representation (a) two-node graph; (b) six-node graph in a link’s availability probability. the extended graph g′ is used by the optimization algorithm to make decisions on forwarding or storing the messages, since it has all the information about the network changes, see fig. 2. table 1 shows the main parameters used in the mathematical model. set t contains the time intervals t that define the validity of the links’ availability probabilities. these time intervals have a duration equal to δt. we are assuming that all time intervals have the same extent and that this time period lasts long enough to send a message between two neighboring nodes. capacities are assumed to be constant, since they depend on the hardware of the nodes and because of this are not so easily changed. figure 2: network graph (a) original graph changes through time; (b) extended graph with the availability probabilities through time represented at once max ∏ (i,j)∈p aij(t)xij(t) (1) max ∑ (i,j)∈e log (aij(t))xij(t) (2) routing optimization for delay tolerant networks in rural applications using a distributed algorithm 105 constraints ∑ j∈n xij(t) − ∑ j∈n xji(t) + xii(t) − xii(t − 1) = bi(t) ∀(i,j) ∈ e,i ̸= j (3) xij(t) ≤ cijδt ∀(i,j) ∈ e (4) xij(t) ∈ z≥0 ∀(i,j) ∈ e,p ∈ p (5) equation (1) shows the objective function that maximizes the network availability probability of the paths from sources to destination. this objective function multiplies the availability probabilities of the links in a path, since we are dealing with probabilities and we want to obtain the path that maximizes these availability probabilities. we are assuming that the internal buffer is always available, so this availability probability (to store a message in its buffer) is always 1. a linear approximation of the objective function is shown in equation (2). decision variable xij(t) determines the amount of information that flows through the link (i,j) using positive integers (5). xij(t) is greater or equal than 1 if the link (i,j) is in the path p for a source to a destination, and 0 otherwise. constraints (3) and (4) are data-flow constraints and they keep the information flow below the channels and buffers capacities and guarantee that the messages reach their destinations. figure 3: network routing solution (a) decision taken at time 0; (b) decision taken at time 1; (c) decision taken at time 2; (d) final path through time fig. 3 shows an example of the optimization algorithm at work. at time 0 node 1 creates a message to be sent to node 6 and all availability probabilities have their initial values. at this moment, node 1 chooses the highest availability probability to its neighbors and decides to send the message to node 2. then, at time 1, all availability probabilities can change and node 2 have to decide where it should send the message or if the message should be stored in its internal buffer to be sent later. node 2 decides to send the message to its neighbor with the highest availability probability, node 5. finally, node 5 sends the message towards its destination, node 6, at time 2. 4 proposed forwarding heuristic this section describes the forwarding heuristic implemented for the results section based on the mathematical optimization algorithm. the heuristic internal working is explained in fig. 4 and the pseudocode is shown in algorithm 1. the heuristic works as a distributed optimization algorithm in which each node has to decide by its own and with local information whether it can send a message to a neighbor or it should store the message in its local buffer until a better path becomes available, see fig. 4. for a node running the heuristic to decide if a link has a 106 c. velásquez-villada, f. solano, y. donoso good enough availability probability it has to compare it against a set parameter (alpha). this forwarding heuristic can be used in a rural scenario that has at least one central, fixed wireless distribution point (ap), at least one mobile distribution point (map) that can be in contact with the local aps periodically (once a day or less in many cases, depending on the transportation means to the town). the aps and maps must have reasonable storage capabilities. the scenario has users in each village where at least an ap is deployed and users requests for information are not real-time ones and can wait for one or more map-ap contacts to be satisfied. users must have the necessary technology to access the network and the appropriate training to use that technology and the applications given to them. figure 4: network distributed heuristic (a) first step at time 0; (b) time 1 (c) time 2 (d) time 3 the forwarding algorithm, shown in algorithm 1, is based on the assumption that every node knows the availability probability of its neighbors at any instant of time, and that if that probability is high enough (over a certain alpha) it can communicate with such neighbor. the alpha parameter is the threshold to take a forwarding action. if an availability probability is below this threshold (alpha) the node can assume that its neighbor is unreachable. nodes do not have access to the whole network graph but they can discover which one is the destination or gateway in the network in order to send their requests to internet. nodes can create a request at any instant of time. in our forwarding strategy we take into account the information available at the node that is taking the forwarding decision; i.e., the availability probabilities of its neighbors. and we also take into account that the node should not return the packet to the node that sent it to him (but it can send it to another node, even if the packet visited that other node earlier). 5 results the proposed heuristic described in section 4 was implemented using java in a 64-bit cpu. five different network configurations, from 6 to 10 nodes, as can be seen in fig. 5, were simulated. in each scenario the last node received messages from all the previous nodes. these nodes created a message to be delivered through the path with the best availability probability but using only routing optimization for delay tolerant networks in rural applications using a distributed algorithm 107 algorithm 1: distributed algorithm for every node in the network local information. all nodes served as relays to convey messages to the destination (i.e., to the final node). nodes had access to the availability probabilities of their links to their neighbors and they had to make a decision (send or store a message) based on this information and the parameter alpha (the threshold to decide if an availability probability is good enough). alpha was varied from 0.1 to 0.9 with 0.1 increases between each simulation run. we ran 10,000 simulations for each scenario and for every alpha to increase the confidence in the averaged results. the results, shown in fig. 6, included the averaged values of all the availability probabilities of the paths from sources to destinations; the hop count and the time steps count until the last message was delivered to the final node. all these variables were measured for every alpha and plotted in a single graph for each scenario shown in fig. 5 (see fig. 6). figure 5: scenarios used for the simulations with (a) 6; (b) 7; (c) 8; (d) 9; e) 10 nodes as can be seen in fig. 6, for alpha values between 0.1 and 0.6 there are no significant differences in all variables. the average hop count does not change; the time to deliver the messages presents an increase of around 7 steps (or a 30% increase); and the average availability probability shows an increase of around 0.1 (or a 30% increase). for alpha values from 0.7 to 0.9 the hop count remains steady but the time steps and the availability probability increase exponentially. the change in time steps from 0.6 to 0.9 alpha is around 200%; and the increase in availability probability is around 0.3 (an increase of around 77%). these results show that choosing an adequate alpha value is a critical decision and that choosing an alpha between 0.6 and 0.8 most likely provides the best trade-off between time to deliver the messages and availability probability of the paths. from fig. 6 we can also see that the hop count does not change with alpha variations and that it depends on the network size (number of nodes) and 108 c. velásquez-villada, f. solano, y. donoso network topology. figure 6: results for the scenarios with (a) 6; (b) 7; (c) 8; (d) 9; (e) 10 nodes 6 conclusions and future work we have proposed a mathematical model for a delay/disruption tolerant network in a rural application based on the dynamic availability probabilities of the links between neighboring nodes. these availability probabilities model the movement of nodes and the opportunistic behavior of the solution. we used a linear approximation of the objective function through a logarithmic function that allows a faster implementation and solution. for an application that can be implemented in a real scenario, we presented a distributed heuristic algorithm where nodes only have access to local information to make decisions. this heuristic was simulated for all the scenarios and it was able to deliver all the messages. the performance of the heuristic regarding the value of the alpha parameter was evaluated. from the results it can be seen that an alpha between 0.6 and 0.8 must be chosen in order to achieve a better availability probability of the paths without a notorious increase in the time needed to deliver all the packets. the hop count is independent of the alpha chosen, it is related to the number of nodes in the network but it also depends on the geometry of the graph and each node’s degree. the dtn implementation proposed here can be used in a rural environment to give internet connectivity to the people living there using any transportation mean available to them. we have shown that the distributed algorithm used to decide whether to forward or store a message works as expected and can deliver all messages in the scenarios tested; however, this algorithm can benefit from recording previous contacts between nodes and sharing this information with other nodes to create a historical record of contacts and choose the nodes that are most likely to deliver the message in short time. for future work we are working with the non-lineal model and the subproblem of path generation to evaluate those paths in the master problem (i.e., find the optimal path based on the availability probabilities of the links in it). routing optimization for delay tolerant networks in rural applications using a distributed algorithm 109 acknowledgments the research leading to these results has received funding from the european union seventh framework programme (fp7/2007-2013) under grant agreement no. 269985. the author carlos velásquez-villada received funding by the colombian “departamento administrativo de ciencia, tecnología e innovación colciencias conv. 528/2011”. bibliography [1] (2013), measuring the information society, international telecommunication union. 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(2014), opportunistic forwarding algorithm for delay tolerant networks in rural applications, international conference on computers communications & control, romania, oradea, baile felix, may 6-10, 2014, abstracts of icccc 2014, issn 1844-4334. 4bologa.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):803-811, december, 2015. a fuzzy-based decision support tool for engineering curriculum design o. bologa, r.e. breaz, s.g. racz octavian bologa, radu-eugen breaz*, gabriel sever racz lucian blaga university of sibiu no.10, victoriei, sibiu, 550024, românia, octavian.bologa@ulbsibiu.ro, radu.breaz@ulbsibiu.ro, gabriel.racz@ulbsibiu.ro *corresponding author: radu.breaz@ulbsibiu.ro abstract: this paper describes a decision support tool which can be used for aiding the academic sraff in making the decision of including a specialty subject in an engineering curriculum. the approach is based on building a list of competences that should be acquired through the study of the specialty subjects. an evaluation of the competences is made by means of questionnaires and finally, a fuzzy model will be run. the output of the fuzzy model reflects the need for the evaluated specialty subject to be included in the curriculum. the proposed method takes into consideration the opinions and experience of both the academic staff and the employers. keywords: curriculum design, decision support engineering studies, fuzzy logic. 1 introduction national regulations state that engineering curriculum in the romanian universities is composed mainly by four categories of subjects: fundamental subjects, domain subjects, specialty subjects and complementary subjects. the fundamental subjects include mathematics, physics, chemistry and other fundamental sciences which provide the future engineer the basic knowledge for operating with the laws and principles of engineering. the domain subjects are intended to provide the technical knowledge related to a specific area of engineering, which at national level is called "domain". for example, some engineering domains in romania are mechanical engineering, industrial engineering, chemical engineering, electrical engineering and many other. the specialty subjects are intended to provide the specific knowledge which individualizes a study programme (also called "specialization") within and engineering domain. for example, machine building technology and machine-tools and production systems are two different specializations within the industrial engineering domain. the complementary subjects are intended to provide the graduates the transversal competences and for engineering study programmes are usually selected from a list that include foreign language, history of technics, sports, culture and civilizations, communication techniques and so other. the national authority responsible with quality assurance within higher education system in romania, aracis (romanian agency of quality assurance in higher education) has established several rules regarding the distribution of fundamental, domain, specialty and complementary subjects within the curriculum. there are rules regarding the percentages of every subject category and there are also lists of subjects for every engineering domain from which the universities have to choose the subjects within the curriculum. however, while the lists of fundamental copyright © 2006-2015 by ccc publications 804 o. bologa, r.e. breaz, s.g. racz and domain disciplines are quite restrictive, for the specialty subjects, the constraints are more flexible and the list of possible "subject-candidates"/options is richer. the broader range of options, which in fact may be considered beneficial, creates a set of problems and difficulties regarding the answer to the question: which specialty subjects have/have not to be included in the curriculum? these problems have to be solved by each university, taking into consideration two aspects: the university autonomy, which encourage every school to customize its curriculum and the rapid evolution of the labor market, which have to be addressed by the universities by rapid adaptation of the curriculum. the research presented in this paper was conducted in order to develop a tool for aiding the university staff in the decision process of designing the engineering curriculum with regards of the specialty subjects. the remaining part of this paper is as follows. in second section, a survey of the state of the art regarding various approaches regarding engineering curriculum design is presented. third section is dedicated to building a competence list, considered necessary for an engineering graduate to possess. in the fourth section the flowchart of the proposed method is presented and the fifth section introduces the fuzzy model used for assessing the percent of introducing an evaluated subject in the curriculum. the last section is dedicated to the conclusion. 2 previous work curriculum design for engineering studies should be oriented to final outcome of the higher education process, providing the labor market with a graduate able to fulfill the requirements of the employers [1]. the problem is that there is a certain conflict between education, which is seen as the main objective by the universities and professional training on the other hand, which is seen as mandatory requirement by the employers. a literature survey shows that competence-based learning [3, 4] is one of the concepts considered as base for higher education curriculum design in order to bridge the gap between education and professional training [2]. competences are defined as a combination of skills and knowledge and it is considered that an individual’s performance at work are hardly influenced by them [3]. moreover, it is considered that competences comprises also entrepreneurial elements which are also needed by a higher education graduate [4, 5]. consequently, university staff and other academic organizations involved in the curriculum design process have to define and select the required competences in order to define and structure a successful engineering study programme. in order to customize a specialization among other, within the same domain, it is very important to define the specific competences and to provide them by means of the specialty subjects, while keeping the curriculum as flexible as possible [6]. however, care has to be taken in order to avoid too much specialization or too much generalization [7]. fuzzy logic was used quite often to build decision support tools related to educational process. many works are related to applications of fuzzy logic in assessing the academic performance of the students [8–10]. the results obtained by using the method presented in [8] were compared to the values produced by statistical means. another research, presented in [11], proposed a fuzzy logic approach to the assessment of student centered learning. with the widespread of e-learning and web-based learning, special software products called learning management systems (lms) are used in order to provide the platform for learning environment. fuzzy logic algorithms are reported to be used for the evaluation of lms systems [12]. competence-based learning had risen the problem of selection, analysis and evaluation the required competences for a higher education study programme. a literature survey revealed a fuzzy-based decision support tool for engineering curriculum design 805 that fuzzy logic was used for this purpose, using a revised dacum method (13). dacum is the abbreviation for developing a curriculuum and was invented by a group of scientists from new start company in canada’s nova scotia. by using fuzzy composite evaluation, the work presented in [13]developed a very effective method of analyzing technical competences for graduates of junior colleges. an approach which uses both fuzzy logic and competence selection and analysis was presented in [14] and [15]. a particular fuzzy model for assisting the decision of including a specialty subject in the curriculum, for a specialization within industrial engineering domain. the approach was developed by a team from "lucian blaga" university of sibiu, which includes the authors of the present paper. 3 the list of competences the approach proposed in this work is intended to have high degree of generality, in order to be applied practically to any engineering study programme which can be related to an industrial process. for example, the method can be applied for machine-building and manufacturing engineers, hydraulic and pneumatic machines engineers, welding engineers, textile and leather engineers, chemical engineers, electrical engineers and many other engineering fields. it is considered that in order to become a specialist in any of the above mentioned branch of engineering, the specialty subjects have to provide the student knowledge in five major components: technology, equipment, automation and control systems, cad/cae/cam (computer aided design/engineering/manufacturing) and research and development, as shown in figure 1. consequently, a generalized list of competences which should be provided by the study of specialty subjects within the curriculum was built and presented below. figure 1: major components of an industrial process competences related to technology are grouped as follows: competence to understand the basic principles of the technology (t1) 806 o. bologa, r.e. breaz, s.g. racz competence to implement the technology and to assembly the logistic chain for its implementation (t2) competence to design the technology (t3) competence to troubleshoot and optimize the technology (t4) competences related to equipment are grouped as follows: competence to understand the structure and operation principles of the equipment (e1) competence to operate and maintain the equipment (e2) competence to design the equipment (e3) competence to troubleshoot and optimize the equipment (e4) competences related to automation and control systems are grouped as follows: competence to understand and apply the basic principles of automation and control systems for the process (a1) competence to identify the components of the process which are suited for automation (a2) competence to design, operate and maintain automation systems for the process (a3) competence to troubleshoot and optimize automation systems for the process (a4) competences related to cad/cae/cam are grouped as follows: competence to operate with cad/cae/cam software developed for the process (c1) competence to build 3d models of single parts and assemblies within the structure of the process components (technology, equipment, automation systems) (c2) competence to realize mathematical models and to simulate the behavior of the process components (c3) competence to manufacture process components by means of computer automated machining (c4) competence to use methods and software for optimizing the process components (c5) competences related to research and development are grouped as follows: competence to investigate and analyze new processes, technologies, equipment and automation and control systems (r1) competence to perform fundamental research for the process (r2) competence to build experimental layouts for applicative researches for the process (r3) competence to produce and test prototype products for the process (r4) competence to write technical reports about the process (r5) in order to exemplify the high degree of generality, for every competences from the list the generalized item was italicized. it can be easily noticed that the italicized words can be replaced with more specific items, related to a specialty subject form the curriculum of an engineering specialization. for example, taking into consideration machine-building engineering as specialization and "plastic deformation technologies and equipment" as specialty subject, the italicized items could be replaced by specific items as follows: technology → plastic deformation technology equipment → plastic deformation equipment process → plastic deformation process. 4 the flowchart of the proposed method the flowchart of the proposed method is presented in figure 2. the preliminary stage consists of two steps: developing a generalized set of competences and building a portfolio of specialty subjects. the final stage consist of four steps. the first one involves the customization of the generalized set of competences for every evaluated subject, according to the specific requirements and characteristics of the study programme. as stated in the previous section, due to the high degree a fuzzy-based decision support tool for engineering curriculum design 807 figure 2: the flowchart of the proposed method of generality of the generalized set of competences, the customization process is quite straightforward. the second step involves the evaluation of the customized set of competences by means of questionnaires. because the method itself, the set of generalized and customized competences and the portfolio of specialty subjects are developed by the academic staff, the questionnaires are distributed to the other main stakeholders of the educational process, the employers. the questionnaire (one for each evaluated subject) includes the set of customized competences, and asks the employers to grade each competence within the list with a grade from 1 to 10. the next step involves the construction of a fuzzy model, also for every evaluated subject, which takes into consideration the results of the questionnaires for fuzzyfing the inputs. finally, after running the fuzzy model, the necessity of including the evaluated subject will be assessed. 5 the fuzzy model the general structure of the fuzzy model is presented in figure 3. the inputs are the competences from the list presented in section 3. for a certain specialty subject, the generalized fuzzy model has to be customized, by customizing the set of competences. moreover, if the evaluated subject does not include one of the five categories of competences (for example a specialty subject with does not include technologyrelated chapters), that category can be removed from the inputs. of course, for an engineering specialty subject it is hard to encounter such a situation, but nevertheless, it could appear. the output variable of the fuzzy model is the "percentage of inclusion of the discipline (pi)" and will fluctuate as a percentage between 0 and 100%. only the disciplines which obtain a percentage of inclusion equal or greater than a threshold (for example 80%) will be considered to be included in the curricula. the linguistic variables used for the fuzzification of the inputs t1, t2, e1, e2, a1, a2, c1, c2, c3 and c4 were chosen as:useless;necessary; mandatory. the linguistic variables used for the fuzzification of the inputs t3, t4, e3, e4, a3, a4, c5, r1, r2, r3, r4 and r5 were chosen as: inefficient; efficient. the linguistic variables for the output were chosen as: low; medium; high. 808 o. bologa, r.e. breaz, s.g. racz the fuzzyfication of the inputs was done by using the membership functions presented in figure 4 ( for t1, t2, e1, e2, a1, a2, c1, c2, c3 and c4) and figure 5 (for t3, t4, e3, e4, a3, a4, c5, r1, r2, r3, r4 and r5). figure 3: the structure for a fuzzy model the membership function used for the fuzzification of the output is shown in figure 6. the membership functions from figures 3-5 were built using only triangular and trapezoidal curves. the triangular curve is a function of a vector x and depends on three scalar parameters a, b and c as given by: f(x, a, b, c) =                0, x ≤ a x − a b − a , a ≤ x ≤ b c − x c − b , b ≤ x ≤ c 0, c ≤ x a fuzzy-based decision support tool for engineering curriculum design 809 figure 4: the membership function for fuzzyfication of the inputs t1, t2, e1, e2, a1, a2, c1, c2, c3 and c4 figure 5: the membership function for fuzzyfication of the inputs t3, t4, e3, e4, a3, a4, c5, r1, r2, r3, r4 and r5 figure 6: the membership function for fuzzyfication of the output 810 o. bologa, r.e. breaz, s.g. racz the trapezoidal curve is a function of a vector x and depends on four scalar parameters a, b, c and d as given by: f(x, a, b, c, d) =                    0, x ≤ 0 x − a b − a , a ≤ x ≤ b 1, b ≤ x ≤ c d − x d − c , c ≤ x ≤ d 0, d ≤ x as development tool, the matlab (with fuzzy logic tcoolbox) was used. the other characteristics of the fuzzy model are: and method minimum; or method maximum; implication method minimum; aggregation method maximum; defuzzification method centroid (center of gravity). 6 conclusion the main goal of this research was to develop a decision support tool, based on fuzzy logic for the decision of including or eliminating a specialty discipline within an engineering curriculum. the proposed tool will help the academic staff to choose from a portfolio of specialty subjects. usually, this portfolio is imposed by the national authorities for higher education in each country. of course, the number of disciplines within the portfolio is higher as the number of disciplines which can be included in the curriculum, so the universities have to choose between them, a process which is often extremely difficult. a list of competences which a future graduate of a four years engineering programme (seen as an industrial processes engineer) should possess after studying the subjects form above-mentioned portfolio was built. the competences within the list were divided into technology related, equipment related, automation and control systems related, cad/cam/cae related and research and development related. the list of competences were distributed (as questionnaires) to industrial companies in order to be assessed. the staff of companies, which are the main employer of the engineering graduates were asked to grade every competence from the list with grades between 1 and 10. using the list of competences and the grades, fuzzy models were built for each subject within the portfolio. the inputs of each fuzzy model were the set of customized competences selected from the list and the output was the percent of inclusion in the curriculum. the fuzzyfication of the inputs was made according to a chosen set of membership functions and to the grades received by each competence. finally, the models were used to assist the decision of including the discipline in the curricula by allowing the user to calculate the percent of inclusion. bibliography [1] national science foundation (1996), shaping the future: new expectations for undergraduate education in science, mathematics, engineering and technology, report nsf 96-139, washington dc. [2] ellstrom p. (1997), the many meaning of occupational competence and qualification, journal of european industrial training, 21(6): 266-273. a fuzzy-based decision support tool for engineering curriculum design 811 [3] sanchez j.(2010), university training for competency of sustainability practitioner. its impact on intention of creation, springer science + bussines media. [4] sanchez j. (2010), university training for entrepreneurial competences. its impact on intention of venture creation, international entrepreneurship and management journal, 7: 239-254. [5] nab j. et al. (2010), authentic competence-based learning in university education in entrepreneurship, international journal of entrepreneurship and small business, 9(11):20-35. [6] pearce h.t. (1997), flexibility in the engineering curriculum: a vital component for the future, proc. of the national seminar on engineering education, south africa, university of cape town, 27-28 september 1997, 198-206. [7] mulder m. et al. (20009), the new competence concept in higher education: error or enrichment, journal of european industrial training, 33(8/9): 755-770. [8] khairul a., qiang s. (2006), data-driven fuzzy rule generation and its application for student academic performance evaluation, journal of applied intelligence, 25: 305-319. [9] bai s.m., chen s.m. (2008); evaluating students’ learning achievement using fuzzy membership functions and fuzzy rules, ieee expert systems with applications, 34(1): 399-410. [10] chen s.m., lee c.h. (1999), new methods for students’ evaluating using fuzzy sets, fuzzy sets and systems, 104(2): 209-218. [11] ma j., zhou d. (2000), fuzzy set approach to the assessment of student-centered learning, ieee transaction on education, 2:237-241. [12] cavus n. (2010), the evaluation of learning management systems using an artificial intelligence fuzzy logic algorithm, advances in engineering software, 41:248-254. [13] chang l.t., hong c.m., shih c.h. (1996); an application of fuzzy theory to technical competency analysis for the entry-level electronic technician, intl. fuzzy systems and intelligent control conference, louisville ky, usa, april 8-10, 1-11. [14] bologa o., beck w., schupp p., breaz r., racz g., ionescu f. (2009); a decision algorithm for optimizing the specialty curricula in machine tools and production systems engineering studies, 5th balkan region conference on engineering and business education & 2nd international conference on engineering and business education, sibiu, romania, 15 17 october, 330-335. [15] beck w., schupp p., bologa o., breaz r., racz g., ionescu f. (2010); mathematical model for aiding the decision of changing the curriculum for higher education in the industrial engineering domain, iceri 2010 intl. conf. of education, research and innovation, 15-17 november, madrid, spain, 6535-6542. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 184-195 maintaining communication links using a team of mobile robots w. zhuang, x. chen, j. tan wei zhuang nanjing university of information science & technology nanjing,210044,p.r.china e-mail: zw@nuist.edu.cn xi chen, jindong tan michigan technological university houghton,mi 49931,usa e-mail: xchen@mtu.edu, jitan@mtu.edu abstract: this paper presents a comprehensive metric to evaluate the link quality and the corresponding control schemes for the distributed control of a team of robots to maintain the communication links. the mobile robots dynamically reconfigure themselves to maintain reliable end-to-end communication links. such applications require online measurements of communication quality in real time and require a mapping between link quality and robot positions. in this paper, we present the empirical results and analysis of a link variability study for an indoor and outdoor environments including received signal strength indicator (rssi), throughput and packet loss rate. the distributed control algorithms consider the environmental constrains and obstacles. moreover, the self-deployment algorithms allow a team of robots to recognize the coverage gap by monitoring link qualities, and deploy the mobile robots for a variety of applications including self-healing, tethering, intelligent relaying. the assessment of link quality acts as the feedback for cooperative control of mobile robots. the experimental results have shown the effectiveness of evaluation for communication links and the related control schemes. keywords: communication link, networked robots, decentralized control, rssi. 1 introduction with the advancement in wireless sensor technologies, a communication network can be rapidly setup by densely deploying sensor nodes in an area of interests. these intelligent wireless sensors will self-organize themselves to form a communication and sensor grid. the quality of communication links are subject to many factors including the distance between neighboring nodes, the topology of the network, the environment attenuation, and the use of battery. the wireless sensors, once deployed, can not be reconfigured to maximize the network performance nor to guarantee end-to-end data transmission. the failure of a few wireless nodes on a critical path may lead to the failure of the entire network. there are many advantages to use mobile robots to setup a temporary communication network whose link qualities can be dynamically maintained by the motion control of mobile robots. fig. 1 shows a scenario where a team of mobile robots can be used to setup a self-organizable communication network in battlefield. these robots can be equipped many sensors for monitoring and reconnaissance of battlefields. military personnel relies on these sensors for situation copyright c⃝ 2006-2012 by ccc publications maintaining communication links using a team of mobile robots 185 figure 1: a large scale battlefield cooperation using networked robotic team awareness. in many circumstances, communication connections could be lost due to many reasons, such as ubiquitous wireless signal noise, angle of signal arrival, natural obstacles, and even a slight movement of pedestrian. the natural settings such as mountains and lakes shown in fig. 1 represents some of the barriers for smooth communication traffic within the network. in these situations, neighboring mobile robots can monitor their link quality and re-position themselves accordingly to the measurement of link quality. a rapidly deployable mobile network with a team of mobile robots has many potential applications in military and disaster relief. the self-deployment and maintaining a team of mobile robots face challenges in both link quality measurement and distributed control a multi-robot system. the quality of an end-end communication link are affected by both many factors including the ambient noise, environment attenuation, battery level, motion of the robots, direction of the antenna, etc. many evaluation metrics such as signal to noise ratio, received signal strength, packet loss, etc. none of these evaluation metrics are easily quantified for control purposes. the second challenge is the distributed control of mobile robots for dynamic link quality improvements. though distributed control of multiple mobile robots have been intensively discussed, the cooperative control of mobile robots for communication links faces new challenges in the coupling of environment and communication factors. the monitoring of communication links has long been a research topic in the communication society, and many approaches for evaluating link quality have been proposed. it is shown that that distance cannot be directly adopted as an assessment for link quality [1]. base on the celebrated shannon-hartley theory, the capacity of communication link is proportional to snr (signal-to-noise ratio). souryal et al. validated that snr can reliably predict the link quality. however, it is difficult to improve the snr measurements with this model due to consideration of external interference in the model [2]. dixon and frew employed snr of the communication links to define an optimal communication chain [3]. apart from using snr to evaluate link quality, researchers adopted wireless signal strength to assess the quality of connectivity in multi-robot system. luthy and grant et al. used rssi measurements to repair disconnected wireless sensor networks [4]. stump et al adopted fielder value (the second-smallest eigenvalue) to describe the degree of connectivity in a communication grid [1]. packet loss rate can also serve as a metric to reflect the performance of communication links. in this work, we analyze a set of comprehensive metrics ,rssi, throughput, packet loss rate and velocity,to explore the possibility of controlling mobile robots for reliable end-to-end communication links. rssi measurement may serve as an main metric to assess the link quality. however, from the results of our experiments, some other metrics such as speed, angle of arrival, can also reflect the link quality. many control algorithms for a large distributed robotic cooperation are presented before. these approaches could be divided into two categories, the centralized and decentralized algo186 w. zhuang, x. chen, j. tan rithms. generally, the question of whether to have centralized or decentralized control comes down to resources [5]. if one of the robots is defined as a central unit which is in charge of the data fusion and decision, the entire system could be brittle when such the manager did not work [6]. additionally, communication overhead and response time are limiting factors for centralized control. in our previous work, a scalable graph model for decentralized control of mobile robots was discussed. the combination of delaunay triangulation and potential field make it possible to adjust the robot positions in a distributed way while a global objective is achieved [7]. similarly, a decentralized mobility control scheme using extremum seeking method was presented to form a linked chain of mobile relays [8]. they focused on maximizing the total link bandwidth. simulation results and movement data from different platforms performing different applications verified the above approaches [9]. in this work, we discuss the control of multiple mobile robots to guarantee reliable end-to-end communication links using a distributed control method. 2 impact of comprehensive metrics in this section, we introduce the analysis of three metrics and related effects on communication links, rssi (received signal strength indicator), throughput and packet loss rate respectively. the shannon-hartley theory stated that the channel capacity c = b log2 (1 + s/n). where, c is the channel capacity, and b is the bandwidth of the channel. s is the total signal power over the bandwidth, and n represents the total noise power over the bandwidth. with a fixed noise distribution and bandwidth, the metric s is proportional to channel capacity which denoted rssi should be adopted as a metric to evaluate the links. throughput as a conventional metric to define quality of end-end communication link is also taken into account. moreover, packet loss rate, a real-time traffic steam detector, is accepted as a threshold to estimate network health status. we take these metrics into account so that the quality of link could be evaluated qualitatively and quantitatively. 2.1 received signal strength indicator (rssi) rssi vs. distance in wireless communication area, received signal strength indicator (rssi) is a measurement of the power present in a received radio signal. rssi measurements from mobile sensor networks could be directly acquired using simple light-weight processing, especially in ieee 802.11 protocol family. generally, rssi is not a stable measurement owning to the multi-path fading and shadowing [10]. it is not linearly proportional to corresponding physical distance. however, it could be considered as a threshold to evaluate the quality of link. fig. 2(a) shows the ubiquitous fluctuation of rssi measurement when the robot is deployed in static. we set the distance between a sender and a receiver as 2 meters in a noisy corridor. two sets of experiments have been done in a noisy corridor and open space outside respectively. it is obvious that the measurements sampled in outdoor are larger than that in a constrained indoor environment. the figure shows the measurements fluctuating all the time. the largest amplitude of measurements reaches -5dbm. another set of experiments aims to test feasibility of rssi as a metric for evaluating links in the same two scenarios. fig. 2(b) shows rssi value varies with the movement of mobile robots. one is set to move forward along a straight line. the results illustrates rssi values varies synchronously with corresponding distance. in a large scale area, if the noise distribution is fixed, we can adopt rssi value as a metric to reflect the link quality inferred from the shannon-hartley theory. maintaining communication links using a team of mobile robots 187 0 5 10 15 20 25 30 35 40 -70 -60 -50 -40 -30 -20 -10 r s s i( db m ) time(s) indoor outdoor (a) fluctuation of rssi in static(indoor and outdoor) 0 5 10 15 20 25 30 -80 -75 -70 -65 -60 -55 -50 -45 -40 -35 -30 r s s i( db m ) distance(m) indoor outdoor (b) rssi vs. distance during the movement of robot figure 2: rssi performance with and without mobility angle of arrival 0 5 10 15 20 -55 -50 -45 -40 -35 -30 -25 -20 r s s (d b m ) time(s) (a) rssi performance with robots being deployed in a line 0 5 10 15 20 25 -70 -65 -60 -55 -50 -45 r s s (d b m ) time(s) (b) rssi performance with robots being deployed in a diagonal figure 3: impact of angle of arrival it have been testified that the angle of arrival could affect the performance of rssi measurements [11]. in this section, we also illuminate this phenomenon during the movement of robots. theoretically, the antenna of 802.11b/g compatible network device is omni-directional, that is, no matter what direction it receives from the signal, the rssi measurers should be the same. but, these off-the-shelf devices do not show the symmetric feature in different orientation. fig. 3(a) and fig. 3(b) shows the variety in different signal received directions when being deployed in a line and diagonal respectively. two robots are deployed in a long corridor, where one is in static and the other is moving. the robot changes its orientation clockwise in turn at the same spot. both of the results show that rssi value has the minimum when the angle turns to 270 degree. the difference of measurements is up to -10dbm. moreover, the rssi difference between 0 degree and the other degrees is larger than that in a line. the results can be useful for the reliable sampling of rssi since the angle between neighbor robots could varied during the task. speed speed is also concerned both in rssi and throughput performance. note the speed we mentioned here is the velocity of mobile robot rather than signal modulation rate. in order 188 w. zhuang, x. chen, j. tan to obtain the real influence on the performance in mobile sensor network, we set up a series of outdoor experiments to testify it. two mobile robots equipped with 802.11b/g compatible network devices are deployed on a long road. we selected the maximum translate velocity of the robots as 700mm/s and the minimum speed as 100mm/s. to avoid the interference of other metrics like angle of arrival, etc., one of the robots is requested to move straightly, the other was stationary. the final trajectory is a straight line. fig. 4(a) shows the connection between rssi and related distance. the black curve (upper line) and the red curve(lower line) denoted the relationship with the speed of 100mm/s and 700mm/s respectively. it was obvious that the rssi measurements with lower speed is always larger than that with higher speed at the same distance except for the startup period. note that the convex curve of the red line was shown in fig. 4(a). theoretically, the curve is proportional to λ−n where λ is distance, and presents monotonic decreasing. however, with higher speed, the rssi measurements could be affected seriously by multi-path fading and reflection of constructions. here, the convex part locates at a special zone in which rssi could be disturbed acutely. meanwhile, the difference between fixed rssi value with two speed modes fluctuated rather than increasing continuously which is shown in fig. 4(b). these could be also explained by the special zones the robot passed in and out. here, the results illuminate that at a higher speed the robot moved, the much more possibility of interference and instability of rssi occurred. 0 5 10 15 20 25 -80 -75 -70 -65 -60 -55 -50 -45 -40 r ss i( db m ) distance(m) 0.1m/s 0.7m/s (a) impact of translate velocity on rssi 0 5 10 15 20 25 -14 -12 -10 -8 -6 -4 -2 0 2 r ss i( db m ) distance(m) rssi difference (b) rssi difference of two speed modes figure 4: rssi vs. distance with different speeds 2.2 throughput performance in this section, we introduce the performance of throughput in our proposed environment. mobility, transmission rate and speed of robots were concerned. throughput as a conventional metric in communication network reflects the performance of communication capacity [12]. as we mentioned before, if the robot moved into a "gray zone" [13] or extremely noisy area, the measurements of rssi could decayed by a small value (dbm) but the link is broken possibly. so, throughput and related parameters like response time or latency are prone to be accepted as compliments for the entire evaluation. mobility compared with computer networks, mobility is one of emphasis in mobile sensor network which can have effects on the throughput of neighbor robots. we deployed two mobile robots (pioneer-3at) in a walled indoor environment. one is set in static, the other moves along the corridor. ixchariot, a tcp/udp packets generator and analyzer is used to obtain real time maintaining communication links using a team of mobile robots 189 figure 5: results of active measurements during the movement table 1: ixchariot parameters of mobility console protocol tcp scripts credits number of timing records 50 size of record to send 100 send buffer size default throughput in our experiments. all the network parameters are listed in table 1. fig. 5 shows the fading of the throughput with movement. the obvious decreasing of throughput occurred at 15.5 meters approximately. based on the theory mentioned above, as long as the mobile robot stays in a circle with the radius less than a value the communication link can retain connection status. if the robot moves out of the circle, it still has ability to exchange data with neighbors but the throughput presents sharply fading. note that deploying in different scenarios (indoor and outdoor) or using different sending packets size, the metric could vary due to the multi-path fading and mac overhead. speed vs. throughput figure 6: comparison of throughput performance with different speeds figure 7: packet loss rate vs. received signal strength another relationship should be concerned is the speed influence on throughput. fig.6 shows the impact of speed on throughput. the upper line and the lower line denote transformation of 190 w. zhuang, x. chen, j. tan throughput as time elapsing with the speed of 100mm/s and 700mm/s respectively. ixchariot is adopted to sample the throughput using the parameters in table 1. there is no obvious difference between the two lines. since the mobile robot always keep connecting with neighbors, as long as the transmission rate stays lower than bandwidth, the traffic could be smooth and stable. the average throughput 546kbps at speed of 700mm/s is adequate for message and low-resolution low fps video transmission. 2.3 packet loss rate packet loss rate can also reflect quality of communication links in wireless network. however, [2] confirm that the low correlation between packet loss rate and distance and show the extent of variation in an indoor office environment. so, we select rssi instead of distance as x domain and corresponding packet loss rate as y domain shown in fig.7. one robot is set to move along the corridor while the other is stationary. 100 packets (522 byte per packet) were transmitted from the base to mobile robot every second continuously. the rssi interval is set as -5dbm, and the range is set from -35dbm to -95dbm. the result indicated the critical area of related rssi lay in −85dbm ± −5dbm. we assumed the point a where the observed rssi value is -80dbm and point b where the sampled rssi value is -90dbm. we define d as the distance between a and b. the mobile robot is arranged to move from a to b repeatedly. the average distance d̃ is less than 1 meter which denoted that it took less than 3.3 seconds at the speed of 300mm/s. generally, the interval is so short that fast responding action such as stop, turn, reverse, etc. could not be achieved in our assumed scenario. so, the packet loss rate can be fit for evaluation of data traffic integrality but not suitable for a feedback for controlling the robotic team. 3 decentralized control algorithms in this section, we introduce a decentralized control scheme adaptive to unconstrained and constrained scenarios. the improved rssi measurement is adopted to be feedback input into control system. base on the delaunay graphic model, we define two different chained form networks describing self-healing and tethering respectively. virtual forces composed of abstractive forces and repulsive forces are exploited to drive the robot to a stable status. 3.1 delaunay triangulation and network model first, we define the graph model for the multi-robot system. the configuration and control input of the whole system could be defined as: q = {q1,q2, ...,qn}t (1) the overall system can be denoted by q̇ = f(q,u), where f is the vector of system dynamics. in a unified inertial coordinate system, p̃i = { ˜xi(t), ˜yi(t)}t is defined as the position of the robot ri. we define pi = {xi(t),yi(t)}t as the position of the robot ri in its local coordinate frame. the delaunay triangulation with a set of nodes is defined such that any additional edge between any two nodes intersected one of the existing edges. the delaunay triangulation define the link properties between one-hop neighbors, which are described by a set of edges. here, we can adopt adjacency matrix to specify the connectivity of the delaunay tessellation using these edges. motivated by the connectivity in multi-robot team [3], we define the quality of communication link between ri and rj which connected with each other through one-hop link as a continuous value. this value is denoted by improved rssi measurement. the quality is set to be: maintaining communication links using a team of mobile robots 191 ψij ,   ∆ij, ∥pij∥ < ρ 0, ∥pij| > r exp[ −5(∥pij∥−ρ) r−ρ ], otherwise (2) where, r means the cut-off rssi measurement which is able to guarantee the link connection and ρ means a saturation rssi measurement where the communication status between robots does not change as they get closer together. the two parameters can be estimated by results of packet loss rate and active throughput respectively. unlike the idea in [1],we define ∆ as a variant which describes the degree of the connectivity of neighbors. all the analysis of the ∆ has been demonstrated in [14],which shows the impact of comprehensive metrics including rssi, active throughput and packet loss rate. we define an adjacency matrix a(t) to specify the connectivity of the delaunay triangulation. here, aij = ψij if ri and rj are one-hop neighbors, otherwise aij = 0. so, the link properties of ri and its one-hop neighbors are denoted by ith column of a(t), that is ai(t) = {ai1,ai2, ...,ain}t . 3.2 graphic model based control algorithm general model for a multi-robot system, the computation of delaunay triangulation is time consuming. a decentralized control method is presented to calculate individually and cooperate to repair the communication link. in this paper, a distributed control method is proposed based on both the potential field method and the delaunay triangulation. for a mobile robot ri, a performance index (candidate lyapunov function) is defined as follows: vi = 1 2 ni∑ j=1 kip(∥pij∥ − cij)2 + 1 2 kiv∥vi∥2 (3) where pij states the improved rssi value between robot ri and robot rj. ∥pij∥ equals to aij which is an entry in adjacency matrix aij, and cij is the actual and desired rssi value between the two neighboring robots. nij is the total number of the one-hop neighbors of rij. kip and kiv are the parameters for the virtual potential energy and kinetic energy of the robot. the control input is derived by: ui = − ∂vi ∂ai − ∂vi ∂vi (4) = − ni∑ j=1 kip(aij − cij) pij aij − kivvi = −fi − kivvi here, we have obtained the necessary conditions for controlling ri to keep the connection. when one node is lost in communication chain, the whole system can repair the network connection using neighboring robots to fill the position automatically. if all the other robots but ri are static, it is easy to prove that the above controller is globally convergent [7]. self-configuration and self-healing self-healing is based on the same theory of self-configuration. in our works before, [7] proposed an control algorithm for self-configuration both in constrained and unconstrained environment. 192 w. zhuang, x. chen, j. tan fig. 8(a) shows the simulation result of self-configuration after 10 random robots implemented the cooperation scheme. fig. 8(b) shows the result of self-healing using the general model when two nodes are disconnected with neighbors. the objective is to fill the coverage gaps the disconnected robots generated before. tethering and intelligent relays tethering is the reverse processing of intelligent relaying which stretches the network to be a chain as long as possible. different with self-healing, the objective is to keep connection with neighbors so as to maximize the end-end distance. theoretically, if the communication range is larger than double of sensing range, the chain should transform into a line in an unconstrained situation while not possible in real environment. an control algorithm named minimum − follow for stretching the network is proposed in this section. the delaunay triangulation could be broken into a chained form network. every robot in this network just have two one-hop links at last. the nearest neighbor with respect to ready-to-move robot could be chosen to take a movement every round in the iterative loop. the offset is determined by the virtual force generated by foregoing robot. each robot is considered to follow its foregoing moving robot. fig. 9 shows the simulation results of tethering algorithm. we set vi as 1m/s and cij as 40. the total processing time was 100 seconds. the start-up scenario was from the stable status after self-healing shown in fig. 8(b). as long as the space is wide enough, finally the chained form network will transform into a line. it is obvious that the cooperation using our algorithm is not optimal for stretching the network using this decentralized algorithm. however, each robot just needs to connect with neighbor robots while a powerful processing unit is able to manage the robots in centralized control. −100 −80 −60 −40 −20 0 20 40 60 80 100 −100 −80 −60 −40 −20 0 20 40 60 80 100 (a) stable status of the delaunay triangulation with 10 robots −100 −80 −60 −40 −20 0 20 40 60 80 100 −100 −80 −60 −40 −20 0 20 40 60 80 100 (b) stable status with 8 robots after selfhealing figure 8: the simulation results of self-healing 4 experiments in this section, we introduce related experiments of indoor and outdoor to test our algorithms. we used pioneer 3-at as our mobile robot which is equipped with one laser detector upper the board and 8 arrays of sonar mounted in front and back flank. a dell-d430 laptop is chosen to be the data processing unit. the real time rssi measurements are collected through wirelessmon. in order to minimize impact of speed, the linear velocity of mobile robot is defined as 110mm/s and the wireless channel is set in 1mbps/s. all the processing and control command codes are implemented in matlab. maintaining communication links using a team of mobile robots 193 −100 −50 0 50 100 −60 −40 −20 0 20 40 60 (a) the snapshot after 10s −100 −50 0 50 100 150 −100 −80 −60 −40 −20 0 20 40 60 80 100 (b) the snapshot after 50s −100 −50 0 50 100 150 200 −100 −80 −60 −40 −20 0 20 40 60 80 100 (c) the snapshot after 150s 0 50 100 150 200 250 300 350 400 −100 −80 −60 −40 −20 0 20 40 60 80 100 (d) the snapshot after 300s figure 9: the snapshots of tethering simulation results. the lead robot is demanded to keep moving at 1m/s, while optional neighbor robots respond. speeds of these ready-to-move robots are determined by virtual forces. finally, every robot only has two one-hop neighbors in the chained form network. table 2: environmental parameters of indoor and outdoor environment ρ r cij γ indoor -65dbm -90dbm -40dbm 20mm outdoor -70dbm -95dbm -50dbm 50mm 4.1 indoor and outdoor the environmental parameters has been listed in table 3. here, ρ means the rssi threshold of smooth traffic without obvious decaying while r means the link cutoff rssi of current environment. cij is the optimal desired rssi value in our system. for obstacles avoidance, γ is designed to denote the safe distance between robots and barriers. when the distance acquired from laser or sonar is larger than γ, the virtual repulsive force would resist the robot as a result of turning. 4.2 link and motion test in order to testify the effectiveness of the proposed metric for our control algorithm, a test of robotic cooperation is implemented using real-time sampled link metric. six robots are deployed in the end of u corridor. first, the team transforms into a chained form network. then, the team leader is demanded to take exploring along the wall. the speed of leader is set at 110mm/s. the desired rssi value cij is set as -40dbm. the parameter kp is set as 25. the objective is to extend the team of robots so as to maintain the end-end communication link during team leader’s 194 w. zhuang, x. chen, j. tan exploration. the sonar sensors are used to detect the range with wall in order to avoid colliding. fig. 10 shows the rssi measurements from every mobile robot. the relationship between rssi and related distance exhibited the rssi variety which could act as a feedback for robotic motion. when the rssi measurement is lager than -40dbm, which means a repulsive force rather than an abstractive force could be loaded, it could result in speed descending sharply or moving back. for example, five sharp spikes in fig. 10(a) reflect the robot 2 keep moving forth and back. note that the speed of each robot varies with respect to the dispersion of desired measurement. this pi control method could guarantee the smooth of robotic motion and cooperative stability. 0 10 20 30 40 50 -52 -50 -48 -46 -44 -42 -40 -38 -36 -34 r s s i( db m ) distance(m) 1-2 (a) robot 1 and 2 0 10 20 30 40 -52 -50 -48 -46 -44 -42 -40 -38 -36 -34 r s s i distance(m) 2-3 (b) robot 2 and 3 0 5 10 15 20 25 30 35 -52 -50 -48 -46 -44 -42 -40 -38 -36 r s s i( db m ) distance(m) 3-4 (c) robot 3 and 4 0 5 10 15 20 25 -50 -48 -46 -44 -42 -40 -38 -36 r s s i( db m ) distance(m) 4-5 (d) robot 4 and 5 0 2 4 6 8 10 12 14 -48 -46 -44 -42 -40 -38 -36 r s s i( db m ) distance(m) 5-6 (e) robot 5 and 6 figure 10: experimental results in an u corridor 5 conclusions a comprehensive metric for evaluating communication link quality and the robotic cooperation algorithms are proposed in this paper. received signal strength indicator has been testified to be an assessment for link quality by a series of empirical experiments. throughput and packet loss rate are the complements for the control system by generating critical point and cutoff point respectively. virtual forces are produced to drive the robot composed of repulsive forces and abstractive forces. two graphic models including self-healing and tethering are proposed to illustrated the kinematic modes of the team of robots. experimental results showed the feasibility of our comprehensive metric for link quality and stability of our control schemes. bibliography [1] e. stump, a. jadbabaie, and v. kumar. connectivity management in mobile robot teams. 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[14] wei zhuang, xi chen, jindong tan, and aiguo song. an empirical analysis for evaluating the link quality of robotic sensor networks. in ieee international conference on wireless communication and signal processing, nanjing, china, november 13-15, 2009., 2009. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 236-245 a network coding based dtn convergence layer reliable transport mechanism over interplanetary networks s. haoliang, l. lixiang, h. xiaohui sun haoliang 1. graduate university of the chinese academy of sciences 19a yuquanlu,beijing, 100049, p. r. china 2. national key laboratory of integrated information system technology, institute of software, chinese academy of sciences 4# south fourth street, zhong guan cun, beijing 100190 p.r. china e-mail: haoliang08@iscas.ac.cn liu lixiang, hu xiaohui institute of software, chinese academy of sciences 4# south fourth street, zhong guan cun street, beijing, china, 100190 abstract: the realization of deep space scientific missions are enabled by the developments in the space technologies. tcp can not provide effective communication service in deep space links because of the long propagation delay and high bers characteristics.scientific community propose delay tolerant network (dtn) for resolving the communication problem between earth and other planet. dtn introduces a new layer in the protocol stack called bundle layer that is placed between application layer and transport layer.dtn calls the transport protocols that it uses to move data across different networks convergence layers. this study present a novel network coding based convergence layer reliable transport mechanism(nc-rtp) in order to provide effective communication service between dtn peers. this mechanism transmit a coded bundle every m original bundles(m is related to the packet error rate of the communication link). the coded bundle is a random linear combination of previous m original bundles. using coded bundle and (m-1)original bundles, the receiver could decode and generate a single lost bundle of previous m original bundles . in this way, nc-rtp could compensate any single lost bundle in (m+1) transmitted bundles(including m original bundles and 1 coded bundle).our theoretical and simulation performance evaluation results reveal that nc-rtp can enhance transmit reliability and make file transfered faster. keywords: dtn,convergence layer, reliable transport, network coding. 1 introduction the realization of deep space scientific missions are enabled by the developments in the space technologies. the future space exploration includes missions to deep space that require communication among planets, moons, satellites, asteroids, robotic spacecrafts, and crewed vehicles. significant amount of scientific data to be delivered to the earth are produced by these missions. the next step in the design and development of deep space networks is expected to be the internet of the deep space planetary networks ,called interplanetary (ipn) internet [1].ipn is an architecture envisioned for interconnecting earth with other planets. the purpose of ipn is to build a communication infrastructure between planets and satellites . interplanetary (ipn) internet, in contrast to conventional internet links, are characterized by: copyright c⃝ 2006-2011 by ccc publications a network coding based dtn convergence layer reliable transport mechanism over interplanetary networks 237 1. high link bit error rates. deep space links have extremely high bit error rates, which may be up to 10 [2], [3]. 2. very high propagation delays. the propagation delay increases with the distance. the propagation delay between earth and mars varies between 8 and 40 minutes depending on the orbital location of the planets [2] [3].and the propagation delays to outer space planets become even higher. 3. bandwidth asymmetry. asymmetry between the forward and the return path bandwidth may be up to 1000:1 [3] [4]. 4. intermittent connectivity. planetary bodies, asteroids or spacecraft may periodically interrupt the communication link between the path endpoints [3] [5]. in this network,classical transport layer mechanisms are not suitable. scientific community propose delay tolerant network (dtn) [6] for resolving the communication problem between earth and other planet such as mars. dtn is an architecture particularly useful in scenarios with very long transmission delay or intermittent connectivity, like ipn. dtn introduces a new layer in the protocol stack called bundle layer that is placed between application layer and transport layer [7]. the bundle layer resolves the high error rates, long delay, asymmetric data rates and intermittent connectivity by using a store and forward mechanism. it sends a bundle of message fragments to the next-hop node with per-hop error control, which increases the probability of data transmission. dtn calls the transport protocols that it uses to move data across different networks convergence layers. a convergence layer should make best use of the intermittent and temporary links in dtn. it transfers as much data as possible while the link between peering dtn nodes is up and available. it is widely accepted that regular tcp cannot operate efficiently as a convergence layer transport protocol for interplanetary networks based on dtn architecture [8]. tcp needs 240 minutes to reach slow start threshold equal to 20 packets [5] over a 40 minute round trip time (rtt) path,and longer rtt paths degrade tcp’s performance further. since tcp was designed to operate over wired links, where the link error rate is insignificant, the protocol cannot cope with high link bit error rates [9]. finally, tcp’s transmission rate depends largely on the receiver’s feedback.tcp sends one acknowledgment (ack) for each successfully received data packet. on the presence of bandwidth asymmetry, the large number of acks will cause congestion on the reverse path,reducing tcp’s transmission rate. as a result, in interplanetary networks based on dtn architecture, a convergence layer transport protocol other than tcp is needed. the remainder of the paper is organized as follows. section 2 introduces current convergence layer protocols in dtn architecture and discusses the drawbacks of them. section 3 proposes our new network coding based convergence layer reliable transport mechanism(nc-rtp). the theoretical evaluation of nc-rtp is presented in section 4. and the experimental evaluation is in section 5. we conclude the paper in section 6. 2 related works there exist already a number of convergence layer transport protocols for interplanetary networks based on dtn architecture . in this section, we briefly review these proposals. saratoga [10]is a reliable rate-based udp/ip file transfer protocol.it is capable of transferring efficiently both small and very large files. it has been developed by surrey satellite technology ltd (sstl) used for mission imaging data. saratoga was designed for dedicated point-to-point links between dtn peers,which focuses on transferring data efficiently to the next hop when link connectivity is available. saratoga achieves efficient transmission by sending out data packets 238 s. haoliang, l. lixiang, h. xiaohui at the line rate. it also uses a negative acknowledgment strategy in order to deal with channel bandwidth asymmetries. saratoga is used as a convergence layer to exchange delay tolerant networking bundles [6], [7] between peer nodes [10]. similarly to saratoga, the licklider transmission protocol (ltp) [11] is a point-to-point protocol applied as a dtn convergence layer. ltp transfer unnamed blocks of data and introduces the concept of partial reliability by dividing each block of data into two parts: the reliable red part and the unreliable green part. moreover, ltp send laconic acknowledgments only upon encountering explicit solicitations for reception reports (checkpoints) in the sequence of incoming data segments of the red part of the block. when the communication link is not available, deferred transmission is possible as well. deep space transport protocol (ds-tp) [12]also can be adapted to serve as an efficient convergencelayer for dtn, by transferring dtn bundles as well as files. it implements a retransmission technique, called double automatic retransmission (dar), which allows for fast and efficient holefilling at the receiver’s buffer. dar sends each bundle twice,importing some delay between the original transmission and the retransmission. more precisely,one redundant bundle is transmitted every 1/e-1 original bundles(e presents the packet error rate per).therefore, in the presence of link errors, corrupted bundles will eventually be replaced by the same correct bundles that arrive later. saratoga and ltp achieve efficient transmission by sending out data bundles at the line rate. this ensures that as much data as possible is transferred to the peering node. ds-tp implements double automatic retransmission(dar) to fast and efficiently compensate lost bundles. but ,according to the algorithm described in [12],dar only retransmit finite bundles((n/(1/e− 1) bundles ,n presents the number of bundles of the file). dar can not provide effective retransmission for all bundles, a more effective retransmission technique is needed to enhance transmit reliability. 3 network coding based convergence layer reliable transport mechanism network coding is an important approach to enhance transmit reliability in lossy link. the central idea of network coding is to transmit a combination of multi-packet to replace a single packet. the receiver could decode and generate needed packets using received packets. it has a good ability to deal with transmit error [13]. the simplest coding approach is random linear coding. we treat packets as vectors over a finite field. to get a coded packet, random linear coding generates a group of random coefficients vector and perform matrix multiplication with the packets vector. the transmission of coded packets are independent of each other. generally, to simplify computer programming and memory accessing, the size of finite field is set to 8,which is the size of a byte. for example, we have two 16 bits data packets to encode, 3a47h and 5f33h(hex), each packet is considered to be a vector with size 2. choose two decimal coefficients randomly,4, 30(the coefficients range from 0 to 255). the coded packet is 43a47h+305f33h=e91ch+27fah=1116h. using network coding in dtn convergence layer can enhance transmit reliability and transmit as many bundles as possible before get any ack information from the receiver. in our study , we transmit a coded bundle every 1/e−1 original bundles(e presents the packet error rate per). the coded bundle is a random linear combination of previous 1/e−1 original bundles.considering original bundles are liner independent with each other,the coefficients can be set to 1 to all original bundles in the coded bundle. theoretically, every 1/e bundles will lost one bundle under per e. using 1/e − 1 original bundles and the coded bundle of 1/e − 1 original bundles, we can decode a network coding based dtn convergence layer reliable transport mechanism over interplanetary networks 239 and generate the lost original bundle with gaussian elimination. thus we can say ,in this way ,the receiver could get all 1/e−1 original bundles using received 1/e−1 bundles(including original and coded bundles). this approach is called network coding based convergence layer reliable transport mechanism(nc-rtp). this mechanism could compensate lost bundle forwardly. we use an example to illustrate nc-rtp. let e = 0.2, so 1/e − 1 = 4. a coded bundle is generated and send every 4 bundles.let bn presents the nth bundle.the transmission sequence is b1, b2, b3, b4, b1,2,3,4(b1,2,3,4presents the coded bundle of b1, b2, b3, b4 and b1,2,3,4 = b1 +b2 +b3 +b4). theoretically, 1 bundle will be lost during the transmission. suppose b3 is lost, and the reciver get b1, b2, b4, and b1,2,3,4. since b1,2,3,4 = b1 + b2 + b3 + b4 and we have b1, b2, b4, b3 = b1,2,3,4 − b1 − b2 − b4,in this way we can decode and generate b3 . compare to ds-tp, nc-rtp could compensate any single lost bundle in 1/e − 1 original bundles, and transmit coded bundles (n presents the number of bundles of the file ) could make all bundles in the file have the chance to be compensated. in this way , nc-rtp can reduce the number of lost bundles during the transport procedure and enhance the transport reliability. moreover, we generate coded bundle using bundles,the coding and decoding delay is neglectable comparing to the propagation delay of interplanetary links. because of the long propagation delay of interplanetary links, using nc-rtp can enhance the transmit reliability and complete file transfers faster. in the next part of this section, we describe in detail the operation process of network coding based convergence layer reliable transport mechanism (nc-rtp),followed by pseudo-code of the algorithm. the initial value of per e can be set by previous transmit experience. in the transmit process, the sender can compute the value of e depending on the acknowledgement information from the receiver and change it. in this study ,we consider the per is fixed during the transmit process to simplify the analysis. sender procedure:the sender transmit bundles in order and transmit a redundant coded bundle every 1/e − 1 original bundles. the coded bundle is a liner combination of previous 1/e − 1 original bundles ,all coefficients are set to 1.when the sender get snack, retransmit lost bundle according to the acknowledgement information. in order to compensate lost bundle, use nc-rt in the retransmit operation, send a redundant coded bundle every 1/e − 1 original bundles. receiver procedure:when the receiver get bundles , it put original and coded bundles into its receiving queue. when it get accumulative ack_delay (ack_delay is set to the ratio of forward and reverse bind with of the link .when the file to be transmitted has few bundles,we can reduce ack_delay to provoke send snack procedure)bundles ,send snack procedure is provoked. send snack procedure:when send snack procedure is provoked ,the sender check original and coded bundles in its receiving queue, decode and generate lost bundle using original and coded bundles, put generated bundle into receiving queue. check the receiving queue again, if there is any lost bundle,write the lost bundle information into the snack bundle and send the snack bundle. the sender side algorithm has to respond to two types of events:the arrival of a bundle from the upper layer, and get snack bundle from the receiver.the receiver side algorithm has to respond to the arrival of a bundle. source side: 1)set snd_count to 0. 2)wait state:if any following events occurs,respond as follows;else,wait. 3)bundle arrives from upper layer: 240 s. haoliang, l. lixiang, h. xiaohui a)send the bundle to the receiver. snd_count ++. b) if(snd_count ==1/e − 1 ) generate a coded bundle with previous1/e − 1bundles. send the coded bundle. snd_count =0. 4)get a snack from the receiver: a)read the snack information,find out which bundle is lost. b)retransmit the lost bundle,go to 3). receiver side: 1)set rcv _count to 0. 2)set ack_delay properly (according to the ratio of the forward and reverse bandwith and the file size). 3)wait state:if get a bundle from lower layer,respond;else,wait. 4)bundle arrives from lower layer: a) rcv _cout ++; add the bundle into the receiving queue,according to the bundle’s id. b) if(rcv _count ==ack_delay ) using the original bundles and the coded bundle to encode the lost bundles. enqueue the generated bundle in to the receiving queue. after the encode operation,if there is any lost bundle,write the lost bundle information into the snack bundle and send the snack bundle. 4 theoretical evaluation we attempt to evaluate, theoretically, the performance of nc-rtp.in order to derive some initial evaluation results regarding the performance of the nc-rtp, we assume that the link error rate remains constant during the file transfer. in particular, we consider the fixed-rate transport protocol (frtp), whose main functionality is summarized as follows: the frtp sender sends data on a fixed rate according to the pre-scheduled line rate. the frtp receiver,responds with snacks in order to signal for lost bundle in the receiving queue. to simplify the analysis,we consider that snacks are sent back to the sender without lost,all acknowledgement information are sent back to the receiver side successfully. we evaluate the performance of the aforementioned protocols over a simple one-hop topology. such topology represents a point-to-point links between dtn peers in interplanetary networks.obviously, the primary metric of interest is the time required for the whole file to be delivered at the receiver side. we define a round to be the end-to-end transmission of a specific amount of data. a file transfer consists of several rounds, during the first round the original file is transmitted, while during the rest of the rounds, the sender retransmits bundles lost in previous rounds. assume the file consists of n bundles. the link per is e. the frtp sender will begin the transmission of the file at the channel rate. after completion of the first round the sender will have transmitted n bundles. during the first round,n · e bundles are lost and will need to be retransmitted during the second round. similarly,n ·e2 bundles are lost during the second round and need to be retransmitted during the third round. during the nth round, the fr-tp sender will need to retransmit n · en bundles. we assume that when n · en < 1 the file transfer is complete. a network coding based dtn convergence layer reliable transport mechanism over interplanetary networks 241 therefore, frtp needs nfrtp rounds in order to complete the file transfer: nfrtp = log en e = log 1 n e = ln 1 n ln e (1) besides frtp,we also consider dstp to evaluate nc-rtp’s performance. according to its operational properties, dstp will transmit both original and redundant bundles at the line rate. more precisely,one redundant bundle is transmitted every 1/e − 1 original bundles. let r1 be the number of transmitted redundant bundles during first round, r1 = n/(1/e − 1). it is to say, there are r1 bundles that were sent twice, and n − r1 bundles that were send only once. we assume that the number of bundles lost during the first round (and need to be retransmitted during the second round) equals a1 where: a1 = (n − r1) · e + r1 · e2 (2) substituting r1 into equation 2, we get that: a1 = n · e · (1 − e) (3) assume that the number of bundles lost during the second round equals a2 where: a2 = n · e2 · (1 − e)2 (4) generalizing equations 3 and 4, we assume that during the nth round, the ds-tp sender will need to retransmit an bundles, where an = n · en · (1 − e)n (5) the file transfer is complete, once the following equation holds: n · en · (1 − e)n < 1 (6) ndstp = log (e·(1−e))n e·(1−e) = log 1 n e·(1−e) = ln 1 n ln (e · (1 − e)) (7) we add nc-rtp on frtp, called frtp-nc. frtp-nc transmits redundant coded bundles every 1/e − 1 original bundles. the coded bundle is a liner combination of previous 1/e − 1 original bundles.according to its operational properties, frtp-nc will transmit both original and coded bundles at the line rate. in ideal condition, every 1/e− bundles (including 1/e − 1 original bundles and one coded bundles) will lost only one bundle. in this way, the redundant coded bundle will compensate the lost bundle, the file transfer wiil complete in just one round. however, when more than one bundles are lost in every 1/e bundles, the redundant coded bundle can not compensate all the lost bundles, the sender needs to retransmit lost bundles. in this case, the coded bundle can replace one lost bundle, and the sender needs to retransmit less bundles. let a = 1/e ,we divide the whole file into n/(a − 1) parts. all parts are independent of each other. in a single part, if only one bundle is lost(including original and coded bundles), no retransmission is needed. if k(k 6 a) bundles are lost, only k − 1 bundles are needed to retransmit. let pk presents the probability of k bundles retransmission in a single part(lost k+1 bundles). pk = c k+1 a · e k+1 · (1 − e)a−k−1 (8) the expectation of k presents the number of bundles needs to retransmit in a single part. e(k) = a−1∑ k=1 k · pk = a−1∑ k=1 k · ck+1a · e k+1 · (1 − e)a−k−1 (9) 242 s. haoliang, l. lixiang, h. xiaohui let k + 1 = j ,we get e(k) = a∑ j=2 (j − 1) · cja · e j · (1 − e)a−j = a∑ j=2 j · cja · e j · (1 − e)a−j − a∑ j=2 cja · e j · (1 − e)a−j (10) because ∑a j=0 j · c j a · ej · (1 − e)a−j = a · e = 1 , ∑a j=0 c j a · ej · (1 − e)a−j = (e + 1 − e)a = 1, and a = 1/e , e(k) = 1 − (1 − e)1/e−1 − [1 − (1 − e)1/e − (1 − e)1/e−1] = (1 − e)1/e (11) consider the whole file consists of n/(a − 1) parts, in the first transmission,e(k) · n/(a − 1) bundles need to retransmit. let y1 be the number of transmitted redundant bundles during first round. y1 = e(k) · n/(a − 1) = (1 − e)1/e · n/(1/e − 1) = n · e · (1 − e)1/e−1 (12) generalizing equation 12, we assume that during the nth round, the frtp-nc sender will need to retransmit yn bundles, where yn = n · en · (1 − e)n·(1/e−1) (13) the file transfer is complete, once the following equation holds: yn = n · en · (1 − e)n·(1/e−1) < 1 (14) nfrtp−nc = log en·(1−e)n·(1/e−1) e·(1−e)(1/e−1) = ln 1 n ln e · (1 − e)(1/e−1) (15) we use two figures to evaluate the performance of frtp, dstp and frtp-nc. figure 1 presents the numbers of rounds of frtp, dstp and frtp-nc in different pers(n = 100000). figure 2 presents the numbers of rounds of frtp, dstp and frtp-nc in different file sizes(per = 0.25). from figure 1 we observe that , when file size is fixed, for small error rates ,the three protocols perform similarly. as the link error rate increases, the rounds that frtp needs grows sharply, and the rounds that frtp-nc and dstp need grow slowly, and frtp-nc always needs less rounds than dstp. only when per = 0.5 ,dstp and frtp-nc have the same performance. at this time ,dstp and frtp-nc operate in the same way(transmit each bundle twice). from figure 2 we observe that ,when the link error rate is fixed, the growth of the file size results in the increment of the rounds that three protocols need, but frtp-nc is the lowest. our theoretical evaluation revealed that, frtp-nc that using nc-rtp needs less time to complete file transmission and it have a better ability to handle high per and large files in interplanetary networks. nc-rtp can be used in convergence layer to achieve efficient transmission between peer nodes . 5 experimental evaluation in this section, we evaluate the performance of nc-rtp experimentally. we use the network simulator "ns-2" [14] for all experiments and simulate. we use a simple one-hop topology, whose propagation delay is 1200s. such topology represents a point-to-point link between dtn peers in interplanetary networks. since congestion control can not respond to the link status in time because of the long propagation delay of deep-space links, the transport protocols we use in the experiment are without congestion control, using fixed rate data transmission. in our experiment a network coding based dtn convergence layer reliable transport mechanism over interplanetary networks 243 0.0 0.1 0.2 0.3 0.4 0.5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 n um be r of r ou nd s packet error rate frtp dstp frtp-nc figure 1: rtt=2400s,file size=100000 bundles,rounds that three protocols need in different pers 0 10 100 1000 10000 100000 1000000 0 1 2 3 4 5 6 7 8 9 10 11 n um be r of r ou nd s bundle quantity frtp dstp frtp-nc figure 2: rtt=2400s,file size=100000 bundles,rounds that three protocols need in different file size ,we use frtp, dstp in[12],and frtp-nc that using nc-rtp to evaluate the performance of nc-rtp. the transmit rate is 1000 bundles per second. in scenario 1,we evaluate the performance of nc-rtp in different pers. the propagation delay is set to 1200s, file size is 100000 bundles and per changes from 0 to 0.5. in scenario 2 ,we evaluate the performance of nc-rtp in different file sizes. the propagation delay is still 1200s, per is 0.25 and file size changes from 0 to 1000000 bundles. from figure 4 and figure 5 we can get,the experimental result generally tally with the theoretical evaluation. frtp-nc that using nc-rtp needs less time to complete file transmission than frtp and dstp. and with the increment of file size , the file transfer complete time of frtp and dstp grow sharply, frtp-nc’s transfer complete time grows slowly. so we can get a conclusion that, nc-rtp can cope with high per in interplanetary networks and more suitable for large files,using nc-rtp can enhance transmit reliability and complete file transfers faster. 244 s. haoliang, l. lixiang, h. xiaohui figure 3: topology of a point-to-point links between dtn peers 0.0 0.1 0.2 0.3 0.4 0.5 0 10000 20000 30000 40000 50000 60000 70000 80000 t ra ns m it t im e packet error rate frtp dstp frtp-nc figure 4: rtt=2400s,file size=100000 bundles,transmit complete time change with pers 0 10 100 1000 10000 100000 1000000 0 5000 10000 15000 20000 25000 30000 t ra ns m it t im e bundle quantity frtp dstp frtp-nc figure 5: rtt=2400s,per=0.25 transmit complete time change with file size 6 conclusions and future works in this study, we present a network coding based convergence layer reliable transport mechanism (nc-rtp),whose main advantage is to compensate lost bundle forwardly .nc-rtp uses fixed rate transmission and generates a redundant coded bundle periodically. the coded bundle is a random linear combination of previous bundles. using original bundles and the coded bundle, we can decode and generate the lost original bundle with gaussian elimination. because of the long propagation delay of interplanetary links, using nc-rtp can enhance the transmit reliability and complete file transfers faster. our theoretical and simulation performance evaluation results reveal that nc-rtp presents high potential for deployability. protocols that use nc-rtp complete file transfers faster than a network coding based dtn convergence layer reliable transport mechanism over interplanetary networks 245 without nc-rt. we can get a conclusion that, nc-rtp can cope with high per in interplanetary networks and more suitable for large files,using nc-rtp can enhance transmit reliability and complete file transfers faster. future works includes, specifically implementation on how to react to change pers and further investigation and evaluation of the performance of nc-rtp when intermittent connectivity events happen 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[14] "ns-2 network simulator," http://www.isi.edu/nsnam/ns/ 911turs6.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):873-888, december, 2015. a hybrid model based on fuzzy ahp and fuzzy waspas for construction site selection z. turskis, e.k. zavadskas, j. antucheviciene, n. kosareva zenonas turskis, edmundas kazimieras zavadskas*, jurgita antucheviciene, natalja kosareva vilnius gediminas technical university sauletekio al. 11, lt-10223 vilnius, lithuania zenonas.turskis@vgtu.lt, edmundas.zavadskas@vgtu.lt jurgita.antucheviciene@vgtu.lt, natalja.kosareva@vgtu.lt *corresponding author: edmundas.zavadskas@vgtu.lt abstract: the purpose of this article is to propose a fuzzy multi-attribute performance measurement (mapm) framework using the merits of both a novel weighted aggregated sum-product assessment method with fuzzy values (waspas-f) and analytical hierarchy process (ahp). the object of this study is to select the best shopping centre construction site in vilnius. a number of conflicting qualitative and quantitative attributes exist for evaluating alternative construction sites. qualitative attributes are accompanied by ambiguities and vagueness. this makes fuzzy logic a more natural approach to this kind of multi-attribute decision making (madm) problems. fuzzy ahp is applied for assigning weights of the attributes and waspas-f method is used to determine the most suitable alternative. keywords: ahp, waspas-f, fuzzy ahp, multi-attribute decision making (madm), key performance attributes, construction site, shopping centre. 1 introduction the increasing competition cause a lot of construction site selection problems. making decisions is a complex process that involves multiple, usually conflicting, objectives or attributes. they are ill-structured. multi-criteria decision aid (mcda, the european school) or multiattribute decision making (madm, the american school) constitutes an advanced field of operations research which is devoted to the development and implementation of decision support methodologies to confront complex decision problems. the problem of how decisions are or ought to be taken by individuals, organisations and institutions was previously discussed by aristotle [1]. later, in the 18th century bernoulli [2] concentrated research on probability theory, borda [3] on social choice procedures. the foundations of mcda can be traced back in the works of von neumann and morgenstern [4] and fishburn [5] on utility theory. multi-attribute utility theory (maut) is an extension of the classical utility theory. the theory underlying multi-attribute performance measurement models was developed in the 1960s, as summarised in keeney and raiffa [6] and zeleny [7]. most of these methods have been developed based on the concepts of accurate measurements and crisp evaluation. the performance measurement parameters cannot be given precisely. the imprecision comes from different sources: unquantifiable information, incomplete information, no obtainable information, and partial ignorance. the real-world problems in performance measurement involve numerous aspects of uncertainty, contain a mixture of fuzzy and crisp data and may have a large number of alternatives and dozens of attributes. the solution is highly dependent on the preferences of the decision maker. it is common that people may not be 100% sure when making subjective judgments. if all or some of the alternatives are imprecise, then fuzzy madm methods are required. zimmermann’s description of uncertainty is as follows: copyright © 2006-2015 by ccc publications 874 z. turskis, e.k. zavadskas, j. antucheviciene, n. kosareva uncertainty implies that in a certain situation a person does not possess the information which quantitatively and qualitatively is appropriate to describe, prescribe or predict deterministically and numerically a system, its behaviour or other characteristics [8]. fuzzy set theory is developed for solving problems, taking into account uncertainty, imprecision, vagueness. a pioneering and outstanding works on fuzzy sets are done by zadeh [9] [12]. the papers introduced a new perspective on the treatment of uncertainty, ambiguity, linguistic variables, and a fundamental aspect of formal languages: fuzzy set theory. fuzzy measures can be introduced for two different uses: either they can represent a concept imprecisely known (although well defined) or a concept which is vaguely perceived such as in the case of a linguistic variable. the basic concepts and algorithms from classical madm methods have been used in the development of the fuzzy madm methods. fuzzy direct aggregation procedures of madm methods generally consist of two stages: 1. the aggregation of the performance scores with respect to all attributes (goals) and per decision alternative, and 2. the rank ordering of the aggregated judgments of decision alternatives determine the optimal alternative. construction site selection is an important task. proper construction site selection can improve project’s success. construction site selection for shopping centre requires a good visibility and accessibility, proper access to suppliers, customers, suitable transportation network and sufficient customer traffic and socio-economic population characteristics. the evaluation data of location performance of the construction site for various subjective attributes, and the weights of the attributes are usually expressed in linguistic terms. this makes fuzzy logic a more natural approach to this kind of problems. there is a limited number of papers evaluating suitable locations. usually gis in combination with decision making methods is applied for selecting the best site for ecologically and economically important objects such as renewable energy systems, including solar or wind farms [13] [15]. prioritizing the best sites for waste management is suggested by applying fuzzy topsis [16] or vikor [17] methods. ahp is applied for warehouse [18] or solar farm [19] construction site selection. also fuzzy mcdm frameworks for locating plants are suggested [20], [21]. application of interval type-2 fuzzy sets for watershed site selection is presented [22]. hybrid approach of fuzzy analytic network process (anp), fuzzy dematel and fuzzy electre for site selection is suggested [23]. the purpose of this article is to propose a novel fuzzy multi-attribute performance measurement framework using the merits of both a novel weighted aggregated sum-product assessment method with fuzzy values (waspas-f) and fuzzy analytical hierarchy process (ahp). 2 methodology the high value of a problem-solving process is that it helps to align human’s thinking and action around a common approach for winning team problem solving to the following nine steps: 1. selecting the problem; 2. exploring the problem and gathering data; 3. establishing success attributes; 4. developing a clear problem statement; 5. generating alternatives; a hybrid model based on fuzzy ahp and fuzzy waspas for construction site selection 875 6. evaluating alternatives; 7. selecting a preferred solution; 8. developing a plan for action; 9. testing and modifying the solution. there is no evidence in the literature of publications evaluating the shopping centre construction sites were applied in lithuania. this is the most powerful motivation to consider the site selection problem. flowchart of proposed problem solving process is shown in fig. 1. the first part of methodology, i.e. the fuzzy ahp was used to calculate attributes weights. in the second part, the waspas-f method was developed and used to rank and select the alternatives. first figure 1: flowchart of the proposed problem solving process of all expert group was formed. the creditability of a group of experts depends on the expertise of the people who are involved and how they can give full play to their professional expertise and make wise and fair decisions. the group of experts was formed based on three main problem solving skills: 1. communications patience (techniques that help members share information and perspectives by working hard to understand one another and working hard to be understood, members with controversial or divergent views are not ignored or blocked); 2. synergy creation (techniques that equip members to expand their thinking by generating many ideas, building on those ideas, and evaluating ideas to create synergistic solutions); 3. disciplined use of a problem solving process (members become disciplined in using a systematic process for analysing data, creating options, and evaluating and selecting preferred solutions). 876 z. turskis, e.k. zavadskas, j. antucheviciene, n. kosareva in the absence of good data, members waste time in pointless debates over opinions, and the problem with opinions is that we all have different ones. the members were asked to generate as many ideas or options as they can without censorship or judgment. ideas were evaluated considering all factors consequences and sequels of all the probable consequences of a particular scenario in the short term, the medium term, and the longer term. the members were asked to identify important criteria for assessing the feasibility of several ideas. 2.1 preliminairies a fuzzy set is a class of objects with a continuum of membership grades. such set is characterized by a membership function which assigns to each object a grade of membership ranging between zero and one [9]. a fuzzy set a defined in space x is a set of pairs: a = {(x, µa(x)), x ∈ x}, (1) where the fuzzy set a is characterized by its membership function µa : x → [0; 1], which associates with each element x ∈ x, a real number µa(x) ∈ [0; 1]. the value µa(x) at x represents the grade of membership of x in a and is interpreted as the membership degree to which x belongs to a . so the closer the value µa(x) is to 1, the more x belongs to a. a crisp or ordinary subset a of x can also be viewed as a fuzzy set in x with membership function as its characteristic function, i.e. µa(x) = { 1 x ∈ a; 0 x /∈ a. (2) the set x is called a universe of discourse and can be written ⊆ x. sometimes a fuzzy set a in x is denoted by list the ordered pairs (x, µa(x)), where the elements with zero degree are usually not listed. thus a fuzzy set a in x can be represented as a = {(x, µa(x))}, where x ∈ x and µa : x → [0; 1]. when the universe of discourse is discrete and finite with cardinality n, that is x = {x1, x2, . . . , xn}, the fuzzy set a can be represented as a = n ∑ i=1 µa(xi) xi = µa(x1) x1 + µa(x2) x2 + . . . + µa(xn) xn , (3) when the universe of discourse x is an interval of real numbers, the fuzzy set a can be expressed as a = ∫ x µa(x) x . (4) a fuzzy number a is defined to be a fuzzy triangular number, with α lower, β modal, and γ -upper values, if its membership function µa : x → [0; 1] is fully described as follows: µa(x) =          x − α β − α if x ∈ [α, β], x − α β − γ if x ∈ [β, γ], 0 otherwise. α 6 β 6 γ. (5) in order to obtain a crisp output, a defuzzification process is needed to be applied. defuzzification is the process of producing a quantifiable result in fuzzy logic, given fuzzy sets and corresponding membership degrees. the output of the defuzzification process is a single number. various types of membership functions are used. the most commonly used membership a hybrid model based on fuzzy ahp and fuzzy waspas for construction site selection 877 functions are the following [24]: triangular, trapezoid, linear, sigmoidal, π-type, and gaussian. a fuzzy number is generally a subjective data given by one expert or several (after collective agreement). the most typical fuzzy set membership function is triangular membership function (fig. 2). figure 2: triangular membership function van laarhoven and pedrycz [25] introduced the basic operations of fuzzy triangular numbers x̃1 and x̃2 (table 1). in the table 1 α is a lower value of fuzzy number, β modal value of fuzzy number, γ -upper value of fuzzy number. values of weight 0 < w̃j < 1, ∑n j=1 w̃j = 1 are usually determined by experts. there are various approaches for assessing weights. decision makers data which cannot be exactly described by means of numerical values, commonly describe the different ways they measure things numerically in terms of scales of measurement, which come in four flavours: nominal, ordinal, interval, or ratio scales. likert items were first introduced in 1932 [26]. likert scales can indeed be analysed effectively as interval or fuzzy scales [27] in which categories are labelled with numerical values. 2.2 fuzzy ahp the earliest work in fuzzy ahp appeared in van laarhoven and pedrycz [25], which compared fuzzy ratios described by triangular membership functions. chang [28] introduced a new approach for handling fuzzy ahp, with the use of triangular fuzzy numbers for pairwise comparison scale of fuzzy ahp, and the use of the extent analysis method for the synthetic extent values of the pairwise comparisons. this study concentrates on a fuzzy ahp approach introduced by chang [28], in which triangular fuzzy numbers are preferred for pairwise comparison scale. this questionnaire sought the satisfaction level of the experts concerning the candidate alternatives using a likert-type five-point scale. the linguistic variables matching tfns are provided in fig.3. fuzzy group weight is determined as follows: after obtaining the attributes weights from ahp the synthesising of ratio judgements is done. w̃ = [w̃1, w̃n] = [w̃j] fuzzy group weights for n attributes are determined as follows (w̃j is fuzzy 878 z. turskis, e.k. zavadskas, j. antucheviciene, n. kosareva table 1: basic operations on fuzzy triangular numbers equation operation x̃1 = (x1α, x1β, x1γ), x̃2 = (x2α, x2β, x2γ) (6) fuzzy triangular numbers x̃1 ⊕ x̃2 =    x1α + x2α, x1β + x2β, x1γ + x2γ    (7) addition x̃1 ⊖ x̃2 =    x1α − x2γ, x1β − x2β, x1γ − x2α    (8) substraction x̃1 ⊗ x̃2 =    x1αx2α, x1βx2β, x1γx2γ    (9) multiplicaction k ⊗ x̃1 =    kx1α, kx1β, kx1γ    (10) multiplicaction by constant x̃1 ÷ x̃2 =    x1α/x2γ, x1β/x2β, x1γ/x2α    (11) division x̃−1 1 =    1/x1γ, 1/x1β, 1/x1α    (12) reverse number x̃x̃2 1 =    x x2γ 1α , x x2β 1β , xx2α 1γ    (13) raising a fuzzy triangular number of the power of another fuzzy triangular number, if x1α 6 x1β 6 x1γ 6 1 and x2α 6 x2β 6 x2γ 6 1 is special for this case study triangular number): x̃j = (xjα, xjβ, xjγ), (14) where yjk is j attribute weight determined by k expert, p is number of experts, wjα = min k yjk, j = 1, n, k = 1, p is minimum possible value, wjβ = ( p ∏ k=1 yjk ) 1/p , j = 1, n is the most possible value and wjγ = max k yjk, j = 1, n, k = 1, p is maximum possible value of j attribute weight. a hybrid model based on fuzzy ahp and fuzzy waspas for construction site selection 879 figure 3: linguistic variables for the importance weight of each attribute. 2.3 a novel fuzzy multi-attribute performance measurement model: weighted aggregated sum-product assessment method with fuzzy values (waspas-f) this subsection extends waspas to the fuzzy environment. the merit of using a fuzzy approach is to assign the relative importance of attributes using fuzzy numbers instead of precise numbers. madm method, namely waspas, was introduced in 2012 by zavadskas et al. [29]. later, modification of the method waspas-ifiv was introduced [30]. there are a number of applications of waspas method, including site selection for wind turbine [31], deciding shopping mall locating [32] or assessing sites for implementation of solar projects [33]. also the method was successfully applied for evaluating alternative technological or design solutions in construction [34] [37], manufacturing [38], business issues [39] or even for performance analysis and ranking of scholarly journals [40]. the waspas method consists of two aggregated parts: 1. the weighted sum model (wsm); 2. the weighted product model (wpm). the wsm method is simple, easy to use and understood. it determines overall score of an alternative as a weighted sum of the attribute values. it is the best known and most widely used method [41]. the wpm is developed in order to avoid alternatives with poor attribute values. it determines score of each alternative as a product of the scale rating of each attribute to a power equal to the importance weight of the attribute [42]. based on the briefly summarized fuzzy theory above, waspas-f steps can be outlined as follows: step 1. forming of fuzzy decision-making matrix (fdmm). the performance values x̃ij and the attributes weights w̃j are entries of a dmm. choose the linguistic ratings. the system of attributes as well as the values and initial weights of attributes are determined by experts. the discrete optimization problem is represented by the preferences for m reasonable alternatives (rows) rated on n attributes (columns): 880 z. turskis, e.k. zavadskas, j. antucheviciene, n. kosareva x̃ =           x̃11 . . . x̃1j . . . x̃1n ... . . . ... . . . ... x̃i1 . . . x̃ij . . . x̃in ... . . . ... . . . ... x̃m1 . . . x̃mj . . . x̃mn           ; i = 1, m, j = 1, n, (15) where x̃ij – fuzzy value representing the performance value of the i alternative in terms of the j attribute. a tilde ˜ is placed above a symbol if the symbol represents a fuzzy set. then the determination of the priorities of alternatives is carried out in several steps. step 2. the initial values of all the attributes x̃ij are normalised – defining values ˜̄xij of normalised decision-making matrix ˜̄x = [˜̄xij]m×n. ˜̄xij =          x̃ij max i x̃ij if max i x̃ij is preferable, min i x̃ij x̃ij if min i x̃ij is preferable; i = 1, m, j = 1, n. (16) step 3a. calculate the weighted normalised fuzzy decision matrix ˜̂xq for wsm: ˜̂ xq =           ˜̂x11 . . . ˜̂x1j . . . ˜̂x1n ... . . . ... . . . ... ˜̂xi1 . . . ˜̂xij . . . ˜̂xin ... . . . ... . . . ... ˜̂xm1 . . . ˜̂xmj . . . ˜̂xmn           ; ˜̂xij = ˜̄xijw̃j, i = 1, m, j = 1, n. (17) step 3b. calculate the weighted normalised fuzzy decision matrix ˜̂xp for wpm: ˜̂ xp =           ˜̄̄x11 . . . ˜̄̄x1j . . . ˜̄̄x1n ... . . . ... . . . ... ˜̄̄xi1 . . . ˜̄̄xij . . . ˜̄̄xin ... . . . ... . . . ... ˜̄̄xm1 . . . ˜̄̄xmj . . . ˜̄̄xmn           ; ˜̄̄xij = ˜̄x w̃j ij , i = 1, m, j = 1, n. (18) step 4. calculate values of the optimality function: a) according to the wsm for each alternative: q̃i = n ∑ j=1 ˜̂xij, i = 1, m, (19) b) according to the wpm for each alternative: p̃i = n ∏ j=1 ˜̄̄xij, i = 1, m. (20) a hybrid model based on fuzzy ahp and fuzzy waspas for construction site selection 881 the result of fuzzy performance measurement for each alternative are fuzzy numbers q̃i and p̃i. the centre-of-area is the most practical and simple to apply for defuzzification: qi = 1 3 (qiα + qiβ + qiγ). (21) pi = 1 3 (piα + piβ + piγ). (22) step 5. the integrated utility function value of the waspas-f method for an alternative could be determined as follows: ki = λ m ∑ j=1 qi + (1 − λ) m ∑ j=1 pi, λ = 0, ..., 1, 0 6 ki 6 1. (23) λ is determined based on the assumption that total of all alternatives wsm scores must be equal to the total of wpm scores: λ = m ∑ i=1 pi m ∑ i=1 qi + m ∑ i=1 pi . (24) step 6. rank preference order. choose an alternative with maximal ki value. 3 construction site for shopping centre site selection in vilnius in this case study, an investor company conducted a feasibility study to establish strategies to locate an appropriate shopping centre in strategic demand areas of vilnius. the identification of feasible sites and the selection of the one will optimize the company’s performance strategically. the problem was solved according to the presented above methodology. the mapm model has been designed by considering the interests and objectives of all stakeholders, customers, local community, employees and suppliers. first of all, expert’s team determined the potential critical errors of construction site location selection (fig. 4). then, based on expert team questionnaire they determined the main problems of construction site selection (fig. 5). figure 4: critical errors of construction site selection. 882 z. turskis, e.k. zavadskas, j. antucheviciene, n. kosareva figure 5: problems of construction site selection. an interview to evaluate the most suitable location alternatives was conducted with seven experts, including stakeholders, business development managers, real estate consultants and academicians. the next step was collection of the information associated to all of the alternative construction sites identified and a description of the most important attributes. the strengths, weaknesses, opportunities, and threats analysis was performed. finally, four location alternatives, which are denoted as a1, a2, a3 and a4 are selected as feasible alternatives. a1 alternative is at the intersection of important roads between the airport and the railway station and the central bus station. a2 is one of the developing dwelling districts. a3 is the most populated residential district. a4 is one of the nearby the centre point of the vilnius. later, the attributes were compared each other using ahp. a likert-type ten-point scale (fig. 3) was used. in the next step the questionnaire was about experts’ satisfaction level toward construction site selection. this questionnaire adopts a likert-type ten-point scale (fig. 6). it has ten different levels from "very bad" (p̃1) to "excellent" (p̃10) on a fuzzy ten-level scale. for example, expert might think the satisfaction the linguistic score of "fair" (p̃5), that score would or correspond to a tfn of (0.4, 0.5, 0.6) respectively. the pairwise comparison matrix set by tfns that matches linguistic statements of data is shown in table 2. each of experts determined attributes weights. integrated results of established weights are shown in table 3. the priority weight vector describes the importance degree of the attributes in decision matrix. after getting the importance degree of attributes, waspas-f method was employed to evaluate alternative locations. in this phase of the study, waspas-f starts establishing fuzzy evaluations of the alternative locations (a1, a2, a3 and a4) with respect to the attributes by using tfns. this is an initial decision making matrix for ranking alternatives and indicates the performance ratings of the alternatives according to the attributes. the alternatives according to linguistic scales and their corresponding fuzzy numbers {(1, 1, 1) – very poor, (2, 3, 4) – poor, (4, 5, 6) – fair, (6, 7, 8) – good, (8, 9, 10) – very good} are compared by experts. table 4 shows comparison of alternatives according to attributes. the normalised decision matrix is obtained by using eq. (16) (table 5). the weighted normalised fuzzy decision matrix for wsm is obtained by using eq. (17) (table 6) and one for wpm is obtained by using eq. (18) (table 7). values of the optimality function values for wsm and wpm are calculated by using eq. (19) and eq. (20) respectively. the integrated utility function value of the waspas-f method for an alternative was determined by using eq. (24), as presented in table 8. as can be seen from table 8, a3 is the best alternative in the wsm, wpm and waspas method. a4 alternative is the worst among considered alternatives. decision maker should choose and implement the a3 alternative. a hybrid model based on fuzzy ahp and fuzzy waspas for construction site selection 883 table 2: pairwise comparisons of site selection attributes for shopping centre via tfn (the first expert). x1 x2 x3 x4 x5 x6 x7 x8 w construction costs x1 1, 1, 1 0.20, 0.33, 1 1, 1, 3 1, 3, 5 5, 7, 9 3, 5, 7 3, 5, 7 3, 5, 7 0.21 economical x2 1, 3, 5 1, 1, 1 1, 1, 3 1, 3, 5 3, 5, 7 1, 3, 5 3, 5, 7 3, 5, 7 0.21 road accesses x3 0.33, 1, 1 0.33, 1, 1 1, 1, 1 0.20, 0.33, 1 1, 3, 5 3, 5, 7 1, 3, 5 1, 3, 5 0.17 competition x4 0.20, 0.33, 1 0.20, 0.33, 1 1, 3, 5 1, 1, 1 1, 3, 5 1, 3, 5 1, 3, 5 1, 3, 5 0.17 population characteristics x5 0.11, 0.14, 0.20 0.14, 0.20, 0.33 0.20, 0.33, 1 0.20, 0.33, 1 1, 1, 1 3, 5, 7 1, 3, 5 1, 1, 3 0.12 environmental impacts x6 0.14, 0.20, 0.33 0.20, 0.33, 1 0.14, 0.20, 0.33 0.20, 0.33, 1 0.14, 0.20, 0.33 1, 1, 1 1, 3, 5 1, 1, 3 0.07 risks x7 0.14, 0.20, 0.33 0.14, 0.20, 0.33 0.20, 0.33, 1 0.20, 0.33, 1 0.20, 0.33, 1 0.20, 0.33, 1 1, 1, 1 1, 1, 3 0.04 attractiveness x8 0.14, 0.20, 0.33 0.14, 0.20, 0.33 0.20, 0.33, 1 0.20, 0.33, 1 0.33, 1, 1 0.33, 1, 1 0.33, 1, 1 1, 1, 1 0.01 table 3: fuzzy weights of attributes experts w̃ e1 e2 e3 e4 e5 e6 e7 wjα wjβ wjγ x1 0.21 0.33 0.29 0.31 0.23 0.35 0.27 0.21 0.28 0.35 x2 0.21 0.23 0.18 0.21 0.23 0.17 0.16 0.16 0.20 0.23 x3 0.17 0.16 0.14 0.17 0.14 0.17 0.16 0.14 0.16 0.17 x4 0.17 0.11 0.09 0.10 0.14 0.12 0.12 0.09 0.12 0.17 x5 0.12 0.07 0.09 0.07 0.08 0.08 0.09 0.07 0.08 0.12 x6 0.07 0.05 0.07 0.05 0.08 0.05 0.09 0.05 0.06 0.09 x7 0.04 0.03 0.07 0.05 0.05 0.04 0.05 0.03 0.05 0.07 x8 0.01 0.02 0.06 0.02 0.05 0.02 0.05 0.01 0.03 0.06 table 4: the initial fuzzy decision making matrix for construction site selection w a1 a2 a3 a4 max α β γ α β γ α β γ α β γ α β γ x̃1 0.21 0.28 0.35 0.5 0.6 0.7 0.6 0.7 0.8 0.6 0.7 0.8 0.6 0.7 0.8 0.8 x̃2 0.16 0.20 0.23 0.6 0.7 0.8 0.6 0.7 0.8 0.8 0.9 1.0 0.5 0.6 0.7 1 x̃3 0.14 0.16 0.17 0.8 0.9 1.0 0.5 0.6 0.7 0.6 0.7 0.8 0.6 0.7 0.8 1 x̃4 0.09 0.12 0.17 0.5 0.6 0.7 0.6 0.7 0.8 0.5 0.6 0.7 0.4 0.5 0.6 0.8 x̃5 0.07 0.08 0.12 0.8 0.9 1.0 0.7 0.8 0.9 0.6 0.7 0.8 0.5 0.6 0.7 1 x̃6 0.05 0.06 0.09 0.5 0.6 0.7 0.8 0.9 1.0 0.6 0.7 0.8 0.8 0.9 1.0 1 x̃7 0.03 0.05 0.07 0.4 0.5 0.6 0.5 0.6 0.7 0.8 0.9 1.0 0.7 0.8 0.9 1 x̃8 0.01 0.03 0.06 0.5 0.6 0.7 0.4 0.5 0.6 0.4 0.5 0.6 0.5 0.6 0.7 0.7 table 5: the normalised fuzzy decision making matrix w̃ a1 a2 a3 a4 α β γ α β γ α β γ α β γ α β γ ˜̄x1 0.21 0.28 0.35 0.63 0.75 0.88 0.75 0.88 1.00 0.75 0.88 1.00 0.75 0.88 1.00 ˜̄x2 0.16 0.20 0.23 0.60 0.70 0.80 0.60 0.70 0.80 0.80 0.90 1.00 0.50 0.60 0.70 ˜̄x3 0.14 0.16 0.17 0.80 0.90 1.00 0.50 0.60 0.70 0.60 0.70 0.80 0.60 0.70 0.80 ˜̄x4 0.09 0.12 0.17 0.63 0.75 0.88 0.75 0.88 1.00 0.63 0.75 0.88 0.50 0.63 0.75 ˜̄x5 0.07 0.08 0.12 0.80 0.90 1.00 0.70 0.80 0.90 0.60 0.70 0.80 0.50 0.60 0.70 ˜̄x6 0.05 0.06 0.09 0.50 0.60 0.70 0.80 0.90 1.00 0.60 0.70 0.80 0.80 0.90 1.00 ˜̄x7 0.03 0.05 0.07 0.40 0.50 0.60 0.50 0.60 0.70 0.80 0.90 1.00 0.70 0.80 0.90 ˜̄x8 0.01 0.03 0.06 0.71 0.86 1.00 0.57 0.71 0.86 0.57 0.71 0.86 0.71 0.86 1.00 884 z. turskis, e.k. zavadskas, j. antucheviciene, n. kosareva table 6: the weighted normalised matrix for wsm a1 a2 a3 a4 α β γ α β γ α β γ α β γ ˜̂x1 0.13 0.21 0.31 0.16 0.25 0.35 0.16 0.25 0.35 0.16 0.25 0.35 ˜̂x2 0.10 0.14 0.18 0.10 0.14 0.18 0.13 0.18 0.23 0.08 0.12 0.16 ˜̂x3 0.11 0.14 0.17 0.07 0.10 0.12 0.08 0.11 0.14 0.08 0.11 0.14 ˜̂x4 0.06 0.09 0.15 0.07 0.11 0.17 0.06 0.09 0.15 0.05 0.08 0.13 ˜̂x5 0.06 0.07 0.12 0.05 0.06 0.11 0.04 0.06 0.10 0.04 0.05 0.08 ˜̂x6 0.03 0.04 0.06 0.04 0.05 0.09 0.03 0.04 0.07 0.04 0.05 0.09 ˜̂x7 0.01 0.03 0.04 0.02 0.03 0.05 0.02 0.05 0.07 0.02 0.04 0.06 ˜̂x8 0.01 0.03 0.06 0.01 0.02 0.05 0.01 0.02 0.05 0.01 0.03 0.06 q 0.78 0.79 0.82 0.75 ∑ qi 3.15 table 7: the weighted normalised matrix for wpm a1 a2 a3 a4 α β γ α β γ α β γ α β γ ˜̄̄x1 0.85 0.92 0.97 0.90 0.96 1.00 0.90 0.96 1.00 0.90 0.96 1.00 ˜̄̄x2 0.89 0.93 0.96 0.89 0.93 0.96 0.95 0.98 1.00 0.85 0.90 0.94 ˜̄̄x3 0.96 0.98 1.00 0.89 0.92 0.95 0.92 0.94 0.97 0.92 0.94 0.97 ˜̄̄x4 0.92 0.97 0.99 0.95 0.98 1.00 0.92 0.97 0.99 0.89 0.95 0.97 ˜̄̄x5 0.97 0.99 1.00 0.96 0.98 0.99 0.94 0.97 0.98 0.92 0.96 0.98 ˜̄̄x6 0.94 0.97 0.98 0.98 0.99 1.00 0.96 0.98 0.99 0.98 0.99 1.00 ˜̄̄x7 0.94 0.97 0.98 0.95 0.97 0.99 0.98 0.99 1.00 0.98 0.99 1.00 ˜̄̄x8 0.98 1.00 1.00 0.97 0.99 1.00 0.97 0.99 1.00 0.98 1.00 1.00 p 0.74 0.75 0.79 0.71 ∑ pi 2.99 table 8: integrated utility function values of the waspas-f method a1 a2 a3 a4 q 0.78 0.79 0.82 0.75 p 0.74 0.75 0.79 0.71 λ 0.49 k 0.76 0.77 0.80 0.73 rank 3 2 1 4 a hybrid model based on fuzzy ahp and fuzzy waspas for construction site selection 885 figure 6: membership functions of linguistic values for criteria rating (likert-type ten-point scale). 4 conclusions 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(1973); one of a kind decisions involving weighted multiple objectives and disparate alternatives, in j. l. cochrane and m. zeleny (eds.), multiple criteria decision making, 657-667, university of south carolina press, columbia, south carolina. ijcccv11n1.dvi international journal of computers communications & control issn 1841-9836, 11(1):142-156, february 2016. a context-aware mhealth system for online physiological monitoring in remote healthcare w. zhang, k. thurow, r. stoll weiping zhang*, kerstin thurow university of rostock celisca, center for life science automation, rostock 18119, germany weiping.zhang@uni-rostock.de,kerstn.thurow@uni-rostock.de *corresponding author: weiping.zhang@uni-rostock.de regina stoll university of rostock institute for preventive medicine, university medical center,rostock 18055, germany regina.stoll@uni-rostock.de abstract: physiological or biological stress is an organism’s response to a stressor such as an environmental condition or a stimulus. the identification of physiological stress while performing the activities of daily living is an important field of health research in preventive medicine. activities initiate a dynamic physiological response that can be used as an indicator of the overall health status. this is especially relevant to high risk groups; the assessment of the physical state of patients with cardiovascular diseases in daily activities is still very difficult. this paper presents a context-aware telemonitoring platform, ipm-mhealth, that receives vital parameters from multiple sensors for online, real-time analysis. ipm-mhealth provides the technical basis for effectively evaluating patients’ physiological conditions, whether inpatient or at home, through the relevance between physical function and daily activities. the two core modules in the platform include: 1) online activity recognition algorithms based on 3-axis acceleration sensors and 2) a knowledge-based, conditional-reasoning decision module which uses context information to improve the accuracy of determining the occurrence of a potentially dangerous abnormal heart rate. finally, we present relevant experiments to collect cardiac information and upper-body acceleration data from the human subjects. the test results show that this platform has enormous potential for use in long-term health observation, and can help us define an optimal patient activity profile through the automatic activity analysis. keywords: decision support systems, telemonitoring, context-aware application 1 introduction for patients with cardiovascular disease, physiological stress is an important index to measure their overall health status [1]. however, the daily physiological stress ration of a patient cannot be carried out through simple laboratory simulation due to the various factors that influence daily life; this makes the standardization work for measuring physiological stress difficult. currently a large amount of research work in the field of preventive medicine is developed around physiological monitoring. with the development of communication and sensor technologies, the mode of (a) real-time physiological parameter acquisition and (b) automatic online analysis provides new technical solutions for effectively resolving long-term monitoring of a patient’s daily activity. in previous work [2] [23] we reported about the potential relationship between cardiac information copyright © 2006-2016 by ccc publications agora university a context-aware mhealth system for online physiological monitoring in remote healthcare 143 and physiological stress. a novel fuzzy modeling based hrv analysis method for stress assessment was proposed in [23]. the method of [23] extracts the features of hrv in time-frequency domain and fuzzy techniques are exploited to render robustness in hrv analysis against uncertainties arising from individual variations. this paper presents a wearable, remote-monitoring system based on patient context information. the monitor’s purpose is to evaluate the physiological parameter combined with the human body status, and detect an anomalous event through the context information. patient context-aware information is an important concept in the mobile health environment. according to the descriptions of dey, abowd, et al., in their thesis’s general definition of context [3], patient context can be defined as the information for a patient’s medical situation, which can be roughly divided into the following categories: (a) patient’s vital parameters, (b) medical symptoms (vomiting ...), (c) risk factors (cholesterol level ...), (d) activities (standing, walking ...), and (e) surrounding environment (room temperature ...) context-aware computing is an important research field in ubiquitous computing [4]. "contextaware system" refers to a system which can perceive a change in the user’s environment and make a corresponding adjustment. under a mobile health environment, the context-aware system connects various medical and perception equipment via the network; access to various resources and information is more convenient for medical staff, thus improving their efficiency. the fusion of environment/position information, patient’s health status, and physiological parameters can provide rich context information for doctors’ decisions and provide an enhanced environment for more-informed medical service. 2 related work in related work various types of relevant data have been collected individually or in some combination for a variety of healthcare purposes [5][6]. physiological profile data has been used exclusively [7] or has been used in conjunction with activity profile data [8] and environmental information [9]. among these papers, special note should be taken of the system described in [10]. this work presented an approach where vital parameter changes of patients are detected in the biosignal recording system in real-time, however, the users are prompted to provide additional input about their daily living activities. therefore, user intervention is required in that system to provide additional context information [11]. in [12] systems are presented that analyze physiological function in conjunction with automated observation, wellbeing, and health status. in [13] a system is described that analyzes the relationship between an activity profile and physiological information. automatic recognition and quantification of human activities using wearable sensors during activities of daily living has been increasingly used in many research works [24][27]. 3 system structure this paper proposes a multi-sensor system which uses an inference decision support system with a basis of rules to improve the accuracy of discovering potentially dangerous heart rate variability. as shown in figure 1, two different sensors at local site of patient are used for acquiring patient context information and physiological parameters. the sensors in the first category include environmental sensors used for monitoring interaction between a patient and the surrounding environment; meanwhile, a wearable acceleration sensor acquires the patient’s activity status. the information acquired in this group of sensors can accurately reflect the time, location, and context of the patient’s activity performing in the environment. the sensors in 144 w. zhang, k. thurow, r. stoll figure 1: institute for preventive medicine mobile health (ipm-mhealth) system structure figure 2: hidalgo equivital muti parameter sensor the second category include vital signs sensors, which is a typical wearable sensor for measuring the patient’s physiological parameters. real-time accurate physiological parameter acquisition is the basis for data analysis; then we can combine the context information of physical activity to carry out more effective physiological status analysis. this paper has the focus on the two core elements of the framework: (1) automated activity recognition, and (2) knowledge-based decision module. • activity recognition can mainly be divided in two ways: visual-based and sensor-based [14] [15]. the activity recognition method based on a 3-axis acceleration sensor belongs to the latter; this is the newly emerged branch of human body activity identification research. compared with traditional activity recognition based on visual sense, it provides advantages such as the acquisition of movement data in a simpler and more human manner, etc. the algorithm designed in this paper focuses on the acceleration signal worn on a patient’s body as a recognition basis. • the decision module represents the intelligent core part of the system. for context processing, modeling should be carried out first for a patient’s context information. (for example: the activity and position in an abnormal heartbeat situation can be included into one context). the experiments uses web ontology language (owl) to construct the model and process it using sample semantic web rule language [16], which can also provide the higher level information about the overall health status of patients through its conditions based engine. a context-aware mhealth system for online physiological monitoring in remote healthcare 145 figure 3: equivital sensor chest belt the system is tested by using the hardware devices: equivital wearable-mutiparametersensor solution developed by hidalgo [22]. one equivital device was placed on the upper body of the participant (as shown in figure 2 and 3). the sensor module with a special chest belt is an appropriate solution for recording the required measurands. it offers the possibility to acquire cardiac function (via three integrated fabric-based silver-coated electrodes), pulmonary function (via an integrated resistance strain gauge),activity function activity(via an integrated accelerometer adxl330) and skin temperature (via an integrated thermistor). the equivital sensor solution, fully charged, offers 24 hours of operation. due to the low overall weight of 175g (electronic module and the sensor 75g; belt 100g) and the dust/water resistance protection rating of ip 67 (0.4 m = 30 min.), the ipm-mhealth system allows subjects to move freely in their familiar work surroundings as an examiner continuously monitors their condition from any location. 4 system design 4.1 automatic activity recognition automated activity analysis is the essential component of the ipm-mhealth system which provides new opportunities for quality long-term studies of various vital functions. activity identification can be defined as a classification problem in the machine learning field. the basic flow of activity identification is as follows. first, 3-axisacceleration sensor data should be acquired and sensor features (mean value, peak value, standard deviation, spectrum energy, etc.) should be extracted within the specific time window. then, classification training for sensor data should be carried out under a different activity status. the activity recognition flow is shown in figure 4: 1. first the accelerometer data will be collected and smoothed. 2. specific sensor values will be extracted within a defined time window (average, maximum, standard deviation, etc.). 3. then the weka tool will be used to classify the measured data relevant to the various states. 146 w. zhang, k. thurow, r. stoll figure 4: flowchart for automatic activity recognition figure 5: direction of the equivital 3-axis accelerometer 4. finally, we use the 1-k-nearest neighbour (knn) classifier to classify the activity status. (1) acceleration data acquisition in this paper, we capture data for ten (10) volunteers performing six (6) different movements, including standing, walking, running, jumping, walking upstairs, and walking downstairs; for each action, the sensor data was collected five (5) times. a total of 300 samples of acceleration data are used as the original acceleration signal. the signals are collected by the equivital sensor described above. the sensor module itself has an integrated three-axis accelerometer, adxl330, and allows detection of orientation and body activity. the device’s integrated 3axis accelerometer was used at a sample rate of 100 hz for each axis, providing synchronous acquisition of motion data. the coordinate-system of the 3-axis acceleration sensors is defined relative to the front of the devicein its default orientation. as figure 5 shows, the x-axis is horizontal and points to the right, the y axis is vertical and points up and the z axis points towards the outside of the front face of the device. in this system, coordinates behind the device have negative z values. all of these data (raw data, calculated data, and message data) can be transferred continuously and wireless from the sensor module, via a bluetooth class interface, to the ipm-mhealth a context-aware mhealth system for online physiological monitoring in remote healthcare 147 figure 6: data transmission process client in realtime. by calling the sensor communication module offered in the ipm-mhealth client, the raw data of equivital acceleration sensors can be transferred and stored. as shown in figure 1, the ipm-mhealth client, running on the smart phones, is responsible for the collection of sensor node data and for sending the data to a remote data processing center. the data processing center, based on a hadoop server cluster, is responsible for data collection, analysis, processing, and data mining. the communication between the ipm-mhealth client and server is implemented through http/https, soap, and socket. http is used in synchronous xml workflow description; soap is used to transmit ontology instance information; the socket connection is used in real-time high-demand, for example, transmitting ecg (1024 bytes/sec) and acceleration (300 bytes/sec) data. the acceleration data transmission process is shown in figure 6.the accelerometer eventlistener class is established to acquire the data; this class inherits the class sensor eventlistener of the android api. it provides the following features: registration of sensor services, recall of sensor services, acquisition of the sensor data, and listening for real changes in the data. after the sensor data is detected, the sensor data of the three directions can be obtained by calling the methods: onsensorchanged-datax, onsensorchanged-datay and onsensorchangeddataz. since the direction has little influence to the following motion identification algorithm. here we carried out a handling, i.e.: the vector sum of the acceleration data in each change should be calculated through the following equation: di= √ (acc_x)2+(acc_y)2+(acc_z)2 the data after each change of acceleration sensor status and the currently relative time (unit: millisecond) are respectively saved in two arrays, time-series[] and data-series[]. the original data time sequence after acquisition is shown in the following figure, the horizontal axis is the time axis, i.e., time-series[], and the vertical axis is acceleration sensor data di, i.e., data-series[].the motion order in the below figure is: (1) walk, then (2) a series of three times of jumps, (3) run, 148 w. zhang, k. thurow, r. stoll figure 7: time sequence diagram of acceleration raw data and (4) slow walk. it can be seen from the original data that different motions differ in the data features of mean value, peak value, frequency, etc. (2) acceleration sensor data smoothing the data acquisition interface of the equivital sensor adopts a passive invoking method, where the data will be sent out only when the acceleration status changes. therefore, the data acquired in the previous step are not evenly distributed at each time point. in order to equalize the time interval between each data point we used the following smoothing algorithm to calculate the mean data value at the time point with the an interval of 40 milliseconds based on the acquired original data time-series[] and data-series[]. the latest two time points in the time-series[] which is nearest to the time point t0 to be calculated are t1 and t2. wherein: t1 <t0, t2 >t0 and the corresponding data on dataseries[] are obtained to be d1 and d2. assume that the acceleration speed of a mobile phone between t1 and t2 is linear, then the data value at t0 is d0, which can be calculated through the following weighted average method: d0= d1∗(t2−t0) +d2∗(t0−t1) t2−t1 new time-series[] and data-series[] can be calculated and set up through the above algorithm. (3) extraction of data features of acceleration sensor in order to accurately capture and identify motions, the data set window can be set to 512*40, i.e., 20,480 milliseconds. therefore, the data-series[] array can save 512 samples. when the array is full, re-save should be carried out from data-series[0] after emptying it. after filling in the array, data extraction with a time domain feature and frequency domain feature should be carried out for another time. (a) time domain feature the time domain feature is the feature value of the data within a certain time window. due to the smoothing of data carried out in the previous step, the acceleration sensor data is with the interval of 40 milliseconds therefore, the time domain feature of the acceleration sensor, within 20,480 milliseconds (about 20 second), can be calculated directly via the mean value, maximum value, and minimum value of all the data within the data-series[] array. by extracting the various features of the acceleration signal within a single time window, several feature vectors can be constructed to characterize the behavior. in this paper, the following feature vectors are used. 1) standard deviation formula: standard deviation is defined as σ= √ √ √ √ 1 n n ∑ i=1 (xi−x) 2 a context-aware mhealth system for online physiological monitoring in remote healthcare 149 where n is the number of samples and x is the sample mean. standard deviation, an often-used statistical characteristic. standard deviation reflects the degree of dispersion of the acceleration sensor data. since the acceleration data are unchanged when subjects are in the static state, standard deviation is near zero. when subjects are moving, the acceleration data are constantly changing and the standard deviation is always much greater than zero. therefore, the standard deviation is an important feature to identify static operation and dynamic action. 2) skewness: skewness is defined as formula sk= n ∑ n i=1 (xi−x) 3 (n−1) (n−2)σ3 where n is the number of samples, x is the sample mean (data), and σ is the sample standard deviation.in probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of the acceleration sensor data about its mean. the skewness of the x axis can effectively distinguish between actions such as walking downstairs. 3) kurtosis: kurtosis is defined as formula k= ∑ n i=1 (xi−x) 4 fi nσ4 where n is the number of samples, x is the sample mean (data) , σ is the sample standard deviation, and fiis the sample interval. kurtosis is any measure of the "peakedness" of the probability distribution of acceleration sensor data, an important statistical characteristic. kurtosis of the y axis can effectively distinguish running and other actions. (b) frequency domain feature the time domain feature alone cannot reflect the obvious characteristics in frequency domain of motion, therefore, characteristics in frequency domain should be obtained via fast fourier transformation. here, fast fourier transformation should first be carried out on the dataseries[] array. fast fourier transformation (fft) is a kind of fast algorithm of discrete fourier transformation (dft), which reduces the number of computations needed for n points from 2n2 to 2nlgn and the data can be efficiently changed from time domain into frequency domain. fast fourier transformation should be carried out on data-series[] (fft operation can be carried out directly if data-series[] array is 512and results in a complex number array in frequency domain space. each complex number in the array includes real and image data. the corresponding fourier component of each frequency within the frequency domain can be obtained through the following relationship, i.e., modulus of the complex number. the energy of characteristics quality in frequency domain, which is required in this paper, is the ratio between the quadratic sum of fourier components within a window and the window size. i.e., frequency domain energy reflected the data periodicity, as for the motion with high periodicity (such as running), the frequency domain energy is obviously high. energy= ∫ w k=0 (mod[k]) 2 w in order to be convenient for feature extraction, this paper uses a windowing method to split the original acceleration signals. the single acceleration signal, after being windowed, includes 512 samples, which is enough for including a single complete motion of patient. if a shorter, rectangular window is adopted, it is not large enough to include information for identifying different motions. if the length of the rectangular window is too long, serious delay phenomenon will occur to the real-time system. (4) classified-learning 150 w. zhang, k. thurow, r. stoll after above procedures obtain the feature values, data excavation tools should be used in the following step. this paper uses weka [18] to classify the learned test data, so as to lay a foundation for the following activity. (5) classification in the experiment, we used 45training samples and 6-11 training samples of each action. volunteers completed the following actions sequentially:(1) walking, (2) jumping, (3) standing, (4) walking up stairs, (5) walking down stairs, and (6) running. every action lasted 30 to 50 seconds to make sure that, for every action, three (3) to five (5) groups of eigenvalues could be collected. finally, we use the 1-k-nearest neighbour (knn) classifier to classify the obtained feature values based on the learning basis in the previous step. its confusion matrix test results are shown as table 1. we can see from the confusion matrix that the classification accuracy of 1-k-nearest neighbour (knn) is high, and the overall accuracy is 95.56%, of which an example could be one jump motion wrongly included into running, and an example of one standing motion included into walking, which shows that these two groups of motions may produce slight confusion to sorting algorithms. table 1. confusion matrix of knn results activity classification jump walking standing downstairs up-stairs running jump 7 0 0 0 0 1 walking 0 11 0 0 0 0 standing 0 1 11 0 0 0 down stairs 0 0 0 6 0 0 upstairs 0 0 0 0 4 0 running 0 0 0 0 0 4 4.2 knowledge representation in order to detect an abnormal situation, the heart rate variability is particularly the most relevant parameter identified by cardiologists [20]. in this experiment, we used equivital multiparameter sensors to collect the ecg data. the changes in the beat-to-beat heart rate, the heart rate variability (hrv),calculated from the electrocardiogram (ecg), is a key indicator of an individual’s cardiovascular condition. assessment of hrv has been shown to aid clinical diagnosis and intervention strategies. an overview of some implemented production rules in the system is shown in the following figure. during the process of acquiring physiological parameters and activity identification an approach based on context information is used in the physiological decision module to detect any abnormal cardiovascular events. it includes the basic medical knowledge required to identify potentially dangerous situations of patients in cooperation with the cardiologists and clinical experts. we first need to construct the context model before processing the context information [19]. the following forms are included in the context models: (subject, predicate, value). if the heart rate value is outside the scope regarded as "normal", or the heartbeat is not regular, we can simply identify hrv. moreover, it can also determine hrv by means of the sdann, the standard deviation of the average r-r intervals calculated over short periods, usually 5 minutes. however, according to the involved cardiologists, the hrv by itself may not have any meaning for identifying an anomalous situation; instead, it should be correlated with other information pertaining the patient, e.g. the physical activity and his/her posture. the following a context-aware mhealth system for online physiological monitoring in remote healthcare 151 physiological parameters are used by the implemented production rules on this aspect: cardiac information • hrmax: patient’s maximum heart rate calculated by karvonen formula [21]; • measuredheartrate: patient’s heart rate measurement; • restingheartrate: patient’s resting heart rate; • timethreshold: time threshold usually 10 sec; • rhrmax : rhr+10; • rhrmin : rhr-10; posture information • lying: datatype [boolean] property detecting whether the patient is lying or not; • standingup: datatype [boolean] property detecting whether the patient is standing up or not; physical activity information • running activity: datatype [boolean] property detect whether the patient is running or not; • walking activity: datatype [boolean] property detect whether the patient is walking or not; environmental information • room temperature: temperature of room where patient lives; for example, we have a patient with heartrate of 70 and id 001, and physical activity was regarded as running, then the context information would be recorded as follows: • (patient001, heartrate, 70), (activity, running, true), 152 w. zhang, k. thurow, r. stoll we can also define the specific conditions for the emergency task to be triggered: e.g. the heart rate of the patient drops below 40, or the blood pressure (systolic) exceeds 170. reasoning may be carried out according to the rules of the language itself, for example: • (?a?p?b),(?prdf : subpropertyof?q)− > (?a?q?b) user defined rules may also be used in the reasoning process, e.g.: • (?patient, equitalbloodpressure001, vl ), ge(?vl, 170), (?patient, equitalbloodpressure, vl), le(?vl, 40) ->(?patient, healthstatus, "danger") • (?patient, healthstatus, "danger"), (?patient, healthstatus, "movementfail"), (emergencytask, taskstate, "close") ->(emergencytask, taskstate, "open") some example rules are shown in table 2, which are used by the system to predict an abnormal situation. when the heart rate is abnormally high, the heartratehigh rule assigns the alarm level. abnormal heart rate in each instance of the heartrate class is specified in the hasminrange and hasmaxrange properties. the system sets the heartrate alarm if a patient’s heart rate is greater than the specified normal maximum. table 2. rules for the system’s alarm management rule description heartratehigh (?patient rdf:type heartrate) , (?par1 hascurrentvalue ?v1) , (?par1 hasmaxrange?max), greaterthan(?v1, ?max) ->(?taskstate hasalarmlevel "hes") heartratelow (?patient rdf:type heartrate) , (?par1 hascurrentvalue ?v1) , (?par1 hasminrange ?min), greaterthan(?v1, ?min) ->(?taskstate hasalarmlevel "les") 5 experimental results in order to identify whether various functions of the ipm-mhealth system are effective or reliable in practical application, we designed an initial experiment. its purpose was to carry out a test and verify the following functions: quality of physiological signals acquired by ipmmhealth; comfort of sensor system; online activity recognition and alarm reliability; reliability of short-distance wireless communication; and reliability of remote wireless communication. five healthy volunteers in the 21 35 age range without cardiovascular disease history participated in this test. in the first test subjects with equivital sensors carried out the motions for walking, jumping, running, standing, climbing the stairs, and walking down the stairs, in proper order; another observer recorded the test subjects’ motions and corresponding times. in the second test, we simulated the abnormal heartbeat event and tested the reliability of the decision-making system under lying-flat and running conditions with the test subjects. the relevant test data and statistical results are shown in table 3 and table 4. in the stability tests, we used ten (10) minutes to teach each volunteer how to put on and operate the system, then let them go home with the wearable device and keep the system running more than 24 hours. the sensor data was collected in real-time and sent to the central ipm-mhealth server; table 4 lists the transmission packet loss rate for 24 hours’ data. we can see that both, the short-distance wireless transmission and remote wireless transmission, operated normally. the expected realtime physiological data transmission was not lost. illegal disconnection conditions did not occur a context-aware mhealth system for online physiological monitoring in remote healthcare 153 in short-distance wireless transmissions during the entire test process. the acquired signal quality was satisfactory. in the automatic activity recognition experiment, volunteers completed totally 300 actions and 10 times for each action. in the abnormal alarm part, good effect was also received in the simulated anomaly detection, of which the conditions of over 150 heartbeats under 10 times of lying flat were correctly identified with alarms, while over 150 heartbeats under running state were included into the normal range. in the activity identification part, 90% motions were correctly identified by the system. the activity identification of standing and jumping had the best accuracy; the activity of climbing and walking down the stairs also had the better accuracy, while running, walking, etc. has misjudgement within 8%. table 3. results of the automatic activity recognition and abnormal situation indicating activity correct recognition rates hr max alert (hr max) std standing 97.8% 170 alarm 10 walking 91.6% 122 none 10 running 92.6% 178 none 10 jump 97.6% 150 none 10 climbing the stairs 96.6% 140 warning 10 walking down the stairs 95.2% 110 none 10 table 4. results of stability test parameter acquisition frequency of sensor data packetloss-rate data transmitted in a 24hour period std heartrate 0.3hz <0.1% 25,920 bytes 24 respiratory 0.3hz <0.1% 25,910 bytes 24 patient temperature 0.3hz <0.1% 25,890 bytes 24 r-r interval 1hz <0.1% 86,390 bytes 24 acceleration x 100hz <0.1% 2,164,986bytes 24 acceleration y 100hz <0.1% 2,164,980bytes 24 acceleration z 100hz <0.1% 2,164,900bytes 24 we also find in the test results that the motions identified within the activity change duration may be misjudged when test subjects conducted multiple motions successively. the causes for this misjudging lie in the selection of the time window when the sensor test data was acquired. the time window selected in this paper is about 20 seconds, the data within 1 second may mix the corresponding acceleration data of two activities. therefore, the data features and user activity did not mutually correspond, it could lead to misidentification between two activities. this problem can be solved by selecting a shorter time window, but the selection of an overly-short time window may create non-obvious problems for the corresponding data features of various activities. 6 conclusion a framework for context-aware physiological analysis with regard to daily activities is proposed in this paper for the detection of abnormal cardiac situations. owl is used in the contextreasoning module to construct target medical tasks and circumstances. the ipm-mhealth system has been used in the institute for preventive medicine at rostock university medical center 154 w. zhang, k. thurow, r. stoll germany for remote monitoring research and about 60 subjects carried out 8-24 hour remote monitoring tests. in the questionnaire, over 90 however, this paper does not address how to process the context conflicts in the process of constructing ontology and reasoning conditions. the reliability and energy consumption problem of remote medical systems will be the focus in further research. as for the energy consumption problem, some new ultra low power transmission protocols that can greatly improve the sustainability of remote monitoring have appeared, such as ant (ant is a proven ultra-low power (ulp) wireless protocol that is responsible for sending information wirelessly from one device to another device, in a robust and flexible manner) and ble (bluetooth low energy). other challenging tasks will include the system recovery mechanism and development of an intelligent error discovery to keep the stability of the system over time. 7 acknowledgement the authors wish to thank the federal ministry of education and research (bmbf, germany) for the financial support (fkz: 01z1kn11). bibliography [1] jovanov, e., milosevic, m., milenkovic, a.(2013); 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[22] hidalgo limited, equivital eq-01 vital signs monitor health care practioner guide. dokumentation, hidalgo ltd., 2006. [23] m. kumar, m. weippert, r. vilbrandt, s. kreuzfeld, and r. stoll, (2007); fuzzy evaluation of heart rate signals for mental stress assessment, ieee transactions on fuzzy systems, 15: 791-808. [24] k aminian, b najjafi, (2002); body movement monitoring system and method, us.patient ep 119513910. [25] fuentes, d., gonzalez-abril, l., angulo, c., ortega, j. a. (2012); online motion recognition using an accelerometer in a mobile device, expert systems with applications, 39(3): 24612465. [26] godfrey, a., bourke, ak., olaighin, gm. (2011); activity classification using a single chest mounted triaxial accelerometer , medical engineering and physics, 33(9): 1127-1135. [27] fleury, a., noury, n., vacher, m. (2009); a wavelet-based pattern recognition algorithm to classify postural transitions in humans, 17th european signal processing conference, 2047 2051. ijcccv7n5.pdf int j comput commun, issn 1841-9836 7(5):798-806, december, 2012. multi-period customer service level maximization under limited production capacity s. babarogić, d. makajić-nikolić, d. lečić-cvetković, n. atanasov sladjan babarogić, dragana makajić-nikolić danica lečić-cvetković, nikola atanasov university of belgrade, faculty of organizational sciences serbia, 11000 belgrade, jove ilića 154 e-mail: sladjan@fon.bg.ac.rs, gis@fon.bg.ac.rs danica@fon.bg.ac.rs, nikola.atanasov@fon.bg.ac.rs abstract: this paper will focus on a make-to-stock multi-period order fulfilment system with random orders from different classes of customers under limited production circumstances. for this purpose a heuristic algorithm has been developed aimed at maximizing the customer service level in any cycle and in the entire multi-period. in this paper, in order to validate the results obtained with this algorithm, a mixed integer programming model was developed that is based on the same assumptions as the algorithm. the model takes into account the priorities of customer groups and the balanced customer service level within the same group. the presented approaches are applied to a real example of fast moving consumer goods. their comparison was carried out in several scenarios. keywords: limited production capacity, customer service level, heuristic algorithm, mixed integer programming. 1 introduction the distribution of available finished products among customer orders requires an efficient distribution system aimed at improving the effectiveness of the entire business. the effectiveness of business is directly related to products quantities and the profits through sales. in addition to profit-oriented decisions on the selection of orders to be met, it is necessary also to take into account the customer service level. according to [6], key performance indicators in manufacturing companies are identified in measuring the customer service level and customer satisfaction. customers whose purchases represent a large share of the company’s sales require special attention and the company should make sure that they achieve the highest possible fulfilment of each order. there are also customers that continuously increase their orders and based on that also expect a corresponding service level. due to their large number, small customers influence the overall sales of manufacturing companies. some of them also represent a potential for future sales growth and increase in revenues of the manufacturing company. these facts underline the importance of making the right decisions when selecting orders to be met. therefore, a heuristic algorithm has been developed [5] that is used for decision-making concerning the customer service level in each cycle by taking into account the priorities of the customers. traditional approaches to fulfil orders based on the make-to-stock (mts) production system are described in [1] by taking into account the available supplies of finished products to satisfy customer orders following the principle of first come first served (fcfs) without assigning priorities to customers and orders. the basic idea of the approach described in [8] is the segmentation of customers in order to increase the total revenue of the manufacturing company by accepting and delivering orders which provide maximum profit. in this paper the customers are clustered into priority groups based on the size of their orders. due to their potential growth, there is tendency to provide protected quantities of products to copyright c© 2006-2012 by ccc publications multi-period customer service level maximization under limited production capacity 799 the customers with the lowest priority. if a manufacturing company has a limited production capacity, it is clear that the company will decide to reject some of the orders received which will have a direct impact on the profitability of the company. the decision to reject orders is made based on a comparison of orders where less profitable orders are rejected. according to [2] this issue has also been defined as dynamic models for managing orders under limited production capacity based on profitability analysis. the problem discussed in this paper refers to meeting the demand in a multi-period, where the unmet demand in one cycle is not compensated in the following cycles, i.e. there are no backorders. demand is a weekly phenomenon which requires dynamic decision-making. the heuristics shown in [9] refer to the problem of replenishment of multiple products in order to meet the demand when the storage capacity is limited. authors in [13] present a mixed integer programming (mip) model that applies to small and medium-sized enterprises with limited available to promise (atp) quantities and which has to decide which customers they will accept and in each cycle which part of the demand of accepted customers they are going to meet. a large number of mip models include unlimited production capacity. the uncapacitated requirement planning model, with demand fulfilment flexibility, is shown in [7]. in each cycle separately a decision is taken regarding the launch of production and the part of demand of each customer that will be met in the respective cycle. a similar mip model is presented in [12]. this model implies that the manufacturer may, in each cycle, decide whether to start the production, which order to fulfil and to what extent. the above mentioned papers consider the maximization of profit as the main criterion. the approach presented in this paper, however, aims at maximizing the customer service level. in [11] the orders of customers are clustered into two groups: small-size orders and large-size (divisible) orders in the presented basic mip model, it is in each cycle determined which of the small-size orders will be fulfilled and what fraction of the large-size order will be met in order to maximize the customer service level. the multiobjective mi nonlinear mathematical model, in which the maximization of average customer service levels is one of the four objectives, is presented in [3]. the remainder of this paper is organized as follows: the second section defines the problem of allocating scarce resources in a manufacturing company. the allocation problem is related to the distribution of limited production capacity to customer orders in order to maximize the customer service level. the third section deals with the computational results and provides an analysis of these two approaches in a real example of fast moving consumer goods (fmcg). in the conclusions section the authors lists the principal advantages of the proposed algorithm for the allocation of limited production capacity, as well as possible directions for the further development. 2 problem definition and model formulation the algorithm has been developed for solving the problem of fmcg industry products. by introducing minor modifications it can also be applied to the product allocation problem in other industries. the basic assumptions of the problem discussed in this paper are the following: • it studies a multi-period and a set of customers that place order in all or almost all of the cycles. demand is uneven and is known only for one cycle in advance; • the production capacity is limited and constant in the entire period; • if the incoming customer orders in a single cycle do not exceed the available stock of finished goods, the allocation is complete and all customer orders are fulfilled, while any surplus products are stored for the next cycle. inventory holding costs are neglected; 800 s. babarogić, d. makajić-nikolić, d. lečić-cvetković, n. atanasov • when the total of all orders exceeds the available stock of products, it is necessary to define the distribution of products i.e. rules based on which the allocation of products will be done according to the received customer orders. the allocation has to maximize the customer service level. • considering the type of products that are being studied, orders that have not been fully met in the reporting cycle shall not be compensated in the subsequent cycles; • order of customer priority is known. these are clustered into priority groups in which they have the same order of priority as the other group members; • the product unit price is the same for all customers. based on the assumptions of problem, in previous research effort we have developed heuristic algorithm that introduces the concepts of partitions and tokens. the algorithm aims to maximize the cumulative customer service level with a balanced customer service level within the same group. the detailed explanation of the proposed algorithm is given in [5]. the important features of the algorithm are: • classification in groups, provides the order of allocation with primarily focus to satisfy customers that are important for the company. • application of mechanism of partitions, ensures certain groups of lower priority within protected partitions to be involved in allocation so that the low-ranked customers would be at least partially satisfied. • application of mechanism of group memory token, allows all customers within a marked group to receive the unsatisfied demand from the previous cycle, with the extended delivery lead time, with which they improve the overall customer service. 2.1 mip customer service level maximization model a mixed integer customer service level maximization model (csl model) model was developed in order to validate the results obtained with this algorithm. the model is based on the same assumption as the algorithm. however, here the total demand of all customers in all cycles is known in advance. given the purpose of the model, these are the assumptions on which the model is based: • an r number of cycles is observed and the demand of any of n customers is known in each of those cycles, ocil, i = 1, . . . , n, l = 1, . . . , r. the demand might not be fulfilled and the demand that was not fulfilled is not compensated for in the next cycle; • production is limited and constant in all cycles and it equals ptl, l = 1, . . . , r. if the production exceeds the total demand of the cycles, the surplus products are stored for the following cycle, so as that the total demand stl in any cycles equals ptl +max{0, ptl−1− otl−1}. inventory holding costs are neglected; • customers are grouped according to priority. lowest-priority customers are a protected group to which the amount of protected products apil, i = 1, . . . , n, l = 1, . . . , r is allocated in each cycle. model variables: multi-period customer service level maximization under limited production capacity 801 • zil customer service level, defined as the fraction of customer order demands ocil delivered on time [10], • acil allocated quantity of the customer i in the cycle l. csl model: max r∑ l=1 n∑ i=1 wizil (1) s.t. acil − zil · ocil = 0, i ∈ {1, . . . , n}, l ∈ {1, . . . , r} (2) n∑ i=1 acil ≤ stl, l ∈ {1, . . . , r} (3) acil ≥ apil, i ∈ {1, . . . , n}, l ∈ {1, . . . , r} (4) zil ≤ 1, i ∈ {1, . . . , n}, l ∈ {1, . . . , r} (5) acil ∈ z + , i ∈ {1, . . . , n}, l ∈ {1, . . . , r} (6) the objective function (1) represents the maximization of the total customer service level, where customers are differentiated by using weights. the wi parameter, which is the weight coefficient, is used to cluster the customers into priority groups. the optimum solution of the csl model is extremely sensitive to the given values of this parameter, in particular when production is significantly less than the total demand. the first constraint (2) refers to the percentage and absolute satisfaction of demand. the second constraint (3) models the total demand and the total fulfilment of demand in each of the cycles. the total demand in a cycle is made up of the production in the given cycle and the eventual surplus from the previous cycle. the third constraint (4) allows the creation of a protected partition. the apil parameter, which is the minimum quantity of products to be delivered to the customer i in the cycle l, represents the reserved quantity for the customer i which is in the protected partition. the value of this parameter for customers outside the protected partition is 0. the value of this parameter has a direct impact on the service level of customers from the protected partition and indirect impact on the service level of customers outside this partition. the last constraint (5) represents the upper bound of the fractional customer service level for the customer i in the cycle l. 3 computational results and discussion in order to analyze the impact of the production capacity to the customer service level, the algorithm and the cls model were tested in three scenarios. all the parameters are the same, except for the production level which equals 1000, 1300 and 1400 fmcg units respectively. for the scenario when the production level reaches 1000 units, the supply is significantly lower than the total demand, for the 1300 units scenario the supply almost meets the minimum total demand. in case of the 1400 units scenario, there are unallocated products only in a few cycles. table 1 shows the demand of nine customers over a period of nine weeks. the customers are clustered into three groups. customers a1 and a2 belong to the first group, while customers b1-b4 make part of the second one. the c-customers are in the third group. the first and the second group of customers are in the first partition, while the third one is in the second, protected partition. 802 s. babarogić, d. makajić-nikolić, d. lečić-cvetković, n. atanasov table 1: input parameters week (cycle) a1 a2 b1 b2 b3 b4 c1 c2 c3 demand w1 330 575 280 110 40 121 100 52 25 1633 w2 360 393 110 170 135 157 74 40 0 1439 w3 220 700 60 100 160 130 100 65 40 1575 w4 230 480 120 140 80 146 74 94 20 1384 w5 270 650 390 110 100 241 140 83 0 1984 w6 381 751 89 140 260 95 110 48 30 1904 w7 320 615 20 120 90 100 46 27 20 1358 w8 390 1.055 120 120 190 130 110 75 0 2190 w9 305 780 290 90 60 110 30 92 11 1768 total 2806 5999 1479 1100 1115 1230 784 576 146 the parameter value kp (protective percentage quota) for all three scenarios is 0.95 for the first partition and 0.05 for the second one. these values are based on the decision of the company management which is founded on the realistic assumption that it is necessary to satisfy even the small customers in order to keep them in the system and take advantage of their potential growth. in this way the dependence on major customers would also be reduced. the given kp parameter values are used to determine the ap (amount of allocated products) parameter value . for each customer, the value of ap parameter is set to 5 for the purpose of benchmarking the presented algorithm it was necessary to set the weights in the csl model that will provide the best balance of the customer service level for the given data. the customers from the protected partition are not included into this sensitivity analysis, as they belong to the lowest-priority group and in any weight distribution their weights are 1. the ap parameter values have a much greater influence on the fulfilment of their demands. the gnu linear programming kit [4] was used to solve the model. the programming kit includes the branch-and-cut algorithm for solving the mip problem. figure 1 shows five value variants of weight coefficients. the first value in brackets represents the weight coefficient of the customers from the first group, while the second value is the weight coefficient of the second group. given the fact that the first group is a higher priority group, the weight coefficients of the customers from this group have to be greater than the weight coefficients of the customers from the second group. the figure shows that the significant difference between weight coefficients has a negative effect on the balancing of the second group. however, small differences have a negative effect on the balancing of the first group. using the bisection method has shown that for the given data it is necessary to use the weight 65 for the first customer group and 10 for the second one. scenario 1 table 2 presents the results obtained through the application of the algorithm, while table 3 contains results of the cls model optimization. the given production capacity in both cases was 1000 fmcg units. both tables show the customer service level for every customer over a period of nine weeks. the last row provides the average customer service level for every customer. by using the cls model, the objective function value, which represents the weighted customer service level, is 1213.77. by weighting the results of the algorithm, the objective function becomes the value of 1092.77 (90.03% of the optimum value). this is an expected result because the cls model maximizes the weighted customer service level. however, from the point of view of the company management it is more important that the customer service level for the customers in the same group is balanced. based on the results for customers in the first group (a1 and a2), it can be concluded that multi-period customer service level maximization under limited production capacity 803 figure 1: cls sensitivity to weights table 2: algorithm results for 1000 units week (cycle) a1 a2 b1 b2 b3 b4 c1 c2 c3 w1 1.000 1.000 0.082 0.082 0.075 0.083 0.280 0.288 0.280 w2 0.786 0.784 1.000 0.594 0.274 0.707 0.432 0.450 w3 1.000 1.000 0.067 0.070 0.063 0.069 0.240 0.246 0.250 w4 0.843 0.846 0.467 0.664 1.000 0.829 0.270 0.266 0.250 w5 1.000 1.000 0.036 0.036 0.030 0.037 0.221 0.229 w6 0.496 0.498 1.000 0.757 0.373 1.000 0.264 0.271 0.267 w7 1.000 1.000 0.050 0.042 0.044 0.050 0.543 0.519 0.550 w8 0.438 0.440 0.158 0.958 0.453 0.731 0.273 0.267 w9 0.846 0.887 0.000 0.000 0.000 0.000 0.367 0.380 0.364 avg 0.797 0.797 0.214 0.400 0.287 0.370 0.293 0.304 0.308 table 3: results of cls model optimization for 1000 units week (cycle) a1 a2 b1 b2 b3 b4 c1 c2 c3 w1 1.000 0.963 0.050 0.055 1.000 0.058 0.170 0.308 0.640 w2 1.000 1.000 1.000 0.053 0.644 0.051 0.230 0.400 w3 1.000 0.794 1.000 1.000 0.050 0.054 0.170 0.246 0.400 w4 1.000 1.000 1.000 0.236 1.000 0.055 0.230 0.170 0.800 w5 1.000 1.000 0.051 0.055 0.080 0.054 0.121 0.193 w6 1.000 0.487 1.000 0.050 0.050 1.000 0.155 0.333 0.533 w7 1.000 0.829 1.000 0.050 1.000 0.050 0.370 0.593 0.800 w8 1.000 0.187 1.000 1.000 0.053 1.000 0.155 0.213 w9 1.000 0.482 0.052 1.000 1.000 1.000 0.567 0.174 1.000 avg 1.000 0.680 0.384 0.343 0.355 0.311 0.195 0.250 0.623 804 s. babarogić, d. makajić-nikolić, d. lečić-cvetković, n. atanasov the use of algorithm keeps the customer service level completely balanced. by using the cls models, customer a1, whose demand is much lower than the one of the customer a2, has a much higher customer service level. this is because the costs of high customer satisfaction within the same group are minimal when demand is the lowest, because the lowest demand causes the highest increase of the objective function. in the second group, when the cls model was used, the customer service level for all nine weeks was more balanced than with the application of the algorithm. however, analyzing customer service level by week, it becomes clear that every week the algorithm assigns a certain amount of products to every customer from the second group and that the balance is very good every odd week. this is primarily due to use of tokens. on the other hand, based on the results of the cls model, it is evident that every week at least one customer from the second group receives protected amount of products. in other words, looking at it by week, the balancing is inadequate. the third group belongs to a protected partition and always gets the guaranteed amount of products. the algorithm provides perfect balancing. however, based on the results of the cls model the last customer in the third group has the highest customer service level due to its low demand which is often less than the guarantied amount of products. this happens due to the maximization of the satisfaction fractions and not the absolute amount of assigned products. scenario 2 the main feature of a scenario with 1300 fmcg units is that the production of each week still does not meet the total demand, but the lack of finished products is lesser than in the first scenario. table 4 provides the average customer service level for each customer over a period of all nine weeks based on the results of the algorithm and the optimization of the cls model. table 4: results of the algorithm and the cls model for 1300 units a1 a2 b1 b2 b3 b4 c1 c2 c3 algorithm 0.937 0.929 0.539 0.706 0.556 0.675 0.434 0.452 0.429 cls model 1.000 0.870 0.412 0.792 0.497 0.891 0.305 0.328 0.808 the weighted overall customer service level based on the results of the cls model equals 1416.17. the results of the algorithm make this value reach 1361.42 (96.13 % of the optimum value). the balancing obtained by applying the algorithm is better than the balancing obtained through the model and it is even more prominent than in scenario 1. scenario 3 when the production reaches 1400 fmcg units, in two weeks (w4 and w7) the supply exceeds the demand and the surplus products are stored for the next week. the average customer service levels are shown in 5. based on the results, the weighted overall customer service level for the cls model equals 1463.77 and 1419.76 for the algorithm (96.99% of the optimum value). table 5: results of the algorithm and the cls model for 1400 units a1 a2 b1 b2 b3 b4 c1 c2 c3 algorithm 0.971 0.968 0.627 0.733 0.575 0.765 0.458 0.542 0.548 cls model 1.000 0.924 0.477 0.879 0.581 0.982 0.383 0.521 0.836 looking at the data from the three scenarios above, it can be concluded that by increasing the production the total value of customer service level obtained through the algorithm, is nearing the optimum value. if observed from the balancing point of view, the advantage of the algorithm is increasingly more prominent compared to the cls model. multi-period customer service level maximization under limited production capacity 805 4 conclusions and future works this paper presents benchmarking of a heuristic algorithm for the dynamic solving of the problem of allocating limited supplies of products to received customer orders with the aim of maximizing the customer service level. in order to validate the algorithm an mip model was developed. the model is used for the distribution of products based on demands that are known in advance for the entire period. computational results have indicated that the proposed algorithm, with the increase in the production capacity, ensures a value of the total customer service level that is closer to the optimum values obtained using the cls model. in addition, in all three scenarios the balancing results of service level for customers from the same group achieved with the algorithm were better than the balancing obtained using the cls model. further analysis of customer demand by week and the obtained customer service levels have shown that the further research might have to be directed towards the analysis of the correlation between the customer service level and the fluctuations in demand. besides, the algorithm could be modified by introducing the assumption that the selling price depends on the customer affiliation to a certain group or on the quantity of products ordered. the growth rate of a customer’s demand is one of the essential elements used by the management in manufacturing companies in planning the sales. the inclusion of this parameter into the problem would require the modification of the algorithm making it a more useful tool in the decision-making process. acknowledgement this research was partially supported by the ministry of education and science, republic of serbia, project number: tr35045. bibliography [1] cederborg o., rudberg m., customer segmentation and capable-to-promise in a capacity constrained manufacturing environment, 16th int. annual euroma conference, goteborg, sweden, 2009, http://www.iei.liu.se/prodek/forskning/iscaps/filarkiv /1.120209/cederborgandrudbergeuroma2009.pdf, accessed 12 january 2010. 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[11] sawik t., integer programming approach to reactive scheduling in make-to-order manufacturing, math comput model, issn 0895-7177, 46(11-12): 1373-1387, 2007. [12] xiao y., taaffe k., satisfying market demands with delivery obligations or delivery charges, comput oper res, issn 0305-0548, 37(2): 396-405, 2010. [13] xiong m.h. et al, a dss approach to managing customer enquiries for smes at the customer enquiry stage, int j prod econ, issn 0925-5273, 103(1): 332-346, 2006. int j comput commun, issn 1841-9836 8(4):571-577, august, 2013. pybneq a tool for computing bayes-nash equilibria i. joldeş, b. pârv, i. parpucea, v. lupşe iulian joldeş, bazil pârv babeş-bolyai university department of computer science romania, 400084 cluj-napoca, 1 m. kogălniceanu str. e-mail: joldesiulian@yahoo.com, bparv@cs.ubbcluj.ro ilie parpucea babeş-bolyai university department of mathematics and statistics romania, 400591 cluj-napoca, 58-60 teodor mihali str. e-mail: ilie.parpucea@gmail.com vasile lupşe technical university cluj-napoca north center baia mare romania, 430083 baia mare, 62/a dr. victor babeş str. e-mail: vasilelupse@ubm.ro abstract: this paper describes pybneq a tool for computing bayes-nash equilibria for games of incomplete information. it is implemented in python and has a graphical user interface, allowing the user to load/save/edit game data, and to find bayes-nash equilibria. currently, pybneq implements porter-nudelman-shoham algorithm for 2-player games and can be considered as a decision support system for solving games of incomplete information. keywords: bayes-nash equilibrium, decision support systems, game with incomplete information. 1 introduction many real-world problems, including ones which contain germs of a crisis, are modeled as games of incomplete information. examples include market competition, currency attacks, bank runs, liquidity crises, as well as military conflicts. this paper describes a tool for computing bayes-nash equilibrium for games with incomplete information. it is structured in 5 sections, as follows. after this introductory section, the section 2 introduces the bayesian games and a short discussion related to the computation of the bayesnash equilibria. section 3 describes the pybneq tool, especially its graphical user interface, while section 4 presents some case studies we used to test our tool. finally, the last section compares our application with the existing ones and presents the future developments. 2 theoretical background in what follows, we consider the definition of a bayesian game given in [1]. such a game is defined as a tuple (n, a, θ, ω, u), where: • n = {1, ..., n} is the set of agents or players; • a = (a1, a2, · · · , an) is the set of agents’ actions, ai being the set of actions available to agent i; copyright c⃝ 2006-2013 by ccc publications 572 i. joldeş, b. pârv, i. parpucea, v. lupşe • θ = (θ1, θ2, · · · , θn), with θi = (θi,1, θi,2, · · · , θi,mi) being the set of types for agent i and θi,j the j-th type of agent i; • ω = (ω1, ω2, · · · , ωn), with ωi = (ωi,1, ωi,2, · · · , ωi,mi) being the set of probabilities assigned to the types of agent i and ωi,j the probability assigned to θi,j; • u = (u1, u2, · · · , un) is the set of utility (payoff) functions, with ui = (ui,1, ui,2, · · · , ui,mi) being the set of utility functions of agent i and ui,j the utility function of θi,j whose arguments are the joint action a = (a1, a2, · · · , an) and the other agents’ types θ−i = (θ1, · · · , θi−1, θi+1, · · · , θn). alternative definitions can be found for example in [3, 8, 9]. the solution of a bayesian game is based on the concept of bayes-nash equilibrium. it is known that the computation of this equilibrium is np-complete, and the usual approach uses two steps [12]: 1. reduce the bayesian game to a complete-information game, and 2. compute the nash equilibrium for the complete-information game obtained in step 1. step 1 above can be addressed in several ways. the paper by ceppi et al. [1] contains a detailed discussion regarding the ways of performing reduction, based especially on the material in [5]. our approach is based on the use of sequence form, due to the smaller payoff matrixes than the ones in normal form. step 2 means computing of the nash equilibria for two-player complete-information games. ceppi et al. paper [1] analyze three algorithms for such games in strategic form: lemke-howson (lh), porter-nudelman-shoham (pns), and sandholm-gilpin-conitzer (sgc), and propose an extension of pns to bayesian games (b-pns), which is implemented in our tool. the b-pns algorithm has the following generic steps: 1. enumerate all the possible joint supports; 2. select a support for each possible type of agent 1; 3. prune the spaces of actions of the agent 2’s types by strict conditional dominance; 4. check the strict conditional dominance on agent 1’s support; 5. select a support for each possible type of agent 2; 6. check the strict conditional dominance on agent 2’s suport; 7. check the feasibility problem. 3 the pybneq tool in order to implement the b-pns algorithm, there were two interrelated design problems to solve: data representation and the individual algorithms for each step. a future paper will describe the design solutions in greater detail. the implementation language is python 2.7 [11], while data representation and numerical computation capabilities of numpy package [6] were extensively used. also, the application makes use of pycplex library [13], a python interface to the ilog cplex callable library [2], for solving the feasibility problem (step 7 of the above-described b-pns algorithm). finally, the graphical user interface was produced by using the wxpython toolkit [14]. the application is designed with a friendly user interface, having the following components: pybneq a tool for computing bayes-nash equilibria 573 • main window contains a menu bar for creating, loading or saving a game and also for running it. when a game is loaded, this window becomes the current game window, where the game parameters are displayed (see figure 1), and the bayes-nash echilibria can then be computed; • new game window, allowing the user to define actions, types and probabilities for both players (see figure 2); • payoffs window, where the payoffs could be added or edited (figure 3). figure 1: the main window of the application the input data are stored in /data sub-directory in .dat files. the structure of input data file is: line1: first agent’s index line2: first agent’s actions line3: first agent’s types line4: first agent’s probabilities line5: second agent’s index line6: second agent’s actions line7: second agent’s types line8: second agent’s probabilities line9 and below: the payoffs of the two players the result consists of the computed bayes-nash equilibria and it’s saved in /output subdirectory, in an .out file. it represents the strategies profile. 4 case studies we tested our tool with three examples of bayesian games: market entry game (see [7]), gift game (see [4], and the example game from [1]. all examples consider two players, whose actions are denoted by ai and bj, respectively. 574 i. joldeş, b. pârv, i. parpucea, v. lupşe figure 2: the add new game window figure 3: the payoffs window pybneq a tool for computing bayes-nash equilibria 575 4.1 market entry game both players have a single type. the following payoff matrix represents the input data: b1 b2 a1 14 , 1 4 3 4 , 0 a2 0, 14 1 2 , 0 a3 14 , 1 4 1 4 , 0 a4 0, 34 0, 0 the application outputs three solutions: (a1, b2), (a2, b1), and (a4, b1). 4.2 gift giving game player 1 has two types (θ1,1, θ1,2), and player 2 has one type (θ2,1). type θ1,1 means p > 12 , while θ1,2 means p < 12 . the input data are represented by the following payoff matrix: b1 b2 a1 0, 0 0, 0 a2 1 p, p 1 p 1, 0 a3 p, p -p, 0 a4 1, 2 · p 1 -1, 0 a) the type combination (θ1,1, θ2,1). player 1 is more likely to be a friend (p > 12 ); the application outputs two solutions: (a1, b2) and (a4, b1). b) the type combination (θ1,2, θ2,1). player 1 is more likely to be an enemy (p < 12 ); the application outputs one solution: (a1, b2). 4.3 third example: the game from [1] player 1 has one type (θ1,1) and player 2 has three types (θ2,1, θ2,2, θ2,3). there are three payoff matrices as input, corresponding to the combinations of players’ types: a) (θ1,1, θ2,1): ω2,1 = 12 b1 b2 b3 b4 b5 a1 0, 2 4, 0 2, 5 3, 2 5, 5 a2 0, 0 5, 0 1, 8 1, 1 2, 3 a3 6, 7 3, 1 2, 6 5, 3 1, 2 a4 0, 0 2, 4 4, 1 3, 5 2, 8 a5 0, 3 5, 0 4, 3 5, 9 3, 3 the solutions are: (a1, b5), (a2, b1), and (a3, b4). b) (θ1,1, θ2,2): ω2,2 = 14 b1 b2 b3 b4 b5 a1 2, 2 0, 5 2, 5 3, 0 5, 5 a2 3, 0 0, 6 1, 8 1, 0 2, 3 a3 4, 7 6, 6 2, 6 5, 1 1, 2 a4 4, 0 0, 4 4, 1 3, 4 2, 8 a5 2, 3 0, 0 4, 3 5, 0 3, 3 576 i. joldeş, b. pârv, i. parpucea, v. lupşe the solutions are: (a1, b3), (a2, b1), (a4, b5), and (a5, b1). c) (θ1,1, θ2,3): ω2,3 = 14 b1 b2 b3 b4 b5 a1 2, 2 4, 5 0, 5 3, 2 5, 0 a2 3, 0 5, 6 0, 8 1, 1 2, 0 a3 4, 7 3, 6 6, 6 5, 3 1, 1 a4 4, 0 2, 4 0, 1 3, 5 2, 4 a5 2, 3 5, 0 0, 3 5, 9 3, 0 the solutions are: (a2, b2), (a3, b2), (a3, b1), (a4, b5), and (a5, b4). 5 conclusions and future work in this paper we described a tool for computing bayes-nash equilibrium for two-player games of incomplete information. the authors of [1] implemented a c version of the b-pns algorithm, while we selected python as the implementation language due to its benefits as rapid application development by using component-based software development and the powerful libraries like numpy, pycplex, as well as the graphical user interface toolkit wxpython. the graphical user interface allows a very convenient way of playing with such games. in the future we would like to extend the tool functionalities so that it can easely be integrated in more elaborate decision support systems, including custom-made guis for different classes of games. also, the application programming interface will be improved and the tool will fully support games generated by the common generators like gamut. acknowledgements this work was supported by the grant id_2586, sponsored by nurc romanian national university research council (cncsis). bibliography [1] ceppi, s., n. gatti, n. basilico, computing bayes-nash equilibria through support enumeration methods in bayesian two-player strategic-form games in wi-iat, proc.of the 2009 ieee/wic/acm int. joint conference on web intelligence and intelligent agent technology, 2: 541-548, 2009, doi: 10.1109/wi-iat.2009.209. [2] cplex reference, ftp://ftp.software.ibm.com/software/websphere/ilog/docs/optimization/cplex/refcallablelibrary.pdf [3] eichberger, i., game theory for economists, academic press, 1993. [4] fehr, e. and k.m. schmidt, theories of fairness and reciprocity evidence and economic applications, in m. dewatripont, l.p. hansen and s.j. turnovsky (eds.) advances in economics and econometrics, econometric society monographs, 8th world congress, 1: 208-257, 2003. pybneq a tool for computing bayes-nash equilibria 577 [5] koller, d., n. megiddo, and b. von stengel, efficient computation of equilibria for extensive two-person games, games and economic behavior, 14(2): 220-246, 1996. [6] numpy home page, http://numpy.scipy.org/ [7] ochs, j. coordination problems. in j.h. kagel and a.e. roth (eds.) handbook of experimental economics, princeton university press, 195-251, 1995. [8] parpucea, i., b. pârv, and t. socaciu, t. modeling uncertainty in a decision problem by externalizing information, int j comput comun, 6(2): 328-336, 2011. [9] pârv, b. and i. parpucea, bayes-nash equilibrium in the presence of information sources: computational issues, studia univ. babes-bolyai, informatica, lvi (2011), 3: 33-38, 2011. [10] porter, r., e. nudelman, and y. shoham, simple search methods for finding a nash equilibrium, proc. of the aaai conference on artificial intelligence (aaai), 664-669, 2004. [11] python home page, http://www.python.org/ [12] shoham, y. and k. leyton-brown, multiagent systems: algorithmic, game theoretic and logical foundations. cambridge, usa: cambridge university press, 2008. [13] pycplex reference, http://www.cs.toronto.edu/ darius/software/pycplex/ [14] wxpython reference, http://wxpython.org/ international journal of computers communications & control issn 1841-9836, 10(4):579-592, august, 2015. a trusted-based cloud computing virtual storage system and key technologies k.h. wu, l. chen, y. li kehe wu, long chen*, yi li school of control and computer engineering north china electric power university no.2 beinong road, changping district, beijing 102206, china lw_ncepu@163.com, easy_cl@163.com, somethingnew1989@163.com *corresponding author: easy_cl@163.com abstract: with the popularity of cloud computing, people become incresingly concern about security problems, especially the data security, which has become the biggest obstacle for the development of cloud computing. in order to protect confidentiality and integrity of user data in cloud computing, this paper firstly studies the relevant research works in fields of trusted computing and cloud computing data protection and secondly introduces the concept of trusted into cloud computing data protection, presents the concept of trusted virtual block storage device (tvbsd) and designs the trusted cloud computing virtual storage system (tccvss). and then, the key technologies such as isolation, block device encryption and two-way authentication are expounded in this paper. finally, the result of experiments shows that the system and the related technologies can not only effectively ensure the security of user data, but also control the consequent performance overhead in a proper range. keywords: trusted, cloud computing, virtual storage, cloud storage, encryption, authentication. 1 introduction cloud computing is an extension of distributed computing and grid computing technology, which offers a variety of services to customers through the network, such as infrastructure as a service (iaas), platform as a service (paas) and software as a service (saas) [1, 2]. compared with the traditional pc service model, the cloud computing model provides almost limitless resources for users, which means inexhaustible, taken on demand and pay per use, and for the companies it can reduce the it infrastructure investment and operation & support costs, so that the companies can gain the maximum economic profits. admittedly, cloud computing owns many merits, but it also has defects. as the computing tasks are performed in the cloud and all user data must be stored in the cloud, so the problems, such as where and how the data would be stored, are transparent to the user, which means that the user loses the absolute control over the data. therefore, the service agreement signed between service providers becomes the only guarantee, and the confidentiality and integrity of data become the primary issues that should be considered [3, 4], and also become the biggest obstacle for the development of cloud computing. in cloud computing there are two main data storage modes. one is distributed file system, which regard the virtual machine as a client for system storage the typical representative include gfs(google file system) [5],hadoop [6] and amazon s3 [7]; and the other is virtual block storage device, the typical representative is amazon ec2. both have their advantages and disadvantages. the former, as its own application software must derectly minipulate user data, is difficult to guarantee the consistency of data; and the latter is more flexible, which is transparent, so that the customers can use it just as they usually use local disk, and thus the users can build their own copyright © 2006-2015 by ccc publications 580 k.h. wu, l. chen, y. li file systems and databases according to different needs. it not only can meet the requirements of massive digital information storage and improve the utilization of storage space, but also can shield the heterogeneity of operating system to realize storage management of automation and intelligentce, so as to better improve the quality of cloud computing services. therefore, it is very significant to provide a safe and reliable data storage system and protection technologies for cloud computing. many existing data storage system has good reliability, flexibility, efficiency and ease of management performance, but it still need to be improved for ensuring data security. though the exsiting storage system has used protecting strategies and technologies, including encryption, firewalls and rights management, it only has solved part of the security problems, which has a degree of limitation [8]. so there are still existing following problems in terms of ensuring the data safety and credibility: (i) the problems of internal attack. using the traditional technologies such as encryption and firewalls can only defend the illegal intrusion from outer, but it is incapable to defend the internal attack. in the cloud computing model, the cloud services administrator (simply administrator for short) has many authority, if the insiders obtain the administrator privileges, he can attack the system from inside by an "legal" method, and then destroy the user data and the system. the administrator has all control permissions on the data, services, network and other important resources, so it can access to user data at any time or change the configuration file of the storage devices. therefore, we must balance distribution of authority between the ordinary users and administrator or restrict administrators access to user data. only in this way can we solve the problems of internal attack, and ensure the confidentiality of user data. (ii) the problems of device management. as the characteristics of cloud computing, all user data are stored in the cloud, and in order to protect the confidentiality of the data, it will be stored with encrypted mode and be decrypted when using. however, as the storage device has no protection, the intruders can attack the device to obtain any data they want, and thus use attack technique the brute force attack to get the data that available to them, resulting in leakage of user data and destruction the confidentiality and integrity of data. therefore, there is a must to practice rational management to storage device, for increasing protection measure of devices, which means to prevent attacks to the devices, so as to better prevent user data from unauthorized intrusion and tampering. (iii) the problems of secure authentication. the following two situations can lead to the disclosure of user data: one is the storage device is remapped to the illegal users or accessed directly by the administrator; and the other is legitimate users stored their confidential data to a non-credible (illegal) storage device. in order to prevent the above situations from occurring, we must provide security authentication, through which, we can bind the storage device with legitimate user, making the legitimate users can not store data on a non-certified storage device, while the trusted storage devices can not be accessed by the unauthenticated user, that means user can only access the storage device belonging to its own. only in this way can we completely prevent the above situations from occurring and thus can prevent data leakage. in order to solve the above problems, this paper presents a trusted cloud computing virtual storage system based on the research of trusted computing and virtual storage technology, and the key techniques is researched and achieved. the main contributions are shown as follows: (i) through the study and research of trusted computing [9] and virtual storage technologies, this paper presents the concept of trusted virtual block storage devices, and then design a trusted cloud computing virtual storage systems , so as to better ensure the confidentiality and the integrity of data. (ii) as for the problems of internal attack, this paper proposes the isolation technology, which separate the management section in user-level and the executive section in kernel of the storage a trusted-based cloud computing virtual storage system and key technologies 581 system, and then place them in different virtual machine, so that even the administrator cannot access the data directorly, only through the interfaces provided by the storage system can he manage the trusted virtual block storage device thus, in this way it can solve the data security problems caused by administrator’s overmuch operating authorities. (iii) as for the problems of device management, this paper puts forward the block device encryption technology, which not only can encrypt the data stored in the trusted virtual block storage device, but also can encrypt the block storage device, so that only the user with the private key of the block storage device can access the device and obtain the data, while the unauthorized user cannot access it. in this way, it can avoid the illegal users obtain user data through attacking the storage devices, and thus well-ensured the security of the data. (iv) as for the problems of secure authentication, this paper raises a two-way authentication technology, namely the user needs to authenticate the storage device whether it is a trusted one, and at the same time, the device also needs to authenticate the user whether it has access, and thus combine the user and device, making the legitimate user can only access the device belongs to its own. that is, the legitimate users can not store data on a non-certified storage device, and the trusted storage devices can not be accessed by the unauthenticated user. (v) the effectiveness and performance of the system has been tested and analyzed, the result shows that the system and the related technologies not only can effectively ensure the security of user data, but also can control the consequent performance overhead in a proper range. the paper is organized as follows. in section 1, we study the relevant research works in fields of trusted computing and cloud computing data protection. in section 2, we introduce the concept of trusted into cloud computing virtual storage, propose the concept of trusted virtual block storage device (tvbsd) and design the trusted cloud computing virtual storage system (tccvss), and then provide highlights of the key technologies, such as isolation, block device encryption and two-way authentication. in section 3 , we conduct test and analyze the effectiveness and performance of the system. the last section is the conclusion of the paper and gives lights to our future work. 2 related work in recent years, as cloud computing technology gradually become mature, there are more and more studies on it., among which the data security become one of the crucial researches. some studies focus on the integrity of software load time and run time, enhancing system security by software integrity verification, so as to ensure the data security. for instance, the seshadri [10] through memory virtualization technology can ensure that only validated code can execute in kernel mode, so as to withstand the attack to the integrity of the kemel caused by code injection and ensure system security. xu et al. [11] propose a method based on the virtual machine monitor to detect and prevent the integrity of kemel, and hence prevent system from illeagal operating intrusion. in addition, some studies mainly through the key management to protect the security of data. for example, pearson et al. [12] use the anti-attack ability of tpm(trusted platform module) and the hardware encryption technology to protect data encryption key, and combine the key with platform, making the key incapable to be used on the other platforms ,so as to protect the confidentiality of the data. wang et al. [13] propose a management approach to key-used times based on trusted platform module in cloud storage, it can also control the times of using key while ensuring the safe storage of key, making the use of the key in user client controlled. cheng et al. [14] present a novel key management scheme based on global logical hierarchical graph (glhg), which is used to enforce correctly the global authorization policies of all users. 582 k.h. wu, l. chen, y. li it can eliminates the redundant to minimize the amount of keys transferred and stored, and to maximum reduce the costs and risks of user key management. as the trusted technology widely used in data security and platform security, there are also some research work applied the trusted technology to cloud computing to solve the credibility problem in virtual storage. liu et al. [15] propose a new storage process of object-oriented properties in trusted storage, which can verify the qos attributes of storage to resist a potential threat to the security and integrity of storage. however, this process only solve the trusted storage problem of physical storage device in single platform. wang et al. [16] present a method to solve the security of data storage in virtualization platform, which is a data encapsulation method based on the properties of component and achieved by using the trusted platform module (tpm). yang et al. [17] design an architecture of cloud storage system based on tpm. this architecture used the symmetric key to encrypt data, and then used the asymmetric key to encrypt symmetric key, and finally used tpm to protect the asymmetric key. only in this way can we manage the process of key storage, backup and share effectively. based on the above studies and inspirations, this paper introduce the trusted technology into the virtual storage of cloud computing, design a trusted cloud computing virtual storage system (tccvss), and put forward the isolation, block device encryption and two-way authentication technologies. meanwhile, by testing and analyzing its effectiveness and performance of the system, the result shows that the system and the related technologies can effectively ensure the security of user data. the following sections describe the system architecture and key technologies in detail. 3 tccvss and key technologies 3.1 the architecture of tccvss based on the research of trusted and storage technologies, this paper designs a trusted cloud computing virtual storage system (tccvss) applied to iaas. each trusted virtual block storage devices (tvbsd) of the system are built on the storage resources based on hard disk and iscsi, which can be used by multiple users but only one owner. tvbsd is a virtualization of the trusted physical storage device, which its storage space can be divided into two parts: one is a private storage space, which mainly used to store the confidential information of tvbsd, such as the key of tvbsd, the integrity measurement value, the access control list and so on; and the other is a user storage space, which mainly used to store the user data. tccvss is a trusted virtual storage system based on the virtualization platform framework of xen, which inherits the functions in traditional storage system including snapshot and mirroring, and also joins the isolation, block device encryption and two-way authentication technologies, making the protection of system data more safe and reliable. fig.1 shows the system architecture diagram of tccvss. among these, the main components are listed as follows: (1) the tvbsd manager tool: implementation of this section is based on logical volume manager (lvm2), and it is mainly used to manage the logical volume, such as creating, deleting, snapshotting, mirroring and dynamic extension capabilities. (2) the tvbsd master: this section can be achieved on the basis of device mapper, which is the new component of linux2.6 kernel and the later versions. device mapper provides a method to unify the ways for creating the virtual layer of block device, so it can facilitate realizes the functions including striping, tandem, mirroring and snapshots in virtual layer for users. this paper use the isolation technology, which will be discussed in later chapters, to separate this section into two parts: tvbsd master front-end and tvbsd master back-end, so that it can a trusted-based cloud computing virtual storage system and key technologies 583 figure 1: system architecture diagram of tccvss effectively prevent the illegal access to data. (3) the tvbsd driver: this section mainly provides system security functions including access control, data encryption and user authentication, which can guarantee the confidentiality and integrity of data. the encryption and user authentication technologies will be discussed in more detail in later chapters. 3.2 isolation technology in the traditional cloud computing model, the administrator has so many authority that if the insiders obtain the administrator privileges, it can attack the system from within, and then destroy the user data and the system. the administrator has all control permissions to the data, services, network and other important resources, so it can access to user data at any time and change the configuration file of the storage devices. therefore, we must balance distribution of authority between the ordinary users and administrator or restrict administrators access to user data. only then can we solve the problems of internal attack, and ensure the confidentiality of user data. in the traditional virtual storage system, such as in xen, the virtual block device (vbd) and its management tool are in the same operating system, as shown in fig.2 by dotted box. in this way, the administrator can access the vbd directly through vbd master tool while the system is running, and then illegal intrude or tamper with the data stored in vbd to destroy the confidentiality and integrity of data. in order to protect the security of user data, there is a need to prevent administrator to access the user data directly, so this paper present the isolation technology to solve this problem. that is, to divide the vbd master into vbd master front-end and vbd master back-end, as shown in fig.3. and then place the vbd master front-end and vbd master tool in the same virtual machine to provide vbd management interface for vbd master tool; and place the vbd master back-end in another virtual machine to receive the orders from vbd master front-end and execute them, as shown in fig.4 by dotted box. in this way, the executing section of vbd is separated from vbd master tool, so the administrator does not have permission to access the virtual machine where the vbd master back-end resides. therefore, the administrator can only access vbd through the interface provided by vbd master front-end instead of accessing 584 k.h. wu, l. chen, y. li figure 2: the organization chart of vbd in xen directly, thus ensuring the confidentiality and integrity of data. figure 3: vbd master divide into vbd master front-end and vbd master back-end 3.3 block device encryption technology since the characteristics of cloud computing, all user data is stored in the cloud, and in order to protect the confidentiality of the data, it will be stored with encrypted mode and be decrypted when using. however, due to the storage device does not make any protection, the intruders can attack the device to obtain any data they want, resulting in leakage of user data and destruction of data confidentiality and integrity. therefore, there is a need for rational management of storage devices, to increase protection measure of devices, which means to prevent attacks on the devices, so as to better protect user data from unauthorized intrusion and tampering. to this end, this paper presents a block device encryption technology, which means assigning a block device encryption key (bdek) for each trusted virtual block storage device(tvbsd). during the initializing phase, the system generates a bdek for each user, which is an asymmetric key consisted of two keys–a public key and a private key. the users own the private key themselves to protect the key from exposure to any other entity; and when the user need to store data, the system creates tvbsd according to user needs, and then the public key certificate issued to each tvbsd. the relationship between user and tvbsd is shown in fig.5 as follows: it can be seen from the figure that there is a one-to-many relationship between the user and the tvbsd, that is each user can have multiple tvbsd and each tvbsd corresponds to only one user. and meanwhile, each tvbsd has just one block device encryption key (bdek), which is used to sign the data information rather than encrypt it. if we used kpri and kpub to a trusted-based cloud computing virtual storage system and key technologies 585 figure 4: the isolation technology in tccvss figure 5: the relationship between tvbsd and user 586 k.h. wu, l. chen, y. li represent private key and public key of bdek, and used the data to represent the ciphertext, then the data read and write operations using bdek as shown below: (1) data write operation: use the public key of bdek to sign the data, which is expressed as follows: data1 = e(data,kpub) (1) where the e(data,kpub) means using the public key kpub to sign the data data, the data means the ciphertext and the data1 means the actual stored information in the block device, which is signed using the public key, and only the one who has the corresponding private key can decrypt the ciphertext to get the useful information. (2) data read operation: we need to isolate the information data from the signed data, which is expressed as follows: data = d(data1,kpri) (2) where the d(data1,kpri) means using the private key to decrypt the signed data data1 = e(data,kpub), and the data means the ciphertext. thus it can be seen that only the one who has the corresponding private key can decrypt the data signed by the public key, so as to protect the data from obtaining by the unauthorized entities. in summary, using the bdek technology can prevent the unauthorized entities from accessing the tvbsd, even under the condition of the device be attacked or lost, so as to ensure the security and credibility of tvbsd and protect the data from being invaded and abused. 3.4 two-way authentication technology the following two situations can lead to the disclosure of user data: one is the storage device is remapped to the illegal users or accessed directly by the administrator; and the other is legitimate users stored their confidential data to a non-credible (illegal) storage device. in order to prevent the above situations from occurring, we must provide security authentication, through which, we can bind the storage device with legitimate user, making the legitimate users can not store data on a non-certified storage device, while the trusted storage devices can not be accessed by the unauthenticated user, that means user can only access the storage device belonging to its own. this paper proposed the two-way authentication technology to solve the above problems, which is mainly shown in the following two aspects: (1) can block the storage requests initiated by the user who does not have the access to trusted virtual block storage device. (2) can reject the unauthorized virtual block storage device as users storage device. in order to better complete the two-way authentication process, this paper proposed a trusted authentication module (tam) and designed three tables, namely: the user information table user_list, the trusted virtual block storage device table tvbsd_list and the corresponding relation table of user and tvbsd is user_tvbsd_list. among this, the user_list is used to store the information of all authenticated users, while the tvbsd_list is used to store the information of all trusted devices, and the user_tvbsd_list is used to store the mapping relationship between the users and their corresponding tvbsd. the specific authentication steps are as follows: the process of two-way authentication is shown in the following fig.6: (1) the user send a data storage request to tam, including the information of user information iduser, device information idtvbsd and so on. (2) when the tam received the data storage request: (a) to search the user information table user_list based on the user information iduser, determining whether the user is an authenticated one. if so, then go to next step; if not, then reject the user’s data storage request and feedback to the user. a trusted-based cloud computing virtual storage system and key technologies 587 figure 6: the two-way authentication (b) to search the tvbsd table tvbsd_list based on the device information idtvbsd, determining whether the device is an authenticated one. if so, then go to next step; if not, then reject the user’s data storage request and feedback to the user. (c) if the user and the device are both authenticated, then continue to search the corresponding relation table of user and tvbsd user_tvbsd_list, determining whether the block storage devices users applied to belongs to users themselves. if so, then allow the data storage request and issue the certificate to the user and the tvbsd user applied to; if not, then reject the user’s data storage request and feedback to the user. (3) to establish a connection between user and tvbsd to complete the two-way authentication: (a) user creates a timestamp t1 and then sent it with user’s certificate to the tvbds. (b) tvbds creates another timestamp t2 and calculate m1 = e(t2||h(data)||e(iduser||t1||t2,kpubu),kprit ) (3) then sent its certificate with timestamp t2 and m1 to the user. among this, h(data) means the hash value of information in this communication, kpubu means the user’s public key, kprit means the tvbds’s private key, and e(data,k) means encrypting the information data with the key k. (c) to verify the tvbsd. firstly, to decrypt the m1 with the public key of tvbsd kpubt to get the information including t2, h(data) and e(iduser||t1||t2,kpubu). and then, to decrypt the e(iduser||t1||t2,kpubu) with private key of user kpriu to get the information including iduser and t1, determining whether the information we got matched the original one. if so, the user accept the tvbsd; if not, the user reject the tvbsd. v erity(iduser||t1||t2,m1) = true (4) (d) the user calculate m2 = e(h(data)||e(idtvbsd||t2,kpriu),kpubt ) (5) and then sent it to tvbsd. (e) to verity the user. firstly, to decrypt the m2 with the private key of tvbsd kprit to get the information including h(data) and e(idtvbsd||t2,kpriu). and then, to decrypt the e(idtvbsd||t2,kpriu) with public key of user kpubu to get the information including idtvbsd and t2, determining whether the information we got matched the original one. if so, the tvbsd accept the user; if not, the tvbsd reject the user. v erity(idtvbsd||t2,m2) = true (6) 588 k.h. wu, l. chen, y. li the whole procedure of two-way authentication is finished after the above steps, which can implement the binding between user and tvbsd, so as to better ensure the data confidentiality and integrity. 4 experiments and analysis 4.1 validity analysis in this paper, we designed four experiments to evaluate the effectiveness of the system, from the perspective of the kind of attack, it can be divided into the following tests: unauthorized user access attack test, illegal equipment mount attack test, data monitoring attack test and physical device attack test. (1) for the unauthorized user access attack test: the access of unauthorized user can be sorted into the following two situations: one is the access of illegal user without authorization; and the other is the access to the storage device, which does not belong to the authenticated user. however, in traditional virtual storage systems, take xen for example, as administrators have too much operating authority, so both of the above users can use the administrator privileges to access to the users storage device directly, or remap the storage device to the unauthorized users, so as to obtain the users’ data. while in the tccvss proposed in this paper, owning to the use of isolation technology, the administrator can only use the interface provided by the tvbsd master front-end to access to the tvbsd, which prevent the administrator from accessing to the virtual block storage devices directly. meanwhile, the trusted authentication module (tam) in two-way authentication technology can verify the identity of a user when the user initiate the request for accessing. the experiments result indicates that only the users, who can pass the validation of tam, can establish a connection with the tvbsd, while the illegal users, who can not pass the validation of tam, can not access to the tvbsd, so that it ensure the security of user data. (2) for the illegal equipment mount attack test: similar to the unauthorized user access, the access of illegal device mounts can also be divided into the following two situations: the first is the mount of equipment without authentication; the second is the authorized device mounted to the user who does not have the access to the device. in the tccvss, the trusted authentication module (tam) in two-way authentication technology can verify the identity of a user when user initiate request for mounting. the experiment suggests that only authenticated devices can establish a connection with user, while the illegal device would be denied by tam, and thus ensure the security of user data. (3) for the data monitoring attack test: in traditional storage systems, vbd management tools and vbd are in the same system, the system administrator can monitor the device’s memory by installing and running various software, and can steal user data by attacking the memory. in the tccvss, with the use of isolation technology, the management section of tvbsd and the actual operation section of tvbsd can be isolated, so that the administrator can only access to the tvbsd through the interface provided by the tvbsd master front-end. the result of testing indicates that the administrator is unable to install monitoring software in the equipment area where the perform section of tvbsd stayed, thereby, he cannot carry out monitoring attack to the device’s memory, and thus ensure the security of the data. (4) for the physical device attack test: in the traditional storage systems, both the attack on a physical storage device and the loss of the physical storage device may cause damage to user data. in the tccvss, with the use of block device encryption technology, the security of the device can be ensured. after testing, the results shows that it is difficult to steal user data by attacking the physical device in the absence of private key, and thus ensure the security of data. a trusted-based cloud computing virtual storage system and key technologies 589 the above experiments indicates that the system can perfectly ensure the confidentiality and integrity of data by using various technologies, such as the isolation, block device encryption and tw-way authentication, etc. compared with the traditional virtual storage system, the tccvss has a higher level of security and reliability. 4.2 performance analysis the experimental environment in this paper is as follows: cpu: inter core 2 @2.7 ghz ram: 2gb system: centos 6.4, the kernel version is 2.6.32 platform: xen v4.2.2 hash function: sha-1 encryption algorithm: aes in openssl0.9.8 test tools: iometer, iozone in order to test the performance of the system designed in this paper, we use the test tools iometer and iozone to measure the effects of this system on the performance of xen virtualization platform. iometer is an i/o subsystem measurement and characterization tool for single and clustered systems. its main functions include testing the performance of the disk and the network controllers, bandwidth and delay capacity of the bus, the throughput of the network, the performance of the shared bus, the performance of the hard drive in system level and the performance of the network in system level, etc [18]. therefore, it can be used as a benchmark and troubleshooting tool and is easily configured to replicate the behavior of many popular applications. iozone is a file system benchmark tool, which can test the read/write performance of file system in different operating system. the benchmark generates and measures a variety of file operations,such as read, write, re-read, re-write, read backwards, read strided, fread, fwrite, random read, pread, mmap, aio_read, aio_write,etc. iozone has been ported to many machines and runs under many operating systems. in order to better measure the performance of this system designed in this paper, we access the following three types of disk in xen: the disk with ecryptfs, the disk with dm-crypt and the disk with tvbsd. the ecryptfs is the enterprise file encryption system in linux; and the dmcrypt is a target driver developed based on the device-mapper, which mainly offers transparent encryption of block devices. first, we use iometer tool to do three experiments under the same operating environment, to compare the throughput of the disk under three cases. the test parameters set as follows: (1) the size of data block: transfer request size is set to two cases: 512bytes and 64kbytes; (2) the method of read and write: percent random/sequential distribution is set to two cases: 100% sequential and 100% random; (3) the percentage of read and write: percent read/write distribution is set to two cases: 100% read and 100% write . the result is shown in fig.7, which is the average of multiple measurement experiments: it can be seen that, compared with xen system without encryption function, the read and write performance of the other three disks has a certain loss, which is caused by adding the encryption function, and thus increase the system overhead. compared with the disk of dmcrypt, the tccvss only has a small portion of performance lost, which is not obvious. but compared with the disk of ecryptfs, the performance improved significantly. meanwhile, in the case of the same block size, the throughput of sequential read and write is higher than that of random read and write. 590 k.h. wu, l. chen, y. li xen ecryptofs dm-crypt tccvss 0 1 2 3 4 5 6 th ro ug hp ut r ag e( m b/ s) read write (a) 100%sequential, block size=512bytes xen ecryptofs dm-crypt tccvss 0 10 20 30 40 50 60 th ro ug hp ut r ag e( m b/ s) read write (b) 100%sequential, block size=64kbytes xen ecryptofs dm-crypt tccvss 0 1 2 3 4 5 th ro ug hp ut r ag e( m b/ s) read write (c) 100%random, block size=512bytes xen ecryptofs dm-crypt tccvss 0 10 20 30 40 th ro ug hp ut r ag e( m b/ s) read write (d) 100%random, block size=64kbytes figure 7: throughout evaluation then we use iozone tool to measure its i/o performance being a file system server. the experiment focus on comparing the change of throughout rate of the original xen system with that of this system under the circumstance of different i/o request size, the requested block size change from 16kb to 16mb. the results show in fig.8: as is shown in this figure, with the increase of i/o requests, the system’s performance gap also increase. this is because with the increase in i/o request, the time system spent in locating the file location and reading the document properties increases small, but the proportion of encryption and certification in the entire operation increases because of joining some technologies, such as the isolation, block device encryption and two-way authentication, so the cpu processing speed of encryption and authentication operations become the bottleneck of system performance, which is not obvious when the i/o request is very small. 5 conclusion based on the research of the trusted computing and storage technologies, this paper introduces the concept of trusted into cloud computing virtual storage, defines the concept of trusted virtual block storage device (tvbsd) and designs the trusted cloud computing virtual storage system (tccvss), and then provides highlights of the key technologies, such as isolation, block device encryption and two-way authentication, with which can we ensure the safety of the user data in the system. we use the simulation experiments to test its effectiveness, the results show that the system can well protect the confidentiality and integrity of the user data. and a trusted-based cloud computing virtual storage system and key technologies 591 16kb 32kb 64kb 128kb 256kb 512kb 1mb 2mb 4mb 8mb 16mb 0 2000 4000 6000 8000 10000 12000 14000 xen tccvss request size of i/o t hr ou gh pu t r at e( k b /s ) (a) the performance comparison of write operations 16kb 32kb 64kb 128kb 256kb 512kb 1mb 2mb 4mb 8mb 16mb 0 2000 4000 6000 8000 10000 12000 t hr ou gh pu t r at e( k b /s ) request size of i/o xen tccvss (b) the performance comparison of read operations figure 8: performance comparison between xen and tccvss at different i/o request size the test tools, such as iometer and iozone have been used to test its performance, the results show that this system, compared with other storage systems, has improved to some extent in protecting the confidentiality and the integrity of user data. however, the system loss increased due to use the technologies, such as isolation, block device encryption and two-way authentication, resulting in a performance degradation within a reasonable range. in addition, because the system is designed in an ideal experiment environment, there are many problems need to be solved in the practical application, so the further work is to improve the performance of the system under the condition of ensuring the confidentiality and integrity of user data, and to implement a more safe and reliable cloud computing virtual storage system, which can be suitable for practical application scenes. bibliography [1] b. tograph, y.r. morgens (2008), cloud computing, communications of the acm, 51(7): 9-11. 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[18] j. chen (2011), design and implementation volume-based hierarchical storage system. huazhong university of science & technology. ijcccv8n1.pdf int j comput commun, issn 1841-9836 8(1):50-60, february, 2013. homeostatic control of sustainable energy grid applied to natural disasters f.m. cordova, f.f yanine felisa m. cordova universidad de santiago de chile, usach av. ecuador 3769, santiago, chile e-mail: felisa.cordova@usach.cl franco f. yanine pontifical catholic university of chile, puc av. vicua mackenna 4860, macul, santiago, chile e-mail: francoyanine@gmail.com abstract: according to seismologists chile has yet to face another big earthquake in the very near future, yet the country remains largely unprepared against massive electric power systems brake-down. the problem lies in the centralized electric power systems and the lack of adequate technologies and back-up/emergency power systems for disaster recovery. the flaws that are built into the very fabric of the presently centralized power systems were on full display in the february 27th earthquake in chile. nowhere it becomes more evident that hugely centralized power generation and distribution systems are extremely vulnerable and ineffective to disruptions from natural disasters, human error or other calamities. the large power networks that once proved very efficient and secure, are now at the center of discussion fueling the need for decentralization and the rapid growth of distributed generation (dg). highly decentralized, diversified and dg-oriented energy matrix is notoriously much better suited to withstand these disasters. in centralized electric power grids, servicing large metropolitan areas, albeit with some but limited differentiation in service, can only be on or off, so either everyone gets power or no one does. this makes recovering power service in an emergency situation a much more difficult task. on the other hand, decentralized power systems (dps) reduce the obstacles to disaster preparation and recovery by allowing the focus to shift first to critical infrastructure and then to flow outward to less integrated outlets. a dg-based model for a smart micro-grid based on hybrid electric power systems (heps) using both renewable energy technologies (ret) and conventional power generation units is presented. the hybrid energy system may be portable or fixed in one place, highly reliable, easy to assemble, modular, flexible and cost-effective solution, that is ready-to-run and go to where it is needed to supply power in natural disaster. keywords: natural disasters, energy sustainability, smart micro-grid, hybrid electric power systems, sustainable blocks, sustainable energy strategy. 1 introduction the days of huge, highly centralized electric power networks paradigm may be numbered based on the current trends in the electric power generation and distribution industries worldwide (see [1], [2], [3], [4], [5], [6], [7], [8] and [9]). in the last 20 years, dg and the decentralization of electric power systems in general has gained substantial ground, especially with the rapid advancement in renewable energy technologies (ret), power electronics for system interfacing, and the continuous efforts and great strides, especially in developed countries, to integrate ncre/alternative energies into their power generation matrices. yet the problem is not trivial when we think that current electric power grids that span hundreds of miles and supply power to very copyright c© 2006-2013 by ccc publications homeostatic control of sustainable energy grid applied to natural disasters 51 large regions which comprise several metropolitan areas, were not built thinking that one day dg plants would require access to these electric distribution networks in order to supply power to local communities of different sizes as part of worldwide efforts towards decentralization in the electric power generation and distribution industry ensue. however, the explanation for this rapid shift to dg and further decentralization in the industry worldwide may not lie only in an effort to cut on fossil fuel consumption, green house gas (ghg) emissions and reduce the strong dependence on foreign oil imports, both in developed and developing countries. yet, despite the profound vulnerability that such situations entail, right along with the dire need to reduce high energy costs and become more efficient in offering electrical energy and heating services to their populations, especially to remote, rural and isolated communities; that as we mentioned earlier, may not be the most pressing issue, at least not anymore. for some time now the authorities around the world have been worried and lately overly concerned about the rapid and drastic changes in the weather (one of the greatest concerns cited by the millennium project 2011 state of the future report [31]), along with the frequency and magnitude of natural catastrophes like large earthquakes, rising sea-levels and solar storms, among other threats to world populations in urban and rural areas, and how to deal with such situations, considering the severity of the disasters and chaos that they may bring to modern mankind (see [30] and [31]). 2 building the case for decentralized power systems (dps) and the penetration of dg natural disasters, like earthquakes, volcano eruptions, wide-spread fires and violent weather phenomena like prolonged periods of intense rain and snow, strong winds and hurricanes are not new to humanity but the difference is that in today’s 21st century world much of our fragile living systems and economic sustainability depend on modern infrastructure of which roads, electric power transmission and distribution networks, and telecommunications are a most vital part, yet increasingly vulnerable when faced with these calamities. since the huge earthquakes and resulting tsunamis in 2010, the first which hit chile with a giant earthquake and the second, which hit japan creating a devastating tsunami, more and more people have come to realize the vulnerability of modern world infrastructure and way of life. although of great concern for millions of people, particularly for those countries where the technology is currently being used, and a grave threat of disastrous implications to humanity should an accident or negligent act were to occur again (like the disasters as a result of the fukushima nuclear accident in japan and the chernobyl nuclear power plant meltdown in the old soviet union), the nuclear power issue and its future standing in today’s world energy matrix is a case of profound implications on its own right, and would require an entire paper to discuss it so we will leave it out. yet if we are to focus too much on power generation technologies like nuclear, fossil fuels or hydroelectricity generation, we may be ignoring a larger concern or at least not giving it its proper place in the scale of concern it deserves. centralized electric power systems, and their still huge concentration on fossil, non-renewable fuels and large hydroelectric projects which require building large dams and the inundation of vast extensions of fertile land are a big problem. another drawback of largely centralized power systems is their inability to differentiate among different end users. a health care facility like a hospital, or a school with small children, or traffic lights across an entire city should be serviced differently than just a regular home, machine shop or convenience store in a neighborhood. in a disaster situation, favoring the power supply to the most critical customers becomes very difficult because the systems are made to provide equal power quality and stability to everyone, even during disaster situations. in a centralized electric 52 f.m. cordova, f.f yanine power grid servicing a large metropolitan area, albeit with some but limited differentiation in service, can only be on or off, so either everyone gets power or no one does, as simple as that. this makes recovering in an emergency situation a much more difficult task, sometimes virtually insurmountable. on the other hand, dps reduce the obstacles to disaster recovery by allowing the focus to shift first to critical infrastructure and then to flow outward to less integral outlets [30]. 3 main benefits and advantages of dps and dg just like in decentralized communication networks, where if one of the stations breaks down, it does not bring the whole network down with it, when faced with a disaster like a hurricane or an earthquake, dps can eliminate the threat of the entire power grid collapsing by creating a series of redundancies in the power system itself. with several decentralized power generation plants and distribution networks, a smart micro grid, powered for example by several combined heat and power (chp) units (see [10] and [11]) can disconnect from a grid experiencing a power outage and continue to operate free of disruption until the main power grid is back on-line. the us has learned its lesson from major disasters over the past decade, especially in areas of recurrent phenomena like in the midwest and in the states of florida and texas, and when making choices on how to rebuild critical pieces of infrastructure, there has been a marked increase in the number of vital facilities like healthcare , schools, industrial parks, airports, large manufacturing plants, military outposts and administrative office buildings developing chp generation systems (see [10] and [11]). these, along with other changes like introducing adequate pieces of legislation and giving tax incentives to small businesses and large size community investments in ret and mini and micro-grid technology may represent the beginning of much needed change which we may see more forthcoming in the following two decades. thus the potential for building a stronger and more resilient energy matrix lies in our hands. we need to have less vulnerable, more flexible and resilient electric power systems, as the next disaster will come for sure, but when? nobody knows. but what we do know is that we ought to be ready and well prepared for it, otherwise we will continue to suffer at the expense of the capriciousness of nature and the unpredictability and absurdity of human error, especially in our most critical systems. notwithstanding this general consensus by experts and authorities in general that something has to be done fast, change, as we all know is slow in coming. if the power grid were to collapse in the immediate future, the only facilities with any power at all will be those equipped with microgrid power plants based on dg technologies, most likely utilizing a combination of both, diesel fueled generators and grid-connected hes employing conventional and unconventional generating technologies like ret (using one or more ncre), and able to work as a stand-alone system as well, to generate their own electric power, just like we see diesel fueled alternators in the basement of just about every apartment building in santiago today. in this regard, those facilities which for example, are equipped with their own chpbased microgrid stand the best chance of maintaining their full power supply regardless of the calamities and climatic menaces that may be lurking in the horizon (see [19] and [26]). 4 energy storage systems it is common to find that during off-peak times, when there is excess power due to high amounts of renewable energy being generated in the system by the different components, a certain amount of excess energy must be dumped if energy storage systems (ess) are not available, to avoid over voltages and damage to equipment. wasted energy that otherwise could very well be used at a later time. homeostatic control of sustainable energy grid applied to natural disasters 53 in [27] three stand-alone solar pv (photovoltaic) power systems, using different energy storage technologies, are modelled and optimized both technically and economically. the proposed modelling of components in [27] facilitates the assessment of the ess capacity and also allows for better computation of system’s overall efficiency. key components including pv modules, fuel cells, electrolyzers, compressors, hydrogen tanks and batteries are modelled in a clear way so as to facilitate the evaluation of the hybrid power system. using energy storage technology, a method of ascertaining minimal system configuration is designed to perform the sizing optimization and reveal the correlations between the system cost and the system efficiency. the three hybrid power systems, i.e., photovoltaic/battery (pv/battery) system, photovoltaic/fuel cell (pv/fc) system, and photovoltaic/ fuel cell/battery (pv/fc/battery) system, are optimized, analysed and compared (see [27], [28] and [29]). the optimal solution comes as a trade-off to the problem as expected, where the proposed pv/fc/battery hybrid power system is found to be the configuration with lower cost, higher efficiency and less pv modules as compared with either single storage system (see [27], [28] and [29]). for homes in general we believe the battery bank as part of the hes is the most convenient choice given the cost and modularity provided by this ess. particularly relevant when it comes to ess technology review are (see [12], [13], [14], [15], [16], [17] and [18]) all presenting important contributions to the subject. likewise it is important to mention the need to carry out a very thorough pre-feasibility study of non-conventional renewable energy (ncre) sources potential present at site, particularly solar and wind, studying load characteristics carefully to identify distinct consumer patterns in power demand, and to do a correct mapping of the electricity distribution network. once the project is decided upon, there comes the need to deal with system sizing, optimization of different system components and of the system as a whole, including energy system design, optimal control of hes. by choosing the right components and the appropriate system configuration, their costs can be minimized through proper equipment sizing and load matching (see [12], [13], [14], [15], [16], [17] and [18]). the bottom line is to make the most efficient use of ncre sources and the heps configuration chosen throughout the lifetime of the system. 5 smart, flexible and modular micro generation systems as means to supply electricity to homes in case of natural disasters smart micro generation systems belong in general to the type of applications called smart microgrids. smart microgrids are understood as the enabling technology, integrating hybrid energy systems and energy storage, which allows for the development and integration of alternative energy sources (see [19], [20], [21], [22], [23], [24], [25] and [26]). it is en essence a micro generation system that uses bidirectional communication systems. the concept of smart microgrid for a group of homes can be utilized to define a diverse group of applications, which fosters the ability to monitor and control an electricity network (us department of energy, 2010) (see [19], [20], [21], [22], [23], [24], [25] and [26]). there is however not one but several definitions of smart grid, even though it is easily distinguished from a conventional power grid. however, one can easily recognize that just about any definition on the subject points to an entirely different system, much more capable, robust and resilient than the traditional electric power systems we grew up with. in general, conventional power generation plants, transmission and distribution networks have a limited capability of monitoring and control. this way, it is common to see control centres far away from power generation communicate with generation centres, power substations and large consumers; control functions are often operated manually (see [19], [20], [21], [22], [23], [24], [25] and [26]). in contrast, a smart grid 54 f.m. cordova, f.f yanine is characterized by a two-way flow of electricity and information and is capable of monitoring everything from power plants to customer preferences to individual appliances. it delivers realtime information and enables a near-instantaneous balance of supply and demand at the device level. it can operate at different scales as long as it is located near the source of energy and near the areas of delivery, ideally used to produce and consume energy locally. it involves changes not only in the technology, but also in the elements such as users’ practices, regulations, industrial networks, infrastructure and symbolic meaning (see [19], [20], [21], [22], [23], [24], [25] and [26]). the distinctive feature of an appropriately designed network control is that it admits local and changing communication networks, is robust with respect to intermittency and latency of its feedbacks, and also tolerates connection and disconnection of network components [32]. however, a small dg has some significant problems of frequency and voltage variation when it is operated in stand-alone mode. therefore, a small dg should be interconnected with the power system in order to maintain the frequency and the voltage ” [32]. for a microgrid it is essential that the energy management system (ems) employed delivers active and reactive power generation by means of set-points for the generation sources, including the battery bank used in this particular case, and sends information to consumers about changes in energy supply and consumption. the paper ”grid-connected hybrid pv/wind power generation system with improved dc bus voltage regulation strategy” [33], authors point out that among the various types of renewable energy sources available, the solar and wind energies are by far the most utilized; because of the inherent complementary nature of the solar and wind energies, the hybrid pv/wind power system has higher reliability to deliver continuous power than either individual source [33]. also aiding this trend is the rapid growth of the power electronics techniques, the photovoltaic (pv) and wind power generations systems have increased rapidly (see [21], [22], and [23]).for a microgrid it is essential that the energy management system (ems) employed delivers active and reactive power generation by means of set-points for the generation sources, including the battery bank used in this particular case, and sends information to consumers about changes in energy supply and consumption. the paper ”grid-connected hybrid pv/wind power generation system with improved dc bus voltage regulation strategy” [33], authors point out that among the various types of renewable energy sources available, the solar and wind energies are by far the most utilized; because of the inherent complementary nature of the solar and wind energies, the hybrid pv/wind power system has higher reliability to deliver continuous power than either individual source [33]. also aiding this trend is the rapid growth of the power electronics techniques, the photovoltaic (pv) and wind power generations systems have increased rapidly (see [21], [22], and [23]). in the hybrid energy system configuration which is part of the microgrid proposed here, a battery bank must be used to draw maximum power output and to safeguard the electricity supply to the homes in case of a power outage. different circuit topologies for the grid-connected hybrid pv/wind power system are shown in the paper. since the output voltage of the pv array is different from the one of the wind turbine and the maximum power point tracking (mppt) feature is demanded, a dc/dc converter and a dc/ac inverter are both needed for the pv/wind power system [33]. as this paper shows, a good configuration option is to have an ac-shunted grid-connected hybrid pv/wind power system using two individual dc/dc/ac converters. each one of them is capable of delivering maximum power produced by the pv array and/or the wind turbine. in general, the use of wind energy is cheaper than that of solar energy. in areas where there is a limited wind source, a wind system has to be over-dimensioned in order to produce the required power resulting in higher plant costs [34]. it is emphasized the importance of the grid-connected pv system regarding the intermittent nature of renewable generation, and the characterization of pv generation with regard to grid code compliance. to meet technical requirements in this case, the dg units in the microgrid must have protection, homeostatic control of sustainable energy grid applied to natural disasters 55 control and communication components to have safe operation. the local electric utility grid, which is connected to the homes (ac loads) through the smart microgrid, is the key player within the system. once the key player is out of the game, the rest of the players, namely the advanced sensing, data measuring and communication systems, the advanced hybrid control system, the advanced human-machine interfacing and decision support systems and the energy storage systems (ess) must all figure out a way to continue to work and coordinate adequately to supply power to the homes in case of a power outage caused by a natural disaster or another adverse condition. figure 1 below shows the schematic diagram of the proposed pv-wind hybrid energy system (hes) with energy storage for power supply to a group of homes in case of a power outage caused by natural disasters. we envision a smart microgrid configuration comprised of one or more hes with the following simple but effective configuration. figure 1: schematic diagram of grid-connected solar pv-wind hybrid energy system (hes) with a battery bank as energy storage to supply electricity to a group of homes for disaster readiness. double pointed arrows on the battery bank connected to the ac bar indicate bi-directional electrical energy flow. the main utility power grid is always connected to the battery bank and recharges the energy storage system (ess) whenever this is needed. likewise, when there is a need to draw electricity from the ess, as it is the case when there is an power outage and no electricity is available from the main grid and in addition, the electric power being drawn from the wind turbines and the solar pv panels may not be enough or may unstable the meta-controller sends the signal to start the flow of electricity from the battery bank. the micro grid system shown in fig. 2 will operate connected to the local electric utility grid, and will have communications linkage among the different systems, namely the hes control system, the utility grid control operator, and the people in their homes whom are being provided electricity both by the microgrid system and by the local electric utility grid. the aim here is for everyone that is part of the system being proposed to have the necessary and relevant information 56 f.m. cordova, f.f yanine on the status and operating conditions of the microgrid and its electricity generation capability throughout the day. ideally the different loads in the homes to which the microgrid supplies power for their operation will have sensing devices, in order for the microgrid controller to sense the power consumption of the loads throughout the time it remains in operation and will also be sensing the data provided by the utility grid status. the task of the hes homeostatic regulator function in the hybrid controller is to regulate the electrical energy flow to and from the microgrid to the homes including the battery bank and also the electrical energy flow from the utility grid once the electric power supply is restored. it is part of the microprocessor-based control set-points, and its response is triggered by signals being sensed by the system (power consumption demand in watts) which tells the system that its capabilities to produce enough power to meet demand are either within the range of its generating capacity or out of range. figure 2: model diagram of grid-connected hybrid energy systems (hes) comprising a small micro grid with energy storage system to supply a group of homes in case of a electric power outage. in figure 2 we have the model diagram showing a solar pv array and a set of small wind generators as renewable energy sources (res) comprising the hybrid system for electricity supply to a group of homes. the generator will most likely be an induction generator (because of its convenient cost and simpler technology) coupled to the set of small wind turbines. the induction generator is then connected to a voltage rectifier and this in turn to the ac load (the homes). everything is connected to the microgrid controller. the solar pv array is connected to a pv energy conversion system or dc/ac power converter and this to the load ac bus and to the ac utility grid as well. likewise the battery bank is connected to a bi-directional dc/ac power inverter and an integrated voltage converter unit to make sure that the voltage signal going in and out of the ess is adequate. as it is said earlier, everything is connected to and controlled by the system controller. the controller coordinates and controls the entire system in a distributive manner, and incorporates homeostatic control of sustainable energy grid applied to natural disasters 57 an innovation element called a hes homeostatic regulator, which acts much like the homoeostatic regulation that takes place in the metabolic system of living organisms like humans and animals. this microgrid system is thus able to adapt to abrupt changes in climate conditions and/or changes in the consumption patterns of the homes or changes in the electricity supplied by the utility grid to which it is connected. thus the system is capable of reacting and at the same time communicating to other systems, namely the people in their homes and the power utility grid control operator the conditions of the system. the hes homeostatic regulator is a vital part of the hybrid controller, and constitutes the means to regulate energy flow from the smart microgrid to the homes and also the electric energy flow from the local utility grid to the homes, an important parameter which is fed into the microgrid controller in real time on a permanent basis. it is in essence an intelligent component based on data set-points enabled by a microprocessor that allows the control system of the smart microgrid to manage the energy flow among systems and also makes the controller ”aware” of what’s happening in the system in regards to its sustainability. the homes can be connected to the utility grid control operator and to the micro grid’s controller through a communications link via ip or lan. the controller coordinates and controls actuating on the different individual controllers and providing status and other vital systems information to users and to the utility grid control center operator. the electric power (in watts) being supplied by the local utility grid, the watts being consumed by the ac loads (homes) and the energy flow within and throughout the small microgrid being drawn from the hes’ operation will all be shown on a pc monitor in each home and in the monitor of the utility grid’s control operator remotely located. ideally voltage and frequency will also be shown to the electric utility control operator since voltage for some given frequency varies as a function of time, and thus both are important parameters of the quality of power that the homes are getting from the microgrid system. hence it is important for those monitoring the microgrid operation at a distance to learn of the quality of the voltage being supplied (looking for possible voltage drops or sagging) and that this is maintained within standards. the system controller will be able to act and respond to disruptions being provoked by internal or external phenomena that may occur during operation, adapting the hes operation to these changes. for example, should the dc/ac inverter fail/break-down, the controller will continue operating with the wind turbines and the battery bank as back-up; or else, should the wind turbines coupled to a generator and this to an ac/ac power converter break-down at any point, preventing the flow of electrical energy to continue operating properly, the controller will continue operating by means of the solar pv panels and the battery bank. 6 conclusions the potential for building a stronger and more resilient energy matrix lies in our hands. we need to have less vulnerable, more flexible and resilient electric power systems, as the next disaster will come for sure, but when? nobody knows. but what we do know is that we ought to be ready and well prepared for it, otherwise we will continue to suffer at the expense of the capriciousness of nature and the unpredictability and absurdity of human error, especially in our most critical systems. notwithstanding this general consensus by experts and authorities in general that something has to be done fast, change, as we all know is slow in coming. if the power grid were to collapse in the immediate future, the only facilities with any power at all will be those equipped with microgrid power plants based on dg technologies, most likely utilizing a combination of both, diesel fueled generators and grid-connected hes employing conventional and unconventional generating technologies like ret (using one or more ncre), and able to work as a stand-alone system as well, to generate their own electric power, just like we see diesel 58 f.m. cordova, f.f yanine fueled alternators in the basement of just about every apartment building in santiago today. in this regard, those facilities which are equipped with their own microgrid stand the best chance of maintaining their full power supply regardless of the calamities and climatic menaces that may be lurking in the horizon (see [19], [20], [21], [22], [23], 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[34] ding j.j., buckeridge j.s. design considerations for a sustainable hybrid energy system, unitech institue of technology-ipenz transactions, 27(1):1-5, auckland, 2000. international journal of computers communications & control issn 1841-9836, 12(2):227-237, april 2017. the model for learning objects design based on semantic technologies d. gudoniene, r. maskeliunas, d. rutkauskiene daina gudoniene*, rytis maskeliunas, danguole rutkauskiene kaunas university of technology studentu str. 50, kaunas, 51392, lithuania *corresponding author: daina.gudoniene@ktu.lt rytis.maskeliunas@ktu.lt, danguole.rutkauskiene@ktu.lt abstract: the paper presents a comparison of state of the art methods and techniques on implementation of learning objects (lo) in the field of information and communication technologies (ict) using semantic web services for e-learning. the web can serve as a perfect technological environment for individualized learning which is often based on interactive learning objects. this allows learners to be uniquely identified, content to be specifically personalized, and, as a result, a learner’s progress can be monitored, supported, and assessed. while a range of technological solutions for the development of integrated e-learning environments already exists, the most appropriate solutions require further improvement on implementation of novel learning objects, unification of standardization and integration of learning environments based on semantic web services (sws) that are still in the early stages of development. this paper introduces a proprietary architectural model for distributed e-learning environments based on semantic web services (sws), enabling the implementation of a successive learning process by developing innovative learning objects based on modern learning methods. a successful technical implementation of our approach in the environment of kaunas university of technology is further detailed and evaluated. keywords: learning object, semantic technologies, web applications, learning environments. 1 introduction a variety of tools and systems could be used for the implementation of learning activities through e-learning processes. when talking about e-learning technologies the most important and painful question we get is: what method should be used for the integration of interactive learning objects (lo) in the development of e-learning? the painful thing about this question is that a usual questioner has often been misled to believe that there is only a single tool that does everything that everybody needs to be done: to create, host, and access e-learning material. successful e-learning strategies and scenarios may require integration of dozens of software products chosen from hundreds of candidates sprawling across multiple categories: development of learning objects, delivery of knowledge, content management, communication and collaboration, live learning and assessment, etc. they can also be categorized according to the possibilities of implementation of curriculum (realization of learning events: imitate, receive information, exercise, explore, experiment, create, self-reflect, debate); technological properties (e.g. synchronous, asynchronous, web based, pc application, mobile application, open source, free service); application domain (language learning, intercultural competences, ict skills, time management skills, study habits skills, etc.). however, independent on the purpose or functionality, all tools and systems are often integrated in the virtual learning environment (vle), which can reflect the discipline by providing a well-designed, visually stimulating interface that genuinely supports the needs of real world learning. a web-based education method suits this by providing more copyright © 2006-2017 by ccc publications 228 d. gudoniene, r. maskeliunas, d. rutkauskiene flexibility and intelligence. however, recent developments in the area of semantic web, while contributing to the solution to these problems, also raise new issues that must be solved [3]. the aim of the research in this paper is to present an architectural model of distributed e-learning environments based on semantic web services (sws) with a goal to improve the effectiveness of a learning process by introducing innovative learning objects that combine variety of multimedia elements and other learning material as well as structural integrity of advanced learning methods. extraction of semantic relations has always been a challenging problem in multimedia data. various architectures of multimedia databases have been developed in the past but the need to refine them still remains in order to get the desired results of users’ interest, to extract semantics from multimedia data in a way the user perceives them. 2 related works interactive learning possibilities were improved with the introduction of internet technologies; however, it still fails to reach the full potential. with new, more interactive internet technologies there is even more to be captured and adopted, such as the public knowledge contained in blogs, wikis, social bookmarking services, social networks, etc. [8, 17]. the ontologies, the semantic web, and the social semantic web offer a new perspective on intelligent educational systems by providing intelligent access to and management of web information, and semantically richer modelling of applications and their users [2-3]. this allows supporting more accurate representations of learners, their learning goals, learning materials and contexts of use, as well as more efficient access and navigation through learning resources with a primary aim to advance intelligent educational systems to achieve improved e-learning efficiency, flexibility, and adaptation for both single users and communities of users. the notion of the social semantic web describes an emerging design approach for building semantic web applications that employs social software techniques. social semantic web systems typically elicit domain knowledge through semi-formal ontologies, taxonomies or folksonomies. techniques related to educational content could be provided in different forms [1, 9, 18] and environments that have relations with semantic web services. as web-based education, it has become a very important branch of educational technology [2, 7, 19]. classroom independence and platform independence of web-based education, availability of authoring tools for developing web-based courseware, cheap and efficient storage and distribution of course materials, hyperlinks to suggested readings, digital libraries, and other sources of references relevant for the course are but a few of a number of clear advantages of web-based education. by analysing design of learning objects and essential characteristics of a range of proven learning activities, we can generate a set of requirements for the is architecture. for example, a proven existing learning activity based on implementation of learning objects might enable students to work simultaneously across a network on a design tool, such as a graphics program, and share the results in separate windows. this learning activity therefore generates the computational requirement to allow the use of any shareable application this way. other researchers [6, 9, 10] have analysed a number of digital resources to be used and reused for learning (learning objects) and concluded that a number of those is constantly increasing. therefore, description of learning objects with metadata is important as it allows enhancing search, retrieval and usage of learning objects and because it is very important in the integration process into any environment to efficiently organize a training process. learning objects can be considered not only as resources providing affiliated materials, but also as methodological resources which include teachers’ experiences, reflections, examples or instructions of usage of content objects and descriptions of learning methods [9, 18]. however, existing standards and specifications for learning objects metadata are not intended for including methodological the model for learning objects design based on semantic technologies 229 figure 1: principal schema of learning objects relations resources and learning method descriptions together with content objects. modeling of learning objects and search of learning objects of the same content in the semantic web is a challenge to every researcher. the metadata and ontologies (see fig. 1) let to find and to reuse learning objects in different situations as they make lo machine-understandable. according to rehman and kifor [21], ontologies are like repositories: they are helpful in exchange of knowledge, reusing existing knowledge, for reasoning and inferring on existing knowledge. dagiene et al. are discussing that digital learning resources by themselves are not as valuable as their target application in the learning process as well as a properly selected learning method. therefore, metadata is indispensable and semantic web services should be intended part of the design of learning objects. the systematically prepared corpus of metadata helps all the parties involved in learning process to cooperate, use and share learning resources [16], [13]. interface of a learning object usually presents the same content and has the same look and feel for every user, regardless of a student’s learning needs and individual characteristics. some interfaces are "customizable"; the user can choose to modify some characteristics of the graphical user interface. however, this does not entirely satisfy the needs of educational content presentation to the users. aspects of content personalization, proper interaction and efficient presentation are also important. some research on a design of learning objects has been already conducted [4]-[6] and some researchers [18] made the effort to combine learning objects and constructivism focus largely on how learning objects can be used in specific constructivist learning environments instead of building a universal generic structure. the results of research presented by gurbuz et al. [5] suggest that a new architectural model for learning objects development must be designed with a common login system that provides 230 d. gudoniene, r. maskeliunas, d. rutkauskiene figure 2: architectural model of the platform information about a person trying to connect (for example, name, address, email address, user code). aroyo at al. [10] and gote et al. [7] have found a key to enable the interoperability and to capitalize on the semantic conceptualization and ontologies, common standardized communication syntax, and large-scale service-based integration of educational content and functionality provision, and usage based on a model of semantic web services for education. the central role in achieving unified authoring support plays the process-awareness of authoring tools which should reflect the semantic evolution of e-learning systems. gurbuz et al. [5] present a system architecture model which is designed and based on serviceoriented architecture technology (soa). this architecture intended to provide an ability to create a flexible system focused on the provision of services with the ability to implement effective services and systems solutions for an integration of standard learning objects into information systems. the platform (see architectural model of the platform in fig. 2) can be structured in the following logical levels [10]-[12]: • user level, consisting of user and system interfaces, implemented according to user specification and system requirements; • the level of external systems; the adapter is provided for managing web services. in addition, a system of components is required for external inclusion; • the data level involves all data management, sanctioning, monitoring, archiving and storage components. when connected to one of the web services by the use of other systems the second time logging is not necessary anymore as the system automatically checks the permission of online users and the model for learning objects design based on semantic technologies 231 figure 3: integration of learning objects into interactive learning courses authorizes them. the architectural model provides main functionality necessary for the design and implementation of learning objects. a central role of achieving unified authoring support plays the process-awareness role of authoring tools which should reflect the semantic evolution of e-learning systems [14], [15], [20]. the researchers are working on the novel architecture of learning objects to solve the problem of unification of learning objects and to start working on standardization of los and to offer templates to users to design content based learning objects for e-learning systems related to semantic web services technologies. the semantic relations with the learning objects are well constructed and to be provided from learning objects’ interface. 3 a proprietary model of building los for semantic e-learning environments (sws) we propose a novel architecture for designing modern learning objects that can be easily applied to various domains. a model is based on identified key learning objects phases that make an influence on the organization of study process as well as on finding technological solutions to design los and to develop technical implementation. we have carried out a comprehensive data analysis on the implementation of e-learning processes starting with the requirement specification to the delivery phase. in the evaluation, we have analysed the design of learning objects, methods of learning (i.e. blended learning method, distance learning method, etc.) and methods of providing massive open online courses. the architectural model (see fig. 4) was designed theoretically (based on the state of the art and best practice scenarios); its implementation started on a step-by-step basis. one of the requirements was to include metadata of each section that could be used to provide some useful information about the task goal, keywords, background, etc. going up to the knowledge granularity level (see fig. 3), multiple sections can form a component. multiple components related to a learning topic can be grouped. when a learning object is generated by an instructor/author, the expert’s experience can be embedded into the configuration of the course, such as selection of section contents, selection of components, sequence of components, display modes of components, etc. when it is generated by the learning environment, certain organizational 232 d. gudoniene, r. maskeliunas, d. rutkauskiene figure 4: architectural model for the design and delivery of learning objects patterns can be applied to generate a course corresponding to the learner’s profile and to the learning goal. the architecture is based on soa principles. the functions will be provided as services; the internal service processes are intended to optimize the use of the provision of direct procedure calls that can be realized directly in the server application procedures. some kind of activities can be performed as services and will be directly integrated into learning objects. through the components of web services (ws), there is a possibility to take an advantage of the vle and its functionality which provides a specific service such as data recording, getting a report, etc. moreover, the service component of our system can be used to work in a workflow. the workflow can be used for both systems and services of vle is provided by external integrated systems. is and all its components are adapted using standard it platforms, operating [13], [16] systems and existing computer network infrastructure. information system can provide nonformal education and implementation of learning objects as well as self-tests carried out in the content management system that assures this essential functionality and the relations by semantic web services directly to content of learning objects, including preparation of learning objects, metadata description and semantic relations to other open resources; development of self-tests, integration into the software facilities; learning objects, learning programs and courses in preparation of and public access to its management; is and user administration. high resolution digital objects are stored in our university’s vle is repository at open.ktu.lt. these objects must then be converted (transformed) into different formats and quality facilities for further implementation and content creation of different types (for different training programmes and courses). is and it’s components are designed to work in the environment which was adapted to communicate with standard it platforms, operating systems and the computer network. looking to the is on student interface we can view learning objects in different ways. one the model for learning objects design based on semantic technologies 233 way is to click “view default” to choose to view the course created by the author/instructor if such a version is available. alternatively, students can choose and have the course generated automatically by the system according to some patterns like some external teaching methodology. there is a way to have a choice to view the learning objects sorted by the profile by which the learning materials will be generated session by session. finally, students can choose to view all of the raw components related to the topic. our approach demonstrates the possibility of using the constructivism learning theory to guide the design of learning objects based upon the original prototype. the collaboration among authors of lo’s is supported, while learners can also actively participate in the construction of learning objects. it provides a way to allow learners to grasp the whole picture of the course quickly. the ease of viewing contents of learning objects iteratively in different ways assists learners to learn efficiently in constructivist learning environments. through the web services (ws) components of the system, users and designer can take an advantage of the vle and its functionality which provides specific services, such as initiating business processes to record data to get a report and so on. besides, the service component of the system can be used to work in the workflow. workflow can be used for both vle is systems and services provided by external integrated systems. some parts of the described system are under constant improvement. changes in system functionality processes have to be implemented and adapted to the system. our proposed process modelling makes it easier to adapt to the emerging new needs, for instance, to decide to adjust the sequence of necessary steps to abandon any of the steps to change the conditions of implementation steps. the workflow can be easily incorporated during the steps carried out by the external system via services (service), as well as allow to easily changing the step executor. for example, a workflow step is carried out by an external system which presents the results of the vle from that system. is functions will assure the insertion of educational content (including different format of learning objects), testing, and content development: development of digital material and metadata description; design of learning objects, design of courses and open access management; development of tests and assignments and relation to the targeted program; virtual learning environment and users administration. key property of the semantic web architecture (common-shared-meaning, machine-processable metadata), enabled by a set of suitable agents, seems to be powerful enough to satisfy the elearning requirements: fast, just-in time and relevant learning. learning material is semantically annotated and it may be easily combined in a new learning course in case of a new learning demand [23]. the process is based on semantic querying and navigation through learning materials enabled by the ontological background. in fact, the semantic web could be treated as a very suitable platform for implementation of e-learning objects since it provides all means for (e-learning) ontology development, ontology-based annotation of learning materials, their composition in learning courses and (pro)active delivery of the learning materials through e-learning portals. in our system, learning objects are distributed on the web but they are also linked to agreed network of ontologies. this enables construction of user-specific courses by allowing semantic querying for various topics of interest. software agents of the semantic web can be used to enable co-ordination between other system agents and proactive delivery of learning materials in the context of actual problems. the semantic web can be as decentralised as possible. this enables an effective co-operative content management. content is determined by an individual user’s needs and aims to satisfy the needs of every user. the users by using personalised agent searches for learning material will 234 d. gudoniene, r. maskeliunas, d. rutkauskiene be customised for her/his needs. the ontology is the link between user needs and characteristics of the learning material. the semantic web enables the use of knowledge, provided in various forms, regarding semantic annotation of content. distributed nature of the semantic web enables continuous improvement of learning materials. it enables the use of distributed knowledge provided in various forms, enabled by semantic annotation of content. distributed nature of the semantic web enables continuous improvement of lo. 4 practical realization of the designed architectural model for learning objects development the developed architectural model for the design of learning objects includes content development tools and social network integration and video lecturing system. the content acquisition and control measures were implemented by using the open-source virtual learning environment moodle, an open source social networking system elgg and open-source drupal cms integrated with a system for video lecture recording as well as the tool ckd dedicated to the development of learning objects. the system allows users to create / edit / delete content, search, use e-learning content and share with other users for evaluation. facility installed and configured user roles and rights connected allow to form a single login system within active web services, in order to produce the information and allow for service centres to carry out the instructions sent by the inclusive media. semantic tools have been implemented to assist the workflow of course creation delivery, to revise a recommendation process of a relevant content and people in the context of the course and the institution, to assist students by recommending resources that match the topics of their assignments and personnel that may be able to support their activities, to help in group formation for collaborative work based on students’ background, personal preferences and successful prior collaboration, to add support for critical thinking and argumentation by visualizing arguments and linking relevant discussions. implementation of semantic technologies allowed enabling integration, searching and matching of nodes of information. large university repositories, like triple stores, where information can be efficiently stored, searched and managed, were also improved by a more efficient semantic model. according to the architectural model presented in the section 2, changes were made to is distance learning management system “mano.ktu.lt”. the platform was created with the idea to aggregate the information distributed by different institutional departments including personal user’s needs (portfolio). this system data of user activity is gathered by metadata service from different e-learning systems. metadata is then structured and saved as a user e-portfolio. semantic analysis service is activated on user elearning event, for example, the user is logging to any of the platform’s systems. semantic analysis service gets e-portfolio data and performs semantic analysis using several methods: user-based collaborative filtering, cosine similarity, person correlation, jaccard tanimoto index, sorensen coefficient. recommendations for learning materials and other users (for promotion of collaboration) are presented to the active user after an analysis is performed in the plugin of the environment where the user is performing his learning activities. for example, if a user is in a video presentation system, s/he gets similar videos based on the analysis of all data on all systems together (semantic proximity between users in elgg, moodle, cdk, mano.ktu.lt environments is analysed) – if two the model for learning objects design based on semantic technologies 235 figure 5: effectiveness of lo integration and more users collaborate in the same elgg group, they might want to check the same videos on a video presentation system. 5 initial experimental evaluation as there is no way to effectively introduce objective measures for other than performance tasks, a specified survey was issued to conceive the effectiveness of current milestone version. the users were requested to answer the questionnaire and express their opinion on the design and delivery of learning objects as well as about practical implementation of los into the institutional system: if it is friendly, easy to use or not. the research was implemented on national institutional level where 94 authors of academic courses were requested to answer the questionnaire. 54 % of respondents were aged between 45-60 and 46 % were aged between 30-45. the respondents were requested to answer the main question which technological solutions dedicated to design learning objects were friendlier to use? from an age perspective, we can claim that age was not a very important factor and the respondents were interested in learning this novel architectural model and were coping easily with a standard template to design interactive learning object that does not require special competences to provide an interactive learning content to students. we have also asked to compare the creation of standard learning objects vs non-standard learning objects used in educational process. the result showed that the users were friendlier with the standard objects (see a comparison of usability of learning objects based on the architectural model and not in table 1) where the number of respondents provided data on the lo usability in the architectural platform. the experimental research on the effectiveness of lo integration (see fig.5) was carried out by means analysing data on the platform developed on the architectural model (see fig. 4) and using of high resolution learning objects to be provided for the study process. in other cases the supplied video objects have proven as a more effective way to work with students who were in different locations, and to leave video records for self-learning as well. this is true even for the development and delivery of massive open online courses in our university. according to the respondents, successful integration of architectural model into practice opened an easy way to teachers and lecturers to make video records of lectures. after tuning a model on the remarks of our experimental participants, a component of video conferencing system was modified into a more user-friendly experience (vips, http://vips.liedm.lt) was developed. is environment became very friendly to use due to a novel combination of modern technologies and established methods of pedagogy. 236 d. gudoniene, r. maskeliunas, d. rutkauskiene conclusions new approaches of the learning objects modelling plays the central role in achieving unified authoring support in the process-awareness of authoring tools which should reflect the semantic evolution of e-learning systems. however, the researchers are working on the architecture on lo design to solve the problem of the unification of learning objects and to start standardization of los and to suggest the templates for users designing content based standard learning objects for e-learning systems based on semantic web services technologies. for conclusions the authors identify that the developed model offers perfect technology and environment for individualized learning based on interactive learning objects not only for teachers but for learners as well, as they can be uniquely identified, content can be specifically personalized, and the learning progress can be monitored, supported and assessed. the ontologies, the semantic web, and the social semantic web offer a new perspective on intelligent educational systems by providing intelligent access to and management of web information, and semantically richer modelling of applications and their users. the semantic web enables the use of distributed knowledge provided in various forms, provided by semantically annotated content. distributed nature of the semantic web enables continuous improvement of learning objects. the research data shows that architecture for the design of learning objects can be applied to various domains and that authors can easily work with in order to design and integrate of learning objects. at the same time, the technological solution for architectural model based on semantic web services cannot be influenced by the age of users as this is not very important and the respondents were interested in architectural model and standard template to design interactive learning object having very easy technologies that do not require special competences to provide to students interactive learning content. bibliography [1] ermalai, i.; dragulescu, b.; ternauciuc, a.; vasiu, r. 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(2014); a conceptual architecture of ontology based km system for failure mode and effects analysis, international journal of computers communications & control, 9 (4): 463-470. international journal of computers communications & control issn 1841-9836, 10(3):357-369, june, 2015. an application of latent semantic analysis for text categorization g. kou, y. peng gang kou school of business administration southwestern university of finance and economics, chengdu, china no.555, liutai ave, wenjiang zone chengdu, 611130, china kougang@swufe.edu.cn yi peng* school of management and economics university of electronic science and technology of china, chengdu, china no.2006, xiyuan ave, west hi-tech zone chengdu, 611731, china *corresponding author: pengyicd@gmail.com abstract: it is a challenge task to discover major topics from text, which provide a better understanding of the whole corpus and can be regarded as a text categorization problem. the goal of this paper is to apply latent semantic analysis (lsa) approach to extract common factors that representing concepts hidden in a large group of text. lsa involves three steps: the first step is to set up a term-document matrix; the second step is to transform the term frequencies into a term-document matrix using various weighting schemes; the third step performs singular value decomposition (svd) on the matrix to reduce the dimensionality. the reduced-order svd is the best k-dimensional approximation to the original matrix. the experiment uses more than fifteen hundreds research paper abstracts from a specific field. because different factor solutions of the lsa suggest different levels of aggregation, this work examines thirteen solutions in the experiment. the results show that lsa is able to identify not only principle categories, but also major themes contained in the text. keywords: latent semantic analysis, topic extraction, text mining, information retrieval. 1 introduction many multidisciplinary fields, such as data mining, bioinformatics, biochemistry, and neuroscience, emerge in the past several decades. since multidisciplinary fields involve theories, methods, and techniques from multiple disciplines, it is not easy to comprehend all the research efforts in these fields. text categorization, which organizes documents into groups based on their underlying structures, can help capturing the large amount of activities and diversity of a multidisciplinary field. the goal of this paper is to apply latent semantic analysis (lsa) approach to detect major research topics and themes of a multidisciplinary field. in particular, it is intended to address three questions: what are the core research areas of the selected field, what are the major research themes, and what is the dynamics of the discipline? lsa is an automatic mathematical and statistical technique for uncovering common factors that representing concepts hidden in text[1,2,3,4]. previous investigations in psychology and computer science have proved that lsa resembles the way the human brain distills meaning from text and is capable of inferring much deeper relations in the text data[3,5]. the rest of the paper is organized as follows. section 2 describes the basic concepts of lsa. section 3 presents the experimental study that was used to identify the core research areas copyright © 2006-2015 by ccc publications 358 g. kou, y. peng and themes. section 4 discusses the results of this analysis, focusing on three important factor solutions of lsa. section 5 summarizes the paper with conclusions and limitations. 2 research method latent semantic analysis (lsa) is a theory of knowledge acquisition, induction and representation[2]. it was first introduced as an information retrieval (ir) technique by [1] and [6]. it is an automatic mathematical learning technique for analyzing the relationships and similarity structures among documents and terms, relying on no human experiences, prior theoretic models, semantic dictionaries, or knowledge bases[3]. similar to factor analysis, principal components analysis, and linear neural networks, the main purpose of lsa is dimension reduction, which is realized through a matrix operation called singular value decomposition (svd). svd is a means of decomposing a matrix indo a product of three simpler matrices. by retaining the k largest singular values, the resulting reduced-order svd provides the best k-dimensional approximation to the original matrix, in the least square error sense[7]. in the results of svd, two sets of factor loadings, one for the words and one for the documents, are generated. each term and document is represented as a k-dimensional vector in the same latent semantic space derived by the svd. thus each latent semantic factor is now associated with a collection of high-loading terms and high-loading documents[5]. high-loading terms and documents are used to interpret and label the corresponding factor. the number of factors is an input parameter that needs to be provided before svd computation. as the number of factors changes, lsa groups key terms or documents into various levels of aggregation. when it is applied to identify important topics of a certain discipline using a collection of representative papers, a higher level of aggregation (e.g., 2 factors) indicates key research areas and a lower level of aggregation (e.g., 100 factors) represents general research themes[5]. the lsa analysis can be summarized in three main steps. the first step is to set up a termdocument matrix in which each row stands for a key word or term and each column stands for a document or context in which the key word appears. an entry in the matrix is the frequency of a key word in the corresponding document. the second step is to transform the term frequencies in a term-document matrix using various weighting schemes. the third step is to perform svd on the matrix to reduce the dimensionality, which is the key feature of the lsa method. in this step only the k largest singular values are retained. the reduced-order svd is the best k-dimensional approximation to the original matrix[7]. extensive experiments have demonstrated that the classification performance of lsa is robust[8] and it is capable of inferring relations in the text [3,5]. it can be used in information retrieval (ir), search optimization, classification, clustering, filtering and other ir-related applications[7]. readers interested in mathematical details of the lsa approach can refer to [1]. 3 experimental study this section describes the data source and the implementation details of lsa analysis that is utilized to identify the core research areas and research themes for the selected field. 3.1 data sources the field of multiple criteria decision making (mcdm) and multiattribute utility theory (maut) has grown exponentially and made remarkable progress since 1960s. as a multidisciplinary field, mcdm/maut has close collaboration with some neighboring disciplines, such as an application of latent semantic analysis for text categorization 359 mathematical programming, organizational behavior, engineering, decision analysis, and negotiation science[9]. during the past twenty years, extensive research papers have been published in mcdm, maut, and related disciplines. in the experiment, lsa is applied to a collection of mcdm/maut publications to extract major research topics and identify the trends of the field. since previous studies, such as [10], [11] and [12], have investigated the major areas and the evolution of mcdm/maut before 1990s, articles published before 1985 were not included in the analysis. a total of 1515 research abstracts published in 16 refereed mcdm-related journals in the english language during the period of 1985 to february 2009 that contain key words: multiple criteria and multicriteria, were collected. as the first and unique journal in multiple criteria decision analysis, articles published in the journal of multi-criteria decision analysis were all collected (from 1992 through 2007). the 16 refereed mcdm journals were selected according to two criteria: (1) journals appeared frequently in the multiple criteria decision aid bibliography on the international society on mcdm website[13]; (2) the most relevant and top-rated mcdm journals listed by [14] and [15]. each article collected in the dataset is stored in microsoft excel as one row with five fields: article title, author(s), journal name, year of publication, and abstract. table 1 lists the journals and the number of abstracts included in the text data. about 34% of the articles were published in the european journal of operational research, with about 19% in the journal of multi-criteria decision analysis and 8.5% in the journal of the operational research society. table1. refereed mcdm journal articles, 1985-2008 journals number of articles european journal of operational research (ejor) 519 journal of multi-criteria decision analysis (jmcda) 292 journal of the operational research society (jors) 130 computers & operations research (c&or) 88 fuzzy sets and systems (fss) 86 computers & industrial engineering (c&ie) 70 decision analysis (da) 64 omega 64 mathematical and computer modelling (m&cm) 41 annals of operations research (aor) 40 decision support systems (dss) 35 management science (ms) 31 operations research (or) 18 journal of optimization theory and applications (jota) 16 theory and decision (td) 11 organizational behavior and human decision processes (obhdp) 10 figure 1 summarizes the number of publications in the field of mcdm from 1985 to february 2009. because text data were retrieved in october 2008, the number of abstracts collected for the year of 2009 can not reflect the real publication trend and therefore is ignored in figure 1. as seen in figure 1, the mcdm publications have been increased rapidly since 1992 and the number of mcdm publications has increased 4.7 times from 1985 to 2008. 3.2 text preprocessing the initial step of lsa analysis is to represent the text as a term-document matrix in which each row stands for a term and each column stands for a document. in order to set up such a matrix, this study started the analysis with text preprocessing procedures that are popular in the information retrieval and text mining[16,17]. 360 g. kou, y. peng figure 1: mcdm publications from 1985 to 2008 the text preprocessing procedure consists of tokenization and term reduction. tokenization divides documents into a set of terms. in this study, each article is represented by its title and abstract. since titles are informative of research papers and normally contain pertinent key words, the weight of titles is set twice as much as abstracts. the 1,515 mcdm research papers generated a dictionary of 9,322 terms. tokenization was implemented using a self-developed c++ program. 3.3 term frequency matrix text preprocessing produced a term-frequency matrix with 1,515 columns (papers) and 3,299 rows (terms). originally, an entry in the matrix contains the number of times a term occurs in a document. a term-frequency matrix measures the association of a term with respect to a given document[17]. there are many methods to define term weights. in this study, the tf-idf, a traditional term-frequency weighting, was used to transform the raw term frequencies in the matrix. the tf-idf weighting scheme combines term frequency (tf) and inverse document frequency (idf) together: wij = tfij × idfi (1) where tfij is term frequency and idfi is the inverse document frequency of term i. inverse document frequency represents the importance of a term and is defined as: idfi = log2(n/dfi) + 1 (2) n is the total number of documents and dfi is the document frequency of term i. idf implies that the discriminative power of a term will be decreased if it occurs in many documents. in other words, the importance of a term will increase if it appears in a limited number of documents. the reasoning behind the tf-idf weighting is that a term occurring frequently in a document but rarely in the rest of the collection is considered to be important. experiments have shown that tf-idf measure works well in many applications[17,22]. the tf-idf weights were calculated using a linguistic analysis tool[23]. 3.4 latent semantic analysis lsa can be considered as an application of reduced-order singular value decomposition (svd)[23]. svd decomposes a term-document matrix x into the product of three other matrices: x = w0s0c(prime)0 (3) an application of latent semantic analysis for text categorization 361 w0 and c0 are the matrices of left and right singular vectors and s0 is the diagonal matrix of singular values. w0 has the same number of rows as the original matrix and c0 has the same number of columns as the original matrix. s0 is a square matrix with non-zero entries only along one central diagonal and sorted in decreasing order[1]. the dimensionality of the original matrix can be reduced by keeping the first k largest coefficients in the diagonal matrix s0 and setting the remaining smaller ones to zero. the zero rows and columns of s0 can then be deleted to get a new diagonal matrix s. similarly, the corresponding columns of w0 and c0 can be removed to obtain w and c respectively. the product of the simplified matrices is a new matrix x̂: x̂ = wsc(prime) (4) x̂ is the k -rank matrix with the best possible least-squares-fit to x [1]. the results of svd include one set of k -factor loading for the terms and one for the documents. high-loading terms and documents of a factor can then be used to interpret and label the factor. for mathematical and technical details of svd, please refer to [1](p. 397-399). the choice of k is a critical issue in svd. an ideal value of k should be large enough to fit all the real structure in the data and small enough to avoid unimportant details[1]. since solutions with different number of factors represent different levels of concept aggregation, we explored 2 through 13, and 100 factors respectively. factor interpretation and labeling was conducted manually by two mcdm researchers. the high-loading terms and documents of 2 through 13 and 100 factor solutions were examined and labeled independently. the next section discusses the results of the lsa analysis. 4 results and discussion 4.1 different factor solutions this work examined 13 solutions, including 2 through 13 and 100 factors, to identify key research areas and major research themes of mcdm. for the rest of the paper, factor x-y is used to indicate the yth factor of the x -factor solution[5]. for example, factor 100-2 refers to the second factor of the 100-factor solution. different factor solutions of lsa show different levels of research themes of the mcdm discipline. the 6-factor and 11-factor solutions describe the evolution of these areas during the past twenty-four years and reveal major research areas of mcdm, including maut, electre methods, analytic network process (anp), multicriteria decision support system (mcdss), heuristics, preference learning, interactive multiple objective programming, mcdm applications, and goal programming. as the number of factors increases, higher level research areas can be partitioned into subareas. for example, preference learning (factor 6-4 from table 2) in the 6-factor solution is represented by preference representation (factor 11-5 from table 3) and preference structure modeling (factor 11-7 from table 3) in the 11-factor solution; and preference modeling (factor 100-55), preference elicitation support (factor 100-77), and preference ordering techniques (factor 100-99) in the 100-factor solution. table2. top 30 high-loading terms for the 6-factor solution factor factor label top 30 terms 6-1 analytic network process (anp) pro,ecis,multi,decis,multipl,pre,riteria,criteria,roc, met,problem,ultipl, gener, rel,ref,model, criterion, valu,ram,set,risk,function, experi, probabl,appli, effect,approach,object, base, altern 6-2 multicriteria decision support system ecis,decis,riteria,criteria,pre,met,refer,ref,valu,method, multi,base, altern, risk,maker, group,appli, experi, prefer,model,multicriteria, util, function, result,theori, regret,multipl,analysi,rel,analys 362 g. kou, y. peng 6-3 multi-attribute utility theory (maut) multipl,method,pro,ultipl,decis,probabl,task,learn,met,hypothes, gener, rel,effect,criterion, fuzzi, program,experi,problem,addit,ram, approach,ecis, linear,function,pre,paper,goal,set,risk,find 6-4 preference learning met,multi,multipl,ultipl,decis,method,object,line,linear, process, program, pre,prefer,refer, ecis,ref,singl,learn, evalu,effici,function, ram, roc, search,task,fuzzi,solut,multiobject,paramet,probabl 6-5 electre methods riteria,criteria,method,ecis,decis,met,valu,multi,analysi, evalu,tri, analys, object,program,ram,multicriteria, perform,solut,linear,fuzzi, multipl, select,sel,algorithm, optim,risk,line,approach,paper,develop 6-6 heuristics riteria,pre,multi,refer,criteria,prefer,ref,multipl,ultipl, ram,met, problem,decis, function,optim,ecis, multicriteria, object,algorithm,program,pro, fuzzi,criterion, roc,solut,system,process,tri,plan,integ the 100-factor solution presents a large variety of research themes studied during the last twenty years by the mcdm and related disciplines (see table 4), including mcdm theories, algorithms, related areas of research, decision support systems, applications, and techniques. it also reveals important mcdm research topics that are not presented in the 6-factor and 11-factor solutions, such as data envelopment analysis (dea) method, genetic algorithms, simulation, behavioral issues, theoretic foundation, and visual tools. the 100-factor solution points out two notable trends in the mcdm publications. the first is the growth in applications of mcdm. in the 100-factor solution, 21 factors are related to mcdm applications. these applications cover not only traditional application areas, such as asset management[24], scheduling problem[25], assignment problem[26], questionnaire survey[27], credit scoring[28,29,30,31], and risk evaluation[32,33,34]; but also emerging novel areas, such as verbal data classification[35], web-based decision support[36], habitual domains[37], electronic commerce systems[38,39], and e-participation[40]. the second trend is that mcdm has entered into some new research areas[41]. for example, supply chain management has utilized mcdm methods to capture multicriteria decision making and decision-making under uncertainty[42]. geographical information systems (gis) and mcdm have been combined to aid spatial decisions[43]. these two results generally agree with [9], [12] and [28]. table3. top 30 high-loading terms for the 11-factor solution factor factor label top 30 terms 11-1 goal programming pro,ecis,multi,decis,multipl,pre,riteria,criteria, ultipl,met,problem,roc, gener,rel,ref,model,criterion, valu,ram,set,risk,function,experi, probabl,appli, effect,approach,object,base,altern 11-2 multiple criteria sorting problem ecis,decis,riteria,criteria,pre,met,refer,ref, prefer,method, multi, base,altern,risk,util,group, appli,maker,experi,valu,model,multicriteria, function, result,theori,regret,multipl,analysi,criterion,rel 11-3 interactive fuzzy multiple objective decision making multipl,method,pro,decis,ultipl,probabl,task,learn, met,gener,ram, hypothes,rel,effect,fuzzi,program, criterion,problem,approach,experi,linear, addit, set,function,goal,risk,ecis,pre,prefer,paper 11-4 ranking alternatives ram,met,multi,multipl,decis,ultipl,method,line,object,process,linear, program, pre,roc,ecis,prefer,refer,ref, evalu,singl,learn,fuzzi, effici, function, search,task,solut,multiobject,analyt,valu 11-5 preference representation riteria,criteria,ecis,method,decis,met,valu,multi,evalu, analysi, analys,object, program,tri,solut,ram,multipl, multicriteria,perform, optim, fuzzi, select,linear, risk,sel,ultipl,line,approach,algorithm,pape 11-6 heuristic approach riteria,pre,multi,refer,criteria,prefer,multipl,ref,ultipl,met, ram,decis, function, problem,optim,ecis,fuzzi, multicriteria,criterion,object,algorithm,pro, process, solut,system,program,singl,risk,prioriti,roc 11-7 preference structure modeling met,method,riteria,ref,refer,pre,criteria,prefer,model,multipl,function, ecis,process,roc,multicriteria,ultipl, weigh,regret,algorithm,decis, weight,group,prioriti, case,gener,goal,methodolog,learn,prior,sel 11-8 machine learning and knowledge discovery multi,multipl,ultipl,model,system,criteria,riteria,object, valu,attribut, analys,met,search,risk,function, research,regret,method,set, analysi,man, pre,problem, effici,theori,compar,ecis,line,polici,paper 11-9 applications tri,riteria,criteria,attribut,met,model,method,prefer,refer,multipl,system, ultipl,problem,evalu,valu,ref, util,ram,man,multi, multicriteria, multiattribut, term, ecis,log,pro,solut,criterion,manag,analys 11-10 multiattribute utility theory model,multi,analys,analysi,man,ultipl,problem,object, decis,solut, multipl, program,valu,manag,ram,ecis, refer,gener,prefer,multiobject, appli,method, maker, interact,system,rel,strateg,log,algorithm,paper an application of latent semantic analysis for text categorization 363 11-11 interactive procedure for mcdm model,tri,man,valu,function,attribut,weigh,line,linear,evalu,weight, criteria, manag,problem,multi,riteria, fuzzi,util,goal,system,plan, search, cost,ram,network, research,multiattribut,decis,altern,process table4. factor labels for the 100-factor solution factor label project selection and scheduling methodology multiple criteria decision making under uncertainty outranking relations gis and mcdm integration exact algorithms multiple criteria linear regression method for ranking alternatives dynamic consistency (dc) optimization techniques monte carlo simulation multi-objective optimization evaluating decision alternatives electronic commerce mcdm in data mining qualitative decision making portfolio selection and management electre methods stochastic goal programming multiple objective ant colony optimization algorithms scheduling problems comparative study of mcdm methods maut model multicriteria classification multiple criteria simulation optimization method bayesian approach heuristic algorithm mavt preference elicitation interactive multiple objective programming procedure artificial intelligence interactive multiobjective optimization manufacturing system neural network for mcdm alternative evaluation models interactive multiobjective sys. aim mcdm in strategic energy policy making multiple criteria decision support system environmental planning assessment and decisions measures of interdependences between the objectives design problem attribute weights determination influence diagram multicriteria expert support system tchebycheff procedure for multiple objective decision making multiple criteria group decision making genetic algorithms tabu search dynamic programming multi-criteria production planning decision maker’s utility function assessment group decision support system (gdss) information systems multiple criteria abc analysis knowledge discovery and mcdm (neural network) flow shop scheduling problem fuzzy set and approximate reasoning vector optimization genetic algorithms tabu search dynamic programming algorithm development internet and public decision making mcdm in cellular manufacturing system system performance measures preference modeling system design problem case study web-based decision support and applications game theory approach industrial facilities layout planning and design anp technique discrete multiple criteria problems operations research multiobjective decision making in military applications topsis facility location problem zionts-wallenius algorithm graphical display tools mcdm and industrial engineering modeling interaction between criteria in mcdm fuzzy mcdm dss applications of heuristic approaches lexicographic goal programming data mining and ml ahp improvements dea philosophy of mcdm parameter determination methods theoretic foundation goal programming metaheuristic algorithm behavioral issues multicriteria location problem smaa optimization algorithms and implementation of mcdm resource allocation model team decision making under uncertainty visual tools simulation modeling table5. top 10 high-loading papers for the 6-factor solution 364 g. kou, y. peng factor high-loading papers factor loading 6-1 jin woo lee, soung hie kim, c&or,2000 0.14 j. m. coutinho et al.,c&or ,1999 0.11 wey, wann-ming,wu, kuei-yang, m&cm,2007 0.11 behnam malakooti, jumah e. al-alwani, c&or, 2002 0.09 minghe sun et al., c&or, 2000 0.08 lorraine r. gardiner, ralph e. steuer, ejor, 1994 0.08 otto rentz, fss, 1996 0.08 taeyong yang et al., fss, 1991 0.08 bernard roy, roman slowinski, aor, 2006 0.07 mark a. coffin, bernard w. taylor, c&or, 1996 0.07 6-2 c. zopounidis, michael doumpos, c&or, 2000 0.14 t. terlaky, ejor, 1985 0.11 v. mousseau et al., c&or, 2000 0.11 taeyong yang et al., fss, 1991 0.11 otto rentz, fss, 1996 0.11 lorraine r. gardiner, ralph e. steuer, ejor, 1994 0.09 n. m. badra, fss, 2002 0.09 e. melachrinoudis, z. xanthopulos, c&or, 2003 0.09 masatoshi sakawa, hitoshi yano, fss, 1989 0.09 john a. aloysius, et al., ejor, 2006 0.08 6-3 jose rui figueira et al., ejor, 2008 0.15 risto lahdelma et al., ejor, 2003 0.15 david l. olson, ejor, 2001 0.15 s. greco, v. mousseau, r. slowinski, ejor, 2008 0.11 stelios h. zanakis, et al., ejor, 1998 0.1 pekka j. korhonen, jukka laakso, ejor, 1986 0.1 gerard colson, c&or, 2000 0.09 silvia angilella, ejor, 2004 0.09 an ngo the, vincent mousseau, jmd,2002 0.09 risto lahdelma, pekka salminen, ejor, 2002 0.08 edmund kieran burke, sanja petrovic, ejor, 2002 0.08 6-4 jose rui figueira et al.,ejor,2008 0.25 bernard roy, roman slowinski, ejor, 2008 0.19 george mavrotas, panagiotis trifillis, c&or, 2006 0.19 theodor j. stewart, ejor, 1986 0.18 peter muller, da, 2006 0.13 kim fung lam, eng ung choo, jors, 1995 0.1 murat koksalan, ahmet burak keha, 2003 0.1 risto lahdelma, pekka salminen, ejor, 2002 0.09 salvatore greco, et al., ejor, 2002 0.09 gregory e. kersten, dss, 1988 0.08 6-5 j.c. leyva-lopez, e. fernandez-gonzalez, ejor, 2003 0.28 salvatore greco et al., ejor, 2008 0.25 bernard roy, roman slowinski, ejor, 2008 0.12 risto lahdelma, pekka salminen, ejor, 2002 0.12 j os c. fodor, marc roubens, jmcda, 1997 0.12 silvia angilella et al., ejor, 2004 0.11 huseyin cavusoglu, srinivasan raghunathan, da, 2004 0.11 salvatore greco, et al., ejor, 2002 0.1 minghe sun, ejor, 2002 0.1 minghe sun, ejor, 2002 0.1 6-6 j. gupta, kruger, lauff, werner, sotskov, c&or, 2002 0.27 j. gupta, k. hennig, f. werner, c&or, 2002 0.24 peter muller et al., da,2006 0.13 sandeep purao et al., dss, 1999 0.12 gregory e. kersten, dss, 1988 0.12 ilia tsetlin, robert l. winkler, da, 2006 0.12 jatinder n. d. gupta, johnny c. ho, c&or, 2001 0.11 vincent t’kindt et al., c&or, 2003 0.11 b. malakooti, c&or, 1989 0.1 julian molina et al., ejor, 2008 0.1 an application of latent semantic analysis for text categorization 365 4.2 different factor solutions table6. factor labels and paper counts for the 6-factor solution factor factor label paper counts 85-89 90-94 95-99 00-04 05-09 6-1 analytic network process (anp) 41 48 54 63 61 6-2 multicriteria decision support system 37 63 51 49 39 6-3 multi-attribute utility theory (maut) 17 44 62 40 42 6-4 preference learning 22 27 34 22 55 6-5 electre methods 38 42 54 59 35 6-6 heuristics 32 36 49 39 45 table7. factor labels and paper counts for the 11-factor solution factor factor label paper counts 85-89 90-94 95-99 00-04 05-09 11-1 goal programming 21 20 37 27 16 11-2 multiple criteria sorting problem 37 63 51 49 39 11-3 interactive fuzzy multiple objective decision making 15 21 13 19 11 11-4 ranking alternatives 22 14 26 24 7 11-5 preference representation 21 16 29 34 30 11-6 heuristic approach 24 22 27 39 29 11-7 preference structure modeling 19 22 37 26 33 11-8 machine learning and knowledge discovery 14 16 25 29 16 11-9 applications 27 29 45 32 26 11-10 multiattribute utility theory 17 19 19 18 20 11-11 interactive procedure for mcdm 18 24 37 28 35 figure 2 suggests that the growth in some research areas, such as preference learning (factor 64), heuristics (factor 6-6), and analytic network process (anp) (factor 6-1) increased considerably from the 1985-1989 period to the 2005-2009 period. in the case of electre methods (factor 6-5), the number of publications maintained a relatively constant increase from 1985-2008. the research interests in multi-attribute utility theory (factor 6-3) grew significantly from 1985-1989 to 1995-1999 and dropped during the 2000-2004 period. the number of publications in maut remained stable since then. multicriteria decision support system (factor 6-2) experienced a rapid growth from 1985-1989 to 1990-1995 and declined slightly during 1995-2008. figure 2: dynamics of major research areas (six-factor solution) 366 g. kou, y. peng figure 3: dynamics of major research areas (eleven-factor solution) 5 conclusions and limitations this paper attempted to identify the major research areas and themes of mcdm field by examining a large body of related research papers using latent semantic analysis. in the experimental study, over fifteen hundred abstracts of mcdm/maut field were collected and analyzed to obtain thirteen factor solutions. the 6-factor and 11-factor solutions of the analysis reveal key research areas of mcdm/maut. maut, electre methods, anp, multicriteria decision support system (mcdss), heuristics, preference learning, interactive multiple objective programming, mcdm applications, and goal programming are among the main streams of thought of the field. the ideas and techniques of mcdm are continuing to integrate into other disciplines. for example, data mining (dm) field used electre methods to cluster opinions[44] and utilized multiple criteria decision aid process to help users to sort association rules[45]. artificial neural networks, an artificial intelligence (ai) method, has been used by mcdm researchers to solve discrete mcdm problem[46] and model decision-makers’ preference structures[47]. geographical information systems (gis) and mcdm have been combined to aid spatial decisions[43]. this study has several limitations. first, since the lsa analysis depends on identifying frequent word usage patterns from a collection of text, it is difficult to capture a research area if it is not well established and has not established consistent terminology among its researchers[5] . second, this study only collected articles published after 1985 because the major areas and the evolution of mcdm and maut before 1990s have been investigated in previous studies[10,11,12]. third, the research abstracts collected 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(1994). a neural network approach to modeling fuzzy preference relations for multiple criteria decision making. computers and operations research, 21(9): 991-1000. ijcccv11n4.dvi international journal of computers communications & control issn 1841-9836, 11(4):457-471, august 2016. dtn routing algorithm for networks with nodes social behavior a.m. dziekonski, r.o. schoeneich andrzej marek dziekonski, radoslaw olgierd schoeneich* institute of telecommunications, warsaw university of technology ul. nowowiejska 15/19, warsaw, 00-665, poland a.m.dziekonski@stud.elka.pw.edu.pl *corresponding author: rschoeneich@tele.pw.edu.pl abstract: this article presents routing algorithm in delay and disruptive tolerant networks (dtn). the main idea of this work is routing method that is based on information about nodes social behavior and their social relations in sparse structure of network. the algorithm takes advantage of friendship relationships between nodes and uses historic information to create groups of friends for each node, which is used in buffer management and forwarding phase of routing. beside the routing method, mechanisms of collecting and exchanging of maintenance information between nodes is described. the algorithm was tested using the one simulation tool especially designed for dtn scenario and compared with miscellaneous popular solutions. keywords: dtn, routing algorithm, social behavior. 1 introduction communication has always been important part of different communities lives, both short and long distance. one of dynamically developing wireless networks are ad-hoc networks, that create connections directly between networks nodes mobile ad-hoc networks (manet) [1]. this is a type of network, in which its nodes are only users and also they are the only elements required for networks existence and operating. one kind from manet networks family are delay tolerant networks (dtn). dtn [2] is a kind of wireless networks that enables communication in sparse and disrupted mobile ad-hoc networks. there are not any central or privileged elements in the means of network operations. depending on networks purpose, there might be nodes collecting data from others, or controlling them, but this is ensured simply by distribution of proper messages, not by other networks mechanisms. dtn network is generally very dynamic its elements can freely move, so constant connections between nodes exist very seldom. also periods between subsequent nodes contacts is not deterministic and usually is very long. one of the essential parts of telecommunication, and also very important in dtn networks, is routing. routing is the decision making process of finding the best paths that messages should follow to reach destination in networks. without this mechanism nodes would not know which messages should be passed to which nodes to provide good network operation. in dtn networks routing is especially important, because contacts between nodes are rare and short lasting, so every opportunity should be perfectly used. unfortunately, for the same reason, it is more complex than in traditional networks, where all connections are known for long periods and it is easier to find proper paths for messages. nodes in dtn networks are devices that move during network operation. some nodes might be means of public transport like communication equipment connected to buses or trams, smartphones, small devices connected to animals etc. there is no limitation of dtn application. still the most interesting and with the biggest possible usage are networks based on smartphones or other equipment held by people. that is why there is a big need for development of solutions copyright © 2006-2016 by ccc publications 458 a.m. dziekonski, r.o. schoeneich directed for networks with human mobility patterns, since people movement and behavior is not random, but more-or-less predictable. 2 state of the art routing protocols for dtn networks are specific solutions due to the necessity of dealing with complex requirements set by the network conditions, mostly in the means of disruption and long message delivery latencies. that is the reason why traditional algorithms from common manet networks, like [3]: ad-hoc on-demand distance vector (aodv) or dynamic source routing (dsr) cannot be used. the easiest solution for delivering messages in dtn networks is epidemic routing algorithm [4]. it floods the network with data without making decisions based on any criteria. node is transmitting all of the messages from his buffer to any met node. the order of choosing messages is random. according to the fact, that it creates many copies of the same message in order to distribute them further, this algorithm belongs to the routing solutions group based on message replication. it is a very good reference point while comparing other solutions efficiency, because it chooses randomly proxy nodes and uses all available network resources. in perfect conditions, that are unlimited buffer capacities and infinite connection bandwidth, epidemic routing acquire biggest possible number of successfully delivered messages to the destination node. the next solution from replication-based types of routing algorithms is spray and wait [5]. it works much alike epidemic routing, but it does not flood the network with unlimited amount of data. each message can be replicated only in specified number of copies, which are transferred to other nodes. after creating and transmission of the last copy of selected message, all of the copies are kept in the buffers and transferred only while meeting the message destination node. spray and wait can work in two modes normal and binary. in normal mode only the node that created the message is distributing copies to other nodes, which later can transfer them only directly to the destination node. in binary mode the node transmitting the message passes half of its lasted copies to the other node, and all the nodes that keep in their buffers more than one copy can pass them to any node until they have only one copy left. prophet algorithm [6] in contrast to spray and wait and epidemic solutions is based on predictions of future nodes contacts. this is done using historic information collected during existence of the network. the main part of this algorithm is calculation of probability of meeting each of the other nodes from the network. in the first phase it calculates the new probability for the node that currently the connection is held. second phase is actualization of the transitive probabilities, that is probability with the use of a proxy node (non-direct probability). prophet solution implements also a mechanism that is prioritizing newest contacts before oldest ones. nodes that are more active in the network, that establish more connections, have higher probabilities and higher priority in other nodes routing data collections. this algorithm creates many copies of the same message, theoretically infinite there are no strict limits, but the network is not flooded with messages to the full extent during contact only those messages are exchanged, that have higher probability of reaching destination while held by the node on the other end of the connection. the algorithm that is best fitted for dtn networks based on complex, natural mobility models is maxprop [7], despite the fact that it was developed for vehicle-based networks. in this solution each node keeps information about the network as a graph, which edges weights are calculated probabilities of the contact two chosen nodes. then algorithm looks for the paths and makes decisions regarding order of the messages in the buffer. the creators did an observation based on many simulations, that transmission of newer messages, so the ones which passed by fewer nodes, in the first place can increase efficiency of routing. that is why in maxprop algorithm dtn routing algorithm for networks with nodes social behavior 459 they developed a mechanism, that messages having hop count less than some calculated border value are transmitted in the first place, and those which passed by more nodes are ordered based on the probabilities calculated with the use of dijkstra algorithm. one of the important elements of the solution is dealing with successfully delivered messages, that enables clearing nodes buffers from those messages and prevents the unneeded its further exchange. many research teams focused on analyzing human mobile patterns for the needs of telecommunication [8–12]. therefore there are also few algorithms that were developed for networks which nodes are behaving like people. one of the algorithms of this type is label routing [13]. it is based on an assumption that people belong to some communities, so they meet with some of the nodes regularly. authors developed a solution that create a label for each node that tells other nodes about his community. when message is supposed to be forwarded, it is passed directly to the destination node or to the node which is in the same community (has the same label) as the destination node. the problem is that messages are exchanged only if labels of potential next-hop and destination nodes match. in other cases messages are stored and waiting for a proper possibility to forward the message. this increases delivery delay and in some cases, for example with low and restricted node mobility, can have a big influence on routing efficiency. another interesting solution focused on social-based dtn networks is bubble rap forwarding [14]. this algorithm takes advantage of two social characteristics community and centrality. they assume that nodes belong to different communities and are active at different level. the algorithm allows each node to belong to more than one group one group can be family, another colleagues at work, another friends from high school etc. in each of those groups nodes are prioritized by calculation of centrality of the node among others. this value is kind of popularity of the node and shows which ones have the highest probability to meet all others from that group. since this value is calculated inside single group, each node has many values of centrality one for each community to which it belongs. this parameter helps to forward message when it is already held by a node from the message destination node community. another problem is to exchange message between different groups. for this purpose another centrality is calculated global one, that takes into account whole network as one group. when new message is created, the first phase is forwarding the message to the destination node community using the nodes global centrality values and then the second phase is exchanging message using local centralities from current group. social based multicasting [15] is a multicast approach, that is creating many copies of the same massage, to dtn routing that uses two social characteristics centrality and community. in this solution, the centrality of the nodes and the cumulative probability of future contacts is calculated based on poisson modeling of social networks. then it uses unified knapsack problem to select relay nodes to assure proper delivery ratio. the main issue is the computational complexity. on the other hand, the authors of sane routing [16] focus on completely different social characteristics interest and similarity. this solution comes from the assumption, that people with similar interests meet each other more often, than the ones that have nothing in common. the interests are represented as a vector, and messages are forwarded to nodes which interest profiles are close to the destination node one. there are few other algorithms that focus on nodes social behavior and its application in order to develop an efficient routing [17–19]. most of them focus on three social characteristics: community, centrality, friendship [20]. first of those assume that nodes can be divided into groups (communities) that has many regular contacts between each other and that will continue in further network operation. thanks to that algorithms can divide its operation into two phases between communities and inside destination group. examples of solutions using community characteristic are described above label and bubble rap, and also friendship based routing 460 a.m. dziekonski, r.o. schoeneich [21]. second of frequently used social characteristics is node centrality, which is a measure of activity and importance of the selected node. this helps to differ nodes inside the network and find most important nodes in the means of potential better forwarding efficiency. the main algorithms taking advantage of centrality is described above bubble rap as well as simbet [22] algorithms. the last social characteristic is friendship, which is measure of the relationship between two nodes. the number of contacts, the periods between consecutive connections, duration of contacts etc. all of those parameters that can help calculate the grade of relation between two nodes can be taken into account and considered as friendship measure. the example of use of this characteristic is friendship based routing algorithm. 3 the social based algorithm this routing algorithm is based on information of nodes social behavior collected during network existence. beside the main algorithm, also mechanisms of collecting and exchanging control information are important for making proper routing decisions. the main assumptions while developing the new solution were that the algorithm should be aimed for networks with human-mobility patterns, but still work well in other types of networks. it should predict future nodes behavior based on historic data collected during network operation. since in that type of networks resources should not be a big problem smartphones and similar devices have large amounts of memory and communication interfaces with fast transmission speed and the main goal is maximizing the number of delivered messages, while minimizing the delivery latency, the proposed algorithm is a full-replication-based type, so there is no limits of created messages copies. secondary goal is also to minimize the length of the paths that messages follow, since it is proven that it helps the network to operate more efficiently [23]. 3.1 information collected and exchanged for routing purposes the key element of the solution are historic information regarding the network needed for making routing decisions. this data can be collected by the nodes themselves, or received from other nodes. each node holds two information regarding whole network. (a.) first of them is maximum popularity value from all network nodes. it is used in mechanism that orders messages to be transfered to other nodes. (b.) second stored data is the time of last reset of nodes popularity values. this helps to determine time, when table aging mechanism, that is the decrease of number of nodes contacts and groups popularities, should be launched. for proper network operation it is needed to handle successfully delivered messages. for this purposes each node stores list of already delivered messages and updates and sends it during each new contact. after such an update node deletes from its buffer all messages that identification numbers are included in the received list. essential for making routing decisions are two tables. first of them stores number of contacts with each node from the network. based on it, the node decides which nodes belong to its friends group. the table is updated after each contact by increasing the number of connections with currently met node. then the border value for belonging to nodes friends group is calculated. identification numbers of the nodes that exceeds this parameter, so the ones that are chosen to friends group, and the popularity of the group, which is defined as calculated border value, are passed to each met node. second essential information is table containing definitions of other nodes friends groups. for each node, with which at least once connection was established, the list of its friends identification numbers and the group popularity value is stored. this data is used in buffer management mechanism and during making decisions related to transferring messages to other nodes. dtn routing algorithm for networks with nodes social behavior 461 except mentioned above, nodes store also information about the time of the last contact and the average period between those contacts for each node from the network. we use that data as one criteria in memory buffer management. nodes gets maintenance information in two ways. some are collected by the nodes themselves during network operation for example connection times with other nodes, number of meetings etc. other information are collected from met nodes. due to limited node communication capabilities it is very important to assure effective and compact way of exchanging maintenance data. after establishing a new connection first and significant for solution effectiveness element of the algorithm is the exchange of successfully delivered messages identification numbers. this data is exchanged in both ways. according to them, nodes update their lists and clears their buffers from appropriate messages. next the information about friends groups are exchanged. after update and rebuilding of nodes friends groups, each of the sides of the connection passes its friends identification numbers and popularity of his group to other node. this helps reduce amount of sent data in this phase of the algorithm. the node that receive this data refresh in its memory information about friends of the node on the other end of the contact. the last collected data from other nodes is information about most active router in the network hub node. the activeness is measured as the number of contacts. identification number of the node is not exchanged, only the value of the popularity of this biggest hub node. 3.2 memory buffer management after establishing a connection between two nodes, exchanging and processing maintenance information, the solution proceeds to the key phase of the algorithm making routing decisions. it is divided into two stages. the first one concerns the management of buffer, the actions that need to be done when the memory is overloading. the second is directly connected with routing it makes decisions in what order messages should be transmitted to connected nodes. this order has essential significance because of limited contact times and bandwidth speed. the first stage is memory management. nodes have limited space in buffers to store messages. it is highly probable situation, when a new message is created or received from other node, but there is no free space in the buffer. in this case the algorithm is launching decision-making procedure to free some space in the memory by choosing older message to be deleted. in the solution there is separate algorithm to compare messages for buffer management purposes. as a result of its operation all messages are sorted and placed in order to be removed, to free enough space for new message. it is done by directly comparing two messages and deciding which of them is less important and should be deleted before the second one. this algorithm is presented on the diagram below. in the buffer management decision-making process there are few criteria implemented. first of them, while choosing messages to delete, is the number of nodes that message already went through number of hops. in case of its different values, the one which went through more nodes is chosen to be deleted before the other one. when compared messages have the same hop count, the algorithm checks predicted time of next contact with destination node. prediction is based on historic information about the time of last contact and average period between them. this predicted time is not very precise, but taking into account non-deterministic nodes movement and the need to minimize resources, that are memory and computation time, it is sufficient to decide which messages should have higher priority to stay in memory. additionally, to compensate inaccuracy, that difference of two predicted contact times has to exceed some border value. in other case the algorithm passes to the last criteria, which is comparison of paths through friends groups. in the case of memory management the distance is calculated and the message which distance is longer is chosen to be deleted. there is no need 462 a.m. dziekonski, r.o. schoeneich figure 1: algorithm to sort messages for buffer-management purposes. dtn routing algorithm for networks with nodes social behavior 463 to add any extra criteria if all of described above steps do not differentiate priority of staying in buffer for two messages, it can be assumed that they are equally important. 3.3 data exchange the main part of developed routing solution is the way of passing messages to other nodes methods of making the decisions about the order of messages to be exchanged with the nodes with whom connection is currently established. the primary assumption in passing the messages is the routing algorithm type, that is replication-based what means that many copies of the same message are created. while spreading many copies it is essential to give messages priorities, that tell which ones should be transmitted in the first place. for this purposes the sorting algorithm is developed. characteristic distinction between sorting method in this stage of routing algorithm compared to the one in buffer management are the items being sorted. in this case not only messages are ordered, but pairs of message-established connection. it helps better estimate probability of delivering data to destination, depending on which node will be next hop on for the message. similarly to buffer management stage, in this phase also few criteria are implemented in order figure 2: algorithm to sort pairs messages-connection for data exchange purposes. to sort elements. first of the criteria is detection, if the node being compared is a hub or not. before checking that, to eliminate situations when both compared nodes are hubs or just the difference between them is very insignificant, the ratio of popularities of both nodes is calculated (bigger value to smaller) and it has to be bigger than border value set as a solution parameter. in case, when this ratio is bigger than the border value, both of the compared nodes are checked if they can be classified as hubs. if only one is successfully classified, his pair, which is 464 a.m. dziekonski, r.o. schoeneich the node and compared with it message, is receiving higher priority in sorting process. in other case, when both or none of them are classified, the algorithm advances to next criteria. the next criteria in routing part of the algorithm is the most important one and makes decisions in most cases distance based on friends groups. the main goal of the solution is working in networks, which nodes are personal equipment held by people, so the mobility is not random. people belong to different communities, have their own friends with whom they meet most often etc. those characteristics are mostly constant or at least long lasting. the algorithm draws conclusions based on observations made during network existence nodes spending most of the time in some location are establishing connections with chosen group of nodes. additionally for each node those groups are different family members spend a lot of time together, but they work in different places, take different routes using public transport or car, have different friends. that is why the creation of common groups is very complex and inefficient. in the solution each node create his own independent friends group and pass its description, containing friends identification numbers and group popularity, to other network elements. group popularity is calculated based on the number of connections required by nodes to belong to the friends group. calculation of the distance based on collection of friends groups is done by looking for a path through those groups to the destination node. in data exchange stage of the solution, the node that currently has an established connection is considered next hop for the message and based on its friends group next nodes on the message path are searched. after finding a path, the number of proxy groups is considered the calculated distance value. furthermore friends groups has different importance active nodes has higher border value that recognizes network elements as its friends, so they are more active and this suggest that this group should have higher priority. popularities of the groups on the predicted path are not added, but averaged geometrically to prevent situations that would preference of the paths with bottleneck group inside, so one group with very small popularity that could decrease efficiency of the path. still the length of the path calculated just as number of proxy groups is more important. that is why the popularity of the path is considered only when the length of compared paths is equal. there is an additional limitation of maximum path length, that the search process is finished after reaching that value and if path to destination is not found, the distance is set to infinity. this limitation was introduced for two reasons (a) to decrease computational complexity, usage of node resources and time needed to find paths, and (b) to introduce next criteria to compare pair message-neighbor node that can have better efficiency than comparison of very long potential message paths. this maximum path length is set as a variable parameter to the algorithm. if any of mentioned above criteria do not resolve which compared pair should have priority, the algorithm passes to final criteria comparison of ttl (time to live) parameter of the message. justification is that messages that will not expire in short future have better probability of reaching the destination. it is worth noticing, that in this criteria only messages are compared the connections in corresponding pairs are not influencing the result. 4 simulations simulations were performed to check the efficiency of the solution using the one simulator [24] a tool developed on aalto university in helsinki. it is used for examination of dtn networks operations, mainly for checking network efficiency with the use of different routing algorithms and different nodes mobility patterns. this tool has many advantages that simplify simulations good programming interfaces to easily add new algorithms, gui that helps finding problems during network operation and implementation of few mobility patterns and most popular existing routing algorithms, i.e. maxprop, prophet, spray&wait, epidemic. dtn routing algorithm for networks with nodes social behavior 465 table 1: values for parameters set for the simulations parameter name value parameter name value popularity 1,25 hub other 0,85 hop limit 4 reset seconds 21600 border value 1,4 reset divisor 2,0 hub max 0,95 predict. time threshold 1,25 the efficiency of routing algorithms was determined by analyzing few parameters. the main ones are: • delivered messages defined as ratio of number of successfully delivered messages to all generated messages. • message delivery latency average time between generation of the message and time of its delivery to destination node. only successfully delivered messages are taken into calculation. dropped, expired or non-delivered are omitted. • message hop count the number of nodes that message went through before reaching destination node. rejected, expired and non-delivered messages are not included in calculation. • message buffer time average time of holding each copy of the message in nodes buffers. • overhead ratio represents the level of flooding network with messages. it is calculated as a ratio of number of all copies of all messages exchanged by nodes, reduced by number of delivered messages, to number of delivered messages. in case of direct delivery algorithm (that passes meessags only when nodes meet destination node) overhead ratio reaches its minimum value 0. 4.1 comparison with chosen existing algorithms in order to compare efficiency of developed solution few simulations were performed, that differs mostly in used mobility patterns and simulation duration. they were executed using five different routing algorithms proposed new solution, maxprop, prophet, spray and wait and epidemic. to compare general efficiency of the new algorithm, values of its parameters that have influence on algorithm operation were set top-down, without looking for their optimal values, the same for all simulation scenarios. this way comparison with other solutions is fair and objective. they are presented in table 1. the values of the parameters were chosen to be universal and work well in different network types. we consider a node to be a hub if its popularity is at least 95% of the largest encountered popularity in the whole network (hub max parameter). this is important only if the difference between two nodes being compared in the message exchange process is larger than 15% (hub other parameter). also each node consider other nodes to be their friends if they meet them at least 40% more often than the average number of all its encounters (border value parameter). the aging of the number of encounters mechanism is set to be executed each 6 hours (reset seconds parameter) and is done by dividing the number by 2 (reset divisor parameter). maximum length of the path through friends groups is set to be 4 (hop limit parameter) and calculated difference in paths popularities must be at least 25% (popularity parameter). the difference of predicted time of the next encounters for two pairs of nodes must be larger than 25% (pred. time threshold parameter). 466 a.m. dziekonski, r.o. schoeneich the two node mobility patterns that were used are shortest-path map-based movement (spmbm) and working day movement (wdm). the second one is a pattern that simulates in the closest way the behavior of people during normal days of their life, so the one that algorithm was aimed for. delivery ratio delivery ratio, that is number of messages that were successfully delivered to destination, is the most important criteria while comparing routing algorithms. on the figure 3 the results of figure 3: delivery ratio for shortest path map based movement scenario. figure 4: delivery ratio for working day movement scenario. simulations for spmbm mobility pattern scenario are presented. the developed solution reaches the best results from all tested algorithms, regardless of the simulation period. the important observation is that the longer simulation lasts, the greater increase in delivery probability this algorithm reaches from all others. also it is easy to observe, that the difference between the best and the worst, which is epidemic routing, is substantial. on the figure 4 the results for wdm scenario are shown. in this case also the best delivery results are reached by described in the article new solution, but in this case the difference is not that significant. the important thing to observe is that in this scenario, so the one with human mobility patterns, with the longer simulation time the results are improving notably. it shows that nodes are making better routing decisions when they have more data collected, so they learn during network operation. delivery latency the second most important criteria while checking efficiency of routing algorithms is delivery latency, because in many situations it is important to deliver messages quick, in other case they can be useless. in the spmbm scenario (fig. 5) the new solution gets the longest average time figure 5: mean delivery latency for shortest path map based movement scenario. figure 6: mean delivery latency for working day movement scenario. for the messages to reach destination. it is not expected result, since one of the goals was to minimize latency. but it is important to look at this result together with the acquired delivery dtn routing algorithm for networks with nodes social behavior 467 ratios. in other case the conclusion would be that the epidemic routing is the most efficient, since the delivery latency is the smallest. the developed solution has big latency values, because it successfully delivered messages, that other algorithms dropped. this messages often take a lot of time to reach destination, so it overstates the measured average delivery latency. in wdm scenario the results look even better, than in spmbm. despite the fact, that new developed solution has the best delivery ratio, the average latency has quite similar values that all other algorithms. this means that not only it delivers more messages than other algorithms, but also average time of the same messages delivered by different algorithms is smaller for described in the article solution. average message hop count the figures 7 and 8 present the results of the average number of proxy nodes on the path (hops) figure 7: mean message hop count for shortest path map based movement scenario. figure 8: mean message hop count for working day movement scenario. that messages followed before reaching destination. in networks using epidemic routing the messages passed many nodes before getting to the destination node. this is caused by the fact, that there is no decision-making process and messages are transferred in random order. the best results for wdm scenario, so the ones when messages go through a small number of nodes, are reached by new developed solution, maxprop and spray&wait. they are very close to each other, so the conclusion is that is almost optimal value. in spmbm scenario also described in this article algorithm and spray&wait allow messages to reach destination in shortest paths calculated in number of proxy nodes. 4.2 different network conditions each algorithm works the best in certain environment. different conditions in network can have significant influence on the obtained results. even if all of the parameters of the network and its elements change the way that network operates, there are few characteristics that have the biggest influence. the main are mobility patterns, that were described in previous chapter, number of hosts, transmission speed and range and buffer sizes. number of nodes in dtn network we performed the simulation with the use of two algorithms the new described in the article and spray&wait to have a reference point to existing solutions. the results are presented on the figure 9. in the small network (100 nodes) both algorithms obtain the same delivery ratio, but spray&wait need less time for that. with the increase of the network size, the developed algorithm is gaining much better results. the delivery ratio is increasing more dynamically and saturates at higher level than the other algorithm. the delivery latency is also changing in the good way spray&wait needs more and more time to deliver messages with the growth of the size of network, while new solution is obtaining smaller delivery latency in bigger networks. it 468 a.m. dziekonski, r.o. schoeneich figure 9: impact of number of hosts in network. shows that the algorithm described in the article works best in big networks, but still get good results in smaller ones. transmission speed and range on the figure 10 and 11 the influence of changes in transmission are shown change in transmisfigure 10: impact of transmission speed. figure 11: impact of transmission range. sion speed, that allows to exchange more data during contact, and change in transmission range, that causes more contacts between nodes. it is clear that with the growth of these parameters, the conditions are becoming better, so the algorithms should work more efficiently. the charts show that new developed solution works exactly as expected with the growth of transmission speed or range, the delivery ratio is increasing and the latency is decreasing. the improvement in the routing efficiency is better in the new developed solution in comparison to prophet algorithm. buffer size the last examined network nodes characteristic that has big influence on the results is the buffer size. the increase of this parameters allows routers to hold more messages without the need to drop some of them. the figure 12 shows that the described in the article algorithm is more efficient when buffers are smaller and reaches its efficiency maximum for the much smaller buffer size than prophet algorithm. this means that the buffer management mechanisms and correlated with it decision making process is very efficient. dtn routing algorithm for networks with nodes social behavior 469 figure 12: impact of buffer size. 470 a.m. dziekonski, r.o. schoeneich 5 conclusion dtn networks are a young field of study and are not standardized. that leaves plenty of space for development new solutions for them. one of the very important part of network operation is routing and in case of dtn networks, in which nodes are connecting with others seldom and for short times, its efficiency is essential. the algorithm that we developed aims for the networks, where nodes follow human mobility patterns, so the ones where networks are made of devices held by people. our algorithm can be divided into three stages. the first one is the exchange and collection of historic control data needed for predicting future contacts and making decisions in further stages of the solution. the second phase is buffer management this consists of processing control data and making decisions of releasing space in buffer when it is overflowed. the last stage is strictly making decision process about the messages to be transferred other nodes. contacts are rare and short-lasting, so it is important to choose which messages to transfer and in what order, because in most cases there is no possibility of exchanging all the messages between nodes during contact. in case of developed solution, which is full-replication based, nodes try to exchange all the messages with other nodes they do not choose which ones but focus on the order in which messages are tried to be send. the developed algorithm gets very good results, especially in the most important criteria, that is successful delivery probability. compared to existing algorithms (spray&wait, epidemic, maxprop, prophet), our new solution if very efficient, especially in the networks with humanmobility patterns. bibliography [1] t.omari; g.franks; m.woodside (2005); on the effect of traffic model to the performance evaluation 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[24] a. keranen; j. ott; t.karkkainen (2009); the one simulator for dtn protocol evaluation, proc. of the simutools: doi: 10.4108/icst.simutools2009.5674. int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 632-644 formation control of multiple agents with preserving connectivity and its application to gradient climbing k.d. do k.d. do department of mechanical engineering, curtin university of technology, perth, wa 6845, australia e-mail: duc@curtin.edu.au abstract: a design of cooperative controllers that force a group of n mobile agents with limited communication ranges to perform a desired formation is presented. the proposed formation control system also preserves initial communication connectivity and guarantees no collisions between the agents. the formation control design is based on smooth step functions, potential functions, and the lyapunov direct method. the proposed formation control system is applied to solve a gradient climbing problem where the gradient average of a distributed field is estimated over a bounded region using the field measurement by the agents. keywords: formation control, collision avoidance, gradient climbing. 1 introduction formation control involves controlling positions of a group of agents such that they perform desired tasks such as optimizing objective functions from measurements taken by each agent, and stabilization/tracking desired locations relative to reference point(s). various methods have been proposed for formation control of multiple agents. here, three popular methods are briefly mentioned. the leader-follower method (e.g., [1], [2]) uses several agents as leaders and others as followers. this method is easy to understand and ensures formation maintenance if the leaders are disturbed. however, the desired formation cannot be maintained if followers are perturbed unless a formation feedback is implemented, [3]. the behavioral method (e.g., [4], [5]), where each agent locally reacts to actions of its neighbors, is suitable for decentralized control but is difficult in control design and stability analysis since group behavior cannot explicitly be defined. the virtual structure method (e.g., [6], [7]) treats all agents as a single entity. this method is amenable to mathematical analysis but is difficult to deal with time-varying formation structure. research works on formation control usually utilize one or more of the above methods in a centralized or a decentralized manner. centralized strategies (e.g., [8], [3]) use a single controller that generates collision free trajectories in the workspace. these strategies guarantee a complete solution but require high computational power and are not robust. decentralized schemes (e.g., [9], [10], [7]) require less computational effort but have difficulties in controlling critical points, especially when collision avoidance between the agents is a must. the control design in the above works did not put hard constraints on the controlled outputs except for those papers considered the problem of collision avoidance. without hard constraints on controlled outputs, overshoot might result in loss of initial communication between agents due to limited communication between the agents. hard constraints on the controlled outputs were applied to design cooperative controllers for mobile agents to preserve initial communication. these constraints on the controlled outputs were obtained through barrier lyapunov or potential functions using non-trivial bump functions or switching control strategies in [11] for the agreement problem, [12] for the centralized approach, and [13] for the swarm aggregation. copyright c⃝ 2006-2012 by ccc publications formation control of multiple agents with preserving connectivity and its application to gradient climbing 633 this paper contributes two main folds. the first one is a design of smooth and bounded cooperative controllers for a group of mobile agents to perform a desired formation task. the desired formation task includes collision avoidance and communication connectivity preservation between the agents, time-varying desired formation shape, and stabilization of the desired formation shape at any reference trajectories with bounded time derivatives. the second contribution is an algorithm for estimating gradient average of a distributed field over a region in two dimensional space. this algorithm uses only the field measurement on the boundary of a region, over which the gradient average is to be estimated. the two contributions are then combined to provide an effective gradient climbing system for a group of mobile agents by allowing the reference trajectory for each agent generated based on the gradient average. 2 preliminaries and formation control objective 2.1 smooth step function this section presents a construction of a smooth step function. the smooth step function is to be embedded into a potential function to avoid discontinuities in the control law due to the agents’ communication limitation in solving collision avoidance and connectivity preserving problems. −1 0 1 2 3 4 −6 −4 −2 0 2 x h (x ,0 ,3 ,2 ) a n d it s d e ri va tiv e s h h’ h’’ figure 1: a smooth step function and its first and second derivatives. definition 1. a scalar function h(x,a,b,c) is said to be a smooth step function if it possesses the following properties where x ∈ r, h′(x,a,b,c) = ∂h(x,a,b,c) ∂x , h′′(x,a,b,c) = ∂2h(x,a,b,c) ∂x2 , a and b are constants such that a < b, and c is a positive constant. lemma 2. let the scalar function h(x,a,b,c) be defined as h(x,a,b,c) = f(τ) f(τ) + cf(1− τ) with τ = x−a b−a , (1) where f(τ) = 0 if τ ≤ 0 and f(τ) = e− 1 τ if τ > 0, (2) with a and b being constants such that a < b, and c being a positive constant. then the function h(x,a,b,c) is a smooth step function. proof. proof of this lemma follows the same lines as the proof of lemma 1 in [7]. an illustration of a smooth step function (a = 0,b = 3,c = 2) is given in figure 1. 634 k.d. do 2.2 problem statement agent dynamics y ( 1) i d q x o ( 1) i d q desired shape od q id l d o idq 1i o agent i -1 agent i 1i o agent i+1 i o i r i r figure 2: formation setup. we assume that the agent i has the dynamics: q̇i = ui, i ∈ n, (3) where n is the set of all agents in the group, ui ∈ rn is the control input vector and qi ∈ rn the position vector of the agent i. formation control objective each agent in the group needs its reference trajectory to track. the reference trajectories can be predefined or determined from measurement data. furthermore, each agent needs to communicate with other agents in the group to perform its cooperative mission. therefore, before stating formation control objective we impose the following assumption on the reference trajectories, communication and initial conditions between the agents in the group: assumption 3. 1) the agent i has a physical safety ball, which is centered at the point oi and has a radius ri, and has a communication ball, which is centered at the point oi and has a radius ri, see figure 2. the radius ri is such that ri ≥ ri + rj + ε1ij, (4) for all j ∈ n,j ̸= i, where ε1ij is a strictly positive constant. 2) the reference trajectory qid for the agent i is generated by qid = qod(sod) + lid, (5) where qod(sod) is referred to as the common reference trajectory with sod being the common trajectory parameter, and lid is to specify a desired formation shape. the trajectory qod has its bounded derivatives. the vectors lid, i ∈ n have bounded derivatives, and satisfy (ri + rj + ε2ij) ≤∥lid − ljd∥≤ min(ri,rj)−ε2ij, (6) for all (i,j) ∈ n, i ̸= j, where ε2ij is a strictly positive constant, and is strictly less than ε1ij 2 . 3)the agent i broadcasts its trajectory, qi, and its reference trajectory qid in its communication ball. moreover, the agent i can receive the trajectory, qj, broadcasted by other agents j, j ∈ n, j ̸= i in the group if the points oj of these agents are in the communication ball of the agent i. 4) at the initial time t0 ≥ 0, all the agents in the group are sufficiently far but not too far away from each other in the sense that the following condition holds: (ri + rj + ε3ij) ≤∥qi(t0)−qj(t0)∥≤ (min(ri,rj)−ε3ij), (7) for all (i,j) ∈ n, i ̸= j, where ε3ij is a strictly positive constant and is strictly less than ε1ij 2 . formation control of multiple agents with preserving connectivity and its application to gradient climbing 635 remark 4. item 2) in assumption 3 defines a desired formation (by vectors lid) and how this desired formation moves (by the common reference trajectory qod). item 3) specifies the way each agent communicates with other agents in the group within its communication range. in figure 2, the agents i and i − 1 are communicating with each other since the points oi−1 and oi are in the communication areas of the agents i and i− 1, respectively. item 4) implies that at the initial time t0 there are no collision between all the agents, and that all the agents are communicating with each other. the conditions (4), (6) and (7) are imposed to avoid conflict when solving collision avoidance and connectivity preserving problems. this is because we will design a formation control system so that qi to track its reference trajectory qid. under assumption 3, for each agent i design the control input vector ui to achieve a desired formation consisting of 1) no switchings in the controllers; 2) no collisions between any agents; 3) asymptotic convergence of each agent’s trajectory qi to its reference trajectory qid; and 4) initial connectivity preservation. mathematically, the objective is to design a smooth ui to achieve: ∥qi(t)−qj(t)∥ > (ri + rj), lim t→∞ (qi(t)−qid(t)) = 0, ∥qi(t)−qj(t)∥ < min(ri,rj), (8) for all ∀t ≥ t0 ≥ 0 and (i,j) ∈ n and j ̸= i. 3 formation control design consider the following potential function φ = n∑ i=1 ( γi + 1 2 βi ) . (9) the aim of the goal function γi is to achieve asymptotic convergence of each agent’s trajectory qi to its reference trajectory qid. as such the function γi puts penalty on the tracking errors between the trajectory qi of the agent i and its reference trajectory qid = qod + lid. we choose the function γi as: γi = 1 2 ∥qi −qid∥2. (10) the purpose of the collision avoidance and connectivity preserving function βi is to force the agent i to move away from other agents, and to maintain communication connectivity between the agent i and other agents in the group. this function is chosen as follows: βi = ∑ j∈ni βij, (11) where ni is the set of all the agents in the group except for the agent i. the function βij = βji is a function of ∥qij∥2/2 with qij = qi −qj , and possesses the following properties: 1) βij = 0, βij′ = 0, βij′′ = 0, ∀ ∥qij∥∈ ( (ri + rj + δij),(min(ri,rj)− δij) ) , 2) βij > 0, ∀ ∥qij∥∈ (( (ri + rj),(ri + rj + δij) ) ∪ ( (min(ri,rj)− δij),min(ri,rj) )) , 3) lim ∥qij∥→(ri+rj) βij = ∞, lim ∥qij∥→min(ri,rj) βij = ∞, 4) βij is smooth for all ∥qij∥∈ ( (ri + rj),(min(ri,rj)) ) , (12) 636 k.d. do where δij is a strictly positive constant and is strictly less than ε2ij specified in assumption 3. the terms βij′ and βij′′ are defined as follows: βij′ = ∞, βij′′ = ∞, if ∥qij∥ = ri + rj, or ∥qij∥ = min(ri,rj), βij′ = ∂βij ∂(∥qij∥2/2) , βij′′ = ∂2βij ∂(∥qij∥2/2)2 , elsewhere. (13) based on the smooth step function in section 2.1, we can find many functions that satisfy all properties listed in (12). as an example, we will use the following function βij: βij =κij [ 1−h ( ∥qij∥2 2 , (ri+rj) 2 2 , (ri+rj+δij) 2 2 ,cij ) ( ∥qij∥2 2 − (ri+rj) 2 2 )2 + h ( ∥qij∥2 2 , (min(ri,rj)−δij)2 2 , min(ri,rj) 2 2 ,cij)( min(ri,rj)2 2 − ∥qij∥ 2 2 )2 ] , (14) where κij and cij are positive constants, and the function h(•) is a smooth step function defined in definition 1. an illustration of βij defined in (14) is given in figure 3 with ri + rj = 1, min(ri,rj) = 11, δij = 2, cij = 1, κij = 1. 0 5 10 0 2 4 6 8 10 ||q ij || β i j figure 3: an illustration of βij. the derivative of φ along the solutions of (3) satisfies φ̇ = n∑ i=1 ωti (ui − q̇id) + n∑ i=1 ( ∑ j∈ni βij′qtij ) l̇id, (15) where ωi = qi −qid + ∑ j∈ni βij′qij. (16) from (15), we design the control input ui to make the sum ∑n i=1 ω t i (ui − q̇id) negative definite as ui = −kψ(ωi) + q̇od + l̇id, (17) where k is a positive constant, and ψ(ωi) denotes a vector of bounded functions of elements of ωi in the sense that ψ(ωi) = [ ψ(ω1i ) ...,ψ(ω l i), ...,ψ(ω n i ) ]t with ωli the l th element of ωi, i.e., ωi = [ω 1 i ...,ω l i...ω n i ] t . the function ψ(x) satisfies 1) |ψ(x)| ≤ m1, 2)ψ(x) = 0 if x = 0, xψ(x) > 0 if x ̸= 0, 3)ψ(−x) = −ψ(x),(x−y)[ψ(x)−ψ(y)] ≥ 0, 4) ∣∣∣ψ(x) x ∣∣∣ ≤ m2, ∣∣∣∂ψ(x) ∂x ∣∣∣ ≤ m3, ∂ψ(x) ∂x ∣∣∣ x=0 = 1, (18) for all x ∈ r,y ∈ r, where m1,m2,m3 are strictly positive constants. some functions that satisfy the above properties are arctan(x) and tanh(x). the above bounds mean that the large control effort problem is avoided when the distance ∥qij∥ between the agent i and an agent j in the group reaches a collision limit ri + rj or a connectivity preserving limit min(ri,rj). to deal with the sum ∑n i=1 ( ∑ j∈ni βij′q t ij ) l̇id in (15), we observe that βij′ = 0 for all ∥qij∥ ∈ ( (ri + rj + δij),(min(ri,rj) − δij) ) , see property 1) of the function βij in (12). this observation motivates us to design an update law for lid so that ∑n i=1 ( ∑ j∈ni βij′q t ij ) l̇id = 0 for all time and l̇id tends to its desired value vid asymptotically. as such, we choose: l̇id = hivid, (19) formation control of multiple agents with preserving connectivity and its application to gradient climbing 637 where hi = ∏ j∈ni h(∥qij∥2/2,(ri + rj + δij) 2/2,(ri + rj + δ v ij) 2/2,cij)× ( 1−h(∥qij∥2/2,min(ri + rj − δvij) 2/2,(ri + rj − δij)2/2,cij) ) , (20) with δvij being a positive constant such that δij < δ v ij < ϵ2ij, and h(•) being a smooth step function defined in definition 1. with the choice of δij < δvij < ϵ2ij, we can see that hi = 1, ∀ ∥qij∥∈ ( (ri + rj + δ v ij),(min(ri,rj)− δ v ij) ) , hi = 0, ∀ ∥qij∥∈ ( (0,(ri + rj + δij))∪ (min(ri,rj)− δij),∞) ) , 0 < hi < 1, elsewhere. (21) obviously, the choice of the update law for lid in (19) with hi being satisfied (21) gives:∑ j∈ni βij′qtij l̇id = 0, ∀ ∥qij∥∈ ((ri + rj),min(ri,rj)). (22) remark 5. 1) a careful look at the control law ui in (17) with ωi in (16) shows that the argument of the bounded ψ (with the negative sign moved in) consists of two parts. the first part is −(qi −qid), and the second part is − ∑ j∈ni βij′qij. the first part together with q̇od + l̇id is referred to as the attractive force plays the role of forcing the agent i to track its reference trajectory. the second part is referred to as the repulsive force takes care of collision avoidance and connectivity preserving for the agent i with the other agents in the group. moreover, the control ui is a smooth function of and depend on only its own state and reference trajectory, and the states of other neighbor agents j if the agents j are sufficiently close to the agent i for collision avoidance, or are sufficiently far away from the agent i for connectivity preserving. 2) the choice of the update law in (19) ensures that when the collision avoidance or connectivity preserving is active, i.e., when the sum ∑ j∈ni βij′qij is non-zero, the vector lid is not updated, i.e., the desired formation shape is not changed. this implies that the control law ui gives priority to the collision avoidance and/or connectivity preserving mission or the desired formation shape updating mission whenever which mission is more important. substituting the control law ui in (17) and the update law l̇id in (19) into (15) gives φ̇ = −k n∑ i=1 ωti ψ(ωi), (23) where we have used (22). on the other hand, substituting the control law the control law ui in (17) into (3) including the update law l̇id in (19) results in the closed loop system: q̇i = −kψ(ωi) + q̇od + l̇id, l̇id = hivid, (24) for all i ∈ n. we now present the main result of our paper in the following theorem. theorem 6. under assumption 3, the smooth control input ui = given in (17) and the update law l̇id in (19) for the agent i solve the formation control objective. in particular: 1) there are no collisions between any agents, connectivity between the agents is maintained, and the closed loop system (24) is forward complete. the first and last inequalities in (8) hold. 2) the reference velocity l̇id approaches its desired reference velocity vid asymptotically. 3) the trajectory qi of each agent i tracks its reference trajectory qid asymptotically, i.e., the limit in the second equation of (8) holds. proof. see appendix 1. 638 k.d. do 4 gradient climbing 4.1 approach in this section, we present an application of our proposed formation control to solve a gradient climbing mission in a distributed environment φ(t,η). to do so, we consider each agent in the group as a mobile sensor and the network as a reconfigurable sensor array. as such, at each time t the agent i with i ∈ n in the group of n agents is equipped with a sensor that can measure φ(t,qi) at the location qi. with φ(t,qi), we estimate/calculate an approximation of the gradient average, ∇φ, of the distributed environment over a region a bounded by a contour c, on which the agents in the group are positioned. after ∇φ is estimated/calculated, we let the gradient of the common reference trajectory qod equal to ∇φ. this means that the common reference trajectory qod is simply generated by q̇od = ∂qod ∂sod ṡod = ∇φṡod, (25) with some initial condition qod(t0), where ṡod specifies how fast the desired formation moves along the common reference trajectory qod. for the case of gradient descent, we can use q̇od = −∇φṡod instead of (25). moreover, we can specify the desired formation shape velocity vid to change (expand/shrink/rotate) the formation shape, i.e., change the shape define vector lid, see (19), to improve the gradient average approximation. we propose the the desired formation shape velocity vid as follows: vid = −k1v(lid − l∗id) + k2vψ(∇φ), (26) where k1v and k2v are diagonal positive definite matrices. the constant vectors l∗id, i ∈ n are chosen so that they specify the minimum desired formation shape, which is such that the condition (6) holds with lid replaced by l∗id for all i ∈ n. the vector function ψ(∇φ) is a bounded vector function of ∇φ, see the paragraph just below (17). once the common reference trajectory qod and the desired formation shape lid are available, the formation control design proposed in section 3 can be used directly to drive the agents in the group. the following section gives a method to estimate an approximation of the gradient average, ∇φ, of the distributed environment φ(t,η) from measurements φ(t,qi) on the boundary, i.e., the contour or surface c, carried out by the agents in the group. therefore, we will present a method to calculate the gradient average of a distributed field in the following subsection. 4.2 average gradient estimate of a distributed field a 1 c 2 c 1 ( )f x 2 ( )f x o y xa b r n t p figure 4: coordinates for a gradient computation we consider a region a, see figure 4, bounded by a contour c, such that any line through a parallel to either one of the coordinate axes intersects c in only two points. the curve c is divided by its leftmost and rightmost points (x = a and x = b) into a lower segment c1, described by y = f1(x), and an upper segment c2 described by y = f2(x). with the position vector to a point p on c given by r = xex + yey, where ex and ey are the unit vector on the ox and oy axes, respectively. the unit tangent vector at p is t = dr ds = dx ds ex + dy ds ey, where ds is the differential length along c, and the unit normal vector is n = t×ez = dydsex − dx ds ey = nxex +nyey. for the function formation control of multiple agents with preserving connectivity and its application to gradient climbing 639 φ(t,x,y) defined in a, consider the area integral∫ a ∂φ ∂y da = ∫ b a ( φ(t,x,f2(x))−φ(t,x,f1(x)) ) dx = ∫ b a ( [φ]c2 − [φ]c1 ) dx. (27) as shown in fig. 4, a positive contour integration corresponds to a counter-clockwise traversal of c. to make the first integral in (27) consistent with this connection, we write∫ a ∂φ ∂y da = − ∫ a b [φ]c2dx− ∫ b a [φ]c1dx = − ∫ c φdx = − ∫ c φ dx ds ds, (28) which combines with n = nxex + nyey to yield ∫ a ∂φ ∂y da = ∫ c φnyds. a similar computation gives ∫ a ∂φ ∂x da = ∫ c φnxds. therefore, we have∫ a ∇φda = ∫ c nφds (29) where ∇φ = [ ∂φ ∂x , ∂φ ∂y ]t . it is of interest to note that the total gradient ∫ a ∇φda of the distributed field φ(t,η) over the region a is completely determined from the integral ∫ c nφds carried out on the boundary c only. from (29), we can calculate the gradient average of φ(t,η) over the region a as ∇φ = ∫ c nφds ωa (30) where ωa is the area of the region a. usually, it is not possible to obtain an explicit result of the integral ∫ c nφds because the distributed field φ is unknown. hence, we approximate this integral from measurement φ(t,qi) at the time t and the location (qi) by each agent i, and approximate the area ωa. we assume that the formation shape is a convex polygon whose vertices are at qi. the steps to calculate an approximate value of the integral ∫ c nφds and the region area ωa are as follows: 1) using a curve fitting method such as spline or least square to find a best fitted and smooth contour c(θ), where θ is the curve parameter, that goes through all vertices at the time t; 2) calculating an approximate value of ∫ c nφds and ωa as follows:∫ c nφds ≈ n∑ i=1 φ(t,qi)n(θi)∆ci, ωa ≈ 1 2 n∑ i=1 ( xiyi+1 −xi+1yi ) , (31) where qn+1 = q1, n(θi) is the unit vector normal to c(θ) at θi corresponding to the position of the vertex qi, and ∆ci is the arc length from the middle point mi−1 between qi−1 and qi an the middle point mi+1 between qi and qi+1, see fig.5. a i xo y x ( ) i n 1i x 1i x i y 1i y 1i y ( )c 1i m 1i m 1i q i q 1i q ci figure 5: coordinates for gradient average calculation. for a special case where the formation shape is a regular simple polygon, which has the center at qod and the vertices at qi, i ∈ n, and that the contour c goes through all the vertices at the time t. moreover, the unit vector n normal to the contour c at qi is in the direction from qod to qi at the time t. the the integral ∫ c nφds and the region area ωa can be approximated as∫ c nφds ≈ n∑ i=1 φ(t,qi) qi −qod ∥qi −qod∥ ∥∥∥∥qi+1 −qi−12 ∥∥∥∥, ωa ≈ 1 2 n∑ i=1 ∣∣∣det([qi,qi+1])∣∣∣, (32) with qn+1 = q1 and q−1 = qn , and det([qi,qi+1]) is the determinant of the matrix [qi+1,qi]. 640 k.d. do 5 simulation results in this section, we a problem of gradient climbing by our proposed formation controller using a group of n = 6 identical agents. each agent i has a physical safety radius ri = 0.5 and a communication radius ri = 10. the control design parameters are taken as k = 4, δij = 0.5, δvij = 0.75, cij = 1, and the bounded function ψ(.) taken as arctan(.). the desired formation shape specification vectors l∗id are chosen as l ∗ id = rf[cos( 2(i−1)π n ), sin( 2(i−1)π n )]t with rf = 3, and the gain k1v = diag(2.5,2.5). this choice of l∗id means that the desired formation configuration is a polygon whose vertices uniformly distribute on a circle centered on the common reference trajectory and with a radius rf . the initial conditions are lid(0) = l ∗ id, qod(0) = [0 0] t , and qi(0) = rf[cos( 2(i−1)π n +π), sin(2(i−1)π n +π)]t . these particular initial qi(0) were chosen to illustrate the collision avoidance capability of our proposed formation control system as all the agents have to across the center of the desired formation shape to track their desired reference trajectories. the distributed environment φ(t,x,y) is taken as φ(t,x,y) = e− (x−15)2+(y−15)2 150 , which has a global maximum value at (x = 15,y = 15). we set k2v = diag(1.5,1.5) to improve the gradient climbing, i.e., the desired formation shape is adapted to the distributed field. simulation results are plotted in figure 6. from these figures, it is seen that our proposed formation is able to achieve the objective of both formation control and gradient climbing. the control inputs ui, see sub-figure 6d, force the agents to move in such a way that collision between the agents is avoided and that communication between the agents is preserved, see sub-figure 6a where trajectories of the agents are plotted in xyplane. these sub-figures also show that our proposed formation control performs the gradient climbing mission very well in the sense that the center of the formation shape, see the polygon of which vertices are the agents, converges to the global maximum location of the function φ(t,x,y). collision avoidance and communication preserving are also confirmed in sub-figure 6c, where the distances ∥q∥1i between the agent 1 and other agents in the group are plotted. these distances are within the range of (1,10) since ri + rj = 1 and min(ri,rj) = 10. −5 0 5 10 15 20 −5 0 5 10 15 20 x y a 0 20 40 60 80 0 0.5 1 1.5 2 2.5 3 3.5 4 time [s] m ea n g ra d. b 0 10 20 30 40 50 60 70 80 0 5 10 time [s] ||q 1i || c 0 10 20 30 40 50 60 70 80 −5 0 5 time [s] u 1 x, u 1 y d figure 6: simulation results with formation shape adaptation. formation control of multiple agents with preserving connectivity and its application to gradient climbing 641 6 conclusions a constructive method has been proposed to design smooth and bounded cooperative controllers for a group of n mobile agents with limited communication to perform a desired formation. novel potential functions encoding desired formation mission tasks with smooth step functions embedded in were constructed to design the controllers that guaranteed all equilibrium (critical) sets, except for the desired set in formation, are unstable. the proposed formation control system is applied to solve a gradient climbing problem. an extension of the proposed formation control design in this paper and those controllers designed for single underactuated ships in [17] to provide a formation control system for a group of underactuated ships is under consideration. 1 proof of theorem 6 proof of no collisions, connectivity preserving, and forward completeness of the closed loop system: it is seen from (23) that φ̇ ≤ 0. integrating φ̇ ≤ 0 from t0 to t and using the definition of φ in (9) with its components defined in (10) and (11) results in φ(t) ≤ φ(t0), (33) where φ(t0) = ∑n i=1(γi(t0) + 1 2 ∑ j∈ni βij(t0)) and φ(t) = ∑n i=1(γi(t) + 1 2 ∑ j∈ni βij(t)), for all t ≥ t0 ≥ 0. from the condition specified in item 4) of assumption 3, and properties of βij, we have the right hand side of (33) is bounded by a positive finite constant depending on the initial conditions. boundedness of the right hand side of (33) implies that the left hand side of (33) must be also bounded. as a result, βij(∥qij∥2/2) must be smaller than some positive constant depending on the initial conditions for all t ≥ t0 ≥ 0. from properties of βij, see (12), ∥qij∥, for all (i,j) ∈ n and i ̸= j, must be in the interval ( (ri + rj),min(ri,rj) ) . hence, there are no collisions between any agents and connectivity between agents is preserved for all t ≥ t0 ≥ 0. boundedness of the left hand side of (33) also implies that of (qi(t) − qid(t)) also bounded for all t ≥ t0 ≥ 0. moreover, from (21) we can see that |hi| ≤ 1. therefore, ∥l̇id(t)∥ ≤ ∥vid(t)∥ for all t ≥ t0 ≥ 0. therefore, the closed loop system (24) is forward complete. equilibrium set: we will use lemma 2 in [7] to find the equilibrium set, which the trajectories of the closed loop system (24) converge to. integrating both sides of (23) yields∫ ∞ 0 ω(t)dt = φ(t0)−φ(∞) ≤ φ(t0), (34) with ω(t) := ∑n i=1 ω t i (t)ψ(ωi(t)), where ωi(t) is given in (16), and the function ψ(ωi(t)) is the bounded vector function of ωi(t) with properties listed in (18). indeed, the function ω(t) is scalar, nonnegative and differentiable. now differentiating ω(t) along the solutions of the closed loop system (24) and using properties of the function βij given in (12) readily show that ∣∣dω(t) dt ∣∣ ≤ mω(t) with m being a positive constant. therefore lemma 2 in [7] results in limt→∞ ω(t) = 0, which implies from the expression of ω(t) and properties of the bounded vector function ψ(ωi(t)) in (18) that limt→∞ ωi(t) = 0. therefore, from the expression of ωi(t) the limit limt→∞ ωi(t) = 0 given in (16) implies that lim t→∞ n∑ i=1 ( qi(t)−qid(t) + ∑ j∈ni βij′(t)qij(t) ) = 0. (35) the limit in (35) implies that q(t) = [qt1 (t) q t 2 (t), . . . ,q t n(t)] t can tend to qd = [qt1d q t 2d, . . . ,q t nd] t denoted by the set ξd, since βij′(t) = 0 at qi = qid and qj = qjd, for all (i,j) ∈ n and i ̸= j or 642 k.d. do tend to the set qc = [qt1c q t 2c, . . . ,q t nc] t denoted by the set ξc as the time goes to infinity, i.e., the equilibrium sets can be ξd or ξc. the equilibrium set ξc is such that ω ∣∣∣ q∈ξc = ( qi −qid + ∑ j∈ni βij′qij )∣∣∣∣ q∈ξc = 0, (36) for all i ∈ n. thus, we have already proved that the trajectory q can approach either the desired equilibrium set denoted by ξd or the undesired equilibrium set denoted by ξc ’almost globally’. the term ’almost globally’ refers to the fact that the agents start from a set that includes the condition (7) and that does not coincide at any point in the undesired equilibrium set ξc. therefore, we now need to prove that ξd is a locally asymptotically stable set and that ξc is a locally unstable set. once this is proved, we can conclude that the trajectory q approaches qd from almost everywhere except for from the set denoted by the condition (7) and the undesired equilibrium set ξc, which is unstable (to be proved below). to prepare for showing that ξd is asymptotically stable and that ξc is unstable. we write the first equation of the closed loop system (24) for all i ∈ n in a vector form as q̇ = −kφ(q,qd) + q̇d (37) where φ(q,qd) = [ψt (ω1), ...,ψt (ωn)]. linearizing (37) around qo = [qt1o, . . . ,q t no] t , and letting the set ξo contain qo results in q̇ = −k ∂φ(q,qd) ∂q ∣∣∣ q∈ξo + q̇d, (38) where ∂φ(q,qd) ∂q = [∆ij] with ∆ij = ∂ψ(ωi) ∂ωi ∂ωi ∂qj and ∂ωi ∂qi = ( 1 + ∑ i∈ni βij′ ) in + ∑ j∈ni βij′′qijqtij, ∂ωi ∂qj = −βij′in×n −βij′′qijqtij, (39) for all (i,j) ∈ n. let n∗ be the set of the agents such that if the agents i and j belong to the set n∗ then ∥qij∥ ∈ ((ri + rj),min(ri,rj). next we will show that the equilibrium set ξd is asymptotically stable and that the equilibrium set ξc is unstable. proof of ξd being asymptotically stable: as mentioned above, to prove that the equilibrium set ξd is asymptotically stable, we just need to show that ξd is locally asymptotically stable. letting ξo be ξd in (38), we obtain q̇ = −k(q −qd) + q̇d, (40) where we have used the fact that βij′ ∣∣ q∈ξd = 0 and βij′′ ∣∣ q∈ξd = 0, see property 1) of the function βij in (12). local asymptotic stability of the equilibrium set ξd follows from (40) since the first time derivative of the function vd = 12∥q−qd∥ 2 along the solutions of (40) satisfies v̇d = −2kvd. proof of ξc being asymptotically stable: let us define q̄ = [qt12, ...,q t 1nq t 23, ...,q t 2n, ...,q t n−1,n] t , q̄c = [q t 12c, ...,q t 1ncq t 23c, ...,q t 2nc, ...,q t n−1,nc] t , βijc′ = βij′ ∣∣ q∈ξc , βijc′′ = βij′′ ∣∣ q∈ξc , qijc = qic −qjc. with the above definitions, we can see that stability of ξc is equivalent to that of ξ̄c = q̄c. define ωijc = ωic − ωjc, ∀(i,j) ∈ n, i ̸= j where ωic = ωi|q∈ξc = 0, see (36). therefore ωijc = 0. hence ∑ (i,j)∈n∗ q t ijcωijc = 0, i ̸= j, which by using (36) is expanded to∑ (i,j)∈n∗ ( qtijc(qijc−qijd)+nβijc′q t ijcqijc ) = 0 ⇒ ∑ (i,j)∈n∗ (1+nβijc′)qtijcqijc = ∑ (i,j)∈n∗ qtijcqijd (41) formation control of multiple agents with preserving connectivity and its application to gradient climbing 643 where i ̸= j. the sum ∑ (i,j)∈n∗ q t ijcqijd is strictly negative since at the point f where qij = qijd, ∀(i,j) ∈ n∗, i ̸= j all attractive and repulsive forces are equal to zero while at the point c where qij = qijc ∀(i,j) ∈ n∗, i ̸= j the sum of attractive and repulsive forces are equal to zero (but attractive and repulsive forces are nonzero). therefore the point o where qij = 0, ∀(i,j) ∈ n∗, i ̸= j must locate between the points f and c for all (i,j) ∈ n∗, i ̸= j. that is the points f , o, and c must be co-linear. hence, there exists a strictly positive constant b such that∑ (i,j)∈n∗ q t ijcqijd < −b, which is substituted into (41) to yield∑ (i,j)∈n∗ (1 + nβijc′)qtijcqijc < −b,i ̸= j. (42) since qtijcqijc > 0,∀(i,j) ∈ n ∗, i ̸= j, there exists a nonempty set n∗∗ ⊂ n∗ such that for all (i,j) ∈ n∗∗, i ̸= j, (1 + nβijc′) is strictly negative, i.e., there exists a strictly positive constant b∗∗ such that (1 + nβijc′) < −b∗∗, ∀(i,j) ∈ n∗∗, i ̸= j. we now define a subspace υ as υ := ( qij −qijc = 0, ∀(i,j) ∈ n\n∗∗ ) ∩ ( qtijc(qij −qijc) = 0, ∀(i,j) ∈ n∗, i ̸= j ) . in the subspace υ, we have v̄c = 1 2 ∑ (i,j)∈n∗∗ ∥qij −qijc∥2, ˙̄vc = −k ∑ (i,j)∈n∗∗ (1 + nβijc′)∥qij −qijc∥2 ≥ 2kb∗∗v̄c (43) where we have used (1 + nβijc′) < −b∗∗, ∀(i,j) ∈ n∗∗, i ̸= j. since the set n∗∗ is nonempty, (43) implies that the equilibrium set ξ̄c is unstable by chetaev’s theorem (theorem 4.3 in [15]). this implies the desired result that the equilibrium set ξc is unstable. we can further explore instability of the equilibrium set ξc based on (43) as follows. from (43), we have∑ (i,j)∈n∗∗ ∥qij(t)−qijc∥≥ ∑ (i,j)∈n∗∗ ∥qij(t0)−qijc∥ekb ∗∗(t−t0), i ̸= j, t ≥ t0 ≥ 0. (44) now assume that the equilibrium set ξc is stable, i.e., limt→∞ ∥qi(t) − qic∥ = di,∀i ∈ n with di a nonnegative constant. note that n∗∗ ⊂ n, we have limt→∞ ∥qi(t) − qic∥ = di,∀i ∈ n∗∗, which implies that limt→∞ ∑ (i,j)∈n∗∗ ∥qij(t) − qijc∥ = d ∗∗,∀(i,j) ∈ n∗∗, i ̸= j with d∗∗ a nonnegative constant, since qij = qi − qj and qijc = qic − qjc. this contradicts (44) for the case∑ (i,j)∈n∗∗ ∥qij(t0)−qijc∥ ̸= 0, since the right hand side of (44) is divergent (so does the left hand side). for the case ∑ (i,j)∈n∗∗ ∥qij(t0)−qijc∥ = 0, there would be no contradiction. however this case is never observed in practice since the ever-present physical noise would cause ∥qij(t∗)−qijc∥ for some (i,j) ∈ n∗∗, i ̸= j to be different from 0 at the time t∗ ≥ t0. proof of theorem 6 is completed. bibliography [1] a. das, r. fierro, v. kumar, j. ostrowski, j. spletzer, and c. taylor, “a vision based formation control framework,” ieee transactions on robotics and automation, vol. 18, no. 5, pp. 813–825, 2002. 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[17] k. d. do and j. pan, control of ships and underwater vehicles: design for underactuated and nonlinear marine systems. springer, 2009. stanojevic_ijcccv11n5.pdf international journal of computers communications & control issn 1841-9836, 11(5):720-733, october 2016. continuous distribution approximation and thresholds optimization in serial multi-modal biometric systems m. stanojević, i. milenković , d. starčević, b. stanojević milan stanojević, ivan milenković, dušan starčević faculty of organizational sciences, university of belgrade, jove ilića 154, 11000 belgrade, serbia (milans, ivan.milenkovic, starcev)@fon.bg.ac.rs bogdana stanojević* mathematical institute of the serbian academy of sciences and arts, kneza mihaila 36, 11000 belgrade, serbia, *corresponding author: bgdnpop@mi.sanu.ac.rs abstract: multi-modal biometric verification systems use information from several biometric modalities to verify an identity of a person. the false acceptance rate (far) and false rejection rate (frr) are metrics generally used to measure the performance of such systems. in this paper, we first approximate the score distributions of both genuine users and impostors by continuous distributions. then we incorporate the exact expressions of the distributions in the formulas for the expected values of both far and frr for each matcher. in order to determine the upper and lower acceptance thresholds in the sequential multi-modal biometric matching, we further minimize the expected values of far and frr for the entire processing chain. we propose a non-linear bi-objective programming problem whose objective functions are the two error probabilities. we analyze the efficient set of the bi-objective problem, and derive an efficient solution as a best compromise between the error probabilities. replacing the least squares approximation of the score distributions by a continuous distribution approximation, this approach modifies the method presented in stanojević et al. [15] (doi: 10.1109/icccc.2016.7496752)a. the results of our experiments showed a good performance of the sequential multiple biometric matching system based on continuous distribution approximation and optimized thresholds. keywords: multi-modal biometrics, sequential fusion, multi-criteria optimization, continuous distribution approximation. areprinted (partial) and extended, with permission based on license number 3938230385072 © [2016] ieee, from "computers communications and control (icccc), 2016 6th international conference on". 1 introduction this paper is an extension of [15] (doi: 10.1109/icccc.2016.7496752). the mathematical model introduced in [15], that derives optimized thresholds for bi-modal biometric systems, is here formulated generally, for n-modal biometric systems. in addition, the linear approximation of the biometric scores, based on the least squares method, is here replaced by a non-linear continuous distribution approximation. we report our new computation results and compare them with previous results. biometrics is the automated recognition of individuals based on their behavioral and biological characteristics [11]. biometric recognition is used for many purposes including criminal copyright © 2006-2016 by ccc publications 722 m. stanojević, i. milenković , d. starčević, b. stanojević identification, secure access control, forensics and so forth. it was intensively researched and widely applied in the last decade [18]. a number of biometric technologies have been developed and several of them are being used in a variety of applications [7]. the most commonly used modalities are fingerprints, face, iris, speech, and hand geometry. due to their strengths and weaknesses, the choice of one or another modality is strictly dependent on the application requirements. in his book [5], kaklauskas presented different methods for analyzing the body language (movement, position, use of personal space, silences, pauses and tone, the eyes, pupil dilation or constriction, smiles, body temperature and the like) for better understanding people’s needs and actions, including biometric data gathering and reading. filip [3], briefly reviewed the book, and emphasized that it addressed two modern research domains: intelligent and integrated decision support systems and biometrics-based human-computer interface. an analysis of a multi-modal biometric system based on level of fusion was presented in [10]. the authors discussed the biometric systems, the limitations of individual biometric, and various fusion levels and methods of multi-modal systems. the parallel fusion mode was first introduced in 1998 [4]. fingerprint and face modalities were simultaneously used for identification. serial fusion of multiple matchers is a good trade-off between the widely adopted parallel fusion and the use of a mono-modal verification system [12]. an alternative to parallel fusion of biometric data is the use of serial fusion. kumar and kumar [6] presented a new approach for the adaptive management of multi-modal biometrics. they employed the ant colony optimization for the selection of the key parameters like decision threshold and fusion rule, to ensure the optimal performance in meeting varying security requirements during the deployment of multi-modal biometrics systems. zhang et al. [18] proposed a novel framework for serial multi-modal biometric systems based on semi-supervised learning techniques. they have promoted the discriminating power of the weaker but more user convenient traits over the use of the stronger but less user convenient traits. in this way, they proposed an alternative to other existing serial multi-modal biometric systems that suggest optimized orderings of the traits deployed and parameterizations of the corresponding matchers but ignore the most important requirements of common applications. their experiments on two prototype systems demonstrated the advantages of their methodology. marcialis et al. [8] proposed a theoretical framework for the assessment of performance of serial fusion multi-modal systems, theoretically evaluated the benefits in terms of performance, and estimated the errors in the model parameters computation. they analyzed the model from the point of view of its pros and cons, and performed preliminary experiments on a benchmark found in the literature. the importance of the use of multi-modal biometrics in the area of secure person authentication is highlighted in a recent study [13]. that study provided a different perception on how to use biometrics on the highest level of the network security with the fusion of multiple biometric modalities. snelick et al. [14] studied the performance of the multi-modal biometric authentication systems using the state-of-the-art commercial off-the-shelf (cots) fingerprint and face biometric systems on a large-scale population. they also proposed new methods of normalization and fusion that improved the accuracy of the biometric systems. the remainder of the paper is organized as follows. in section 2 we formulate the problem that we wish to solve. two solving methods are presented in section 3: the linear approximation method is briefly presented in section 3.1, and our novel approach is introduced in section 3.2. our computational results on random generated instances are given in section 4.1, and the numerical results on nist-bssr1 and nist-bssr2 databases are reported in section 4.2. the conclusions and some directions for future work are included in section 5. continuous distribution approximation and thresholds optimization in serial multi-modal biometric systems 723 2 the formulation of the problem multi-modal biometric verification systems use information from several biometric modalities to verify an identity of a person. the false acceptance rate (far) and false rejection rate (frr) are metrics generally used to measure the performance of such systems. the far is the probability that the system incorrectly matches the input pattern to a non-matching template in the database. it measures the percent of invalid inputs which are incorrectly accepted. in case of similarity scale, if the person is an impostor in reality, but the matching score is higher than the threshold, then he is treated as genuine. the far depends on the threshold value: the far increases as the threshold decreases. the frr is the probability that the system fails to detect a match between the input pattern and a matching template in the database. it measures the percent of valid inputs which are incorrectly rejected. it also depends on the threshold value: the frr increases as the threshold value increases. the visual characterization of the tradeoff between the far and frr is generally given by a graphic representation of the genuine acceptance rate (gar=1-frr) with respect to the false acceptance rate. in general, a matching algorithm performs a decision based on a threshold. the threshold determines how close to a template the input needs to be for it to be considered a match. if the threshold is reduced, there will be fewer false non-matches but more false accepts. conversely, a higher threshold will reduce the far but increase the frr. our goal is to find the system’s thresholds that assure a good compromise between the minimizations of the false acceptance rate and false rejection rate. pato and millett, in their book [11], emphasized that the biometric recognition systems are inherently probabilistic. in their opinion, the biometric recognition involves matching, within a tolerance of approximation, of observed biometric traits against previously collected data for a subject. the approximate matching is required due to the variations in biological attributes and behaviors both within and between persons. let us assume that the multi-modal biometric system consists of n matchings. after any of the first n − 1 matchings (indexed in the formulas from 2 to n), one of the following three decisions will be made: accept the person as genuine, reject the person as impostor, or demand another matching. naturally, after the last matching (that uses the index 1 in formulas), only two decisions will be possible: to accept, or to reject the person. for each modality in the system, we collect data from n persons. for each person we take m samples, and construct an m × n matrix m of input information. for real-life databases the components of matrix m are vectors derived by using classic protocols, that extract biometric features from the real data collected (images, videos, speeches). in the beginning we restrict our attention to one of the first n−1 matchings. using the matrix m we compute the distances between each two collected samples, and derive the distributions for the genuine users and impostors. the distribution of the genuine users is constructed using the distances between each two components of the same column of the matrix m. the distribution of the impostors uses the distances between each component of matrix m and each component of matrix m that lies on different columns. two samples obtained from the same person are highly expected to have a small distance between them. thus, the genuine distribution will generally have a range of smaller values than the impostor distribution. a graphical representation of such distributions is shown in figure 2 (top) using normalized histograms. since the number of distances computed for the genuine distribution is significantly less then the number of distances computed for the impostor distribution, and the number of intervals used to construct the histograms is the same for both distributions, the height of the genuine distribution is significantly greater than the height of the impostor distribution. let ak and bk denote the minimal distance in the impostors distribution and the maximal 724 m. stanojević, i. milenković , d. starčević, b. stanojević figure 1: the areas involved in computing the far and frr on the first n − 1 match levels (top), and on the last match (bottom) continuous distribution approximation and thresholds optimization in serial multi-modal biometric systems 725 distance in the genuine distribution, respectively, for the k-th matching (we assume that ak < bk, otherwise the decision is trivial, and the system is error-free). when one of the first n − 1 matchings of the biometric system is used to verify a person, a decision is made according to two thresholds xq, xr ∈ [ak, bk], q = 2k − 2, r = 2k − 1 as follows: if the distance between the given sample and the sample in the database is less than xq the person is accepted as genuine; if it is greater than xr the person is rejected as impostor; but if the distance belongs to the uncertainty region [xq, xr], the verification process demands another matching (see figure 1, top). for the last matching in the sequence, the distributions of both genuine users and impostors are constructed in the same way; two values a1 and b1 are specified, with the same meaning as ak and bk, k = 2, . . . , n from the first matchings; but the decision is based on a single threshold denoted by x1, that lies between a1 and b1 as follows: if the distance is less than x1 the identity of the verified person is accepted as a genuine user, otherwise it is rejected as impostor (see figure 1, bottom). the main problem is to find proper values for the thresholds involved in the given sequence of matchings. our goal is to provide optimized values for these thresholds, in sense of minimizing both false acceptance and false rejection errors, far and frr, respectively. 3 solving methods we first have to evaluate the false acceptance and false rejection errors with respect to the thresholds xk, k = 1, . . . , 2n − 1. having a graphical representation of the score distributions of both genuine users and impostors, let ak (xq), q = k − 2 denote the area under the impostor distribution bounded to the right by the vertical line that passes through xq; and let bk (xr), r = 2k −1 denote the area under the genuine distribution bounded to the left by the vertical line that passes through xr – for the k-th matching (see figure 1, top). similarly, let a1 (x1) denote the area under the impostor distribution bounded to the right by the vertical line that passes through x1; and b1 (x1) denote the area under the genuine distribution bounded to the left by the vertical line that passes through x1 – for the last matching (see figure 1, bottom). the probability of a false match error based on the k-th matching is ak (xq), and it is a1 (x1) for the last matching. similarly, the probability of a false non-match error based on the k-th matching is bk (xr), and it is b1 (x1) for the last matching. the function farn, that describes the probability of a false match error in the general case of a biometric system with n modalities is computed using the recurrent formula fark(x1, ..., x2k−1) = ak (xq) + [ak (xr) − ak (xq)] fark−1(x1, ..., x2k−3), (1) where q = 2k − 2, r = 2k − 1, far1(x1) = a1 (x1). similarly, the function frrn, that describes the probability of a false non-match error in the general case of a biometric system with n modalities is computed using the recurrent formula frrk(x1, ..., x2k−1) = bk (xq) + [bk (xq) − bk (xr)] frrk−1(x1, ..., x2k−3), (2) where q = 2k − 2, r = 2k − 1, frr1(x1) = b1 (x1). in order to find proper bounds to the uncertainty regions involved in the verification process, we minimize both probabilities of error. since a low false match error means a high false nonmatch error and reverse, we have to search for a good compromise between the two errors. such 726 m. stanojević, i. milenković , d. starčević, b. stanojević compromise is achieved by solving the bi-objective programming problem min farn(x1, ..., x2n−1), min frrn(x1, ..., x2n−1), s.t. ak ≤ x2k−2 ≤ x2k−1 ≤ bk, k = 2, . . . , n a1 ≤ x1 ≤ b1. (3) model (3) is the generalization of model (2) given in [15]. 3.1 local linearization approximation in this section we briefly present the local linearization approach used in [15] to approximate the areas involved in formulas (1) and (2). the discrete score distributions of both genuine users and impostors were approximated using the least squares method. a graphical representation of the discrete distributions (points) and their linear approximations (the straight lines) may be seen in figure 4. the scores for genuine users are on the left, and the scores for the impostors are on the right. the representation is restricted to the region significant to the decision, i.e. to the intersection of the distributions. in [15] the theoretical presentation of the approach was restricted to bi-modal systems, and quadratic expressions with respect to the thresholds x1, x2 and x3, were derived. combining them, a polynomial expressions of degree 4 were obtained, for the objective functions of the optimization model. the bi-objective optimization problem formulated in [15] is a particular case of model (3), obtained for n = 2. for a general multi-modal matching system with n modalities, the expressions of the objective functions obtained by the linear approximation are polynomials of degree 2n. 3.2 continuous distribution approximation in this section we propose a new way to approximate the discrete score distributions of both genuine users and impostors. in the previous approach, the discrete data collected from the users was grouped in intervals, relative frequencies were computed, and the discrete set of pairs (frequency, midpoints of the intervals) was continuously and linearly approximated using the least squares method. we now propose to use a continuous distribution that approximate the initial discrete collection of data. we need to compute the mean and the variance of the data, separately for each modality, genuine users and impostors, and try to identify a continuous distributions that best fit the distance frequencies. figure 2 shows how the continuous distributions approximate the original score distributions for both genuine users and impostors, for one modality. in this representation the gamma distribution was used for the genuine scores and the normal distribution for the impostors. for the same data set – representing the genuine users scores distribution – we performed a fit distribution test, that identified as best fit distributions the gamma, generalized gamma (4p), and johnson sb. the goodness of fit was performed for 61 well-known distributions. among those distributions, the kolmogorov-smirnov test identified the generalized gamma (4p) distribution as the best fit, the anderson-darling test identified johnson sb distribution as the best fit, while the chi-squared test identified the gamma distribution as the best fit. figure 3 graphically shows how the input data (with all values grouped in a histogram) may be approximated by gamma, normal, and beta probability density functions. we were restricted to choose among gamma, normal, beta, erlang, and chi-square distributions due continuous distribution approximation and thresholds optimization in serial multi-modal biometric systems 727 figure 2: the approximation based on gamma distribution for the genuine scores, and on normal distribution for the impostors scores versus the original (discrete) score distributions figure 3: probability density functions for the continuous approximations using gamma, normal, and beta distributions. the characteristics of the distributions are given in table 1 table 1: fitting results for the probability density functions of the gamma, normal, and beta distributions presented in figure 3 distribution parameters gamma α = 3.1919; β = 52049 normal σ = 92990; µ = 166130 beta α1 = 1.4696; α2 = 4.7366; a = 29877; b = 616440 728 m. stanojević, i. milenković , d. starčević, b. stanojević table 2: the goodness of fit for the gamma, normal, and beta distributions (presented in figure 3), using kolmogorov-smirnov, anderson-darling and chi-squared statistics tests. the rank is given out of 61 distribution kolmogorov-smirnov anderson-darling chi-squared statistic / rank statistic / rank statistic / rank gamma 0.04247 / 15 1.26490 / 19 6.4661 / 1 normal 0.11578 / 37 11.63 / 36 50.698 / 38 beta 0.06484 / 25 4.3973 / 27 22.072 / 25 to the toolbox of the programming language we used for the optimization. despite the fact that these distributions are not the best fit, they behave well during experiments since they approximate relatively well the initial score distributions in along the intervals relevant to the optimization. we used easyfit 5.6 standard software (http://www.mathwave.com) for goodness fitting distribution. table 1 reports the parameters that describe the fitting results. these parameters were computed with respect to the mean and variance of the original score distribution. table 2 shows the results for the statistic tests kolmogorov-smirnov, anderson-darling and chi-squared applied to the data graphically represented in figure 3. it also includes the ranks of the chosen distributions in the ranking list of 61 well-known distributions. figure 4 shows that the probability density function of the continuous distribution (the nonlinear graph) better approximates the original set of data than the linear function (the straight line) obtained by the least squares method presented in the previous section. on the top side of the figure the approximations for the genuine users scores is given. for the impostors scores the approximations are given on the bottom side. according to the new proposed approximation method, we use the cumulative density functions (cdf) to express the probabilities ak and bk, k = 1, . . . , n, in model (3). thus, ak(x) = cdfi k (x), k = 1, . . . , n in far (1) for impostors, and bk(x) = 1 − cdf g k (x), k = 1, . . . , n in frr (2) for genuine users. model (3) is a nonlinear bi-objective problem independent on the approximation used. one way to solve problem (3) is to aggregate the two objective functions, and optimize the obtained function. we propose the weighted sum method, with the initial weights (1, 1), to aggregate the objectives. in this way we optimize the total error rate (ter), that is one parameter used in analyzing the performance of an biometric system. by changing the set of weights, we favor one or another type of error. in order to chose certain weights to aggregate the far and frr, the analyst must know/model the cost functions for false rejections and false acceptances. there are more system specific factors that may influence the priorities in favoring one or another of the error rates. analyzing such characteristics is beyond the scope of this work. our approach provides optimized thresholds from the point of view of minimizing the total error rate. we solved the single objective optimization problem min farn(x1, ..., x2n−1) + frrn(x1, ..., x2n−1), s.t. ak ≤ x2k−2 ≤ x2k−1 ≤ bk, k = 2, . . . , n a1 ≤ x1 ≤ b1. (4) numerically. a starting point was chosen by taking the midpoint of the feasible interval for the threshold x1, the point that is at one third from the left bound of the feasible interval for the x2k−2, and the point that is at one third from the right bound of the feasible interval for the threshold x2k−1, k = 2, . . . , n. we optimized one variable at a time, fixing others to either their continuous distribution approximation and thresholds optimization in serial multi-modal biometric systems 729 figure 4: the approximation based on the least square method versus the approximation based on continuous distributions for both genuine users and impostors 730 m. stanojević, i. milenković , d. starčević, b. stanojević initial values or previously obtained optimal values. the optimization process was stopped when the distance between two consecutive solutions dropped under a given threshold. the advantage of finding optimized thresholds, to be used by the decision-maker in constructing the sequential multi-modal system, resides in yielding the needed information in the a priori stage of the decision. generally, the trade-off between two conflicting objectives, and particularly the trade-off between far and frr, may be a subject of a wider discussion. the usefulness of a priori, a posteriori, and interactive methods in multiple objective optimization are highly emphasized in the literature (see for instance [1]). a visualization technique for accessing the solution pool in the interactive methods of multiple objective optimization can be found in [2]. we used gnu-octave (https://www.gnu.org/software/octave/) for the optimization step. 4 computation results 4.1 experiments using random generated instances in order to test the performance of our method we organized the experiments, as in [15]. first, the input data, necessary to construct the score distributions, were randomly generated according to the rules that made them proper data for biometric tests. more precisely, we have generated a set of vectors with l real positive components. each of these vectors simulates the essential information that in real situations is extracted from the taken pictures of a person during a biometric measurement. the first sample of each user is generated as a vector of uniformly distributed random numbers. the mean and standard deviation were varied through instances. each subsequent sample of the same user is generated as a vector of random numbers with normal distribution, keeping the same mean and variance as for its corresponding first sample. in this way we provided that the samples of one person are more similar to each other than when compared to samples of another person. we computed the euclidean distances between each two generated vectors, and split them in two categories to be used for the construction of the genuine and impostor distributions. for the continuous distribution approximations we used the gamma distribution for the genuine users and the normal distribution for the impostors. we computed the bounds ak, bk, k = 1, 2; used the approximations to evaluate the errors of false acceptance and false rejection; and constructed the bi-objective optimization model. in order to find the optimized thresholds we added the two error functions, and minimized the total error. each triple (x1, x2, x3), including the triple of optimized thresholds, defines a bi-modal biometric system, whose performance will be evaluated. in order to estimate the far and frr of each system using the same data as in constructing the genuine and impostor distributions, we successively collected the answers of the system, obtained when each person i = 1, . . . , n claims that he/she is the person k = 1, . . . , n, and he/she is verified with all samples j = 1, . . . , m of the person k, according to the specific thresholds of the system. each time when the system accepts a person i as being the person k, the numerator of the ratio far increases with 1 unit. similarly, each time when the system rejects a person i when he/she claims that he/she is person i the numerator of the ratio frr increases with 1 unit. the nominator of far is n(n − 1)m2, while the nominator of frr is m(m − 1)n/2. finally, we compute the genuine acceptance rate (gar) as 1-frr. when biometric samples of a control group are available, we collect the system’s answers obtained by checking the control samples instead of the initial samples. the numerical results are grouped in two tables. table 3 contains the results obtained by both methods ls (based on least square approximation), and cd (based on continuous distribution approximation). comparing the two methods we note that, considering the total error rate, cd continuous distribution approximation and thresholds optimization in serial multi-modal biometric systems 731 table 3: comparison of ls and cd for certain instances with given characteristics instances characteristics far gar ter ls cd ls cd ls cd n = 100, m = 5, l = 3 9.4% 0.52% 99.6% 98.5% 9.80% 2.03% n = 100, m = 5, l = 5 2.2% 0.77% 100% 99.5% 2.20% 1.26% n = 100, m = 5, l = 10 0.05% 0.19% 99.2% 99.8% 0.85% 0.35% n = 200, m = 5, l = 10 0.001% 0.05% 99.5% 99.9% 0.50% 0.15% table 4: the numerical results obtained by running cd on more instances with given characteristics instances characteristics far gar ter l = 5 l = 10 l = 5 l = 10 l = 5 l = 10 n = 100, m = 10 0.91% 0.08% 99.6% 99.94% 1.31% 0.14% n = 200, m = 10 0.96% 0.08% 99.7% 99.97% 1.31% 0.11% n = 500, m = 10 0.92% 0.08% 99.6% 99.98% 1.34% 0.10% performs better than ls in all cases. analyzing separately the values of the false acceptance rate and genuine acceptance rate, cd is better for two sets of instances, and ls is better for other two sets of data. the results reported in table 4 were obtained by running the cd method on instances with 100, 200 and 500 users. for each user 10 different samples were available. the samples were described by vectors of length 5 and of length 10. it is obvious that the number of genuine users used to train the system does not influence the error rates. contrary, the length of the vectors used to describe the individuals is very important, as expected, since the error rates are much smaller in the case of l = 10 than in the case of l = 5. more precisely, when the length of the vectors is greater, more biometric information is enclosed in them, thus a more clear separation between individuals exists. consequently, the score distributions of genuine users and impostors are less overlapped, the uncertain regions are smaller, and the total error rate is smaller. contrary, a small vector length correspond to a wide overlapping of the score distribution, and to a biometric system with greater error rates. 4.2 experiments using the nist-bssr matching score sets the nist bssr1 multi-modal database contains scores from 517 users. for each user, the database contains one score set from the comparison of two right index fingerprints, one score set from the comparison of two left index fingerprints, and two score sets (from two separate matchers) from the comparison of two frontal faces. the score sets from the left (right) indexes are referred as “li” (“ri”). each matching set contains 517 genuine scores and 266, 772 (i.e. 516 × 517) impostor scores. we transformed the given scores into distances, i.e. a great (small) score representing a similarity (non-similarity) between two collected samples is transformed to a small (great) distance between the same two samples. as a part of our experiments we derived the optimized thresholds for the bi-modal systems developed from the bssr1 database; and considered the li-ri and ri-li 2-matcher combinations. the nist bssr2 multi-modal database contains scores from 6000 users. for each user, the database contains one score set from the comparison of two right index fingerprints, and one score 732 m. stanojević, i. milenković , d. starčević, b. stanojević table 5: computation results for nist-bssr1 instances match ls cd gamma-normal cd gamma-gamma 1st 2nd far gar ter far gar ter far gar ter li ri 0.15% 94.59% 5.57% 0.62% 94.39% 6.23% 0.69% 95.36% 5.33% ri li 0.11% 93.82% 6.30% 0.35% 94.78% 5.58% 0.73% 95.30% 5.37% table 6: computation results for nist-bssr2 instances match ls cd gamma-normal cd gamma-gamma 1st 2nd far gar ter far gar ter far gar ter li ri 0.96% 95.87% 5.09% 1.03% 94.59% 6.46% 0.61% 95.42% 5.19% ri li 1.55% 96.12% 5.4% 0.75% 94.44% 6.40% 0.62% 95.44% 5.19% set from the comparison of two left index fingerprints. the score set from the left (right) indexes are referred as "li" ("ri"). each matching set contains 6000 genuine scores and 35,994,000 (i.e 5999 × 6000 ) impostor scores. as for the bssr1 dataset, we transformed the similarity scores into distances; derived the optimized thresholds; and considered both possible combinations. tables 5 and 6 report the numerical results obtained by running cd on nist-bssr1 and nist-bssr2. in these tables we include the results obtained by running ls in order to compare the performances, and the results obtained by two versions of the cd. the first version used gamma distribution for both genuine users and impostors, while the second version used gamma distribution for the genuine users and the normal distribution for the impostors. according to these experiments, it is better to choose the continuous distribution gamma for both genuine users and impostors. for bssr1 instances, the total error rate for cd gamma-gamma is considerably smaller than the total error rate obtained by ls method. for bssr2 instances the total error rate obtained with cd gamma-gamma is better then the one obtained by ls, in the case when checking the left fingerprint is the first modality, and checking the right fingerprint is the second modality in the biometric system. many papers referred to the same matching score dataset (see for instance [6], [9], [16], and [17]). searching for papers that report experimental results to compare with, we faced two main issues. first issue is related to the fact that there is no consistent way to deal with this database. for example, some authors randomly selected the scores for the system training, and used the rest for evaluation, thus making impossible to repeat their experiments; and/or discarded some scores due to apparent template acquisition errors, but without explaining which scores were discarded [16]. the second one is related to the fact that we propose a set of thresholds to be used in the multi-modal system; but we do not generate a roc curve, thus the equal error rate (eer) cannot be employed straightforward to validate our approach. 5 conclusions and future works in this paper we proposed a novel approach to determine the upper and lower acceptance thresholds in sequential multi-modal biometric matching systems. the new approach uses continuous distribution for the score distributions approximations of both genuine users and impostors. it improved the results obtained by the least square approximation approach. in the present paper we introduced the general mathematical model that may be used to minimize the total error function, and derive the thresholds in a general multi-modal system with n modalities. we solved the non-linear optimization problem numerically. the paper is an extension of [15], where the linear approximation of the biometric scores based on the least squares method was continuous distribution approximation and thresholds optimization in serial multi-modal biometric systems 733 introduced, and the optimization model was given biometric systems with 2 modalities. one of the advantages of the new approach is the fact that it relays on less input parameters than the previous method. the method based on the least square approximation included the computation of the frequencies needed for constructing the histograms for the score distributions. the number of the intervals for the histogram is theoretically uncertain for guaranteeing the best results. generally, the optimization of the thresholds for a serial multi-modal system serves the a priori need of the decision maker when building a convenient multi-modal biometric system. our method that provides optimized thresholds for a multi-modal biometric system is fast, and relatively simple to implement. for a biometric system that works in real time, the existence of multiple matchings, and the possibility that the unproblematic genuine users pass the system after the first match with a low false rejection rate, offers the advantage of an increased speed of the matching process. moreover, if the first match is based on a face image, or a video record that may be taken even without user’s will, then the process is even faster. the numerical results of our experiments were reported in the paper. for the majority of our experiments on random generated instances, the pair (far, gar), obtained by our method, dominates at least one pair (far, gar) from the set of the final results obtained by the fusion based method. the experiments showed a good performance of the sequential bi-modal biometric matching system based on optimized thresholds and continuous approximation of the distribution scores. our numerical results on real life datasets were also included in the paper. we referred to the nist-bssr1 and bssr2 data sets, and intend to extend our experiments to more benchmark data from the literature. we performed some experiments with multi-modal biometric systems with more than two matchings, but the instances were not yet statistically relevant. the research may advance by refining the approximation step needed for obtaining the expressions for the false acceptance and false rejection rates to be used for finding the optimized thresholds. one direction is to search for the fitting distributions that approximate well the initial score distributions just along the uncertain region. it is also possible to involve other metrics, instead of the euclidean distance, to compare the samples vectors. acknowledgment this research was partially supported by the ministry of education and science, republic of serbia, project numbers tr32013 and tr36006. bibliography [1] ehrgott, m. ; (2000); multicriteria optimization, berlin, germany: springer-verlag, isbn 3-540-21398-8. 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[16] tulyakov, s. ; li, j.; govindaraju, v.; (2008), enrolled template specific decisions and combinations in verification systems, biometrics: theory, applications and systems, 2008. btas 2008. 2nd ieee international conference on, arlington, va, 2008, isbn: 978-1-42442729-1, 1-7. [17] villegas, m.; paredes, r.; (2009), score fusion by maximizing the area under the roc curve”, pattern recognition and image analysis, lncs 5524, issn: 1054-6618, 473-480. [18] zhang, q.; yin, y.; zhan, d.-c.; peng, j.; (2014), a novel serial multimodal biometrics framework based on semisupervised learning techniques, ieee transactions on information forensics and security, issn: 1556-6013, 9:1681-1694. int j comput commun, issn 1841-9836 7(5):832-839, december, 2012. public discourse semantics. a method of anticipating economic crisis d. gîfu, d. cristea daniela gîfu alexandru ioan cuza university of iaşi, faculty of computer science 16, general berthelot st., 700483 iaşi, romania e-mail: daniela.gifu@info.uaic.ro dan cristea 1. romanian academy the iasi branch, institute for theoretical computer science 2, t. codrescu st., 700481 iaşi, romania and 2. alexandru ioan cuza university of iaşi, faculty of computer science 16, general berthelot st., 700483 iaşi, romania e-mail: dcristea@info.uaic.ro abstract: this paper provides a proof that anticipation of an economic crisis by analysing public discourses (in particular, speeches on economic issues) is feasible. it proposes a method of text classification and semantic interpretation based on natural language processing techniques that could be used to trace, over a period of time, the print press discourses, with the aim to valuate the perspective of occurrence of crises. classification is the task of assigning tags (words, expressions) to the texts that make up a corpus. in our case, we were interested to identify among the texts under scrutiny those belonging to classes like financial, economic, nationalism, etc. this approach is sustained by the fact that public discourses can be characterized from a rhetorical perspective, depending on the specific strategies their authors have chosen: orientation to change opinions or to determine action, ratio between rational (logos) and emotional (pathos), etc. we are sugesting an automatic analysis of the content of the public language, by using quantitative measures. our purpose was to develop a computational tool able to offer to researchers in the economic, social or political sciences, but, not less, to the public at large, the possibility to measure the acuity of different accents of a written public discourse (financial, emotional, etc.), as mean to anticipate the threat of financial waves. such a tool could help the processes of decision making in the analysis of crisis. although our analysis used as data the journalistic and economic environments of romania, it could easily be extrapolated to other languages/countries. keywords: public language, text categorization, semantic analysis, economic crisis. 1 introduction in the atempt to divulge ante-factum crises in public discourse, primarily the voices of those entities must be listen to which are most influencial on the financial and economic domains. these entities, clearly, are: the romanian national bank (in the internal context) and the world bank (in the international context)1. the voices of these entities are best listen to in the public speeches of governors and, in many cases, of journalists specialised on economic-financial issues. a public discourse arguing on some extremely important moment-related issue is, most of time, an amalgam of arguments, rational forms, descriptions, stylistic procedures, which are 1"in times of internal or international crisis (...), we talk about managing various symbolic aspects of the role of: guardian of institutions, guarantor of national unity, moderator." [3] copyright c⃝ 2006-2012 by ccc publications public discourse semantics. a method of anticipating economic crisis 833 intended to inform or to prepare a receptor in front of a problematic reality. but, as close to the subject a discourse would be, often it hides, in subtle ways, the true nature of the subjective thinking of the emitter. for instance, an exaggerated trust in the fresh energy of the society, in the benefits the loans on mortgage could bring to ordinary people, on the exceptional rise of the rate of interests, or on the incredible high bonuses certain banks are offering to their highly ranked employees could simultaneously bring the negative news, that something wrong is in the air, that a crisis is insinuating. decoding of this hidden message, which is most of the time transmitted unintentionally, could be done only by someone extremely sensible to all facets of the financial and economic life. signals for economic crises are issued by the central banks (e.g. federal reserve system, central bank of u.s., european central bank, etc.). during the period 2001-2008, when the banking system issued large but artificially cheap credits, there have been many public rhetoric appearances favourable to this behaviour which tried to set up an economic development investment with questionable prudence. slogans like "a home for every american" (u.s.) or "credit with only an id" (romania), addressing a wide range of borrowers but having extremely low interest rates, could have been taken as signals of an economic potential crisis. in this study we address the question: can an economic crisis be anticipated by evaluating public discourses from a lexical-semantic perspectives? we are interested to pursue a content analysis of the public language, using for that investigation tools that belong to the domain of natural language processing (nlp) and addressing: vocabulary (key words, frequent words), semantics (classes of concepts arranged in a hierarchy) and rhetorical-pragmatic discursive strategies (presence of the person i, preference for vague statements, generalities, etc.). in u.s., the tradition of quantitative analysis is very strong, its roots being defined by lasswell [5]. in europe the interest grew more towards theoretical investigation of the semiotics of discourse ( [1], [10], [1]). modern content analysis is not only an illustration of a theory of text, but, should be rooted on empirical data. on the other hand, the american analysis is often neutral, technical, comparative, while the european analysis (especially the critical discourse analysis model2) has a critical component and a strong enough ethicist. in the perspective of our study, we are interested on public discourses (speeches), in written form, given by specialists on economy or by journalists, on economic issues. it is known that economy crises succeeds either a period of economic thrive or, as happened recently, a previous crisis. in our investigation we have used texts produced by most pertinent spokesmen which appeared in press materials issued by the romanian national bank (bnr), the most legitimate voice on economy issues in romania. the other major filter in selecting the texts that should populate our corpus was the economic context (e.g. economic stability vs. economic crisis). a text categorization application filtered a stream of news that was considered of interest for our research. some of the topics of interest have been: "credit id only", "real estate boom", "mortgages", and "transactions with land or housing". on another hand, at the base of our quantitative investigation was laid a lexical-semantic database. in order to assure generality, in acquiring it we had to use rather neuter sources, not necessarily tight to our specific corpus of texts. as such, the lexicon and the semantic classes have been collected from different sources usually dealing with economy themes: the bnr publications, already mentioned, but also a collection of dailies, ziarul financiar, curierul naţional, bursa, that have been monitored for a long period of time. current empirical approaches in analysing the public language put at work nlp techniques, 2"critical theories, thus also cda, are afforded special standing as guides for human action. they are aimed at producing enlightenment and emmancipation. such theories seek not only to describe and explain, but also to root out a particular kind of delusion. even with differing concepts of ideology, critical theory seeks to create awareness in agents of their own needs and interests." [11] 834 d. gîfu, d. cristea by which a multitude of features of the discourse were extracted and interpreted. the domain of nlp includes a theoretically motivated range of computational techniques for analyzing and representing naturally occurring texts at one or more levels of linguistic analysis for the purpose of achieving human-like language processing for a range of tasks or applications. in this paper we describe a platform (discourse analysis tool dat) specialised in the interpretation of the public discourse, which integrates a range of language processing tools with the intent to build complex characterisations of the public discourse. the idea behind it is that the vocabulary betrays discursive tonalities, this way allowing interpretations over the speakers orientation. the paper is structured as follows. section 2 shortly describes the previous work. section 3 presents the dat software and section 4 discusses an example of comparative analysis of economic discourses, elaborated during one year (2007-2008). finally, section 5 highlights interpretations anchored in our analysis and presents conclusions. 2 previous work the aim of an interdisciplinary approach such as analysing the language of public speeches is to define and explain different discursive contexts, in our case, reflected in the print media. the studies in this direction have mainly concentrated on three tasks. the first had to do with a cognitive side and, often, with an emotional side, of how humans acquire, produce, and understand language. the second aimed at understanding the relationship between the linguistic utterance and the world, and the third at understanding the linguistic structure of the language as a communication device. linguistics has usually treated language as an abstract object which can be accounted for without reference to social or political concerns of any kind [9]. as we will see, one aspect of the platform that we present touches a lexical-semantic functionality, which has some similarities with the approach used in linguistic inquiry and word count (liwc), an american software used to analyse the elections in united states in 2008. there are, however, important differences between the two platforms. liwc-2007 basically counts words and increments counters associated with their declared semantic classes. dat performs part-of-speech (pos) tagging and lemmatization of words. the lexicon contains a collection of lemmas (over 8800) for the pos categories of verb, noun, adjective and adverb, each being associated with one or more semantic classes. in the context of the lexical semantic analysis, the pronouns, numerals, prepositions and conjunctions, considered to be semantically empty, have been left out. then, a special section of the lexicon includes expressions. an expression is defined as a sequence of stems of words. dat includes now 33 semantic classes, chosen to fit optimally with the necessities of interpreting the public discourse, five of them having been added recently (failures, nationalism, moderation, firmness, spectacular). then, another range of differences between the two platforms regards the user interface. in dat, the user is served by a friendly interface, offering a range of services: opening and displaying one or more files, editing and saving the text, functions of undo/redo, functions of editing the lexicon, visualization of the mentioning of occurrences of certain semantic classes in the text, etc. the menus offers a whole range of output visualization functions, from the tabular form to graphical representations and to printing services. and finally, and most importantly, to help the user to interpret different authors simultaneously, she/he can chose among a collection of formulas that facilitate comparative studies. public discourse semantics. a method of anticipating economic crisis 835 figure 1: the dat interface: in the left window appear the selected files, in the middle window the text from the selected file, and in the right window, information about the text (language, word count, dominant class, etc.). bellow, a plot chosen from a range of graphical styles is displayed. by selecting a specific class in the middle window, all words assigned to that class are highlighted in the text. 3 the dat platform the discourse analysis tool (dat, currently at version 3) considers the public discourse from two perspectives: lexical and semantic. we describe shortly our platform which integrates a range of language processing tools, with the intent to build complex characterisations of the public discourse. the concept behind this method is that the vocabulary used by a speaker betrays the authors sensibility, her/his level of culture, her/his cognitive world, and, by this, to the semantic spectrum of her/his speeches, while the syntax may reveal the level of culture, intentional persuasive attitudes towards the public, etc. some of these means of expression are intentional, aimed to deliver a certain image to the public, while others are unintentional. figure 1 shows a snapshot of the interface showing a semantic analysis, during a working session. to display the results of the lexical-semantic analysis, the platform incorporates two alternative views: graphical (pie, function, columns and areas) and tabular (microsoft excel compatible). the vocabulary of the platform covers 33 semantic classes (swear, social, family, friends, people, emotional, positive, negative, anxiety, anger, sadness, rational, intuition, determine, uncertain, certain, inhibition, perceptive, see, hear, feel, sexual, work, achievements, failures, leisure, home, financial, religion, nationalism, moderation, firmness, spectacular), considered to fulfil optimally the necessity of interpreting the public discourse in different contexts. some of these categories are placed in a hierarchical relation. linguistic processing begins by tokenization, part of speech tagging and lemmatization. only the words belonging to the lexicon are considered relevant and therefore count in establishing the weights of different semantic classes. in response to the text being sent by the user, the system returns a compendium of data which includes: the language of the document, the number of words, and the type of discourse detected, a unique identifier (usually the file name), and a 836 d. gîfu, d. cristea table 1: examples of phrases on economy issues, on bnr editorials classes original in romanian english equivalent financial positive creşterea pib, expansiunea economiei mondiale, investiţii, scăderea ratei şomajului, expansiunea economică pib growth, global economic growth, investments, unemployment has declined, economic growth negative moderarea ritmului de creştere a salariilor, gradul de incertitudine, turbulenţe pe pieţele financiare, efectul inhibitor asupra consumului şi investiţiilor moderate the wage growth, uncertainty, financial markets turmoil, dampening impact on consumption and investment report of the lexical-semantic analysis. our interest went mainly in determining those discursive attitudes able to betray an approaching recession. but the system can be parameterised to fit also other conjunctures: the user can define at will her/his semantic classes, which, as indicated, are partially placed in a hierarchy. thus, for example, for the lemma economist, the following classes are assigned: 2 = social and 5 = people. the class people, is a subclass of the class social. these classes and their hierarchy are defined in a xml-like manner: <class name="social" id="2"> <class name="people" id="5" parent="2"> whenever an occurrence belonging to a lower level class is detected in the input file, all counters in the hierarchy, from that class to the root, are incremented. in other words, the lexicon assigned to superior classes includes all words/lemmas of its subclasses. 4 a comparative study 4.1 the corpus the corpus used for our investigation was configured to allow a comparative study over the discursive characteristics of economic-financial themes, by including economy texts published on the bnr site in three different periods: 1. april-june 2007, when romania crossed a period of economic stability. 2. april-june 2008, when romania was near the economic crisis. 3. july 2008, when the romanian president declared the economic recession. table 1 presents examples of phrases in the economy domain that exhibit two different discourse moods: positive emotional and negative emotional. the analyzed texts were essentially dealing with the topics social and financial. after processing the texts with the dat software, the following classes proved to have preponderant occurrences: financial, social, work, emotional (positive and negative), rational (intuition, determine, uncertain, certain and inhibition) and nationalism. to stress the distinguishing features, only these classes were finally left on the graphics. 4.2 the lexical-semantic analysis we show in this section the results outputted by dat when analysing the streams of textual data belonging to the three sections of the corpus (presented in section 4.1). for that, we have public discourse semantics. a method of anticipating economic crisis 837 figure 2: difference between the occurrence of semantic classes in bnr editorials: one year before the economic recession versus three months before. used the dat feature of performing comparative studies. the values are supposed to reflect correctly the indicated classes, because they were computed by averaging on the whole collections of texts, not just a single text. the graphics considered for the interpretation computed one-toone differences, as given by formula 1, included in the dat mathematical functions library: diff1−1x,y = average(x) − average(y) (1) where x and y are two streams; average(x) and average(y) are the average frequencies of x and y over the whole stream, and the difference is computed for each selected class. since a difference can lead to both positive and negative values, these particular graphs should read as follows: values above the horizontal axis are those prevailing at the first element more than at the second element, and those below the horizontal axis show the reverse prominence. a zero value indicates equality. our experience showed that values below the threshold of 0.5% should be considered as irrelevant and, therefore, were ignored in the interpretation. so, the graphical representation in figure 2, in which the editorials (apr.-jun. 2007) are compared against the editorials (apr.-jun. 2008) should be interpreted as follows: in 2007 the bnr discourse was extremely optimistic (high difference values of the class positive) and they were giving high importance to romanian specific aspects (class nationalism), while in 2008 (nearly recession time) the bnr discourse had become rather pessimistic (class negative) and speculative (class intuition) with respect to the romanian economic future. in the following we will compare the same 2007 discourse against their discourse immediately after the recession. the graphical representation in figure 3, in which the editorials (apr.-jun. 2007) are compared against the editorials (july 2008) should be interpreted as follows: the difference in optimism between the bnr discourse one year before the recession and that of the moment the crisis was officially declared (class positive) is more pregnant (1.25% here versus 0.89% in figure 2). however, although the pessimistic tone (class negative) is more pronounced in july 2008 than in the period of stability, it has weakened in intensity. we could say that bnr is caution to push too much on the distress pedal, because its voice could influence the fixing and, by that, worsen 838 d. gîfu, d. cristea figure 3: difference between the occurrence of semantic classes in bnr editorials: one year before the economic recession versus one month after the economic recession. the financial market even more. moreover, bnr is offering a possible immediate solution, by accenting on the job sphere (class work). 5 conclusions and future work in this paper we presented a quantitative method and an application that strengthen the idea that crises can be anticipated by monitoring public speeches produced by representative entities. we are aware that some of the differences which we have evidenced in our comparative study should partially be attributed to idiosyncratic rhetorical styles. however, when the traits inventoried acquire the regularities of patterns, then they could be used as measure apparatuses and, properly used, could emit useful signals to a receptive society. there are a number of ways in which we think our research could be continued. first, we want to add new features to the platform, with a special emphasis on the syntactic and rhetorical levels of analysis. the new release of dat should help the user to identify and count patterns of use at the syntactic and rhetorical level. another line to be continued regards the evaluation metrics, which have not received enough attention till now. we are currently studying other statistical metrics able to give a more comprehensive image on different facets of the public discourse. a weakness of the present system is the fact that the unequal sizes of the lexicons characteristic to semantic classes can influence the decisions: the more entries in the lexicon a certain class contains, the higher its influence could be foreseen. to this problem, the solution is not to balance the classes in their number of entries, because the language makes them intrinsically unequal, but to find calibration techniques that bring their values on equivalent ranges, irrespective of the dimensions of the lexicons. let’s note that in the present study we have counterbalanced somehow this skew by using the difference-based formulas (and thus avoiding absolute values). surely, the problem of characterising public speeches receives no final solution with our approach. we believe, however, that our method sheds an interesting light on possibilities of automatically interpreting discourses and, equally, it opens new perspectives. public discourse semantics. a method of anticipating economic crisis 839 acknowledgements in pursuing this research the authors had partial support from the projects posdru-63663, ict-psp 250467-atlas and ict-psp 270893-metanet4u. bibliography [1] bürger, c.,textanalyse als ideologiekritik, zur rezeption zeitgenössischer unterhaltungsliteratur, frankfurt am main, athenäum, 1973. [2] cristea, d., răschip, m., forăscu, c., haja, g., florescu, c., aldea, b., dănilă, e., the digital form of the thesaurus dictionary of the romanian language, in proceedings of sped 2007 speech technology and human-computer dialogue, iaşi, may 10-12, 2007. [10] dijk van, t. a., sémantique générale et théorie des textes, linguistics, 62, 66-95, 1970. [3] gerstlé, j., comunicarea politică, trad. gabriela cămară ionesi, institutul european, iaşi, 94, 2002. [4] gîfu, d., cristea, d., computational techniques in political language processing: anadip2011, in j.j. park, l.t. yang, and c. lee (eds.), futuretech 2011, part ii, ccis 185, 188-195, 2011. [5] lasswell, h. d., politics: who gets what, when, how, mcgraw-hill, new york, 1936. [6] lazarsfeld, p. f., berelson, b., hazel, g., the peoples choice: how the voter makes up his mind in a presidential campaign, 3d ed., new york, columbia university press, 1944. [7] perelman, c., olbrechts-tyteca, l., traité de l’argumentation, éd. de l’institut de sociologie de l’université libre de bruxelles, 72, 1972. [1] plett, h. f., ştiinta textului şi analiza de text, trad. speranţa stănescu, ed. univers, bucureşti: 72, 1983. [9] romaine, s., language in society. an introduction to sociolinguistics, oxford university press inc., new york, 1994. [11] wodak, r., critical linguistics and critical discourse analysis, handbook of pragmatics, benjamins, 2006. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 550-564 improving tracking performance of predictive functional control using disturbance observer and its application to table drive systems t. satoh, k. kaneko, n. saito toshiyuki satoh, kotaro kaneko, naoki saito akita prefectural university department of machine intelligence and systems engineering 84-4, aza-ebinokuchi, tsuchiya, yurihonjo, akita, japan e-mail: tsatoh@akita-pu.ac.jp, m12a011@akita-pu.ac.jp naoki_saito@akita-pu.ac.jp abstract: a practical approach for improving tracking performance of the predictive functional control (pfc) is proposed. the disturbance observer is utilized to nominalize the actual plant and to reduce the predicted output error in the pfc algorithm by canceling not only constant but also high-order disturbances. the proposed control scheme is experimentally validated on a single axis table drive system and is compared with the standard pfc and the industrial cascade control. the experimental results prove the effectiveness of the proposed disturbance observer-based pfc scheme. keywords: predictive functional control, disturbance observer, table drive system, model predictive control 1 introduction the model-based predictive control (mpc) has been successfully applied mainly in the petrochemical industry since it was put into practical use in the 1970’s. in general, the quadratic optimization problem must be solved on-line to compute the optimal control sequence in the standard constrained multivariable mpc algorithm [14]. when the mpc is applied to the control of mechatronic servo systems, the timeconsuming optimization may be problematic since the sampling period of such systems is usually less than a few milliseconds and may be too short to complete the optimization. on the other hand, a simple mpc scheme called the predictive functional control (pfc) [11–13] is widely used. unlike the standard mpc, the pfc is primarily intended to single-input-single-output systems, and no on-line optimization is required since the control input is expressed as a linear combination of time-dependent basis functions. although the pfc algorithm is much simpler than the standard mpc algorithm, it gives a similar performance to the full mpc. accordingly, a variety of successful industrial applications have been reported (e.g., [2–5, 7, 8, 10, 20]). the tracking performance attained by the pfc heavily depends on the accuracy of the internal model of the actual plant. so, if the predicted output error caused by disturbances or model uncertainties is significant, the tracking performance may deteriorate, and the designed response cannot be obtained on the actual system. another weakness of the pfc is that it cannot cope with high-order disturbances, such as ramp or parabola disturbances. assume that disturbances entering the system are the 1/sk-type (k > 1). then, for non-integrating plants, it is known that the pfc is offset-free for stepwise (i.e., k = 1) disturbances, which means that the stepwise disturbance is asymptotically rejected. however, this property does not hold true for high-order (i.e., k > 2) disturbances 1. also, for integrating plants, even stepwise disturbances cannot be rejected in the pfc scheme. 1among high-order disturbances, ramp disturbances are especially worth considering. examples of ramp disturbances include the viscous friction under uniform accelerated motion in mechatronic systems, or flow fluctuations in petrochemical plants. copyright c⃝ 2006-2012 by ccc publications improving tracking performance of predictive functional control using disturbance observer and its application to table drive systems 551 the purpose of this paper is to propose a practical way to overcome the above-mentioned drawbacks. to this end, we construct a dual loop control structure which consists of an inner loop formed by the disturbance observer (dob) [15, 16, 19] and an outer loop formed by the pfc. the dob is known to be an effective compensation mechanism which reduces the influence of disturbances, uncertainties and nonlinearities in the plant and enforces the nominal input/output behavior on the actual plant especially in the low frequency range where the frequency of the reference signal concentrates. accordingly, the actual plant behaves as if it is the nominal plant by introducing the dob. in the dob design, we can specify the number of integrators introduced in the loop transfer function. hence, the dob has the ability to asymptotically reject high-order disturbances as well as stepwise disturbances. the contributions of the paper are as described below. first, it offers a way of improving the prediction accuracy within the pfc algorithm. this leads to the improvement of the transient performance of the pfc in the presence of disturbances and/or modeling errors since the dob nominalizes the actual plant especially at low frequencies. second, it solves the inherent weakness of the pfc against the highorder disturbances. when the 1/sk-type (k > 2) disturbances are applied, those disturbances cannot be rejected by the standard pfc controller. however, due to the introduction of the dob, such high-order disturbances can be asymptotically rejected since the dob has the specified number of integral action. we should mention here that the idea of the combination of the pfc and the disturbance observer has originated in the recent application to the control of a pneumatic artificial muscle actuator by the authors [18]. however, in-depth argument about the advantages of the proposed scheme was not provided there. the present paper states the advantages that were left unclear in [18]. in addition, the proposed method is validated on a single axis table drive system and compared with the standard pfc and the industrial cascade control which consists of the inner proportional-plus-integral control loop and the outer proportional control loop. we also examine the performance differences between two different control systems that have the same pfc controller but different dobs. 2 predictive functional control enhanced with disturbance observer 2.1 predictive functional control in this subsection, we give a brief overview of the predictive functional control (pfc). for more details of the pfc algorithm based on the state-space model, see, for example, s. abu el ata-doss et al. [1]. figure 1 shows the basic concept of the pfc. suppose that the current time is labeled as time step k. a set-point trajectory is defined as a command signal which the process output yp should follow, and the value of the set-point trajectory at the current time step is denoted by c (k). also shown is a reference trajectory denoted by yr. this trajectory starts at the current process output yp (k) and defines a desired trajectory along which the process output yp should approach the set-point trajectory. on the reference trajectory, there are a few coincidence points on which the performance index is defined so that the process output yp will coincide with the reference trajectory yr. as an example, three coincidence points are drawn in figure 1. the optimal control input trajectory is then computed on the basis of the predicted output. once we have computed a future control input trajectory, we apply only the first element to the process. at the next time step, we repeat the whole cycle from the definition of the reference trajectory to the application of the first element of the optimal control input trajectory. we call this way of control a receding horizon control. next, we show the basic pfc algorithm which is a slightly modified formulation for handling time delay. if we set d = 0 in the following description, the control law accords with the one without time delay. in the following, let rn and zn be the set of all n-dimensional real vectors and the set of all ndimensional integer vectors, respectively. now assume that the plant is stable and has the time delay of l and that the sampling period is t s. the development of the pfc algorithm is based on the following 552 t. satoh, k. kaneko, n. saito siso discrete-time linear state-space model of the plant: xm (k + 1) = am xm (k) + bm u (k) ,ym (k) = cm xm (k) (1) where xm ∈ rn is the state vector, u ∈ r is the control input, ym ∈ r is the model output, respectively. here, the model output ym (k) is used to predict the future plant output ŷp (k + d) where d ∈z is defined as the nearest integer of l/t s. assume that the following condition holds: det am − i bm cm 0  , 0. (2) then the reference trajectory is defined as follows: yr (k + d + i) := c (k + d + i) −αi (c (k + d) − ŷp (k + d)) , i = 0,1, . . . (3) where α ∈r is a parameter which adjusts the approaching ratio of the reference trajectory to the set-point (0 < α < 1). for example, dieulot et al. [3] have chosen the parameter α as α = e−3t s/tcltr along with the following three coincidence points: ( h1 h2 h3 ) = ( tcltr 3t s tcltr 2t s tcltr t s ) (4) where tcltr ∈r is constant and called the desired closed-loop time response, which is taken as the time required to reach 95% of the final value [13]. in many cases, the performance index is defined as the quadratic sum of the errors between the predicted process output ŷp and the reference trajectory yr as follows: j (k) := nh∑ j=1 { ŷp ( k + d + h j ) −yr ( k + d + h j )}2 (5) where h j ∈ z ( j = 0,1, . . . ,h) and nh ∈ z are respectively the coincidence time point and the number of coincidence points. in the pfc, the future control input computed at each sampling instant is assumed to be the sum of weighted basis functions, and a time-dependent polynomial basis is usually employed. then the optimal control input that minimizes the performance index (5) is given by u (k) = k0 {c (k + d) −yp (k)}− de∑ m=1 kmem (k + d) + ν̃ t x xm (k) + ν̃ t xd xm (k − d) (6) figure 1: concept of the predictive functional control. improving tracking performance of predictive functional control using disturbance observer and its application to table drive systems 553 where de ∈z and em ∈z respectively denote the degree of the polynomial and unknown coefficients that are used when the predicted error at the future time step k + d + i is approximated by a time-dependent polynomial, and k0 ∈r, km ∈r, ν̃x ∈rn and ν̃xd ∈rn are respectively given by k0 = ν t  1 −αh1 1 −αh2 ... 1 −αhnh  , km = ν t  hm1 hm2 ... hmnh  , ν̃x =−  cm ( ah1m −α h1 i ) cm ( ah2m −α h2 i ) ... cm ( a hnh m −α hnh i )  t ν, ν̃xd =−  ( αh1 − 1 ) cm( αh2 − 1 ) cm ...( αhnh − 1 ) cm  t ν. (7) in (7), ν ∈rhnh is given by ν= ( yb (h1) · · · yb ( hnh ))t  nh∑ j=1 yb ( h j ) yb ( h j )t  −1 ub (0) (8) where yb ( h j ) = ( yb1 ( h j ) · · · ybnb ( h j ))t ∈ rhnh×nb and ub (0) = ( 1 0 · · · 0 )t ∈ znb . here, ybl (i) ∈r is the forced response to the basis function of the form il−1 (l = 1,2, . . . ,nb). the second term in the right-hand side of (6) denotes the term to compensate for the prediction error due to disturbances and/or uncertainties. the unknown coefficients em (k + d) (m = 1,2, . . . ,de) are determined in the following fashion. first, the number of steps hc used for the polynomial approximation is specified. then, at each sampling instant, the following vectors φ ∈ rhc , θ ∈ rde and a matrix h ∈zhc×de are defined: φ :=  e (k + d) − e (k + d − hc) e (k + d − 1) − e (k + d − hc) ... e (k + d − hc + 1) − e (k + d − hc)  , h :=  hc h2c · · · h de c hc − 1 (hc − 1)2 · · · (hc − 1)de ... ... ... ... 1 1 · · · 1  , θ :=  e1 (k + d) e2 (k + d) ... ede (k + d)  . (9) the problem is to approximate φ by hθ in the least-squares sense. the vector of unknown coefficients θ that minimizes the square error j (θ) := (φ− hθ)t (φ− hθ) is hence given by θ= ( ht h )−1 htφ. (10) once the number of steps hc is specified, the matrix ( ht h )−1 ht can be preliminarily computed off-line. therefore, the optimal coefficients em (k) (m = 1,2, . . . ,de) can be determined by just updating the vector φ and computing (10) on-line. the mechanism which extrapolates the past prediction error to the future prediction horizon by using the coefficients given in (10) is called the auto-compensation. the use of the auto-compensation is optional, so the pfc is often utilized without the second term in the right-hand side of (6). 2.2 disturbance observer the disturbance observer (dob) has the structure depicted in figure 2 where p (s) is the transfer function of the real plant, pn (s) is the nominal model of the plant, and q (s) is a proper and stable filter. the nominal model pn (s) is supposed to be minimum-phase in the following. on the assumption that disturbances acting on the system, modeling errors and nonlinearities can be regarded as an equivalent 554 t. satoh, k. kaneko, n. saito figure 2: structure of the disturbance observer. figure 3: equivalent form of the disturbance observer. disturbance d at the plant input, the dob computes the estimate d̂ of the current disturbance d, which is subtracted from the input u to cancel the disturbance. the designed parameter of the dob is the filter q (s). this filter is closely related to the sensitivity properties of the dob. for example, the transfer function from the disturbance d to the measurement output y is given by gyd (s) = (1 − q (s)) p (s) pn (s) pn (s) + (p (s) − pn (s)) q (s) . (11) accordingly, to reject the disturbance d asymptotically, the gain of q (s) should be unity at low frequencies. on the other hand, the transfer function from the measurement noise n to the measurement output y is given by gyn (s) = −p (s) q (s) pn (s) + (p (s) − pn (s)) q (s) . (12) hence, to suppress the measurement noise, the gain of q (s) should be 0 at high frequencies. therefore, q (s) should be a low-pass filter with the dc gain of 1. since the closed-loop transfer function from the input u to the measurement output y in figure 2 is given by gyu (s) = p (s) pn (s) (1 − q (s)) pn (s) + p (s) q (s) , (13) the transfer property approximates the nominal plant pn (s) at low frequencies if q (s) is chosen as stated above. this means that the dob can nominalize the real plant at low frequencies. when the plant has no unstable zeros, the low-pass filter q (s) can be selected as [19] q (s) = 1 + ∑nq−ρq m=1 fm s m 1 + ∑nq m=1 fm s m (14) where nq ∈ z, ρq ∈ z and fm ∈ r are respectively the order of q (s), the relative degree of q (s) and unknown coefficients to be determined. to make q (s) pn (s) −1 proper, ρq must be chosen as ρq > ρp where ρp ∈ z is the relative degree of pn (s). figure 3 shows the equivalent form of the disturbance observer. in the figure, (1 − q (s))−1 can be written as 1 1 − q (s) = 1 + ∑nq m=1 fm s m sni ( fni + ∑ρq m=1 fni+m s m ) (15) where ni = nq −ρq + 1, if the q-filter of the form given in (14) is used. hence, we can see that the loop transfer function includes ni integrators in figure 3, which means that the dob can asymptotically reject up to the 1/sni -type disturbances. various types of analogue filters can be utilized to realize q (s), and in many cases, the butterworth or the binomial low-pass filters are used. more specifically, the butterworth filter is a reasonable option improving tracking performance of predictive functional control using disturbance observer and its application to table drive systems 555 figure 4: structure of the unknown input disturbance observer. figure 5: structure of the proposed control system. when nq = ρq, which means that the degree of the numerator polynomial of q (s) is 0. otherwise, the binomial filter of the following form is useful: q (s) = 1 + ∑nq−ρq m=1 am ( sτnq )m 1 + ∑nq m=1 am ( sτnq )m (16) where am (m = 1,2, . . . ,nq) are the binomial coefficients (i.e., am = nq!/m!(nq − m)!) and τnq is a design variable to be determined. aside from the dob, another type of disturbance observer based on the unknown input observer, which is referred to as the unknown input disturbance observer (uidob), has also been proposed [6]. the uidob is a natural extension of the luenberger observer, and it is based on the following state-space models of the plant and the fictitious disturbance generator: plant  ẋ (t) = ax (t) + b (ua (t) + d (t)) ,y(t) = c x (t) , disturbance generator  ż (t) = adz (t) ,d (t) = cd z (t) (17) where x ∈ rn is the state variable of the plant, ua ∈ r is the control input, y ∈ r is the plant output, z ∈ rnd is the state variable of the disturbance generator and d ∈ r is the disturbance. notice that the eigenvalues of ad are not restricted to those on the origin (i.e., disturbances are not necessarily the 1/sktype). it is assumed that the pair (c, a) and (cd, ad ) are both observable and that no eigenvalues of ad coincide with zeros of the plant. the uidob is based on the augmented system obtained from (17) and is given by   ˙̂x (t)˙̂z (t)  = a bcd 0 ad  x̂ (t) ẑ (t)  + b 0  ua (t) + l y(t) − (c 0) x̂ (t) ẑ (t)   ,x̂ (t) d̂ (t)  = i 0 0 cd  x̂ (t) ẑ (t)  (18) where x̂, ẑ and d̂ are the estimates of x, z and d, respectively. l ∈ rn+nd is the observer gain which is chosen to stabilize the observer system. figure 4 shows the structure of the uidob where au = a bcd 0 ad  , bu = b 0  , cu = (c 0) , x̂u (t) = x̂ (t) ẑ (t)  . (19) the estimate d̂ is fed back to the control input to compensate for the input disturbance d. unlike the dob, the uidob estimates x̂ as well as d̂, so the observer-based state feedback technique can be applicable to stabilize the plant. 556 t. satoh, k. kaneko, n. saito the equivalence and difference between the dob and uidob has been already investigated by schrijver and van dijk [9]2, and they have shown that the dob is equivalent to the uidob for some specific design choices. actually, the dob is more general than the uidob for the following reasons [9]: • given any uidob designed for the 1/sk-type disturbance model, we can always find exactly the same dob as the given uidob. • it is possible to design a dob by freely specifying the relative degree of the q (s). however, in the uidob case, the relative degree always becomes ρp + 1 where ρp is the relative degree of the plant. hence, it is generally impossible to convert a given dob into an equivalent uidob structure. • there is no freedom to choose the order of q (s) in the uidob structure, and the order of the uidob is higher than that of the dob for plants with stable zeros. we therefore use the dob in this paper. refer to schrijver and van dijk [9] for more details about the dob and uidob. 2.3 structure of proposed control system the structure of the overall control system we propose is shown in figure 5 where p represents the actual plant, c is the set-point, yp is the plant output, d is the disturbance, n is the noise, and d̂ is the estimated disturbance. the control system consists of an inner loop formed by a disturbance observer and an outer loop formed by a pfc controller. the advantages of using the disturbance observer along with the predictive functional control are twofold. first, an improvement of prediction accuracy in the pfc algorithm is expected since the input/output behavior of the actual plant approaches its nominal characteristics by using the dob. this can be understood in the following way. as explained in the previous subsection, the design parameter q (s) is a low-pass filter, and its dc gain is usually chosen to be 1 (= 0 db) to reject the 1/sk-type disturbances asymptotically. hence, in the low-frequency band on which disturbances generally concentrate, the transfer function from the control input to the plant output given in (13) can be approximated by gyu (s) = p (s) pn (s) (1 − q (s)) pn (s) + p (s) q (s) ≃ pn (s) (20) since q (s) ≃ 1. this means that the behavior of the actual plant equipped with the dob shown in figure 2 is close to that of the nominal model pn (s) as long as the bandwidth of q (s) is properly designed. although gain mismatches or modeling errors between the internal model and the real plant are unavoidable in real-world applications, the pfc has tolerance for such uncertainties to some extent. however, the transient performance may deteriorate even though the steady-state response is satisfactory. for instance, suppose that the real plant p (s) and the internal model pn (s) are both stable first-order systems and given by as follows: p (s) = k s + p , pn (s) = kn s + p (21) where k > 0, kn > 0 and p > 0. if there exists a gain mismatch between these two transfer functions such that k < kn, then the closed-loop response is slower than the designed response based on the internal model pn (s). this difference is due to the inaccuracy of the prediction in the pfc algorithm. the use of 2the dob is denoted as the disturbance estimation filter (def) in [9]. improving tracking performance of predictive functional control using disturbance observer and its application to table drive systems 557 table 1: specifications of linear actuator (left) and dc motor (right). description value description value ball screw lead 10 mm nominal voltage 24 v stroke 500 mm rated current 2.96 a rail length 670 mm rated torque 0.28 nm maximum rotational speed 500 mm/s rated speed 1810 r/min figure 6: single axis table drive system. figure 7: schematic diagram of experimental setup. the disturbance observer improves the prediction accuracy and, in consequence, leads to the improvement of the transient response in the pfc scheme. second, the disturbance observer can compensate for high-order (e.g., ramp, parabola, etc.) disturbances. it is known that, in the case of non-integrating processes, the steady-state error caused by stepwise disturbances becomes zero in the pfc scheme. unfortunately, this property does not hold true for high-order disturbances. also, for integrating processes, the steady-state error remains even though the disturbance is stepwise. to eliminate the steady-state error in such cases, one or more integrators must be added in the loop transfer function. this can be accomplished within the local loop formed by the disturbance observer when ni > 1 in (15), and the disturbance is asymptotically eliminated in the inner-loop shown in figure 5. consequently, the disturbance rejection property of the pfc can be enhanced, and the above-mentioned difficulty is remedied by the simultaneous use of the disturbance observer. 3 controller design for single axis table drive system 3.1 plant description figure 6 shows the external view of the single axis table drive system which is used in the experiment shown in section 4. also, figure 7 shows the schematic diagram of the experimental setup. as is clear from figure 7, the system is controlled by the semi-closed-loop control method. the core of this experimental apparatus is a single axis linear actuator (nsk ltd., monocarrier mcm08050h10k), which is driven by an 80 watt dc servo motor (maxon motor ag, f2260.885) equipped with an optical encoder with the resolution of 1000 pulses/revolution. the dc motor is driven by a dc servo amplifier (maxon motor ag, ads 50/5). the specifications of the linear actuator and dc motor are summarized in table 1. the encoder pulse is received by a 24-bit encoder counter board (interface co., ltd., pci-6204) and counted by quad edge evaluation. the command signal to the dc servo amplifier is sent from a 12bit digital-to-analogue converter, and the current monitor signal from the dc servo amplifier is received by a 12-bit analog-to-digital converter. those d/a and a/d converters are both implemented on one analogue i/o board (interface co., ltd., pci-3521). the control pc is equipped with dual 1.7 ghz intel xeon r⃝ processors and is running on microsoft windows 2000 r⃝ operating system. a real-time control environment is constructed by using matlab r⃝, simulink r⃝ and real-time windows target r⃝ (math558 t. satoh, k. kaneko, n. saito works, inc.), and the table drive system is controlled at the sample rate of 1 khz. two s-function blocks for the use of pci-6204 (encoder counter board) and pci-3521 (a/d and d/a board) were developed by the authors since simulink is not shipped with blocks for these boards. we use the dc servo amplifier in the current control mode to command and control the motor torque instead of directly manipulating the motor current. hence, on the assumption that the table drive system can be approximated by a one-inertia system, its equation of motion subject to friction torque is given by θ̈ (t) = ks j ea (t) − 1 j td (t) (22) where θ denotes the angle of the motor shaft, ea is the command voltage to the dc servo amplifier, j is the equivalent moment of inertia, ks is the conversion factor from the command voltage to the motor torque, and td is the nonlinear friction torque. notice that td includes the viscous friction torque. in the case of our experimental apparatus, the conversion factor is given by ks = 0.0801 nm/v, and the nominal value of the moment of inertia is identified as j = 1.6928 × 10−4 kg m2 by using a standard system identification technique. we define the state vector and output variables as (x1 x2) t := (θ θ̇)t and y := θ, respectively. also, the time delay of the experimental apparatus can be negligible as long as the sampling period is greater than or equal to 1 ms. then the state-space description of the single axis table drive system is given by ẋ1 (t) ẋ2 (t)  = 0 1 0 0  x1 (t) x2 (t)  +  0 ks/j  ea (t) +  0−1/j  td (t) , y(t) = ( 1 0 ) x1 (t) x2 (t)  . (23) here, the nonlinear friction characteristics of this single axis table drive system can be approximated by, for example, the general kinetic (gk) friction model [17] of the form td (t) =  { tc + (tb − tc) e − ∣∣∣∣ θ̇(t)θ̇str ∣∣∣∣2 } sgn θ̇ (t) + dθ̇ (t) , if θ̇, 0, te (t) , if θ̇= 0 and |te|< t s, tb sgn te (t) , if θ̇= 0 and |te|> t s (24) where θ̇str is the stribeck velocity, tc is the coulomb friction level, tb is the breakaway torque, d is the viscous friction coefficient, te is the external torque generated by the driving motor. however, to evaluate the effectiveness of the disturbance observer, and to compare the prediction accuracy in the pfc algorithm with or without the disturbance observer, we treat the friction torque as unknown disturbance in this paper. 3.2 design of disturbance observer the nominal transfer function from the applied voltage to the angular velocity can be described as pn (s) = 473.2 s . (25) hence, the relative degree of the q-filter must be chosen as ρq > 1, and we take ρq to be 1 in this paper. the required number of the integral action introduced by the dob depends on the nature of disturbances, and it is difficult to decide its appropriate number beforehand. so, we will design two dobs to see the difference due to the number of integral action under the following design conditions: improving tracking performance of predictive functional control using disturbance observer and its application to table drive systems 559 10 0 10 1 10 2 10 3 10 4 10 5 −70 −60 −50 −40 −30 −20 −10 0 10 frequency (rad/s) g ai n (d b ) q 1 (s) q 2 (s) 1−q 1 (s) 1−q 2 (s) figure 8: frequency characteristics of qi (s) and 1 − qi (s) (i = 1,2). 1. relative degree ρq = 1, number of integral action ni = 1. 2. relative degree ρq = 1, number of integral action ni = 2. since the transfer function from the input voltage ea to the rotor angle θ is given by the double integrator model pn (s)/s = 473.2/s2, the closed-loop system equipped with the dob can asymptotically reject the stepwise disturbance when ni = 2. the order of the q-filter nq can be determined by the relation nq = ni +ρq − 1, so nq = 1 for the former case, and nq = 2 for the latter case. first, we design a first order filter q1 (s) as an analogue butterworth low-pass filter. the target cut-off frequency ωc is chosen to be 40 hz. then, q1 (s) is given by q1 (s) = 251.33 s + 251.33 . (26) next, we design a second order filter q2 (s). in this case, we use the prototype of the binomial filter given in (16). for the case of q2 (s), the binomial coefficients a1 and a2 are given as a1 = 2 and a2 = 1, respectively. to determine the parameter τnq , we numerically solve the following optimization problem: min τnq n∑ i=1 ( |qd (ωi)| − ∣∣∣∣q2 ( jωi,τnq )∣∣∣∣)2 (27) where |qd (ω)| is the gain of the ideal low-pass filter whose value is 1 over the frequency range from 0 to ωc and is 0 at higher frequencies than ωc, and ωi (i = 1,2, . . . , n) are n logarithmically-spaced points between decades 10−1 rad/s and 104 rad/s. solving the optimization problem with n = 3000, we obtain the optimal value of the parameter τnq = 0.015, which results in the following second order filter: q2 (s) = 133.38 (s + 33.35) (s + 66.69)2 . (28) figure 8 shows the gains of qi (s) and 1 − qi (s) (i = 1,2). we can see form figure 8 that the property of the disturbance suppression of the dob using q2 (s) is better than that using q1 (s). 3.3 pfc parameters using the control structure depicted in figure 5, we implement the pfc algorithm in the position control loop with the double integral internal model pn (s)/s. also, the time delay steps d is fixed to 0 in the control law given in (6). 560 t. satoh, k. kaneko, n. saito to cope with non-constant references, both the step and ramp basis functions are employed which will be expected to achieve better tracking performance than the step basis function alone. we choose the desired closed-loop time response tcltr to be 0.02 s to make a compromise between the stability of the closed-loop system and the tracking performance, and the sampling period t s of the system is fixed to 0.001 s. the same approaching ratio and the coincidence points as dieulot et al. [3] have used, which are already shown in subsection 2.1, are employed. so, the approaching ratio becomes α= 0.8607, and the three coincidence points given in (4) are defined as (h1 h2 h3) = (6 10 20). in the experiment shown in the next section, we will also use the standard pfc control scheme with the auto-compensation for comparison, and the predicted error is approximated by a first degree polynomial. the number of steps used for the polynomial approximation in (9) is taken to be hc = 20. it can be seen that the tracking performance is improved as hc becomes small. however, in exchange for the improvement, the control effort becomes large and shows extremely oscillatory behavior. the number of steps 20 is therefore the result of compromise between the tracking performance and control effort. 3.4 design of industrial controller as well as the pfc plus auto-compensation scheme, we will use an industrial controller for comparison in the experiment. it consists of a proportional-integral controller in the inner speed loop and a proportional controller in the outer position loop. the current loop is neglected since the current is already controlled in the motor driver. the pi controller in the speed loop is defined as cpi (s) = kpi 1 + dj s  (29) where kpi is the proportional gain. this pi controller is constructed to cancel the plant dynamics and force the open-loop transfer function to be ks kpi/j s. it follows that the viscous friction is theoretically canceled. here, the proportional gain kpi is chosen to be 1. as the value of kpi is increased, the speed of response is improved, but the maximum tracking error is not. also, the stability of the inner loop is sensitive to the change of this gain and easily collapses when kpi > 1. hence, kpi is fixed to 1. the p controller in the position loop is defined as cp (s) = kp. on the simulation model, we can increase the proportional gain up to 100. however, due to uncertainties in the actual plant and the nonlinear friction, the responses of the closed-loop system becomes highly oscillatory, and the large control effort is applied to the system when kp is greater than 20. hence, kp is fixed to 20 in the experiment. 4 experimental results to test the proposed control scheme, an experiment was conducted using a sinusoidal reference input. the amplitude and the angular frequency are respectively 5 rad (7.96 mm in displacement) and π/4 rad/s. figure 9 shows the reference and the motor angles obtained by different kinds of control schemes. in this and the subsequent figures, the correspondence between the legends and control schemes are as follows: ‘pfc’ is the standard pfc, ‘pfc+ac’ is the pfc with the auto-compensation, ‘pfc+dob1’ is the pfc combined with the disturbance observer using q1 (s), ‘pfc+dob2’ is the pfc combined with the disturbance observer using q2 (s), ‘p+pi’ is the industrial control. we can see from the top graph that only the standard pfc can hardly actuate the table drive system and cannot track the reference angle. this is mainly because the stiction torque (breakaway torque) is larger than the driving torque generated by the motor and, thus, the table drive system does not move. every control scheme except the standard pfc tracks the reference, and it is hard to distinguish from each other in the top graph. so, a detailed improving tracking performance of predictive functional control using disturbance observer and its application to table drive systems 561 0 1 2 3 4 5 6 7 8 −10 −5 0 5 10 p os it io n (m m ) plant output reference pfc pfc+ac pfc+dob1 pfc+dob2 p+pi 0.5 0.502 0.504 0.506 0.508 0.51 2.75 2.8 2.85 2.9 2.95 3 3.05 3.1 p os it io n (m m ) 1.5 2 2.5 7.6 7.7 7.8 7.9 8 time (s) p os it io n (m m ) reference p+pi pfc+acpfc+dob1pfc+dob2 pfc+ac reference pfc+dob1 pfc+dob2 p+pi pfc figure 9: comparison of control performances (top graph) and its closeups (middle and bottom graphs). 0 1 2 3 4 5 6 7 8 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 time (s) p os it io n (m m ) tracking error pfc+ac pfc+dob1 pfc+dob2 p+pi p+pi pfc+ac pfc+dob1 pfc+dob2 figure 10: comparison of tracking errors. view near 0.5 seconds where the coulomb friction torque and the viscous friction torque respectively act as the stepwise disturbance and the ramp disturbance is shown in the middle graph of figure 9. in addition, a detailed view around 2 seconds where a change of direction of the movement occurs and therefore the friction torque takes the maximum is shown in the bottom graph of figure 9. it can be seen from figure 9 that the pfcs along with the disturbance observer track the reference better than other control schemes without the disturbance observer. figure 10 shows the tracking error of each control scheme where the result of the pfc is excluded from the graph since its tracking error is significant. comparing the two pfc plus disturbance observer schemes, we see that the tracking performance of pfc+dob2 is better than that of pfc+dob1. the difference arises from the number of integrators in the velocity loop, and, as a result, the disturbance observer using q2 (s), which adds two integrators in the loop, has lower sensitivity and better disturbance rejection property at low frequencies. we also see that pfc+ac is a simple but effective control scheme, considering the fact that it uses no active disturbance estimation or cancellation. however, the peak-to-peak value of its tracking error is about twice as large as that of pfc+dob1. also, since the auto-compensation is based on the past prediction errors and extrapolation, a certain amount of degradation of the tracking error is unavoidable when the reference signal changes its direction; see around 2 seconds in figure 10. finally, we can see that the industrial controller (p+pi) provides the worst tracking performance. figure 11 shows the control effort of each control scheme. basically, the magnitude is similar to each other. every control effort presents oscillatory behavior during the phase of initiation of motion (see from 0 to 2 seconds), though that of the industrial controller especially draws our attention. it can be seen from the bottom graph that, with the disturbance observer, the control effort quickly responds to the change of direction of the reference to cancel the disturbance torque. this leads to the improvement of the tracking error on the time interval between 2.1 s and 2.2 s. figure 12 shows the actual plant output and the predicted output of the inner model. here, to compare the prediction accuracy inside the pure pfc algorithm, the pfc scheme using the auto-compensation (pfc+ac) is excluded from the comparison. the top graph is in the case of the pfc without the distur562 t. satoh, k. kaneko, n. saito 0 1 2 3 4 5 6 7 8 −1 −0.5 0 0.5 1 1.5 v ol ta ge ( v ) control effort pfc+ac pfc+dob1 pfc+dob2 p+pi 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 −0.8 −0.4 0 0.4 0.8 time (s) v ol ta ge ( v ) p+pi pfc+ac pfc+dob1 pfc+dob2 figure 11: comparison of control inputs (top graph) and its closeup (bottom graph). 0 1 2 3 4 5 6 7 8 −10 0 10 p os it io n (m m ) pfc plant output predicted output (1/200) 0 1 2 3 4 5 6 7 8 −10 0 10 p os it io n (m m ) pfc+dob1 plant output predicted output 0 1 2 3 4 5 6 7 8 −10 0 10 p os it io n (m m ) time (s) pfc+dob2 plant output predicted output figure 12: comparison of prediction accuracy. table 2: root integrated squared values. control scheme tracking error control effort pfc 11.45 1.78 pfc+dob1 0.14 2.12 pfc+dob2 0.10 2.13 p+pi 0.48 2.23 pfc+ac 0.32 2.19 bance observer. since the amplitude of the predicted output is too large in this case, two-hundredth of its value is drawn in the figure. as we have already seen in figure 9, the ordinary pfc cannot overcome the friction torque, and the table drive system does not start to move. hence, the predicted output is completely different from the plant output. on the other hand, we can see from the middle and bottom figures that the predicted output approximates the actual plant output. also, the disturbance observer using q2 (s) provides a more accurate result than the one using q1 (s). in the middle figure, the magnitude of the predicted output is larger than the plant output, which, roughly speaking, indicates that the gain of the nominal model is larger than the actual plant. it follows that the speed of response of the plant becomes slower than the designed response. this can be confirmed by comparing the response of the pfc using q1 (s) with that of the pfc using q2 (s) in figure 9. even though the specified desired closed-loop time response tcltr is the same in both cases, the former slightly lags behind the latter, which is very close to the designed response owing to the accurate prediction. to see the difference of the control performances quantitatively, we show the root integrated squared values of the tracking error e and that of the control effort u computed on the time interval between 0 s and 8 s in table 2. clearly, the pfc plus the disturbance observer using q2 (s) provides the best performance of all the control schemes. on the other hand, in terms of the control effort, we can hardly recognize a difference apart from the standard pfc. as a consequence, we can see that, in this experiment, the pfc combined with the disturbance observer successfully improved the tracking performance without putting extra control energy into the system. improving tracking performance of predictive functional control using disturbance observer and its application to table drive systems 563 5 conclusions we have presented a disturbance observer-based approach for improving the tracking performance of the predictive functional control. the role of the disturbance observer is to reject unknown disturbances including the plant-model mismatch or nonlinearities that can be regarded as an input disturbance and to nominalize the real plant. owing to the disturbance observer, the predicted output error in the pfc algorithm is reduced, and, as a result of this, the tracking performance is improved. the proposed control scheme was implemented and validated on a single axis table drive system. it was confirmed that the predicted output error was effectively reduced and that better tracking performance was provided than the standard pfc using the auto-compensation mechanism and the conventional p plus pi control scheme even in the severe environment where the system was not driven by the standard the pfc scheme due to friction. future works involves the extension to non-minimum phase linear systems. acknowledgment this work was supported by the japan society for the promotion of science under grant-in-aid for scientific research (c) 21560247. bibliography [1] s. abu el ata-doss, p. fiani and j. richalet, “handling input and state constraints in predictive functional control,” proc. of the 30th conference on decision and control, pp. 985-990, brighton, england, 1991. 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[20] a. vivas and p. poignet, model based predictive control of a fully parallel robot, control engineering practice, vol. 13, no. 7, pp. 863-874, 2005. international journal of computers communications & control issn 1841-9836, 11(5):613-630, october 2016. efficient historical query in hbase for spatio-temporal decision support x.y. chen, c. zhang, b. ge, w.d. xiao xiao-ying chen, chong zhang*, bin ge, wei-dong xiao science and technology on information systems engineering laboratory national university of defense technology changsha 410073, p.r.china chenxiaoying1991@yahoo.com, leocheung8286@yahoo.com gebin1978@gmail.com, wilsonshaw@vip.sina.com *corresponding author: leocheung8286@yahoo.com abstract: comparing to last decade, technologies to gather spatio-temporal data are more and more developed and easy to use or deploy, thus tens of billions, even trillions of sensed data are accumulated, which poses a challenge to spatio-temporal decision support system (stdss). traditional database hardly supports such huge volume, and tends to bring performance bottleneck to the analysis platform. hence in this paper, we argue to use nosql database, hbase, to replace traditional back-end storage system. under such context, the well-studied spatio-temporal querying techniques in traditional database should be shifted to hbase system parallel. however, this problem is not solved well in hbase, as many previous works tackle the problem only by designing schema, i.e., designing row key and column key formation for hbase, which we don’t believe is an effective solution. in this paper, we address this problem from nature level of hbase, and propose an index structure as a built-in component for hbase. stehix (spatio-temporal hbase index) is adapted to two-level architecture of hbase and suitable for hbase to process spatio-temporal queries. it is composed of index in the meta table (the first level) and region index (the second level) for indexing inner structure of hbase regions. base on this structure, three queries, range query, knn query and gnn query are solved by proposing algorithms, respectively. for achieving load balancing and scalable knn query, two optimizations are also presented. we implement stehix and conduct experiments on real dataset, and the results show our design outperforms a previous work in many aspects. keywords: spatio-temporal query, hbase, range query, knn query, gnn query, load balancing. 1 introduction nowadays, either organizations or common users need sophisticated spatio-temporal decision support system (stdss) [1] for countless geospatial applications, such as urban planning, emergency response, military intelligence, simulator training, and serious gaming. meanwhile, with the development of positioning technology (such as gps) and other related applications, huge of spatio-temporal data are collected, of which volume increases to pb or even eb. consequently, this necessarily poses a challenge to stdss applications. traditionally, these data are stored in relational database, however, since the database can’t resist such a huge volume, such architecture would bring performance bottleneck to the whole analysis task. hence, the new structural storage system should back up stdss. in this paper, we argue that hbase [2] is capable to accomplish such task, since hbase is a key-value, nosql storage system, which can support large-scale data operations efficiently. on the other hand, from system point of view, an ideal geospatial application designed to formulate and evaluate decision-making questions for stdss should contain efficient presentation copyright © 2006-2016 by ccc publications 614 x.y. chen, c. zhang, b. ge, w.d. xiao of a basic set of spatio-temporal queries, such as: find doctors who can carry out rescue in a certain area, recently, find 5 flower shops nearest to tony, a group of friends spreading over different places want to find nearest restaurant to them, aggregately, i.e., the sum of distances to them is minimum. these operations are supported well in relational database, however, they are not supported by hbase in a straightforward way. the main reason is that hbase do not natively support multi-attribute index, which limits the rich query applications. hence in this paper, we explore processing for basic spatio-temporal queries in hbase for stdss. from a variety of applications, we mainly address three common and useful spatiotemporal queries as follows: • range query: querying data in specific spatial and temporal range. for instance, in realtime monitoring and early warning of population, query the number of people in different time intervals within a specific area. • knn query (k-nearest neighbor): querying data to obtain k nearest objects to a specific location during a certain period. for instance, in the past week, find 5 nearest uber taxis to a given shopping mall. • gnn query (group nearest neighbor): querying data to obtain k nearest objects aggregately (measured by sum of distances) to a group of specific locations during a certain period. for instance, during last month, find the nearest ship to the given three docks. as an example, figure 1 shows the spatial distribution of users during two time interval [1, 6] and [7, 14]. for range query, find the users who are in the spatial range marked by the dashed line rectangle within time period [1, 6], apparently, {u1, u3} is the result. for 1nn query, if we want to find the users who are nearest to p1 during time period [1, 6] and [7, 14], respectively, the result is u2 for [1, 6] and u1 for [7, 14]. for gnn query, if we want to find the user who are nearest to p1 and p2 by summing the distances during time period [1, 6], the result is u2. p1 (u1,1) (u1,4) (u1,6) (u2,6) (u3,3) (u3,5) (u3,6) (u2,1) x y (u1,3) p1 (u1,9) (u1,14) (u2,12) (u3,10) (u3,14) (u2,14) x y t [1,6] t [7,14] p2 p2 (u2,6) (u1,6) (u1,4) (u3,5) (u1,3) (u1,1) (u3,6) (u3,3) (u2,1) ||p1,ui || ||p2,ui || sum (u1,9) (u2,12) (u1,14) (u3,10) (u3,14) (u2,14) ||p1,ui || ||p2,ui || sum 10 units figure 1: an example for range, knn and gnn query efficient historical query in hbase for spatio-temporal decision support 615 1.1 motivation our motivation is to adapt hbase to efficiently process spatio-temporal queries as basic operations for spatio-temporal decision support system. although some previous works propose distributed index on hbase, but these works only consider spatial dimension, more critically, most of these works only concern how to design schema for spatial data, which do not tackle the problem from the nature level of hbase, except one, md-hbase [5] is designed to add index structure into the meta table, however, it doesn’t provide index to efficiently retrieve the inner data of hbase regions. our solution, stehix (spatio-temporal hbase index), is built on two-level lookup mechanism, which is based on the retrieval mechanism of hbase. first, we use hilbert curve to linearize geo-locations and store the converted one-dimensional data in the meta table, and for each region, we build a region index indexing the storefiles in hbase regions. we focus on range query, knn query and gnn query for such environment in this paper. 1.2 contributions and paper organization we address how to efficiently answer range query, k nearest neighbor (knn) query and gnn query on spatio-temporal data in hbase. our solution is called stehix (spatio-temporal hbase index), which fully takes inner structure of hbase into consideration. the previous works focus on building index based on the traditional index, such as r-tree, b-tree, while our method constructs index based on hbase itself, thus, our index structure is more suitable for hbase retrieval. in other way, stehix considers not only spatial dimension, but also temporal one, which is more in line with user demand. we use hilbert curve to partition space as the initial resolution, the encoded value of which is used in the meta table to index hbase regions, then we use quad-tree to partition hilbert cells as the finer resolution, based on this, we design region index structure for each region, which contains the finer encoded values for indexing spatial dimension and time segments for indexing temporal dimension. and later, we show such two-level index structure, meta table + region index, is more suitable for hbase to process query in the experiment. based on our index structure, algorithms for range query, knn query and gnn query are devised, and load balancing policy and optimization to knn query are also presented to raise stehix performance. we compare stehix with md-hbase on real dataset, and the results show our design philosophies make stehix to be more excellent than the counterpart. in summary, we make the following contributions: • we propose stehix structure which fully follow inner mechanism of hbase and is a new attempt on building index for spatio-temporal data in hbase platform. • we propose efficient algorithms for processing range query, knn query and gnn query in hbase. • we carry out comprehensive experiments to verify the efficiency and scalability of stehix. the rest of this paper is organized as follows. section 2 reviews related works. section 3 formally defines the problem and prerequisites. section 4 presents stehix structure. in section 5, algorithms for range query knn query and gnn query are presented. section 6 reports the optimizations to the index. and we experimentally evaluate stehix in section 7. finally, section 8 concludes the paper with directions for future works. 616 x.y. chen, c. zhang, b. ge, w.d. xiao 2 related works to overcome the drawbacks of traditional rdbms, as an attractive alternative for largescale data processing, cloud storage system currently adopts a hash-like approach to retrieve data that only support simple keyword-based queries, but lacks various forms of information search. for data processing operations, several cloud data managements (cdms), such as hbase, are developed. hbase, as nosql databases, is capable to handle large scale storage and high insertion rate, however, it does not offer much support for rich index functions. many works focus on this point and propose various approaches. nishimura et al. [5] address multidimensional queries for paas by proposing md-hbase. it uses k-d-trees and quad-trees to partition space and adopts z-curve to convert multidimensional data to a single dimension, and supports multi-dimensional range and nearest neighbor queries, which leverages a multi-dimensional index structure layered over hbase. however, md-hbase builds index in the meta table, which does not index inner structure of regions, so that scan operations are carried out to find results, which reduces its efficiency. hsu et al. [6] propose a novel key formulation scheme based on r+-tree, called kr+-tree, and based on it, spatial query algorithm of knn query and range query are designed. moreover, the proposed key formulation schemes are implemented on hbase and cassandra. with the experiment on real spatial data, it demonstrates that kr+-tree outperforms md-hbase. kr+tree is able to balance the number of false-positive and the number of sub-queries so that it improves the efficiency of range query and knn query a lot. this work designs the index according to the features found in experiments on hbase and cassandra. however, it still does not consider the inner structure of hbase. zhou et al. [7] propose an efficient distributed multi-dimensional index (edmi), which contains two layers: the global layer divides the space into many subspaces adopting k-d-tree, and in the local layer, each subspace is associated to a z-order prefix r-tree (zpr-tree). zpr-tree can avoid the overlap of mbrs and obtain better query performance than other packed r-trees and r∗-tree. this paper experimentally evaluates edmi based on hbase for point, range and knn query, which verifies its superiority. compared with md-hbase, edmi uses zpr-tree in the bottom layer, while md-hbase employs scan operation, so that edmi provides a better performance. han et al. [8] propose hgrid data model for hbase. hgrid data model is based on a hybrid index structure, combining a quad-tree and a regular grid as primary and secondary indices, supports efficient performance for range and knn queries. this paper also formulates a set of guidelines on how to organize data for geo-spatial applications in hbase. this model does not outperform all its competitors in terms of query response time. however, it requires less space than the corresponding quad-tree and regular-grid indices. hbasespatial, a scalable spatial data storage based on hbase, proposed by zhang et al. [9]. compared with mongodb and mysql , experimental results show it can effectively enhance the query efficiency of big spatial data and provide a good solution for storage. but this model does not compare with other distributed index method. all the previous works we have mentioned above only consider the spatial query. for moving objects, a certain type of geo-spatial applications, requires high update rate and efficient realtime query on multi-attributes such as time-period and arbitrary spatial dimension. du et al. [10] present hybrid index structure based on hbase, using r-tree for indexing space and applying hilbert curve for traversing approaching space. it supports efficient multi-dimensional range queries and knn queries, especially it is adept at skewing data compared with md-hbase and kr+-tree. as this work focus on moving objects, it is different for our goal, and it also does not take the inner structure of hbase into account. efficient historical query in hbase for spatio-temporal decision support 617 to address the shortcoming which have mentioned above, the stehix structure which fully follow inner mechanism of hbase and is a new attempt on building index for spatio-temporal data in hbase platform is proposed. 3 problem definition and prerequisites in this section, we first formally describe spatio-temporal data, and then present the structure of hbase storage. for simplicity, only two-dimensional space is considered in this paper, however, our method can be directly extended into higher dimensional space. a record r of spatio-temporal data can be denoted as 〈x, y, t, a〉, where (x, y) means the geo-location of the record, t means the valid time when the data is produced, a represents other attributes, such as user-id, object’s shape, descriptions, and etc. we give the descriptions for structure of storage and index in hbase [11], [12], for simplicity, some unrelated components, such as hlog and version, are omitted. usually, an hbase cluster is composed of at least one administrative server, called master, and several other servers holding data, called regionservers. logically, a table in hbase is similar to a grid, where a cell can be located by the given row identifier and column identifier. row identifiers are implemented by row keys (rk), and the column identifier is represented by column family (cf) + column qualifier (cq), where a column family consists of several column qualifiers. the value in a cell can be referred to as the format (rk, cf:cq). table 1 shows a logical view of a table in hbase. for instance, value v1 can be referred to as (rk1, cf1:cq1). table 1: logical view for hbase table cf1 cf2 cq1 cq2 cq3 cqa cqb rk1 v1 v2 v3 v4 v5 rk2 v6 v7 v8 v9 v10 physically, a table in hbase is horizontally partitioned along rows into several regions, each of which is maintained by exactly one regionserver. the client directly interacts with the respective regionserver when executing read or write operations. when the data, formally as 〈rk, cf:cq, value〉 (we alternatively use term key-value data in rest of the paper), are written into a region, the regionserver first keeps the data in a list-like memory structure called memstore, where each entry is pre-configured with the same fixed size (usually 64kb) and the size of a certain number of entries is equal to that of the block of the underlying storage system, such as hdfs. when the size of memstore exceeds a pre-configured number, the whole memstore is written into the underlying system as a storefile, the structure of which is similar to that of memstore. further, when the number of storefiles exceeds a certain number, the regionserver will execute the compaction operation to merge storefiles into a new large one. hbase provides a two-level lookup mechanism to locate the value corresponding to the key (rk, cf:cq). the catalog table meta stores the relation {[table name]:[start row key]:[region id]:[region server]}, thus given a row key, the corresponding regionserver can be found, and then the regionserver searches the value locally according to the given key (rk, cf:cq). figure 2 shows an example of hbase two-level lookup structure. from above descriptions, we can see that hbase only provides a simple hierarchical index structure based on the meta table, and the corresponding regionserver must do scan work to refine the results, which would be inefficient to handle spatio-temporal queries. 618 x.y. chen, c. zhang, b. ge, w.d. xiao tablet1, rk1, regiona-->serveri tablet2, rk2, regionb-->serverii tabletn, rkn, regiony-->serverx regoina storefilememstore serveri regoinb serverii regoiny serverx meta regoin ... regoin ... regoin ... storefilememstore storefilememstore (rk1,cf1:cq1,value1) (rk1,cf1:cq2,value2) (rk1,cf1:cq3,value3) 64kb figure 2: hbase two-level lookup 4 stehix structure in this section, we present the structure of our index, stehix (spatio-temporal hbase index). the following philosophies are considered during index design, 1) for applications, it is not necessary for users to dedicatedly to design schema for query spatio-temporal data, i.e., our index should add no restriction on schema design, but a inner structure associated with hbase, 2) the index should be in accordance with the architecture of hbase as identical as possible, 3) the index should be adaptive to data distribution. for design rule 1), we don’t care the schema design and generalize each record to be a keyvalue data in storefile(memstore), formally (rk, cf:cq, r), where r=〈x, y, t, a〉. for design rule 2), our index is built on the two-level lookup mechanism. in particular, we use hilbert curve to linearize geo-locations and store the converted one-dimensional data in the meta table, and for each region, we build a region index to index the storefiles. figure 3 shows an overview of stehix architecture. 4.1 meta table organization we use hilbert curve to partition the whole space as the initial granularity. according to the design rationale of hbase, the prefix of row key should be different so that the overhead of inserting data could be distributed over regionservers. and such design is able to satisfy this demand. hilbert curve is a kind of space filling curve which maps multi-dimensional space into onedimensional space. in particular, the whole space is partitioned into equal-size cells and then efficient historical query in hbase for spatio-temporal decision support 619 meta data for other purpose [hs1, he1], region a->serveri [hsn, hen], region y->serverx regoin a storefile serveri meta [hs2, he2], region b->serverii s-index t-index storefile storefile regoin b serverii s-index t-index regoin y serverx s-index t-index figure 3: overview of stehix a curve is passed through each cell for only once in term of some sequence, so that every cell is assigned a sequence number. different space filling curves are distinguished by different sequencing methods. due to information loss in the transformation, different space filling curves are evaluated by the criteria, locality preservation, meaning that how much the change of proximities is from original space to one-dimensional space. hilbert curve is proved to be the best locality preserved space filling curve [13]. with hilbert curve, any object in the original space is transformed into [0, 22λ − 1] space, where λ is called the order of hilbert curve. figure 4 shows four hilbert curves in two-dimensional space with λ=1, 2, 3 and 4. (a) =1 (b) =2 (c) =3 (d) =4 p1 p2 r1 figure 4: hilbert curves we describe three functions for hilbert curve, first one is mapping a point in the original space to a value in one-dimensional space, the second is mapping a range window to a series of intervals, and the third is retrieving proximity cells of a point. specifically, for a hilbert curve with order=λ, • coortocell(p). given a point p=(x1, x2, . . . , xn) in n-dimensional space s, coortocell(p) returns a cell number (between 0 and 22λ − 1) referring the cell where p lies within s. 620 x.y. chen, c. zhang, b. ge, w.d. xiao • recttointervals(r). given a range window r=(xl1, x l 2, . . . , x l n, x u 1 , x u 2 , . . . , x u n) in ndimensional space s, where xli and x u i (1 ≤ i ≤ n) are the lower and upper bound of the ith-dimension, respectively, recttointervals(r) returns a series of intervals representing the cells intersecting with r in s. • getneighborcells(p). given a point p=(x1, x2, . . . , xn) in n-dimensional space s, getneigh borcells(p) returns a list of cell numbers referring the cells which are neighbors of the cell coortocell(p). for instance, in figure 4 (b), coortocell(p1) = 2, coortocell(p2) = 13, recttointervals(r1) = {[1,2], [7,8], [11,15]}, and getneighborcells(p2)={1, 2, 7, 8, 11, 12, 15, 14}. based on above descriptions, we use hilbert cell value as row key in the meta table to index spatio-temporal data as first level, thus, each record can be placed into the corresponding region according to hilbert value of spatial part of the record. in particular, the following mapping structure is built in the meta table (for simplicity, table name is omitted): {[start hilbert cell, end hilbert cell]:[region id]:[region server]}. initially, assuming there are n regions across m regionservers, we can uniformly assign hilbert cells to these regions, for instance, the first entry could be {[0, ((22λ − 1)/n) − 1] : regiona : serveri}, and the second {[((22λ − 1)/n), (2 ∗ (22λ − 1)/n) − 1] : regionb : serverii}. 4.2 region index structure for retrieving local data efficiently, we design the region index which is kept in memory like memstore. considering memstore is always kept in memory, region index is only to index storefile, however, for answering a query, memstore must be scanned to guarantee the completeness of results. region index is a list-like in-memory structure, each entry of which points to a list of addresses referring to key-value data in the storefile. the region index consists of two parts, one is called s-index indexing spatial component of data, the other is called t-index indexing the temporal part, and such design is able to benefit query efficiency as we will see in next section. for constructing s-index, the space is further partitioned at a finer granularity, i.e., each hilbert cell is recursively divided by quad-tree and the resulting tiles are encoded with binary zorder. such consideration is able to deal with the skewed data, i.e., when a hotspot is detected, quad-tree can be used recursively until the hotspot is eliminated. later, we will use this idea to design an adaptive load balancing policy. after partitioning the hilbert cell, each tile is corresponding to an entry in the s-index, i.e., the entry points to the key-value data whose geolocations lie in that tile. for instance, figure 5 shows an example of meta table and region index, where in the meta table, hilbert cells [0, 1] indexes regiona : serveri and [2, 3] for regionb : serverii, respectively. for regiona, hilbert cells 0 and 1 are divided using quad-tree into 11 tiles, 7 of which are 2-bit tiles and 4 are 4-bit tiles, and for each entry in s-index, the identifier is a combination of hilbert value and binary z-order value, for instance, entry 0-10, where 0 is the number of hilbert cell 0 and 10 is the code of lower-right tile in hilbert cell 0, points to a list containing two addresses referring to two key-value records in storefile. for building t-index, we use a period t to bound the length of the list of t-index, and such consideration is based on the fact that there may be some cycle for the spatial change of objects. the period t is divided into several segments, each of which is corresponding to an entry in t-index. each entry points to a list of addresses referring to key-value data in storefile, whose temporal component modulo t lies in the segment. continuing the example, figure 5 shows the structure of t-index. let t=24, which means a period of 24 hours is a cycle, and let each segment = 3 hours, which means t is divided into 8 segments, and entry [3, 6) points to 8 key-value data whose temporal value modulo 24 between 3 and 6. efficient historical query in hbase for spatio-temporal decision support 621 00 01 10 11 01 10 11 0000 0010 0001 0011 [0, 1], region a->serveri meta regoin a serveri 0-10 0-11 1-0000 1-0001 1-01 [0, 3) [3, 6) s-index t-index [21, 0) storefile [2, 3], region b->serverii q 1-0010 1-0011 #addr #addr #addr #addr #addr #addr #addr #addr #addr #addr #addr #addr #addr #addr #addr figure 5: region index structure 5 query processing in this section, the processing algorithms for range query, knn query and gnn query are presented. 5.1 range query a range query q=(xl, yl, xu, yu, ts, te), aims to find all the records, whose geo-locations lie in the range (xl, yl, xu, yu) during time [ts, te]. the basic work flow for processing a range query q is described as follows, first, using hilbert curve, spatial predicate (xl, yl, xu, yu) is converted into a set of one-dimensional intervals iq, then according to mapping relation in the meta table, the involved regionservers are informed to search the corresponding regions locally, utilized by region index. here we propose a query optimization, i.e., using s-index and t-index to calculate selectivity, which is helpful to choose the high-selectivity to filter more unrelated data, in particular, the spatial predicate is recursively divided by quad-tree, the results of which are intersected with the entries in s-index, and then the number of addresses to key-value data can be calculated, say sn, similarly, using t-index can also calculate a number, tn, then if sn is less than tn, s-index is followed to retrieve results, other wise t-index is used. algorithm 1 describes the range query processing for stehix. in line 1, the spatial predicate is converted into one-dimensional intervals iq, and the temporal predicate is converted into [0, t ] interval in line 2. in line 3, function findregions() finds the involved regions which intersect with iq. from line 4 to 11, each corresponding region index is inspected to retrieve results, in 622 x.y. chen, c. zhang, b. ge, w.d. xiao particular, s-index and t-index is used to calculate selectivity for the query, which is implemented by function getcard(), and the index with the lower cardinality is chosen to retrieve the results. algorithm 1 range query processing require: q=(xl,yl,xu,yu, ts, te) ensure: qlist //result list 1: iq = recttointervals(xl,yl,xu,yu) 2: keys = ts mod t , keye = te mod t 3: regions=findregions(iq) /*the following processing is executed separately in each region*/ 4: for each region ∈ regions do 5: sn=region.s-index.getcard(xl,yl,xu,yu) 6: tn=region.t-index.getcard(keys,keye) 7: if sn ≤ tn then 8: qlist←region.s-index.seachindex(q) 9: else 10: qlist←region.t-index.seachindex(q) 11: end if 12: end for 13: return qlist figure 5 shows an example for range query processing in stehix. the spatial bound of q is depicted with dashed line and we assume that temporal predicate of q is [3, 6]. then hilbert cells 0 and 1 are intersected with q, thus, two entries in the meta table are examined, namely, {[0, 1] : regiona : serveri} and {[2, 3] : regionb : serverii}. for instance, in regiona, the entries in s-index are intersected with spatial predicare of q, resulting 0-10, 0-11, 1-0001, 1-0011, 1-01 these 5 entries, which refer to totally 7 addresses to key-value data, and similarly, entry [3, 6) of t-index refers to 8 addresses, consequently s-index is followed to retrieve the results. 5.2 knn query a knn query could be formally defined as: given a set r of spatio-temporal data records, a knn query q=(xq, yq, ts, te, k), aims to find a set r(q) ⊆ r , such that |r(q)|=k, and d(o, (xq,yq)) ≤ d(o′, (xq,yq)), ∀o ∈ r(q), o′ ∈ r\r(q), and o.t, o′.t ∈[ts, te], where d() is the euclidean distance function. we don’t want to use n range queries to accomplish the knn query, which means continuously enlarging spatial range of the query until k records are obtained [14], because we believe such a method would cause heavy querying overhead. we propose an approach utilized by incremental retrieval idea [15]. the basic work flow is, proximity objects of point (xq, yq) are constantly, incrementally retrieved until k results are found. in particular, first, hilbert cell h containing point (xq, yq) is located, then the corresponding region index is utilized to retrieve all records lie in h, meanwhile, neighbor cells of h are also retrieved, and these records and hilbert cells are all enqueued into a priority queue where priority metric is the distance from (xq, yq) to record or hilbert cell. then top element is constantly dequeued and processed, either being added to result list or being followed to retrieve neighbor cells to be enqueued, until k results are found. algorithm 2 presents knn query processing. the first line initializes a priority queue pq where each element is ordered by the distance from (xq, yq) to the element. the element can be hilbert cell or record, and if it is a hilbert cell, the distance is mindist [16], other wise, the distance is the euclidean distance from (xq, yq) to geo-location of the record. in line 2, the hilbert cell containing (xq, yq) is gained, and is enqueued in line 3. from line 4, the procedure constantly retrieves top element e from pq (line 5) and processes it, in particular, if e is a hilbert cell (line 6), find the corresponding region rg from the meta table (line 7), and then the corresponding region index is searched to retrieve all the records satisfying temporal predicate (line 8), which are enqueued into pq (line 9 to 11), after that, the neighbor cells of e are obtained and enqueued into pq (line 12 to 15); other wise, i.e., if e is a record (line 16), which means e is a result, e is added into qlist (line 17), and the above procedure is looped until the size of efficient historical query in hbase for spatio-temporal decision support 623 algorithm 2 knn query processing require: q=(xq, yq, ts, te, k) ensure: qlist //result list 1: pq=null //initial a priority queue 2: h=coortocell(xq, yq) 3: pq.enqueue(h, mindist((xq, yq), h)) 4: while pq 6= φ do 5: e=pq.dequeue() 6: if e is typeof cell then 7: rg=findregions(e) 8: rs=rg.findrecords(e, (ts, te)) 9: for each record ∈ rs do 10: pq.enqueue(record, dist((xq, yq), record)) 11: end for 12: cellset=getneighborcells(e.center) 13: for each cell ∈ cellset do 14: pq.enqueue(cell, mindist((xq, yq), cell)) 15: end for 16: else if e is typeof record then 17: qlist←e 18: if qlist.size()=k then 19: return qlist 20: end if 21: end if 22: end while qlist reaches k (line 18 to 20). 5.3 gnn query a gnn query in our work could be formally defined as: given a set r of spatio-temporal data records and a set of location point(s) p , a gnn query q=(p , ts, te, k), aims to find a set r(q) ⊆ r , such that |r(q)|=k, and the point(s) of r(q) with smallest sum of distances to all points in p (|p |=n), i.e. ∑n i=1 d(o, (xi,yi)) ≤ ∑n i=1 d(o′, (xi,yi)), ∀o ∈r(q), o′ ∈r\r(q), and o.t, o′.t ∈[ts, te], where d() is the euclidean distance function. different from knn query, gnn query aims to finds a group of point(s) that nearest to a set of points. in knn query processing, firstly hilbert cell h containing point (xq, yq) is located, while in gnn processing, we firstly find the ideal nearest neighbor p, which could not exist in the dataset r. this approach is that the nearest neighbor is the point(s) "near" p. let (x,y) be the coordinates of ideal 1nn point p and (xi,yi) be the coordinates of point pi ∈p , p minimizes the sum of distance function: sumdist(p,p) = n∑ i=1 √ (x−xi)2 + (y −yi)2 (1) partially calculate the derivation of function sumdist(p,p) with respect to variables x and y, let them equal to zero, we have:  ∂sumdist(p,p) ∂x = n∑ i=1 x−xi√ (x−xi)2 + (y −yi)2 = 0 ∂sumdist(p,p) ∂y = n∑ i=1 y −yi√ (x−xi)2 + (y −yi)2 = 0 (2) however, this equations can not be solved when n > 2. according to the method in [18], we start with the arbitrary initial coordinates x = ∑n i=1 xi n , y = ∑n i=1 yi n , then modifies as follows: x = x−η∂sumdist(p,p) ∂x ,y = y −η∂sumdist(p,p) ∂y (3) where η is a step size. the process is repeated until the distance function sumdist(p,p) converges to a minimum value. we call this processing p = getnearest(p). the range around p in which we should look for points of r(q). 624 x.y. chen, c. zhang, b. ge, w.d. xiao the basic work flow is similar to knn query processing which introduce above. algorithm 3 presents gnn query processing. in particular, first, hilbert cell h containing point p (x, y) is located, then the corresponding region index is utilized to retrieve all records lie in h, meanwhile, neighbor cells of h are also retrieved, and these records and hilbert cells are all enqueued into a priority queue where priority metric is the sum of distance from p to record or hilbert cell. then top element is constantly dequeued and processed, either being added to result list or being followed to retrieve neighbor cells to be enqueued, until k results are found. algorithm 3 gnn query processing require: q=(p , ts, te, k) ensure: qlist //result list 1: pq=null //initial a priority queue 2: p = getnearest(p) 3: h=coortocell(p) 4: pq.enqueue(h, summindist ((p), h)) 5: while pq 6= φ do 6: e=pq.dequeue() 7: if e is typeof cell then 8: rg=findregions(e) 9: rs=rg.findrecords(e, (ts, te)) 10: for each record ∈ rs do 11: pq.enqueue(record, sumdist((p), record)) 12: end for 13: cellset=getneighborcells(e.center) 14: for each cell ∈ cellset do 15: pq.enqueue(cell, summindist ((xq, yq), cell)) 16: end for 17: else if e is typeof record then 18: qlist←e 19: if qlist.size()=k then 20: return qlist 21: end if 22: end if 23: end while 6 optimizations in this section, we propose two methods for raising performance of stehix from the aspects of load balancing and query optimization. 6.1 adaptive load balancing for achieving design rule 3), adaptive load balancing is considered. our spatial partition procedure contains two phases, first is hilbert curve, and the second is quad-tree. and load balancing is based on the second phase and region split, in particular, when the volume of a region exceeds a limit due to the hotspot in spatial dimension, the procedure detects which hilbert cell is the hotspot, and uses a quad-tree to divide it into four subspaces, thus the original region is split into five regions, i.e., four corresponds to the four subspaces and one corresponds to the undivided hilbert cell(s). after that, the meta table is also updated to renew the mapping information as well as the region index. figure 6 shows an example of region split. we can see when a hotspot is generated in hilbert cell 0, the cell is divided into four subspaces by quad-tree, and the corresponding region is split into five, namely, 0-00, 0-01, 0-10, 0-11 and 1, and the meta table and new regions are updated accordingly. 6.2 optimization for knn query from knn algorithm we can see, each time for retrieving the records of a hilbert cell, the meta table must be searched to locate the corresponding region, which would increase overhead of the query. to deal with such a problem, we add modifications to region index, in particular, each region index ri is connected to the regions whose hilbert cells are the neighbors of ri’s efficient historical query in hbase for spatio-temporal decision support 625 region’s hilbert cells. thus, when getneighborcells() method is invoked, the current region is able to retrieve records from proximity regions, however, not all the records can be retrieved, and for this case, the meta table should be searched. nevertheless, this optimization would reduce the overhead of querying the meta table. 00 01 10 11 [0, 1], region a->serveri meta 0-00, region r1->serverii meta 0-01, region r2->serverii 0-10, region r3->serverx 0-11, region r4->serverx 1, region a->serveri figure 6: load balancing 7 experimental evaluation we evaluate our algorithms on real dataset, which contains trajectories of taxis in beijing1. in particular, the dataset contains about 100 million records, and temporal range is from nov. 1st to 3rd, and each record in the dataset contains vehicle id, geo-location, recording time stamp, etc. our algorithms are implemented in hadoop 2.5.1 and hbase 0.98.6, and run on a cluster with size varied from 5 to 33, in which each node is equipped with intel(r) core(tm) i3 cpu @ 3.40ghz, 4gb main memory (for master 16gb), and 500gb storage, and operating system is centos release 6.5 64bit, and network bandwidth is 10mbps. for comparison, we choose md-hbase due to the similar function. 1http://activity.datatang.com/20130830/description 626 x.y. chen, c. zhang, b. ge, w.d. xiao 3 10 15 30 50 1000 2000 3000 4000 5000 6000 7000 8000 d e la y (m s ) �(%) stehix md-hbase (a) effect of selectivity 5 9 17 33 2000 3000 4000 5000 6000 7000 8000 9000 d e la y (m s ) cluster size stehix md-hbase (b) effect of cluster size figure 7: experimental results for range queries 7.1 range queries first, we evaluate the algorithm for range queries. and we introduce two parameters to test the algorithm under various conditions. one is selectivity θ defined as: θ = l(ts,te) lt · arq as where l(ts,te) means the length of query temporal range (ts, te), lt means the length of temporal extent of the dataset, arq means the area of query spatial range rq, and as means the area of the whole space. selectivity specifies the size of the query range, and the larger θ is, the more spatio-temporal records are involved. in this experiment, the default values of θ and cluster size are 10% and 9, respectively. for each value of θ or size, we issue 10 queries with different temporal ranges and spatial ranges, and collect the average response time as the measurement of performance. first, we vary θ from 3% to 50% and figure 7(a) shows the results. we can see that response time increases with θ for both methods. this is because a larger selectivity would access more records to be retrieved and examined, which increases the processing time. however, we can see stehix outperforms md-hbase, which can be explained by the design of region index. although md-hbase builds index in the meta table, it doesn’t index inner structure of regions, thus, scan operations are carried out to find results, which cost heavily. our stehix is adapted to the two-level architecture of hbase, and is able to use region index to efficiently search each region, which highly improve performances. next, we vary cluster size from 5 to 33, and figure 7(b) shows the results. it is apparent that stehix is excellent due to its nearly horizontal response time and good scalability. when the number of cluster size is increased, more regionservers take part in the processing and use their region indexes parallel. however, due to lack of indexing storefiles, the scalability of md-hbase is not good. efficient historical query in hbase for spatio-temporal decision support 627 4 8 16 32 0 500 1000 1500 2000 2500 3000 d e la y (m s ) k stehix md-hbase (a) effect of k 5 9 17 33 500 1000 1500 2000 2500 3000 d e la y (m s ) cluster size stehix md-hbase (b) effect of cluster size figure 8: experimental results for knn queries 7.2 knn queries in this experiment, the default values of k and cluster size are 8 and 9, respectively. first, we vary k from 4 to 32, and figure 8(a) shows that stehix outperforms md-hbase. when k is increased, both methods need more time to process queries. stehix uses less time to retrieve k results, which can be explained by the same reason, i.e., the region index embedded in hbase region. and then cluster size is varied from 5 to 33, still, stehix is better than md-hbase, figure 8(b) shows the fact. 7.3 gnn queries in this experiment, we vary the size of location set p and cluster to measure performance of stehix. note that md-hbase does not study gnn query, so we just simply use our virtual centroid method to apply to it. we vary n (size of p) from 3 to 9, and figure 9(a) shows the results. we can see with increasing of n, the response time is also increased, this is because a larger size of p would cause more time to calculate the virtual centroid, however, we can the delay time does not increase very steeply, due to the fact that computing the virtual centroid only cost cpu time. similarly, our stehix still outperforms md-hbase in both varying n and cluster size. 7.4 effect of optimizations we examine the effect of optimizations to stehix in this experiment, and figure 10 show the results. first, we use maximum imbalance load ratio [17] as metric, and test our adaptive load balancing policy, the results of comparison with non-load balancing are plotted in figure 10 (a). we can see with cluster size increased, both ratios are raised, this is because the more nodes participate in the cluster, the more difficult is to distribute load uniformly, however, we can see our load balancing method indeed takes effect, i.e., when load balancing policy is used, the ratio is averagely around 6, while the counterpart shows the performance about 38 to 70. next, we test the effect of knn optimization, from figure 10 (b), we can see the connections among region indexes give chances to reduce querying overhead. 628 x.y. chen, c. zhang, b. ge, w.d. xiao 3 4 5 6 7 8 9 1000 1200 1400 1600 1800 2000 2200 2400 2600 d e la y (m s ) n stehix md-hbase (a) effect of size of p 5 9 17 33 1000 1500 2000 2500 3000 d e la y (m s ) cluster size stehix md-hbase (b) effect of cluster size figure 9: experimental results for gnn queries 4 8 16 32 0 200 400 600 800 1000 1200 1400 1600 1800 2000 d e la y (m s ) k stehix stehix optimization (a) effect of k 5 9 17 33 0 10 20 30 40 50 60 70 m a x im b a la n c e lo a d ra ti o (% ) cluster size load balancing non load balancing (b) effect of cluster size figure 10: experimental results for optimizations efficient historical query in hbase for spatio-temporal decision support 629 8 conclusion and future works with development of positioning technology, more and more spatio-temporal data need to be processed. to equip hbase with efficient and scalable spatio-temporal querying capability will benefit the whole spatio-temporal decision support system. in this paper, we argue that many previous works fail to tackle this problem due to lack of deep design for hbase, while we address the problem by proposing a novel index structure adapted to two-level architecture of hbase, which is suitable for hbase to process queries. algorithms for range query, knn query and gnn query are designed, what’s more, the optimizations for load balancing and knn query are also proposed. we carry out extensive experimental studies for verifying our index, and the results show that our approach for hbase is more efficient and scalable than the previous work. in the future, we plan to utilize this idea to efficiently store and retrieve graph data and apply to social networks. acknowledgment this work is supported by nsf of china grant 61303062 and 71331008. we would like to thank peijun he for helping with the implementation. bibliography [1] van orshoven et al. 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(2009), histogram-based global load balancing in structured peer-to-peer systems, knowledge and data engineering, ieee transactions on, 21(4): 595-608. [18] hochreiter, s., younger, a. s., conwell, p. r. (2001), learning to learn using gradient descent. artificial neural networks-icann 2001, springer berlin heidelberg. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 21-32 an automatic face detection system for rgb images t. barbu tudor barbu institute of computer science, romanian academy, iaşi branch, iaşi, romania e-mail: tudbar@iit.tuiasi.ro, www.iit.tuiasi.ro/ tudbar abstract: we propose a robust face detection approach that works for digital color images. our automatic detection method is based on image skin regions, therefore a skin-based segmentation of rgb images is provided first. then, we decide for each skin region if it represents a human face or not, using a set of candidate criteria, an edge detection process, a correlation based technique and a threshold-based method. a high face detection rate is obtained using the proposed method. keywords: color image, color space, rgb, hsv, skin region, face detection, cross-correlation coefficient, edge detection, template matching, threshold. 1 introduction this paper approaches an important digital image analysis domain. face detection represents a computer technology that determines the locations and sizes of human faces in arbitrary digital images. face detection can be regarded as a specific case of object-class detection. in object-class detection, the task is to find the positions and sizes of all objects in an image that belong to a given class [1]. while being a sub-domain of the object detection field, face detection represents a generalization of face localization. in face localization, the task is to find the location and size of a given input image, while in face detection one does not have any information about the human faces [2]. the most important application area of face detection is biometrics. face finding is often considered the first step of the face recognition process [3, 4]. thus, most facial recognition systems, and the more complex biometric systems including face recognition components, use face detection techniques. video surveillance represents another important application domain of face detection. a robust face detection task consists of identifying and locating all the faces in an image, regardless of their position, scale, pose, orientation and illumination [2, 3]. early face-detection techniques focused on the detection of frontal human faces only, and did not consider the rotation problem. the newer methods attempt to solve the more general and difficult problem of multiview face detection. these algorithms take into consideration the two types of face rotation: pose, representing the out-of-plane rotation, and orientation, representing the in-plane rotation [2]. also, there are several factors which could transform the human face finding process into a difficult task, such as: the structural components (presence or absence of beards, moustaches, glasses or other elements), the facial expressions (smiling, laughing, crying and others), occlusions (the faces can be occluded by other objects) and imaging conditions (lighting, camera characteristics) [2]. a robust face detection approach must take into consideration the presence of these factors. there are several known categories of face detection approaches: knowledge-based techniques [5], feature-based methods [2,6,7], appearance-based approaches [8–13] template matching methods copyright c⃝ 2006-2011 by ccc publications 22 t. barbu [14]. the knowledge-based methods encode human knowledge of what constitutes a typical face, usually the relationships between facial features. a face is represented using a set of human-coded rules. these rules are then used to guide the face search process. the advantages of the knowledge-based techniques are: the easy rules to describe the face features and their relationships, and the good results obtained for face localization in uncluttered background. their disadvantages are: the difficulty to translate the human knowledge in rules precisely and the difficulty to extend these methods to detect faces in different poses, respectively [5]. the feature based approaches aim to detect invariant face features. these are structural features of a face that exist even when the pose, viewpoint or lighting conditions vary. we could mention here the random graph matching based approaches [6] and the feature grouping techniques [7]. the main advantage of the feature oriented face detection approaches consists in the fact that these features are invariant to rotation changes. their main drawback is the difficulty to located facial features in a complex background. appearance-based techniques train a classifier using various examples of faces. the classifiers which can be used in the training process include: neural networks (multilayer perceptrons) [8], hidden markov models [9], bayes classifiers [10], support vector machines (svm) [11], sparse network of winnows (snow) [12], principal component analysis (pca) [3] and boosting algorithms (ada-boost) [13]. the template matching based techniques use stored face templates [2, 14]. usually, these approaches use correlation operations to locate faces in images [15]. the templates are handcoded, not learned. also, these templates have to be created for different poses. we propose a template matching based face detection method in this paper, too. our detection technique works for rgb color images only and it is based on the skin regions of the image. thus, in the first stage, our approach performs a skin segmentation process, extracting the human skin regions from the analyzed image. the proposed skin detection technique is described in the next section. next, the technique identifies the human faces, by performing an analysis of the previously obtained skin segments. the face identification method is provided in the third section. in the fourth section, the experiments performed using the proposed human face detection system, are discussed. the paper ends with a conclusions section and the references section. 2 a skin detection approach for rgb images human skin color is proven to represent a very useful face detection and localization tool [2, 14, 16, 17]. a skin-based face finding approach identifies the skin regions of the image, then determine those of them which represent human faces. besides face detection, there exist other important application areas of skin detection, such as image content filtering and finding illegal internet content [16], content-aware video compression or image color balancing. many skin color localization techniques have been developed in recent years. a robust and very known skin finding method is the algorithm proposed by fleck and forsyth in 1996, that uses a skin filter [16]. we are interested in color images only and do not perform skin and face detection in grayscale images. obviously, the color images are usually in the rgb format. while it is one of the most used color spaces for processing and storing of digital image data, rgb is not a favorable choice for skin color analysis, because of the high correlation of its three channels and the mixing of luminance and chrominance data [17]. for this reason, most skin segmentation algorithms work with other color spaces, such as the normalized rgb, hsv (and other hue saturated based spaces) and ycrcb formats [2, 17]. an automatic face detection system for rgb images 23 we propose a skin detection technique using the hsv and ycrcb color spaces. first, a denoising process should be performed on the input rgb image, i. usually, these images are affected by gaussian noise. therefore, a 2-d gaussian smoothing filter has to be applied to them, to remove detail and noise [18]. then, the smoothed image is converted into the hue saturation value format, by computing the three components using the known conversion equations. we obtain the components, h, s and v , as three matrices with coefficients in the [0, 1] interval. we are interested mainly in the hue value, h. the ycrcb color model represents a family of color spaces [17]. in fact, it is not an absolute color space, but a way of encoding the rgb information. in this format, y represents the luminance, while cr and cb are the blue-difference and red-difference chroma components. these three components of the color space are computed as linear combinations of r, g and b components of the image. thus, the computation formulas of the chroma components, cr and cb, have the general form α · r + β · g + γ · b + 128, where coefficients α, β, γ ∈ [−0.5, 0.5]. we choose empirically some proper values for these coefficients and get the following components:{ cr = 0.15 ·r −0.3 ·g + 0.45 ·b + 128 cb = 0.45 ·r −0.35 ·g−0.07 ·b + 128 (1) then, we apply a set of restrictions on these two components and on the hue, to identify the skin regions. thus, we have determined a skin related interval for each component. in our approach, each pixel of the image i belongs to a human skin segment if the corresponding values in cr, cb and h are situated in those intervals. we create a binary image sk, having the same size as i, whose white regions correspond to the skin segments. the proposed skin segmentation process is modeled by the following relation: sk = { 1, if cr(i, ju) ∈ [150, 165] ∝ cb(i, j) ∈ [145, 190] ∝ h(i, ju) ∈ [0.02, 0.1] 0, otherwise (2) where i, j ∈ [1, m] and j ∈ [1, n], i representing an [m ×n] image. the connected components from image sk, computed by (2), represent the detected skin regions. the proposed detection method provides good results, although some skin identification errors could appear. that means some non-skin image regions could be detected as skin segments, but this fact will not affect the final goal, human face detection. therefore, we are satisfied with the obtained skin finding results. in figure 1 (a), there is displayed an rgb image depicting human persons. the result of the hsv conversion is displayed in figure 1 (b). the skin detection process is performed by applying the equations (1) and (2). the resulted skin regions are those depicted in figure 2. 3 a face finding technique the skin regions detected in the previous section are used in the human face identification process. for each skin segment we have to decide if it represents a face, or it is a non-facial skin region. we will propose an automatic template matching scheme for face detection. before applying the matching procedure, our face finding approach performs several necessary preprocessing steps. 24 t. barbu figure 1: digital color image conversion: rgb to hsv figure 2: skin detection result an automatic face detection system for rgb images 25 3.1 skin region pre-processing a task we try to solve is the separation of human faces from adiacent or occluding skin regions. our detection task cannot identify properly the faces which are occluded by other skinlike objects in the images. usually, this situation appears in group photos, like that displayed in figure 3 (a). therefore, we provide a separation technique involving some morphological operations [19], performed on the corresponding binary image, sk. thus, we will apply two successive erosions on it. first, the binary image is eroded with a structuring element l, representing a vertical line, having a length of 5 pixels: sk′ = sk ⊖l = ∩ ℓ∈l sk−ℓ (3) figure 3: face separation then, another erosion operation is performed on sk′, using a structuring element sq, representing a small square area (for example containing a single pixel): sk′′ = sk′ ⊖sq = ∩ p∈sq sl′−p (4) in the figure above, one can see a skin separation example using the proposed method. in figure 3 (b), the skin segments corresponding to the faces from figure 3 (a) are conjoined into a single region. the result of the morphology-based process, given by the relations (3) and (4), is displayed on figure 3 (c). the two greatest skin regions are clearly separated in the final binary image, sk′′. obviously, there could be situations, provided by big occlusions for example, when the face separation is not possible in the binary image. the binary image sk′′ contains a set of skin segments, which represent connected sequences of white pixels. let this set of regions be {s1, ..., sn}. now, we have to decide which of these regions could qualify as face candidates for the template matching process. so, we have established a set of candidate criteria for si segments. first condition is size related. the skin region set is usually very large because of the many small white regions which could be present in image sk′′. we have decided to not take into consideration these small area white spots, because they cannot represent serious face candidates. 26 t. barbu it is still possible to exist very small faces in an image, like those in a very large crowd, but we consider them irrelevant and do not try to detect them. so, if a white region area (the number of pixels) is below a given threshold value, that region is labeled as a non-facial segment. another condition is related to the shape of the skin regions. obviously, a face region should have a rectangular-like or ellipse-like shape. thus, a rigorous shape analysis can determine which skin segments cannot represent human faces. we propose a less complex approach to this task. a connected component si has to be rejected as non-face, having a non-facial shape, if its solidity, representing the ratio between its area and its bounding box area is below an established threshold. a facial skin-region is usually characterized by a high ratio, close to 1, but the solidity value may become lower because the region’s area is affected by the presence of many black holes in the face region. these holes could represent human face components, such as eyes, eyebrows, mouth, nose, ears and wrinkles, or some skin detection errors. therefore, we perform a black hole filling process on the binary image sk′′, first, then compute and use the areas of the filled si segments. also, a human face is characterized by some limits of its width to height ratio. none of the two dimensions of a face, the width and the height, can be much larger than the other. for this reason, we set another condition, requiring the width to height ratio of the face candidates to be restricted to a certain interval. the ratio between the width of the region’s bounding box and the height of the bounding box should be between two properly chosen threshold values. the white regions satisfying the proposed restrictions represent the face candidates. for each si, i ∈ [1, n], the described face candidate identification process is formally expressed as follows: area(si) ≥ tiα area(fill(si)) area(box(si)) ≥ t2α width(box(si)) height(box(si)) ∈ [t3, t4] =⇒ si = candidate (5) where area( ) computes the number of white pixels of the region received as argument, fill( ) performs the filling process, box( ) returns the bounding rectangle, width( ) and height( ) returning the dimensions of a rectangle. we have considered the following proper values for the thresholds in equation (5): the area threshold t1 = 130, the solidity threshold t2 = 0.65, and the width to height thresholds t3 = 0.6 and t4 = 1.8. a face candidate identification example is described in figure 4. in the rgb image from figure 4 (a) one can see a boy flexing his muscles. figure 4 (b) represents the corresponding binary image resulted after skin detection, erosion operations, small region removing and hole filling process. the bounding boxes of the three remaining skin regions are depicted in pictures c), d) and e). the one representing the right arm is rejected because of its low solidity percent (meaning a wrong shape), the skin segment of the left arm is rejected because a wrong width to height ratio, and the one representing the skin of head and neck is accepted as a right face candidate. 3.2 template matching process in its next stage, our facial detection approach determines which of the face candidates represent human faces. first, one converts the denoised rgb image i into a 2d grayscale form, let it be i′. if there is a set of k face candidates, where k ≤ n, then we determine the set of the sub-images of i′ corresponding to the bounding boxes of these candidates. the face detection process can be affected by the head hairline of the persons and by the skin zone corresponding to the neck and the upper chest. therefore, a narrow upper zone and a narrow bottom zone from each image are removed. in our tests, the height of each removed zone represents one eleventh of the bounding box height. let the set of the truncated skin images be {i1, ..., ik}, where ii ⊂ i′, ∀i ≤ k. then, we perform a correlation-based template matching process on this set. our template-based approach an automatic face detection system for rgb images 27 figure 4: face candidate identification example works like a supervised classification algorithm. we create a face template set, containing human faces of various sizes, orientations and poses, and representing both male and female people, of various ages and races. let the template set be {f1, ..., fn}, with n large enough, where each fi represents a grayscale image. next, an edge detection operation is performed on both the skin image set and the face template set. a canny filtering technique is used for image edge extraction, because this detector is less likely than the others to be affected by noise [20]. first, it computes the gradient of the image, using the derivative of a gaussian filter. then, it finds edges by looking for local maxima of this gradient. the canny method uses two thresholds, to detect strong and weak edges. it takes into consideration only the truly weak edges, representing those connected to the strong ones. thus, for each skin image ii and each face image fj, a binary image representing its edges is determined. let us note iei and f e j the edge images corresponding to images ii and fj respectively. then, for each candidate (skin image), one computes the 2d cross-correlation coefficients [21] between its edge image and the edge images of the templates, and the average value of this sequence of coefficients. let us note v(ii), this mean value corresponding to image ii. each time a correlation operation is performed the edge image of the candidate has to be resized to the size of the template. the best solution to the face detection task is a threshold-based one. the computed two-dimensional mean correlation coefficient corresponding to a facial skin image, must exceed a properly chosen threshold value. the face identification process is expressed mathematically as follows: ∀i ∈ [1, k], ii = face ⇐⇒ v(ii) ≥ t (6) 28 t. barbu where t represents the chosen threshold and v(ii) = 1 n n∑ j=1 σxσy(i e i (x, y)−µ(i e i ))(f e j (x, y)−µ(f e j )) (σxσy(i e i (x, y)−µ(i e i )) 2)(σxσy(f e j (x, y)−µ(f e j )) 2) (7) where µ( ) computes the mean of a matrix. the threshold value is determined empirically. from the performed experiments, we have got a satisfactory threshold value, t = 0.185. if t is not exceeded for any ii, then the color image i contains no human faces. we propose a no-threshold automatic face finding approach, too. the threshold can be replaced with a clustering procedure that uses the values v(ii) computed by (7) as feature vectors. thus, the value set {v(i1), ..., v(ik)} is divided into two classes. a region-growing algorithm can be used in this case. there is also a more simple way to perform the clustering: to sort the set in ascending order, then to find the greatest difference between two successive values. that pair of successive correlation-based values marks the dividing point between the two clusters. obviously, the cluster containing the high values is the one corresponding to the facial images. this method works satisfactory when the values v(ii) related to faces are much greater than those corresponding to non-facial images. some face detection errors could be produced. for this reason, the threshold based approach is preferred by us. after determining those ii images representing faces, if such images exist, the corresponding facial sub-images of the rgb image i are provided to the output of our face detection system. let us return to the example described in the first two figures. if the described face finding technique is applied to the skin detection results depicted in figure 2, we get the results displayed in figure 5. in figure 5 (a) there are displayed the main skin regions of the image, obtained after performing the morphological operations, the hole filling and the small region removing processes on the binary image depicted in figure 2. one of these skin regions is rejected because of its low solidity, the remaining regions being accepted as face candidates, as one can see in figure 5 (b). the template matching process is performed using the template face set represented in figure 6. one can see the resulted average correlation coefficient values in figure 5 (c), those greater than 0.185 corresponding to the detected faces, surrounded by black rectangles in that grayscale image. the final face detection result for the rgb image is displayed in figure 5 (d), the human faces being marked by red bounding boxes. 4 experiments we performed a lot of face detection experiments using the described system. our tests involved tens of rgb images containing human faces and produced satisfactory results. a high face detection rate is obtained. we created a template face set that contains 25 grayscale images of various scales and imaging conditions, for our experiments. as one can see in the pictures below, these templates represent both male and female faces, and people of various ages and races. these faces are also characterized by various orientations and poses, some of them have structural elements, too. the template set can be extended, by adding new faces, but although a large set could improve the detection results, it also produces a high computation complexity. as mentioned in the previous section, the threshold-based face finding approach provides much better results than the clustering-based one. our face detection technique is characterized not only by a high detection rate, that is approximately 90% and indicates a low number of false negatives (missed faces), but also by a low number of false positive (non-facial image regions declared to be faces). an automatic face detection system for rgb images 29 figure 5: face detection example the performances of the proposed face detection system are comparable with those of the face detection approaches mentioned in the introduction. it achieves better detection results for the frontal faces, than for faces characterized by various orientations. 5 conclusions a skin segmentation based face detection system for rgb color images has been proposed in this paper. the main contributions of this work are the proposed skin detection approach and face identification technique. the skin regions resulted from the skin segmentation process are analyzed to determine which of them could represent human faces. a set of face candidate criteria was proposed by us, to reduce the set of face candidates and the computation volume and complexity. we used a template matching method for face detection and provided a cross-correlation 30 t. barbu figure 6: template face set based skin region discrimination approach. unlike other template matching algorithms, our procedure uses the edges of the skin region and not the region itself. we choose to perform an edge detection first, because we consider that face features are contained mainly in the edge image. the good face detection results obtained in our experiments prove the effectiveness of our technique. our future work will focus on developing robust face recognition and more complex biometric systems, using the face detector proposed in this paper. we approached the face recognition domain in our previous works [4], and we want to unify the research in these two areas. acknowledgment the research described here has been supported by the grant pncdi ii, idei program having the cncsis code 70/2008. bibliography [1] c. papageorgiou, m. oren, t. poggio. a general framework for object detection, international conference on computer vision, bombay, india, pp. 555-562, jan. 1998. an automatic face detection system for rgb images 31 [2] m.h. yang, d. kriegman, n. ahuja. detecting faces in images: a survey, ieee transactions on pattern analysis and machine intelligence (pami), vol. 24, no. 1, pp. 34-58, jan. 2002. 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[21] a.l. edwards, an introduction to linear regression and correlation, san francisco, ca: w.h. freeman, pp. 33-46, 1976. international journal of computers communications & control issn 1841-9836, 9(5):555-569, october, 2014. a fairness load balancing algorithm in hwn using a multihoming strategy y. donoso, c. lozano-garzon, m. camelo, p. vila yezid donoso universidad de los andes bogotá, colombia, south america ydonoso@uniandes.edu.co carlos lozano-garzon universidad de los andes bogotá, colombia, south america & universitat de girona, girona, spain. calozanog@ieee.org miguel camelo, pere vila universitat de girona, girona, spain. miguel.camelo@udg.edu, pere.vila@udg.edu abstract: due to the growth of the number of intelligent devices and the broadband requirements, between others technical requirements, of the new applications, suppose a new challenge in planning, maintenance and resource allocation in mobile networks for the telecommunication operators. service providers must ensure a quality of service for users in a new environment based in heterogeneous wireless networks (hwn). a good way to achieve this goal is to prevent the quantity of services of each mobile users being connected to the same access networks and therefore reducing the possibility of overloading it. this paper presents a load balancing optimization scheme that enables operators to make decisions about re-allocation of each of the services in different access networks, keeping the required quality of service (qos). in this paper, we propose 1) a mathematical model addressed as a fairness resource allocation in order to obtain a global load balancing, and 2) a two-step algorithm based on the anchor-adjustment heuristic to solve it. our algorithm contribute to unload the network with maximum load while at the same time, the other networks are balanced. as a result, we show that our algorithm finds (near)-optimal solutions while keeps low complexity. keywords: fairness, load balancing, multihoming, quality of service, heterogeneous wireless networks (hwn). 1 introduction nowadays, mobile operators have a great challenge in planning, maintenance and optimization of their complex network infrastructure [2]. many of these challenges are related to the continuous growth of both mobile subscriptions and mobile traffic; in [3] the global mobile suppliers association estimates that there are around 1.3 billion of broadband mobile subscriptions in the second quarter of 2013, and that the monthly traffic was around 885 petabyte in 2012 and is expected to reach to 11.2 exabyte in 2017 [4]. this growing, together with the need of allowing a quality of service that ensures connectivity and mobility to its users, regardless of the access technology, constitute the main topic of our study. copyright © 2006-2014 by ccc publications 556 y. donoso, c. lozano-garzon, m. camelo, p. vila current infrastructure deployed by the majority of mobile operators is made up by a combination of radio access technologies (rat). it is possible that during the network operation of the network some of these access channels could be overloaded due to a sudden growth of traffic. hence it is necessary to perform a good management of the resource allocation, specifically bandwidth in this case, over all the access infrastructures. the final aim is to ensure a balance between the use of network resources and the number of user connections; likewise, operators must allow user mobility between technologies without users perceiving the change [5]. thus, it is essential for operators to use decision-making algorithms to manage both their network resources and the connection of the services. this decision could be based on the qos requirements, among other parameters, and it can be seen as a rat selection process. combining the advantage of the existence of user equipment and network protocols to provide connections through multiple interfaces to different kind of networks [10], it would provide network benefits such as ubiquitous access, reliability, load balancing, among others [6]. specifically, multihoming could separate a flow between multiple points of attachment (simultaneously active or not) of a node, usually by choosing the less loaded connection or according to preferences on the mapping between flows and interfaces. following the latest idea, we initiated our study about the always best connected (abc) problem in hwn with an approach based on the possibility that the mobile user could make the decisions about which network it wants to be connected [7]. we designed a vertical handover (vho) decision algorithm that allows the user terminal to start a proactive re-allocation of the mobile based on parameters such as: user preferences, qos requirements, and network conditions. this process has as aim to avoid the over burdening of any interface. in this paper, we propose an efficient decision-making algorithm to perform a load balancing by re-allocation of connected services among networks by using multihoming as main strategy. however, the decisions are made on the operator side by using both local and global information. the proposed algorithm is based on the anchor-adjustment heuristic proposed by tversky and kahneman in [8], which runs in quadratic time proportional to the number of mobiles in the network. this paper extends the conference paper [1]. the key additions of this journal version are as follows. first, section 2 describes several works about the use of multihoming in this research scenario. second, this paper contains an additional explanation of the proposed model in section 3 and the algorithm designed in section 4. finally, this paper contains an additional scenario composed by seven networks and a variable number of mobiles; our obtained results are contrasted with the results of round robin and least connected algorithms, two ore the most used load balancing algorithms. the remainder of this paper is organized as follows. in section 2 we present the related work on load balancing in cellular networks using multihoming as main strategy. in section 3 we introduce the mathematical model that encode the objective function to obtain a global load balancing among hwn under qos constraints. in section 4 the load balancing algorithm based on the anchor-adjustment heuristic is presented. the experimental results about the performance of our proposal compared to round robin and least connected algorithms are shown in section 5. finally, concluding remarks and directions for further research are given in section 6. 2 related work multihoming and load balancing strategies have been topics of several research projects. a mathematical model to load balancing is presented in [9]. this model aims to minimize the load of network by re-allocating services from the more loaded network. sousa, pentikousis and curado present in [10] the architectural goals and system design principles for multihoming, and review a fairness load balancing algorithm in hwn using a multihoming strategy 557 different approaches. in addition, they show in a survey, how multihoming is supported at the different levels of the osi layers, covering all recent proposals based on a locator/identifier split approach. a mathematical model for heterogeneous network and its performance, considering both multihoming and network coding, is presented in [11]. they propose an optimal resource allocation by deciding which data should be transmitted on which interface and, simultaneously, which coding parameters should be used on the data. they showed that the combination of multihoming and network coding can improve significantly the service rate of access points (wifi) and base stations (cellular system) and reduce the system delay. an alternative technique based on traffic splitting using common radio resource management for long term evolution (lte) and high-speed downlink packet access (hsdpa) networks is presented in [12]. they propose a mathematical model to solve the traffic split problem. in their research, they found that to maximize the throughput it is necessary minimize the transmission delay between both networks. then, the split ratios can be dynamically adjusted according to the channel qualities and the load status of the networks. some other research studies about the use of multihoming in the cellular heterogeneous networks are related with the vho process. in [13], authors present a transport-layer scheme to support vho between universal mobile telecommunications system (umts) and wireless local area network (wlan) using stream control transmission protocol (sctp). their method is based in the multihoming capability and the dynamic address reconfiguration extension of sctp; through it, they obtain a decrease in handover delay and improve throughput performance. the study of liu, boukhatem, martins and bertin in [14] propose a multihoming approach to implement a seamless vho for umts and wimax over integrated and tight coupling architectures. based on these architectures, authors design a sublayer of osi layer 2 to be added to the radio network controller (rnc) and mobile station (ms); this sublayer implements a dual retransmission queue scheme to enable a soft handover that can eliminate packet losses and reduce handover latency significantly. paik et al. [15] also designed a seamless vho mechanism using multihoming for mobile networks. they implement a multihomed mobile access point, which was tested over heterogeneous networks formed by wireless broadband network (wibro) and hsdpa access technologies. as a result, they obtained a significant reduction of handover latency by reducing ip connection latency. authors in [16] also use the seamless handover and multihoming techniques but their aim was to increase the access availability, which is fundamental for qos and critical services. they propose one method to calculate and distribute network topology information with estimated availability, which allows to predict the overall availability of accessible networks. this is a criteria, among others, used for a handover decision in the imh 802.21 framework. to test the proposal they used three different access technologies, wlan, wimax and umts. finally, other researchers have studied the use of multihoming as strategy to make a better distribution of the bandwidth charge. sungwook and varshney [17] worked on a dynamic on-line bandwidth reservation algorithm for some multimedia services over cellular networks. this algorithm was designed to control bandwidth according to the priority of traffic services and current network traffic conditions. later, in [18] they proposed another adaptive on-line algorithm for the multimedia services but it is based on the minimization of the maximum available bandwidth in each cell in order to keep the load balancing. they compared the performance of both proposed schemes with the abr scheme and the cac provision scheme, obtaining an appropriate performance balance between contradictory requirements. in this paper, we propose both a generic mathematical model to load balancing in hwn, which uses as a main objective function the concept of fairness over the networks, and a scalable two-step algorithm with low computational complexity that can be implemented by using vho. 558 y. donoso, c. lozano-garzon, m. camelo, p. vila 3 a mathematical model for load balancing since the resources are limited, an incorrect allocation could impact both the performance of the network and the satisfaction perception of the users. therefore, it is necessary to define a mathematical model that encodes the requirements of the user, the environment constraints and the main target: to balance the load among different networks while ensuring the qos requirements are met. in the following sections, we define the variables, functions and parameters of the mathematical model, and a solution that gives us an optimal distribution of loads across multiple networks under qos constraints. 3.1 network load let n, m and s be the sets of n networks, m mobiles and s services that compose a cellular system, respectively (see figure 1). additionally, let yj,k ∈ [0,1] be a binary parameter that indicates if the service k of the mobile j is activated or not. we calculate the load of the network i (αi) as the sum of demanded bandwidth (dk) of each connected service (k), for each mobile (j) over the total capacity of the network channel (ci). αi = ∑m j=1 ∑s k=1 dk ·x k i,j ·yj,k ci ,∀i (1) where xki,j = 1 if the service k of the mobile j is connected to the network i, or 0 otherwise. mobile device 1 services 1 services 2 services s mobile device 2 mobile device m services serverrat 1 rat 2 rat n core network services server internet services 1 services 2 services s services 1 services 2 services s figure 1: multihoming cellular system 3.2 load balancing: jain’s index functions fairness is a concept related with “equality" in the resource allocation widely used in many research fields, including those in wireless networks [19]. the jain’s index function [20] can be formulated to measure the fairness of resource allocation among networks, i.e. it can give us an accurate measurement of how well-distributed is the network load in the cellular system. this function can be formulated as follows: f1(α) = [ ∑n i=1 αi] 2 n · ∑n i=1 α 2 i ,αi ≥ 0,∀i (2) a fairness load balancing algorithm in hwn using a multihoming strategy 559 f1(α) is a continuous map in to the real interval [0,1], where a value closer to 0 represent a unbalanced load in the system, whilst a value near to 1 represents a fairer allocation. 3.3 constraints in this model we need to consider several constraints in order to ensure the adjustment to the real-life networks. some constraints are related to qos requirements and others to the service connectivity. qos constraints we consider only two qos parameters in our modeling: bandwidth and signal strength. however, it is important to remark that the model can be extended by including any other qos requirements. the bandwidth is a resource that is not unlimited, therefore, it is necessary to ensure that all the services connected to a given network receive the requested bandwidth, otherwise the network must reject the connection. in other words, the service k of the mobile j can be connected to the network i if and only if: dk ·xki,j ≤ abi ∀i ∈ n,∀j ∈ m,∀k ∈ s (3) where abi is the available bandwidth of the network i. the received signal strength indication (rssi) is the relative received signal strength in a wireless environment. it measures the power level being received by the antenna, where higher values of the rssi imply stronger received signals. if this value is below a certain threshold rssith, we assume that the quality of the communication between the mobile and the base station is very poor. thus, we can define that the service k of the mobile j can be connected to the network i if and only if: xki,j ≤ z k i,j ∀i ∈ n,∀j ∈ m,∀k ∈ s (4) where the parameter zki,j = 1 if rssiki,j rssith ≥ 1, or 0 otherwise. actived services constraint this constraint ensures that all activated services of each mobile device j must be connected to some network in n. xki,j ≤ yj,k ∀i ∈ n,∀j ∈ m,∀k ∈ s. (5) connectivity constraint through this constraint we ensure that each service used by the mobile j is connected to only one network. n∑ i=1 xki,j = max 1≤i≤n {zki,j ·yj,k} ∀j ∈ m,∀k ∈ s. (6) 560 y. donoso, c. lozano-garzon, m. camelo, p. vila summary a summary of the proposed mathematical model is presented as follows: maximize f1(α) = [ ∑n i=1 αi] 2 n· ∑n i=1 α 2 i ,αi ≥ 0 (2) subject to dk ·xki,j ≤ abi, ∀i ∈ n,∀j ∈ m,∀k ∈ s (3) xki,j ≤ z k i,j, ∀i ∈ n,∀j ∈ m,∀k ∈ s (4) xki,j ≤ yj,k, ∀i ∈ n,∀j ∈ m,∀k ∈ s (5)∑n i=1 x k i,j = max 1≤i≤n {zki,j ·yj,k}, ∀j ∈ m,∀k ∈ s (6) xki,j ∈{0,1}, ∀i ∈ n,∀j ∈ m,∀k ∈ s (7) 4 proposed algorithms the resource allocation problem has been proved to be np-complete [21], therefore, an efficient algorithm that solves this problem is not known yet. in addition, our model includes an objective function (see equation 2) that can only be solved by mixed-integer nonlinear fractional programming methods [22], which also difficult the possibility of finding an optimal solution even in small problems. thus, it is necessary to implement heuristics that either solve the problem quickly or find an approximate solution when classic methods fail. based on the anchor-adjustment heuristic proposed in [8], we present a two-step algorithm that solves the above mathematical model in quadratic time proportional to the number of mobiles in the network. the proposed algorithm (see algorithm 1) finds a (near)-optimal load balancing among networks by performing a re-allocation of connected services. roughly speaking, the first step of the algorithm tries to balance the load of the network by distributing the services from the more loaded network to the one with minimum load. second step performs an optimization on the result obtained from the first one by using a load distribution method with local information. algorithm 1 two-step algorithm based on anchor-adjustment heuristic require: list of the set of available networks anj,k = {t1, . . . , tp},∀j ∈ m, ∀k ∈ s and p ≤ n require: set of actual connection of mobiles and their services x = {x11,1, . . . ,x s n,m} ensure: a (re) allocation of each used service by each connected mobile device. 1: call anchor algorithm based on a max-min strategy at network level (algorithm 2) 2: call adjustment algorithm based on local information (algorithm 3) 3: return a set of connections of mobiles and their services x = {x11,1, . . . ,x s n,m} we assume that for each mobile j ∈ m and each service k ∈ s of j, we have access to the set of available networks anj,k = {t1, . . . , tp},∀j ∈ m, ∀k ∈ s and p ≤ n, which is derived by selecting those networks that meet both requirements the rssi threshold and the available bandwidth (see constraints 3 and 4). we will see below that the time complexity of this two-step algorithm is bounded by o(m2), when m >> n and m >> s. a fairness load balancing algorithm in hwn using a multihoming strategy 561 4.1 first step: anchor based on a max-min strategy at network level given the set n of networks and a valid set of connections x = {x11,1, . . . ,x s n,m}, i.e. set of connections that meet the constraints 5 and 6, the algorithm finds the network imax ∈ n with maximum load αimax and selects a random service k connected to it. then, k is moved to the available network with minimum load imin ∈ n that is available for k. in case that there are no alternative networks to connect s, i.e. either there are not more reachable networks to connect the service or all the networks have the same load, the service is not re-allocated. note that although the algorithm 2 can obtain an optimal load balancing among networks in most cases, the addition of constraints into the mathematical model may permit that the algorithm can be stuck in local minimum. for example, if all services connected to imax ∈ n have not alternative networks to move, then the algorithm can not perform a load balancing among any network. the load balancing is obtained by the assignation of each service to the feasible network with the minimum load at that moment. algorithm 2 anchor based on a max-min strategy at network level require: list of the set of available networks anj,k = {t1, . . . , tp},∀j ∈ m, ∀k ∈ s and p ≤ n require: set of actual connections of mobiles and their services x = {x11,1, . . . ,x s n,m} ensure: a (re) allocation of each used service by each connected mobile device. 1: imax ← 0, imin ← 0, l ← 0; 2: repeat 3: compute αi,∀i ∈ n; 4: imax ← i | i ∈ n and max i {αi}; 5: select any xki,j = 1 | i = imax; 6: imin ← t | t ∈ anj,k and min t {αt}; 7: if αimin < αimax then 8: xkimax,j = 0, x k imin,j = 1; 9: end if 10: l = l + 1 11: until l = m ·s 12: return a set of connections of mobiles and their services x = {x11,1, . . . ,x s n,m} the complexity of the algorithm 2 is θ(m2 ·s2), where m is the number of mobile devices, and s the number of services. remark that although the main loop has θ(m ·s) iterations, step 6 of the algorithm implies to find the services connected to a specific network. this operation takes at most θ(m·s) steps, i.e. we need to classify each mobile and its services regarding which network they are connected to. 4.2 second step: adjustment based on local information algorithm 4.2 uses the result of the algorithm 2, and tries to improve the allocation of services by using an iterative process of adjustment. the algorithm iterates on each j ∈ m and moves each one of its service k to its available network with minimum load. in case that there are no alternative networks to connect the service k, i.e. either there are not more reachable networks to connect the service or all the networks have the same load, the service is not re-allocated. observe that algorithm 3 permits to leave local optimal from algorithm 2 in a greedy way: in each iteration, the service k ∈ s of each mobile j ∈ m is moved to its less loaded available network (local information). however, we can not ensure an optimal solution at the end of algorithm 3 because in each iteration, the actual load of the networks has been influenced by 562 y. donoso, c. lozano-garzon, m. camelo, p. vila both the initial allocation of services (from step 1) and the allocation of services performed before the current iteration. algorithm 3 adjustment based on local information require: list of the set of available networks anj,k = {t1, . . . , tp},∀j ∈ m, ∀k ∈ s and p ≤ n require: set of actual connection of mobiles and their services x = {x11,1, . . . ,x s n,m} ensure: a (re) allocation of each used service by each connected mobile device. 1: for 1 ≤ j ≤ m do 2: for 1 ≤ k ≤ s do 3: iact ← i | xki,j = 1; 4: imin ← t | t ∈ anj,k and min t {αt}; 5: if αimin < αiact then 6: xkiact,j = 0, x k imin,j = 1; 7: end if 8: end for 9: end for 10: return a set of connections of mobiles and their services x = {x11,1, . . . ,x s n,m} we found that the complexity of the algorithm 3 is θ(m·s), where m is the number of mobile devices, and s the number of services. note that this algorithm has a linear time complexity with respect to the total number of mobiles. in general, the combination of algorithm 2 and 3 gives us a time complexity equal to θ(m ·s + m2 ·s2). however, since the number of mobiles is usually larger than the number of services and networks, the time complexity of the algorithm 1 has as upper bound o(m2). 5 experimental results we propose an experimental environment composed for seven different radio access technologies and a finite number of mobile devices, each one with at most three different network interfaces to supporting data, voice, and video services. table 1: access network bandwidth network edge hspa wimax hspa+ wifi g wifi n lte bandwidth(mbps) 0.384 14.4 37.0 42.0 54.0 100 100 table 2: requested bandwidth for service id service required bandwidth (mbps) 1 voice 0.012 2 data 0.028 3 video 0.128 tables 1 and 2 show the characteristics of each access network and the minimum required bandwidth to ensure qos for each service. to verify the efficiency and effectiveness of our algorithm, we compare the solutions found by our algorithm with respect to those obtained by solving the mathematical model and by using both the round robin and least connected algorithms. the mathematical model was implemented in gams [23], and it was solved by using the global optimization solver bonmin (basic open-source nonlinearmixed integer programming) [24]. two different scenarios to evaluating the proposed algorithm are presented in the following sub-sections. a fairness load balancing algorithm in hwn using a multihoming strategy 563 5.1 scenario 1: three networks and 10 mobiles the first scenario is composed of three networks (wimax, edge and hspa) and 10 mobile devices, where each one of them has two activated services: data and voice. mobiles are randomly distributed among networks and the rssi threshold is set to 10. figure 2, and table 3 show the distribution of mobiles among the networks and the rssi values of each mobile according to a random distribution. table 4 (a) presents an initial connection matrix, which is the result of a random selection process on the mobile rssi matrix, which ensures the required bandwidth of each service. figure 2: distribution of mobiles in scenario 1 mobile wimax edge hspa k1 0 3 26 k2 9 27 23 k3 16 19 29 k4 5 15 12 k5 29 4 12 k6 14 17 24 k7 24 8 29 k8 15 14 18 k9 28 15 0 k10 19 21 11 table 3: rssi for each mobile in scenario 1 (a) mobile id service 1 service 2 k1 hspa hspa k2 hspa hspa k3 edge edge k4 hspa hspa k5 hspa hspa k6 edge edge k7 hspa hspa k8 edge edge k9 edge edge k10 edge edge jain’s index = 0.3510 (b) mobile id service 1 service 2 k1 hspa hspa k2 hspa hspa k3 wimax wimax k4 hspa hspa k5 hspa hspa k6 wimax wimax k7 hspa hspa k8 wimax wimax k9 wimax wimax k10 wimax wimax jain’s index = 0.5586 (c) mobile id service 1 service 2 k1 hspa hspa k2 edge hspa k3 wimax wimax k4 hspa hspa k5 wimax wimax k6 wimax wimax k7 wimax wimax k8 hspa wimax k9 wimax wimax k10 wimax wimax jain’s index = 0.6653 table 4: connection of services: (a) initial, (b) obtained from algorithm 2, and (c) obtained from algorithm 3 from the initial connection matrix, the computed jain’s index (see function (2)) was 0.3510 (see table 4 (a)). once we ran the algorithm 2, this value raised up to 0.5586 (see table 4 (b)). finally, by using the connection matrix resulting from algorithm 2 as input for 3, the jain’s index increased to 0.6653 (see table 4 (c)), while the optimal value of such function calculated by bonmin was 0.7070 in 17 seconds (see table 5). we emphasize that the solution found by our proposal has only a relative error of 5.9% with respect to the optimal one, and its execution time was less than 1 second. 564 y. donoso, c. lozano-garzon, m. camelo, p. vila mobile id service 1 service 2 k1 hspa hspa k2 hspa hspa k3 wimax wimax k4 hspa hspa k5 hspa wimax k6 wimax hspa k7 hspa hspa k8 edge hspa k9 wimax wimax k10 wimax wimax jain’s index = 0.7070 table 5: optimal connection of services 5.2 scenario 2: seven networks and a variable number of mobiles in this scenario, we show the behavior of the proposed algorithm when the number of mobiles is increased. the scenario is composed for seven different radio access networks (edge, hspa, wimax, hspa+, wifi g, wifi n, and lte), and a set of a distributed randomly mobile devices over those networks, where each one has three activated services and the number of them is increased from 10 to 1000. we also implement both the round robin (rr) and least connected (lc) algorithms [25] to compare their performance with respect to our proposed algorithm. the pseudo-codes of these algorithms are described in algorithms 4 and 5. it is important to note that our version of rr and lc also include a random selection process when the next network in the list can not be reached by a given mobile. algorithm 4 round robin algorithm require: list of the set of available networks anj,k = {t1, . . . , tp},∀j ∈ m, ∀k ∈ s and p ≤ n require: set of actual connection of mobiles and their services x = {x11,1, . . . ,x s n,m} ensure: a (re) allocation of each used service by each connected mobile device. 1: inew ←−1; 2: for 1 ≤ j ≤ m do 3: for 1 ≤ k ≤ s do 4: iact ← i | xki,j = 1; 5: inew ← (inew + 1) mod (n−1) + 1; 6: if tinew ∈ anj,k then 7: xkiact,j = 0, x k inew,j = 1; 8: else 9: i ← select a random network index from anj,k; 10: xkiact,j = 0, x k i,j = 1; 11: end if 12: end for 13: end for 14: return a set of connections of mobiles and their services x = {x11,1, . . . ,x s n,m} a fairness load balancing algorithm in hwn using a multihoming strategy 565 algorithm 5 least connected algorithm require: list of the set of available networks anj,k = {t1, . . . , tp},∀j ∈ m, ∀k ∈ s and p ≤ n require: set of actual connection of mobiles and their services x = {x11,1, . . . ,x s n,m} ensure: a (re) allocation of each used service by each connected mobile device. 1: inew ←−1; 2: for 1 ≤ j ≤ m do 3: for 1 ≤ k ≤ s do 4: iact ← i | xki,j = 1; 5: inew ← network with less number of connections; 6: if tinew ∈ anj,k then 7: xkiact,j = 0, x k inew,j = 1; 8: else 9: i ← select a random network index from anj,k; 10: xkiact,j = 0, x k i,j = 1; 11: end if 12: end for 13: end for 14: return a set of connections of mobiles and their services x = {x11,1, . . . ,x s n,m} # mobiles optimal without vertical handover relative error proposed algorithm relative error 10 0.852 0.231 0.729 0.717 0.158 30 0.890 0.173 0.806 0.890 0.000 50 0.991 0.179 0.819 0.767 0.226 70 0.999 0.174 0.826 0.769 0.230 100 0.955 0.176 0.816 0.882 0.076 200 1.000 0.176 0.824 0.784 0.216 300 1.000 0.172 0.828 0.841 0.159 400 1.000 0.170 0.830 0.878 0.122 500 0.945 0.169 0.821 0.939 0.006 600 0.975 0.171 0.825 0.973 0.002 700 0.991 0.172 0.826 0.990 0.001 800 0.998 0.172 0.828 0.997 0.001 900 1.000 0.173 0.827 0.999 0.001 1000 1.000 0.173 0.827 0.998 0.002 table 6: computed jain’s index: optimal, without vertical handover and using the proposed algorithm # mobiles round robin relative error least connected relative error 10 0.162 0.810 0.174 0.796 30 0.198 0.778 0.248 0.721 50 0.216 0.782 0.256 0.742 70 0.244 0.756 0.307 0.693 100 0.490 0.487 0.338 0.646 200 0.500 0.500 0.409 0.531 300 0.659 0.341 0.515 0.485 400 0.624 0.376 0.589 0.411 500 0.682 0.278 0.646 0.316 600 0.709 0.273 0.686 0.296 700 0.783 0.210 0.712 0.282 800 0.815 0.183 0.758 0.240 900 0.829 0.171 0.781 0.219 1000 0.860 0.140 0.737 0.203 table 7: computed jain’s index: round robin and least connected algorithms 566 y. donoso, c. lozano-garzon, m. camelo, p. vila figure 3: effectiveness of the proposed algorithm figure 4: relative error of the proposed algorithm table 8: final network load and the computed jain’s index for the instances of 30, 500 and 1000 mobiles number of mobiles 30 500 1000 network pa rr lc pa rr lc pa rr lc hspa 2.14% 5.22% 11.31% 24.83% 96.08% 99.89% 48.89% 93.81% 99.11% hspa+ 1.52% 1.90% 1.78% 23.80% 32.72% 41.53% 48.25% 76.98% 70.10% wimax 1.74% 3.07% 2.53% 24.22% 39.37% 67.36% 47.89% 90.36% 99.63% lte 1.35% 0.70% 0.50% 23.89% 14.57% 6.01% 48.16% 29.38% 19.41% edge 3.23% 68.75% 48.96% 43.75% 45.83% 94.79% 54.08% 54.17% 98.96% wifi n 1.36% 0.99% 0.70% 24.18% 12.46% 5.36% 48.26% 29.07% 14.02% wifi g 1.35% 0.74% 0.61% 24.18% 27.11% 28.72% 48.01% 55.69% 99.27% jain-s index 89.05% 19.84% 24.84% 93.94% 68.23% 64.61% 99.82% 86.01% 79.73% a fairness load balancing algorithm in hwn using a multihoming strategy 567 tables 6 and 7 show the computed jain’s index and the relative error of the solutions found by round robin (rr), least connected (lc) and the proposed (pa) algorithms. it is important to remark that for the instances between 100 and 1000 mobile devices, we relaxed the variable xki,j to accept real values between 0 and 1 in order to obtain a lower bound for those instances by nonlinear programming. as you can see in figure 3 our proposed algorithm obtains a near optimal solutions when the number of mobiles is increased. compared to rr and lc, we can see in table 8 that our algorithm converge to the optimal values obtaining a fairness load balancing. the results obtained shows that the convergence of rr and lc to the optimal values of jain’s index is due to the networks being loaded up to 100% instead of a fairness load balancing in fact. it is appropriate to note that without vertical handover, the jain’s index of the networks was low, and therefore the initial network load is unbalanced. once we executed our algorithm, we observed that the resulting resource allocation had given us solutions with relative error less than 23% in the first instances. however, when the size problem increases, our algorithm presents a better convergence towards the optimal solution. figure 4 shows how the relative error converges to zero when the number of mobiles is increased. 6 conclusions and future work in this paper, we have presented a load balancing optimization model using multihoming approach in heterogeneous wireless networks. in this approach, we have worked with different wireless access networks like hsdpa, hspa+, edge, wimax, wifi and lte. furthermore, in this paper we designed and implemented a vertical handover (vho) algorithm following the always best connected scheme. it allows to give a solution, in a proactive way, for re-allocation of services when a new access network is available. it is also best to provide the required resources. our proposed algorithm gives a solution to global optimality. the resource allocation problem has been proved to be np-complete and we have proposed an algorithm that calculates the best load balancing solution in heterogeneous wireless networks using a multihoming approach in polynomial time o(m2). the mathematical optimization model was computed by using the solver called bon-min (basic open-source nonlinear mixed integer programming) and their results were compared with our proposed algorithm. simulation results showed that without vertical handover, the initial value of the jain’s index was very low, which means that the initial load balancing of the network was poor. however, the proposed algorithm solved the load balancing optimization model with nearly the same values as the ones given by the optimal solution obtained with the bonmin solver. finally, when the number of mobiles was increased, the results obtained by our algorithm were very close to the optimal ones from the mathematical optimization model. in further studies, we will consider researching the applicability of evolutionary algorithms for a multi-objective load-balancing scheme. in the technical scheme, we are going to work to implement this algorithm like a functional protocol. 568 y. donoso, c. lozano-garzon, m. camelo, p. vila acknowledgments the authors acknowledge to the administrative department of science, technology and innovation (colciencias) for the financial support to carlos lozano-garzon through the 528 2011 national call for doctoral studies in colombia, and to the comissionat d’universitats i recerca (cur), which is part of the departamento de innovacion, universidades i empresas (diue) of the generalitat de catalunya, the roger project (tec 2012-32336) of the spanish government, the csi project (sgr-1202) of the generalitat de catalunya, and the european social fund through the financial support given in the grant fi-dgr 2011 to miguel camelo. bibliography [1] donoso y., lozano-garzon c., camelo m., vila p.(2014); a multihoming load balancing algorithm for a fairness resource allocation in heterogeneous wireless networks, international conference on computers, communications & control, romania, oradea, baile felix, may 6-10, 2014, abstracts of icccc 2014, issn 1844-4334, 4: 43. 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(1975); non-linear fractional programming, bulletin of the australian mathematical society, 12(3) : 391-397. [23] gams development corporation, the general algebraic modeling system (gams), retrived november 2013, from http://www.gams.com/. [24] computational infrastructure for operations research, bonmin and bonminh solvers, available at http://www.gams.com/dd/docs/solvers/coin.pdf. [25] teo y. m., ayani r. (2001); comparison of load balancing strategies on cluster-based web servers, simulation, 77(5-6): 185-195, 2001. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 101-114 optimal tuning of pid controller using adaptive hybrid particle swarm optimization algorithm s. morkos, h. kamal sawsan morkos gharghory electronics research institute dokki, cairo, egypt e-mail: sawsan@eri.sci.eg hanan ahmed kamal faculty of engineering, cairo university giza, egypt e-mail: hanan−ak2003@yahoo.com abstract: particle swarm optimization (pso) has proved its ability as an efficient search tool in many optimization problems. however, pso is easy to be trapped into local minima due to its mechanism in information sharing. under this circumstance, all the particles could quickly converge to a position by the attraction of the best particle; all particles could hardly be improved. to overcome premature convergence of the standard pso algorithm, this paper presents an adaptive hybrid pso, namely (ahpso) by employing an adaptive mutation operator for local best particles instead of applying the mutation operator to the global best particle as has been done in previous work. the developed algorithm is a new approach which allows the swarm to be more diverse by making better exploration of the local search space instead of global search space investigated by previous researchers. the proposed algorithm holds on the properties of simple structure, fast convergence, and at the same time enhances the variety of the population, and extends the search space. it is applied to self-tuning of proportional-integral-derivative-(pid) controller in the ball and hoop system which represents a system of complex industrial processes. the results are compared with those obtained by applying standard pso, and adaptive hybrid pso based on global best particles. it has been shown that the developed ahpso local best algorithm is faster in convergence and the obtained results are proved to have higher fitness than the other two algorithms. keywords:pso, adaptive mutation, pid controller, and ball and hoop system. 1 introduction the pid controller was the most popular controller of this century because of its remarkable effectiveness, simplicity of implementation and broad applicability. in practice, it is hard to obtain optimal tuning for pid controller. most of pid tuning is done manually which is difficult and time consuming. in order to use pid controller better, the optimal tuning of its parameters have become an important research field [1]. people have made lots of research, and proposed some advanced pid control methods, such as expert pid control based on knowledge inference[2], self-learning pid control based on regulation, neural network pid control based on connection mechanism[3], and intelligent pid control based on fuzzy logic[4,5]. genetic algorithm (ga) has copyright c⃝ 2006-2012 by ccc publications 102 s. morkos, h. kamal been applied to self-tuning of pid parameters, too [6]. however, ga has the disadvantages of premature and slow convergence rate, and the need to set up many parameters. recently, the computational intelligence has proposed particle swarm optimization (pso) [7, 8] as opened paths to a new generation of advanced process control. the pso algorithm, proposed by kennedy and eberhart [7] in 1995, was an evolution computation technology based on population intelligent methods. in comparison with genetic algorithm, pso is simple, easy to realize and has very deep intelligent background. it is not only suitable for scientific research, but also suitable for engineering applications in particular. thus, pso received widely attentions from evolution computation field and other fields. now the pso has become a hotspot of research. many efforts on the enhancement of traditional pso have been proposed, by combining the pso with other techniques, especially evolutionary computation techniques. the research effort in [9] has developed a hybrid method combining two heuristic optimization techniques, ga and pso, for the global optimization of multimodal functions. the work in [10], obtained better results by applying pso first followed by applying ga in their profiled corrugated horn antenna optimization problem. the effort in [11] has introduced a new integrated genetic swarm optimization algorithm (igsa), combining the strengths of pso with ga. it is applied in the tuning of pid controllers for the ball and hoop system. a genetic programming based adaptable evolutionary hybrid particle swarm optimization algorithm, have presented in [12], for avoiding premature convergence to local minima by the introduction of diversity in the swarm. in addition to incorporate evolutionary algorithms into pso, another research trend is to merge evolutionary operators like selection, crossover and mutation to the pso. by applying selection operation in pso, the particles with the best performance are copied into the next generation; therefore, pso can always keep the best performed particles [13]. by applying crossover operation, information can be swapped between two individuals to have the ability to "fly" to the new search area as that in evolutionary programming and ga [14]. among the three evolutionary operators, the mutation operators are the most commonly applied evolutionary operators in pso. the purpose of applying mutation to pso is to increase the diversity of the population and the ability to have the pso to escape from the local minima. one approach is to mutate pso parameters such as the position of the best neighborhood, as well as the inertia weight [15]. another approach is to prevent particles from moving too close to each other so that the diversity could be maintained and therefore escape from being trapped into local minima. in [16], the particles are relocated when they are too close to each other. in [17], collisionavoiding mechanisms are designed to prevent particle from colliding with each other and therefore increase the diversity of the population. in [18], deflection and stretching techniques as well as a repulsion technique are incorporated into the original pso to avoid particles to move toward the already found global minima, so that the pso can have more chances to find as many global minima as possible. chen [19] presented a gaussian mutation operator with adaptive mutation probability. wang [20] proposed an adaptive mutation on the basis of average velocity of swarm. pant [21] used an adaptive cauchy mutation operator in pso, which was based on beta distribution. tang [22] proposed local search pso, namely lspso by applying an adaptive mutation operator which dynamically adjusts the step size of local search in terms of the size of current search space in order to improve the global search ability of pso. he introduced one technique for mutating the global best particle by a value which based on the difference between the maximum and minimum value for each dimension of the search space. however, the results given by lspso as well as the standard pso, pso with both gaussian and cauchy mutation fall into local optima for some types of test functions. in the present work, a new adaptive hybrid pso, called ahpso local best is proposed by applying an adaptive mutation operator, which differs from the above mutation techniques by using three types of mutation operators instead of using one technique. the main idea optimal tuning of pid controller using adaptive hybrid particle swarm optimization algorithm 103 of ahpso local best is to generate an operator that can adaptively select the most suitable mutation method in each generation according to each stage of the problem. three types of mutation operators are used in this work, gaussian, cauchy, and levy mutation operators. in the proposed algorithm, the local best particles are to be mutated by the selected mutation operator instead of applying the mutation operator to the global best particle as in the previous literatures. this can be accomplished by searching the neighborhood of the global best particles in each generation, resulting in more exploration of the search space and increasing the diversity of the population and the ability to have the pso to avoid the local optima. the new developed algorithm is carried out for the optimal tuning of pid controller to the ball and hoop system. the performance of the system is compared with the standard pso and adaptive pso using the global best particle. experimental studies on tuning the parameters of pid controller for the ball and hoop problem show that ahpso local best performs better than the standard pso and adaptive pso based on global best particle search technique. the obtained results have higher fitness and faster convergence. the rest of the paper is organized as follows. section 2 describes the standard pso. in section 3, pso with adaptive mutation operator is described and the proposed ahpso local best algorithm is presented in detail. an overview of the control problem which will be solved is provided in section 4. experimental results and discussions are presented in section 5. finally, section 6 concludes the whole work. 2 particle swarm optimization pso is a stochastic optimization technique [7] which operates on the principle of social behavior like bird flocking or fish schooling. like other evolutionary algorithms, pso is also a population-based search algorithm and stats with an initial population of randomly generated solutions called particles which fly through the search space. each particle represents a candidate solution to the optimization problem, and has a velocity and a position. the position of a particle is influenced by the best position visited by itself i.e. its own experience and the position of the best particle in its neighborhood i.e. the experience of neighboring particles. the best particle in the population is denoted by (global best), while the best position that has been visited by the current particle is denoted by (local best). consequentially, each particle is influenced by the best performance of any member in the entire population due to the sharing information between them. the performance of each particle is measured using a fitness function that varies depending on the optimization problem. each particle in the swarm is represented by the following characteristics: xi : the current position of the particle i. vi: the current velocity of the particle i. pi : the best position of particle i so far,and pg is the best position found in the whole swarm so far. equations (1) and (2) are used for updating both of the velocity and the position of each particle. vi = wi.vi + c1.r1.(pi − xi) + c2.r2.(pg − xi) (1) xi = xi + vi (2) where: c1 and c2 are the cognitive coefficients and r1, and r2 are random real numbers drawn from u (0, 1), ω is the inertia weight which is used to achieve a balance in the exploration and exploitation of the search space and plays very important role in pso convergence behavior. the 104 s. morkos, h. kamal inertia dynamically reduces during a run from 1.0 to near 0 in each generation which facilitates a balance in the exploration and exploitation of the search space, it is determined as follows: wi = wmax − wmax − wmin itermax .iter (3) where iter_max, is the maximum number of iterations, and iter is the current number of iteration. several topologies exist in literature for the particles to communicate with one another. the topologies are ring, star, pyramid and master-slave topologies [23]. among the topologies, the star is the best topology. 3 adaptive particle swarm optimization different types of mutation operators can be used to increase the diversity of the population and to help pso jump out of local minima. the type of mutation operator may be more effective or worse depending on the stage of optimization process. in the present work, three types of mutation operators are applied at different stages of the problem for more exploration of search space. an adaptive method for selecting the mutation operator that is suitable for each stage of the problem was proposed in this paper. also, the developed approach search for the best neighborhood of the global best particle to be mutated by the selected mutation operator. the proposed mutation operators are presented in the following section as follow: -cauchy mutation operator vg = vg exp(δ) (4) xg = xg + vgδg (5) where: xg and vg represent position and velocity of the global best particle. δ and δg denote cauchy random numbers with the scale parameter of 1. -gaussian mutation operator vg = vg exp(n) (6) xg = xg + vgng (7) where: xg and vg represent position and velocity of the global best particle. n and ng are gaussian distribution numbers with the mean equals 0 and the variance equals 1. -levy mutation operator vg = vg exp(l(α)) (8) xg = xg + vglg(α) (9) l(α) and lg(α) are random numbers generated from levy distribution with a parameter α which is set to 1.3 [24]. optimal tuning of pid controller using adaptive hybrid particle swarm optimization algorithm 105 3.1 the adaptive mutation operator the method proposed for adaptive mutation uses the three mutation operators described above. initially, the selection ratio is set equal to 1/3 for the three mutation operators, by this ratio; the number of particles mutated by each operator is calculated. then, each mutation operator is applied to the swarm particles according to its selection ratio and finally, the resulting offspring fitness is evaluated. the mutation operators that result in higher fitness values of offspring have the most chance to be selected than the other one with lower fitness values of offspring. gradually the most suitable mutation operator will be chosen automatically and control all the mutation behavior in the whole swarm. the steps for selecting the best mutation operators are described as follows [24]: 1the progress value for each operator at each generation is evaluated as: progi(t) = mi∑ j=1 f(p ij (t)) − min(f(p i j (t)),f(c i j(t))) (10) where: p ij (t) , and c i j(t) denote the fitness of a parent and its child produced by mutation operator i at generation t, and mi is the number of particles that select mutation operator i to mutate. 2the reward value for each operator is calculated as: rewardi(t) = exp( progi(t)∑n j=1 progj(t) α + si mi (1 − α)) + cipi(t) − 1 (11) where: si is the number of particles whose children have a better fitness than themselves after the mutation by operator i, pi(t) is the selection ratio of mutation operator i at generation t, α is a random weight between (0,1), n is the number of mutation operator, and ci is a penalty factor for mutation operator i, and is defined as: ci = { 0.9 if si = 0 and pi(t) = maxnj=1(pj(t)) 1, otherwise (12) the mutation operator with maximum reward has the best chance to mutate the best local particles selected during each generation. 3the selection ratio to the next generation for the mutation operator is updated as follows: pi(t + 1) = rewardi(t)∑n j=1 rewardj(t) (1 − n − γ) + γ (13) where, γ is the minimum selection ratio for each mutation operator and is set equal to 0.01 in our problem. the selection ratio for the next generation depends on four factors: the progress value, the minimum selection ratio, the previous selection ratio, and the ratio of successful mutation operator. the selection of each mutation operator may be updated each generation or after a fixed number of generations, in this paper we update the selection ratio after each generation. 106 s. morkos, h. kamal 3.2 the proposed ahpso local best particles algorithm the standard pso was inspired by the social and cognitive behavior of swarm. according to the analysis given in [25], particles are largely influenced by its previous best particles and the global best particle. once the best particle has no change in a local optimum, all the rest particles will quickly converge to the position of the best particle. the present work proposes searching neighbors of the global best particle to be mutated in each generation, rather than selecting the global best particle for mutating. as a result, it would be helpful for the best particles to jump out the local minima, and the whole swarm would move to better position. this can be accomplished by applying the adaptive mutation operator described above to the neighborhood of the global best particle in each generation. the framework of pso algorithm with one of the three mutations operators according to its selection ratio to mutate the best neighborhood particles of the global best particle is given as follows: 1generate the initial position and velocity for each particle in the swarm randomly. 2evaluate the fitness of each particle, and determine the local and the global best fitness for each particle in the swarm. 3set the initial selection ratio equal 1/3. 4update each particle according to equation (1) and (2) 5for each particle i, if its fitness is smaller than the fitness of its previous best position (pi ) update pi . 6update the fitness of the best position (pg) of all particle if there is a particle with fitness smaller than the current best fitness pg . 7apply each one of mutation operator to number of particles according to its selection ratio. 8evaluate the progress and the reward values to select the best one from the three above mutation operators, and then update the selection ratio of each operator for the next generation. 9mutate the best neighborhood particles of the global best particle with the best mutation operator (with maximum reward), and select the best one from the mutated best neighborhood to produce (p⋆g ). 10compare p⋆g and pg to select the better to reproduce in the next generation. 11stop if the stop criterion is satisfied otherwise, go to step 4. 4 plant system the ball and hoop system illustrates the dynamics of a steel ball that is free to roll on the inside of a rotating circular hoop. there is a groove on the inside edge of the hoop so that a steel ball can roll freely inside the hoop. this introduces the complexity of the rolling radius of the ball being different to the actual radius of the ball as illustrated in figure 1where angle θ is the hoop angular position. the position of the ball is given by: 1γ is the position of the ball on the hoop periphery with respect to a datum point. 2-ψ is the slosh angle which measures the deviation of the ball from its rest position. a fourth order system for the ball and hoop system with the following transfer function[26] is: g(s) = 1 s4 + 6s3 + 11s2 + 6s (14) the ball and hoop apparatus is difficult to control optimally using a pid controller because the system parameters are constantly changing. the parameters of pid controller will be tuned offline separately, using pso, ahpso global best, and ahpso local best algorithms as shown in figure 2. optimal tuning of pid controller using adaptive hybrid particle swarm optimization algorithm 107 r ball point a hoob figure 1: the ball and hoop system _ tuning algorithms pso, ahpso global, and ahpso local plantpid controller output input + error figure 2: the structure of the proposed algorithms in tuning pid controller for the plant system various objective functions based on error performance criterion are used to evaluate the performance of the above algorithms. each objective function is fundamentally the same except for the section of code that defines the specific error performance criterion being implemented to optimize the performance of a pid controlled system. the performance index is calculated over a time interval t. performance indices used to estimate the best parameters of pid controller are given by: integral of the square of the error (ise) iise = ∫ t 0 e2(t)dt (15) mean of the square of the error (mse) imse = 1 n n∑ i=1 (e(t))2 (16) integral of absolute magnitude of the error (iae) iiae = ∫ t 0 |e(t)|dt (17) 108 s. morkos, h. kamal where: e is the error calculated over a time interval t. the effectiveness of the proposed ahpso local best algorithm in comparison with the other two algorithms is tested using the above three performance indices. 5 simulation result to evaluate the performance of ahpso based on local best particles, experiments have been carried out for optimal tuning of pid controller to the ball and hoop system. the performance results of pid controller tuned by ahpso local best search in comparison with pso, and ahpso global best particle in the swarm is analyzed using iae, ise, and mse performance indices. cost functions achieved by each algorithm are averaged over 10 runs for 30 generations. the resulted time response and cost function for the three algorithms using three performance indices are shown in figures 3-8 respectively. tables 1-3 give comparison of cost function values and the transient response characteristics for pso, ahpso global best, and ahpso local best algorithms using iae, ise and mse performance indices. 0 5 10 15 20 25 30 0 0.2 0.4 0.6 0.8 1 1.2 1.4 time(sec) a m p li tu d e pso alone ahpso global best ahpso local best figure 3: system response using iae for pso, ahpso global particle, and ahpso local particle optimal tuning of pid controller using adaptive hybrid particle swarm optimization algorithm 109 0 5 10 15 20 25 30 0 0.2 0.4 0.6 0.8 1 1.2 1.4 time(sec) a m p lit u d e pso alone ahpso global best ahpso local best figure 4: system response using ise for pso, ahpso global particle, and ahpso local particle 0 5 10 15 20 25 30 0 0.2 0.4 0.6 0.8 1 1.2 1.4 time(sec) a m p lit u d e pso alone ahpso global best ahpso local best figure 5: system response using mse forpso, ahpso global particle and ahpso local particle 110 s. morkos, h. kamal 2 4 6 8 10 12 14 16 18 20 5 10 15 20 25 30 35 40 45 generation c o s t f u n c ti o n pso alone ahpso global best ahpso local best figure 6: cost function using iae for pso, ahpso global particle, and ahpso local particles 2 4 6 8 10 12 14 16 18 20 5 10 15 20 25 30 35 40 45 50 generation c o s t f u n c ti o n pso alone ahpso global best ahpso local best figure 7: cost function using ise for pso, ahpso global particle, and ahpso local particles optimal tuning of pid controller using adaptive hybrid particle swarm optimization algorithm 111 2 4 6 8 10 12 14 16 18 20 0.025 0.03 0.035 0.04 0.045 generation c o s t f u n c ti o n pso alone ahpso global best ahpso local best figure 8: cost function using mse for pso, ahpso global particle, and ahpso local particles table 1: transient response charteristics using iae criteria criteria iae standard pso ahpso global ahpso local rise time tr 1.12 1.29 1.17 peak value mp 25% 15.5% 14% settling time ts 7.2 7.58 5.1 peak time tp 1.27 2.30 2.25 cost function 13.39 13.38 13.36 table 2: transient response charteristics using ise criteria criteria ise standard pso ahpso global ahpso local rise time tr 1.02 0.97 0.83 peak value mp 25.95% 28% 25.89% settling time ts 9.54 9.29 9.14 peak time tp 1.72 1.72 1.67 cost function 7.46 7.49 7.43 112 s. morkos, h. kamal table 3: transient response charteristics using mse criteria criteria mse standard pso ahpso global ahpso local rise time tr 1.012 1.01 0.84 peak value mp 25.9% 29% 25.6% settling time ts 9.59 9.4 9.32 peak time tp 1.72 1.72 1.69 cost function 0.0248 0.0248 0.0247 simulation results demonstrate the superiority of ahpso based on local search comparing to the other algorithms. in terms of overshoot (peak value), ahpso local search has a lower overshoot by 1.3% than ahpso global search and 8.8% than pso for iae performance index. in ise performance index, the improvement is about 1.6% over ahpso global search and has 0.047 over pso. for mse, the improvement is about 0.23% over pso, and about 2.6% over ahpso global search using mse performance index. for cost function the results are as follows: the improvement is about 0.22% over pso, and 0.149% over ahpso global search using iae performance index. for ise performance index it is about 0.4% over pso, and 0.8% over ahpso global search. the improvement using mse performance index is about 0.68% over pso, and 0.4% over ahpso global search. as for settling time, ahpso local search has minimum settling time to reach the final minimum cost function. the improvement is about 29% over pso, and 32.7% over ahpso global search using iae. it equals 4.2% over pso, and is 1.6% over ahpso global search for ise. also, it is about 2.8% over pso, and is 0.85% over ahpso global search using mse metric. 6 conclusion this paper is concerned with developing adaptive operator for the selection of best mutation technique of three investigated mutation techniques: cauchy, gaussian, and levy techniques. instead of applying single mutation operator, several mutation operators are applied at different stages for best performance. a new particle swarm algorithm based on adaptive mutation operator to local best particles namely ahpso local best is proposed in this paper. instead of applying the best mutation operators to the global particle, it is applied to the neighbors’ of global best particle. besides, the paper investigates the use of ahpso based on local best particles for tuning pid controller parameters for the ball and hoop system and compares the system performance with the standard pso and ahpso based on global best particles. the performance of the three algorithms is analyzed based on three performance indices; iae, ise, and mse. experimental results show the superiority of the ahpso local search over the other techniques for the optimal tuning of pid controller to the ball and hoop system. also, it has been shown that the developed algorithm is faster in convergence and gives higher fitness value than the other algorithms, at the same time enhances the variety of the population, and extends the search space. also, the time response characteristics of the proposed algorithm are better than other techniques. in future research, we intend to apply the technique to different set of practical constrained problems to show the robustness of the technique. also, comparison of the effectiveness of different mutation operators on particles velocity for different types of problems will be studied. optimal tuning of pid controller using adaptive hybrid particle swarm optimization algorithm 113 bibliography [1] astroöm k., and hagglund t., "the future of pid control". control engineering practice, 9, pp. 1163-1175, 2001. 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"an adaptive mutation operator for particle swarm". proceedings of the 2008 uk workshop on computational intelligence, pp. 165-170, 2008. [25] h. wang, y. liu c. h. li, and s. y. zeng, "a hybrid particle swarm algorithm with cauchy mutation," ieee swarm intelligence symposium, honolulu, hawaii, pp. 356-360, 2007. [26] i. griffin, "on-line pid controller tuning using genetic algorithms", msc. thesis school of electronic engineering dublin city university, 2003. int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 388-395 a reliability level list based sdd algorithm for binary cyclic block codes b. yamuna, t.r. padmanabhan b.yamuna assistant professor,dept of ece amrita vishwa vidyapeetham, amrita school of engineering amrita nagar, coimbatore. 641 112 tamil nadu, india e-mail: b_yamuna@cb.amrita.edu dr. t.r.padmanabhan professor emeritus, dept of it amrita vishwa vidyapeetham, amrita school of engineering e-mail: trp@amrita.edu abstract: soft decision decoding (sdd) provides a better coding gain by making use of the unquantized channel output. in this paper we introduce the concept of a reliability level list (rll); based on the rll a new sdd algorithm for binary phase shift keying (bpsk) based binary cyclic block codes is proposed. the algorithm guarantees to extract the most reliable codeword in an iterative manner. the formation of the rll involves a search for the next possible entry into the rll based on the error probability which is a reflection of the reliability values of the bits of the received word obtained from the channel. the procedure for the formation of rll which is the central idea of the paper is given as a structured algorithm. keywords: cyclic block codes, reliability based decoding, soft decision decoding, probability of error. 1 introduction soft decision decoding (sdd) algorithms in general focus on the extraction of the codeword from the received word by making use of the reliability information available at the channel output which is otherwise discarded in hard decision decoding. sdd algorithms have increased complexity compared to hard decision decoding but they trade off complexity for better error performance. the idea of exploiting the real channel output to the maximum possible extent instead of quantizing it to 0’s and 1’s as with hard decision decoding has been the essence of sdd vis-â-vis binary cyclic block codes. different sdd algorithms have been proposed in the recent years-each with its own trade off between complexity and error performance[13]. the reliability based sdd algorithms aim at decoding a received word which is more reliable even under low signal-to-noise ratio (snr) conditions. researchers are focusing on making the best use of the channel output and improving decoding reliability. in these algorithms the channel output is used to quantify reliability values of the bits of the received word. the bits are processed with respect to their reliability values. they can be based on processing the bits from the least reliable end as with chase decoding algorithms and generalized-minimum distance (gmd) decoding algorithms [4], [5] or they can be based on processing from the most reliable copyright c⃝ 2006-2012 by ccc publications a reliability level list based sdd algorithm for binary cyclic block codes 389 end as with ordered statistics decoding algorithms[6]. a sdd algorithm is evolved in this paper. the algorithm uses the channel output reliability in the true sense. the significance of the work is in the fact that the target codeword identified through the algorithm here is the best that reliability based sdd algorithm can lead to. the method involves searching in a space of 2n words for the most reliable codeword. but the extent of actual search is confined to a much shorter range as brought out in the paper. the focus of the paper is two-fold: (1) proving that the codeword extracted is the best possible one from a soft decision point of view and (2) giving a structured algorithm for the extraction of the codeword. 2 preliminaries of the rll based sdd algorithm the problem of sdd is one of starting with the received word and identifying a codeword which will be the most reliable one; this codeword is called the ’target codeword’ here. the process essentially amounts to arranging all the possible 2n binary bit sequences in the order of increasing reliability and picking the first codeword from it. the sequence so arranged is called the reliability level list (rll) here. hence rll represents the search sequence to be followed to find the most reliable codeword. for an n bit sequence let the pair {(bi, mi)}, 0 ≤ i ≤ n represent the sets of the bit values and the corresponding reliability magnitudes. each bit of hard decision has an associated probability of error given as ∫ ∞ mi 1√ 2π e− x2 2 dx. let q(mi σ )= ∫ ∞ mi 1√ 2π e− x2 2 dx, σ being the noise variance. arrange the set {q(mi σ )} in descending order of magnitudes and assign integer values k to them such that k =1 for the bit with the largest value of q(mi σ ); let q[1] represent this q(m σ ) value. similarly k = 2 for the next one with q[2] representing the corresponding q( m σ ) value and so on until k = n for the bit with the smallest value of q( mi σ ). let {q[k]} be the rearranged set of numbers and {p[k]} = {1 − q[k]}. with an (n,k) code all the 2k possible codewords are included in the total space of 2n words. all these codewords appear in the rll each with its own associated probability of being the target codeword; the very first codeword to appear in the list being the most reliable of the codewords is the target codeword; those further down the rll being less reliable need not be examined. the first few entries in rll as defined above can be seen to be as given below: • the topmost entry is the term ∏15 k=1 p[k] having the largest magnitude. this represents the received word itself. i.e., none of the bits is in error. • the next entry in rll is the product q[1] ∏15 k=2 p[k] having the next higher magnitude. this represents the received word with the least reliable bit with k =1 being in error. hence the received word with the least reliable bit flipped is examined and if found to be a codeword it is the target codeword and the search stops. if it is not a codeword then the subsequent entry in rll has to be made and the process of examining the entry for identification of target codeword is to be continued. the process is thus repeated until target codeword is identified. the third and fourth entries are given below for clarity. • the third entry in rll is the term q[2]p[1] ∏15 k=3 p[k] • the fourth entry in rll is either q[3]p[2]p[1] ∏15 k=4 p[k] or p[3]q[2]q[1] ∏15 k=4 p[k] based on whichever candidate has the higher magnitude. in general the position of a word in rll is decided by the magnitude of the corresponding product∏ ki p[ki] ∏ kj q[kj] (1) 390 b. yamuna, t.r. padmanabhan where ki and kj represent the set of k values for which the p[ki] and q[kj] values respectively appear in the product. this is the crux of selecting the next entry in to the rll at every stage. withs[k ] = q[k] p[k] define f = ∏ kj q[kj]∏ ki p[ki] (2) using f the product ∏n τ=1 p [τ]×f can be used in place of the product in (1) above. this directly leads to the following lemma. lemma 1: let v represent the rank of an entry in rll and fv the corresponding f value as defined in equation (2). then the vth entry in rll is such that fv−1 ≥ fv ≥ fv+1 for 0 ≤ v ≤ n the above inequality means that the problem of identifying the rank of an entry in the rll is the same as computing the fv′s and arranging them in descending order of magnitudes. since log fv is a monotonically increasing function of f, the inequality implies log fv−1 ≥ logfv ≥ log fv+1 for 0 ≤ v ≤ n. if an entry in rll is known its log fv is known. the problem of deciding the very next entry can be looked upon as the problem of identifying a set of log s[k] such that the corresponding σ log s[k] satisfies the following two conditions 1. it should be smaller than the σlog s[k] for the present entry. 2. it should be the largest in magnitude amongst the set yet to be entered in the rll. with w =σ log s[k], an entry with rank v in rll is characterized by its wv. with this the above result can be formalized as a theorem: theorem 1: for any v ∈ {0, 2n−1} the corresponding entry in rll has a wv such that wv−1 ≥ wv ≥ wv+1 log s[k] being a negative quantity, w =σlog s[k] is also a negative quantity. it is more convenient to work with log ( 1 s[k] ) = (-log s[k]) and the corresponding sum m = -σ log s[k]. with this, theorem 1 implies lemma 2 below which is more convenient to work with. lemma 2: for any v ∈ {0, 2n−1 } the corresponding entry in rll has a mv such that mv−1 ≤ mv ≤ mv+1 3 procedure for forming the rll the procedure involves forming sequences of n bit binary numbers which would be the entries in the rll. let v denote the rank of an entry in the list with v ranging from 0 to 2n. the step by step procedure for making the entries in the rll is follows. 1. m0, m1, m2, where m0 =0,m1 = log s[1], m2 = log s[2] are formed as explained earlier in section 2 and the corresponding binary sequences designated n0, n1, n2 are entered in the rll. 2. define a ’pending list’ pl as the collection of all contenders for the next entry to the rll. 3. the entries in the pl are characterized by the following. (a) each entry is a collection of indices. (b) each entry has an associated characteristic magnitude m. a reliability level list based sdd algorithm for binary cyclic block codes 391 4. once the pl is fully known, the next entry to the rll can be decided by comparing the m values for each entry in the pl. it also implies that the left over elements in the pl will also be members for the pl for the subsequent entry in the rll. 5. once an entry to the rll is decided, the pl for the next entry to the rll is formed through the following steps. (a) all the entries left over after the previous entry to the rll are also to be in the new pl. (b) additional sets are to be added to the new pl depending on the present entry to the rll. this essentially amounts to scanning the indices in the set for the present entry and altering them. a perusal of the set of indices shows that the additional element sets can be formed as follows: i. if the least index k1 for the set is not 1 add 1 to the index set. ii. for every index xi in the set if xi+1 < xi+1 replace xi with xi+1. this amounts to adding a set to the pl where the contribution to m by xi is increased by the least possible amount in the neighborhood of xi. the procedure to form the rll as a sequence of 2n binary numbers explained above can be cast as structured algorithm. the list of notations used in the algorithm is given below: • k: reliability index assigned to the bits of the received word. • nv : binary number in rll with rank v. • s[k] = q[k] 1−q[k] • n : block length of the codeword • pl : pending list after updating • pu : pending list before updating • ti : ith set in pl • jmax :total number of ti′s in pl • βi: number of elements in ti • f : flag • ui, µ, ∆: temporary symbols used for ti′s, ui , and jmax respectively 4 rll based sdd algorithm 1. input: {-log s[k]}, n. 2. output: sequential entries in rll 3. initial condition: n0 = {0}, n1 = {1}, n2 = {2}, pl = {t1, t2} where t1 = {3}, t2 = {1, 2},jmax = 2, f = 0. 4. do for v=3 to 2n 392 b. yamuna, t.r. padmanabhan 5. do for i=1 to jmax compute mvi = − ∑βi α=1 log(s[xα]) enddo i 6. mmin = min{ mvi for i = 1 to jmax} 7. let im = the i for which mvi is minimum 8. form a binary sequence nv whose 1 bits are decided by the indices in the set tim 9. βv = βim 10. do for i=1 to jmax if(i ̸= im) ui = ti enddo i (removing tim from the pending list and reassigning the ti’s to the ui’s.) 11. pu = {0} 12. do for i = i to im−1 pu = { pu :ui} enddo i 13. do for i = im to jmax (a) ui = ui+1 (b) pu = { pu :ui} (c) βi = βi+1 enddo i 14. in nv (updating the pending list by forming candidate entries; processing done with present entry) 15. do for d = 1 to n (a) if k1 ̸= 1, (if lsb of nv is 0 add 1 to the present entry) { i. µ = { nv :1} ii. if µ is not in pu , iii. ui = µ for i = jmax iv. ∆ = jmax v. βjmax = βjmax + 1 vi. f = 1 } (b) if kd +1 < kd+1 { if f =1 { i. form µ with kd replaced by kd +1 in nv ii. if µ is not in pu , a reliability level list based sdd algorithm for binary cyclic block codes 393 iii. u∆+1 = µ iv. β∆+1 = βv } else { 15.2.5 form µ with kd replaced by kd +1 in nv 15.2.6 if µ is not in pu 15.2.7 u∆ = µ 15.2.8 β∆ = βjmax } 16. increment ∆ } 17. jmax = jmax +1 18. enddo d 19. pl = pu for i =1 to ∆ (pending list updated) 20. f = 0. 21. enddo v 5 example extensive simulation with a variety of binary cyclic block codes like (15, 7), (31, 16), and (127, 64) have been carried out; in each case a few thousand transmissions over an awgn channel was done for different values of σ and the erroneous received words decoded using the proposed algorithm. in all the cases the resultant target codeword was found to be the most reliable one. details of a representative set are discussed briefly here. a (15, 7) binary cyclic block code with dmin = 5 and t =⌊ dmin/2 ⌋ = 2 is considered. the message 030h has been encoded as 304eh and transmitted over an awgn channel with σ= 0.8. the received word is 78cch. there are 4 errors at bit positions 1, 7, 11, 14. with the input as the set {-log s[k]}, n = 15 and the initial condition as:n0 = {0} n1 = {1} , n2 = {2}, pl = {t1, t2} where t1 = {3}, t2= {1, 2},jmax = 2, f = 0, following the steps of the algorithm the rll was formed. a representative segment of the rll is given in table 1. the table gives the rank v, k value, mmin , and the contents of ti with the corresponding mvi (both separated by a hyphen in the table). as seen in table 1 in each row the specific ti in italics has the minimum value of mvi and forms the candidate identified for the next entry. further in each row the entries in bold represent the updated candidates that are formed by applying the rules in row 15 in the algorithm given above in section 4. for example considering the row with rank 82, the entry t36= {2,3,6,7 0.977} with k=2, k = 3, k = 6, k = 7 as 1′s and σ log s[k] as 0.977 is the next entry with rank as 83 ; and this is the one with minimum of mvi . the entries in the same row with rank 82, namely t38 = {1,4,6,7 1.198} and t39 = {1,3,6,8 1.4992}are the ones formed on applying the rules in rows 15.1 and 15.2.1 of the algorithm given in section 4 respectively. 394 b. yamuna, t.r. padmanabhan table 1: partial rll rank v k m min details of t i‘s in the updated pending list pl 1 1 0.0832 2 2 0.0957 t1= {3 0.1223 }, t2= {1,2 0.1789} 3 3 0.1223 pl= { t1, t2, t3}; t1= {1,2 -0.1789 }, t2= {1,3 -0.2055}, t3= {4,-0.3558} 4 1,2 0.1789 t1= {1,3 0.2055 }, t2= {4 0.3558} 5 1,3 0.2055 t1= {4 0.3558}, t2= {2,3 0.218}, t3= {1,4 0.439} . . . . . . . . 82 1,3,6,7 0.9645 t1= {1,8 0.9978}, t2= {2,8 1.0103}, t3= {3,8 1.0369}, t4= {1,2,8 1.0935}, t5= {1,3,8 1.1201}, t6= {2,3,8 1.1326}, t7= {1,2,3,8 1.2158}, t8= {4,8 1.2704}, t9= {5,8 1.2727}, t10= {6,8 1.2937}, t11= {1,4,8 1.3536}, t12= {1,5,8 1.3559}, t13= {2,4,8 1.3661}, t14= {2,5,8 1.3684}, t15= {1,6,8 1.3769}, t16= {2,6,8 1.3894}, t17= {3,4,8 1.3927}, t18= {4,5,6 1.093}, t19= {4,5,7 1.0938},t20= {3,5,8 -1.395}, t21= {4,6,7 1.1148}, t22= {3,6,8 1.416}, t23= {1,2,4,8 1.4493}, t24= {1,2,5,8 1.4516}, t25= {1,2,3,4,5 1.0151}, t26= {1,2,6,8 1.4726}, t27= {1,9 1.0262}, t28= {10 1.0421}, t29= {1,2,3,4,6 1.0361}, t30= {1,2,3,4,7 1.0369}, t31= {2,3,4,8 1.4884}, t32= {1,2,3,5,6 1.0384}, t33= {2,4,5,6 1.1887}, t34= {1,2,3,5,7 1.0392}, t35= {2,4,5,7 1.1895}, t36= {2,3,6,7 0.977}, t37= {2,3,5,8 1.4907}, t38= {1,4,6,7 1.198 }, t39= {1,3,6,8 1.4992 } 83 2,3,6,7 0.977 t1= {1,8 0.9978}, t2= {2,8 1.0103}, . . . . . . . . . . . . . . . .t37= {1,4,6,7 1.198 }, t38= {1,3,6,8 1.4992 },t39={1,2,3,6,7 1.0602}, t40={2,4,6,7 1.2105}, t41={2,3,6,8 1.5117}. a reliability level list based sdd algorithm for binary cyclic block codes 395 with each such entry to the rll the corresponding candidate word is formed by complementing the bits indicated by the entry. the candidate word is examined to check if it is a codeword; if yes it is the desired target codeword. once the target codeword is identified the process of filling the rll can be discontinued since any codeword found below this in the rll is less reliable. for the specific case here the target code word is obtained at the rank of 83 with errors at bits 7, 1, 11, and 14 with the corresponding k values as 2, 3, 6, and 7 respectively. 6 conclusion a structured, iterative, and reliability based soft decision decoding algorithm is proposed. the algorithm uses the reliability value of the received word as a soft metric and outputs the desired target codeword through an ordered search in the 2n word space. the algorithm identifies an entry in the rll such that the corresponding word is the most reliable yet. for each entry in the rll the corresponding word formed has to be checked to see if it is a code word and if so it is the desired target codeword. the proposed algorithm guarantees to return the best in terms of reliability because of the very fact that any other codeword found below the identified one will be less reliable. the structured algorithm can be easily coded and used for decoding any (n,k) binary cyclic block code. bibliography [1] wenyi jin and marc.p.c.fossorier,fellow,ieee, reliability-based soft-decision decoding with multiple biases , ieee trans. inform. theory, vol. 53, pp. 105-120, jan. 2007 [2] ye liu, member, ieee, shu lin, fellow, ieee, and marc.p.c.fossorier,fellow,ieee, map algorithms for decoding linear block codes based on sectionalized trellis diagrams, ieee trans. communications, vol. 48, pp. 577-587, apr. 2000 [3] yuansheng tang, member, ieee, san ling and fang weifu, on the reliability based soft decision decoding algorithms for binary linear block codes, ieee trans. inform. theory, vol. 52, pp. 328-335, jan. 2006 [4] david chase, member, ieee, a class of algorithms for decoding block codes with channel measurement information, ieee trans. inform. theory, vol. it-18, pp.170-182, jan 1972 [5] g. d. forney, jr., generalized minimum distance decoding, ieee trans. inform. theory, vol. it-12, pp. 125-131, apr. 1966 [6] marc p. c. fossorier, member, ieee, and shu lin, fellow, ieee, soft-decision decoding of linear block codes based on ordered statistics,ieee trans. inform. theory, vol. 41, pp. 1379-96, sep 1995 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 525-531 fingerprints identification using a fuzzy logic system i. iancu, n. constantinescu, m. colhon ion iancu, nicolae constantinescu, mihaela colhon department of informatics university of craiova, al.i. cuza street, no. 13, craiova ro-200585, romania e-mail: i_iancu@yahoo.com, nikyc@central.ucv.ro, mghindeanu@yahoo.com. abstract: this paper presents an optimized method to reduce the points number to be used in order to identify a person using fuzzy fingerprints. two fingerprints are similar if n out of n points from the skin are identical. we discuss the criteria used for choosing these points. we also describe the properties of fuzzy logic and the classical methods applied on fingerprints. our method compares two matching sets and selects the optimal set from these, using a fuzzy reasoning system. the advantage of our method with respect to the classical existing methods consists in a smaller number of calculations. keywords: fuzzy models, fingerprint authentication, cryptographic signature model. 1 introduction fingerprint identification is the most mature biometric method being implemented at an early level since 1960. the recognition of a fingerprint can be done with two methods: ”one-to-one” (verification) and ”one-to-many” (1 : n identification). the first method is applied when we have two fingerprints and we want to verify if they belong to the same person. the second one is used when we have one fingerprint and we search it in a data base. the verification is much easier and faster because we have the two fingerprints and we just need to compare them. on the other hand, the identification implies more time for extracting the fingerprint because there are needed much more details. the fingerprints are not compared with images, they use a method based on characteristic points named ”minutiae”. these points are characterized by ridge ending (the abrupt end of a ridge), ridge bifurcation (a single ridge that divides in two ridges), delta (a y-shaped ridge meeting), core (a u-turn in ridge pattern), etc. all these features are grouped in three types of lines: line ending, line bifurcation and short line. after the minutiae points are localized, a map with all their locations on the finger is created. every minutiae point has associated two coordinates (x,y), an angle for orientation and a measure for the fingerprint quality. the matching of two fingerprints depends on the position and on the rotation. for this reason, every fingerprint is represented, not only, as a group of points with two coordinates, but also, as a group of points with coordinates relative to other points. this allows obtaining an unique positioning of a point regarding to other three points. the three selected points must not be collinear. when two fingerprints are compared, first are compared the relative coordinates. if this stage ends successfully, these coordinates are transformed in 2d coordinates and verified. after verifying the fingerprints, the result will tell us if they are from the same person with a high probability. still, the cases when the belonging probability of a fingerprint is 0 (false) or 1(true) are rarely. in most of the cases, the probability will be a number p ∈ [0,1]. this fact leads to a fuzzy logic. the values in fuzzy logic can range between 0 and 1 (1 is for absolute truth, 0 for absolute falsity). a fuzzy value for an element x will express the degree of membership of x in a set x . it is essential to realize that fuzzy logic uses truth degrees as a mathematical model of the vagueness phenomenon while probability is a mathematical model of randomness. copyright c⃝ 2006-2010 by ccc publications 526 i. iancu, n. constantinescu, m. colhon 2 state of the art two fingerprints are similar if n out of n points match. to verify this, freedman et al. introduced the fuzzy matching protocols [3]. using these protocols, the information about the fingerprint we want to identify (or verify) will not be revealed if no match is found. to describe the fuzzy private matching problem we will take a set of words x = x1 ...xn where {xi} are the letters. two words x = x1 ...xn and y = y1 ...yn match only if: n ≤ |{k : xk = yk | 1 ≤ k ≤ n}| and this relation is denoted with x ≈n y . in the subsequent we will name the set x as the total set for selection. the input of the protocol will be two sets of words (x = x1 ...xm for the client and y = y1 ...ys for the server) and the parameters m,s,n and n. while the output of the server is empty, the output of the client will be a set {yi ∈ y |∃xi ∈ x : xi ≈n yi}, where a ≈n b means that the points a and b are very close. this set is, in fact, the intersection of the two input sets [1]. it was demonstrated that this protocol leads information about the input even if no match is found [1]. another protocol, based on freedman’s protocol, was presented in [1]. it uses σ as a combination of n different indices γ1,γ2 ...γn and σ(x) = xγ1||... ||xγt for a word x . after the parameters and the public key are sent, the client constructs a polynomial representation of the points set: pσ = (x − σ(x1))∗(x − σ(x2))∗...∗(x − σ(xm)) this is a feedback polinomial value for a set of fingerprints. then he sends {pσ }k2 to the server. the server analyzes every received polynomial {pσ } at the point σ(yi) and computes {wσi }k2 = {r ∗ pσ (σ(yi))+yi}k2 , where r is a random value. after all the calculations, the server sends {wσi }k2 to the client. the client will decrypt all the messages and if wσi matches with any word from x then it is added to the output set. {wσi }k2 is a combination between fingerprint points value and the parameter which characterizes the common information between a base set and the current set of collected values. a particular scheme of fingerprint authentication describes a method which is not based on the minutiae points [12], but by the texture of the finger, called fingercode. such a fingercode is a vector composed from 640 values between 0 and 7. the vector is ordered and stable in size. the method uses euclidean distance to find the matching. after estimating the block orientation, a curvature estimator is designed for each pixel. its maximal value is, in fact, the morphological searched center. using a properly tuned gabor filter ( [11, 13]) we can catch ridges and valleys from the fingerprint. the fingercode is computed as the average absolute deviation from the mean of every sector of each image. error-correction for the fingercode would never be efficient enough to recognize a user. in [12], the method proposed uses a secret d +1− letter word , which correspond to the d +1 coefficients of a polynomial p of degree d. the public key will be extract from (f, p), where f is the fingercode. then, we choose n random point of p. these points will be hidden like in a fuzzy commitment scheme. to find the polynomial p, each point is decoded. if at least d +1 points are decoded then p can, also, be retrieved. a method based on the minutiae points and, also, on the pattern of the finger was presented in [4]. all the ridges that cross a line (x,y) where x and y are minutiae points are counted. then, are presented all the possible combinations of three minutiae points and the ridges crossing that line. such a combinations’ list needs c3n entries, where n is the number of minutiae points. this method is more complex because before all the calculations are done we need to identify the minutiae points and then combine them. 3 our method 3.1 system description a commercial fingerprint-based authentication system requires a very low false reject rate (frr) for a given false accept rate (far) where far is the probability that the system will incorrectly identify and frr is the probability of failure in identification. fingerprints identification using a fuzzy logic system 527 our method is, also, based on the minutiae points of the fingerprints. we can identify at least 40 minutiae points on a fingerprint, depending on its quality. in general, the number of the minutiae points varies from 0 to 100. all the methods mentioned above can be applied to a fingerprint verification. but, for an identification we need an algorithm with a low level of complexity because the data bases used in practice have millions of fingerprints. to reduce the search time and complexity, we first propose to classify the fingerprints, and then, to identify the input fingerprints only in one subset of the data base. to choose the right subset the fingerprint is matched at a coarse level to one of the existing types. after that, it is matched at a finer level to all the fingerprints of the subset. the fbi in the united states recognize eight different types of patterns [5]. for example, we have an input fingerprint and we want to identify it in a data base with 15000 entries. we will take the minim number of minutiae points, 40. if no classification is made we have to do at least 40 × 15000 = 600000 operations. but, if we use a classification with eight types (each subset has the same number of fingerprints 15000/8 = 1875) we will have at least (8+1875)×40 = 75320 calculations. this is because we will first compare the input fingerprint with each group and after that it will be compared with each element of the chosen group. as we can see, the calculations are reduced to only 12,5%. the classification of the fingerprints is preferred to have more than three types of subsets. this is because a higher accuracy is achieved. such a classification, also, helps to reduce the number of calculations with a higher percentage. 3.2 fuzzy mathematical background a fuzzy set a in x is characterized by its membership function: µa : x → [0,1] where µa(x) ∈ [0,1] represents the membership degree of the element x in the fuzzy set a. we will work with membership functions represented by trapezoidal fuzzy numbers. such a number n = (m,m,α,β ) is defined as µn (x) =   0 f or x < m − α x − m + α α f or x ∈ [m − α,m] 1 f or x ∈ [m,m] m + β − x β f or x ∈ [m,m + β ] 0 f or x > m + β the rules are represented by fuzzy implications. let x and y be two variables whose domains are u and v , respectively. the rule i f x is a then y is b is represented by its conditional possibility distribution ( [14], [15]) πy /x : πy /x (v,u) = µa(u)→ µb(v), ∀u ∈ u, ∀v ∈ v where → is an implication operator ( [2]) and µa and µb are the membership functions of the fuzzy sets a and b, respectively. one of the most important implication is lukasiewicz implication [2], il(x,y) = min(1− x + y,1). 3.3 proposed fuzzy logic system fuzzy control provides a formal methodology for representing, manipulating and implementing human’s heuristic knowledge about how to control a system. in a fuzzy logic controller, the expert knowledge is of the form 528 i. iancu, n. constantinescu, m. colhon if (a set o f conditions are satis f ied) t hen (a set o f consequences are in f erred) where the antecedents and the consequences of the rules are associated with fuzzy concepts (linguistic terms). the most known systems are: mamdani, tsukamoto, sugeno and larsen which work with crisp data as inputs. a mamdani type model which works with interval inputs is presented in [10]. in this paper we use a version of fuzzy logic control (flc) system from [9] in fingerprints identification. this version is characterized by: • the linguistic terms (or values), that are represented by trapezoidal fuzzy numbers • lukasiewicz implication, which is used to represent the rules • the crisp control action of a rule, computed by middle-of-maxima method • the overall crisp control actions, computed by discrete center-of-gravity. we assume that the facts can be given by crisp data, intervals and/or linguistic terms and a rule is characterized by: • a set of linguistic variable a, having as domain an interval ia = [aa,ba] • na linguistic values a1,a2,...,ana for each linguistic variable a • membership function µ0ai(x) for each value ai, where i ∈ {1,2,...,na} and x ∈ ia. according to the structure of a flc, the following steps are necessary in order to work with our system. firing levels we consider an interval input [a,b] with aa ≤ a < b ≤ ba. the membership function of ai is modified ( [10]) by membership function of [a,b] as follows ∀x ∈ ia, µai(x) = min(µ 0 ai(x), µ[a,b](x)) where µ[a,b](x) = { 1 i f x ∈ [a,b] 0 otherwise it is obvious that, any t-norm t can be used instead of min (see, for instance, [6–8]). the firing level, generated by the input interval [a,b], corresponding to the linguistic value ai is given by: µai = max{µai(x)|x ∈ [a,b]}. the firing level µai , generated by a linguistic input value a ′ i is µai = max{min{µ 0 ai(x), µa′i (x)}|x ∈ ia}. the firing level µai , generated by a crisp value x0 is µ 0 ai (x0). fuzzy inference we consider a set of fuzzy control rules ri : i f x1 is a 1 i and ... and xr is a r i then y is ci where the variables x j, j ∈ {1,2,...,r}, and y have the domains u j and v, respectively. the firing levels of the rules, denoted by {αi}, are computed by αi = t (α1i ,...,α r i ) where t is a t-norm and α ji is the firing level for a j i , j ∈ {1,2,...,r}. the conclusion inferred from the rule ri, using the lukasiewicz implication is c ′i (v) = i(αi,ci(v)),∀v ∈ v. fingerprints identification using a fuzzy logic system 529 figure 1: conclusion obtained with lukasiewicz implication defuzzification the fuzzy output c ′i of the rule ri is transformed in a crisp output zi using the middle-of-maxima operator. the crisp value z0 associated to a conclusion c ′ inferred from a rule having the firing level α and the conclusion c represented by the fuzzy number (mc,mc,αc,βc) is: z0 = mc + mc +(1− α)(βc − αc) 2 the overall crisp control action is computed by the discrete center-of-gravity method: if the number of fired rules is n then the final control action is z0 = ( n∑ i=1 αizi ) / n∑ i=1 αi where αi is the firing level and zi is the crisp output of the i-th rule. 4 an application in fingerprint identification for the proposed flc we consider rules with two inputs and one output. the input variables are σ1 = {xi|xi ∈ x} and σ2 = {x j|x j ∈ x,xi ̸= x j,∀i, j} where x is the set defined in section 2. by σ1 we represent the values set of basic input data and σ2 is a user data to be evaluated and authenticated. these values sets will be denoted by s1 and s2 respectively. the σ set values denote the optimal points set to be used for the fuzzy matching authentication, according with s output variable. the fuzzy rule-base consists of r1: if s1 is low and s2 is very low then s is low r2: if s1 is very low and s2 is low then s is low r3: if s1 is very low and s2 is very low then s is very low r4: if s1 is very low and s2 is very low then s is low r5: if s1 is very low and s2 is middle then s is middle r6: if s1 is very low and s2 is middle then s is low r7: if s1 is high and s2 is very high then s is high r8: if s1 is very high and s2 is high then s is high r9: if s1 is very high and s2 is very high then s is high r10: if s1 is low and s2 is very high then s is middle the value very low for a variable s1, s2 or s represents a minimum degree of trust while the value very high represents the maximum degree. there are five linguistic values for every variable {very low, low, middle, high, very high}. we consider the universes of discourse [0,10]. the membership functions corresponding to the linguistic values are represented by the following trapezoidal fuzzy numbers: 530 i. iancu, n. constantinescu, m. colhon • for the variable s1: {(0,1,0,1.5),(3,4,1,0.5),(5,6,1,0.5),(7.5,8.5,3,1),(9.5,10,0.5,0)} • for the variable s2: {(0,1.5,0,1),(2.5,4,0.5,0.5),(5,6,2,0),(6.5,8,1.5,0),(8.5,10,0.5,0)}, • for the variable s: {(0,1,0,1.5),(2.5,4,0.5,0.5),(5,7,2.5,0.5),(8,8.5,2,0.5),(9.5,10,1,0)} we consider the following interval input values: [1.5,2.2] for s1 and [3.2,4.2] for s2. the positive firing levels corresponding to the linguistic values of the input variable s1 are µverylow = 0.666, µlow = 0.2 and the positive firing levels corresponding to the linguistic values of the input variable s2 are: µlow = 1, µmiddle = 0.6 the fired rules and their firing levels, computed with t-norm product t (x,y) = xy, are: r2 with firing level α2 = 0.666, r5 and r6 with α5 = α6 = 0.3996. the fired rules give the following crisp values as output: z2 = 3.25, z5 = 5.3996, z6 = 3.25; then the overall crisp control action is z0 = 3.836. these values represent the matching approach for every subset points which are candidate to be in the final set, and are computed using a fuzzy merging comparison between selection sets σ1 and σ2. the optimal selection set (which has less points) is represented by the output variable σ . 5 conclusions among all the biometric techniques, the identification based on fingerprints is used in the most applications. the uniqueness of the fingerprint can be determinate by the pattern of ridges and the minutiae points. for identifying an input fingerprint, the proposed method uses a fuzzy classification of the data. the proposed system is much more efficient than the flc presented in [8]. this is because, in order to reduce the necessary points number, we find the minutiae points by using a fuzzy logic reasoning system which compare two points sets matching values. in a practical application, it is recommended to use the proposed system with a set of implications and aggregate the results given by every implication, in order to obtain the overall output; in this way can be obtained a stronger base for more accurate results of our system. we intend to use these results in a future work, by maping their relative placement on the finger, and comparing all its points with the ones of the fingerprints for the right subset. bibliography [1] l. chmielewski and j. h. hoepman, fuzzy private matching (extended abstract), ares ’08: proceedings of the 2008 third international conference on availability, reliability and security, ieee computer society, pp. 327–334, 2008. [2] e. czogola and j. leski, on equivalence of approximate reasoning results using different interpretations of fuzzy if-then rules, fuzzy sets and systems, vol. 117, no. 2, pp. 279–296, 2001. fingerprints identification using a fuzzy logic system 531 [3] m. freedman, k. nissim and b. pinkas, efficient private matching and set intersection, advances in cryptology, eurocrypt 2004, springer-verlag, pp. 1–19, 2004 [4] r. s. germain, a. califano and s. colville, fingerprint matching using transformation parameter clustering, ieee comput. sci. eng., vol. 4, no. 4, pp. 42–49, 1997. [5] m. r. hawthorne, fingerprints. analysis and understanding, crc press, 2009 [6] i. iancu, t -norms with threshold, fuzzy sets and systems. international journal of soft computing and intelligence, vol. 85, no. 1, pp. 83–92, 1997. [7] i. iancu, operators with n-thresholds for uncertainty management, journal of applied mathematics & computing, springer berlin, vol. 19, no. 1-2, pp. 1–17, 2005. [8] i. iancu, generalized modus ponens using fodor’s implication and t -norm product with threshold, international journal of computers, communications & control (ijccc), vol. 4, no. 4, pp. 330–343, 2009. [9] i. iancu, extended mamdani fuzzy logic controller, the fourth iasted international conference on computational intelligence ci 2009, acta press, vol. 5, pp. 143–149, 2009. [10] f. liu, h. geng and y. q. zhang, interactive fuzzy interval reasoning for smart web shopping, applied soft computing, elsevier, vol. 5, no. 4, pp. 433–439, 2005. [11] d. g. radojevic, fuzzy set theory in boolean frame, international journal of computers, communications & control (ijccc), vol. 3, no. 5, pp. 121–131, 2008. [12] v. v. t. tong, h. sibert, j. lecoeur and m. girault, biometric fuzzy extractors made practical: a proposal based on finger codes, advances in biometrics, springer berlin / heidelberg, pp. 604–613, 2009. [13] t. vesselenyi, s. dzitac, i. dzitac and m.j. manolescu, fuzzy and neural controllers for a pneumatic actuator, international journal of computers, communications & control (ijccc), vol. 4, no. 2, pp. 375–387, 2007. [14] l. a. zadeh, a theory of approximate reasoning, machine intelligence 9, elsevier, pp. 149–194, 1979. [15] l. a. zadeh, fuzzy sets as a basis for a theory of a possibility, fuzzy sets and systems, vol. 100, pp. 9–34, 1999. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 469-476 hierarchical and reweighting cluster kernels for semi-supervised learning z. bodó, l. csató zalán bodó, lehel csató department of mathematics and computer science babeş–bolyai university kogălniceanu 1, 400084 cluj-napoca, romania e-mail: {zbodo, lehel.csato}@cs.ubbcluj.ro abstract: abstract: recently semi-supervised methods gained increasing attention and many novel semi-supervised learning algorithms have been proposed. these methods exploit the information contained in the usually large unlabeled data set in order to improve classification or generalization performance. using data-dependent kernels for kernel machines one can build semi-supervised classifiers by building the kernel in such a way that feature space dot products incorporate the structure of the data set. in this paper we propose two such methods: one using specific hierarchical clustering, and another kernel for reweighting an arbitrary base kernel taking into account the cluster structure of the data. keywords: kernel methods, semi-supervised learning, clustering 1 introduction extracting information from large data collections is an important research topic in mathematical modeling: it helps designing automated inference procedures with limited or no user intervention [9]. the resulting algorithms are used in various domains like bioinformatics or natural language processing, both involving the processing of large data sets. data sets are usually labeled; this manual labeling is done before the automated information extraction procedure takes place. the limitation of the procedure is that the total number of items cannot be labeled. in this scenario the semi-supervised learning (ssl) develops methods that handle partially labeled data sets where only a small portion has labels, the rest of it is collected but unlabeled. since unlabeled data is ubiquitous, in semi-supervised learning we jointly handle both the labeled and the unlabeled parts to improve the performance of the algorithm. in the following we only consider semi-supervised classification, i.e. those methods that assign single or multiple labels to a given input. to use the unlabeled part of the data set, some assumptions have to be made [5]: (i) smoothness assumption, (ii) cluster assumption, (iii) manifold assumption. most ssl methods are built on the top of the supervised algorithms by using these assumptions together with estimates of the input distribution. we use the input distribution to define a change in the input metric, leading to a modified distance between items. we study ssl methods comprising the following two steps: first we determine the new distance – dot product or kernel function – between the learning examples, and in the second step with a supervised method we obtain the decision function by using the new distance obtained in the first step. in this paper we focus on methods that exploit the induced changes in distances and characterize this induced distance measure with kernels [3]. kernel methods constitute a powerful tool to rewrite a linear algorithm into a non-linear one. they are based on a symmetric positive semi-definite (kernel) function, that is a dot product in a high-dimensional space [10]. the kernel in the first step of the generic ssl method mentioned above is data-dependent. data-dependent kernels combine kernel algorithms and semi-supervised learning by providing a new representation for the examples that uses both the labeled copyright c⃝ 2006-2010 by ccc publications 470 z. bodó, l. csató and the unlabeled parts of the data set. a formal definition of the data-dependent kernel is the following: if d1 ̸= d2, k(x1,x2;d1) m k(x1,x2;d2) where “m” reads as “not necessarily equal” and the semicolons denote conditioning. it is important that these kernel functions are conditioned on the data sets, nevertheless we omit it in the following: it will be clear from the context if a kernel is data-dependent. we propose two data-dependent kernels in this paper: (i) a kernel using the distances induced by hierarchically clustering the labeled and unlabeled data; (ii) a reweighting kernel based on the hadamard product of some base kernel matrices. the paper is structured as follows: section 2 outlines the notations, in section 3 the hierarchical and graph-based hierarchical cluster kernels for semi-supervised classification are described. in section 4 we introduce the reweighting kernels based on data clustering and kernel combination. in section 5 we present the experiments and results and in section 6 we present the conclusions and discussions on the proposed kernels. 2 notation we denote with d = {(xi,yi) |i = 1,2,...,ℓ}∪{xi |i = ℓ+1,...,ℓ+ u} the training data, with the first set being the labeled and the second the unlabeled data set. we further assume xi ∈ x with x metric space, yi ∈ y , and the set of labels is of finite cardinality, i.e. |y | < ∞. a key assumption is that the size of labeled data is much smaller than the available unlabeled part, i.e. ℓ ≪ u. in the paper n denotes the size of the entire training set, n = ℓ + u. we use the scalar k to denote the number of clusters, where needed. boldface lowercase letters denote vectors, boldface capitals are matrices, all other variables are scalars; a′ denotes the transpose. for a matrix a, ai j is its element in the i-th row and j-column, and ai· and a· j denote the vectors corresponding to the i-th row and j-th column. 3 hierarchical cluster kernels in this section we introduce the proposed hierarchical cluster kernels. we propose the use of distances induced by different clustering algorithms instead of the original distance measure in the input space. if unlabeled data is added to the relatively small labeled data set, we expect that the new distance, obtained via clustering and the use of unlabeled data, induces a better representational space for classification. for clustering we use special hierarchical clustering techniques – the ones that result in ultrametric distance matrices – leading to positive semi-definite kernel matrices. our method is based on the connectivity kernel [8] and we extend on this kernel construction by involving the unlabeled data and allowing any hierarchical clustering method leading to ultrametric distance matrices. for data sets where the manifold assumption is expected to hold, we construct the hierarchical cluster kernel using distances induced by the k-nn and ε -nn data graphs. 3.1 hierarchical clustering and ultrametricity hierarchical clustering builds a tree in successive steps, where the nodes of the tree represent nested partitions of the data, in contrary to partitional clustering methods, which result in a single partition. for the proposed hierarchical cluster kernel we use special agglomerative clustering methods. to fully specify a hierarchical clustering algorithm, cluster similarities have to be measured; these are called linkage distances. based on the choice of the linkage distance measuring distances between clusters in agglomerative clustering, one can design a large variety of clustering methods. hierarchical and reweighting cluster kernels for semi-supervised learning 471 single linkage: d(c1,c2) = min{d(x1,x2) | x1 ∈ c1,x2 ∈ c2} (1) complete linkage: d(c1,c2) = max{d(x1,x2) | x1 ∈ c1,x2 ∈ c2} (2) average linkage: d(c1,c2) = 1 |c1||c2| ∑ x1i∈c1 ∑ x2 j∈c2 d(x1i,x2 j) (3) the linkage distances are based on d(x1,x2), the pointwise distance in the input space that usually is the euclidean distance d(x1,x2) = ∥x1 − x2∥2. in this paper we experiment only with the three linkage distances presented above; for a detailed discussion of these and other distances see [7]. all these three methods lead to ultrametric hierarchical clustering. the property is used for constructing positive semi-definite kernels from clusters: suppose that we choose to merge three clusters, c1, c2 and c3 in the following order: we first merge c1 with c2 resulting in c12, and then we merge it with c3. now if d(c1,c2) ≤ d(c1,c3) and d(c2,c1) ≤ d(c2,c3)   then d(c1,c2) ≤ d(c12,c3) (4) based on an agglomerative clustering method that uses ultrametric linkage distance, we can define an ultrametric distance matrix, based on that a kernel function that can be used for a better representation. 3.2 the connectivity kernel our method of constructing hierarchical cluster kernels is based on [8]. the authors propose a twostep clustering: map the points to a new representational space based on the effective dissimilarities, and cluster them using the new representation. the method is an approximation to pairwise clustering. to compute the effective dissimilarities used in pairwise clustering, the authors build a graph of the data; they assume that on the path between two points belonging to different clusters there will be an edge with large weight, representing the weakest link on the path. the effective dissimilarity will be represented by this value. they approximate the effective dissimilarities using a kruskal-style algorithm [8]. our method can be viewed as a generalization of the connectivity kernel, since if the ultrametric property is satisfied, we can use an arbitrary linkage distance when performing the agglomerative clustering. moreover we propose to use the kernel in semi-supervised learning settings, when only a small portion of the data labels is known, and we propose a manifold-based extension of the kernel too. 3.3 constructing the kernel the hierarchical clustering results in a dendrogram, whose nodes are labeled with the distance between the clusters that were merged at the respective node. we build a distance matrix by taking the label attached to the lowest common ancestor of the points in the tree. in order to transform distances to dot products we use a method similar to multi-dimensional scaling (mds) [2]: k = − 1 2 jmj with j = i − 1 n 11′ where m contains the squared distances based on the dendrogram and j is the centering matrix built from the identity matrix i and the tensor product of the vector with all elements 1. the resulting matrix contains the dot products between a set of vectors {zi}ni=1 with squared euclidean distances ∥zi − z j∥ 2 2 = mi j [8]. 472 z. bodó, l. csató in what follows, we construct the cluster kernel using linkage distances from hierarchical clustering. thus we map the points to a feature space where the pointwise distances are equal to the cluster distances in the input space. the steps are shown in algorithm 1. algorithm 1 hierarchical cluster kernel 1: perform an agglomerative clustering with ultrametric linkage distances from section 3.1 on the labeled and unlabeled data. 2: define matrix m with mi j = linkage distances of xi and x j; mii = 0. 3: define the kernel matrix as k = −1 2 jmj. the resulting kernel k is obviously data-dependent. we use the unlabeled data in the clustering step to determine better pointwise distances, leading to the kernel; we expect to obtain better similarities then using only the labeled part. it is important that in order to compute the kernel function for the test set we include them into the unlabeled set. this means that if the test point is unavailable at training time, the whole clustering process should be repeated, slowing down the classification. to efficiently compute the kernel for unseen points is left as a future realization and is discussed in section 6. 3.4 hierarchical cluster kernel with graph distances in building the hierarchical cluster kernel, we only used the cluster assumption. here we extend the above kernel to also exploit the manifold assumption, mentioned in section 1, using a graph-based hierarchical cluster kernel. we approximate distances by using shortest paths based on k-nn or ε -nn graphs, similar to isomap [11]. in this process we substitute the graph distances for the pointwise distances d(·,·). the result is that the hierarchical clustering algorithm is preceded with the steps shown in algorithm 2. algorithm 2 graph-based hierarchical cluster kernel -2: determine the k-nearest neighbors or an ε -neighborhood of each point, and let distances to other points equal to ∞. -1: compute shortest paths for every pair of points – using for example dijkstra’s algorithm. 0: use these distances for the pointwise distance in eqs. (1), (2), or (3). we deliberately started the numbering from −2 to emphasize that these steps precede the algorithm from the previous subsection. we emphasize that these steps are optional: should only be used if the manifold assumption holds on the data set. we use here the shortest path distances computed on the k-nearest neighbor or the ε -neighborhood graph of the data, thus – if the data lie on a low-dimensional manifold – approximating pointwise distances on this manifold. the graph built from the k-nearest or the ε -neighborhoods may contain several disconnected components, for example if k or ε is too small. if this scenario happens we use a method similar to the one described in [13] for building a connected graph. 4 reweighting kernels combining kernels to improve classification performance was thoroughly studied [10]. in the following – based on kernel combination – we propose three techniques to reweight a base kernel using the cluster assumption of semi-supervised learning. hierarchical and reweighting cluster kernels for semi-supervised learning 473 we make use of the following properties: for any k1 and k2 positive semi-definite matrix and any positive scalar value a > 0, the following combinations are positive semi-definite matrices: k1 + k2, a k1, k1 ⊙ k2, where ⊙ denotes the hadamard, or direct product. we develop techniques that reweight the kernel matrix by exploiting the cluster structure of the training data. thus, if two points are in the same cluster, their similarity obtains a high weight, while lying in different clusters induces a lower weight; the resulting kernel is called the reweighting kernel, or krw(x1,x2). the similarity weights are combined with the values of the base kernel kb(x1,x2), thus forming the final kernel matrix. to sum up, the new cluster kernel is k(x1,x2) = krw(x1,x2)kb(x1,x2) where krw(·,·) is the reweighting and kb(·,·) is the base kernel. in matrix form it can be written as k = krw ⊙ kb we are faced with two problems in the construction of the above cluster kernel: (i) the reweighting kernel must be positive semi-definite, (ii) the base kernel matrix has to be positive semi-definite and positive. the first requirement is obvious: it is needed to guarantee the positive semi-definiteness of the resulting kernel. the second condition is crucial, since for negative values in the base kernel matrix a quite different reweighting should be performed. to avoid complications due to negativity, we require a positive base kernel, kb(x1,x2) ≥ 0. the bagged cluster kernel, proposed in [12], reweights the base kernel values by the probability that the points belong to the same cluster. for computing this probability the bagged cluster kernel uses k-means clustering, together with its property that the choice of the initial cluster centers highly affects the output of the algorithm. assuming we have n data items and k clusters, the kernel is constructed by running k-means t times, each time with different initialization resulting in different cluserings. the resulting kernel is called the bagged kernel. the final cluster kernel is the hadamard product of the base kernel and the bagged kernel. borrowing the underlying idea of the bagged cluster kernel in the following we develop reweigthing kernels based on various clustering algorithms. 4.1 gaussian reweighting kernel suppose that we are given the output of a clustering algorithm, the cluster membership matrix u of size k × n. we assume that two points belong to the same cluster(s) if their cluster membership vectors are similar or close to each other. we define similarity via the gaussian kernel in the following way: krw(x1,x2) = exp ( − ∥u·x1 − u·x2∥ 2 2σ 2 ) (5) where u·x denotes the cluster membership vector of point x, i.e. the column of x in u. we know that the resulting matrix is positive semi-definite [10] with each element between 0 and 1. in this case the parameter σ defines the amount of separation between similar and dissimilar points: if σ is large, the gap between the values expressing similarity and dissimilarity becomes smaller, while for smaller σ these values get farther from each other. 4.2 dot product-based reweighting kernels another possibility of using the cluster membership vectors is to define the following reweighting kernel: krw = u′u + α n 11′ (6) 474 z. bodó, l. csató where u denotes the cluster membership matrix and α ∈ [0,1). the first term scores point similarity according to the cluster memberships, and the second term is used to avoid zero similarities: if two membership vectors are orthogonal, leading to a zero in the dot product matrix. we may assume that the obtained clustering is not too confident, i.e. we should use a small value α , the crisp cluster membership is thus alleviated with the term (α/n)11′. shoving that the reweighting kernel from equation (6) is positive semi-definite is straightforward: both the first and the second term is an external product, thus positive semi-definite, and owing to the properties defined in section 4, results that the kernel is indeed positive semi-definite. another version of the kernel in (6) is krw = β u′u + 1 n 11′ (7) where β ∈ (0,∞). here the kernel values for which the dot product matrix of cluster membership vectors correspond to zero, by (1/n)11′, remain the same, however if the points lie in the same cluster β u′u gives a weight greater than zero, thus this kernel value will be increased. the above equations could clearly be merged, but we left them as separate reweighting kernels to differentiate between the underlying ideas. 5 experiments and results in this section we present the results obtained using our cluster kernels, and we compare it to other data-dependent kernels. for learning we used support vector machines (svms) [3], namely the libsvm (version 2.85) implementation [4]. the data sets used for evaluating the kernels were the following: usps, digit1, coil2, text. the detailed descriptions of these sets can be found in [5]. each data set has two variations: one with 10 and one with 100 labeled data; furthermore each data set contains 12 labeled/unlabeled splits of the data. we used only the first split from each set. the columns having labels 10 and 100 in the tables showing the obtained result indicate which version of the data set was used, i.e. they show the number of labeled and unlabeled examples used. table 1 shows the accuracy results obtained using different kernels. we used accuracy as the evaluation measure, and the results are given in percentage. for each data set we indicated the best, second best and third best results obtained. the first two rows contain the baseline results obtained with linear and gaussian kernels. principally we wanted to improve on these results. the hyperparameter for the gaussian kernel was set using a cross-validation procedure. the following 7 rows show the results obtained using the isomap kernel [11], the neighborhood kernel [12], the bagged cluster kernel [12], the multi-type cluster kernel with different transfer functions [6] and laplacian svm [1], respectively. the next 15 rows – below the second horizontal line – show the results obtained using our kernels. hck and ghck denote the hierarchical cluster and graph-based hierarchical cluster kernels from section 3, respectively. rck1, rck2 and rck3 denote the reweighting cluster kernels defined in equations (5), (6) and (7), respectively. here we experimented with three clustering techniques: k-means, hierarchical and spectral clustering. because of lack of space we omitted the description of setting the parameters. 6 discussion as the results show we obtained good results with the proposed hierarchical and reweighting cluster kernels, and in many cases the results provided by our kernels are very close to the best results, as shown hierarchical and reweighting cluster kernels for semi-supervised learning 475 usps digit1 coil2 text 10 100 10 100 10 100 10 100 linear 72.82 86.43 81.07 90.86 60.74 80.43 58.26 67.86 gaussian 80.07 89.71 56.11 93.86 57.38 82.50 59.06 56.43 isomap 85.10 86.71 94.43 97.43 62.62 80.64 59.80 72.43 neighborhood 76.31 94.14 87.11 94.21 64.43 84.43 51.68 62.79 bagged 87.38 92.79 93.29 96.93 71.28 85.57 63.29 66.14 multi-type, step 80.07 92.86 91.01 91.29 55.77 84.86 53.56 74.79 multi-type, linear step 80.07 92.86 91.01 91.36 55.77 84.86 53.02 75.29 multi-type, polynomial 80.07 80.29 48.86 65.07 54.23 82.29 50.60 56.71 lapsvm, gaussian 81.95 95.93 84.50 97.64 76.64 97.71 63.42 62.50 hck, single 80.07 81.79 48.86 70.21 67.85 96.00 66.78 73.14 hck, complete 82.01 89.50 60.67 89.71 55.64 86.36 50.27 49.57 hck, average 81.48 92.86 71.75 93.79 68.05 91.71 64.63 50.14 ghck, single 80.07 81.79 48.86 70.21 60.60 93.86 66.78 73.14 ghck, complete 88.26 95.64 75.50 93.71 68.52 88.79 56.17 67.71 ghck, average 89.26 95.64 94.70 95.21 60.54 90.64 47.32 66.86 rck1, k-means 84.45 92.98 83.14 94.28 58.55 83.76 – – rck1, hierarchical 86.17 95.29 89.06 94.94 62.08 85.93 62.35 68.07 rck1, spectral 81.43 90.87 88.32 95.20 58.22 83.83 63.26 66.93 rck2, k-means 83.86 92.45 84.58 94.08 58.95 83.76 – – rck2, hierarchical 86.11 95.50 89.06 95.29 62.08 85.64 61.28 71.14 rck2, spectral 81.63 91.39 88.32 94.64 58.03 83.37 61.50 70.07 rck3, k-means 83.66 92.59 84.13 92.96 58.60 83.28 – – rck3, hierarchical 84.97 95.29 89.06 94.57 62.95 86.07 59.13 71.21 rck3, spectral 81.16 91.56 88.32 94.73 55.83 83.20 59.26 71.00 table 1: accuracy results using different kernels. the results are given in percentage. for each data set the best three results were formatted in boldface. in table 1. individually lapsvm outperformed every other method, possibly because of the careful selection of its parameters, but also because it is a very powerful technique. thus the results show that the proposed kernels for semi-supervised classification can be used for different types of data sets, and they provide better performances compared to simple, data-independent kernels, e.g. the gaussian kernel. moreover with data-dependent kernels any supervised kernel method can be easily turned into a semi-supervised method, without changing the underlying learning algorithm. in order to compute the kernel for the test points one needs to include these points in the unlabeled data set. that is one can say that the methods resemble transductive learning, where the decision function is computed only on the points in question. thus if a new point arrives the whole process must be repeated. to overcome this costly process approximation methods could be implied, but this is left as a future work. we also plan to develop methods or heuristics for automatically choosing the parameters of the proposed kernels. acknowledgments the authors acknowledge the partial support of the romanian ministry of education and research via grant pnii 11-039/2007. bibliography [1] mikhail belkin, partha niyogi, and vikas sindhwani. manifold 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[8] bernd fischer, volker roth, and joachim m. buhmann. clustering with the connectivity kernel. in sebastian thrun, lawrence k. saul, and bernhard schölkopf, editors, nips. mit press, 2003. [9] imre j. rudas and jános fodor. intelligent systems. int. j. of computers, communication & control, iii(suppl. issue: proceedings of icccc 2008):132–138, 2008. [10] b. schölkopf and a. j. smola. learning with kernels. the mit press, cambridge, ma, 2002. [11] j. b. tenenbaum, v. de silva, and j. c. langford. a global geometric framework for nonlinear dimensionality reduction. science, 290(5500):2319–2323, december 2000. [12] jason weston, christina leslie, eugene ie, and william stafford noble. semi-supervised protein classification using cluster kernels. in olivier chapelle, bernhard schölkopf, and alexander zien, editors, semi-supervised learning, chapter 19, pages 343–360. mit press, 2006. [13] quan yong and yang jie. geodesic distance for support vector machines. acta automatica sinica, 31(2):202–208, 2005. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 317-327 classification performance using principal component analysis and different value of the ratio r j. novakovic, s. rankov jasmina novakovic “faculty of computer science” megatrend university belgrade serbia, 11000 belgrade, bulevar umetnosti 29 e-mail: jnovakovic@megatrend.edu.rs sinisa rankov megatrend university belgrade bulevar umetnosti 29 e-mail: rankovs@megatrend.edu.rs abstract: a comparison between several classification algorithms with feature extraction on real dataset is presented. principal component analysis (pca) has been used for feature extraction with different values of the ratio r, evaluated and compared using four different types of classifiers on two real benchmark data sets. accuracy of the classifiers is influenced by the choice of different values of the ratio r. there is no best value of the ratio r, for different datasets and different classifiers accuracy curves as a function of the number of features used may significantly differ. in our cases feature extraction is especially effective for classification algorithms that do not have any inherent feature selections or feature extraction build in, such as the nearest neighbour methods or some types of neural networks. keywords: feature extraction, linear feature extraction methods, principal component analysis, classification algorithms, classification accuracy. 1 introduction data dimensionality reduction is an active field in computer science. it is a fundamental problem in many different areas, especially in forecasting, document classification, bioinformatics, and object recognition or in modelling of complex technological processes. in such applications datasets with thousands of features are not uncommon. all features may be important for some problems, but for some target concept only a small subset of features is usually relevant. to overcome the curse of dimensionality problem, dimensionality of the feature space should be reduced. this may be done by selecting only the subset of relevant features, or creating new features that contain maximum information about the class label from the original ones. the former methodology is named feature selection, while the latter is called feature extraction, and it includes linear (pca, independent component analysis (ica) etc.) and non-linear feature extraction methods. finding new features subset are usually intractable and many problem related to feature extraction have been shown to be np-hard ( [1]). feature extraction brings the immediate effects for application: speeding up a data mining algorithm, improving the data quality and thereof the performance of data mining, and increasing the comprehensibility of the mining results. it has been a fertile field of research and development since 1970’s in statistical pattern recognition ( [2] and [3]), machine learning and data mining. some classification algorithms have inherited ability to focus on relevant features and ignore irrelevant ones. decision trees are primary example of a class of such algorithms ( [4] and [5]), but also multi-layer perceptron (mlp) neural networks with strong regularization of the input copyright c⃝ 2006-2011 by ccc publications 318 j. novakovic, s. rankov layer may exclude the irrelevant features in an automatic way ( [6]). such methods may also benefit from independent feature selection or extraction. on the other hand, some algorithms have no provisions for feature selection or extraction. the k-nearest neighbour algorithm (k-nn) is one family of such methods that classify novel examples by retrieving the nearest training example, strongly relaying on feature selection or extraction methods to remove noisy features. 2 pca pca is a standard statistical technique that can be used to reduce the dimensionality of a data set. it ( [7]) is known as karhunen-loeve transform, has proven to be an exceedingly useful tool for dimensionality reduction of multivariate data with many application areas in image analysis, pattern recognition and appearance-based visual recognition, data compression, time series prediction, and analysis of biological data to mention a few. the strength of pca for data analysis comes from its efficient computational mechanism, the fact that it is well understood, and from its general applicability. for example, a sample of applications in computer vision includes the representation and recognition of faces ( [8], [9], [10] and [11]), recognition of 3d objects under varying pose ( [12]), tracking of deformable objects ( [13]), and for representations of 3d range data of heads( [14]). pca is a method of transforming the initial data set represented by vector samples into a new set of vector samples with derived dimensions. the basic idea can be described as follows: a set of n-dimensional vector samples x = {x1, x2, x3, ..., xm} should be transformed into another set y = {y1, y2, ..., ym} of the same dimensionality, but y-s have the properties that most of their information content is stored in the first few dimensions. so, we can reduce the data set to a smaller number of dimensions with low information loss. the transformation is based on the assumption that high information corresponds to high variance. if we want to reduce a set of input dimensions x to a single dimension y , we should transform x into y as a matrix computation y = a · x (1) choosing a such that y has the largest variance possible for a given data set. the single dimension y obtained in this transformation is called the first principal component. this component is an axis in the direction of maximum variance. the first principal component minimizes the distance of the sum of squares between data points and their projections on the component axis. in practice, it is not possible to determine matrix a directly, and therefore we compute the covariance matrix s as a first step in features transformation. matrix s ( [15]) is defined as sn×n = 1 n − 1 n∑ j=1 (xj − x′)t · (xj − x′) (2) where x′ = 1 n n∑ j=1 xj (3) in the next step, the eigenvalues of the covariance matrix s for the given data should be calculated. finally, the m eigenvectors corresponding to the m largest eigenvalues of s define a linear transformation from the n-dimensional space to an m-dimensional space in which the features are uncorrelated. to specify the principal components we need the following additional explanations about the notation in matrix s: 1) the eigenvalues of sn×n λ1, λ2, ..., λn, where λ1 ≥ classification performance using principal component analysis and different value of the ratio r 319 λ2 ≥, ..., ≥ λn ≥ 0 and 2) the eigenvectors e1, e2, ..., en correspond to eigenvalues λ1, λ2, ..., λn, and they are called the principal axes. principal axes are new, transformed axes of n-dimensional space, where the new variables are uncorrelated, and variance for the i-th component is equal to the i-th eigenvalue. most of the information about the data set is concentrated in a few first principal components. in this paper we research how many of the principal components are needed to get a good representation of the data. in other words, we try to find the effective dimensionality of the data set. for this purpose we analyze the proportion of variance. dividing the sum of the first m eigenvalues by the sum of all the variances (all eigenvalues), we will get the measure for the quality of representation based on the first m principal components. the result is expressed as a percentage. the criterion for features selection is based on the ratio of the sum of the m largest eigenvalues of s to the trace of s. that is a fraction of the variance retained in the m-dimensional space. if the eigenvalues are labeled so that λ1 ≥ λ2 ≥, ..., ≥ λn, then the ratio can be written as r = m∑ i=1 λi n∑ i=1 λi (4) all analyses of the subset of m features represent a good initial estimate of the n-dimensionality space if the ratio r is sufficiently large, it means greater than the threshold value. this method is computationally inexpensive, but it requires characterizing data with the covariance matrix s. in implementation, the transformation from the original attributes to principal components is carried out through a process by first computing the covariance matrix of the original attributes and then, by extracting its eigenvectors to act as the principal components. the eigenvectors specify a linear mapping from the original attribute space of dimensionality n to a new space of size m in which attributes are uncorrelated. the resulting eigenvectors can be ranked according to the amount of variation in the original data that they account for. typically, the first few transformed attributes account for most of the variation in the data set and are retained, while the remainders are discarded. pca is an unsupervised method, which makes no use of information embodied within the class variable. because, the pca returns linear combinations of the original features, the meaning of the original features is not preserved. over the years there have been many extensions to conventional pca. for example, independent component analysis (ica) ( [16] and [17]) is the attempt to extend pca to go beyond decorrelation and to perform a dimension reduction onto a feature space with statistically independent variables. other extensions address the situation where the sample data live in a low-dimensional (non-linear) manifold in an effort to retain a greater proportion of the variance using fewer components ( [18], [19], [20], [21], [22] and [23]) and yet other (related) extensions derive pca from the perspective of density estimation (which facilitate modeling non-linearities in the sample data) and the use of bayesian formulation for modeling the complexity of the sample data manifold ( [24]). 3 classification algorithms four supervised learning algorithms are adopted here to build models, namely, ib1, naive bayes, c4.5 decision tree and the radial basis function (rbf) network. this section gives a brief overview of these algorithms. 320 j. novakovic, s. rankov 3.1 ib1 ib1 is nearest neighbour classifier. it uses normalized euclidean distance to find the training instance closest to the given test instance, and predicts the same class as this training instance. if multiple instances have the same (smallest) distance to the test instance, the first one found is used. nearest neighbour is one of the simplest learning/classification algorithms, and has been successfully applied to a broad range of problems ( [25]). to classify an unclassified vector x, this algorithm ranks the neighbours of x amongst a given set of n data (xi, ci), i = 1, 2, ..., n, and uses the class labels cj (j = 1, 2, ..., k) of the k most similar neighbours to predict the class of the new vector x. in particular, the classes of these neighbours are weighted using the similarity between x and each of its neighbours, where similarity is measured by the euclidean distance metric. then, x is assigned the class label with the greatest number of votes among the k nearest class labels. the nearest neighbour classifier works based on the intuition that the classification of an instance is likely to be most similar to the classification of other instances that are nearby to it within the vector space. compared to other classification methods such as naive bayes’, nearest neighbour classifier does not rely on prior probabilities, and it is computationally efficient if the data set concerned is not very large. if, however, the data sets are large (with a high dimensionality), each distance calculation may become quite expensive. this reinforces the need for employing pca and information gain-based feature ranking to reduce data dimensionality, in order to reduce the computation cost. 3.2 naive bayes this classifier is based on the elementary bayes’ theorem. it can achieve relatively good performance on classification tasks ( [26]). naive bayes classifier greatly simplifies learning by assuming that features are independent given the class variable. more formally, this classifier is defined by discriminant functions: fi(x) = n∏ j=1 p(xj | ci)p(ci) (5) where x = (x1, x2, ..., xn) denotes a feature vector and cj, j = 1, 2, ..., n, denote possible class labels. the training phase for learning a classifier consists in estimating conditional probabilities p(xj | ci) and prior probabilities p(ci). here, p(ci) are estimated by counting the training examples that fall into class ci and then dividing the resulting count by the size of the training set. similarly, conditional probabilities are estimated by simply observing the frequency distribution of feature xj within the training subset that is labeled as class ci. to classify a class-unknown test vector, the posterior probability of each class is calculated, given the feature values present in the test vector; and the test vector is assigned to the class that is of the highest probability. 3.3 c4.5 decision tree different methods exist to build decision trees, but all of them summarize given training data in a tree structure, with each branch representing an association between feature values and a class label. one of the most famous and representative amongst these is the c4.5 tree ( [27]). the c4.5 tree works by recursively partitioning the training data set according to tests on the classification performance using principal component analysis and different value of the ratio r 321 potential of feature values in separating the classes. the decision tree is learned from a set of training examples through an iterative process, of choosing a feature and splitting the given example set according to the values of that feature. the most important question is which of the features is the most influential in determining the classification and hence should be chosen first. entropy measures or equivalently, information gains are used to select the most influential, which is intuitively deemed to be the feature of the lowest entropy (or of the highest information gain). this learning algorithm works by: a) computing the entropy measure for each feature, b) partitioning the set of examples according to the possible values of the feature that has the lowest entropy, and c) for each are used to estimate probabilities, in a way exactly the same as with the naive bayes approach. note that although feature tests are chosen one at a time in a greedy manner, they are dependent on results of previous tests. 3.4 rbf networks a popular type of feed forward network is rbf network. usually, the rbf network consists of three layers, i.e., the input layer, the hidden layer with gaussian activation functions, and the output layer. each hidden unit essentially represents a particular point in input space, and its output, or activation, for a given instance depends on the distance between its point and the instance-which is just another point. intuitively, the closer these two points, the stronger the activation. this is achieved by using a nonlinear transformation function to convert the distance into a similarity measure. a bell-shaped gaussian activation function, whose width may be different for each hidden unit, is commonly used for this purpose. the hidden units are called rbfs because the points in instance space for which a given hidden unit produces the same activation form a hypersphere or hyperellipsoid. the output layer of an rbf network takes a linear combination of the outputs of the hidden units and-in classification problems-pipes it through the sigmoid function. the parameters that such a network learns are (a) the centers and widths of the rbfs and (b) the weights used to form the linear combination of the outputs obtained from the hidden layer. one way to determine the first set of parameters is to use clustering, without looking at the class labels of the training instances at all. the simple k-means clustering algorithm can be applied, clustering each class independently to obtain k basis functions for each class. intuitively, the resulting rbfs represent prototype instances. then the second set of parameters can be learned, keeping the first parameters fixed. this involves learning a linear model using one of the techniques such as, linear or logistic regression. if there are far fewer hidden units than training instances, this can be done very quickly. rbf networks ( [28]) are a special class of neural networks in which the distance between the input vector and a prototype vector determines the activation of a hidden neuron. prototype vectors refer to centers of clusters obtained during rbf training. usually, three kinds of distance metrics can be used in this network, such as euclidean, manhattan, and mahalanobis distances. the rbf network provides a function y : rn → rm, which maps n-dimensional input patterns to m-dimensional outputs ({(xi, yi) ∈ rn × rm, i = 1, 2, ..., n}). assume that there are m classes in the data set. the m-th output of the network is as follows ( [29]): ym(x) = k∑ j=1 wmj θj(x) + wm0bm (6) in this case x is the n-dimensional input pattern vector, m = 1, 2, ..., m, and k is the number of hidden units. m is the number of classes (outputs), wmj is the weight connecting the j-th 322 j. novakovic, s. rankov hidden unit to the m-th output node, bm is the bias, and wm0 is the weight connecting the bias and the m-th output node. the radial basis activation function θ(x) of the rbf network distinguishes it from other types of neural networks. several forms of activation functions have been used in applications ( [29]): • θ(x) = e −x2 2σ2 (7) • θ(x) = (x2 + σ2)−β, β > 0 (8) • θ(x) = (x2 + σ2)β, β > 0 (9) • θ(x) = x2 ln(x) (10) here σ is a parameter that determines the smoothness properties of the interpolating function. a disadvantage of rbf networks is that they give every feature the same weight because all are treated equally in the distance computation. hence they cannot deal effectively with irrelevant features. 4 experiments and results real datasets called "statlog (australian credit approval)" and "statlog (german credit data)" for tests were used, taken from the uci repository of machine learning databases. these datasets were used to compare the classification performance using ib1, naive bayes, rbf networks and c4.5 decision tree classifiers, in conjunction with the use of pca and different value of the ratio r. the classification performance is measured using ten-foldcross-validation. german credit data this dataset classifies people described by a set of features as good or bad credit risks. data set characteristics is multivariate, feature characteristics are categorical and integer. number of instances is 1000, number of features is 20, and there are no missing values. australian credit approval this file concerns credit card applications. data set characteristics is multivariate; feature characteristics are categorical, integer and real. number of instances is 690, number of features is 14, and there are missing values. this dataset is interesting because there is a good mix of features continuous, nominal with small numbers of values, and nominal with larger numbers of classification performance using principal component analysis and different value of the ratio r 323 values. there are also a few missing values. australian credit approval data classification accuracy without pca naive bayes 77,7 c4.5 decision tree 86,1 ib1 classifier 81,2 rbf network 79,7 table 1: classification results for australian credit approval data without using pca. the number of input components produced australian credit approval data 20 22 25 29 29 30 32 33 r 0,8 0,85 0,9 0,95 0,96 0,97 0,98 0,99 table 2: the number of input components produced using pca at various ratio r values for australian credit approval data. figure 1: the classification performance using naive bayes, c4.5 decision tree, ib1 and rbf network classifiers, in conjunction with the use of pca and different value of the ratio r. statlog (australian credit approval) data set classification results without using pca as a standard statistical technique that can be used to reduce the dimensionality of a data set, for australian credit approval are presented in table 1, and for german credit data in table 3. table 2 and 4 show the number of input components produced for each ratio r value investigated. it can be observe from the tables that the number of input components reduces with decreasing values of the ratio r used. in figure 1 classification performance for naive bayes and rbf network are significantly better with pca. for ib1 and c4.5 decision tree classifiers the results are better without pca. 324 j. novakovic, s. rankov for greater value of the ratio r classification accuracy of ib1 classifier is better. the others types of classifier in our experiment have the better results with greater value, but when value reached some boundaries performance of classifier are the same or worst. german credit data classification accuracy without pca naive bayes 75,4 c4.5 decision tree 70,5 ib1 classifier 72 rbf network 74 table 3: classification results for german credit data without using pca. the performance of some classifiers depends on its generalization capability, which in turn is dependent upon the data representation. one important characteristic of data representation is uncorrelated. this is because correlated data reduce the distinctiveness of data representation and thus, introduce confusion to the classifier model during the learning process and hence, producing one that has low generalization capability to resolve unseen data. the results demonstrated that the elimination of correlated information in the sample data by way of the pca method improved naive bayes and rbf network classification performance (figure 1). at point 0.95% of the ratio r value with 29 input components, all classifiers significantly improved the classification accuracy. after that the ratio r value, the classification accuracy is about the same with little variations between classifiers. it suggests that this number of inputs is sufficiently optimal for the all classifiers to learn distinct features in the data and perform better input/output mapping. the number of input components produced german credit data 31 34 38 42 43 44 45 46 r 0,8 0,85 0,9 0,95 0,96 0,97 0,98 0,99 table 4: the number of input components produced using pca at various ratio r values for german credit data. german credit data doesn’t consist of correlated information caused by overlapping input instances. without correlation in sampled data there is not confusion in classifiers during the learning process (figure 2) and thus, no degrades their generalization capability. in this case all classifiers’ classification performance doesn’t improved by pca. for greater value of the ratio r classification accuracy of ib1 classifier is better. naive bayes classifier has the worst results with greater values of the ratio r. classification accuracy of rbf network have oscillation values. classification accuracy for c4.5 decision tree doesn’t change too much with different values of the ratio r. this final part of the comparative study is set to investigate the differences between different classifiers, in terms of their classification ability. it is clear from figures 1 and 2 that on average, naive bayes and rbf network classifiers tend to significantly outperform the decision tree and ib1 classifiers. 5 conclusions feature extraction leading to reduced dimensionality of the feature space. pca is one of the most popular techniques for dimensionality reduction of multivariate data points with application classification performance using principal component analysis and different value of the ratio r 325 figure 2: the classification performance using naive bayes, c4.5 decision tree, ib1 and rbf network classifiers, in conjunction with the use of pca and different value of the ratio r. statlog (german credit data) data set areas covering many branches of science. this is especially effective for classification algorithms that do not have any inherent feature selections or feature extraction builds in, such as the nearest neighbour methods or some types of neural networks. pca has been used for feature extraction with different values of the ratio r, evaluated and compared using four different types of classifiers on two real benchmark data sets. accuracy of the classifiers is influenced by the choice different values of ratio r (figure 1 and figure 2). there is no best value of the ratio r, for different datasets and different classifiers accuracy curves as a function of the number of features used may significantly differ. but, in more cases, the value of 0.95 gave the best results. several improvements of the feature extraction method presented here are possible: • the algorithms and datasets will be selected according to precise criteria: different algorithms with pca as linear feature extraction method, and several datasets, either real or artificial, with nominal, binary and continuous features. • ica and others linear feature extraction methods may be included. • problem of data dimensionality reduction may be analysed with non-linear feature extraction methods. these conclusions and recommendations will be tested on larger datasets using various classification algorithms in the near future. 326 j. novakovic, s. rankov bibliography [1] a.l. blum, r.l. rivest, training a 3-node neural networks is np-complete, neural networks, 5:117 127, 1992. 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[29] l. wang, x. fu, data mining with computational intelligence, springer-verlag berlin heidelberg, germany, pages 9-14, 2005. ijcccv7n5.pdf int j comput commun, issn 1841-9836 7(5):807-815, december, 2012. memetic engineering for permanent education in line with sustainable growth c.i. brumar, r.d. fabian, m.-j. manolescu, v. chiş cristina i. brumar, ralf d. fabian lucian blaga university of sibiu romania, 550024 sibiu, 10 bd.victoriei e-mail: crista.brumar@gmail.com, ralf.fabian@gmail.com mişu-jan manolescu agora university, romania, piata tineretului, 8, 410526 oradea, romania, e-mail: mmj@univagora.ro violeta chiş aurel vlaicu university of arad faculty of exact sciences department of mathematics-informatics romania, 310330 arad, 2 elena dragoi e-mail: viochis@yahoo.com abstract: given the recent point of view of the european commission regarding the implementation of a new strategy for sustainable growth and jobs, this paper emphasises the opportunity and urgency of supporting the eu 2020 strategy, providing an appropriate educational tool for the knowledge society. the main objectives are: a. adapting memetic engineering expressed in terms of general system theory to the teaching component of permanent education based on bounded rationality and "just in time" as key tools for fighting cognitive chaoplexity in the post-industrial era; b. facilitating the use of memetic engineering based on its double-faceted nature: as both positive and negative feedback; c. extending the applicability of memetic engineering to ecology as source of memes; d. exemplifying the above in primitive metamodels applying memetic engineering in ecology and highlighting the relevant design-space dimensions. among the conclusions: a. to be sustainable in the long run permanent education must be modelled in line with learner bounded rationality, since bounded rationality is a psychological lasting feature; b. sustainable development depends on affordable permanent education; c. as a result, e-teaching should be systematically revisited through intense transdisciplinary research. keywords: sustainable growth, permanent education, bounded rationality, chaoplexity, memetic engineering. 1 introduction given the recent point of views of the european commission (march 2, 2012, brussels [13]) regarding the implementation of a new strategy that "pursues both continued fiscal consolidation and determined action to boost growth and jobs; sustainable growth and jobs cannot be built on deficits and excessive debt levels" [13], this paper follows the requirements and urgency of implementing the strategy [13], namely strengthening and development of actions to achieve continuous growth of services and economy. at the european level, after renewing the lisbon strategy through the eu 2020 strategy, lifelong learning is regarded as key pillar of sustainable growth. permanent education based on skills acquired through dynamic knowledge, recognizes the failure of static knowledge-based lifelong learning through learning objects repositories [16]. copyright c© 2006-2012 by ccc publications 808 c.i. brumar, r.d. fabian, m.-j. manolescu, v. chiş since education is seen as a complex service in a post-industrial era, this paper aims to extend to ecology the general educational tool for knowledge society as proposed in [20], [19], [16], [6]. dominant features of the post-industrial era are summarized and explained in [10] as: "a) growing speed of change (due to the intense positive feedback entailed by moore’s law outcomes: internet, broad-band technology, semantic web, google, etc.). b) growing complexity (architectural, cognitive, structural). c) globalization (expressed in it context mainly through the modern enterprise paradigms). [...] thus, modern it environments, except for simple applications, move towards open and heterogeneous (resources are unalike and their availability is not warranted), dynamic (the pace of exogenous and endogenous changes is high) and uncertain (both information and its processing rules are revisable, fuzzy, and uncertain). most situations to be controlled are complex and uncertain, and involve parallel processes." [10]. thus, after updating related work in section 2, the paper tracks the first objective in section 3 investigating the main concepts related to memetic stability. on this groundwork, as bounded rationality and "just-in-time" were tools for e-teaching [16], section 4 is build as paper core: it launches the concept of memetic engineering regarded as tool for research in ecology since it matches with e-teaching environmental science. section 5 highlights the design-space dimensions of a (primitive) metamodel for the "integral ecology" [12]. conclusions and future work (section 6) close the paper. 2 rationale and related work considering the far range target of sustainable growth, the milestones are set up by the europe 2020 strategy: "and if europe is to emerge stronger from the crisis, we need more than ever to stimulate smart and green economic growth, underpinned by knowledge and innovation as its key drivers. [...] we need to focus on policies that give a chance to everyone to develop one’s skills and live in dignity. [...] our common aim is to provide a sustainable response to the many challenges facing us to transform the european union into a knowledge-based, resource efficient and low-carbon economy" [2]. that means that the students have to learn how to be skilled (chaoplexity teaching for permanent education based on bounded rationality), how to reduce the ecological damage made by economic growth (ecological systems based on memetic engineering), and we need to prepare them for what comes next (students have to live based on very different principles). as regards the concept of meme, a short definition might be: a meme is a unit of information residing in the brain and is the mutating replicator in human cultural evolution (richard dawkins, [8]). slogans, riddles, songs, signs, inventions and fashions are typical memes. an idea or information pattern is not a meme until it causes someone to spread, to transmit it to another. "why another new "....tics"? memetics is a key zeitgeist-component and is transdisciplinary par excellence; the "self" memotype shows a cognitive complexity similar to the structural one of the genotype; the "self-*" memeplex invaded modern it ("star" = awareness) [...]; the thick-time meme is vigorous and old (preceded ai itself)" [1]. "according to the theory memetic engineering is, simply put, the analysis of an individual or individual’s behaviour, the selection of specific memes and the distribution or propagation of those memes with the intent of altering the behaviour of others". [http://en.wikipedia.org/wiki/ memetic_engineering]. including memetics in this paper (as shown in [16]) is reasonable because most paradigms in modern artificial intelligence have a memetic character and this phenomenon is ever more important in an increasing chaoplexic world [16]. memes are transdisciplinary par excellence and they originate from myths that represent valuable sources of metaphors an obvious source of stability. hence, the research about the role of permanent education for sustainable growth as well as memetic engineering for permanent education in line with sustainable growth 809 the role of memetic engineering in permanent education [19], [16], [20], [6] has been continued in line with the following objectives: a) adapting memetic engineering expressed in terms of general system theory to the teaching component of permanent education based on bounded rationality and "just-in-time" as key tools for fighting cognitive chaoplexity in post-industrial era [16]; b) facilitating the use of memetic engineering based on his double faceted nature: as negative feedback (through memetic stability) and positive feedback (through intense spreading of memes); c) extending the applicability of memetic engineering from myths and metaphors, including (paleo)linguistics [16], to ecology as source of memes; d) exemplifying the above in primitive metamodels applying memetic engineering in ecology and highlighting the relevant design-space dimensions. sustainable growth depends on permanent education; this involves memetic engineering and bounded rationality, and requires changes in the three referred fields: cognitive psychology, it, and higher education. for the sake of self-containment the paper summarises some results of [16], [6], [20] underlining the transdisciplinary aspects related to: psychology, semantics, vital service, skill oriented and self-organizing (lifelong) learning, corpus of knowledge, intensely dynamic and uncertain environments. 3 main concepts related to memetic stability norbert n. seel in "encyclopedia of the sciences of learning" [22] proposed an interdisciplinary overview about learning and the connections between different fields of sciences: "over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, how they acquire, organize and deploy knowledge and skills. [...] as the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and as a result of the emergence of computer technologies especially also in the field of computer sciences and artificial intelligence." [22]. three memetic features should be focused on to ease applying them in permanent education [16]: a) lastingness (they are a leitmotif in cultural history); b) ubiquitousness (they permeate all cultures); c) effectiveness (they are active now in education) [16], [6]. even without prior knowledge of psychology it is well-known that behaviours are extremely lasting. hence, they can illustrate the role of bounded rationality as "educational mechanism" much more convincingly than widespread topics albeit very famous. the three steps are similar to those above: a) choosing the pervasive habit of scoring (instead of counting) scoring was and still is certainly easier than counting; b) investigating the related memeplex; c) proposing a boundedly rational way to exploit simplicity in e-teaching. memetic engineering for permanent education tries to integrate transdisciplinary sciences. in recent papers [19], [20] bounded rationality and "just-in-time" were used as tool for e-teaching; this paper aims at proving that memetic engineering for permanent education is a tool for eteaching environmental science. ecologists use memetic stability because they want to preserve the environment. as regards the concept of chaoplexity, it reveals its value in military operational research: "chaoplexic warfare draws on the study of nonlinear phenomena of self-organization to propose a radical decentralization of armed forces through the adoption of the network form. [...] information remains the central concept, and in this sense chaoplexity is an outgrowth of cybernetics; but the focus on change, evolution and positive feedback breaks with the cybernetic pioneers’ 810 c.i. brumar, r.d. fabian, m.-j. manolescu, v. chiş concern for stability" [4]. to add a flavour to the syntagm "educational chaoplexity" (edchy): "in association with the respective technologies of the clock, engine and computer, the scientific theories of mechanism, thermodynamics, and cybernetics have all in turn been recruited to shape distinct approaches to the challenges of imposing order on the chaos of the battlefield. today, it is on the basis of the new sciences of chaos and complexity that the latest regime of the scientific way of warfare is being erected" [4], [19]. in the abbreviation edchy, the focus is on the letters d and h because education has to take into account that we live in a dynamic and heterogonous environment. concerning ecology, even if the time span is larger implied in edchy regarding learning, climatic changes take place during thousands of years. hence "just-in-time" measures should be taken to ensure environment preservation an obvious chaoplexic issues. in short: permanent education relies on bounded rationality and "just-in-time". bounded rationality can be used as a tool to educational chaoplexity and as common denominator for the two facets of permanent education, namely e-teaching and e-learning. "just-in-time", as connotation of "real time", is related with the role of response time (vital in any teacher-learner interaction) [5]. permanent education involves "just-in-time" synchronization between e-teaching and e-learning and is basic for the shift from product-based to a service-based society [16], [6]. the links to memetic stability will become evident in the next section. 4 double faceted nature in line with common linguistic usage, positive and negative has connotations different from the scientific ones: stability (as negative feedback) has positive effect while creativity (as positive feedback) could become extremely risky if not restricted. 4.1 negative feedback through memetic stability "negative feedback helps to maintain stability in a system in spite of external changes. it is related to homeostasis. for example, in a population of foxes (predators) and rabbits (prey), an increase in the number of foxes will cause a reduction in the number of rabbits; the smaller rabbit population will sustain fewer foxes, and the fox population will fall back." [http://en.wikipedia.org/wiki/feedback]. on the same sight appear other correct sentences, but using the contrasting connotation: "in an electronic amplifier feeding back a negative copy of the output to the input will tend to cancel distortion, making the output a more accurate replica of the input signal" or even "positive feedback amplifies possibilities of divergences (evolution, change of goals); it is the condition to change, evolution, growth; it gives the system the ability to access new points of equilibrium" [wikipedia]. [...] some assertions need badly to be improved (e.g., "negative feedback, which tends to reduce the input signal that caused it, is also known as a self-correcting or balancing loop. [...] the terms negative and positive feedback can be used loosely or colloquially to describe or imply criticism and praise, respectively. this may lead to confusion with the more technically accurate terms positive and negative reinforcement, which refer to something that changes the likelihood of a future behaviour." [wikipedia]). almost always in nature (e.g., homeostasis in living beings) and very often in technology (e.g., reducing noise and message distortion in communication systems), the target looked for is preservation (e.g., in living systems to prevent decay), or stability (e.g., in artificial systems to prevent deterioration). this basic kind of feedback is called for obvious historical and physical reasons negative feedback [16]. for permanent education, negative feedback is sine qua non, above all for the teaching process because it is corrective, promotes stationariness, stability, and reversibility; (e.g. ecologists are based on traditions and, some time, on people’s inertia). memetic engineering for permanent education in line with sustainable growth 811 in short: it and memetic engineering provides to ecologists tools to handle environmental issues and to decide what is sustainable or not sustainable for them. memetics helps indirectly in the sense of tradition, because ecology implies environment stability. 4.2 positive feedback through spreading of memes positive feedback means creativity; using positive feedback, the parameter values are increased, the whole process is evolutive, innovative, generating chain reactions that increase instability and involve irreversibility. concerning ecology, positive feedback refers to the countermeasures to be taken to oppose/face environment degradation. (e.g. since the environment is preserved by fighting/opposing nuclear energy, specialists have to be creative to find methods for supplying energy without destroying the environment). with no doubt language and culture are close related and have been subject of sociological, anthropological and memetic research [3], [8]. language is determined by culture (ancient civilisations did not have words for computer, internet, phone, television, radio, etc.) but culture is determined by language too, if memes are regarded as fundamental replicators for their evolution. richard dawkins emphasised in "the selfish gene" [8] the importance of thinking about evolution in terms of information that can be transferred from one person to another via imitation [8]. on this groundwork susan blackmore in "the meme machine" asserts: "just as the design of our bodies can be understood only in terms of natural selection, so the design of our minds can be understood only in terms of memetic selection." [3] according to her, language developed as result of memetic evolution and is the principal medium used for spreading memes, i.e. replicating themselves in as many minds as possible. traditionally, spreading of memes was through word-of-mouth followed by written word and printed word. today memetic propagation via language takes advantages of globalization and of a wide media spectrum (i.e., radio, television, internet, etc.) and of increasing propagation speed due to intense positive feedback (id est., moore’s law and the new technologies based on it) [10]. talking about internet, it is impossible to ignore the meme spreading power of search engines. without them information would be hard to reach. moreover, by email, newsgroups, message boards and social networks people can circulate their memes allowing adherents to unpopular/popular memes to come together ideologically. companies already promote memes this way to advertise their products by viral marketing strategies. internet and it&c in general "is becoming a tool for social interaction bridging the strands between online and offline activities, respectively, digital and social behaviour. [...] information and communication technologies (ict) experienced in people’s everyday life sets a milestone for an active participation in the knowledge society" [17]. at european level, it is still a basic pillar of the strategy for growth: "high-speed internet underpins all sectors of the economy and will be the backbone of the digital single market. for every 10% increase in the broadband penetration the economy grows by 1 to 1.5%." [14]. of course, this stimulates the speed of meme propagation. memes are spread by copying and suffer modifications in their evolution similar to genes. thus, the newness, the sparkle of creativity is within mutation, since it exceeds the possibilities of simple copying from crossover (when archimedes came out of bath exclaiming "eureka" he was not copying anyone). thinking in terms of genetic algorithms, crossover is perceived as negative feedback, preserving stability by selecting and copying fittest the properties from parents to their offspring, whereas positive feedback manifests itself creatively in mutations, giving rise to new unpredictable, hence non-deterministic outcome. corollary: memetic evolution (and also genetic algorithms) involves intrinsically non-determinism. that is why non-deterministic software is 812 c.i. brumar, r.d. fabian, m.-j. manolescu, v. chiş used in it [16] to be able to exploit positive feedback too. in short, technology in first place, only eases rapid spreading and gives no clue about the effect of a meme. to decide if a meme is toxic or not, is still up to the end-user. the contradiction between negative feedback fundamental for stability required by ecologists and positive feedback fundamental for growth can be solved only by approaching the problem through general system theory (gst). any sectorial, partial, local or parochial solutions for solving the problems are inappropriate, because these solutions are not only for a limited biocenosis but have to be for a global ecosystem. consequently, holistic analyses of situations are sine qua non and the tools for handling these situations must also be holistic [16]. examples and explanations inspired by integral ecology [12] are abridged below. based on a large public acceptance of renewable energy, the hype around wind energy developed significantly in the last years, promoting wind turbines and wind farms as one ecological sustainable solution for green energy [18]. a recent report praises the wind energy as "recessionbusting industry" for its impact as "green growth" on jobs and economy [15]. beside benefits of green energy, e.g. no air pollution, moderate capital cost, low ongoing costs, energy independence, several side-effects came to fore. wind farms need to be placed in wide areas for great efficiency. reports of bird and bat mortality argue that wind turbines are artificial structures that interfere and destroy ecosystems, especially in regions where there are important flight paths for migratory bird [21], [9]. furthermore, aesthetic impact, sound emission or even increase of night time temperatures [7] are part of the price to pay when it comes to green energy. to which extend the ecological impact is significant or not is still in question but it clearly leads to an "environmental paradox" [11] that ecologists have to deal with. as regards environment, preservation is a legitimate endeavour whereas wild altering proved to be harmful. memetic stability is consistent with the requirement for stability of environment preservation whereas creativity, in unconventional technological approaches, is shaping hope for finding proper solutions. climate change is linked subjective and objective to "just-in-time". that is, ecology has as main target conservation whereas climate change is perceived memetically as slowly. in this context "just-in-time" could be understood as a secondary restriction since there is enough time to fight climate change and find solution. such arguments are perhaps convenient for emerging industries, pushing limits, conventions and boundaries as far as possible in the future (e.g. kyoto protocol). on the other hand, relying on the meme that climatic changes are slowly and considering that the last significant climate change of human history, the glacial period, dates of approximate 20 millenniums ago, means ignoring heavy side effects, like hurricane katrina or el niño phenomena, with evolutions visible even in periods shorter than a human lifespan. hence, solutions are addressable only through bounded rationality, "just-in-time" and chaoplexity together. that is why holistic approaches are needed; any reductionist view leads to solutions that could hardly satisfy ecosystem stability and sustainable growth. 5 metamodel nowadays, the world’s natural resources are under pressure. actions as soil erosion, acid rain, the extinction of species, have all contributed to the environmental system damage. "economic production influences the environment in many ways, through the consumption of energy and natural, often non-renewable resources, and the production of pollution, toxic wastes, etc. they further stressed (not without opposition) that present environmental problems require a new type of development process which harnessed the benefits of economic growth without the damaging consequences which growth can have on the environment. up to now, technology has been memetic engineering for permanent education in line with sustainable growth 813 developed for the sole purpose of increasing economic and social standards, with little or no regard for its potential negative impact on the environment (e.g. exhaustion of non-renewable resources, extinction of species, eutrophication, acidification, ozone-depletion, etc.)" [23]. understanding human diversity through cultural value and thinking, enables an approach to education that explains different outcomes of choices people make. the challenge is for explaining the thinking that led to certain behaviour and not for behaviour itself. since memes being decision-systems transcending culture, society and evolution, the struggle is with the memes in humans that are at rumour. to name some memes in ecologic field: nuclear power plant fukushima economic efficiently but ecological disaster; whaling coastal communities have long tradition and industrial level emerged as technology increased, but whaling is immoral, unsustainable, and should be banned. as regards memetic engineering as antidote to vicious memes, it has been shown that "folklore can be harmful when myths are not dismantled before being disseminated as memes. the need to counteract them is obvious and memetic engineering is just the newest arm in the well assorted panoply of persuasive means, from ancient rhetoric to modern aggressive advertising." [16]. with the growing economy, we use a lot of resources which will regenerate in years, and therefore, if there are not taken certain measures from an ecological point of view, we exhausted all resources. today’s youth must be trained to meet this challenge, and for that we need to "e-teach" environmental science. the field of ecology is manifold and should be treated from a transdisciplinary point of view. hence, it is appropriate to create a primitive metamodel for ecology (its relevant design-space metadimensions are shown in figure 1). sean esbjörnhargens in his article [12] wrote about the existence of four correlated dimensions to approach ecological issues. "integral ecology inquires into all for quadrants, or four terrains: behavioral terrain (behaviors at all levels of organization), experience terrain (experiences at all levels of perception), systems terrain (systems at all levels of ecological and social intersection), and cultural terrain (cultures at all levels of mutual resonance and understanding)" [12]. in short: memetic engineering based on bounded rationality and "just-in-time" fits in all four terrains, but of course is the right and the duty of the end-user ( i.e. researchers in ecology) to choose and to prioritise those terrains or maybe some blend of them. 6 conclusions and future work starting of the outcomes of [16], the conclusions are: a) to be sustainable in the long run permanent education must be modelled in line with learner bounded rationality, since bounded rationality is a psychological lasting feature; b) for sustainable development e-teaching should 814 c.i. brumar, r.d. fabian, m.-j. manolescu, v. chiş be systematically revisited through intense transdisciplinary research; c) in line with the eu 2020 strategy, education for sustainability involves also to develop metamodels in the field of integral ecology using memetic engineering to alleviate the apparent opposition between economic growth and environment preservation; d) time has to be dealt with carefully since teaching and learning take place in different temporal frameworks. any educational process that is changing and adapting every day has to be in accordance with the new strategy; memetic stability helps creating a new king of e-teaching. some corollaries are: a) it provides ecologists, in their attempt to preserve environment, a useful tool: memetic engineering is applied to put to work useful memes and to avoid vicious memes; b) to be sustainable on the long run lifelong learning needs eteaching based on innovation and "out of the box" thinking; c) it tools avoiding nondeterministic software are inefficient because of the evolutionary unpredictable processes, both memes and genes are based on. as regards future work, to be reliable, the research should be separated regarding research object (ecology) and method (memetic engineering). in this respect, the intention is to develop methods based on memetic engineering where memes are more appropriate applied, namely in decision making adapting the way decisions will be described in a future work dedicated to strategic decisions for environment preservation. bibliography [1] bărbat, b.e., newton, husserl, wiener: a temporal golden braid (invited paper at int. conf. on comput. commun. and control, icccc 2010, oradea), in abstracts of icccc papers, issn 1844-4334, pp. 12. 2010. 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[23] ulhoi j. p., henning m., sustainable development and sustainable growth: conceptual plain or points on a conceptual plain? http://www.systemdynamics.org/conferences/1999/papers/para197.pdf. ijcccv11n4.dvi international journal of computers communications & control issn 1841-9836, 11(4):553-566, august 2016. a hybrid model for concurrent interaction recognition from videos m. sivarathinabala, s. abirami m. sivarathinabala* department of information science and technology, anna university, chennai, india. *corresponding author: sivarathinabala@gmail.com s. abirami department of information science and technology, anna university, chennai, india. abirami_mr@yahoo.com abstract: human behavior analysis plays an important role in understanding the high-level human activities from surveillance videos. human behavior has been identified using gestures, postures, actions, interactions and multiple activities of humans. this paper has been analyzed by identifying concurrent interactions, that takes place between multiple peoples. in order to capture the concurrency, a hybrid model has been designed with the combination of layered hidden markov model (lhmm) and coupled hmm (chmm). the model has three layers called as pose layer, action layer and interaction layer, in which pose and action of the single person has been defined in the layered model and the interaction of two persons or multiple persons are defined using chmm. this hybrid model reduces the training parameters and the temporal correlations over the frames are maintained. the spatial and temporal information are extracted and from the body part attributes, the simple human actions as well as concurrent actions/interactions are predicted. in addition, we further evaluated the results on various datasets also, for analyzing the concurrent interaction between the peoples. keywords: pose prediction, interaction recognition, layered hmm 1 introduction human interaction analysis involves human activities that are happening between two or more persons to understand the interaction. the automation is required in video surveillance to detect activities from the videos and infer some useful information without the intervention of human beings. the type of human activities detected mainly depends upon the domain in which surveillance system has been employed. human activity recognition may be used for behavior pattern observation or for suspicious activity detection. the activities can either be detected or reported during the event when it takes place or it can be predicted in advance. a system or framework to understand human interaction from surveillance videos involves the following key components: a) low level components for background modeling, feature extraction and object tracking, b) middle level components for object classification c) high level components for semantic interpretations ( ie, understanding actions, interactions between two / multiple people). significant works have been progressing in the literature for each level in this framework. this work mainly focuses on higher level components in order to predict the human interactions. human interaction recognition (hir) involves activity recognition of humans to understand their behaviors. human activity recognition has been presented by ( [2], [18]) as, single layered and hierarchical approaches like spacetime volumes, spacetime trajectories, space-time features and state based models such as hmm and dbn. from these approaches, human activity such copyright © 2006-2016 by ccc publications 554 m. sivarathinabala, s. abirami as shaking hands, punching, pushing, pointing, picking up the object, throwing are recognized. there exist some limitations such as difficulty in recognizing the interactions that happened between multiple people in varying time difference in when he/she re-enters in the scene, difficulty in distinguishing the poses when transformation and scaling has been performed, variation in the performance of body part detection, difficulty to recognize the interactions in complex environments. 2 related works activity analysis involves two fold actions: (i) analysis of motion patterns (ii) understanding of high level descriptions of actions/interactions happening place among humans or in an environment. activities can be recognized [2] in single layered approaches and in hierarchical approaches. in single layer approaches human activities could be recognized based on the image sequences and it is suitable for gesture/action recognition. in contrast, hierarchical approaches recognize high level activities which are complex in nature. it has been observed from the literature that the hierarchical approach well suits to recognize high level activities (interactions). thus, many researchers proposed a level of hmm differently as hierarchical hmm, semihmm, 2dhmm, factorial hmm, coupled hmm, asynchronous io hmm etc., this paper attempts to analyze the interactions between two or more persons in a new fashion of hidden markov model . hidden markov model (hmm) plays a vital role in determining activities which are vision based. the research works ( [11], [13], [13], [12], [19])proves that hmm is one of the most appropriate models for person activity prediction. [9] stated that, a layered hidden markov model (lhmm) can obtain different levels of temporal details while recognizing human activity. the lhmm is a cascade of hmms, in which each hmm has different observation probabilities processed with different time intervals. [22] has proposed layered hidden markov model (lhmm) as a statistical model which is derived from the hidden markov model (hmm). in hmm, the activities are recognized with less temporal correlated frames and over fitting problem occurs during the calculation of observation probability. the activities that take place in long temporal difference cannot be identified with good accuracy. moreover, the use of single variable state representation makes more difficult to model the complex activities involving multiple interacting agents. in order to solve the over fitting problem in the hmm, human action recognition [8] has been done using three layered hmm. their system was capable of recognizing six distinct actions, including raising the right arm, raising the left arm, stretching, waving the right arm, waving the left arm, and clapping. from the observations made in the survey process, most of the previous work centered on identification of particular activities in the particular scenario and less effort has been done to recognize interactions. many research works ( [20], [21], [3], [13]) have been done using graphical models such as hmm and crf. a conventional hmm can have many mathematical structures and has proved its simplicity in recognizing temporal events. its only limitation in conventional hmm is, it cannot capture high temporal correlated frames since the output depends only on the current states. our work is different in recognizing interactions (i.e., actions between two or more persons) in the group. when there are multiple persons in the scene, one person may stand still (or) not doing any actions (or) not interacting with any other persons in the group. that particular person’s activity may be considered as abnormal activity. in order to identify the abnormal activity in the particular frame, we are interested in concurrent interactions between a group of peoples. as a result, a three layered hmm has been designed in our work to recognize the actions and interactions in the group. in the first layer, pose of the persons has been identified and in the second layer ie., the action layer in which actions of the individual persons has been recognized a hybrid model for concurrent interaction recognition from videos 555 and in the third layer, the actions of the first person and the second person are coupled in order to identify the interaction of the people. this three layered hmm able to handle temporal correlations between the frames. this paper has been motivated to design a human interaction analysis system which can overcome these limitations and to design an automated smart surveillance system which could predict actions/interactions taking place in public environments. as motivated by the above challenges the following contributions arose in our work: a) learning model has been designed and implemented in order to learn the complex activity/interaction, b) joint form of lhmm and chmm provides concurrent interactions between the persons c) proposed model has been validated using different interaction recognition datasets and self generated data set. 3 preprocessing and feature extraction pre-processing includes two phases called background modeling and foreground segmentation. this phase highlights the portion of the frame which is under motion. background modeling [5] is a process which tries to model the current background of the scene precisely and aids to segment the foreground objects from the background. foreground segmentation has been performed to obtain the foreground image from the actual image. feature extraction starts with contour extraction [19] and the region properties of the frame have been considered, the number of objects in each frame has been found out. object centroid and object area have been calculated from the region properties as the next set of features. the human body parts need to be modeled before performing the pose estimation. here, to employ a silhouette based approach ( [6], [14], [15], [13]) in body part modeling, convex hull technique has been adapted. here, the convex hull points have been obtained for the whole blob to construct the skeleton. a minimum of 5 dominant points such as head, left hand, right hand, left leg and right leg that lie on the convex hull polygon has been chosen in our observation. body part modelling [4] differs for each and every pose since the pose stand may completely vary from the pose sit. in order to predict every pose precisely, the height of the human is divided into four quadrants, upper most quarter, upper middle quarter, lower middle quarter, and lower most quarters. from the contour (sketch) of the human body, the location of the hands, head and legs have been exactly predicted using the convex hull co-ordinates that lie in the arrangement of the four regions mentioned above. this type of body part modelling can better suit to any kind of pose prediction. 4 human interaction recognition in our work, three layers of hmm have been framed in order to identify the group activity in varying time intervals. figure 1 shows the overview of the human interaction recognition system. this layered representation provides outputs at different levels and decompose the levels in different time granularity. hidden markov model (hmm) [7] is the most successful approach to modeling and classifying dynamic behaviors. layered hidden markov model (lhmm) and coupled hidden markov model (chmm) are combined together to enhance the robustness of the interaction analysis system by reducing the training parameters. effective learning process has been carried out by combining max-belief algorithm and baum-welch algorithm. max-belief algorithm is used to derive the most likely sequence of results in observation activities and the baum-welch algorithm reduces the inference errors. 556 m. sivarathinabala, s. abirami figure 1: overview of human interaction recognition system 5 proposed learning model the visual features are extracted from the videos and the feature vector has been derived from the observations and shown in figure 2. feature vector observation includes spatial information si(t) and temporal information tn where t corresponds to the time stamp within the set of frames and tn represents the trajectory information for n number of frames. z is the feature set and it is represented as z = s1(t),s2(t),s3(t), .....sn (t),t1,t2,t3, .......tn . here, spatial representations have been given as joint locations. let the layered hmm1 models the observations of the person 1 and layered hmm2 models the observation of person 2 respectively. the observations of the person 1 and 2 have been given as input to the layered hmm1 and layered hmm2 respectively. let p1,p2, .....pn represents the pose of the person that has been shown in pose layer (p-hmm) and defined as layer 1i and layer 1j for person 1 and 2 respectively. the action layer (a-hmm) for person 1 and 2 is defined in layer 2i and layer 2j. pose as the feature vector has been given as input to this layer and action of the person has been identified. in order to provide high level information action attributes has been added to the action. after identifying the action attributes, it has been manually labeled. action and attributes of person 1 and action and attributes of person 2 has been coupled together to recognize the new interaction. the interaction layer (i-hmm) has been considered as layer 3 and interactions are represented as i1,i2,i3, .....in.because of coupling the a1hmm and a2-hmm are coupled so that interactions between two persons have been recognized. 6 layers in hir model 6.1 pose layer (low level layer) pose is defined as the preliminary motion sequences that are obtained from the observations. let the input observations be o1,o2,o3, .....onas n represents the number of observations. high dimensional pose vector, pi = l1,l2,l3,l4,l5 where li represents the joint locations of each part of an image frame.pi ∈ r2 where r2 represents the 2d space. pi ∈ p where p represents the pose of the body.p(p|z) represents the inference framework to estimate the posterior probability a hybrid model for concurrent interaction recognition from videos 557 where p represents the pose and z represents the feature set. the desired posterior probability has been calculated using likelihood and prior. p(p|z) ∝ p(z|p)p(p). maximum a posteriori solution can give a high likelihood. in the layer 1, the pose of the person in the particular time interval has been identified and the output has been given as input to the next level. pmap = argmax(p(p|z)) (1) figure 2: hmm model for hir system 6.2 action layer (mid level layer) action is defined as the stream of successive pose that happened in the particular time interval. actions may be defined as the event that takes place for a single person. action,a = p1,p2,p3, .....pn (2) the action of the individual person includes pose and action class labels. ie.,action,a ∈ p,ai(t) maximizing the log likelihood probabilities, the inferential result has been calculated and the actions such as walking, running, jogging, loiter and get hurt has been identified. after training, the likelihood of the action class in the layer 2i and layer 2j is calculated separately. let at = at1,a t 2, ......a t pn ∈ rpn denotes the pose vector in a continuous space of dimension equal to the number of individual actions.this layered approach directly outputs the probability ptk for each individual action model mk where k = p1,p2,p3, .....pn as input to action hmm where atk = p t k for all k. to calculate the probability of model mk for the given sequence x t 1 is computed in the following manner: xt1 = x1,x2, ....xt represents the sequence of model. let us define forward variable α(i,t) = p(xt1,qt = i) which is the probability of having generated the sequence xt1 being in state i at time t. in asynchronous hmm,α(i,t) can be replaced by a corresponding factor ( [9], [23]). assume ∑ns j=1 p(qt = j) = 1 where ns is the number of states for all models. probability p(qt = i|xt1) of state i is given as p(qt = i|xt1) = p(qt = i|xt1) p(xt1) = p(qt = i|xt1) p(qt = j|xt1) = α(i,t) ∑ns j=1 α(j,t) (3) 558 m. sivarathinabala, s. abirami from this, the probability of model mk can be computed as pkt = ∑ i∈mk (p(qt = i|xt1)) = ∑ i∈mk α(i,t) ∑ns j=1 α(j,t) (4) where i is the state of the model mk, i ∈ states of all models and ns denotes the total number of the states. in this work, individual action recognition vectors with the action attributes as observations given to the interaction level hmm. attribute selection criteria human interactions can be recognized with the help of action attributes (aa). the purpose of the attributes is to provide high level knowledge about actions. actionattributeset,aa = a1,a2,a3,a4,a5 (5) attributes have been manually labeled where low level features are given a class label. here, in this work, five attributes have been manually labeled. stretching arm, withdrawing arm, stretching legs, withdrawing leg, hand contact and body contact are defined as the attributes. the presence or absence of each attribute is approximated by the confidence value (0 or 1). attribute classifiers are learned from training data sets. in the multi-level modeling approach [24] , the knowledge of actions and attributes gives interaction in a more accurate way. 6.3 interaction layer (high level layer) interaction is defined as successive actions between two persons that are integrated together. i represent the possible interactions between all possible co-existing pairs a1 and a2 where a1 and a2 denote the action of the person 1 and 2 respectively. a1 and a2 are coupled together to identify the new interaction i(1,2) = [((a1 + aa)(t1)......(a1 + aa)(tz))u((a2 + aa)(t1).....(a2 + aa)(tz))] (6) where iij represents the interactions between 2i layer, 2j layers respectively, and t1,t2...tz represents the time frames from 1 to z. the interaction layer (i-hmm) is defined as the third layer in which the observations have been done using log-likelihood of the action layer and its inferential results. the interactions such as handshaking, pushing, hugging, fighting and meeting have been recognized. the knowledge of both the layers has been coupled to recognize the interaction between two or more persons. this layer uses spatio-temporal constraints as features. a chmm model (λ) is defined by the following parameters. [16] πoc (i) = p(q c 1 = si) (7) aci|j,k = p(q c t = si|qa1t−1 = sj,qa2t−1 = sk) (8) bct (i) = p(o c t ||qct = si) (9) where c ∈ (action1,action2)and qct represent the state of coupling nodes in the cth stream at time t. in coupled hmm, the output observed from layer 2i and layer 2j are given as inputs. the observed sequence has been given as, o = a1t ,a2t where a1t = a11,a12,a13,a1t are the observations of the first person and a2t = a21,a22,a23,a2t are the observed sequence of second person. the observation a11 consists of a1 + aa. here, the observation sequence consists of actions of each person (a) and action attribute (aa) of each person. the state sequence a hybrid model for concurrent interaction recognition from videos 559 has been given us, s = xt1 ,x t 2 where x t 1 = x11,x12,x13, ....x1t x1 ∈ 1, ...m are the state sequence of first observations and xt2 = x21,x22,x23, ....x2t x2 ∈ 1, ...mare the state sequence of second observations. state transition probabilities of the first chain of observations have been represented as, p(x1t+1|x1t,x2t) and for the second chain as p (x2t+1|x1t,x2t). p (x1) and p (x2) are the prior probabilities of first and second chain respectively. p(a1t|x1t) and p(a2t|x1t) are the observation densities assumed to be multivariate gaussian with mean vectors µx , µy and covariance matrices σx,σy. expectation maximization (em) algorithm finds the maximum likelihood that estimates the model parameters by maximizing the following function (10). parameter λ , contains parameters of transition probability, prior probability, and parameters of observation densities. m(λ) = p(x1)p(x2)πnt−1p(a1t|x1t)p(a2t|x2t)p(x1t+1|x1t,x2t) p(x2t+1|x1t,x2t),1 ≤ t ≤ n (10) 7 concurrent interaction recognition the interactions that happened between groups of people simultaneously at a particular time interval are defined as concurrent interactions. example of concurrent interaction is hugging and handshaking in a single scenario. here, in this work four persons have been considered and concurrent interactions between them have been identified. ci(1,2,3,4) = i(1,2) + i(3,4) (11) the training of chmm differs from standard hmm in the expectation step (e) while they are both identical in the maximization step (m) which tries to maximize the equation (10).the expectation step of chmm is defined in terms of forward and backward recursion. for the forward recursion, we define a variable for both observation chains at t=1, α person1(a1) t=1 = p(a1|x1)p(x1) (12) α person2(a2) t=1 = p(a2|x2)p(x2) (13) then the variable α is calculated incrementally at any arbitrary moment t as follows. α person1(a1) t+1 = p(a1t+1|x1t+1) ∫ ∫ (α person1(a1) t )(α person2(a2) t p(x1t+1|x1t,x2t),dx1tdx2t (14) α person2(a2) t+1 = p(a2t+1|x2t+1) ∫ ∫ (α person1(a1) t )(α person2(a2) t p(x2t+1|x1t,x2t),dx1tdx2t (15) in the backward direction, there is no split in the calculated recursions which can be expressed as: β person1(a1),person2(a2) t+1 = p(o n t+1|st) = ∫ ∫ p(a1nt+1,a2 n t+1|x1t+1,x2t+1) p(x1t+1,x2t+1|x1t,x2t)dx1t+1dx2t+1 (16) after combining both forward and backward recursion parameters, an interaction will be tested on the trained model, generating the equivalent interaction that most likely fit the model. the generated interaction sequence is determined when there is a change in the likelihood. 560 m. sivarathinabala, s. abirami 8 results and discussion 8.1 datasets and experimental setup the proposed system has been implemented in matlab version13a, with no special requirements in hardware. the proposed work has been analyzed and evaluated using four video datasets. they are ut-interaction dataset, behave dataset, self generated dataset and kth dataset. the ut-interaction dataset consists of different two person interaction patterns like shake hands, hug, point, kick, punch and push. the behave dataset consist of the outdoor environment and it consists of activities like group formation, crossing each other, depart, approach, move closer, move farther, etc. the generated dataset consists of single person activities and interactions that happen between two persons are taken under indoor environment. the activities covered in this dataset are walking, running, jogging, loitering and getting hurt. in kth dataset, the walking, jogging and running scenarios have been taken for evaluation. in experiment 1, the ut-interaction dataset [12] contains videos of continuous executions of 6 classes of human-human interactions: shake-hands, point, hug, push, kick and punch. there are a total of 20 video sequences whose lengths are around 1 minute. the videos are taken with the resolution of 720*480 and 30fps. high level interactions have been recognized using four participants. results from this dataset has been shown in figure 3,4,5. figure 3: activity recognition approach and group formation, departing, collide and divert figure 4: interaction recognition ( kick, hug and push) figure 5: concurrent interaction recognition (hug and punching, pushing and approaching, handshaking and pushing) in experiment 2, training and testing has been carried out using behave dataset [17] comprises of two views of various scenario’s of people acting out various interactions. the data is captured a hybrid model for concurrent interaction recognition from videos 561 at 25 frames per second. the resolution is 640 * 480. the following ten interactions have been recognized in the behave dataset. in group, approach, walk together, meet, split, ignore, chase, fight, run together and following are the interactions. the results from this dataset have been shown in figure 6. figure 6: concurrent interaction recognition (in group and crossing each other, walk together and walking) 8.2 performance analysis in this research, metrics such as accuracy, precision, recall, positives such as true positive (tp), false positive (fp) and negatives such as true negative (tn), and false negative (fn) have been used to measure the performance of the system. the performance metrics have been calculated for the poses, activity and interactions. figure 7: pose recognition rate precision and recall values for each pose such as stand, sit, lie, bend and crawl have been shown in figure 7. stand and sit poses has more precision and recall values. lie, bend and crawl poses has more precision values than recall values. figure 8 shows the precisionrecall curve for activity recognition rate. the performance for the activities such as walk, run, jog, loiter and get hurt has been shown. walk, run and loiter activities have high recall value than precision value. the activities walk, run and loiter are slightly confusing due to temporal difference. the other activities such as jog and get hurt have high precision values. figure 9 show the precision-recall curve for interaction recognition rate. the interactions such as handshaking, hugging, kicking, punching and pushing have been predicted. handshaking, punching and pushing interactions have high precision values. hugging and kicking have low precision values than other interactions. the recognition rate of poses, activity, interaction 562 m. sivarathinabala, s. abirami figure 8: activity recognition rate and concurrent interaction in different datasets has been shown in table 1. these concurrent interactions have been identified using the proposed learning model. single person action has been recognized from kth dataset and recognition rate has been obtained as 87.62%. figure 9: interaction recognition rate table 2 shows the comparison of the previous works that are carried out in this activity recognition. in learning methodologies, the joint form of lhmm and chmm outperforms other learning models. the spatial relations and the levels of temporal granularity have been considered. usually, the hmm can handle temporal variations in the video. in this paper, the interaction between neighboring states also been considered. chmm has the capacity to underlying synchronization of two different processes. the two actions have been coupled using coupling probabilities with proper weights and the concurrent interactions have been recognized. table 1. recognition rate across datasets (in terms of accuracy) datasets poses single person action twoperson interaction concurrent interaction kth 87.62 ut-interaction 83.90 81.54 behave 71.56 self-generated 91.55 93.47 84.23 88.36 a hybrid model for concurrent interaction recognition from videos 563 table 2. comparison of activities recognized with previous works previous works learning model proposed concentrated on activities recognized recog. rate(% ) sunyoung cho et al.,2013 [25] visual and textual information as features,graphical model using structured learning with latent variables activity recognition high five, handshake, hug and kiss. 78.4 m.j.marin jimenez et al.,2013 [26] dictionary learning, support vector machines with ϕ2 kernel. activity recognition hug, kiss and hand shake. 78 hejin yuan et al.,2015 [27] semi supervised learningk-means clustering, skeleton features. (cumulative skeleton image(csi), silhouette history image(shi)) activity recognition single person basic actions weizmann dataset (10 basic actions) bend, jump, jump, jack, wave, wave1, wave 2, side, walk, run. 90 fadime sener et al .,2015 [4] multiple instance learning process. shape and motion features two person interactions. ut interactions dataset and tv interaction dataset 75.60 proposed system three layer hmm along with coupled hmm concurrent interaction with four persons complex activities in group, walking together, chase, fight, following, handshaking and hugging. 88.36 8.3 discussion our interaction recognition system is based on hierarchical hidden markov model (hhmm) which combines layered and coupled hmm that tries to find the concurrent interactions. the three layers such as pose layer, action layer and interaction layer has been modeled. in order to identify the interaction, action of the two persons has been modeled independently. as a preprocessing step, we have been deployed body part modeling and location based trajectory tracking to aid the localization of the people in frames. the action sequence is composed of parallel states presenting the poses and each pose is composed of the specific number of observations (m= 5 in our case). the action sequence of person 1 and 2 has been modeled individually in the layered fashion. interaction sequence is composed of the action sequence of person 1 and the action sequence of person 2. (m=10). after training lhmm, the observation sequence from the databases, the system tries to find a corresponding sequence of poses based on the learning during the training phase. the generated pose sequence is the sequence that achieves the maximum likelihood estimation with the poses. thus the observed pose is given as input 564 m. sivarathinabala, s. abirami to the next layer hmm. in the second layer hmm, the system tries to find the corresponding action sequences. the action of person1 is identified from the maximum likelihood estimation of the action sequences. in the same way, the action of person 2 also identified using two layered hmm. coupled hmm (chmm) couples both the action sequence of person1 and person2 and the system tries to find the interaction in a better way. chmm in lag1 condition can couples the observation channels. each channel has its own action sequence. from both the action sequences the next state emission probability has been generated. based on all previous works, the specific activity has been recognized using the specific learning model. hmm is the most successful framework in speech and video applications and it is well suited for computing with uncertainties. here, in this work to demonstrate the concurrent interactions, the hmm learning model has been extended in the joint form of layered hmm and coupled hmm. layered hmm models the non-causal symmetric influences and chmm to model the temporal and asymmetric conditional probabilities between observation chains. conclusion in this work, a hybrid learning framework is designed to recognize the concurrent interactions between multiple peoples. the spatial and temporal information and body part attributes are considered as features. the poses and actions are recognized in a layered fashion. the actions of multiple persons are coupled to recognize the interactions and concurrent interactions. the joint form of lhmm and chmm has been used for providing concurrency. the interactions between neighboring persons has also been recognized. here, the activity recognition has been done for the continuous events and this could be extended in future to discrete event recognition mechanisms also. further work will focus on identifying interactions and behavior in different person to person interaction contexts that will allow the system to recognize the interactions under different conditions.this system can act as a smart surveillance to recognize the actions/interactions of multiple people without human intervention in the environments such as meeting hall, discussion groups, public places, banking sectors, where multiple people could interact with each other. acknowledgment the work reported in this paper has been supported by anna university,chennai by providing anna centenary research fellowship. we also acknowledge the anonymous reviewers for comments that lead to clarification of the paper. bibliography [1] alexandros andre chaaraoui, pau climent-perez, francisco florez-revuelta(2013); silhouette-based human action recognition using sequences of key poses, pattern recognition letters, 34(15): 1799-1807. 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[28] hejin yuan (2015); a semi-supervised human action recognition algorithm based on skeleton feature, journal of information hiding and multimedia signal processing, 6(1): 175-181. int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 264-272 a decision-making perspective for designing and building information systems f.g. filip florin gheorghe filip the romanian academy: bar and ince, and the nat. inst. for r&d in informatics -ici, bucharest, romania ffilip@acad.ro abstract: this paper aims at highlighting several aspects and associated decision situations which may be met in the process of designing and building modern information systems, such as: choosing the approach and methods to be utilized for building the system and selecting the it tools, integrating the system into the enterprise and evaluating the project. a particular emphasis is put on evaluation criteria to be utilized in solving the various decision problems. keywords: decision criteria, it&c tools, methodology, prototype, saas, standards. 1 introduction a large number of models and corresponding solvers have been proposed and reported in the literature with a view to getting optimal solutions for the academic test-problems or real-world management and control applications. they can be found in the technical literature. in many cases, in practical applications, a necessary condition to make the models and the corresponding solvers utilized is to incorporate them into adequate information systems (is). in the same time, the it&c (information technology and communication) vendors release to the market ever more modern hardware and software products. new trends can be noticed on the software market [21], such as "merger mania" (consisting in mergers, acquisitions, partnerships and strategic alliances between business software vendors), functional expansion, a clear dominance of a few (three) databases (oracle, microsoft sql server, and ibm db2), increased usage of internet, viewing it/software as a service and so on. exhaustive services for project management in the software field to serve those managers who want to save time are already available on the market [18]. one can notice, during the process of designing and implementing information systems, sequences of decisions which should be made with respect to the choice of the most adequate alternatives concerning several critical aspects, such as system orientation, composition of the team, method to be adopted, it&c tools to be utilized, resources to be allocated and so on. in this context, this paper aims at surveying from a decision-making perspective several methodological and practical aspects of designing effective (usable, useful, and actually utilized ) is. the paper extends, details and up-dates an earlier preliminary version of the paper [9]. the remaining part of this paper is organized as it follows. in the second section, several factors which may influence decisions meant to design and implement information systems in organizations are reviewed. then, several design and implementation critical aspects are presented, such as the design approach adopted and the selection of the it&c tools. the paper concludes with a discussion of technical and non-technical integration issues and evaluation aspects. throughout the paper the evaluation criteria to be used in various decisions are highlighted. copyright c⃝ 2006-2012 by ccc publications a decision-making perspective for designing and building information systems 265 2 main influence factors there are several factors which can influence the process of designing and implementing an information system, such as: people involved, the orientation and the purpose of the system, the organizational setting, standards utilized and so on [8]. those influence factors should be taken into consideration by the management of the target enterprise and the designer as well, when a decision on introducing and creating an information system is to be made. figure 1: influence factors (adapted from [9]) the people involved in the is design and implementation should cooperate closely to form a virtual team who aims at obtaining the best solution for the allocated resources (time, manpower, money). there are several classes of people who should take part in various extents of involvement and contributions to the process for the first moment of discussing the idea of is until its "steady state" operation and impact evaluation. one can identify the following generic classes:"clients", designers, and it&c vendors. the members of the "client" class include the project "sponsor" (a manager) and the project "champion" who represents the interests of the future direct (’hands-on") or indirect ("beneficiary") actual users ( who may be also involved in design and implementation of the is). the "project champion" possesses the necessary knowledge of the application domain. the "project sponsor" possesses the authority since he/she represents the interests of the organization and, consequently, is empowered to accept or reject the project solutions and allocate the necessary resources. the designers can be members of a group of people of the organization or/and a team of analysts and it&c professionals from a consultancy firm who master the design techniques and who are aware of the it&c products available on the market. the it&c manufacturers and vendors can adapt and alter the it&c products to be utilized. the information system may be oriented to serve a certain generic class of users ("roles") or to help a specific group of persons with names, identities and specific it skills ("actors"). the system purpose might be either to facilitate and make more comfortable the work of 266 f.g. filip the users, or to promote the change. the normative model of the change [14] includes several steps of cooperation between users and designers which can be met in the process of creating an information system, such as: • pioneering (to evaluate the needs of the final users and necessary competences of the systems constructors); • acceptance (to establish the objectives to be agreed by the users and the designers); • diagnosis (to collect the data, define the problem and estimate the necessary resources); • planning (to set up the work plan and allocate the corresponding resources); • action (to design and implement the information system and train its users); • evaluation of the process and project impact. the target organization where the information system is to be implemented may create a context which strongly influences the solution and the process of the system building. there might be constraints caused by a) the insufficient it&c knowledge and skills of the future users and b) scarce available data or/and limited internal data access rights of the external consultants. several integration problems may show up caused by the "legacy it systems" or/and the operating procedures permitted within the organization. standards should play a central role in design. the international standard organization (iso) is an excellent source of documents to be utilized to set the stage for useful, usable and used solutions. the standards for usable (traditionally called "user-friendly") interfaces, such as those of the series [13]iso 9241 ("ergonomics of human-system interaction"), are recommended and can contribute to obtaining a user-centered solution. the quite recent standards, such as iso 9241-171.2008 ("guidance in software accessibility") and iso 9241-151.2008 ("guidance www user interface"), are of a particular importance in the context of modern information systems which are ever more oriented to use www technologies. suduc [23] gives a comprehensive analysis of the design methods to be utilized in the low-cost interface design. cojocaru [5] proposes the intelligent interface concept. other aspects, such as previous experience, industry competitors’ moves, legislation and, the most serious one, available budgets and intended due dates, may also influence the is construction process. 3 design and implementation approaches there are various approaches to designing, building and implementing an information system. they can be grouped in accordance with several criteria, such as: • it&c tools and platforms which will be utilized (general-purpose products vs integrated suites/generators/shells); • buying it or using it as a service-itas [11], or software as a service -saas [4] and [25]; • place for construction (within the target organization or at the consultant’s site); • method utilized (the lifecycle method or the evolving/adaptive design which is based on the use of the prototype). the lifecycle-based method requires several steps, such as system analysis, design, implementation, and operation which are carried out in sequential ("cascade") manner. it also implies that the well defined procedures and checkpoints are strictly observed and the solutions adopted are well documented. it is, consequently, recommended for large-scale applications. a decision-making perspective for designing and building information systems 267 the origins of the prototype-based method [20] in the field of is could be traced back in mid 70’s in the empiric remark that 80% of the design ideas in the field are wrong [17]. consequently, in order to avoid the waste of resources, the prototype permitted to spend 20% of the resources in the early stages of design and construction for identifying the 80% wrong ideas, so that the remaining 80% of resources should be utilized to implement the remaining 20% of ideas which are likely to be correct. when adopting the prototype-based method, there are a few basic principles which are to be observed such as: • the process starts with approaching the most critical problems of the target organization, so that the user’s confidence could be gained as early as possible. in some cases, a "demo script", which includes a critical business scenario [21], can be also utilized to select the most adequate product from a short list (see the next section); • the early requirements can be formulated in collaboration with the user in a "quick and dirty "simplified manner; • the information system is developed in several cycles which include operations, such as experiment, evaluation, modification. the cycles should be as short as possible and the cost of the first version must be very low, in order not to lose the user’s interest and confidence; • the evaluation of the effects of the usage of the preliminary version is carried out on a permanent time basis. two main types of prototypes have been commonly utilized [22]: a) the "throwaway" prototype, and b) the "evolving one". while the former is only utilized to test the design ideas and then is discarded ( the next versions re-designed by using new technologies), the latter consists in a series of refinements of the initial version (fig. 2). a decision choice should be made on which type to be utilized. figure 2: types of prototypes (legend: pr= prototype, sv= system version, ex= experiment, ev= evaluate, mo= modify) the prototype-based (adaptive/incremental/iterative) methods allow for obtaining a good well customized, early utilizable and helpful solution, even if the information on organization 268 f.g. filip and its context is scarce and uncertain. on the other hand, methods may stimulate the tendency to continually modify the solution or, on the contrary, to adopt too early a solution which is imperfect or incomplete. in [6] and [8] the story of constructing dispatcherr, a family of decision support systems (dss) which is meant to assist logistics and production control decisions to be made in the context of the continuous process industries and related fields, is described. the dispatcherr project started in early 80’s as an optimization model and software for production scheduling. since then, under the influence of several factors such as the users’ changing needs and improvement of their it&c skills, specific characteristic features of target enterprises, and new products and technologies released in the field of it&c, several application versions were designed and installed in refineries, pulp and paper mills, chemical plants, and water systems. dipatcherr has evolved towards a complex solution, a dss generator which could be adapted to new business models (such as the "extended"/networked/"virtual" enterprise), to support new functions and usages. it includes new constituents, such as a threelevel modeling scheme of the plant (expressed in terms of final users, analysts and programmers, respectively), ai (artificial intelligence)-based solvers and model experimentation tools( fig. 3). figure 3: evolution of dispatcher system (adapted from [6] and [9]) an important decision consists in making a choice between buying or leasing it&c products. for example, in recent years, the approach to use saas (software as a service) model has become ever more popular. this new business model means that the software vendors host on their servers the applications to be accessed, via internet, by client organizations only when it is necessary. the pricing scheme is based on paying monthly lease fees, instead of initial license cost and annual maintenance fee. the saas scheme can be of particular interest for smaller companies that have limited it infrastructure and skilled personnel. on the other hand, when a decision is to be made one should take into account long run costs and security issues. availability of necessary it infrastructure, ease of usage are the main evaluation criteria when a decision is to be made. 4 selection of the it&c tools the selection of the it&c tools should be viewed as a multi-attribute decision-making (madm) problem ( [12], [7], [19], [15]). the general criteria to be used in selecting and ranking the possible it&c products which can be found on the market can be grouped as it follows: • adequacy: informational transparency, accuracy of expected results, robustness to errors and low quality uncertain input data, response time; • quality of implementation: scalability, flexibility, easy integration with the "legacy systems", functional transparency, documentation completeness; a decision-making perspective for designing and building information systems 269 • delivery quality: price, delivery time, provider’s general reputation, easy adaptation, degree on dependence on the technical assistance from the provider’s specialists for implementation and usage). a particular attention has been received by the software selection. a set of criteria for filtering the software products from the initial long list is recommended by software resources [21], such as: a) budget for the new system, b) unique functionality (i.e. multi-currency, multi-company, budgeting, workflow), c) technology preferences, d) reporting, e) scalability, and f) vendor stability. as a subsequent step to make a choice from the short list software resources recommends the "scripted (or the standard) demo" in order to determine the software vendor or the var (value added reseller ) to modify their product demo to show how the specific business needs of the client can be solved. a systematic methodology for software evaluation and selection through the usage of madm was proposed by [16] and an experimental expert system was proposed by [27]. an interesting list of pitfalls to be avoided when selecting the software is provided by software resources [21]. it includes the following 12 "deadly mistakes": "a) buying the same software as the competitors, b) buying software based on features alone, and overlooking other critical factors (scalability, flexibility, excessity, technology and cultural fit, affordable cost, insufficient technical support and infrastructure), c) neglecting the proper consideration of the vendor reputation, d) buying software without focusing on the implementation partner, e) taking into consideration only the low initial costs and overlooking significantly higher ongoing costs, f) buying software using input from an elite group without getting buy-in from the organization at large, g) choosing the popular software without considering all the posible and affordable options h) buying software that is too complex, i) making a choice without properly defining your requirements, j) buying software that is either at the end or at the beginning of its product lifecycle, k) buying software that is based on dying technology, l) selecting a software only to fix the current business problems" (instead of implementing the change). comprehensive on-line independent support for software evaluation and selection can be obtained from specialized consulting firms, such as technology evaluation center [24], software resources [21], project perfect [18]. 5 integration in many cases a new information systems should be integrated into the existing or planned it&c infrastructure of the target organization. several principles are recommended by [26] which are still valid for technical integration, such as: • adopting an "open system" architecture; • neutralizing the information which can be achieved by using standardized data formats; • semantic unification which means a symbol has a unique meaning throughout the whole system. there are, however, several new problems which can show up due to non-technical causes, for example: • wrong orientation of the solution which does not facilitate solving the central problems of the organization; this may be associated with informational opacity (the system provides more/less than necessary outputs); 270 f.g. filip • functional opacity which means that he user is not given the necessary information and incentives to understand how the system works; • frustration of the "hands-on" user due to a long response time or an un-adequate (insufficient/excessive) number of functions to perform his/her task. 6 evaluation evaluation of is has been subject of interest for long time [10]. there are several main principles to be observed in the process of designing, building and implementing an information system, such as: • evaluation is necessary all over in the design and implementation process to support making a decision choice from the set of possible alternatives (project continuation, giving up, allocating some additional resources and so on); • the objectives and the degree of detail of evaluation depend on various factors, as : a) the project scope, b) technical complexity, c) duration and cost of the project, d) the person who requested the evaluation, e) overall state of the target enterprise; • involving the designer into the evaluation team is necessary especially in the case of a large project. as previously stated, the evaluation is meant to support a decision-making process. consequently, a set of evaluation criteria should be set up, namely: • impact on the efficiency of users’ professional performance in accomplishing his/her tasks and quality of life (intellectual development, possible additional stress caused, comfort of performing the task); • impact on target enterprise general evolution; • implementation and further running costs. a set of more detailed set of criteria which was used in a specific context is given by [1]. there are several methods which can be utilized for evaluation, for example: a) benefits/cost analysis, though the nvp ("net present value") of the investments, b) value analysis, c)"rating and scoring", d) event logging and so on. agouram [2] gives a useful methodology to assess the is success. 7 conclusion the activities of designing and implementing an information system form, in practical applications, a process which may include many decisions to be made at different stages. there are several critical aspects, both of the technical and non-technical nature, which should be taken into consideration. among the main aspects which might cause problems are the evolution of the technical constituents associated with the increased requirements for the solution quality set by the users who are ever more informed and skilled and have to face an ever more fierce competition. multi-attribute decision models could be effectively utilized to solve the decision situations which can be encountered. a decision-making perspective for designing and building information systems 271 bibliography [1] al-adaileh r. m, an evaluation of information systems success: a user perspective the case of jordan telecom group, european journal of scientific research, 37(2):226-239, 2009 [2] agouram h., defining information system success in germany, international journal of information management, 29:129-137, 2009 [3] bizoi, m., sisteme support pentru decizii bazate pe comunicatii (communication-based decision support systems), ph.d. thesis, (in romanian), (http://www.racai.ro /doctorate/bizoi_rezumat_teza.pdf, 2010, accessed on 09.07.2011) [4] carraro g., chong f., 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[16] moriso m., tsoukias a., jusware: a methology for evaluation and selection of software products, iee proc-softw. eng., 144(2):162-174, 1997 272 f.g. filip [17] ness d.n., decision support systems: theory and design, in wharton office of naval research on dss, philadelphia, november, pp. 4-7, 1975 [18] pp: project perfect: project management software, 2012 http://www.projectperfect.com.au/, 2012, accessed on 09.02.2012) [19] resteanu c., somodi m., alexe b., multi-attribute decision-making; e-course, ici, bucharest, 2007 [20] shelly g. b., cashmanan t. j., rosenblatt h.j., systems analysis and design, 8th ed., thomson course technology, boston, mass, 2010 [21] sr: soft resources: empowering software decisions, 2012, (http://www.softresources.com/, accessed on 06.02.2012) [22] sprague jr. r. h., carlson e. d., building effective decision support systems, prentice hall, englewood cliffs, n. j., 1982 [23] suduc a.m., interfete avansate pentru sisteme support pentru decizii("advanced interfaces for dss"), phd thesis, (in romanian), 2010 (http://www.racai.ro/doctorate/suduc_ rezumat_teza.pdf , accessed on 09.07.2011) [24] tec: technology evaluation center, 2011, (http://www.technologyevaluation.com/software/, accessed on 09.07.2011) [25] trumba , white paper: five benefits of software as a service, 2007 (http://www.trumba.com/ connect/knowledgecenter/software_as_a_service.aspx, accessed on 09.02.2012) [26] vernadat a., enterprise modeling and integration principles and applications, chapman & hall, london, 1996 [27] vlahavas i., stamelos refanidis i., tsoukias a., esse: an expert system for software evaluation, knowledge-based systems, 12(4):183-197, 1999 int j comput commun, issn 1841-9836 7(5):945-956, december, 2012. cooperative robot structures modeled after whale behavior and social structure i.c. res.ceanu, g.c. călugăru, c.f. res.ceanu, n.g. bîzdoacă ionut. cristian res.ceanu cristina floriana res.ceanu nicu-george bîzdoacă university of craiova faculty of automation, computers and electronics romania, 200440 craiova, decebal blvd., no. 107 e-mail: {resceanu, cristina, nicu}@robotics.ucv.ro george-cristian călugăru university of craiova faculty of automation, computers and electronics romania, 200440 craiova, decebal blvd., no. 107 e-mail: calugaru.george.nds@gmail.ro abstract: this paper analyses the communications and social structure of whale pods and tries to apply their principles on cooperative robot structures which can be guided to perform a certain task. the communication patterns and social structure are presented at first in order to define real, natural phenomena which can then be translated through cooperative robotic structures. whale communication has suffered modifications in the latter years mostly because of the increase of noise in the ocean. problems regarding communications between the cooperative structures chosen for modelling whale behavior are solved by data fusion techniques. in the last part of the paper the problem of dynamic compensation of disturbances is studied with regard to cooperative structures. keywords: cooperative structures, data fusion, manipulative robots. 1 introduction the order cetacea (cetus, whale, from greek) includes the marine mammals commonly known as whales, dolphins and porpoises. cetus is latin and is used in biological names to mean "whale"; its original meaning, "large sea animal", was more general. it comes from ancient greek (ketos), meaning "whale" or "any huge fish or sea monster". in greek mythology the monster perseus defeated was called ceto, which is depicted by the constellation of cetus. cetology is the branch of marine science associated with the study of cetaceans. suborders of the cetacea order are mysticeti, odontoceti and archaeoceti (extinct ancient whales, depict the evolution of whales throughout time). cetaceans are mammals that evolved throughout the ages for aquatic environments. their body is fusiform (spindle-shaped) and the forelimbs are modified into flippers as a result of this evolution. the tiny hindlimbs are vestigial; they do not attach to the backbone and are hidden within the body. the tail has horizontal flukes. cetaceans are nearly hairless, and are insulated from the cooler water they inhabit by a thick layer of blubber. some species are noted for their high intelligence. 1.1 whale social structure whale family structures are fascinating. one of the most important facts about whales is that they are particularly intelligent mammals and like humans, place much value on their families copyright c⃝ 2006-2012 by ccc publications 946 i.c. res.ceanu, g.c. călugăru, c.f. res.ceanu, n.g. bîzdoacă and the role that each member plays within the unit. notably, the individual families also travel and migrate together in pods. each family member continues to play a vital role within that pod, as a greater unit of the family. these groups demonstrate the sociable nature of whales and their unspoken cooperation with one another is evidence of the insight and sense of responsibility inherent to these animals. another interesting whale fact: whales tend to separate themselves into pods according to age and sex. the whale cows and their calves travel together in pods of up to 30 members at a time, accompanied by one dominant bull. cows without their calves, or whose calves are mature enough, act as midwives to pregnant and nursing mothers. they assist them with their birth by ensuring that the newborn reaches the surface of the water for air. cows are also babysitters to the other mother’s calves in her absence, and assist her with the care of her new baby in a general sense. the calves stick close to their mothers for an average of three to six years. but this period can be even longer, depending on the individual calf and their species. even after they have left their mother’s side, they may still return to the main pod to visit her. females are also known for returning to their first pod when they become pregnant with their own calf. the youths of the group eventually branch off into a smaller juvenile pod. they will move into larger pods once they reach sexual maturity and begin to calve. in some cases, the juvenile pod is either replaced or broken away from by a ’bachelor pod’, consisting of only the young bulls. whales are considered ’juveniles’ from about three years of age to approximately thirteen. there is a dominant bull in each core pod, and he is responsible for the pod in which he resides. he is sexually mature and cares for his harem of cows and calves. the other males tend to stick to themselves, traveling separately from the rest of the main pod, as to respect the ’property’ of the dominant bull. this familyand pod structure is designed to protect the weak and the young of the group. because whale calves do not mature as quickly as some other mammals do, they require time to grow and develop within a protected environment. the organization of the dominant male and the group of mothering cows ensures that calves are isolated from the dangers of the deep. traveling in this way also ensures that whale migrations remain orderly and safe for all involved, preventing smaller family units from drifting off course or facing the dangers that come with isolation. of course, whales are also social creatures and benefit from the close interaction with others of their sort. this level of mutual understanding and cooperation is another indication of the brilliance of the whale creation and instinct. [12] 1.2 whale communication whales can communicate through a very intriguing method called echolocation. echolocation, also known as biosonar, is the biological sonar used by several animals such as shrews, most bats and most cetaceans. the term was first used by donald griffin, whose work with robert galambos was the first to conclusively demonstrate its existence in bats. two bird groups also employ this system for navigating through caves, the so called cave swiftlets in the genus aerodramus (formerly collocalia) and the unrelated oilbird steatornis caripensis. [13] echolocating animals emit calls out to the environment and listen to the echoes of those calls that return from various objects in the environment. they use these echoes to locate, range and identify the objects. echolocation is used for navigation and for foraging (or hunting) in various environments. echolocation makes use of active sonar, using sounds made by an animal. ranging is done by measuring the time delay between the animal’s own sound emission and any echoes that return from the environment. the relative intensity of sound received at each ear provides information cooperative robot structures modeled after whale behavior and social structure 947 about the horizontal angle (azimuth) from which the reflected sound waves arrive. unlike some sonar that relies on an extremely narrow beam to localize a target, animal echolocation relies on multiple receivers. echolocating animals have two ears positioned slightly apart. the echoes returning to the two ears arrive at different times and at different loudness levels, depending on the position of the object generating the echoes. the time and loudness differences are used by the animals to perceive distance and direction. with echolocation, the bat or other animal can see not only where it is going but also how big another animal is, what kind of animal it is, and other features. the sounds produced by whales can travel for miles as objects found in the water can help amplify them. the sounds will echo back to the whale that emitted them. this form of communication has an estimated speed of 1 mile/second. sounds made by whales are in fact very unique. the clicks are a basic part of this communication. the clicks also help the whales navigate through the waters as well as being a language feature. different species of whales communicate in various ways. this is due to the fact that they don’t have an inner ear. the humpback whales have a very particular way of communication called singing. this signing mainly consists of periodical sound patterns. these songs can be up to 30 minutes in length and can travel up to 100 miles. sperm whales have only been heard making clicks, while toothed whales use echolocation that can generate sounds of 30000 watts at 163 decibels. whales create small pods with various communication patterns within. researchers established that whales from different locations in the world have different terminology. this terminology is also different at whales in captivity in comparison with free whales. it is believed that whales have amplified their songs in the past few decades. whale songs can travel thousands of miles, but new research shows that their ability to communicate is severely affected by the increase of the amount of noise in an ocean. thus the whales adapted by making their songs louder. [11] 2 using data fusion for process surveillance and diagnosis in the modelling of whale communication terms like data fusion, multi-sensor data fusion, sensor fusion and information fusion or multi-sensor integration are used frequently in literature to depict a variety of techniques, technologies, systems and applications which use data provided by multiple sources. the data fusion applications are various from real-time sensor fusion applied to mobile robots navigation to the fusion of strategic intelligence developed for military purposes. [6] data fusion is used in the development of modern applications regarding process surveillance and diagnosis. using specific algorithms, damaged sensors can be determined and in the same time signals can be synthesized in order to replace the erroneous information. data fusion techniques combine data from a multitude of sensors and related information to obtain more specific results than in the case of one sensor. the data fusion concept can find its equivalent in nature. throughout their evolution, people and animals developed their ability to use multiple senses in order to survive. for example, establishing if a food is eatable or not cannot be determined by using only the sight sense; the combination of sight, touch, smell and taste is far more effective. in a similar manner, when the ability to see is blocked by built structures or vegetation, the hearing sense can offer advanced warnings in case of imminent danger. thus, multi-sensor data fusion is performed naturally by animals and people in order to evaluate the configuration of the surrounding environment and 948 i.c. res.ceanu, g.c. călugăru, c.f. res.ceanu, n.g. bîzdoacă in order to detect possible threats. even though the concept of data fusion is well established, the development of new sensors, new techniques of processing and hardware made real-time data fusion viable. recent progresses made in the development of computers and sensors offer the ability to emulate the natural abilities regarding data fusion for both people and animals through hardware and software means. nowadays, data fusion systems are used in military applications such as: target recognition (e.g. intelligent weapons), vehicle guidance and remote detection like iffn systems (identification friend-foe-neutral systems). non-military applications include monitoring of manufacturing processes, condition based maintenance for complex machinery, robotics and medical applications. 2.1 multi-sensor advantages another frequent term is that of multi-sensor integration which signifies the synergic use of sensor data to perform a specific task. sensor fusion is different from multi-sensor integration because the first includes the current combination of sensor information in a single representation format. multiple sensor data fusion offers multiple advantages in comparison with information prelevated through one sensor. first, if multiple sensors are used (e.g. identical radars following an object in motion) combining all the determinations will provide a better estimate of the motion and position of the object. a statistical advantage is obtained by adding n independent determinations (e.g. estimation of the position and speed of the target is improved by a factor of ), with the assumption that data are combined in an optimal manner. the same result could be obtained by adding n independent determinations to a single sensor. figure 1: data fusion and multiple sensor integration configurations a second advantage implies the use of relative placement and motion of sensors to improve the process of observation. for example, 2 sensors that measure the angular motion of an object can be coordinated in order to determine the position of an object through triangularization. this technique is used for surveillance and commercial navigation. a third advantage is the improved observability. the initial increase in physical observability may lead to significant improvements. 2.2 control architectures for system control, the following types of data fusion can be defined with regard to sensors, [1]: • complementary data fusion the fusion of multiple sensors spread across an area provides partial information about the environment; e.g. the fusion of multiple sensors which meacooperative robot structures modeled after whale behavior and social structure 949 sure distances or video cameras pointing in different directions. this type of fusion solves the problem of incomplete information. • competitive fusion the fusion of uncertain data obtained from multiple sensors e.g. a radar and a video camera which detect the same object. through data fusion, the distance to the object can be obtained with a higher accuracy. this type of fusion is used to reduce the effect of uncertainties and erroneous measurements. • cooperative fusion the fusion of different sensors in which one of the sensors is based on determinations of another sensor in order to obtain its own set of data. for example, when a tactile sensor supplies information about the shape of an object previously estimated by a proximity sensor. this type of fusion is used to diminish the uncertainty effect, measurement errors as well as the incomplete state of the data. three basic alternatives can be used for multi-sensor data: 1. direct fusion of data provided by sensors 2. representation of sensor data through characteristic vectors with subsequent fusions of them. 3. processing of each sensor in order to obtain high level control decisions which are later combined. each of these approaches will use different data fusion techniques. if multi-sensor data are proportional (if sensors measure the same physical phenomenon) then data from the sensors can be directly combined. the techniques for fusion of sheer data imply classical estimation methods like kalman filtering. however, if sensor data is not proportional the data must be merged at the level of the characteristic/state vector or at a decision level. data fusion at the characteristics level implies the extraction of the representative characteristics from the sensor. it has been demonstrated that people use a cognitive function based on characteristics in order to determine objects. in case of data fusion at the characteristics level, the characteristics are extracted from multiple sensor observers and combined in a single vector which is subjected to pattern recognition techniques like neural networks or grouping algorithms. decision level data fusion combines sensor information after every sensor performed a preliminary determination of the entity location, attributes and identification. 2.3 a model for processing data fusion in order to improve communications between military researchers and system developers, jdl (joint directors of laboratories), established in 1986, began an effort to define the specific terms regarding data fusion. the result of the effort led to the creation of a process model for data fusion and specific terminology. the jdl process model is set to be very general and useful in multiple application domains, identifies processes and categories of techniques applicable to data fusion. the model is hierarchical with 2 levels. at the upper level, the data fusion process is described by the sensor inputs, the interaction between man and computer, database management, source preprocessing and 4 key sub-processes: level 1 processing (object refining) combines sensor data to obtain the most reliable and accurate estimation of the position of the object, its velocity, attributes and identity. level 2 processing (scenario refining) tries dynamically to develop a description of the current relationship between entities and events taking place in their surrounding environment. level 3 processing (threat refining) projects the current scenario into the future in order to draw the inferences about for threats, about friends and the foe vulnerabilities and opportunities of action. 950 i.c. res.ceanu, g.c. călugăru, c.f. res.ceanu, n.g. bîzdoacă figure 2: a. direct data fusion of sensors; b. representation of sensor data through characteristic vectors and subsequent fusions of them; c. the processing of each sensor in order to obtain high level inferences and decisions which are then combined level 4 processing (process refining) is a meta-process which monitors the global process of data fusion in order to evaluate and improve the real-time system. for each of these sub-processes, the jdl hierarchical model identifies specific function and technique categories (in the second level of the model) and specific techniques (in the lower layer of the model). the implementation of the data fusion systems integrates and correlates those functions in a general work flow. 3 the dynamic model of a manipulative robot used in modelling whale behavior the cooperative structure used to model whale behavior contains manipulative robots. the dynamic model of a such a robot is presented below. for more details on the principles of bioinsipired computing and robots see [10]. also for more details on cooperative robot structures see [3]. the dynamic model is given by: j′1θ̈1 + j∗cos(θ1 − θ2)θ̈2 + j∗sin(θ1 − θ2)θ̇22 + m′1cosθ1 = m1 (1) j′2θ̈2 + j∗cos(θ1 − θ2)θ̈1 − j∗sin(θ1 − θ2)θ̇21 + m′2cosθ2 = m2 (2) cooperative robot structures modeled after whale behavior and social structure 951 figure 3: the jdl process model for data fusion figure 4: the kinematical model of the manipulative robot 3.1 determining the operation span in fault conditions the present day migration courses of whale pods are affected by naval traffic. for instance, in 2007 a mother whale and her calf were trying to return from the sacramentosan joaquin delta to the pacific ocean. the two whales had deviated off course a week earlier. the change in course is believed to be influenced by the sounds of tug boats met along the way. the two whales appeared to have been wounded by a ship’s propeller. in case the whale swimmers are affected, the whale will try to maintain its course using the wounded swimmers with a smaller operation span. in this paragraph the operation span in fault conditions is determined. according to the kinematical configuration of the robot system considered, it has an area of operation in the shape of an annulus sector as depicted in the figure below: in the case of a fabrication line which contains a number of robots with the same kinematical configuration, trespassing over these admissible operation areas can lead to interference problems. in order to avoid this, all areas that can be trespassed are eliminated apriori and thus each robot can have its own distinct area. so, for every robot a rectangular operation area (called an operation cell) will be defined. fig. 5.a) depicts the three parameters that describe the area of operation: maximum angle φ , minimum range rmin and respectively, the maximum range rmax. in figure 6.b) a rectangular operation cell is defined where x and y represent the height and width of the cell. between the 2 regions the following relations are defined: 952 i.c. res.ceanu, g.c. călugăru, c.f. res.ceanu, n.g. bîzdoacă figure 5: definition of the area of operation for the manipulative robot: a) area of operation b) definition of the operation cell y 2 < rmintgφ (3) r2max = (rmin + y) 2 + (y 2 )2 (4) 3.2 manipulator fault due to a jammed articulation it is assumed that the fault which appears at the manipulator is due to a jammed articulation. it is also assumed that the value of the angle of the jammed articulation is known. even though the blocking position is not available from the sensor attached to every articulation, this can be calculated based on the position of the griper of the faulted manipulator by solving backward kinematics problems. an articulation fault when the first articulation is jammed, the faulted manipulator can only move its third joint by means of the second joint. depending on the configuration of the system at the moment the fault occurred, the faulted manipulator can be placed in a position similar to the one below: figure 6: aerial view of the manipulator with the first articulation blocked the admissible region from the area of operation is, in this case, projected on a line of the arc as can be seen in figure 8. in this case, the terminal of the faulted manipulator can be placed in one of the two possible positions, a and a’, from its trajectory. the kinematical constraints that can guarantee the existence of these two placement positions are defined like this: cooperative robot structures modeled after whale behavior and social structure 953 figure 7: the kinematical constraints on case of first articulation failure y 2 + d ≤ r ≤ r′ (5) where r is the radius of the radius of the arc and r’ is the distance between the point in which the arm attaches itself to the support and the limits of terminal of the manipulator. r′ = 1 2 √ x2 + (y + 2d)2 (6) r = a1cosθ̂1 + a2cosθ2 (7) where θ̂1 is the angle of the jammed articulation. note the fact that r is identical to the length of the projection of the manipulator on the area of operation. y 2 + d ≤ a1cosθ̂1 + a2cosθ2 ≤ 1 2 √ x2 + (y + 2d)2 (8) 4 dynamic delay compensation modelled after whale communication habits this part of the paper deals with dynamic compensation of disturbances, [7] in the last decades, the amount of noise present in the ocean has increased significantly in the ocean. this is largely due to the increase of naval traffic, the exploitation of certain resources(e.g. oil through oil platforms) and the intensive use of sonars. the whales themselves have adapted by increasing the intensity of their sounds. with regard to robotic structure communication a dynamic delay compensation structure is necessary. thus, alternative smith predictor structures were used for the dynamic delay compensation, [2], [4], [5], [9], [8]. the following simulations were performed using smith predictor structures on a robotic arm acted on by a quanser srv-02 servo-motor. 954 i.c. res.ceanu, g.c. călugăru, c.f. res.ceanu, n.g. bîzdoacă figure 8: modified smith predictor structure figure 9: system response to a signal generator figure 10: system response in case of delay occurence figure 11: system response when using a modified smith predictor cooperative robot structures modeled after whale behavior and social structure 955 figure 12: improved smith predictor structure figure 13: position control of the quanser srv-02 plant using an improved smith predictor figure 14: step response of the system using the improved smith predictor and a pi controller 956 i.c. res.ceanu, g.c. călugăru, c.f. res.ceanu, n.g. bîzdoacă 5 conclusions this paper analyses the possibility of modelling cooperative robot structures after the behavior and communication habits of whale pods. certain key aspects are studied like the modelling of whale communication using data fusion techniques, determining the operation span in fault conditions and the dynamic delay compensation modelled also after whale communication. aspects like communication and social structure of whale pods are well documented in literature. what this paper is trying to establish is that there is the possibility of implementing this complex behavior in a cooperative robot structure with a well determined purpose. further research and the study of other key behavior aspects should lead to the appearance of another class of problems based on real-life phenomena-the evolution of whale pods in matters of communication. acknowledgement. this work was supported by the strategic grant posdru/89/1.5/s/61968, project id61968 (2009), co-financed by the european social fund within the sectorial operational program human resources development 2007-2013 bibliography [1] k.j. astrom, b. wittenmark, computer controlled systems: theory and design, prenticehall, englewoods cliffs, nj, 1984 [2] k.j. astrom, c.c. hang, b.c. lin, a new smith predictor for controlling a process with an integrator and long dead-time, ieee transactions on automatic control, 39(2), pp. 343-345, 1994 [3] y.u. cao, a.s. fukanaga, a.b. kahng, cooperative mobile robotics: antecedents and directions, autonomous robots, 4, 1-23, kluwer academic publishers, boston, 1997 [4] d. feng, d.,wencai, l. zhi, new smith predictor and nonlinear control for networked control systems, proceedings of the international multiconference of engineers and computer scientists, march 18-20, hong kong, vol. 2, pp. 1184-1188, 2009 [5] d. feng, q. qian, study of ncs with improvement smith predictor and fuzzy immune control, proceedings of the international conference on intelligent systems and knowledge engineering, chengdu, china, 2007 [6] d.l. hall, j. llinas,handbook of multisensor data fusion, crc press, new york, usa, 2001 [7] j.g. truxal, m.j. shooman, w.r. blesser, j.w. clark, remote control, in the handbook of telemetry and remote control, mc-graw hill, new-york, pp 1-169, 1967 [8] j. velagic, design of smith-like predictive controller with communication delay adaptation, world academy of science, engineering and technology, no. 47, pp 199-203, 2008 [9] m. veronesi, performance improvement of smith predictor through automatic computation of dead time, yokogawa technical report english edition, pp 25-30, 2003 [10] x. yang, bio-inspired computing and networking, crc press, 2011. [11] http://www.eoearth.org/article/whale_communication_and_culture [12] http://www.napali.com/na_pali_coast/hawaii_whale_watching/social_communication.html [13] http://www.whales.org.za/facts_communication.aspx international journal of computers communications & control issn 1841-9836, 11(3):428-440, june 2016. checking multi-domain policies in sdn f.a. maldonado-lopez, e. calle, y. donoso ferney a. maldonado-lopez systems and computing engineering department universidad de los andes, bogotá, colombia fa.maldonado1897@uniandes.edu.co eusebi calle bcds, broadband communication and distributed systems universitat de girona, spain e.calle@udg.edu yezid donoso* systems and computing engineering department universidad de los andes, bogotá, colombia *corresponding author: ydonoso@uniandes.edu.co abstract: programmable network like sdn allows administrators to program network infrastructure according to service demand and custom-defined policies. network policies are interpreted by the centralized controller to define actions and rules to process the network traffic on devices that belong to a single domain. however, actual networks are multi-domain where several domains are interconnected. then, because sdn controllers in a domain cannot define nor monitor policies in other domains, network administrators cannot ensure that their own policies, origin policies are being enforced by the domains not directly managed by them (i.e. foreign domains). we present audit, a multi-domain sdn policy verifier that identifies whether an origin policy is enforced by foreign domains. audit comprises (1) model for network topology, policies, and flows, (2) an audit protocol to gather information about the actions performed by network devices to carry the flows of interest, and (3) a validation engine that takes that information and detects security policy violations, and (4) an extension to the openflow protocol to enable external auditing. this paper presents our approach and illustrates its application using an example considering multiple sdn networks. keywords: network operating systems, software-defined networking, network management, policy verification 1 introduction in software-defined networking (sdn), network administrators use software languages to define how network traffic is processed and delivered. they use these languages to implement network policies, concrete rules about how a network must deal with specific types of traffic known as flows. for instance, these languages can be used to specify which users or network machines can connect to specific servers; which network devices must be used to deliver specific types of traffic, or which bandwidth can be assigned to specific flows. sdn is based on the separation of control and data planes. on one hand, the control plane remains on a centralized server called controller that makes decisions about how the traffic is processed. the controller is responsible for managing connections, addressing, and routing protocols. applications at the controller use specific-sdn protocols such as openflow [14] to instruct elements in the data plane how to process and deliver the network traffic. the data plane consists of network devices, or datapaths, responsible for packet forwarding and switching. copyright © 2006-2016 by ccc publications checking multi-domain policies in sdn 429 in the sdn architecture, a single controller manages policies and behavior of a network domain. only the domain controller has access to the rules used by each network device in its own domain. thus, neither a network device nor a controller can access information about rules from other network domains. although this is perfect to deal with policies in a single domain, a network administrator cannot observe how a external network, out of its domain, handles the traffic. with the current sdn architecture, the administrator is not able to enforce nor monitor multidomain policies. that is because forwarding rules must be implemented on multiple domains. for instance, today is very common that network traffic is delivered using a internal network such as lans, and external networks such as wans and internet. if the network administrator wants to enforce or monitor a network policy, she can define applications in her own network domain but cannot do it in the external domains. unfortunately, she cannot check if external domains enforce a network policy because she cannot determine how the traffic is delivered in those external networks. this situation can be specially critical in network security policies. for example, essential traffic that is delivered through external networks can be duplicated or redirected to other network machines using a simple application in the external domain sdn controllers. due to network administrators cannot get access to the rules in the external networks, they are disabled to detect these situations neither validate if a policy is achieved. we propose a mechanism to audit network policies in multiple domains called audit. our approach overcomes these sdn limitations and allows to network administrators to validate if the network policies are enforced by a external domain. audit comprises three modules: an extension to the openflow protocol to enable external auditing, the audit interface for network devices that gathers information about the actions performed in external domains to carry the flows of interest, and a validation engine that runs into the internal network controller and detects policy violations. other mechanisms were suggested to check network policies. for example, hinrichs [4] developed a declarative language called flow-based management language (fml) to describe network policies and configuration in a high-level and declarative approach. this fml is a high-level declarative language, based on flows, that checks the first packet of every flow against the policy. lately, monsanto et. al. [16] introduce a declarative language called netcore. it is a high-level declarative language that describes the desired behavior of the network but does not deepen the implementation of that behavior. with netcore is possible to express packet forwarding policies for sdn. afterwards, soulé et. al. present merlin [21]. merlin is also a declarative language based on logical predicates and regular expressions with which a network administrator can write network policies. in contrast to previous works, we propose to express network policies as predicates but use a sat solver and a model finder to evaluate predicates, find inconsistencies and detect policy violations. audit uses alloy [6] to describe the network topology, policies and network traffic. mirzaei et al. proposed used alloy to verify network properties in [15]. in this case they model internal states of a network and openflow switches. in summary, we introduce the foreign controller verification problem, we define multi-domain policies in programmable networks, a mechanism to gather information from external sdn domains, and a validation engine that uses gathered information to check if a network policy is enforced by the external domain. rest of this paper is organized as follow: section 2 explains the problem of auditing own policies at external domains; then we present a model to illustrate network topology, paths, forwarding rules and policies in section 3. then, section 4 introduces audit protocol and functionality, and presents an example. finally, section 5 concludes the paper and presents 430 f.a. maldonado-lopez, e. calle, y. donoso future work. 2 auditing policies in multi-domain networks a network policy is a set of conditions, constraints, and settings about how a specific type of traffic must be managed by a network. it also includes which users and hosts are authorized to create connections, and the circumstances under which they can or cannot connect. network policies are the accurate and unambiguous way to specify the traffic behavior. initially, stone et al. proposed a path-based policy language (ppl) that abstracts topological (physical) paths and flows to check network properties [22]. now, with programmable networks as sdn, new network policy abstractions are under development, therefore the challenges in policy checking open a rich field of study. high-level declarative language were proposed to represent network policies with more expressiveness. declarative languages such as fml [4, 5] express network policies in terms of flows. for general purposes, hinrichs developed a declarative language called flow-based management language (fml) to describe network policies and configuration in a high-level and declarative approach [4]. fml is based on flows, and checks the first packet of every flow against the policy. fml identifies a network flow by: source and target for users, hosts, and access points, in addition to protocols and requests a flow is the specification of a traffic, sometimes is called a session, that contains common attributes such as source, destination, protocol, but also can specify more granular characteristics as duration, valid time, users, data format and so on. then those policies are processed using datalog to find matching flows. other languages were designed for sdn are merlin and netcore. merlin [21] is a framework to write network policies for sdn. netcore [16] is a language for describe forwarding rules and it is integrated with another framework called frenetic, a project from cornell and princeton universities. these languages allow network administrators to define policies in a single-domain networks. they did not contemplate checking policy enforcement on a third-domain. in contrast to previous approaches, audit offers not only the ability to write and check network policies, it is unified with the controller and extract forwarding data and check it. audit also uses the flow specification and checks if the set of flows is valid for a given topology. moreover, audit reports inconsistencies in terms of flows not only as instructions at the hardware implementations. for example, suppose that a network policy defines only computers assigned to members of it department can get access to database servers. we can write this policy as: allow(src,target) | src ∈ it ∧ target ∈ database (1) deny(src,target) | src /∈ it ∧ target ∈ database (2) expression (1) means that the network must allow flows from it to database servers. due to this policy must be closed, (2) denies any flow from other machines to the same servers. in sdn networks, policies are enforced by its domain controller which rules the behavior of every forwarding device –switch– in its domain. the big question is: can the network administrator monitor that this policy is achieved? 2.1 policies in multi-domain networks in multiple-domain networks, each domain is managed by its own controller. in our example scenario depicted in figure 1.a, the domain a is ruled by its controller ca, and operates the it department and its users. the external domain b, managed by the controller cb, operates checking multi-domain policies in sdn 431 s3 it s4 s6 s5s2 ca cb s1 db s1 flowtable match action src = it ∧ dst = db ⟨ fwd s3 ⟩ s3 flowtable match action src = it ∧ dst = db ⟨ fwd s4 ⟩ a) multi-domain network scenario. b) flowtables for devices in domain a. figure 1: domain a may send a policy to be implemented in domain b, but there is not guarantee b implements the policy correctly. the database servers. network administrator supervises her own controller ca, and may install forwarding rules on devices s1, s2, and s3 to deal traffic generated by the it department. network controller ca cannot access the rules in forwarding devices in the domain b neither compel controller cb to install required forwarding actions into the devices s4, s5, and s6. a multi-domain policy must be enforced by own and external domains. the controller ca may share the policy with the controller cb, and awaits that cb implements the policy in its devices. however, there is not certainty that delivered traffic in the external domain b obeys any policy defined by a. following the example, the administrator of domain a cannot enforce policies related to deliver traffic to the database servers, because the domain b is external. 2.2 challenges in multi-domain networks each network controller is in charge of configuring switching devices on its domain. figure 1.b shows the required configuration installed on devices in the path of domain a that process the flow until it reaches the next domain. network configuration are rules that implements the policy. in this case, the configuration conducts the flow traffic from it department to database servers. this traffic arrives to switch s1, then is forwarded to switch s3, and finally it is forwarded to domain b interface, switch s4. clearly, a controller unknowns and cannot handle implemented configurations in external domains. however, administrator want to know if their policies are enforced in external domains. because of database servers are located in external network, for instance it is hosted by another company, the above policy redirects flows from the it department to external servers but deny the flows originated from sources other than it department. however, usually companies rely on external networks such as wans and internet to deliver network flows. since the configuration of network device is protected information, it is only accessed by its own domain controller, and administrator wants to check if the external controller applies a policy on its domain, we have identified a main challenge: how to detect if a policy is enforced by a external domain? and how to audit the policy enforcement without reveal risky information? 2.3 policies in programmable networks in sdn environment there are some languages to describe network policies. for example, netcore is a high-level declarative language that describes the desired behavior of the network but does not deepen the implementation of that behavior. with netcore is possible to express packet forwarding policies for sdn networks [16]. another work, merlin [21] is also a declarative language but based on logical predicates and regular expressions which can be solved using linear programming to determine forwarding paths. 432 f.a. maldonado-lopez, e. calle, y. donoso verification of sdn configurations is focused on check network properties that follow a rule. for instance veryflow [10] creates a network-wide invariants and checks them against rules. fattire [19] uses regular expressions and writes policies in this way to be able to validate. other works attend to find conflict rules, rules that contradict earlier ones. in such a way fortnox [18] checks new flow-rules against a flow-constraint set, and authenticates the source of rules by means of digital signatures. another illustration is netplumber [9] that searches if a candidate rule introduces network misconfigurations or policy violations . it executes a procedure called header space analysis (hsa) over dependency graphs to find conflicts. these approaches examine the forwarding tables from each network device and could check if they conform with the specified policy. however, none of these approaches support the validation of policies in external domains. from policies to flow-rule implementation network applications – or functionality– run on a controller and define the general behavior or policies by installing specific configurations on each switching device. regularly, those programs use openflow (of) [17] to communicate controllers and forwarding devices, and install, modify, or get flow-rules that specify how a device deals with specific traffic. a flow-rule is a pair <match,action> map on the device’s flowtable. a flow-rule defines which action is performed once a packet header matches the match pattern. of defines a set of messages to control the internal information on each device, and rules used to process a flow. in summary, of messages can add, modify, and query rules from device’s flowtable. actions also include: dropping a packet (drop), forwarding a packet to a specific port (fwd), or report the set of installed rules (status). the rule-set is closed, and the packet is reported to the controller if its header does not match any rule. 3 topology and policy models we describe a model that involves the physical topology and paths; and network operation definitions such as flows, policies, and conflicts. first, we use the following specification for networks. we will use the relation of correspondence later on when we write the model in alloy. definition 1 (network graph). a network graph g is a duple (n,l) such that n is the set of nodes, and links l := (n,n,c) is the correspondence c with domain and co-domain n → n. considering the links relation l, we write the function links(n) to denote {m|(n,m) ∈ l}, the set of all the nodes m connected to a node n. in addition, for convenience, we write n → m to express from n to m sometimes instead of (n,m) ∈ l. a well-formed network g must satisfy the following rules: 1) network is connected, ∀n ∈ n| links(n) ̸= ∅, i.e. there are not isolated nodes; 2) no self-loops, @n ∈ n|n → n, i.e. there are not links from a node to itself; and, 3) for all link, there is an arrival node, @n ∈ n|links(n) ∩ n = ∅. it is important to note that nodes in our model does not represent a network device – router nor switch–, a node denotes a device port. then under this abstraction, the link relation represents forwarding rules, not just physical links in the topology. definition 2 (path). a path p is a tuple (s,t,np,lp). a source node s, a target node t, a subset of nodes np = {n1,n2, . . . ,nk}, and a subset of links that creates the sequence lp = {(s,n1),(n1,n2), . . . ,(nk−1,nk),(nk, t)}. a path, can be described as a list of nodes that maintains a sequence. path nodes np ⊂ n, path links lp ⊂ l. a well-formed path satisfies: 1) all implicated nodes in the links belong to node set, ∀(na,nb) ∈ lp =⇒ {na,nb} ∈ np, 2) {s,t} ∈ n, source and target nodes are in the network, and 3) source node opens and target node finishes a path, @(n1,n2) ∈ lp|n2 = s∨n1 = t. checking multi-domain policies in sdn 433 at this point is appropriated to describe the transitive closure. we use this concept to tackle the reachability property when describe a path. a binary relation r is transitive if contains tuples in the way a → b and b → c, but also contains a → c. this relation is noted as r+ and contains r. finally, a path has no loops, considering the relation l and the function links+(n) is the set of all nodes that can be reached from n. then a path has no loops if @n ∈ np|n ∈ links+(n). also for convenience, we denote a path as a node sequence as ⟨s,n1,n2, . . . , t⟩. we include a wildcard symbol (∗) to denote any unspecified node or sequence of nodes. for example, the path p = ⟨a,∗,c⟩ is the path that starts at node a and ends at c. 3.1 traffic flows flow is the fundamental abstraction for our model. for the reader it is similar to communication session supported by a set of paths and device configurations. traffic flow defines the high-level network parameters needed to create a competent communication channel. a flow provide enough detail to describe a set of feasible sessions, and provides a form to group and manage these sessions. definition 3 (flow). a flow is a sequence of traffic constraints f = (f1, . . . ,fn). each term fi is a restriction over a traffic characteristic, strongly related to filters on packet fields. due to we illustrate flows as traffic constraints. reader should note we indicate packet-field match as those constraints. the used definition allows us to construct flexible and composed communication flows. a term of flow involves transport-layer protocol, source / destination at third layer, or applications. also, we use set operators over these packet fields to define the flow. for instance, flowa = {protocol = tcp,src_ip = 192.168.5.10,dst_ip = 192.168.7.10} details a traffic flow between those ip addresses and tcp as transport protocol. note that this flow only defines the traffic in one way. it means, the other direction is not under this definition. however, flow definition is only associated to communication characteristics and packet fields, but not the set of paths that supports the flow. 3.2 policies, conflicts and semantics in order to define the set of paths that implements a policy and then identify policy conflicts and violation we follow the guidelines of harel and rumpe [8] to specify a modeling language ł describing the syntactic domain ll, the semantic domain sl and the semantic function ml : ll → sl, also traditionally written j·kl. the policy is a set of rules that achieves a management procedure. forwarding rule is the action that a node executes to forward a packet into a computer network. rules are described in terms of flows, by the previous definition. definition 4 (policy). a network policy is a tuple π = (f,p,c,α) s.t. f is the target flow composed of packet-field values, p is the set of paths that support the flow, c is a set of conditions, over the flow f or path p , and α is an action, regularly {permit,deny}. essentially, a policy resolves whether allow the flow f over the set p conclude on a specific action α. it is decisive and produces a configuration network that allows or deny the traffic flow. for example, the network administrator wants to apply the policy: ana is a user with profile of it member, who is in the subnetwork 192.168.5.*/24 (s1), is allowed to access the database at subnetwork 192.168.7.*/24 (s6) and port 1521, and her traffic must go through the router s3. now the manager has to detail the policy. in order to do that she solves the following steps: 1. path s1_s6 := <s1,*,s3,*,s6>, 434 f.a. maldonado-lopez, e. calle, y. donoso 2. transport protocol: = tcp, 3. port number: = 1521; 4. the conditions user = ana, and ana ∈ it member; 5. finally the policy decision: permit in this way, we can find ω, the set of configurations and instructions that implements the paths and the policy π. note that ip addresses, user groups, traffic class and protocols should be modeled as sets. on the other hand, ordered items such as time are modeled as sequences to be able to compare them using ≤ and ≥ operators. ω = impl(p : path|(s1_s6) ∧ protocol = tcp ∧ port = 1521 ∧ ana : user ∈ it member)(3) this representation of a policy, utilizing logical conjunctions, allows us to express this policy as a conjunctive normal form predicate (cnf), and logically solve it. moreover, we are able to check a formal solution using a model finder as alloy [7], compare solutions, or find inconsistencies. definition 5 (policy semantic). a semantic of a policy jπ,gk is the set of paths that implements the flow over a path on g and achieves the policy. jυk = ω is the semantic of all network policies and produces the set of all paths implemented on the network. the complete network configuration is denoted by ω. the semantic functionjπ,gk of a policy contains the sets of paths, flow definitions, conditions and the network g that satisfies the policy π. obviously, the policy π is valid in a network g, if jπ,gk is not empty. definition 6 (policy conflict). a policy conflict occurs when a set of policies are not implemented by any path or there are inconsistencies that prevent the generation of a path. essentially, if the semantic of a policy is empty, means that there is not set of configuration of paths that satisfies the policy. given two valid policies π1 and π2, they are not conflicting in the network g if jπ1 ∪ π2,gk is not empty. that is, two policies are not conflicting if there is a set of paths, flows, and restrictions in the network g that satisfies both policies. in contrast, we say that two policies π1 and π2 are conflicting in g if |jπ1 ∪ π2,gk| = 0. definition 7. (minimal diagnosis) given a set of policies π ⊆ υ such that jυ \ πk ̸= ∅, the minimal set π is the minimal diagnosis. we refer to this minimal set as the littlest configuration applicable without conflicts. now, we need a tool, called a verifier, able to find (calculate) the semantic function jπ,gk and verify if that set is empty and the minimal diagnosis of that set. we use similar tools also for validating paths on network infrastructure [12], and recently we show how to use of minimal diagnosis to detect and prevent firewall-rule conflicts on software-defined networking [13]. 4 checking multi-domain policies with audit audit is an auditing extension for openswitch protocol and openflow controller that allows domain controllers to validate security policies on a foreign domain. our proposal creates a language definition and transformation to audit network policies. we use alloy to obtain a set of tuples that satisfy the policies (exactly the semantic function). if alloy does not find any element (the set is empty), the policy set is invalid or conflicting. checking multi-domain policies in sdn 435 foreign alloy sat solver owner domain figure 2: audit architecture audit works as a validation protocol that allows a controller to gather auditing information from external domains and validate the origin policy. it performs two phases: gathering network information and validation process. first, the controller in the origin domain gets information we called audit packets that is routed through the network as regular traffic. then, when auditor packet reaches devices in the external domain, these network devices report a subset of its own flowtable to the controller in the origin domain. finally, the controller in the origin domain process the gathered flowtables to obtain all the processing rules related to the flows of interest, and executes the validation engine that checks if the external domain is accomplishing the security policy. 4.1 audit extension openflow specifies a set of control messages between controller and forwarding devices. control messages include: modify-state to add or delete flowtables in the device, collect-statistics to read counters and device statistics, managing groups of flowtables. controller is also able to request device status, where the device reports the flowtable to the controller. audit uses regular controller primitives to request information from the flowtable on devices of external domain. information gathering once the controller enables audit on each network device, and the audit packet arrives, the device invokes ofpmp_table_features and the header match to filter a subset of the rule table that matches the header. thereby, it extracts a set of all the related flowtable entries (rfe). rfe = {e|e.src ⊙ p.src ∪ e.target ⊙ p.target} (4) for simplicity, our example only considers ip addresses and masks in the it-database scenario. our rfe are defined by (4). where p is the policy and ⊙ is the match relation. audit message figure 3 shows the structure of an audit message. it comprises the same flow header in order to be routed through the same path; moreover, it includes origin controller identifier, controller authentication data, other audit settings, and the list of fields and rules to be filtered by the device. 436 f.a. maldonado-lopez, e. calle, y. donoso flow header origin controller id audit settings controller signature list of fields list of policies figure 3: structure of an audit packet. the list of policies are constraints over packet fields. 4.2 audit protocol figure 4 shows the proposed protocol that allows controllers to enable audit protocol, gather information from foreign devices, and check policies. 1. involved domains subscribe an audit agreement that specifies the permission to create, send and process audit packets. then, all implicated domains update their module that recognizes the audit request and overwrites audit_enable variable. 2. origin domain a shares the traffic policy over it’s traffic with b. security policy described in section 1 2: database is only accessed from it department. external-domain controller cb enforces the security policy in its network, translates the policy into rules applicable to its infrastructure. 3. origin controller creates an audit packet. audit packet contains all packet fields of the flow traffic. this procedure request information about how the traffic is delivered. thus, foreign network devices process the audit packet as they process regular data flow, or use a interface to return the related flowtable entries (rfe). 4. foreign devices reply the audit packet with the rfe. the list of entries from its flowtable. 5. at the origin, the controller of a executes the validation engine, determines if there is a subset of rules that violates the policy, and writes a conflict report. s3 it s4 s6 ca cb s1 db audit agreement tra�c policy audit request forwarding rules con�gura�on set veri�ca�on engine conflict report figure 4: audit protocol execution. devices from domain b report packet rules to ca, then a verifies traffic policy and generates an auditing report. 4.3 multi-domain policy checking at the end, origin controller owns all rules related with the traffic policy that comes from the external domain, and validates the set of related flowtable entries (rfe) against the policy to identify violations. figure 5 shows the set rfe that ac gathers from domain b. it is a list of rules related with the traffic policy defined in expressions 1 and 2. then, the validation engine checking multi-domain policies in sdn 437 determines if this subset of rules violates the policy. this policy-rule validation engine could be similar to [9]. s4 flowtable s6 flowtable match action match action src = it ∧ dst = db ⟨ fwd s6 ⟩ src = it ∧ dst = db ⟨ fwd db ⟩ figure 5: related flowtables entries from devices in domain b. 4.4 inference engine based on sat we develop an inference engine able to check implementation procedures against network policy. topology is defined as a set of nodes. links is a closure relation of arity two over the node set. specifically for this project, we model device ports as nodes, and links by a closure relation over nodes. figure 6 shows how the topology is represented in terms of device ports. a forwarding rule, the simplest instruction that redirects a packet from one port to another is represented as part of the path. under this perspective, the configuration is part of the topology. forwarding rules are shown in the figure as dotted lines. these soft-links are considered as regular topology once the model is built. an optimization opportunity arises due to forwarding rules create soft-links that are interpreted as part of the topology. if the traffic policy is quite specific, the resultant topology is disconnected graph, even is reduced to some paths. this abstraction of nodes as device ports, and soft-links can reduce the complexity at evaluation time. s1 s2 s3 s4 s5 s6 db it figure 6: representation of a network topology based on ports from the original deployment. blocks are network devices and circles are ports. forwarding rules are depicted as dotted lines that connect two ports. flow is depicted as the list of constraints over packet header, traffic movement sense, and topological considerations. for example, the flowtable described in figure 1.b is the interpretation of constraints, source and destination addresses, over fields of a packet header. reader should note that flowtable also denotes the soft-link between two ports generated by the forwarding rule. nevertheless, this soft-links are part of the topology equally as wired links do. in other words, the model does not discerns one from another. communication details such as protocols or port numbers are considered sets if these elements are part of the packet header. due to our work uses set theory notion of order is not considered in this model, for that reason we cannot have policies with arithmetic conditionals. for example, the expression if the number port is greater that 1024, then ... is invalid in our approach. our inference machine is implemented on alloy [6]. it is fed with external-domain information gathered by the audit procedure or through services that exposes forwarding information. 438 f.a. maldonado-lopez, e. calle, y. donoso 5 conclusions and future work we presented openflow audit, a mechanism that checks if foreign domains are enforcing multi-domain network policies. audit helps to overcome policy-checking limitations of the sdn architecture. it comprises (1) an extension to the openflow protocol to enable external auditing, (2) an audit protocol to gather information about rules applied to specific network flows, and (3) a validation engine that uses flow information and determines if the external network is enforcing specific traffic policies. additionally, audit can identify policy violations. it informs the network configuration, rule of flow that infringes the policy and its identifier. in general terms, audit allows network administrators to gather information from external domains and determine if network policies are enforced in multiple domains. 5.1 experiments and results we implement and test our audit protocol using the floodlight controller 1. test cases are divided into two groups: information gathering, and violation inferencing. we run the controller on a server and deploy a test-network using mininet 2, which operate as external domain and implement the example topology used by sethi in [20]. from another terminal, which operates as owner domain, we run our audit interface and extract traffic information from the controller. audit implementation creates a topology representation, a policy inventory, and a configuration repository. thereafter, the inference engine is executed. audit writes, policies, configurations and topology as alloy instructions and executes the satisfiability solver. 0 500 1.000 1.500 10.703 13.518 16.333 19.148 time (ms) p ri m a ry v a ri a b le s found solutions minisatprover jni minisat jni figure 7: solutions from alloy implementation. the same implementation is evaluated using two solvers: minisat and minisatprover with minimal unsatisfiable core. figure 7 shows two evaluations over the same topology and set of policies. we implement the same fattree topology also described in [20] to compare our approach. audit takes less than a second using the minisat solver, which only finds if an instance accomplishes the set of policies. on the other hand, if the network administrator wants to determine the set of policies violated by the external domain, she executes audit with the minisatprover option and could take up to 1.5s. these measures are lower than the values reported on [20], for the same fat tree topology composed of 20 switches, 16 hosts, and 48 links. we test forwarding and reachability on a intel i5 at 3.0 ghz, with 3.74 gb ram. with the intension to show how state explosion and variable affect the performance we test audit for 930k, 1.5m, 2.2m and 2.8m of states, which are represented on primary variables shown in figure 7. however, audit does not have complete information about the network as opposed to netplumber [9]. moreover, audit requires the deployment of our openflow extensions into the 1 http://www.projectfloodlight.org/floodlight/ 2 mininet.org checking multi-domain policies in sdn 439 network devices in those external domains. commercial products (i.e. switches from companies such as ibm or hp) do not support the deployment of new extensions without a firmware update. we expect that future experimental implementation shows the benefits of audit and can be a foundation to introduce multi-domain policy validation into the standard. letting external domains gather information about network flow processing may represent a potential security risk for external controllers. in addition, controllers in external domains may include programs that hide information or mimic policy enforcement. future work focuses on evaluating security risks on our experimental implementation in order to determine which additional mechanisms are required to ensure safe auditing of multi-domain policies. acknowledgment ferney a. maldonado-lopez is a recipient of a the fellowship n528–2011 for doctoral studies by the colombian department of science, technology and innovation colciencias. we would like to thank to comit members for their reviews and meaningful recommendations. bibliography [1] al-shaer, e.; marrero, w.; el-atawy, a.; elbadawi, k. 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(2001); network policy languages: a survey and a new approach, ieee network, 15: 10-21. international journal of computers communications & control issn 1841-9836, 10(1):112-122, february, 2015. energy-efficient design for relay-aided mimo-ofdm cognitive radio networks b. wu, j. zuo, l. zhao, c. zou bin wu*, jiakuo zuo, li zhao, cairong zou school of information science and engineering, southeast university, nanjing, 210096, china njwubin@seu.edu.cn, zuojiakuo85418@gmail.com zhaoli@seu.edu.cn, cairong@seu.edu.cn *corresponding author:njwubin@seu.edu.cn abstract: with the explosive growth of high-rate multimedia services and promptly boomed energy consumption in wireless networks, energy-efficient design is become more and more important. in this paper, we investigate energy-efficient design for relay-aided multiple-input multiple-output-orthogonal frequency division multiplexing (mimo-ofdm) cognitive radio networks. we formulate an energy-efficient power allocation problem, which takes a form of nonlinear fractional programming. to solve the problem, we first make a joint concave approximation to the original problem which facilitates the optimal algorithm development. then, we derive an equivalent parametric optimization problem of the approximated problem. finally, an iteration energy-efficient power allocation algorithm is presented. numerical results reveal that the proposed algorithm can improve energy efficiency over traditional capacity maximization method. keywords: cognitive radio, power allocation, mimo, relay, fractional programming. 1 introduction cognitive radio (cr) and multiple-input multiple-output-orthogonal frequency division multiplexing (mimo-ofdm) communications have been considered as a promising scheme to solve the spectrum scarcity problem and improve the quality of wireless communications [1]. recently, to reduce the multi-path fading and improve the channel capacity, cooperative relaying technique is considered as a potent means to be adopted in the cr networks. thus, the researches of relay aided mimo cr networks are being received a growing attention in recent years [2, 3]. in [4], the relay selection and beamforming problem for the non-regenerative mimo cognitive multi-relay network was considered and an optimal scheme was proposed via maximizing the capacity of the sus by selecting the best cognitive mimo relay. [5] studied a new paradigm for cr networks, which allowed the secondary users (sus) to cooperatively relay the traffic for the primary users (pus) while simultaneously transmitting their own traffic, and proposed a novel mimo cooperative cognitive radio networks framework. [6] considered the power allocation problem for mimo two-way cr sytem under a specturm sharing scenario, and presented an analytical expression of the optimal power allocation to each antenna of the treminals. [7] studied the power and channel allocation, and relay assignment for mimo-ofdm based cooperative cr networks and proposed an optimal complexity algorithm and a sub optimal low complexity algorithm. [8] investigated subcarrier pairing and power allocation for mimo-ofdm relay-aided cr networks and used environmental learning algorithm to mitigate the interference of the pus. in the previous works, most researches intend to improve the throughput of relay aided cr systems. however, the energy efficiency (ee) has been considered more and more important in future wireless communication networks. the wireless devices and equipments consume about 9% of the total energy of information technology, the communication and information technology already contributes to about 2% of the global carbon dioxide emissions [9, 10]. therefore green copyright © 2006-2015 by ccc publications energy-efficient design for relay-aided mimo-ofdm cognitive radio networks 113 communication, which emphasizes on ee in wireless communication networks, is attracting more and more attention [11, 12]. a large amount of work has bee reported on energy-efficient design for cr networks [13-15]. for mimo cr networks, [16] studies ee optimization problem of mimo cr broadcast channels to improve the system throughput for unit energy consumption. in [17], the throughput and energy efficiency optimization under quality-of-service (qos) constraints for mimo cr systems are studied. in [18], a promising framework of spectrum sharing strategy selection based on ee is proposed for mimo cr interference channels. in this paper, we focus on energy-efficient power allocation for relay-aided mimo-ofdm cr networks. we formulate an optimization problem related to maximization of ee of the consider network under total power constraints of cognitive source node and cognitive relay node, and interference constraints of primary users. since the original optimization problem is difficult to solve directly, we first get an approximated problem of the original problem, and then transform the approximated problem into an equivalent convex optimization problem. a new iterative energy-efficient power allocation scheme is presented at last. the rest of this paper is organized as follows: in section 2, we introduce the system model and formulate an energyefficient power allocation problem. in section 3, the double-loop iterative method is presented. finally, simulation results and conclusions are presented in section 4 and 5. the following notations are used in this paper, cm×n denotes m × n complex matrix, (·)h denotes the conjugate transpose, (·)+ means max (0, ·), the distribution of a circularly symmetriccomplex-gaussian vector with mean vector x and covariance matrix y is denoted by cn (x,y) , diag (·) returns a square matrix with the elements of (·) on the diagonal. sr ng ssn srn sdn pu 1 pu l rd ng s p 1 n g h s p 1 n g s p l n g h s p l n g d p1 n g h d p 1 n g h d p l n g d p l n g r p 1 n g h r p l n g r p l n g hr p 1 n g figure 1: relay-aided mimo-ofdm cognitive radio network 2 signal model and problem statement consider a two-hop relay-aided cognitive radio (cr) network shown in fig.1, there are a secondary source node (ssn), a secondary destination node (sdn), and a secondary relay node (srn). the relay-aided cr network coexists with l licensed primary users (pus). the ssn communicates with sdn through srn, and they share the whole spectrum with pu. ssn, sdn, and srn are equipped with ms antennas, each pu is equipped with mp (mp ≤ ms )antennas. the relay-aided cr network adopts ofdm modulation for transmission, and the total number of available subcarriers for cr network is n. let gsr (n) ∈ cms×ms and grd (n) ∈ cms×ms denote the channel matrices from ssn to srn and srn to sdn over the n-th subcarrier respectively. let gspl (n) ∈ cmp×ms , grpl (n) ∈ cmp×ms and gdpl (n) ∈ cmp×ms denote the 114 b. wu, j. zuo, l. zhao, c. zou channel matrices from ssn to l-th pu, from srn to l-pu, and from sdn to l-pu, respectively. the channel matrix from l-th pu to ssn, sdn, and srn are ghspl (n), g h rpl (n) and g h dpl (n). assume there is no cooperation between cr network and pus, environmental learning (el) method [19] is performed to control the interference to the pus. via el learning method, secondary nodes estimate the null space information of the channels between secondary nodes and pu. assume the cognitive beamforming (cb) matrices at ssn, srn, sdn for the n-th subcarrier are uspl (n) ∈ cms×(ms−mp ) , urpl (n) ∈ cms×(ms−mp ) and udpl (n) ∈ cms×(ms−mp ), respectively. these cb matrices satisfy uhspl (n) gspl (n) = 0, u h rpl (n) grpl (n) = 0 and uhdpl (n) gdpl (n) = 0. however, the accurate cb matrices are difficult to be acquired, therefore in practical applications, the estimated cb matrices ũspl (n), ũrpl (n) and ũdpl (n) are used. in the first hop, the received signal at crn in the n-th subcarrier is given by: yr (n) = gsr (n) ũspl (n) xs + g h rpl (n) w 1 p + zr (n) (1) where xs is the transmitted signal of ssn, w1p is the pu interference to srn in the first hop. zr (n) ∼ cn ( 0,σ2rims ) is the additive white gaussian noise (awgn) at srn. in the second hop, srn first filters received signal yr (n) with ũ h rpl (n), and then precodes the filtered signal by forwarding matrix b (n), finally precodes the resultant signal by ũdpl (n). therefore, the received signal at sdn in the n-th subcarrier is: yd (n) = grd (n) ũrpl (n) b (n) ũ h rpl (n) yr (n) + g h dpl (n) w 2 p + zd (n) (2) where w2p is the pu interference to sdn in the second hop, zd (n) ∼ cn ( 0,σ2dims ) is additive white gaussian noise (awgn) at sdn in the n-th subcarrier. finally, the cr-dn does receive cb by filtering yd (n) with ũ h dpl (n), we have y = d2 (n) b (n) d1 (n) xs + d(n)2b (n) n1 (n) + n2 (n) (3) where d1 (n) = ũ h dpl (n) grd (n) ũrpl (n) and d2 (n) = ũ h rpl (n) gsr (n) ũspl (n), n1 (n) = ∆uhrpl (n) g h rpl (n) w 1 p +ũ h rpl (n) zr (n), n2 (n) = ∆u h dpl (n) g h dpl (n) w 2 p +ũ h dpl (n) zd (n), ∆uhrpl (n) = ũ h rpl (n) − u h rpl (n), ∆u h dpl (n) = ũ h dpl (n) − u h dpl (n) denote the first-order perturbations of the cb matrices due to imperfect environmental learning [8, 19]. let the singular value decomposition of {dk (n)}k=1,2 be dk (n) = uk (n) λk (n) v h k (n) (k = 1,2), and define qm,n = √ prm,n psm,nλ sr m,n+σ 2 r , where psm,n and p r m,n are the transmit power of ssn and srn, λsrm,n is the eigenvalue of gsr (n). thus, the forwarding matrix can be defined as b (n) = v h2 (n) q (n) u1 (n). multiplying y with u h 2,n at sdn , we have y = λ2 (n) σ (n) λ1 (n) v 1 (n) xs + λ2 (n) σ (n) u h 1 (n) n1 (n) + u h 2 (n) n2 (n) (4) where σn = diag (q1,n,q2,n, · · · ,qm,n). according to formula (4), the mimo-ofdm channel between ssn and sdn can be decomposed into n × ms parallel independent channels, therefore, the throughput of the relay-aided mimo-ofdm network is ctp (p) = 1 2 m∑ m=1 n∑ n=1 log2 ( 1 + psm,nαm,np r m,nβm,n 1 + psm,nαm,n + p r m,nβm,n ) (5) where αm,n = λsrm,n σ2 r +ψ1 , βm,n = λrdm,n σ2 d +ψ2 , λrdm,n is the eigenvalue of grd (n), ψ1 and ψ2 are constants and linear with 1 nel ( nel is the number of samples in el stage), p = { psm,n,p r m,n } is power energy-efficient design for relay-aided mimo-ofdm cognitive radio networks 115 vector. the overall power consumption at ssn and sdn can be expressed respectively as follows: pssn = τs m∑ m=1 n∑ n=1 psm,n + p s c (6a) psrn = τr m∑ m=1 n∑ n=1 prm,n + p r c . (6b) where τs and psc are the reciprocal of drain efficiency of power amplifier and circuit power at ssn. τr and prc are the reciprocal of drain efficiency of power amplifier and circuit power at sdn. the ee of the cognitive relay network while selecting the l-th crn for transmitting is defied as: ξee (p) = 1 2 m∑ m=1 n∑ n=1 log2 ( 1 + psm,nαm,np r m,nβm,n 1+psm,nαm,n+p r m,nβm,n ) τs m∑ m=1 n∑ n=1 psm,n + τ r m∑ m=1 n∑ n=1 prm,n + p s c + p r c (7) since we use the estimated cb matrices, the interferences to pus cased by ssn and srn are inevitably, the interferences cased by ssn and srn to l-th pu are ispl = µs σ2l m∑ m=1 n∑ n=1 psm,n (8a) irpl = µr σ2l m∑ m=1 n∑ n=1 prm,n. (8b) where µs and µr are constants and linear with 1nel , σ 2 l is the transmit power of the l-th pu signal. from (7), the objective of energy-efficient power allocation problem for the relay-aided mimo-ofdm cr network can be expressed as: op1 max psm,n,p r m,n≥0 1 2 m∑ m=1 n∑ n=1 log2 ( 1 + psm,nαm,np r m,nβm,n 1+psm,nαm,n+p r m,nβm,n ) τs m∑ m=1 n∑ n=1 psm,n + τ r m∑ m=1 n∑ n=1 prm,n + p s c + p r c (9) subject to   c1 : m∑ m=1 n∑ n=1 psm,n ≤ psth c2 : m∑ m=1 n∑ n=1 prm,n ≤ prth c3 : µs σ2 l m∑ m=1 n∑ n=1 psm,n ≤ ith, l = 1,2, · · · ,l c4 : µr σ2 l m∑ m=1 n∑ n=1 prm,n ≤ ith, l = 1,2, · · · ,l. where psth and p r th are the total power budgets of ssn and srn respectively, ith is the interference threshold of pus. c1 and c2 are transmission power constraints of ssn and sdn, c3 and c4 are the interference constraints of the pus. duo to lack of convexity, it is difficult to solving op1 directly. in the following, we make a joint concave approximation to op1 and introduce a new equivalent optimization problem via nonlinear fractional programming (nfp) [21]. 116 b. wu, j. zuo, l. zhao, c. zou 3 energy efficient power allocation algorithm to make op1 more tractable, the throughput ctp can be approximated at the high signalto-noise ratio (snr) as c̃tp (p) = 1 2 m∑ m=1 n∑ n=1 log2 ( 1 + psm,nαm,np r m,nβm,n psm,nαm,n + p r m,nβm,n ) (10) note: as in [20], c̃tp (p) is joint concave with psm,n and p r m,n. thus, we can also get the approximation of ee as: ξ̃ee (p) = c̃tp (p) ptotal (p) (11) where ptotal (p) = pssn + psrn. substitute ξ̃ee into op1, we get the approximated optimization problem op2 max psm,n,p r m,n≥0 ξ̃ee (p) (12) subject to c1 ∼ c4 for notational simplicity, we define ℵ as the set of feasible solution of op2, and let p ={ psm,n,p r m,n } be variable vector. define the maximum ee ρ∗ of network as follows: ρ∗ = c̃tp (p∗) ptotal (p∗) = max p∈ℵ c̃tp (p) ptotal (p) (13) where p∗ is the optimal solution of op2. introducing a new parametric optimization problem op3 op3 max psm,n,p r m,n≥0 { c̃tp (p) − ρptotal (p) } (14) subject to c1 ∼ c4 where ρ is non-negative parameter. since c̃tp (p) is joint concave with psm,n and p r m,n, for a given ρ, op3 is a convex optimization problem. next, introduce a theorem based on nfp [21]: theorem the optimal solution achieve the maximum ee if and only if max p∈ℵ { c̃tp (p) − ρ∗ptotal (p) } = c̃tp (p∗) − ρ∗ptotal (p∗) = 0 (15) with c̃tp (p) ≥ 0, ptotal (p) > 0. proof: similar proof can be found in [21]. the theorem implies that for fractional op2, there is an equivalent problem whose objective function is in subtractive form, e.g. c̃tp (p) − ρ∗ptotal (p) . therefore, solving op2 is equivalent to solve problem op3 for a given ρ and then update ρ until the theorem is satisfied. energy-efficient design for relay-aided mimo-ofdm cognitive radio networks 117 an alternative method solving op3 is through deriving the lagrange dual [22] of the optimization problem op3. the lagrange function of op3 is defined as bellow: lag ( p,θ1,θ2,{χl} l l=1 ,{ηl} l l=1 ) = 1 2 m∑ m=1 n∑ n=1 log2 ( 1 + psm,nαm,np r m,nβm,n psm,nαm,n+p r m,nβm,n ) −ρ [ τs m∑ m=1 n∑ n=1 psm,n + τ r m∑ m=1 n∑ n=1 prm,n + p s c + p r c ] −θ1 ( m∑ m=1 n∑ n=1 psm,n − psth ) − θ2 ( m∑ m=1 n∑ n=1 prm,n − prth ) − l∑ l=1 χl ( µs σ2 l m∑ m=1 n∑ n=1 psm,n − ith ) − l∑ l=1 ηl ( µr σ2 l ms∑ m=1 n∑ n=1 prm,n − ith ) (16) where θ1, θ2 , χl and ηl are the lagrange multipliers. therefore, the lagrange dual function of the primal problem op3 can be written as: dual ( θ1,θ2,{χl} l l=1 ,{ηl} l l=1 ) = max p≥0 lag ( p,θ1,θ2,{χl} l l=1 ,{ηl} l l=1 ) (17) the corresponding lagrangian dual problem of op3 can be expressed as: min θ1,θ2,χl,ηl≥0 dual ( θ1,θ2,{χl} l l=1 ,{ηl} l l=1 ) (18) the problem (17) is convex, according to the karush-kuhn-tucker condition: ∂lag ∂psm,n = 0 and ∂lag ∂prm,n = 0, then we have: 1 2 ln 2 αm,nβ 2 m,n ( prm,n )2( psm,nαm,n + p r m,nβm,n )( psm,nαm,n + p r m,nβm,n + p s m,np r m,n ) = ρτs + θ1 + µs l∑ l=1 χl σ2l (19) 1 2 ln 2 α2m,nβm,n ( psm,n )2( psm,nαm,n + p r m,nβm,n )( psm,nαm,n + p r m,nβm,n + p s m,np r m,n ) = ρτr + θ2 + µr l∑ l=1 ηl σ2l (20) solving the above two equations, we get the optimal power allocation solutions as: psm,n = 1 √ αm,nx1 (√ x1 αm,n + √ x2 βm,n )(1−(√ x1 αm,n + √ x2 βm,n )2)+ (21) prm,n = 1√ βm,nx2 (√ x1 αm,n + √ x2 βm,n )(1−(√ x1 αm,n + √ x2 βm,n )2)+ (22) where [x]+ = max (0,x), x1 = ρτs + θ1 + µs l∑ l=1 χl σ2 l and x2 = ρτr + θ2 + µr l∑ l=1 ηl σ2 l . note: (21) and (22) show that psm,n and p r m,n are either both positive or both zero, this implies that if power allocated to the n-th subcarrier in the first hop is zero, then no power is allocated to its corresponding subcarrier in the second hop, which meets the intuition very well. 118 b. wu, j. zuo, l. zhao, c. zou table 1: algorithm: approximated energy-efficient power allocation 1 initialization: initial ρ, ϖθ1, ϖ θ 2, ϖ χ l and ϖ η l , the maximum tolerance δ 2 repeat 3 repeat 4 update psm,n and p r m,n according to (21) and (22) 5 update ϖθ1, ϖ θ 2, ϖ χ l and ϖ η l according to (23) 6 until ϖθ1, ϖ θ 2, ϖ χ l and ϖ η l converge 7 update ρ = ξ̃ee (p) via (11) 8 until ∣∣∣c̃tp (p) − ρptotal (p)∣∣∣ ≤ δ the optimal dual variables can be obtained from the dual problem (18) using the subgradient method[23]. the dual variables could be updated as: θ1 = ( θ1 + ϖ θ 1 ( psth − m∑ m=1 n∑ n=1 psm,n ))+ (23a) θ2 = ( θ2 − ϖθ2 ( prth − m∑ m=1 n∑ n=1 prm,n ))+ (23b) χl = ( χl − ϖ χ l ( ith − µs σ2l m∑ m=1 n∑ n=1 psm,n ))+ (23c) ηl = ( ηl − ϖηl ( ith − µr σ2l m∑ m=1 n∑ n=1 prm,n ))+ (23d) where ϖθ1, ϖ θ 2, ϖ χ l and ϖ η l are the step length. according to the aforementioned analysis, we propose a two loop iterative algorithm to solve the approximated energy-efficient power allocation problem op2, which is termed as aee-pa and tabulated as in table 1. note: [23] shows that the subgradient algorithm can converge to the optimal solution of convex optimization problems within a small range. therefore, the inner loop can converge to the optimal solution of the dual problem (18) with in a small range. since op3 is convex optimization problem, the duality gap for op3 is zero, the inner loop also converges to the optimal solution of op3 within a small range. the detailed proves of the convergence of the outer loop, i.e. nfp can be found in [21]. 4 performance simulations we perform numerical simulations to evaluate the present some numerical experiments to evaluate the performance of our proposed scheme. without loss of generality, the channel gains are assumed to be rayleigh fading with an average power gain of 1db, and set the parameters n = 10, l = 2 , ms = 4, mp = 2, σ2r = σ 2 d = 10 −6w , σ2l = 1w , τ s = τr = 1, psc = p r c = 10 −2w , psth = p r th = pmax. since ψ1, ψ2, µs and µr are linear with 1 nel , for energy-efficient design for relay-aided mimo-ofdm cognitive radio networks 119 simplicity, let , , and are equals with . all the results have been averaged over 500 iterations. we compare the proposed algorithm with the traditional throughput maximum problem. change the objective function ξee (p) in op1 with the throughput ctp (p) in (5) and change the approximated objective function ξ̃ee (p) in op2 with the approximated throughput c̃tp (p) in (10), then we formulate the traditional throughput maximum problem. since c̃tp (p) is joint concave in with psm,n and p r m,n, the throughput maximum problem is convex problem which can be solved by many standard convex optimization algorithms [22]. we name the method to solve the throughput maximum problem as tm-pa. in the following, we compare the proposed algorithm with the tm-pa scheme. since the proposed aee-pa consists of two loops, we only consider the affect of the number of outer loop iterations to and set the number of inner iterations large enough to guarantee that the inner loop can find the optimal solution of op3. fig.2 shows the ee versus the outer loop iterations to for different total power budget under ith = 1w , nel = 500 . it can be observed in fig.2 that aee-pa converges to the optimal value within eleven iterations for all considered value of total power budgets. the maximum ee can be improved when there are more total power budgets. fig.3 depicts the ee versus total power budget pmax for different interference thresholds under nel = 500. as shown in fig.3, the ee of the both algorithms increases with the increasing of the total power budget, however the proposed aee-pa has a higher ee than the non energy efficiency scheme tm-pa. the ee versus interference threshold ith for different total power budgets under nel = 500 is evaluated in fig.4. it is shown in fig.4 that the ee of the both algorithms grows with the growth of the interference threshold. this is because that the lower the interference threshold is, the more the cr network suffers outage. we also evaluate the impact of nel (the number of samples in el stage) on the proposed algorithm. in fig.5, ee versus interference threshold ith for different nel under pmax = 0.5w is depicted. obviously, the algorithm has a better performance with lager nel than small nel. this is because smaller nel performs poor learning and yields large interference to pus. 1 2 3 4 5 6 7 8 9 10 11 7 8 9 10 11 12 13 t o e n e rg y e ff ic ie n cy ( b it/ s/ jo u le ) p max =1w p max =0.8w p max =0.6w figure 2: energy efficiency versus the outer iterations for different total power budget 120 b. wu, j. zuo, l. zhao, c. zou 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 p max (w) e n e rg y e ff ic e n cy ( b it/ s/ jo u le ) aee−pa i th =1w tm−pa i th =1w aee−pa i th =0.01w tm−pa i th =0.01w figure 3: energy-efficiency versus total power budget for different interference threshold 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 2 3 4 5 6 7 8 i th (w) e n e rg y e ff ic e n cy (b it/ s/ jo u le ) aee−pa p max =1w tm−pa p max =1w aee−pa p max =0.5w tm−pa p max =0.5w figure 4: energy efficiency versus interference threshold for different total power budget 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 3.5 4 4.5 5 5.5 i th (w) e n e rg y e ff ic e n cy (b it/ s/ jo u le ) n el =1000w n el =500w n el =100w figure 5: energy efficiency versus interference threshold for different nel energy-efficient design for relay-aided mimo-ofdm cognitive radio networks 121 5 conclusions in this paper, we investigated the power allocation for relay-aided mimo-ofdm cognitive radio networks from energy efficiency perspective. different from traditional throughput maximizing methods, we solve the power allocation problem via maximizing the energy efficiency measured by "joule per bit" metric. however, the formulated problem is nonconvex. to make it solvable, we first make an approximation to the original problem. indeed, the approximated problem is a fractional programming problem. then, the approximated problem is transformed into a parametric convex optimization problem. finally, 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[23] stephen boyd, l. xiao and a. mutapcic, subgradient methods, online: https://web.stanford.edu/class/ee392o/subgrad_method.pdf. international journal of computers communications & control issn 1841-9836, 10(4):471-479, august, 2015. efficient design and deployment of aqua monitoring systems using wsns and correlation analysis s. babu chandanapalli, e. sreenivasa reddy, d. rajya lakshmi suresh babu chandanapalli* associate professor, department of cse, gudlavalleru engineering college, gudlavalleru-521356, andhrapradesh, india. *corresponding author: sureshdani2004@gmail.com dr e. sreenivasa reddy professor of cse, anu college of engineering & technology, acharya nagarjuna university, guntur-522510, andhrapradesh, india. edara_67yahoo.com dr d. rajya lakshmi professor, hod of cse, jntuk university college of engineering, vizianagaram535002, andhrapradesh, india. jrajyalashmi@gmail.com abstract: the roots of innovation are extending towards every field to provide ace solution. we cater an ace solution for aquaculture, where their yields (shrimp, fish, etc.) depends on the ponds water characteristics. the parameters depending on water must kept at certain optimal levels for better cultivation of aqua. the parameters of water extremely project alterations during the day and also alter depending upon the environmental conditions i.e., it is necessary to monitor these parameters with high frequency. we adopt wireless sensor networks to monitor aqua forms. this system consists of two modules, they are transmitter and receiver station. we navigate data to database at receiver station through the gsm. the graphical user interface was designed in such a manner that the observations are forwarded to the farmer as message in their respective local languages to their mobile phones. that alerts them in unhygienic environmental conditions for adopting suitable measures. keywords: aquaculture, wireless sensor networks, iar-kick, ph; 1 introduction aquaculture is one of the widely extending industry attributable to the rapid demand for fish and seafood all over the world. the term aquaculture is referred as the cultivation of fish,plants and animals in various types of environments that includes rivers, ponds and oceans. aquaculture consists of two types i.e. one is marine aquaculture that is nothing but the cultivation of species in ocean and another is freshwater aqua culture where species are cultivated in native water bodies. in shrimp culture, it is observed that samples that are taken into consideration to predict low levels in dissolved oxygen, temperature, salinity and ph levels. taking all these parameters into consideration deploying sensors in shrimp culture for monitoring water quality and alert regarding contaminants in water will yield exceptional results. [1] [8]. the analysis of water quality desires consistent observation of water depended parameters in significant catchments. the various parameters which we consider as ph, dissolved oxygen, water temperature and water levels [2] at various depths. making use of various ideal instruments like sensors and wireless sensor networks will produce better results. the adoption of zigbee standard for short range and low cost module of wireless sensor network is developed in real time information system [3], in copyright © 2006-2015 by ccc publications 472 s. babu chandanapalli, e. sreenivasa reddy, d. rajya lakshmi which it consists of small sensor nodes, coordinator or gateway node and personal computer. in this system the smart sensor node monitor the ecological parameters such as water level, ph and dissolved oxygen and transmits it to the coordinator or gateway node from which data is again transferred to the personal computer where it is visualized for human-computer interface. the application of wireless sensor networks in various fields for detection of ecological parameters and transferring data to database using network. yet, the wireless sensor networks have many constraints like memory, processing power and limited batter energy but the efficient utilization of energy is a decisive issue. we propose the implementation of wireless sensor networks to have a distributed collection of sensor nodes networked together to transfer the raw data to a central location known as base station through gsm. every sensor node consists of a micro-controller, some sensors and a radio transceiver for communication [3]. the micro-controller is used for in-network processing for transferring needed information instead of raw data. the information which is transferred is preserved in a database and analyzed for further process. after analysis the data from database is forwarded to the farmers as a message to their mobile in their respective languages to alert them about the unhygienic environmental conditions. the proposed system makes farmers aware about the vulnerabilities so as to resolve them. 2 motivation figure 1: paper cutting for problems in aquaculture in aquaculture, the yields(shrimp, fish etc) depends on the water characteristics of the aquaculture pond. parameters must be maintained at certain levels for better cultivation of fish yields. the parameters asph level, salinity, turbidity, alkalinity and nutrition level alter from day and environmental issues. from early years, considerable amount of research and experimentation have processed in this field but till there is no perfect solution of this issue. there are certain possible wireless sensor network solutions that act as a perfect solution for the problem but to their drawbacks in power utilization, configuration, communication failures, environmental influences and scalability inspired us to perform further explorations in aquaculture. [1]. fig.2.shown given below, certain innovations those were carried out in aquaculture for their detection in 2011 the innovation of traceability enciphers for recirculation aquaculture and in 2012 creation of water checking framework where we adopt zigbee and gprs for transmission of gathered data [4]. in this existing system we use a ph meter where we use a probe to testing the parameters and transferring the results using wireless sensor networks. in this manual testing there are some efficient design and deployment of aqua monitoring systems using wsns and correlation analysis 473 drawbacks as arrangement of ph meter at some frequency,maintains of probe, time for calculation of ph and manually testing is not possible in unhygienic environmental conditions. the earlier systems are not made up of gsm for aqua monitoring system for remote connectivity and sensing parameters. figure 2: manual testing of ph 2.1 proposed system description we propose the automatic system for checking water characteristics of aqua ponds in hygienic and unhygienic conditions. the proposed system consists of two modules, they are transmitter and receiver station and a personal computer which is used as analyzing station. the following list contributes overall framework of the aqua monitoring system: • embedded system design • radio frequency integrated circuits. • wireless sensor network. • the collected data will be analyzed by using data mining tool. • convert the output into local language using local language. • user interface design in local language 2.2 design of transmitter station we propose the implementation of wireless sensor networks to have a distributed collection of sensor nodes networked together to transfer the raw data to a central location known as base station through gsm. every sensor node consists of a micro-controller, sensors and radio transceiver for communication. the micro-controller is used for in-network processing for transferring needed information instead of raw data. as the sensor node are powered by battery and thereby reduction of total n umber of bits will save the battery life. research activities are 474 s. babu chandanapalli, e. sreenivasa reddy, d. rajya lakshmi figure 3: overall architecture of aqua monitoring system processing for the change of sensor node replacement for optimal coverage, topology formation, routing, data processing techniques to reduce communication costs, operating system design etc. it is also to estimate the impact of the present sensors in aquaculture as it needs few more crop cycles for generation of accurate results [6] [11]. in the proposed system, we introduce a sensor that makes use of the off-the-shelf available sensor nodes and we didnt make any specific efforts in hardware design cost reduction. the sensor is embedded in the transmitter station for monitoring of aqua forms depending on parameters like ph levels, dissolved oxygen levels, temperature inside and outside water, ammonia levels etc.the user interface was designed in vb and .net. so that farmers and investigators may analyze and investigate the data. [7] the transmitter consists of: 1.sensor nodes 2.gsm modems 3.micro-controllers 4.arm processor 2.3 sensor nodes sensors are the electronic devices that sense the alterations of physical environmental conditions as sound, temperature, pressure etc.the sensor works at particular voltage and continuously transmit the signal in analog form. these signals are converted into digital form using converters. the sensors are of small size, consume low power and operate in high densities. we make use of following sensors: • temperature sensor for measuring the altering conditions of water • ph sensor • humidity sensor. sensor selection • temperature sensor for outside water: we make use of ds18b20 i-wired digital sensor from maxim ic. efficient design and deployment of aqua monitoring systems using wsns and correlation analysis 475 figure 4: block diagram of transmitter • temperature sensor for inside water: we make use of hobo u-series sensor with ux 120-006m external channel data logger. • ph sensor : we make use of a glass electrode for ph sensing. • humidity sensor : we use to measure the humidity. gsm modems we make use of gsm modems for long range communication as the aquaculture ponds are far away to villages and the supply of power is not available to ponds therefore data collection nodes are not placed near to pond. we place the receiver node at data collection center in villages or towns and transmits message to the mobile phones with the help of a sim, that supports 2g and g technologies, hsupa, umts, wcdma, hsdda, gpra and edge. these modems are embedded both at transmitter and receiver station.in this prototype we adopt at89c52 micro-controller that is having the capability of showing high-performance by making use of cmos-8 bit microcomputer having 8k bytes flash programmable and erasable read only memory.we make use of the ace arm processor that comes with all the well known features and functionality.system software of the aqua monitoring system gains the sensor data and verifies with the data records and preserves the data in the database to construct a complete database. the constructed database consists of all the analyzed patterns depending on the altering environmental changes of fish ponds [5]. the sensor data is correlated with the ideal conditions present in the database. any alteration in the ideal conditions will transmit a warning message to the farmer for adopting suitable measures. communication process module is connected to the pc via serial port rs-232. we use iar or ram for controlling the wireless sensor system in the embedded software that is programmed in c. we port the hex document to the flash memory of at-mega8 micro-controller via usb interface. the project is created with subroutine modules for sensor indicator digitization, engine transfer and value hand-off control, remote information procurement and transmission, universal and asynchronous receive transmit. the analog and digital converter of micro-controller performs cyclic scans for the sensors and considers the varying check rate that is programmable [9]. the sensor information might be contrasted and set-point qualities put away in memory. the transmission of information through the gsm to the base collector is done at serial correspondence at 9600 baud rate. 476 s. babu chandanapalli, e. sreenivasa reddy, d. rajya lakshmi tab.1. technical specifications of communication protocol parameters zigbee bluetooth wi-fi gsm range 30m 1.6 km 30 3000ft 100 150 ft 30 35 km power consumption 0.2 ma(one node) 1 watts 10.80 watts 230 volts frequency 2.4g 2.4g 2.4g 900 and 1800 mhz cost $350 $325 $500 $250 3 system implementation the system was implemented in gudlavalleru, krishna district. the intensive aquaculture monitoring system is acquiring data from march-2014 to august-2014.this system analyzes all the appropriate operations and assesses the feasibility. we had taken a pond area of three acres and divided into four fish ponds. we deployed four sensor nodes and a transmitter node consisting of ph, humidity and water temperature at the ponds. the whole system was tested appropriately with the connection of sensors to their modules and monitoring computer to its receiver nodes in the control room with the proper installation of sensor nodes in the fish ponds and their operations. figure 5: installation of transmitter station & receiver station at pond 4 results and discussion tab.2. summary of sensor data between mar-2014 to july-2014 parameters mar-14 apr-14 may-14 min max stdiv min max stdiv min max stdiv *w. i.temp. 23 26 2.12 23 25 1.4 18 22 2.83 **w .o.temp. 25 27 1.41 25 25 0 25 26 0.7 humidity 44.1 50.2 4.3 43.5 46 1.73 43.6 46.6 2.12 ph 6.1 6.3 0.14 5.8 6.2 0.28 5.8 6.2 0.28 fish mortality 1.4 1.1 1 parameters june -14 july-14 aug-14 min max stdiv min max stdiv min max stdiv *w. i.temp. 22 24 1.41 23 23 0 23 24 0.71 **w .o.temp. 25 27 1.41 25 26 0.71 25 27 1.41 humidity 45.9 46.9 0.71 46.8 48.7 1.34 42 47.6 3.96 ph 5.8 6.3 0.35 5.7 6 0.21 5.7 7.8 1.48 fish mortality 1.2 1 1.3 *-water inside temperature **-water outside temperature efficient design and deployment of aqua monitoring systems using wsns and correlation analysis 477 figure 6: monitoring screen and user interface of aqua monitoring system figure 7: water inside and outside temperature,humidity,ph variation from a period of six months 478 s. babu chandanapalli, e. sreenivasa reddy, d. rajya lakshmi correlation co-efficients correlation co-efficient may termed as the measure of the strength of linear association among two variables or parameters. the value of correlation always lies in between -1.0 to +1.0.if the value of correlation, c(r), is positive then we retrieve positive relationship, if negative we retrieve negative relationship [7] [10]. formula: c(r) = [ n ∑ xy − (∑ x )(∑ y )] /sqrt ([ n ∑ x2 − (∑ x )2][ n ∑ y 2 − ( y )2]) where, n = the total number of values or elements x = first score y = second score∑ xy = sumoftheproductoffirstandsecondscores∑ x = sumoffirstscores∑ y = sumofsecondscores∑ x2 = sumofsquarefirstscores∑ y 2 = sumofsquaresecondscores tab.3. summary of correlation coefficients parameters ph humidity *w.i.temp **w.o.temp ph 1 0.75 0.85 0.95 humidity 0.75 1 0.65 0.75 *w.i.temp 0.85 0.65 1 0.70 **w.o.temp 0.95 0.75 0.70 1 *-water inside temperature **-water outside temperature taking the available parameters in the table 3.i.e. ph, water inside temperature, water outside temperature and humidity calculate the value for the variables x and y. 5 conclusions and future works a wireless sensor network for aquaculture monitoring and control based on virtual instruments is presented. we implemented the hardware design of smart sensor nodes, transmitter/receiver with software design and prototype system of four sensor nodes with the embedding of transmitter/receiver with proper design structure. the collected data provides an accurate analysis of successful operation of the system. the proposed work can be utilized in various fields as greenhouse monitoring and control, hydrological water conservation and farm land irrigation. future development efforts should involve enhancing the wsn by adding gprs modules on the smart sensor nodes to monitor aquaculture environment on-line through the internet. the monitoring program should be linked to the web server through a passive ip address in one port of the receiving computer, thus making the architecture more scalable and robust. 6 acknowledgment this work was supported by university grants commission minor research project for teachers, southern eastern regional office, india and is also sanctioned fund in-house r & d by gudlavalleru engineering college. efficient design and deployment of aqua monitoring systems using wsns and correlation analysis 479 bibliography [1] http://www.nmfs.noaa.gov/ [2] a. harun, d. l. ndzi, m. f. ramli, a. y. m. shakaff, m. n. ahmad, l. m. kamarudin, a. zakaria, and y. yang (2012); signal 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[11] glasgow h.b et al.(2004); real-time remote monitoring of water quality: are view of current applications, and advancements in sensor, telemetry and computing technologies, journal of experimental marine biology and ecology, issn: 0022-0981, doi: 10.1016/j.jembe.2004.02.022, 300:409-448. int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 744-758 minimum cycle time analysis of ethernet-based real-time protocols j. robert, j.-p. georges, e. rondeau, t. divoux jérémy robert, jean-philippe georges, eric rondeau, thierry divoux centre de recherche en automatique de nancy, université de lorraine, cnrs umr 7039 campus sciences, bp 70 239, f-54506 vandœuvre-lès-nancy, france e-mail: jeremy.robert@univ-lorraine.fr, eric.rondeau@univ-lorraine.fr, jean-philippe.georges@univ-lorraine.fr, thierry.divoux@univ-lorraine.fr abstract: the ethernet standard is a standard solution for interconnecting industrial devices despite its intrinsic drawbacks, particularly its nondeterministic medium access method. many ethernet-based commercial solutions available (cots components off the shelves) on the market guarantee time performance. this means that user selection of one particular solution is a critical decision, but the choice often depends more on political strategizing with an industrial device manufacturer than on the intrinsic performance of ethernet-based interfaces. the objective of this paper is to provide a formal behavioural analysis of each ethernet-based solution, in order to facilitate comparison. keywords: real-time systems, performance analysis, embedded systems. 1 introduction fieldbuses interconnecting industrial equipment are typically designed by programmable logic controller (plc) manufacturers. this is mainly attributable to specific constraints on industrial communication, which require a high degree of expertise. fieldbuses must be robust in the noisy environments produced by the plant (physical layer), deterministic in guaranteeing data refresh during controller cycle periods (link layer), and capable of exchanging information between all types of industrial devices (application layer). manufacturers provide different solutions to satisfy the first two constraints, without necessarily considering the final constraint to be relevant in business terms. this has resulted in the specification of a large number of fieldbus standards as described in [1]. a new trend endorsed particularly by the iaona (industrial automation open networking alliance) consortium was to promote the ethernet network as a standard for industrial communications. the expected benefits are less costly network installations, because equipment is available off-the-shelf, and the avoidance of interoperability problems, because ethernet technology is broadly used. other advantages are that ethernet is a well-known protocol, which is widely implemented, and its performance improves continuously with technological evolution (especially bandwidth). however, access to the ethernet medium relies on the nondeterministic csma/cd algorithm, which applies a stochastic method to resolving collisions and cannot guarantee that message transmissions will be received in bounded time. consequently, the native ethernet protocol cannot be directly implemented in a plant with severe time constraints, and many ethernetbased solutions have been proposed to overcome this issue, as mentioned in [2–4]. however, if these ethernet-based solutions are adapted to industry requirements, they lead to two types of problem. the first is that different solutions are not interoperable because different fieldbuses are copyright c⃝ 2006-2012 by ccc publications minimum cycle time analysis of ethernet-based real-time protocols 745 developed by each manufacturer. the second problem is that time performances are insufficiently evaluated or compared. according to [5], different ethernet products can be summarily classified into three main categories: the native ethernet standard (ethernet/ip, modbus/tcp), ethernet solutions using the priorities defined in ieee802.1d/q, and ethernet-based solutions that incorporate new scheduling features in asic/fpga (ethercat, profinet irt). the last approach enables the elimination of all collisions and simplifies transmission time estimation, as described in [5, 6]. the authors compared ethercat with profinet irt in a simplified context using analytic models. this analysis was improved and refined in section 2 and extended to two other well-known industrial ethernet products: the modbus/tcp solution and ethernet/ip. in section 3, we present several scenarios that use the analytic models to facilitate assessment of different ethernet products. 2 estimation of minimum cycle time 2.1 introduction the general objective of this study was to compare the time performance of the major industrial ethernet products available on the market. this comparison could be achieved only in a common application context. thus, the specification of the communication scenario was based on one controller (for example, a plc), interconnecting sensors, and actuators in an ethernet network. the controller was treated as the communication master that initiated all dialogues with slave nodes (sensors and actuators). the controller was characterized by its controller cycle time period, which was divided into three steps, as shown in figure 1: the sensor data refresh time, in the controller memory, the processing time, and the actuator update time. steps (1) and (3) represent communication periods, which should be less than the difference between the controller cycle time period and the processing time period. thus, the time performance of each ethernet product was compared according to a constraint named minimum cycle time, which was defined as: the minimum cycle time was the communication time required by the controller to both collect and update the data memories of all sensors and actuators. figure 1: plc cycle time in the following section, the analytic models of minimum cycle time are elaborated for ethercat, profinet irt, modbus/tcp, and ethernet/ip. these models all used the following parameters: the transmission delay, the network device latency, the propagation delay, the link capacity, the payload, and the number of slaves. the notations of these are given in table 1. it was assumed that there were no transmission errors and that the network was dedicated to the plc application and was not shared with other applications. 746 j. robert, j.-p. georges, e. rondeau, t. divoux table 1: notations terms notation units minimum cycle time γ s delivery time (from frame i) di s transmission delay τ s network device latency ℓ s propagation delay δ s link capacity c bits/s payload x bytes number of network devices (slaves) n – 2.2 ethercat the ethercat network was developed by the beckhoff company (type 12 in standard iec 61158, [7, 8]). in theory, ethercat cards are standard ethernet interfaces. in practice, specific hardware (fpga, field-programmable gate array, or asic, application-specific integrated circuit) is used to mitigate the frame forwarding delay. the ethercat network adds a master/slave protocol over the ethernet. a frame is sent by the master and slaves can read and write data on the fly. the duration of reading or writing operations corresponds only to the network device latency ℓ, which is independent of frame size and the same for all slaves. a logical ring is defined between the slaves such that when a frame reaches the last slave in the ring it is returned to the master via all the slaves. the space–time diagram shown in figure 2 illustrates the behaviour of ethercat communications. figure 2: ethercat space-time diagram the ethercat protocol can support both line and ring topologies. because the line topology is used mainly in the industrial framework, this topology was investigated in our study (figure 3). the ethercat datagram is directly encapsulated inside the basic ethernet frame as shown in figure 4. an ethercat frame is composed of a header specifying the length of the frame and a list of datagrams. the number of datagrams depends on the number of slaves. a datagram is minimum cycle time analysis of ethernet-based real-time protocols 747 figure 3: ethercat line topology defined for each slave, and it contains the command type and associated data. figure 4: ethercat frame (field lengths are given in bytes) in this study, our analysis of ethercat performance considered the following hypothetical test scenario: the topology was a line, the initialization step was ignored and only cyclic communication was studied, the master sent only one frame per cycle, and the payload x was the same for each slave. the minimum cycle time shown in figure 2 was determined for this scenario. the link transmission delay is the ratio between frame size and link capacity c. the total frame size can be divided into two parts: a constant value that equals the sum of the ethernet protocol (26 bytes), the interframe gap (corresponding to the time of 12 bytes), and the ethercat header (2 bytes) and a variable value that depends on the slave number n, the amount of data to transport x, and the header (12 bytes). the link transmission delay is: τ = 8 (40 + max (44,n(12 + x))) c . (1) the term 44 in the equation (1) was added to ensure the minimum data size defined by the ethernet protocol. if the ethercat telegram length was less than 46 bytes, an equivalent amount of padding was inserted in the ethernet frame. the ethercat telegram already included a 2 bytes header, which meant that there was no padding requirement when the length of the datagram sequence was larger than 44 bytes. as shown in figure 2, the cycle time was estimated using the expression: γ = (2n−1)ℓ + 2nδ + τ = (2n−1)ℓ + 2nδ + 8 (40 + max (44,n(12 + x))) c . (2) it should be noted that (2) considers only one frame. because the ethernet payload size depends directly on the number of slaves and the ethernet frame size cannot exceed 1 526 bytes 748 j. robert, j.-p. georges, e. rondeau, t. divoux (and therefore the data field 1 500 bytes), (2) is only valid if the number of devices interconnected to the network is less than: n ≤ nmax = ⌊ 1500−eh 12 + x ⌋ , where eh is the ethercat header size (2 bytes) and nmax is the maximum number of datagrams, of length x, which can be included in a single frame (in the following, we have assumed that x ≤ 1486 bytes). in general, the number of slave devices on the network can be greater than the frame size capacity. this means that the controller has to send more than one frame in a cycle time. in fact, the number of ethernet frames required to support n devices with a constant payload x is given by: k = ⌈ n nmax ⌉ . consequently, (2) now integrates a different transmission time for each frame and finally gives: γ = (2n−1)ℓ + 2nδ + 8 c ( 40k + (k −1)nmax (12 + x) ) + 8 c max ( 44,(n− (k −1)nmax) (12 + x) ) (3) the final term of expression (3) was used to differentiate cases where the last frame generated padding. similar results are given in [5] but, in contrast to this earlier work, expression (3): • takes into account the on the fly minimum cycle time mechanism proposed by ethercat; the main advantage of this is that a device can begin frame forwarding before complete reception of a frame (in contrast to, e.g., store-and-forward mode), which significantly reduces the forwarding time as shown in figure 2, • considers the use of padding, as defined by ethernet, • integrates the time required to forward the information sent from devices to the controller, as shown in figure 2, and • considers cases where the number of slaves and their payload requires the utilization of several frames. the accuracy of the ethercat synchronization mechanism was reported in [9], which shows that this issue need not be considered because it was estimated as equal to a few nanoseconds. 2.3 profinet irt introduction the profinet protocol was developed by the siemens company (type 10 in standard iec 61158, [7, 8]). profinet irt manages real-time communications. however, standard ethernet cards cannot be used because profinet irt requires the operation of specific hardware on slaves (asic type – 2 or 4 ports inbuilt switch). profinet irt is based on the time-slice mechanism, which specifies two modes, the asynchronous mode and the isochronous mode. the asynchronous and isochronous modes are used for unconstrained traffic and real-time traffic, respectively. our study dealt only with real-time traffic. thus, only the isochronous mode was analysed. for more information on these two modes, the reader can refer to [10]. in minimum cycle time analysis of ethernet-based real-time protocols 749 the isochronous mode, the master sends one data frame to each device and each device replies to the master. profinet can support line, star, and ring topologies. only the line topology (see figure 5), with full-duplex links, was analysed in this study (as for ethercat). figure 5: profinet line topology in this study, the profinet irt used the slipstreaming effect, where the controller began by sending frames to the most remote slave in the line topology, and then to the second remote slave slave and so on, until it reached the nearest slave. this mechanism enabled a reduction in the cycle time by minimizing the transmission time. the slipstreaming effect was also applied to exchanges from slaves to the controller. the links were set up in full-duplex mode. the global communication scheme is given in figure 6. figure 6: profinet irt space-time diagram with slipstreaming effect the space–time diagram shown in figure 6 illustrates the optimum use of the profinet irt protocol. devices were assumed to be synchronized using a clock synchronization protocol, such as the ieee 1588 standard. the ieee 1588 standard generates synchronization frames (ptp) between devices, but these frames were not relevant to this study. minimum cycle time estimation the slave devices periodically sent their messages to the controller at the same times as messages were sent by the controller to slaves. only the positive characteristics of the slipstream effect were considered. this required that τ ≥ δ + ℓ, as noted by [5]. in such cases, the minimum cycle time was given by [5, 6] as the sum of: • the latency ℓ that crosses all devices plus the propagation delay δ for each link and 750 j. robert, j.-p. georges, e. rondeau, t. divoux • and the link transmission time τ for each frame sent by the controller. hence, the minimum cycle time is written as: γ = δ + ℓ + nτ. (4) equation (4) was developed by analysing the transmission delay τ. the profinet datagram is encapsulated in the ethernet frame as shown in figure 7. figure 7: profinet irt frame (field lengths are given in bytes) three fields are added to the ethernet frame: a data identifier (2 bytes), the data value (with the assumption that x ≤ 1494 bytes), and an information status (4 bytes). when considering the constraint of the minimal ethernet frame size, the transmission delay of a profinet frame was given by: τ = 8 38 + max (46,6 + x) c . (5) the term 38 in the equation (5) corresponds to the size of the ethernet layer (26 bytes) plus the interframe gap (12 bytes). as with ethercat, the term 46 was added in order to ensure the minimum data size as defined by the ethernet protocol. if the ethercat telegram length was less than 46 bytes, an equivalent amount of padding was inserted in the ethernet frame. the final form of equation (4) was expressed as: γ = δ + ℓ + n 8 c ( 38 + max (46,6 + x) ) comments the ethercat minimum cycle (3) was less than the profinet irt one (4), because the profinet irt transmission delay was multiplied by the number of devices. the profinet irt analysis assumed that all devices had the same clock reference. figure 6 shows that all devices were synchronized because of the ieee 1588 protocol. they shared the same clock and periodically sent their messages at the same time. hence, it may be expected that clock synchronization errors will increase the minimum cycle time. this paper aims only at comparing optimal performances of cots ethernet-based protocols, i.e. profinet irt nodes sharing the same clock reference. next studies will hence aim at reporting this synchronization issue. 2.4 modbus/tcp modbus is a serial communication protocol developed by modicon in 1979. modbus/tcp is a variant of the modbus protocol (type 15 in standard iec 61158, [7,8]), which uses the ethernet physical and link layers [12]. modbus/tcp encapsulates a modbus frame into a tcp frame as minimum cycle time analysis of ethernet-based real-time protocols 751 figure 8: general modbus/tcp frame and function specific modbus application frame (field lengths are given in bytes) shown in figure 8. the modbus datagram is composed of the modbus application protocol header (mbap), the function (read/write), and the data value. modbus/tcp is a pragmatic approach that works on several types of configurations that impact on its performance. parameters, including topology, exchange management in the application layer, and the processing capacity of devices, change the modbus/tcp time behaviour. modbus/tcp is based on connection-oriented transactions and it can use different exchange models, such as master/slaves, producer/consumer, or client/server. the objective of this study was to compare several ethernet products, so it was necessary to use similar contexts for all ethernet products. because ethercat and profinet irt were previously analysed using a master/slaves model, this model was also used for modbus/tcp evaluation. the master/slaves model also simplified the analysis because all the devices were synchronized by network events (none clock synchronization protocol is required). the communication scheme defined in this study followed these steps. the master sent a frame to one slave and when the slave received this frame, it sent a reply to the master. when the master received the reply, it repeated the same procedure with another slave. all the slaves were processed by the master using a round-robin method. this communication scheme was implemented in the application layer of the osi model. the request/reply protocol is shown in figure 9. modbus/tcp not only supports application data but also tcp pdu, and it comprises opening and closing tcp connections when acknowledging segment reception. the acknowledgment can be achieved either immediately a segment is received, after the reception of several segments, or inside the next data transmission (piggybacking). in practice, tcp behaviour changes according to the operating system, the tcp configuration, and whether or not the nagle algorithm is used. in this study, we assumed that the timeout to send the acknowledgement was 0.5 s, meaning that only piggybacking was analysed, as shown in figure 9. the transient states of the tcp opening and closing steps were not considered in the modelling. modbus/tcp supports line, star, and ring topologies. complex architectures based on switches can be used, especially when the network is shared by several applications, although switches induce additional costs. however, because only the master/slaves communication 752 j. robert, j.-p. georges, e. rondeau, t. divoux figure 9: modbus/tcp space-time diagram scheme was considered in this study, a bus infrastructure was used. it was assumed that each device was interconnected through a common hub, and the propagation delay was equal to δ. figure 9 shows that the cycle time was equal to the number of devices n multiplied by the time required to poll one device. given previous assumptions, this can be written as: γ = n(τreq + τrep + 2 (2δ + ℓ)) . (6) in the worst case, where a tcp acknowledgment is sent for each segment, the minimal cycle time would be equal to:gamma = n(τreq + τrep + 3× (2δ + ℓ) + 2τack) where τack = 672/c and 672 is the minimal size in bits for an ethernet frame (interframe gap included). when the architecture was composed of several hubs, the propagation delay increased and (6) was slightly different. the frame transmission time was composed of: • a constant part related to the sum of the ethernet protocol (38 bytes with the interframe gap), the ip header (20 bytes without options), the tcp header (20 bytes without options), and the modbus/tcp header (7 bytes for the modbus application protocol header), • a variable part related to the type of modbus message, with the size changing according to the type of data (function code) and the transaction model state (request or reply). consequently, the analysis of modbus/tcp was only valid for an application framework. in this study, we considered only write requests. • a variable part proportional to the payload (because the byte count field was stored as a single byte, this indicates that the date field length was limited to x ≤ 255 bytes). the sum of ip, tcp, and modbus header sizes was larger than the minimal ethernet data length. thus, no added padding was required. the delay is directly given by: τreq10h + τrep10h = 8 91 + x c + 8 90 c such that (6) corresponds to: γ10h = n ( 8 181 + x c + 2 (2δ + ℓ) ) . (7) minimum cycle time analysis of ethernet-based real-time protocols 753 2.5 ethernet/ip introduction ethernet/ip (ip, industrial protocol) is a network developed by rockwell automation in 2001 and supported by odva (open devicenet vendor association) [13, 14]. ethernet/ip (type 2 in standard iec 61158, [7, 8]) uses the common industrial protocol (cip) for off-theshelf ethernet products and tcp-udp/ip stack. ethernet/ip is a connection-based network. a cip connection defines the type of packet sent to the network. there are two types of connections: the explicit messaging connection and the i/o (or implicit) connection. explicit messaging provides generic and multi-purpose communication paths between two nodes, whereas i/o messaging is specific to application i/o data and provides serial purpose communication paths. when the application is time-constrained, i/o messaging is the preferred mode because it employs udp rather than tcp sockets. cip uses the producer/consumer model and requires broadcast exchanges encapsulated in udp. this study evaluated only i/o connections. because ethernet/ip relies on cots, no particular topology was specified. busor switchbased architectures are both possible. switches are interesting because they break the collision domain, allowing the support of vlan and the classification of service mechanisms [15]. switched architectures are recommended for exchange management with time-critical (implicit) messaging. however, several switched ethernet architectures are possible. in this study, a linear switched topology was selected, as shown in figure 10. this can be viewed as an extension of the experimental set-up considered in [16], where the number of switches varied according to the number of ports per switch. figure 10: ethernet/ip switched linear topology in contrast to modbus/tcp, utilization of a slipstreaming effect on ethernet/ip does not facilitate the use of a bus topology. indeed, it is possible that two messages can be on the network at the same time (as shown in figure 11). in order to compare the other protocols with ethernet/ip, a similar exchange scenario was proposed. the controller sent a frame to each slave device and each slave device produced data that were sent to the controller. figure 11 shows this behaviour. initially, ethernet/ip did not support medium access synchronization points, as found in the master/slaves technique used in modbus/tcp. this meant that any device could access the network at any time. however, the time synchronization mechanism can be used because of the support of ethernet/ip by the ieee 1588 [17] protocol (implemented by the cipsync profile). consequently, devices can send messages using the same clock reference. we used the slipstreaming effect for the controller and a common departure time for the devices, as shown in figure 11. obviously, this profile corresponded to an ideal case related to the minimal cycle time for this architecture, because lock synchronization errors would lead to another profile inducing a longer cycle time and the use of the slipstreaming effect requires (see profinet irt) that τ ≥ δ+ℓ. 754 j. robert, j.-p. georges, e. rondeau, t. divoux figure 11: ethernet/ip space-time diagram for 4-ports switches minimum cycle time considering the profile given in figure 11, the minimum cycle time corresponded to the sum of: the latency ℓ for crossing only one switch plus twice the propagation delay δ, between a controller/device and a switch and the link transmission time τ for each frame sent by the controller. thus, the minimum cycle time can be written as: γ = 2δ + ℓ + nτ. (8) the term given in (8) was valid only if τ ≥ δ + ℓ, meaning that switches did not work in the store-and-forward mode. the link transmission time was computed according to the encapsulation format of i/o messages. figure 12 shows that 18 bytes were added by the cip protocol, 8 bytes by udp, 20 bytes by ip, and finally 38 bytes by ethernet. in this case, there was no padding. thus, under the assumption that x ≤ 1454 bytes, the link transmission time was given by: τ = 8 38 + 20 + 8 + 18 + x c . (8) can be modified as: γ = 2δ + ℓ + 8n 84 + x c . (9) the profile given in figure 11 can be optimized if several “items” (data) are encapsulated inside one frame, as specified in the cip protocol. it induces a mitigation of the minimum cycle time. minimum cycle time analysis of ethernet-based real-time protocols 755 figure 12: ethernet/ip i/o messaging frame (field lengths are given in bytes) since we employed the same approach that for profinet irt (in particular the slipstreaming effect), next studies will hence aim at reporting the synchronization effect on the minimum cycle time. in the next section, the results obtained for ethercat, profinet irt, modbus/tcp, and ethernet/ip are compared in different application contexts. 3 comparisons the objective was to analyse and compare the behaviour of different ethernet-based solutions. the topologies used in each assessment were linear. two bandwidths were studied: 100 mb/s and 1 gb/s. we analysed two payload sizes introduced by [6] as being representative of an industrial context: 16 bytes and 100 bytes. the network device latencies ℓ were defined in [5, 6] and those used in our study are described in table 2. it was assumed the performances of switches used in ethernet/ip were the same as those used by profinet irt. clearly, alternative assumed values would yield different results. thus, the results for the study using ethernet/ip are only valid when τ ≥ δ + ℓ. the link propagation delay was 50 ns and corresponded to a distance of 10 m between two devices. the comparison is related to the minimum cycle time without synchronization errors. table 2: latencies protocol fastethernet (100 mb/s) gigaethernet (1 gb/s) ethercat 1.35 µs 0.85 µs profinet irt 3 µs 0.6 µs modbus/tcp 1 µs (hub) ethernet/ip 3 µs 0.6 µs figure 13 shows the minimum cycle times (in ms), according to the number of slave devices with a payload equal to 100 bytes the first observation was that modbus/tcp provided the worst results, whatever the payload. this was because of its medium access mechanism, which is based on polling at the application level. in the case of a small payload, ethercat provided the best results in the fastethernet mode. the impact of bandwidth on profinet irt and ethernet/ip was very significant, because it 756 j. robert, j.-p. georges, e. rondeau, t. divoux figure 13: minimum cycle time as a function of the number of devices with a constant payload of 100 bytes per device and two bit rates of 100 mb/s and 1 gb/s enabled a reduction in the minimum cycle time. this benefit was lower with ethercat. the explanation is simple: ethercat sends only one frame for communicating with all its slaves and so ethercat provides only one link per transmission. in contrast, profinet irt and ethernet/ip sent n frames to dialogue with n slaves, which provides n times the link transmission. the bandwidth was the most crucial parameter in reducing the time cycle. moreover, 2n latencies must be considered with ethercat because profinet and ethernet/ip cycle times were composed of only one switch latency ℓ, because of the slipstreaming effect. profinet irt provided the best results at 1 gb/s. figure 13 shows that all the ethernet solutions, with the exception of fastethernet modbus/tcp, could interconnect with more than 60 slaves in cycle times of less than 1 ms. this cycle time constraint, and the number of slaves and the frame size, enables adequate coverage for most industrial applications. performances were similar when the payload was increased. the only difference was that the ethercat minimal cycle time was larger than profinet when the number of devices was increased. this was because the ethercat telegram had to be fragmented, which decreased protocol performance. an increase in frame size means that the choice of the fastethernet solutions must be carefully considered because the cycle time grows quickly. the gigaethernet solution eliminated this problem. 4 conclusion this study analysed the time performance of industrial ethernet protocols. the general conclusion was that all ethernet protocols perform suitably with a bandwidth of 1 gb/s for interconnected real-time systems. at 100 mb/s, special attention is required by engineers in the selection of ethernet-based protocols. other considerations have to be taken into account in the selection of ethernet-based products, including persistence in the market and interoperability minimum cycle time analysis of ethernet-based real-time protocols 757 with other industrial equipment and network. regarding interoperability concerns, ethernet/ip is a standardized solution and provides acceptable performance for controlling industrial systems. ethernet/ip can also implement priority mechanisms (ieee 802.1p), which is important when a network is shared with other applications because this facility enables the differentiation of network services offered by real-time traffic and unconstrained traffic. moreover, multicast communications may be interesting in industrial applications. it is hence important to know what is the capacity of the analysed solutions to enhance nodes operation using multicasting techniques. as ethernet/ip relies on cots, multicast addresses and vlan techniques may be used to create multicast communications. because of its transport protocol (tcp), modbus/tcp does not seem to be suitable to multicast communications (due to multiple acknowledgement issue). profinet irt considers only multicast communications in asynchronous mode. finally, ethercat supports vlan switches in its topology. as a conclusion, all solutions excepted modbus/tcp make it possible to support multicast communications. future works are related to the clock synchronization issue, especially for solutions based on the slipstreaming effect. the objective is to deal with non optimal situations for profinet irt and ethernet/ip where synchronization errors appear. bibliography [1] m. felser, t. sauter, the fieldbus war: history or short break between battles?, 4th ieee international workshop on factory communication systems, pp.73-80, 2002. [2] m. alves, e. tovar, and f. vasques, ethernet goes real-time: a survey on research and technological developments, technical report hurray-tr-2k01, ipp-hurray, polytechnic institute of porto (isep-ipp), 2000. [3] j.-d. decotignie, a perspective on ethernet-tcp/ip as a fieldbus, 4th ifac international conference on fieldbus systems and their applications, pp.138-143, 2001. [4] j.-d. decotignie, ethernet-based real-time and industrial communications, proceedings of the ieee, 93(6):1102-1117, 2005. [5] j. jasperneite, m. schumacher, k. weber, limits of increasing the performance of industrial ethernet protocols, 12th ieee conference on emerging technologies and factory automation, pp.17-24, 2007. [6] g. prytz, a performance analysis of ethercat and profinet irt, 13th ieee conference on emerging technologies and factory automation, pp.408-415, 2008. [7] iec, digital data communications for measurement and control ? fieldbus for use in industrial control systems: part 3: data link service definition, iec standard 61158, part 3, 2006. [8] iec, digital data communications for measurement and control ? fieldbus for use in industrial control systems: part 4: data link protocol specification, iec standard 61158, part 4, 2006. [9] g. cena, i c. berlotti, s. scanzio, on the accuracy of the distributed clock mechanism in ethercat, 4th ieee international workshop on factory communication systems, pp.43-52, 2010. [10] j. jasperneite , e. elsayed, investigations on a distributed time-triggered ethernet realtime protocol used by profinet, 3rd international workshop on real-time networks, 2004. 758 j. robert, j.-p. georges, e. rondeau, t. divoux [11] the modbus organization. modbus. [12] modbus-ida, modbus application protocol specification v1.1b, 2006. [13] p. brooks, ethernet/ip-industrial protocol, 8th ieee international conference on emerging technologies and factory automation, vol.2, pp.505-514, 2001. [14] v. schiffer, the common industrial protocol (cip) and the family of cip networks, technical report pub00123r0, open devicenet vendor association, inc. (odva), 2006. [15] a. modlovansky, utilization of modern switching technology in ethernet/ip networks, 1st international workshop on real-time lans in the internet age, pp.35-37, 2002. [16] e. alessandria, l. seno, s. vitturi, performance analysis of ethernet/ip networks, 7th ifac international conference on fieldbuses and networks in industrial and embedded systems, vol.7, 2007. [17] ieee computer society, ieee standard for a precision clock synchronization protocol for networked measurement and control systems, ansi/ieee standard 1588-2002, 2002. int j comput commun, issn 1841-9836 8(1):61-69, february, 2013. crcwsn: presenting a routing algorithm by using re-clustering to reduce energy consumption in wsn a.g. delavar, a.a. baradaran arash ghorbannia delavar, amir abbas baradaran department of computer engineering and information technology, payam noor university, po box 19395-3697, tehran, iran a_ghorbannia@pnu.ac.ir, amirbaradaran24@yahoo.com abstract: in this paper, we have presented an algorithm, based on genetics and re-clustering, to reduce energy consumption in wireless sensor networks. algorithm crcwsn could be best used by selected chromosomes in different states. in this algorithm, a new technique of selecting cluster head(ch) has been initially used by genetic algorithm. these chs have been used individually in each round to transmit data. in this research, considering distance and energy parameters, we have created a target function having more optimum conditions, compared to previous techniques. the created target function has been evaluated by input chromosome, and the combination of chromosomes has been done by a new technique having more efficiency compared to previous similar techniques. consequently, the timing of generation repeat is based on local distribution in chromosomes, and their using in sending data from source to destination that decrease generations’ repeat, compared to previous methods .results by simulation show that, at the end of each round, the number of alive nodes in the suggested algorithm increases, compared to previous methods, which increases network’s lifetime. keywords: genetic algorithm, wireless sensor network (wsn), routing, reduce energy consumption, re-clustering. 1 introduction recently, wireless sensor networks have widely been considered[1].these networks include some nodes receiving environment data and then sending them to a base station(bs)[2,3].generally, in these kinds of networks, nodes have batteries of limited energy which guarantee network’s lifetime[1,2,3,4]. the most important problem in wireless sensor networks is to create effective routing protocols in order to decrease energy consumption and increase network’s lifetime[2,4,5]. so many methods, having advantages and limitations have been presented , to find the best route and transmit data to bs. one of the most effective ways of finding optimum route and data transmission from common nodes to bs, is using genetic algorithm(ga)[2,6]. an important advantage of routing by using ga, is a multipurpose search rather than a point to point search, which leads to using all points in all processes of running ga. this causes non-optimum points in previous stages, to be attended in next stages[2]. to solve the problems, ga uses a group of chromosomes related to one population. in each running of ga, current chromosomes take a genetic operation for next generation to be appeared. this operation includes selection, composition and mutation[2,3,5]. optimum route is created by ga, after running several generations. in this research, we try to present a genetic and re-clustering based routing algorithm to reduce energy consumption of sensor network. routine is comparing to previous methods. 2 related works one of the most important re-clustering algorithms is leach which is based on some rounds[2,3,5]. each round includes two phases; setup and steady. in setup phase, clusters are copyright c⃝ 2006-2013 by ccc publications 62 a.g. delavar, a.a. baradaran formed and common nodes and chs are determined and in steady phase, data are transmitted from common node to ch and from ch to bs. in leach algorithm; in each round, each node can be a ch or a common node. whether a node can be a ch or not depends on the following threshold[2,7,8,9]: t(n) = { p 1−p∗(r mod 1 p ) if n ∈ g, 0 otherwise (1) in which : p: ch decision percentage( percentage of being ch, for example p=0.05.); r: current round; g: a set of rounds not being ch in 1/p of current round; another routing way by ga, is the algorithm presented by annie s.wu,ming zhou, shiyuan jin[10]. in this algorithm, selecting and clustering nodes is done by ga. of other methods of routing by ga, we can refer to the rgwsn algorithm [2]. in this algorithm, clustering and selection of optimum chs are done by ga. another geneticbased method, presented by jianming zhang, yaping lin, cuihong zhou, jingcheng ouyang could reduce energy consumption of sensor networks by considering distance and energy parameters[11]. 2.1 genetic algorithm ga is a method, based on natural inspiration, which numerically does direct and random search. finding an optimum solution is based on repeat and differs from other search methods in applying natural selection[2]. ga includes bit strands named chromosomes, each bit of which is called a gene. chromosomes, represent the entire set of variables and ga, in each repeat, will use non-optimum points of previous repeat[2,12]. one important advantage of ga , in comparison with other searching methods, is multi-point selection rather than one-point selection, in searching space. so ga, in searching and finding an optimum solution, is less likely to converge into a local maximum. ga includes some stages. each stage in ga is called a generation and a series of solutions is called a population[2,5,9]. ga initiates with initial population and after operations of genetic operators including selection, composition and mutation, a new population is created. the creation of new population is done by fitness function , i.e. after applying fitness function on created population , by termination of each generation, a new population is created. the processes are running through different generations to find optimum solution. generally, ga includes the following stages[2,6,12]: 1. selection: in this stage, two chromosomes having higher fitness, are selected as parent. 2. crossover: in this stage, two parents selected in the previous stage, are composed and new children are created. 3. mutation: in this stage, the child having mutation conditions, mutates. after this stage, children are decoded and compared to fitness function. if, by regarding fitness function, conditions are not optimum, new children will be used in initial population and algorithm proceeds. in this stage, generated chromosomes are treated as initial population and answers of low fitness are omitted, and algorithm proceeds by n chromosomes. under following conditions ga can come to an end[2,6]: 1. the best degree of fitness for children is achieved. 2. no improvement achieved by running the algorithm during several generations. 3. mean value of fitness function reaches a fixed measure as per several repeats(e.g. during 50 generations). 4. the number of generations reaches a fixed value after several repeats. 5. a combination of the abovementioned items occurs. crcwsn: presenting a routing algorithm by using re-clustering to reduce energy consumption in wsn 63 2.2 network model all sensor nodes and bs are motionless and after establishment can not be added or omitted. also the base energy of nodes differs and sensor nodes are informed of situation , i.e. they need hardware ,s such as gps to do this. 2.3 radio model sensors, when receiving data or transmitting data, consume energy[7,13]. standard radio model used in wsn, uses free space and multi-pass fading models depending on the distance between sender and receiver . this distance is the shortest crossover distance dcrossover[7,9]. transmit power equals[14]: pr(d) = ptgtgrλ 2 (4πd)2 (2) in which: pt: transmit power; gt: gain of transmit antenna; λ: wave length of carrier signal (in meter). when receiver distance is longer than dcrossover, transmit power equals[7,9]: pr(d) = ptgtgrh 2 t h 2 r (d)4 (3) ht: height of sender antenna (in meter). hr: height of receiver antenna (in meter). to transmit an n-bits message in d meter distance, radio energy consumption equals[7,9]: etx(n,d) = n(eelect+ ∈fs d2) d < dcrossover etx(n,d) = n(eelect+ ∈mp d4) d ≥ dcrossover (4) to receive n-bits message radio energy equals[9]: erx(n) = neelect (5) ∈fsand ∈mp are parameters depending on sensitivity(intelligence)of receiver and noise shape and eelectis an electric energy depending on some factors as digital code, modulation, filtering[9]. 3 the new presented algorithm crcwsn the new proposed algorithm includes some rounds each of which has two setup and steady phases. clustering is formed in setup phase and data transmission is in steady phase. setup phase initiates with an initial message from bs, including the nodes’ position and initial energy, and after running ga, some generations having lower fitness than other generations , are selected (optimum generations), and common nodes and chs are selected from available nodes in selected generations. (for example, if ga stops after running 50 generations, the 5 generations having lower fitness than other generations are selected). in the proposed method, a binary coding system has been used and we suppose 0 bits representing common nodes and 1 bits representing chs. after selection of common nodes and chs, data are transmitted in steady phase. the routine in setup phase is as follows: the distributive environment of nodes is divided into separated areas called grid. then some nodes, likely to be optimum ch, from each grid are selected. the way of selecting nodes of each grid is based on the distance from gravity center of nodes of each 64 a.g. delavar, a.a. baradaran grid, and the initial energy thereof, i.e. the nodes having more initial energy and short distance to gravity center are better selected. then the nodes selected as chromosomes ’bits are attended in ga. for example, if the environment is divided into 10 grids and 4 nodes from each grid are selected, the chromosomes attended in ga will have 40 bits(0,1). routine in ga is as follows: firstly, from initial chromosome which is initial population, some random populations are created and then randomly by using genetic operators of selection , composition and mutation, new children are created binarily from created populations. for example, if we create 50 new populations from initial populations, we will have 100 new children after applying genetic operators. after creating children, we apply fitness function on new population to select some populations(for example 5 populations) having lower fitness , as optimum generations. this trend will be proceeded for several generations until ga stops. (when ga reaches a fixed number of generations). for proposed methods, fitness function equals mean energy consumed by entire network in each population. fitness function is calculated , regarding heinzelman model. heinzelman has stated, in a model, that each node, to transmit l bits of data in d distance from itself, consumes etenergy. et = leelect + l ∈fs d2 d < d0 et = leelect + l ∈mp d4 d ≥ d0 (6) in which: d0: the shortest crossover distance. eelect: energy required to activate electronic circuits. ∈mp, ∈fs: parameters related to receiver’s sensitivity and noise shape. also the energy to receive l bits of data equals: er = leelect (7) in the proposed algorithm and setup phase, we compute fitness value for the bits in final selection, and suppose that 0 bits representing common nodes and 1 bits representing chs. the total consumed energy of network equals: e = e1 + e2 + e3 + e4 (8) in which: e1: energy necessary to send from common node to ch; e2: energy necessary to receive ch data from common nodes; e3: aggregation energy in ch; e4: energy necessary to send from ch to bs. which we have:   e1 = leelect + l ∈fs d2distoch ddistoch < d0 or e1 = leelect + l ∈mp d4distoch ddistoch ≥ d0 (9) in which: ddistoch: distance from common node to ch; l: number of bits. e2 = leelect × n_common (10) n_common: common node number e3 = leag × n_ch (11) crcwsn: presenting a routing algorithm by using re-clustering to reduce energy consumption in wsn 65 eag: aggregation energy in ch n_ch: chs number (bits 1) e4 = leelect + l ∈fs d2distobs ddistobs < d0 or e4 = leelect + l ∈mp d4distobs ddistobs ≥ d0 (12) ddistobs: distance from ch to bs. in setup phase, after applying fitness function on final populations(population of selected optimum generations) and specifying common nodes and chs,e1for common nodes(0 bits) and e2,e3, e4for 1 bits, are calculated. finally in steady phase nodes’ energy is reduced, based on transmissions. for example if we want to run algorithm in 1000 rounds and 5 optimum populations (population resulted by 5 optimum populations)are selected in setup phase, second round with second population, third round with third population, fourth round with fourth population, and fifth round with fifth population, is run. similarly, the sixth round with first population and the seventh round with second population is run. code i shows the proposed method. also chart 1 shows flowchart of the proposed algorithm. code i. proposed method: 1. state = normal 2. grid 3. node selection of each grid 4. create initial population 5. create a random population of initial population 6. running ga 7. selection 5 optimum selected generation 8. running steady phase 9. if round = 0 go to 10 else go to 6 10. end. 4 simulation the proposed algorithm analysis has been done by matlab software. in this analysis, some indices as alive nodes at the end of each round, number of grids, number of selected nodes in each grid are considered to compose initial population. also initial nodes’ energy, are random measures between 0.3 to 0.5. other parameters used in simulation are as follow: 1. nodes are randomly placed in a square –shaped environment; 2. bs position is variable; 3. eelect: 50nj/bit; 4. ∈fs: 10pj/bit/m2; 5. ∈mp: 0.0013pj/bit/m4; 6. eag: 5nj/bit/signal; 7.d0 = √ ∈fs ∈mp 66 a.g. delavar, a.a. baradaran figure 1: flowchart of the proposed algorithm figure 2: total number of alive nodes in the rgwsn, leach,gsaga,rcsdn,rgwsn the proposed method has been compared to leach, rcsdn, gsaga , and rgwsn methods. figure 1 shows the number of alive nodes at the end of 1400 rounds and figure 2 shows fitness function for population of 5 optimum selected generation. table 1 shows simulation parameters. total number of cluster in different round in leach. crcwsn: presenting a routing algorithm by using re-clustering to reduce energy consumption in wsn 67 figure 3: the energy consumed by entire network for 5 optimum selected generation figure 4: total number of cluster in different round in leach figure 5: total number of cluster in different round in crcwsn 68 a.g. delavar, a.a. baradaran table i.simulation parameters value parameter 100*100 m network size 50,50 m base station location rand [0.3,0.5] j initial energy for node 50nj/bit eelec 10pj/bit/ m2 εfs 0.0013pj/bit/m4 εmp 5nj/bit/signal data aggregation energy 100 nodes number 6 grids number 5 nodes number of each grid 87m d0 1. the energy consumed by entire network for 5 optimum selected generation. as shown by diagrams, after running 1400 rounds, the alive nodes in the proposed algorithm are more than those in leach, rcsdn,gsaga, and rgwsn methods. so the network’s lifetime increases. in this method, we also compare the number of clusters in various rounds, to leach methods. figures 3,4 show the number of clusters in leach and proposed methods. total number of cluster in different round in crcwsn. as shown by diagrams, in the proposed method, clusters formation in different rounds is more balanced than in the leach method. 5 conclusion in this paper, we presented a new method of clustering to transmit data from common nodes to ch and from ch to bs in sensor networks. selection of optimum cluster play an effective role in increasing sensor network’s lifetime. we show, by multi simulations, that the proposed algorithm differs from other proposed algorithm in reducing energy consumption and can significantly increase network’s lifetime compared to similar previous methods. bibliography [1] gao de-yun, zhang lin-juan, wang hwang-cheng, energy saving with node sleep and power control mechanisms for wireless sensor networks,in: national engineering laboratory for next generation internet interconnection devices, school of electronics and information engineering, beijing jiaotong university, china, 18(1):49-59, 2011. [2] a. g. delavar, a. abbas baradaran, j. artin, rgwsn: presenting a genetic-based routing algorithm to reduce energy consumption in wireless sensor network,international journal of computer science issues, vol. 8, issue 5, no 1, 54-59, september 2011. [3] ]y. zhu, w. wu, j. pan, y. tang, an energy-efficient data gathering algorithm to prolong lifetime of wireless sensor networks, comput. commun., 33:639-647, 2010. crcwsn: presenting a routing algorithm by using re-clustering to reduce energy consumption in wsn 69 [4] cheng hong-bing, yang geng, nhrpa: a novel hierarchical routing protocol algorithm for wireless sensor networks, journal of china universities of posts and telecommunications, 15(3): 75-81, 2008. [5] a.h. mohajerzadeh, m.h.yaghmaee, h.s.yazdi,a.a.rezaee, a fair protocol using generic utility based approach in wireless sensor networks, ultra modern telecommunications & workshops, 2009. icumt ’09. international conference on, pp. 1-4, 2009. [6] s.yussof, r.z. razali, o.h.see, a parallel genetic algorithm for shortest path routing problem, 2009 international conference on future computer and communication, doi 10.1109/icfcc.2009.36, 2009. [7] a.g. delavar,j.artin,m.m.tajari, rcsdn : a distributed balanced routing algorithm with optimized cluster distribution, icsap 2011, 3rd international conference onsignal acquisition and processing , 26-28, february, 2011, singapore [8] a.g. delavar, j.artin, m.m.tajari, prwsn: a hybrid routing algorithm with special parameters in wireless sensor network, in: a. özcan, j. zizka, and d. nagamalai (eds.): wimo/coneco 2011, ccis 162, pp. 145–158, 2011. [9] heinzelman, w.r., chandrakasan, a., balakrishnan, h., energy efficient communication protocol for wireless sensor networks, proc. of the 33rd hawaii international conference on system science, vol. 2, doi: 10.1109/hicss.2000.926982, 2000. [10] shiyuan jin, ming zhou, annie s. wu, sensor network optimization using a genetic algorithm, school of eecs university of central florida orlando, fl 32816 [11] jianming zhang,yaping lin,cuihong zhou,jingcheng ouyang, optimal model for energyefficient clustering in wireless sensor networks using global simulated annealing genetic algorithm, doi 10.1109/iita.workshops.2008.40 [12] v.purishotham reddy, g.michael, m.umamaheshwari, coarse-grained parallelgeneticalgorithm to solve the shortest path routing problem using genetic operators, indian journal of computer science and engineering, issn : 0976-5166, 2(1):39-42, 2011. [13] wang, q., yang, w. energy consumption model for power management in wireless sensor networks, in 4th annual ieee communications society conference on sensor, mesh and ad hoc, communications and network, doi:10.1109/sahcn.2007.4292826, 2007. [14] t. rappaport, wireless communications: principles & practice, nj, prentice hall, 1996. ijcccv8n1.pdf int j comput commun, issn 1841-9836 8(1):8-17, february, 2013. forecasting chaotic series in manufacturing systems by vector support machine regression and neural networks m.d. alfaro, j.m. sepúlveda, j.a. ulloa miguel d. alfaro, juan m. sepúlveda jasmín a. ulloa department of industrial engineering, university of santiago of chile 3769 ecuador ave. santiago, chile. e-mail: miguel.alfaro@usach.cl, juan.sepulveda @usach.cl jasmin.ulloa@usach.cl abstract: currently, it is recognized that manufacturing systems are complex in their structure and dynamics. management, control and forecasting of such systems are very difficult tasks due to complexity. numerous variables and signals vary in time with different patterns so that decision makers must be able to predict the behavior of the system. this is a necessary capability in order to keep the system under a safe operation. this also helps to prevent emergencies and the occurrence of critical events that may put in danger human beings and capital resources, such as expensive equipment and valuable production. when dealing with chaotic systems, the management, control, and forecasting are very difficult tasks. in this article an application of neural networks and vector support machines for the forecasting of the time varying average number of parts in a waiting line of a manufacturing system having a chaotic behavior, is presented. the best results were obtained with least square support vector machines and for the neural networks case, the best forecasts, are those with models employing the invariants characterizing the system’s dynamics. keywords: chaos; forecast; neural networks; vector support machines; manufacturing systems 1 introduction manufacturing systems are conceived as complex ones; although the complex term does not have a unique definition [1] it is possible to distinguish two kinds of complexities in production systems: a) structural complexity or static complexity dealing with the number of system’s components and their relationships, and b) dynamic complexity dealing with the uncertainty in the systems behavior [2]. it may seem paradoxical that an artificial system engineered for making a set of given tasks had its own laws as if it was a natural system. this is due to the fact that production systems are becoming everyday more complex by the technological progress and the transformation of the supply chain. flexible manufacturing machinery, global markets, and supply network relationships are typical examples of such changes. managing these systems to bring them under control is today a difficult task. the dynamic complexity of production systems has been demonstrated by the kinds of behavior that they can exhibit, among these a chaotic behavior [3], [4], [5]. several metrics have been proposed for measuring the complexity of manufacturing systems [2], [6], [7], [8]. these studies relate metrics of performance with metrics of complexity. in this article, a way of control by forecasting the system’s behavior is shown. for this purpose, a time series of the average number of parts in the waiting line of a chaotic manufacturing system is utilized [3]. as forecasting methods, support vector machines (svms) and artificial neural networks (anns) have been selected because these methods can distinguish chaotic patterns and therefore they can predict the evolution of an observed control variable. in [9] svms have been used for support vector regression (svr) analysis of several exchange rates with respect to the us dollar. in the present work, a least square support vector regression copyright c© 2006-2013 by ccc publications forecasting chaotic series in manufacturing systems by vector support machine regression and neural networks 9 (ls-svr) with less computational effort than the one reported in [10] is proposed. in [11] an ann model for forecasting the observed error of monitoring units of the sea level in singapore is presented. in [12] an ann is constructed for forecasting the behavior of a diode having a chaotic pattern; in this case the local dimension and the time delay are proposed for determining the network architecture. the originality of this work consists of the study of the performance of two forecasting techniques: ls-svm and ann as applied to chaotic series. similar studies have been made but for series that are not chaotic [13], [14]. also, it is a novel application in the manufacturing area. the paper is organized as follows: section 2 shows in a summarized way the manufacturing system from which the time series was obtained; section 3 presents the methods utilized for the forecasting; section 4 shows the results of the analysis of the time series by using nonlinear dynamic systems (nlds) theory. forecasting results are detailed in section 5. finally, conclusions and research directions are given. 2 system under study 2.1 variable to be analyzed the system under study is described in [3]. the variable to be analyzed is the average in time of the number of parts in the waiting line of a flexible manufacturing system. by utilizing a time series of this variable the system’s dynamics is characterized by means of the theory of non-linear dynamic systems. the machining shop is formed by three different machines producing three types of parts. each part has a set of operations which can be executed in different machines according to the operations sequence. figure 1 shows the layout of the system under study. figure 1: machining shop layout. 2.2 operation of the system • the arrivals rate and service rate are such that the system is under equilibrium; that is, the number of parts neither tends to zero nor infinity. • upon arrival of a part, a function f assigns the part to a machine able to perform the operation and that has the least number of parts in queue. • the priority of the queue of each part type at any machine is first-in first-out (fifo). 10 m.d. alfaro, j.m. sepúlveda, j.a. ulloa • the machine works cyclically with a kind of part, during a time interval equivalent to the time needed to complete the stock of that kind of part at the k-th machine. the function gk manages the execution cycle according to the part type. the values of plant parameters are shown in table 1, where βj is the arrival rate of the j-th part type (number of parts/time unit) and oij is the i-th operation of the j-th part. the values in the table represent the operation time at each machine. 3 forecasting methods utilized research on forecasting models has received considerable attention over the last 50 years. currently, there exist numerous forecasting methods [15]. for the case of chaotic systems, the very theory on nlds provides forecasting methods [16]. in this article, nlds theory is used as a base for constructing ann and vsm models. these techniques are proposed due to their capacity for recognizing chaotic patterns. 3.1 artificial neural networks an artificial neural network (ann) is a computational model of the brain. it consists of a limited number of connected elements (neurons) and it is distributed in an input layer, one or more hidden layers, and an output layer.an ann is a mathematical structure that allows pattern recognition; in this work we use a back-propagation type of network, as shown in figure 2. as it is known that the system is chaotic, it is proposed as the number of neurons in the input layer the dimension of the phase space. this value is obtained by the method of false neighbors. the temporal distance between the input variables corresponds to the time delay in the construction of the systemďż˝s attractor. this value is obtained from the average of the mutual information [16]. the transfer function is of the sigmoidal type and the network is defined by the equation (1). xt = β0 + n ∑ i=1 βif(sωi0 + d ∑ j=1 ωijxt−j) (1) where n is the number of neurons in the hidden layer, d is the number of neurons in the forecasting chaotic series in manufacturing systems by vector support machine regression and neural networks 11 figure 2: neural network for forecasting input layer, s is the standard deviation of the weights matrix and βi, ωij are the weights. the number of neurons in the hidden layer is due to the following empirical relationship 2: nobs 10 ≥ (ne + 1)nc + (nc + 1)ns (2) where nobs is the number of observations, ne, nc and ns are the number of neurons in the input layer, hidden layer, and output layer, respectively. 3.2 support vector machines for least squares regression support vector machines (svm) algorithms emerged from the artificial intelligence field and they have been successfully used in a variety of applications for problems of classification and regression. the least-squares vector support machine is a modified version of a standard vsm for regression; the model is trained by solving a linear system instead of a quadratic programming optimization model [10]. the ls-svm are closely related with regularization networks and gaussian processes, buy they emphasize and exploit its interpretation from the viewpoint of the optimization theory. the general formulation for a ls-svr is shown in (3): y = wt f(x) + b (3) where x is the input vector of the data series, y is the output vector. parameters w and b are obtained from the optimization problem given by equations (4), (5). min τ(w, b, e) = 1 2 ‖w‖2 + γ 1 2 n ∑ i=1 e2i (4) s.t. (yi − (〈w, xi〉) + b) = ei ∀i = 1, ...n (5) in (4), γ is an arbitrarily chosen parameter. for learning, the kernel utilized is the radial base function (rbf) (6). k(xi, xj) = exp(− ‖yi − xi‖ 2 σ2 ) (6) 12 m.d. alfaro, j.m. sepúlveda, j.a. ulloa the architecture of the ls-svr [17] is shown in figure 3. figure 3: architecture generated by regression svm 4 characterization of the system’s dynamics it has been stated that the system has a chaotic behavior; in figures 4, 5, 6, 7, 8, 9 the analysis confirming this assumption is shown. in figure 4 the time series plot is seen, figure 5 shows the fourier spectrum where the erratic nature of the series is verified; figure 6 presents the time delay corresponding to the first minimum of the mutual information average. figure 7 shows the phase space dimension d = 5, which has been obtained by the percentage of false neighbors [16]. however, figure 8 shows that the local dimension is 3 (correlation dimension 2.784). finally, in figure 9 is observed that the highest lyapunov’s exponents value is 0.41, which corroborates the signal’s chaotic character. figure 4: average of the number of parts in time figure 5: fourier power spectrum forecasting chaotic series in manufacturing systems by vector support machine regression and neural networks 13 figure 6: mutual information average figure 7: dimension of phase space figure 8: correlation dimension figure 9: lyapunov’s exponents 5 forecasting models results in order to measure the performance of the models, the apropiability index (ia) and the normalized root of quadratic error (rms) are used, as defined by (7) and (8) respectively: ia = 1 − ∑n i=1(yi − y , i )2 ∑n i=1 (|yi| + |y , i |)2 (7) rms = √ ∑n i=1(yi − y , i )2 ∑n i=1 (yi)2 (8) where, yi is the value of the average number of parts at period i, y , i is the value predicted by the model, and n is the number of forecasted periods. the ia indicates the proportion of the variance that is explained by the model; values greater than 0.9 are expected. whereas, the rms compares the error between the desired output and the one generated by the model; values close or below to 0.1 are expected. the series has 12001 data or periods, which correspond to the time persistent average number of parts in a time interval of 0.15 time units. a 70% is used for training and the remaining 30% for validation. 5.1 experimental results for ann for the forecasting model, a neural network is designed based on the time delay τ and the dimension d which were obtained from the chaotic system’s characterization. five forecasting models are constructed with 1, 3, 5, 7 and 10 neurons in the input layer, with a time delay τ = 4 in each model. the number of neurons in the hidden layer is obtained by the relationship (2) for 14 m.d. alfaro, j.m. sepúlveda, j.a. ulloa each model; thus it is obtained: 279, 167, 119, 93, 69 neurons, respectively. all of the models were implemented by using the matlabtmann toolbox. figures 10, 11, and 12 show the results for the cases of 3, 5 and 10 neurons in the input layer. figure 10: forecast with three neurons in the input layer figure 11: forecast with five neurons in the input layer figure 12: forecast with ten neurons in the input layer table 2 show the values of ia and rms indicators for the different models. it is observed that the model of 10 neurons is superior in 0.03% to the model with five neurons in the input layer. however, if the rms of the same models are compared, the model with 10 neurons is greater in 3.65%. in a similar analysis between the models with three and ten neurons, it can be seen that the difference of the ias is 0.05% and the rms’s 0.67%. then, it is possible to state that the best models can be found between the ones with three and five neurons in the input layer. these values are exactly those corresponding to the local dimension and the global dimension in the phase space. 5.2 experimental results for ls-svr the values of the rbf kernel parameters associated with the optimization problem described by equations (4), (5) and (6) are γ = 256 and σ = 8. as time delay in the input variables τ = 4 has been used. likewise the ann case, five models have been constructed with 1, 3, 5, 7 and 10 input vectors. the models were implemented by using matlabtm lssvmlab1.7. figures 13, 14 and 15 show results for 3, 5 and 10 input vectors. forecasting chaotic series in manufacturing systems by vector support machine regression and neural networks 15 figure 13: forecast with three input vectors figure 14: forecast with five input vectors figure 15: forecast with ten input vectors table 3 shows the values of the indicators ia and rms for the different models. the results in table 3 are not the expected ones; the best results correspond to seven and ten input vectors but not between dimensions three and five as with anns. the same experiment was executed without time delay, that is τ = 1 was assumed. the results are shown in table 4. again, it is observed that best results are for seven and ten input vectors. thus, for this specific case, it is verified that does not exists a behavior pattern based on nlds characterization. 5.3 comparison between experimental results for ann and ls-svr even though the difference between the ia of the best ann and ls-svr models is 0.11% in these models (five neuron for ann and seven input vector for ls-svr) the lssvr’s rms value is practically a half the ann’s. hence, it is possible to conclude that the best model is the one constructed with ls-svr. nevertheless, it is observed that in general terms both approaches perform adequately. 16 m.d. alfaro, j.m. sepúlveda, j.a. ulloa 6 conclusions as seen, the best results were obtained with least square support vector machines. similar results have been reported for svr and ann for non-chaotic series [13], [14]. notwithstanding, it can be stated that both models are efficient for the forecasting of a chaotic series obtained from a flexible manufacturing system. according to the results, it can be concluded that the system’s behavior can be predicted in one time step, that is 0.15 time units. as research directions, it is suggested to develop models able to predict a longer time interval. for this purpose, ongoing work by the authors is addressing the use of the inverse of the lyapunov’s exponent in order to determine the number of neurons in the output layer for the ann case, and of the number of output vectors for the ls-svr. acknowledgments this research has been supported by dicyt ( scientic and technological research bureau) of the university of santiago of chile (usach) and department of industrial engineering. bibliography [1] eimaraghy h., kuzgunkaya 0., urbanic r., manufacturing systems configuration complexity, cirp annals-manufacturing technology, issn 0007-8506, 54(1): 445-450, 2005. 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[11] sun y., babovic v., chan e., multi-step-ahead model error prediction using time-delay neural networks combined with chaos theory, journal of hydrology, issn 00221694, 395(1): 109-116, 2010. [12] hanias m., karras d. on ef?cient multistep non-linear time series prediction in chaotic diode resonator circuits by optimizing the combination of non-linear time series analysis and neural networks, engineering applications of artificial intelligence issn 0952-1976, 22(1): 32-39, 2009. [13] vanajakshi l., rilett l.,a comparison of the performance of artificial. neural networks and support vector machines for the prediction of traffic speed, ieee intelligent vehicles symposium isbn 0-7803-8310-9, 194-199, parma, italy june 2004. [14] yoon h.,jun s, ,hyun y.,bae g., lee k.,a comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, journal of hydrology, issn 0022-1694, 396(1): 128-138, 2011. [15] scott j., it principles of forecasting, university of pennsylvania, kluwer academic publisher 2001. [16] abarbanel h., analysis of observed chaotic data. new york, springer-verlag, 1996. [17] vapnik v., statistical learning theory, wiley. springer, new york. usa. 1998. int j comput commun, issn 1841-9836 9(3):253-260, june, 2014. solving continuous trajectory and forward kinematics simultaneously based on ann c.k. ang, s.h. tang, s. mashohor, m.k.a.m. arrifin chun kit ang*, sai hong tang, syamsiah mashohor, mohd khairol anuar mohd arrifin universiti putra malaysia department of mechanical and manufacturing engineering, faculty of engineering, 43400 upm, serdang, selangor darul ehsan. email: ack_kit@hotmail.com, saihong@upm.edu.my, syamsiah@upm.edu.my, khairol@upm.edu.my *corresponding author abstract: robot movement can be predicted by incorporating forward kinematics (fk) and trajectory planning techniques. however, the calculations will become complicated and hard to be solved if the number of specific via points is increased. thus, back-propagation artificial neural network is proposed in this paper to overcome this drawback due to its ability in learning pattern solutions. a virtual 4-degree of freedom manipulator is exploited as an example and the theoretical results are compared with the proposed method. keywords: artificial neural network (ann), back propagation neural network, forward kinematics, continuous trajectory. 1 introduction trajectory planning can be considered as an important issue in robot industry. without an appropriate trajectory planning, a robot’s motion will become unpredictable and it may collide with obstacles or go through undesirable points. thus, an appropriate trajectory planning enables us to determine the required workspace and provides us an opportunity to avoid obstacles. basically, there are two types of trajectory planning, point to point and continuous trajectory (with via points). however, continuous trajectory planning will be more practical compare to point to point trajectory because it allows robot to pass through various specific points and it is useful for real time robot. conventionally, there are a few methods that can be used for continuous trajectory planning such as bezier function, linear segments with parabolic blends (lspb), and high order polynomials. unfortunately, those methods require extensive calculations when the number of specific points is increased and thus leading to the emergence of a large number of formulas ( [1][5]). hence, it is advisable to develop a method which is able to overcome this drawback. 2 preliminaries forward kinematics (fk) can be used to determined the position and orientation of a robot’s hand if all the configurations are known and the equations can be derived by using denavithartenberg (dh) method because it is the standard way of modelling the robot motion due to its simplicity of modelling robot links and joints that is applicable for any robot configuration s regardless of its complexity. the total transformation for a robot manipulator from base to hand will be copyright © 2006-2014 by ccc publications 254 c.k. ang, s.h. tang, s. mashohor, m.k.a.m. arrifin rth = rt 11 t2 2t3... n−1tn = a1a2a3...an =   nx ox ax px ny oy ay py nz oz az pz 0 0 0 1   (1) and the end effector can be defined as: re(t) = fe(θ(t)) (2) where re ∈ rm in cartesian space is related to the joint space vector, m is dimensional position vector of end effector. in fact, what we are actually interested in is the inverse kinematics (ik) because it allows us to determine the value of each joint in order to place the arm at a desired position and orientation. the inverse kinematic problem is to find the joint variable θ for any givenre(t). unfortunately, it is impossible to find an analytic solution due to the nonlinearity off(θ(t))in reality. the inverse kinematics problem of manipulators is thus usually solved at the velocity level. differentiating re(t) = fe(θ(t)) with respect to time yields a linear relation between the cartesian velocity ṙeand the joint velocityθ̇: je(θ)θ̇ = ṙe (3) where je(θ) ∈ rm×n is the end effector jacobian matrix which is defined as je(θ) = ∂f(θ) ∂θ [613]. however, the derived ik equations are not necessarily available when the degeneracy and singularity problems ( [6], [9], [12][14])] occurred and thus we need to use some new methods to solve it such as artificial neural network, genetic algorithm, neural fuzzy and etceteras ( [7][15]). generally, high order polynomial is used to plan a trajectory with the intention to let the robot’s hand to pass through all the specific points. however, solving the high order polynomial equations requires extensive calculations. instead, it is advisable to use combinations of lower order polynomials for different segments of the trajectory and blend them together to satisfy all required boundary conditions ( [1][5]). θ (t) = c0 + c1t + c2t 2 + ....... + cn−1t n−1 + cnt n (4) for an example, for a 4-3-4 trajectory, a fourth order polynomial is used to plan a trajectory between the initial point and the first via point, followed by a third order polynomial to plan a trajectory between two via points, lastly another fourth order polynomial is implemented to plan the trajectory between the last via point and final angle. θ (t)1 = a0 + a1t + a2t 2 + a3t 3 + a4t 4 θ (t)2 = b0 + b1t + b2t 2 + b3t 3 θ (t)3 = c0 + c1t + c2t 2 + c3t 3 + c4t 4 in short, ik allows us to allocate the robot’s hand at desired location and the motion of robot’s hand can be determined by incorporating the fk and trajectory techniques. sadly, the calculations will become tedious and hard to be solved when the number of specific points is increased. solving continuous trajectory and forward kinematics simultaneously based on ann 255 3 theoretical and simulation results experiments were performed on a simulated 4-degree of freedom (dof) robot manipulator which shown in figue 1, ik was used to determine the starting and ending configurations for the robot’s hand while the high order polynomials and fk were used to track the motion of the robot’s hand from beginning to the end in cartesian space. it was specified that the robot need to pass through two specific points. at the same time, velocities and accelerations constraints need to be taken into consideration. figure 1: simulated 4 dof manipulator assuming that a manipulator needs to spend tf to complete the moving path and a manipulator joint needs to reach θaat interval time ta then θbat interval time tb where ta < tband ta, tb ∈ [0, tf]. one may need to match the position, velocity, and accelerations of the two segments at each point to plan a continuous trajectory. hence, higher order polynomials in the form of θ (t) = c0 + c1t + c2t2 + ... + cn−1tn−1 + cntn are required. however, it is an extensive calculation process to solve high order polynomial and it is advisable to use the combinations of lower order polynomials for different segment of the trajectory such as 4-3-4 trajectory or 3-5-3 trajectory. the velocity and acceleration for initial and final point are zero. in order to fulfill the entire initial, final, and via points requirement, a matrix form can be generated where 256 c.k. ang, s.h. tang, s. mashohor, m.k.a.m. arrifin tab = tb − taand tc = tf − ta − tb.  θ1 θ̇1 θ̈1 θ2 θ2 0 0 θ3 θ3 0 0 θ4 θ̇4 θ̈4   =   1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 1 ta t 2 a t 3 a t 4 a 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 2ta 3t 2 a 4t 3 a 0 −1 0 0 0 0 0 0 0 0 0 2 6ta 12t 2 a 0 0 −2 0 0 0 0 0 0 0 0 0 0 0 1 tab t 2 ab t 3 ab 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 2tab 3t 2 ab 0 −1 0 0 0 0 0 0 0 0 0 0 2 6tab 0 0 −2 0 0 0 0 0 0 0 0 0 0 0 1 tc t 2 c t 3 c t 4 c 0 0 0 0 0 0 0 0 0 0 1 2tc 3t 2 c 4t 3 c 0 0 0 0 0 0 0 0 0 0 0 2 6tc 12t 2 c   ×   a0 a1 a2 a3 a4 b0 b1 b2 b3 c0 c1 c2 c3 c4   or [θ] = [m] [c] (5) by solving those constant values in matrix[c], the joint turning angle at any interval time can be obtained where θ (t) = a0 + a1t + a2t 2 + a3t 3 + a4t 4 0 < t ≤ ta θ (t) = b0 + b1t + b2t 2 + b3t 3 t ∈ [ta, tb] , t = t − ta θ (t) = c0 + c1t + c2t 2 + c3t 3 + c4t 4 t ∈ [tb, tf] , t = t − ta − tb the equation which is used to represent the robot’s hand at any interval time can be denoted as: re (ti) =   cos (θ1 (ti)) [ l1 × cos (θ2 (ti)) + l2 × cos ( 3∑ k=2 θk (ti) ) + l3 × cos ( 4∑ k=2 θk (ti) )] sin (θ1 (ti)) [ l1 × cos (θ2 (ti)) + l2 × cos ( 3∑ k=2 θk (ti) ) + l3 × cos ( 4∑ k=2 θk (ti) )] l1 × sin (θ2 (ti)) + l2 × sin ( 3∑ k=2 θk (ti) ) + l3 × sin ( 4∑ k=2 θk (ti) )   (6) in fact, all these calculations can be solved simultaneously by back-propagation artificial neural network. the back-propagation neural network can be activated by presented a training set data with inputs x1(p), x2(p)...xn(p)and desired outputsyd,1(p), yd,2(p), ..., yd,n(p). assume that the back-propagation network consists of three layers which are input layer, hidden layer, solving continuous trajectory and forward kinematics simultaneously based on ann 257 and output layer. the actual outputs of the neurons in the hidden layer will be yj(p) = sigmoid [ n∑ i=i xi(p) × wij(p) − θj ] where n is the number of inputs for neuron j in the hidden layer, and sigmoid is the sigmoid activation functiony sigmoid = 1/(1+e−x). for the output layers, the actual outputs of the neurons will be yk(p) = sigmoid [ m∑ j=1 xjk(p) × wjk(p) − θk ] where m is the number of inputs for neuron k in the output layer. an error signal ek(p) = yd,k(p) − yk(p)will be generated from neuron kat output layer and propagated backward for updating the weights. for updating the weights at the output neurons with learning rate, α: error gradient,δk(p) = yk(p) × [1 − yk(p)] × ek(p) ek(p) = yd,k(p) − yk(p) weight correction, ∆wjk(p) = α × yj(p) × δk(p) update weight, wjk(p + 1) = wjk(p) + ∆wjk(p) similar to output layer, for updating the weights at the hidden neurons with learning rate, α: error gradient,δj(p) = yj(p) × [1 − yj(p)] × 1∑ k=1 δk(p) × wjk(p) weight correction, ∆wij(p) = α × xi(p) × δj(p) update weight,wij(p + 1) = wij(p) + ∆wij(p) the same procedures can be repeated by increasing the iteration puntil the output error criterion is satisfied. a training data set which consists of inputs and outputs is presented to the networks for learning purpose. inputs consists of the final angle of each joint θf,n and interval time variable ti where ti ∈ [0, tf] and tf is the time for the manipulator to complete the moving path. the outputs will be the coordinates of robot’s hand at any interval time with respect to ti,re(ti) = [px py pz] t . there are two set of results in table 1. the theoretical results which obtained by using conventional fk and high order polynomial trajectory were compared to the artificial neural network (ann) results. the robot started moving from its initial position (θ1=θ2=θ3=θ4=0) and passed through two via points before it reached its destinations. it shows that the robot still able to pass through those specific points even though the destination point was changed and the moving path could be tracked by using ann. the theoretical and ann results were almost the same and the errors were less than ±0.3unit (less than 1%). this can be used as an evidence to proof that artificial neural network is able to solve fk and continuous trajectory simultaneously due to its adaptive learning ability. since back-propagation neural network is able to track the motion of end effector, it can be used to track the position of each joint in cartesian space as well. referring to figure 1, joint 1 and joint 2 are located at the origin of reference frame, (0,0,0). for tracking the position of joint 3, the training data set inputs and outputs will be [ti θ1(ti) θ2(ti)] t and re,3(ti). correspondingly, for joint 4, the training data set inputs and outputs will be [ti θ1(ti) θ2(ti) θ3(ti)] t and re,4(ti). once the joins location of all joints are computed, the manipulator link equations can be derived 258 c.k. ang, s.h. tang, s. mashohor, m.k.a.m. arrifin by using parametric equations. assuming that the location and vector for joint m and joint n at any interval time ti: re,n(ti) = [px,n py,n pz,n] t , −−→ opn = [an bn cn] t and re,m(ti) = [px,m py,m pz,m] t , −−−→ opm = [am bm cm] t . the link equation that connecting joint n and m will be x−px,n an−am = y−py,n bn−bm = z−pz,n cn−cm = ζ or [x y z] t = [px,n py,n pz,n] t + [an − am bn − bm cn − cm]t ζ for the robot manipulator which is mentioned in this paper, the robot link equations can be denoted as: link 1: x−px,3 a3−a2 = y−py,3 b3−b2 = z−pz,3 c3−c2 = ζ2 0 ≤ z ≤ pz,3 link 2: x−px,4 a4−a3 = y−py,4 b4−b3 = z−pz,4 c4−c3 = ζ3 pz,3 ≤ z ≤ pz,4 link 3: x−px,5 a5−a4 = y−py,5 b5−b4 = z−pz,5 c5−c4 = ζ4 pz,4 ≤ z ≤ pz,5 conclusively, the link equation can be derived as long as the coordinates of two successive joints are provided by using back-propagation neural network and the links parametric equations can be derived based on the vector of the two successive joints. once the equation of each manipulator link is known, a person is able to predict or compute the entire motion of a robot manipulator. besides, obstacle collision can be detected if the link equations are known. assuming that there is a rigid body obstacle enclosed in a sphere which possesses a r radius and center locates at (xo, yo, zo), the sphere equation will be (x − xo)2 + (y − yo)2 + (z − zo)2 = r2 the intersection points between the link and obstacle can be obtained by solving the link and sphere surface equation. for an example, a line that pass through the point (e, f, g)possessing a same direction as vector[a b c]t , the equation for the link will be: x−e a = y−f b = z−g c = ζor   x y z   =   e f g   +   a b c   ζ substituting the link equation into sphere surface equation yields, (aζ + e − xo)2 + (bζ + f − yo)2 + (cζ + g − zo)2 = r2 simplified and yields, aζ2 + 2bζ + c = 0 where, a = a2 + b2 + c2 b = a(e − xo) + b(f − yo) + c(g − zo) c = (e − xo)2 + (f − yo)2 + (g − zo)2 − r2 solving continuous trajectory and forward kinematics simultaneously based on ann 259 by examining the discrimination roots, the intersection points between link and sphere surface exists when∆ ≥ 0, [a(e − xo) + b(f − yo) + c(g − zo)]2 ≥ 4 [ a2 + b2 + c2 ] [ (e − xo)2 + (f − yo)2 + (g − zo)2 − r2 ] the intersection point between the manipulator links and obstacle can be determined by solving the constant ζ. table 1: comparison of conventional and ann techniques in tracking robot end effector moving path 4 conclusions this paper shows that back-propagation neural network is able to solve trajectory and kinematic problems simultaneously. the moving path of the robot end effector still able to be tracked even though the ending point is changed. besides, this method can be used to track the position of robot joints which can be used to derive the robot link equations. in comparison to conventional continuous trajectory planning methods, ann is much easier to be applied and provide high accuracy results. 260 c.k. ang, s.h. tang, s. mashohor, m.k.a.m. arrifin bibliography [1] saeed b. n. 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(2002). approximating a robot inverse kinematics solution using fuzzy logic tuned by genetic algorithms. int. j. adv. manuf. technol., 20: 375–380. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 246-257 multi-objective optimization for the m-pdptw: aggregation method with use of genetic algorithm and lower bounds i. harbaoui dridi, r. kammarti, m. ksouri, p. borne imen harbaoui dridi lagis : ecole centrale de lille, villeneuve d’ascq, france lacs : ecole nationale des ingénieurs de tunis, tunis belvédère. tunisie e-mail: imenharbaoui@gmail.com ryan kammarti, mekki ksouri lacs : ecole nationale des ingénieurs de tunis, tunis belvédère. tunisie e-mail: kammarti.ryan@planet.tn, mekki.ksouri@insat.rnu.tn pierre borne lagis : ecole centrale de lille, villeneuve d’ascq, france e-mail: p.borne@ec-lille.fr abstract: the pdptw is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers in purpose to satisfy precedence, capacity and time constraints. we present, in this paper, a genetic algorithm for multi-objective optimization of a multi pickup and delivery problem with time windows (m-pdptw), based on aggregation method and lower bounds. we propose in this sense a brief literature review of the pdptw, present our approach to give a satisfying solution to the m-pdptw minimizing the compromise between total travel cost and total tardiness time. keywords: pdptw, multi-objective, aggregation method, lower bounds. 1 introduction the vehicle routing planning and traffic management are considered a major logistical challenge in terms of supply, inter-plant transport or distribution transport. [1] many studies have been directed mainly towards solving the vehicle routing problem (vrp). it’s an optimization vehicle routing problem to meet travel demands. other researchers became interested on an important variant of vrp which is the pdptw (pickup and delivery problem with time windows) with capacity constraints on vehicles. the pdptw is divided into two categories: 1-pdptw (single-vehicle) and m-pdptw multivehicle). in the m-pdptw problem which we are interested in, we consider a vehicles fleet vk of capacity qk and a set of goods to transport providers to different destinations. the goal is to provide a set of customers under certain constraints concerning vehicles and their capacity, precedence between nodes, and this by minimizing the compromise between the total travel cost and total tardiness time. in this paper we present a literature review of the pdptw followed by the proposed approach for the optimization of pick-up and delivery problem with time window, using the genetic algorithms, aggregation method and lower bounds. copyright c⃝ 2006-2011 by ccc publications multi-objective optimization for the m-pdptw: aggregation method with use of genetic algorithm and lower bounds 247 2 litterature review 2.1 vehicle routing problem the vehicle routing problem (vrp) represents a multi-goal combinatorial optimization problem which has been the subject of many works and variations in the literature [2] [3]. the theory of the vrp is formulated as follows: given a depot d and a set of customers orders c = (c1, ... , cn), to build a package routing, for a finite number of vehicles, beginning and ending at the depot. in this routing, a customer must be served only once by a single vehicle and vehicle capacity transport for a routing should not be exceeded [4] [5]. the meta heuristics were also applied to solve the vehicle routing problem. among these methods, we can include ant colony algorithms, which were used by montamenni, r et al for the resolution of dvrp [6], and by sorin c. negulescu et al to solve the vehicle route allocation problem (vrap) [7]. kaoru hirota et al have presented a computational intelligence approach to vrsdp (vehicle routing, scheduling, and dispatching problems). the objective of the vrsdp is to produce a delivery schedule for a group of vehicles, with respect to multiple users, so that while satisfying constraints delivery cost corresponding to users’ order is minimized [8]. savelsbergh et al have shown that the vrp is a np-hard problem [9]. 2.2 the pdptw: pickup and delivery problem with time windows the pdptw is a variant of vrptw where in addition to the existence of time constraints, this problem implies a set of customers and a set of suppliers geographically located. every routing must also satisfy the precedence constraints to ensure that a customer should not be visited before his supplier. [10] a dynamic approach for resolving the 1-pdp without and with time windows was developed by psaraftis, h.n considering objective function as a minimization weighting of the total travel time and the non-customer satisfaction [11]. jih, w et al have developed an approach based on the hybrid genetic algorithms to solve the 1-pdptw, aiming to minimize combination of the total cost and total waiting time. [12] another genetic algorithm was developed by velasco, n et al to solve the 1-pdp bi-objective in which the total travel time must be minimized while satisfying in priority the most urgent requests. in this literature, the method proposed to resolve this problem is based on a no dominated sorting algorithm (nsgaii). [13] kammarti, r et al treat the 1-pdptw, minimizing the compromise between the total travel distance, total waiting time and total tardiness time, using an evolutionary algorithm with special genetic operators, tabu search to provide a set of viable solutions. [14] [15]. this work has been extended, by proposing a new approach based on the use of lower bounds and pareto dominance method, to minimize the compromise between the total travel distance, total waiting time and total tardiness time. [16] about the m-pdptw, sol, m et al have proposed a branch and price algorithm to solve the m-pdptw, minimizing the vehicles number required to satisfy all travel demands and the total travel distance. [17] quan, l et al have presented a construction heuristic based on the integration principle with the objective function, minimizing the total cost, including the vehicles fixed costs and travel expenses that are proportional to the travel distance. [18] a new metaheuristic based on a tabu algorithm, was developed by li, h et al to solve the m-pdptw. [19] li, h et al have developed a "squeaky wheel" method to solve the m-pdptw with a local search. [20] a genetic algorithm was developed by harbaoui dridi i et al treating the m-pdptw to minimize the total travel distance and the total transport cost [21]. this work has been extended, by proposing a new approach based on the use of pareto dominance method to give a set of satisfying solutions to the m-pdptw minimizing total travel cost, total tardiness time and the vehicles number. [22] [23] 248 i. harbaoui dridi, r. kammarti, m. ksouri, p. borne 3 mathematical formulation our problem is characterized by the following parameters: • n: set of customers, supplier and depot vertices, • n′: set of customers and supplier vertices, • n+: set of supplier vertices, • n−: set of customers vertices, • n: size of the initial population, • k: vehicle number, • dij: euclidian distance between the vertex i and the vertex j. if dij = ∞ then the road between i and j doesn’t exist, • tijk: time used by the vehicle k to travel from the vertex i to the vertex j, • [ei, li]: time window of the vertex i, • si: stopping time at the vertex i, • qi: goods quantity of the vertex i request. if qi > 0, the vertex i is a supplier; if qi < 0, the vertex i is a customer and if qi = 0 then the vertex was served. • qk: capacity of vehicle k, • i = 0...n : predecessor vertex index, • j = 0...n : successor vertex index, • k: 1...k: vehicle index, • xijk = { 1 if the vehicle travel from the vertex i to the vertex j 0 else • ai: arrival time of the vehicle to the vertex i, • di: departure time of the vehicle from the vertex i, • yik: the goods quantity in the vehicle k visiting the vertex i, • ck: travel cost associated with vehicle k, • a vertex is served only once, • a vertex is served only once, • the capacity constraint must be respected, • the depot is the starting and finishing vertex for the vehicle, • the vehicle stops at every vertex for a period of time to allow the request processing, • if the vehicle arrives at a vertex i before its time windows beginning date it waits. multi-objective optimization for the m-pdptw: aggregation method with use of genetic algorithm and lower bounds 249 the function to minimize is given as follows: minimizef =   λ1c1 ∑ k∈k ∑ i∈n ∑ j∈n ckdijkxijk+ λ2c2 ∑ k∈k ∑ i∈n ∑ j∈n max(0, di − li)xijk   (1) where λi and ci are weights and scaling coefficients. subject to: n∑ i=1 k∑ k=1 xijk = 1, j = 2, ...n (2) n∑ j=1 k∑ k=1 xijk = 1, i = 2, ...n (3) ∑ i∈n xi0k = 1, ∀k ∈ k (4) ∑ j∈n x0jk = 1, ∀k ∈ k (5) ∑ i∈n xiuk − ∑ j∈n xujk = 0, ∀k ∈ k, ∀u ∈ n (6) xijk=1⇒yjk=yik+qi,∀i,j∈n;∀k∈k (7) y0k=0,∀k∈k (8) qk ≥ yik ≥ 0, ∀i ∈ n; ∀k ∈ k (9) dw ≤ dv, ∀w ∈ n+; ∀v ∈ n− (10) d0 = 0 (11) xijk = 1 ⇒ di + tijk ≤ dj∀i, j ∈ n; ∀k ∈ k (12) the constraint (2) and (3) ensure that each vertex is visited only once by a single vehicle. the constraint (4) and (5) ensure that the vehicle routing is beginning and finishing in the depot. the constraint (6) ensures the routing continuity by a vehicle. (7), (8) and (9) are the capacity constraints. the precedence constraints are guaranteed by (10), (11) and (12). 4 genetic algorithm for optimization of the m-pdptw 4.1 generation of the initial populations in our case, we generate two types of populations. a first population noted pnode (figure 1), which represents all nodes to visit with all vehicles, according to the permutation list coding. the second population noted pvehicle (figure 2) indicates nodes number visited by each vehicle. 250 i. harbaoui dridi, r. kammarti, m. ksouri, p. borne figure 1: the permutation list coding figure 2: example of individual from the population pvehicle 4.2 correction procedures before beginning the construction of the population pnode/vehicule, we proceed to the correction procedures of precedence and capacity between nodes. we consider the following couples customer / supplier: (1,5), (2,8), (9,7), (10,3) and (4,6), noting that qk max= 60 and q = 20, we present, respectively, in figures 3 and 4 the principle of correction precedence and capacity. figure 3: correction precedence figure 4: correction capacity 4.3 computation procedure taking into account the population pnode, correction procedures and pvehicle we illustrate in figure 5 an example of an individual from the population pnode/vehicule . knowing that it is necessary to verify that a couple is visited by only one vehicle. [24] with: n ′ = 10 and k = 2 figure 6 represents the process to determine the population pnode/vehicule. the principles of different genetic operations such as crossover and mutation operator are detailed in our work [21]. multi-objective optimization for the m-pdptw: aggregation method with use of genetic algorithm and lower bounds 251 figure 5: example of an individual from the population pnode/vehicule 4.4 multi-criteria evaluation a multi-objective problem is defined as an optimization vector problem, which seeks to optimize several components of a vector function cost. pareto dominance method a multi-criteria problem p is composed of n variables, m inequality constraints, p equality constraints and k criteria that can be formulated as follows: p ⇒   minf(x)= [f1, f2, f3, .....fk(x)] gi(x)≤0i=0...m gj(x)=0j=0...p (13) however, it is necessary to find solutions representing a possible compromise between the criteria. the pareto optimality concept introduced by the economist v. pareto in the twentieth century is frequently used [25]. v. pareto formulated the following concept: in a multi-criteria problem, there is equilibrium so that we can not improve one criterion without deteriorating at least one other. this equilibrium has been called pareto optimal a solution is noted pareto optimal if it is dominated by any other point in solutions space. these points are noted non dominated solutions. a point x ∈ e dominates y ∈ e if:{ ∀i, fi(x) ≤ fi(y) and ∃ j, such asfj(x) < fj(y) (14) figure 7 shows an example where we seek generations of the initial populations to minimize f1and f2. the points 1, 3 and 5 are not dominated. on the contrary point 2 is dominated by point 3, and point 4 is dominated by point 5. aggregation method in the resolution of mop (multi objectives problem), several traditional methods are transforming the mop into a single objective problem. among these methods we find the aggregation method. this is one of the first methods used to generate pareto optimal solutions. it is to transform the problem (mop) in a problem (pmoλ) which combines the different cost functions of the problem into a single objective function f generally linear [26]: f(x) = n∑ i=1 ciλifi(x) (15) where λi and ci are weights and scaling coefficients, according to the application, that the different objectives are not necessarily commensurable. the constants ci are usually initialized to 1 fi(x∗) where fi(x∗) is the optimal solution associated to the objective function fi considered 252 i. harbaoui dridi, r. kammarti, m. ksouri, p. borne figure 6: computation procedure multi-objective optimization for the m-pdptw: aggregation method with use of genetic algorithm and lower bounds 253 figure 7: dominance example separately. the idea of the aggregation method (figure 8) is to fix a weight vector i.e. find a hyper-plane in the objective space (a line for a bi-criteria problem) with a fixed orientation. the pareto optimal solution is the point where the hyper-plane has a common tangent with a feasible space. the advantage of the aggregation method is to produce a single solution and thus do not require interaction with the decision maker. figure 8: multi-objective optimization and computing of lower bounds the computing of lower bounds has been studied in literature for several scheduling problems which we quote: the problems with a machine [27], with parallel machines [28] and [29], the hybrid flow-shop problems [30] and the flexible job shop problems [31]. these proposed methods for computing of the lower bounds are generally based on the relaxation of constraints (preemption of tasks, constraints related resources ...) to minimize one or more criteria for optimal scheduling. based on these methods and seen that we don’t have information on the optimal solutions associated with different cost functions fi for our problem we should compute the minimum value to determine the scaling constants ci . for this objective, we use the relaxation of various constraints. to find a minimum value associated with the criterion of total travel cost f1 =∑ k∈k ∑ i∈n ∑ j∈n ckdijxijk, we have treated this problem to the travelling salesman problem when we try to minimize ∑ i∈n ∑ j∈n dijxij. we subsequently determine the routing crossed by a single vehicle, minimizing the total travel distance by incorporating the constraints and capacity precedence. what gives us dmin .p.c. 254 i. harbaoui dridi, r. kammarti, m. ksouri, p. borne dmin .p.c = min( ∑ i∈n ∑ j∈n dijxij) (16) by setting k, the number of vehicles used, we get: dmin k.p.c = dmin .p.c k (17) consequently, we acquire a value f1b that represents a minimum value of the total travel cost. f1b = ∑ k∈k ckdmin k.p.c (18) to determine the minimum value of the total tardiness time, we fix k the vehicles number and find out the population pnode/vehicule . thus, we calculate the total tardiness time for each individual and determine the minimum. f2b = ∑ k∈k ∑ i∈n ∑ j∈n max(0, di − li)xijk (19) knowing that for the criterion of tardiness, a better lower bound is zero. we will have therefore:{ c2 = 1 f2b s.c.f2b ̸= 0 (20) 4.5 computational results to test our approach, we use benchmark problem instances generated by li and lim [19] from solomon’s ones [32]. corresponding to solomon’s classification of c1, c2, r1, r2, rc1 and rc2, their data sets were also generated in six classes: lc1, lc2, lr1, lr2, lrc1 and lrc2. the lc problems are clustered whereas in the lr problems, providers and customers are randomly generated. therefore in the lrc problems the providers and the customers are partially clustered and partially randomly distributed. while lc1, lr1 and lrc1 problems have a short scheduling horizon, lc2, lr2 and lrc2 have longer scheduling one. [33] in our work, we consider a vehicle number k ranging between 1 and 25. table 1 shows the results of our simulation using the parameters of the problem lrc1. of course, for every given solution, we note the corresponding routing, crossed by each vehicle. nsol: represents the number of non dominated solutions. nk: represents the vehicles number used. we observe that our approach generates a multiple number of solutions that give flexibility of choice for the decision maker and that by using two different methods to determine the vehicles number used, minimizing the compromise between the total travel cost and the total tardiness time. we also observe that we obtain a total tardiness equal to zero with a tolerable cost. 5 conclusion in this paper, we have presented our approach to solve the m-pdptw, based on pareto dominance method, with use of genetic algorithm and lower bounds. our purpose was in a first part a brief literature review on the vrp, 1-pdptw and m-pdptw. the mathematical formulation of our problem is detailed in second part. then, we have detailed the use aggregation multi-objective optimization for the m-pdptw: aggregation method with use of genetic algorithm and lower bounds 255 lrc1 nsol nk f1b f2b f1 f2 f(x) lrc101 4 11 67971,97 39,08 187085,73 2,173 1,09 192150,78 2,09 192843,93 1,32 194911,78 0,651 lrc101 1 25 67971,97 0 216261,26 0 2,49 lrc103 2 11 645,1144 0 1946,2465 0 1,09 1711,304 0,429 lrc103 1 25 645,1144 0 2081,1978 0 2,49 lrc105 3 9 697,0005 0,823 1862,0582 4,28 0,89 1965,2465 0,823 1947,6537 4,27 lrc105 2 25 697,0005 0 2171,1193 0,107 2,49 2246,6729 0 lrc107 1 11 647,345 0 1803,9515 0 1,09 lrc107 1 25 647,345 0 2171,1006 0 2,49 table 1: results for the lrc1 problem method and lower bounds to determine a set of solutions, minimizing our objective functions. simulation was presented in a last part by using benchmark’s data. bibliography [1] h. ezzedine t. bonte c. kolski and c. tahon. integration of traffic management and traveller information systems: basic principles and case study in intermodal transport system management. international journal of computers, communications & control (ijccc), issn 1841 9836, e-issn 1841-9844 vol. iii, no. 3, pp. 281-294, 2008. 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[31] i. kacem. ordonnancement multicritères des job shops flexibles : formulation, bornes inferieures et approche éévolutionniste coopérative. thèse en automatique et informatique industrielle ŕ l’université de lille 1, 2003. [32] solomon m.m., "algorithms for the vehicle routing and scheduling problem with time window constraints", operations research, 41, 469-488, 1987. [33] kammarti. r. hammadi. s. borne. p. and ksouri. m. "lower bounds in an hybrid evolutionary approach for the pickup and delivery problem with time windows". ieee international conference on systems, man and cybernetics. 2005. volume 2, p 1156-1161, oct 2005. international journal of computers communications & control issn 1841-9836, 9(4):453-462, august, 2014. cloud service management system for innovative clusters. application for north-west region of romania d. popescu, a. dodescu, p. filip daniela popescu, anca dodescu* university of oradea economy department romania, 410610 oradea, university street, 1 depopescu@uoradea.ro *corresponding author: adodescu@uoradea.ro petru filip 1. dimitrie cantemir christian university, romania, 040042 bucharest, splaiul unirii, 176 2. agora university of oradea, romania, 410526 oradea, piata tineretului, 8 3. university of oradea romania, 410610 oradea, university street, 1 pfilip@uoradea.ro abstract: in order to stimulate and optimize the organization and management of innovative clusters from value chain perspective and guide their planning activities towards a differentiation strategy in which cluster members cooperate, we propose a cloud service management system (csms) that provides it services for these innovative clusters companies that can be customized for both enterprises with the associated clusters. within such a system, actors begin to depend one on another and to take advantage of the local knowledge base. each cluster is designed to have a different profile which will integrate all the companies mapped with it, with the objective of keeping the profile and data for each company. for the existing companies the idea is to migrate their services into the related cluster for integration within csms. thus, our proposed csms will consider and meet different quality of services (qos) parameters of each individual enterprise and service which will be included in specific service level agreements (slas), after the negotiation between the cloud service provider and the csms. realizing that technological progress is at the heart of regional development and decision-makers could support the development of technology clusters towards transforming them into regional innovative clusters, the application of our proposal aims to overcome existing bottlenecks in terms of business strategies and regional development policies in the north-west region of romania. keywords: cloud computing, service oriented architecture, open cloud architecture, it services, innovative clusters, supply chain management 1 introduction the paper proposes a cloud service management system (csms) that provides it services for the innovative clusters companies in order to develop collaborative mechanisms specific for innovative clusters aimed to solving the problems identified in the economic development of the north-west region of romania. although in the past 20 years numerous studies have been conducted regarding the importance of innovative clusters for the regional economic development, the number of innovative clusters in romania, in general, and in the north-west region, in particular, is surprisingly small, many of the existing clusters not being functional, due to lack of experience in organizing and managing copyright © 2006-2014 by ccc publications 454 d. popescu, a. dodescu, p. filip collaborative mechanisms specific for innovative clusters and insufficient and ineffective interconnection of companies in the same sector of activity through services and related technologies which enable an efficient group management and fostering innovation. surprisingly small is also the number of networks of firms in romania, the explanation being related to the lack of confidence of economic actors in the collaborative mechanisms and the lack of investments in building confidence at regional level, the lack of good governance practices of networks of firms that lead to bottom-up clustering as well as the lack of patterns of structured planning and development of innovative clusters. in this context, the practical relevance of the problem sought to be solved through the present paper is the proposal of applying a cloud service management system (csms) that provides it services for innovative clusters which produces an effective group management, able to stimulate cooperation between cluster members in terms of the value chain on high value added niches, increasing competitiveness and innovation within the cluster and hence diversified smart specialization and regional development in order to increase the number and ensure long-term sustainability of innovative clusters in the north-west region of romania. proposing a functional model for the management of innovative clusters to compensate for functional limits of collaborative mechanisms within networks of companies and existing regional clusters, research involves an interdisciplinary cooperation of specialists in information technology, management and economics, the solutions targeted being positioned in the interference areas of the information technology with knowledge management and regional development. 2 cloud service management system for innovative clusters given the advantages of cloud which allows the delivery of scalable resources on demand, we propose a cloud service management system (csms) that provides it services for the innovative clusters companies that can be customized for both enterprises with the associated clusters. in this sense, csms will be built within a private cloud with multiple clusters. we are focused to deliver broad range of cloud services as the ones described by david linthicum in [2] due to their spread across industry. these are: storage-as-a-service, database-as-a-service, informationas-a-service, process-as-a-service, application-as-a-service, platform-as-a-service, integrationas-a-service, security-as-a-service, management / governance-as-a-service, testing-as-a-service, infrastructure-as-a-service. these services can be rent by customers depending on their needs and used as computing utilities. the advantage is the decreasement of the initial investment and that billing changes accordingly with the computing requirements for an individual or an organization changes, without incurring any additional cost. regarding the security of the cloud service management system, we must point out that besides the stages of a service level agreement (sla) content [1] [4], our services provided within each cluster of the csms must enhance availability, confidentiality, data integrity, control and audit. within such a system, actors begin to depend one on another and to take advantage of the local knowledge base, if this interdependence causes a continuous flow of product and process innovations, diffusion of knowledge and collective learning processes at local or regional level, public-private partnerships being the synergistic factors that lead to innovative clusters. each cluster is designed to have a different profile (e.g. it, automotive, apparel, tourism, health services, green energy etc.) which will integrate all the companies mapped with it, with the objective of keeping the profile of each company (figure 1). for the existing companies the idea is to migrate their services into the related cluster for integration within csms. eventually, csms will bring together all the involved services provided by the enterprise applications. moreover, in order to make the cloud computing approach to work as an efficient cloud sercloud service management system for innovative clusters. application for north-west region of romania 455 figure 1: the csms/ vice management for the innovative clusters, and to deliver the business agility and it flexibility promised by web services, it is required the creation of a service oriented environment (soe) [3].our approach is to deliver value to customers through the services provided using the soe without implying any additional cost and risk from the customer perspective. the services with csms are supposed to bring to customers: simplicity, flexibility maintainability, reusability and independence from technology. services can be published, discovered and used in a technology neutral, standard form by using the web services protocols. thus, through a common interface, separate services for companies within each cluster are created and managed with the purpose of achieving the objectives of each company that has its own assets, employees, suppliers, partners (and information about them) or existing it infrastructure [2] [6]. the huge increase in it system interoperability that soa can bring, not only at enterprise level, but also at the innovative cluster level is based on the use of smaller, modular services that have interface descriptions and contracts that ensure the business agility. these services are identified, defined and described in the context of the innovative cluster business activities and they are performed by the it system and managed at the csms level. for each service is clearly set what it does and it is stipulated in a contract. the development of effective and flexible solutions is ensured by the use of soa techniques like: service composition, discovery, message-based communication, and model-driven implementation [3]. soa represents not only the architecture of services seen from a technology perspective, but also the policies, practices, and frameworks by which we ensure for the entire innovative cluster the right services are provided and consumed in order to ensure a best business outcome for the innovative cluster [2]. the software services used by the innovative cloud business operations are supported by a cloud infrastructure that, together with the it services, improves information flow within the companies and between the companies from the innovative cluster and all of them end the outside. the access to all of these services frequently involves a human-computer interface, often implemented as a web interface using portals (figure 2), etc. thus, our proposed csms will consider and meet different quality of services (qos) parameters of each individual enterprise and service which will be included in specific service level agreements (slas), after the negotiation between the cloud service provider and the csms. 456 d. popescu, a. dodescu, p. filip figure 2: the infrastructure (adapted from [3])/ realizing that technological progress is at the heart of regional development and decision-makers could support the development of technology clusters towards transforming them into regional innovative clusters, the application of our proposal aims to overcome existing bottlenecks in terms of business strategies and regional development policies in the north-west region of romania in order to stimulate and optimize the organization and management of innovative clusters from value chain perspective and guide their planning activities towards a differentiation strategy in which cluster members cooperate with high value added niches (smart diversified specialization) and, consequently, to create regional growth and development. to get the ideal agile in collaborating environments (such as business) is needed that the it infrastructure necessary to access the functionality by services be able to be configured by the user without the need to become experts in the field. a well suited solution can be the cloud computing open architecture (ccoa), proposed in [5]. our csms solution for the management of services of the innovative cluster is developed based on the cloud computing open architecture, which we have to customize in order to ensure the services needed to migrate the companies services into the related cluster for integration within csms. so, at the second level of the ccoa model [5] we develop the innovative clusters using an hpc infrastructure. level 3, structured as soa, will be used to define the profiles and the services for each company included in each cluster. level 4 will be specific to each cluster, namely profile. level 5 provides the granular services, while level 6 will have custom profile services for each cluster (figure 3). 3 innovative clusters in north-west region of romania there is currently a broad consensus to consider that economic agglomeration effects in a given location is the result of the balance of opposed agglomeration forces (centripetal and centrifugal), generally or in a particular field, branch of industry, assuming technological networking, mainly for efficiency and flexibility, or by propensity for innovation [7]. the identification of endogenous determinants of knowledge spillover and collective learning process within innovative clusters represent one of the most advanced theoretical perspectives on regional development [8]. initially defined by michael e. porter as business strategy in a global economy context, as a source of competitive advantage for the participating companies [9], and later as "geographic cloud service management system for innovative clusters. application for north-west region of romania 457 figure 3: the csms model/ concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries and associated institutions (eg universities, standardization agencies, professional associations) in a particular field that compete but also cooperate" [10], by encouraging the formation of some relational regional assets external to individual firms, but with major influence on the performance of their competitiveness [9], [12], the porterian cluster has turned from the most influential business strategy into the most popular regional development strategy. innovation has become a critical factor for survival in a competitive spatial economy and also the factor that determines the direction and the rhythm of regional development. in this context, the propensity to form innovative clusters characterizes development policies at present, due to regional and local impact (providing a successful combination of infrastructure, highly skilled workforce and flexible corporations for development, offering well-paid and high quality jobs, have prospects for long-term growth and development, are dynamic and dominated by innovative smes because continuous research and development is vital for survival, it accelerates the technological and innovative process at local or regional level etc.) [13]. the linkages between firms and organizations that form an innovative clusters the institutional density at local level, the unsold interdependency network that go beyond market transactions, the mechanisms by which technological progress within the cluster turns into a collective learning system, have been the subject of numerous research based, especially, on case studies of successful technology clusters [14], the cambridge technology cluster with the involvement of cambridge university and science parks: melbourne science park, cambridge science park and st john’s innovation park being frequently analyzed. close working relationship between specialized smes, the involvement of higher education and research institutions, the free movement of highly skilled labor and availability of specialized consulting services within the cluster are considered the main explanatory mechanism for the success of these clusters. in this context, the classical concept of network of companies, which designates a form of cooperation between companies legally independent, geographically dispersed, but with common interests in economic terms has evolved into the concept of collaborative networks of innovative technological systems with respect to network configuration referring to coordinate cooperation between the various organizations that pursue a common goal: the development of a region; and the concept of technological cluster evolved into the "pôle de compétitivité" (in french ap458 d. popescu, a. dodescu, p. filip proach) or innovative cluster facilitated by the availability of the nature of information technology to transform into a cumulative process and "institutional density" which facilitates cooperation and technological knowledge flow, leading to the accumulation of "strong local base of knowledge" and create a system like "innovative millieu" [15], [16], [17]. within such a system, smes begin to depend one on another and to take advantage of the local knowledge base, if this interdependence does not stuck on the "old, competitive and unstable paths" [18], but causes a continuous flow of product and process innovations, diffusion of knowledge and collective learning processes at local or regional level, public-private partnerships being the synergistic factors that lead to innovative milieux [19], toward learning regions [20], creative regions, knowledge based regions etc. [8], [13]. realizing that technological progress is at the heart of regional development and decision-makers could support the development of technology clusters towards transforming them into regional innovative clusters, learning environments and innovative regions or areas of knowledge, quickly penetrated regional growth and development policies of the eu member states [15], [16], closely related with the pulses set in the lisbon strategy on "knowledge based economy" and later with the approach of the territorial agenda 2020 and the specific smart growth objectives of europe 2020 strategy. the current financial and economic crisis is pressing even more to stimulate the innovation process and increase the role of regions in this context. the eu reports and strategic documents demonstrates that innovation policy should focus not only on research and development or science and technology, almost exclusively concentrated in a few regions of the eu, especially capital regions, but "to anticipate" appropriate policy mix for innovation covering all the dimensions of innovation, including those concerning organizational process, creativity, design, etc. and all the regions of europe [21], [22], [23]], and the reality of a high concentration of technological capabilities in the developed center of the eu must change gradually and create space for regions and companies with a lower absorption capacity. but how to change this in favor of the less favored regions in conditions of economic crisis? the most common response is by stimulating both innovation and entrepreneurship through innovative regional clusters [22], [23]]. according to the 2013 european cluster observatory star rating [24], there are 92 clusters in romania that have received at least one star depending to the extent to which clusters have achieved critical mass by employing measures in terms of size, specialization and focus [25]. in this context, the clustering potential of the north-west region of romania is sensitively high in the figure 4 mentioned industries, where we represented the number of employees in the stars classified industries: with a diverse economic structure, characterized by the shift from predominant development of strong labor intensive sectors to the services sector, particularly the intelligence intensive and high value-added industries, that require skilled and specialized labor force, north west region of romania has a certain degree of regional specialization for the following industries: furniture industry, electrical equipment, metallurgy, leather and footwear industry. for the following industries: furniture industry, electrical equipment, metallurgy, leather and footwear industry [26], according with zaman’s methodology for calculating apparent or revealed regional comparative advantage indicator, based on the top main regional trade exporters in the eu and between the eu extra trade [27]. cloud service management system for innovative clusters. application for north-west region of romania 459 figure 4: star clusters in northwest vest region of romanial/ (source: star clusters in romania, center for strategy and competitiveness, csc stockholm school of economics, april 2011, http: // www. clusterobservatory. eu/ common/ galleries/ downloads/ star_ clusters_ romania. pdf) regarding economic agglomeration, according with most recent regional development strategy 20142020 draft plan [26], in the north-west region there were 6 registered clusters, out of which only 4 are active as shown in table below: table 1. clusters situation in north-west region of romania cluster field of activity location information about association initiative under cluster structure and status romanian water cluster water energy industry clujnapoca information not found unfunctional geothermal cluster renewable energies, services in tourism oradea created in 2009 through clustherm (creating a central european thermal water research cluster) project, financed through fp7, regions of knowledge programme (regions2007-2), coordinator: innova szak-alfld regional development and innovation nonprofit ltd (hu), http://www.clustherm.eu/. unfunctional trec transnational renewable energies cluster renewable energies clujnapoca created in 2012, through trec (transnational renewable energies cluster) project, financed through smart +, interreg ivc programme, coordinator: northwest regional development agency, romania, http: //www.nord-vest.ro/document_files/noutati/00001234/ciic6_ 1.%20trec%20-%20concept,%20activitati,%20viziune.pdf. active cluster mobilier transilvan furniture clujnapoca created in 2012, bottom up, as a non-governmental organization through legal association of 18 firms from furniture industry, 3 catalyst organizations, 1 public institutions and 1 university, website: http://transylvanianfurniture.com/ active cluj it cluster it & c clujnapoca created in 2012, bottom up, as a non-governmental organization through legal association of 28 it & c companies, 6 public institutions and catalyst organizations, 4 universities, website: http://www.clujit.ro/ active agro-food-ind napoca agro-food clujnapoca created in 2013, bottom up, as a non-governmental organization through legal association of 13 companies and associations from agro-food industry, 2 catalyst institutions, 3 public administration institutions, 2 universities; website: http://www.agrotransilvaniacluster.ro/ active source: authors’ processing looking at figure 4 and table 1, we should notice that, despite its sensitively high clustering 460 d. popescu, a. dodescu, p. filip potential in apparel, farming and animal husbandry, agricultural products, footwear, textiles, tobacco, building fixtures, equipment and services, construction, construction materials, leather products, paper products [26], only furniture industry is covered with active cluster in the region. surprising is the absence of a cluster in the apparel, footwear, textiles industries, despite existing industrial agglomerations and tradition in the region. according to the cluster programme and policy stress test for north-west regional development agency regional innovation strategy [28], the score of the north-west region is of 9 points out of 50 points, which places the region in the "yellow area" of the "league of clusters" traffic light, the minimum limit for compliance with the "green zone" being of 40 points. the weaknesses identified in the north west region of romania after applying the policy stress test are related to the management approach of clusters and the internationalization of their activity, the malfunction integration of regional programs for clusters in the general context of industrial policy and economic development policy, the critical points being related to the implementation, monitoring, evaluation and impact of regional programs for clusters and the absence of a national policy for clusters. 4 conclusions the analysis of economic agglomerations in north-west region of romania shows the predominance of economic agglomerations based on simple agglomeration economies (especially, industrial parks) and high difficulties in the implementation and ensuring sustainability on long term of economic agglomerations based on economies of dynamic agglomeration (especially, innovative clusters) and highlights the need of instruments to improve the management approach of clusters and to stimulate the innovative process, and also collective learning processes through cooperation and relationality. in this context, the proposed cloud service management system model aims to represent an important support for the development of existing clusters and to find solutions for the functionality and sustainability of new clusters in the north-west region for valuing high regional clustering potential. in this respect, broad range of cloud services that can be rent by firms from same industry, aligning cluster members on value chain. a number of services can be adapted to the cluster specificity: project planning, marketing planning, strategy, business plan, kpi’s system etc.; knowledge management, open virtual library, e-learning, working groups, application procurement, legislation info, match-making, funding opportunities portal, common eu projects, etc. within such a system, actors begin to depend one on another and to take advantage of the local knowledge base, if this interdependence causes a continuous flow of product and process innovations, diffusion of knowledge and collective learning processes at local or regional level, public-private partnerships being the synergistic factors that lead to innovative clusters. the proposed model will help to tackle the functionality and long-term sustainability issues of innovative clusters in the north-west region of romania and will represent the base of an integrated network that will bring together human resources trained within the project in interdisciplinary applied research and smes networks from the north-west region of romania and will contribute decisively to the accumulation of the "critical mass" of resources for research, development and innovation for increasing regional competitiveness. through the development of some model for structured planning and development of innovative clusters, our future steps will be focused on testing of model on a pilot cluster in order to stimulate learning, relationships and cooperation within innovative clusters, the development of the following innovative services: methodologies and methods for planning and structured development of innovative clusters, handbook for the management of innovative clusters, system of integrated software for managing clusters and knowledge, and fostering cooperation within an innovative cluster. cloud service management system for innovative clusters. application for north-west region of romania 461 bibliography [1] lonea, a.m., tianfield, h. and popescu, d.e. (2013). identity management for cloud computing, new concepts and applications in soft computing, 417: 175-199. [2] linthicum, d. s. (2009); cloud computing and soa convergence in your enterprise: a step-by-step guide, addison-wesley information technology series. [3] open group, soa and enterprise architecture, available online: http://opengroup.org/ [14.12.2013]. [4] lonea, a.m., popescu, d.e. and prostean, o. (2012); the overall process taken by enterprises to manage the iaas cloud services, proceedings of the 6th european conference on information management and evaluation, university college, cork, ireland, 13-14 september 2012. [5] vitek, a. j. & morris, m.n, (2012); service oriented cloud computing architectures, umm csci senior seminar conference, available online: https://wiki.umn.edu/ [07.11.2013]. [6] entreprise concept, available online: http://enterprise-concept.com/ [05.12.2013]. [7] sövell, ö., lindqvist, g. & ketels, ch. (2003); the cluster initiative greenbook, stockholm: bromma tryk ab. [8] capello, r. & nijkamp, p. (eds.) (2009); handbook of regional growth and development theories, edward elgar publishing. [9] porter, m. e. (1990); the competitive advantage of nations, new york: free press. [10] porter, m.e. (1998); on competition, boston: harvard business school press. [11] porter, m.e. (1998); clusters and the new economics of competition, harvard business review, november-december, 77-90. [12] mccann, p. (2001); urban and regional economics, oxford: oxford university press. [13] armstrong, h. & taylor, j. (2000); regional economics and policy, third edition. oxford: blackwell. [14] karlsson, c. (2008); handbook of research on clusters, edward elgar publishing house. [15] ketels, ch. & svell, a. (2006); innovation clusters in the 10 new member states of the european union, european communities, available online: http://www.isc.hbs.edu/ [22.07.2013]. [16] european commission (2007); innovation clusters in europe. a statistical analysis and overview of current policy support, europe innova / pro inno europe paper no. 5, dg enterprise and industry report. [17] hamdouch, a. (2008); conceptualizing innovation clusters and networks, international conference: innovation networks, tacoma-seattle, washington, usa, may 14-16, 2008, available online: http://rrien.univ-littoral.fr/ [22.07.2013]. [18] armstrong, h. & taylor, j. (2000); regional economics and policy, third edition. oxford: blackwell. 462 d. popescu, a. dodescu, p. filip [19] camagni, r. (ed.) (1991); innovation networks: spatial perspectives, london: belhaven press. [20] morgan, k. (1997); the learning region: institutions, innovation and regional renewal, regional studies, 31(5);491-503. [21] european commission (2011); the urban and regional dimension of europe 2020.seventh progress report on economic, social and territorial cohesion, report from the commission, available online: http://ec.europa.eu/ [24.09.2013]. [22] european commission (2012); state of the innovation union report 2012 accelerating change, communication from the commission to the european parliament, the council, the european economic and social committee and the committee of the regions swd(2013) 75 final, available online: http://eur-lex.europa.eu/ [24.09.2013]. [23] european commission (2013); innovation union progress at country level 2013, available online: http://ec.europa.eu/ [24.09.2013]. [24] european cluster observatory (2013);available online: http://www.clusterobservatory.eu/ [18.02.2014]. [25] european cluster observatory (2011); star clusters in romania, center for strategy and competitiveness, csc stockholm school of economics, april 2011, available online: http://www.clusterobservatory.eu/ [13.11.2013]. [26] romanian north-west regional development agency (2013); 2014-2020 regional development plan (draft, september 2013), available online: http://www.nord-vest.ro/ [18.02.2014]. [27] zaman, gh. (2010); sistemul de indicatori ai competitivitatii romanesti in contextul dezvoltarii durabile, communication for afer summer school, university of oradea, september 2010. [28] lammer-gamp, k. (2013); results cluster programme and policy stress for north-west regional development agency regional innovation strategy, european secretariat for cluster analysis, berlin int j comput commun, issn 1841-9836 8(4):578-587, august, 2013. information security engineering: a framework for research and practices m. li, m. tang meng’gang li china center for industrial security research, beijing jiaotong university, china morganli@vip.sina.com mincong tang international center for informatics research, beijing jiaotong university, china mincong@bjtu.edu.cn abstract: information security is not a new topic in academics and industry. however, through a comprehensive literature review, we found that most research in information security focus on technical perspectives including evaluation methods and mathematical approaches for securities, risk mitigation algorithms, with some research focus on economic perspective of information security and even a few talked about social engineering of information security. there is not a unique framework to integrate different types of research in information security. we believe that information security research apply the theories and methodologies in systems engineering to investigate the problems, that is, information security engineering. in this paper, we propose a conceptual framework of information security engineering. this framework explicitly illustrates the methodological system, content system, procedures and strategies for information security engineering research and practices. keywords: computer science, information security engineering (ise), systems engineering, information systems. 1 introduction information security problem has been coming along with the development of our society. in the past two decades, information technologies (it) and internet have changed our world in an unbelievable speed. they bring to us many conveniences as well as risks and uncertainties. thus information security problem becomes very prominent under such circumstance and it now plays a decisive position in national securities of a country. information security mainly involves governments, enterprises, organizations, associations and individuals. it spreads with a very wide time domain and covers all fields of the society including politics, economy, culture, military etc. compared with the securities of politics, economy and military, information security has the following features: 1) information security is the core of national security in information era; 2) the nature of information security is information resource security; 3) information security relies more and more on the securities of technological systems; 4) attack sources of information security are characterized as spectral and concealed. information security nowadays is no longer an isolated and scattered but a very complicated systems engineering problem. thus we propose that it’s necessary and urgent to conduct in-depth and comprehensive studies to examine information security from the viewpoint of engineering. we define this as information security engineering (ise): ise is a kind of systems engineering which is based on information technology and takes information security management as its approach and information security laws and policies as its assurance. as a subset of systems engineering, ise is the embodiment of information system securities for systems security engineering, systems engineering and system acquisitions. ise is a product of the integration copyright c⃝ 2006-2013 by ccc publications information security engineering: a framework for research and practices 579 of security engineering, information management and information systems development. the contents of ise mainly include: connotations, contents and objectives of ise; information security risk analysis and evaluation procedures, methods and tools; requirement analysis methods, security strategies, security architecture, security solutions, security implementation standards, security test and operations, emergency measures and methods, security education and training. the thinking of engineering has been well applied into software development and security management, from which two subjects: software engineering and security engineering have been formed and developed. though these two subjects are relatively completed, studies of information security have been affected by the knowledge structures of researchers, rapid development of our society and other factors, the findings of information security research are scattered and the theory lags behind the demand of applications. on the other hand, though the sense of security has been strengthen in the past 10 years, from installing anti-virus software to procuring security products, the understanding of security still stays at the very stage of ‘treat only where the pain is’. the problem of information security cannot be solved by pure technology, nor can it be solved by putting security products together. it has to rely on complicated systems engineering— information security engineering (ise). therefore, there is a need to conduct in-depth research in information security from a systems engineering perspective. we organize this paper in the following ways: in the next section we present a comprehensive literature review, which forms the foundations of ise. followed by is the processes of ise in section 3. after that we propose a framework of ise, the framework is used to suggest future trends within ise research and practices in section 4. 2 foundations of ise as developed from software engineering and security engineering, ise follows the foundations of systems engineering but its context is limited to information security and information systems. different from typical systems engineering, ise research focuses on the development of methodology and architecture for information security. it intends to unite the diversities in information security research and makes it an interdisciplinary research without limiting the analysis to a particular discipline (i.e. computer sciences, information systems etc.). by comprehensively reviewing existing information security literature, we mainly found that four issues of information security have been well studied. these four issues are: 1) security management (sm): sm refers to the ways like information systems planning and evaluation, to maintain secure information systems within organizations. backup, recovery, contingency management are included in sm. 2) communication security (cs): cs refers to the measures adopted to ensure secure communication achieved between people. 3) access of information and systems (ais): ais refers to the measures that control people’s access to information and systems. 4) secure information systems development (sisd): sisd refers to the methods, policies and procedures that lead a secured information system to be developed. according to the differences in research approaches of the literatures we reviewed, we only 580 m. li, m. tang incorporate those studies after 2000 for the purpose of most updated knowledge in these areas, we summarized the findings from existing literature as table 1. table 1: summary of existing literature in information security issue source propositions/findings approach security management dhillon & backhouse (2000) responsibility, integrity, trust and ethicality (rite) principles hold the key for successfully managing information security in addition to confidentiality, integrity, and availability (cia). conceptual eloff & eloff (2003) to successfully secure the information and technology related assets of an organization, management should aim towards establishing an information security management system (isms) conceptual & organizational solms & solms (2004) 10 essential aspects like not realizing that information security is a corporate governance responsibility and not realizing that information security is a business issue and not a technical issue and so on must be taken into account in an information security governance plan to make it a success conceptual solms (2005) a separate information security compliance management department is needed when talking about information security management conceptual saintgermain (2005) for organizations to fall into several regulatory realms, they need to establish a comprehensive, flexible framework for implementing cost-effective compliance, deployed via a governing system that maintains security policies and controls organizational (case study) tsoumas & gritzalis (2006) the separation of security requirements from their technical implementations facilitates security management technological chang & lin (2007) organization culture will significantly influence the effectiveness of implementing information security management organizational (survey) ashenden (2008) human challenge of information security management has largely been neglected and people need to look at the skills needed to change organizational culture, the identity of the information security manager and effective communication between information security managers, end users and senior managers. conceptual werlinger et al. (2009) 18 challenges like technical complexity, mobility and so on can affect it security management within organizations organizational (survey) communication security chaddoud et al. (2001) baal protocol as a scalable solution to group key management problems and it can resolve the user’s revocation problem technological khadra et al. (2003) impulsive synchronization of two chaotic systems is very robust, this robustness is useful in designing chaos based cryptosystems, which is used to ensure secure communication technological information security engineering: a framework for research and practices 581 issue source propositions/findings approach yang (2004) impulsive synchronization is more robust than continuous synchronization. based on a combination of both conventional cryptographic method and impulsive synchronization of chaotic systems, a new chaotic secure communication scheme is proposed, which is to ensure secure communication technological liang et al. (2008) secrecy capacity region of the gaussian bcc complements the secrecy capacity region of the discrete memoryless bcc. this will enhance secure communication. technological kiani-b et al. (2009) the proposed encoding chaotic communication has achieved a satisfactory, typical secure communication scheme. results show that security is enhanced based on spreading the signal in frequency and encrypting it in time domain in the proposed system. technological access of information and systems kagal et al. (2001) trust is added as a new dimension to pervasive computing, allowing greater flexibility in designing policies and providing more control over accessing services and information conceptual bonatti, & samarati (2002) an approach for regulating service access and information disclosure on the web is proposed, which consists of a uniform formal framework to formulate and reason about – both service access and information disclosure constraints. this approach ensures communicating users’ requirement while disclosing no private information. technological whitman (2003) ‘understanding the enemy’ is believed to be an important component of information protection conceptual gritzalis & lambrinoudakis (2004) a security architecture based on a role-based access scheme is proposed and found to be effective to identify different users’ access to local sources and other sites conceptual karyda et al. (2005) contextual factors including organizational culture, management support etc. for the application of is security policies have been discussed, these factors are to be taken into consideration when implementing information security policies including access to information and systems. conceptual cheng et al. (2007) a new model for risk-based access control is proposed. this model is based on fuzzy multi–level security access control and found to be more effective than traditional bell–lapadula model. technological secure is development jurjens (2001) uml is used to express security requirements in system development. technological georg et al. (2002) an aspect-oriented approach to modeling is proposed to allows developers to encapsulate design concerns like security, availability of services, and timeliness so that they can be woven into a design in a systematic and consistent manner technological 582 m. li, m. tang issue source propositions/findings approach jones & rastogi (2004) security has to be “baked in” to the overall systems development life-cycle process. technological villarroel et al. (2005) eleven secure systems design methodologies have been compared to see how they should be adopted in system development conceptual mouratidis et al. (2005) an approach considering security concerns as an integral part of the entire system development process is proposed to be necessary organizational (case study) mellado et al. (2007) security has to be dealt with at all stages of is development, especially in the establishment of security requirements to achieve a robust is. conceptual cheng et al. (2008) a new concept of security engineering environment (see) is proposed, see concept with high security requirements can provide a base for designers, developers, users, and maintainers with standard, formal, and consistent supports technological mouratidis & jurjens (2010) two prominent approaches, a goal-oriented security requirements engineering approach called secure tropos and an mbse approach called umlsec have been integrated to help how elicited security requirements can be realized in the design stage and how the developed design can be verified against the security requirements of the system technological based on the discussion from existing literature, we believe that these four issues of information security above consist of the major contents of ise (figure 1) and form the foundations of ise. specifically, the four issues in ise can be elaborated as followings. security management communication security access of information & systems secure is development figure 1: fours issues in information security studies while the contents of information security mainly cover the four issues, ise research issues will include but not limits to: philosophical foundations of information security, definitions information security engineering: a framework for research and practices 583 of information security, mathematical foundations of information security, safety rheology and mutation laws, physiological and psychological issues in information security, ise methods of analysis, forecasting and control, information security risk evaluation and information security management methods, human-computer environment analysis and design etc. these research issues interact with the four issues of information security. 3 processes of ise as a subset of systems engineering, ise mainly follows the processes of systems engineering, which is illustrated by figure 2. discover requirement define system design system implement system users/representatives of users evaluate effectiveness figure 2: processes of systems engineering the processes described above are usually carried out in the following ways: 1) discover tasks or requirements; 2) define functionalities of system; 3) design system; 4) implement system; 5) evaluate effectiveness for ise, the processes above are customized to specific information securities. the key is to fulfill the requirements of information protection by implementing systems engineering processes. ise can facilitate the development of system products and process solutions to satisfy the users’ requirements. thus, the processes of ise becomes: 1) discover requirements of information security: ise will first investigate users’ requirement, policies, standards, vulnerabilities and threats regarding of information. then ise will mark the users of information and systems, their roles and responsibilities in information security. 2) define information security system: users’ requirements of information protection and description of information system environment are interpreted as the objectives and functionalities of information security system. in this stage, ise will define what can be done by information protection system and the executions of information security system as well as the internal and external interface of information security system. 3) design information security system: ise will design the architecture of information security system and detail the design scheme of information security system. 584 m. li, m. tang 4) implement information security system: according to the requirements of information security, this stage aims to development, procure, integrate, test and verify the collections of configuration of information security system. similar with the corresponding stages in systems engineering, ise will conduct implementation and testing in this stage. 5) evaluate effectiveness of information security system: ise emphasizes the capabilities of providing confidentialities, integrities, availabilities and non-repudiation for information. ise processes emphasize marking, conceiving and controlling information security risks and optimize these risks to protect potential losses due to various possible threats and attacks. 4 a framework for ise research and practices in oxford dictionary, framework is defined as a structure upon or into which contents can be put and further relates it to thoughts that are directed for a purpose. the ise framework proposed in this study provides academics and practitioners with an understanding of how to conduct an information security research and practices from an engineering point of view so as to align ise theories with applications. figure 3: a framework for information security engineering research and practices information security engineering: a framework for research and practices 585 as illustrated in figure 3, there are four levels in the framework: definition, basic theory, methodology and application. at the definition level, four elements are included. we propose that objectives, definitions, research subjects and relations with other disciplines belong to this level. for ise research, these four elements must be clearly defined. at the basic theory level, three elements are included. philosophical foundations of ise refer to those philosophical issues like unity and contradiction of security and risks, accuracy and fuzziness of information security etc., these provide the highest level of understanding of ise. mathematical foundations of ise provide theoretical and analytical methods to solve the problems of ise while physiological and psychological problems of ise focus on the behavioral perspectives of ise research. at the methodology level, different techniques (methods) for analysis and evaluation are included. these do not only include analytical methods (i.e. event tree, fault tree etc.), but also include causal analysis methods which are widely used in behavioral research. at the application level, two categories are proposed to provide guideline for practitioners in ise, which include information security management and backbone technologies for information security. this framework provides guidelines and directions for researchers in information security areas. first, it proposes three elements in the basic theory level. these elements cover a very large section of the existing literature in information security especially for the mathematical foundations. second, from a conceptual approach point of view, the philosophical foundations cover many discussions in literature. third, physiological and psychological problems, which are still gaps in literature of information security, they are areas calling for further studies. for practitioners, this framework provides insights for their practices in information security and management. behaviorally, three managerial perspectives are proposed: standards, processes, policies and regulations. technologically, three issues of information security have been proposed, they are regarded as backbone technologies through which the objectives of information security could be achieved from a technological view. 5 concluding remarks information security engineering is a comprehensive and cross-disciplined subject which covers the knowledge in mathematics, physics, telecommunication, computer sciences and management. it is not a simple combination of various tools of security technologies, nor does it equal to a series of managerial regulations and safety standards. it is a complicated system engineering. by conducting a comprehensive literature review, we have a body of knowledge on information security research, which forms the basics and contents of ise. establishing information security engineering as an independent subject is critical to assure national information securities and improve the levels of information security training. moreover, it guides theoretical research and practical applications in this area and make it possible to integrate the critical security technologies and standards and subsequently create a unique and effective system for information securities. acknowledgement this paper is part of the project ‘research on china industrial security index’ (no. b09c1100020) funded by the ministry of education, china. we are grateful for the suggestions and comments from two experts who reviewed this article for us. 586 m. li, m. tang bibliography [1] ashenden, d., information security management: a human challenge?, information security technical report,13(4): 195-201, 2008 [2] bonatti, p. & samarati, p., a uniform framework for regulating service access and information release on the web, journal of computer security, 10(3):241-271, 2002 [3] chrisment, i. & schaff, a., dynamic group communication security, proceedings of sixth ieee symposium on computers and communications, 49-56, 2001 [4] chang, s. & lin, c., exploring organizational culture for information security management, 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abdelkrim faiçal hamidi, mohamed naceur abdelkrim unité de recherche modélisation, analyse et commande des systèmes, enig, 6029 gabes, tunisie. email: faicalhamidi@yahoo.fr, naceur.abdelkrim@enig.rnu.tn houcem jerbi university of hail, college of engineering p.o.box 2440, hail, kingdom of saudi arabia. email: h.jerbi@uoh.edu.sa wahiba aggoune ecs-lab, equipe commande des systèmes, ensea,6 avenue du ponceau, 95014 cergy, france. email: aggoune@ensea.fr mohamed djemaï univ. lille nord de france, f-59000 lille, france. uvhc, lamih, cnrs, umr 8201, campus du mont houy f-59313, valenciennes, france. email: mohamed.djemai@univ-valenciennes.fr abstract: this paper addresses the problem of enlarging the domain of attraction (da) based on a generalized eigenvalue problem (gevp) approach. the main contribution is the maximization of the (da) while characterizing the asymptotic stability region by a lyapunov function. such result is obtained using a genetic algorithm (ga). a theoretical proof of the validity of the obtained domain is developed. an illustrative example ends the paper. keywords: nonlinear polynomial systems, lmi, genetic algorithm, stability. 1 introduction the problem of enlarging the domain of attractions (da) has been the topic of an important number of research works (see for example [2] [4], [5], [6] [11], [13] and the references cited therein). the da is defined as the set of initial conditions from which the states converge to the asymptotically stable equilibrium point [7]. as a result, it is essential to identify the shape of this region whenever one has to study the stability of a system. for this purpose, we exploit the fundamental theory of lyapunov stability (see [3], [12]). indeed, for a particular lyapunov function, the largest estimated region of asymptotic stability can be defined as the largest level set of the lyapunov function included in the region where its derivative is negative. in a recent work [3], the author proposes a static nonlinear feedback input, which allows enlarging the da. the proposed controller is polynomial in the measurable output; it exploits relaxations based on the sum of squares of polynomials in order to prove that the lower bound of the maximum achievable largest estimated domain of attraction and a corresponding controller can be computed via a generalized eigenvalue problem. the main advantage of the methodology is that the problem is formulated as a quasi-convex linear matrix inequalities (lmi) ( [1], [3], [10]). the main purpose of this work is to develop an exact method allowing the maximization of the da. the objective of this work is to improve the approach adopted in [3] by combining the copyright c© 2006-2013 by ccc publications enlarging the domain of attraction in nonlinear polynomial systems 539 genetic algorithm (ga) as an advanced optimization strategy to the lmi technique in order to maximize the da. the parameter optimizing approach will simultaneously deal with the lyapunov function and the control input parameters. based on the reverse trajectory method (rtm) one can accurately determine a preliminary maximal region of asymptotic stability and thereafter define the parameter of the maximal lyapunov function ( [8], [9], [12]). this allows to define precisely the initial values and the constraints related to the required parameters of the investigated lyapunov function. 2 problem statement and notations consider the continuous-time polynomial system ẋ = f(x) + g(x)u; y = h(x̃) (1) where f(.), g(.) and h(.) are polynomial functions such that f(0) = 0, g(0) = 0 and h(0) = 0. (the equilibrium point is the origin). in what follows, we assume that x ∈ rn is the state vector, u ∈ rp is the input vector and y ∈ rq is the measurable output. the control input is supposed to be a polynomial function of the form u = uφ (h (x̃)) (2) where φ(.) is a given polynomial function of the output and h (x̃) and u are defined by : h (x̃) = [ x̃[1], x̃[2], · · · , x̃[q] ] ; u = q∑ i=1 uix̃ [i] (3) with x̃[i] is the non-redundant kronecker power of the state vector x to the ith order, q is a truncation order and u ∈ rp×r is a matrix belonging to the interval matrix: u = { u = [u1, . . . , uq] : ui ∈ ( u−i , u + i ) , i = 1, .., q } . for the seek of simplicity and for explaining our approach, we consider the case of q = 2. we obtain u1 = [u1, u2], u2 = [u3, u4, u5] and consequently u = u1x1 + u2x2 + u3x 2 1 + u4x1x2 + u5x 2 2 the domain of attraction of the controller is the set of states which can be steered towards the terminal region. this paper is devoted to enlarge this domain. the size of the da depends on the control parameters, and the chosen lyapunov function. a wise and optimal choice of both of these may yield a bigger domain of attraction. the size of the region depends on the computed controller, the constraints on the system and the procedure used to compute it. thus, the most used procedure to enlarge the domain of attraction are based on a polynomial control. this leads to a greater number of parameters and therefore, to a greater computational effort. in this paper, a formulation of the problem, focused on enlarging the domain of attraction without increasing the computational effort is presented. the optimization problem formulation, and hence the computational effort is similar to the original one but with a larger domain by using the genetic algorithm approach. for this purpose, we proceed in three steps : -first, we exploit the method described in [3] to derive an initial da. 540 f. hamidi, h. jerbi, w. aggoune, m. djemaï, m.n. abdelkrim -second, we implement a ga combined with a linear matrix inequalities lmi approach to determine explicitly a maximal parameterized lyapunov function. -third, the implementation of the rtm leads to a maximized asymptotic stability region, while giving an accurate idea on numerical values of the lyapunov function parameters. the second step is finally reapplied in order to define the maximal lyapunov function and its corresponding da. a parameter optimization will cover in this step both the lyapunov function parameters and those of the polynomial control input. 3 preliminaries results before proceeding further, we will give some preliminary results. let v (x) ∈ r be a positive definite, radially unbounded and continuously differentiable function. the bounded set ω(c) = {x ∈ rn/v (x) ≤ c} (4) is an estimate of the region of attraction if ω ⊂ d where d = {x ∈ rn/v̇ (x, u) < 0} ∪ {0}. the time derivative of v (x, u) along trajectory of system (1) is given by v̇ (x, u) = ∂v (x) ∂x f(x) + ∂v (x) ∂x g(x) uφ(h(x̃)) = lfv (x) + lgv (x)uφ(h(x̃)) (5) where lfv (x) (resp. lgv (x)) is the lie derivative of v (x) along the polynomial function vector f(x) (resp. g(x)). in what follows, we shall denote lgv (x)uφ(h(x̃)) by l(g,u)v (x) for simplicity; i.e. l(g,u)v (x) = lgv (x)uφ(h(x̃)). the largest estimate of the da is given by ω(c∗(u)) where : c∗(u) = inf x∈rn v (x) such as v̇ (x, u) = 0 for each matrix u ∈ u. (6) the optimal value of c∗(u) is obtained by c∗ = sup u∈u c∗(u). (7) in [3], it has been proven that for any given c ∈ r , c ≤ c∗ if there exists u ∈ u and s(x) a positive definite polynomial such that v̇ (x, u) + (c − v (x)) s (x) < 0 (8) then the polynomial degrees of v (x) and v̇ (x, u) are 2δv and δl respectively. if we choose s(x) degree to be 2δs such that δs ≥ δl 2 − δv (9) it follows that the degree of the polynomial t(x, u, c, s(x)) = v̇ (x, u) + (c − v (x))s(x) (10) is equal to 2δm where δm = δv + δs. an approach based on both square matricial representation (smr) and complete square matrix representation (csmr) of polynomials is used in order to determine an appropriate enlarging the domain of attraction in nonlinear polynomial systems 541 optimization problem [3]. the csmr provides all the possible representations of a polynomial in terms of a quadratic form. the csmr matrix of t(x, u, c, s(x)) is given by t(α, u, c, s) = df (α) + dg(u) + cw1(s) − w2(s) (11) where df(α) is the csmr of lf v (x), α ∈ rτ(n,δm), dg(u) is the smr of l(g,u)v (x), w1(s) and w2(s) are the smr of s(x) and v (x)s(x). the condition (8) with (11) implies that if ĉ∗ = sup u∈u,α,s>0 c such that t(α, u, c, s) < 0; then ĉ∗ ≤ c∗ (12) theorem 1. ( [3]) the lower bound ĉ∗ is given by ĉ∗ = −λ∗ (u) 1 + µλ∗ (u) (13) where λ∗ (u) is the solution of the following gevp λ∗ (u) = inf u∈u,α,s>0,λ λ (14) such that    1 + µλ > 0; u ∈ u; s > 0 λw (s) > df (α) + dg (u) − w2 (s) with µ being any positive scalar and w(s) = kt (( 1 0 0 µv ) ⊗ s ) k. (15) the symbol ⊗ is the kronecker’s product and the matrix k satisfies ( 1 x{δv } ) ⊗ x{δs} = kx{δm} (16) where x{δm} ∈ rς(n,δm), α ∈ rτ(n,δm), x{δv } ∈ rς(n,δv ), x{δs} ∈ rς(n,δs), k ∈ rς(n,δs)(ς(n,δv )+1)×ς(n,δm) and the quantities ς (n, δm) and τ (n, δm) are given by ς (n, δm) = (n + δm)! n!δm! − 1 τ (n, δm) = 1 2 ς (n, δm) (ς (n, δm) + 1) − ς (n, 2δm) + n. 4 main results in this section, we present the main results of the paper. for this, we go back to the rtm introduced in [12], for estimating the region of asymptotic stability (ras) through reversing the system trajectory flow. our main aim is to enlarge the ras resulting form the method described in [3]. the idea consists in determining the maximal ras of the system obtained via the implementation of the rtm. this allows determining an analytical expression of the maximal da included in the ras. such a result is not given by the rtm, which finally gives an interesting graphical representation of the ras. we want to compute an analytical expression of 542 f. hamidi, h. jerbi, w. aggoune, m. djemaï, m.n. abdelkrim the ras in terms of a lyapunov function. we consider, for this purpose, a quadratic lyapunov function of the form: v (x) = xt px, where p = p t > 0 (17) for the sake of simplicity, we assume that p ∈ r2, with p = [ p1 p2 p2 p3 ] , so that : v (x) = p1x 2 1 + 2p2x1x2 + p3x 2 2. the results are generalizable for matrices of larger sizes. by using such a lyapunov function we can express the ras in terms of an ellipsoid in (x1, x2) plane which will be contained in the ras obtained by the trajectory reversing method. the idea consists in estimating the parameters pi, (i = 1, 2, 3) and ui, (i = 1, 2, 3, 4, 5) via a genetic algorithm. by combining this algorithm with an lmi optimization we can obtain the largest ellipsoid contained in the ras. the candidate solutions pi, (i = 1, 2, 3) and ui, (i = 1, 2, 3, 4, 5) are chosen arbitrarily and can be regarded as individuals. each variable can be considered as a gene and the different steps of the genetic algorithm can be expressed as follows : 1. initial population : each individual in the population is represented by a chromosome that is coded in binary form. 2. selection : in the selection process, strings must be of suitable fitness to be selected as a factual member in creating a new population. 3. replication : in this process, the best strings which have a greater probability than others will be member of the next generation. 4. crossover : two random chromosomes are selected in recreation process. they will be exposed to the crossover rate and they will swap from the crossover points. the experienced boundary for crossover rate is from 0.6 to 0.9. 5. mutation : we underline mutation relevance in just one way : for each bit we generate a random number and if it is less than the specified mutation probability, we flip the bit. if it is "one" we change it to "zero" and vice versa. the values, which are the boundaries for mutation rate, are between 0.1 and 0.3. in order to estimate, the parameter, by means of a genetic algorithm, we must encode the vector into binary code in order to let the parameters with genetic algorithm easier. theorem 2. in this work, a ga is used to estimate the parameters pi, (i = 1, 2, 3) and ui, (i = 1, ..., 5) where the next constraint is satisfied by : p1 > 0, p3 > 0 and p1.p3 > (p2) 2. (18) theorem 3. each iteration of the ga optimization routine, results in new parameters values pi, (i = 1, 2, 3), ui, (i = 1, ..., 5) and ĉ ∗ which is a solution of the lmi corresponding to this set of parameters. the genetic algorithm leads to the best set of parameters pi, (i = 1, 2, 3) and ui, (i = 1, ..., 5) which are used in the computation of the best solution ĉ ∗ (defined by (13)) which is the solution of the lmi (14). • the set of variables {pi, i = 1, 2, 3} and {ui, i = 1, ..., 5} are encoded into the gene candidate. • the fitness value is given by the maximum c∗ for which there exists a feasible solution of the lmi optimization. enlarging the domain of attraction in nonlinear polynomial systems 543 • the global optimization of the variables pi, (i = 1, 2, 3) and ui, (i = 1, ..., 5) is performed by the genetic operators (selection, recombination and mutation). the fitness function is evaluated in two steps as follows : first step second step: set pi, (i = 1, ..., 3) as gene candidates while p1 > 0, p3 > 0 and p1.p3 > (p2)2 apply the lmi optimization if cj > ci, j > i; i, j = 1, ..., n write pj1, pj2, pj3 end end return ĉ∗ = cj as fitness value. set pi, (i = 1, ..., 3) as gene candidates set ui, (i = 1, ..., 5) as gene candidates while : p1 > 0, p3 > 0 and p1.p3 > (p2)2 apply the lmi optimization if cj > ci, j > i; i, j = 1, ..., n write pj1, pj2, pj3, uj1, uj2, uj3, uj4, uj5 end end return ĉ∗ = cj as fitness value. the following flowchart, in figure 1, presents the main steps needed to implement the synthesized algorithm, according to which, we can maximize the region of asymptotic stability. the obtained solution is specified by the definition of a maximal quadratic lyapunov function. figure 1: flowchart of advanced lmi optimization algorithm for maximizing the da 5 illustrative example consider the following polynomial nonlinear system [3]: { ẋ1 = x2 ẋ2 = −x1 − x2 + x22 + x21x2 + u (19) 544 f. hamidi, h. jerbi, w. aggoune, m. djemaï, m.n. abdelkrim consider a controller that is linear in y1 = x1; that is, u = u1x1, with : u = {u = u1 : −2 ≤ u1 ≤ 2}. in order to find the shape of the da, we employ a lyapunov function of the form : v (x) = p1x 2 1 + 2p2x1x2 + p3x 2 2 (20) since the degree δl of v̇ (x, u) is 4, we can select δs = 1 which implies δm = 2. vectors x{δv}, x{δs} and x{δm} are selected as: x{δv } = x{δs} = (x1, x2) t and x{δm} = ( x1, x2, x 2 1, x1x2, x 2 2 )t which implies that: df (α, p1,2,3) =   −2p2 (p1 − p2 − p3) 0 α1 α2 + p2 (p1 − p2 − p3) (2p2 − 2p3) −α1 −α2 p3 0 −α1 0 p2 α3 α1 −α2 p2 −2(α3 − p3) 0 α2 + p2 p3 α3 0 0   dg (p1,2,3) =   2p2u1 p3u1 0 0 0 p3u1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   ; s = ( s1 s2 s2 s3 ) w (s, p1,2,3) =   s1 s2 0 0 0 s2 s3 0 0 0 0 0 µp1s1 µα 0 0 0 µα µα µγ 0 0 0 µγ µp3s3   ; w2 (s, p1,2,3) =   0 0 0 0 0 0 0 0 0 0 0 0 p1s1 α 0 0 0 α β γ 0 0 0 γ p3s3   with α = p1s2 + p2s1, β = p1s3 + 4p2s2 + p3s1 and γ = p2s3 + p3s2. we propose to encode the parameters pi into 7 bits code : pi = (p0i, p1i, p2i, p3i, p4i, p5i, p6i), and put them into a chromosome as follows : { pop_size = 50; crossover_rate = 0.65; mutation = 0.1; max_generations = 100. (21) note that we consider the lmi representation as a fitness function for the genetic program. then, we obtain: p1 = 3.5; p2 = 1.5; p3 = 5.5. this result justifies a domain of attraction that is larger than that obtained through the direct method [3]. the result of this study is shown in fig. 2 where µ = 1 100 . in this figure, solid line v̇ (x, u) = 0, and v (x) = 2.5244 obtained by the ga method. dashed line the v̇ (x, u) = 0 and v (x) = 1.2324 obtained by the method developed in [3]. when we apply the rtm described in ( [12]), the result given in fig. 3 represents the region of asymptotic stability of the system under consideration. hence, the equation of the largest da is given by v (x) = 9x21 + 4.4x1x2 + 15.5x 2 2 = 9.5. when implementing our proposed method based on the lmi and the ga, we obtain the following result, with input u = 0.4864x1: v (x) = 9.5x21 + 4x1x2 + 15.5x 2 2 = 8.5066 enlarging the domain of attraction in nonlinear polynomial systems 545 the result of this study is depicted in fig. 4 which shows that the resulting domain of attraction is larger than the initial one given by [3]. in this figure, solid line indicates the v̇ (x, u) = 0 and v (x) = 8.5066 obtained via the proposed method with ga, the dashed line indicates the v̇ (x, u) = 0 and v (x) = 1.2324 result in [3]. now, we investigate the maximization of the da by estimating parameters p1, p2 and p3 of the lyapunov function and u1, u2, u3, u4 and u5 of the control input. consequently, we have u = u1x1 + u2x2 + u3x 2 1 + u4x1x2 + u5x 2 2 (22) with u = {u = [u1, u2, . . . , u5] : −2 ≤ ui ≤ 2, i = 1, ..., 5} we propose to encode the parameters pi and ui into 7 bits code pi = (p0i, p1i, p2i, p3i, p4i, p5i, p6i) and ui = (u0i, u1i, u2i, u3i, u4i, u5i, u6i) and put them into a chromosome as described in equation (21). this implies that df (α, p1,2,3), s, w (s, p1,2,3) and w2 (s, p1,2,3) remains unchanged, while dg (u1,2,3,4,5, p1,2,3) is defined as : dg (u1,2,3,4,5, p1,2,3) =   2p2u1 (p3u1 + p2u2) p2u3 (p3u3 + p2u4) 0 (p3u1 + p2u2) 2p3u2 0 (p2u5 + p3u4) p3u5 p2u3 0 0 0 0 (p3u3 + p2u4) (p2u5 + p3u4) 0 0 0 0 p3u5 0 0 0   the result of this study yields v (x) = 9.5x21 + 3.9x1x2 + 15.5x 2 2 = 38.6183 which means that the input is written as u = 0.6348x1 − 1.9996x2 + 0.0006x21 − 0.0015x1x2 − 1.0044x22 in figure 5, we represent the different da(s) by continuous lines (which allow elliptic forms). the dash-dotted curves represent the constraints. clearly, the largest (da) is obtained when we optimize both parameters of the lf and those of the control input. in this figure, the da ( blue line) illustrates the results obtained through the optimization of parameters p1, p2, p3, u1, u2, u3, u4 and u5 by combining ga and lmi. da with red line : obtained results while optimizing parameters p1, p2, p3, u1 by combining the ga and lmi [2]. da with the black line : obtained results by optimizing parameters p1, p2, p3, u1, with lmi [3].this result demonstrates the consistency of the proposed method. 6 conclusion in this paper, the problem of enlarging the attraction domain for nonlinear controlled systems is investigated. we were particularly interested in the class of nonlinear polynomial systems which represents a large class of physical nonlinear dynamics that can be approximated by polynomials using taylor series expansions. we rely on an optimization approach based on linear matrix inequality (lmi) to compute an initial region of attraction. the main contribution consists in the determination of an explicit da by using a parameterized lyapunov function. the parameters of both the lyapunov function and nonlinear control input are computed by combining the genetic algorithm and an lmi approach. the implementation of the reverse trajectory method leads to represent the largest elliptic shape of the ra. an illustrative example has demonstrated the efficiency of the established results. 546 f. hamidi, h. jerbi, w. aggoune, m. djemaï, m.n. abdelkrim acknowledgements this work has been supported, for m. djemaï, by ari-projets and by international campus on safety and intermodality in transportation, the european community, the delegation regionale a la recherche et a la technologie, the ministere de l’enseignement supérieur et de la recherche, the region nord pas de calais and the centre national de la recherche scientifique. figure 2: v̇ (x, u) = 0, and v (x). figure 3: largest ellipsoid included in ras figure 4: v̇ (x, u) = 0 and v (x). figure 5: da by combining ga and lmi. bibliography [1] d. rosinova and v. vesely,robust pid decentralized controller design using lmi, int j comput commun, issn 1841-9836, 2(2):195-204, 2007. 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[13] y. fujisaki, and r. sakuwa. estimation of asymptotic stability regions via homogeneous polynomial lyapunov functions, international journal of control, 79(6): 617-623, 2006. 5du.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):812-824, december, 2015. fuzzy robust tracking control for uncertain nonlinear time-delay system z.-b. du, t.-c. lin, t.-b. zhao zhenbin du yantai university yantai, shandong, 264005, china zhenbindu@126.com tsung-chih lin feng-chia university taichung, 40724, taiwan tclin@fcu.edu.tw tiebiao zhao university of california merced, ca, 95343, usa tzhao3@ucmerced.edu abstract: the problem of fuzzy robust tracking control is investigated for uncertain nonlinear time-delay systems. the nonlinear time-delay system is modeled as fuzzy takagi-sugeno (t-s) system, and fuzzy logic systems are used to eliminate the uncertainties of the system. a sufficient condition for the existence of fuzzy controller is given in terms of linear matrix inequalities (lmis) and adaptive law. based on lyapunov stability theorem, the fuzzy control scheme guarantees the desired tracking performance in sense that all the closed-loop signals are uniformly ultimately bounded (uub). simulation results of 2-link manipulator demonstrate the effectiveness of the developed control scheme. keywords: fuzzy t-s model; fuzzy logic systems; nonlinear system; time-delay; tracking control. 1 introduction fuzzy control approach offers a powerful and systematical control methodology to handle nonlinear system. owing to the superior approximation and reasoning abilities of the fuzzy controller, fuzzy control approach has been applied in different applications. with the extensive efforts of the researchers working on the fuzzy control discipline, fruitful stability analysis results have been obtained to aid the design of stable fuzzy controllers. in [1], a fuzzy t-s model was employed to represent the system dynamics of the nonlinear system. the fuzzy t-s model represents the nonlinear system as a weighted sum of some linear subsystems. this particular structure offers a general framework to represent the nonlinear system which is favorable for system analysis. fuzzy controllers [2-4] were proposed to handle the nonlinear system represented by the fuzzy t-s model. to avoid the effect of the uncertainties, a matching condition is assumed in [5–7], and an upper bound on uncertainties is introduced in [8–10]. the matching condition and the upper bound in dealing with the uncertainties are effective and feasible. however, there exists certain conservatism. the matching condition is a very conservative assumption and the upper bound may be too big or too small, which adds some difficulties to the controller design. on the other hand, it is well known that fuzzy logic systems can uniformly approximate nonlinear continuous functions to arbitrary accuracy. thus, fuzzy logic systems are used to model uncertain nonlinear systems in [11–13]. copyright © 2006-2015 by ccc publications fuzzy robust tracking control for uncertain nonlinear time-delay system 813 time delays are frequently encountered in engineering systems. the existence of time delays usually becomes the source of instability and degrading performance of systems. therefore, stability analysis and controller synthesis for nonlinear time-delay systems are important both in theory and in practice. by using fuzzy t-s model and fuzzy logic systems, we propose a novel robust tracking control scheme for a class of uncertain nonlinear time-delay system. fuzzy t-s model is used to approximate the nonlinear system, and a fuzzy state feedback controller is designed to guarantee the stability of the fuzzy system. a compensator based fuzzy logic systems is introduced to eliminate the uncertainties of the system. the fuzzy control scheme ensures the desired tracking performance in sense that all the closed-loop signals are uniformly ultimately bounded (uub). the rest of the paper is organized as follows. section 2 provides the problem formulation. section 3 develops a procedure of the controller design. section 4 gives the main result. section 5 presents simulation examples to illustrate the effectiveness of the proposed method. these are followed by conclusions in section 6. 2 problem formulation consider the following uncertain nonlinear time-delay system: ẋ1 = x2, · · · ẋ(β1−1) = xβ1, ẋβ1 = f1(x, x(t − τ1), · · · , x(t − τr), u) + f̃1(x, x(t − τ1), · · · , x(t − τr), u) + d1, ẋ(β1+1) = x(β1+2), · · · ẋn = fm(x, x(t − τ1), · · · , x(t − τr), u) + f̃m(x, x(t − τ1), · · · , x(t − τr), u) + dm, (1) where x = [x1, · · · , x (β1−1) 1 , · · · , x(n−βm+1), · · · , x (βm−1) (n−βm+1) ]t ∈ rn with β1+β2+· · ·+βm = n and u ∈ rm are the system state and control input, respectively. fi (i = 1, · · · , m) are known smooth nonlinear functions, f̃i (i = 1, · · · , m) are unknown nonlinear uncertainties, τi(i = 1, · · · , r) are time delays, and di (i = 1, · · · , m) are external bounded disturbances. the control objective of this paper is to find a fuzzy tracking controller such that, while maintaining all the closed-loop signals uub, the system states of nonlinear system (1) follow those of the given stable reference model. 3 fuzzy model, reference model and fuzzy controller a fuzzy-model-based control system, formed by a fuzzy model, a reference model, and fuzzy controller connected in a closed-loop, is introduced. 3.1 fuzzy model a fuzzy dynamic model has been proposed by takagi and sugeno to represent a nonlinear system. the fuzzy dynamic model is described by the following fuzzy if-then rules and will be employed here to deal with the control design problem for the nonlinear system in (1). 814 z.-b. du, t.-c. lin, t.-b. zhao plant rule i: if z1(t) is f i 1 and, · · · , and zs(t) is f i s,then ẋ(t) = aix(t) + r ∑ l=1 ailx(t − τl) + biu(t) + d, i = 1, · · · , l (2) where z1(t), · · · , zs(t) are the premise variables, f i j (j = 1, · · · , s) are the fuzzy sets, l is the number of if-then rules, ai,bi and ail are some constant matrices with compatible dimensions, bi=[0, · · · , b t i1, · · · , 0, · · · , b t im] t ∈ rn×m with bi1 ∈ r m, · · · , bim ∈ r m , and d = [0, · · · , d1, · · · , 0, · · · , dm] t . then, the final output of the fuzzy system is inferred as follows: ẋ(t) = l ∑ i=1 µi[aix(t) + r ∑ l=1 ailx(t − τl)] + l ∑ i=1 µibiu(t) + d, (3) where µi = vi(z(t)) / l ∑ i=1 vi(z(t)), vi(z(t)) = s ∏ j=1 f ij (zj(t)) (4) for all t ≥ 0, and f ij (zj(t)) is the grade of membership of zj(t) in f i j . it can be seen that l ∑ i=1 vi(z(t)) > 0, and vi ≥ 0(i = 1, · · · , r) for all t ≥ 0. we have µi ≥ 0(i = 1, · · · , r), l ∑ i=1 µi = 1. hence, the nonlinear system (1) can be rearranged as the following equivalent system : ẋ(t) = l ∑ i=1 µi[aix(t) + r ∑ l=1 ailx(t − τl)] + l ∑ i=1 µibiu(t) + b∆(x, x(t − τ)) + d, (5) where b∆(x, x(t−τ)) = b∆(x, x(t−τ1), · · · , x(t−τr)) denotes the uncertainties between the nonlinear system (1) and the fuzzy model (3), and b = diag[b1, · · · , bm] with bi = [0, · · · , 0, 1]t ∈ rβi . 3.2 reference model the system states of nonlinear systems (1) are driven to follow those of the following stable reference model ẋr(t) = arxr(t) + r(t), (6) where xr(t) is a reference state, r(t) is a bounded reference input, and ar is an asymptotically stable matrix. 3.3 fuzzy controller a fuzzy controller is chosen as u(t) = ul(t) − uf(t), (7) where ul(t) denotes the fuzzy state feedback control based on t-s model, and uf(t) is the adaptive compensator based on fuzzy logic systems. the former is used to stabilize the linear part of system (11), and the latter is used to compensate the uncertainties. ul(t) and uf(t) are designed as (8) and (10), respectively. for the fuzzy model represented by (2) or (3), fuzzy state feedback control ul(t) shares the same if parts with the following structure. fuzzy robust tracking control for uncertain nonlinear time-delay system 815 control rule i: if z1(t) is f i 1 and, · · · , and zs(t) is f i s , then ul(t) = ki(x(t) − xr(t)), i = 1, · · · , l. hence, the overall state feedback controller ul(t) is given by ul(t) = l ∑ i=1 µiki(x(t) − xr(t)), (8) where ki(i = 1, 2, · · · , l) are matrices with proper dimensions and satisfy the following inequalities ātijp + pāij + r ∑ l=1 α −1 l pāilā t ilp + r ∑ l=1 αli + 1 ρ2 pp + q̄ < 0, i, j = 1, · · · , l, (9) where āij = [ ai + bikj −bikj 0 ar ] , āil = [ ail 0 0 0 ] , q̄ = diag{2q, 2q}, p and q are some symmetric and positive definite matrices, and αl(l = 1, · · · , r) are positive constants. the adaptive compensator based on fuzzy logic systems uf(t) are as follows: uf(t) = { e−1û(x, x(t − τ)|θ), if e is nonsigular et (i + eet )−1û(x, x(t − τ)|θ), if e is sigular (10) where ei = [b t i1, · · · , b t im] t ∈ rm×m,e = l ∑ i=1 µiei, and û(x, x(t − τ)|θ) is constructed by fuzzy logic systems. the weight θ is an adaptive parameter, which is adapted by θ̇ = η1ψ t (x, x(t − τ))b̄t px̃, (11) where η1 is a positive constant, ψ(x, x(t−τ)) is a fuzzy basis-function matrix, and x̃ = [x t , xtr ] t . in the following, we explain the solution of the inequalities (9) and the construction of fuzzy logic systems û(x, u|θ). 1) by schur complements, the inequalities (9) are transformed into the lmis. for the convenience of design, p is chosen as the formp = diag{p1, p2}, where p1, p2 are some symmetric and positive definite matrices. the inequalities (9) are equivalent to the following matrix inequalities    s11 −p1bikj 0 −(bikj) t p1 s22 p2 0 p2 −ρ 2i    < 0, i, j = 1, 2, · · · , l, (12) where s11 = p1(ai + bikj) + (ai + bikj) t p1 + r ∑ l=1 α −1 l p1aila t il p1 + r ∑ l=1 αli + 1 ρ2 p1p1 + 2q, s22 = p2ar + a t r p2 + r ∑ l=1 αli + 2q. the matrix inequalities (12) imply s11 < 0. let w = p −1 1 and yj = kjw . s11 < 0 is equivalent to the lmis with prescribed q and αl(l = 1, · · · , r),   s w w −( r ∑ l=1 αli + 2q) −1   < 0, i, j = 1, 2, · · · , l (13) 816 z.-b. du, t.-c. lin, t.-b. zhao where s = aiw + wa t i + biyj + (biyj) t + r ∑ l=1 α −1 l aila t il + (ρ2)−1i. by solving the lmis (13), p1 and kj(j = 1, 2, · · · , l) could be obtained. and then, by substituting p1 and kj(j = 1, 2, · · · , l) into (12), (12) becomes standard lmis. we can easily solve p2 from (12). therefore, the common solution p and kj(j = 1, 2, · · · , l) could be found. remark 1: either the matching condition or the upper bound is related to a large number of matrix operations. without the matching condition and the upper bound, the dimension of the lmis of this paper is reduced. 2) fuzzy adaptive systems consist of four main components: fuzzy rule base, fuzzy inference engine, fuzzifier and defuzzifier [11]. the fuzzy rule base is composed of a collection of if-then inference rules: rl: if x1 is a l 1, · · · , xn is a l nłźthen y is g l(l = 1, · · ·p) where ali(i = 1, · · · , l) and g l(l = 1, · · ·p) are fuzzy sets. the kth element of ∆(x, x(t−τ)) is of the following form: ∆̂k(x, x(t − τ)|θk) = ξ t k (x, x(t − τ))θk, where θk = (θ 1 k , · · · , θ p k )t ∈ rp, ξt k (x, x(t − τ)) = (ξ1 k , · · · , ξ p k ) ∈ rp, ξlk = n ∏ i=1 µf l i (xi, xi(t − τ)) / p ∑ l=1 n ∏ i=1 µf l i (xi, xi(t − τ)), µf l i (xi, xi(t−τ)) = µf l i (xi) r ∏ j=1 µf l i (xi(t − τj)), and µf l i (xi)(i = 1, 2, · · · , n) are the membership functions. in this paper, fuzzy logic systems are constructed to eliminate the uncertainties ∆(x, x(t−τ)). the approximation form is given as follows: ∆̂(x, x(t − τ)|θ) = ψ(x, x(t − τ))θ, (14) where ψ(x, x(t − τ)) = diag[ξt1 (x, x(t − τ)), · · · , ξ t m(x, x(t − τ))], θ = [θ t 1 , θ t 2 , · · · , θ t m] t . define the optimal the parameter θ∗ as θ∗ = arg min θ∈ω1 [ sup x∈u1 |û(x, x(t − τ)|θ) − ∆(x, x(t − τ))|], (15) where u1 = {x ∈ r n : ‖x‖ ≤ n}, ω1 = {θ ∈ r pm: ‖θ‖ ≤ m}. u1, ω1 denote the sets of suitable bounds on x,θ respectively, n, mare upper bounds. the approximation error for the function ∆(x, x(t − τ))can be expressed as ∆̂(x, x(t − τ)|θ) − ∆(x, x(t − τ)) = ψ(x, x(t − τ))θ̃ + w, (16) where θ̃ = θ − θ∗ the estimation error for θ, w = [w1, · · · , wm] t is a residual term. remark 2: in order to guarantee ‖θ‖ ≤ m, the adaptive law (11) must be modified by the projection algorithm [11] as follows: θ̇ = { η1ψ t (x, x(t − τ))b̄t px̃, if(‖θ‖<m)or(‖θ‖ =m and x̃t pb̄ ψ(x, x(t − τ))θ ≤ 0) pθ[.], if ‖θ‖ =m and x̃ t pb̄ ψ(x, x(t − τ))θ > 0 where pθ[.]=η1ψ t (x, x(t − τ))b̄t px̃ − η1 x̃t pb̄ ψ(x,x(t−τ))θ ‖θ‖2 . fuzzy robust tracking control for uncertain nonlinear time-delay system 817 4 stability analysis substituting (11) into (11) yields ẋ(t) = l ∑ i=1 µi[aix(t) + r ∑ l=1 ailx(t − τl)] + l ∑ i=1 l ∑ j=1 µiµjbikj(x(t) − xr(t)) −b(û(x, x(t − τ)|θ) − ∆(x, x(t − τ))) + d. (17) let x̃(t) = [xt (t), xtr (t)] t , andb̄ = [ b t 0 ]t . by using (11) and (17), a new extended closedloop system is as follows: ˙̃x(t) = l ∑ i=1 l ∑ j=1 µiµj[āijx̃(t)+ r ∑ l=1 āilx̃(t − τl)]+b̄(−(û(x, x(t−τ)|θ)−∆(x, x(t−τ)))+d′, (18) where d′ = [dt , rt (t)]t . when fuzzy logic systems û(x, x(t−τ)|θ) could eliminate ∆(x, x(t−τ)), the closed-loop system (18) is stable. by denoting w′ = [w̄t , rt (t)]t , w̄ = [0, · · · , d1 − w1, · · · , 0, · · · , dm − wm] t and using (14), the closed-loop system (18) could be rewritten as ˙̃x(t) = l ∑ i=1 l ∑ j=1 µiµj[āijx̃(t) + r ∑ l=1 āilx̃(t − τl)] + b̄(−ψ(x, x(t − τ))θ̃) + w′. (19) from the above analysis, we have the following conclusion. theorem 1. given a matrixq > 0, scalarsρ > 0,αl(l = 1, · · · , r) > 0, η1 > 0.if there exist matricesp > 0,kj(j = 1, 2, · · ·, l) such that the inequalities (9) hold. if the updating law for fuzzy logic systems is chosen as (11). then there exists a controller (11) with the fuzzy state feedback controller (8) and the adaptive compensator (10) such that, while maintaining all the closed-loop signals uub, the following tracking performance(20) is achieved ∫ t 0 (x(t) − xr(t)) t q(x(t) − xr(t))dt ≤ x̃ t (0)px̃(0) + 1 η1 θ̃t (0)θ̃(0) + ρ2 ∫ t 0 (w′t w′)dt. (20) proof: consider the following lyapunov-krasoviskii candidate v = 1 2 x̃t px̃ + 1 2 r ∑ l=1 ∫ t t−τl αlx̃ t (v)x̃(v)dv + 1 2η1 θ̃t θ̃, (21) where v̇ = v̇1 + v̇2, v̇1and v̇2 are given in (22) and (26), respectively. v̇1 = 1 2 ( l ∑ i=1 l ∑ j=1 µiµj[āijx̃(t) + r ∑ l=1 āilx̃(t − τl)]) t px̃(t) + 1 2 x̃t (t)p( l ∑ i=1 l ∑ j=1 µiµj[āijx̃(t) + r ∑ l=1 āilx̃(t − τl)]) + 1 2 w′t px(t) + 1 2 xt (t)pw′ + 1 2 r ∑ l=1 αlx̃ t (t)x̃(t) − 1 2 r ∑ l=1 αlx̃ t (t − τl)x̃(t − τl) ≤ 1 2 ( l ∑ i=1 l ∑ j=1 µiµj[x̃ t (t)ātijpx̃(t)+x̃ t (t)pāijx̃(t)+ r ∑ l=1 α −1 l x̃t (t)pāilā t ilpx̃(t)+ r ∑ l=1 αlx̃ t (t − τl)x̃(t − τl)] 818 z.-b. du, t.-c. lin, t.-b. zhao − 1 2 ( 1 ρ px(t) − ρw′)t ( 1 ρ px(t) − ρw′) + 1 2 ρ2w′t w′ + 1 2ρ2 x̃t (t)ppx̃(t) + 1 2 r ∑ l=1 αlx̃ t (t)x̃(t) −1 2 r ∑ l=1 αlx̃ t (t − τl)x̃(t − τl) ≤ 1 2 l ∑ i=1 l ∑ j=1 µiµjx̃ t (t)(ātijp +pāij + r ∑ l=1 α −1 l pāilā t ilp + r ∑ l=1 αli + 1 ρ2 pp)x̃(t)+ 1 2 ρ2w′t w′. (22) substituting (9) into (22) yields v̇1 ≤ − 1 2 x̃t (t)q̄x̃(t) + 1 2 ρ2w′t w′. (23) by using (11), v2 = [x̃ t pb̄(−(ψ(x, x(t − τ))θ̃) + 1 η1 θ̃t θ̇] = 0. (24) from (23)-(24), v̇ ≤ − 1 2 x̃t (t)q̄x̃(t) + 1 2 ρ2w′t w′. (25) when‖x̃(t)‖ > ρ λmin(q̄) ‖w′‖,v̇ < 0.thus, the closed-loop system consisting of (1), (11), (8) and (10) is uub . ✷ note that ∫ t 0 (x(t) − xr(t)) t q(x(t) − xr(t))dt ≤ ∫ t 0 x̃t (t)q̄x̃(t)dt. integrating the above equation (25) from t = 0 to t yields (20). 5 simulation example in this section, we provide an example to verify the effectiveness of the proposed control scheme. example: consider the following 2-link manipulator system in [14] q̈(t) + c(q, q̇)q̇(t) + g(q) = b(q)u(t) + r ∑ i=1 ξi(t)q(t − τi) + d′, (26) where c(q, q̇) = h−1(q)c′(q, q̇), g(q) = h−1(q)g′(q), b(q) = h−1(q),d′ = h−1(q)d,q = [q1, q2] t , ξi(t)(i = 1, · · · , r)are uncertain and bounded, and dis the external bounded disturbance. the reference model is as follows: ẋr(t) = arxr(t) + r(t), (27) wherear = diag{ar1, ar2},ar1 = ar2 = [ 0 1 −6 −5 ] , r(t) = [0, r1(t), 0, r2(t)] t , r1(t) = r2(t) = 3 sin(2t). fuzzy robust tracking control for uncertain nonlinear time-delay system 819 step1: denote x1 = q1, x2 = q̇1, x3 = q2, and x4 = q̇2. then, (26) can be written as a fourthdimension system. a nine-rule fuzzy t-s model is used to approximate the nonlinear 2-link manipulator system at x1 = − π 2 , 0, π 2 and x3 = − π 2 , 0, π 2 , where a1 =       0 1 0 0 5.927 −0.001 −0.315 −0.0000084 0 0 0 1 −6.859 0.002 3.155 0.0000062       , a2 =       0 1 0 0 3.0428 −0.0011 −0.1791 −0.0002 0 0 0 1 −3.5436 0.0313 2.5611 0.0000114       , a3 =       0 1 0 0 6.2728 0.003 0.4339 −0.0001 0 0 0 1 −9.1041 0.0158 −1.0574 −0.000032       , a4 =       0 1 0 0 6.4535 0.0017 1.2427 −0.0002 0 0 0 1 −3.1873 0.0306 −5.1911 −0.000018       , a5 =       0 1 0 0 11.1336 0 −1.8145 0 0 0 0 1 −9.0918 0 9.1638 0       , a6 =       0 1 0 0 6.1702 −0.001 1.687 −0.0002 0 0 0 1 −2.3559 0.0314 4.5298 −0.000011       , a7 =       0 1 0 0 6.1206 0.0041 0.6205 0.0001 0 0 0 1 8.8794 0.0193 −1.0119 0.000044       , a8 =       0 1 0 0 3.6421 −0.0018 0.0721 0.0002 0 0 0 1 2.429 −0.0305 2.9832 −0.000019       , a9 =       0 1 0 0 6.2933 −0.0009 0.2188 −0.000012 0 0 0 1 −7.4649 0.0024 3.2693 −0.0000092       , a11 = a21 = a31 = a41 = a51 = a61 = a71 = a81 = a91 =       0 0 0 0 0.01 0 0 0 0 0 0 0 0 0 0 0       , a12 = a22 = a32 = a42 = a52 = a62 = a72 = a82 = a92 =       0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0       , b1 = [ 0 1 0 −1 0 −1 0 2 ]t , b2 = [ 0 0.5 0 0 0 0 0 1 ]t , b3 = [ 0 1 0 1 0 1 0 2 ]t , b4 = [ 0 0.5 0 0 0 0 0 1 ]t , b5 = [ 0 1 0 −1 0 −1 0 2 ]t , b6 = [ 0 0.5 0 0 0 0 0 1 ]t , 820 z.-b. du, t.-c. lin, t.-b. zhao b7 = [ 0 1 0 1 0 1 0 2 ]t , b8 = [ 0 0.5 0 0 0 0 0 1 ]t , b9 = [ 0 1 0 −1 0 −1 0 2 ]t . the membership functions are adopted as the triangle type. step 2: on the basis of theorem1, withα1 = 0.005, α2 = 0.005, andρ = 1,we have k1 = [ -76.9685 -42.9566 -19.6919 -8.9116 6.0025 -0.4619 -51.4252 -25.0336 ] , k2 = [ -77.7828 -42.8754 -13.6211 -5.9413 8.7179 1.2251 -50.6614 -24.6859 ] , k3 = [ -76.8347 -42.9089 -19.8204 -8.9785 5.8595 -0.5257 -51.3739 -25.0109 ] , k4 = [ -77.7828 -42.8754 -13.6211 -5.9413 8.7179 1.2251 -50.6614 -24.6859 ] , k5 = [ -77.7828 -42.8754 -13.6211 -5.9413 8.7179 1.2251 -50.6614 -24.6859 ] , k6 = [ -77.7828 -42.8754 -13.6211 -5.9413 8.7179 1.2251 -50.6614 -24.6859 ] , k7 = [ -79.8424 -43.4072 -6.0780 -2.2626 12.7745 3.6898 -50.2150 -24.4989 ] , k8 = [ -77.7828 -42.8754 -13.6211 -5.9413 8.7179 1.2251 -50.6614 -24.6859 ] , k9 = [ -80.1162 -43.5088 -5.8152 -2.1328 13.0509 3.8147 -50.3242 -24.5472 ] . step 3: in fuzzy adaptive compensator, the membership functions are selected as µf 1 i (xi) = 1 1 + exp[5(xi + 0.8)] , µf 2 i (xi) = exp[−(xi + 0.6) 2], µf 3 i (xi) = exp[−(xi + 0.4) 2], µf 4 i (xi) = exp[−(xi) 2], µf 5 i (xi) = exp[−(xi − 0.4) 2], µf 6 i (xi) = exp[−(xi − 0.6) 2], µf 7 i (xi) = 1 1 + exp[5(xi − 0.8)] , i = 1, 2, · · · , 4. step 4: some parameters are choose as η1 = 10, r = 2, τ1 = 0.5, τ2 = 1, ξ1(t) = 5 + 20sin(5t), andξ1(t) = 1 + 15cos(5t), θ(0) = [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2], (x1(0), x2(0), x3(0), x4(0), xr1(0), xr2(0), xr3(0), xr4(0)) = (0.4, 0,−0.4, 0, 0, 0, 0, 0). by using the method in theorem 1, the tracking performances of x1(t), x2(t), x3(t), x4(t) are shown in fig.1,and the control efforts u1(t) and u2(t) are given in fig.2,respectively. fuzzy robust tracking control for uncertain nonlinear time-delay system 821 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s ta te 1 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s ta te 2 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s ta te 3 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s ta te 4 x 1 x r1 x 2 x r2 x 3 x r3 x 4 x r4 figure 1: the responses of x1,x2,x3,x4, xr1,xr2,xr3andxr4 0 2 4 6 8 10 −80 −60 −40 −20 0 20 40 t(sec.) c o n tr o l1 0 2 4 6 8 10 −10 0 10 20 30 40 50 60 70 c o n tr o l2 t(sec.) figure 2: the control inputs u1,u2 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s ta te 1 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s ta te 2 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s ta te 3 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s ta te 4 x 1 x r1 x 2 x r2 x 3 x r3 x 4 x r4 figure 3: the responses of x1,x2,x3,x4, xr1,xr2,xr3andxr4 822 z.-b. du, t.-c. lin, t.-b. zhao 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s a te 1 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s a te 2 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s a te 3 0 2 4 6 8 10 −1 −0.5 0 0.5 1 t(sec.) s a te 4 x 1 x r1 x 2 x r2 x 3 x r3 x 4 x r4 figure 4: the responses of x1,x2,x3,x4, xr1,xr2,xr3andxr4 0 2 4 6 8 10 −0.1 0 0.1 0.2 0.3 0.4 t(sec.) s ta te 1 0 2 4 6 8 10 −0.8 −0.6 −0.4 −0.2 0 0.2 s ta te 2 t(sec.) 0 2 4 6 8 10 −0.4 −0.3 −0.2 −0.1 0 0.1 t(sec.) s ta te 3 0 2 4 6 8 10 −0.5 0 0.5 1 t(sec.) s ta te 4 x 4 x r4 x 2 x r2 x 1 x r1 x 3 x r3 figure 5: the responses of x1,x2,x3,x4, xr1,xr2,xr3andxr4 0 2 4 6 8 10 −15 −10 −5 0 5 t(sec.) c o n tr o l1 0 2 4 6 8 10 −5 0 5 10 15 t(sec.) c o n tr o l2 u 1 u 2 figure 6: the control inputs u1,u2 fuzzy robust tracking control for uncertain nonlinear time-delay system 823 whenτ1 = 1,τ2 = 1, simulation results are shown in fig.3.whenτ1 = 1,τ2 = 2, simulation results are shown in fig.4. when r1(t)andr2(t) are square waves having an amplitude ±0.2 with a period of 2π, the tracking performances of x1(t), x2(t), x3(t), x4(t) are shown in fig. 5, and the control efforts u1(t) and u2(t) are given in fig.6. simulation results illustrate that the proposed controller design is effective and feasible. 6 conclusion based on fuzzy technique, a novel tracking control scheme is presented for uncertain nonlinear time-delay system. as main contribution of this paper, we design a novel fuzzy tracking controller, which is independent of the matching condition or the upper bound for the uncertainties. furthermore, the tracking control design for discrete nonlinear systems is also developed. acknowledgment this work was supported by the national natural science foundation of china(61203320,61273128), shandong natural science foundation(zr2014fl023) and 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(2004); tracking-based adaptive fuzzy-neural control for mimo uncertain robotic systems with time delays, fuzzy sets and systems, issn 0165-0114,146(3): 375-401. 912wangshi6.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):889-903, december, 2015. homeomorphism problems of fuzzy real number space and the space of bounded functions with same monotonicity on [-1,1] h.-d. wang, s.-c. guo, s.m. hosseini bamakan, y. shi hua-dong wang 1. key laboratory of big data mining and knowledge management, chinese academy of sciences, beijing 100190, china 2. research center on fictitious economy & data science, chinese academy of sciences, beijing 100190, china 3. college of mathematical sciences, university of chinese academy of sciences, beijing, 100190, china huadw2012@163.com si-cong guo college of sciences, liaoning technical university, fuxin 123000, china seyed mojtaba hosseini bamakan 1. key laboratory of big data mining and knowledge management, chinese academy of sciences, beijing 100190, china 2. research center on fictitious economy & data science, chinese academy of sciences, beijing 100190, china 3. school of management, university of chinese academy of sciences, beijing, 100190, china s_mojtabahossini@yahoo.com yong shi* 1. key laboratory of big data mining and knowledge management, chinese academy of sciences, beijing 100190, china 2. research center on fictitious economy & data science, chinese academy of sciences, beijing 100190, china *corresponding author: yshi@ucas.ac.cn abstract: in this paper, based on the fuzzy structured element, we prove that there is a bijection function between the fuzzy number space ε1 and the space b[−1, 1], which defined as a set of standard monotonic bounded functions with monotonicity on interval [−1, 1]. furthermore, a new approach based upon the monotonic bounded functions has been proposed to create fuzzy numbers and represent them by suing fuzzy structured element. in order to make two different metrics based space in b[−1, 1], hausdorff metric and lp metric, which both are classical functional metrics, are adopted and their topological properties are discussed. in addition, by the means of introducing fuzzy functional to space b[−1, 1], we present two new fuzzy number’s metrics. finally, according to the proof of homeomorphism between fuzzy number space ε1 and the space b[−1, 1], it’s argued that not only does it give a new way to study the fuzzy analysis theory, but also makes the study of fuzzy number space easier. keywords: fuzzy numbers; fuzzy structured element, standard monotonic bounded functions, fuzzy functional, homeomorphism copyright © 2006-2015 by ccc publications 890 h.-d. wang, s.-c. guo, s.m. hosseini bamakan, y. shi 1 introduction fuzzy numbers, which are a generalization of real numbers, have been perfectly applied to model and show the fuzzy data. recently, application of fuzzy numbers in data mining algorithms has been an interesting topic to the researchers in this domain, for instance, clustering [1, 2], classification [3] and regression [4, 5]. generally, the efforts have been done in study of fuzzy mathematical analysis and its application falls into two main categories: first, studies on constructing fuzzy number metrics based on the fuzzy numbers and their topological properties. many researchers proposed different metrics and many discussions on them have been proposed. for example, hausdorff metric [6], lp metric [7] and sendograph metric [8], were proposed as some of the most well-known widely used metrics. the second category consists of those studies which addresses the relationship between the fuzzy number space and other topological spaces, study the properties of the fuzzy number space and develop some new methods in the proposed spaces. among these studies, goetschel and voxman(2003) introduced a homeomorphic mapping from θ-crisp fuzzy number space to hilbert space ℓ2, which ranges in a convex cone (see [9]). later, gerg [10] generalized this mapping by extending the θ-crisp fuzzy number space to a more general one. in order to apply the functional analysis to the fuzzy-valued functions studies, in which variables are real numbers and function values are fuzzy numbers, puri and ralescu [11], proposed an embedding theorem that the fuzzy number space ε1 can be embedded into a banach space x, with the help of the radstrom embedding theorem of compact convex set. this theorem establishes the theoretical link between the fuzzy number space and the banach space. however, because of do not considering any specific structure of banach space, it is not easy to implement (it is not applicable anymore). thus, by adopting the mapping of goetschel and voxman, wu and ma [12, 13] embedded fuzzy number space into the concrete banach space c[0, 1] × c[0, 1] (c[0, 1] = {f : f is a bounded left-continuous function on (0, 1], and f has right limit on (0, 1], especially f is right-continuous at 0}), and present a specific isometrically isomorphic operator. although the proposed embedding operator is proved to be as same as the embedding operator given by puri and ralescu [11] in the sense of isometrical isomorphism, the embedding operator has a specific form. this paper is organized as follows: section 2 and section 3 introduced definitions and notations employed throughout the paper. in section 4, we introduced a specific fuzzy number, namely; fuzzy structured element. then two important theorems which are the local mapping theorem and the structured element representation theorem of fuzzy number, are proved. as a result, we obtain a conclusion that there exist an one-one mapping from b[−1, 1] with the same order standard monotonic bounded function family on [0, 1] to the fuzzy real number space ε1. in section 5, we introduced lp metric and hausdorff metric into b[−1, 1] and some of its topological properties, such as completeness and separability are discussed. in section 6, by means of a fuzzy functional induced by fuzzy structured element, two fuzzy number metrics induced in the given metrics of b[−1, 1]. this section discussed the homeomorphism problems between b[−1, 1] and the space ε1. finally, we conclude and provide future works in section 7. 2 notion of the fuzzy numbers fuzzy numbers are the natural generalization of real and crisp numbers. a fuzzy number is a set of the real line with the upper semi-continuous and quasi-concave membership function. the definition implies that α-cut (aα) of a fuzzy subset a is a closed interval in [a α l , a α r ] for any α ∈ (0, 1]. the support of a fuzzy number a is a crisp set so that suppa = cl({x : a(x) > 0}) = [a0l , a 0 r](the closure of the support of a). thus, by supposing suppa to be a bounded closed homeomorphism problems of fuzzy real number space and the space of bounded functions with same monotonicity on [-1,1] 891 interval, a defined as a bounded fuzzy number. denote all bounded fuzzy numbers on real line r as ñc(r)(or ε 1). theorem 1. [12] if u ∈ ñc(r), then let u(α) = inf{x : x ∈ uα}, u(α) = sup{x : x ∈ uα}, here u(α) and u(α) are two functions that satisfy the following conditions (1)–(4) on [0, 1]: (1) u(α) is a bounded left continuous nondecreasing function on (0, 1]; (2) u(α) is a bounded left continuous nonincreasing function on (0, 1]; (3) u(α) and u(α) are right continuous at α = 0; (4) u(α) ≤ u(α). conversely, if functions u(α) and u(α) satisfy the conditions (1)–(4) on [0, 1], then there exists an unique u ∈ ñc(r) such that uα = [u(α), u(α)] for each α ∈ [0, 1]. the theorem says that for any fuzzy number a, it can be uniquely determined by two monotonic functions u(α), u(α) on interval [0, 1]. 3 notions of the extended set-valued function and general inverse function let f be a monotonic and bounded function on [a, b] and x0 ∈ (a, b) be a discontinuous point in f. by considering f as a monotone increasing function, f can be a surjective function from [a, b] to (−∞, +∞) by the following formula: f(x0) = [f(x0−), f(x0+)], f(a) = (−∞, f(a+)], f(b) = [f(b−), +∞), here, we denote a new function f̂, which f̂ is a monotonic set-valued function extended by f and it also called extensional set-valued function of f. furthermore, we denote all the family of function f which are bounded and have the same monotonicity on [a, b], by d[a, b]. 3.1 discontinue monotonic function with set-valued extensional at discontinuity for discontinue monotonic increasing function f, x0 is a discontinuous point in the range of [−1, 1]. here, f(x0 − 0) = m1 and f(x0 + 0) = m2, by considering our default suppose that f is an increasing function, then m1 < m2 and f(x0) is an interval number between [m1, m2]. if functional values of all discontinuities redefined as closed interval with left-hand and righthand limited values, then this new function is called monotonic bounded set-valued function extensional from f that we denote it by f̂. obviously, inverse function f̂−1 of f̂ exist. 3.2 continuous non-strictly monotonic function suppose f is a non-strictly increasing function, then there exists at least one pair points {x1, x2} on [−1.1] such that value of f is equal to constant c = f(x1) = f(x2) on interval [x1, x2]. and suppose x1, x2 are two endpoints so that increasing function f is equal to constant, that is, when x < x1, f(x) < c and when x > x2, f(x) > c. here, we define inverse function f −1(x) which is continue close to 0 at discontinuity, i.e. when x2 ≤ 0, f̂ −1(c) = limx→c+0 f −1(x) = y+; 892 h.-d. wang, s.-c. guo, s.m. hosseini bamakan, y. shi when x1 ≥ 0, f̂ −1(c) = limx→c−0 f −1(x) = y−; when 0 ∈ [x1, x2], define f −1(c) as set of two points {y−, y+}, denote by f̂ −1(c) = {y−, y+}. it is quite straightforward to verify that, if f is an increasing and bounded function on [−1, 1] and f̂ is the extensional set-valued function of f, then the inverse function of f̂ can be equivalently defined as: f̂−1(x) =      sup{t : f̂(t) = x,−1 6 t < 0}, −∞ < x 6 f(0−) 0, f(0−) 6 x 6 f(0+) inf{t : f̂(t) = x, 0 < t 6 1}, f(0+) 6 x < +∞ . (1) example 2. to make the above concept more understandable, let consider f as a monotonic bounded function on [0, 2], f(x) = { x, 0 6 x 6 1 1 + x, 1 < x 6 2 . then its extensional set-valued function f̂(x) and its inverse function f̂−1(x) are defined as the following: f̂(x) =      x, 0 6 x < 1 [1, 2], x = 1 1 + x, 1 < x 6 2 , f̂−1(x) =      x, 0 6 x < 1 1, 1 6 x < 2 x − 1, 2 6 x 6 3 . the f(x) and f̂(x) can be illustrated by figure 1 y x21 0 1 2 y x21 0 1 2 ( )f x � ( )f x figure 1: set-valued function f̂ extended by f 4 fuzzy structured element and transformation in order to establish the relationship between the fuzzy real number space ε1 and the monotone function space on interval [−1, 1], we introduce a method, namely the fuzzy structured element, which was proposed by sicong guo in [14]. definition 3. let e be a fuzzy set on real line r and e(x) is membership function of e. then, e is called a fuzzy structured element, if e(x) satisfies the following properties: 1) e(0) = 1; 2) e(x) is monotonic increasing and right-continuous on [−1, 0), monotonic decreasing and leftcontinuous on (0, 1]; homeomorphism problems of fuzzy real number space and the space of bounded functions with same monotonicity on [-1,1] 893 3) for any x ∈ (−∞,−1) ∪ (1, +∞), e(x) = 0. further, e is called a normal fuzzy structured element if the fuzzy structured element e satisfies: (1) e(x) > 0 for all x ∈ (−1, 1); (2) e(x) is continuous, strictly monotonic increasing on [−1, 0) and also continuous, strictly monotonic decreasing on (0, 1]. according to definition 3, it is easy to know that the fuzzy structured element is a special fuzzy number on real line r, which can be used to express the concept of fuzzy zero 0̃. let e be a fuzzy number. e is called a triangular structured element if it has membership function µe(x), where µe(x) =      1 − x, x ∈ [0, 1] 1 + x, x ∈ [−1, 0] 0, otherwise (2) as it is shown in figure 2. obviously, e is a special fuzzy structured element. 1 11! o x ( )e x figure 2: triangular structured element e based on the fuzzy structured element, we can give the following two theorems: theorem 4 (local mapping theorem). suppose e is a fuzzy structured element on r with membership function e(x). f(x) is monotonically bounded on [−1, 1] and f̂(x) is extensional setvalued function of f(x). then f̂(e) is a bounded closed fuzzy number and membership function of f̂(e) is e(f̂−1(x)), where f̂−1(x) is the inverse function of f̂(x) (if f(x) is strictly increasing and continuous on [−1, 1], then f̂−1(x) is a ordinary inverse function of f(x)). proof: let a = f̂(e), a(y) defined as a membership function of f̂(e). suppose that f(x) is increasing and bounded on [−1, 1]. by extension principle, we have a(y) = ∨ y∈f̂(x) e(x), f̂(e) = ⋃ x∈r e(x) ∗ f̂(x), where e(x) ∗ f̂(x)(y) = { e(x), y ∈ f̂(x) 0, otherwise . from the former equation, the membership function of f̂(e) is f̂(e)(y). when y ∈ f̂(x), counterpart membership degree defined as e(x). denote α–cut of e by eα = [e − α , e + α ]. it follows from the concept of fuzzy structured element that e0 = [e − 0 , e + 0 ] ⊆ [−1, 1]. since f(x) is increasing bounded on interval [−1, 1] and f̂(x) is surjection on r, it follows that for α ∈ (0, 1], [f̂(e)]α = f̂(eα) = f̂[e − α , e + α ] = [f(e − α ), f(e + α )], 894 h.-d. wang, s.-c. guo, s.m. hosseini bamakan, y. shi for α = 0, [f̂(e)]0 = suppf̂(e) = ∪α∈(0,1][f̂(e)]α = ∪α∈(0,1][f̂(eα)] = [f(e − 0 +), f(e + 0 −)] (3) thus eα, α ∈ [0, 1] are bounded closed sets. for all α1, α2 ∈ (0, 1], if α1 ≤ α2, then eα1 ⊆ eα2, that is e − α2 ≤ e−α1, e + α1 ≤ e+α2 . since f(x) is monotone increasing, we have f(e−α1−) ≤ f(e − α2 −), f(e+α2+) ≤ f(e + α1 +) therefore, [f̂(e)]α1 ⊆ [f̂(e)]α2 . furthermore, it follows from eq.(3) that [f̂(e)]0 = ∪α∈(0,1][f̂(e)]α ⊇ [f̂(e)]α1, it means that f̂(e) is a convex set on real line r. since e is a fuzzy number and 1-cut set e1 of e is nonempty and [f̂(e)]1 = f̂(e1) also is nonempty, hence we can say that f̂(e) is a normal fuzzy number. from definition of bounded closed fuzzy number, we know f̂(e) ∈ ñc(r). since f̂(e)(y) = ∨ y∈f̂(x) e(x) = e(xy) as y ∈ f̂(x), where xy = f̂ −1(y). it follows that f̂(e)(y) = e(f̂−1(y)), or f̂(e)(x) = e(f̂−1(x)). if f(x) is monotonic decreasing on [−1, 1], the proof can be shown in a similar manner. ✷ theorem 5 (theorem of structured element expression of fuzzy number ). for a given regular fuzzy structured element e and any bounded fuzzy number a, there exists a monotonic bounded function f on [−1, 1] such that a = f(e) (strictly, exists a extended set-valued function f̂ such that a = f̂(e)). we called it fuzzy number a generated by the fuzzy structured element. proof: from fuzzy number expression theorem, fuzzy number u can be expressed by a family set {uα : uα = [u(α), u(α)], α ∈ [0, 1]}. therefore, we just need to prove that there exists a monotone bounded function f(x) on [−1, 1] such that f(e) = u, that is, for all α ∈ [0, 1], [f(e)]α = [u(α), u(α)]. let f(x) = { u(e(x)), x ∈ [−1, 0] u(e(x)), x ∈ (0, 1] . it follows from theorem 1 that f(x) is a monotone increasing bounded function on [−1, 1]. from the local mapping principle, f(e) is a bounded closed fuzzy number. it follows from the extension principle that [f(e)]α = f(eα). denote e(x) on [−1, 0] as le(x) and e(x) on [0, 1] as re(x). since e is strictly increasing on [−1, 0] and is also a bijection from [−1, 0] to [0, 1] , e is strictly decreasing on [0, 1] and is also a bijection from [0, 1] to [0, 1], so le(x), re(x) inverse and are denoted by l−1e (α), r −1 e (α), then eα = [e(α), e(α)] = [l −1 e (α), r −1 e (α)]. since u(α), u(α) are left-continuous on (−1, 0] and are right-continuous at α = 0, also e(x) is continuous, we know that u(e(x)) = u(le(x)) is left-continuous on (−1, 0] and is right-continuous at x = −1, u(e(x)) = u(re(x)) is right-continuous on [0, 1) and is left-continuous at x = 1. since homeomorphism problems of fuzzy real number space and the space of bounded functions with same monotonicity on [-1,1] 895 f(x) is increasing, it follows that for all α ∈ (0, 1], f(eα) = f[l −1 e (α), r −1 e (α)] = [f(l −1 e (α)−), f(r −1 e (α)+)] = [u(e(l−1e (α)+)), u(e(r −1 e (α)−))] = [u(e(l−1e (α))), u(e(r −1 e (α)))] = [u(α), u(α)] for α = 0, f(e0) = f[l −1 e (0), r −1 e (0)] = [f(−1+), f(1−)] = [u(e(−1+)), u(e(1−))] = [u(e(−1)), u(e(1))] = [u(0), u(0)] therefore, we conclude that [f(e)]α = [u(α), u(α)] for all α ∈ [0, 1]. that’s to say that f(e) = u. we complete the proof of this theorem. ✷ when no confusion can arise in the following discussions, we will use f(x) to denote the extended function f̂(x) and use f(e) to instead of f̂(e), respectively. theorem 6. let f be a monotonic bounded function and e be a fuzzy structured element on r and fuzzy number u = f(e). for all α ∈ [0, 1], eα = [e − α , e + α ]. then (1) if f(x) is increasing on [−1, 1], then α-cut of fuzzy number u is closed interval uα = { [f(e−α−), f(e + α +)], α ∈ (0, 1], [f(e−α +), f(e + α−)], α = 0, . (4) (2) if f(x) is monotonic decreasing function on [−1, 1], then α-cut of u is closed interval uα = { [f(e+α−), f(e − α +)], α ∈ (0, 1], [f(e+α +), f(e − α−)], α = 0, (5) proof: based on theorem 4 and function f satisfies the monotone condition of local mapping theorem, it follows that for any α ∈ (0, 1], we have [f(e)]α = f(eα). since f is monotone on closed interval eα = [e − α , e + α ] ⊆ [−1, 1], it follows that: if f is increasing, for α ∈ (0, 1], we have uα = f(eα) = f[e − α , e + α ] = [inf{y : y ∈ f[e−α , e + α ]}, sup{y : y ∈ f[e − α , e + α ]}] = [f(e−α−), f(e + α +)], and for α = 0, it holds that u0 = suppf(e) = f(e)0̇ = f(e0̇) = lim α→0 [f(e−α−), f(e + α +)] = [f(e − 0 +), f(e + 0 −)]. 896 h.-d. wang, s.-c. guo, s.m. hosseini bamakan, y. shi if f is decreasing, we have uα = f(eα) = f[e − α , e + α ] = [inf{y : y ∈ f[e−α , e + α ]}, sup{y : y ∈ f[e − α , e + α ]}] = [f(e+α +), f(e − α−)], ✷ from the local mapping theorem 4, we know that given a fuzzy structured element e, it will be transformed into an fuzzy number a = f(e) with any a monotonic function f on [−1, 1]. when f is not a monotonic function, the fuzzy set f(a) can not be guaranteed to be a fuzzy number. theorem 5 show us that for any bounded fuzzy number a, we always can find a monotonic bounded function f on [−1, 1] such that f(e) = a. therefore, the two theorems reveal to us that there exists a deep relationship between the family of bounded monotonic function on [−1, 1] and the fuzzy number space. 5 the same order standard monotonic bounded function classes b[−1, 1] let f be monotonic bounded function on [−1, 1]. if for any discontinuity x in [−1, 1], we have f(x) = 1 2 [f(x+) + f(x−)], (6) where f(x+)(f(x−)) is the right-limit(left-limit) of f(x) at the point x, then f(x) is called a standard monotonic bounded function on [−1, 1]. all same order standard monotonic bounded function on [−1, 1] is denoted by b[−1, 1]. it is obvious that a continuous monotonic bounded function on d[−1, 1] is also a standard monotonic bounded function. definition 7. suppose that f ∈ d[−1, 1], we define f̌(x) =        f(−1+), x = −1 [f(x−) + f(x+)]/2, x ∈ (−1, 1) f(1−), x = 1 (7) where f̌(x) is as a standardized form of f(x). obviously, f̌ ∈ b[−1, 1]. if f is a standard monotonic bounded function, then f̌ = f. in the following we introduced two distance formulas: dp(f, g) = [ ∫ 1 −1 |f(x) − g(x)|pdx ]1/p , for all f, g ∈ b[−1, 1], (8) dh(f, g) = sup x∈[−1,1] |f(x) − g(x)|, for all f, g ∈ b[−1, 1]. (9) where 1 ≤ p < +∞. theorem 8. let e be a normal fuzzy structured element, k is a bounded closed interval on r. denote bf(k) = {f : f ∈ b[−1, 1] and [f(−1), f(1)] ⊆ k}, (10) metric spaces (b[−1, 1], dh) and (bf(k), dp) both are complete. homeomorphism problems of fuzzy real number space and the space of bounded functions with same monotonicity on [-1,1] 897 proof: first, we prove completeness of space (b[−1, 1], dp). suppose each elements in (b[−1, 1], dp) are increasing, for decreasing situation has similarity conclusions. suppose given sequence {xn(t)}, where xn(t) ∈ (b[−1, 1], dp), n = 1, 2, · · · . let dp(xn, xm) → 0, (as n, m → ∞), that is, sequence {xn(t)} satisfies cauchy uniformly convergence conditions. suppose x0(t) is limit of sequence xn(t),i.e. lim n→∞ xn(t) = x0(t) for all t ∈ [−1, 1]. since all xn(t1) is monotonic increasing function, then xn(t1) ≤ xn(t2) for all t1, t2 ∈ [−1, 1]. so lim n→∞ xn(t1) ≤ lim n→∞ xn(t2), and x0(t1) ≤ x0(t2). thus x0(t) is monotonic increasing function. now we prove g(t) is standard function on [−1, 1]. suppose t0 is a discontinuous point of g(t). we might as well suppose that g(t) is’t standard, that is g(t0) 6= 1 2 [g(t0+) + g(t0−)]. letδ = ∣ ∣g(t0) − 1 2 [g(t0+) + g(t0−)] ∣ ∣ . furthermore, since fn(t), n = 1, 2, · · · , are standard, we have e(t0+) |fn(t0+) − g(t0+)| < ε, e(t0−) |fn(t0−) − g(t0−)| < ε, e(t0) |fn(t0) − g(t0)| < ε. take ε = ε0 < δ/2. when n ≥ n0(ε0), we have |g(t0) − 1 2 [g(t0+) + g(t0−)]| ≤ |fn(t0) − g(t0)| + 1 2 |fn(t0+) − g(t0+)| + 1 2 |fn(t0−) − g(t0−)| ≤ ε0 + ε0 = 2ε0 < δ. a contradiction. thus, g(t) is standard on [−1, 1]. 2) now we prove the metric space (bf(k), dp) is complete. suppose that fn is a cauchy sequence in bf(k), dp, then for any ε > 0, there exists a positive integer n such that for any m, n > n, we have dh(fm, fn) = [ ∫ 1 −1 |fm(t) − fn(t)| p dx ]1/p < ε. this indicates that {fn} is a cauchy sequence of lp[−1, 1]. we know that lp[−1, 1] is complete space, so {fn} is converse in lp[−1, 1]. suppose h is a limit of sequence {fn}. similar to the proof in 1) that h(x) is increasing and bounded in interval [−1, 1]. therefore, h(−1+) and h(1−) exist. let f(x) =      h(−1+), x = −1 [h(x+) + h(x−)] /2, x ∈ (−1, 1) h(1−), x = 1 . it is obvious that f(x) ∈ b[−1, 1] and also is a limit of cauchy sequence fn. f(x) and h(x) have different values which happened only on discontinuity, so we have dp(fn, f) = dp(fn, g). thus, f is a limit of cauchy sequence in (bf(k), dp), that is, {fn} converges in (bf(k), dp). hereby, the completeness of (bf(k), dp) have been proved. ✷ 898 h.-d. wang, s.-c. guo, s.m. hosseini bamakan, y. shi theorem 9. let e be a normal fuzzy structured element and k ∈ i(r) be a nonempty set, denoted by: bf(k) = {f : f ∈ b[−1, 1] and [f(−1), f(1)] ⊆ k}, then the metric space (bf(k), dp) is complete. example 10. the metric space (b[−1, 1], dp) is not complete. for example, fn ∈ b[−1, 1] is defined by fn+1(x) = { fn(x), x ∈ [−1, 1 − 1/n 2] n, x ∈ (1 − 1/n2, 1] , (n ≥ 1), where f1(x) = 0, x ∈ [−1, 1]. it is obvious that fn(x)(n ≥ 1) are bounded functions. suppose m ≤ n, we have dp(fm, fn) = [ ∫ 1 −1 |fm(t) − fn(t)| p dt ]1/p < [ 1 (n + 1)2 + 1 (n + 2)2 + · · · + 1 m2 ]1/p < [ 1 n − 1 m ]1/p < 1 n → 0(m, n → ∞) thus, {fn} is a cauchy sequence, their standard function sequence {f̌n(x)} is a cauchy sequence in b[−1, 1]. it is easy to understand that {f̌n(x)} do not convergence to any upper bounded function. theorem 11. metric space (b[−1, 1], dh) is not a separable space and metric space (b[−1, 1], dp) is a separable space. proof: 1) it is sufficient to construct an uncountable set bf of b[−1, 1] with the property that if f, g ∈ bf , then dh(f, g) = 1. for each t ∈ (−1, 1), define ft ∈ b[−1, 1] by ft(x) =      0 x ∈ [−1, t) 0.5 x = t 1 x ∈ (t, 1] . denote bf = {ft : t ∈ (−1, 1)}. consequently, if t1 6= t2, then dh(ft1, ft2) = 1. 2) since lp[−1, 1] is a separable space with respect to the metric dp and b[−1, 1] ⊂ lp[−1, 1], then b[−1, 1] is separable with respect to metric dp (for any separable metric space x, any nonempty subset of this space is also separable). ✷ in general, f ∈ b[−1, 1],−f ∈ b[−1, 1] unless f is a constant-valued function. because, if f isn’t constant-valued function, despite −f is also monotonic function, but it is not same order with f. hence, b[−1, 1] cannot form group with respect to operation of addition,just can form a semigroup. it should be noted that each element in b[−1, 1] is not a closed form with respect to ordinary subtraction operator. we can take an example, function obtain by two monotonic function subtracted may be non-monotonic. therefore, b[−1, 1] can’t form linear space with respect to addition and number multiply operation. theorem 12. b[−1, 1] is a convex cone with 0 as its vertex. the theorem is obvious, so the proof is omitted. homeomorphism problems of fuzzy real number space and the space of bounded functions with same monotonicity on [-1,1] 899 6 topological relationship between b[−1, 1] and ñc(r) 6.1 two types of fuzzy number metric spaces induced by the fuzzy structured element let e be a symmetrical regular fuzzy structured element on real line r and ñc(r) be the set of all bounded closed fuzzy numbers. for given function f ∈ b[−1, 1], there exists corresponding unique fuzzy number such that af = f(e). in other words, fuzzy structured element determines a mapping from b[−1, 1] to ñc(r). denote he : b[−1, 1] → ñc(r), f → he(f) = f(e) ∈ ñc(r). then he is called fuzzy functional induced by fuzzy structured element e. using metrics dp and dh on b[−1, 1], mapping he induces distances dnp(a, b) = dp(h −1 e (a), h −1 e (b)), (11) dnh(a, b) = dh(h −1 e (a), h −1 e (b)), (12) on ñc(r), where h −1 e (a), h −1 e (b) are preimage of mapping he at a and b, respectively. suppose a = f(e), b = f(e), where f, g ∈ b[−1, 1], then eq.(11) and eq.(12) can also rewrite as dp(f, g) = dp(he(f), he(g)), (13) dh(f, g) = dh(he(f), he(g)), (14) (ñc(r), dnp) and (ñc(r), dnh) are said to be distance space induced by (b[−1, 1], dp) and (b[−1, 1], dh), respectively. it is easy to understand that he is an isometric bijection of b[−1, 1] onto ñc(r). using isometric bijection he, we can translate metric of elements in fuzzy number space to metric between the same order standard monotonic bounded functions in range of [−1, 1]. then, what is the relationship between those metrics and the other metrics on fuzzy numbers? before discussing the relationship, a lemma need to be presented here: for u ∈ ñc(r), e is a normal fuzzy structured element. if g ∈ b[−1, 1] such that u = g(e), as defined in the following: gu(x) = { u(e(x)) −1 ≤ x ≤ 0 u(e(x)) 0 < x ≤ 1 . lemma 13. suppose that e is a normal fuzzy structured element, fuzzy number u ∈ ñc(r), uα = [u(α), u(α)](α ∈ [0, 1]). if u = f(e), f ∈ b[−1, 1], then f(x) = ǧu(x), x ∈ [−1, 1], where ǧu is the standard function of gu. proof: the proof has been provided in the following two steps: 900 h.-d. wang, s.-c. guo, s.m. hosseini bamakan, y. shi 1) first, we prove ǧu = u. according to the decomposition theorem, we only need to prove that [ǧu(e)]α = uα for any α ∈ (0, 1]. denote eα = [e − α , e + α ]. from the extension principle and g(x) is a increasing function, it follows that [ǧu(e)]α = ǧu(eα) = ǧu([e − α , e + α ]) = [ǧu(e − α−), ǧu(e + α +)]. since u(α), u(α) are continuous on (0, 1], we have u(e(x)) is left-continuous on (−1, 0] and u(e(x)) is right-continuous on [0, 1). therefore, [ǧu(e − α−), ǧu(e + α +)] = [gu(e − α−), gu(e + α +)] = [ u ( e(e−α ) ) , u ( e(e+α ) )] = [ u ( α ) , u ( α )] that is, [ǧu(e)]α = uα. thus, ǧu = u. 2) here we prove f is unique in b[−1, 1]. suppose f1, f2 ∈ b[−1, 1] such that f̂1(e) = f̂2(e) = u, then f̂ −1 1 = f̂ −1 2 . furthermore, we have f̂1 = f̂2, then f1 = f2. thus, f is unique in b[−1, 1]. therefore, f(x) = ǧu(x), x ∈ [−1, 1]. the proof is complete. ✷ the following theorem shows the relation between the induced fuzzy number metrics dnh, dnp and the previous metrics of fuzzy numbers. theorem 14. let e be regular structured element, u, v ∈ ñc(r), there are f, g ∈ b[−1, 1] such that u = f(e), v = g(e). denote uα = [u(α), u(α)], vα = [v(α), v(α)], then dnp(u, v) = [ ∫ 1 −1 |f(x) − g(x)|pdx ]1/p = [ ∫ 1 0 |u(α) − v(α)|p de(α) + |u(α) − v(α)|p de(α) ]1/p (15) dnh(u, v) = sup x∈[−1,1] |f(x) − g(x)| = sup x∈[−1,1] (|u(α) − v(α)|∨ |u(α) − v(α)|) (16) proof: 1) we have from lemma 13 that dnp(u, v) = dp(f, g) = dp(f̌u, ǧv), and ǧu(x) = gu(x) and ǧv(x) = gv(x) are bounded almost everywhere on [−1, 1] respectively, it follows that dp(f̌u, ǧv) = dp(fu, gv). therefore, dnp(u, v) = dp(fu, gv) = [ ∫ 1 −1 |fu(x) − gv(x)| p dx ]1/p = [ ∫ 0 −1 |u(e(x)) − v(e(x))| p dx + ∫ 1 0 |u(e(x)) − v(e(x))| p dx ]1/p denote e(x) = le(x) for x ∈ [−1, 0] and re(x) = e(x) for x ∈ [0, 1]. since e is a regular fuzzy structured element, we know that le is bijective from [−1, 0] to [0, 1], re is bijective from [0, 1] to [0, 1]. thus, we can say that l−1e , r −1 e exist and they are monotone bijections. it obvious that e = l−1e and e = r −1 e both are differentiable almost everywhere. therefore, we have dnp(u, v) = [ ∫ 1 0 |u(α) − v(α)| p dl−1e (α) + ∫ 0 1 |ū(α) − v̄(α)| p dr−1e (α) ]1/p = [ ∫ 1 0 |u(α) − v(α)| p de(α) − ∫ 1 0 |ū(α) − v̄(α)| p dē(α) ]1/p homeomorphism problems of fuzzy real number space and the space of bounded functions with same monotonicity on [-1,1] 901 2) from lemma 13, we obtain dnh(u, v) = dh(f, g) = dhf̌u, ǧv), furthermore, fu(x), gu(x) both are left-continuous on (−1, 0] and right-continuous on [0, 1), and are right-continuous at x = −1 and left-continuous at x = 1, we have |fu(x) − gv(x)| ≤ |fu(x−) − gv(x−)|∨ |fu(x+) − gv(x+)|, x ∈ (−1, 1) |fu(x) − gv(x)| = |fu(x) − gv(x)|, x ∈ {−1, 1} since f̌u(x), ǧu(x) are standard functions of fu(x), gv(x) for x ∈ (−1, 1), we have |f̌u(x−) − ǧv(x−)| = |fu(x−) − gv(x−)|; |f̌u(x+) − ǧv(x+)| = |fu(x+) − gv(x+)|. it follows that dh(f̌u, ǧv) = dh(fu, gv). moreover, we have dh(fu, gv) = sup x∈[−1,1] {|fu(x) − gv(x)|} = sup x∈[−1,0] {|u(e(x)) − v(e(x))|}∨ sup x∈[0,1] {|ū(e(x)) − v̄(e(x))|} let α = e(x) on [−1, 0] and [0, 1], respectively. since e is a regular fuzzy structured element, it follows that sup x∈[−1,0] {|u(e(x)) − v(e(x))|} = sup α∈[0,1] {|u(α) − v(α)|} , sup x∈[0,1] {|ū(e(x)) − v̄(e(x))|} = sup α∈[0,1] {|ū(α) − v̄(α)|} , completing the proof of the theorem. ✷ since integral variables e(α), ē(α) in the eq.(6.1) are two general functions, the definite integral is a riemann-stieltjes integral. when the e is a triangular structured element (2), we have de(α) = d(α − 1) = dα, dē(α) = d(−α + 1) = −dα. then the eq.(2) becomes the following form: dnp(u, v) = [ ∫ 1 0 |u(α) − v(α)|p dα −|u(α) − v(α)|p dα ]1/p . from eq.(16) in the theorem 14, we also note that the induced fuzzy number metric dnh(u, v) is the same as the hausdorff metric(diamond,1989) [6]. 6.2 homeomorphism between the fuzzy number space ñc(r) and b[−1, 1] proposition 15. suppose that (x, dx), (y, dy ) are two metric spaces. f is an isometric bijection from (x, dx) to (y, dy ). then f is continuous and inverse mapping f −1 of f exists and is also continuous. 902 h.-d. wang, s.-c. guo, s.m. hosseini bamakan, y. shi proof: since f is a bijection of (x, dx) into (y, dy ), there exists inverse mapping f −1 which is also one-to-one mapping. by definition of continuous mapping, for all x0 ∈ x and any positive number ε, there always exists a positive number δ such that dy (f(x), f(x0)) < ε as dx(x, x0) < δ. since dx(x, x0) = dy (f(x), f(x0)), given ε, it is sufficient by taking δ ≤ ε (for instance, take δ = ε/2).hence, f is continuous. similarly, we can also prove inverse mapping f−1 which is also continuous. ✷ given a bounded closed interval k, let the uniformly bounded fuzzy number set as ñc(k) = {u : u ∈ ñc(r) and supp u ⊆ k}, since there exists a bijection he of b[−1, 1] into ñc(r) and he and inverse function h −1 e are continuous, thus we have conclusions as follows: theorem 16. metric spaces (b[−1, 1], dh) and (ñc(r), dnh) are homeomorphic. metric spaces (bf(k), dp) and (ñc(k)), dnp) are homeomorphic. since space (ñc(r), dnh) and (b[−1, 1], dh) are homeomorphic, that is, both metric spaces are topologically equivalent. so elements in both of them have consistent properties on metrics. there are one-to-one relationship between fuzzy number sequence {un} of (ñc(r), dnh) and function sequence {fn}, fuzzy number sequence on (ñc(r), dnh) and function sequence on (b[−1, 1], dh) have completely same properties. similarly, fuzzy number sequence on (ñc(k), dnp) and function sequence on (bf(k), dh) have completely same properties. therefore, the properties of convergence sequence of general metric spaces are also founded to the convergence fuzzy number sequence. thus, they are trivial to the following corollaries. corollary 17. fuzzy number metric space (ñc(r), dnh) is complete and (ñc(r), dnp) is not complete. corollary 18. for any nonempty closed interval k on r, (ñc(k), dnp) is a complete metric space. corollary 19. for any fuzzy number sequence {un} of fuzzy number space (ñc(r), dn) only has a limit almost everywhere. that is, the limit of convergence sequence is unique.(where dn represents dnh or dnp). corollary 20. suppose that {un} is a fuzzy number sequence of (ñc(r), dnh), {fn} is a function sequence on (b[−1, 1], dh). for all n ≥ 1, un = fn(e). then fuzzy number sequence {un} converge if and only if function sequence {fn} converge. let lim n→∞ un = u0, lim n→∞ fn = f0, then u0 = f0(e). if using dnp , dp, bf(k) (defined as 10) instead of dnh, dh and b[−1, 1] in corollary 20, respectively. the corollary is still founded. 7 conclusion by using monotonic mapping of the fuzzy structured element, we have proved that the bounded fuzzy number space is homeomorphic to the space b[−1, 1] of monotonic bounded function with same monotonicity on [−1, 1]. therefore, the problem of the fuzzy number space can be transformed to one’s of space b[−1, 1], such as the convergence of sequence of fuzzy numbers, the convergence of the fuzzy series , continuous of fuzzy-valued function and so on. to some extent, our study provides a new way for the study of fuzzy analysis. homeomorphism problems of fuzzy real number space and the space of bounded functions with same monotonicity on [-1,1] 903 acknowledgements this work has been partially supported by grants from national natural science foundation of china (grant no.71331005, no.71110107026). bibliography [1] m.-s. yang, c.-h. ko (1996), on a class of fuzzy c-numbers clustering procedures for fuzzy data, fuzzy sets and systems,84(1):49–60. 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[14] s. guo(2002), method of structuring element in fuzzy analysis, journal of liaoning technical university, 21(5):670–673. int j comput commun, issn 1841-9836 8(6):791-799, december, 2013. association rule mining using path systems in directed graphs s. arumugam, s. sabeen s. arumugam* 1. national centre for advanced research in discrete mathematics (n-cardmath) kalasalingam university anand nagar, krishnankoil-626126, india. 2. school of electrical engineering and computer science the university of newcastle nsw 2308, australia. *corresponding author: s.arumugam.klu@gmail.com s. sabeen department of computer applications jaya engineering college chennai-600054, india. sabeens@rediffmail.com abstract: a transaction database (tdb) consists of a set i of items and a multiset d of nonempty subsets of i, whose elements are called transactions. there are several algorithms for solving the popular and computationally expensive task of association rule mining from a tdb. in this paper we propose a data structure which consists of a directed graph d (loops and multiple arcs are permitted) and a system of directed paths in d to represent a tdb. we give efficient algorithms for generating the data structure, for extracting frequent patterns and for association rule mining. we also propose several graph theoretic parameters which lead to a better understanding of the system. keywords: directed graphs, path system, in-degree, out-degree, association rule mining, frequent patterns, data mining. 1 introduction the task of association rule mining in a large database of transactions was proposed by agarwal et al. [1]. since then this problem has received a great deal of attention and association rule mining is one of the most popular pattern discovery methods in knowledge discovery from database (kdd). a broad variety of efficient algorithms such as apriori algorithm [3], fpgrowth [4], fp-tree [4], setm [6], dic [5] have been developed during the past few years. in this paper we propose a data structure consisting of a directed graph d and a multiset of directed paths in d to represent a database of transactions. we give an algorithm which generates the directed graph d and which also simultaneously computes several other measures such as indegree, out-degree, total number of arcs, length of a largest transaction, frequency of occurrence of various nodes and the number of occurrences of each arc in d. the second algorithm generates all the patterns of the transaction database using the above data structure. this algorithm can be modified to extract frequent patterns. the third algorithm deals with association rule mining. in this process we scan the database exactly once and the digraph is constructed dynamically. 2 directed graphs and path systems a directed graph d = (v,a) consists of a finite nonempty set v and a multiset a of ordered pairs of elements of v. the elements of v are called vertices and the elements of a are called arcs. an ordered pair (v,v) is called a loop at v. we allow both loops and multiple arcs (that copyright © 2006-2013 by ccc publications 792 s. arumugam, s. sabeen is, an arc (u,v) appearing more than once) in d. if a = (u,v) is an arc in d, then u is called the tail of a and v is called the head of a. for basic terminology in directed graphs we refer to the book by chartrand and lesniak [2]. a directed path in d is a sequence of distinct vertices p = (v1,v2, . . . ,vk) such that (vi,vi+1) is an arc in d for all i,1 ≤ i ≤ k − 1. a directed cycle in d is a sequence of vertices c = (v1,v2, . . . ,vk,v1) such that (vi,vi+1) is an arc in d for all i,1 ≤ i ≤ k − 1, (vk,v1) is an arc in d and the vertices v1,v2, . . . ,vk are distinct. a directed graph is called acyclic if it contains no directed cycle. for any vertex v, the in-degree id(v) is defined to be the number of arcs of the form (u,v) in d and the out-degree od(v) is defined to the numbers of arcs of the form (v,u) in d. we observe that a loop at v contributes 1 to both id(v) and od(v) and an arc (u,v) contributes 1 to od(u) and 1 to id(v). a vertex v such that no arc is incident with v or all the arcs incident at v are loops is called an isolated vertex of d. we denote by d1 the subdigraph of d obtained by removing all the loops in d. a path system or a path cover in a directed graph d is a multiset ψ of directed paths in d such that every arc of d is in exactly one path in ψ. we adopt the convention that all loops in d are members of ψ. if d contains multiple arcs, a directed path in d may occur more than once in ψ. 3 digraph model for transaction database let i = {1,2, . . . ,n} be a set of objects whose elements are called items. we impose on i the natural ordering of the set of positive integers. any nonempty subset of i is called a transaction. a transaction database d is a multiset of transactions in i. thus a subset x of i may occur more than once in d. we assume that for each i ∈ i, there is at least one transaction in t ∈ d with i ∈ t. in otherwords ∪ t∈d t = i. we say that a transaction t ∈ d supports an item set x ⊆ i if x ⊆ t. the support of x is defined by supp(x) = |t∈d:x⊆t||d| . thus supp(x) is the fraction of transactions supporting x. if we fix a threshold value s, then any subset x of i with supp(x) ≥ s is called a frequent pattern. an association rule is an implication of the form x ⇒ y where x,y ⊆ i and x ∩ y = ∅. the support of a rule x ⇒ y is defined to be supp(x ∪ y ). the confidence of the rule is defined as conf(x ⇒ y ) = supp(x∪y ) supp(x) . the support and confidence values are usually normalized so that these values occur between 0% and 100% instead of 0 to 1.0. normally we generate association rules for frequent patterns. for further terminology in tdb and association rule mining we refer to the book by han and kamber [7]. in this paper we propose a data structure which consists of a directed graph d and a system of directed paths in d for representing a transaction database. the vertex set of the directed graph is the item set i. we sort the elements of each transaction t ∈ d in the increasing order and represent t as a directed path in d. thus the direction on each edge is given by the orientation from low-to-high. any transaction t of the form {x} is represented as a loop at x. we illustrate this database with a small example. example 1. let i = {1,2,3,4,5}. let d = {t1,t2,t3,t4,t5,t6} where t1 = {1,2,5},t2 = {1,4},t3 = {1,2,4},t4 = {3,5},t5 = {2},t6 = {3} and t7 = {3,4,5}. the directed graph of d is given in figure 1. association rule mining using path systems in directed graphs 793 � ^ � � i o � w s ss ss : : 2 3 1 4 5 figure 1 since every transaction t in d gives a directed path in d, it follows that the multiset d of all transaction forms a path system in d. we observe that the directed graph d1 which is obtained by removing all the loops in the directed graph d representing a tdb is an acyclic directed graph. 4 directed graph of a transaction data base (dgtdb) in this section we propose an algorithm to construct the directed graph representing a tdb. the algorithm scans the data exactly once, dynamically constructs the digraph d and simultaneously computes several parameters such as frequency of occurrence of each node, number of loops at each node, number of occurrence of each arc uv, total number of arcs in d, the maximum length of a transaction, in-degree and out-degree of each node. the algorithm first creates all nodes of d, one node for each item, with support count 0. then each transaction is scanned and the directed path in d representing the transaction is constructed. if (i1, i2, . . . , ik) is a transaction, the arc (ij, ij+1) is represented as a linked list. the header of this list has two fields. one field is used to store the list of vertices (i1, i2, . . . , ij+1), which is called the label of the arc (i,j) and the other field is used to store the frequency of occurrence of the arc (i,j). for example in the digraph given in figure 3, the arc (3, 5) occurs with values 2, (1,3,5) and also with values 1 (2,3,5), indicating that it occurs twice as part of (1,3,5) and occurs once as a part of (2,3,5). in general if an arc (i,j) has labels k,(i1, i2, . . . , ir, i,j) it means that the arc (i,j) appears k items as part of (i1, i2, . . . , ir, i,j). dynamic memory allocation method is used for storing these values. the pseudo code for the construction of dgtdb is given in figure 3. this algorithm also generates further informations which are given in the output. algorithm: construction of dgtdb input: transaction database tdb, n: number of distinct items in tdb, m: number of transactions. output: dgtdb: directed graph of tdb. method: create a node for each item and initialize the values f(i), out-degree (i), in-degree (i), in-edge(i), out-edge (i) lc(i) to zero. initialize predecessor = ∅; and x = ∅; 1: for each transaction tj and for each each item i in tj, do 2: x = ∪ {i}; f(i) + +; 3: if (|x| > k) then 794 s. arumugam, s. sabeen 4: k = |x|; 5: end if 6: if (|tj| = 1) then 7: lc(i) + +; 8: if (lc(i) = 1) then 9: // if an edge from i to i does not exist so far 10: createedge(i, i); 11: set label (ec, i) = ⟨x⟩; 12: f(ec, i) = 1; 13: n(e) + +; 14: out-degree(i) + +; 15: in-degree(i) + +; 16: in-edge(i) + +; 17: out-edge(i) + +; 18: end if 19: end if 20: if (lc(i) > 1) then 21: f(ec, i) + +; 22: out-degree(i) + +; 23: in-degree(i) + +; 24: end if 25: if (predecessor ̸= ∅) and (x /∈ any label (ec, i)) then 26: createedge (predecessor,i); 27: n(e) + +; 28: set label(ec, i) = ⟨x⟩ 29: f(ec, i) = 1; 30: out-degree(predecessor) + +; 31: in-degree(i) + +; 32: in-edge(i) + +; 33: out-edge(predecessor) + +; 34: end if 35: if (x ∈ label (ej, i)) then 36: f(ej) + +; 37: out-degree(predecessor) + +; 38: in-degree(i) + +; 39: end if 40: predecessor = i; 41: end for 42: return dgtdb; figure 2. pseudo code for dgtdb construction we illustrate the algorithm dgtdb with a transaction database consisting of 12 items and 30 transactions, which is given in table 1. association rule mining using path systems in directed graphs 795 tid products in each tid products in each transaction transaction t001 1, 3, 7, 8 t016 2, 4, 6 , 8, 10, 12 t002 1, 2, 3, 8 t017 1, 3,5, 7 t003 2, 4, 5, 6 t018 9, 11 t004 2, 3, 5, 6 t019 10, 11,12 t005 1, 4, 5, 11 t020 6 , 7, 8,12 t006 3, 4, 5, 12 t021 3 t007 1, 2, 3, 4 t022 10, 11, 12 t008 4, 5, 11 t023 2, 4,6 , 8, 10,12 t009 1, 4, 5, 8 t024 1, 3, 5, 7, 9, 11 t010 3, 4, 10 t025 2, 4, 6, 8, 10, 12 t011 2, 3, 4 t026 3 t012 3 t027 2, 4, 6 , 8, 10, 12 t013 3, 6 , 9, 12 t028 3 t014 1, 10, 12 t029 12 t015 3, 6 ,9 ,12 t030 3 table 1. transaction database, tdb the dgtdb for the above transaction database is given in figure 3. figure 3. directed graph of tdb in table 1. the number given at each node represents the frequency of the corresponding item. 5 algorithm for extracting frequent patterns (xofp) in this section we present an algorithm for extracting the set l of all frequent patterns from dgtdb constructed in section 4. for each node i, the algorithm generates all frequent patterns l1 with i as the last item of l1. if in-edge (i) = 0, then {i} is the only pattern with i as the last item. otherwise for each edge ec with i as head we consider all subsets x of label (ec, i) such that |x| ≥ 2 and i ∈ x. then the frequency of x is the sum of the frequencies of all the edges ec with i as head for which x ⊆ label(ec, i). if this frequency is greater than or equal to the minimum support threshold, then x is added to the set of frequent patterns. the algorithm xofp is given in figure 4. algorithm: xofp, extraction of frequent patterns from dgtdb. input: s: minimum support threshold; dgtdb: directed graph of tdb. output: l: the set of all frequent patterns mined from the dgtdb. method: 796 s. arumugam, s. sabeen 1: l = ∅; m = 0; 2: // initialize set of frequent patterns 3: for each node i do 4: if (f(i) ≥ s) then 5: l = l ∪ {i} 6: return {i},f(i) 7: m + +; 8: end if 9: if (in-edge(i) > 0) then 10: f = 0; 11: for each label (ec, i), 1 ≤ c ≤ in-edge(i) do 12: f = f(ec, i); 13: w = label(ec, i); 14: if (w /∈ l) then 15: for each x ⊆ w, |x| ≥ 2 and i ∈ x do 16: for each label (ey, i) do 17: if (x ⊆ label ey(i) then 18: f = f + f(ey, i)); 19: end if 20: end for 21: end for 22: if (f ≥ s) then 23: l = l ∪ {x}; 24: return {x},f 25: m + +; 26: end if 27: end if 28: end for 29: end if 30: end for 31: return (l) figure 4. xofp, extraction of patterns from dgtdb example 2. by applying xofp to the dgtdb given in figure 3, the set of frequent patterns obtained using the items 1,2,3 and 4 along with the respective frequencies are given in table 2. node id frequent patterns frequency 1 {1} 8 2 {2} 9 {1,2} 2 3 {3} 16 {1,3} 5 {2,3} 4 {1,2,3} 2 4 {4} 12 {1,4} 3 {2,4} 7 {3,4} 4 {2,3,4} 2 table 2. extracted frequent patterns from the nodes 1, 2, 3 and 4 of dgtdb where s = 2. association rule mining using path systems in directed graphs 797 6 algorithm for generating association rules (gear) in this section we present an algorithm for generating association rules and strong association rules from the set of frequent patterns mined from the given tdb. an association rule which satisfies both minimum support threshold and minimum confidence threshold is called a strong association rule. for each frequent pattern x and for each nonempty proper subset y of x the algorithm computes the support and confidence of the association rule y ⇒ x − y. algorithm: gear, generating association rules from the frequent patterns input: l: set of all frequent patterns; cminimum confidence threshold of rule; sminimum support threshold. output: r: set of all strong association rules; r′: set of all association rules not in r. method: 1: r = ∅;r′ = ∅;n(r) = 0;n(r′) = 0; 2: for each x,y where x ∈ l with |x| > 1 and y ⊆ x,y ̸= ∅,y ̸= x do 3: generate rule (y ⇒ (x − y )) 4: compute conf = f(x) f(y ) 5: // f(x) is the frequency of pattern x. f(y ) is the frequency of pattern y. 6: if (conf ≥ c) then 7: r = r ∪ {y ⇒ (x − y ) : support=s, confidence = conf}; 8: n(r) + +; 9: else 10: r′ = r′ ∪ {y ⇒ (x − y ) : support=s, confidence = conf}; 11: n(r′) + +; 12: end if 13: end for 14: return (r) and return (r′); figure 5. gear, algorithm for generating association rules example 3. from table 2 we have x = {1,2,3} is a frequent pattern with frequency 2. the set of all association rules generated from this pattern by using gear along with the confidence and support for each rule is given in table 3. we have taken the minimum confidence threshold c and the minimum support threshold s as 50 and 6 respectively. s.no association rules confidence support of r or r′ of the rule the rule 1 {1} ⇒ {2,3} 25 6 r′ 2 {2} ⇒ {1,3} 22.2 6 r′ 3 {3} ⇒ {1,2} 12.5 6 r′ 4 {1,2} ⇒ {3} 100 6 r 5 {1,3} ⇒ {2} 66.67 6 r 6 {2,3} ⇒ {1} 50 6 r table 3. association rules mined from the frequent pattern {1,2,3}. for the tdb given in table 1 we have generated 188 frequent patterns. the number of frequent patterns generated with various support counts is given in table 4. the total number 798 s. arumugam, s. sabeen of associate rule generated is 1588. the number of association rule with various support counts is given in table 5. the breakup of the number of association rules generated with various levels of confidence is given in table 6. s.no support % no. of patterns s.no support % no. of patterns generated generated 1 3 188 10 30 3 2 6 113 11 33 3 3 9 84 12 36 3 4 12 72 13 39 1 5 15 25 14 42 1 6 18 14 15 45 1 7 21 12 16 48 1 8 24 9 17 51 0 9 27 5 18 54 0 table 4. number of patterns with various support counts s.no support % ≥ no. of association s.no support % ≥ no. of association rules generated rules generated 1 3 1588 10 30 0 2 6 746 11 33 0 3 9 484 12 36 0 4 12 292 13 39 0 5 15 38 14 42 0 6 18 16 15 45 0 7 21 10 16 48 0 8 24 2 17 51 0 9 27 0 18 54 0 table 5. number of association rules with various support counts s.no confidence % no. of association rules generated 1 < 50 465 2 50-59 162 3 60-69 81 4 70-79 36 5 80-89 95 6 ≥ 90 749 table 6. number of strong association rules with various confidence levels 7 conclusion in this paper we have proposed a new data structure consisting of a directed graph d and a path system in d for representing a tdb. we have presented algorithms for constructing d, for generating frequent patterns using d and for generating association rules. during the entire process the data is scanned exactly once. further it is possible to get several information about the tdb by using graph theoretic parameters. for example if in-degree(i) = 0 in the directed association rule mining using path systems in directed graphs 799 graph d1 obtained from d by removing all the loops, then the item i always appears as the first item in every transaction t with i ∈ t. similarly if out-degree(i) = 0, then the item i always appears as the last item in every transaction t with i ∈ t. our algorithm can be used to identify all such items. use of other graph theoretic parameters to extract new knowledge about the tdb and the comparison of the performance of this algorithm with other existing algorithms in the literature using real data set will be reported in a subsequent paper. acknowledgment the first author is thankful to the department of science and technology, new delhi for its support through the n-cardmath project no. sr/s4/ms:427/07. bibliography [1] r. agrawal, t. imielinski and a. swami, mining association rules between sets of items in large database, in proc. of the acm sigmod international conference on management of data (acm sigmod 93), washington, usa, 22(2)207-2016, may 1993. [2] g. chartrand and l. lesniak, graphs and digraphs, chapman and hall, crc, 4th edition, 2005. [3] r. agrawal and r. srikant, fast algorithms for mining association rules, in proc. of the 20th international conference on very large database (vldb’ 94), santiago, chile, 487499, june 1994. [4] j. han, j. pei and y. yin, mining frequent patterns without candidate generation, in proc. of the 2000 acm sigmod international conference on management of data, dallas, texas, usa, 29(2):1-12, may 2000. [5] s. brin, r. motwani, j.d. ullman and s. tsur, dynamic itemset counting and implications rules for masket basket data, in proc. of the acm sigmod international conference on management data, 26(2):255-264, 1997. [6] m. hontsma and a. swami, set oriented mining for association rules in relatrend database, the technical report rj9567, ibm almaden research centre, san jose, california, october 1993. [7] j. han and m. kamber, data minining, concepts and applications, elsevier inc., (2006). int j comput commun, issn 1841-9836 7(5):840-848, december, 2012. visual depth perception of 3d cad models in desktop and immersive virtual environments f. gîrbacia, a. beraru, d. talabă, g. mogan florin gîrbacia, andreea beraru doru talabă, gheorghe mogan transilvania university of brasov romania, 500036 brasov, eroilor, 29 e-mail: garbacia@unitbv.ro, aberaru@unitbv.ro talaba@unitbv.ro, mogan@unitbv.ro abstract: in this paper is presented an experimental study that aims to compare the depth perception of virtual prototypes in immersive virtual environments with the depth perception of cad models using 2d lcd display. first, a multipurpose solution of a large-scale interactive multi wall projected virtual environment named holo-cave is described and then the conducted experiments are presented. the experiments carried out highlight that perceived depth values estimated for virtual prototypes are significantly influenced by the 3d stereoscopic visualization. another interesting result of the study is that the estimated depth accuracy increases with the depth size that has to be perceived. the results of experimental study illustrate that the use of immersive stereoscopic visualization is useful during computer aided design related activities. keywords: virtual reality, computer aided design, immersive 3d systems. 1 introduction current computer aided design (cad) systems offer extremely rich modeling features and functions for the development of 3d virtual prototypes, which increase the productivity of the new products design. while the geometrical database is 3d since long ago, the user interaction within this software has not significantly changed. at present time cad tools use standard wimp (window, icon, menu, pointer) desktop-based graphical user interfaces (gui), and the interaction is made through keyboard, mouse and crt/lcd display which are solely 2d devices. in the last years, virtual reality (vr) technology became a dynamic field of research that has began to be used to a certain extent in industrial applications. an important goal of the current worldwide research efforts is to facilitate the implementation of vr in industrial product development processes and to asses the impact and its feasibility into the workplace and everyday life contexts in terms of cost-effectiveness, human-machine interaction and side-effects on the users, as well as their impact on the actual working environment, at both individual and organizational level. vr provides new perspectives for user interaction with cad tools. it enhances the immersion feeling and the depth perception of 3d objects, providing information with less perceptive ambiguities. this opportunity is important for a cad application where users must have a direct and thus better appreciation of object shapes and dimensions. many research activities are currently focused to integrate cad architecture inside vr-systems in order to enhance the immersion feeling and the user interaction interface [1], [7], [10]. in many applications, vr technologies are used only for visualization and analysis of previously created cad models [7], [10]. another emerging category of vr design applications are the vr-cad integrated systems, which allow the creation, modification and manipulation of 3d models directly in the vr environment [1]. despite of the intensive research activities, none of them produced a significant impact for the development of the next generation of cad systems. copyright c⃝ 2006-2012 by ccc publications visual depth perception of 3d cad models in desktop and immersive virtual environments 841 therefore, it is necessary to develop various experimental researches in order to evaluate the impact of virtual reality technologies in the design process, and analyze their advantages and shortcomings. the last generation of commercial cad systems still uses 2d crt/lcd displays for visualization in most of the cases. the disadvantage of these devices is the lack of depth perception of 3d models. generally, the commercial visualization systems offer better visualization conditions comparing to the reduced possibilities of 2d displays. on the other hand, the performance of various technical solutions is different, each of them being appropriate to be used in special defined situations. therefore, regarding cad related activities, the development of an evaluation study of the visual perception is necessary, that will highlight the impact of vr technologies on visual perception of 3d cad models during the design process of products. there are previous extensive researches on depth perception of 3d models using immersive or volumetric 3d environments [3], [4], [5], [6], [8], [9], but the novelty of this experimental study is the comparison of the depth perception of cad models using two distinct display modalities: monoscopic desktop system and 3d stereoscopic immersive environment. the conducted experimental study analyzes the depth perception appreciation (value dimension along z axis) of 3d cad models using immersive cave-like stereoscopic visualization systems compared to the usage of 2d traditional display. it is known that the real perception of dimensions in computer aided design related activities plays an important role in the decisionmaking process of a design solution. this experimental study is helpful in evaluating the benefits of immersive visualization system compared with monoscopic visualization using traditional 2d lcd/crt equipment. 2 description of immersive holo-cave system the conventional cad systems use for the visualization of the generated cad model a traditional crt/lcd 2d display. the disadvantage of this type of display for cad systems is the lack of depth cues. immersive virtual reality cave-like systems [2] are 3d stereoscopic displays that significantly improve the way users can visualize, navigate and interact in virtual environments. compared to other devices like head mounted display (hmd) or volumetric displays, the cave-like systems offer several advantages: improvement of the immersion awareness; obtaining high-definition stereoscopic images; large filed of view; collective visualization; collaboration between several users. a multipurpose architecture was developed at vr lab of transilvania university of brasov, that is able to provide both possibilities for the 3d visualization: four walls cave-like and holobench [12] functionality. therefore, the system is called "holo-cave" (figure 1). this solution allows making experiments related to the study of product engineering tasks that are performed by a human operator in the posture "seated" in the case of holobench functionality or, alternatively, in a "standing" posture when the system is configured as cave. the developed system enables the visualization of large scale, high-resolution 3d stereoscopic images with a large field-of-view. another advantage is the improvement of immersion awareness and the possibility to visualize the cad models in their natural dimensions. the physical structure of the holo-cave has the dimensions of 2.8 x 2.8 x 3 meters. the hardware architecture of the vr holo-cave is presented in figure 2. the used screens were screen-tech type, rigid back-projection with the dimension of 2.7 x 2 meters. the mechanical frame of the system was constructed from wood material because it was the simplest and easiest construction free of magnetic field that could be built. the screens were attached to the frames using tubes and glue. considering the high price and the variety of manufacturers, it was decided to use eight hitachi cpx1350 high-end projectors (two for each screen for displaying the passive 842 f. gîrbacia, a. beraru, d. talabă, g. mogan figure 1: the holo-cave immersive system stereoscopic images) capable of displaying images with a resolution of 1400 x 1050. the physical projection distance between the projector and the screen is of 5.2 of meters. mirrors have been used to cut down the required distance. in order to calculate the exact locations and dimensions of the mirrors and the projectors a cad model was used. a pc cluster was used as a computer system, composed of one server with a dual 2.4 ghz cpu and eight pc with 3 ghz cpu and dedicated video cards. figure 2: architecture of the holo-cave immersive system the holo-cave software is capable to load and display in a synchronized way a 3d scene on the multi-wall display environment, to display passive stereo 3d images and plug in different vr devices. it also provides methods by which the user can manipulate, add or remove objects in virtual environment. the holo-cave software architecture is designed as a distributed highly modular network based on the strict separation of its vr system management into two layers: a multi user server that performs the administration of the 3d model, the users’ interaction and a virtual environment server that coordinates local projections and navigation devices. the 3d visual depth perception of 3d cad models in desktop and immersive virtual environments 843 representation is full vrml2.0 (virtual reality modeling language [11]) thus compliant with all vrml sensors, events and sounds that can be used. because vrml is the data format, vrml events are used for communication. based on this approach, all vrml sensors, thus environmental sensors (time, proximity, visibility, and collision node), pointing device sensors (plane, cylinder, sphere, anchor, touch) and embedded javascript/ecma script can be used. 3 experiment description this study tries to answer the following research questions: 1. is immersive 3d visualization useful for the design engineers? 2. what is the performance of 2d display devices compared to 3d immersive visualization for perception of dimensions of 3d cad models? 3. which is the most intuitive and natural interface for the visualization of 3d cad models? we have devised and conducted two experiments to measure and record the estimated depth value of several cad models using two types of displays. the former is the traditional desktop workspace with 2d input (keyboard and mouse) and 2d output (computer screen) peripherals. the latter consists of a multimodal immersive interface of an integrated vr-cad system that uses the immersive 3d holo-cave system. the results of these experiments will allow answering the three research questions. 4 experiment procedure in order to evaluate the perception of depth in a cad model an experiment was conducted, involving eight subjects (three women and five men) with the average age of 28 years and with a healthy sense of vision. none of the subjects used vr immersive stereoscopic 3d visualization for the perception of 3d cad models before. instead, they had extensive experience in using cad software and good computer skills. in the conducted experiment six 3d cad models were used, each of them composed from a parallelepiped part with variable dimensions. in order to give the subjects the opportunity of appreciating depth, the models were placed on a virtual table with the size of 300 x 200 x 150 cm. the solid models were visualized using two types of devices: a universal 2d lcd display with the diagonal of 15.4" for desktop interface (figure 3a) and a holo-cave system for immersive perception (figure 3b). to display the 3d environment in the first case a solidworks cad system was used and in the second case dedicated software was used: bscontact stereo vrml visualization player integrated in the holo-cave. in the beginning, each subject was informed about the purpose of the experiment and specific instructions were given regarding the method of depth estimation. the subjects were asked to assess the depth of six objects using centimeter as measurement unit. in order to estimate the depth of cad models, the subjects were informed about the size of the virtual table where virtual objects were positioned. in the case of holo-cave immersive system, the distance between the viewpoint of the user and the projection screen was kept constant, 2 m (figure 3b). for each subject were displayed in a random order the cad models. each subject that participated to this experiment filled a questionnaire in which they were asked to provide information about age, experience of using vr equipment, experience in using cad systems and computer skills. half of the users estimated first the depth of virtual objects using the traditional desktop cad system, then, after a break of 20 minutes, they were asked to estimate the depth of virtual cad objects using the holo-cave stereoscopic visualization system. simultaneously, the other half of subjects estimated first the depth of 3d cad objects using stereoscopic system and then 844 f. gîrbacia, a. beraru, d. talabă, g. mogan using monoscopic desktop system. the value of the estimated depth was recorded in a text file that was used afterward for the assessment of the results. figure 3: the subject estimating depth using 2d display(a) the subject standing inside the cave-like visualization system(b) 5 results evaluation figure 4a presents the difference between the estimated values of the depth and the real dimensions of objects. after analyzing the data, the drawn conclusion is that for small values of depth (less than 35 cm) the subjects overestimated the depth of cad 3d models. another significant result is that for higher values of depth, the average of estimated depth was more accurate when using stereoscopic 3d immersive visualization. an interesting result obtained by using the immersive 3d visualization, was that for all models the subjects overestimated the depth value. figure 4b shows the accuracy of depth estimation that was obtained by using the value of relative error. the relative error was calculated using the following formula: er = (dp − dr)/dr (1) in which er is the value of the calculated relative error, dp the value of the estimated depth, dr the real value of virtual objects depth. figure 4: difference between the estimated values of the depth and real object dimensions(a); accuracy of depth estimation(b) visual depth perception of 3d cad models in desktop and immersive virtual environments 845 if the value of relative error is positive, the subject overestimated the depth of the cad models, and if the value of relative error is negative then the subject underestimated the depth of the cad models. the highest value of the relative error was obtained for the depth value of 35 mm and was due to the overestimation of depth. the conclusion drawn from this experiment is that the precision of depth estimation for stereoscopic viewing is lower for cad models with small depth values, but increases significantly when cad models depth value is higher. after conducting the experiments, each of the subjects was asked what viewing equipment he/she prefers. most subjects would use the immersive stereoscopic system because of the superior intuitive way of visual perception. however, few subjects considered as a shortcoming the need to wear glasses for passive stereoscopic visualization. we can conclude that the subjects estimated the depth of 3d cad models with greater accuracy using the holo-cave stereoscopic immersive visualization compared to monoscopic traditional desktop display. another interesting result drawn from the experiment was the increasing of estimated depth with the dimensions of the 3d cad models. in order to emphasize the results of the experiments described above, there was a new series of experiments conducted on the same experimental set-up already presented, namely the holo-cave system. the experiment was dedicated to assert the variation of stereopsis depth perception. the observers viewed the image by wearing polarizing glasses. the position of the observer was tracked by using a magnetic ascension flock of birds tracking system with 6 dof. the observer was standing inside the cave-like visualization system facing the screen (fig. 5). the viewing distance was set to three predefined values (1.5, 2.0, and 2.5 meters). the stereoscopic image consisted of two cubes, a red and a blue one having the sides of sizes 50 and 35 cm respectively. the arrangement of the cubes was such that the smaller one was set to a distance of 2 m (behind) with respect to the bigger one. the cube displayed to the left eye had a range of disparities added to it by shifting its horizontal position. the values of the disparities were 1, 6, and 11 cm. when the observer fuses left and right image he always perceives the cubes being in front of the screen. each observer was tested individually having the task to estimate the depth of the scene, namely which is the distance he perceives to the red cube and to the blue cube. the dimensions of the cubes and the relative position of one to the other were not made known to the participants. free eyes movement and as much time as required to estimate the depth of the scene were allowed. each observer for all values of the viewing distance and disparities repeated the task. figure 5: the observer standing inside the cave-like visualization system depth of a scene can be determined by using a simple arrangement as in figure 6, where e 846 f. gîrbacia, a. beraru, d. talabă, g. mogan is the interpupillary distance, d is the viewing distance, d is the disparity distance and l is the depth distance from the observer. the following equation expresses the depth l as function of variable d: l = ed/(e + d) (2) for d and e constants, the depth is affected only by the variation of disparity d. therefore, when disparity becomes smaller the object tends to be farther away from the user and vice versa. this is also illustrated in figure 7 where the predicted depth is represented as a function of the disparity distance for all the three cases of the viewing distance. for the calculation, an average value of the interpupillary distance of 6 cm has been considered. figure 6: determination of depth distance figure 7: calculated depth the results of these experiments are summarized in figures 8a and 8b. the graph in figure 10 shows a good correspondence between the average values of the perceived depth as a function of disparity with the calculated values of the depth for the same value of the viewing distance d = 2m. the open circles represent the average values of the perceived depth whereas the full circles are the calculated values of the depth. for d = 1.5m, one can found the same good correspondence between the theoretically estimated values of depth and the perceived values. for visual depth perception of 3d cad models in desktop and immersive virtual environments 847 figure 8: depth as a function of disparity(a);depth as a function of the viewing distance(b) d = 2.5m the agreement between theoretical and experimental values is not so good anymore, in this case the observers reporting difficulties in estimation of the depth. in figure 8b it is displayed the dependence of the perceived depth on the viewing distance for a constant value of the disparity d = 1 cm. for the sake of the comparison, the calculated depth is displayed too. the same trend it is observed for all the other values of the disparity. concerning the precision of the depth perception of the viewer, it is observed that the users presented more accurate stereopsis when the value of the disparity is small while increasing the disparity value leads to more imprecise stereopsis. 6 conclusions and future works realistic perception of the models depth in computer aided design plays an important role in decision making of design engineers. in this paper was presented an experiment aimed to estimate the depth of 3d cad models. from the performed experiment, we can emphasize that the perception of cad model depth is significantly influenced by the stereoscopic visualization. the subjects estimated depth of 3d models with greater accuracy using the immersive stereoscopic holo-cave system compared to traditional desktop display. the accuracy of depth perception is not considerably improved when the depth of cad models is small, but it increases significantly corresponding to a higher depth. as a general conclusion, we can declare that the alternative of replacing the 2d desktop systems with 3d vr visualization systems can be considered a viable alternative. 7 acknowledgments the research activities where supported by the sectoral operational programme human resources development (sop hrd), financed from the european social fund and by the romanian government under the contract number posdru/89/1.5/s/59323. bibliography [1] bourdot, p.; convard, t.; picon, f.; ammi, m.; touraine, d.; vezien, j.-m.(2010); vr-cad integration: multimodal immersive interaction and advanced haptic paradigms for implicit edition of cad models, comput. aided des, 42(5): 445-461. 848 f. gîrbacia, a. beraru, d. talabă, g. mogan [2] cruz-neira, c.(1995); virtual reality based on multiple projection screens: the cave and its applications to computational science and engineering, ph.d. dissertation, university of illinois at chicago, chicago, il, usa. umi order no. gax95-32383. [3] foley, l. m.(1991); stereoscopic distance perception, pictorial communication in virtual and real environments, stephen r. ellis (ed.). taylor & francis, inc., bristol, pa, usa, 558-566. [4] grossman, t.; balakrishnan r.(2006); an evaluation of depth perception on volumetric displays, proceedings of the working conference on advanced visual interfaces (avi ’06), acm, new york, ny, usa, 193-200. [5] hoskinson, r.; akai c.; fisher, b.; dill, j; po b.(2004); causes of depth perception errors in stereo displays, proceedings of the 1st symposium on applied perception in graphics and visualization, acm, new york, ny, usa, 164-164. [6] lang, m.; hornung, a.; wang, o.; poulakos, s.; smolic, a.; gross, m.(2010); nonlinear disparity mapping for stereoscopic 3d, acm transactions on graphics, 29(4): 1-10. [7] raposo, a.; soares, l; wagner, g.; corseuil, e.; gattass, m.; santos i.;(2009); environ: integrating vr and cad in engineering projects, ieee comput. graph. appl., 29(6): 91-95. [8] reichelt, s.; haussler, r.; futterer, g.; leister, n.(2010); depth cues in human visual perception and their realization in 3d displays, three dimensional imaging, visualization and display, bahram javidi and jung-young son(ed.), proc spie 7690, 134-144. [9] svarverud, e.; gilson, s.j.; glennerster, a. (2010); cue combination for 3d location judgements, journal of vision, 10(1): 1-13. [10] weidlich, d.; cser, l.; polzin, t.; cristiano, d.; zickner, h. (2009); virtual reality approaches for immersive design, int. j. on interactive design and manufacturing, 3: 103-108. [11] www.web3d.org/x3d/specifications/vrml/iso-iec-14772-vrml97/ [12] http://www.barco.com/fr/virtualreality/product/961 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 477-482 the avatar in the context of intelligent social semantic web a. braşoveanu, m. nagy, o. mateuţ-petrişor, r. urziceanu adrian braşoveanu lucian blaga univeristy of sibiu, romania e-mail: adrian.brasoveanu@gmail.com mariana nagy aurel vlaicu university of arad, romania e-mail: mnagy62@yahoo.com oana mateuţ-petrişor, ramona urziceanu agora university, oradea and r&d agora ltd. cercetare dezvoltare agora oradea, romania e-mail: ganearamona2002@yahoo.com, oana_mateut@yahoo.com abstract: when the first articles about the semantic web (sw) appeared, there were hardly any signals that the next revolution would be related to social networking. social networking services (sns) have grown after myspace, linkedin and facebook were launched in 20032004 and combine text, images, movies, music, animations and all sorts of lists to create personal presentation pages for users, means to connect to real or virtual friends from all over the world and recommendations based on trust. the rise of the social semantic web and the convergence of different media to create rich experiences is one of the most interesting paradigm shift in the last decades because the probable effect of this movement is the fact that in one day virtual meetings will become legitimate in all aspects of our daily lives (if they are not already). the most important question in this context (the one that we try to answer in this paper) is related to how people will try to shape and use their avatars. in order to understand this, we will study the links between multimodal ontologies, affective interfaces, social data portability and other recent findings. this paper starts with a survey of the current literature of the field, examines some social semantic web mechanisms that changed the way we think about sns and in the end discusses some methods of connecting emotions with the social semantic web which pose some interesting questions related to the use of avatars. between conclusions, one of the most interesting is the one that states that the use of affective interfaces adds value to the multimodal ontologies, while another suggests that the avatar must be a mediator between different technologies. keywords: avatar, semantic web (sw), social networking services (sns), affective interfaces, human-computer interaction (hci). 1 introduction the semantic web (sw) has evolved into a technology we use on daily basis, sometimes without even being aware of this, but dreams like those described by world wide web creator timothy berners-lee and his collaborators in the 2001 article [4] are still not common place. this is often the case when new technologies are presented to the general public while still in their infancy. the original article has been revised 5 years later [5] and berners-lee admits that we are still very far from his original vision of agents replacing humans for several tasks like buying tickets or making appointments to the doctor. almost a decade later, the web has changed and the information is no longer presented through classic text and pictures. social networking services (sns) like facebook, linkedin, myspace combine text, images, movies, music, animations and all sorts of lists to create personal presentation pages for users, different means to connect to real or virtual friends from all over the world and recommend things based on trust. these days even the way we make a search is going through a paradigm shift, because it is widely believed that the recommendations of a copyright c⃝ 2006-2010 by ccc publications 478 a. braşoveanu, m. nagy, o. mateuţ-petrişor, r. urziceanu circle of trusted friends are more valuable than those of a search engine. this is clearly a human response to the strategies of increasing the ranks of the pages in search engines. as it was stated by berners-lee, the original vision of the semantic web was to "enable machines to comprehend semantic documents and data, not human speech and writings" [4]. because of the chaotic developments of the last decade, machines will not only need to comprehend human speech and writings, but will also need to be capable to search through videos almost in the same way we do. such capabilities go well beyond the initial meaning of the semantic web, imply reasoning and emotions and since they are similar to the way we think, allow us to use the term "intelligent" before the terms "social semantic web". in this paper we will try to see how the developments in different areas like hci, sns or sw reshape old concepts on the fly. we have chosen the avatar in order to express some of our thoughts. 2 rationale and approach: why the avatar? to explain the rationale behind choosing the avatar for presenting some of our ideas, we have to examine a brief history of the links between the semantic web and social networking. we will also need to look at the trends from these areas and hci. constraints force us to think creatively, is the mantra of the agile development community (and of ruby on rails in particular), but also the phrase that defines the last decade in the it industry. when we look back to the year 2001, we do not see wtc but rather a fragile industry which tried to recover from the "dot com bubble". in this climate of uncertainty when the seminal article about semantic web by berners-lee and his research group [4] was published it sparked a lot of debates and started a series of innovations which did not stop to date. in those days it was almost impossible to predict that any company related to the it would be sold for half a billion or more, given the fact that many companies failed to bring cash to the investors. few years later, when myspace was sold for a considerable amount to newscorp and google acquired youtube, everybody understood that something changed in the world of it. somewhere between 2002 and 2005, without knowing it, the world has become social. historically social networks were viewed as the preserve of the rich and even as a sort of social divide between the rich and the poor, but with the rise of the social networking services they became useful for everyone [26] [27]. the user centric social network, where everything is a link from or to a friend, is necessary to everyone who tries to find a job or his old friends from college these days. social networking services added new layers not only to the social interaction but to the semantic web as well. some ideas like mika’s community-based ontology extraction from web pages [21] would have never emerged without the rising of sns. the field of ontology was connected with it for several decades, but the first definition dates back to gruber’s 1993 trial, an ontology being an "explicit specification of a conceptualization" [14]. this definition has been revised several times by different authors [15], but all these revisions are still based on [14]. between 2001 and 2007 a lot of ontologies have been created with the purpose of connecting different sns or as extensions to applications that were built around data extraction from web applications [18] [22] [24]. rich visualization techniques [22] like those generally used in research appeared on the web with the huge success of the adobe flash technology (1998-2005) and the introduction of ajax and tag clouds (2004-2005). the use of labels for annotating different pieces of information has created a new field for knowledge representation called folksonomies [26]. the use of folksonomies is often linked with the use of ontologies since both concepts aim to offer a way to retrieve information. the folksonomy will give us some ideas about the most valuable words for a certain group of users, while the ontology will also try to model the relations between different topics (as we can see in figure 1). the various methods of bridging folksonomies and ontologies to enable better knowledge representation are presented in [18]. the links between social networking and semantic web are discussed in berners-lee revision of [4] in the 2006th article [5] and in [6], while the history of the sns is examined in [8]. when it comes to sns we usually agree with the chronology proposed by boyd in [8], but when we deal with bridging sns and sw we propose a simple timeline: • 2001 2004: the first attempts to link sw and sns; • 2004 2007: the explosion of sns and the first important results in bridging sw and sns like katrina peoplefinder [21]; • 2008 present day: sns are now present in all aspects of our lives and research is focused on rather more advanced topics like affective interfaces. perhaps the most interesting conclusion from the early years of research regarding the links between sns and the sw (2001-2007) belongs to mika from his most cited article from [21]:"it seems that ontologies are us: inseparable from the context of the community in which they are created and used". the research in recent years the avatar in the context of intelligent social semantic web 479 figure 1: a) foaf ontology visualization with welkin; b) tag cloud for facebook. (2008 2010) has rendered mika’s thesis as true. some interesting problems related to social networking (similarity problems usually phrased like "find all users which share certain interests"; semantic concept clustering and many others) have been modeled on various datasets (from small predefined datasets selected for special topics to large online datasets), but the emergence of the semantic web tools on large scale, mostly in the last six years (in the same time with the sudden growth of interest in the area of sns), enables us to look at them from new perspectives. our approach to the subject consists in making connections between different recent findings in the fields of sns and sw. the most interesting trends in social software (2008-2009) we investigated are social data portability [7] [23], live social semantics [1], mass interpersonal persuasion also known as mip [15], and the growth of interest surrounding affective interfaces [10] [19]. social data portability is a classic problem in the field of social software since its inception. it might have started as something which was interesting only for people with large social networks in real life, but as soon as the companies discovered that sns had the potential of attracting clients with clear profiles, something that tv or written media does not guarantee, it became the norm to be present on all the major sns. the growing number of social networking sites in different niches is a problem for the users or companies interested in more than one field of knowledge who want to share their data. porting data between different sns or platforms like facebook and opensocial or defining standards for presenting the articles from magazines or blogs is the work of researchers like uldis bojars and john g. breslin from deri, ireland, authors of the sioc ontology [7]. live social semantics is a concept proposed by a team of researchers from uk, france and italy lead by harith alani [1]. the main idea is to "integrate data from the semantic web, online social networks, and a real-world contact sensing platform" [1]. their system which integrated the social semantic web mechanisms with the real world was tested at eswc 09. live social semantics is something that benefits when being linked with social data portability, because in the same room we will have people connected to more than one sns in most of the cases. mass interpersonal persuasion or mip is the generic name given to those techniques of persuading millions of people to join to a certain initiative [12]. before facebook gathering millions of people was not an easy task, therefore it is not a surprise that in this case the theory appeared only after several applications succeeded. its creator, bj fogg is the same man who introduced captology and coined the term persuasive technology [13]. the main idea behind mip is that we should design new systems with mip and the huge social graph in mind if we want to change something in this world. when it comes to mip, the technology, the topic or the creator’s initial intent does not matter much. what is important is that mip gives to a creator the possibility of reaching a wider than expected audience. fogg stated that: "we are at the start of a revolution in how individuals and cultures make decisions and take action" [13]. affective interfaces have been proposed since 1980s but recently there has been a renowned interest. beyond fogg’s phd work and his seminal book [13], today affective computing is sometimes related to the semantic web. between the most interesting concepts is the use of multimodal ontologies for describing emotions [10] [19]. the use of affective interfaces adds value to the multimodal ontologies because they improve communicability. other interesting topic in the field is the way in which emotional experiences from sns affect us in ordinary life as presented in [22] and [24]. while work in 3d graphics is not necessarily connected with the semantic web, when it comes to affective interfaces, the research related to the animation of the emotional facial expressions [25] can very easily be combined with the ontologies for describing emotions [10] [19]. the rise of the social semantic 480 a. braşoveanu, m. nagy, o. mateuţ-petrişor, r. urziceanu web and the convergence of different media to create rich experiences is one of the most interesting paradigm shifts in the last decades. one of the effects of this movement is the fact that in one day virtual meetings will become legitimate in all aspects of our daily lives (if they are not already). in such a context the role of the avatar is to replace a human being, as it was supposed to be, but the trends we have examined also suggest new approaches to the concept of the avatar and will also enable us to get closer to the visions described in [4]. 3 the avatar in the intelligent social semantic web for the purpose of this paper we use the meaning of the term avatar which represents an agent that is a double for a real person, a double that takes care of our social self, its virtual ego [20]. it can very well be even a pseudoavatar, not necessarily as it is viewed in berners-lee’s paper [4]. it can represent a person, an organization or a fictional character that needs to be in the social space and it can also have a graphical representation be it 2d or 3d. the first question that comes in mind is clear: what is the purpose of the avatar in the intelligent social semantic web at which all of the concepts presented in the previous section are aimed toward? are there any links between those concepts? figure 2: some of the functions of the avatar in the intelligent social semantic web: a) maintain communication with its human counterpart (or organization); b) act as replacement for the human in virtual meetings; c) use the web, ontologies and other sources to find out news about the master’s fields of interest; d) update the social network or the sites of its human counterpart. one link was already shown in the previous section by connecting social data portability and the live social semantics. another one is the idea that only emotional agents are believable [13], proposed by fogg. but why would we need an avatar that is an emotional being? even though the work presented in [11] [12] [16] [18] [25] is good for avatars that represent human beings, which are emotional in the real world, it is debatable if the same can be applied to organizations. advertising is often misleading because a firm only uses emotions to sell products to produce emotional reactions which can lead to the decision of choosing a certain brand not because it really has emotions. people that work for a certain organization do have emotions, but the organization itself does not. there should be clear differences between the avatars that represent organizations and the avatars that represent human beings, but these are not in our sights for the moment. an avatar that represents an organization uses sns to achieve mip. without mip and the targeted advertising social networking has no value for organizations. the main philosophical problem that arises when the avatars try to use mip is related to trust and it is expressed very well in [3]: "whereas trust is generic to human communication and implies evaluative aspects, social presence is aiming at mediated communication and is more descriptive by nature". we can build different associations between the research areas we have mentioned in the previous section, but it should be enough to analyze the parts of the expression from the title (intelligent social semantic web) to the avatar in the context of intelligent social semantic web 481 understand the purpose of the avatar in this medium. sns represent the social part of the equation, while the sw is represented through ontologies and their applications, so it should be clear that the avatar should be a part of the intelligence. in fact it is in the same time a part of the intelligent side of the equation, as well as of the social part (if we limit ourselves to the meaning of the avatar in the current sns like facebook, myspace, linkedin). for each major field of interest of a human person or organization several ontologies already exist or will be developed. the main problem that an avatar will face will be to wisely choose those ontologies or even perform ontology matching [11] [16] and use them to extract the meaningful data from the web (like in [28]) or create content that could help us (humans or organizations) to fulfill our objectives. it has to work for us when we sleep and alert us when something critical for our activity happens. the avatar will be credible only if it will be emotional, because it’s not easy to wake up a man at 3:00 am and tell him that in other part of the world something happened and it will change his life. in any other way it would be impossible to have any impact in an open dynamic environment [2]. we might also need to change the way we design socio-technical systems [9] in order to enable the avatars to automate different tasks. when we will have this, the vision from [4] will be closer to us than ever, if not reality [17]. 4 conclusions and future work predicting the future is not an easy task as we have seen. any technology needs several iterations before achieving its goal so we should not be surprised that it will take some time until the results of our work will be implemented and validated. the avatar of the future will have some difficult tasks to solve (like choosing the proper ontologies) if we are to benefit from its use. it will also need to have emotions if we want it to be believable because the use of affective interfaces improves communicability. the problem of differentiating between the avatars of the real persons and the avatars of the organizations will remain an open problem until the use of avatars will be the subject of standards committees or international law. the future work will consider implementing new mechanisms for linking the multimodal ontologies and affective interfaces with recent research in semantic web and hci in a 3 years interval (during the phd studies of the first author). the objectives are to be fulfilled involving european teams of researchers interested in this kind of projects. acknowledgements this work was partially supported by the strategic grant posdrui88i1.5isi60370 (2009) on "doctoral scholarships" of the ministry of labour, family and social protection, romania, cofinanced by the european social fund investing in people. bibliography [1] h. alani, m. szomszor, c. cattuto, w. van den broeck, g. correndo, a. barrat. live social semantics. in: 8th international semantic web conference (iswc), october 2009, us, 2009. 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[6] t. berners-lee, w. hall, j.a. hendler, k. o’hara, n. shadbolt, d.j. weitzner. a framework for web science. foundations and trends in web science, 1 (1), pages 1-130, 2006. 482 a. braşoveanu, m. nagy, o. mateuţ-petrişor, r. urziceanu [7] u. bojārs, a. passant, j.g. breslin, s. decker. social networks and data portability using semantic web technologies. the 2nd workshop on social aspects of the web (saw 2008) at the 11th international conference on business information systems (bis 2008), innsbruck, austria, may 2008. [8] d. m. boyd, n. b. ellison. social network sites: definition, history, and scholarship. in journal of computermediated communication, 13(1). http:lljcmc.indiana.edulvol13lissue1lboyd.ellison.html, 2007. [9] v. bryl, p. giorgini, and j. mylopoulos. designing socio-technical systems: from stakeholder goals to social networks. requirements engineering, 14(1):47-70, 2009. 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[16] harth, s. kinsella, s. decker. using naming authority to rank data and ontologies for web search. in proc. international semantic web conference, iswc’09, washington, usa, october 2009, 2009 [17] d. j. lewis. intelligent agents and the semantic web. developing an intelligent web. retrieved from http:llwww.ibm.comldeveloperworkslwebllibrarylwa-intelligentagel. 2008. accessed: december 2009. [18] f. limpens, f.gandon, and m. buffa. linking folksonomies and ontologies for supporting knowledge sharing: a state of the art. technical report, eu project, isicil, 2009. [19] j. m. lopez, r. gil, r. garcia, i. cearreta, n. garay. towards an ontology for describing emotions. wsks (1) 2008: 96-104. [20] p. messinger, x. ge, e. stroulia, k. lyons, k. smirnov, m. bone. on the relationship between my avatar and myself. journal of virtual worlds research 1 (2). http:lljournals.tdl.orgljvwrlarticlelviewl352l. (accessed december 2009) [21] p. mika. social networks and the semantic web, springer, 2007 [22] d. petrelli, s. mazumdar, a-s. dadzie, f. ciravegna. multivisualization and dynamic query for effective exploration of semantic data. in proc. international semantic web conference, iswc’09, washington, usa, october 2009, 2009. [23] l. răzmeriţa, m. jusevičius, rokas firantas. new generation of social networks based on semantic web technologies: the importance of social data portability. in: workshop on adaptation and personalization for web 2.0, umap’09, june 22-26, 2009. [24] c. sas, a. dix, j. hart, s. ronghui. emotional experience on facebook site. in: chi ’09: chi ’09 extended abstracts on human factors in computing systems, 4-9 april 2009, boston, ma. [25] r.j.s. sloan, m. cook, b. robinson. considerations for believable emotional facial expression animation. 2nd international conference on visualization, barcelona, spain, 2009. [26] t. vanderwall. folksonomy coinage and definition. 2007. retrieved from http:llvanderwal.netlfolksonomy.html. accessed: december 2009. [27] m. webb. 2004. on social software. http:llinterconnected.orglhomel2004l04l28l. [28] s.y. yang. developing an ontological faq system with faq processing and ranking techniques for ubiquitous services. proc. of the first ieee international conference on ubi-media computing, lanzhou, china, 2008, pp. 541-546. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 565-573 packet-layer quality assessment for networked video h. su, f. yang, j. song honglei su, fuzheng yang, jiarun song state key laboratory of integrated service networks xidian university, xi’an, shaanxi 710071, china e-mail:{hlsu,fzhyang}@mail.xidian.edu.cn, sjrxidian@hotmail.com abstract: to realize real-time and non-intrusive quality monitoring for networked video, a content-adaptive packet-layer model for quality assessment is proposed. considering the fact that the coding distortion of a video is dependent not only on the bit-rate but also on the motion characteristic of the video content, temporal complexity is evaluated and incorporated in quality assessment in the proposed model. since very limited information is available for a packet-layer model, an adaptive method for frame type detection is first applied. then the temporal complexity which reflects the motion characteristic of the video content is estimated using the ratio of the bit-rate for coding i frames and p frames. the estimated temporal complexity is incorporated in the proposed model, making it adaptive to different video content. experimental results show that the proposed model achieves an advanced performance in comparison with the itu-t g.1070 model. keywords: packet-layer model, networked video, video quality assessment, coding distortion, temporal complexity. 1 introduction recently, with the development of advantage multimedia processing technologies [1], multimedia services such as videophone, mobile conference and internet protocol television (iptv) have gained significant popularity in our daily life. however, the quality of these applications cannot be guaranteed in an ip network due to its best-effort delivery. it is therefore crucial to establish an objective model for video quality assessment targeting system design, qos (quality of service) planning and quality monitoring [2], [3]. objective video quality assessment can be categorized into media-layer models, bitstream-layer models, packet-layer models, parametric models and hybrid models from the viewpoint of the input information. to estimate the perceptual quality of service (qos) for users, the media-layer models use media signals [4], where characteristics of the video content and decoder strategies such as error concealment are usually taken into account. the bitstream-layer models, on the other hand, perform an analysis on the bitstream without resorting to a complete decoding [5], which can be used in situations where one does not have access to decoded video sequences. the packet-layer models exploit the packet headers to obtain information about the service quality [6], making them well suited for in-service non-intrusive monitoring. the parametric models employ parameters from the network or the application [7], [8]. parameters from the network may include the packet loss rate and the delay information, while those from the application usually cover the coding bit rate, frame rate, and so on. the hybrid models use a combination of information from the bitstream and the media data, and therefore have an advanced performance as well as combined features of the other models [9]. since the packet-layer model only utilizes information from packet headers, it is very efficient in quality monitoring due to its low complexity, especially suitable for quality monitoring at network internodes. the other advantage is that the packet-layer model does not need decryption and decoding, copyright c⃝ 2006-2012 by ccc publications 566 h. su, f. yang, j. song making it favorable when packet payloads are encrypted. in this paper, a packet-layer model is proposed for efficient quality assessment for networked video. utilizing the limited information which can be provided by packet headers, the frame type and temporal complexity are estimated based on the coding bit-rate. the temporal complexity is incorporated in the proposed model to make it content-adaptive. the remainder of this paper is organized as follows. the framework for proposed packet-layer video quality assessment is introduced in section 2. section 3 discusses the relationship between the coding distortion and the bit-rate. the proposed packet-layer model for video quality assessment is described in section 4. the experimental results are presented in section 5. this paper closes with conclusions given in section 6. 2 packet-layer model for video quality assessment packet-layer model for video quality assessment is especially suitable for application scenarios like in-service video quality monitoring and network service planning. it predicts the networked video quality from packet-header information, without resorting to any media-related payload information. since only the packet header is exploited, the packet-layer model is very useful at network inter-nodes due to its low complexity, where it can monitor thousands of video streams at the same time. the other advantage is that the packet-layer model does not need decryption and video decoding, making it favorable when packet payloads are encrypted. as an example, figure 1 shows the structure of a packet in rtp/udp/ip protocol stacks. in this case, the ip (internet protocol) header, the udp (user datagram protocol) header, and the rtp (real-time transport protocol) header can be accessed by a packet-layer model. the length of payload is easily obtained since the udp length field indicates the length of the udp header [10], the rtp header and the payload [11]. the marker bit in the rtp header indicates the end of a video frame, and all packets related to one video frame are with the same rtp timestamp. using this information, the packets can be assembled to frames. figure 1: the structure of a packet under rtp/udp/ip protocol stacks by analyzing packet headers, the information needed by the parametric model can be obtained and used to estimate the video quality [12]. apart from the parameters of bit-rate, frame rate, and packet loss rate which are usually employed in parametric models [13], [14], other information can also be employed in a packet-layer model, such as coding parameters (e.g., the frame type, and the bit-rate of each frame), information about the video content characteristics (e.g., the ratio of the bit-rate for coding i frames and p frames), and the detailed positions of lost packets in a video. the framework of the proposed packet-layer model is shown below in figure 2. firstly, after packet header analysis, the bit-rate for coding each frame can be obtained. then, it is employed to detect the frame type and calculate the ratio of the bit-rate for coding i frames and p frames. this ratio is employed in the proposed model to estimate the temporal complexity which reflects the motion characteristic of the video content. finally, the coding distortion of networked video is evaluated using the bit-rate information and the estimated temporal complexity. packet-layer quality assessment for networked video 567 packet header analysis bitstream frame type detection coding distortion evaluation temporal complexity estimation bit-rate video qualityframe type temporal complexity figure 2: framework of the proposed packet-layer model 0 0.1 0.2 0.3 0.4 0.5 0.6 1.5 2 2.5 3 3.5 4 4.5 bit−rate(bits/pixel) m o s container coastguard sign−irene football figure 3: relationship between the mos and the bit-rate for each sequence 0 0.1 0.2 0.3 0.4 0.5 0.6 0 5 10 15 20 25 30 35 bit−rate(bits/pixel) r at io o f bi t− ra te f or i a nd p f ra m es coastguard container football news sign−irene tempete figure 4: the ratio of the bit-rate for coding i frames and p frames 3 coding distortion and bit-rate the bit-rate is a key parameter for estimating the coding distortion. it has been well recognized that there is a relationship between the bit-rate and the average mean opinion score (mos) for different video sequences. therefore, several functions have been proposed to approximate the relationship to predict the average coding distortion using the bit-rate, such as the computational model proposed in the itu-t recommendation g.1070 [15] and its enhancement [16]. other model forms can also be found, such as the "m-n model" [17], as well as the exponential model [12], [18]. a detailed performance comparison of those models is provided in [19], where superior performance of the enhanced g.1070 model is observed. although the average mos can be predicted using models, the subjective quality of individual videos cannot be well formulated when provided only with the coding bit-rate. considering the video quality for each sequence, the relationship between the bit-rate and the mos is shown in figure 3. it is observed that there are obvious differences in the video quality at a same bit-rate for different sequences. therefore using the bit-rate only is not suitable for estimating the quality of a certain video service. it has now been widely acknowledged that content features must be taken into account for an accurate prediction of the perceived video quality [20]. building on this argument, video clips are classified into three classes according to the subjective movement content (high, medium and low movement content), and the model parameters are calculated for each class [18], [19]. however, it is not described how to obtain the information about movement content for each video clip based on objective parameters [18]. although the average sad (sum of absolute differences) can be employed in [19] to reflect the motion characteristic of video content, this value is not available for a packet-layer model. according to figure 3, it can be seen that the video clip which has a higher motion complexity such as "football" has a comparatively lower quality at the same bit-rate. correspondingly, "container" having a lower motion has a higher quality over the others under the same coding bit-rate. therefore, the temporal complexity estimated from the packet headers is expected to reflect the motion extent of video clips. how to measure this variable and then establish a packet-layer model based on video content is proposed in the next section. 568 h. su, f. yang, j. song 0 0.5 1 1.5 2 1 4 7 10 13 16 19 22 25 28 31 34 b it -r a te ( b it s/ p ix e l) frame number i p (a) football 0 0.5 1 1.5 2 1 4 7 10 13 16 19 22 25 28 31 34 b it -r a te ( b it s/ p ix e l) frame number i p (b) news figure 5: bit-rate histogram of different frame type at 0.202(bits/pixel) 4 packet-layer model based on video content figure 4 shows the values of the ratio of the bit-rate for coding i frames and p frames for different video clips. this ratio is always comparatively lower for the "football" sequence due to its high temporal complexity which results in more bit-rates in coding the p frames. on the other hand, the values of this ratio for the "container" sequence are consistently larger because of its lower temporal complexity. as the observation, low values of this ratio may correspond to a high temporal complexity. therefore, the temporal complexity can be roughly estimated using the ratio of the bit-rate for coding i frames and p frames. for a packet-layer model, however, the frame type information is not readily available. consequently, a frame type detection method based on the bit-rate distribution for each frame is introduced as follows. 4.1 frame type detection as a general principle, video coding exploits spatial redundancy using intra coding and temporal redundancy using inter coding, where the inter coding modes are usually more efficient in removing redundancy. accordingly, the bit-rate for coding an i frame is usually much higher than that for a p frame, as shown in figure 5. consequently, a threshold-based method is proposed to detect the frame type using the information about the coding bit-rate. however, figure 5 also shows that the bit-rate related to a certain frame type varies with the video content. because of the high motion complexity of "football", more bit-rates are distributed to p frames. as the result, at a same bit-rate, the values of bit-rate coding i frames of "football" are lower than the values of "news" which has a lower motion complexity under the same coding bit-rate. therefore, to make the detection more effective, the threshold for video clips of higher temporal complexity should be lower than the threshold for clips of lower temporal complexity. so fixed thresholds may fail in detecting for different video clips. dynamic thresholds which are adaptively adjusted [21] are applied in this paper. 4.2 temporal complexity estimation after frame type detection, the ratio of the bit-rate for coding i frames and p frames can be calculated using the information about the frame type and bit-rate of each frame. this ratio is defined as: r = ri rp , (1) where ri is the average bit-rate for coding i frames in a certain duration and rp is the average bit-rate for coding p frames in the same duration. however, it can be seen from figure 4 that there is obvious packet-layer quality assessment for networked video 569 0 0.2 0.4 0.6 0.8 1 0.6 0.8 1 1.2 1.4 1.6 bit−rate(bits/pixel) e st im at ed t em po ra l co m pl ex it y coastguard container football news sign−irene tempete figure 6: relationship between estimated temporal complexity and bit-rate 0.6 0.8 1 1.2 1.4 1.6 0 0.05 0.1 0.15 temporal complexity v 4 figure 7: relationship between v4 and temporal complexity differences in the values of r at different bit-rates for each sequence. due to the fact that the temporal complexity should be evaluated for the sequence with a given related value, r can not be directly applied as the measure of temporal complexity. consequently, a mathematical mapping is used to unify the values of r for all the range of bit-rates for each sequence. firstly, the natural logarithm function acts on the values of r to eliminate the enormous differences due to their distribution of different orders of magnitude. then, adaptively adjusted factors which are related to the average bit-rate are introduced to make the values generated by the first step for each sequence as near as possible. both of these two steps are implemented without influencing the relatively ranking of the curves of different sequences. however, to give the temporal complexity a practical significance (a high value corresponds to a higher motion extent sequence), the inverse function is employed at last. so the temporal complexity is formulated as: σt = a1 · ln(r) + b1 ln(ri ) − ln(rp) , (2) where r is the average bit-rate, and a1 and b1 are constants obtained by the experiments. as shown in figure 6, the values of estimated temporal complexity calculated by formula 2 for each sequence are roughly consistent for all bit-rate. and different sequences have different average values which can reflect the motion characteristic of the video content. so the estimated temporal complexity can be employed for the packet-layer model based on video content. 4.3 proposed model for quality assessment it is obvious that the average mos for different video sequences increases as the bit-rate increases and saturates at the maximum mos, which has been formulated in itu-t recommendation g.1070 as: vq = 1 + v3 · (1 − 1 1 + ( r v4 )v5 ), (3) where vq is the video quality, r is the bit-rate, and v3, v4, v5 are empirical parameters. this model can estimate the average video quality for different contents at each bit-rate. however, video quality strongly depends on the video content. best values for v3, v4 and v5 are calculated for the video sequences are presented in table 1. it can be found from figure 6 and table 1 that a higher value of v4 corresponds to a video sequence with higher temporal complexity (e.g., the "football" sequence). on the contrary, a video sequence whose temporal complexity is low usually has a low value of v4 (e.g., the "container" sequence). figure 7 shows the relationship between v4 and the temporal complexity, and a linear model approximates this relationship well as follows, v4 = a2 ·σt + b2, (4) 570 h. su, f. yang, j. song where a2, b2 are obtained by the experiments, and v4 is not a constant but a variable varied with σt . table 1: the values of v3, v4 and v5 for each video sequence video sequence v3 v4 v5 container 3.546 0.027 1.237 news 3.371 0.040 2.232 coastguard 3.464 0.063 1.733 tempete 3.456 0.072 1.687 sign-irene 3.546 0.091 1.884 football 3.481 0.134 2.230 in addition, table 1 shows that there is relative small difference between the values of v3 for different sequences, which is the maximum mos of the sequence. though there is a relative large difference between the values of v5 for different sequences, the difference influences the value of vq slightly. therefore, v3 and v5 are set as constants for all video clips in the proposed model and both of them are obtained by the experiments. consequently, submitting formula 4 into formula 3, the proposed model is established as: vq = 1 + v3 · (1 − 1 1 + ( ra2·σt+b2 ) v5 ). (5) apart from the bit-rate, the temporal complexity, which reflects the motion characteristic of the video content, is considered in this model to make the evaluation more accurate. 5 experimental results the video sequences chosen for experiments covered a wide range of scenes from high motion to low motion events. specifically, standard video sequences of "carphone", "coastguard", "container", "football", "foreman", "hall_monitor", "mother&daughter", "news", "paris", "sign_irene", "silent", "soccer" and "tempete" were used for performance evaluation. the sequences were all in the common intermediate format (cif) at 25 frames per second (fps), and encoded using x264 coder [22] with a gop (group of picture) structure of "ippp" sized of 30. for each sequence, the first 8 seconds were used for evaluation. the subjective scores were collected for comparison purposes. the guidelines specified by the video quality experts group (vqeg) in [23] were followed for the subjective tests. twenty-five non-expert viewers were involved in these tests, using the absolute category rating (acr) with a 5-point scale to obtain the moss of reconstructed sequences [24], [25]. the parameters were obtained according to the experiments, and their values are shown in table 2. these parameters were set fixed for all carried experiments. however, if applied to videos generated by the other codecs, they may need to be adjusted. table 2: parameter values v3 v5 a1 b1 a2 b2 3.477 1.834 -0.334 1.137 0.142 -0.065 pearson correlation coefficient (pcc) and the root-mean-squared error (rmse) were used to evaluate the performance of the proposed model. by comparison with the g.1070 model, the proposed model gets an increment about 0.024 in pcc and a decrement about 0.082 in rmse, as shown in table 3. the packet-layer quality assessment for networked video 571 0 1 2 3 4 5 0 1 2 3 4 5 scores by proposed model m o s (a) proposed model 0 1 2 3 4 5 0 1 2 3 4 5 scores by g.1070 model m o s carphone coastguard container football foreman hall−monitor mother&daughter news paris sign−irene silent soccer tempete (b) g.1070 model figure 8: scatter plot of moss vs objective scores scatter plots of the objective scores versus the subjective scores are shown in figure 8, from which the same conclusion can be drawn that using the proposed model the perceived coding distortion can be more accurately measured. table 3: performance comparison of proposed model and g.1070 model video quality assessment model pcc rmse proposed model 0.9582 0.3250 g.1070 model 0.9338 0.4067 6 conclusions a packet-layer model based on characteristics of the video content is proposed in this paper to measure the perceived coding distortion for networked video. without resorting to the payload information, the temporal complexity is estimated using the ratio of the bit-rate for coding i frames and p frames to reflect the motion characteristic of the video content. based on analysis of the parameters in the original g.1070 model, the measure of temporal complexity is integrated in the proposed model. extensive experimental results have demonstrated that the proposed model shows an advanced performance in comparison with the g.1070 model. further work may include the application of the proposed model to practice by considering both coding distortion and packet loss. acknowledgement this work was supported by the national science foundation of china (60902081, 60902052), the fundamental research funds for the central universities (72004885), the international science and technology cooperation program of china (2010dfb10570), and the 111 project (b08038). 572 h. su, f. yang, j. song bibliography [1] c. grava, a. gacs¨˘di, i. buciu, "a homogeneous algorithm for motion estimation and compensation by using cellular neural networks", international journal of computers communications & control, issn 1841-9836, vol. 5, no. 5, pp.719-726, 2010. 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[25] itu-r recommendation bt.500-11, "methodology for the subjective assessment of the quality of television pictures", 2002. international journal of computers communications & control issn 1841-9836, 10(3):390-402, june, 2015. a reference dataset for network traffic activity based intrusion detection system r. singh, h. kumar, r.k. singla raman singh*, harish kumar and r.k. singla university institute of engineering and technology panjab university, chandigarh, india raman.singh@ieee.org, harishk@pu.ac.in, rksingla@pu.ac.in *corresponding author: raman.singh@ieee.org abstract: the network traffic dataset is a crucial part of anomaly based intrusion detection systems (idss). these idss train themselves to learn normal and anomalous activities. properly labeled dataset is used for the training purpose. for the activities based idss, proper network traffic activity labeled dataset is the first requirement, however non-availability of such datasets is bottlenecked in the field of ids research. in this experiment, a synthetic dataset "panjab university intrusion dataset (pu-ids)" is created. the purpose of this study is to provide the researchers a reference dataset for the performance evaluation of network traffic activity based idss. university of new brunswick network security laboratory knowledge disscovery in databases (nsl-kdd) is a benchmark dataset for anomaly detection but it does not contain activity based labeling. so basic characteristics of this dataset are taken for the generation of the new synthetic dataset with various activities based labels. the dataset is first categorized as per protocol and service. thereafter, as per minimum & maximum values of attributes, activity profiles are synthetically generated. this paper also discusses various statistical characteristics of pu-ids. the total number of 198533 instances along with 273 of activity profiles are created. this dataset also contain different 98 protocol_service profiles. keywords: intrusion detection system, network traffic dataset, network traffic profiling, behavioral profiling, traffic activity profiling 1 introduction anomaly based intrusion detection system (ids) is an emerging research interest for network security researchers and professionals. in order to detect intrusions, idss learn the network traffic activities/ behaviors of malware rather than rely on virus definitions and security updates. well labeled network traffic dataset is used for this training. this dataset is crucial and only properly labeled dataset can serve this purpose. profiling based anomaly detection is a new and emerging field of network security research, but, no standard network traffic activities based labeled dataset is available for the training and performance testing. some available datasets are internet traces and un-labeled while others are only single level labeled (normal or anomalous). despite the wide availability of these datasets, non-availability of network traffic activities based datasets is hindrance in the intrusion detection research. in the anomaly based idss, normal behavior of networks may differ for different kind of organizations. for example, network game packets (online or intranet games) may be malicious for consultancy companies while they are absolutely normal for game development and testing companies. in the same manner within an organization, different departments may have different network traffic patterns. there is need of development of such anomaly detection techniques which can identify such network traffic activities/behaviors and create normal (and/or anomalous) profiles for intrusions detection. no such dataset exists for this purpose. the synthetic dataset should be generated so as to be used as a reference dataset for testing and performance evaluation. in this paper, university of new brunswick network security laboratory knowledge disscovery copyright © 2006-2015 by ccc publications a reference dataset for network traffic activity based intrusion detection system 391 in databases (nsl kdd) network traffic dataset is taken as a base and "panjab university intrusion dataset (pu-ids)" network traffic dataset is generated synthetically. nsl kdd is benchmark dataset but does not contains network traffic activities based labels. these labels are introduces in pu-ids. this paper is divided into five sections; section 1 introduces the topic, section 2 discusses the various available network traffic datasets. this section also discusses available network traffic profiling based threat detection techniques. section 3 describes the dataset used. this section also explain the methodology of synthetic dataset generation. in section 4, statistical analysis of synthetic dataset is carried out. finally, section 5 discusses the conclusions and future works. 2 network traffic dataset and profiling : related work 2.1 network traffic dataset in the last few years, some datasets are either generated or collected for research purpose. these datasets are used by researchers worldwide. the cyber systems and technology group (formerly the darpa intrusion detection evaluation group) of mit lincoln laboratory has collected network traffic dataset in 1998 and 1999. this dataset is collected by simulating various attacks like denial of services (dos), remote to local (r2l), user to remote (u2r) etc. on different platforms like windows, unix etc. this dataset become benchmark kdd dataset for research in ids [1]. later, some of the problems like redundant and duplicate records present in this dataset are removed and more efficient dataset become available for researcher which is known as nsl kdd dataset [2]. this dataset is widely used for performance analysis of various intrusion detection techniques. in this dataset each instances are labeled as normal or anomalous. in another version of this dataset, instances are classified as par various attacks like dos,u2r, l2r, probe and others. the drawback of this dataset is that, no further network traffic activities/behaviors based labels are available. the stanford network analysis project created 50 network traffic datasets by using various nodes of social networks, internet work, web graphs, etc. some of this collection of datasets is labeled while others are un-labeled. profiling based classification is missing in this dataset [3]. the dataset is created by the center for applied internet data analysis (caida) at various topologically and geographically separated locations. anonymized internet traces along with other worms related dataset is created by ensuring privacy preservation from the year 2008 to the year 2013. this is a collection of various datasets like anonymized internet traces and attack specific dataset. the internet traces dataset is un-labeled and so may not be useful directly for performance analysis of ids. it needs to be the first pre-processed for labeling [4]. cert synthetic sendmail system dataset is created by the university of new mexico using sun sparc stations. some system call instances are live while others are synthetic. normal instances and intrusion traces are provided separately. further level of labeling is not provided in this dataset [5]. real traffic of http, smtp, ssh, imap, pop3, and ftp are used to create a network traffic dataset in the university of new brunswick information security ? centre of excellence (unb iscx) lab. this dataset which contains seven days of normal and malicious instances is known as unb iscx dataset [6]. this dataset is available on request for university researchers. this dataset includes normal and malicious instances. profiling level labeling is not available for this dataset. internet traces of the sub-network of panjab university network (pucan) is captured from july 2011 to august 2011 in order to create a network traffic dataset. this dataset consists of un-labeled internet traces which needs further processing for labeling [7]. internet traffic traces like lan/wan ethernet traffic, tcp traffic, http traffic, http logs etc., are available in other dataset. the collection of datasets includes lan/wan traces, various 392 r. singh, h. kumar, r.k. singla logs and web client traces. a further level of classification and labeling is not present in these datasets [8]. from the literature studied it has been found that most of the datasets are not labeled on the basis of network traffic activities/behaviors. these datasets are labeled on single level profiling. therefore, there is a need for the development of a dataset which is based on the behavior profiling that consider network traffic activities and various user’s behaviors. 2.2 network traffic profiling researchers are using clustering and profiling in order to detect intrusions. network traffic activities based idss are the future of network security. in this sub-section various techniques which are used to detect intrusions are discussed. clustering is used in the network traffic dataset in order to cluster various instances into different activity profiles. these profiles are used to determine normal and malicious instances [9]. behavioral distance based anomaly detection for real time traffic analysis is also proposed. horizontal and vertical distance metrics are used for different attributes of network traffic datasets [10]. behavioral foot printing method along with content based signature is used to profile self-propagating worms. the worm’s dynamic infection sequence is learned to detect it [11]. clustering based anomaly detection technique is proposed for modeling user’s normal behavior in [12]. traffic causality graphs (tcgs) are used to analyze and visualize temporal and spatial causality of flows to profile network traffic. the advantage of this technique is that there is no need of payload inspection [13]. personal and application profiles are created by tagging network traffic. traffic from known source is profiled with role tag and application tag [14]. ip to ip communication graph and information is used to profile internet backbone traffic. this technique is known as profiling by association [15]. data mining and entropy based techniques are used to profile the behavior of internet end to end hosts [16]. researchers also proposed behavior based tracking to create long term profiles of user?s interest. this methodology is tested on dns traffic [17]. these techniques can be used on network traffic activity based datasets in order to detect various normal and anomalous network traffic activities/behaviors. in the state of the art idss, the normal behavior of user’s groups needs to be learned to effectively detect intrusions. 3 materials and methods unavailability of traffic activity/ behavior level network traffic dataset motivates this experiment of generation of synthetic dataset. the network traffic dataset used for generation of the synthetic dataset is discussed as below: 3.1 network traffic dataset used benchmarked nsl-kdd network traffic dataset is used as a base and its various characteristics of different attributes are used to synthetically generate instances. the various characteristics used are minimum and maximum values of different attributes. table 1 shows the list of attributes of this dataset. the same attributes are taken in pu-ids. these attributes are either continuous or categorical type. continuous attributes are those in which the value belongs to indefinite set while in categorical attribute values are assigned from a definite set. attribute number 2, 3, 4, 7, 12, 14, 15, 21, 22 and 42 are categorical attributes while all others are continuous attributes. a reference dataset for network traffic activity based intrusion detection system 393 table 1: list of attributes of nsl kdd and synthetically generated dataset sr. no. feature sr. no. feature sr. no. feature 1 duration 15 su attempted 29 same srv rate 2 protocol type 16 num root 30 diff srv rate 3 service 17 num file creations 31 srv diff host rate 4 flag 18 num shells 32 dst host count 5 source bytes 19 num access files 33 dst host srv count 6 destination bytes 20 num outbound cmds 34 dst host same srv rate 7 land 21 is host login 35 dst host diff srv rate 8 wrong fragment 22 is guest login 36 dst host same src port rate 9 urgent 23 count 37 dst host srv diff host rate 10 hot 24 srv count 38 dst host serror rate 11 number failed logins 25 serror rate 39 dst host srvserror rate 12 logged in 26 srvserror rate 40 dst host rerror rate 13 num compromised 27 rerror rate 41 dst host srvrerror rate 14 root shell 28 srvrerror rate 42 class label 3.2 synthetic dataset generation methodology figure 1 shows the methodology of synthetic dataset generation. it describes the procedures followed in the experiment to generate instances of synthetic dataset. this methodology is implemented using matlab [18] as a tool. the various steps are described as below: separation of dataset in the first step, nsl kdd training and testing dataset is clubbed into a single set. this dataset is then separated into two categories of normal and anomalous sub-datasets. protocol_service profile creation in the second step, protocol_service profiles are created by integrating protocol and service attributes for both categories of sub-datasets (normal and anomalous sub-datasets). all protocol_service profiles created in the first level of profiling are shown in table 2. thereafter, all instances are separated as per protocol_service profiles for both normal and anomalous sub-datasets. extraction of basic characteristics in the third step, all continuous attributes of all protocol_service profiles of both sub-datasets are considered and basic characteristics like minimum and maximum values are extracted. unique values for each categorical attributes are also calculated. calculation of cluster_gap and number_of_instances_per_cluster in the fourth step, for each protocol_service profile, number of clusters and the number of instances to be created are taken as input from the user. as per equation 1 cluster_gap and as per equation 2 number_of_instances_per_cluster is calculated for each protocol_service profile. 394 r. singh, h. kumar, r.k. singla figure 1: synthetic dataset generation methodology a reference dataset for network traffic activity based intrusion detection system 395 table 2: list of protocol_service profiles icmp_eco_i tcp_courier tcp_gopher tcp_link tcp_pop_2 tcp_systat icmp_ecr_i tcp_csnet_ns tcp_harvest tcp_login tcp_pop_3 tcp_telnet icmp_red_i tcp_ctf tcp_hostnames tcp_mtp tcp_printer tcp_time icmp_tim_i tcp_daytime tcp_http tcp_name tcp_private tcp_uucp icmp_urh_i tcp_discard tcp_http_2784 tcp_netbios_dgmtcp_remote_jobtcp_uucp_path icmp_urp_i tcp_domain tcp_http_443 tcp_netbios_ns tcp_rje tcp_vmnet tcp_irc tcp_echo tcp_http_8001 tcp_netbios_ssntcp_shell tcp_whois tcp_x11 tcp_efs tcp_imap4 tcp_netstat tcp_smtp udp_domain_u tcp_z39_50 tcp_exec tcp_iso_tsap tcp_nnsp tcp_sql_net udp_ntp_u tcp_aol tcp_finger tcp_klogin tcp_nntp tcp_ssh udp_other tcp_auth tcp_ftp tcp_kshell tcp_other tcp_sunrpc udp_private tcp_bgp tcp_ftp_data tcp_ldap tcp_pm_dump tcp_supdup udp_tftp_u in these equations xmax is the maximum and xmin is the minimum value extracted in step three for a particular protocol_service profile. kcluster is the number of clusters required and number_of_instances_protocol_service is the number of instances required for particular protocol_service profile which are taken from the user. cluster_gap = xmax − xmin kcluster (1) number_of_instances_per_cluster = round{ number_of_instances_protocol_service kcluster } (2) table 3 shows various parameters like kcluster and number_of_instances_protocol_service used to generate synthetic dataset. in table 3, cluster_n (kcluster) and insta_n (number_of_instances_protocol_service) corresponds to the number of clusters and instances to be created for normal dataset respectively. cluster_a and insta_a is the number of clusters and instances to be created for anomalous dataset respectively. 396 r. singh, h. kumar, r.k. singla table 3: synthetic dataset generation parameters protocol service profiles cluster _n insta _n cluster _a insta _a protocol service profiles cluster _n insta _n cluster _a insta _a icmp_eco_i 3 1215 6 4200 tcp_link 1 5 4 1536 icmp_ecr_i 2 456 4 3120 tcp_login 0 0 3 855 icmp_red_i 1 125 0 0 tcp_mtp 0 0 3 858 icmp_tim_i 1 220 1 42 tcp_name 0 0 4 780 icmp_urh_i 1 150 0 0 tcp_netbios_dgm0 0 4 2920 icmp_urp_i 4 1200 1 45 tcp_netbios_ns 0 0 3 960 tcp_irc 3 900 1 50 tcp_netbios_ssn 0 0 2 1048 tcp_x11 1 175 1 124 tcp_netstat 0 0 3 1305 tcp_z39_50 0 0 5 2800 tcp_nnsp 0 0 4 2600 tcp_aol 0 0 1 50 tcp_nntp 0 0 2 708 tcp_auth 2 450 4 952 tcp_other 2 460 5 1850 tcp_bgp 0 0 6 2100 tcp_pm_dump 0 0 1 135 tcp_courier 0 0 7 2100 tcp_pop_2 0 0 1 235 tcp_csnet_ns 0 0 5 3270 tcp_pop_3 1 350 1 115 tcp_ctf 0 0 4 800 tcp_printer 0 0 1 354 tcp_daytime 0 0 6 1800 tcp_private 1 22 6 28000 tcp_discard 0 0 4 1680 tcp_remote_job 1 5 1 154 tcp_domain 1 255 3 405 tcp_rje 0 0 1 204 tcp_echo 0 0 4 1040 tcp_shell 1 16 1 165 tcp_efs 0 0 5 1850 tcp_smtp 5 9600 2 386 tcp_exec 0 0 3 1395 tcp_sql_net 0 0 2 840 tcp_finger 3 375 5 1750 tcp_ssh 1 20 2 400 tcp_ftp 4 1616 4 1800 tcp_sunrpc 0 0 2 856 tcp_ftp_data 6 3006 5 2000 tcp_supdup 0 0 3 1404 tcp_gopher 0 0 3 570 tcp_systat 0 0 2 1206 tcp_harvest 0 0 1 26 tcp_telnet 5 5750 3 1836 tcp_hostnames 0 0 2 500 tcp_time 1 170 2 680 tcp_http 6 50010 4 2800 tcp_uucp 0 0 3 2085 tcp_http_2784 0 0 1 12 tcp_uucp_path 0 0 3 2346 tcp_http_443 0 0 3 999 tcp_vmnet 0 0 3 1875 tcp_http_8001 0 0 1 14 tcp_whois 0 0 3 2256 tcp_imap4 1 160 3 888 udp_domain_u 5 11825 1 14 tcp_iso_tsap 0 0 4 820 udp_ntp_u 1 320 0 0 tcp_klogin 0 0 3 924 udp_other 4 2624 2 304 tcp_kshell 0 0 2 650 udp_private 3 1212 4 2600 tcp_ldap 0 0 2 360 udp_tftp_u 1 35 0 0 synthetic instances generation in the fifth step, for each continuous attributes of various protocol_service profiles, kcluster numbers of traffic activity/ behavior profiles are created by using xmin, xmax and kcluster. for categorical attributes of various protocol_service profiles kcluster number of traffic activity/ behaviors are created by considering cate_unique_values. for each traffic activity profile, number_of_instances_per_cluster numbers of instances are a reference dataset for network traffic activity based intrusion detection system 397 generated by using same characteristics used for generation of various traffic activity/ behavior profiles. values of continuous attributes for each cluster is calculated as given in equation (3) value_conti_attribute = max_value_previous_cluster + cluster_gap (3) subject to: max_value_previous_cluster = xmin, for first cluster and xmin ≥ value_conti_attribute ≤ xmax for other clusters where value_conti_attribute represents value of a particular continuous attribute and max_value_previous_cluster represents the maximum value of previous cluster of that particular attribute. for categorical attributes cate_unique_values are extracted by taking each unique value for each categorical attribute separately as shown in equation (4). value_cate_attribute = div{cate_unique_values(kcluster)} (4) function div means values of particular categorical attributes are obtained by dividing cate_unique_value into cluster groups and then for each group/ cluster number_of_instances_per_cluster instances are created. 3.3 illustrative example let?s say for tcp_http protocol_service profile, 3 clusters (kcluster) and 1000 instances (number_of_instances_protocol_service) are required to be generated. so the values of xmin and xmax for all continuous attributes are extracted. also assume the values of xmin and xmax for attribute source_bytes are 30 and 255. so cluster_gap and number_of_instances_per_cluster are calculated as per equation (1) and (2) respectively as shown in example 1. example 1. cluster_gap = (255-30) /3 = 75 number_of_instances_per_cluster = round (1000/3) = 333 range of first cluster value_conti_attribute1 = (from 30 up to 30+75=105) range of second cluster value_conti_attribute2 = (from 105 up to 105+75=180) range of third cluster value_conti_attribute3 = (from 180 up to xmax = 255) for each continuous attribute , 333 instances per cluster are created and the same procedure is followed for all continuous attributes (last cluster may have one additional instance to sum up total instances to 1000). example 2 explain the assignment of values of different clusters for categorical attributes (like flag). example 2. cate_unique_values = s0, s1 ,s2, s3, s4, s5, s6, s7, s8 ( assumed value) for first cluster, value_cate_attribute1 = s0, s1, s2 for second cluster, value_cate_attribute2= s3, s4, s5 for third cluster, value_cate_attribute3= s6, s7, s8 now for each categorical attribute, 333 instances per cluster are created by taking value from value_cate_attribute for all categorical attributes (last cluster may have one additional instance to sum up total instances to 1000). these procedures are followed for each continuous 398 r. singh, h. kumar, r.k. singla and categorical attributes of all protocol_service profiles and for both categories of sub-datasets. both categories of normal and anomalous synthetic sub-datasets are integrated to obtain one synthetically generated dataset. this dataset has two levels of profile labeling. one level is protocol_service while other is traffic activity/ behavior level. 4 results and analysis the nsl kdd (training + testing) dataset taken for experimentation has 148517 instances and 72 protocol_service profiles. the same numbers of protocol protocol_service are created in synthetic dataset. table 4 shows the comparative statistical analysis of nsl kdd and pu-ids dataset. table 4: basic statistics of nsl kdd and pu-ids dataset statistics nsl kdd dataset pu-ids normal anomaly normal anomaly numbers of instances 77054 71463 92727 105806 numbers of protocol_service profiles 30 68 30 68 numbers of network traffic activity profiles not present not present 72 201 in nsl kdd dataset, 30 normal and 68 anomalous protocol_service profiles are present. some of these profiles are common in both normal and anomalous sub-datasets. total 198533 instances are generated synthetically out of which 92727 are normal while other are anomalous. in pu-ids, the generated instances are more than the base dataset. these excess instances provide an opportunity of better and effective training of ids as dimensions of normal and anomalous behavior is increased. these instances also helps in performance testing of ids in broad spectrum. equal numbers of protocol_service profiles as of base dataset are created in pu-ids dataset. generated dataset has equal first level of profiles. in nsl kdd dataset, traffic activity labeling are not present and hence this second level of labeling is synthetically generated. different 273 traffic activities in datasets are synthetically generated as shown in table 4. 72 normal and 201 anomalous behavior profiles are generated. this second level of labeling will help the researcher to train and test anomaly detection techniques, where nsl kdd provides limited opportunity. figure 2 shows the number of normal instances generated for each protocol_service profile. 53.93% of the generated normal instances are of tcp_http protocol_service. this profile is the biggest contributor in normal instances. the different significant normal protocol_service profiles are udp_domain_u (12.75%), tcp_smtp (10.35%), tcp_telnet (6.2%), tcp_ftp_data (3.24%), udp_other (2.83%), tcp_ftp (1.74%), icmp_eco_i (1.31%), udp_private (1.31%) and icmp_urp_i (1.29%). other protocol_service (tcp_irc, tcp_other, icmp_ecr_i, tcp_auth, tcp_finger, tcp_pop_3, udp_ntp_u, tcp_domain, icmp_tim_i, tcp_x11, tcp_time, tcp_imap4, icmp_urh_i, icmp_red_i, udp_tftp_u, tcp_private, tcp_ssh, tcp_shell, tcp_link, and tcp_remote_job) collectively contribute 5.04% of total normal instances. only 0.0053% of tcp_remote_job instances are generated which is lowest in normal instances. a reference dataset for network traffic activity based intrusion detection system 399 figure 2: distribution of normal instances as per protocol_service profiles figure 3: distribution of anomalous instances as per protocol_service profiles 400 r. singh, h. kumar, r.k. singla figure 3 shows the number of anomalous instances generated for each protocol_service profile. in the anomalous instances the tcp_private profile has largest share, which is 26.46%. the various significant anomalous protocol_service profiles are icmp_eco_i (3.97%), tcp_csnet_ns (3.09%), icmp_ecr_i (2.95%) and tcp_netbios_dgm (2.76%). other anomalous protocol_service profiles (like tcp_http_443, tcp_netbios_ns, tcp_auth, tcp_klogin, tcp_imap4, tcp_mtp, tcp_sunrpc, tcp_login, tcp_sql_net, tcp_iso_tsap, tcp_ctf, tcp_name, tcp_nntp, tcp_time, tcp_kshell, tcp_gopher, tcp_hostnames, tcp_domain, tcp_ssh, tcp_smtp, tcp_ldap, tcp_printer, udp_other, tcp_pop_2, tcp_rje, tcp_shell, tcp_remote_job, tcp_pm_dump, tcp_x11, tcp_pop_3, tcp_irc, tcp_aol, icmp_urp_i, icmp_tim_i, tcp_harvest, tcp_http_8001, udp_domain_u, and tcp_http_2784 ) contribute 16.29% of total anomalous instances. least number of anomalous instances of tcp_http_2784 protocol_service (only 0.011%) are generated. figure 4 shows the comparative study of various protocols like tcp, udp and icmp, present in nsl-kdd and synthetically generated pu-ids dataset. in the base dataset 121569 of tcp, 17614 figure 4: numbers of instances of tcp, udp and icmp protocols of udp and 9334 of icmp instances are present. in pu-ids, the numbers of synthetically generated instances of tcp, udp and icmp are 168826, 18934 and 10773 respectively. 5 conclusions and future works the network traffic dataset is an important part of intrusion detection system as it learns normal and anomalous behavior of computer networks. perfect training of idss depends on the proper labeled dataset. the well-known benchmark nsl kdd dataset is widely used in ids research, but this dataset is very old and only has one level of labeling. the other available unlabeled datasets has little importance in performance evaluation of malware detection techniques. traffic activity labeled datasets are not present for research purpose. activity/ behavioral based network traffic datasets are needed for state of the art idss. a new synthetic network traffic dataset (pu-ids) with two levels of labeling is generated by taking basic characteristics of the nsl kdd dataset. ”protocol service” level and ”traffic activity/ behavior” level labeling is created so as this dataset can be used for performance evaluation. this will overcome limited utility of nsl kdd network traffic dataset. pu-ids consists of total numbers of 198533 instances along a reference dataset for network traffic activity based intrusion detection system 401 with 72 protocol profiles. it also consists 273 synthetically created traffic activity/ behavioral profiles which is the novelty of this dataset. in the future, an organizational network with different departments and various user groups should be set up to create the simulated dataset. users within one organization should exhibit similar network activities/ behaviors as they are working on the same set of software. behavior based idss should identify these similar network activities and create normal or anomalous model. acknowledgment this research work is supported by world bank funded technical education quality improvement program ? phase ii (teqip ii) in the form of research assistantship. this research is revised and extended version of paper presented at international conference on computers, communications and control (icccc 2014) held at oradea, romania on may 7-9, 2014. bibliography [1] http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html [2] http://nsl.cs.unb.ca/nsl-kdd 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(2011); packet tagging system for enhanced traffic profiling. ieee 5th int. conf. on internet multimedia systems architecture and application (imsaa) : 1-6. 402 r. singh, h. kumar, r.k. singla [15] iliofotou, m.; gallagher, b.; eliassi-rad, t.; xie, g.; faloutsos, m.(2010); profiling-byassociation: a resilient traffic profiling solution for the internet backbone. proc. of the 6th int. conference co-next’10 : doi: 10.1145/1921168.1921171. [16] xu, k.; zhang, z.l.; bhattacharyya s.(2008); internet traffic behavior profiling for network security monitoring. ieee/acm trans. on networking, 16(6): 1241-1252. [17] herrmann, d.; banse, c.; federrath, h.(2013); behavior-based tracking: exploiting characteristic patterns in dns traffic. computers & security, 39 (part a): 17-33. [18] http://www.mathworks.in/products/matlab int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 115-122 switched nonuniform and piecewise uniform scalar quantization of laplacian source a.v. mosic, z.h. peric, s.r. panic aleksandar v. mosić faculty of electronic engineering, university of niš aleksandra medvedeva 14, 18000 niš, serbia e-mail: mosicaca@yahoo.com zoran h. perić faculty of electronic engineering, university of niš aleksandra medvedeva 14, 18000 niš, serbia e-mail: zoran.peric@elfak.ni.ac.rs stefan r. panić faculty of natural science and mathematics, university of priština lole ribara 29, 38200 kosovska mitrovica, serbia e-mail: stefanpnc@yahoo.com abstract: in this paper switched nonuniform and piecewise uniform scalar quantization of laplacian source are analyzed. this scalar quantization techniques are used in order to obtain higher signal quality by increasing signal-to-quantization noise ratio (snrq) with respect to it’s necessary robustness over a broad range of input variances in a wide range of signal volumes. we observe µ-law compandor implementation to achieve compromise between high-rate digitalization and variance adaptation. the main contribution of this model is kipping almost the same quality as the nonuniform compandor model, with simpler realization structure and the possibility of it’s applying for digitalization of wide range continuous signals. keywords: switching quantization, µ-law companding, variance adaptation, laplacian source. 1 introduction quantization denotes the heart of analog to digital (a/d) conversion and efficient technique of data compression. quantizers play an important role in the theory and practice of modern day signal processing. they are applied for the purpose of storage and transmission of continual signals. in a number of papers like this one the quantization of laplacian source was analyzed since the probability density function (pdf) of the instantaneous speech signal values for higher number of digitalization samples is better represented by laplacian then the gaussian function [1], [2], [4], [7], [10]. analysis of nonlinear quantization optimization in wide volume range [5], [6], for gaussian source is given in [8], [9]. in this paper we analyze robust and switched nonuniform and piecewise uniform scalar quantization of laplacian source to achieve compromise between high-rate digitalization and variance adaptation. the aim of our research is to find a simple way to realize a quantizer system having high quality performance but maintaining robustness in wide range of input signal. the goal of the paper is designing piecewise uniform scalar quantizer based on the µ-law compandor which satisfy g.712 standard. in this purpose, we must to find optimal values for parameter µ and number of quantizers. proposed solution copyright c⃝ 2006-2012 by ccc publications 116 a.v. mosic, z.h. peric, s.r. panic has the same complexity as the g.711 standard, because we use 8 bits, but it satisfy g.712 standard with 7 bits/sample. important issues from the engineer’s point of view are the design and implementation of quantizers to meet performance objectives. when the required hardware solution it is better to use piecewise uniform scalar quantizers, because they can be realized using linear electronic circuits. but if the required software solution it is better to use nonuniform scalar quantizers. this paper is organized as follows. in chapter 2. we have developed expressions for granular and overload distortion of nonuniform scalar quantization, using bennett’s integral on laplacian distribution. then in chapter 3. a switching nonuniform model and numerical results for µlogarithmic compandor are presented. in chapter 4. our switching piecewise uniform model and numerical results for µ-logarithmic compandor are presented. last two chapters show how the increase of number of quantizers, in switching scheme, affects the sqnr dependence of input power. also we have discussed how constant µ should be chosen in order for total distortion to be as minimum as possible in the wide volume range of input signal. finally, in conclusion we have discussed obtained results, and on these bases, we derived conclusions about the possibilities of this switched quantization application in speech processing. 2 distortion for nonuniform µ-logarithmic compandor scalar quantizers are the only types of quantizers considered in this paper, so we just briefly recall their properties. an n-point fixed rate scalar quantizer is characterized by the set of real numbers t1, t2, . . . , tn , called decision thresholds, which satisfy −∞ = t0 < t1 < · · · < tn−1 < tn = +∞, and set y1,y2, . . . ,yn , called representation levels, which satisfy yj ∈ αj = (tj−1, tj], for j = 1, . . . ,n. sets α1,α2, . . . ,αn form the partition of the set of real numbers r and are called quantization cells. the quantizer is defined as many-to-one mapping q : r → r, q(x) = yj where x ∈ αj. in practice, input signal value x is quantized to the value yj. cells α2,α3, . . . ,αn−1 are inner cells (or granular cells) while α1 and αn are outer cells (or overload cells). in such way, cells α2,α3, . . . ,αn−1 form granular while cells α1 and αn form an overload region. let input signal is characterized by continuous random variable x with pdf p(x). the first approximation to the long-time-averaged pdf of amplitudes is provided by a two-sided exponential or laplacian model. waveforms are sometimes represented in terms of adjacentsample differences. the pdf of the difference signal for an image waveform follows the laplacian function [4]. laplacian source can be also used for modelling of the speech signal. in the rest of the paper we assume that information source is laplacian source with memoryless property and zero mean value. the pdf of that source is given by: p(x) = 1 √ 2σ2 e− |x| √ 2 σ , (1) where x is zero-mean statistically independent laplacian random variable of variance σ2. the quality of the quantizer is measured by distortion of resulting reproduction in comparison to the original one. mostly used measure of distortion is mean-squared error. it is defined by: d(q) = e(x − q(x))2 = n∑ i=1 ∫ ti ti−1 (x − yi)2p(x)dx. (2) the n-point quantizer q is optimal for the source x if there is no other n-point quantizer q1 such that d(q1) < d(q). we also define granular distortion dg(q) and overload dol(q) switched nonuniform and piecewise uniform scalar quantization of laplacian source 117 distortion by: dg(q) = n−1∑ j=2 ∫ tj tj−1 (x − yj)2p(x)dx, (3) dol(q) = ∫ t1 −∞ (x − y1)2p(x)dx + ∫ +∞ tn−1 (x − yn)2p(x)dx. (4) obviously follows that d(q) = dg(q) + dol(q). the companding technique is one of the commonly used techniques for the construction of nearly optimal quantizers for large number of quantization levels. it forms the core of the itut g.711 standard [3]. it is a nonuniform pcm standard recommended for encoding speech signals. the recommendation is based on digitally linearizable companding, which permits a precise control of quantization characteristics. the compression and expansion characteristics are piecewise linear approximations to µ-law , where µ = 255, with 8 bits/sample are adopted, leading to a bit-rate of 64 kbps at 8 khz of sampling frequency. companding procedure consists of following steps: i) compressing the input signal x by applying the compressor function c(x). ii) applying the uniform quantizer qu on the compressed signal. iii) expanding the quantized version of the compressed signal using an inverse compressor function c−1(x). as explained the corresponding non-uniform quantizer consisting of a compressor, a uniform quantizer, and an expander in cascade is called companding quantizer (compandor). hence, the companding quantizer can be represented as q(x) = c−1(qu(c(x))), where qu(x) is uniform quantizer in the interval [−1,1]. let us denote by tu,i and yu,i decision thresholds and representation levels of the uniform quantizer qu(x). corresponding values ti and yi of the companding quantizer q(x) could be determined as the solutions of the following equations: c(ti) = tu,i = −1 + 2i n , c(yi) = yu,i = −1 + 2i − 1 n . (5) there are several ways how to choose the compressor function c(x) for compression law. in practice, a piecewise linear approximation of the logarithmic compression characteristic is used. there are two different ways. in north america, a µ-law compression characteristic is used, which is defined as follows: c(x) =   xmax ln(1+µ) ln ( 1 + µ x xmax ) , 0 ≤ x ≤ xmax − xmax ln(1+µ) ln ( 1 − µ x xmax ) , −xmax ≤ x ≤ 0 . (6) substituting (1), (5) and (6) in (3) and (4), and considering that yn can be approximated with xmax, granular and overload distortions are defined as: dg(q) = σ2 ln2(1 + µ) 3n2 [ 1 µ2 x2max σ2 + xmax σ √ 2 µ + 1 ] , (7) dol(q) = σ 2e− √ 2xmax σ . (8) since we now know how to calculate total distortion for quantization of a laplacian source that has variable average power in a wide range: d(q) = σ2 ln2(1 + µ) 3n2 [ 1 µ2 x2max σ2 + xmax σ √ 2 µ + 1 ] + σ2e− √ 2xmax σ , (9) 118 a.v. mosic, z.h. peric, s.r. panic we can find the signal power-to-total-distortion ratio (db), which is denoted as sqnr: sqnr = 10 lg σ2 d(q) = 10 lg 1 ln2(1+µ) 3n2 [ 1 µ2 x2max σ2 + xmax σ √ 2 µ + 1 ] + e− √ 2xmax σ . (10) on the basis of this expression we will design the desired model below. 3 switching nonuniform scalar quantization and numerical results for µ-logarithmic compandor classical quantizer with µ-law compression characteristic (see fig. 1, µ = 255) has limited range of input variances. we will solve that problem with switching quantization application. one simple technique is switched codebook adaptive scalar quantization. the basic scheme of robust and switched codebook adaptation is shown in [8]. this technique uses a classifier that looks at the contents of the input frame buffer and decides that the next block of samples belongs to a particular statistical class of samples from a finite set of k possible classes. namely, the index specifying the class is used to select a particular codebook from a redesigned set of k codebooks. in addition, this index is transmitted as side information to the receiver. then, each sample in the block is encoded by the scalar quantizer, which performs a search through the selected codebook. one frame has length of m. the index to identify the class is sent on the end of block. if each of the k codebooks has size n, the bit rate per sample is: r = log2 n + log2 k m . (11) codebook size n depends on number of bits that are used for the encoding n. the relation between n and n is n = 2n, where n is the number of bits per sample. we will use this switching technique for our problem solving. we have k codebooks, i.e., k nonuniform scalar quantizers designed for particular values σ0j to cover input power range σ20j/σ 2 0 ∈ [σ 2 1j/σ 2 0,σ 2 2j/σ 2 0),where σ0 denotes referent value of input power and ∪ k j=1[σ 2 1j/σ 2 0[db], σ22j/σ 2 0j[db]) = [−20,20). maximal amplitude for each quantizer xmaxj (each codebook j) is chosen in a way, that for each input power range σ20j/σ 2 0 ∈ [σ 2 1j/σ 2 0,σ 2 2j/σ 2 0) the total distortion has a minimum. the procedure is as follows: we optimize total distortion (9) to have a minimum. the optimization is going over parameter c, witch denotes ratio xmax/σ. after finding copt, for corresponding µ, which satisfies the following term: ∂d(q) ∂c = 0 ⇒ c = copt. (12) we can easily evaluate xmaxj for each input power range σ20j/σ 2 0 ∈ [σ 2 1j/σ 2 0σ 2 2j/σ 2 0), from the expression xmaxj = coptσ0j. each particular value σ0j can be represented as σ0j = kjσ0, where kj is called adaptation factor. during the switched quantizer design, a particular type of memory is needed. each input class j = 1,2, . . . ,k requires one quantizer, for which we know adaptation factor kj and input power range [σ21j/σ 2 0,σ 2 2j/σ 2 0) for which the quantizer is designed. also we have to store in memory the corresponding µ and copt. first, let us examine switched codebook adaptive scalar quantization model with only one quantizer present. here, only parameter that can be optimized, for achieving high quality of transmission by increasing snrq, in a wide range of signal volumes (variances) with respect to switched nonuniform and piecewise uniform scalar quantization of laplacian source 119 it’s necessary robustness over a broad range of input variances is the ľ parameter in expression for snrq. parameter µ can be optimized, for the case when expression for snrq has his maximum, which means that expression (9) for total distortion should have his minimum. optimization of total distortion is derived in two steps. first, we accomplish adaptation on maximal amplitude of input signal, or the optimization for parameter c in corespondents to µ, which is described as: ∂d(q) ∂c = 0 ⇒ c = copt(µ). (13) then in the second step, we find required µopt, for witch total distortion should have his minimum, which is described as ∂d(q) ∂µ ∣∣∣∣ c=copt(µ) = 0 ⇒ µ = µopt, d(q)(µopt) = dmin(q). (14) these two steps can be represented as the following equation system: ∂d(q) ∂c = σ2  −√2e−√2c + ( 2c µ2 + √ 2 µ ) ln2(1 + µ) 3n2   = 0 (15) ∂d(q) ∂µ ∣∣∣∣ c=copt(µ) = σ2  2 ( 1 + c2opt(µ) µ2 + √ 2copt(µ) µ ) ln(1 + µ) 3n2(1 + µ) − ( 2c2opt(µ) µ3 + √ 2copt(µ) µ2 ) ln2(1 + µ) 3n2   = 0. (16) for n1 = 128 and n2 = 256, numerical solutions for presented systems are copt1 = 8.8, µopt1 = 15 and copt2 = 9.9 and µopt2 = 17, respectively. if there are not restrictive limitations about memory size and sample bit rate for the transmission system, then there is a possibility to choose optimal number of quantizers in our model, for which we can achieve high quality measured by sqnr, in a wide range of signal volumes (variances) with respect to it’s necessary robustness over a broad range of input. if we increase number of quantizers k, there is a way to flatten the sqnr dependence of input power in such a way that, if the memory size isn’t the limiting factor, with data compression being disregarded, we will achieve a signal-to-noise ratio that does not have a large variation during input power changes which is shown in figs 1 and 2. in fig 2, we can see that sqnr varies from it’s peak value, for maximum 0.282 db and 0.313 db for each input power range [σ21j/σ 2 0,σ 2 2j/σ 2 0), ∪ k j=1[σ 2 1j/σ 2 0[db],σ 2 2j/σ 2 0j[db]) = [−20,20), for which the quantizer is designed, in case of codebook size of 128 and 256, with 16 codebooks. there is a conclusion, that if we want to satisfy the same standard for varying of sqnr in twice larger input power range of [-40db,40db], we will have to use same codebook size for 32 codebooks. if we want to satisfy less restrictive standards of sqnr variance for each input power range [σ21j/σ 2 0,σ 2 2j/σ 2 0), we can use smaller number of codebooks, and if we want to achieve smaller peak value of sqnr, we can use smaller size of each codebook, for each input power range. if we analyze bit sample rate in function of frame length with respect to number of quantizers k, we can se that for relatively small frame length of 80 samples, bit sample rate rapidly convergates to the value of bit sample rate of transmission without side information. so we can derive conclusion that memory size is much more restrictive limitation for multi-quantizer implementation, than sample rate is. 120 a.v. mosic, z.h. peric, s.r. panic from fig. 2, we can see that presented nonuniform switched quantizer with optimized µ outperform classical µ = 255 switched quantizer characteristic for 3 db and 2.7 db in the cases of n = 128 and n = 256, respectively. 4 switched piecewise uniform scalar quantization and numerical results for µ-logarithmic compandor this paper utilizes the basic concept of the jayant quantizer model in order to provide the development of the adaptive piecewise uniform scalar quantizer [4]. particularly, the model avails the jayant manner of maximum amplitude adaptation, which means according to the one word memory. finally, since one of the main goals when designing quantizers is to provide as high as possible quality, i.e. as low as possible distortion, an analysis will be conducted in order to provide optimal values of µ. in this section, it is showed that segmental µ-law better than method presented in section 3, when the required hardware solution because they can be realized using linear electronic circuits, and provide almost the same results for quality. switched piecewise uniform scalar quantization of memoryless laplacian source is asymptotically analyzed for the case where the power of input signal varies in a wide range. one possible solution for encoder design is given for the same quantizer. switched quantization is used in order to give a higher quality in a wide range of signal volumes (variances). these systems, although not optimal, may have asymptotic performance arbitrarily close to the optimum. furthermore, their analysis and implementation can be simpler than those of optimal systems. we also define granular distortion dg(q) and overload dol(q) distortion by: dg(q) = 2 l∑ i=1 ∆2i 12 pi(σ) (17) dol(q) = 2 ∫ +∞ xmax (x − yl,nunif ) 2p(x)dx. (18) where are amplitudes quant in the i-th segment defines es: ∆i = xi − xi−1 n 2l (19) and yl,nunif is representation layer in the last cell of l-th segment: yl,nunif = xmax − ∆l 2 ≈ xmax (20) finally p(x) is the pdf of inner signal on quantizer, which define probability pi(σ) that quantum of signal belong the i-th segment: pi(σ) = ∫ xi xi−1 p(x,σ)dx. (21) as we know d(q) = dg(q) + dol(q). after sam basic mathematical operations we can get that: d(q) = x2max 12(n/2l)2µ2 l∑ i=1 (1 + µ) 2(i−1) l ((1 + µ) 1 l − 1)2e− √ 2xmax µσ ((1+µ) i−1 l −1) × ( 1 − e− √ 2xmax µσ ((1+µ) i l −(1+µ) i−1 l ) ) + σ2e− √ 2xmax σ . (22) switched nonuniform and piecewise uniform scalar quantization of laplacian source 121 optimizing the previous expression in the same manner as in section 3, we get the results given in the table 1 and figure 2. in figure 2 we can observe that our l = 4 switching piecewise uniform method with n1 = 128 and n2 = 256 outperforms the classical µ = 255 law method for 2.5 db and 2.2db, respectively. comparatione is also made with g.711 standard, and it is also shown that our model satisfy the g.712 standard with 1 bit compression obtained. our switching piecewise uniform model, with l = 8 segments and l = 16 segments (see table 1), gives almost same results as switching nonuniform method, with much more simpler realization structure of hardware. figure 1: improvement of quality of transmission (snrq), for model implementations with two quantizers over robust quantization. figure 2: comparatione of quality of transmission (sqnr), for model implementations with sixteen quantizers for standard and optimized value of parameter µ. 5 conclusions and future works we have suggested switched nonuniform and piecewise uniform scalar quantzation of laplacian source that solve the problem of variable input power in a wide range. we also have presented 122 a.v. mosic, z.h. peric, s.r. panic l sqnrn=128 sqnrn=256 2 32.76 38.21 4 34.35 39.96 8 34.73 40.37 16 34.82 40.48 64 34.85 40.51 128 34.85 40.52 table 1: comparison between values of sqnr of piecewise uniform scalar quantizers for different values of bitrate r, i.e. n1 = 128, µ1 = 15 and n2 = 256, µ2 = 17. how they can accomplish high quality of snrq by optimization on maximal amplitude of input signal, by optimization on parameter µ, and by adaptation on input variance range length. they can be applied for coding of speech signals and other continuous signals. the dependence of quality and robustness of quantized signals is analyzed over the broad range of input variances and corresponding number of codebooks with respect to system memory and sample bit rate. presented switching piecewise uniform model gives almost same results as switching nonuniform method, with much more simpler realization structure of hardware. bibliography [1] r. gray, quantization and data compression, lecture notes, stanford university, 2004. [2] d. hankerson, g. a. harris, p. d. jr. johnson, introduction information theory and data compression, 2nd ed., chapman & hall/crc, 2004. [3] itu-t, recommendation g.711, pulse code modulation (pcm) of voice frequencies, international telecommunication union, 1972. [4] n. s.jayant, p. noll, digital coding of waveforms, prentice-hall, new jersey, 1984. [5] z. h. perić, i. djordjević , m. stefanović, s. bogosavljević, combined source and channel coding of speech signal in wide volume range, proceedings of the iasted, las vegas, nevada, usa, 1998. [6] z. h. perić, quantization optimizations of speech signal in wide volume range, electroniccs and electrical engineering, vol.45, pp.41-48, 2003. [7] z. h. perić, j. nikolić, an effective method for initialization of lloyd-max’s algorithm of optimal scalar quantization for laplacian source, informatica, vol.18, pp.1-10, 2007. [8] z. h. perić, a. v. mosić , s. r. panić, robust and switched nonuniform scalar quantization of gaussian source in a wide dynamic range of power, journal of communications technology and electronics, avt journal, vol.6, pp.74-84, 2008. [9] z. h. perić, a. v. mosić , s. r. panić, coding algorithm based on loss compression using scalar quantization switching technique and logarithmic companding, journal of information science and engineering, vol.26, pp.967-976, 2010. [10] k. sayood, introduction to data compression, 3rd ed., elsevier inc, 2006. international journal of computers communications & control issn 1841-9836, 9(5):570-583, october, 2014. trust model in cloud computing environment based on fuzzy theory l. gu, j. zhong, c. wang, z. ni, y. zhang lichuan gu, chengji wang, youhua zhang school of computer and information, anhui agricultural university no.130 changjiang road, hefei, anhui, 230036 china glc@ahau.edu.cn, wang_chengji@qq.com, yhzhang@ahau.edu.cn jinqin zhong university school of international business no.420 linquan road, hefei, anhui, 230031 china. jinqinzhong@163.com zhiwei ni, lichuan gu school of management, hefei university of technology no.9 tunxi road, hefei, anhui, 230009 china gdnzw@hfut.edu.cn abstract: recent years have witnessed the development of cloud computing. however, there also come some security concerns in cloud computing environment, such as emerging network attacks and intrusions, and instable cloud service provision due to flexible cloud infrastructure and resources. to this end, we research on the trusted computing in cloud computing environment. specifically, in this paper, we propose a trust model based on virtual machines, with two considerations. first, we introduce timeliness strategy to ensure the response time and also minimize the idle time of servers. second, we extend the linear trust chain by differentiating the trust of the platform domain and user domain. besides, we develop a fuzzy theory based method to calculate the trust value of cloud service providers. we also conduct some experiments to evaluate our method. keywords: trust model, fuzzy theory, cloud computing. 1 introduction recent years have witnessed the development of cloud computing, which is an integration of parallel computing, grid computing and distributed computing [1, 2]. with massive computing and storage capability, cloud computing provides various resources to end users through trusted and reliable services. in this way, users can be relieved from trivial management routines and stay focused on the interesting business only. for example, cloud services can help to reduce the complexity of enterprise informatization process, improve the efficiency of companies’ operation, and facilitate the utilization of computer resources [3]. however, although cloud computing brings us extremely convenience, there also comes some security concerns [4, 5]. the security flaws are growing ever since the complexity of system softwares increases. also, the increasingly development of internet, as well as emerging network attacks and intrusions [6], leads to more security events. besides, the flexibility of cloud infrastructure and resources increases the difficulty of management and brings instability. moreover,there might be single point failure in demanding for high quality trusted service, and thus the cloud service delivery could be delayed or failed. for example, suitable authentication is required for access to bank accounts, healthrecords, intellectual property and business or politically sensitive information to reduce the security risks of cloud computing infrastructure [7]. copyright © 2006-2014 by ccc publications trust model in cloud computing environment based on fuzzy theory 571 to this end, trusted computing [8,9] is proposed by the trusted computing group (tcg) [10]. with trusted computing technologies, computers can be safer and less prone to viruses and mal ware, and therefore hardware and software can consistently behave in expected manner. one way to ensure the functionality of cloud infrastructure through trusted computing is to leverage the idea of chains of trust. in this paper, we propose a trust model and its evaluation method in cloud computing environment. specifically, (1) the trust model in this study is built in cloud computing environment instead of traditional scenarios; (2) since time factor is significant for both qos in requesting cloud services and also maximizing the utilization of cloud resources, we consider timeliness strategy in choosing trusted cloud services; (3) we extend the traditional linear trust chain as a tree-like structure to differentiate the trust of the platform domain and user domain; and (4) the evaluation of trusted computing is based on the fuzzy theory. the remains of this paper are organized as follows. in section 2 we provide some related work. section 3 presents our proposed trust model and the fuzzy theory based evaluation method. then experiments are conducted in section 4. finally, the paper is concluded in section 5. 2 related work in this section, we present some related work. generally, there are three categories: theoretical research on trusted computing (tc),architecture and implementation of tc, and tc for virtual machines environment. the first category is theoretical efforts on trusted computing. blaze et al. [11] first proposed the concept of trust management in 1996. then, josang et al. [12–14]proposed a trust model based on subjective logic, and introduced evidence space and opinion space to measure the trust relationship. beth et al. [15] classified trust into direct trust and indirect trust, and proposed to measure the trust based on the degree of task completion. fault tolerance capability in trusted computing within the whole life cycle of software development was also discussed [16–18]. smith et al. [19] developed a outbound authentication model using ibm secure coprocessors. abadi et al. [20] provided a formal description of the access control process in ngscb system using secure logical language. chen et al. [21]described the process of secure bootstrap in trusted computing using predicate logic. there are also many efforts on the architecture and implementation of tc. ibm 4758 secure coprocessors [22] are one of the most earliest secure hardware. the design of ibm 4758 is to provide an isolated running environment to ensure the computing and storage capability even when something happens to the operating system or the main processors. stanford university developed an architectural support for copy and tamper resistant software, called xom [23]. suh et al. [24] developed aegis, an architecture for a single-chip aegis processor which can be used to build computing systems secure against both physical and software attacks. chen et al. [25] designed another secure processor cerium. bear [26, 27] constructs trusted computing in commercial trusted platforms in linux, and extends trust chain to the folder layer by checking the integrity of folders when they are first opened. also, ibm proposed an architecture for trusted computing called ima [28]. the last category of related work is trusted computing for virtual machines. garfinkel et al. [29] developed a virtual machine-based platform for trusted computing, called terra. it allows applications with a wide range of security requirements to run simultaneously on commodity hardware. also, ibm implemented vtpm [30] to support trusted computing for multiple virtual machines, by introducing a virtual layer and extending the trust chain for virtual machines based on typical trusted platform module (tpm). 572 l. gu, j. zhong, c. wang, z. ni, y. zhang 3 proposed trust model in typical cloud computing environment, the service model is multi-layered. that is, the cloud service provider (csp) not only provides services to end users, but also to the upper layer csps, which forms a trust chain of service providers and consumers. as shown in figure 1, the arrow denotes from providers to consumers. for example, the service of end users might be provided by a saas csp directly, or first paas csp then saas csp, or from iaas csp downstream to end users. figure 1: trust transitivity service providers and consumers there are two kinds of trust in cloud computing environment: direct and indirect trust. direct trust means the impression of consumer users on the service quality of csp, while indirect trust denotes the aggregated impression of all other previous consumers who have used the service or other csps who have connections with current csp. suppose user ua wants to use the cloud service from csp x .the objective is to calculate the trust value of x and determine if x is trusted. denote imp(a,b) as the impression of b on a. therefore, the trust value of x for ua can be calculated as: imp(x,ua) = c1 ∑ ui∈u αiimp(x,ui) + c2 ∑ y βiimp(x,y ) (1) where imp(x,ui) is the impression value of ui on csp x , imp(x,y ) is the impression of other csps on x, and c1, c2, αi, βi are coefficients. 3.1 timeliness strategy in a cloud computing environment, host data nodes provide trusted services with high credible and stable resources, where timeliness is a significant indicator. generally, the process of trusted cloud services is as follows. first, if there exist idle host data nodes, then evaluate the timeliness of the node. second, calculate the trust value of data node based on the timeliness and trust model. if the trust value is satisfactory, then the node is assigned for cloud services; otherwise, repeat the process for the next service request. we assume that the trust value is related to time, and the more recent the evaluation is, the more contribution it has to the current trust calculation. let imp(x,ui)l be the trust value at time l therefore, the trust value of x for ua can be represented by rewriting equation (1) through introducing timeliness strategy: trust(x,ua) = imp(x,ua)l −∆imp ·f(t−l) (2) where imp(x,ua)l is the current impression value at time l , and ∆imp ·f(t−l) indicates the affect of previous trust value. suppose there are k historical impression values before l, and imp(x,ua)l is defined as: imp(x,ua)l = k∑ i=1 imp(x,ua)iwi (3) trust model in cloud computing environment based on fuzzy theory 573 where k∑ i=1 wi = 1. since the history impression evaluation happens randomly, we equally split the history time period into m segments in order to differentiate the importance of each historical evaluation. historical impressions within the same time window w are assigned an identical weight ωw . therefore, inspired by [31], weight wi can be calculated as: wi = n∑ j=1 m−1 √ ωw1(m− j)ωwm(j −1) n (4) where n is the number of values within i-th historical impression. substitute equation (4) into (3), we get imp(x,ua)l = k∑ i=1 imp(x,ua)i n∑ j=1 m−1 √ ωw1(m− j)ωwm(j −1) n (5) suppose the experience distribution of ∆imp follows gumbel distribution [32]. therefore the estimate of ∆imp is: ĝ(impi) = exp { − exp { − impi − û δ̂ }} (6) where û, δ̂ is the maximum likelihood estimates for gumbel distribution. time based function f(t−l) follows exponential distribution: f(t−l) = ∫ +∞ l (t−l)f(t)dt = exp(−λl) λ (7) 3.2 tree structured trust chain in this section, we consider the trust between a specific csp and users. the objective is to measure the trust value from the csp hardware to user defined software. trust relationship in cloud environment is more complicated than the traditional scenarios. for example, the services are typically running in virtual machines with larger management and user domain, which is hardly measured by the traditional linear structure. moreover, for some business requirements, services are assembled across multiple user domains, which also increase the difficulty of measuring the trust. note that by management domain, we mean the set of objects, typically refers to the component in traditional trust chain; by user domain, we mean that different users that request for the service. typically, cloud users don’t have controls over the hardware devices, and thus the safety is only ensured by service-level agreement (sla). however, users wish to somehow control the virtual computing resources, i.e., virtual machines (vm), so that user-defined security strategy can be passed over vm and therefore ensure the safety of cloud resources. one popular solution is to use trusted platform module (tpm), which is a specialized chip that can securely store information, such as passwords and encryption keys, with independent execution cpu unit. the typical architecture of tpm is shown in figure 2. tpm provides trusted computing by ensuring security to operating systems and tcg software stack (tss). tss is a support software of tpm, and its architecture is shown in figure 3. in cloud computing environment, trusted computing is typically implemented by virtualizing the trusted platform module (vtpm). however, vtpm is only a virtual instance of physical tpm in user domain, which calls the physical tpm resource to provide tpm service, as shown 574 l. gu, j. zhong, c. wang, z. ni, y. zhang figure 2: tpm component architecture figure 3: architecture of tss trust model in cloud computing environment based on fuzzy theory 575 in figure 4. there are some limitations of this simple structure. first, the whole structure is dependent on limited physical tpm, which is not scalable for large scale virtual machines in cloud platform. second, evaluation of each individual vtpm is sequential due to the limited physical resource, and therefore it is not dynamic and elastic. last, vtpm relies on physical tpm, that is, the trusted capability is ensured by the cloud infrastructure only. accordingly, users cannot specify their personalized security strategies on demand. moreover, we observe that existing trust models, where the evaluation is all performed by one single node, are inappropriate especially in cloud computing environment [33]. figure 4: illustration of user virtual machine to this end, in this section, we propose a tree structured trust model by distributing the evaluation work to multiple nodes. specifically, we combine the trust evaluation from physical platform and user domain. as shown in figure 5, there are two stages in the evaluating of trust value. first, there is a chain of trust in physical tpm, i.e., crtm’bios’grub’vmm. the integrity and security of the system is ensured by the isolation mechanism of physical tpm and cloud infrastructure. second, tpm controller creates tpm for each user, i.e., utpm, which is responsible for the evaluation and security of software components in the user domain. each utpm is created for a user virtual machine, and holds the results of integrity evaluation and reports to users. there are two kinds of trust transitivity in this model. (1) pass the trust from physical tpm to tpm controller, from hardware to user virtual machine. in this way, we can combine the trust of platform and the trust of user virtual machine together, to provide a complete trust chain. (2) pass the trust from user to virtual machine. by virtualizing tpm, an independent vtpm is created for each user virtual machine, i.e., utpm. in figure 5, each node in the upper physical trust chain is responsible for evaluating trust, and the evaluation is performed for each user in parallel in the lower user domain trust chain. theorem 1. the tree structured trust model in figure is trusted. 576 l. gu, j. zhong, c. wang, z. ni, y. zhang figure 5: illustration of tree structured trust model proof: for ease of description, we simplify the tree structured trust model in figure 6. denote the trust value of node a for b as t(a,b) . inferred by the trust transitivity principle, we have t(a,b) = min{t(a,b),t(b,c), . . . ,t(n −1,n)} . as proved in [24], the left part chain a → b → c →···→ n is trusted. now we consider the right part chain. each node ui represents a trust chain of each user virtual machine, and therefore ui itself is trusted. there are multiple direct paths, i.e., t1(n,u1),t2(n,u2), . . . ,tn(n,un). the final trust value t(n,u) is no less than max { t1(n,u1),t2(n,u2), . . . ,tn(n,un) } . therefore the right part chain is trusted. 2 figure 6: illustration of tree structured trust model 3.3 fuzzy theory based trust model as used in many suppose the confidence level for trust is u = {u1,u2,u3,u4} = {‘not trusted’, ‘somehow trusted’, ‘normal trusted’, ‘complete trusted’}, and the confidence vector v = {v1,v2,v3,v4}, where vi(i = 1,2,3,4) denotes the degree of membership of each level ui. figure 7: illustration of trust transitivity trust model in cloud computing environment based on fuzzy theory 577 the trust between initial entity and target entity is evaluated through the stepwise evaluation and the transitivity between them. as shown in figure 7, where a is observed entity, b is evaluation entity, and r1,r2, . . . ,rn are the intermediate entities. the corresponding trusts between nodes are vt1,vt2, . . . ,vtn,vd , and the trust vector from a to b is vt . the integrity of direct measurement is mainly affected by the measuring capability of the current evaluation node. now we consider the timeliness factor. let dt(ri,rj,t) be the direct trust of rj in ri at time t , and it can be calculated as: dt(ri,rj, t) = srjri srjri + frjri δ(t,t0) (8) where srjri denotes the number of successful historical integrity evaluation of rj on ri, frjri is the number of failure evaluation, and t, t0 denote the current and first evaluation time respectively. δ(t,t0) is the time decay function, defined as: δ(t,t0) = 1− t− t0 t ξ (9) where ξ ∈ [0,1] is the adjustment factor of the decay. the value of direct trust dt(ri,rj, t) can be transformed into a fuzzy vector v (i,j) = (v1,v2,v3,v4) , where v1 =   1−2 ( dt 0.5 )2 , 0 ≤ dt ≤ 0.25; 2 ( 0.5−dt 0.5 )2 , 0.25 < dt < 0.5; 0, 0.5 < dt < 1. (10) v2 =   0, 0 ≤ dt ≤ 0.25; 1−2 ( dt−0.25 0.5 )2 , 0.25 < dt ≤ 0.25; 2 ( 0.75−dt 0.5 )2 , 0.5 < dt ≤ 0.75; 0, 0.75 < dt ≤ 1. (11) v3 =   0, 0 ≤ dt ≤ 0.25; 2 ( dt−0.25 0.5 )2 0.25 < dt ≤ 0.5; 1−2 ( 0.75−dt 0.5 )2 0.5 < dt ≤ 0.75; 0 0.75 < dt ≤ 1. (12) v4 =   0, 0 ≤ dt ≤ 0.5; 2 ( dt−0.5 0.5 )2 0.5 < dt ≤ 0.75; 1−2 ( dt−1 0.5 )2 0.75 < dt ≤ 1. (13) suppose there are n evaluation metrics z = {z1,z2, . . . ,zn}, we get the evaluation matrix for entity ri : vi =   v1,1 v1,2 · · · v1,n v2,1 v2,2 · · · v2,n v3,1 v3,2 · · · v3,n v4,1 v4,2 · · · v4,n   (14) 578 l. gu, j. zhong, c. wang, z. ni, y. zhang therefore, the indirect trust of ri is calculated as: iti = wi ·vi (15) where wi = (w1,w2,w3,w4) is the weights of each metric, and wi ∈ [0,1], 4∑ i=1 wi = 1. we employ a fuzzy reasoning method based on similarity. suppose there exist a rule: r : if a then b,λ,w (16) where a is the antecedent component, b is the consequent component of the rule, and λ is a threshold which decides the rule to be executed or not. the similarity between two entities is calculated as: s(a′i,ai) =   m(a′i∩ai) m(a′i)∨m(ai) , if a′i ⊆ ai or a′i ⊇ ai a′i∩ai m(a′i) , else. (17) where a′i, ai are fuzzy set, m(ai) = ∑ x∈xi µai(x), xi is the mathematical domain of discourse for a′i, ai. as shown in figure, in our case, the antecedent components are vt1,vt2, . . . ,vtn, and the consequent component is vt. that is, the rule is: r : if the measurement of a for r1 is vt1 and the measurement of r1 for r2 is vt2 and · · · and the measurement of rn for b is vd then the measurement of a for b is vt (18) given the observed values are v ′t1,v ′t2, . . . ,v ′tn, the goal is to calculate the observed v ′t. the process of inference is as follows. step 1: calculate the similarity between observed s(v ′ti,vti), i = 1,2, · · · ,n; step 2: if s(v ′ti,vti) > λ , calculate the overall similarity: sw (v ′ti,vti) = n∑ i=1 s(v ′ti,vti)∗ wi n∑ j=1 wj (19) step 3: compute the inference results: θ1 = k1∑ i=1 s(v ′ti,vti)∗wi k1∑ i=1 wj (20) where j ∈{i : m(v ′ti) ≥ w(vti)} , and θ2 = k2∑ i=1 s(v ′ti,vti)∗wi k2∑ i=1 wj (21) trust model in cloud computing environment based on fuzzy theory 579 where j ∈ { i : m(v ′ti) < w(vti) } . if θ ̸= 0 and θ2 ̸= 0, then v ′t =   vt∗θ1 θ2 , θ1 ≤ θ2; min { 1, vt∗θ1 θ2 } , otherwise. (22) if θ1 = 0 or θ2 = 0, then vt =  vt ∗sw , θ1 ≤ θ2; min { 1, vt sw } , otherwise. (23) to sum up, in this section we presented the proposed trust model, which works as follows. when user a wants to use some service, for each qualified candidate csp x, he/she first inquires the information from other users and csps who have interactions with x. then, for each x, a tree structured trust chain is constructed, where the upper part is the trust chain for physical tpm of x, and the lower part is built for each possible user. after that, an evaluation method based on fuzzy theory is performed to calculate the trust value of a for x. once all the trust value is learned, user a can determine if a csp x is trusted or not. 4 experiment in this section, we evaluate the efficiency of our proposed tree structured trust model. the configuration of pc is as follows. intel core i5 2.8 ghz cpu with four cores, 4 gb memory, 500 g hard disk. we use xen virtualization platform for virtualization implementation, cloudsim [34] for cloud computing platform simulation, and matlab for fuzzy system implementation. 4.1 evaluating trusted cloud service selection figure 8 shows the trust value of four types of csps with different number of transactions. we have the following observations. first, for complete trusted and normal trusted csps, the trust value is near linearly growing since they are offering real trusted services. second, for somehow trusted csps, the trust value is unstable. third, the value of not trusted csps decreases quickly, and they would not be selected as the cloud service provider. 4.2 evaluating timeliness we compare our method with traditional dynamic level scheduling (dls) algorithm [35] for scheduling cloud services with the consideration of timeliness factor. the results are reflected as the average of 100 executions. figure 9 shows the ratio of successful execution with different numbers of tasks. we can observe that the successful execution tends to increase when the number of tasks is growing. however, for dls algorithm, the maximum success ratio is around 0.65. the reason is that it does not consider the node failure at specific time. by contrast, our method performs better because we consider the timeliness factor in all historical evaluations. figure 10 shows the average schedule length with different numbers of tasks. the average schedule length grows when there are more tasks. also, the average schedule length of dls is smaller than that of our method. therefore, we can see that our method achieves larger success ratio by sacrificing the scheduling time. although the time cost increases, our method can help to select more successful trusted cloud services. 580 l. gu, j. zhong, c. wang, z. ni, y. zhang figure 8: trust value of four types with different number of transactions figure 9: the ratio of successful execution with different numbers of tasks trust model in cloud computing environment based on fuzzy theory 581 figure 10: average schedule length with different numbers of tasks 5 conclusion in this paper, we proposed a trust model in cloud computing environment. specifically, the trust model is designed for virtual machines with the consideration of timeliness factor. moreover, we employ a fuzzy theory based method to calculate the trust value of specific csp. in our experiments, we exhibit the trust value of four pre-defined confidence levels, and also evaluate the efficiency of the timeliness consideration. we find that our method can improve the successful response of selecting cloud services at the expense of average schedule length. however, in future works, we might want to explore a more optimal balance between efficiency and effectiveness. acknowledgements this work was supported by the national natural science foundation of china ( grant no.313715 33, grant no. 71271071 ), national science and technology support program ( grant no. 2013baj10b12 ), the key technologies r & d program of anhui province (grant no.1301032169) and by the natural science foundation of anhui province ( grant no.1308085mf89 ). bibliography [1] armbrust, michael, et al. 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(2012); dynamic trust evaluation and scheduling framework for cloud computing. security and communication networks, 5(3): 311–318. int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 645-660 a multimodel approach for complex systems modeling based on classification algorithms n. elfelly, j-y dieulot, m. benrejeb, p. borne nesrine elfelly, jean-yves dieulot, mohamed benrejeb, pierre borne université des sciences et technologies de lille (ustl), ecole polytechnique de lille, ecole nationale d’ingénieurs de tunis -lara automatique (enit), ecole centrale de lille (ec lille) laboratoire d’automatique, génie informatique et signal ecole centrale de lille, cité scientifique bp 48 59651 villeneuve d’ascq cedex, france e-mail:nesrine.elfelly@ed.univ-lille1.fr, jean-yves.dieulot@polytech-lille.fr, mohamed.nerejeb@ec-lille.fr,pierre.borne@ec-lille.fr abstract: in this paper, a new multimodel approach for complex systems modeling based on classification algorithms is presented. it requires firstly the determination of the model-base. for this, the number of models is selected via a neural network and a rival penalized competitive learning (rpcl), and the operating clusters are identified by using the fuzzy k-means algorithm. the obtained results are then exploited for the parametric identification of the models. the second step consists in validating the proposed model-base by using the adequate method of validity computation. two examples are presented in this paper which show the efficiency of the proposed approach. keywords: complex systems , multimodel , system modeling, classification 1 introduction the multimodel approach has been recently developed in several science and engineering domains, with typical applications in the mechanical and chemical engineering areas, with application to modelling, control and/or fault detection e.g. [1–3]. it was introduced as an efficient and powerful method to cope with modelling and control difficulties when complex non linear and/or uncertain processes are concerned. the multimodel approach assumes that it is possible to replace a unique non linear representation by a combination of simpler models thus building a so-called model-base. usually, each model of this base describes the considered process at a specific operating point. the interaction between the different models of the base through normalized activation functions allows the modelling of the global non-linear and complex system. therefore, the multimodel approach aims at lowering the system complexity by studying its behavior under specific conditions. the multimodel principle is given in figure 1. the different models of the base could be of different structures and orders but no model can represent the system in its whole operating domain. the decision unit allows the estimation of the weight of each model and thus the selection of the most relevant models at each time. as for the output unit, controlled by the decision unit, it allows the computation of the multimodel output which is obtained by the contribution of the different models’ outputs. in spite of its success in many fields (academic, biomedical, etc), the multimodel approach remains confronted with several difficulties, in particular the determination of the number and parameters of the different models representative of the system and the choice of an adequate method of validities computation used for multimodel output deduction. last years, many authors [4–7] have been copyright c⃝ 2006-2012 by ccc publications 646 n. elfelly, j-y dieulot, m. benrejeb, p. borne figure 1: multimodel approach principle. interested by this approach. the main differences between the proposed studies are the selected method for models identification and the type of models. linear models are mainly used, e.g. fuzzy takagi-sugeno models [8, 9] which are often obtained using linearization methods. however, the multimodel identification is more difficult to work out when the models of the base should be determined from only input/output data. some results are given in [10]. in the latter case, the number of models must firstly be determined. then, the data are classified and the models’ parameters are estimated. last, the validities of the different models are computed according to the adequate method. the issue of the selection of the appropriate method of validities’ estimation was discussed in [11]. many authors propose to apply classification algorithms in order to handle a set of dynamical models. for example, neural networks have been used to represent and control complex systems [7, 12–15]. in another hand, thanks to their ability to classify data and their simplicity, k-means algorithms have proved to be efficient for data clustering e.g. [16, 17]. in short, whereas many architectures using multiple models and neural networks have been proposed, there has not been much work on clustering techniques, based on neural networks and k-means algorithms [18], applied to traditional multimodel representation using only input/output data. the most tedious issues are related to the model-base size and the clustering procedure which aims to the determination of the operating domains. this paper thus proposes a multimodel approach for complex processes modelling based on classification algorithms. the efficiency of the proposed study is illustrated by two examples for which simulations are proposed. the issue of the determination of the number of models in the base is solved through the application of the rival penalized competitive learning (rpcl), which is an extension of the work in [19] . in the following sections, the different steps of model-base building are detailed knowing that only input/output data are available. 2 model-base determination using classification algorithms the proposed approach allows the determination of the number, structure and parameters of the different models of the base. firstly, a neural network and a rival penalized competitive learning are used for the selection of the adequate number of models. the second step consists in clustering system data by using fuzzy k-means. the classification results are then used for a multimodel approach for complex systems modeling based on classification algorithms 647 the parametric identification of the models. 2.1 determination of the number of models with a rival penalized competitive learning (rpcl) the proposed approach allows the construction of the model-base by using two clustering algorithms. the application of this approach requires firstly the determination of the number of models which will be handled by using a rival penalized competitive learning (rpcl) [20]. secondly, the application of the fuzzy k-means algorithm for data clustering and then the characterization of the different base-models is carried out by exploiting the clustering results. finally, the validation of the modeling strategy is considered by the use of an adequate method for validity computation allowing the generation of the multimodel output, compared to the real system output for a different set of inputs. the models number determination requires experimental data obtained by applying an appropriate input sequence. in order to generate the different operating domains of the process, the measurements must be merged into a set of clusters by the use of a classification algorithm with unsupervised learning. most existing clustering algorithms [21, 22] do not handle the selection of the appropriate number of clusters, which is, however, essential to the estimation and classification performance in the multimodel approach when no information is available about the operating domains and their number. however, many experimental results have shown that the rpcl algorithm automatically allocates an appropriate number of units for an input data set when they are used for clustering. the selection of the number of models in the multimodel representation requires that the excitation signal must be rich enough (a sine curve for example with the adequate frequency and amplitude added to a random signal) to take into consideration the non-linear aspect of the considered process. for tackling the issue of determination of this number, via input-output data, it is proposed to apply the learning algorithm called rpcl which allows the selection of the adequate number of operating clusters for an input data set. thus, the extra units are gradually driven far away from the distribution of the data set when the number of units is larger than the real number of clusters in the input data set. rpcl is an unsupervised learning strategy (proposed by xu [23] and renewed by tambe [24]), that automatically determines the optimal number of clusters. the principle underlying rpcl can be considered as an extension of the competitive learning based on kohonen rule [25]. its specificity lies in the modification, for each input vector, not only of the winner weights, but also of the weights of its rival (called second winner) so that the rival will be moved or penalized. the rate, at which the rival is penalized, is much smaller than the learning rate, e.g. [26]. given a competitive learning neural network (figure 2), i.e. a layer of units with the output ui of each unit and its weight vector wi for i = 1 . . .n; n is the number of output units, the rpcl algorithm can be described by the following steps. 1. initialize weight vectors wi randomly. 2. take a sample x from a data set d, and for i = 1 . . .n, let ui =   1 if i = c; −1 if i = r; 0 otherwise; (1) 648 n. elfelly, j-y dieulot, m. benrejeb, p. borne figure 2: competitive learning neural network. with: γc∥x−wc∥2 = min j γj∥x−wj∥2; (2a) γr∥x−wr∥2 = min j ̸=c γj∥x−wj∥2; (2b) ∥∗∥: euclidean distance; c: index of the unit which wins the competition (winner); wc: weight vector of the winner; r: second winner (rival) index; wr: weight vector of the rival; γj: conscience factor (relative winning frequency) used to reduce the winning rate of the frequent winners. it is useful to develop a set of equiprobabilistic features or prototypes representing the input data. γj is calculated as follows [27]: γj = nj∑n i=1 ni ; (3) where nj refers to the cumulative number of occurrences the node j has won the competition (uj = 1). 3. update the weight vectors as follows: wj(k + 1) = wj(k) + ∆wj; (4) with: ∆wj =   αc(k)(x−wj(k)) if uj = 1; −αr(k)(x−wj(k)) if uj = −1; 0 otherwise; (5) 0 ≤ αc(k) and 0 ≤ αr(k) ≤ 1 are respectively the winner learning rate and the rival delearning rate. in practice, the rates are fixed small numbers or depend on time (starting from not so small initial values and then reduced to zero in some way) with αc(k) ≫ αr(k) at each step. several empirical functions have been proposed for the update of the learning and de-learning rates [27, 28]. 4. repeat steps 2 and 3 until the whole learning process has converged. referring to the learning results, only the units enclosed within the data set should be considered and the number of clusters could be deduced as equal to the number of the selected units. a multimodel approach for complex systems modeling based on classification algorithms 649 2.2 identification of the operating clusters after the selection of the appropriate number of clusters, let us consider the problem of splitting up the measurements to generate the different operating domains from which the basemodels will be identified. data classification by using fuzzy k-means algorithm the fuzzy k-means classification algorithm has been chosen for data classification, according to its performance and easy working out. fuzzy k-means algorithm fuzzy k-means algorithm (developed by dunn [29] and improved by bezdek [30]) is a data clustering technique which allows each data point to belong to more than one cluster with different membership degrees (between 0 and 1) and vague or fuzzy boundaries between clusters. the aim of this method is to find an optimal fuzzy k-partition and corresponding prototypes minimizing the following objective function: jm = m∑ i=1 k∑ j=1 umij∥xi − cj∥ 2, 1 ≤ m < ∞; (6) with: ∥∗∥: any norm expressing the similarity between any measured data and a cluster centre; m: weighting exponent (real number greater than 1) which is a constant that influences the membership values; uij: degree of membership of xi to cluster j, such as uij ∈ [0,1], ∑k j=1 uij = 1 ∀i and 0 <∑m i=1 uij < m ∀j; xi: ith data point; cj: center vector (node) of the cluster j; m: number of observations; k: number of clusters (2 ≤ k < m). fuzzy partitioning is carried out through an iterative optimization of the objective function shown above, with the update of membership uij and the cluster centers cj [31]. this procedure will stop when max i,j {|uij(k + 1)−uij(k)|} < ε and converges to a local minimum of jm, where ε is a termination criterion belonging to [0,1] and k the iteration step. the algorithm is composed of the following steps. 1. initialize the matrix u = [uij], u(0). 2. at k-step, calculate the centers vectors cj;j = 1 . . .k: cj = ∑m i=1 u m ij xi∑m i=1 u m ij . (7) 3. update the matrix of membership degrees u(k) according to the new centers positions u(k + 1): uij = [ k∑ l=1 ( ∥xi − cj∥ ∥xi − cl∥ ) 2 m−1 ]−1 . (8) 650 n. elfelly, j-y dieulot, m. benrejeb, p. borne 4. while max i,j {|uij(k + 1)−uij(k)|}≥ ε , return to step 2. in our study, the final clustering result is obtained by considering that a given data point belongs to the cluster to which it presents the greatest membership degree. parametric identification of the models in the base the application of the clustering algorithm results in some repartition of the data set. each cluster is represented by a set of input/output measurements which will be exploited for the identification of the different models in the base. the application of the classification algorithm results in some repartition of the data set. each cluster is represented by a set of input/output measurements which will be exploited for the identification of the different models in the base. for this, the models orders are first estimated by using the so-called instrumental determinants’ ratio-test. this method is mainly based on the conditions concerning a matrix called "information matrix" which contains the input/output measurements [32]. this matrix is described as follows: qm = 1 nob nob∑ k=1   u(k) u(k + 1) ... u(k −m + 1) u(k + m)   [ y(k + 1) u(k + 1) · · · y(k + m) u(k + m) ] ; (9) where nob is the number of observations. the instrumental determinants’ ratio (idr) is given by: idr(m) = ∣∣∣∣ det(qm)det(qm+1) ∣∣∣∣ . (10) for each value of m,(m ≥ 1) the determination procedure of the order consists in building the matrices qm and qm+1 and in evaluating the ratio idr(m); the retained order m is the value for which the ration idr(m) quickly increases for the first time. given those different orders of models, the parametric identification issue consists in calculating the values of the parameters of the corresponding model-equation, given several experimental measures which describe the dynamic behavior of the model. for this, the recursive leastsquares method (rls) [32] is applied to achieve the parameters estimation. 3 validity computation and validation of the proposed modelling scheme the steps described in the previous paragraphs allow the design of the model-base. the purpose is to test the efficiency of the proposed modeling. for this, a validation step is worked out for some inputs different from those used for clustering. then, the multimodel output ymm, computed and compared to the real output of the studied process, is obtained through a fusion of the k models’ outputs yi weighted by their respective validity indexes vi, as illustrated by the a multimodel approach for complex systems modeling based on classification algorithms 651 system 11 and figure 3. ymm(k) = k∑ i=1 yi(k)vi(k); (11a) k∑ i=1 vi(k) = 1. (11b) figure 3: fusion principle. the validity index is a real number belonging to the interval [0,1]. it represents the relevance degree of each model calculated at each instant. in the literature, several methods have been proposed to deal with the validity issue. in our study, the residues’ approach is adopted for the calculation of validities. this method is based on the distance measurement between the outputs of the process and of the considered model. for example, the residue can be given by the following expression: ri = |y −yi|; i = 1, . . . ,k; (12) with: y: process output; yi: output of the model mi. if this residue value is equal to zero, the corresponding model mi represents perfectly the process at that time; if not, the model represents the process partially. between the methods proposed for the calculation of validities [4, 33], only the simple and the reinforced validities approaches are here considered. in general, the expression of the validities is given by: vi = 1−r′i ; (13) where r′i represent the normalized residues and are given by: r′i = ri∑k j=1 rj . (14) simple validities: the normalized simple validities vsimpi are defined so that their sum must be equal to 1 at each time: v simp i = vi k −1 . (15) 652 n. elfelly, j-y dieulot, m. benrejeb, p. borne reinforced validities for this type of validities, the reinforcement expression v′renfi is introduced as: v ′renf i = vi k∏ j =1,j ̸=i (1−vj) . (16) the normalized reinforced validities vrenfi could be written as follows: v renf i = v ′renf i∑k j=1 v ′renf j . (17) the comparative study between the two considered validities [11] has shown that the selection of the suitable approach depends on clustering results i.e. the clusters structure and repartition. in fact, when there are important variations in the same cluster and when an overlapping between clusters occurs, it is worth to use the simple validities’ method since it takes account of different models’ outputs referring to the expression 15. in this case, no model could represent ideally the process at any time. but when the clusters present very few variations and are well separated, the reinforced validities’ method is better adapted. thanks to the reinforcement expression 16, the application of this method promotes the contribution of the most dominant model which represents at best the process behavior. validation of the proposed modeling scheme once the appropriate method of validity computation selected, the validation of the global modeling scheme is carried out through a comparison between the real and the multimodel outputs for different input sequences. 4 simulation examples 4.1 modelling of a mechanical manipulator in order to underline the interest and the efficiency of the proposed approach, a first example of nonlinear system is considered, which consists of a two-link manipulator (figure 4) [34, 35] that can be described by the following equations: figure 4: mechanical system (simulation example). a multimodel approach for complex systems modeling based on classification algorithms 653   ẋ1 = x2, ẋ2 = −mgl i sin(x1)− mgli (x1 −x3), ẋ3 = x4, ẋ4 = k j (x1 −x3) + 1j u. (18) the considered system is composed of two rotating bars, where: • x1,x2: angular position and angular velocity of bar 1; • x3,x4: angular position and angular velocity of bar 2; • u: torque applied to bar 2; • g = 9.8 m ·s−2: gravity constant; • i = 1 kg ·m2: moment of inertia of bar 1; • j = 1 kg ·m2: moment of inertia of bar 2; • l = 1 m: half of the length of bar 1; • m = 1 kg: mass of bar 1; • k = 1 nm ·rad−1: elastic rigidity at the link between bars 1-2. the normal form of the nonlinear model of the system can be written as follows:  ż1 = z2, ż2 = z3, ż3 = z4, ż4 = a(z) + b(z)u, (19) where a(z) = mgl i sin(z1)z 2 2 − mgl i cos(z1)z3 − (ki + k i )z3 − kmglij sin(z1) and b(z) = k ij . the variables zi are related to the variables xi through the following equations:  x1 = z1, x2 = z2, x3 = z1 + 1 k z3 + mgl k sin(z1), x4 = z2 + 1 k z4 + mgl k cos(z1)z2. (20) in the remainder of the study, u will be considered as the input and x1 as the output of the system. first, the system is excited by an adequate signal u(k) in order to collect the measurements x1(k) and x1(k − 1) at different instants. these numerical data are used for the determination of the appropriate number of operating clusters by using a neural network and the rpcl algorithm. figure 5, which gives the results of the learning procedure, shows that by considering six units in the input layer, two centres move away from the observation space, which permits to conclude that the adequate number of clusters can be chosen equal to four. then, the fuzzy k-means algorithm is carried out in order to select the different operating clusters (figure 6). referring to each of the four data sets relative to the clusters resulting from the implementation of the proposed classification algorithm, the orders and the parameters of the transfer 654 n. elfelly, j-y dieulot, m. benrejeb, p. borne figure 5: determination of the number of clusters (n=6). figure 6: classification results. functions relative to the four models of the base are estimated by using respectively the idr and the rls methods. the study presented in [11,19] leads to the selection of the reinforced validities method since the classification results (figure 6) present well-separated clusters with few variations. in order a multimodel approach for complex systems modeling based on classification algorithms 655 to give prominence to the capacity of the identified models to reproduce the operating system in different domains, many input sequences have been considered, two of which are presented in this paper and are described by the following equations: u1(k) = sin(0.5k). (21) u2(k) = 1 + 0.5sin(0.6k). (22) referring to the designed model-base, the multimodel output is computed for each input sequence (by fusion of the models’ outputs weighted by the reinforced validities) and is then compared to the real system output. the results are given in figure 7 and figure 8 where x1 is the real output, and x1mm the multimodel output obtained by using the fuzzy k-means algorithm. figure 7: real and multimodel outputs for the mechanical manipulator (input sequence u1). figure 8: real and multimodel outputs for the mechanical manipulator (input sequence u2). introducing the nrmse (normalized root mean square error): nrmse = √∑n i=1(ymm−y)2 n ymax −ymin , (23) where ymm,y are the multimodel and process output and n is the number of samples; one obtains an accuracy of nrmse = 0.001. a fair comparison was made in a previous paper [39] with an 656 n. elfelly, j-y dieulot, m. benrejeb, p. borne academic system which was controlled in [6] with a multimodel method where 64 models were proposed instead of only 4 for the method proposed in this paper. the results where only 3 times better for the method in [39] which needed 16 times more models and complicated workout and tuning, but both methods outperformed the results. it can be noticed that the implementation of the proposed multimodel approach allows an acceptable modelling of the studied system respective to its complexity. in fact, by considering the first input sequence u1, the multimodel output follows the real output and the error between the two signals shaves over the time. besides, even with a more complicated system output (obtained by considering the input sequence u2) with important oscillations and for a longer time interval, the results given by the proposed modelling approach (figure 8) are satisfactory. 4.2 modeling of a bioreactor this second example shows the relevance of our method compared to a "black-box" multimodel technique with a large model base as provided in the work by [6], illustrated on the same benchmark, a bioreactor model. this model has been used in several nonlinear modeling or control issues [6, 36, 37]. substrate is fed continuously with a constant feedrate f to the well-mixed bioreactor which has a constant volume v . microbial growth follows a contois model [38], the microorganisms and substrate concentration, respectively x1 and x2, are supposed to be small. the discrete-time mass-balance equations are derived as follows:  x1(k + 1) = x1(k) + 0.5 x1x2 x1+x2 −0.5u(k)x1(k), x2(k + 1) = x2(k)−0.5 x1x2x1+x2 −0.5u(k)x2(k) + 0.05u(k), y(k) = x1(k) (24) where y(k) is the model output, u(k) = f/v is the dilution rate, normalized to 1. as in the previous example, it is assumed that no a priori information is given on the possible (real) model structure and the system is adequately excited to generate a set of input/output measures which will allow to describe the whole process dynamics. as was explained before, the rival penalized competitive learning algorithm is applied, and the number of models was found to be equal to 10. experimental measurements are then classified by using the fuzzy k-means algorithm. based on the learning results, where small overlapping between clusters was found, the reinforced validities were embedded in the multimodel structure. in terms of modelling accuracy, the obtained results were found to be quite similar to those given in [6] where 196 models were needed to represent the system dynamics . this enlightens the interest of the proposed approach as a high number of models yields a considerable computational burden. figure 9 provides the real output y and the multimodel output ymm by considering the following input sequence: u(k) = 0.5 + 0.1sin(2k). the results are confirmed through another input sequence u2 shown on figure 10 and figure 11 shows a good agreement between multimodel and real outputs. 5 conclusion in this paper, a new approach for complex systems modelling is proposed and an experimental validation is presented. this approach is applicable when dealing with complex, strongly nonlinear and uncertain systems. it allows the determination of the base of models, which are representative of the system in different operating domains, by using two classification algorithms and two methods of structural and parametric identification. the issue of selection of the models’ number is solved by using a neural network and a rival penalized competitive learning (rpcl). once this number determined, the data collected on the system are then clustered by using a multimodel approach for complex systems modeling based on classification algorithms 657 figure 9: real and multimodel outputs for the bioreactor model (input sequence u1). figure 10: bioreactor input sequence u2. figure 11: real and multimodel outputs for the bioreactor model (input sequence u2). the fuzzy k-means algorithm. the classification results are exploited for the identification of models. finally, a validation procedure is worked 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[39] n. elfelly and j.-y. dieulot and m. benrejeb and p. borne, multimodel control design using unsupervised classifiers, studies in informatics and control, issn 1220-1766, vol. 21 (1), pp. 101-108, 2012. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 2 (june), pp. 328-336 modeling uncertainty in a decision problem by externalizing information i. parpucea, b. pârv, t. socaciu ilie parpucea babes-bolyai university faculty of economic sciences and business administration romania, 400591 cluj-napoca, 58-60 teodor mihali e-mail: ilie.parpucea@econ.ubbcluj.ro bazil pârv babes-bolyai university faculty of mathematics and computer science romania, 400084 cluj-napoca, 1 m. kogălniceanu e-mail: bparv@cs.ubbcluj.ro tiberiu socaciu stefan cel mare university faculty of economic sciences and public administration romania, 720229 suceava, 13 universităţii e-mail: socaciu@seap.usv.ro abstract: this paper deals with decision problems under uncertainty. the solution of a decision problem involves observation, processing, and modeling of statistical data in order to quantify the uncertainty. better data measurement and estimation of uncertainty add more consistency to the solution of a decision problem. the paper proposes a new way of predicting the bayesiannash equilibrium which uses information sources to measure new information received by information consumers. thus, the estimation of uncertainty is based on a more solid mathematical foundation, needed (as in the case of artificial intelligence) to produce logical inferences. from another perspective, the externalization of information helps the software designers to produce better software architectures for decision support systems. an theoretical example illustrates a market situation with a small number of firms, each firm’s output being likely to have a large impact on the market price. keywords: bayesian-nash equilibrium, information source, conditional probability distribution. 1 introduction game theory, founded by von neumann and morgenstern (1947), studies situations in which multiple agents or players interact in order to each maximize an objective (payoff) function. the payoff function of a player is determined not only by its own actions, but also by the actions of other players. in a game with incomplete information, the payoffs also depend on information that is private to the individual agents. this information is known as an agent’s type. bayesian decision theory is concerned with the question of how a player (decision maker) should choose a particular action from a set of possible choices if the outcome of the choice also depends on some unknown state (from the states of the world). in our approach, the decision maker is modeling the information received by the system (i.e. new information) as an information source ( [2]). a decision problem involves one or several information sources. we copyright c⃝ 2006-2011 by ccc publications modeling uncertainty in a decision problem by externalizing information 329 assume that each person is able to represent his beliefs, as the likelihood of the different n states of the information source, by a subjective discrete probability distribution ( [5]). the structure of this paper is as follows. after this introductory section, the next one introduces the teoretical background related to games with incomplete information and information sources. the third section presents the bayes-nash equilibrium in the presence of information sources, and the fourth discusses a market-related example. the last section compares the proposed approach with the classical one, and outlines future work. 2 theoretical background this section introduces the main concepts discussed in this paper. it starts with some definitions stating the context, and then defines the concept of information source, both taken from [1] and [3]. the last sub-section introduces the new concept of bayesian-nash equilibrium based on information sources. in what follows, information means a message about an event that has occurred, will occur, or is likely to occur. the received information regarding a possible realization of an event is extremely important. information is a particular case of reflection, as an interaction between two processes; one’s properties (the process that generates or produces information) will be reproduced in another process or several other processes (that consume information). interaction between two or more processes involves an exchange of information. 2.1 games with incomplete information definition 1. a game with incomplete information ( [1]), is denoted by: γt = (i, (fi)i∈i, (p i t(f, θ))i∈i, (θi)i∈i, µt), (1) where: • i is the set of players, |i| = m, • fi is the strategy set for player i, i = 1, m, and f = f1 × f2 × · · · × fm is the the set of all possible strategy profiles; • f = (f1, f2, · · · , fm) ∈ f is a joint strategy or strategy profile; • θi is the set of types for the player i, and θ = θ1 × θ2 × · · · × θm is the joint type space; • θ = (θ1, θ2, · · · , θm) ∈ θ is the joint type of all players; • pit(f, θ) is the payoff function for player i at the moment t if the strategy f and the type combination θ are chosen. note that the payoff for the player i may depend not only on its type θi, but also on the other players’ type, denoted by θ−i. • µt the probability distribution on the set θ at the moment t. in our exposition, we assume that type sets θi are finite; consequently, θ is a finite set also. µt(θ), θ ∈ θ denotes the probability of chosing type combination θ at the moment t. as in [4], we assume, without loss of generality, that players have incomplete information about their opponents’ payoffs but have complete information about the strategies of all other players. the following definition, taken from [1, 3], introduces the classical bayes-nash equilibrium of a game with incomplete information. 330 i. parpucea, b. pârv, t. socaciu definition 2. a strategy profile f(θ) = (f1(θ1), f2(θ2), · · · , fm(θm)) constitutes a bayes-nash equilibrium of a game γt with incomplete information if the following inequality: ∑ θ−i∈θ−i pit(f ∗ i (θi), f ∗ −i(θ−i), θi, θ−i)µt(θ−i|θi) ≥ ∑ θ−i∈θ−i pit(fi(θi), f ∗ −i(θ−i), θi, θ−i) · µt(θ−i|θi) (2) holds for all possible players i ∈ i and all types θi ∈ θi and all strategies fi ∈ fi. 2.2 information sources a process (information producer or consumer ) is specified by a set of n variables, denoted by v = {v1, v2, · · · , vn}, where vj is the set of values for the jth variable: vj = {v1j , v 2 j , · · · , v nj j }. the state of a process at a certain moment is given by the vector v = {v1, v2, · · · , vn}, vj ∈ vj, j = 1, n. future values of all variables are random, and the realization of the states depends on received information. this information is in message form, decreasing or increasing the uncertainty of the realization of an event. an information source is a way of specifying the states of a process, regarding one or several variables. the information source assigned to the jth variable is denoted by sj, and the set of distinct values vkj ∈ vj, k = 1, nj, represents a complete space of events. the simultaneous realization of two events is impossible, and the union of the events represents a certain event. a state skj of the information source s j is assigned to each event vj = vkj . for a bounded interval of time, only information sources with a finite number of states will be taken into account. consider the following assumptions: • each player is able to represent his beliefs, as to the likelihood of the different nj states of the information source sj, by a subjective discrete probability distribution. • the information source sj has discrete states and the individual is supposed to be able to assign to each state skj a degree of belief, in the form of (normalized) numerical weights p k j , between zero and one and whose sum is one: ∀j = 1, n : 0 ≤ pkj ≤ 1, ∀k = 1, nj; nj∑ k=1 pkj = 1. (pkj is the probability that the state s k j occurs). if the information source sj has nj states, the set of states and probabilities defined at a moment t, forms a discrete random variable denoted by: s j t : ( skj pkj (t) ) k=1,nj , j = 1, n. a simple information source is an information source defined with respect to a single variable vj. a complex information source is an information source defined with respect to two or more variables, which can be independent or dependent. in the second case (i.e. the variables are related to one another), the mathematical model of the complex information source needs to contain this dependency. in order to illustrate how a complex information source is constructed, let’s consider for the beginning the simplest case of two independent variables, v1 ∈ v and v2 ∈ v and assign to each variable a simple information source, s1t and s 2 t respectively: s1t : ( sk1 pk1(t) ) k=1,n1 , s2t : ( sl2 pl2(t) ) l=1,n2 , where sk1, k = 1, n1 are the states of information source s 1 t and s l 2, l = 1, n2 are the states of information source s2t . modeling uncertainty in a decision problem by externalizing information 331 the complex information source sc1,2t , built with respect to the variables v1 and v2 at the moment t, has the following mathematical model: sc 1,2 t : ( sk1s j 2 pk1(t)p j 2(t) ) k=1,n1,j=1,n2 . now let us consider the case of two dependent variables, with the simple information source s1t assigned to the independent variable v1, and the simple information source s 2 t assigned to the dependent variable v2. the discrete random variable s1t is: s1t : ( sk1 pk1(t) ) k=1,n1 . for a state of the source s1t , denoted by s k 1, the information source s 2 t conditioned by the state sk1 is defined as follows: s 2/1 t (s k 1) : ( sl2 p k,l 2 (t) ) l=1,n2 , where pk,l2 (t) = p(s 2 t = s l 2|s 1 t = s l 1) is the probability of occurence of state s l 2 conditioned by state sk1. the complex information source sc2/1t , constructed by considering the two variables, has the following form: sc 2/1 t : ( sk1s l 2 pk1(t) · p k,l 2 (t) ) k=1,n1,l=1,n2 , where the probability of occurence of the state sk1s l 2 is: p(s1t = s k 1; s 2 t = s l 2) = p(s 1 t = s k 1) · p(s 2 t = s l 2|s 1 t = s k 1) = p k 1(t) · p k,l 2 (t). if s2t is a discrete probability distribution and s 1 t is an information source, then, according to the above discussion, we can say that s2t is a distribution conditioned by information source s1t . therefore we have a probability distribution updated by an information source. 3 bayes-nash equilibrium in the presence of information sources let us consider now the probability distribution µt defined on the discrete set θ and a information source st (simple or complex), common to all players. according to the probability distribution conditioned by an information source discussed above, we have: p(µt = θ; st = s j) = p(st = s j) · p(µt = θ|st = sj), where θ ∈ θ and sj ∈ st. 3.1 notations considering the following notations: • p(µt, st) the joint probability of µt and st occurring simultaneously, referred to as the historic probability distribution, • p(µt/st) the probability of µt occurrring conditional on st having occurred (i.e. the conditional probability of µt given st), also known as the probability distribution of information source, and • p(st) the marginal probability of st, referred to as the posterior probability distribution, the following equation holds (as in [7]): p(µt/st) = p(µt,st) p(st) . according to the above, posterior means historical updated with information, i.e. the probability distribution µt conditioned by the information source st, denoted by µct, is the probability distribution µt updated by the information source st. 332 i. parpucea, b. pârv, t. socaciu the information source st is a possible probability distribution of states at the moment t+1. denoting the game with incomplete information at the moment t+1 and based on st with γt+1, it can be defined recursively as follows: γt+1 = γt(st), where γt+1 = (i, (fi)i∈i, (pit(f, θ))i∈i, (θi)i∈i, µct). the game γt+1 is the updated game γt based upon st. this information source updates probability distribution µt on θ and thus the equilibrium of γt is modified. 3.2 decision functions and strategies if a player receives information about his/her own type, then he/she can choose a particular strategy to maximize his/her expected payoff. definition 3. a decision function of player i ∈ i, denoted by fi(.), is a function that, for each type θi ∈ θi, specifies the strategy fi(θi) ∈ fi this player will choose if his/her type turns out to be θi. let µcjt(θ−i|θ(i)) be the updated probability obtained by using bayesian updating rule, of a particular type combination for the opponents θ−i, given that player i has type θi. for each type profile θ ∈ θ, there are updated beliefs for each player, i.e. a list of conditional probability distributions ( µc1t (θ−1|θ1), · · · , µcmt (θ−m|θm) ) . players’ beliefs after they have received information about their types, are no longer identical. definition 4. the strategy combination of all players except player i, that will be played according to the decision functions f−i(.), if type combination θ−i occurs, is a list of decision functions f−i(.) = (f1(.), · · · , fi−1(.), fi+1(.), · · · , fi(.)) for all players (other than player i) and θ−i(.) = (θ1, · · · , θi−1, θi+1, · · · , θi), a type combination for the other players, f−i(θ−i), that is: f−i(θi) = (f1(θ1), · · · , fi−1(θi−1), fi+1(θi+1), · · · , fm(θm)). 3.3 bayes-nash equilibrium in the presence of information sources the above definitions allow us to give the following: definition 5. the bayes nash equilibrium of the game γt+1 is a list of decision functions (f∗1 (.), · · · , f ∗ i (.)), such that for all possible players i ∈ i and all types θi ∈ θi:∑ θ−i∈θ−i pit ( f∗i (θi), f ∗ −i(θ−i), θi, θ−i ) ·µct(θ−i|θi) ≥ ∑ θ−i∈θ−i pit ( fi, f ∗ −i(θ−i), θi, θ−i ) ·µcit(θ−i|θi) (3) holds for all strategies fi ∈ ft. the equilibrium of γt+1 can differ from the equilibrium of γt due to the information in st. for a given player i ∈ i the updated equilibrium of γt+1 is: f∗i (.) = ∑ j f∗ij(.) · p j(t), where f∗ij(.) is the equilibrium of player i for the state s j of the information source st and pj(t) is the probability that st = sj. the above equation represents the updated equilibrium as a weighted average of all equilibria for the states of information source. 4 a market-related example consider a game with incomplete information, where the players are two firms supplying slightly different products (produced with zero production costs, as in [1]), with prices denoted modeling uncertainty in a decision problem by externalizing information 333 by p1 and p2. as a result of new received information, the variation of each price around the average price can be modeled using two simple information sources, s1t and s 2 t , as it follows: s1t : ( s11 : p1 ≤ p1 s 2 1 : p1 > p1 π1 1 − π1 ) ; s2t : ( s12 : p2 ≤ p2 s 2 2 : p2 > p2 π2 1 − π2 ) where p1 represents the average price of the first firm, and p2 the average price of the second firm. both information sources have two states, (s11, s 2 1) and (s 1 2, s 2 2), with the probabilities (π1, 1 − π1) and (π2, 1 − π2), respectively. 4.1 notations for our example, consider the following problem-specific notations: • the demand functions for the goods of the two firms: d1(p1, p2) = a · ∆p1 + b · ∆p2, d2(p1, p2) = c · ∆p1 + d · ∆p2, where ∆pi = pi − pi, i = 1, 2, are the deviation of price pi from the average price pi. firm one does not know parameters c and d; firm two does not know parameters a and b. • the sets of possible types of the two players: θ1 = {(ai, bj), i = 1, 2; j = 1, 2} , θ2 = {(ci, dj), i = 1, 2; j = 1, 2}; • the payoff functions of the two players: π1 (p1, p2, (ai, bj)) = (ai · ∆p1 + bj · ∆p2) · p1, π2 (p1, p2, (ci, dj)) = (ci · ∆p1 + dj · ∆p2) · p2. 4.2 building the complex information source with the above notations, the complex information source is rewritten as: sct+1 : ( p1 ≤ p1 ∧ p2 ≤ p2 p1 ≤ p1 ∧ p2 > p2 p1 > p1 ∧ p2 ≤ p2 p1 > p1 ∧ p2 > p2 π1 · π2 π1 · (1 − π2) (1 − π1) · π2 (1 − π1) · (1 − π2) ) , no matter what dependency relation is between the two prices considered. the information source sct+1 describes the behavior of the market of the both products considering the variation of their prices in the next period, as a result of the received information. in a bayesian-nash equilibrium, each firm is supposed to choose a type contingent strategy, that is decision functions p1(.) and p2(.) respectively, which is the best response to the opponent’s decision function. in this example, µct is a probability distribution defined on θ1 × θ2 and conditioned by the information source sct. for a state sk1s l 2 of source sct we build two conditional distributions, µc1t and µc 2 t as can be seen next. 4.3 computing the best response for the firm one consider p2(.) = (p2(c1, d1), p2(c1, d2), p2(c2, d1), p2(c2, d2)) as given (fixed) and suppose that firm one has just learned that it has the demand parameters (a1, b1). firm one’s expected payoff can be rewritten as: π1 (p1(a1, b1), p2(c1, d1)) · µc1t ((c1, d1)|(a1, b1)) + +π1 (p1(a1, b2), p2(c1, d2)) · µc1t ((c1, d2)|(a1, b1)) + +π1 (p1(a2, b1), p2(c2, d1)) · µc1t ((c2, d1)|(a1, b1)) + +π1 (p1(a2, b2), p2(c2, d2)) · µc1t ((c2, d2)|(a1, b1)) = = a1 · p21(a1, b1) + p1(a1, b1) (b1 · p2(cidj|a1b1) − a1p1 − b1p2) . (4) 334 i. parpucea, b. pârv, t. socaciu in the above equation we used the following notation for the average price p2 conditioned by state (a1, b1): p2(ci, dj)|a1b1) = p2(c1, d1) · µc1t ((c1, d1)|(a1, b1)) + p2(c1, d2) · µc 1 t ((c1, d2)|(a1, b1)) + +p2(c2, d1) · µc1t ((c2, d1)|(a1, b1)) + p2(c2, d2) · µc 1 t ((c2, d2)|(a1, b1)). the payoff function (4) is continuously differentiable in firm one’s strategy p1(.). therefore, any p1 satisfying the first order condition for a maximum will be the best response to the type-contingent strategy p2(.), previously considered. solving the first order condition for the maximum of the expected payoff function (4), one obtains the following best response p∗1(a1, b1) for firm one of type (a1, b1) to the decision functions (p2(c1, d1), p2(c2, d2), p2(c2, d1), p2(c2, d2)) of firm two: p∗1(a1, b1) = 1 2 (p1 + b1 a1 (p2 − p2(cidj|a1b1))). (5) in a similar way, one obtains for all other types of firm one: p∗1(a1, b2) = 1 2 (p1 + b2 a1 (p2 − p2(cidj|a1b2))), (6) p∗1(a2, b1) = 1 2 (p1 + b1 a2 (p2 − p2(cidj|a2b1))), (7) p∗1(a2, b2) = 1 2 (p1 + b2 a2 (p2 − p2(cidj|a2b2)). (8) 4.4 computing the best response for the firm two now consider that firm two learns that its type is (c1, d1). for a fixed type-contingent strategy of firm one p1(.) = (p1(a1, b1), p1(a1, b2), p1(a2, b1), p1(a2, b2)), the expected payoff of firm two will be as follows: π2 (p1(a1, b1), p2(c1, d1)) · µc2t ((a1, b1)|(c1, d1)) + +π2 (p1(a1, b2), p2(c1, d2)) · µc2t ((a1, b2)|(c1, d1)) + +π2 (p1(a2, b1), p2(c2, d1)) · µc2t ((a2, b1)|(c1, d1)) + +π2 (p1(a2, b2), p2(c2, d2)) · µc2t ((a2, b2)|(c1, d1)) = = d1 · p22(c1, d1) + p2(c1, d1) (c1 · p1(aibj|c1d1) − c1p1 − d1p2) . (9) in the equation (9) of the payoff function for the second firm, the average price p1 conditioned by the state (c1, d1), is given by: p1(ai, bj)|c1d1) = p1(a1, b1) · µc2t ((a1, b1)|(c1, d1)) + p1(a1, b2) · µc 2 t ((a1, b2)|(c1, d1)) + p1(a2, b1) · µc2t ((a2, b1)|(c1, d1)) + p1(a2, b2) · µc 2 t ((a2, b2)|(c1, d1)). first-order condition gives the best response function for a firm of type (c1, d1): p∗2(c1, d1) = 1 2 (p2 + c1 d1 (p1 − p1(aibj|c1d1))). (10) a similar calculation yields firm two’s best response for all other types of the following typecontingent strategies: p∗2(c1, d2) = 1 2 (p2 + c2 d1 (p1 − p1(aibj|c1d2))), (11) p∗2(c2, d1) = 1 2 (p2 + c1 d2 (p1 − p1(aibj|c2d1))), (12) p∗2(c2, d2) = 1 2 (p2 + c2 d2 (p1 − p1(aibj|c2d2))). (13) modeling uncertainty in a decision problem by externalizing information 335 4.5 conclusion in order to find a bayesian-nash equilibrium, one has to solve the system of equations given by the best response functions. with two players and four types for each player, this leads to a system of eight equations. the solution, (p∗1(.), p ∗ 2(.)), is the bayesian -nash equilibrium. the probability of realization of equilibrium prices (p∗1(.), p ∗ 2(.)), as a result of information received, is equal to the probability of realization of state sk1s l 2 of complex source sct+1. 5 conclusions and future works 5.1 our approach vs other approaches the main points of our approach are as follows: • the complexity of uncertainty is given by the great (huge) number of variables; when the complexity of a decision problem (and the number of components dominated by uncertainty) grows, it is recommended to use a bayesian network ( [6]); in our case, we use a simple bayesian network, subject to a learning algorithm; • the original idea is to separate the set of problem components into two disjoint subsets: (a) deterministic components, and (b) components dominated by uncertainty; the separation of game information into external and internal can be done for each decision problem dominated by uncertainty; • this separation allows you to study the influence of each individual factor to the solution of the game in a more efficient way; also, it suggests some architectural patterns (styles) to be used when designing a decision support system. the paper [8] discusses this issue in more detail. the essential difference between the classic approach and those proposed in this paper is given by the separation of the information external to the game from the game-specific information. this separation follows the separation of responsibilities principle. this way, both external and internal elements of the game are easier to model and understand. the classical approach does not make any difference between these two categories of information; more precisely, the influence of external information on the uncertainty that dominates the game is not taken into account/quantified. by splitting the game information into external and internal, the former being modeled by information sources, the influence of external environment on the variation of the solution is better captured and quantified. this provides a better evaluation of the contribution of individual factors to the predicted equilibrium. another advantage of this separation is that it allows a better, easier calibration of the model, by comparing the computed equilibrium with real solution, taken from historical data. 5.2 future work our future efforts are directed to apply this general algorithm to various games with incomplete information and to build decision support systems based on it. acknowledgements this work was supported by the grant id_2586, sponsored by nurc romanian national university research council (cncsis). 336 i. parpucea, b. pârv, t. socaciu bibliography [1] eichberger, i., game theory for economists, academic press, 1993. [2] florea, i., parpucea, i., economic cybernetics (romanian), babeş-bolyai university, 1993. [3] fudenberg, d., tirole, j., game theory, mit press, 1991. [4] harsanyi, j.c., games with incomplete information played by bayesian players, parts i, ii, iii, management science, 14, 1967, pp. 159-182, 320-334, 486-502. [5] hirshleifer ,j., riley j. g., the analytics of uncertainty and information, cambridge university press, 1995. [6] jensen, f.v., nielsen, t.d., bayesian networks and decision graphs, springer, 2nd ed., 2007 [7] koop, g., bayesian econometrics, wiley, 2003. [8] pârv, b., parpucea, i., computing bayes-nash equilibrium for games with incomplete information by using information sources, submitted to ijccc. [9] reiz, b., csato, l. tree-like bayesian network classifiers for surgery survival chance prediction, int. j. of computers, communications & control, iii, 2008, pp. 470-474. international journal of computers communications & control issn 1841-9836, 10(4):551-566, august, 2015. analytical modelling and performability evaluation of multi-channel wlans with global failures y. kirsal-ever, y. kirsal, e. ever, o. gemikonakli yoney kirsal-ever, orhan gemikonakli computer and communication engineering middlesex university london, hendon, uk y.kirsal@mdx.ac.uk,o.gemikonakli@mdx.ac.uk yonal kirsal electrical and electronic engineering european university of lefke north cyprus, mersin 10, turkey ykirsal@eul.edu.tr enver ever* computer engineering middle east technical university northern cyprus campus, guzelyurt, mersin 10, turkey *corresponding author: eever@metu.edu.tr abstract: wireless local area networks (wlans) which are based on ieee 802.11 standard are used widely in existing local area network configurations. ieee 802.11 offers multiple non-overlapping channels to increase the capacity of the network. there are strong evidences that wlans are prone to impairments. in order to improve the quality of service (qos) and to evaluate the performance of wlans realistically, the availability of the systems should be considered. this paper studies performability evaluation of a multi-channel wlan using analytical modelling approach. unlike the existing studies, the failures of the overall system, where a critical function unit fails making all the channels unavailable are considered. a new term is introduced as global failures. it is possible to solve the models considered using matrix geometric method where system parameters and minimal non negative solution r is computed by an iterative method. however spectral expansion method is a well-known alternative where the iterative calculations for solving r is avoided using eigenvalues and eigenvectors. the exact spectral expansion method is employed to obtain performability measures such as mean queue length and blocking probability. iterative refinements are employed in solution of simultaneous equations. keywords: analytical modelling, 2-dimensional markov chain analysis, performability evaluation, availability, spectral expansion, multi-channel wlans. 1 introduction the demand of new multimedia services, increase in bandwidth available to users, high traffic densities, and ubiquitous connectivity have caused the rapid growth of wireless and mobile communication systems. wireless lans (wlans) based on the ieee 802.11 standard have gained widespread popularity mainly because they provide users unlimited access to the internet with relatively high data rates. the ieee 802.11 specifications provide a multi-rate capability at the physical layer (phy) to accommodate mobile users with diverse wireless channel conditions [1]. the ieee 802.11b specification supports data rate up to 11 mbps. the subsequent versions of wlans such as 802.11a, 802.11g, 802.11e and the latest version, 802.11n, offers maximum copyright © 2006-2015 by ccc publications 552 y. kirsal-ever, y. kirsal, e. ever, o. gemikonakli rate of 54 mbps and 500-600 mbps respectively [2, 3]. the classical ieee 802.11 mac protocol considers a single shared channel. nevertheless, multiple channels can also be used [1,5,20,22,24]. the recent wlans based on ieee 802.11 standards permit simultaneous operation on multiple non-overlapping channels at the physical layer. extensive research has been carried out on multi-channel mac protocols for ieee 802.11 based wlans [1, 21, 23, 24]. these approaches proposed multi-channel wlans to improve overall network performance depending on different channel assignment schemes. in other words, the wireless access points (aps) can be considered as multi-channel systems with multiple wireless radios each operating independently on different channels. these multiple radios and multiple channels facilitates dynamic selection of channels for the users to transmit and receive packets without interfering each other [1,2,4,5,8,14]. thus, these new features improve the qos such as throughput, blocking probability, response time etc. [15, 21–24]. in [20] two cross-layer models have been proposed to access transmissions channels. the first model is single class type traffic. the traffic divided into two classes as high and low priority traffic in the second model. the performance parameters for both single and multi-channel wireless networks, like the requests throughput, acceptance probability are calculated. wlans suffer especially when the users with different and high transmission rates share the same physical channel. in order to solve this problem different transmission channels have been proposed in [21] to achieve better qos. in addition, an 802.11 based multi-hop wireless ad-hoc network architecture have been proposed in [22] where it employs multiple radio channels simultaneously in the system. multiple channels along with the bi-directional channel reservation policy of ad hoc networks based on ieee mac protocol considered is considered in [23] using analytical modelling. the paper also considered two different channel schemes for further analysis. in [24] existing multi-channel protocols are proposed to enhance the qos by classifying them into single-radio protocols, multi-radio single-hop protocols, and multi-radio multi-hop protocols. the existing studies in the literature propose modified mac layer architecture which supports multichannel networks. the main aim is to find an optimal channel for a single packet transmission to improve qos. in order to improve qos and to maximize the performance of such systems analytical modelling and evaluation is still one of the key issues in the performance characterization of wireless communication systems. various approaches exist in the literature for the performance evaluation of wlans [6–8, 12, 14, 20]. however, most of the existing work has been done on pure performance analysis of ieee 802.11 distributed coordination function (dcf) as well as the analysis of the ieee 802.11 mac protocol. the traditional pure performance models are too optimistic. they ignore failure and recovery behaviour and only focus on system’s ability to perform. system availability models, that consider failure and repair, should be taken into account in order to obtain more realistic qos measures. the performability modelling and evaluation is a combination of performance and availability models [9–11, 13]. in practice, wlans are prone to system failures such as failure of an ap and base station (bs) like any other physical system [10, 11, 13, 15, 16, 19]. the signal strength, fading, reflection and refraction of signals, and man-made noise etc. are some important factors that can be the cause of the channel unavailability in wlans [5, 10, 11, 13, 15]. in wlans, failing of ap/bs and/or the components of networks may cause the failure of all the available channels. this fault is termed as global system failure of wlan in this paper. examples of system failure scenarios in wlans would include failure of aps and/or bss, loss of the link between aps and users etc. this type of failures may occur when an ap fails or the communication links between an ap and service providers do not function, or operate. failures may occur due to software, hardware, human error, or a combination of these factors in wlans. different kind of failures, reasons, and failure affects can be found in [5, 9, 11–13, 16, 19]. channel failures due to the environmental interference as analytical modelling and performability evaluation of multi-channel wlans with global failures 553 well as from other wireless devices are considered in [15]. moreover, in [13], channels are defined as virtual channels and each channel is subject to failure for transmission because of multipath fading in the wlans. in case of a system failure, all the channels, resources, services, and the components become unavailable to the users. system and channel failures occurring in system is lead to degradation of the entire system performance. however, the existing studies do not consider the channel unavailability with global failures which can be quite critical for qos measurements. one of the main objectives in this paper is to model and analyse the system performance considering global failures and recovery in multi-channel wlans. thus, modelling and analysis of the system performance with global failures and repairs is considered with potential channel failures in multi-channel wlans. in addition, mobility related factors are considered and analysed. therefore the proposed model attempts to understand, and improve system performance in terms of system capacity, mobility and blocking probability of packets. the channel assignment scheme employed is another major issue in the performance characterisation of wireless communication systems. there have been extensive studies on dynamic channel access in multi-channel wlans recently [1, 3, 4, 14, 20, 22, 23]. this is because dynamic channel access scheme is appropriate and efficient scheme to improve qos of multi-channel wlans. there is no fixed relationship between channels and systems in dynamic channel access. all channels are assigned into a central pool (e.g bandwidth). the channels are assigned to the incoming packet requests dynamically and channel returns to the central pool after the service [3, 4]. in order to meet the future demands and increase available bandwidth for each user within wlans, dynamic channel access is also taken into consideration in this paper. this is done by (assuming the packets are accepted with certain bandwidth assigned) optimal allocation of wlan bandwidth for the number of channels required. all the channels used by the same wlan are perfectly coordinated and synchronised. spectral expansion method is employed for the exact solution of the analytical model considered [9,10]. using the proposed model and an exact solution, important performability measures, such as mean queue length and blocking probability can be computed. the rest of the paper is organized as follows: section ii describes the proposed model. the two dimensional modelling approach and exact steady state solution are explained in section iii and section iv respectively. in section v, numerical results computed by using an exact solution approach are presented. some pure performance results are also presented for comparison. conclusions and recommendations are provided in section vi. 2 the system considered and the analytical model the wlan presented is subject to failures. the entire system failures (e.g ap or bs) are considered as global failures that may be caused by power cut-off or resetting ap etc. the model covers mobility issues and various queue capacities as well. the system has multiple identical channels. allocation of packets is usually done by availability of channels and in this regard, it is well known that, in terms of efficiency the common queue is more suitable than individual queues in the queuing theory [7, 10, 11, 17]. the proposed model considered for performability evaluation of multi-channel wlans is shown in fig. 1. the system consists s identical channels, numbered 1, 2, 3, · · · , s with a common queue. the common queue is bounded with a capacity of w (w ≥ s). the maximum number of calls in the system is equal to the number of calls assigned to the channels plus the queuing capacity. this is given by l where, l = s+w . the superposition of all arrival streams (potential arrivals from other wireless technologies can be incorporated) follows a poisson process. in other words, inter-arrival times of the incoming call requests are assumed to follow an exponential distribution with mean rate of λ similar to studies [7–11], [17]. if the channels are available and idle in the 554 y. kirsal-ever, y. kirsal, e. ever, o. gemikonakli figure 1: the queueing system considered for proposed multi-channel wlan wlan, the packet arrivals can be assigned to any channel. otherwise, in case of a channel failure, or a busy channel, the incoming packet request is queued. similarly, the service times of the packet serviced by channel s (s=1, 2, · · · , s) are distributed exponentially. in addition to that, the dwell time, which is the time that mobile users spend in the wlan coverage area, is assumed to be distributed exponentially with mean 1/µwd [9, 10]. equation (1) is used to calculate µwd. µwd = e[v]f πo (1) where, e[v] denotes the expected value of the random variable v, which is the average speed of the mobile users. since the coverage area of the wlans are relatively small and most of the users are almost static, v is taken only for pedestrians (e.g e[v]=2km/h). f is the length of the perimeter of cell (a wlan with an arbitrary shape is assumed), and o is the area of the wlan [9], [10], [11]. tch is the average packet holding time in the wlan and follows an exponential distribution with mean e[tch]=1/µch. since f is small for wlan, it would be impractical to consider high speeds as the user will be out of range before they are able to use the connection. hence in this study, near stationary speeds are considered. t can be defined as the total channel holding time of a packet, which is exponentially distributed with mean e[t ] = 1/µ, where µ = µch + µwd (2) channel assignment schemes can be implemented in many different ways depending on the medium access control (mac) and the system architecture. wlan (the ieee 802.11) mac assures fair sharing of wlan bandwidth/channels stations since wlans use a shared medium [1, 4, 6, 7, 14, 19, 23]. traffic load makes channels unreliable and unavailable for transmission. this causes severe degradation in the performance of wlans. in order to solve the performance degradation problem in wlans, frequency channels should be assigned to aps in an appropriate and efficient manner. hence, in this paper, an analytical model developed maximizes the bandwidth usage according to the number of mobile users within the wlan. in other words, considering the behaviour of mac mechanism, the users fairly share wlan bandwidth. this sharing can be thought as a dynamic bandwidth allocation in wlan. this is analytical modelling and performability evaluation of multi-channel wlans with global failures 555 done by (assuming the call is accepted with certain bandwidth assigned) optimal allocation of wlan bandwidth for the number of channels required. the main objective of using dynamic bandwidth allocation is to improve the overall qos of an entire wlan by sharing the available bandwidth. when dynamic channel allocation scheme is employed, the channel does not change for different numbers of packets in the performance model employed. figure 2: the operative states of the proposed system the failure and repair behaviour of each channel are also represented by exponentially distributed failure and repair times with means 1/ξ and 1/η, respectively [?], [16]. a single repairman facility is assumed for all of the channels but the models provided can easily be extended for systems with multiple repairmen facilities. an inoperative period of a channel would also include the possible waiting time for a repairman. no operative channel can be idle if there are packets waiting, and no repairman can be idle if there are failed channels waiting for repair. on the other hand, the system (ap) can fail, and the repair facility is provided with means 1/ξg and 1/ηg, respectively. in case of global failures (ξg is the respective global failure rate) the priority of repair is given to the ap because the channels cannot be used when the ap is inoperative. all inter-arrival, service, failure/repair of channels and global failure and repair time variables are distributed exponentially and are independent of each other. the operative state of such a system is illustrated in fig. 2. the states 1, 2, 3, · · · , s are the working states of the multi-channel wlan. in state 0 there are no channels available due to the channel failures and in state g there are no channels available due to the ap failures. 3 two dimensional markov representation of the proposed model the state of the system at time t can be described by a pair of integer valued random variables, i(t) and j(t), specifying the channels plus global failure/recovery configurations and the number of packets present, respectively. here, channel configuration, and hence the range of i(t), refers to the operative states of the channels. in general, there are s + 2 configurations, represented by the values i(t) = 0, 1, · · · , s, g. the first s + 1 configurations represent the number of available channels in the system (from 0 to s). the (s+2)th state is used to represent the system in case the ap is down (state g). when the ap is repaired with repair rate ηg, the system resets with s operative channels. j(t) is the number of jobs in the system, j(t) = 0, 1, . . ., l. z = [i(t),j(t)]; t ≥ 0 is an irreducible markov process on a lattice strip (a qbd process), that models the system. its state space is, (0, 1, · · · , s + 1) x (0, 1, · · · , l). let the possible operative states of both channels and ap be represented in the horizontal direction (the number of channels available and g for the ap) and the number of packets in the vertical direction of a lattice strip. here, a is the matrix of instantaneous transition rates from state (i,j) to state (k,j), (i=0, 1, · · · , s +1; k=0, 1, · · · , s +1; i ̸= k; j=0, 1, · · · , l), with zeros 556 y. kirsal-ever, y. kirsal, e. ever, o. gemikonakli on the main diagonal, caused by a change in the state. these are the purely lateral transitions of the process z. matrices b and c are transition matrices for one-step upward and one-step downward transitions respectively [9], [10]. b is the matrix of one-step upward transition rate from state (i,j) to state (k,j + 1), (i=0,1, · · · , s + 1; k=0,1, · · · , s + 1; and j=0,1, · · · , l), caused by a packet arrival. clearly, the elements of a depend on the parameters s, ξ, η, ξg, ηg. the transition matrices of a system with s channels are of size (s + 2)×(s + 2). it is possible to specify the numbering of the matrices as (0, 1, 2, 3, · · · , s + 1) for states (0, 1, 2, 3,· · · , s, g) respectively. the state transition matrices a, aj, b, and bj, can be given as follows: a = aj =   0 η 0 0 0 0 0 ξg ξ 0 η 0 0 0 0 ξg 0 2ξ 0 η 0 0 0 ξg 0 0 3ξ 0 . . . 0 0 ξg 0 0 0 . . . 0 η 0 ξg 0 0 0 0 (s − 1)ξ 0 η ξg 0 0 0 0 0 sξ 0 ξg 0 0 0 0 0 0 ηg 0   b = bj =   λ 0 0 0 0 0 λ 0 0 0 0 0 . . . 0 0 0 0 0 λ 0 0 0 0 0 λ   on the other hand, c is the matrix of one-step downward transition rate from state (i,j) to state (k,j − 1), (i=0, 1, · · · , s + 1; k=0, 1, · · · , s + 1; and j=0, 1, · · · , l), caused by the departure of a serviced packet. the transition rate matrices do not depend on j for j ≥ m, where m is a threshold having an integer value. the elements of c and cj depend on the parameter µ and can be given as follow: c = cj =   0 0 0 0 0 0 0 0 µ 0 0 0 0 0 0 0 2µ 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 (s − 1)µ 0 0 0 0 0 0 0 sµ 0 0 0 0 0 0 0 0   please note that the stations considered within the coverage are of the wlan can be mobile. when such a scenario is considered, the transition rate matrix c depends on j since the departure rate caused by mobility depends on the number of packets in the queue [9, 10]. therefore, the threshold m is taken as m = l. if the number of packets in the system is less than the number of available channels, a channel is assigned for each packet. therefore, the downward transition rate is chosen as the minimum of number of packets and number of available channels. on the other hand, if the number of packets is greater than the number of available channels, all of the available channels are assigned to incoming calls and the calls in the queue have the departure rate µwd [9, 10] due to mobility. the matrix c is defined below, together with cj matrices for two different regions explained above: analytical modelling and performability evaluation of multi-channel wlans with global failures 557 c =   0 0 0 0 0 0 0 µ + wµwd 0 0 0 0 0 0 . . . 0 0 0 0 0 0 (s − 1)µ + wµwd 0 0 0 0 0 0 sµ + wµwd 0 0 0 0 0 0 0   for j > s cj =   0 0 0 0 0 0 0 µ + (j − s)µwd 0 0 0 0 0 0 . . . 0 0 0 0 0 0 (s − 1)µ + (j − s)µwd 0 0 0 0 0 0 sµ + (j − s)µwd 0 0 0 0 0 0 0   for j ≤ s cj =   0 0 0 0 0 0 0 0 min(1, j)µ 0 0 0 0 0 0 0 min(1, j)µ 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 min(1, j)µ 0 0 0 0 0 0 0 min(1, j)µ 0 0 0 0 0 0 0 0   this system can be solved and the steady state probabilities, pi,j can be obtained using the steady state solution presented in the next section. 4 steady state solution the solution is given for systems with bounded queueing capacities. the steady-state probabilities of the system considered can be expressed as: pi,j = limt→∞ p(i(t) = i,j(t) = j); (3) 0 ≤ i ≤ s + 1, 0 ≤ j ≤ l 4.1 spectral expansion solution it is possible to use spectral expansion or matrix-geometric methods to solve the system considered. when matrix-geometric method is employed, a non-linear matrix equation is formed from the system parameters and an iterative method is employed in order to compute the minimal non-negative solution r of this equation. one of the main disadvantages of this method is that it is not possible know the number of iterations needed to compute r for a specified accuracy. in this study spectral expansion method is employed for steady state solution. let’s define certain diagonal matrices of size (s + 2)× (s + 2) as follows: daj (i, i) = s+1∑ k=0 aj(i,k); d a(i, i) = s+1∑ k=0 a(i,k); (4) 558 y. kirsal-ever, y. kirsal, e. ever, o. gemikonakli dbj (i, i) = s+1∑ k=0 bj(i,k); d b(i, i) = s+1∑ k=0 b(i,k); (5) dcj (i, i) = s+1∑ k=0 cj(i,k); d c(i, i) = s+1∑ k=0 c(i,k); (6) and q0 = b, q1 = a−da −db −dc, q2 = c. all state probabilities in a row can be defined as: vj = (p0,j,p1,j, · · · ,ps+1,j);j = 0,1,2 . . . ,l (7) the steady-state balance equations for bounded queuing systems (0 ≤ j ≤ l) can now be written as follows: v0[d a 0 + d b 0 ] = v0a0 + v1c1 (8) vj[d a j + d b j + d c j ] = vj−1bj−1 + vjaj + vj+1cj+1; (9) 1 ≤ j ≤ m −1 vj[d a + db + dc] = vj−1b + vja + vj+1c; (10) m ≤ j ≤ l vl[d a + dc] = vl−1b + vla (11) the normalizing equation is given as follows: l∑ j=0 vje = l∑ j=0 s+1∑ i=0 p(i,j) = 1.0 (12) from the equations above, one can write vjq0 + vj+1q1 + vj+2q2 = 0 (13) (m −1) ≤ j ≤ (l−2) furthermore, the characteristic matrix polynomial q(λ) can be defined as: qλ = q0 + q1λ + q2λ 2; q̄β = q2 + q1β + q0β 2; (14) where ψqλ = 0; |qλ| = 0; ϕq̄β = 0; |q̄β| = 0; (15) analytical modelling and performability evaluation of multi-channel wlans with global failures 559 λ and ψ are eigenvalues and left-eigenvectors of qλ and β and ϕ are eigenvalues and lefteigenvectors of q̄β, respectively. note that, ϕ is a vector defined as ϕ = ϕ0,ϕ1, . . . ,ϕs+1 (16) and β as: β = β0,β1, . . . ,βs+1. (17) furthermore, vj = s+1∑ k=0 (akψkλ j−m+1 k + bkϕk(i)β l−j k ),m-1≤j≤ l and in the state probability form, pi,j = s+1∑ k=0 (akψkλ j−m+1 k + bkϕk(i)β l−j k ) (18) m −1 ≤ j ≤ l where λk(k = 0,1, . . . ,s + 1) and βk(k = 0,1, . . . ,s + 1) are s + 2 eigenvalues each, that are strictly inside the unit circle and ak, bk (k=0, 1,. . ., s + 1) are arbitrary constants which can be scalar or complex-conjugate [9], [10]. all the ak, bk values and the remaining vj vectors can be obtained using the process in [9] and [10]. 4.2 lu factorization with partial pivoting for the solution of real linear equations to obtain the scalars ak, and bk, (k=0, 1,. . ., s + 1) the set of linear equations are considered with a single right-hand side, using an lu factorization with partial pivoting, and iterative refinement [25]. lu factorization of the matrix employed is computed first with partial pivoting (l is lower triangular and u is unit upper triangular). an approximation for the answer vector x is then found by forward and backward substitutions. using additional precision, the residual vector is calculated in turn. an iterative refinement approach is employed until full machine accuracy is obtained. once all the steady state probabilities pi,j (i=0, 1, . . ., s + 1; j=0, 1, . . ., l) are computed, a number of steady-state performance measures can be obtained. for numerical results and discussions, the mean queue length (mql) and blocking probabilities (pb) of multi-channel wlan are considered as: mql = l∑ j=0 j s+1∑ i=0 pi,j (19) pb = s+1∑ i=0 pi,l (20) 560 y. kirsal-ever, y. kirsal, e. ever, o. gemikonakli 5 numerical results and discussions numerical results are presented in this section for the performability analysis of multi-channel wlans with global failures. the exact spectral expansion solution is employed to obtain performability measures. numerical results are presented for performability measures of multichannel wlan (e.g. mql and pb). the parameters used are mainly taken from the relevant literature and are e[tch] = 60sec, e[v]=2km/h, r=100m [7, 8, 10, 11, 13, 18]. the results in fig. 3 show that for low channel failure rates, the system can perform as good as systems without failures. however, as the channel failure rate increases the performance degradation becomes more evident (for ξ=0.1, the mql values become up to twice as much as the mql values calculated for the other rates). the mql and pb measures are presented in fig. 4 and fig. 5, respectively. the parameters used are the same as previous computations with various λ values. figure 3: mql results for systems with various channel failure rates fig. 4 and fig. 5 show that the global failures (ap failures) affect the system significantly. the effects of global failures are more evident for blocking probabilities, since as mean arrival rate and global failure rate increase, the limiting effect of queue capacity becomes more evident. the effects of global failures are considered in fig. 6 as well. this time a 25 channels system is considered (s=25) together with w=100. mql values are presented as a function of mean arrivals. the other parameters used are same as previous computations. fig. 6 shows that when failure rate of the access point is taken as 0.1 the difference in mql values is quite significant. however for systems with ξg=0.01/h and ξg =0.001/h the difference is not as significant. this is mainly due to good repair facilities provided. since the ηg=2/h the mean repair time is taken as half an hour for the access point. the effects of having various repair times are considered in fig. 7. the other parameters considered are the same as the ones used for fig. 6. the repair time of the access point affects the performance of the system significantly. fig. 7 clearly shows that systems with mean repair time equal to one hour performs significantly better then systems with 1/ηg=1.5h and 1/ηg=2h. in the model considered the priority of repair is given to the access point. however, since the repair time also includes delay factors such as the time needed for transportation of the repairman, configuration of access point etc., the repair facility should be carefully considered in order to meet expected levels of performability. the results demonstrate that the systems may become overwhelmed because of long repair times, analytical modelling and performability evaluation of multi-channel wlans with global failures 561 figure 4: mql results for systems with various global failure rates figure 5: blocking probabilities for systems with various global failure rates especially if heavy loads of packet requests are expected. fig. 8 shows the effects of having various numbers of channels in a wlan with various mean incoming arrival rates. the other parameters used are the same as the ones used for fig. 7. fig. 8 shows that the number of channels affects the overall performability of the system significantly. when λ=0.25 is considered, the mql takes values close to l, such as 104.32 for five channel systems and it is around 1.042 for systems with 25 channels. systems with different number of available channels may perform similarly for heavy loads and light loads, however, it is obvious that as the number of channels employed increases, the system performs better. 562 y. kirsal-ever, y. kirsal, e. ever, o. gemikonakli figure 6: mql as a function of λ for s=25 and w=100 for various ξg values figure 7: mql as a function of λ for s=25 and w=100 for various ηg values finally, fig. 9 shows the mql as a function of mean rate for incoming packet requests. two different scenarios are considered for the computations. in the first scenario the mobile stations waiting in the queue for channels are static, whereas in the second scenario the mobile stations are pedestrians with e[v]=2km/h. the other parameters are s=10, w=50, ξ=0.01/h, η=2/h, ξg=0.001/h, ηg=2/h. the results show that, although the velocity of pedestrian users is relatively low, in case of high arrival rates, the mean number of users leaving the system due to mobility is quite significant. for example, for λ=0.2 mql is 28.3 for systems with static users in the queue, and 9.92 for systems with pedestrian users in the queue. analytical modelling and performability evaluation of multi-channel wlans with global failures 563 figure 8: mql as a function of s for various λ values figure 9: mql as a function of λ, for mobile users waiting for service 564 y. kirsal-ever, y. kirsal, e. ever, o. gemikonakli 6 conclusions in this paper, multi-channel wlan is considered for performability evaluation. a two dimensional modelling approach and an exact solution approach where iterative refinements are used to increase the accuracy for solution of system of linear equations are employed. the multichannel failures/repairs, and global failures/repairs of wlans modelled for exact solution. the dynamic channel allocation scheme is also considered in an attempt to maximise the performance of the underlying infrastructure. the proposed model is used to analyse qos measures such as mean queue length and blocking probabilities. the presented examples were kept simple due to the introductory nature of the proposed model for the multi-channel wlans. obviously, the analysis can be expanded upon for more informed decisions, since the multi-channel wlans are common today. recent wlans have multiple wireless radios each operating independently on different channels. the analysis of the multi-channel wlans is an important issue in order to achieve better qos measurements in future wireless and communication systems. in this paper, in order to obtain more realistic qos measures, considerable amount of focus is given operative states of multi-channel as well as global failures of wlans with the proposed analytical model and its exact solution. the results mainly show that, channel and global failures affect the qos of the system significantly. therefore, the system availability is important for the future system design and modelling. results also show that the failures cause significant performance degradations in the system, and the channel and global recovery are important parameters for the system’s performance. the systems with higher number of channels perform better as expected. as is widely agreed, future mobile communication systems, fourth-generation (4g) systems, will be heterogeneous networks, which provide ubiquitous access and seamless mobility across heterogeneous network technologies, such as cellular, wlan, and broadcast access. in 4g wireless networks, mobility management is still one of the most important issues that need to be considered. thus, results provided in fig. 9 are very important in that sense. the results mainly show the analytical model and the exact steady state solution approach presented is very useful in future systems since the recent wlans technologies operate in multichannel basis (e.g hiperlan2 or wimax). the model 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[25] numerical algorithms group (2005), nag c library manual, nagnp3660/8 december 2005 isbn:978-1-85206-206-4 (isbn 1-85206-206-1). int j comput commun, issn 1841-9836 9(3):305-312, june, 2014. development of mcdm methods – in honour of professor edmundas kazimieras zavadskas on the occasion of his 70th birthday o. kaplinski, f. peldschus, l. tupėnaitė oleg kaplinski poznań university of technology piotrowo 5, 60-965 poznań, poland oleg.kaplinski@put.poznan.pl friedel peldschus leipzig university of applied science 132 karl liebknecht st, 04227 leipzig, germany friedel@peldschus.net laura tupėnaitė* vilnius gediminas technical university sauletekio ave. 11, vilnius, lt-10223, lithuania e-mail: laura.tupenaite@vgtu.lt *corresponding author abstract: multiple criteria decision making (mcdm) substantially evolved during the past decades and became one of the most important areas in operational research/management science. the article presents a review of extensive scientific work of professor edmundas kazimieras zavadskas on development of mcdm methods on the occasion of his 70th birthday. the article also highlights his research carrier, and lists some of his publications. keywords: decision support, mcdm, methods, development, edmundas kazimieras zavadskas. 1 introduction multiple criteria decision making (mcdm) methods have substantially evolved since 1970s, and had various types of real world applications. new mcdm methods have been developed, and existing methods improved, showing that research in the decision-making is still critical and valuable. one of the early and exceptional authors, continuously working on the development and improvement of mcdm methods since 1976, is professor edmundas kazimieras zavadskas (fig. 1). this article is an attempt to summarise his research and achievements in development of the mcdm methods on the occasion of professor˙s 70th birthday. 2 achievements in development of mcdm methods edmundas kazimieras zavadskas was born on the 12th of may 1944 in vilnius. he presented his phd in 1973 – he researched the applications of polymer resins in reinforced concrete. this was the time when he took interest in optimising constructions, technologies and organisations. a selection of decision-making solutions dominated his research. this is how he significantly developed some elements of rational decisions theory. as a synthesis of research results, in 1987 e. k. zavadskas defended his post-doctoral (habilitation) thesis where mcda/madm methods (topsis, saw, electre, entropy, game theory, utility theory, permutation copyright © 2006-2014 by ccc publications 306 o. kaplinski, f. peldschus, l. tupėnaitė method, judgement methods) were applied for construction tasks solutions. furthermore, these methods were used for development of decision support systems [1]. there is a monograph summarising his achievements from that period [2]. this monograph has had a strong influence on research conducted by young academics working towards their phd theses in number of countries, from uzbekistan through russia, poland, germany, denmark, and cuba to syria. another monograph [3] strengthened the professor˙s position as a leader in this part of europe, conquering the area of multi criteria decision aiding methods, and operational research applications in construction industry. synthetic works on multiple criteria decision support systems in construction have been published in individual monographs or in collaboration with his colleagues [4] – [6]. new methods of performing multiple criteria analysis in a project have been developed by professor and his team, including: • a method of complex proportional assessment (copras) [5], complex proportional assessment of alternatives with grey relations (copras-g) – presented in publication [7] which was nominatd the new hot paper for january 2010 by science watch (thomson reuters) in the field of engineering (see http://archive.sciencewatch.com/dr/nhp/2010/ 10jannhp/10jannhpzavaet/) and complex proportional assessment of alternatives applying fuzzy sets (copras-f) [8]; • additive ratio assessment (aras) method [9], additive ratio assessment applying attributes values determined in intervals (aras-g) method [10] and applying fuzzy sets (aras-f) [11]; • a selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (swara) [12]; • topsis method applying mahalanobis distance measure (topsis-m) [13]; • a new normalization method in games theory [14]; • a method of weighted aggregated sum product assessment [15]; • algorithm of maximising the set of common solutions for several mcdm problems [16]. furthermore, variety of the new mcdm methods and software was developed in collaboration with academic colleagues from abroad, i.e.: • software for multiple criteria evaluation [17]; • a method of multi-objective optimisation on the basis of ration analysis (moora) [18]; • a method multimoora (moora plus full multiplicative form) [19]; • copras method for group decision making in an interval-values intuitionistic fuzzy environment [20]; • extensions of linamp model for multi criteria decision making with grey numbers [21]; • fuzzy dea approach based on parametric programming [22]; • intuitionistic fuzzy dea for efficiency evaluation under uncertainty [23]; • stepwise dea analysis and grey incidence analysis [24]. all the above listed methods had wide real world applications in such areas as: sustainable development in civil engineering, building life cycle, modelling of construction and real estate sector, quality control of construction projects, etc. professor e. k. zavadskas continuously develops new and researches existing mcdm methods for further improvements. researches results, among many others, as illustration of professor˙s works can be distinguished: • measuring congruence of ranking results applying particular mcdm methods [25]; • evaluation of ranking accuracy in multi-criteria decisions, presented in paper [26] which was titled as fast breaking paper for june 2009 by science watch (thomson reuters) in development of mcdm methods – in honour of professor edmundas kazimieras zavadskas on the occasion of his 70th birthday 307 the field of mathematics (see http://archive.sciencewatch.com/dr/fbp/2009/pdf/09junfbpvysh.pdf); • verification of robustness of methods when assessing alternative solutions [27] – [31]; • proposal of multi-criteria assessment model of technologies [32] – [39] . professor e. k. zavadskas, in collaboration with his colleagues, has also developed hybrid decision making methods by combining mcdm methods topsis, saw, electre, ahp and the methods proposed by the professor (see, i.e. [40] – [45]). about 20 papers were nominated as hot papers in thomson reuters database. in one of these papers there is a published interview with the author (see http://archive.sciencewatch.com/dr/nhp/2011/11maynhp/11maynhpzavaet/). professor e. k. zavadskas was granted a lithuanian award for research achievements for a cycle of works ´multiple criteria assessment of construction projects and technological solutionsą (1980–1996) in 1996, and for a cycle of works ´modelling in construction (methods, simulation, decision support and information systems, web-based technologies, practical application)ą (1996–2003) – in 2004. in 1996 he also was awarded the 4th class medal of the lithuanian grand duke gediminas. 3 biography and general data edmundas kazimieras zavadskas (fig. 1) was born in 1944 vilnius, lithuania. he graduated from an elementary school in vorkuta, ussr and secondary school in 1962, dūkštas, lithuania. e. k. zavadskas studied at faculty of construction economics in vilnius branch of kaunas polytechnic institute (visi) (1962–1967) (now – vilnius gediminas technical university, vgtu). in 1968 he became an assistant, 1969–1972 he was a phd student at visi, lecturer – in 1974, associate professor in 1977 and became a professor in 1988. the professor became a rector of vilnius civil engineering institute (visi) in 1990. during the period between february-october 1990, he successfully reorganized the institute, which became the vilnius technical university, and was nominated a rector for period of 1990–1996. later the university was renamed as vilnius gediminas technical university (vgtu) and prof. e. k. zavadskas became a rector for the period of 1996–2002. in 2002–2011, he was a vicerector of vgtu. in this time, he worked towards making the university one of the largest universities in lithuania, taking a leading position in technical and engineering education and research. since 1986 till now professor zavadskas has been a head of the department of construction technology and management, civil engineering faculty, vgtu. his impressive academic carrier started in 1973 when he became a phd student at the vilnius civil engineering institute (visi, now vgtu) and defended his post-doctoral (habilitation) thesis at moscow civil engineering institute in 1987 (on multi attribute decision making in construction). in 1993, he won laurea doctorali ad habilitationem at technical sciences at vgtu. professor e. k. zavadskas was an expert member (1991–1993), corresponding member (1993–2011) and in 2011, became a full member of the lithuanian academy of sciences. furthermore, he has been granted a title of honorary doctor of three universities: poznań, kiev, and st. petersburg (2001–2003) and the honorary international professor of the national taipei university of technology. he presides and participates in a number of scientific corporations and editorial boards of scientific publishing houses. he is a member of two russian academies, the ukrainian academy of cybernetics, and many (17) scientific and research organisations, from melbourne to brussels. he also represents the baltic states in international organisations. the professor was a president of operational researchers society in lithuania and the baltic states 308 o. kaplinski, f. peldschus, l. tupėnaitė figure 1: r. j. slowiński, b. roy, e. k. zavadskas at the 52nd meeting of the euro working group multicriteria aid for decisions (mcda), 6–7 october, 2000, vilnius, lithuania (organised by h. pranevičius, l. sakalauskas, e. k. zavadskas, a. kaklauskas) in 2001–2012. the professor˙s research and teaching includes fields of construction materials, materials resistance, construction technology and management, operational research methods, decision support systems, life cycle analysis, etc. prof. e. k. zavadskas has published over 50 books, including 5 textbooks and 16 monographs as single author, or in collaboration with other authors, 10 popular science books, over 400 research articles as well as several hundreds of articles on various social and cultural topics. he has edited over 20 collective volumes. professor e. k. zavadskas has set up three famous international scientific journals: ´technological and economic development of economyą (editor-in-chief since 1994), ´journal of civil engineering and managementą (editor-in-chief since 1995) and ´international journal of strategic property managementą (editor-in-chief since 1997 till 2011). since 2008 all three journals have been referred in thomson reuters web of science database, and since 2010 – have impact factor (if). furthermore, since 2010 these journals are published by vgtu publishing house ´technikaą in collaboration with a famous publishing house – taylor & francis. the professor is also a member of editorial boards of 16 international journals referred in thomson reuters web of science database and 17 other journals. on various occasions special issues of journals and collective volumes were dedicated to professor˙s works, i.e. journal of management and decision making: ´normalisation in decision making methodsą (2007), international journal of environment and pollution (2007, 2008), automation in construction (2010), informatica (2001), ecology (2007). professor e. k. zavadskas was a chairman and member of organizing committees of numerous international conferences, as well as editor of conference proceedings, including: • modelling and simulation of business systems (vilnius, lithuania 2003); • 33rd symposium international fesf strasbourg: recent developments in environmental protection (vilnius, lithuania, 2003); • simulation and optimization in business and industry: international conference on operdevelopment of mcdm methods – in honour of professor edmundas kazimieras zavadskas on the occasion of his 70th birthday 309 ational research (tallinn, estonia, 2006); • the 20th international conference euro mini conference ľconstruction optimization and knowledge-based technologies´ (europt’2008) (neringa, lithuania, 2008); • the 25th international symposium on automation and robotics in construction (isarc 2008) (vilnius, lithuania, 2008); • international conference on modelling of business, industrial and transport systems (riga, latvia, 2008); • international conference ´modern buildings, materials and structuresą, (vilnius, lithuania, 2004, 2007, 2010, 2013), etc. professor e. k. zavadskas was one of the main initiators of international german – lithuanian – polish colloquium dedicated to operational research (or) in civil engineering. the first one was held in 1986, leipzig, germany. the colloquia are organized every two years. since the first one, 14 colloquia have already been organized. on the basis of collaboration during 11th and the 12th colloquia, the idea of setting up of a new euro working group ´or in sustainable development and civil engineering (ewgorsdce)ą was presented. on the initiative of professor zavadskas, the working group was established during the 23rd european conference on operational research ´or creating competitive advantageą, which took place in bonn, germany 5–8 july 2009 (http://www.euroonline.org/web/ewg/32/ewg-orsdce-or-in-sustainable-development-and-civil-engineering). prof. e. k. zavadskas is a chairman of this working group. under e. k. zavadskas supervision, 33 phd dissertations were presented (four of his former students were awarded the title of full professor). on the day of his jubilee, we would like to congratulate professor edmundas kazimieras zavadskas – an exceptional scientist of greatest format. we wish the professor good health and creativity in further contributions to mcdm methods. 4 conclusions professor edmundas kazimieras zavadskas has greatly contributed to development and practical applications of mcdm methods. this is why a part of the article was devoted to his achievements. his numerous articles and, most of all, authorship or co-authorship of books contribute to the mcdm theory and practice, as well as encourage continuous innovations in this field. bibliography [1] zavadskas, e.k. (1987); multiattribute decision making in construction. dr.sc. dissertation. moscow: institute of civil engineering. 720 p. (in russian). [2] zavadskas, e. (1987); complex estimation and choice of resource-saving decisions in construction. vilnius: mokslas. 210 p. (in russian). [3] zavadskas, e.k. (1991); system of estimation of technological solutions in building construction. leningrad: stroizdat. 256 p. (in russian). [4] zavadskas, e.; peldschus, f.; kaklauskas, a. (1994); multiple criteria evaluation of projects in construction. vilnius: technika. 226 p. [5] zavadskas, e.k.; kaklauskas, a. (2007); mehrzielselektion für entscheidungen im bauwesen. stuttgart: irb verlag. 276 p. (in german). 310 o. kaplinski, f. peldschus, l. tupėnaitė [6] zavadskas, e.; kaplinski, o.; kaklauskas, a.; brzezinski, j. (1995); expert systems in construction industry. trends, potencial & applications. vilnius: technika. 180 p. [7] zavadskas, e.k.; kaklauskas, a.; turskis, z.; tamosaitiene, j. (2008); selection of the effective dwelling house walls by applying attributes values determined at intervals, journal of civil engineering and management, 14(2): 85-93. [8] zavadskas, e.k.; antucheviciene, j.; (2007); multiple criteria evaluation of rural building’s regeneration alternatives, building and environment, 42(1): 436-451. [9] zavadskas, e.k.; turskis, z. (2010); a new additive ratio assessment (aras) method in multicriteria decision-making, technological and economic development of economy, 16(2): 159-172. [10] turskis, z.; zavadskas, e.k.; (2010); a novel method for multiple criteria analysis: grey additive ratio assessment (aras-g) method, informatica, 21(4): 597-610. [11] turskis, z.; zavadskas, e.k. (2010); a new fuzzy additive ratio assessment method (aras-f). case study: the analysis of fuzzy multiple criteria in order to select the logistic centers location, transport, 25(4): 423-432. [12] keršulienė, v.; zavadskas, e.k.; turskis, z. (2010); selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (swara), journal of business economics and management, 11(2): 243-258. [13] antuchevičienė, j.; zavadskas, e.k.; zakarevičius, a. (2010); multiple criteria construction management decisions considering relations between criteria, technological and economic development of economy, 16(1): 109-125. [14] zavadskas, e.k.; turskis, z. (2008); a new logarithmic normalization method in games theory, informatica, 19(2): 303-314. [15] zavadskas, e.k.; turskis, z.; antucheviciene, j.; zakarevicius, a. (2012); optimization of weighted aggregated sum product assessment, electronics and electrical engineering = elektronika ir elektrotechnika, 122(6): 3-6. [16] dadelo, s.; krylovas, a.; kosareva, n.; zavadskas, e.k.; dadeliene, r. (2014); algorithm of maximizing the set of common solutions for several mcdm problems and it˙s application for security personnel scheduling, international journal of computers, communications & control (ijccc), 9(2): 140-148. [17] zavadskas, e.k.; ustinovičius, l.; peldschus, f. (2003); development of software for multiple criteria evaluation, informatica, 14(2): 259-272. [18] brauers, w.k.m.; zavadskas, e.k. (2006); the moora method and its application to privatization in a transition economy, control and cybernetics, 35(2): 445-469. [19] brauers, w.k.m.; zavadskas, e.k. (2010); project management by multimoora as an instrument for transition economies, technological and economic development of economy, 16(1): 5-24. [20] razavi hajiagha, s.h.; hashemi, s.s.; zavadskas, e.k. (2013); a complex proportional assessment method for group decision making in an interval-valued intuitionistic fuzzy environment, technological and economic development of economy, 19(1): 22-37. development of mcdm methods – in honour of professor edmundas kazimieras zavadskas on the occasion of his 70th birthday 311 [21] razavi hajiagha, s.h., hashemi, s.s.; zavadskas, e.k.; akrami, h. (2012); extensions of linmap model for multi criteria decision making with grey numbers, technological and economic development of economy, 18(4): 636-650. [22] razavi hajiagha, s.h.; mahdiraji, h.a.; zavadskas, e.k.; hashemi, s.s. (2013); a fuzzy data envelopment analysis approach based on parametric programming, international journal of computers, communications & control (ijccc), 8(4): 594-607. [23] razavi hajiagha, s.h.; akrami, h.; zavadskas, e.k.; hashemi, s.s. (2013); an intuitionistic fuzzy data envelopment analysis for efficiency evaluation under ucertainty: case of a finance and credit institution, e&m economics and management = e&m economie a management, 1: 128-137. [24] razavi hajiagha, s.h.; zavadskas, e.k.; hashemi, s.s. (2013); application of stepwise data envelopment analysis and grey incidence analysis to evaluate the effectiveness of export promotion programs, journal of business economics and management, 14(3): 638-650. [25] antuchevičienė, j.; zakarevičius, a.; zavadskas, e.k. (2011); measuring congruence of ranking results applying particular mcdm methods, informatica, 22(3): 319-338. [26] zavadskas, e.k.; zakarevicius, a.; antucheviciene, j. (2006); evaluation of ranking accuracy in multi-criteria decisions, informatica, 17(4): 601-618. [27] zavadskas, e.k.; antucheviciene, j.; saparauskas, j.; turskis, z. (2013); mcdm methods waspas and multimoora: verification of robustness of methods when assessing alternative solutions, journal of economic computation and economic cybernetics studies and research (ececsr), 47(2): 5-20. [28] brauers, w. k. m.; zavadskas, e. k. (2012); robustness of multimoora: a method for multi-objective optimization, informatica, 23(1): 1-25. [29] brauers, w. k. m.; kildienė, s.; zavadskas, e. k.; kaklauskas, a. (2013); the construction sector in twenty european countries during the recession 2008-2009 – country ranking by multimoora, international journal of strategic property management, 17(1): 58-78. [30] yazdani-chamzini, a.; yakhchali, s. h.; zavadskas, e. k. (2012); using a integrated mcdm model for mining method selection in presence of uncertainty, ekonomska istraživanja economic research, 25(4): 869-904. [31] brauers, w. k. m.; zavadskas, e. k. (2012); a multi-objective decision support system for project selection with an application for the tunisian textile industry, e&m economie a management, 15(1): 28-43. [32] zavadskas, e. k.; turskis, z.; volvačiovas, r.; kildienė, s. (2013); multi-criteria assessment model of technologies, studies in informatics and control, 22(4): 249-258. [33] hashemkhani zolfani, s.; sedaghat, m.; zavadskas, e. k. (2012); performance evaluating of rural ict centers (telecenters), applying fuzzy ahp, saw-g and topsis grey, a case study in iran, technological and economic development of economy, 18(2): 364-387. [34] hashemkhani zolfani, s.; zavadskas, e. k.; turskis, z. (2013); design of products with both international and local perspectives based on yin-yang balance theory and swara method, ekonomska istraživanja economic research, 26(2): 153-166. 312 o. kaplinski, f. peldschus, l. tupėnaitė [35] yazdani-chamzini, a.; fouladgar, m. m.; zavadskas, e. k.; moini, s. h. h. (2013); selecting the optimal renewable energy using multi criteria decision making, journal of business economics and management, 14(5): 957-978. [36] hashemkhani zolfani, s.; esfahani, m. h.; bitarafan, m.; zavadskas, e. k.; arefi, s. l. (2013); developing a new hybrid mcdm method for selection of the optimal alternative of mechanical longitudinal ventilation of tunnel pollutants during automobile accidents, transport, 28(1): 89-96. [37] staniunas, m.; medineckiene, m.; zavadskas, e. k.; kalibatas, d. (2013); to modernize or not: ecological-economical assessment of multi-dwelling houses modernization, archives of civil and mechanical engineering, 13(1): 88-98. [38] kracka, m.; zavadskas, e. k. (2013); panel building refurbishment elements effective selection by applying multiple-criteria methods, international journal of strategic property management, 17(2): 210-219. [39] brauers, w. k. m.; kracka, m.; zavadskas, e. k. (2012); lithuanian case study of masonry buildings from the soviet period, journal of civil engineering and management, 18(3): 444456. [40] zavadskas, e.k.; vainiūnas, p.; turskis, z.; tamošaitienė, j. (2012); multiple criteria decision support system for assessment of projects managers in construction, international journal of information technology & decision making, 11(2): 501-520. [41] hashemkhani zolfani, s.h.; aghdaie, m.h.; derakhti, a.; zavadskas, e.k.; varzandeh, m.h.m. (2013); decision making on business issues with foresight perspective; an application of new hybrid mcdm model in shopping mall locating, expert systems with applications, 40(17): 7111-7121. [42] fouladgar, m. m.; yazdani-chamzini, a.; zavadskas, e. k.; haji m., hamzeh, s. (2012); a new hybrid model for evaluating the working strategies: case study of construction company, technological and economic development of economy, 18(1): 164-188. [43] rezaeiniya, n.; hashemkhani zolfani, s.; zavadskas, e. k. (2012); greenhouse locating based on anp-copras-g methods – an empirical study based on iran, international journal of strategic property management, 16(2): 188-200. [44] hashemkhani zolfani, s.; rezaeiniya, n.; pourhossein, m.; zavadskas, e. k. (2012); decision making on advertisement strategy selection based on life cycle of products by applying fahp and topsis grey: growth stage perspective; a case about food industry in iran, inžinerinė ekonomika engineering economics, 23(5): 471-484. [45] zavadskas, e. k.; sušinskas, s.; daniūnas, a.; turskis, z.; sivilevičius, h. (2012); multiple criteria selection of pile-column construction technology, journal of civil engineering and management, 18(6): 834-842. international journal of computers communications & control issn 1841-9836, 11(6):804-818, december 2016. the particle swarm optimization algorithm with adaptive chaos perturbation l. mengxia, l. ruiquan, d. yong li mengxia 1. school of information and mathematics, yangtze university jingzhou hubei 434023, china 2. petroleum engineering college, yangtze university wuhan hubei 430100, china 3. the branch of key laboratory of cnpc for oil and gas production, yangtze university wuhan hubei 430100, china 4. key laboratory of exploration technologies for oil and gas resources, yangtze university wuhan hubei 430100, china limengxia81@126.com liao ruiquan 1. petroleum engineering college, yangtze university wuhan hubei 430100, china 2. the branch of key laboratory of cnpc for oil and gas production, yangtze university wuhan hubei 430100, china 3. key laboratory of exploration technologies for oil and gas resources, yangtze university wuhan hubei 430100, china liaoruiquan@263.net dong yong* 1. school of information and mathematics,yangtze university jingzhou hubei 434023, china 2. the branch of key laboratory of cnpc for oil and gas production, yangtze university wuhan hubei 430100, china 3. key laboratory of exploration technologies for oil and gas resources, yangtze university wuhan hubei 430100, china *corresponding author: dongyong80@126.com abstract: aiming at the two characteristics of premature convergence of particle swarm optimization that the particle velocity approaches 0 and particle swarm congregate, this paper learns from the annealing function of the simulated annealing algorithm and adaptively and dynamically adjusts inertia weights according to the velocity information of particles to avoid approaching 0 untimely. this paper uses the good uniformity of anderson chaotic mapping and performs chaos perturbation to part of particles based on the information of variance of the population’s fitness to avoid the untimely aggregation of particle swarm. the numerical simulations of five test functions are performed and the results are compared with several swarm intelligence heuristic algorithms. the results shows that the modified algorithm can keep the population diversity well in the middle stage of the iterative process and it can improve the mean best of the algorithm and the success rate of search. keywords: particle swarm optimization, inertia weight,population diversity, expected velocity, chaos perturbation. 1 introduction particle swarm optimization (pso) is an evolutionary algorithms (eas). it stems from the simulation of group behavior for birds swarm’s foraging [1, 2]. the parameters and structures of copyright © 2006-2016 by ccc publications the particle swarm optimization algorithm with adaptive chaos perturbation 805 pso are very simple and are apt to be realized. it has parallelism essentially. it has no requirement to the properties of objective functions. it has good adaptation and gains wide concern. it is applied in many fields such as decision feedback equalizer [3, 4], parameter identification [5, 6], power dispatch [7] and mechanical control [8,9], etc. the particle swarm of the standard pso is apt to get into local best position and occurs premature convergence phenomenon. the premature convergence has two characteristics that the velocity of particle swarm approach 0 and the particles aggregate in a small region. in order to avoid early approaching to 0 for the velocity of particle swarm, the inertia weights of standard pso are modified from fixed values to variation values in the references [10-15], and the performance of pso is improved in some extent. the reference [2] proposed a method that the inertia weight decreases within the increasing of iterations. it emphasizes the global search in the earlier stage of evolution and intensifies the local exploitation in the later stage of evolution. but the effect on the complex problem is not obvious. the reference [10] use the fuzzy controller to adjust the inertia weight, but it is complex to build the fuzzy rule. the reference [11] proposed the method to set the inertia weight randomly and obtained a greater effect on pursuing the optimization. the reference [12] used the chaotic mapping to change the inertia weight and the inertia weight changes according to the logistic chaotic mapping. the reference [13, 14] used different exponential forms to build the setting method of the inertia weight which deceases according to the exponential rule. the reference [15] uniformly adjusted the inertia weight for the all particles according to the mean velocity of the particle swarm, and the numerical results showed that it can keep the population diversity in the middle stage of evolution. for pso algorithm, the particle,s position is different. the global best particle and the personal best particle are in the leading position. in the process of evolution, the other particles are affected by the two particles and are close to them. the global best particle and the personal best particle are considered as on group which is called the dominant group. the other particles are taken as another group which is called the ordinary group. obviously, it is appropriate to deal with the dominant group and the ordinary group respectively. this paper uses the smaller inertia weight to develop the local development capabilities for the particles in the dominant group. for the particles in the ordinary group, the method in reference [15] that adjusts the inertia weight based on the mean velocity of swarm is modified and the inertia weight corresponding to each particle is adjusted respectively according to the expected values of velocity of particle swarm to avoid the swarm velocity early approaching 0. for the second characteristics of the premature convergence, the reference [16] considered that the variance of the population’s fitness can reflect the concentration degree of population. if the value of is less than some specified threshold, the particle swarm aggregate and the premature convergence occurs. σ2 = n∑ k=1 ( fk −fm f )2 (1) f = max { 1,max k {|fk −favg|} } (2) in eq. 1 and eq. 2, fkdenotes the fitness value of the k-th particle.fmdenotes the mean value of the variance of the population’s fitness. fdenotes the normalized parameter. the reference [16] didn’t point out how to determine the appropriate threshold value. to analyze a large number of numerical simulations, the results showed that the variances σ2keep stabilization and basically equal in the subsequent several evolutionary when the premature convergence occurs. hence, this paper compares the corresponding variancesσ2between two 806 l. mengxia, l. ruiquan, d. yong adjacent iterations. if the change of σ2value is very small, it considers that the premature convergence occurs and needs to set the position of particle swarm to advance the population diversity. one common method is to use the logistic chaotic mapping performing perturbation to part of particles. it is easy to verify that the distribution of the chaotic sequence generated by the logistic chaotic mapping has the characteristic that is big at both ends and small in the middle and the uniformity of the chaotic sequence is poor. but the chaotic sequence generated by the anderson chaotic mapping has better uniformity. so it is appropriate to use the anderson chaotic mapping to perform the perturbation to part of particles. based on the above analysis, this paper firstly considers the expected value of population velocity, and changes the inertia weights respectively corresponding to the particles according to the category of particles. and then it performs chaos perturbation to part of particles based on the difference of the variances of the population’s fitness between two adjacent iterations. finally, the numerical simulations are performed and the results show that the new algorithm has higher performance. 2 standard pso algorithm pso algorithm is simulating the foraging process of the bird flock. particle denotes the individual. it has position and velocity, but has no volume and mass. multiple particles constitute the particle swarm which denote the bird flock. the objective function value corresponding to the particle’s position is called the fitness of the particle which is used to evaluate the good points and the bad points of the particle. the best position which the individual particle went through is called the personal previous best position. the personal previous best position of the kth particle is denoted by pk.the previous best position of the particle swarm is called the swarm best position which is denoted by pg.the evolution of the particle is realized through tracing the personal previous best position and swarm best position. the evolution of standard pso algorithm is divided into the evolution of the velocity and the evolution of the position. the evolution equations are shown in eq. 3 and eq. 4 [1, 2] as below. vt+1k = ω ×v t k + c1 ×r1 × (pk −xtk) + c2 ×r2 × (pg −xtk) (3) xt+1k = x t k + v t+1 k (4) in eq. 3 and eq. 4, vtkdenotes the velocity of the kth particle in the tth evolution. x t kdenotes the position of the kth particle in the tth evolution. r1 and r2 denote the independent random numbers on the interval [0, 1]. c1 and c2 denote the learning factors which generally equal 2. ω denotes the inertia weight which takes fixed value between 0.1 and 0.9. the value ranges of the particle’s velocity and position are usually restricted. if the maximum of the position is determined by xmax, the maximum of the velocity is vmax = k × xmax for 0.1 ≤ k ≤ 1.0 [16]. 3 modification of the inertia weight the mean velocity vta of the particle swarm in the kth evolution is denoted by eq. 5. vta = ( n∑ i=1 m∑ k=1 ∣∣∣v(t)k,i∣∣∣)/(n×m) (5) the particle swarm optimization algorithm with adaptive chaos perturbation 807 n is the dimension of the problem which is the dimension of the search space. m is the total number of the particles. v(t)k,i is the velocity of the kth particle in the ith dimensionality as the particle experiences the tth evolution. v(t)a denotes the evolution amplitude of the particle swarm or the evolution step. the larger the value of v(t)a is, the larger the evolution step is. it means that the search capability of the particle swarm is great. if the value of v(t)a is small, it means that the search of particle swarm focuses on the local exploitation. in the earlier stage of evolution, the global search capability is expected to be stronger. it means that the value of v(t)a is expected to be larger. in the later stage of the evolution, the capability of local exploitation is expected to be intensified to increase the capability of finding the maximal solution by the algorithm, and the value of v(t)a is expected to be smaller. this paper firstly introduce the expected value of the mean value of particle swarm in the evolution which is shown in eq. 6 as follow. vte = v0 ×exp(−(λ1 × t/tmax)λ2 ) (6) in eq. 6, v0 denotes the mean velocity of the initial particle swarm. λ1 and λ2 are adjustable parameters which is to control the change rule of the expected value vte of the particle swarm shown in figure 1. tmax denotes the maximal evolution generations. figure 1: the change rule of vte under different parameters from figure 1, it can be seen that the value of vte as λ1 = 1.8 and λ2 = 5 is suitable to be the expected mean velocity of particle swarm. in this case, in the earlier stage of evolution, the expected value of mean velocity of particle swarm is larger and it helps to keep the global search capability. in the last stage of evolution, the expected value of mean velocity approaches 0 and it helps to improve the local exploitation capability. ω0 denotes the initial inertia weight. vte denotes the actual velocity of the kth particle. ω t k denotes the inertia weight of the kth particle in the tth evolution. ωt+1k denotes the inertia weight of the kth particle in the (t + 1)th evolution. ωt+1k is determined by the following rule. (1) if the particle is the better particle, ωt+1k =ωmin; (2) if the particle is the trivial particle, then, define v (t) k,a = ( n∑ i=1 ∣∣∣v(t)k,i∣∣∣)/n (7) 808 l. mengxia, l. ruiquan, d. yong there are three cases to be considered as below. (2.1) if v(t)k,a > v (t) e ,then, ω (t+1) k = ω (t) k ×p1. (2.2) if v(t)k,a < v (t) e ,then, ω (t+1) k = ω (t) k ×p2. (2.3) if v(t)k,a = v (t) e ,then, ω (t+1) k = ω (t) k . in the above process, p1 and p2 are constants with 0 < p1 < 1 and p2 > 1. it also can take p1 = 1/p2. if ω (t+1) k > ωmax, p2 is taken to be 1.05 according to the reference [8] or is determined by the experiment. then taking ω(t+1)k = ωmax. ωmax and ωmin denote the upper limit and lower limit of the inertia weight, respectively. 4 anderson chaotic mapping this paper uses the rule that the difference of σ2 between the adjacent evolution process is less than some given value se to recognize the premature convergence, for example, taking se = 10−3. if there exists premature convergence, then it needs to perform the perturbation for the current particle to improve the population diversity. generally, the researchers use the logistic chaotic mapping to perform perturbation [18-21], but the sequence generated by the logistic chaotic mapping has bad uniformity [22]. the logistic chaotic mapping is shown in eq. 8 as below. yn+1 = µ×yn × (1 −yn) (8) when µ = 4, eq. 8 will generate the chaotic sequence whose value is taken between 0 and 1. this paper introduce anderson chaotic mapping to generate chaotic sequence which has good uniformity [23]. anderson chaotic mapping is shown in eq. 9 as below. yi = (ln(xi + 1/2) + ln(2)))/ln(3) (9) where, xn+1 = { 3 2 ×xn + 1/4 0 6 xn < 12 1 2 ×xn − 1/4 12 6 xn < 1 (n = 1, 2, ...) (10) the sequence yi is uniform distribution on (0, 1). in order to compare the uniformity of the two chaotic mapping, the initial value is taken arbitrarily. and then iterate 1000 times according to eq. 8 and eq. 9. the interval [0, 1] is divided into 10 subintervals with equal width. the frequency that the elements of the chaotic sequence locate at each subinterval is considered and shown in figure 2. if the other initials are taken except that the value of 0.25 and 0.75 cannot be taken because of the occurring of fixed points for logistic chaotic mapping. the obtained frequency graph is similar to figure 2. this means that anderson chaotic mapping has stable uniformity. 5 modified pso algorithm the objective function is expressed in eq. 11. minf(x) = f(x1,x2, ...,xn) (11) where, n is the dimension of the problem. the particle swarm optimization algorithm with adaptive chaos perturbation 809 0 0.5 1 0 200 400 600 800 1000 1200 f re qu en cy h is to gr am f ro m a nd er so n subinterval 0 0.5 1 0 500 1000 1500 2000 2500 f re qu en cy h is to gr am f ro m l og is ti c subinterval figure 2: comparison of the uniformity between two chaotic mappings 5.1 initial particle swarm generated by the chaotic mapping firstly, one m-dimensional vector is generated randomly and its element is between 0 and 1. secondly, each element is taken as initial value. and using anderson chaotic mapping iterates n−1 times to generate ms n-dimensional vector which are denoted by y1,y2, ...,ym. finally, the value range of element of yi for i = 1, 2, ...,m is transformed to the search space from the interval [0, 1]. suppose y ∈ [a,b],then x ∈ [c,d]. and the relation between x and y is shown in eq. 12. x = c + y −a b−a (d− c) (12) the initialization of the velocity can be finished analogously. 5.2 modification for evolution process in figure.3, after finishing the tth evolution process, the variance σ2(t) is obtained by calculating the variance of the population’s fitness. and then it is compared with the variance σ2(t− 1) in the previous evolution process. if ∣∣σ2(t) −σ2(t− 1)∣∣ 6 se, the particles are ordered from small to large according to the fitness value. the particles which account for 61.8% of total particle number after ordering are performed chaotic perturbation. the number of foregoing particles is denoted by s. that is, the positions of ss particles are initialized again. 5.3 modified pso algorithm step 1 give the initial value of the inertia weight ω0 = ωmax = 0.95,ωmin = 0.05 learning factors c1 = c2 = 2, the number of particles m = 40, the maximal evolution generations tmax = 10000, the maximal evolution generations n, the control value for starting the chaotic perturbation se = 10−3, the search space [xmin,xmax] and the upper limit of velocity vmax. step 2 randomly generate a m-dimensional particle on [0, 1].use the anderson mapping shown in eq. 9 and eq. 10 to get ms n-dimensional particles. the ms n-dimensional particles which are transformed into the search space based on eq. 12 are denoted by xi for i = 1, 2, ...,m. 810 l. mengxia, l. ruiquan, d. yong figure 3: the flow chart of chaos perturbation the particle swarm optimization algorithm with adaptive chaos perturbation 811 similarly, generate the particle’s velocity. take vmax = xmax. let the evolution generations be 0, and then turn to step 3. step 3 calculate the fitness of each particle and the variance of the population’s fitness. determine the swarm best position pg, the personal previous best position pi for i = 1, 2, ...,m. turn to step 4. step 4 adjust ω according to the contents in section 3. evolve the particle’s velocity and position according to eq. 3 and eq. 4. and then add one to the evolution generations. update pg and pi.calculate the variance of current population’s fitness. compare the calculating variance with the variance of fitness value before evolution. if the difference is less than se, turn to step 5. otherwise, turn to step 6. step 5 determine the number of particles which need to be performed perturbation, s. use anderson mapping to generate ss particles to replace the ss particles whose fitness value order are back. then turn to step 6. step 6 if the evolution generations is less than tmax, turn to step 4. otherwise, turn to step 7. step 7 output the results: pg and f(pg). 6 numerical simulation in order to test the performance of the pso algorithm with chaos perturbation proposed in this paper, this paper selects five benchmark function shown in table 1. table 1: benchmark functions and part parameters function dimensionality search space optimal value convergence criteria sphere function 30 [−100, 100]n 0 0.01 rosenbrock’s function 30 [−30, 30]n 0 0.01 rastrigin’s function 30 [−5.12, 5.12]n 0 0.01 griewanks’s function 30 [−600, 600]n 0 0.01 schwefel function 30 [−100, 100]2 0 0.01 table 1 gives some parameters of the algorithm, such as dimensionality, search space, theoretical optimum and convergence criteria. the theoretical optimums of these benchmark functions are all zeros. because the theoretical optimum is difficult to obtained, so this paper uses the convergence criteria to determine the astringency of the algorithm. the convergence criteria equals to 0.01. aiming at the above benchmark functions, this paper compares the optimal performance of the following algorithms with each other: the algorithm acpso proposed in this paper, the standard pso algorithm denoted by spso, the algorithm in the reference [12] denoted by ref. 12, the algorithm in the reference [15] denoted by ref. 15, and the algorithm in the reference [20] denoted by ref. 20. the algorithm acpso proposed in this paper tests the number of two kinds of particles. the spso algorithm is shown in the reference [2]. the algorithm ref. 12 generates the random number of the velocity equation for the spso algorithm by using logistic chaos. the algorithm ref. 15 uniformly adjusts the inertia weight according to the average velocity of the all particles. the algorithm ref. 20 is the firefly algorithm. for ref. 20, it takes the number 812 l. mengxia, l. ruiquan, d. yong of particles 40, the evolution generations 1000, initial attractiveness 0.728, the initial absorption coefficient 0.345 and the randomization parameter 0.25. using the parameters shown in table 2, each algorithm is run 20 times respectively. table 2: the parameters for each algorithm algorithm number of particles evolution generations initial ω p2 se chaos update rate r1 r2 acpso 40 1000 0.95 1.05 1 61.8% / / acpso 400 1000 0.95 1.05 1 61.8% / / spso 40 1000 0.7298 / / / / / ref. 12 40 1000 0.7298 / / / 0.2 0.8 ref. 15 40 1000 3 1.05 / / / / the performance of each algorithm is compared by adapting the following criterions. (1) the convergence rate ir. it is the ratio of running time as achieving the convergence criteria to the total running time 20. (2) optimal value fbest. it is the minimum value of the results in 20 times run. (3) the mean optimal value denoted by fav. it is defined by the arithmetic mean value of the optimal fitness in 20 times run. (4) mean squared deviation denoted by std. it is the mean squared deviation for some algorithm in 20 times run. (5) elapsed time. it is the consuming time for each run. (6) the schematic diagram of mean convergence. firstly the evolution generations and the corresponding optimal value are extracted. and then take the arithmetic mean value for the optimal value in 20 times run for each evolution generation. finally, draw the graph by taking the evolution generations and the mean optimal value of each evolution generation to be the abscissa and ordinate respectively. (7) the schematic diagram of optimal convergence. aiming at 20 times run, take the running process corresponding the optimal result. and draw the graph by taking the evolution generations and the optimal value of each evolution generation to be the abscissa and ordinate, respectively. it gives the result respectively for each benchmark function from table 3 to table 7 as follow. table 3: the result of sphere function algorithm ir /% fbest fav std elapsed time /s acpso 0 0.7204 2.68 1.11 1.66 acpso 1 7.83e− 13 6.60e− 12 4.77 15.23 spso 0 2164.84 6705.48 2772.00 0.97 ref. 12 0 22237.05 55486.04 14306.06 0.97 ref. 15 0 41.8138 438.52 448.86 1.14 ref. 20 1 0.00174 0.00268 0.000495 5.12 the benchmark functions adapted in this paper are all high-dimensional functions of 30dimension. from figure 4 to figure 13, it can be seen that the acpso algorithm proposed in this paper has better search capability, especially in the earlier stage of the evolution. from table 3 to 7, it can be seen that the acpso algorithm shows better stability. on the whole, the particle swarm optimization algorithm with adaptive chaos perturbation 813 0 200 400 600 800 1000 −12 −10 −8 −6 −4 −2 0 2 4 6 8 generations lo g 1 0f itn es s acpso 40 pso ref. 12 ref.15 ref.20 acpso 400 figure 4: average evolutionary process for sphere function 0 200 400 600 800 1000 −12 −10 −8 −6 −4 −2 0 2 4 6 8 generations lo g 1 0f itn es s acpso 40 pso ref. 12 ref.15 ref.20 acpso 400 figure 5: best evolutionary process for sphere function table 4: the result of rosenbrock’s function algorithm ir/% fbest fav std elapsed time s acpso 0 38.10 174.31 128.14 1.64 acpso 1 16.90 23.19 10.99 14.74 spso 0 6.65e + 5 3.79e + 6 3.16e + 6 0.90 ref. 12 0 1.40e + 8 2.26e + 8 4.57e + 7 1.03 ref. 15 0 138.44 3.75e + 3 4.86e + 3 1.05 ref. 20 1 28.07 1.76e + 5 7.00e + 5 5.27 table 5: the result of rastrigin’s function algorithm ir/% fbest fav std elapsed time s acpso 0 44.32 105.88 39.94 1.63 acpso 0 29.85 80.35 29.34 15.05 spso 0 205.15 262.46 39.06 0.93 ref. 12 0 304.99 392.91 41.42 0.94 ref. 15 0 29.01 62.14 24.09 1.09 ref. 20 0 17.39 34.47 9.39 4.96 814 l. mengxia, l. ruiquan, d. yong ! " # $ % ! " # $ % %! %" &'(')*+,-(. /0 % 1 ,+( '. . 23456 " 456 7'189%! 7'18%: 7'18! 23456 " figure 6: average evolutionary process for rosenbrock’s function ! " # $ % ! " # $ % %! %" &'(')*+,-(. /0 % 1 ,+( '. . 23456 " 456 7'189%! 7'18%: 7'18! 23456 " figure 7: best evolutionary process for rosenbrock’s function ! " # $ % %&' ! !&' ( (&' " "&' ' '&' # #&' )*+*,-./0+1 20 3 % 4 /.+ *1 1 56789 " 789 :*4&;%! :*4&%' :*4&! 56789 " figure 8: average evolutionary process for rastrigin’s function the particle swarm optimization algorithm with adaptive chaos perturbation 815 ! " # $ % % ! & " ' # ( )*+*,-./0+1 20 3 % 4 /.+ *1 1 56789 " 789 :*4;<%! :*4;%' :*4;! 56789 " figure 9: best evolutionary process for rastrigin’s function table 6: the result of griewanks’s function algorithm ir/% fbest fav std elapsed time s acpso 0 0.7952 0.9675 0.0068 1.87 acpso 35 5.66e− 10 0.0214 0.0169 19.52 spso 0 19.303 51.182 18.643 1.10 ref. 12 0 278.85 527.87 116.75 1.11 ref. 15 0 1.2985 6.2802 5.9002 1.36 ref. 20 85 1.04e− 4 0.0041 0.0082 6.21 ! " # $ % &' &! &% % ! ' ()*)+,-./*0 1/ 2 % 3 .-* )0 0 45678 " 678 9)3:;%! 9)3:%< 9)3:! 45678 " figure 10: average evolutionary process for griewanks’s function table 7: the result of griewanks’s function algorithm ir/% fbest fav std elapsed time s acpso 0 1.0486 2.0469 0.6447 1.84 acpso 1 0.00038 0.00038 1.91e− 08 17.08 spso 0 207.8326 453.7501 130.34 1.10 ref. 12 0 2014.7359 3771.971 852.53 1.11 ref. 15 0 12.2179 44.2053 32.499 1.27 ref. 20 0 4621.4715 6014.141 767.825 5.38 816 l. mengxia, l. ruiquan, d. yong ! " # $ % &" &' &! &% % ! ' ()*)+,-./*0 1/ 2 % 3 .-* )0 0 45678 " 678 9)3:;%! 9)3:%< 9)3:! 45678 " figure 11: best evolutionary process for griewanks’s function ! " # $ % &" &' &! &% % ! ' " ( )*+*,-./0+1 20 3 % 4 /.+ *1 1 56789 " 789 :*4;<%! :*4;%( :*4;! 56789 " figure 12: average evolutionary process for schwefel function ! " # $ % &" &' &! &% % ! ' " ( )*+*,-./0+1 20 3 % 4 /.+ *1 1 56789 " 789 :*4;<%! :*4;%( :*4;! 56789 " figure 13: best evolutionary process for schwefel function the particle swarm optimization algorithm with adaptive chaos perturbation 817 the search capability of the algorithm proposed in this paper is superior to the standard pso algorithm and is better than the algorithm ref. 20 for part benchmark functions. conclusions (1) the algorithm proposed in this paper introduces the anderson chaotic mapping to realize the initial of the particle swarm and the perturbation of part particles. it also introduces the concept of the expected mean velocity of particle swarm to realize the adjustment of the inertia weight of particles. (2) the results of benchmark functions show that the algorithm proposed in this paper reflects preferable search ability. 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[23] r. anderson (1996); industrial cryptography, ieee rev., 118-120. international journal of computers communications & control issn 1841-9836, 12(3), 307-322, june 2017. a rating-based integrated recommendation framework with improved collaborative filtering approaches s. cheng, b. zhang, g. zou shulin cheng 1. school of computer engineering and science, shanghai university 99 shangda road, baoshan district, shanghai, 200444, pr, china chengshulin@shu.edu.cn 2. school of computer and information, anqing normal university 1318 jixian north road, anqing, anhui province, 246133, pr, china chengshl@aqnu.edu.cn bofeng zhang*, guobing zou school of computer engineering and science, shanghai university 99 shangda road, baoshan district, shanghai, 200444, pr, china *corresponding author: bfzhang@shu.edu.cn guobingzou@gmail.com abstract: collaborative filtering (cf) approach is successfully applied in the rating prediction of personal recommendation. but individual information source is leveraged in many of them, i.e., the information derived from single perspective is used in the user-item matrix for recommendation, such as user-based cf method mainly utilizing the information of user view, item-based cf method mainly exploiting the information of item view. in this paper, in order to take full advantage of multiple information sources embedded in user-item rating matrix, we proposed a rating-based integrated recommendation framework of cf approaches to improve the rating prediction accuracy. firstly, as for the sparsity of the conventional item-based cf method, we improved it by fusing the inner similarity and outer similarity based on the local sparsity factor. meanwhile, we also proposed the improved user-based cf method in line with the user-item-interest model (uiim) by preliminary rating. second, we put forward a background method called user-item-based improved cf (uibcf-i), which utilizes the information source of both similar items and similar users, to smooth itembased and user-based cf methods. lastly, we leveraged the three information sources and fused their corresponding ratings into an integrated cf model (inte-cf). experiments demonstrate that the proposed rating-based inte-cf indeed improves the prediction accuracy and has strong robustness and low sensitivity to sparsity of dataset by comparisons to other mainstream cf approaches. keywords: personalized recommendation, collaborative filtering, rating integration. 1 introduction the recommender system [21] has been studied by many researchers in the past decade, which are widely applied in many fields like information retrieval [5], item recommendation [22], e-commerce [13]. recommender system obtained relatively promising results and facilitated users, in which collaborative filtering (cf) recommendation methods are classic and useful ones, and do well in recommending items with ratings such as products, movies, music. cf recommendation approaches [21] [19] [23] can be divided into memory-based approaches and model-based approaches. memory-based approaches are heuristic and comprises item-based and user-based approaches, while model-based approaches are built based on machine learning theory. item-based and user-based approaches both leverage the idea of neighbors to generate recommendation by measuring the similarities between the target item and other items, or, between copyright © 2006-2017 by ccc publications 308 s. cheng, b. zhang, g. zou the target user and other users. and the similarities are viewed as weights between items or users in the process of rating prediction. but many of item-based and user-based approaches predict unknown ratings from single perspective of either users or items, in which only partial information embedded in user-item matrix is utilized. traditional approach [22] [19] with single view has relatively low performance due to the poor ability against the sparsity of user-item matrix except few ones like recommendation in amazon [13]. naturally, some researchers studied the imputation of missing data [6] [16] which produced relatively good performance. but they did not consider the information of multiple sources embedded in user-item rating matrix. therefore, we study an integrated recommendation framework of cf approaches using the information of multiple sources from user-item matrix. in this paper, we propose a rating-based integrated recommendation framework inte-cf with improved cf methods. our integrated framework is, to some extent, similar but different to hybrid recommendation approach which usually combined cf methods with content-based approaches applying the strategies of pre-fusion or/and post-fusion, or built linear combination of different cf methods. our framework could directly obtain the values of optimal fusion parameters by one time of learning, whereas other methods like [16] [24] found out the suitable values of combination parameters by many times of learning and manual comparisons. in our framework, an objective optimization function for predicting unknown ratings is put forward by considering three varying information sources from different perspectives of improved traditional cf approaches. the framework can implement more accurate recommendations through learning the optimization parameters, whose advantages are fully leveraging the information embedded in user-item matrix from three different perspectives, reducing the dependence on missing data and balancing three cf methods by optimization parameters. the remainder of the paper is organized as follows. we first summarized the related works in section 2. the rating-based integrated recommendation framework is presented in section 3. section 4 presented the improved approaches of traditional itemand user-based methods. we designed a background rating prediction method based on both similar users and similar items in section 5. the details of integrated recommendation framework are demonstrated in section 6. the experimental results of the proposed scheme are discussed in section 7. finally, we discussed the findings of our work along with the future work in the last section. 2 related works since the recommender systems were generated, cf recommendation has been viewed as the most successful recommender method including memory-based heuristic approaches cf methods and model-based learning approaches [14]. there have been many cf recommender applications in academia and industry. to the best of our knowledge, tapestry system [9] identifying likeminded user is the earliest real cf recommender system. and amazon web site [13] is a famous application of cf approach. in order to increase the recommendation accuracy, many scholars tried to improve cf approaches by varying similarity calculations between users or items. breese [3] etc. compared the prediction accuracy of several similarity algorithms including correlation coefficient-based algorithm, vector-based algorithm and statistical bayesian algorithm. choi [4] etc. proposed a new similarity function for selecting neighbors for each target item. others like conditional probability-based similarity algorithm [7] of item similarity and genetic algorithm [2] of user similarity were also studied. good similarity computation method as a kind of enhancement of cf methods indeed improves the recommendation accuracy to some extent. but it is sensitive to the data quality like the sparsity of dataset. despite the success of cf approaches, sparsity is still a major challenge and heavily affects a rating-based integrated recommendation framework with improved collaborative filtering approaches 309 recommendation accuracy. the fact is that a large volume of entries’ value in user-item matrix is missing. therefore, some solutions were proposed to address the issues of sparsity. the simplest ways [6] are using either the value of zero or the average rating of users or items. obviously, these two ways are too coarse and imprecise. later on, some relatively better methods were adopted, such as dimensionality reduction based on matrix factorization [20], imputation based on preliminary rating prediction of missing data [16] [8]. in recent years, some other information related to users or items was adopted to alleviate sparsity, like the trust and distrust relationships between users, which were studied in open dataset of epinions [1]. the information in social networks such as friend relations, social influence, is also researched to alleviate sparsity by some scholars [27]. indeed, these information is useful, but they are not always obtained like in the movielens dataset, unless in social networks. it is straightforward that the information that could be utilized in recommendation depends on the questions of a certain specific domain. therefore in this paper, we consider only the information embedded in user-item rating matrix and the semantic information of items to reduce the sparsity according to the limited available information. in addition to taking full advantage of improvements of similarity computation, dimensionality reduction and other related information sources, there is another important improvement way called hybrid filtering which combines cf with other recommendation approaches. lu [15] etc. proposed the ccf approach for the news topic recommendation in bing, which combined cf approach and content-based filtering method. in e-commerce, song [24] etc. leveraged both demographic recommendation techniques and cf algorithms to put forward a hybrid algorithm in order to improve recommendation accuracy. ma [16] etc. proposed a linear combination of userand item-based methods based on the missing value prediction by finding suitable combination parameters and obtained better performance. moin [17] etc. suggested feature hybrid weighting schemes for improving the precision of neighborhood based cf algorithms, while it increases the complexity of computation. from the perspective of optimization, nilashi [18] etc. proposed hybrid recommendation for cf method based on multi-criteria to improve prediction accuracy. hybrid recommendations absorb the advantages of each recommender algorithm and do improve the precision of recommendation. they effectively alleviated sparsity and solved the problem of cold start to some extent, especially the combination of content-based method and cf approach. in this paper, we also leverage a similar but different idea of content-based method to improve the item-based cf approach, which utilize inner similarity of items and is discussed in subsection 4.1. to sum all, cf is applied successfully into all kinds of recommendation fields and obtained a lot of improvements, which focuses on the similarity improvements either in user-based or in item-based methods and combinations with other types of recommendation algorithms. in this paper, we emphasis on the improvement of accuracy by cf-self integration based on three types of ratings deriving from three perspectives of users, items and both users and items. the integrated model is proved to be more accurate, effective and interpretive than some other mainstream cf methods demonstrated in our experiments. what’s more, the model is easy to be paralleled to improve the running efficiency. 3 integrated recommendation framework of cfs our proposed integrated cf recommendation framework, namely, inte-cf is shown in fig.1, which comprises four core parts. the first part is to generate the first type of rating (rating 1) from the perspective of item by improving conventional item-based cf approach based on the fusion of two kinds of similarities of item. the second part is in charge of generating the second type of rating (rating 2) from the perspective of user by improving the traditional 310 s. cheng, b. zhang, g. zou user-based cf (ubcf) approach with uiim extracted from user-item matrix. the third part demonstrates a combination model of generating the third type of prediction rating (rating 3) based on both similar items and similar users from the two perspectives of item and user. the three types of ratings are integrated together to build an objective function f in the part 4, which is our proposed integrated optimal model. it is tuned by the optimization parameters which are learned by training sample data. the parts of 1, 2, and 3 serve the part 4. the details of generating each type of rating are demonstrated in the subsequent sections. preliminary ratings compute user similarity users ratings interest model user-based cf rating 1 rating 3 based on both similar users and similar items rating 2 4 integrate ratings objective function f: min training optimal parameter vector integrated predicting user-item matrix 2 objective function training for ratings integration and predicting u1 i1 i2 i3 ... ij ... in u2 u3 ... ui ... um r1,1 ... ... rm,1 r3,1 r1,n r2,n ... ri,n ... rm,n r1,2 r2,2 ri,2 ... r2,3 ... ri,3 ... rm,3 r3,3 ... .. ... ... r1,j r2,j ... ... rm,j ... ... ... ... inner similarity local sparseness factor outer similarity items ratings semantic information item-based cf fusion similarity 1 3 figure 1: the rating-based integrated recommendation framework of cf approaches 4 improvements of traditional item-based and user-based cf item-based and user-based cf approaches have similar rationale to predict the unknown ratings in user-item matrix. firstly, the neighbors of target user or item are obtained by similarities in the two cf approaches. then the unknown rating of each entry related to the target user or item in user-item matrix is predicted by these neighbors whose similarities to the target user or item are viewed as weights in calculation. lastly, the top-k recommendation list is generated in accordance with the predicted ratings. the details of them can refer to [7,13,19,23]. 4.1 improved item-based approach by similarity fusion similarities between items the performance of recommender system partially depends on the computation of similarities between items. according to the principles related to dialectics, the relevance between things is determined by inner factors and outer factors. in conventional item-based cf approaches (ibcf), the similarities which are calculated in line with the ratings in user-item matrix are measured from outer factors, namely, the perspective of user evaluation. actually, the similarities between items are also influenced, to a large extent, by the inner factors such as the properties of items, which embody item’s inherent semantic information [26]. in other words, the similarities between a rating-based integrated recommendation framework with improved collaborative filtering approaches 311 items depend both on inner factors and outer factors. in this paper, inner factors denote the properties of item, which are utilized to characterize items and depend on the specific objects. for instance, if the object is movie, the properties could be genres etc.; if the object is product or commodity, the properties could be appearance, genres, color, function, price, quality etc. therefore, it is necessary to take the two kinds of factors into account to measure the similarities between items. for convenience, the similarity produced by outer factors is called outer similarity and the similarity produced by inner factors is called inner similarity. the outer similarity is calculated by the ratings of items in user-item matrix showed in eq. (1). the inner similarity is computed by the properties of items showed in the eq.(2). simiout(i,j) = ~ii · ~ij ||~ii||× ||~ij|| (1) simiin(i,j) = ∑ k=1 ϕ(k)sim(θ(k), i,j) (2) where θ denotes the property set of item i and item j , sim(θ(k), i,j) represents the similarity of item i and item j on property k in θ, ϕ(k) is the weight of property k like the genre property of a movie. item local sparsity factor user-item matrix is commonly heavily sparse. the existing sparsity called global sparsity degree is measured by the ratio that is equal to the number of unknown ratings over the number of total entries in user-item matrix. when calculating the item similarity, we defined a local sparsity factor, i.e., item local sparsity factor which is used to describe the sparsity of the set of co-ratings from the local perspective of item. definition 1. (item local sparsity) let uii be the set of users who rated on item i and u i j be the set of users who rated on item j, then the item local sparsity is defined as: spii,j = 2 ∗ |uii ∪u i j|− (|u i i + |u i j|) 2 ∗ |uii ∪u i j| (3) fusion of inner similarity and outer similarity of item according to the aforementioned analysis, it is reasonable to fuse the inner similarity and outer similarity of items. the item local sparsity factor can be used to balance the outer similarity and inner similarity. therefore, we define the weight function between inner and outer similarities incorporating the item local sparsity by sigmoid function as follows: f(spii,j) = { 1 1+e−sp i 0 ≤ spii,j<1 1 spii,j = 1 (4) clearly, spii,j is between 0 and 1, and f(sp i i,j) belongs to 0.5 and 1, which guarantees inner similarity always be in the resulting item similarity, because inner similarity between two items is always useful and works. when spii,j equals 0, namely, two items have the complete common rating users and the set of co-rating users is full, the value of f(spii,j) is 0.5, which means inner and outer similarities have same weights. when spii,j equals 1 that means item i and item j have 312 s. cheng, b. zhang, g. zou no common rating users, f(spii,j) is set to 1, i.e., the similarity between item i and item j only depends on the inner similarity. the resulting similarity after being fused based on f(spii,j) is as follows: simi(i,j) = f(spii,j)∗sim i in(i,j) + (1−f(sp i i,j))∗sim i out(i,j) (5) the resulting similarity of items embodies the two aspects of inner factors and outer factors, effectively alleviates the dependence on the sparsity of user-item matrix and overcomes the item cold start problem. f(spii,j) balances the inner factors and outer factors. therefore, ibcf can be improved by the item fusion similarity and called ibcf-i. 4.2 improved user-based cf for rating prediction based on user item interest model although conventional ubcf method can predict rating with some extent accuracy, it still has the space of improvement. the key of conventional ubcf approach is to find quality neighbors of a target user. so the similarity calculation between users is important. but due to the heavy sparsity of initial user-item matrix, sometimes the similarity calculation like cosine similarity in conventional ubcf method has relatively low accuracy, even not correct occasionally. in order alleviate the sparsity, deng [6] etc. proposed an approach of preliminary rating of unknown rating entries, which, while, still suffered from the sparsity, since it only used the existing known ratings. different from deng [6], we propose a preliminary rating model (prm) based on user-item-interest to conquer the sparsity, which is similar to imputation. and the rating prediction method ubcf-i is put forward based on prm. applying cf method, user-item matrix is the information source leveraged to make study and analysis. the uiim model is built based on knn cluster approach using inner similarities between items. generally, user rates similar items with similar ratings. therefore, items that user has rated can be clustered into k clusters in line with their inner similarities for building uiim. then the nearest cluster to the target item with unknown rating entry is selected. lastly, it is utilized to make preliminary rating for the unknown rating entry. so there are more coratings between users, which are used to calculate the similarities between users. obviously it can produce better accuracy of user similarity than traditional ubcf method. the detailed process is discussed as follows. let ip be the known rating item set of user up, iq be the known rating item set of user uq, i∪p,q be the union set of ip and iq, and i∩p,q be the intersect set of ip and iq with co-ratings namely, i∪p,q = ip ∪iq, i∩p,q = ip ∩iq. then the unknown rating item sets np and nq of user up and user uq are np = i∪p,q − ip and nq = i∪p,q − iq, respectively. the process of preliminary ratings of np and nq are similar, here we take an example for np. assume item ij ∈ np, firstly, compute the semantic distances of item ij to the k clusters in uiim of the user up, and sort them by ascend. the cluster which is the nearest to the item ij is selected as the neighbors called in. then calculate the preliminary rating r′p,j of unknown rating entry of user up on item ij according to the neighbors in, as follows: r′p,j = ∑ l∈in sim i(j, l) ∗rp,l∑ l∈in sim i(j, l) (6) so far, each entry in the union set i∪p,q has co-ratings of user up and user uq either known rating or preliminary rating. the resulting similarity between user up and user uq is quality. therefore, in user space the similarities between user up and other users can be calculated effectively. the nearest neighbor user set nup used to calculate the similarities to the target user up is formed in lines with the rule of top-k. finally, ubcf-i is applied to predict the unknown ratings of user a rating-based integrated recommendation framework with improved collaborative filtering approaches 313 up. ubcf-i method has some advantages (1) searching similar items for preliminary ratings is in a small item scope rather than in the whole item space, which derived from the most related items in uiim; (2) avoiding the sparsity of computing the item similarity for making preliminary rating, especially for the case two users have many ratings but few common ones, since all the missing values between two users are imputed in our method when computing their similarity. although ubcf-i has some strong points, we don’t intend to deeply research the serious user cold start problem in which the number of user ratings is close or equal to 0. to well solve the problem needs some other information like user social information [12,27], trust relationships [1] and etc., which should be deeply studied but not always be achieved in traditional dataset such as movielens, unless in social network. therefore, as for the very serious user cold start problem in which clustering is ineffective, considering averaging user’s average rating and the item’s average rating as the preliminary rating is a good selection [8]. 5 rating based on both similar users and similar items ibcf-i and ubcf-i predict ratings from the perspectives of similar items and similar users, respectively. but only depending on one of them is undesirable [22,23]. it is necessary to think about that taking both similar users and similar items into account, which correspond to the rows and columns in user-item matrix, respectively, can provide more effective information sources for predicting ratings. that means similar users making similar item ratings provides an extra and useful information source for prediction. but how to make full use of the information that derives from both similar users and similar items? firstly, reorder user-item matrix according to the similarities of users and similarities of items. second, generate the predictive ratings by fusing the two similarities towards the target user and the target item related to the entries with unknown ratings in user-item matrix. therefore, we proposed one kind of cf method using the compound similarity based on the two-dimension coordinates to address the problem. for the convenience of expression, we call this method uibcf-i. the rationale of uibcf-i is shown in fig.2 as follows. users itemsi1 i2 i3 ... ij ... in r1,1 ... ... rm,1 r3,1 r1,n r2,n ... ri,n ... rm,n ? r1,2 r2,2 ri,2 ... r2,3 ... ri,3 ... rm,3 r3,3 ... .. ... ... r1,j r2,j ... ... rm,j ... ... ... ... u1 i1 i2 i3 ... ij ... in u2 u3 ... ui ... um r1,1 ... ... rm,1 r3,1 r1,n r2,n ... ri,n ... rm,n ? r1,2 r2,2 ri,2 ... r2,3 ... ri,3 ... rm,3 r3,3 ... .. ... ... r1,j r2,j ... ... rm,j ... ... ... ... u1 u2 u3 ... ui ... um b expression of re-ranked matrix in coordinatea origin user-item matrix :f ratings figure 2: principle of predicting rating based on uibcf-i the part (a) denotes the original user-item matrix and part (b) represents the rebuilding and mapping of user-item matrix in two-dimension coordinates. horizontal axis denotes item and vertical axis represents user. the entries of user-item matrix correspond to the points in coordinates. all the users and items are ordered in descend by the magnitudes of similarities to the target user and the target item. the question mark denotes the entry of unknown rating related to the target user ui and the target item ij. the top-k most similar users uss and top-m 314 s. cheng, b. zhang, g. zou most similar items iss to the user ui and the item ij are selected, respectively. the predictive rating can be calculated by the compound similarity of similar users and similar items in eq.(7). rpss(i,j) = ∑ k∈uss ∑ m∈iss simss(i,j,k,m) ∗r(k,m)∑ k∈uss ∑ m∈iss simss(i,j,k,m) (7) where simss represents the compound similarity of similar users and similar items, which is computed in eq. (8) as follows: simss(i,j,k,m) = λ1sim u(i,k) + λ2sim i(j,m) (8) λ1 and λ2 are tuning parameters, whose values are commonly denoted by 0.5 respectively. 6 integrated cf recommendation model by ratings fusion 6.1 overview the core task of a recommendation algorithm is to predict which items a user relatively most likes based on his/her observed feedback which denotes ratings on items here. so far, we have obtained three types of ratings, namely, ratings of 1, 2 and 3 according to the aforementioned content. they are obtained by three different methods from three varying information sources. each of them has its own strength and weakness. how to combine the three types of ratings represents a novel challenge. we proposed the optimal integration framework inte-cf based on the three types of ratings by learning relevant parameters. in inte-cf model, the ratings predicted by ibcf-i, ubcf-i and uibcf-i from three varying perspectives and using different information sources which complement each other. and uibcf-i could also be viewed as a background method of ibcf-i and ubcf-i and smooth the rating predictions generated by ibcf-i and ubcf-i. therefore, the integration of the three types of ratings not only leverages the three varying information sources but also can reduce the dependence on data sparsity. let u = {u1,u2, ...,um} be the set of m users and i = {i1, i2, ..., in} be the set of n items. rui,r̂ui denote the rating and predicted rating of user u on item i, respectively. r̂ (1) ui , r̂ (2) ui , r̂ (3) ui represent user u′s predicted ratings by ibcf-i, ubcf-i and uibcf-i on item i, respectively. ~̂rui is a predicted rating vector composed of r̂ (1) ui , r̂ (2) ui , r̂ (3) ui . we also use r ∈ r m×n to represent the matrix of observed ratings and ~w = (w1,w2,w2) to denote a parameter vector. for convenience, we use s∗u ⊆ u ×i to denote the set of user-item pairs of user u, for which the observed ratings are available. 6.2 integration we have obtained user u′s three predicted ratings of r̂(1)ui , r̂ (2) ui , r̂ (3) ui on item i, which are leveraged to predict user u′s preference on item i from three different perspectives. in order to achieve more accurate user u′s predictive rating on item i, we proposed an algorithm of combining them with an integrated model to implement the aforementioned framework inte-cf as follows: r̂ui = r̂ (1) ui ∗w1 + r̂ (2) ui ∗w2 + r̂ (3) ui ∗w3 = ~̂rui~w t (9) actually, it is an optimization problem of the following general form: min ~w (`(rui, r̂ui) + r(~w)) (10) a rating-based integrated recommendation framework with improved collaborative filtering approaches 315 here `(rui, r̂ui) is a loss function measuring the discrepancy between the observed rating and the predicted rating of user u′s on item i. the regularization function r(~w) overly penalizes the model to suppress overfitting. the goal of the model is to make the predicted rating r̂ui as close to the observed rating rui as possible. the common and good selection for the loss function is to use squared error loss form: `(rui, r̂ui) = 1 2 ∑ (u,i)∈s∗u (rui − r̂ui)2 (11) certainly, there are several other forms of loss function such as [14]. here we used squared error form for loss function due to its simplicity and easiness of implementation. we used the frobenius norm of parameters to build the regularization function r(~w), which was adopted by koren [10] et al. due to its smooth differentiable property. r(~w) = 1 2 λ||~w||2f (12) where the parameter λ ≥ 0 is used to control the strength of regularization and helps to balance between training error and model complexity. so the training model can be rebuilt as follows according to the equations of (10), (11) and (12): f(~̂rui, ~w) = min ⇀ w (`(rui,~rui) + r(~w)) = 1 2 min ~w ( ∑ (u,i)∈s∗u (rui − ~̂rui~wt ) 2 + λ||~w||2f ) (13) for convenience, we convert eq.(13) into eq. (14):  fmin = 1 2 ( ∑ (u,i)∈s∗u (rui − ~̂rui~wt ) 2 + λ||~w||2f ) s.t. ||~w||1f = 1, wj ≥ 0, j ∈{1, 2, 3} (14) obviously, this is an optimization problem and could be solved by dynamic program with constraints. we adopted the stochastic gradient descent (sgd) [11] method to learn the parameters in order to accelerate the optimization process. the optimization procedure is shown in algorithm 1. the algorithm takes as input the matrix r of observed ratings, error ε and a group of vectors ~̂ruis which derive from the three types of predicted ratings. 7 experiments in order to verify our proposed integrated model inte-cf, we experimented on the classic dataset of movielens1 and eachmovie2. due to the high similar results on the two datasets, we only report the experiment results of movielens (out of space consideration). the movielens dataset is comprised with 943 users, 1682 movies (items) and 100,000 ratings (1-5 scales) with the global sparsity of 0.93695, where each user has rated at least 20 items. to better validate our proposed inte-cf model, we conducted 4 groups of experiments corresponding to 200, 400, 600 and 800 users and relevant data extracted from the dataset at random. the purpose of dividing the dataset into 4 groups is to find out the difference of optimal parameters which, we thought, depend on the information of specific dataset, such as the size, sparsity. the experiments were finished in line with 10-fold cross-validation. we have two goals to conduct the experiments. one is to validate the higher prediction accuracy and more effectiveness of inte-cf model. the other is to find out the component variation law of optimal parameter vector with varying scale dataset. 1http://www.grouplens.org/ 2http://www.research.digital.com/src/eachmovie/ 316 s. cheng, b. zhang, g. zou algorithm 1 the optimization of inte-cf model input: the rating matrix r, error ε and a group of vectors ~̂ruis of three types of predicted ratings. output: model parameter vector ~w = (w1,w2,w3). begin 1: initialize the vector ~w = (0.333, 0.333, 0.334), k ← 1 and f(0) = 0; 2: calculate f(1); 3: while |f(k) −f(k−1)| > ε do 4: k → k + 1; 5: s (k) 1 ←− ∇f(w1) ||∇f(w1)|| , s(k)2 ←− ∇f(w2) ||∇f(w2)|| ; 6: w1 ← w1 + αks (k) 1 , w2 ← w2 + αks (k) 2 ;//αk is learning parameter 7: w3 ← 1 −w1 −w2; 8: calculate f(k); 9: end while 10: return vector ~w = (w1,w2,w3) end 7.1 preliminary here we conducted the experiments on the dataset of movielens. in order to calculate the inner similarities between items (movies) discussed in the sub-section of 4.1, we need to quantify the information of item’s genre properties which characterize the movie’s inherent features. the genre of each movie in the dataset is multi-valued and has 20 possible values such as drama, action and comedy. in general, more than one of these genres present with different degree in a movie. some of them with high presence are called major or dominating genres for that movie. for example for the movie "copycat" as presented in the movielens dataset [28] has "crime/mystery/thrill/drama", crime is the most dominating genre value; mystery is the second one, etc. the rest in 20 possible genres do not present. therefore, in order to quantify the presence degree, we utilize a gaussian-like function [28] to compute it. µ(gi,ij) = ri/2 √ α∗nj∗(ri−1) (15) where gi denotes the genre i of item ij, nj represents the total number of the present genres, rj denotes the rank position that indicates the magnitude of presence of gi and 1 ≤ ri ≤ nj. the rank positions of those genres with no presence equal 0. and α > 1 is a constant threshold which controls the difference in presence degree of the gi in the item ij. here we set α = 1.2 which makes the calculation perfect [28]. due to no information of rank positions of genres in the dataset, we complemented them by crawling the information from the online movie database(http://www.imdb.com/). 7.2 metrics as for assessing the accuracy of a recommender system with prediction ratings, one of the most popular evaluation metrics is mean absolute error (mae) [21, 27], which measures the average absolute deviation between the real rating assigned by the user and the predicted rating calculated by a certain recommendation algorithm. therefore, we use mae to measure the prediction quality of our proposed integrated framework inte-cf with other mainstream cf methods, which is defined as follows. a rating-based integrated recommendation framework with improved collaborative filtering approaches 317 mae = ∑ (u,i)∈rtest |ru,i − r̂u,i| |rtest| (16) where rtest is the set of all user-item pairs (u,i) in the test set. the smaller mae value means a better performance. 7.3 preliminary experiments of verifying ubcf-i and ibcf-i methods we first conducted a preliminary experiment to verify the effectiveness of our proposed improved cf methods ibcf-i and ubcf-i, which are compared to the conventional methods of ubcf and ibcf so that we could proceed to do the next further experiments of inte-cf model. we randomly selected half of data of the movielens dataset to conduct the preliminary experiment. the data was split into two parts, namely, training set 80% and prediction set 20%. the related users and items are 444 and 1605, respectively. the global sparsity is 0.9298. the experimental results are showed in fig.2. figure 3: preliminary experiment about comparisons of mae between improved methods and conventional methods obviously, ubcf-i and ibcf-i methods are more accurate than the conventional methods of ubcf and ibcf from overall view. ubcf-i obtains maximum 19.57% and average 17.25% increases than ubcf, respectively. similarly, ibcf-i obtains maximum 18.93% and average 18.12% increases than ibcf method, respectively. they all get lower mae value with the increase of neighbors since ubcf-i approach benefits from uiim and ibcf-i method benefits from fusion similarity composed of inner and outer similarities. we achieved the significant improvements of ubcf-i and ibcf-i approaches on mae and not planned to further analysis and conduct the preliminary experiments. we emphasized on the subsequent experiments of our integrated framework model inte-cf. 7.4 experiments for predictive accuracy we conducted 4 experiments in which users are divided into 4 groups of 200, 400, 600 and 800. for convenience, we call them g2, g4, g6 and g8, respectively. we compared our integrated model inte-cf to two individual predictors, namely, ubcf-i and ibcf-i, and other two combination predictors, namely, our proposed uibcf-i method and a linear combination method [16], 318 s. cheng, b. zhang, g. zou table 1: comparison to other cf methods: a smaller value means a better performance groups g2 g4 g6 g8 inte-cf 0.792 0.744 0.731 0.711 uibcf-i 0.887 0.827 0.774 0.748 ubcf-i 0.845 0.775 0.763 0.759 ibcf-i 0.873 0.778 0.786 0.764 ui-linear 0.861 0.819 0.762 0.728 table 2: the value of average optimal vector in 4 groups w g2 g4 g6 g8 w3 0.625 0.697 0.711 0.725 w2 0.204 0.192 0.174 0.166 w1 0.171 0.111 0.115 0.109 which, for convenience, is called ui-linear showed at the last row in table 1. the optimal number of neighbors was 35 selected by many tests. table 1 summarizes the results, showing the how inte-cf approach outperforms the other methods in all 4 groups of experiments. inte-cf approach is the best recommendation method in table 1. ubcf-i and ibcf-i have relatively low performance compared to inte-cf, although they are improved based on standard cf method. uibcf-i similar to ui-linear is less accurate than ubcf-i and ibcf-i in g2 and g4 because of the less data when both considering similar users and similar items. but their accuracy increases fast with more users and items. if there are enough users and items, they will outperform ubcf-i and ibcf-i just like in g6 and g8, since they benefit from the strengths of combination. inte-cf has the best performance which fuses three information sources deriving from ubcf, ibcf and uibcf-i, and absorbs the advantages of them. we also found that the performances of all the methods have been improved with more users in dataset. it is evident that more users and items produce more ratings on whole, which provide more accurate prediction when applying enhanced cf methods. 7.5 discussion about optimal parameter vector each group of experiments generates 10 optimal parameter vectors in 10-fold cross-validation experiments. in order to demonstrate the overall changes of vectors and proportions of ubcf-i, ibcf-i and uibcf-i, we select the average optimal parameter vector of each group of experiments for the comparisons. table 2 and fig.3 show the changes of value derived from all the 4 groups of average optimal parameter vectors. each vector contains three components which are w1, w2 and w3 corresponding to the weights ω1, ω2 and ω3 of ibcf-i, uibcf-i and uibcf-i in parameter vector, respectively. in each group of experiment, w1, w2 and w3 have the similar situations. w3 is dominant value and plays an important role in predicting ratings, especially in more rating data. w1 and w2 decreased with the increase of rating data, maybe since both more similar users to the target user and more similar items to the target item result in great influence on uibcf-i. the optimal value of w3 is in vibration around 0.7 in most cases. combining table 1 and fig.3, our proposed model of inte-cf makes full use of the three kinds of information sources of ibcf-i, ubcf-i and uibcf-i from varying views and obtains the best performance. a rating-based integrated recommendation framework with improved collaborative filtering approaches 319 figure 4: three components of average optimal parameter vector in 4 groups table 3: statistics information of 4 groups groups g2 g4 g6 g8 users 200 400 600 800 items 1409 1484 1596 1655 existing data count 22378 41826 64384 85697 theoretical data count 281800 553600 957600 1324000 global sparsity 0.9206 0.9296 0.9328 0.9353 320 s. cheng, b. zhang, g. zou table 3 gives the statistics information of 4 groups of experiments. the global sparsity in each group is adjacent regardless of the increase of the rating data, and is close to the global sparsity 0.93695 of the whole dataset. the three components of the optimal parameter vector changes a little in 4 groups. the background method of uibcf-i plays a great role in 4 groups, especially in g8 whose global sparsity is relatively large. the optimal parameter vector is low sensitive to the data size. the component weights of uibcf-i in 4 groups are all high since compound similarity between items does work and uibcf-i makes full use of both similar users and similar items. 8 conclusions and future work as for the shortage of individual predictors of conventional item-based and user-based cf recommendation approaches utilizing single information source, we proposed a rating-based integrated framework to combine three cf recommendation methods of ibcf-i, ubcf-i and uibcf-i. uibcf-i is considered as a background method to smooth the rating predictions of ubcf-i and ibcf-i. meanwhile, we improved traditional item-based cf by inner similarity and outer similarity, and user-based cf by preliminary ratings based on uiim. furthermore, we built an optimal learning model inte-cf of the framework by dynamic program with constraints to find out the optimal parameter vector in rating predictions. the experiments showed that our new integration framework of cfs is effective in improving the prediction accuracy of cf recommendation approaches. that is to say our integrated model inte-cf leveraging the three kinds of information sources achieves the best performance. but inte-cf pays the price of a little more running time which is not avoided but worthy. fortunately, some calculations which accelerate the overall execution process are off line or incremental, including inner similarity of between items, clustering of uiim, and the predictions of three ratings (rating 1, 2 and 3) can be parallel processing. in the future work, we will compare inte-cf model to more other methods such as sf [25] and evaluate it on more metrics. the parallelization of inte-cf is also interesting when encountered big data. and we will continue to optimize inte-cf by considering the rating biases of users and items and the influence of time. acknowledgment we would like to thank all of the anonymous reviewers for their insightful comments and useful suggestions that must lead to a much higher quality of our manuscript.this work was partially supported by the national science natural foundation of china (nos. 61303096). bibliography [1] anand d., bharadwaj k.k. 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(2010); user preferences discovery using fuzzy models, fuzzy sets and systems, 161(23), 3044-3063, 2010. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 8-19 a non-fragile h∞ output feedback controller for uncertain fuzzy dynamical systems with multiple time-scales w. assawinchaichote wudhichai assawinchaichote department of electronic and telecommnunication engineering king mongkut’s university of technology thonburi 126 prachautits rd., bangkok 10140, thailand e-mail: wudhichai.asa@kmutt.ac.th abstract: this paper determines the designing of a non-fragile h∞ output feedback controller for a class of nonlinear uncertain dynamical systems with multiple timescales described by a takagi-sugeno (ts) fuzzy model. based on a linear matrix inequality (lmi) approach, we develop a non-fragile h∞ output feedback controller which guarantees the l2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value for this class of uncertain fuzzy dynamical systems with multiple time-scales. a numerical example is provided to illustrate the design developed in this paper. keywords: fuzzy control, linear matrix inequality (lmi), non-fragile h∞ output feedback control, multiple time-scale systems. 1 introduction in the last few years, the problem of control design for dynamical systems with multiple timescale has been intensively studied by a number of researchers; see [1][12]. this is due not only to theoretical interest but also to the relevance of this topic in control engineering applications. singularly perturbed systems are dynamical systems with multiple time-scales. singularly perturbed systems often occur naturally due to the presence of small “parasitic” parameter, typically small time constants, masses, etc. indeed multiple time-scales phenomena are almost unavoidable in “real-life” systems. examples of such systems abound and include convection-diffusion systems, diffusion-drift motion systems, power systems, scheduling systems, economic models, telecommunication systems and bifurcations. presently, many researchers have studied the h∞ control design for a general class of linear singularly perturbed systems due to a great practical importance; see [4,5,7]. the main purpose of the singular perturbation approach to analysis and design is the alleviation of high dimensionality and ill-conditioning resulting from the interaction of slow and fast dynamics modes. the separation of states into slow and fast ones is a nontrivial modelling task demanding insight and ingenuity on the part of the analyst. in state space, such systems are commonly modelled using the mathematical framework of singular perturbations, with a small parameter, say ε, determining the degree of separation between the “slow” and “fast” modes of the system. although many researchers have studied linear singularly perturbed systems for many years, the h∞ control design of nonlinear singularly perturbed systems remains as an open research area. this is because, in general, nonlinear singularly perturbed systems can not be separated into slow and fast subsystems. over the past two decades, there has been rapidly growing interest in application of fuzzy logic to control problem. researches have been focused on its application to industrial processes and a number of successful results have been reported in the literature. in spite of these successes, there are many basic issues remain to be addressed. one of them is how to achieve a systematic copyright c⃝ 2006-2012 by ccc publications a non-fragile h∞ output feedback controller for uncertain fuzzy dynamical systems with multiple time-scales 9 design that guarantees closed-loop stability and performance. recently, a great amount of effort has been devoted to describing a nonlinear system using a takagi-sugeno fuzzy model; see [16][29]. the takagi-sugeno (ts) fuzzy model represents a nonlinear system by a family of local linear models which smoothly blended together through fuzzy membership functions. unlike conventional modelling techniques which uses a single model to describe the global behavior of a nonlinear system, fuzzy modelling is essentially a multi-model approach in which simple sub-models (typically linear models) are fuzzily combined to described the global behavior of a nonlinear system. based on this fuzzy model, a number of systematic model-based fuzzy control design methodologies have been developed. the aim of this paper is to design a non-fragile h∞ output feedback controller for a uncertain nonlinear dynamical system with multuple time-scales. based on an lmi approach, we develop the fuzzy non-fragile h∞ output feedback controller that guarantees the l2-gain of the mapping from the exogenous input noise to the regulated output to be less than or equal to a prescribed value for this class of fuzzy dynamical systems. in order to alleviate the ill-conditioned linear matrix inequalities resulting from the interaction of slow and fast dynamic modes, the illconditioned lmis are decomposed into ε-independent and ε-dependent lmis. the ε-independent lmis are not ill-conditioned and the ε-dependent lmis tend to zero when ε approaches to zero. it can be shown that when ε is sufficiently small, the original ill-conditioned lmis are solvable if and only if the ε-independent lmis are solvable. the proposed approach does not involve the separation of states into slow and fast ones, and it can be applied not only to standard, but also to nonstandard singularly perturbed systems. this paper is organized as follows. in section 2, system descriptions and definition are presented. in section 3, based on an lmi approach, we respectively develop a technique for designing a non-fragile h∞ output feedback controllers such that the l2-gain of the mapping from the exogenous input noise to the regulated output is less than a prescribed value for the system described in section 2. the validity of this approach is demonstrated by an example from a literature in section 4. finally, conclusions are given in section 5. 2 system descriptions and definitions in this section, we consider the ts fuzzy system with multiple time-scales to represent a ts fuzzy multiple time-scale system with parametric uncertainties as follows: eεẋ(t) = ∑r i=1 µi(ν(t)) [ [ai + ∆ai]x(t) + [b1i + ∆b1i]w(t) + [b2i + ∆b2i]u(t) ] z(t) = ∑r i=1 µi(ν(t)) [ [c1i + ∆c1i]x(t) + [d12i + ∆d12i]u(t) ] y(t) = ∑r i=1 µi(ν(t)) [ [c2i + ∆c2i]x(t) + [d21i + ∆d21i]w(t) ] (1) where eε = [ i 0 0 εi ] , ν(t) = [ν1(t) · · · νϑ(t)] is the premise variable vector that may depend on states in many cases, ε > 0 is the singular perturbation parameter, µi(ν(t)) denotes the normalized time-varying fuzzy weighting functions for each rule (i.e., µi(ν(t)) ≥ 0 and ∑r i=1 µi(ν(t)) = 1), ϑ is the number of fuzzy sets, x(t) ∈ ℜn is the state vector, u(t) ∈ ℜm is the input, w(t) ∈ ℜp is the disturbance which belongs to l2[0,∞), y(t) ∈ ℜℓ is the measurement, z(t) ∈ ℜs is the controlled output, the matrices ai,b1i,b2i,c1i,c2i,d12i and d21i are of appropriate dimensions, and r is the number of if-then rules. the matrices ∆ai,∆b1i,∆b2i,∆c1i,∆c2i,∆d12i and ∆d21i represent the uncertainties in the system and satisfy the following assumption. 10 w. assawinchaichote assumption 1. ∆ai = f(x(t), t)h1i, ∆b1i = f(x(t), t)h2i, ∆b2i = f(x(t), t)h3i, ∆c1i = f(x(t), t)h4i, ∆c2i = f(x(t), t)h5i, ∆d12i = f(x(t), t)h6i and ∆d21i = f(x(t), t)h7i where hji, j = 1,2, · · · ,7 are known matrix functions which characterize the structure of the uncertainties. furthermore, the following inequality holds: ∥f(x(t), t)∥ ≤ ρ (2) for any known positive constant ρ. next, let us recall the following definition. definition 1. suppose γ is a given positive number. a system (1) is said to have an l2-gain less than or equal to γ if∫ tf 0 zt (t)z(t)dt ≤ γ2 [∫ tf 0 wt (t)w(t)dt ] , (3) for all tf ≥ 0, x(0) = 0 and w(t) ∈ l2[0,tf]. note that for the symmetric block matrices, we use (∗) as an ellipsis for terms that are induced by symmetry. 3 non-fragile h∞ output feedback controller the nature of the information of the state available to the controller has a major effect on the complexity of the designing problem and of the resulting controller. the state-feedback control design problem is an easier problem in which all information are available. however, in most real physical systems, the state is not perfectly known, and so we must estimate it. the process of estimating the system state from the measurement output that are available is called the estimator design. by utilizing the state estimator, the output feedback problem is converted to the state-feedback problem for a new problem. this new problem employs the estimated state as its own state variable and the solution of the new state-feedback problem leads to the solution of the dynamic output feedback control problem. basically, the dynamic output feedback is a coupling of control and estimation. this section aims at designing a full order dynamic non-fragile h∞ fuzzy output feedback controller of the form eε ˙̂x(t) = ∑r i=1 ∑r j=1 µ̂iµ̂j [ âij(ε)x̂(t) + b̂iy(t) ] , u(t) = ∑r i=1 µ̂iĉix̂(t) (4) where x̂(t) ∈ ℜn is the controller’s state vector, âij, b̂i and ĉi are parameters of the controller which are to be determined, and µ̂i denotes the normalized time-varying fuzzy weighting functions for each rule (i.e., µ̂i ≥ 0 and ∑r i=1 µ̂i = 1), such that the inequality (3) holds. clearly, in real control problems, all of the premise variables are not necessarily measurable. thus, in this section, we consider the designing of the non-fragile h∞ output feedback control into two cases as follows. in subsection 3.1, we consider the case where the premise variable of the fuzzy model µi is measurable, while in subsection 3.2, the premise variable which is assumed to be unmeasurable is considered. a non-fragile h∞ output feedback controller for uncertain fuzzy dynamical systems with multiple time-scales 11 3.1 case i–ν(t) is available for feedback the premise variable of the fuzzy model ν(t) is available for feedback which implies that µi is available for feedback. thus, we can select our controller that depends on µi as follows: eε ˙̂x(t) = ∑r i=1 ∑r j=1 µiµj [ âij(ε)x̂(t) + b̂iy(t) ] , u(t) = ∑r i=1 µiĉix̂(t). (5) before presenting our next results, the following lemma is recalled. lemma 1. consider the system (1). given a prescribed h∞ performance γ and a positive constant δ, if there exist matrices xε = xtε , yε = y t ε , bi(ε) and ci(ε), i = 1,2, · · · ,r, satisfying the following ε-dependent linear matrix inequalities:[ xε i i yε ] > 0 (6) xε > 0 and yε > 0 (7) ψ11ii(ε) and ψ22ii(ε) < 0, i = 1,2, · · · ,r (8) ψ11ij (ε) + ψ11ji(ε) and ψ22ij (ε) + ψ22ji(ε) < 0, i < j ≤ r (9) where ψ11ij (ε) =   ( e−1ε aiyε + yεa t i e −1 ε + e −1 ε b2icj(ε)e −1 ε +e−1ε cti (ε)b t 2j e−1ε + γ −2e−1ε b̃1ib̃ t 1j e−1ε ) (∗)t[ yεc̃ t 1i + e−1ε cti (ε)d̃ t 12j ]t −i   (10) ψ22ij (ε) =   ( ati e −1 ε xε + xεe −1 ε ai +bi(ε)c2j + c t 2i btj (ε) + c̃ t 1i c̃1j ) (∗)t[ xεe −1 ε b̃1i + bi(ε)d̃21j ]t −γ2i   (11) with b̃1i = [ δi i δi 0 b1i 0 ] , c̃1i = [ γρ δ ht1i 0 γρ δ ht5i √ 2λρht4i √ 2λct1i ]t , d̃12i = [ 0 γρ δ ht3i 0 √ 2λρht6i √ 2λdt12i ]t , d̃21i = [ 0 0 0 δi d21i i ] and λ =  1 + ρ2 r∑ i=1 r∑ j=1 [ ∥ht2ih2j ∥ + ∥h t 7i h7j ∥ ] 1 2 , then the system (1) has the prescribed h∞ performance γ > 0. furthermore, a suitable controller is of the form (5) with âij(ε) = eε [ y −1ε − xε ]−1 mij(ε)y −1ε b̂i = eε [ y −1ε − xε ]−1 bi(ε) and ĉi = ci(ε)e−1ε y −1ε (12) where mij(ε) = −ati e −1 ε − xεe −1 ε aiyε − xεe −1 ε b2iĉjyε − [ y −1ε − xε ] e−1ε b̂ic2jyε − c̃ t 1i [ c̃1jyε + d̃12jĉjyε ] −γ−2 { xεe −1 ε b̃1i + [ y −1ε − xε ] e−1ε b̂id̃21i } b̃t1je −1 ε . (13) 12 w. assawinchaichote proof: the proof can be carried out the same technique used in lemma 1. 2 remark 1. the lmis given in lemma 3.1 may become ill-conditioned when ε is sufficiently small, which is always the case for the multiple time-scale systems. in general, these ill-conditioned lmis are very difficult to solve. thus, to alleviate these ill-conditioned lmis, we have the following ε-independent well-posed lmi-based sufficient conditions for the uncertain fuzzy multiple time-scale systems to obtain the prescribed h∞ performance. theorem 1. consider the system (1). given a prescribed h∞ performance γ > 0 and a positive constant δ, if there exist matrices x0, y0, b0i and c0i, i = 1,2, · · · ,r, satisfying the following ε-independent linear matrix inequalities:[ x0e + dx0 i i y0e + dy0 ] > 0 (14) ext0 = x0e, x t 0 d = dx0, x0e + dx0 > 0 (15) ey t0 = y0e, y t 0 d = dy0, y0e + dy0 > 0 (16) ψ11ii and ψ22ii < 0, i = 1,2, · · · ,r (17) ψ11ij + ψ11ji and ψ22ij + ψ22ji < 0, i < j ≤ r (18) where e = ( i 0 0 0 ) , d = ( 0 0 0 i ) , ψ11ij = ( aiy t 0 + y0a t i + b2ic0j + c t 0i bt2j + γ −2b̃1ib̃ t 1j (∗)t[ y0c̃ t 1i + ct0id̃ t 12j ]t −i ) (19) ψ22ij = ( ati x t 0 + x0ai + b0ic2j + c t 2i bt0j + c̃ t 1i c̃1j (∗) t[ x0b̃1i + b0id̃21j ]t −γ2i ) (20) with b̃1i = [ δi i δi 0 b1i 0 ] , c̃1i = [ γρ δ ht1i 0 γρ δ ht5i √ 2λρht4i √ 2λct1i ]t d̃12i = [ 0 γρ δ ht3i 0 √ 2λρht6i √ 2λdt12i ]t , d̃21i = [ 0 0 0 δi d21i i ] and λ =  1 + ρ2 r∑ i=1 r∑ j=1 [ ∥ht2ih2j ∥ + ∥h t 7i h7j ∥ ] 1 2 , then there exists a sufficiently small ε̂ > 0 such that for ε ∈ (0, ε̂], the prescribed h∞ performance γ > 0 is guaranteed. furthermore, a suitable controller is of the form (5) with âij(ε) = [ y −1ε − xε ]−1m0ij (ε)y −1ε , b̂i = [y −10 − x0]−1b0i, and ĉi = c0iy −10 (21) where m0ij (ε) = −a t i − xεaiyε − xεb2iĉjyε − [ y −1ε − xε ] b̂ic2jyε −c̃t1i [ c̃1jyε + d̃12jĉjyε ] − γ−2 { xεb̃1i + [ y −1ε − xε ] b̂id̃21i } b̃t1j (22) xε = { x0 + εx̃ } eε and y −1ε = { y −10 + εnε } eε (23) a non-fragile h∞ output feedback controller for uncertain fuzzy dynamical systems with multiple time-scales 13 with x̃ = d ( xt0 − x0 ) and nε = d ( (y −10 ) t − y −10 ) . proof: suppose the inequalities (14)-(16) hold, then the matrices x0 and y0 are of the following forms: x0 = ( x1 x2 0 x3 ) and y0 = ( y1 y2 0 y3 ) with x1 = xt1 > 0, x3 = x t 3 > 0, y1 = y t 1 > 0 and y3 = y t 3 > 0. substituting x0 and y0 into (23), respectively, we have xε = { x0 + εx̃ } eε = ( x1 εx2 εxt2 εx3 ) (24) y −1ε = { y −10 + εnε } eε = ( y −11 −εy −1y2y −1 3 −ε(y −1y2y −13 ) t εy −13 ) . (25) clearly, xε = xtε , and y −1 ε = (y −1 ε ) t . knowing the fact that the inverse of a symmetric matrix is a symmetric matrix, we learn that yε is a symmetric matrix. using the matrix inversion lemma, we can see that yε = e −1 ε { y0 + εỹ } (26) where ỹ = y0nε(i + εy0nε)−1y0. employing the schur complement, one can show that there exists a sufficiently small ε̂ such that for ε ∈ (0, ε̂], (7) holds. now, we need to show that ( xε i i yε ) > 0. (27) by the schur complement, it is equivalent to showing that xε − y −1ε > 0. (28) substituting (24) and (25) into the left hand side of (28), we get[ x1 − y −11 ε(x2 + y −1 1 y2y −1 3 ) ε(x2 + y −1 1 y2y −1 3 ) t ε(x3 − y −13 ) ] . (29) the schur complement of (14) is[ x1 − y −11 0 0 x3 − y −13 ] > 0. (30) according to (30), we learn that x1 − y −11 > 0 and x3 − y −1 3 > 0. (31) using (31) and the schur complement, it can be shown that there exists a sufficiently small ε̂ > 0 such that for ε ∈ (0, ε̂], (6) holds. next, employing (24), (25) and (26), the controller’s matrices given in (12) can be re-expressed as follows: bi(ε) = [ y −10 − x0 ] b̂i + ε [ nε − x̃ ] b̂ib0i + εbεi ci(ε) = ĉiy t0 + εĉiỹ t c0i + εcεi. (32) 14 w. assawinchaichote substituting (24), (25), (26) and (32) into (10) and (11), and pre-post multiplying (10) by( eε 0 0 i ) , we, respectively, obtain ψ11ij + ψ11ij and ψ22ij + ψ22ij (33) where the ε-independent linear matrices ψ11ij and ψ22ij are defined in (19) and (20), respectively and the ε-dependent linear matrices are ψ11ij = ε   aiỹ t + ỹ ati + b2icεj + ctεibt2j (∗)t[ ỹ c̃t1i + c t εi d̃t12j ]t 0   (34) ψ22ij = ε   ati x̃ + x̃t ai + bεic2j + ct2ibtεj (∗)t[ x̃b̃1i + bεid̃21j ]t 0   . (35) note that the ε-dependent linear matrices tend to zero when ε approaches zero. employing (17)-(18) and knowing the fact that for any given negative definite matrix w, there exists an ε > 0 such that w + εi < 0, one can show that there exists a sufficiently small ε̂ > 0 such that for ε ∈ (0, ε̂], (8)-(9) hold. since (6)-(9) hold, using lemma 2, the inequality (3) holds. 2 3.2 case ii–ν(t) is unavailable for feedback the output feedback fuzzy controller is assumed to be the same as the premise variables of the fuzzy system model. this actually means that the premise variables of fuzzy system model are assumed to be measurable. however, in general, it is extremely difficult to derive an accurate fuzzy system model by imposing that all premise variables are measurable. in this subsection, we do not impose that condition, we choose the premise variables of the controller to be different from the premise variables of fuzzy system model of the plant. in here, the premise variables of the controller are selected to be the estimated premise variables of the plant. in the other words, the premise variable of the fuzzy model ν(t) is unavailable for feedback which implies µi is unavailable for feedback. hence, we cannot select our controller which depends on µi. thus, we select our controller as (4) where µ̂i depends on the premise variable of the controller which is different from µi. let us re-express the system (1) in terms of µ̂i, thus the plant’s premise variable becomes the same as the controller’s premise variable. by doing so, the result given in the previous case can then be applied here. note that it can be done by using the same technique as in subsection. after some manipulation, we get eεẋ(t) = ∑r i=1 µ̂i [ [ai + ∆āi]x(t) + [b1i + ∆b̄1i]w(t) + [b2i + ∆b̄2i] ] u(t) z(t) = ∑r i=1 µ̂i [ [c1i + ∆c̄1i]x(t) + [d12i + ∆d̄12i]u(t) ] y(t) = ∑r i=1 µ̂i [ [c2i + ∆c̄2i]x(t) + [d21i + ∆d̄21i]w(t) ] (36) where ∆āi = f̄(x(t), x̂(t), t)h̄1i, ∆b̄1i = f̄(x(t), x̂(t), t)h̄2i, ∆b̄2i = f̄(x(t), x̂(t), t)h̄3i, ∆c̄1i = f̄(x(t), x̂(t), t)h̄4i, ∆c̄2i = f̄(x(t), x̂(t), t)h̄5i, ∆d̄12i = f̄(x(t), x̂(t), t)h̄6i and ∆d̄21i = f̄(x(t), x̂(t), t)h̄7i a non-fragile h∞ output feedback controller for uncertain fuzzy dynamical systems with multiple time-scales 15 with h̄1i = [ ht1i a t 1 · · ·a t r h t 11 · · ·ht1r ]t , h̄2i = [ ht2i b t 11 · · ·bt1r h t 21 · · ·ht2r ]t , h̄3i = [ ht3i b t 21 · · ·bt2r h t 31 · · ·ht3r ]t , h̄4i = [ ht4i c t 11 · · ·ct1r h t 41 · · ·ht4r ]t , h̄5i = [ ht5i c t 21 · · ·ct2r h t 51 · · ·ht5r ]t , h̄6i = [ ht6i d t 121 · · ·dt12r h t 61 · · ·ht6r ]t h̄7i = [ ht7i d t 211 · · · dt21r h t 71 · · ·ht7r ]t and f̄(x(t), x̂(t), t) = [ f(x(t), t) (µ1−µ̂1) · · · (µr−µ̂r) f(x(t), t)(µ1−µ̂1) · · · f(x(t), t)(µr−µ̂r) ] . note that ∥f̄(x(t), x̂(t), t)∥ ≤ ρ̄ where ρ̄ = {3ρ2 + 2} 1 2 . ρ̄ is derived by utilizing the concept of vector norm in the basic system control theory and the fact that µi ≥ 0, µ̂i ≥ 0, ∑r i=1 µi = 1 and ∑r i=1 µ̂i = 1. note that the above technique is basically employed in order to obtain the plant’s premise variable to be the same as the controller’s premise variable; e.g. [28]. now, the premise variable of the system is the same as the premise variable of the controller, thus we can apply the result given in case i. theorem 2 consider the system (1). given a prescribed h∞ performance γ > 0 and a positive constant δ, if there exist matrices x0, y0, b0i and c0i, i = 1,2, · · · ,r, satisfying the following ε-independent linear matrix inequalities:[ x0e + dx0 i i y0e + dy0 ] > 0 (37) ext0 = x0e, x t 0 d = dx0, x0e + dx0 > 0 (38) ey t0 = y0e, y t 0 d = dy0, y0e + dy0 > 0 (39) ψ11ii and ψ22ii < 0, i = 1,2, · · · ,r (40) ψ11ij + ψ11ji and ψ22ij + ψ22ji < 0, i < j ≤ r (41) where e = ( i 0 0 0 ) , d = ( 0 0 0 i ) , ψ11ij = ( aiy t 0 + y0a t i + b2ic0j + c t 0i bt2j + γ −2 ˜̄b1i ˜̄bt1j (∗) t[ y0 ˜̄ct1i + c t 0i ˜̄dt12j ]t −i ) (42) ψ22ij = ( ati x t 0 + x0ai + b0ic2j + c t 2i bt0j + ˜̄ct1i ˜̄c1j (∗) t[ x0 ˜̄b1i + b0i ˜̄d21j ]t −γ2i ) (43) with ˜̄b1i = [ δi i δi 0 b1i 0 ] , ˜̄c1i = [ γρ̄ δ h̄t1i 0 γρ̄ δ h̄t5i √ 2λ̄ρ̄h̄t4i √ 2λ̄ct1i ]t , ˜̄d12i = [ 0 γρ̄ δ h̄t3i 0 √ 2λ̄ρ̄h̄t6i √ 2λ̄dt12i ]t , ˜̄d21i = [ 0 0 0 δi d21i i ] and λ̄ =  1 + ρ̄2 r∑ i=1 r∑ j=1 [ ∥h̄t2ih̄2j ∥ + ∥h̄ t 7i h̄7j ∥ ] 1 2 , then there exists a sufficiently small ε̂ > 0 such that for ε ∈ (0, ε̂], the prescribed h∞ performance γ > 0 is guaranteed. furthermore, a suitable controller is of the form (4) with âij(ε) = [ y −1ε − xε ]−1m0ij (ε)y −1ε , b̂i = [y −10 − x0]−1b0i, and ĉi = c0iy −10 (44) 16 w. assawinchaichote where m0ij (ε) = −a t i − xεaiyε − xεb2iĉjyε − [ y −1ε − xε ] b̂ic2jyε − ˜̄ct1i [ ˜̄c1jyε + ˜̄d12jĉjyε] − γ−2{xε ˜̄b1i + [y −1ε − xε]b̂i ˜̄d21i} ˜̄bt1j (45) xε = { x0 + εx̃ } eε and y −1ε = { y −10 + εnε } eε (46) with x̃ = d ( xt0 − x0 ) and nε = d ( (y −10 ) t − y −10 ) . proof: since (36) is of the form of (1), it can be shown by employing the proof for theorem 3.1. 2 4 example consider the tunnel diode circuit where the tunnel diode is characterized by id(t) = −0.2vd(t)− 0.01v3d(t). assume that ε is a “parasitic” inductance in the network. let x1(t) = vc(t) be the capacitor voltage and x2(t) = il(t) be the inductor current. then, the circuit can be modelled by the following state equations: cẋ1(t) = 0.2x1(t) + 0.01x 3 1(t) + x2(t) εẋ2(t) = −x1(t) − rx2(t) + u(t) + 0.1w2(t) y(t) = jx(t) + 0.1w1(t), z(t) = [x1(t) x2(t)] t (47) where u(t) is the control input, w1(t) is the measurement noise, w2(t) are is the process noise which may represent un-modelled dynamics, y(t) is the measured output, z(t) is the controlled output, j is the sensor matrix, x(t) = [xt1 (t) x t 2 (t)] t and w(t) = [wt1 (t) w t 2 (t)] t . note that the variables x1(t) and x2(t) are treated as the deviation variables (variables deviate from its desired trajectories). the parameters in the circuit are given by c = 100 mf and r = 1 ± 0.3% ω, with these parameters (47) can be rewritten as ẋ1(t) = 2x1(t) + (0.1x 2 1(t)) · x1(t) + 10x2(t) εẋ2(t) = −x1(t) − (1 ± ∆r)x2(t) + u(t) + 0.1w2(t) y(t) = jx(t) + 0.1w1(t), z(t) = [x1(t) x2(t)] t . (48) for the sake of simplicity, we will use as few rules as possible. assuming that |x1(t)| ≤ 3, the nonlinear network system (48) can be approximated by the following ts fuzzy model: 1 0 1 2 m (x ) m (x ) x 1 1 1 −3 3 figure 1: membership functions for the two fuzzy set. a non-fragile h∞ output feedback controller for uncertain fuzzy dynamical systems with multiple time-scales 17 plant rule 1: if x1(t) is m1(x1(t)) then eεẋ(t) = [a1 + ∆a1]x(t) + b1w(t) + b21u(t), z(t) = c1x(t), y(t) = c21x(t) + d21w(t). plant rule 2: if x1(t) is m2(x1(t)) then eεẋ(t) = [a2 + ∆a2]x(t) + b1w(t) + b22u(t), z(t) = c1x(t), y(t) = c22x(t) + d21w(t) where x(0) = 0, x(t) = [xt1 (t) x t 2 (t)] t , w(t) = [wt1 (t) w t 2 (t)] t , a1 = [ 2 10 −1 −1 ] , a2 = [ 2.9 10 −1 −1 ] , b1 = [ 0 0 0 0.1 ] , b21 = b22 = [ 0 1 ] , c1 = [ 1 0 0 1 ] , c21 = c22 = j, d21 = [ 0.1 0 ] , ∆a1 = f(x(t), t)h11, ; ∆a2 = f(x(t), t)h12 and eε = [ 1 0 0 ε ] . note that, the plot of the membership functions is the same as in figure 1. now, by assuming that in (2), ∥f(x(t), t)∥ ≤ ρ = 1 and since the values of r are uncertain but bounded within 30% of their nominal values given in (47), we have h11 = h12 = [ 0 0 0 0.3 ] . note that by employing the results given in lemma 1 and the matlab lmi solver, it is easy to realize that when ε < 0.03, the lmis become ill-conditioned and the matlab lmi solver yields the error message, “rank deficient". using the lmi optimization algorithm and theorems 3.1-3.2 with ε = 0.01, γ = 1 and δ = 1, we obtain the following results as shown in table 1. table 1: the performance index γ of the system with different values of ε. the performance index γ ε output feedback case i case ii 0.01 0.316 0.346 0.15 0.574 0.922 0.16 0.600 > 1 0.28 0.989 > 1 0.29 > 1 > 1 remark 2. for a sufficiently small ε, the non-fragile output feedback controllers guarantee that the l2-gain, γ, is less than the prescribed value. the disturbance input signal, w(t), which was used during the simulation is the rectangular signal with magnitude 0.1 and frequency 1 hz. for an example, when ε is 0.01, the output feedback controller in case i where γ = 0.316 and in case ii where γ = 0.346, all are less than the prescribed value 1. thus, table 1 shows the result of the performance index γ with different values of ε. 5 conclusion this paper has considered the problem of designing a non-fragile output feedback controller for a ts fuzzy system with multiple time-scales. sufficient conditions for the existence of nonfragile fuzzy controllers are derived in terms of a family of ε-independent linear matrix inequalities. the proposed approach does not involve the separation of states into slow and fast ones, 18 w. assawinchaichote and it can be applied not only to standard, but also to nonstandard multiple time-scale systems. a numerical simulation example has been presented to illustrate the effectiveness of the designs. acknowledgment this work is financed by thailand national research university funding (nru). the author also would like to acknowledge to the department of electronic and telecommnunication engineering, king mongkut’s university of technology thonburi for their supports in this research work. bibliography [1] m. suzuki, and m. miura, “stabilizing feedback controller for singularly perturbed linear constant systems," ieee trans. automat. control, vol. ac-21, pp. 123–124, 1976. 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[29] h. j. lee, j. b. park, and g. chen, “robust fuzzy control of nonlinear system with parametric uncertainties," ieee. trans. fuzzy syst., vol. 9, pp. 369–379, 2001. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. v (2010), no. 4, pp. 540-550 solving vertex cover problem by means of tissue p systems with cell separation c. lu, x. zhang chun lu key laboratory of image processing and intelligent control department of control science and engineering huazhong university of science and technology wuhan 430074, hubei, people’s republic of china e-mail: luchun.et@gmail.com(corresponding author) xingyi zhang school of computer science and technology, anhui university hefei 230601, anhui, people’s republic of china e-mail: xyzhanghust@gmail.com abstract: tissue p systems is a computing model in the framework of membrane computing inspired from intercellular communication and cooperation between neurons. many different variants of this model have been proposed. one of the most important models is known as tissue p systems with cell separation. this model has the ability of generating an exponential amount of workspace in linear time, thus it allows us to design cellular solutions to np-complete problems in polynomial time. in this paper, we present a solution to the vertex cover problem via a family of such devices. this is the first solution to this problem in the framework of tissue p systems with cell separation. keywords: membrane computing, tissue p system, cell separation, vertex cover 1 introduction membrane computing is an emergent branch of natural computing, which is inspired by the structure and the function of living cells, as well as the organization of cells in tissues, organs and other higher order structures. the devices in membrane computing, called p systems, provide distributed parallel and non-deterministic computing models. since gh. păun introduced the p system in [10], this area has received important attention from the scientific community, such as computer scientists, biologists, formal linguists and complexity theoreticians. in the last years, many different models of p systems have been proposed (a comprehensive bibliography can be found in [14]). the most studied variants are the cell-like models of p systems, where membranes are hierarchically arranged in a tree-like structure. various models of cell-like p systems have been successfully used to design solutions to np-complete problems in polynomial time (see [4]). these solutions are obtained by generating an exponential amount of workspace in polynomial time and using parallelism to check simultaneously all the candidate solutions. in general, cell division, cell creation and cell separation are the three efficient ways to obtain exponential workspace in polynomial time, thus obtaining three corresponding variants of p systems: cell division, where the new workspace is generated by membrane division, cell creation, where the new membranes are created from objects, and cell separation, where the new workspace is generated by membrane separation. it has been proved that all of the three models can efficiently solve np-complete problems, but technically they are pretty different in the way of designing solutions. another interesting class of p systems is known as tissue p systems, where membranes are placed in the nodes of a graph. this variant has two biological inspirations (see [6]): intercellular communication copyright c⃝ 2006-2010 by ccc publications solving vertex cover problem by means of tissue p systems with cell separation 541 and cooperation between neurons. the common mathematical model of these two mechanisms is a net of processors dealing with symbols and communicating these symbols along channels specified in advance, based on symport/antiport rules [9]. tissue p systems can also efficiently solve np-complete problems provided that some ingredients are added into such systems, as in the case of cell-like p systems. the first attempt in this respect is to consider cell division in tissue p systems, yielding tissue p systems with cell division [12]. in this model, the two new cells generated by a division rule have exactly the same objects except for at most a pair of different objects. this model was shown to efficiently solve np-complete: sat [12], 3-coloring [1], subset sum [2], vertex cover [3], etc. recently, another class of tissue p systems is proposed based on cell separation, that is, tissue p systems with cell separation, and a polynomial-time solution to the np-complete problem sat is given in [8]. in this model, the contents of the two new cells evolved from a cell by separation rules can be different, thus leading to a significant difference in specific techniques for designing solutions to concrete np-complete problems. in this paper, we shall explore the possibility of using such a model to solve another np-complete problem–vertex cover. specifically, a family of tissue p systems with cell separation is constructed, in which each system can solve all instances of vertex cover of a fixed size in a polynomial time. although the vertex cover problem has been considered in the framework of other models in membrane computing (for instance, cell-like p systems with active membrane, tissue p systems with cell division, and so on), here the first solution for this problem is presented in the framework of tissue p systems with cell separation. the paper is organized as follows: in sections 2 and 3 preliminaries and the definition of tissue-like p systems with cell separation are recalled, respectively. in section 4, recognizer tissue p systems are briefly described. a polynomial-time solution to vertex cover problem is presented in section 5, including a short overview of the computation and of the necessary resources. finally, some conclusions and new open research lines are presented. 2 preliminaries an alphabet, σ , is a finite and non-empty set of abstract symbols. an ordered sequence of symbols is a string. let σ be a (finite) alphabet; then σ ∗ is the set of all strings over σ . the number of symbols in a string u is the length of the string, and it is denoted by |u|. as usual, empty string (with length 0) is denoted by λ . the set of strings of length n built with symbols from the alphabet σ is denoted by σ n and σ ∗ = ∪n≥0σ n. let a be a (finite) set, a = {a1,··· ,an}. then a finite multiset m over a is a function f : a → in. if m = (a, f ) is a multiset then its support is defined as supp(m) = {x ∈ a | f (x) > 0}. the size of the multiest m is |m| = σx∈a f (x). a multiset is empty (resp. finite) if its support is the empty set (resp. finite). a multiset m over a can also be represented by any string x that contains exactly fm(ai) symbols ai for all 1 ≤ i ≤ n, e.g., by a f (a1) 1 a f (a2) 2 ...a f (ak) k . thus, superscripts indicate the multiplicity of each element, and if f (x) = 0 for any x ∈ a, then this element is omitted. we suppose that the reader is already familiar with the basic notions and the terminology of p systems. for details, see [11]. 3 tissue p systems with cell separation according to the first works on tissue p systems [5, 6] the membrane structure did not change along the computation. a new model based on the cell-like model of tissue p systems with cell separation is presented in [7]. the biological inspiration of them is clear: alive tissues are not static network of cells, since membrane fission generates new cells in a natural way. 542 c. lu, x. zhang formally, a tissue p system with cell separation of initial degree q ≥ 1 is a construct π = (γ ,o1,o2,w1,...,wq,e,r,io), where: 1. γ is the alphabet of objects, γ = o1 ∪ o2, o1,o2 ̸= /0, o1 ∩ o2 = /0; 2. w1,...,wq are strings over γ , describing the multisets of objects placed in the cells of the system at the beginning of the computation; 3. e ⊆ γ is the set of objects present in the environment in arbitrarily copies each; 4. r is a finite set of rules of the following forms: (a) (i,u/v, j), for i, j ∈ {0,1,2,...,q},i ̸= j, u,v ∈ γ ∗; communication rules; 1,2,··· ,q identify the cells of the system, 0 is used as the label of the environment. this rule (i,u/v, j) can be applied over two cells i and j such that u is contained in cell i and v is contained in cell j. the application of this rule means that the objects of the multisets represented by u and v are interchanged between the two cells; (b) [a]i → [o1]i[o2]i, where i ∈ {1,2,...,q} and a ∈ γ ; separation rules; under the influence of object a, the cell with label i is separated into two cells with the same label; at the same time, the object a is consumed; the objects from o1 are placed in the first cell, those from o2 are placed in the second cell; 5. io ∈ {0,1,2,...,q} is the output region. rules are used in the non-deterministic maximally parallel manner as customary in membrane computing. in each step, all cells which can evolve must evolve in a maximally parallel way (in each step a multiset of rules which is maximal is applied, no further rule can be added). this way of applying rules has only one restriction: when a cell is separated, the separation rule is the only one which is applied for that cell in that step; the objects inside that cell do not evolve by means of communication rules. the daughter cells will participate to the interaction with other cells or with the environment by means of communication rules in the next step, if they are not separated once again. their labels precisely identify the rules which can be applied to them. a sequence of transitions which starts from the initial configuration is called a computation with respect π . a computation is completed only if it halts and the computations give a result, and result is the multiset of objects present in region io in the halting configuration. 4 recognizer tissue p systems with cell separation np-completeness has been usually studied in the framework of decision problems. let us recall that a decision problem is a pair (ix ,θx ) where ix is a language over a finite alphabet (whose elements are called instances) and θx is a total boolean function over ix . the notions from classical computational complexity theory are adapted for membrane computing to study the computing efficiency for solving decision problems. recognizer tissue p systems are introduced in [12] for tissue p systems with the same idea of recognizer p systems introduced into cell-like p systems [13]. a recognizer tissue p system with cell separation of degree q ≥ 1 is a construct π = (γ ,o1,o2,σ,w1,...,wq,e,r,iin,io) where: solving vertex cover problem by means of tissue p systems with cell separation 543 • (γ ,o1,o2,w1,...,wq,e,r,io) is a tissue p system with cell separation of degree q ≥ 1 (as defined in the previous section). • the working alphabet γ has two distinguished objects yes and no, at least one copy of them present in some initial multisets w1, . . . , wq, but not present in e. • σ is an (input) alphabet strictly contained in γ . • iin ∈ {1,...,q} is the input cell. • the output region io is the environment. • all computations halt. • if c is a computation of π , in the last step of the computation either the object yes or the object no (but not both) have to be send out to the environment. the computations of the system π with input w ∈ σ ∗ start from a configuration of the form (w1,w2,...,wiin w,...,wq;e), that is, after adding the multiset w to the contents of the input cell iin. we say that the multiset w is recognized by π if and only if the object yes is sent to the environment, in the last step of the corresponding computation. we say that c is an accepting computation (respectively, rejecting computation) if the object yes (respectively, no) appears in the environment associated to the corresponding halting configuration of c. definition 1. a decision problem x = (ix ,θx ) is solvable in polynomial time by a family of recognizer tissue p systems π = {π(n) | n ∈ in} with cell separation, if the following holds: • the family π is polynomially uniform by turing machines, that is, there exists a deterministic turing machine constructing π(n) from n ∈ in in polynomial time. • there exists a polynomial-time coding (cod,s) form ix to π such that: − for each instance u ∈ ix , s(u) is a natural number and cod(u) is an input multiset of the system π(s(u)); − the family π is polynomially bounded with regard to (x,cod,s), that is, there exists a polynomial function p, such that for each u ∈ ix every computation of π(s(u)) with input cod(u) is halting and, moreover, it performs at most p(|u|) steps; − the family π is sound with regard to (x,cod,s), that is, for each u ∈ ix , if there exists an accepting computation of π(s(u)) with input cod(u), then θx (u) = 1; − the family π is complete with regard to (x,cod,s), that is, for each u ∈ ix , if θx (u) = 1, then every computation of π(s(u)) with input cod(u) is an accepting one. we denote by pmct s the set of all decision problems which can be solved by means of recognizer tissue p systems with cell separation in polynomial time. 5 a solution to the vertex cover problem the vertex cover of a non-directed graph is a subset of its vertices such that for each edge of the graph at least one of its endpoints belongs to that subset. the size of the vertex cover is the number of vertices in the subset. the vertex cover problem considered in this paper is formulated as follows: given a non-directed graph, g = (v,e), and a natural number k ≤ |v |, determine whether or not g has a vertex cover of size at most k. 544 c. lu, x. zhang we shall prove that vertex cover can be solved in linear time (in the number of nodes and edges of the graph) by a family of recognizer tissue-like p systems with cell separation. we construct a family π = {π(⟨n,m,k⟩) | n,m,k ∈ in} where each system of the family will process every instance u of the problem given by a graph with n vertices and m edges, and by a size k of the vertex cover (that is, s(u) = ⟨n,m,k⟩, where ⟨a,b⟩ = (a+b)(a+b+1) 2 + a and ⟨a,b,c⟩ = ⟨⟨a,b⟩,c⟩. in order to provide a suitable encoding of these instances, we will use the objects ai j, with 1 ≤ i < j ≤ n, to represent the edges of the graph, and we will provide cod(u) = {ai j | 1 ≤ i < j ≤ n ∧(vi,v j) ∈ e} as the initial multiset for the system. with an instance u of the vc problem, the system π(s(u)) with input cod(u) decides that instance by a brute force algorithm, implemented in the following four stages: • generation stage: the initial cell labeled by 2 is separated into two new cells; the separations are iterated until a cell has been produced for each possible candidate solution. • pre-checking stage: after obtaining all possible subsets of vertices encoded in cells labeled by 2, this stage only select the subsets of size k. • checking stage: for each of these subsets, it is checked if there exists an edge of the graph for which none of its endpoints is in the subset. • output stage: the system sends to the environment the right answer according to the results of the previous stage. π(⟨n,m,k⟩) = (γ (⟨n,m,k⟩),σ(⟨n,m,k⟩),w1,w2,r(⟨n,m,k⟩),e(⟨n,m,k⟩),iin,i0), for each n,m,k ∈ in. the family π contains the following systems: • γ (⟨n,m,k⟩) = o1 ∪ o2, o1 = {ci, j,ai, j,zi, j,pi, j | 1 ≤ i < j ≤ n}∪{ ji | 1 ≤ i ≤ 2n +1} ∪{ai,bi,b′i ,c ′ i ,ti,f ′ i | 1 ≤ i ≤ n}∪{di | 1 ≤ i ≤ n +1} ∪{di, j | 1 ≤ i, j ≤ n}∪{a1,i,b1,i,d1,i,gi,hi,li,ei | 1 ≤ i ≤ n −1} ∪{ai | 1 ≤ i ≤ 5n + m +⌈lg n⌉+9}∪{a2,i,b2,i,d2,i | 2 ≤ i ≤ n −1} ∪{ai, j,k,bi, j,k,di, j,k | 1 ≤ i < j ≤ n,1 ≤ k ≤ n −1} ∪{ci, j,bi, j | 1 ≤ i ≤ n,1 ≤ j ≤ m}∪{li | 1 ≤ i ≤ m +⌈lg n⌉+7} ∪{pi | 1 ≤ i ≤ m +⌈lg n⌉+6}∪{hi | 1 ≤ i ≤ ⌈lg m⌉+1} ∪{gi | 1 ≤ j ≤ ⌈lg n⌉+1}∪{b,z, f1,y,s,e0,e1,e2,t,n,yes,no}, o2 = {c′i, j,a ′ i, j,z ′ i, j} | 1 ≤ i < j ≤ n}∪{t ′ i ,fi | 1 ≤ i ≤ n}∪{y ′,z′, f ′}. • σ(⟨n,m,k⟩) = {ci, j,ai, j,a′i, j | 1 ≤ i < j ≤ n}. • w1 = a1a1,1g1ai, j,1yes no. • w2 = ci, jai, ja1. • r(⟨n,m,k⟩) is the set of rules: 1. separation rule: r1 ≡ [s]2 → [o1]2[o2]2. 2. communication rules: r2,i ≡ (1,ai/ai+1,0) for 1 ≤ i ≤ 5n + m +⌈lg n⌉+8; r3,i, j,k ≡ (1,ai, j,k/bi, j,k,0) for 1 ≤ i < j ≤ n,1 ≤ k ≤ n −1; r4,i, j,k ≡ (1,bi, j,k/c2i, jd 2 i, j,k,0) for 1 ≤ i < j ≤ n,1 ≤ k ≤ n −1; r5,i, j,k ≡ (1,di, j,k/ai, j,k+1,0) for 1 ≤ i < j ≤ n,1 ≤ k ≤ n −2; solving vertex cover problem by means of tissue p systems with cell separation 545 r6,i ≡ (1,gi/hi,0) for 1 ≤ i ≤ n −1; r7,i ≡ (1,hi/l2i a 2 i+1,0) for 1 ≤ i ≤ n −1; r8,i ≡ (1,li/gi+1,0) for 1 ≤ i ≤ n −2; r9,i ≡ (1,a1,i/b1,i,0) for 1 ≤ i ≤ n −1; r10,i ≡ (1,b1,i/c2d21,ie 2 i ,0) for 1 ≤ i ≤ n −1; r11,i ≡ (1,d1,i/a1,i+1,0) for 1 ≤ i ≤ n −2; r12,i ≡ (1,ei/a2,i+1,0) for 1 ≤ i ≤ n −2; r13,i ≡ (1,a2,i/b2,i,0) for 2 ≤ i ≤ n −1; r14,i ≡ (1,b2,i/c2d22,i,0) for 2 ≤ i ≤ n −1; r15,i ≡ (1,d2,i/a2,i+1,0) for 2 ≤ i ≤ n −2; r16,i, j ≡ (2,ci, jai, j/zi, jz′i, jai, ja ′ i, j,0) for 1 ≤ i < j ≤ n; r17,i, j ≡ (2,ci, ja′i, j/zi, jz ′ i, jai, ja ′ i, j,0) for 1 ≤ i < j ≤ n; r18,i ≡ (2,cti/zz′tit ′i ,0) for 1 ≤ i ≤ n −1; r19,i ≡ (2,ct ′i /zz ′tit ′i ,0) for 1 ≤ i ≤ n −1; r20,i ≡ (2,cfi/zz′fif ′i ,0) for 1 ≤ i ≤ n −1; r21,i ≡ (2,cf ′i /zz ′fif ′i ,0) for 1 ≤ i ≤ n −1; r22 ≡ (2,an/tnfn f1 f ′1s,0); r23,i ≡ (2,ai/tifiyy′zz′s,0) for 1 ≤ i ≤ n −1; r24,i ≡ (2,y/ai,1) for 2 ≤ i ≤ n; r25,i ≡ (2,y′/ai,1) for 2 ≤ i ≤ n; r26 ≡ (2,z/c,1); r27 ≡ (2,z′/c,1); r28,i, j ≡ (2,zi, j/ci, j,1) for 1 ≤ i < j ≤ n; r29,i, j ≡ (2,z′i, j/ci, j,1) for 1 ≤ i < j ≤ n; r30 ≡ (1,z/λ ,0); r31 ≡ (1,z′/λ ,0); r32,i, j ≡ (1,zi, j/λ ,0) for 1 ≤ i < j ≤ n; r33,i, j ≡ (1,z′i, j/λ ,0) for 1 ≤ i < j ≤ n; r34 ≡ (2, f / j1d1,0); r35 ≡ (2, f ′/ j1d1,0); r36,i, j ≡ (2,d jti/di, j,0) for 1 ≤ i, j ≤ n; r37,i, j ≡ (2,d jt ′i /di, j,0) for 1 ≤ i, j ≤ n; r38,i, j ≡ (2,di, j/bid j+1,0) for 1 ≤ i, j ≤ n; r39,i ≡ (2, ji/ ji+1,0) for 1 ≤ i ≤ 2n; r40 ≡ (2, j2n+1dk+1/e0,0); r41 ≡ (2,e0/l1e1,0); r42,i ≡ (2,li/li+1,0) for i = 1,...,m +⌈lg n⌉+6; r43 ≡ (2,e1/p1e2,0); r44 ≡ (2,e2/g1h1,0); r45,i ≡ (2,pi/pi+1,0) for i = 1,...,m +⌈lg n⌉+5; r46,i ≡ (2,gi/g2i+1,0) for i = 1,...,⌈lg n⌉; r47,i ≡ (2,hi/h2i+1,0) for i = 1,...,⌈lg m⌉; r48,i, j ≡ (2,ai, jh⌈lg m⌉+1/pi, j,0) for 1 ≤ i < j ≤ n; r49,i, j ≡ (2,a′i, jh⌈lg m⌉+1/pi, j,0) for 1 ≤ i < j ≤ n; r50,i ≡ (2,g⌈lg n⌉+1bi/ci,0) for i = 1,...,n; r51,i ≡ (2,ci/ci,1bi,1,0) for i = 1,...,n; r52,i, j ≡ (2,bi, j/bi, j+1b′i ,0) for i = 1,...,n and j = 1,...,m; r53,i, j ≡ (2,ci, j/ci, j+1c ′i ,0) for i = 1,...,n and j = 1,...,m; r54,i, j ≡ (2,b′i pi, j/λ ,0) for 1 ≤ i < j ≤ n; 546 c. lu, x. zhang r55,i, j ≡ (2,c ′jpi, j/λ ,0) for 1 ≤ i < j ≤ n; r56,i, j ≡ (2,pm+⌈lg n⌉+5pi, j/n,0) for 1 ≤ i < j ≤ n; r57 ≡ (2,lm+⌈lg n⌉+7pm+⌈lg n⌉+6/t,0); r58 ≡ (1,b/t,2); r59 ≡ (1,a5n+m+⌈lg n⌉+9b/n,2); r60 ≡ (1,t yes/λ ,0); r61 ≡ (1,n no/λ ,0); • e(⟨n,m,k⟩) = γ (⟨n,m,k⟩)−{yes,no}. • iin = 2 is the input cell. • io = 0 is the output region. we will show that the family π = {π(⟨n,m,k⟩) | n,m,k ∈ in} defined above is polynomially uniform by turing machines. to this aim it will be proved that π(⟨n,m,k⟩) is built in polynomial time with respect to the size parameter n, m and k of instances of vertex cover problem. it is easy to check that the rules of a system π(⟨n,m,k⟩) of the family are defined recursively from the values n, m and k. the necessary resources to build an element of the family are of a polynomial order, as shown below: • size of the alphabet: n2 +5mn +26n +7m +4⌈lg n⌉+⌈lg m⌉+27 ∈ o(n2 + mn). • initial number of cells: 2 ∈ o(1). • initial number of objects: 3m +6 ∈ o(m). • number of rules: 5mn +3n2 +26n +10m +4⌈lg n⌉+⌈lg m⌉+6 ∈ o(n2 + mn). • maximal length of a rule: 6 ∈ o(1). therefore, a deterministic turing machine can build π(⟨n,m,k⟩) in a polynomial time with respect to n, m and k. 5.1 an overview of the computation a family of recognizer tissue p systems with cell separation is constructed in the previous section. in the following, we informally describe how the recognizer tissue p system with cell separation π(s(γ)) with input cod(γ) works. let us start with the generation stage, where all the possible subsets of the vertices of the graph are generated. this stage has several parallel processes, which we describe in several items. – in the cells with label 2, in the presence of ci, j, by the rules r16,i, j, r17,i, j, the objects ci, jai, j, ci, ja′i, j introduce the objects zi, jz′i, jai, ja ′ i, j, respectively. in the next step, primed objects and non-primed objects are separated into the new daughter cells with label 2. the objects zi, j and z′i, j in cells with label 2 are exchanged with the objects ci, j in the cell with label 1 by the rules r28,i, j and r29,i, j. in this way, the cycle of duplication-separation can be iterated. – in parallel with the above duplication-separation process, the objects c are used to duplicate the objects ti, t ′i , fi and f ′ i by the rules r18,i – r21,i (in general ti(t ′ i ) and fi(f ′ i ) correspond to the values true and f alse of vertex ai); the rules r26 and r27 take care of introducing the object c from the cell with label 1 to cells with label 2. solving vertex cover problem by means of tissue p systems with cell separation 547 – in the initial configuration of the system, the cell with label 2 contains an object a1 (ai encodes the i-th variable in the propositional formula). the objects t1, f ′1 , z, z ′, y, y′ and s are brought in the cell with label 2, in exchange of a1, by the rule r23,i. in the next step they are separated into the new daughter cells with label 2 by separation rule, because (t1,f ′1 ) ∈ o1 and (f1,t ′1 ) ∈ o2. the object s is used to activate the separation rule r1, and is consumed during the application of this rule. the objects y and y′ are used to introduce a2 from the cell with label 1, and the process of truth-assignment for variable v2 can continue. in this way, in 3n −1 steps, we get 2n cells with label 2, and each one contains one of the 2n possible truth-assignments for the n variables. – in parallel with the operations in the cells with label 2, the objects ai, j,k+1 from the cell with label 1 are traded for objects bi, j,k+1 from the environment at the step 3k +1 (0 ≤ k ≤ n −3) by the rule r2,i, j,k. in the next step, each object bi, j,k+1 is traded for two copies of objects ci, j and di, j,k+1 by the rule r3,i, j,k. at step 3k + 3 (0 ≤ k ≤ n − 3), the object di, j,k is traded for object ai, j,k+2 by the rule r4,i, j,k. especially, at step 3n−5, ai, j,n−1 is traded for bi, j,n−1 by the r2,i, j,k, at step 3n−4, each copy of object bi, j,n−1 is traded for two copies of ci, j by the r4,i, j. after step 3n − 4, there is no object ai, j,k appears in the cell with label 1, and the group of rules r3,i, j,k – r5,i, j,k will not be used again. note that the subscript k of the object ai, j,k grows by 1 in every 3 steps until reaching the value n −1, and the number of copies of ai, j,k is doubled in every 3 steps. at step 3k +3 (0 ≤ k ≤ n −2), the cell with label 1 contains 2k+1 copies of object ci, j. at the same time, we have 2k+1 cells with label 2, and each cell with label 2 contains one copy of object zi, j (or z′i, j). due to the maximality of the parallelism of using the rules, each cell with label 2 gets exactly one copy of ci, j from the cell with label 1 by the rules r28,i, j and r29,i, j. the object ci, j in cell with label 2 is used for duplication as described above. – the objects a1,i and a2,i in the cell with label 1 has a similar role as object ai, j,k in cell 1, which introduces appropriate copies of object c for the duplication of objects ti, t ′i , fi and f ′ i by the rules r9,i – r15,i. note that at step 3k + 3 (0 ≤ k ≤ n − 2), there are (k + 1)2k+1 copies of object c which, by the maximality of the parallelism of using the rules, ensures that each cell with label 2 gets k +1 copies of object c . – the object gi+1 in the cell with label 1 is traded for hi+1 from the environment at step 3i + 1 (0 ≤ i ≤ n −3) by the rule r6,i. in the next step, the object hi+1 is traded for two copies of objects li+1 and ai+2 by the rule r13,i. at the step 3i + 3 (0 ≤ i ≤ n − 3), the object li+1 is traded for two copies of gi+2, so that the process can be iterated, until the subscript i of gi reaches n −1. at step 3n −5, object gn−1 is traded for hn−1 by the rule r6,i. at step 3n −4, each object hn−1 is traded for two copies of an. after step 3n − 4, no object gi appears in the cell with label 1, and the group of rules r15,i – r18,i will not be used again. at the step 3i + 3 (0 ≤ i ≤ n − 2), the cell with label 1 contains 2i+1 copies of ai+2, and we have 2i+1 cells with label 2, each of them containing one copy of object y or one copy of object y′. due to the maximality of the parallelism of using the rules, each cell with label 2 gets exactly one copy of ai+2 from cell 1 by the rules r24,i and r25,i. in this way, the truth-assignment for the vertex ai+1 can continue. – the objects zi, j, z′i, j, y, y ′, z and z′ in the cell with label 1 are removed by the rules r28,i, j, r29,i, j, r30, r31. note that this non-deterministic generation stage is performed by the successive application of the separation rules, and at the end of the stage the same configuration is always reached. thus, the system is confluent in this stage and performs 3n +1 steps. now that all the subsets of vertices of the graph are generated, the pre-checking stage selects only those of size k. this stage is activated by rules r34 and r35, which interchange the object f (or f ′) of each 2-cell (recall that there are 2n of them) from the environment, and then each of the latter in each 2-cell 548 c. lu, x. zhang introduces an object d1 and an object j1 from the environment (recall that there are infinitely many of them). the objects d1 and j1 start two processes of counting in each 2-cell. the first process counts the steps of this stage with counter ji using rules r39,i. the second process counts the number of vertices in the subset. it is performed using rules r36,i, j and r37,i, j, which interchange the objects ti in the 2-cells by objects bi (indicating this way that the corresponding vertex has been counted) and increase the counter d j (the only purpose of the objects di j is to reduce the length of the rules). note that this is a non-deterministic process, since the vertex "counted" in each step is chosen in a non-deterministic way. however, as the size of the subsets of vertices is bounded by n, after 2n steps of this process, the same configuration is always reached, so the system is also confluent in this stage. for the counter d j of a 2-cell to increase, it is necessary and sufficient that in that cell there exist objects bi left. this means that at the end of the process explained in the previous paragraph, the only 2-cells that contain objects encoding subsets of vertices of size k are those containing the object dk+1. at this moment, those cells also contain the counter j2n+1, which then in two steps cause (using rules r40 and r41, and the intermediate object e0 for rules size reduction) the object dk+1 to be interchanged by objects l1 and e1 from the environment. the total number of steps of the pre-checking stage is 2n +2. the checking stage starts now, but before checking if any of the subsets of vertices of size k selected in the previous stage is a vertex cover of the graph, we need some preparation steps. first of all, the objects li will be used as a counter, controlled by rules r42,i, of the number of steps performed. on the other hand, rule r43 introduces another counter pi, controlled by rules r45,i, which runs in parallel, but with a delay of one step. also, in each 2-cell encoding a subset of vertices of size k objects g1 and h1 are introduced by rules r43 and r44, and are then multiplied by rules r45,i and r46,i until obtaining n copies of the former and m copies of the latter. the objects h⌈lg m⌉+1 are used by rules r48,i, j and r49,i, j to change into objects pi j encoding the edges of the graph. on the other hand, rules r50,i, r51,i, r52,i, j and r53,i, j produce, from objects g⌈lg n⌉+1 and bi and by successive interchanges of objects between the 2-cells and the environment, m copies of objects b′i and c ′ i for each and all of the vertices in the subset encoded into the 2-cell. as the copies of objects b′i and c ′ i are being produced, rules r54,i, j and r55,i, j eliminate from the 2cell, in a non-deterministic way, edges of the graph (encoded by objects pi j) such that at least one of its endpoints is contained in the subset encoded in the corresponding 2-cell. once this stage has performed m +⌈lg n⌉+6 steps, we are sure that if there is any object pi j left in the 2-cell, then the subset of vertices encoded in that cell is not a vertex cover of the graph, and rule r56,i, j eliminates the counter pi in an additional step. the answer stage starts at step 5n + m +⌈lg n⌉+9, when the object lm+⌈lg n⌉+7 appears in every 2-cell encoding a subset of vertices of size k. if the counter p has survived in any of these 2-cells, it means that it encoded a vertex cover of the graph, and rule r57 interchanges the two counters with an object t from the environment, which is then sent to the 1-cell of the system by rule r58. then, rules r59, r60 and r61 control if this cell has received at least one object t from any of the 2-cells of the system. if this is the case, it is detected at step 5n + m + ⌈lg n⌉ + 9, when an object yes is sent to the environment and the system halts. otherwise, it is detected at step 5n + m +⌈lg n⌉+10, when an object no is sent to the environment and the system halts. 5.2 main results from the discussion in the previous section, the family π is polynomially bounded, sound and complete with regard to (vc,cod,s). we have the following result: theorem 5.1. vertex cover ∈ pmct s. solving vertex cover problem by means of tissue p systems with cell separation 549 corollary 2. np ∪ co − np ⊆ pmct s. proof: it suffices to make the following observations: the vertex cover problem is np-complete, vertex cover∈ pmct s and this complexity class is closed under polynomial-time reduction and under complement. 6 discussion the main purpose of this paper is to provide a polynomial time solution for vertex cover problem based on tissue p systems with cell separation. we showed that the membrane separation is an important feature that could hold the power to solving computationally hard problems in polynomial time. following this direction, it remains as further work to describe classical complexity classes below pspace with this framework. 7 acknowledgements the authors acknowledge the support of national natural science foundation of china (60674106, 30870826, 60703047, and 60533010), program for new century excellent talents in university (ncet05-0612), ph.d. programs foundation of ministry of education of china (20060487014), chenguang program of wuhan (200750731262), hust-srf (2007z015a), and natural science foundation of hubei province (2008cdb113 and 2008cdb180). bibliography [1] d. díaz-pernil, m. a. gutiérrez-naranjo, m. j. pérez-jiménez, a. riscos-núñez. a linear–time tissue p system based solution for the 3–coloring problem. electronic notes in theoretical computer science, vol. 171, pp. 81–93, 2007. [2] d. díaz-pernil, m. a. gutiérrez-naranjo, m. j. pérez-jiménez, a. riscos-núñez. solving subset sum in linear time by using tissue p systems with cell division. in: j. mira, j. r. alvarez, j. r. ivarez (eds.) 2nd international work-conference, iwinac 2007, interplay between natural and artificial computation lecture notes in computer science, vol. 4527, pp. 170–179, 2007. [3] d. díaz-pernil, m. j. pérez-jiménez, a. riscos-núñez, a. romero. computational efficiency of cellular division in tissue-like membrane systems. romanian journal of information science and technology, vol. 11(3), pp. 229–241, 2008. [4] m. a. gutiérrez-naranjo, m. j. pérez-jiménez, f. j. romero-campero. a linear solution for qsat with membrane creation. lecture notes in computer science, vol. 3850, pp. 241–252, 2006. [5] c. martín vide, j. pazos, gh. păun, a. rodríguez-patón. a new class of symbolic abstract neural nets: tissue p systems. lecture notes in computer science, vol. 2387, pp. 290–299, 2002. [6] c. martín vide, j. pazos, gh. păun, a. rodríguez-patón. tissue p systems. theoretical computer science, vol. 296, pp. 295–326, 2003. [7] l. pan, t.-o. ishdorj. p systems with active membranes and separation rules. journal of universal computer science, vol. 10(5), pp. 630–649, 2004. 550 c. lu, x. zhang [8] l. pan, m. j. pérez-jiménez. efficiency of tissue p systems with cell separation. in m. a. martínez-del-amor, e. f. orejuela-pinedo, gh. păun, i. pérez-hurtado, a. riscos-núñez, seventh brainstorming week on membrane computing, sevilla, report rgnc 02/2009, 169–196, 2009. [9] a. păun, gh. păun. the power of communication: p systems with symport/antiport. new generation computing, vol. 20(3), pp. 295–395, 2002. [10] gh. păun. computing with membranes. journal of computer and system sciences, vol. 61(1), 108–143, 2000. [11] gh. păun. membrane computing, an introduction, springer–verlag, berlin, 2002. [12] gh. păun, m. j. pérez-jiménez, a. riscos-núñez. tissue p system with cell division. in gh. păun, a. riscos-núñez, a. romero-jiménez, f. sancho-caparrini (eds.), second brainstorming week on membrane computing, sevilla, report rgnc 01/2004, 380–386, 2004. [13] m. j. pérez-jiménez, a. romero-jiménez and f. sancho-caparrini, a polynomial complexity class in p systems using membrane division, in e. csuhaj-varjú, c. kintala, d. wotschke and gy. vaszyl (eds.), proceedings of the 5th workshop on descriptional complexity of formal systems, dcfs 2003, pp. 284–294, 2003. [14] the p system web page: http://ppage.psystems.eu chun lu is a ph.d candidate in huazhong university of science and technology, wuhan, china. he received his master degree in systems engineering from huazhong university of science and technology in 2008. currently, his main research interests cover membrane computing, neural computing, automata theory and its application. xingyi zhang was born in china on june 6, 1982. he received his doctor degree at huazhong university of science and technology in 2009. currently, he works in school of computer science and technology, anhui university. his main research fields are formal language theory and its applications, unconventional models of computation, especially, membrane computing. he has published several scientific papers in international journals. int j comput commun, issn 1841-9836 7(5):957-967, december, 2012. function approximation with artmap architectures l.m. sasu, r. andonie lucian m. sasu 1. transilvania university of braşov mathematics and computers department romania, 500091 braşov, iuliu maniu, 50 lmsasu@unitbv.ro 2. siemens corporate technology romania, 500096 braşov, 15 noiembrie, 46 e-mail: lucian.sasu@siemens.com răzvan andonie 1. computer science department usa, central washington university, ellensburg 400 east university way ellensburg, wa 98926, usa 2. transilvania university of braşov electronics and computers department romania, 500024 braşov, politehnicii, 1 e-mail: andonie@cwu.edu abstract: we analyze function approximation (regression) capability of fuzzy artmap (fam) architectures well-known incremental learning neural networks. we focus especially on the universal approximation property. in our experiments, we compare the regression performance of fam networks with other standard neural models. it is the first time that artmap regression is overviewed, both from theoretical and practical points of view. keywords: fuzzy artmap, universal approximation, regression. 1 introduction the approximation of functions that are known only at a certain number of discrete points is a classical application of neural networks. almost all approximation schemes can be mapped into some kind of network that can be dubbed as a “neural network” [1]. a neural network has the universal approximation property if it can approximate with arbitrary accuracy an arbitrary function of a certain set of functions (usually the set of continuous function) on a compact domain. the drawback is that such an approximation may need an unbounded number of “building blocks” (i.e., fuzzy sets or hidden neurons) to achieve the prescribed accuracy. therefore it is reasonable to make a trade-off between accuracy and the number of the building blocks, by determining the functional relationship between them. historically, of fundamental importance was the discovery [2] that a classical mathematical result of kolmogorov (1957) was actually a statement that for any continuous mapping f : [0, 1]n ⊂ ℜn −→ ℜm there must exist a three layered feedforward neural network of continuous type neurons that implements f exactly. this existence result was the first step. cybenko [3] showed that any continuous function defined on a compact subset of ℜn can be approximated to any desired degree of accuracy by a feedforward neural network with one hidden layer using sigmoidal nonlinearities. many other papers have investigated the approximation capability of three layered networks in various ways. in addition to sigmoid functions, more general functions can be used as activation functions of universal approximator feedforward networks [4]. copyright c⃝ 2006-2012 by ccc publications 958 l.m. sasu, r. andonie girosi and poggio proved that radial basis function (rbf) networks also have universal approximation property [1]. hartman and kowalski [5] proved that a one hidden layer neural network with gaussian hidden nodes is a universal approximator for real-valued maps defined on convex, compact sets of ℜn. additional related papers are [6] and [7]. the fuzzy artmap (fam) family of neural networks is one of the best known incremental learning systems. there are many variations of carpenter’s et al. [8] initial fam model, including gaussian artmap (gam) [9], probart [10], famr [11], gart [12], [13], and appart [14]. compared to fam classification, the function approximation (regression) capability of fam was less frequently addressed. it is our goal here to discuss fam regression capability for different fam architectures. the fam maps subsets of ℜn to ℜm, accepting both binary and analogue inputs in the form of pattern pairs. the initial fam, probart, and the famr architectures have been used for incremental regression estimation. since the initial fam was proved to be universal approximator [15], it is reasonable to believe that members of the fam family may also have the universal approximation capability. however, since some of the fam variations are quite different than the initial fam, each model should be considered individually. the bayesian theory allows for elaboration of general neural network training methods [16].recently, vigdor and lerner have combined the bayesian theory and the fam introducing the bayesian artmap (ba) [17]. like the gam and the gart networks, during training, the ba uses gaussian categories and fam competitive learning. however, the ba prediction phase is very different than the fam competitive algorithm, being a bayesian approach. vigdor and lerner have compared the ba performance with respect to classification accuracy, learning curves, number of categories, sensitivity to class overlapping and risk with those of the fam. generally, the ba outperformed the fam in classification tasks. up to our contribution, the ba regression capability was not discussed or tested. our paper is the first overview of both theoretical and practical aspects of fam regression, considering several major fam architectures: the initial fam of carpenter et al., probart, famr, ba, gam, and appart. we discuss universal approximation capabilities of these fam models. in our experiments, we compare the regression performance of fam networks with standard neural networks: multi layer perceptron (mlp), rbf, general regression neural network (grnn), and fasback. section 2 reviews the main notations and paradigms of fam. in section 3, we discuss the universal approximation capability of the following fam architectures: the original fam, probart, famr, ba, and appart. we synthesize our comparative experiments in section 4. section 5 contains the final remarks. 2 fuzzy artmap a fam consists of a pair of fuzzy art modules, arta and artb, connected by an inter-art module called mapfield, f ab. arta contains a preprocessing layer f a0 , an input (or short-term memory) layer f b1 and a competitive layer f b 2 . the following notations apply: ma is the number of nodes in f a1 , na is the number of nodes in f a 2 , and w a is the weight vector between f a1 and f a 2 . we say that a node – also called a category – from f a2 is uncommitted if it has not learned yet an input pattern, and committed otherwise. analogous layers and notations are used in artb. each node j from f a2 is linked to each node from f b 2 via a weight vector w ab j from f ab, the jth row of the matrix wab, 1 ≤ j ≤ na. all weights are initialized to 1. all input vectors are complement-coded by the f a0 layer in order to avoid category proliferation [8], [18], [19]: the input vector a = (a1, . . . , an) ∈ [0, 1]n produces the normalized vector a = (a1, . . . , an, 1 − a1, . . . , 1 − an). during pattern processing, the operator ∧ used is the fuzzy function approximation with artmap architectures 959 and operator defined as (p ∧ q)i = min(pi, qi), where p = (p1, . . . , pn) and q = (q1, . . . , qn). | · | denotes the l1 norm. before learning a normalized input vector a, the vigilance parameter factor ρa is reset to its baseline value ρa and each input category is considered as not inhibited, competing for the current input pattern. a fuzzy choice function is computed for every arta category: tj(a) = |a∧waj | αa+|waj | , for 1 ≤ j ≤ na. the non-inhibited node of index j having the maximum fuzzy choice function value is further checked whether it passes the resonance condition, i.e. if the input is similar enough to the winner’s prototype: |a ∧ waj|/|a| ≥ ρa. if this condition is not fulfilled, then the node having index j is inhibited and another non-inhibited node maximizing the fuzzy choice function is considered as above. if no such node exists, a new node with index j is created to represent the input vector. in parallel, a similar step is performed in the artb module; we obtain output vector yb = (δik)1≤i≤nb , where k is the index of the output winner node (1 ≤ k ≤ nb) and δij is kronecker’s delta. if input node j is newly added, then we associate it with the current output: wabjk = δkk and this association becomes permanent. each time input node j is activated, it predicts as output value the only index k for which wabjk = 1. if node j is not new, then we check whether its predicted value is k. if the prediction is incorrect, a new activity (called match tracking) is triggered in arta solely. otherwise, learning occurs in both arta and artb: w a(new) j = βa ( a ∧ wa(old)j ) + (1 − βa)w a(old) j (1) where βa ∈ (0, 1] is the learning rate parameter. a similar learning step takes place in artb. the match tracking raises the ρa threshold for the current input pattern: ρa = δ + |a ∧ waj|/|a|. if ρa > 1 then the current input pattern is rejected; otherwise, the search for an appropriate input category is continued, as described above. for each f a2 category we have the following geometrical interpretation. node w a j is a hyperrectangle rj inside the n-dimensional hypercube, having size n − |wj| [8]. learning, as in equation (1), is equivalent to expanding the hyperrectangle towards the current input pattern, unless this pattern is not already in rj. if βa = 1, then rj expands to rj ⊕ a, the minimal hyperrectangle containing both rj and input pattern a. a similar geometrical interpretation applies to artb. 3 fam architectures used in regression 3.1 the initial fam for regression the fam regression capability was first tested by carpenter et al. for univariate real functions [8]. input categories were considered to predict not real values, but real intervals. the experiments targeted the study of predicted output intervals’ geometry and the number of resulted categories for various values of ρb. for the test set, the authors counted the matchings between predicted output categories and actual output values. a matching between f(a) and the predicted output category (a rectangle) rbk was established if the size of r b k ⊕ f(a) did not exceed (1 − ρb). as expected, the number of matchings increased with ρb. verzi et al. [15] proved that a slightly modified fam version can be used to universally approximate any measurable function in lp ([0, 1]). more specifically, given 1 ≤ p < ∞, for every f ∈ lp ([0, 1]), f ≥ 0, a series of fam computable functions sn with the following property were determined: functions sn approximate f in the limit and sn are dense in lp ([0, 1]). one can extend this result to the initial fam. 960 l.m. sasu, r. andonie 3.2 probart for function approximation probart is a modification of fam motivated by empirical findings on the operational characteristics of fam under certain conditions [10]. the authors replaced the mapfield update rule fam by wabj = { yb + wabj if the j-th f a 2 node is active and f b 2 is active wabj if the j-th f a 2 node is active and f b 2 is inactive (2) thus, wabjk indicates the number of associations between the j-th arta node and k-th artb node. initially, wabjk = 0, i.e. no association has been made yet. there is no match tracking phase. the predicted value for an input pattern activating the jth arta category is µjl = 1 |wabj | nb∑ k=1 ϵklw ab jk, 1 ≤ l ≤ mb (3) where µjl is the expected value of the l-th component of the predicted output pattern associated with the current input pattern, |wabj | is the total number of associations of the j-th arta category and each category from artb, and ϵkl represents the kth artb category. specifically, for probart the authors considered ϵkl as the lth component of the kth artb category exemplar. only the first m components of each output category wbk are meaningful for computing the prediction corresponding to the current input pattern. equation (3) can be written as µjl = ∑nb k=1 ϵklpjk, where pjk is the empirically estimated association probability between the jth arta category and the kth artb category: pjk = wabjk/|w ab j |. 3.3 the famr model for function approximation the famr (fuzzy artmap with relevance factor), a version of the fam, has a novel learning mechanism. we will review here the famr basic notations (details in [11]) and discuss its function approximation capabilities. the main difference between the famr and the initial fam is the update method of the wabjk weights. the famr uses the following updating formula [11]: w ab(new) jk =   w ab(old) jk if j ̸= j w ab(old) jk + qt qnew j ( 1 − wab(old)jk ) w ab(old) jk ( 1 − qt qnew j ) if k ̸= k (4) where qt is the relevance assigned to the t-th input pattern (t = 1, 2, . . . ) and qnewj = q old j + qt. the relevance qt is a real positive finite number directly proportional to the importance of the experiment considered at step t. initially, each qj (1 ≤ j ≤ na) has the same initial value q0. to maintain the stochastic nature of each wabj row in mapfield, we modified the mapfield dynamics: when a new input category is created, a new row filled with 1/nb is added to wab; when a new artb category indexed by k is added, each existing input category is linked to it by wabjk = q0 nbqj , and the rest of elements wabjk are decreased by wab jk nb−1 , for 1 ≤ j ≤ na, 1 ≤ k ≤ nb, k ̸= k. the update in eq. (4) preserves the stochastic property of each row. finally, the vigilance test is changed to: nb wabjk ≥ ρab. according to [11], this wabjk approximation is a correct biased estimator of posterior probability p(k|j), the probability of selecting the k-th artb category after having selected the j-th arta. function approximation with artmap architectures 961 to estimate the corresponding output value for a given input pattern, famr uses the same formula as in eq. (3), but in this case ϵk contains the coordinates of the kth artb category centroid. during the famr training process, the l-th component of the centroid can be updated by kohonen’s learning rule: ϵb(new)kl = ϵ b(old) kl + (bl − ϵ b(old) kl )/size b j . this rule incorporates an idea from [20]. the value sizebj is the number of output vectors of the k-th artb category and bl is the l-th component of b, the output vector of the current training pair (a, b). 3.4 the bayesian artmap function approximation algorithm in ba, in contrast to fam, waj is not a weight vector (a prototype), but simply a category label. also, the art categories are gaussians, similar to the gam. each ba category j is characterized by the n-dimensional vector µ̂aj (mean), the n × n covariance matrix σ̂ a j , and the count number of training patterns clustered to category j, naj . analogous notations appear in artb, where one provides m-dimensional vectors. the associations between input and output categories are stored inside the mapfield module, as probart does, and one can approximate the conditional probability p(wbk|w a j ) as p̂(wbk|w a j ) = w ab jk/ ∑nb l=1 w ab jl . the following description uses arta notations; analogous notations are used for artb. all existent arta categories compete to represent the current input pattern. the posterior probability of category j given input a is estimated according to bayes’ theorem: p̂(waj |a) = p̂(a|waj )p̂(w a j ) na∑ i=1 p̂(a|wai )p̂(w a i ) (5) where p̂(waj ) is the estimated prior probability of the j-th arta category, p̂(w a j ) = n a j / ∑na i=1 n a i . the conditional probability p(a|waj ) is estimated using all patterns already associated with gaussian category waj : p̂(a|waj ) = 1 (2π)n/2 ∣∣∣σ̂aj∣∣∣1/2 · exp { − 1 2 (a − µ̂aj ) t(σ̂aj ) −1(a − µ̂aj ) } (6) during the category choice step in arta, the winning category j is the one maximizing the posterior probability p̂(waj |a). the following vigilance test is performed: saj ≤ s a max, where s a j = ∣∣∣σ̂aj∣∣∣ is the hyper-volume of the winning category, and samax is an upper bound threshold. during processing a training pattern, samax may decrease from its initial value s a max. in contrast, s b max remains unchanged. every newly recruited category inside an arta (artb) module is centered in the current pattern and has the initial covariance matrix set to λ(sbmax) 1/m ·im (and λ(sbmax) 1/n ·in, respectively), where λ is a small positive constant. this is done when none of the categories fulfills the vigilance test. adding a new input (output) category triggers the addition of a new zero-filled line (column) to the association matrix wab. if the connection strength p̂(wbk|w a j) between winning categories w a j and w b k is below a fixed threshold pmin, then samax is slightly decreased under the current winner input category’s sj , and the quest for another input category is continued. otherwise, if the current winner input category was not newly added during processing the current pattern, arta learns the current pattern: 962 l.m. sasu, r. andonie µ̂aj(new) = naj naj + 1 µ̂aj(old) + 1 naj + 1 a , (7) σ̂aj(new) = naj naj + 1 σ̂aj(old) + 1 naj + 1 (a − µ̂aj(new))(a − µ̂ a j(new)) t ∗ in (8) naj = n a j + 1 (9) unless wbk is a newly added category for the current training pattern, an analogous learning process in artb takes place. finally, the mapfield association counter wabjk is updated. after learning, the ba can be used for prediction. we estimate the probabilistic association of an output category wbk with input test pattern a: p̂(wbk|a) = na∑ j=1 p̂(wbk|w a j )p̂(a|w a j )p̂(w a j ) nb∑ l=1 na∑ j=1 p̂(wbl |w a j )p̂(a|w a j )p̂(w a j ) (10) as in [21], we assume the conditional independence of activating categories wbk and w a j , given input pattern a. for function approximation the following average formula is used: f̂(a) = nb∑ k=1 p̂(wbk|a) · µ̂ b k (11) since, under certain mild conditions on the kernel function, rbf networks are universal approximators [1], [5], [6], [7], and the fam also has universal approximation capability [15], it looks natural for the ba, which is essentially a fam architecture with gaussian categories, to be universal approximator. however, this statement can not be directly deducted from the rbf and fam results. this is was a good reason for us to proof the following theoretical result [22]: theorem 1. ba is a universal approximator on a compact set x ⊂ ℜn. 3.5 appart: hybrid stable learning for universal function approximation appart [14] is an art-based neural network model that incrementally approximates continuousvalued multidimensional functions through a higher-order nadaraya–watson regression. an input pattern x is feedforwarded from input layer f1 to the f2 layer. the f2 layer consists of n categories, modeling a local density of the input space using gaussian receptive fields with mean µj and standard deviation σj. a match criterion is used to detect whether the current leaning pattern activates an existing f2 category or a new one should be added. the match function is: gj = exp ( − 1 2 n∑ i=1 ( xi − µji σji )2) , 1 ≤ j ≤ n (12) if all gj values are below threshold ρf2, a new node is recruited to represent the current input pattern. otherwise, the input strength of each f2 node is computed as gj = i(gj > ρf2) · (ηjgj/ ∏n i=1 σji), where ηj is a measure of the prior activation probability of the jth category, and i is the binary indicator function: i(p) = 1 iff p is true. the activation values vj of the f2 nodes are obtained by normalizing gj. one can use vj as an approximation of the posterior probability p(j|x) of category j given input pattern x. function approximation with artmap architectures 963 the p and o layers together compute the prediction of the network. in the p layer, there are m + 1 nodes whose corresponding values are computed as: ak = ∑n j=1 αkjvj (1 ≤ k ≤ m), b =∑n j=1 βjvj where αkj and βj are weights connecting each f2 category to the each node in the p layer. each αkj is the sum of values of output feature k, learned when the jth f2 node was active. βj counts how many patterns the jth f2 category has learned. output layer o has m output nodes, whose predictions are ok = i(b > 0) · ak/b. incorrect predictions are detected by comparing a threshold ρo with the degree of closeness between the prediction of the network and the desired output. if an incorrect prediction is produced, a match tracking mechanism (similar to the one in fam) is triggered. this might produce a new f2 node or find a more suitable node for the current input pattern. the learning process takes place for µj, σj, ηj, αj and βj: ηj(t + 1) = ηj(t) + vj, µji(t + 1) = (1 − η−1j vj)µji(t) + η −1 j vjxi λji(t + 1) = (1 − η−1j vj)λji(t) + η −1 j vjx 2 i , σji(t + 1) = √ λji(t + 1) − µji(t + 1)2 αkj(t + 1) = αkj(t) + ϵ −1vjyk, βj(t + 1) = βj(t) + ϵ −1vj a common value γi = γcommon may be used for the standard deviation in case of all input features. an important theoretical result of appart is [14]: theorem 2. appart with ρf2 = 0, ρo = 0 and γi = γcommon, 1 ≤ i ≤ n behaves as grnn. since the grnn can be viewed as a normalized rbf expansion, one can transitively apply to appart two important properties of rbf networks: the universal approximation and the best approximation properties [1]. 4 experimental results for the first test, we consider function [10] f : [0, 1] → [0, 1] defined by f(x) = (10 +∑7 t=1 sin(10tx))/20. we use independent, randomly generated datasets for training, validation and testing, consisting of 800, 200, and 1000 patterns, respectively. each training pattern is a (x, f(x)) inputoutput pair. the testing set was not used in the training phase, but only to assess generalization performance. the ba parameters samax, s b max, and pmin are optimized on the validation set by trial and error, for samax, s b max ∈ {10 −3, 5·10−4, 10−4, 5·10−5, 10−5, 5·10−6} and pmin ∈ {0, 0.1, . . . , 0.9}. the ba with optimized parameters (i.e., generating the lowest rmse on the validation set) was trained on the training+validation dataset. the generalization performance of the trained ba was assessed on the testing set in two ways (see table 4): 1. the “ba(1)”, corresponds to a ba network with unbounded number of categories. 2. for “ba(2)”, we considered only ba models with similar number of input categories as for probart. 964 l.m. sasu, r. andonie arta categories no. artb categories no. rmse fam 312 53 0.0074 probart 110 53 0.0169 ba(1) 185.6 57.8 0.0076 ba(2) 111.0 35.8 0.0106 table 1: fam, probart, and ba performance for regression on data generated by function f. the rmse for ba(1) and ba(2) were each averaged for five different runs, using each time randomly generated training, validation, and test sets. the results for probart and fam are from [10]. fam in our experiments is carpenter’s initial fam version. the ba(1) results are very similar to the fam results, but for a considerably smaller number of input categories. on average, ba(2) produced one more input category than probart, while improving the rmse by 40.23%. it is quite difficult to directly compare the resulted ba(2) and fam, since ba(2) has 64.42% less input categories than the fam. considering both the rmse score and the number of input categories, we may conclude that, for this experiment, the ba performs better than the fam and probart. in the second test, we use the fifth-order chirp function [14]: g(x) = 0.5+0.5 sin(40πx5). marti et al. have experimentally compared the function approximation performance of the following neural models [14]: appart, multi layer perceptron (mlp), rbf, general regression neural network (grnn), fam, gam, probart, and fasback [23]. the reported score was the mean squared error (mse). the authors run the training algorithms for several epochs. the data set consisted of 10000 points x ∈ [0, 1], of which 70% were used for training and the rest for testing. the cited paper does not fully describe the parameter values used for each of the networks. in our experiment, we partition a dataset of 10000 patterns into a 4000 patterns training set, a 3000 validation set, and a 3000 patterns testing set. we perform a trial and error search for samax, s b max ∈ {10 −4, 10−3, 10−2, 10−1}, pmin ∈ {0, 0.1, . . . , 0.9}. the values producing the best mse on the validation set are used to train the ba on the train+validation dataset, and the testing set mse was reported. the above procedure are repeated five times, for randomly generated datasets. we only use single epoch training. table 2 contains the results for mlp, rbf, grnn, fam, gam, probart, fasback, appart and ba. for the first eight neural networks the results are from [14]. ba produces a very good mse score for this regression task, most likely due to the optimized parameter values obtained by trial and error. comparing the mse ba score, obtained by single epoch training, and those reported in [14], where multi-epoch training was used, we can state that the ba clearly performs better. 5 conclusions theoretical universal approximation results were obtained for several fam architectures: • explicit results were obtained for a slight variation of the initial fam and for the ba. • implicit results, derived by association with other networks: famr, probart, and appart. function approximation with artmap architectures 965 model mse training epochs mlp 0.4362 30000+ rbf 0.2701 10000 grnn 0.1540 150 fam 0.1802 140 gam 0.1521 45 probart 0.1435 50 fasback 0.0915 10000 appart 0.0803 30 ba 0.0086 1 table 2: ba vs. other neural networks generalization performance for data generated by function g. the result showing fam networks to be universal approximators is an important fact in establishing the utility of fam architectures. a learning algorithm which is known to be a universal approximator can he applied to a large class of interesting problems with the confidence that a solution is at least theoretically available. experimentally, fam architectures performed well compared to other neural function approximators. the fam model, as well as other universal approximators, suffer from the curse of dimensionality, as defined by bellman [24]: an exponentially large number of art categories may be required to reach a final solution. therefore, the universal approximation capability of a network is an generally an existential result, not a constructive procedure to obtain a guaranteed compact network approximation of an arbitrary function. an important problem we have not addressed here is that of determining the network parameters so that a prescribed degree of approximation is achieved (see [25]). the fam and its offsprings are incremental learning models. therefore, they may be used for fast approximation of massive streaming input data. this may be a serious plus when compared to other neural predictors. how could a neural posterior probability estimator, like the ba, be used in risk 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[26] duda, r.o.; hart, p.e.; david g.s (2000); pattern classification, 2nd edition. international journal of computers communications & control issn 1841-9836, 12(3), 415-428, june 2017. compensation of time-varying delay in networked control system over wi-fi network h.-c. yi, c.-j. an, j.-y. choi hyun-chul yi, cheol-jin an, joon-young choi* department of electronics engineering pusan national university, pusan, korea 2, busandaehak-ro 63beon-gil, geumjeong-gu, pusan, 46241, korea hcy@pusan.ac.kr, cja@pusan.ac.kr *corresponding author: jyc@pusan.ac.kr abstract: in this study, we design a state predictor-based output feedback controller that compensates for unavoidable time-varying network delays in networked control systems (ncss) over wi-fi networks. we model time-varying network delays as timevarying input delays of ncss over wi-fi networks. the designed controller consists of a linear quadratic regulator (lqr), a full-order observer, and a time-varying stepahead state predictor. the state predictor plays a key role in compensating for the time-varying input delay by providing the lqr with an estimation of future states ahead by the current network delay time. the time-varying network delays are acquired in real time by measuring the time differences between sent and received control data packets. we verify the stability and compensation performance of the designed controller by performing extensive experiments for an ncs in which a rotary inverted pendulum is controlled over wi-fi networks. keywords: networked control system (ncs), wi-fi network, time-varying delay, state predictor, rotary inverted pendulum. 1 introduction networked control systems (ncss) are spatially distributed systems in which sensors, actuators, and controllers exchange i/o information through a shared band-limited digital communication network. ncss have been applied to a broad range of areas such as wireless sensor networks (wsns), remote surgery, haptics collaboration over the internet, automated highway systems, and unmanned aerial vehicles (uavs) [4,10,25]. in particular, wireless networked control systems (wncss) have been increasingly applied in different fields because of the need for mobile operations, flexible installations, and rapid deployment in many applications. as an alternative solution to wired ncss, wncss are considered as primary solutions of networked control applications because of their simple configuration and mobility. however, the reliability and real-time performance of wncss are lower than for wired ncss because with wireless networks, the sizes of network delays abruptly changes over time owing to the dynamic state variation of wireless networks. it is commonly known that even a small time delay in control system feedback loops can make the whole system oscillating or unstable [4,15,25], and it is obvious that fluctuating time-varying delays severely degrade the stability and performance in wncss. in order to deal with the network delays, various control schemes for wncss are proposed in [1–3, 5–9, 11–14, 16–23, 26, 27]. these include: fuzzy-based control [6, 22], predictor-based control [13, 27], pid-based control [7, 14, 19, 20], h∞ filter-based control [1, 5, 17, 21, 23], faulttolerant-based control [3, 9], linear quadratic regulator (lqr)-based control [8], linear matrix inequality (lmi)-based control [18], kalman filter-based control [12,16,26], and observer-based control [11]. copyright © 2006-2017 by ccc publications 416 h.-c. yi, c.-j. an, j.-y. choi when constructing these control schemes, it is necessary to model the network delays. in [1,3,8,16,22], the network delays are modeled as constant-valued delays or bounded time-varying delays that do not suitably describe the rapidly fluctuating time-varying delays in wncss. in [2,5–7,9,11–14,17–21,23,26,27], the network delays are modeled as time-varying delays, but the stability and performance of proposed control schemes are demonstrated only by performing network or numerical simulations using opnet++, truetime network simulators, or matlab/simulink. considering dynamically changing states of wireless networks, the stability and performance of controllers designed for wncss must be verified by performing experiments over real wireless networks. moreover, all of the controllers in [1–3, 5–9, 11–14, 16–23, 26, 27] were designed under the assumption that the network delays are known in advance in the form of constant-valued or time-varying delays for all operation times. however, this is not the case in real operations of wncss, but time-varying network delays must be measured in real time, and should be used for the control action to compensate for the time-varying delays. hence, a real-time measurement method for time-varying network delays must be designed and be a part of wncss. in order to cope with the problems in the existing results of wncss, we design a state predictor-based output feedback controller to compensate for the time-varying network delays of ncss over wi-fi networks. we model wi-fi network delays as time-varying input delays, and we design a control scheme consisting of an lqr, a full-order observer, and a time-varying step-ahead predictor. moreover, we design a real-time measurement method for time-varying network delays, and we use the measured delays to construct the state predictor in real time. to apply the designed control scheme, we construct a wncs hardware platform where the rotary inverted pendulum is controlled over wi-fi networks, and we conduct extensive experiments to verify the stability and performance of the designed predictor-based controller. this paper is organized as follows. in section 2, we formulate the control problem. in section 3, we propose the design of a state predictor-based output feedback controller. in section 4, we propose a delay-measurement method in a wncs hardware platform. in section 5, we conduct experiments and discuss the results. in section 6, we conclude the paper. 2 problem formulation we consider a wncs over a wi-fi network, where the plant belongs to the class of singleinput multi-output systems, and the controller output is transferred via a wi-fi network, as depicted in fig. 1. in order to precisely describe the dynamic changes in the network state of wncs, we model the network delays as time-varying delays, d(k), and we assume that the measured values of the plant output are directly available for the construction of the controller. with respect to the models of the plant and controller, we choose discrete-time models because modern controllers are usually implemented using digital computers [2], and continuous-time controllers inevitably involve a degrading discretization process during implementation. the discrete-time linear plant including the network delays is described by the following model. x(k + 1) = ax(k) + bu(k −d(k)) y(k) = cx(k), (1) where x(k) ∈ rn is the system state, u(k) ∈ r is the control input, d(k) is the time-varying input delay, y(k) ∈ rq is the plant output, and it is assumed that the pair (a,b) are controllable and (a,c) are observable. the goal of this paper is summarized as follows. first, we design a state predictor-based output feedback controller for a discrete-time linear system (1) in order to compensate for the compensation of time-varying delay in networked control system over wi-fi network 417 figure 1: structure of wncs over wi-fi network. time-varying input delay and regulate the system output to a desired reference. second, we develop a real-time method to measure the time-varying network delays in wi-fi networks. the measured network delays are used for the implementation of the designed controller in real time. finally, we construct the hardware platform of a wncs with a rotary inverted pendulum as the plant for experiments of the designed controller. 3 design of predictor-based feedback controller we design the predictor-based feedback controller by performing the following three steps. in the first step, we design a full-state feedback controller for the plant with no input delay by applying discrete-time lqr theory. in the second step, we design a full-order observer that estimates the plant states. in the last step, we design a state predictor for the compensation of the time-varying input delay. first, we design a static state feedback controller u(k) = −kx(k) for the plant (1) with no input delay (i.e., with d(k) = 0) such that the designed controller can stabilize the plant (1); that is, the matrix (a−bk) becomes schur stable. the existence of the stabilizing state feedback gain, k, is guaranteed by the assumption that the pair (a,b) is controllable [24]. by applying the discrete-time lqr theory, we can easily obtain the optimal state feedback controller u(k) = −kx(k), (2) which stabilizes the plant system (1) with no input delay and minimizes the following quadratic cost function: j (u) = ∞∑ k=1 ( x(k) t qx(k) + ru2(k) ) , (3) where the state-cost matrix, q, and the performance index constant, r, are design parameters [24]. the implementation of the state feedback controller (2) requires knowledge of all state variables, and it is necessary to estimate the system state, x(k), from the measurement of the system 418 h.-c. yi, c.-j. an, j.-y. choi output y(k). for this purpose, we design the full-order observer as follows. x̂(k + 1) = ax̂(k) + bu(k −d(k)) + l (y(k) − ŷ(k)) ŷ(k) = cx̂(k), (4) where x̂ ∈ rn is the estimated state, ŷ ∈ rq is the estimated output, and l ∈ rn×q is the observer gain, which is designed such that the matrix (a−lc) becomes schur stable; that is, the designed observer becomes exponentially stable [24]. then, for the designed observer (4), it holds that x̂(k) exponentially converges to x(k), and it is proven that the estimated state, x̂(k), can be used to construct the state feedback controller (2) instead of the unavailable actual state, x(k). finally, we seek the final controller that is constructed with the estimated state as u(k −d(k)) = −kx̂(k), (5) which can be alternatively written as u(k) = −kx̂(k + d(m)), (6) where m satisfies m−d(m) = k, and it is non-implementable because it requires future values of state. however, the d(m)-step-ahead predictor is designed in [2] as x̂(k + d(m)) = ad(m)x̂(k) + k−1∑ j=k−d(m) ak−j−1bu(j), (7) which yields the implementable predictor-based feedback controller u(k) = −k  ad(m)x̂(k) + k−1∑ j=k−d(m) ak−j−1bu(j)   . (8) the closed-loop system with the designed controller (8) is globally exponentially stable in the sense of the norm ( |x̂(k)|2 + ∑d−1 j=0 |u(k + j −d(m + j))| 2 )1/2 , where d is an upper bound of the time-varying input delay [2]. remark 1. in order to implement the designed controller (8), we must find m satisfying m − d(m) = k at each time k, which requires some knowledge of future input delay, d(m). practically, it is difficult to know the future time-varying input delay in advance. we overcome this problem by measuring the time-varying delay at the plant input, and adopting the measured delay as an estimate of d(m). the overall structure of the wncs with the designed controller is depicted in fig. 2. as shown in fig. 2, the delayed input value is applied to the plant input, and the measured delays in real time are used as the time-varying input delay, d(m), for the state predictor. once the timevarying input delay, d(m), is estimated, the state predictor can compensate for the time-varying delay and stabilize the entire closed-loop system. compensation of time-varying delay in networked control system over wi-fi network 419 predictor controller plant observer delay figure 2: wncs with the predictor-based feedback controller. 4 delay measurement method in wncs hardware platform as emphasized in remark 1, in order to implement the designed controller (8), we need to measure the time-varying delay in real time that occurs in wncs. in this section, we design a delay-measurement method for wncs over wi-fi networks by extending the method proposed in [25] to measure wsn (wireless sensor network) delays in real time. sender receiver time time = = -th packet -th packet = = received time of -th packet i[�sending time of -th packet figure 3: measurement method for time-varying network delay. the basic idea of the delay-measurement method is to measure the transmission delay of signal packets from the controller output to the plant input. when controller output packets are sent, the time of sending is appended to the packets; when they are received at the plant input, the transmission delay is calculated by subtracting the time of sending from the present time. 420 h.-c. yi, c.-j. an, j.-y. choi figure 4: structure of wncs hardware platform. then, the calculated transmission delay is used to generate the next time-step controller output as the estimated delay of d(m) in (8). this basic operation principle is depicted in fig. 3 in the level of transmission packets. in order to implement and verify the designed controller and the delay-measurement method, we construct the wncs hardware platform, as depicted in fig. 4, where the rotary inverted pendulum is chosen as the physical plant to be controlled. the rotary inverted pendulum is well known to be nonlinear and unstable, and it is commonly accepted as a test plant to demonstrate the stability and performance of designed controllers [4]. as shown in fig. 4, the pendulum is connected through a data interface board to an intel i7-6700 3.4 ghz desktop pc (control pc), which acquires the pendulum position data, computes the control input, and transmits the control input to another intel i5-3570 3.4 ghz desktop pc (relay pc) over a wi-fi network. the relay pc retransmits received packets from the control pc to the control pc over the wi-fi network, which results in the time-varying delay in the wncs. the delay-measurement method is implemented on the control pc such that the sending time is added to each sending packet, and the received time of each packet from the relay pc is recorded. then, by subtracting the sending time from the received time, the current network delay is obtained and used as an estimate of d(m) in (8). 5 experiment 5.1 implementation of designed controller as mentioned in chapter 4, we choose the rotary inverted pendulum as the physical plant; fig. 5 shows the photograph and schematic diagram of the pendulum’s movement, and the actual parameters of the pendulum are listed in table 1. in order to apply the designed controller (8) to the pendulum over the wi-fi network, we first linearize the continuous-time nonlinear model of the pendulum. then, we convert the continuoustime linear model into the discrete-time linear model because the controller is designed based on a discrete-time linear system. we compute the linearized equations using the taylor series expansion, and the discretized equations using the zero-order hold discretization method. based on the parameters in table 1 and the sampling period of 2ms, we obtain the following discretecompensation of time-varying delay in networked control system over wi-fi network 421 (a) photograph of rotary inverted pendulum pendulum rotary arm load gear : rotary arm position : pendulum position load gear (b) schematic diagram and coordinate system figure 5: rotary inverted pendulum. table 1: parameters of rotary inverted pendulum. parameters description value lr the length of the rotary arm 21.6cm jr the moment of inertia of the rotary arm 9.98 × 10−4kg.m2 mr the mass of the rotary arm 0.257kg lp the length of the pendulum 33.7cm jp the moment of inertia of the pendulum 0.0012kg.m2 mp the mass of the pendulum 0.127kg ηg the efficiency of the gearbox 0.90(±10%) kg the total gear ratio of the high-gear 70 ηm the efficiency of the motor 0.69(±5%) kt the current-torque constant of the motor 7.68 × 10−3n −m/a km the back-emf constant of the motor 7.68 × 10−3v/(rad/s) rm the armature resistance of the motor 2.6ω(±12%) bp the viscous damping coefficient of the pendulum 0.0024nm(s/rad) br the viscous friction torque of the pendulum 0.0024nm(s/rad) 422 h.-c. yi, c.-j. an, j.-y. choi time linear model for the rotary inverted pendulum system. x(k + 1) =   1 0.0002 0.0019 0 0 1.0002 −0.0001 0.0020 0 0.1554 0.9125 −0.0016 0 0.2368 −0.0842 0.9975  x(k) +   0.0002 0.0002 0.1594 0.1533  u(k) y(k) = [ 1 0 0 0 0 1 0 0 ] x(k). (9) where the state vector x(k) and output vector y(k) consist of the rotary arm position φ, the pendulum position θ, and their velocities as x(k) = [ φ(k) θ(k) φ̇(k) θ̇(k) ]t y(k) = [ φ(k) θ(k) ]t , (10) and the input, u(k), is the motor input voltage. now, we are ready to construct the designed controller (8). we obtain the state feedback gain by applying the lqr method to the plant model (9) using the following parameters in the cost function (2): q =   30 0 0 0 0 30 0 0 0 0 0 0 0 0 0 0   and r = 1, (11) which yields the state feedback gain as k = [ 5.3456 −24.8038 2.9490 −3.3639 ] , (12) which assigns the eigenvalues of (a−bk) to {0.9114, 0.9699, 0.9902 + 0.0039j, 0.9902−0.0039j} and makes (a−bk) schur stable. with respect to the observer, the eigenvalues of (a−lc) must be selected much faster than the eigenvalues of (a−bk). considering the eigenvalues of (a−bk) from the state feedback, we select the desired eigenvalues of (a − lc) as {−0.2000,−0.2100,−0.2200,−0.2300}, which yields the following observer gain: l =   2.3441 −0.0100 −0.0955 2.4262 666.0953 −6.2360 −77.0342 735.4454   . (13) using (9), (11), and (13), we can implement the predictor-based feedback controller as u(k) = −k  ad(m)x̂(k) + k−1∑ j=k−d(m) ak−j−1bu(j)   . (14) compensation of time-varying delay in networked control system over wi-fi network 423 figure 6: wncss in the second and the third case. 0 20 40 60 80 100 120 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 time (s) w i− f i d e la y (s ) figure 7: time-varying delay in wi-fi network. 424 h.-c. yi, c.-j. an, j.-y. choi 0 20 40 60 80 100 120 −0.5 0 0.5 time (s) φ ( ra d ) 0 20 40 60 80 100 120 −0.1 −0.05 0 0.05 0.1 time (s) θ ( ra d ) 0 20 40 60 80 100 120 −5 0 5 time (s) u ( v ) (a) without the state predictor 0 20 40 60 80 100 120 −0.5 0 0.5 time (s) φ ( ra d ) 0 20 40 60 80 100 120 −0.1 −0.05 0 0.05 0.1 time (s) θ ( ra d ) 0 20 40 60 80 100 120 −5 0 5 time (s) u ( v ) (b) with the state predictor using the constant delay 0 20 40 60 80 100 120 −0.5 0 0.5 time (s) φ ( ra d ) 0 20 40 60 80 100 120 −0.1 −0.05 0 0.05 0.1 time (s) θ ( ra d ) 0 20 40 60 80 100 120 −5 0 5 time (s) u ( v ) (c) with the state predictor using the time-varying delay figure 8: response of rotary inverted pendulum (φ is the rotary arm position, θ is the pendulum position, and u is the input voltage) compensation of time-varying delay in networked control system over wi-fi network 425 time-varying figure 9: rms values of state and input variables. 5.2 discussion of experiment results in our experiments, we consider three cases. the first case is the wncs only with the state feedback (12) and the observer (13) without the state predictor (14). the experimental result of the first case is shown in fig. 8(a), which demonstrates that the pendulum cannot be controlled because it falls down at around 6s. this is because the time-varying delay is not compensated and destroys the stability. in order to verify the stability and performance of the designed controller for wncss over wi-fi networks, we conduct several extensive experiments on the hardware platform constructed in chapter 4. a detailed configuration of the experimental set-up is depicted in the block diagram of fig. 6. before presenting the experiment results for the controller performance, we discuss the true extent of time-varying delays that occurred in the wncs by measuring the real delays in wi-fi networks for a 120s period with a 2ms sampling rate. the measurement delays are shown in fig. 7 and it is obvious that the delays in wi-fi networks fluctuate extensively and are relatively large, and they must be compensated to realize stable operation of the wncs. the maximum, average, and minimum delays in fig. 7 are 260.5ms, 14.1ms, and 1.3ms, respectively. the second case is the wncs with the state feedback (12), the observer (13), and the state predictor (14), but the time-varying delay is approximated to a constant delay such that d(m) is selected as a constant in (14). that is, the state predictor (14) with a constant d(m) is applied to the wncs with time-varying delays, and fig. 8(b) shows the experimental result when d(m) = 10, which demonstrates that the pendulum is adequately controlled, and is kept in the upright position with small oscillations of the state variables, with only a constant-delay compensation. however, we observe that when we conduct the same experiments with various constant-delay values, only the delay within the range of 5 ≤ d(m) ≤ 41 can stabilize and control the pendulum kept in the upright position. the third case is the wncs with the state feedback (12), the observer (13), and the state predictor (14) using the measured time-varying delay, d(m). fig. 8(c) shows the experimental result for the third case, which demonstrates that the pendulum is adequately controlled, and is kept in the upright position with small oscillations of the state variables, as expected. 426 h.-c. yi, c.-j. an, j.-y. choi from fig. 8(b) and 8(c), we observe that the amplitudes in fig. 8(b) are larger than those in fig. 8(c) for both state and input variables, which demonstrates that the time-varying delay compensation exhibits better performance than the constant-delay compensation. in order to highlight the better performance quantitatively, in fig. 9, we display the root mean square (rms) values of state and input variables for both the time-varying delay compensation and several constant-delay compensations that are computed for the experiment interval ranging from 0s to 120s. considering the performance in terms of both the state and input variables, fig. 9 demonstrates that the time-varying delay compensation achieves the best performance. 6 conclusion we designed a state predictor-based output feedback controller for ncss over wi-fi networks, and we constructed the wncs hardware platform for many different experiments. in order to design a feedback controller to compensate for time-varying network delays, we proposed a three-step design process. we designed a state feedback controller based on the lqr theory, the observer estimates the full-state of the system, and the predictor predicts the future state for the plant input. we acquired the time-varying network delays in real time by measuring the time difference between sending and received control data packets. we used the measured delays for the designed controller in real time, which allows the controller to precisely compensate time-varying delays. a distinct feature compared to other studies involving wnscs is that we verified the stability and performance of wncss even by considering the time-varying delays in wi-fi networks and conducting real experiments on the wncs hardware platform. the designed controller can be applied to a specific class of wncss over wi-fi networks, where only the controller output is connected to the plant input via wi-fi networks. this limitation provides the motivation for carrying out a challenging future study, in which we aim to extend the results obtained in this paper to realize the control of more general wncss over wi-fi networks, where the plant input and output are connected to the controller output and input, respectively, through wi-fi networks. in this case, 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(2015); robust time delay compensation in a wireless motion control system with double disturbance observers, 2015 american control conference(acc), isbn 978-1-4799-8685-9, 5294-5299, 2015. int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 273-284 a general approach for minimizing the maximum interference of a wireless ad-hoc network in plane v. haghighatdoost, m. espandar vahid haghighatdoost, maryam espandar computer and electrical engineering department, shahed university, opposite holy shrine of imam khomeini, khalij fars expressway, tehran, iran. {haghighatdoost, espandar}@shahed.ac.ir abstract: the interference reduction is one of the most important problems in the field of wireless sensor networks. wireless sensor network elements are small mobile receiver and transmitters. the energy of processor and other components of each device is supplied by a small battery with restricted energy. one of the meanings that play an important role in energy consumption is the interference of signals. the interference of messages through a wireless network, results in message failing and transmitter should resend its message, thus the interference directly affect on the energy consumption of transmitter. this paper presents an algorithm which suggests the best subgraph for the input distribution of the nodes in the plane how the maximum interference of the proposed graph has the minimum value. the input of the application is the complete network graph, which means we know the cost of each link in the network graph. without any lose of generality the euclidean distance could be used as the weight of each link. the links are arranged and ranked according to their weights, in an iterative process the link which imposition minimum increase on the network interference with some extra conditions which is proposed in future sections, is added to resulting topology and is eliminated from list until all nodes are connected together. experimental results show the efficiency of proposed algorithm not only for one dimensional known distribution like exponential node chain, but also for two dimensional distributions like two exponential node chains and α-spiral node chains. keywords: wireless ad-hoc network, sensor network, interference, spanning tree. 1 introduction wireless ad-hoc networks consist of mobile nodes equipped with, among other components, a processor, some memory, a wireless radio, and a power source. due to physical constraints, nodes are primarily powered by a weak battery, so energy is a scarce resource in wireless ad-hoc networks. in a general way, topology control can be considered as the task of, given a network communication graph, constructing a subgraph with certain desired properties while minimizing energy consumption. the subgraph needs to meet some requirements, the minimum requirement being to maintain connectivity and it should be a spanner of all nodes in original graph; additionally, symmetric links are desired as they permit simpler higher-layer protocols [1]. one of the foremost approaches to achieve substantial energy conservation is by minimizing interference between network nodes. the concept of topology control restricts interference by reducing the transmission power levels at the network nodes and cutting off long-range connections in a coordinated way. at the same time transmission power reduction has to proceed in such a way copyright c⃝ 2006-2012 by ccc publications 274 v. haghighatdoost, m. espandar figure 1: the interference model of a graph with 5 vertexes that the resulting topology preserves connectivity. some other works focused on topology control algorithms emphasizing locality while exhibiting more and more desirable properties [2–4], sometimes presenting distributed algorithms that optimize various design goals concurrently. all these approaches have in common, however, that they address interference reduction only implicitly. the intuition was that a low minimizing the maximum degree of nodes of graph would solve the interference issue automatically. as depicted in [1] this intuition was proved wrong in [5], starting a new thread that explicitly studies interference reduction in the context of topology control [6–8]. the general interference model introduced in [9], proposes a natural way to define interference in ad-hoc networks. the general question is: how can one connect the nodes such that as few nodes as possible disturb each other? in the following, we discuss the network and interference model presented in [9]. so far, not many results have been published in the context of explicit interference minimization. for networks restricted to one dimension the authors in [9] present a 4 √ n-approximation of the optimal connectivity preserving topology that minimizes the maximum interference. for the two dimensional case, the authors in [10] propose an algorithm that bounds the maximum interference to o( √ n). if average interference of a graph is considered, there is an asymptotically optimal algorithm achieving an approximation ratio of o(logn) [11]. kevin buchin in [12] proved that this problem is np-complete. in the following sections the proposed algorithm is explained briefly. the time complexity of producing the spanning tree with minimum interference is o(n5). 2 interference model of network the network is modeled as a geometric graph g = (v,e). as mentioned in previous section the links between nodes are symmetric and have not direction, it means that a message sent over a link can be acknowledged by sending a corresponding message over the same link in the opposite direction. so the matrix e is symmetric. let nu denote the set of all neighbors of a node u ∈ v in the resulting topology. then, each node u features a value ru defined as the distance from u to its farthest neighbor. more precisely ru = maxv∈nu{|u − v|}, where |u − v| denotes the euclidean distance between nodes u and v. since we assume the nodes to use omnidirectional antennas, d(u,ru) denotes the disk centered at u with radius ru covering all nodes that are possibly affected by message transmission of u to one of its neighbors. then the interference of a node v is defined as the number of other nodes that potentially affect message reception at node v. definition 1. given a graph g = (v,e), the interference of a node v ∈ v is defined as: i(v) = ∣∣∣∣{u|u ∈ v �{v},v ∈ d(u,ru)} ∣∣∣∣ (1) a general approach for minimizing the maximum interference of a wireless ad-hoc network in plane 275 a) exponential node chain b) two exponential node chains figure 2: two special node distributions note that even though each node is also covered by its own disk, we do not consider this kind of self-interference. the graph interference is the maximum interference occurring in a graph: definition 2. the interference of a graph g = (v,e) is defined as: i(g) = maxv∈v i(v) (2) as shown in figure 1 the interference of nodes is as follow: node: a b c d e interference: 2 2 2 2 1 according to definition 2 the interference of graph i(g) is 2. 3 the nearest neighbor forest in the first view of the interference problem, one may say the nearest neighbor forest or minimum spanning tree is the best subgraph which results in minimum interference. in this section, it is shown that this is already a substantial mistake, as thus interference becomes asymptotically incomparable with the interference-minimal topology. for some special distribution the nearest neighbor forest results in the worst interference. authors in [13], introduced an instance which seems to yield inherently high interference: the so called exponential node chain is a one-dimensional graph g = (v,e) where the distance between two consecutive nodes grows exponentially from left to right as depicted in figure 2(a). that is, the distance between nodes vi and vi+1 is 2i for i = 0,1,2, ...,n − 1. so as shown in figure 3(a) the nearest neighbor forest results in the interference of ω(n). also in [9] they introduced the two exponential node chains as shown in figure 2(b), on the bottom, there is a horizontal chain of nodes vi with exponentially growing distances, the same as the one dimensional exponential chain, thus distance between vi and vi+1 is 2i. each of these nodes vi has a corresponding node ti vertically displaced by a little more than vi’s distance to its left neighbor, that is, |vi − ti| > di where di = |vi − vi−1| = 2i−1. note that the nodes ti also form a (diagonal) exponential node chain. finally, between two of these diagonal nodes ti−1 and ti an additional helper node ci is placed such that |vi −ci| ≥ |vi −ti|. the nearest neighbor forest for this node distribution is shown in figure 3(b). the nearest neighbor forest for the distributions in figure 2 and their disk graph is shown in figure 3. the algorithm proposed in [9] finds a subgraph for the exponential node chain (figure 2(a)) with i(g) ∈ o( √ n). and they proved bellow theorem: 276 v. haghighatdoost, m. espandar a) the nearest neighbor forest and disk graph for exponential b)the nearest neighbor forest and disk graph for chain, i(g) = n − 2 = ω(n) two exponential node chains, i(g) = ⌊ n 3 ⌋ + 2 = ω(n) figure 3: the disk graph and the interference of nearest neighbor forest a) the best spanning tree for the two exponential node b)the disk graph of the tree that shows the constant chains where i(g) always is equal to 3 interference figure 4: suggested topology for two exponential node chains in [9] theorem 1: given an exponential node chain g = (v,e) with n = |v | , √ n is a lower bound for the interference i(g). they also proposed a topology with constant interference for the two exponential node chains which is depicted in figure 4 but there is no algorithmic method which generate automatically similar subgraph. according to the construction of the exponential node chain, only nodes connecting to at least one node to their right increase the v1’s (leftmost node) interference. they called such a node a hub and define it as follows [9]: definition 3. given a connected topology for the exponential node chain g = (v,e). a node vi ∈ v is defined to be a hub in g iff there exists an edge (vi,vj) with i < j. the aexp algorithm which is proposed in [9] for exponential chain is as follows: the algorithm starts with a graph gexp = (v,eexp), where v is the set of nodes in the exponential node chain and eexp is initially the empty set. following the scan-line principle, aexp processes all nodes in the order of their occurrence from left to right. initially, the leftmost node is set to be the current hub h. then, for each node vi, aexp inserts an edge {h,vi} into eexp. this is repeated until i(gexp) increases due to the addition of such an edge. now node vi becomes the current hub and subsequent nodes are connected to vi as long as i(gexp) the overall interference does not increase. figure 5 shows the resulting topology when aexp algorithm is used for the exponential node chain with 17 nodes and i(g)=6. a general approach for minimizing the maximum interference of a wireless ad-hoc network in plane 277 figure 5: the result topology of aexp algorithm for exponential node chain. the interference of the exponential node is bounded by o( √ n). for clarity of representation edges are depicted as arcs and x dimension is shown in logarithmic scale. the interference of each node is wrote under the node position in the next section a new algorithm which is the extension of aexp is proposed for the nodes in the plane. this algorithm finds the best sub graph with minimum interference. the output of proposed algorithm for exponential chain is very similar to aexp’s output with equal complexity. also it shows the subgraph with constant interference for two exponential node chains. 4 proposed algorithm (apln) the following algorithm apln is the extension of aexp for finding a spanning tree with minimum interference of nodes distributed in the plane. the apln the same as aexp is an iterative algorithm which generates the resulting graph in n steps. where n is the number of nodes. in the beginning the result graph has no edge and in each step of algorithm one edge is added to result graph until the connected result graph is generated. suppose gin = (vin,ein) be the input graph, where vin = {v1,v2, ...,vn} is the set of n separate nodes in the plane. (xi,yi) is the coordinate of vi. also ein is the n×n adjacent matrix of gin with einij elements. e in ij is the euclidean distance between i’th and j’th nodes. einij = |vi − vj| = √ (xi − xj)2 + (yi − yj)2 ∀i,j = 1,2, ...,n (3) the steps of generating the spanning tree with minimum interference from gin are as follows: step 1) preparing data: generate the adjacent list of gin’s edges. the adjacent list of edges of a graph is a list of triples (i,j,eij); where i points to i’th vertex and j points to j’th vertex of graph and eij) is the weight of edge (in our problem it is the euclidean distance). gin is a complete graph and the adjacent matrix ein is symmetric so the adjacent list would have n(n−1) 2 elements. step 2) preparing data: sort the elements of adjacent list according to the weight of each edge and call the new list srte1 . thus the elements of the srte are arranged as follow: edgelength(srtei) ≤ edgelength(srtei+1) ∀i = 1,2, ..., n(n − 1) 2 (4) step 3) find start point: find the smallest edge from srte list; set emin = srte1. step 4) initialization: suppose that gpln = (vpln,epln) is the result graph. set vpln = vin and epln contains of only the smallest edge emin = (h,k,ehk) , in other word all elements of epln are valued with zero instead of e pln hk and e pln kh . initialize the maximum interference of result 1srte: obtained from sorted edge list 278 v. haghighatdoost, m. espandar graph maxipln with:   a) vpln = vin b) e pln ij = 0 ∀i,j = 1,2, ...,n c) e pln hk = e pln kh = e in hk d) maxipln = 1 (5) step 5) initialize the active vertexes list: the nodes which are in sub connected graph are named as active vertexes. at the beginning sub connected graph consist of only the smallest edge. the active vertex list av is a 1×n array; iff the node vi be an active vertex the i’th element of av (avi) gets value 1 otherwise its value is 0. av = { 1 if i = h,k 0 if otherwise ∀i = 1,2, ...,n. (6) in other word: 1 2 ... k ... h ... n av = 0 0 0 1 0 1 0 0 (7) where k,h are the corresponding nodes of smallest edge. step 6) check the status: while all nodes of gpln are not connected together repeat steps 7 to 9. step 7) generate candidate edges: according to av list, generate the active edge list (ae). ae is a subset of srte; an edge from srte exist in ae iff only one of its endpoints have value 1 in av . for each srtek ∈ srte if (avi = 1 ⊕ avj = 1) → add srtek to ae wheresrtek = (i,j,eij) (8) the xor symbol (⊕) in the predicate part of relation (8) ensures the intolerance of multi selecting one edge and recursion in final subgraph. according to (4) and (8) we can write: edgelength(aei) ≤ edgelength(aei+1) ∀i = 1,2, ...,size(ae) − 1 (9) step 8) find the best edge: select the edge aem = (p,q,einpq) from ae , which leads to minimum increase on maxipln when is added to gpln. after adding the aem to gpln , update the av and maxir. the algorithm of finding the aem will be explained in next section. the specification of aem is as follow: ∃aem ∈ ae|∀aej ∈ ae → i(gpln,epln ∪ aem) ≤ i(gpln,epln ∪ aej) (10) i(g,e) determines the maximum interference of graph g according to adjacent matrix e. updating the variables is done as follow: (a) suppose aem = (p,q,e in pq) (b) epln = epln ∪ aem means−−−−→ e pln pq = e in pq,e pln qp = e in qp (c) maxipln = i(gpln,epln) (d) avp = 1,avq = 1 (11) go to step 6 for checking the status. step 9) finalizing: the obtained gpln = (vpln,epln) is the spanning tree with minimum interference for the input distribution of nodes vpln in the plane. step 10) finish a general approach for minimizing the maximum interference of a wireless ad-hoc network in plane 279 a) connected component consist of 7 nodes and 3 new vertex with b)three vertexes have maximum interference and 21 possible edge are candidate to joint to connected component their interference is 4. figure 6: the topology of graph after 6 step with i(g)=4 5 find best edge algorithm in step 8 of the apln algorithm we have addressed an algorithm which has found the best edge from ae list. the brief introduction of finding the best edge from ae list is as follow: suppose that the current state of algorithm is a connected sub graph with m vertex and (n−m) disconnect nodes which are shown in figure 6(a) we want to expand the connected sub graph with minimum possible increase of maxi. lemma 1) adding a new vertex to connected sub graph in the worst case result in two unit increase of the current maximum interference. proof) suppose ri determines the distance from vi to its farthest neighbour and di is a disk with radius ri and centered by vi. the disk di shows the domain of i’th transmitter and the nodes inside the di affect by vi. when a new vertex vk is added to the sub graph by connecting to vj, the new disk dk is added to current disks and the radius of dj may increase. thus in the worst case dj and dk will dominate all other vertexes. so the current max-interfered node is affected by two new transmitter signals and for this reason we will have two unit increase of maxi. and in the best case maxi not changes. adding a new edge in the worst case → maxinew = maxiold + 2 (12) figure 6 shows the increase of interference when a new vertex is added to sub graph. the algorithm of finding the best edge is as follows: step 1) as lemma 1 determined, in the worst case we will have 2 unit increase on maximum interference of the graph so repeat the bellow steps for △i = 0,1,2 step 2) for each aei from ae list repeat bellow step 3) set maxinew = i(gpln,epln ∪ aei) step 4) if maxinew is equal to (maxinpln + △i) determine the aei as the best edge and go to step 6, else check for next edge. step 5) set △i = △i + 1 and go to step 2. step 6) finish. 280 v. haghighatdoost, m. espandar figure 7: relation of iteration and complexity of find the best edge for n=100. the complexity of determining the best edge is as follow: complexity = o(3 ∗ length(ae) ∗ o(i(g,e))) = o(3 ∗ m(n − m) ∗ n2) = o(mn3 − m2n2) (13) where m is number of active vertexes and n is the number of total vertexes. the relation (13) shows that in the first iteration (m = 1) the complexity of finding the best edge is o(n3) and in the final step (m = n − 1) it is also o(n3) but in the middle iteration when m = n/2 the complexity is as follow: if(m = n/2) → o(mn3 − m2n2) = o( n4 2 − n4 4 ) = o( n4 4 ) = o(n4) (14) the relation between complexity of determination of best edge and iteration is depicted in figure 7. applying the best edge algorithm for the node distribution which was shown in figure 6 is depicted in figure 8. 6 experimental results at first the proposed algorithm apln is compare with aexp. as depicted in figure 9 the interference of the exponential chain for both algorithms are equal but the topologies are different. for aexp the order of nodes is important but the apln does not have any precondition on the distribution. in following the strength of apln for finding the spanning tree with minimum interference for two exponential node chains and some special similar distributions is depicted. figure 10 depicts the resulting topology if apln is applied to the two exponential node chains.the most important note on this topology is that the apln algorithm does not know this distribution but the resulting topology is the same as best topology which is created by human brain in [9]. for random distribution of nodes in plane the apln algorithm always suggest a better topology with equal or smaller interference value rather than nearest neighbor forest. another test case is α-spiral node chain. in α-spiral node chain which is shown in figure 12, the k’th node is placed in (2k cos(ak),2k sin(ak)). a general approach for minimizing the maximum interference of a wireless ad-hoc network in plane 281 a) the best edge which results in one unit increase on maxi b)two disks are different in this topology rather than is selected to expand the connected sub graph. previous topology, which is shown in figure 6. figure 8: the topology of graph after 7 step with i(g)=5 a) the aexp result for exponential chain with 9 nodes b)the apln result for exponential chain with 9 nodes c) the aexp result for exponential chain with 12 nodes d)the apln result for exponential chain with 12 nodes figure 9: the spanning trees obtained by aexp,apln, algorithms for exponential chain. for clarity of representation edges are depicted as arcs and x dimension is shown in logarithmic scale. the interference of each node is wrote under the node position the results of applying the apln and nearest neighbor forest (amst ) to some α-spiral node chains are depicted in figure 13. 7 conclusion in this paper a general algorithm named apln for finding the spanning tree of separate nodes in the plane has been proposed. the apln algorithm presents an iterative routine for minimizing the maximum interference of the resulting spanning tree. at the beginning the resulting tree has only one edge which is the smallest edge in the input graph, until all input nodes are not connected together, the algorithm adds a new edge to the resulting tree. for adding a new edge to sub graph the best edge which imposes minimum increase on the interference of all nodes from all available edges is selected. in section 6 the experimental result of using apln and aexp for some special distributions are depicted and all of them show the good performance of the proposed algorithm. the apln is a general algorithm for any two dimensional 282 v. haghighatdoost, m. espandar figure 10: constant interference for two exponential node chains obtained by apln algorithm. the interference of each node is wrote beside the node position a) result of amst for random distribution of 20 nodes. b)result of apln for random distribution of 20 nodes c) result of amst for random distribution of 15 nodes d)result of apln for random distribution of 15 nodes figure 11: result of applying nearest neighbor forest and apln algorithms on random distribution of nodes. distribution and it has no limit or special conditions for the input distribution and there is no need to inform it about the input distribution, the algorithm itself finds the best spanning a general approach for minimizing the maximum interference of a wireless ad-hoc network in plane 283 figure 12: α-spiral node chain. a)the apln suggestion for 45-spiral node chain.i(g)=13 b)the amst suggestion for 45-spiral node chain.i(g)=38 c)the apln suggestion for 30-spiral node chain.i(g)=18 d)the amst suggestion for 30-spiral node chain.i(g)=78 figure 13: 45-spiral node chain with 40 and 30-spiral node chain with 80 nodes and proposed topologies with apln and amst . note that for clarity of representation, the k′th node is positioned in (kcos(αk), ksin(αk)) instead of (2kcos(αk), 2ksin(αk)). the interference of each node is wrote beside the node position. tree with minimum interference. in this paper we can not compute the final interference order according to the input size but for some special distributions which are criterions for interference problem the resulting topology is generated and all resulting topologies are satisfactory. for the 284 v. haghighatdoost, m. espandar future work by mathematical relations we will find the order of final topology according to the number of input nodes for the worst case of node distribution. bibliography [1] t. locher, p. von rickenbach, and r. wattenhofer: sensor networks continue to puzzle: selected open problems. in proc. 9th internat. conf. distributed computing and networking (icdcn), 2008 [2] p. santi: topology control in wireless ad hoc and sensor networks. wiley , 2005 [3] x.y. li , w.z. song , w. wan: a unified energy efficient topology for unicast and broadcast. in: proc. of the 11th int. conf. on mobile computing and networking (mobicom), 2005 [4] m. damian, s. pandit, s. pemmaraju: local approximation schemes for topology control. in: proc. of the 25th 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[11] t. moscibroda, r. wattenhofer: minimizing interference in ad hoc and sensor networks. in: proc. of the 3rd acm jointworkshop on foundations of mobile computing (dialmpomc), 2005 [12] k. buchin: minimizing the maximum interference is hard. corr 2008 and sited in arxiv/0802.2134 , 2008 [13] n. clark, c. j. colbourn, d.s. johnson: unit disk graphs. discrete mathematics, 86:165177, 1990 [14] m. heide, c. schindelhauer, k. volbert, m. gruenewald: energy: congestion and dilation in radio networks. in proc. of the 14th annual acm symposium on parallel algorithms and architectures (spaa), pages 230-237, 2002 international journal of computers communications & control issn 1841-9836, 11(6):789-803, december 2016. analytical modelling of a new handover algorithm for improve allocation of resources in highly mobile environments y. kirsal yonal kirsal* electrical and electronic engineering european university of lefke north cyprus, mersin 10, turkey *corresponding author: ykirsal@eul.edu.tr abstract: wireless and mobile communication systems have evolved considerably in recent years. seamless mobility is one of the main challenges facing mobile users in wireless and mobile systems. however, highly mobile users lead to a high number of handover failures and unnecessary handovers due to the limited resources and coverage limitations with a high mobile speed. the traditional handover models are unable to cope with high mobile users in such environments. this paper proposes, an intelligent handover decision approach to minimize the probability of handover failures and unnecessary handovers whilst maximizing the usage of resources in highly mobile environments. the proposed approach is based on modelling the system using a markov chain to enhance the system’s performance in terms of blocking probability, mean queue length and transmission delay. the results are compared with the traditional handover model. simulation is also employed to validate the accuracy of the proposed model. numerical results have shown that the proposed method outperforms the traditional algorithm over a wide range of handover failures and significantly reduced the number of such failures and unnecessary handovers. the results of this study show that quality if service (qos) measures of such systems can be evaluated efficiently and accurately using the proposed analytical model. however, the performance results have also shown that it is still necessary to explore an effective model for operational spaces. in addition, the proposed model can also be adapted to various types of networks considering the high speed of the mobile user and the radius of the network. keywords: analytical modelling, mobility, handover decision algorithm, quality of service (qos), highly mobile environments. 1 introduction with the rapid development and deployment of wireless technologies, next-generation wireless networks are expected to provide seamless mobility and ubiquitous access to the networks [1,2,4]. researchers have focused on the improved quality of service (qos) and performance evaluation of 4g/5g networks in highly mobile environments [6,7,9]. one of the main challenges for seamless mobility in next-generation wireless networks is the availability of the resources in the networks which allow mobile users to roam among heterogeneous environments [4, 9]. mobility between wireless networks may lead to a high number of unnecessary handovers and handover failures when a mobile user is in highly mobile environments [10,11]. the mobile user requires less time to across the coverage area of the network when the speed increases. thus, the mobile user does not have enough time to acquire the network resources to do the handover. in addition, mobile users can join the queue by requesting resources from the system. however, mobile users will never get access to a channel for communication due to the user’s velocity as well as the lack of resources. hence, the queue will have unnecessary handovers by allowing such users to access the system. unnecessary handover occurs when the mobile user’s travelling time within the system is copyright © 2006-2016 by ccc publications 790 y. kirsal less than the handover process from the neighbouring networks to the system. thus, the mobile user leaves the network coverage area before the handover process is executed [2, 9, 10]. this causes network connection breakdown [11] and interrupts the service [10]. unnecessary handover is undesirable because it wastes network resources [7, 10]. on the other hand, if the user’s call holding time is equal to or less than the total time of handover into and out of the system, then handover failure occurs [11]. in this situation, the mobile user does not transmit or receive any data packet to system; however, the mobile user might enter the system just after triggering the handover process. in other words, the system will be available after a short time when the mobile user triggers the handover process. many network characteristics (such as power consumption, received signal strength, and network conditions) affect the on handover process in wireless and mobile systems. most existing decision algorithms have focused on the vertical handover process, such as optimization problems [12], a policy-enabled schemes [13,14], and fuzzy logic [15]. on the other hand, in [16] the main focus is various mathematical models used in vertical handover decisions for heterogeneous networks. however, most previous works consider vertical handover decision algorithms. more recently, the literature has proposed various handover decision mechanisms [1–6] but none of the existing works consider user’s the high mobility in the handover decision procedure. in [1] two-dimensional (2-d) markov queuing models have been constructed to enhance multiple service requirements in the lte network. the proposed models decrease the call blocking rate, especially for handover users. however, the mobility issues are not considered in [1]. the authors in [2] demonstrated their overall approach by describing the vanet testbed in vehicular environments. the results obtained in [2] showed that it is necessary to consider a new handover model based on a probabilistic rather than traditional approach. in addition, proactive queueing approach for handover is also considered in [2], but the parameters (e.g., two channels in the systems) and scenario used for the analysis are rather simple in order to obtain realistic qos results in highly mobile environments. in [4] a simple and robust two-step vertical handover decision algorithm is proposed for wireless and mobile networks. the new call blocking probability of the proposed model is modelled as m/m/bi/bi based on the erlang-b model. however, [5] showed that the erlang-b model is not suitable for the handover schemes of highly mobile environments due to user’s mobility in the system. in addition, as the reserved bandwidth may not be utilized effectively in low handover rates, the traditional reservation-based schemes are not efficient for future networks, especially in 4g vehicular networks [16]. in [17] the time before the vertical handover and the network dwell time are calculated and presented for any network topology. however, both parameters have not been used in previous studies to improve unnecessary handovers and handover failures. in order to achieve seamless handover, when the mobile user has a high speed, it is important to predict the network availability, the time before handover and network dwell time with coverage boundaries. the time before handover is the time after which the handover occurs, and the network dwell time is the time that the mobile user spends in the coverage area of the network. these two parameters are important in order to obtain the best time and place for the handover for mobile users. it is also possible to improve resource allocation in mobile and wireless systems by using these two parameters. traditional handover models have been studied for wireless and mobile systems [1,3–5,8,9] but they lead to the degradation of the qos due to the network’s small coverage area and the velocity of the users, especially in highly mobile environments. hence, the new handover approach is necessary for providing ubiquitous communication in next-generation systems. an analytical modelling and performance evaluation of mobile and wireless system using queueing theory has recently been performed [1–6, 8, 9]. modelling limited resources and enhancing the qos of the systems are the basis of the queuing phenomenon. thus, the queuing theory can be used to model and analyse such problems [1, 7, 9]. therefore, developing an analytical modelling of a new handover algorithm for improve allocation of resources in highly mobile environments 791 analytical model of a new handover approach and resource allocation model for such systems would be the best option for obtaining more efficient qos measurements. this paper develops a new analytical model for handover and resource allocation in highly mobile environments based on the time the mobile user needs to acquire network resources for the handover. the main contribution of the paper can be summarized as follows: • the qos degradation of the traditional approach due to the handover failures and unnecessary handovers can be improved by the proposed algorithm considering call holding time, mobile user dwell time and time before handover. • the performance improvement of high mobile users and management of the high mobile users within the cell and/or between neighbouring cells can be based on the acceptance factor. • the results show that the proposed method gives better performance results than the traditional approach. however, a statistical model is still necessary to predict the degree of contention in highly mobile environments. the main purpose of this paper is to develop a useful analytical model based on time before handover, call holding time and network dwell time by using a new decision algorithm to improve the qos of real networks. the analysis done in this paper is based on modelling the system using a markov chain to enhance the system performance in terms of blocking probability, mean queue length and transmission delay. this proposed model is applicable for most wireless communication systems. the rest of the paper is organised as follows: section ii presents the traditional handover approach. section iii describes the proposed handover approach. section iv discusses the performance evaluation of the proposed algorithm with the traditional approach and simulation results. finally, section v provides the conclusions and future works. 2 the traditional approach this section explains and represents the traditional handover approach for wireless and mobile environments. figure 1 shows the handover process in such environments. the traditional handover approach introduces two types of threshold circles in the coverage area of the system [3,8,9,17]. the handover threshold and the exit threshold circles are shown in figure 1 for the traditional handover approach. figure 1: the traditional handover approach with threshold circles based on the traditional approach, the cell can be divided into different regions depending on the radius. the continuous circle with radius r2 represents the handover threshold circle. in addition, the dotted circle with r1 represents the exit threshold circle as shown in figure 1. the exit threshold circle is the starting point for the the handover process in the traditional handover 792 y. kirsal approach. in order to process successful mobility, mobile users have to finish the handover before reaching the classic handover threshold circle. if the mobile user is not handed over successfully before the circle, the mobile user will lose the connection. the traditional handover approach is currently being used in [1,3–5,8,9] and [17] for wireless and mobile systems. 2.1 traditional handover queueing model in mobile and wireless communication networks, the service is provided by the base station (bs) and/or access point(ap) depending on the network. mobile users communicate via radio links with bss/aps [3,5]. a single and arbitrary shape of cell is assumed. there are s channels that the system can provide for the service. in addition, the length of the queue is q. the maximum number of calls allowed into the system is a combination of the number of users being served (s) and the number of users in the queue (q). hence, the maximum number of calls in the system is given by l where l = s + q. the traditional handover queueing model is given in figure 2 [3,8,9]. figure 2: traditional handover queueing model the originating calls and handover calls are two kinds of arrival rates defined in the system, with mean arrival rates given as λo and λh, respectively. λo is newly generated calls in the system and λh is the mobile calls from one cell to another. if the channels are available and idle in the system, both call’s arrival can be assigned to any channel. otherwise, the incoming call request is added to the queue if the channels are busy [3,8,9]. in addition, if the queue is full, the incoming call is blocked. the channel requests in the system are served by first in first out (fifo) rule. the inter-arrival times of the incoming call requests are assumed to follow an exponential distribution. λ is defined as the total arrival rate of calls in the cell, where λ = λo + λh. in traditional handover, the mobile users are moving at a velocity v, and there is a probability that it can also leave the network when being served due to the mobility. moreover, a mobile user is placed in the queue waiting for the channel to be served. however, the mobile user can leave the system due to the mobility shown in figure 2. a formula is given in [3] and [9] for λh. in traditional handover, tc is call holding time in the system. an exponentially distributed tc with a mean rate of µc is assumed. in addition, tdwell is the dwell time, indicating the time that mobile users spend in the cell. this is also assumed to be exponentially distributed with a mean rate of µdwell. the equation 1 is used in the literature for the dwell time in wireless and mobile systems [3, 8, 9] for traditional handover queuing model. thus, µdwell can be calculated and described as follows: µdwell = e[v ] ·l π ·a (1) where e[v ] is the average of the random variable, v is the speed of mobile users, l is the length of the perimeter of cell (a cell with an arbitrary shape is assumed), and a is the area of the analytical modelling of a new handover algorithm for improve allocation of resources in highly mobile environments 793 cell [3,8,9]. the total channel holding time of a call is exponentially distributed with mean 1/µ where, µ = µc +µdwell. the state transition diagram of the traditional handover queueing model is shown in figure 3. the states are defined as i (i=0,1,2,· · · ,s+q) the number of calls in the system at time t. figure 3: the state diagram of the traditional handover queueing model ρ is the traffic intensity in the system, where ρ=λ/µ. assuming a system in a steady state, the state probabilities, pi’s, can be obtained as in equation 2 [3,8,9]. pi =   (λo+λh) i i! ·p0 0 ≤ i ≤ s (λo+λh) s s! ·(λo+λh)i−s·p0 i∏ j=s+1 [sµ+(j−s)µdwell] s < i ≤ s + q (2) in equation 2, pi is the probability that there are i calls in the system. p0 can be defined as follows: p0 =   s∑ i=0 (λo + λh) i i! + s+q∑ i=s+1 (λo+λh) s s! · (λo + λh)i−s i∏ j=s+1 [sµ + (j −s) µdwell]   −1 (3) once all the steady state probabilities pi are computed, the rest of the performance measures can be easily obtained. more information about the traditional handover queueing model can be found in [3,8,9]. 3 the proposed approach this section presents an abstract intelligent handover algorithm for high mobile environments applying queuing theory. the accurate knowledge of network availability, coverage boundaries (radius of the cell) and the velocity of mobile users are fundamental factors that play an important role in correct decision making during the handover. hence, in the proposed model, the proposed algorithm determines the time that a mobile user needs before performing a handover. as mentioned in the previous sections, the proposed scheme is based on the current time tcurrent, network dwell time tdwell and estimated time before handover testimated of the mobile users. in order to reduce the number of unnecessary handovers and handover failures, the proposed algorithm determines the required time for the mobile user whether, admitting it into the system or performing a handover as shown in figure 4. assuming a mobile user is moving at a velocity v towards the system at tcurrent, the user can request a channel for communication. the user 794 y. kirsal needs a channel at testimated and releases the channel at (testimated + tdwell). based on the call holding time (tc) of the calls, three possible conditions are proposed and analysed in figure 4. hence, the proposed approach can improve the resource allocation, especially in highly mobile environments. • first condition: (testimated + tdwell)n−1 < (testimated)n if the channel needs time of the current user (n) is higher than the channel release time of the user being served (n-1), then the mobile user can enter the system seamlessly. this shows that the mobile user has enough time to get a place in the system to be served. in other words, unnecessary handovers (tcurrent + tdwell) < (testimated) and partial handovers tc ≤ (tdwell + testimated) do not occur. • second condition: (testimated)n−1 < (testimated)n and (testimated + tdwell)n−1 < (testimated + tdwell)n for the second condition, the users are currently using the channels or waiting in the queue to be served. if the channels’ release time of the users and/or waiting time in the queue (n-1) are higher than the channel release time of the current user (n), then the system will be partially busy by the time the current user reaches the system. this means that the current user might be admitted to the system after a short time. in other words, the system will be available soon for the service after the current user requests a channel. hence, there is a partial contention tc ≤ (tdwell + testimated) in the system. • third condition: (testimated)n−1 < (testimated)n and (testimated + tdwell)n−1 > (testimated + tdwell)n if the channel release time of the users being served (n-1) is greater than the channel release time of the current user (n), then the system will be busy during the travel of the current user. hence, the current user will never get access to the system. the channels and queue will no longer be available and the mobile user will be handed over to another network. thus, the mobile user leaves the network coverage area before the handover process is executed (tcurrent + tdwell) < (testimated) [11]. this causes a network connection breakdown [11] and interrupts the service [10]. in summary, in the event of the third condition, current mobile users will never join the system. the unnecessary handovers occur due to the high speed of the user as well as the radius of the network. thus, the proposed algorithm passes the mobile user to the next available network via the acceptance factors. when the first condition is identified, the system (channels plus queue) can be used by the mobile user. in addition, when the second condition is identified and notified before the current user reaches the system, the contention can be signalled and the mobile user might be passed to other available networks nearby instead of waiting for the service. this approach should result in better network performance. 3.1 the proposed handover queuing model in the proposed approach, the decision algorithm decides whether the mobile user will be admitted to the system based on the analysis described above section (see figure 4). it is clearly seen that the proposed algorithm ensures that mobile users do not wait and leave the system unserved because of mobility. in other words, all mobile users will be allowed into the system depending on the analysis. otherwise, the mobile user at a high speed towards the system will not have enough time to enter the system. hence, the mobile users move at a high speed will be handed over to the another available network. thus, mobile users do not wait long and leave the system without service. the proposed handover queueing model is shown in figure 5. analytical modelling of a new handover algorithm for improve allocation of resources in highly mobile environments 795 figure 4: the proposed handover decision algorithm the proposed handover queueing model (similar to the traditional handover) considers s number of channels and can allow i requests at time t as shown in figure 5. the queueing capacity of the system is q. the arriving requests may be sent from different users to the 796 y. kirsal figure 5: the proposed handover queueing model system. hence, the inter-arrival time of consecutive requests follows the poisson process which can be distributed as an exponential distribution with arrival rate λ. according to [3], for a two-dimensional fluid model, the arrival rate of handover calls can be obtained as follows: λh ≈ µdwell µc λo (4) the decision algorithm distinguishes the calls (λo/λh) and decides to send them into the system depending on the acceptance factors. α and β are the acceptance factors of the originating calls and handover calls, respectively. for the purpose of the proposed analytical model, α and β are taken as constant. it is assumed that the originating calls can join the system with an arrival rate of λo (1-α). similarly, the handover calls can join the system with an arrival rate of λh (1-β). hence, the total arrival rate is λ = λo(1−α)+λh (1-β). as the requests are rejected from entering the system, especially into the queue, the queue can be treated as a normal queuing system. hence, the service rate is µ = µc + µdwell. figure 6: the state diagram of the proposed handover model it is clearly seen that the m/m/c/k queuing model is fit for the proposed model for the performance evaluation. thus, the proposed system can be illustrated by the given one-dimensional markov chain as shown in figure 6. let’s define the states i (i=0,1,2,· · · ,s+q) as the number of calls in the system at time t. the arrival rate can be taken as constant for all requests regardless of the number of users in the system. hence, the arrival rate is the birth rate in the proposed model and can be obtained as [λo (1-α) + λh (1-β)]. in contrast, the rate of service completions in the proposed scheme depends on the number of calls in the system based on the analysis. if there are s or more requests in the system, then all s channels are busy. as each channel services users at the rate µc + µdwell, the combined service rate for the system is s(µc + µdwell). if there are fewer than s requests in the system, i < s, only i of the s channels are busy and the combined service rate for the system is i(µc + µdwell), as shown in figure 6. hence µi can be calculated as follows: µi =   i(µc + µdwell) 0 ≤ i < s s(µc + µdwell) s ≤ i ≤ s + q (5) analytical modelling of a new handover algorithm for improve allocation of resources in highly mobile environments 797 assuming the system is in a steady state, then using the well-known birth and death process, the steady state probabilities pi can be obtained and are given in equation 6: pi =   [λo(1−α)+λh(1−β)]i i!(µc+µdwell) i ·p0 0 ≤ n < s [λo(1−α)+λh(1−β)]i si−ss!(µc+µdwell) i ·p0 s ≤ i ≤ s + q (6) in order to find p0 the normalization condition used since the probabilities must sum to 1, which gives: p0 = [ s−1∑ i=0 [λo(1 −α) + λh(1 −β)]i i!(µc + µdwell) i + s+q∑ i=s [λo(1 −α) + λh(1 −β)]i si−ss!(µc + µdwell)i ]−1 (7) the average number of packets in the system, mql can then be calculated as mql = s+q∑ i=0 i ·pi which gives: mql = [ s−1∑ i=0 i [λo(1 −α) + λh(1 −β)]i i!(µc + µdwell) i + s+q∑ i=s i [λo(1 −α) + λh(1 −β)]i si−ss!(µc + µdwell)i ] ·p0 (8) similarly, the blocking probability pb can be calculated as: pb = p(s + q) = [λo(1 −α) + λh(1 −β)]s+q sqs!(µc + µdwell) s+q ·p0 (9) in addition, the average queue length lq is: lq = s+q∑ i=s+1 (i−s) ·pi (10) using little’s formula, the mean waiting time of channel requests in the queue can be calculated as follows: e[tw] = lq (1 −pb)[λo(1 −α) + λh(1 −β)] (11) hence, the average value of time of a call in a cell is: e[ts] = mql (1 −pb)[λo(1 −α) + λh(1 −β)] (12) let us define mqlh as the mean number of handovers per user during its lifetime which can be calculated as: mqlh = mqlhe[tw]e[tw] e[ts] (13) therefore, the mean transmission delay of packet is calculated: e[td] = mqlhe[tw] (14) 798 y. kirsal 4 performance evaluation this section presents numerical results in order to show the accuracy and effectiveness of the proposed analytical model of the new handover algorithm to improve resource allocation in highly mobile environments. in addition, the results obtained from the solution of the traditional and proposed approaches are validated by using discrete event simulation (des). the simulation tool is mainly used for the validation of both the traditional and proposed models. as the simulation simulates the actual scenario rather than the markov models presented in this paper, it can also be used for the performance evaluation of such systems. the des developed considers the stochastic processes for all types of mobile users’ arrivals and departures. mobile users’ arrivals and departures occur one at a time in a random, discrete, event-triggered fashion when an arrival enters the system and service is completed, respectively. in addition, the users waiting in the system are served based on first come first serve (fcfs) basis in the order of their arrival. the channel (and/or channels) becomes idle or remains busy with requests stored in the queue when the service event is completed. while a particular event is handled, the next event is generated. the results obtained from the simulation runs are within the 5% confidence interval with a 95% confidence level [9]. the simulation model was adopted for the scenario considered and implemented in c++ language. in order to validate the proposed analytical model, the results obtained from the analytical model and the simulation results for different performance measures are presented and compared. the numerical study focuses on mql, pb and transmission delay of the proposed models. the mean arrival and mean service rates are mainly application dependent. the assumptions in [3, 5, 7, 8] and [9] are employed in this paper as well for consistency, unless stated otherwise. 4.1 key parameters the system parameters used are mainly taken from [3, 5, 7, 8] and [9] based on the relevant literature [1,2,4,6,10–13,16]. the system has a fixed number of identical channels: s=16. q is the queuing capacity which represents the number of packets waiting for service. it is assumed that the moving direction of the mobile users can be detected by the bs/ap using a control channel. in addition, a mixed traffic pattern is also assumed, as in [2] where on average a minimum of 2 slots are 0.5 ms. hence, the rates are translated into packet per second in order to use consistent values. the service rate of the mobile users µdwell is calculated using equation 1. the requests are handed over or rejected from entering the system due to the proposed analysis; thus, the arrival rate is λ = λo(1 −α) + λh(1 −β). however, in this paper α is taken as 0.01 because λh passing through in a unit time with a high speed is larger than λo. in other words, λo calls are assumed to be allocated by the system as they are newly generated in the system. the analysis of α could be explored in future work. 4.2 results the figures 7 and 8 show mql and pb results, respectively, as a function of the originating calls λo in the system. the parameters are s=16, q=50, e[v]=40m/s (144km/hr), r=1000m, e[tc]=120 packets/sec, and α=0.01 and the λo rate per user varies from 0.01 packets per second. the figures clearly show that the proposed approach works far better than the traditional approach. in the traditional approach, due to the high mobile users, most users will leave the system without being served. in addition, the handover calls from the neighbour cells will request channel allocation at the same time, especially for heavy traffic loads (e.g., λo=0.08). this causes an increase in mql as well as the pb of the system. thus, β has an impact on the analytical modelling of a new handover algorithm for improve allocation of resources in highly mobile environments 799 figure 7: mean queue length results as a function of originating calls λo with different β values figure 8: blocking probability results as a function of originating calls λo with different β values system. it is clear from the figures that β significantly affects the system performance. hence, β is an important parameter for the handover management in highly mobile environments. figure 9 shows transmission delay as a function of the originating calls λo with different β values. in wireless and mobile networks, transmission delay is another important qos parameter criterion. it can be clearly seen that transmission delay increases rapidly for the traditional handover when λo increases due to the number of unnecessary handovers allowed in the system. such handovers will leave the system without being served. the system is then busy with unnecessary handovers and the transmission delay increases. it can be observed from the graphs in figures 7, 8 and 9 that the proposed approach gives better qos results in terms of mean calls in the system, blocking probability and transmission delay, respectively, when β increases. this means that, according to the acceptance factor, highly mobile users are handed over to the neighbour cell and/or served without wasting the network resources. table 1 illustrates blocking probability results as a function of queue size. parameters used 800 y. kirsal figure 9: transmission delay as a function of originating calls λo with different β values table 1: blocking probability results as a function of queue size (q) q traditional approach proposed approach, β = 0.2 proposed approach, β = 0.4 proposed approach, β = 0.6 proposed approach, β = 0.8 30 0.089398 0.005699 1.57e-05 3.01e-09 1.35e-14 50 0.0863125 0.001211 6.89e-08 9.82e-14 6.58e-22 70 0.0858134 0.000269 3.03e-10 3.20e-18 3.20e-29 90 0.0857308 0.0000601 1.33e-12 1.04e-22 1.56e-36 for figures 7, 8 and 9 are used for the results presented in table 1 as well. the parameters are as follows: s=16, λo=0.1, e[v]=40m/s, r=1000m, e[tc]=120 packets/secs and α=0.01. the blocking probability decreases slightly in the traditional handover because (especially for a loaded system) highly mobile users make the system busy due to the unnecessary handover as well as the handover failures. however, this is not the case when the proposed algorithm is employed. the blocking probability decreases rapidly when q increases. this means that the proposed approach can handle unnecessary handovers and handover failures. in other words, the proposed approach gives better resource usage than the traditional approach. on the other hand, mql results as a function of originating calls for low mobile users are given in figure 10. the results show that the proposed model performs better than the traditional approach when the system utilisation (u = λ/sµ) is less than 0.72. however, as the velocity decreases, the traditional approach outperforms the proposed approach in some situations, especially for a heavy-loaded system (e.g., u =0.88). this is mainly because at such low velocity no one leaves the system due to the mobility. then, large mql results are experienced in the proposed system when β = 0.1 and 0.3. however, the proposed model gives better results when higher values of β are considered (i.e., β = 0.7). in addition, even at low velocity, most of the mobile users leave the queue without being served in the traditional approach. the numerical results obtained from the proposed model are also validated by the simulation in table 2 and figure 11. the parameters used in table 2 and figure 11 are the same parameters used in figures 7 and 8. table 2 shows the pb results of the traditional and proposed approaches with different β. it is obvious in table 2 that numerical results obtained from the proposed model show agreement with the results obtained from the simulation as the discrepancies are less than 5%. the numerical results show the effectiveness of the proposed model. the mql results for both approaches as a function of λo are shown in figure 11 and validated by simulations. the results of the proposed analytical approach and simulation results show good agreement. the maximum discrepancy between the analytical model and simulation is 3.42% which is well within analytical modelling of a new handover algorithm for improve allocation of resources in highly mobile environments 801 figure 10: mean queue length results as a function of originating calls λo with different β values for low mobile users the 5% confidence interval of the simulation. figure 11: the analytical and simulation mql results of both traditional approach (ta) and proposed approach (pa) as a function of originating calls λo with different β values 802 y. kirsal table 2: validation of pb results as a function of λo for both traditional and proposed approaches. (d is discrepancy). pb,traditional approach pb,proposed approach β= 0.3 pb,proposed approach β =0.5 λo analytical simulation d(%) analytical simulation d(%) analytical simulation d(%) 0.085 0.2471 0.2490 0.78 0.0335 0.0340 1.40 1.20e-12 1.20e-12 0.04 0.09 0.2889 0.2900 0.38 0.0805 0.0810 0.57 2.89e-11 2.89e-11 0.02 0.095 0.3263 0.3180 2.62 0.1282 0.1240 3.42 5.63e-10 5.62e-10 0.05 0.1 0.3600 0.3610 0.28 0.1718 0.1750 1.85 9.06e-09 9.05e-09 0.07 0.105 0.3905 0.3900 0.12 0.2112 0.2130 0.84 1.22e-07 1.22e-07 0.03 0.0.11 0.4182 0.4120 1.50 0.2471 0.2400 2.94 1.39e-06 1.39e-06 0.22 0.115 0.4435 0.4430 0.11 0.2798 0.2790 0.28 1.34e-05 1.35e-05 0.51 0.12 0.4667 0.4660 0.14 0.3098 0.3090 0.26 1.09e-04 1.10e-04 0.91 0.125 0.4880 0.4800 1.67 0.3374 0.3370 0.12 7.23e-04 7.30e-04 0.93 0.13 0.5077 0.5000 1.54 0.3629 0.3620 0.25 3.76e-03 7.70e-03 1.63 0.135 0.5259 0.5250 0.18 0.3865 0.3860 0.13 1.38e-02 1.3e-02 0.23 0.14 0.5429 0.5400 0.53 0.4084 0.4080 0.10 3.433e-02 3.43e-02 0.10 0.145 0.5586 0.5500 1.57 0.4288 0.4300 0.28 6.233e-02 6.23e-02 0.07 0.15 0.5733 0.5700 0.58 0.4478 0.4490 0.26 9.21e-02 9.39e-02 1.96 0.155 0.5871 0.5800 1.22 0.4657 0.4656 0.01 1.21e-01 1.22e-01 0.81 conclusions and future work this paper proposed a new analytical modelling approach and qos management for handovers based on a new handover admission control mechanism in highly mobile environments. the analysis of the handover is an important issue in order to achieve better performance, especially in highly mobile environments. the proposed handover admission control mechanism is useful for achieving better performance in such systems. it offers the perspective of considering the current time tcurrent, network dwell time tdwell and estimated time before handover testimated of the mobile users. the system is modelled as an open queuing network using a markov chain with continuous time to determine the state probabilities. based on the proposed approach developed in this paper, computer simulations are also used to assess the accuracy for the proposed model. the proposed model can be used to analyse qos measures such as mql, pb and transmission delay. the presented examples were kept simple for performance evaluation due to the introductory nature of the proposed model for highly mobile environments. the proposed method successfully reduced the number of handover failures and unnecessary handovers to the system by using the proposed algorithm compared to the traditional approach for highly mobile users. it minimizes the number of handover failures and unnecessary handovers to the system by enhancing the usage of the resources. with this approach, resource allocation can be improved in such systems with highly mobile environments. however, there are still specific operational aspects that need to be explored where the proposed approach can be applied to get the best effect. in addition, considering the availability, modelling the proposed model could be considered for future work. bibliography [1] chen, y.; yang, s.; xu, s.; xue, p.; zhou, x.; (2012); queuing theory based handover resource self-management in lte networks, international conference on wireless communications, networking and mobile computing (wicom):1-4. 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(2009); exploring efficient imperative handover mechanisms for heterogeneous networks, international symposium on emerging ubiquitous and pervasive systems, 286-291. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 482-493 a fast and scalable re-routing algorithm based on shortest path and genetic algorithms j. lee, j. yang jungkyu lee cyram inc., 516 seoul national university research park naksungdae-dong, gwanak-gu, seoul 151-919 koera e-mail: jklee@cyram.com jihoon yang department of computer science, sogang university 1 sinsu-dong, mapo-gu, seoul 121-742 koera e-mail: yangjh@sogang.ac.kr abstract: this paper presents a fast and scalable re-routing algorithm that adapts to dynamically changing networks. the proposed algorithm, dga, integrates dijkstra’s shortest path algorithm with the genetic algorithm. dijkstra’s algorithm is used to define the predecessor array that facilitates the initialization process of the genetic algorithm. then the genetic algorithm keeps finding the best routes with appropriate genetic operators under dynamic traffic situations. experimental results demonstrate that dga produces routes with less traveling time and computational overhead than pure genetic algorithm-based approaches as well as dijkstra’s algorithm in largescale routing problems. keywords: evolutionary algorithm, routing in dynamic networks, car navigation system. 1 introduction the car navigation system has become a very useful tool for many drivers. when a driver turns on a car navigation system and inputs where he or she wants to go, the system searches the map and finds the best route (e.g. shortest path) to the destination. recently, in addition to such a basic functionality, car navigation systems are equipped with real-time traffic information services like tpeg (transport protocol experts group) [1–6]. here, the navigation system is provided with the traffic information on current road conditions, with which it re-computes the best route with minimal expected travel time. unfortunately, such traffic information is not truly real-time but delivered from a central server at certain intervals. in addition, updating the entire map with the new information delivered from the server causes an exorbitant overhead. in this paper, we propose a novel approach to deal with these problems and to produce the best route dynamically. our algorithm integrates dijkstra’s shortest path algorithm [7] with a genetic algorithm [8], and thus named dga. the former is for incorporating useful prior knowledge on the network (e.g. distance between two places) and facilitating the initialization process of the genetic algorithm, and the latter is for finding the best routes. (detailed descriptions on dga will be given in section 3.) dga re-computes the routes quickly whenever new real-time traffic information is available. a car is assumed to send the traffic information (e.g. its speed) to the vehicles it meets during the trip via wireless communication. this direct and local communication among vehicles provides genuine real-time information and obviates the use of the expensive central server. this paper is organized as follows: section 2 briefly introduces a representative genetic algorithmbased approach to the shortest path problem proposed by ahn [9] which will be compared with dga. section 3 describes dga. section 4 presents the results of the experiments designed to evaluate the performance of dga. section 5 concludes with a summary and future research directions. copyright c⃝ 2006-2012 by ccc publications a fast and scalable re-routing algorithm based on shortest path and genetic algorithms 483 2 related work: genetic algorithm for shortest path problem the genetic algorithm (ga) is one of the global search heuristics inspired from biology and has been successfully applied to a variety of optimization problems [8, 10]. a great deal of research on ga-based shortest path search has been carried out in various communication network applications [9, 11–15], among which [9, 14, 15] are related to car navigation systems. ahn’s method [9] is one of the most representative applications of ga to the shortest path routing problem. however, though ahn’s method was able to find a good solution with solid theoretical results, it worked only for moderate-sized networks. in fact, our experiments with the algorithm failed to produce solutions within a reasonable period of time for networks with more than 10,000 nodes. considering real-world networks where there exist huge number of nodes (or places), ahn’s approach is thus far from applicable. there exists another ga-based approach for the car navigation system proposed by kanoh [14, 15]. kanoh’s approach is similar to dga in that it computes routes by considering dynamic road conditions and initializing the population using dijkstra’s algorithm. however, the motivation of their algorithm is quite different from ours and not fully comparable: they use ga for improving the quality of solution in terms of multi-objective criteria (e.g. traveling time, route length, number of signals, number of right turns, etc.), whereas our algorithm focuses on re-routing. in addition, kanoh’s approach was evaluated only with small networks (of less than 20,000 nodes) though the computational overhead was claimed to be low. the main contribution of this paper is the design of an efficient algorithm that can be deployed in a car navigation system and be used frequently for re-routing in a large-scale network only with locallytransmitted traffic information. to the best of our knowledge, there does not exist a ga-based algorithm to the shortest path problem that can cope with dynamic situations in a huge network. since dga has characteristics common with ahn’s ga, we briefly introduce the method here. (see [9] for detailed descriptions.) 2.1 genetic representation a chromosome (representing a candidate solution path) is variable-length and consists of the sequence of positive integers that represent the ids of nodes through which a routing path passes. the gene at the first and the last loci are reserved for the source and the destination nodes, respectively. 2.2 population initialization the chromosomes are initialized randomly. starting from the source, a chromosome encodes a routing path by successively selecting the next node at random among the neighboring nodes that are linked to the current node. note that the chance for generating a valid path is fairly slim due to the randomness, which makes the approach infeasible for large networks, as verified in section 4. 2.3 fitness function the fitness function of the ith chromosome, fi, is defined as fi = ( mi∑ i=1 c(gi( j),gi( j + 1))) −1 (1) where mi is the length of the chromosome, gi( j) represents the gene at the jth locus, and c(gi( j),gi( j+1)) is the link cost between node gi( j) and gi( j + 1). 484 j. lee, j. yang 2.4 selection the tournament selection without replacement is used. in other words, non-overlapping random sets of 2 chromosomes are chosen from the population, and the chromosome with higher fitness was selected from each set to survive in the next generation. 2.5 crossover the concept of crossover is depicted in fig. 1. first, the crossover points are determined by randomly choosing a common gene appearing in both parent chromosomes. then the chromosomes are interchanged with respect to the crossover points and the offsprings are generated. figure 1: the concept of crossover. 2.6 mutation typically, ga performs mutation by changing or flipping the genes in the candidate chromosome, thereby maintaining the genetic diversity. here, the mutation operation attempts to maintain the diversity in the population by modifying the current path represented by a chromosome. for a chromosome, a gene is randomly selected as a mutation point. starting from the mutation point, a sequence of neighboring nodes are randomly chosen to define a complete path (i.e. until the node chosen last is the destination). the concept of mutation is depicted in fig. 2. figure 2: the concept of mutation. 2.7 repair function note that a chromosome produced by the crossover operation may contain a loop in the path it represents, which is an invalid solution. to make the path valid, the repair function was proposed. as shown in fig. 3, the repair function eliminates a loop by finding the intersection (or repeated) node and removing the intermediate nodes in the loop. figure 3: the concept of repair function. for example, assume that the following chromosome is produced: 1,2,3,4,5,6,3,7,8 a fast and scalable re-routing algorithm based on shortest path and genetic algorithms 485 then the repair function finds (and keeps a single occurrence of) the intersection node 3 and removes the intermediate nodes 4, 5 and 6. the resulting chromosome is: 1,2,3,7,8 2.8 the overall algorithm now, ahn’s ga for finding the shortest path is described in algorithm 1. algorithm 1. ahn’s ga 1: initialize the population; 2: repeat until convergence 3: calculate the fitness of individuals in the population; 4: do selection; 5: do crossover; 6: remove loops by repair function; 7: do mutation; 8: end the condition for the convergence of the algorithm is if all chromosomes are identical. 3 dga we describe our algorithm, dga, in this section. the purpose of dga is to adapt to the dynamically changing networks and to re-route the shortest path fast. as aforementioned, dga inherits the characteristics of both dijkstra’s shortest path algorithm and ga. the former is to initialize the population in the latter with meaningful candidate solutions (i.e. paths) instead of random ones. for instance, the chromosomes can be generated based on useful information such as the distance or average vehicle speed between two nodes. among the various data structures used for dijkstra’s algorithm, the overflow bag introduced by cherkassky et al. [16] was adopted in our work. (cherkassky et al. had developed the overflow bag to reduce the memory requirement of dijkstra’s algorithm with the bucket data structure proposed by dial [17].) instead of dijkstra’s algorithm, any single-source shortest path algorithm (e.g. bellman-ford algorithm [18]) can be also used for dga. 3.1 population initialization the random population initialization of ahn’s ga does not work well for large-scale networks since the chance for generating invalid paths becomes very high as explained in section 2. to overcome this problem, dga makes use of dijkstra’s algorithm and produces a predecessor array as described in fig. 4. first, from the start (source) node, the shortest paths to all the other nodes including the goal (destination) node are computed by dijkstra’s algorithm. then, for the shortest path from an arbitrary node a to the goal node, all the links on the path are stored in the form of a (direct) predecessor array pred which is s sequence of nodes constituting the path in a reverse order (i.e. from the goal to a). fig. 4(a) shows an example of pred. once pred is constructed, the shorted path from the goal to a can be easily obtained by a call getpath(pred,goal,a) defined as follows, which makes fast initialization of the population possible: subroutine 1. getpath( pred, x, y) // compute the path from x to y using pred. 1: set current node scur = x and path = [scur]; 2: repeat 486 j. lee, j. yang figure 4: example of reverse graph and predecessor array. 3: scur = pred(scur); 4: path = [path scur]; 5: until (scur = y) 6: return path; let g(n, e) be a directed graph with the set of nodes n and the set of edges e. we define the reverse graph of a directed graph g(n, e) as the graph grev(n, erev) with erev = {(u, v)|(v,u) ∈ e} (2) for example, if we reverse all the edges of g(n, e) in fig. 4(a), we get the reverse graph grev(n, erev) in fig. 4(b), with which we can compute the shortest paths from the goal node to all the other nodes by dijkstra’s algorithm. then we can compute a reverse-pred for grev(n, erev) which is a series of nodes constituting the path from an arbitrary node to the goal node as shown in fig. 4(b). now, if we consider the original graph g(n, e) with reverse-pred computed with resect to the reverse graph grev(n, erev) as in fig. 4(c), we can see that reverse-pred contains pointers to the optimal node to travel from any node in g(n, e) to reach the goal. suppose that an agent travels around the graph to arrive at the destination node. even if the agent deviates from the optimal path, it can eventually reach the destination by following the next node that reverse-pred points. in other words, reverse-pred serves as a guide to the lost or wandering agents in the network. for instance, in fig. 4(c), if an agent on node 0 moves to node 1 which is not on the optimal path, it can adapt to the situation and follow the optimal path from node 1 by consulting reverse-pred. like this, if an agent deviates from the shortest path on any node, it can rectify its plan and follow the optimal path to the goal. in the field of reinforcement learning, such a reverse-pred is called the optimal policy [19]. the optimal policy π∗ : n 7→ n is the mapping from the current node to the next node that is on the optimal path. as a scheme to apply the optimal policy π∗, the ϵ-greedy method is used which picks the best move most of the times but allows a random move with a small probability of ϵ. this can be summarized as a fast and scalable re-routing algorithm based on shortest path and genetic algorithms 487 ϵ-greedy(s,π∗) =  π ∗(s) if ζ < ϵ random move otherwise (3) where π∗(s) is equivalent to reverse-pred(s), s is the current node, and ζ is a random number generated between 0 and 1 to be compared with ϵ. now, the population can be initialized by subroutine 2: subroutine 2. populationinit(π∗, start, goal) 1: for i = 0 to populations ize − 1 2: set scur = start and chromosome[i] = [scur]; 3: repeat 4: scur = ϵ-greedy(scur,π∗); 5: chromosome[i] = [chromosome[i] scur]; 6: until (scur = goal) 7: end 8: return chromosome; there are several advantages in our population initialization method. first, the amount of data needed in a random initialization method (like ahn’s ga) is even larger than in our algorithm since the former requires the information on the overall network topology while the latter only refers to the optimal policies in the predecessor array. therefore, in real-world situations where the size of the network is huge, dga has significantly less overhead than ahn’s ga. second, our initialization method increases the probability of generating valid chromosomes while the probability with random population initialization is inversely, exponentially proportional to the length of the path. this is theoretically proved in claim 1, and experimentally verified by large networks wherein valid chromosomes could not be generated within reasonable time. claim1. let x be a random variable drawn from bernoulli(m) distribution defined as follows: if an agent reaches the destination node in reasonable time by selecting the next node randomly, then x = 1, otherwise x = 0. that is, the probability p(x = 1) is m. the agents is assumed to makes l independent selections of next nodes. we claim that the probability prand (l) of generating a valid path (chromosome) with length l in reasonable time with random population initialization is prand (l) = m l (4) meanwhile, let y be a random variable drawn from bernoulli(1) defined as follows: if an agent reaches the destination node in reasonable time by executing the optimal policy, then y = 1, otherwise y = 0. that is, the probability p(y= 1) is 1. we now claim that the probability pϵ-greedy(l) of generating a valid path with length l in reasonable time with ϵ-greedy selection is pϵ-greedy(l) = m l(1−ϵ) (5) proof: prand (l) = l︷ ︸︸ ︷ p(x = 1) × p(x = 1) × ·· ·× p(x = 1) = l︷ ︸︸ ︷ m × m ×·· ·× m = ml 488 j. lee, j. yang pϵ-greedy(l) = l︷ ︸︸ ︷ p(x = 1) · · · p(x = 1) p(y= 1) · · · p(y= 1)︸ ︷︷ ︸ lϵ = l︷ ︸︸ ︷ m · · ·m · 1 · · ·1︸︷︷︸ lϵ = ml−lϵ = ml(1−ϵ) � for example, let m = 0.995, l = 50, ϵ = 0.5. then, prand (l) = (0.995) 50 = 0.7783 pϵ-greedy(l) = (0.995) 25 = 0.8822 pϵ-greedy(l)/prand (l) = 1.1335 however, if l = 1000, prand (l) = (0.995) 1000 = 0.0067 pϵ-greedy(l) = (0.995) 500 = 0.0816 pϵ-greedy(l)/prand (l) = 12.1791 3.2 fitness function since the purpose of the proposed algorithm is to re-route the shortest path considering dynamic traffic situations, the fitness of each chromosome is based on the traveling time instead of the physical distance between the source and the destination. so we redefine c(x,y) in eq. (1) with the traveling time from node x to node y, and represent the costs as a hash table. we can define the set of all edges comprising the chromosomes as ω= {(yi, j,yi, j+1)|yi, j} (6) where yi, j is the jth gene in the ith chromosome in the population. then the hash table contains the edge (x,y) ∈ ω with its associated cost. this scheme provides fast access of the edge costs, and requires less communication overhead of real-time traffic information only for the edges in ω instead of all the edges in the network. 3.3 selection although the time complexity of tournament selection without replacement used in ahn’s ga is not costly (o(|chromosomes|) where |chromosomes| is the number of chromosomes in the population), it has a problem that good chromosomes can dropout early if they are met with chromosomes with higher fitness values in the tournament. we devised the following selection method to solve the problem. 1. the average fitness of all chromosomes in the population is calculated. 2. the chromosomes with above-average fitness survive in the next generation, and the chromosomes with below-average fitness are weeded out. 3. the deleted chromosomes are replaced by the survived ones at random. each step of the above selection method has time complexity of o(|chromosomes|), which also makes the total complexity of o(|chromosomes|). with asymptotically the same computational overhead, our selection method can overcome the early dropout problem. a fast and scalable re-routing algorithm based on shortest path and genetic algorithms 489 3.4 crossover as described earlier, the crossover operator in ahn’s ga finds all genes that appear in both parent chromosomes and then chooses one of them randomly. let α and β be the lengths of such two chromosomes, respectively. then the time required to find the crossover point is o(αβ). if α and β increase for large networks, the cost for searching the crossover point become expensive. to optimize the process of crossover point search, we use the following subroutine. subroutine 3. searchcrosspoint(x1, x2) // x1, x2 are two chromosomes to crossover. 1: s1 = rand % size(x1); 2: if (s1 == 0) e1 = size(x1) − 1 else e1 = s1 − 1; end 3: s2 = rand % size(x2); 4: if (s2 == 0) e2 = size(x2) − 1 else e2 = s2 − 1; end 5: for i = s1 to e1 6: for j = s2 to e2 7: if (x1[i] == x2[ j]) return i, j; end 8: if ( j == size(x2) − 1) j = 0; end 9: end 10: if (i == size(x1) − 1) i = 0; end 11: end note that s earchcrosspoint(x1, x2) determines the random crossover point of common genes starting from arbitrary positions of two chromosomes x1 and x2, and keeps comparing the genes in a circular way (i.e. after considering the last gene, it starts from the first gene of the chromosome). as soon as the first match occurs, the subroutine returns the genes. otherwise, it repeats the comparisons for all possible pairs of positions. the remaining steps of the crossover operation (i.e. generation of offsprings from the crossover point and application of the repair function) remain the same as ahn’s ga. 3.5 mutation as described in section 3.2, only the edge (x,y) ∈ ω can appear in the chromosomes. if an edge (x′,y′) <ω appears in the chromosomes in a new generation as a result of mutation, the traffic information on (x′,y′) needs to be fetched to compute the shortest path, which causes additional communication. to prevent this overhead, the mutation is omitted in our algorithm. in our preliminary experiments, there was no significant difference in performance (in terms of the path quality and the convergence speed) between two approaches where the mutation was applied or not. 3.6 the overall algorithm dga can be summarized as algorithm 2. algorithm 2. dga 1: construct π∗ (from reverse-pred) and the initial population y (section 3.1). 2: remove loops in chromosomes in y by repair function (section 2.7). 3: construct hash table for edges (x,y) ∈ω (section 3.2). 4: repeat until convergence 5: calculate the fitness of population y (section 3.2). 6: do selection (section 3.3), crossover (section 3.4), and remove loops in y (section 2.7). 7: end 8: return y 490 j. lee, j. yang table 1: parameter settings. network id network size ϵ population size #1 50 0.5 20 #2 100 0.5 20 #3 200 0.5 20 #4 400 0.5 20 #5 800 0.5 20 #6 2000 0.5 20 #7 4000 0.5 20 #8 8000 0.5 20 #9 20000 0.5 30 #10 40000 0.6 30 #11 80000 0.6 40 #12 160000 0.6 40 #13 320000 0.6 40 #14 640000 0.7 40 #15 800000 0.7 40 #16 1000000 0.7 40 #17 1200000 0.7 50 as in ahn’s ga, the condition for the convergence of the algorithm is if all chromosomes are identical. 4 experiments 4.1 setup dga is implemented in c and all the experiments were conducted on intel core2quad processors (2.40ghz clock rate). we generated strongly connected random networks of size (i.e. number of nodes) ranging 50-1,200,000, and the distance dist(i, j) between node i and j is assigned with a random integer in [1-9,999]. there are two parameters in dga: ϵ (of ϵ-greedy strategy) and the population size, except the crossover probability which was set to 1. the lower ϵ and the higher population size we set, the greater the diversity in a population will be. we applied parameter settings for each network as illustrated in table 1. to evaluate the performance of dga under real-time traffic conditions, we also constructed a traffic simulator as follows: 1. a vehicle travels around the networks (generated as in table 1) to arrive at the destination node. 2. whenever a vehicle makes a move from the current to the next node, all the edge costs of the network are changed dynamically by c(i, j) = dist(i, j) vi j , for each node i, j (7) where vi j is velocity of vehicles on the link (road) between node i and j which is drawn from two normal distributions with the same mean but different standard deviations (i.e. n(80km/s, 20km/s) and n(80km/s, 40km/s)) to see the behavior of the algorithms in different situations. 3. a vehicle re-routes the path whenever the edge costs are changed. a fast and scalable re-routing algorithm based on shortest path and genetic algorithms 491 (a) a comparison of average cpu time (low variation) (b) a comparison of average traveling time (low variation) (c) a comparison of average cpu time (high variation) (d) a comparison of average traveling time (high variation) figure 5: simulation results. 4. there are three types of vehicles implementing three algorithms: dijkstra’s algorithm (implemented with the overflow bag structure as in [16]), ahn’s ga, and dga. 5. for 300 randomly generated source-destination node pairs, the performance (in terms of the cpu time and the traveling time) are measured and averaged. 4.2 results the experimental results are shown in fig. ?? where (a), (b) are for the networks with less drastic changes in the velocity of vehicles (i.e. standard deviation of 20km/s), and (c), (d) are with more drastic changes (i.e. standard deviation of 40km/s). the x-axis of the graphs represents the network id of table 1. the y-axis represents the average cpu time in (a) and (c), and the average traveling time in (b) and (d). the results of ahn’s ga for above 20,000 node-sized networks are not included due to the excessive running time. as shown in fig. ??(a) and (c), it is impossible for ahn’s ga to find the path in reasonable time. for networks with less than 40,000 nodes, the average cpu time of dijkstra’s algorithm and dga are similar. however, as the size of the network increase, dga outperforms dijkstra’s algorithm by a large margin. this is because dijkstra’s algorithm computes a new path over the entire nodes for each traffic condition, while dga adjusts the path based on the locally updated traffic condition with the predecessor array. as shown in fig. ??(b) and (d), the quality of the path (i.e. average traveling time) of ahn’s ga is even inferior to other algorithms. fig. ??(b) verifies that dga produces paths as good as the ones produced by dijkstra’s algorithm for less dynamic networks. however, dga outperforms dijkstra’s algorithm for highly dynamic networks as shown in fig. ??(d). this is because dijkstra’s algorithm sticks to the current traffic conditions too much and might make inefficient changes in the path (e.g. 492 j. lee, j. yang detours), while dga makes local adjustments to the current path and produces stable solutions. this verifies the feasibility of dga in real-world car navigation systems where traffic conditions are constantly and possibly drastically changing. 5 conclusion we have presented a fast and scalable re-routing algorithm, dga, that adapts to dynamically changing networks. in addition to the theoretical soundness, the experimental results have also shown the outstanding performance of dga on large networks. dga is a good candidate for intelligent car navigation systems since it is capable of re-routing the optimal path swiftly whenever new traffic information is available. in addition, dga has a significant merit of requiring the minimal traffic information and reducing the communication cost between the car navigation system and the central server, or among the navigation systems in each vehicle. we have not tested dga with real-world maps and traffic information due to the lack of required infrastructures (e.g. communication, information collection). therefore, dga needs to be deployed and fully evaluated when the infrastructures become available. also, dga can be extended to work in the unexplored environment where the agent does not have the global picture on the environment where it belongs. in such an environment, markov decision processes (mdps) [20] and reinforcement learning approaches [19, 21] can be useful to learn the optimal routing policy, as attempted in [22]. in addition, dga can be also extended to consider additional criteria for navigation similar to [15, 23, 24]. some of these research issues are currently in progress. acknowledgments this research was supported by basic science research program through the national research foundation of korea(nrf) funded by the ministry of education, science and technology (2009-0076594) to jihoon yang, the corresponding author. bibliography [1] ebu bpn. 027-1 ňtransport protocol experts group (tpeg) specifications. 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[10] d. e. goldberg. genetic algorithms in search and optimization. addison-wesley, 1989. a fast and scalable re-routing algorithm based on shortest path and genetic algorithms 493 [11] q. zhang and y. w. leung. an orthogonal genetic algorithm for multimedia multicast routing. ieee transactions on evolutionary computation, 3(1):53–62, 1999. [12] f. xiang, l. junzhou, w. jieyi, and g. guanqun. qos routing based on genetic algorithm* 1. computer communications, 22(15-16):1392–1399, 1999. [13] y. leung, g. li, and z. b. xu. a genetic algorithm for the multiple destination routing problems. ieee transactions on evolutionary computation, 2(4):150–161, 1998. [14] h. kanoh. dynamic route planning for car navigation systems using virus genetic algorithms. international journal of knowledge-based and intelligent engineering systems, 11:65–78, 2007. [15] h. kanoh and k. hara. hybrid genetic algorithm for dynamic multi-objective route planning with predicted traffic in a real-world road network. in proceedings of the conference on genetic and evolutionary computation, pages 657–664. acm, 2008. [16] b. v. cherkassky, a. v. goldberg, and t. radzik. shortest paths algorithms: theory and experimental evaluation. mathematical programming, 73(2):129–174, 1996. [17] r. b. dial. algorithm 360: shortest-path forest with topological ordering [h]. communications of the acm, 12(11):632–633, 1969. [18] r. bellman. on a routing problem. quarterly of applied mathematics, 16(1):87–90, 1958. [19] r. s. sutton and a. g. barto. reinforcement learning. mit press, 1998. [20] r. bellman. a markovian decision process. journal of mathematics and mechanics, 6, 1957. [21] l. p. kaelbling, m. l. littman, and a. w. moore. reinforcement learning: a survey. journal of artificial intelligence research, pages 237–285, 1996. [22] j. a. boyan and m. l. littman. packet routing in dynamically changing networks: a reinforcement learning approach. proceedings of the advances in neural information processing systems, pages 671–671, 1994. [23] m. stanojević, m. vujošević, and b. stanojević. number of efficient points in some multiobjective combinatorial optimization problems. international journal of computers, communications & control, 3(suppl.): 497-502, 2008. [24] i. harbaoui dridi, r. kammarti, m. ksouri, and p. borne. multi-objective optimization for the m-pdptw: aggregation method with use of genetic algorithm and lower bounds. international journal of computers, communications & control, 6(2): 246-257, 2011. international journal of computers communications & control issn 1841-9836, 9(4):463-470, august, 2014. a conceptual architecture of ontology based km system for failure mode and effects analysis z. rehman, s. kifor zobia rehman* 1. faculty of engineering and management lucian blaga university of sibiu, romania 2. department of computer science comsats institute of information technology, pakistan *corresponding author: zobia.rehman@gmail.com stefania kifor lucian blaga university of sibiu, romania kifors@yahoo.com abstract: failure mode and effects analysis (fmea) is a systematic method for procedure analyses and risk assessment. it is a structured way to identify potential failure modes of a product or process, probability of their occurrence, and their overall effects. the basic purpose of this analysis is to mitigate the risk and the impact associated to a failure by planning and prioritizing actions to make a product or a process robust to failure. effective manufacturing and improved quality products are the fruits of successful implementation of fmea. during this activity valuable knowledge is generated which turns into product or process quality and efficiency. if this knowledge can be shared and reused then it would be helpful in early identification of failure points and their troubleshooting, and will also help the quality management to get decision support in time. but integration and reuse of this knowledge is difficult because there are number of challenges e.g., unavailability of unified criteria of fmea knowledge, lack of semantic organization, natural language text based description of knowledge, most of the times fmea is started from scratch instead of using existing knowledge that makes it incomplete for larger systems, and above all its success depends on the knowledge which is stored in the brains of perfectionists in the form of experience which may or may not be available anytime anywhere. in this article we are proposing an information and communication technology (ict) based solution to preserve, reuse, and share the valuable knowledge produced during fmea. in proposed system existing knowledge available in repositories and experts head will be gathered and stored in a knowledge base using an ontology, and at the time of need this knowledge base will be inferred to make decisions in order to mitigate the probable risks. ontology based approaches are best suited for the knowledge management systems, in which human experts are required to model and analyze their expertise in order to feed them in a conceptual knowledge base for its preservation and reuse. keywords: knowledge management (km), ontology, failure mode and effects analysis (fmea). 1 introduction knowledge is often defined as "justified true belief". facts, information, and skills acquired by practical experience or education are also called knowledge. knowledge is the information combined with experience, context, interpretation, and reflection. it is a high-value form of information that is ready to apply to decisions and actions [3]. in this era knowledge is the core asset of any organization. it is important for the organizations to know what they know. nowadays sustainability of an organization depends on what it knows, how efficiently it utilizes what it knows, and how quickly it acquires and utilizes new knowledge [3], and in other words success of copyright © 2006-2014 by ccc publications 464 z. rehman, s. kifor any organization relies on the effective management of its knowledge. organizational knowledge can reside in the minds of experts and experienced employs of organizations, files in cabinets, or in huge volumes of databases on servers. since ages human beings and organizations have been creating, acquiring, communicating, and re-utilizing their knowledge. knowledge management can be defined as doing what is needed to get the most out of the knowledge resources [6]. it is also the collection of activities of knowledge gathering, structuring, refinement, and dissemination. nowadays ict is helping to make these activities more effective for organizations. knowledge management systems have been and are being designed and developed to facilitate organizations in managing their knowledge and making it useful in the absence of concerned experts. but knowledge management systems are very difficult to build. it is difficult to find experts who are willing to share their knowledge, and allow experiencing how they solve problems. it is difficult to gather, compile and refine the knowledge across the organization from person to person, documenting the way how decisions are reached, and structuring it to represent on machine. a project risk is defined as an uncertain and undesired event or condition which affects the objectives of a project in positive or negative way [15].risk management is an endeavor to identify and manage internal events and external threats that can affect the likelihood of a project’s success. it identifies risk events, plans how to minimize the consequences or impact of the risk event, lists all anticipated tasks that can be completed in order to avoid undesired events, and builds contingency plans to handle such events after their occurrence. it is a proactive approach to reduce surprises and negative consequences. it provides better control over the future, makes a project manager ready to take the advantage of appropriate risks; and increases the probability of meeting project objectives within budget and time [8]. successful project management is based on effective risk planning and management. risk management helps a project manager to actively handle all expected and unexpected risks on projects. there are various other factors that affect a project’s success but inadequate risk management increases the possibility of project failure. risk management process is based on four major actions; risk identification, risk assessment, risk response development, and risk response control [1]. in risk identification phase all probable risks are considered which can potentially make a process or product fail to achieve its objectives; brain storming, problem identification, and risk profiling are effective approaches for this phase. in risk assessment phase, the value of risk associated to a product or process is determined by calculating the magnitude and probability of an undesired event. there are different tools for risk assessment, e.g. scenario analysis for event probability and impact, risk assessment matrix, fmea, probability analysis; and semi-quantitative scenario analysis. risk response development phase is responsible for deciding an appropriate response for assessed risk. this response could be mitigation, avoidance, transfer, or retention of the risk. to control risk, response strategies are designed and executed, events are monitored, contingency plans are initiated and new risks are identified; all these tasks are completed in risk response control phase. fmea is an effective systematic technique for failure analysis during product life cycle. its purpose is to identify all potential failure modes related to a system, product or process in advance, so that experts can think about their remedies and can take actions to reduce the probability of their occurrence or severity of their impacts [18]. for the very first time fmea was used in the 1960’s in the aerospace industry during the apollo missions. in 1974, the us navy developed mil-std-1629 about the use of fmea. in the late 1970’s, the automotive industry started using fmea and they realized about the advantages of using this tool to reduce risks in order to improve quality of the product [18]. now this inductive reasoning analysis is in practice in almost every field of engineering and technology and especially it has become a preliminary step of quality engineering, reliability engineering, and safety engineering. fmea report outlines the a conceptual architecture of ontology based km system for failure mode and effects analysis 465 findings which have been developed from worksheets. the findings focus on the failure modes which can have significant effects on the system and categorize them as catastrophic, critical, down to minimal, nuisance value, or etc. an fmea covering the complete system includes its constituent fmeas by a reviewing and analyzing the interfaces between the subsystems. during the analysis some failure modes are difficult to assess, so fmea is responsible for the assessment of such failure modes by executing practical tests and generating their results. in time prepared comprehensive fmea provides technical support in identification of weak points in a system, reduces the cost of modification along the product life cycle [2][4].organizations spend a lot of effort and high cost to apply fmea method but still the knowledge they acquire during this analysis is neither reused nor shared. major reason is that the failure modes are not semantically organized. their interpretation varies from person to person and from situation to situation. even there is a possibility that a team prepared an fmea in one situation, might not be able to apply the same to some other situation. also reusing of this information becomes imprecise and unproductive because of its larger size and its production again and again [12]. so an it based solution is needed to store, share, reuse, and infer the knowledge produced by fmea. artificial intelligence (ai) has been supportive in not only storing and manipulating data but also in acquiring, manipulating, and representing knowledge. knowledge based systems based on the concept of ontologies and expert systems have been popular for addressing knowledge management problems. in this article we will focus on the ontology based knowledge management systems which have been formulated for fmea. we also propose a conceptual model of an ontology based knowledge management system for fmea, which we believe is able to solve all above mentioned problems. ontology is an axiomatic theory, designed for a specific domain composed of objects, and is made explicit by means of a formal specific language [21]. 2 related work there are different technologies which have been helpful for applying knowledge, but ai played major role in enabling these systems to work. ai systems are based on the belief that intelligence and knowledge are closely related to each other. knowledge is associated with the human’s capability to manipulate cognitive symbols, whereas human intelligence deals with our ability to learn and communicate our understandings in order to solve problems. human beings are born with a certain degree of intelligence, by using this they learn and acquire new knowledge. there are some ai systems which are known as knowledge based systems and their purpose is to mimic problem solving capabilities of the experts of a specific domain. these intelligent systems and their associated technologies assist us in every phase of knowledge management [6]. four ai techniques have been widely used to build knowledge management systems, which are case based reasoning (cbr), rule based expert systems, fuzzy logic, and ontologies. ontologies are found better than rest of the three, as some disadvantages are associated with rule-based systems (fuzzy or crisp). for example in many situations, a large number of rules are required to represent the domain knowledge, and it could make the inference mechanism quite complex and time consuming, as it adds difficulty in coding, verifying, validating, and maintaining these rules. moreover they do not have any ability to learn [6]. whereas the major disadvantage associated with cbr is that all cases are needed to be stored in case library with additional care about their references and attributes, efficient methods are needed to search the cases and identify their search attributes, and they do not provided well presented information to their users [11]. purpose of ontology development is to provide semantically shared domain knowledge for intelligent inferencing, since ontologies are famous for domains like semantic web, system engineering, bio-medical informatics,software engineering, e-commerce, knowledge engineering, knowl466 z. rehman, s. kifor edge management, artificial intelligence, natural language processing, etc. ontologies play a distinct role in information retrieval and information systems, they better serve as system models and help in storing and retrieving knowledge semantically. ontology engineering is a sub-field of knowledge engineering. its aim is to make knowledge explicit, which is hidden in software applications, business procedures, and in enterprise repositories [16]. while constructing an ontology for any domain, knowledge acquisition is the major bottleneck, so in the knowledge management systems. but there are some automated knowledge acquisition techniques which can be applied e.g., linguistic techniques to acquire knowledge from unstructured text documents, machine learning techniques to acquire knowledge from structured documents, crawling through web, and requesting experts for required knowledge according to a template [14]. some work has been found in which ontologies have been helpful in managing knowledge produced by fmea. authors in [12] applied ontology based knowledge retrieval approach for distributed knowledge sharing and reuse in the pfmea domain. using this strategy the authors were able to construct a complex search patterns, combining many complex roles of pfmea, and connecting them to a particular instance. this system helped them to establish actions with adequate knowledge retrieval and semantic organization. in [5] the authors realized that traditional way (text based) to represent knowledge produced and used during fmea, makes it difficult to reuse. that’s why they developed an ontology, based on the standard iso-15926 to improve the representation of knowledge used and produced during fmea. in [13], authors are trying for lead free soldered, for its accomplishment they need to carry an fmea to find all critical failures associated to this process and their impact on the quality of lead free soldered joints. they suppose that ontology is suitable to store the knowledge produced during fmea as it offers the common understanding of the domain of their focus and its procedures; and it is also a computationally processed model to represent knowledge. authors in [4] believe that fmea is highly effective method to prevent the failure of a system in early phases of its development but a lot of effort is needed to develop an fmea and it is difficult to earn the advantages immediately. to get more advantages from fmea it is needed to shorten its development process by reusing the knowledge gathered in already developed fmea. knowledge produced by fmea is in natural language, that’s why systematized components, functions and failure modes are not explicit and are hard to reuse. most of the times the meanings of such natural language documents, depend on the understanding of the team who prepare these and may not be clearly interpret-able for a different one. authors found that ontology could help to integrate the related knowledge by providing uniform semantic knowledge of a domain for solving a fore mentioned problems. in [7] authors tried to transform scattered knowledge of extended functional model to fmea sheets using ontology. functional model included the knowledge of functional structure and faulty behavior. they developed the ontology by identifying the knowledge models in various forms and mapped the ontology by identifying correspondence relations between the concepts of two ontologies. authors were also trying to transform this knowledge to fta. authors in [20] found that fmea knowledge structure lacks unified standards because of this it cannot be collected during manufacturing process. because of this for many organizations fmea is not an effective and feasible method for improvement in manufacturing process. they proposed a system to extract and convert fmea existing knowledge to develop a systematic, structured ontology-based fmea knowledge management system. this system was helpful for intelligent inquiry and reasoning from fmea knowledge in order to make decisions timely, to handle quality issues in manufacturing process. similarly [4] [9][19]used ontology to smooth the progress of fmea proceedings. they all have done a lot for reusing the knowledge gathered during fmea using ontologies for different industries but still some very important things are missing from the scene. for the very first time [10] presented an approach to bind fmea and ontologies together but this model a conceptual architecture of ontology based km system for failure mode and effects analysis 467 lacks the facility of inference because according to authors ontology is just a conceptual model without having any rules. authors in [4] focused on the deficiencies left by [10] by combining knowledge management and quality management systems to reuse the existing fmea knowledge easily but according to them this work still misses a commonsense ontology to provide the parts of standardized and functional taxonomy. authors in [13] proposed a method for better fmea procedure representation using ontologies for lead free soldering but still his work is under research. moreover no specific ontology is found to address the fmea knowledge sharing and reuse for automotive domain. in this article we are proposing a conceptual architecture for ontology based knowledge management system specifically for automotive domain. 3 proposed conceptual architecture working of knowledge management systems is based on four major actions; knowledge gathering, knowledge organization and structuring, knowledge refinement, and knowledge distribution. figure 1, depicts a conceptual architecture for a knowledge management system and in the following subsections it is described in terms of four major knowledge management actions. 3.1 knowledge gathering knowledge gathering is the set of activities which are responsible for acquisition of the knowledge to be managed. according to the given model it will be accomplished by gathering existing fmea reports completed by experts and also by gathering the rationales and heuristics of the knowledge of domain experts. 3.2 knowledge organization and structuring from information science perspective an ontology is described as formal description of a domain, in terms of instances and relationship between those instances. according to the given model it will describe the basic structure for the instances which will be stored in the knowledge base. knowledge to be structured and stored will be provided to ontology populator after its extraction from existing fmea reports and document files composed on the heuristics and rationales of experts. ontology populator will learn the structure of instances and their relationships from ontology and will populate the knowledge in the knowledge base. to extract knowledge from existing reports some natural language processing techniques will be applied, but depending on the accuracy of the techniques some human intervention would be needed. 3.3 knowledge refinement once the existence knowledge is gathered the next phase is about its refinement. for refinement of this knowledge some experts will be invited to query the system for specific problems of domain. purpose of this querying would be to validate the knowledge in the knowledge base that if it is correct or enough to solve all probable problems expected in the domain. these experts will be authorized to update the knowledge base according to their level of expertise. they can add new instances and also can update the existing ones. 468 z. rehman, s. kifor 3.4 knowledge distribution once the knowledge base is updated with appropriate and refined knowledge, it is ready to assist its users via browser interface. inference engine will depict the rationales of experts and will provide intelligent conclusions after inferring through knowledge base. this conceptual architecture is equally useful in multi-user environment, where number of authenticated users can interact with the system via internet. figure 1: conceptual architecture of ontology based knowledge management system for failure mode and effects analysis (fmea) 4 required tools and techniques collection of fmea reports would be in a formal way by requesting the organizations to provide these. heuristics of experts over fmea can be gathered by interviewing them about a given scenario and cross validating their views. these views can be stored in machine readable documents. once the basic knowledge is gathered, gate (general architecture for text engineering) can be used to extract required knowledge, it is an open source infrastructure to develop and implement software components to process human languages, it helps in keeping data separate from application. we will use protege to create ontology and its instances. protege is an open source software tool to develop domain models and knowledge based systems with ontologies. it provides both of the main ways to model ontologies e.g., frame based and owl. it provides full reasoning support to compute the inferred ontology class hierarchy and for consistency checking. owl-lite can be used to get full support of existing description logic reasoners for inference engine. jena can be used to query the ontology and to update its instances. a conceptual architecture of ontology based km system for failure mode and effects analysis 469 5 conclusion strategic management of any organization revolves around the risk management. good risk management focuses on the identification and treatment of the risk in time, to increase the probability of the success and decrease the probability of the failure in achieving overall objectives of an organization. fmea is a systematic way to identify probable failure modes and their overall effects on a system, if this analysis is timely completed, it helps to reduce the probability of risk occurrence. in this article we proposed a conceptual architecture for ontology based knowledge management system to eliminate the problems regarding poor management of the knowledge produced during fmea, to timely generate fmea reports even in the absence of domain experts, and to reuse and share existing fmea knowledge. ontologies are like repositories to organize formal knowledge and information represented by common vocabulary of a language to support reasoning, they are becoming the core component for many applications e.g., knowledge portals, knowledge management and integration systems, and e-commerce applications. ontologies are helpful in exchange of knowledge, reusing existing knowledge, for reasoning and inferring on existing knowledge [17]. as ontologies use common understanding of concepts of a domain this is why fmea knowledge stored in ontology will be general, comprehensive, machine readable and explicit. in future we will materialize this conceptual model for automotive domain. bibliography [1] carbone, t. a.; tippett, d.; (2004); project risk management using the project risk fmea, engineering management journal, 16, 28-35. [2] chang, k.h.; ching, c.h.; (2010); a risk assessment methodology using intuitionistic, international journal of systems science, 1457–1471. [3] davenport , t. ; prusak, l.; (1998); working knowledge, boston, ma : harvard business school press. [4] dittmann, l. et al (2004); performing fmea using ontologies, 18th international workshop on qualitative reasoning, 209–216. [5] ebrahimipour, v. et al (2010); an ontology approach to support fmea studies, elsevier expert systems with applications, 671-677. [6] fernandez, b.i.; saberwal, r.; (2010); knowledge management systems and processes, m.e. sharpe. [7] koji, y. et al (2005); ontology-based transformation from an extended functional model to fmea, international conference on engineering design, melbourne. [8] larson, e. w.; gray, c. f.; (2011); project management: the managerial process (5th edition ed.), mcgraw hill. [9] laaroussi, a. et al (2007); ontology-aided fmea for construction products, j. ieee press, 189-194. 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[21] zuniga, g.l.; (2001); ontology: its transformation from philosophy to information system, proceedings of international conference on formal ontology, 187-197. international journal of computers communications & control issn 1841-9836, 10(1):123-135, february, 2015. tractable algorithm for robust time-optimal trajectory planning of robotic manipulators under confined torque q. zhang, s.-r. li, j.-x. guo, x.-s. gao qiang zhang, shu-rong li automation department china university of petroleum(east china) e-mail:zhangqiangupc@gmail.com, lishuron@upc.edu.cn jian-xin guo, xiao-shan gao* academy of mathematics and systems science chinese academy of sciences e-mail:guojianxin@amss.ac.cn *corresponding author: xgao@mmrc.iss.ac.cn abstract: in this paper, the problem of time optimal trajectory planning under confined torque and uncertain dynamics and torque parameters along a predefined geometric path is considered. it is shown that the robust optimal solution to such a problem can be obtained by solving a linear program. thus a tractable algorithm is given for robust time-optimal path-tracking control under confined torque. keywords: robust optimal control, time minimum trajectory planning, parameter uncertainties, tractable algorithm. 1 introduction in order to maximize productivity, the problem of minimum time motion planning of robotic manipulators is widely studied. in practical applications, the motion planning problem can be solved by two kinds of methods: the coupled method and the decoupled method [1]. as the name implies, the coupled methods determine the geometric path and the velocity along the path simultaneously. on the other hand, the decoupled methods determine the geometric path first according to certain criteria, and then determine the velocity along the path to minimize the path traversal time. this paper follows the decoupled approach due to the following reasons. simultaneous optimization of the geometric path and the velocity is computationally difficult [2], especially when one wants to find a tractable algorithm. also, in many industrial applications, the geometric path is predefined and does not need to be optimized. many efficient approaches for the time minimum trajectory planning (tmtp) problem along a predefined geometric path have been proposed, including the phase plane analysis approaches [3–6], the pontryagin maximum principle and shooting algorithms [7], the convex optimization [2, 10], the path reshaping [8], and the direct search method [9]. the recent works [2, 11] are significant in which general and efficient methods are proposed. in [2], the second-order cone program is applied to solve the corresponding convex optimization problem. in [11], the sequential linear programming is used to solve the tmtp, which has better computational complexity than that of the phase plane method given in [4]. the above approaches assume that the dynamics model of the robotic manipulator is precisely known and equal to some nominal values. in practice, the dynamic model always has uncertainties. for example, to simplify the solution procedure, the viscous friction is often neglected and the uncertainties caused by the payload variation are often ignored. many robust trajectory planning methods were proposed to handle these uncertainties. one of the commonly used approach is the min-max optimization approach which obtains the time minimum solution in the case of worst disturbances [12–14]. due to its bi-level structure, min-max optimization problems pose copyright © 2006-2015 by ccc publications 124 q. zhang, s.-r. li, j.-x. guo, x.-s. gao challenges for efficient numerical solution in many cases. other interesting approaches include the stochastic optimization approach [15] and the predictive control [16, 17]. in this paper, a tractable offline algorithm is given for the robust tmtp of robotic manipulators under torque limits and bounded dynamic parameter uncertainties and torque disturbances based on the min-max optimization approach. to solve the problem, it is shown that the tmtp problem for robotics under confined torque is equivalent to a linear optimal control problem meaning that both the objective function and the constraints are linear in the control and state variables. when disturbances are added, a robust linear optimal control problem is obtained, which is formulated as a min-max optimization problem. furthermore, it is shown that when the trajectory is approximated with a quadratic b-spline curve, the min-max optimization problem is reduced to a min-max linear optimization problem which in turn can be reduced to a plain linear programming problem and thus is tractable, meaning that the problem can be solved in polynomial time. numerical simulations are used to show that the method gives high precision approximate solutions to the robust tmtp very efficiently. comparing to existing approaches [12–14], the proposed method seems to be the first to convert the robust tmtp for robotics into a linear program. linear program was also used in [12], where the objective function is not equivalent to time-minimality. the rest of the paper is organized as follows. in section 2, the nominal tmtp problem is reduced to a linear control problem. in section 3, methods of parametrization is given. in section 4, a tractable algorithm for the robust tmtp problem is given. in section 5, numerical examples are presented. conclusions are presented in section 6. 2 linear formulation for tmtp under confined torque in this section, we will show that the tmtp problem can be formulated as a linear optimal control problem meaning that the objective function and the constraints of the problem are linear in the control and state variables. consider an n dof robotic manipulator satisfying the following dynamic equation [2] τ = m (q) q̈+q̇t c (q) q̇+g (q) , (1) where τ ∈ rn is the vector of actuator torques, q ∈ rn is the vector of joint angular positions, m (q) ∈ rn×n is the inertia matrix of the manipulator, c (q) ∈ (rn)n×n is a matrix, whose elements are in rn, representing the coefficients of the centrifugal and coriolis forces, and g (q) ∈ rn is the vector of gravitational torques. in this paper, the given path to be tracked in task space is denoted by r (s) = [x(s) ,y (s) ,z (s)] t ,s ∈ [0,1] , (2) and the corresponding joint path q (s) in the joint space is also assumed to be determined by considering task requirement, obstacle avoidance, singularity avoidance, etc. hence, a kinematics map between r (s) and q (s) exists. here the path singularity problem is not considered since it can be avoided by properly adjusting the joint path. following [4], each element of r (s) is assumed to be a piecewise analytic real-valued function and has at least c1 continuity at the connection points. furthermore, r is assumed to be nonsingular, that is, for any s ∈ [0,1] there exists at least one l ∈ {x,y,z} such that dl(s) ds ̸= 0. denote ′ = d ds and ˙ = d dt , where t represents time. then the joint velocity and joint acceleration can be formulated as q̇ = q′ṡ, q̈ = q′s̈+q′′ṡ2. (3) tractable algorithm for robust time-optimal trajectory planning of robotic manipulators under confined torque 125 following (1) and (3), the dynamics model can be written as a linear function in s̈ and ṡ2: τ = m (s) s̈+c (s) ṡ2+g (s) , (4) where m (s) = m (q (s)) q′ (s) ∈ rn,c (s) = m (q (s)) q′′ (s) +q′(s)t c (q (s)) q′ (s) ∈ rn, g (s) = g (q (s)) ∈ rn. the tmtp problem can be described as the following optimal control problem [2]: min b(s) jt = ∫ 1 0 1√ a(s) ds s.t. a′ (s) = 2b(s) , ∀s ∈ [0,1], a(s) > 0, ∀s ∈ (0,1) ,a(0) = a0,a(1) = a1, m (s)b+c (s)a+g (s) = τ (s) , τmin ≤ τ (s) ≤ τmax, (5) where a = ṡ2, b = s̈, τmin and τmax are the bound vectors for the torque, and the boundary values a(0) and a(1) can be obtained from (3) and the boundary values of the joint velocities: q̇ (0) = q̇0, q̇ (1) = q̇f. when b(s) = s̈ is known, ṡ and q(t) can be uniquely determined. problem (5) has the following important property. theorem 1. the optimal solution ṡo(s),s ∈ [0,1] of problem (5) is unique and is maximum at any parameter value. that is, let ṡf(s) be another parameter velocity satisfying the constraints of (5). then, ṡf(s) ≤ ṡo(s) for all s ∈ [0,1]. theorem 1 follows from the proof of theorem 2 in [3] and the proof of theorem 3 in [4]. in both cases, an admissible parameter velocity ṡo(s),s ∈ [0,1] is first constructed. to show ṡo(s) is a solution to problem (5), the authors actually proved that ṡo(s) is maximum at any parameter value. the constraints of problem (5) are linear in a and b. it will be shown that the nonlinear objective function of problem (5) can be replaced with a linear function in a. the idea is to maximize the total feedrate for the end effector of the robots instead of minimizing the traversing time. the feedrate of the end effector of the robot is fd (s) = ∥r′ (s)∥ √ a(s), where ∥r′ (s)∥ =√ x′2 (s) + y′2 (s) + z′2 (s). consider the optimal control problem. max b(s) jf = ∫ 1 0 ∥r′ (s)∥2a(s)ds s.t. a′ (s) = 2b(s) , ∀s ∈ [0,1], a(s) > 0, ∀s ∈ (0,1) ,a(0) = a0,a(1) = a1, m (s)b+c (s)a+g (s) = τ (s) , τmin ≤ τ (s) ≤ τmax. (6) since the objective function of the above problem is linear in a(s), problem (6) is a linear optimal control problem meaning that both the objective function and the constraints are linear in a and b. we thus have theorem 2. problem (6) and problem (5) have the same unique optimal solution. proof: from [3] and [4], problem (5) has an optimal solution ao(s) = ṡo(s)2,s ∈ [0,1]. since the two problems have the same constraints, ao(s) is also an admissible pseudo feedrate to problem (6). from theorem 1, ao(s) achieves maximum values among all a(s) satisfying the constraints of (6) at any parametric value. since the parametric path r(s) is assumed to be non-singular, ∥r′(s)∥2 > 0 for all s ∈ (0,1). thus ao(s) is also the optimal solution to problem (6). 2 126 q. zhang, s.-r. li, j.-x. guo, x.-s. gao 3 parametrization and tractable algorithm for nominal tmtp in this section, a method of parametrization is given to reduce the infinite dimensional optimal control problem (6) to a linear program. the parametrization method will also be used in the next section to solve the robust tmtp. the first step is to use a special type quadratic b-spline function to approximate the function a(s). we first define the b-splines used in this paper. let k and p be positive integers and si = i k−p+2, i = 0, . . . ,k − p + 2. consider the following knot sequence with the first p + 1 and last p + 1 knots “clamped". s =  0, . . . ,0︸ ︷︷ ︸ p+1 ,s1, . . . ,si, . . . ,sk−p+1,1, . . . ,1︸ ︷︷ ︸ p+1  . (7) the b-spline curve of degree p in the parameter interval [0,1] is defined as ã(s) = k+1∑ i=0 ni,p (s)âi, (8) where âi are parameters and ni,p (s) is the i-th spline basis function of degree p defined as ni,0 (s) = { 1 if s(i) ≤ s < s(i + 1) 0 otherwise , i = 0, . . . ,k + p + 1 ni,k (s) = s − s(i) s(i + k) − s(i) ni,k−1 (s) + s(i + k + 1) − s s(i + k + 1) − s(i + 1) ni+1,k−1 (s) , k = 1, . . . ,p;i = 0, . . . ,k + p − k + 1. the derivative of the b-spline basis function is given below n ′i,p (s) = d ds ni,p (s) = p s(i + p) − s(i) ni,p−1 (s) − p s(i + p + 1) − s(i + 1) ni+1,p−1 (s) . (9) now, function a(s) is approximated by a quadratic b-spline function a(s) ≈ ã(s) where ã(s) is from (8) for p = 2. set â0 = a0, âk+1 = a1. due to the multiple knots in the b-splines, we have have ã(0) = â0 and ã(1) = âk+1 [18]. the control points x = [â1, . . . , âk]t will be optimized. the variable b(s) is b(s) = a′ (s)/2 ≈ 1 2 k+1∑ i=0 n ′i,p (s)âi, (10) where n ′i,p (s) is given in (9). the second step is to reduce the continuous optimal control problem (6) to a finite state optimal problem by using pointwise constraints to replace the continuous torque constraints. the constraint knot points are s̄j =   1 2 s1,j = 1 1 2 (sj−1+sj) ,j = 2, . . . ,k − 1 1 2 (sk−1+1) ,j = k (11) with these knot points, the objective function can be approximated as jf = ∫ 1 0 ∥r′ (s)∥2a(s)ds ≈ bx (12) tractable algorithm for robust time-optimal trajectory planning of robotic manipulators under confined torque 127 where b = (b1, . . . ,bk) with bi = k∑ j=1 ∥r′ (s̄j)∥2ni,p (s̄j) ∆s̄j for ∆s̄j = sj − sj−1 = 1/k. from (4), (8), and (10), the torques of the l-th joint at the knot points are τ̂l = alx + ĝl, l = 1, . . . ,n, (13) where τ̂l = [τl (s̄1) , . . . ,τl (s̄k)] t with τl (s̄j) = 12ml (s̄j) k+1∑ i=0 n ′i,p (s̄j) âi +cl (s̄j) k+1∑ i=0 ni,p (s̄j) âi + gl (s̄j), al ∈ rk×k with alji = 12ml (s̄j)n ′ i,p (s̄j) + cl (s̄j)ni,p (s̄j), and ĝl = [ gl (s̄1) + ξ1, . . . , gl (s̄k) + ξk]t with ξj = 12ml (s̄j) (n ′ 0,p (s̄j)a0 + n ′ k+1,p (s̄j)a1) + cl (s̄j) (n0,p (s̄j) a0 + nk+1,p (s̄j)a1). with the above parametrization, problem (6) is reduced to the following linear program max x bx s.t. ã(s̄j) = njx + n0,p(s̄j)a0 + nk+1,p(s̄j)a1 > 0,j = 1, . . . ,k, τl,mini ≤ alx + ĝl ≤ τl,maxi, l= 1, . . . ,n, (14) where i = (1, . . . ,1)t ∈ rk, b is from (12), nj = [n1,p(s̄j), . . . ,nk,p(s̄j)], and al, ĝl are from (13). note that the boundary conditions a(0) = â0 = a0 and a(1) = âk+1 = a1 are automatically satisfied. properties of the above problem will be discussed in the next section. 4 tractable algorithm for robust tmtp in this section, we will show that the robust tmtp can be reduced to a linear program by using proper parametrization and methods of robust optimization. suppose that the dynamics model (4) has uncertainties and the real dynamics model is τ (s) = (m (s) + ∆m (s)) s̈+ (c (s) + ∆c (s)) ṡ2 + (g (s) + ∆g (s)) − ∆d (s) , (15) where ∆m (s) ,∆c (s) ,∆g (s) , and ∆d (s) are the disturbances for m (s) ,c (s) ,g (s), and the torque, respectively. the disturbances are assumed to satisfy the following constraints: d(∆m,∆c,∆g,∆d) :   |∆ml (s)| ≤ bm (l,s) , l = 1, . . . ,n |∆cl (s)| ≤ bc (l,s) , l = 1, . . . ,n |∆gl (s)| ≤ bg (l,s) , l = 1, . . . ,n |∆dl (s)| ≤ bd (l,s) , l = 1, . . . ,n   (16) where ∆ml (s) ,∆cl (s) ,∆gl (s), and ∆dl (s) are the elements of the vectors ∆m (s) ,∆c (s) ,∆g (s), and ∆d (s), respectively. using the min-max approach [12, 13], the robust tmtp can be formulated as the following bi-level optimization problem: max b(s) jf = ∫ 1 0 ∥r′ (s)∥2a(s)ds (17) s.t. a′ (s) = 2b(s) ,a(s) > 0,s ∈ (0,1), a(0) = a0, a(1) = a1, max d(∆m,∆c,∆g,∆d) ( (m (s) + ∆m (s))b(s)+ (c (s) + ∆c (s))a(s)+ (g (s) + ∆g (s)) − ∆d (s) ) ≤ τmax min d(∆m,∆c,∆g,∆d) ( (m (s) + ∆m (s))b(s)+ (c (s) + ∆c (s))a(s)+ (g (s) + ∆g (s)) − ∆d (s) ) ≥ τmin where d(∆m,∆c,∆g,∆d) is defined in (16). 128 q. zhang, s.-r. li, j.-x. guo, x.-s. gao using the parametrization procedure presented in section 3, the robust tmtp problem (17) is reduced to the following bi-level optimization problem max x bx s.t. njx + n0,p(s̄j)a0 + nk+1,p(s̄j)a1 > 0,j = 1, . . . ,k max ∆aml,∆acl,∆gl,∆dl alx + ĝl + ∆amly + ∆aclx + ∆ĝl − ∆d̂l ≤ τl,maxi, min ∆aml,∆acl,∆gl,∆dl alx + ĝl + ∆amly + ∆aclx + ∆ĝl − ∆d̂l ≥ τl,mini, l = 1, . . . ,n, (18) where y = [n′1, . . . ,n ′ k] t x is a newly defined variable with n′j = [ n ′1,p (s̄j) , . . . ,n ′ k,p (s̄j) ] , b, nj, al, and ĝl are the same as in (14), ∆ĝl = [∆gl (s̄1) , . . . ,∆gl (s̄k)] t is the disturbance of the gravitational torque, ∆d̂l = [∆dl (s̄1) , . . . ,∆dl (s̄k)] t is the torque disturbances, ∆aml ∈ rk×k and ∆acl ∈ rk×k are the uncertainty matrixes of the l-th joint with elements ∆amlji = 1 2 ∆ml (s̄j) and ∆aclji = ∆cl (s̄j)ni,p (s̄j) respectively. and the uncertainty constraints are ∆aml : |∆ml(s̄j)| ≤ bm (l, s̄j) , (19) ∆acl : |∆cl(s̄j)| ≤ bc (l, s̄j) , (20) ∆gl : |∆gl(s̄j)| ≤ bg(l, s̄j), ∆dl : |∆dl(s̄j)| ≤ bd(l, s̄j),j = 1, . . . ,k. based on a result of soyster about robust optimization [19], the bi-level optimization problem (18) can be relaxed to the following linear program max x,z1,z2 bx s.t. njx + n0,p(s̄j)a0 + nk+1,p(s̄j)a1 > 0,j = 1, . . . ,k, alx + ĝl + ∆bmlz1 + ∆bclz2 + ∆bgl + ∆bdl ≤ τl,maxi, alx + ĝl − ∆bmlz1 − ∆bclz2 − ∆bgl − ∆bdl ≥ τl,mini, −z2 ≤ x ≤ z2,−z1 ≤ y ≤ z1,z1 ≥ 0,z2 ≥ 0, y = [n′1, . . . ,n ′ k] t x, l = 1, . . . ,n, (21) where z1 ∈ rk and z2 ∈ rk are new sets of variables, ∆bml ∈ rk×k and ∆bcl ∈ rk×k are matrixes with elements ∆bmlji = 12bm (l, s̄j) and ∆bclji = ni,p (s̄j)bc (l, s̄j) respectively, ∆bgl = [bg(l, s̄1), . . . ,bg(l, s̄k)] t , and ∆bdl = [bd(l, s̄1), . . . ,bd(l, s̄k)]t . the number of variables in problem (21) is 3k and the number of constraints is (2n + 7)k. according to karmarkar’s result [20], problem (21) can be solved with o((nk)3.5) floating point arithmetic operations. we briefly discuss the convergency of the method, that is, whether the solutions of the linear programs (14) and (21) convert to the optimal solutions of the optimal control problems (6) and (17), respectively, when k becomes sufficiently large. from the approximation theory, quadratic splines can approximate the solution of optimal control problems to any given precision when k is large enough [21]. notice that the method of using quadratic splines to approximate the control variable is a special case of the ritz method, the convergence of which has been proved only for special types of constraints [21–23]. convergency for problems like the one in this paper seems open. in the next section, numerical results will be used to demonstrate that the method gives the optimal solution with very high precision in less than one second. tractable algorithm for robust time-optimal trajectory planning of robotic manipulators under confined torque 129 5 numerical examples the experimental robot is a six-dof puma 560 manipulator which is modeled using the robotics toolbox for matlab [24]. the torque limits of the six joints are set to be [97.6; 186.4; 89.4; 24.2; 20.1; 21.3]n.m. initial and end feedrates of the end effecter are zeros. the path to be traced in the robot work space is shown in fig.1(a) with the following parametric formula   x(s) = 0.5, y (s) = 0.25 ( cos (2aπs) − cos (2bπs)3 ) , z (s) = 0.25 ( sin (2aπs) − sin (2bπs)3 ) , s ∈ [0,1] . where a = 2,b = 1. the corresponding joint path is shown in fig.1(b). −0.4 −0.2 0 0.2 0.4 0.6 −0.4 −0.2 0 0.2 0.4 y(m) z (m ) start point (a) 0 200 400 600 800 1000 −1.5 −1 −0.5 0 0.5 1 1.5 path parameter(−) d is pl ac em en t (r ad ) joint 1 joint 2 joint 3 joint 4 joint 5 joint 6 (b) figure 1: (a): the task path on y-z plane. (b): the corresponding joint path. first, problem (14) is used to show that the proposed method is efficient and correct. the proposed linear program (lp) approach will be compared to the convex optimization (cp) approach [2] for efficiency. the approach in [2], which is realized by executing the second order cone program (socp), was the fastest existing numerical method by now. both algorithms are implemented by using optimization software package sedumi [25] in matlab on a pc with a 2.6ghz cpu and 2g memory. table 1: comparison of computation times for different k number k cpu time(s) optimal motion time(s) lp cp in [2] lp cp in [2] 100 0.3964 1.2460 1.6473 1.6449 200 0.6830 2.2970 1.6495 1.6491 500 0.4630 3.6260 1.6506 1.6506 700 0.9048 4.2620 1.6508 1.6507 1000 0.9300 3.4430 1.6508 1.6508 2000 1.1960 6.3440 1.6508 1.6508 130 q. zhang, s.-r. li, j.-x. guo, x.-s. gao 0 0.2 0.4 0.6 0.8 0 0.2 0.4 0.6 0.8 1 path parameter(−) p ar am et er v el oc it y( − ) vlc nominal case1 case2 case3 figure 2: optimized parameter velocity curves of all four cases for k=1000. 0 0.2 0.4 0.6 0.8 1 −200 0 200 jo in t to rq ue (n .m ) joint 1 joint 2 joint 3 0 0.2 0.4 0.6 0.8 1 −20 0 20 path parameter(−) jo in t to rq ue (n .m ) joint 4 joint 5 joint 6 figure 3: joint torques of the planned nominal minimum time motion for k=1000. 0 0.2 0.4 0.6 0.8 1 −200 0 200 jo in t to rq ue (n .m ) joint 1 joint 2 joint 3 0 0.2 0.4 0.6 0.8 1 −20 0 20 path parameter(−) jo in t to rq ue (n .m ) joint 4 joint 5 joint 6 figure 4: joint torques of robust minimum time motion in case 1 for k=1000. in table 1, the computation time and the robot motion time are given for different values of k. from table 1, we can see that the optimal motion times based on formulation (6) and formulation (5) are almost the same, which is consistent with theorem 2. however, the computation time of our approach is faster than that of [2]. also note that the actually computational complexity of our approach is sub-linear, meaning less than o(k), which might be due to the special structure of al in (14). the practical computational complexity of this approach is better than the complexity o(k2) of the method given in [11]. the planned joint torques are given in fig. 3. from [3], the optimal solution of problem (5) is bang-bang-singular in the sense that for any s ∈ [0,1], the torque of at least one joint reaches its boundary values in the constraint τmin ≤ τ (s) ≤ τmax. this property is often used to verify whether a numerical solution is the optimal solution and whether k is large enough. in fig. 3, the torque is clearly bang-bang-singular: the torques of joints 2, 1, 2, 1, and 2 reach their maximal or minimal values in the intervals [0,0.17], [0.17,0.35], [0.35,0.41], [0.41,0.78], [0.78,1], respectively. this indicates that for k = 1000, the solution obtained with our method most probably gives high precision approximation to the optimal solution of the original problem (5). now, it will be shown that the solution of problem (21) is indeed robust. suppose that the payload holden by the end effector is the only uncertain parameter. as mentioned in [24], the maximum payload capability of the puma 560 is 2.5 kg. in this section, the robust trajectory tractable algorithm for robust time-optimal trajectory planning of robotic manipulators under confined torque 131 0 0.2 0.4 0.6 0.8 1 −200 0 200 jo in t to rq ue (n .m ) joint 1 joint 2 joint 3 0 0.2 0.4 0.6 0.8 1 −20 0 20 path parameter(−) jo in t to rq ue (n .m ) joint 4 joint 5 joint 6 figure 5: joint torques of robust minimum time motion in case 2 for k=1000. 0 0.2 0.4 0.6 0.8 1 −200 0 200 jo in t to rq ue ( n .m ) joint 1 joint 2 joint 3 0 0.2 0.4 0.6 0.8 1 −20 0 20 path parameter(−) jo in t to rq ue ( n .m ) joint 4 joint 5 joint 6 figure 6: joint torques of robust minimum time motion in case 3 for k=1000. planning is implemented under three cases of maximum payload uncertainty assumptions, respectively. the assumptions are 0.5kg for case 1, 1.25kg for case2, and 2.5kg for case 3. the disturbed ∆m, ∆c, and ∆g are estimated by using the dynamics computation function of the robotic toolbox for matlab. the computation performance of the proposed robust trajectory planning algorithm is tested by implementing case 2 under different values of k. the results are listed in table 2. from this table, we can see that the calculated optimal solution is convergent when k increases. also in this example, the actually computational complexity of our approach is less than o(k). table 2: computation performance of robust algorithm for case 2 with different k number (k) 100 200 500 700 1000 2000 cpu time 0.4330 0.7080 1.0720 1.1290 1.6920 2.6680 optimal motion time 1.7355 1.7395 1.7407 1.7408 1.7409 1.7409 the planned joint torques of the robust minimum time motions in case 1, case 2, and case 3 are given in fig. 4, fig. 5, and 6, respectively. the optimized parameter velocities ṡ for all four cases are shown in fig. 2. comparing to fig. 3, the torques for the robust time-minimum motion are reduced to reserve enough space to reject uncertainties and disturbances without causing torque saturation. to demonstrate the robustness of the obtained trajectories compared to the nominal one, consider 10 equidistantly distributed mass values of payload in [0,2.5]kg. the planned feedrates in the cases of fig. 3, fig. 4, fig. 5, and fig. 6 are used to traverse the path, and the corresponding torques are given in fig. 7, fig. 8, fig. 9, and fig. 10, respectively. note that the torque bounds are [97.6; 186.4; 89.4; 24.2; 20.1; 21.3] n.m. from fig. 7, fig. 8 and fig. 9, we can see that the actual torques pass the given bounds for about 25%, 19% and 11%, respectively. from fig. 10, it can be seen that the actual torques are all within the given bound, which is consistent with the fact that the robust trajectory in this case is planned under the condition of maximum 2.5kg payload. the robust trajectories for the three cases of uncertainty assumptions are used to traverse 132 q. zhang, s.-r. li, j.-x. guo, x.-s. gao 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 −200 0 200 s im ul at ed j oi nt t or qu e (n .m ) joint 1 joint 2 joint 3 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 −20 0 20 time (s) s im ul at ed j oi nt t or qu e (n .m ) joint 4 joint 5 joint 6 figure 7: joint torques for 10 difference payload masses along the nominal reference trajectory. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 −200 0 200 s im ul at ed j oi nt t or qu e (n .m ) joint 1 joint 2 joint 3 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 −20 0 20 time (s) s im ul at ed jo in t to rq ue ( n .m ) joint 4 joint 5 joint 6 figure 8: joint torques for 10 difference payload masses along the robust reference trajectory of case 1. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 −200 0 200 s im ul at ed jo in t to rq ue ( n .m ) joint 1 joint 2 joint 3 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 −20 0 20 time (s) s im ul at ed j oi nt t or qu e (n .m ) joint 4 joint 5 joint 6 figure 9: joint torques for 10 difference payload masses along the robust reference trajectory of case 2. 0 0.5 1 1.5 −200 0 200 s im ul at ed j oi nt t or qu e (n .m ) joint 1 joint 2 joint 3 0 0.5 1 1.5 −20 0 20 time (s) s im ul at ed j oi nt t or qu e (n .m ) joint 4 joint 5 joint 6 figure 10: joint torques for 10 difference payload masses along the robust reference trajectory of case 3. the path using the ten values of the payload. detailed data of the experiments are given in table 3, where ctime stands for the computation time, mtime stands for the optimal motion time, rti stands for the rate of time increase compared with the nominal case, mtv stands for the maximum torque violation, atv stand for the average torque violation, and rtv stands for the rate of torque violation. from table 3, we can see that when the 2.5kg payload uncertainty assumption is used, along the generated robust trajectory, the actual torques do not pass the given bounds. by contrast, along the non robust nominal trajectories, at almost all times at least one actual torque passes the given bound and the maximum torque violation reaches 25% of the given bound. the optimal motion time of this robust trajectory increases about 10.7% compared with the nominal case, which is a reasonable price to pay for robustness. tractable algorithm for robust time-optimal trajectory planning of robotic manipulators under confined torque 133 table 3: results of trajectory robustness test for k=1000 ctime(s) mtime(s) rti(%) mtv(n.m) atv(n.m) rtv(%) nominal 1.4508 1.6508 0 46.39 3.99 99.16 case 1 1.9810 1.6872 2.2 35.65 2.87 98.96 case 2 1.6920 1.7409 5.5 21.04 1.75 82.35 case 3 1.8600 1.8281 10.7 none none 0 6 conclusions in this paper, a linear program based robust time minimum trajectory planning approach is presented. the robust version of the tmtp problem is constructed to overcome the parameter uncertainties and torque disturbances of the dynamic system. to make our approach tractable, the tmtp problem is proven to be equivalent to a linear optimal control problem by considering the total motion feedrate of the end-effector of the robot as the objective function. quadratic b-spline curves are used to parameterize the linear optimal control problem into a linear program which can be solved in polynomial time. the approach theoretically ensures that the generated velocity is time optimal and for every possible realization of the uncertainties and disturbances within the bound, the robust trajectory will not violate any joint torque constraints. though the proposed algorithm is designed to execute offline, online use is also possible because of the good computation performance. acknowledgment this research was partially supported by a national key basic research project of china (2011cb302400), a grant from nsfc (60821002) and a grant from upc (120501a). bibliography [1] katzschmann, r.; kroger, t.; asfour, t.; khatib o.(2013); towards online trajectory generation considering robot dynamics and torque limits, in intelligent 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[25] sturm, j.f.(1999); using sedumi 1.02, a matlab toolbox for optimization over symmetric cones, optimization methods and software, issn 1055-6788, 11(1-4): 625-653. ijcccv11n1.dvi international journal of computers communications & control issn 1841-9836, 11(1):51-66, february 2016. personnel ranking and selection problem solution by application of kemira method n. kosareva, e.k. zavadskas, a. krylovas, s. dadelo natalja kosareva, edmundas kazimieras zavadskas*, aleksandras krylovas, stanislav dadelo vilnius gediminas technical university sauletekio al. 11, lt-10223 vilnius, lithuania natalja.kosareva@vgtu.lt, edmundas.zavadskas@vgtu.lt aleksandras.krylovas@vgtu.lt, stanislav.dadelo@vgtu.lt *corresponding author: edmundas.zavadskas@vgtu.lt abstract: in this study kemeny median indicator rank accordance (kemira) method is applied for solving personnel ranking and selection problem when there are two subgroups of evaluating criteria. each stage of kemira method illustrated with the examples. in the first stage kemeny median method is applied to generalize experts’ opinions for setting criteria priorities. medians were calculated for all experts opinions generalization and for experts majority opinions generalization. in the second stage criteria weights calculated and alternatives ranking accomplished simultaneously by indicator rank accordance method. the obtained solutions compared with the results received in previous work of authors. keywords: multiple criteria decision making, kemeny median, criteria priority, optimization problem. 1 introduction personnel ranking and selection process in companies is focused on testing and evaluating of human resource potential, skills and personal characteristics. developed personnel selection and evaluation systems are usually oriented to companies operating cost reduction in order to optimize human resources demand and layout planning [8]. personnel selection and placement of the necessary positions is seen as the most important factor affecting organization’s security, stability and development [20]. proper recruitment has influence on organization’s climate directly or influencing it through mediators [34]. security personnel selection process requires identification of specific criteria for the occupied position and setting of their weights values [12]. solution of this problem is relevant to many companies and organizations. at the same time it is necessary to create unified (being in accordance with uniform standards) personnel selection algorithms [3]. personnel selection deployment process is directed to human resource potential, skills and personal features, i.e. factors affecting professional efficiency, testing and evaluation. research has revealed that the main factors affecting the professional ability to work are of different origin: physical, psychological, cognitive, social/behavioral, workplace factors and factors outside the workplace [18]. this investigation has not determined the strength (weight) of identified factors influence on professional working capacity. it can be assumed that these factors have a different impact on the effectiveness of different professional activities. experts’ assessment applied for weight identification is often a subjective process based on stereotypes, attitudes, sympathy and so on [15]. the results of such evaluation often do not meet the expectations of the ultimate goal. from the other hand application of specialized tests (objective assessments) for weight identification meets the uncertainty factor. it is not clear how much test results will be concerned with the quality of the work results in the future [22]. according to the authors the most reliable methodology of professional selection is integrating several fields of criteria copyright © 2006-2016 by ccc publications agora university 52 n. kosareva, e.k. zavadskas, a. krylovas, s. dadelo and sub-criteria systems [41]. it was found that data obtained with the objective (internal) and subjective (external) methods correlate with each other at about 0.3 [11], so harmonization of objective and subjective criteria schemes allow to expect a higher efficiency of the staff selection process [13]. solution of this relevant problem is often subject to the multiple criteria decision making methods (mcdm). mcdm is rapidly evolving methodologies direction, does have considerable researchers attention. decision-making methods face new requirements for modernization. there is a variety of mcdm methods. a monograph [39] provides readers with a capsule look into the existing methods, their characteristics and applicability to analysis of multiple attribute decision making (madm) problems. models for madm, transformation of attributes, fuzzy decision rules, and methods for assessing weights are presented in this work. the paper [42] presents a panorama of decision making methods in economics and summarizes the most important results and applications which have appeared in the last decade. [33] added several new important concepts and trends in the mcdm field for solving actual problems. they provided comments on a previously published paper of [42] that could be thought of as an attempt to complete the original paper. despite the intensive development worldwide, few attempts have been made to systematically present the theoretical bases and developments of mcdm methods. the article [43] describes the situation with reviews of mcdm/madm methods. the goal of the paper [31] is to propose an approach to resolve disagreements among mcdm methods based on spearman’s rank correlation coefficient. the authors showed that the proposed approach can resolve conflicting mcdm rankings and reach an agreement among different mcdm methods. sometimes, there are situations where mcdm methods generate very different results. the results proposed in [35] proved that mcdm methods are useful tools for evaluating multiclass classification algorithms and the fusion approach of mcdm methods is capable of identifying a compromised solution when different mcdm methods generate conflicting rankings. however, mcdm techniques for recruitment and layout planning areas are not consistently structured and improved, this is an obstacle to their wide application. new mcdm methods are developing and new fields are looking for their application. the fuzzy multimoora for group decision making (multimoora-fg) enables to aggregate subjective assessments of the decision-makers and offer an opportunity to perform more robust personnel selection procedures [4], [5]. the selection process often contains imprecise data and linguistic variables. the model proposed in [6] combines fuzzy analytic hierarchy process (fahp) and technique for order preference by similarity to ideal solution (topsis) approaches to overcome these problems. the personnel selection problem is suitable to be dealt with by the linguistic vikor method because it includes some conflicting criteria and needs to consider the relative competitiveness of each candidate [9], [16]. the hybrid mcdm models which employ analytic network process (anp) and modified topsis and combination of the fuzzy delphi method, anp, and topsis demonstrated the effectiveness and feasibility of the proposed models for personnel selection [14] and for investment in stock exchange [19]. the model based on fuzzy dematel (decision making trial and evaluation laboratory) and fuzzy anp is proposed in [25] to to cope with the interdependencies between evaluation criteria. in the mentioned paper, a fuzzy dematel-anp model is proposed for selection of snipers. an approach enabling combination of fuzzy anp, fuzzy topsis and fuzzy electre techniques were enabled for solving of the same problem [26]. other authors proposed other mcdm methods, like fuzzy ahp adopted for triangular fuzzy numbers (tfn) [37] or such as electre iii and promethee ii [27], [36] in the process of staff selection and recruitment. other methods used for solving similar problems are grey relational analysis (gra) based methods [41]. a gra-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection is proposed in [23]. in this paper intuitionistic fuzzy weighted averaging personnel ranking and selection problem solution by application of kemira method 53 (ifwa) operator is utilized to aggregate individual opinions of decision makers and intuitionistic fuzzy entropy is used to obtain the entropy weights of the criteria. the hybrid approach applying ahp and topsis grey as an effective decision aid to improve human resources management in various areas of economic activities is proposed in [23]. a personnel selection system based ahp and complex proportional assessment of alternatives with grey relations (copras-g) method was proposed in [24]. the use of hesitant fuzzy sets as a powerful technique has been studied in [20]. this paper explores aggregation methods for prioritized hesitant fuzzy elements and their application on personnel evaluation. the comparison of various methods in the selection of personnel may help in finding out the accuracy, appropriateness, suitability, fairness and practicality efficiently [37]. using several independent sub-criteria for personnel selection we face with the lack of appropriate mcdm solutions. the purpose of this article is to offer methods of this problem solution. studies have shown that the subjective and objective assessments are different [2], [21]. in order to effective solutions, it is necessary to synthesize the objective and subjective assessments, this opens up new opportunities and improve the quality of the selection process [10]. most previous studies did not provide clear criteria for grouping and ranking process patterns [30]. the proposed new kemira method allows to combine criteria structuring and evaluating on the basis of subjective and objective levels. this method can be used to solve unstructured evaluation tasks when consensus among the experts on the importance of sub-criteria is not obligatory or when criteria of different nature (genesis) are used. it occurs in those areas of human activity where it is necessary the evaluation of experts from different specializations (personnel, finance, manufacturing and technology, etc.). using this algorithm for decision-making, the decision-making process takes on a higher quality and greater integration levels to include in the evaluation and selection process a lot of important criteria of different nature, incomparable among themselves and couldn’t be combined. for example, external and internal criteria; subjective and objective criteria; physical, mental and psychological indicators, etc. the proposed kemira criteria selection and structuring model extends the mcdm approach. the advantage of proposed kemira method is its efficiency for solving mcdm problems when the set of criteria is divided into a few subgroups having different origin. nevertheless, kemeny median method, which is a part of kemira method, is applicable for much wider class of mcdm problems when it is necessary to establish priority of criteria. it must be mentioned that criteria weights are found at the same time as the process of mcdm problem solution (ranking the alternatives) goes on. 2 problem formulation and scheme of solution this work continues the series of works designed to solve mcdm problem of elite selection from security personnel, when two separate groups of criteria describing security guards are presented. in general case it may be more than two groups of criteria. external assessment values and internal measurements are given for each subject. additionally, information about experts independently set preferences for criteria in each criterion group is known. the purpose of the study is selection of 10% best subjects according to the given information. usually mcdm problems are being solved in the following order. at first stage of the problem solution data normalization procedure is performed. then criteria weights are determined and finally mcdm methods applied for selecting best objects and/or objects ranking [29]. each problem solution phase can be performed by different methods. our proposed kemeny median indicator rank accordance (kemira) [32] method consists of other three phases: 1. data normalization and standardization; 2. determination of criteria preferences reflecting the aggregated experts opinion; 54 n. kosareva, e.k. zavadskas, a. krylovas, s. dadelo 3. minimization of the target functions value, determination of criteria weights and objects ranking. the second step is determination priority of criteria by kemeny median method. in this stage we’ll use the information about criteria preferences set by experts. note that the third step combines two phases into one determination of criteria weights and objects ranking will be carried out simultaneously. this process is dynamic, so both goals will be achieved at the end of optimization procedure. brief scheme of problem solution is depicted in figure 1. comprehensive description of the method steps is provided below. at first introduce some notations: m – number of internal criteria (x), n – number of external criteria (y ), k – number of alternatives, s – number of experts. initial decision-making matrix: d =          x (1) 1 . . . x (1) j . . . x (1) n y (1) 1 . . . y (1) j . . . y (1) m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x (i) 1 . . . x (i) j . . . x (i) n y (i) 1 . . . y (i) j . . . y (i) m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x (k) 1 . . . x (k) j . . . x (k) n y (k) 1 . . . y (k) j . . . y (k) m          . (1) criteria preferences estimated by experts: expert x1 . . . xl . . . xn y1 . . . yl . . . ym 1 i (1) 1 . . . i (1) l . . . i (1) n j (1) 1 . . . j (1) l . . . j (1) m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s i (s) 1 . . . i (s) l . . . i (s) n j (s) 1 . . . j (s) l . . . j (s) m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . s i (s) 1 . . . i (s) l . . . i (s) n j (s) 1 . . . j (s) l . . . j (s) m figure 1: scheme of kemeny median indicator rank accordance method (kemira) application for selecting the best objects. step 1. normalization of elements of decision making matrix (1). all criteria are representing a benefit, i.e. the bigger is a value, the better is the respective alternative. elements of decisionpersonnel ranking and selection problem solution by application of kemira method 55 making matrix are normalized by formulas: x (i)∗ j = ( x (i) j −x (i) min ) / ( x(i)max −x (i) min ) , y (i)∗ j = ( y (i) j −y (i) min ) / ( y(i)max −y (i) min ) . (2) step 2. determining priority of criteria x and y components. criteria x and y components priority is established independently by choosing priority which minimizes sum of distances to the priorities set up by all s experts: ra = arg min r s ∑ j=1 ρa ( r,r(j) ) . (3) the result of this step is the median criteria components priority: xj1 ≻ xj2 ≻ ··· ≻ xjn, yi1 ≻ yi2 ≻ ··· ≻ yim. step 3. fixing initial weights of criteria satisfying median criteria components priority set in step 2 and normalizing condition: αj1 ≻ αj2 ≻ ··· ≻ αjn, βi1 ≻ βi2 ≻ ··· ≻ βim, ∑n r=1 αjr = ∑m s=1 βis = 1. (4) step 4. calculation of functions ϕ(x) and ψ(y ) values and total value ϕ(x) + ψ(y ) for each alternative. ϕ(x) = n ∑ r=1 αjrxjr, ψ(y ) = m ∑ s=1 βisyis. (5) step 5. calculation of ranks of alternatives according to the total value ϕ(x) + ψ(y ): r(k)(α) and r(k)(β), k = 1,2, . . . ,k. step 6. calculation number of elements having ranks lower than kx according to the criteria x and lower than ky according to the criteria y : ∣ ∣ ∣ akx ∩bky ∣ ∣ ∣ . step 7. calculation sum of squared ranks differences for the alternatives satisfying conditions of step 6, i.e. denote akx ∩bky = {k ∈{1,2, . . . ,k} : r (k) (α) 6 kx ,r (k) (β) 6 ky}. then ∑ k ∈ {akx ∩ bky } ( r(k) (α) −r(k) (β) )2 . (6) steps 4–7 are repeated until number of elements in step 6 will reach its maximum value and the value of target function in step 7 reach its minimum value. the obtained values of criteria weights αj1,αj2, · · · ,αjn,βi1,βi2, · · · ,βim are used for calculation of total value ϕ(x) + ψ(y ) and mcdm problem solution. 3 determining priority of criteria say that priority of criteria x = (x1,x2, . . . ,xn) and y = (y1,y2, . . . ,ym) was estimated by s experts. each expert sorted criteria in the priority descending order: x (s) i1 ≻ x (s) i2 ≻ . . . ≻ x (s) in , y (s) i1 ≻ y (s) i2 ≻ . . . ≻ y (s) im , (7) here s ∈{1,2, . . . ,s} – number of experts. we must ascertain generalized experts opinion. this task will be accomplished by kemeny median method. suppose that permutations (j1,j2, . . . ,jn) of the set of natural numbers {1,2, . . . ,n} determine priorities of vector (criteria) x components: xj1 ≻ xj2 ≻ ··· ≻ xjn. it means that for the set {x1,x2, . . . ,xn} a strict order relationship r = (xj1,xj2, . . . ,xjn) is defined. this relationship can be defined by the square matrix ar = ‖aij‖, 56 n. kosareva, e.k. zavadskas, a. krylovas, s. dadelo which elements are: aij = { 1, if xi < xj, 0, if xi > xj. , where aii = 0 and aij = 1 −aji, for i 6= j. define function ρa ( r(1),r(2) ) = n ∑ i=1 n ∑ j=1 ∣ ∣ ∣ a (1) ij −a (2) ij ∣ ∣ ∣ . (8) function (8) is a certain measure of difference between two relationships and its values coincide with values of kemeny distance function [28]. for example, if ranks of criteria x = (x1,x2,x3,x4) components are established by the first expert as r(1) = (3,2,4,1) and by the second expert as r(2) = (2,1,4,3), consequently priorities of criteria components are set accordingly x4 ≻ x2 ≻ x1 ≻ x3 and x2 ≻ x1 ≻ x4 ≻ x3. then corresponding matrices are: a(1) =      0 0 1 0 1 0 1 0 0 0 0 0 1 1 1 0      , a(2) =      0 0 1 1 1 0 1 1 0 0 0 0 0 0 1 0      . function (8) in this case is gaining value ρa ( r(1),r(2) ) = 4 ∑ i=1 4 ∑ j=1 ∣ ∣ ∣ a (1) ij −a (2) ij ∣ ∣ ∣ = 1+1+0+2 = 4. suppose that s experts established priorities r(1),r(2), . . . ,r(s). most consistent with these estimates will be priority ra, which is called median: ra = arg min r s ∑ j=1 ρa ( r,r(j) ) . (9) notice that we can analyze functions ρa analogues, i.e. another distances between two relationships. the solution of (9) is not necessary a unique value. sometimes we can obtain several medians which are solutions of problem (9). if we are interested in the majority experts opinion only (not all experts) then we apply cluster analysis procedure, that helps to distinguish a cluster of experts majority. then the median is sought among selected group of experts. let’s us consider an example. priority of criteria x = (x1,x2,x3,x4) and y = (y1,y2,y3) was estimated by 5 experts. results of estimation presented in the table 1. first expert set table 1: criteria x and y components preferences established by 5 experts. expert x1 x2 x3 x4 y1 y2 y3 1 3 2 4 1 1 3 2 2 2 1 4 3 1 2 3 3 3 2 1 4 1 2 3 4 2 3 4 1 2 1 3 5 3 4 2 1 2 3 1 criterion x components priorities as follows: x4 ≻ x2 ≻ x1 ≻ x3 and criterion y components priorities: y1 ≻ y3 ≻ y2. notice that medians must be sought independently for criterion x and y components. let’s calculate elements of the matrices a (i) x : a (1) x =      0 0 1 0 1 0 1 0 0 0 0 0 1 1 1 0      , a (2) x =      0 0 1 1 1 0 1 1 0 0 0 0 0 0 1 0      , a (3) x =      0 0 0 1 1 0 0 1 1 1 0 1 0 0 0 0      , personnel ranking and selection problem solution by application of kemira method 57 a (4) x =      0 1 1 0 0 0 1 0 0 0 0 0 1 1 1 0      , a (5) x =      0 1 0 0 0 0 0 0 1 1 0 0 1 1 1 0      . note, that we must search the median rax among all 24 = 4! components priorities options. median is the priority, which minimize functions (9) value. in this case solution was found among priorities r(1) −r(5) (for other priorities options value of function (9) is bigger): 5 ∑ j=1 ρa ( r(1),r(j) ) = 0 + 4 + 10 + 2 + 6 = 22, 5 ∑ j=1 ρa ( r(2),r(j) ) = 4 + 0 + 6 + 6 + 10 = 26, 5 ∑ j=1 ρa ( r(3),r(j) ) = 10 + 6 + 0 + 12 + 8 = 36, 5 ∑ j=1 ρa ( r(4),r(j) ) = 2 + 6 + 12 + 0 + 4 = 24, 5 ∑ j=1 ρa ( r(5),r(j) ) = 6 + 10 + 8 + 4 + 0 = 28 ... median components priority is rax = r (1) = (3,2,4,1) or x4 ≻ x2 ≻ x1 ≻ x3. next we find criterion y components priorities among all 6 = 3! options. calculate elements of matrices a (i) y : a (1) y =    0 1 1 0 0 0 0 1 0    , a (2) y = a (3) y =    0 1 1 0 0 1 0 0 0    , a (4) y =    0 0 1 1 0 1 0 0 0    , a (5) y =    0 1 0 0 0 0 1 1 0    , a (6) y =    0 0 0 1 0 1 1 0 0    , a (7) y =    0 0 0 1 0 0 1 1 0    . here r(6) = (3,1,2),r(7) = (3,2,1). 5 ∑ j=1 ρa ( r(1),r(j) ) = 0 + 2 + 2 + 4 + 2 = 10, 5 ∑ j=1 ρa ( r(2),r(j) ) = 5 ∑ j=1 ρa ( r(3),r(j) ) = 2 + 0 + 0 + 2 + 4 = 8, 5 ∑ j=1 ρa ( r(4),r(j) ) = 4 + 2 + 2 + 0 + 6 = 14, 5 ∑ j=1 ρa ( r(5),r(j) ) = 2 + 4 + 4 + 6 + 0 = 16, 5 ∑ j=1 ρa ( r(6),r(j) ) = 6 + 4 + 4 + 2 + 4 = 20, 5 ∑ j=1 ρa ( r(7),r(j) ) = 4 + 6 + 6 + 4 + 2 = 22. median components prioritiy is ray = r (2) = r(3) = (1,2,3) or y1 ≻ y2 ≻ y3. 4 calculation of weights and mcdm problem solution suppose that there are known objective measurements (x) and subjective expert evaluations (y) of k test takers t(j),j = 1,2, . . . ,k : x (j) 1 , x (j) 2 , . . ., x (j) n , y (j) 1 , y (j) 2 , . . ., y (j) m , 0 6 x (j) i ,y (j) i 6 1: t(j1) � t(j2), if (∀i) x (j1) i > x (j2) i & y (j1) i > y (j2) i . (10) suppose that 0 6 wxi,wyi 6 1 are weighted coefficients: n ∑ i=1 wxi = m ∑ i=1 wyi = 1. then under conditions (10) the following inequalities take place: w(j1)x > w (j2) x & w (j1) y > w (j2) y . (11) here w(j)x = n ∑ i=1 wxix (j) i , w (j) y = m ∑ i=1 wyiy (j) i . (12) 58 n. kosareva, e.k. zavadskas, a. krylovas, s. dadelo it means that if all measurements values are bigger for object t(j1) than for object t(j2) then each linear combination of measurements for t(j1) will also be bigger than for t(j2) provided that weighted coefficients are nonnegative. in practice more often we have encountering situation when results of the measurements are as follows: x (j1) i1 > x (j2) i1 and x (j1) i2 < x (j2) i2 . then it isn’t possible to apply criteria (10) for selecting better alternative. the idea of current study is to choose such values of weights wxi, wyi which will guarantee proximity of values w (j) x and w (j) y for 10% of best security guards. the measure of closeness of these values would be sum of squares of ranks differences. let’s denote r (j) x and r (j) y positive integers r (j) x,y ∈{1,2, . . . ,k} satisfying condition: r (j1) x,y < r (j2) x,y , when w (j1) x,y > w (j2) x,y , i.e. they are ranks of numbers w (j) x,y. let’s define akx and bky as subsets of the set {1,2, . . . ,k}: akx = { {j1,j2, . . . ,jkx} : r (ji) x 6 kx } , bky = { {j1,j2, . . . ,jky} : r (ji) y 6 ky } including objects which have lowest ranks according to the corresponding criteria (x and y ) and having kx and ky elements respectively. note, that the lower is the rank, the better is the alternative. the best alternatives selection task according to both criteria (12) is equivalent to the task of finding the intersection of sets akx ∩bky , which has the required number of elements ∣ ∣akx ∩bky ∣ ∣. if the number of elements ∣ ∣akx ∩bky ∣ ∣ is insufficient, it is necessary to increase numbers kx and ky. preferences of the selected alternatives could be determined by the following criteria expressed as sum of criteria (12): w(j) = w (j) x + w (j) y . consider the following example. suppose that 5 objects have 4 attributes of one type (x) and 3 attributes of other type – (y ). values of attributes are given in the table 2. let’s calculate table 2: attributes xi and yj values for the 5 given objects. x1 x2 x3 x4 y1 y2 y3 1 0.4 0.6 0.7 0.3 0.5 0.4 0.2 2 0.5 0.3 0.7 0.3 0.6 0.7 0.2 3 0.2 0.4 0.6 0.5 0.7 0.7 0.4 4 0.5 0.3 0.5 0.4 0.6 0.8 0.2 5 0.6 0.2 0.8 0.8 0.4 0.5 0.8 values of criteria (12) for the first object when weights are as follows: wx,1 = 1 2 , wx,2 = 1 4 , wx,3 = 1 8 , wx,4 = 1 8 , wy,1 = 5 8 , wy,2 = 1 4 , wy,3 = 1 8 . we obtain criteria (12) values: w (1) x = 1 2 0.4 + 1 4 0.6 + 1 8 0.7 + 1 8 0.3 = 0.475,w (1) y = 5 8 0.5 + 1 4 0.4 + 1 8 0.2 = 0.4375. similarly calculate criteria values for the remaining 4 objects. all criteria values and their ranks are presented in the table 3. previously defined sets akx and bky are those including table 3: criteria w (j) x and w (j) y and their ranks values for the 5 given objects. j w (j) x w (j) y r (j) x r (j) y 1 0.475 0.4375 2 5 2 0.45 0.575 3 3 3 0.3375 0.6625 5 1 4 0.4375 0.60 4 2 5 0.55 0.475 1 4 respectively kx and ky elements with lowest ranks r (j) x and r (j) y : a1 = {5}, a2 = {1,5}, a3 = {1,2,5}, a4 = {1,2,4,5}, b1 = {3}, b2 = {3,4}, b3 = {2,3,4}, b4 = {2,3,4,5}, a1 ∩b1 = ∅, a2 ∩b2 = ∅, a3 ∩b3 = {2} and so on. personnel ranking and selection problem solution by application of kemira method 59 calculation of criteria weights is performed simultaneously with the decision of mcdm problem. suppose that there are known criteria-referenced assessments for certain alternatives x(k) = ( x (k) 1 ,x (k) 2 , . . . ,x (k) n ) , y (k) = ( y (k) 1 ,y (k) 2 , . . . ,y (k) m ) , k = 1,2, . . . ,k and criteria x, y priorities are determined: xj1 ≻ xj2 ≻ ··· ≻ xjn, yi1 ≻ yi2 ≻ ··· ≻ yim. (13) according to the established criteria x, y priorities (13) functions ϕ(x) and ψ(y ) are determined as follows: ϕ(x) = n ∑ r=1 αjrxjr, where αj1 > αj2 > . . . > αjn > 0, (14) ψ(y ) = m ∑ s=1 βisyis, where βi1 > βi2 > . . . > βim > 0. (15) for identifying criteria weights we use the heuristic described in [13]. require that the weighting coefficients αj and βi satisfy normalizing condition (4). denote s(j1,j2,...,jn) class of all convolutions constructed on the base of weighted averages and having priority feature (j1,j2, . . . ,jn), i.e. satisfying condition (14). analogous s(i1,i2,...,im) is a class of all convolutions having priority feature (i1, i2, . . . , im) (satisfying condition (15)). suppose that ϕ and ψ are criteria x and y convolutions having corresponding priority features (13). for each alternative ( x(k),y (k) ) we’ll calculate values of both criteria convolutions ϕ ( x(k) ) , ψ ( y (k) ) . numbering them in ascending order we get ranks of alternatives: r (k) x , r (k) y . denote akx and bky sets of the best alternatives – i. e. those subsets of the set {1,2, . . . ,k}, whose elements ranks satisfy the inequalities r (k) x 6 kx and r (k) y 6 ky. in the set akx there are kx the best alternatives according to criteria x, similarly in the set bky there are ky the best alternatives according to criteria y . numbers kx ir ky are chosen so that the intersection of sets akx ∩bky have not less than 10 % of the best alternatives. then we search such functions ϕ and ψ that the number of elements of sets akx and bky intersection should be the maximum: max ϕ ∈s(j1,j2,...,jn) ψ ∈s(i1,i2,...,im) ∣ ∣ ∣akx ∩bky ∣ ∣ ∣ . (16) condition (16) means that we must select convolutions ϕ and ψ, which maximize criteria x and y compatibility. the number of convolutions ϕ and ψ, maximizing (16) can be great, therefore additional optimization problem must be formulated. denote r(k)(α) and r(k)(β) ranks of the numbers { ϕ ( x(1) ) ,ϕ ( x(2) ) , . . . ,ϕ ( x(k) )} and { ψ ( y (1) ) ,ψ ( y (2) ) , . . . ,ψ ( y (k) )} respectively (k = 1,2, . . . ,k). we’ll apply indicator rank accordance method for minimizing ranks discrepancy function, i. e. sum of squares of the highest ranks differences according to criteria x and y : crkx,ky (α,β) = min ϕ ∈s(j1,j2,...,jn) ψ ∈s(i1,i2,...,im) ∑ k ∈ {akx ∩ bky } ( r(k) (α) −r(k) (β) )2 . (17) here kx and ky are chosen so that the number of elements in the intersection akx ∩bky will be equal to the desired number of selected objects. 60 n. kosareva, e.k. zavadskas, a. krylovas, s. dadelo for example, suppose that for the data given in table 2 five experts proposed criteria priorities presented in table 1. in chapter 3 for these data we have determined criteria priority features as follows: x4 ≻ x2 ≻ x1 ≻ x3, y1 ≻ y2 ≻ y3. therefore, we’ll search for weights values, satisfying conditions α4 > α2 > α1 > α3, β1 > β2 > β3 (18) and normalizing conditions (4) which maximize number of elements in the intersection of sets (16) and minimize value of function (17). let’s choose kx = ky = 3 and initial values of coefficients as follows: α1 = 1 12 , α2 = 1 3 , α3 = 1 12 , α4 = 1 2 , β1 = β2 = β3 = 1 3 . values of functions ϕ(x(i)) and ψ(y (i)) and their ranks for each alternative are given in table 4. table 4: values of functions ϕ(x(i)) and ψ(y (i)) and their ranks values for the 5 given objects. j w (j) x w (j) y r (j) x r (j) y 1 0.4417 0.33 3 5 2 0.35 0.45 5 4 3 0.45 0.54 2 1 4 0.3833 0.48 4 3 5 0.5833 0.51 1 2 a3 = {1,3,5}, b3 = {3,4,5}, a3 ∩b3 = {3,5}, |a3 ∩b3| = 2. so, in the sum (17) we have only 2 summands: cr3,3 (α,β) = (2−1) 2 + (1 −2) 2 = 2. in the next step we choose other values of coefficients satisfying conditions (18) and (4). if the number of elements in the intersection of sets (16) is greater or equal than 2, we calculate function (17) value. if this value is less than in previous step, values of these new coefficients are considered to be the best solution before the next step. after the last step when all coefficients satisfying conditions (18) and (4) are verified we’ll receive values of coefficients α1,α2,α3,α4,β1,β2,β3 which are the solution of mcdm problem. finally, values of functions ϕ(x) and ψ(y ) are calculated and objects are ranked according to the criteria ϕ(x) + ψ(y ). 5 case study the present problem has been solved in [13]. in the mentioned paper weighted coefficients of criteria were calculated as the proportions of the collected scores to the total score. in the current research the weights obtained by kemira method. results of selecting the best 10% security guards will be compared with previous results. moreover, guard ranking will be performed. 118 security guards were randomly selected from the company g4s lietuva. 22 leader managers (experts) ranked the competences described below. personnel elite – 10% the best employees of the private security company – are selected according to six internal assessment criteria (x1−x6) and nine external evaluation criteria (y1−y9). 6 internal criteria are objective tests and measurements: x1 is employee’s theoretical and practical preparation; x2 – professional activity, x3 – mental qualities; x4 – physical development; x5 – motor abilities (personal physical conditions allowing to carry out physical tasks); x6 – fighting efficiency. 9 external criteria are evaluation of subordinate by his immediate superior: y1 – specialty knowledge, professionalism, y2 – diligence and positive attitude to work, y3 – behavior with colleagues and supervisors; y4 – reliability at work; y5 – quality of work; y6 – workload performance; y7 – image; y8 – development rate; y9 – being promising (potential to make a career). fragment of security guards evaluation criteria structure is given in table 5. table 5 data are given in the paper [13]. note that all 15 criteria are associated with benefit and their personnel ranking and selection problem solution by application of kemira method 61 table 5: security guards internal and external evaluation criteria. security guards internal criteria external criteria x1 x2 . . . x6 y1 y2 . . . y9 a1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a118 . . . . . . . . . . . . . . . . . . . . . . . . greater value is better. therefore criteria values were transformed to values xj∗i ,y j∗ i , belonging to the range [0; 1] by the transformations (2). 22 independent experts determined preferences separately for internal and external evaluation criteria. criteria preferences established by experts are presented in the table 6. a higher grade means that the criterion is more important. criterion x priority feature is determined from expert estimates of the form (j1,j2, . . . ,j6). for example, first expert set criterion x components priorities as follows: (x1,x3,x6,x4,x5,x2). here x1 is the most important and x2 – the least important component. criterion y priority feature is established similarly. for example, first expert set them in this order: (y2,y4,y1,y7,y5,y3, y6,y8,y9). generalized experts opinion expressing criteria priority feature is established by calculating median with the respective metric (8) to calculate distances between priorities. calculation of medians was performed by full re-selection of options, which is respectively 6! = 720 and 9! = 362880. medians were calculated separately for criterion x components xj and criterion y components yi by minimizing function (9) values. two solutions were obtained for criterion x components xj priority features: (1,5,3,6,4,2), (1,3,5,6,4,2). criterion y components yi priority features were determined uniquely: (2,4,5,3,1,7,6,8,9). therefore, criteria x and y components order was established respectively: x1 ≻ x5 ≻ x3 ≻ x6 ≻ x4 ≻ x2 or x1 ≻ x3 ≻ x5 ≻ x6 ≻ x4 ≻ x2, (19) y2 ≻ y4 ≻ y5 ≻ y3 ≻ y1 ≻ y7 ≻ y6 ≻ y8 ≻ y9. (20) table 6: criteria x and y components preferences established by experts. expert x1 · · · x6 y1 · · · y9 1 615324 794853621 2 523164 795682341 3 513264 594681732 4 615342 597863421 5 615234 796853421 6 514263 596873421 7 423165 485972631 8 625341 685973421 9 546321 794863512 10 546123 674983521 11 413265 586972431 expert x1 · · · x6 y1 · · · y9 12 624153 695784321 13 645132 596873421 14 635142 786954321 15 413265 597863241 16 413265 798654231 17 614235 687945231 18 634152 698745231 19 415263 498671532 20 624153 596783421 21 526341 687954321 22 416325 697854231 finally, cluster analysis [17] was applied to the data presented in the table 6 to distinguish a group of experts whose opinions are very close to each other and which form the majority of the experts. by applying between groups, ward’s and furthest neighbor methods the group of experts with numbers {1,4,5,8,9,10,12,13,14,17,18,20,21,22} was selected. further only those experts priorities were examined to get criteria priority preferences, because we did not want to distort the opinion of this group by the remaining minority group of experts. kemeny median method was applied for this reduced data array. for criterion x components xj the solution 62 n. kosareva, e.k. zavadskas, a. krylovas, s. dadelo minimizing function (9) received as follows: (1,3,5,6,2,4). results for criterion y components yj were the same as in (20). therefore, for the majority of experts criteria x and y components order was established respectively: x1 ≻ x3 ≻ x5 ≻ x6 ≻ x2 ≻ x4 (21) and (20). from the priority features (19), (21) and (20) it follows, that it is necessary to search for convolutions ϕ and ψ in the form: ϕ1(x) = α1x1 + α5x5 + α3x3 + α6x6 + α4x4 + α2x2, α1 > α5 > α3 > α6 > α4 > α2 > 0, ϕ2(x) = α1x1 + α3x3 + α5x5 + α6x6 + α4x4 + α2x2, α1 > α3 > α5 > α6 > α4 > α2 > 0, ϕ3(x) = α1x1 + α3x3 + α5x5 + α6x6 + α2x2 + α4x4, α1 > α3 > α5 > α6 > α2 > α4 > 0, ψ(y ) = β2y2 + β4y4 + · · · + β9y9, β2 > β4 > β5 > · · · > β8 > β9 > 0. we define parameter values for our problem: number of investigated objects k = 118, number of criterion x components n = 6, number of criterion y components m = 9. values kx = 21 and ky = 21 were chosen to fulfill the condition ∣ ∣ ∣akx ∩bky ∣ ∣ ∣ = 12, since the goal is to select the top 12 security guards. in the case of functions ϕ1(x) and ψ(y ) the lowest criterion (17) value was found cr21,21 (α,β) = 331 with the following values of weighted coefficients: α1 α5 α3 α6 α4 α2 β2 β4 β5 β3 β1 β7 β6 β8 β9 0.202 0.202 0.197 0.191 0.11 0.098 0.32 0.149 0.14 0.12 0.085 0.064 0.053 0.035 0.034 the best 12 security guards, belonging to intersection of sets a21 ∩ b21 and ranked according to the criterion w (j) 1 = ϕ1(x (j)) + ψ(y(j)) as follows: a56 ≻ a76 ≻ a91 ≻ a81 ≻ a21 ≻ a47 ≻ a36 ≻ a106 ≻ a34 ≻ a111 ≻ a102 ≻ a77. in the case of functions ϕ2(x) and ψ(y ) the lowest criterion (17) value cr21,21 (α,β) = 362 was obtained with these weighted coefficients values: α1 α3 α5 α6 α4 α2 β2 β4 β5 β3 β1 β7 β6 β8 β9 0.194 0.192 0.188 0.182 0.132 0.11 0.29 0.24 0.094 0.089 0.082 0.078 0.055 0.055 0.017 the same 12 security guards got to the intersection of sets a21 ∩b21. alternatives ranked according to the criterion w (j) 2 = ϕ2(x (j)) + ψ(y(j)) as follows: a56 ≻ a76 ≻ a91 ≻ a81 ≻ a21 ≻ a47 ≻ a36 ≻ a106 ≻ a111 ≻ a34 ≻ a102 ≻ a77. two alternatives have exchanged places: a111 and a34. the obtained results coincide with the results got in [13], since in the case of ϕ2(x) and ψ(y ) criteria priorities were set in the same order as in the mentioned article. for the generalized opinion of the experts majority determined by cluster analysis the solution was sought between the functions of the form ϕ3(x) and ψ(y ). the minimum (17) value cr19,19 (α,β) = 373 was obtained with these weighted coefficients values: α1 α3 α5 α6 α2 α4 β2 β4 β5 β3 β1 β7 β6 β8 β9 0.2 0.19 0.19 0.19 0.133 0.097 0.25 0.249 0.12 0.1 0.081 0.07 0.05 0.05 0.03 the top 12 security guards are: a21, a34, a36, a47, a56, a72, a76, a81, a91, a102, a106, a111. the difference from the previous cases when solutions were sought amongst functions ϕ1(x) or ϕ2(x) is that a77 was changed by a72. alternatives ranked according to the criterion w (j) 3 = ϕ3(x (j))+ ψ(y(j)) as follows: a56 ≻ a91 ≻ a76 ≻ a81 ≻ a21 ≻ a36 ≻ a111 ≻ a47 ≻ a106 ≻ a34 ≻ a102 ≻ a72. ranking results differ from those accomplished by criteria w (j) 1 and w (j) 2 much more. personnel ranking and selection problem solution by application of kemira method 63 6 discussion and conclusions many authors studied the relationship between the set of qualitative indicators of human resources and business performance. recently, this phenomenon is gaining considerable economic importance. a lot of recruitment methods, whose main aim is to help organizations make the best personnel management decisions, are being created. many authors have expressed concern about the human resources performance assessment process because it can be biased due to the non-compliance of methods [1]. traditional human resource evaluation and selection methods are usually based on a statistical analysis for evaluation of objective indicators thought to reflect the realities. however, it is important to assess not only known but also unknown, hard-diagnosable factors. solution of this problem requires modern techniques involving large amounts of uncertain and subjective information [38]. for the assessment of indicators it is necessary to apply a taxonomy based sorting principles [7]. the different types of equally important factors should be grouped into separate sub-criteria [41]. there can be two or more sub-criteria. in selection process sub-criteria are given the same weights. this paper presents an algorithm which opens possibilities for recruitment in compliance with the above requirements. sub-criteria applied to the evaluation of candidates reflect subjective and objective information about the candidates. in the current paper two equally important hierarchical structures were generated and a new method for ranking proposed. this method combines expert (subjective) evaluation and testing indicators (objective evaluation) hierarchical layout. in the article a new approach to mcdm problem, when objects are evaluated by two groups of criteria having different origin, is presented. in the first stage criteria components preferences are established separately in each group by applying the novel method of kemeny medians. method of kemeny medians can be used when information about criteria components preferences established by independent experts is available. in the second stage criteria weights are determined and mcdm problem is solved by applying proposed indicator rank accordance method. principle of this method is to choose criteria weights values from the set of all possible values which maximize number of elements having lowest ranks according to the both criteria (16) and minimize sum of squared ranks differences 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[44] s. zhang, s. liu (2011); a gra-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection. expert systems with applications, 38(9): 11401– 11405. int j comput commun, issn 1841-9836 8(1):70-78, february, 2013. detecting ddos attacks in cloud computing environment a.m. lonea, d.e. popescu, h. tianfield alina madalina lonea "politehnica" university of timisoara, faculty of automation and computers b-dul vasile parvan, nr. 2, 300223, timisoara, romania e-mail: madalina _ lonea@yahoo.com daniela elena popescu university of oradea, faculty of electrical eng. and information tech. universitatii street, nr. 1, 410087, oradea, romania e-mail: depopescu@uoradea.ro huaglory tianfield school of engineering and built environment, glasgow caledonian university cowcaddens road, glasgow g4 0ba, united kingdom e-mail: h.tianfield@gcu.ac.uk abstract: this paper is focused on detecting and analyzing the distributed denial of service (ddos) attacks in cloud computing environments. this type of attacks is often the source of cloud services disruptions. our solution is to combine the evidences obtained from intrusion detection systems (idss) deployed in the virtual machines (vms) of the cloud systems with a data fusion methodology in the front-end. specifically, when the attacks appear, the vm-based ids will yield alerts, which will be stored into the mysql database placed within the cloud fusion unit (cfu) of the front-end server. we propose a quantitative solution for analyzing alerts generated by the idss, using the dempster-shafer theory (dst) operations in 3-valued logic and the fault-tree analysis (fta) for the mentioned flooding attacks. at the last step, our solution uses the dempsters combination rule to fuse evidence from multiple independent sources. keywords: cloud computing, cloud security, distributed denial of service (ddos) attacks, intrusion detection systems, data fusion, dempster-shafer theory. 1 introduction cloud computing technology is in continuous development and with numerous challenges regarding security. in this context, one of the main concerns for cloud computing is represented by the trustworthiness of cloud services. this problem requires prompt resolution because otherwise organizations adopting cloud services would be exposed to increased expenditures while at a greater risk. a survey conducted by international data corporation (idc) in august 2008 confirms that security is the major barrier for the cloud users. there are two things that cloud service providers should guarantee all the time: connectivity and availability, and if there are not met, the entire organizations will suffer high costs [1]. this paper is focused on detecting and analyzing distributed denial of service (ddos) attacks in cloud computing environment. this type of attacks is often the source of cloud services disruptions. one of the efficient methods for detecting ddos is to use the intrusion detection systems (ids), in order to assure usable cloud computing services [2]. however, ids sensors have the limitations that they yield massive amount of alerts and produce high false positive rates and false negative rates [3]. copyright c⃝ 2006-2013 by ccc publications detecting ddos attacks in cloud computing environment 71 with regards to these ids issues, our proposed solution aims to detect and analyze distributed denial of service (ddos) attacks in cloud computing environments, using dempstershafer theory (dst) operations in 3-valued logic and fault-tree analysis (fta) for each vmbased intrusion detection system (ids). the basic idea is to obtain information from multiple sensors, which are deployed and configured in each virtual machine (vm). the obtained information is integrated in a data fusion unit, which takes the alerts from multiple heterogeneous sources and combines them using the dempster’s combination rule. our approach quantitatively represents the imprecision and efficiently utilizes it in ids to reduce the false alarm rates. specifically, our solution combines the evidences obtained from intrusion detection systems (idss) deployed in the virtual machines (vms) of the cloud system with a data fusion methodology within the front-end. our proposed solution can also solve the problem of analysing the logs generated by sensors, which seems to be a big issue [4]. the remainder of this paper is organized as follows: section 2 introduces dempster-shafer theory. section 3 presents the related work of ids in cloud computing and the related work of ids using data fusion. section 4 introduces the proposed solution of detecting ddos attacks in cloud computing. finally, in section 5 the paper presents the concluding remarks. 2 dempster-shafer theory (dst) dempster-shafer theory is established by two persons: arthur dempster, who introduced it in the 1960’s and glenn shafer, who developed it in the 1970’s [5]. as an extension of bayesian inference, dempster-shafer theory (dst) of evidence is a powerful method in statistical inference, diagnostics, risk analysis and decision analysis. while in the bayesian method probabilities are assigned only for single elements of the state space (ω),in dst probabilities are assigned on mutually exclusive elements of the power sets of possible states [6], [7]. according to dst method, for a given state space (ω) the probability (called mass) is allocated for the set of all possible subsets of ω, namely 2ω elements. consequently, the state space (ω) is also called frame of discernment, whereas the assignment procedure of probabilities is called basic probability assignment (bpa) [6], [7], [8]. we will apply the particular case of dst, i.e., the dst operations in 3-valued logic using the fault-tree analysis (fta), adopted by guth (1991) and also used in popescu, et al. (2010). thus, if a standard state space ω is (true, false), then 2ω should have 4 elements: { ϕ, true, false, (true, false) }. the (true, false) element describes the imprecision component introduced by dst, which refers to the fact of being either true or false, but not both. dst is a useful method for fault-tree analysts in quantitatively representing the imprecision [8]. another advantage of dst is it can efficiently be utilized in ids to reduce the false alarm rates by the representation of ignorance [6], [7], [10]. for the reason that in dst the [sum of all masses] = 1 and m(ϕ) = 0,we have the following relation: m(true) + m(false) + m(true,false) = 1 (1) in order to analyze the results of each sensor we’ll use the fault tree analysis, which can be realized by boolean or gate. table 1 describes the boolean truth table for the or gate. from table 1 we have: m(a) = (a1,a2,a3) = {m(t),m(f),m(t,f)} (2) 72 a.m. lonea, d.e. popescu, h. tianfield table 1: boolean truth table for the or gate b1 b2 b3 ∨ t f (t,f) a1 t t t t a2 f t f (t,f) a3 (t,f) t (t,f) (t,f) m(b) = (b1,b2,b3) = {m(t),m(f),m(t,f)} (3) ⇒ m(a ∨ b) = (a1b1 + a1b2 + a1b3 + a2b1 + a3b1;a2b2;a2b3 + a3b2 + a3b3) (4) m(a ∨ b) = (a1 + a2b1 + a3b1;a2b2;a2b3 + a3b2 + a3b3) (5) at the last step, our solution applies the dempster’s combination rule, which allows fusing evidences from multiple independent sources using a conjunctive operation (and) between two bpa’s m1 and m2 , called the joint m12 [11]: m12(a) = ∑ b ∩ c=a m1(b)m2(c) 1 − k , (6) when : a ̸= ϕ m12(ϕ) = 0 and k = ∑ b ∩ c=ϕ m1(b)m2(c) the factor 1-k, called normalization factor, is constructive for entirely avoiding the conflict evidence. data fusion is also applied in real world examples: robotics, manufacturing, remote sensing and medical diagnosis, as well in military threat assessment and weather forecast systems [12]. sentz and ferson (2002) demonstrated in their study that dempster’s combination rule is suitable for the case that the sources of evidences are reliable and a minimal conflict or irrelevant conflict is generated. 3 related work 3.1 intrusion detection systems (ids) in cloud computing one of the ids strategies proved reliable in cloud computing environments is its applicability to each virtual machine. this is the method we’ll choose for our proposed solution. mazzariello, et al. (2010) presented and evaluated this method in comparison with another ids deployment strategy, which uses single ids near the cluster controller. ids applied to each virtual machine in cloud computing platform eliminates the overloading problem, because in a way the network traffic is split to all idss. thus, applying ids to each virtual machine gets rid of the issue of the ids strategy near the cluster controller, which tends to be overloaded because of its necessity to monitor all the supposed traffic from the cloud computing infrastructure. another advantage of this strategy as described by roschke, et al. (2009) is the benefit of reducing the impact of the possible attacks by the ids sensor vms. however, the limitation of ids strategy applied to each virtual machine is the missing of the correlation phase, which is suggested in the future work by mazzariello, et al. (2010). detecting ddos attacks in cloud computing environment 73 the correlation phase will be included in our proposed solution, because beside the ids for each virtual machine, our ids cloud topology will include a cloud fusion unit (cfu) on the front-end, with the purpose of obtaining and controlling the alerts received from the ids sensor vms as presented by roschke, et al. (2009) in their theoretical ids architecture for cloud, which utilizing an ids management unit. compared to roschke, et al. (2009) who suggested the utilization of idmef (intrusion detection message exchange) standard, a useful component for storage and exchange of the alerts from the management unit, the alerts in our proposed solution will be stored into the mysql database of cloud fusion unit. the cloud fusion unit will add the capacity to analyze the results using the dempster-shafer theory (dst) of evidence in 3-valued logic and the faulttree analysis for the ids of each virtual machine and at the end the results of the sensors will be fused using dempster’s combination rule. a similar method of using a ids management unit is proposed in dhage, et al. (2011), who presented a theoretical model of an ids model in cloud computing, by using a single ids controller, which creates a single mini ids instance for each user. this ids instance can be used in multiple node controllers and a node controller can contain ids instances of multiple users. the analysis phase of the mini ids instance for each user takes place in the ids controller. compared with roschke, et al. (2009) where the emphasis is on how to realize the synchronization and integration of the ids sensor vms, in dhage, et al. (2011) the focus is to provide a clear understanding of the cardinality used in the basic architecture of ids in cloud infrastructure. applying the ids for each virtual machine is an idea suggested also by lee, et al. (2011), who increases the effectiveness of ids by assigning a multi-level intrusion detection system and the log management analysis in cloud computing. in this sense the users will receive appropriate level of security, which will be emphasized on the degree of the ids applied to the virtual machine, and as well on the prioritization stage of the log analysis documents. this multi-level security model solves the issue of using effective resources. lo, et al. (2010) proposed a cooperative ids system for detecting the dos attacks in cloud computing networks, which has the advantage of preventing the system from single point of failure attack, even if it is a slower ids solution than a pure snort based ids. thus, the framework proposed by lo, et al. (2010) is a distributed ids system, where each ids is composed of three additional modules: block, communication and cooperation, which are added into the snort ids system. 3.2 ids using dempster-shafer theory dempster-shafer theory (dst) is an effective solution for assessing the likelihood of ddos attacks, which was demonstrated by several research papers in the context of network intrusion detection systems. dissanayake (2008) presented a survey upon intrusion detection using dst. our study is to detect ddos attacks in cloud computing environments. dempster-shafer theory (dst) is used to analyze the results received from each sensor (i.e. vm-based ids). data used in experiments using dst vary: yu and frincke (2005) used darpa ddos intrusion detection evaluation datasets, chou et al. (2008) used darpa kdd99 intrusion detection evaluation dataset, chen and aickelin (2006) used the wisconsin breast cancer dataset and iris plant data, while others scientists generated their own data [7]. the data to be used in our proposed solution will be generated by ourselves, by performing ddos attacks using specific tools against the vm-based ids. siaterlis, et al. (2003) and siaterlis and maglaris (2005) performed a similar study of detecting ddos using data fusion and their field was an operational university campus network, while in our solution the ddos attacks are proposed to be detected and analyzed in our private cloud 74 a.m. lonea, d.e. popescu, h. tianfield computing environment. additionally, we consider to analyze the attacks generated against the tcp, udp, icmp packets, like siaterlis, et al. (2003) and siaterlis and maglaris (2005). however, instead of applying dst on the state space ω = {normal,udp − flood,sy n − flood,icmp − flood}, our study uses dst operations in 3-valued logic as suggested by guth (1991) for the same flooding attacks: tcp-flood, udp-flood, icmp-flood, for each vm-based ids. like siaterlis and maglaris (2005), chatzigiannakis, et al., (2007) chosen the same frame of discernment, while hu, et al. (2006) used a state space: {normal, tcp, udp and icmp}. furthermore, compared with the study performed by siaterlis, at al. (2003) and siaterlis and maglaris (2005), who use a minimal neural network at the sensor level, our proposed solution will assign the probabilities using: dst in 3-valued logic, the pseudocode and the fault tree analysis. whilst the computational complexity of dst is increasing exponentially with the number of elements in the frame of discernment [12], the dst 3-valued logic proposed to be used in our research will not encounter this issue, which will meet the efficiency requirements in terms of both detection rate and computation time [15]. finally, the data fusion of the evidences obtained from sensors studied by siaterlis and maglaris (2005) will be used in our study. the data fusion will be realized using the dempstershafer combination rule, which was demonstrated in siaterlis and maglaris (2005) for its advantages, i.e., maximization of ddos true positive rates and minimization of the false positive alarm rate, by combining the evidence received from sensors. therefore, the work of cloud administrators will be alleviated, whereas the number of alerts will decrease. 4 proposed solution in order to detect and analyze distributed denial of service (ddos) attacks in cloud computing environments we propose a solution as presented in figure 1. for illustration purpose, a private cloud with a front-end and three nodes is set up. whilst the detection stage is executed within the nodes, more precisely inside the virtual machines (vms), where the intrusion detection systems (idss) are installed and configured; the attacks assessment phase is handled inside the front-end server, in the cloud fusion unit (cfu). the first step in our solution includes the deployment stage of a private cloud using eucalyptus open-source version 2.0.3. the topology of the implemented private cloud is: a front-end (with cloud controller, walrus, cluster controller, storage controller) and a back-end (i.e. three nodes). the managed networking mode is chosen because of the advanced features that it provides and xen hypervisor is used for virtualization. then, the vm-based ids are created, by installing and configuring snort into each vm. the reason of using this ids location is because the overloading problems can be avoided and the impact of possible attacks can be reduced [2], [13]. these idss will yield alerts, which will be stored into the mysql database placed within the cloud fusion unit (cfu) of the front-end server. a single database is suggested to be used in order to reduce the risk of losing data, to maximize the resource usage inside the vms and to simplify the work of cloud administrator, who will have all the alerts situated in the same place. a similar idea of obtaining and controlling the alerts received from the ids sensor vms using an ids management unit was presented by roschke, et al. (2009) as a theoretical ids architecture for cloud. a similar method of using an ids management unit is proposed in dhage, et al. (2011). however, our solution adds the capacity to analyse the results using the dempster-shafer theory of evidence in 3-valued logic. as showed in figure 1, the cloud fusion unit (cfu) comprises 3 components: mysql database, bpas calculation and attacks assessment. detecting ddos attacks in cloud computing environment 75 figure 1: ids cloud topology i. mysql database the mysql database is introduced with the purpose of storing the alerts received from the vm-based ids. furthermore, these alerts will be converted into basic probabilities assignments (bpas), which will be calculated using the pseudocode below. ii. basic probabilities assignment (bpa’s) calculation for calculating the basic probabilities assignment, first we decide on the state space ω. in this paper we use dst operations in 3-valued logic {true, false, (true, false)} guth (1991) for the following flooding attacks: tcp-flood, udp-flood, icmp-flood, for each vm-based ids. thus, the analyzed packets will be: tcp, udp and icmp. further, a pseudocode for converting the alerts received from the vm-based ids into bpas is provided. the purpose of this pseudocode is to obtain the following probabilities of the alerts received from each vm-based ids: (mudp (t),mudp (f),mudp (t,f)) (mtcp (t),mtcp (f),mtcp (t,f)) (micmp (t),micmp (f),micmp (t,f)) 76 a.m. lonea, d.e. popescu, h. tianfield figure 2: bpa’s calculation pseudocode for converting the alerts into bpa’s: for each node begin for each x ∈ {udp; tcp; icmp}: begin 1: query the alerts from the database when a x attack occurs for the specified hostname 2: query the total number of possible x alerts for each hostname 3: query the alerts from the database when x attack is unknown 4: calculate the belief (true) for x, by dividing the result obtained at step 1 with the result obtained at step 2 5: calculate the belief (true, false) for x, by dividing the result obtained at step 3 with the result obtained at step 2 6: calculates belief (false) for x: 1belief (true) belief (true, false) end end furthermore, after obtaining the probabilities for each attack packet (i.e. udp, tcp, icmp) for each vm-based ids, the probabilities for each vm-based ids should be calculated following the fault-tree as shows in figure 2. figure 2 reveals only the calculation of the probabilities (i.e. ms1(t),ms1(f),ms1(t,f)) for the first vm-based ids. thus, using the dst with fault-tree analysis we can calculate the belief (bel) and plausibility (pl) values for each vm-based ids: bel(s1) = ms1(t) (7) pl(s1) = ms1(t) + ms1(t,f) (8) iii. attacks assessment the attacks assessment consists of data fusion of the evidences obtained from sensors by using the dempster’s combination rule, with the purpose of maximizing the ddos true positive rates and minimizing the false positive alarm rate. ms1,s2(t) can be calculated using table 2 and equation (6). detecting ddos attacks in cloud computing environment 77 table 2: boolean truth table for the or gate ms1(t) ms1(f) ms1(t,f) ms2(t) ms1(t) ms2(t) ms1(f) ms2(t) ms1(t,f) ms2(t) ms2(f) ms1(t) ms2(f) ms1(f) ms2(f) ms1(t,f) ms2(f) ms2(t,f) ms1(t) ms2(t,f) ms1(f) ms2(t,f) ms1(t,f) ms2(t,f) 5 conclusions to detect and analyze distributed denial of service (ddos) attacks in cloud computing environments we have proposed a solution using dempster-shafer theory (dst) operations in 3-valued logic and the fault-tree analysis (fta) for each vm-based intrusion detection system (ids). our solution quantitatively represents the imprecision and efficiently utilizes it in ids to reduce the false alarm rates by the representation of the ignorance. whilst the computational complexity of dst is increasing exponentially with the number of elements in the frame of discernment [12], the dst 3-valued logic in our solution does not have this issue, which meets the efficiency requirements in terms of both detection rate and computation time. at the same time, the usability requirement has been accomplished, because the work of cloud administrators will be alleviated by using the dempster rule of evidence combination whereas the number of alerts will decrease and the conflict generated by the combination of information provided by multiple sensors is entirely eliminated. to sum up, by using dst our proposed solution has the following advantages: to accommodate the uncertain state, to reduce the false negative rates, to increase the detection rate, to resolve the conflicts generated by the combination of information provided by multiple sensors and to alleviate the work for cloud administrators. acknowledgment this work was partially supported by the strategic grant posdru/88/1.5/s/50783, project id50783 (2009), co-financed by the european social fund investing in people, within the sectoral operational programme human resources development 2007-2013. bibliography [1] perry, g., minimizing public cloud disruptions, techtarget, [online]. available at: http://searchdatacenter.techtarget.com/tip/minimizing-public-cloud-disruptions, 2011. 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[10] esmaili, m., dempster-shafer theory and network intrusion detection systems. scientia iranica, vol. 3, no. 4, sharif university of technology, 1997. [11] sentz, k. and ferson, s., combination of evidence in dempster-shafer theory. sandia national laboratories, sandia report, 2002. [12] dissanayake, a., intrusion detection using the dempster-shafer theory. 60-510 literature review and survey, school of computer science, university of windsor, 2008. [13] mazzariello, c., bifulco, r. and canonico, r., integrating a network ids into an open source cloud computing environment. in sixth international conference on information assurance and security, pp. 265-270, 2010. [14] dhage, s. n., et al., intrusion detection system in cloud computing environment. in international conference and workshop on emerging trends in technology (icwet 2011) ’ tcet, mumbai, india, pp. 235-239, 2011. [15] lo, c-c. , huang, c-c. and ku, j., a cooperative intrusion detection system framework for cloud computing networks. in 39th international conference on parallel processing workshops, pp.280-284, 2010. [16] yu, d. and frincke, d., alert confidence fusion in intrusion detection systems with extended dempster-shafer theory. acm-se 43: proceedings of the 43rd acm southeast conference, pp. 142-147, 2005. [17] chou, t., yen, k.k., luo, j., network intrusion detection design using feature selection of soft computing paradigms. international journal of computational intelligence, 4(3):102105, 2008. [18] chatzigiannakis, v., et al., data fusion algorithms for network anomaly detection: classification and evaluation. proceedings of the third international conference on networking and services (icns’07), 2007. [19] hu, w., li, j. and gao, q., intrusion detection engine based on dempster-shafer’s theory of evidence. communications, circuits and systems proceedings, 2006 international conference, 3:1627-1631, 2006. ijcccv11n4.dvi international journal of computers communications & control issn 1841-9836, 11(4):472-479, august 2016. direct evolutionary search for nash equilibria detection r.i. lung rodica ioana lung babeş-bolyai university of cluj-napoca faculty of economics and business administration t. mihali 58-60, cluj-napoca rodica.lung@econ.ubbcluj.ro abstract: a direct method of computing mixed form nash equilibria of a normal form game by using a simple evolutionary algorithm is proposed. the direct evolutionary search algorithm (des) uses a generative relation for nash equilibria with binary tournament selection and uniform mutation. numerical experiments are used to illustrate the efficiency of the method. keywords: mixed form nash equilibria, evolutionary algorithms, generative relation 1 introduction the problem of computing nash equilibria of normal form games is one of the most challenging problems faced by computer scientists. the complexity of this problem varying hugely from a type of game to another is still studied. normal form games can be solved by modern heuristics by transforming them into an optimization or fixed point problem [2]. an interesting challenge consists on designing a method that solves the game directly, as a game and not as a corresponding optimization problem. the main problem in directly approaching the nash equilibria is caused by the fact that there does not exist an order (or even preorder) relation defined for game situations that can guide a search operator towards the nash equilibrium. while any kind of optimization endeavor is driven by a corresponding order (or preorder) relation defined in the objective space, in the case of nash equilibria the lack of such a relation limits the design possibilities of computational heuristics. however, recently [1] a generative relation for nash equilibria called the nash ascendancy relation has been proposed. the nash ascendancy relation is defined on strategy profiles. even if it does not induce an actual order, numerical results indicate that it is capable of guiding a search operator towards nash equilibria. nash equilibria definition and generative relation nash equilibrium [4] is the most popular solution concept in noncooperative game theory. a finite strategic game is defined by a set o players, a set of strategies available to each player and a set of payoff functions for each player and denoted by γ = (n,s,u) where: • n represents the set of players, n = {1, ....,n}, n is the number of players; • for each player i ∈ n, si represents the set of actions available to him, si = {si1,si2, ...,simi} where mi represents the number of strategies available to player i and s = s1×s2×...×sn is the set of all possible situations of the game; • for each player i ∈ n denote by pij the probability that he selects its j-th action, j ∈ {1, ...,mi}. then pi = (pi1, ...,pimi) represents a probability distribution over the set of actions of player i and p = (p1, ...,pn) represents a mixed strategy profile for the game, where pij ∈ [0,1] and ∑mi j=1 pij = 1, ∀i = 1,n and ∀j = 1,mi; copyright © 2006-2016 by ccc publications direct evolutionary search for nash equilibria detection 473 • for each player i ∈ n, ui(p) represents the expected payoff for the mixed strategy p ; denote by (qi,p∗−i) the strategy profile obtained from p ∗ by replacing the probability distribution of player i with qi i.e. (qi,p ∗ −i) = (p ∗ 1 ,p ∗ 2 , ...,p ∗ i−1,qi,p ∗ i+1, ...,p ∗ n). a strategy profile p ∈ s for the game γ represents a nash equilibrium [2, 4] if no player has anything to gain by unilaterally changing his own strategy while the others do not modify theirs. several methods to compute ne of a game have been developed. for a review on computing techniques for the ne see [2] and [6]. nash ascendancy relation a generative relation for nash equilibria is a relation between two strategy profiles that enables their comparison with respect to the nash solution concept, i.e. it evaluates which one is "closer" to equilibrium. in [1] such a generative relation has been introduced and shown that solutions that are non-dominated/ascended with respect to this relation are exactly the nash equilibria of the game. consider two mixed strategy profiles p and q and the operator κ that associates the cardinality of the set κ(p,q) = |{i ∈{1, ...,n}|ui(qi,p−i) ≥ ui(p),qi 6= pi}| to the pair (p,q), i.e. the number of players i that would benefit from unilaterally switching their strategies from pi to qi. the operator κ can be used to induce a relation on the set of mixed strategy profiles in the following manner: we can say that the strategy profile p nash ascends the strategy profile q in and we write p ≺n q if the inequality κ(p,q) < κ(q,p) holds, i.e. there are les players that can increase their payoffs by switching their strategy from p to q than vice-versa. it can be said that strategy profile p is more stable (closer to equilibrium) then strategy q. regarding the nash ascendancy relation, two strategy profiles p and q may be in the following situation: 1. p ascends q: p ≺n q (κ(p,q) < κ(q,p)) 2. q ascends p : q ≺n p (κ(p,q) > κ(q,p)) 3. or κ(p,q) = κ(q,p) and p and q are considered indifferent (neither p ascends q nor q ascends p). the strategy profile p∗ is called non-ascended in nash sense (nas) if there does not exist any mixed strategy profile q such that q 6= p∗ and q ≺n p∗. a very important result in [1] shows that for pure strategies all non-ascended strategies are ne and also all ne are non-ascended strategies. the proof in the case of mixed strategies is 474 r.i. lung similar and direct. thus the nash ascendancy relation can be used to characterize the equilibria of a game and can be considered as a generative relation for nes. the nash ascendancy concept was introduced with the purpose to compare two strategy profiles [1] during the search of an evolutionary algorithm in order to compute nes of a game. consider two strategy profiles p∗ and p from ∆. then k : ∆ × ∆ → n associates the pair (p∗,p) the cardinality of the set k(p∗,p) = card{i ∈{1, ...,n}|ui(pi,p∗−i) > ui(p∗),pi 6= p∗i }. this set is composed by the players i that would benefit if given the strategy profile p∗ would change their strategy from p∗i to pi. it is obvious that for any p∗,p ∈ s, we have 0 ≤ k(p∗,p) ≤ n. definition 1. let p,q ∈ ∆. we say the strategy profile p nash ascends q and we write p ≺ q if the inequality k(p,q) < k(q,p), holds. remark 1.1. two strategy profiles p,q ∈ ∆ can have the following relation: 1. either p nash ascends q, 2. either q nash ascends p, 3. if k(p,q) = k(q,p) then p and q are indifferent definition 2. a strategy profile p∗ ∈ s is called non-dominated with respect to the nash ascendancy relation (nns) if ∄q ∈ ∆,q 6= p∗such that q ≺ p∗. definition 3. the set of all nash nondominated strategy profiles with respect to the nash ascendancy relation is the set containing all nondominated strategies i.e. nnd = {s ∈ s|s nash non-dominated with respect to the nash ascendancy relation} proposition 1. a strategy profile p∗ ∈ ∆ is a ne iff the equality k(p∗,q) = 0,∀q ∈ ∆, holds. proof: let p∗ ∈ ∆ be a ne. suppose there exists q ∈ ∆ such that k(p∗,q) = w, w ∈ {1, ...,n}. therefore there exists i ∈ {1, ...,n} such that ui(qi,p∗−i) > ui(p∗) and qi 6= p∗i , which contradicts the definition of ne. for the second implication, let p∗ ∈ ∆ such that ∀q ∈ ∆,k(p∗,q) = 0. this means that for all i ∈{1, ...,n} and for any qi ∈pi we have ui(qi,p∗−i) ≤ ui(p∗). it follows that p∗ is a ne. ✷ proposition 2. all ne are nash nondominated solutions (nnd) i.e. ne ⊆ nnd. direct evolutionary search for nash equilibria detection 475 proof: let p∗ ∈ ∆ be a ne. suppose that there exists a strategy profile p ∈ ∆ such that p ≺ p∗. it follows that k(p,p∗) < k(p∗,p). but k(p∗,p) = 0, therefore we must have k(p,p∗) < 0 which is not possible since k(p,p∗) denotes the cardinality of a set. ✷ proposition 3. all nash nondominated solutions are ne, i.e. nnd ⊆ ne. proof: let p∗ be a nondominated strategy profile. suppose p∗ is not ne. therefore there must exist (at least) one i ∈{1, ...,n} and a strategy pi ∈pi such that ui(pi,p ∗ −i) > ui(p ∗), holds. let’s denote by q = (pi,p∗−i). it means that k(p ∗,q) = 1. but k(q,p∗) = 0. therefore k(q,p∗) < k(p∗,q) which means that q ≺ p∗ thus the hypothesis that p∗ is nondominated is contradicted. ✷ using propositions 2 and 3 it is obvious that the next result holds: proposition 4. the following relation holds: ne = nnd, i.e. all ne are also nash nondominated and also all nash nondominated strategies are ne. direct evolutionary search the direct evolutionary search (des) algorithm is a simple evolutionary algorithm based on tournament selection and uniform mutation designed for nash equilibria detection. individuals represent strategy profiles of the game. real valued encoding is used. each individual is represented as a vector composed of ( ∑n i=1 mi) components between 0 and 1. for each individual n corresponding payoffs are computed. selection binary tournament selection is used in the following manner: for each individual i, another one k is selected randomly. if individual k nash ascends individual i, k is selected and copied in a separate population of children p ′. mutation uniform mutation with probability pm is applied to all children in p ′. with probability pm all strategies are modified (±) with ε. if the resulting value is lower than 0, it is set to 0. if it is higher than 1, it is set to 1. termination condition des runs either a maximum number of generations which is a parameter of the algorithm and depends on the problem, either until no child can replace a parent for 100 generations. for this the variables countreplacements and control in algorithm 1 are used. 476 r.i. lung algorithm 1 direct evolutionary search algorithm randomly generate population; evaluate population; countreplacements = 1; control = true; for nrgen = 0; ((nrgen < maxnogenerations) and (control));nrgen + + do apply selection; apply mutation(pm); replace children; countreplacements+ =no of children that replace a parent; if nrgen = 100 then if countreplacements = 0 then control = false; else countreplacements = 0; end if end if end for return non-ascended solutions; =0 table 1: description of the seven games tested name of game no. of players no. of strategies type of ne game1 2 2,2 totally mixed game2 2 2,2 totally mixed matching pennies 2 2,2 totally mixed g1 3 2,2,2 totally mixed g2 3 3,3,3 mixed o’neill 2 4,4 totally mixed poker 2 4,2 totally mixed direct evolutionary search for nash equilibria detection 477 table 2: descriptive statistics of numerical results obtained by des using the following parameters: population size 50, pm = 0.5, ε = 0.5. game1 avg. dist: 0 st dev: 0 avg. no. gen.: 844.33 st dev: 476.57 avg. eval.: 447,117.47 st dev: 271909.88 game3 avg. dist: 7.75094e-17 st dev: 3.13e-17 avg. no. gen.: 501.00 st dev: 141.42 avg. eval.: 243,736.93 st dev: 74416.31 matching pennies avg. dist: 0 st dev: 0 avg. no. gen.: 497.67 st dev: 125.12 avg. eval.: 233,854.53 st dev: 56641.84 g1 avg. dist: 4.04061e-11 st dev: 2.25e-17 avg. no. gen.: 11,941 st dev: 13424.39 avg. eval.: 15,541,134 st dev: 17317631 g2 avg. dist: 4.04061e-11 st dev: 6.98e-18 avg. no. gen.: 931 st dev: 324.70 avg. eval.: 1,076,041 st dev: 374858.2 oneill avg. dist: 2.79146e-05 st dev: 0.0001 avg. no. gen.: 8,217.63 st dev: 26628.89 avg. eval.: 6,556,652.7 st dev: 21598601 poker avg. dist: 3.70074e-18 st dev: 1.99e-17 avg. no. gen.: 794.33 st dev: 240.73 avg. eval.: 419,827.93 st dev: 126713.48 2 numerical results des was first tested on a set of simple well known 2 or 3-players with up to 4 actions available to them. the games have been chosen from the gambit [3] set of normal form games. the characteristics of the games are presented in table 1. table 2 presents descriptive statistics of the results obtained using des for the seven games: average and standard deviation values for the minimum distance to the ne of the game for the non ascended solutions in the final population, number of evaluations and of generations until des stopped the search. the second set of games that was used to test if des was generated by using the gamut distribution [5]. the distribution names, characteristics and rates of success of des are presented in table 3. 478 r.i. lung table 3: gamut distributions and results obtained by des. rate of success represents percent of runs in which des detected a ne. parameters of des: population size 100, pm = 0.1 distribution no. of players no. of actions rate of success bertrand 2 10/player 100% ologopoly 2 20/player 100% 2 50/player 100% 2 100/player 100% 5 2/player 100% 5 4/player 100% 5 6/player 100% bidirectional leg 2 10/player 100% complete graph 2 20/player 100% 2 50/player 100% 2 100/player 100% 5 2/player 100% 5 4/player 100% 5 6/player 100% bidirectional leg 2 10/player 100% random graph 2 20/player 100% 2 50/player 100% 2 100/player 100% 5 2/player 100% 5 4/player 100% 5 6/player 100% bidirectional leg 2 10/player 100% star graph 2 20/player 100% 2 50/player 100% 2 100/player 100% 5 2/player 100% 5 4/player 100% 5 6/player 100% covariance game 2 10/player 100% ρ = 0.9 2 20/player 100% 2 50/player 100% 2 100/player 100% 5 2/player 100% 5 4/player 100% 5 6/player 100% direct evolutionary search for nash equilibria detection 479 conclusion the presented results indicate that it it possible for an evolutionary algorithm to directly detect nash equilibria of normal form games. a population composed of situations of the game is randomly generated; using selection and mutation operators and the nash ascendancy relation individuals of the populations are guided towards the nash equilibrium of a normal form game. two sets of problems have been chosen to test the method: the first one consists of games presenting a mixed nash equilibrium. results indicate that des is capable of locating mixed equilibria for the selected games. the second group of games is used to test the scalability of the method: the number of actions available to each player, as well as the number of players are increased for five distributions available in the gamut package. results also indicate the potential of the method, inspite of well known scalability issues associated with evolutionary algorithms. acknowledgment this work was supported by a grant of the romanian national authority for scientific research and innovation, cncs uefiscdi, project number pn-ii-ru-te-2014-4-2560. bibliography [1] rodica ioana lung and d. dumitrescu (2008); computing nash equilibria by means of evolutionary computation, intrenational journal of computers communications & control, suppl. issue, 3(5):364-368. [2] richard d. mckelvey and andrew mclennan (1996); computation of equilibria in finite games. in h. m. amman, d. a. kendrick, and j. rust, editors, handbook of computational economics, volume 1 of handbook of computational economics, chapter 2, pp. 87-142, elsevier, 1996. [3] richard d. mckelvey, andrew m. mclennan, and theodore l. turocy (2010); gambit: software tools for game theory, technical report. [4] john f. nash (1951) non-cooperative games, annals of mathematics, 54:286-295. [5] eugene nudelman, jennifer wortman, yoav shoham, and kevin leyton-brown(2004); run the gamut: a comprehensive approach to evaluating game-theoretic algorithms, proceedings of the third international joint conference on autonomous agents and multiagent systems, washington, dc, usa, 2004. ieee computer society, aamas04, 2: 880-887. [6] ryan porter, eugene nudelman, and yoav shoham (2008); simple search methods for finding a nash equilibrium, games and economic behavior, 63(2): 642-662. ijcccv8n1.pdf int j comput commun, issn 1841-9836 8(1):18-29, february, 2013. broadcast scheduling problem in tdma ad hoc networks using immune genetic algorithm d. arivudainambi, d. rekha d. arivudainambi, d. rekha department of mathematics anna university, chennai, india e-mail: arivu@annauniv.edu, rekhadurai@gmail.com abstract: in this paper, a new efficient immune genetic algorithm (iga) is proposed for broadcast scheduling problem in tdma ad hoc network. broadcast scheduling is a primary issue in wireless ad hoc networks. the objective of a broadcast schedule is to deliver a message from a given source to all other nodes in a minimum amount of time. broadcast scheduling avoids packet collisions by allowing the nodes transmission that does not make interference of a time division multiple access (tdma) ad hoc network. it also improves the transmission utilization by assigning one transmission time slot to one or more non-conflicting nodes such a way that every node transmits at least once in each tdma frame. an optimum transmission schedule could minimize the length of a tdma frame while maximizing the total number of transmissions. the aim of this paper is to increase the number of transmissions in fixed ad hoc network with time division multiple access (tdma) method, with in a reduced time slot. the results of iga are compared to the recently reported algorithms. the simulation result indicates that iga performs better even for a larger network. keywords: ad hoc networks, broadcast scheduling, genetic algorithm, immune genetic algorithm. 1 introduction a wireless ad hoc network is a collection of nodes, which communicate with each other using radio transmissions. in wireless networks, nodes can communicate directly if they are within the transmission range of each other. when a source node is out of the transmission range of destination node then it uses intermediate nodes for routing their message. in ad hoc networks, there are no base stations to act as routers and the nodes themselves perform routing. therefore, data should be delivered from source to destination through multiple hops. ad hoc networks rely on multihop transmission among the nodes on the same channel. we have proposed how efficiently broadcasting can be done in ad hoc network. in this paper, we consider a fixed ad hoc networks with tdma and its topology can be represented by a graph. tdma is divided into frames where each frame is further divided into time slots that can be assigned to different nodes. in a single frame, all nodes must be allowed to transmit packets at least once. tdma scheduling algorithms can be categorized into link scheduling algorithm and broadcast/node scheduling algorithm [10]. in ad hoc network, a link can be represented as (t, r), where t is a transmitter and r is a receiver. in link scheduling, the transmission in every slot is assigned to certain links, where as in broadcast scheduling the transmission in every slot is assigned to certain nodes. the aim of the algorithm is to activate all the nodes at least once, to improve the utilization factor of the network channels, i.e., to increase the number of transmissions within minimum number of time slots. ahmad et al., [1] proposed an algorithm based on finite state machine synthesis that determines the minimum frame length with the maximum slot utilization. a maximal compatible set of nodes is produced and these are chosen such that the nodes in that set do not have conflicts copyright c© 2006-2013 by ccc publications broadcast scheduling problem in tdma ad hoc networks using immune genetic algorithm19 with one another. a tight lower bound derived from set of maximal incompatibles forms the basis for deriving minimum frame length. the algorithm applies set of rules on the maximal compatibles in order to maximize utilization of slots. genetic algorithm (ga) is population-based stochastic optimization method with an iterative process of generation-and-test. it has been recognized that ga is promising approach for nphard or np-complete problems. ga solves many search and optimization problems effectively. a standard genetic algorithm approach is given by chakraborty in 1998 for scheduling problem in packet radio networks, though the algorithm able to solve small problems but performs poorly for large networks. this is because classical crossover and mutation operations create invalid population that goes through several generations and delay the progress of search for valid solutions. special crossover and mutation operations for elite population method is defined by chakraborty [3], such that members of the population always remain valid solutions for the problem. even though, it produces optimal solution in less number of generations and it reduces invalid solutions but the computation time is not reduced. gunasekaran et al., [4] proposed two different algorithms for spatial reuse in wimax networks. first, a dynamic programming (dp) method is adopted to produce maximal collision free set of nodes, but suffered from high memory requirements. the second one is genetic algorithm approach, it is more scalable than dp approach but does not guarantee optimality. two kinds of population are used in co-evolutionary genetic algorithm that act as competitors for each other and hence increase the evolving rates. this approach seems to have a better performance than classical genetic algorithm approach, which did not produce good result for large networks. the main drawback of co-evolutionary genetic algorithm is every member of test population has to be compared with every member of solution population. this requires many comparisons, calculations and hence may slow down the procedure when population sizes are vast. a novel hysteretic noisy chaotic neural network (hncnn) by controlling noises of the equivalent model for broadcast scheduling problem in packet radio networks is proposed by ming sun et al., [7]. they combine the hncnn with the gradual expansion scheme to find the minimal frame length in the first phase, and to maximize the conflict-free transmission in the second phase. ngo and li [8] proposed an approach based on a modified ga called genetic-fix. they formulate the problem based on a within-two-hop connectivity matrix and proposed a centralized scheduling algorithm using a modified genetic-fix algorithm. traditional ga generates subsets of all possible sizes whereas genetic-fix algorithm generates fixed-size subsets i.e., in binary representation number of one’s is fixed. a mixed tabu-greedy algorithm has been implemented to solve the broadcast scheduling problem in packet radio networks is given by peng et al., [9]. improvements are achieved in terms of both channel utilization and packet delay by using a two-step algorithm. the problem of determining an optimal minimum length tdma schedule for a general multihop radio network is np-complete for both link and broadcast scheduling [10]. both link scheduling and broadcast scheduling are considered in their approach. salcedo et al., [11] propose a procedure that combines hopfield neural network for the constraints satisfaction and genetic algorithm for achieving maximal throughput. their approach to solve broadcast scheduling problem is by dividing problem in two sub problems. the first is to find minimum frame length without interference using discrete hopfield neural network. the second increases the throughput for given frame length is done by combining hopfield neural network with genetic algorithm. a linear integer programming formulation for the composite problem of maximizing channel utilization while minimizing the length of the frame is given by syam menon [12] that performs in reduced computation time but maximum number of stations taken in their approach is 50 20 d. arivudainambi, d. rekha stations. in [13], wang and ansari propose a broadcast scheduling algorithm based on mean field annealing neural networks. they proposed a three-step algorithm first step reduces solution space by presetting some neurons according to the topology of the scheduling network. second step executes mfa procedure to maximize channel utilization. a heuristic approach is the final step to arrange transmissions of unassigned stations. a competent permutation encoded genetic algorithm is executed to solve optimum time division multiple access broadcast scheduling problem for mobile ad hoc networks is proposed by wu et al., [14]. the problem search space is reduced mostly and genetic algorithm becomes more capable in searching the optimum solutions. in genetic algorithm, the mutation creates new genes for the population and crossover operator orients seeking the best solution from genes in the population. however, they may drop into local optimal solutions or they may find optimal solution by low convergence speed and ga blindly wanders over search space. in ga, a normal mutation operator takes chance to change a best solution obtained from previous operation. to overcome these problems, we used immune concept to enhance the ga. immune genetic algorithm (iga) does not perform mutation in a normal way and mutation operator is carried out by two steps in this algorithm 1) immune selection and 2) vaccination. immune selection performs reduction of time slots whereas vaccination gets the knowledge from hop matrix to mutate a bit. iga increases number of transmission in a reduced time slot. the aim of proposed immune genetic algorithm is to activate all nodes at least once, while minimizing number of time slots. another goal is to increase utilization factor of network channels, i.e., the usage of available channels should be maximized by increasing number of transmissions. the method based on immune genetic algorithm for the broadcast scheduling problem in ad hoc networks has not been studied so far. a comparison with existing methods for the test instance reported in the literature shows that our algorithm identifies optimal solution in less number of generations and the average time delay is reduced even for large network. this paper is organized as follows: section 2 gives a formal definition of broadcast scheduling problem, along with constraints to be satisfied. an immune genetic algorithm approach is provided in section 3. simulation, results and analysis of the proposed algorithm are provided in section 4. section 5 concludes the paper. 2 formulation of broadcast scheduling problem ad hoc topology can be represented as undirected graph g = (n, e), where n is the set of nodes or mobile stations and e is the links or transmissions assumed bidirectional. a link (i, j) exists between nodes i and j if they are within transmission range of each other. when node i transmits data, other nodes within the transmission range of i will receive it. if both i and j transmit packet in the same time slot then it leads to primary conflict. when a node receives two or more packets from directly connected different nodes in a single time slot then it cause to secondary conflict. for a collision free transmission primary conflict and secondary conflict should not occur. two mobile stations can transmit in the same time slot without mutual interference, if they are located more than two hops apart. three important matrixes used in this study are connectivity matrix [cm] is to represent a direct link between nodes, hop information matrix [hm] is to represent one-hop and two-hop connectivity information of each node and tdma frame matrix [tm] is to represent the allotted time slots of given network without any interference. the connectivity matrix in fig.2.(a) represents direct link between nodes given in the network fig.1. each column represents the nodes of network and row represents link existence between nodes. in fig.2.(a), first row says about connectivity information of node 1, likewise for remaining broadcast scheduling problem in tdma ad hoc networks using immune genetic algorithm21 figure 1: a sample 6-node network figure 2: (a) connectivity matrix and (b) hop information matrix for a 6-node network figure 3: (a), (b), (c) and (d) sample solution tdma frames created for a 6-node network nodes. the matrix has value 0 or 1, where 1 represents existence of a link. the hop information matrix for the given 6-node network is shown in fig.2.(b), where row value represents one-hop and two-hop information between nodes. the matrix takes value 0 or 1 from first row, it is identified nodes 2 and 3 is either one-hop or two-hop away from node 1. the tdma frame matrix is a |m|x|n| matrix where |m| is the number of time slots and |n| = {n1, n2, ..., nx} is total number of nodes in the network. for the 6-node network, possible tdma frame matrix is shown in fig.3. the first [tm] is a trivial solution where there is no chance to get conflict since each node is assigned in each time slot. fig.3.(b) & (c). represents solution frame with reduction in time slot but it is not an optimal solution, where as the solution frame in fig.3.(d) is generated with 4 time slots which is optimal solution for the given 6-node network. the optimal solution is determined based on fitness criteria tight lower bound and channel utilization variable. tight lower bound is md = max nǫ|n| |ds (n)| (1) let ds(n) be the degree set of n nodes, md represents maximum degree of the network, based on this value tight lower bound is generated as, ∆ = |m|− md ≥ 1 (2) if ∆ = 1 then the solution is optimal. channel utilization variable for entire network ρ = 1 |m| ∗ |n|   |m| ∑ i=1 |n| ∑ j=1 [tmij]   (3) the total number of transmission taken by each node is calculated using ρx = |m| ∑ i=1 [tmix] (4) 22 d. arivudainambi, d. rekha applying the criteria in the sample tdma solutions shown in fig.3 it is identified solution in fig.3.(d) satisfies tight lower bound value with high channel utilization value compared to solution in fig.3.(a).(b) & (c). md value for the given 6-node network is 3, so ∆ = 1 for solution in (d) and channel utilization of entire network is 0.33, where as in solution (a) channel utilization ρ is 0.17, for (b) and (c) ρ is 0.2. 3 proposed iga 3.1. analysis of genetic algorithm genetic algorithm is a heuristic search technique that simulates the processes of natural selection and evolution. genetic algorithms are effective, robust search procedure for np-complete problems. ga is a nontraditional search and optimization method. it works for the problem that has large number of solutions, out of which some are feasible and some are infeasible. the task is to get best solution out of feasible solutions. standard ga starts with a set of solutions called population. a population is a collection of chromosomes. solutions for one population are taken and used to form a new population, which are selected according to their fitness. this is repeated until some conditions for improvement of best solution are satisfied. three main operators are used to create a new population 1) selection, 2) crossover, and 3) mutation. the new population is further evaluated and tested for termination. if termination criteria are not satisfied, the population is iteratively operated by three operators and evaluated. one sequence of these operations and subsequent evaluation procedure is a generation in ga. 3.1.1. initial population tdma scheduler matrix is represented as bit string chromosome containing 0’s and 1’s in broadcast scheduling problem. each row and column of scheduler matrix represents to time slot and node transmission. the value 1 in position (i, j) in scheduler matrix indicates jth node is allowed for transmission in ith time slot. in classical ga approach, chromosome contains random string of 0’s and 1’s that does not perform well when number of nodes exceeds 40 [3]. the reason is when number of generations increases number of invalid individuals in population keeps increasing as the validity of every individual is not ensured while following classical ga crossover and mutation operations. finally, invalid members dominate total population and thus optimal solution is not found. this was overcome by changing initial population, crossover and mutation methods to keep members of population valid by chakraborty [3]. in our algorithm, initial tdma frames are constructed using elite population method of chakraborty [3]. 3.1.2. selection the selection operators for parent selection and survivor selection follow darwinian principle of survival of the fittest. for parent selection two chromosomes is selected randomly from the population to serve as parents for reproduction process. second, survivor selection applies the principle of survivor of the fittest. only fittest individuals selected as parents for next-generation, to achieve this k-tournament selection is done. survivor selection is also called as elitism, which is to retain some of the best individuals in each generation. in this study, a small percentage of best fitness individuals retained to next generation. it increases the performance of algorithm, by preventing loss of best found solution. from each generation 10% of best solution is retained to next iteration. 3.1.3. crossover and mutation the selected chromosomes for reproduction are gathered in mating pool. the single-point crossover operator is done on rows of the population. once a crossover point is identified, a random row from first parent pr1 is crossed over with a random row from second parent pr2. the resultant chromosome ch1 is replaced with pr1 and ch2 is replaced with pr2. after replacing, if the solution violates constraints then it is removed from population. the broadcast scheduling problem in tdma ad hoc networks using immune genetic algorithm23 mutation operator behaves in a different manner depending on the fitness of selected gene. the mutation operator changes one bit in selected chromosome depending on individual fitness. simple mutation is done by flipping a bit. at every bit of all members of population, a random number between 0 and 1 is generated. if it is less than or equal to mutation probability, it is flipped from 0 to 1 and vice versa. 3.1.4. fitness function the fitness function evaluates quality (fitness) of candidate solutions. the fitness function for scheduling problem is based on the variables tight lower bound and channel utilization. the termination point determines whether optimal solution is determined in that generation or not. the optimal solution is the one, which satisfies both criteria. when the generation of evolution reaches this termination point, algorithm stops and output optimal solution for the given network, else elitism method is done on populations and proceeds to next generation. at the end of iteration before evaluating fitness function, populations produced in the generation are taken for duplicate row elimination i.e., time slot which is repeated is removed from the population in order to produce optimized tdma frame. 3.2. optimization properties of immune genetic algorithm compared to ga in ga two main genetic operators, crossover and mutation, not only give each individual’s the evolutionary chance to obtain global optimum but also cause the degeneracy to some extent because of random and unsupervised searching during the entire process. on the other hand, ga is lack of capability of making use of some basic and obvious characteristic or knowledge in pending problem. based on the considerations above, immune genetic algorithm is proposed. algorithm 1 shows the structure of immune genetic algorithm. the solution after crossover is taken for immune operations. iga is an intelligent optimization algorithm, which mainly constructs an immune operator accomplished by two steps: immune selection and vaccination. the initial populations are created using elite population method of chakraborty [3]. random selection method for parent selection and 10% of best solution is taken to next generation. the knowledge added iga algorithm performed in the following way. algorithm 1. immune genetic algorithm immune genetic algorithm(g=(v,e), psel, pcr, psize, maxgen) { generate compatibility and hop information matrix; find the degree of the network; for loop=1:psize population = generate initial population using elite population; end for while (until reaches maxgen or optimal solution is identified ) // two parents are selected based on the selection probability spop = selection (psel, population); // single point crossover is done on the selected parents cpop = crossover (pcr, spop); // the chromosomes after reproduction is taken for immunization immunization (cpop) { // solutions that satisfies primary and secondary constraints are taken by immune selection isel = immuneselection (cpop); // resulting solutions are arranged according to channel utilization variable // vaccination is performed based on hop information matrix vpop = vaccination (isel); } 24 d. arivudainambi, d. rekha evaluate the fitness of each vpop if optimal solution is identified then break; // come out of the while loop and print the solution else // replace the best few offspring with the initial population and continue the loop survival (initial population, vpop); end if end while } 3.2.1. crossover the modified crossover operator given by chakraborty [3] is performed in iga. crossover is done on rows of the tdma cycle. rows from members of the population, i.e., different tdma frames are selected using predetermined crossover probability and are marked to be members of mating pool. a pair of rows pr1 and pr2 selected randomly from the mating pool for crossover. the objective of crossover is to create an offspring with a better schedule for a time slot with more transmissions by combining parent schedules. by crossover operation, only one child is created which may or may not replace the parent, depending on how good it is. first, pr1 is logically and with pr2, then logically exclusive or operator is performed for pr1 and pr2. both new populations are scanned from left to right, the first occurred one is copied to same position of new row. the next one is checked with corresponding row of hop information matrix if it does not make conflict then it is copied to new row else, it is rejected. in the same way, it will continue until end of both population and a new row is created from pr1 and pr2. the offspring may replace with pr1 and pr2 based on the condition set given by chakraborty [3]. 3.2.2. mutation random mutations alter a certain percentage of bits in the list of chromosomes. a single point mutation changes 1 to 0, and vice versa. increasing number of mutations increases algorithm’s freedom to search outside the current region of variable space. it also tends to distract the algorithm from converging on a correct solution so mutations are not allowed for best solutions. they are designated as elite solutions destined to propagate unchanged. such elitism is very common in gas. why throw away a perfectly good solution? however, in previous algorithm solutions generated after crossover is taken for mutation operation and random mutation is done on populations, which may change best population. to avoid this and to make algorithm knowledgeable, immune concept is added to genetic algorithm. a normal mutation operation is not done in iga instead, knowledge added two steps are performed in place of mutation. the two steps of iga are immune selection and vaccination. a. immune selection the newly created population after crossover, which satisfies the primary and secondary constraints, is selected for reduction of time slots. those populations selected are stored in vaccine pool. from the vaccine pool, one population is taken and the algorithm finds repeated time slot in that population. if so then the repeated time slot is deleted from the population. this step is carried out for the remaining populations that are in vaccine pool. the resulting time slot reduced populations are arranged according to the channel utilization variable and stored in the vaccine pool by replacing the old populations. b. vaccination vaccination is used to improve fitness by modifying the genes of an individual population with prior knowledge to gain higher fitness with greater probability. a chromosome from vaccine pool is taken for vaccination. iga identifies a node transmits first in the population in a time slot. during same time slot some other node, which does not create interference with transmitting broadcast scheduling problem in tdma ad hoc networks using immune genetic algorithm25 node can be allowed to transmit in the same time slot. to perform this a node is selected randomly and checked with hop information matrix whether it creates an interference with currently transmitting node, if not node value is mutated to one allowing the selected node to transmit in same time slot. the genes of selected chromosome are modified based on the knowledge obtained from hop information matrix of given network. hence, the vaccination process increases number of transmissions. 4 solving bsp using iga: simulation and results a series of simulations are carried to evaluate the performance of iga to solve broadcast scheduling problem, in comparison with finite state approach [1], ga [3], ga with collision free set (gacfs) [4], mean field annealing [13] and competent permutation genetic algorithm [14]. the performance of the ga and iga were tested for a large number of times, using ad hoc networks of different sizes. in the following sections, we discuss simulation results regarding the number of nodes |n|, the number of timeslots |m| and the computation time. the iga was tested by 100 randomly generated graphs, each representing an ad hoc network topology. the simulation results are based on population size 50, maximum number of generations 500, crossover rate 0.30, mutation probability 0.001 and on the measures, 1 tight lower bound ∆ value is one. 2 channel utilization variable to find the improvement in number of transmission. 4.1 results obtained from ga the purpose this simulation was to investigate performance of genetic algorithm for different networks. the number of nodes taken for simulation ranges from five to hundred. smaller node networks executed with more number of transmission in an acceptable generation. however, for a 100 node network with 200 edges identifies the optimal solution tdma frame after 489 generations. the average number of generations for 100-node network is 410. this has to be decreased in order to reduce execution time. 4.2 results obtained from iga the simulation results based on iga is given in the following table 1. compared to genetic algorithm, knowledge added iga could improve the searching ability, adaptability and greatly increase the converging speed. during vaccination process, selected antigen is improved with more number of transmissions so channel utilization is increased. comparing simulation results of iga with ga number of generations is reduced and utilization of each network is improved. even for large network, the solution is identified with acceptable generation. the values of table 2 clearly imply iga performs better than standard ga. one main aim is to reduce number of time slots so there is a steady decrease in utilization index as the number of nodes increases. 4.3 optimum schedule performance with other methods the channel utilization of iga is high compared to the channel utilization of elite population genetic algorithm given by chakraborty [3] is shown in fig.4. similarly, for networks with nodes 14, 16 and 40 number of transmissions generated by iga is somewhat closer to ga [3] whereas for large networks iga has improved on an average of 20 to 30 transmissions is shown in fig.4. table 3 gives a detailed comparison between iga and existing algorithms. it represents maximum number of transmissions produced by some recent algorithms for the given number of nodes and time slots. the results indicate for smaller networks the number of transmission differs slightly, whereas for a 100-node network number of transmission generated by iga create a variation of 10 to 20 transmissions. two benchmark problems discussed by [13] are solved using iga and results are compared with other algorithms such as, the finite state machine based algorithm fsma [1], co-evolutionary 26 d. arivudainambi, d. rekha genetic algorithm for collision free set gacfs [4], gradual hysteretic noisy chaotic neural network g-hncnn [7], mean field annealing algorithm mfa [13] and the competent permutation genetic algorithm cpga [14] is shown in table 4. 30 nodes with 70 edges is analyzed in problem instance # 1 the channel utilization is largely improved compared to other algorithms similarly the time delay is also reduced reasonably by iga compared to other methods. 40 nodes with 66 edges and maximum degree of 7 is analyzed in problem instance # 2 to this the channel utilization is increased moderately and the time delay is reduced by iga. the results of other algorithm ghncnn, gacfs, fsma, cpga and mfa are given in [7], [4], [1], [14] and [13]. the algorithm gacfs and cpga have not calculated the average time delay, which is represented by hyphen in table 4. the average time delay is calculated by η = |m| |n| |n| ∑ i=1 ( 1 ∑|m| j=1 |tmij| ) (5) the average time delay η for each node represents the average availability of the network, and the minimal η is very important for the optimal ad hoc network design and its evaluation is described by ming sun et al., [7]. in fig.5 (a) the input graph topology for a 100-node network with 200 edges with average degree of 4 and its optimum solution produced by iga is shown in fig.5 (b). the solution is identified with 9 time slots and 152 transmissions compared to recent algorithms the number of transmissions produced by iga is largely improved that attain the aim of this paper. problem no. of average no. of time total channel computation number nodes degree slots |m| transmissions utilization ρ time 1 14 3 6 24 0.285 0.5 sec. 2 16 3.6 5 23 0.287 0.5 sec. 3 40 4 8 67 0.209 2.1 sec. 4 100 4 10 152 0.152 5.2 min. 5 200 4 9 282 0.157 15.7 min. 6 300 3 10 489 0.163 36.1 min. 7 400 4 10 623 0.156 69 min. table 1: simulation results of iga with total number of transmission, channel utilization ρ, and the computation time. problem no. of average no. of time channel number nodes degree slots |m| utilization ρ 1 10 3 4 0.3 2 20 3.6 7 0.286 3 30 4 8 0.208 4 50 4 9 0.2 5 100 4 10 0.152 6 120 4 9 0.149 table 2: results obtained by iga for varying number of nodes, number of time slots and channel utilization ρ. broadcast scheduling problem in tdma ad hoc networks using immune genetic algorithm27 no. of no. of time iga gacfs ga finite state component nodes slots |m| approach permutation ga 15 8 20 18 17 20 20 30 9 37 28 33 35 37 40 8 69 65 65 64 64 100 9 152 139 133 134 136 table 3: comparison of time slots |m| and total number of transmissions using iga with other competitive algorithms. instance parameter iga g-hncnn gacfs fsma cpga mfa |m| 10 10 10 10 10 12 #1 ρ 0.19 0.1233 0.093 0.1167 0.1233 0.1056 avg. time delay 7.54 8.83 — 9.2 — 10.5 |m| 8 8 8 8 8 9 #2 ρ 0.237 0.2125 0.203 0.200 0.200 0.197 avg. time delay 5.212 5.7056 — 6 — 6.9 table 4: comparison of time slot |m|, channel utilization ρ and average time delay of each node by iga with other competitive algorithms. figure 4: (a) channel utilization vs. number of nodes, (b) number of transmissions vs. number of nodes for ga [3] and iga 5 conclusion the basic genetic algorithm and knowledge added immune genetic algorithm are discussed to improve broadcast scheduling problem in ad hoc. according to our knowledge, this paper is the first one to study immune genetic algorithm to the broadcast scheduling problem. compared to ga, iga actively aims on improving solutions, while ga blindly wanders over search space. immune genetic algorithm gets knowledge from hop matrix during vaccination process and increases number of transmissions in a reduced time slot in an acceptable computation time compared to recently proposed algorithms. the simulation results confirm advantages of iga in terms of channel utilization, number of generations and running time. the outcome validates the effectiveness and efficiency of iga for broadcast scheduling problem. further research may 28 d. arivudainambi, d. rekha figure 5: (a) graph topology with 100 nodes, 200 edges and average link degree of 4, (b) solution found by iga for the same network be performed to improve the performance of iga by finding variations in crossover and altering the immune operator by adding refined knowledge to vaccination. acknowledgement we gratefully acknowledge department of science and technology, india for providing financial support to carry out this research work under purse scheme. bibliography [1] i. ahmad, b. al-kazemi, and a.s. das. 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[14] x. wu, b.s. sharif, o.r. hinton, and c.c. tsimenidis.(2005); solving optimum tdma broadcast scheduling in mobile ad hoc networks: a competent permutation genetic algorithm approach, iee proceedings: communications, 152(6) : 780-788. int j comput commun, issn 1841-9836 8(6):863-868, december, 2013. direct method for stability analysis of fractional delay systems m.a. pakzad, m.a. nekoui mohammad ali pakzad* department of electrical engineering science and research branch, islamic azad university tehran, iran *corresponding author: m.pakzad@srbiau.ac.ir mohammad ali nekoui faculty of electrical and computer engineering k.n.toosi university of technology, seyed-khandan p.o. box 16315-1355, tehran, iran manekoui@eetd.kntu.ac.ir abstract: in this paper, a direct method is presented to analyze the stability of fractional order systems with single and multiple commensurate time delays. using the approach presented in this study, first, without using any approximation, the transcendental characteristic equation is converted to an algebraic one with some specific crossing points. then, an expression in terms of system parameters and imaginary root of the characteristic equation is derived for computing the delay margin. finally, the concept of stability is expressed as a function of delay. an illustrative example is presented to confirm the proposed method results. keywords: control, fractional delay systems, stability windows, root-locus. 1 introduction time delay is an inherent part of many dynamical and physical systems. the device delay often appears in computer based control systems, the wireless and web-based control systems, communication systems and so on. also, the existence of various sensors and actuators in the feedback loop is the cause of delay in many systems. moreover, taking an automatic computerbased control system or a process control with networked transmission for example, it is necessary and more reasonable to simultaneously consider the possible transmission delay for the corresponding control law and the device delay due to computing or processing. recently, much attention has been paid to the subjects of stability, stabilization and control of the time delay systems [1] [5]. fractional order models would be more accurate than integer order models. in fact, real world processes generally or most likely are fractional order systems. in a generic sense, the fractional-order systems are identified by non-integer powers of the laplace variable s. when time delays and fractional-order derivative are involved in dynamical systems, we have fractional-delay systems. the stability of fractional-delay systems can be determined by the root location of the characteristic equations. the most natural way to find the location of the roots of a linear fractional order system is to solve its corresponding characteristic equation. but in our case this will be a transcendental one, being thus generally impossible to solve it directly. for this reason, most of the existing approaches study stability of such systems by finding the crossings of poles through the imaginary axis [8]. there has been a large effort to deal with this problem, as can be seen by the large quantity of articles dealing with it for the standard case (integer order systems); see [5], and others. in [6], the necessary and sufficient conditions for the bibo stability of fractional order delay systems have been introduced. from the numerical analysis point of view, the effective numerical algorithms have been discussed in [7] and [8] for the evaluation of bibo stability of fractional copyright © 2006-2013 by ccc publications 864 m.a. pakzad, m.a. nekoui order delay systems. in [9] a heavy computation scheme based on the cauchy’s integral has been proposed to test the stability of such systems, and in [10], a technique based on the lambert w function was used for the same purpose. in this work, an analitical direct approach to determine all possible stability regions in the parametric space of delay is proposed. the original idea in this strategy is derived based on the method reported in [5] to achieve the stability criteria of integer order delay systems. the proposed method is an analytically elegant procedure that first converts the transcendental characteristic equation into an algebraic equation without the transcendentality by eliminating the highest degree of commensuracy terms successively. the resulting algebraic equation without the transcendentality also enables us to easily determine the delay dependency of the system stability and the sensitivities of crossing roots (root tendency) with respect to the time delay. 2 preliminaries and defintions a general class of fractional order lti (linear time invariant) systems with multiple commensurate delays of retarded type is taken into account: d 1/α t x(t) = d1/αx(t) dt1/α = a0 + n∑ ℓ=1 aℓx(t − ℓτ) the fractional delay characteristic equation of above system can be expressed in the general form of c(α √ s,τ) = det ( s1/αi − a0 − n∑ ℓ=1 aℓe −ℓτs ) = n∑ ℓ=0 pℓ( α √ s) e−ℓτs = 0 (1) where parameter τ is non-negative, such that τ ∈ r+ and pℓ(α √ s) for ℓ ∈ nn is a real polynomial in the complex variable α √ s (where α ∈ n). note that the zeros of characteristic equation (1) are in fact the poles of the system under investigation. we find out from [6] that the transfer function of a system with a characteristic equation in the form of (1) will be h∞ stable if, and only if, it doesn’t have any pole at ℜ(s) ≥ 0. for fractional order systems, if a auxiliary variable of v = α √ s is used, a practical test for the evaluation of stability can be obtained. by applying this auxiliary variable in characteristic equation (1), the following relation is obtained: cv(v,τ) = n∑ ℓ=0 pℓ(v) e −ℓτvα (2) this will transform the domain of the system from a multisheeted riemann surface into the complex plane, where the poles can be easier calculated. in this new variable, the instability region of the original system is not given by the right half-plane, but in fact by the region described as: |∠v| ≤ π 2α (3) with v ∈ c . let us assume that ; s = ±jω or in other words, s = ωe±jπ/2 are the roots of characteristic equation (1) for a τ ∈ r+. then for the auxiliary variable, the roots are defined as follows: v = α √ s = α √ ω e±jπ/2α (4) direct method for stability analysis of fractional delay systems 865 3 crossing position the main objective of this section is to present a new method for the evaluation of stability and determination of the unstable roots of a fractional delay system. the proposed method eliminates the transcendental term of the characteristic equation without using any approximation and converts it into a equation without the transcendentality such that its real roots coincide with the imaginary roots of the characteristic equation exactly. if the characteristic equation (1) has a solution of s = jωc then c(α √ −s,τ) = 0 will have the same solution. c(α √ −s,τ) = n∑ ℓ=0 pℓ( α √ −s) eℓτs = 0 (5) characteristic equation (5) can be written in terms of the auxiliary parameter v as: c(v,τ) = n∑ ℓ=0 pℓ(v) e ℓτvα = 0 (6) a recursive procedure should be developed to achieve that purpose. therefore, let us define c(1)(v,τ) = n−1∑ ℓ=0 [p0(v)pℓ(v) − pn(v)pn−ℓ(v)] e−ℓτv α = n−1∑ ℓ=0 p (1) ℓ (v) e −ℓτvα = 0 (7) then, we have c(1)(v,τ) = n−1∑ ℓ=0 [p0(v)pℓ(v) − pn(v)pn−ℓ(v)] eℓτv α = n−1∑ ℓ=0 p (1) ℓ (v) e ℓτvα = 0 (8) where p (1) ℓ (v) = p0(v)pℓ(v) − pn(v)pn−ℓ(v) (9) we can easily repeat this procedure to eliminate commensuracy terms successively by defining a new polynomial p (r+1) ℓ (v) = p (r) 0 (v)p (r) 0 (v) − p (r) n−r(v)p (r) n−r−ℓ(v) (10) and an augmented characteristic equation c(r)(v,τ) = n−r∑ ℓ=0 p (r) ℓ (v) e −ℓτvα = 0 (11) by repeating this procedure n times, we eliminate the highest degree of commensuracy terms and obtain the following augmented characteristic equation c(n)(v) = p (n) 0 (v) = 0 (12) where p (n) 0 (v) = p (n−1) 0 (v)p (n−1) 0 (v) − p (n−1) 1 (v)p (n−1) 1 (v) (13) it should be emphasized that that if s = jωc is the solution of (1) for some τ, then it is also a solution of (12). if we substitute v = (e−jπ/α)v and v = α √ ωc e jπ/2α in (13), we get the following equation in ω d(ω) = ( p (n−1) 0 (e −jπ/α v) p (n−1) 0 (v) − p (n−1) 1 (v) p (n−1) 1 (e −jπ/α v) )∣∣∣ v=α √ ωc ejπ/2α (14) 866 m.a. pakzad, m.a. nekoui the corresponding value of time delay is then computed by τ∗ = 1 ωc tan−1 ( ℑ[(p(n−1)0 (v))/(p(n−1)1 (v))] ℜ[−(p(n−1)0 (v))/(p (n−1) 1 (v))] )∣∣∣ v=α √ ωc e jπ 2α + 2kπ ωc ;k ∈ z+ (15) theorem 1. a system with characteristic equation (1) has finite crossing points for any τ ∈ r+. proof: let assume s = jωc be a pair of roots for c(α √ s,τ) then v = α √ s = α √ ωc e ±jπ/2α would be a pair of roots for d(ω). since d(ω) is a finite degree polynomial with maximum degree of (p (n) 0 (v)) then the number of crossing points of (1) that are the real roots of d(ω) is finite. 2 the whole ω values, for which s = jω is a root of equation (1) for some non-negative delays, is defined as the crossing frequency set. ω = { ω ∈ r+ ∣∣∣ c(α√s,τ) = 0, for some τ ∈ r+} (16) corollary 2. if the system given as (1) is stable for τ = 0 (i.e. system without delay) and ω = ϕ, then the system will be stable for all positive values of τ ∈ r+. proof: from the fact that there are no roots crossing the imaginary axis. 2 4 direction of crossing after the crossing points of characteristic equation (1) from the imaginary axis are obtained, the goal now is to determine whether each of these root crossings from the imaginary axis is a stabilizing cross or a destabilizing cross. assume that (s,τ) is a simple root of c(α √ s,τ) = 0. the root tendency for each ωcm and τmk is defined as: root tendency = rt |s=jωc = sgn ( ℜ ( ssτ|s=jωcm τ=τmk )) = sgn ( ℜ ( − ∂c/∂τ ∂c/∂s ∣∣∣∣ s=jωc )) (17) if it is positive, then it is a destabilizing crossing, whereas if it is negative, this means a stabilizing crossing. notice that root tendency represents the direction of transition of the roots at jωcm as τ increases from τmk−ϵ to τmk−ϵ , 0 < ϵ ≪ 1. theorem 3. the root tendency at a crossing, jωc is invariant with respect to time delay τmk . proof: one can find ds/dτ for simple roots of (1) as follows: ds dτ = − ∂c/∂τ ∂c/∂s = ∑n ℓ=0 pℓ( α √ s) ℓ s e−ℓτs∑n ℓ=0 pℓ( α √ s) ds e−ℓτs − ∑n ℓ=0 pℓ( α √ s)ℓτse−ℓτs (18) based on (18) and definition given in (17), the root tendency of each time delay τmk is written as follows: rt|τs=jωc = sgn ( ℜ ( ssτ|s=jωc )) = sgn ( ℜ ( − ∂c/∂τ ∂c/∂s ∣∣∣∣ s=jωc )) = sgn ( ℜ ( ∑n ℓ=0 pℓ( α √ s)ℓse−ℓτs∑n ℓ=0 pℓ( α √ s) ds e−ℓτs − pℓ(α √ s)ℓe−ℓτs )) = sgn ( ℜ ( ∑n ℓ=0 pℓ( α √ s) ds e−ℓτs∑n ℓ=0 pℓ( α √ s) ℓ s e−ℓτs − τ s )−1) = sgn ( ℜ ( ∑n ℓ=0 pℓ( α √ s) ds e−ℓτs∑n ℓ=0 pℓ( α √ s)ℓse−ℓτs )) = sgn ( ℑ ( ∑n ℓ=0 pℓ( α √ s) ds e−ℓτs∑n ℓ=0 pℓ( α √ s)ℓe−ℓτs ) )∣∣ s=jωc τ=τm1+ 2kπ ωc (19) the root tendency in each time delay τmk is independent from the time delay itself and constant for each crossing frequency, because e−ℓτs and pℓ(α √ s) do not depend on τmk. 2 direct method for stability analysis of fractional delay systems 867 5 illustrative example we present an example case, which display all the features discussed in the text. example 4. this example has been taken from [3] and [8]. consider the following linear timeinvariant fractional order system with one delay: c( √ s,τ) = ( √ s)3 − 1.5( √ s)2 + 4( √ s) + 8 − 1.5( √ s)2e−τs (20) this system has a pair of poles (s = ±8j) on the imaginary axis for τ = 0. a very involved calculation scheme based on cauchy’s integral has been used in [9] to show that this system is unstable for τ = 0.99 and stable for τ = 1. our objective in this example is to find all the stability windows based on the method described in this article for this system. using auxiliary variable v = √ s into (20) the characteristic equation is obtained as: cv(v,τ) = v 3 − 1.5v2 + 4v + 8 − 1.5v2 e−τv 2 (21) by applying the criterion expressed in the previous section, we can eliminate exponential term from (21) as follows: jv6 + 1.5(1 − j)v5 + 14(1 + j)v3 − j16v2 + 32(1 − j)v + 64 = 0 (22) by inserting expression v = √ ω ej π 4 = a(1 + j) in the above equation , we get: 8a6 − 12a5 − 56a3 + 32a2 + 64a + 64 = 0 (23) where a = √ ω/2. the real solutions of (23) for a is a = 1.82 , v2 = 1.82(1 + j) , ω = 6.6248 (24) a = 2 , v1 = 2(1 + j) , ω = 8 (25) the corresponding infinite countable time delays of the cross points in (24) and (25) are obtained with regards to relation (15) as τ1k = 0.0499 + 0.3019kπ and τ2k = 0.25kπ, respectively. by applying the criterion expressed in the previous section, it is easy to find out that a destabilizing crossing of roots (rt = +1) has occurred at τ = 0.25kπ for s = ±j6.6248 and a stabilizing crossing (rt = −1) has taken place at τ = 0.0499 + 0.3019kπ for s = ±j8 for all values of k ∈ z+. therefore, we will have 5 stability windows as follows: 0.0499 < τ < 0.7854 , 0.9983 < τ < 1.5708 , 1.9486 < τ < 2.3562 , 2.8953 < τ < 3.1416 and 3.8437 < τ < 3.9270 which agree with the results presented by [3] and [8]. note that at τ = 3.9269 , an unstable pair of poles crosses toward the right half-plane, and before this unstable pole pair can turn to the left half-plane at τ = 4.7922 , another unstable pair of poles goes toward the right half-plane at τ = 4.7123 ; and thus, the system can not recover the stability. to get a better understanding of the properties of this system, its root-locus curve has been plotted as a function of delay in fig.1. 6 conclusions an efficient method to analyze the bibo stability of a large class of time-delayed fractional order systems for both single and commensurate-delay cases is proposed. the method introduces an augmented equation whose real roots give the finite values of crossing frequencies at which stability feature of the system change. according to the infinitely countable time delays corresponding to each crossing point, the parametric space of τ is discretized to investigate stability in each interval. finally, an illustrative example is presented to highlight the proposed approach. 868 m.a. pakzad, m.a. nekoui figure 1: root-loci for c( √ s,τ) until τ=3.9 bibliography [1] stojanovic, s.b.; debeljkovic,d.lj.; dimitrijevic, n. 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(2013); stability analysis of multiple time delayed fractional order systems, american control conference, washington, dc, 170-175. [8] a. r. fioravanti, c. bonnet, h. ozbay and s. i. niculescu, (2012), a numerical method for stability windows and unstable root-locus calculation for linear fractional time-delay systems, automatica, 48(11):2824-2830. [9] hwang, c.; cheng, y.c. (2006), a numerical algorithm for stability testing of fractional delay systems, automatica, 42(5): 825-831. [10] hwang, c.; cheng, y.c. (2005), a note on the use of the lambert w function in the stability analysis of time-delay systems, automatica, 41(11):1979-1985. international journal of computers communications & control issn 1841-9836, 10(5):654-666, october, 2015. mining periodic traces of an entity on web x. huang, x. wang, y. zhang, j. zhao xinyan huang 1. shandong university 2. shandong university of finance and economics num 1500, sunhua road in high tech industrial development zone ji’nan, china 20063462@sdufe.edu.cn xinjun wang*, yan zhang, jinxin zhao shandong university num 1500, sunhua road in high tech industrial development zone ji’nan, china wxj@sdu.edu.cn, zy@sdu.edu.cn, zjx@sdu.edu.cn *corresponding author: wxj@sdu.edu.cn abstract: a trace of an entity is a behavior trajectory of the entity. periodicity is a frequent phenomenon for the traces of an entity. finding periodic traces for an entity is essential to understanding the entity behaviors. however, mining periodic traces is of complexity procedure, involving the unfixed period of a trace, the existence of multiple periodic traces, the large-scale events of an entity and the complexity of the model to represent all the events. however, the existing methods can’t offer the desirable efficiency for periodic traces mining. in this paper, firstly, a graph model(an event relationship graph) is adopted to represent all the events about an entity, then a novel and efficient algorithm, tracesmining, is proposed to mine all the periodic traces. in our algorithm, firstly, the cluster analysis method is adopted according to the similarity of the activity attribute of an event and each cluster gets a different label, and secondly a novel method is proposed to mine all the star patterns from the event relationship graph. finally, an efficient method is proposed to merge all the stars to get all the periodic traces. high efficiency is achieved by our algorithm through deviating from the existing edge-by-edge pattern-growth framework and reducing the heavy cost of the calculation of the support of a pattern and avoiding the production of lots of redundant patterns. in addition, our algorithm could mine all the large periodic traces and most small periodic traces. extensive experimental studies on synthetic data sets demonstrate the effectiveness of our method. keywords: event, periodic trace, pattern. 1 introduction an event is something that happens at some specific time. nowadays, due to the popularity of the internet, an increasing number of events about an entity are reported on web every day. however, since these events are usual scattered and redundant, meaningful information can not be extracted by people, such as a behavior trajectory of an entity which is named a trace. periodicity is one of the most common phenomena that these traces show, such as the launch of new products, the product promotion and so on, which are named periodic traces by us. in addition, a periodic trace can be loosely defined as the repetitive events series with several kinds of event relationship between them, such as causal, part of and following relationship etc. periodic traces can provide an insightful and concise explanation over the long development history of one entity, which are very valuable in the prediction of future events’ happening of an entity. copyright © 2006-2015 by ccc publications mining periodic traces of an entity on web 655 unfortunately, mining periodic traces from an entity’s long and noisy history data is a challenge, which includes the following major issues: firstly, the period of a trace are usually unfixed. however, in previous work, li z [8,10] studied the frequent periodic behaviors for moving objects with the period fixed. unfortunately, the fixed period is unfit for the periodic trace. for example, the period of the launch of new products is usually varying with the changes of the competitive environment or others factors. secondly, the model to represent so large events set is a complex issue. considering the big scale of the events and the relatively complex relationship between them, a big graph is employed, in which the events are represented as vertices and the events relationship (such as, causal relationship, following relationship etc) are represented as edges. and the big graph is named the event relationship graph of the entity. at the same times, both the structure of periodic traces and the mining of periodic traces become more complex. the problem of mining periodic traces is transformed into mining frequent subgraph from a big graph. for nowadays, the general methods of frequent subgraph mining from a single graph are based on edge-by-edge (i.e., incremental) pattern-growth [12], which are more fit for a small graph than our big graph with several thousand vertices. in this paper, the problem of periodic traces mining is solved through the following steps: firstly, the cluster analysis method is adopted according to the similarity of the activity attribute of an event and each cluster gets a different label. secondly, a novel method is proposed to mine all the vertice-edge-vertice patterns from the event relationship graph firstly and then all the star patterns are mined based on the preceding patterns. finally, an efficient method is proposed to merge all the stars to get all the periodic traces of the entity. in our paper, we address an important problem of mining periodic traces. in our algorithm, high efficiency is achieved through deviating from the existing edge-by-edge pattern-growth framework. at the same time, the heavy cost of the calculation of the support of a pattern is reduced and the production of lots of redundant patterns can be avoided. in addition, our algorithm could mine all the large periodic traces and most small periodic traces. the rest of the paper is organized as follows. in section 2, some related work is discussed. section 3 gives the problem formulation. section 4 and section 5 provides an outline of our algorithm to discover the periodic traces. we report our experimental results in section 6, conclude our study in section 7. 2 related work at present, some studies [20] about how to organize events and events relationship have been investigated. however, less studies aim to mine some meaningful information from the events data of entities. an event relationship migration graph was proposed by zhaoman zhong in [1] to organize events, in which a typical event is defined as a behavior sequence or a series of state changes. a concept of event network is proposed by zongtian liu [3], which is used to organized the events and the relationship between them in an article. christopher c. yang [4] introduces the concept of event evolution graph in which events are organized by the time sequence, which can help to efficiently browse the whole of event evolution process. heng ji [5] proposes to identify the “centroid entities” which are frequently involved in events and then link the events involving the same centroid entity along a time line. however, no more studies are done based on the work ahead. zhenhui li proposed an concept of periodic behavior in [8,10], which is mined from the spatiotemporal data over a long histoy and propose a two-stage algorithm, periodica, to detect 656 x. huang, x. wang, y. zhang, j. zhao the periods in complex movements and to mine periodic movement behaviors. similar to method in literature [3], all events of an entity is firstly linked according to three types of relationships between them. but the difference is that their goal is to create an event ontology but not to mine the meaningful information on the event ontology. however, our main goal is to organize the events of an entity for a long history and to mine the meaningful periodic traces, so much more vertices are involved than [3]. the model of a periodic trace adopted by us is a graph structure which is different from surprising periodic patterns [13] and the periodic behavior in [8,10] which is characterized as a probabilistic model. subdue [15] is probably the most well-known algorithm for mining frequent subgraphs in a single graph, however, it tends to mine small patterns with high frequency.the spidermine algorithm in [7] is proposed to mine top-k largest frequent patterns from a single massive network. subdue and spidermine are both approximate algorithms, which aren’t fit for our problem to mine all the periodic traces.although moss is an algorithm to mine the complete pattern set, it suffers from a significantly runtime complexity issue as the input graph size grows [7,17]. so we propose an efficient method fit for our problem. our algorithm is to mine the complete pattern set, which achieves its efficiency through reducing the heavy cost of calculation of the support of a pattern and avoiding the production of lots of redundant patterns. 3 problem formulation all the following work is based on the assumption that the event relationship graph g = (v,e) has been established already. the mining of periodic traces is to mine the periodic events series with three kinds of event relationship from the events data of an entity. with the help of the event relationship graph, the main task of periodic traces mining is now transformed to mine the frequent subgraph from the event relationship graph. we formally define the notions related with the mining of periodic traces as follows: definition 1 (event). an event is something that happens at some specific time, and often some specific place, which is usual a phrase or sentence in the web pages including an activity which is the main word most clearly expressing an event occurrence. similar to [2], in our paper, the event of an entity is defined as a model e with five attributes, described as follows: e⟨subject, activity,{object}, time,{location}⟩. among them, subject, activity and time elements are required. definition 2 (event relationship). the dependent relationship from an event to another is named an event relationship. there are three kinds of relationships that we considered in this paper, which are causal relationship [9], part of relationship and following [19] relationship. for these three kinds of relationships, we consider an event relationship as a logical dependency between two events. if an event e1 is a component of an event e2, then there is a part of relationship between e2 and e1. besides, if the occurrence of an event e2 always depends on the occurrence of an event e1, then there must be a causal relationship or a following relationship from e1 to e2. if e1 surely leads to the occurrence of e2, then there is a causal relationship between e1 and e2. the rest are following relationships in which e2 always occurs after e1. mining periodic traces of an entity on web 657 definition 3 (event relationship graph). event relationship graph is to link all the events of an entity according to the relationship between them. an event relationship graph is denoted as a directed graph g = (v,e), where v are the vertices representing events and e are the directed edges representing event relationships. a fragment of an event relationship graph is shown in fig 1, in which rc represents causal relationship, rf represents part of relationship and rp represents part of relationship. definition 4 (trace). a trace is denoted as a directed graph g∗ = (v ∗,e∗), which represent a behavior trajectory of the entity. definition 5 (periodic trace). a periodic trace is also denoted as a directed graph g′ = (v ′,e′) composed of periodicly happening events as the vertices v ′ and periodicly happening event relationships as the directed edges e′ between vertices v ′, where v ′ ∈ v , e′ ∈ e. and the support of g′ in g is beyond σ, σ is the minimum support that we set. the problem of mining periodic traces is to mine all the periodic traces g′ = (v ′,e′) from an event relationship graph g, and the occurrence of g′ in g is beyond σ. we will solve the problem in the following sections. 4 find all the event classes figure 1: a fragment of an event relationship graph a fragment of an event relationship graph is shown in fig 1, in which all the events is represented by it’s activity attribute. in this section, our main goal is to find all the event classes ec = {ec1,ec2, . . . ,ecd} from all the events, eci is an event class in which all the events has the similar activity and is represented like eci = {e2,e6,ej, . . . ,en}. in this section, a clustering method is employed. with the help of wordnet, all the events are clustered into groups according to semantic similarity of activity attribute of them, because the activity attribute could mostly express an event, hence the same kind of events get together and get an identical label, such as a, b etc. we name the events with the similar activity as an event class. for example, a launch event class, a promotion event class etc. 658 x. huang, x. wang, y. zhang, j. zhao then the event relationship graph is turned to a labeled directed graph, in which there are three kind of edges labeled as 1, 2, 3 etc. 5 results and discussion in this section, we will describe the periodic trace mining algorithm, tracesmining, which could mine all the periodic traces from the event relationship graph. the main algorithm of mining periodic traces is shown in algorithm 1. algorithm 1 the periodic traces mining algorithm require: input all the event classes ec = {ec1,ec2, . . . ,ecd}, the event relationship graph g of an entity, support threshold σ. ensure: all the periodic traces s. 1: initialize s ← φ; 2: t ′← minsmining(g,ec,σ); 3: /* mine all the minimum patterns– vertice-edge-vertice;*/ 4: s′← starsmining(g,t ′,σ); 5: /* mine all the patterns–stars based on t ′;*/ 6: s ← starsmerging(s′,σ); 7: /* merge all stars whenever possible*/ 8: return s; 5.1 mining all the frequent patterns of vertice-edge-vertice in the minsmining(g,ec,σ) algorithm, for every two event classes,we should get all the patterns of vertice-edge-vertice and all their corresponding instances in g, here an instance of an pattern is just a physical occurrence of the pattern. as is shown in fig 2, a rf−−→ b is a vertice-edge-vertice pattern, the frequency that the physical events in one event class a has the event relationship rf with the physical events in another b in the event relationship graph g must be beyond the threshold σ, rf is one of the three relationships we defined ahead. the generation of a pattern a rf−−→ b and it’s instances is illustrated in fig 2, in which ei refers to an physical event, eci refers to an event class the label of which is b and σ is set to 2. 5.2 mining all the patterns of stars in the algorithm 1, the starsmining(g,t ′,σ) algorithm is to generate all the patterns of stars and their corresponding instances based on all the veritice-edge-vertice patterns produced ahead. just as fig 3 illustrated, the so called star is just a pattern the number of occurrence of which in the event relationship graph is beyond σ and in which a vertice serves as the center, 1 is the radius, the edge represent one of three kinds of event relationships and the direction of edge is optional which can point to or be away from the center. in fig 3, the label of e1 and e2 is b, the corresponding label of e7 and e8 is d etc. then we propose an efficient method to mine all the stars based on all the vertice-edge-vertice patterns, as shown in algorithm 2. in the algorithm 2, in line 5, the function find(c,li) is to find all the vertice-edge-vertice pattern that have a vertice with the label li. in line 10, the function count(t[i],t [j]) is to mining periodic traces of an entity on web 659 figure 2: generation of a vertice-edge-vertice pattern and it’s instances count the number of the same physical events that the label li in t[i] and t[j] stands for and judge whether the number is beyond σ. in line 13, the function merge(t[i],t [j],t ′) is to merge t[i] and t[j] if the number is beyond σ. in line 14, the function check(t,s′) is to put all the patterns which has never been merged into s′. ultimately, the s′ stores all the stars centered by li, for example, the stars centered by label a are named like star(a1), star(a2) etc according to the different physical events set that the label a stands for. in the algorithm 2, we shall finally find all the star patterns. in this paper, stars could help efficiently mine the periodic traces due to the following reasons: 1. stars reduce the heavy cost of the calculation of support of a pattern. judging whether two patterns can be merged, what we only need to do is to determine which star the merging bases on and further confirm the number of the same physical events that the center label of the star stand for. the number is the support of the merged pattern, which could greatly reduce the heavy cost of the calculation of support of a pattern. 2. stars reduce the complexity of merging. the inherent character of stars with one label as the center makes the merging of pattern convenient. to merge a star, a pattern is just to judge whether one of the borders of the pattern can merge with the star. and the merging makes the patterns grow by stars 660 x. huang, x. wang, y. zhang, j. zhao figure 3: a star pattern and it’s instances deviating from the existing edge-by-edge pattern-growth way [14], which greatly improve the efficiency of merging. the last and most important step of generating the periodic traces is to merge the stars, details is shown in 5.3. 5.3 generating periodic traces to discover all the periodic traces, the last step is to merge all the stars. then an effecient method starsmerging() is proposed to merge all the stars that can be merged until no more frequent patterns can be found. although the number of underlying periodic traces is usually unknown, the algorithm starsmerging() could at the same time determine the optimal number of periodic traces while generating all the periodic traces, details shown in. algorithm 3. s is implemented as a queue into which all the stars are put. star(s) (line 3) points to the current star to be dealt with. p.border (line 6) points to all the boundaries of the pattern p. the function compare(b,s) (line 10) is to count the number of the same physical events that the vertice b and the center of s stand for. at line 4–13, each current star as far as possible merges the stars in s through checking it’s borders one by one until no stars can be merged to. the function cleanup(s,σ) (line 21) is employed to clean all the stars that has been merged and the number of remainder physical events of the center vertice is not beyond σ from s. compared with the algorithm in literature [7], our algorithm is more effective and efficient. in our algorithm, a pattern with 4 stars just need 3 merging without additional patterns generated, which does not generated any redundant patterns and does not need any additional merging, mining periodic traces of an entity on web 661 algorithm 2 the starsmining algorithm require: input all the vertice-edge-vertice pattern c{c1,c2,c3, . . . ,ci, . . . ,ce}, all the instances of c, all the labels l{l1,l2,l3, . . . ,li, . . . ,lf} appears in c, support threshold σ. ensure: all the stars s. 1: initialize s ← φ; 2: for i = 1 to f do 3: s′← φ; 4: flag = false; 5: t ← find(c,li) /*find all the vertice-edge-vertice patterns that have a vertice with the label li*/ 6: repeat 7: t ′← φ; 8: for i = 0 to t .length do 9: for j = i + 1 to t .length do 10: n ← count(t[i],t[j]); /*to count the number of the same physical events that the label li in t[i] and t[j] stands for*/ 11: if n ≥ σ then 12: flag = ture; 13: merge(t[i],t [j],t ; ); /*add the merged pattern to t ′*/ 14: end if 15: end for 16: end for 17: check(t,s′) /*all the pattern in which the number of the physical events that label li stand for is beyond σ and which hasn’t been merged are put into s′*/ 18: t = t ′ 19: until flag /* the merger is completed*/ 20: s′ = s′∪t ; 21: s = s ∪s′; 22: end for 23: return s while the algorithm in [7] averagely needs 5 additional patterns generated and needs 6 merging. with the number of stars growing, more obvious advantages are shown in our algorithm. more details of the comparison with [7] are shown in section 6. 6 experiments in this section, we performed extensive experiments to evaluate the performance of our algorithm of mining the periodic traces on synthetic data. all experiments were done on a 2.8ghz intel pentium iv pc with 1gb main memory, operating system windows xp. our algorithm is implemented in python 2.7. the mining of periodic traces is experimented on a single graph which is generated by the erdős rényi random network model. the erdős rényi model is a well-known model to generate random graphs. using the g(n, p) function, our synthetic single graph is constructed. 662 x. huang, x. wang, y. zhang, j. zhao algorithm 3 starsmerging require: all the stars queue s, support threshold σ. ensure: the periodic traces s′. 1: s′← φ; 2: while s.length > 0 do 3: p = star(s); /* select one star from s*/ 4: while true do 5: flag = false; /*a flag whether merge happened*/ 6: border ← p.border; /*check the borders of p one by one*/ 7: for b in border do 8: ss = lookups(b); /*find all the stars of which the label of the center vertice is b*/ 9: for s in ss do 10: n = compare(b,s) /*count the same physical events that the vertice b and the center vertice of s stand for*/ 11: if n > σ then 12: p.merge(s); 13: flag = true; 14: end if 15: end for 16: end for 17: if !flag then 18: break; 19: end if 20: end while 21: s′ = s′∪p; /*put these patterns that don’t grow to s′*/ 22: cleanup(s,σ); 23: end while 24: return s′ however, the graph generated by the erdős rényi model is an undirected graph, which is different from our event relationship graph which is a directed graph. so some changes made based on the erdős rényi model is that 0.5 probability is adopted to decide the direction of the edge. besides, the labels of vertices and the labels of edges are distributed randomly under the condition that labels of any two adjacent vertices can’t be identical. in the experiment, in order to calculate the recall and precision ratio, a set of large patterns as well as a set of small patterns are injected into the graph. our goal is to find all the large frequent patterns and all the small patterns from the big graph. nowadays despite lots of studies in graph mining, few algorithms are capable of the mining task in a big single graph due to the exponentially high combinatorial complexity and support computation. and since spidermine [7] has been compared with the other algorithms (seus [16] (version 1.0), moss [17] (version 5.3) and origami [18]) and shows a tremendous advantage in efficiency and effectiveness, we just compare our algorithm with spidermine and the well-known subdue [15] (version 5.2.2), which is a classic approximate algorithm on a single graph. firstly, we generate 4 different data sets (labeled data 1 to 4) with varied parameter settings referring to [7]. the description of the data sets is given in fig 4. the details of the parameters is given as follows. |v | is the number of vertices. lv is the number of vertice labels and le is mining periodic traces of an entity on web 663 data |v | lv le d m |vl| el n |vs| es 1 400 70 3 2 5 30 2 5 3 2 2 600 130 3 4 5 30 2 20 3 2 3 1000 250 3 4 5 30 2 5 3 20 4 1500 350 3 4 10 30 2 10 3 2 figure 4: data sets the number of edge labels. d is the average degree. |vl| (or |vs|) is the number of vertices of each injected large (resp. small) pattern. m (or n) is the number of large (resp. small) patterns injected. el (or es) is the number of instances of each large (resp. small) pattern injected. we implement the spidermine [7] according to it’s algorithms in python. but a little changes are made to spidermine, because spidermine is designed for undirected graph without labels for it’s edges while our graph is directed with labeled edges. at same times, spidermine is to mine top k large patterns from a big graph. here we set k = 5, dmax = 4. figs 5 to 8 show the distribution of patterns mined by spidermine, subdue and our algorithm tracesmining for different data sets in fig 4. the minimum support threshold is set to 2. figure 5: data 1 figure 6: data 2 figure 7: data 3 figure 8: data 4 in figs 5, 6, 7 and 8, we can see that compared with spidermine() and subdue, our algorithm, tracesmining could mine all the large periodic traces and most small periodic traces injected into the graph.while spidermine() focus on the large patterns in four different data sets and subdue tends to mine smaller patterns . 664 x. huang, x. wang, y. zhang, j. zhao figure 9: large pattern figure 10: small pattern run time (seconds) data tracesmining spidermine 1 0.686 0.704 2 2.248 3.951 3 10.089 13.041 4 13.190 16.272 figure 11: run time in figs 9 and 10, tracesmining shows a tremendous advantage compared with spidermine and subdue in both large and small patterns mining. spidermine is good at mining large patterns and subdue tends to mine smaller patterns with the growing of graph, which fully illustrate that spidermine() and subdue don’t suit our task to find all the periodic traces. since spidermine has compare the run time with subdue in [7], the runtime comparison of spidermine and tracesmining is shown in fig 9 on the three data sets. we can see that in runtime, our algorithm has a clear advantage over spidermine. 7 conclusion in this paper, we address an important and difficult problem: mining periodic traces of an entity on web. we propose a novel and efficient framework to solve the aforementioned problem. our algorithm achieves its efficiency through deviating from the existing edge-by-edge patterngrowth framework and reducing the heavy cost of the calculation of the support of a pattern and avoiding the production of lots of redundant patterns. in addition, our algorithm could mine all the large periodic traces and most small periodic traces. experiments demonstrate the efficiency as well as scalability of our algorithm. acknowledgement the first author acknowledges the support by the natural science foundation of china (no. 61303005, no. 61303089, no. 61202151, no. 61103117). mining periodic traces of an entity on web 665 bibliography [1] z. zhong, z. liu, z. wen(2009), the model of event relation representation, journal of chinese information processing, 23(6): 56–60. 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[20] i.c. resceanu, g.c. călugăru, c.f. resceanu et al(2012), cooperative robot structures modeled after whale behavior and social structure, international journal of computers communications & control, 7(5): 945–956. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 574-585 bionic wavelet based denoising using source separation m. talbi, a.b. aicha, l. salhi, a. cherif mourad talbi, lotfi salhi, adnene cherif faculty of sciences of tunis, laboratory of signal processing, university campus, 2092 el manar ii, tunis, tunisia e-mail: mouradtalbi196@yahoo.fr, lotfi.salhi@laposte.net, adnane.cher@fst.rnu.tn anis ben aicha université de carthage, ecole supérieure des communications laboratoire de recherche cosim route de raoued 3.5 km, cité el ghazala, ariana, 2083, tunisie, tél. : +216 71 857 000 fax : +216 71 856 829 e-mail: ben.aicha.anis@gmail.com abstract: we consider the problem of speech denoising using source separation. in this study we have proposed a hybrid technique that consists in applying in the first step, the bionic wavelet transform (bwt) to two different mixtures of the same speech signal with noise. this speech signal is corrupted by a gaussian white noise with two different values of the signal to noise ratio (snr) in order to obtain those two mixtures. the second step consists in computing the entropy of each bionic wavelet coefficient and finds the two subbands having the minimal entropy. those two subbands are used to estimate the separation matrix of the speech signal from noise by using the source separation. our proposed technique is evaluated by comparing it to the denoising technique based on source separation in time domain. keywords: bionic wavelet transform, blinde source separation, entropy, speech enhancement. 1 introduction in signal processing, the source separation constitutes an attractive problem. its goal is to extract from many signals mixture, the meaningful signals. this is performed with minimum a priori information on the mixture process. in the case of instantaneous mixture, many approaches employing the ica algorithm can solve the problem of the source separation. one of those approaches permits to estimate the unmixing matrix by minimizing the mutual information between the separated sources [1, 2]. others exploit the non-gaussianity of the source signals and perform separation by maximizing this non-gaussianity [2]. for example, a technique using a subband decomposing in combination with ica, has been developed by tanaka et al [3]. kisilev et al [4] have employed geometric algorithms for separating mixed signals. rachid moussaoui et al [5] have proposed an algorithm using the idea of applying a preprocessing in the transformed domain but the separation is performed in the time domain. in this paper we have used the source separation with bionic wavelet transform (bwt) for enhancement of speech signal corrupted by white noise. the source separation is performed with ica and instead of using the wavelet packet transform as used in the technique proposed by rachid et al [5], we have used in this work the bwt. copyright c⃝ 2006-2012 by ccc publications bionic wavelet based denoising using source separation 575 2 restrictions of ica the ica standard formulation needs at least as many sensors as sources. therefore, we suppose in this paper that the source number is equals to the sensor number. in the instantaneous mixture case, the sources are not directly observed but as a linear combination such as: xi(t) = j=n∑ j=1 ai js j(t), (1) where x are the observed signals, s are the source signals and a = [ai j] is unknown full rank mixing matrix. figure 1: ica principle. figure 1 shows the ica principle. the ica aim is practically to find the inverse matrix of a, which is the unmixing matrix w = a−1. to make an estimation of w, certain assumptions have to be made and some restrictions have to be imposed [2]: we assume that the individual components si(t) are statistically independent over the observation time and the individual components must have non gaussian distributions. in comparison to previous work, the novelty of the approach of rachid moussaoui et al [5] resides in the preprocessing implementation before the source separation process in to: relax the previous restrictions by increasing the non-gaussanity which is a pre-requirement for ica. initiate a preliminary separation by decreasing the mutual information between the resultant signals from the preprocessing. the preprocessing transforms the observed signals to find an adequate representation where the signals distributions are non-gaussian. for this reason, the wavelet transform is used in order to emphasize the non gaussian nature of the observed signals. once we have found the inverse matrix w with the wavelet packets based ica then, the separation is performed in the time domain [5]. figure 2 illustrated an overview of the system proposed by rachid moussaoui et al [5]. in this paper we have chosen s2(t) to be a white noise that corrupted the clean speech signal s1(t) with two different values of the signal to noise ratio (snr). 576 m. talbi, a.b. aicha, l. salhi, a. cherif figure 2: overview of the source separation system proposed by rachid moussaoui et al [5]. 3 the proposed technique the proposed speech enhancement system, illustrated in figure 4, is inspired from that of rachid moussaoui et al [5]. the latter is conceived for multi-channel source separation and is based on wavelet based independent component analysis. it comprises two modules shown in dotted boxes in figure 4. the first module (pre-processing) extracts appropriate signals from the observed signals in order to facilitate the separation of the speech and noise signals. for this, the observed signals are projected on suitable bases, more specifically on bionic wavelet bases. the second module (speech and noise separation) performs the source separation using standard ica [1]. the input of this module is the extracted signals from module 1 and the observed signals. its output is the cleaned or the enhanced speech signal ŝ1(t). figure 4 illustrated an overview of the proposed speech enhancement system which is summarized by the following steps: i decompose the observed signals (the two noisy speech signals) into bionic wavelet subbands by applying the bwt. ii compute the entropy value of each subband and select the two subbands having the minimum entropy. iii use those two subbands as the inputs of the ica system in order to estimate the separation matrix, a−1. iv estimate the enhanced speech signal ŝ1(t) by applying a−1 to the temporal mixtures x1(t) and x2(t) . the used entropy in this work, is the shannon entropy which is defined for each subband w·, j, 1 ≤ j ≤ 30 as: h( j) =− ∑ pilog( pi). (2) bionic wavelet based denoising using source separation 577 note that in the expression w·, j, 1 ≤ j ≤ 30, · is replaced by 1 if we apply the bwt to x1(t) and is replaced by 2 if we apply the bwt to x2(t). the probability pi is expressed as: pi = w·, j(i)2 ∥w∥2 , 1 ≤ i ≤ n and n is the number of samples in the subband w·, j and w is obtained by concatenating all the subbands w·, j, 1 ≤ j ≤ 30. 4 the bionic wavelet transform j. yao and y. t. zhang have proposed the bionic wavelet transform (bwt) as a new time-frequency technique by referring to the perceptual model [6]. the term "bionic" means that the bwt is guided by an active biological mechanism [7]. moreover, the bwt decomposition is both perceptually scaled and adaptive [8]. the initial perceptual aspect of the transform comes from the logarithmic spacing of the baseline scale variables which are designed to match basilar membrane spacing [8]. then, two adaptation factors control the time-support employed at each scale, based on a non-linear perceptual model of the auditory system [8]. the basis of this transform is the giguerre -woodland non-linear transmission line model of the auditory system [9, 10], an active-feedback electro-acoustic model incorporating the auditory canal, middle ear, and cochlea [8]. the model yields estimates of the time-varying acoustic compliance and resistance along the displaced basilar membrane, as a physiological acoustic mass function, cochlear frequency-position mapping, and feedback factors representing the active mechanisms of outer hair cells. the net result can be seen as a technique for the estimation of the time-varying quality factor qeq of the cochlear filter banks as the input sound waveform function [8]. the references [6–9] give the complete details on the elements of this model. the bwt adaptive nature is ensured by a timevarying linear factor t (a,τ) which represents the scaling of the cochlear filter bank quality factor qeq at each scale over time [8]. for each scale and time, the adaptation factor t (a,τ) of bwt is computed by using the update equation [8]: t (a,τ+∆τ) = 1[ 1 − g1 gsgs+|xbwt (a,τ)| ] [ 1 + g2| ∂∂t xbwt (a,τ)| ] (3) where cs is a constant (typically cs = 0.8) that represents non linear saturation effects in the cochlear model [6, 8]. the quantities g1 and g2 are respectively the active gain factor, which represents the outer hair cell active resistance function, and the active gain factor representing the time-varying compliance of the basilar membrane [8]. practically speaking, the partial derivative in equation (3) can be approximated by using the first difference of the previous points of the bwt at that scale [8]. xbwt (a,τ) represents the bionic wavelet transform (bwt) of the signal x(t) and it is given by: xbwt (a,τ) = 1 t (a,τ) √ a ∫ x(t) · φ̃∗ [ t −τ a · t (a,τ) ] · e− jω0( t−τ a )dt, (4) where a denotes the parameter of scale, τ is the shifting parameter in time and φ̃ is the mother wavelet envelop given by [7]: φ(t) = 1 t (a,τ) √ a φ̃ [ t t (a,τ) ] · e jω0t (5) where ω0 is the base fundamental frequency of the unscaled mother wavelet. in practice ω0 is equals to 15165.4 for the human auditory system [6]. the discretization of the scale a is achieved by employing a pre-determined logarithmic spacing across the desired frequency range, so that at each scale the center frequency is expressed by [8]: ωm = ω0 (1.1623)m ,m = 0,1,2, . . . . (6) 578 m. talbi, a.b. aicha, l. salhi, a. cherif based on yao and zhang’s original work for cochlear implant coding [9], coefficients at 22 scales, m = 7,. . . ,28, are calculated employing numerical integration of the continuous wavelet transform. these 22 scales correspond to center frequencies logarithmically spaced from 225 hz to 5300 hz. (although the scales used here match those from yao and zhang’s original work, empirical variation of the number of scales and frequency placement showed minimal effect on the overall enhancement results). for this implementation, we have used coefficients at 30 scales. in the formula (4), the role of first factor t (a,τ) multiplying √ a is to ensure that the energy remains the same for each mother wavelet. the role of second factor t (a,τ) is to adjust the envelop φ̃(t) without adjusting the central frequency of φ(t) [7]. thus, the main difference between (bwt) and the continuous wavelet transform (cwt) is based on the fact that the time-frequency resolution achieved by (bwt) can be adjusted in an adaptive manner not only by frequency variation of the signal but also by instantaneous amplitudes of this signal. it is the mother wavelet which makes the continuous wavelet transform adaptive, while the adaptive characteristic of the bwt comes from the mechanism of active control in the human auditory model. which adjusts the mother wavelet associated to (bwt) according to the analyzed signal. basically, the idea of the (bwt) is inspired from the fact that we need to make the mother wavelet envelop variable in time according to the signal characteristics. the employed mother wavelet φ(t) in [7] is the morlet wavelet and its envelop φ̃(t) is given by [8]: φ̃(t) = e [ −( tt0 ) 2 ] (7) where t0 denotes the initial time-support. figure 3 illustrated the real and the imaginary parts of the complex morlet mother wavelet. figure 3: the morlet wavelet. it can be shown [7, 9] that the obtained bwt coefficients, xbwt (a,τ) are derived by using the following formula [8]: xbwt (a,τ) = k(a,τ)xwt (a,τ), (8) where k(a,τ) is given by: k(a,τ) = √ π c t0√ 1 + t 2(a,τ) (9) where c represents a normalizing constant calculated from the squared mother wavelet integral. bionic wavelet based denoising using source separation 579 this representation yields to an effective computational technique for calculating in direct manner, the bwt coefficients from those of the wavelet transform. this is performed without using the bwt definition given by equation (4). there are some key differences between the discretized cwt employing the morlet wavelet used for the bwt and a filterbank based wpt using an orthonormal wavelet. one of them is that the wpt provides a perfect reconstruction, while the discretized cwt is an approximation whose exactness depends on the number and placement of frequency bands selected [8]. figure 4: overview of the proposed system. 5 criterion of evaluation for evaluating our proposed technique, we have compared it to the temporal technique based on runica. the evaluation is based on snr, ssnr, isd and pesq computation. these parameters are defined as follow: signal-to-noise ratio the signal-to-noise ratio (snr) of the enhanced speech signal is defined by: s nrdb = 10log10  ∑n−1 n=0 x(n) 2∑n−1 n=0 (x(n) − x̂(n)) 2  , (10) where x(n) and x̂(n) represent respectively the original and the enhanced speech signals, and n is the samples number per signal. segmental signal to noise ratio the segmental signal-to-noise ratio (segsnr) is calculated by averaging the frame based snrs over the signal: segs nrdb = 1 m m−1∑ m=0 10log10  ∑nm+n−1 n=nm x(n)2∑nm+n−1 n=nm (x(n) − x̂(n))2  , (11) 580 m. talbi, a.b. aicha, l. salhi, a. cherif where m designates the number of frames, n is the size of frame, and nm is the beginning of the m− th frame. as the snr can become negative and very small during silence periods, the segsnr values are limited to the range of [-10db, 35db] as per [10]. itakura-saito distance the distance of itakura-saito (isd) measures the spectrum changes and can be computed employing the coefficients of linear prediction (lpc) and this according to the following equation: is d(a,b) = (a − b)t r(a,b) at ra , (12) where a represents the lpc vector of the original speech signal x(n). r is the matrix of autocorrelation and b is the lpc coefficients vector of the enhanced speech signal x̂(n). in this work, a 10th order lpc based measure is employed. perceptual evaluation of speech quality the perceptual evaluation of speech quality (pesq) algorithm [11, 12] is an objective quality measure, that is approved as the itu-t recommendation p.862. it is a tool of objective measurement conceived to predict the results of a subjective mean opinion score (mos) test. it was proved [13, 14] that the pesq is more reliable and correlated better with mos than the traditional objective speech measures. 6 results and discussion from table 1 to table 8, we report the obtained results from the application of our proposed speech enhancement technique and the temporal technique based on runica on eight noisy sentences taken from the timit database. table 1: sentence 1. parameters proposed method temporal method snri of the first mixture 0.7672 0.7672 snri of the second mixture -3.4638 -3.4638 snrf(db) 69.5528 58.7147 ssnri of the first mixture -3.5715 -3.5715 ssnri of the second mixture -6.3509 -6.3509 ssnrf(db) 34.8736 34.1366 pesqi of the first mixture 1.3110 1.3110 pesqi of the second mixture 1.0983 1.0983 pesqf 4.4989 4.4936 isdi of the first mixture 2.4029 2.4029 isdi of the second mixture 3.9475 3.9475 isdf 4.181 10−10 4.948 10−8 these results show clearly that our proposed technique outperforms the temporal technique of source separation using standard ica [1]. fig. 5 and fig. 6 illustrate an example of speech enhancement using our proposed technique. bionic wavelet based denoising using source separation 581 table 2: sentence 2. parameters proposed method temporal method snri of the first mixture -2.1038 -2.1038 snri of the second mixture -6.6325 -6.6325 snrf(db) 66.5296 48.9184 ssnri of the first mixture -5.8749 -5.8749 ssnri of the second mixture -7.8815 -7.8815 ssnrf(db) 34.2126 30.7198 pesqi of the first mixture 1.4577 1.4577 pesqi of the second mixture 1.2445 1.2445 pesqf 4.4981 4.4535 isdi of the first mixture 2.8500 2.8500 isdi of the second mixture 4.6523 4.6523 isdf 1.4043 10−9 3.1847 10−6 table 3: sentence 3. parameters proposed method temporal method snri of the first mixture 4.2400 4.2400 snri of the second mixture -0.2606 -0.2606 snrf(db) 77.4719 49.5281 ssnri of the first mixture -1.4316 -1.4316 ssnri of the second mixture -4.6465 -4.6465 ssnrf(db) 34.7279 31.6486 pesqi of the first mixture 1.9873 1.9873 pesqi of the second mixture 1.7460 1.7460 pesqf 4.4999 4.4621 isdi of the first mixture 1.8622 1.8622 isdi of the second mixture 3.1204 3.1204 isdf 2.5837 10−10 8.2210 10−6 7 conclusion in this paper, we have proposed a new speech enhancement technique that consists in applying in the first step, the bionic wavelet transform (bwt) to two different mixtures of the same speech signal with gaussian white noise with two different values of signal to noise ratio (snr). the second step consists in computing the entropy of each bionic wavelet coefficient and finds the two subbands having the minimal entropy. those two subbands are used to estimate the separation matrix of the speech signal from noise by employing the source separation. the obtained results from the snr, ssnr, isd and pesq computation, show clearly that the proposed speech enhancement technique outperforms the temporal technique of source separation using standard ica. 582 m. talbi, a.b. aicha, l. salhi, a. cherif table 4: sentence 4. parameters proposed method temporal method snri of the first mixture 1.6366 1.6366 snri of the second mixture -2.7143 -2.7143 snrf(db) 61.8042 55.4325 ssnri of the first mixture -4.3027 -4.3027 ssnri of the second mixture -6.3345 -6.3345 ssnrf(db) 31.4223 29.4299 pesqi of the first mixture 1.5414 1.5414 pesqi of the second mixture 1.2476 1.2476 pesqf 4.4816 4.4506 isdi of the first mixture 2.5180 2.5180 isdi of the second mixture 4.0632 4.0632 isdf 7.4591 10−8 8.9142 10−7 table 5: sentence 5. parameters proposed method temporal method snri of the first mixture -0.7340 -0.7340 snri of the second mixture -5.7034 -5.7034 snrf(db) 59.5033 57.0129 ssnri of the first mixture -5.6047 -5.6047 ssnri of the second mixture -7.6901 -7.6901 ssnrf(db) 31.4463 30.8201 pesqi of the first mixture 1.3907 1.3907 pesqi of the second mixture 1.1391 1.1391 pesqf 4.4814 4.4748 isdi of the first mixture 2.6384 2.6384 isdi of the second mixture 4.2292 4.2292 isdf 2.4528 10−8 1.0487 10−7 table 6: sentence 6. parameters proposed method temporal method snri of the first mixture 2.5544 2.5544 snri of the second mixture -2.1241 -2.1241 snrf(db) 62.4473 62.2521 ssnri of the first mixture -3.3643 -3.3643 ssnri of the second mixture -5.8605 -5.8605 ssnrf(db) 31.4816 31.4398 pesqi of the first mixture 1.3805 1.3805 pesqi of the second mixture 1.0564 1.0564 pesqf 4.4929 4.4926 isdi of the first mixture 2.6047 2.6047 isdi of the second mixture 4.1036 4.1036 isdf 9.3330 10−8 1.0324 10−7 bionic wavelet based denoising using source separation 583 table 7: sentence 7. parameters proposed method temporal method snri of the first mixture -0.0411 -0.0411 snri of the second mixture -4.9428 -4.9428 snrf(db) 69.0167 67.9430 ssnri of the first mixture -5.7362 -5.7362 ssnri of the second mixture -7.8201 -7.8201 ssnrf(db) 33.9383 33.8069 pesqi of the first mixture 1.6015 1.6015 pesqi of the second mixture 1.4326 1.4326 pesqf 4.4978 4.4974 isdi of the first mixture 2.5867 2.5867 isdi of the second mixture 4.1275 4.1275 isdf 4.6205 10−10 6.7472 10−10 table 8: sentence 8. parameters proposed method temporal method snri of the first mixture -1.7018 -1.7018 snri of the second mixture -6.5995 -6.5995 snrf(db) 68.1109 52.2155 ssnri of the first mixture -5.8031 -5.8031 ssnri of the second mixture -7.8610 -7.8610 ssnrf(db) 33.2544 29.8972 pesqi of the first mixture 1.3814 1.3814 pesqi of the second mixture 1.1889 1.1889 pesqf 4.4968 4.4504 isdi of the first mixture 3.3273 3.3273 isdi of the second mixture 5.1147 5.1147 isdf 3.1514 10−9 5.4382 10−6 584 m. talbi, a.b. aicha, l. salhi, a. cherif figure 5: (a) clean speech signal, (b) enhanced speech signal. figure 6: (c) first mixture, (d) second mixture. bionic wavelet based denoising using source separation 585 bibliography [1] a. j. bell, t. j. sejnowski, an information maximization approach to blind separation and blind deconvolution, neural computation, vol.7, pp.1004-1034, 1995. 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[14] e. zavarehei, s. vaseghi, q. yan. interframe modeling of dft trajectories of speech and noise for speech enhancement using kalman filters, speech communication, vol.48, no.11, pp.1545-1555, 2006. int j comput commun, issn 1841-9836 8(6):825-837, december, 2013. localization of wireless sensor network based on genetic algorithm n. jiang, s. jin, y. guo, y. he nan jiang*, sixin jin, yan guo college of information engineering, east china jiaotong university nanchang, jiang xi 330013, p.r.china jiangnan@ecjtu.jx.cn, aquajsx@gmail.com, guoyan997@gmail.com *corresponding author: jiangnan@ecjtu.jx.cn yueshun he college of information engineering, east china institute of technology nanchang, jiang xi 330013, p.r.china hys8418@163.com abstract: this paper proposes a novel localization approach based on genetic algorithm for wireless sensor networks. in this method, we use a new way to approximate the distance between anchor node and unknown node which is out of the anchor nodes’ communication radius. in addition, we use self-adapting genetic algorithm into localization, which will ensure it can produce the result as similar as its real position in any environment. the experiments on various network topologies show the better results. in comparison, we find that previous anchor node free localization approach cannot work well in the unidealization environment. the demonstration explains that the approach can help unknown nodes obtain high accuracy position whether in open space, the environment with obstruction, or even the unconnected well environment. keywords: wireless sensor networks(wsns), genetic algorithm(ga), localization 1 introduction localization is one of key supporting technologies to wireless sensor networks (wsns). it could provide accurate position information for kinds of expanding application. in this paper, we study the localization problem for a 2d large-scale wsns with obstruction. if we equip gps for every sensor node, it will raise the cost much more, and it also brings a new problem of nodes’ power support. can we only use less gps for large-scale wsns to obtain the position of nodes, in obstruction or unconnected well environment? the major challenge is reducing the influence of obstruction between pairs of nodes. how to achieve high accuracy and reliability localization scheme for wsns that is always the research subject to researcher around the world, in the environment with obstruction or unconnected well. at the beginning of the localization study on wsns, received signal strength indicator(rssi) [1], tine of arrival(toa) [2], time difference of arrival(tdoa) [2] [3],angle of arrival(aoa) [4]and other range-based localization have been proposed. in addition, centroid algorithm [5],adhoc positioning system(aps) [6], amorphous [7], apit [8] and other range-free localization also have been proposed. through literature [1] [2] [3] [4] [5] [6] [7] [8], we found that these algorithms only could provide low accuracy position of nodes for us, and couldn’t meet the needs of expanding application on wsns. there are many approaches with only network connectivity to calculate the position of nodes such as multidimensional scaling (mds) [9]. it gives us an o.k. localization results on a wellconnected dense network (show in figure 7). but it does not have any well results in the environment with obstruction (show in figure 9). more recently, there are many researchers put graph rigidity theory into localization of wsns [10] [11] [12], they have made a lot of contribution on it. the methods proposed by s. lederer copyright © 2006-2013 by ccc publications 826 n. jiang, s. jin, y. guo, y. he et al. just use the network connectivity information to get the position of unknown nodes. in order to obtain the nodes’ position, they partition the network into voronoi cells with using delaunay triangles closest to the shape of region of concern. these kinds of methods are good choice of localization plan for underwater, underground or indoor environment, but in these way we can’t localization in the environment with obstruction or unconnected well. apart from this, the locations of landmarks should know before localization. how to obtain the nodes’ position in obstruction or unconnected well environment also is the research hotspot for us. our contribution. we propose a novel localization algorithm, it uses genetic algorithm(ga) [13] [14] into localization of wsns, the approach causes nodes in the environment with obstruction or unconnected well could get high accuracy position by themselves. we assume wsns has been deployed in a region with obstruction, in the network only fewer nodes equipped gps which could localization by themselves, but others are not. all nodes only could communicate with others in its communication radius, unknown nodes could get the position of anchor node and distance between with anchor node. when there are obstruction stop communicating pairs of nodes, unknown nodes could make use of other nodes in its communication radius to get the position of anchor nodes, and they could obtain distance with anchor nodes through the method provide in section 4. at last, every unknown node could estimate the position by the global search feature of ga. through the simulation result in this paper, we prove that the method provided in this paper has a well property in the environment with obstruction than any other methods, especially it’s could localization well in the environment where is separated into several parts like figure 1. figure 1: localization environment with obstruction, these pictures come from google maps. the outline of the paper is as follows: section 2 presents the theoretical foundations of genetic algorithm. the localization approach based on self-adapting genetic algorithm is proposed in section 3. section 4 presents the evaluation of system and compare simulate results use a different approach. section 5 concludes this paper. 2 theoretical foundations genetic algorithm (ga) is a bionics optimize algorithm. it has been put forward by john holland on 1975 [13] [14]. it is based on darwin’s biological evolutionism which is "survival of the fittest" and mendel’s genetic variation principle which is "biological genetic evolution mainly take place in the chromosome". ga through selection, crossover and mutation to die out the low-fitness individuals and maintain high-fitness individuals like in the real nature, it can make the population towards to optimize and converge to a global optimum individual. table 1 shows the pseudocode of the genetic algorithm. in this paper, we use the self-adapting ga which has been proposed in literature [15], the algorithm could change the operate points by the different multiformity of group. we should set up some parameters like code length l, group size n, cross probability pc, mutation probability localization of wireless sensor network based on genetic algorithm 827 table 1: pseudocode of genetic algorithm step 1: initialization the number of individuals, nind; the maximum number of generation, maxgen; the precision of variables, preci; the generation gap, ggap; initialized the times of generation gen= 0; step 2: coding initialize the group by gray, p(t); step 3: evaluate evaluate the fitness of each individual in the group p(t); step 4: genetic iteration while t < maxgen selecting the individuals for crossover; crossing the selected individuals by certain probability; mutating the individuals of group by certain probability; evaluating the fitness of the new group; producing a new group after the evolution, p(t); partial best = min(evaluate p(t)); update the times of generation, t = t + 1; end step 5: obtain the result global best = partial best; pmand so on. using ga into the node localization approach of wsns, we make use of the partial search ability and the global search ability of ga. the partial search ability will help nodes in class i(show in section 3.1) to obtain its position; the global search ability will help nodes in class ii(show in section 3.1) to obtain its position, these two kinds of abilities are supplied by cross probability and mutation probability. 3 algorithm description the localization approach based on genetic algorithm (gal) proposed in this section is divided into two phases: firstly unknown nodes obtain the distance to adjacent anchor nodes; secondly unknown nodes use genetic algorithm to estimate unknown node’s position. 3.1 obtain the distance after all sensor nodes been deployed, all anchor nodes begin broadcasting their position information. then unknown nodes could obtain messages with anchor nodes’ position directly which is in its radius, and they also could obtain other anchor nodes’ position through other nodes by routing forwarding. at last, unknown nodes metric distance with adjacent anchor nodes through rssi. after these steps, unknown nodes are classified into two categories: definition 1. consider an unknown node has obtained the distance to three or more anchor nodes called class i nodes. we say that this kind of nodes can use genetic algorithm to calculate its own position directly. 828 n. jiang, s. jin, y. guo, y. he the calculate method will proved in section 3.2. definition 2. consider an unknown node hasn’t obtained the distance of which to three or more anchor nodes called class ii nodes. we say that this kind of nodes cannot use genetic algorithm to calculate its own position directly. now we are ready to explain how to estimate the distance for class ii nodes. shown in figure 2. figure 2: two types of unknown nodes in the wsns. the red points mean anchor nodes and the black means unknown nodes. definition 3. there are two kinds of nodes, one is anchor node such as node a, the other is unknown node such as node c. node k is either kind of nodes in the network. node k can communicate with node a and c. let node k to node a farther than to node c. node b also is an assume node on the circle k, the radius of this circle is the distance of between node k and node c. shown in figure 3. figure 3: how to estimate the distance to anchor nodes for class ii nodes. every node has the same communication radius, and it is marked by gray area. definition 4. node d an assumed node which is the intersecting point of the extended line of line ak and circle k. θ is the included angle of line ak and kb, then φ = π − θ. shown in figure 4. lemma 5. for isoceles triangle abk, it has sin θ 2 = √ ak2 + (ck − ak)2 4ak2 (1) lemma 6. for the small circle of concentric circles k, it has b̂d = φ � bk (2) localization of wireless sensor network based on genetic algorithm 829 figure 4: how to estimate the distance to anchor nodes for class ii nodes. figure 5: class ii unknown nodes get the distance to anchor nodes. based on lemma 5. and lemma 6. we can conclude that: theorem 7. for class ii nodes like the node c in the figure 5, the distance between node a and c is equal to the distance between node a and c’ . in this paper, we use the sum of ab and the length of b̂c to similar the distance between node a and c’. as point c is an uncertain node, point c’ is also, it could appear on any position of b̂d. because of this, we use the half length of b̂d to measure b̂c′ . according this, we estimate the distance between anchor node and class ii unknown node as following ac = ac′ ≈ ab + b̂c′ ≈ ab + 1 2 (b̂d) (3) proof: node c’ is the mirror image of node c, it subjects to uniform distribution on b̂d. so if c ∼ u [b,d], then the probability distribution of node c subinterval [b,d], p (b ≤ c ≤ d) = ∫ d b 1 b − d dx = d − b b − d (4) in order to estimate the distance between node a and c’, we use the expectation of the length b̂c′ similar as the longer part of ac’ than ab. the expectation of the length b̂c′ is e ( b̂c′ ) = 0 + b̂d′ 2 (5) 2 3.2 compute unknown node’s position by genetic algorithm this paper measure the multiformity of group by f (t) × ρ(t), to guarantee ga group’s multiformity and it could expand search range during the previous period of iterate, moreover, optimize well during the later period of iterate. in this way, the self-adapting ga makes this localization approach can deal with the unconnected environment well. 830 n. jiang, s. jin, y. guo, y. he 0 10 20 30 40 50 60 70 80 90 100 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 figure 6: the function of ρ(t), t=100. definition 8. in this paper, the element of measuring the group’s multiformity is multiformity function f (t), it is considered that f (t) = 1 − h(t) l (6) where the average hamming distance is h (t) = n−1∑ i=1 n∑ j=i+1 h (xi (t) ,xj (t)) n−1∑ i=1 (n − i) (7) where the hamming distance between individual xik (t) and xjk (t) is h (xi (t) ,xj (t)) = l∑ k=1 |xik (t) − xjk (t)| (8) in the equation (8),xik (t) means the t generation’s k (k = 1,2, · · · ,l) bit of individual i(i = 1,2, · · · ,n). definition 9. in the multiformity measure, the other element is ρ(t), it is considered that ρ(t) = exp ( −t2/2σ2 ) ,σ = t/3 (9) in the equation (9) of definition 9, t means the last generation of ga iteration, when t=100, the figure of ρ(t) like figure 6. moreover, we also can’t use the encoding method for simple genetic algorithm. the encoding method of simple genetic algorithm is simply binary code, as this way, it will make great hamming distance between neighbor encode elements, the distance is called hamming cliff [16].the hamming cliff is too difficult to crossover or mutation, therefore we initialize encoding by gray code to avoid this problem. the most important part of genetic algorithm is fitness function. it is used to evaluate the fitness of every node’s estimate position. definition 10. in this paper, consider the fitness function f(j) = |dist(i,j) − dist(n,i)| + f(j)last−iteration (10) localization of wireless sensor network based on genetic algorithm 831 table 2: pseudocode of localization based on genetic algorithm step 1: initialization the number of individuals, nind; the maximum number of generation, maxgen; the precision of variables, preci; the generation gap, ggap; initialized the times of generation gen= 0; the number of anchor nodes, nodenum; the number of aimnodes, aimnum; the distance between anchor and aimnode, d; step 2: coding generate the position of aimpiont by gray code randomly, aimp(t); step 3: evaluate for n = 1; n < aimnum; n++ t = 0; for j = 1; j < nind; j++ f(j) = 0;%f(j) is the fitness function to the group for i = 1; i < nodenum; i++ f(j) = dist( nodep(i) − aimp(j) ) − d(n,i) + f(j); end end end step 4: select, crossover, mutation, reproduce while t < maxgen evaluating the fitness of the new group; selecting the individuals for crossover; crossing the selected individuals by certain probability; mutating the individuals of group by certain probability; evaluating the fitness of the new group; producing a new group after the evolution, aimp(t); partial best = min(f(j)); update the times of generation, t = t + 1; end step 5: obtain the result aimp = decode(partial best); dist(i,j) be the distance between anchor node i and unknown node j, calculating it needs the coordinate of node i and j. how to get the coordinate of anchor node i has proposed in section 3.1, and the coordinate of unknown node j could estimate by genetic algorithm. dist(i,j) be defined as: dist(i,j) = √( xnodep(i) − xnodep(j) )2 + ( ynodep(i) − ynodep(j) )2 (11) the pseudocode of the localization method proposed in this paper is showed in table 2. 832 n. jiang, s. jin, y. guo, y. he 4 simulation in this paper, we develop matlab toolbox to verify the effectiveness of our approach, and then contrast the experiment results by using different methods in different environment. during the follow experiment, there are 300 nodes deployed in the area 1000m × 1000m, the communication radius of anchor nodes and unknown nodes both are 200m. in this paper, the values of main parameters as follow, the ggap is 0.7, cross probability is 0.85 and the mutation probability is 0.1, it is similar in other application as usual. 4.1 localization effect in different environment from the following results, we could easily find that using mds to get the position of unknown nodes is a good choice for wsns, which is deployed in the open space environment (show in figure 7). while we using gal to localization for this environment, we can find that unknown nodes also could obtain its position accurately, if there are enough anchor nodes around it (show in figure 7). 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e figure 7: the first row figures are produced by mds; the second row figures are produced by gal. the left col figures show the nodes deploy in the open space; the middle col figures show the neighbor relationship have been built; the right col figures show the localization error. to verify the localization ability of gal in the environment with obstacles, we put rectangle, triangle, circle, wall into the region of consideration. when anchor nodes are 20% of all, the localization results are shown in figure 8. from the simulate result of figure 8, it is not difficulty to see that obstacles can’t influence localization accuracy much, unknown node also could obtain its position within accepted range. in the same environment as figure 8, we use mds to obtain unknown nodes’ position again, the experiment results show in figure 9. from figure 9 it’s not hard to find that localization use mds is much worse than localization using gal. in the environment like figure 9 (l) and (o) we can’t use mds to get the position of unknown nodes. it is well know that mds has the better localization accuracy than any other classical methods, in order to make this experiment completed we also use dv-hop and centroid algorithm to get the unknown nodes’ position in a few environments appeared in figure 8 and 9. the results localization of wireless sensor network based on genetic algorithm 833 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te figure 8: these pictures show the system evaluation by gal in the environment with obstacles or unconnected. the left col figures show the nodes deploy in different environment; the middle col figures show the neighbor relationship have been built; the right col figures show the localization error of gal. 834 n. jiang, s. jin, y. guo, y. he 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e −400 −200 0 200 400 600 800 1000 −200 0 200 400 600 800 1000 1200 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c o o rd in a te 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 x coordinate y c oo rd in at e figure 9: these pictures show the system evaluation by mds in the environment with obstacles. the left col figures show the nodes deploy in different environment; the middle col figures show the neighbor relationship have been built. the right col figures show the localization error of mds. but in the last two environment, we can’t use mds to get nodes’ position. localization of wireless sensor network based on genetic algorithm 835 of localization error is shown in table 3. 4.2 contrast gal with mds on characters in section 4.1 we have presented the result of localization using gal and mds in different environment with obstacles. in order to compare with two localization algorithms quantify and objectively, we make experiment in each environment and use different anchor nodes ratio, then analysis the localization error of these results. figure 10 shows the localization error use different anchor percentage. 6 8 10 12 14 16 18 20 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 anchor percentage(%) e s ti m a ti o n e rr o r( r ) gal mds 6 8 10 12 14 16 18 20 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 anchor percentage(%) e s ti m a ti o n e rr o r( r ) gal mds 6 8 10 12 14 16 18 20 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 anchor percentage(%) e s ti m a ti o n e rr o r( r ) gal mds (a).open space (b). rectangle obstacle (c). the area like “c” 6 8 10 12 14 16 18 20 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 anchor percentage(%) e s ti m a ti o n e rr o r( r ) gal mds 6 8 10 12 14 16 18 20 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 anchor percentage(%) e s ti m a ti o n e rr o r( r ) gal mds (d). triangle obstacle (e). pentagram obstacle figure 10: these pictures show the localization error in different anchor percentage by mds and gal. in these results, each point is the average result of ten experiments. figure 10 (a) shows the localization error in different anchor percentage in the open space. mds has higher accuracy than gal, and it just need little number of anchor nodes. the more anchor nodes, the more accuracy of gal, but the accurate of mds hardly change. when anchor nodes up to 20%, the localization accuracy of gal is close to mds. figure 10 (b) to (e) show the localization error comparison of two methods in the environment with obstacles, from these figures we can find that the more anchor nodes percentage, the more localization accuracy of gal, but the accurate of mds also hardly to be changed. when percentage of anchor nodes less than 8% in the rectangle or pentagram obstacle environment, or less than 11% in the triangle obstacle environment, mds could get better localization accuracy than gal, but the localization error is too high to application. however, gal could make localization error less than 15%, when percentage of anchor nodes reaches 13%. therefore, gal has highly robustness and practicality than any other localization algorithms in the environment not well for communicating. table 3: the localization error of dv-hop and centroid method without obstruction triangle obstruction five pointed star obstruction dv-hop 0.34246 0.375053 0.32259 centroid 0.318298 nan nan 836 n. jiang, s. jin, y. guo, y. he 5 conclusion a novel localization approach is proposed in this paper, in this method unknown nodes through their near anchor nodes to obtain their position. in order to reduce error during localization, we use new means to approximate the distance between unknown nodes and anchor nodes when it is larger than node’s communication radius. moreover, we use self-adapting genetic algorithm to calculate the similar position of nodes, it makes the localization error much lower than the common method. through the contrast experiments in this paper, we find this localization method can work well, whether in the environment with obstruction or not. for the aim of localization in non-planar environment like interior of tall building, we would develop a 3-d localization algorithm in the future study. acknowledgment this work is supported by national natural science foundation of china under grant no.61063037 and no. 51364001, key projects in the science and technology pillar program of jiangxi province of china under grant no. 20111bbg70031-2 and 20133bbe50033, and foundation for young scientists of jiangxi province of china under grant no. 20133bcb23016. bibliography [1] girod, l.; bychkovskiy, v.; elson, j.; estrin, d. 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[16] k. deep; m. thakur(2007); a new crossover operator for real coded genetic algorithms, applied mathematics and computation, issn 0974-4665, 188:895-911. international journal of computers communications & control issn 1841-9836, 11(3):372-380, june 2016. forecasting gold prices based on extreme learning machine s. kumar chandar, m. sumathi, s.n. sivanadam s. kumar chandar* associate professor, christ university, bangalore, india *corresponding author: kumar.chandar@christuniversity.in m. sumathi associate professor, sri meenakshi government college for arts for women (autonomous), madurai, india sumathivasagam@gmail.com s.n. sivanandam professor emeritus, karpagam college of engineering, coimbatore, india sns12.kit@gmail.com abstract: in recent years, the investors pay major attention to invest in gold market because of huge profits in the future. gold is the only commodity which maintains its value even in the economic and financial crisis. also, the gold prices are closely related with other commodities. the future gold price prediction becomes the warning system for the investors due to unforeseen risk in the market. hence, an accurate gold price forecasting is required to foresee the business trends. this paper concentrates on forecasting the future gold prices from four commodities like historical data’s of gold prices, silver prices, crude oil prices, standard and poor’s 500 stock index (s &p500) index and foreign exchange rate. the period used for the study is from 1st january 2000 to 31st april 2014. in this paper, a learning algorithm for single hidden layered feed forward neural networks called extreme learning machine (elm) is used which has good learning ability. also, this study compares the five models namely feed forward networks without feedback, feed forward back propagation networks, radial basis function, elman networks and elm learning model. the results prove that the elm learning performs better than the other methods. keywords: feed forward neural networks, extreme learning machine, gold price forecasting. 1 introduction gold is the major commodity in the economic and monetary market. india and china are the major importers among the world and consumes 60 % of the global gold. every day, the value of the gold increases and cannot be controlled. nowadays, people tend to invest in gold owing to huge profits in future. the gold prices are closely related with other commodities. a hike in oil prices will have positive impact on gold prices and vice versa. when there is a hike in equities, gold prices goes down. this is because when there is a boom in the stock market, the investors tend to invest the gold money in the equities. hence, an accurate gold price forecasting is required to foresee the business trends in future. soft computing techniques like neural networks, fuzzy logic, genetic algorithms, particle swarm optimization and simulation annealing can be used to forecast the gold price. among the above, artificial neural networks are very accurate and predicts the future very well. some of the recent studies on gold price forecasting are discussed below. copyright © 2006-2016 by ccc publications forecasting gold prices based on extreme learning machine 373 gary et al. [1] used neural networks for forecasting standard & poor’s 500 stock index and gold futures prices. their forecast was based on the historical prices of the stock index and gold prices. malliaris et al. [2] used times series techniques and artificial neural networks for forecasting the prices of gold, oil and euro. they gave an interrelationship among the three and proposed ann technique to forecast the individual variables. and they concluded that both short term and long term relationship exist between the three variables. mehdi bijari et al. [3] proposed a hybrid arima model using fuzzy logic and artificial neural network for forecasting exchange rates and gold prices. fuzzy logic and ann was hybridized with the arima model in order to get accurate results. the results explained that the proposed technique predicts the future prices accurately than the other methods. ali et al. [4] uses multilayer perceptron neural network model for predicting the changes in stock prices and gold prices. the data used in this study was tehran’s stock exchange (t.s.e). the results showed that the ann models perform better than the traditional statistical techniques. deepika et al. [5] proposed autoregressive integrated moving average (arima) models for forecasting the monthly gold price from period 1980 to 2012. this paper also finds the factors influencing the gold price using multiple regression analysis. lazim abdullah [6] used auto-regressive integrated moving average (arima) model for forecasting the selling prices of gold bullion coins. they forecasted that the selling prices are in the upward direction and the investors can invest money in the gold bullion coins. trian et al. [7] explored gold equivalent for forecasting steel prices in pipeline projects. this paper elaborates on how the steel prices depend on the gold price using a regression model of the historical data. massarrat ali khan et al. [8] used box-jenkins, arima model for forecasting the gold prices. the period used for the study was from january 2003 to march 2012. they concluded that arima model was the suitable model for forecasting the gold price. bai li [9] proposed improved artificial bee colony algorithm (abc) for forecasting the gold price modelling using wavelet neural networks. the experimental results showed that the improved abc algorithm works more effective than the conventional abc algorithm. fengyi zhang et al. [10] proposed the methods for forecasting gold price using radial basis function (rbf) neural networks and hybrid fuzzy clustering algorithm. principal component analysis was used to unite technical indicators namely moving average, receive operator characteristics and p-accuracy rate. the results showed that the hybrid fuzzy clustering algorithm works better than the rbf neural network. hossein mombeini et al. [11] developed a defined model for forecasting gold prices. the performance measures are used to access the accuracy of the model. they have presented gold price forecasting using two models namely artificial neural networks and arima models and showed that the ann model works better than the arima model in terms of performance metrics. this paper concentrates on forecasting the future gold prices from four commodities like historical data’s of gold prices, silver prices, crude oil prices, standard & poor’s 500 stock index (s & p 500) index and foreign exchange rate. the period used for the study is from 1st january 2000 to 31st april 2014. in this paper, a learning algorithm for single hidden layered feed forward neural networks called extreme learning machine (elm) is used which has good learning ability. also, this study compares the three models namely feed forward networks without feedback, feed forward back propagation networks and elm learning model. the organization of the paper is as follows: section 1 summarizes on the introduction and literature survey. section 2 illustrate on the research data used in this study, section 3 explains the elm algorithm. section 4 explains on the application of elm algorithm to gold price forecasting. section 5 discusses on the results, section 6 concludes the paper and section 7 enlighten on the references used in the study. 374 s. kumar chandar, m. sumathi, s.n. sivanadam 2 research data the gold prices are normally related with other commodities like crude oil, stock prices, silver prices etc. the period of the study is from 1st january 2000 to 31st april 2014. the monthly data are used to forecast the future gold prices. in this study, the future gold prices are forecasted from four commodities like historical data’s of gold prices, silver prices, crude oil prices, standard & poor’s 500 stock index (s & p 500 index and foreign exchange rate. the gold price and silver price data was gathered from http://www.bullion-rates.com/gold /inr/2014-4-history.htm, crude oil price data was collected from http://www.indexmundi// .com/commodities, bombay stock exchange data was accumulated from http://bseindia.com website and foreign exchange data was collected from http://fxtop.com/en/historical-exchangerates.phpma=1. in the sample data set, the input attributes contains the average monthly data of gold price per oz, the monthly average data of crude oil, the monthly average data of silver price, the monthly average value of us dollars in terms of indian rupees the monthly average values of s & p 500 stock data and the target attribute has the actual gold prices. the summary statistics of the research data is calculated from the input data by finding the minimum, maximum, mean and standard deviation of these values. table 1 statistics of the research data input data maximum minimum mean standard deviation gold price 95,194.24 12,100.23 39,317.532 8,592.43 crude oil 6,928.11 887.42 3091.0277 1716.6898 silver price 1,899.62 197.92 695.25052 517.21883 usd 63.90 39.26 47.4437 5.037152 s&p 500 stock 8,592.43 850.56 4398.487849 2500.74607 3 extreme learning machine single layer feed forward network (slfn) is one of the most common network architecture. it is widely used in many applications like classification and regression. gradient descent learning methods like slfn networks are time consuming and have serious issues such as over fitting, local minima problem and some parameters need to be tuned manually. some researchers have discovered many possibilities for slfn networks. among all, huang et al. [12–14] proposed that randomly chosen input weights and the bias weights in all the iterations with utmost n hidden neurons will bring desirable accuracy. moore-penrose generalized pseudo inverse is used for calculating the output weights. the learning algorithm is faster and has good generalization ability. this type of learning is known as ’extreme learning machine’ (elm). the salient features of elm are specified in a simple three step algorithm. in contrast to gradient descent methods, elm need not see the training data before generating the hidden node parameters. elm algorithm works for all piecewise continuous activation function. elm tries to find solution for many problems like local minima, time consuming and tuning the parameters. it works easier than other learning algorithms such as neural networks and support vector machine. the brief overview on elm algorithm is discussed below: 3.1 extreme learning machine a single layer feed forward network with x1,x2, ...,xm, input nodes, h1,h2, ...,hn, hidden nodes and ti be the target node is shown in fig.1. forecasting gold prices based on extreme learning machine 375 figure 1: single layer feed forward network let (ai,bi) be the weights connecting from input layer to hidden layer and β1,β2, ...,βn be the weights of the nodes connecting from hidden layer to the output layer. let ’g’ be the piecewise continuous activation function. the hidden layer outputs are given as n∑ i=1 [βig(ai,bi,xj)] = tj,j = 1, ...,n (1) equation (1) can be rewritten as βh = t .here h is called the hidden layer output matrix , which can be expressed as follows, h(a1, ...,an;b1, ...,bn;x1, ...,xm) = (2)   g(a1,b1,x1) . g(an,bn,x1). . . g(a1,b1,xm) . g(an,bn,xm)   β = [β1β2...βn] t and t = [t1t2...tn]t (3) in the hidden output matrix, each value represents the hidden output values of their corresponding node. the three step elm learning algorithm is as follows: in a single layer feed forward neural network, (xi, ti) be the training pair and g(a,b,x) be the hidden node output function. step 1: initially hidden nodes are chosen randomly, (ai,bi) where i = 1,2,3, ...,n step 2: calculate the hidden layer output matrix h step 3: calculate the output weights î˛ where î˛= t. here represents the moore-penrose inverse of hidden layer output matrix h. 376 s. kumar chandar, m. sumathi, s.n. sivanadam 4 an application of elm learning to gold price prediction in this section, elm training methodology is applied to gold price prediction. the proposed architecture is shown in fig.2. the gold prices are normally related with other commodities like crude oil, stock prices, silver prices etc. the period of the study is from 1st january 2000 to 31st april 2014. in this figure, monthly average price of five parameters like gold price, crude oil, silver price, usd and s&p 500 stock price are taken as inputs for elm learning network. the output of this network gives the next month’s gold average price. all the hidden nodes weights are chosen randomly. first, the hidden node output values are calculated from equation (1). then the output of this network is calculated by multiplying outputs of the hidden nodes with the weights of the hidden node and the output nodes. figure 2: architecture of the proposed system 5 results and discussions all the implementations are carried out using matlab r2009 running in an ordinary pc with pentium iv processor with 2.40 ghz speed and 256 mb of ram. . the period of the study is from 1st january 2000 to 31st april 2014. the monthly data are used to forecast the future gold prices. in this study, the future gold prices are forecasted from four commodities like historical data’s of gold prices, silver prices, crude oil prices, standard & poor’s 500 stock index (s & p 500) index and foreign exchange rate. among the data, 70% of the data is used for training, 15% is used for validation and 15% of the data is used for testing. the total number of trading days is 86 from (1st jan 2000 31st apr 2014). all the ann models are predicting next month’s average gold value. the details of the historical datasets for all the prediction models are listed in table 2 table 2 details of historical dataset forecasting gold prices based on extreme learning machine 377 details of the dataset total number training validation testing of trading days sample sample sample monthly average gold price monthly average silver price monthly average crude oil price monthly average s&p 500172 120 26 26 stock index monthly average usdollars in indian rupees elm learning algorithm is implemented and compared with other four existing neural networks such as feed forward neural network, back propagation network, radial basis function and elman networks. 5.1 feed forward networks without feedback a network is said to be feed forward network when the outputs are not directed back as inputs to the same or preceding layer. in this network, the information moves only in one direction and there is no feedback. all the layers are trained using levenberg-marquardt (trainlm) function and initnw and trains functions are used for adaptation of weights. during training process, this network has been given with 1000 epochs. but, it achieves the best performance goal at 13th iterations. the training time, training accuracy, testing time and testing accuracy of this network is given in table 3. 5.2 feed forward back propagation networks back propagation networks are multi-layered feed forward networks trained with respect to the back propagation error algorithm. this is the most widely used network. here, the outputs are directed back as inputs to the same or preceding layer nodes. hence in this model, the function ’mapminmax’ is used to reduce the noise in the dataset. training is done with the levenberg-marquardt ’trainlm’ training function and back propagation weight/bias learning is done using ’learngdm’ function. adaption is done with ’trains’ which updates weights with the specified learning function. the transfer function ’tansig’ function is used for hidden layers and ’purelin’ function for output layer. performance is measured according to the mean square error ’mse’ performance function. this model runs up to 11 iterations and achieves the best performance goal. the training time, training accuracy, testing time and testing accuracy of this network is given in table 3. 5.3 radial basis function network radial basis function network is a particular type of feed forward network used for approximating the functions and recognizing the patterns. the architecture of the network consists of three layers namely, the input layer, hidden layer and the output layer. 5.4 elman networks the elman networks are a form of recurrent neural networks. a three layer feed forward network where a set of ’context units’ are attached to the hidden layer. at each time step, a copy of hidden layer unit is copied onto the context unit having a weight of one. thus the network is learnt by the current input signals, a copy of previous hidden layer unit’s i.e context 378 s. kumar chandar, m. sumathi, s.n. sivanadam units and the output of the network. the context unit can be considered as a one of the inputs to the hidden layer. 5.5 elm learning algorithm in this study, the weights of the hidden nodes are chosen randomly and the number of hidden nodes is varied from 5 to 20. but the best training and testing accuracy was achieved when the number of hidden nodes was 10. when compared with the other two networks, elm achieves 97.5% for training and 93.82% for testing. the performance of elm algorithm with respect to gold price prediction is shown is table 3. table 3 performance comparison of different neural networks training testing algorithm training training testing testing time accuracy time accuracy elm 3.1682 97.65 % 0.05272 93.82 % feed forward networks without feedback 4.1184 93.82 % 0.3772 91.14 % zabir haider khan et al. [5] feed forward back propogation networks 7.6752 92.41 % 0.5389 90.02 % malliaris et al. [2] radial basis function 5.1589 95.37 % 0.1839 92.58% fengyi zhang et al. [10] elman networks 6.9384 94.28% 0.20915 91.67% figure 3: comparison graph of predicted monthly average gold value vs target gold price forecasting gold prices based on extreme learning machine 379 the graph is drawn between monthly average gold value and time series which is shown in fig.3. in this graph, the predicted gold prices are mapped and it is compared with the actual gold prices. from the graph, it is understood that this proposed model predicts the future gold prices more accurately with the actual gold prices. 6 conclusions an accurate gold price forecasting is necessary for predicting the future gold price. hence in this paper, a new learning algorithm called elm learning is applied which has good learning ability and generalization capability. the period used for the study is from 1st january 2000 to 31st april 2014. since, the gold price is related with other commodities, four commodities like like historical data’s of gold prices, silver prices, crude oil prices, standard & poor’s 500 stock index (s & p 500) index and foreign exchange rate are given as inputs to the single layer feed forward network also, this study compares the three models namely feed forward networks without feedback, feed forward back propagation networks and elm learning model. the results clearly explain that the elm algorithm achieve approximately 3% increase in the training and testing efficiency when compared with the other networks. bibliography [1] gary grudnitski, larry osburn, forecasting s &p and gold futures prices: an application of neural networks, the journal of futures markets, 13(6) : 631-643, 1993. [2] a.g. malliaris and mary malliaris, time series and neural networks comparison on gold, oil and the euro, proceedings of international joint conference on neural networks, atlanta, georgia, usa, june 14-19, 2009. [3] mehdibijari, gholam aliraissi ardali, improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (anns), neurocomputing, 72 : 956-967, 2009. [4] ali ghezelbash, predicting changes in stock index and gold prices to neural network approach, the journal of mathematics and computer science, 4 : 227-236, 2012. [5] deepika m g, gautam nambiar and rajkumar m, forecasting price and analysing factors influencing the price of gold using arima model and multiple regression analysis,international journal of research in management, economics and commerce, 2(11) : nov 2012. [6] lazim abdullah, arima model for gold bullion coin selling prices forecasting, international journal of advances in applied sciences, 1(4) : 153-158, 2012. [7] trian hendro asmoro, exploring gold equivalency for forecasting steel prices on pipeline projects, 2(5) : 1-22, may 2013. [8] dr. m. massarrat ali khan, forecasting of gold prices (box jenkins approach), international journal of emerging technology and advanced engineering, 3(3) : 2013. [9] bai li, research on wnn modelling for gold price forecasting based on improved artificial bee colony algorithm, hindawi publishing corporation computational intelligence and neuroscience vol. 2014, pp. 1-10, 2014. 380 s. kumar chandar, m. sumathi, s.n. sivanadam [10] fengyi zhang and zhigao liao, gold price forecasting based on rbf neural network and hybrid fuzzy clustering algorithm, proceedings of the seventh international conference on mangement science and engineering management, lecture notes in electrical engineering, vol. 1, pp. 73-84, 2014. [11] hossein mombeini and abdolreza yazdani-chamzini, modeling gold price via artificial neural network, journal of economics, business and management, 3(7) : 2015. [12] guang-bin huang, lei chen, chee-kheong siew, universal approximation using incremental networks with random hidden computational nodes ieee transactions on neural networks, 17(4) : 879-892, 2006. [13] guang-bin huang, qin-yu zhu, chee-kheong siew, extreme learning machine: theory and applications,neurocomputing, 70 : 489-501, 2006. [14] guang-bin huanga, lei chena, convex incremental extreme learning machine, neurocomputing, 70 : 3056-3062, 2007. int j comput commun, issn 1841-9836 7(5):849-856, december, 2012. strategic decision models cross-validation by use of decision reports information extraction l. hancu lucian hancu 1. "babes-bolyai" university of cluj-napoca romania, cluj-napoca, 1 m. kogalniceanu, and 2. softproeuro ltd. cluj-napoca romania, 400614 cluj-napoca, 1 lacul rosu e-mail: lhancu@softproeuro.ro abstract: from all the events in the life of a business entity, the mergers and acquisitions transactions are one of the most challenging ones, as they drastically affect the life of the involved entities, but also their business stakeholders (like clients or suppliers). the merger transaction can be seen as a growth crisis in the life of the buyer entity and a strive for survival in the life of the acquired company. studying such transactions are being a constant preoccupation for both academia and practitioners, modeling mergers in order to predict them one of the most ambitious task. in this paper, we present our technique of cross-validating the results of our model and use several boosting methods for improving the computed decisions scores. keywords: mergers and acquisitions, quantitative models, cross-verification, boosting algorithm, growth crisis, business survival 1 introduction the strategic decisions of the type mergers and acquisitions are of crucial importance for the life of both the entities involved in such a process and their stakeholders ones (clients, suppliers, or even competitors). predicting such transactions are, thus, of great importance for the participants to the economic activities, as the changes in the market conditions can drastically affect the entities, especially the small competitors. bearing this in mind, we have previously built a model of predicting future mergers and acquisitions based on the financial statements of the entities involved in such a strategic process and on the correlations between the two entities activity’s codes (the so-called business dependencies map [4]). in addition, data regarding previously completed acquisitions are available on the web and can be easily downloaded and analyzed. information extraction from such data can be of great help in cross-verifying the quantitative models for mergers and acquisitions. in this paper, we apply a cross-validation mechanism in order to correlate data manually extracted from the competitors council merger decisions reports from 2003 up to 2008 of the type buyer (the entity who bought another entity) target (the entity who was bought) seller (the entity who sold its ownership of the target entity to the buyer entity) with the results of the mavoc (mergers, acquisitions, virtualizations or conservations) quantitative model. the cross-validation occurs only when the buyer and target are both romanian entities and their financial statements are present in the previously-computed database of the top of the romanian entities, so it is possible to compute the mavoc quantitative score. prior to performing such a step, an automatic cleansing is performed on the data extracted from the competition’s council decisions reports, that assures that the entities are found on the databases collecting the financial statement. the cleansing step is crucial as many entities change names after the completion of an acquisition transaction, which makes difficult (or even impossible) the finding of the financial information regarding the specified entity. copyright c⃝ 2006-2012 by ccc publications 850 l. hancu in addition to the cross-validation task, a boosting algorithm is used in order to improve the results of the mergers and acquisitions mavoc model. the boosting algorithm is based on the risk profile of both buyer and the seller and it takes into consideration the risk associated with the two entities activity codes. the boosting algorithm has the scope of improving the acquisition score and downgrading the other scores (especially the inverse acquisition aand conservation c), so as the mavoc model would output acquisition for the two companies extracted from the decision reports. this boosting technique is required when the acquiring company’s financial strength is similar to (or weaker than) the acquired company’s financial strength, which would conduct to a false inverse acquisition (a-) recommendation if used alone without boosting. the paper is organized as follows. in the following section we provide a brief introduction to the mergers and acquisitions research. this research usually is the result of consultant companies and it takes several years of investigation, when analyzing transactions from several decades. in the subsequent section, we briefly explain our methodology of modeling mergers and the technique of extracting data from decisions reports from the competition council, that are later used in cross-validating the model. the fourth section describes our boosting techniques aimed at improving the results of the decision model, whereas the paper ends with a brief summarization of the discussed topics and depicts directions for future work. 2 mergers and acquisitions transactions a thorough analysis of more than 20 years of italian mergers and acquisitions transactions is done in [7]. the research takes into consideration the interval between 1998 and 2010, in which the authors analyze transactions from various sectors including banking and public services like electricity and gas. it is analyzed the context of such transactions the italian economy in the analyzed period, in which the small and medium entities occupy a large percentage of the total amount of business entities. the context is, to some extent, similar to the one of the romanian market: some transactions were done as privatizations, between the state agencies and private entities (most of them being foreign entities), others being transactions between foreign entities (one which previously acquired the company and which is now willing to exit from the investment). a special role in these transactions are occupied by investment funds, business entities that acquire several percentages of companies, develop a new business, then sell the company to a third-party investor. while transactions between two companies (competitors, clients or suppliers) are easier to be analyzed as one could extract features like financial indicators, position on the market, coverage of the market, the case of private funds (also known as investment funds or equity funds) remain a distinct subject of research and it shall be left behind during our research. the main motives of mergers transactions are depicted in [2]: to affect more rapid growth, gain economies of scale, increase market percentage, expand in the territory, increase stock market value, expand or improve the mix of products, spread risk through diversification, enhance the power and influence of the entity, invest the entity’s idle capital, acquire technical knowledge and expertise, counter cyclical of seasonal revenues, obtain managerial talent, gain from tax advantages, obtain more control over the supply sources and/or the retail outlets or to defend against a possible takeover. some of these motives are summarized also in [1]. the main aim of the merger transaction is to realize value, by managing risk and exercising power. the mergers transactions from the market power perspective are also analyzed in [3] and [8]. the former research states that markets are passing through several stages in their way to consolidation, when having almost 90% of the market power concentrated into the industry giants. the latter research, instead, focuses on strategic decision models cross-validation by use of decision reports information extraction 851 exploting niche markets for mergers transactions. the research literature of mergers and acquisitions coming from both practitioners but especially from academic researchers is crucial in deriving the criteria for future modeling of mergers and acquisitions transactions. by analyzing the research literature, we have figured out that several mergers motives can be modeled by quantitative variables, while others (denoting especially human resource-related questions) focus on more qualitative results. in this article, we shall concentrate on modeling mergers through quantitative models and improve them by crossvalidations with data previously extracted from the decisions reports of the national competition council, that are published through their public web information systems. 3 gathering decision reports and cross-verification of merger model modeling mergers decisions, based on the financial indicators of the analyzed entities and the business dependencies of the various business activities (which constitutes the business dependency map [4], has been first explained in [5]. the mergers acquisitions virtualizations or conservations mavoc model consists in extracting the financial indicators of the involved entities from the available public web sources (like the ministry of finance or registry of commerce). the model makes use of the two-business entities activity codes, number of employees, financial resources (turnover, tangible assets, intangible assets), market share whether the entities are part of the local (or national) top of the entities corresponding to the county of each business entity. according to the relations between the financial indicators of the two business entities, a score for each alternative (mavoc) is computed, the score which is higher ranked is returned as the suggested alternative for the two business entities for the specified year. one method of verifying the quantitative mergers model would be to extract data from the decision reports published by the competition council and cross-verifying the results of the model with the data extracted from these decision reports. in the following paragraph, we briefly explain the methodology we use in extracting data from these reports. 3.1 extracting information from decision reports strategic decisions like mergers or takeovers are analyzed by the country’s competition council (in the case that the two entities are from the same country) or by each country’s specific competition council (in the case that the two business entities are coming from two different countries). upon each analysis, a decision report is issued (and usually available using web information systems) which describes the details of the transaction and the council’s acceptance or rejection. the technique of extracting information from these reports has been thoroughly explained in [6]. we extract information from these reports then cross verify the extracted data with our data sources, in order to obtain relationships of the type buyer (the entity which bought another entity) target (the entity who was bought) seller (the entity which previously owned the target). in some cases, the relationship is restricted to only buyer target, as there is no entity which owns the target company. 3.2 cross-verification of the model with previously-extracted data the extraction of the data resulted in 740 relationships of the form buyer target seller, from which 20, 27% of them were business entities coming from romania. in order to further 852 l. hancu perform cross-validation tasks, we had to check that these entities are business entities (and not public agencies owned by the state), so that we can obtain the financial indicators from the external information sources. the results of the cross-validation baseline experiment are depicted in the first row of the table 1 and in the figure 1. the table synthesizes the percents of the final results of the model for the input coming from the decisions reports: 2, 67% were classified as mergers, 14% were classified as inverse acquisitions (if a and b are the entities, we denote direct acquisition or a+ the acquisition of b made by a and inverse acquisition or athe acquisition of a made by b). in addition, 26, 67% were classified as virtualizations (creation of short-term virtual entities for profiting from a business momentum), 30% as direct acquisitions and 26, 67% as errors. the errors are due to the fact that several values are missing in our information systems which contain financial data on business entities. instead of adding an auto-updater tool to our information systems for correcting these errors, we have performed several boosting strategies in order to correct or limit these errors, strategies that are explained in the next section. the last two columns of the table 1 represent the relative number of correct answers with respect to all the output of the model including the errors, and the relative number of those answers with respect to the output excluding the errors. the former is computed by dividing the m, a+ and v answers to the m, a+, a-, v and error answers, while the latter is computed by dividing the m, a+ and v answers to the m, a+, aand v answers. as previously noted in our experiments, with the introduction of the 250 top of the entities (instead of the previous top of 10 entities in the original model [5]) the number of conservations (c) results is zero. we consider here the m, a+ (direct acquisitions) and v as correct answers for the cross-validations with the decision reports data extraction. during our boosting experiments we shall concentrate in minimizing the number of errors and the number of inverse acquisitions a-. in the figure 1 we depict the analytical scores of the model the maximum between the m, a+ and v scores in comparison with the aand c scores. the image serves at an overview of the baseline experiment. we shall improve the score and present an updated version of the score, during the boosting section. 4 boosting techniques several approaches were required for improving the results of the decisions scores. in this section, we briefly summarize the boosting approaches that we are using. experiment 2 downward model in order to improve the number of correct answers, we apply a downward strategy to our model: we take the initial year of the transaction and the fiscal ids of the two entities from the extracted decision reports (see previous paragraph for the methodology). if the model outputs an error, we apply the same model to the previous years from the range 2003..2008 in order to obtain better results (non-error result of the model). the reason for applying this boosting technique is: several transactions completed at the end of a fiscal year are analyzed only in the next year, when one of the entity might not be a valid entity any more, in the mean time being absorbed by the other entity, and, thus, making impossible the gathering of its financial indicators for the current year. the results are depicted in the second row of table 1 and reveal a slight improvement of the results of the experiments. experiments 3 upward model a similar mechanism of upward strategy is also used, when the model outputs the same error (indicating that one or both business entities are missing from the information systems business data), with the sole difference that we analyze the years after the transaction and output the model decision score, when available. strategic decision models cross-validation by use of decision reports information extraction 853 figure 1: model score before boosting experiments 4 and 5 using total incomes instead of turnover into the model. by manually inspecting the false negative results from our experiments, we have discovered that a number of entities had much higher total incomes with respect to the turnover, which suggested us to use the other indicator into the model in order to improve the results. the difference between the two experiments is a small change in the importance of the business classes of the two entities, that affected the results of our experiments; we shall use this strategy in the boosting techniques, discussed below. risk-based boosting: experiments 6-12 the main idea of this paper was to apply riskbased boosting techniques in order to improve the decision scores of our model. the motivation for this approach relies on the fact that businesses coming from certain business sectors are more willing to accept higher risks than businesses coming from the more conservative business sectors. we have previously developed various techniques for computing the dependency-based risks from the virtualized supply chains; in this article we shall limit at applying boosting techniques to the high risk-tolerant business classes, without insisting in the classification low-medium-high risk business class. thus, higher risks from mergers and acquisitions transactions are supposed to be accepted for the high risk-tolerant business classes. the risk-based boosting techniques are summarized below: model’s parameters when the business class of the a company is one of the high risktolerant business classes, we use different thresholds for the parameters k1, k2, k3, k4 (in the relations 1, 2, 3, 4, as follows: for the first relation we use the lower-bound parameter for higher risk business classes, allowing that weaker business entities acquire stronger ones 854 l. hancu m av a+ err proc1 proc2 1 2,67 14 26,67 30 26,67 59,34 80,91 2 2,67 16 30,67 36 14,67 69,34 81,25 3 2,6 14,94 31,17 39,61 11,69 73,38 83,08 4 2,6 16,23 30,52 38,96 11,69 72,08 81,62 5 2,6 12,34 31,82 41,56 11,69 75,98 86,03 6 1,3 12,99 31,17 42,86 11,69 75,33 85,29 7 5,23 13,07 26,8 43,14 11,76 75,17 85,19 8 4,55 12,34 26,62 44,81 11,69 75,98 86,03 9 1,3 10,39 37,01 39,61 11,69 77,92 88,23 10 12,34 7,14 29,22 39,61 11,69 81,17 91,91 11 7,14 7,14 29,22 44,81 11,69 81,17 91,91 12 2,6 5,19 29,87 50,65 11,69 83,12 94,12 table 1: boosting experiments (but within the specified threshold between the intangible assets aca and the sum between tangible and intangible assets aimb and acb. the relation 2 specifies that the lowest parameter should be use in less risk-tolerant business classes, whereas the highest should be used in the higher risk-tolerant ones. we point out that the more risk-tolerant business class refers to the business class of a and not to the risk class of b. similar relations are 3 and e4 when we use the number of employees. more risk-tolerant business classes are those that are willing to assume a higher risk during the acquisition transaction. this could be contracting a credit for the acquisition of the company, or having a weaker company willing to acquire a stronger one for gaining market share. aca > k1 ∗ (aimb + acb), k1 ∈ {0.5, 1.0} (1) acb > k2 ∗ (aima + aca), k2 ∈ {1.0, 2.0} (2) ema > k3 ∗ emb, k3 ∈ {0.5, 2.0} (3) emb > k4 ∗ ema, k4 ∈ {2.0, 4.0} (4) importance of criteria the criteria used in our mavoc model can be divided into two categories: risk-dependent ones and non-risk-dependent ones. for the risk-dependent ones like the financial criteria, human resources criteria and business classes criterion we use different importance weights than in the standard risk classes. this assures that the risk-dependent variables are more weighted in constructing the final decision score. weights of alternatives when using the risk-dependent criteria we provide various weights for the 5 alternatives, that are tuned during the boosting experiments 6-12. higher weights are given to the alternatives that express a direct acquisition, whereas gradually lower weights are given to the ones expressing inverse acquisition a-. strategic decision models cross-validation by use of decision reports information extraction 855 figure 2: model score after boosting the results from our experiments are summarized in table 1 and figure 2. the figure shows that the cross-validated results are better than the initial ones. the reason for summing the results of the scores m, a+, v is that we can consider the merger and virtualization score as similar to the acquisition score. as stated in the experiments from [5], the merger score is given to almost equal in financial and human strength of business entities with similar activity codes, whereas the virtualization score is given to the same kind of business entities, but having different and correlated activity codes. 5 conclusions and future works in this paper we have presented a technique for boosting the results of the merger scores obtained by cross-validating the mavoc model decision scores when the business classes are ones from more risk-tolerant business classes. several factors limit the results of our experiments: the relatively high number of transactions in which one or two of the businesses from the buyer-target-seller relationship are foreign entities (or local businesses hidden behind foreign offshore companies). in these cases, it was not possible to apply the decision score and boost its results. in our future works, we plan to also investigate the possibility to include foreign companies into the model, by enhancing the information systems database which contains the financial statements of the analyzed business entities. 856 l. hancu acknowledgment this work was partially supported by the cncs-uefiscdi under the grant pn ii-rupd-397/2010. we would like to thank the combinations of companies m.sc. students and their academic mentors from the faculty of economic sciences and business administration for their contribution in manually extracting the mergers and acquisitions informations from the decisions reports. bibliography [1] d. angwin, mergers and acquisitions, blackwell publishing, 2007, pg. 21. [2] h. k. baker, t. o. miller, b. j. ramsperger, a typology of mergers motives, in akron business and law review, 12(4), 1981, 24-29, reprinted in j. a. krug, mergers and acquisitions, sage, 2008, pp. 67-76. [3] g.k. deans, f. kroeger, s. zeisel, winning the merger endgame, a playbook for profiting from industry consolidation, a.t. kearney, 2003, pp. 22-95. [4] l. hancu, data-mining techniques for supporting merging decisions, in international journal of computers, communications and control, suppl. issue, 2008, pp. 322-326. [5] l. hancu, pruning decision trees for easing complex strategic decisions, in annals of the tiberiu popoviciu seminar, volume 6, 2008, pp. 194-203. [6] l. hancu, mining strategic decisions information systems for predicting future market concentrations, in proceedings of the international iadis information systems conference, march 2012, berlin, germany. [7] kpmg, 20 anni di m&a fusioni e acquisizioni in italia dal 1988 al 2010 (20 years of m&as mergers and acquisitions in italy from 1988 to 2010), egea, 2010. [8] f. kroeger, a. vizjak, m. moriarty, beating the global consolidation endgame, a. t. kearney, 2008. international journal of computers communications & control issn 1841-9836, 9(4):408-418, august, 2014. simulation experiments for improving the consistency ratio of reciprocal matrices d. ergu, g. kou, y. peng, x. yang daji ergu southwest university for nationalities 4th section,yihuan nanlu,chengdu, 610041, china, ergudaji@163.com gang kou ∗ school of business administration southwestern university of finance and economics no.555, liutai ave, wenjiang zone,chengdu, 610054, china *corresponding author kougang@swufe.edu.cn yi peng school of management and economics university of electronic science and technology of china no.2006, xiyuan ave, west hi-tech zone, chengdu, 611731, china pengyicd@gmail.com xinfeng yang school of statistics, southwestern university of finance and economics no.555, liutai ave, wenjiang zone, chengdu, 610054, china, 315425159@qq.com abstract: the consistency issue is one of the hot research topics in the analytic hierarchy process (ahp) and analytic network process (anp). to identify the most inconsistent elements for improving the consistency ratio of a reciprocal pairwise comparison matrix (pcm), a bias matrix can be induced to efficiently identify the most inconsistent elements, which is only based on the original pcm. the goal of this paper is to conduct simulation experiments by randomly generating millions numbers of reciprocal matrices with different orders in order to validate the effectiveness of the induced bias matrix model. the experimental results show that the consistency ratios of most of the random inconsistent matrices can be improved by the induced bias matrix model, few random inconsistent matrices with high orders failed the consistency adjustment. keywords: reciprocal random matrix, consistency ratio, induced bias matrix, simulation experiment; analytic hierarchy process (ahp)/analytic network process (anp) 1 introduction in the analytic hierarchy process (ahp) and analytic network process (anp), the pairwise comparison matrix (pcm hereinafter) originated by thurstone [1] is one of the most important components, which is used to compare two criteria or alternatives with respect to a given criterion , then a matrix a = (aij)n×n is built to reflect the direct and indirect judgment relations between pairs of criteria or alternatives with respect to a given criteria, where aij > 0, aij = 1aji . all pcms are then used to derive the priority vectors, and the alternatives can be ranked by aggregating the local priority vectors [2–4]. however, the decision made based on the final priority vectors is effective only if the paired comparison matrices pass the consistency test [5]. in practice, it is usually difficult to obtain a matrix that satisfies the perfect consistency condition (i.e. aij = aikakj for i, j, k = 1, 2, . . . , n). therefore, saaty [6, 7] proved that the maximum eigenvalue λmax of matrix a always satisfies λmax ≥ n and the equality holds if and only if a is perfectly copyright © 2006-2014 by ccc publications simulation experiments for improving the consistency ratio of reciprocal matrices 409 consistent. based on this property, saaty proposed the consistency ratio (cr) to measure the consistency of a matrix, i.e. the consistency of a matrix is acceptable if the cr is less than 0.1. however, this condition sometimes cannot be satisfied with because of the limitations of experiences and expertise, prejudice as well as the complexity nature of the decision problem [8]. to improve the consistency ratio of a matrix, many models and methods have been proposed over the past few decades. for instance, harker [9] regarded the largest absolute value(s) in matrix { viωj − a2jivjωi } for all i, where i > j , as the most inconsistent element(s). saaty [6] constructed the deviation differences matrix b = [bij] = [|aij − ωi/ωj|] to identify the most inconsistent entry, where ωi and ωj are any two subjective priority weights in the ω = (ω1, · · · , ωn) . based on these models, xu and wei [10] generated a near consistent matrix b = (aλij(ωi/ωj) 1−λ)n×n to improve the consistency, where λ is a parameter of the auto-adaptive algorithm. besides, saaty [7] and cao et al. [11] introduced hadamard operator “ ◦ ” to build a perturbation matrix e and a deviation matrix a, in which e = (aij) ◦ (ωj/ωi)] and a = [ωi/ωj] ◦ [aij/(ωi/ωj)] , to identify the most inconsistent entry. there is a common feature in the previously reviewed models, that is, these models are dependent on the priority weights ωi and ωj, but there exist more than 20 priority derivation methods [12–14], and the final priority weights obtained from different methods might be different when the matrix is inconsistent. therefore, ergu et al. [8] proposed an induced bias matrix to identify the most inconsistent entry in the original inconsistent matrix a. to do so, three major steps containing seven specific steps were proposed and several numerical examples were used to validate the proposed model. in this paper, we attempt to conduct simulation experiments to further validate the effectiveness of the proposed induced bias matrix (ibm) model by generating randomly millions number of the reciprocal positive matrices with different orders. the step 6 and step 7 proposed in ergu et al. [8] are further quantified and detailed in order to implement automatically modification. the remaining parts of this paper are organized as follows. the next section briefly describes the induced bias matrix model. the simulation experiments and some algorithms are performed and proposed in section 3. section 4 concludes the paper as well as future research directions. 2 the induced bias matrix model in ergu et al. [8], the theorem of induced bias matrix and two corollaries were proposed to identify the most inconsistent entries in a pcm and improve the consistency ratio. for the readers’ convenience, we first briefly describe the related theorem and corollaries of the ibm model as preliminary of ibm model. the theorem 1 says that "the induced matrix c=aa-na should be a zero matrix if comparison matrix is perfectly consistent". based on this theorem, if comparison matrix a is approximately consistent, corollary 1 derived that "the induced matrix c=aa-na should be as close as possible to zero matrix". however, if the pairwise matrix is inconsistent, corollary 2 says that "there must be some inconsistent elements in induced matrix c deviating far away from zero". by this corollary, the largest value in matrix c can be used to identify the most inconsistent element in the original matrix a. some of the identification processes are presented next. the procedures of the ibm model proposed in ergu et al. [8] include three major steps, containing seven specific steps (details are referred to ergu et al. [8]). the first five steps are easy to be implemented by matlab software in practice, i.e. 1) construct an induced matrix c=aa-na; 2) identify the largest absolute value(s) of elements and record the corresponding row and column; 3) construct the row vector and column vector using the recorded location; 4) calculate the scalar product f of the vectors; 5) compute the deviation elements between aij 410 d. ergu, g. kou, y. peng, x. yang and vectors f. however, for steps 6-7, the definitions are not easy to be quantified and it needs the decision makers to determine when we should use method of maximum,method of minimum, and method for adjusting aij to identify the most inconsistent entries. in the following section, we combine these identification methods to perform the simulation experiment by generating randomly reciprocal matrix in order to validate the effectiveness of the induced bias matrix model. 3 simulation experiments 3.1 design of simulation experiments the simulation experiments were performed to confirm the effectiveness of the induced bias matrix model using random inconsistent reciprocal matrices. we generated randomly 106 set of reciprocal matrices with orders 3 to 9, and 105 set of reciprocal matrices with orders 10-12, i.e. the entries above the main diagonal of a reciprocal matrix is generated randomly from the 17 numbers (1/9, 1/8, 1/7, . . . , 1, 2, 3, . . . , 9) in order to satisfy the saaty’s fundamental 9point scales, the entries below the main diagonal of the pcm is filled automatically with the corresponding reciprocal value. then calculating the consistency ratio by the formula proposed by saaty [7], where λmax is the maximum eigenvalue of matrix a, and n is the order of matrix a. if the cr < 0.1, discard the generated matrix, if the cr ≥ 0.1, then applying the ibm model to modify the inconsistent entry and improve the consistency ratio by the six steps and the combined algorithm, as shown in figure 1. if the consistency ratio of the generated randomly reciprocal pairwise comparison matrix cannot be reduced to be lower than 0.1, then record the corresponding matrix. the specific procedures of this simulation experiment are shown in figure 1. figure 1: procedures of simulation experiment by ibm model for the matrices with cr ≥ 0.1, the formula of steps 1-6 presented in ergu et al. [8] are directly used to identify and modify the most inconsistent entries in matrix a as well as improving the consistency ratio. however, the method of maximum, method of minimum and method for simulation experiments for improving the consistency ratio of reciprocal matrices 411 identifying aij proposed in step 6 and step 7 involve qualitative observation and judgment, for instance, how many absolute values in vector f can be regarded as more absolute values that are around zero? how to measure the absolute values of aij, aik and akj are too large or too small by quantifying terms? therefore, the following algorithm is used to combine the previously mentioned identification processes. 3.2 algorithms of simulation experiments in order to simulate the induced bias matrix model, the program codes with two input parameters, n and n1 were written by matlab 7.0 to randomly generate reciprocal matrices, in which n denotes the numbers of random reciprocal matrix, while n1 represents the number of simulation. for the space limitations, we omitted the first five steps, and the following algorithm is used to combine the method of maximum,method of minimum, and method for adjusting aij. algorithm 1: improving the consistency ratios of the random reciprocal matrices with cr ≥ 0.1 input: matrix order, n; number of simulation, n1 output: matrices with cr ≥ 0.1 process: 01. c=aa-na % matrix a is the generated randomly reciprocal matrix with cr ≥ 0.1 02. if cij < 0 03. adjust aij using aij = 1n−2 ∑n k=1 aikakj 04. end % method for identifying aij(1) 05. if cij > 0 & & min(f) == 0 % we can obtain that aij is inconsistent whether it is too large or too small, in which f is the vector product 06. adjust aij using ãij = 1n−2 ∑n k=1 aikakj 07. end % method for identifying aij(2) 08. if cij > 0 % aij and aik (or akj ) might have problematic 09. [m, k] = max(f); % m is the element with the largest value in vector f, while k is the corresponding location. 10. if cik < 0 & & ckj ≥ 0 % aik is problematic (too large). 11. adjust cik using aik + cik/(n − 2). 12. break 13. end 14. if cik ≥ 0& & ckj < 0 % akj is problematic and large 15. adjust akj using akj + ckj/(n − 2) 16. break 17. end 19. if cik < 0 & & ckj < 0 % cik and ckj are problematic 20. if abs(cik) >= abs(ckj) 21. adjust aik using aik + cik/(n − 2) 22. break 23. else 24. adjust akj using akj + ckj/(n − 2) 25. break 26. end 27. end 412 d. ergu, g. kou, y. peng, x. yang 28. if f(k) > 0 & & cik >= 0 & & ckj >= 0 % it is unreasonable to occur simultaneously, if it does occur, then go to adjust the second largest value. 29. cij = 0; 30. end 31. end 32. calculate the cr; % see algorithm 2 33. if cr < 0.1 34. break 35. end. algorithm 2: calculating the consistency ratio of the modified matrix b input: modified random matrix b output: consistency ratio cr. process: 01. n=length(b); % b is reciprocal matrix 02. ri=[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59]; 03. [a,b]=eig(b); 04. [c1,d1]=max(b); % c1 is maximum value in each column, d1 is the corresponding row of each element 05. [e1,f1]=max(c1); % e1 is the largest element, f1 is the corresponding column. 06. ci=(e1-n)/(n-1); 07. cr=ci/ri(2,n); 3.3 experimental results in this section, we do not attempt to optimize the program codes for speed, therefore, we set the matrix order n to be 3-12, and the simulation number n1=106 for the matrices with orders 3-9. for the matrix with orders 9-12, we only simulated 105 numbers of randomly reciprocal matrices. the results of simulation experiments are shown in table 1. it can be seen that some of the random reciprocal matrices with orders from 3 to 6 passed the consistency test, for instance, 206130 random matrices with order 3 passed the consistency test among 106 matrices, while 73 random matrices with order 6 passed the consistency text. however, all random matrices with orders 7-12 did not pass the consistency test. for the random matrices with cr ≥ 0.1, the proposed ibm model was used to modify the most consistent entries and improve the consistency ratio. table 1 shows that the consistency ratios of all the inconsistent random matrices with cr ≥ 0.1 and orders 3-7 have been improved and lower than 0.1 after the proposed ibm model is used to modify the random matrices, as shown in figures 2-7, while some matrices still failed the consistency test, the numbers are 3 for order 8, 5 for order 9, 1 for order 10, 2 for order 11 and 13 for order 12, as shown in figures 8-12. the corresponding simulation figures for 106 random matrices with orders 3 to 9, and 105 random matrices with orders 10-12 are shown in figures 2-10. simulation experiments for improving the consistency ratio of reciprocal matrices 413 table 1 simulation experiments for randomly generated matrices with different orders matrix order number of simulation number of matrices with cr ≥ 0.1 failed matrices succeeded matrices 3 1000000 793870 0 793870 4 1000000 968083 0 968083 5 1000000 997518 0 997518 6 1000000 999927 0 999927 7 1000000 1000000 0 100000 8 1000000 1000000 3 999997 9 1000000 1000000 5 999995 10 100000 100000 1 99999 11 100000 100000 2 99998 12 100000 100000 13 99987 0 2 4 6 8 10 x 10 5 0 0.5 1 1.5 2 2.5 cr>0.1 t h e v a lu e o f c r the number of simulation 0 2 4 6 8 10 x 10 5 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 cr<0.1 t h e v a lu e o f c r the number of simulation figure 2: simulation experiment for 106 randomly generated matrices with order 3 0 5 10 x 10 5 0 0.5 1 1.5 2 2.5 cr>0.1 t h e v a lu e o f c r the number of simulation 0 5 10 x 10 5 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 cr<0.1 t h e v a lu e o f c r the number of simulation figure 3: simulation experiment for 106 randomly generated matrices with order 4 414 d. ergu, g. kou, y. peng, x. yang 0 5 10 x 10 5 0 0.5 1 1.5 2 2.5 cr>0.1 t h e v a lu e o f c r the number of simulation 0 5 10 x 10 5 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 cr after adjustment t h e v a lu e o f c r the number of simulation figure 4: simulation experiment for 106 randomly generated matrices with order 5 0 5 10 x 10 5 0 0.5 1 1.5 2 2.5 cr>0.1 t h e v a lu e o f c r the number of simulation 0 5 10 x 10 5 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 cr after adjustment t h e v a lu e o f c r the number of simulation figure 5: simulation experiment for 106 randomly generated matrices with order 6 0 5 10 x 10 5 0 0.5 1 1.5 2 2.5 cr>0.1 t h e v a lu e o f c r the number of simulation 0 5 10 x 10 5 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 cr after adjustment t h e v a lu e o f c r the number of simulation figure 6: simulation experiment for 106 randomly generated matrices with order 7 simulation experiments for improving the consistency ratio of reciprocal matrices 415 3 4 5 6 7 8 9 10 x 10 5 x: 9.816e+005 y: 0.1768 cr>0.1 the number of simulation x: 3.154e+005 y: 0.1743 x: 2.589e+005 y: 1.85 x: 6.034e+005 y: 1.811 0 1 2 3 4 5 6 7 8 9 10 x 10 5 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 x: 6.259e+005 y: 0.0109 cr<0.1 t h e v a lu e o f c r the number of simulation x: 4.691e+005 y: 0.01213 x: 4.669e+004 y: 0.01128 figure 7: simulation experiment for 106 randomly generated matrices with order 8 3 4 5 6 7 8 9 10 x 10 5 cr>0.1 the number of simulation x: 9.508e+005 y: 0.2731 x: 2.771e+005 y: 0.2575 x: 9.83e+005 y: 1.685 x: 4.352e+005 y: 1.7 x: 1.862e+005 y: 1.708 0 1 2 3 4 5 6 7 8 9 10 x 10 5 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 cr<0.1 t h e v a lu e o f c r the number of simulation figure 8: simulation experiment for 106 randomly generated matrices with order 9 2 3 4 5 6 7 8 9 10 x 10 5 cr>0.1 the number of simulation 0 1 2 3 4 5 6 7 8 9 10 x 10 5 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 cr<0.1 t h e v a lu e o f c r the number of simulation figure 9: simulation experiment for 105 randomly generated matrices with order 10 416 d. ergu, g. kou, y. peng, x. yang 3 4 5 6 7 8 9 10 x 10 4 x: 2.42e+004 y: 1.436 cr>0.1 the number of simulation x: 5.303e+004 y: 1.467 x: 8.631e+004 y: 1.48 x: 1.849e+004 y: 0.5031 x: 9.352e+004 y: 0.4573 x: 2.631e+004 y: 0.4731 0 1 2 3 4 5 6 7 8 9 10 x 10 4 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 x: 1.612e+004 y: 0.03592 cr<0.1 t h e v a lu e o f c r the number of simulation figure 10: simulation experiment for 105 randomly generated matrices with order 11 2 3 4 5 6 7 8 9 10 x 10 4 cr>0.1 the number of simulation 0 1 2 3 4 5 6 7 8 9 10 x 10 4 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 cr<0.1 t h e v a lu e o f c r the number of simulation figure 11: simulation experiment for 105 randomly generated matrices with order 12 simulation experiments for improving the consistency ratio of reciprocal matrices 417 4 conclusions in this paper, some of the identification processes proposed in ergu et al. [8] were combined to implement the programming. based on these combinations, an algorithm was proposed and simulation experiments on random reciprocal matrices with different orders were conducted to validate the effectiveness of the induced bias matrix model. we found that some matrices generated randomly could pass the consistency test, and the higher the orders of matrices are, the less the matrices have cr < 0.1. when the orders of random matrices increase to 7, all matrices generated randomly have cr ≥ 0.1, and they need to be adjusted. after the algorithm of the induced bias matrix (ibm) model was applied to these matrices, all the consistency ratios of random matrices with orders 3-7 were improved and less than the threshold 0.1, while fewer matrices with order higher than 8 still could not be modified satisfactorily. however, we believe that the consistency of the pairwise comparison matrices provided by experts will be much better than the consistency of random matrices, thus the proposed ibm model is capable of dealing with the consistency of a pcm. although the results of the simulation experiments show the effectiveness of the ibm model, the experimental findings also reveal the failed tendency will increase with the increase of the matrices order. the failed matrices should be paid more attention to and analyze the reason why it failed the consistency test, we leave it for further research in future. acknowledgements this research was supported in part by grants from the national natural science foundation of china (#71222108 for g. kou, #71325001 and #71173028 for y. peng, #71373216 for d. ergu, #91224001 for s.m.li);program for new century excellent talents in university #ncet12-0086 for y. peng, and the research fund for the doctoral program of higher education (#20120185110031 for g. kou). we thank prof. waldemar w. koczkodaj for encouraging us to conduct simulation experiments in order to further validate the effectiveness of ibm model. bibliography [1] thurstone, l. (1927), a law of comparative judgment, psychological review, 34(4): 273-273. [2] saaty, t. l. 1986. axiomatic foundation of the analytic hierarchy process, management science 32(7): 841-855. [3] saaty, t. l. (1994), how to make a decision: the analytic hierarchy process, interfaces, 24: 19-43. [4] siraj, s.; mikhailov, l.; keane, j. (2012), a heuristic method to rectify intransitive judgments in pairwise comparison matrices, european journal of operational research, 216: 420-428. [5] kou, g., ergu,d., shang,j. (2014). enhancing data consistency in decision matrix: adapting hadamard model to mitigate judgment contradiction, european journal of operational research, 236(1):261-271. [6] saaty, t. l. (1980), the analytical hierarchy process, new york: mcgraw-hill. [7] saaty, t. l. (2003). decision-making with the ahp: why is the principal eigenvector necessary, european journal of operational research, 145(1): 85-91. 418 d. ergu, g. kou, y. peng, x. yang [8] ergu, d. ; kou, g.; peng, y.; shi, y. (2011). a simple method to improve the consistency ratio of the pair-wise comparison matrix in anp, european journal of operational research, 213(1): 246-259. [9] harker, p. t. 1987. derivatives of the perron root of a positive reciprocal matrix: with applications to the analytic hierarchy process, applied mathematics and computation, 22: 217-232. [10] xu, z., wei, c. (1999),a consistency improving method in the analytic hierarchy process, european journal of operational research, 116: 443-449. [11] cao, d., leung, l. c., law, j. s. (2008). modifying inconsistent comparison matrix in analytic hierarchy process: a heuristic approach, decision support systems, 44: 944-953. [12] choo, e., wedley, w. (2004). a common framework for deriving preference values from pairwise comparison matrices, computer and operations research, 31 (6): 893-908. [13] lin, c. (2007). a revised framework for deriving preference values from pairwise comparison matrices, european journal of operational research, 176 (2): 1145-1150. [14] kou,g, lin,c. (2014). a cosine maximization method for the priority vector derivation in ahp, european journal of operational research, 235:225-232. int j comput commun, issn 1841-9836 8(6):891-900, december, 2013. rsea-aodv: route stability and energy aware routing for mobile ad hoc networks p. srinivasan, p. kamalakkannan p. srinivasan* mahendra institute of technology, namakkal tamil nadu, india *corresponding author: salemsrini4u@gmail.com dr. p. kamalakkannan department of computer science, government arts college, salem tamil nadu, india abstract: frequent changes in network topology, confined battery capacity of nodes and unreliable nature of wireless channels are the main challenges for reliable routing in mobile ad hoc network (manet). selecting a long lasting route is a critical task in manet. in this paper, we propose a new protocol, route stability and energy aware ad hoc on-demand distance vector (rsea-aodv) protocol which is an enhancement of ad hoc on-demand distance vector (aodv) protocol. it designs a bi-objective optimization formulation to compute the reliability factor based on stability and residual energy of nodes. the route with the highest reliability factor value is selected for data transmission. this protocol is compared with other similar routing protocols: perra and aodv. we use ns-2 for simulation. our simulation results show that, the proposed protocol increases the node expiration time by 12 32 % and accomplishes 7 13 % higher packet delivery ratio compared to perra or aodv. the packet delay and control overhead of the proposed protocol is comparable to that of aodv. keywords: manet, reliable routing, route stability, biobjective optimization. 1 introduction mobile ad hoc networks (manet) are groups of wireless mobile devices, which can communicate with each other without any infrastructure support. it is a self-configured and selfmaintained network with no central authority. every node in manet acts as both a host and a router. dynamic topology, limited bandwidth, battery, cpu resources and multi-hop communication are the characteristics that put special challenges in routing protocol design. several routing protocols have been proposed for manets. based on the route discovery principle, we can classify them into either proactive or reactive. proactive routing protocols update routes for every pair of nodes at regular intervals irrespective of their requirement. the reactive or on-demand routing protocols, determine route only when there is a need to transmit a data packet, using a broadcasting query-reply (rreq-rrep) procedure. most of these protocols use min-hop as the route selection metric and found that the routes discovered by these protocols are not stable. the stability based routing protocols [1][4] are designed to choose stable route passing through stable links. these protocols improve route lifetimes and packet delivery ratio compare to the shortest path routing protocols. the energy-aware routing protocols [5, 6] are designed considering factors like residual energy, total transmission power or both. these protocols avoid over using of certain nodes and reduce total energy consumptions. but, there exist a very few protocols in the literature [6] [8] that consider both stability and energy metric during route discovery and maintenance. copyright © 2006-2013 by ccc publications 892 p. srinivasan, p. kamalakkannan in this paper, our objective is to design a routing scheme based on route stability and residual energy metrics during route discovery and maintenance. this scheme compute the link stability based on measurement of received signal strength of successive packets and route stability is computed as the product of link stability of all links that make up the route. this scheme allows nodes that satisfy required energy metric to act as intermediate nodes. 2 related work aodv [9] a reactive routing protocol establish a route to a destination only on demand.. the advantage of aodv is reduced control overhead. but, multiple rrep packets in response to a single rreq packet can lead to heavy control overhead. presence of stale routes and unnecessary bandwidth consumption due to periodic beaconing are the drawbacks of aodv. link stability is a measure of how stable the link is and how long it will last. signal strength, pilot signals, link duration distributions, residual energy of the nodes and relative speed between nodes are the parameters used for the computation of link stability. stability based routing protocols use link stability factor and path stability factor calculated using the above specified parameters to select stable path for data transmission. the lifetime of a network is one of the important factors to be considered in designing a manet routing protocol. maximizing the network lifetime by minimizing the power consumption for the data transfer is the main aim of energy aware routing protocols [5]. optimizations carried out in these protocols are classified in the following schemes. (i) minimize the total energy consumed along the route (ii) avoid using node with minimum residual energy and (iii) minimize the total battery cost along the route. they reduce the energy wastage ensuing from retransmission due to bit error rate, frame error rate and link failures due to energy depletion. in [4], the authors propose route stability based qos routing (rsqr) where they use the received signal strength of the packets to calculate link stability and route stability. it considerably reduces the number of route breakages by restricting traffic admission. however, the above described protocols do not consider energy metrics during route discovery. in [7], the authors propose link-stability and energy aware routing protocol (laer). it considers joint metric of link stability and energy drain rate into route discovery. it balances the traffic load on the nodes and considerably decreases the control overhead. however, laer does not able to discriminate between links of the same age. in [8], the authors propose power efficient reliable routing protocol for ad hoc networks (perra), a reactive routing protocol, which accounts for both link stability and power efficiency on route discovery. it considerably reduces control overhead by constrained flooding of route requests. however, perra does not able to discriminate longer link from lifetime point of view as it does not consider link lifetime and its statistical behavior. 3 route stability and energy aware (rsea) model 3.1 stability aware metric rsea model considers signal strengths and mobility for computing the probability of link failures. it computes link stability (ls) using signal strength values receivd from the mac layer. any link e has an associated link stability ls(e) [4] and it is given by lsi,j = u2 − dssi,j u2 − u1 (1) rsea-aodv: route stability and energy aware routing for mobile ad hoc networks 893 where dss is the differentiated signal strength to decide whether the signals are getting stronger or weaker. it is computed as follows. dssi,j = sscuri,j − ssnewi,j (2) a path between source s and destination d is given as p(s, d) = (s, e(s, x),x, e(x, y),y, ... , e(z, d), d). formally, a path between two nodes s and d is a set of all feasible path between them and can be represented as p(s, d) = p0, p1, . . . , pn, where each pi is a feasible path between s and d. we define the stability of the path p, by the product of link stability of its edges as follows stability(p) = ∏ e∈p ls(e) (3) the path with higher path stability value contains more stable links and choosing it will considerably reduce the probability of link failure. 3.2 energy aware metric it is assumed that all wireless nodes come with residual power detection device. the energy required to transmit a packet (etx) [8] can be computed as etx = psize ∗ ptx bw (4) where psize is the packet size, ptx is the packet transmitting power and bw is the bandwidth of the link. the transmitting energy is directly proportional to the distance between nodes. the source application layer communicates n value to the network layer, for selecting nodes that meet the energy requirement. it avoids link breakages due to energy depletion. the total energy required (reqe) for data packet transmission is given by reqe = n ∗ (etx + eproc) (5) where eproc is the energy required for packet processing and n is the number of packet . the energy metric (em) of the path is given by em(p) = n∏ i=1 ( ri fi ) (6) where ri(t) is remaining battery capacity and fi is full battery capacity of intermediate node i, at time t. the goal of this metric is to maximize em. it takes the product of the residual battery of the intermediate nodes to select a path that has nodes with maximum residual energy among the path that just meet the basic energy requirement reqe. 3.3 problem formulation the problem can be stated as, ‘to find a reliable path for data communication based on route stability and residual energy metrics’. the above bi-objective optimization problem can be transformed into a single objective problem, by providing importance factor (i.e. w1 and w2) for each criterion of the objective. we combine the objectives into a single objective function to calculate the reliability factor (rf) of the path p, can be mathematically stated as rf(p) = w1 · stability(p) + w2 · em(p) (7) 894 p. srinivasan, p. kamalakkannan where the parameters w1 and w2 are chosen based on the network dynamics and application requirements. in this study, in order to give equal importance to both stability and energy metrics, we assign 0.5 to both w1 and w2, such that w1+ w2=1 condition is satisfied. consequently, the sum of the objectives has to be maximized and maximum reliability factor (mrf) can be computed by mrf = max(rf(p1),rf(p2)...,rf(pn)) (8) the path with mrf value is selected as a reliable path for data transmission. 4 route stability and energy aware (rsea) routing in manet 4.1 route discovery when a node s needs to send packets to a destination d, it searches for the route in its route table. if a route to the destination d is not available, then the source s will broadcast a route request (rreq) message to its neighbors. the rreq of rsea-aodv is an extension of a rreq packet of aodv routing protocol. three new fields accumulated path stability (aps), accumulated energy metric (aem) and required energy (reqe) are added to the rreq packet. it initializes the values to the added fields as follows: aps, aem with 1. the required energy is calculated using equation 5 and is initialized to reqe field. 4.2 route discovery at intermediate nodes if the strength of the rreq packet is poor, then it drops the rreq. then node i checks whether its residual energy will meet the required energy reqe specified in the rreq packet. if the above conditions are satisfied, then node i make a reverse route entry in the routing table (rt). then it calculates ls. if the signal strength is above sthr1, then it assigns 1 to ls. it implies that nodes are close and link is sufficiently stable. otherwise, it calculates ls using equation 1. energy metric is calculated using equation 6. after these steps, it updates the aps and aem fields such that the updated values contain the route stability and energy metric of the explored route up to the current node. it enters the relevant information from rreq into route request forward table (rft). then, node i broadcast the rreq to its neighbor. in case of duplicate packet, if it contains better values for aps or aem, then it makes an entry in rft and discards the packet. on receiving a rrep packet, node i measures the strength of rrep. if its strength is poor, then it will drop the rrep packet. it looks up rft for the corresponding rreq entries, to select the node with the highest rf value. it forwards the rrep packet to the node with the highest rf value. it is shown in algorithm 2. it makes an entry in the rt. 4.3 route selection at destination destination node will receive rreq packets from different possible routes. on receiving the first rreq packet, the node d starts a timer t1 for the duration of route reply latency (rrl) time. it stores all the rreq that arrives, in its routing table. it computes rf value for the path explored by the rreq. if destination node receive more than one rreq before the timer t1 expires, then it forwards the rrep packet to the node with the highest rf value. it is shown in algorithm 3. this considerably reduces the amount of control overhead incurred during the route establishment due to multiple rrep for single rreq as in aodv. the destination node makes an ft entry for the flow. in case, if it does not receive any data packets within the timeout period, then it will delete the respective entry from the ft table. rsea-aodv: route stability and energy aware routing for mobile ad hoc networks 895 896 p. srinivasan, p. kamalakkannan 4.4 route maintenance in dynamic mobile ad-hoc networks, link-breaks occur frequently. rsea-aodv comes with make-before-break route maintenance mechanism. this mechanism quickly adapt to the link breakage likely to occur due to the mobility and energy drain. it is depicted in algorithm 4. it executes the mechanism after every t2 seconds, to monitor the status of the route established. if an intermediate node is in the critical battery status or it is receiving weaker signal packets, then it creates an hlp message with time to live (ttl) set to 1 and broadcasts it to its neighbors. neighbors on receiving the hlp packet, check for route availability in its routing table for the destination specified in the hlp packet. if the route is available, then it returns the route to the downstream node of the node broadcasting the hlp packet. the downstream node on receiving the route it updates its routing table. it routes the data packets in the new route available, preventing packet losses due to link breakage. if the node with critical battery is the destination node, then it will send the stop traffic intimation to the source node, to avoid future packet drops and wastage of resources. if there is no alternate route available with the one-hop neighbors, then after the expiry of timer i.e. timeout, the node will send the route change request (rcr) to the source node. the source node on receiving the rcr will go for the re-route discovery to the destination. rsea-aodv: route stability and energy aware routing for mobile ad hoc networks 897 4.5 simulation parameters we have simulated a scenario of 50 mobile nodes in a rectangualr toplpogy of 1000m * 500m, with each node having a transmission range of 250m. the values of sthr1 and sthr2 are set to 1.5 * rxthr and 1.2 * rxthr, respectively. we simulate it in ns2 2.31.the simulation has been run for 600 seconds. the results show 95% confidence interval on all observed metrics. the first experiment evaluated the overall performance of rsea-aodv, perra and aodv. during simulation, we generated 12 cbr connections producing 3 packets /second. table 1: simulation results parameters rsea-aodv perra aodv pdr 96.42 90.54 86.29 coh 0.8342 1.0871 1.1956 delay(sec) 0.0698 0.0732 0.0659 table 1 presents the results of the simulations. we observe that the pdr of rsea-aodv is improved by 6.5% and 13.2 % relative to perra and aodv, respectively. this is because of node selection and, route maintenance strategy carried on by the rsea-aodv. it chooses the intermediate nodes considering the stability and residual energy issues. in case of aodv, it chooses a path with the shortest route. it may contain low energy nodes which lead to disconnections of sessions. the control overhead of rsea-aodv is reduced by 22 % and 30.2% relative to perra and aodv, respectively. this is due to reduction in path reconstruction and constrained flooding of control packets. as rsea-aodv selects the most reliable path, it considerably reduces the total number of required messages for route reconstruction. the packet delay for rsea-aodv is comparable to aodv and perra. this is due to following opposing factors. (i) finding a reliable route, considering the stability and energy metrics, increases the delay on the one hand. (ii) on the other hand, route selection and route maintenance procedure of rsea-aodv increases the lifetime of the routes and the bottle-neck nodes. it significantly reduces the need for packet retransmissions. figure 1: node expirationtime figure 2: connection expiration time the plots of net and cet are presented in figures 1 and 2. it is observed that rseaaodv extends the node lifetime by between 12%-24% over perra and by between 20%-32% over aodv. this is because of rsea-aodvs route maintenance mechanism, in which nodes stop transmitting data traffic if they are about to drain and find an alternate route. aodv exhibits 898 p. srinivasan, p. kamalakkannan the worst performance in terms of the nodes expiration time because intermediate nodes continue packet forwarding regardless of the remaining battery until battery depletion. aodv exhibits a longer lifetime of connections despite a shorter lifetime of nodes. it is noted that, aodvs connection expiration times being anywhere between 25%-5% better than rsea-aodv or perra. this is because, in rsea-aodv: (i) a source node tries for route reestablishment only fixed number of times after route change request or link failures, and (ii) nodes reject request for new session once their residual energy is below energy threshold. but aodv keeps retrying and so has a higher chance of finding an alternate route and keeps connection alive for a longer time. figure 3: average number of path reconstructions figure 3 shows the number of path reconstructions due to node mobility and energy shortages. rsea-aodv chooses the more reliable route and thus, reduces the need for route reconstructions. in addition, it decreases the control overhead and offers some energy benefits. the route discovery and maintenance mechanism carried on by perra considerably reduced the number of route reconstructions compared to aodv. in the second experiment, the stability weight (w1) was provided with the values 0.1, 0.3, 0.5, 0.7 and 0.9 (lowest to highest) and performance of the protocols are observed. the nodes moved at the maximum speed of 5 meters / second. from figure 4, it is observed the packet delivery ratio of rsea-aodv was higher than that of perra and aodv. it is because it uses more stable routes and presence of enhanced route maintenance mechanism. perra showed comparatively better performance than aodv, due to its route discovery mechanism and maintenance of alternative route. as aodv does not discriminate the link in stability issue, it does not show any deviation. the variance of residual node energy is depicted in figures 5. this parameter shows the load balancing capability of the protocols. it is noted that as stability weight factor increases, there is an increase in energy variance. as aodv overloads certain nodes and shows a big variance between remaining energies of the node. figure 6 shows that there is a gradual increase in the hop count as stability weight increases in both rsea-aodv and perra. the increase in hop count is due to the selection of short and stable link as the stability weight increases. the average physical length of the hops chosen by rsea-aodv is 45-55% of the transmission range. it is 50-60% in the case of perra and 55-65% in the case of aodv. the third experiment measured the average residual energy of the nodes on the chosen path. rsea-aodv: route stability and energy aware routing for mobile ad hoc networks 899 figure 4: packet delivery ratio vs. stability figure 5: residual energy vs stability figure 6: hop count vs. stability figure 7: residual energy vs. energy the average residual energy of nodes was set at 30 [j] and with some randomness as in [8] ree = e[ree] ± γ (9) where e[ree] is the residual energy. 10 50 % randomness in the residual energy for the nodes was provided in this simulation. for example, in the case of 10 % randomness, each node had 27 33 [j] residual energy. the sources energy requirement was 30j. figures 7 depicts the average energy of the nodes on the chosen path by the protocols. it is noted that rsea always had higher residual energy compared to perra and aodv protocols. this was due to the consideration of nodes residual energy during route selection. but, aodv showed lower residual energy as it goes for the shortest path selection and does not consider residual energy during route discovery. 5 conclusions and future work in this paper, we proposed a new route stability and energy aware routing scheme. it establishes route based on the joint metric of link stability and residual energy. during the route discovery process, it constructs the route with nodes that satisfy the sources energy requirement. among the possible routes, rsea selects the route with the highest reliability factor for data 900 p. srinivasan, p. kamalakkannan transmission. this scheme can be incorporated in any of the existing manet routing protocols. using the ns2 simulator, we compared the performance of rsea-aodv with perra and aodv. the simulation results highlight that rsea-aodv was outstanding in terms of node expiration time, delivery ratio and path reconstruction overhead. we intend to evaluate the performance of the protocol under different node densities and traffic loads, as part of our future work. bibliography [1] abid, m.; belghith, a. (2011); stability routing with constrained path length for improved routability in dynamic manets, personal and ubiquitous computing, 15: 799-810. [2] hui, z.; ning, d.y. (2007); a novel path stability computation model for wireless ad hoc networks, ieee signal process. letter, 14: 799-810. [3] sarma, n.; sukumar nandi. (2010); multipath qos routing with route stability for mobile ad hoc networks, iete technical review, 27: 380-397. [4] nandi, s.; sarma, n. (2009); route stability based qos routing in mobile ad hoc networks. wireless pers. commun, springer, 54: 203-224. [5] tan, w.c.w.; bose, s.k. (2012); power and mobility aware routing in wireless ad hoc network. commun, iet, 6: 1425-1437. [6] lee, s.; park. (2008); a routing protocol for extent network lifetime through the residual battery and link stability in manet, in proc. acc 08, 199-204. [7] rango, f. de.; fazio, p.; (2012); link stability and energy aware routing protocol in distributed wireless networks, ieee trans. on parallel and distributed systems, 23: 713726. [8] kim, k.j.; sang, y. (2005); power-efficient reliable routing protocol for mobile ad hoc networks, ieice trans. on commun., 88: 4588-4597. [9] perkins, c.e.; royer , e.m.; and das, s.r. (2003); ad hoc on-demand distance vector (aodv) routing, ietf rfc 3561. international journal of computers communications & control issn 1841-9836, 9(6):686-693, december, 2014. mathematical decision model for reverse supply chains inventory l. duta, c.b. zamfirescu, f.g. filip luminita duta automation and computer science department valahia university of targoviste romania, 130083, targoviste, 24, unirii ave. duta@valahia.ro constantin-bala zamfirescu dept. of computer science and automatic control lucian blaga university of sibiu romania, 550024, sibiu, 10, victoriei ave. zbc@acm.org florin g. filip the romanian academy romania, bucharest, 010071, 125 calea victoriei ffilip@acad.ro abstract: in the reverse supply chain inventory theory, inventory models are concerned with the demand of reusable parts, stock replenishment, ordering cycle, delivery lead time, number of disassembled products, ordering costs. the particularity of these models consists in the occurrence of high uncertainties of the quantity and quality of the returned products and resulting parts. to overcome the problem, an inventory model that incorporates decision variables at proactive and reactive levels is derived and discussed in this paper. keywords: reverse supply chains, decision aid, inventory models, bayesian networks 1 introduction a reverse supply chain is the process of moving end of life (eol) products from customer to distributor or manufacturer for the purpose of capturing the market leftover value, or for proper disposal. reverse supply chains operations have a strong impact on forward supply chain stocks and transportation capacities. customers act as suppliers in reverse supply chains. after sorting, the collected items will be moved through the reverse supply chain. some used products are sold for recycling, usually after destroying their physical structure. others are used as source of spare parts on the secondary market. another common use of returns is as spare parts for warranty claims in order to reduce the cost of providing these services to customers. defective and used products are moved to manufacturer for reusing or remanufacturing or for disposal. remanufactured or refurbished products are sold for additional revenue to a secondary market. part of the reverse supply chain, the reverse logistics has to deal with product transportation, production planning and inventory management. in reverse supply chain inventory, two problems might occur: a) a high level of uncertainty provided by the product end-of-life state of the eol product and b) the irregular supply for remanufacturing or refurbishing. in this respect, two types of inventory replenishment models have been identified in literature: a deterministic model with stationary or dynamical demand and a stochastic model with continuous or periodical review [1]. this paper addresses the stochastic models with variability of the demand. copyright © 2006-2014 by ccc publications mathematical decision model for reverse supply chains inventory 687 in the sequel the paper is organized as it follows. the state of art from the second section of this paper describes various reverse supply chain inventory models found in literature. in the third section we present a brief review of supply chain inventory policies and the positioning of our work. in the fourth section, an approach based on a mathematical decision model to deal with the stochastic aspect of the reverse supply chain inventory is described. decision variables are introduced on two levels: a proactive level and a reactive level. results and discussions are highlighted in the fifth section. conclusions and future work perspectives are presented in the last part of the paper. 2 literature review early researches in the domain of interest of this paper concerned the economic order quantity logic which was exploited by using deterministic models with stationary demand. in [2] a survey on quantitative models for reverse logistics is given. in such systems, authors show that the objective of the inventory management is to control the component recovery process to guarantee a required level of maintenance service and to minimize fixed and variable costs. in [3] kiesmuller and sherer determinate, for the first time, an optimal policy for a remanufacturing system with dynamic demand, by assuming that there are no backorders and lead times. in [4], authors develop two heuristic procedures to investigate the effect of stochastic yields on a disassembly to order system. in [5], imtavanich and gupta use heuristics to address different stochastic parameters of a disassembly to order (dto) system and they propose a goal programming procedure to calculate the number of returned products that satisfy various goals. a dto system is concerned with the process of finding how many products have to be disassembled in order to fulfil the demand of reusable parts or subassemblies that will be used in remanufacturing. bayindira and et al. [6] investigate the desired level of recovery under various inventory control policies when the success of recovery is probabilistic. all used and returned items go into a recovery process that is modelled as a single stage operation. the recovery effort is represented by the expected time spent for it. using brand new parts may represent an alternative or/and complementary solution to ensure the necessary stocks for satisfying the orders received in case the recovered parts do not satisfy the demand. four inventory control policies that differ in timing of and information used in purchasing decision could be envisaged. the objective is to find the recovery level together with inventory control parameter that minimizes the long-run average total cost. a numerical study covering a wide range of system parameters is carried out. finally computational results are presented with their managerial implications. a dto model with deterministic yields is presented in [7]. the dto system is represented by an integer programming model. the objective function is to minimize the total costs. the authors propose three decision variables: one v1 for the amount of the end of life eol products to be acquired in view of disassembling, one v2 for the amount of new parts to be acquired in order to meet the overall demand and one v3 for the number of the parts that need to be disposed. the authors made several assumptions: there is no lead time for acquisition or disassembly, products are completely disassembled, there is a continuous supply of eol products and the demand for parts is completely fulfilled. in [8], a decisional dto decision model is presented. the author separated decomposes the decision process into two stages. in the first stage one the goal is to minimize the costs by deciding how many items must be disassembled to fulfil the reusable parts demand. in the second stage the goal is to minimise the number of disposed parts and the procurement costs of the new parts. another model for dto systems with stochastic yields is presented by kongar and gupta in [9]. the quantity of reusable parts is obtained from the number of eol products disassembled multiply by a weight coefficient. the method uses models in which linear programming encompasses a degree of stochasticity. decisions are split 688 l. duta, c.b. zamfirescu, f.g. filip into two groups: proactive decisions and reactive decisions. while proactive decisions determine the number of eol products to disassemble, reactive decisions take into account the uncertain variables (as the quality of the disassembled part, the lead time or the disassembly time) and their probability of occurrence. kongar and ilgin propose a linear physical programming solution based methodology which deals with tangible and intangible financial, environmental and performance measures of dto systems [10]. two types of decision depending on the disassembly operations are taken into consideration. if the item (the eol product) is subjected to resale or storage, a non-destructive disassembly process is performed. otherwise, if the item is subjected to recycling or disposal, a destructive disassembly is applied [9]. in 2010, ahiska and king [11] extend kiesmullerand sheres’ approach [3] by considering different lead times for manufacturing and remanufacturing. recently, godichaud [12] demonstrates the relevance of bayesian networks (bn) in a spare parts inventory model which deals with costs and time uncertainties. the same tool (bn) is used in [13] to highlight the temporal dependences between variables in a model for optimal disassembly policy. 3 inventory policies for reverse supply chain the economic order quantity model (eoq) is one of the most widely known inventory control methods [14]. it is used to set the quantities to order in replenishing inventories so that a trade-off between inventory holdings and ordering/set up costs is achieved. some assumptions are associated with this model: a) constant and known demand, instantaneous receipt of inventory, b) constant and known time intervals between order placement and receipt of the order, stock outs are avoided by placing orders at the right time. it is also assumed that the unit procurement price remains constant irrespective of the number of units purchased. this model induces four inventory basic policies according to the moment of the inventory position review: with continuous review or with periodical review. used notations: q = order quantity, s = order-up-to level, s = reorder point, t = review period [15]: • the (s, q) policy: whenever the inventory position (inventory level plus quantity on order) drops to a given level limit, s, or below, an order is placed for a fixed quantity, q. • the (s, s) policy: whenever the inventory position (inventory level plus quantity on order) drops to a given level s, or below, an order is placed for a sufficient quantity to bring the inventory position up to a given level, s. • the (t, s) policy: inventory position is reviewed at regular discrete time moments spaced at intervals of length t time units. at each review, an order is placed for a sufficient quantity to bring the inventory position up to a given level, s. • the (t, s, s) policy: inventory position is reviewed at regular instants spaced at time intervals of length t. at each review, if the inventory position is at level s or below, an order is placed for a sufficient quantity to bring the inventory position up to a given level s. if the inventory position is above s, no order is placed. remarks: • when t=0 in the (t, s, s) policy, one obtains the (s, s) policy. so, the (t, s, s) policy can be regarded as a periodic version of the (s, s) policy, which, in turn, may be viewed as a special case of the (t, s, s). • the (t, s) policy represents a special case of the (t, s, s) policy in which s = s mathematical decision model for reverse supply chains inventory 689 4 the decision model since the demand for spare parts is variable and is depending on the needs and the time when an order for replenishment is placed until the replenishment arrives (the lead time), the (t, s, s) policy was considered in this paper. in such a system, the period of review is fixed and the ordered quantity changes as per demand or rate of consumption. the period of review t is decided such as the ordered quantity is economical for purchasing the items. the problem is to coordinate the two processes disassembly and remanufacturing so as to meet the demand of items. another problem is the disassembly depth that deals with how completely a product should be disassembled. in this context, the researcher must weigh not only the costs of the process, whether is destructive or not, but also consider which reusable parts are already in stock, how many will be obtained through disassembly and will be accumulated in inventory and how many parts will have to be disposed of. it is obvious that a reverse chain inventory model has to include different decision variables. 4.1 notations ndi = number of items i to acquire and disassemble pki = amount of part k in item i p rki = amount of part k in item i to reuse p dki = amount of part k in item i to dispose p hki = amount of part k in item i to hold (to stock) cai = acquisition cost of the item i cdi = disassemble cost of the item i crk = reusing cost of the part k of the item i cdk = disposal cost of the part k of the item i chk = holding cost of the part k of the item i psc = probability of scenario sc occurring tc = total inventory cost to simplify the analytical model, the following assumptions are made: only a single type of product to disassembly is considered; there is no lead times for acquisition or disassembly; eol items to disassembly are always available; a single disassembly scenario (psc = 1) is occurring; products are completely disassembled; there are only two types of eol options: reusing and disposal; two types of disassembly operations are considered: destructive and non-destructive; all costs are deterministic and constant; the interval to acquire is deterministic (at the first slice of time t); the amount of reusable, disposable and holding parts is subjected to uncertainties; the number of items to acquire at the second slice of time have a probabilistic distribution; the model is periodically reviewed [8]. 690 l. duta, c.b. zamfirescu, f.g. filip 4.2 the bayesian network model to implement the correct model, bayesian networks were used so as to determinate all influences and causalities between decision variables. the results will show how information influences decisions and how these decisions cause the change of information. bayesian networks (bn) have the ability of capturing both qualitative knowledge through their network structure, and quantitative knowledge through their parameters [14]. a static bayesian network can be extended to a dynamic bayesian network (dbn) by introducing relevant temporal dependencies to capture the dynamic behaviors of the system at different moments. to validate the model, the bayesialab® software is used ( [16] and [17]). the software is able to seize degrees of probability. once validated, probabilities are used jointly with the probability distribution for giving a new probability distribution. bayesialab® allows the temporal dimension integration in a bayesian network. thus, a bn can be easily transformed into a dbn. temporal nodes at instants t and t+1 can be represented and connected by temporal arcs. the parameters evolution of the dbn nodes can be so tracked in time.decision nodes are marked by squares. the amount of part k found in item i influences the amount to reuse, to dispose or to hold. further, these decisional variables change the number of products to be acquired at the next slice of time (moment t+1 ). the two temporal nodes nid(t) and nid(t+1) are linked by a temporal arc. figure 1 shows background calculation of the total cost. the objective function is included in the utility node tc. in the figure, two decision nodes are represented: da decision to acquire used products, dd decision to disassembly. figure 1: dynamic bayesian network model 4.3 the mathematical model the total cost of the disassembly phase (tc) is composed of product acquisition cost, disassembly operations costs, eol options costs and inventory cost. the value of the total cost is given by the equation (1). the first term is the cost of product acquisition and disassembly so mathematical decision model for reverse supply chains inventory 691 as to meet the demand of parts for remanufacturing, while the second term is the inventory cost of the disassembled parts. this function is a multi-objective one, since the aim is to find not only the optimal quantity of products to acquire to meet the demand of parts, but also to find the best eol strategy or scenario so as to optimize the number of parts to reuse in a remanufactured product. for the moment we are interested on quantity to acquire to meet the demand, while scenarios probabilities are introduced by the software to reduce the objective function to a mono-objective one. the objective function to be minimized is: min(tc) = min (∑ i (cai + c d i ).n d i + ∑ i ∑ sc psc(c r k.p r ki + c d k.p d ki + c h k .p h ki) ) (1) in equation 1 the unknown variables are ndi , p r k , p d k , p h k . this objective function is subjected to the following constraints: dk 6 ∑ i ∑ sc psc.(pki −p dki) (2) ndi ≥ 0 and integer (3) pki, p r k , p d k , p h k ≥ 0 and integer (4) ∑ sc psc = 1 (5) where dk is the demand (the number of k parts needed). one can note that equations (1) to (5) form a linear integer mathematical model where decision variables can be treated as continuous in order to satisfy the integer value of the demand. 5 results figure 2: (t, s, s) policy decisions( [13], p 117) figure 3: reactive decisions 692 l. duta, c.b. zamfirescu, f.g. filip computations were performed with the help of fico xpress® optimization tool [18] which provides a mixed integer solver and framework for constraint integer programming. supposing a periodic review inventory with a security stock at the beginning of the period, the classical (t, s, s) policy is modeled in figure 2. the planning horizon was fixed to a month, and the review period is established to ten days. results show that at the end of each review period, the number of disassembled products arrives to a minimal value and the tc to a maximal one. for the eoq model with constant demand, a decision to acquire products to disassemble is taken whenever the inventory level reaches the reorder point. running simulations on the previous model, the decision to acquire is taken before the beginning of each new review period (fig. 3). in other words, starting with a periodical review inventory model, which usually provides orders at constant periods of time, we have reached to a mixed model where orders are given in accordance with the reactive decisions (i.e. in real time). 6 conclusions and future works an inventory model for reverse supply chains was presented. this model is deduced from the classical inventory model with constant review period and variable demand, and it encompasses decision variables structured on two decisional levels: the proactive level and the reactive level. to implement the approach, dbn was found to be an appropriate decision aid tool. using a dbn, one can determine the optimal disassembly inventory policy dealing with stochastic aspects of the system. proactive decisions aim to determine the initial number of items to disassembly to fulfill the demand of parts. reactive decisions take into consideration disassembly scenarios and the endof-life options of the disassembled parts. the model optimizes the quantity of used products to acquire so as to minimize the total inventory cost. future work will integrate the results above in a decision support system [19]. this issue needs further investigation in real-world settings where the increased cognitive complexity of using different models (i.e. dbn, integer programming), most likely by a collective decision-maker [20], will play a major role in adopting the proposed solution. bibliography [1] gupta s. m. (2013); reverse supply chains: issues and analysis, crc press, taylor&francis [2] fleischmann m, bloemhof-ruwaard j. m., dekker r. (1997); quantitative models for reverse logistics: a review, european journal of operational research , 103: 1-17. [3] kiesmuller g p, scherer c w (2003); computational issues in a stochastic finite horizon one product recovery inventory model, european journal of operational research 146(3): 553-579. [4] inderfurth k, langella i. m. (2006); heuristics for solving disassembly to order problems with stochastic yields, or spectrum, 28 (1): 73-99 [5] imtanavanich p, gupta s. m. (2006); calculating disassembly yields in a multicriteria decision making environment for a disassembly to order system, application of management science, elsevier science, amsterdam, 12: 109-125 [6] bayindira z.p., dekkerb r, porrasb e. (2006); determination of recovery effort for a probabilistic recovery system under various inventory control policies, the international journal of management science, 34: 571 584. mathematical decision model for reverse supply chains inventory 693 [7] inderfurth k, langella i. m., (2008); planning disassembly for remanufacture to order systems, environment conscious manufacturing, gupta and lambert eds.,crc press, boca raton, fla. [8] langella i. m (2007), planning demand driving disassembly for remanufacturing, deutscher universitäts-verlag. [9] kongar e, gupta s m, (2009) solving the disassembly to order problem using linear physical programming, international journal of mathematics in operational research, 1(4): 504-531. [10] gupta , s.m., ilgin, m. i. ( 2012), physical programming; a review of the state of the art, studies in informatics and control, 21(4): 349-366. [11] ahiska s s., king r e (2010); inventory optimization in a one product recoverable manufacturing system, international journal of production economics, 124(1): 11-19. [12] godichaud m. (2010); outils d’aide à la décision pour la sélection des filières de revalorisation des produits issus de la déconstruction des systemes en fin de vie. thèse de doctorat, université de toulouse. [13] duta l., addouche s.a. (2012); dynamic bayesian network for decision aided disassembly planning, studies in computational intelligence, springer, 402: 143-154. [14] blumenfeld, d.e. (2008), operations research calculation handbook, crc press, taylor and francis group. [15] ghorbel n., duta l., addouche s.a., el mhamedi a. (2011), decision aided tool for sustainable inventory control, the 21th international conference on production research, sttutgart, germany. [16] conrady s, (2011), introduction to bayesian networks, conrady applied science, llc bayesia’s north american partner for sales and consulting. [17] http://www.bayesia.com/en/products/bayesialab.php (last consulted in april 2014) [18] fico xpress optimization suite (http://www.fico.com/en/products/fico-xpressoptimization-suite/) [19] filip f.g. (2008), decision support and control for large-scale complex systems. annual reviews in control, 32(1):61-70. [20] zamfirescu c.b., duta l., iantovics b. (2010),on investigating the cognitive complexity of designing the group decision process, studies in informatics and control, 19 (3):263-27. 6krein6.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):825-833, december, 2015. why fuzzy cognitive maps are efficient v. kreinovich, c. stylios vladik kreinovich* department of computer science university of texas at el paso 500 w. university el paso, texas 79968, usa *corresponding author: vladik@utep.edu chrysostomos d. stylios laboratory of knowledge and intelligent computing department of computer engineering technological educational institute of epirus 47100 kostakioi, arta, greece stylios@teiep.gr abstract: in many practical situations, the relation between the experts’ degrees of confidence in different related statements is well described by fuzzy cognitive maps (fcm). this empirical success is somewhat puzzling, since from the mathematical viewpoint, each fcm relation corresponds to a simplified one-neuron neural network, and it is well known that to adequately describe relations, we need multiple neurons. in this paper, we show that the empirical success of fcm can be explained if we take into account that human’s subjective opinions follow miller’s seven plus minus two law. keywords: fuzzy cognitive maps, neural networks, seven plus minus two law. 1 introduction: fuzzy cognitive maps and their puzzling success need for (imprecise) expert estimates. to characterize a real-life system, we must know its properties. some of these properties come from measurements and are thus represented by real numbers. however, in many cases, a large amount of information comes from expert estimates. for example, to describe the current state of a patient, it is rarely enough to collect the corresponding measurement results – such a temperature, blood pressure, etc. medical doctors supplement this information by providing imprecise ("fuzzy") estimates, such as "somewhat soft", "small", "rather high", etc. similarly, to adequately describe the financial situation of a company or of a country, it is important to supplement the corresponding numbers with expert estimates describing how probable is a default or, vice versa, how probable in an increase in profitability (and how big this increase can be). a typical expert’s opinion sounds like this: "a vert big increase is improbable, but it is reasonable to expect a modest increase in reasonable time". fuzzy techniques as a natural way to describe imprecise expert estimates. when an expert completely agrees or completely disagrees with a precise statement (such as "the price of this stock will increase by at least 5% in a year"), in the computer, the resulting expertestimated truth value of a statement is either "true" or "false". in the computer, "true" is usually represented as 1, and "false" as 0. when the statement is imprecise, like the one above about a modest increase, the expert is not 100% sure that the price will increase by 5%. instead, the expert has some degree of copyright © 2006-2015 by ccc publications 826 v. kreinovich, c. stylios confidence in this 5% increase. since full confidence in a statement is described by the number 1, and full confidence in its negation is described by the number 0, a reasonable way to describe the expert’s partial confidence is by using numbers between 0 and 1: the higher the value, the larger the expert’s degree of confidence. the use of numbers from the interval [0, 1] for describing the experts’ degrees of confidence is the main idea behind fuzzy logic [6, 9, 15]. from individual fuzzy properties to fuzzy cognitive maps. fuzzy properties describing a system are often interrelated, in the sense that some properties imply others. for example, in medicine, if a person is overweight and not very physically fit, this increases the possibility that this person may get diabetes and thus, may be in a pre-diabetic stage. in financial situations, if a company has many new patents, especially patents in a "hot" area like advanced bioinformatics, it is usually a good indication of its future financial prosperity, etc. fuzzy cognitive maps (fcm) are a way to describe the relations between different fuzzy properties. to describe these relations, for each property p , we first need to list all the properties p1, . . . , pn that directly affect the property p . once this list is produced, we need to describe how the numerical values x1, . . . , xn ∈ [0, 1] of the properties pi affect the value x of the property p . in computational terms, we need to come up with an algorithmic function f(x1, . . . , xn) that predicts the value x based on the known values x1, . . . , xn. which functions should we choose? a natural idea is to start with the simplest functions. the simplest possible functions are linear functions, in which case we have x = w0 + w1 · x1 + . . . + wn · xn. (1) however, we cannot simply use general linear functions: • the predicted value should be within the interval [0, 1], but • for different combinations of weights, the above linear expression can be any real number, not necessarily a number between 0 and 1. a reasonable idea is that, after we get the above linear combination, we then apply an additional transformation s(x) that maps the whole real line to a number from the interval [0, 1]. in other words, instead of the linear expression (1), we use a slightly more complex expression x = s(w0 + w1 · x1 + . . . + wn · xn), (2) where s(x) is a pre-selected function that maps the real line into a unit interval [0, 1]. this function s(x) is called an activation function. this is the main idea behind fuzzy cognitive maps (fcm); see, e.g., [2,3,7,8,11,12,16–20,22, 24–29]. the fcm model is used when experts provide estimates only for some of the properties. in this case, the values of other properties are estimates by using the corresponding formulas of type (2). which activations functions are used? several different activation functions s(x) have been used in fcm; the most frequently used is the sigmoid function s(x) = 1 1 + exp(−x) . (3) the main reason why this function is used is the same reason why the same function is used in artificial neural networks: our goal is to describe human reasoning, and the sigmoid function provides a good approximate description of how similar processing in performed by the biological neurons in the brain; see, e.g., [1]. why fuzzy cognitive maps are efficient 827 comment. there are also theoretical reasons explaining why the sigmoid function is, in some reasonable sense, optimal; see, e.g., [10, 14, 23]. these theoretical reasons may also explain why evolution resulted in selecting this particular function in the actual brain – since this function is indeed optimal. fuzzy cognitive maps are efficient. in many practical applications, fuzzy cognitive maps have led to a reasonably good description of human reasoning; see, e.g., [2, 3, 7, 8, 11, 12, 16–20, 22, 24–29]. this empirical success is puzzling. from the theoretical viewpoint, this empirical success is puzzling. indeed, as we have mentioned, the output (3) of each corresponding fuzzy rule is the same as the output of a standard non-linear neuron [1, 24]. it is known that a 3-layer neural network has the universal approximation property; see, e.g., [1]. this means that if we use several (k) nonlinear neurons, with the outputs x(k) = s ( w (k) 0 + w (k) 1 · x1 + . . . + w (k) n · xn ) , (4) and then use an additional linear neuron to combine these output into a single combination x = w (0) + k ∑ k=1 w (k) · x(k), (5) then, for each continuous function x = f(x1, . . . , xn) on any box – in particular, on the box [0, 1]×[0, 1] – and for every ε > 0, we can find the values of the weights w(k) i and w (k) for which, for every inputs, the final output (5) is ε-close to the desired value f(x1, . . . , xn). it is also known that we need several neurons to get the universal approximation property, a single neuron does not have this property; see, e.g., [9]. and here, we have an opposite phenomenon: in many practical cases, already a single neuron provides a good approximation for the desired dependence! this is very puzzling. comment. the fact that a single neuron does not have a universal approximation property can be explained if we take into account that when the dependence x = f(x1, . . . , xn) is described by the formula (2), then for every i, we get ∂f ∂xi = s′ · wi, where s′(x) is the derivative of the activation function s(x). thus, for every i 6= j, we have ∂f ∂xi = const · ∂f ∂xj , where the constant is the ratio wi wj . this property is already not satisfied by the simplest nonlinear operation of multiplication f(x1, x2) = x1 · x2, for which ∂f ∂x1 = x2 6= const · ∂f ∂x2 = const · x1. what we do in this paper. in this paper, we provide a possible explanation for the (puzzling) empirical success of fuzzy cognitive maps. 828 v. kreinovich, c. stylios 2 possible explanation for the puzzling empirical success of fcms main idea behind our explanation. our main idea is to take into account the following difference between the general universal approximation property (as used in neural network theory) and what we want in fuzzy cognitive maps. the difference is that in the general applications of neural networks, the values x1, . . . , xn, and x are usually well-defined physical quantities, quantities which can be, in principle, measured with an arbitrary accuracy ε. for example, if we use neural networks to design an appropriate control, we want the resulting control value x to be as close to the optimal value f(x1, . . . , xn) as possible. in contrast, in fuzzy cognitive maps, all the variables x1, . . . , xn, and x are degrees of confidence describing expert opinions. these degrees are, by definition, imprecise, so computing them with too much for an accuracy simply does not make sense. an expert may be able to mark his or her degree of confidence by marking 6 on a scale from 0 to 10 – which corresponds to the degree of confidence 0.6 – but a normal expert cannot meaningfully distinguish between degrees of confidence 0.61 and 0.62. let us show that this difference can explain the puzzling empirical success of fuzzy cognitive maps. how accurate are expert estimates: 7 ± 2 rule. psychologists have found out that we usually divide each quantity into 7 plus plus minus 2 categories – this is the largest number of categories whose meaning we can immediately grasp; see, e.g., [13, 21] (see also [30]). for some people, this "magical number" is 7 + 2 = 9, for some it is 7 − 2 = 5. this rule is in good accordance with the fact that in fuzzy logic, to describe the expert’s opinion on each quantity, we usually use 7±2 different categories (such as "small", "medium" , etc.). since on the interval [0, 1], we can only have 7±2 meaningfully different degrees of confidence, the accuracy of these degrees ranges from 1/9 (for those who use 9 different categories) to 1/5 (for those who use only 5 different categories). what is the overall accuracy of the corresponding degrees. a fuzzy cognitive map usually combines knowledge of a large number of experts. since we have a large number of experts, it is practically certain that these experts include experts of all types: namely, those who can estimate their degree of confidence with a higher accuracy of 1/9, as well as those who can only estimates their degree of confidence with a much lower relative accuracy of 1/5 = 20%. in general, if we process a large amount of data of different accuracy, the accuracy of the result is determined by the lowest accuracy of the inputs. for example, if we estimate the overall amount of money m = m1 + m2 + m3 owned by three people, and we know m1 and m2 with an accuracy of 1 cent, but we only know m3 with an accuracy of 50% (i.e., we only know the ballpark estimate for m3), then clearly our estimate for the sum m will be very inaccurate as well. from this viewpoint, since fcm contains lower-accuracy data, with the accuracy 20%, we cannot expect the estimation results to be more accurate than that. how accurate should our predictions be? based on the above arguments, it makes sense to estimate the dependence of x on x1, . . . , xn with accuracy 20%. attempts to get a more accurate estimation would be, in general, a useless computational exercise which is not related to the desired problem – of estimating the expert’s degrees of confidence. for example, if the expert’s degree is 0.6, and our formula predicts 0.65, it is a very good match, and there is no need to come up with a formula that predicts exactly 0.6. so, how many neurons do we need to make predictions with this accuracy: let us start our analysis. let us show that in general, if we want predictions with accuracy 20%, why fuzzy cognitive maps are efficient 829 then one neuron is sufficient. specifically, we will show that if, instead of taking the neuron that provides the largest contribution to the prediction, we consider both neurons, then – within the given accuracy – the result will not change. it makes sense to treat the outputs of two neurons as random variables. as we have mentioned, for a general neural network, the result is a sum of the terms corresponding to different neurons. let t1 and t2 be terms corresponding to the two neurons. in general, these terms depends on many factors, so it makes sense to treat them as random variables. as usual in statistics, we can somewhat simplify the problem by subtracting the means e[ti] from the corresponding variables. in precise terms, instead of the original random variables ti, we can consider the differences di def = ti − e[ti] for which the mean value is 0: e[di] = 0. what we compare. we compare the two situations: • a situation in which we consider the sum d1 + d2 of both neural terms, and • a situation in which we only have a single neuron, the one that provides the largest contribution: – we consider d1 if |d1| ≥ |d2|, and – we consider d2 if |d2| ≥ |d1|. it is reasonable to assume that the variables corresponding to different neurons are independent. since we have no reason to believe that the variables corresponding to different neurons are correlated, it makes sense to assume that the variables t1 and t2 – and thus, the corresponding differences d1 and d2 – are independent. this conclusion is in line with the general maximum entropy approach to dealing with probabilistic knowledge: if there are several possible probability distributions consistent with our knowledge, it makes sense to select the one which has the largest uncertainty (entropy; see, e.g., [4, 5]), i.e., to select a distribution for which the entropy s = − ∫ ρ(x) · ln(ρ(x)) dx attains the largest possible value, where ρ(x) is the probability density function (pdf). in particular, for the case when for two random variables, we only know their marginal distributions, with probability densities ρ1(x1) and ρ2(x2), the maximum entropy approach selects the joint probability distribution with the probability density ρ(x1, x2) = ρ1(x1) · ρ2(x2) that corresponds exactly to the case when these two random variables are independent. this independence make perfect sense for neural networks: when we train a neural network, we want to get a model which is as accurate as possible, and if we use two highly correlated neurons, we waste the second neuron to describe what the first neuron describes already. how can we estimate the size of each random variable? for a random quantity with 0 mean, a natural measure of its size is its standard deviation σ. if we only consider the term ds corresponding to a single neuron, then this size can be described by the corresponding standard deviation σs. if we consider both neurons, then the size of the sum d1+d2 can be similarly characterized by its standard deviation σ12. since the variables are independent, the variance σ212 of this sum is equal to the sum σ 2 1 + σ 2 2 of the corresponding variances. thus, the standard deviation σ12 of the sum has the form σ12 = √ σ21 + σ 2 2. (6) 830 v. kreinovich, c. stylios what we plan to prove. we plan to prove that the change caused by adding the second neuron is, in general, below the desired accuracy bound, i.e., that ∣ ∣ ∣ ∣ σ12 − σs σs ∣ ∣ ∣ ∣ ≤ 0.2. (7) let us estimate the sizes σs and σ12 corresponding to two possible situations. we do not have much information about the size of the signals di corresponding to different neurons. we may guess some bounds d ≤ di ≤ d. if all know about the probability distribution is that its values are always located on the interval [ d, d ] , then the maximum entropy approach recommends that we select a uniform distribution on this interval. this recommendation is in perfect accordance with common sense: if we have no reason to believe that some values from this interval are more probable or less probable then others, then it is reasonable to assume that all these values have the exact same probability, i.e., that the distribution is indeed unform. for a uniform distribution on the interval [ d, d ] , the mean value is known to be equal to the midpoint d + d 2 of this interval. since we are interested in random variables di with 0 mean, this means that this point must be equal to 0, i.e., that we have d = −d. since the mean is 0, the variance is equal to the expected value of d2i . here, d 2 i = a 2 i , where by ai def = |di| we denoted the absolute value of the di. one can easily check that this absolute value ai is uniformly distributed on the interval [0, d], with a constant probability density ρi(x) = 1 d , so its variance σ2 = ∫ x2 · ρ(x) dx is equal to σ2i = ∫ d 0 x2 · 1 d dx = 1 3 · ( d )3 · 1 d = 1 3 · ( d )2 . (8) due to the formula (6), we thus have σ12 = √ 1 3 · ( d )2 + 1 3 · ( d )2 = √ 2 3 · d. (9) now, we need to estimate the variance σ2s of the case when we only select one of the neurons, i.e., the expected value of the square of the selection ds. similarly to the one-neuron case, since d2s = |ds|2, this variance is equal to the expected value of a2s, where we denoted as def = |ds|. by definition, as = |ds| = max(|d1|, |d2|) = max(a1, a2). we know that a1 and a2 are two independent random variables which are uniformly distributed on the interval [ 0, d ] . the distribution of the maximum can be described in terms of the cumulative distribution functions (cdf) f(x) def = prob(x ≤ x). for the uniformly distributed variable a1, we have f1(x) = prob(a1 ≤ x) = x d . similarly, f2(x) = prob(a2 ≤ x) = x d . for the maximum as = max(a1, a2), we have fs(x) = prob(as ≤ x) = prob(max(a1, a2) ≤ x). since the maximum of the two numbers is smaller than or equal to x if and only both of these numbers are ≤ x, we conclude that fs(x) = prob((a1 ≤ x) & (a2 ≤ x)). the variables a1 and why fuzzy cognitive maps are efficient 831 a2 are independent, so fs(x) = prob(a1 ≤ x) · prob(a2 ≤ x) = x d · x d = x2 ( d )2 . (10) from this cdf, we can compute the corresponding pdf ρs(x): ρs(x) = dfs(x) dx = 2x ( d )2 . (11) thus, the desired variance σ2s has the form σ2s = ∫ d 0 x2 · 2x ( d )2 dx = 2 ( d )2 · ∫ d 0 x3 dx = 2 ( d )2 · 1 4 · ( d )4 = 1 2 · ( d )2 . (12) thus, σs = √ 1 2 · d. (13) final step: checking that the desired inequality (7) is indeed satisfied. now that we have expressions (9) and (13) for the sizes σ12 and σs, we can plug them into the inequality (7) and check that this inequality is satisfied – i.e., that within the desired accuracy of 20%, adding the second neuron, on average, does not matter. indeed, substituting expressions (9) and (13) into the left-hand side of the formula (7) and dividing both the numerator and the denominator by the common factor d, we get the ratio r = ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ √ 2 3 − √ 1 2 √ 1 2 ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ . dividing both terms in the numerator by the denominator, we get r = ∣ ∣ ∣ ∣ ∣ √ 4 3 − 1 ∣ ∣ ∣ ∣ ∣ = ∣ ∣ ∣ ∣ 2√ 3 − 1 ∣ ∣ ∣ ∣ = ∣ ∣ ∣ ∣ 2 3 · √ 3 − 1 ∣ ∣ ∣ ∣ . for √ 3 = 1.73 . . ., we get r = ∣ ∣ ∣ ∣ 2 · 1.73 . . . 3 − 1 ∣ ∣ ∣ ∣ = ∣ ∣ ∣ ∣ 3.46 . . . 3 − 1 ∣ ∣ ∣ ∣ = |1.15 . . . − 1| = 0.15 . . . < 0.2. the statement is proven. 3 conclusion thus, we have explained why fuzzy cognitive maps (i.e., 1-neuron neural networks) are adequate for describing the dependence between the experts’ degree of confidence, when the relative accuracy of 20% is quite sufficient. 832 v. kreinovich, c. stylios comment. we have proven that, on average, the relative error does not exceed 20%. this explains why fuzzy cognitive maps are efficient in many practical situations. however, the fact that this inequality is satisfied on average does not necessarily mean that it is satisfied always. there may be cases when fuzzy cognitive maps do not work that well – in this case, it makes sense to describe the corresponding dependencies x = f(x1, . . . , xn) by generic (multi-neuron) neural networks. acknowledgment this work was supported in part by the national science foundation grants hrd-0734825 and hrd-1242122 (cyber-share center of excellence) and due-0926721. this work was performed when c. stylios was a visiting researcher at the university of texas at el paso. bibliography [1] c.m. bishop (2006), pattern recognition and machine learning, springer, new york. 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[31] l.a. zadeh (1965), fuzzy sets, information and control 8: 338-353. int j comput commun, issn 1841-9836 9(3):356-369, june, 2014. a feedback-corrected collaborative filtering for personalized real-world service recommendation s. zhao, y. zhang, b. cheng, j.-l. chen shuai zhao*, yang zhang, bo cheng, jun-liang chen state key laboratory of networking and switching technology beijing university of posts and telecommunications beijing, 100876, china *corresponding author: zhaoshuaiby@bupt.edu.cn abstract: the emergence of internet of things (iot) integrates the cyberspace with the physical space. with the rapid development of iot, large amounts of iot services are provided by various iot middleware solutions. so, discovery and selecting the adequate services becomes a time-consuming and challenging task. this paper proposes a novel similarity-measurement for computing the similarity between services and introduces a new personalized recommendation approach for real-world service based on collaborative filtering. in order to evaluate the performance of proposed recommendation approach, large-scale of experiments are conducted, which involves the qos-records of 339 users and 5825 real web-services. the experiments results indicate that the proposed approach outperforms other compared approaches in terms of accuracy and stability. keywords: internet of things, service recommendation, similarity measurement, collaborative filtering. 1 introduction 1.1 background internet of things (iot) seamlessly integrates user requirement, cyberspace and physical space, which enables the dynamic cooperation of ą°human-machine-thingąą. as the adopting of soa (service oriented architecture) paradigm in iot environment [1], real-world devices will be able to offer their functionality via service interfaces, which enable other components to interact with them dynamically. the functionalities provided by these devices ( e.g. the provisioning of online sensing data) are referred to as real-world services because they are provided by embedded systems that are directly related to the physical world [2]. with the developments of iot, lots of middleware solutions like openiot [3], gsn [4], cosm [5] are proposed which act as service provision platforms. these platforms access real-world resources around the world and provide their capability in form of millions of real-world services, which enable sharing, monitoring and controlling environmental data on the web. however, these existing platforms only provide limited functions for service selecting and recommendation. as the rapid increase of available services, selecting the appropriate services becomes challenging and time-consuming [6]. therefore, service discovery becomes the critical issues of iot development. other than web-service discovery based on functional property which has been deeply studied [7], the studies of discovery based on non-functional property are far from mature, i.e. it is difficult to differentiate services with similar or identical functions. as the user-received performance of service is tightly related to the personalized information of specific users, identifying the optimal one for service users is difficult and costly in the case of many services with equivalent functions. effective personalized service selection and recommendation based on non-functional property become more and more important [8]. quality of service (qos) (e.g. observation-accuracy, round-trip time (rtt), etc.) copyright © 2006-2014 by ccc publications a feedback-corrected collaborative filtering for personalized real-world service recommendation 357 is served as key non-functional property, which acts as important element considered when discovering and selecting services [9, 10]. the values of qos are usually influenced by the specific environment (such as network quality) of users and tend to vary with each user. because conducting actual service invocation is time and resource consuming, it is unpractical to evaluate the qos of all candidate services for every user [11]. so, the idea of making personalized qos prediction for users using a small amount of available qos value is extremely useful. based on the predicted qos values, personalized service recommendation is available for service-users. it enables users to select the service with optimal qos from a number of services which are function-equivalent. 1.2 motivation collaborative filtering (cf) are widely used in recommender systems [12]. the algorithms of cf can be divided into two categories: memory based and model based. cosine-based approach (cos) [14, 15] and pearson correlation coefficient (pcc) [12, 13] are two of the most popular memory-based approaches [16] to calculate the similarity between items. a number of works that employ cos-cf (cosine based collaborative-filtering) or pcc-cf (pearson correlation coefficient based collaborative filtering) for qos based service recommendation and selection have been proposed recently [11] [17–19]. however, the performance that using pcc and cos to measure similarity leaves much to be desired and the prediction accuracy of these works cannot satisfy the requirement of practical application. moreover, the experiments of these existing works are not convincing enough. the existing service recommendation [17, 19] approaches are short of sufficient-scale and systematic evaluation to verify their recommendation results. some of them employ item dataset (such as movielens [20]) instead of real service dataset to evaluate their approaches. in order to address these issues, this paper proposes a novel similarity-measurement for computing the similarity between service users and introduces a new personalized recommendation approach for real-world service based on collaborative filtering, which named feedback-corrected tan-ned (tanimoto normalized euclidean distance). the contributions of this paper are summarized as the following aspects: • this paper proposes a novel similarity measurement for memory based cf, which avoids the shortcomings of existing approaches for service recommendation and takes the characteristics of real-world service qos into account. therefore, it finds similar users more accurately and obtains more accurate qos-prediction. • a feedback-corrected cf approach is proposed, which significantly improves the performance of service recommendation in comparison with existing approaches. • to evaluate the performance of proposed approach, we conduct comprehensive evaluative experiments based on a large-size real-service qos dataset which includes 5825 real webservices and 339 users. the following parts of this paper are organized as follows. in section 2, we review existing similarity measurement approaches for memory-based cf. section 3 presents the proposed feedback-corrected tan-ned cf approach. the experiment results of proposed approach are discussed in section 4. section 5 concludes the work and discusses the future work. 358 s. zhao, y. zhang, b. cheng, j.-l. chen 2 existing similarity measurement the service similarity measurements are divided into two types, namely, functional-measurement and non-functional-measurement. our previous work [21] focuses on measuring the functional similarity between real-world services based on semantic model. other than functional similarity, this paper focuses on non-functional similarity, i.e. qos similarity. the idea of memory-based cf is inspired by the fact that users trust the recommendations from the one who have similar context and preference. these methods predict the qos of a particular service for a user based on the qos obtained from users who have similar context and preference. in memory-based cf, cosine-based approach (cos) [14,15] and pearson correlation coefficient (pcc) [12,13] are two of the most popular algorithms to measure the similarity. assuming that a service recommender system includes n services and m users, then we obtain an m ×n user-service matrix, in which the entry rm,n denotes the qos value of service n observed by user m. if the entry rm,n = ∅, it indicates that user m has never invoked service n. pcc-cf can be used to calculate the similarity between user u and user v by following formula: sim(u, v) = ∑ i∈i(ru,i − ru)(rv,i − rv)√∑ i∈i(ru,i − ru)2 √∑ i∈i(rv,i − rv)2 (1) where i = iu ∩ iv is the set of services that are co-invoked by users u and v, ru,i denotes the quantized qos value of service i observed from the view of user u, and ru is the average value on the qos of services in i observed by user u. the values of pcc range from −1 to 1 according to the definition of equation (1). in cos cf, the similarity between users can be measured by calculating the cosine similarity of the vectors between them: sim(u, v) = ∑ i∈i ru,irv,i√∑ i∈i r 2 u,i √∑ i∈i r 2 v,i (2) table 1: an example of user-service qos matrix service1 service2 service3 service4 service5 user1 2 4 2 4 5 user2 1 2 1 2 5 user3 2 4 2 ∅ ∅ user4 2 2 2 1 4 user5 5 5 5 4 ∅ user6 3 2 3 3 1 user7 3 2 3 4 ∅ user8 3 1 ∅ ∅ ∅ table 1 is an example of qos matrix which contains 5 services (service1 to service5) and 8 users (user1 to user8). the values from 1 to 5 are the minimum to the maximum qos-values of the user-service matrix. ∅ denotes the user has never invoked the corresponding service before. we calculate similarity value adopting cos approach (eq. (2)), and get the following arithmetic expression: sim(user3, user1) = sim(user3, user2) a feedback-corrected collaborative filtering for personalized real-world service recommendation 359 it indicates that user1 is similar to user3 as much as user2 is. actually, user1 is more similar to user3 than user2 , which can be easily observed according to the values in table 1. hence, calculation results and facts are contradictory. we can also compute the similarity between user4 and user5: sim(user4, user5) = 0.9885 we can draw conclusion that user4 and user5 are very similar according to this computation result. however, this is in conflict with the fact shown in table 1, since user4 and user5 almost have the opposite qos-values. user5’s qos-values are approximated to the maximum qos-value 5 in the user-service matrix while user4’s values are approximated to the minimum qos-value 1 of the matrix. this contradiction arises from that when measuring the similarity between two vectors, cos only considers the angle between two vectors and does not consider the length of vector. letążs consider another example. if we employ pcc defined in eq.(1) to measure the similarity between users, we can get the following result: sim(user7, user6) < sim(user7, user8) it indicates that user6 is less similar to user7 than user8 is. actually, user6 is more similar to user7 than user8, because there are the same value in three dimensions and a difference of 1 in one dimension for user7 and user6, and there are the same value in one dimension and a difference of 1 in one dimension for user7 and user8. therefore, the calculation results are in conflict with the facts. this contradiction arises from that pcc does not consider the number of services co-invoked which implies the similarity of selection preference and style between users. in addition to above mentioned approaches, some other similarity measurements are also proposed such as rated-item pools (rips) user similarity [22], proximity impact popularity (pip) measure [13], and mean squared difference [14], which are either for special purposes, or for special situations, or not used widely. among the traditional similarity measurement approaches, the approaches that we elaborated above are strongly representative. 3 feedback-corrected tan-ned cf 3.1 tan-ned similarity measurement in order to address the issues of existing similarity measurement, this paper proposes a novel similarity measurement named tanimoto [23] normalized euclidean distance (tan-ned). compared with the traditional similarity measurement approaches, our approach measures the similarity based on normalized euclidean distances of difference multidimensional vector spaces. although euclidean-distance approach can also be employed to measure similarity, tan-ned is completely different from it. since the number co-invoked services is different deal with different couples of users in a recommender system, the euclidean distances of different couples of users tend to compute in the different dimensions of vector spaces. moreover, the maximal values of euclidean distances in different vector spaces are usually very different. a value which denotes the maximal value in one vector space may be a very small euclidean distance in another vector space. therefore, putting them together to measure similarity is meaningless. for instance, if users a, b both invoked the same 10 services while users c, d both invoked the same 300 services, dist(a, b) is the euclidean distance between the two users a and b, dist(c, d) is the euclidean distance between the two users c and d, then it will be meaningless to mention dist(a, b) and dist(c, d) in the same breath, because dist(a, b) is calculated in 10-dimension vector space, and 360 s. zhao, y. zhang, b. cheng, j.-l. chen dist(c, d) is computed in 300-dimension vector space. therefore, to measure the similarity of vectors we should consider the difference of dimension-number. our ned normalizes the qos values of different users to the same range in order to address the different maximal-value issue. then, it unifies the similarity metrics of different vector spaces. before measuring the similarity between users, it uses the maximal and minimal qos-value of each row to normalize every value of the same row in the original matrix m. after that, the qos-values of each row are normalized to [0, 1]. in consequence, the original qos value matrix m is mapped into a row-normal matrix mnu. assuming that the number of co-invoked services by user u, v is num, and user u, v have the observed qos-value vectors u⃗, v⃗ respectively in matrix mnu. then, the euclidean distance between vector u⃗ and v⃗ in the num dimensions can be calculated by dist(u, v). besides, the maximal euclidean distance of the num dimensions are calculated by distmax. since the matrix mnu has been normalized, each dimension ranges from 0 to 1, and the maximal distance of each dimension is 1. in mnu, nr(u,i), nr(v,i) are the qos-value of service i towards user u, v respectively. the similarity between user u and v can be calculated by ned as follows: nru,i = ru,i − rumin rumax − rumin , nrv,i = rv,i − rvmin rvmax − rvmin dist(u⃗, v⃗) = √∑ i∈i (nru,i − nrv,i)2 simned(u, v) = 1 − dist(u⃗, v⃗) distmax = 1 − √∑ i∈i( ru,i−rumin rumax−rumin − rv,i−rvmin rvmax−rvmin )2√∑|i| k=1(1 − 0)2 i.e., simned(u, v) = 1 − √∑ i∈i( ru,i−rumin rumax−rumin − rv,i−rvmin rvmax−rvmin )2√ |i| (3) where i = iu ∩iv is the set of services which is co-invoked by user u and v; |i| is the number of i; rumin and rumax are the minimal and the maximal qos-values of user u in the original matrix m, r(u,i) denotes the qos-value of service i towards user u in m. the results of eq.(3) range from 0 to 1, i.e., simned(u, v) ∈ [0, 1], where simned(u, v) = 0 represents that two users are dissimilar and simned(u, v) = 1 indicates that these two user are exactly similar even the same. further, in order to use more information of the two users, we also take the number of invoked services by each user and that by both users which implies the qos preference and style of users into account. we propose tan-ned which combines tanimoto similarity coefficient [23] with ned. the formula of tan-ned is as follow: sim(u, v) = |i| |iu| + |iv| − |i| × simned(u, v) i.e., sim(u, v) = |i| |iu| + |iv| − |i| × (1 − √∑ i∈i( ru,i−rumin rumax−rumin − rv,i−rvmin rvmax−rvmin )2√ |i| ) (4) all the contractions mentioned in section 2 can be eliminated by our tan-ned. adopting eq.(4) we get the following results which are consistent with the facts: a feedback-corrected collaborative filtering for personalized real-world service recommendation 361 sim(user3, user1) > sim(user3, user2) sim(user4, user5) = 0.3381 sim(user7, user6) > sim(user7, user8) 3.2 feedback-corrected tan-ned collaborative filtering tan-ned can calculate the similarity between two users, based on it, a novel memory-based cf approach named tan-ned cf is proposed. tan-ned cf predicts the unknown qos-value r(u,i)′ of service i towards user u based on the already available qos-values of service i towards other users that are similar with user u. the more similar user v to user u is, the greater user v’s qos-value influences on r(u,i)′. the normalized predicting value r̂(u,i)′ can be calculated by eq.(5), and then we recovers the normalized value to the original scale of user u by the maximal and minimal values of user u. the qos-value predicted by tan-ned cf is defined as follow: r̂(u,i)′ = ∑ v∈u sim(u, v) × nrv,i∑ v∈u sim(u, v) (5) r(u,i)′ = rumin + (rumax − rumin)r̂(u,i)′ (6) u is the set that contains the similar users to user u. each element v ∈ u has also invoked service i. nrv,i denotes the normalized qos-value of user v on service i in matrix mnu which is row-normal. sim(u, v) can be calculated by eq.(4), and rumax and rumin are the maximal and minimal qos-values of user u in the original matrix m. then we employ the feedback (i.e., the difference value between prediction qos value and real qos value of invoked services) to correct the following prediction. the correction is a continuous process, so uncertain abnormal real qos values may appear which cause "dirty" feedback to the correction process. for example, if a users router is congested then the rtt of service invoked by this user will become very long, which will not response the real qos of service. in order to reduce the impact of "dirty" feedback, we adopt a gaussian distribution coefficient to control the correction efforts of feedback. the qos value prediction of feedback corrected tan-ned is as follow: ru,i = ∆r · 1 √ 2π e − (∆r− ∆rmin+∆rmax 2 )2 2σ2 + ru,i′ (7) where ∆r denotes the difference value between real qos and prediction qos of the previous service invoked by user u, i.e., ∆r = rreal − rpred. ∆rmin is the minimum value of historical ∆r and ∆rmax is the maximum value of historical ∆r. σ is the standard deviation of gaussian distribution which determines the amplitude of distribution, and an appropriate σ value will be obtained by experiments. the feedback correction is a continuous recursive process along with every service invoking. 3.3 feedback-corrected tan-ned cf for service recommendation in the case of candidate services having equivalent functions, the predicted qos-values which are calculated by feedback-corrected tan-ned can be used for service recommendation, and the service that has the best predicted qos performance will be recommended to the corresponding user. then, our approach enables personalized service recommendation using a small amount of available qos value without the time-consuming and costly actual service invocation. in service recommender system, either services or users may be remotely distributed in different 362 s. zhao, y. zhang, b. cheng, j.-l. chen location. besides, the network performances which influence the qos of services are highly dynamic. hence, the qos styles and preferences of user are quite different from each other. since the proposed approach considers the diversity of qos style and preference and adopts the information of similar users to make the prediction, it is applicable to a variety of environments. 4 evaluative experiments 4.1 qos dataset in order to has sufficient data to evaluate our approach, we use the web-service qos set [24] which contains 1873838 real rtt (route trip time) records on 5825 real web-services from 339 distributed service users. to our knowledge, this is the largest dataset in the domain of service-computing. in order to collect the data, zheng et al. monitor 5825 web-services using 339 distributed planet-lab computers. assuming that a m × n user-service matrix contains n services, m users and t non-null records, then the density of this matrix can be defined as density = t (m×n) . according to this concept, the density of user-service matrix used for evaluation is 94.9%. 4.2 evaluation metric we adopt the mae (mean-absolute-error) metric to evaluate the prediction accuracy of proposed approach. mae denotes the average-absolute-deviation between the ground-truth values and the predictions values. it is defined as follow: mae = ∑ u,i |r̄u,i − ru,i| n (8) ru,i is the prediction rtt of web-service i towards user u, r̄u,i is the real rtt of i observed by u, n is the number of predicted rtts. the less the value of mae is, the better the accuracy of prediction is. 4.3 experimental setup the experiments are divided into three parts, namely, performance of similarity measures, impact parameters of prediction, and performance of prediction approaches. first, we compare the performance of our tan-ned with other similarity measures. in this experiment, we use the original user-service matrix. the rtt records of matrix are divided into two parts: 80% of the records as the training set and 20% of the records as the test set. then, we measure the impact of σ (controls the correction effort of feedback), neighbour size k (top-k similar users to calculate the prediction qos value), and density of matrix. in the third part, we compare the performance of our feedback-corrected tan-ned with other prediction approaches. in order to evaluate the accuracy of rtt value prediction by different prediction algorithms, the user-service rtt records in the original matrix is removed randomly to generate ten sparse matrices. as section 4.1 defined, the densities of these ten matrices are incremental with the step-size of 2%, their densities range from 2% to 20%. we adopt these small density matrices in order to get closer to the practical situation that a user may only invokes limited number of services in large amount of available services. so, the real user-service matrix is generally very sparse. we divide each of the ten matrices into three parts: 70% of the rtt records as the training set, 10% as the feedback set, 20% as the test set. a feedback-corrected collaborative filtering for personalized real-world service recommendation 363 4.4 performance evaluation of similarity measurements in order to validate the validity of proposed tan-ned similarity measurement (eq.(4)), we compare its performance with other two well-known similarity measurements: cos (eq.(2)) and pcc (eq.(1)). then, we combine tan-ned, cos, and pcc with formula eq.(6) to predict the missing rtt values respectively. figure 1 presents the accuracy of predictions by different similarity measurements. as figure 1 shows, the proposed tan-ned consistently outperforms other compared approaches under different k-values; even the worst-case of tan-ned still overmatch the best cases of the compared approaches. therefore, compared with cos and pcc, the proposed tan-ned significantly improves the accuracy of prediction. figure 1: performance comparison of similarity measures 4.5 impact of parameters impact of σ the parameter σ determines the amplitude of distribution which controls the correction efforts of feedback. the higher the value, the more flat the distribution, which means that the probability distribution of ∆r (rreal −rpred) is dispersive. then, the correction efforts of different ∆r value are approximate. whereas, the lower the value, the steeper the distribution. it means that a few values which close to the median of ∆r have strong efforts, however, other values have relatively weak efforts. in this experiment, we increase the parameter σ from 0.1 to 1.0 with the step-size of 0.1 in order to study the impact of śň on the prediction result. the original user-service matrix is adopted, and the neighbour size k is set to 30. the influence of parameter σ on prediction is presented in figure 2. as it shows, the values of mae first slightly decline and then sharply rise. when σ = 0.4, it hits the bottom. this experiment result indicates that the prediction accuracy can be improved by adjusting the amplitude of correction-effort distribution. impact of neighbour size k in the proposed tan-ned approach, the neighbour size k determines the number of similar users used for missing value prediction. it acts an important role in the prediction performance. if the value of k is too low, many similar users will be filtered out. if the value of k is too high, dissimilar usersąż records will be considered to calculate the prediction qos value. the neighbor size k is increased from 10 to 100 with the step-size of 10 in order to study the impact 364 s. zhao, y. zhang, b. cheng, j.-l. chen figure 2: impact of sigma of k. the original user-service matrix is employed, and the parameter σ is set to 0.4. the impact of k on the prediction of tan-ned is presented in figure 3. as it shows, the value of mae first slightly declines and then slightly rises, indicating that our tan-ned achieves best performance for this dataset when k = 50. the deviation between the highest value and the lowest value is only 0.041, which means that our approach is not sensitive to the neighbour size. it is because that our approach uses the value of similarity degree to restrict the effects of each similar user when calculating the prediction qos value (shown in eq.(5)). figure 3: impact of neighbour size impact of user-service matrix density matrix density denotes the proportion of non-null records that can be used for missing value prediction in the matrix. this section studies the impact of the matrix density on the accuracy of tan-ned. in this experiment, the density is increased from 0.04 to 0.2 with the step-size of 0.02. the parameter σ is set to 0.4 and k is set to 50. the impact of matrix density on the accuracy of tan-ned prediction is presented in figure 4. as it shows, the value of mae declines as the density increase. the result of this experiment denotes that the prediction of tan-ned becomes more accuracy as the matrix density increase. the reason is that a more intensive a feedback-corrected collaborative filtering for personalized real-world service recommendation 365 matrix provides more reference information for tan-ned to predict missing value. figure 4: impact of user-service matrix density 4.6 performance evaluation of prediction approaches we compare the proposed feedback corrected tan-ned with other three prediction approaches: upcc (user-based cf adopting pcc), umean (user-mean) and wsrec (webservices recommender) in order to validate its effectiveness. upcc refers the information of similar users to predict the missing value [7, 19]. wsrec [11] is a novel memory-based cf for web-service recommendation, which achieves a relatively good performance. umean uses the average rtt values of other web-services from the same user to make missing value prediction. the predictions of these four approaches are influenced by the neighbour size k. the neighbour size is increased from 10 to 50 with the step-size of 20 in this experiment. the parameter σ of tan-ned is set to 0.4 and the confidence weight of wsrec is set to 0.11. figure 5 presents the prediction accuracy measured by mae of the evaluated approaches. the three subfigures of figure 5 correspond to the neighbour-size of 10, 30, and 50 respectively. we increase the density of matrix from 0.02 to 0.2 with the step-size of 0.02. each subfigure shows the value of mae with the matrix density changes. as shown in figure 5, our feedback corrected tan-ned is significantly superior to other compared approaches. when the density of user-service matrix becomes sparser, the co-valued dimensions between vectors decrease. it means that the number of available values used for missing value prediction is limited, which expands the gap of performance between tan-ned and compared approaches. as the increase of density, the improvement rate of tan-ned declines due to each approach has enough available values for prediction. however, in practical situation, the user-service matrix is usually very sparse. moreover, as each subfigure shows, the deviation between the maximum mae and the minimum mae of tan-ned is small. it means that tan-ned keeps a stable mae performance under the different density of user-service matrix. the results of this experiment indicate two features of tan-ned: 1) compared with other existing approaches, the sparser the user-service matrix is, the more superior of tan-ned is; 2) the performance of tan-ned is insensitive to the decrease of matrix density, namely, even with limited available qos records tan-ned can also make relatively accurate prediction. these two features of tan-ned are suitable for the actual situation of real-world service environment. in 1the confidence weight in wsrec denotes the impact of user-based method on the final prediction result. as [11] discussed, when confidence weight is set to 0.1, it achieves the best performance. 366 s. zhao, y. zhang, b. cheng, j.-l. chen (a) set the number of neighbour to 10 (b) set the number of neighbour to 30 (c) set the number of neighbour to 50 figure 5: the performance of compared prediction approaches a feedback-corrected collaborative filtering for personalized real-world service recommendation 367 real-world service recommendation, the density of matrix is generally very sparse. therefore, compared with existing approaches, our approach can make more accurate and stable prediction for qos-value. 5 conclusion this paper proposes a feedback corrected tan-ned approach to solve the issue of real-world service personalized recommendation. it studies the features of the qos-values of real-world services, and proposes a novel similarity measurement which seeks similar users more accurately and provides a basis for accurate qos-value prediction. then, the proposed approach can use a small number of available qos-values from similar users to predict the service qos-value for the user according to his personalization. in the service recommender system, the proposed approach assists service users to select the service with optimal qos from a number of functionequivalent services instead of conducting the costly actual service invocation. in order to evaluate the performance of feedback corrected tan-ned, this paper conducted comprehensive evaluative experiments using a real-world web-service dataset which has sufficient qos records. experiment results indicate that compared with existing approaches the proposed approach improves the accuracy of qos-value prediction significantly. since dynamic is a new feature of iot environment, the qos of real-world service changes with time frequently. in the future work, we will focus on enhancing the efficiency of our approach to handle the dynamic qos issue. it may be addressed by using the latest advanced technologies of machine learning. acknowledgement this study is supported by 973 program of national basic research program of china (grant no. 2011cb302704 and 2012cb315802). national natural science foundation of china (grant no. 61001118, 61171102); program for new century excellent talents in university (grant no. nect-11-0592); project of new 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(2011). qos-aware web service recommendation by collaborative filtering, ieee trans on service computing, issn: 1939-1374, 4(2): 140-152. international journal of computers communications & control issn 1841-9836, 10(1):30-37, february, 2015. a comprehensive approach to off-line advanced error troubleshooting in intelligent manufacturing systems l.s. csokmai, r.c. ţarcă, c. bungău, g. husi lehel szabolcs csokmai, constantin bungău*, radu cătălin ţarcă mechatronics department, university of oradea romania, 410087 oradea, universitatii st., 1 cs.lehel@gmail.com, rtarca@uoradea.ro *corresponding author: bungau@uoradea.ro géza husi department of electrical engineering and mechatronics university of debrecen hungary, debrecen, dembinszky ut. husigeza@eng.unideb.hu abstract: the errors recovery in the production systems will be always an open issue. therefore, the fmss have to be endowed with tools and techniques allowing an automatic recovery of errors. the objective of this work consists in proposing an off-line version of the software framework for error troubleshooting in a flexible manufacturing system [1]. the main difference between the on-line and off-line version is that the error database is stored on the mobile device and the frame marker device is connected directly to the fms components without the need of the pc.). our framework system is designed to solve the failures in the functioning of the fms and to generate self-training from previous experience. keywords: frame maker, fms(flexible manufacturing system), error, troubleshooting, advanced decision systems. 1 introduction to improve their products quality, many companies used the six sigma approach to capture measureand eliminate the defects in manufacturing process in last years. nowadays, in flexible manufacturing systems, smart products and fmss components (equipped with embedded smart devices) are wirelessly networked and remotely monitored in a real-time, using for this intelligent control systems. consequently, using these systems, we can achieve real time data gathering, remote monitoring and analysing the results acquired from smart product and fms components, the purpose being: to control the manufacturing quality and to detect quality degradation, that will allows assessment of the failure situation and taking of appropriate corrective actions accordingly. the error handling issue can be observed from two points of views. the former (out-of order) is related to the hole system-level point of view and deals with unexpected events as: fms’s components breakdowns, changes in task priorities, and all others which can be identified by the system’s controller. the latter (out-of-ordinary events) is related to the errors at the cell level. exceptions (differences between the actual and the expected state of the system) in a flexible manufacturing cell can be classified as: (1) unexpected events [2] (like time-out on expected process report or occurrence of unexpected reports), (2) errors [3] (like positioning errors), (3) unpredictable failures [9] (such as resource’s out of orders, cameras failures, equipment failures, tool breakages, human errors, material handling problems, collisions, obstructions and handling failures). the diagnosis issue is widely spread in the fms research community. the diagnosis task can be split in the following sub-tasks [5], [6], [9]: copyright © 2006-2015 by ccc publications a comprehensive approach to off-line advanced error troubleshooting in intelligent manufacturing systems 31 • detection, the goal of this task is to identify that something is not going as expected; • isolation, the goal of this task is to determine the exact location of the disturbance; • identification, the goal of this task is to determine the disturbance magnitude and impact; • error diagnosis and prognosis, that means to identify the responsible for the system degradation and the future degradation consequences; • error or failure recovery a procedure that allows to restart the error of failure in a way that either eliminates or minimizes it. from the perspective of the automated control system, real-time status and data gathering of the various fms components or manufacturing process and information transfer to the manufacturing process control centre or a corrective maintenance intervention upon fmss components represents the close loop control and management of the manufacturing system (figure 1). figure 1: disturbance management system figure 1 illustrates the informational flow in a disturbance management system. the monitoring devices is continuously observe the fmss component status and manufacturing process, performing a comparison with the existing states and plans to verify if a deviation occurs. if a deviation is detected, a diagnostic is generated, so the causes for the disturbance are identified; then its dynamics is evaluated, the possible actions to be executed to prevent a dynamic evolution of the disturbances consequences [7] ; all these with the aim to recover the system from this status. if the diagnostic is generated, the diagnostic system starts a self-repair procedure; if it is not possible, the system requests a corrective maintenance intervention to the maintenance department. in the same time this department generates updates info about occurred disturbances. in [8] was developed a prototype system, using rfid monitoring system and zigbee wireless transmission as a close loop, to monitor the manufacturing process for a fms. in [9] is presented a new disturbance management system approach, based on adacor holonic control architecture [10]. 32 l.s. csokmai, r.c. ţarcă, c. bungău, g. husi 2 system structure to monitoring the fms system developed at the university of oradea and presented in figure 2, our researchers have conceived and realized an off-line frame marker device for error troubleshooting. figure 2: fms structure the main components of the fms are: the 5-axis machine-tool tma al 550 equipped with fanuc cnc, two abb irb 1600 robots with schunk png 100 grippers, an asrs (to storage pallet with work-piece, finite product and tools) and a conveyor. 3 frame maker device the starting point in using frame markers to detect errors in fms is the ease of recognition of their patterns fiducial markers by vuforia sdk frame marker software which can run on any smart phone. these fiducial markers (called frame markers) present a very predictable and specific pattern and are generated by frame marker device (fmd) realized by authors. the frame marker’s id, used to recognize the marker in its environment by the camera during run-time, is generated by fmd which has three zones as follows: a continuous outline black square, an area with light and dark blocks which follow the outline border and the last one the inside area a black square (figure 3). the position and arrangement of blocks from second zone are distinct, offering possibility to develop a computer vision algorithm for frame markers detection. the id is encoded four times (on each edge of the frame) using a different base pattern of dark and light blocks. this redundancy increases the robustness of marker in detection and tracking. to realise the code generator device of the fmd two major components were used:: • led stripe with 36 leds individually addressable; a comprehensive approach to off-line advanced error troubleshooting in intelligent manufacturing systems 33 figure 3: frame marker device • as signal generator for led stripe was used an atmega 1280 micro-controller . each led is powered separately because of the high energy consumption. if all 36 leds are set to white colour the consume should be around 12 watts ( 2.5 amps and 5 volts) each led is provided with an control chip (shift register) which uses one pin for input and one for output. from this reason the used protocol is very timing-specific and the micro-controller has to work with high repeatability (100ns timing precision) and at least on 8mhz. for each led, the colour can be set with 8-bit pwm precision (24-bit colour / led). the atmega 1280 micro-controller can be programmed with the processing/wiring open source programming language and the integrated development environment (ide). the programming language is built on java but uses a simplified syntax. 4 structure of the off-line error troubleshooting system the structure of the off-line error troubleshooting system is presented in figure 4. the frame maker device is connected through factory server to each fms component’s control module, and also receive information about the fms’s components status directly from the monitoring devices. if any error appears in fms (e.g. failure at any fmss component, or at the fms control modules or at the communications level) the error troubleshooting system will try to solve this problem searching in the error database for a possible solution. if the error can be manage automatically by the system it will be solve by fms software. if not, the code generator maker of the frame marker device will generate and deliver an error code to the fmd and the operator will be notified about the need of troubleshooting the error through a visual and sound alarm. the error id can be read, from the frame maker device, using a portal device ccd camera, which should be directed toward the fmd (figure 5). the image acquired by ccd camera is decoded by vuforia sdk to an error id. on the portable device screen will appear an error message, with a brief description (which corresponds to that error id figure 6). 34 l.s. csokmai, r.c. ţarcă, c. bungău, g. husi figure 4: structure of the off-line error troubleshooting system errors types for different cases of failures is presented in figure 7 (with red for abb irb robot, with blue for asrs system, with purple for conveyor). in some cases when the portable device screen is touched, detailed information of the error can be supplied, if exists. after one error was successfully corrected, the operator should communicate the intervention via wifi with fms’s error database. another application was also developed by authors: to check the functional status of fms components by orienting the portable device towards the marker device; if a message on a green background appears on portable device screen, it means that the fms component works properly; in case that a message on a purple background appears on screen, it means that the fms component is off-line. a comprehensive approach to off-line advanced error troubleshooting in intelligent manufacturing systems 35 figure 5: image acquired by ccd camera figure 6: error message 36 l.s. csokmai, r.c. ţarcă, c. bungău, g. husi figure 7: errors types for different cases of failures 5 conclusions and future works the contribution of this paper is to propose an off-line version of the software framework using a frame marker device conceived by the authors, which can offer effectiveness control activities in the frame of a disturbance management system. the fms components status is controlled by the software framework which transfers the information about the need of error troubleshooting from the fms component to the operator, via a smartphone. with the off-line error database we can create a more compact and network free error troubleshooting system. the only drawback of the system is the updates are not instant; they can be applied when the mobile system connects to the fms control software when recharging. the initial error database can be dynamically extended with new items corresponding to unpredictable errors developed during the manufacturing process. bibliography [1] csokmai,l.; moldovan, o.; tarca, i.; tarca, r. 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(2008), fault diagnosis system for a robot manipulator through neuro-fuzzy approach, international journal of modelling, identification and control, 3(2):181-192. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 53-62 effective retransmission in network coding for tcp j. chen, l.x. liu, x.h. hu, w. tan jing chen information center the ministry of science and technology of the people’s republic of china 15#b, fuxing road, beijing, 100862, p.r. china e-mail: dongdcj@gmail.com lixiang liu, xiaohui hu institute of software, chinese academy of sciences 4# south fourth street, zhong guan cun street, beijing, china, 100190 wei tan baidu, inc e-mail: tanweildd@gmail.com abstract: incorporating network coding into tcp has the advantage of masking packet losses from the congestion control algorithm. it could make a lossy channel appear as a lossless channel for tcp, therefore the transport protocol can only focus on handling congestion. however, most schemes do not consider the decoding delay, thus are not suitable to be implemented in practical systems. we propose a novel feedback based network coding (fnc) retransmission scheme which has high throughput and quite low decoding delay without sacrificing throughput. it uses the implicit information of the seen scheme to acquire the exact number of packets the receiver needs for decoding all packets based on feedback. we also change the encoding rules of retransmission, so as to decode part of packets in advance. the scheme can work well on handling not only random losses but also bursty losses. our scheme also keeps the end-to-end philosophy of tcp that the coding operations are only performed at the end hosts. thus it is easier to be implemented in practical systems. simulation results show that our scheme significantly outperforms the previous coding approach in reducing decoding delay, and obtains the throughput which is close to the scenarios where there is zero error loss. it is particularly useful for streaming applications. keywords: network coding, tcp, decoding delay, retransmission. 1 introduction it is well known that tcp suffers poor performance in lossy wireless networks. even if a perfect congestion control algorithm can avoid congestion loss in transmission, there are still noncongestion losses (including random losses with a fixed bit error rate and bursty losses due to bad weather or signal shadowing, etc.), which necessarily degrade the transmission performance. network coding has emerged as an important potential approach in the operation of communication networks [1]. the core idea is that the sender transmits coded packets combined with unacknowledged original packets rather than transmitting individual packets. thus sending a packet can be seen as adding a packet to the pool and acknowledgement as removing the received packets from the pool. each transmission is not affected by any other losses. thus, incorporating copyright c⃝ 2006-2011 by ccc publications 54 j. chen, l.x. liu, x.h. hu, w. tan network coding with tcp is a natural way to enhance the robustness and effectiveness of data transmission in lossy channels. the arq for network coding (anc) scheme presented in [2] defines the seen packet as an abstraction for the case in which a packet has not yet been decoded, but can be safely removed from the sender’s buffer. the expected queue size of the scheme is reduced from the traditional length ω((1−ε)−2), to ω((1−ε)−1) where is the erasure probability. however, the weak receivers are unlikely to recover from erasures in reasonable time, since the decoding delay becomes very large. tcp/nc [3] uses seen scheme to mask losses from the congestion control algorithm. it aims to make a lossy channel appear as a lossless channel to tcp, so the congestion control protocol does not need to pay attention to the error losses and thus can focus solely on the congestion. in fact, masking losses from tcp was considered earlier by using link layer retransmission [4]. yet it has been noted in [5], [6] that the interaction between link layer retransmission and tcp’s retransmission is complicated and the performance may suffer due to independent retransmission protocols at different layers. tcp/nc uses a constant redundancy factor for retransmission in order to compensate for the loss rate of the channel and match tcp’s sending rate. however, if it suffers bursty losses due to bad weather or signal shadowing, the algorithm must wait a long time to decode all packets. in fact, most coding schemes do not take decoding delay into consideration. the receiver has to wait for a considerable number of packets before it can decode the data. consequently, it is hard for them to deploy in a real system in spite of the benefits in terms of throughput and robustness the network coding can bring [7], [8]. the work by barros et al. [9] redesigns the encoding rules and stages of the anc scheme to decrease decoding delay, which is useful for streaming applications with special delay requirements. however, it reduces delay by sacrificing some of the throughput. and its expected queue size increases from ω((1−ε)−1) in anc to ω((1−ε)−2), since the sender cannot discard a packet after it is being seen by all senders. therefore, when incorporating the existing coding mechanisms with tcp in wireless environment, we faced two main problems: (1) retransmission schemes which are triggered by a static parameter such as a fixed delay threshold or a redundancy factor, can not balance both throughput and decoding delay. this is shown in sec v., fig. 3 [9], and sec 3 of our paper. (2) the retransmission schemes require the fixed loss rate and are deeply influenced by it. as a result it cannot handle bursty losses well. to overcome the disadvantages in existing approaches, we provide the end-to-end feedback based network coding (fnc) retransmission scheme which makes use of the implicit information of the seen scheme to obtain the exact number of packets needed by the receiver to decode all data as soon as possible. we also change the encoding rules to decode part of the packets in advance. our effective retransmission scheme can mask losses better than the previous scheme, and hence significantly improve the performance of decoding delay and throughput under both random losses and bursty losses. simulation results show that our scheme significantly outperforms the previous coding approach in reducing decoding delay while increasing throughput. it obtains the throughput which is close to the scenarios where there is zero error loss. the remainder of the paper is organized as follows. section 2 introduces the terminology and describes basic ideas of network coding in tcp with the seen scheme. section 3 proposes our new network coding retransmission scheme fnc. the corresponding simulation results are presented in section 4. we conclude the paper in section 5. effective retransmission in network coding for tcp 55 2 essential background we treat packets as vectors over a finite field fq of size q. the kth packet generated by the source has an index k and is denoted as pk. when the source is allowed to transmit, it sends a random linear combination of all packets instead of the original packet. we will firstly explain the seen scheme with logical description and then illustrate its implementation in the existing protocol stacks. 2.1 arq for network coding the arq for network coding (anc) scheme presented in [2], is designed to handle loss in a multicast environment. in the scheme, the decision of which packets to combine relies on the concept of the seen packets. a packet pk is said to be seen by a receiver if it has enough information to compute a linear combination in the form of pk + q, in which q is a linear combination of packets that are newer than pk, i.e. q = ∑ l>k αl ·pl, with αl ∈ fq for all l > k. the receiver acknowledges the oldest unseen packet, so the sender always transmits a packet that is a combination of the oldest unseen packets of each receiver. a packet can be dropped from the sender queue when it is seen by all receivers. this provides an efficient method to keep the sender’s queue sizes small, although the receiver may not decode all packets. it is demonstrated to be throughput optimal for the case of poisson arrivals, perfect feedback, and identical erasure probabilities on all channels, because each reception is innovative. 2.2 network coding for tcp the reference system for our scheme is the tcp/nc protocol [3], which is designed with respect to a single source that generates a stream of packets to one sink. it incorporates the seen scheme with congestion control and introduces a new network coding layer between the transport layer and the network layer in the protocol stack, which masks packet losses from congestion control algorithm. there are several modifications on anc to be fit for end-to-end connection. first, the sender transmits random linear combinations of packets in the coding window, instead of combinations of the oldest unseen packets of each receiver. the receiver acknowledges the oldest unseen packet although it may not be decoded yet. there will never be any duplicate acks as each reception is innovative. every ack will cause the congestion window to advance, so it is not proper to apply fast retransmit/recovery algorithms which use three duplicate acks as the packet loss indication. therefore tcp/nc chooses tcp vegas [12] as the congestion control approach and introduces a novel rtt estimation algorithm. it matches the newly arrived ack with the transmission that occurred after the one that triggered the previous ack, rather than the transmission that triggered this ack. for example, in figure 1(c), rtt2 matches the 2nd transmission, rather than the 4th transmission. in the implementation, tcp/nc embeds the network coding operations in a separate layer below tcp and above ip on two end nodes in order to naturally add network coding to the current protocol stack. in the source side, when a packet arrives at the coding layer from the transport layer, the coding layer generates a random linear combination of the packets in the coding window and sends it to the sink. in order to compensate for the loss rate of the channel and to match tcp’s sending rate, for every packet from tcp, r linear combinations are sent to ip on average, where r is the constant redundancy factor equal to the reciprocal of the probability of successful reception. as an example shown in figure 1(a), assuming packet loss rate e to be 20%, hence r is 1/0.8=1.25, which means that the sender makes a retransmission after sending four combinations triggered by tcp. the retransmission does not include any new 56 j. chen, l.x. liu, x.h. hu, w. tan 1: p1 2: p1+6p2 3: p1+3p2+ 4p3 4: 7p2+ p3+3p4 5: 4p2+p3+3p4 6: 3p3+3p4+2p5 s: p1; d: p1 s: p2,3 s: p4 rtt1 rtt2 rtt3 1: p1 2: p1+6p2 3: p1+3p2+ 4p3 4: 7p2+ p3+3p4 5: 4p2+p3+3p4 6: 3p3+3p4+2p5 s: p1; d: p1 s: p4; d: p4 s: p5; d: p,5 s: p2; d: p2 s: p3; d: p3 1: p1 2: p1+6p2 3: p1+3p2+ 4p3 4: 7p2+ p3+3p4 5: 4p2+p3+3p4 6: 3p3+3p4+2p5 s: p1; d: p1 s: p3,4; d: p3,4 s: p2 s: p5; d: p,5 (a) proper retransmission (b) redundant retransmission (c)insufficient retransmission figure 1: example of the transmission with a redundancy factor original packet from tcp. in figure 1(a), the 5th transmission is a retransmission which only encodes p2, p3 and p4, without p5. it compensates the loss of the 2nd transmission, makes p3, p4 to be seen and p3, p4, p5 decodable. however, the retransmission with a fixed rate may result in the following two problems: 1) the retransmission may be useless. for example, in figure 1(b), the 5th retransmission is useless since no losses happened before it. we call this redundancy or redundant retransmission. 2) the retransmission may be helpless. in figure 1(c), the 5th retransmission is valid but still can not help the receiver to decode all packets as there are two combinations lost before. therefore, it is natural to think about using a feedback based retransmission scheme to replace the scheme with a fixed retransmission rate. 3 network coding retransmission scheme to reduce the decoding delay and redundancy, we propose the feedback based network coding (fnc) retransmission scheme. it acknowledges the total number of coding packets required to repair a loss and then decodes all packets. in the receiver, the difference between the number of seen packets and the largest packet index in the coefficient matrix implies the number of packets that the source needs to retransmit. so we maintain two variables in the receiver side: one is the number of seen packets seen_cnt ; the other is the largest index of the received packets max_seq. for each ack, the receiver embeds in the header not only the sequence number that equals the oldest unseen packet, but also the difference between max_seq and seen_cnt , which is called diff . for example, suppose the source transmits the following linear combinations: x = p1, y = (p1 + p2) and z = (p1 + p2 + p3). the second transmission y is lost. so the sink only receives the linear combinations x and z. as p1 and p2 have been seen, seen_cnt is 2, the largest index max_seq is 3, and hence diff = 3 − 2 = 1. thus diff indicates the number of packets the receiver needs to change into the decodable state which means the receiver can decode all packets. in the sender side, when an ack arrives, if the diff is larger than zero, the sender uses this value to decide to retransmit in the following two steps: first, it checks to see if the difference between the current time tnow and the time of the last retransmission tlast is greater than the timeout value. if it is, the sender retransmits diff linear combinations of first diff packets in the coding window. this avoids retransmission more than once for losses that occurred during one rtt interval. if it is not, the sender compares the diff received this time with the previous retransmission last _diff . if the current diff is greater than the last _diff , there is new packet loss and it retransmits (diff − last _diff) random linear combinations of the first (diff − last _diff) packets in coding window. if we try to retransmit the combinations of all packets in the coding windows, as in tcp/nc [3], then the average decoding delay of our scheme is indeed lower than nc. however, it is still effective retransmission in network coding for tcp 57 1: p1 2: p1+6p2 3: p1+3p2+ 4p3 4: 7p2+ p3+3p4 5: 4p2+p3+3p4 6: p2+3p3+3p4+p5 s: p1,diff=1 s: p2,diff=2 s: p3,diff=1 s: p4,diff=1 1: p1 2: p1+6p2 3: p1+3p2+ 4p3 4: 7p2+ p3+3p4 5: 4p2 6: p2+3p3+3p4+p5 s: p1,diff=1 s: p2,diff=2 s: p3,diff=1; d: p2,3 s: p4,diff=1 (a) coding with all packets (b) coding with the first several packets figure 2: retransmission based on feedback much higher than our expectation. given an example in figure 2(a), if the sender retransmits the coded packet combined with p2, p3 and p4 in the current coding window in the 5th transmission, the receiver is still in undecodable state and cannot decode any packets, because the diff has changed to 2 due to the loss of the 3rd transmission. therefore, we improve the encoding rule in which retransmission packets are only combined with first diff (or diff − last _diff ) packets in the coding window in order to make a part of packets decodable. although we do not decode all packets together, we significantly reduce the number of undecodable packets as well as the decoding delay. additionally, encoding less packets can reduce the overhead and complexity of encoding and decoding operations. consequently, in the example of fnc in figure 2(b), we only retransmit the combination containing p2 (as diff equals to 1) in the 5th transmission, and thus p1 and p2 are decodable. additionally, tcp/nc simply retransmits packets through redundancy factor to compensate for error losses, rather than for congestion losses. the retransmission in the coding layer does not consume the congestion window. however, our previously mentioned algorithm treats congestion losses and error losses the same, which may bring or exacerbate congestion. we can solve this problem by limiting the current total number of retransmission packets in the coding layer with no more than the product of the total number of transmitted packets n and the loss rate e. in contrast with tcp/nc that retransmits dispersedly, our policy can retransmit the appropriate number of packets together while they are required, and does not mask congestion losses at the same time. in other words, nc (i.e. tcp/nc) is a static retransmission scheme whereas fnc is a dynamic one. however, regarding those near-zero congestion loss algorithms such as vcp [10] and mlcp [11], it is unnecessary to adopt this policy. thus, it can handle such losses much better, when it encounters burst or inconstant loss rate conditions. the improved algorithm is specified below using pseudo-code: source side: initialization: set last _diff and tlast to 0. ack arrives from receiver: the source side algorithm removes the seen packet from the buffer and retrieves diff from ack header. if this is the fourth uninterrupted time that diff is larger than 0: 1) if (tnow −tlast > rtt ) a) set last _diff =0; b) goto 3); 2) if diff <= last _diff goto 5). 3) repeat the following (diff −last _diff ) times: a) generate a random linear combination of the packets in the coding window. b) deliver the packet to the ip layer. 58 j. chen, l.x. liu, x.h. hu, w. tan 4) update tlast to the current time; 5) update last _diff to the diff of the new arrival. receiver side: initialization: set seen_cnt and max_seq to 0. packet arrives from source side: 1) performs gaussian elimination to update the set of seen packets. 2) update seen_cnt and max_seq. 3) add the network coding ack header to tcp ack, consisting of the value of diff which is the difference between max_seq and seen_cnt . the algorithm not only avoids the redundant transmissions when the receiver is in the decodable state, but also retransmits the appropriate number of packets so as to make a part of packets decoded in advance. it significantly reduces the decoding delay, which is particularly useful for streaming applications with stringent delay requirement. also, as we do not change the acknowledgment scheme of seen packet, the expected queue size for our scheme remains ω((1−ε)−1) rather than ω((1−ε)−2) in snc [9] and tcp. in contrast with traditional retransmission schemes, such as sack etc., our scheme obtains the total number of packets for decoding. it significantly reduces overheads in the ack header. it is also more robust and effective. compared with tcp/nc [3], fnc has a relative small decoding delay. it can handle both random losses and unknown bursty losses. furthermore, our scheme respects the end-to-end philosophy of tcp that coding operations are only performed at the end hosts while achieving the aim of masking losses from congestion control algorithm at the same time. 4 simulation results we evaluate the performance of different coding algorithms by means of the network simulator "ns-2" [13]. the basic setting is a tandem network consisting of 4 hops. the source and sink nodes are at opposite ends of the chain. the packet size is 1000 bytes. we incorporate network coding with tcp vegas. the vegas parameters are set as α = 28, β = 30, γ = 2. the performance metrics are the throughput, the average decoding delay and the maximum decoding delay. the throughput with network coding is calculated as the total number of seen packets, rather than the decoded packets, divided by the simulation time. all simulations are run for at least 200s to ensure that the system reaches its steady state. 4.1 random losses we first evaluate the performance of fnc under the case of a fixed packet loss rate. the basic setting is a 10mbps link capacity, an 80ms round-trip time, and a 5% packet loss rate. we compare the throughput of nc, fnc and vegas under a fixed loss rate with the standard vegas under no loss rate to evaluate their performance on masking losses. each scenario runs 20 times and we get the mean value. 4.2 impact of packet loss rate we first study the variation of throughput with loss rate from 0% to 15%. figure 3(a) shows that the throughput of vegas falls rapidly as losses increase. nc performs better than vegas but it is worse than fnc since it only successfully masks part of losses. in contrast, fnc is very robust to losses. it maintains over 88% throughput as if there is no error loss. our effective retransmission in network coding for tcp 59 2 4 6 8 10 12 14 16 0 20 40 60 80 100 0.1 t hr ou gh pu t ( % ) packet loss rate (%) fnc nc vegas 2 4 6 8 10 12 14 16 0.0 0.4 0.8 1.2 1.6 2.0 fnc nc a ve ra ge d ec od in g de la y (s ) 0.1 packet loss rate (%) 2 4 6 8 10 12 14 16 0 4 8 12 16 20 fnc nc m ax im um d ec od in g de la y (s ) packet loss rate (%) 0.1 (a) throughput (b) average decoding delay (c) maximum decoding delay figure 3: performance as a function of the loss rate variation 10 100 1000 0 20 40 60 80 100 50050 rtt (ms) t hr ou gh pu t ( % ) fnc nc vegas 10 100 1000 0 2 4 6 8 10 fnc nc rtt (ms) a ve ra ge d ec od in g de la y (s ) 50050 10 100 1000 0 4 8 12 16 20 24 28 rtt (ms) fnc nc m ax im um d ec od in g de la y (s ) 50050 (a) throughput (b) average decoding delay (c) maximum decoding delay figure 4: performance as a function of rtt variation scheme also keeps quite a low decoding delay and the average value does not increase as the loss rate increases. this is because fnc is according to the actual losses to retransmit packets dynamically. in contrast, nc is deeply affected by the loss rate since its retransmission is static. in some cases, the average decoding delay of nc is even higher than the maximum decoding delay of fnc. 4.3 impact of feedback delay next, we evaluate the performance across a wide range of propagation delay from 10ms to 1000ms. as illustrated in figure 4(a), our scheme performs much better than nc and vegas in every case. the throughput in fnc is at least 2 times more than in nc. however, the fnc’s throughput degrades significantly when rtt increases to 1000ms. this is because our retransmission scheme is based on feedback. nc may perform better than fnc when rtt becomes much larger. thus our retransmission scheme can not be applied in deep space communication of which rtt may be hundreds of seconds, whereas in most real cases, rtt is no more than 1000ms. figure 4(b) shows that the average decoding delay of nc is 2 to 10 times of fnc. the decoding delay for both schemes increases quickly when rtt grows beyond 500ms. when rtt is 500 ms, fnc’s average decoding delay is 1.00165s, and when rtt is 1000ms, its average decoding delay is 2.35303s. they are approximately 2 times that of rtt, which is in accordance with our expectations. the average decoding delay of nc is 3.0087s and 8.93286s respectively, much larger than fnc. 60 j. chen, l.x. liu, x.h. hu, w. tan 0.1 1 10 100 0 20 40 60 80 100 500 fnc nc vegas t hr ou gh pu t ( % ) bandwidth (mbps) 0.1 1 10 100 0 4 8 12 16 fnc nc 500 a ve ra ge d ec od in g de la y (s ) bandwidth (mbps) 0.1 1 10 100 0 10 20 30 40 50 500 m ax im um d ec od in g de la y (s ) fnc nc bandwidth (mbps) (a) throughput (b) average decoding delay (c) maximum decoding delay figure 5: performance as a function of the bandwidth variation 4.4 impact of link capacity we fix the round-trip time to 100ms with a packet loss rate of 5%, and vary the link capacity from 0.1mbps to 1000mbps. as shown in figure 5(b) and (c), the maximum decoding delay for nc is very high in every case, whereas both the maximum and the average decoding delay for fnc are quite low. when the bandwidth is lower than 1mbps, the congestion control algorithm itself leads to congestion or even loss, which increases the delay. if the bandwidth is very high, one loss can lead to great throughput decrease. it is shown in figure 5(a) that due to the limitation of vegas itself, the performance of all three schemes is not very high when the bandwidth is larger than 100mbps. since our scheme can mask packet losses more effectively, it has the highest throughput in contrast with nc and vegas without incorporating with network coding. overall, the decoding delay for fnc depends on the rtt, the overhead of operation of encoding and decoding rules, and the congestion control algorithm, but it is not deeply affected by the loss rate. it seldom has a long undecodable chain thus its maximum decoding delay is endurable. furthermore, fnc masks random losses more effectively than nc does, and hence obtains higher throughput in most cases. 4.5 bursty loss all previous simulations focus on the behavior of fnc under the fixed loss rate. now, we investigate its performance in an unknown environment with unfixed loss rate. the setting of this scenario is a 10mbps link capacity with 80 ms rtt, where the background loss rate is 0.1%. at t=40s, the loss rate is changed to 50%, and lasts for 5 seconds until t=45s. at t=60s, the loss rate is changed to 100% which means the signal is fully shadowed by obstacles, and it lasts for 2 seconds until t=62s. the tested protocols vegas and fnc do not know these loss rate changes. figure 6 clearly shows that fnc can quickly and effectively handle the sudden bursty losses. before t=40s, fnc almost masks all random losses whereas vegas without network coding is seriously affected by random losses. when the loss rate is changed to 50%, vegas could hardly work whereas fnc still has a relatively high throughput. fnc does not mask all losses because the retransmission packet also has a 50% probability to be lost. after around t=63s, the sending rate of fnc has a sudden increase because the sender retransmits all lost packets together. due to the limitation of the congestion control algorithm vegas itself, the stable sending rate after this burst does not recover as before. the maximum decoding delay of fnc is 3.33053s, which contains 2 seconds during which the connection is broken. the average decoding delay is 0.0839042s, a little more than one rtt . we do not test nc because its retransmission scheme depends on the estimation of the loss rate, and it is hard to give an effective estimation algorithm. effective retransmission in network coding for tcp 61 0 20 40 60 80 100 0 200 400 600 800 1000 th ro ug hp ut (p kt/ s) time (secs) fnc vegas figure 6: performance under unknown bursty losses 5 conclusions network coding is a powerful tool in fighting against non-congestion losses. however, its redundancy and decoding delay can significantly impair transmission performance so that most schemes cannot be implemented in practical systems. in our work, we propose a novel dynamic network coding retransmission scheme which makes use of the information implied in the seen scheme to acquire the exact number of packets the receiver wants instantly. as we do not retransmit packets with a stable rate or a constant redundancy factor, our approach can handle not only random losses, but also unknown bursty losses. simulation results show that our scheme significantly outperforms the previous coding approach in reducing decoding delay and masking losses. it obtains the throughput which is close to the scenarios where there is zero error loss. the remaining issue in our research is to evaluate the performance of incorporating network coding with other congestion control algorithms, such as those load factor-based algorithms, vcp and mlcp, etc. furthermore, we intend to implement our algorithm in a linux protocol stack to asses its strengths and limitations in practice. bibliography [1] t. ho, networking from a network coding perspective, phd thesis, massachusetts institute of technology, dept. of eecs, may 2004. 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[7] s. katti, h. rahul, w hu, databi, m. mcdard, and j. crowcrofg, xors in the air: practical wireless network coding, in ieee/acm transactions on networking, 16(3): 497-510, 2008. [8] c. fragouli, j.-y. le boudec, and j. widmer, network coding: aninstant primer, acm computer communication review, jan. 2006. [9] j. barros, r. a. costa, d. munuaretto, and j. widmer, effective delay control in online network coding, in ieee infocom, 2009, san francisco, usa, apr 2009. [10] yong xia, l. subramanian, i. stoica, s. kalyanaraman, one more bit is enough, in ieee/acm trans. networking, 16(6):1281-1294, dec 2008. [11] i. a. qazi, and t. zuai, on the design of load factor based congestion control protocols for next-generation networks, in ieee infocom, apr 2008. [12] l. s. bramko, s. w. o’malley, and l. l. peterson, tcp vegas: new technichques for congestion detection and avoidance, in proceedings of the sigcomm ’94 symposium, august 1994. [13] ns-2 network simulator, http://www.isi.edu/nsnam/ns/ int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 661-673 the research of differentiated service and load balancing in web cluster a. gao, q. pan, y. hu ang gao, quan pan,yansu hu school of automation northwest polytechnical university xi’an 710072, china e-mail: gaoang@nwpu.edu.cn, quanpan@nwpu.edu.cn, huyansu@gmail.com abstract: differentiated service, as a key solution to meet the heterogenicity of web clients’ qos requirements, has been widely used to optimize the server utilization without over-providing resources. based on the relative differentiated service, this paper treats the application of proportional delay as a optimal control problem, and focuses on the cluster-side architecture improvement as well as qos controller design. a load balancing web cluster architecture supported differentiated service is proposed and implemented. by system identification and resource optimal control, the frontend dispatcher could adjust the resource quotas assigned to different classes in every single back-end server, and multi-class based maximum idle first load balancing strategy is designed to ensure a fair resource consumption among back-end nodes. as a result, the end-to-end delay is controlled and proportional delay is guaranteed. the experiments demonstrate that no matter using round-robin, least connection scheduling or maximum idle first load balancing strategy, the proposed resource optimal controller could hold the relationship among different classes. compared to round-robin and least connection first scheduling, maximum idle first strategy increases the cluster throughput by 33% and reduces the average delay by 21%. keywords: differentiated service, maximum idle first, load balancing, proportional delay guarantee 1 introduction with the dramatic explosion of online information, the internet is undergoing a transition from a data communication infrastructure to a service intergraded utility. the increase of web applications, web clients and http requests on the internet makes the web server systems often suffer from huge pressure of heavy workload. the deployment of web cluster system keeps increasing to meet the demand for availability, scalability and stability of the diversified performance demands of clients. web cluster organizes a number of web servers as a logical entirety to enhance the storage and processing capacity. the cluster also shows a good expansibility, whose capability can be easily tuned by changing the number of back-end server, which are connected by high speed local area network. clients’ http requests are well-proportioned and transparently dispatched to back-end. server nodes work concurrently and the responsiveness as well as the reliability of web sites are improved (see [17]). to take full use of every server’s processing resources, a major issue is how to arrange each server node appropriate requests according to its capability. however, the cluster system scale is limited by the financial cost of web sits and idc (internet digital center), and the load characteristics of web sites is often affected by the browsing habits, geographic distribution and breaking news. it is impossible to accurately predict the peak load and prepare enough copyright c⃝ 2006-2012 by ccc publications 662 a. gao, q. pan, y. hu computing resources. so it is not cost-effective to allocate excessive computing resources for a web site to accommodate the potential peak. even the large-scale clusters, there will still be the case of overload. now, the internet has become a commercial product, the growth of ecommerce is creating demand for services with financial incentives for the service providers, i.e., the economic transaction is more important than a simple browsing, and the premium users also expect better quality services. so, the other major problem web cluster system facing is how to meet the service level agreements(slas) with their clients without excessively over-provisioning resources (see [21]). as an initial effort, a feedback control mechanism is designed to achieve proportional delay differentiation service and load balancing (pdds-lb) in a web cluster system. first, according to the general web cluster system framework and the http processing procedure, a load balancing web cluster architecture is proposed. second, considering the residue delay1 is the main factor affecting the users’ experience in a web application, with the aid of system identification and optimal control, we design a feedback controller, which periodically re-allocates the processing resources to keep the residue delay ratio around the set point. finally, we present multi-class based maximum idle first load balancing strategy(mif) to achieve efficient and fair resource consumption. experiment results show that our mechanism is effective, the total throughput increases by 33%, and the average delay reduces by 23%. 2 the overview of cluster 2.1 the architecture and forwarding technology figure 1 gives the framework of web cluster.the front-end is called dispatcher, which is the entrance of the cluster system. the clients’ http requests first reach dispatcher, then are distributed to back-end servers according to the load balancing strategy. the the dispatcher can select a back-end according to the request-url in http message and/or other information in entity-header fields, such as user-agent, from, host, etc. according to the layer of dispatcher, clusters can be divided into three types (see [17]): l4/2 cluster-l4 switcher,l2 forwarding; l4/3 cluster-l4 swithcer,l3 forwarding; l7 clusterapplication layer forwarding. http request dispatcher server m server 1 browser internet router http response figure 1: the processing of http request in web cluster the dispatcher should establish tcp connection with clients and back-ends concurrently. although compared with the hardware-accelerated forwarding method used by l4/2 and l4/3 cluster, l7 cluster has the limitation of larger processing overhead. but it is still a promising implementation of web server cluster (see [11]). it could not only combine l4/2, l4/3 forwarding technology (see [3]), but also take usage of application information to enforce the content-aware dispatcher and combine the priority scheduling with the processing/threads-based web qos control scheme (see [18]). 1residue delay is consist of connecting time and processing time. the research of differentiated service and load balancing in web cluster 663 from the view of forwarding technology used by dispatcher, there are also three types of clusters: reverse proxy, tcp splicing and tcp handoff. the most widely-used is reverse proxy which is shown in figure 1 arrow ¬. the dispatcher distributes http requests to backends and transmits the responses back to clients. in order to avoid the redundant data copies and enhance the forwarding efficiency of reverse proxy, tcp splicing is enforced in operation system kernel. as arrow ­ shown in figure 1, by means of tcp handoff protocol, the tcp connection established between dispatcher and clients is transfer to back-ends, and the response is directly sent back to the clients without relaying of dispatcher, which increases the potential throughput of the system. tcp splicing and tcp handoff are both proposed to enhance the forwarding efficiency. however, none of them is software-compatible because the implements require the amendments of tcp/ip protocol stack and system kernel in dispatcher and/or back-end servers. for the reasons above, our research focus on l7 cluster whose dispatcher running as reverse proxy. 2.2 the related work the current related work mainly focus on the load balancing strategy and algorithm. while the researches of l4 cluster are just on how to uniformly spread the requests. besides this, l7 cluster considers the contents of the request as well. typical l4 load balancing strategies are as follows: round robin (rr), i.e. executes (i + 1) mod n for every request and selects back-end according to the result; weighted round robin (wrr), i.e. every back-end servers’ processing capacity is in accordance with its weight. the larger weight the more times requests be sent; least connection scheduling (lcs) and weighted least connection scheduling (wlcs), which forward the requests to the server with least active tcp connections; source hashing scheduling (shs) and destination hashing scheduling (dhs) are statics mapping algorithms, the ip source address or destination address of http request is hashed and mapped to a certain back-end server. there are two common scheduling strategy used in l7, client-aware policy (cap) (see [4]) and locality aware request distribution (lard)(see [13]). cap is actually a kind of rr scheduling supported client-side classification. same kind of requests are spreading uniformly without considering their content. lard is server-status based load balancing strategy, in the threshold of stability, the same url are forwarding to the same server to achieve a higher hit rate. however, no matter l4 or l7 cluster research does not concern the issue of differentiated service and the related study is few. [21] gives the demand-driven service differentiation(ddsd) approach, which improves web cluster model into a multi-queueing system, such as m/m/1/∞ and m/g/1/∞ models proposed by [19,20]. by selecting stretch factor, it treats the re-allocation of cluster resources as a slas constrained optimization problem. however, because queueing model is based on the premise of poisson arrival rate and exponential distribution of processing time, queueing model can not accurately describe the of http traffic feature when the arrival rate and leave rate does not match(see [2, 9]). dynamic partitioning (dynamicpart) algorithm is proposed by casalicchio (see [1]). by feedback control, the back-ends is dynamically servicing different kinds of requests, which transforms a best-effort web cluster into a qos-enhanced system. but the performance isolation is enforced at the level of server host, it can only provide coarse-grain control at host-level, which disturbs the on-line extension and the resources utilization. our research purpose is to provide a differentiated services to increase the resources utilization of web cluster at the level of processing/thread. first, the resource consumption of every backend should be coordinated and load-balanced. then the resource management and scheduling 664 a. gao, q. pan, y. hu should meet the qos requirement (see [5] ). 3 qos-enhanced web cluster 3.1 pdds-lb architechure the processing/thread-based web qos control is used in the researches of single web server. the processing or thread pool is partitioned into several parts to support the performance isolation. the processing or thread number in each part is called quota. the http requests are classified into different business flows (1...n). by adjusting the quotas of every class, the performance isolation and differentiation service are achieved (see [8,14]). the initial ideal of pdds-lb is a unified scheduling of all back-ends’ processings/threades and a fair consumption of different servers’ resource to the same flows. as shown in figure 2, pdds-lb supports differentiated service in two layers: clients request dispatcher quotas allocation qos monitor optimal controller ydesire y front-end local area network back-end delay request response x request classifier request distribution layer resource allocation layer figure 2: pdds-lb architecutre in the resource allocation layer, the qos monitor perceives the average residue delay of each class 2 ℓi,(1 ≤ i ≤ n) while the back-ends report their status periodically (detailed in section 5). the optimal controller adjusts every back-ends’s quota ci,j,(1 ≤ j ≤ m) assigned to different business flows. in the request distribution layer, the requests are separated into different priorities according to the specified strategy and slas. the request dispatcher selects a back-end for each request by the load balancing algorithm. the front-end dispatcher is the key element of the cluster, which establishes tcp connection with back-end and clients simultaneously. back-end servers deploy the same contents and can service all the requests. classification strategy back-ends used is consistent with the front-end. different requests flows passed into the corresponding connecting queueing wait to be served in the manner of first-in-first-out(fifo). heartbeat is original used to make a high amiability of cluster infrastructure (front-end and back-ends)(see [15,16]). in pdds-lb, it is referenced as a daemon to provide the communication between client infrastructure. at every heartbeat time, the status reporter sends the status messages to front dispatcher and the quotas of business flows are adjusted according to the optimal controller’s output. 2residue delay is the sum of connecting time w and processing time χ. the research of differentiated service and load balancing in web cluster 665 3.2 pdds-lb software component considered our previous work (see [8,9]), we implement and deploy pdds-lb in the apache plantform. the back-end server is modified into the multi-processing queue architecture. on the dispatcher, mod_proxy (see in [6]) is activated as reverse proxy, and mod_proxy_balancer is used to translate the request url into absolute url in back-end server. 1. mod_cluster is modified to achieve communication between dispatcher and back-end servers. at every heartbeat time, back-end servers initiatively report their status. meanwhile, mod_clsuter in dispatcher implements the function of resource allocation layer. by optimal control, the refreshed quotas of classes are sent back to back-end servers. 2. mod_proxy_balancer is modified to enforce the functions of request distribution layer. mod_cluster calculates the busyness of every back-end server according to the their status and urls are redirected to a appropriate server. then apache httpd obtains responses from it. 4 cluster resource management 4.1 system model identification supposed the cluster consists of m back-end servers, serving n classes of business flows. every back-end runs cj(j = 1, . . . ,m) service threads: cj = n∑ i=1 ci,j, ςi = m∑ j=1 ci,j,1 ≤ i ≤ n,1 ≤ j ≤ m, (1) where ci,j is the number of threads assigned to class i in jth back-end, i.e. the quota that server j assigns to class i. ςi is the number of threads assigned to class i in the whole cluster. at every sampling time, the optimal controller adjusts ci,j to hold equation (2): ℓi ℓl = δi δl ,1 ≤ i ≤ n,1 ≤ l ≤ n, (2) where δi is the class i’s priority assigned by sla. the smaller δi, the higher its priority. as shown in figure 2,the controlled object is threads of the whole cluster. for the constrain of equation (1), the input has i = n ×m independent variables. at the kth sampling time, the input is: x(k) = [c1,1(k),c1,2(k), . . . ,c1,m(k), . . . ,cn−1,1(k),cn−1,2(k), ...,cn−1,m(k)] t. define yi(k), yidesire are the normalized residue delay and its expected value: yi(k) = ℓi(k) n∑ l=1 ℓl(k) ,yidesire = δi n∑ l=1 δl ,1 ≤ i ≤ n. (3) because ∑n i=1 yi(k)= 1 and ∑n i=1 yidesire= 1, the output has o = n − 1 independent variables. so let y(k) = [y1(k),y2(k), . . . ,yn−1(k)] t, ydesire = [y1desire,y2desire, . . . ,yn−1desire] t, 666 a. gao, q. pan, y. hu where y(k) is the measurement output. according to the derivation e(k) between y(k) and ydesire, the optimal controller adjusts the threads quotas of every classes to guarantee the relative relationship constant. strictly speaking, we require a discrete and nonlinear model for pdds-lb. however, such a nonlinear model is not amenable to the straight forward theoretical design and analysis (see [12]). so the following linear model is used to approximate the system. supposing r-order could be precise enough, the corresponding difference equation is: y(k) = r∑ j=1 [ajy(k − j) + bjx(k − j)], (4) and the z-domain transformation is: a(z−1)y(k) = b(z−1)x(k) + ε(k), (5) where ε(k) = [ε1(k),ε2(k), ...,εn−1(k)]t is o-order not related white noise sequence with mean zero. a(z−1) = i−a1z−1 − ...−arz−r,ai ∈ ro×o,0 < i ≤ r. b(z−1) = b1z −1 + ... + brz −r,bj ∈ ro×i,0 < j ≤ r. because of observation error and system noise, define ε(k) = [ε1(k),ε2(k), ...,εn−1(k)]t be o-order white noise sequence. equation (5) can be rewritten as: y(k + 1) = θφ(k) + ε(k + 1), (6) where θ = [b1...,br,a1, ...ar],k ≥ r −1, φ(k) = [xt(k), ...,xt(k −r + 1),yt(k), ...,yt(k − r + 1)]r, θ ∈ ro×[o×2×r]. recursive least square (rls) estimate algorithm is used to calculate parameter matrix θ, and the white noise-similarity of pseudo-random sequence is used as impulse to fully stimulate the system . in the system identification experiment, the cluster is composed of 4 back-end server, each of which runs 100 threads serving two classes of business flows. i.e. n = 2, m = 4 ,cj = 100,j = 1, . . . ,4, ydesire = [y1desire] = [1/3]. in order to fully stimulate the system, at every sampling time, the quotas assigned to every classes x(k + 1) = [c1,1(k + 1),c1,2(k + 1), ...,c1,4(k + 1)] t are adjusted according to the current value of pseudo-random sequence. the pseudo-random sequence is generated as equation (7). set p = 7, q = 12, the relationship between ϵ(k) and x is shown in table 1. ϵ(k) = ϵ(k −p) + ϵ(k −q)(mod4) (7) table 1: relation of ϵ(k) and x when p = 7, q = 12 ϵ(k) c1,j(k + 1) c2,j(k + 1) 0 25 75 1 40 60 2 60 40 3 75 25 the system identification experiment lasts 5000 seconds with the sampling time t = 30s and we got 150 sets of effective data. supposing θ̂q is the estimation of θ from the former q (q ≥ r−1) the research of differentiated service and load balancing in web cluster 667 sampled data. after the q + 1th sampling time, θ̂q can be revised as: θ̂q+1 = θ̂q + [y(k + 1)− θ̂qφ(k)]φt(k)pq λ + φt(k)pqφ(k) , (8) where p−1q+1 = p −1 q + [1 + (λ−1) φt(k)pqφ(k) (φt(k)φ(k))2 ]φ(k)φt(k), pq is covariance matrix and λ is forgetting factor. φ, y are measured by qos monitor. by selecting an appropriate θ̂0 and p0, we could get the estimation of parameter matrix. the criteria for θ̂0 and p0 is{ θ̂0 = ι,ι is sufficiently small real vector p0 = α 2i,α is sufficiently large real number (9) the loss function is defined as equation (10) to describe the variance between identified residue delay proportion and its measured value. j(m) = m+m−1∑ k=m ||y(k + 1)− θ̂qφ(k)||2, (10) where || • || is vector norm, and r is sample size. the system order r is desided by f-test. supposed m1 and m2 are adjacent orders of system, statistics variable h is constructed as follows: h(m1,m2) = j(m1)− j(m2) j(m2) m −2m2 2(m2 −m1) . (11) if m is large enough and m2 > m1, h(m1,m2) obeys f-distribution. h ∼ f(2(m2 −m1),m −2m2). the following pseudo-code used for j(m) will be generated: 1. begin 2. set m be the maximum possibel order, i.e. m = mmax . 3. q = 0, k = m−1, chose an appropriate θ̂0 and p0. 4. φ(k) is constructed according to the former [k −m + 1,k] sample data. 5. θ̂q+1 and pq+1 is calculated as equation (8). 6. k = k + 1, q = q + 1. 7. if k ≤ m, goto 4 ; else θ̂m−m+1 is the rls estimation of this m-order system. 8. j(m) is calculated as equation (10). 9. m = m−1. 10. if m ≥ 1, goto 3. 11. end. then j = [j(1),j(2), ...,j(mmax)]t is obtained. given the degree of confidence α = 5%, where is f0.05(2,144) ≈ 3.05. because h(2,3) = 2.29 < f0.05(2,144), which means there is no 668 a. gao, q. pan, y. hu significant reduction of the loss function when system order changes from 2 to 3. so the pddslb model can be modeled as a second-order linear time-invariant system. the corresponding rls estimation of parameter matrix θ is: θ̂ = [b̂0,b̂1,â1,â2], b̂1 = [b1,1,b1,2,b1,3,b1,4] = [−0.0004,−0.0005,−0.0004,−0.0004] b̂2 = [b2,1,b2,2,b2,3,b2,4] = [0.0049,0.0060,0.0056,0.0050] â1 = a1 = 0.4528 â2 = a2 = 0.0922 figure 3 is the compare of identified value vs. actual measurement of y (k). the identified value of y (k) is closed to the measured value, so 2-order linear mimo model is appropriate to describe pdd-lb. 0 20 40 60 80 100 120 140 160 0 1 2 3 time/30s re si d u e d el a y ra ti o identified value measured value figure 3: the identified value of y (k) is closed to actual measurement 4.2 optimal controller design the resource reallocation of process/thread can be treated as an optimization problem which minimizes the deviation between yi(k) and yidesire , while penalizes large changes in the control variables to save the overhead (see in [12]). so we construct the following quadratic cost function: l = e{||w [y (k + 1)−ydesire]||2 + ||q[x(k)−x(k −1)]||2}, (12) where w is o × o positive-definite weighting matrix and q is i × i positive-definite penalty matrix. diagonal elements of w represent the priorities of the corresponding classes, the smaller wi,i, the more discriminated against the class i . in our application, w , q are diagonal matrix and unit matrix. the derivative of l about x is zero when at its minimum, so there is(the derivation of equation see in [10, 12]): ∂l ∂x(k) = 2(w b̂0) tw [θ̂φ̃(k)−ydesire] + 2(w b̂0)tw b̂0x(k) + 2qtqx(k)−2qtqx(k −1) = 0, (13) by solving the equation (13), we can obtain the following optimal control law at the kth sampling time x∗(k) = [(w b̂0) tw b̂0+q tq]−1{(w b̂0)tw [ydesire − θ̂φ̃(k)] + qtqx(k −1)}. (14) the research of differentiated service and load balancing in web cluster 669 5 maximum idle first the resource optimal control is used to calculate the quotas ςi assigned to class i, while the load balancing strategy is to assure a fair assumption of threads, avoiding some back-end starvation or overload. most researches of load balancing are on the promise of possion arrival distribution and the exponential service time distribution, such as rr and lcs. however, the tendency that web pages is on a going to contains more and more dynamic objects and database operation makes processing time hard to meet the exponential distribution, and all of these strategies can not support differentiated service. in order to precisely estimate the load status of each classes in back-end servers and dispatch new coming requests equally, we propose a load balancing strategy called maximum idle first (mif). the status of jth back-end server are n sequence of two-tuples < ni,j,τi,j >��i = 1, . . . ,n, which includes the connection queue length nij and the number of idle threads in the server pool τij. the residue delay ℓi is proportional to the queue length ni,j and inverse proportional to the number of service threads, i.e. τi,j (see in [8, 14]). for the clients supported http 1.1 , a web session is always a sequence of http requests on a consistent tcp connection as a manner of pipeline, so there is ℓi,j = αni,je[ρi,j]/τi,j, (15) idlei,j ∝ 1/ℓi,j. (16) let idlei,j be the idle degree of service for class i in server j, which is inverse proportional to the residue dalay. α is a planform dependence translation parameter. e[ρi,j] is the mathematic expecting of web session size. (see in [7]),there is : e[ρi,j] = e[vi,j]e[ui,j],1 ≤ i ≤ n,1 ≤ j ≤ m (17) vi,j,ui,j are the size and the number of the embedded requests from a web session. because every back-end servers deploys the same content and the self-similarity of the requests, e[ρi,j] is a constant value. when a request belonged to class i arrivals at the front-end dispatcher, mod_proxy_banlancer calculates each back-end’s idleij, then redirects this request to the server, which has the maximum idle degree of service. 6 experiment evaluation 6.1 configuration of experiment the test-bed is developed to evaluate the pdds-lb mechanism, which consists of 9 computers connected together via 1gbps ethernet. there are 4 back-ends running apache-2.0.53, which each has 100 concurrent threads, and 4 linux machines simulate 120 × 4 different clients using surge-1.00a. considering the front-end may be a new bottleneck, we configure the apache2.2.63 on the front-end with 1000 concurrent threads, which is large enough. in our experiment, http requests are classified into 2 business flows based on the clients’ ip address,i.e. class 1 and class 2. set the expected delay ratio is ℓ1/ℓ2 = 1/2, ydesire = [1/3], i.e. the class 1’s residue delay should be the half of the class 2’s. define the relative variance ψ(y ) be a control performance metric. a smaller ψ(y ) indicates a better stability that controller can keep y (k) at ydesire. ψ(y ) = √∑i k=1 ||y (k)−ydesire||2/i ydesire , (18) 670 a. gao, q. pan, y. hu 6.2 result and analysis we design two sets of contrast experiments to evaluate the pdds-lb cluster’s ability of differentiation,throughput and dalely under different load balancing strategy. firstly, we evaluate the optimal controller’s ability of differentiation under different load balancing strategy and sampling time. as shown in figure 4(a), 4(b) and 4(c), each of them are the results of t = 20s and t = 30 when using rr, lcs and mif algorithm. the optimal controller launches at 800 seconds, and the measured residue delay ratio of class 1 and class2 is gradually settled around the expected value. according to the equation (18), ψ(y ) are calculated and shown in figure 4(d). compared figure 4(a), 4(b) and 4(c), the following conclusion can be summarized: 1. no matter using which load balancing algorithms or which sampling time, our optimal controller can provide proportional delay services. this proves the correctness and feasibility of pdds-lb cluster model. 2. ℓ−800i and ℓ +800 i are used respectively to compare average residue delay before and after 800 sec, there is: ℓ+8002 − ℓ −800 2 ≥ ℓ −800 1 − ℓ +800 1 , (19) it means that the increase of class 2’s delay is more than the decrease of class 1’s. nevertheless, it is worth making for the reasons in section 1. 3. in equilibrium state, i.e. y (k) = ydesire, there are ℓ+8001,mif < ℓ +800 1,rr ≈ ℓ +800 1,lcs, (20) ℓ+8002,mif < ℓ +800 2,rr ≈ ℓ +800 2,lcs, (21) where ℓ+800i,rr , ℓ +800 i,lcs, ℓ +800 i,mif are the average residue delay of class i in stable sate under rr, lcs and mif strategies, equation (20)and (21) proves that mif has less delay in the stable state. 4. as shown in figure 4(d), when sampling time t = 20s, the delay ratio jitters severely. the possible reason is the delay jitter caused by large files, and which are more apparent in small sampling time. then, a further comparation is enforced to evaluate the different balancing strategy on the system throughput and residue delay. when t = 20, as the increase of the client’s tcp connection requests arrival rate, the throughput and residue time also enhance, while the two classes of business still keep differentiate relationship. the figure 5(a) and 5(b) are the histogram of throughput and the residue delay with the width of 20 tcp connections/sec. as can be seen, when the tcp connection arrive rate is 25/sec the system is saturated. both in lcs and rr, the throughput of class 1 and 2 are 80 requests/sec and 40 requests/sec respectively, while in mif, the throughput of class 1 can increase to 120 requests/sec. at the same time, in lcs and rr, the average delay of class 1 and 2 are 100ms and 280ms, while the average delay of class 2 reduce to 200ms in mif. this is not only because mif consider the impact of the connection queue length and idle threads on the residue delay, but also due to the fine-grained state feedback. the status of back-ends contain details of every class , we can design a better dispatch strategy. although lcs and rr can dispatch requests to all back-ends evenly, since lack of the fine-grained state feedback, they cannot maximize the resource utilization. for example, if the thread quotas of class 1 will be exhausted, or its connection queue will overflow on back-end j, while the resources the research of differentiated service and load balancing in web cluster 671 0 100 200 300 400 0 1000 2000 3000 0.5 1.5 2.5 3.5 re s id u e d e la y ( m s ) d e la y r a ti o time(sec) t=20 class 1 dealy class 2 delay delay ratio 0 100 200 300 400 0 1000 2000 3000 0.5 1.5 2.5 3.5 re s id u e d e a ly ( m s ) d e la y r a ti o time(sec) t=30 class 1 dealy class 2 delay delay ratio (a) rr 0 100 200 300 400 0 1000 2000 3000 0.5 1.5 2.5 3.5 re s id u e d e a ly ( m s ) d e la y r a ti o time(sec) t=20 class 1 dealy class 2 delay delay ratio 0 100 200 300 400 0 1000 2000 3000 0.5 1.5 2.5 3.5 re s id u e d e a ly ( m s ) d e la y r a ti o time(sec) t=30 class 1 dealy class 2 delay delay ratio (b) lcs 0 100 200 300 400 0 1000 2000 3000 0.5 1.5 2.5 3.5 re s id u e d e la y ( m s ) d e la y r a ti o time(sec) t=20 class 1 dealy class 2 delay delay ratio 0 100 200 300 400 0 1000 2000 3000 0.5 1.5 2.5 3.5 re s id u e d e a ly ( m s ) d e la y r a ti o time(sec) t=30 class 1 dealy class 2 delay delay ratio (c) mif ψ(y ) rr lcs mif 20s 0.3843 0.3810 0.31925 30s 0.3831 0.3759 0.28000 (d) ψ(y ) figure 4: differentiation effects under different load balancing strategy and sampling time of other classes are idle, lcs and rr tend to redirect requests to this back-end. now, there is a new request, which is exactly belongs to class 1, resource shortage on back-end j will further deteriorate, however, other back-ends may be in idle status. 7 conclusion in this paper, we design a feedback control mechanism to achieve proportional delay differentiation service and load balancing in a web cluster system. by recursive least square estimation and f-test, pdds-lb is model as a second-order liner time-invariant system. we construct the cost function and obtain the optimal law by derivation of cost function. experimental evaluations have shown that our mechanism achieves pdds, while the low priority class is not over-sacrificed. with the aid of mif, we maximize the resource utilization of the cluster system. however, our experiments are just in the condition of ideal transport layer, in the real network, link status is complex and changeable ,the factors that influence qos interweave each other, such as bandwidth, cache, i/o etc. as part of out ongoing work, we are exploring a integrated resource management to adapt to more complex environment. 672 a. gao, q. pan, y. hu 0 20 40 60 80 100 120 140 10-30 30-50 50-70 70-90 90-110 10-30 30-50 50-70 70-90 90-110 t h ro u g h p u t (/ s e c ) tcp connections (/sec) class 1 class 2 mif lcs rr (a) throughput 0 50 100 150 200 250 300 10-30 30-50 50-70 70-90 90-110 10-30 30-50 50-70 70-90 90-110 re s id u e d e la y ( m s ) tcp connections (/sec) class 1 class 2 mif lcs rr (b) delay figure 5: compared with lcs and rr, the average delay reduces 21% and the total throughput increases 33% in mif. bibliography [1] m. andreolini, e. casalicchio, m. colajanni, and m. mambelli. a cluster-based web system providing differentiated and guaranteed services. cluster computing, 7(1):7–19, 2004. 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[21] h. zhu, h. tang, and t. yang. demand-driven service differentiation in cluster-based network servers. in ieee infocom, volume 2, pages 679–688. citeseer, 2001. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 123-134 an ims architecture and algorithm proposal with qos parameters for flexible convergent services with dynamic requirements m. navarro, y. donoso miguel navarro universidad de los andes bogotá, colombia e-mail: mignavarro@egresados.uniandes.edu.co yezid donoso universidad de los andes bogotá, colombia e-mail: ydonoso@uniandes.edu.co abstract: quality of service (qos) provisioning is one of the main requirements in the 3gpp ip multimedia subsystem (ims) and it has been addressed in different works since the beginning of the ims standardization process. as a result of the fixed and mobile networks evolution, the parameters standardized in ims have changed constantly until the specification of the policy and charging control (pcc) architecture that integrates ims qos and charging functionalities. however, current ims qos specifications still have some limitations to handle service flexibility that is required to provide internet services over ims. in this work, we propose an enhanced ims qos architecture to support efficient qos providing for flexible services with dynamic requirements. this proposal is compared against different approaches to evaluate their behavior under network saturation conditions. simulations results show that the architecture we propose achieves efficiency and flexibility, maintaining the number of blocked and active sessions, and increasing the number of high priority sessions activated in a saturated network. keywords: convergent services, ip multimedia subsystem, quality of service 1 introduction networks for convergent services are the result of an evolution process followed by fixed and mobile networks. as a result of different trends of evolution, the ip multimedia subsystem (ims) was introduced as the accepted network architecture for convergent services with guaranteed requirements, such as qos, security, charging, and roaming. qos provision on ims networks is a problem that has been studied since the first ims standardization given by the 3rd generation partnership project (3gpp) in release 5 [1]. ims was first introduced as the subsystem in charge of session control for ip services in 3g networks, and for this reason, its evolution process towards ip in mobile networks could be compared to the next generation network´s(ngn) evolution process in fixed networks. however, since ims was already considering session control features, it was accepted as the unifying standard core network (cn) for ip convergent services, increasing its initial scope to include fixed networks as an additional access network [2] [3]. currently, with the specification of the fourth generation (4g) in mobile networks, 3gpp introduced the program named evolved packet system (eps), which combines the long term evolution (lte) program, copyright c⃝ 2006-2012 by ccc publications 124 m. navarro, y. donoso and the evolved packet core (epc) program. ims objectives continued having a leading role in eps, since lte is considered as a new access network that may be integrated to the network architecture, and the core in eps integrates ims architecture. at this point, networks working with these programs are referred as next generation mobile networks (ngmn) [4] [5]. in ims, the problem regarding qos provision at the ip media transport layer is the same as it is defined for the internet. several authors have already covered this problem and the models of integrated services (intserv) and differentiated services (diffserv) have been studied under different contexts. both models apply for ims networks; nevertheless, diffserv model’s ability to keep minimal information about the network state makes it more scalable compared to intserv. as a result, 3gpp defined diffserv as the qos model for the ip media transport layer [1] [6]. for upper layers, 3gpp has also specified the mechanisms for providing qos. since ims release 7 specification, 3gpp introduced the policy and charging control (pcc) architecture, which continued until release 9 as the mechanism for determining qos and charging for convergent services. although, the pcc architecture specification gives the definition of the entities involved and their basic functions, there is still much work to do in order to cover all possible scenarios and to guarantee qos requirements. in [7], 3gpp standardized the qos parameters applied in the service level, and also introduced the concepts of service priority and pre-emption capability and vulnerability, which support conflict handling between services in a state of network saturation. in spite of this concept and function definitions, their relation to the main functional entities in ims layered architecture is still an ongoing process. the main objective in this work is to define an enhanced ims qos architecture, in order to support qos providing for flexible services with dynamic requirements in an efficient way. then, we defined an architecture that supports service relocation between different qos levels, based on information about priority, pre-emption and the service capability to be flexible. to achieve this, we defined a new qos parameter called the service flexibility bit (sfb) and a new entity named the qos level relocation function (qos-lrf)in the pcc architecture. the remainder of this paper is organized as follows. in section 2, we describe some related work. in section 3, we present the pcc architecture. in section 4, we propose an enhanced qos architecture and a heuristic algorithm to validate the architecture. in section 5, we present the architecture and performance evaluation. the discussion of the results is presented in section 6. finally, section 7 contains our conclusions and directions for further study. 2 related work related work about qos in ims has been presented prior the standardization of the pcc architecture in ims release 7. the main focus is on the heterogeneity introduced by different access networks and discrepancies between qos classes in all of them. this problem is analyzed in [8], where authors present the work developed by 3gpp and etsi tispan in qos provisioning for ims. with regard to the session control layer from the ims architecture, authors emphasize on the importance of the policy decision function (pdf) for 3gpp specifications, a function that is later performed by the pcc architecture. the transport layer is also considered, presenting the benefits and weaknesses’ in diffserv core networks. in the end, a practical implementation on a real network is stated and given for further study. after the pcc architecture specifications where given, several works have been presented focusing on enhancements for charging and qos functions. in ims, qos may be studied according to the different architectural layers, starting with the session control layer and their effects on the application and service layers. in [9], authors propose an approach to ims policy control based on session policies. in this work, they present service integration using common functions provided by ims, and horizontal integration as the methodology applied for multimedia service development. with this methodology, they an ims architecture and algorithm proposal with qos parameters for flexible convergent services with dynamic requirements 125 are allowed to combine service functions together in order to provide a specific functionality, in contrast to the traditional vertical service integration, which basically provides all the functionality with one service module. there are more studies concerning different problems in qos on ims, like [10], [11], and [12]; however, the problem introduced by dynamic qos requirements, service level relocation, and their effect in the transport network, has not been considered. 3 quality of service in ims the ims pcc architecture specified for release 9 in [7] comprises high-level functions for both charging and qos. this architecture associates functions previously carried by the flow based charging (fbc) and the service-based local policy (sblp) mechanisms, which were separated in previous releases. the evolution process that leads to the pcc architecture starts in release 5, with a policy framework specification based on the ietf’s policy management architecture standardized in [13], and the common open policy (cops) protocol defined in [14]. then, in release 6, 3gpp specifies the service-based local policy (sblp) mechanism to differentiate qos parameters in the service level. later, in release 7, the pcc architecture was first introduced, including charging functions related to the qos decisions and the allocated resources. finally, in release 9 the pcc architecture includes some new specifications. the functions included in the pcc architecture to control the qos are the following: resource allocation, event triggering, media flow establishment, and gating control. the pcc architecture includes the specification of four service-level qos parameters: qos class identifier (qci), allocation and retention priority (arp), guaranteed bit rate (gbr), and maximum bit rate (mbr). these parameters define qos features that will be taken into account for further implementations of functions performed by pcc entities [7]. qos class identifier (qci) the qci is a scalar number associated to a network element and it is used to describe the packet forwarding treatment in terms of performance characteristics. this value needs to be pre-configured by the operator directly into the element. since there may be many characteristics associated to the qci values, 3gpp standardized four characteristics: resource type, priority, packetdelaybudget, and packeterrorlossrate. allocation and retention priority (arp) the arp parameter incorporates information about the priority level, pre-emption capability (pec) and pre-emption vulnerability (pev). the priority level has a range of values from 1 to 15, in which 1 is the highest possible value. in the same way, values from 1 to 8 should be assigned to services with priority treatment in the network, and values from 9 to 15 should be used for roaming services. in the case of pec and pev, they are defined as the capability of a session to get resources that are already assigned to another session with lower priority lever, and as the vulnerability of a session to allow the loss of resources that are already assigned from another session with higher priority level, respectively. the values of the pec and pev parameters are set as "yes" or "no". guaranteed bit rate (gbr)/non-guaranteed bit rate (non-gbr) this parameter indicates whether a session has reserved bit rate resources or not. it is associated to the resource type characteristic of the qci. maximum bit rate (mbr) the mbr parameter indicates the maximum bit rate authorized for a session. up to this point, we have presented the specifications given by 3gpp for qos provisioning at a service level involving the ims session control and multimedia services layers. as mentioned 126 m. navarro, y. donoso earlier, diffserv is the qos model defined for the ims media transport layer, therefore an association is needed between diffserv’s parameters and the service-level qos parameters discussed in the previous subsection. to define that association, 3gpp includes qos classes for umts networks in the qos concept and architecture specification given in [6]. there are four umts qos classes: conversational, streaming, interactive, and background. the principal characteristic that differentiates between these classes is delay sensitivity, going from the most sensitive (conversational class), to the less sensitive (background class). having a characteristic to differentiate between classes, many services could be classified according to their specific requirements.the relation between umts qos classes and diffserv parameters is presented in [2], based on the gsma specification for the gprs roaming exchange (grx). this relation includes additional distinguishing factors in addition to delay sensitivity, such as jitter, packet loss, and service data unit (sdu) error ratio. 4 proposed architecture in the previous section we presented the qos specifications in ims on a service level and how they are associated todiffserv parameters in the media transport layers. we focus on congested networks that need diverse mechanisms to solve conflicts between the different sessions trying to access the network. in current ims specifications, these mechanisms are based on information contained in the arp qos parameter: priority, pec, and pev. nevertheless, it is not completely specified how these parameters are used to solve conflicts, and because of this, configurations may be applied according to each carrier on its own convenience. the problem when this information is not specified, is that each carrier may apply its own configuration following 3gpp indications about service priority levels, but missing to have congruent configurations will lead to increase the probability of rejecting incoming and active user sessions. diffserv assigns a percentage of the network capacity to each per-hop behavior (phb), based on previous information the carrier knows about their users demands [15]. despite having accurate information about their users demands, the dynamism introduced by ims services, makes it very difficult to collect that information for one operator, and when relations between different operators are also introduced, there may be several scenarios in which many sessions will be rejected. at the same time that ims introduces dynamic services, those services allow some flexibility in their qosrequirements. flexibility could be used to define mechanisms that not necessarily resolve session conflicts by blocking or canceling sessions when there are not enough resources. we use the concepts blocking and canceling to differentiate the time when a session is rejected from the network; when a new session is trying to enter the network and that request is denied, we name it blocking the session, and when the session is already activated by the time it is removed from the network, we name it canceling the session. we define an enhanced ims qos architecture that supports flexible services and their relocation in the qos level assigned at the ip media transport layer. first, relying on the service-level qos parameters standardized for the pcc architecture [7], we specified a new parameter named the service flexibility bit (sfb) that reflects the service capability of being relocated in a different qos level. the sfb can be set to "1" or "0", when a session accepts being relocated or not, respectively. the enhanced pcc architecture that we propose is depicted in figure 1. this architecture introduces a new entity called qos level relocation function (qos-lrf), which is in charge of making decisions about session relocation in the qos levels. the qos level relocation function (qos-lrf) uses the information given by the sfb, priority level, pec, and pev, in addition to parameters about the transport network state, to decide whether a session is going to be relocated and where. in order to define how the qos-lrf uses the information to make the decision, we define the mapping of these parameters an ims architecture and algorithm proposal with qos parameters for flexible convergent services with dynamic requirements 127 figure 1: the enhanced pcc architecture according to the standardized qci characteristics [7] and umts qos classes and their relation to diffserv parameters [2]. first, we take the qos transport levels defined in diffserv by each phb and we assign them a priority level between 1 and 9, which is the specified range of values. to assign these values, we joined the corresponding services, starting with ims signaling that has the highest priority value, and then the different services according to their qos level. after that, we defined the information required from each service andthat is considered by the qos-lrf to make the relocation decisions. at this point, we divided qos parameters in two classes: parameters associated to the qos level and parameters associated to the session. finally, we reduced the qos level parameters to the bandwidth (bw) requirement in order to reduce the problem complexity, maintaining the relation between umts qos classes and diffserv parameters, as described in the previous section. according to the qos level classification and the services that would be using each of these levels, we define the session relocation as the possibility of reserving the required network resources on a different qos level, and transferring the session to a different level in order to provide the service according to the qos parameters specified for the new level. the main objective of this feature included in the qos-lrf is to benefit the session with higher priority in each qos level, and also to optimize network resources offering the possibility to use other qos level resources, we use the pre-emption functions specified with the pcc architecture, the pec and pev parameters, which give us the possibility to use other session’s resources and reserve them for a different session with higher priority level. the introduction of the sfb gives us the possibility of using the pre-emption functions in the other qos levels before blocking the activation of a new session, or before canceling an active session with lower priority level. the heuristic algorithm used by the qos-lrf is given in algorithm 1. as seen in algorithm 1, when a new session is going to be relocated, the pec, pev and sfb values are saved as historical values in order to recover them when resources become available at the original qos level. in addition, when those values are saved, the new values assigned depends on how the relocation is being done; for example, if a new af session finds enough resources at the ef level, its pev and sfb parameters are set to "1", so that if a new ef session enters the networks, the af session could be relocated in a different level or rejected, but just until the ef level resources are required. on the other hand, when ef and af sessions are relocated, they go to a lower qos level, then the pec parameter is set to "1" and the sfb is set to "0", so that the session that is being relocated can use resources from sessions with lower priority and with the pev parameter activated. in addition, when the session is relocated it cannot be relocated again. this means that a session cannot be transferred two levels below its initial qos level and we will not find ef sessions in the be level. the relocation algorithms for ef and af sessions are given in algorithm 4. finally, when users leave the network and finish their sessions, if they forced other sessions relocation and those sessions are still active, they can be relocated at their 128 m. navarro, y. donoso algorithm 1 new sessions entering the network a new session enters the network if qos-level = ef then if availability in ef then resources are reserved in ef/ the new ef session is activated in ef else if pec is activated and enough resources from ef users with lower priority and pev activated then resources are released from the selected ef users/ ef sessions are relocated in af (*)/ released resources in ef are reserved for the new ef session/ the new ef session is activated in ef else if sfb is activated then the pec, pev and sfb values from the new ef session are saved as historical values/ pec = 1 / pev = 0/ sfb = 0 if availability in af then resources are reserved in af for the new ef session/ the new ef session is activated in af else if pec is activated and enough resources from af users with lower priority and pev activated then resources are released from the selected af users/ af sessions are relocated in be (*)/ released resources in af are reserved for the new ef session/ the new ef session is activated in af else the new ef session entering the network is rejected end if else the new ef session entering the network is rejected end if else if qos-level = af then if availability in af then resources are reserved in af/ the new af session is activated in af else if availability in ef then the pec, pev and sfb values from the new ef session are saved as historical values/ pec = 0/ pev = 1/ sfb = 1/ resources are reserved in ef/ the new af session is activated in ef else if pec is activated and enough resources from af users with lower priority and pev activated then resources are released from the selected af users/ af sessions are relocated in be (*)/ released resources in af are reserved for the new af session/ the new af session is activated in af else if sfb is activated then the pec, pev and sfb values from the new ef session are saved as historical values/ pec = 1/ pev = 0/ sfb = 0 if availability in be then resources are reserved in be for the new af session/ the new af session is activated in be else if pec is activated and enough resources from be users with lower priority and pev activated then resources are released from the selected be users/ the sessions from the selected be users are rejected/ released resources in be are reserved for the new af session/ the new af session is activated in be else the new af session entering the network is rejected end if else the new af session entering the network is rejected end if else if qos-level = be then if availability in be then resources are reserved in be/ the new be session is activated in be else if availability in af then the pec, pev and sfb values from the new ef session are saved as historical values/ pec = 0/ pev = 1/ sfb = 1/ resources are reserved in af/ the new be session is activated in af else if the new be session priority level is 8 (highest priority in the be level) and enough resources from be users with lower priority and pev activated then resources are released from the selected be users/ the sessions from the selected be users are rejected/ released resources in be are reserved for the new be session/ the new be session is activated in be else the new ef session entering the network is rejected end if end if an ims architecture and algorithm proposal with qos parameters for flexible convergent services with dynamic requirements 129 initial qos level with the historic pec, pev and sfb values. algorithm 2 ef and af session relocation algorithms ef session relocation the pec, pev and sfb values from the ef session are saved as historical values pec = 1 / pev = 0 / sfb = 0 if availability in af then resources are reserved in af for the ef session the ef session is activated in af else if pec is activated and enough resources from af users with lower priority and pev activated then resources are released from the selected af users af sessions are relocated in be (*) released resources in af are reserved for the ef session / the ef session is activated in af else the ef session rejected end if af session relocation the pec, pev and sfb values from the ef session are saved as historical values / pec = 1 / pev = 0 / sfb = 0 if availability in be then resources are reserved in be for the af session the af session is activated in be else if pec is activated and enough resources from af users with lower priority and pev activated then resources are released from the selected be users the sessions from the selected be users are rejected released resources in be are reserved for the af session / the af session is activated in be else the af session is rejected end if 5 architecture and performance evaluation the evaluation presented in this section is based on simulations of architectural models in different scenarios. we define three architectural models in order to have different values to compare results and to have the opportunity to observe improvements given by the session relocation feature and the sfb. then, the scenarios present different network states, varying times and service requirements for sessions entering the network. 5.1 architectural models the first architectural (m1) model is the reference point that gives standard values to compare results obtained with models 2 (m2) and 3 (m3). this model implements neither the session relocation feature, nor the sfb functionality, and for this reason its behavior under congestion conditions is similar to current 3g networks. it looks if there are enough resources and if there are not, the new session is rejected. this reference to current networks is based on the analysis presented on [8] regarding diffserv networks and qos resource management. the second architectural model, m2, implements the session relocation feature in case there are resources available in a higher level, and it also implements the pre-emption functions for using resources in the same qos level. the sessions, which resources are released to be used by a higher priority session, are rejected. in this model, a session can only be upgraded to a higher level, so that the qos provided is not reduced from the original requirements. the third architectural model, m3, comprises all the functionality that we propose for the qos-lrf. besides implementing the second model’s functionality, it implements the sfb that allows using the pre-emption functions in a lower level before rejecting the session. in this model before rejecting any session, even sessions with lower priority and the pev parameter activated, if the sfb is activated there is a possibility to use resources from a lower qos level. the relevant information we want to obtain from simulations is the number of rejected and active sessions; with this information we are able to analyze the benefits of the proposed architecture. the first architectural model gives standard values to calculate a percentage error for other models using (1). 130 m. navarro, y. donoso (a) deterministic parameters variable description n number of monte carlo simulations λ[users/time] process rate t simulation time even_num = λ ∗ t [sessions] number of sessions cap_ef level ef capacity cap_af level af capacity cap_be level be capacity (b) services priority level and bandwidth requirements priority level qos level service bandwidth 2 ef voip 32 kbps 3 ef video conference 1 mbps 4 af streaming 512 mbps 5 af transactional services 1 mbps 6 af web browsing 64 kbps 7 af telnet 8 kbps 8 be e-mail 1 mbps 9 be web browsing 1 mbps (c) ranges and distributions for random parameters parameter distribution ranges arrival time uniform [1, t ] session lengh normal n(µ, σ) according to the scenario qos level (type of service) according to the scenario ef, af, be priority level uniform according to the qos level bandwidth uniform according to the qos level and the priority level pev/pec uniform 0, 1 sfb uniform 0, 1 table 1: simulation parameters δ = 100 (vexp − vstd) vstd (1) we simulate implementations of the three architectural models and the network behavior. arrival of network users is simulated as a poisson stochastic process with a rate parameter λ, using monte carlo simulations, and we define the simulation deterministic parameters as shown in table 1(a). table 1(b) shows the bandwidth requirements defined for services in the different priority levels. afterwards, we specify the random parameters of the simulation, such as arrival time, length of the session, qos level, priority levels, session requirements, pec/pev, and sfb; table 1(c) shows the ranges and distributions for the random parameters. 5.2 simulation scenarios simulation parameters were fixed for all scenarios. they were selected to achieve the objective of simulating the architecture in a saturated network and therefore, having the opportunity of studying the model’s behavior in that state. the process rate λ was set to 0.95 simulations per period. then, the simulation time was set to 2000 sec; it can also be interpreted as any other consistent unit of time. with this values, for each simulation 1900 users try to access the network with a random service, and a service duration time following a normal distribution with µ=300 sec and σ=200 sec. afterwards, levels capacities are set to 20 mbps and according to the bandwidth requirements from table 0(b), the state of network saturation may be achieved with at least 60 sessions in a worst-case scenario. finally, type of service is selected as the parameter that changes for each scenario and all the other parameters are defined as random with equal probability for every value in its range. figure 2 presents a basic description of each simulation scenario, and the results obtained for the number of rejected users and the percentage of usage for each qos level. 6 discussion previous simulations give information about the number of rejected and active sessions for three models in the four scenarios we selected. the four scenarios were chosen to test the models varying the type of sessions entering the network, a parameter that we selected as the most sensitive to the algorithm proposal because inthe simulation it determines how the qos level resources are used, and it also gives information about the type of carrier. that allows us to analyze the results according to the behavior a carrier would expect. an ims architecture and algorithm proposal with qos parameters for flexible convergent services with dynamic requirements 131 (a) scenario 1 (b) scenario 1 (c) scenario 2 (d) scenario 2 (e) scenario 3 (f) scenario 3 (g) scenario 4 (h) scenario 4 figure 2: simulation scenarios. scenario 1: basic scenario, sessions generated with the same probability. scenario 2: sessions generated with 60% probability for ef, 20% for af, 20% for be. scenario 3: sessions generated with 60% probability for af, 20% for ef, 20% for be. scenario 4: sessions generated with 60% probability for be, 20% for ef, 20% for af. (a), (c) (e) and (g) show the accumulated percentage of rejected sessions, and (b),(d), (f) and (h) show the percentage of usage of each qos-level in the network. the first graphics presented, for each scenario, depict the behavior of rejected sessions. as we defined it previously, a blocked session refers to a session that could not be activated in the network due to the lack of resources. a canceled session is counted when a session that was already active in the network is removed from it at because a new session, with the pec parameter activated and with a higher priority, is going to use the resources from the canceled session. then, rejected sessions refer to the total number of sessions that leave the network, adding the blocked and canceled values. comparing results from the different scenarios in the previous section, we can see that m3 reduces the number of blocked sessions in all scenarios compared to m2 and m1. this results presented in figure 3(a), indicates a benefit from implementing m3 over 132 m. navarro, y. donoso (a) (b) (c) figure 3: simulation results. part (a) shows the blocked sessions relative error comparison and, it shows the blocked sessions absolute values comparison at t=2000sec. part (b) shows the rejected sessions relative error comparison, and the rejected sessions absolute values comparison at t=2000sec. part (c) shows the active sessions relative errors to model 1 at t=2000sec, and the number of active sessions at t=2000sec. m1 and m2. despite having this result for the number of blocked sessions, it is very important for the algorithm not to increment abruptly the canceled session percentage because m1 does not cancel any session. according the definition of m1, once a session reserves resources they cannot be released until the user finishes the session. the small effect of canceled sessions may be confirmed with the percentage of rejected sessions, as depicted in figure 3(b). looking at the results in all scenarios, the number of canceled sessions in m3 may be considered to have an effect that we may consider as small if we take into account that m2 always ends having a mayor percentage of canceled sessions than m3. the small effect of canceled sessions may be confirmed with the percentage of rejected sessions form figure 2. in scenarios 2 and 4 there is no significant difference for the percentage of rejected sessions between m1 and m3; however, in scenarios 1 and 3 there are relative errors of 6% and 10%, respectively. as expected, m2 increases the total percentage of rejected sessions compared to m1 and m3, and it is also very important to remark that the behavior of m3 may maintain the percentage of rejected sessions obtained in m1, the reference model. it is not enough to demonstrate that our proposal maintains the percentage of rejected sessions to validate it, because there would be no reason to select m3 over m1. the value added given by this work comes from the number of active sessions and how they are distributed among the qos levels. in this point, m3 algorithm’s behavior is better for scenarios 2 and 4 than for scenarios 1 and 3, but it still has a negative relative error, which means that m3 may reduce the number of active sessions. unlike blocked session data, the information in figure 3(c) about active sessions is considered instantaneous and in absolute values, not accumulated percentages as before. then, taking into account the final values is not enough. if we consider the results for the number of active sessions in each qos level presented in the previous section, we can observe there is a consistent behavior for m3 increasing the number of ef active sessions. therefore, results obtained with m3 are according to the objectives, increasing the number of active sessions that have the highest priority level, and m2 does not increase the number of active ef session compared to m1 in any scenario. under saturation conditions, the number of ef active sessions in m1 is higher than m2, validating the importance of the sfb implementation in m3. bringing previous observations together, the simulations results show that our proposal, implemented in m3, may be a feasible implementation for the four considered scenarios, although an ims architecture and algorithm proposal with qos parameters for flexible convergent services with dynamic requirements 133 it has a better behavior in scenarios 2 and 4. in scenario 2, it is very important to see that m3 maintains the same values as m1 for both rejected and active sessions. there is significant reduction of the number of active af users, but since ef sessions are arriving with three times af session’s probability, it may be considered as an accepted tradeoff. then, scenario 4 gives important results because m3 also maintains very small differences with m1 in rejected and active sessions. in this scenario, the number of be sessions entering the network is higher compared to the other qos level sessions. finally, scenarios 1 and 3 present higher differences between m1 and m3; nevertheless, we consider them feasible scenarios because they maintain the model’s objective and increase the number of high priority sessions with a higher tradeoff in the number of sessions rejected from the network. analyzing the complexity of the algorithms in a worst case basis, it is evident that for every incoming session, the running time will be o(n), where n is the number of active sessions in the network. considering concurrent sessions entering the network, the running time will be o(mn), where m is the number of sessions entering the network. 7 conclusions in this work we present an efficient and enhanced ims qos architecture to support qos providing for flexible services with dynamic requirements. our approach follows the 3gpp qos specifications and is based on the pcc architecture. we propose an architecture including new features in the pcrf entity given by the concept of session relocation and the introduction of the qos-lrf and sfb. the proposed heuristic algorithms for the qos-lrf use information already available at the pcrf according to the pcc architecture specifications. according to the three model simulations, our proposal overcomes the first two models, which offer a valid implementation of current 3gpp pcc architecture specifications. the results obtained for the number of rejected and active sessions validates it, and for this reason, our proposal would have a good performance for carriers with customers requesting more ef and be services. furthermore, for carriers with customers requesting all types or services at the same rate, or requesting more af services, the algorithm achieve the objectives but with some tradeoffs for its implementation that would need to be evaluated. the architecture proposal achieves the objectives of efficiency and flexibility. efficiency may be analyzed according to how network resources are used. the objective validation is given by the simulations showing that implementing our proposal, the number of rejected and active sessions is maintained and at the same time, the number of high priority sessions is increased, then network resources are properly assigned according to the priority level. flexibility is achieved with the definition of the sfb and the algorithms implementations. they offer the possibility of relocating a session in a lower qos level, before it is rejected from the network. other important contributions of this work is that carriers would have the possibility of assigning different priorities to the same service within the same qos level, and offer the service at different rates controlled by the pcc architecture charging mechanisms. finally, the worst case running time of the algorithms is o(n), where n is the number of active sessions in the network, and if the possibility of having m concurrent sessions entering the network is considered, the worst case running time is o(mn). for further study, we will continue with the message flow analysis required to implement our proposal and a prototype implementation. we will also study scenarios involving different carriers and roaming services, which could be implemented in the prototype. 134 m. navarro, y. donoso bibliography [1] 3gpp, "ip multimedia subsystem (ims); 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[13] r. yavatkar, d. pendarakis, and r. guerin, "rfc2753 a framework for policybased admission control", network working group, january 2000. http://www.rfceditor.org/rfc/rfc2753.txt [14] d. durham, j. boyle, r. cohen, s. herzog, r. rajan, and a. sastry, "rfc 2748 the cops (common open policy service) protocol", network working group, january 2000. http://www.rfc-editor.org/rfc/rfc2748.txt [15] c. filsfils and j. evans, deploying diffserv in backbone networks for tight sla control, ieee internet computing, volume 9, issue 1, pp. 66-74. january 2005 international journal of computers communications & control issn 1841-9836, 10(2):263-279, april, 2015. qeam: an approximate algorithm using p systems with active membranes g. zhang, j. cheng, m. gheorghe, f. ipate, x. wang gexiang zhang*, jixiang cheng school of electrical engineering, southwest jiaotong university chengdu, 610031, p.r. china *corresponding author: zhgxdylan@126.com marian gheorghe faculty of engineering and informatics, university of bradford, bradford, west yorkshire bd7 1dp, uk, m.gheorghe@bradford.ac.uk florentin ipate faculty of mathematics and computer science, university of bucharest academiei 14, bucharest, romania florentin.ipate@ifsoft.ro xueyuan wang school of information engineering southwest university of science and technology mianyang 621010, p.r.china abstract: this paper proposes an approximate optimization approach, called qeam, which combines a p system with active membranes and a quantum-inspired evolutionary algorithm. qeam uses the hierarchical arrangement of the compartments and developmental rules of a p system with active membranes, and the objects consisting of quantum-inspired bit individuals, a probabilistic observation and the evolutionary rules designed with quantum-inspired gates to specify the membrane algorithms. a large number of experiments carried out on benchmark instances of satisfiability problem show that qeam outperforms qeps (quantum-inspired evolutionary algorithm based on p systems) and its counterpart quantum-inspired evolutionary algorithm. keywords: membrane computing, active membranes, approximate optimization approach, quantum-inspired evolutionary algorithm; satisfiability problem. 1 introduction in the last decades, natural computing has been intensively studied and a wide range of applications in computer science and many other areas have been produced. as a well established branch of natural computing, membrane computing, using models called p systems, has made a significant impact on the development of various disciplines [22], such as theoretical computer science, biology, linguistics, etc. the first variants of p systems were proposed in 1998 by g. păun [19]. they represent a new distributed-parallel framework for designing cell-like or tissue-like computing models, handling multisets of abstract objects in a compartmentalized arrangement of membranes. the membrane structure delimits compartments in a hierarchical or network manner. objects are arranged as multisets and dispersed across these compartments. rules are usually associated to the regions enclosed by membranes and control the evolution of objects inside in a maximally parallel way. the main characteristics of p systems are the hierarchical or network architecture of membranes, type of rules (transformation, communication etc.) and intrinsic parallelism, which are all very effective from a computational point of view copyright © 2006-2015 by ccc publications 264 g. zhang, j. cheng, m. gheorghe, f. ipate, x. wang and attractive and suitable for modelling various problems. until now, p systems have been developed principally from a mathematical and computational point of view, building a great variety of computing models and studied for their computational power, complexity aspects and potential solutions to np-complete problems, and have been utilized for modelling real-world problems in graphics, linguistics, biology. however, the issue of adapting p systems for solving practical problems remains a fundamental aspect of the research in this field, and fortunately a burgeoning interest in this respect for many researchers has been noticeable in the last years [7]. the application of p systems to such problems is still in a developmental phase [22], as compared to evolutionary computation. inspired by the evolution in natural selection and molecular genetics, evolutionary algorithms (eas) have become the most successful metaheuristic search techniques [2]. the great success of eas in various applications, such as evolutionary optimization and machine learning, can be attributed to two outstanding characteristics: practicability and robustness. eas are regarded as blind search methodologies without domain specific knowledge [23], suitable for a variety of complex problems in real-world applications. as population-based search tools, eas usually sample multiple points of the search space in a single step and consequently are quite robust with respect to the objective function landscapes containing many peaks. developing potential efficient solutions for specific problems is a challenging and attractive topic for researchers from a wide range of areas. quantum-inspired evolutionary algorithms (qieas), one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention [28]. a qiea is a new evolutionary algorithm for a classical computer rather than for quantum hardware. qieas use quantum-inspired bits (q-bits), quantum-inspired gates (q-gates) and observation processes to specify their structure and steps. more specifically, q-bits are applied to represent genotype individuals; q-gates are employed to operate on q-bits to generate offspring; and the genotypes and phenotypes are linked by a probabilistic observation process. even though p systems and eas use different rules and computational strategies to handle different objects, both of them are paradigms of natural computing and employed to solve complex problems such as np-complete problems [27, 34]. p systems represent a suitable formal framework for parallel-distributed computation and eas are very effective for implementing different algorithms to solve many problems. thus, the possible interplay between p systems and eas is very promising for further exploration and represents a fertile research field. being the successful instances of this interaction, membrane algorithms can be regarded as a class of hybrid optimization algorithms using the concepts and principles of metaheuristic search methodologies and the hierarchical or network structures of membranes and, to some extent, rules of p systems. when a p system is considered as a parallel-distributed framework for metaheuristic search techniques, it is investigated in terms of optimization results and computation framework, instead of computing power and efficiency. according to the investigations in the literature, there are two main types of membrane algorithms in terms of membrane structures: hierarchical and network. in [30], a tissue membrane system with a network structure was used to appropriately organize five representative variants of differential evolution algorithms. three principal categories, nested membrane structure (nms), one-level membrane structure (olms) and hybrid membrane structure, were reported with respect to the membrane algorithms with hierarchical membrane structures. in [16], a membrane algorithm with nms was proposed by using a genetic algorithm and a local search method to solve travelling salesman problems. this kind of membrane algorithms was also applied to solve the min storage problem [13], dna sequence design problem [24, 25] and the proton exchange membrane fuel cell model parameter estimation problems [26]. in [27], a membrane algorithm integrating olms with a qiea, called qeps, was proposed to solve knapsack problems and the experiment-based comparisons qeam: an approximate algorithm using p systems with active membranes 265 between olms and nms were drawn, implying that the choice of the membrane structure is very important for membrane algorithms. this membrane structure was also combined with a qiea and tabu search [32], differential evolution [3], ant colony optimization [29], particle swarm optimization [33] and multiple qiea components to solve radar emitter signal time-frequency atom decomposition, numerical optimization problems, travelling salesman problems, broadcasting problems in p systems and image processing, respectively. in [11], a dynamic multi-objective optimization algorithm using a membrane system with a hybrid structure was developed to design a controller for a time-varying unstable plant. the dynamic behavior analysis in [31] indicates that the membrane algorithm, qeps, has a stronger capability to balance exploration and exploitation than its counterpart approach, qiea. it is worth pointing out that păun has made a clear claim that membrane algorithms represent a research directions with a well-defined practical use [22], and therefore further studies are very necessary to prove the use of p systems for solving real-world applications. p systems with active membranes can produce an exponential growth of membranes and consequently can solve a class of np-complete problems, such as the satisfiability (sat) problem [1, 20] and the knapsack problem [18], in a linear or polynomial time. the two types of complete problems were discussed in [1,18,20] and in many other places from a mathematical perspective. to the best of our knowledge, no evolutionary algorithm using p system with active membranes has been devised to approximately solve the two aforementioned kinds of problems. this paper proposes an approximate algorithm combining a p system with active membranes model and a qiea, called qeam. this approach is based on the hierarchical arrangement of the compartments and developmental rules (e.g., membrane separation, merging, transformation/ communication-like rules) of a p system with active membranes model, and the objects consisting of q-bit individuals, a probabilistic observation and the evolutionary rules designed with qgates to specify the membrane algorithms. in the experiments, the application of qeps to sat problems is first discussed, and then qeam is tested on 65 benchmark sat problems. extensive experiments show that qeam achieves much better results than qeps and its counterpart qiea. also, the parametric and non-parametric tests show significant differences. 2 qeam in this section, we start by introducing some concepts related to p systems with active membranes and qieas and then describe in detail the proposed qeam algorithm. 2.1 p systems with active membranes in this subsection, we give a brief description of p systems with active membranes without polarizations due to [20] and [17], where more details can also be found. a membrane structure is a rooted tree represented by a venn diagram and is identified by a string of correctly matching parentheses, with a unique external pair of parentheses; this external pair of parentheses corresponds to the external membrane, called the skin. a membrane without any another membrane inside (the leaves of the tree) is said to be elementary. for example, the structure in fig. 1 contains 8 membranes; membranes 3, 5, 6 and 8 are elementary. the string of parentheses identifying this structure is µ = [1[2[5]5[6]6]2[3]3[4[7[8]8]7]4]1. all membranes are labelled; here we have used the numbers from 1 to 8. we say that the number of membranes is the degree of the membrane structure, while the height of the tree associated in the usual way with the structure is its depth. in the example above we have a membrane structure of degree 8 and of depth 4. 266 g. zhang, j. cheng, m. gheorghe, f. ipate, x. wang the membranes delimit regions, precisely identified by the membranes (the region of a membrane is delimited by the membrane and all membranes placed immediately inside it, if any such a membrane exists). in these regions we place objects, which are represented by symbols of an alphabet. several copies of the same object can be present in a region, so we work with multisets of objects. a multiset over an alphabet v is represented by a string over v , together with all its permutations: the number of occurrences of a symbol a ∈ v in a string x ∈ v ∗ (v ∗ is the set of all strings over v ; the empty string is denoted by λ) is denoted by |x|a and it represents the multiplicity of the object a in the multiset represented by x. a polarizationless p system with active membranes is a construct π = (v, t, h, µ, w1, . . . , wm, r), where 1. m ≥ 1 (the initial degree of the system); 2. v is an alphabet (the working alphabet of the system); 3. t ⊆ v (the terminal alphabet); 4. h is a finite set of labels for membranes; 5. µ is a membrane structure consisting of m membranes, labelled (not necessarily in a oneto-one manner) with elements of h; 6. w1, . . . , wm, are strings over v , describing the multisets of objects placed in the m regions of µ; 7. r is a finite set of developmental rules, of the following forms: (a) [ha → v]h, for h ∈ h, a ∈ v, v ∈ v ∗; (object evolution rules, associated with membranes and depending on the label, but not directly involving the membranes, in the sense that the membranes are neither taking part in the application of these rules nor are they modified by them); (b) a[h]h → [hb]h, for h ∈ h, a, b ∈ v ; (communication rules; an object is introduced in the membrane, possibly modified during this process); (c) [ha]h → [h]hb, for h ∈ h, a, b ∈ v ; (communication rules; an object is sent out of the membrane, possibly modified during this process); (d) [h]h[h]h → [h]h, for h ∈ h; (merging rules for elementary membranes; in reaction of two membranes, they are merged into a single membrane; the objects of the former membranes are put together in the new membrane); (e) [hw ]h → [hu]h[hw − u]h, for h ∈ h, u ⊂ w ; (separation rules for elementary membranes; the membrane is separated into two membranes with the same labels; the objects from u are placed in the first membrane, those from w − u are placed in the other membrane); for a detailed description on how to use these rules, refer to [17, 20]. it is worth pointing out that these rules are used in the non-deterministic maximally parallel manner, i.e., in any given step, one or more rules of type (a), such that no unallocated object to rules can be allocated to any rule, and/or at most one rule of types (b)-(e) can be applied to each membrane. in this way, we get transition from a configuration of the system to the next configuration. a sequence of transitions forms a computation. a computation is halting if no other rules can be employed in its last configuration. qeam: an approximate algorithm using p systems with active membranes 267 6 5 2 3 8 7 4 1 1 8 7 432 5 6 figure 1: a membrane structure the beginning. begin 1t (i) initialize q(t) while (not termination condition) do (ii) make p(t) by observing the states of q(t) (iii) evaluate p(t) (iv) update q(t) using q-gates (v) store the best solutions among p(t) 1t t end end fig. 2. pseudocode algorithm for qiea [13] figure 2: pseudocode algorithm for qiea [10] 2.2 quantum-inspired evolutionary algorithms the interaction of quantum computing and evolutionary algorithms has produced three research avenues: evolutionary-designed quantum algorithms using evolutionary algorithms to design new quantum algorithms, quantum evolutionary algorithms implementing evolutionary algorithms in a quantum computing environment and qieas [28]. qiea is employed to describe the computational methods using concepts and principles of quantum computing for solving various problems in the context of a classical computer [14]. based on the concepts and principles of quantum computing, such as quantum bit (qubit), quantum gate and superposition, a qiea is developed as a novel evolutionary algorithm for a classical computer. narayanan and moore [15] introduced a preliminary idea of a qiea and han and kim [10] proposed its practical algorithm. a qiea is characterized by a q-bit representation, a probabilistic observation and a q-gate evolutionary rule. in recent years, qieas have become a promising and rapidly growing branch of evolutionary computation. in qieas, a q-bit is defined by a pair of complex numbers (α, β) as [α β]t , where |α|2 and |β|2 are probabilities that the observation of the q-bit will render a ’0’ or ’1’ state. normalization requires that |α|2 + |β|2 = 1. note that qieas just need real numbers for amplitudes. besides ’0’ and ’1’ states, a q-bit can also be in a superposition of the two states. a q-bit individual is represented as a string of l q-bits [ α1|α2| · · · |αl β1|β2| · · · |βl ] , (1) where |αi|2 + |βi|2 = 1 (i = 1, 2, · · · , l). a q-gate in a qiea is defined as a variation operator for updating the q-bit individuals such as to guarantee that they also satisfy the normalization condition |α|2 + |β|2 = 1 [10]. the basic pseudocode algorithm for a qiea is shown in fig. 2 and the description for each step is as follows. 1. in the "initialize q(t)" step, a population q(1) with n q-bit individuals is generated, q(t)={qt1, q t 2, · · · , q t n}, at generation t, where qti (i = 1, 2, · · · , n) is an arbitrary individual in q(t), which is represented as qti = [ αti1|α t i2| · · · |α t il βti1|β t i2| · · · |β t il ] , (2) where l is the number of q-bits, i.e., the string length of the q-bit individual. in the initial population, that is when t = 1, we have αtij = β t ij = 1/ √ 2 for all i = 1, 2, · · · , n 268 g. zhang, j. cheng, m. gheorghe, f. ipate, x. wang and j = 1, 2, · · · , l. this means that all possible states are superposed with the same probability at the beginning. 2. by observing the states q(t), binary solutions in p(t), where p(t)={xt1, x t 2, · · · , x t n}, are produced at step t. according to the current probability, either |αtij| 2 or |βtij| 2 of qti, i = 1, 2, · · · , n, j = 1, 2, · · · , l, a classical bit 0 or 1 is generated. thus, l classical bits can construct a binary solution xti (i = 1, 2, · · · , n). 3. the fitness value for each binary solution xti (i = 1, 2, · · · , n) is calculated by using an evaluation function. 4. in this step, all the q-bit individuals in q(t) are updated by applying q-gates. to be specific, the jth q-bit in the ith q-bit individual qti, j = 1, 2, · · · , l, i = 1, 2, · · · , n, is updated by applying the current q-gate gtij(θ). as usual, qieas use a quantum rotation gate as a q-gate; this is given by gtij(θ) = [ cos θtij − sin θ t ij sin θtij cos θ t ij ] , (3) where θtij is an adjustable q-gate rotation angle. 5. the best solutions among p(t) are selected and stored into b(t). in qieas, the q-bit representation, which can describe simultaneously multiple genotype states using a linear superposition of states in a probabilistic way, makes the algorithm rather good with respect to population diversity. q-gate evolutionary rules are executed in the qbit probability space to avoid the selection pressure problem of conventional genetic algorithms with selection, crossover and mutation operators. as compared with local search methods and conventional genetic algorithms, a qiea has good balance between exploration and exploitation so as to obtain stronger global search capability and better convergence. furthermore, a qiea is able to exploit the search space for a global solution with a small number of individuals, even with one individual; q-gate evolutionary rules, which are only related to searching the best solution, are easy to implement in a parallel distributed structure because little information needs to be transmitted and exchanged. 2.3 qeam this section will introduce the membrane algorithm, qeam, combining p systems with active membranes and qieas. qeam uses a dynamic p systems-like framework, which is initially randomly produced and then may be changed in the process of evolution. this framework directly uses some of the elements of a p system with active membranes, whereas others are slightly adapted for this evolutionary algorithm. the objects employed will be organized in multisets of special strings built either over the set of q-bits or {0, 1}. the rules will be responsible to make the system evolve and select the best fit q-bit individuals. more precisely, the dynamic p system-like framework will consist of: 1. a dynamic structure [0[1]1, [2]2, · · · , [m]m]0 with m regions contained in the skin membrane, denoted by 0, where m is a number varied during the evolution process; 2. an alphabet that consists of all possible q-bits and the set {0, 1}; 3. a set of terminal symbols, t = {0, 1}; qeam: an approximate algorithm using p systems with active membranes 269 4. initial multisets w0 = λ, w1 = q1q2 · · · qn1 , w2 = qn1+1qn1+2 · · · qn2 , · · · , wm = qn(m−1)+1qn(m−1)+2 · · · qnm , where qi, 1 ≤ i ≤ n, is a q-bit individual; nj, 1 ≤ j ≤ m, is the number of individuals in wj; ∑m j=1 nj = n, where n is the total number of individuals in this computation; 5. rules include the types of (a)-(e) in p systems with active membranes and their use will be given in the following description. in what follows, we summarize the steps of qeam by using a pseudocode notation, shown in fig. 3, to help presenting the membrane algorithm. begin input n, gmax, tmax 1t (i) initialize the membrane st ruct ure (m t elementary membranes) and object s; (ii) while (not t erminat ion condit ion) do (iii) produce g (t); (iv) perform object evolut ion rule (a) in elementary membranes; (v) perform communicat ion rule (c); (vi) perform object evolut ion rule (a) in t he skin membranes; 1t t ! (vii) det ermine the number mt+1 of element ary membranes; if (mt+1< m t) (viii) p erform membrane merging rule (d); el se i f (mt+1> m t) (ix) p erform membrane separat ion rule (e); en d (x) p erform communicat ion rule (b); en d output : the best individual en d figure 3: pseudocode algorithm for qeam. 1. in the initialization of qeam, a one level membrane structure [0[1]1, [2]2, · · · , [m]m]0 consisting of a skin membrane denoted by 0 and m elementary membranes delimiting m regions inside the skin membrane is constructed as the membrane structure at iteration t = 1, where m is a random number ranged from 1 to n, where n is the number of q-bit individuals. each q-bit individual forms an object. thus, n objects are randomly scattered across the m elementary membranes in a non-deterministic way to make sure that each elementary membrane contains at least one object. so the number of objects in each elementary membrane varies from 1 to n − m + 1. 2. the termination condition for qeam could be a prescribed number of maximal iterations or the algorithm searches the optimal or close-to-optimal solution. 3. this step determines the numbers g(t) = (g1, g2 . . . , gm), of evolutionary generations for independently performing object evolution rule (a) in the m elementary membranes, where gi (i = 1, 2, . . . , m) for the ith elementary membrane is generated randomly between 1 and a certain integer number gmax. 4. the steps (ii) to (v) of the qiea shown in fig. 2 are performed independently in each elementary membrane to evolve the objects inside. the termination condition for the ith 270 g. zhang, j. cheng, m. gheorghe, f. ipate, x. wang begin if 2 [0,1)random then 0x else 1x end figure 4: observation process in qiea begin 0t ; initialize tabu search; while ( max t t ) do 1t t ; search the neighborhood; evaluate candidate solutions; update tabu list. end end figure 5: pseudocode algorithm for tabu search elementary membrane is the maximal number gi(i = 1, 2, · · · , m) of evolutionary generations. it is worth pointing out that the observation process in qieas, illustrated in fig. 4, is applied to build a connection between a q-bit [α β]t and a classical bit and hence to build a link between q-bit individuals and binary solutions. the q-gate update procedure[ α′ β′ ] = g(θ) [ α β ] (4) is used to transform a current q-bit [α β]t into the corresponding q-bit [α′ β′]t at the next generation. the rotation angle θ in the q-gate g(θ) in (4) is defined as θ = s(α, β) · ∆θ, where s(α, β) and ∆θ can be obtained from the lookup table in [10]. 5. the communication rule is employed to send the best binary solution in each elementary membrane out to the skin membrane. this step is helpful to exchange information among the objects in the elementary membranes and the skin membrane because the qiea employs q-gates, which are related to only the best individual searched, to generate the offspring. after this step there are m binary solutions in total in the skin membrane. 6. in the skin membrane, a local search, tabu search [8, 12], is performed on the best binary solution selected from the m binary solutions, which are sent from the m elementary membranes (see step (v)). the pseudocode algorithm for tabu search is shown in fig. 5. in the "initialize tabu search" step, an empty tabu list is constructed and tabu length is set to a value. at each iteration, the neighborhood of the best binary solution in the skin membrane is explored to obtain candidate solutions. next, the candidate solutions are evaluated by using the fitness function and the best of them is selected to update the tabu list. 7. the number mt+1 of elementary membranes at iteration t+1 is produced randomly between 1 and n, which will directly determine the membrane structure at the next iteration. 8. if mt+1 < mt, the (mt−mt+1) elementary membranes will be merged into the mt+1 elementary membranes. the merging process is shown in fig. 6, where em represents elementary membranes. in each merging operation, we first choose any two arbitary elementary membranes i and j from m elementary membranes, i.e., 1 ≤ i, j ≤ m and i ̸= j; and then we merge the elementary membranes i and j into a single membrane and put the objects in the elementary membranes i and j into the merged membrane. the initial value of m is mt. thus, multiple membranes may be merged into a single membrane. so this rule is a multi-merging one. qeam: an approximate algorithm using p systems with active membranes 271 begin t m m ; while ( 1tm m !" ) do choose any t wo arbit ary elementary membranes; perform the merging rule (d); 1m m # ; en d en d d figure 6: merging process of ems begin t m m ; while ( 1t m m ! $ ) do choose any one elementary membrane; while (|w|<2) do choose any one elementary membrane; end perform the separation rule (e); 1m m ! ; end end figure 7: separation process of ems 9. if mt+1 > mt, the (mt+1 − mt) elementary membranes will be separated into two membranes. the separation process is illustrated in fig. 7, in which |w | is the number of objects in the pre-separation membrane. we choose any one elementary membrane i which has at least two objects from m elementary membranes, i.e., 1 ≤ i ≤ m. the initial value of m is mt. when the separation rule is performed, |u| (|u| < |w |) objects are placed in the first membrane and the |w | − |u| objects are placed in the other membrane. thus, a single membrane may be divided into several membranes. so, this rule is a multi-separation one. 10. by performing the communication rule (b) in the skin membrane, this step sends the fittest binary solution to each elementary membrane for the further evolution steps. 3 experimental results to test the performances of the presented algorithm, qeam, we will use the satisfiability problem, which is a well-known np-complete problem, to conduct the experiments. we start from the description of the satisfiability problem, and then turn to use qieas and qeps as benchmark algorithms to solve 65 representative instances of the satisfiability problem. finally, qeam is tested on the same instance of the satisfiability problem to draw conclusions. 3.1 satisfiability problem the satisfiability problem (sat) is a fundamentally paradigmatic problem in artificial intelligence applications, automated reasoning, mathematical logic, and related research areas [5]. sat can be described as follows: given a boolean formula in conjunctive normal form (cnf), determine whether or not it is satisfiable, that is, whether there exists an assignment to its variables on which it evaluates to true, i.e., a sat instance is to search a variable assignment x so that a boolean formula f(x) becomes true, where x is a set of boolean variables x1, x2, · · · , xn, i.e., xi ∈ {0, 1}, i = 1, 2, · · · , n and the propositional formula f(x) is in a conjunctive normal form, i.e., f(x) = c1(x) ∧ c2(x) ∧ · · · ∧ cm(x), (5) where each clause cj(x), j = 1, 2, · · · , m, is a disjunction of literals, and a literal is a variable or its negation [9]. a sat instance is called satisfiable if such x exists, and unsatisfiable otherwise. in this paper only 3-sat problems, in which each clause has exactly three literals, will be considered 272 g. zhang, j. cheng, m. gheorghe, f. ipate, x. wang 2 5 10 15 20 25 30 35 40 45 50 0 20 40 60 80 100 number of membranes s u c c e s s fu l ra te s ( % ) sat1 sat2 sat3 sat4 sat5 sat6 sat7 sat8 sat9 sat10 figure 8: successful rates 2 5 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 3.5 x 10 4 number of membranes a e s ( 1 /5 0 ) sat1 sat2 sat3 sat4 sat5 sat6 sat7 sat8 sat9 sat10 figure 9: aes because a number of other problems, such as the travelling salesman problem and the n-queens problem, can be reformulated with respect to 3-sat problems. in [4] it is shown that 3-sat problem is np-complete. in membrane computing, various types of p systems with membrane division are frequently investigated from a mathematical point of view to obtain an exponential working space in a linear time to solve the sat problem [1, 20]. this paper will use an approximate algorithm to solve the sat problem, in which the number of clauses that are not satisfied by the variable assignment x is considered as the evaluation function. 3.2 results of qeps and qiea according to the study in [27], the number of elementary membranes has a significant impact on the qeps performances. so we first focus on how to set the number of elementary membranes in an empirical way. ten benchmark 3-sat problems1, each of which has 20 boolean variables and 91 clauses, are applied to conduct the experiments. the fitness function is the number of clauses that are not satisfied by the variable assignment. the population size n is set to 50. the values of 2, 5, 10, 15, 20, 25, 30, 35, 40, 45 and 50, for the number m of elementary membranes, are used in the experiments. according to previous investigations regarding the effect of the number gi(i = 1, 2, . . . , m) of iterations on the qeps performances [27], the parameter gi(i = 1, 2, . . . , m) is set to a uniformly random integer ranged from 1 to 10. the algorithm stops when either 2.75 × 106 evaluation steps are made or the sat problem solution is found, i.e., the minimal fitness value 0 is attained. the performances of the above 11 cases are evaluated by using the successful rate of 30 independent runs (the percentage of the runs making the sat problem satisfiable) and the average number of evaluations to solutions (aes) over the successful runs. the experimental results are listed in fig. 8 and fig. 9, which illustrate that the successful rates and the aes vary with the number of elementary membranes. as shown in fig. 8 and fig. 9, the successful rates and the aes show a broad range of variability with respect to the number of different elementary membranes; this indicates that the number of elementary membranes has a significant impact on the qeps performances. in order to obtain a balance between the successful rates and the aes, the number of elementary membranes could be fixed at 15. 1satlib the satisfiability library, http://www.satlib.org/ qeam: an approximate algorithm using p systems with active membranes 273 qiea is also applied to conduct the experiments on the 10 sat problems. in these experiments, qiea employs the same population size and stopping criteria as the qeps. the statistical results of 30 independent runs for each problem are listed in table 1. the best experimental results of qeps are also shown in table 1, where each of which has 20 boolean variables and 91 clauses; sr and aes represent successful rates and average number of evaluations to solutions, respectively. to perform convincing comparisons between qiea, qeps and qeam, additional fifty-five 3-sat benchmark problems are employed to carry out experiments. both qeps and qiea use 50 individuals as a population, the prescribed number of 2.75 × 106 evaluations to solutions as the stopping criterion and the number of clauses that are not satisfied by the variable assignment as the fitness function. in qeps, the parameter gi(i = 1, 2, . . . , m) is set to a uniformly random integer ranged from 1 to 10, and the number of elementary membranes is assigned to 15. the performances of the two algorithms are evaluated by using the following criteria: the mean of the solutions over 15 runs and their standard deviations. it is worth pointing out that the experiments are very time-consuming and therefore only 15 independent runs are performed for each sat problem. the number of boolean variables, the number of clauses in each boolean formula and the experimental results are provided in table 2. table 1: comparisons of qiea, qeps and qeam on 10 sat problems qiea qeps qeam problems sr(%) aes sr(%) aes sr(%) aes sat1 77 572750 100 528850 100 220804 sat2 70 496700 90 701200 100 300468 sat3 53 638250 73 949400 97 279174 sat4 100 218250 100 90300 100 59978 sat5 87 575900 87 582300 100 179445 sat6 57 469650 83 701700 100 336728 sat7 53 1137750 67 907300 93 527795 sat8 27 1120300 50 410300 100 354903 sat9 87 684800 100 405450 100 128633 sat10 93 281050 100 572750 100 115876 3.3 results of qeam in the experiments for testing qeam performance, the population size and the prescribed number of evaluations of solutions as the stopping criterion are set to 50 and 2.75 × 106, respectively, which are the same as those in qiea and qeps. qeam applies the same gmax as qeps. additionally, the tabu length and tmax in qeam are 5 and 100, respectively. for each of the first 10 benchmark 3-sat problems shown in table 2, we performed 30 independent runs and recorded the successful rate and the aes over successful runs. the experimental results are provided in table 1. the qeam performance is further investigated by using the remaining 55 3-sat benchmark problems. we record the average solution and the standard deviations over 15 runs for each of them. the experimental results are listed in table 2, where each of the first ten, the second ten, the third ten, the fourth ten, the fifth ten and the last five problems has 50, 75, 100, 125, 150 and 250 boolean variables and 218, 325, 430, 538, 645 and 1065 clauses, respectively; mean and std represent the mean of the best solutions and the standard deviation of the best solutions, respectively; (+) represents significant difference. 274 g. zhang, j. cheng, m. gheorghe, f. ipate, x. wang table 2: comparisons of qiea, qeps and qeam using 55 instances of the sat problem (to be continued). qiea qeps qeam qeamvs.qiea qeamvs.qeps sat mean std mean std mean std t-test imp.(%) t-test imp.(%) 1 7.67 0.82 6.60 1.18 0.93 0.26 5.23e-23(+) +87.87 5.30e-17(+) +85.91 2 8.27 2.12 7.40 1.12 1.53 0.64 2.32e-12(+) +81.50 1.13e-16(+) +79.32 3 7.40 1.40 6.27 0.88 1.00 0.53 5.90e-16(+) +86.49 5.64e-18(+) +84.05 4 7.87 1.19 6.33 0.98 1.00 0.38 7.31e-19(+) +87.29 5.74e-18(+) +84.20 5 7.13 1.41 6.20 0.86 0.07 0.26 1.30e-17(+) +99.02 2.50e-21(+) +98.87 6 7.27 0.80 5.93 0.80 0.20 0.41 5.39e-23(+) +97.25 1.53e-20(+) +96.63 7 7.73 1.16 6.40 1.18 1.07 0.46 1.73e-18(+) +86.16 8.26e-16(+) +83.28 8 8.73 0.70 7.67 1.18 1.53 0.83 5.99e-21(+) +82.47 6.01e-16(+) +80.05 9 7.87 1.13 6.87 1.19 1.47 0.64 1.27e-17(+) +81.32 2.84e-15(+) +78.60 10 8.33 0.82 6.93 0.88 1.07 0.59 5.74e-22(+) +87.15 7.33e-19(+) +84.56 11 16.67 1.11 15.40 0.99 2.00 0.93 5.05e-26(+) +88.00 9.32e-26(+) +87.01 12 15.07 1.49 14.60 0.74 1.47 0.74 1.76e-23(+) +90.25 1.38e-28(+) +89.93 13 16.33 0.82 14.80 2.01 2.07 0.88 6.61e-28(+) +87.32 1.84e-19(+) +86.01 14 14.00 1.20 12.87 1.60 1.87 0.64 1.53e-24(+) +86.64 1.41e-20(+) +85.47 15 15.07 1.03 14.87 0.92 1.60 1.06 9.13e-25(+) +89.38 3.01e-25(+) +89.24 16 16.13 1.06 14.87 1.19 2.20 0.68 4.29e-27(+) +86.36 5.80e-25(+) +85.21 17 15.33 1.40 14.53 1.64 1.67 0.90 1.54e-23(+) +89.11 2.00e-21(+) +88.51 18 15.73 1.44 14.53 1.51 2.20 0.86 2.54e-23(+) +86.01 8.03e-22(+) +84.86 19 14.93 1.49 13.80 1.97 1.80 0.41 6.02e-24(+) +87.94 9.25e-20(+) +86.96 20 14.40 1.45 13.93 1.22 2.00 0.76 1.48e-22(+) +86.11 1.19e-23(+) +85.64 21 24.53 1.81 22.93 1.39 3.0 0.93 1.44e-26(+) +87.77 5.29e-28(+) +86.92 22 24.20 1.21 22.80 2.14 3.33 0.62 4.78e-31(+) +86.24 3.08e-24(+) +85.39 23 24.27 1.28 22.93 1.79 4.20 0.94 1.15e-28(+) +82.69 6.05e-25(+) +81.68 24 23.40 1.68 22.80 1.08 3.53 0.92 2.63e-26(+) +84.91 1.51e-29(+) +84.52 qeam: an approximate algorithm using p systems with active membranes 275 table 2 comparisons of qiea, qeps and qeam (continued) qiea qeps qeam qeam vs. qiea qeam vs. qeps sat mean std mean std mean std t-test imp.(%) t-test imp.(%) 25 24.27 1.22 22.80 1.47 3.73 0.88 1.45e-29(+) +84.63 4.13e-27(+) +83.64 26 24.00 1.51 22.47 1.60 3.60 0.74 3.53e-28(+) +85.00 1.06e-26(+) +83.98 27 24.13 1.06 23.40 1.35 3.87 0.74 2.99e-31(+) +83.96 1.08e-28(+) +83.46 28 24.00 1.85 23.40 1.30 3.73 0.80 6.33e-26(+) +84.46 6.40e-29(+) +84.06 29 25.13 1.68 24.13 2.00 4.27 1.28 1.06e-25(+) +83.01 9.18e-24(+) +82.30 30 22.93 2.15 22.27 1.98 3.40 0.74 4.81e-24(+) +85.17 1.64e-24(+) +84.73 31 33.53 1.96 32.87 1.51 5.67 0.90 6.07e-29(+) +83.09 3.87e-31(+) +82.75 32 33.80 1.90 32.07 2.12 5.40 1.06 6.07e-29(+) +84.02 2.76e-27(+) +83.16 33 34.47 1.81 33.73 1.33 6.07 1.33 4.37e-28(+) +82.39 1.85e-30(+) +82.00 34 34.06 1.84 33.27 2.34 5.13 0.74 1.13e-30(+) +84.94 1.78e-27(+) +84.58 35 34.67 1.76 33.60 2.20 5.20 1.32 2.25e-29(+) +85.00 4.32e-27(+) +84.52 36 34.80 2.21 33.93 1.44 6.20 1.47 9.51e-27(+) +82.18 1.93e-29(+) +81.73 37 32.80 2.86 32.93 1.33 5.27 1.28 2.49e-24(+) +83.93 1.05e-30(+) +84.00 38 32.80 1.97 32.47 1.19 5.27 1.10 3.02e-28(+) +83.93 4.14e-32(+) +83.77 39 34.20 2.08 33.40 1.76 5.67 0.98 1.78e-28(+) +83.42 1.09e-29(+) +83.02 40 33.93 1.67 33.20 2.34 5.67 0.98 1.96e-30(+) +83.29 7.19e-27(+) +82.92 41 42.60 1.50 41.20 2.01 6.87 1.13 1.29e-33(+) +83.87 1.13e-30(+) +83.33 42 43.07 1.67 40.00 2.67 5.40 0.91 4.16e-34(+) +87.46 2.66e-28(+) +86.50 43 41.73 1.71 41.53 1.06 6.27 1.75 2.55e-30(+) +84.97 2.08e-32(+) +84.90 44 42.80 3.28 41.07 1.94 6.47 1.25 2.73e-26(+) +84.88 1.01e-30(+) +84.25 45 43.93 1.67 42.60 2.64 7.13 1.06 2.38e-33(+) +83.77 1.66e-28(+) +83.26 46 43.00 3.23 42.07 1.62 7.60 1.30 4.55e-26(+) +82.33 6.09e-32(+) +81.93 47 43.20 2.04 42.73 1.87 7.87 1.30 2.12e-30(+) +81.78 5.60e-31(+) +81.58 48 44.27 2.19 43.60 2.32 7.47 1.06 7.50e-31(+) +83.13 4.97e-30(+) +82.87 49 44.67 2.26 43.53 2.10 8.93 1.67 9.21e-29(+) +80.01 6.37e-29(+) +79.49 276 g. zhang, j. cheng, m. gheorghe, f. ipate, x. wang table 2 comparisons of qiea, qeps and qeam (continued) qiea qeps qeam qeam vs. qiea qeam vs. qeps sat mean std mean std mean std t-test imp.(%) t-test imp.(%) 50 43.13 1.55 41.47 2.50 6.53 0.99 3.87e-34(+) +84.86 5.45e-29(+) +84.25 51 83.07 3.45 81.27 2.91 12.93 1.33 1.48e-33(+) +84.43 5.51e-35(+) +84.09 52 83.40 3.44 82.73 2.96 15.27 1.67 8.05e-33(+) +81.69 4.07e-34(+) +81.54 53 83.00 2.73 81.60 2.50 12.67 1.50 1.05e-35(+) +84.73 3.04e-36(+) +84.47 54 85.13 3.23 83.60 3.14 15.80 1.66 1.15e-33(+) +81.44 1.14e-33(+) +81.10 55 84.87 2.83 80.80 2.31 14.20 1.52 2.22e-35(+) +83.27 1.76e-36(+) +82.43 according to these experimental results, we employ statistical techniques to analyze the behaviour of the three algorithms over the 55 instances of the sat problem. there are two statistical methods: parametric and non-parametric [6]. the former, also called single-problem analysis, uses a parametric statistical analysis t-test to analyse whether there is a significant difference between the two algorithms solving the optimization problem. the latter, also called multiple-problem analysis, applies non-parametric statistical tests such as wilcoxon’s and friedman’s tests, to compare different algorithms whose results represent average values for each problem, regardless of the inexistence of relationships among them. therefore, a 95% confidence student t-test is first applied to check whether the number of false clauses of the two pairs of algorithms, qeam vs. qiea and qeam vs. qeps, are significantly different or not. furthermore, the percentage of improvement (%) in the average number of false clauses due to the qeps algorithm over qiea and qeps is also listed in table 2. then two non-parametric tests, wilcoxon’s and friedman’s tests, are employed to check whether there are significant differences between the two pairs of algorithms, qeam vs. qiea and qeam vs. qeps. the level of significance considered is 0.05. the results of wilcoxon’s and friedman’s tests are shown in table 3. the symbols + and in tables 2–3 represent significant difference and no significant difference, respectively. in the experiments carried out on the qeam, the qeps and the qiea, we also record the average elapsed time for the first ten sat problems over 30 independent runs and for the remaining 55 instances of the sat problem over 15 independent runs. the comparisons of the three algorithms are illustrated in fig. 10. the x-axis and y-axis represent the number of sat problems and the elapsed time, respectively. as shown in table 1, the qeam greatly outperforms the qiea and the qeps in terms of the successful rates and the average number of evaluations. also, table 1 shows that qeps obtains higher successful rates and smaller average number of evaluations than qiea. it can be seen from the experimental results of 55 sat bench problems in table 2 that the qeam achieves much better results than the qiea and the qeps. the qeps obtains better results than the qiea in 54 out of 55 cases. the t-test results demonstrate that there are 55 significant differences between the two pairs of algorithms, qeam vs. qiea and qeam vs. qeps. the p-values of the two non-parametric tests in table 3 are far smaller than the level of significance 0.05, which indicates that the qeam really outperforms the qiea and the qeps by introducing the framework and some rules of p systems with active membranes. it is worth noting that the study in [6] shows that the non-parametric statistical tests are more appropriate than parametric statistical tests in the analysis of the behaviour of the evolutionary algorithms over multiple optimization problems. the qeps uses the framework and some evolution rules of p systems. each elementary membrane evolves for a certain number of generations in a non-deterministic way, and then all elementary membranes communicate in the skin membrane. thus, the qeps has better population diversity and the capability to balance exploration and exploitation. consequently the qeps obtains better results and smaller elapsed time, shown in fig. 10, than the qiea. the qeam goes further and applies the framework and some evolution rules of p systems with active membranes. the good performance of the qeam is qeam: an approximate algorithm using p systems with active membranes 277 1 10 20 30 40 50 60 65 0 2500 5000 7500 10000 12500 15000 no. of sat problems e la p s e d t im e ( s ) qeam qeps qiea figure 10: comparisons of elapsed time. due to the combination of independent evolution of each elementary membrane in a non-deterministic way, communication in the skin membrane, membrane separation and merging, and a local search in the skin membrane. figure 10 shows that the qeam and qeps consumes less time than qiea, which indicates that the use of evolution rules of p systems in the qeam and qeps has little effect on the overall computational load. furthermore, the qeam and qeps may use a slightly smaller number of evaluations of the solutions than the qiea because of the randomness of evolutionary generations for each elementary membrane. additionally, as a result of the use of membrane separation and division, the qeam consumes slightly more time than the qeps, which is shown in fig. 10. table 3: results of non-parametric statistical tests for the two pairs of algorithms, qeam vs. qeps and qeam vs. qiea, in table 2. the symbol + represents significant difference. tests qeam vs. qiea qeam vs. qeps wilcoxon test (p-value) 1.21e-13 (+) 1.21e-13 (+) friedman test (p-value) 1.11e-10 (+) 1.11e-10 (+) 4 conclusions membrane algorithms, defined by carefully mixing selected ingredients of p systems and metaheuristic search methodologies, and the interaction between p systems and quantum computing, are highly promising and give rise to challenging research issues, which are mentioned as open problems and research topics in [7, 21]. benefiting from the cross-fertilization of ideas from p systems, evolutionary computation and quantum computing areas, this paper discussed a novel membrane algorithm combing p systems with active membranes and qiea to solve satisfiability problem. a large number of experiments show that qeam performs better than qeps and qiea. as further work, we aim to investigate other interactions between the three disciplines and their applications to specific problems. acknowledgments the work of gz is supported by the national natural science foundation of china (61170016, 61373047), the program for new century 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[34] zhang, x.; zeng, x.; luo, b.; zhang, z.(2012); a uniform solution to the independent set problem through tissue p systems with cell separation, front comput sci, issn 2095-2228, 6(4):477-488. international journal of computers communications & control issn 1841-9836, 9(5):584-592, october, 2014. choice of countermeasures in project risk management using fuzzy modelling d. kuchta, d. skorupka dorota kuchta* 1. wroclaw university of technology poland, 50-370 wroclaw, wybrzeze wyspianskiego 27, dorota.kuchta@pwr.wroc.pl 2. tadeusz kosciuszko military academy of land forces poland, 51-150 wroclaw, ul. czajkowskiego 109, *corresponding author dariusz skorupka tadeusz kosciuszko military academy of land forces poland, wroclaw abstract: the paper proposes a new method for project risk management. it is proposed how, after risk identification, the countermeasures for risk mitigation and elimination can be selected, taking into account the cost and effort linked to them as well as the weights assigned by the decision maker to risk attributes, such as probability or consequences, and the values of those attributes. the risk attributes and weights, as well as the maximal total risk and the maximal total effort of risk mitigation accepted by the decision maker for the project are expressed as fuzzy numbers, which in turn constitute models for linguistic expressions. keywords: project risk, risk mitigation, risk transfer, risk elimination, fuzzy logicbased optimisation. 1 introduction project risk management is a very important subject and there is a lot of literature connected to it. there are also a lot of risk definitions (it is assumed here, after [1], that risk is defined as an event which may happen and if it does, it will have negative consequences on at least one of project success determinants1) and risk management methods or techniques (e.g. [1]). most of them can be summarized as follows: i. risk identification; ii. risk evaluation the risks identified in point i. are evaluated on the basis of their attributes (like probability and consequences); iii. risk elimination, transfer or mitigation taking some steps or countermeasures which eliminate some risks or decrease the evaluation of others; iv. risk control during the project execution; v. lesson learned recording. the problem to which this paper wants to contribute is point iii. of the above procedure. there are no formal methods in the literature, apart from one presented in [10], helping the decision maker in this step. normally only informal procedures are proposed (e.g. [6]). and in [10] the authors assume among others that the probability of the risks cannot be explicitly 1project success determinants are discussed later in the paper. copyright © 2006-2014 by ccc publications choice of countermeasures in project risk management using fuzzy modelling 585 influenced by the countermeasures, but only the expected value of monetary losses, which in our opinion is wrong in reality usually each risk attribute (e.g. probability, consequences, etc.), can be individually influenced by individual countermeasures. the authors of [10] assume also that the goal is the maximization of the monetary expected value of risk reduced by the countermeasures, whereas we assume that the goal should be the minimization of countermeasures total cost needed to attain a fixed total risk level of the project, which we think to be more realistic. also, in [10] crisp values are required for risk attributes, ever for quality, whereas we assume linguistic expressions (fuzzy numbers) for risk attributes, like it is done e.g. in [4] and [9], as we think crisp values would usually be difficult to obtain from the decision maker. also, basing ourselves on [9], it is assumed here that different risk attributes may have various weights in the eyes in the decision maker, which in [10] is ignored. for one decision maker mostly the consequences may count (this is the case for projects where consequences are injuries or deaths of human beings), for some decision makers mostly the probabilities (it is so for many public projects, where each problem, even a relatively small one, is noticed by the media who make a big deal of it). what is more, nowhere in the literature the notion of risk levels (e.g. project level, closest environment level, further environment level, national level, international level etc., introduced in [8]) and the effort linked to an effective application of countermeasures on each of the different levels are combined with the choice of risk countermeasures, and this is done in the present paper. 2 basic notions and notation as it was mentioned above, project risk is an event which might happen and if it happens, it will have negative consequences on one of the success determinants of the project. there are several determinants of project success (for a review see e.g. [2]), like time, quality, cost, customer satisfaction, etc. let us denote those determinants as dl, l = 1, . . . ,l. the risks may concern several levels ( [8]), e.g. the project level, the closer environment level (for construction projects this would be the construction market level), the further environment level (for construction projects this would be the national level), etc. the various levels are denoted as lt, t = 1, . . . ,t . the higher the level index, the harder accessible and the less possible to influence the level is, thus the less manageable the risks linked to this level are. it is assumed that (point i. of the project risk management procedure given above) p lt risks for each level lt, t = 1, . . . ,t and for each determinant dl, l = 1, . . . ,l have been identified (the risks will be denoted as rt,lk , k = 1, . . . ,p l t ). each risk r t,l k , if it happens, it concerns level lt (thus, if one wanted to mitigate or eliminate it, one would have to act on the respective level) and will influence determinant dl, l = 1, . . . ,l of project success. it is also assumed that each risk is linked to exactly one level and to exactly one determinant, but this assumption would not be difficult to be given up. each risk rt,lk will have several attributes. usually two or at the most three risk attributes are considered: the probability of occurrence, the consequences of the occurrence (for the project success determinant linked to the specific risk, i.e. risks may have consequences for time, cost, quality, customer satisfaction etc.) and sometimes the difficulty in an early detection or in early forecasting of the risk occurrence (e.g. [6]). sometimes additional attributes may be important, like the probability of the occurrence of consequences once the risk has happened, the degree of influence we have on the occurrence of the risk etc. the latter attribute is especially important in case we have to consider several levels on which the risks may occur: the further the level, the less effective our countermeasures may be. let us denote the number of attributes as a, and the attributes themselves as va, a = 1, . . . ,a. the attributes are functions of risks, thus the value of the a-th attribute of risk rt,lk will be 586 d. kuchta, d. skorupka va(r t,l k ), a = 1, . . . ,a,l = 1, . . . ,l, t = 1, . . . ,t, k = 1, . . . ,p l t . va(r t,l k ) will be all measured in the same scale, their values will be given by experts. as mentioned above, various risk attributes may have various weights in the eyes of the decision maker. each attribute va(r t,l k ) will be thus assigned a weight wa(r t,l k ), also according to a given scale. finally, it is possible to use the defined parameters to evaluate each risk (point ii. of the project risk management procedure). the evaluation rev (rt,lk ) of each risk may be defined using one of the formulae proposed e.g. in [6]2, e.g. the following one: rev (r t,l k ) = a∑ a=1 wa(r t,l k ) ·va(r t,l k ) a∑ a=1 wa(r t,l k ) (1) as a further step (point iii. of the project risk management procedure), it is proposed to try to identify certain countermeasures to eliminate or mitigate the risks. it is assumed that each measure may concern only one level, one project success determinant and one risk attribute. this assumption seems natural: one countermeasure can e.g. be effective only on the national level and affect only the time, i.e. a possible delay being the consequence of a certain risk, another countermeasure may concern only the project level and affect the cost and the probability of an event which would increase cost. let us introduce also a simplifying assumption that each attribute of each risk can be affected by only one measure. thus, for each l = 1, . . . ,l, t = 1, . . . ,t and a = 1, . . . ,a let us define a set of measures (mt,l,ah ,h = 1, . . . ,h l,a t ). then, for each attribute va(r t,l k ), a = 1, . . . ,a of each risk r t,l k (k = 1, . . . ,p l t) it is identified which measure among the measures { m t,l,a h , h = 1, . . . ,h l,a t } can be applied, and the index of this measure will be denoted as m(va(r t,l k )). the value of each attribute of the risk r t,l k after the application of the measure indexed by m(va(r t,l k )) will be equal to m(va(r t,l k )). of course it is true m(va(r t,l k )) < va(r t,l k ). for some risks and attributes no countermeasure may exist then it is set everywhere m(va(r t,l k )) = va(r t,l k ). the evaluation of each risk after the application of the measures will be rev (r t,l k ) = a∑ a=1 wa(r t,l k ) ·m(va(r t,l k )) a∑ a=1 wa(r t,l k ) (2) each measure will of course be linked to a cost, measured in monetary values, denoted as c(m t,l,a h ), h = 1, . . . ,h l t, l = 1, . . . ,l, t = 1, . . . ,t, a = 1, . . . ,a. apart from the cost, the implementation of each measure will mean an effort, problems of various kind, the necessity to seek the access to certain people, to ask for permissions, uncertainty as far the effect of the measure is concerned, etc. usually the bigger, the higher the level index is. thus it is assumed that the effort depends on the level, not on the individual measures (again, it is only a simplifying assumption). it will be denoted as et, t = 1, . . . ,t and evaluated by an expert in a fixed scale. each single measure mt,l,ah , h = 1, . . . ,h l t, l = 1, . . . ,l, a = 1, . . . ,a will be thus linked to effort et. 2in [6] there is a discussion showing that from the practical point of view all the formulae are in fact equivalent. choice of countermeasures in project risk management using fuzzy modelling 587 3 model optimising the choice of countermeasures in project risk management in the model we assume that the project manager wants, if possible, to achieve a small project overall risk level at the minimal cost. also, the effort linked to the measures should be taken into account. thus in fact three objective functions should be present in the model: the overall risk level, the cost of applying risk mitigation or elimination measures and the overall effort of applying those measures. in order to solve this multicriteria problem, let us choose the easiest approach, where the level of all but one criteria has to be fixed, so that only one criteria formally remains as an objective function. in our opinion in most cases the decision maker will have to maintain a certain level of overall risk, and this level would be fixed beforehand. the decision maker would thus seek a way to achieve this level at the minimal cost. also, he would like to minimize the effort, but probably rather as a secondary goal, trying to control it and be aware of it than to search for an absolute minimum. that is why the following model is proposed, but of course other formulations of this multicriteria problem would be possible too.∑ h=1,...,hlt, l=1,...,l, t=1,...,t,a=1,...,a c(m t,l,a h ) ·x t,l,a h → min (3) ∑ l=1,...,l,t=1,...,t, a=1,...,a,k=1,...,plt a∑ a=1 wa(r t,l k ) · (1−y t,l k,a) ·va(r t,l k ) + a∑ a=1 wa(r t,l k ) ·y t,l k,a ·m(va(r t,l k )) a∑ a=1 wa(r t,l k ) ≤ rl (4) ∑ k∈{1,...,plt} and m(va(rt,lk ))=h y t,l k,a ≤ m ·x t,l,a h (5) for each h = 1, . . . ,hlt, l = 1, . . . ,l, t = 1, . . . ,t, a = 1, . . . ,a∑ h=1,...,hlt, l=1,...,l, t=1,...,t,a=1,...,a e(m t,l,a h ) ·x t,l,a h ≤ el (6) where xt,l,ah , h = 1, . . . ,h l t, l = 1, . . . ,l, t = 1, . . . ,t, a = 1, . . . ,a, will be equal to 1 if the corresponding measure should be applied and to 0 otherwise, yt,lk,a, l = 1, . . . ,l, t = 1, . . . ,t, a = 1, . . . ,a, k = 1, . . . ,p lt will be binary variables fulfilling the constraint y t,l k,a = 0 (thus, being able to be eliminated from the model) if the value of the a-th attribute of risk rt,lk would not be changed thanks to the corresponding countermeasure. the constant rl stands for the admissible overall project risk level, the constant el for the admissible effort level linked to the selected countermeasures and the constant m for a sufficiently big number. actually, in practice most of the parameters in the above model may be impossible to determine in an exact way and may require a fuzzy formulation. because of the limited scope of the paper, we assume, basing ourselves on [4, 5, 7, 9], only the risk attributes and the weights of the attributes to be fuzzy, as well the constants rl and el and cost values will be crisp. for the fuzzy modeling, let us use here the language from [9], where the authors consider three risk attributes (probability of occurrence, severity of consequences, early detection difficulty) whose values can take five fuzzy vales each (vh very high, h high, m moderate, l 588 d. kuchta, d. skorupka low, vl very low). the same 5 values are assigned in [9] to the attributes weights. the human language assumed in [9] at the background is as follows (ã = (a1,a2,a3) stands for the triangular fuzzy number with support [a1,a3] and a2 as the value with the membership degree 1): table 1: linguistic scale for risk attributes, their weights and the effort linked to the countermeasures [9] linguistic term corresponding triangular fuzzy number very low (vl) (0, 0, 0.25) low (l) (0, 0.25, 0.5) moderate (m) (0, 0.5, 0.75) high (0, 0.75, 1) very high (vh) (0.75, 1, 1) for rl and el we will use fuzzy numbers ã = (a1,a2,a3) with a2 = a3. the decision maker might be supported in the choice of the fuzzy numbers for rl and el by means of questions similar to the following ones: • for rl: how many serious risks at the maximum would he accept, and how many serious risk he thinks have to be linked to each project at the minimum (as there are no projects without risk and accepting risks opens new possibilities)? • for el: similar questions, but regarding difficult countermeasures. with fuzzy parameters denoted by ,̃ we would have a mathematical programming problem with fuzzy parameters in constraint (4), which would become ∑ l=1,...,l,t=1,...,t, a=1,...,a,k=1,...,plt a∑ a=1 w̃a(r t,l k ) · (1−y t,l k,a) · ṽa(r t,l k ) + a∑ a=1 w̃a(r t,l k ) ·y t,l k,a · m̃(va(r t,l k )) a∑ a=1 w̃a(r t,l k ) ≤ r̃l (7) the rest of model (3) (6) will remain unchanged. constraint (7) is not unequivocal. it may be interpreted in many ways, according to the way the decision maker chooses to compare fuzzy numbers. also, the fuzzy numbers on the left hand side have to be multiplied with a constant and added one to another. let us thus discuss shortly basic arithmetical operations on fuzzy numbers and the simplest approaches of comparing them. let us thus consider two triangular fuzzy numbers ã and b̃, based, respectively, on the following triples of crisp numbers: (a1,a2,a3) and (b1,b2,b3). then we have: • ã + b̃ = (a1 + b1,a2 + b2,a3 + b3) (8) • ã · b̃ = (a1b1,a2b2,a3b3) if all the parameters are positive (9) • for each crisp number r we have: r · ã = ã ·r = (ra1,ra2,ra3) (10) if it comes to the inequality ã ≤ b̃, it may be interpreted depending on the pessimism/optimism degree of the decision maker. e.g. three interpretations listed below are possible, where the first one is the most pessimistic (it is most difficult for ã to satisfy it) and the last one the most optimistic: choice of countermeasures in project risk management using fuzzy modelling 589 • a3 ≤ b1 (11) • a3+a2 2 ≤ b2+b1 2 (12) • a2 ≤ b2 (13) • a2+a1 2 ≤ b3+b2 2 (14) • a1 ≤ b3 (15) for other possibilities see e.g. [3]. choosing (in cooperation with the decision maker) one of the approaches, we can turn constraint (7) into a crisp constraint. 4 example let us consider a real world construction project, presented in detail in [8]. there are over 30 risks there and a similar number of countermeasures. here let us consider only a selection of the data. thus we have the following risks: • on the project level, determinant time: r1,11 erroneous identification of soil; • on the project level, determinant cost: r1,21 accidents during the construction; • on the construction market level, determinant time: r2,11 non-availability of work force at the budgeted cost; • on the construction market level, determinant cost: r2,21 non-availability of required equipment; • on the national level, determinant cost: r3,21 unfavorable inflation. the values of the risk attributes were assessed by the experts, as well as the weights of the attributes (we assume in the example that the weights are the same for all the risks). table 2: risks attributes and their weights in the example risk r1,11 r 1,2 1 r 2,1 1 r 2,2 1 r 3,2 1 weight of the attribute probability of occurrence m vh m h h l consequences of occurrence h vh h m vh vh difficulty in early detection vh h l l l m difficulty in influencing the risk by countermeasures l l h h vh m five countermeasures have been identified, together with their cost and effort: 590 d. kuchta, d. skorupka table 3: countermeasures, their cost, efforts and effects for the example countermeasure risk and attribute influenced effect on the attribute cost of the countermeasure (in monetary units denoted $) effort linked to the countermeasure having ready an alternative technology which can be used immediately when the soil problem is detected, m1,1,21 r 1,1 1 consequences h → l 40 $ l additional check of all the equipment and workers training, m1,2,11 r 1,2 1 , probability vh→h 30 $ l training offered to pupils which are about to graduate a construction high school, m2,1,21 r 2,1 1 , probability m→l 50 $ h renting or reserving the needed equipment already now, m2,2,11 r 2,2 1 , consequences m→vl 40 $ h using a more stable currency in all the transactions, m3,2,11 r 3,2 1 , probability h→l 20 $ vh for the example we have: • l = 1,2 (we consider two project success determinants: time and cost) • t = 1,2,3 (we have three levels: the project level, the construction market level, the national level); • a = 1,2,3,4 (we have four risk attributes corresponding to the rows of table 2); • hl,at = 1 for the following triples (t, l,a), which can be read off from the rows of table 3: (1,1,2),(1,2,1),(2,1,1),(2,2,2),(3,2,1). otherwise it is equal to 0; • decision variables (binary) are as follows: x1,1,21 ,y 1,1 1,2,x 1,2,1 1 ,y 1,2 1,1,x 2,1,2 1 ,y 2,1 1,1,x 2,2,2 1 ,y 2,2 1,2, x 3,2,2 1 ,y 3,2 1,1. let us assume rl = el = (2,3,3). we get then the following model (using values from table 1,2 and 3 and formulae (8)-(10)): 40x 1,1,2 1 + 30x 1,2,1 1 + 50x 2,1,2 1 + 40x 2,2,2 1 + 20x 3,2,2 1 → min (16) choice of countermeasures in project risk management using fuzzy modelling 591 a =(0,75,1,1)(1−y1,11,2)(0,0,75,1) + (0,75,1,1)y 1,1 1,2(0,0,25,0,5) + (0,0,25,0,5)(1−y1,21,1)(0,75,1,1) + (0,0,25,0,5)y 1,2 1,1(0,0,75,1) + (0,0,25,0,5)(1−y2,11,1)(0,0,5,0,75) + (0,0,25,0,5)y 2,1 1,1(0,0,25,0,5) + (0,75,1,1)(1−y2,21,2)(0,0,5,0,75) + (0,75,1,1)y 2,2 1,2(0,0,0,25) + (0,0,25,0,5)(1−y3,21,1)(0,0,75,1) + (0,0,25,0,5)y 3,2 1,1(0,0,25,0,5) =(0,0,75,1)(1−y1,11,2) + (0,0,25,0,5)y 1,1 1,2 + (0,0,25,0,5)(1−y 1,2 1,1) + (0,0,1875,0,5)y 1,2 1,1 + (0,0,125,0,375)(1−y 2,1 1,1) + (0,0,0625,0,25)y 2,1 1,1 + (0,0,5,0,75)(1−y2,21,2) + (0,0,0,25)y 2,2 1,2 + (0,0,1875,0,5)(1−y 3,2 1,1) + (0,0,0625,0,25)y 3,2 1,1 (17) b = (0,75,1,1) + (0,0,25,0,5) + (0,0,25,0,5) + (0,75,1,1) + (0,0,25,0,5) = (1,5,2,75,3,5) (18) a b ≤ (2,3,3) (19) y 1,1 1,2 ≤ mx 1,1,2 1 ,y 1,2 1,1 ≤ mx 1,2,1 1 ,y 2,1 1,1 ≤ mx 2,1,2 1 ,y 2,2 1,2 ≤ mx 2,2,2 1 ,y 3,2 1,1 ≤ mx 3,2,2 1 (20) (0,0,25,0,5)x 1,1,2 1 + (0,0,25,0,5)x 1,2,1 1 + (0,0,75,1)x 2,1,2 1 + (0,0,75,1)x 2,2,2 1 + (0,75,1,1)x 3,2,2 1 ≤ (2,3,3) (21) assuming the method (12) of comparing fuzzy numbers and combining (17), (18) i (19), we get the following crisp linear programming model: 40x 1,1,2 1 + 30x 1,2,1 1 + 50x 2,1,2 1 + 40x 2,2,2 1 + 20x 3,2,2 1 → min 0,875(1−y1,11,2) + 0,375y 1,1 1,2 + 0,375(1−y 1,2 1,1) + 0,34375y 1,2 1,1 + 0,25(1−y 2,1 1,1) + 0,15625y 2,1 1,1 + 0,625(1−y 2,2 1,2) + 0,125y 2,2 1,2 + 0,34375(1−y 3,2 1,1) + 0,15625y 3,2 1,1 ≤ 2,5 (22) y 1,1 1,2 ≤ mx 1,1,2 1 ,y 1,2 1,1 ≤ mx 1,2,1 1 ,y 2,1 1,1 ≤ mx 2,1,2 1 ,y 2,2 1,2 ≤ mx 2,2,2 1 ,y 3,2 1,1 ≤ mx 3,2,2 1 0,375x 1,1,2 1 + 0,375x 1,2,1 1 + 0,875x 2,1,2 1 + 0,875x 2,2,2 1 + x 3,2,2 1 ≤ 2,5 (23) solving model (16),(20),(22),(23) we get the information that measures m1,1,21 and m 2,1,1 1 should be applied, at the cost of 80. however, if we change the weight of the risk attributes in the last column of table 2, e.g. if the probability gets the weight vh, there is no solution and we have to accept a higher risk level: rl = (3,4,4). in that case the cheapest way of attaining this risk level would be the application of measures m1,2,11 and m 3,2,1 1 . 592 d. kuchta, d. skorupka 5 conclusions a new method of selecting risk countermeasures to attain the desired risk level has been proposed. it allows to choose risk countermeasures taking into account such elements as various risk attributes and various weights assigned to them by the decision maker, various risk levels (the ones closer to the project, where we have more influence, and the ones further away), the monetary cost linked to the countermeasures as well as the immeasurable effort their application implies. the information required from the decision maker may be given in linguistic terms, modeled by fuzzy numbers. a real world example has been presented too. further research is necessary to verify the approach in other real world projects as well as to support the decision maker in the choice of the definition of fuzzy inequalities, which may be interpreted in many ways. the choice of the interpretation of fuzzy constraints has to correspond to the preferences of the decision maker, otherwise the model will not lead to a satisfying solution. bibliography [1] courtot h. (1988), la gestion des risques dans les projets, ed. economica, paris. [2] camilieri e. (2011), project success: critical factors and behaviours, gower publishing company. [3] liu x. (2001), measuring the satisfaction of constraints in fuzzy linear programming, fuzzy sets and systems, 122(2): 263-275. [4] mandal s., maiti j. (2013), risk analysis using fmea: fuzzy similarity value and possibility theory based approach, expert systems with applications, 41(7): 3527-3537. [5] meyer a., zimmermann, h. (2011); applications of fuzzy technology in business intelligence. international journal of computers communications & control, 6(3): 428-441. [6] norman t.l. (2010), risk analysis and security countermeasure selection, crc press. [7] shang k., hossen z. (2013); research paper: applying fuzzy logic to risk assessment and decision-making, casualty actuarial society, canadian institute of actuaries, society of actuaries, 1-59. [8] skorupka d. (2012), method of construction projects risk assessment, lambert academic publishing. [9] wang y.-m., chin k.-s., poon g. k. k., yang j.-b. (2009), risk evaluation in failure mode using fuzzy weighted geometric mean, expert systems with applications, 1195-1207. [10] zhang y., zhi-ping p. (2013); an optimization method for selecting project risk response strategies, international journal of project management, 32(3); 412-422. int j comput commun, issn 1841-9836 7(5):968-975, december, 2012. the specification of etl transformation operations based on weaving models m. vučković, m. petrović, n. turajlić, m. stanojević milica vučković, marko petrović, nina turajlić, milan stanojević university of belgrade, faculty of organizational sciences serbia, 11000 belgrade, jove ilića 154 e-mail: {milica.vuckovic, marko.petrovic, nina.turajlic, milan.stanojevic}@fon.bg.ac.rs abstract: in the etl process the transformation of data is achieved through the execution of a set of transformation operations. the realization of this process (the order in which the transformation operations must be executed) should be preceded by a specification of the transformation process at a higher level of abstraction. the specification is given through mappings representing abstract operations specific to the transformation process. these mappings are defined through weaving models and metamodels. a generated weaving metamodel (gwmm) is proposed giving the complete mapping semantics through specific link types (representing the abstract operations) and appropriate ocl constraints. weaving models specifying the actual mappings must be in accordance with this proposed gwmm. keywords: etl process, mdd, weaving models. 1 introduction in crisis management the tracking of a large amount of information (regarding people, material, financial, medical and other resources) is crucial. the establishment of a data warehouse, in which all of the relevant information could be easily stored, processed and analyzed, would enable the crisis management coordinators to make efficient decisions. one of the most demanding phases in the data warehouse design process is the design of the process for transforming the source data into a form suitable for its further analysis (the extract-transform-load process). mistakes made during this phase may lead the whole project to failure. since it is usually very complex and time-consuming it is necessary to provide data warehouse designers with adequate techniques to aid them in overcoming this complexity. the etl process consists of the extract, transform and load phases. the focus of this paper is the transform phase of the etl process. this transformation process involves the execution of a set of operations through which the actual transformations are achieved. most of the existing approaches directly define the order in which the transformation operations must be executed during the transformation process. we consider this to be too complex because it involves the definition of the process realization at a low level of abstraction and propose that this phase should be preceded by a specification of the transformation process at a higher level of abstraction. the main focus of this paper is the specification of the key abstract operations specific to the transformation process. these abstract transformation (at) operations denote the semantics related to the different possible types of correspondences that exist between the source models and the target model and are the basis for the specification of mappings. we propose an approach in accordance with model driven development (mdd) which is based on the premise that the most important product of software development is not the source code itself but rather the models representing knowledge about the system that is being developed. the main goal of mdd is to automate software development through the successive copyright c⃝ 2006-2012 by ccc publications the specification of etl transformation operations based on weaving models 969 application of model transformations, starting from the model representing the specification of the system and ending in a model representing the detailed description of the physical realization, from which the executable code can ultimately be generated. in accordance with mdd a special kind of model i.e. a weaving model (wm) is used for the specification of mappings between heterogeneous models [1–3]. through these weaving models the correspondences between individual elements of different models (called woven models) are defined. in compliance with the omg mda an appropriate metamodel (i.e. a weavingmetamodel wmm) is defined and weaving models must conform to it. the wmm actually defines the types of correspondences which may occur between particular concepts of concrete models. however, metamodel concepts (e.g. relational metamodel concepts) cannot be used in the definition of the wmm, hence only new link types can be defined. this implies that the corresponding wm i.e. the mappings between concepts of concrete models (e.g. a concrete relational model) cannot be semantically controlled. in this paper mapping models and metamodels are used for specifying the at operations. in the proposed solution we provide for the explicit introduction of metamodels into the weaving model approach as well as an appropriate metamodel for the semantic mapping between these metamodels (the generated wmm or gwmm). the at operations are represented through the concepts of this gwmm. these concepts allow the establishing of semantic correspondences only between those metamodel concepts on which the respective abstract operations can be applied. since weaving models must conform to the gwmm this implies that both the syntax and the semantics of the correspondences between concepts of concrete models can be controlled. the paper is organized as follows. the next section describes the main issues of the design of the etl processes and briefly presents the related work regarding the different approaches to its design. section 3 describes the existing mdd approach used in the eclipse modeling framework for the specification of model mappings. the proposed solution for the specification of the gwmm as well as several examples demonstrating its application, are given in section 4. finally, a conclusion is given detailing the main benefits of the proposed approach. 2 design issues one of the main issues in data warehouse is the problem of data integration, i.e. the integration of heterogeneous data sources into a single source. to this end first a global model i.e. a reconciled model should be created giving a unified view of the relevant source data. the main benefit of this approach is that it creates a common reference data model for the whole organization [7]. after the reconciled model has been created the next step is the population of the data warehouse (dw) with the actual data. if the chosen architecture for dw design presumes that the reconciled model is materialized, first the reconciled data layer will be populated from the sources and then the dw we be populated from the reconciled data layer. we have adopted such an approach in this paper since it enables the clear separation of source data extraction and integration from dw population [7]. therefore, the data extracted from the sources first needs to be transformed into a format compliant with the defined reconciled model. assuming that a single reconciled model has previously been created on the basis of the source models (e.g. by overlapping the source models) we propose that the first step in the transformation process design should be the specification of the correspondences between the source model concepts and the reconciled model concepts at the highest level of abstraction. at this stage conflicts can occur both at the structure and the instance levels (i.e. the data level). structure level conflicts are caused by the fact that different structures, and relationships among them, are used to represent the same real world concepts in different source models. instance level conflicts are more complex and may be the result of different granularity levels (e.g. events 970 m. vučković, m. petrović, n. turajlić, m. stanojević recorded on a daily or weekly level) or different formats in which the data is recorded. at the structure level we can differentiate between mappings at two different granularity levels i.e. the element and attribute level. element level mappings are used to define the correspondences between source model and reconciled model concepts by which same real world concepts are modeled. these mappings represent different abstract transformation (at) operations (e.g. join, equivalence, etc.) by which the semantics of the correspondences between the related elements are defined and which are easily understandable from the end-user viewpoint. the at operations are not executable and will be transformed, in the subsequent phases of the etl process design, into one or more actual operations (e.g. sql join, union, etc.), though this transformation is not in the scope of this paper.the attribute level mappings are at a lower granularity level and give the details of the established element level mappings. attribute mappings represent at operations that transform the values of one or more source model attributes into one or more reconciled model attribute values (e.g. equals, concatenate, split, add, etc.). most of the existing approaches to etl design proposed in literature or implemented in commercial tools do not provide concepts which allow the explicit and formal definition of the semantics of the element or attribute mappings. in [11] mechanisms for the specification of the most common etl process operations (e.g. join, filter, etc.) are provided and a set of corresponding uml stereotypes is defined. these mechanisms are related through uml dependencies, and attribute mappings are defined by notes attached to the dependencies. data mapping diagrams based on the data mapping uml profile are introduced in [10] to trace the flow of data and are organized into four levels (database, dataflow, table and attribute levels) through the use of uml packages. at the table level only data relationships are specified and not the actual processes therefore, these mappings do not carry any semantics. at the attribute level the semantics of the mappings are given either as uml notes attached to the target attributes if the relationship is represented as an association, or, if the relationship is represented as a mapping object, by the tag definition of the mapping object. in [12] a static conceptual model of the etl process is proposed for identifying the transformations in the etl process and it includes the transformation entity (an abstraction of modules of code executing a single task related to filtering, cleaning or transformation operations), etl constraints (regarding the necessary data requirements), notes (explaining the semantics of the applied functions: the type or expression/condition). in these approaches most of the transformations semantics are given through notes, often in a natural language, which does not represent a formal specification. on the other hand, most approaches directly define the order in which the transformation operations must be executed during the transformation process i.e. the dataflow, as in [10, 11]. also in [9] etl process models are designed in accordance with the introduced bpmn4etl metamodel (based on the business process modeling notation) which defines the dataflow. we consider that the specification of the transformation process realization should be preceded by a specification of the process at a higher level of abstraction. these specifications should be formal enough to enable the designer to move to lower specification levels resulting in the dataflow specification, through certain model transformations (in accordance with mdd). to this end, in [12] a static conceptual model of the etl process is proposed which can be mapped into a logical model representing the workflow. in [8] a uml profile is proposed which introduces the mappingoperator stereotype (among others) to tag classes defining the functions that can be used for specifying the mapping expression of a particular mapping. therefore, we propose that the specification of the dynamics of the etl process should be preceded by a static specification in which the mappings are defined through concepts which explicitly represent the semantics of the transformation operations. this specification should be formal enough to allow its transformation into the dataflow specification (in accordance with mdd), it should be extensible to allow the designers to add their own concepts, and its concepts the specification of etl transformation operations based on weaving models 971 should be easily understandable from the end-user viewpoint. 3 the amw approach in accordance with the mdd motto that everything should be treated as models, the weaving model approach treats mappings between heterogeneous models also as models [1, 2] and is supported by the atlas model weaver (amw) toolkit [3] within the eclipse modeling framework (emf) environment. in the amw approach weaving models (wm) are used for defining the correspondences between individual concepts of different models (called woven models). in the context of the amw approach mapping specifications can be regarded at different abstraction levels (illustrated in figure 1.), which actually correspond to the different levels in the omg mda standard [4]. taking into account the relationships which must exist between models from different abstraction levels in the omg mda, a mapping model at a given level of abstraction serves as a metamodel for mapping models from the lower abstraction level. or, in other words, every mapping model must conform to a mapping metamodel from the higher abstraction level. it should be noted that in this paper we concentrate only on the metamodel (m2) and model (m1) levels. ������������������ ���� �� �� ���� �������� �� ���� �� ���������� ��� ���������� ������������� ��� ���������� ��� ���������� ��� ���������� ��� ���������� ��� ����� ��� �� ��� ���� � ��� ������� ���� � ��� ��������� � ��� ����� figure 1: the amw approach in accordance with the previous statements, a wm must also conform to a particular weaving metamodel (wmm). this wmm is actually an extension of the generic wmm i.e. the core wmm, which is based on the meta-metamodel namely, the ecore meta-metamodel in the emf environment. since the concepts of the core wmm do not give the definition of the specific semantics of the correspondences, it should be extended [1] to provide the semantics relevant for the specific context in which the models are used (i.e. it should be extended to include concepts specific to a certain domain). therefore, the wmm will actually define the types of correspondences which may exist between concepts of woven models, and consequently correspondences specified in a wm must only be instances of the types defined in the wmm. however, a wmm cannot specify the mappings between metamodel concepts (e.g. the relational metamodel, the xsd metamodel concepts etc.), it merely defines new correspondence types. more precisely, the semantics of an introduced correspondence type are given only through the definition of its name without specifying the type of metamodel concepts that may participate in that type of correspondence. thus, one of the main drawbacks of the amw approach is the fact that a wmm does not include the necessary semantic rules for establishing mappings between metamodels so mappings between meta-concepts cannot be defined. instead it is only possible to define mappings between concepts of concrete models. consequently, only the syntax of these mappings can be controlled and not their semantics. in other words, it is up to the designer to know these semantic rules and ensure they are fulfilled when defining the mappings. in [5,6] a solution is proposed for overcoming this problem in the context of the specification of mappings between heterogeneous schemas. this solution is based on the explicit introduction of 972 m. vučković, m. petrović, n. turajlić, m. stanojević meta schemas into the wm approach as well as an appropriate wmm for the semantic mapping between the concepts of these meta schemas. the details of this approach and it application for the specification of at operations are given in the following section. 4 the proposed solution to take into account the assertions made in the previous section an extension of the core wmm is proposed which includes the concepts necessary for providing the semantics of the correspondences in the context of the transformation process. in figure 2. this extended wmm is shown using the uml class diagram. new mapping link types representing the identified at operations (join, equivalence, equals, concatenate, etc.) are defined as specializations of the wlink concept. it should be emphasized that every identified at operation is represented by a separate mappinglink type in our extended wmm though we have only depicted those relevant for the following examples. these new mappinglink types are used for the specification of mapping between the concepts of different metamodels (the actual concepts are represented by the mappinglinkend subclass of the wlinkend class). references source and target enable the defining of many-to-many mappings between the concepts of different metamodels. mappingmodelref and mappingelementref represent references to concrete uml models and their elements. ���������� ���������������������� ��������� � ��� ����������� � � ������ ���� ���� ���� ���� ������ �� ��� � ������ ��� ��������� ���������� � �������������� �� ������� �������� �� �������� � �������� �� ���� ����� � �������� �� ��������� �������� �� ������� �� ���� ����� ������������ ����� �� ����������� �� � figure 2: extended weaving metamodel (simplified) as explained in the previous section the extended wmm merely defines new mapping link types (whose semantics are only given through their names). since each specific type of mapping assumes certain constraints regarding the number and type of model concepts that may participate in that type of mapping (e.g. the join operation may only be used for mapping elements and must map at least two source model elements to a single reconciled model element) these constraints should be included in a wmm to prevent structural and semantic mistakes in wms. the procedure for generating the gwmm is based on the transformation given in [5, 6]. the transformation is accomplished by defining an individual wm for the mappings between metamodel concepts (the mm-wm) and then transforming it into a generated wmm with appropriate ocl constraints (e.g. for checking whether the type of model concepts involved in each mapping are valid). specifically, a wm conforming to the proposed extended wmm is created in which the mappings are defined through a set of mapping links which are instances of the introduced mapping link types (e.g. tablejoin is an instance of the join link type). this wm is then transformed into a new generated wmm (gwmm) in which the semantics of these mapping links are represented by appropriate ocl constraints. this gwmm now serves as a metamodel for weaving models at a lower level of abstraction (the m1 level in the omg mda). more precisely put, while the extended wmm defines the new mapping link types (representing the at operations), it is the gwmm with ocl constraints that gives the complete semantics the specification of etl transformation operations based on weaving models 973 of the defined mappings (regarding the metamodel concepts on which those operations can be applied). this process is illustrated in figure 3. it is assumed that all of the models (both source and reconciled) are expressed using the same formalism i.e. they are based on the same metamodel, which is common practice in dw design. for the purpose of this paper we have chosen the relational metamodel. the concrete models are represented as uml class diagrams using the appropriate uml stereotypes defined by the relational metamodel (the definition of these uml stereotypes is omitted from this paper due to space constraints). �������������� ��� � ��� � ���������� � ���� ��� ��� � ��� � ���������������� ������ � ��� ������� � ���� ���������� ����� �� ��� � ��� ������ ������ ��� ��� ��� ��������� ���� ����� ����� � ���������! "#������ ���� �� ��� � �� � �� ��� ����� ����� � � �� ����� � $$� �% ��� � && $$� �% ��� � && $$�����% ��� � &&� ��� �������� figure 3: proposed solution next we give several examples of concrete weaving models conforming to the proposed gwmm. it should be emphasized that the first step in creating a particular wm is to define the element mappings. subsequently, the attribute mappings are defined detailing the established element mappings. all mappings are defined by using the concepts of the proposed gwmm. in figure 4. an example is given illustrating the abstract tableequivalence operation. the mapdoctor mapping is an instance of the tableequivalence mapping link which states that the mapped elements medicalworker and doctor are equivalent in the sense that they describe the same real world concept at the same abstraction level. the details of the mapdoctor element mapping are given through the child attribute mappings. the attribute mapping mapfullname is given by the columnconcatenate mapping link whose semantic indicates that the values of the firstname and lastname attributes of the medicalworker element should be concatenated to obtain the value for the fullname attribute of the corresponding doctor element. the mapssn mapping is given by the columnequals mapping link whose semantic indicates that attributes ssn of the medicalworker and doctor elements exactly coincide. another example is given in figure 4. (on the right) illustrating the situation in which the medicalworker element of the fist source model and the doctor element of the second source model represent the same real world concept but record different information about it. therefore they are represented by a single doctor element in the reconciled model which includes all of the relevant information contained in both of the source models. for defining this type of mapping the abstract tablejoin operation is used whose semantic indicates that the medicalworker and doctor elements of the two different source models should be joined to obtain all of the relevant information for the doctor element in the reconciled model (the mapdoctor mapping link). in addition to defining the corresponding child attribute mappings it is also necessary to define the condition by which the elements should be joined. this is given by the columncondition mapping link, also a child of the defined element mapping, which specifies that the join should be performed on the basis of the ssn attributes. the columnconstraint link, again, also a 974 m. vučković, m. petrović, n. turajlić, m. stanojević ��������������� �� �� � ����� �� � ������� ��� �� � ����� �� � ����� �� ��� �� � ������� ��� �������� �� ����� ��� ���������� ������� ������ ����� ��������� ��� ������ ��� ������ ��� ���� �� ������������������ ��� ������ ��������������� �� �� � ����� �� ��� �� � ����� �� � ������� ��� �������� �� �� � ����� �� � ��������� � �!�� � ������� ��� �� � ����� �� � ��������� � �!�� � ������� ��� �������� �� ���� �� �������" ��� ��� ��� ��� ����� ����� � ��� ��� ������ ��� ��� � ��� ����#�� ��� ����� ��� ���� ��� figure 4: illustrations of the proposed approach child of the defined element mapping indicates that only those instances of the doctor element which satisfy the defined constraintyear regarding the required dateofbirth will be included in the transformation. the child attribute mappings giving the details of the mapdoctor element mapping have been omitted from because they would render the picture unnecessarly complex. 5 conclusion to overcome the compexity of etl process design they should be designed gradually through the development of a series of models and the corresponding transformations between them (in accordance with mdd). the aim of this paper is to facilitate the defining of the transformation process at the highest level of abstraction. the specification of the transformation is given through mappings between the source models and the reconciled model which represent abstract operations specific to the transformation process. in the proposed solution the specification of the at operations is based on the weaving model approch. to this end, first a description of the existing model weaving approach in the emf environment is given and its drawbacks are discussed. then, a solution is proposed which actually extends the existing amw approach to overcome the identified drawbacks. the proposed solution is based on the introduction of a special generated wmm with ocl constraints through which the mapping links between concepts of concrete models are semanticaly controlled. namely, the at operations of the etl process are actualy represented through appropriate semantic mapping links between metamodel concepts.therefore, the main benefit of the proposed approach is the introduction of a formal specification of the semantics of the transformation operations giving a static view of the transformation process which is extensible and easily understandable from the end-user viewpoint. it is also platform independent so it can be used to complement other approaches dealing with the dynamic aspects of the transformation. on the basis of these specifications the designer would, through certain model transformations (in accordance with mdd) move to lower specification levels which would result in the data flow specification i.e. the specification of the process dynamics (the at operations would be transformed into one or more actual operations e.g. sql join, union, etc.). therefore, further work in this area would be aimed at the realization of the proposed solution in the emf environment. the specification of the at operations given through weaving models would be used for obtaining the transformation models in a given transformation language (atl, qvt or xslt). these transformation models can be used for the automatic generation of code for any target platform. the specification of etl transformation operations based on weaving models 975 acknowledgment the research presented in this paper was partially supported by the ministry of education and science of the republic of serbia, grant iii-44010 and tr32013. bibliography [1] del fabro, m.d., bézivin, j., jouault, f., valduriez, p., applying generic model management to data mapping. in proc. of base de donnes avances (bda 2005), france, 2005. [2] del fabro, m.d., valduriez, p., semi-automatic model integration using matching transformations and weaving models. in proc. of symposium on applied computing, acm, 2007. [3] del fabro, m.d., bézivin, j, and valduriez, p., weaving models with the eclipse amw plugin. in: eclipse modeling symposium, eclipse summit europe 2006, germany, 2006. [4] miller, j., mukerji, j., model driven architecture (mda). http://www.omg.org; 2001. [5] nešković, s., vučković, m., aničić, n., on using weaving models to specify schema mappings. 2nd intl workshop on future trends of model-driven development, portugal, 2010. [6] aničić, n., nešković, s., vučković, m., cvetković, r., specification of data schema mappings using weaving models. paper accepted for publication in comsis journal, march 2012. [7] golfarelli m., rizzi, s., data warehouse design: modern principles and methodologies. mcgraw-hill, isbn-13: 978-0071610391, 2009. [8] kurz, s., guppenberger, m., freitag, b., a uml profile for modeling schema mappings. in proc. of the intl. conf. on advances in conceptual modeling, (pp. 53–62), springer, 2006. [9] el akkaoui, z., zimányi, e., mazón, j.-n., trujillo, j., a model-driven framework for etl process development. in proc. of 14th intl. workshop on dw and olap, acm, 2011. [10] lujan-mora, s., vassiliadis, p., trujillo, j., data mapping diagrams for data warehouse design with uml. in proc. 23rd intl. conf. on conceptual modeling, springer, 2004. [11] trujillo, j., luján-mora, s., a uml based approach for modeling etl processes in data warehouses. conceptual modeling er 2003, (pp. 307–320), springer-verlag, 2003. [12] simitsis, a., mapping conceptual to logical models for etl processes. in proc. of the 8th acm international workshop on data warehousing and olap, (pp. 67–76), acm, 2005. int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 312-324 an intelligent and pervasive surveillance system for home security a. longheu , v. carchiolo, m. malgeri, g. mangioni alessandro longheu, vincenza carchiolo, michele malgeri, giuseppe mangioni dipartimento di ingegneria elettrica, elettronica ed informatica facoltà di ingegneria università degli studi di catania viale a. doria 6, i 95125 catania italy {alessandro.longheu, vincenza.carchiolo, michele.malgeri, giuseppe.mangioni}@dieei.unict.it abstract: domotics is a promising area for intelligent and pervasive applications that aims at achieving a better quality of life. harnessing modern technologies is valuable in many contexts, in particular in home surveillance scenario, where people safety or security might be threatened. modern home security systems endorse monitoring as well as control functions in a remote fashion, e.g. via devices as a laptops, pdas, or cell phones, thus implementing the pervasive computing paradigma; moreover, the intelligence is now often embedded into modern applications, e.g. surveillance systems could adapt to the environment through a self-learning algorithm. this work presents an intelligent and pervasive surveillance system for home and corporate security based on the zigbee protocol which detects and classifies intrusions discarding false positives, also providing remote control and cameras live streaming. results of tests in different environments show the effectiveness of the proposed system. keywords: home surveillance systems; pervasive systems; zigbee protocol. 1 introduction home automation [1], [2] exploits the latest technologies to provide an intelligent control of lighting, air conditioning, plumbing systems, home appliances and security systems, achieving comfort, safety, efficiency, costs/energy savings and, in summary, a better quality of life [3], [4]. one of the first scenarios where the penetration of domotics occurred is home surveillance, where sensors, actuators, alarms, controllers or even robots [5] increase safety through continuously environment scanning, sending alarms and recording incidents upon detecting any abnormal event, based on the consolidated principles of corporate security [6]. the amazing part of modern home security systems is that monitoring and control can be performed remotely via devices as a laptops, pdas, or cell phones. according to this criterion, home security systems can be classified into four categories [7]: • hardware-based, the simplest systems where both monitoring and control are implemented in hardware, • passive systems, where only the monitoring is remote (the control is manual), • phone based systems, with monitoring and control performed through the phone (wired and/or cellular) network • web-based systems, identical to phone-based but using the internet as the communication infrastructure. copyright c⃝ 2006-2012 by ccc publications an intelligent and pervasive surveillance system for home security 313 hardware-based systems in general offer high performances but with higher costs than other solutions, and they might be subjected to proprietary solutions thus with potentially drawbacks as less interoperability and less flexibility; conversely, lower performances but greatest flexibility at limited costs is achieved with phoneand web-based systems, which are now more and more adopted for home surveillance. moreover, the use of these systems together with the cutting-edge hardware and software technologies of mobile devices allows a more effective implementation of the intelligent [8] and pervasive (or ubiquitous) computing paradigma [9], [10]. the intelligence is now often embedded into modern applications, for instance the surveillance system could adapt to the environment through self-learning [11] or it can automatically start some countermeasure as some critical event occurs [12]. the concepts of ubiquitous and pervasive computing, sometimes considered as similar [13] or even overlapped with mobile or embedded computing [14], are implemented on one hand thanks to the advanced pc-based hardware (sensors, actuators etc.) available for home security, in accordance with the ubiquitous computing paradigm where computers ”vanish into the background” [16], [15], on the other hand the use of smatphones or pdas as monitor/controller devices gives users the possibility of accessing information and services anytime from anywhere, as promoted by the pervasive computing approach [17]. another relevant issue concerning home security systems is the underlying transmission network, indeed a consolidated standardization is still missing [18], whilst the need for a quick deployed and cost-effective wireless solution to support easy remote control and affordable data transmission is emerging rapidly [19]. currently, several home wireless networks are available as infrared technology (irda), bluetooth and zigbee protocol. irda operates over short distances and is subjected to high error rate, whereas the bluetooth technology is limited by network capacity and performances. the most promising standard for wireless home and personal area networks is then represented by the zigbee technology [20], [21], which comes with features as low complexity, low data error rate, low power and low-cost [22], [23]. the work presented in this paper falls into the scenario outlined above, in particular we propose an intelligent surveillance system for home security that receives intrusion attempts detected by sensors and cameras connected through a zigbee-based network and classifies such intrusions with a customizable algorithm in order to exclude false positive cases (e.g. leaves moved by the wind); this paper is an extended version of [24]. potential alarms can be managed via an iphone r⃝ application that receives alarms, and allows users to use the iphone as a system remote controller and as a monitoring console to view real-time camera images. the paper is organized as follows: section 2 introduces the architecture of the system, while in section 3 we describe in detail the application that manages devices, processes alarms detection and implements the push notification service. section 4 shows the calibration that helps false positive intrusion detection as well as the system at work; finally, section 5 presents our conclusions and further works. 2 the physical layer the home surveillance system we propose is represented in figure 1; its components come from several goals to be addressed: • first, we want to detect of unauthorized accesses to the perimeter, i.e. whenever a potential intrusion occurs it should be detected using infrared radars and ip cameras) 314 a. longheu , v. carchiolo, m. malgeri, g. mangioni • the system must be able to classify the events detected, distinguishing between real intrusions and false positives e.g. due to leaves moved by the wind or to animals; this goal is achieved through a customizable algorithm [25] that can be tailored to both indoor and outdoor environments • finally, once real intrusions are recognized, the system provides a notification via an iphone r⃝ application so the user is immediately warned; he should also be able to view real-time images from cameras, so real-time countermeasures as siren activation or police action request can be taken. figure 1: logical schema of the proposed suirveillance system referring to figure 1, the system key component is the supervisor which manages the network, the data coming from devices (cameras and radars) as well as remote iphone connections and finally processes video images for intrusions detection. the supervisor used in our experiments was a medium-performance notebook, including a pentium r⃝ dual-core cpu @ 2.30ghz and 2gb ram, however the system does not require significant cpu/bandwidth resources, so it can be easily implemented on a low-cost 32-bit microcontroller provided with a serial interface (to allow communications with the coordinator module). the coordinator acts as the interface between the supervisor and the zigbee network; it is implemented using a custom zigbee module, it manages the low-level network and can be configured and managed via standard serial interface. moreover, we used only devices that provide standard ttl output, in order to easily achieve interoperability with any programmable digital circuit. to manage the communication between the devices and the zigbee module we used a microcontroller that listens to events from the device and controls the zigbee module. for instance, the pir (passive infrared) based component, i.e. a typical residential pir motion detector with multi-fresnel lens cover, needs to be interfaced with the zigbee network; to this purpose, we used the pic16f628a microcontroller (see fig.2), identical to the well known pic16f84 except for its additional usart module, which allows a high level management of serial communication. in addition to standard pins connection, as clock and reset, also the rb0/int (pin 6) and rb2/tx/ck (pin 8) were connected, in particular the former was used to receive asynchronous interrupt signals coming from the pir radar, whilst the latter was set up as an output for the pic16f628a and used to connect to the zigbee network. the microcontroller run at a 16mhz frequency clock in order to ensure the compatibility with the zigbee module the microcontroller waits for signals coming from sensors ttl output; as soon as it receives a signal i.e. a potential alarm it alerts the coordinator (via the zigbee module) that controls the camera using the rs232 protocol. note that each module acts as a zigbee end device, so they spend most of the time in a sleep state, thus saving energy. this however also requires to ”wake up” the device in order to an intelligent and pervasive surveillance system for home security 315 figure 2: interfacing a pir motion detector with the zigbee network using the pic16f628a communicate with it, so the pic16f628a was programmed so that the device changes from the sleep to the ready state before sending a command, and the same controller allows the device going into the sleep state again after the command has been executed. finally, note that for the pir motion detector we also detect potential tampering attempts by connecting the device tamp pin to the pic16f628a so that an alarm is sent even in this case. 3 the application layer the architecture described in the previous section operates thanks to the supervisor application, a software that manages devices and evaluates alarms, helping to prune false positive ones. such application is arranged into the following modules: • control module: it controls the physical connection with devices and is based on the qextserialport project [27]; this module automatically detects new devices (for instance, a new radar) and allows the supervisor user to add it to the zigbee network • zigbee management module: it is an higher layer manager of the zigbee devices as cameras and radars; • alarm management module: it processes potential alarms to remove false positives and manages the remote alarm notifications as well as control. all these modules are implemented in c++ within the qt framework [26], whereas the opencv libraries [28] were used to process images coming from ip camera. in particular, the available libraries to communicate with the coordinator were developed as an ocx control, so just windows r⃝-based systems can interface with the system. to overcome this limitation, we develop a c++ version of such libraries; this not only provides more compatibility, but also can be easily interfaced with computer vision (cv) softwares, which are generally written in c language. the reason for choosing the qt framework is that it ensures platform portability and operating system independence; its only drawback is the phonon framework used by qt to manage the multimedia layer. phonon is an abstract framework whose implementation depends on the operating systems, in particular it leverages the quicktime r⃝ libraries for mac os-x, directshow r⃝ libraries within the windows r⃝ platform and gstreamer for linux os. phonon does not allow 316 a. longheu , v. carchiolo, m. malgeri, g. mangioni an easy single frame management of a video stream, so we exploited the qtkit and quicktime frameworks to write a class that provided these functionalities. a complete uml diagram of the supervisor application is depicted in fig.3, in particular: figure 3: uml diagram of the proposed system • the zbdevice class represents a generic zigbee device with its standards parameters, as the mac address, the type of device and so on • three classes are derived from the zbdevice, i.e. the zbnetworkcoordinator, the zbradar and the zbcamera, whose roles are clearly understandable; in particular, the coordinator is the only device that can access the physical network via the serialporthandler class • the zbnetwork class describes the underlying zigbee network, whereas asynchronous signals in the network are managed by the serialportreceiver, that forwards such signals to the right device, for instance as soon as the coordinator is turned on, it sends a message to its supervisor with its mac address in order to be registered following a process mediated by the serialportreceiver which also exploits the serialporthandler to provide message acknowledgements a snapshot of the supervisor application is presented in fig.4. in the following we discuss how the alarms are detected, whereas the subsection 3.2 shows how the remote notification occurs whenever a relevant alarm is detected. 3.1 alarms detection the mp4 video stream generated by the ip camera is provided to the supervisor’s alarm processing module through a real time streaming protocol (rtsp) server connected via the wireless connection; this allows the evaluation of whether an intrusion occurred and an alarm should be generated or not. the received frames are evaluated through the algorithm (details can be found in [25]) in order to assign a precision (i.e. a numeric value) thus establishing whether a false positive has been detected. to do this, the first (trivial) solution we adopted was to evaluate the difference between the current frame and the previous one, applying a threshold to detect a binary pattern; this worked an intelligent and pervasive surveillance system for home security 317 figure 4: snapshot of the supervisor application in almost static background scenarios, as for indoors environments (e.g. a room) with constant lighting, but it is not suitable when frames background is not static, for instance when tree leaves are moved by the wind. the next step was to apply the gaussian background model [29], citelee2:2005, in order to effectively remove the (even dynamic) background; the algorithm implemented in the supervisor application works following the steps illustrated in fig. 5: it first converts colour images into a black and white format for binary processing (1), then it detects the foreground (2) and extracts the corresponding bounding rectangle (3), i.e. the area where the gaussian model detected relevant information. figure 5: alarms sources extraction process of the supervisor algorithm bounding rectangles across subsequent frames are compared for a given time interval and if something is detected with a specified precision, an alarm is generated; the system stores in xml format any relevant event, should it determine an alarm or not, in accordance with a set of severity levels. 318 a. longheu , v. carchiolo, m. malgeri, g. mangioni note that alarm detection is completely customizable by setting algorithm parameters, so a proper configuration allows to correctly distinguish between false positives/negatives and real alarms; for instance, the threshold can be reduced for indoor environments (where the background is almost static) so even a little movement will be detected; similarly, the minimum bounding rectangle can be increased, in order to discard cats/dogs movement detection in outdoor environments and so on. parameters should be tailored to the actual scenario the system will be installed into (section 4 shows a typical calibration session); in fig. 6 the c struct implementing such parameters is shown, together with the screenshot of the supervisor application used to set up their values. details about the meaning of parameters and how they affect the cv recognition process can be found in the opencv documentation [28]. figure 6: cv parameters set up 3.2 alarm notifications and remote control as soon as a (possibly real) alarm is detected, the system sends notification to the user, also providing a remote control of overall systems functionalities. these are implemented making use of the well-known push technology [31], citepohja:2009, in particular we used the apple push notification service (apns in the following) [33]. the supervisor application is first registered at the server providing apns; then, whenever the supervisor needs to send a notification, it establishes a secure connection to the apns using the certificate obtained by the apns during the registration, and finally data are transmitted. the format of the packet being sent is illustrated in figure7, where the devicetoken is a 32 byte device identifier and the payload represents the notification, created according to the json format, a lightweight data-interchange format based on a subset of the javascript language [34]. during our tests notifications were received within about 4 seconds since the request, that can be considered a good response time for this kind of application. to manage notifications and provide remote control functionalities, a mobile application operates in conjunction with the supervisor’s software counterpart. an intelligent and pervasive surveillance system for home security 319 figure 7: the format of notifications sent by the supervisior this application was developed in objective-c within the cocoa touch framework [35], and it displays notifications received from the push server, also allowing the management of the zigbee network (e.g. enabling or disabling devices) and the possibility of remotely examining current camera video stream from the mobile phone, checking whether it deals with a real intrusion on not and applying proper countermeasures, as an alarm activation or a police call for on-site actions. finally, note that the system also performs a complete log of all events, each stored with a severity level, a timestamp and a description, so that it will be possible to analyze who, why and when a given alarm notification has been triggered. 4 experiments and results in order to evaluate the performance of the intrusion detection system presented in previous sections, we performed two types of test, the former to assess how the calibration affects alarms classification and the latter is about the behaviour of the zigbee network, in particular for what concerns the largest area that can be effectively kept under surveillance. to assess false positive alarms pruning, we used a panasonictm standard vga resolution camera in two different scenarios, an indoor environment (a room) with scarce illumination and the an outdoor environment (a garden in front of a house) with strong lighting. for each scenario we performed 50 test sessions, 25 with an intruder walking in front of the camera and the remaining 25 without intrusion. finally, results from each scenario have been evaluated first without any configuration for the detection algorithm parameters (i.e. with default values) and then providing calibration for such values (details are here omitted [25]) in the following we present the test results using diagrams that show the number of tests according to the percentage of accuracy reached (ranging from 0 to 100%). the measures for the indoor scenario with no calibration (see figure 8 a) show unsatisfactory results. in the set of 25 tests with an intruder, 20% of them shown negative results, i.e. the system did not detect the intruder. the same scenario with no intruder shows incorrect results for 44% of tests (a non-existent intruder is detected); this was due to missing calibration and also by changes in the light coming from the windows (which lead to false positive detection). after the calibration, results significantly improved as figure 8 b) shows. the algorithm indeed detected the intruder with high accuracy (no false negative occurred), and similarly in the case of absence of the intruder (no false positive have been detected). the second scenario concerns an outdoor environment with strong lighting (a garden in front of a house in the morning); results are displayed in figure 9. the significantly increasing in illumination allow better results even with no calibration (see fig. 9 a); the algorithm indeed shown high precision in detecting an intruder (no false negatives), whilst we have just 16% of false positives in the case of no intrusion. after the calibration results were further improved (false positives were completely prevented). the second set of tests was about the zigbee network, in particular we want to assess the largest area that can be effectively kept under survelliance. to quantify this effectiveness, we 320 a. longheu , v. carchiolo, m. malgeri, g. mangioni figure 8: indoor scenario used the link quality indicator (lqi), an 8 bit sequence representing a figure of the quality of the link between two zigbee nodes [36]. to perfom such tests we used a system made up of a coordinator module and a router module which was positioned at different locations (increasing the distance from the coordination) along a given perimeter. at each position we performed 25 measures; results are shown in fig. 10, where the value of lqi for distance from 0 to 12m is plotted, in particular the solid line represents the theoretical lqi value whereas the other line represents measured lqi values. each point is the average of 25 measures; in the same figure we report some lqi measurements together with corresponding variance values; note that variance remains limited until a distance of 4 meters, whereas it increases significantly over this distance, this is due to the presence of a wall at 5 mt, which hinders the communication between zigbee modules. tests revealed worse performances than those declared on the component datasheets, indeed the system worked well up to a distance of 20 meters even if the lqi decreased significantly since a distance of 5 meters. however, we noted that even with a low lqi the communications between zigbee modules was acceptable, in particular some connection losses were detected but the system was able to restore the connection in few seconds. 5 conclusions and future works several intelligent and pervasive applications are being developed within the domotics context. in particular, an intelligent surveillance system for home security was presented in this paper. it exploits the zigbee protocol and it can detect and classify intrusions to discard false positive and negative alarms, also providing remote control functions and cameras live streaming to allow users to analyze who and why is triggering alarms. we also presented results about both the detection algorithm effectiveness and the zigbee network performances. some future works include the following issues: • the application currently was tested on windows or macos x operating systems; to increase portability, we are planning to test it in linux-based platforms; an intelligent and pervasive surveillance system for home security 321 figure 9: outdoor scenario figure 10: lqi versus distance between coordinator and router zigbee modules • the system includes just a coordinator and a router with a camera; adding other components, e.g. smoke and gas sensors, household appliances etc., allows to build a system that guarantees security and safety in a global fashion as electronic keys or biometric sensors to improve the system’s capability. • the algorithm we used to evaluate intrusions was quite simple, and it should be intended as the best choice for first experiments (it indeed did not affect on the overall system performance); better yet slower algorithm should be tested. 6 acknowledgement this paper was inspired by a work developed in cooperation with valueteam it consulting and solutions (http://www.valueteam.com). particularly, we thank francesco consoli as senior manager at security division, for his guidance and support to this work. we also thank danilo torrisi for the implementation and test of the system. 322 a. longheu , v. carchiolo, m. malgeri, g. mangioni bibliography [1] jacobson, j., understanding home 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sahinoglu, reliable broadcast in zigbee networks, in proceedings of secon ieee conference, 2005 int j comput commun, issn 1841-9836 8(6):800-811, december, 2013. adaptive network coding scheme for tcp over wireless sensor networks y.-c. chan, y.-y. hu yi-cheng chan*, ya-yi hu department of computer science and information engineering, national changhua university of education no.2, shi-da road, changhua city 500, taiwan ycchan@cc.ncue.edu.tw, m9954016@mail.ncue.edu.tw *corresponding author: ycchan@cc.ncue.edu.tw abstract: the purpose of this paper is to develop a network coding scheme to enhance tcp performance in wireless sensor networks. it is well known that tcp performs poorly over wireless links which suffer from packet losses mainly due to the bad channel. to address this problem, it is useful to incorporate network coding into tcp, as network coding can offer significant benefits in terms of throughput, reliability, and robustness. however, the encoding and decoding operations of network coding techniques will bring an additional delay that has a negative effect on applications of wireless sensor networks. in this paper, we propose an adaptive network coding (anc) scheme which contains two major aspects: the adjustment of the redundancy factor r and the adjustment of the coding window size cw. we dynamically adjust these two parameters depending on the measured packet loss rate, so that the proposed anc can effectively mask packet losses and reduce the decoding delay of network coding. the performance of our scheme is evaluated by simulations using ns-2 simulator. compared to other schemes, the anc not only achieves a good throughput but also has the lowest average delay and the lowest maximum delay in all experimental environments. keywords: network coding, tcp, delay, wireless sensor networks. 1 introduction the transmission control protocol (tcp) is the main transport protocol that provides reliable transmission in the current internet. it has been developed for many years. most applications on the internet depend on tcp to ensure safe delivery of data, such as ftp (file transfer protocol), http (hypertext transfer protocol), smtp (simple mail transfer protocol), and so on. tcp performs well in wired networks where packets losses mainly occur due to congestion. however, the performance of tcp degrades very fast in wireless networks. in a wireless environment, there is not only congestion but also numerous other reasons for packet losses exist. traditional tcp treat a packet loss event as the indication for network congestion, and then decrease its congestion window size. this may severely impair the throughput when the tcp runs on a wireless channel. therefore, it is very important to improve the performance of tcp in wireless networks. and this goal can be achieved by combining network coding with tcp. network coding is a new transmission paradigm originally proposed by ahlswede et al [1]. in recent years, it has received much attention and has been generated huge research in communication networks [2]. tcp/nc [3] is the first one that incorporates network coding into tcp with minor changes to the protocol stack. they present a solution which embeds the network coding operation in a separate layer below transport layer and above network layer on the source and receiver side. the idea of network coding is that, instead of transmitting individual packets, the sender takes several packets and combines them together for transmission. consequently, successful reception of information does not depend on receiving specific packet content but rather copyright © 2006-2013 by ccc publications adaptive network coding scheme for tcp over wireless sensor networks 801 on receiving a sufficient number of combinations [4]. then it can compensate for the packets in the presence of random losses, as long as the sent redundant combinations are enough. this characteristic is very attractive for tcp to improve the robustness and effectiveness of data transmission over lossy wireless networks. wireless sensor networks (wsns) are usually composed of a large number of radio-equipped sensor devices to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion, or pollutants. these devices typically include some strong constraints in terms of energy, memory, computational speed and communication bandwidth. in fact, most applications on wireless sensor networks prefer faster and reliable packet delivery to higher throughput [5]. generally, these applications are not only loss-sensitive that require successful transmission of all packets or at a certain success ratio but also delay-sensitive that require timely delivery of data [6]. for these applications, packet loss will lead to retransmission and the inevitable consumption of additional battery power. in addition, if the delay time is too long, the monitored data may be outmoded. to overcome these problems it is beneficial to use network coding by transmitting redundant packets to mask packet losses. thus, the number of retransmissions and timeouts can be reduced. in this paper, we propose a new network coding scheme to improve tcp performance over wireless sensor networks which is called adaptive network coding (anc). the concept of anc is divided into two parts. in the first part, we adjust the redundancy parameter dynamically according to the network situation. as a result, the event of packet loss can be masked from the congestion control algorithm of tcp by sending enough redundant combinations. in the second part, we change the coding window size contingent on the measured packet loss rate to obtain the optimal throughput-delay trade-off. the results of simulation show that our scheme achieves higher throughput and lower delay in difference network scenarios. the remainder of this paper is organized as follows. in section 2 we provide an overview of related work. section 3 we propose our schemes and algorithms focus on the redundancy parameter and the coding window size of network coding. in section 4 we present an experimental evaluation of the performance, and finally, our conclusions as well as the future work are discussed in section 5. figure 1: an example of random linear network coding 2 related work random linear network coding is one of the major techniques in the network coding. the encoding coefficients are randomly chosen from a set of coefficients of a finite field. a linear equation of packets is then performed to generate a coded packet, and the receiver only needs to receive a sufficient number of linear equations in the forms of the coded packets to successfully decode the original packets. for instance (see figure 1), suppose that the sender buffers the first 802 y.-c. chan, y.-y. hu n packets p1, . . . ,pn in its coding buffer, then the sender chooses n coefficients c1, . . . ,cn from a finite field and mates n coefficients with n packets. next, the sender encodes these pairs into a linear combination x, x = c1p1 + c2p2 + · · · + cnpn, then sends it to the receiver. if the receiver receives enough number of these combinations then it can decode the original packets. in recent years, there have been many studies make use of random linear network coding to improve tcp performance. the main aim of tcp/nc [3] is to mask losses from tcp using random linear coding. the source transmits random linear combinations of packets currently in the congestion window, and the receiver acknowledges every innovative linear combination it receives, even if it cannot decode an original packet immediately. this scheme gives a new interpretation of acks, and brings a new concept that “seen packet” which is defined in [7] as an abstraction for the case in which a packet cannot yet be decoded but can be safely removed from the coding buffer at the sender. tcp-vegas is chosen for the transport layer protocol in tcp/nc, as it is more compatible with their modifications. when losses are effectively masked, tcp-vegas can infer congestion from increased rtts. additionally, tcp/nc uses a constant redundancy parameter r to compensate for the loss rate of the channel, for every packet arrives from tcp, r linear combinations are sent to the ip layer on average. however, in a wireless environment, the packet loss rate is very likely to not a constant value. a fixed redundancy parameter r may damage network performance, since it is not always suitable for all network conditions. if the value of parameter r is too small, then the losses are not effectively masked from the tcp layer and will result in timeout. on the other hand, if the value of parameter r is too large, the source may send too many linear combinations that consume network resources. thus, the value of r should be dynamically adjusted depending on the estimated packet loss rate. the feedback based network coding (fnc) [8] uses the implicit information behind of the seen scheme to find out the exact number of packets needed by receiver to decode all data. the receiver computes the diff value by two variables: the number of seen packets and the largest packet index in the coefficient matrix, and then embeds this value into the ack header. when the sender receives an ack, it uses this value to decide how many random linear combinations should be retransmitted and how many original packets should be combined in a linear combination. in this way, the fnc retransmission scheme can reduce the decoding delay and the number of redundant retransmissions. but the fnc retransmission scheme highly relies on feedback, this will greatly impair the throughput when the network with a large round-trip time. furthermore, if the ack is lost, the sender cannot repair the loss of linear combinations through instant retransmissions. the sanc-tcp protocol [9] is designed primarily to optimize the tcp/nc protocol. the redundancy factor r of tcp/nc is constant, while sanc-tcp adjusts the redundancy factor r adaptively based on existing network conditions. in order to implement this approach, sanctcp adds some information in the ack header to indicate the current network state, thus enable the sender to dynamically change the value of r according to the real system. our scheme is similar to the sanc-tcp that with dynamic adjustment of redundancy factor r, but in different ways. moreover, we limit the coding window size and dynamically change it depending on the packet loss rate to reduce the delay of network coding. 3 proposed method in this section, we describe our adaptive network coding scheme (anc) which consists of two parts. first, we adjust the redundancy factor r dynamically by estimating the packet loss rate in current network, so that the value of r can represent the actual network state. second, in order to reduce the decoding delay of network coding, we limit the coding window size and adaptive network coding scheme for tcp over wireless sensor networks 803 dynamically adjust it contingent on the measured packet loss rate. 3.1 adjustment of the redundancy factor r before adjusting the redundancy factor r, we have to find the packet loss rate of current network. in our scheme, we insert two variables into the header of ack, which are send_count and seen_count. the variable send_count refers to the number of packets that have been sent from the sender and the variable seen_count refers to the number of seen packets at the receiver. then, the sender can use these and other related variables to calculate packet loss rate. for example, when the ack arrives from receiver, the sender retrieves the variables send_count and seen_count from ack header and compares seen_count with a threshold t which is the variable used to determine if it is time to adjust the value of r. if seen_count is more than or equal to t , the sender starts to calculate the packet loss rate by the following equations: diff_send = send_count − send_old, (1) diff_seen = seen_count − seen_old, (2) loss_rate = diff_send − diff_seen diff_send , (3) where send_old and seen_old are the previous value of send_count and seen_count respectively. the initial send_old and seen_old is set to 0. the diff_send refers to the number of packets that have been sent during this calculation cycle while diff_seen refers to the number of seen packets during this calculation cycle. therefore, the loss_rate means the packet loss rate of current network. through the above equations, we can obtain the accurate packet loss rate to conduct the correct adjustment of redundancy factor r. after calculating the packet loss rate, the sender adjusts the redundancy factor r accordingly. if current t is equal to init means this is the first time for the sender to adjust the value of r. in the simulation, we choose init = 20 which is the same as initial coding window size. the equation below shows the calculation of the redundancy factor r for the first time: rcurrent = 1 1 − loss_rate , (4) where rcurrent is current redundancy factor r. on the other hand, if it is time to adjust the value of r after the first calculation, the value of r can be obtained as follow: rcurrent = a ∗ 1 1 − loss_rate + b ∗ rold,0 ≤ a,b ≤ 1,a + b = 1. (5) the above equation is calculated using moving average to take into account the previous trends on rold, where rold is the last value of redundancy factor r. the values of variables a and b can be set based on the network situations. if the random loss rate of the network environment tends to change frequently, the value of a should be set greater than b. in contrast, if the random loss rate of the network environment tends to change occasionally, the value of a should be set less than b. 3.2 adjustment of the coding window size the coding window size represents that the largest number of original packets can be encoded in a linear combination. to reduce the decoding delay of network coding, we limit the coding window size and adjust it according to the packet loss rate. 804 y.-c. chan, y.-y. hu after the sender computed the redundancy factor r, the next process for the sender is to adjust the coding window size. the following equation describes the adjustment of coding window size for the first time: cw = 20 + ⌊loss_rate ∗ 10⌋, (6) where cw refers to the coding window size. in our scheme, the coding window size is dynamically adapted depending on the packet loss rate. this means that the coding window size increases as the packet loss rate increases, and the coding window size decreases as the packet loss rate decreases. after the first calculation, the value of coding window size can be obtained as follow: cw = 20 − ⌊ 1 − rcurrent rcurrent ∗ 10 ⌋ . (7) the above equation shows that the packet loss rate estimation for adjusting the coding window is derived from the value of rcurrent rather than the measured packet loss rate at this period. that is because the value of rcurrent is used to predict the packet loss rate in the next period. finally, we must update several related variables, such as rold to rcurrent, send_old to send_count and seen_old to seen_count. the value of t also should be reset by the following equation: t = cw + seen_count. (8) table 1: coding window size adjustment in difference loss rates dynamic coding window adjustment cw loss rate 0% loss rate 10% loss rate 20% loss rate 30% loss rate 40% 10 585.1 0.06828 617.2 0.07108 515.7 0.07460 346.3 0.07648 91.1 0.07476 15 877.6 0.06828 849.3 0.08667 733.7 0.09681 602.5 0.10356 341.6 0.10394 20 998.6 0.07997 898.6 0.11153 793.3 0.13131 681.6 0.14362 509.7 0.15326 25 998.6 0.09997 899.9 0.14440 798.9 0.17561 695.2 0.19556 589.3 0.20850 30 998.6 0.11993 899.9 0.17740 799.0 0.21640 696.1 0.25118 592.8 0.27962 35 998.6 0.13986 899.9 0.21272 799.0 0.21640 696.1 0.31536 592.8 0.27962 40 998.6 0.15976 899.9 0.24271 799.0 0.21640 696.1 0.37298 592.8 0.27962 anc 998.6 0.07997 899.9 0.13830 799.0 0.18977 696.1 0.23014 592.4 0.26464 cw 20 24 27 28 29 3.3 analysis of the coding window size the initial value of coding window size in our scheme is acquired by the experimental results presented in table 1. for this experiment, the topology and experimental parameters are the same as that in the subsection 4.1.1. table 1 shows the data of throughput (tp) and delay which are obtained by adjust the coding window size in difference loss rates. the data that have an outline border is the optimal trade-off value when compared with others data in the same loss rate. the last two rows in table 1 are the experimental results and the coding window adjustments of our scheme anc. for the initial setting, we assume the packet loss rate at beginning of network is zero, so we set the initial coding window size to 20 that is the optimal value in table 1 when the loss rate is 0%. after this, the sender adjusts the coding window size in accordance with the measured packet loss rate. this experimental result (see table 1) indicates that the throughput adaptive network coding scheme for tcp over wireless sensor networks 805 of our scheme at last row can reach the optimal value in most cases while has a shorter delay, that is, the adjustment of the coding window size in our scheme is suitable. as mentioned before, in order to reduce the decoding delay of network coding, we limit the coding window size in our scheme. when the coding window size is limited, the coding coefficient matrix can become smaller and the complexity of decoding can become lower. hence, the time of packets remain in the decoding buffer is decreased, that is, packets can be discarded earlier from the decoding buffer. for these reasons, our scheme can effectively reduce the decoding delay of network coding. nevertheless, a smaller coding window size does not mean to be better. if the size of coding window is too small, the coding window will restrict the use of available network resources which could cause a poor throughput. as a consequence, we must take into account the trade-off between throughput and delay to get higher throughput and lower delay. the algorithm of our scheme is specified using pseudo-code that shown in tables 2 and 3. table 2: the operations of network coding layer at the sender event pseudo-code initialization: 1)set num,send_count, send_old and seen_old to 0. 2)set init to 20. let t = init. when the packets arrive from tcp sender: 1)if the packet is a control packet used for connection management, deliver it to the ip layer then doing nothing; else, move to state 2). 2)if the packet not already in the coding buffer, add it to the coding buffer. 3)set num = num + r. (r = redundancy factor) 4)repeat the following ⌊num⌋ times: 1.generate a random linear combination of the packets in the coding window. 2.count the send_count = send_count + 1. 3.add the network coding header specifying the set of packets in the coding window and the coefficients used for the random linear combination. 4.add the variable send_count to the network coding header. 5.deliver the packet to the ip layer. 5)set num = fractional part of num. when the ack arrives from receiver: 1)pick up the variables seen_count and send_count from ack header. 2)if seen_count ≥ t start to adjust the values of r and cw ; else, move to state 3). 1.compute the diff_send = send_count − send_old and the diff_seen = seen_count − seen_old. 2.compute the loss_rate = (diff_send − diff_seen)/diff_send. 3.if t = init: a)rcurrent = 1/(1 − loss_rate). b)cw = 20 + ⌊loss_rate ∗ 10⌋. c)move to state 5. 4.if t > init: a)rcurrent = a ∗ 1/(1 − loss_rate) + b ∗ rold. b)cw = 20 − ⌊(rcurrent − 1)/rcurrent ∗ 10⌋. 5.update rold to rcurrent, send_old to send_count and seen_old to seen_count. 6.reset t = cw + seen_count. 3)remove the acked packet from the coding buffer and hand over the ack to the tcp sender. 4 performance evaluation our adaptive network coding scheme is evaluated by means of the network simulator (ns-2) under various conditions such as different link bandwidths, packet sizes, and random loss rates. evaluation metrics in performance test are the throughput, the average delay, and the maximum 806 y.-c. chan, y.-y. hu table 3: the operations of network coding layer at the receiver event pseudo-code initialization: 1)set seen_count to 0. when a packet arrives from sender: 1)count the seen_count = seen_count + 1. 2)remove the network coding header, then retrieve the coding vector and the variable send_count. 3)add the coding vector as a new row to the existing coding coefficient matrix, and perform gauss-jordan elimination to update the set of seen packets. 4)add the payload to the decoding buffer. perform the operations corresponding to the gauss-jordan elimination, on the buffer contents. if any packet gets decoded in the process, deliver it to the tcp sink and remove it from the buffer. 5)generate a new ack with sequence number equals to that of the oldest unseen packets and add two variables send_count and seen_count to the ack header. when an ack arrives from the tcp sink: 1)if the ack is a control packet for connection management, deliver it to the ip layer; else, ignore the ack. delay. we compare our scheme anc with sanc-tcp and tcp-vegas under both fixed and unfixed loss rate. we use tcp-vegas as the transport layer protocol, and set the parameters of tcp-vegas to α = 28, β = 30, γ = 2. this setting is identical with tcp/nc [3]. the values of a and b in our scheme are set to a = 0.2 and b = 0.8 individually. figure 2: a tandem network consisting of 6 hops 4.1 results for fixed random loss rates the topology in this simulation is a tandem network consisting of 6 hops, as shown in figure 2. the sender and the receiver are at opposite sides of the chain. in contrast to traditional wireless networks, wireless sensor networks generally have a lower bandwidth for data transmission. we experiment with two different bandwidth settings: (a) bandwidth = 1 mbps and (b) bandwidth = 250 kbps which is the channel bandwidth of ieee 802.15.4. in the following experiments we assume that the source always has data to send. set the link bandwidth to 1 mbps in this experiment, each link has a bandwidth of 1 mbps, and a propagation delay of 10 ms. the buffer size on the links is set to 200 packets. the packet size is 1000 bytes, and the random loss rate is varied from 0% to 20% on each link. the simulation time is 1000 seconds. the throughput obtained corresponding to different random loss rates is plotted in figure 3. our scheme anc and sanc achieve a similar throughput, but the throughput of vegas decrease rapidly when the random loss rate increases. the adjustment of the redundancy factor r that we proposed in subsection 3.1 makes the sender to send out the proper number of redundant linear combinations so that the random loss rate can be masked effectively. as a result, we can get a fairly well throughput. adaptive network coding scheme for tcp over wireless sensor networks 807 0 200 400 600 800 1000 0 2 4 6 8 10 12 14 16 18 20 t h r o u g h p u t( k b p s) random loss rates (%) sanc anc vegas figure 3: throughput 0 50 100 150 200 250 300 350 0 2 4 6 8 10 12 14 16 18 20 d e la y (m s) random loss rates (%) sanc anc figure 4: average delay figure 4 shows the average delay of anc and sanc. compared with the sanc, our scheme have lower average delay in every case, moreover, the average delay of our scheme is almost half of the average delay of sanc. this is because we limit the coding window size which would decrease the complexity of decoding and reduce the time that packets remain in the decoding buffer. thus, we can have a lower delay time than that of sanc. 0 50000 100000 150000 200000 250000 0 2 4 6 8 10 12 14 16 18 20 n u m b e r o f p a c k e ts random loss rates (%) sanc anc vegas figure 5: the total number of packets 0 200 400 600 800 1000 1200 1400 1600 1800 0 2 4 6 8 10 12 14 16 18 20 d e la y (m s) random loss rates (%) sanc anc figure 6: maximum delay figure 5 shows the total number of packets that have been send for sanc, anc, and vegas. this figure is essentially identical to figure 3 except the y-axis represents the total number of sent packets. as shown in figure 5, the metric of total number of sent packets for anc and sanc outperform vegas as the loss rate increases. with regard to the simulation of delay, we also retrieve the maximum delay at various loss rates. as illustrated in figure 6, the maximum delay of sanc rises significantly with loss rate, while our scheme rise slightly. furthermore, the maximum delay of sanc is at least two times more than anc in most cases. set the link bandwidth to 250 kbps the channel bandwidth of ieee 802.15.4 is 250 kbps [10]. many wsns adopt ieee 802.15.4 for communicating among nodes. the characteristics of ieee 802.15.4 technology includes low rate, low transit distance, low power, low cost, simple architecture, small size, etc. all these characteristics are applied to the applications of wsn. the experimental parameters in this simulation are identical to the setting used in subsection 4.1.1, except that the bandwidth of each link is 250 kbps, and the buffer size on the links is set to 50 packets. besides, we simulate the effect of two different packet sizes on the performance. the packet sizes are set to 1000 bytes and 200 bytes. 808 y.-c. chan, y.-y. hu 0 50 100 150 200 250 0 2 4 6 8 10 12 14 16 18 20 t h r o u g h p u t( k b p s) random loss rates (%) sanc anc vegas (a) throughput 0 100 200 300 400 500 600 700 800 0 2 4 6 8 10 12 14 16 18 20 d e la y (m s) random loss rates (%) sanc anc (b) average delay figure 7: the throughput and delay when packet size is 1000 bytes 0 50 100 150 200 250 0 2 4 6 8 10 12 14 16 18 20 t h r o u g h p u t( k b p s) random loss rates (%) sanc anc vegas (a) throughput 0 20 40 60 80 100 120 140 160 180 200 0 2 4 6 8 10 12 14 16 18 20 d e la y (m s) random loss rates (%) sanc anc (b) average delay figure 8: the throughput and delay when packet size is 200 bytes as the link capacity and the buffer size become smaller, our scheme anc still has high throughput and low delay, as shown in figure 7. however, it must take a longer delay to transmit a large packet (1000 bytes) if the link capacity becomes smaller. this is demonstrated through the simulation results, when the loss rate is 20%, anc’s average delay is 189.77 ms in figure 4 where the bandwidth is 1 mbps, while anc’s average delay is 627.58 ms in figure 7(b) where the bandwidth is 250 kbps. then, we change the packet size to 200 bytes. figure 8(a) demonstrates that when the loss rate is 0%, the throughput of our scheme anc is lower than sanc and vegas. this is because that the coding window size we adjust according to subsection 3.2 is not big enough in such a network environment, thus the available network resources is restricted by coding window and result in lower throughput. that is to say, when the packet size is very small, we must have a sufficient coding window size to take full advantage of network resources. in figure 8(b), the average delay of our scheme is lower than sanc in every case. in addition, the average delay in figure 8(b) is lower when compared with the figure 7(b) because the smaller packet size has the lower transmission time. 4.2 results for unfixed random loss rates all previous simulations focus on the behavior of anc under the fixed loss rate. now, we evaluate its performance in an unknown environment with unfixed loss rate. we use the same topology and parameters as 4.1.1. the background loss rate in this scenario is 2%. the loss rate adaptive network coding scheme for tcp over wireless sensor networks 809 is changed to 6% from 200 to 400 second, and changed to 10% from 600 to 800 second. total simulation time is 1000 seconds. in figure 9, the x-axis represents the simulation time, and the y-axis represents the average throughput. it clearly shows that anc and sanc can quickly and effectively handle the sudden bursty losses by adjusting the redundancy factor r dynamically, but vegas is seriously affected by bursty losses. in figure 10, we show the comparison of delay between anc and sanc. the delay of sanc increases rapidly when the loss rate is suddenly changed at 200 second and 600 second. in contrast to sanc, anc can still maintain quite low latency under the changed loss rate. this is because our scheme anc can constantly measure the packet loss rate of network and further adjust the coding window size to get low delay. based on the above experimental results, we can demonstrate that even under the unknown environment with bursty losses, our scheme anc still can send enough linear combinations to compensate the lost packets. thus, anc can reach high throughput and keep low delay through the adjustment of r and cw . 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 t h r o u g h p u t( k b p s) time(s) snac anc vegas figure 9: the average throughput under bursty losses figure 10: the delay evolution under bursty losses 4.3 results in a more realistic environment in this subsection, we compare our scheme with sanc-tcp and tcp-vegas in a wireless topology shown in figure 11. there are five nodes (four hops) in this topology. the distance between each node is 30 meters. table 4 is a parameter table which shows the parameter settings that used in the performance test. the simulation time is 100 seconds. the ftp flow stars at 1 second. in this experimental environment, some packets can be lost for reasons other than congestion. figure 11: wireless topology the experimental results show that the throughput of anc and sanc are 272.0 kbps and 271.9 kbps respectively. both of them are outperform vegas whose throughput is 260.5 kbps. the comparison of rtt between anc and sanc is shown in figure 12. at the beginning of this experiment, we can see the rtt for both schemes are rising rapidly, then the rtt of anc is maintained between 9 to 14 milliseconds that is lower than the rtt of sanc which is maintained between 15 to 20 milliseconds. figure 13 presents the average rtt and maximum 810 y.-c. chan, y.-y. hu table 4: parameter table parameter value packet size 200 bytes buffer size 50 packets buffer management scheme droptail link bandwidth 2 mbps transmission range 40 meters carrier sensing range 90 meters mac protocol csma/ca routing algorithm dsr rtt of anc and sanc. it is clear anc has lower average rtt and maximum rtt than sanc. as mentioned before, this is because we limit the coding window size, and then the complexity of decoding can be decreased. thus, packets will be discarded earlier from the decoding buffer. according to the experimental results, our scheme anc can also have a good performance in such an environment. 0 5 10 15 20 25 0 10 20 30 40 50 60 70 80 90 100 r t t (m s) time(s) sanc anc figure 12: rtt of anc and sanc 0 5 10 15 20 25 average rtt maximum rtt r t t (m s) anc sanc figure 13: average rtt and maximum rtt of anc and sanc 5 conclusions in this paper, we introduced a network coding scheme called adaptive network coding (anc) that can be applied on wireless sensor networks. the objectives of our study are to mask packet losses by transmitting redundant linear combinations and to reduce the decoding delay of network coding by limiting the coding window size. we compare our scheme with sanc-tcp and tcp-vegas in difference random loss rates, different link bandwidths, and difference packet sizes. from the experimental results obtained, our scheme has a lower delay than sanc-tcp in all experimental environments without sacrificing throughput. besides, both the throughput of our scheme and sanc-tcp are significantly better than that of tcp-vegas in most cases. it is important to note that the adjustment of coding window size can be investigated in more detail so as to improve the throughput when tcp runs on a harsh network environment. a possible direction for further study is to allow intermediate nodes to perform the encoding and decoding whenever they receive packets. that means encoding and decoding operations are done in a hop-by-hop manner. in this way, the network coding can be used for a wide variety of topologies of wireless sensor networks. adaptive network coding scheme for tcp over wireless sensor networks 811 bibliography [1] r. ahlswede, n. cai, s.-y. r. li, and r. w. yeung, network information flow. ieee trans. on information theory, 46(4):1204-1216, jul. 2000. [2] d. silva and f. r. kschischan, universal secure network coding via rank-metric codes, ieee trans. on information theory, 52(2): 1124-1135, feb. 2011. [3] j. k. sundararajan, d. shah, m. medard, m. mitzenmacher, and j. barros, network coding meets tcp, 2009 proceedings of ieee infocom, 280-288, apr. 2009. [4] c. fragouli, j.-y. le boudec, and j. widmer, network coding: an instant primer, acm sigcomm computer communication review, 36(1): 63-68, jan. 2006. [5] yao-nan lien, hop-by-hop tcp for sensor networks, international journal of computer networks & communications, 1(1):1-16, apr. 2009. [6] c. wang, k. sohraby, b. li, m. daneshmand, and y. hu, a survey of transport protocols for wireless sensor networks. ieee network magazine, 20(3): 34-40, jun. 2006. [7] j. k. sundararajan, d. shah, and m. medard, arq for network coding, in proc. of ieee international symposium on info. theory (isit), 1651-1655, jul. 2008. [8] j. chan, l. liu, x.hu, and w. tan, effective retransmission in network coding for tcp. int j comput commun, issn 1841-9836, 6(1):53-62, 2011. [9] s. song, h. li, k. pan, j. liu, and shuo-yen robert li, self-adaptive tcp protocol combined with network coding scheme, in international conference on systems and networks communications (icsnc), 20-25, oct. 2011. [10] f. xia, a. vinel, r. gao, l. wang, and t. qiu, evaluating ieee 802.15.4 for cyber-physical systems, eurasip j. wireless commun. and networking, 1-15, feb. 2011. international journal of computers communications & control issn 1841-9836, 12(4), 507-518, august 2017. an approach for construction of augmented reality systems using natural markers and mobile sensors in industrial fields d. gomes jr., p. reis, a. paiva, a. silva, g. braz jr., m. gattass, a. araújo daniel lima gomes júnior* 1. federal institute of maranhão, são luís, ma, brazil 2. applied computing center federal university of maranhão, são luís, ma, brazil *corresponding author: daniellima@ifma.edu.br paulo roberto jansen dos reis, anselmo cardoso de paiva, aristófanes corrêa silva, geraldo braz junior applied computing center federal university of maranhão, são luís, ma, brazil jansen@nca.ufma.br, paiva@deinf.ufma.br, ari@dee.ufma.br, geraldo.braz@ufma.br marcelo gattass tecgraf institute/puc-rio, rio de janeiro, rj, brazil mgattass@inf.puc-rio.br antônio sérgio de araújo hydroeletric company of são francisco, recife, pe, brazil asergio@chesf.gov.br abstract: this paper presents a methodology for the development of augmented reality (ar) visualization applications in industrial scenarios. the proposal presents the use of georreferenced natural markers detected in real time, which enables the construction of ar systems. this use of augmented visualization allows the creation of tools that can aid on-site maintenance activities for operators. ar use makes possible including information about the equipment during a specific procedure. in this work, the detection of natural markers in the scene are based on haar-like features associated with equipment geolocalization. this approach enable the detection of equipment in multiple user’s viewpoints in the industrial scenario and makes it possible the inclusion of real information about those equipment in real time as ar annotations. in this way, beyond a methodology approach, this paper presents a new way for power system information visualization in the field that can be used in both for training and for control operations. keywords: augmented reality, data visualization, natural markers. 1 introduction augmented reality (ar) is associated with the insertion of additional information or virtual objects superimposed or combined into a visualization of the real world [3]. the use of ar techniques allows to magnify user’s visual experience with the addition of information to the visualized real scene. it contributes to a potential increase of user’s comprehension about the physical environment where the user stands. therefore, information or images generated by computers need to be registered to the real scene. it means that they need to be precisely and contextually positioned in the real world visualization. in addition, it allows a big variety of applications on several areas such as medicine, education, and industrial activities. industrial companies demand for applications that use ar technology for training and also for control and operation procedures. this technology has been used in a quite limited form, copyright © 2006-2017 by ccc publications 508 d. gomes jr., p. reis, a. paiva, a. silva, g. braz jr., m. gattass, a. araújo in areas such as architecture, engineering, and civil construction [6]. the main limitation in these areas are related to high precision processing in real time. another limiting factor is the complexity of the detection process and the object tracking in these scenes. this demands the use of strategies and algorithms of computer vision (cv) which permit a suitable time of processing for the visualization. a strategy generally used to reduce processing time during object detection is the use of fiducial markers in the physical environment [25]. this is the insertion of objects or marks of low complexity, highly distinct of the present objects in the scene that allow to decrease the necessary computing effort for the correct spatial orientation in an ar environment. although the use of these markers increase robustness and reduce significantly the computational cost, it has the disadvantage of requiring the inclusion of artificial marker in the real environment. besides this, there are safety restrictions in industrial environments and incidental displacement or destruction of these markers result in the immediate inoperability of the ar system. using another type of ar marker, known as natural marker, the elements that already exist in the scene are detected directly without the need of fiducial markers inclusion. this approach can use the equipment of the industrial environment as markers for an ar application in an industrial plant, for example. the detection of these objects require extraction of the image features and comparison with patterns previously registered. this is considered one of the most complex tasks for the creation of ar applications. although there are techniques which address the feature detection problem and object recognition [17] [14], processing time is still a challenge for these systems, since in ar applications, detection needs to be done in real time. also there is a demand for ar applications that offer support to operators during real maintenance procedures of equipment in the field. in general, in these situations, data from equipment sensors are sent to a distant control room. the operators make decisions based on these information and in some circumstances there is the need of operators to move from the site to get access to the information. this could generate a situation in which information can be modified during the route between control room and maintenance site. this methodology proposes a solution for the creation of ar applications which enables a visualization of these information in the maintenance site, enabling a decentralization of control rooms. this work presents a methodology which is composed by three main steps: the creation of an ar marker using a computer vision technique, followed by using sensors (gps and compass) from mobile device to perform the correct identification of an equipment. finally, it is presented the integration with equipment data. in our case study it is used the communication and integration with supervisory control and data acquisition systems/energy management system (scada/ems). so, this methodology enables the creation and use of ar in industrial outdoor scenarios. the investigation method is based on the association of location data sensors and the detection of real scene objects that are used as natural markers. this way, we search to contribute for the use of full potential of ar application in industrial environments aiming the improvement of maintenance tasks in these scenarios. this paper is organized as follows. the section 2 presents and discuss several related works and in section 3 the technical background on cv and ar. from section 4 to section 6 we present the methodology steps described above. in section 7 we present the results from a case study related to substation scenario followed with conclusion and future works. 2 related work in [31] the concept of ar as a novel way to enhance visualization is used for educational purposes. in this work the authors highlight the need of registration, because the change of an approach for construction of augmented reality systems using natural markers and mobile sensors in industrial fields 509 camera viewpoint changes the position that the virtual element have in the scene. this is an obstacle to build ar mobile applications. the use of applications in mobile devices in real time has grown rapidly and several applications for these device formats have been developed. in [12], simple techniques of image processing are used in smartphones for detection of fiducial markers to include 3d elements in real scenes in real time. this related research are enabled by the evolution of the devices and its processing capabilities in the last years. also, according to [7], the correct position identification to include virtual information is the key for ar. in [29] there is a strategy presented for detection of ships on the african coast which uses cascade classifiers based in haar-like features. the work shows a detection system which generally uses the vessel’s transponder for monitoring, but in sabotage cases or in transponder shutdown, the solution is to detect it with radar images and a combination of techniques. the use of techniques such as template matching demonstrate a creation approach of ar environments based in image processing. besides that, the adoption of mobile devices brought a great advance for ar systems and, according to [26], generally techniques similar to surf (speededup robust features) [?] have been used for natural markers detection. object detection is a fundamental task for ar applications to be used in outdoor environments. the detection results may be used for objects recognition, tracking, and construction of environment maps. in [11] the detection is applied for the use in security robots of the substation with use of object detection algorithm with processing based in cloud computing. another interesting strategy presented is to reduce the comparison area with costly algorithms such as surf and sift [17] to optimize the processing. several works like [16] and [8] point to the question of data communication and architecture questions for electric power systems. although the emergence of new devices suggest possibilities of using newer technologies in these scenarios. our work present an approach for new ways of visualization in power system substations. despite the importance of new visualizations for power systems, accordingly to [20], in the last 20 years there were few proposals or new ideas in visualization approaches for electric system data. even though some works were done like [18], [19], [23], [33] and [24]. the above mentioned analysis concludes that visualizations must have the intention of replacing textual data or numeric information and must be explored the visual patterns for a mapping, the most natural possible way, aggregating meaning of the data for the visualization. information must be understood naturally and colors must be used, but carefully not to generate discomfort for system operators. a more recent work [1] proposed the use of qr code for ar markers and the use of iec 61850 communication for automation integrated with scada information. according to the authors, many solutions use ar applications for simulation and operator training, and there are several situations where it might be advantageous to have the capability of annotating the real process with information. besides all important contributions, the above-mentioned solution still needs to include the fiducial markers in real equipment, differently from proposed in our work. 3 computer vision and augmented reality we can define ar as a system that combines real world visualization with virtual information objects, requiring real time interactivity and 3d world registration. tracking and registration is one of the fundamental tasks for ar systems. these systems must work in real time and, aim to get a credible scene for the user, the real and virtual cameras should be mapped in such a way that both environments precisely match. thus ar system can take advantage of cv techniques. because ar can rely on visual features that are naturally present on the real scene, avoiding the need for engineering the environment. 510 d. gomes jr., p. reis, a. paiva, a. silva, g. braz jr., m. gattass, a. araújo cv aims to obtain geometrical, topological or physical information from an image and the objects which are present in that image. these information may allow the recognition of patterns, object classification, robot movement, among other possibilities. furthermore, the digital images carry with them information such as colors, light intensity that permit an image analysis through image processing. to suply the ar need for tracking, fiducial markers are commonly used. a fiducial marker is an easily detection used as a point of reference to an object targeted for tracking. an options is to use natural information of the scene (natural markers). thus, computer vision techniques can be used with additional information such as georeferencing data, gyroscope data, thermal sensors, among others sensors. these natural markers generally are specific for each type of application and the object type to be identified. for processing the inclusion of 3d virtual elements, there is a need to align the object coordinate system to the world coordinate system for a proper virtual information placement in the real scene. 4 natural 3d marker for detection of objects using haar-like features to use natural markers, we need to detect the objects that are in the scene image. this depends on the correct selection of the representation model. there are several techniques to detect objects in scene and each different kind of object changes the detection approach. in some situations it is necessary to modify the whole training, even using the same detection technique. the proposed methodology uses a technique proposed in [32] and extended by [15]. this is a a robust framework for the construction of fast object detectors, using machine learning, that reach high detection rates. the version used includes a rectangular feature rotation, enriching the detection algorithm and keeping calculation efficiency. combining the aspects of the works mentioned above, the proposed 3d marker uses cascade classifier based on haar features. the process is based on two main stages of training. firstly, the classifier is trained with images containing the object to be detected and these images are called positive examples. during processing these images are resized into a commom size defined empirically. the second stage is responsible for training the negative examples, which are arbitrary images that do not contain the object trained in first step. our approach have used several images from real industrial environment in both stages. this is because there is the requirement to detect an object regardless of where the user stands, the proposed work used images from every side of the equipment for the object detector’s training. figure 1 shows that each image matches an equipment side that is used in training process. the overall process of training and detection is presented in figure 2. the equipment detection process is performed for each frame acquired from the camera. it is initially performed necessary adjustments in the image related to camera position configuration and operational system, then it executes related procedures for feature detection in image with cascade classifier. this work have used transformer initially as a case study for detection, since this one is one of the most important equipment inside the substation. as result of the training, a three dimensional natural marker is created, in which object detection is performed independently of user’s viewpoint. using this approach, it is possible to build ar environments with use of 3d natural markers, without the need to include fiducial markers. the advantages of using natural markers can be seen in table 1. it is worth mentioning that an approach for construction of augmented reality systems using natural markers and mobile sensors in industrial fields 511 figure 1: image acquisition from different sides of object for training the cascade classifier figure 2: processes from training to detection of objects in camera the natural marker corresponds to an object that already exists on scene without modification of it. table 1: comparison of ar markers technical feature fiducial marker qr code natural marker training independence no yes no model storing local internet local or internet environment remains unchanged no no yes multiple viewpoint detection with a single loaded marker no no yes the first step for the creation of the natural 3d marker is the detection. after the correct detection of the object for each camera frame of camera in real time, the next steps consist in associating this information with predefined information about the objects existing in scenario and the use of sensors to be combined with result of detection. 5 sensors for automatic identification of equipment associated to detection after the equipment detection we need to identify it. this is a required task to allow the query of the equipment information from automation and control systems. the methodology 512 d. gomes jr., p. reis, a. paiva, a. silva, g. braz jr., m. gattass, a. araújo proposed herein suggests the integrated use of more than one sensor type. the sensors are used in identifying the natural 3d markers. most devices, such as smartphones and digital cameras, have auxiliary sensors for geolocation and orientation. for applications that do not need big precision, only using gps sensor is possible to solve location problem that has been used mainly in situations with map localizations. in outdoor applications, such as detection of an equipment inside an industrial area, the use of gps data does not only constitute a reasonable solution. due to the error inherent to gps localization (up to 7.8 meters with 95% of confidence). but, this error can present variation depending on atmospheric effects, reception quality and sky blockage [10]. there are some frameworks for construction of ar environment that use just gps data as main source of information. these frameworks add virtual information to the real world but this gps error variation causes annotation out of place in real application. besides gps, this methodology also uses compass or magnetometer sensor. this sensor works like a pointer to the north pole. after the transformer detection, the algorithm considers three main variables: the operator location at the time of the image acquisition from mobile device, the transformer’s location present in the substation and the compass orientation sensor for field of view (fov) calculation from the user point of view, as shown in figure 3a. the white circles in figure 3b are user position or equipment inside the substation. (a) image from real substation (b) field of view calculated with equipment inside figure 3: the proposed method for equipment identification from gps integrated information, compass, user’s location, transformers location and image obtained from camera. the fov is computed from intrinsic parameters of the camera, as described in equation 1. the α angle is half of fov and used to identify which equipment are inside the obtained image. α = arctan s 2f s = sensor dimension f = focal length (1) besides that, we use camera information such as sensor size and focal distance to compute the fov to verify objects inserted inside the camera frame. by obtaining this fov, the objects position is analyzed in relation to the central line of fov and the boundary lines, right and left, in relation to user’s viewpoint. with this information it is possible to identify which equipment were detected in the image. with this intention the distance is calculated to the central line and the object is detected at the right side or at the left of the observer (figure 4). this approach still allows to identify more than one detected object on the same image. an approach for construction of augmented reality systems using natural markers and mobile sensors in industrial fields 513 figure 4: identification of objects inside the observer viewpoint. due to degree of reliability of gps data in low cost sensors, this proposal can be used on outdoor environments. in indoor scenarios the gps error increases and the application behavior depends on these data. the strategy of joining 3d natural markers associated with georeferenced data and compass minimizes the error compared to use only gps data. this strategy works as an auxiliary information layer, containing detection data of the desired object, inside the ar system. 6 communication and integration to scada/ems after detection and identification of the equipment inside the ar scenario, a data request is performed about the equipment to scada/ems communication system based on identified equipment tag. each equipment has a unique tag inside the open system for energy management (sage) and through this tag the associated information is obtained. with this methodology is possible to build ar systems, including real information which could help the system operators in control process and problem identification inside the energy substation, on the field. besides that, critical information may be specified, showing just alert states based on defined alarms in the system. decreasing the data quantity observed by operator. this integration architecture is based in agitoserver [27], in which control systems are accessed through interfaces via remote calls (rpc) provided by sage and operator training simulator (ots). integration with database provide access and control to modify data via tcp/ip protocol available in socket format. after that, it is possible to send and receive messages with data of the power system in json format (figure 5). this architecture allows perform queries to equipment data, such as equipment electric parameters, operation state (open or closed switch), among others. through message exchange model, this solution allows to obtain the desired information request about the equipment and add it to the visualization. furthermore, the ots training environment is similar to real environment, only with simulated data of operation state of the systems and chosen obviously in this way for safety reasons of the electric system and enabling consultation application tests to the data. 514 d. gomes jr., p. reis, a. paiva, a. silva, g. braz jr., m. gattass, a. araújo figure 5: architecture of acquisition of integrated data to the sage/ots 7 results and case study: ar visualization in energy substation the evaluation of the proposed methodology was applied using mobile devices, which allow rendering different visualization formats. it might enable the construction of two augmented reality versions of application: a tablet view version with a single display and a head-mounted display (hmd) version using smartphone integrated to google carboard sdk [9]. this possibility of including a mobile tool for operators enables that the user can perform information acquisiton in the operation field, with no need of returning to operation center to access a given system which contains desired information. by using the auxiliary use of ar it is possible to add information about the task being executed during operation. images used in this study were obtained from real operating environments of chesf. more specifically at chesf substations suape ii and suape iii in recife-pe, and at chesf substation extremoz in natal-rn, all located in brazil. the approach used cascade classifier based in haar-like features for transformer detection. in this approach the classifier training is performed with negative and positive images set and detection quality depends on the amount of training images. the obtained results initially used a base of positive images (transformer pictures) and negative images, which include basically areas around the substation itself do not containing the transformer. the results can be seen in table 2 using amount of p = 419 (positive images) and n = 270 (negative images) during training and also, for the detection test, a total of 40 pictures of equipment were used. for elimination of false-positives, the haar cascade method demands an image base as large as possible, and this statement is valid both for positive images as for negative ones. these tests show that as we increase the quantity of stages, the algorithm minimizes incorrect detection features. but, to be able to increase equipment detection in these conditions is necessary to increase the training base. applications have been developed for devices with android operational system and they have been installed in tablet version (single view) as shown in figure 6 and hmd combined with an approach for construction of augmented reality systems using natural markers and mobile sensors in industrial fields 515 table 2: equipment detection with haar cascade classifier cascade stages false positives (fp) total features detection rate (%) 14 95 222 78.05% 15 73 181 78.05% 16 54 131 80.49% 17 36 120 80.49% 18 22 99 82.93% 19 13 87 82.93% 20 11 73 80.49% 21 10 66 82.93% 22 1 46 70.73% 23 0 33 53.66% 24 0 31 48.78% 25 0 24 43.90% 26 0 18 34.15% smartphone (using stereoscopic view) in figure 7a and figure 7b. figure 6: ar application visualization in tablet (single view) the work proposes real data integration after stages taken related to equipment detection system. the sage system is widely used by several electric power system companies in brazil. according to [5] the sage system may be used in substations and power plant and it supports several hardware, including different manufacturers. in addition to sage, the use of ots has permitted an environment for training operators without the necessity of being connected to real equipment. thus, it enables running several simulations of possible energy system scenarios. 516 d. gomes jr., p. reis, a. paiva, a. silva, g. braz jr., m. gattass, a. araújo (a) stereoscopic view of object detection (b) augmented user visualization integrated with ots data figure 7: ar application visualization in hmd (stereoscopic view) 8 conclusion and future work this work presents two main contributions: the first one, and most important, it is a methodology for creation of ar systems using georeferenced natural 3d markers. it uses image processing techniques and sensor information present in mobile devices to provide identification for detected objects in outdoor environments. this contribution has applicability in others scenarios and several areas of application. the second contribution is the application constructed itself using the natural marker and integration with mobile sensors, as a case study. the application integrated with scada/ems and equipment’s real information create a new visualization format in power system operational environment. this last innovation intends to improve the data visualization inside industrial environments. this new visualization is applied in stereoscopic view display, which enables the operator to modify the real world through addition of virtual elements (information or auxiliary data of equipment state). it allows the effective creation of an innovative ar solution and the use of this technology can be used no only for training but also for operation tasks. so, the next step will be experimenting this methodology in real industrial environments. there are many points to test about its usability, mainly on the stereoscopic visualization, due the need of reduce operator’s visual discomfort infringed by the use of hmd for several hours. we also will conduce experiments to detect the pose of the object and allow a virtual analysis of it. bibliography [1] antonijević m.; sučić s.; 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(2000); voltage contours for power system visualization, ieee transactions on power systems, 15(1), 404-409, 2000. international journal of computers communications & control issn 1841-9836, 10(1):136-146, february, 2015. design of robust fuzzy sliding-mode controller for a class of uncertain takagi-sugeno nonlinear systems x.z. zhang, y.n. wang x.z. zhang* 1. school of electrical and information engineering hunan university china, 410082 changsha, yuelushan 2. cooperative innovative center of wind power hunan institute of engineering china, 411104 xiangtan, fuxing road 88 *corresponding author: zxz@hnie.edu.cn y.n. wang school of electrical and information engineering hunan university china, 410082 changsha, yuelushan yaonan@hnu.edu.cn abstract: this paper presents the fuzzy design of sliding mode control (smc) for nonlinear systems with uncertainties, which can be represented by a takagi-sugeno (t-s) model. there exist the parameter uncertainties in both state and input matrices, as well as the matched external disturbance. the key feature of this work is the great ability of the controller to deal with systems without assuming that the control matrices of each local t-s model to be same and knowing the priori information of the upper norm-bounds of uncertainties. a sufficient condition for the existence of the desired fuzzy smc is obtained by solving a set of linear matrix inequalities (lmis). the reachability of the specified sliding surface is proven. a numerical example is illustrated in order to show the validity of the proposed scheme. keywords: uncertain t-s nonlinear system, sliding mode control, linear matrix inequalities (lmi), system stabilitybooks, examples. 1 introduction it has been shown that takagi sugeno (t-s) fuzzy model is a powerful and efficient tool to handle complex nonlinear system, and be employed in most model-based fuzzy analysis approaches [1, 2]. since the parallel distributed compensation (pdc) approach is formulated by tanaka and wang [3], the stability analysis and stabilized controller design for t-s fuzzy systems become achievable and have been applied in a wide range of areas such as electrical/mechanical systems [4, 5], process control [6], robot [7] and time-delayed systems [8]. a lot of research outcomes on this field have been appeared in literatures. practically, a real nonlinear system may contain various kinds of uncertainties such as parameter variations, modeling errors and external disturbances, etc. in this case, the pdc-based controllers [3] may perform well no longer. then, the robustly stabilized control to uncertainties of nonlinear fuzzy systems is required. based on the t-s fuzzy model, wu studied the robust h2 fuzzy observer-based control problem for discrete-time nonlinear systems with parametric uncertainties [9]. lin, et.al, investigated the mixed h2/h∞ controller for uncertain t-s fuzzy systems with discrete time-varying delay [10]. it’s well known that the sliding mode control (smc) is an effective means to design robust controllers for nonlinear systems with uncertainties bounded by known scalar-valued functions. recently, the smc has been successfully applied into solving the stabilization problems for uncertain fuzzy systems [11, 12]. specially, under the smc control the state trajectories would copyright © 2006-2015 by ccc publications design of robust fuzzy sliding-mode controller for a class of uncertain takagi-sugeno nonlinear systems 137 enter into the pre-designed sliding-mode motion within finite time and after that keep staying on it; thus, the system dynamics is not sensitive to parameter variations and external disturbances any more [9, 13]. basically, the feedback gains of the smc control are often determined by the feasible solution of a set of linear matrix inequalities (lmi). choi presented a robust stabilization of uncertain fuzzy systems using smc system approach [14]. zhang and wang proposed a mixed smc-h∞ controller for time-delay system with unmatched uncertainties [15]. although many researchers have proposed a variety of t-s fuzzy smc-based methods [4, 7– 10,14], by far there still two problems yet remained to be well-solved: (1) the suitable relaxation on the assumption that all the control matrices of the nominal sub-systems’ models are identical; it is practically difficult to satisfy this assumption. in practice, this assumption is very strict and insufficient to model various uncertainties/nonlinearities in most of actual systems such as nonlinear stirred tank reactor, fourth-order cart-pole system, and active queue management in tcp networks and two-link robot manipulator; (2) the reasonable assumption that the system uncertainties/perturbations are unknown but norm-bounded. for the smc-based methods, if uncertainties are known, it’s easy to choose proper switching gains, which are bigger than the upper norm-bounds of uncertainties, to ensure the reachability of sliding-mode motion. unfortunately, the information of the upper bound of uncertainties/perturbations may not easily be obtained in practice. therefore, it’s supposed to adopt parameter identification or adaptive control approach to estimate the bounds of uncertainties on-line [16]. motivated by the above discussion, it is meaningful to design fuzzy variable structure controller in this paper such that the closed-loop system is asymptotically stable without (1) assuming the control matrices of each local linear model to be same; and (2) knowing the priori information of the upper norm-bounds of uncertainties. the existence condition of linear sliding surfaces and the asymptotical stability of the reduced-order equivalent sliding-mode dynamics are firstly derived by the lmi optimization technique. after the establishment of the local t-s model, the upper norm-bounds of uncertainties and modeling errors between the original and local systems are estimated. then, the designed smc controller composes of a state-feedback control term and an adaptive switching-feedback control term, which are achieved based on the lyapunov function method. as a result, the aforementioned problems are well solved in the proposed scheme. finally, a numerical example is illustrated in order to show the effectiveness of the proposed methods. 2 problem formulation and preliminaries as stated in introduction, t-s fuzzy models can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input output sub-models. hence, in this work, a class of nonlinear systems is represented by a t-s model. as in [3], the t-s fuzzy system with uncertainties is described by fuzzy if then rules, which locally represent linear input-output relations of nonlinear systems. the i-th rule of the fuzzy model is formulated in the following equation: plant rule i: if z1 is mi1 and z2 is m i 2 · · · and zp is m p 1 , then ẋ(t) = aix(t) + biu(t) (1) where mij is the fuzzy set, z(t) = [z1(t),z2(t), · · · ,zp(t)] t is the premise variable vector, r is the number of rules of this t-s fuzzy model. x(t) ∈ rn is the state vector, u(t) ∈ rm is the control input vector. ai,bi are known real constant matrices with appropriate dimensions. 138 x.z. zhang, y.n. wang the overall fuzzy model achieved by fuzzy blending of each plant rule is represented as follows: ẋ(t) = r∑ i=1 hi(z)[aix(t) + biu(t)] (2) where hi(z(t)) = wi(z(t))∑r j=1 wi(z(t)) ,wi(z(t)) = ∏p j=1 m i j(z(t)), in which m i j(z(t)) is the membership grade of zj(t) in mij. according to the theory of fuzzy sets, we have m i j(z(t)) ≥ 0. therefore, it implies that hi(z(t)) ≥ 0 and ∑r i=1 hi(z(t)) = 1. in practical applications, the system (2) usually exists the parameters variation and the external disturbances. after considering the perturbation, the global fuzzy model of system (2) can be rewritten as ẋ(t) = r∑ i=1 hi(z)[(ai + ∆ai)x(t) + (bi + ∆bi)u(t) + di(t,x,u)] (3) where ∆ai, ∆bi are unknown time-varying matrices representing parameter uncertainties, and di(t,x,u) denotes the external disturbance. in this work, the control matrices doesn’t satisfy b1 = b2 = ... = br and we define the weighted nominal matrix as b = ∑r i=1 hi(z)bi. moreover, it is required that b is a nonzero matrix with full-column (or full-row) rank. the uncertain matrices are assumed to be matched, i.e. there exist certain functions d(t), e(t) and f (t) such that ∆ai(t) = bid(t), ∆bi(t) = bie(t) and di(t,x,u) = bif (t) hold. as a result, the system (3) can be rewritten as ẋ(t) = r∑ i=1 hi(z)aix(t) + b[u(t) + g(t,x,u)] (4) where the time-varying function g(t,x,u) = d(t)x(t)+e(t)u(t)+f (t), then g(t,x,u) contains all the perturbation in system (3). before proceeding, some assumptions and lemmas are given as following, which are useful for the development of our result. (assumption.1) the time-varying uncertainties g(t,x,u) is assumed to be norm-bounded, that is, ∥g∥ ≤ ∑n−1 k=0 γk∥x(t)∥ k, where γk is unknown coefficient and n is a positive integer. remark 1: assumption 1 is a standard assumption in the study of variable structure control. (lemma.1 choi [14]): give any matrix x, y , z with appropriate dimensions, and y > 0. then we have −xtz − ztx ≤ xty x + zty −1z. (lemma.2 schur’s complement [3]): given the matrix inequality [ s11 s12 st12 s22 ] < 0, where s11 and s22 are invertible symmetrical matrices, it’s equal to each of the following inequalities: (i) s11 < 0, s22 − st12s −1 11 s12 < 0; (ii) s22 < 0, s11 − s12s −1 22 s t 12 < 0. 3 controller design the objective of this work is to design an smc law such that the desired control performance for the resulting closed-loop system is obtained despite of parameter uncertainties and unmatched external disturbance. in this section, an smc law is synthesized such that the closed-loop systems are robustly asymptotically stable. it is also proven that the reachability of the specified switching (sliding) surface s(t) = 0 can be ensured by the proposed smc law. thus, it is concluded that the synthesized smc law can guarantee the state trajectories of uncertain system (4) to be driven onto the sliding surface, and asymptotically tend to zero along the specified sliding surface. design of robust fuzzy sliding-mode controller for a class of uncertain takagi-sugeno nonlinear systems 139 3.1 design of the sliding function and stability analysis of the sliding motion essentially, a vsc design is composed of two phases: hyper-plane design and controller design [9]. there are various methods for designing hyper-plane, however in this paper the switching surface is defined as s(t,x) = cx(t) (5) where c ∈ rm×n is the designed coefficient. according to the previous works [2], for the system (2), there are two prerequisites to find the switching surface (5) that is (p-1): the matrix cb is invertible for any hi(z) satisfying hi(z) ≥ 0 and ∑r i=1 hi(z) = 1. (p-2): the reduced sliding-mode motion of the system dynamics restricted on the switching surface is asymptotically stable to all admissible uncertainties. in this part, we analyze the dynamic performance of the system described by (4), and derives some sufficient conditions for the asymptotically stability of the sliding dynamics via lmi method. the following theorem shows that system (4) with the switching surface as in (5) is asymptotically stable. theorem 1. consider the fuzzy uncertain systems (4) with assumptions (1). the switching function is given by (5). if there is feasible solution q such that the lmis shown in (6) hold for ∀i,j,k ∈ 1, ...,r, the proper sliding-mode coefficient c exists and c = (btq−1b)−1bt .  aiq + qa t i qb t j a t i btj q −b t j bk 0 ai 0 −i   < 0 (6) where the invertible matrix q ∈ rn×n is decisive variable. proof: first, the existence of the coefficient c is proved. since q is designed to be invertible, the inequalities bt b ̸= 0 and bt q−1b ̸= 0 hold; thus, its easy to prove that the achieved matrix cb is invertible and (cb)−1 = (bt b)−1(bt q−1b). that is to say, if the coefficient is chosen as c = (bt q−1b)bt , both the sliding surface and the equivalent control exist. with the chosen sliding surface, once the n-order system enters the m-order sliding surface s = cx(t), the system dynamic of (4) is equivalent to the (n−m)-order sliding motion, and the system states will asymptotically converge to zero with proper switching gains. in the following, we will derive the equivalent control and the sliding mode as well as the stability analysis. now, the linear transformation t is carried on the states to separate the m-th sliding-mode states and the reduced (n − m)-order states t = [ t1 t2 ] = [ (kt qk)−1kt q (bt q−1b)−1bt ] (7) where t1 ∈ r(n−m)×n, t2 ∈ rm×n and k ∈ rn×(n−m is an orthonormal basis for the null space of bt such that bt k = 0 and kt k = i satisfy. we have z = t x(t) = [ z1 z2 ] (8) in (8), it’s obvious that z2 = (bt q−1b)bt x(t) = cx(t) is the sliding-mode state and z1 is the reduced-order state. its easy to obtain the inverse transformation t −1 = [k,q−1b]. 140 x.z. zhang, y.n. wang by differentiating the above transformation, we can obtain ż = t x(t) = r∑ i=1 hi(z) [ t1 t2 ] ai [ k q−1b ] t x(t) + [ t1 t2 ] b[u + g(t,x,u)] = r∑ i=1 hi(z) [ t1aik t1aiq −1b t2aik t2aiq −1b ][ z1 z2 ] + [ t1b t2b ] [u + g(t,x,u)] (9) in (9), it’s not difficult to verify that t1b = (kq−1k)kt b = 0 and t2b = cb. according to the sliding mode theory, let ṡ = s = ż2 = 0, we have ż2 = r∑ i=1 hi(z)(t2aikz1 + t2aiq −1bz2) + t2b[u + g(t,x,u)] = r∑ i=1 hicaix(t) + t2b[g(t,x,u)] (10) from (16), the following equivalent control can be derived ueq(t) = −g(t,x,u) − r∑ i=1 hi((z)(t))(cb) −1caix(t) (11) substitute ueq(t) in (11) into (10), the reduced-order sliding motion in the switching surface can be obtained as ż1 = r∑ i=1 hi(z(t))(k t qk)−1kt [i − b(cb−1c)]aikz1 (12) to analyze the stability of the sliding-mode dynamics (8), we consider the fuzzy uncertain system (4) with lmis in (6) and choose the lyapunov functional candidate v1 = zt1 pz1, where p = kt qk is a positive matrix. by differentiating the function v1, we obtain the differential along the trajectories as v̇1 = z t 1 p ż1 + ż t 1 pz1 = r∑ i=1 hi(z) [ zt1 p t1(i − b(cb) −1cai)kz1 + z t 1 k t ati (i − b(cb) −1c)t t t1 p tz1 ] = r∑ i=1 hi(z) [ zt1 k t q(i − b(cb)−1c)aikz1 + (∗) ] = r∑ i=1 hi(z) [ wt (t)q(i − b(cb)−1c)aikw(t) + (∗) ] = r∑ i=1 hi(z)w t (t) {[ qai + (∗) ] − [ qb(cb)−1cai + (∗) ]} w(t) (13) where the defined new state vector w(t) = kz1 ∈ rn×1. by now, the stability of the states x(t) in the switching surface is equivalent to the stability of the news states w(t). noticing that b(cb)−1c = b(bt b)−1bt , it is easy to have v̇1 < 0 if the following inequality holds[ qai + (∗) ] − qb(bt b)−1bt ai − ati b(b t b)−1bt q ≤ 0 (14) design of robust fuzzy sliding-mode controller for a class of uncertain takagi-sugeno nonlinear systems 141 according to lemma 2, let x = qb(bt b)−1bt , z = ai and y = i > 0, the inequality in (21) implies that [ qai + (∗) ] + qb(bt b)−1bt + ati ai ≤ 0 (15) according to the inequalities (14), (15), we have [ qai + (∗) ] − [ qb(bt b)−1bt ai + a t i b t (bt b)−1bt q ] ≤ 0 (16) by using schur’s complement formula and noticing that bt b ≥ 0, we can rewrite the above inequality as   aiq + qa t i qb a t i bt q −bt b 0 ai 0 −i   < 0 (17) from (17) and noticing the definition of the matrix b and bt b = ∑r j=1 ∑r k=1 hj(z)hk(z)b t j bk, we can deduce the following inequality v̇1 = r∑ i=1 r∑ j=1 r∑ k=1 hi(z)hj(z)hk(z)w t (t)   aiq + qa t i qb t j a t i btj q −b t j bk 0 ai 0 −i  w(t) ≤ 0 (18) and this leads to for ∀i,j,k ∈ 1, ...,r   aiq + qa t i qb t j a t i btj q −b t j bk 0 ai 0 −i  w(t) ≤ 0 (19) this means that the sliding surface (5) and the equivalent control (11) for the fuzzy system (4) with assumption 1 exist and the sliding motion is asymptotically stable. 2 3.2 design of controller and adaptive laws as the last step of design procedure, we will further design the vsc controller ensures the reachability of the specified switching surface. the adaptive vsc controller is represented in a set of fuzzy rules as following controller rule i: if z1 is mi1 and z2 is m i 2 · · · and zp is m i p, then u(t) = −(cb)−1 [ caix(t)+∥n(t)∥∥ḣα∥∥x(t)∥sgn(s)+∥cb∥(ρ̂0+ρ̂1∥x(t)∥)sgn(s)+εsgn(s) ] (20) the global fuzzy vsc and the adaptation laws are designed as u(t) = − r∑ i=1 hi(z)(cb) −1[caix(t) + φ̂(t,x)sgn(s)] (21) with the following adaptive laws 142 x.z. zhang, y.n. wang   φ̂(t,x) = ∥n(t)∥∥ḣα∥∥x(t)∥ + 1∑ k=0 ρ̂k∥cb∥∥x(t)∥k + ε ˙̂ρ0 = γ0∥st (t)∥ ∥∥ r∑ i=1 hi(z)cbi ∥∥ ˙̂ρ1 = γ1∥st (t)∥ ∥∥ r∑ i=1 hi(z)cbi ∥∥∥x(t)∥ (22) where the adaptation rates γi > 0, the constant ε > 0, and the functions n(t), hα will be defined in the following theorem, which gives the stability analysis when the controller (30) is enforced on the system (4) and the reachability of the specified sliding surface s(t) = 0 can be obtained. theorem 2. for the uncertain fuzzy systems (4) with the switching function (5), q is the feasible solution of lmis (6). then it can be shown that the state trajectories of the system (4) will be driven onto the switching surface s(t) = 0 and asymptotically converge to zero by the adaptive smc law in (20),(21) and (22). proof: for purpose of design integrity, a simple stability analysis based on lyapunov direct method is carried out. define the lyapunov function candidate v2(t) = 1 2 st s + 1∑ k=0 1 2γk ρ̃2k ≥ 0 (23) where the estimation error ρ̃k = ρ̂k − ρk. by differentiating the switching surface s(t), we have ṡ(t) = c(t)ẋ(t) + ċ(t)x(t) (24) in (24), by following from lemma 1 we have ċ(t) = d dt (htαphα) −1bαhα = n(t)ḣα(t) (25) where hα(t) = [h1, ...,hr], bα(t) = [b1, ...,br]t , p = bαq−1btα , n(t) = [ −(htαphα)−1htα(p + p t )(htαphα) −1bαhα + (h t αphα) −1bα ] then, it follows from (4) that we have ṡ(t)s(t) = c(t)ẋ(t)c(t)x(t) + n(t)ḣα(t)x(t)c(t)x(t) (26) by substituting the controller (21) into (26), we have st (t)ṡ(t) = st (t) [ r∑ i=1 hi(z)caix(t) + cbu(t) + n(t)ḣα(t)x(t) + r∑ i=1 hi(z)cbig(t,x,u) ] = st (t) [ n(t)ḣα(t)x(t) + cbg(t,x,u) − φ̂(t,x)sgn(s(t)) ] ≤ ∥st (t)∥∥n(t)∥∥ḣα∥∥x(t)∥ + ∥st (t)∥∥cb∥ 1∑ k=0 ρk∥x(t)∥k − φ̂(t,x)∥s(t)∥ = ∥st (t) [ ∥cb∥ 1∑ k=0 ρk∥x(t)∥k − ∥cb∥ 1∑ k=0 ρ̂k∥x(t)∥k − ε ] (27) design of robust fuzzy sliding-mode controller for a class of uncertain takagi-sugeno nonlinear systems 143 by differentiating the function v2 and substituting the adaptive laws (22) into (27), the simplified expression of (27) can be obtained as v̇2(t) = s t ṡ + ˙̃ρ0 γ0 (ρ̂0 − ρ0) + ˙̃ρ1 γ1 (ρ̂1 − ρ1) = −ε∥st (t)∥ (28) noticing that ϵ > 0, thus the derivative v̇2(t) < 0 when s(t) ̸= 0, which implies that under the controller (21) and (22) the reachability of the specified switching surface is guaranteed, and the trajectories of the fuzzy uncertain system (3) are globally driven onto the specified switching surface s(t) ̸= 0. moreover, it is seen that the estimation error ρ̃k will converge to zero. 2 4 numerical simulation to show the effectiveness of the proposed controller design techniques, the inverted pendulum with parametric uncertainties, which is taken from wu and juang [16], is formulated for simulation. the control objective is to drive its state trajectories to the origin. the equations of motion for the inverted pendulum device are  ẋ1(t) = x2(t) ẋ2(t) = −f1mlx2cosx1 − m2gl2sinx1cosx1 + (j + ml2)[mlx22sinx1 − f0x4 + u(t) + d(t)] m̄(j + ml2) − m2l2cos2x1 ẋ3(t) = x4(t) ẋ4(t) = −f1m̄x22 − m 2l2x22sinx1cosx1 + f0mlx4cosx1 + m̄mgsinx1 − mlcosx1[u(t) + d(t)] m̄(j + ml2) − m2l2cos2x1 where x1 denotes the angle (rad) of the pendulum from the vertical,x2 is the angular velocity (rad/s), x3 is the displacement (m) of the cart, and x4 is the velocity of the cart.g = 9.8m/s2 is the gravity constant, m is the mass (kg) of the pendulum, m is the mass (kg) of the cart, f0 is the friction factor (n/m/s) of the cart, f1 is the friction factor (n/rad/s) of the pendulum, l is the length (m) from the center of the mass of the pendulum to the shaft axis,j is the moment of inertia (kg.m2) of the pendulum round its center of mass, and u(t) is the force (n) applied to the cart. the model parameters are given as: m̄ = m + m,m = 1.3282kg, m = 0.22kg, f0 = 22.915n/m/s, f1 = 0.007056n/rad/s, l = 0.304m, j = 0.004963kg.m2 in the numerical simulation. it’s assumed that d(t) is bounded by d(t) ≤ ρ0 + ρ1∥x∥, where ρi is unknown parameter. the fuzzy model of system is described as the following two rules: plant rule i: if x1(t) is mi1, then ẋ(t) = ai + bi [ u(t) + g(t,x). the model parameters are given as a1 =   0 1 0 0 29.2529 −0.3149 0 44.1811 0 0 0 1 −1.2637 0.0136 0 −16.7096  , a2 =   0 1 0 0 22.0587 −0.2872 0 20.1425 0 0 0 1 −0.4765 0.0062 0 −15.2361  , b1 =   0 −1.9280 0 0.7292  , b2 =   0 −0.8790 0 0.6649  . the membership functions are selected as m1(x) = [1 − 1/(1 +e−7(x1−π/24))]/(1 +e−7(x1+π/24)), m2(x) = 1−m1(x).due to b2 ≤ b1, the stabilization result (zheng et al., 2002) is invalid. the perturbation is set to be g(t,x) = [0.1sint,0.05sint]t , the initial states x(0) = [π/3,0,π/5,0]. to assess 144 x.z. zhang, y.n. wang the effectiveness of our fuzzy controller, we apply the controller to the original system (12) with nonzero d(t). we choose the adaptation parameters γ0 = 0.001, γ1 = 0.1.via lmi optimization with (19), we obtain the feasible solutions and the switching suafrace. the simulation results are given in figures 1-2. it is seen that the reachability of the sliding motion can be guaranteed. the system enters sliding-mode motion after about t=0.8 second. from figure 1, one can see that the system states converge to zero fast, furthermore, the simulation results also show that our present design effectively attenuates the effect of both parameter uncertainties and external disturbances. in figure 2, the control effort is shown and approaches to be stable after a short-term adjustment in the initial stage. 0 0.5 1 1.5 2 −10 −5 0 5 10 15 20 time(s) st a te t ra je ct o ri e s x x1 x2 x3 x4 figure 1: trajectories of states x1, x2, x3 and x4 0 0.5 1 1.5 2 −250 −200 −150 −100 −50 0 50 time(s) co n tr o l i n p u t figure 2: control input design of robust fuzzy sliding-mode controller for a class of uncertain takagi-sugeno nonlinear systems 145 5 conclusions and future works this paper has generalized the t-s fuzzy model to represent a class of nonlinear systems which includes parameter uncertainties or external disturbances. a novel adaptive vsc control scheme has been proposed for the uncertain model, which relaxes the restrictive assumption that the input matrices of the local sub-models are identical and needs no information of uncertainties. the overall fuzzy vsc controller of the system is achieved by fuzzy blending of the local vsc controller. the existence condition of linear sliding surfaces guaranteeing asymptotic stability of the equivalent dynamics is derived as well as the stability analysis. finally, a numerical design example is illustrated in order to show the effectiveness of our scheme. acknowledgment the authors are grateful to the support of the national natural science foundation of china (61203019), the china postdoctoral science foundation funded project (2012m521518), the key projects of chinese ministry of education (no.212122), the department of education (14a032) and innovative research team in higher educational institutions of hunan province, and the natural science foundation of hunan provincial (no.13jj9019). bibliography [1] takagi, t.; sugeno, m. (1985); fuzzy identification of systems and its applications to modeling and control, ieee trans. syst., man, cybern., issn 0018-9472, 15(1): 116-132. [2] feng, g. (2006); a survey on analysis and design of model-based fuzzy control systems, ieee trans. fuzzy syst., issn 1063-6706, 14(5): 676-697. [3] tanaka, k.; ikeda, t.; wang, h.o. (1998); fuzzy regulators and fuzzy observers: relaxed stability conditions and lmi-based designs, ieee trans. fuzzy syst., issn 1063-6706, 6(2): 250-265. [4] zhang, x. z.; wang, y. n. (2009); fuzzy variable structure control based on a takagisugeno model for permanent magnet synchronous motors, journal of systems and control engineering, issn 0959-6518, 223(6): 773-783. [5] besheera, a. h.; emarab, h. m.; azizb, m. m. a.(2009); wind energy conversion system regulation via lmi fuzzy pole cluster approach, electric power system research, issn 03787796, 79(6): 531-538. 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[10] lin, y.c.; lo, j.c.(2005); robust mixed h2/h∞ filtering for discrete-time delay fuzzy systems, international journal of system science, issn 1841-9836, 36(15): 993-1006. [11] wang y.n.; zhang x.z.et.al.(2011); position-sensorless hybrid sliding mode control of electric vehicles with brushless dc motor, ieee trans. veh. technol., issn 0018-9545, 60(2): 421-432. [12] zheng, f.; wang, q. g. et.al.(2002); output tracking control of mimo fuzzy nonlinear systems using variable structure control approach, ieee trans. fuzzy syst., issn 1063-6706, 10(3): 686-697. [13] cao, w. j.; xu, j. x.(2004); nonlinear integral-type sliding surface for both matched and unmatched uncertain systems, ieee trans. automat. contr., issn 0018-9286, 49(8): 13551360. [14] choi, h. h.(2008); robust stabilization of uncertain fuzzy systems using variable structure system approach, ieee trans. fuzzy syst., issn 1063-6706, 16(3): 715-724. [15] zhang, x.z.; wang, y.n.et.al.(2010); h∞ robust t-s fuzzy design for uncertain nonlinear systems with state delays based on sliding-mode control, international journal of computers communications & control, issn 1841-9836, 5(4): 592-602. [16] wu, t.z.; juang, y.t.(2008); design of variable structure control for fuzzy nonlinear systems, expert systems with applications, issn 0957-4174, 35(3): 1496-1503. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 403-417 identification of erd using fuzzy inference systems for brain-computer interface i. dzitac, t. vesselényi, r.c. ţarcă ioan dzitac aurel vlaicu university of arad department of mathematics-informatics elena dragoi st., 2, complex m arad, romania e-mail:ioan.dzitac@uav.ro tiberiu vesselényi, radu cătălin ţarcă university of oradea faculty of management and technological engineering universitatii st. 1, 410087 oradea, romania e-mail:tvesselenyi@yahoo.co.uk, rtarca@uoradea.ro abstract: a brain-computer interface uses measurements of scalp electric potential (electroencephalography eeg) reflecting brain activity, to communicate with external devices. recent developments in electronics and computer sciences have enabled applications that may help users with disabilities and also to develop new types of human machine interfaces. by producing modifications in their brain potential activity, the users can perform control of different devices. in order to perform actions, this eeg signals must be processed with proper algorithms. our approach is based on a fuzzy inference system used to produce sharp control states from noisy eeg data. keywords: event related desynchronization (erd), brain-computer interface (bci), electroencephalography (eeg), fuzzy inference system. 1 introduction devices that are using brain (cortical or scalp) electric potentials which allow brain function signals to control machines (or computers) are called brain-computer interfaces (bci). it had been shown that bci systems using steady state visual evoked potentials have allowed healthy subjects to communicate with computers [1]. non-invasive, electroencephalogram (eeg)-based bci technologies can be used to control a limb prothesis, for the use of a computer program, and for other functions such as environment control or entertainment. bci technologies can improve the lives of people with neurological disorders and also can restore motor control by guiding activity-dependent brain plasticity [2]. a review of the advantages of bcis which shows that they can provide even a good candidate for space applications is presented in [3]. some bci implementations are using sensory-motor rhythms as it is presented in [4]. the interest shown for bci technology is motivated by the applicability for helping disabled, for gaming, and as a tool in cognitive neuroscience [5]. in [6] a model is proposed which includes a set of definitions of the typical entities encountered in a bci, diagrams which explain the structural correlations among them and a detailed description of the timing of a trial. in this paper we present some of our original experiments with bci, and eeg data processing methods applied. copyright c⃝ 2006-2011 by ccc publications 404 i. dzitac, t. vesselényi, r.c. ţarcă 2 bci applications important application for bcis can be found in the medical field. a good example for this is presented in [7] where bci methodology based on self-regulation of slow-cortical potentials of the eeg is used. this kind of two choice task is called "brain-switch" [8], but there are other applications in which multiple choices are employed. another important application of bci is represented by cognitive experiments during which the researchers try to better understand some sequences of brain functions. in [9] for example the researchers use information derived from previous eeg recordings to inform the analysis of functional magnetic resonance imaging (fmri) data collected for the same behavioral task. robotics and environment control applications are also emerging in the studies of this area. for example in [10] a study was made in order to determine the most discriminative features for a bci system based on statistically significant differences between two energy density maps calculated from eeg signals during two different motor tasks. in [11] it is shown that simple rehabilitation tasks can be performed and household robotic devices can be easily controlled by means of bci technology. robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for noninvasive bci applications. a review of current robotic technologies that are relevant to bci is presented in [12]. as it can be seen from the references in this field, employment of prothesis and rehabilitation is a largely studied area in the field of bci applications [13], [14], [15]. several other applications of bci are designed for entertainment, gamming and learning to concentrate or to achieve a certain desired mental state. 3 considerations on eeg signals and signal processing methods used in bci most of the bci devices developed by now are based on the electric potentials of scalp known as eeg, which is noninvasive opposing to those based on cortical potentials where the electrodes are in direct contact with the cortex (electrocorticography ecog). a special method is using eeg signals combined with an appropriate model of volume conduction and of neuro-electrical sources (high-resolution electroencephalography) [16]. in order to intentionally generate the electrical potentials of the brain two kinds of tasks are widely used which are also correlated with different regions of the brain: the sensory-motor (or sometimes motor imagery) tasks and those tasks which imply some kind of processing called mental or cognitive-related tasks (i.e. simple syntactic analysis of words). the sensory-motor tasks are by far mostly used because their direct implication in limb movement [17], [4], [18]. examples of mental or cognitive-related task studies are given in [19] and [20]. in sensory-motor tasks an often used method is to employ event-related desynchronization (erd) which is a reduction in eeg signal amplitude in a specific frequency band (8 12 hz) as a result of a particular event. when a subject is making a hand movement, or even just imagine it, a reduction in sensory-motor signal amplitude can be observed in certain parts of the scalp. in order to use this in a bci application the proper components of the signal must be found. the signals measured by sensors, particularly those used in noninvasive techniques, are known to be very noisy due to a series of potentials generated by other biological or artificial sources than brain potentials. one of the major challenges of bci devices is to filter out the unwanted components of the signals. here both hardware and software solutions are to be applied. the other major task is to uniquely recognize a specific pattern in the useful component of the signals which is meant to trigger a specific command. the number of sensors placed in specific areas identification of erd using fuzzy inference systems for brain-computer interface 405 of the scalp can vary from 1 to 36 which are read in on different channels of the device. each of these channels can be filtered separately to monitor a specific, larger or narrower, frequency domain. researchers applied a wide range of techniques to filter, preprocess and analyze signals and to produce decisions. statistical approach. in [21] the researchers studied spike sorting by introducing a mathematical model consisting of three gaussian waveforms, which appropriately represents the general shapes of action potentials. then they searched for the best-fit waveform for each noise-corrupted spike based on the model, using peak fitting method. the performance of the proposed method was assessed with synthesized neural recordings composed by spike templates and white gaussian noise in various signal to noise ratio environments. gaussian process (gp) classification to binary discrimination of motor imagery eeg data is described in [22]. a robust bayesian linear regression algorithm is presented in [23] that automatically detects relevant features and excludes irrelevant ones, all in a computationally efficient manner. spectral methods. in [24] the researchers investigated spectral power changes both in intracranial ecog recordings in epilepsy patients and in non-invasive eeg recordings optimized for detecting high gamma band (a specific frequency domain in eeg signals) activity in healthy subjects. in order to characterize the non-gaussian information contained within the eeg signals, a new feature extraction method based on "bispectrum" is proposed in [25] and applied to the classification of right and left motor imagery. in [26] a novel method is presented for detecting frequency-frequency coupling between the electrical output of cortical areas as measured by ecog, electroencephalography eeg and magnetoencephalography meg, the biphase-locking value. the method is specifically sensitive to non-linear interactions, i.e. quadratic phase coupling across frequencies. this method was employed to study signals measured from pre-motor area, in the motor cortex. wavelet methods. wavelet transforms are applied in [27] to study motor imagery tasks. in this work the continuous wavelet transform (cwt) was applied together with student’s twosample t-statistics for 2d time-scale feature extraction, where features are extracted from eeg signals recorded from subjects performing left and right motor imagery. in [28] a novel method was proposed for the feature extraction of electroencephalogram (eeg) based on wavelet packet decomposition (wpd). in [29] the assessment of wavelet transform (wt) as a feature extraction method was used in representing the electrophysiological signals. genetic algorithms. positive potentials at a latency of about 300 ms in eeg (p300) are largely used to study brain functions. this pattern is used as sign of cognitive function in decision making processes. in [30] p300 detection approach based on some features and a statistical classifier was implemented. the optimal feature set was selected using a genetic algorithm from a primary feature set including some morphological, frequency and wavelet features and was used for the classification of the data. artificial neural networks and support vector machines. in [31] the authors investigated if an artificial neural network-based model for closed-loop-controlled neural prostheses could use neuromuscular activation recorded from individuals with impaired spinal cord to predict their end-point gait parameters (such as stride length and step width). this ann-based model allows a seamless incorporation of neuromuscular activity, detected from paralyzed individuals, to adaptively predict their altered gait patterns, which can be employed to provide closed-loop feedback information for neural prostheses. support vector machine implementations are used in [32], [33], [34]. the results indicate that the proposed algorithms are promising for future use of rehabilitative bci applications in neurologically impaired patients. fuzzy logic. in [35] a new method is introduced, which combines information from different classic time series similarity measures, using a fuzzy fusion framework. this method is accurate 406 i. dzitac, t. vesselényi, r.c. ţarcă and reliable in p300 detection. this framework is used to combine two computationally simple signal detection methods: "peak picking" and "template matching". fusion takes place in the last step (decision-making step) by means of a fuzzy rule-base. in order to predict the gripping force from the eeg signals in [36] a methodology that uses subsequent signal processing methods is used: filtering, principal component analysis, and the phase-demodulation method. a fuzzy inference system is then used to predict the gripping force from the processed eeg data. neuro-fuzzy methods are also to be considered as a highly efficient way to deal with uncertain information. the paper [37] introduces a number of modifications to the learning algorithm of the selforganizing fuzzy neural network (sofnn) to improve computational efficiency. an analysis of the sofnn effectiveness when applied in an electroencephalogram (eeg)-based brain-computer interface (bci) involving the neural-time-series-prediction-preprocessing (ntspp) framework is also presented, where a sensitivity analysis of the sofnn hyper-parameters was performed using eeg data recorded from three subjects during left/right-motor-imagery-based bci experiments. the aim of this analysis was to eliminate the need to choose subject and signal specific parameters for eeg preprocessing. the results indicate that a general set of ntspp parameters chosen provide the best results when tested in a bci system. a new brain-computer interface design using fuzzy artmap (fa) neural network, as well as an application of the design is proposed in [38]. the objective of this bci-fa design is to classify the best three of the five available mental tasks for each subject using spectral amplitude values of electroencephalogram (eeg) signals. the findings show that the average bci-fa outputs gave very low error using the best triplets of mental tasks identified from the classification performances of fa. in [39] a subject-based feature extraction method using the fuzzy wavelet packet in brain-computer interfaces (bcis) is presented. in [40] is presented a new method introduced in [34], which combines information from different classic time series similarity measures, using a simple fuzzy fusion framework. this method is accurate and reliable in p300 (a positive event-related component occurring 300ms after stimulus onset) detection. fusion takes place in the decision-making step by means of a fuzzy rule-base. compared to similar works on electroencephalogram-based (eeg-based) bci datasets, in spite of being computationally simple, this new technique’s performance is comparable to very complicated methods. in [41] an analyze of electroencephalogram (eeg) signals of imaginary left and right hand movements, an application of brain-computer interface (bci) is presented. the researchers propose here to use an adaptive neuronfuzzy inference system (anfis) as the classification algorithm. anfis has an advantage over many classification algorithms in that it provides a set of parameters and linguistic rules that can be useful in interpreting the relationship between extracted features. the continuous wavelet transform is used to extract highly representative features from selected scales. 4 method and results the 10-14 hz frequency band of eeg signals is, among others, associated with those cortical areas that are most directly connected to the brain’s normal motor output channels. movement or preparation for movement is typically accompanied by a decrease of amplitude in this band over sensory motor cortex. this decrease has been labeled "event-related desynchronization" or erd [42]. opposite to erd, amplitude increase, or "event-related synchronization" (ers) occurs in the post-movement period and with relaxation. furthermore, and most relevant for bci applications, erd and ers occur also with motor imagery (i.e., imagined movement) and they do not require actual movement. in this research our goal is to analyze eeg signals by observing erd and ers characteristics identification of erd using fuzzy inference systems for brain-computer interface 407 and find a suitable algorithm to automatically detect these characteristics by using a fuzzy inference system. we consider that given the high noisiness of eeg signals a fuzzy or neurofuzzy approach would be desirable. in our experiments subjects were asked to perform several hand movements at irregular times (with random intervals between two movements) during a session of 40 seconds. the hand movement consists of pushing a switch button. the subjects were also asked to concentrate on the action (hand movement) imagining it before. the eeg signals collected from a single electrode placed in c3 point on the scalp were registered. electrode placement location is shown in figure 2. the switch on-off states were also registered using a separate acquisition device which is triggered to start data acquisition at the same time as the eeg signal acquisition, so the eeg signals and the switch on off states data covers the same time period. figure 1: theoretical eeg signals in 12 hz frequency band. a) resting hand; b) moving hand (amplitude decrease erd). a total of five healthy male adults (aged 25-48) participated in this study. none of the subjects had participated in the same experiments before and the subjects had a short basic training for the task. the subjects were informed about the nature and purpose of the experiments and consented to participate. eeg signals were acquired using a bst112 8-channel amplifier from veb messgeratewerk zvoniz and for a/d conversion and transmission of data to a pc, a national instruments ni-usb 6251 daq board had been used. the accuracy of the measuring system is of 1.5259µv. the ag-agcl electrode was placed in the c3 point on the scalp, using a conductive gel. as voltage reference, the a1 point was used (figure 2) and the ground was located on the right leg. data were sampled at a sample rate of 1000 hz and were transmitted through the usb port to a pc for storage and processing. the data acquisition and processing programs were written in matlab language. every subject has made 3 training sessions to accommodate with the environment and the equipment and then 4 sessions of 40 seconds each were recorded. figure 2: location of electrode on the scalp in point c3. the diagram of a sample acquisitioned signal is given in figure 3. as it can be observed the signal is affected by low level noise resulting in a variable offset across it’s whole length. 408 i. dzitac, t. vesselényi, r.c. ţarcă figure 3: a sample of acquisitioned eeg signal. this can be eliminated by using a running average filter which gives local average values which then are extracted from the original signal (seeg1). if n is the length of the signal v (in samples) and m is the running average filter length given by the vector u, then the length of the average will be m+n-1. the running average w is given by convolution of the filter vector with the signal as it is shown in formulae 1: w(k) = ∑ j u(j) · v(k − j + 1) (1) where k is the index of the resulting signal w and j is the index of the filter u. in this case all values of u are equal to 1/m. in this case we used m = 200, which for a sampling rate of 1 ks/sec will not generate errors for the studied frequency band (centered on 12 hz). the running average values are shown in figure 4 (red). according to the convolution theorem, that convolution of signal and the digital filter’s impulse response is nearly the same as multiplying their fourier transforms. this can be used to speed up the calculations if it can be shown that the results are accurate up to an acceptable limit. figure 4: detail of the acquisitioned signal for a 1 second time span (black). running average values of the signal (red). after extraction of the running average the resulting signal (seeg2), is free of low frequency noises as it can be seen in the diagram given in figure 5. a further enhancement of the signal can be obtained by reducing the random peaks, employing a running average filter of the same form as before, but this time keeping the resulting average values. this has the effect of smoothing the signal without modifying the studied components. identification of erd using fuzzy inference systems for brain-computer interface 409 figure 5: the signal given in figure 3, with running average values extracted. this time we used m = 20. the resulting signal (seeg3) is shown in detail for a time span of 1 second in figure 6. studying this signal we can observe that the dominating frequency is around 12-14 hz but still having some low frequency components. figure 6: detail of processed signal given in figure 5, for a time span of 1 second. in order to further analyze the signal we have to switch to frequency domain analysis because time domain diagrams can no longer be enhanced. in case of frequency domain analysis of acquisitioned digital signals spectral amplitudes are computed using the fast fourier transform (fft which is a variant of the fourier transform used in numeric computing). a fft amplitude value is given by formulae 2: a(k) = n∑ j=1 a(j)ω (j−1)(k−1) n (2) where: ωn = e (−2π·i) n , n number of samples; a(j) amplitude of jth sample in the signal. in order to obtain the required spectrum we will use only the real part of the obtained fft amplitudes (real(a(k))). as we want to analyze changes in the signal the fft algorithm should be applied not on the entire length of the signal but with a suitably sized window which is moved step by step on the whole length of the signal. in our case we used a window of size nfft = 256. the number of partitions is np = integer(m/nfft). some samples at the end of the signal will be lost but these can be only a quarter of a second which in our case is reasonable (given a 40 seconds long signal). 410 i. dzitac, t. vesselényi, r.c. ţarcă representing the result we will obtain a diagram of fft amplitudes as a function of time and frequency as it is shown in figure 7. the results had been interpolated using a cubic spline interpolation method in order to smooth the borders between partitions. figure 7: time frequency diagram of the processed signal. the frequency band centered on 12 hz is clearly observable. if we select a slice of this diagram at a specific moment in time we ca obtain the spectra of the signal. such spectra are given in figures 8 (resting hand) and 9 (moving hand). figure 8: fft amplitudes of the eeg signal for resting hand. figure 9: fft amplitudes of the eeg signal for moving hand. if we represent a slice of the time-frequency diagram for a specific frequency we will obtain identification of erd using fuzzy inference systems for brain-computer interface 411 the variation in time of fft amplitude at that frequency. as erd and ers are occurring in a frequency band of 10-14 hz choosing a center frequency of 12 hz the resulting diagram is shown in figure 10 (black). on this diagram we had superimposed the switch on-off states (red). we can observe that the "on" states (which are showing the real movement of the hand) mainly corresponds to the drop of signal amplitude of the chosen frequency. this should show the occurrence of erd. also there can be observed a rise of the curve corresponding with time intervals when the switch is not activated (the subject is relaxed). this should show the occurrence of ers. in case of erd the anticipation of the real movement, the fact that the amplitude is dropping earlier than the hand movement occurs shows that it happens due to motor imagery. figure 10: fft amplitude versus time for the processed signal at the frequency of 12 hz (black) and switch on-off states diagram (red). as it can be seen in figure 10, and it had been observed in almost all of the studied cases, these characteristics can not be expressed with simple parameters (i.e. value of amplitude or the slope of the amplitude drop). hence, in order to automatically detect erd occurrence in fft amplitude signals some fuzzy or neuro-fuzzy algorithms can be applied which may be capable of recognizing these kinds of amplitude drops. fuzzy logic is widely used in modelling and identification problems and in applications where data sets are obtained with a greater or smaller degree of uncertainty [43], [44], [45]. in the followings we will use an adaptive neuro-fuzzy inference system (anfis) to solve this issue. anfis is implemented in the fuzzy logic toolbox of the matlab environment and uses a hybrid learning algorithm in order to identify parameters of sugeno-type fuzzy inference systems [46]. it applies a combination of the least-squares method and the backpropagation gradient descent method for training membership function parameters to emulate the given training data set. the data sets obtained and processed, as it has been described, will be used for training and checking of the anfis system. after training, this system can be used to identify portions of the eeg signal which meet the erd characteristics. from the acquisitioned and processed data we had chosen 14 intervals which represents true erd characteristics and which we will use to train, check and validate the fuzzy identification algorithm. in order to apply the algorithm the amplitudes must be normalized and the time intervals must be scaled so all the datasets representing erd characteristics will have the same amplitude domain and the same number of samples. for normalization the datasets were divided with the maximum value of each dataset and for time scaling a linear interpolation algorithm had been used. two of the datasets were chosen as input data for training and checking. these datasets are shown in figure 11. another dataset had been chosen as output dataset shown in figure 12. 412 i. dzitac, t. vesselényi, r.c. ţarcă figure 11: normalized fft amplitudes versus scaled time for two erd characteristics; a. training input, b. checking input. figure 12: normalized fft amplitudes versus scaled time, output dataset. in the training process the initial membership functions are modified to fit the training input output dataset and in the checking process these membership functions are tuned in correspondence with the checking input-output datasets. the diagrams of the membership functions obtained after training are given in figure 13.a. and after tuning in figure 13.b. figure 13: membership functions obtained after training (a.) and after tuning (b.). the fuzzy inference system parameters are: number of nodes: 12; number of linear parameters: 4; number of nonlinear parameters: 6; number of fuzzy rules: 2. once the fuzzy inference system was trained and tuned we used 14 erd characteristic identification of erd using fuzzy inference systems for brain-computer interface 413 datasets to verify their outputs (vo) compared to the results obtained for the dataset used as output in the training process (to) which is used as reference. system output of the two different validation datasets are given as examples in diagrams of figure 14. figure 14: validation and training system output data diagrams. for each validation dataset the root mean square (rms) values of the difference between vo and to datasets were computed. these values are shown in the diagram in figure 15. figure 15: rms values of difference between vo and to for 14 validation datasets. in order to identify if a dataset is a true erd component or not the rms values of difference between vo and to can be used as a classifier. on the basis of rms values span obtained for known true erd components we can distinguish erd components in real time acquired signals using simple or complex threshold techniques. 5 conclusions and future works in this work we described our experiments and the development of data processing for eeg signals, focusing on identification of erd characteristics which can be used in brain computer interfaces. the experiments showed that eeg signals had to be carefully acquired and processed in order to identify erd components. data acquisition equipment quality, careful selection of subjects and subject focus capacity are of major importance in achieving successful results. data preparation had been done considering simple and time effective algorithms which can be used for real time processing of eeg signals. 414 i. dzitac, t. vesselényi, r.c. ţarcă further studies must be made to validate the described fuzzy inference system on a larger number of datasets for both true and false erd components in order to establish it’s robustness. there is still in discussion whether to use output in the training process dataset as reference for erd identification or to find another more appropriate reference. in a future work we will try to answer these questions. acknowledgements we thank our colleagues simona dzitac, cristina hora and ioan moga for her/his practical contributions in this research. also we thank the professors hariton costin and horia-nicolai teodorescu for the positive and constructive comments and recommendations on our manuscript, which significantly contributed to improving the quality of this paper. we are very honored to thank professor lotfi a. zadeh for his generous openness in collaborations with romanian researchers and for his visits in romanian cities arad (2003: he was granted as doctor honoris causa of aurel vlaicu university of arad) and oradea (2008: as keynote speaker in our conference icccc 2008 and exploratory workshop on natural language computationewnlc 2008). dear professor zadeh, 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[42] pfurtscheller, g.: eeg event-related desynchronization (erd) and event-related synchronization (ers), in e. niedermeyer, f.h. lopes da silva (eds.) electroencephalography: basic principles, clinical applications and related fields, 4th edition, williams and wilkins, baltimore, md, 958-967, 1999 [43] zadeh l. a., the role of fuzzy logic in modeling, identification and control, modeling, identification and control, 15(3):191-203, 1994 [44] zadeh l. a., fuzzy logic and the calculus of fuzzy if-then rules, proceedings of synapse’91, tokyo, japan, 1991 [45] zadeh l. a., toward extended fuzzy logic-a first step, fuzzy sets and systems, 160: 31753181, 2009 [46] ***, matlab, user guide, mathworks, 2008 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 562-576 axiomatic theory of complex fuzzy logic and complex fuzzy classes d.e. tamir, a. kandel dan e. tamir texas state university, cs department 601 university drive san marcos, texas 78666 e-mail: dt19@txstate.edu abraham kandel university of south florida, cse department 4202 e. fowler ave tampa, florida 33620 e-mail: kandel@cse.usf.edu abstract: complex fuzzy sets, classes, and logic have an important role in applications, such as prediction of periodic events and advanced control systems, where several fuzzy variables interact with each other in a multifaceted way that cannot be represented effectively via simple fuzzy operations such as union, intersection, complement, negation, conjunction and disjunction. the initial formulation of these terms stems from the definition of complex fuzzy grade of membership. the problem, however, with these definitions are twofold: 1) the complex fuzzy membership is limited to polar representation with only one fuzzy component. 2) the definition is based on grade of membership and is lacking the rigor of axiomatic formulation. a new interpretation of complex fuzzy membership enables polar and cartesian representation of the membership function where the two function components carry uncertain information. moreover, the new interpretation is used to define complex fuzzy classes and develop an axiomatic based theory of complex propositional fuzzy logic. additionally, the generalization of the theory to multidimensional fuzzy grades of membership has been demonstrated. in this paper we propose an axiomatic framework for first order predicate complex fuzzy logic and use this framework for axiomatic definition of complex fuzzy classes. we use these rigorous definitions to exemplify inference in complex economic systems. the new framework overcomes the main limitations of current theory and provides several advantages. first, the derivation of the new theory is based on axiomatic approach and does not assume the existence of complex fuzzy sets or complex fuzzy classes. second, the new form significantly improves the expressive power and inference capability of complex fuzzy logic and class theory. the paper surveys the current state of complex fuzzy sets, complex fuzzy classes, and complex fuzzy logic; and provides an axiomatic basis for first order predicate complex fuzzy logic and complex class theory. keywords: fuzzy logic, fuzzy class theory, complex fuzzy logic, complex fuzzy class theory. 1 introduction in 1965, l. a. zadeh has established the theory of fuzzy sets where the degree of membership of an item in a set can get any value in the interval [0, 1] rather than the two values {/∈,∈} [1,2]. copyright c⃝ 2006-2011 by ccc publications axiomatic theory of complex fuzzy logic and complex fuzzy classes 563 in addition he introduced the notion of fuzzy logic [1][4]. which is a multilevel extension of classical logic where propositions can get truth-values in the interval [0, 1], and are not limited to one of the two values {true, false} (or {0, 1}) [3]. the four decades that followed his pioneering work has shown a multitude of research work and applications related to signal processing [5], knowledge representation [6], control theory [4], reasoning [7][9], and data mining [10]. in 1975 zadeh introduced the concept of linguistic variable and the induced concept of type-2 (type-n) fuzzy sets [11, 12]. in recent years, type-1 and type-2 along with interval type1/type-2 fuzzy logic and fuzzy systems have been applied in many areas including signal processing [12], fuzzy clustering [13], data mining [10], and software testing [14]. complex fuzzy sets, classes, and logic have an important role in applications, such as prediction of periodic events and advanced control systems, where several fuzzy variables interact with each other in a complex way that cannot be represented effectively via simple fuzzy operations such as union, intersection, complement, negation, conjunction and disjunction. the initial formulation of these terms stems from the definition of complex fuzzy grade of membership. ramot et al. propose an extension of fuzzy set theory and fuzzy logic where the range of degrees of membership and the range of truth-values is the complex unit circle [15, 16]. these definitions, however, have several constraints. first, the derivation relies on a complex fuzzy membership which is limited to polar representation and carries fuzzy information only in the magnitude component of the function. second, they do not provide an axiomatic approach for their theory. finally, the derivation of complex fuzzy logic assumes complex fuzzy relations thereby presumes the existence of complex fuzzy sets. dick has extended the work by ramot et al., [17], yet his approach is also limited to polar representation with single fuzzy component. tamir et al., provide further generalization of the concept of complex fuzzy membership function and use a cartesian complex fuzzy membership function where both the real part and the imaginary part can be fuzzy functions. alternatively, polar representation where both the magnitude and the phase values of the complex membership function convey fuzzy information, can be utilized [18]. furthermore, they provide a new interpretation of complex fuzzy grades of membership as a representation of a complex fuzzy class along with complex fuzzy class operations. while this formulation has several advantages over previous formalisms, it is still based on the definition of grade of membership which is a limiting factor in the ability to provide a rigorous, axiomatic based, theory. in ref. [19], tamir et al., develop an axiomatic based propositional complex fuzzy logic theory which is independent of complex fuzzy sets, classes and relations. in addition, they demonstrate the potential use of this formalism for inference in complex systems. in this paper we expand the definition of complex fuzzy logic to include axiomatic based definition of first order predicate complex fuzzy logic and use the new formalism to establish an axiomatic framework for complex fuzzy classes. the new theory is compatible with classical logic, as well as with first order predicate fuzzy logic [20][24]. furthermore, the new theory supports cartesian as well as polar representation of complex logical fuzzy propositions with two components of ambiguous information. hence, this form significantly improves the expressive power and inference capability of complex fuzzy logic. the paper reviews the current state of complex fuzzy sets theory, provides a brief overview of complex fuzzy classes, and complex fuzzy logic; and introduces a new and generalized complex fuzzy propositional logic theory. the new formalism can be used in advanced complex fuzzy logic systems and provides ways for extension into multidimensional fuzzy propositional and first order logic. furthermore, it can be used for inference with type 2 (or higher) fuzzy sets [11, 12]. the rest of the paper is organized in the following way: section 2 introduces current research based on the concepts of complex fuzzy sets [15][18]. section 3 provides the axiomatic definition of first order complex fuzzy predicate logic [19]. based on the formulation in section 3, section 564 d.e. tamir, a. kandel 4 contains an axiomatic theory of complex fuzzy classes while section 5 includes conclusions and directions for further research. 2 current and related research on complex fuzzy sets ramot et al., observe that the expressive power of fuzzy set theory and fuzzy logic and the utility of derived applications can be significantly improved via the introduction of complex fuzzy sets [15,16]. their observation is mainly motivated by fuzzy processes that contain periodical behavior such as the cycles in economic markets. in order to capture these phenomena in reasoning, they introduce a complex grade of membership and derive the definition of complex fuzzy sets. later, they introduce complex fuzzy logic via relations on complex fuzzy sets. their formalism, however, is limited due to the fact that they restrict the membership function to representation using polar coordinate system where only the magnitude carries fuzzy information. motivated by similar considerations, tamir et al., extends the formalism proposed by ramot et al., and introduce complex class theory where both component of a complex fuzzy grade of membership carries fuzzy information [18]. this enables reasoning about processes with multi-dimensional components where each component is carrying fuzzy information and the interaction between the components cannot be decomposed and represented via primitive, one dimensional, fuzzy set theory and fuzzy logic operations such as union, intersection, conjunction, and disjunction. the current formulation of complex fuzzy sets, complex fuzzy classes, and propositional fuzzy logic is based on the definition of grade of membership. this section briefly reviews the first two formalisms [15][18]. the next section introduces axiomatic fuzzy logic and complex fuzzy logic theory and includes a discussion on propositional complex fuzzy logic [19]. 2.1 membership-grade based definition of complex fuzzy sets a complex fuzzy set s on a universe of discourse u is defined by a complex-valued grade of membership function µs(x); (ramot et al. [15, 16]): µs(x) = rs(x)e jωs(x) (1) where j = √ −1. this definition utilizes polar representation of complex numbers along with conventional fuzzy set definition; where rs(x), the amplitude part of the grade of membership, is a fuzzy function defined in the interval [0, 1]. and ωs(x) is a real number standing for the phase part of the grade of membership. ramot et al. propose the following operations on complex fuzzy sets: 1) a directional fuzzy complement which induces the following membership function c(µs(x)) = c(rs(x))e j(ωs(x)+θ) (2) where c(f(x))=1-f(x). 2) the union function, ∪, of two complex fuzzy sets a and b induces a membership function µa∪b(x) defined to be: µa∪b(x) = [ra(x) ⊕ rb(x)] · ejωa∪b(x) (3) where ⊕ represents a t-conorm function [24]; and ωa∪b(x) is a real function. 3) the intersection function ∩, of two complex fuzzy sets a and b induces a membership function µa∩b(x) defined to be: µa∩b(x) = [ra(x) ⊕ rb(x)] · ejωa∩b(x) (4) axiomatic theory of complex fuzzy logic and complex fuzzy classes 565 where ⊕ represents a t-conorm function [24]; and ωa∩b(x) is a real function. dick expands the research on complex fuzzy sets [17], [25][30]. in addition, he observes the relations between complex fuzzy sets and complex neural networks where the excitation, the outputs, and weights can obtain complex values [31][35]. buckley introduces the definition of complex fuzzy numbers [36, 37]. complex fuzzy numbers have been utilized in several numerical applications [38, 39]. nevertheless, the concept of complex fuzzy numbers is different from the concept of complex fuzzy sets, classes, and logic [15][19], it is also different from the concepts presented in the current paper. buckley is concerned with generalizing number theory, while references [15][19] as well as the current paper are concerned with the generalization of fuzzy set theory and fuzzy logic. 2.2 membership-grade based definition of complex fuzzy classes in the following sections, a class is denoted by an upper case greek letter, a crisp set is denoted by an upper case latin letter, and a member of a set is denoted by a lower case latin letter. the cartesian and polar representation of a complex grade of membership, respectively are given by: µ(v,z) = µr(v ) + jµi(z),µ(v,x) = r(v )e jσϕ(z) (5) where µr(v ),µi(z),r(v ) and ϕ(z), the real and imaginary components as well as the amplitude and phase components of the complex fuzzy grade of membership, are real value functions over the interval [0,1]. the scaling factor σ is in the interval (0,2π]. it is used to control the behavior of the phase within the unit circle according to the specific application. we adopt the definition of fuzzy classes given by bĕhounek et al., and define a complex fuzzy class γ to be a fuzzy class of order 2 [44,45]. formally, let u be a universe of discourse and let 2u be the power set of u. let f1 be a function from 2u to [0, 1] and let f2 be a function that maps elements of u to the interval [0, 1]. for v ∈ 2u and z ∈ u define µγ(v,z) to be: µγ(v,z) = µr(v ) + jµi(z) = f1(v ) + jf2(z) (6) then, µγ(v,z) defines a fuzzy class of order two, where for every v ∈ 2u, and for every z ∈ u, µγ(v,z); is the degree of membership of z in v and the degree of membership of v in γ. hence, a complex fuzzy class γ can be represented as the set of ordered triples: γ = {v,z,µγ(v,z)|v ∈ 2u,z ∈ u} (7) depending on the form of µγ() (cartesian or polar), µr(), µr(), r(), and ϕ() denote the degree of membership of z in v and / or the degree of membership of v in γ. the role of the real and imaginary parts of the membership function can be interchanged, the same applies to the components of the polar representation. furthermore, the usual definition of coordinate transformation: µγ(v,z) ⇔ r(v )ejϕ(x);µγ(v,z) ⇔ r(v ) + jϕ(x) (8) maintains the semantics of complex fuzzy classes. thus, without loss of generality we assume that: µr(v ) and r(v ) denote the degree of membership of v in γ and that µi(z) and ϕ(z) denote the degree of membership of membership of z in v for the cartesian and the polar representations respectively. one can consider the grade of membership based definition of a fuzzy class of order 1 as a mapping into a one dimensional space and the grade based definition of fuzzy class of order 2 (see [40]) as a mapping into a two dimensional space. hence, it is 566 d.e. tamir, a. kandel possible to consider a degree of membership of order n as a mapping into an n-dimensional space. operations on complex fuzzy classes. consider the two complex fuzzy classes: 1. γ = {v,z,µγ(v,z)|v ∈ 2u,z ∈ u}. 2. ψ = {t,z,µψ(t,z)|t ∈ 2u,z ∈ u}. where v and t are fuzzy classes. assume that γ and ψ are defined over a universe of discourse u and let 2u denote the power set of u. further assume that the degree of membership of an object z ∈ v, and an object y ∈ t is given by: µγ(v,z) = µγr(v ) + jµγi(z);µψ(t,y) = µψr(t) + jµψi(y) respectively, where µγr(α),µψr(α),µγi(α) and µψi(α) stand for the real and imaginary parts of µγ(v,x) and µψ(t,y). finally, let w ∈ 2u, let ⊕ denote a t-conorm operation, and let ⊕ denote a t-norm operation. 1. the complex fuzzy class complement is defined to be: c(µγ(v,z)) = c(µr(v )) + jc(µi(z)) (9) 2. the complex fuzzy class union is defined to be: µγ∪ψ(w,z) = (µγr(v ) ⊕ µψr(t) + j(µγi(z) ⊕ µψi(z))) (10) 3. the complex fuzzy class intersection operation is given by: µγ∩ψ(w,z) = (µγr(v ) ⊕ µψr(t) + j(µγi(z) ⊕ µψi(z))) (11) reference [18] includes several examples of complex fuzzy classes and their applications. 3 axiomatic complex fuzzy logic two main considerations are motivating this section. the first relates to the observations stated in section 2. namely, there are multidimensional fuzzy processes which cannot be decomposed into simple single dimensional logical operations. second, many researchers has observed that the grade based membership approach is limited in its capability to deliver a concise and precise formalism for fuzzy logic [20][24], [40]. instead, current research in fuzzy logic, fuzzy class theory, fuzzy mathematics, and its applications is based on axiomatic theory. in ref. [19] we have presented an axiomatic framework for propositional complex fuzzy logic (cfl). in this section we extend the definition to include first order predicates cfl. the presented framework is based on the basic fuzzy propositional and predicate logic (bl) along with the fuzzy łukasiewicz (ł) and fuzzy product (π) logical systems [21, 22, 40]. we refer to the propositional logic system as łπ and to the first order predicate fuzzy logic system as łπ∀. this section introduces the łπ and łπ∀ fuzzy logic system as well as the łπ and łπ∀ cfl. we use the following notation: a variable is denoted by a lower case latin letter and a predicate/proposition is denoted by an upper case latin letter. 3.1 propositional fuzzy logic several axiom based logical systems have been investigated [20][24], [40]. bĕhounek, et al., ( [40]) use the łπ/ łπ∀ as the basis for the definition of fuzzy class theory (fct). in this section we closely follow łπ the system used by bĕhounek, et al. for clarity, we reintroduce some of the important notions, notations, and concepts from that paper. a fuzzy proposition p can get any truth-value in the real interval [0,1], where ’0’ denotes “false,” and ’1’ denotes “true.” furthermore, the relation ≤, over the interval [0,1] implies a monotonically increasing ordering on the truth-values associated with the proposition. a fuzzy interpretation of a proposition p is an assignment of a fuzzy truth-value to p. let p and q axiomatic theory of complex fuzzy logic and complex fuzzy classes 567 denote fuzzy propositions and let i(p) denotes the fuzzy interpretation of p. table 1, includes the basic connectives of łπ. table 2 includes connectives that can be derived from the basic connectives. the constant 0 is assumed; and the constant 1 can be derived from 0 and the basic connectives. operation interpretation ł-implication i(p→l q) = min(1,1-i(p)+i(q)) π-implication i(p→π q) = min(1,i(p)/i(q)) π-conjunction i(p ⊗ q) = i(p) · i(q) table 1: basic łπ connectives operation interpretation ł-negation i(¬p) = 1 i(p) π-delta ∆(i(p)) = 1 if i(p)=1; else ∆(i(p))=0 equivalence i(p ↔ q) = i(p →l q) ⊗ i(q →l p) p⊖ q i(p ⊖ q) = max(0,i(p) i(q)) table 2: derived łπ connectives bĕhounek, et al., use the basic and derived connectives along the truth-constants and the following set of axioms [40]: 1) the łukasiewicz set of axioms [21]. 2) the product set of axioms [21]. 3) the łukasiewicz delta axiom: ∆(p →l q) →l (p →π q) (12) 4) the product delta axiom: ∆(p →π q) →l (p →l q) (13) 5) the axiom: r ⊗ (p ⊖ q) ↔l (r ⊗ p) ⊖ (r ⊗ q) (14) the rules of inference are: 1) modus ponens. 2) product necessitation (infer ∆q from p). reference [40] includes several theorems that follow from the definition of łπ propositional fuzzy logic. in the next section, we define łπ first order predicate fuzzy logic (łπ∀). 3.2 first order predicate fuzzy logic following classical logic, łπ first order predicate fuzzy logic, referred to as łπ∀, extends łπ propositional fuzzy logic. the primitives include constants, variables, arbitrary arity functions and arbitrary arity predicates. formulae are constructed using the basic connectives defined in table 1, derived connectives such as the connectives presented in table 2, the truth-constants, the quantifier ∀ and the identity sign =.the quantifier ∃ can be used to abbreviate formulae derived from the basic primitives and connectives. a fuzzy interpretation, of a proposition p(x1, ...,xn) 568 d.e. tamir, a. kandel over a domain m is a mapping that assigns a fuzzy truth-value to each n-tuple of elements of m. as in the case of łπ, we closely follow the system used in ref. [40]. assuming that y can be substituted for x in p and x is not free in q the following axioms are used. 1) instances of the axioms of łπ obtained through substitution. 2) universal axiom i: (∀x)p(x) → p(y) (15) 3) universal axiom ii: (∀x)(p →l q) → (p →l (∀x)q) (16) 4) identity axiom i: x = x (17) 5) identity axiom ii: (x = y) → ∆(p(x) ↔ p(y )) (18) modus ponens, product necessitation, and generalization are used for inference. in the next section, we define propositional and first order predicate cfl. 3.3 propositional and first order predicate complex fuzzy logic as noted, the main motivation for developing cfl is twofold: first, we have observed that many multidimensional processes cannot be adequately represented as a combination of single dimension processes expressed via single dimension fuzzy logic. second, we are interested in an axiomatic based formal system that can be used for a rigorous definition of complex fuzzy classes. a complex fuzzy proposition p is a composition of two propositions each of which can accept a truth-value in the interval [0,1]. in other words; the interpretation of a complex fuzzy proposition is a pair of truth-values from the cartesian interval [0,1] × [0,1]. alternatively, the interpretation can be formulated as a mapping to the unit circle. formally a fuzzy interpretation of a complex fuzzy proposition p is an assignment of fuzzy truth-value of the form i(pr) + j · (pi), or of the form i(r(p))ejσi(θ(p)), where σ is a scaling factor in the interval (0,2π], to p. for example, consider a proposition of the form “x ... a ... b ...," along the definition of linguistic variables and constants. namely, a linguistic variable is a variable whose domain of values is formal or natural language words [1]. generally, a linguistic variable is related to a fuzzy set such as {very young male, young male, old male, very old male} and can get any value from the set. a linguistic constant has a fixed and unmodified linguistic value i.e., a single word or phrase from formal or natural language. thus, in a proposition of the form “x ... a ... b ...," where a and b are linguistic variables, i(pr)(i(r(p))) can be assigned to the term a and i(pi)(i(θ(p))) can be assigned to term b. propositional cfl extends the definition of propositional fuzzy logic and first order predicate cfl extends the notion of first order predicate fuzzy logic. nevertheless, since propositional cfl is a special case of first order predicate cfl we only present the formalism for first order predicates cfl here. reference [19] presents propositional cfl. hence, this section augments ref. [19] with first order predicate cfl. it is the first work that provides this formalism. tables 3 and 4 present the basic and derived connectives of łπ∀ cfl. in essence, the connectives are symmetric with respect to the real and imaginary parts of the predicates. axiomatic theory of complex fuzzy logic and complex fuzzy classes 569 operation interpretation ł-implication i(p→l q) = min(1,1 − i(pr) + i(qr)) + j · min(1,1 − i(pi) + i(qi)) π-implication i(p→π q) = min(0, i(pr)/i(qr)) + j · min(0,1(pi)/i(qi)) π-conjunction i(p ⊗ q) = i(pr) · i(qr) + j · (i(pi) · i(qi)) table 3: basic łπ∀ cfl connectives operation interpretation ł-negation i(¬p) = 1 + j1 i(p) π-delta ∆(i(p)) = 1 if i(p)=1 + j1; else ∆(i(p))=0 + j0 equivalence i(p ↔ q) = i(pr →l qr) ⊗ i(qr →l pr) + j · (i(pi →l qi) ⊗ i(qi →l pi)) p⊖ q i(p ⊖ q) = max(0,1(pr) − i(qr)) + j · max(0, i(pi) − i(qi)) table 4: derived łπ∀ cfl connectives following classical logic, łπ∀ cfl extends, łπ cfl. the primitives include constants, variables, arbitrary arity functions and arbitrary arity predicates. formulae are constructed using the basic connectives defined in table 3, derived connectives such as the connectives presented in table 4, the truth-constants, the quantifier ∀ and the identity sign =. the quantifier ∃ can be used to abbreviate formulae derived from the basic primitives and connectives. a fuzzy interpretation of a proposition p(x1, ...xn) = pr(x1, ...xn)+j ·pi(x1, ...xm) over a domain m is a mapping that assigns a fuzzy truth-value to each (n-tuple) × (m-tuple) of elements of m. as in the case of łπ fuzzy logic, we closely follow the system used in ref. [40]. the same axioms used for first order predicate fuzzy logic are used for first order predicate complex fuzzy logic, and modus ponens as well as product necessitation, and generalization are the rules of inference. complex fuzzy propositions and connectives examples. one form of a fuzzy proposition is: “x ... a ...;" where a is a linguistic variable such as “young male" or “tall person" and ‘...’ denote natural language constants such as “moses." “is," “portfolio," “mutual fund," etc. for example, under one interpretation, the fuzzy truth-value associated with the fuzzy proposition: p = “moses is young male" can be 0.3, and under another interpretation, the fuzzy truth-value associated with the proposition p can be 0.9. in this case, the linguistic variable is “young male," and it is distinguished from the fuzzy constants “moses" and “is" by its italics font. one form of a complex fuzzy is: “x ... a ... b ..." where a and b are values assigned to linguistic variables and ‘...’ denotes natural language constants. for example, under one interpretation, the complex fuzzy truth-value associated with the complex fuzzy proposition: “x is a volatile stock in a strong-portfolio," can be 0.1 + j0.5. alternatively, in another context, the same proposition can be interpreted as having the complex truth-value 0.3ej0.2. consider the following propositions(p and q respectively): 1) “x is a volatile stock in a strong-portfolio." 2) “x is a stock in a decline-trend in a strong-portfolio" hence, p is of the form: “x is a a in a b " and q is of the form “x is a stock c in b " in this case, “volatile stock," “a strong-portfolio," and “a decline-trend " are values assigned to the linguistic variables {a,b,c}. assume that the complex fuzzy interpretation (i.e., degree of confidence or complex fuzzy truth-value) of p is i(pr) + j · (pi), while the complex fuzzy interpretation of q is i(qr) + j · (pi). thus, the truth-value of “x is a volatile stock " is i(pr), the truth-value of “x is in a strong-portfolio" is i(pi), and the truth-value of “x is in a decline-trend " is i(qr) suppose 570 d.e. tamir, a. kandel that the term “non-volatile" stands for “not volatile” the term “weak ” stands for “not strong,” and the term “rising” stands for “not declining,” then, the complex fuzzy interpretation of the following composite propositions is: 1) i(¬p)= (1 − i(pr)) + j · (1 − i(pi)). that is, ¬p denotes the proposition “x is a non-volatile stock in a weak portfolio.” the confidence level in ¬p is (1 − i(pr)) + j(1 − i(pi)); where the fuzzy truth-value of the term “x is a non-volatile stock” is (1 − i(pr)) and the fuzzy truth-value of the term “weak portfolio" is (1 − i(pi)). 2) i(¬p →l ¬q) = min(1,1 + i(pr) − i(qr)) + j · min(1,1 + i(pi) − i(qi)). thus, (¬p→l ¬q) denotes the proposition “if x is a non-volatile stock in a weak portfolio, then x is a stock in a rising-trend in a strong-portfolio." the truth-values of individual terms, as well as the truth-value of ¬p→l ¬q are calculated according to table 3. 3) i(p ∨ q) = max(i(pr) · i(qr)) + j · max(i(pi) · i(qi)). that is, (p∨¬q) denotes the proposition “x is a volatile stock in a strong-portfolio" or “x is a stock in a rising-trend in a week portfolio." the truth-values of individual terms, as well as the truth-value of p∨¬q are calculated according to tables 3 and 4. 4) i(¬p ⊗ q) = min(1 − i(pr), i(qr)) + j · min(1 − i(pi), i(qi)). that is, (¬p ⊗ q) denotes the proposition “x is a volatile stock in a strong-portfolio" and “x is a stock in a rising-trend in a strong-portfolio." the truth-values of individual terms, as well as the truth-value of ¬p ⊗ q are calculated according to table 3. complex fuzzy inference example. assume that the degree of confidence in the proposition r = ¬p (p is defined above) is i(rr) + j · (ri) let s = ¬q and assume that the degree of confidence in the fuzzy implication t = r →ls is i(tr) + j · (ti). then, using modus ponens: r r → s s one can infer s with a degree of confidence min(1,i(rr) · i(tr)) + j · min(1,i(ri) · i(ti)). in other words one using: “x is a non-volatile stock in a weak portfolio." if “x is a non-volatile stock in a weak portfolio," then “x is a stock in a rising-trend in a weak-portfolio." “x is a stock in a rising-trend in a weak-portfolio" then, one can infer “x is a stock in a rising-trend in a weak-portfolio" with a degree of confidence min(1,i(rr) · i(tr)) + j · min(1,i(ri) · i(ti)). 4 complex fuzzy class theory many natural phenomena are complex and cannot be modeled using one dimensional classes and / or one dimensional variables. for example, in pattern recognition, objects can be represented by a set of measurements and are considered as vectors in a multidimensional space. these patterns might be clustered into several clusters where different clusters relate to different subsets of features. often, it is not practical to assume that this multidimensional information can be represented via a one-dimensional combination of variables and operators. as a relatively simple example consider the stock market; at any given time each stock can be evaluated by its current value and trend. each of these parameters can be represented by a fuzzy set and inference can be carried by fuzzy logic. nevertheless, ignoring the current state of the entire market and axiomatic theory of complex fuzzy logic and complex fuzzy classes 571 many other parameters provides a very limited capability to make sound decisions. moreover, often the relations between the current value of the stock and the current market trends cannot be adequately represented via simple fuzzy logic operations over individual parameters. other examples for the need in a mechanism that can represent complex objects and complex sets occur in signal processing where many signals are represented using complex variables. in a noisy environment these signals might form a “complex fuzzy class." finally, consider a set of values where each value is a member of a fuzzy set. this set referred to as fuzzy set of type-2 cannot be represented by basic operations on fuzzy sets of type-1 [11, 12]. this type of sets however, can be represented via complex classes. motivated by these considerations ramon et al., and dick propose complex fuzzy sets. the limitation of their approach is described in section 2. tamir et al. introduce a new interpretation of complex fuzzy grade of membership and derive the concept of complex fuzzy classes using the framework of a complex fuzzy grade of membership [18]. this form is limited since it cannot be easily axiomatized and rigorously formalized. in this paper we provide an alternative formulation for complex fuzzy classes which is based on first order predicate complex fuzzy logic (łπ∀ cfl). based on the logic system presented in section 3.1, the next section introduces the axiomatic based formulation of fuzzy class theory (fct) as developed in ref. [40]; section 4.2 provides the extension of fct to complex fuzzy class theory (cfct). 4.1 axiomatic based fuzzy class theory the axiomatic fuzzy logic can serve as a basis for establishing an axiomatic fct. similarly, axiomatic based complex fuzzy logic can serve as the basis for formal definition of complex fuzzy classes. several variants of fct exists, most of them use a similar approach and mainly differ in the selection of the logic base. another difference between various approaches is the selection of class theory axioms. bĕhounek, et al., present and analyze a few variants of fct. in ref. [40] they present a łπ∀ based fct. their formalism serves as the starting point for the complex fct presented in this paper. next, we provide an overview of the łπ∀ based fct. the main components of fct are: 1) variables. a. variables denoting objects; b. variables denoting crisp sets, i.e., a universe of discourse and its subsets; c. variables denoting fuzzy classes of order 1; d. variables denoting fuzzy classes of order n, that is, fuzzy classes of fuzzy classes of order n-1. we use the following notation for variables: objects are denoted by lower case latin letters, crisp sets are denoted by upper case latin letters, fuzzy classes of order n are denoted by upper case greek letter of the form γn (n is omitted when n=1). 2) the łπ∀ fuzzy logic system along with its variables, connectives, predicates, and axioms defined in section 3.1 and ref. [40]. 3) additional predicates. a. a binary predicate ‘x ∈ γ’ denoting membership of objects in fuzzy classes. 4) additional axioms. a. instances of the comprehension schema (further explained below) (∃γ)∆(∀x)(x ∈ γ ↔ p(x)) (19) b. the axiom of extensionality (∀x)∆(x ∈ γ ↔ x ∈ ψ) → γ = ψ (20) 572 d.e. tamir, a. kandel note that a grade of membership is not a part of the above specified terms; yet it can be derived or defined using these terms. the comprehension schema is used to “construct" classes. it has the basic form of: (∀x)(x ∈ γ ↔ p(x)). intuitively, this schema refers to the class γ of all the objects x that satisfy the predicate p(). instances of this scheme have the generic form: (∃γ)(∀ x)(x ∈ γ ↔ p(x)). associated with this schema are comprehension terms of the form: y ∈ {x|p(x) ↔ p(y)}. the ∆ operation introduced in equation 20, is used to produce precise instances of the extensionality schema and ensure the conservatism of comprehension terms. fixing a standard model over the fct, enables the definition of commonly used terms, set operations, and definitions, as well as proving fct theorems [45]. some of these elements are listed here: 1) the characteristic function χx∈γ ≡ χγ and the grade of membership function µx∈γ ≡ µγ. 2) class constants, α-cuts, iterated complements, and primitive binary operations such as union and intersection etc. these operations are constructed using the schema op(γ) ≡ {x|p(x ∈ γ)}. table 5 lists some of these elements. 3) uniform and supreme relations defined in ref. [40] enable the definition of fuzzy class relations such as inclusion. 4) theorems on axioms, primitive fuzzy class operations, and fuzzy class relations [40]. term symbol p comments empty class θ 0 universal class φ 1 strict complement \γ ∼ ∼ stands for gödel(g) negation class intersection ∩ ⊕ ⊕ stands for a g, ł, or π conjunction t-norm class union ∪ ∨ ∨ stands for a g, ł, or π disjunction table 5: primitive fuzzy class operations 4.2 axiomatic based complex fuzzy class theory the axiomatic fuzzy logic can serve as a basis for establishing an axiomatic fct. similarly, axiomatic based complex fuzzy logic can serve as the basis for formal definition of complex fuzzy classes. in this section we provide a formulation of complex fuzzy class theory (cfct) that is based on the logic theory presented in section 3.2. this is the first work that provides this formalism. a new concept that is introduced in this section is the concept of complex objects. intuitively this may sound odd since we are used to think about set objects as one-dimensional or dimensionless. nevertheless, in reality, most of the objects of interest in many applications and research projects are represented by a set of measurements and are considered as multidimensional. in the context of this paper, a multi-dimensional object is an object that is associated with (or perceived by) more than one measurement or stimuli. a simple example is human color perception which associates the “qualities" of hue and saturation with many colors. a complex object is represented by two measurements or stimuli also referred to as features. the main components of fct are: 1) variables. a. variables denoting objects (potentially complex objects); b. variables denoting crisp sets, i.e., a universe of discourse and its subsets; c. variables denoting complex fuzzy classes of order 1; axiomatic theory of complex fuzzy logic and complex fuzzy classes 573 d. variables denoting complex fuzzy classes of order n, that is, complex fuzzy classes of complex fuzzy classes of order n-1. 3) additional predicates. a. a binary predicate ‘x ∈ γ’ denoting membership of objects in complex fuzzy classes. to elaborate, assume that γ is a complex class, and let x be a complex object of the form x = xr + j · xi, then the predicate x ∈ γ yields a complex truth-level of the form x ∈ γ = [xr ∈ γ + j · xi ∈ γ]. intuitively this truth-value represents fuzzy validity of the compounded assertion “[xr,xi] belongs to γ," or “[xr,xi] is a member of γ”. 4) additional axioms. a. instances of the comprehension schema (further explained below) (∃γ)∆(∀x)(x ∈ γ ↔ p(x)) (21) where x is a complex fuzzy object, γ is a complex fuzzy class, and p() is a complex fuzzy predicate. b. the axiom of extensionality (∀x)∆(x ∈ γ ↔ x ∈ ψ) → γ = ψ (22) again, x is a complex fuzzy object, γ is a complex fuzzy class, and p() is a complex fuzzy predicate. note that a grade of membership is not a part of the above specified terms; yet it can be derived or defined using these terms. the comprehension schema is used to ”construct" classes. it has the basic form of: (∀x)(x ∈ γ ↔ p(x)). intuitively, this schema refers to the class γ of all the objects x that satisfy the predicate p(). instances of this scheme have the generic form: (∃γ)(∀x)(x ∈ γ ↔ p(x)). associated with this schema are comprehension terms of the form: y ∈ {x|p(x) ↔ p(y)}. the ∆ operation introduced in equation 22, is used to produce precise instances of the extensionality schema and ensure the conservatism of comprehension terms. fixing a standard model over the cfct enables the definition of commonly used terms, set operations, and definitions, as well as proving cfct theorems. some of these elements are listed here: 1) the complex characteristic function χx∈γ ≡ χγ and the complex grade of membership function µx∈γ ≡ µγ. 2) complex class constants, α-cuts, iterated complements, and primitive binary operations such as union and intersection etc. these operations are constructed using the schema op(γ) ≡ {x|p(x ∈ γ)}. table 6 lists some of these elements. 3) uniform and supreme relations defined in ref. [40] enable the definition of fuzzy class relations such as inclusion. 4) theorems on axioms, primitive fuzzy class operations, and fuzzy class relations [40]. following the axiomatic based definition of grade of membership equations (9-11) can be used as a basis for the definition of “membership-grade based” complement, union, and intersection. we are currently performing research aimed to provide a comprehensive list of theorems, complex class operations, and relations. complex fuzzy propositions and connectives examples. in order to provide a concrete example we define the following complex fuzzy classes using the comprehension schema. let the universe of discourse be the set of all the stocks that were 574 d.e. tamir, a. kandel term symbol p comments empty complex class θ 0 universal complex class φ 1 strict complement \γ ∼ ∼ stands for gödel(g) negation complex class intersection ∩ ⊕ ⊕ stands for a g, ł, or π conjunction t-norm complex class union ∪ ∨ ∨ stands for a g, ł, or π disjunction table 6: derived primitive class operations available for trade on the opening of the new york stock exchange (nyse) market on january 3, 2011 along with a set of attributes related to historical price performance of each of these stocks. consider the following complex propositions: 1) p(x)≡ “x is a volatile stock in a strong-portfolio," 2) q(x)≡ “x is a stock in a decline-trend in a strong-portfolio," then, the proposition: (∃γ)∆(∀x)(x ∈ γ ↔ (p(x) ⊗ q(x)) where x is any member of the universe of discourse defines a complex fuzzy class γ that can be “described" as the class of “volatile stocks in a decline-trend in strong-portfolios,” on the other hand, the proposition: (∃γ)∆(∀x)(x ∈ γ ↔ (¬p(x)∨q(x)) where x is any member of the universe of discourse, defines a complex fuzzy class γ that can be “described" as the class of “non-volatile stocks in a decline-trend in strong-portfolios." 5 conclusions and directions for further research a new and innovative formal definition of complex fuzzy logic (cfl), referred to as łπ∀ cfl as well as a formalism of complex fuzzy class theory (cfct) that is based on cfl, is presented in this paper. the new form significantly improves the expressive power and inference capability of cfl and cfct based systems. in addition, it enables axiomatic and rigorous development of the relevant theory. in the future, we plan to extend the theory. in addition, we plan to apply the new formalism to multidimensional fuzzy propositional and predicate logic; and further explore its potential for usage in advanced complex fuzzy logic systems as well as inference with type-2 (or higher) fuzzy sets. bibliography [1] zadeh, l.a., the concept of a linguistic variable and its application to approximate reasoning part i. information sciences, 1975, 7: p. 199-249. 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[40] bĕhounek, l., cintula, p., fuzzy class theory. fuzzy sets and systems, 2005, 154(1): p. 34-55. int j comput commun, issn 1841-9836 8(1):105-110, february, 2013. flexible gps/gprs based system for parameters monitoring in the district heating system a. peulic, s. dragicevic, z. jovanovic, r. krneta aleksandar peulic, snezana dragicevic, zeljko jovanovic, radojka krneta university of kragujevac, technical faculty serbia, 32000 cacak, e-mail: apeulic, snezad, zeljko.jovanovic, radojka@tfc.kg.ac.rs abstract: energy consumption for heating purposes accounts for a significant part of the budgets of individual and collective users. this increases the importance of issues related to the monitoring of heating energy flows, analysis of flow parameters, verification of fees and, in the first place, minimization of energy consumption. the goal of this paper is to develop, by employing global positioning system receivers, measurement techniques that are suited to the continuous monitoring of the heating substation parameters. this paper presents the design and implementation of gps/gprs (global positioning system/general packet radio service) system for low power data acquisition using msp430 texas instruments microcontroller for monitoring of the heating substation parameters. the system is implemented in heating stations for a temperature and pressure monitoring. it contains gps/gprs gateway and 8 analog sensor inputs. acquisition module and the server base station are suitable for industrial applications, home applications and for other appliances. the proposed measurement procedures, which are different from commercially available measurement units, are based on general-purpose acquisition hardware and processing software, thus guaranteeing the possibility of being easily reconfigured and reprogrammed according to the specific requirements of different possible fields of application and to their future developments. keywords: distributed measurement systems, gps/gprs, computer data acquisition, low power microcontroller. 1 introduction an adequate control of all relevant parameters of the heating process is one of the most significant means of power consumption optimization. district heating substations, as a link between hot-water network and internal heating installations in buildings, are used to adapt highpressure hot water to temperature and pressure conditions required by space heating systems of buildings as well as by the systems for the preparation of hot service water in buildings. to control the energy transfer in the district heating substation, some kind of control system is needed. the overall efficiency of district heating could clearly be improved by using new strategies for measurement and control. to maximize energy efficiency in the district heating network it is essential to have a large temperature drop across the substation between supplies and return pipes in the distribution network. a larger temperature drop will contribute to more possible customers in available district heating networks without increasing the production power. an efficient system will reduce the amount of wasted energy while maintaining comfort, and indirectly reduce co2 emissions for heating purposes, which accounts for 30% of the worlds current co2. very rough estimates shows savings of more than 1 million year when increasing the temperature drop across the substation between by 5 ◦c in a 760 gwh district heating system. today, in most substation control a system focuses on indoor comfort and do not generally consider temperature copyright c⃝ 2006-2013 by ccc publications 106 a. peulic, s. dragicevic, z. jovanovic, r. krneta drop across the substation, since it is not measured by the control system. the rapid progress in microprocessor and communication technologies over the last ten years or so has provided great potential for innovative applications in the field of protection and substation control [1]. there are a number of applications of different strategies of monitoring and control of district heating system components: a new control and communication architecture based on wsn and soa for district heating substations are developed in [2], [3]. reference [4] shows the issue of integrating of intelligent electronic devices (ied) data recording by different ied types and focuses on how to facilitate the use of the integrated data; the water temperature control of a district heating substation using soft computing methods, based on fuzzy logic, is presented in [5]. fuzzy logic control is implemented and the good performance of the fuzzy control proves that this can be an alternative to the classic control. the control and monitoring system for the heat distribution network with a multi-layer structure, which integrates several state-of-the-art technologies and standards applied in modern industrial automatics, are presented in [6]. the applied control system and supervisory control algorithms have result in power savings. reference [7] proposes an alternative approach to the problem of district heating monitoring parameters selection. the wireless technology comparing with non-wireless technology has some important benefits, for example the system cost reduces and easier the installation and maintenance. some of the most popular low power wireless sensors networks are zigbee, bluetooth, distance between sensors and the base station is limited to about 1500 m are presented in [8], [9]. this paper presents gps/gprs based wireless acquisition system. the global positioning system (gps), which is a satellite based system, is the main synchronizing source that is used to provide a time reference on the communication networks, and its widespread availability makes it possible to obtain, at each point of the tested system, a clock signal that is synchronized with the one generated in other remote places. currently, gps is the only satellite system with sufficient availability and accuracy for most distributed monitoring and control applications in distribution systems. alternatives will eventually become available, with galileo being the most promising at this time [10]. 2 the remote acquisition system the basis of hardware part of gprs based system for data acquisition (gprsuc) from remote locations consists of low power msp430f147 microcontroller and telit gm-862 gprs/ gsm/gps module. they communicate with each other in the process of collecting and sending data to remote server. the main objective of the microcontroller is to sample data from eight multiplexed analog inputs, and to form data blocks with time stamp from gps sentences. telit module is used to send those data blocks to the server base over gprs system. besides the standard functions of the devices used in m2m (machine to machine) communication, this module has a gps receiver, as well as the gps dedicated port on which the data obtained from gps are shown in nmea(national marine electronics association) format. system’s operating range is from -10 ◦c to + 55 ◦c, which can be potential problem if gprsuc is used in environments with very low temperatures. the gsm modem is made in a way that the rf transmission is not continuous else, it is packed into bursts at a base frequency about 216 hz. firmware is written in c, and its structure is shown in fig. 1. telit module acts as a slave carrying out the commands that are sent by the microcontroller. since the platform is designed to be suitable for the monitoring of the mobile location, gps accuracy is very important. after the initialization, moving system variable need to be set. complete algorithm depends on the value of this variable. after checking the network status system is trying to catch gps signal (see fig. 1). when there is no gps signal telit module flexible gps/gprs based system for parameters monitoring in the district heating system107 figure 1: flow chart of the firmware returns null in latitude and longitude fields. this routine repeats until it is established that the gps signal is captured. last good gps position is saved and used in case that gps signal is lost due the impact of various barriers, which is especially important when system is moving. if the system is not moving then saved gps position is used. time is extracted from gps sentence in short routine and it is used even though the system is not moving. 3 communication resources used for remote aquisition general packet radio services standard allows data transfer in a completely different way from the circuit switch data (csd) type of transmission. in csd, data is transmitted by establishing connection with other, remote modem, directly, so all devices in between are used to provide simulation of the physical connection between the final points (point-to-point connection). besides the obvious disadvantage in terms of low utilization of network resources, there are also problems of long delays in establishing a connection and high fees for using network resources, based on time period of established link, not amount of data like in gprs [11]. one message from remote acquisition system consists of eight measured parameters, time stamp and identification field, and has near 110 bytes, so it is appropriate to use gprs system. mobile operators provide fixed ip address service, and it is possible to achieve communication in both directions with changes in overall software of the system. in realization of gprsuc prototype this service is not used in order to make remote acquisition platform as cheap as possible. as practical part of this project is regarded, its main goal is to presents the data collected in a way, which was explained in the previous chapter. the entire software solution has been realized using the open source j2ee technology. one of the reasons why the j2ee technology is selected is the possibility of extension to mention the system used on mobile phones in a j2me applications. completed software solution can be roughly divided into two parts, which exchange data with hardware and presents results to the client the part of the software used to exchange data with hardware, is the interface with the hardware support. this part of the software has no visual interpretation and is executed only when the http request is passed to the servlet by a device 108 a. peulic, s. dragicevic, z. jovanovic, r. krneta that forwards data. it has been develop as a java servlet, which call by hardware is using the http get request. to the appropriate data servlet that performs the same processing and saves them in the mysql database. the server confirms a successful reception of data and it is able to send the parameters which correct the way that device work. since the servlet can be accessed with the use of http requests from anywhere in the world, this way of communication gives this project one global level. there is the ability of usage in different cities, countries and even continents. the preference listed in the realization of communication in addition to the great advantages is also a defect that opens a project to the attacks and simulation of some other persons who would like to emulate values of the passed parameters. as the container, apache tomcat version, 6.0.16 is used. the project is implemented as a web application, which is located on the server in laboratory. it provides current monitoring of more parameters from one station and displays their values in real time. since this system has a role in monitoring, the value of parameters for easier viewing, the marker may be green, yellow or red depending on the values of parameters, i.e. whether they allow or do not allow critical range, respectively. in addition to transferring data, hardware has a role that does the processing of data, and as a parameter sends its status. processing is not on the server from the simple reason that for n stations which monitoring m parameters would representing an (m x n) processing every few seconds. it is very important that every measured value is stored in a mysql database and thanks to that it could be seen whenever it is needed. this is important because the further analysis of data is possible in a very easy way. since a system sends gps coordinates, gis support is used for better data presentation. this is especially important with moving systems. it is also interesting using gis presentation when there is more then one system for tracking and their positions are easy to monitor. as a geographical support in the project, the google maps api is used. it provides great opportunities thanks to the entire globe coverage with satellite and aero-photo shots of high resolution. the principle of working with google maps api is that a complete gis system is on google’s server. the user passes the coordinates and parameters for the display, to the corresponding server that replies by sending the required graphic content. google maps usage is free of charge and only google key is needed. the google supplies the key and a gmail account (mail) is required. in order to get the key, it is needed to enter the url of the web server on which google maps will be used. mouse click on the marker surface displays current parameter values for wanted system (see fig. 2). figure 2: multiple systems monitoring with wanted system data presentation if live monitoring for one system is needed, web application offers functionality (see fig. 3) where user activity is not needed since ajax is refreshing a web page after any data value change. alarm values are shown with red background color. there is also a possibility for history observation of measured parameters values with table or chart view. flexible gps/gprs based system for parameters monitoring in the district heating system109 figure 3: live temperature and pressure monitoring in laboratory area 4 conclusions and future works in this paper, a flexible measurement system for parameters monitoring in district heating system is proposed. this system is able to react rapidly to any incidental parameters changes and alarm responsible server in base station. gsm network is widespread, reliable and cheap. gprsuc system describes an attempt of integration low power microcontroller, telit module and server applications into a distributed system for data acquisition and monitoring remote measurement sites. the flexibility of system arises from the use of general-purpose acquisition hardware, which allows the system to be easily upgraded and/or reconfigured according to the specific measurement needs existing and evolving in modern district heating systems. communication of the system is complete wireless, easily operable and low power. to maximize the energy efficiency in the district heating network, it is essential to have a large temperature drop across the substation between supplies and return pipes in the distribution network and the proposed system has and economic reasons for implementation. the proposed measurement systems could be further improved simply by using more sophisticated acquisition hardware. bibliography [1] p. bornard, power system protection and substation control: trends, opportunities and problems,international journal of electrical power and energy systems, 10(2):101-109, 1998. [2] j. gustafsson, distributed wireless control strategies for district heating substations, licentiate thesis, dept. of comp. sci. and elect. eng., lulea university of technology, lulea, sweden, 2009. [3] j. v. deventer, j. gustafsson, j. delsing, j. eliasson, wireless infrastructure in a district heating substation,ieee int. sys. conf., pp. 139-143, vancouver, canada, 2009. [4] m. kezunovic, t. popovic, substation data integration for automated data analysis systems,ieee pes general meeting, pp. 1-6, tampa, florida, 2007. [5] l. mastacan, i. olah, c. c. dosoftei, district heating substations water temperature control based on soft computing technology,6th int. conf. on electromechanical and power systems, pp. 172-175, rep.moldova, 2007. [6] w. grega, k. kolek, monitoring and control of heat distribution,int. carpathian control conf. iccc, pp.439-444, malenovice, czech republic, 2002. 110 a. peulic, s. dragicevic, z. jovanovic, r. krneta [7] p. malinowski, p. ziembicki, analysis of district heating network monitoring by neural networks classification,journal of civil engineering and management, 12(1):21-28, 2006. [8] j. a. gutierrez, m. naeve, e. callaway, m. bourgeois, v. mitter, b.heile, ieee 802.15.4: a developing standard for low-power low-cost wireless personal area networks,ieee network, 15(5):12-19, 2002. [9] a. z. alkar, an internet based wireless home automation system for multifunctional devices, ieee trans. consumer electronics, 51(4):1169-1174, 2005. [10] ieee standard for synchrophasors for power systems, ieee std. c37.118-2005 (revision of ieee std. 1344-1995), 2006. [11] a. alheraish, design and implementation of home automation system, ieee trans. consumer electronics, 50(4):10871092, 2004. ijcccv4n3draft.pdf int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. iv (2009), no. 3, pp. 301-310 introducing a space complexity measure for p systems antonio e. porreca, alberto leporati, giancarlo mauri, claudio zandron università degli studi di milano-bicocca dipartimento di informatica, sistemistica e comunicazione viale sarca 336, 20126 milano, italy e-mail: {porreca, leporati, mauri, zandron}@disco.unimib.it received: april 5, 2009 accepted: may 30, 2009 abstract: we define space complexity classes in the framework of membrane computing, giving some initial results about their mutual relations and their connection with time complexity classes, and identifying some potentially interesting problems which require further research. keywords: membrane computing, complexity theory. 1 introduction until now, research on the complexity theoretic aspects of membrane computing has mainly focused on the time resource. in particular, since the introduction of p systems with active membranes [5], various results concerning time complexity classes defined in terms of p systems with active membranes were given, comparing different classes obtained using various ingredients (such as, e.g., polarizations, dissolution, uniformity, etc.). other works considered the comparisons between them and the usual complexity classes defined in terms of turing machines, either from the point of view of time complexity [8, 3, 11], or space complexity classes [10, 1, 9]. despite the vivid interest on this subject, up to now no investigations concerning space complexity classes defined in terms of p systems have been carried out in formal terms. of course, the evident relation between time and space in p systems with active membranes is informally acknowledged: all results concerning solutions to nnnppp-complete problems are solved using an exponential workspace obtained in polynomial time. nonetheless, there is no formal definition of space complexity classes for p systems and, as a consequence, no formal results concerning the relations between space and time. in this paper, we make the first steps in this direction, first by defining the space requirements for a given p system on a specific computation, and then by formally defining space complexity classes for p systems. we will then give a first set of results concerning relations among complexity classes for p systems, some of them directly following from the definitions, and others which can be derived by considering space requirements of various solutions proposed in the literature which make use of p systems with active membranes. in what follows we assume the reader is already familiar with the basic notions and the terminology underlying p systems. for a systematic introduction, we refer the reader to [6]. a survey and an up-todate bibliography concerning p systems can be found at the web address http://ppage.psystems.eu. the rest of the paper is organized as follows. in section 2 we give basic definitions for membrane systems which will be used throughout the rest of the paper. in section 3 we give formal definitions of space complexity classes in terms of p systems. in section 4 we present some results concerning such complexity classes, which follow immediately from the definitions, while in section 5 we present some results which can be obtained by considering known results for time complexity classes in the framework of p systems with active membranes. section 6 concludes the paper by presenting some conjectures and open problems concerning space complexity. copyright c© 2006-2009 by ccc publications 302 antonio e. porreca, alberto leporati, giancarlo mauri, claudio zandron 2 definitions we begin by recalling the formal definition of p systems with active membranes and the usual process by which they are used to solve decision problems. moreover, we recall the main definitions related to time complexity classes in this framework. definition 1. a p system with active membranes of degree m ≥  is a structure π = (γ ,λ , µ, w, . . . , wm, r) where • γ is a finite alphabet of symbols or objects; • λ is a finite set of labels; • µ is a membrane structure (i.e. a rooted, unordered tree) of m membranes, labeled with elements of λ ; different membranes may be given the same label; • w, . . . , wm are multisets over γ describing the initial contents of the m membranes in µ ; • r is a finite set of developmental rules. the polarization of a membrane can be + (positive), − (negative) or  (neutral); each membrane is assumed to be initially neutral. developmental rules are of the following six kinds: (a) object evolution rule of the form [a → w]αh it can be applied inside a membrane labeled by h, having polarization α and containing an occurrence of the object a; the object a is rewritten into the multiset w (i.e. a is removed from the multiset in h and replaced by the multiset w). (b) communication rule of the form a [ ]αh → [b] β h it can be applied to a membrane labeled by h, having polarization α and such that the surrounding region contains an occurrence of the object a; the object a is sent into h becoming b and, simultaneously, the polarization of h is changed to β . (c) communication rule of the form [a]αh → [ ] β h b it can be applied to a membrane labeled by h, having polarization α and containing an occurrence of the object a; the object a is sent out from h to the surrounding region becoming b and, simultaneously, the polarization of h is changed to β . (d) dissolution rule of the form [a]αh → b it can be applied to a membrane labeled by h, having polarization α and containing an occurrence of the object a; the membrane h is dissolved and its content is left in the surrounding region unaltered, except that an occurrence of a becomes b. (e) elementary division rule of the form [a]αh → [b] β h [c] γ h it can be applied to an elementary membrane labeled by h, having polarization α and containing an occurrence of the object a; the membrane is divided into two membranes having label h and polarizations β and γ ; the object a is replaced, respectively, by b and c while the other objects in the initial multiset are copied to both membranes. introducing a space complexity measure for p systems 303 (f) non-elementary division rule of the form [ [ ]+h ···[ ]+hk [ ] − hk+ ···[ ]−hn ]α h → [ [ ]δh ···[ ] δ hk ]β h [ [ ]εhk+ ···[ ] ε hn ]γ h it can be applied to a non-elementary membrane labeled by h, having polarization α , containing the positively charged membranes h, . . . , hk and the negatively charged membranes hk+, . . . , hn; no other non-neutral membrane may be contained in h. the membrane h is divided into two copies with polarization β and γ ; the positive children are placed inside the former, their polarizations changed to δ , while the negative ones are placed inside the latter, their polarizations changed to ε . any neutral membrane inside h is duplicated and placed inside both copies. note that here we have used the original definition of division rules introduced in [5]. afterwards, other papers have proposed several alternatives for non-elementary division rules; nonetheless, in this paper these variants are not considered. a configuration of a p system with active membranes π is given by a membrane structure and the multisets contained in its regions. in particular, the initial configuration is given by the membrane structure µ and the initial contents of its membranes w, . . . , wm. a computation step leads from a configuration to the next one according to the following principles: • the developmental rules are applied in a maximally parallel way: when one or more rules can be applied to an object and/or membrane, then one of them must be applied. notice that an object or membrane may remain inactive, even if it can trigger a rule, only when its use is inhibited by the application of another rule. • each object can be subject to only one rule during each step. also membranes can be subject to only one rule, except that any number of object evolution rules can be applied inside them. • when more than one rule can be applied to an object or membrane, then the one actually applied is chosen nondeterministically. thus multiple, distinct configurations may be reachable by means of a computation step from a single configuration. • when a dissolution or division rule is applied to a membrane, the multiset of objects to be released outside or copied is the one after any application of object evolution rules inside such membrane. • the skin membrane cannot be divided or dissolved, nor any object can be sent in from the environment surrounding it (i.e. an object which leaves the skin membrane cannot be brought in again). a sequence of configurations, each one reachable from the previous one by means of developmental rules applied according to the above principles, is called a computation. due to nondeterminism, there may be multiple computations starting from the initial configuration, thus producing a computation tree. a computation halts when no further configuration can be reached, i.e. when no rule can be applied in a given configuration. families of recogniser p systems can be used to solve decision problems as follows. definition 2. let π be a p system whose alphabet contains two distinct objects yes and no, such that every computation of π is halting and during each computation exactly one of the objects yes, no is sent out from the skin to signal acceptance or rejection. if all the computations of π agree on the result, then π is said to be confluent; if this is not necessarily the case, then it is said to be non-confluent and the global result is acceptance iff there exists an accepting computation. definition 3. let l ⊆ σ ⋆ be a language, d a class of p systems and let πππ = {πx | x ∈ σ ⋆} ⊆ d be a family of p systems, either confluent or non-confluent. we say that πππ decides l when, for each x ∈ σ ⋆, x ∈ l iff πx accepts. 304 antonio e. porreca, alberto leporati, giancarlo mauri, claudio zandron complexity classes for p systems are defined by imposing a uniformity condition on πππ and restricting the amount of time available for deciding a language. definition 4. consider a language l ⊆ σ ⋆, a class of recogniser p systems d, and let f : n → n be a proper complexity function. we say that l belongs to the complexity class mmmccc⋆ d ( f ) if and only if there exists a family of confluent p systems πππ = {πx | x ∈ σ ⋆} ⊆ d deciding l such that • πππ is semi-uniform, i.e. there exists a deterministic turing machine which, for each input x ∈ σ ⋆, constructs the p system πx in polynomial time; • πππ operates in time f , i.e. for each x ∈ σ ⋆, every computation of πx halts within f (|x|) steps. in particular, a language l ⊆ σ ⋆ belongs to the complexity class pppmmmccc⋆ d iff there exists a semi-uniform family of confluent p systems πππ = {πx | x ∈ σ ⋆} ⊆ d deciding l in polynomial time. the analogous complexity classes for non-confluent p systems are denoted by nnnmmmccc⋆ d ( f ) and nnnpppmmmccc⋆ d . another set of complexity classes is defined in terms of uniform families of recogniser p systems: definition 5. consider a language l ⊆ σ ⋆, a class of recogniser p systems d, and let f : n → n be a proper complexity function. we say that l belongs to the complexity class mmmcccd( f ) if and only if there exists a family of confluent p systems πππ = {πx | x ∈ σ ⋆} ⊆ d deciding l such that • πππ is uniform, i.e. for each x ∈ σ ⋆ deciding whether x ∈ l is performed as follows: first, a polynomial-time deterministic turing machine, given the length n = |x| as a unary integer, constructs a p system πn with a distinguished input membrane; then, another polynomial-time dtm computes a coding of the string x as a multiset wx, which is finally added to the input membrane of πn, thus obtaining a p system πx accepting iff x ∈ l. • πππ operates in time f , i.e. for each x ∈ σ ⋆, every computation of πx halts within f (|x|) steps. in particular, a language l ⊆ σ ⋆ belongs to the complexity class pppmmmcccd iff there exists a uniform family of confluent p systems πππ = {πx | x ∈ σ ⋆} ⊆ d deciding l in polynomial time. the analogous complexity classes for non-confluent p systems are denoted by nnnmmmcccd( f ) and nnnpppmmmcccd. 3 a measure of space complexity for p systems in order to define the space complexity of p systems, we first need to establish a measure of the size of their configurations. the first definition we propose is based on an hypothetical implementation of p systems by means of real biochemical materials (cellular membranes and molecules). under this assumption, every single object takes some constant physical space: this is equivalent to using a unary coding to represent multiplicities. definition 6. let c be a configuration of a p system π , that is, a rooted, unordered tree µ representing the membrane structure of π , whose vertices are labeled with the multisets describing the contents of each region. the size |c| of c is then defined as the sum of the number of membranes in µ and the total number of objects they contain. an alternative definition focuses on the simulative point of view, i.e. on the implementation of p systems in silico, where it is not necessary to actually store every single object (using a unary representation), but we can just store their multiplicity as a binary number, thus requiring exponentially less space for each kind of symbol. introducing a space complexity measure for p systems 305 definition 7 (alternative). let c be a configuration of a p system π , that is, a rooted, unordered tree µ representing the membrane structure of π , whose vertices are labeled with the multisets describing the contents of each region. the size |c| of c is then defined as the sum of the number of membranes in µ and the total number of bits required to store the objects they contain. in the following discussion we will assume the first definition; however notice that the actual results might or might not depend on the precise choice between definitions 6 and 7 (a thorough analysis of the differences involves a clarification of the relative importance of the number of membranes and the number of objects in various classes of p systems, and it is left as an open problem). once a notion of configuration size is established, we need to take account of all possible computation paths which can develop even in confluent recogniser p systems; the following definitions are given in the spirit of those concerning time complexity for p systems [7]. definition 8. let π be a (confluent or non-confluent) recogniser p system, and let ~c = (c, . . . , cm) be a halting computation of π , that is, a sequence of configurations starting from the initial one and such that every subsequent one is reachable in one step by applying the rules in a maximally parallel way. the space required by ~c is defined as |~c| = max{|c|, . . . , |cm|}. the space required by π itself is then |π | = max{|~c| such that ~c is a halting computation of π }. definition 9. let πππ = {πx | x ∈ σ ⋆} be a uniform or semi-uniform family of recogniser p systems, each πx deciding the membership of the string x in a language l ⊆ σ ⋆; also let f : n → n. we say that πππ operates within space bound f iff |πx| ≤ f (|x|) for each x ∈ σ ⋆. we are now ready to define space complexity classes for p systems. definition 10. let d be a class of confluent recogniser p systems; let f : n → n and l ⊆ σ ⋆. then l ∈ mmmcccssspppaaaccceee⋆ d ( f ) iff l is decided by a semi-uniform family πππ ⊆ d of p systems operating within space bound f . the corresponding complexity class for uniform families of confluent p systems is denoted by mmmcccssspppaaaccceeed( f ), while in the non-confluent case we have the classes nnnmmmcccssspppaaaccceee ⋆ d ( f ) and nnnmmmcccssspppaaaccceeed( f ) respectively. as usual, we provide a number of abbreviations for important space classes. definition 11. the classes corresponding to polynomial and exponential space, in the semi-uniform and confluent case, are pppmmmcccssspppaaaccceee⋆ d = ⋃ k∈n mmmcccssspppaaaccceee⋆ d (o(nk)) eeexxx pppmmmcccssspppaaaccceee⋆ d = ⋃ k∈n mmmcccssspppaaaccceee⋆ d ( o(n k) ) . the definitions are analogous in the uniform and non-confluent cases. 4 basic results from the above definitions, some results concerning space complexity classes and their relations with time complexity classes follow immediately. we state them only for semi-uniform families, but they also hold in the uniform case. the first two propositions can be immediately derived from the definitions. 306 antonio e. porreca, alberto leporati, giancarlo mauri, claudio zandron proposition 12. the following inclusions hold: pppmmmcccssspppaaaccceee⋆ d ⊆ eeexxx pppmmmcccssspppaaaccceee⋆ d nnnpppmmmcccssspppaaaccceee⋆ d ⊆ nnneeexxx pppmmmcccssspppaaaccceee⋆ d . proposition 13. mmmcccssspppaaaccceee⋆ d ( f ) ⊆ nnnmmmcccssspppaaaccceee⋆ d ( f ) for each f : n → n, and in particular pppmmmcccssspppaaaccceee⋆ d ⊆ nnnpppmmmcccssspppaaaccceee⋆ d eeexxx pppmmmcccssspppaaaccceee⋆ d ⊆ nnneeexxx pppmmmcccssspppaaaccceee⋆ d . the following results mirror those which hold for turing machines, and they describe closure properties and provide an upper bound for time requirements of p systems operating in bounded space. proposition 14. the classes pppmmmcccssspppaaaccceee⋆ d , nnnpppmmmcccssspppaaaccceee⋆ d , eeexxx pppmmmcccssspppaaaccceee⋆ d , and nnneeexxx pppmmmcccssspppaaaccceee⋆ d are all closed under polynomial-time reductions. proof. let l ∈ pppmmmcccssspppaaaccceee⋆ d and let m be the turing machine constructing the family πππ that decides l. let l′ be reducible to l via the polynomial-time computable function f . now let m ′ be the following turing machine: on input x of length n compute f (x); then behave like m on input f (x), thus constructing π f (x). since | f (x)| is bounded by a polynomial, m ′ operates in polynomial time and π f (x) in polynomial space; but then πππ ′ = {π f (x) | x ∈ σ ⋆} is a polynomially semi-uniform family of p systems deciding l′ in polynomial space. thus l′ ∈ pppmmmcccssspppaaaccceee⋆ d . the proof for the three other classes is analogous. proposition 15. mmmcccssspppaaaccceee⋆ d ( f ) is closed under complement for each function f : n → n. proof. simply reverse the roles of objects yes and no in order to decide the complement of a language. proposition 16. for each function f : n → n mmmcccssspppaaaccceee⋆ d ( f (n)) ⊆ mmmccc⋆ d ( o( f (n) log f (n)) ) nnnmmmcccssspppaaaccceee⋆ d ( f (n)) ⊆ nnnmmmccc⋆ d ( o( f (n) log f (n)) ) . proof. let l ∈ mmmcccssspppaaaccceee⋆ d ( f (n)) be decided by the semi-uniform family πππ of recogniser p systems in space f ; let πx ∈ πππ with |x| = n and let c be a configuration of πx. then c can be described with a string of length at most k f (n) log f (n) over a finite alphabet, say with b ≥  symbols, for some constant k: • we need k f (n) symbols in order to represent the membrane structure and the objects it contains, as well as the polarizations. • we also need to encode the labels of the membranes: even if they do not contribute to the space required by the p system, nonetheless each assignment of labels gives rise to a different configuration. since all labels might be distinct, and there are at most f (n) of them, k f (n) log f (n) symbols are needed. notice that there are less than bk f (n) log f (n)+ such strings. since πx is a recogniser p system, by definition every computation halts: then it must halt within bk f (n) log f (n)+ steps in order to avoid repeating a previous configuration (thus entering an infinite loop). this number of steps is o( f (n) log f (n)). the same proof also works in the non-confluent case (only the acceptance criterion is different). introducing a space complexity measure for p systems 307 5 space complexity of p systems with active membranes in this section we provide a brief review of part of the ample literature on complexity results about p systems with active membranes; our aim is to analyse existing polynomial-time solutions to hard computational problems in order to obtain space complexity results. we first consider the class of p systems with active membranes which do not make use of membrane division rules, usually denoted by nam. it is a well known fact that such p systems are able to solve only problems in ppp (the so-called milano theorem [11]); on the other hand, they can be used to solve all problems in ppp with a minimal amount of space, when a semi-uniform construction is considered: proposition 17. ppp ⊆ mmmcccssspppaaaccceee⋆ nam (o()). proof. let l ∈ ppp. then there exists a deterministic turing machine m deciding l in polynomial time. now consider the family of p systems πππ = {πno, πyes}, where πno (resp. πyes) is the following trivial p system with active membranes: • the membrane structure consists of the skin only, labelled by h; • in the initial configuration, exactly one object a is located inside the skin; • the only rule is [a]h → [ ]  h no (resp. [a]  h → [ ]  h yes). it is clear that such p systems halt in one step and that the space they require is independent of the size of the instance they decide. the family of p systems πππ can be constructed in a semi-uniform way in order to decide l by a deterministic turing machine which first simulates m (it can do so, since m operates in polynomial time), then outputs one of πyes, πno according to the result (acceptance or rejection, respectively). one of the most powerful features of p systems with active membranes is the possibility of creating an exponential workspace in polynomial time by means of elementary membrane division rules; we denote the class of such p systems by eam. this feature was exploited for solving nnnppp-complete problems in polynomial (often even linear) time. in terms of space complexity, this can be stated as follows: proposition 18. nnnppp ∪ cccooonnnppp ⊆ eeexxx pppmmmcccssspppaaaccceee⋆ eam . proof. in [11] a polynomial-time semi-uniform solution to sat is described; the number of membranes and objects required is exponential with respect to the length of the boolean formula. the result then follows from closure under reductions and complement of eeexxx pppmmmcccssspppaaaccceee⋆ eam . this result can be improved when the use of non-elementary membrane division rules is allowed; indeed, all problems in pppssspppaaaccceee can be solved by such class of p systems with active membranes, denoted by am. proposition 19. pppssspppaaaccceee ⊆ eeexxx pppmmmcccssspppaaaccceee⋆ am . proof. in [10] a polynomial-time uniform solution to qbf (also known as qsat), the canonical pppssspppaaaccceeecomplete problem, is described; the space required by each p system is still exponential, and the result follows from the closure properties. in [1] a uniform solution for the same problem was achieved, with the same space requirements; this provides a tighter upper bound to pppssspppaaaccceee: proposition 20. pppssspppaaaccceee ⊆ eeexxx pppmmmcccssspppaaaccceeeam. 308 antonio e. porreca, alberto leporati, giancarlo mauri, claudio zandron since standard p systems with active membranes are very powerful when division rules are allowed, but very weak otherwise, another line of research involves removing some other features, such as polarizations. polarizationless p systems with active membranes have been proved able to solve qsat uniformly in polynomial time by making use of both elementary and non-elementary division rules [2]. since the space requirements are once again exponential, the following result is immediate: proposition 21. pppssspppaaaccceee ⊆ eeexxx pppmmmcccssspppaaaccceeeam , where am  is the class of polarizationless p systems with active membranes and both kinds of division rules. 6 open problems in p systems with active membranes, division rules are usually exploited by producing an exponential number of membranes in linear time, which then evolve in parallel; for instance, several solutions to nnnpppcomplete problems explore the full solution space (e.g. generating every possible truth assignment and then checking whether one of them satisfies a boolean formula). it appears that membrane division may become much less useful when a polynomial upper bound on space is set; or, in other words, conjecture 22. the three complexity classes pppmmmcccssspppaaaccceee⋆ nam , pppmmmcccssspppaaaccceee⋆ eam and pppmmmcccssspppaaaccceee⋆ am coincide. an idea which might be useful in proving this conjecture is precomputing the “final” membrane structure (which is obtained via division rules) during the construction phase. while this is straightforward when considering membrane divisions which always occur, the matter might be much more difficult in the case of “conditional” division (i.e. division rules are applied only when certain conditions are met) or when the p system exhibits a recurring behaviour (e.g. a membrane divides, then one of the two copies is dissolved, and the process is repeated continuously). another interesting problem involves the relations between time and space complexity classes for p systems with active membranes. we know that turing machines, once a polynomial space bound is fixed, are able to solve more problems in exponential time than in polynomial time (at least when ppp 6= pppssspppaaaccceee is assumed). this fact has not been investigated yet in the setting of membrane computing, as all solutions to decision problems presented until now (up to the knowledge of the authors) require only a polynomial amount of time. formally, the question we pose is the following: problem 23. is pppmmmccc⋆ d 6= pppmmmcccssspppaaaccceee⋆ d for any class of p systems d among nam, eam, am? that is, do problems which can be solved in polynomial space but not in polynomial time exist? another important property of traditional computing devices is described by savitch’s theorem: nondeterministic space-bounded turing machines can be simulated deterministically with just a polynomial increase in space requirements, and as a consequence pppssspppaaaccceee = nnnpppssspppaaaccceee holds. the proof does not appear to be transferable to p systems in a straightforward way; nonetheless, an analogous result might hold even in this setting: problem 24. does pppmmmcccssspppaaaccceee⋆ d = nnnpppmmmcccssspppaaaccceee⋆ d hold for any class of p systems d among nam, eam, am? the classes of p systems with active membranes we have considered in all the previous problems are only defined according to which kinds of membrane division rules are available (none, just elementary or both elementary and non-elementary). the same questions may be also worth posing about other restricted classes, such as p systems without object evolution or communication [12, 4], p systems with division but without dissolution, or even purely communicating p systems, with or without polarizations. finally, we feel that the differences between p systems and traditional computing devices deserve to be investigated for their own sake also from the point of view of space-bounded computations. we formulate this as an open-ended question: introducing a space complexity measure for p systems 309 problem 25. what are the relations between space complexity classes for p systems and traditional ones, such as ppp, nnnppp, pppssspppaaaccceee, eeexxx ppp, nnneeexxx ppp, and eeexxx pppssspppaaaccceee? 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[10] p. sosík: the computational power of cell division in p systems: beating down parallel computers?, natural computing, vol. 2(3), pp. 287–298, 2003. [11] c. zandron, c. ferretti, g. mauri, solving np-complete problems using p systems with active membranes, in: i. antoniou, c. calude, m. j. dinneen, eds., unconventional models of computation, 2nd international conference, umc’2k, brussel, belgium, springer, 2001. [12] c. zandron, a. leporati, c. ferretti, g. mauri, m. j. pérez-jiménez, on the computational efficiency of polarizationless recognizer p systems with strong division and dissolution, fundamenta informaticae, vol. 87(1), pp. 79–91, 2008. 310 antonio e. porreca, alberto leporati, giancarlo mauri, claudio zandron antonio e. porreca was born on september 18, 1982 in monza, italy. he got his b.sc. and m.sc. in computer science, in 2005 and 2008 respectively, from university of milano-bicocca, where he is now a ph.d. student. his current research interests focus on complexity-theoretic aspects of membrane computing. alberto leporati obtained a ph.d. in computer science from the university of milano (italy) in 2002. since 2004, he is assistant professor at the university of milano-bicocca. his research interests are in membrane computing, theoretical computer science and computational complexity. giancarlo mauri is full professor of computer science at the university of milano-bicocca. his research interests are mainly in the area of theoretical computer science, and include: formal languages and automata, computational complexity, bioinformatics and unconventional computing models, in particular membrane systems. on these subjects, he published more than 200 scientific papers in international journals, contributed volumes and conference proceedings. claudio zandron was born in 1972 in milan, italy. he received his m.sc. in computer science from the university of milano in 1996, and a ph.d. degree in 2002 from the same university. since 2006 he is associate professor at the university of milano-bicocca, department of informatics, systems, and communication. his research interests concern the areas of formal languages, molecular computing, and computational complexity. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 592-602 a non-cooperative game algorithm for task scheduling in wireless sensor networks l. dai, y.l. chang, z. shen liang dai school of electronic and control engineering chang’an university xi’an 710064, china e-mail: ldai1981@gmail.com yilin chang, zhong shen state key laboratory of integrated service networks xidian university xi’an 710071, china e-mail: ylchang@xidian.edu.cn, zhshen@mail.xidian.edu.cn abstract: scheduling tasks in wireless sensor networks is one of the most challenging problems. sensing tasks should be allocated and processed among sensors in minimum times, so that users can draw prompt and effective conclusions through analyzing sensed data. furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. but sensors may refuse to take pains to carry out the tasks due to the limited energy. to solve the potentially selfish problem of the sensors, a non-cooperative game algorithm (ngtsa) for task scheduling in wireless sensor networks is proposed. in the proposed algorithm, according to the divisible load theory, the tasks are distributed reasonably to every node from sink based on the processing capability and communication capability. by removing the performance degradation caused by communications interference and idle, the reduced task completion time and the improved network resource utilization are achieved. strategyproof mechanism which provide incentives to the sensors to obey the prescribed algorithms, and to truthfully report their parameters, leading to an effient task scheduling and execution. a utility function related with the total task completion time and tasks allocating scheme is designed. the nash equilibrium of the game algorithm is proved. the simulation results show that with the mechanism in the algorithm, selfish nodes can be forced to report their true processing capability and endeavor to participate in the measurement, thereby the total time for accomplishing the task is minimized and the energy-consuming of the nodes is balanced. keywords: wireless sensor networks; task scheduling; divisible load theory; game algorithm; mechanism design. 1 introduction & motivation wireless sensor networks are constituted by a large number of nodes, and without fixed infrastructure and centralized control mechanisms, so the task scheduling needs of mutual cooperation in all nodes. the energy of sensors is limited, so not all the sensors are willing to participate in collaborative efforts. lack of coordination between the nodes in wireless sensor networks is a new problem[1]. owing to the sensors with limited energy, the task should be completed within the shortest possible amount of time, so that users can draw useful conclusions through analyzing sensed copyright c⃝ 2006-2011 by ccc publications a non-cooperative game algorithm for task scheduling in wireless sensor networks 593 data. furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. divisible load theory [2] provides an effective solution to wireless sensor networks for task scheduling [3-6]. different from other heuristic solutions of task scheduling problem in wireless sensor networks [7, 8], this scheme can get not only the optimal solution, but also the analytic solution, thus ensuring the consistency of the results of scheduling. divisible load scheduling algorithms were applied to wireless sensor networks in [3-6]. although the authors derived closed-form solutions to obtain the optimal finish time, the network topology discussed in those papers is single-level tree structure. while in wireless sensor networks, as compared with the single-level tree structure, clustered structure (multi-level tree structure) has a great of advantages [9]. those algorithms mentioned above designed on the premise that sensors manipulate the algorithms and perform a task to the best of their abilities. but the sensors may be owned by autonomous rational organizations or people that have no a priori motivation for cooperation. consequently, they will manipulate the algorithms if it benefits them to do so. therefore, we present a non-cooperative game algorithm for task scheduling in wireless sensor networks. the goal of this algorithm is to minimize the overall execution time (hereafter called makespan) and fully utilize network resources, by finding an optimal strategy of splitting the original tasks received by sink into a number of sub-tasks as well as distributing these subtasks to the clusters in the right order, and through the proposed mechanism to encourage collaboration. 2 mechanism design the energy of sensors is limited, if we do not integrate contributions with benefits, not all nodes willing to participate in collaborative efforts to perform tasks. this selfish, non-cooperative behavior will result in poor performance. if we follow the principle of more labor and more benefits, some selfish sensors, driven by the interests, will intentionally exaggerate its datameasuring capacity to increase their income. such dishonest behavior will result in that it’s difficult to determine which sensor reporting its actual capacity, and which said a lie, so we can not globally optimize tasks allocation according to each sensor’s capacity. these dishonest or selfish behavior, has seriously affected the efficiency of wireless sensor network. therefore, we should provide incentives to the nodes to obey the prescribed algorithms and to truthfully report their parameters, leading to an efficient allocation and execution. this problem can be solved by applying mechanism design of game theory. mechanisms are generally composed of allocation o = o(a1, a2, · · ·, an) and payment p = (p1(a), p2(a), · · ·, pn(a)) rules. allocation rules also are called output function. in mechanism design, there are usually n agents, and each agent has some private information, which is known as the type of agent or true value. the private information is only known by agent, and is confidential for the external, for example, the type of a agent ti can be the cost executing an assigned task. vi(ti, o) is the value function of an agent, said the cost of performing a task. pi(·) is the payment function for agent performing a task. ui(·) is the utility function of an agent, that ui(·) = pi(·) − vi(ti, o). to maximize the utility function value is the goal of agents. mechanism should be designed to maintain unity with the goal the agent seeking for. mechanism can induce the agent’s choice or behavior by means of payment. if in a mechanism, the only optional behavior of all agents is directly stating their preferences, then we say that the mechanism is a direct revelation mechanism. in a direct revelation mechanism, if all agents will tell their true preferences in order to maximize their utility, then we say that the mechanism is incentive compatibility. the nature of incentive compatibility allows 594 l. dai, y.l. chang, z. shen sink c1 ck c3 c2 n11 nknk nk2 nk1 n1n1 n13 n12 l11 l13 l12 cluster1 l1n1 lknk lk2 lk1 clusterk l1 l2 l3 lk figure 1: network topology us to design a mechanism to overcome the selfishness of agents, because in an incentive compatible mechanism, the agent will always choose to say their true private information in order to maximize their utility. in a direct revelation mechanism, if it’s a dominant strategy that they gave their true preferences, then we claimed that the mechanism is strategyproof. if each agent is honest, and each agent’s income is not less than zero, then the mechanism satisfies the voluntary participation condition. 3 scheduling algorithm 3.1 network model the network topology discussed in this paper is shown in fig.1. wireless sensor networks construct clusters several times in its life cycle. each cluster will have a set-up phase and a steady-state phase[11]. assuming that there are k clusters (clusteri, i = 1, ...., k) in the network during a steady-state phase. each cluster head is expressed as ci. within the clusteri, there are ni nodes expressed as ni,j (i = 1, ....k; j = 1, ...ni), respectively. communication links between cluster heads and sink are expressed as li, i = 1, ...., k, respectively. communication links between intra-cluster nodes and cluster head ci are expressed as li,j (i = 1, ....k; j = 1, ...ni), respectively. the following notations will be used throughout this paper: αi: the total fraction of load that is assigned by sink to cluster head ci; αi,j: the fraction of load that is assigned to intra-cluster node ni,j in clusteri by ci; by definition we can see: k∑ i=1 αi = 1 (1) ni∑ j=1 αi,j = αi (2) ωi: a constant that is inversely proportional to the processing (data fusion) speed of cluster head ci; yi,j: a constant that is inversely proportional to the measuring speed of intra-cluster node ni,j; a non-cooperative game algorithm for task scheduling in wireless sensor networks 595 zi: a constant that is inversely proportional to the speed of link between sink and cluster head ci; zi,j: a constant that is inversely proportional to the speed of link between the cluster head ci and the intra-cluster node ni,j; tcp: data fusion intensity constant. tcp is the time that takes to fuse all the load on a cluster head when ωi = 1. the entire load can be fused on cluster head ci in time ωitcp; tms: measurement intensity constant. tms is the time that takes the intra-cluster node ni,j to measure the entire load when yi,j = 1. the entire assigned measurement load can be measured on the intra-cluster node ni,j in time yi,jtms; tcm: communication intensity constant. this is the time it takes to transmit all the processing loads over a link when zi = 1. the entire load can be transmitted over the ith link in time zitcm; φi: the information utility constant [8,10] of cluster head ci. information utilization constant is based on a technique of information accuracy estimation. through estimating accuracy of information, cluster head can know the approximate percentage of data fusion. tf : the finish time of a given task. in this paper, we formulate the problem of task scheduling in a large scale sensor network as an optimization problem to minimize tf . 3.2 scheduling strategy the proposed task scheduling algorithm is based on leach [11] protocol. leach was firstly proposed clustering protocol in wireless sensor network, and its certain governing ideas breathe through many other clustering protocols, such as teen and daea. leach protocol structures the single-hop networks. each cluster’s intra-cluster nodes share the same channel, while each cluster head has its own channel to sink. the original tasks received by sink are divided into two stages: inter-cluster task scheduling and intra-cluster task scheduling. first, inter-cluster task scheduling partitions the entire tasks into each cluster, and then the sub-tasks in a cluster are assigned to each intra-cluster node by intra-cluster task scheduling. 1) intra-cluster task scheduling fig.2 illustrates the timing diagram for a set of intra-cluster nodes, indexed from n1 to nk, in one cluster. from fig.2, it can be observed that there is no time gap between every two successive nodes because the divisible workload can be transferred in the cluster. all nodes start to measure data at the same time. once the previous node finishes transmitting data, the other one completes its measuring task and starts to report its data. as a result, the proposed timing diagram minimizes the makespan by scheduling the measuring time and reporting time of each node. moreover, since the intra-cluster scheduling tries to avoid the transmission conflicts at the cluster head, energy spent on retransmission is conserved. for cluster i, based on the timing diagram shown in fig.2, one can write the following set of equations about tasks assigned to the intra-cluster nodes in clusteri: αi,j−1yi,j−1tms = αi,jyi,jtms + αi,jzi,jtcm, i = 2, 3, ...k (3) a general expression for the above set of recursive equations can be written as: αi,j=si,jαi,j−1 (4) where si,j = yi,j−1tms/(yi,jtms + zi,jtcm). 596 l. dai, y.l. chang, z. shen n1 n2 ti n3 nk ,1 ,1i i cmz tα,1 ,1i i msy tα ,2 ,2i i cmz tα,2 ,2i i msy tα ,3 ,3i i cmz tα,3 ,3i i msy tα , ,i ni i ni cmz tα, ,i ni i ni msy tα measurement data reporting figure 2: timing diagram for intra-cluster task-processing using eq.2 and eq.4, the above recursive equation for αi,1 can be rewritten in terms of αi only as αi,1 = αi/(1 + ni∑ j=2 j∏ k=2 si,k) (5) the cluster head ci will use the above value of αi,1 to obtain the amount of data that has to be measured by the rest of the ni − 1 sensors by using αi,j = αi j∏ k=2 (si,k)/(1 + ni∑ j=2 j∏ k=2 si,k) (6) then from fig.2, the minimum time for measuring, reporting sink’s sub-task αi can be given as ti=αi(yi,1tms + zi,1tcm)/(1 + ni∑ j=2 j∏ k=2 si,k) (7) 2) inter-cluster task scheduling after cluster heads fusing the intra-cluster nodes’ measured data, cluster heads can send the fused data to sink concurrently because each cluster head has a separate channel to sink. in order to remove the performance degradation caused by idle, and to improve efficiency, as shown in fig.3, we can get ti + αiwitcp + φiαizitcm = tj + αjwjtcp + φjαjzjtcm, i ̸= j (8) in eq.7, we make (yi,1tms + zi,1tcm)/(1 + ni∑ j=2 j∏ k=2 si,k) = gi, then substitute gi into eq.8, we can get αiri = αjrj (9) where ri = gi + witcp + φizitcm. now using the eq.1 and eq.9, one can solve for αi as αi=(1/ri)/ k∑ i=1 (1/ri) (10) a non-cooperative game algorithm for task scheduling in wireless sensor networks 597 intra-cluster scheduling reporting data to sink 1t 2t 3t 4t kt 1 1 cpwtα 2 2 cpw tα 3 3 cpw tα 4 4 cpw tα k k cpw tα k k k cmz tϕ α 1 1 1 cmztϕ α 2 2 2 cmz tϕ α 3 3 3 cmz tϕ α 4 4 4 cmz tϕ α c1 ck c4 c3 c2 s i n k tf data fusion figure 3: timing diagram for inter-cluster task scheduling hereto, we can get the fraction of load cluster head ci and the intra-cluster nodes within it received from sink (αi and αi,j). and the total task execution time is as follow: tf=ti + (witcp + φizitcm)(1/ri)/ k∑ i=1 (1/ri) (11) for the homogeneous networks, the parameters(measurement speed, communication speed, data-fusing speed, information utility constant) of the network are the same and there are n sensors in each cluster. sink can evenly distributed loads to each cluster. the sub-loads each cluster receiving is as follows: αi=1/k (12) in the homogeneous networks, the minimum time-consuming for the total task is: tf=(d + wtcp + φztcm)/k (13) where d= (ytms + ztcm)/n. with the increasing density of wireless sensor networks, the number of the clusters is higher with the current clustering algorithms. from eq.13, it can be known that tf approaches 0 with k approaching infinite. 4 mechanism design for ngtsa in this section, we propose strategyproof mechanism for task scheduling of wireless sensor networks. the ith cluster is composed of ni strategic sensors. each task-performing sensor ni,j is characterized its true value yi,j, which is equal to the time to measure the unit task. we denote by yi = (yi,1, yi,2, ···, yi,ni) the vector of true measuring speed. measuring speed yi,j is private to ni,j. as the sensor ni,j with rationality may give a bid bi,j which is not equal the true measuring speed yi,j, if it benefits it to do so. cluster heads calculated the fraction of load that is assigned to intra-cluster nodes αi(bi) = (αi,1(bi), αi,2(bi), ···, αi,ni(bi)), according to the bids of intra-cluster nodes, where ni∑ j=1 αi,j(bi) = αi, and bi = (bi,1, bi,2, · · ·, bi,ni) is the bid vector of intra-cluster nodes. 598 l. dai, y.l. chang, z. shen as the selfishness of sensors, the sensor may complete the measurement task at a relatively low speed ỹi,j, where ỹi,j ≤ yi,j. after intra-cluster nodes completed their task, cluster head node will get the actual measuring speed. the value function of ni,j under task allocation αi(bi) is defined as follow: vi,j(αi(bi), ỹi,j) = −αi,jỹi,j (14) the value of vi,j(αi(bi), ỹi,j) is equivalent to the negation of the actual time required for ni,j to complete αi,j of total load. it can be regarded as the cost of ni,j to complete its task. the mechanism will provide payment for involving in measurement, so each node will select the appropriate strategy to maximize its benefits. the utility function of ni,j is defined as follow: ui,j(bi, ỹi) = qi,j(bi, ỹi) + vi,j(αi(bi), ỹi,j) (15) where qi,j(bi, ỹi) is the payment cluster head ci paid to ni,j, ỹi = (ỹi,1, ỹi,2, · · ·, ỹi,ni) is the vector of each node’s actual measurement speed. utility function equals the payment the node got from mechanism minus the cost for completing its tasks. designing such mechanisms involves finding an allocation and a payment scheme that minimize the makespan according to the sensors’ bid bi and motivate all the intra-cluster sensors to bid their true value yi,j and measure the task at their full measuring capacity ỹi,j = yi,j. define the payment function for mechanism is as follow: qi,j(bi, ỹi) = ci,j(bi, ỹi) + bi,j(bi, ỹi) (16) where ci,j(bi, ỹi) = −vi,j(αi(bi), ỹi,j) is the compensation to the node ni,j. the bonus of ni,j is defined as follow: bi,j(bi, ỹi) = t−i,j(αi(b−i,j), b−i,j) − t(αi(bi), (b−i,j, ỹi,j)) (17) where t−i,j(α(b−i,j), b−i,j) is the optimal task completion time when ni,j is not involved in measurement, that is, the bonus of intra-cluster node is equal to its contribution in reducing the task completion time because of its participation for measurement. theorem (strategyproofness). the mechanism proposed in this paper is strategyproof. proof. according to the bids of intra-cluster nodes in cluster i, bi = (bi,1, bi,2, · · ·, bi,ni), we can get the utility of ni,j is as follow: ui,j(bi, ỹi) = qi,j(bi, ỹi) + vi,j(αi(bi), ỹi,j) = t−i,j(α(b−i,j), b−i,j) − t(αi(bi), (b−i,j, ỹi,j)) + αi,j(b)ỹi,j − αi,j(b)ỹi,j = t−i,j(α(b−i,j), b−i,j) − t(αi(bi), (b−i,j, ỹi,j)) (18) when ni,j perform a task to the best of their abilities (ỹi,j = yi,j), and bid the measurement as bi,j = yi,j, the utility of ni,j is as follow: uti,j = t−i,j(α(b−i,j), b−i,j) − t(αi(bi), (b−i,j, ỹi,j)) = t−i,j(α(b−i,j), b−i,j) − t ti,j (19) where t ti,j is the shortest possible amount of time to finish total tasks according to divisible load theory. if ni,j bids a lower value, that is, bi,j < yi,j ,the makespan is increased by a small amount as the load allocated to the other intra-cluster nodes is increased. therefore, only when ni,j voluntarily gives the true measuring speed and be assiduous in its duty, they can get the maximum benefit. because t−i,j is the optimal task completion time when ni,j is not involved in measurement, its value must not be less than when all the nodes involved in the tasks. so, we can get uti,j ≥ 0, that is, all nodes within one cluster will try to do their best to carry out a task. a non-cooperative game algorithm for task scheduling in wireless sensor networks 599 5 wireless energy use in this section, the energy model of the ngtsa algorithm is presented in detail and the equations of energy consumption of individual sensor nodes are derived. the model is based on first-order radio model [11]. there are three kinds of energy consumption in the wireless sensor network: measurement, data fusion, and communication. because nodes in the sensor network cooperate with each other via data transmission. energy consumption of communications exist in sensor nodes, cluster heads and sink. it is not necessary for cluster heads and sink to perform any sensing task. thus, there is no energy cost for cluster heads due to the measurement of these nodes, while the additional energy cost of cluster heads attributes to data fusion. the energy to sense, fuse, and transmit a unit sensory data are denoted by es, ep, and etx, respectively. sensor nodes also consume the energy of erx to receive one unit of data. the distance between the sender and the receiver is d. the energy use for each kind of nodes is outlined as follows: energy use for individual sensor nodes j in cluster i: ei,j = αi,j(es + etxd 2), i = 1, · · ·, k , j = 1, · · ·, ni (20) energy use for individual cluster head: ei = αi(erx + ep + φietxd 2), i = 1, · · ·, k (21) energy use for sink: esink = k∑ i=1 αiφietx (22) 6 performance evaluation in the above sections, we have obtained the close-form solution of the task completion time and the formula for the energy consumption of each node. in this section, we investigate the effects of different cases under homogeneous network environment on the makespan, energy consumption of every intra-cluster nodes, and the payment/utility of the cheating nodes. we consider the wireless sensor network comprising 10 clusters. within each cluster, there are 20 intra-cluster nodes. assume that only the first intra-cluster node cheats by reporting values different than its true measuring speed, and by measurement at a different speed than the true speed in each cluster. the speed of communication link between each node were 0.05; the measuring speed of each intra-cluster node is 0.2; the data fusion speed of each cluster head is 0.1, and the information utility constant of each cluster head is 0.5. in the simulation, the following energy parameters are adopted: transmitting a unit of sensor reading over a unit distance takes etx=200nj, receiving one unit of sensor reading consumes erx=150nj, measuring one unit of sensor reading needs es=100nj, fusing one unit of observation consumes ep=20nj and the distance between the sender and the receiver is d=100m. in the simulation, we supposed that: tms=tcm=tcp= 1. we simulated the following eight cases: 1⃝ yi,1 = bi,1 = ỹi,1; 2⃝ yi,1 = bi,1 < ỹi,1; 3⃝ yi,1 < bi,1 = ỹi,1; 4⃝ yi,1 = ỹi,1 < bi,1; 5⃝ bi,1 < yi,1 = ỹi,1; 6⃝ yi,1 < ỹi,1 < bi,1; 7⃝ yi,1 < bi,1 < ỹi,1; 8⃝ bi,1 < yi,1 < ỹi,1. where bi,1 states the bid of the first intra-cluster node; yi,1 represents the node’s actual value; ỹi,1 is the true value of the measurement speed. the bid bi,1 and the measurement value ỹi,1 for each case are presented in tables 1: 600 l. dai, y.l. chang, z. shen table 1: bids and execution values case 1 2 3 4 5 6 7 8 bi,1 0.2 0.2 0.3 0.3 0.1 0.4 0.3 0.1 ỹi,1 0.2 0.3 0.3 0.2 0.2 0.3 0.4 0.3 figure 4: makespan when the first intra-cluster node cheats the simulation results are shown in fig.3 and fig.5. firstly, fig.3 shows the makespan for the eight cases. notice that case 1 (yi,1 = bi,1 = ỹi,1) results in the minimum makespan, while all other cases result in a larger makespan. in that case, each node honestly bids, and makes effort to measure. when ỹi,1 ≤ bi,1, the case 3,4, and 7, the makespan is increased by a small amount as the load allocated to the other intra-cluster nodes is increased. the effect of cheating is dispersed throughout the remaining nodes. in the remaining case (ỹi,1 > bi,1),the case 2,5,6, and 8, the network performance dramatically degrades as the first intra-cluster nodes is overloaded, and the other nodes are underutilized. in these cases, the first intra-cluster nodes is slowing down the entire network. the increase in makespan is large, and it is due to the impact of ỹi,1. next, the second simulation is about the energy consumption of intra-cluster sensor nodes for the 8 cases. sink and cluster heads are not taken into account, because generally, sink has no energy constraint and the chosen cluster heads have the possibly enough energy. the network is configured with 20 clusters. without loss of generality, the intra-cluster sensor nodes in the first cluster are chosen to study the energy consumption, as shown in fig.5. in each case, the energy consumption of sensor nodes monotonically decreases due to the reduced workload. in the case (yi,1 = bi,1 = ỹi,1), each node honestly bids, and make effort to measure, so the energy-consuming of each intra-cluster node is most evenly. in the case 2,5,6, and 8, because the cluster head allocates more tasks on the first intra-cluster node as it bids a rate slower than its true rate, the energy-consuming of the first intra-cluster nodes increases noticeably, which will run out power and affected the entire network performance. then, the third simulation is about the payment and utility of intra-cluster nodes. fig.6 shows the payment and utility for the first intra-cluster nodes in the 8 cases. as we expected, case 1 (yi,1 = bi,1 = ỹi,1), maximizes the first intra-cluster node’s utility. in all the other cases, utility is lessened. when ỹi,1 > bi,1, the utility is negative due to the impact of ỹi,1 on the a non-cooperative game algorithm for task scheduling in wireless sensor networks 601 figure 5: energy consumption when the first intra-cluster node cheats figure 6: payment and utility of the first intra-cluster node when it cheats makespan. in the remaining cases, payment and utility are reduced as αi,1. 7 conclusions as the node in wireless sensor network has limited energy, the tasks should be completed as quickly as possible, and the network resources should be fully utilized. in this paper, we present a task scheduling algorithm (ngtsa) in clustered wireless sensor networks. the goal of this algorithm is to minimize the makespan and fully utilize network resources, by finding an optimal strategy of splitting the original load received by sink into a number of chunks as well as distributing these chunks to the clusters in the right order. as the gradual development of the wireless sensor networks and ubiquitous networks, this scheduling model using divisible load theory needs further study and validation. in addition, the validation of divisible load theory in wireless sensor networks still remains in the simulation phase. there are short of scanty of large-scale, practical applications to demonstrate its true potential, so applying the divisible load theory in actual wireless sensor network should be an important research direction for the next step. 602 l. dai, y.l. chang, z. shen acknowledgement the authors thank the editors and the anonymous reviewers for their valuable comments that helped to improve the paper. the work was supported by the national natural science foundation of china (no.60972047), and the 111 project (no.b08038). bibliography [1] c. pandana, h. zhu, l. ray, cooperation enforcement and learning for optimizing packet forwarding in autonomous wireless networks. ieee transactions on wireless communications, vol.7, no.8, pp.3150-3163, 2008. 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[11] w. heinzelman, a. chandrakasan, an application-specifid protocol architecture for wireless microsensor networks. ieee transaction on wireless communications, vol.1, no.4, pp. 660-670, 2002. international journal of computers communications & control issn 1841-9836, 10(5):732-745, october, 2015. a modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning x.y. wang, g.x. zhang, j.b. zhao, h.n. rong, f. ipate, r. lefticaru xueyuan wang 1. school of electrical engineering southwest jiaotong university chengdu 610031, p.r. china 2. school of information engineering southwest university of science and technology mianyang 621010, p.r.china 121053406@qq.com gexiang zhang*, junbo zhao, haina rong school of electrical engineering southwest jiaotong university chengdu 610031, p.r. china gexiangzhang@gmail.com, junbozhao55589@gmail.com, ronghaina@126.com *corresponding author: gexiangzhang@gmail.com florentin ipate, raluca lefticaru faculty of mathematics and computer science university of bucharest academiei 14, bucharest, romania florentin.ipate@ifsoft.ro, raluca.lefticaru@fmi.unibuc.ro abstract: to solve the multi-objective mobile robot path planning in a dangerous environment with dynamic obstacles, this paper proposes a modified membraneinspired algorithm based on particle swarm optimization (mmpso), which combines membrane systems with particle swarm optimization. in mmpso, a dynamic double one-level membrane structure is introduced to arrange the particles with various dimensions and perform the communications between particles in different membranes; a point repair algorithm is presented to change an infeasible path into a feasible path; a smoothness algorithm is proposed to remove the redundant information of a feasible path; inspired by the idea of tightening the fishing line, a moving direction adjustment for each node of a path is introduced to enhance the algorithm performance. extensive experiments conducted in different environments with three kinds of grid models and five kinds of obstacles show the effectiveness and practicality of mmpso. keywords: membrane computing, evolutionary membrane computing, particle swarm optimization, variable dimensions, mobile robot path planning, membrane systems. 1 introduction as a branch of natural computing, membrane computing(mc), initiated by păun in 1998, aims to abstract distributed and parallel computing models, also called p systems or membrane systems, from the compartmentalized structure and interactions of living cells [1, 2]. there are three main research directions in this area [3]: theoretical study including computing models and their computing power and efficiency; applications such as modeling biological processes and approximately solving engineering optimization problems [4]; software and hardware realization. in the past seventeen years, much attention has been paid to the theoretical aspects, but the applications are worth further discussing, especially for solving real-world engineering problems. copyright © 2006-2015 by ccc publications a modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning 733 evolutionary algorithms (eas) are a class of probabilistic search methods with many advantages such as flexibility, convenient application and robustness. while mc can provide flexible evolution rules and parallel-distributed framework [5], which is very beneficial to produce the membrane-inspired evolutionary algorithms (mieas). until now, different kinds of mieas have been proposed. in [6], a certain number of nested membrane structures in the skin membrane were combined with eas for multi-objective optimization problems. a novel miea, called qeps, combining quantum-inspired evolutionary algorithms with p systems to solve image processing problems and knapsack problems were proposed in [7, 8]. particle swarm optimization (pso) [9] with one-level membrane structure (olms) was used to solve broadcasting problems of p systems and radio spectrum allocation, respectively [10, 13]. in [11], xiao et al. applied the bio-inspired algorithm based on membrane computing for engineering design problems. the investigations verify the usefulness of the introduction of p systems into eas to solve various real-world applications. to the best of our knowledge, there is not any work focusing on the use of a miea to solve mobile robot path planning problems (mr3p), which is one of very important real-world applications. in this paper, a modified membrane-inspired algorithm based on particle swarm optimization (mmpso) is proposed to solve mr3p. the main contributions of this paper can be summarized as follows: (1) in this study, the solving process of mr3p is considered as a dimension-reducing optimization procedure and therefore a pso with variable dimensions (vpso) is introduced into mmpso, and further a dynamic double olms (d-olms) with membrane division and dissolution is presented to combine with vpso to arrange the particles and execute the communications between regions delimited by membranes. (2) mobile robot path planning is a multi-objective optimization problem. this study considers three objectives, distance, safety and smoothness, instead of a single objective (path length) [15–17] or bi-objectives (path length and risk degree) as previously considered in the literature [18, 21]. a point repair algorithm and a smoothness approach are presented to effectively trade-off multiple objectives and speed-up the mmpso convergence. (3) inspired by the idea of tightening the fishing line, a moving direction adjustment for each node of a path is introduced to enhance the algorithm performance, together with the point repair algorithm. (4) extensive experiments are carried out by considering various environments with different grid models and different obstacles to verify the effectiveness and practicality of mmpso. the rest of this paper is organized as follows. section 2 describes mr3p. in section 3, we present mmpso for solving mr3p. section 4 discusses parameter setting and provides experimental results. conclusions are drawn in section 5. 2 mobile robot path planning problems this section gives a brief description of mr3p and then summarizes the related works. 2.1 problem statement mr3p aims to find a reasonable collision-free path for a mobile robot from the starting position to the target position through an environment containing static or dynamic obstacles. it is proved that mr3p is an np-complete problem [19]. mobile robots are very useful for the dangerous or hostile environments that humans are not able to reach. so, in recent years, mobile robotics field is a very hot and also challenging research area. as one of important research themes in the mobile robotics field, mr3p, launched at the 1960s, has become an attractive area. 734 x.y. wang, g.x. zhang, j.b. zhao, h.n. rong, f. ipate, r. lefticaru generally, the criterion for planning a mobile path has to consider many factors, such as the shortest distance, safety degree, smoothness, the lowest energy cost and minimum time, based on the characteristics of a special robot with the minimal turning radius, acceleration and the limited velocity and the features of the environment, such as the distances between obstacles, the shapes of obstacles, the occurrence probabilities of dynamic obstacles. thus, the optimization of a mobile robot path in a static or dynamic environment is very complicated. to realize the real time robot path planning in a dynamic environment or the consideration that a robot can dynamically track the motion target, at least three aspects should be carefully considered together due to their interactions among each other. (i) an efficient and effective optimization approach is very important for planning a good mobile robot path. aiming to solve mr3p in a dynamic environment, this study proposes mmpso. (ii) a simple and good objective function is very important for planning a good mobile robot path. in this study, the objective function aims to minimize the path length, to maximize the smoothness and the distances between a robot and the obstacles or dangerous sources, and can be expressed as f = kd ·dis + kf ·s + ks ·sd (1) where kd, kf , ks are the weighing factors of path length, smoothness and safety degree, respectively. the detailed description of path length, smoothness and safety degree are as follows: 1. path length: path length dis is the sum of distances between n nodes from the starting point to the end point and can be described as dis = n−1∑ i=0 l(i, i + 1) (2) where l(i, i + 1) = √ (xi+1 −xi)2 + (yi+1 −yi)2 is the distance between nodes i and i + 1, where xi and xi+1 are the x-axis values of nodes i and i + 1; yi and yi+1 are the y-axis values of nodes i and i + 1, respectively. 2. smoothness: smoothness refers to the sum of the reflection angles formed by any three neighboring nodes of a path. as usually calculating directly the smoothness is a timeconsuming process, this study uses an indirect approach, i.e., it uses the ratio sc of the number of deflection angles less than the given expected value to the total number of deflection angles and the ratio sp of the number of path segments more than the number of the segments in the path with the smallest number of path segments in a group to the total number of path segments to evaluate the smoothness of a path. smoothness can be calculated by using the following formula: s = α ·sc + β ·sp (3) where sc = 1− dalnf −1 ; sp = 1− smin nf , where nf is the total number of path segments; dal is the number of deflection angles greater than the expected value; smin is the number of the segments in the path with the smallest number of path segments in a group; α and β are two weighting coefficients. 3. safety degree: safety degree (sd) is the sum of deviation degrees ci (i = 1,2, . . . ,n) between any segment in a path and its nearest obstacle. sd is defined as sd = n−1∑ i=1 ci = { 0, di ≥ λ∑n−1 i=1 e λ−di, di < λ (4) a modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning 735 where di is the minimal distance between the ith segment and its nearest obstacle; λ is the threshold of safty degree. (iii) the establishment of an environment model is the foundation of mr3p and decides the environment feature (static or dynamic), and how to choose an evaluation method and an optimization approach to implement the path planning for a mobile robot. there are three main environment models: vector (obstacles represented by polygons), grid (occupancy cell) and graph (voronoi diagram or visibility graph). as compared with vector and graph, grid has the advantages of simple and flexible. this study uses a grid environment. there are two ways of representing a grid-based environment. one is a x-y coordinates plane [15] and the other is an orderly numbered grid, which has been widely used. we adopt the latter approach, in which a square environment is evenly divided into a certain number of squares, i.e., the x-axis and y-axis are divided equally into m parts, thus, we get m×m grids, where one or more grids are used to represent the obstacles. an example of the 7×7 grids is shown in fig. 1(a), where the grid map is encoded by using matlab and the grey shadow grids represent obstacles. the mapping relations between coordinates (x,y) and the serial number p beginning from one can be identified by the following formula: p = fix(y/sog) ·noc + fix(x/sog) (5) where noc is the number of columns; sog is the size of a grid; the function fix(t) rounds t to its nearest integer towards zero. 2.2 related work since the pioneering work of lozano-pérez [12], a number of algorithms for solving the path planning problems have been reported in the past thirty years. these algorithms can be generally divided into two main classes: classical [14] and heuristic [19]. although the classic approaches can be used to solve this problem, they may suffer from some drawbacks, such as easily falling into local minima, high complexity in high dimensions. in order to overcome these problems of classic methods, heuristic algorithms have been developed. the representative heuristic approaches for solving mr3p are neural networks, genetic algorithms [15], ant colony optimization, fuzzy logic [16], simulated annealing [17], pso [21], probabilistic road maps, rapidly exploring random trees, etc. although heuristic methods do not guarantee to find an optimal solution, they may be faster and may have higher efficiency than classical methods [19]. the studies in [20, 21] have shown that the interest in pso-based meta heuristics algorithms is growing in mobile robotics. particularly interesting is the work in [20,21] for solving the static or dynamic mr3p. according to the reports in the literature, the dimensions of the search space are set to a fixed value and remain constant throughout the entire optimization process in almost all of pso-based algorithms for solving mr3p; consequently, the solving ability of the algorithms is limited to a single individual’s dimension and the algorithm cannot find the optimal solutions. in mr3p, the dimensions of the search space decide the number of nodes of the optimal path. high dimensions may result in the decrease of the searching efficiency, while low dimensions may cause the case in which it is impossible to get barrier-free paths. in order to find a proper dimension for a path and improve the search efficiency to selfadapt the dynamic environment with randomly appearing or disappearing obstacles, mmpso with variable dimensions is introduced to solve mr3p and will be presented in the next section. 736 x.y. wang, g.x. zhang, j.b. zhao, h.n. rong, f. ipate, r. lefticaru 3 mmpso for mr3p this study considers a grid-based environment, in which moving obstacles or dangerous sources may appear or disappear. to adapt the hostile environment, mmpso uses a variable dimension pso and a dynamic membrane structure with membrane division and dissolution. to improve the mmpso performance such as effectiveness, efficiency and extensibility, we introduce a point repair algorithm, a smoothness approach and a moving direction adjustment technique. in what follows, we first present the variable dimensions and then describe the point repair algorithm and the smoothness approach. finally, we summarize the mmpso algorithm. 3.1 variable dimensions in mmpso, each particle represents a feasible path, instead of an infeasible path in other heuristic approaches such as genetic algorithms. if the dimension of each particle is fixed, the search efficiency is often low due to the following reasons: (1) population initialization, i.e., obtaining a population of initially feasible paths through randomly searching each node row by row, is time-consuming, especially for large grids or complex environments with circuitous route phenomenon. (2) the search process of the algorithm with fixed dimensions is also timeconsuming, as compared with the dimension-reducing methods, because the variable dimensions in this study consider the removal of redundant information at each iteration. (3) due to the complex or hostile environment, the optimization algorithms with fixed dimensions have low efficiency and poor adaptability. to overcome these shortcomings, a set of high-dimension particles are needed at the beginning and the dimension of the best solution, i.e., the optimal path, is usually quite low. thus, the dimensions of each particle in mmpso are considered to be variable. in mmpso for solving mr3p, the initial population p of particles (feasible paths) is classified into several subpopulations pmin,. . . ,pmax, where pmin is the subpopulation with lower dimensions, which represent shorter paths, that pass around fewer obstacles, and pmax is the subpopulation with higher dimensions, which denote longer paths, passing around more obstacles. at the beginning, the population size smin of pmin may be similar to the one smax of pmax, and the particles in pmax may search a feasible path through passing around external obstacles, while the particles in pmin may go to the contrary case. as the algorithm goes forward, smin will increase and smax will decrease. in general, a particle with low dimensions produces a shorter path, while a particle with high dimensions corresponds to a longer path. however, there are still some exceptions. but in mmpso, the point repair algorithm, the smoothness approach and the moving direction adjustment technique can rectify the exceptions. the implementation of variable dimensions motivates the dynamic membrane structure of mmpso. 3.2 point repair algorithm in the process of searching the optimal path, some nodes may move into obstacles and some path segments may cross obstacles, which results in infeasible paths and it is necessary to repair them. this study introduces a point repair algorithm to change the infeasible paths into feasible paths. we first define some special grids in the environment model. in fig. 1(a), p1, which is surrounded by the three peripheral grids, 8, 9 and 15, and p2, which is surrounded by the three peripheral grids, 29, 36 and 37, are the vertexes of obstacle o. all the peripheral grids have two kinds of coefficients, γ1 and γ2, which are randomly selected and are controlled by the weighing factors, kd,ks and kf in (1). the coefficient γ1 related to lateral grids {9,15,29,37} is mainly controlled by kd. the coefficient γ2 related to the diagonal grids {8,36} is mainly controlled by ks and kf , where kd + ks + kf = 1, 0.6 ≤ kd ≤ 1, 0 ≤ γ1 ≤ 1, 0 ≤ γ2 ≤ 1, γ1+γ2 = 1. the a modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning 737 relationship between γ1 and γ2 is shown in fig. 1(b). for example, if kd = 0.8, ks = kf = 0.1, we obtain γ1 = 0.5 and γ2 = 0.5. if kd = 1, ks = kf = 0, we get γ1 = 1 and γ2 = 0. (a) 1 g2g d k (b) figure 1: definition of grids o 1 3 4 ! 2 "! "# # (a) o 1 3 4 ! " #$ #% l (b) o 2 3 2 !" !# 4 1 (c) o 4 3 2 1 ! " (d) (e) figure 2: an example for point repair and smoothness algorithms two types of infeasible paths are shown in fig. 2(a)-(b), where d1 is the point to point distance between node 2 and p1; d2 is the point to point distance between node 2 and p2; d3 is the point to line distance between p1 and l; d4 is the point to line distance between p2 and l; d1, d2, d3 and d4 decide which peripheral grids will be selected. in fig. 2(a), the node 2 in the path {1,2,3,4} (type 1) must be repaired. in fig. 2(b), the path segment l crossing the obstacle (type 2) must be broken. the point repair process is as follows. step 1 : evaluate a path found. if it is feasible, we skip the repair process, otherwise, we perform the repair process. step 2 : judge the type of an infeasible path, types 1 or 2. step 3 : if the infeasible path is type 2, go to step 4, otherwise, conduct the following steps: (a) calculate the distance between the infeasible point p0 and the vertex pi of the obstacle, then get the value(s) of di, i = 1 (the obstacle is in the corner) or i = 1,2 (the obstacle is located at the edge of the map) or i = 1,2,3,4 (the obstacle is located in the middle of the map). next, sort di in an increasing order. (b) select unused pi by using the corresponding smallest value in di and get the peripheral grids pjg_i,j = 1,2,3, and randomly select unused p j g_i by using γ1 and γ2. (c) replace the value of the infeasible node with selected pjg_i. (d) judge the path again. if it is feasible, this process terminates, otherwise, go to step 2. step 4 : type 2 is repaired according to the following steps: (a) calculate the distance between the infeasible path segment l and the vertex pi of the obstacle, get the value(s) of d′i, i = 1 (the obstacle is in the corner) or i = 1,2 (the obstacle is located at the edge of the map) or i = 1,2,3,4 (the obstacle is located in the middle of the map). next, sort d′i in an increasing order. (b) select unused pi by using the corresponding smallest value in d′i and get the peripheral grids pjg_i,j = 1,2,3, and randomly select unused p j g_i by using γ1 and γ2. 738 x.y. wang, g.x. zhang, j.b. zhao, h.n. rong, f. ipate, r. lefticaru (c) insert the selected pjg_i between the two nodes of the infeasible path segment and get two new path segments paths_1 and paths_2. (d) judge each of the two paths paths_1 and paths_2. if one of them is not feasible, go to step 4, otherwise, the repair process terminates. there are three cases of infeasible paths shown in fig. 2(e) (dash line) and fig. 2(a)-(b). we use the introduced point repair algorithm to process the three cases and obtain the corresponding results shown in fig. 2(e) (solid line) and fig. 2(c)-(d), respectively. e.g., the path segment {3,4} across the obstacle o in the infeasible path represented by the nodes {1,2,3,4,5,6} in fig. 2(e) should be modified. if γ1 ≫ γ2, the path segment {3,4} is replaced by two path segments {3,8} and {8,4} in the first modification, but the modified path segment {3,8} is still infeasible and must be modified further. in the second modification, the segment {3,8} is replaced by the path segments {3,7} and {7,8}. thus, all the path segments are feasible and the new path is {1,2,3,7,8,4,5,6}. in fig. 2(c)-(d), the new feasible path is {1,2′,2′′,3,4} or {1,2′,3,4} under the condition γ1= 0,γ2 = 1 or γ1= 1,γ2 = 0. 3.3 smoothness algorithm the smoothness algorithm is used to get rid of those redundant nodes of a feasible path. the smoothness process is described as follows: step 1 : sort the nodes in a path from the starting node to the goal node and get a sequence ni, i = 1,2, . . . ,m, where m is the dimension of a particle; n1 and nm are the starting and goal nodes, respectively. step 2 : judge the path segment lij between ni and nj (at the beginning, i = 1,j = 3), if lij is infeasible, insert the nodes i and j − 1 into the node set pf of the smoothed path, i.e., pf ={i,j − 1} and let i = j − 1 and j increase 1, the algorithm continues to judge the path segment lij, otherwise, let j increase 1, continue to judge the feasibility of the path segment lij till it is infeasible, insert the nodes i and j − 1 into the node set pf of the smoothed path and let i = j −1. repeat this step till j = m. as shown in fig. 2(e), we use the introduced smoothness algorithm to remove the redundant nodes in the path {1,2,3,7,8,4,5,6} and obtain the smoothed path {1,8,6}. similarly, the smoothed paths {1,2′,2′′,4} and {1,2′,4} come from the paths {1,2′,2′′,3,4} and {1,2′,3,4}, respectively, as shown in fig. 2(c)-(d). 3.4 mmpso in mmpso, a dynamic membrane structure (specifically, olms alternates with d-olms shown in fig. 3(a)) is introduced to arrange a population of particles, each of which is a feasible path for a mobile robot, and specify various rules, such as membrane division, transformation and communication-like rules, and membrane dissolution. the dimension of each particle is variable in the process of evolution. the point repair algorithm described above is used to change infeasible paths into feasible ones. the repair process may increase the dimensions of each particle. the smoothness algorithm presented in this section is applied to remove the redundant nodes of a path and the process may decrease the dimensions of each particle. in addition, a moving direction adjustment technique is presented to accelerate the algorithm convergence. the pseudocode algorithm of mmpso is shown in fig. 3(b), where each step is described in detail as follows: step 1 : an olms [[ ]1, . . . , [ ]m]0 composed of a skin membrane denoted by 0 and m + 1 regions inside the skin membrane is constructed. the way in which the parameter m is chosen will be discussed in section 4. a modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning 739 olms d-olms (a) (1) initialize membrane structure (2) initialize populations (3) produce new particles in each elementary membrane (4) while (not termination condition) do (5) divide each elementary membrane (6) evaluate every particle (7) find (8) find local best particle (9) execute communication rules (a) (10) find global best particle (11) execute communication rules (b) (12) update particle’s velocity v(t) (13) update particle’s position x(t) (14) execute point repair algorithm (15) execute smoothness algorithm (16) adjust each particle’s moving direction (17) dissolve elementary membrane end end +1t t¬ begin: 1t ¬ ( ) b d g t ( ) id best p t ( ) b ij g t (b) figure 3: membrane structures and the pseudocode algorithm for mmpso step 2 : a particle swarm x with m particles in a d-dimensional search space is randomly generated and each particle is put inside an elementary membrane in olms, where d represents the number of nodes in a feasible path; x = {x1,x2, . . . ,xm}, where xi is an arbitrary individual in x and denotes a feasible path, xi = (xi1,xi2, . . . ,xid). step 3 : in this step, a moving direction adjustment technique is introduced to produce n particles inside each elementary membrane. to be specific, we modify the velocity of the particle inside each elementary membrane to generate a new particle by using (6) and (7), v (g + 1) = ρ1 ·vr(g) + ρ2 ·vf(g) (6) x(g + 1) = x(g) + v (g + 1) (7) where ρ1 and ρ2 are the inertia weighting factors and usually are set to larger values for exploring the global solutions; vr(g) is the randomly produced velocity of the gth particle (at the beginning for each elementary membrane, g=0); vf(g) is the adjusted velocity of the gth particle by using the idea of tightening fishing line and the moving directions of each node in the gth particle is shown in fig. 4(a); v (g + 1) is the velocity of the (g + 1)th particle; x(g) and x(g + 1) are positions of the gth and (g + 1)th particles. inspired by the idea of tightening fishing line, we consider a feasible path as a fishing line and tighten the line from the target node, thus, each node except for the target one in the path will show a moving direction toward the next node (the target node is the first one). the moving directions of all the nodes in the path construct the velocity vf(g). this step is repeated for n times to produce n new particles for each elementary membrane and used together with the point repair algorithm and smoothness algorithm. the dimensions of new particles may be greater than or less than d. thus, the swarm will have m×n particles in total. fig. 4(b) shows an example that one particle with 20 dimensions (in thick line) inside a certain elementary membrane produces 10 particles with 6–10 dimensions (in thin line). compared to the random approach, the production of the new particles can remove redundant nodes of a path and has better adaptability in hostile environment, especially in the circuitous route environment. step 4 : the maximal number of iterations is used as the termination condition. 740 x.y. wang, g.x. zhang, j.b. zhao, h.n. rong, f. ipate, r. lefticaru s t (a) (b) figure 4: an example of the generation of new particles and direction of each dimension of the individual step 5 : this step first classifies the n particles inside the ith elementary membrane into ki clusters according to the dimension of each particle and then divides the ith elementary membrane into ki membranes, each of which contains the particles with the same dimension, i = 1,2, . . . ,m. thus, olms becomes d-olms. step 6 : each particle is evaluated by using (1) and assigned a fitness value. step 7 : find p idbest(t), which is the best solution of each particle in its history with respect to the fitness values. step 8 : find the locally best solution gbij(t) in the jth elementary membrane inside the ith membrane, i = 1,2, . . . ,m,j = 1,2 . . . ,ki, in terms of fitness values. step 9 : perform communication rules (a), which first send all the locally best solutions gbij(t) (j = 1,2 . . . ,ki) out into the ith submembrane, i = 1,2, . . . ,m, and further send out into the skin membrane. step 10 : find the globally best solution gbd(t) by comparing g b ij(t) with the same dimension d, d ∈{1,2, . . . ,d}, i = 1,2, . . . ,m,j = 1,2 . . . ,ki. step 11 : perform communication rules (b), which send gbd(t) back into the elementary membrane containing d-dimension particles across certain submembrane. step 12 : update the velocities of the d-dimension particles using (8). vid(t + 1) = δ1 · ( ρ ·vid(t) + c1 ·r1 · ( p idbest(t)−xid(t) ) + c2 ·r2 · ( gbij(t)−xid(t) ) +c3 ·r3 · ( gbd(t)−xid(t) )) + δ2 ·v f id(t) (8) where vid(t) and vid(t + 1) are the velocities of the particle at generation t and t+1, respectively; p idbest(t) is the best solution of the particle at generation t; xid(t) is the position the particle at generation t; gbij(t) is the locally best solution with the same dimension d at generation t; g b d(t) is the globally best solution with the same dimension d at generation t; v fid(t) is the adjusted velocity of the particle at generation t; δ1 and δ2 are proportion coefficients; ρ is an inertia weighting factor; r1 , r2 and r3 represent the functions that generate independently random numbers, which are uniformly distributed between 0 and 1; c1, c2 and c3 are acceleration coefficients. step 13 : update the positions of the d-dimension particles using (9). xid(t + 1) = xid(t) + vid(t + 1) (9) where xid(t) and xid(t + 1) are the positions of the particle at generation t and t+1, respectively; vid(t + 1) is the velocity of the particle at generation t + 1. step 14 : execute point repair algorithm for each particle. step 15 : execute smoothness algorithm for each particle. a modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning 741 step 16 : adjust the moving direction of each particle by using the moving direction adjustment technique. step 17 : this step dissolves all the elementary membranes and releases their particles into their corresponding submembranes. thus, d-olms becomes the original olms. 4 experimental results the mmpso performance is tested by using mr3p. we first discuss how to set the number m of elementary membranes in olms by using 20 × 20 grid model environment with 6, 8 and 10 obstacles, respectively. then, 16 × 16 grid model environment with 9 static obstacles are applied to compare mmpso with its counterpart vpso and ga [15]. subsequently, the complex environments, 32×32 and 64×64 grid model environments with 20 static obstacles, are applied to further test the mmpso performance. in these experiments, one dynamic obstacle representing a moving obstacle or a dangerous source occurring suddenly is employed to analyze the mmpso behavior. (a) 6 obstacles (b) 8 obstacles (c) 10 obstacles 2 4 6 8 10 12 14 16 18 20 0 0.5 1 1.5 2 2.5 3 number of elementary membranes e la p s e d t im e obstacle 6 obstacle 8 obstacle 10 (d) results figure 5: the near shortest paths in three environments (20 × 20 grid with obstacles 6, 8 and 10, respectively) and experiment results 4.1 parameter setting in this subsection, we use 20 × 20 grid model environment with three kinds of obstacles shown in fig. 5 to discuss how to choose m. fig. 5(a)-(c) have 6, 8, 10 static obstacles (shaded areas), respectively. in the following experiments, the population size is set to 100; in (8), c1 = c2 = c3 = gsize/vmax, where gsize = 0.08 is the size of a grid and vmax is the maximal distance allowing a node to move in a step; the proportion coefficients δ1 = 0.65, δ2 = 0.35; ρ is defined as a variable, which varies from 0.246 to 0.157 along the logarithm function log10(y). in (6), ρ1 and ρ2 are set to random values between 0.4 and 0.6. in (3), α = 0.6, β = 0.4. in (4), λ is set to the robot radius. s t r1=0,r2=1 r1=1,r2=0 (a) ! (b) s t r1=0,r2=1 r1=1,r2=0 (c) figure 6: experimental results of mmpso in the environments 16×16 grids, os = 9 and od = 1. 742 x.y. wang, g.x. zhang, j.b. zhao, h.n. rong, f. ipate, r. lefticaru s t r1=0,r2=1 r1=1,r2=0 (a) 32 × 32 s r1=0,r2=1 r1=1,r2=0 (b) 32 × 32 s t (c) 32 × 32 s t r1=0,r2=1 r1=1,r2=0 (d) 64 × 64 figure 7: experimental results of mmpso in the environments 32 × 32, 64 × 64 grids, os = 20 and od = 1. in what follows, m varies from 2 to 20 by the interval 2, thus, we first generate m particles in step 2 and in step 3 for the first m− 1 elementary membranes, we produce round(100/m) particles and 100− (m−1)∗round(100/m) particles for the mth elementary membrane, where round(.) is a function for rounding its element towards nearest integer. in the experiments, if a given near-optimal solution is reached, mmpso stops. because the optimal solution to mr3p is usually unknown, we set kd = 1,ks = kf = 0 in (1) and independently perform mmpso for 30 times, where the terminal condition is such that the maximal number of iterations is set to 2000, in order to find the near-optimal solution. fig. 5(a)-(c) show the near shortest paths of the model environment with different obstacles, 6, 8 and 10, respectively. the mmpso performance for each of the 19 cases is evaluated by using the mean of the elapsed time in 30 independent runs. the experimental results are shown in fig. 5(d), where the elapsed time for three environments first decreases and then increases as the value of m goes up. these experimental results indicate that m could be assigned as 13 by considering the three environments. 4.2 mr3p experimental results to investigate the mmpso performance, this subsection uses three grid models, 16 × 16, 32 × 32 and 64 × 64, to carry out the experiments and considers five environments: 16 × 16 with 9 static obstacles (os = 9), 16 × 16 with os = 9 and one dynamic obstacle (od = 1), 32 × 32 with os = 20, 32 × 32 with os = 20 and od = 1, 64 × 64 with os = 20. the place for the possible occurrence of the dynamic obstacle is set to the near center, which is very likely to block the feasible paths. the model with 16 × 16 grids is applied to compare mmpso with its counterpart pso (vpso) and ga in [15]. the models with 32 × 32 and 64 × 64 grids are used to further discuss the mmpso performance in different complex environments. the setting of the parameters in mmpso except for kd,ks,kf is the same as in subsection 4.1. m=13. the termination condition is designated as the maximal number 2000 of iterations. all the experiments are run on the pc with cpu 1.7ghz, 512mb ram, and the software platform matlab7.4 and windows xp os. we first use the model with 16 × 16 grids to compare mmpso with vpso (when m = 1, mmpso becomes vpso) and ga in [15]. we consider three cases for kd,ks,kf as follows: (1) case 1: kd = 1,ks = kf = 0,γ1 = 1,γ2 = 0; (2) case 2: kd = 0.6,ks = kf = 0.2,γ1 = 0,γ2 = 1; (3) case 3: kd = 0.8,ks + kf = 0.2,γ1 + γ2 = 1. the experimental results of mmpso are shown in fig. 6. in fig. 6(a), the blue line is the best path in case 1 considering only one objective, path length, and the red line is the best result in case 2 by trading-off safety and smoothness. fig. 6(b) illustrates 8 near optimal paths (8 colors) through balancing the path length, safety and smoothness. the paths in fig. 6(c) a modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning 743 table 1: comparisons of three methods in the environment(fig. 6 (a)) method noo nono noi fv gn st ga[15] 9 78 13 24.68 16 1.68 vpso 83 108 0 24.95 65 2.97 mmpso 94 239 0 24.26 27 0.84 table 2: comparisons of three methods in the environment(fig. 6 (c)) method noo nono noi fv gn st ga[15] 32 68 0 24.71 12 0.69 vpso 81 103 0 28.56 73 3.12 mmpso 92 235 0 27.43 34 0.97 are obtained by considering one dynamic obstacle and the blue line is the best path in case 1 considering only one objective, path length, and the red line is the best result in case 2 by trading-off safety and smoothness. to draw a comparison with ga in [15] and vpso, let kd=1 and the experiment is executed for 100 independent runs. tables 1 and 2 show the experimental results of ga, vpso and mmpso for the environments with static obstacles and the environments with static and dynamic obstacles. in tables 1-3 , noo, nono, noi, fv, gn, st represents the number of optimal solutions, the number of near optimal solutions, the number of infeasible solutions and the fitness value in 100 trials, the average generations for finding the optimal solution and the mean of the elapsed time (s) in each trial, respectively. as it can be clearly seen from table 1 and fig. 6, mmpso finds much more optimal paths and near optimal paths, while it spends smaller computing time than ga. there are some infeasible solutions in ga, while there is not any infeasible solution in vpso and mmpso because the point repair algorithm have repaired the infeasible path. on the other hand, vpso also finds more optimal paths and near optimal solutions than ga, but the elapsed time is far larger than ga. mmpso is better than vpso with respect to optimal and near optimal solutions and the elapsed time, which indicates the advantage of the combination of a membrane system with pso. tables 2 shows similar conclusions to those in tables 1. to further analyze the mmpso performance in more complex environments, more experiments are conducted in the environments with 32 × 32 and 64 × 64 grids containing 20 or 21 obstacles, as shown in fig. 7 (a-d). the environment with 32×32 grids and 20 static obstacles are shown in fig. 7 (a). fig. 7(b)-(c) show the environment with 20 static obstacles and one dynamic obstacle. in fig. 7 (c), the three objectives, path length, smoothness and safety, are considered. the parameters of mmpso are the same as above except for the population size table 3: experimental results of mmpso in different environments in fig. 7 environment noo nono fv gn st 32×32, os = 20, od = 0 86 242 28.79 36 1.72 32×32, os = 20, od = 1 82 225 31.53 45 1.93 64×64,os = 20, od = 0 83 247 28.14 59 2.68 744 x.y. wang, g.x. zhang, j.b. zhao, h.n. rong, f. ipate, r. lefticaru 150 and m = 15. all the tests are executed for 100 independent runs. table 3 shows the results. it can be seen from tables 1-3 that the optimal solutions of mmpso drop from 94 to 83, the elapsed time rises from 0.84 to 2.68 and the average generations vary from 27 to 59 as the number of grids increases from 16×16 to 64×64 and the static obstacles go up from 9 to 20. the elapsed time and average generations increase a little with the dynamic obstacle. to sum up, as the number of model grids increases by 4n (n = 1,2,3 . . .) and the static obstacles double, the increase of the elapsed time is quite small, instead of an exponential increase. mmpso maintains good search capability to find the optimal solution in both static and dynamic environments, which indicates mmpso has good adaptability to mr3p under complex environments. 5 conclusions this paper discusses a feasible combination of membrane systems and pso to solve mr3p. the outstanding novelty is to justify the introduced dynamic membrane structure, which proves to be suitable for solving mr3p with variable dimensions. mmpso uses the alternation of olms and d-olms to integrate a pso with variable dimensions, point repair algorithm, smoothness algorithm and moving direction adjustment. a large number of experiments are carried out on several mr3p with various environments and the results show that mmpso can achieve much better solutions than its counterparts pso and ga, as reported in the literature. this paper considers only the planar (two dimensions) environments. following this work, some issues need to be further investigated, such as how to extend mmpso to three dimensional spaces, how to use mmpso to solve more difficult path planning problems (mobile robots follow the tracks of moving targets in a hostile environments), how to combine mmpso with numerical p systems to control mobile robots and how to apply the idea of variable dimension pso to solve more engineer application problems. acknowledgment the work of xw, gz, jz and hr was supported by the national natural science foundation of china (61170016, 61373047), the program for new century excellent talents in university (ncet-11-0715) and swjtu supported project (swjtu12cx008); the work of fi and rl was supported by a grant of the romanian national authority for scientific research, cncsuefiscdi, project number pn-ii-id-pce2011-3-0688. bibliography [1] g. păun, g. rozenberg, a. salomaa (eds.) 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[21] d.w. gong, j.h. zhang (2011); multi-objective particle swarm optimization for robot path planning in environment with danger sources, j comput, 6(8): 1554-1561. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 20-38 controlling the double rotary inverted pendulum with multiple feedback delays v. casanova, j. salt, r. piza, a. cuenca v. casanova, j. salt, r. piza, a. cuenca departamento de ingeniería de sistemas y automática instituto universitario de automática e informática industrial universitat politécnica de valencia camino de vera s/n 46022 valencia (spain) e-mail: {vcasanov, julian, rpiza, acuenca}@isa.upv.es abstract: the aim of this work is the development and implementation of a control structure for the double rotary inverted pendulum, suitable to be used in a networked control system environment. delays are quite common in this kind of systems and, when controlling multivariable plants, it is possible that different delays are applied to the multiple inputs and outputs of them. a control structure that allows compensating individually each one of the multiple loop delays would be useful when one of these delays changes. inverted pendulums are quite sensitive to delays and for this reason are appropriated plants to be used in these conditions. the control structure is developed modifying the control in no-delay conditions with a generalized predictor able to deal with unstable and non-minimum plants as the chosen one is. the proposed structure has been simulated and implemented to control a real double rotary inverted pendulum. keywords:control applications, delay compensation, distributed control, multi-loop control, multi-variable feedback control, transport delay. 1 introduction the main goal of this paper is to propose a control structure that deals with multivariable plants when different delays are applied to the feedback signals. consider the scenario in figure 1. a multivariable (mimo) plant is going to be controlled by a mimo controller. the plant outputs are feedback into the controller and the control actions are calculated by it, following some predesigned control law. these actions are applied to the plant. if the communication links between controller and plant is ideal, no delays are present. when a real link is used at least one sample time delay must be considered in each communication link. the influence of these delays must be removed to reach the desired behavior: the one in ideal (no-delay) conditions. in the proposed scenario sensors and actuators (i.e. ad and da conversions) are supposed to be separated by long distances among them and from the controller. the communication follows different paths for output signals and control actions, suffering different transport delays. the controller used in ideal conditions must be modified to compensate these delays. this is a common situation in networked control systems (ncs) when a shared medium is used to communicate a number of sensors and actuators with the main control. the longer the distance is, the greater the delay must be removed. the delay compensator becomes a mimo structure to deal with the delay in each pair action/signal. in wide-area ncs it is usual to have a great number of ad and da conversions involved in the same control structure. the communication is performed through a shared medium instead of using point-to-point links. this allows to reduce costs (wiring, maintenance...) and copyright c⃝ 2006-2012 by ccc publications controlling the double rotary inverted pendulum with multiple feedback delays 21 p l a n t d / a a / d a / d d / a c o n t r o l l e r c o n t r o l o u t p u t … … d a1 d an d s1 d sm figure 1: problem scenario to increase flexibility of the control system. the main drawbacks are the delay uncertainty and the bandwidth limitation. if there are different distances between sensors/actuators and the controller and/or the priority of the devices attached to the network is different, multiple delays can arise in the control structure. in the most general case different number of sample time delays are applied to each one of the signals towards the actuators (δan being n the nth control action) and to the signals from the sensors (δsm being m the mth measured variable). the control structure becomes a collection of nxm loops each one of them includes a δan+δsm sample time delay, from the point of view of the controller (loop delay). this is the delay to be compensated in the controller of each one of these loops in the control structure. by removing the influence of the delays, the ideal behavior is intended to be reached. the goal of this work is to use the same controller, designed without considering the delays, to deal with the problem when they are present. the presence of multiple delays in a ncs environment has been considered in many descriptions of this kind of systems ( [1], [2], [3], [4], [5], [6], [7]) and several previous works have dealt with this problem. in [8] and [9] there is a description of the problem of multiple delays in ncs when they are smaller than one sampling period. in [10], [11] and [12] a ncs with multiple distributed delays is modeled and an optimal control is proposed. [13] proposes a model of an ncs with multiple successive delay components in the state. [14] deals with the problem of observability and optimal control in these control systems. [15] analyses the maximum allowable delay bound in control loops with network induced delays. [16] considers the problem of multiple delays in ncs from the point of view of fault diagnosis. some works ( [17], [18]) have considered the problem as a multivariable control design in which multiple input-output delays are included in the plant model. [19] uses an observer to provide state information, comparing the system behaviour with the smith predictor. the proposed solution is going to be applied to a classical control problem: the double rotary 22 v. casanova, j. salt, r. piza, a. cuenca inverted pendulum (also known as double furuta pendulum, see figure 2). this is a classical textbook example of non-linear, non-minimum phase plant. the plant to be controlled has one input (the control action to the motor) and three outputs (the angular position of the motor shaft, the angular position of the first rod of the inverted pendulum and the angular position of the second rod). the goal of this control problem is to keep the pendulum angles as close to zero as possible, avoiding disturbances and to follow a certain reference with the shaft angle. so, four signals are involved between control and plant and four different time delays are going to be considered. as it is well known, this plant is quite sensitive to delays between controller and plant becoming unstable even with small delays. this sensitivity makes the furuta pendulum quite appropriated to the proposed environment. the controller used in ideal conditions (i.e. no delays) must be properly modified to remove the delay influence in the feedback loop. in addition, the plant is an unstable and non-minimum phase one which makes more difficult the delay compensation. the single and double inverted pendulums have been widely used as a test-bed for different control strategies ( [20], [21], [22]). figure 2: the double rotary inverted pendulum the paper begins with a description of the plant to be controlled (section 2), followed by the controller used in ideal conditions (section 3). in section 4 the controller is modified to remove the influence of the delays, considering different control-plant delays for each one of the signals involved. simulated results are presented in these sections to compare the behavior in ideal conditions, the nearly-unstable behavior when delays are included and the behavior when delay influence is removed from the loops. results from a real plant are presented in section 5 showing how the proposed structure works when real disturbances and noise are present in the system. finally, in section 6 conclusions and future work are presented. 2 double rotary inverted pendulum model the plant that has been chosen to implement the proposed control structure is the double rotary inverted pendulum (double furuta pendulum) due to its sensitivity to feedback delays. the theoretical model of this plant is described in this section and it is particularized to model controlling the double rotary inverted pendulum with multiple feedback delays 23 a real one, the double rotary inverted pendulum developed by quanser consulting inc. (shown in figure 2 in its upwards position). the equations describing the behavior of the double inverted pendulum can be derived, using the euler-lagrange method. a complete development of this model can be found in [23] and [24]. by applying the euler-lagrange equation to the lagrangian (i.e. the difference between kinetic and potential energy) the development of the mathematical model is greatly simplified. figure 3 shows a schematic representation (top and front view) of the plant. the variables considered in the model are: • θ, θ̇, θ̈: angular position, velocity and acceleration of the motor shaft, around the vertical axis. • α, α̇, α̈: angular position, velocity and acceleration of the first rod, around the motor shaft axis. • γ, γ̇, γ̈: angular position, velocity and acceleration of the second rod, around the motor shaft axis. • τ: torque applied to the motor shaft. • u: control action applied to the dc motor (voltage). figure 3: significant variables. first, the torque applied by the motor must be related with the angular positions. the three equations defining the non-linear dynamics of the mechanical part of the pendulum can be expressed as follows: 24 v. casanova, j. salt, r. piza, a. cuenca τ(t) = [j1l 2 1(m2 + m3)]θ̈(t) − [l1(m2l2 + m3l2) cosα(t)]α̈(t) + [m3l3l1 cosγ(t)]γ̈(t) + (1) +b1θ̇(t) + [l1(m2l2 + m3l2) sinα(t)]α̇(t) 2 + [m3l3l1 sinγ(t)]γ̇(t) 2 0 = −[l1(m2l2 + m3l2) cosα(t)]θ̈(t) + [j2 + m2l22 + m3l 2 2]α̈(t) + (2) +[m3l3l2 cos (γ(t) − α(t))]γ̈(t) + b2α̇(t) − −[m3l3l2 sin (γ(t) − α(t))]γ̇(t)2 − [g(m2l2 + m3l2) sinα(t)] 0 = −[m3l3l1 cosγ(t)]θ̈(t) + [m3l3l2 cos (γ(t) − α(t)]α̈(t) + [j3 + m3l23]γ̈(t) + (3) +[m3l3l2 sin (γ(t) − α(t))]α̇(t)2 + b3γ̇(t) − [gm3l3 sinγ(t)] the parameters (whose values are provided by the manufacturer) in these equations are the lengths, masses, moments of inertia and friction coefficients of the elements in the pendulum. • j1: moment of inertia around the center of rotation of the motor arm. • j2: moment of inertia around the center of rotation of the first rod of the pendulum. • j3: moment of inertia around the center of of the second rod of the pendulum. • l1: length of the motor arm. • l2: length of the first rod of the pendulum. • l2: length between the centers of rotation and gravity of the first rod of the pendulum. • l3: length between the centers of rotation and gravity of the second rod of the pendulum. • m2: mass of the first rod of the pendulum. • m3: mass of the second rod of the pendulum. • b1: viscous friction in the joint of the motor arm. • b2: viscous friction in the joint of the first rod of the pendulum. • b3: viscous friction in the joint of the second rod of the pendulum. • g: acceleration of the gravity. the non-linear model must be linearized to reach a mimo linear model to be used in the design of the linear controller. the setting point for the linearization will be around α = 0 and γ = 0, , corresponding this values to the upwards position, in which the pendulum is desired to remain stable. the signal derivatives must be zero at the settling point to keep the pendulum stable. in these conditions, the three linear (linearized) equations are: τ(t) = [j1l 2 1(m2 + m3)]θ̈(t) − [l1(m2l2 + m3l2)α̈(t) + [m3l3l1]γ̈(t) + b1θ̇(t) (4) 0 = −[l1(m2l2 + m3l2)]θ̈(t) + [j2 + m2l22 + m3l 2 2]α̈(t) + [m3l3l2]γ̈(t) + (5) +b2α̇(t) − [g(m2l2 + m3l2)α(t)] 0 = −[m3l3l1]θ̈(t) + [m3l3l2]α̈(t) + [j3 + m3l23]γ̈(t) + b3γ̇(t) − [gm3l3]γ(t)] (6) to implement the controller the input must be the voltage applied to the motor that moves the arm that carries the pendulum rods. the relationship between the voltage applied and the torque can be stated from the typical dc motor: controlling the double rotary inverted pendulum with multiple feedback delays 25 τ(t) = ηgηmkgkt rm (u(t) − kgkmθ̇(t)) (7) the mechanical parameters in this fourth equation are: • ηg: gearbox efficiency. • ηm: motor efficiency. • kg: total gear ratio. • kt: motor torque constant. • km: motor back-emf constant. • rm: motor armature resistance. solving the system of four equations, the linear continuous state-space model of the plant to be controlled can be obtained. the model must have the motor voltage as input signal and the three angular positions as output signals. the three angular velocities are not included as outputs because they are not going to be measured in the real plant. the development is omitted here for space reasons. as the goal is to control a real pendulum, the model has to be particularized with the real parameters (dimensions, masses, inertias, frictions, motor constants...). these parameters have been provided by the manufacturer. this information can be reached from the quanser documentation [25]. as the control is going to be designed and implemented in discrete time, the continuous model must be discretized. the sample time is going to be 10 ms, small enough to control the plant and large enough to become unstable with a couple of sample time delays. this model is the one to be used when designing, first, the controller in ideal conditions and, after that, the predictor to remove the influence of the loop delays. the behavior, with and without the delays, must be similar in both, simulated and real systems. the delay compensation is going to be different (because the delays are different) for the three controlled variables. the mimo system can be divided into three siso systems one for each of the output signals, (θ, α and γ as described before): gθ(z) = θ(s) u(s) = 0.0015763(z + 0.9446)(z − 0.9432)(z − 0.8953)(z − 1.061)(z − 1.145) (z − 1)(z − 0.9716)(z − 0.9077)(z − 1.084)(z − 1.18)(z − 0.7665) (8) gα(z) = α(s) u(s) = 0.0018286(z + 0.9477)(z − 0.912)(z − 1)(z − 1.114) (z − 0.7665)(z − 0.9077)(z − 0.9716)(z − 1.084)(z − 1.18) (9) gγ(z) = γ(s) u(s) = −0.0019976(z + 0.9538)(z − 0.9968)(z − 1)2 (z − 0.7665)(z − 0.9077)(z − 0.9716)(z − 1.084)(z − 1.18) (10) as can be seen these transfer functions describe the dynamical behaviour of unstable and non-minimum phase systems. the mimo model will be used in the following section to design the controller in ideal conditions. the three siso models will be used in section 4 to design the predictors for delay compensation purposes. 26 v. casanova, j. salt, r. piza, a. cuenca 3 control structure in no delay conditions to reach the desired behavior, a linear-quadratic regulator (lqr) controller is going to be used control the plant in absebtia of delays. the controller must keep the angles of the two rods as close to zero as possible (unstable equilibrium upwards position), canceling the unavoidable disturbances. in addition the system must follow a certain reference for the motor shaft angle. the control structure is not robust enough to cope with delays, becoming unstable, even though they are very small. this ideal control will be appropriately modified to remove loop delays in the following section. in order to improve the steady-state error of the shaft position and the robustness of the controller, the six-state model is increased including an integration term. with this term the motor shaft can follow step references with zero error, even with the small dead zone present in the real plant. the integral of θ(t) will be computed in the controller from the measured angle. this augmented state is now: x̃ = ( θ(t) α(t) γ(t) θ̇(t) α̇(t) γ̇(t) ∫ θ(t)dt )t (11) the six-state mimo model obtained in the previous section is modified, including this new state: ẋ = ax + bu −→ ˙̃x = ( a 0 0 0 ) x̃ + ( b 0 ) u (12) using matlab lqr command, the seven feedback control gains for the seven states from the augmented plant model are: k = ( 1.2824 −42.4077 −101.7583 1.8735 −11.8712 −11.5716 0.3162 ) (13) although is not included in the model and, so, not considered when designing the controller, the real control action saturated ±10 volts. this saturation has been included in the simulation model. however, in ideal conditions and when the delay is compensated, the control action always remains in this range and the saturation can be ignored. this controller needs the seven states as inputs to generate the control action to be applied to the plant. in the real plant only the first three of them will be available, by means of sensors measuring the angular positions. there will not be sensors to measure the other states (angular velocities and integral error). these states are going to be computed in the control structure using a filtered discrete derivative and a discrete integrator. so, the controller becomes a three inputs-one output discrete system to be implemented in the control device. as it has been described in the introduction, the three inputs of the controller are arriving through different paths and different delays are applied to them. to compensate the delays independently it will be useful to decompose the controller into three sub-controllers, each one of them concerned for one of the three measured angles. the transfer functions of these individual controllers are as follows: cθ(z) = uθ(z) θ(z) = k(1) + k(4)drv(z) + k(7)int(z) = k(1) + k(4) 50z − 50 z − 0.6065 + k(7) 0.01 z − 1 (14) cα(z) = uα(z) α(z) = k(2) + k(5)drv(z) = k(2) + k(5) 50z − 50 z − 0.6065 (15) cγ(z) = uγ(z) γ(z) = k(3) + k(6)drv(z) = k(3) + k(6) 50z − 50 z − 0.6065 (16) controlling the double rotary inverted pendulum with multiple feedback delays 27 using drv(z) and int(z) as discrete derivator and integrator respectively, all the states can be considered measurable. the control structure with these three sub-controllers is shown in figure 4. figure 4: control structure without delay compensation. the inner loop tries to keep γ angle as close to zero as possible, avoiding disturbances. the middle loop does the same with α angle. the combination of these two loops keep the pendulum in the upwards position as desired. the outer loop tries to follow the desired reference with θ angle and drives the integral of the error to zero. as the three plant-to-control signals and the control-to-plant signal are separated, different delays will be applied to the four communication links to study its influence in the system performance. with the described control structure, three sub-control actions are generated and the addition of them is the control action to be sent and then applied to the plant. closing the loop with the model from the previous section and the gain matrix from the lqr, the simulated results are shown in figures 5 and 6. a ±45 degrees step reference has been applied to be tracked by the shaft angle (θ) and a small random noise has been added to the measured pendulum angles (α and γ). no loop delay is present. figure 5 depicts the output variables (θ, α and γ angles in degrees). as can be seen, the reference is followed properly (with zero steady-state error) by the shaft angle and the pendulum angles are kept close to zero, despite the noise and the changes in the main reference. figure 6 shows the control actions of the three sub-controllers and the addition, which is the final control action to be applied to the plant. note that α and γ control actions are clearly influenced by the noise, added to the measured angles. 4 control structure with delay compensation delays in the communication between controller and plant are a well-known source of instability. this is critical when controlling processes as unstable as the furuta pendulum is. the influence of the delay in the control loop must be removed to assure that the system remains stable. figure 7 shows the behavior of the three controlled angles when a single sample time delay is included in the control action (with ±10 volts saturation in the control action as in the real plant). another sample time delay in one of the feedback signals makes the system completely unstable. the sensitivity of the plant to loop delays makes this plant the ideal one for the work in this paper. the proposal is to modify the original delay-free controller to handle with delays even when they are different in the signals transmitted to and from the plant. 28 v. casanova, j. salt, r. piza, a. cuenca 30 35 40 45 50 55 60 65 70 reference vs. theta angle time (sec) 30 35 40 45 50 55 60 65 70 alpha angle time (sec) 30 35 40 45 50 55 60 65 70 gamma angle time (sec) figure 5: theta, alpha and gamma angles: ideal conditions. 30 35 40 45 50 55 60 65 70 theta control action time (sec) 30 35 40 45 50 55 60 65 70 alpha control action time (sec) 30 35 40 45 50 55 60 65 70 gamma control action time (sec) 30 35 40 45 50 55 60 65 70 control action time (sec) figure 6: control actions: ideal conditions. controlling the double rotary inverted pendulum with multiple feedback delays 29 26 28 30 32 34 36 38 40 42 44 reference vs. theta angle time (sec) 26 28 30 32 34 36 38 40 42 44 alpha angle time (sec) 26 28 30 32 34 36 38 40 42 44 gamma angle time (sec) figure 7: theta, alpha and gamma angles: delayed. smith predictor has been traditionally used to overcome delays for stable plants without integral behavior. several modifications ( [26], [27], [28]) of the original idea have been proposed to be used for multivariable plants, with different input-output delays. the limitation is that they usually cannot be used with non-minimum phase, unstable plants with integral behavior. the classical smith predictor has been modified ( [29], [30], [31]) to deal with unstable plants. if all the controlled variables (i.e. all the loops in the control system) have the same delay, one of these modified predictors could be used. the plant to be controlled in this paper is an unstable and non-minimum mimo (one input and three outputs) plant but it can be easily decomposed into three siso plants. in addition, in the scenario considered in this work, each one of the siso plants is influenced by a different loop delay to be compensated. parameter δu is the number of sample time delays for the control action (controller-to-plant signal). parameters δθ, δα and δγ are the delays for the shaft and pendulum angles (plant-to-controller signals). the most general case is given when the four delays are different, as shown in figure 4, there are three loops in the systems. the loop delay (from the output to the input of the controller) for each one of the loops is given by the addition of the control action delay and the corresponding output signal delay. these are the delays to be compensated in each one of the sub-controller. only constant and integer multiple of the sample time delays are considered in this work but the idea can be extended to fractional and variable delays. in [32] and [33] a delay compensation is proposed, suitable to be used with unstable and nonminimum phase siso plants. the predictor proposed and described in these references (generalized predictor, gp, figure 8) is stable for unstable/stable, minimum/non-minimum plants. the inputs of the gp are the plant input (control action) and output (controlled variable). the output of the gp is the predicted signal. depending on the quality of the plant model used to design the predictor, the predicted signal will be equal to the controlled variable without the delay. it is the same behavior than with the classical smith predictor but, in this case, it can be used with the transfer functions of the inverted pendulum. from the (unstable nonminimum phase) transfer function and the known loop delay the gp can be designed. this gp 30 v. casanova, j. salt, r. piza, a. cuenca figure 8: generalized predictor. will provide the information to be used by the controller, removing the influence of the delay in the feedback. the predicted (undelayed) output will be used instead of the measured one, by the same controller designed in ideal (no-delay) conditions. as usual, the system behavior depends on the uncertainty of the model and the presence of significant disturbances. to generate this predicted signal, transfer functions f1(z) and f2(z) must be calculated. being g(z) the plant to be controlled, this transfer function can be divided in two parts: g(z) = gmp (z)gnmp (z) (17) gmp (z) is a transfer function including al the poles (stable and unstable) and the minimum phase zeros. gnmp (z) is a polynomial including the non-minimum phase zeros. gmp (z) can be expressed as a polynomial quotient or as a minimum order state space model: gmp (z) = nmp (z) dmp (z) = cmp (zi − amp ) −1 bmp (18) a new transfer function can be defined, including the delay (δ) in the minimum phase one: g∗mp (z) = n∗mp (z) dmp (z) = cmp (zi − amp ) −1 aδmp bmp (19) from these definitions the two transfer functions of the gp can be calculated as follows: f1(z) = ( cmp δ∑ i=1 aδmp bmp z −i ) gnmp (z) (20) f2(z) = n∗mp (z) nmp (z) (21) the predicted output (i.e. the estimation of the plant output without delay) is calculated by the gp as follows: yp (z) = u(z)f1(z) + y (z)f2(z) (22) in the proposed scenario, three nested loops are present in the system. three different loop delays are considered and had to be compensated. three different predictors must be designed and included in the control structure. figure 9 shows the control structure including the three controlling the double rotary inverted pendulum with multiple feedback delays 31 figure 9: control structure with delay compensation. predictors to provide undelayed information for the controllers. note that the controllers in this structure are the same used in figure 3, when no delay was present in the system. to compare the information provided by the plant (measured) and by the gp’s (predicted), figure 10 shows a detail of the measured angles (in radians) compared with the predicted ones. measured (red) and predicted (blue) signals can be easily distinguished as the measured is delayed signal respect to the predicted one. undelayed signals are not equal to the plant outputs due to the noised included in the simulation. it can be seen that the delay compensated by the predictors are different for θ, α and γ angles. in the simulation one sample period delay has been included in the control action, two sample delays in the γ angle, three sample delays in the α angle and four sample delays in the θ angle. so, the loop delay for the gpγ predictor is δu + δα = 3 and for gpα δu + δθ = 4. these delays can be seen in the time between predicted and measured signals. 25.05 25.075 25.1 25.125 25.15 25.175 25.2 25.225 25.25 25.275 25.3 25.325 25.35 25.375 25.4 25.425 25.45 25.475 25.5 theta angle: predicted vs. measured time (sec) 25.05 25.075 25.1 25.125 25.15 25.175 25.2 25.225 25.25 25.275 25.3 25.325 25.35 25.375 25.4 25.425 25.45 25.475 25.5 alpha angle: predicted vs. measured time (sec) 25.05 25.075 25.1 25.125 25.15 25.175 25.2 25.225 25.25 25.275 25.3 25.325 25.35 25.375 25.4 25.425 25.45 25.475 25.5 gamma angle: predicted vs. measured time (sec) figure 10: measured and predicted angles. the delay-free predicted signals are used as inputs for the three independent controllers to generate the control action to be applied to the plant. figures 11 and 12, show simulation results in these conditions. comparing these results with the ones in figures 5 and 6, it can be seen that the system recovers the ideal conditions behavior, even with different loop delays in the signals 32 v. casanova, j. salt, r. piza, a. cuenca between controller and plant. real results are presented in the following section. 5 experimental results the results presented in the previous section have been obtained from a simulation model. it is easier to see how the control structure works in simulated results without (unbounded) noise and/or uncertainty. nevertheless, the work would remain uncompleted if not implemented over a real plant to reach real results. the proposed structure has been implemented to control a real plant, the quanser double rotary inverted pendulum (figure 2). the plant is endowed with a potentiometer to measure the shaft angle (θ) and a couple of incremental encoders to measure the angles of the two rods of the pendulum (α and γ). it has also a tachometer to measure the shaft angular velocity but it has not been used in the implementation. 30 35 40 45 50 55 60 65 70 reference vs. theta angle time (sec) 30 35 40 45 50 55 60 65 70 alpha angle time (sec) 30 35 40 45 50 55 60 65 70 gamma angle time (sec) figure 11: measured angles: delayed+compensated. the signal applied to the plant saturates at ±10 volts but the action calculated by the controller is almost always inside this range. the aim is to consider a plant in which the different (and long enough) distance between each one the three sensors and the device implementing the control introduces non-negligible delays. however, with the dimensions of the used prototype, the multiple delays between controller and plant had to be simulated and included in the control structure. matlab/simulink real-time windows target has been used to implement the control structure, using quanser q4 board for the a/d and d/a conversions. as noted in section 2, a sample time t = 10ms has been used, being large enough for real-time requirements. with this sample time, two sample time delays in the inner loop of the proposed structure are enough to make the system unstable. figure 13 shows the real measured angles when the delays appear, being the pendulum in its upwards stable position. the conventional control cannot bear this delay and the pendulum falls. to overcome this delay influence, three gp’s has been included in the control structure, as controlling the double rotary inverted pendulum with multiple feedback delays 33 30 35 40 45 50 55 60 65 70 theta control action time (sec) 30 35 40 45 50 55 60 65 70 alpha control action time (sec) 30 35 40 45 50 55 60 65 70 gamma control action time (sec) 30 35 40 45 50 55 60 65 70 control action time (sec) figure 12: control actions: delayed+compensated. 60 65 70 reference vs. theta angle time (sec) 60 65 70 alpha angle time (sec) 60 65 70 gamma angle time (sec) figure 13: real results: unstable behavior. 34 v. casanova, j. salt, r. piza, a. cuenca shown in figure 9.as in the simulation results, the gp’s have been calculated to compensate delays δu = 1, δθ = 3, δα = 2 and δγ = 1. the predictor in the inner loop, gpγ offers a prediction for the γ angle with a z−2 delay. in the middle loop, gpα has been designed for a z−3 delay. finally, in the outer loop gpθ predicts the shaft angle removing a z−4 delay. with these delays in the loops, the control is absolutely unstable if the measured angles are used. figure 14 shows the results in these conditions, using the predicted signals instead of the measured ones. as can be seen, the shaft angle is following a ±45 degrees reference and keeps the rods angles close to zero, to hold the pendulum in its upwards position, despite noises and disturbances. it is quite difficult to see differences between these results and the ones in ideal conditions (without delays) because the behavior depends deeply on the initial setup to put the plant in its working point. figure 15 shows the behavior with some mechanical disturbances in the second rod angle. the control structure keeps the pendulum in its upwards position despite the multiple delays in the plant-control communications. due to the noise in the measured angles it is difficult to compare the real with the predicted signals. to see the operation of the predictors, both measured and predicted signals, have been low-pass filtered to remove the noise. these filtered signals are shown in figure 16 for θ, α and γ angles in radians. although it is not easy to measure because the signals are slightly different, the time between predicted and measured signals is 40, 30 and 20 ms, respectively. these are the loop delays between the plant ant the three sub-controllers in the control structure. as the predictors remove the delay influence from the feedbacks, the system behaves as in ideal conditions. 60 65 70 75 80 85 90 95 reference vs. theta angle time (sec) 60 65 70 75 80 85 90 95 alpha angle time (sec) 60 65 70 75 80 85 90 95 gamma angle time (sec) figure 14: real results: delayed+compensated. 6 conclusions and future works the aim of this work was to propose a multiloop control structure for a multivariable plant with different delays in each one of the signals between controller and plant. the double inverted rotary pendulum has been chosen for a practical implementation of the proposed control. this controlling the double rotary inverted pendulum with multiple feedback delays 35 60 65 70 75 80 85 90 95 reference vs. theta angle time (sec) 60 65 70 75 80 85 90 95 alpha angle time (sec) 60 65 70 75 80 85 90 95 gamma angle time (sec) figure 15: real results: disturbances. 5.54 5.56 5.58 5.6 5.62 5.64 5.66 5.68 5.7 5.72 5.74 5.76 5.78 5.8 5.82 5.84 5.86 5.88 5.9 5.92 5.94 5.96 5.98 6 theta angle: predicted vs. measured time (sec) 5.54 5.56 5.58 5.6 5.62 5.64 5.66 5.68 5.7 5.72 5.74 5.76 5.78 5.8 5.82 5.84 5.86 5.88 5.9 5.92 5.94 5.96 5.98 6 alpha angle: predicted vs. measured time (sec) 5.54 5.56 5.58 5.6 5.62 5.64 5.66 5.68 5.7 5.72 5.74 5.76 5.78 5.8 5.82 5.84 5.86 5.88 5.9 5.92 5.94 5.96 5.98 6 gamma angle: predicted vs. measured time (sec) figure 16: real results: measured and predicted angles. 36 v. casanova, j. salt, r. piza, a. cuenca is a classic example of multivariable plant whose stability is seriously worsened even with small delays. there are four links in the system: one control-plant link (actuator) and three plantcontrol ones (sensors). the scenario assumes that this four links are a submitted to different communication delays which increases the difficulty to remove its influence on the stability. a generalized predictor has been used to provide delay-free information for each one of the three loops in the control structure. each one has been designed attending to each loop delay. depending on the quality of the model used in the predictor, the system behaves as in ideal conditions, even with one different delay in each of the links. the validity of the proposed control structure has been shown by means of simulated results. the real implementation using the quanser double rotary inverted pendulum shows how the proposed structure works when a certain degree of uncertainty, non-negligible noises and disturbances are present. the delays in this work have been included in the control structure but they should be caused by the communication between sensors/actuators and the device in which the control algorithm is implemented. the goal is to close the loops through a shared communication medium (fieldbus, wireless network...), following different paths for each one of the links. this will cause different transport delays in each loop that may be solved with the proposed structure. in this more realistic implementation an additional problem will arise. the unavoidable lack of synchronization between the devices (sensors, controllers and actuators) will make the delay variable and, probably unknown. some kind of delay identification must be performed or the control structure must be modified to be adaptive to compensate variable (maybe random) delays. another improvement of this proposal is to use a plant with more than one input. this will increase the number of communication links and the number of feedback loops. the 2d inverted pendulum is a good choice to be used in this improvement. this plant increases the degrees of freedom allowing movement in the xy plane. it has two inputs for a planar movement of the cart carrying the pendulum. two measurements of the cart position must be used in the controller. the rod can bend in two directions giving two angular measurements. so, the plant offers a multivariable model (two inputs, four outputs) and, as is also quite sensitive to delays, is an appropriated plant to extend the work in this paper. acknowledgement this work was supported by the spanish ministerio de ciencia y tecnologa project 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[32] p. garcía, p. albertos, t. hägglund, control of unstable non-minimum-phase delayed systems, journal of process control, vol. 16, no. 10, pp. 1099-1111, 2006 [33] p. albertos, p. garcía, robust control design for long time-delay systems, journal of process control, vol. 19, no. 10, pp. 1640-1648, 2009. int j comput commun, issn 1841-9836 8(3):469-476, june, 2013. on the characteristic functions of fuzzy systems h.n.l. teodorescu horia-nicolai l. teodorescu 1. institute of computer science, romanian academy iasi branch romania, iasi, carol i, 8 2. gheorghe asachi technical university of iasi romania, iasi, str. d. mangeron, 67 hteodor@etti.tuiasi.ro abstract: we provide several properties of the input-output function of the siso / miso sugeno fuzzy systems with center of gravity defuzzification. the properties analyzed are related to continuity under various conditions for the input membership functions, including the case when the input space is a topological space. keywords: sugeno fuzzy system , characteristic function, continuity, domain. 1 introduction the progress of knowledge on fuzzy systems historically followed a bifurcated path. on one way, the engineering applications drove the development [8] (tong), with the underlying foundations following. on the second way, logic and mathematics produced advances with limited reference to the applications. that left several area in-between the two paths partly unclarified, especially regarding fuzzy logic systems (fls) foundations. we address several properties of the characteristic function of the siso / miso (single input single output / multiple input single output) fuzzy systems with defuzzification, for sugeno fuzzy systems. as far as we know, these properties have been explicitly addressed until now only sporadically. the main issues concern the continuity of the characteristic functions of the fuzzy systems with center of gravity (c.o.g.) defuzzification, as continuity is essential in understanding the applicability and constraints when using fuzzy systems in control, system modeling (approximation), predictors and other current applications. the approximation properties of fuzzy logic systems with defuzzification have been extensively studied starting in the 1990s [6] (teodorescu 1990), [1] [2], [3], [7]. however, while somewhat elementary, the properties that guarantee that these systems have derivable or continuous input-output functions have not been investigated in detail, except cases discussed in [4], [9]. the continuity of flss is essential in many applications, and is effectively assumed in most control applications, as in [13] (even when not stated, e.g., as in many robotic system papers [10], [15]), and in interpolations and approximation with flss, as in [11], [12]. the topic was intensively studied, see e.g. [14], [9], [4] etc. the organization of the paper is as follows. in the next section we recall a few definition and results and state several working hypotheses that are used in the paper. the third section addresses the continuity of sugeno-type fuzzy systems. the last section is conclusive. 2 definitions and hypotheses consider two spaces, s ⊆ x named input space and s′ ⊆ y named output space, and applications µ : s → [0, 1] ⊂ r named input membership functions and respectively η ...s′ → [0, 1] ⊂ r named output membership functions. the definition domain of all input membership functions is s; the subset of s where the membership functions have values larger than 0 is named the support of the function. copyright c⃝ 2006-2013 by ccc publications 470 h.n.l. teodorescu we work under a set of very general hypotheses that simplify the presentation of the main results; most results remain valid without these hypotheses, with slightly more elaborate conditions, but their proofs become tedious and lengthy. the hypotheses are as follows. all input and output membership functions (h1) are normalized, that is, there is at least one point where the membership functions have value 1. (h2) have a finite set of α-intervals for every α. (we will not use h2 when building an example in section 3.) (h3) have compact supports, that is, compact sets where they are larger than zero. (h4) have bounded supports. (h5) the union of all the supports of the input membership functions is compact. this hypothesis has no significant impact on the results, but simplifies the discussion. hypothesis h3 is typically accepted for fuzzy sets, yet the hypothesis can be removed by defining two (or several) fuzzy sets with compact support, with their union equal to the given non-compact support set. we will still require that the number of disjoint sets of the support is finite. hypothesis h4 (boundedness of the support) is only used to avoid studying one more trivial case for membership functions. it can be removed with no influence on the results. hypothesis h5 (compactness of the union of the supports) plays no role except of avoiding splitting the problem into sub-problems. notice that in many respects, the type of input space is not essential, as far as it is a topological space where continuity of the mappings can be defined. in general, we assume that an input membership function is an application from a compact set in a hausdorff topological space to the unit interval of the real line, µ : s ⊆ x → [0, 1] ⊆ r. we assume that the input membership functions are not singletons on x. consider a finite set of real numbers, named singletons, {βi}i=1,..,q. we recall a few definitions. definition 1. a siso zero-order sugeno fuzzy system is a construction comprising (i) a set of input membership functions µk : s → [0, 1], k = 1, · · · , m, (ii) a set of output singletons, {βh}; (iii) an application i(.) : {1, a, m} → {1, · · · , q}, that associates to any k ∈ 1, · · · , m a value i(k) ∈ {1, · · · , q}; each such individual association, that is a value i(k) for a specified k, is named rule; (iv) an application s → r defined by x → y = ∑ k βi(k)µk(x)∑ k µk(x) , βi(k) ∈ r . the part (iii) in the construction is equivalent to saying that there is a set of m rules, r(k): if input is µk, then output is βi(k) , that is, one singleton is assigned to each input membership function. a definition a little more general allows for several singletons assigned to one input membership function, (iii*) an application i(.) : {1, · · · , m} → ℘{1, · · · , q}, where ℘ denotes the set of parts (power set); i(k) for a specified k is named rule; (iv) an application s → r defined by x → y = ∑ k βh maxjs.t.i(j)=h µj(x)∑ k maxjs.t.i(j)=h µj(x) . the part (iii*) in the construction is equivalent to saying that there is a set of rules as r(k): if input is µk, then output is βi1(k), βi2(k), · · · , βir(k) , that is, several singletons may be assigned simultaneously to a single input (non-univalent, multivalued / multivocal association). equivalently, one can allow for several rules with the same antecedent and different consequents connected by or, as r(k): if input is µk, then output is βi1(k) , or on the characteristic functions of fuzzy systems 471 r(k): if input is µk, then output is βir(k). the application x ∈ s ⊆ x → y ∈ y ⊆ r is named characteristic or input-output function of the fls. this function is defined in all points where at least one input membership function is not null, for sugeno-type flss. this condition is required by the c.o.g. defuzzification method, specifically by the condition that the denominator in the c.o.g. is not null. for ease, we will say that a fuzzy system is continuous if its characteristic function is continuous. 3 properties of continuity siso sugeno systems the simplest way to analyze the continuity of sugeno-type system is to use the formula of the output according to the definition in the previous section and to consider that all input membership functions are defined on the whole domain, x. when an input membership function is defined on a subset of the input space, µ : s ⊆ x → [0, 1], the last condition is enforced by extending the definition of the input membership functions to the whole x by µ(x ∈ x \s) = 0. we will study the cases when all input membership functions are continuous on x. because we assume that all membership functions are continuous everywhere, moreover because the maximum, minimum and rational functions of continuous functions are continuous (wherever they are defined), we directly have the basic result: proposition 1. sugeno siso fuzzy systems with continuous membership functions over the whole input space are continuous input-output applications in the subset of the input space where the system is defined. the input space can be whatever topological space x that allows us building continuous applications from it to [0, 1]. beyond this very general condition, all the other conditions regard the interval [0, 1] and functions defined on it; hence, the generality of the result in proposition 1. while the above result is very general, easy to obtain, in addition easily extendable to all sugeno systems (of whatever order), it sheds little light on the internal mechanisms of these systems and on what happens outside the subset of the input space where the system is defined. therefore, we will provide another proof for the continuity, studying along the proof the properties of the frontier of the definition space of the system. the analysis will be developed first for the simplest case of flss, namely when the input membership functions are defined on r. then, we deal with x as a general topological space. we first introduce a few concepts that simplify the explanations. consider single-input single-output (siso) fuzzy systems with input membership functions defined on the real axis (mono-dimensional input functions). we assume that any input membership function is not null on an opened interval and null outside it. denote by ik = (xk, xk) the opened interval corresponding to the membership function µk, with µk(x) > 0 for all x ∈ ik and µk(x) = 0 for all x ∈ r \ ik. the case xk = xk is not allowed, as the membership function would be void everywhere except a point, reducing it to an input singleton. while not essential, for ease of exposition consider that the union of all closures ik of the intervals ik is an interval,i = ∪kik . consider that the intersection of the closures of two specified intervals, ik and ij, is not void. the following cases will play a role in the discussion. the intersection of two intervals is a single point. assume that ik ∩ij = xk = xj or ik ∩ij = xj = xk. the two intervals are adjacent, non-overlapping. (see fig. 1 a). then, in the respective point xk = xj or xj = xk, both functions have zero value, therefore another membership function must have non-zero value in that point, for the fuzzy system is defined in that point. in the respective point (x3 in fig. 1 (a)), the system is not defined. if the two membership functions are 472 h.n.l. teodorescu associated with different singletons, the system will have different values at left and at right of x3, making it impossible to achieve continuity of the system simply by extending the characteristic function of the system to one of the two values. the cases ik ∩ij = xk = xj and ik ∩ij = xk = xj are not possible, because we required that xk ̸= xk and that the intersection reduces to a single point. therefore, this case is not allowed under normal circumstances (sugeno systems are not using input singletons). the intersection is an interval. when ik ∩ ij = ik or ik ∩ ij = ij, the membership functions may look as in fig. 1 b. we will take into account this case. the typical case in applications is an intersection that is an interval but not equal to one of the two, ik, ij. figure 1: (a) not allowed case, if no other function is non-zero in x3; (b) allowed case. lemma 1. in the case (ii) above, when ik ̸= ij, there is at least one point inside ik ∩ ij such that the two membership functions have equal values. proof. indeed, as the membership functions are assumed continuous, so is their difference, µk − µj. denote ik ∩ ij = [a, b]. then, either (µk − µj)(a) < 0 and (µk − µj)(b) > 0, or (µk−µj)(a) > 0 and (µk−µj)(b) < 0. therefore, there is a point x ∈ [a, b] such that (µk−µj)(x) = 0. consider all points where two membership functions have equal values. denote these points by ek and create the ordered set comprising all distinct ek, xk, xk points. denote this set of ordered points by pc = ah. the set of intervals [ao, a1), · · · , [ah, ah+1), · · · , [an, an+1] will be named canonical partition of the interval i. the points ek play a role in considerations only regarding the derivability of sugeno systems and regarding mamdani systems; for sake of generality we use them here too. one could define the canonical partition without the points ek and introduce them only when needed. lemma 2. for any interval in the canonical partition, a single rule (iii) or a single subset of rules (iii*) simultaneously apply (are active) in a single-input fuzzy logic system. proof. consider x changing inside some interval of the canonic partition. because such an interval is included or at most equal to any support (basis interval) for a membership function, moreover no point inside such an interval, x ∈ (ah, ah+1), represents an edge of the support of an input membership function, the activated rules will remain the same for all inside points. we recall that the minimum and maximum of two continuous functions are continuous functions, that is, if f and g are continuous functions in some interval, min(f(x), g(x)) and max(f(x), g(x)) are continuous functions on that interval. consequently, the truncation operation applied to a continuous membership function µ, min(µ(x), γ), as used for mamdani systems, is continuous for any γ ∈ r, in the given interval of continuity of µ. we introduce the notion of support of a fuzzy systems defined by the union of the supports of all the input membership functions, i = ∪kik . we now can prove proposition 2. any siso zero-order sugeno system with continuous input membership functions defined on r is continuous inside the support of the system. proof. according to lemma 2, a single subsystem of rules always apply inside an interval of the canonical partition, thus the set of integers k for which µk(x) ̸= 0 in x → y = ∑ k βi(k)µk(x)∑ k µk(x) on the characteristic functions of fuzzy systems 473 is uniquely defined. as the functions µk(x) are continuous and none null, the denominator is nowhere null inside the interval and both the nominator and denominator are continuous, thus the function x ∈ i → y ∈ r is continuous. in the points ending the interval for x, x ∈ (ah, ah+1), the subset of active rules changes because one or several membership functions become zero. consider the point ah, which is not on the frontier of the support of the fuzzy system, ah ∈ i \ i. at the limit, we have lim x→ah,x>ah ∑ k βi(k)µk(x)∑ k µk(x) . some of the membership functions tend to zero, while others tend to some non-zero values. those membership functions not tending to zero remain non-null for x < ah in some vicinity of ah, because of continuity. at the same time, other membership functions are non-zero for x < ah and tend to zero in ah. therefore, the limit at right and at left of ah are equal, lim x→ah,x>ah ∑ k βi(k)µk(x)∑ k µk(x) = lim x→ah,x<ah ∑ k βi(k)µk(x)∑ k µk(x) and the application is continuous in ah. because in the whole closed interval [ah, ah+1] the system is continuous, it is continuous in the whole definition domain. the only case we still need to analyze is on the frontier of the union of the supports. because all the subsequent considerations do not make use of the specific fact that the input membership functions are defined on r, we will continue the discussion in a more general setting. denote u = ∪ksk , where sk is the support of the membership function µk. the frontier is γ = u \ u. because on the frontier all membership functions are null, the fls is not defined on γ , yet it can be extended on γ if the system function has a limit on every point on γ. lim x→x0∈γ,x∈u ∑ k β(i(k))µk(x)∑ k µk(x) = lim x→x0∈γ,x∈u ∑ k βi(k)µk(x) mink µk(x)∑ k µk(x) mink µk(x) . in the second limit, the denominator is larger than 1, because all quantities are positive and for h = arg minkµk(x), (µh(x))/ mink µk(x) = 1. when a single membership function has values larger than zero in a vicinity of x0, that is, when x ∈ vx0 ∩ u{x0} implies that there is some unique h such that µh(x) > 0 and µk ̸=h(x) = 0, then the limit is βi(h). this is probably the most frequent case in applications. figure 2: evolution near zero of the system described in example 1 however, in general the limit does not exist. example 1. consider µ1(x) = max(1, x|sin(1/x)|), µ2(x) = max(1, x|sin(1/(2x))|), β(i(1)) = 1, βi(2) = 3. notice that these functions do not obey the requirement in the hypothesis (h2). the limit 474 h.n.l. teodorescu lim x→0,x>0 (max(1, x|sin(1/x)|) + √ 3 max(1, x|sin(1/ √ 2x)|) max(1, x|sin(1/x)|) + max(1, x|sin(1/ √ 2x)|) does not exist. however, this is not a case of practical interest when choosing membership functions for typical control problems. figure 3 shows the variation of the above function around zero, at two scales. on the other hand,if for any point x on the frontier of the support of the fuzzy system with continuous input membership functions on x, there is a membership function µh such that for all the other membership functions µi ̸=h there is a finite limit lim u→x µi(u) µh(u) = li,h(x)(x), then the characteristic function of the sugeno system can be extended on the frontier as x → y = ∑ k βi(k)µk(x)∑ k µk(x) for x inside s∗ x → y = β(i(h(x))) + ∑ ( k ̸= h(x))l(i, h(x))(x)) 1 + ∑ ( k ̸= h(x))l(i, h(x))(x) , x ∈ γ(s∗)). the extension is obtained as limu→x ∑ k β(i(k))µk(x)∑ k µk(x) = βi(h(x)) + ∑ k=h(x) βi(k)µk(x))/(1 +∑ k=h(x) li,h(x)(x). the limit is obtained by factoring in the nominator and in the denominator by µh(u). the possibility to choose several membership functions for µh does not modifies the result, as it is easy to check. example 1. consider a sugeno system with only two membership functions, both defined on [0, 0.1], µ1(x) = x, µ2(x) = x2, β1 = 1, β1 = 3. in this case, h = 1. then, inside [0, 0.1], x → y = (x+3x2)/(x+x2), thus limx→0(y) = limx→0(x+3x2)/(x+x2) = 1. remark 1. the characteristic function of the system so defined is continuous in the closure of the support, s∗ if the input membership functions are continuous in x. this property results from the above discussion. remark 2. consider the more general case when x is a multidimensional space, for example x ∈ x and x = r2 and γ(s∗) is a curve in the plane. it is easy to see that the function x → y = (βi(h(x)) + ∑ k ̸=(x) li,h(x)(x)/(1 + ∑ k ̸=h(x) li,h(x)(x) defined on γ(s ∗) may be discontinuous if there are membership functions that are discontinuous even in only one of the variables of the plane. because the standard definition of the fuzzy logic systems forces us to exclude the definition of the characteristic function on the support of the system, that is, forces the support to be an open set, we suggest the next extension of the definition for siso sugeno systems by replacing condition (iii) in the definition: (iii**) the output of the system is x → y = lim u→x ∑ k βi(k)µk(u)/ ∑ k µk(u), whenever the limit exists. this definition generalizes (iii) covering it as a specific case. in addition, it allows us to use membership functions as in fig. 1 (b) and still have the system defined in the closed interval corresponding to the bases of the triangles. moreover, the definition completes the domain of definition of the system to a closed set, and makes it continuous (when the input membership functions are continuous and satisfy the other conditions regarding the limit) on its whole (closed) domain. of course, outside the reunion of the input membership functions support, the system is undefined. on the characteristic functions of fuzzy systems 475 the case of multi-input single-output miso case, with membership functions defined on r for each input (mono-dimensional input functions) is dealt similarly. we do not study it here because of space limits. 4 conclusions we provided several properties of the input-output (characteristic) function of the siso and miso sugeno fuzzy systems with defuzzified output by the center of gravity method. these properties regard the continuity of the characteristic functions of the fuzzy systems and are essential in understanding the applicability and constraints when using fuzzy systems in control, system modeling (approximation), predictors and other current applications. we made a clear difference between the domain of definition of the input membership functions and the domain of the characteristic (input-output) function of the fuzzy system, stressing that they are not identical. the continuity inside the system domain was proved for input topological spaces that allow the definition of the membership functions. next, we showed cases when it is possible to and explained how to extend the definition of sugeno fuzzy systems to a closed domain, preserving the continuity of the system. while we provided a proof of continuity for the opened domain of the systems, proof that is valid for general topologic spaces, we worked out a second proof, which is longer but has the advantage of detailing the internal mechanics of sugeno systems. the second way of dealing with the continuity problem has the additional advantage of showing a path toward the extending of the domain of the systems to a closed domain, for a case that may be of interest in applications. bibliography [1] buckley, j.j., hayashi, y. 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(editor), fuzzy signals and systems. amse press, lyon, france, 1990 [7] teodorescu, h.n., taylor and bi-local piecewise approximations with neuro-fuzzy systems, studies in informatics and control, 21(4):367-376, 2012. [8] tong, r.m., a control engineering review of fuzzy systems, automatica, 13(6):559-569, 1977. [9] yu-ru s., sugianto, l.f., lee, e.s., a note on continuity and semicontinuity of fuzzy mappings. fuzzy information processing society, 2007. nafips ’07. annual meeting of the north american , 24-27 june 2007, 106 111. 476 h.n.l. teodorescu [10] a. bazoula, m.s. djouadi, h. maaref, formation control of multi-robots via fuzzy logic technique, int j comput commun, issn 1841-9836, suppl. issue, 3(s):179-184, 2008. [11] d.dubois, h. prade, on fuzzy interpolation, int j of general systems, 28(2-3):103-114, 1999. [12] e.p. klement, l.t. koczy, b. moser, are fuzzy systems universal approximators?, int j of general systems, 28(2-3): 259-282, 1999. [13] pierre borne, mohamed benrejeb, on the representation and the stability study of large scale systems, int j comput commun, issn 1841-9836, suppl. issue, pp. 3(s):55-66, 2008. [14] p.j.s.g. ferreira, m.j.c.s. reis, fuzzy information on discrete and continuous domains: approximation results, int j of general systems, 33(5):583-591, 2004. [15] s.b. cononovici, a. curaj, an approach to walking robots planning and control, proceedings of the romanian academy, series a, 11(1):75-82, 2010. int j comput commun, issn 1841-9836 9(2):217-226, april, 2014. a metamodel for an adaptive control system f. valles-barajas fernando valles-barajas universidad regiomontana, information technology department 15 de mayo 567 pte., c.p. 64000 colonia centro, monterrey, nuevo león, méxico, tel. +52 81 8220-4733 e-mail: fernando.valles@acm.org, fernando.valles@ieee.org abstract: in this paper a metamodel for an adaptive control system (acs) is described. this metamodel was built employing use, which is a uml-based specification environment. the main goal of the metamodel is to complement other models describing different views of an acs. as the reader will notice, the metamodel is composed of a graphical and a mathematical model. weak constraints are specified in the graphical model using a unified modeling language (uml) class diagram, while strong constraints are defined in the mathematical model using the object constraint language (ocl). keywords: adaptive control, metamodels, ocl, uml 1 introduction a software process describes who is doing what, how, and when [9]. one of the phases of a software process is design. in this phase a model of a system, that will later be implemented, is constructed [15]. this model is useful to detect flaws but also for documenting and to establish a communication channel with system’s user. depending on the process being constructed designers can use text or mathematics or a combination of both to build models. an advantage of using text, which is an informal technique, is that the resulting design is easy to understand and can be rapidly constructed. its disadvantage is that sometimes a model made using an informal technique can lead to a misunderstanding. formal models, which rely on mathematics, do not have this disadvantage. on the other hand, control systems are used in industry to assist control engineers in maintaining processes in a desired state (see [3], [25], [10]). control algorithms are embedded in control systems. sometimes control systems are applied to control critical systems, whichrequire free errors designs and because of one of the part of control systems is software, recently control engineers are applying software processes in the building of control algorithms [18]. proposal of the paper : usually a design covers one view of the system being modeled. according with [7], the design views of system are: structural, procedural and behavior. while making the design’s structural view, the designer should document constraints affecting system entities and the relations between them [7]. this documentation can be made using text or a formal language to get a more precise specification. in this paper, the author explains how a semi formal modeling language and a formal language can be used to specify constraints affecting the parts of an adaptive control system as well as constraints affecting the relations between them. the unified modeling language (uml) [5], [14] and the object constraint language (ocl) will be used to specify these constraints. previous works: the first step in a software process is to gather requirements system. in [17] a requirements process for control system software is presented. this process is based mainly in copyright © 2006-2014 by ccc publications 218 f. valles-barajas the rational unified process (rup). as is recommended in personal software process (psp) [7], once the requirements are captured by software engineers, the next step is to make a pre-design for the purpose of prediction and planning. in [19] a proposal for the building of pre-design for control systems is presented. further details, not covered in the pre-design, can be specified in the design phase. psp proposes that design can be analyzed from several perspectives. in [20] a proposal, based on psp, for modeling the structural view of control systems is introduced; in this view the entities of the system, its attributes and relations among the system entities are modeled. once the entities of system are specified, software designer should detect entities having several states and model them using a state machine. in [21] the authors model an adaptive control system using a state machine. the design views proposed in psp are a subset of the uml diagrams, which model systems in more detail. in [23] a survey of the application of uml to model mechatronics systems is presented. once software design is made, code must be built. in [18] and in [22] the authors explain best practices in using programming languages at the moment code for control system software is made. related works: the z language is a formal modeling language based on first-order logic and set theory. this modeling language has been applied to the specification of critical systems. in [8], this language is used to specify a control program for a radiation therapy machine. another application of the z modeling language in critical systems is described in [15], where a system that monitors the blood glucose level of diabetics and automatically injects insulin when it is required is specified in the z language. the disadvantage of the z language in comparison with ocl, which is the modeling language used in this paper, is that ocl complements uml and it does not use mathematical symbols to make specifications. the latter characteristic could be attractive to control engineers not familiar with mathematical logic. control systems are usually modeled using differential equations and analyzed using, for example, lyapunov stability theory. in [2] a novel approach based on hoare logic for reasoning about control systems is presented. in [12] the authors present intent-specifications model for a robotic software control system. according to the authors, an intent-specification is composed of seven levels that as a whole model the entire software control system. each level models the system from a different perspective and, in particular, in level 4 a design representation of the system is included. the unified modeling language (uml) has also been used to model software for control systems. in [24] the authors propose a methodology for generating code for programmable logic controllers (plcs) from uml diagrams. the application of uml for process control was evaluated from a usability and cognitive science point of view. the authors performed an experiment to evaluate the acceptation of uml for control engineers. some researchers are studying mapping between traditional tools used by control engineers for software modeling and uml. for example, in [16] the author analyzed mapping between function blocks, which are defined by the international electro-technical commission as the basic construct for distributed control applications and uml. when compared with uml, function blocks do not consider all the benefits of object oriented theory. another paper including functional blocks and uml is [13]; in that paper the authors apply uml activity diagrams to model function blocks. theorem provers have also been used to specify and verify the correctness of control software. a metamodel for an adaptive control system 219 for example, in [6] the authors apply the interactive theorem prover pvs to model the light control system, which is a benchmark in formal methods. outline of the paper : in this section, the motivation of this work has been explained. section 2 contains a brief description of an adaptive control system. section 3 describes the tools used in this paper to specify the constraints of an adaptive control system. in section 4 the adaptive control system design is included. the last section contains concluding remarks. 2 adaptive control systems fig. 1 shows the configuration of an indirect adaptive control system. in this kind of adaptive control, a model of the process (gp(z−1)) is obtained based on a set of input-output measurements (u(k),y(k)) and then the controller (gc(z−1)) is designed using this model [10]. the paa gp(z −1)gc(z −1)σ y(k) y(k)u(k)e(k)yref (k) θ̂p aa(k) desired performance controller σ ? d2(k) σ ? d1(k) supervisor block σ� n(k) --? 6 � � 6 ? u ? on/off θ̂cd(k) design figure 1: block diagram for an adaptive control system parameter adaptation algorithm (paa) block is the responsible for obtaining the parameter vector of the process (see θ̂paa(k)) in fig. 1). the controller design block specifies the parameters of the controller (θ̂cd(k)) based on the model obtained by the paa and on the desired performance specified by the system operator. an adaptive control system must control a process in spite of disturbances d1(k), d2(k), noise n(k) and the parametric variations of the process. the supervisor block is in charge of detecting any event that may provoke a decreasing in the performance of system; in these cases the supervisor will turn off the controller gc in fig. 1, yref(k) is the reference, e(k) is the control error, u(k) is the manipulated variable and k is the kth sampling time. 3 uml & ocl: tools to model software systems uml is a modeling language, based on object oriented theory, developed by the three amigos with the aim of modeling complex software [14]. every of the uml diagrams models a different view of a system; for example class diagrams specify system entities and the relations between 220 f. valles-barajas figure 2: class diagram for an adaptive control system them. uml has acquired a good acceptation in the software community, though sometimes models created in uml lead to miscommunication. in these cases it is recommended to complement uml models with an ocl specification, which is a formal modeling language based on logic and set theory. one of the advantages of ocl is that it does not use mathematical symbols to build models; instead it uses a textual representation. for designers without strong background in logic, this characteristic of ocl may result attractive. 4 uml/ocl specification for an adaptive control system in this section the adaptive control system metamodel is presented. first of all, a graphical model is described using uml and as will be shown this model contains weak constraints on the elements composing an adaptive control system. with the introduction of the uml model the disadvantages of only using graphical models to specify an adaptive control system are demonstrated. a model complementing the graphical model is then constructed using ocl. as the reader will see the weak constraints established in the uml model will be strengthened by using ocl. 4.1 uml specification fig. 2 contains a uml class diagram for the acs of fig. 1. for simplicity, this diagram does not contain all of the details of an acs; for example, the supervision block and the controller design block are not considered in this diagram. fig. 3 and 4 contain the use specification of the class diagram of fig. 2. as the reader may notice a composition relation between classes gp (process model), gc (controller) and paa (parameter adaptation algorithm) was specified (see the composition acs in the class diagram of fig. 2 and its definition in the use specification of fig. 4). also, an association class between classes gp and gc was defined. as can be seen in the use specification of fig. 4, this class was defined as an association class because it defines attributes that do not belong to a particular class; for example ts (time sampling) is associated to the entire system and not to a particular class. a reflexive relation, named cascade, was defined in the class gc. master and slave roles of this relation are specified in fig. 4. a metamodel for an adaptive control system 221 model adaptivecontrolsystem enum externalagent {d1, d2, n} enum paa_state {on, off} enum paa_fault {poorexcitation, perturbation, lowm} enum tuning {quarterdecayratio, stepresponse, iae, itae, other} abstract class gc attributes id : integer isinautomatic: boolean r: sequence(real) s: sequence(real) t: sequence(real) tuningtype: tuning indicator : string constraints inv updatedisplay: isinautomatic implies indicator = ’automatic’ inv validid: id >= 1 end class paa attributes theta: set(real) phi: set(real) lambda: real state : paa_state faults : set(paa_fault) operations updategp( b: sequence(real), a: sequence(real) ) constraints inv: if faults −>notempty then state = #off else state = #on endif end class pid < gc attributes tao_d: real tao_i: real kc: real operations assigngp(gp: gp) end abstract class gp attributes b: sequence(real) a: sequence(real) d: integer na: integer nb: integer np: nyquistplot operations ismonichurwitzpolynomial (): boolean constraints inv sructure3: d>0 and na>0 and nb>0 inv structure: na >= nb inv structure2: a−>size() = na and b−>size() = nb end class fop < gp attributes k: real theta: real tao: real operations initgpfop() fromcontinoustodiscrete() constraints inv structure: nb = 1 and na = 2 end class sop < gp constraints inv structure: nb = 2 and na = 3 end figure 3: use specification for an adaptive control system (part i) 222 f. valles-barajas class process attributes identifiedgp : gp affected : set(externalagent) end class frequency attributes value: real end associationclass gpgc between gp [1] role model2; gc [1] role controller ; attributes ts: integer affectedby: set(externalagent) faulthistory : bag(externalagent) gainmargin: real phasemargin: real modulusmargin: real delaymargin: real operations registerafault( f : externalagent) end association tune between paa [1] role tunner; gc [1] role controller2 ; end association corresponds between frequency [1] complexnumber [1] end class complexnumber end class criticalpoint < complexnumber end class nyquistplot attributes w: sequence(frequency) c: set(complexnumber) end association cascade between gc [0..1] role master; gc [0..1] role slave ; end association validzone between process [1] role plant; gp [1..∗] role model; end association identifies between paa [1] role identifier ; process [1] role process; end composition acp between paa [1] gp [1] gc [1] end figure 4: use specification for an adaptive control system (part ii) the model defined in fig. 2, 3 and 4 contain some flaws; for example this model allows that a controller gc1 plays at the same time master and slave role in the cascade association. another design flaw occurs between classes gc and gp as is explained as follows. pid controllers have demonstrated to have a good performance when the process being controlled can be modeled as a first or second order process (see [11]), however the class diagram of fig. 2 allows that any kind of process may participate in the relation between gc and gp. these design flaws justify the addition of ocl constraints to the acs class diagram model shown in fig. 2 and defined in fig. 3 and 4. 4.2 ocl specification in this section, invariants on the class diagram defined in fig. 2 are defined. some invariants affect more than two elements of the class diagram. invariants are explained in the same order they appear in fig. 5 and 6. fig. 5 defines three constraints on the class controller, gc. invariant onlyonerole assures that a controller gc1 cannot be at the same time, both a master controller and a slave controller. invariant differentids constraints that in a cascade relation the controller playing the master role has a different id that the controller playing the slave role. the last invariant of the class gc specifies that if the type of controller is a pid controller, then an appropriate tuning type must be selected (quarterdecayratio, stepresponse, iae or itae a metamodel for an adaptive control system 223 context gc inv onlyonerole: master−>notempty implies slave−>isempty inv differentids : (master−>notempty implies self.id <> master.id) and (slave−>notempty implies self.id <> slave.id) inv validgp: self .oclistypeof( pid ) implies tuningtype <> #other and (model2.oclistypeof( fop ) or model2.oclistypeof( sop )) and model2.oclistypeof( fop ) implies model2.oclastype( fop ).theta < gpgc.ts context pid::assigngp( gp: gp ) pre: fop.allinstances−>union( sop.allinstances )−>includes( gp ) post: model2.plant.identifiedgp = gp context nyquistplot inv orderedfreq1: let integers = sequence{1 .. w−>size()} in integers−>forall(e1, e2: integer | w−>at(e2).value > w−>at(e1).value implies e2 > e1) inv orderedfreq2: −− another way to specify invariant orderedfreq1 w−>forall(f1, f2: frequency | w−>indexof(f2) > w−>indexof(f1) implies f2.value > f1.value) inv stability : w−>forall(f: frequency | criticalpoint.allinstances−>excludes(f.complexnumber)) inv completeness: w−>forall(f: frequency | complexnumber.allinstances−>exists(cn | f.complexnumber=(cn))) figure 5: use constraints for an adaptive control system (part i) among others, see the enumeration tuning defined in fig. 3). besides this, the process being controlled should be modeled as a first order process or as a second order process. if the process is a first order process then the dead time, θ, should be smaller than ts. a method to assign a process model to a pid controller is defined in fig. 5. this method forces that the model, gp, should be either a first order process or a second order process. four invariants are defined on the class nyquistplot. invariants orderedfreq1 and orderedfreq2 declares that the frequencies specified in a nyquist plot should be ordered from low to high. invariant stability defines that a nyquist plot should not contain the critical point. the last invariant of class nyquistplot specifies that every frequency in a nyquist plot has a complex number assigned to it. fig. 6 declares a method of the class gp, which constraints that the polynomial a of gp should be monic; in other words the leading coefficient should be 1. the method updategp defined on the class paa, updates the model gp as long as the model identified by the paa is inside the valid zone of the process being controlled and there is not fault affecting the entire system. invariant stabilitycriteria defines the standard values to get a robust controller recommended in [3]. method registerafault defined on the association class gpgc updates the logging of system faults. 224 f. valles-barajas context gp :: ismonichurwitzpolynomial(): boolean pre: a−>first() = 1.0 post: result = true context paa::updategp( b: sequence(real), a: sequence(real) ) pre: state = #on pre: process.model−>exists(gp: gp | gp.a = a and gp.b = b) pre: set{faults, process.model.gpgc.affectedby}−>isempty post: process.identifiedgp .a = a and process.identifiedgp.b = b context gpgc inv stabilitycriteria : gainmargin >= 6 and modulusmargin >= −6 and sequence{ 30, 31 .. 59, 60 }−>includes(phasemargin) and delaymargin = 0.1∗ts context gpgc::registerafault(f : externalagent) post: faulthistory = faulthistory@pre−>including(f) context process inv not_a_pid: let ip = identifiedgp in let fop = ip.oclastype( fop ) in ip .oclistypeof( fop ) and (fop.theta > fop.tao∗0.25 and fop.theta < ip.gpgc.ts) implies pid.allinstances−>excludes( ip.controller ) figure 6: use constraints for an adaptive control system (part ii) the last invariant of fig. 6 constrains the class process. if the process is modeled as a first order process then θ should be less than 0.25τ (τ is the constant time of a first order process) and less than ts. 5 conclusions in this paper an informal modeling language (uml) and a formal modeling language (ocl) have been applied to model an adaptive control system. uml was used to specify weak constraints whereas ocl was applied to strength the model made in uml. the author of this paper believes that the model included in this work, will be useful for control engineers that want to have a better understanding of how to apply uml and ocl in the modeling of control systems. a simple example (an adaptive control system) was used in this paper to illustrate how control system software can be specified using an informal notation (uml) together with a formal modeling language (ocl). the technique proposed in this paper should be used to model software for complex control systems, e.g. nuclear plants, robots, planes among others. the future lines of research based on this paper are: • due to the fact that uml is a general purpose modeling language, in some cases a more specialized language gives a more precise specification than that obtained using uml. this drawback of uml has been studied and analyzed by the uml community and to overcome this problem uml extension mechanisms have been proposed; one of these are profiles. a profile based on uml and ocl will be studied in future papers. • there are some tools to prove if some constraints are fulfilled by one specification (see for example [1] and [4], among others). building one tool with this characteristic would be useful to check the models created by the technique proposed in this paper. a metamodel for an adaptive control system 225 • every modeling technique specifies a system from a particular perspective. in this paper, a uml class diagram was used to model the entities composing a control system along with the constraints among them; strong constraints were specified using ocl. other perspectives not considered in this paper could model the interaction between the entities composing the system and the interaction between the user of a system and the system itself. a study of other models complementing the perspectives of control system software will be made in a future research. bibliography [1] r. arthan, p. caseley, c. o’halloran and a. smith (2000). clawz: control laws in z, third ieee international conference on formal engineering methods (icfem 2000), 2000. 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[25] b. wittenmark, k. j. åström, and k. e. årzén. computer control: an overview, ifac professional briefs, 2002. int j comput commun, issn 1841-9836 vol.7 (2012), no. 3 (september), pp. 494-508 mining temporal sequential patterns based on multi-granularities n. li, x. yao, d. tian naiqian li, xinhui yao, dongpin tian department of computer science baoji university of arts and sciences baoji 721016, shaanxi, china e-mail: xalnq@hotmail.com, baojiyxh@163.com, tdp211@163.com abstract: sequential pattern mining is an important data mining problem that can extract frequent subsequences from sequences. however, the times between successive items in a sequence is typically used as user-specified constraints to pre-process the input data or to prune the pattern search space. in either cases, the times cannot be used to identify item intervals of sequential patterns. in this paper, we introduce a form of multi-granularity sequence patterns, which is a sequential pattern where each transition time is annotated with multi-granularity boundary interval and average time derived from the source data rather than the user-predetermined time interval or only a typical time. then we present a novel algorithm, mg-prefixspan, of multiple granularity sequential patterns based on prefixspan[, which discovers all such patterns. empirical evaluation shows that mg-prefixspan scales up linearly as the size of database, and has a good scalability with respect to the length of sequence and the size of transaction. keywords: data mining algorithm, sequential pattern mining, sequential data, time granularity, temporal patterns. 1 introduction among various types of data mining applications [1, 2], the sequential pattern mining, which discoveries interesting sequential patterns hidden in sequence of events, is an important data mining problem with broad applications, including market analysis, decision support, the prediction of occurrences of recurrent illnesses, system performance analysis and telecommunication network analysis etc. the problem of mining sequential patterns was first proposed by agrawal and strikant [3]: given a data set of sequences, each sequence is a list of transactions, where each transaction is a set of items. the sequential pattern mining is to find all subsequences that is more frequent than a user-specified minimum support threshold while maintaining their item occurrence order. for example, in the database of a book-club, a sequential pattern might be “5 percent of customers bought ‘foundation’, then ‘foundation and empire’, and then ‘second foundation”’ [4]. although the discovered sequential patterns reveal what items are frequently bought together and in what order, they cannot reveal how long time the items will be bought after the preceding items. unfortunately, not knowing the time means that we cannot exactly predict when the next purchase will happen. in addition, some sequential patterns could occur in different periods with different time granularities. for example, “hp stock could rise within 5 days after ibm stock rose" and “hp stock could fall within 6 month after ibm stock rose", which reveal hp stock rise or fall with respect to ibm stock rise at different time granularities (day and month), are two useful different patterns. however, these traditional sequence patterns can only tell us “hp stock could rise after ibm stock rose" and “hp stock could fall after ibm stock rose". this situation means that the two patterns are useless. another situation may be “hp stock copyright c⃝ 2006-2012 by ccc publications mining temporal sequential patterns based on multi-granularities 495 could rise 5 days later after ibm stock rose" and “hp stock could rise 6 months later after ibm stock rose". although these two patterns are completely different with regard to different time granularities, these traditional sequence patterns could treat the two patterns as the same pattern “hp stock could rise after ibm stock rose", which make the extracted pattern less precise and some useful information lost. given the above reasons, in this paper we generalize the problem definition given in [1, 8, 9, 13] to incorporate the maximum, minimum and average time between successive transactions, which are derived from the source data, and different time granularities in traditional sequential patterns. we present a novel algorithm of multiple granularity sequence patterns based on prefixspan [6] , called mg-prefixspan, which discovers all such patterns. empirical evaluation shows that mg-prefixspan scales up linearly as the size of database, the length of sequence and the size of transaction. the rest of this paper is organized as follows. related work is discussed in section 2. we give a formal description of the problem of mining temporal sequence patterns based multiple granularities in section 3. in section 4, we describe mg-prefixspan, an algorithm for finding such patterns, and then section 5 provides empirical evaluation of the performance of mg-prefixspan. finally, we conclude the paper in section 6. 2 related work sequential pattern mining, in general, can be grouped into two categories. one category, called un-temporal sequence pattern mining or traditional sequence pattern mining, considers only the item occurrence order in a sequential pattern, but does not deal with time-related data [1, 3–6]. the other category, called temporal sequence pattern mining, consider not only the item occurrence order in a sequence pattern, but also the time between successive items in a sequential pattern, such as [8, 9, 11–13]. 2.1 un-temporal sequence pattern mining agrawal and strikant [3] introduced the notion of sequential pattern mining, and based on the property that any sub-pattern of a frequent pattern must be frequent, three apriori-based algorithms were proposed: apriorisome, dynamicsome and aprioriall. two of these algorithms were designed only to find maximal sequential patterns. the third algorithm, aprioriall, finds all patterns. briefly, aprioriall is decomposed into two phases: (1) generating candidate sequences; (2) scanning the sequential database to check the support of each candidate to determine frequent sequence patterns according to minimal support threshold. although aprioriall is not efficient, it is the basis of many efficient algorithms developed later. spads [5] is an algorithm proposed to find frequent patterns using efficient lattice search technology and simple joins. it decomposes the original search space (lattice) into smaller pieces (sub-lattices), which can be processed independently in the main memory. due to adopting a vertical id-list database format to count the number of frequent patterns, all the sequential patterns are discovered with only a few passes over the database. spads outperforms apriorall [2,3]. prefixspan[6] is another more efficient algorithm for mining sequential patterns comparing with the aprior-based algorithm apriorall and spads, especially in dealing with very large databases. it mainly adopts a projection-based, sequential pattern-growth method to make the database for next pass much smaller and consequently make the algorithm more speedy. also in prefixspan there is no need to generate candidates, only recursive projection of database according to their prefix. our method is based on the prefixspan algorithm. some have tried to exert constraints on the mining of sequential patterns so that only those sequential patterns interesting to users are discovered rather than the whole possible sequential patterns[2,5,6,7]. strikant and agrawal [4] generalized their definition of sequential patterns in [3] to integrate with time constraints, sliding time window, and user-defined taxonomy, and proposed algorithm gsp. mannila et al. [7] proposed a method of mining frequent episodes in a sequence of events, in which episodes are essentially constraints on events in form of acyclic graphs. garofalakis et al. [8] proposed a family of 496 n. li, x. yao, d. tian spirit algorithms of mining user-specified sequential patterns by using regular expression constraints. pei et al. [9] developed an extended framework based on a sequential pattern growth for constraint-based sequential pattern mining. although time between successive items is typically used as a user-specified constraint to shrink the pattern search space to make the computation more efficient, it is not used in the output frequent sequence patterns. 2.2 temporal sequence pattern mining yoshida et al.[10] proposed a notation of delta pattern, which is a temporal sequence pattern with temporal constraints in the form a [0,7] → b [3,5] → c of bounding intervals. an example of delta pattern has the form denoting a sequential pattern a → b → c that frequently appears in the database with transition times from a to b and from b to c that contained in [0, 7] and in [3, 5] respectively. however, yoshida et al. only provided a heuristics for finding some frequent delta patterns, and did not investigate the problem of finding all of them. along the same direction, chen et al. [11] introduced a form of temporal sequence pattern by inserting pseudo items into the original sequential pattern. pseudo items are userdefined time interval segmentations in advance. when counting the support of a sequential pattern, only the sequence, in which the pseudo item between successive items is same, supports the sequential pattern. two algorithms, i-apriori and i-prefixspan, were proposed. i-apriori is based on apriori algorithm [12], and i-prefixspan is based on prefixspan[6]. hirate et al. [13] proposed generalized sequential pattern mining with item intervals. they extended sequences which are defined by inserting pseudo items based on the interval itemization function and exerting four interval constraints on items. however, as they adopted some user-predefined pseudo items or constraints on the time intervals between successive items, it is difficult for a user to specify optimal constraints related to item interval and cannot reveal the time interval between successive items in the patterns precisely, it may result in some useful sequential patterns not being found. giannotti et al. [14] introduced another form of sequential pattern, called temporally-annotated sequence, tas in short, where each transition is annotated with temporal information representing a typical time derived from the source data. for instance, tas a t1→ b t2→c denotes the fact that a sequential pattern a → b → c frequently appears in the database and that the time for getting from event a to b is close to t1 and from event b to c is close to t2. although this method using typical time to annotate transition between two successive events can reveal how much time the event will occur after the preceding event, they cannot distinguish completely patterns with regard to different time granularities. for example, from event a to b is close to t1 days, other from event a to b may be close to t2 months, these two different patterns are treated as the same. it is useful to be able to distinguish the patterns to understand not only what event will follow, but also when these events will occur. finally, we mention the work in [15], where event structures that have temporal constraints with multiple granularities are introduced. event structures essentially are used as a flexible user-defined constraint specification to define the pattern discovery problem with these structures that enable users to focus on their interested sequential patterns. this method can only find the patterns that satisfy the event structures. furthermore, an event structure consists of a number of variables representing events and temporal constraints among these variables, an efficient event structure is difficult to define beforehand even if you are a domain expert. in our work, we introduce a new form of sequential pattern with multi-granularities, which is a sequential pattern where each transition is annotated with multi-granularity boundary interval and average time derived from the source data rather than user-predetermined time intervals [8,9] or only a typical time [14]. we also define the pattern discovery problem involving multi-granularities for these pattern and study efficient algorithm based on prefixspan to solve it. mining temporal sequential patterns based on multi-granularities 497 3 problem formulation 3.1 time granularity in order to formally define temporal sequence pattern that involves time granularities, we first review the notion of a time granularity [15]. definition 1. a granularity is a mapping µ from the set of the positive integers ( the time ticks) r to r2 (the set of absolute time sets) such that for all positive integer i and j with i ≤ j, the following two condition are satisfied: (1) µ(i) , ∅∧µ( j) , ∅ implies that each number in µ(i) is less than all the numbers in µ( j), and (2) µ(i) , ∅ implies µ( j) , ∅. each set µ(i), if non-empty, is called a granule of the µ. property (1) says that granules do not overlap and the order on time ticks follow the order on the corresponding granules. property (2) says that the subset of the time ticks corresponding to the granules forms a set of contiguous integers. the set µ(i) of reals is said to be the ith tick of µ, or tick i of µ . for example, hour, day, week, month, and year, satisfy the above definition. we can also define more complex granularities like business-week, weekend and so on. when dealing with temporal types, it is needed to determine the tick (if any) of a temporal type µ that covers a given tick z of anther temporal type v. formally, for each positive integer z and temporal types µ and v, if ∃ z’ (necessarily unique) such that v(z) ⊆µ(z’) then ⌈z⌉µv = z′, otherwise ⌈z⌉ µ v is undefined[15]. in this paper, all timestamps in a sequence are assumed to be in terms of a fixed granularity g0, and abbreviate ⌈z⌉µv as ⌈z⌉µ if v= g0. definition 2. a multi-granularity schema is a tuple of the form gm = (gm,gm−1, · · · ,g2,g1), where each gi (1 ≤ i ≤ m) is a granularity. for simplicity, we require that in gm and g0, each unit of gi is contained in a unit gi+1 (0 ≤ i ≤ m−1). 3.2 temporal sequence and multi-granularity sequence pattern let i = {i1, i2, · · · , in} be a set of items. an itemset si is a non-empty subset of items (without loss of generality, we assume that items of an itemset are sorted alphabetically) denoted as i1, · · · , ik, where i j (1 ≤ j ≤ k) is an item. a traditional data sequence is an ordered list of itemsets, which is sorted by the order of priority of the transaction time and denoted as (s1 → s2 →···→ sn), where si (1 ≤ i ≤ n) is an itemset [1,3]. in practice, a data sequence of a customer is also formed as an ordered list of itemsets and time stamps [11], we call it a temporal sequence. definition 3. a temporal sequence s is represented as ⟨(s1, t1) → (s2, t2) → ··· → (sn, tn)⟩, where si is an itemset and ti stands for the time at which si occurs, ti < t j for 1 ≤ i < j ≤ n. when adding the itemset time information in the sequence, the time interval value between any two elements in the sequence can be computed as follows: ti j = t j − ti,where 1 ≤ i < j ≤ n we now formally define multi-granularity schema, multi-granularity sequence patterns and its’ support, then formalize our novel mining problem as the discovery of all frequent multi-granularity sequence patterns in the database d. definition 4. given a multi-granularity schema gm = (gm,gm−1, · · · ,g2,g1), a multi-granularity sequence pattern is represented as follows: α=α1 [l1,m1,u1]µ1→ α2 [l2,m2,u2]µ2→ ··· [ln−1,mn−1,un−1]µn−1→ αn 498 n. li, x. yao, d. tian where (1) αi (1 ≤ i ≤ n) is an itemset; (2) µi ∈ {g j| j = 1, · · · ,m} (1 ≤ i ≤ n − 1) a granularity; (3) li, mi, and ui (1 ≤ i ≤ n − 1) are respectively the lower bound, average time and upper bound of the time that αi+1 occurs after α1, α2, · · · and αi with the granularity µi. [li, mi,ui]µi is called temporal annotation of αi+1, and the tuple ([li, mi,ui]µi,αi+1) also called a temporal item (1 ≤ i ≤ n − 1). example α = α1 [1,25,5]day → α2 [2,33,4]week → α3 is a multi-granularity sequential pattern, where α1,α2,α3 are itemsets or events, day and week are time granularities. the pattern indicates that α2 occurs in 1 day to 5 days or average 2.5 days after α1, then 2 weeks to 4 weeks or average 2.5 weeks later, α3 occurs. the total number of items in a multi-granularity sequence pattern is referred as the length of the pattern. a multi-granularity sequence pattern whose length is k is also represented as k-multi-granularity sequence pattern. definition 5. given a temporal sequence s = (s1, t1) → (s2, t2) →···→ (sn, tn) and a multi-granularity schema gm = (gm,gm−1, · · · ,g2,g1). let α be a multi-granularity sequence pattern α = α1 [l1,m1,u1]µ1→ α2 [l2,m2,u2]µ2→ ··· [lk−1,mk−1,uk−1]µk−1→ αk, where µi ∈ g j| j = 1, · · · ,m (1 ≤ i ≤ k − 1). s is said to support( or contain ) α if and only if there exists integers 1 ≤ i1 < i2 < · · ·< ik ≤ n such that (1) ∀ j1 ≤ j ≤ k, αi ⊆ si j (2) ∀ j1 ≤ j ≤ k−1, if µ j = gh (1 ≤ h ≤ m−1) then gh(1) ≤ ti ji j+1 <gh+1(1), otherwise ti ji j+1 ≥ gm(1). the condition (2), in fact, is a constraint which divides the time between successive items into nonoverlap partitions according to the sizes of different time granules. without this constraint, the too long or too short time interval between successive items can be treated as the same in a pattern, and may result in some useful patterns not being found. definition 6. let α = α1 [l1,m1,u1]µ1→ α2 [l2,m2,u2]µ2→ ··· [lk−1,mk−1,uk−1]µk−1→ αk be a multi-granularity sequential pattern, gm be a multi-granularity schema, and s l be a set of temporal sequence that support αl+1 = α1 [l1,m1,u1]µ1→ α2 [l2,m2,u2]µ2→ ··· [ll,ml,ul]µl→ αl+1 (1 ≤ l ≤ k − 1) in database d. the annotation [l1, m1,u1]µl of αl+1 is defined as follows: ul = max s∈s l max i1,··· ,il+1 ⌈ tilil+1 ⌉µl ll = min s∈s l min i1,··· ,il+1 ⌈ tilil+1 ⌉µl ml = 1 |s l| ∑ i1,··· ,il+1 ⌈ tilil+1 ⌉µl where i1, i2, · · · , il, il+1 are the indexes of the elements in s ∈ s l that match the elements of αl+1, µl ∈ gm. definition 7. let d be the database of temporal sequence, gm be a multi-granularity schema. for a multi-granularity sequence pattern α, its support in database d is defined by: s upportd(α) = |{s|s ∈ d ∧ s supports α}| |d| α is said to be frequent if its support is no less than the user-specified minimum support threshold min-sup i.e. s upportd(α) ≥ min − sup. given a temporal sequence database d, multi-granularity schema gm and user-specified minimum support threshold min-sup, the task of mining multi-granularity sequence pattern is to find all frequent multi-granularity sequence patterns in the database d. mining temporal sequential patterns based on multi-granularities 499 4 algorithm for mining multi-granularity sequence patterns in this section, we propose a novel multi-granularity sequence pattern mining algorithm based on the well-known prefixspan[6], called as mg-prefixspan. the prefixspan algorithm solves the pattern mining problem by a divide-and-conquer, pattern-growth principle as follows: for each frequent item a, a projection of the initial sequence database d is created, denoted by d|a, i.e., which (1) contains only the suffix of sequences in d with respect to a; (2) contains only the frequent items; (3) in general is much smaller than d. then, for each frequent item b in d|a, appends b to a to form a sequential pattern a′ which starts with a. finally a new, smaller projection d|a′ can be constructed recursively and used for finding longer patterns starting with a′. however, although the prefixspan algorithm is very efficient for mining sequential pattern, it cannot be used straightforwardly to find multi-granularity sequence patterns. we extend the sequence database projection operation in prefixspan to be able to deal with the temporal relationship between successive items in the sequential pattern based on multi-granularities. before introduction of the algorithm mg-prefixspan, we first extend definitions of prefix, suffix, projection and projected database based on prefixspan[6]. definition 8. given a temporal sequence s = ⟨(s1, t1) → (s2, t2) →···→ (sn, tn)⟩ and a multi-granularity schema gm = (gm,gm−1, · · · ,g2,g1). a multi-granularity sequence pattern α=α1 [l1,m1,u1]µ1→ α2 [l2,m2,u2]µ2→ ··· [lk−1,mk−1,uk−1]µk−1→ αk (k ≤ n) is a multi-granularity sequence pattern prefix of s if and only if (1) αi = si for 1 ≤ i ≤ k − 1; (2) αk ⊆ sk and all the items in (sk −αk) are alphabetically after those in αk; (3) ∀i1 ≤ i ≤ k − 1, if µi = gh (1 ≤ h < m) then gh(li) ≤ tii+1 ≤ gh(mi). for example, let multi-granularity schema be g2 = (hour, minute), g0 = second. if there is a temporal sequence α= ⟨(c,5) → (abc,80) → (ab,320) → (d,5400) → (c f,6400)⟩, β1 = (c) is a multi-granularity sequence pattern prefix of and so is β2 [1,2.4,4]minute → (ab). definition 9. given a temporal sequence s and a multi-granularity sequence pattern α such that s supports α. a subsequence β of s is called a projection of s with respect to α if and only if (1) β has a multi-granularity sequence pattern prefix α, and (2) there exists no proper super-sequence β′ of β such that β′ is a subsequence of s and also has a multi-granularity sequence pattern prefix α. if a temporal sequence α = ⟨(c,5) → (abc,80) → (ab,320) → (d,5400) → (c f,6400)⟩ is projected with respect to β2 [1,2.4,4]minute → (ab), then two projection are ⟨(c,5) → (abc,80) → (ab,320) → (d,5400) → (c f,6400)⟩ and ⟨(c,5) → (abc,80) → (ab,320) → (d,5400) → (c f,6400)⟩. if α is projected with respect to β1 = (c), then three different projections are ⟨(c,5) → (abc,80) → (ab,320) → (d,5400) → (c f,6400)⟩, ⟨(c,5) → (abc,80) → (ab,320) → (d,5400) → (c f,6400)⟩ and ⟨(c f,6400)⟩ respectively. in the above, a temporal sequence α that is projected with respect to a prefix produces more than one projection. to differentiate these projections from the same temporal sequence, the tag [sid, item, t] is attached to each projected sequences, where sid is the identifier of the temporal sequence, item is last item in the multi-granularity sequence pattern and t is the time of element in temporal sequence that matches the last element of the multi-granularity sequence pattern. consequently, if sequence α = ⟨(c,5) → (abc,80) → (ab,320) → (d,5400) → (c f,6400)⟩ is projected with respect to the β2, then different projections are presented as follows: [sid, b, 80]: ⟨(c,5) → (abc,80) → (ab,320) → (d,5400) → (c f,6400)⟩; [sid, b, 320]: ⟨(c,5) → (abc,80) → (ab,320) → (d,5400) → (c f,6400)⟩. 500 n. li, x. yao, d. tian definition 10. given a temporal sequence s and a multi-granularity schema gm = (gm,gm−1, · · · ,g2,g1). let α = ⟨(s1, t1) → (s2, t2) → ··· → (sn, tn)⟩ be the projection of s with respect to a multi-granularity sequence pattern prefix α=α1 [l1,m1,u1]µ1→ α2 [l2,m2,u2]µ2→ ··· [lk−1,mk−1,uk−1]µk−1→ αk,(k ≤ n) temporal sequence γ = ⟨(sk′′, tk) → (sk+1, tk+1) → ··· → (sn, tn)⟩ is called the suffix of s with respect to α, denoted as γ= s/α, where sk′′= sk − sk′. note, if α is not a multi-granularity sequence pattern prefix of s, the suffix of s with respect to α is empty. this definition indicates that the suffix of a temporal sequence can be obtained by directly removing the prefix from its projection. in the example above, if the sequence α = ⟨(c,5) → (abc,80) → (ab,320) → (d,5400) → (c f,6400)⟩ is projected with respect to the β2, then three different suffix are obtained as follows: [sid, b, 80]: ⟨(c,80) → (ab,320) → (d,5400) → (c f,6400)⟩; [sid, b, 320]: ⟨(d,5400) → (c f,6400)⟩. definition 11. let α be a multi-granularity sequence pattern in a temporal sequence database d, the αprojected databases, denoted as d|α, is the collection of suffixes of temporal sequences in d with respect to α. based on the above description, the newly proposed algorithm, mg-prefixspan, is shown as following: input: d: temporal sequence database; gm: multi-granularity schema; min-sup: minimal support threshold. output: the complete set of frequent multi-granularity sequence patterns. method: (01) scan d once, to find all frequent items, which are denoted by l1. (02) for each a l1 do begin (03) construct candidate temporal items α= (0,a) and add α in l1; (04) end (05) output l1; (06) for each s ∈ d do begin (07) for each α ∈ l1 do begin (08) for each β suffix of s with respect to α do begin (09) if β, null null then add β and its tag to d|α; (10) end (11) if d|α, null null then call mg-prefixspan (α,1, d|α); (12) end (13) end subroutine mg-prefixspan (α,1, d|α) parameter: α a multi-granularity sequence pattern; k the length of α; d|α the α-projected database. (20) if lk = null then return; (21) for all a ∈ lk and all µ ∈ gm do begin (22) construct candidate temporal item (a, a), where a=[l, m, u]µ or 0, add it in ck; (23) end (24) for each s s ∈ d|α with tag [sid, b, t] do begin (25) for each (a,a) ∈ ck do begin (26) for each (si, ti) in s where a a ∈ si do begin (27) if ti == t, then add 1to the counting number of (0, a) for different sid; mining temporal sequential patterns based on multi-granularities 501 (28) if g j(1) ≤ ti − t <g j+1(1) or gm(1) ≤ ti − t for 1 ≤ j ≤ m, then do begin (29) increase the counting number of (a, a) for different sid; (30) let l = min{l,⌈ti − t⌉g j}, u = max{u,⌈ti − t⌉g j}, m = m + ⌈ti − t⌉g j ; (31) end (32) end (33) end (34) end (35) for each (a,a) ∈ ck do begin (36) if counting number of (a, a) ≥ min-sup |d|, then do begin (37) let m=m/the total number of pulsing m (a,0); (38) append (a, a) to α to form k+1-multi-granularity sequential pattern γ, add a in lk+1; (39) let γ. support=min α. support, counting number of (a, a)/|d|, add γ in lk+1; (40) end (41) end (42) output lk+1 (43) for each γ ∈ lk+1 do begin (44) for each s ∈ d|α do begin (45) for each suffix of s with respect to γ: β do begin (46) if β, null then add β and its tag to d|γ; (47) end (48) end (49) if d|γ= null then return; (50) call mg-prefixspan (γ,k + 1, d|γ); (51) end (52) return the overall mg-prefixspan algorithm is summarized in the above. the most importance difference lies in that mg-prefixspan algorithm is able to deal with the temporal relationship between successive items in the patterns based on multi-granularities. steps 21-34 handle the frequent items in d|α and correspond annotations with multi-granularities, where steps 21-23 construct candidate temporal items, steps 24-30 derive the low bound, upper bound and average time of each candidate temporal item from the projected database d|γ. if a candidate temporal item is frequent, then it can be appended to the prefix to form k+1-multi-granularity sequence pattern γ in the steps 35-41, where when a annotation of a item is 0, it only insert the item into the last element of the prefix, otherwise appends the temporal item to the prefix as an new element. steps 43-48 construct a new, smaller projection d|γ, recursively find temporal items in d|γ and yield longer multi-granularity sequence patterns until all the multi-granularity sequence patterns are found. 5 experimental results and performance analysis in this section, we provide an experimental assessment of the proposed algorithm on synthetic data sets. the purpose is to test the performance of our algorithm mg-prefixspan. we will analyze the effects of input parameters on execution times and compare the performances with those of the most efficient algorithm prefixspan [6] and i-prefixspan [11]. the first algorithm being the fastest algorithm for sequential pattern mining without time intervals [6] and the second algorithm the most effective algorithm in finding sequential patterns with user-defined time intervals, it is natural to compare our algorithm with its. 502 n. li, x. yao, d. tian table 1: parameters used in the generation of dataset |d| number of customers |c| average number of transactions of per customer |t| average number of items per transaction |s| average length of maximal potentially large sequences |i| average size of itemsets in maximal potentially large sequences |ns| number of maximal potentially large sequences |ni| number of maximal potentially large itemsets |n| number if items |tt| average length of time intervals table 2: parameter settings name |c| |t| |s| |i| |d| size (mb) c10t2s4i1 10 2 4 1 10k 1.733 c10t2s4i2 10 2 4 2 10k 1.743 c10t2s8i1 10 2 8 1 10k 1.746 c10t4s4i1 10 4 4 1 10k 2.509 c20t2s4i1 20 2 4 1 10k 3.317 5.1 evaluation environment the two algorithms are implemented by sun java language and experiments were conducted on a 1.7mhz intel pentium ibm laptop with 512mb main memory, running microsoft windows xp and borland jbiulder 9.0 as the java execution environment. detailed algorithm implementation of prefixspan and i-prefixspan is according to the algorithms described in [4,9], but with the pseudo projection turned off; mg-prefixspan is implemented as described in this paper. 5.2 synthetic dataset generation in this work we extended the ibm synthetic generator described in [1,10] to generate synthetic data sets. basically, each data-sequence is a list of transactions, where each transaction is a set of items, called itemset. however, the transaction data is extended so that the items in different itemsets are assigned different time values and that those in the same itemsets are assigned the same time values. the interval time value between successive itemsets for each customer obeys a poisson distribution with mean w. the value w is drawn repetitively from a poisson distribution with mean tt for this particular customer [11]. after that, we determine the time t for each transaction of this customer by summing the interval times before the transaction. table 1 lists the parameters used in the generation of the simulating dataset. the parameters except for the last one are the classical ones used in the previous research. the parameter tt is a new one used to generate the time for each transaction. we generate datasets by setting ns = 500, ni = 2500, n = 1000 and tt = 60. table 2 summarizes the dataset parameter settings. mining temporal sequential patterns based on multi-granularities 503 table 3: part of the exacted patte extracted patterns support(%) < (80) > 22.89 < (80) [1,3,6]day → (80) > 1.71 < (80) [1,2,5]day → (80) > 1.97 < (80) [1,3,6]day → (80) [1,2,5]day → (80) > 0.54 < (80) [2,3,6]day → (569) > 0.62 < (80) [1,2,4]day → (569) > 0.84 < (80,432) > 1.19 < (80,432) [1,2,3]day → (944) > 0.60 table 4: part of the exacted pattern extracted patterns support(%) < (80) > 22.89 < (80) → (80) > 3.03 < (80) → (80) → (80) > 0.71 < (80) → (569) > 1.39 < (80,432) > 1.19 < (80,432) → (944) > 0.62 5.3 time granularity selection in the experiment, we use multi-granularity schema: g3 = (week, day, hour), and all the timestamps in the synthetic data sets are hour, i.e. g0 = hour. in order to compare the performance of algorithm i-prefixspan to that of mg-prefixspan, all the intervals between successive items are partitioned into five intervals: {l0, l1, l2, l3, l4}, where l0 : t = 0 : 0 < t < 1, l2 : 1 ≤ t < 24, l3 : 24 ≤ t < 168, and l4 : 168 ≤ t <∞. 5.4 comparison of extracted sequential pattern quality the first test is a comparison of the quality of extracted patterns by algorithm prefixspan[6], iprefixspan[11] and mg-prefixspan. the test is designed using the data set c10t4s5i2 and all minimum support thresholds is set to 0.005(0.5%). table 3 shows part of the extracted frequent multi-granularity sequence patterns using mg-prefixspan algorithm, and tables 4 and 5 show part of the extracted frequent sequential patterns using the algorithm prefixspan and i-prefixspan, respectively. as shown in table 3, we can made the following observation: (1) once item 80 occurs, then item 80 will occur again within 1 day to 6 days, or with average time 3 days, with probability (1.71/22.89) × 100% = 7.4%; within 1 week to 5 weeks, or with average time 2 weeks, with probability (1.97/22.89) × 100% = 8.6%. (2) once item 80 occurs and item 80 occurs within 1day to 6 days, or with average time 3 days, then item 80 will occur again within 1 week to 5 weeks, or with average time 2 weeks, with probability (0.54/1.19)100% = 45.4%. 504 n. li, x. yao, d. tian 0 500 1000 1500 2000 2500 3000 0.005 0.01 0.015 0.02 su pport thre shol d r u n ti m e (i n s ec o n d s) mg-prefixspan prefixspan i-prefixspan figure 1: performance of the of the algorithms on data set c10t2s4i1. 0 500 1000 1500 2000 2500 3000 0.005 0.01 0.015 0.02 su pport thre s hol d r u n ti m e (i n s ec o n d s) mg-prefixspan prefixspan i-p refixspan figure 2: performance of the algorithms on data set c10t2s4i2. (3) once item 80 occurs, then item 569 will occur within 2 days to 6 days, or with average time 3 days, with probability (0.62/22.89) × 100% = 2.7%; within 1 week to 4 weeks, or with average time 2 weeks, with probability (0.84/22.89) × 100% = 3.7%. (4) once item 80 and 432 occur, then item 94 will occur within 1 days to 3 days, or with average time 2 days, with probability (0.60/1.19) × 100% = 50.4%; as shown in table 4, although the patterns are similar to those in table 3, the time intervals between successive itemsets are not tighter than those in table 3. thus users cannot precisely predict when items 569 and 944 occur and 80 occurs again. on the other hand, as shown in table 5, there is no item annotation information based on multi-granularities in extracted sequences. thus, users are not able to predict how long time item 569, 944 will occur and item 80 will occur again. furthermore, the pattern < (80) → (569) > also makes users unable to distinguish periods of item 80 and 569 occurrence. these results indicate that the multi-granularity sequence patterns mined by algorithm, mg-prefixspan, are more useful than those mined by algorithms, i-prefixspan, or prefixspan. 5.5 comparison of the execution time the second test of the three algorithms would compare the run times for different minimum supports. the comparison is on the five data sets shown in the table 2, where the minimum support threshold is varied from 0.5% to 2%. fig.1 to fig. 5 summarizes the results. it is clearly show that how the performance of the three algorithms changes as varying of the parameters |c|, |t|, |s| and |i|, and the differences among the three algorithms. mining temporal sequential patterns based on multi-granularities 505 0500 1000 1500 2000 2500 3000 0.005 0.01 0.015 0.02 s u pport th re sh ol d r u n ti m e (i n s ec o n d s) mg-prefixspan prefixspan i-prefixspan figure 3: performance of the algorithms on data set c10t2s8i1. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0.005 0.01 0.015 0.02 s upport th re sh ol d r u n ti m e (i n s ec o n d s) mg-p refixspan prefixspan i-p refixspan figure 4: performance of the algorithms on data set c10t4s4i1. the results in the fig. 1 fig. 3 indicate that the processing time of algorithm mg-prefixspan is at different support threshold is no significant difference. the efficiency of mg-prefixspan is little less than that of the i-prefixspan. this result matches our expectation, because the mg-prefixspan algorithm does some more complicated computes for the bounds and average interval time in different granularities than that of the i-prefixspan. on the other hand, the results in the fig. 1, fig. 2 and fig. 3 show that the run time of three algorithms at different support threshold is also no significant difference. the speed of mg-prefixspan and i-prefixspan are little less than that of prefixspan. it is correct, because the algorithm prefixspan does not some complicated computes for interval time. when we see the fig. 3, fig. 4 and fig. 5 we can find that the run time of the three algorithms is increase rapidly as the minimum support threshold values vary from small to large. it is correct, because larger number of frequent sequential patterns could be found as the minimum support threshold value became smaller. however, it is worth note that the algorithm mg-prefixspan and i-prefixspan take less time than the prefixspan algorithm and the time difference of processing become larger as the minimum support threshold declines although the mg-prefixspan and i-prefixspan are more complicated than the prefixspan. in order to find the reason, let us note the data sets. fig. 3 test is performed on the data set c10t2s8i1, where the parameter average length of maximal potentially large sequences s increased from 4(fig.1) to 8. on average, a potentially frequent sequential pattern consists of 8 transactions, which mean that although the average number of transactions in a potentially frequent sequential pattern is set to 8, the number of transactions in a sequence is still set to 10. this situation can not cause the number and length of frequent sequential pattern increase considerably, because the data set c10t2s8i1 (1.746mb) is as sparse as c10t2s4i1 (1.733mb); fig.4 on the data set c10t4s4i1, where the parameter average number 506 n. li, x. yao, d. tian 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 0.005 0.01 0.015 0.02 support threshold r u n ti m e (i n s ec on d s) mg-prefixspan prefixspan i-prefixspan figure 5: performance of the algorithms on data set c20t2s4i1. 0 1000 2000 3000 4000 5000 6000 10 15 20 25 30 database si z e (i n k se qu e n ce s) r u n ti m e (i n s ec o n d s) threshold=0.01 threshold=0.015 figure 6: scalability test of the algorithm mg-prefixspan on data set t2s4i1. of items per transaction t increased from 2 (fig.1) to 4, which mean, on average, a sequence consists of 10 transactions, each transaction composed of 4 items. it may result in the number of frequent sequential pattern increasing significantly; fig.5 on the data set c20t2s4i1, where the parameter average number of transactions of per customer c increased from 10 (fig.1) to 20, which mean, on average, a sequence consists of 20 transactions, each transaction composed of 2 items. it may result in the length of frequent sequential pattern increasing greatly. this both situations may cause the number and length of frequent sequential pattern increase considerably, because the data sets c10t4s4i1 and c20t2s4i1 are denser than the c10t2s8i1 and c10t2s4i1. when we use prefixspan to find all frequent sequential patterns, much more time would be spent in dealing with these more frequent sequential patterns although many of frequent sequential patterns may be useless. on the contrary, to the mg-prefixspan and i-prefixspan, although adding multiple time granularities or pseudo items to the traditional sequential pattern may make each of them to produce the several multiple granularities or time-interval patterns and need spend some time to does some complicated computation, many of them may not be frequent and some patterns found frequent by prefixspan may be infrequent by mg-prefixspan and i-prefixspan too. this reason reducing the numbers of frequent sequential patterns is why the mg-prefixspan and i-prefixspan are faster than the prefixspan. 5.6 related global performances the final test is the scalabilities of the mg-prefixspan algorithm. four tests are designed using the data set c10t2s4i1 and all minimum support thresholds are set to 0.01 and 0.015. in each test, test how the runtime of the mg-prefixspan algorithm scales as one parameter is increased. fig. 6 shows the result mining temporal sequential patterns based on multi-granularities 507 010002000 3000 4000 5000 6000 5 10 15 20 average number of transaction per customer r u n ti m e( in s ec on d s) threshold=0.01 threshold=0.015 figure 7: performance of the algorithms on data set t2s10i1. 01000 2000 3000 4000 5000 6000 2 3 4 5 ave rage n u mbe r of tran sacti on pe r cu stome r r u n ti m e( in s ec o n d s) threshold=0.01 threshold=0.015 figure 8: performance of the algorithms on data set c10s10i1. of scalability of the algorithm as the data set size grows from 10000 to 30000; fig.7 shows the result as the average number of transactions of per customer varies from 5 to 20; fig. 8 shows the result as the average number of items per transaction varies from 2 to 5. the results indicate that the mg-prefixspan algorithm has good scalabilities for its runtime increases linearly as each parameter varies from small to large respectively. 6 conclusions in this paper, we have proposed a novel approach for mining multi-granularities sequence patterns based on prefixspan [6], called mg-prefixspan. the multi-granularities sequence pattern not only reveals what items occur frequently together and in what order, but also the time interval that the next items occur after the preceding items. we use boundary interval and average time with multi-granularities, which are derived from the source data, rather than user-predetermined the time interval or only a typical time to annotate time interval between successive items in the patterns. the performance analysis shows that mg-prefixspan scales up linearly as the size of database, and has a good scalability with respect to length of sequence and the size of transaction. the future work along this line of research includes several aspects as follows: (1) validation on large, real databases; (2) low-level optimizing mechanism of the algorithm. acknowledgment this work was supported by the department of education of shaanxi province of china under grant 05jk137 and the natural science foundation of shaanxi province of china under grant 2005f11. 508 n. li, x. yao, d. tian bibliography [1] constantinescu, z, marinoiu, c, vladoiu, m, “driving style analysis using data mining techniques,” international journal of computers communications and control, vol.5, no.5, pp. 654663, dec 2010. [2] andonie, r, “extreme data mining: inference from small datasets,” international journal of computers communications and control, vol.5, no.3, pp. 280-291, sep 2010. [3] r. agrawal and r. srikant, “mining sequential patterns,” proc. of the 7th international conference on data engineering (icde’95), pp. 3-14, march, 1995. [4] r. srikant and r. agrawal, “mining sequential patterns: generalizations and performance improvements,” proc. of the 5th international conference on extending database technology, pp. 3-17, march, 1996. [5] m. zaki, “an efficient algorithm for mining frequent sequences,” machine learning, vol. 40, pp. 31-60, 2000. [6] j. pei, j. han and h. pinto et al, “mining sequential pattern-growth: the prefixspan approach,” ieee transactions on knowledge and engineering, vol.16, no.11, pp. 1424-1440, 2004. [7] h. manila, h. toivonen, and a.i. verkamo, “discovery of frequent episodes in event sequences,” data mining and knowledge discovery, vol. 1, no. 3, pp. 256-289, 1997. [8] m.n. garofalakis, r. rastogi and k. shim, “spirit: sequential pattern mining with regular expression constraints,” proc. of the 25th international conference on very large data bases (vldb’99), pp. 223-234, september, 1999. [9] j. pei, j. han and w. wang, “constraint-based sequential pattern mining: the pattern-growth methods,” journal of intelligent information systems, volume 28, issue 2, pp. 133-160, april, 2007. [10] m. yoshida et al. “mining sequential patterns including time intervals”, proc. of spie conf.dmkd, pp. 213-220, april, 2000. [11] y.-l. chen, m.-c. chiang and m.-t. ko, “discovering time-interval sequential patterns in sequence databases,” expert system with applications, volume 25, issue3, pp. 343-354, october, 2003. [12] r. algawal and r. srikant, “fast algorithm for mining association rules in large databases.,” proc. of the 20th international conference on very large data bases (vldb’94), pp. 487-499, september 1994. [13] y. hirate, h. yamana, “generalized pattern mining with item intervals,” journal of computers, vol.1, no3, pp. 51-60, june, 2006. [14] f. giannotti, m. nanni, and d. pedreschi. “efficient mining of temporally annotated sequences,” proc. of the 6th siam international conference on data mining, pp. 346-357, april, 2006. [15] c. bettini, x.s. wang and s. jajodia et al, “discovering temporal relationships with multiple granularities in time sequences,” ieee transactions on knowledge and data engineering, vol. 10 (2), pp. 222-237, 1998. international journal of computers communications & control issn 1841-9836, 12(5), 715-727, october 2017. fast motion estimation algorithm using hybrid search patterns for video streaming application r. vani, n. ushabhanu, m. sangeetha vani rajamanickam* meenakshi college of engineering, chennai, india *corresponding author: ecehod@mce.edu.in ushabhanu nageswaran valliammai engineering college, srm nagar, chennai, india ushabhanun.ece@valliammai.co.in sangeetha marikkannan bharath university, chennai, india abstract: the objective of the paper is to develop new block matching motion estimation (me) algorithm using hybrid search patterns along the direction of best match. the search efficiency for sequences with fast motions and high resolutions is improved by proposing new cross diagonal-hexagon search (ncdhs) algorithm which involves a novel multi half-hexagon grid global search pattern and a cross diagonal-hexagon local search pattern. the new search pattern enables the proposed algorithm to perform better search using 9.068 search points on an average, to obtain optimal motion vector with slight improvement in quality. this inturn reduces me time upto 50.11%, 47.12%, 32.99% and 43.28% on average when compared to the existing diamond search (ds), hexagon search (hs), new cross hexagon search (nhexs) and enhanced diamond search (eds) algorithms respectively. the novelty of the algorithm is further achieved by applying the algorithm proposed for live streaming application. the ncdhs algorithm is run on two matlab sessions on the same computer by establishing the connection using transmission control protocol (tcp) /internet protocol (ip) network. the me time obtained is 14.5986 seconds for a block size 16×16, is less when compared to existing algorithms and that makes the ncdhs algorithm suitable for real time streaming application. keywords: hybrid search patterns, motion estimation time, search points, peak signal-to-noise ratio (psnr). 1 introduction the h.264/advanced video coding (avc) standard was developed by joint video team (jvt) in the year 2003 [6]. the h.264 standard adopts the variable block size motion estimation (vbsme) feature which enables the standard to find its importance in various applications such as high definition digital video disc (hd-dvd), digital video broadcasting and mobile applications as discussed by chen et al. [2]. the variable block size motion estimation (vbsme) feature plays a vital role in the h.264 video coding standard. the input video sequence is processed as frames and each frame is divided into macroblocks. as the human visual system (hvs) is more sensitive to luma than the chroma components, the h.264 standard uses 4:2:0 sampling format as mentioned by richardson [8], in which each macroblock consists of one block of 16 × 16 pixels for representing the luminance component and two blocks of 8 × 8 pixels for representing the chroma components as shown in fig. 1. the 16×16 block known as macroblock (mb) is the basic building block of the video coding standard which is subdivided into 16×8 or 8×16 or 8×8 sub-blocks. an 8×8 sub-macroblock copyright © 2006-2017 by ccc publications 716 r. vani, n. ushabhanu, m. sangeetha figure 1: macroblock (4:2:0) can be further divided into 4×4 or 8×4 or 4×8 blocks. according to wen et al. [9], for objects with different motions and directions, the vbsme feature has improved the coding performance in h.264 standard than the fbsme method adopted in the earlier standards. this feature has enabled the motion estimation algorithms in the h.264 standard to find its applications in video conferencing, video streaming, surveillance, video telephony, multimedia networks, etc as mentioned in li & yang [7]. 1.1 concepts involved in block matching algorithms in the encoder design, the me process occupies about 53% of the time when one reference frame is used and about 70.20% of the total encoding time, when four reference frames are used, as mentioned in huayi et al. [5]. it increases the overall computational complexity in the encoder design. the probability of mv distribution has to be analyzed to obtain better match accuracy with less number of search points in the me algorithms. in a video sequence, more than 70% of the blocks are considered as either stationary at the location (0,0) or quasi stationary at the location (±1,0) or (0,±1). as discussed by zhao & xu [10], in the 5×5 search area as shown in fig. 2, the higher possibility of mv occurs at the centre position a, which is about 45.49%. figure 2: probability of mv distribution in the 5 × 5 search area at a pixel distance of ±1 from the centre, the mv is distributed for about 23.52% which is indicated as position b. at a pixel distance of ±2 from the centre, the possibility of distribution is about 5.73% indicated by position c. at the positions d, e and f, the possibility of mv distribution is very less and the sum of probability is about 7.051%. fast motion estimation algorithm using hybrid search patterns for video streaming application 717 1.2 limitations of the existing algorithms the diamond search (ds) algorithm proposed by zhu & ma [12] starts searching with large diamond search pattern (ldsp) and this process is repeated a number of times until the best match is obtained. then final refinement is done using small diamond search pattern (sdsp) which is applied only once. zhu et al. [11] proposed hexagon search (hs) algorithm that achieves better speed improvement than the existing ds algorithm with similar distortion performance. it is used for implementing the video codec standards such as h.261, mpeg-1 and mpeg2. cheung & po [3] proposed cross-diamond-hexagonal search (cdhs) algorithm which uses cross shaped pattern followed by hexagon search pattern. it performs faster searching than ds algorithm. the new cross hexagon search (nchexs) hybrid algorithm proposed by belloulta et al. [1] is developed for h.264 standard. it utilizes hybrid search patterns such as the cross search and the hexagon search patterns for search path analysis. the performance of motion estimation algorithms are influenced by the number of search points for many video based applications. the traditional me block matching algorithms used in the earlier standards are based on fixed block size and are not able to incorporate different motions in the video frames. for real time video sequences with different motions, the h.264 standard adopts variable block size for video processing to address the complexity problems. the algorithms mentioned in the literature adopt fixed and hybrid search patterns which have reduced the complexity in me time. it is necessary to develop the motion estimation algorithm based on motion vector characteristics with quality improvement. the objective of the paper is to propose new efficient me algorithm for improving the search efficiency using hybrid search patterns and to have less time consumption. the rest of the section is organised as follows: section 2 explains about the proposed algorithm and its search path analysis. in section 3, the performance analysis of the proposed and existing algorithm is done using matlab software. section 5 illustrates the live streaming of the proposed algorithm. finally, section 6 concludes the future possibilities of the work. 2 proposed ncdhs algorithm the ncdhs algorithm involves both coarse grain and fine grain search to find the best matching between the successive frames. initially, we consider the motion vector point (mvp) to be at the position (0,0). the first step is designed using unsymmetrical cross pattern with step size ±1, and followed by modified asymmetric hexagon with step size ±1 and ±2. the last step in which the fine grain is done which utilises either a cross or diagonal hexagon depends on the minimum cost with step size ±1 from centre point. the steps for proposed algorithm are given below: step 1: the coarse grain search starts with the unsymmetrical cross search pattern with pixel distance ±1 as shown in fig. 3. the minimum cost point is obtained using sad computation. if the minimum cost is found to be at centre point, then the search stops else the search continues to step 2. step 2: the half hexagon search is performed with pixel distance ±1 and ±2 as shown in fig. 4 as a second search with minimum cost point from step 1 acting as a centre point. this reduces the number of search points as the grid chooses the minimum cost either in the upper half or lower half of the hexagon pattern. step 3: switch the search from the coarse to the fine resolution inner search to find the best motion vector. 718 r. vani, n. ushabhanu, m. sangeetha figure 3: first search (i) a small cross pattern with best search point obtained from global search is formed as a centre. if the current minimum sad is at centre, the search stops. else it moves to next step 2. (ii) the same cross pattern is repeated based on the minimum sad position. if the current and the previous minimum sad positions are same, then the selected search point is the best match point else move to step 3. (iii) diagonal-hexagon search pattern is formed with previous minimum sad point as centre. the algorithm steps are iterated until the final best vector is obtained as shown in fig. 5. the proposed ncdhs algorithm adopts the pseudo code as shown in algorithm 1 to find the minimum cost. 2.1 theoretical search point analysis based on the assumption, let the best match block occur at the position (3,0) as shown in fig. 6. initially the search starts with unsymmetrical cross search pattern with seven check points. if the mbd point is at the location (1, 0), the algorithm searches for minimum motion vector using half hexagon grid with two check points (2,-1), (3,0) in addition with the pixels at the location (1,0), (0,-1) and (-1,0) that is reused from the previous search. with the minimum mbd point found at location (3,0) from second search, the ncdhs algorithm finally searches for four check points around it, to form a small cross pattern. the search point located at (3,0) is found by using 12 search points by adopting ncdhs algorithm. 3 performance analysis to evaluate the performance of the ncdhs algorithm in terms of me time, search points and psnr, simulation is done using matlab for the proposed and the existing algorithms at a frame rate of 30 fps. the test sequences used are "foreman" and "mobile" with a resolution fast motion estimation algorithm using hybrid search patterns for video streaming application 719 figure 4: second search of 352 × 288, "bqmall" and "mobisode" with a resolution of 832 × 480, "people on street" and "traffic" with a resolution of 2560 × 1600. the block size is chosen as 16 × 16 with sum of absolute differences (sad) as the distortion metric. the sad metric at the search location (m,n) is calculated as given in eq. (1). sad(m,n) = n−1∑ x=0 n−1∑ y=0 |i1(x,y) − i2(x + m,y + n)| (1) where n × n denotes block size, i1(x,y) indicates the pixel intensity at the location (x,y) in the current frame, i2(x,y) indicates the pixel intensity at the location (x,y) in the reference frame and (m,n) denotes the displacement vector. the lowest sad estimates the best position of prediction occurred within the search window. 3.1 motion estimation time table 1 summarises the simulation results obtained for the me time taken by the proposed ncdhs and the existing algorithms. for the mobile sequence, the me time obtained for proposed ncdhs algorithm requires 10.161 seconds and the me time obtained for the existing ds algorithm proposed by zhu & ma [12], hs algorithm proposed by zhu et al. [11] and nchexs algorithm proposed by belloulata et al. [1] are 19.881, 19.448 and 13.321 seconds respectively. the results show that the proposed ncdhs algorithm has reduced the me time when compared to the other existing algorithms. 3.2 search points the average search points for each block in a frame are calculated for different sequence in encoding 30 frames. table 2 shows that the proposed ncdhs algorithm consumes the smallest number of search points compared to other algorithms. the results show that the average number of search points in ncdhs algorithm is 9.068. in order to demonstrate the performance of the proposed algorithm, the fig. 7 gives the result of the average number of computations per block for different video sequences as the frame number increases. for mobisode sequence, the search 720 r. vani, n. ushabhanu, m. sangeetha figure 5: search path analysis of ncdhs algorithm table 1: simulation results showing me time of the ncdhs and the existing algorithms test sequences me time obtained for existing and proposed algorithms (in seconds) ds [12] hs [11] nchexs [1] proposed ncdhs foreman 19.387 19.86 15.233 9.48 mobile 19.881 19.448 13.321 10.161 bqmall 61.207 54.765 48.776 31.506 mobisode 77.945 69.422 57.707 37.461 video codec standards h.261, h.263 h.261, mpeg-1,2 h.264 points obtained for ncdhs algorithm is 8.174 which is less when compared to the existing algorithms. it clearly manifests the superiority of the proposed ncdhs algorithm to the other methods in terms of number of search points used. 4 me time reduction and psnr improvement the ∆me time and ∆psnr denotes the cpu time reduction in me process and improvement of peak signal to noise ratio (psnr) of the encoded image respectively. they are defined as in eqs. (2)–(4): ∆me time = metproposed −metoriginal (2) %∆me time = (metoriginal −metproposed) metoriginal × 100 (3) ∆psnr = psnrproposed −psnroriginal (4) fast motion estimation algorithm using hybrid search patterns for video streaming application 721 algorithm 1 pseudocode of ncdhs algorithm input: initialize a large cross pattern such that sph = 2 ∗ (spv) output to find minimum cost 1: begin 2: if (mvp = minsad) then 3: mv = mvp 4: else 5: with p = minsad, select large half hexagon with range {p + 2, p−2} calculate the overall minsad (minsadcg) 6: end if 7: with minsadcg, initialize fine-grain search with small cross with 5 points 8: if (minsadcurrent = minsadprev) then 9: mv = minsad(x,y) 10: else if (minsad is in the same row or column) then 11: cross slides to next position 12: else 13: with minsad(x,y), a diagonal-hexagon pattern is formed 14: if (minsadprev = minsadcurrent) then 15: mv = minsad(x,y) 16: end if 17: end if 18: end table 2: average number of search points per block sequence average number of search points (computations) for each block ds [12] hs [11] nhexs [1] eds [4] proposed foreman 16.839 12.716 12.084 12.757 10.48 mobile 14.323 12.73 11.341 11.634 9.396 bqmall 15.519 11.439 11.204 11.307 8.225 mobisode 18.793 13.31 12.982 13.86 8.174 average computations 16.368 12.548 11.902 12.3895 9.068 where metproposed and psnrproposed denotes the me time and psnr of the proposed algorithm. the metoriginal and psnroriginal represents the me time and psnr of ds, nhexs, hs and eds algorithms. table 3 shows the improvement in psnr and the average reduction of me time for proposed algorithm when compared to existing ds, hs and nhexs search patterns. it is clear that the ncdhs algorithm reduces an average of 50.11% of time for motion estimation compared to diamond search (ds) consequently 47.12% of motion estimation time compared to hexagon search (hs), 32.99% of motion estimation time compared to new hexagon search (nhexs) and 43.28% of motion estimation time compared to enhanced diamond search (eds). this indicates that the average reduction in me time is greatly achieved by the proposed algorithm with slight improvement in psnr, when compared to the excising algorithms. the comparison of the existing and the proposed algorithms is shown in fig. 8 highlighting 30 frames of different video sequences. when compared to the existing algorithms, the proposed scheme can reduce the me time of the encoded image, as the frame number increases. 722 r. vani, n. ushabhanu, m. sangeetha figure 6: ncdhs algorithm search point analysis table 3: simulation results showing psnr improvement and average reduction of me time sequence psnr improvement (db) (∆psnr) ∆me time (in%) proposed vs ds proposed vs hs proposed vs nhexs proposed vs eds proposed vs ds proposed vs hs proposed vs nhexs proposed vs eds foreman 0.57 0.11 0.06 0.15 -51.10 -52.26 -37.76 -51.83 mobile 0.64 0.34 0.1 0.33 -48.89 -47.75 -23.72 -46.99 bqmall 0.62 0.24 0.02 0.05 -48.52 -42.47 -35.40 -39.07 mobisode 0.92 0.84 0.24 0.59 -51.93 -46.03 -35.08 -35.23 average % time savings of the ncdhs algorithm -50.11 -47.12 -32.99 -43.28 5 video streaming application based on ncdhs algorithm the block diagram of video streaming system for the proposed ncdhs algorithm is shown in fig. 9. the ncdhs server and the ncdhs client run on two matlab sessions on the same computer as shown in fig. 10. after establishing the connection using transmission control protocol (tcp) /internet protocol (ip) network, the server and client becomes ready for streaming. at the server end, the input video frames of frame size 352×288 are captured by the camera and is displayed on the pc screen. the proposed ncdhs algorithm starts estimating the motion between the frames by computing sad values. the motion vectors are transmitted to the client over the network. the reconstruction of the video frames is done using ncdhs algorithm and the video frames are displayed on the client screen. fast motion estimation algorithm using hybrid search patterns for video streaming application 723 figure 7: performance comparison of ds, hs, nhexs, eds and the proposed ncdhs algorithm for (a) foreman sequence (b) mobile sequence (c) mobisode sequence (d) bqmall sequence in terms of average number of search points 724 r. vani, n. ushabhanu, m. sangeetha (a) foreman sequence (b) mobile sequence (c) mobisode (d) bqmall figure 8: performance comparison of ds, hs, nhexs and the proposed ncdhs for (a) foreman sequence (b) mobile sequence (c) mobisode sequence (d) bqmall sequence in terms of time reduction in me process figure 9: video streaming using ncdhs algorithm fast motion estimation algorithm using hybrid search patterns for video streaming application 725 figure 10: snapshot of ncdhs server and client sessions using ncdhs figure 11: sample of live input and the reconstructed output in the server and client using ncdhs algorithm 726 r. vani, n. ushabhanu, m. sangeetha table 4: simulation results showing me time of the ncdhs and the existing algorithms video codec standard h.264 algorithms nchexs [1] eds [4] ncdhs me time (in seconds) 17.18488 19.1599 14.5986 the sample video input and the reconstructed output is as shown in fig. 11. for a frame size 352×288 and block size 16×16, the live video frames are captured using laptop camera. for the first thirty frames, the motion estimation time taken for the proposed ncdhs and the existing algorithms for block size 16 × 16 is as shown in table 4. for the live video sequence, the me time obtained for ncdhs algorithm is 14.5986 seconds, for the nchexs algorithm introduced by belloulata et al. [1] is 19.1599 seconds and for the eds algorithm discussed by devi et al. [4] is 17.18488 seconds. the simulation results show that the me time obtained by the ncdhs algorithm is less when compared to the existing algorithms. the speed of the algorithm can be further improved by reducing the motion estimation time when implemented on dedicated hardware as suitable for real time streaming applications. 6 conclusion the proposed new cross diagonal-hexagon search (ncdhs) algorithm has adopted novel half-hexagon grid global search pattern and a cross diagonal-hexagon local search pattern. the hybrid global and local search pattern utilises less search points to obtain optimal motion vector and this inturn reduces the motion estimation time, when tested on live sequences. the search points reduction has reduced the computational complexity when compared to the existing algorithms with little improvement in quality. the new block matching motion estimation algorithm using hybrid search patterns achieves less me time when compared to the existing algorithms, which makes it suitable for live streaming application. the algorithm proposed can be extended to high efficiency video coding (hevc)/h.265 standard by considering the reduction in motion estimation time and by extending the block size to meet the requirements of the standard. bibliography [1] belloulata k., zhu s., tian j., shen x. (2011); a novel cross hexagon search algorithm for fast block motion estimation, international workshop on systems, signal processing and their applications, 1-4, 2011. [2] chen t.c., lian c., chen l.g. (2006); hardware architecture design of an h.264/avc video codec, ieee asia and south pacific conference on design automation, 8, 2006. [3] cheung c.h., po l.m. (2005); novel cross-diamond-hexagonal search algorithms for fast block motion estimation, ieee transactions on multimedia, 7(1), 16-22, 2005. [4] devi s.r., rangarajan p., perinbam j.r.p., paul r. (2013); vlsi implementation of high performance optimized architecture for video coding standards, acta polytechnica hungarica, 10(6), 237-249, 2013. [5] huayi l., lini m., hai l. (2010); analysis and optimization of the umhexagons algorithm in h. 264 based on simd, second international conference on communication systems, networks and applications, 1, 239-244, 2010. fast motion estimation algorithm using hybrid search patterns for video streaming application 727 [6] itu-t rec.h.264, iso/iec 14496-10 avc. (2003); joint video team draft itu-t recommendation and final draft international standard of joint video specification, 2003. [7] li z., yang q. (2012); a fast adaptive motion estimation algorithm, international conference on computer science and electronics engineering, 3, 656-660, 2012. [8] richardson i.e. (2011); the h. 264 advanced video compression standard, john wiley & sons, 2011. [9] wen x., au o.c., xu j., fang l., cha r., li j. (2011); novel rd optimized vbsme with matching highly data re-usable hardware architecture, ieee transactions on circuits and systems for video technology, 21(2), 206-219, 2011. [10] zhao w., xu s. (2012); research and optimization of umhexagons algorithm based on h.264, ieee 4th international conference on multimedia information networking and security, 600-603, 2012. [11] zhu c., lin x., chau l.p., lim k.p., ang h.a., ong c.y. (2001); a novel hexagonbased search algorithm for fast block motion estimation, ieee international conference on acoustics, speech, and signal processing, 3, 1593-1596, 2001. [12] zhu s., ma k.k. (2000); a new diamond search algorithm for fast block-matching motion estimation, ieee transactions on image processing, 9(2), 287-290, 2000. international journal of computers communications & control issn 1841-9836, 9(6):694-702, december, 2014. detecting emotions in comments on forums d. gîfu, m. cioca daniela gîfu "alexandru ioan cuza" university of iaşi 16 general berthelot st., iaşi, 700483, românia daniela.gifu@info.uaic.ro marius cioca "lucian blaga" university of sibiu 10, victoriei bd., sibiu, 550024, românia marius.cioca@ulbsibiu.ro abstract: the paper presents one of the most important issues in natural language processing (nlp), emotion identification and classification to implement a computational technology based on existing resources, open-source or freely available for research purposes. furthermore, we are interested to use it for establishing gold standards in sentiment analysis area, such as sentiwordnet. in this sense, we propose to recognize and classify the emotions (sentiments) of the public consumer from the written texts which appeared on the various forums. we analyse the writing style which refers to how consumers construct sentences together when they write comments to indicate their passion about an entity (persons, brand, location, etc.). we present in this paper a method for integrating romanian lexical resources from emotional perspective, in developing, which can be used in sentiment analysis. this study is intend to help direct beneficiaries (public consumer, marketing managers, pr firms, politicians, investors), but, also, specialists and researchers in the field of natural language processing, linguists, psychologists, sociologists, economists, etc. keywords: sentiment analysis, language resources, emotions levels, semantic classes, forums. 1 introduction in our context, emotion in writing refers to how public consumers express a personal opinion of their experience about entities (products, persons, tourism objectives, etc.). when we say public consumer, actually, we say any commentator who is interested in a range of information about a particular entity.the option for such a topic, known as sentiment analysis (sa) or opinion mining1, encountered in texts circulated on different forums, and comes from the need to clarify descriptive consumer behavior, affected by the amount of promotional messages, regardless of their nature and purpose. at the present time, sentiment analysis is one of the most studied natural language processing (nlp) issues. the hypothesis of this paper is that by observing the emotional orientation of the commentators over time (visible in writing style) on forums can help us to build a database with information on topics, services, products, etc. for the public interest, which can serve to implement a nlp tool, useful to predict potential consumer needs. the paper is structured in five sections. after a brief introduction about the importance of this study, the section 2 mentions some important works focused on sa. the section 3 describes 1opinion mining originates from the information retrieval (ir) community, and aims at extracting and processing users’ opinions about entities (products, movies, etc.). sentiment analysis was initially formulated as the nlp task of retrieval of sentiments expressed in texts. looking closely, these two issues are similar in their own essence and fall under the area of subjectivity analysis. copyright © 2006-2014 by ccc publications detecting emotions in comments on forums 695 four units of sentiment analysis some of the most coomonly used in sa, and section 4 describes the our tool functionality. the last section highlights conclusions and mentions the future work, one of the projects of nlp-group@uaic-fii. 2 state of the art nowadays, forum becomes a long-term instrument that can consolidate the public sphere, habermas’s concept [9] and civil society. in opposite to the instrumental view of liberalization of the internet, the new dimension can be classified as environmental. the ubiquity of forums affects the marketing mechanisms to respond to the challenges imposed by it. if the landscape of communication becomes denser, more complex and more participative, then the network population gets increased access to information, achieving multiple opportunities by engaging in public speech and putting in motion collective actions. but, a problem appears. more information, more opinions reflected mostly in writing style. in fact, any difference in writing reflects the heterogeneity in reviewers culture, education, occupation and so on. this heterogeneity can be quantified in sentiments. the sentiment is the overall emotion towards the subject matter expressed by the reviewer. in general terms, sa consists of extracting opinions from text. it is assimilated as subjectivity analysis [2] or evaluating affection [1]. sa defines the processing search results from an article, generating a list of attributes product (quality, characteristics, etc.) and aggregating opinions for each of them (e.g. poorly, good). moreover, sa has been interpreted as including various types of analysis and evaluation [14], [15], [17], [18]. another important dimension of sa is researching objectivity in a text, finally resulting a text classification into two classes objective and subjective -, frequently more difficult to undertake than for a polarity one [16]. in 2001, sentiment analysis was the subject of two researches by das and chen [3], and tong [1], concerned on the opinions on the market sales. out attention is also take up by the classification of the degree of positivity of a text (document, sentence/clause, etc.), consisting in opinion words (e.g. angry, happy). for instance, in elections, we established two classes, positive and negative, each of them with other three subclasses for determining the intensity of sentiment [7]. moreover, in the sentiment analysis area there are approaches that consider, also, the neutral class (value 0), assigning words with one value from -5 to +5, with two classes more than the first author [8]. this paper describes a method with a shorter scale of values, from -1 to +1, as the authors are interested to discover the sentiment extracted from their comments. 3 units of sentiment analysis sa offers organizations the possibility to monitor opinions about products/ services and their reputation (e.g. measuring feedback with statistical software packages sas statistical analysis system, spss statistical package for the social sciences or superior performing statistical software), on various forums platforms in real time and to act accordingly. we describe below four lexical units for sa. 3.1. document as the unit of analysis it is the simplest form of sa and assumes that the document contains an opinion on one main message expressed by the commentator. we will stop at two approaches of sentiment analysis from the document. a) supervised the document must be classified in a finite set of classes, the training data are assigned to each class. this is for the simple case, when there are two classes: positive and 696 d. gîfu, m. cioca negative. also, a neutral class can be added or a numeric scale can be considered from which the document has to be reported (for instance, sentiwordnet). esuli and sebastiani [6] reports three sentiment scores: positivity, negativity and objectivity. the system learns a classification model based on the training data, using an algorithm of classification, such as svm (support vector machines) or knn (k-nearest neighbors). then, this classification is used for mapping new documents in their different sentiment classes. good precision is achieved even when each document is represented as a bag of words [13]. b) unsupervised the document is based on determining the semantic orientation (so) of specific phrases. if the average so of these phrases is above a predefined threshold, the document is classified as positive. otherwise, it is considered negative. for instance, a set of predefined part-of-speech (pos) models can be used to select those sentences [21] approach taken into consideration in this study or to create an opinion lexicon structured in words and syntagmas used by the first author since 2009. 3.2. sentence as the unit of analysis for a more refined analysis of opinions about an entity (organization, product, political actor, etc.) we must move to the sentence level. it is assumed that there is only one opinion (sentiment) in each sentence. to prove it, each sentence is splitted in clauses (a fragment with a predicative verb) and every clause contains only one opinion which we classified it in subjective or objective. only the subjective clauses will be analyzed. for instance, the approach is based on minimal reductions [19], as the premise is that the neighboring clauses should have the same subjective classification. then the sentences can be classified as either positive or negative. 3.3. comparative sentiment analysis in many cases, users do not offer a direct opinion about a product, preferring instead comparable opinions such as: dacia logan arată mult mai bine decât dacia solenza2. in this case, the purpose of the sentiment analysis system is to identify opinions of the sentence containing the comparative views, as well as to extract there from the preferred entity. authors like jindal and liu [12] describe this analytical method. using a relatively small number of words as comparative adverbial adjectives mai mult, mai puţin, uşoare3, superlative adjectives and adverbs mai, cel puţin, cele mai bune4, additional clauses favoare, mare, preferă, decât, superioară, inferior, numărul unu, împotriva5, we can cover 98 % of the comparative opinions. for these words/groups of words which frequently appear in texts, but with low precision, a classifier6 can be used to filter phrases that do not contain comparative views. ding, liu and zhang [4] present a simple algorithm for identifying preferred entities relating to the type of comparisons used and the presence of negation. 3.4. sentiment lexicon as we have seen so far, the lexicon is the most important resource for the majority of the sentiment analysis techniques. there are three options in order to create a lexicon of sentiments: a) manual approaches, when researchers create a manual lexicon, consisting of a set of words selected from explanatory dictionaries that will be subsequently extended by using existing lexical resources (synonyms and antonyms for enrichment). we have already mentioned wordnet. this process requires a laborious effort, especially that each domain needs its own lexicon. a handy algorithm is proposed by kamps, j., marx, m., mokken, r.j. and de rijke, m. (2004). 2en. dacia logan looks much better than dacia solenza. 3en. more, less, easy. 4en. more, at least, the best, etc. 5en. favour, high, prefer, rather than, superior, inferior, the number one, against. 6for example, naive bayes classifier, a statistical method for forms classification and recognition, where each document represents a collection of words and word order is considered irrelevant. detecting emotions in comments on forums 697 b) corpus-based approaches, in which a set of words/phrases extracted from a relatively small corpus is extended by using a large corpus of documents of a single domain. the main disadvantage of any dictionary-based algorithm (a) is that the acquired lexicon is too general and therefore does not capture the specific features of a particular area. advanced approaches based a lexicon are reported in dragut et al. [5]. if we want to create a specific sentiment lexicon, we have to use a corpus-based algorithm. a classical work in this area [10] highlights the concept of sentiments consistency allowing the identification of complex polar adjectives. in other words, a set of linguistic connectors şi, sau, nici, fie, sau7 has been used to find the adjectives that are connected to the adjectives with well-known polarity. for example: bărbat puternic şi armonios8. if we admit that puternic is a positive word, we can assume that the word armonios is also positive thanks to the use of the connector şi. 4 the tool description this version of our tool9 is able to detect and to explain the appreciations about some entities (persons, products, brands, etc.). this tool is based on information like labeling of parts of speech (e.g. the xml example), extracting of interest nominal groups, automatic extracting of entities and anaphoric connections. <?xml version="1.0" encoding="utf-8" standalone="no"?> <document> <p id="1"> <s id="1"> <w extra="notindict" id="11.1" lemma="" msd="vmip3s" mood="indicative" number="singular" pos="verb" person="third" tense="present" type="predicative" offset="0"></w> <np headid="11.2" id="0" ref="0"> <w case="direct" gender="masculine" id="11.2" lemma="nimic" msd="pz3msr" number="singular" pos="pronoun" person="third" type="negative" offset="1">nimic</w> <w id="11.3" lemma="mai" msd="rg" pos="adverb" offset="7">mai</w> <w case="direct" definiteness="no" gender="masculine" id="11.4" lemma="odios" msd="afpmsrn" number="singular" pos="adjective" offset="11">odios</w> <w id="11.5" lemma="," msd="comma" pos="comma" offset="16">,</w> <w id="11.6" lemma="mai" msd="rg" pos="adverb" offset="18">mai</w> <w id="11.7" lemma="oribil" msd="rg" pos="adverb" offset="22">oribil</w> <w case="direct" definiteness="no" extra="notindict" gender="masculine" id="11.8" lemma="decat" msd="afpmsrn" number="singular" pos="adjective" offset="29">decât</w> </np> <np headid="11.9" id="1" ref="1"> <w case="direct" definiteness="yes" gender="masculine" id="11.9" lemma="pantof" msd="ncmpry" number="plural" pos="noun" type="common" offset="35">pantofii</w> <np headid="11.10" id="2" ref="2"> <w case="direct" definiteness="no" gender="masculine" id="11.10" lemma="sport" 7en. and, or, not, either. 8en strong and harmonious man. 9the version previous of this tool, called eat (emotional analysis tool), is still in testing phase. 698 d. gîfu, m. cioca msd="ncmsrn" number="singular" pos="noun" type="common" offset="44">sport</w> <w id="11.11" lemma="cu" msd="sp" pos="adposition" offset="50">cu</w> <np headid="11.12" id="3" ref="3"> <w case="direct" definiteness="yes" gender="feminine" id="11.12" lemma="platform" msd="ncfsry" number="singular" pos="noun" type="common" offset="53">platforma</w> </np> </np> </np> </document> moreover it was developed an important ontology of entities, categories and values. in figure 1 we have the interface of our tool. we describe briefly work methodology: figure 1: the interface of the computational tool 1. a corpus of texts (50 texts) is manually annotated using palinka10, in order to build triplets of the form: <entitate><categorie><valoare>. 2. the text is preprocessed using uaic romanian part of speech tagger11 [20]. this tagger combines a statistical model to one based on rules. the morphological dictionary was largely extracted from dexonline and contains 1.25 milion distinct words. the result is an xml file, each word has been tockenized and annotated according to the pos that it represents. 3. noun phrases are detected and annotated with np-chunker12 [20]. this chunker is used in 10http://clg.wlv.ac.uk/trac/palinka/ 11pos tagger has a precision of 96,6%8, considered on the corrected version of the novel "1984" (george orwell).(http://instrumente.infoiasi.ro/webposro/). 12chunker receives as input the tokenized text, in xml, formed by suitable groups in text, and the output is another xml file where each nominal interest group will be annotated xml with np label (http://instrumente.infoiasi.ro/webposro/). detecting emotions in comments on forums 699 many applications to resolve the ambiguities or to extract information. for example, the newest work studies based on machine translation use texts in two languages (parallel corpora) to derive the appropiate transfer models. 4. proper names of entities are automatically extracted using a named entity recognizer techonology gate13 open source (annie)14. 5. anaphoric links (especially, pronouns) are extracted from the text using rare (robust anaphora resolution engine implemented by eugen ignat [11].). this process makes appreciations that the text expresses about those entities (coreferences) to be aggregated to the same entity (reference). 6. entities, categories and values from the ontologies that have been already created are recognized in the text using ner (named entity recognition) which extracted the entities automatically. ner recognizes entities such as persons, organizations or geographic locations, receiving as input a natural language text and the output is a text file which contains entities as a string that uses separators to delimit named entities. 7. a set of rules is written for the recognition of values and the connections such as <entity><category><value> are established. 8. graphical inteface reveals the extracted information and global scores. of the recorded, our tool is able to detect and explain qualitative appreciations about entities. in figure 2 is profiled the architecture of this software as follows: building an anthology of entities, categories and values, useful to obtain a correct and complete result; preprocessing text, meaning annotation, splitting text into entities (words, symbols or tokens); noun phrase chuncking (np-chunk), meaning splitting text into sequences of syntactically correlated words (nominal groups); recovering anaphoric connections, important not to lose any reference to a particular entity, using rare. extracting entities, using ner module. it receives a file .txt (input). the output file contains only the entities mentioned in the analyzed text. for instance: " vodafone românia oferă cea mai bună conectivitate pentru serviciile de date dintre toate reţelele mobile gsm / umts / cdma din românia". the output file contains the following entities: vodafone, românia, vodafone românia, gsm, umts, cdma. if an entity appears more than once, it will be found only once in the output file. as an exemplification, here is a part of the xml output-file: <entity type="company">vodafone românia</entity> <category>conectivitate pentru serviciile de date</category> <value ="1">bună</value> recognizing categories, values and relationships with entities. considering the resulting files, once the previous phases have been completed, it will automatically extract the categories, values and relationships with entities using a set of rules (regular expression). these regular expressions use parentheses (round, square brackets) that form rules for constructing words. the most frequent use of regular expressions consists in recognizing if a string contains or not words or sub-string, that can be formed by that regular expression. for instance: the string p[oa]t can be interpreted as pot and pat. 13http://gate.ac.uk/ 14http://services.gate.ac.uk/annie/ 700 d. gîfu, m. cioca figure 2: the architecture of the computational software basically, the tool completes the following steps: it identifies opinion words and phrases; it assigns to every positive or negative word a value (+1) for the positive one and (-1) for the negative one; the words which depend on context get also a value (0). for instance: dacia logan este mai fiabilă decât orice opel . <entity type="brand">dacia logan</entity> <category>capacitatea sistemelor tehnice de a funcţiona </category> <value ="1">fiabilă</value> 5 conclusions and future work this paper presents an automatic method able to detect and explain opinions on certain entities (peoples, companies, products, etc.) identified in a text, regardless of its nature (advertising, political, journalistic, etc.) based on a lexicon of opinions resulted from manual annotation (presented in other papers) of an initial corpus (consisting of opinion words and syntagmas). moreover, in addition to this lexicon, we focused on the semantic role of negations and pragmatic connectors like "dar" ("but"). this application seeks to support the development of a complex lexical resource, necessary to interpret qualitative assessments found in any text. we are convinced that this analyze manner may be an important support for marketing managers, pr firms, politicians, online buyers, but, also, for specialists in nlp, linguistics, etc. until now, we observed the fact that when a variable of neutralizing sentiments appears, it is not enough to cover only the summarizing operation of values for each opinion sentence. because of that, we propose to add degrees of intensity and power in expressing opinions. in romanian language, the superlative amplify semantically the convictions of the person who opines on an issue. in the sentence vodafone românia oferă cea mai bună conectivitate pentru serviciile de date dintre toate reţelele mobile gsm/ umts/ cdma din românia. the word bună gets +1. the detecting emotions in comments on forums 701 superlative cea mai expands the scale of values. it can get the degree of positivity (or negativity). it depends on which word follows. so, cea mai bună gets (+2). also, due to pragmatic connectors, we have to give up on summarizing values. acknowledgments in order to perform this research the first author received financial support from the erasmus mundus action 2 emerge project (2011 2576 / 001 001 ema2). i am also grateful to the nlp-group@uaic-fii for offering me support in using some tools for automatic interpretation of romanian language. bibliography [1] ardeleanu, i. (2013); extragerea de opinii din texte, lucrare de licenta coord. de prof.univ.dr. dan cristea, universitatea alexandru ioan cuza din iasi. [2] dave, k.; lawrence, s. and pennock d.m. (2003); mining the peanut gallery: opinion extraction and semantic classification of product reviews in proceedings of www. [3] das, s.; chen, m. (2001); yahoo! for amazon: extracting market sentiment from stock message boards in proceedings of the asia acific finance association annual conference (apfa). [4] ding, x., liu, b. and zhang, l. (2009): entity discovery and assignment for opinion mining applications. in proceedings of acm sigkdd international conference on knowledge discovery and data mining. [5] dragut, e.c., yu, c., sistla, p. and meng, w. (2010): construction of a sentimental word dictionary. in proceedings of acm international conference on information and knowledge management. [6] esuli, a.; sebastiani, f. (2006); determining term subjectivity and term orientation for opinion mining in proceedings of eacl-06, 11th conference of the european chapter of the association for computational linguistics, trento, it. forthcoming. [7] gifu, d. (2012); political text categorization in humanities and social sciences review, vol. 1, no. 3, university publications.net, usa, part of the paper presented in the international journal of arts and sciences’ (ijas) international conference for academic disciplines, harvard university, cambridge, massachusetts, 27-31 may 2012. [8] gifu, d. (2013); temeliile turnului babel. o perspectiva integratoare asupra discursului politic, ed. academiei romane, bucuresti. [9] habermas, j. (1962); strukturwandel der ofentlichkeit: untersuchungen zu einer kategorie der burgerlichen gesellschaft. neuwied, luchterhand. [trad. rom.: sfera publica si transformarea ei structurala, bucuresti, ceu, 1989.] [10] hatzivassiloglou, v. and mckeown k. r. (1997): predicting the semantic orientation of adjectives. proceedings of acl-97, 35th annual meeting of the association for computational linguistics, madrid, es, association for computational linguistics. 702 d. gîfu, m. cioca [11] ignat, e. (2011); rare-uaic (robust anaphora resolution engine), resursa gratuita pe meta-share, universitatea "alexandru ioan cuza" din iasi, 2011. [12] jindal, n. and liu, b. (2006): identifying comparative sentences in text documents. in proceedings of acm sigir conf. on research and development in information retrieval. [13] kamps, j., maarten, m., r. ort.mokken and maarten de rijke. (2004): using wordnet to measure semantic orientation of adjectives in proceedings of lrec-04, 4th international conference of language resources and evaluation, vol. iv. [14] liu, b. (2010); sentiment analysis and subjectivity. handbook of natural language processing. n. indurkhya and f.j. damerau, eds. [15] liu, b. (2012); sentiment analysis and opinion mining. synthesis lectures on human language technologies, morgan claypool publishers. [16] mihalcea, r.; banea c.; wiebe, j. (2007); learning multilingual subjective language via cross-lingual projections in 45th annual meeting of the association for computational linguistics (acl-2007). [17] pang, b.; lee, l. (2008); opinion mining and sentiment analysis in foundations and trends in information retrieval, 2. [18] pang, b.; lee, l.; vaithyanathan, s. (2002); thumbs up? sentiment classification using machine learning techniques in proceedings of emnlp-02, 7th conference on empirical methods in natural language processing (philadelphia, pa). association for computational linguistics, morristown, nj. [19] pang, b.; lee, l. (2004); a sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts in proceedings of the association for computational linguistics. [20] simionescu, r. (2011); pos-tagger hibrid, lucrare de disertatie coord. de prof.univ.dr. dan cristea, universitatea "alexandru ioan cuza" din iasi. [21] turney, p. (2002); thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews in proceedings of the association for computational linguistics. [22] tong, r.m. (2001); an operational system for detecting and tracking opinions in on-line discussion in workshop note, sigir 2001 workshop on operational text classification. international journal of computers communications & control issn 1841-9836, 12(3), 403-414, june 2017. an extension of the vsm documents representation l. vinţan, d. morariu, r. creţulescu, m. vinţan lucian vinţan, daniel morariu, radu creţulescu*, maria vinţan lucian blaga university of sibiu romania, sibiu, emil cioran, 4 lucian.vintan@ulbsibiu.ro, daniel.morariu@ulbsibiu.ro radu.kretzulescu@ulbsibiu.ro, maria.vintan@ulbsibiu.ro *corresponding author: radu.kretzulescu@ulbsibiu.ro abstract: in this paper we will present a new approach regarding the documents representation in order to be used in classification and/or clustering algorithms. in our new representation we will start from the classical "bag-of-words" representation but we will augment each word with its correspondent part-of-speech. thus we will introduce a new concept called hyper-vectors where each document is represented in a hyper-space where each dimension is a different part-of-speech component. for each dimension the document is represented using the vector space model (vsm). in this work we will use only five different parts of speech: noun, verb, adverb, adjective and others. in the hyper-space each dimension has a different weight. to compute the similarity between two documents we have developed a new hyper-cosine formula. some interesting classification experiments are presented as validation cases. keywords: documents representation, vector space model, hyper-vectors, documents similarity, classification, clustering. 1 introduction one of the main goals of information retrieval is organizing and retrieving information from a large number of text-based documents. typically, an information retrieval problem is to locate relevant documents based on the user’s input, such as keywords or sample documents. usually information retrieval systems include on-line library catalog systems and on-line document management systems. an information retrieval system [8] based on similarity finds similar documents using a set of common keywords. the output for this system is based on the degree of relevance measured according to keywords closeness and the relative frequency of the keywords [2,5]. in some cases, it is difficult to give an accurate measure of the relevance between the keywords set. in modern information retrieval systems, keywords for document representation are automatically extracted from the documents. this system often associates a stopword list with the set of documents. a stopword list is a set of words that are considered "irrelevant" for the representation of the document set and can vary when the document set varies. another issue is the extraction of the word stem. a group of different words may share the same word stem. a text retrieval system needs to identify groups of words where the words in a group have small syntactic variants, and collect only the common word stem per group. the most common method used in document representation for classification or clustering algorithms is the vector of word frequencies [5]. this method is quite rapid and reliable because it does not require huge computing power. however, the latest approaches for the representation of documents in current applications (which require speed and fast response time such as the majority of the online applications) are not always feasible due to computational cost and the waiting time needed for processing large results. copyright © 2006-2017 by ccc publications 404 l. vinţan, d. morariu, r. creţulescu, m. vinţan in this article, we intend to present a new approach for the representation of text documents, which augments the classical vsm (vector space model) representation with some semantic information such as the parts of speech [6] of the words (or other classification of the corresponding words). this new representation we have called h-vsm (hyper vector space model). the reason is natural because in this case the new document representation, instead of containing just a single vector of scalars (representing its words occurrences), like in the vsm representation, it contains a hyper-vector containing parts of speech vectors. such a vector might contain, for example, all the noun-words; another one contains all the verb-words and so on. this idea brings in a new light, a more general vsm representation form, containing new information (the words classes parts-of-speech in our case). it may open a new effective approach to document classification or clustering using computational techniques. despite the semantic approaches emergence, these computational methods are still of great interest in documents classification research. the reasons are multiple: they are simpler, easier to implement, faster, independent of the research field, more feasible in some implementations (for example in internet browsers) than the semantic approaches (ontology, nlp, etc.), whose complexities are enormous and the run time, too. 2 the classical vsm representation model for text documents in the classical vsm representation, a text document is represented as a vector of frequencies of its words [2, 5]. thus, in this approach for each set of data a dictionary of words (typically the stem-words) will be generated. let us consider a set of d documents and a set of t terms used for representing the documents into the information retrieval system. we can represent each document as a vector v in the t -dimensional space rt. the ith coordinate of v[] is a number that measures the association of the ith term with respect to the given document: it is generally defined as 0 if the document does not contain the term, and nonzero otherwise. the vi element from a v[] vector, can indicate the frequency of the word in the document. for defining the frequency of the terms (term-weighing) for the nonzero entries in such a vector there are also used many methods [5, 7]. for example, it can be simply defined vi = 1 if the ith term occurs in the document, or let vi be the term frequency, or normalized term frequency. the term frequency is the number of occurrences of the ith term in the document. there are different ways to weight the term frequency for the nonzero entries in such a vector. we can simply set value 1 if the term t occurs in the document (binary normalization), or use term frequency or the relative them frequency, that is, the term frequency divided by total number of occurrences of all the terms in the document (nominal normalization). another way to normalize the term frequency is cornell smart representation that uses the following formula: tf(d,t) = { 0 if freq(d,t) = 0 1 + log (1 + log (freq(d,t))) othervise (1) where freq(d,t) is the frequency of term t in the document d. based on this representation there is another measure called inverse document frequency that represents the scaling factor for a term t relative to the frequency of term in all documents from the dataset. idf(t) = log 1 + |d| |dt| (2) where d is the document collection and dt is the set of documents containing term t in a complete vector space model tf and idf are combined together forming the tf − idf measure: an extension of the vsm documents representation 405 tf − idf(d,t) = tf(d,t) × idf(t) (3) we expect that similar documents will have similar relative terms frequencies, and we can measure the similarity among a set of documents. there are many metrics for measuring the document similarity. a common used measure is the euclidean distance but the most used in the literature is the cosine similarity defined as: sim(v1,v2) = v1 ·v2 ‖v1‖ ‖v2‖ (4) 3 the new h-vsm model 3.1 the rationale for a new vector model representation let v be a vector, which represents a document in the vsm. let us agree that all related words to v are from the syntactic point of view only of two types (x and y). keeping the vsm representation, the vector v can be written as v = vx&vy where & is the concatenation operator. but if we consider vx and vy being two orthogonal axes, we could represent v as: v = vx ·hi + vy ·hj (5) where hi and hj are the "hyper-unit vectors" of this "plane", because vx and vy are vectors (although in formula 5 they are denoted as simple scalars just to simplify the notations). we note the vector v in this representation a "hyper-vector" (representing a vector of vectors). 3.2 a first particular model of hyper-vector similarity let consider two hyper-vectors represented in a 2d hyper-plane as: hv1 = vx1 ·hi + vy1 ·hj hv2 = vx2 ·hi + vy2 ·hj (6) where (vx1,vy1) and (vx2,vy2) are the projections of hv 1 respectively hv 2 on the orthogonal axes. in text documents representation the vectors vx1, vx2 could be, for example, the verb vectors for the document 1 and document 2 and the vectors vy1, vy2 could be the noun vectors for the same documents (syntactic extensions are immediate). as we already pointed out, a common metric used to measure the similarity between two vectors in the vsm representation is the cosine distance. the problem is to consistently define a representation of this distance for 2 "hyper-vectors". we will call this distance "hyper-cosine" and we will note it hcos(hv 1,hv 2), even if it is, in fact, the cosine between two vectors. to solve this problem, we will write the "scalar" product of the "hyper-vectors" as: hv1 ·hv2 = (vx1 ·hi + vy1 ·hj) · (vx2 ·hi + vy2 ·hj) = = vx1 ·vx2 + vy1 ·vy2 (7) but: vx1 = vx11 · i + vx12 · j and vx2 = vx21 · i + vx22 · j (8) 406 l. vinţan, d. morariu, r. creţulescu, m. vinţan where vx11,vx12 represent the frequencies of occurrence of the syntactic part x (verbs for example) in the vectors vx1 respectively vx2 . analogous for the vectors vy1 respectively vy2 (representing nouns, for example). here i and j are the well-known orthogonal unit vectors. substituting the 8 equalities in formula 7 we obtain: hv1 ·hv2 = vx11 ·vx21 + vx12 ·vx22 + vy11 ·vy21 + vy12 ·vy22 (9) it follows that: hcos ( hv1,hv2 ) = hv1 ·hv2 |hv1| · |hv2| = = vx11 ·vx21 + vx12 ·vx22 + vy11 ·vy21 + vy12 ·vy22√ v 2x1 + v 2 y1 · √ v 2x2 + v 2 y2 (10) but: v 2x1 = (vx11 · i + vx12 · j) 2 = v 2x11 + v 2 x12 (11) we represent v 2y1, v 2 x2, v 2 y2 in a similar way. replacing in 10 we obtain: hcos(hv1,hv2) = vx11 ·vx21 + vx12 ·vx22 + vy11 ·vy21 + vy12 ·vy22√ v 2x11 + v 2 x12 + v 2 y11 + v 2 y12 · √ v 2x21 + v 2 x22 + v 2 y21 + v 2 y22 (12) we notice that v 2x11 + v 2 x12 = ∣∣vx1∣∣2 and analogues. based on this notation, 12 relationship can be written more concisely: hcos(hv1,hv2) = vx1 ·vx2 + vy1 ·vy2√∣∣vx1∣∣2 + ∣∣∣vy1∣∣∣2 · √∣∣vx2∣∣2 + ∣∣∣vy2∣∣∣2 (13) note: the formulas 12 and 13 are "different" representations for the cosine between two vectors. indeed, if all the words belong to a single syntactic category (and not two, x and y) formula 13 becomes the well-known formula for the cosine: cos(v1,v2) = v 1 ·v2∣∣v1∣∣ · ∣∣v2∣∣ (14) cos(v̄1, v̄2) = 1 being equivalent with v̄1 = kv̄2. 3.3 a generalization of the similarity between hyper-vectors considering two hyper-vectors having "n" orthogonal dimensions: hv1 = n∑ k=1 vxk1 ·hik hv2 = n∑ k=1 vxk2 ·hik (15) formula 13 becomes: hcos(hv1,hv2) = n∑ k=1 vxk1 ·vxk2√ n∑ k=1 ∣∣vxk1∣∣2 · √ n∑ k=1 ∣∣vxk2∣∣2 (16) an extension of the vsm documents representation 407 further, in order not to complicate the notation, we are considering in this first model that all vectors vxk, · · ·k = 1,n have the same length "n". of course, different lengths for each vector "mk" can also be considered (as we do in the next section). in order to generalize formula 12 we can use the following notation: vxk1 = m∑ p=1 v p xk1 ·~ip, ∀k = 1,n (17) (where "p" is considered an index, not a power). for instance: vx11 represents the "syntactic" vector of verbs from document number 1 (k=1), vx21 represents the "syntactic" vector of nouns from document number 1 (k=2), etc. vxk1 ·vxk2 = m∑ p=1 v p xk1 ·v p xk2 (18) also: ∣∣vxk1∣∣2 = m∑ p=1 ( v p xk1 )2 (19) ∣∣vxk2∣∣2 = m∑ p=1 ( v p xk2 )2 (20) replacing 18, 19 and 20 in 16 we obtain: hcos(hv1,hv2) = n∑ k=1 m∑ p=1 v p xk1 ·v p xk2√ n∑ k=1 m∑ p=1 ( v p xk1 )2 ·√ n∑ k=1 m∑ p=1 ( v p xk2 )2 (21) formula 21 represents the generalization of formula 12. the formulas 16 and 21 represent similarities between documents that are represented in a space "syntactically richer" than the vsm, namely a hyper-space which generalizes consistently the vsm representation. normalized weights of classes of words (x1,x2, . . .xn) are possible and they lead to authentic generalizations of cosines, meaning: hv1 = n∑ k=1 αk ·vxk1 ·hik hv2 = n∑ k=1 αk ·vxk2 ·hik with n∑ k=1 αk = 1 (22) αk > 0, will be chosen larger or smaller depending on the greater or lower "semantic importance" of a certain (k) hyper-dimension. unlike the cases presented so far, in this case we would obtain a h-cos type similarity formula, different from the classical cos type, which could have positive consequences in improving the accuracy of document classification algorithms (see formula 23). hcos(hv1,hv2) = n∑ k=1 (α2k · m∑ p=1 v p xk1v p xk2)√ n∑ k=1 (α2k · m∑ p=1 (v p xk1) 2 ) · √ n∑ k=1 (α2k · m∑ p=1 (v p xk2) 2 ) 6= cos(hv1,hv2) (23) 408 l. vinţan, d. morariu, r. creţulescu, m. vinţan the αk coefficient can be simplistically computed for instance using: αk = nk m∑ j=1 nj , for k = 1,m (24) nk represents the length of a certain dimension k (k = 1,m). in this case we consider that a longer vector for a given type of a part of speech could be more important than a shorter one. also there can be used other formulas for computing the αk coefficient. it remains to be proven by experiments that this new representation of the text documents with the new similarity metrics will improve the accuracy of the document classification in accordance with our scientific hypothesis. this hypothesis is based on a rational intuition: including in document representation some new "morphologic" information offers a greater discriminatory power. 3.4 generalization for "mk" length different for each vxk vector we are considering in this paragraph that our document is represented in a hyper-space with m dimensions (e.g. the document is represented for m different parts of speech). each space of this hyper-space has nk dimensions (any 2 spaces can have different sizes ni 6= nj). for example n1 represents the dimension of the nouns vector; n2 represents the dimension of the verbs vector, etc. any two documents are represented in the same hyper-space m and have the same dimension in each space. thus: hv1 = m∑ k=1 vk1 ·hk hv2 = m∑ k=1 vk2 ·hk (25) we try to simplify the notations as much as possible. in the formula 21 we have replaced xk directly with k. the scalar product of two documents vectors in this hyper-space will be (generalizing formula 9): hv1 ·hv2 = m∑ k=1 nk∑ p=1 vk1p ·vk2p (26) the norm of the hyper-vector becomes: ∣∣hv1∣∣ = √√√√ m∑ k=1 nk∑ p=1 v 2k1p (27) using the formula 14 representing the cosine between two vectors and generalizing formula 21 the hyper-cosine in the hyper-space defined in this way becomes: hcos(hv1,hv2) = m∑ k=1 nk∑ p=1 vk1p ·vk2p√ m∑ k=1 nk∑ p=1 v 2k1p · √ m∑ k=1 nk∑ p=1 v 2k2p (28) this formula is similar with formula 21 (using a different notation style). an extension of the vsm documents representation 409 under these circumstances if we consider that the hyper-vector is a single vector with the dimension equal with n1 + n2 + · · · + nm, than the cosine value should be identical. the initial idea from which we have started was that the vectors are represented as follows: hv1 = m∑ k=1 ( αk ·vk1 ·hk ) hv2 = m∑ k=1 ( αk ·vk2 ·hk ) (29) where ∑ αk = 1 (equilibrium relationship). then the hyper-cosine becomes: hcos(hv1,hv2) = m∑ k=1 ( α2k · nk∑ p=1 vk1p ·vk2p ) √√√√ m∑ k=1 ( α2k · nk∑ p=1 v 2k1p ) · √√√√ m∑ k=1 ( α2k · nk∑ p=1 v 2k2p ) (30) in this case the value for the hcos can be different from the value obtained by a simple concatenation of the part-of speech vectors. again the αk coefficient may be computed for instance as in the following formula: αk = nk m∑ j=1 nj , for k = 1,m (31) this idea could bring in a new light the vsm representation. the alpha coefficients for each part of speech depend more on the semantics of the documents than on the percentage for a certain part of speech (as we have previously suggested in 24 formula). therefore choosing the optimal alpha weights remains an open problem. in this article, we have tried finding, in an empirical way, the coefficients’ values so that the classification accuracy increases, without claiming to have found the optimal values. 3.5 other generalized formulas used to measure the similarity between two vectors considering that all the vxk, · · ·k = 1,n, vectors have the same length "n", the euclidian hyper-distance becomes: hdeucl(hv1,hv2) = √√√√ m∑ k=1 n∑ p=1 ( v p xk1 −v p xk2 )2 (32) as well it would be possible to weight classes of words, depending on their importance, leading to consistent generalizations of the euclidean distance, with potentially positive influences on the document classification algorithms. in this case the euclidean distance for different space dimension mk becomes: hdeucl(hv1,hv2) = √√√√√ m∑ k=1  α2k nk∑ p=1 (vk1p −vk2p) 2   (33) the dot product between two vectors can be expressed as: 410 l. vinţan, d. morariu, r. creţulescu, m. vinţan hv1 ·hv2 = m∑ k=1  α2k nk∑ p=1 vk1p ·vk2p   (34) similarly other distances can be expressed using weight classes generalization (city-block distance, etc.). hdcb ( hv1,hv2 ) = m∑ k=1  α2k nk∑ p=1 |vk1p −vk2p|   (35) 4 improvements brought to document classification 4.1 tagging the reuters dataset for validating the above presented theoretical results, based on a set of documents, we have tried a separation of words according to their part of speech. after that step, we have then performed a vector representation of documents as vectors of frequencies of words but we have taken into account also the part of speech for the given words. in this way, we have obtained an augmented vsm representation. in this new representation each vector in the hyper-vector space represents a part of speech component. we want to compare the results of the new representation with the results obtained by us in previous experiments where documents were represented only by frequencies of words vectors [9]. we have used the support vector machine (svm) algorithm for classifying text documents. because svm is a supervised learning algorithm, we need a set of data that is tagged in terms of parts of speech and classified in terms of documents belonging to classes. these classes are defined axiomatically according to the content of documents. in initial experiments, we have used the reuters 2000 dataset [10] which contains documents that are pre-classified by the reuters news agency but without having any information about the part of speech of words contained. in our experiments, regarding the parts of speech, we have used the brown corpus [1] that is a corpus labeled in terms of part of speech but not classified into categories (classes) by document contents. therefore, in the first step we have tried to label the words from the documents contained in the reuters database with their corresponding parts of speech. for this purpose, using the brown corpus we have evaluated several known tagging (labeling the part of speech) applications and also some tagging applications which were developed by us [3], in order to find the best suited tagging application / applications for labeling the documents. the entire experiment for the tagger selection was explained in our article published in [4]. in [4] we have performed a number of experiments for selecting the "best" tagger in order to tag the reuters dataset. analyzing the obtained results we have decided to use, in the first experiments, only the tree tagger tool [11]. this tool has obtained the best results for three of the tested parts of speech: noun, verb and adverb. hopefully, in further experiments, we’ll combine the results from more taggers (through meta-tagging) in order to increase the probability to obtain the correct part of speech for a word in a given context. after tagging our reuters dataset and representing each document as a vector of words frequencies, where each word is separated based on its part of speech, we have obtained 27240 different words (for all 5 parts-of speech taken into consideration). in the next table we present for each part of speech the number of words discovered by the tagger in the reuters dataset. after the prediction of the part of speech using the tree tagger tool, we have extracted the stem of the words and after that we have recorded the words’ frequencies. we have obtained an extension of the vsm documents representation 411 table 1: pos for the reuters data part of speech # of words % of total word nouns 16820 61.75 verbs 3668 13.47 adjectives 5555 20.39 adverbs 1001 3.67 others 196 0.72 total 27240 27240 different words, which mean in the hyperspace to have a representation with five different spaces, each space having a different dimension. for example, the space of nouns has 16820 dimensions. it is remarkable that the tree tagger has labeled in the "other" category only 196 stem of words that represents less than 1% from all stem of words extracted from the reuters dataset. 4.2 obtained results with svm classifier we have started some initial experiments using our tagged reuters data set in order to determine if there are some improvements in the classification accuracy using this new representation. in the svm classifier, we have decided to use both polynomial and gaussian kernels as it was presented in [9]. the results obtained using the svm classifier and all 27240 features are presented in the table 2. these results were compared with previous results obtained using a vector with 18424 features but without pos. (the new document representation has a bigger dimension because a certain word could belong to multiple parts of speech). this was the biggest dimension obtained for the words frequencies vectors representation. in [9] it was demonstrated that if the vector dimension decreases and fewer features are chosen through some feature selection techniques, than the noise introduced in the classification algorithm is smaller and the learning is better. with this new representation it was interesting to observe that although the number of features was higher, which would theoretically induce more noise in the document representation, the classification results are at average with 0.85% better for the polynomial kernel and with 1.1% at average better for the gaussian kernel. these results give us hope for future better results especially for the gaussian kernel. in the table 2 we have marked with bold the highest values for the two developed experiments. when the part of speech was introduced, the results improved (even if we have weights equal to 1 for the hyper-vectors) especially for small degrees of the kernels (so there is no need for a shift into a higher space). also the results are more equilibrate for different kernel dimensions that mean that we can obtain better results searching only in few spaces. further we have selected only the best 1309 features from all 27240 features using the information gain as feature selection method [7]. in table 3 we present the number of words obtained, according to their part of speech. in the table 4 we present the results of our classification experiments where we have applied the formula 34 for computing the dot product between two hyper-vectors, considering that each hyper-vector consists of five vectors with different sizes for each part of speech. in these experiments we have weighted with 0.8 the verbs and the adverbs, with 0.9 the nouns and the adjectives and with 0.6 the "other" category. the obtained results are shown in the table 4. the values of coefficients were chosen close to 1 (value that is used in case of computing the classical dot product between two vectors for example, the formula 14). 412 l. vinţan, d. morariu, r. creţulescu, m. vinţan table 2: vsm versus vsm-with-pos for all features using svm classifier polynomial kernel gaussian kernel degree of kernel data representation for 18428 features (vsm) for 27240 features (vsm with pos) degree of kernel data representation for 18428 features (vsm) for 27240 features (vsm with pos) p1.0 bin 83.03 83.41 c1.0 bin 82.01 82.22 p1.0 nom 86.22 86.98 c1.0 smart 81.75 84.22 p1.0 smart 82.52 84.01 c1.3 bin 82.69 82.86 p2.0 bin 85.79 85.16 c1.3 smart 82.39 84.43 p2.0 nom 85.50 85.67 c1.8 bin 82.86 83.03 p2.0 smart 85.92 86.39 c1.8 smart 82.60 84.26 p3.0 bin 83.96 76.05 c2.1 bin 82.56 82.77 p3.0 nom 84.94 85.92 c2.1 smart 82.43 84.18 p3.0 smart 77.16 85.03 average 82.41 83.50 p4.0 bin 53.64 64.44 p4.0 nom 82.99 82.56 p4.0 smart 59.34 55.47 average 75.25 80.09 table 3: number of words selected according to their pos part of speech # of words % of total word nouns 683 52.18 verbs 289 22.08 adjectives 188 14.369 adverbs 63 4.81 others 86 6.57 total 1309 an extension of the vsm documents representation 413 table 4: vsm versus vsm-with-pos for 1309 features using svm classifier polynomial kernel gaussian kernel degree of kernel data representation for 1309 features (vsm) for 1309 features (vsm with pos) degree of kernel data representation for 1309 features (vsm) for 1309 features (vsm with pos) p1.0 bin 81.45 80.99 c1.0 bin 82.99 84.05 p1.0 nom 86.69 86.39 c1.0 smart 82.99 84.77 p1.0 smart 80.99 81.07 c1.3 bin 83.74 84.05 p2.0 bin 86.64 85.11 c1.3 smart 83.57 84.56 p2.0 nom 85.03 84.94 c1.8 bin 83.24 83.96 p2.0 smart 87.11 85.03 c1.8 smart 84.30 84.47 p3.0 bin 85.79 85.33 c2.1 bin 83.11 84.05 p3.0 nom 84.35 82.52 c2.1 smart 83.83 84.13 p3.0 smart 86.51 85.71 average 83.47 84.25 p4.0 bin 74.61 83.03 p4.0 nom 81.54 79.20 p4.0 smart 71.84 81.67 average 82.71 83.41 as a first observation, it is interesting to point out that the average classification accuracies for the 1309 features representation are significantly better (up to 7.46%) than those obtained using all features representation (see table 4 vs. table 2). regarding the polynomial kernel, most results are slightly lower than that obtained without the pos. interesting, all the vsm with pos results are close to a certain value for both small degrees and high degrees kernel values suggesting that the new representation would not be affected so much by shifting into another higher space. however, at average, the new representation gets better results with 0.70% for the polynomial kernel. using the new representation with the gaussian kernel, we have obtained constantly slightly better results than with the classical vsm representation. more precisely, at average the svm with gaussian kernel obtained, using the new representation, an improvement of 0.78% using only 1309 features, compared to the simple frequency of words vector representation. as we have already mentioned, the selection for the weighting values for each vector from the hyper-space remains an open problem because it is difficult to determine what contribution has each part of speech regarding the quality of the classification accuracy. in future experiments we will try to find different methods and strategies for computing the optimal values for the weights. 5 conclusions and future works in our paper, we have presented a possible improvement for the vsm representation used in text documents classification adding some new morphological information, transforming the vectors of documents into hyper-vectors, which contain information about the part of speech of the words (just in our case presented here). we have also developed a new formula for the cosine between two hyper-vectors starting from the well-known formula for the cosine distance between two vectors. in fact, the proposed model is more general, because it tries to augment the classical 414 l. vinţan, d. morariu, r. creţulescu, m. vinţan representation which leads to a separation of the representations that can have different weights. considering these first experiments we have observed that such a representation which adds supplementary information helps to achieve better classification results. we intend to improve this information trying to find the most optimal representation. anyway, even if the classification algorithms will have no improvement with this representation, this does not necessarily mean that the idea of "representation by hyper-vectors" is bad; this means that the used hyper-space is inappropriate for the purpose (classification). the chosen representations might not lead to a better discrimination. if we choose otherwise, it may lead to a better classification. what representation should be used to obtain a better classification? well, that nobody knows, we can only make assumptions based on intuition (common-sense). as a further work idea, instead of using the parts of speech for words we can consider to use the parts of the sentence (subject, predicate, attribute, etc.) or, more generally, we can consider to group information in other quasi-orthogonal categories (these categories could be whatever: syntactic, morphologic, etc.) and weighted each category separately and compute the similarity. (for example we can have "beautiful" words and "ugly" words. if this involves better classification accuracy, why not?) bibliography [1] brown university standard corpus of present-day american english (brown corpus), [online] http://icame.uib.no/brown/bcm.html, accessed in april 2014. [2] chakrabarti s.(2003); mining the webdiscovering knowledge from hypertext data, morgan kaufmann press, 2003. [3] cretulescu r., david a., morariu d., vintan l. (2014); part of speech tagging with naive bayes methods, proceedings of the 18-th international conference on system theory, control and computing, sinaia (romania), doi: 10.1109/icstcc.2014.6982457, 446-451, 2014. [4] cretulescu r., david a., morariu d., vintan l. (2015); part of speech labeling for reuters database, proc. of the 19-th international conference on system theory, control and computing, gradistea (romania), doi: 10.1109/icstcc.2015.7321279, 117-122, 2015. [5] han j., kamber m. (2001); data mining: concepts and techniques, morgan kaufmann publishers, 2001. [6] manning d., schütze h. (1999); foundations of statistical natural language processing, mit press, isbn: 987–0–262–133360–9, 1999. [7] mitchell t. (1999); machine learning, mcgraw hill publishers, 1997. [8] mitkov r. (2005); the oxford handbook of computational linguistics, oxford university press, 2005. [9] morariu d. (2008); text mining methods based on support vector machine, matrixrom, bucharest, 2008. [10] reuters corpus, [online] http://about.reuters.com/researchandstandards/corpus/, released in november 2000. [11] tree tagger, [online] http://www.cis.uni-muenchen.de/ schmid/tools/treetagger, accessed in april 2014. international journal of computers communications & control issn 1841-9836, 11(6):755-775, december 2016. incremental and decremental svm for regression h. gâlmeanu, l.m. sasu, r. andonie honorius gâlmeanu* 1. siemens corporate technology honorius.galmeanu@siemens.com 2. faculty of mathematics and informatics transilvania university of braşov *corresponding author: galmeanu@unitbv.ro lucian mircea sasu 1. faculty of mathematics and informatics transilvania university of braşov lmsasu@unitbv.ro 2. siemens corporate technology lucian.sasu@siemens.com răzvan andonie 1. computer science department central washington university, ellensburg, usa andonie@cwu.edu 2. electronics and computers department transilvania university of braşov abstract: training a support vector machine (svm) for regression (function approximation) in an incremental/decremental way consists essentially in migrating the input vectors in and out of the support vector set with specific modification of the associated thresholds. we introduce with full details such a method, which allows for defining the exact increments or decrements associated with the thresholds before vector migrations take place. two delicate issues are especially addressed: the variation of the regularization parameter (for tuning the model performance) and the extreme situations where the support vector set becomes empty. we experimentally compare our method with several regression methods: the multilayer perceptron, two standard svm implementations, and two models based on adaptive resonance theory. keywords: support vector machine, incremental and decremental learning, regression, function approximation 1 introduction the approximation of continuous functions that are known only at a certain number of discrete points (also known as regression), a standard procedure in statistics, can be also approached from a machine learning perspective. in the context of supervised training, incremental learning means learning each input-output sample pair, without keeping it for subsequent processing. the topic addressed in this paper is supervised incremental/decremental learning of regression models, in particular svm models. the fundamental issues in incremental/decremental learning are: i) how can a learning system adapt to new information without corrupting or forgetting previously learned information (the stability-plasticity dilemma addressed by carpenter and grossberg [2]), and ii) how can a learning system "forget" information without corrupting previously learned information and without adding new information. in other words, how can a system learn without unlearning and unlearn without learning. copyright © 2006-2016 by ccc publications 756 h. gâlmeanu, l.m. sasu, r. andonie support vector regressions (svrs) are svm learning models used for regression. these algorithms solve the quadratic optimization problem using a decomposition method based on sequential minimum optimization [4], or on an incremental learning method. the incremental method considers that input patterns are added to the solution (or removed from the solution), one at a time, without affecting the learning process of the other patterns [13,14]. after introducing some basic notations, we aim review in this introductory section related work on incremental/decremental svrs and also highlight our contribution. 1.1 svr notations for a non-linear svr, regression translates into the following optimization problem [14]: approximate a given set of training pairs (xi,yi), i = 1 . . .n, xi ∈ rm, yi ∈ r, with an svm defined by g(xi) = w t φ(xi) + w0, (1) where φ(·) is a non-linear function and w0 is an offset. the objective function g(x) : rm → r requires the minimization [21] of min w,w0,ξi,ξ ∗ i j(w) = min w,w0,ξi,ξ ∗ i ( 1 2 ‖w‖2 + c n∑ i=1 (ξi + ξ ∗ i ) ) , (2) and the constraints are given by −�− ξ∗i ≤ g(xi) −yi ≤ � + ξi for ξi,ξ∗i ≥ 0, i = 1 . . .n (3) svr matches the output value of the objective function g(xi) as close as possible to the given yi. the output value is constrained to lay inside a "tube" of values, given by −� + yi ≤ g(xi) ≤ � + yi, where � is a given constant. the slack variables ξi and ξ∗i allow the system to cope with patterns that do not fall inside the "tube" and also to minimize the acceptable error for such patterns. to minimize the expression of j(w) in equation (2) with respect to w and w0, after several manipulations, the lagrangian can be written as: l(w,w0,αi,α ∗ i ) = − 1 2 ∑ i ∑ j (α∗i −αi)φ(xi)t φ(xj)(α∗j −αj) + ∑ i (α∗i −αi)yi − ∑ i (α∗i −αi)w0 − ∑ i (αi + α ∗ i )� (4) subject to αi,α∗i ≥ 0. the karush-kuhn-tucker (kkt) conditions (imposed by minimizing the lagrangian) are given by: w = ∑ i (α∗i −αi)φ(xi) (5)∑ i (αi −α∗i ) = 0 0 ≤ αi ≤ c 0 ≤ α∗i ≤ c (6) incremental and decremental svm for regression 757 the wolfe dual form states that this minimization with respect to w and w0 can be transformed into a maximization with respect to αi and α∗i . this leads to the minimization of l′(w,w0,αi,α ∗ i ) = −l(w,w0,αi,α∗i ). the expression of l′(w,w0,αi,α∗i ) depends only on the inner product φ(xi) t φ(xj). therefore, we use the notation qij = φ(xi)t φ(xj). however, svm uses the "kernel trick": the inner products of the vectors φ(x), belonging to an infinite-dimension feature space, are replaced by the non-linear kernel function k(xi,xj) = e−σ(xi−xj) 2 = φ(xi) t φ(xj) [19]. generally, kernel functions are used for non-linear svms, so we use the notation qij = k(xi,xj) to designate the kernel function. the extremum conditions for l′(w,w0,αi,α∗i ), considering also the conditions (5, 6), are given by: ∂l′ ∂αi = − ∑ j qij(α ∗ j −αj) −w0 + yi + �−δi + ui = 0 (7) ∂l′ ∂α∗i = ∑ j qij(α ∗ j −αj) + w0 −yi + �−δ∗i + u∗i = 0 (8) with the following additional kkt conditions: δi,δ ∗ i ≥ 0 δiαi = 0, δ∗i α∗i = 0 (9) ui,u ∗ i ≥ 0 ui(c −αi) = 0, u∗i (c −α∗i ) = 0 (10) from equation (1), using the condition (5) and the notation α∗j −αj = θj, the expression of the "margin" function hi becomes: hi = g(xi) −yi = ∑ j qijθj + w0 −yi (11) where −c ≤ θi ≤ c (12) from equations (7) (12), observing in the kkt conditions that at most one of the αi, α∗i values is nonzero, it follows that there are three possible vector sets: • error vectors (e), for the following cases: hi < −� (on θi = c), or hi > � (on θi = −c); • support vectors (s), for the cases: hi = −� (on 0 < θi < c), or hi = � (on −c < θi < 0); • other vectors (o), for the cases where −� < hi < �, with θi = 0. svms can be trained in an incremental/decremental way. using equation (11), we can decompose hi into two parts: the contribution of other vectors and the contribution of its own vector: hi = ∑ j 6=i qijθj + qiiθi + w0 −yi (13) given that qii > 0, an increasing negative contribution of other vectors θj (for j 6= i) would be compensated by increasing the value for θi up to the point where θi = c. from this point onward, since the increasing of negative contribution for θj would no longer be counteracted by 758 h. gâlmeanu, l.m. sasu, r. andonie the increase of θi, the net result is that hi would decrease past the −� value, and xi would be treated as an error vector. in equation (13), only the error and support vectors contribute to the expression hi. for all the sets, the associated threshold value θi is bounded by the regularization parameter c (equation (12)). r = {e ∪o} is the set of reserve vectors. we denote by r, s, and e the reserve, support and error vectors, respectively. the whole training set is defined as {e ∪s ∪o}. 1.2 related work and our contribution the adiabatic incremental/decremental training algorithm for svms (and svrs) was introduced in [13,14] and it follows from a method proposed by cauwenberghs and poggio [3,5]. for each pattern being part of the solution (called support vector), the associated threshold value is θi ∈ {−�,�}. following equation (13), as one new vector is "learned", its associated threshold θi increases/decreases, starting from 0, on the expense of other vectors’ thresholds. during this process, the rest of the vectors migrate between the sets of support, error and other vectors. when the same vector is "unlearned", its corresponding threshold value θi is decreased (when positive) or increased (when negative), in order to reach 0 (when the vector is removed). during unlearning a vector, the vectors which previously migrated between sets when learning that vector will now migrate in the reverse order. an implementation of the adiabatic algorithm can be found in [12], with further developments in [7]. during incremental training, while changing the threshold of the newly introduced vector, the vectors will migrate among sets. the value of the threshold increment determines how many vectors will migrate. therefore, we are interested to determine the largest threshold increment ∆θi which is small enough to cause only one vector to migrate. proceeding with fixed small increments of ∆θi is not an efficient search technique. we discussed in [7] the conditions for vector migrations between the sets and how to determine the optimal increment. we gave the incremental/decremental context, but we did not describe particular situations, like the empty support vector set situation and the variation of the regularization parameter. an incremental/decremental svr, which can simultaneously add batches of new samples and also remove the obsolete ones, was applied for online time series prediction [11]. the ν-svr, presented in [21], is an incremental/decremental svr which determines a priori the proportion ν of support vectors from the total set. the algorithms in [9,11] are variations of the adiabatic algorithm. for a classification problem, an incremental/decremental svm operates in the following way: to add vector xc to the solution, the associated threshold θc is set from 0 to c, whereas to remove xc, θc is set to 0. for svrs, this operations are not symmetric anymore: to add xc, θc is set from 0 to c (increase) or to −c (decrease); to remove xc, θc is also increased or decreased. the direction (incremental or decremental) is not directly given by the intention adding or removing the vector, but by the margin hc. we will discuss this aspect in section 2. we name our method incremental/decremental support vector machine regression (idsvmr). it is based on the adiabatic training algorithm. compared to [13,14], the idsvmr contains all aspects needed for an implementation: a) we provide the relations that give the exact amount needed for increasing the threshold θc for the new vector, before any vector migrates; b) we determine the exact threshold variation that leads from one migration to the next. this produces a robust incremental algorithm, that creates a sequence of vector migrations, as part of the learning process; c) we deduct the expressions for the variation of the threshold θc, from one migration to the next. during training, the support vectors’ set may become empty. we give the exact expressions needed to increase θc also considering this particular case. compared to [7], we contribute with: i) a complete description of the increment/decrement incremental and decremental svm for regression 759 operations of the regularization parameter, showing that the expressions to be maximized (minimized) are similar, and ii) we expand the results from [12] by giving an exact procedure on how to continue the migration in the empty support vector set cases, for both the regular procedure and the increment/decrement of the regularization parameter. section 2 describes the expressions of the threshold variables θi and the expression of the vectors margins before the first migration. section 3 introduces the expressions of the maximum threshold variations that occur before vectors migrations. section 4 analyzes the maximum variation of the regularization parameter. section 5 discusses the variations of the free parameter w0 and the regularization parameter in case of an empty support vector set. section 6 presents the results of the idsvmr implemented as a weka [10] plugin, compared with the multilayer perceptron (mlp), two models based on adaptive resonance theory, and two classical svm– derived regression models. finally, section 6 contains our conclusions. 2 incremental and decremental updates of thresholds we start this section with summarizing the adiabatic training algorithm, which is at the core of the idsvmr method. then, we will introduce idsvmr implementation details which are not included in the original adiabatic algorithm. to adapt to a new vector xc, the adiabatic algorithm migrates specific vectors between the sets, in order to reestablish the kkt conditions [5]. at the beginning, xc has the threshold θc = 0. the threshold can evolve both toward positive or negative directions. first, this direction is determined. then, the threshold is modified considering, with each update, the migration between the sets. the migration is identified by observing the maximum (or minimum) variation of the margins hi for each vector. considering the kkt conditions, specifically the conditions for the thresholds (6) and margin function (11), their variations can be expressed as: ∆hi = ∑ j qij∆θj + ∆w0 (14) 0 = ∑ j ∆θj (15) when adding vector xc to the set, the migrations that take place strive to keep kkt conditions in place. thus, the variations of thresholds and margins can be further expanded to: ∆hi = ∑ j qij∆θj + ∆w0 + qic∆θc (16) 0 = ∑ j ∆θj + ∆θc (17) these relations can be written more compact using e, the vector of ones:  ∆hs ∆hr ∆hc 0   =   es qss er qrs 1 qcs 0 ets   [ ∆w0 ∆θs ] + ∆θc   qsc qrc qcc 1   (18) the modification of ∆θc would be absorbed by the modification of ∆θs (the set of support vectors), ∆w0, and the variation of margin functions. the margins of support vectors do not change: they are either −� or +�. hence, we have ∆hs = 0 and we obtain: 760 h. gâlmeanu, l.m. sasu, r. andonie [ ∆w0 ∆θs ] = − [ 0 ets es qss ]−1 [ 1 qsc ] ︸ ︷︷ ︸ β ∆θc (19) considering the definitions of ∆w0 and ∆θs, the margin functions of the current and reserve vectors are: [ ∆hc ∆hr ] = [ 1 qcs er qrs ] β + [ qcc qrc ] ︸ ︷︷ ︸ γ ∆θc (20) to prevent the recalculation of the inverse matrix in equation (19), the sherman-morrisonwoodbury formula was used in [12]. if used repeatedly, this leads to error accumulation. therefore, we prefer to calculate the inverse by lu decomposition, which is in o(n3) but numerically stable [8]. 3 learning direction and extremum increment/decrement before vector migration in what follows, we describe the conditions for vector migration between the sets and establish migration conditions for all possible situations. we explain how we prevent immediate cycling, mentioned in [12]. although laborious, we give full coverage of all particular situations. to "learn" a new vector xc, the training process starts with the initial value θc = 0. its margin, hc, is first determined using relation (11). since ∆hc = γc∆θc, the aim is to modify θc such that the margin function is updated to fit as well as possible into the "tube" according to relation (3). the following cases occur: 1. if hc > �, then the margin decreases to fit into the −� < hc < � "tube" and ∆hc < 0: • if γc > 0, then ∆θc < 0, θc decreases to −c, and the direction is decremental; • else, if γc < 0, then ∆θc > 0, θc increases to c, and the direction is incremental; 2. if hc < −�, then the margin increases and ∆hc > 0: • if γc > 0, then ∆θc > 0, θc increases to c and the direction is incremental; • else, if γc < 0, then ∆θc < 0, θc decreases to −c and the direction is decremental; from these relations we can establish the direction: • if sgn(hc) 6= sgn(γc), the approach is incremental and ∆θc > 0; • if sgn(hc) = sgn(γc), the approach is decremental and ∆θc < 0. to "unlearn" a vector xc from the support or error sets, the direction only depends on the value of θc: if θc < 0, the direction is incremental; if θc > 0, the direction is decremental. once the direction (the variation of θc) is established, we have to determine the variation increment ∆θc. we have the following cases (details are provided in appendix a): incremental and decremental svm for regression 761 1. migration of support (s) vectors. considering only the ∆θs components from equation (19) and following the β notation, the expression of the increment for only one support vector is ∆θs = βs · ∆θc. it gives the limits for the θs updates (we considered βs to be the specific component from the β vector). on the other hand, considering the allowed variation interval for θs, we can determine the limit for θc before the support vector associated with θs leaves the set: • the incremental case: – if sgn(hs) = sgn(βs), θs may reach 0, so we have the migration to set o: ∆θc ≤− θs βs (s → o) – if sgn(hs) 6= sgn(βs), θs may reach c or −c: ∆θc ≤ sgn(βs) ·c −θs βs (s → e) • the decremental case: – if sgn(hs) 6= sgn(βs), θs may reach 0: ∆θc ≥− θs βs (s → o) – if sgn(hs) = sgn(βs), θs may reach c or −c: ∆θc ≥ −sgn(βs) ·c −θs βs (s → e) 2. migration of other (o) vectors. these are the vectors with −� < he < �: • for the incremental case: ∆θc ≤ sgn(γr) · �−hr γr (o → s) • for the decremental case: ∆θc ≥ −sgn(γr) · �−hr γr (o → s) 3. migration of error (e) vectors. these are the vectors with he < −� or he > �: • for the incremental case: – if sgn(hr) 6= sgn(γr): ∆θc ≤ −sgn(γr) · �−hr γr (e → s) – if sgn(hr) = sgn(γr): vector does not migrate • for the decremental case: – if sgn(hr) = sgn(γr): ∆θc ≥ sgn(γr) · �−hr γr (e → s) 762 h. gâlmeanu, l.m. sasu, r. andonie – if sgn(hr) 6= sgn(γr): vector does not migrate 4. migration of current xc vector. the current vector xc newly added to the set is checked for possible migration to the support set (s): • for the incremental case: – if sgn(hc) 6= sgn(γc): ∆θc ≤ −sgn(γc) · �−hc γc (e → s) – if sgn(hr) = sgn(γc): vector does not migrate • for the decremental case: – if sgn(hc) = sgn(γc): ∆θc ≥ sgn(γc) · �−hc γc (e → s) – if sgn(hc) 6= sgn(γc): vector does not migrate for removing xc from the training set, the objective is to increase/decrease θc to zero, so that the variation of the margin does not determine vector migration. 5. the variation of θc from/to zero should be −c ≤ θc ≤ c, and we have the following conditions: • for adding xc: – for the incremental case, ∆θc ≤ c −θc – for the decremental case, ∆θc ≥−c −θc • for removing xc: – for the incremental case, where θc < 0, ∆θc ≤−θc – for the decremental case, where θc > 0, ∆θc ≥−θc the maximum variation of ∆θc for the incremental case, before the vectors change sets, is determined by computing the minimum of all these values. conversely, the minimum variation for the decremental case is established by determining the maximum of all these (negative) values deduced in the five cases presented above. 4 incrementing and decrementing the regularization parameter training a svm also requires at some stage a fine-tuning of the regularization parameter. many times, this is done by trial-and-error. in order to avoid re-training from scratch, the adiabatic algorithm modifies the regularization parameter in an incremental or decremental way, in small increments, watching for vectors migrations between sets. in the following, we aim to determine the maximum values for these increments. starting from equations (14) and (15), and adopting the notation θk = bk ·c for the threshold of error vectors, the relations for the margin functions (when only the regularization parameter is modified) can be written as: ∆hi = ∑ j∈s qij∆θj + (∑ k∈e qikbk ) ∆c + w0 (21) incremental and decremental svm for regression 763 0 = ∑ j∈s ∆θj + (∑ k∈e bk ) ∆c (22) or, more compact:  ∆hs∆hr 0   =   es qsser qrs 0 ets   ·[ ∆w0 ∆θs ] +   ∑ k∈e bkqsk∑ k∈e bkqrk∑ k∈e bk   · ∆c (23) since the margins for the support vectors remain constant, ∆hs = 0, the variations of support vectors threshold and the margin variations can be computed as [ ∆w0 ∆θs ] = − [ 0 ets es qss ]−1 · [ ∑ k∈e bk∑ k∈e bkqsk ] ︸ ︷︷ ︸ η︸ ︷︷ ︸ β ∆c (24) ∆hr = ([ er qrs ] β + ∑ k∈e bkqrk ) ︸ ︷︷ ︸ γ ∆c (25) let us summarize the migration conditions for each vector type (details can be found in appendix b). 1. migration of support vectors. • for incremental (∆c > 0, cumulative conditions): – if sgn(βs + sgn(hs)) 6= sgn(hs), then the limit is imposed by (migration s → e): ∆c ≤ −sgn(hs) ·c −θs βs + sgn(hs) – if sgn(βs) = sgn(hs), then the limit is imposed by (migration s → o): ∆c ≤−θs βs • for decremental (∆c < 0, cumulative): – if sgn(βs + sgn(hs)) = sgn(hs), then the limit is imposed by (migration s → e): ∆c ≥ −sgn(hs) ·c −θs βs + sgn(hs) – if sgn(βs) 6= sgn(hs), then the limit is imposed by (migration s → o): ∆c ≥−θs βs 2. migration of other vectors. for other (o) vectors, −� < hr < �: 764 h. gâlmeanu, l.m. sasu, r. andonie • for incremental (∆c > 0): ∆c ≤ sgn(γr)�−hr γr • for decremental (∆c < 0): ∆c ≥ −sgn(γr)�−hr γr 3. migration of error (e) vectors. • for incremental (∆c > 0): (a) if sgn(hr) 6= sgn(γr): ∆c ≤ −sgn(γr)�−hr γr (b) if sgn(hr) = sgn(γr), the limit is not active; • for decremental (∆c < 0): (a) if sgn(hr) = sgn(γr): ∆c ≥ sgn(γr)�−hr γr (b) if sgn(hr) 6= sgn(γr), the limit is not active; 4. variation of c is also limited by a target value ctarget: • for incremental: ∆c ≤ ctarget −c • for decremental: ∆c ≥ ctarget −c when varying c, the maximum variation before the vectors change sets is given by the minimum of these previously described ∆c values for the incremental scenario, or as the maximum, for the decremental case. 5 migration when the support vector set is empty during training process, the support vector set may become empty. in this circumstance, the variations of the free parameter w0 and the regularization parameter are very particular. 5.1 regular training when the support vector set is empty for this case, equations (19) and (20) cannot be used to determine new values for the threshold parameters. equation (16) can be written considering that there are no support vectors and that the threshold values for the error and other vectors do not change: ∆hc = ∆hr = ∆w0 (26) the variation of the margin for the current vector is the same as the variation for the margins of all vectors. imposing limits on the allowed change in margins, before some vector change sets, will lead to determine the specific vector that migrates first. depending on desired direction (increase or decrease of θc), we have two cases: incremental and decremental svm for regression 765 1. if hc < −�, then θc > 0 increases, we have the following limits for margins: • for other vector xo, −� < ho < �, ∆ho ≤ �−ho; • for error vector xe with he > �, the restriction is not active; • for error vector xe with he < −�, ∆he ≤−�−he; • for xc, ∆hc ≤−�−hc. 2. if hc > �, then θc < 0 decreases, we have the following limits for margins: • for other vector xo, −� < ho < �, ∆ho ≥−�−ho; • for error vector xe with he > �, ∆he ≥ �−he; • for error vector xe with he < −�, the restriction is not active; • for xc, ∆hc ≥ �−hc. the rules are valid for either the incremental or the decremental case. the margin of the xc vector is sought to either increase (first situation) or decrease (second). it is sufficient to take ∆w0 to be the smallest (or largest, depending on the desired direction) of these quantities. this way of varying the margins would always guarantee that some of them would be equal to −� or � limit, ensuring that the support vector set would not become empty. 5.2 updating the regularization parameter when the support vector set is empty when the support vector set is empty, the regularization parameter cannot modify on the expense of the variation of support vectors’ threshold values. relations (21) and (22) will change to: ∆hi = (∑ k∈e qikbk ) ∆c + ∆w0 (27) 0 = ∑ k∈e bk∆c (28) relation (28) is always valid, regardless of c’s variation. as opposed to how we proceeded previously, we assume that w0 parameter is kept unchanged, thus ∆hi = µi∆c, where we made the notation µi = ∑ k∈e qikbk. 1. when c increases (∆c > 0, incremental): (a) for other vectors xr ∈ o, −� < hr < �: • if µr > 0 then ∆hr > 0, thus ∆hr ≤ � − hr or µr∆c ≤ � − hr, which leads to ∆c ≤ �−hr µr ; • if µr < 0 then ∆hr < 0, thus ∆hr ≥−�−hr or µr∆c ≥−�−hr, which leads to ∆c ≤ −�−hr µr ; (b) for error vectors xr ine and θr = c, hr < −�: • if µr > 0 then ∆hr > 0, thus ∆hr ≤−�−hr or µr∆c ≤−�−hr, which leads to ∆c ≤ −�−hr µr ; • if µr > 0 then ∆hr < 0, the condition is not active; (c) for error vectors xr ine and θr = −c, hr > �: • if µr > 0 then ∆hr > 0, the condition is not active; 766 h. gâlmeanu, l.m. sasu, r. andonie • if µr < 0 then ∆hr < 0, thus ∆hr ≥ � − hr or µr∆c ≥ � − hr, which leads to ∆c ≤ �−hr µr ; in brief, a minimum will be computed among all the thresholds computed for vectors, imposed by conditions like: ∆c ≤ set ·sgn(µr)�−hr µr (29) where set = 1 if xr ∈ o and set = −1 if xr ∈ e. the condition will be inactive if sgn(µr) = sgn(hr) for xr ∈ e. 2. when c decreases (∆c < 0, incremental): proceeding in the same manner, a maximum will be computed among all the thresholds determined by similar conditions: ∆c ≥ −set ·sgn(µr)�−hr µr (30) where set is defined like above. the condition will be inactive if sgn(µr) 6= sgn(hr), for xr ∈ e. 6 experimental results in our experiments, we compare the idsvmr with the mlp, two svm–based regression models, and two incremental learning models derived by us in previous work from adaptive resonance theory: the fuzzy artmap with relevance (famr) and the bayesian artmap for regression (bar). since the last two models are less known, we included here a short description of them. famr was introduced in [1], as an extension of the classical fuzzy artmap (fam) [15] and probart [16]. famr can be used as classifier, regression model and posterior probability estimator. each training pattern can come with its own relevance factor assigned to it, which influences the probabilistic links formed between the input and output categories. a relevance factor allows for ranking of sample pairs according to the confidence one has in the information source. famr builds one–to–many mappings between input and output categories through maximum likelihood, approximating the probability of associations between input and output categories. famr comes with a stochastic approximation procedure, which allows making use of the relevance factor associated to the training patterns. under some mild constraints, famr’s mapfield values converge both in mean square and with probability one to the posterior probability p(k|j) between the jth input category and the kth output category. bayesian artmap (ba) [17] is a neural architecture which uses a combination of fam competitive learning and bayesian learning. ba uses gaussian categories and fam competitive learning. ba modifies some of the characteristics of the fam algorithm mainly by replacing the hyperrectangular categories with gaussian categories. the categories may grow or shrink, and they are probabilistically associated to classes. ba probabilistically infers the classes associated to input categories, by using all input categories, unlike the competitive approach used by fam and famr. in [18], the ba is extended for function approximation. the resulted architecture – bayesian artmap for regression (bar) – generalizes the ba algorithm using the clustering functionality of both input and output modules. the bar has the universal approximation capability and also the best approximation property; this is very important in regression [18]. both the famr and the bar can be trained incrementally, but not decrementally. this is typical for fam models. incremental and decremental svm for regression 767 for benchmarking, we use the following public datasets [6]: cpu computer hardware (cpu), boston housing (bh), wisconsin breast cancer (wbc), and communities and crime (cc). for the cpu datasets we removed the following input features: vendor name, model name, and estimated relative performance. from the wbc dataset, we removed the four instances with missing values and also the outcome attribute. the first five features of the cc dataset are acknowledged (by the dataset donors) not to be predictive. we removed them, together with the features with missing values. the original datasets are described in [6]. the filtered datasets, as explained above1, are synthetically described in table 1. to support performance comparisons across various benchmarks, both input and output values are independently scaled between 0 and 1. table 1: synthetic description of the four benchmark datasets used. some instances and/or attributes were removed from the original datasets dataset input attributes instances cpu 6 209 bh 13 506 wbc 32 194 cc 99 1994 we compare the regression capabilities of idsvmr, bar, and famr. we are also interested in comparing these incremental models with the popular mlp model and two classical svmbased regression algorithms, namely ε-svr [20] and ν-svr [21]. this is of interest because idsvmr, ε-svr and ν-svr have overlapping inductive bias, and beyond that they cover incremental and non-incremental adaptive models. we include mlp in the group of non–incremental models. even if is trained with mini–batches of data, the mlp does not learn new information without possibly corrupting previously learned information (the stability-plasticity condition is not addressed). for famr, bar and idsvmr, we use our own weka plugins, whereas for ε-svr, ν-svr and mlp we use the weka–provided implementations [10]. we use ten random permutations (shuffles) of each datasets. for each permutation, 66% of the data is used as training/validation set and the rest serves exclusively as test set. a ten–fold crossvalidation on the training/validation set is performed, for fine-tuning the hyperparameters of each model. for mlp, bar and famr, paper [18] enumerates the sought hyperparameters and their corresponding search ranges. for idsvmr, the hyperparameters are: the regularization coefficient c (shown in equation 2) and σ, the width of the gaussian kernel2. for the cc dataset, we seek an optimal value of c in {4, 4.5, . . . , 8}, and σ is sought over the candidate values {0.5, 0.7, 0.9}. for the other three datasets, c is sought in the set {0.2, 0.4, . . . , 10}, and the candidate values for σ are in the set {0.5, 0.6, . . . , 0.9}. the same hyperparameters and corresponding search ranges as for idsvmr are used for both ε-svr and for ν-svr. after cross-validation, we train the optimized models on the whole learning/validation set, and their generalization capability is assessed on the test set. the test set is used solely in this final assessment. the reported scoring values are the root mean squared error (rmse) and the mean absolute error (mae). table 2 contains the average rmse and mae values over the ten permutations, measured on the test sets. we split the table into two sections: the former contains only incremental models, the latter is devoted to mlp and to the two svm–based regression models. 1available at http://www.liaad.up.pt/~ltorgo/regression/datasets.html 2note that section 1.1 and the subsequent ones "hide" this hyperparameter under the notation qij. nevertheless, one ought to seek for a proper value of it as well. 768 h. gâlmeanu, l.m. sasu, r. andonie the results for mlp, famr, and bar are the ones found in [18]. all the steps for crossvalidation, learning, and performance assessment are performed under the weka experimenter framework [10]. table 2: the performance assessment results for ten random permutations of each of the four benchmarks datasets. the results are split into two groups: the former refers the incremental models (famr, bar and idsvmr), the latter presents the scores for ε-svr, ν-svr and mlp. every cell of the table contains the pair of rmse and mae values averaged on the test sets. the boldfaced values are optimal in their corresponding group. the loss values for mlp, famr and bar are the ones reported in [18]. model cpu bh wbc cc famr 0.09, 0.06 0.15, 0.12 0.31, 0.27 0.21, 0.14 bar 0.12, 0.06 0.17, 0.12 0.27, 0.23 0.20, 0.14 idsvmr 0.12, 0.05 0.09, 0.05 0.26, 0.22 0.14, 0.09 ε-svr 0.07, 0.05 0.09, 0.06 0.26, 0.22 0.16, 0.12 ν-svr 0.05, 0.02 0.09, 0.05 0.26, 0.22 0.15, 0.11 mlp 0.19, 0.14 0.15, 0.11 0.30, 0.25 0.15, 0.12 for all four datasets, in the group of incremental models, the minimum mae is obtained by idsvmr. except for the cpu dataset, the lowest rmse is produced by idsvmr; for cpu, idsvmr produces the median rmse value, at tie with the bar. for the bh dataset, idsvmr largely outperforms the other models. for all four datasets, idsvmr exhibits a consistent tendency to produce the lowest error scores. in case of non–incremental models, ν-svr always obtains the lowest scores. except for the cpu dataset, the ε-svr and ν-svr models have a similar performance. mlp’s scores are close to the ones obtained by ν-svr for cc, while for the other ones mlp shows rather modest performances. finally, we compare the incremental and non–incremental models. for cpu, ν-svr obtains better performance scores than any incremental model. for bh and wbc, ν-svr is at par with idsvmr. for cc, idsvmr outperforms any non–incremental model. conclusion the idsvmr proves to be a performant and functional implementation of the adiabatic incremental/decremental svr model. we have described it here with complete implementation details and we have implemented it as a weka plugin. in conclusion, the newly–introduced idsvmr shows itself as a promising incremental regression model, favorably comparing with the other three incremental algorithms in terms of performance. due to the differences between the hyperparameters number and ranges, it is rather meaningless to consider execution time for the cross-validation stage used for hyperparameter optimization. svms (and svrs) are known be relatively slow during training. using incremental/decremental training is therefore very attractive when dealing with large datasets and with data streams. incremental and decremental svm for regression 769 bibliography [1] andonie, r.; sasu, l. 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(2000); new support vector algorithms, neural computation, 12 (5): 1207–1245. appendix a migration of vectors on learning and unlearning 1. migration of support (s) vectors. from equation (19), ∆θs = βs · ∆θc, the following cases are possible: (a) hs = �, for −c < θs < 0: i. if incremental (∆θc > 0) and βs > 0: ∆θs > 0, θs can reach 0, βs · ∆θc = ∆θs ≤ 0 −θs, thus ∆θc ≤−θsβs (s → o) ii. if incremental (∆θc > 0) and βs < 0: ∆θs < 0, θs can reach −c, βs · ∆θc = ∆θs ≥ −c − θs, thus ∆θc ≤ −c−θsβs (s → e) iii. if decremental (∆θc < 0) and βs > 0: ∆θs < 0, θs can reach -c, βs · ∆θc = ∆θs ≥ −c − θs, thus ∆θc ≥ −c−θsβs (s → e) iv. if decremental (∆θc < 0) and βs < 0: ∆θs > 0, θs can reach 0, βs · ∆θc = ∆θs ≤ 0 −θs, thus ∆θc ≥−θsβs (s → o) (b) hs = −�, for 0 < θs < c: i. if incremental (∆θc > 0) and βs > 0: ∆θs > 0, θs can reach c, βs ·∆θc = ∆θs ≤ c−θs, thus ∆θc ≤ c−θsβs (s → e) ii. if incremental (∆θc > 0) and βs < 0: ∆θs < 0, θs can reach 0, βs · ∆θc = ∆θs ≥ 0 −θs, thus ∆θc ≤−θsβs (s → o) iii. if decremental (∆θc < 0) and βs > 0: ∆θs < 0, θs can reach 0, βs · ∆θc = ∆θs ≥ 0 −θs, thus ∆θc ≥−θsβs (s → o) iv. if decremental (∆θc < 0) and βs < 0: ∆θs > 0, θs can reach c, βs ·∆θc = ∆θs ≤ c−θs, thus ∆θc ≥ c−θsβs (s → e) 2. migration of other (o) vectors. these are the vectors with −� < he < �, θr = 0, ∆hr = γr · ∆θc. (a) if incremental (∆θc > 0) and γr > 0: ∆hr > 0, hr can reach �, γr · ∆θc = ∆hr ≤ �−hr, thus ∆θc ≤ �−hrγr (o → s) (b) if incremental (∆θc > 0) and γr < 0: ∆hr < 0, hr can reach −�, γr ·∆θc = ∆hr ≥−�−hr, thus ∆θc ≤ −�−hrγr (o → s) incremental and decremental svm for regression 771 (c) if decremental (∆θc < 0) and γr > 0: ∆hr < 0, hr can reach −�, γr ·∆θc = ∆hr ≥−�−hr, thus ∆θc ≥ −�−hrγr (o → s) (d) if decremental (∆θc < 0) and γr < 0: ∆hr > 0, hr can reach �, γr · ∆θc = ∆hr ≤ �−hr, thus ∆θc ≥ �−hrγr (o → s) 3. migration of the error (e) vectors. (a) for hr > �, θr = −c, ∆hr = γr · ∆θc: i. if incremental (∆θc > 0) and γr > 0: ∆hr > 0, hr increases even further, vector does not migrate; ii. if incremental (∆θc > 0) and γr < 0: ∆hr < 0, hr may reach �, γr · ∆θc = ∆hr ≥ �−hr, ∆θc ≤ �−hrγr (e → s) iii. if decremental (∆θc < 0) and γr < 0: ∆hr > 0, hr increases even further, vector does not migrate; iv. if decremental (∆θc < 0) and γr > 0: ∆hr < 0, hr may reach �, γr · ∆θc = ∆hr ≥ �−hr, ∆θc ≥ �−hrγr (e → s) (b) for hr < −�, θr = c, ∆hr = γr · ∆θc: i. if incremental (∆θc > 0) and γr > 0: ∆hr > 0, hr may reach −�, γr ·∆θc = ∆hr ≤−�−hr, ∆θc ≤ −�−hrγr (e → s) ii. if incremental (∆θc > 0) and γr < 0: ∆hr < 0, hr decreases even further, vector does not migrate; iii. if decremental (∆θc < 0) and γr > 0: ∆hr < 0, hr decreases even further, vector does not migrate; iv. if decremental (∆θc < 0) and γr < 0: ∆hr > 0, hr may reach −�, γr ·∆θc = ∆hr ≤−�−hr, ∆θc ≥ −�−hrγr (e → s) 4. migration of the current xc vector. the current newly added vector xc is checked for possible migration to the support set (s): (a) for hc > �: i. if incremental (∆θc > 0) and γc > 0: ∆hc > 0, hc increases even further, vector does not migrate; ii. if incremental (∆θc > 0) and γc < 0: ∆hc < 0, hc may reach �, γc · ∆θc = ∆hc ≥ �−hc, ∆θc ≤ �−hcγc (e → s) iii. if decremental (∆θc < 0) and γc > 0: ∆hc < 0, hc may reach �, γc · ∆θc = ∆hc ≥ �−hc, ∆θc ≥ �−hcγc (e → s) iv. if decremental (∆θc < 0) and γc < 0: ∆hc > 0, hc increases even further, vector does not migrate; (b) for hc < −�: i. if incremental (∆θc > 0) and γc > 0: ∆hc > 0, hc may reach −�, γc ·∆θc = ∆hc ≤−�−hc, ∆θc ≤ −�−hcγc (e → s) ii. if incremental (∆θc > 0) and γc < 0: ∆hc < 0, hc decreases even further, vector does not migrate; iii. if decremental (∆θc < 0) and γc > 0: ∆hc < 0, hc decreases even further, vector does not migrate; iv. if decremental (∆θc < 0) and γc < 0: ∆hc > 0, hc may reach −�, γc ·∆θc = ∆hc ≤−�−hc, ∆θc ≥ −�−hcγc (e → s) 772 h. gâlmeanu, l.m. sasu, r. andonie appendix b migration of vectors on varying regularization parameter 1. migration of the support vectors. (a) for ∆c < 0, when c is decreasing (decremental): i. for support vector with 0 ≤ θs ≤ c, hs = −�, the variation of c is limited by 0 ≤ θs + ∆θs ≤ c + ∆c: a. for βs ≤ 0: • βs · ∆c = ∆θs ≥ 0, θs + ∆θs ≥ 0 is always fulfilled; • θs + βs · ∆c ≤ c + ∆c; so (βs − 1)∆c ≤ c −θs leads to ∆c ≥ c−θs βs−1 , ∆θs ≥ 0, θs can reach c (migration s → e) b. for 0 ≤ βs ≤ 1, both conditions are active: • 0 ≤ θs + βs∆c, leading to ∆c ≥−θs βs , ∆θs ≤ 0, θs can reach 0 (migration s → o) • θs + βs∆c ≤ c + ∆c, (βs − 1)∆c ≤ c −θs, leading to ∆c ≥ c−θs βs−1 , ∆θs = βs∆c, θs can reach c (migration s → e) c. for βs ≥ 1: • θs + βs∆c ≥ 0 leads to ∆c ≥−θs βs , θs decreases (migration s → o) • θs + βs∆c ≤ c + ∆c, or (βs − 1)∆c ≤ c −θs, is always fulfilled. summary: • if βs − 1 ≤ 0, we have s → e migration: ∆c ≥ c −θs βs − 1 • if βs ≥ 0, we have s → o migration: ∆c ≥−θs βs ii. for support vector with −c ≤ θs ≤ 0, hs = +�, the variation of c is limited by −c − ∆c ≤ θs + ∆θs ≤ 0: a. for βs ≤−1: • −c − ∆c ≤ θs + βs∆c, or −c −θs ≤ (βs + 1)∆c is always fulfilled; • θs + βs∆c ≤ 0 leads to ∆c ≥−θs βs , ∆θs ≥ 0, θs can reach 0 (migration s → o) b. for −1 ≤ βs ≤ 0, both conditions are active: • −c − ∆c ≤ θs + βs∆c, leading to ∆c ≥ −c−θs βs+1 (migration s → e) • θs + βs∆c ≤ 0 leads to ∆c ≥−θs βs (migration s → o) c. for βs ≥ 0: • −c − ∆c ≤ θs + βs∆c, or −c −θs ≤ (βs + 1)∆c, leading to ∆c ≥ −c−θs βs+1 (migration s → e) • θs + βs∆c ≤ 0 is always fulfilled. incremental and decremental svm for regression 773 summary: • if βs ≤ 0, we have s → o migration: ∆c ≥−θs βs • if βs + 1 ≥ 0, we have s → e migration: ∆c ≥ −c −θs βs + 1 (b) for ∆c > 0, when c is increasing (incremental): i. for support vector with 0 ≤ θs ≤ c, hs = −�, the variation of c is limited by 0 ≤ θs + ∆θs ≤ c + ∆c: a. for βs ≤ 0: • θs + βs∆c ≥ 0, or βs∆c ≥−θs leads to ∆c ≤−θs βs (migration s → o) • θs + βs∆c ≤ c + ∆c or (βs − 1)∆c ≤ c −θs is always fulfilled; b. for 0 ≤ βs ≤ 1: • θs + βs∆c ≥ 0, or βs∆c ≥−θs is always fulfilled; • θs + βs∆c ≤ c + ∆c or (βs − 1)∆c ≤ c −θs is also always fulfilled; c. for βs ≥ 1: • θs + βs∆c ≥ 0, or βs∆c ≥−θs is always fulfilled; • θs + βs∆c ≤ c + ∆c or (βs − 1)∆c ≤ c −θs leads to ∆c ≤ c−θs βs−1 (migration s → e) summary: • if βs ≤ 0, we have s → o migration: ∆c ≤−θs βs • if βs − 1 ≥ 0, we have s → e migration: ∆c ≤ c −θs βs − 1 ii. for support vector with −c ≤ θs ≤ 0, hs = +�, the variation of c is limited by −c − ∆c ≤ θs + ∆θs ≤ 0: a. for βs ≤−1: • −c − ∆c ≤ θs + βs∆c, or −c −θs ≤ (βs + 1)∆c leads to ∆c ≤ −c−θs βs+1 (migration s → e) • θs + βs∆c ≤ 0 or βs∆c ≤−θs is always fulfilled; b. for −1 ≤ βs ≤ 0: • −c − ∆c ≤ θs + βs∆c, is always fulfilled; • θs + βs∆c ≤ 0 is also always fulfilled; c. for βs ≥ 0: • −c − ∆c ≤ θs + βs∆c is always fulfilled; • θs + βs∆c ≤ 0 or βs∆c ≤−θs leads to ∆c ≤−θs βs (migration s → o) 774 h. gâlmeanu, l.m. sasu, r. andonie summary: • if βs + 1 ≤ 0, we have s → e migration: ∆c ≤ −c −θs βs + 1 • if βs ≥ 0, we have s → o migration: ∆c ≤−θs βs for migration of support vectors, these conditions are summarized in section 4. hence, we have (conditions may be cumulative): • for incremental (∆c > 0): – if sgn(βs + sgn(hs)) 6= sgn(hs), then the limit is imposed by (migration s → e): ∆c ≤ −sgn(hs) ·c −θs βs + sgn(hs) – if sgn(βs) = sgn(hs), then the limit is imposed by (migration s → o): ∆c ≤−θs βs • for decremental (∆c < 0): – if sgn(βs + sgn(hs)) = sgn(hs), then the limit is imposed by (migration s → e): ∆c ≥ −sgn(hs) ·c −θs βs + sgn(hs) – if sgn(βs) 6= sgn(hs), then the limit is imposed by (migration s → o): ∆c ≥−θs βs 2. migration of other vectors. for other (o) vectors, −� < hr < �, where θr = 0; ∆hr = γr · ∆c. (a) for ∆c < 0, when c is decreasing (decremental): i. for γr > 0: ∆γr < 0, hr may reach −�, γr∆c ≥−�−hr, it leads to ∆c ≥ −�−hrγr (migration o → s) ii. for γr < 0: ∆γr > 0, hr may reach �, γr∆c ≤ � − hr, it leads to ∆c ≥ �−hrγr (migration o → s) (b) for ∆c > 0, when c is decreasing (decremental): i. for γr > 0: ∆γr > 0, hr may reach �, γr∆c ≥ � − hr, it leads to ∆c ≤ �−hrγr (migration o → s) ii. for γr < 0: ∆γr < 0, hr may reach −�, γr∆c ≥−�−hr, it leads to ∆c ≤ −�−hrγr (migration o → s) resuming the o → s migration: incremental and decremental svm for regression 775 • for incremental (∆c > 0): ∆c ≤ sgn(γr)�−hr γr • for decremental (∆c < 0): ∆c ≥ −sgn(γr)�−hr γr 3. migration of error (e) vectors. (a) for error vectors with hr > �, where θr = −c; ∆hr = γr · ∆c: i. for ∆c < 0 and γr < 0, or ∆c > 0 and γr > 0, then ∆hr > 0, hr increases even further, there is no migration; ii. for ∆c < 0 and γr > 0, then ∆hr < 0, hr may reach �; γr∆c ≥ �−hr leads to ∆c ≥ �−hr γr (migration e → s) iii. for ∆c > 0 and γr < 0, then ∆hr < 0, hr may reach �; γr∆c ≥ �−hr leads to ∆c ≤ �−hr γr (migration e → s) (b) for error vectors with hr < −�, where θr = c; ∆hr = γr · ∆c: i. for ∆c < 0 and γr > 0, or ∆c > 0 and γr < 0, then ∆hr < 0, hr decreases even further, there is no migration; ii. for ∆c < 0 and γr < 0, then ∆hr > 0, hr may reach −�; γr∆c ≤−�−hr leads to ∆c ≥ −�−hr γr (migration e → s) iii. for ∆c > 0 and γr > 0, then ∆hr > 0, hr may reach −�; γr∆c ≤−�−hr leads to ∆c ≤ −�−hr γr (migration e → s) resuming the e → s migration: • for incremental (∆c > 0): (a) if sgn(hr) 6= sgn(γr): ∆c ≤ −sgn(γr)�−hr γr (b) if sgn(hr) = sgn(γr), the limit is not active; • for decremental (∆c < 0): (a) if sgn(hr) = sgn(γr): ∆c ≥ sgn(γr)�−hr γr (b) if sgn(hr) 6= sgn(γr), the limit is not active; 4. variation of c is also limited by a target value ctarget: • for incremental: ∆c ≤ ctarget −c • for decremental: ∆c ≥ ctarget −c int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 204-217 application of chaos embedded pso for pid parameter tuning o.t. altinoz, a.e. yilmaz, g.-w. weber o. tolga altinoz hacettepe university bala vocational school electronics technology department, ankara, turkey taltinoz@hacettepe.edu.tr a. egemen yilmaz ankara university electronics engineering department, ankara, turkey aeyilmaz@eng.ankara.edu.tr g. wilhelm weber middle east technical university institute of applied mathematics, ankara, turkey gweber@metu.edu.tr abstract: proportional-integral-derivative (pid) control is the most common method applied in the industry due to its simplicity. on the other hand, due to its difficulties, parameter tuning of the pid controllers are usually performed poorly. generally, the design objectives are obtained by adjusting the controller parameters repetitively until the desired closed-loop system performance is achieved. this allows researchers to use more advanced and even some heuristic methods to achieve the optimal pid parameters. this paper focuses on application of the chaos embedded particle swarm optimization algorithm (cpso) for pid controller tuning, and demonstrates how to employ the cpso method to find optimal pid parameters in details. the method is applied to optimal pid parameter tuning for three typical systems with various ordered, and comparisons with the conventional pso and the ziegler-nichols methods are performed. the numerical results from the simulations verify the performance of the proposed scheme. keywords: particle swarm optimization (pso); chaos theory; pid control; multidimensional optimization. 1 introduction numerous control techniques have so far been introduced and applied in industrial problems. on the other hand, since its invention in 1910, proportional-integral-derivative (pid) control has become the best known controller due to its simplcitiy and efficiency in various control problems. industrial implementation of the pid controller is remarkable that its tuning still presents a challenge in many applications. primary problem associated with such systems is to obtain the optimal pid controller parameters for a satisfactory control performance, which is a quite difficult task. therefore, enormous amount of research results have been reported in the literature, among which the well-known tuning method was proposed by ziegler and nichols in 1942. despite the huge number of proposed approaches for pid parameter tuning, most of them (as well as the ziegler-nichols method) occasionally yield poor performance in practice. generally, conventional tuning methods use the root locus and frequency response for pid parameter tuning. early presentation of the most common conventional tuning method, the ziegler-nichols method, was based on the open-loop step response of the system. with such copyright c⃝ 2006-2012 by ccc publications application of chaos embedded pso for pid parameter tuning 205 definition, the performance was observed to be inadequate and poor. later, the authors improved the method by making it dependent on the frequency response of the close-loop system. the procedure imposed by this method can be summarized as follows: i) first, the system is assumed to be under proportional control only. the proportion parameter is increased until the system becomes critically stable. the proportion parameter yielding this condition is recorded as well as the corresponding oscillation period; ii) based on these values, other parameters (i.e. corresponding to the integral and derivative operations) are determined via some look-up-tables which can be found any control theory textbook. however, this method yields unsatisfactory phase and gain margins. in addition, due to its off-line unautomatized nature, the ziegler-nichols method is not quite applicable to processes in the working systems in practice. for this reason, recently, heuristic methods have been employed for pid parameter tuning in order to improve the controller performance. in the last decade, heuristic approaches have received increased attention from researchers dealing with engineering problems. over the years, several heuristic methods have been proposed for pid parameter tuning. in 1995, a novel heuristic approach called the particle swarm optimization (pso) was introduced by eberhart and kennedy [7]. compared to other optimization algorithms, pso has a very simple mathematical definition yielding easy-to-implement and short computer programs, which can generate high-quality solutions with reasonable speed. however, like other heuristic methods, conventional pso suffers from premature convergence especially in higher order complicated problems. in order to improve pso, so far numerous variants have been introduced. compared to the conventional pso, most of these variants show relatively better performance. much research is still in progress for improvement of the performance of pso especially in complicated problems. in this study, we present a simple method for pid controller parameter tuning by using the chaos embedded particle swarm optimization algorithm (cpso). pid parameter tuning achives in such a way that parameters are adjusted in real-time when the controller is active, which is called on-line tuning or the parameters are measured than the tuned controller is performed on a plant, which is called off-line tuning. hence, in this study, the cpso is applied to overall system to obtained the design objectives by adjusting the controller parameters at each iteration repetitively until the desired closed-loop system performance is achieved. this attitude is off-line tuning. the performance of the closed-loop system can defined in terms of rise time, overshoot, settling time and steady state error. in general, the system with fast rise and settling time under no steady-state error and almost zero overshoot is desired. hence, in this study to provide a desired performance, mean value of squared error signal, which is different between reference signal and the system output, is minimized by using cpso and pso. these methods (cpso, pso and ziegler-nichols) are applied to three typical systems of different orders. the first system under investigation is the acceleration model of an object in a frictional environment. the second one is the inverted pendulum control problem, and the last issue is the field-controled of a dc motor problem. when these systems are controlled via pid controller, the closed-loop performance is sensitively dependent to the pid parameters. hence, determination of these parameters becomes quite critical. therefore, pid parameter tuning methods should be performed on the closed-loop system. in section 2 of the paper, the descriptions and formulations of the controller and problems under investigation are presented. section 3 contains the definitions of the pso and cpso algorithms; and finally, section 4 presents the numerical results and section 5 contains the comparisons together with discussions and concluding remarks. 206 o.t. altinoz, a.e. yilmaz, g.-w. weber table 1: increase in each pid parameter and its impact on transient response parameter rise time overshoot settling time steady state error kp decrease increase increase decrease ki decrease increase increase decrease kd decrease decrease decrease slight change 2 problem statement in this study, pid control is discussed and three systems of different orders are investigated for the performance comparison of the proposed cpso method with the ziegler-nichols method and conventional pso. each system is summarized via pictorial description as well as its transfer function in the upcoming subsections. 2.1 the proportional-integral-derivative (pid) control the basic form of the pid controller composes from sum of the multiplication, integration and differentiation of the error signal, and each operator is multiplied by three control parameters. time domain representation of pid control equation is presented in eq. 1, meanwhile the transfer function is given in eq. 2: u(t) = kp e(t) + ki ∫ e(t)dt + kd de(t) dt , (1) g(s) = u(s) e(s) = kds 2 + kps + ki s , (2) where kp , ki and kd are the control parameters, u(t) is the controller output/system input, and e(t) is error signal. in frequency domain, the transfer function of the pid controller has one pole at the origin, and two zeros with locations depending on the tuning strategy. table 1 presents the separate and isolated impact of change in each parameter on the system performance. however, in reality, the parameters must be mutually tuned. therefore, these impacts listed in table 1 should not be considered as rules-of-thumb, and applied directly for parameter tuning. 2.2 a system of first order: acceleration control of an object in a frictional environment the first order system in this study results from the acceleration of an object against the friction due to the ground contact, which is one of the fundamental models in vehicular control. the figure 1 depicts the pictorial description of the relevant system. figure 1: accelerated object model in a frictional environment. the transfer function of the system is given in eq. 3. the model presents the equation of motion for the speed of an object with mass m. assuming that the input u imposes a force; application of chaos embedded pso for pid parameter tuning 207 table 2: setup1a and setup1b descriptors descriptor symbol setup1a setup1b friction constant (n.sec/m) b 50 50 mass of the object (kg) m 100 50 desired velocity of the object (m/sec) v 1 1 meanwhile the friction force bv restricts the movement of the object, where v is the velocity of the object as well as the output of the overall system. the aim of the controller is to make the object to reach to a desired velocity. v (s) u(s) = 1/m s + b m (3) for this particular type of problem, two different setups are constructed and investigated. from now on, these setups will be referred to as setup1a and setup1b. the descriptor parameters of these setups are given in table 2. 2.3 a system of second order: inverted pendulum the inverted pendulum is one of the benchmark problems of control theory for illustration and comparison of different control methodologies. the problem comes from the motivation on development of missiles, rockets, robots and other transportation means. in general, the aim in the inverted pendulum problem is to maintain the pendulum at upright position. therefore, in addition to the pid control, various techniques (such as optimal control [15], linear [9] control, nonlinear control [5], intelligent and adaptive control methods [3]) have also been applied to this problem. figure 4 shows the general model of the inverted pendulum. the pendulum is composed of a free moving pendulum with the mass mp and length l attached to the cart with mass mt, where a force f is applied to this setup. figure 2: inverted pendulum model. eq. 4 shows the transfer function of the inverted pendulum system. the moment of inertia i about the pendulum’s mass center is defined as i = (1/3)mpl2. θ(s) u(s) = mpl ((i + mpl2) − m2pl2)s2 − mpgl(mp + mt) . (4) for this particular type of problem, four different setups are constructed and investigated. from now on, these setups will be referred to as setup2a, setup2b, setup2c and setup2d. the descriptor parameters of these setups are given in table 3. 208 o.t. altinoz, a.e. yilmaz, g.-w. weber table 3: setup2a setup2d descriptors descriptor symbol setup2a setup2b setup2c setup2d mass of the cart (kg) mt 0.5 0.5 1 1 mass of the pole (kg) mp 1 1 1 1 length of the pole (m) l 0.5 0.5 2 2 initial value of the rod angle (rad) θ 0.4 0.8 0.4 0.8 table 4: setup3a and setup3b descriptors descriptor symbol setup3a setup3b motor constant (n.m/a) km 50.10−3 50.10−3 friction constant (n.sec/m) b 50.10−3 50.10−3 resistance (ω) rf 1 10 field time effect (msec) τf 1 10 rotor time constant (msec) τl 100 10 rotor angle (rad) θ 2 2 2.4 a system of third order: field-controlled dc motor the third problem under investigation is the field-controlled dc motor problem depicted in figure 1, which has a third order transfer function. the main purpose of this type of systems are to increase speed while reduce the torque. eq. 5 gives the transfer function of the system. figure 3: field-controlled dc motor model. θ(s) vf(s) = km/(brf) s(τfs + 1)(τls + 1) , (5) where km is the motor constant, b is the friction constant, τf = lf/rf is the field time effect, τl = j/b is the rotor time constant, and j is the rotor inertia, vf is the input and θ is the output of the system. for this particular type of problem, two different setups are constructed and investigated. from now on, these setups will be referred to as setup3a and setup3b. the descriptor parameters of these setups are given in table 4. 3 particle swarm optimization (pso) the pso algorithm depends on motions of particles (swarm members) searching for the global best in an n-dimensional continuous space. the position of each particle is nothing but a solution candidate, and every time, the fitness of this candidate is re-evaluated. in addition application of chaos embedded pso for pid parameter tuning 209 to its exploration capability (i.e. the tendency for random search throughout the domain), each particle has a cognitive behavior (i.e. remembering its own good memories, and having tendency to return there); as well as a social behavior (i.e. observing the rest of the swarm and having tendency to go where most other particles go). the original pso formulation of kennedy and eberhart [7] depends on the update of the position xi[k] and the velocity vi[k] of the ith particle (swarm member) at the kth iteration as follows: vi[k + 1] = vi[k] + c1 × rand() × (pbesti − xi[k]) + c2 × rand() × (gbesti − xi[k]), (6) xi[k + 1] = xi[k] + vi[k + 1] × ∆t, (7) where c1 and c2 are measures indicating the tendencies of approaching to pbest and gbest, which are the best positions achieved personally by the ith particle and the whole swarm, respectively. in other words, c1 and c2 are the measures of the cognitive and the social behaviors (called cognitive and social parameters), respectively. rand() is a uniformly distributed pseudo-random number generator which produces random numbers between 0.0 and 1.0; and the time step size ∆t is taken to be unity for simplicity. this optimization algorithm demonstrates an outstanding performance under complicated problems. but still, it occasionally faces problems such as getting stuck at local optima and stagnation for multi-dimensional and complex problems. thus, various improvements have been proposed in order to get rid of these problems. one of the important innovations was introduced by shi and eberhart [11], who proposed a term called "inertial weight" in order to improve the performance of the method (used for controlling local and global exploration behavior of the population). by introduction of this term, which puts an additional control on the current velocity of the particles, eq. 6 is modified as: vi[k + 1] = w[k] × vi[k] + c1 × rand() × (pbesti − xi[k]) + c2 × rand() × (gbesti − xi[k]), (8) which is referred to as the inertial weight pso, and is currently accepted as the de-facto pso formulation. moreover, in a following study [11], shi and eberhart showed that the ideal choice for the inertial weight is to decrease it linearly from 0.9 to 0.4. the following pseudocode presents the pso algorithm. pso algorithm initialize random velocity and position do for i = 1 to swarm size 1) calculate fitness function (fit) aimed to be minimized. this is the function that the mean squre error of the difference between reference signal and the output of the system if fit < best pattern ith particle best position = ith particle position best pattern = fit end if end for find min best pattern and corresponding particle for i = 1 to swarm size update velocity 210 o.t. altinoz, a.e. yilmaz, g.-w. weber update position end for check the limits of the maximum velocity while maximum iterations are not exceeded or minimum error is not achieved 4 chaos embedded particle swarm optimization (cpso) after pso has been presented, various hybrid and innovative methods were introduced in order to get rid of its drawbacks such as premature convergence and stagnation for multidimensional and complicated problems. one of the ideas for preventing premature convergence is to embed chaotic maps into pso. in recent years, chaotic maps have been utilized for generating pseudo-random numbers, and they have been applied to many areas like communication and control theory, since they can produce sufficiently random numbers enhancing the overall performance of the systems. for pso, the chaotic maps can be helpful for prevention of premature convergence [2]; moreover, it can improve the global/local searching capabilities of the algorithm. implementation of chaotic maps in pso can be categorized into two different approaches: in the first approach, variables and/or random number generators are reinforced with the chaotic maps without any radical changes in the algorithm. in a similar manner, any stochastic algorithm can be improved by using chaotic maps. for pso, the performance of the algorithm is greatly dependent on the parameters w, c1, c2 as well as the random number generators. thus, any improvement performed on these variables would slightly have influence on the overall performance. in the second approach, chaos is used in order to interact with the pso algorithm for searching the solution space. the modification in the pso algorithm must be fulfilled. in addition to the conventional algorithm, the add-on code is evaluated in order to find a solution candidate with better fitness. at each iteration, the search space boundary is reduced for increasing the searching efficiency. by this way, the particles are kept away from local optimum. this phenomenon is called chaos search. in this study, due to its effective performance, the second approach is preferred to the first approach. the chaos embedded particle swarm optimization (cpso) [8] differs from the conventional pso with four new operators, which are: chaotic map, variable mapping, inverse variable mapping, and search range operators. 4.1 chaotic maps the discrete-time dynamical system in the iteration form in eq. 9 is called chaotic mapping or chaotic map. cxi+1 = f(cxi,p), (9) where f : s → s and s ∈ ℜ , s = [0,1] or s = [−1,1]. p is the chaos parameter, cx is a vector and f is a nonlinear transformation. the equation starting from the initial value cx0 in the iterative map is obtained. chaotic maps have sensitivity on initial value and they produce pseudo random sequences based on cx0. one of the most common chaotic maps is the logistic map. the logistic map which is a model of population biology, is frequently used with pso [8]. cxi+1[k] = µcxi[k](1 − cxi[k]), (10) where µ (which is called bifurcation parameter) is set to 4 for ergodicity as seen in figure 4. there are two fixed points (if the condition cx∗ = f(cx∗,p) holds cx∗ is called fixed point) exists application of chaos embedded pso for pid parameter tuning 211 at 3/4 and 0. moreover, if the chaotic map will be used with cpso, the fixed points must be checked at the algorithm. figure 4: bifurcation diagram of logistic map. the chaotic map is a function producing a random number based on the initial value. in cpso, the initial value is the current position for a particle and chaotic map produces a new position. if the fitness value related to the new position is optimal than the initial position, then the position of a particle is changed as the new position. thus, it is essential that the chaotic orbit must be ergodic, which means the initial value of the chaotic map must be varied. however, the range of the particle position is not between [−1,1] or [0,1]. hence, the particle position should be mapped into chaotic space. therefore, the variable mapping operator is defined for this purpose. 4.2 variable mapping the position of the particle should be mapped into the chaotic domain by using carrier equation as defined in eq. 11 [14]. cxi[k] = (xi[k] − xmin)/(xmax − xmin), (11) where cxi is decision variable, which is the initial value of the chaotic map, x is the position of the particle and xmin and xmax are the boundaries of the search space. this function maps the range [xmin,xmax] to the range of chaotic variable [0,1]. the procedure; variable mapping a particle position and then using this position as an initial value of a chaotic map is called chaos search. however, when the optimal point is obtained, it should be mapped to a search space. hence, the inverse mapping operator is produced. 4.3 inverse variable mapping after determination of the chaotic variable from a chaotic map with the initial value from variable mapping, the chaotic variable is converted into the particle position by using eq. 12. this function maps the range of chaotic variable [0,1] to the range of solution space [xmin,xmax]. xi[k] = xmin + cxi[k](xmax − xmin) (12) the position, which is taken from the inverse mapping, is used in order to evaluate the new solutions. if the new solution is better than the non-chaos-search one, the new solution is put pursuant to chaos search. 212 o.t. altinoz, a.e. yilmaz, g.-w. weber 4.4 search range if the search space extends in a wide area, the searching cannot be completed in the optimal area in a short time. hence, in order to obtain high performance, the chaotic search is run in a small range. this search area is changed in the current optimal solution neighborhood [4]. xmin = max(xmin,xg − r(xmax − xmin)), (13) xmax = min(xmax,xg + r(xmax − xmin)), (14) where xg is the global best position so far and r is the variable between 0 and 1. in this study, r is chosen as 0.45, and xg is chosen as 0 for simplicity. decreasing the range of the chaotic search will increase the searching efficiency. the outline of the cpso used in this study can be listed as follows: chaos pso algorithm initialize random velocity and position do for i = 1 to swarm size 1) calculate the fitness function (fit) aimed to be minimized. this function is nothing but the mean square error of the difference between reference signal and the output of the system if fit < best pattern ith particle best position = ith particle position best pattern = fit end if end for find min best pattern and corresponding particle for i = 1 to swarm size update velocity update position end for check the limits of the maximum velocity 1) execute variable mapping that maps the positions into chaos variables (section 4.2) 2) use logistic map and find new chaos variable (section 4.1) 3) execute inverse variable mapping that converts chaos variable into positions (section 4.3) if (new position < old position) position = new position break end if 4) change the search area (section 4.4) end for while maximum iterations are not exceeded or minimum error is not achieved application of chaos embedded pso for pid parameter tuning 213 table 5: optimization algorithm parameters for the simulation property value number of particles in population 10 number of iterations (maxiter) 50 social and cognitive parameters (c1 and c2) 1.494 inertia weight (wmax and wmin) 0.9 down to 0.4 table 6: pid controller parameter values for setup1a parameter value obtained value obtained value obtained by cpso by pso by ziegler-nichols kp 100 22.64 30.52 ki 38.745 100 109.01 kd 1 0.1 30.52 5 simulation results in this study, a simulation environment is constructed in order to apply the tuning methods and make performance comparisons. figure 5 shows the overall setups for all cases; and the selected parameters for the optimization algorithms are given in table 5. figure 5: overall setups for all cases. the brief summary of the solution strategy to the control problems in this study is as follows: 1) initialize the overall system with random pid parameters; 2) obtain the error, which is the transient response of the system where the reference signal is zero; 3) calculate the fitness function; 4) update the parameters by using ziegler-nichols, pso or cpso; 5) return to step 2 until the maximum iteration is achieved. 5.1 results for setup1 for this type of problem, the pid parameters are tuned by using pso, cpso and zeiglernichols methods. figure 6 shows the transient responses of the closed loop system for setup1a and setup1b. table 6 and 7 present the obtained pid parameters for setup1a and setup1b by means of different methods. it can be observed from the figure that for setup1a, pso yields the worst pid parameters. cpso has no overshoot and better rise time compared to pso and ziegler-nichols. for setup1b, ziegler-nichols yields the worst performance. even though cpso seems to outperform to pso, it can be concluded that cpso performance is almost the same with pso. in summary, for this particular type of problem, cpso presents better performance compared to pso and ziegler-nichols. 214 o.t. altinoz, a.e. yilmaz, g.-w. weber figure 6: transient response of the system for a) setup1a and b) setup1b. table 7: pid controller parameter values setup1b parameter value obtained value obtained value obtained by cpso by pso by ziegler-nichols kp 33.86 24.89 0.85 ki 49.3 45.57 43.6 kd 1 1 0.85 5.2 results for setup2 for this type of problem, again, the pid parameters are tuned by using pso, cpso and ziegler-nichols methods. tables 8 and 9 give the obtained pid parameters, and figure 3 shows the transient responses of designs. the results as seen in figure 3 indicates that cpso outperforms to pso and ziegler-nichols. however, in case 2a and case 2b cpso performance is similar to that of ziegler-nichols and pso. from the simulations, it is observed that, cpso is better than pso; on the other hand, it should be noted that for this particular type of problem, ziegler-nichols outperforms to conventional pso. this result emphasizes the importance of the efforts spent for improvement of the conventional pso. 5.3 results for setup3 the pid parameters obtained for this type of problem are presented in tables 10 and 11, and illustrated in figure 8. table 8: pid controller parameter values for setup2a and setup2b parameter value obtained value obtained value obtained by cpso by pso by ziegler-nichols kp 93.6226 60.0136 60.3788 ki 10.4097 0 4.997 kd 10.1 3.6553 4.977 application of chaos embedded pso for pid parameter tuning 215 figure 7: transient response of the system for a) setup2a, b) setup2b, c) setup2c and d) setup2d. table 9: pid controller parameter values for the setup2c and setup2d parameter value obtained value obtained value obtained by cpso by pso by ziegler-nichols kp 162.9 169.7 190 ki 10 79.03 4.9 kd 10 10 5.12 table 10: pid controller parameter values for setup3a parameter value obtained value obtained value obtained by cpso by pso by ziegler-nichols kp 395.33 219.20533 1.08 ki 1.1 55.2488 0.0314 kd 41.6245 1 6.22 216 o.t. altinoz, a.e. yilmaz, g.-w. weber figure 8: transient response of the system for a) setup3a and b) setup3b. table 11: pid controller parameter values for the setup3b parameter value obtained value obtained value obtained by cpso by pso by ziegler-nichols kp 28.73 62.7446 0.2442 ki 186.95 1.7 87.205 kd 8.7 1 1.7 10−4 the results once more indicate the efficiency of the cpso algorithm. at this point, the following remark shall be made: even though for setup3b pso seems to have a better rise time as well as a settling time compared to cpso, it yields a steady state error. therefore, it can still be claimed that cpso has a better overall performance compared to pso and ziegler-nichols. 6 conclusion and future works in this study, the chaos embedded particle swarm optimization algorithm (cpso) is developed, and applied to the some popular engineering problems. the parameter tuning of the pid controller is performed by applying cpso to three systems. at each iteration, the pid parameters are found off-line; then, the optimized controller is applied to the system. the effectiveness of the proposed method is exposed by comparing it to pso and ziegler-nichols methods. it is observed and concluded that cpso outperforms to pso and ziegler-nichols, and it is quite efficient for usage in other systems. the scope of the current study is limited to off-line determination of the pid parameters; but simple and parallelizable structure of pso (and also of cpso) gives the impression that it is feasible to have real-time implementations of pso (and also cpso) in industrial applications for on-line pid controller parameter tuning. application of chaos embedded pso for pid parameter tuning 217 bibliography [1] l.s. coelho, a novel quantum particle swarm optimizer with chaotic mutation operator, chaos, solitons and fractals, vol.37, pp. 1409-1418, 2008 [2] l.s. coelho, v.c. mariani, a novel chaotic particle swarm optimization approach using henon map and implicit filtering local search for economic load dispatch, chaos, solitons and fractals, vol.39, pp. 510-518, 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with dry friction, ieee control systems magazine, 13(4):44-50, 1993 914xun6.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):936-951, december, 2015. pi and fuzzy control for p-removal in wastewater treatment plant h. xu, r. vilanova hongyang xu*, ramón vilanova dept. de telecomunicació i enginyeria de sistemes escola d’enginyeria, universitat autónoma de barcelona, carrer de es sitges, 08193 bellaterra, bcn, spain xu.hongyang@e-campus.uab.cat, ramon.vilanova@uab.cat *corresponding author: xu.hongyang@e-campus.uab.cat abstract: due to the complex and non linear character, wastewater treatment process is difficult to be controlled. the demand for removing the pollutant, especially for nitrogen (n) and phosphorus (p), as well as reducing the cost of wastewater treatment plant is an important research theme recently. thus, in this paper, the benchmark proposed default control strategy and 10 additional control strategies are applied on the combined biological p and n removal benchmark simulation model no.1 (bsm1-p). in addition, according to the results of applying pi controllers, as usual, we also chose the group with the better performance, as well as the default control strategy, to replace the pi controllers with fuzzy controllers. in this way, it can be seen that in all cases the quality of effluent of the controlled process could be improved in some degree; and the fuzzy controllers get a better phosphorus removal. keywords: wastewater treatment plant, pi controllers, fuzzy control, p removal. 1 introduction as the human society develops rapidly, the demand for water resources is playing a more and more important role in the civil life and industrial production. nowadays, stringent legislation for wastewater treatment plants (wwtps) is currently a top driving force for the development of new treatment technologies and for the optimisation of the existing ones. meeting stringent concentration requirements for carbon (c), nitrogen (n) and phosphorus (p) discharge with minimal costs has raised the need of a more efficient operation. however, as a large nonlinear system, wwtps are subject to complex disturbances, where complicated biological and hydrodynamic phenomena are taking place. thus it is difficult to achieve the aim to meet the standard of wwtps effluent quality (eq) and minimize the operational cost (oc) simultaneously. many control strategies have been proposed in the literature but their evaluation and comparison are difficult, either practical or based on simulation. different control algorithms for wwtps have been introduced over the years. for instance, sufficient nitrification can be maintained by applying a constant aeration flow rate, by controlling the dissolved oxygen (do) level at a pre-selected set-point or by using a variable do set-point controller based on ammonia concentration in the last aerobic reactor of the plant [1, 2]. on the other hand, the denitrification process is usually controlled by manipulating the external carbon flow rate or internal recirculation flow rate based on nitrate concentration in the last anoxic reactor or in the last aerobic reactor [3, 4]. unfortunately, various plant configurations, influent characteristics and evaluation criteria have been used in the assessment of control algorithms. as all of these factors influence the choice of a control strategy, it is difficult to say which control algorithm is the most appropriate with respect to minimal oc and best eq, and whether the implementation of complex control algorithms is really necessary. this is caused by many reasons: the variability of influent (both the volume and the chemical component of copyright © 2006-2015 by ccc publications pi and fuzzy control for p-removal in wastewater treatment plant 937 influent), the weather condition, and the complexity of biochemical and physical phenomena, the large range of time constants and the lack of standard evaluation criteria, among others. to deal with this problem, in recent years the benchmark simulation model no.1 (bsm1) [5, 6] has been proposed as a standard platform for comparing different control strategies in the community of wastewater treatment processes. this benchmark model can be used for simulating an effective wwtp to reduce the chemical oxygen demand (cod) of the polluted water as well as to remove the nitrogen (n), both of which are key standards for a wwtp. in addition to cod and n, another major concern is p. since p has been identified as the key element responsible for eutrophication in the aquatic environment, reducing p release to the environment is an important issue for protecting water resource. among those activated sludge systems for p removal, enhanced biological phosphorus removal (ebpr) system was notable since it was introduced [20]. in the ebpr system, the phosphate accumulating organisms (paos ) are responsible for the active of p removal and are enriched to accumulate large quantities of polyphosphate (poly-p) in their cells. in this way the biological p removal is enhanced. however, the paos have a stricter requirement of cyclic anaerobic, anoxic and aerobic conditions than n removal and cod removal, thus the process of p removal is more difficult and complex. although the bsm1 modelling tool has been widely used in the wwtp research community, it has a structural limitation that it does not involve the p removal that should be taken into account for achieving a more realistic simulation model. to fill this gap, gernaey and jorgensen [7] developed a simulation benchmark which models the combined biological p and n removal suitable for the anaerobic-anoxic-oxic (aao) processes, which could be regarded as benchmark simulation model no.1 including p removal (bsm1-p). two pi controllers have been designed and tested for this process and defined as the default control (dc). but since there are many potential combinations of control variables, in this paper we propose 10 pi control strategies to compare the control performance. among them, 5 strategies are basic ones with no more than three controllers, whereas the other 5 strategies are combinations of those 5 basic ones. besides the traditional pi controller, fuzzy logic controller was drawing more attention for improving the performance of wwtps due to its model free and easily understandable character. fuzzy control can be regarded as a viable alternative control strategy in comparison with the conventional control in some certain situations, e.g. the control process with nonlinear characters which may lead to difficult mathematical modelling and controller tuning. so in this paper, we also give some examples of applying several fuzzy controllers on the wwtp process, and compare the control performance with these referred pi controllers. 2 description of bsm1-p the description could be seen in fig.1. resembling the bsm1 model, the bsm1-p has a process layout of seven biological reactors and one settler. as it is showed in the fig.1, the plant lay-out consists of 7 bio-reactors in series followed by a sedimentation tank. here, qin means influent, qe means effluent, qint means nitrate recycle, qr means sludge recycle and qw means waste sludge. the total volume of the biological tanks is 6749 m3, the volumes of tanks 1, 2, 3 and 4 are 500 m3, 750 m3, 750 m3 and 750 m3 respectively, which four of them are fully mixed, but not aerated. tanks 5, 6 and 7 are fully mixed as well as aerated, and their volumes are 1333 m3. aeration of tanks 5, 6 and 7 is achieved by using a maximum kla of 10 h −1, here kla means oxygen transfer coefficient. in the openloop situation, default klas of tanks 5 and 6 are equally set to 10 h−1, and that of tank 7 is set to 2.5 h−1. dissolved oxygen (do or so2 ) saturation in arebic tanks is 8 g(-cod)/m3. the volume of sedimentation tank is 6000 m3, with area of 1500 m2 and a depth of 4 m; the sedimentation tank is feed at the point of 2.2 m above the bottom. two internal recycles are also included: qintr from tank 7 to tank 3 at a default flow 938 h. xu, r. vilanova rate of 300% of the influent flow rate, and qr from the underflow of the sedimentation tank to the inffluent of tank 1. the default qr is equal to the qin. besides of qr, the underfolw of the sedimentation tank is also devided to waste sludge qw, and the default qw is 400 m 3/d. more detailed explanation about the configuration of combined n and p removal plant can be seen in [7]. figure 1: lay-out of the benchmark plant for evaluation of control strategies on combined n and p removal processes 2.1 influent composition the influent for bsm1-p was generated from the asm1 influent composition [6, 7]. besides the concentration of pollutant, another important parameter that will affect the operation of wwtp and should be considered is the volumn of influent, which is affected significantly by weather condition. thus, in bsm1-p, three weather conditions are taken into account: dry weather, rain weather and storm weather. 2.2 plant performance criteria it is also necessary to build up a number of indexes to evaluate the performance of the simulated benchmark wwtp studied in this paper. similar to the original bsm1, the effluent quality index (eqi) for bsm1-p was included p, as calculated by eqs. (1) and (2). eqi = 1 1000(tf − t0) ∫ tf t0 pu(t)qe(t) dt (1) pu(t) = putss(t) + pucod(t) + pubod(t) + putkn(t) + puno3(t) + puptot(t) (2) in eq. (1), t0 and tf represent the start time and end time of the period of evaluating eqi separately. the pollutant load puk (kg/d) corresponding to each component k is estimated by eq. (3). puk = βkck (3) the factors βk are weighting factors that are attributed to each effluent component. in this paper, the factors were chosen as follows: βtss = 2; βcod = 1; βbod = 2; βtkn = 20; βno3 = 20; βptot = 20. furthermore, the instantaneous concentrations of the different pollutants ck are calculated by eqs. (4)-(10). ctss = xtss (4) pi and fuzzy control for p-removal in wastewater treatment plant 939 ccod = sf + sa + si + xi + xs + xh + xpao + xpha + xa (5) cbod = 0.25(sf +sa+(1−fsi )xs +(1−fxih)xh +(1−fxip )(xpao+xpha)+(1−fxia)xa) (6) ctkn = snh4 + in,sf sf + in,sisi + in,xixi + in,xsxs + in,bm(xh + xpao + xa) (7) cno3 = sno3 (8) cntot = ctkn + cno3 (9) cptot = sp o4 +ip,sf sf +ip,sisi +ip,xixi +ip,xsxs +ip,bm(xh +xp ao+xa)+xp p +(1/4.87)xmep (10) here, fsi means fraction of si from hydrolysis, fxia, fxih, fxip represent fraction of inert cod from xa, xh and xpao, respectively. in addition, in,k and ip,k represent n and p fraction in organic component k (k = sf , si, xi, xs, xh, xa or xpao), respectively. the influent quality index (iq) is calculated in the same way as eqi, but the bod coefficient in eq. (6) is modified from 0.25 to 0.65. similar to bsm1 the limits for certain components should be provided to evaluate the performance of wwtp in detail. by comparing the simulation output with these limits, we could calculate the number of times that the effluent concentration of a pollutant exceeded the limit during the evaluation period. the limit for p is based on the danish wwtp effluent standard [7], whereas other limits are same to the bsm1, i.e., cptot =1.5 g p/m 3, cntot =18 g n/m 3, cbod=10 g/m 3, ccod=100 g cod/m 3, ctss=30 g/m 3 and snh4 =4 g n/m 3. in addition, to quantify the cost of wwtp operation, the operating cost index (oci) was introduced [21]: oci = αeqieqi + αaeae + αpepe + αsldgpsldg (11) in eq. (11), eqi is the effluent quality index caculated by eq. (1), ae is aeration energy consumption rate which happens in aerobic tanks 5, 6 and 7, pe is pumping energy consumption rate to maintain wastewater flowing. the unit for ae and pe is kwh/d. psldg is the sludge production rate (kg/d). values for ae, pe and psldg are calculated in a similar way to bsm1 [6]. the αi coefficents are oci weighting factors. in this paper, αi values are suggested in [21], i.e. αeq=50 (euro/year)/eqi; αae = αpe=25 (euro/year)/(kwh/d); αsldg=75 (euro/year)/(kg tss/d). 3 fuzzy logic control fuzzy control makes use of so-called fuzzy controllers (fcs) or fuzzy logic controllers to ensure a nonlinear input-output static configuration can be designed/changed according to designer’s mind. compared to the conventional control, fuzzy control could take sufficient advantage of the experience of a human operator, because fuzzy control has the ability to introduce this experience in a more accurate way by applying linguistic variables. the mathematical foundation 940 h. xu, r. vilanova of fuzzy logic control was set by zadeh in his paper about forty years ago [8]. after that, as the computer science and the tools for dealing with mathematical problems were developing rapidly, madamni and assilian applied the first fuzzy control application on a small steam engine [9, 10]. afterwards, in japan and usa, and later, in europe, the fuzzy logic control became more and more popular [19]. until now, fuzzy controllers have been successfully used in the area of process industries [11–17]. this control method based on human’s experience is achieved in fcs by expressing the control requirements and expounding the control signal in terms of the if-then linguistic rules which belong to the set of rules: if(conditions)then(consequent) where the conditions means the present situation of the controlled process dynamics (compared usually with the desired dynamics), and the consequent (conclusion) refers to the action which should be taken under the form of the control input u in order to follow the desired dynamics. the set of rules makes up the rule base of the fc. a typical fuzzy control system is as followed (fig. 2): figure 2: typical fuzzy control system in the present study, different forms of fuzzy logic systems for designing fc have been implemented: mamdani fuzzy inference systems and sugeno fuzzy inference systems. in a mamdani fuzzy inference systems [18], given fuzzy rules: (1) if x is a1 and y is b1 then z is c1, (2) if x is a2 and y is b2 then z is c2; and the fact: x is x1, y is y1, here x1 and y1 are crisp inputs. fig. 3 shows how to determine the fuzzy output (dark aera). different from the conventional mathematical set, which only has two relationships with a certain element (belong to or not belong to), a fuzzy set could also be partly belonged to by an element. in the theory of fuzzy, to describe the relationship between a fuzzy element and a fuzzy set, a grade of membership µ(x) is introduced, and µ(x) = 1 means that the element x totally belongs to a fuzzy set, while µ(x) = 0 means the element x not belongs to the fuzzy set at all. for example, in the fig. 3, x1 partly belongs to set a1 and partly belongs set a2, and the grade membership is described by µ(x1|a1) and µ(x1|a2) separately. similarly, µ(x1|a1) and µ(y2|b2) represent the grade of membership for y1 to fuzzy set b1 and b2. this is how to convert crisp inputs to fuzzy inputs, i.e. fuzzification. next, we should consider how to determine the conclusions. to do this, we will first consider the recommendations of each given fuzzy rule independently. the membership function for the conclusion reached by rule (1), which we denote by µ(1), is showed in fig. 3 and is given by µ(1)(z) = min{µ(x1|a1), µ(x1|a1)} this membership function µ(1)(z) can be explained as how big part we should take into consider for the fuzzy set c1. and similarly, we can reach the other membership function for the conclusion by rule (2). and as the present situation (x is x1 and y is y1) is affected by both rule (1) and rule (2), thus the final decision should be a combination of both membership funcion, as showed in fig.3. according to centroid way, to defuzzify the fuzzy output, we only need to calculate the centroid point of the gray aera: let bi denote the center of the membership function of the pi and fuzzy control for p-removal in wastewater treatment plant 941 sonsequent of rule (i), and let ∫ µ(i) denote the area under the membership function µ(i), the centroid method computes z1 to be z1 = ∑ i bi ∫ µ(i)∑ i ∫ µ(i) and z1 is the defuzzified output, as showed in fig. 3. figure 3: mamdani fuzzy inference systems to design a fuzzy controller, at first the input and output variables of the fuzzy controller should be chosen. normally the input variables are feedback error and higher order derivatives of feedback error, in this paper the feedback error (e) and first order derivative of feedback error (ec) were chosen as inputs of the fuzzy controller. on the other hand the output variables should be controlled inputs of the controlled plant (u), therefore in this case, the kla of the aerobic tanks was chosen as output of the do fuzzy controller, and the internal recycle rate was chosen as the output of the internal recycle fuzzy controller. the next step is to choose the membership functions for input and output variable. the shape of membership functions here we chose the triangle functions both for the input variables and for the output variables. it was concluded that in the case of tank 5 the range of e could be fixed from -1.5 g/m3 to 1.5 g/m3, the range of ec could be fixed from -15g/ (m3d−1) to 15 g/ (m3d−1). the kla of tank 5 could range from 160 d −1 to 280 d−1. in addition, the number of parameters of the membership function was chosen as 7, which included nb, nm, ns, o, ps, pm, pb. here n meant negative, o meant zero, p meant positive, b meant big, m meant medium, s meant small. to express in a more clear way, fig.4 presents the membership function of the feedback error for the fuzzy controller of the tank 5. similarly, this could also be applied on the do fuzzy controller of tank 6, tank 7 and that of the internal recycle. but in the case of tank 6 and 7, e and ec range the same way, because 942 h. xu, r. vilanova table 1: decision table for fuzzy controllers (a) for do fuzzy controllers nb nm ns o ps pm pb nb pb pb pb pb pm o o nm pb pb pb pb pm o o ns pm pm pm pm o ns ns o pm pm ps o ns nm nm ps ps ps o nm nm nm nm pm o o nm nb nb nb nb pb o o nm nb nb nb nb (b) for internal recycle nb ns o ps pb nb pb pb pb ps o ns ps ps ps o ns o ps ps o ns ns ps ps o ns ns ns pb ns nb nb nb nb according the experiments the range of e and ec did not affect the control performance significantly, and the u ranged from 120 d−1 to 240 d−1 and from 60 d−1 to 180 d−1 separately. in the case of internal recycle, the e ranged from -1 g/m3 to 1 g/m3, the ec ranged from -20 g/ (m3d−1) to 20 g/ (m3d−1) and the u ranged from 5000 m3/d to 45000 m3/d. the number of parameters for u of the internal recycle is adjusted to 5, which means that only exist nb, ns, o, ps and pb. the next step was deciding the fuzzy inference mechanism. in the case of 7 parameters of linguistic terms, there are totally 49 control rules formed by if-then clauses. table.1(a) shows the detail of control rules, which are same for the entire 3 do fuzzy controller, whereas table.1(b) shows the fuzzy control rules for the internal controller. figure 4: membership of e of tank 5 4 control configurations in this paper, at the first part, a series of control strategies are applied on the bsm1-p. as given in [7], a default control (dc) is simulated as a reference as well as a test for the updated simulation plant. then 10 additional pi-based control strategies (s1-s10) are applied to compare the performance: the first 5 control strategies (s1-s5) are basic ones, including the do controller, cascade do controller, internal recycle flow rate controller, extra carbon resource controller and the waste sludge amount controller, whereas the other 5 control strategies (s6s10) are generated by combining these basic ones. a detailed description of each control strategy follows below. pi and fuzzy control for p-removal in wastewater treatment plant 943 4.1 default control (dc) strategy similar as with the original bsm1, the dc strategy consists of two pi-based control loops: a do controller in tank 7 and an internal recycle controller. the measured variables are dissolved oxygen of the tank 7 and the concentration of nitrate nitrogen (no3) of the tank 3 respectively. the controlled variables are same as measured variables, and the set points are do=2 g/m3 and no3=1 g/m 3 respectively. the manipulated variables are kla7 and internal recycle qint respectively. as showed in fig. 5. figure 5: configuration of dc 4.2 control strategies configurations a set of 5 basic control strategies (s1 to s5) has also been implemented by using the following control loops respectively: • s1: pi controllers of the dissolved oxygen concentration (do) in the 3 aerobic tanks by regulating the oxygen transfer coefficients (kla) simultaneously, and the set points are all 2 g/m3.internal recycle loop is left as in the openloop. the configuration is shown in fig. 6. figure 6: configuration of s1 • s2: fig.7 shows the cascade pi control of the ammonia nitrogen of the effluent by manipulating the do set points in all the aerobic tanks. the set point of effluent ammonia nitrogen concentration is 1 g/m3, and the controlled variables are also the klas of the aerobic tanks. internal recycle loop is left as in the openloop. • s3: fig.8 shows the control of nitrate nitrogen concentration (sno3) in the tank 4 by manipulating the internal recycle flow rate (qint). the set point is 1 g/m 3. and the klas of aerobic tanks are left constant as in the openloop. 944 h. xu, r. vilanova figure 7: configuration of s2 figure 8: configuration of s3 • s4: fig.9 shows the control of sno3 in the tank 4 by manipulating the extra addition carbon resource (qcarb) into the tank 3. the set point is also 1 g/m 3. and the internal recycle loop was left as openloop. figure 9: configuration of s4 • s5:fig.10 shows the control of total suspended solids concentration (xtss) in tank 7 by manipulating the wastage sludge flow rate (qw). the set point is 4000 g/m 3. and the internal recycle loop was left as openloop. furthermore, 5 extra pi-based control strategies (s6 to s10) generated by combining these basic control strategies are also tested. in detail, the control strategy s6 is to control the dos in the 3 aerobic tanks as well as internal recycle flow rate by applying s1 and s3 simultaneously. as can be seen in the simulation result, the performance of s2 is not beneficial for the phosphorus removal, which is mainly considered in this work, consequently in all the combined control strategies, none is included s2. the control strategy s7 is obtained by combining s1 and s4, pi and fuzzy control for p-removal in wastewater treatment plant 945 figure 10: configuration of s5 which means control the do in 3 aerobic tanks and the extra carbon resource in tank 3. similarly, the control strategy s8 is to combine s1 and s5, s9 is to combine 3 control strategies (s1, s4 and s5). finally, the last control strategy s10 is generated by combining s1, s3 and s5. 5 results and discussions the important information of simulation results of pi-based control strategies is showed in the table.2. for comparison, it included the simulation result of open loop, default control loop and s1 to s10 control strategies. the effluent quality indexes (eqis) are showed in the table to judge the overall performance. in detail, the mainly considered components of a certain wastewater treatment plant: ammonia nitrogen (nh4), total amount of nitrogen (ntot), phosphate (spo4 ), the amount of chemical oxygen demand (cod) and the total suspended solids (tss) being another main factor to evaluate the performance of a wwtp are showed in the table. in addition, the operation cost index (oci), as a consequence of consuming aeration energy (ae), pumping energy (pe), sludge production and the added carbon volume and metal volume (in this paper, 0 in all case), is also given in the table to compare the control strategies. table 2: results of pi-based control strategies (s1-s5) average effluent default s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 concentration component limit (g/m3) snh4 4 3.16 3.36 2.17 2.84 3.62 2.30 3.36 4.02 2.61 3.68 2.60 ntot 18 17.15 15.19 15.06 15.47 12.59 15.16 15.36 12.91 14.82 12.66 15.01 spo4 2 1.86 2.55 3.99 3.150 0.85 2.97 2.27 0.85 2.19 0.82 1.97 cod 125 45.44 45.46 45.58 45.52 45.90 46.17 45.45 45.79 46.04 46.05 46.02 tss 35 14.15 14.05 13.93 14.01 14.54 14.58 14.08 14.47 14.59 14.66 14.60 global plant performance eqi(kg/d 4495 4741 5618 5468 4502 5403 4643 4087 4672 4096 4597 oci(euro/d) 19919 19664 19779 19343 24858 18954 19774 24231 19532 24373 19648 since bsm1-p is an updated version of bsm1, from the results it can be seen that in all cases the ammonia nitrogen (nh4), total amount of nitrogen (ntot) and the chemical oxygen demand (cod) are all under limit amount. in case of s4 and those that included it: s7 and s9, all the limit concentrations for pollutant components concerned are satisfied. the reason is that extra carbon resource can promote the growth and ability of denitrifying organism and phosphorus accumulating organism (pao) simultaneously. however because of the added carbon resource, the operational cost indexes 946 h. xu, r. vilanova (oci) in these 3 cases are higher than any other case. in the other cases of pi-based control strategies, it is obvious that, by comparing with the case of openloop, a lower level of ammonia nitrogen concentration in the effluent corresponds a higher level of phosphorus concentration. this is because without extra carbon resource, the only way to decrease the ammonia is to increase the explosion of air, but this will lead a higher level of nitrate nitrogen, which is harmful for the accumulation of phosphorus. however, the case s5 is an exception, where both snh4 and spo4 are lower than the case of openloop. although the phosphorus is still above the limitation, but considering the concentration of ammonia nitrogen is relatively lower than many cases, the consequence is also beneficial. this is because by controlling the waste sludge flow rate more suspended solids, including the organisms, remain in the treatment plant circumstance, which is beneficial for both nitrogen and phosphorus removal. correspondingly, the cod and tss in the effluent are higher than other cases. furthermore, the oci is the lowest. fig.11 shows the tradeoff among oci and eqi for the different pi-based control strategies. in fig.11, the horizontal axis means the operational cost index (oci) and the vertical axis represents the effluent quality index (eqi). basically, higher operational cost leads to lower effluent quality, which means that the performance of treatment plant is better. from fig.11, it is easy to conclude that those strategies including extra carbon flow (s4, s7, s9) cost much more than other ones, but achieve lower effluent pollutant concentration. in fact, the performance of s4 is the best among the 5 basic pi-based control strategies. among all, s7 and s9 possess the lowest effluent pollutant load without significant difference. however, when analyzing the control strategies combined with the operational cost, the default control, s1, s6, s8 and s10 show a good balance. but the main flaws of them are as followed: the average level of phosphorus in s6 and s8 exceeded the limit amount, and the instant level of phosphorus amount in all the 5 cases violated the limitation for a great partial of the total evaluated time (64.43%, 36.01%, 49.55%, 46.88% and 38.69%, respectively). figure 11: the oci against eqi graph of pi controllers in fig.11, the points that are closer to the origin mean lower effluent quality index with less operational cost, therefor better tradeoff, when choosing the appropriate control, these ones should receive more interest. hence it is obvious that s10 made the best balance enter oci and eqi. fig.12 and fig.13 show a dynamic plant performance of wwtp for n and p under the pi-based control strategy s10. to make a clear comparison with the fuzzy controller, in these three figures we also added in the time response of certain components of effluent by using fuzzy controllers, which will be anylized below. pi and fuzzy control for p-removal in wastewater treatment plant 947 figure 12: total amount of nitrogen of effluent of s10 figure 13: total amount of phosphorus of effluent of s10 figure 14: the comparison of oci to eqi between pi-based and fuzzy-based controllers 948 h. xu, r. vilanova figure 15: total amount of nitrogen of effluent of s1 figure 16: total amount of phosphorus of effluent of s1 table 3: comparison of pi and fuzzy control strategies average effluent open default s1 s6 s8 s10 concentration loop pi fuzzy pi fuzzy pi fuzzy pi fuzzy pi fuzzy component limit (g/m3) snh4 4 2.79 3.16 5.77 4.43 6.99 3.36 6.54 2.61 6.55 2.60 7.11 ntot 18 15.50 17.15 17.04 15.19 15.91 15.36 17.73 14.82 16.42 15.01 18.10 spo4 2 3.69 1.86 1.52 2.55 1.27 2.27 1.08 2.19 1.46 1.97 1.15 cod 125 45.54 45.44 45.36 45.46 45.40 45.45 45.32 46.04 44.80 46.02 44.83 tss 35 13.95 14.15 14.12 14.05 14.17 14.08 14.12 14.59 13.62 14.60 13.69 eqi(kg/d) 5596 4496 4314 4741 4224 4643 4141 4672 4226 4597 4121 % of variation -19.7% -22.9% -15.3% -24.5% -17.0% -26% -16.5% -24.5% -17.9% -26.4% oci(euro/d) 19175 19920 20047 19665 20057 19774 20203 19532 20283 19648 20416 % of variation +3.9% +4.5% +2.6% +4.6% +3.1% +5.4% +1.9% +5.8% +2.5% +6.5% pi and fuzzy control for p-removal in wastewater treatment plant 949 fig.12 and fig.13 show the most concerned component of waste water, nitragen (n) and phosphorus (p) separately. from fig.12, we can see that although the average amount of ntot is satisfied with the requirement according to table.2, in some moments the ntot of effluent violates the limitation. however, the overall performance is rather good. when come to fig.13, the outcome is in contrast, the average amount of p of effluent is higher than the desired limit, and neither is there much time satisfying the limitation. so this is the major defect of s10. but considering the overall control performance and the cost of wwtp, s10 still draws a good attention. so in the next step, we focus on replacing the pi controllers of s10 by fuzzy controllers. in addition, since df, s1, s6 and s8 also get a good comparison of oci and eqi, as well as a good ability for p removal, as can be seen in the table.2 and table.3, it is also necessary to build up a fuzzy-based controller for these control strategies to see the performance. the procedure and important information of designing fuzzy controller are mentioned in the second part. the simulation results of fuzzy control strategy are showed in table.3, to make a clear comparison, in table.3 we also repeat the situation of applying pi-based controllers. from this table, we can see that by applying fcs, the average concentration of p in effluent becomes much lower and satisfies with the requirement. but since the favorable condition for p removal is contrary against the one of n removal, by applying fcs the amount of n rises. however, the average concentration of total n in effluent is still under the limitation (except fuzzy-based s10), this means that fcs are able to satisfy with the requirement of total n and total p simultaneously. in addition, from the table we can also see that by applying fcs, the eqi is lower, but the oci is higher. to make a clear comparison, table.3 also gives a percent (%) of variation of each control strategy against openloop to see in how much degree oci enlarged as well as eqi reduced. from table.3, we can see that in dc by applying fuzzy controllers eqi reduced about 3% more than by applying pi controllers, and oci only gained 0.6%. in other control configurations, by applying fuzzy controllers, eqi reduced 8%-9% comparing with pi controllers, and oci gained about 2%-4%. this means fuzzy logic controllers are able to improve the wwpt performance by increasing operational cost, however the degree of improvement is greater than the increase in cost. fig.14 gives a more clear way to see this conclusion. to see the dynamic plant perform, we can refer to fig.12 and fig.13. it can be concluded that by applying fuzzy controllers in every moment p gets a better removal. another phenomenon that is exihibited in fig.15 and fig.16 needs to be mentioned, which show effluent concentrations of n and p of wwtp controlled by s1.since s1 only contains do controllers, these two pictures can reveal the effect of do on n and p removal process, because despite of the same setpoint for both pi-based and fuzzy-based controllers, the regulated instantaneous do could vary according to different type of controllers. from fig.15, we can see that the pi controllers are beneficial for the n removal, but in some period (such as day7 to day8 and day13 to day 14) the concentration of n did not change much, but from fig.16 we can see that all the time the amount of p by using fuzzy controllers is below the one by using pi controllers. from this point, the eqi of fuzzy controllers is lower than the pi controllers, as showed in table.3. it can be assumed that the process of p removal is more sensitive than the n removal. based on this assumption, the real concentration of do in the fuzzy-based s1 at the moment that n keeps the same could draw more interest, because under this condition we can get better removal of p and at the same time do not affect the removal of n. from the simulation results, we can see that by applying fuzzy control strategies the performance of wwtp is improved, and fuzzy control strategy could get a better operation result for the wastewater treatment process in some degree. 950 h. xu, r. vilanova 6 conclusions in this paper, at first a set of pi control strategies has been applied on the bsm1-p waste water treatment plant to maintain the pollution component of effluent within regulations specified limits. good performance was achieved; it could be proved that the bsm1-p is efficient to simulate the combined p and n removal wwtp. among the 10 designed pi control strategies, we chose a group that could make a better balance between oci and eqi (dc, s1,s6, s8 and s10) to test fuzzy logic controllers. from the results we could conclude that by applying fcs the p removal consequent was enhanced. in this way, the most focused on component (p) of the effluent was controlled under the required limit. the results show fc could be efficiently used to control the wwtp, especially for the p removal. however, in this paper, although a set of fuzzy control strategies was tested on the bsm1p, the operation was only replacing the fcs to the pi controllers. the control loop was not changed at all and the set points were also the same. but as one of the most studied advanced control strategies, fuzzy logic control could make a better improvement. therefore, the future work will not only concern to replace the fc to the pi controllers, but also make a combination of fuzzy logic control and pi control. fcs could act as a higher level to make the important decision as a human being, and the basic control loop could be accomplished by pi controller. for example, when we focus on the concentration of p of effluent, it is not necessary to fix the do of three aerobic tanks to 2 mg/l; high level of do is beneficial for the p removal, but harmful to the denitrification process which is important for the n removal. although according to the reference, 2 mg/l of do is an ideal amount for making the balance between p removal and n removal, however in the real situation there are so many disturbances in the wwtp, it would get a better control performance by adjusting the do according to the specific situation. in this way, we could get a better balance between p removal and n removal, as well as between oci and eqi. acknowledgments this work was supported by the spanish ministry of economy and competitiveness program under grant dpi2013-47825-c3-1-r. the work of xu hongyang is covered by the china scholarship council. bibliography [1] ingildsen, p. 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(2002); robustness and economic measures as control benchmark performance criteria, water science and technology, 45(4-5): 117-126. international journal of computers communications & control issn 1841-9836, 12(1):116-130, february 2017. feature analysis to human activity recognition j. suto, s. oniga, p. pop sitar jozsef suto* department of information systems and networks university of debrecen, debrecen, hungary *corresponding author: suto.jozsef@inf.unideb.hu stefan oniga 1. department of information systems and networks university of debrecen, debrecen, hungary oniga.istvan@inf.unideb.hu 2. department of electronic and computer engineering technical university of cluj-napoca, north university center at baia mare, baia mare, romania stefan.oniga@cunbm.utcluj.ro petrica pop sitar department of mathematics and informatics technical university of cluj-napoca, north university center at baia mare, baia mare, romania petrica.pop@cunbm.utcluj.ro abstract: human activity recognition (har) is one of those research areas whose importance and popularity have notably increased in recent years. har can be seen as a general machine learning problem which requires feature extraction and feature selection. in previous articles different features were extracted from time, frequency and wavelet domains for har but it is not clear that, how to determine the best feature combination which maximizes the performance of a machine learning algorithm. the aim of this paper is to present the most relevant feature extraction methods in har and to compare them with widely-used filter and wrapper feature selection algorithms. this work is an extended version of [1]a where we tested the efficiency of filter and wrapper feature selection algorithms in combination with artificial neural networks. in this paper the efficiency of selected features has been investigated on more machine learning algorithms (feed-forward artificial neural network, k-nearest neighbor and decision tree) where an independent database was the data source. the result demonstrates that machine learning in combination with feature selection can overcome other classification approaches. keywords: human activity recognition, feature extraction, feature selection, machine learning. areprinted and extended, with permission based on license number 3958150787732 [2016] ieee, from "computers communications and control (icccc), 2016 6th international conference on" 1 introduction this paper is an extended version of our previous work where only artificial neural networks have been used to the efficiency investigation of feature selection in the human activity recognition (har) problem [1]. however, in this paper by the involvement of more machine learning techniques we can examine the relation between classifier and feature selection methods. the appearance of data mining was a milestone of modern biomedical applications. har is an interesting and rapidly expanding part of this area. in this type of problem, we want to determine copyright © 2006-2017 by ccc publications feature analysis to human activity recognition 117 the activity of people from the information which comes from one or more accelerometer-based data collector devices. generally, sensors are placed to different parts of the body and provide information about the functional ability and lifestyle of an observed person. although many articles have been presented in this topic, some questions are unanswered yet. which feature selection algorithm generate the best feature combination that maximize the recognition rate of a machine learning algorithm? does a general feature combination which similarly efficient independently of the person exist? can a machine learning algorithm in combination with feature selection overcome other classification approaches?. the aim of this study is to give reliable answers for the above questions. the rapidly growing rate of elderly population in our society has a huge impact to health care systems. obviously, everyone wants to stay in a familiar environment where they feel comfortable during the observation. this new challenge motivated the development of different kind of home care services, assistive systems and wearable sensor networks in order to increase the autonomy and life quality of an observed person [2,3]. today, the miniaturized sensor technology (mems) makes it possible for a person to wear data acquisition devices on predetermined body segments. in the past decade, more research groups developed different kinds of devices for har purposes which ensure continuous observation in both indoor and outdoor environments [4][7]. such a wearable sensor network was used to the construction of the ward 1.0 database. it is a benchmark database to the har research which was collected at university of california, berkeley [7]. this public data set gives an opportunity for qualitative comparison of existing har algorithms. the database contains information about 13 different activities which were acquired from 20 people aged between 19 and 75 years. during the data acquisition, 5 sensor nodes were placed at multiple body locations of each person: left and right forearm, waist, left and right ankle. in this study we used the data of only one sensor which has been placed on the right ankle because ertugrul et al. and oniga et al. demonstrated that one sensor is enough for appropriate har recognition while preece et al. claimed that the ankle is an optimal placement for single sensor [8,28,31]. beyond data acquisition, efficient algorithms are also necessary to interpret the collected data. previous works have shown that, machine learning algorithms are efficient for human movement classification. khan et al., oniga et al. and yang et al. used artificial neural network (ann) to their har research and archived 97.9%, 95% and 99% recognition rates (with single sensor), respectively [9][12]. duarte et al. and preece et al. used k-nearest neighbor (knn) method and measured 97.8% and 95% recognition rates [8,13]. finally, gao et al. and maurer et al. reached similarly good results with decision tree 96.4%, 92.8% [4,5]. it was the main motivation to use those three machine learning methods in this study. 2 raw data pre-processing and classification 2.1 feature extraction many machine learning application require feature extraction and feature selection; see for example [23, 25]. feature extraction can be seen as a data pre-processing step where different kinds of features will be extracted from the raw data. in the first step the raw data (time series) will be split into short intervals windows. usually, a window covers one or two seconds long time interval and its size depends on the sampling frequency. for instance, preece at al. used 2 seconds long window with 50% overlapping while gao et al. and karantonis et al. used 1 second long window without overlapping [4, 8, 14]. in our case, a window contains 32 samples and there is a 50% overlapping between windows in the training phase and no overlapping in the test phase. this size covers 1.6 seconds time interval because the sampling frequency of the 118 j. suto, s. oniga, p. pop sitar table 1: most common feature extraction methods in har category feature abbreviation references time mean m [4,8] variance v [4,8] mean absolute deviation mad [5,15] root mean square rms [5,15] zero crossing rate zcr [4,5] interquartile range iqr [5,15] 75’th percentile pe [5,8] kurtosis ks [15,16] signal magnitude area sma [4,15] min-max mm [17,18] frequency spectral energy se [4,8] spectral entropy e [8,15] spectral centroid sc [4,19] principal frequency pf [8,20] other correlation between axis corr [4,17] autoregressive coefficients ar1, ar2 [9,19] tilt angle ta [9,18] ward database is approximately 20 hz. after the windowing step, features will be extracted from each window. in previous activity classification studies, the researchers used different kinds of features from different domains. most of them come from the time a frequency domains but exist some other types of features. table 1 summarizes the most common features from the literature and their references. some features are redundant and those were omitted from the table. for instance, standard deviation because variance is in the table. moreover, wavelet features have been similarly omitted because time-frequency features are more efficient than wavelets, see [8]. at the rest of this subsection we give a short description to the methods in table 1. in the equations t indicates the window size, f is the number of frequency components, i refers to the accelerometer dimensions (x,y,z), ai(t) is an element of time series and ai(f) is a frequency component. mean, variance, mean absolute deviation and root mean square are statistical indicators which give information about sample distribution. zcr measures sing changes along the time series. in the formula, the i{x} function returns 1 if its argument is true and 0 otherwise. zcri = 1 t − 1 t−1∑ t=1 i{ai(t)ai(t + 1) < 0}. (1) quartiles (q1, q2 and q3) divide an ordered time series into quarters. iqr is the difference between the upper and lower quartiles: iqr = q3 q1. it measures the spread of a data set over a range. percentiles are similar as quartiles, except that those divide a data set into arbitrary parts (given in percentage). therefore, the 25th percentile is equal to the lower quartile (q1) and the 75th percentile is equal to the upper quartile (q3). in this study we used only the 75th percentile. kurtosis measures the peakedness of probability distribution of collected data. ksi = 1 t ∑t t=1(ai(t) −mi) 4 ( 1 t ∑t t=1(ai(t) −mi)2)2 . (2) feature analysis to human activity recognition 119 sma equals to the normalized sum of accelerometer components. sma = 1 t t∑ t=1 |ax(t)| + |ay(t)| + |az(t)|. (3) min-max is the difference between the maximum and the minimum values of time series. generally, signal energy is the area under the squared signal. in this case, se measures the sum of the squared frequency components. (since, the spectrum is symmetric, in the following three formulas f can be replaced by f/2). sei = f∑ f=1 |ai(f)|2. (4) the entropy is a measure for uncertainty. the following formula is the entropy of the normalized power spectrum density. ei = − f∑ f=1 |ai(f)|2∑f j=1 |ai(j)|2 log2( |ai(f)|2∑f j=1 |ai(j)|2 ). (5) spectral centroid measures the average frequency, weighted by the amplitude of spectrum. sci = ∑f f=1 f|ai(f)|∑f j=1 |ai(j)| . (6) principal frequency refers to the most significant frequency component which has the highest amplitude (dc component has been omitted). pfi = maxf|ai(f)| f 6= 0. (7) autoregressive model is another representation of signal. in the model, an element in the time series can be estimated by a linear weighted sum of previous elements where the weights are the coefficients. in (8), (9) and (10) p indicates model order, φk is the ar coefficient, ε(t) is the noise and τk refers to the autocorrelation. in table 1, ar1 and ar2 abbreviations refer to the first and second autoregressive coefficients, respectively. ar(p) −→ ai(t) = p∑ k=1 φkai(t−k) + ε(t). (8) φ =   1 r1 r2 · · · rp−1 r1 1 r1 · · · rp−2 ... ... ... ... ... rp−1 rp−2 rp−3 · · · 1   −1 ∗   r1 r2 ... rp   . (9) rk = 1 t ∑t−k t=1 (ai(t) −mi)(ai(t−k) −mi) vi . (10) correlation measures linear relationship between two axes. actually, corri,j is the cosine of angle between normalized vectors. therefore, -1 ≤ corri,j ≤ 1. the sing indicates the 120 j. suto, s. oniga, p. pop sitar direction of correlation. if corri,j is near to ± 1 implies that there is strong linear correlation between vectors. corri,j = ∑t t=1(ai(t) −mi)(aj(t) −mj)√∑t t=1(ai(t) −mi)2(aj(t) −mj)2 . (11) finally, tilt angle indicates the relative tilt of the sensor. it is the angle between z-axis and gravitational vector. in this survey, the absolute values of tilt angles have been summarized. ta = arccos( az(t)√ ax(t)2 + ay(t)2 + az(t)2 ). (12) 2.2 feature selection feature selection, also called feature reduction, is the process of choosing a subset of original features according to a well-defined evaluation criterion. it is a frequently used dimensionality reduction technique which removes irrelevant and redundant features. this approach has more useful effects for real applications because it accelerates algorithms, improves the performance and simplifies the model. in contrast to other dimensionality reduction techniques like linear discriminant analysis (lda) or principal component analysis (pca) which are based on projection, feature selection does not alter the original representation of feature sets, see [21]. according to the training data which are may be tagged, untagged or partially tagged, there have been developed three categories of algorithms: supervised, unsupervised and semisupervised feature selection where a tag refers to a given class. in addition, depending on the feature evaluation process, feature selection algorithms belong to three different groups: filter, wrapper and embedded. filter algorithms calculate scores for all features and select features according to the score. wrapper methods require a predefined classification technique and use its performance as ranking criteria of feature subsets. in embedded models, feature selection takes place at the training process. in this article we confine on the supervised category and particularly we are interested in filter and wrapper methods. in real applications filter methods are frequently used for feature selection because they have some significant advantages. firstly, those methods are independently applicable with any types of machine learning techniques. secondly, filter methods are faster than wrappers. however, wrapper methods are more efficient than feature ranking algorithms in some cases because they take into consideration the classifier hypothesis. this also means that, wrapper techniques can handle feature dependencies. so, both types of feature selection methods have advantages and disadvantages [22]. essentially, independently of categories and groups, the goal is to find the most appropriate hyperplane of the n-dimensional feature space in all cases where the sample distributions can be separable. for example, fig. 1 illustrates a proper separation between six 2 dimensional sample distributions (as the colour indicates) where each distribution (samples) comes from different classes. obviously it is an ideal case and the classification will be 100%. more information about feature selection and their application opportunities in bioinformatics can be found in [24,25]. today’s, a reach literature exists concerning the feature (or variable) selection. during the last decades, there have been developed a large amount of filter algorithms. the proposed algorithms are based on different kinds of approaches such as statistical, information theory, rough set, etc. [23]. however, to the best of our knowledge, the number of articles which utilizes feature selection algorithm on har is very small. we found only two papers where the minimum redundancy maximum relevance and the correlation feature selection techniques have been utilised, see [5, 26]. therefore, we collected the most relevant filter techniques and examined them on the har problem. fortunately, zhao et al. proposed a generally applicable repository to feature feature analysis to human activity recognition 121 figure 1: an ideal separation between sample distributions. selection research which contains 13 supervised and unsupervised filter methods [27]. moreover, the repository suggests references and implementations (in matlab) to the algorithms which have been applied in this investigation. in addition, we similarly utilised the naive bayesian wrapper method from [1]. so, in this work the following 9 feature selection methods have been tested: (1) correlation feature selection (cfs); (2) chi square (chi); (3) fast correlation-based filter (fcbf); (4) fisher score (fis); (5)information gain (ig); (6) kruskal-wallis (kw); (7) minimum redundancy maximum relevance (mrmr); (8) t-test; (9) naive bayesian (bayes). 2.3 classification a classification system has to use an algorithm that is capable to learn and tolerate errors which come from noise. previous studies have shown that artificial neural network (ann), knearest neighbor (knn) and decision tree (dt) are well applicable for har, see for example [5, 11, 13]. therefore, to the efficiency measurement of selected features those classifiers have been applied. as the name indicates, in feed-forward networks the input data go through all layers where the incoming data will be modified according to the weights and biases of a layer. the ann theory gives some advices to architecture construction but general rules do not exist. finding the right architecture of an ann for a specific purpose is a time-consuming task because it requires lots of simulations. our ann architecture design is based on the work of oniga et al. where the authors demonstrated that a simple feed-forward ann with only one hidden and one output layers is enough to har [28]. therefore, in this study, some feed-forward anns were generated with the same architecture as in their work. the number of neurons on the input, hidden and output layers ere equal to i, 2i and c respectively where i indicates the number of inputs and c is the number of activities (classes). the activation functions on the hidden and output layers were sigmoid and linear and the training algorithm was the levembergmarquardt. the knn classifier generation is based on the work of duarte et al. [13]. in this research, the authors reached approximately 98% recognition rate by a 1nn classifier with the euclidean distance metrics. finally, we used the default decision tree in matlab without any modification. 122 j. suto, s. oniga, p. pop sitar 3 results at the beginning of the investigation, 7 volunteers were selected from the ward database with different and similar ages and their raw data were the input of the feature extraction step in the training and test phases: (subject 1) 19 years old; (subject 2) 75 years old; (subject 3) 27 years old; (subject 4) 29 years old; (subject 5) 20 years old; (subject 6) 29 years old; (subject7) 34 years old. in table 1 two features are one dimensional while others are multi-dimensional according to the axis of the accelerometer sensor. therefore, multi-dimensional features were separated into one dimensional. this step generated 50 one dimensional features from the 17 feature extraction methods. after feature extraction, we got a feature matrix (windows x features) where rows contain the features of the windows. this matrix was the input of each feature selection method and the selected feature vectors were the input of the classifiers. in this study we applied the first 5 and 6 selected features because gao et al. and oniga et al. demonstrated that approximately 5 or 6 features are enough to an effective classification [4,28]. finally, according to the selected features the performance of the classifiers was measured. table 2-10 contain the selected features by the methods in selection order while the measured recognition rates (in percentage) can be seen in table 11-17. table 2: selected features by cfs subject features subject 1 mx, mz, madx, rmsx, rmsy, rmsz subject 2 mx, my, madz, rmsx, rmsy, rmsz subject 3 mx, my, vz, mady, rmsx, rmsz subject 4 mx, my, mz, rmsy, rmsz, iqrx subject 5 mx, my, mz, vx, vz, rmsx subject 6 mx, my, mz, mady, rmsx, rmsz subject 8 mx, my, mz, mady, rmsx, iqry table 3: selected features by chi subject features subject 1 pey, pez, mmz, mmy, pex, sma subject 2 pey, mmx, pex, mmy, mmz, rmsx subject 3 pey, mmx, mmz, mmy, pex, pez subject 4 pey, mmx, mmy, pez, mmz, pex subject 5 mmy, pey, mmx, mmz, pez, pex subject 6 mmy, pey, mmz, mmx, pex, pez subject 8 pey, mmx, mmy, pex, mmz, pez feature analysis to human activity recognition 123 table 4: selected features by fcbf subject features subject 1 rmsx, pey, rmsy, mz, rmsz, scx subject 2 sex, pex, sez, rmsy, my, sma subject 3 pey, sma, rmsx, my, rmsz, mmx subject 4 2pey, sey, sex, mz, scx, madx subject 5 rmsx, pey, mx, ta, ey, scx subject 6 sma, rmsz, pex, pey, mx, madz subject 8 pey, rmsx, mz, mady, scx, iqrx table 5: selected features by fis subject features subject 1 rmsx, madx, mx, pex, scz, ey subject 2 sez, rmsx, mx, ta, pex, mz subject 3 rmsx, pex, mx, madx, ey, rmsz subject 4 mmx, madx, mady, mmy, scx, scz subject 5 ey, scz, rmsx, scy, ez, mx subject 6 rmsx, pex, mx, sex, scy, mmz subject 8 mmx, mady, rmsx, iqry, vy, sex table 6: selected features by ig subject features subject 1 zcrz, zcrx, mmx, ex, ez, ar2x subject 2 ar2z, ksz, pfz, ar1y, zcry, corry subject 3 zcrz, iqrz, ex, ar1x, ar2x, ar2y subject 4 zcry, zcrz, ex, ey, ar1x, ar2x subject 5 ksz, zcrx, zcrz, zcry, ex, ar1x subject 6 ar1z, ar2y, pfz, ar2z, zcry, zcrz subject 8 ar2y, zcrz, ex, ey, ar1x, ar2x table 7: selected features by kw subject features subject 1 ey, scy, scz, scx, iqrx, madx subject 2 scz, scy, ey, iqry, ksz, mady subject 3 scy, scz, iqrx, iqry, mmy, madx subject 4 scx, scy, scz, iqrx, iqrz, madx subject 5 ey, ez, scy, scz, scx, iqry subject 6 scy, scz, ey, ez, madx, pey subject 8 scy, scz, scx, my, iqry, iqrz 124 j. suto, s. oniga, p. pop sitar table 8: selected features by mrmr subject features subject 1 pey, ta, ar2y, ar2z, ar2x, ar1z subject 2 pey, ta, scz, scy, ey, ez subject 3 mmx, ta, ar2z, ar1z, ar2x, ar2y subject 4 pex, ta, ar2y, ar2z, ar2x, ar1z subject 5 mmy, ta, ar2z, ar2y, ar2x, ar1z subject 6 mmy, ta, ar2z, ar2y, scz, ez subject 8 pey, ta, ar2z, ar2y, ar2x, ar1z table 9: selected features by t-test subject features subject 1 mmx, ex, ez, ar2x, sez, rmsz subject 2 zcrz, rmsz, ta, sey, sez, rmsy subject 3 iqrz, ex, ar1x, ar2x, ar2y, rmsz subject 4 ex, ey, ar1x, ar2x, zcry, zcrz subject 5 ex, ar1x, rmsy, rmsz, sey, sez subject 6 ta, sez, rmsz, zcry, ar2x, rmsy subject 8 ey, ez, ar1x, ar2x, zcrz, zcry table 10: selected features by bayesian subject features subject 1 pez, sma, corry, ksx, corrx, mmy subject 2 rmsy, my, mmz, ksx, pez, pex subject 3 pey, mmx, sma, pfy, iqrx, ey subject 4 pey, mmx, pez, mmy, mx, pfy subject 5 pey, ta, corry, mmx, pfy, mmz subject 6 pex, pey, pfy, pez, mmx, mmy subject 8 pey, pex, mmx, iqrx, sma, pfx table 11: recognition rates for subject 1 ann 1nn dt n=5 n=6 n=5 n=6 n=5 n=6 cfs 93.1 94.7 98.4 99.7 94.2 95.9 chi 84.9 92.6 100 100 98.6 98.2 fcbf 86.2 90.7 99.7 99.9 90.9 96.3 fis 87.2 93.4 91.9 94.6 90.1 91.8 ig 18.0 24.7 12.5 12.5 18.0 24.7 kw 83.9 89.0 97.4 99.6 87.2 89.7 mrmr 87.9 90.8 95.9 98.7 93.5 95.0 t-test 12.7 51.7 43.5 77.4 44.0 73.9 bayesian 92.2 93.0 96.3 98.3 80.7 94.3 feature analysis to human activity recognition 125 table 12: recognition rates for subject 2 ann 1nn dt n=5 n=6 n=5 n=6 n=5 n=6 cfs 96.5 97.8 100 100 85.1 86.6 chi 96.6 97.9 99.9 100 97.6 85.8 fcbf 82.5 85.5 99.7 99.9 97.1 97.8 fis 71.3 84.7 81.7 83.4 94.0 94.6 ig 63.2 69.0 95.9 88.0 73.6 74.1 kw 89.9 92.2 99.8 99.9 93.2 94.1 mrmr 94.4 94.3 100 100 95.1 96.5 t-test 43.1 72.8 98.5 99.8 93.6 96.0 bayesian 96.4 98.6 98.7 99.4 96.9 97.5 table 13: recognition rates for subject 3 ann 1nn dt n=5 n=6 n=5 n=6 n=5 n=6 cfs 95.0 96.0 88.5 99.8 79.0 87.1 chi 93.7 94.6 100 100 99.7 99.8 fcbf 96.2 96.1 99.0 99.9 88.8 90.0 fis 94.0 89.4 76.2 93.9 92.6 94.0 ig 62.3 65.4 62.2 62.9 62.2 63.1 kw 88.1 89.7 99.4 99.4 97.1 97.4 mrmr 82.6 83.9 79.5 79.7 93.0 93.0 t-test 62.3 65.5 62.2 66.4 62.2 70.6 bayesian 95.3 96.3 99.9 99.9 97.8 98.9 table 14: recognition rates for subject 4 ann 1nn dt n=5 n=6 n=5 n=6 n=5 n=6 cfs 92.1 94.6 99.9 100 94.2 97.2 chi 92.2 93.4 100 100 98.9 99.5 fcbf 62.6 84.5 84.2 87.8 88.9 89.5 fis 75.0 87.4 71.0 77.1 72.1 76.7 ig 57.9 66.7 51.5 62.5 57.9 66.7 kw 82.3 83.0 68.1 68.9 65.7 66.2 mrmr 82.4 86.9 85.2 89.7 88.3 88.6 t-test 62.7 66.8 62.5 66.5 62.8 66.8 bayesian 93.7 95.0 100 100 99.4 99.6 126 j. suto, s. oniga, p. pop sitar table 15: recognition rates for subject 5 ann 1nn dt n=5 n=6 n=5 n=6 n=5 n=6 cfs 90.7 95.5 99.6 99.5 96.9 96.7 chi 92.3 94.5 100 100 99.5 99.6 fcbf 93.1 94.2 70.0 74.1 84.0 62.1 fis 85.4 92.6 93.5 97.5 92.6 94.2 ig 38.4 47.9 26.2 59.3 38.4 58.3 kw 82.8 87.8 88.2 95.6 82.8 88.7 mrmr 85.8 88.2 62.7 74.4 44.1 49.6 t-test 59.5 61.1 82.0 92.3 76.3 87.7 bayesian 94.3 96.5 70.6 82.9 46.1 50.3 table 16: recognition rates for subject 6 ann 1nn dt n=5 n=6 n=5 n=6 n=5 n=6 cfs 93.0 97.6 100 100 97.7 97.8 chi 93.8 96.9 100 100 99.5 99.6 fcbf 92.2 95.3 100 100 97.3 97.8 fis 58.7 62.3 83.1 91.8 88.6 91.5 ig 46.9 67.3 58.1 70.4 53.8 56.6 kw 81.4 91.9 85.5 95.7 81.9 90.6 mrmr 82.6 85.3 87.4 90.7 84.8 88.6 t-test 66.0 68.1 93.6 99.5 83.6 90.4 bayesian 94.5 96.5 100 100 99.7 97.7 table 17: recognition rates for subject 8 ann 1nn dt n=5 n=6 n=5 n=6 n=5 n=6 cfs 92.5 93.8 99.2 99.7 91.4 91.9 chi 92.4 89.8 100 100 99.7 99.5 fcbf 91.8 95.7 94.7 98.8 93.7 96.7 fis 88.0 70.5 98.3 99.9 98.2 98.4 ig 47.9 57.3 42.5 48.6 47.9 52.9 kw 87.8 91.0 83.3 87.7 80.0 81.6 mrmr 84.4 87.0 96.7 99.0 94.6 95.0 t-test 47.9 57.9 42.5 46.9 47.9 57.9 bayesian 95.8 96.8 100 100 99.5 99.6 feature analysis to human activity recognition 127 4 discussion table 18: average recognition rates ann 1nn dt n=5 n=6 n=5 n=6 n=5 n=6 cfs 93.3 95.7 97.9 99.8 91.2 93.3 chi 92.3 94.2 100 100 99.1 97.4 fcbf 86.3 91.7 92.5 94.3 91.5 90.0 fis 79.2 82.9 85.1 91.2 89.7 91.6 ig 47.8 56.9 49.8 57.7 50.3 56.6 kw 85.2 89.2 88.8 92.4 84.0 86.9 mrmr 85.7 88.1 86.8 90.3 84.8 86.6 t-test 50.6 63.4 69.3 78.4 67.2 77.6 bayesian 94.6 96.1 95.1 97.2 88.6 91.1 the results in table 2-17 provide large amount of information about features, feature selection and classification algorithms. moreover, table 18 shows the average recognition accuracies where the best rates are highlighted by red. according to the results, we summarized our observations into the following points: • a generally applicable feature set does not exist because the selected features for the subjects are just partially overlapping. • more features do not guarantee performance improvement, see chi and ig in table 12. • the performance of some feature selection methods is person dependent. for instance, in the case of subject 2 the machine learning algorithms with mrmr reached higher recognition rate than in the case of subject 3. • the performance of machine learning algorithms is feature selection and person dependent. for example, in the case of subject 5, the mrmr algorithm reached higher recognition rates with ann while in other cases, the 1nn or dt were better. in addition, the 1nn produced better results than ann and dt with chi in all cases. • as table 18 shows, a relation exists between machine learning and feature selection algorithms. the 1nn and dt reached the highest recognition rate with the chi while the ann in combination with the bayesian was the most efficient. • already 5 features are enough for good classification, because the difference of recognition rates with 5 and 6 features is rather small. • table 18 shows that, the 1nn produced the best result however the decision of the 1nn is more slower than in the case of ann or dt, see for example [4]. therefore, the usage of dt with chi can be a good decision when the calculation capacity is strongly limited. • with only one training, the performance of the ann is not as good as we expected. the outcome of an ann training depends on more hyperparameters, therefore a right ann construction requires hyperparameter search and more trainings. however, it is a very time-consuming process. • since chi used only time domain features, in this case frequency domain features are negligible. 128 j. suto, s. oniga, p. pop sitar • the result does not show strong relation between selected features and measured recognition rates for subjects of similar age. conclusion in this article, we investigated the performance of feature selection methods on the har problem in combination with three well-known machine learning algorithms. to the survey two external sources have been utilised: ward 1.0 database was the data source and the feature selection methods derived from an open source repository. at the beginning of the article, the most common feature extraction methods from time, frequency and other domains have been collected from the literature. thereafter we selected 7 volunteers with different ages from the ward and applied the feature extraction methods on their data. the selected features were the input of the ann, 1nn and dt classifiers and the recognition rates have been archived. duarte et al., gao et al., maurer et al., khan et al., oniga et al. and yang et al. used similar research conditions and environment to their study (they used single tri-axial accelerometer attached to one part of the body; they applied machine learning algorithm(s); they split time series into windows; etc.) and reached 95%, 97.9%, 99%, 97.8%, 96.4% and 92.8% recognition rates, respectively [4, 5, 9, 10, 12, 13]. as table 10-16 demonstrate, we reached approximately 100% recognition rates in each cases. it is better than previous results and clearly indicates the efficiency of the feature selection and machine learning combination. pinardi et al., su et al. and yang et al. are also used the ward database in their research [7,29,30]. they archived 97.8%, 98.5% and 93.5% recognition rates with five sensors and different kinds of classifiers (majority voting, distributed sparsity and support vector machine). against their works, we showed that less sensors are enough for good classification and a general 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(2016); determining the optimal number of body-worn sensors for human activity recognition. soft computing, issn 1433-7479, doi: 10.1007/s00500-0162100-7, 20(2):1-8. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 681-700 a novel parallel transmission strategy for data grid q. ming-cheng, w. xiang-hu, y. xiao-zong qu ming-cheng, wu xiang-hu, yang xiao-zong school of computer science and technology, harbin institute of technology harbin, heilongjiang 150001, china e-mail: qumingcheng@126.com, wuxianghu@hit.edu.com, yangxz@hit.edu.com abstract: creation of multi-copies accelerates data transmission and reduces network traffic, but it causes overhead storage and additional network traffic. a variety of parallel transmission algorithms based on gridftp and multi-copy can be used to accelerate data transmission further, but they can not adapt to a wide range of network, and they can not be used to solve the problems of storage space and network traffic waste. gridtorrent combined with bittorrent and gridftp has compatibility with grid and has flexible scalability, but the speed is very slow when there are few peers, to solve this problem multicopy is needed also. to achieve multiple optimization objectives of storage space saving, suitable for two kinds of application modes(i.e. parallel transfer based on gridftp and bittorrent), adaptability for wide range of network and higher performance when there are fewer peers, based on the idea of gridtorrent, a distributed storage model, parallel transfer algorithm and virtual peer strategy are proposed. in experiments the performance is compared among the verification system vpg-torrent and original parallel transfer algorithm (dcda) only based on gridftp & multi-copy and gridtorrent. when the same amount of data is deployed vpg-torrent has better performance than dcda, and when there are fewer peers vpg-torrent also exceed gridtorrent, which prove the effectiveness of vpg-torrent. keywords: data grid, distributed storage model, parallel transmission. 1 introduction data grid is used during data-intensive computing applications to facilitate the efficient use of distributed data resources. it focuses on the management of data in a wide, heterogeneous, and distributed environment; acquisition of data from a variety of heterogeneous data resources, and extraction of useful information from the data source through collaboration and geographical distribution operation [1]. data grid has been widely used in many fields currently. such as in scientific computing, physics, biology, astronomy, oceans, atmosphere, manufacturing and so on [2–4], and its desktop operation interface has been implemented well [5–7]. combining computing grid and data grid for data-intensive computing applications has become a trend now [8–11]. however, to meet time requirement of data-intensive computing applications, how to accelerate transmission is the key problem faced by data grid [12]. in order to improve the user’s access speed and reduce the network load effectively, much work has been done in recent years on how to create a reasonable number of copies and locate them [13, 14] efficiently. data transmission speed is improved and network traffic is reduced. however, these studies overlooked the mass properties of data in data grid, and storage and network overhead were caused by the creation of multiple copies. if there is a huge amount of data, then it is more harmful than good to create multi-copies [15, 16]. bittorrent is a typical application example of p2p technology. in bt system every peers should upload data when they download. the download speed mainly depends on the number copyright c⃝ 2006-2011 by ccc publications 682 q. ming-cheng, w. xiang-hu, y. xiao-zong of simultaneous peers. now bt is mainly used in file sharing of public community where there are mass accesses. in scientific computing areas there are a few users in general, and computing tasks have strict time requirements in most cases, so bittorrent reveals some shortcomings. oversim is a flexible overlay network simulation framework. all oversim protocol implementations can be used without code modifications in real networks. several applications like i3, scribe, p2pns, and simmud based on these protocols are available. all these implementations can be used for both simulation as well as real world networks. oversim utilizes the graphical interface of omnet++ to display overlay and underlay topology and all network packets. the paper [17, 18] shows that with oversim simulations of overlay networks with up to 100,000 nodes are feasible. moreover it provides a fully configurable network topology with realistic bandwidths, packet delays and packet losses. 2 related works and ideas gridftp provides a striped transmission mode to enable the gridftp client to download the same data in different data blocks from multiple gridftp servers in parallel mode, see figure 1. based on gridftp protocol and multi-copy technologies, many parallel transmission algorithms have been developed [19–22], in these studies, a common feature is to divide original data into sub-blocks, then download these sub-blocks from different nodes through some decision-making in parallel mode, when download is finished the sub-blocks are merged into original data. figure 1: striped transmission mode of gridftp for these parallel transfer algorithms based on client/server(c/s) mode, the performance will decrease dramatically as the amount of access increase, so new copy must be deployed to solve this problem. in spite of this defect, this c/s mode is still necessary for scientific computing community and computing tasks which have higher requirement in reliability, security and some other special data service. but the problems of storage resource waste and network traffic caused by the the deployment of multiple copies can not be neglected. so, how to use a less redundant storage and make full use of gridftp protocol to improve data transmission speed is a challenge faced by data grid now. bittorrent has better adaptability in a wide range of network. but its performance also has much to be desired when there are a few peers or the peers do not allow to upload or upload speed is strictly limited. integrated the advantages of gridftp and bittorrent, some scholars proposed gridtorrent as shown in [23–25]. gridtorrent (see figure 2) is an implementation of the popular bittorrent protocol designed to interface and integrate with well-defined and deployed data grid components and protocols (e.g. gridftp, rls). just like bittorrent, gridtorrent is based on peer-to-peer technique, that a novel parallel transmission strategy for data grid 683 figure 2: gridtorrent allow clients to download files from multiple sources while uploading them to other users at the same time, rather than obtaining them from a central server. by dividing files into fragments, gridtorrent can combine the best out of the two protocols [25]. the data in bittorrent community is generally obtained from other people’s works, like music or movie file, whereas every scientific data is generated in scientific community. therefore, scientific data are more sensitive than data used in bittorrent community. so the number of concurrent peers will be very limited. from figure 2, when the sum of peers is small, the performance of gridtorrent will be degraded as gridftp. so like other parallel transfer algorithms based on gridftp and multicopies, a number of copies should be deployed to enhance the transmission speed. accordingly storage space and network traffic waste are serious. bittorrent algorithms do not rely on global data storage knowledge to schedule. because the number of participating peers, network speed, peer-owned data blocks are dynamically changing. so if we directly divide overall data into sub-blocks of the same size and then deploy them to some servers, the parallel transmission capacity of the servers can not be maximized. and for the previous parallel transmission algorithms, it will inevitably lead to some of the nodes complete their download task first, i.e., all the data stored by the nodes have been downloaded. it can not guarantee each task continuously download from start to finish and all the tasks finish at or tend to the same time. this paper is based on the idea of gridtorrent, and study how to achieve higher speed using less storage when the number of participating peers is small or p2p is not allowed, so that speed does not depend on the number of concurrent peers. to solve the problem from three levels: first, p2p is not allowed (only download from gridftp servers); second, p2p is allowed, but the number of peers is small; third, large-scale peer transmission. therefore, a data grid distributed storage model which uses less data redundancy and can ensure the reliability of data is proposed in this paper. a parallel scheduler and a parallel transmission algorithm based on the model and gridtorrent are thus presented. main contributions: a distributed storage model is put forward, the model can ensure data reliability using less storage. a scheduler is presented based on model, and further a parallel transmission algorithm (ptabm) is put forward based on model and scheduler. comparative 684 q. ming-cheng, w. xiang-hu, y. xiao-zong experiments are carried, our algorithm ptabm achieved the same performance compared with dcda, meanwhile the strategy proposed in this paper has absolute advantage in storage space, and also network load can be reduced greatly during copy deployment and migration. virtual peer(vpeer) is presented in section 4.4 based on ptabm and the idea of gridtorrent. during the scheduling, vpeer is regarded as a general peer, only the transmission from vpeer is based on ptabm. this system is called vpg-torrent, and it can adapt to the situation of large-scale networks. compared with gridtorrent, when the number of peers is small (dozens of peers), vpgtorrent make the average speed of every peers be higher, as the number of peers increases gradually, the performance of them gets closer gradually, it is because that bittorrent accounts for a dominant role when large-scale peers exist. the outcome achieved by this paper is:using less storage space achieved objective of rapid transmission; and the performance is not affected by the sum of concurrent peers; vpg-torrent can met two kinds of application modes, i.e., one is parallel transmission only based on gridftp, the other one is bittorrent service in large-scale network. this is the lack of previous studies. the experiments include two parts: parallel transmission only based on gridftp, simulation of vpg-torrent. (1)the first part is to verify the performance of vpg-torrent when bittorrent is not allowed. we implemented a data dispatcher, rls server and ptabm, and built environment platform composed of 7 computers. good results were achieved compared with previous parallel transmission algorithm (dcda). (2)the second part is to verify the performance of vpg-torrent when bittorrent is allowed, and compare with gridtorrent. in order to simulate large-scale concurrent access, oversim is used in simulation experiment, and also good results were achieved. in following chapters, firstly a distributed storage model is put forward, then a scheduler based on model is proposed, further a parallel transmission algorithm is given, and then expounded the verification system modules and work process, at last experiments and analysis are given. 3 distributed storage model in this section we will put forward a block-devision and block-storage strategy (called distributed storage model), so some necessary definitions will be given first. definitions 1-6 are given to explain how to divide data file and how to storage the blocks into grid nodes. definitions 7-10 are used to show the properties of distributed storage model definition 1. (metasum partition and metadata) let m represent the total amount of data. divide the overall data into sub-blocks of equal size, so that metablks=k(k-1)*metasum, and k is the number of copies, and metablks is divided shares. firstly the data is divided into k(k-1) shares, then each share is divided into metasum shares, and metasum is a variable parameter. so let the amount of data for each share be metadata, and can be expressed as: metadata = m k(k − 1) ∗ metasum (1) definition 2. (local data) the k(k-1)*metasum shares of data defined in definition 1 are evenly distributed among k nodes. so each node contains (k-1)*metasum shares of data. these data is called local data lndi of node ni. a novel parallel transmission strategy for data grid 685 definition 3. (local data virtual group) the local data of ni is classified into some virtual groups.the metasum shares of data is classified as one group, and the classified groups are numbered uniquely within nodes. from definition 2, we can see that the local data of each node can be classified into (k-1) virtual groups. let the virtual group be gij (0≤ i≤ k-1, 0≤ j≤ k-2). the sum of data blocks that one group contains is metasum. let gi represent all the vitural groups that node ni contains, and g represents the current vitural group. definition 4. (remaining node set) after excluding node ni, all remainder grid nodes is called remaining node set: n e i (0 ≤ e ≤ k − 2) and k−2∪ y=0 n y i = k−1∪ j=0,j ̸=i nj , n y i = nj, { y = j if j < i y = j − 1 if j > i definition 5. (cross storage) croup gi of ni is distributed into n e i , that is gi → n e i , and( w∪ r=e ( gri → n e i )) ∣∣∣∣k−2 e=0 {w = (e + (p − 1)) mod k, p ≤ k − 1} , where p is the specified constant, and symbol → means ’storage into’. this is why such storage rule is called cross storage. definition 6. (distributed storage model) all the data andi that node ni contains includes local data lndi and cross storage data ondi. from the definitions 3 to 5 above: andi=(lndi)∪ (ondi)= ( k−2∪ j=0 g j i ) ∪ ( k−1∪ (a=0,a ̸=i) ( w∪ r=e gra ) ) { e = i − 1(a < i) e = i (a > i) &{ w = (e + (p − 1)) mod k p ≤ k − 1 . p is a specified constant, so such storage mode is called distributed storage model. (note: ond represents local data and ondi represents local data of node ni ) theorem 1. (p integrity) if data storage meets distributed storage model, when there are arbitrary p nodes are not available, the merger of data at the remaining k-p nodes is still equivalent to m, that is, the data is complete, this property is called p integrity. proof: for an arbitrary local data gxi , i ∈ (0, k − 1), x ∈ (0, k − 2), where g x i represents arbitrary one local virtual group of one arbitrary node ni. from definition 5, we can know that e ∈ (x−(p−1), k −2), r ∈ (e, w), and w=(e+(p-1) mod k), p≤k-1. so from e to w there are p numbers. retrieve a subset e and e ∈ (x − (p − 1), x) ⊂ (0, k − 2).because of each value of e, r starts from e will get p values. when e varies from x-(p-1) to x, r is in the range of { (x (p 1),x),...,(x,x + (p 1))} .the ’x’ is repeated p times because e has undergone p changes, that is,gxi has been stored into node n e i for p times. here e∈(x-(p-1),x). adding the node that virtual group gxi lies in, there are p +1 nodes where store g x i therefore, if there are arbitrary p nodes are unavailable, a node store gxi is available. as g x i is arbitrary, so all the virtual groups of all the nodes meet the above derivation. the proof is completed. 2 lemma 1. if the data storage met the distributed storage model, then the overall amount of stored data can be expressed as: k(1+p)(k-1)*metasum. proof: derivated from definition 6: k−1∑ i=0 andi = k−1∑ i=0 ((k − 1) ∗ metasum|lndi + (1 + p) ∗ metasum ∗ (k − 1)|ondi) = k(k − 1)(1 + p) ∗ metasum 686 q. ming-cheng, w. xiang-hu, y. xiao-zong the proof is completed. 2 definition 7. (storage space usage rate:ssur) the total amount of data that stored by distributed storage model divided by the total amount of data that stored by k-complete copies is defined as ssur. from definition 1 and lemma 1: ssur = k(k − 1)(1 + p) ∗ metasum k ∗ k(k − 1) ∗ metasum = 1 + p k (2) definition 8. (limit speed ratio: vratio) let vratio=vi/vj represents the limit ratio of the amount of data that downloaded from nodes ni and nj seperately. then: 1 (k − 1)(1 + p) ∗ metasum ≤ vratio ≤ (k − 1)(1 + p) ∗ metasum (3) definition 9. (maximum speed ceiling: vtop) during the period of downloading data, the amount of data downloaded from any node can not excess andi. then: ( vmax/ ∑k−1 i=0 vi ) k(k − 1) ∗ metasum ≤ (k − 1)(1 + p) ∗ metasum ⇒ vtop ≤ (1+p) k ( k−1∑ i=0 vi). vtop is called maximum speed ceiling. example 1 given: k=4,p=2,metasum=4. questions: (a), give the calculus course that distributes the data to grid nodes according to model. (b). calculate ssur. solution a: (1) from definition 1: the data divided shares=4*(4-1)*4=48. these data blocks are as follows: (1), (2),..., (48). (2) from definition 2: lnd0=(1),(2)...(12); lnd1=(13),(14)...(24); lnd2=(25),(26),...,(36); lnd3= (37),(38),...,(48) (3) from definition 3: (a) g00=(1),(2),(3),(4), g 1 0=(5),(6),(7),(8), g 2 0=(9),(10),(11),(12); (b) g01=(13),(14),(15),(16), g 1 1=(17),(18),(19),(20), g 2 1=(21),(22),(23),(24); (c) g02=(25),(26),(27),(28), g 1 2=(29),(30),(31),(32), g 2 2=(33),(34),(35),(36); (d) g03 =(37),(38),(39),(40), g 1 3=(41),(42),(43),(44), g 2 3=(45),(46),(47),(48). (4) from definition 4: (a)n 0 0=n1, n 1 0=n2, n 2 0=n3; (b)n 0 1=n0, n 1 1=n2, n 2 1=n3; (c)n 0 2=n0, n 1 2=n1, n 2 2=n3; (d)n 0 3=n0, n 1 3=n1, n 2 3=n2; (5) from definition 5:(a) g0 → n 0 0 = g0 → n1 = {g00, g 1 0} → n1, g0 → n 1 0 = g0 → n2 = {g10, g 2 0} → n2, g0 → n 2 0 = g0 → n3 = {g20, g 0 0} → n3; (b) g1 → n 0 1 = g1 → n0 = {g01, g 1 1} → n0, g1 → n 1 1 = g1 → n2 = {g11, g 2 1} → n2, g1 → n 2 1 = g0 → n3 = {g21, g 0 1} → n3; (c)(d) omitted solution b: from definition 7: ssur = (1+p) k = 1+2 4 = 0.75. from the calculus above we can know each node stores 36 shares of data, and 4 nodes in all. so all the shares of data are 36*4, 36*4/(48*4)=0.75. the theoretical value equals to the actual value, that is, ssur=0.75. 4 parallel scheduler 4.1 the basic idea for scheduler definition 10. (local data remainder: si) let xi represent the ideal data downloaded from node ni, where xi = vi/( ∑k−1 j=0 vj). let si = xi − lndi, it is called local data remainder. a novel parallel transmission strategy for data grid 687 this indicator reflects the node load. if si tends to or equals to 0, then it shows that each node can roughly finish downloading at the same time. if si is positive, then it shows that the download speed of the current node is fast and it can share the load of other nodes whose si is negative. from definition 9 we can see that under ideal circumstances, there is ∑ si = 0. examples (1) given k=4, p=1, metasum=3, v1=12, v2=10, v3=4, v4=28. scheduling objective: as far as possible to ensure that each node finishes downloading at the same time, and the data is non-repeated. we first give final results of scheduling algorithm as shown in table 1. download the gray data blocks from node ni. the ideal download data blocks from each node is (8, 6.7, 2.7, 18.7), the scheduling result is (8, 7, 3, 18). the load(si) of each node happens (-1, -2, -6, 10) both before (i-a) after scheduling , that is, (0, -0.3, -0.3, 0.7), the load is balanced more or less. as the sum of data blocks is an integer, so the actual scheduling result is rounded off. here, ssur=50%, compared with the full copy deployment, only 50% storage space is used. table 1 example (1) from table 1 we can see that for the fast nodes they not only complete lnd download mission, but they also share simultaneously the download mission of the slow nodes from their ond. for example, the data blocks (25)(26)(27) lie in lnd of node n3, but they are downloaded from the ond of node n4. 4.2 description of scheduler objective: optimize the si of each node, make them close to or tend to 0, to make load balance for each node. explanation: for any two nodes zn and fn, if the si of zn is positive and there are remainder parts in lnd of fn that are not shared by the ond of zn, then zn can share the load of fn. when a block is shared, do zn.si–, fn.si++. if a data block is shared, then the flag which points out download position is marked, which contains the same data block stored at other nodes. so the same block at other nodes will not be operated. input: k, p, vi , and the data distributed according to storage model. output: program for how to download data blocks. (1) build list zsi_list consisted with the nodes whose si are positive, and sort zsi_list in descending order. (2) build list fsi_list consisted with the nodes whose si are negative, and sort zsi_list in ascending order. /* deal with each node in fsi_list. */ (3) for every node fn in fsi_list deal from first /* let the nodes in zsi_list share the load of nodes in fsi_list*/ (4) for every node zn in zsi_list deal from first (5) if fn.si≥0 then fsi_list.move_to_next, go to(3) (6) if zn.si >0 then (a)let the data blocks in ond of zn share the load in lnd of fn. according to the sharing sum, update zn.si. share one block each time, and judge the 688 q. ming-cheng, w. xiang-hu, y. xiao-zong below two conditions timely: (b)if fn.si ≥0 then fsi_list.move_to_next, go to(3); (c) if zn.si ≤0 then zsi_list.move_to_next, go to(4). (7) in stages (1)-(6) above, let the fast node share the load of slow node. if either list has reached the tail, then exit. (8) if there are still some nodes(fn) whose si are still negative in fsi_list, then deal with zsi_list from first. if the intersection for ond(non-processed) of zn and lnd(non-processed) of fn is not null, then let zn share the load of fn until the intersection is null, in this process regardless of whether the si of zn is already negative. (9) there are two circumstances for the nodes in fsi_list, their si is positive or non-positive. let the nodes whose si are positive share the load of negative ones until they can not share. now the nodes in zsi_list exist in two cases, their si is positive or non-positive. for every node in zsi_list, repeat (1)-(6). then output the scheduling sequence. 5 parallel tranfer algorithm: ptabm if the transmission speed is stable, then the scheduler can be used directly. as network transmission speed changes dynamically, a parallel transmission algorithm, which is based on scheduler and can meet the dynamic speed, is proposed in this chapter. 5.1 model-based storage policy assuming there are four nodes. as shown in figure 3, let the data meet sub-block model storage, that is, each data block meets distributed storage model separately. in fig.1 data groups < b1i , b 2 i , b 3 i , b 4 i > (1 ≤ i ≤ 4) and bi = b 1 i ∪ b 2 i ∪ b 3 i ∪ b 4 i meet storage model separately(every group meets the model separately). figure 3: storage policy sample 5.2 parallel transmission algorithm based on model (ptabm) in fig.1, as any bji is only part of bi, so if download bi from four nodes in parallel, the following restriction must be met: (a) uninterrupted download data from the four nodes at the same time; (b) non-repeated download. as the speed changes dynamicly, let ∆ti represent the time interval from the time of first finishing downloading bi−1 to the time of first finishing downloading bi. for example, the time of completion of bi−1 downloaded by n1 is ti−1, the time of completion of bi downloaded by n3 is ti, so the time interval ∆ti =ti − ti−1. as the speed changes dynamically, the size of each time interval may also be different. the amount of bi data (xi) (0 ≤ i ≤ k − 1) that each node should commit is determined as follows:(a)take the vi at ti−1 (0 ≤ i ≤ k − 1) as the input speed of scheduler; (b)the remainder data that every node did not complete is mi ; (c)formula (2) must be met in theory, that is, as far as possible to ensure that each node fulfils its download mission at the same time. a novel parallel transmission strategy for data grid 689 m0 + x0 v0 = m1 + x1 v1 = ... = mk−1 + xk−1 vk−1 (4) formula (2) is changed into formula (3) through identity theorem. from formula (3), xi is deduced as formula (4). take xi as the input value of scheduler to calculate si. ( mi + xi vi )k−1 i=0 = k−1∑ i=0 (mi + xi) k−1∑ i=0 vi = k−1∑ i=0 mi + k−1∑ i=0 xi k−1∑ i=0 vi = k−1∑ i=0 mi + m k−1∑ i=0 vi = λ (5) xi = λvi − mi (0 ≤ i ≤ k − 1) (6) according to formulas and ristriction in this section, the schematic diagram of parallel transmission architecture is shown in figure 4 the client side consists of two parts. at the end of every ∆t download unit passes vi and mi to scheduler unit. scheduler unit calculates xi and the data block bji , and then output them to download unit. download unit downloads data from the nodes according to bji , and real-time observe whether there is a new generation of ∆t, and then repeat the process above. figure 4: schematic diagram of parallel transmission 5.3 reliability analysis the reliability of algorithm includes two aspects, which are scheduler reliability and algorithm reliability. (1) from theorem 1, if there are random p nodes fail, then the data ,the other k-p nodes contain, is integrated. (2) if there are nodes fail during the download period, then (a) get vi of current normal nodes; (b) let the speed of failure nodes be 0; (c) mark the downloaded blocks of bi, and the undownloaded blocks are b‘i; (d) take the amount of data(m’) of ∑ b‘i , vi and mi as the input values of scheduler; (e)use scheduler to recompute the scheduling sequences. 5.4 verification system (vpg-torrent) verification system contains four parts, as shown in figure 5: data dispatcher, rls server, virtual node (vpeer), peer node. data dispatcher: data dispatcher take p, k, list of servers and original data as input, through model computing unit, original data was divided and deployed into servers. meanwhile, the storage information of data blocks(m-table) was sent to rls server. rls server: the replica location service (rls) keeps track of the physical locations of files. the information stored in the replica location service defines the number of replicas for each file as well as the physical location of the actual data. the physical location is identified by a unique physical file name (pfn) such as a gridftp url. to enable the use of the gridtorrent protocol we introduce the gridtorrent url. 690 q. ming-cheng, w. xiang-hu, y. xiao-zong figure 5: data dispatcher and download virtual peer (vpeer): in the process of data download, gridftp servers are regarded as a virtual peer node (vpeer). so vpeer has a fast transmission speed. the other peers regard vpeer as a general peer to schedule. of course, the transmission of vpeer is based on ptabm. peer node: each peer contains scheduling algorithm of bittorrent and a download unit. scheduling algorithm treat vpeer as a normal peer. download unit responsible for data transmission from other peers or vpeer. each new peer must first request access to rls server to get rls and m-talbe information, and upload the information of data blocks they owned to rls server periodically. as vpeer has fast speed, so when the number of peers is small, its contribution to transmission will be greatest. when download from vpeer ptabm must be used. verification system consists of two major experiments: real experiment 5-3 is to verify ptabm, and compare with previous algorithm. simulation experiments 5-4, 5-5 are to verify the performance of large-scale concurrent access, so experiment is carried out in oversim. 6 experiments 6.1 purpose from figure 5, (a)if there are no peers willing to upload, then vpg-torrent will degenerate into the most common parallel transmission system based on gridftp and multi-copy, this transmission mode plays an irreplaceable role in some applications in data grid. therefore, the performance between vpg-torrent and traditional dadc must compared; (b) for large-scale network where bittorrent plays an important role, the performance of vpg-torrent must be verified as the number of concurrent peers increase from small to large, and should be compared with gridtorrent. there is a natural experiment. if storage model is not used, but the data is directly divided into uniform shares, and then stored them into multiple nodes, so the bandwidth is also increased. if so, vpeer is not exist, accordingly ptabm can not be used too, but only bittorrent can be used. as bittorrent algorithms do not rely on global data storage knowledge to schedule, so it leads to the service capacity of servers can not be maximized. this experiment is carried out in 5-4(2). simulation experiments (1) (2) (3) are made to verify the performance of scheduler; real experiments (4) (5) are made to verify the performance of parallel transmission algorithm based on storage model & full copies and compare with the performance, experiment (6) (7) are a novel parallel transmission strategy for data grid 691 made to verify the performance vpg-torrent. experiment 5.5 is made to verify the reliability of sheduler and algorithm. in experiment (4) (5) the client host lies in education network, and the other four grid nodes are all telecommunication network access. gridftp is used as transmission protocol. in experiment the concurrent connections to vpeer is limited to 20. the maximum connection speed of every server in vpeer is 200k, and rated bandwidth is 5m. each peer can start up 15 connections at the same time, the upload rate is up to 20kb. 6.2 basic definitions and explanations if the speed is determined, the ideal amount of data downloaded from each node can be: m ∗ vi/( ∑k−1 j=0 vj) (0 ≤ i ≤ k − 1). the result that scheduler can give and the ideal amount of download data are compared during experiments (1) (2) (3). literature [11] combines basic parallel transmission mode of gridftp and p2p technologies, but the prerequisite is that there are multiple clients requesting; the research of parallel transmission algorithm in literature [9] [10] is based on gridftp only, its dynamic collaborative algorithm (dcda) achieved good performance. the performances of ptabm and dcda are compared in experiments (4) (5). definition 11. (the percentage of time that scheduling results exceeds ideal value:trer) given the ideal download time is tid, the download time outputted by scheduler is tre. let trer = (100(tre − tid)/ti)%. this parameter is used to detect the performance of scheduler. definition 12. (unit speed variance:d(v)) variance divided by the sum of the speed is called unit speed variance, that is: e(v ) = (∑k−1 i=0 vi ) /k and d(v ) = (∑k−1 i=0 (vi − e(v ))2 ) / ( k (∑k−1 i=0 vi )) (7) from definition 9 we can see that when k is fixed and for the same group vi, vtop increases by (∑k−1 i=0 vi ) /k when p increases by 1. from definition 7 we can see that the storage space usage rate increases by 1/k. 6.3 scheduler performance test (1) this experiment is to verify the impact of d(v), vtop, vratio on trer. let p=1, k=4, metasum=15, the range for the speed distribution is (1,100), then the theoratical vratio=90. consecutively record the results of algorithm for 20 times, the impact of d (v), vratio, and vtop on trer is listed in table 2. calculate the average of d (v), that is, ed(v). classified the observational results that meet d(v)>ed(v) into a group, and d(v)<ed(v) as another group. in table 2 the experiments numbered by 1-9 are of d (v)>ed (v). trer is 4.40. the experiments 1-9 and 10-20 calculate the average trer respectively, the value are 7.40, 1.87 and they are greater or less than the overall average of 4.40. experiment (1) shows that if the distribution of node speed is very uneven and the difference between maximum and minimum speed is large, trer may be increased. if vtop exceeds the theoratical maximum value, then trer will have a substantial increase. (2) this experiment is made to verify the impact of p and speed range on trer. let k=10, and change the range of p & speed, as shown in table 3. there are nine combinations, observe ten times for every combination. calculate the trer average of every combination, the histogram is shown in figure 6. let k=2,p=2, observe the impact of speed range on trer. observing ten times of algorithm results in every speed range, and calculates trer average. the curve is shown in figure 7. 692 q. ming-cheng, w. xiang-hu, y. xiao-zong table 2 impact of d(v), vratio, vtop on trer table 3 experimental methods a novel parallel transmission strategy for data grid 693 from fig.6 we can see that when p=1, the change of speed has greater impact on trer. but when p=2,3 as the speed range increases, trer are almost unaffected. scheduling results are approximately equivalent to the ideal download speed. it can be seen from figure 7, trer gradually increases as the speed range increases. however, when the speed range is in 5-150 and trer are in the vicinity of one, the effect is good. figure 6: mpact of p and speed range on trer when k=10 figure 7: impact of speed range on trer when k=10, p=2 experiment (2) shows that in a random speed range, if p has already guaranteed that trer is maintained at an ideal value. even when p continues to increase, trer will not decrease. (3) this experiment is done to verify the impact of k and speed range on trer. figure 8: impact of k on trer when p=2, vi ∈(1,50) figure 9: impact of k on trer when p=2,vi ∈(5,50) let the amount of data at every node is 300. in order to ensure the metadata will not be affected when k and the overall amount of data change, do as follows: when k=4 and metasum=100, from formula (1) metasum of other nodes can be calculated, as shown in table 4. 694 q. ming-cheng, w. xiang-hu, y. xiao-zong because the sum of nodes increases, if the amount of data doesn’t increase, then the download time will decrease, so the trer will become inaccurate. in order to guarantee experimental parameters consistency, a special treatment is needed. table 4 value of k, m and metasum let p=2, vi ∈(1,50), observe the effect of changes of k on trer. for every k, observe 10 times, then calculate the average of trer. from figure 8 we can see that trer increases gradually as k increases. let p=2, vi ∈(1,50), observe the effect of the change of k on trer. for every k, observe 10 times, then calculate the average of trer. from figure 9 we can see that as k increases trer changes little. when vi ∈(1,50), the difference among the speeds of nodes is larger, and the maximum theoretical vtop=50; while vi ∈(5,50), there is little difference among the speeds of nodes, and the maximum theoretical vtop=10 only. experiment (3) shows that the larger speed range affects trer greatly. if the speed range is not large, then k has little effect on trer. figure 10: distribution of sub_data points when matadata=5m) figure 11: distribution of sub_data points when matadata=5m,10m it is found through experiments that the value of trer near one is caused by the sub-block. the ideal download data of each node is based on an arbitrary partition for the overall data, and the error generated by the scheduler can be reduced by increasing the metasum appropriately. therefore for the value of trer near one we can consider that the scheduler has achieved the ideal scheduling result. 6.4 ptabm performance test a novel parallel transmission strategy for data grid 695 (4) if there are no peers willing to upload, then vpg-torrent will degenerate into the most common parallel transmission system based on gridftp and multi-copy. this experiment is made to compare the transmission speed between ptabm and dcda, as well as compare the amount of download data at the same time of the two algorithms. let metadata be 5m and 10m, analyze the performance of ptabm. let matadata=5m, observe the amount of data downloaded by dcda and ptabm separately ten times at different time. let sub_data equal the amount of download data by sub_data minus ptabm’s. the point distribution is shown in figure 10. let matadata=5m,10m, observe the amount of data that downloaded by dcda, ptabm separately ten times at different time. let sub_data equal the amount of data downloaded by dcda minus ptabm’s. two type point distribution is shown in figure 11. figure 10 in experiment (4) shows that sub_data does not increase as the time increases, and shows the oscillation effects. it shows that sub_data is non-cumulative. so as the amount of download data increases, the ratio of sub_data and overall data tends to zero, so the performance of ptabm tends to dcda. fig.8. shows that when matadata is larger, sub_data is also larger. but as time increases, it also shows the oscillation effects. (5) this experiment is made to compare the performance between ptabm and dcda in download time and amount. let one time unit contain 10 seconds and matadata=15m. observe the amount of download data and calculate the ratio of data downloaded by dcda and ptabm, take the percentage (dr). the curve for the relationship between dr and time units is shown in figure 12. let metadata=5m,10m,20m, p=1. separately observe the time that taken by dcda and ptabm to download 960m and 1920m data. then calculate the ratio of ptabm finishing time and dcda’s, take the percentage (tr). the tr bar graph is shown in figure 113. figure 12 in experiment 5 shows that as the download time increases the speed of ptabm tends to dcda. at 40 50s their speeds are similar and are approximately equivalent at 100s. from figure 13 we can see that smaller matadata can achieve better effects than larger one. when matadata=5 transfer 1920m data the ratio of the time used by ptabm and dcda is 1.05. so for vast data the two algorithms can achieve similar performances. 6.5 vpg-torrent verification (6) this experiment is made to verify performance of vgp-torrent when p=1, p2p is allowed or not allowed (download from gridftp servers), and compare performance with gridtorrent. let original data size be 1g, and the amount of data permitted to be deployed is 2g. within 20 minutes specific amount of peers are generated randomly. each peer will continue to permit to be downloaded by other peers for 40 minutes from its completion. firstly, deploy two copies for gridtorrent system; secondly, given p=1, k=5,6,7 as the input of data dispatcher of vpg-torrent, so the information of data storage is stored into rls server later. when p2p is allowed, from figure 14, in the performance curves of different peers, the curve gt of gridtorrent is located in the bottom. when k=5,6,7, the curves of vpg-torrent increased gradually. and the curves are all concave. it is because that when the number of peers is small, parallel transmission only based on gridftp servers play an important role, as the number increases gradually, then bittorrent plays an important role. when p2p is not allowed, i.e., the transmission is only based on gridftp. from figure 15, vpg-torrent has a great advantage over bittorrent in average speed of peers. most importantly, the storage space used by them is the same. (7) this experiment is made to test how impact of storage method on gridtorrent and compare performance with vpg-torrent. 696 q. ming-cheng, w. xiang-hu, y. xiao-zong figure 12: download data ratio at the same time when matadata=15m figure 13: impact of matadata on download speed for gridtorrent we process the original data as follows: let original data size be 1g, and the amount of data permitted to be deployed is 2g. the original data were evenly divided into 3 shares, and are distributed to 6 nodes, so that there are two nodes have the same data. these six nodes will treated as normal peers for any new peers. experiments were carried out in two conditions: p2p is allowed, and not allowed. when p2p is not allowed, there will be six threads downloading data concurrently, and the completion time depends on the node which complete transmission task at last. in figure 16, k=6 is the performance curve of vpg-torrent, as is the curve of gridtorrent when storage method is adopted, and gt is the curve of gridtorrent obtained in experiment 6-5-(6). obviously as is better than gt. so for bittorrent to increase performance, the better way is to increase the bandwidth by deploying original data to multiple nodes under the condition of using the same size of storage space. however, the performance of as is a little worse than vpg-torrent’s. it is because that bittorrent will not utilize the storage knowledge to schedule, accordingly it can not make full use of server bandwidth. in figure 17, when p2p is not allowed, ’m’ is the column of vpg-torrent, and ’a’ is the column of gridtorrent. obviously the transmission time of vpg-torrent is less than gridtorrent’s. in this experiment vpg-torrent can ensure that all the download tasks can be finished almost at the same time, therefore, it can make the load balance. instead, bittorrent can not. although the storage space used is the same, vpg-torrent exceeds bittorrent in transmission time. 6.6 reliability test this experiment is made to verify the performance of scheduler at normal conditions and when node failed, verify the performance of ptabm when one node failed, and compare the performance of ptabm and dcda at this condition. let p=(1,2), vi ∈(5,25), k=(4,5,6,7), observe the performance of scheduler in normal and failure circumstances. in figure 18 ’f=1’ represents that one node is failed. draw the curve of k and trer . from figure 18 we can see that when p=1, there is one failure node and the performance of scheduler is a little worse than in normal circumstances. while p=2, there is also one failure node, but the performance of scheduler is good, with trer kept near 1. let p=1, vi ∈(5,25), metasum=5, observe the performance of ptabm and dcda in normal a novel parallel transmission strategy for data grid 697 figure 14: impact of peers on average speed when bittorrent is allowed figure 15: impact of peers on average speed when bittorrent is not allowed figure 16: performance compares for different storage methods when p2p is allowed figure 17: performance compares for different storage methods when p2p is not allowed 698 q. ming-cheng, w. xiang-hu, y. xiao-zong figure 18: comparison of scheduler performance between normal and failure curcumstances figure 19: comparison of algorithm performance between normal and failure curcumstances and failure circumstances, make the ratio(tr) of ptabm and dcda. in figure 19 ’n’ represents normal circumstance while ’f’ represents failure circumstance. from the fig we can see when the amount of data is 1920m the download time of the two algorithms is similar. it shows that ptabm has good reliability. 6.7 supplementary analysis of experimental results the large speed range in experiment 6.3 is to verify the performance of scheduler in the extreme distribution of speed. so in practice as long as taking appropriate p value according to the speed range, the smaller trer can be achieved. at the same time, given a reasonable matadata value, the performance of ptabm can be close to or approximately equal to the performance of dcda. the most important is saving storage space and reducing the network traffic caused by the creation of multi-copies. fortunately, the distributed storage model and ptabm meet those requirements. in experiment 6.4 p=1, matadata=5m, the effects are very good. derived from definition 7: when p=1, the ssur and k are calculated as in table 5. when k=4, the ssur is 50%; but when k=10, the ssur is only 20%. as achieved, the objectives of rapid data transmission, the storage space and network traffic are also significantly optimized at the same time. table 5 relationship between p=2, k and ssur from experiment 6.5, whether p2p is allowed or not, the performance of vpg-torrent is higher than gridtorrent’s, even if increasing the bandwidth by adopting uniform storage method. 7 conclusions parallel transmission algorithms based on multi-copy cause a waste of storage space seriously and are difficult to adapt to a wide range of network access. instead, bittorrent for wide range of file-sharing has a unique advantage. although gridtorrent combined the two above, a novel parallel transmission strategy for data grid 699 the performance is poor when the peers are few. in this paper we achieved multiple optimization objectives: storage space saving, suitable for two kinds of application modes (i.e. parallel transfer based on gridftp and bittorrent), adaptability for wide range of network and higher performance when there are fewer peers. the proposed storage model, scheduler, parallel transmission algorithm, virtual peer and vpg-torrent verification system achieved very good result, and the system proposed has certain advantages compared with the previous algorithms. bibliography [1] chen lei, li san-li. a calking dynamic replication distribution algorithm in data grid. acta electronica sinica, 34(11):1-4, 2006 [2] xie xiao-lan, liu yu, zhou de-jian. research on manufacturing grid data access and integration key technology. journal of wuhan university of technology, 31(6):1-4, 2009 [3] zhang guangzhi, he jieyue. application research on biological data grid. computer engineering,(2):1-4, 2004 [4] qin xin, luo ze, nan kai etal. design and implementation of problem solving environment for astronomy application based on science data grid. application research of computers,(4):1-4, 2009 [5] h.a. james, k.a. hawick. scientific data management in a grid environment. journal of grid computing,3: 39-51, 2005 [6] mingwei wang, shusheng zhang, jingtao zhou etal. an architecture of semantic desktop data grid. proceedings of the 10th international conference on computer supported cooperative work in design,ieee computer society ,1-6, 2006 [7] s. fiore, m. mirto, cafaro. a grelc based data grid management environment. 21st ieee international symposium on computer-based medical systems, ieee computer society, 355360,2008 [8] richard mcclatchey, ashiq anjum etal. data intensive and network aware (diana) grid scheduling. journal of grid computing,5:43-64, 2007 [9] h. liu, et al., scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm, future generation computer systems:1-8,2009 [10] xiangang zhao, bai wang, nan du. qos-based algorithm for job allocation and scheduling in data grid. proceedings of the fifth international conference on grid and cooperative computing workshops (gccw’06), ieee computer society:1-7,2006 [11] nhan nguyen dang, soonwook hwang, sang boem lim. improvement of data grid’s performance by combining job scheduling with dynamic replication strategy. the sixth international conference on grid and cooperative computing(gcc 2007), ieee computer society:1-8,2007 [12] esther pacitti. patrick valduriez. marta mattoso. grid data management: open problems and new issues. journal of grid computing,5:273-281, 2007 700 q. ming-cheng, w. xiang-hu, y. xiao-zong [13] jiang jianjin, yang guangwen. replication strategies in data grid systems with clustered demands. journal of computer research and development,46(2):1-8,2009 [14] w u chang-ze, chen shu-yu, ti an dong. the strategy of creating replica based on cost shared in data grid. huazhong univ. of sci. & tech. 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[4] proposed a probabilistic model where the frame of discernment contained the individual clusters and the ignorance that was modeled by the union of all individual clusters. in that work, the authors derived the mass value of ignorance from the mixture of distributions of the individual clusters composing it. guan et al. [5] came up with three methods to construct the bpa function based on gray correlation analysis, fuzzy sets, and attribute measure respectively. chen et al. [6] and li et al. [7] used back-propagation (bp) neural network to obtain basic probability assignment. according as neural network can gain stronger generalization ability, the measured data being processed by neural network can be used as the bpa value of every sensor. xu et al. [8] used the difference matrix of deviation degree to represent quantitatively the degree of similarity between interval numbers, and constructed an expression of basic probability assignment function. since the basic probability assignment of evidence theory obtained by using neural network has high computational complexity, zuo et al. [9] put forward a method of rough set theory based on random set and bp neural network to obtain the basic probability assignment. in the framework of random set, the ability of attribute reduction of rough set was made use of to reduce the neural network input dimension. in papers [10-13], in order to solve the different practical problems, we proposed several different copyright © 2006-2015 by ccc publications 334 w. jiang, y. yang, y. luo, x.y. qin approaches to obtain bpa. these more pragmatic methods are proposed to generate bpas from uncertain information. in this paper, an improved method to determine the similarity measure between generalized fuzzy numbers is presented. then, a new method to obtain basic probability assignment (bpa) is proposed based on the improved similarity measure between generalized fuzzy numbers. an experiment of iris data classification is used to illustrate the efficiency of our method. 2 preliminaries 2.1 dempster shafer evidence theory. evidence theory first supposes the definition of a set of hypotheses θ = {h1, h2, · · · , hn} called the frame of discernment. it is composed of n exhaustive and exclusive hypotheses. let us denote p(θ), the power set composed with the 2n propositions a of θ: p(θ) = {∅, {h1}, {h2}, · · · , {hn}, {h1 ∪ h2}, {h1 ∪ h3} · · · , θ} (1) where ∅ denotes the empty set. the n subsets containing only one element are called singletons. a key point of evidence theory is the basic probability assignment (bpa). a bpa is a function from p(θ) to [0, 1] ,and which satisfies the following conditions:∑ a∈p(θ) m(a) = 1; m(∅) = 0, (2) dempster’s rule of combination (also called orthogonal sum), noted by m = m1 ⊕ m2, is the first one within the framework of evidence theory which can combine two bpas m1 and m2 to yield a new bpa: m(a) = ∑ b∩c=a m1(b)m2(c) 1 − k and k = ∑ b∩c=∅ m1(b)m2(c) (3) where k is a normalization constant, called conflict because it measures the degree of conflict between m1 and m2. k = 0 corresponds to the absence of conflict between m1 and m2, whereas k = 1 implies complete contradiction between m1 and m2. the belief function resulting from the combination of j information sources sj defined as m = m1 ⊕ m2 · · · ⊕ mj · · · · · · ⊕ mj, (4) as can be seen from above, multi source information can be easily fused in the framework of evidence theory, if we can obtain the bpa functions. 2.2 generalized fuzzy numbers a generalized fuzzy number ã = (a, b, c, d; w) is described as any fuzzy subset of the real line r with membership function µ ã which has the following properties [14]: (1) µ ã is a continuous mapping from r to the closed interval in [0, w], 0 ≤ w ≤ 1; (2) µ ã (x) = 0 for all x ∈ (−∞, a]; (3) µ ã (x) is strictly increasing in [a, b]; (4) µ ã (x) = w for all x ∈ [b, c], where w is a constant and 0 < w ≤ 1; (5) µ ã (x) is strictly decreasing in [c, d]; (6) µ ã (x) = 0 for all x ∈ [d, +∞). determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers 335 where 0 < w ≤ 1, a, b, c and d are real numbers. especially, a generalized trapezoidal fuzzy number can be defined as ã = (a, b, c, d; w), where a ≤ b ≤ c ≤ d, 0 ≤ w ≤ 1, its membership function is defined by µ ã (x) =   (x − a) b − a a ≤ x ≤ b w b ≤ x ≤ c (x − c) d − c c ≤ x ≤ d 0 else (5) if w = 1, then the generalized fuzzy number ã is called a normal trapezoidal fuzzy number, denote as ã = (a, b, c, d). if a = b and c = d, then ã is called a crisp interval. if b = c, then ã is called a generalized triangular fuzzy number. if a = b = c = d, then ã is called a real number. 1.0 0.8 0.1 0.2 0.3 0.4 w a=(0.1,0.2,0.3,0.4;1.0) b=(0.1,0.2,0.3,0.4;0.8) figure 1: two generalized trapezoidal fuzzy numbers ã and b̃ figure 1 shows two different generalized trapezoidal fuzzy numbers ã = (0.1, 0.2, 0.3, 0.4; 1.0) andb̃ = (0.1, 0.2, 0.3, 0.4; 0.8). compared with normal fuzzy numbers, the generalized fuzzy numbers can deal with uncertain information in a more flexible manner. for example, in decision making situation, the values w1 and w2 represent the degree of confidence of the opinions of the decision-makers’ ã andb̃, respectively, where w1 = 1 and w2 = 0.8. 2.3 a review of the existing similarity measures between fuzzy numbers in this section, we briefly introduce some existing similarity measures between fuzzy numbers from chen [15], lee [16], chen and chen [17], wei & chen [18] and hejazi, et al. [19]. let ã and b̃ be two trapezoidal fuzzy numbers, where ã = (a1, a2, a3, a4) and b̃ = (b1, b2, b3, b4), as shown in figure 2. chen [15] presented a similarity measure between fuzzy numbers ã and b̃ based on the geometric distance, where the degree of similarity s(ã, b̃) between the fuzzy numbers ã and b̃ is calculated as follows: s(ã, b̃) = 1 − ∑4 i=1 |ai − bi| 4 . (6) where s(ã, b̃) ∈ [0, 1]. the larger the value of s(ã, b̃), the more the similarity between the fuzzy numbers ã and b̃. if ã and b̃ are two triangular fuzzy numbers, where ã = (a1, a2, a3) and b̃ = (b1, b2, b3). the degree of similarity s(ã, b̃) between the triangular fuzzy numbers ã and b̃ is calculated as 336 w. jiang, y. yang, y. luo, x.y. qin figure 2: trapezoidal fuzzy numbers ã and b̃ follows [15]: s(ã, b̃) = 1 − ∑3 i=1 |ai − bi| 3 . (7) wheres(ã, b̃) ∈ [0, 1]. the larger the value of s(ã, b̃), the more the similarity between the fuzzy numbers ã and b̃. lee [16] presented a similarity measure between trapezoidal fuzzy numbers, where the degree of similarity s(ã, b̃) between the trapezoidal fuzzy numbers ã and b̃ is calculated as follows: s(ã, b̃) = 1 − ∥∥∥ã − b̃∥∥∥ lp ∥u∥ × 4−1/p. (8) where ∥∥∥ã − b̃∥∥∥ lp = ( 4∑ i=1 (|ai − bi|)p )1/p . (9) and ∥u∥ = max(u) − min(u). (10) in order to optimally aggregate experts’ fuzzy opinions, chen and chen [17] presented a similarity measure between generalized trapezoidal fuzzy numbers. first, they calculate the center-of-gravity (cog) point (x∗ ã , y∗ ã ) and (x∗ b̃ , y∗ b̃ ) of the generalized trapezoidal fuzzy numbers ã and b̃, respectively. the cog point (x∗ ã , y∗ ã ) of the generalized trapezoidal fuzzy numbers ã = (a1, a2, a3, a4; wã) is calculated as follows: y∗ ã =   w ã × ( a3 − a2 a4 − a1 + 2 ) 6 if a1 ̸= a4 and 0 < wã ≤ 1, w ã 2 if a1 = a4 and 0 < wã ≤ 1, (11) x∗ ã = y∗ ã (a3 + a2) + (a4 + a1)(wã − y ∗ ã ) 2w ã . (12) then the degree of similarity s(ã, b̃) between the two generalized trapezoidal fuzzy numbers ã and b̃ can be calculated as follows [17]: s(ã, b̃) = [ 1 − ∑4 i=1 |ai − bi| 4 ] × ( 1 − ∣∣∣x∗ ã − x∗ b̃ ∣∣∣)b(sã,sb̃) × min(y∗ã, y∗b̃) max(y∗ ã , y∗ b̃ ) (13) where b(s ã , s b̃ ) are defined as follows: determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers 337 b(s ã , s b̃ ) = { 1 s ã + s b̃ > 0 0 s ã + s b̃ = 0 (14) where s ã = a4 − a1 and sb̃ = b4 − b1 are the lengths of the generalized trapezoidal fuzzy numbers ã and b̃. the larger the value of s(ã, b̃), the more the similarity measure between two fuzzy numbers. wei & chen [18] proposed a method for calculating the similarity of two fuzzy numbers ã and b̃, where ã = (a1, a2, a3, a4; wã) and b̃ = (b1, b2, b3, b4; wb̃). if 0 ≤ a1 ≤ a2 ≤ a3 ≤ a4 ≤ 1 and 0 ≤ b1 ≤ b2 ≤ b3 ≤ b4 ≤ 1, then the degree of similarity s(ã, b̃) between the generalized trapezoidal fuzzy numbers ã and b̃ is calculated as follows: s(ã, b̃) = [ 1 − ∑4 i=1 |ai − bi| 4 ] × min(p(ã), p(b̃)) + min(w ã , w b̃ ) max(p(ã), p(b̃)) + max(w ã , w b̃ ) (15) where s(ã, b̃) ∈ [0, 1]; p(ã) and p(b̃) are defined as follows: p(ã) = √ (a1 − a2)2 + w2 ã + √ (a3 − a4)2 + w2 ã + (a3 − a2) + (a4 − a1). (16) p(b̃) = √ (b1 − b2)2 + w2 b̃ + √ (b3 − b4)2 + w2 b̃ + (b3 − b2) + (b4 − b1). (17) p(ã) and p(b̃) are the perimeters of generalized trapezoidal fuzzy numbers ã and b̃, respectively. the larger the value of s(ã, b̃), the more the similarity measure between two fuzzy numbers. hejazi, etc.[19] presented an improved similarity measure between two fuzzy numbers ã and b̃ combining the concept of geometric distance, height, areas and perimeters of generalized fuzzy numbers. the degree of similarity s(ã, b̃) between the generalized trapezoidal fuzzy numbers ã and b̃ is calculated as follows: s(ã, b̃) = [ 1 − ∑4 i=1 |ai − bi| 4 ] × min(p(ã), p(b̃)) max(p(ã), p(b̃)) × min(a(ã), a(b̃)) + min(w ã , w b̃ ) max(a(ã), a(b̃)) + max(w ã , w b̃ ) (18) p(ã) and p(b̃) are the perimeters of two generalized trapezoidal fuzzy numbers which are calculated by eqs.(16),(17).on the other hand they have a(ã) and a(b̃) which are the areas of the two fuzzy numbers and that are calculated as follows: a(ã) = 1 2 w ã (a3 − a2 + a4 − a1). (19) a(b̃) = 1 2 w b̃ (b3 − b2 + b4 − b1). (20) the larger the value of s(ã, b̃), the more the similarity measure between two fuzzy numbers. 3 an improved similarity measure of generalized fuzzy numbers many similarity measures between fuzzy numbers have been proposed [15-19]. however, it has been found that the existing methods cannot correctly calculate the degree of similarity between 338 w. jiang, y. yang, y. luo, x.y. qin two generalized fuzzy numbers in some situations. in this section, we present an improved method to calculate the degree of similarity between generalized fuzzy numbers[20],which gives consideration to the horizontal center of gravity, the perimeter, the height and the area of the two fuzzy numbers. the proposed similarity measure can overcome the drawbacks of the existing methods. assume there are two generalized trapezoidal fuzzy numbers ã and b̃, where ã = (a1, a2, a3, a4; w ã ) and b̃ = (b1, b2, b3, b4; wb̃), 0 ≤ a1 ≤ a2 ≤ a3 ≤ a4 ≤ 1 and 0 ≤ b1 ≤ b2 ≤ b3 ≤ b4 ≤ 1. then the degree of similarity s(ã, b̃) between the generalized trapezoidal fuzzy numbers ã and b̃ is calculated as follows: s(ã, b̃) = [ 1 − ∣∣∣x∗ ã − x∗ b̃ ∣∣∣] × [1 − ∣∣wã − wb̃∣∣] × min(p(ã), p(b̃)) + min(a(ã), a(b̃)) max(p(ã), p(b̃)) + max(a(ã), a(b̃)) (21) where x∗ ã and x∗ b̃ are the horizontal center-of-gravity (cog) of the generalized trapezoidal fuzzy numbers ã and b̃, respectively. the cog point (x∗ ã , y∗ ã ) of the generalized trapezoidal fuzzy numbers ã is calculated as follows: y∗ ã =   w ã × ( a3 − a2 a4 − a1 + 2 ) 6 if a1 ̸= a4 and 0 < wã ≤ 1, w ã 2 if a1 = a4 and 0 < wã ≤ 1, (22) x∗ ã = y∗ ã (a3 + a2) + (a4 + a1)(wã − y ∗ ã ) 2w ã , (23) p(ã) and p(b̃) are the perimeters of two generalized trapezoidal fuzzy numbers which are calculated as follows: p(ã) = √ (a1 − a2)2 + w2 ã + √ (a3 − a4)2 + w2 ã + (a3 − a2) + (a4 − a1) (24) p(b̃) = √ (b1 − b2)2 + w2 b̃ + √ (b3 − b4)2 + w2 b̃ + (b3 − b2) + (b4 − b1) (25) a(ã) and a(b̃) are the areas of two generalized trapezoidal fuzzy numbers which are calculated as follows: a(ã) = 1 2 w ã (a3 − a2 + a4 − a1), (26) a(b̃) = 1 2 w b̃ (b3 − b2 + b4 − b1), (27) the larger the value of s(ã, b̃) is, the more the similarity measure between two generalized trapezoidal fuzzy numbers ã and b̃ will be. in the following sections, we will introduce some of properties that our model has: theorem 3.1. two generalized trapezoidal fuzzy numbers ã and b̃ are identical if and only if s(ã, b̃) = 1. proof: (i) if ã and b̃ are identical, x∗ ã = x∗ b̃ , w ã = w b̃ , min(p(ã), p(b̃)) = max(p(ã), p(b̃)), min(a(ã), a(b̃)) = max(a(ã), a(b̃)). determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers 339 the degree of similarity between two generalized trapezoidal fuzzy numbers is calculated as follows: s(ã, b̃) = [ 1 − ∣∣∣x∗ ã − x∗ b̃ ∣∣∣] × [1 − ∣∣wã − wb̃∣∣] × min(p(ã), p(b̃)) + min(a(ã), a(b̃)) max(p(ã), p(b̃)) + max(a(ã), a(b̃)) = [1 − 0] × [1 − 0] × 1 = 1 (28) (ii) if s(ã, b̃) = 1, then s(ã, b̃) = [ 1 − ∣∣∣x∗ ã − x∗ b̃ ∣∣∣] × [1 − ∣∣wã − wb̃∣∣] × min(p(ã), p(b̃)) + min(a(ã), a(b̃)) max(p(ã), p(b̃)) + max(a(ã), a(b̃)) = 1 (29) it implies that x∗ ã = x∗ b̃ , w ã = w b̃ , min(p(ã), p(b̃)) = max(p(ã), p(b̃)) and min(a(ã), a(b̃)) = max(a(ã), a(b̃)). therefore, the generalized trapezoidal fuzzy numbers ã and b̃ are identical. 2 theorem 3.2. s(ã, b̃) = s(b̃, ã). proof: because s(ã, b̃) = [ 1 − ∣∣∣x∗ ã − x∗ b̃ ∣∣∣] × [1 − ∣∣wã − wb̃∣∣] × min(p(ã), p(b̃)) + min(a(ã), a(b̃)) max(p(ã), p(b̃)) + max(a(ã), a(b̃)) (30) s(b̃, ã) = [ 1 − ∣∣∣x∗ b̃ − x∗ ã ∣∣∣] × [1 − ∣∣wb̃ − wã∣∣] × min(p(b̃), p(ã)) + min(a(b̃), a(ã)) max(p(b̃), p(ã)) + max(a(b̃), a(ã)) (31) we can see that ∣∣∣x∗ ã − x∗ b̃ ∣∣∣ = ∣∣∣x∗ b̃ − x∗ ã ∣∣∣, ∣∣wã − wb̃∣∣ = ∣∣wb̃ − wã∣∣, min(p(ã), p(b̃)) = min(p(b̃), p(ã)), min(a(ã), a(b̃)) = min(a(b̃), a(ã)), and max(a(ã), a(b̃)) = max(a(b̃), a(ã)). therefore, s(ã, b̃) = s(b̃, ã). 2 theorem 3.3. if ã = (a1, a2, a3, a4; wã) and b̃ = (b1, b2, b3, b4; wb̃) are two generalized trapezoidal fuzzy numbers with the same geometric shape and height, then s(ã, b̃) = 1 − d, where d = ∣∣∣x∗ ã − x∗ b̃ ∣∣∣ is the offset between ã and b̃. proof: because w ã = w b̃ , and based on eq.(24) eq.(27), we can get min(p(ã), p(b̃)) = max(p(ã), p(b̃)) and min(a(ã), a(b̃)) = max(a(ã), a(b̃)); therefore, due to eq.(21), the degree of similarity between ã and b̃ is calculated as follows: s(ã, b̃) = [ 1 − ∣∣∣x∗ ã − x∗ b̃ ∣∣∣] × [1 − ∣∣wã − wb̃∣∣] × min(p(ã), p(b̃)) + min(a(ã), a(b̃)) max(p(ã), p(b̃)) + max(a(ã), a(b̃)) = [ 1 − ∣∣∣x∗ ã − x∗ b̃ ∣∣∣] × [1 − 0] × 1 = 1 − d (32) 2 340 w. jiang, y. yang, y. luo, x.y. qin 4 a comparison of the similarity measures in this section, we extend 15 sets of fuzzy numbers presented by wei & chen [18] into 18 sets of fuzzy numbers, as shown in figure 3, and compare the calculation results of the proposed method with the results of the existing similarity measures, as shown in table 1. from figure 3 and table 1, we can see the drawbacks of the existing similarity measures: (1) from figure 3, we can see that set 3 and set 4 are different sets of fuzzy numbers. however, from table 1, we can see that if we apply chen’s method (chen, 1996) and lee’s method (lee, 2002), set 3 and set 4 get the same degree of similarity, respectively. (2) from set 5 of figure 3, we can see that ã and b̃ are different generalized fuzzy numbers. however, from table 1, we can see that if we apply chen’s method (chen, 1996) and lee’s method (lee, 2002), their result is a degree of similarity equal to 1, respectively, which is an incorrect result. (3) from set 6 of figure 3 and table 1, we can see that if we apply lee’s method (lee, 2002), we cannot calculate the degree of similarity between two identical real values due to the fact that the denominator will become zero, such that s(ã, b̃) = ∞, which is an incorrect result. (4) from set 7 of figure 3 and table 1, we can see that if we apply lee’s method (lee, 2002), we can see that lee’s method cannot correctly calculate the degree of similarity between two identical real values due to the fact that the degree of similarity of the real values become zero, which is an incorrect result. (5) from set 8 and set 9 of figure 3, we can see that they are two different sets of fuzzy numbers. however, from table 1, we can see that if we apply chen’s method (chen, 1996), they get the same degree of similarity, respectively, which does not coincide with the intuition of human being. (6) from set 10, set 11 and set 12 of figure 3, we can see that they are different sets of generalized fuzzy numbers. however, from table 1, we can see that if we apply chen’s method (chen, 1996), they get the same degree of similarity, respectively, which does not coincide with the intuition of human being. (7) from set 7, set 9 and set 13 of figure 3, we can see that ã and b̃ have the same shape and the offset d = 0.1 in the x-axis, respectively. by applying the proposed method, we can see that the proposed method has the good property that the degree of similarity between ã and b̃ is equal to1 − |d| = 1 − 0.1 = 0.9. however, from table 1, we can see that if we apply chen and chen’s method (chen & chen, 2003), the degree of similarity is equal to 0.81, which is an incorrect result. (8) from set 14 of figure 3, using chen’s method (chen, 1996) and lee’s method (lee, 2002), the result is a degree of similarity equal to 1, respectively, which is an incorrect result. (9) from set 14 and set 15 of figure 3, we can see that set 14 is more similar than set 15 by the intuition of human being. however, from table 1, we can see that if we apply chen and chen’s method (chen & chen, 2003), we can see that it gets an incorrect result. (10) from figure 3, we can see that set 10 and set 16 are different sets of generalized fuzzy numbers and set 10 is more similar than set 16 by the intuition of human being. however, from table 1, we can see that if we apply the methods presented by chen (1996), lee (2002) and hejazi et al. (2011), set 10 and set 16 get the same degree of similarity, respectively, and if we apply the method presented by wei & chen (2009), the result shows that set 16 is more similar than set 10. they are not the correct results. (11) from figure 3, we can see that set 11 and set 17 are different sets of generalized fuzzy numbers and set 11 is more similar than set 17 by the intuition of human being. however, from table 1, we can see that if we apply the methods presented by chen (1996), lee (2002) and hejazi et al. (2011), set 11 and set 17 get the same degree of similarity, respectively, and if we determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers 341 apply the method presented by chen & chen (2003) and wei & chen (2009), the result shows that set 17 is more similar than set 11. they are not the correct results. (12) from figure 3, we can see that set 11 and set 18 are different sets of generalized fuzzy numbers. however, from table 1, we can see that if we apply the methods presented by chen (1996), lee (2002), hejazi et al. (2011) and wei & chen (2009), set 11 and set 18 get the same degree of similarity, respectively. in summary, from figure 3 and table 1, we can see that the proposed method can overcome the drawbacks of the existing similarity measures. table 1: the fuzzy model of attribute sl sets chen’s lee’s chen & chen’s wei & chen’s hejazi et al. proposed method method method method method method (1996)[15] (2002)[16] (2003)[17] (2009)[18] (2011)[19] set 1 0.9750 0.9617 0.8357 0.9500 0.9004 0.9473 set 2 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 set 3 0.7000 0.5000 0.4200 0.6820 0.6465 0.6631 set 4 0.7000 0.5000 0.4900 0.700 0.7000 0.7000 set 5 1.0000 1.0000 0.8000 0.8248 0.6681 0.6659 set 6 1.0000 * 1.0000 1.0000 1.0000 1.0000 set 7 0.9000 0.0000 0.8100 0.9000 0.9000 0.9000 set 8 0.9000 0.5000 0.5400 0.8411 0.3700 0.3896 set 9 0.9000 0.6667 0.8100 0.9000 0.9000 0.9000 set 10 0.9000 0.8333 0.9000 0.7833 0.6261 0.7731 set 11 0.9000 0.7500 0.7200 0.8003 0.6448 0.7938 set 12 0.9000 0.8000 0.8325 0.8289 0.7361 0.7478 set 13 0.9000 0.7500 0.8100 0.9000 0.9000 0.9000 set 14 1.0000 1.0000 0.7000 0.7209 0.5113 0.5104 set 15 0.9500 0.7500 0.9048 0.6215 0.3830 0.4242 set 16 0.9000 0.8333 0.7425 0.8140 0.6261 0.7321 set 17 0.9000 0.7500 0.8911 0.8380 0.6448 0.7432 set 18 0.9000 0.7500 0.6976 0.8003 0.6448 0.7144 note. "*" means that the similarity measure cannot calculate the degree of similarity between two generalized fuzzy numbers and the results that are not satisfactory are given in bold. 5 a new method to obtain bpa in fact, some samples exist in many systems, which often approximatively submit the triangular distribution. therefore, we use the existing sample data to build a triangular distribution to describe model of attribute categories, and then generate the bpa function based on the similarity between the collected attribute and the model attribute. in order to be understood easily, the following iris data classification problem shows the detailed approach of the proposed method. in the iris data, there are 3 species of iris flower, i.e., setosa, versicolor, and virginica [21]. the iris data contain 150 instances, and each species contains 50 instances. there are four 342 w. jiang, y. yang, y. luo, x.y. qin 1.0 0 0.1 0.2 0.50.40.3 a=(0.1,0.2,0.3,0.4;1.0) b=(0.1,0.25,0.25,0.4;1.0) set 1 w 1.0 0 0.1 0.2 0.50.40.3 a=(0.1,0.2,0.3,0.4;1.0) b=(0.1,0.2,0.3,0.4;1.0) set 2 w 1.0 w 0 0.1 0.2 0.50.40.3 0.7 0.80.6 a=(0.1,0.2,0.3,0.4;1.0) b=(0.4,0.55,0.55,0.7;1.0) set 3 1.0 w 0 0.1 0.2 0.50.40.3 0.7 0.80.6 a=(0.1,0.2,0.3,0.4;1.0) b=(0.4,0.5,0.6,0.7;1.0) set 4 1.0 0 0.1 0.2 0.50.40.3 a=(0.1,0.2,0.3,0.4;1.0) b=(0.1,0.2,0.3,0.4;0.8) set 5 w 0.8 1.0 0 0.1 0.2 0.50.40.3 a=(0.3,0.3,0.3,0.3;1.0) b=(0.3,0.3,0.3,0.3;1.0) set 6 w 1.0 0 0.1 0.2 0.50.40.3 a=(0.2,0.2,0.2,0.2;1.0) b=(0.3,0.3,0.3,0.3;1.0) set 7 w 1.0 0 0.1 0.2 0.50.40.3 a=(0.1,0.2,0.2,0.3;1.0) b=(0.3,0.3,0.3,0.3;1.0) set 8 w 1.0 0 0.1 0.2 0.50.40.3 a=(0.1,0.2,0.2,0.3;1.0) b=(0.2,0.3,0.3,0.4;1.0) set 9 w 1.0 w 0 0.1 0.2 0.50.40.3 0.7 0.80.6 a=(0.1,0.4,0.4,0.7;1.0) b=(0.3,0.4,0.4,0.5;1.0) set 10 1.0 w 0 0.2 0.50.40.3 0.70.6 a=(0.2,0.3,0.5,0.6;1.0) b=(0.3,0.4,0.4,0.5;1.0) set 11 1.0 w 0 0.1 0.2 0.50.40.3 0.7 0.80.6 a=(0.4,0.4,0.4,0.8;1.0) b=(0.3,0.4,0.4,0.5;1.0) set 12 1.0 w 0 0.1 0.2 0.50.40.3 0.7 0.80.6 a=(0.2,0.3,0.4,0.5;1.0) b=(0.3,0.4,0.5,0.6;1.0) set 13 1.0 0 0.1 0.2 0.50.40.3 a=(0.1,0.2,0.2,0.3;1.0) b=(0.1,0.2,0.2,0.3;0.7) set 14 w 0.7 1.0 0 0.1 0.2 0.50.40.3 a=(0.1,0.2,0.2,0.3;1.0) b=(0.2,0.2,0.2,0.2;0.7) set 15 w 0.7 1.0 w 0 0.1 0.2 0.50.40.3 0.7 0.80.6 a=(0.1,0.4,0.4,0.7;0.825) b=(0.3,0.4,0.4,0.5;1.0) set 16 0.825 1.0 w 0 0.2 0.50.40.3 0.70.6 a=(0.2,0.3,0.5,0.6;0.79205) b=(0.3,0.4,0.4,0.5;1.0) set 17 0.79205 1.0 w 0 0.2 0.50.40.3 0.70.6 a=(0.2,0.3,0.5,0.6;1.0) b=(0.2,0.3,0.3,0.4;1.0) set 18 figure 3: eighteen sets of generalized fuzzy number ã and b̃ determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers 343 attributes in the iris data, i.e., sepal length (sl), sepal width (sw), petal length (pl), and petal width (pw). we randomly chose 40 instances from setosa, the min(sl)=4.30; the average(sl)=5.04; the max(sl)=5.80 can be obtained. hence, we can construct the fuzzy model of sl attribute of setosa in figure 4. in the same way, we can construct fuzzy models of sepal length(sl) of versicolor and virginica, as shown in table 2 and figure 4. as can be seen from figure 4, there are some crossing areas. for example, the crossing area of fuzzy number of setosa and versicolour can be shown in figure 5. all the crossing areas can be modeled as generalized fuzzy numbers shown in table 3. table 2: the fuzzy model of attribute sl species setosa(s) versicolor(c) virginica(v) attribute (sl) (4.30,5.04,5.80;1.0) (5.0,5.90,6.8;1.0) (5.6,6.59,7.90;1.0) setosa versicolor virginica 4.5 6.565.55 7 7.5 84 0 0.2 0.4 0.6 0.8 1.0 m e m b e r fu n c ti o n sl(cm) figure 4: the fuzzy number representation of sl attribute of each species setosa versicolor virginica 4.5 6.565.55 7 7.5 84 0 0.2 0.4 0.6 0.8 1.0 m e m b e r fu n c ti o n sl(cm) figure 5: the generalized fuzzy number model of crossing area of setosa and versicolour in a similar way, the fuzzy models of sepal width (sw) attribute, petal length (pl) attribute, and petal width (pw) attribute of each species can be constructed. 344 w. jiang, y. yang, y. luo, x.y. qin table 3: the generalized fuzzy number model of crossing area of three species iris flowers species s&c s&v v&c s&c&v attribute (sl) (5.00,5.43,5.80;0.48) (5.60,5.71,5.80;0.11) (5.60,6.23,6.80;0.63) (5.60,5.71,5.80;0.11) we randomly chose a datum from iris source; for example, a new instance (ni) of setosa could be shown as (5.1cm, 3.5cm, 1.4cm, 0.2cm). in figure 6, the relation between sl attribute of ni and the fuzzy number representation of sl attribute of each species is distinctly shown. to calculate the similarities between ni and each generalized fuzzy number and normalize the obtained similarities, the bpa can be obtained, as shown in table 4. 4.5 6.565.55 7 7.5 84 0 0.2 0.4 0.6 0.8 1.0 m em b er f u n ct io n setosa versicolor virginica sl(cm) ✕★✣✢ figure 6: the relation between the new instance and the model of sl attribute of each species table 4: the bpa of an instance attribute sl m(s) m(c) m(v) m(s,c) m(v,s) m(c,v) m(s,v,c) sl 0.2955 0.2605 0.2215 0.0842 0.0046 0.1291 0.0046 in the same way, the four attributes and their relative bpas can be obtained, as shown in table 5. with the combination rule in eq.(3) used, the fusion results can be shown as follows: m(s) = 0.5324, m(c) = 0.2607, m(v ) = 0.1879, m(s, c) = 0.0112, m(v, s) = 0.0015, m(c, v ) = 0.0063, m(s, v, c) = 0.0001 hence, the instance can be classed as setosa. the result is consistent with the actual situation. the algorithm of our proposed method can be listed step by step as follows. step 1: use the existing sample data to obtain the min, average and max value to construct the triangular fuzzy models, which describe the model attributes of instances. step 2: calculate the similarity between the collected attribute and the model attribute. step 3: normalize the similarity measure to obtain the bpa function. we randomly selected 120 instances, 40 instances for each of the 3 species, to construct species models. the remaining 30 instances, 10 instances for each of the 3 species, were used as collected instances whose class was unknown. by applying the method proposed in the above section to classify the 30 instances, the correct rate of iris data classification was calculated as 96.67%. determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers 345 table 5: the bpa of an instance m(s) m(c) m(v) m(s,c) m(v,s) m(c,v) m(s,v,c) sl 0.2955 0.2605 0.2215 0.0842 0.0046 0.1291 0.0046 sw 0.1727 0.1614 0.1687 0.0925 0.1510 0.1611 0.0925 pl 0.2198 0.1193 0.0731 0.1943 0.1943 0.0049 0.1943 pw 0.1921 0.0988 0.0499 0.2143 0.2143 0.0162 0.2143 further, we applied the proposed method 10 times; the average correct rate of iris data classification was up to 95.67%. it can be seen that our proposed method has good results in classification problem. 6 conclusions the estimation of bpa plays a very important role in the application of dempster-shafer theory in complex uncertain problems. the fusion performance depends on the method of bpa construction. to solve this problem, firstly, the paper presents an improved method to calculate the degree of similarity between generalized fuzzy numbers, which gives consideration to the horizontal center of gravity, the perimeter, the height and the area of the two fuzzy numbers. the method can overcome the drawbacks of the existing similarity measures. then, a new method to obtain bpa is proposed based on the improved similarity measure between generalized fuzzy numbers. the proposed method to obtain bpa can effectively overcome the problem of subjectivity, which also has strong generality. the classification of iris data is used to illustrate the efficiency and the low computational complexity of the proposed method. the proposed method provides a simple technique that will help to use the classical dempster combination rule effectively. acknowledgment this work was supported in part by a grant from national natural science foundation of china (no. 61104214) and foundation for fundament research of northwestern polytechnical university, grant no.jc20120235. bibliography [1] a. dempster(1967), upper and lower probabilities induced by multivalued mapping, annals of mathematical statistics, issn 0003-4851, 38(2): 325-339. 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[21] r.a. fisher(1936), the use of multiple measurements in taxonomic problems, annals of eugenics, issn 1469-1809, 7(2): 179-188. international journal of computers communications & control issn 1841-9836, 13(1), 24-38, february 2018. selective feature generation method for classification of low-dimensional data s.-i. choi, s.t. choi, h. yoo sang-il choi, haanju yoo department of computer science and engineering dankook university 152, jukjeon-ro, suji-gu, yongin-si gyeonggi-do, 16890, korea choisi@dankook.ac.kr, haanju.yoo@gmail.com sang tae choi* department of internal medicine chung-ang university college of medicine 102 heukseok-ro, dongjak-gu seoul, 06974, korea. *corresponding author: beconst@cau.ac.kr abstract: we propose a method that generates input features to effectively classify low-dimensional data. to do this, we first generate high-order terms for the input features of the original low-dimensional data to form a candidate set of new input features. then, the discrimination power of the candidate input features is quantitatively evaluated by calculating the ‘discrimination distance’ for each candidate feature. as a result, only candidates with a large amount of discriminative information are selected to create a new input feature vector, and the discriminant features that are to be used as input to the classifier are extracted from the new input feature vectors by using a subspace discriminant analysis. experiments on low-dimensional data sets in the uci machine learning repository and several kinds of low-resolution facial image data show that the proposed method improves the classification performance of low-dimensional data by generating features. keywords: feature generation, input feature selection, feature extraction, discriminant distance, low-dimensional data, data classification. 1 introduction advances in information technology have resulted in a rapid increase in the amount of digital data that is available, and a significant amount of research has been carried out to develop tools to extract useful and necessary information from vast amounts of data. such tools are currently being applied in various fields, including biometrics (e.g., iris, fingerprint and face recognition), data mining, diagnosis systems and pattern classification [22,26]. when working with data samples, which are represented as ‘input features’, feature extraction methods can effectively improve classification performance by extracting useful information. when there are input features in a data sample, feature extraction methods find projection vectors to get new features containing the maximal information for problem solving [4,14,15,27,31]. then, an input data sample is represented by a set of new features (feature vector), each of which is a linear combination of the input features. the different feature extraction methods have different properties, and the appropriate method must be used corresponding to the characteristics of the data and the problem that is to be solved, e.g., data representation, classification, restoration, etc. common feature extraction methods such as principal component analysis (pca) [27] and linear discriminant analysis copyright ©2018 cc by-nc selective feature generation method for classification of low-dimensional data 25 (lda) [15] have been the basis to develop other methods, including null space lda (nlda) [4], biased discriminant analysis (bda) [31], etc. in these methods, data is stored in vector form, and the appropriate features are extracted using a covariance matrix which is appropriately defined depending on the problem to be solved. methods such as matflda [5], two-dimensional lda (2dfld) [29], composite lda (c-lda) [18] and composite bda (c-bda) [17] use an image covariance matrix instead of the covariance matrix. these image covariance-based methods can be used effectively for data in which input features are strongly correlated [17]. c-lda can be viewed as a generalized image covariance-based method because c-lda becomes identical to the 2dlda or matflda form when the composite vector is defined as a row or column vector. in classification problems, an object is described as an array of attributes to search for the underlying patterns in the object. these attributes are represented as numerical values, which are stored in a vector form (input feature vector) [8]. for example, for blood test data for a person in a hospital, the dimension of the data is the number of test items. even when using the same object, the attributes can be defined in different ways depending on the problem that is to be classified. for example, when classifying a dog, attributes such as food or skeletal structure can be used to classify species of mammals, amphibians, and the like, and when distinguishing individual objects belonging to the same group of animals, attributes such as hair color, size, age, etc. can be used. however, when expressing an object with attributes in this manner, the number of attributes is limited, and it is usually represented using low-dimensional data. on the other hand, temporal sensing data such as speech, or spatial data such as images is usually stored as high-dimensional data. even such data is often reduced and stored as low-dimensional data such as a thumbnail image in order to effectively use the data in a small device, which has a relatively small computing power. most feature extraction methods mentioned above use a statistical correlation of input features and extract features from the shape information of the pixels constituting the image, so their classification performance is limited when the number of input features is too small and is affected by the resolution of the image. in the case of the dcv method, which offers a high performance for generic high-dimensional data, the dimension of the null space may decrease or disappear when the dimension of the data decreases. therefore, it is necessary to generate meaningful features from the input features to effectively utilize the existing data classification techniques with low-dimensional data. in this paper, we propose an input feature generation method for classification of lowdimensional data. according to the theorem of cover [10], if data samples are not distributed linearly and separably, they can be made into a linearly separated distribution through conversion into higher dimensions. many methods use kernel functions to convert low-dimensional data into higher dimensions [9,24,28,30]. these methods use a kernel matrix instead of directly computing kernel functions because doing so would require extensive computation. however, in this case, since the value of the high-dimensional data that is created can not be confirmed, even if the feature corresponding to the individual dimension of the high-dimensional data includes unnecessary information that do not help in classification, they can not be removed or separately used. in the proposed method, new input features are generated by adding a higher order term of individual input features, and the separability power for the original input features and the generated input features is measured using the discriminant distance scale [21]. then, only features with high discrimination information are selectively used during data classification. the new input features improves the performance of existing discriminant feature extraction methods especially when classifying low-dimensional data. we recently investigated the feature generation method for face recognition and presented preliminary results in [6]. in this paper, we provide a more detailed analysis of the method, as well as an extensive discussion, and we apply the method to other classification problems other than face recognition. through experiments on 26 s.-i. choi, s.t. choi, h. yoo various low-dimensional data sets, we confirmed that the classification performance is improved when using the proposed input feature generation method. the results of the experiment for low-resolution facial images show that the proposed method offers a higher recognition rate than when the resolution of such images is increased via interpolation. this paper is organized as follows. in the next section, we examine the effect of the data dimension on the classification performance. then, we describe the feature generation method and the optimal input feature selection method. finally, the experimental results are described and the conclusion follows. 2 effect of data dimensionality on classification performance 2.1 subspace discriminant analysis subspace discriminant analysis methods represent a data sample as an n-dimensional vector x. lda, nlda and bda are representative methods of these subspace discriminant analysis methods. when there are n data samples with c classes and ni samples for each class ci(i = 1, ..., c ), the within class scatter matrix sw and the between class scatter matrix sb can be defined as follows: sw = c∑ i=1 ∑ xk∈ci (xk −µi)(xk −µi)t , sb = c∑ i=1 ni(µi −µ)(µi −µ)t (1) where µi is the average of the samples in class ci and µ is the average of all n samples. lda finds a projection matrix wfisher = [µ1, · · · ,wc−1] consisting of projection vectors wl(l = 1, · · · ,c − 1) that satisfies the following objective function. this means that the lda constructs a feature space that maximizes the covariance between the other classes while minimizing the covariance between the same classes in the range space of sw [15]. wlda can be obtained by calculating the eigenvectors of s−1w sb. wfisher = argmaxw ∣∣wtsbw∣∣ |wtsww | (2) unlike the lda, which uses the range space of sw , the nlda uses the null space of sw containing more discriminating information [4]. that is, a projection matrix wdcv satisfying the following objective function is obtained in a space of ∣∣wtsww∣∣ = 0 and ∣∣wtsbw∣∣ 6= 0. wdcv = argmax t |wt sw w |=0 ∣∣wtsbw∣∣ (3) nlda shows good performance especially when the number of input features of the data samples and the null space of sw are large. bda is a modified form of lda. unlike lda, which maximizes the distance of the mean values of classes in a multi-class classification problem, the bda aims to classify one class of interest and the rest [31]. the bda constructs a positive sample in the form of a normal distribution, and negative samples constitute a feature space that is distributed away from the mean of positive samples, and has the following objective function. assuming that (i) the data samples xp and xn are positive and negative samples, respectively, (ii) their numbers are np selective feature generation method for classification of low-dimensional data 27 and nn, respectively, and (iii) the average of the positive samples is µp , the scatter matrix of the positive samples sp and the scattering matrix for the negative samples are defined as shown in eq. (4). the objective function of bda is defined as shown in eq. (5). sp = np∑ k=1 (xpk −µ p )(xpk −µ p )t sn = nn∑ k=1 (xnk −µ p )(xnk −µ p )t (4) wbda = argmaxw ∣∣wtsnw∣∣ |wtspw | (5) to avoid the small sample size problem [11], we use ν and γ instead of srn = (1−ν)sn + ν n tr[sni] and srp = (1 −γ)sp + γ n tr[spi] by using a regularization factor sn and sp for each scattering matrix [31]. after investigating classification rates for various values of ν and γ, we set ν and γ to 0 and 0.1, respectively. in the subspace-based analyses, after finding w in the training phase, the feature vector (y ∈ rm×1, m < n) for a given sample x can be obtained through a linear transformation as y = wt x. also, the problem is effectively solved by defining the covariance matrices and objective function according to the particular type of problem. however, the number of input features should be secured for the covariance analysis of the input features to be successful. besides, some methods, such as nlda, may not be able to conduct an analysis if the number of input features is less than the number of samples. therefore, to more efficiently use subspace discriminant analysis, it is necessary to ensure a certain number of input features. 2.2 classification performance over data dimensionality to confirm the effect of the dimension of the data sample on the classification performance in the subspace discriminant analysis, it is necessary to examine how the classification rate changes with respect to the data representing the same object with vectors of different dimensions. as an example, we performed recognition experiments on facial images with various resolutions [6]. we have experimented on images with 120×100, 60×50, 30×25, 24×20 and 15×12 resolution for the feret database [25], cmu-pie database [1], yale b [12] and yale database [32] database (fig. 1). the nlda method was used for 120×100, 60×50, 30×25, and 24×20 images, and the lda method [2] was used for 15×12 images because there is no null space of sw . as can be seen in fig. 2, the recognition rate decreases as the resolution decreases in all databases. the recognition rate of the 15×12 images, which can not use the nlda method, is significantly lower than that for the 120×100 to 24×20 images because the applicable classification methods are limited when the dimension of the data is low. as a result, when data is a dimension higher than a certain level, it is possible to attempt effective classification using various methods. on the other hand, the variations in illumination and facial expression in facial images can be regarded as a kind of noise. in this sense, the feret database, which has less variation in images than the cmu-pie, yale b and yale databases, can be regarded as relatively noiseless. the results of the experiment for the feret database show that the recognition rates for 120×100, 60×50 and 24×20 images are almost the same. this indicates that, when the influence of the noise is not large, if the dimension of the data becomes larger than a certain level, there is no further advantage in classification accuracy, and the amount of unnecessary calculation 28 s.-i. choi, s.t. choi, h. yoo (a) (b) (c) (d) figure 1: examples from (a) cmu-pie database. (b) yale b database. (c) feret database. (d) yale database. 120x100 60x50 30x25 24x20 15x12 40 50 60 70 80 90 95 image size r ec o gn it io n r at e (% ) cmu-pie yale b feret yale figure 2: face recognition rates for various face image resolutions. increases due to data redundancy. therefore, to efficiently classify the data, it is necessary to construct appropriately sized data. 3 feature generation and construction of optimal features as noted above, low-dimensional data samples may have limitations when classified only with the original input features. therefore, to improve the performance of the data classification, it is desirable to increase the separability of the samples by converting the dataset with the samples into a high-dimensional space through a non-linear transformation ϕ(·) (cover’ theorem [10], fig. 3). one simple way to increase the dimension of the input feature space is to create and add a higher order term from the input features of the data sample. in this paper, we use the correlation between the input features as a new feature by adding the quadratic term (xixj, (i,j = 1, ...,n)) of the input features (pixels) of the data sample (x = [x1, ...,xn] t ). the dimension of the data increases through the addition of a higher order nnew as follows. selective feature generation method for classification of low-dimensional data 29 (low-dimensional) original space (high-dimensional) new space non-linear transformation figure 3: cover’s theorem. nnew = ( n + 2 − 1 2 ) + n = (n + 2 − 1) ! 2 !(n− 1) ! + n = (n2 + 3n) 2 (6) since the dimension of the input feature space increases through feature generation, the accuracy of the whole classification can be improved. however, at the same time, the amount of computation needed in the classifier increases due to the increase in dimensionality. furthermore, if the dimension of the feature space increases beyond a certain level, the classification accuracy would be rather reduced due to overfitting or the like. this phenomenon is called the "curse of dimensionality" [23]. this happens because as the dimension of the feature vector increases, the volume of the feature space increases exponentially, so the number of data samples required to effectively utilize the huge feature space also increases. however, there is a limit to collecting the necessary data samples in reality. since all generated input features do not have a positive effect on the classification performance, creating a feature is not itself a solution to the problem. for example, for an image with a size of 100×120, according to eq. (6), 16290 input features can be created by adding a quadratic term, and some of these features are useful for classification, while others have little effect in solving the classification problems. therefore, to obtain the optimal classification performance, it is necessary to generate only useful input features to construct a new input feature space of the appropriate dimensions. using the proposed method, the amount of discriminative information of individual features is quantitatively measured before using the original input features and the generated input features in the classification process. then, based on the results of the measurement, only features with a large amount of discriminative information are selected to construct a new input feature vector, and the discriminant features that are to be used for classification are extracted using subspace discriminant analysis on the new input feature vectors xsfg. the separability of the individual features is measured using the discriminant distance scale [21]. the distance between the different classes and the class can be defined as follows for a j-th component (feature) of xfg, where xfg ∈ rnnew×1 is a data sample including newly generated input features. 30 s.-i. choi, s.t. choi, h. yoo evaluate individual features discriminant distance optimal feature set (new input features) discriminant features (low-dimensional) original input features generated input features subspace discriminant analysis (e.g. lda or bda) feature generation feature pool classifier (classification) figure 4: overall procedure of the proposed method. d j w = c∑ i=1 ni n(ni − 1) ∑ xf g kj ∈ci (xfgkj −µ j i ) 2 d j b = c∑ i=1 ni n (µ j i −µ j)2 (7) , where µji and µ j are the j-th component of the mean of class ci and all training data samples, respectively. the discriminant distance of the j-th feature from eq. (7) can be defined as (1/σj)[d j b −βd j w ], σ j = (1/(n − 1)) ∑n k=1(x fg kj −m j)2 [21], which can be used as a measure of the amount of discriminative information possessed by the j-th feature. β can be determined according to the distribution of the data samples as a user coefficient. in the case where the distribution within a class is large but the class separability is relatively good, it is preferable to reduce the value of β, which means a penalty of djw . we set the value of β to 2 in this paper. then, a measurement vector s = [s1,s1, ..,snnew ] t , sj = (1/σj)[d j w −βd j b] of the same size as nnew is defined and the new input feature vector xsfg is constructed with the features corresponding to the large sj. the entire process of the proposed method is shown in fig. 4. 4 experimental results and discussion to show the effectiveness of the proposed method, we applied the proposed method to various real world problems. through experiments on face databases and the uci machine learning repository [3], we show that the proposed method works effectively for various kinds of lowdimensional data sets and for low-resolution images. selective feature generation method for classification of low-dimensional data 31 table 1: datasets from uci machine learning repository used in the experiments dataset no. of classes no. of instances no. of original input f. no. of new input f. (for lda/bda) breast cancer 2 683 9 40/6 pima 2 768 8 27/9 bupa 2 345 6 20/15 monk3 2 432 6 4/4 balance 3 625 4 6/2 wine 3 178 13 28/61 glass 6 214 9 6/21 car 4 1728 6 13/20 table 2: classification rates for uci data sets feature extraction lda bda datasetinput features xori xiv s xf g xcom xsf g xori xiv s xf g xcom xsf g breast. 95.9 96.0 95.7 96.5 96.0 95.1 95.8 95.3 96.8 95.8 pima 68.9 69.1 69.8 68.6 70.7 69.3 70.0 69.5 68.7 70.4 bupa 59.8 59.8 63.7 57.7 64.1 64.1 65.5 62.7 63.7 64.8 monk3 87.4 100.0 91.2 99.6 99.9 68.6 100 68.4 99.4 99.8 balance 87.7 87.7 94.1 88.9 99.2 84.3 84.3 85.3 96.3 99.8 wine 98.7 98.7 96.4 98.7 98.6 98.0 98.8 99.3 98.6 99.7 glass 61.8 71.2 64.5 71.7 71.5 71.2 77.6 70.0 72.3 70.6 car 83.5 90.7 91.9 87.3 94.9 95.3 95.3 95.3 87.0 95.5 aver. 80.4 84.1 83.4 83.6 86.8 80.7 85.9 80.7 85.3 87.0 4.1 uci machine learning repository we applied the propose method to several data sets in uci machine learning repositories. brief summaries of eight data sets that have been used in many other studies are given in table 1. for each data set, we performed 10-fold cross validation 10 times and computed the average classification rate. each input feature in the training set was normalized to have zero mean and unit variance, and the input features in the test set were also normalized using the means and variances of the training set. the one nearest neighbor rule was used as a classifier and the l2 norm was used to measure the distance between two samples. lda and bda were used to extract the discriminant features from the input feature vectors. lda is a supervised learning method that is extensively used in data classification. in addition, as shown in table 1, most of the data sets used in the experiments have binary classes, so we evaluated the classification performance using the bda developed for one-class problems as well. we should find ways to extend bda to multi-class problems in order to apply it to a few data sets having more than two classes, such as an iris data set, balance data set, glass data set and car data set. one of the simplest ways [20] to extend the bda to d-class classification problems is to construct d data sets with only two classes (positive and negative). in constructing the i-th data set, the samples from the i-th class are regarded as positive samples, and the rest are regarded as negative samples. then, we obtain d feature spaces by applying bda to each of these data sets. during the test of a sample, a combined feature vector, which is concatenated with d resulting feature vectors from d feature spaces as in [19] is used with the classifier. the necessary parameters for clda and cbda, i.e., the length of a composite vector and the number of composite features, were set to the values with which each classification method exhibited the best performance, as in [17]. 32 s.-i. choi, s.t. choi, h. yoo table 2 shows the classification performance using lda and bda for new input feature vectors obtained by applying various methods to input features. the values in the column corresponding to xori are the classification rates obtained by applying lda or bda to the original data. xiv s are the data samples containing only some input features selected by the ivs method [8] among the original input features, and xfg are data samples with quadratic terms added to original input features using eq. (6). columns corresponding to xcom are the results of clda and cbda using a composite vector, which is a subset of input features. for the last row, the average classification rate of nine data sets was reported for each method. from the results in the table, the proposed method that selectively generated new input features (xsfg) provided the best classification performance in most data sets, showing that the average classification rates were 6.% and 6.3% higher in the lda and bda, respectively, than when using the original input features. the effects of the proposed method are prominent in the monk3 and balance data sets. in particular, for the balance data set, both the lda and bda classification results showed that when new features were selectively generated using the proposed method, the classification rate increased by more than 10% when using the original input features intact. the common characteristic of these two data sets is that the input features have fewer types of values. in both the monk3 and balance data sets, input feature values can only be four and five kinds of integers, respectively. in this case, when new features are generated using the proposed method, not only the dimension of the data but also the kinds of values that the input feature can have increases, so the data samples can be distributed more effectively in the feature space. on the other hand, in the case of the monk3 data set, lda and bda showed 87.4% and 68.6% of the original input features, respectively. however, when some input features were removed using the ivs method, both lda and bda showed 100%, respectively. this means that among the original input features, unnecessary features were included that would disturb the classification. as a result, the performance of xfg, including all quadratic terms generated by these unnecessary input features, increased slightly (in the case of lda) or was even lower than the for xori (in the case of bda). however, in the case of the selective feature generation using the proposed method (xsfg), the classification rate can be seen to have increased to nearly 100% because the unnecessary input features were effectively filtered. 4.2 face database and preprocessing we also applied the proposed method to a face recognition problem. the feret, cmu-pie database, yale b, and yale databases, which are used in the experiments, are widely used in face recognition research (table 3, fig. 2). in order to represent each database’s degree of variation, we selected an image taken under normal conditions (no illumination and expression variations) for each subject as a reference image and computed the psnr of the subject’s other images. as shown in table 3, the psnr of the feret database is higher compared to the other databases; thus, the images in the feret database exhibited a relatively small variation. for the feret database, images for 792 subjects were used, and two images (‘fa’, ‘fb’) taken from the front of each person were used, that is, a total of 1584 images [7]. of 792 participants, 200 images for 100 subjects were used as training images to evaluate the recognition performance, and the remaining images for 692 subjects were used as test images. for the test, the ‘fa’ image was used as a gallery image and the ‘fb’ image was used as the probe image. among the frontal pose images of the cmu-pie database, the ‘illum’ category includes 21 images with different lighting conditions for a total of 68 subjects. in this experiment, we used 21 images for 65 subjects, that is, 1365 images in total, except for images of people who have some shooting defects or do not include all 21 kinds of illumination variations. we used three images (‘27_06’, ‘27_07’, ‘27_08’) for each subject, i.e., 195 total images that have a relatively selective feature generation method for classification of low-dimensional data 33 table 3: characteristics of each face database used for the experiments database feret cmu-pie yale b yale no. of subjects 992 65 10 15 no. of images per subject 2 21 45 11 illumination variation none large large small expression variation small none none large occlusion none none none glasses no. training / test 200 / 1384 195 / 1170 70 / 380 10-fold cv degree of variations (avr.psnr) 16.9 12.6 12.4 14.1 4790 7290 9790 12290 14790 70 75 80 85 90 95 100 nf r ec o gn it io n r at e (% ) cmu-pie yale b feret yale figure 5: recognition performance for various nf. small variation in illumination as training images, and the ‘27_20’ image from the front lighting was used as a gallery image. the remaining images for each subject (total 65 pieces x17 = 1105 pieces) were used as proof images. the yale b database contains images for 10 subjects, and each subject’s image consists of 45 kinds of images with illumination variations. the images are divided into subsets 1, 2, 3, and 4 according to the degree of variation in the illumination. in this experiment, the images for the subset 1 with less variation in illumination were used as training images and gallery images, and the images for remaining subset 2, 3 and 4 were used as probe images. the yale database contains 165 gray images of 15 subjects, with different facial expressions, with or without glasses, and under different illumination variations. in order to evaluate the recognition rates, we performed 10-fold cross validation 10 times and computed the average classification rate. for face recognition experiments, facial images should be aligned to have the same size. for this, the whole face image is cropped based on the distance between the two eyes using manually detected eye coordinates and is then down scaled to a size of 120×100 [8], and the 60×50, 30×25, 24×20, and 15×12 images are downscaled versions of the 120×100 image again. all images were pre-processed for histogram equalization [13] and all pixels were normalized to have zero mean and unit standard deviation [7, 8]. the face recognition rates were evaluated from the 15×12 image (i180), for which the recognition rate decreased sharply in fig. 2, to iip12000 which is resized from the i180 to the 120x100 size via the bicubic interpolation [16], ifg, to which the features 34 s.-i. choi, s.t. choi, h. yoo cmu-pie yale b feret yale 41 65 70 75 80 85 90 95 i1 8 0 i 1 2 0 0 0 i p i f g i n f sfg database r ec og n it io n r at e (% ) ~~ figure 6: comparison of recognition rates for i180, iip12000, i fg and isfgnf (proposed method). generated using eq. (6) from i180 are added. isfgnf (proposed) consists of the optimal features selected by the discriminant distance scale from 16,290 features of ifg. the optimal number of features (nf) is experimentally determined because it depends on the nature of the database [8]. as in fig. 5, after we investigated the recognition rates by changing nf, we set nf to 12290, 10290, 16290, and 14290 for the feret, cmu-pie, yale b, and yale databases, respectively. among the appearance-based face recognition methods, the dcv method was used for feature extraction and the fisherface method was used only for i180 where the sss problem occurred. the nn (nearest neighborhood) method was used as a classifier, and the euclidean distance was used as the distance measurement. fig. 6 shows the recognition rates for i180, iip12000, i fg, isfgnf for various databases. fig. 6 shows that the recognition rate for the cmu-pie database and the feret database was improved when the image size (i.e., the number of pixels) was increased using the interpolation method (iip12000), but in the case of the yale database, the recognition rate for i ip 12000 is less than i180 because the pixels (input features) generated via the interpolation method have brightness values estimated from the spatial relationship of adjacent pixels in the existing image, and thus the generated pixels do not help extract features using linear discriminant analysis. on the other hand, isfgnf , which is composed of the selected features by the discriminant distance scale among features generated in a non-linear way, showed a higher recognition rate than i180 for all databases. compared to iip12000, the recognition rates of i sfg nf were significantly improved in the cmupie, yale b, and yale databases than in the feret database. the images of the feret database, which have a relatively small variation compared to the cmu-pie, yale b and yale databases, are less likely to suffer a loss of identity information due to image reduction. since the images of the cmu-pie, yale b and yale databases have already lost much of the identity information in the original image due to the variations such as in illumination and facial expressions, the reduced image (i180) includes many pieces of face identification information as well as distortion information. bicubic interpolation uses 16 adjacent pixels in i180 to determine the brightness value of a new pixel when expanded from i180 to iip12000, so if any one of the 16 pixels contains distorted information (variation), the distortion is also reflected in the generated pixels. consequently, in the case of the cmu-pie, yale b, and yale databases, the improvement in the recognition rate through the use of iip12000 is not large or is rather worse than using i180. in selective feature generation method for classification of low-dimensional data 35 contrast, the features generated by using the high order terms of the input features are relatively low in the distortion ratio of the identity information, and as a result, the recognition rate of ifg is higher than i180 in all databases. in addition, even if distorted information is included in the generated features, all features are evaluated using the discriminant distance scale. using only features with a high separability based on this (isfgnf ), an additional improvement in the recognition rate can be obtained. 5 conclusions in pattern recognition problems, data for an object is represented vector composed of input features. the dimensions of data sample are determined by the attributes of the object samples are often stored as low-dimensional vectors according to the nature of the problem. several discriminant feature extraction methods developed for data classification use statistical correlation of input features, but their performance is limited when the dimension of data is small or the range of values input features is small. also, in the case of high-dimensional data such as image data, the image taken from a high-resolution camera converted into low-resolution image to reduce the calculation for data processing and effectively use the storage space. however, the performance may when a low-resolution image is used for recognition due to loss of information occur reducing the dimension of data. in this paper, we propose an input feature generation method effectively low-dimensional data to solve these problems. first, by generating high-order terms of the input features of the low-dimensional data samples, information on the correlation between the input features used as a new feature candidate group. then, using the discriminant distance scale, new data samples were constructed with only input features with high separability by removing the features that are not helpful or obstructive to classification among the original input features and newly generated features. the experimental results on various low-dimensional data sets of uci machine learning repository and several kinds of low-resolution facial images showed that the classification performance improved by selectively generating input features using the proposed method. acknowledgments this work was supported by the human resources program in energy technology of the korea institute of energy technology evaluation and planning (ketep) granted financial resource from the ministry of trade, industry and energy, republic of korea (no. 20174030201740), and also supported by the msit (ministry of science and ict), korea, under the itrc (information technology research center) support program (iitp-2017-2015-0-00363) supervised by the iitp (institute for information and communications technology promotion). author’s contributions conceived and designed the experiments: s.-i. choi (sic), h. yoo (hy), s.t. choi (stc). performed the experiments: sic, stc. analyzed the data: sic, hy, stc. contributed reagents/materials/analysis tools: sic, hy. wrote the paper: sic, hy. revised the manuscript critically for important intellectual content: sic, hy, stc. 36 s.-i. choi, s.t. choi, h. yoo bibliography [1] baker, s.; sim, t.; bsat, m. 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[32] center for computational vision and control, yale university, the yale facedatabase, http://cvc.cs.yale.edu/cvc/projects/yalefaces/yalefaces.html. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 577-580 on the meaning of approximate reasoning − an unassuming subsidiary to lotfi zadeh’s paper dedicated to the memory of grigore moisil − h.-n. l. teodorescu horia-nicolai l. teodorescu gheorghe asachi technical university, iasi, romania institute for computer science, romanian academy, iasi branch abstract: the concept of “approximate reasoning” is central to zadeh’s contributions in logic. standard fuzzy logic as we use today is only one potential interpretation of zadeh’s concept. i discuss various meanings for the syntagme “approximate reasoning” as intuitively presented in the paper zadeh dedicated to the memory of grigore c. moisil in 1975. keywords: logic, truth value, natural language, inference. 1 introduction i perceive three central ideas in zadeh’s wide conceptual construction in his work until now. the first one is that words and propositions in natural languages, consequently human thoughts are not representable by standard sets and standard binary predicates. the second central idea in many of zadeh’s papers is that humans perform computations in an approximate manner that real numbers can not represent well. the third idea, which zadeh presented in his more recent works, is that of granularity of human mental representations and reasoning. these three strong points were represented by zadeh in various forms and synthesized in the title of his paper on “computing with words” [1]. most frequently and zadeh himself is doing no exception authors cite as the initial paper clearly stating the approximate reasoning model the one published in the journal information sciences, july 1975 [2]. however, another paper published in synthese [3], during the same year, deals with the main ideas in approximate reasoning, not to mention a conference paper published in 1974. the paper published in synthese is important in several ways. first, it offers clear explanations of the meanings associated by zadeh to the syntagme “approximate reasoning”. second, the paper was published in a well-established journal that “spans the topics of epistemology, methodology and philosophy of science,” thus boldly reaching to a large audience in a set of fundamental disciplines and daringly affirming the importance of the new concepts. third, the paper is important for the romanian scientists because it re-enforces the understanding of grigore moisil’s contributions and the deep connections between zadeh and moisil. for an in-depth coverage of the last topic, see [4], [5]. zadeh starts the paper [3] from the common-sense remark that “it is a truism that much of human reasoning is approximate rather than precise in nature.” from that remark, he builds a broad program for research. the program that zadeh establishes remains, in my opinion, unfinished. in the abstract of the paper discussed here, zadeh states “since t [the truth-value set] is not closed under the operations of negation, conjunction, disjunction and implication, the results of an operation on truth-values in t requires, in general, a linguistic approximation by a truth value in t .” with copyright c⃝ 2006-2011 by ccc publications 578 h.-n. l. teodorescu this clarification, classic fuzzy logic as we know today is not a proper representation of zadeh’s original ideas. there are two ways to interpret the above quotation. the first interpretation is in the frame of the classic thinking and runs as follows. because language is a set of propositions (we restrict the discussion to truth-valuable propositions only), under whatever logic, all simple and composed propositions have a truth value. denoting the set of truth values by t , the above remark by zadeh has no effect. moreover, when defining the truth-valuation function we already need to know the set of truth values, which again makes ineffective zadeh’s remark. standard fuzzy logic pursue this direction of thinking, as it starts with . under this approach, as t is given, what is needed is to define the logic operators for the respective logic. the second interpretation is constructive. it may start with the assumption that language is not predetermined and must be constructed as a recursive, dynamic process, as poets, writers, other language professionals, and laymen do every day. whenever a new proposition is invented, it is assigned a meaning. the meaning includes what we conveniently name “truth”, a coverage degree of the reality that we need for making inferences. the truth may or may not be numerically representable. moreover, the truth of a composed proposition may not be representable by the truth of each of the initial propositions. therefore, the “set of truth values” evolves continuously. that, in turn, creates a stumbling block. because we are supposed to know the truth of the original sentences, but not of the result, how are we supposed to infer? the answer proposed by zadeh is that we still have an approximate truth in the initial set of truth values, approximating the truth of the new proposition. he hypothesizes that there is always an inverse application, which i will name projection, from the new truth set to the original one, indicating a truth degree in the original set that approximates the truth degree of the composed proposition. that makes our reasoning possible, if approximate. this way of thinking, more or less directly suggested by zadeh, opens the door to several formal descriptions. i sketch below a loose formalism for the recursive approach. consider a language l = ({p},c1,c2, · · ·cr), where p denotes extant propositions and ck logic operators (either unary or binary). notice that l is an initial language, meaning that the initial set {p} is evolvable, that is, it is recursively increased by adding propositions correctly formed from the initial ones through logic operations. assume that any proposition has a characteristic named truth value. thus, there is some set t of truth values (i use for this set the same notation as zadeh’s), as well as an application such that for any proposition p there is a truth value in t . also assume that any propositions can be concatenated to produce a new or extant proposition. whenever such proposition is new in the language, it has a characteristic truth value which is not necessarily in t . we can regard the creation of new propositions (including those used for reasoning) as producing an application t → t ′ , as for the negation operator, or t ×t ×· · · → t ′, as for the connectives. the new t−set which includes the original one. moreover, for satisfying zadeh’s hypothesis, there is a projection operation ϑ. notice that whenever a new proposition is produced, l is modified. because of t non-closure to logic operations, we can not talk about a t (proper) set for a language. instead, we can conceive an object named t∗-set that can be seen as a string of sets, each generated recursively from the previous t-set or from a product of t-sets. the string must be complemented by a string of applications from a t-set (or from a product of such sets) to the next one in the sequence. next, we need to allow for a construction where the term approximate makes sense. several mathematical constructions may be used in this respect, namely the concept of topological space, where approximate may mean “in a vicinity of”, the concept of metric space or one of its variants, as pseudo-metric space, where the term approximate may be interpreted as “at a distance less than epsilon”, or a measurable space, where the truth may be conceived as elements of the space and the approximation is interpreted as on the meaning of approximate reasoning − an unassuming subsidiary to lotfi zadeh’s paper dedicated to the memory of grigore moisil − 579 “the difference between the measures of the respective two truth values (here, a value can be represented for example by a measurable set) is less than epsilon.” the version based on the measurable spaces corresponds to the probabilities. the same version is adopted below, but with no direct connection to probabilities. the main requirements for the t∗-set are: i) the truth-attribute of a proposition is represented by an application θ : l → t , where t is measurable. ii) for any two extant propositions p1 and p2 and for any connective c, the new proposition p1cp2 has a truth degree such that t∪{θ(p1cp2)} is measurable and includes the measurable space t . iii) for any valid linguistic construction that uses logic operators, there is an application ϑ : t ′ → t ′ , named back-projection. iv) for any connective c, there is a formula fc such that ϑ(θ(p1cp2)) and fc(θ(p1),θ(p2)) are two points close enough in t . standard fuzzy logic obviously satisfies the above conditions, with t = t ′. replacing the measurable space with a metric one, we obtain another construction, which may be closer to zadeh’s intuition in [3]. using distances d : t → t , the conditions to satisfy are: d(ϑ(¬p),1 − θ(p)) < ε d(ϑ(p ∧ q),min(θ(p),θ(q)) < ε d(ϑ(p ∨ q),max(θ(p),θ(q)) < ε, where ε is the allowed approximation error. as grigore moisil has done, zadeh emphasizes in all his work how much language is fundamentally creative. by stating that “ t · · · is not closed under the logic operations,” he highlights that there is always generation of new meaning, or at least an evolution of the meaning, whenever a sentence is uttered. i believe that this is one of the fundamental contributions made by zadeh’s work until now. understanding the creative process in languages and in reasoning was significantly elucidated by zadeh’s work, yet much remains to be done for deriving conclusions and theoretical developments in the directions pointed to by his works. the developments informally suggested above may indicate several such directions, yet others may be put forward in a more formal manner during the years to come. acknowledgment. i thank several colleagues and the referees for their comments and suggestions. the note was not supported by, but is related to research performed in the frame of the romanian academy. bibliography [1] l.a. zadeh, fuzzy logic = computing with words. fuzzy systems, ieee transactions on, volume: 4 issue 2, pp. 103–111 (may 1996) [2] l.a. zadeh, the concept of a linguistic variable and its application to approximate reasoning i, information sciences, vol. 8, no. 3, pp. 199-249, july 1975. [3] l.a. zadeh, fuzzy logic and approximate reasoning (in memory of grigore moisil). synthese, vol. 30, (1975), pp. 407-428, d. reidel publishing co, dordrecht, holland 580 h.-n. l. teodorescu [4] g. georgescu, a. iorgulescu, s. rudeanu, grigore c. moisil (1906 1973) and his school in algebraic logic. int. j. computers, communications & control, vol. i (2006), no. 1, pp. 81-99 [5] z. lemnaouar and a. abdelaziz, on the representation of l-m algebra by intuitionistic fuzzy subsets. arima j., volume 4 (2006), pp. 72-85 int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 63-71 software components for signal fishing based on ga element position optimizer n. crisan, l.c. cremene, m. cremene nicolae crisan, ligia chira cremene, marcel cremene technical university of cluj-napoca romania, cluj-napoca, memorandumului nr. 28, 400114 e-mail(s): nicolae.crisan@com.utcluj.ro, ligia.cremene@com.utcluj.ro, marcel.cremene@com.utcluj.ro abstract: long-term adaptation solutions do not receive much attention in the design phase of a wireless system. a new approach is proposed, where the antenna takes an active role in characterising and learning the operation environment. the proposed solution is based on a signal fishing mechanism. several software components, among which a genetic optimizer, implement the processing stages of autonomous design of the antenna array during operation. keywords: smart antennas, signal fishing, long-term dynamic adaptation, ga optimizer 1 introduction wireless receivers incorporate more and more adaptive techniques in the attempt to compensate the effects of the radio channel. technical challenges for current wireless technologies include limited bandwidth and transmit power, interference, signal fading and other propagation impairments. a multitude of adaptive techniques are nowadays implemented at different levels of a wireless transmission chain, as depicted in figure 1. at the physical layer, data link and network layers there are many adaptive techniques that deal with modulation and coding schemes, equalization, filtering, arq mechanism, fragmentation, routing [9], [10]. these techniques have more or less independent approaches, and their joint operation is not much studied. adaptive solutions are usually specific solutions, having a local effect. most of the existing adaptive solutions are hardcoded and therefore not able to evolve. the main concerns regarding wireless receiver performance are link availability and link capacity. tens of adaptive techniques are trying to dynamically adapt the transmission to the changes in the wireless environment. these are short-term, instant adaptation methods/mechanisms. long-term adaptation solutions are more complex and their design and operation are time consuming. the majority of the adaptation methods considering the spatial component of the propagation channel use highly-parameterized, yet simplified channel models [3], [4]. usually fixed parameters, such as mean values of statistical channel parameters are involved [5]. also, processing methods that consider time and frequency variability of the channel exploit only partially the spatial component information. an approach for developing a long-term dynamic adaptation solution for wireless receiver chains is proposed. unlike current approaches the antenna takes an active role in characterising and learning the operation environment, namely the wireless propagation channel. the proposed solution is based on a spatial characterization of the fading allowing the detection of signal maxima in the antenna environment. the antenna is described as an mxn copyright c⃝ 2006-2011 by ccc publications 64 n. crisan, l.c. cremene, m. cremene rectangular array. only two elements in the array are active at a given time. in order to search for the best positions of the active elements a genetic optimizer is used. this paper is organized as follows: section 2 discuses the interdisciplinary aspect of the smart antenna field. in section 3 we describe several software components that implement the processing stages of the autonomous design of the antenna array during operation. the proposed solution is based on a signal fishing mechanism [6] and uses a genetic optimizer. experimental results are presented in section 4 together with our conclusions. figure 1: adaptive techniques operating at different levels of the wireless tx-rx chain 2 smart antenna interdisciplinary aspect classical smart antennas are based on antenna arrays of different configurations and controlled by means of a specialized signal processor. their beamforming and beamsteering mechanisms address capacity and reliability issues of wireless systems [14], [15]. the subject of smart antennas, also known as adaptive antennas, becomes a widely interdisciplinary field as it bridges several disciplines: electronics, electromagnetics, channel&propagation modelling, communication techniques – signal processing, control&adaptive systems, random processes, and evolutionary computing. antenna, communications, and control engineers tend to view adaptive antennas from quite different perspectives. some are inclined to focus on electromagnetic features (e.g. radiation patterns and levels), others on communication-link parameters like snr (signal-to-noise ratio) and ber (bit error rate), and others on various control algorithms. there are at least three issues that computational intelligence (especially evolutionary computing) can help solve in smart antenna systems. the first direction is generating new, optimized antenna geometries, capable of multi-mode operation. the other two are dynamic optimization issues that concern signal detection and tracking and statistical signal processing. we identify the antenna as a pivotal element in determining and assessing quality in wireless communication systems. actually, the user-perceived network or equipment quality relies, ultimately, on antenna performance. moreover, until recently, the antenna influence on channel measurements for channel modelling was considered a bias, now it can be used to a benefit by integrating it into the channel analysis [3], [4]. the distance between transmitter and receiver, their speed, their transmit powers, obstacles, all have an impact on the channel matrix [7], [2], [1]. these are difficult to control factors. yet, there is a system that has a great influence on the channel matrix, a system that is under the control of the receiver: it is the receiver antenna array. the position of the antenna array in software components for signal fishing based on ga element position optimizer 65 the field and the spacing between array elements are the main parameters that can influence the channel matrix. 3 proposed solution: signal fishing based on ga element position optimizer signal fishing (sf) is a new concept [6] that addresses fading mitigation in adaptive multipleantenna receivers. the sf mechanism is based on spatial fading characterization relaying on the antenna array itself. the main idea is to detect and exploit channel signal maxima. fading is a random fluctuation of amplitude and phase of the propagated signal. amplitude fluctuation in a multipath environment is usually described by a rayleigh distribution. a useful fading characterization will turn the selective-fading channel problem into a flat-fading problem, and will enable the receiver signal processing part to perform better [6]. antenna selection techniques generally consider a fixed number of elements in the antenna array allowing only regular configurations. signal fishing allows a random activation of a variable number of array elements which is equivalent to dynamically modifying the element spacing d and the orientation angle ω. figure 2 a) illustrates the description of the receive rectangular array which is part of a transmit-receive system operating in a mimo 2x2 configuration. each element is a sierpinski gasket (fig. 2b). two elements are selected at a specific moment of time and six wave-fronts are considered. the two array elements operate as receive antennas; the other elements are used for channel estimation and spatial fading characterization and are uniformly spaced (d is constant only for these np-2 elements). spatial fading characterization involves estimation or measuring of the angle of arrival θi„ phase shift φi, and amplitude ai of the incoming signal which is performed during the training sequence. when the training sequence elapses, the channel is considered quasi-static for about 448 symbols. by changing the element displacement (element perturbation) we can introduce a phase difference between arriving signals rx1 and rx2. figure 2: a) 32x32 rectangular antenna array ϕ = 900, 0 ≤ θ ≤ 900, dx = dy = λ4 , m = n = 32. b) sierpinski gasket multi-band antenna the signal equations for each of the two receive elements, rx1 and rx2, are: rx1 (a, b) = np∑ i=1 ρ(θi,900)fiexp [j ((a−1) (dxcos θi )) + j (b−1) (dysin θi )] (1) rx2 (c, d) = np∑ i=1 ρ(θi,900) fiexp [j ((c−1) (dxcos θi )) + j (d−1) (dysin θi )] 66 n. crisan, l.c. cremene, m. cremene where: ρ is the antenna element factor fi = |fi|exp(jϕi) is the complex value associated to wavefront i. a, b, c, and d are the coordinates of the activated receive elements rx1 and rx2 β is the phase constant θi is the angle of arrival (aoa) of the ith front. figure 3 presents an illustration of the signal fishing concept – exploiting the spatial component information of the channel in order to detect and use the signal maxima. signal maxima are estimated based on the optimum element spacing dopt = λ ∑np i=1 |hi| 4k±1 4sinθi∑np i=1 |hi| (2) where k is a positive integer; the array grows wider with k. figure 3: signal fishing with two antennas figure 4: signal fishing based on ga element position optimizer figure 4 shows the block diagram of the signal fishing mechanism realization. the main components are: an np-element antenna array, two receiver chains, an array controller, a baseband processing component and a genetic optimizer. the array controller plays a key role in the antenna selection mechanism. the array controller can be located between the antenna array and the receiver rf chain. antenna selection control is based on a switching algorithm that activates two elements at a time. the array controller is the software component that translates the coordinates a, b, c, d into a binary signal suited for on/off element switching. this is done via a microcontroller located on a software defined radio board. this algorithm can be implemented in the context of the sdaa (software defined autonomous antenna), described by the authors in [11]. software components for signal fishing based on ga element position optimizer 67 benefits of adaptive antenna spacing and angle diversity are discussed in [16]. passive antenna diversity alone is not enough to ensure significant capacity improvements in mimo systems. however, spacing and angle reconfiguration of the antenna array is enough to significantly increase the system data rate [8]. classical digital beamforming and beamsteering algorithms can achieve this but with limited degrees of freedom and introducing noise. activating the appropriate two array elements results in maximization of rx1 and rx2 signals. activating certain elements in the array is equivalent to complex coefficient multiplication (complex weighting) in baseband processing [2]. this spatial method is meant to maximize the snr by spatial decorrelation of the incoming fronts. from the adaptive mechanism point of view, the baseband processing component includes two important software components: the per (packet error rate) calculation component and the rxx correlation matrix estimation component. the per component counts the dropped packets and statistically evaluates the packet error rate after thousands of received data packets. this software component is available in most wireless equipments and its value is readable using a software register of the sdr. the per value is used as a trigger for the genetic search of array element coordinates a,b,c and d. the rxx component provides the correlation matrix at the receiver; the matrix is not readily available and is computed based on pilot signal information. the rxx correlation matrix of the receiver (mxm) offers the mean power delay profile and power angular profile. the h channel matrix coefficients and angles of arrival (aoa) can be further extracted. rxx = e { _ x _h x } = arssa h + rnn (3) where e is the expectation function and _h x is the hermitian of the received signal matrix − x (mx1 ) m is the number of elements that are used for power and angle profile estimation a is the array of the steering vectors (mxd) (one vector for each angle of arrival, d propagation paths are considered and d<m ). rssis the corellation matrix of the source (dxd) and rnn is the correlation matrix of the noise (mxm ). computation of the h matrix is implemented in many wireless standards, such as ieee 802.11n, 802.16e. the h matrix represents the average energy of the channel between receive antenna j and transmit antenna i. the angles of arrival are estimated based on the available rxx matrix, using the esprit method [1]. the computation time is not critical in this case because rxx computation is also a statistical problem. rxx computation is an ergodic process that depends on the signal-to-noise ratio. ga element-position optimizer. genetic algorithms (gas) entered the electromagnetic domain in the 1990s, and have been successfully used in antenna design to evolve new antenna geometries [12], [13]. unlike most approaches, where gas are involved, our proposal takes into account solution search during device operation. the ga search is based on spatial fading characterisation data. the signal fishing (sf) adaptive mechanism needs to find the maximum of the mean received signal power p = |h11|2 + |h22|2, after the mrc signal processing. therefore the sf mechanism problem reduces to the maximization of the objective function sf : (mxn)2− > r+, where: sf(a, b, c, d) = |rx1(a, b)|2 + |rx2(c, d)|2 (4) sf(a, b, c, d) → max 68 n. crisan, l.c. cremene, m. cremene solution encoding. candidate solutions are encoded as x = (a,b,c,d), where a, b ,c ,d are expressed as binary strings. fitness assignment. the fitness of solution x is: eval(x) = 1/sf(a, b, c, d) (5) population model. a generational model where the offspring completely replace the parent population at each generation is considered. stopping criterion. the search process stops when the maximum number of generations is reached. search operators. recombination is based on uniform crossover operator. a mask is randomly generated for each pair. one bit mutation is considered. parameter setting. population size – we consider the initial population of 100 chromosomes randomly generated for the first generation. number of generations – between 100 and 300 generations. mutation rate – 0.01. the number of mutations is 19 for each generation and is computed [13] with: nrmut = mutrate∗nbits∗nvar ∗ (npop−1) (6) where: mutrate is the mutation rate (0,01), nbits is the number of bits/chromosome (5), nvar is the number of genes for each chromosome (4 a,b,c,d) and npop is the total population number (100 chromosomes). nrmut is the number of mutations per generation. the search process follows the flowchart described in figure 5. figure 5: ga optimizer flowchart 4 experimental results figure 6 a) illustrates the ga search process. the solution pair is circled in red rx1 and rx2. it indicates the best coordinates (a,b) for receiving element rx1 and (c,d) for rx2. the corresponding array elements to be activated are shown in figure 6 b). the evolution of the objective function for each generation is tracked in figure 7 a), revealing many local maxima. the global maximum is marked in figure 7 a). software components for signal fishing based on ga element position optimizer 69 figure 6: a) ga search process and solution, b) corresponding activated array elements figure 7: a) objective function over generations, b) signal-fishing mechanism gain 70 n. crisan, l.c. cremene, m. cremene from the antenna point of view the local maxima are also important because each can result in a signal gain. so whenever a tradeoff between speed and gain is necessary these local maxima can be used. the gain is calculated as the difference between the mean signal power for a fixed two-element array and the mean signal power for the signal fishing mechanism. 5 conclusions a long-term adaptation solution for wireless receivers is proposed. it is based on a systemic approach of the wireless receiver chain and gives the antenna array a central role in learning the environment, namely the wireless propagation channel. an implementation of the proposed signal-fishing concept using a ga optimizer for element positioning shows that the maximum signal levels of the channel can be detected and used to increase the mean received signal power. the channel fading effects are mitigated starting at the antenna level, an approach that does not introduce additional noise like current baseband processing techniques. acknowledgments this work was supported by cncsis-uefiscsu, pnii-idei, project number 1062/2007. bibliography [1] f. gross, smart antennas for wireless communications, mcgraw hill, 2005 [2] a.b. gershman, n.d. sidiropoulos, space-time processing for mimo communications, wiley&sons, 2005 [3] 3gpp, “spatialchannel model for multiple input multiple output mimo simulations,” technical specification group radio access network tr 25.996 v6.1.0, 3gpp, sept. 2003. [4] j. salo, g. del galdo, j. salmi, et al., “matlab implementation of the 3gpp spatial channel model,” tech. rep. tr 25.996, 3gpp, jan. 2005. [5] l. mucchi, claudia staderini, j. ylitalo, p. kyosti, “modified spatial channel model for mimo wireless systems”, eurasip j. on wireless comm. and netw., vol. 2007, pp.1-7. [6] n. crisan, ligia c. cremene, “antenna-based signal fishing”, the fifth int. conf. on wireless and mobile communications – icwmc’09, pp.152-156, ieee computer society press, cannes, 2009. [7] g. tsoulos, mimo system technology for wireless communications, crc press cityplaceboca raton, stateflorida, 2006. [8] n. crisan, ligia c. cremene, "a novel combining technique for adaptive antenna arrays", acta technica napocensis electronics and telecommunications, vol. 49/2, pp.27-34, mediamira science publisher, cluj-napoca, 2008. [9] m-s. alouini, adaptive and diversity techniques for wireless digital communications over fading channels, ph.d. thesis, california institute of technology, 1998. [10] l. hanzo, c. h. wong, m. s. yee, adaptive wireless transceivers, john wiley & sons, ny, 2002. software components for signal fishing based on ga element position optimizer 71 [11] ligia c. cremene, n. crisan, “towards cognitive antenna systems based on antennachannel co-evolution” 3rd int. workshop on soft computing applications sofa2009, pp.165-169, szeged, 2009. [12] d. dumitrescu, b. lazzerini, l. c. jain, a. dumitrescu, evolutionary computation, crc press, ny, 2000. [13] r. l. haupt, sue ellen haupt, practical genetic algorithms, john wiley & sons, inc., 2004. [14] s. d. blostein, h. leib, “multiple antenna systems: their role and impact în future wireless access”, ieee communications magazine, vol.41, no.7, pp.94-101, july 2003. [15] angeliki alexiou, m. haardt, “smart antenna technologies for future wireless systems: trends and challenges”, ieee communications magazine, vol.42, no.9, pp. 90-97, 2004. [16] j. d. boerman, j. t. bernhard, “performance study of pattern reconfigurable antennas in mimo communication systems”, ieee trans. on ant. and propag., vol.56, no.1, jan.2008. int j comput commun, issn 1841-9836 8(2):304-311, april, 2013. bio-eco-analysis for risk factors using gis software r. serbu, s. borza, b. marza razvan serbu, borza sorin bogdan marza lucian blaga university of sibiu 10, victoriei bd., sibiu, 550024, românia e-mail: razvan.serbu@ulbsibiu.ro, borza.sorin@ulbsibiu.ro, bogdan.marza@ulbsibiu.ro abstract: agriculture is a business sector ideally suited for the application of geographic information systems (gis) because it is natural resource based, requires the movement, distribution, and/or utilization of large quantities of products, goods, and services, and is increasingly required to record details of its business operations from the field to the marketplace. nearly all agricultural data has some form of spatial component, and a gis allows you to visualize information that might otherwise be difficult to interpret. environment has a major impact on agriculture. in this paper we presented how gis software can be used to analyze risk factors that influence agricultural production naturally. natural risk factors were taken into account are: land degradation, flooding, humidity, action on farmland of the wildlife. the conclusions drawn from this paper using gis allows the adoption of important measures on a short or long time to reduce natural risk factors on agricultural production. the advantage of this model is possibility to bee extended to national, regional and global area. keywords: geographic information systems (g.i.s.), query, information and communication technologies (ict), spatial analysis. 1 introduction from several existing studies that debate relationship between environmental sustainability, economic performance and competitiveness has been debated strongly for many years and still remains unclear. we have to bring to attention the dr javier carrillo-hermosilla’s book named "eco-innovation". here are the two main views that the literature [1], [2], [3], [4] gives us of the link between environmental and economic performance, which give rise to rather different perspectives on this relationship. they are: 1. the ’traditionalist’, or neoclassical, view of a trade-off between environmental performance and competitiveness. according to this view, the purpose of environmental regulation is to maximize social welfare, making polluting firms responsible for the costs of the negative externality they produce, thereby correcting the market failure. as a consequence, environmental policies may have an adverse impact on competitiveness, insofar as this regulation imposes additional costs to firms. this burden may be of particular concern in industries with substantial environmental impact, where the share of environmental costs in total production costs is considerably higher than for the manufacturing sector on average. [5] a defensive business strategy and the adoption of end-of-pipe technologies may be expected. [6] 2. the ’revisionist’ view adopts a more dynamic perspective of the relationship between sustainability and competitiveness, and assigns a central role to technological change and innovation. better environmental performance can lead to lower production costs and enhance competitiveness through efficiency, productivity and new market opportunities. [7], [8], [9], [10], [11], [12] according to the so-called ’porter hypothesis’, [13] stringent environmental regulation could copyright c⃝ 2006-2013 by ccc publications bio-eco-analysis for risk factors using gis software 305 force polluting firms to seek innovations to reduce the cost of compliance and production, improving the firm’s competitiveness and leading to a positive relationship between environmental and economic performance. additionally, companies can obtain ’first mover advantages’ by marketing the innovation itself and through the creation of new markets or market segments. [14] hence properly designed environmental policies may help firms discover their inefficiencies and sources of comparative advantage, promoting innovation and creative thinking. [15] setting aside these theoretical discussions, and in more practical terms, it is clear that technological development and institutional considerations play an important role in the transition of the economic system towards sustainability. [16] in other words, technological change is probably a necessary, albeit insufficient, condition for achieving sustainability. institutional changes, including changes in routines, social norms, formal regulations, etc., are needed not only to induce the required technological changes, but also to encourage behavioral changes at all levels of society in more sustainable directions. today’s major environmental problems, such as climate change, the destruction of the ozone layer, loss of biodiversity, the degeneration and erosion of soil and water pollution are characterized by their delocalization, considerable uncertainty, irreversibility and extreme complexity in terms of consequences and the likelihood that they will occur. [17] 2 the analize of environmental risk in agriculture with gis aplications the analyze of environmental risk in agriculture with gis applications needs a multidisciplinary approach, with input and expertise required from many fields civil and chemical engineering, physics, life sciences, ecology, geology, hydrology and statistics being some of them. a wide range of simple to complex, spatial as well as non-spatial, and quantitative as well as qualitative, input data sets is used in environmental risk assessment and analysis process. the analyze of environmental risk in agriculture process involves preparation and use of the processed information derived and presented in various ways for example, comparative (or relative) risk analysis, cost-benefit analysis, scenario analysis, probabilistic analysis, decision matrix, sensitivity analysis etc. due the need for using and analyzing a huge volume of the spatial as well as non-spatial environmental hazards and exposure data in a fast and reasonably accurate way, gis based software applications using a variety of modeling techniques serve as powerful tools for effective environmental risk assessment and management. [18] such applications can be used for a diverse environmental risk assessment and analysis purposes. these applications can ranges from development of databases/inventory systems for simple to complex gis layers overlays, to complex spatial decision-making systems for study of the impact of air, water and soil pollution, ecological imbalance, and natural disasters on the natural and man-made environment, including living beings, properties, infrastructure, vegetation and ecology. these systems could also be interlinked with other related systems, providing online and real-time input data feeds or communication systems, to allow continuous monitoring and tracking of environmental risks in an integrated way. normally, it is good to start with a prototype application first, which could be expanded further based on the budgetary allocation, user needs and the user feedback obtained from the prototype’s implementation. such a system would allow the users to develop possible scenarios using gis and graphical icons for example, a symbol of a polluting industry planned can be placed at a user defined locations on a given regional map, showing the terrain, rivers, soil, vegetation, population, employment, infrastructure, land-use and wild life attributes, and to evaluate the different aspects of the environmental risk, for a set of industry locations scenarios. 306 r. serbu, s. borza, b. marza it can help in developing a prior understanding of the potential risks and for arriving at the best possible alternatives, within the given constraints. there actions could be a combination of human actions taking place in that region; for example, establishment of an agricultural zone, clearing of some forest area to obtain news terrains for agriculture. this entire situation involves a complex set of multiple actions over a wide geographic region, so there would a need for the system to be able to analyze & manage the environmental risks posed by the combination of these actions. f. capra states that by developing and using some systemic biology, each organic part of the common live is an integral whole and therefore a lively system regardless if we refer to individual systems or social ones, to ecosystems we coexist with and we develop. the economy as live organism is a system composed by human beings and social ones interacting with one another and the ecosystems that are around us and our life depend on. it is from the perspective of the systemic vision that it comes out the understanding the problems with interrelationships that are coming up at the level of the whole common live economy is part of. to look at the economic life apart from the environment, from people’s life, families and communities, from the life of the organizations and institutions means to fail understanding that economy is a live (vivid) system in a continuous change and evolution, dependent on the change of the ecologic and social systems it finds it self related. it is from understanding the economy as a live (vivid) organism that a radical change results in the way the processes of economic growth and development are being conceived, mechanisms backed by the institutions that govern and manage the crisis. this change resulted by the essence of systemic wisdom has the origin in understanding the wisdom of nature and is the substance of the ecologic consciousness bateson talked about, the fact that our natural environment is not only live but conscious too. a new economy as a live vivid organism entails a powerful investing in human resources, developing the human wisdom in such a way as to enforce a new directing of science and technology towards the organic, in a gentle, non violent, elegant way. 3 spatial analysis of natural and hazards factors of risk in agriculture using geomedia professional software the risk analysis of natural and hazard factors, we started with some definitions [19]: hazard is "a threatening event or the likelihood in a region in a given period of a natural phenomenon potentially harmful (damage, environmental damage, human casualties) risk is defined as "the potential number of casualties, injuries, property damage of any kind, produced during a reference period in a given region, where there is a particular natural phenomenon". natural disaster is "a serious disruption of functioning of a society, causing loss of life, materials and environment, which the company can not exceed in-house". the vulnerability is "the degree of loss (0-100%), a phenomenon resulting from potentiality to produce casualties and material damage", depending on the vulnerability of socio-economic development of the area concerned. in addition to the above definitions, there are many approaches that are intended to complement and enhance the significance of the terms set [20]. analysis of risk factors in sibiu county was based on a map using gis software, geomedia professional. we obtain the map with major risk factors [21]. the areas where they occur are presented in map: bio-eco-analysis for risk factors using gis software 307 figure 1: the map with major risk factors. • relatively stable areas with different risk of flooding and embankment works without clogging and regulate rivers and streams of water and maintenance of beds; • stable areas but with the risk of water stagnation, due to low permeability of soil; • risk areas due to excessive soil moisture by raising the groundwater or irrigation; • moderate to strong erosion areas with high risk of landslides activation when heavy rain, deforestation or work on slopes; • strong unstable areas affected by erosion, excessive, coupled with active gulling and landslides, torrents, and springs coastal; • unstable areas at high risk of landslides, collapses and collapsing; • seismic zone, (msk scale); • area of seismic risk as normative p100-1992 (f=0,008;e=0,12;d=0,16) • areas affected by landslides; • areas affected by floods due to overflowing rivers; • areas affected by floods due to leakage from the slopes. the legend shown in the figure above, appears in figure 2. all these risk areas are represented in the map made. gis product provides great opportunities for spatial analysis [22]. you can see the weight of each risk factor in the whole county. the analysis of the digital map shows that: • high risk factor for landslides is relatively high in sibiu county, this having a negative influence on agriculture and human habitat; • soil erosion with negative influences on agriculture is another important factor which covers a large area in the county of sibiu; • flood risk is important in the county. on the map are located in areas where floods occurred with disastrous effects. in sibiu floods occurred in the years: 1956, 1958, 1960, 1962, 1965, 1967, 1969, 1970, 1971, 1975, 1982, 1985, 1998, 2005; • in general the county is a hilly area, the beds of watercourses characterized by gently sloping broad flood plain areas, without equipped with storage or flood defense and without extraction 308 r. serbu, s. borza, b. marza figure 2: the legend of the major risk factors map. opportunity ballast; • the analysis of the map also notes the significant share that has seismic risk factor in sibiu county. gis software allows obtaining custom information using spatial analysis and the particular attribute and spatial queries [23]. so you can analyze the influence of risk on a certain area, using buffer zones. for example in the figure below we present a buffer area for flood risk factor. in the map made, we may introduce new risk factor such as, referring to the influence of wildlife on agricultural production. although the number of wild animals in sibiu county decreased by about 50% in last years, it continues to represent a significant risk on agricultural production, in rural areas given the issues outlined above, figure 4 presents a map that allows the analysis of other natural factors of risk. the map has 3d features. 4 conclusions and future works spatial analysis is very importance to all areas and in agriculture also. the spatial analysis can determine the relevant institutions to take certain measures to be taken as: • make works of dams and abundant vegetation cleaning; • reforestation areas with landslides; • cant dams; • repair of retaining walls; • desalting of water courses; • desalting of culverts sections. motivations of people who live within rural areas, to adopt and use information technologies and those of communication, can differ from the reasons people have within the urban environbio-eco-analysis for risk factors using gis software 309 figure 3: the 3d map of risk factors in the talmaciu area. figure 4: the 3d map of risk factors in the sibiu county area . 310 r. serbu, s. borza, b. marza ment. regarding the implications of geographic information systems in bio-economic analyses of some natural factor of risk, the people from rural area should be more interested because now they can minimize the gap between rural and urban development. researches suggest that information and communication technologies (ict) can eliminate the handicap of distance concerning distance and social interaction. donald janelle used the expression "convergence time-space at the end of year ‘60 in order to describe the capacity of transport, of technologies, in order to approach different places" [18]. the risk we discus about, are between the one that minimize the profit and the good living of people living in the rural area. these people can take advantage of these technologies, and information and communication technologies (ict) offer this way entirely. using information and communication technologies might have greater impact on some persons within the rural area, than another one who lives in the urban environment, because a person who lives in rural area can now access information, goods and services and very important, information that they couldn’t before. now when the global crisis is being considered as a complex, multi dimensional one with faces that reach each aspect of our life health and living conditions, quality of the environment and social, economic, technological, political relationships... a crisis of intellectual, moral, spiritual dimensions , a vast crisis as there has not been before in humanity, human respirituality as sustainable ground of that of the institutions representing a redefining of the human nature from the perspective of the fact the revolution of the means must be subordinated permanently to expectations , only out of their harmony results the health of the entire common live formed of people, communities, organizations, families and institutions. acknowledgements this paper was co-financed from the european social fund through sectoral operational programme human resources development 2007 2013, project number posdru /89/1.5/s/63258 "postdoctoral school for zootechnical biodiversity and food biotehnology based on the eco-economy and the bio-economy required by eco-san-genesys." bibliography [1] xepapadeas, a. and de zeeuw, a., environmental policy and competitiveness: the porter hypothesis and the composition of capital, journal of environmental economics and management, 37: 165-182, 1999. [2] simpson, r. d. and bradford, r. l. taxing variable cost: environmental regulation as industry policy, journal of environmental economics and management, 30: 282-300, 1996. [3] palmer, k. w., oates, w. e. and portney, p. r. tightening environmental standards: the benefit-cost or the no-cost paradigm, journal of economic perspectives, 9(4): 119-132, 1995. [4] walley, n. and whitehead, b., it’s not easy being green, harvard business review, 72(3): 36-44, 1994. [5] luken, r., the effect of environmental regulations on industrial competitiveness of selected industries in developing countries, greener management international, 19: 67-78, 1997. bio-eco-analysis for risk factors using gis software 311 [6] faucheux, s. and nicolai, i., les firmes face au development soutenable: changement technologique et gouvernance au sein de la dynamique industrielle, revue d’economie industrielle, 83: 127-145, 1998. [7] sinclair-desgagné, b., remarks on environmental regulation, firm behaviour and innovation, scientific series 99s-20, 1999. [8] porter, m. and van der linde, c., green and competitive: ending the stalemate, harvard business review, 120-134, 1995. [9] porter, m. and van der linde, c., toward a new conception of the environmentcompetitiveness relationship, journal of economic perspectives, 9(4): 97-118, 1995. [10] shrivastava, p. ecocentric management for a risk society, academy of management review, 20(1):118-37, 1995. [11] porter, m., america’s green strategy, scientific american, 264(4):96, 1991. [12] gabel, l. h. and sinclair-desgagné b., managerial incentives and environmental complianc, journal of environmental economics and management, 24: 940-55, 1993. [13] porter, m. and van der linde, c., green and competitive: ending the stalemate, harvard business review, 120-134, 1995. [14] esty, d. and porter, m., industrial ecology and competitiveness: strategic implications for the firm, journal of industrial ecology, 2(1): 35-43, 1998. [15] jaffe, b., peterson r., portney r. and stavins r., environmental regulation and the competitiveness of us manufacturing: what does the evidence tell us?, journal of economic literature, 33: 132 -163, 1995. [16] wced, our common future, oxford university press for the world commission on environment and development, 1987. [17] dr javier carrillo-hermosilla’s, dr pablo del río gonzález, dr totti könnölä, ecoinnovationt, palgrave macmilla, 2009. [18] pierce , f., clay, f., gis applications in agriculture, crc press, taylor & francis group, 2007. [19] bryant e.a., natural hazards, cambridge university press, 1992. [20] tanislav d., costache a., geografia hazardelor naturale şi antropice, editura transversal, târgovişte, 2007. [21] borza s., realizarea aplicaţiilor gis, folosind geomedia professional, ed. univ. "lucian blaga" din sibiu, isbn 978-606-12-0261-4, 2012. [22] bălteanu d., natural hazards in romania, revue roumaine de géographie, 36: 47-55, 1992. [23] donald g. janellea, spatial reorganization: a model and concept, annals of the association of american geographers, 59(2): 348-364, june 1969. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vii (2012), no. 1 (march), pp. 135-146 adaptive neuro-fuzzy controler with genetic training for mobile robot control o. obe, i. dumitrache olumide obe "politehnica" university of bucharest, romania e-mail: oluobes@gmail.com ioan dumitrache "politehnica" university of bucharest, romania e-mail: ioan.dumitrache@cncsis.ro, idumitrache@ics.pub.ro abstract: in this paper, we investigate the use of adaptive techniques in the optimization of navigation of khepera mobile robot in an unstructured and dynamic environment. we optimize the performance of our simplified fuzzy controller using neural network that utilizes genetic algorithm learning. the adaptation of the system involves the tuning of the control rules thereby trimming the control actions, and adjusting the fuzzy controller output gain. we realised an improved performance in our adaptive neuro-fuzzy controller with genetic training for various implemented behaviours on the robot. keywords: khepera, fuzzy controller, neuro-fuzzy controller, navigation, genetic algorithm. 1 introduction navigation and obstacle avoidance are very important issues for the successful use of an autonomous mobile robot in a dynamic and unstructured environment. mobile robot researchers aim to build an autonomous and intelligent robot which can plan its motion in a dynamic environment. a successful use of an autonomous mobile robot depends on its controller. mobile robot control is difficult as they are subjected to non-holonomic (non-integrable) kinematic constraints involving the time derivates of configuration variables [12] and dynamic constraints. both analytical like potential field method as well as graph-based techniques have been used to solve the navigation problems of robot involving both static and dynamic obstacles. but, all such methods may not be suitable for on-line implementations due to their inherent computational complexity and limitations. mobile robot researchers have carried out various researches in this direction using various intelligent techniques methods such as fuzzy logic, neural network and genetic algorithm and their different hybrids. because of the non-linear kinematics of the robot, the uncertainty in sensors readings, and unstructured environmental constrains in the control of mobile robot navigation; researchers have found fuzzy logic as one of the best intelligent technique for handling the constraints. however, fuzzy logic needs tuning for optimal performance. hand tuning is very difficult and time consuming therefore there is need for automation of the tuning process. the process of tuning requires learning brought about by training or adaptation of the robot to adapt to its dynamic environment. the poor learning capability of fuzzy logic is compensated for by hybridizing fuzzy logic with other soft computing techniques with excellent learning features such as neural network. in this paper, we present an adaptive neuro-fuzzy controller with genetic algorithm learning for the navigation of khepera mobile robot. copyright c⃝ 2006-2012 by ccc publications 136 o. obe, i. dumitrache 2 state-of-the-art of control of mobile robots a mobile robot is a situated and embodied agent endowed with mobility; and operates autonomously, communicating with, and exploring its environment. mobile robots are complex systems functioning in real world environments thus, making it uneasy to design an adaptive control system that can control robots to act as desired. task based decomposition of robot control by brooks’ [8] subsumption architecture that makes easy design of robots to realize multiple behaviours, respond to multiple sensors and incrementally extension has been successfully applied in mobile robot [9].researchers in robotics use theories and concepts from intelligent control theory described as behaviour-based control as an alternative to conventional robot control since real world cannot be accurately modeled. because of the need to operate in unknown environments, mobile robots demand much higher level of intelligence in order to learn to adapt successfully to its ever changing environment. intelligence in a mobile robot is considered as an adaptive behaviour that makes a robot adapt to and acts intelligently in its environment. intelligent control is an act of directing a complex system to a goal [1].the intelligence is the property of a system that emerges when procedures of focusing attention, combinatorial search and generalization are applied to the input information so as to receive the output results [2].consequently, murphy [3]defines an intelligent mobile robot as a situated agent with a mechanical structure that operates autonomously. intelligent systems strategies like fuzzy logic, neural network, and genetic algorithms have been used to endow robot with intelligent capability to navigate its environment autonomously. various hybrids of these strategies have also been implemented successfully on mobile robots. in this work, we employ hybrid geno-neuro-fuzzy intelligent strategies to control khepera mobile robot. 3 fuzzy control of mobile robot fuzzy control is one of the intelligent control techniques that pertain to the realization of intelligent control systems. fuzzy control provides a mechanism for incorporating human-like reasoning capabilities and computation in control systems. the linguistic variables are used to mimic the human action into a system more closely than traditional control. fuzzy logic is a logical system that aims at a formalization of approximate reasoning [10].these can be represented as the concept of a linguistic variable, canonical from, fuzzy if-then rule, fuzzy quantifiers, and modes of reasoning. 3.1 fuzzy controller design in this paper, we design a simple fuzzy logic controller for khepera mobile robot for obstacle avoidance and wall following behaviours. our fuzzy logic controller architecture is as shown in figure 1: where, r = sensors input from the robot and uext = [5000,5000] i.e. the maximum robot’s motor speed for stability (for our design), which is equal to 35mms−1 real speed.ufc is the fuzzy controller output; u = the resultant speed for the robot; and y = the robot output control signal. adaptive neuro-fuzzy controler with genetic training for mobile robot control 137 figure 1: architecture of our fuzzy logic controller for the mobile robot showing the number of inputs and outputs parameters at each processing stage. obstacle avoidance fuzzy controller we design the obstacle avoidance controller to allow the robot to avoid colliding with any object along its path to its target. the controller will empower the robot to maneuver its way around any obstacles without colliding with them. by using mamdani fuzzy logic approach, we define three sensor input variables representing relative distance between the robot and any object in its environment. these include: left distance (ld), right distance (rd), and front distance (fd). since the robot rarely uses the back sensors because, the robot usually moves forward, we exclude back sensor readings in our design. the physical domains over which these input variables are defined are determined as: ld = max(s2,s3); fd = max(s4,s5); rd = max(s6,s7). where si(i = 2,3...7) are the sensor values normalized within the interval [0,1]. all these input variables have the same base variable length. figure 2.1 shows our defined membership function distributions of the input variables. for simplicity, we use two types of shapes for our membership function. these include the z-shaped and s-shaped membership functions shape types. two linguistics variables (or grades of distance) from the robots’ sensors are considered: not detected (nd), and detected (d). we use z-shaped membership function type for the nd grade and s-shaped membership function type for the d membership function. these membership function types are the variant of gaussian membership functions that have been proved experimentally by [11] to be of better performance for the same robot navigation problems. the output fuzzy variables are the motor speed of the robot’s wheels, represented by left velocity, lv and right velocity, rv. for the output fuzzy variables, we use three linguistic variables (s-slow, z-zero, and f-fast) for each of the fuzzy output variables. we use gaussian membership function type for each of the linguistic variables. the simplicity in the design is to reduce complexity. this is at the cost of granularity which we belief would be compensated for by the optimization of fuzzy controller using neural network with genetic training. this design invariably reduces the number of fuzzy rules as expected, to 8 rules. some of the 8 rules are shown in table 1. 4 neural network design a layer network is used for our neural network as shown in figure 2. where, xi = sensors input (the context and the last two history inputs each of three pair) y = f(w ∗ [ 1 x ] ) + bias), bias=1 138 o. obe, i. dumitrache figure 2: fig.2.1 fuzzy controller design fig.2.2: input mfs plot fig.2.3: output mfs plot. input output sensors motor wheels if then rule no. ld fd rd lv rv 1 nd nd nd z z 2 nd nd d z f 3 d nd nd f z 4 nd d – s f 5 d d – f s table 1: fuzzy rule table for khepera iii fuzzy controller f(s) = 1 1+e−s s = w01 + w11 ∗ x1 + · · · + w19 ∗ x9 w ∈ u5,10(r) (5 neurons for fuzzy weights with 9 sensors inputs and a bias). we design a network of 5 neurons representing the 5 fuzzy rules weight to be optimized. the inputs are the sensor readings from the 3 fuzzy input variables (ld, fd, and rd). in other to ensure that our robot collides not with the obstacles considering the simplicity in our fuzzy design, we decided to keep history of the last two sensors readings with the context readings of the robot as the input to our nn. this allows the robot to keep history of the last two previous inputs values from the sensors thereby increasing accuracy of decision of eliciting obstacle avoidance bahaviour. the input of our nn is the sensor values from the robot and the output is the 5 optimized rule weights to optimize fuzzy logic controller performance for obstacles and wall following behaviours. figure 3: a layer neural network architecture for our network design adaptive neuro-fuzzy controler with genetic training for mobile robot control 139 genetic algorithm for training we employ ga as the learning algorithm of neural network weight which invariably optimize nn performance resulting in nn optimizing fuzzy logic rules weights and thereby optimizing the robot navigation in terms of wall following and obstacle avoidance behaviours. initially, 50 random weight matrix of 20 population size for each individual are generated and made to evolve for 100 generations. this was tested on the robot to get new 50 different weights. for each generation, a performance index, ϵ to evaluate the fitness of each chromosome is computed in terms of the number obstacles avoidance performed successfully. we set a fixed path to be traversed by the robot for each generation. each chromosome is evaluated based on this performance measure. the evaluation of the chromosome fitness determines either to accept the chromosome as potential part of the new population for reproduction in the next generation. the fit chromosomes are set as part of the new population. for the next generation, 20 new populations are generated by using genetic operators like crossover and mutation. two point crossovers were used and the mutation factor was set at 0.05. with the best 10 chromosome and 10 new chromosomes, the robot is tested. in this way the algorithm continues till the optimum obstacle performance is obtained. figure 4 below shows the flow chart for our genetic algorithm implementation for the optimization of nn weights. figure 4: flow chart for our genetic algorithm implementation for the purpose of optimization of nn weights. 5 neuro-fuzzy control of mobile robot with genetic training design we design a neuro-fuzzy controller for our mobile robot control using genetic training(figure 5). the overall design of our neuro-fuzzy controller with genetic algorithm learning is as illustrated in figure 9 above. 140 o. obe, i. dumitrache figure 5: geno-neuro-fuzzy controller for khepera mobile robot figure 6: a schematic diagram showing flowchart of the the khepera mobile robot motion planning scheme of our robot control using geno-neuro-fuzzy controller. adaptive neuro-fuzzy controler with genetic training for mobile robot control 141 the overall neuro-fuzzy controller with genetic training has the sensors values of the robot from the fuzzy variable parameters as the reference input, all initialized to zero. the sensor readings from the robot are the inputs to the fuzzy controller and the neural network. the outputs of the fuzzy controller are the speed values for the left and right motors of the robot. the external speed input, uext represents the possible maximum set hardware speed for the khepera robot in other to obtain stable performance. the resultant output speed, u is used for the navigation control of the robot. the difference in the robot output speed y and input speed is computed and feed back as error, ϵ. this error serves as the fitness function to measure the fitness of each individual chromosome in the ga. 6 result and discussion in the developed neuro-fuzzy approach, training is done off-line with the help of genetic algorithm. the computer simulation is carried out by considering an arena (kiks arena) with number of obstacles. apart from smoothness in path traced by the robot, we have decided to use other index of performance measures to evaluate the performance of the optimization results. these include the error measure implemented in our program to monitor the difficulties in obstacles avoidance. each experiment is run in the same environment with equal number of obstacles along the robot’s path. hence, we can measure performance in terms of difficulties in circumventing obstacles by the robot by penalizing or rewarding the actions. this measure is what we call error penalty (ep) in this work. the objective function is to minimize this error. hence, the smaller the error, the better the performance output. fuzzy controller obstacle avoidance simulation results figure 7: fl controller output (ep = 57.99) figure 8: fl controller output after manual tuning of rule base (ep =12.58) 142 o. obe, i. dumitrache from figure 7, it is obvious that even though our fuzzy controller succeeds in avoiding obstacles, the robot was trapped initially and could not move forward until after several loops. in other to improve the robot’s performance, we decided to manually tune our fuzzy controller by adjusting some rules consequences of the rule base. the result of the manual tuning is made obvious by the improved performance observed in figure 11 below. the robot can now move forward to the target without being caught in a loop. we equally observed reduced error measured from 57.99 to 12.58.we further improve on the fuzzy controller by manually adjusting the membership sets of the fuzzy controller. we notice improved performance in the path traced by the robot and the number of error encountered in obstacle avoidance behaviour (from 57.99 errors to 12.58 errors). this result is shown in figure 9 below. figure 9: fuzzy controller after hand tuning of rule base and membership function sets (ep = 12.58) from the fuzzy controller performance results presented above, we could not reach better performance in terms smoothness of path traced by the robot by manual tuning. the lack in granularity is responsible for the zig-zag movement of the robot along its path. therefore, we decided to employ neural network with genetic training to fine tune the navigation performance. because of the simplicity in the fuzzy design, in terms of number of rules in the rule base, we observed that the optimal result would be better realized by deciding on what rules to be executed at an instant of time instead of further tuning of rule base and membership function parameters. hence, we focus on the automatic tuning of the rule weights. the idea is based on the fact that we can train an nn model using a genetic training to evolve an optimal rule weights for the navigation control, wall following, and obstacle avoidance behaviours for our robot. the neuro-fuzzy controller with genetic training designed in this paper was put to test and we have realized the results below. neuro-fuzzy with genetic training for obstacle avoidance result in this section, we present the implementation result of our neuro-fuzzy controller with genetic training. the first weights set generated by our neuro-fuzzy with genetic training were used in the neuro-fuzzy controller. the simulated output result of this implementation is shown in figure 13. apart from smoothness in path traced by the robot and error penalty (ep), we equally utilize the readings as observed by the simulation trace output report window of the kiks simulator as shown in figure 14. the report gives information on the number of forward, backward, and straight moves of adaptive neuro-fuzzy controler with genetic training for mobile robot control 143 the robot. with this information, we can easily measure the performance based on number of straight moves made by the robots and also find the ratio of straight moves to forward moves (called performance ratio in this work) in other to compare the performance of each set of weights set generated in each generation. this performance ratio helps to further justify path smoothness traced by the robot. hence, our overall desire is to obtain performance output result with the smoothest path traced by the robot; minimum error penalty (ep); and maximum performance ratio (pr). figure 10: geno-neuro-fuzzy controller output after first evolution of rule base weights. error penalty: 64.89; forward: 181cm; straight: 84.6cm; performance ratio: 0.46 the result of implementation of the first weight set output of the neuro-fuzzy controller with genetic training on the robot navigation is shown in figure 10. by comparing figure 9 of manual tuned fuzzy controller with figure 10, there is no much difference in terms of path traced smoothness of the robot. figure 11: geno-neuro-fuzzy controller output after 50 evolutions.error penalty: 12.79; forward: 181.0cm;straight: 84.6cm; performance ratio: 0.46 after several evolutions, we tested the best rule weight output on the robot and observed the performance output in figure 14. we noticed a greater improved performance in terms of path traced by the robot and error penalty (from 64.89 to 12.79). the performance output after further evolutions of rule weights by the genetic algorithm for the nn learning results in greater improved performance measures in terms of error penalty (from 12.79 to 9.7390), and performance ratio (0.46 to 0.66) as indicated in figure 15. further evolution results show a little drop in performance as shown in the figure 16. after several generations by the ga, we could not get a rule weights set of better performance than 144 o. obe, i. dumitrache figure 12: geno-neuro-fuzzy controller at 95th evolutions. error penalty: 9.7390; forwards: 146.7cm; straight: 96.6cm; performance ratio = 0.66 figure 13: geno-neuro-fuzzy controller output after 100 evolutions of rule weights.error penalty: 12.20; forward: 154.3cm;straights: 96.8cm; performance ratio = 0.63 figure 14: kiks window to monitor simulation details adaptive neuro-fuzzy controler with genetic training for mobile robot control 145 the one shown in figure 15 (95th generation). this shows that we have reach the optimum performance for the rule weight generation for our experiment. hence, we can use the nn model for this optimal rule weight set to obtain an optimal performance in terms of obstacle avoidance behaviour and navigation of our khepera robot. the nn model with this optimal netweight values for rule weights is judged as the best nn model for our research result. 7 conclusion in this paper, a simple neuro-fuzzy strategy with genetic training has been proposed to drive a khepera mobile robot. with this approach, we have been able to tune the rule weights of our fuzzy controller in order to guide our robot. the proposed neuro-fuzzy controller is a layer neural network along with an evolutionary (genetic) learning algorithm. this system has been successfully implemented in simulation obtaining satisfactory results which is better than fuzzy controller output. the lack of granularity in the membership functions has been to a large extent compensated for by the adaptive hybrid neuro-fuzzy with genetic training controller. this is obvious from the smoothness of path traced by the hybrid system compared to the path traced by the fuzzy controller outputs. we have equally adapted the neuro-fuzzy controller with genetic training to work on real khepera iii robot. the result was satisfactory. the simulation and real implementation on the khepera iii robot have shown the efficacy of using hybrid systems in improving mobile robot navigational performances in terms of obstacles, wall following and path tracking behaviours. bibliography [1] i. dumitrache, 1996, intelligent control of industrial robots, mediamira press, cluj, 1996. [2] i. dumitrache, 2000, intelligent autonomous systems, revue roumaine des sciences techiniques electrotechique et energetique, vol. 45, no.3, pp. 439-453, bucharest. [3] r.r. murphy, 2000, introduction to ai robotics,mit press. [4] i. dumitrache and m. dragocea, 2006, some problems of advanced mobile robot control,ceai, vol. 7, no. 4, pp. 11-30 [5] g. campion , g. bastin, 1996, structural properties and classification of kinematics and dynamic model of wheeled mobile robots, ieee transaction on robotics and automation, vol. 12,no. 1. [6] h.r. everett, 1995, sensors for mobile robots,a.k. peters, ltd. [7] x.q. chen, y.q. chen, and j.g. chase, mobiles robots-past present and future, intech. [8] r. a. brooks, 1986, a robust layered control system for a mobile robot. ieee journal of robotics and automation, ra-2(1):14-23. [9] r. a. brooks, 1999, cambrian intelligence: the early history of the new ai, the mit press, cambridge, massachusetts. [10] l.a. zadeh, 1994, fuzzy logic, neural networks, and soft computing, communications of the acm, 37(3):77-84. 146 o. obe, i. dumitrache [11] o.o.obe and i. dumitrache, fuzzy control of autonomous mobile robot, u.p.b. sci. bull., series c, vol. 72,iss. 3, 2010 [12] m.jackson phinni et. al, obstacle avoidance of a wheel mobile robot: a genetic-neurofuzzy approach, iisc centary-international conference on advances in mechanical engineering (ic-icame),bangalore, india, july, 2008 international journal of computers communications & control issn 1841-9836, 9(6):721-729, december, 2014. framework for automated reporting in eu funded projects a. mihăilă, d. benţa, l. rusu alin mihăilă*, lucia rusu faculty of economics and business administration babes-bolyai university of cluj-napoca alin.mihaila@econ.ubbcluj.ro, lucia.rusu@econ.ubbcluj.ro *coresponding author: alin.mihaila@econ.ubbcluj.ro dan benţa faculty of law and economics agora university of oradea dan.benta@univagora.ro abstract: reporting processes in eu funded projects involve a huge amount of financial operations. this time consuming procedure can be easily automated as it involves repetitive operations. the novelty of the paper consists in the presentation of autofistate software application for automatic financial data capture in projects financed through esf (european social fund) in romania. the autofisate application was developed on the basis of results obtained in the fields of automated testing and scripting languages. the use of the autofistate software application leads to maximum effectiveness in how esf funds are used by reducing the time needed to draw up the financial reports and the related labor costs. using scripting languages to develop such reporting-support programs we can improve reporting and save employees effort and time. keywords: automatic financial data capture, automated testing, scripting languages 1 introduction it is widely known that romania has a very low degree of eu funds absorption. there are many reasons for the failure to absorb the eu’s so-called structural funds and it is not the objective of this paper to discuss them. we will like to address just one of the problems and to suggest a possible solution in order to have projects with greater capacity of absorption. from our own experience with these kinds of projects, one of the main problems in eu funded projects conducted by the universities is the reporting procedure of financial evidence when the requests for reimbursement are made. this is a time consuming activity that needs high cognitive skills such attention to small details. in general, in a reporting period, several eu funded projects are handled by a single person, the financial official from projects’ beneficiary. moreover, financial evidence and reporting should be done by people with great expertise and most of the time these types of tasks cannot be transferred to another team member. we had to experience all this problems and that is why we were interested in this field and finally we managed to develop a software solution to divide reporting tasks in order to facilitate the work of financial officials in beneficiary offices. test automation [1], [2], [3], [4] is a significant area of investment and the market awareness of highly automated testing is very high. as some white papers present [5], [6] you can get the most benefit out of your automated testing efforts by automating: repetitive tests that run for multiple builds, tests that are highly subject to human error, tests that require multiple data sets, frequently-used functionality that introduces high risk conditions, tests that are impossible to perform manually, tests that run on several different hardware or software platforms and copyright © 2006-2014 by ccc publications 722 a. mihăilă, d. benţa, l. rusu configurations, tests that take a lot of effort and time when doing manual testing. moreover, authors [6] describe best practices for script automation or for cross-site scripting attacks [7]. as reporting operations are repetitive tasks, using scripting languages to develop reportingprograms proved to be a great solution for many other businesses, inclusive for our own projects. in a market research report made in 2013 by musier & javed [8], automated testing delivers business benefits in multiple areas for most business companies. more than 4 out of 5 firms using test automation identified business benefits of test automation in multiple areas (86%), with most respondents identifying 3 to 6 different areas of benefits. the 5-top ranked areas of value were: 1) greater staff efficiency and time savings; 2) early identification of defects before business users are impacted; 3) higher quality in business processes and the software that supports them; 4) greater accuracy in catching more defects; 5) faster deployment of innovation and new features for business users. the authors also mention that testing is increasingly seen as an essential competency for most of the global companies. more than that, harvesting economic benefits will continue to drive the industry’s shift toward highly automated testing and away from manual approaches, as companies continue to push for higher quality execution and greater business agility at lower cost. beside the aspects mentioned, an important aspect for any work field is the dynamics of the group. this concept is an essential factor in long term projects as it can affect the project performance. because some staff is short time employed and the same task may be allocated to several employees during the implementation period, aspects of group dynamics become even more important [9]. the reason tackled in this paper is the fulfilment of financial statement demanded when applications for reimbursement (refund) are submitted. the current legislation requires that, for each project implemented by a public institution, each application for reimbursement (refund) should be submitted no later than three months after the first payment is made [10]. drawing up a funding application is a complex and time-consuming activity. in big organizations, and not only, where esf-funded projects are implemented, the job of financial officials who draw up funding applications is very difficult and demanding. to support them during the stage of financial recording, the present paper proposes the automation of the process of data input in the reporting system of the managing authority in romania for the sectorial operational programme human resources development. the reporting system used in romania by the managing authority for the sectorial operational programme human resources development is an online software application called action web. consequently, the automation of the process of data input into the reporting system represents the automation of the process of filling in a web form which, in turn, is similar to automated testing that is appropriate for [5] tests that are highly subject to human error and/or tests that take a lot of effort and time when performed manually. the main advantage of automatizing the financial reporting operations lies in greater staff efficiency and time saving [8]. the novelty of the paper consists in the presentation of autofistate software application for automatic financial data capture in projects financed through esf in romania. to develop the autofistate software application for the automatic data input into the action web to prepare the financial statement, use was made of results in the fields of automated testing [1], [2], [3], [4], [5], [6], [8] and scripting languages [6], [7], [11]. the structure of the present paper is as follows: section 2-problem statement; section 3-presentation of the solution to the problem and of the autofistate software application; section 4-presentation of tests and results; section 5-conclusions. within this framework and taking into consideration that a project can be very difficult to manage without proper instruments, we present our solution for data validation and reporting framework for automated reporting in eu funded projects 723 process in order to avoid human reporting mistakes and to reduce data input time in eu projects. we also consider that our solution helps with the problems that can arise with the short time employment, helps to improve the dynamic of employees and to build trust between partners in eu funded projects. 2 problem statement in the case of esf-funded projects implemented by public institutions, the lead partner must submit, no later than three months after the first payment is made during the reimbursement period, an application for reimbursement consisting of several documents among which is the financial statement. the financial statement contains data about all the expenditures made by all partners in the project during the reimbursement (refund) period. the data obtained from the financial officials of all partners are then entered by the project coordinator into the action web software which automatically generates the financial statement in pdf file format (figure 1). for each type of expenditure made in the project (human resources, participants, other costs, indirect expenses), data must be entered for the documents certifying the commitment of expenditures (date of issue, number of documents, supplier, tax identification number, description, annual budget, budget line expenditure category, payer, currency, vat-inclusive price, vat-exclusive price, vat-exclusive project costs, vat) and for the documents certifying the payment was made (date of payment, number of payment document, currency, vat-inclusive price, vat-exclusive price). figure 1: input and output for reporting given that: • data input into the action web application is a time-consuming operation, • the data to be entered into action web follow the same structure/format for all types of expenditures, and • the action web application does not feature the automatic data capture from a file of a particular format/structure, the opportunity was opened up to develop a software application entitled autofistate that will be used to automatically enter data into action web. 724 a. mihăilă, d. benţa, l. rusu by using the autofistate software application: • the time needed to enter data into action web is reduced, and • the time needed to identify and correct errors made when entering data into action web is also reduced. as a direct consequence thereof, the labor costs for drawing up each financial statement are reduced. 3 software design and implementation the preparation of a financial statement using the autofistate software application is accomplished in three stages (figure 2): • the preparatory stage during which the financial officials of all partners in the project prepare the financial data related to all expenditures made by partners and the project coordinator centralizes financial data collected from the financial officials of the partners; • the autofistate stage during which the project coordinator automatically enters the centralized financial data into the action web application; • the final stage during which the financial official of the lead partner (beneficiary) generates the financial statement for the entire project and prepares it to be approved by the legal representative of the project. figure 2: financial statement generation phases all mentioned steps involve more than one employee and the beneficiary financial officials’ job is to check the whole evidences and costs asking for supporting documents. in our case, we have developed and tested this solution, for the first time, in an eu funded project with five partners (our university as beneficiary and four other partners). we are describing in detail the performed operations in a reporting period. a) during the preparatory stage (figure 3), the financial officials of each partner draw up their own financial statements by entering the data about each category of project expenditures into an excel template file. some data must be entered into the excel template in the format required by the action web application. for instance, calendar dates must be entered in the "dd/mm/yyyy" format and the numeric data should not use any separator except for the dot (".") character. the financial official of the beneficiary receives the financial statements from each partner and checks the eligibility of all project expenditures on the basis of supporting documents. after the eligibility of expenditures for all partners has been checked, the financial official of the beneficiary forwards the excel templates containing the related financial statements to an operator who automatically enters the data into action web using the autofistate application. b) during the autofistate stage (figure 4), an operator of the beneficiary checks whether the data in the excel template file containing the financial statements of each partner comply with the formats of the action web application. if some data are entered not according to the required format, the operator processes the data so that they may comply with the format required by the action web application. after the data in all excel template files have been found to comply framework for automated reporting in eu funded projects 725 figure 3: the preparatory stage with the format required by the action web application, they then are transferred to a central excel file. all fields of the central excel file are set at identical height and width because the autofistate script uses the screen coordinates to copy data from the central excel file into the action web application. while the operator inputs all the records, beneficiary financial official can perform other mandatory operations and prepare the requested for the reimbursement (for instance, documents for the external auditor). figure 4: the autofistate stage the autofistate application reads the central excel file row by row so that at a given moment a single row is active or current. initially, the current row is represented by the first row of the central excel file. the autofistate application copies/captures the data from the current row into the action web software. the data from the current row are copied cell by cell, from the left to the right of the row. after having copied the data from the current row, the autofistate application waits until the data are saved on the server of the managing authority and sets the next row of the central excel file as the current row. the new current row is set by clicking on the 726 a. mihăilă, d. benţa, l. rusu vertical scroll bar so that the next row of the central excel file becomes the first row displayed on the screen. this process is repeated until all rows of the central excel file are copied into the online application of the managing authority. the autofistate application should be run under supervision because either the computer or the action web application or both are sometimes slow in responding. in this case, the autofistate application will be closed and then reopened from the record that failed. closing and reopening the autofistate application is made using the hotkeys/predefined keys from autoit scripts. to develop and use the autofistate application, the following software applications were used: • microsoft office excel, 2010 version, for data preparation by the financial officials of all partners to be entered into the action web software; • mozilla firefox, version 15.0.1, to access the action web application; • auto it scripting package, version v3.3.8.1, to develop and use effectively the autofistate application (during the development stage, window info cursor position tool proved extremely helpful). in order to properly use the autofistate application, several prerequisites/conditions must be met: • only microsoft excel file and reporting platform should be opened for our test we used office 2010 package; • use mozilla firefox to open reporting platform because full screen result differs when ie or google chrome browsers are used; • reporting platform should run in full screen (clicking coordinates were defined in an full screen operation); • minimize the ribbon should be activated in microsoft excel 2010; • auto-hide the taskbar option should be checked in taskbar and start menu properties. those restrictions assure a proper script running and reporting according to excel file records. with this pre-settings made, the script runs. main operations performed by reporting script (figure 5) are coordinated by hotkeys in autoit scripts. a hotkey press for such scripts calls a user function that may pauses or interrupts current autoit function. most used and defined hotkeys in autoit scripts are for pause: hotkeyset("pause", "togglepause") and for terminate: hotkeyset("esc", "terminate"). in our case, we have used "esc" and terminate function to stop script running (figure 5a). in case of processing delays or server slow response, the script can be stopped and restarted from the last selected record. a message window with some information is defined, then, the script reporting phase is initialized (from 0 to n-1, where n is the number of records from excel file). further, the script runs and each record from excel file is inputted in the reporting web form (figure 5b). after last record, an increased waiting time (sleep time) is defined in order to secure the web server response time and loading reporting form to enter the next record (figure 5c). the application crosses financial evidence file in row order. then, all specific operations for the first row are applicable to the next one. to assure this, after finishing a row, the next row should be repositioned. for this to happen, the application clicks once the roll down bar from excel so that second row became first displayed row. this process repeats until the last record is inputted in the reporting web form. c) the automatic financial data capture from the central excel file into the action web application is followed by the final stage (figure 6) during which the financial official of the beneficiary generates the financial statement from the online application of the managing authority and submits it for approval by the legal representative. before report generation, incomes must be inputted. later, along with other documents, the reimbursement (refund) request is send to authorities. framework for automated reporting in eu funded projects 727 figure 5: autoit script: a. safety settings; b. automated reporting; c. save and repeat. figure 6: the final stage 4 tests results the autofistate application has been used with four esf projects financed through the sectorial operational programme human resources development: only the autofistate software application was used to enter the data because, for each project, the amounts of data were relatively large and the available time needed to draw up a funding application and prepare a financial statement was not sufficient as regards entering the data into the action web application both manually and automatically. leaving aside the time needed to discover possible mistakes due to manual data entry and correct them, it has been noticed that 142 hours have been saved by using the autofistate application. since the gross hourly salary of a financial official amounts to 150 lei, then the saving amounts to 21300 lei. 728 a. mihăilă, d. benţa, l. rusu table 1: data test project id no. of partners no. of budget (in million status months euros) 1 5 36 5 closed 2 2 29 4,5 ongoing 3 2 36 3,5 closed 4 9 36 5 closed table 2: test results project manual time* autofistate manual autofistate no. of records id (in hours) time (in hours) error error 1 111 40 ≥ 0% 0% 4767 2 30 11 ≥ 0% 0% 1301 3 23 8 ≥ 0% 0% 978 4 57 20 ≥ 0% 0% 2435 5 conclusions automated reporting shortened our reporting period and improve the quality of reporting tasks. the current version we developed is good enough to make the reporting procedure of all stored records in an efficient and clear manner. there are various testing and automation software solutions that may improve such repetitive reporting tasks. the implemented solution constitutes a solid foundation for our future work. as web browsers update is regular made, sometimes defined coordinated doesn’t perfect match and should be redefined. a solution to resolve this problem may be implemented. this solution have two major advantages: saved financial official time with more than 50% and offers dynamic collaborative framework for projects’ partners. it can be applied to all eu funded projects that need this reporting procedure and can also be applied successfully in national research projects (ancs, cncsis etc.). reporting procedures in eu funded projects needs careful planning and design work as a huge amount of time and effort is involved. using this reporting-support application, a full range of requests is considered. the software enhances the reporting process by increasing data validation and efficiency, removing reporting complexity and lowering costs. it can be adapted and customized for different reporting procedures in projects when repetitive data input operations are needed. the use of the autofistate software application leads to maximum effectiveness in how esf funds are used by reducing the time needed to draw up the financial reports and the related labor costs. the autofistate software application can be improved to the point where it no longer depends on the regular updates of the web browser. the autofistate application can be used for all esf-funded projects in romania, whose financial reports are forwarded to the managing authority via the action web application. the autofistate software can also be slightly adjusted so that it can be used successfully in other eu-funded or government-funded projects. acknowledgement the work presented has been founded by the research grant fp7 aal nitics and was financed by it center for science and technology, romania, as a partner in this program. framework for automated reporting in eu funded projects 729 bibliography [1] jureczko, m. (2008). the level of agility in the testing process in a large scale financial software project. software engineering techniques in progress, oficyna wydawnicza politechniki wroclawskiej, 139-152. [2] li, k., & wu, m. (2004). available gui testing tools vs. proposed tool. effective gui test automation: developing an automated gui testing tool. sybex. [3] myers, g. j., badgett, t., thomas, t. m., & sandler, c. (2004). the psychology and economics of program testing. the art of software testing-second edition, john wiley & sons, isbn 0-471-46912-2.. [4] dustin, e. (2003). automated testing tools. effective software testing: 50 specific ways to improve your testing. pearson education, isbn 0-201-79429-2, 137-154. [5] juniper networks (2008), increasing network availability with automated scripting, available on-line http://support.neoteris.com/solutions/literature/white_papers/200252.pdf [6] smartbear software (2013), 6 tips to getting started with automated testing, white paper, available on-line http://smartbear.com/smartbear/media/pdfs/6_tips_for_automated_test.pdf [7] van acker, s., nikiforakis, n., desmet, l., joosen, w., & piessens, f. (2012). flashover: automated discovery of cross-site scripting vulnerabilities in rich internet applications, asiaccs ’12, may 2-4, 2012, seoul, korea, acm. [8] musier, r., javed, s. (2013). 2013 trends in automated testing for enterprise systems, pp. 3-4, available on-line http://www.worksoft.com/files/resources/worksoft-research-report2013-trends-in-automated-testing.pdf [9] levi, d. (2011). group dynamics for teams. sage publications, 3rd edition, isbn: 9781412977623 [10] romanian government emergency ordinance no 120 from december 23, 2010, art. 5∧1. [11] autoit, http://www.autoitscript.com/site/autoit ijcccv8n1.pdf int j comput commun, issn 1841-9836 8(1):127-135, february, 2013. a novel method for service differentiation in ieee 802.15.4 : priority jamming s.y. shin soo young shin school of electronic engineering, kumoh national institute of technology, room 112, digital building, yanghodong, gumi, gyeongbuk, korea e-mail: wdragon@kumoh.ac.kr abstract: ieee 802.15.4 employs carrier sense multiple access with collision avoidance (csma/ca), which is known to have difficulty for supporting quality of service (qos). in this paper, a new priority scheme called priority jamming (pj) is proposed for service differentiation for ieee 802.15.4. the main idea of the proposed scheme is deferring the low priority packet transmission for the high priority packet. clear channel assessment of ieee 802.15.4 is modified to support the proposed pj. the efficiency of the proposed scheme is validated by comparing the delay, throughput, and energy-per-bit with those of standard csma/ca. simulations results showed that pj improves the delay and throughput simultaneously while maintaining marginal difference in energy efficiency. keywords: csma/ca, qos, priority jamming, service differentiation, ieee 802.15.4 1 introduction ieee 802.15.4 is originally designed for low duty cycle and low rate applications such as wireless personal area network (pan) [1]. however, it can be adopted for some delay-sensitive applications such as emergency detecting, intruder alarming, health care, and so on. for supporting these applications, qos (quality of service) requirements need to be supported. ieee 802.15.4 adopts carrier-sense multiple access with collision avoidance (csma/ca) mechanism. therefore, every node has a statistically equal chance to use the wireless medium and to transmit packets. because of unpredictable and undeterministic nature of csma/ca cased by collisions and random backoff algorithm, to provide qos in csma/ca is generally known to be difficult. some works such as [2], [3], [4] studied the qos support in ieee 802.15.4. in [2], guaranteed time slot (gts) mechanism is exploited for the real-time service when enabling beacon mode. the gts uses the contention-free period (cfp), which is optional feature in ieee 802.15.4 standard. in addition, if the available resources are not sufficient, i.e., the number of nodes requires real-time service is large, the gts may not be sufficient because of limited number of gts slots. in [3], differnet backoff exponent(be) and contention window(cw) are used to provide service differentiation in ieee 802.15.4. however, nodes with different priorities can have the same value of be and/or cw based on slotted carrier-sense multiple-access with collision avoidance (csma/ca) algorithm of ieee 802.15.4, which leads to collisions among the nodes with different priorities and increased delay for successful packet transmission. in [4], the frame tailoring (frt) and the priority toning (prt) are proposed to support qos. using the frt, i.e. padding zeroes, ieee 802.15.4 nodes with high priority performs one clear channel assessment (cca) only. however, the frt causes a packet overhead and the acknowledgement packet of the normal priority node and the data packet of the high priority node can collide. to prevent collisions among high priority and normal priority packets, the priority toning (prt) is used to defer the normal priority packets by allocating some active portion of active period for the copyright c© 2006-2013 by ccc publications 128 s.y. shin table 1: parameters of ieee 802.15.4 parameters values aminbe 3 (default) amaxbe 5 (default) cw 2 (default) macmaxcsmabackoffs 5 (default) slot duration (unitbackoffperiod) 20 symbols cca duration of low priority packet 20 symbols cca duration of high priority packet 8 symbols jamming signal duration 8 symbols high priority packet transmissions. however, if the number of the high priority packets is large and network load is high, collisions may frequently occur among the high priority packets in the allocated portion by the prt. moreover, those methods are designed for the beacon-enabled ieee 802.15.4 network only. to support service differentiation, a novel method for supporting priority called priority jamming (pj) is proposed in this paper. the core idea of the pj is deferring the normal priority packet transmissions using a jamming signal transmitted for high priority nodes who have packets ready to transmit. because the proposed method exploit channel sensing part of ieee 802.15.4, i.e., clear channel assessment (cca), it can be applied both beacon and non-beacon enabled network. although the proposed method considers slotted csma/ca in this paper, it can be used both slotted and unslotted versions of ieee 802.15.4 csma/ca.the paper is organized as follows. the suggested pj is described in the next section. section 3 evaluates the performance of pj and compares with standard csma/ca. finally, we draw our conclusions in section 4. 2 priority jamming in this paper, non-beacon enabled network with slotted csma/ca is assumed. let’s examine the operation of standard csma/ca of ieee 802.15.4 first. ieee 802.15.4 csma/ca works as follows. three variables are maintained at each device for a channel access: nb, cw and be. nb is the number of times the csma/ca backoffs while attempting the current transmission, and is reset to 0 for each new data transmission. cw is the contention window length, which is reset to 2 either for a new data transmission or when the channel is found to be busy. be is the backoff exponent, related to the backoff periods a device should wait before attempting carrier sensing. when a device needs to transmit, it delays for a random number of backoff periods (up to 2be − 1 periods) and then determines if the channel is clear. if the channel is busy, the mac increases both nb and be by one, and resets cw to 2. if nb is less than or equal to macmaxcsmabackoffs, the csma/ca delays for a random time again, otherwise it terminates with a failure. if the channel is assessed to be idle, it must ensure that the contention window is expired before starting transmission. for this, the mac sublayer first decrements cw by one. if cw is not equal to 0, it must go to another channel sensing step. otherwise, it starts transmission on the boundary of the next slot period [1]. in an ieee 802.15.4 network, it is assumed that there are two data categories, namely, high and normal priority packets. the main idea of pj is to provide high priority packets with greater possibility to access the channel compared to normal priority packets. figure 1 describes the operation of the proposed scheme and some parameters related to both pj and standard mac a novel method for service differentiation in ieee 802.15.4 : priority jamming 129 be=aminbe, nb=0, cw=2 locate backoff period boundary delay for random [0,2 be -1] unitbackoffperiods high priority packets? ready for transmit perform cca channel idle? cw=2, nb=nb+1, be=min(be+1, amaxbe) transmission failure cw=cw-1 nb= macmaxcsmabackoffs? cw=0? transmission success transmit priority jamming signal y n n nn yy y figure 1: operation of priority jamming in ieee 802.15.4 130 s.y. shin operation are summarized at table 1. to transmit either a high or normal priority packet, nodes will contend to access channel using csma/ca of ieee 802.15.4 as described earlier. to introduce pj into ieee 802.15.4, grey-colored portion is changed or modified compared to standard csma/ca of ieee 802.15.4 as shown in figure 1. when a node is ready to transmit high priority packet, it not only performs cca to see whether the channel is busy or idle, but also set aside some slot time to send a jamming signal to notify other nodes that a high priority packet is ready to be transmitted. in other words, a node with high priority packets, performs cca during 8 symbol times as defined in ieee 802.15.4 standard and transmits a jamming signal. for jamming signal, any signal whose duration is less than (slot duration-8) symbols can be used. in this paper, we used the preamble of ieee 802.15.4 packet for the jamming signal. with pj, a node with normal priority packets performs cca for entire slot time (20 symbol time), not for just 8 symbol times. because a node with normal priority packets listens entire slot time and then the channel will be assessed to be busy due to jamming signal by the node with high priority packets ready to be transmitted. then, a node with normal priority packets will defer its transmission and perform another random backoff procedure. by deferring the transmissions of normal priority packets, the collision probability among nodes with high and normal priority packets will be reduced compared to standard csma/ca. in other words, the effective node numbers which contend for the channel will be reduced by adopting pj. note that if all the packets have the same priority level, i.e., under either 100% high priority or normal priority traffic, the behavior of pj nodes will be identical to standard csma/ca. also, when nodes with standard csma/ca coexist with nodes using pj, they are considered as nodes with high priority packets. this means that pj guarantees backward compatibility with legacy ieee 802.15.4 standard. data ackchannel node with normal priority backoff backoff c c a 1 c c a 1 c c a 1 c c a 0 transmission priority jamming i c c a 0 data backoff node with high priority (i+2)(i+1)(i-1) figure 2: an example of priority jamming in slotted csma/ca of ieee 802.15.4 figure 2 shows an example of pj operation with one node with a high priority packet and one node with normal priority packet. as illustrated, a node with normal priority packet finishes backoff at (i−1)-th slot and performs the first cca, cca1. at this time, the channel is assessed to be idle and (cw = cw −1). at i-th slot, a node with high priority packet finishes its backoff and performs cca1 with pj. at the same time, the node with normal priority starts the second cca, cca0. now the channel is assessed to be busy because of the jamming signal transmitted a novel method for service differentiation in ieee 802.15.4 : priority jamming 131 by the node with high priority packet. then, at (i+1)-th slot, another random backoff and cca0 are performed by the node with normal priority and the node with high priority, respectively. finally, the node with high priority has a chance to transmit packet at (i + 2)-th slot. 3 performance evaluation to evaluate the performance, we developed an opnet simulation model of the slotted csma/ca and priority jamming of ieee 802.15.4. star topology network with one coordinator and 20 end devices is used for the simulations as illustrated in figure 3. figure 3: simulation scenario of ieee 802.15.4 csma/ca with priority jamming 132 s.y. shin all end devices send data packets to coordinator, which responds with corresponding ack packets. all end devices generate 102 bytes long packets based on exponential distribution with mean 1/λ. two simulation parameters are inter-arrival time, i.e. λ, and ratio of high priority packets in the total traffic. the λ varies from 0.15 to 0.4, which means the traffic introduced to the network varies from 108.8 to 40.8 kbps. the ratio of high priority in the traffic, is one of 10, 30, and 50 %. as the measure of performance, delay, throughput, and energy efficiency are used in this paper. then, the performances of pj are compared to those of standard slotted csma/ca. 0.15 0.2 0.25 0.3 0.35 0.4 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 packet inter−arrival time (sec) d e la y ( s e c ) std high (50%) normal(50%) high(30%) normal(30%) high(10%) normal(10%) figure 4: delay vs. inter-arrival time figure 4 shows packet delays as the inter-arrival time increases. here, (x %) in the legend of figure 4 means that x percentage of the total traffic introduced to ieee 802.15.4 network is high priority packets. the parallel lines to y-axis means the delay doesn’t converge. in other words, there are so many collisions that the packet delay increases. for example, with λ < 0.2, the delay of standard csma/ca doesn’t converge. however, when pj is applied, the delays of high priority packets are bounded even for λ = 0.15. this is because the collision probability of high priority packets decreases due to service differentiation provided by pj. for example, at λ = 0.2, the collision probabilities of the high and normal priority packets are 0.007, 0.026, 0.049, and 0.111, 0.089, 0.060 for 10, 30, 50 % cases, while that of the standard is 0.129. as the ratio of high priority packets increases, the collision probabilities of high priority packets and those of normal priority packets increase and decrease because the node numbers of high priority and normal priority increases and decreases, respectively. note that the delays of normal priority packets with 10% high priority packages at a novel method for service differentiation in ieee 802.15.4 : priority jamming 133 λ = 0.18 are about 5 second. for λ ≥ 0.18, the delays of the high priority packets increases at the ratios of the high priority packet increases. as λ decreases, the channel will be more crowded, even for the cca of the high priority nodes. then, channel could be assessed as "busy" and may cause many random back-offs. 1 as the packet inter-arrival time decreases, the channel occupation by packet transmissions is more dominant than collisions. for larger λ, the channel is not frequently occupied, the delay is more dependent on the collision probability. hence, the delay of high priority packet decreases as the ratio of high priority decreases. note that the delay experienced by both high and normal priority packets in pj is less than that of standard slotted csma/ca (std). 0.15 0.2 0.25 0.3 0.35 0.4 0 20 40 60 80 100 120 packet inter−arrival time (sec) th ro u g h p u t (k b p s ) std pj total(50%) high (50%) normal(50%) pj total(30%) high(30%) normal(30%) pj total(10%) high(10%) normal(10%) figure 5: throughput vs. inter-arrival time fig. 5 illustrates throughput of pj with high priority, low priority, and total (sum of high and normal priority throughput) and compares to throughput of standard ieee 802.15.4. the throughput for high priority packets are monotonically increases as the inter-arrival time decreases. this is because the high priority packets really have priority to access the channel by priority jamming. the total throughput with pj outperforms that of standard ieee 802.15.4 due to decreased collision probability as mentioned earlier. why the delay with the standard csma/ca diverges when λ = 0.18? the reason is that the throughput is 81.2 kbps, smaller than the generated traffic, 90.7 kbps. contrarily, throughput with pj are 91.8 kbps (50% of high 1in standard csma/ca, channel is already saturated at λ = 0.2. 134 s.y. shin priority), 92.6 kbps (30% of high priority), 92.9 kbps (10% of high priority), which are higer that the generated traffic, so delays with pj are still bounded. 0.15 0.2 0.25 0.3 0.35 0.4 0 0.5 1 1.5 2 2.5 3 3.5 x 10 −4 inter arrival time (sec) e n e rg y p e r b it ( µ j /b it ) std high (50%) normal(50%) high(30%) normal(30%) high(10%) normal(10%) figure 6: energy-per-bit vs. inter-arrival time fig. 6 compares the energy efficiency of pj and standard csma/ca using the concept of energy consumption for transmitting a bit. the power consumption of idle, transmit and receive states are set to pidle=712 µw , ptx= 31.32 mw , and prx=35.28 mw , respectively [5]. because pj scheme has energy consuming elements such as jamming signal (node with high priority packets) and longer cca time (node with normal priority packets), the energy consumption of pj is higher than that of standard csma/ca. however, the µj/bit differences are relatively small because throughput with pj are increased and energy-per-bit values are the same order of magnitude. 4 conclusion in this paper, a new service differentiation scheme which is called priority jamming (pj) is proposed for ieee 802.15.4. the main idea of the proposed priority jamming is deferring the transmissions of normal priority packets using a jamming signal transmitted for high priority nodes who have packets ready to transmit. by using the proposed scheme, the average delay of high priority packets is reduced. in addition, the net throughput of ieee 802.15.4 is increased and delay of normal priority packets is decreased because of lowering the collision probabilities among ieee 802.15.4 nodes. a novel method for service differentiation in ieee 802.15.4 : priority jamming 135 although pj consumes more energy for a packet transmission because energy consuming factors such as jamming signal transmission and long cca time, pj shows better performance both in the delay and the throughput. therefore, the differences in pj and standard ieee 802.15.4 are relatively small. the proposed algorithm can be used both beacon-enabled and non-beacon enabled mode of ieee 802.15.4 and guarantee backward compatibility with legacy ieee 802.15.4 standard. by providing the service differentiation using pj, this paper may contribute to enlarge the delay-sensitive application area of ieee 802.15.4 such as as emergent alarms and intruder detections. acknowledgments this research was supported by basic science research program through the national research foundation of korea(nrf) funded by the ministry of education, science and technology(2012r1a1a1009442). bibliography [1] ieee 802.15 tg, ieee std.802.15.4: ieee standard for wireless medium access control (mac) and physical layer (phy) specifications for low-rate wireless personal area networks (lr-wpans), ieee standard, 2006 [2] a. koubaa, m. alves, e. tovar, guaranteeing real-time services for industrial wireless sensor networks with ieee 802.15.4, ieee transactions on industrial electronics, 57(11):38682876, 2010. [3] m. j. kim, c. h., priority-based service-differentiation scheme for ieee 802.15.4 sensor networks in nonsaturation environments, ieee transactions on vehicular technology, 59(7):3524-3535, 2010. [4] t. h. kim, s. choi, priority-based delay mitigation for event monitoring ieee 802.15.4 lr-wpans, ieee communications letters, 7(3):213-215, 2006. [5] b. bougard, f. catthoor, d. daly, a. chandrakasan, w. dehaene, energy efficiency of the ieee 802.15.4 standard in dense wireless microsensor networks: modeling and improvement perspectives, ieee proceedings of date, 196-201, 2005 international journal of computers communications & control issn 1841-9836, 11(5):602-612, october 2016. language processes and related statistics in the posts associated to disasters on social networks s.c. bolea speranţa cecilia bolea institute of computer science of the romanian academy iasi branch romania, iasi, carol i, 8 cecilia.bolea@iit.academiaromana-is.ro abstract: this paper provides a detailed and long-period statistics of the use of synonyms in posts related to specific events on social networks (sns), an extended analysis of the correlations of the flows of the synonyms in such posts, a study of the applicability of zipf’s law to posts related to specific events on sns, and an analysis of the dynamics of the fluxes of synonyms in the posts. the paper also introduces the study of the distances in the phase space for the characterization of the dynamics of the word fluxes on social networks. this article is a partial report on recent research performed for a deeper analysis of social networks and of processes developing on social networks, including used lexicon, dynamics of messages related to a specific type of topic, and relationships of the processes on sns with external events. keywords: social networks, disaster, analytics, language statistics, correlative analysis, zipf’s law, dynamics. 1 introduction analytics aimed to social networks have explosively developed during the last few years, with some of them focusing on the rescue from disasters [3], [5], others proposing means for disaster management [6], [10], [26] and others on disaster prevention and mitigation [1,2], [11], [15], [22], [27]. the main purpose of this paper is fact-finding about the language of the social networks and related posts, for messages connected to dramatic events such (fires and earthquakes). in subsidiary, we are interested in the distributions of the most used words and of the synonyms. a hypothesis we made in connection with the word probability distribution is that it departs from the typical distribution of the language, which is a zipf’s distribution. this article is a partial report on recent research related to the deeper analysis of social networks and of processes developing on sns; the analysis refers to the lexicon used, dynamics of messages related to a specific type of topic, and the relationships of the processes on sns with external events. the general lexicon was studied in [24], and issues related to the use of synonyms in tweets were discussed in [4], [20], and [24]. the main contributions of this paper include the detailed and long-period statistics of the use of synonyms in posts related to specific events on sns, the extended analysis of the correlations of the flows of the synonyms [29] in those posts, a study of the applicability of zipf’s law to posts related to specific events on sns, and the detailed analysis of the dynamics of the fluxes of synonyms in the posts. the research was performed in the frame of a larger project, being a part of it (see acknowledgments). we advance the study of correlation between time series of the number of words occurrences especially of synonyms in messages related to disasters effects on sns, and compare the sns from this point of view. also, we apply a recently proposed nonlinear technique of analysis of time series for better characterizing, and differentiating specificities of sns. copyright © 2006-2016 by ccc publications language processes and related statistics in the posts associated to disasters on social networks 603 one of the purposes of this article is to present a full year data statistics for synonyms usage in posts related to two types of potentially disastrous events, namely earthquakes and fires, and to analyze in detail the dynamics of the words most used in such posts. the utility of this study is multifold and includes the optimization of searches in software applications for disaster monitoring and management, and better understanding the language use under and in relation with such events [18]. the results may also be of interest to psycho-linguistics, beyond descriptive linguistics. a special emphasis is placed on the correlative study of the dynamics of the flows of synonyms in the posts. this research associates with other analyses of the language used in sns reported recently in the literature, for example with the vast literature on sentiment analysis on sns [7], [12], including the time of disaster events [16], [17]. both the mentioned project and this study relates to previous interests of our research group in computational linguistics and speech technology. the research in the project and partly in this study also reflects the interest in detecting attitudes in messages and to a standing interest in detecting emotions in speech and texts as in the studies [9], [13], [25], [28]. 2 data collection and processing method the data collection process was described in [24]; data comprises all posts, including articles and blogs pointed to by messages on twitter and google+, during the period march 2015 and march 2016, as detected with queries using the keywords (cutremur or seism) and vrancea, respectively incendiu or foc. an important difference between the dataset we used and the data in other papers is that we have not restricted ourselves only to the messages, but we also downloaded the media articles or blogs having addresses in the collected messages. therefore, the most or at least a large part of the text analyzed comes from the media, not from the respective social network (sn) messages. thus, a larger lexicon was included in the database. yet, the most frequent words found beyond stop words have been those intimately related to the search condition, which is an interesting and somewhat unexpected fact. the text was lemmatized before performing the statistics. in doing so, words with various grammatical forms were reduced to their main representative. we used the free lemmatizer for the romanian language from racai institute, available at [30]. the statistic of words in this study actually represents the statistic of lemmas in the dataset. the dataset was conventionally split by sn and by months and is reported as such in the paper, for example in the correlative analysis of synonyms and in the analysis of their dynamics of occurrences. the descriptive statistics, including zipf’s law detection, refers to the whole database, on each sn, thus extending the results in [24]. the dataset includes 3.715 posts for the earthquake type of event (database named cutremurdb) and 1.106 for fire type (database named focdb). we reiterate that the only events of interest here are the earthquakes and the fires. the total number of posts, words (without stopwords), and the number of characters (with spaces) are given in table 1 for the two classes of posts. the stopwords are the words such as "0 retweets 0 likes", "0 retweets 2 like", "view summary" and "newline", and their form depends on the type of browser used (google chrome, mozilla firefox). the focdb database is larger than cutremurdb because fires are much more frequent events, thus produce more posts on sns. interestingly, the posts related to earthquakes have a small number of lemmas (table 1). the average number of words per post are: 126 words for google+ and 124 words for twitter in cutremurdb, and 317 words for google+ and 131 words for twitter in focdb. 604 s.c. bolea table 1: the number of posts and lemmas posts lemma no. char with of lemmas spaces google+(cutremurdb) 318 6.872 40.196 174.030 twitter(cutremurdb) 941 12.229 116.667 632.979 google+(focdb) 245 15.041 77.909 466.618 twitter(focdb) 279 10.396 36.665 231.293 both databases, cutremurdb and focdb have the same structure and contain the fields: year, month, posts (number of posts from the month), lemma (number of lemmas), no. of lemmas (number of lemmas occurrences), char with spaces (number of characters including spaces) and lemma1, lemma2, · · · , lemman, where lemmai, i ∈ {1,n} represents the significant words referring earthquake and fire (nouns, verbs, adjectives, adverbs). the other words such as pronouns, prepositions, conjunctions, numerals and punctuations have no relevance in our study, and they do not appear in our databases. 3 main findings 3.1 descriptive statistics of synonyms used in relation to specific events this study contributes to the descriptive statistics of the sn language by determining the probabilities of the most frequent words and synonyms in posts related to earthquakes and fires on social networks, on extended periods. the main results are summarized in table 2, where lemma denotes the number of lemmas and no. of lemmas the number of lemma’s occurrences. table 2: the probabilities of the most important lemmas for cutremurdb and focdb cutremurdb cutremur seism seismic magnitudine richter vrancea epicentru adâncime produce p for google+ 0,0310 0,0123 0,0069 0,0158 0,0149 0,0244 0,0041 0,0079 0,0133 p for twitter 0,0311 0,0130 0,0064 0,0165 0,0137 0,0244 0,0054 0,0085 0,0153 focdb incendiu foc izbucni pompier suferi stinge flacără pericol arde p for google+ 0,0092 0,0069 0,0018 0,0027 0,0003 0,0006 0,0012 0,0002 0,0012 p for twitter 0,0260 0,0164 0,0055 0,0098 0,0005 0,0024 0,0047 0,0007 0,0027 the best correlation found (in cutremurdb) is for the lemmas: cutremur vrancea and cutremur produce (see table 3). this is because the messages about earthquakes found on sns have a very similar "un cutremur cu magnitudinea de 3, 5 grade pe scara richter s-a produs luni seara în zona seismică vrancea...", "an earthquake with a magnitude of 3.5 on the richter scale occurred monday evening in vrancea seismic zone...". the correlations between cutremur and seism synonyms is a little smaller than the above. the synonyms incendiu and foc have a higher value of correlation than other pairs of words (see table 3). it was shown in [19], [23] that there is a strong correlation between the number of posts related to potentially or actually disastrous events and the intensity of the event. precisely, in the cited papers it was found that the peak of the number of posts per day (post flux) related to the event correlates with the number of victims in several types of disasters; when the event is only potentially disastrous, such as a small seism, the peak of the post flux was found to correlate with the magnitude of the earthquakes. in addition, it was shown in [19], [23] that the flux of posts related to such events is well represented by a pulse with exponential growth and decay, in the form language processes and related statistics in the posts associated to disasters on social networks 605 table 3: examples of correlations in the use of words: correlations of the time series of the monthly number of occurrences of the lemmas word pairs correlation value word pairs correlation value cutremurdb google+ twitter focdb google+ twitter cutremur seism 0,87 0,96 incendiu foc 0,99 0,95 cutremur seismic 0,78 0,90 incendiu izbucni 0,90 0,66 cutremur magnitudine 0,89 0,96 incendiu pompier 0,69 0,92 cutremur richter 0,86 0,93 incendiu suferi 0,77 0,69 cutremur vrancea 0,97 0,97 incendiu stinge 0,82 0,26 cutremur epicentru 0,81 0,86 incendiu flacără 0,87 0,36 cutremur adâncime 0,82 0,99 incendiu pericol 0,61 0,37 cutremur produce 0,96 0,98 incendiu arde 0,83 0,75 cutremur grad 0,87 0,98 incendiu distruge 0,79 0,08 cutremur intensitate 0,61 0,85 incendiu răni 0,93 0,04 cutremur românia 0,83 0,92 incendiu românia 0,81 0,80 n(t) = { ae λ(t−t0) aeλ(tm−t0)e−κ(t−tm) (1) above, t is time, n(t) is the number of posts per day (flux) at time t, t0 is the moment of the event, tm is the moment when the peak occurs, and λ and κ are coefficients. a similar evolution was verified for the words cutremur and foc in our database, see figure 1. notice that during the months of autumn and winter the number of fires increases compared with the other months of the year. yet the summer heat may cause vegetation fires. these facts are mirrored by the number of related messages on sns, see foc in figure 1. 3.2 zipf’s law and the lexicons of posts on sns recall that zipf’s law [14] states that the logarithm of the probability of a word in a language is related to the rank of the word, log(p(w)) = α×rank(w) + β (2) zipf-like laws, that is, power laws, are well known in statistical linguistics, see for example [14], and in many other domains such as populations of cities, economy, and biology. explained in more detail, this law says that, when ordering the words of any language, including romanian, according to their frequency of apparition in a large corpus, the logarithm of their apparition probability decreases linearly with their rank. this means that selecting at random words in a specified language, the law should not apply. we have been interested, see [24], to find if zipf’s law is relevant to the database related to disasters. our initial hypothesis was that the law would not be valid because of the very restrictive set of words used in such posts, their low rank in the language, and their supposedly almost random choice from the lexicon of the global lexicon of the language. yet, our hypothesis was overturned [24], in that at least the most frequently used words obeyed zipf’s law, while words with higher rank confirmed the hypothesis. the results of log-lin representation of the probabilities of the most frequent 8 words, topic seism for google+ (3), and twitter (4) are: y = −0, 1969x − 3, 3642; r2 = 0, 9555 (3) y = −0, 1592x − 3, 0524; r2 = 0, 9494 (4) 606 s.c. bolea figure 1: time series of keywords occurrences: a. google+, b. twitter language processes and related statistics in the posts associated to disasters on social networks 607 the main results are shown in fig. 2, indicating a good agreement (r2 > 0.7) with zipf’s law. notice that on google+ the slope slightly differs from that on twitter, indicating a lexiconbased personality of the two sns. additional results are given on the srol website described in [9]. figure 2: log-lin representation of the probabilities of the most frequent 14 words, topic seism analyzing the equations 3, 4 and figure 2, the first ranked words (up to 8) obey the zipfs law. in case of focdb, the first ranked words(up to 8 or 14) obey zipf’s law (see (5), (6) and figure 3). figure 3: log-lin representation of the probabilities of the most frequent 14 words, topic incendiu y = −0, 33x − 4, 6241; r2 = 0, 8933; in google+ sn (5) y = −0, 3481x − 3, 5163; r2 = 0, 9489; in twitter sn (6) 3.3 dynamics of the fluxes of synonyms the notion of dynamics of sns originates from the papers [19], [23], where it was shown that the flow of messages streaming from an event on sns is predictable and has a dynamics that has characteristics of nonlinearity. it was shown that the time series of the number of messages related to events correlate with the level of seismic activity [19], [23]. one of the issues only slightly addressed in [24] is that of the dynamics of the use of keywords and synonyms. in many respects, this dynamics resembles with the dynamics of the number of messages addressing a certain disaster event, as dealt in [19], [23]. the interest in the dynamics is multifold and includes the expectation of correlation of the query based on keywords and the number of returned messages related to a specified event (probability of finding messages related to an event when the search uses a given keyword), and the dynamics of the joint use of synonyms in messages. on the other hand, the study [24] proved that, while the time series of the number 608 s.c. bolea of occurrences of the words "cutremur" and "seism" are strongly correlated, they have dynamics that are clearly differentiated in the phase space. in this sub-section, we study the difference in the dynamics of synonyms, continuing [24], by generating the phase-space plots (maps) of the time series of the synonyms and then by applying the method of windows in the time space, as proposed in [21]. that method is essentially an approximate way of determining the probability that the attractor of the time series has a point in a specified rectangular region of the phase space. four windows were defined in the rectangular coverage of the attractors, as suggested in [24], and the counts for the respective four windows was performed, producing a vector with four components for each time series. then, the distance between the vectors was determined (method due to hn teodorescu). we found that the euclidean distance is low only for the pair of synonyms cutremur and seism on the twitter sn and for google+ sn, this distance is a little larger. the phase space, in a simplified case of the phase plane for the dynamics x(t), is simply defined as the plane (x,ẋ), dx dt = ẋ, with the corresponding graphical representation. the characterization method introduced in [21], consists in determining the frequency of a point in the phase space that lies in the 2d interval (rectangle) [a1,a2] × [b1,b2], for a set of nonoverlapping rectangles defined in the phase space. let us consider the attractor of the time series of the flux of a specified word, nw(tk), for a given time duration t1, · · · , tp framed in the rectangle [minj∈{1,p}nw(tk),maxj∈{1,p}nw(tk)] × [minj∈{1,p}dnw(tk),maxj∈{1,p}dnw(tk)], where dnw(tk) = nw(tk)−nw(tk−1). divide this rectangular subspace of the phase space into four equal rectangles, overlapping only on one of their edges, r1 = [minj∈{1,p}nw, minjnw + maxjnw) 2 × [minjdnw, minjdnw + maxjdnw 2 ] (7) where everywhere above min and max are for j ∈ {1,p}; similarly one defines r2,r3,r4, see fig. 4. denote the number of points that fall in these rectangles by n1(w1), · · · , n4(w1). form the vector −→ω1 = (n1(w1),n2(w1),n3(w1),n4(w1)). perform the same operations for the specified word, w2, whose dynamics we wish to compare with −→ω1, and find −→ω2. then, the distance d(−→ω1,−→ω2) between the two dynamics is the distance between −→ω1 and −→ω2 (personal communication hnt): d(−→ω1,−→ω2) = ∑ k=1−4 |nk(w1) −nk(w2)|, or the euclidean distance would be d(w1,w2) = √∑ k=1−4(nk(w1) −nk(w2))2. a further improvement is to use relative frequencies in the distances, dividing by the total number of points in the phase diagrams, νk = nk/p, where p = ∑ k nk. the distances between the attractors in the phase plane provides a quantitative, simple characterization of the similarity of the dynamics of the two time series compared, supplementing the information given by the correlation. the phase diagrams for four of the most frequent words found on google+ are shown in figure 4; the corresponding euclidean distances between the dynamics are given in table 4, which stands for the matrix [d12], where d12 denotes the distance between words. the results presented in table 4 show that the larger euclidean distance is between the dynamics of the words magnitudine and vrancea, and the smallest distance is between seism and vrancea respectively cutremur and magnitudine, on google + (both distances 1.41). comparatively, the time series for the word pair cutremur and magnitude have the third higher correlation (see table 3), only slightly higher than the correlation for the pair cutremur seism. even less favorably compares the value of the distance between the dynamics of cutremur and vrancea d = 3.75, the third largest, with the correlation of their time series, which is the highest, language processes and related statistics in the posts associated to disasters on social networks 609 figure 4: example of dynamics in the phase plane of the series for cutremur table 4: euclidean distances between the dynamics of the words cutremur, seism, vrancea, and magnitudine (relative frequencies) words cutremur seism vrancea magnitudine cutremur 0 2,83 3,75 1,41 seism 0 1,41 4,24 vrancea 0 5,10 magnitudine 0 c = 0.97. this shows that distances between the dynamics and the value of the correlation of the respective time series carry at least partly different information and help differentiate between the processes represented by the time series. 4 discussion the use of synonyms has also a psycho-linguistic background [24]; as such, it relates to emotions and attitudes expressed in the posts. it would be interesting to follow this study by a research of the uttering of the synonyms on voice-enabled sns in view of detecting their emotional charge using various methods of characterization, such as those in [8], [9], [13], [28] that report on emotion recognition tools specifically for the romanian language. not all messages posted on sns contain diacritics. this can present a problem in recognizing lemmas. in this study, all the words with and without diacritics were taken into account. there is a difference between the same messages on different browsers and sns. so, the stopwords for twittersn are (in google chrome): "no retweets no likes", "reply", "retweet", "more", "like" "newline", and in case of mozilla’s browser the stopwords are "no retweets no likes" and "newline", where no is a number. the stopwords in google+sn are: "adauga un comentariu...", "newline". this posts were saved first in .docx, than transformed in .txt. the earthquake’s posts contain information about the earthquake from a specific day. in almost all messages there are dates about the biggest earthquake from that year, or the previous years. this is the reason for the cutremurdb contain a small numbers of words than focdb. the data bases include only the following words: nouns, verbs, adjectives, adverbs; we deleted the conjunctions, prepositions, numerals. 610 s.c. bolea 5 conclusions one of the interesting findings first detected in the preliminary study [24] and confirmed by this ampler database is that zipf’s law is still valid for the most important most frequent 6-7 words used in the posts related to disaster. this is surprising because the words are not among the most used in a language, that is, they are among the rare words in common language; moreover, they are not selected in any specific way preserving the distribution in the common language, so there is no reason that these words still obey a zipf-type law. however, the law breaks down when we try to expand the set of the most frequent words beyond the number of eight. these facts may indicate that the word selection mechanism in the common language scales down to the lexicon used when dealing specific topics. this scaling property of language statistics (zipf’s law) was never been pointed out before [24], according to our knowledge. social networks have their own specificity, with features that could be likened to the personality of humans; the specific use of synonyms is one of that. the parameters in zipf’s law for the lexicons used in posts related to various types of events may help to automatically identify the sn from a set of posts, only knowing the related type of event eliciting the messages. the posts which have been studied for one year are mostly messages sent by newspapers and tv’s and have an informal structure of mass-media type. acknowledgments this work was supported in part by the sps nato program under grant g4877 /sfp 984877. the author acknowledges the help of horia-nicolai teodorescu (hnt), who, as the pi of the cited grant, established the principles and methods of this research, proposed the main ideas, proposed the structure of this paper and suggested some of the text. this paper reports detailed and complete results complementing [24], where the research was largely done by hnt. bibliography [1] abbasi, m.a. et al. 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[30] http://www.racai.ro/en/tools/text/ international journal of computers communications & control issn 1841-9836, 10(1):38-48, february, 2015. intelligent design environment for second-order positioning systems s. dale, h. silaghi, d. zmaranda, u. rohde sanda dale, helga silaghi, doina zmaranda* university of oradea romania, 410087 oradea, universitatii, 1 sdale@uoradea.ro, hsilaghi@uoradea.ro *corresponding author: dzmaranda@uoradea.ro ulrich rohde tuniversity of technology cottbus germany, 03048 cottbus, lipezker st. 47 ulrich.rohde@tu-cottbus.de abstract: from the designer perspective, in real-time applications as mechatronic systems the control algorithm has to be easy to understand and to implement, easy to optimize and last but not least, to ensure the best possible behavior for the controlled system. the paper presents a design environment which provides the possibility to choose between a classical control algorithm (as state feedback stabilization represents) and a new approach (interpolative controllers) with the possibility to modify the designed parameters such as to obtain the optimum behavior for the controlled system. this opportunity is given through the inner structure and operating laws of the algorithm and exploited using genetic algorithm-based techniques. interpolative-type controller category covers the controllers based on fuzzy, neural and pure interpolative algorithms, due to their common capability to make an approximate reasoning. the first ones, fuzzy and neural algorithms, are already well-known in the control area. a pure interpolative controller presumes to contain at least one block, placed no matter where in his structure, in which interpolation as mathematical operation to have place. the approach presented in the present paper uses this kind of blocks to reproduce and to optimize the behavior of an already existing controller (in this case the state feedback one) [1]. this type of controllers meets all the requirements stated at the beginning: they are easy to implement, easy to understand, they need reduced calculus time and gives notable results in specific cases. the above mentioned kind of controllers will be presented with all the necessary details in the paper. the design environment is presented as a collection of matlab functions, simulink configurable schemes and a user interface. a case study ends the presentation with some experimental results obtained through a simulation for a specific second order positioning system. keywords: interpolative control algorithms, intelligent optimization, design environment, mechatronic positioning system. 1 introduction a mechatronic system is generally an electromechanical system equipped with sensors which detect the state of the environment, actuators which modify the state of the environment and a control system which controls the actuators based on the environment, as described by the sensors. mechatronical systems are implemented in all kind of man-made machines as autonomous robots, industrial robots or flexible manufacturing cells. in many mechatronical applications, the control functions are implemented through a microcontroller. if in the past microprocessors and microcontrollers have been used especially by the copyright © 2006-2015 by ccc publications intelligent design environment for second-order positioning systems 39 electronic and automation engineers (due to the difficulties in design, programming, testing and usage and due to the fact that the amount of needed information for the applications was very large and the given degree of software and hardware integration), today the microcontroller can be used even by people that have minimum knowledge of electronics and programming. there are many brands that offers developing systems with integrated microcontrollers at good prices, well documented and with numerous applications in different fields. some of them have an active policy in attracting the non-specialists in realization of applications with microcontrollers. the main advantage of such an evolution is the possibility to realize, in a relative easy way a sum of practical applications in various physical areas. in this context there isnt surprising the increasing amount of applications from the mechatronics field based on the usage of microcontrollers which implements various control techniques. the actual approaching manner of such applications, focused on miniaturization, has stimulated the efforts in this direction. nowadays, the microcontrollers have better and better performances (memory, speed etc). there is a need to continuous increase of the performances for those products, leading to a larger volume of software applications. testing of the applications becomes more important. the usage of microcontrollers in mechatronical applications leads to the appearance of new directions of research in the field. integrated systems based on microcontrollers are low cost solutions and have the advantage that can be used in applications where miniaturization (from the mass, volume, energy consumption point of view) is an important requirement. as a consequence, the researches on the implementation possibilities for control algorithms in applications from mechatronics as well as motion control, mini-robotics etc., constitutes a necessity. the main disadvantage of using the microcontroller in such applications (comparative with plc’s) its related to the potential difficulties in software development. in this context, development of performant control algorithms easy to use and to implement contributes to reduce and eventually eliminate this inconvenient: among them, some of those knew as intelligent control algorithms. as it is well known, intelligent control is a class of control techniques, that use various ai computing approaches like bayesian probability, approximate reasoning techniques based on expert knowledge or interpolative-type algorithms (as neural networks, fuzzy logic, pure interpolative algorithms), machine learning, evolutionary computation and genetic algorithms. in this branch of control engineering, the control techniques are extended beyond classical mathematical methods based on integral-differential calculus. difficulties that appear in complex non-linear systems control with uncertain models can be eliminated by new approaches based on an operator experience. hence, one way to develop control algorithms is to generate them based on expert knowledge. in that way, they are easier to understand for humans, and this can be considered as the second major requirement for the control algorithms used in mechatronic devices design. intelligent control systems must treat information referring their own state and the environment state and that implies both heuristic and algorithmic programming methods. intelligent control implies also a decisional strategy based on synthetic thesis from system theory, advanced mathematical modeling, computers and knowledge based linguistic methods, as in [2] and [7]. from both perspectives exposed above which can be expressed as two major desiderates or requirements for the control algorithms implemented on microchips and designated to mechatronic devices there are a few categories of appropriate intelligent algorithms. fuzzy and neural ones, due to their complexity, are not one of them. in [3] and [4] some comparative experimental studies was made from the implementation on microcontroller point of view, between pid, fuzzy and interpolative algorithms. the results allow to consider pure interpolative algorithms implemented as interpolative blocks and used in control structures as candidates for implementation on microcontrollers. one of the advantages of using such blocks are, mainly, based on simplifying the solutions (easier implementation and reduced calculus time, meaning the first requirement 40 s. dale, h. silaghi, d. zmaranda, u. rohde is fulfilled) and the possibility to ensure for the control systems, in a relative easy way, some robustness properties [5]. in the same time, as in [4], [6] and [9], the controllers based on interpolation table, on the same form as in drechsels rip (regelbasierte interpolation) controllers introduced in [12], can be developed in much more situations than those based on linguistic rules. they can be conceived starting from solutions deducted in different ways, solutions that, finally, can be improved. from this perspective, interpolative algorithms meet the second requirement also, the possibility to be understood and designed by automation engineers or non-specialists with no loses from the control systems performances point of view. the results achieved in this field in the last years authorise the answer at two major questions. why an interpolative algorithm? because these algorithms belong to the intelligent control techniques category, beeing one of the new-generation performant algorithms. on the other hand, due to the internal structure based on interpolation they are improvable, very simple to understand, use and implement, making them a good choice for the implementation on microcontrollers by specialist either non-specialist. in this way, the interpolative algorithms can present interes for the mechatronic applications field. why a ga-based optimization technique? because it seems to be very appropriate for optimizing the performances of the control structure with interpolative controllers, due to the fact that they represent themselves improvement methods based on natural and genetical mechanisms, which combine artificial surviving of best individuals with specific natural operators. some procedural aspects of an intelligent design environment which combines interpolative control with ga-based optimization are presented in [1]. for the intelligibility of the presentation, in the second chapter interpolative algorithms will be presented from a large perspective, in a descriptive-conceptual manner. the functionality and the structure of the proposed design environment is described in the third chapter that ends with a case study made on a second order positioning system. some relevant conclusions resume the presentation. 2 design procedures for controllers with interpolative blocks optimized through ga-based algorithms the controllers with interpolative blocks as introduced in [5] and [9] represents an alternative to the controllers obtained by various design procedures, that are replaced with controllers containing interpolation tables and algorithms and participate to the partial or integral control law elaboration. the idea of using interpolative blocks, named also table-type blocks its not a new one. they are frequently used in real time programs in order to reduce the calculus time in case of operating with multivariable complex functions. the interpolation technique is usual in robotic control systems. the concept of controller with interpolative blocks brings as a new element the elaboration method of such a controller which can be implemented quite easy using digital techniques. these techniques allow the real time operation with complex tables. the concept maintains actual the design methods for the linear or non-linear control algorithms, whose results constitute the starting point for interpolative controller synthesis and at the same time improves their performances. those controllers are synthesized based on a very well known principle in the quality systems domain: continuous improvement of the activities flux of an already existing system, designated to ensure a specific quality function by which are expressed the system performances [10]. for the interpolative controllers this principle is translated as follows: to obtain, for a certain control system, a better controller than the initial one, designed through usual methods. the synthesis of an interpolation block (implemented by an interpolation table) is accomplished in two stages intelligent design environment for second-order positioning systems 41 (as in figure 1): • primary synthesis stage necessits other two steps: initial control algorithm synthesis and interpolation table construction based on the command hyper-surface of the initial control algorithm • correction stage consists in one or more steps of on-line or off-line correction, operated on the interpolation table figure 1: interpolative controller synthesis the interpolation table is determined through support points extraction from the initial controller hyper-surface. support points set must be structurated as a cartesian grid and has to cover all variations domain for the input variables. the support points ensemble has to embed, from a qualitative point of view, all the inflection points of the command hyper-surface. support points generation is made point by point or based on an analytical function, when such a function is available. if the locations number on the table is too large, the support points extraction can be incomplete; the empty spaces will be filled by shepard interpolation. once the interpolation table was obtained describing approximatively the behavior of the initial block it will replace the initial block, followed by a correction stage. the correction is realized by successive modifications operated on interpolation table with a specific aim: to improve the performance of the control system. the effective correction can be made by modification on the values in table locations and/or by granularity modification on the inputs and/or output and it is an iterative process. at principle level, an interpolative controller contains an interpolative processing block (ipb), usually a non-dynamic non-linear block, implementing the relation in (1). u = int(a1, ....an) (1) the input and output filters (if and of respectively) ensure together the controller dynamics and they adapt the inputs at the ipb and the outputs to the execution element connected after the controller. control system performances (csp) can be improved when an improvement criterion is chosen as in [10] or they can be optimized if an optimization, well defined criterion is considered, as in [6] and [8], where ga are used for a so-called intelligent optimization. an ipb implementation is a digital one and its realized through n-dimensional interpolation tables, made in soft manner (programs capable to execute the operations corresponding to the association of table locations for input a and the calculus of command c based on an interpolation algorithm between the table values) or hard (using plcs, microcontrollers or dspace modules). all implementation modalities offer the possibility of real-time operating. 42 s. dale, h. silaghi, d. zmaranda, u. rohde interpolative controller design has, starting from the correction stage, a heuristic character, mostly empirical. this fact represents in many cases an advantage: brings to the light the creative, innovative, intuitive side of the designer, without forcing him to manipulate a complex mathematical instrument, on models affected by uncertainties. in the present paper the interpolative blocks will be synthesized through the method exposed above, as in [5] and [9], followed by off-line ga-based correction, as in [6] and [4]. ga-based techniques seems very appropriate in such cases due to the fact that gas represent themselves improvement methods based on natural and genetic selection, compare and sorting mechanisms, which combine artificial surviving of best individuals with specific natural operators and form a searching mechanism, usable in various purposes. for this kind of application optimization of an interpolation control table a ga modifies a table population ti which implements the control block for the interpolative structure (look-up table block). the starting point represents an initial set made from interpolation tables tinitial capable to ensure command characteristics. during the synthesis operation the performances of the interpolative control systems are permanently evaluated by simulations made with the simulink control schemes, for preset scenarios (eg. unit step response). a fitness function is used to provide a measure of performances related to the time requirements imposed to the control system. the adopted objectiv function induces the desired performances by using appropriate restrictions associated to the control system’s response. through techniques which are specific to ga, at every generation are preserved a rising number of performant individuals, where the individual performance is associated with a rise in the system performance. at the time when the convergence criterion (cc) consisting on minimization of the error between an imposed trajectory and the current trajectory of the system is accomplished, the best individual is extracted and will be used in the effective implementation, being considered the optimal solution, regarded from the used criterion point of view. the entire process can be synthesized by the schema in figure 2. figure 2: ga-based optimization the main limitations of the interpolative controllers are represented by the fact that they cannot guarantee the robustness and stability for control structures during the operating regimes not provided (included) in their synthesis. this issue represents a common problem for all the systems with non-linear controllers [11]. 3 structure presentation of the intelligent design environment the proposed design environment consists in a collection of matlab function and simulink schemes managed through a user interface. simulink schemes are configurable by user in order to correct and to justify some values or even to reconfigure some blocks if it is needed. the environment addresses to second order positioning systems, as simple mechatronic eleintelligent design environment for second-order positioning systems 43 ments, and offers the possibility to design an interpolative control structure in three stages: 1 state feedback control structure design, 2 interpolative control structure design, 3 optimized ga-based interpolative structure design. every stage, if the user considers it, can be viewed as a final and sufficient stage or it can be seen only as a step on a complex design issue, having as a final result the optimized interpolative structure. the functionality of the environment can be followed through the organigrame presented in figure 3. figure 3: intelligent design procedure as depicted in the organigrame, the usage of the design environment presumes a sum of steps, described in detail in the following paragraphs. 3.1 prelevation of process data transfer function of the positioning system and the requirements for the control systems, in terms of damp and rising time are introduced by the user. based on that information, a preset simulink scheme for the mm-isi model of the second order system is updated. 3.2 design of state feedback control structure consider the feedback state stabilization law with the folowing trajectory: u(t) = lc · uc + uf(t) = lc · uc − l1 · x1(t) − l2 · x2(t) (2) the calculus implied in state feedback control law design is implemented by a matlab function that use the ackermann formula (for l1 and l2) and transmits the results to the simulink scheme (see figure 4) of the general state feedback control structure. with some manual corrections made on lc parameter by trial-error efforts the results are evaluated through a step response. 44 s. dale, h. silaghi, d. zmaranda, u. rohde figure 4: initial state-feedback control loop (for second order processes) 3.3 interpolative controller design based on initial data, significant information are extracted from the command characteristic of the state feedback control structure, as support points. a table which consists in a number of row and columns chosen by the user with linguistic terms meaning for the variables x1(t) and x2(t) is filled with the information organized as a cartesian grid. the command law remains the same, only expressed through an interpolation table, as in table 1. table 1: interpolation table approximating the control law x1/x2 lt2−n lt2−n+1 .... lt20 .... lt2n−1 lt2n lt1−n lt1−n+1 ... lt10 uf = −l1x1 − l2x2 ... lt1n−1 lt1n the interpolation table is implemented as a 2d look-up table in the simulink scheme of the interpolative control structure, replacing the summation element with the irrespective coefficients (which implements the effective state feedback law) in control structure in figure 4. the interpolative control structure can be seen in figure 5. after some corrections made on lc parameter and on table locations made by trial-error efforts the results are evaluated through a step response. if the user is satisfied with the results, the process can be stopped here. 3.4 optimization of the interpolative control algorithm implemented as an interpolation table through ga-based techniques as described in section 2 of the present paper, a ga is used to optimize the performances of the interpolative control structure, starting from a population of initial tables {ti} with the restriction: ti ∈ [-150% · tinitial + 150% · tinitial], i.e. all the support values corresponding to the support points should not surpass, point by point, the âą150% centered on the initial support values obtained at step 3 from the state feedback command surface. during the evolution of the algorithm (realized by matlab functions) the best individuals are stored as tables kept in especially created directories, out of which, in the end, one will replace the interpolation table tinitial. in this case, a target function was formed, by which was imposed that the system intelligent design environment for second-order positioning systems 45 figure 5: interpolative control loop (for second order processes) response to be ideal, meaning to be identical to the standard (unit step). from here appears the necessity of introducing the following criterion, which in fact is a minimizing one: the smaller the difference between ideal response and the tested system, the better the tested system will be. ji(ti) = abs[ ∫ +tmax 0 [yi(t) − yideal]dt] (3) , where yideal is the standard. the genetic algorithm uses a fitness function that is a sum of conditions related to the error polarity, speed of the response, monotony a.s.o. the fitness function is implemented in matlab-simulink, such that it allows the imposing of the abovementioned conditions on the whole considered time, or only on certain intervals. at the end of the procedure, the results evaluation is made and if they are satisfactory to the user, the process stops here or. if the results are considered unsatisfactory, the fitness function implemented in the simulink optimization scheme can be modified and the process can be resumed from the beginning of the 4th step. the interpolation table considered as the best individual at step 3.3 will replace the look-up table in the interpolative control structure. with this action, the entire design process ends. as it can be observed from the description above, the usage of the proposed design environment presumes that the user has some minimal knowledge about control systems and their performances evaluation (for reconfiguration of the schemes and parameters correction) without implying detailed knowledge regarding control system design. the user interface provides a friendly and easy communication and usage of the environment. a text containing instructions for the user is available, describing in detail minimum requirements, design steps, matlab functions and simulink schemes involved in the designing process. 4 case study: second order positioning system as a study case a second order positioning system was chosen, given by the transfer function in (4) and the state-space model in (5). h(s) = 0.5 s2 (4)   x1(t) = −0.5 · x2(t) x2(t) = −u(t) y(t) = x1(t) (5) 46 s. dale, h. silaghi, d. zmaranda, u. rohde the performance requirements for the control system were set at ζ = 0.9 damping and tr = 0.8 sec rising time. after the first step data introduction the design procedure begins with the first stage: state feedback stabilization structure design. for l matrix of the state controller calculus, the following results were provided: l=[l1 l2] = [0.8889 19.7531] . the value for the lc coefficient of the trajectory following component was manually set on the simulink scheme at lc = 39.5. the simulation results of the step response in (6) are presented in figure 6 (continuous line) and the command surface in figure 7. uc(t) = 1,2 · σ(t) (6) figure 6: step response figure 7: command surface in the next stage, significant support points from the command surface of the state feedback intelligent design environment for second-order positioning systems 47 controller were chosen and a 2d look-up table replaces the initial state feedback controller. the simulation results for the step response of the interpolative control system are depicted also in figure 6 (point-dashed line). finally, the ga-based optimization was applied to the interpolative controller. a simple ga was used tournament selection, simple crossing and uniform mutation. the size of the population was 50. the probability of crossing and mutation were considered 0.45 and 0.01 respectively, using the grefenstelte references. the initial populations evolved for 50 generations. the simulation results for the step response of the optimized interpolative control system are depicted also in figure 6 (dashed line). the results obtained through simulation for the three control structures, for the same scenario, present sensible differences between them, with a small advance for the optimized interpolative controller, that justify the demarche made by developing the design environment. 5 conclusions and future works an intelligent design environment development can appear as a daring purpose. the result is a collection of matlab functions and simulink schemes, unified by a quite explicit user interface that offers the possibility to design an optimized interpolative structure for any second order positioning system. the procedure is semi-automatic there are operations which have place in a non-transparent manner (they are done by the functions embeded in the environment without interacting with the user) and also other actions that involve the user in a more or less specialized interaction with the environment. the design environment proposed in the article offers the possibility, for engineers and also for non-specialists, to design in three steps control systems for second order positioning systems and also to simulate their behavior. at the beginning, a state feedback stabilization algorithm can be designed which will constitute the base for a second step, the procedure to develop an interpolative structure. this one will be, in a third step, optimized through a ga-based technique. the environment consists in matlab functions, configurable simulink schemes united by a user interface. the user can study and compare the behaviors of the system in each design stage, having hence the possibility to provide some conclusions related to feasibility, implementability and resources for the involved algorithms. some observations can be made based on the results obtained in previous work and on the study case from the present article: the combination interpolative algorithm plus ga-based optimization techniques offers easy to implement optimized controllers without loses in control performances and in specific cases even better from the performances point of view. encouraged by the results obtained through simulation and dspace experiments, in future work, assisted with the proposed design environment, real-time applications on more complex systems will be developed. bibliography [1] s. dale, h. silaghi, d. zmaranda, u. rohde (2014), procedural aspects concerning intelligent design environment for real-time applications, abstracts of icccc papers, 5th international conference on computers, communications & control, icccc2014, baile felix, romania, issn 1844-4334, 4:41. [2] f.g. filip (2012), a decision-making perspective for designing and building information systems, international journal of computers communication & control, issn 1841-9836, 7(2):264-272. 48 s. dale, h. silaghi, d. zmaranda, u. rohde [3] s. dale, g. gabor, c. gyorodi, d. zmaranda (2010), interpolative control algorithm applied on a 3d-mechatronic system, proceedings of 4th international workshop of soft computing applications, arad, romania, 2010, 229-233. [4] a. bara, s. dale, t.z. nagy (2009), comparative real time experimental study case for control algorithms from the implementation point of view, proceedings of the 8th wseas international conference on system science and simulation in engineering (icossse09), genova, italy, 302-306. [5] s. dale, t.l. dragomir (2009), interpolative-type control solutions, studies in computational intelligence, berlin, springer verlag, 196: 169-203. [6] s. dale (2009), interpolative control for a d.c. motor drive with genetic algorithm-based tuning, proceedings of emes09 in journal of computer science and control systems, oradea, 93-96. [7] a. bara (2006), an algorithm for fuzzy relation identification, proceedings ieee-tttc 2006, tome1, aqtr 2006, cluj-napoca, 96-102. [8] t.l. dragomir, e. vladu, s. dale (2003), interpolative-type controller synthesis using genetic algorithms, proceedings of cscs14, bucuresti, 70-75. [9] t.l. dragomir, i. silea, s. nanu (2002), control performances improving by interpolator controllers, proceedings of the 6th world multiconference on systemics, cybernetics and informatics, july 14-18 2002 orlando, florida, 6: 208-213. [10] g.f. kamische, j.p.brauer (1999), qualittsmanagement von a bis z, hanser. [11] m.m. seron, et all (1997), fundamental limitation in filtering and control, springer-verlag, 1997. [12] d. drechsel (1996), regelbasierte interpolation und fuzzy control, vieweg, 1996. international journal of computers communications & control issn 1841-9836, 13(1), 39-49, february 2018. the integrated environment for learning objects design and storing in semantic web v. dagiene, d. gudoniene, r. bartkute valentina dagiene, daina gudoniene* institute of mathematics and informatics vilnius university akademijos 4, lt-08663 vilnius, lithuania *corresponding author: daina.gudoniene@mii.stud.vu.lt reda bartkute informatics faculty kaunas university of technology studentu 50, lt-51367 kaunas, lithuania abstract: there is a variety of tools and environments for learning objects (los) design and delivery as well as learning object repositories (lor) but the researchers could not find a repository that includes both functions: creation and storing of los. a number of different integrated learning systems are suggested for users that demonstrate the variety of e-learning methods and semantic capabilities. lo repository oer.ndma.lt/lor, that we are going to present, is very friendly and interoperable to use and assure lo design, search in semantic web, adaptation of the re-used objects and storing. there are no more existing lo repositories with the functionality presented by researchers. transformation of closed education into open one without existence of well-structured, multifunctional and integrated environment becomes problematic. authors will present an integrated environment for the lo design, search in semantic web, adaptation and storing of newly designed or re-designed lo. measures will support the transformation of closed education into open and will assure effective design, re-usability and adaptation of lo in the integrated environment. keywords: learning objects (lo), models, semantic web, semantic technologies. 1 introduction the authors of the paper explore the existing learning objects repositories throw the open education and transformation of education into openness where it is a need to have an integrated environment for lo design, search and storing in the semantic web [4, 5]. bellow presented different scientific papers and authors have analyzed technological challenges for lor and is implementation into learning design process. in this paper, learning objects (los) are referred as "small, modular, discrete units of learning design for electronic delivery and use" [27]. all learning objects can be identified by these features: interoperability, reusability, manageability, flexibility, accessibility, durability and scalability [22]. analyzing learning objects from the technological perspective, they are based on the "paradigm of object orientation" [14] which means that the parts of a learning object can be used, changed or created repeatedly. this feature allows the user to create new and unique learning objects that can be used multiple times. in this process, the semantic web plays an important role. the semantic web is "the extension of the world wide web that enables people to share content beyond the boundaries of applications and websites" [12]. in other words, the semantic web can be understood as a web of related meanings. the authors of the paper analyze the opportunities of the semantic web in the perspective of the search of a learning object. in this copyright ©2018 cc by-nc 40 v. dagiene, d. gudoniene, r. bartkute case, semantic web broadens the search and provides more and better fit with the search requests on the various aspects. the context of semantic web, most of the practical implementations and use of standards are related to the marking of learning objects, which creates a lot of additional requirements for the successful use of standards [16]. when searching for ways to improve the courses using semantic web technologies, it is very important to develop simple methods and tools for lo labeling, separation of the objective and subjective metadata to create metadata sets and schemes from a variety of sources, to integrate production and labeling, to include formal semantics into existing standards and dynamically associate metadata with different lo. these are the challenges that are expected to be solved. for sharing reusable learning objects, repositories are required so that these learning objects could be stored and delivered . the role and functions of learning objects’ repository are described in the ims digital storage compatibility specification [11]. a variety of learning object repositories are existing, however metadata studies show that the majority of metadata of currently existing learning objects repositories (lors) is only a general description of the content and settings [21]. such data is difficult to use for program agents. therefore, it is important to create semantic relations in the repositories so that learning objects would be fully integrated and linked [10]. a semantic learning object repository is a system containing the educational resources and metadata (or metadata only) which provides search interface to people or other systems [2]. the analysis shows that the existing lors and is analysed and presented by the authors do not assure effective e-learning objects (elo) design, search in semantic web and adaptation, as well as there is no suggested model, which will assure elo adaptation in semantic web and automatically will integrate elo to be re-used. 2 a research methodology the research methodology was prepared in the frames of constructive method. the constructive research method takes 5 phases (see fig.1): (1) literature review; (2) problem identification; (3) theoretical framework; (4) practical implementation and (5) experiment. (figure 1). figure 1: constructive research method a systematic review of related research works and analytical research methods were used for revealing the advantages of the use of semantic web technologies for integrated environment and for raising issues related to the semantic learning objects’ use in semantic education as well the integrated environment for learning objects design and storing in semantic web 41 as for exploring existing los design approaches and models and for extracting initial data from our model linked to a theoretical framework. the theoretical framework is designed to explain the integrations in the environment and the need of the new technological solutions after problem identification. practical implementation phase evaluated during the experiment phase. 3 related works on learning object repositories implementation the comparative analysis of smart e-learning systems and architectures [26] [25] highlights the challenges of e-learning: (1) development of mobile agent’s architecture in semantic web-based e-learning systems, while the agent knowledge is improved; (2) increased number of visualized educational resources; (3) new cooperation and communication with the learner to apply artificial intelligence achievements and the updating of internal and online databases, wikis etc. 3.1 learning object repositories learning object repositories are operating online by providing the users a lot of different los by covering different educational levels and topics and also developed by variety of technologies and having different metadata described with the aim to classify los [19]. there are a lot of different well known learning object repositories like: cloe; mit open courseware (ocw); vcilt; careo; oli; commonwealth of learning object repository; edclicks; encore; gem; lola; merlot (specialized in microworlds); ndma (www.oer.ndma.lt/ lor/) repository for lo and different educational activities design and store. most of these lors follow ieee-lom metadata standard and metadata annotation is done manually. however, the learning object repositories are used not only for storing but also for sharing, reusing and lo design [18]. mohan and brooks [23] discussed a situation where learning objects are embedded within learning systems. today we have an analogous situation with mainframe systems. learning systems today are essentially centralized, with learning objects (data) managed at a single place by a single system. however, with the growth of learning object repositories on the semantic web, it is necessary to find a model for the lo search, exchange and design where a learning object may also contain links to other courses or content packages, where it has been used before to support searches by software and human agents on the semantic web [17]. goncales and pimenta [13] analyzed the lo’s integration into virtual learning environment and made a conclusion that in a dynamic environment of content sharing and educational practices the lor has to integrate, thus creating dynamic learning environments to allow the interoperable user to retrieve them by searching through federated repositories, with the ability to modify those objects and compose lessons out of them. the integration aspects were discussed by shen, ullrich and borau [28] who outlined the importance of metadata in the lor implementation process. lor uses metadata to describe los and helps to improve the search for users. learning object metadata lower barriers for repository growths as the server or learning environment does not contain the whole object but the metadata. this feature allows to have big learning object repositories with less expenses. the model based on metadata of learning objects were implemented in proposed lor as well. 3.2 learning object design models the researchers worked on the integrated environment for lo design and search in the semantic web. 42 v. dagiene, d. gudoniene, r. bartkute table 1: comparative analysis of the lo models models features of the model verbert and duval model the model components are: content parts, learning objects, content objects. the model explains how the authors distinguish the fragments of content, content and learning objects (text, audio and video), presents individual resources [29]. meyer model lo components are designed on the basis of a clearly defined concept. lo components are well defined and focused on reusing. lo has one or more evaluation criteria and their usage areas includes several lessons [3]. boyle model structural generative learning model (glo) of an object contains inner and outer structures. glo behavioral model consists of development tool, an xml file, and player program. as the main characteristic of the model, boyle distinguishes the ability to change the flexibility of the xml file, using authoring tools to create instances of glo layouts [8]. santiago and raabe generative model generative learning objects of higher levels (aglo) come from the advanced options of heterogeneous meta-programming technology that allows to convey many aspects of learning (such as content, didactic, social and technological aspects) clearly through parameterization [7]. "learnativity" content model the model defines five levels of content hierarchy [29]: 1. the raw data and media items belong to the lowest level. 2. information object includes raw data as well as media items and focuses on one piece of information. 3. on one task basis, information objects are connected to the third level software objects. learning objects are sets of information objects. 4. the fourth level complex sets for larger (end) tasks. 5. lessons or sections can be combined into larger sets. netg lo model this model defines the rate as a matrix, divided into three main components: subjects (vertical), lessons (horizontal) and topics (fields) [1]. bntopm model widely used set of specifications for creating learning content independently from specific content delivery platform. bntopm includes content model compounds, consisting of shared content objects and containing compound [14]. "navy" content model "navy" content model is an improved bntopm content model that offers more specific content definitions for detail levels that are necessary for the "navy interactive learning environment". the content of "navy" is compatible with bntopm. "navy" distinguishes lo compounds, final los, enabling los [29]. "cisco" dnmo/dnio model cisco classifies each dnio. possible content element classifications are: definition, example, overview, further steps, analogy, topology illustration, block scheme, additional resources, pie charts, teacher’s notes, introduction, a key wording, illustration, importance, plan, facts, list, objectives, contrast, table, working scenario, conditions, guidelines, procedures’ table, decisions’ table, demonstration, table of prepared or combined [9]. the integrated environment for learning objects design and storing in semantic web 43 dynamic learning content management system model the aim is to increase learning content reusability providing a modular design strategy together with a structured description [20]. the system includes a component model that defines three circuit levels: 1. the asset is media elements: pictures, video clips, animations, and simulation. 2. the content elements are defined as small, modular learning content units, which: (1) form the basis of learning content, (2) can be combined into larger, didactically based learning units (3) are independent, (4) are based on a single didactic content type (5) can be reused in different teaching contexts and (6) can be made of assets. learning unit is defined as a set of elements. alocom model it defines different levels of detail of contemporary content models as well as their mutual relations. it is based on owl language, which uses ontologies for relating the content models [24]. integrated modeling method conceptual, educational and didactical model it is a sustainable model developed to model the educational content. it consists of several models: conceptual, educational and didactic. each of them defines specific learning object development aspects. the conceptual model is the basis for the field of knowledge, because it provides teaching concepts and determine their mutual relations [6]. the analysis shows that currently there is no model where lo functionality would be directly semantically tied to lo repository and lo development environment, where different learning objects are created. 4 the integrated environment for learning object’s design and storing traditional teaching must be re-evaluated and adapted in a way to of open education. we cannot forget the educational aspects of the lo, which have a direct impact on a successful organization of learning process, and for efficient lo development. for the creation of such learning object, the educational model of lo planning, development and delivery is applicable. the essential functionality is related with lo design, delivery and search for additional material by using semantic web [15] i.e. search in other repositories of open educational resources for improving the designed lo. semantic network software agents can use contractual language service that allows agents to work together and actively introduce the learning material in the context of current problems. the aim to create links between learning objects and semantic network is very important in lo adaptation or improvement (figure 2). the framework for the user profile is close related with the learning program and a different types of lo distributed in the integrated environment having also functions of learning object repository (lor) (figure 3). the context is complex and dynamic, composed of a variety of problematic situations. according to the lom specifications, context is the environment in which the lo may be created. all contextual components are directly related to the tasks, content structure, available resources and contextual model, bringing together all these resources into the whole with the help of lo repository. the aim is to provide the necessary information to the consumer, and to carry out a search of electronic sources using keywords. the new framework links semantic web technologies and learning objects: the technologies that facilitate search and re-use of learning objects in lo repositories are developed. the idea of search in the semantic web, is to find similar content lo not only in national but also internation44 v. dagiene, d. gudoniene, r. bartkute figure 2: integrated environment for learning objects design and storing in semantic web figure 3: framework for the user profile in the lor al/external learning object repositories, directing into storage that support certain educational topics (figure 4). lo search is performed from the user development environment and directly channeled into educational learning object repositories. figure 4 shows the lo search algorithm in semantic web. such a search cannot be carried out without using, i.e. each lo must be described and assigned to a specific storage area containing the various technologies and content objects (see table 2). for example, when modeling the lo repository, we distinguish the following entities: book abstract publication in a repository; catalogue systematic lo catalogue, in which the knowledge area includes a part of books or lo of the repository; teacher lo developer, etc. the designed environment’s functionality allows to users to develop different types of lo (see figure 5) and the integrated environment for learning objects design and storing in semantic web 45 to upload it directly to the repository www.oer.ndma.lt. figure 4: the framework of integrated environment for lo design and delivery table 2: meta data for lo description type of file format text (plain, richtext, html, xml) images (bmp, gif, jpeg, png, tiff) video records (avi, mpeg, quicktime) other formats (pdf, doc, xls, ppt, java, x-shockwave-flash, zip, scorm) figure 5: learning object design tools in the environment (oer.ndma.lt/lor) search lo in the semantic web is assured by relations with external repositories of open educational resources, giving open access to it. in a search in the semantic web the meaning of the words used in the query is considered in the search process what involves, for instance, the understanding of the intention of the user and the context of the search term, either on the newly designed lor (figure 5). 46 v. dagiene, d. gudoniene, r. bartkute 5 experiment on effectiveness of the integrated environment for lo design and delivery the distributed nature of the semantic web enables continuous improvement of learning materials. it enables the use of distributed knowledge provided in various forms, enabled by semantic annotation of content. for example, if user is in a video presentation system, he gets similar videos based on analysis of all data on systems together. the created environment enables personalized lo development and delivery as well as its integration into different learning environments, regardless of their origin and type. this means that it assures minimal time consumption and gives an opportunity for teachers to create learning objects, complement them semantically with other educational content, and integrate them into personalized learning environments, integrated learning environments, repositories and teaching management environments. it allows improve educational module’s fitness for the learner, and to increase learning efficiency and quality as well as the effectiveness of different learning environments. the proposed framework and its practical application is useful for course developers, when designing and developing distance learning courses, massive open online courses, and other ictbased content. table 3: comparison of existing is for lo design and the newly designed features of the systems/environments integrated environment virtual learning environment information system los form the basis of learning content x x x los can be combined into larger x x los are independent x x los are based on a single didactic content type x x x los can be reused in different teaching contexts x x los can be searched in the semantic web x x los described in metadata x x x learning domain ontology x x curricular structure ontology x x pedagogical strategies ontology x x environment have categorization functions x x environment have a semantic search for other external lo x existing user interface x x x existing query / update x x existing web services facilities x x the newly designed framework identifies user’s need to develop and organize the learning the integrated environment for learning objects design and storing in semantic web 47 content. content modeling activities can be carried out at different levels of abstraction from coordination to instructional and educational level. lo models defined a formal framework, where learning objects can be modelled defining their formats, functions, participants and activity sequences. high-resolution digital objects are stored in the repository (oer.ndma.lt/lor). however, using them for further development of the learning process and creating educational programs and courses, requires these objects to be transformed into different formats and to ensure their reusability in other content or courses. information systems and their components designed to work in an environment that is adapted to communicate with standard it platforms, operating systems and computer networks. 6 conclusions and future studies the authors have proposed a friendly and interoperable integrated environment framework, which is able to use and assure lo design, search in semantic web, adaptation of the re-used objects and storing. there are no more existing lo repositories with the functionality presented by researchers. the integrated environment will contribute to the transformation of education into openness. the integrated environment for the lo design, search in semantic web, adaptation and storing of newly designed or re-designed lo is identified as an effective to use. the created environment enables personalized lo development and delivery as well as its integration into different learning environments, regardless of their origin and type. the authors of the 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[online] www.semanticweb.org int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 418-427 uncertain fractional order chaotic systems tracking design via adaptive hybrid fuzzy sliding mode control t.c. lin, c.h. kuo, v.e. balas tsung-chih lin feng-chia university, 40724, taichung, taiwan e-mail: tclin@fcu.edu.tw chia-hao kuo ph.d program in electrical and communications engineering feng-chia university, taichung, taiwan e-mail: peterqo022@hotmail.com valentina e. balas aurel vlaicu university of arad, romania b-dul revolutiei 77, 310130 arad, romania e-mail: balas@drbalas.ro abstract: in this paper, in order to achieve tracking performance of uncertain fractional order chaotic systems an adaptive hybrid fuzzy controller is proposed. during the design procedure, a hybrid learning algorithm combining sliding mode control and lyapunov stability criterion is adopted to tune the free parameters on line by output feedback control law and adaptive law. a weighting factor, which can be adjusted by the trade-off between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive fuzzy controller and direct adaptive fuzzy controller. to confirm effectiveness of the proposed control scheme, the fractional order chaotic response system is fully illustrated to track the trajectory generated from the fractional order chaotic drive system. the numerical results show that tracking error and control effort can be made smaller and the proposed hybrid intelligent control structure is more flexible during the design process. keywords: fractional order chaotic systems; fuzzy logic control, adaptive hybrid control. 1 introduction in domain due mainly to its demonstrated applications in numerous seemingly diverse and widespread fields of science and engineering, fractional calculus has gained considerable popularity and importance during past three decades [1][2]. in control system, due to the fact that the theoretical aspects are well established, fractional order controllers are successfully used to enhance the performance of the feedback control loop. it is observed that the description of some systems is more accurate when the fractional derivative is used. nowadays, many fractional-order differential systems behave chaotically, such as the fractional-order chua’s system [3], the fractionalorder duffing system [4], the fractional-order system, the fractional-order chen’s system [5], the fractional-order cellular neural network [6], the fractional-order neural network [7]. the tracking problem of fractional order chaotic systems is first investigated by deng and li [21] who carried out tracking in case of the two fractional lü systems. afterwards, they studied chaos tracking of the chen system with a fractional order in a different manner [22]-[24]. based on the universal approximation theorem, [9][20] (fuzzy logic controllers are general enough to perform any nonlinear control actions) there is rapidly growing interest in systematic copyright c⃝ 2006-2011 by ccc publications uncertain fractional order chaotic systems tracking design via adaptive hybrid fuzzy sliding mode control 419 design methodologies for a class of nonlinear systems using fuzzy adaptive control schemes. like the conventional adaptive control, the adaptive fuzzy control is classified into direct and indirect fuzzy adaptive control categories [9], [17][19]. a direct adaptive fuzzy controller uses fuzzy logic systems as controller in which linguistic fuzzy control rules can be directly incorporated into the controller. on the other hand, an indirect adaptive fuzzy controller uses fuzzy descriptions to model the plant in which fuzzy if-then rules describing the plant can be directly incorporated into the indirect fuzzy controller. moreover, a hybrid adaptive fuzzy controller can be constructed using a weighting factor to sum together the control efforts from indirect adaptive fuzzy controller and direct adaptive fuzzy controller. although the concept of sliding mode control (smc) and the theory of fractional order system are well known, their integration, fractional sliding mode control, is an interesting filed of research dwelt on this paper with some applications [8]. the motivation of this paper stands on two driving forces: one, most systems in the reality display behavior characterized best in time domain of fractional operators, the other, the uncertainties on the process dynamics can appropriately be alleviated by utilizing smc technique. in this paper, by combining the approximate mathematical model, linguistic model description and linguistic control rules into a single adaptive fuzzy controller, an adaptive hybrid fuzzy controller is proposed to achieve prescribed tracking performance of fractional order chaotic systems. a new adaptive hybrid fuzzy smc algorithm incorporated lyapunov stability criterion is proposed so that not only the stability of adaptive fuzzy control system is guaranteed but also the influence of the approximation error and external disturbance on the tracking error can be attenuated to an arbitrarily prescribed level. this paper is organized as follows: in section2, an introduction to fractional derivative and its relation to the approximation solution will be addressed. section 3 generally proposes adaptive hybrid fuzzy smc of uncertain fractional order systems in presence of uncertainty and its stability analysis. in section 4, application of the proposed method on fractional order expression chaotic system is investigated. finally, the simulation results and conclusion will be presented in section 5. 2 basic definition and preliminaries for fractional order systems the concept of fractional calculus is popularly believed to have steamed from a question raised in the year 1695 by marquis de l’hoptial to gottfried wilhelm leibniz. it is a generalization of integration and differentiation to non-integer order fundamental operator, denoted by ad q t , where a and t are the limits of the operator. this operator is a notation for taking both the fractional integral and functional derivative in a single expression defined as [1] ad q t =   dq dtq , q > 0 1 q = 0∫ a t (dτ)−q, q < 0 (1) there are some basic definitions for the general fractional and the commonly used definitions are grunwald-letnikov and riemann-liouville [1]. the grunwald-letnikov definition is expressed as ad q t f(t) = lim h→0 [t−ah ]∑ j=0 (−1)j ( a b ) f(t − jh) (2) 420 t.c. lin, c.h. kuo, v.e. balas where [.] is the integer part. the simplest and easiest definition is riemann-liouville definition given as ad q t f(t) = 1 γ(n − q) dn dtn ∫ t 0 f(τ) (t − τ)q−n+1 dτ (3) where n is the first integer which is not less q, i.e., n − 1 < q < n, and γ is the gamma function. the numerical simulation of a fractional differential equation is not simple as that of an ordinary differential equation. in this paper, the algorithm which is an improved version of adams-bashforth-moulton algorithm to find an approximation for fractional order systems based on predictor-correctors is given. consider the following differential equation ad q t y(t) = r(y(t), t), 0 ≤ t ≤ t and y (k)(0) = y(k)o ,k = 0,1,2, ...,m − 1 (4) where ad q t y(t) =   1 γ(m−q) ∫ t 0 f(m)(τ) (t−τ)q−m+1 dτ, m − 1 < q < m dm dtm y(t), q = m (5) and m is the first integer larger the q. the solution of the equation (4) is equivalent to volterra integral equation [1] described as y(t) = [q]−1∑ k=0 y (k) 0 tk k! + 1 γ(q) ∫ t 0 (t − λ)q−1r(y(λ),λ)dλ (6) let h=t/n, tn = nh, n=0,1,2,· · · n . then (6) can be discretized as follows. yh(tn+1) = [q]−1∑ k=0 y (k) 0 tkn+1 k! + hq γ(q + 2) r(y p h(tn+1), tn+1) + hq γ(q + 2) n∑ j=0 aj,n+1r(yh(tj), tj) (7) where predict value yph(tn+1) is determined by y p h(tn+1) = [q]−1∑ k=0 y (k) 0 tkn+1 k! + hq γ(q) n∑ j=0 bj,n+1r(yh(tj), tj) (8) and aj,n+1 =   nq+1 − (n − q)(n + 1)q, j = 0 (n − j + 2)q+1 + (n − j)q+1 − 2(n − j + 1)q+1 1 ≤ j ≤ n 1 j = n + 1 (9) bj,n+1 = hq q ((n + 1 − j)q − (n − j)q) (10) the approximation error is given as max j=0,1,2,···n |y(tj) − yh(tj)| = o(hp) (11) where p=min(2,1+q). therefore, the numerical solution of a fraction order chaotic system discussed in this paper can be obtained by applying the above mentioned algorithm. uncertain fractional order chaotic systems tracking design via adaptive hybrid fuzzy sliding mode control 421 3 adaptive hybrid fuzzy sliding mode control of uncertain fractional order chaotic systems in this section, we study adaptive hybrid fuzzy tracking control of uncertain fractional order chaotic systems, i.e., to force output trajectory which is obtained by the algorithm mentioned in section 2 of the response system to track output trajectory of the drive system. consider a fractional order chaotic dynamic system x(nq) = f(x,t) + g(x,t)u + d(t), y = x1 (12) where x = [x1,x2, ...,xn]t = [x,x(q),x(2q), ...,x((n−1)q)]t is the state vector, f(x,t) and g(x,t) are unknown but bounded nonlinear functions which express system dynamics, d(t) is the external bounded disturbance, |d(t)| ≤ d, and u(t) is the control input. the control objective is to force the system output y to follow a bounded reference signal yd which is the output trajectory of a drive system, under the constraint that all signals involved must be bounded. to begin with, the reference signal vector y d and the tracking error vector e will be defined as y d = [ yd,y (q) d , ...,y ((n−1)q) d ]t ∈ rn, e = y d − x = [ e,e(q),e(2q), ...,e((n−1)q) ]t ∈ rn,e(iq) = y(iq)d − x (iq) = y (iq) d − y (iq) in general, in the space of the error state a sliding surface is defined by s(x,t) = −(ke) = − ( k1e + k2e (q) + ... + kn−1e (n−2)q + e(n−1)q ) (13) where k = [k1,k2, ...,kn−1,1] in which the ki’s are all real and are chosen such that h(r) =∑n i=1 kir (i−1)q,kn = 1 is a hurwitz polynomial where r is a laplace operator. the tracking problem will be considered as the state error vector e remaining on the sliding surface s(x,t) = 0 for all t ≥ 0. the sliding mode control process can be classified into two phases, the approaching phase with s(x,t) ̸= 0 and the sliding phase with s(x,t)= 0 for initial error e(0) = 0. in order to guarantee that the trajectory of the state error vector e will translate from the approaching phase to the sliding phase, the sufficient condition s(x,t)ṡ(x,t) ≤ −η > 0 (14) must be satisfied. two type of control law must be derived separately for those two phases described above. in the sliding phase, it implies s(x,t) = 0 and s(q)(x,t) = 0. in order to force the system dynamics to stay on the sliding surface, the equivalent control u can be derived as follows: if f(x,t) and g(x,t) are known and free of external disturbance, i.e., d(t)=0, taking the derivative of the sliding surface with respective to time, we get s(q) = − ( n−1∑ i=1 cie (iq) + e(nq) ) = − ( n−1∑ i=1 kie (iq) + y (nq)−y(nq) d ) = − ( n−1∑ i=1 kie (iq)−f(x,t)−g(x,t)ueq ) − y(n)d = − n−1∑ i=1 kie (i) + f(x) + b(x)u(t) − x(n)d = 0 (15) therefore, the equivalent control can be obtained as 422 t.c. lin, c.h. kuo, v.e. balas u = 1 g(x,t) ( n−1∑ i=1 kie (iq) − f(x,t) + y(nq)d ) (16) on the contrary, in the approaching phase, s(x,t) ̸= 0, an approaching-type control uap must be added in order satisfy the sufficient condition (4) and the complete sliding mode control will be expressed as u = u − uap, uap = ψhsgn(s) (17) where ψh ≥ η > 0. to obtain the sliding mode control (17), the system functions f(x,t),g(x,t) and switching parameter ψh must be known in advance. however, f(x,t) and g(x,t) are unknown and external disturbance, d(t) ̸= 0, the ideal control effort (16) cannot be implemented. we replace f(x,t), g(x,t) and uap by the fuzzy logic system f(x|θf), g(y|θg) and h(s|θh) in specified form as [9], [17][19], i.e., f(x|θf) = ξ t (x)θf,g(x|θg) = ξ t (x)θg, h(s|θh) = ∅ t (s)θh (18) let |h(s|θh)| = d + ψh + ωmax when s(x,t) is outside the boundary layer. here the fuzzy basis functions ξ(x) and ∅(s) depend on the fuzzy membership functions and is supposed to be fixed, while θf,θg and θh are adjusted by adaptive laws based on lyapunov stability criterion. therefore, depending on plant knowledge and control knowledge, a hybrid adaptive fuzzy controller can be constructed by incorporating both fuzzy description and fuzzy control rules using a weighting factor α to combine the indirect adaptive fuzzy controller and the direct adaptive fuzzy controller. based on the trade-off between plant knowledge and control knowledge, the weighting factor α ∈ [1,1] can be adjusted. therefore, the total control effort can be expressed as uc = αui + (1 − α)ud (19) where the direct adaptive fuzzy controller ud and the indirect adaptive fuzzy controller ui are given as follows: ud(x) = ud(x|θd) − h(s|θh) g(x,t) and ui(x) = 1 g(x|θg) [ n−1∑ i=1 kie (iq) + y (nq) d − f(x, |θf) − h(s|θh) ] (20) where ud(x|θ) is obtained by fuzzy logic system specified as ud(x|θd) = ξ t (x)θd (21) the optimal parameter estimations θ∗f,θ ∗ g, θ ∗ h and θ ∗ d are defined as θ∗f = arg minθf ∈ωf [ sup x∈ωx |f(x|θf) − f(x,t)| ] , θ∗g = arg minθg∈ωg [ sup x∈ωx |g(x|θg) − g(x,t)| ] θ∗d = arg minθd∈ωd [ sup x∈ωx |ud(x|θg) − u| ] , θ∗h = arg minθh∈ωh [ sup x∈ωx |h(s|θh) − uap| ] uncertain fractional order chaotic systems tracking design via adaptive hybrid fuzzy sliding mode control 423 where ωf,ωg,ωd and ωx are constraint sets of suitable bounds on θf,θg,θ ∗ h,θd and x respectively and they are defined as ωf = {θf|θf| ≤ mf}, ωg = {θg|θg| ≤ mg}, ωd = {θd|θd| ≤ md}, ωh = {θh|θh| ≤ mh} and ωx = {x||x| ≤ mx}, where mf,mg,md,mh and are positive constants. by using (20), (21), sliding surface equation (15) can be rewritten as s(q) = ω + α [ f(x|θ∗f) − f(x|θf) ] + α [ g(x|θ∗g) − g(x|θg) ] ui − (1 − α)h(s|θ∗h) −αh(s|θh) − (1 − α)g(x) [ud(x|θ ∗ d) − ud(x|θd)] + αh(s|θ ∗ h) − αh(s|θ ∗ h) (22) +(1 − α)h(s|θ∗h) − (1 − α)h(s|θ ∗ h) + d(t) where the minimum approximation errors is defined as ω = α [ f(x) − f(x|θ∗f) ] + α [ g(x) − g(x|θ∗g) ] ui + (1 − α) [ud(x|θ∗) − ud] (23) if θ̃f = θf − θ ∗ f , θ̃g = θg − θ ∗ g and, θ̃d = θd − θ ∗ d, we have s(q) = −(1 − α)h(s|θ∗h) + ω − αθ̃ t h ∅(s) − αθ̃ t f ξ(x) − αθ̃ t g ξ(x)ui +(1 − α)g(x)θ̃ t dξ(x) − αh(s|θ ∗ h) − (1 − α)θ̃ t h ∅ + d(t) (24) following the proceeding consideration, the following theorem can be obtained. theorem: consider the fractional order siso nonlinear chaotic system (12) with control input (19), if the fuzzy-based adaptive laws are chosen as θ (q) f = r1sξ(x), θ (q) g = r2sξ(x)ui, θ (q) d = r3s∅(s) and θ (q) h = −r4sg(x)ξ(x) (25) where ri > 0, i = 1 ∼ 4. then, the overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded and the tracking error will converge to zero asymptotically. proof: in order to analyze the closed-loop stability, the lyapunov function candidate is chosen as v = 1 2 s2 + α 2r1 θ̃ t f θ̃f + α 2r2 θ̃ t g θ̃g + α 2r4 θ̃ t h θ̃h + (1 − α) 2r3 θ̃ t dθ̃d (1 − α) 2r4 θ̃ t h θ̃h (26) taking the derivative of the (26) with respect to time, we get v (q) = ss(q) + α r1 θ̃ t f θ̃ (q) f + α r2 θ̃ t g θ̃ (q) g + α r4 θ̃ t h θ̃ (q) h + (1 − α) r3 θ̃ t θ̃ q + (1 − α) r4 θ̃ t h θ̃ (q) h = −(1 − α)sh(s|θ∗h) + sω − αsθ̃ t h ∅(s) − αsθ̃ t h ξ(x) − αsθ̃ t g ξ(x)ui + (1 − α)sg(x)θ̃ t ξ(x) −ash(s|θ∗h) − (1 − α)sθ̃ t h ∅ + sd(t) + α r1 θ̃ t f θ̃ (q) f + α r2 θ̃ t g θ̃ (q) g + α r4 θ̃ t h θ̃ (q) h + (1 − α) r3 θ̃ t θ̃ q + (1 − α) r4 θ̃ t h θ̃ (q) h ≤ α r1 θ̃ t f ( θ̃ (q) f − r1sξ(x) ) + α r2 θ̃ t g ( θ̃ (q) g − r2sξ(x)ui ) + 1 r4 θ̃ t h ( θ̃ (q) h − r4s∅(s) ) − αs(d + η)sgn(s) + 1 − α r3 θ̃ t ( θ̃ (q) + r3sg(x)ξ(x) ) − (1 − α)s(d + ψh)sgn(s) + sd(t) + sω (27) from the robust compensator ua and the fuzzy-based adaptive laws are given (25), after simple manipulation, we have v (q) ≤ sω − sψhsgn(s) = sω − |s|ψh (28) using the corollary of barbalat’s lemma [16]-[19], we have limt→∞ |s(x,t)| = 0. therefore, limt→∞ |e(t)| = 0. the proof is completed. 424 t.c. lin, c.h. kuo, v.e. balas 4 simulation example in this section, we will apply our adaptive hybrid fuzzy sliding mode controller to force the fractional order chaotic gyro response system to track the trajectory of the fractional order chaotic gyro drive system. example: the fractional order chaotic gyro drive and response systems are given as follows: drive system: { y (q) 1 = y2 y (q) 2 = −100 ( y1 4 ) + y31 12 − 0.5y2 − 0.05y32 + sin(y1) + 35.5 sin(2t)y1 − x31 6 + d(t) response system: { x (q) 1 = x2 x (q) 2 = −100 ( x1 4 ) + x31 12 − 0.7x2 − 0.08x32 + sin(x1) + 33 sin(2t)x1 − x31 6 + ∆f(x1,x2) + d(t) + uc(t) where structured uncertainty ∆f(x1,x2) = −0.1sin(x1) and external disturbance d(t) = 0.2cos(πt). the main objective is to control the trajectories of the response system to track the reference trajectories obtained from the drive system. the initial conditions of drive and response systems are chosen as [y1(0),y2(0)]t = [1,−1]t and [x1(0),x2(0)]t = [1.6,0.8]t , respectively. for q=0.95, α = 0.7 and all design constants are specified as k1 = k2 = 1,r1 = 150,r2 = 20,r3 = 1,r4 = 1 and step size h = 0.01. the phase portrait of the drive and response systems for free of control input is given in figure 1. it is obvious that the tracking performance is bad without control effort supplied to response system. figure 1: phase portrait of chaotic drive and response systems the membership functions for xi i=1,2 are selected as follows: µf i1 (xi) = exp [ −0.5 ( xi−4 2 )2] , µf i2(xi) = exp [ −0.5 ( xi−2.7 2 )2] , µf i3(xi) = exp [ −0.5 ( xi−1.2 2 )2] , µf i4 (xi) = exp [ −0.5 ( xi 2 )2], µf i5(xi) = exp[−0.5(xi+1.22 )2], µf i6(xi) = exp[−0.5(xi+2.72 )2], µf i7 (xi) = exp [ −0.5 ( xi+4 2 )2] , from the adaptive laws (25)-(28), the control effort of the response system can be obtained as uc = αui + (1 − α)ud (29) figure 2 shows the trajectories of the states xi,yi and x2,y2, respectively. control effort trajectory is given in figure 3 and phase portrait, tracking performance, of the drive and response uncertain fractional order chaotic systems tracking design via adaptive hybrid fuzzy sliding mode control 425 figure 2: the trajectories of the states xi,yi and x2,y2 figure 3: trajectory of the control effort figure 4: phase portrait, tracking performance, of the drive and response systems systems is shown in figure 4. trajectory of the sliding surface is given is figure 5. the maximum value of v (q)(t) is -1.711e-4 which is always negative defined and consequently is stable. in order to show the robustness of the proposed adaptive hybrid fuzzy sliding mode control, the control effort is activated at 5 second. the phase portrait, tracking performance, of the drive and response systems is given in figure 6. figure 7 shows the trajectories of the states xi,yi and x2,y2 respectively. we can see that a fast tracking of drive and response is achieved as the control effort is activated. control effort trajectory is given in figure 8. trajectory of the sliding surface is given is figure 9. the maximum value of v (q)(t) is -1.732e-4 which is always negative defined and consequently is stable. figure 5: trajectory of the sliding surface figure 6: phase portrait, tracking performance, of the drive and response systems 426 t.c. lin, c.h. kuo, v.e. balas figure 7: the trajectories of the states xi,yi and x2,y2 figure 8: trajectory of the control effort figure 9: trajectory of the sliding surface 5 conclusions a novel adaptive hybrid fuzzy sliding mode controller is proposed to achieve tracking performance of fractional order chaotic systems in this paper. it is a flexible design methodology by the trade-off between plant knowledge and control knowledge using a weighting factor ? adopted to sum together the control effort from indirect adaptive fuzzy controller and direct adaptive fuzzy controller. based on the lyapunov synthesis approach, free parameters of the adaptive fuzzy controller can be tuned on line by output feedback control law and adaptive laws. the simulation example, the output trajectory of the fractional order chaotic response system to tracking the trajectory of the fractional order chaotic drive system, is given to demonstrate the effectiveness of the proposed methodology. bibliography [1] m. s. tavazoei, m. haeri, synchronization of chaotic fractional-order systems via active sliding mode controller, physica a, vol. 387, pp. 57-70, 2008. 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[20] l. a. zadeh, knowledge representation in fuzzy logic, ieee trans. knowledge and data engineering, 1 (1), pp. 89-100, 1989. int j comput commun, issn 1841-9836 8(2):206-219, april, 2013. digital perception functionality for institutional design: the forest policy issue of "loch lomond & the trossachs national park" of scotland as a case study n.d. hasanagas nikolaos d. hasanagas university of kavala institute of technology, greece e-mail: nikolaos.hasanagas@gmail.com abstract: motivation for this research was the need to perceive the relations among actors involved in the rural-forest policy networks. the problem of an objective analysis of formal and informal power and information flow structure measurement and visualization in policy-making is tried to be solved. the policy network of "loch lomond & the trossachs national park" is used as a case study as it includes a great variety of actors. an answer to the much-discussed problem of digital perception of politico-administrative power are in part the results about: (1) how complexity of policy making can be measured and what this practically means, (2) who controls the communication, (3) where the most important information resources are located, and (4) who is most trustworthy in this network. it is concluded that: "important" information is not a source of trust, but rather inversely, through trust one can impose information as being important. between actors of relatively high trust status, the most trusted one does not need to control information in order to strengthen its position. a less trusted actor may supplement the lack of power status with information control. the leading role that forestry sector may play even in cross-sectoral issues is pointed out in this case study. keywords: forest-environmental policy, cross-sectorality, forestry & rural development, network analysis software. 1 introduction 1.1 aim aim of this research is to present an example of policy arena perception through the social network analysis software visone [1] [2] and to propose the enrichment of software which is engineered for social network analysis, with the algorithm of complexity. the design of such a software product is decisive for the perceptional output of a policy arena [4]. it is going to be shown that different algorithms reveal different structures (such as hierarchies of trust or information) which can empirically and qualitatively interpreted. the discussion will be extended on the role of the informal hierarchy of trust and information exchange and the complexity of a network. a particular environmental network is characterized by a single hierarchy of trust in which each actor is embodied. it will be shown that the position of each actor in this hierarchy is significant for the "importance" of the information that the actor distributes to the other actors of the network. understanding informal structures is important because these structures, and not always the formal ones, determine policy outputs (e.g. roles, winners and losers, decisions, etc). the following questions will be addressed: how complexity of policy making can be measured and what this practically means, who controls the communication, where the most important information resources are located, and who is most trustworthy in this network. copyright c⃝ 2006-2013 by ccc publications digital perception functionality for institutional design: the forest policy issue of "loch lomond & the trossachs national park" of scotland as a case study 207 1.2 literature review "smart" algorithms which should be abstract and simultaneously functional and meaningful for a wide range of heterogeneous policy fields, from gis up to marketing and e-learning, is still a challenging issue in software engineering [3] [5] [13] [14] [16]. the examples suggested until now are mainly related to concrete fields such as environmental, spatial, industrial, commercial or military issues [6] [23]. steps to more abstract fields of interactions concerning influence potential, administration and innovation have also been made [3] [9] [10] [11]. however, they depict a path leading to a more "immaterial" perception of social networks and thereby to sharper perception toward the institutional infrastructure of the reality. many approaches have been presented in data mining and databases utilization [10] [11] [19] but only focused on classical statistics, without strong attention to the conception and perception of social structures. such a perception of structures may also be a database with adaptive learning objects suitable for tentative policy planning and e-learning of lobbyists and policy-makers. a basic hypothesis of decision making theory is that information does not produce power, but inversely, power seems to condition the role of information. trust is a form of power: an actor who has gained the trust of the others can, in general, (mis)lead them with his/ her plausible arguments. the other dimension of power is trust: the trustor follows the trustee [12] [19] [20] [21] [22] [5] [6] [7] [8] [13] [15]. trust is defined as the extent to which an actor is willing to let another actor make a decision for the former without exerting any control. in this way, a trustee may persuade the others that his/ her information is the most "important" and/or control the distribution of information (communication control). 1.3 paper’s innovation at theoretical level, this paper presents an approach of perceiving the institutional relations of the policy arena through software engineered for quantifying and visualizing policy networks. the system approach is not a method to analyze the reality but rather a pattern of perceiving it. the practical added value lies in analyzing hierarchies structured in the framework of a real policy-making case. the results can be interesting for environmental policy-makers, lobbyists and policy analysts. the complexity algorithm is regarded as a software tool for policy analysis, which optimizes the functionality of digital perception and is suggested to be integrated in software products. this indicator is useful for examining which function of an actor is expected to be most or least important for a successful involvement in the network (the leading organs, the public relation officer, or the experts). 2 case study: "loch lomond & the trossachs national park" the "loch lomond & the trossachs national park" was selected as a case study as it is a policy issue directly related to forestry: typical conflicts between environmental groups and forest groups, agencies or enterprises (e.g. whether native or commercial species should be planted in this area) which appear in several european and american regions appear also in this case. a public forest actor appears to be the strongest trustee and has achieved to be considered as the most "important" information source in this cross-sectoral policy issue. it has been argued [22] that forestry actors should avoid joining cross-sectoral policy networks or letting other sectors be involved in forestry issues because in this way the forestry actors become weaker. however, this case study offers a critical feedback to this hypothesis, as a public forest actor in scotland can lead actors of several sectors (recreation, wood production, water and wildlife 208 n.d. hasanagas management etc) by gaining their trust and controlling scientific information, enhancing selfregulation of forest policy and forest policy analysis [13] [21] [14] [15]. 3 the proposed methodology complete quantitative network analysis [17] [18] is applied. the empirical data have been collected through standardized telephone interviews with directors, chairpersons and campaign officers. the standardized questionnaire was based on previous intensive expert interviews. the interviewees were asked to name the other actors which they had contact to in the process of the policy network. afterwards, they were asked to cross-assess all actors which they had named regarding different variables, as for example: to what degree they trusted the other actors, who they considered had been their supplier of general and scientific information, amongst other variables etc. the separation of scientific knowledge from socially-constructed norms that are usually mixed in environmental policy [14] [16] was based on cross-assessment between the participants. qualitative (in-depth) interviews with field experts have been conducted for the interpretation of the quantitative results. for reasons of discretion the actors will not be named but only depicted as described in figure1: figure 1: legend of actors in "loch lomond & the trossachs national park" networks 3.1 analysis of the socio-grams of "loch lomond & the trossachs national park" the policy network approach is a form of system theory appropriately operationalized for policy analysis. according to the system theory, the interaction between the actors determines the properties of its actor (trust, importance etc) and the policy output (i.e. certain winners and losers). the system is in this case the policy network. the elements of this system are the participating actors (forest and land owner associations, enterprises, agencies, ngos, universities etc) and the interactions between them are relations of exchange: offering and reception of information or trust. the policy output is derived from these interactions: the policy (issue) network approach assumes that the structure derived from these interactions explains policy outcomes (results of negotiations or other decision-making procedures, losers and winners). a network is specified through: a. the issue of interest, b. the time in which the interactions take place, c. the content of the relations (in our case, trust and information exchange etc). digital perception functionality for institutional design: the forest policy issue of "loch lomond & the trossachs national park" of scotland as a case study 209 three hierarchies (also to be considered as ’sub-networks’) concerning "loch lomond & the trossachs national park" in 2002 are analyzed using visone [1] [15]: exchange of trust, of general information and of scientific information. general information means monitoring information (political-administrative events and evaluations). scientific information means ’objective’ description of functions and facts using expertise. a basic mathematical entity for the following formulas is the link from actor i to actor j. if there is a link (e.g. information exchange) from actor i (e.g. forest service) to actor j (e.g. a certain environmental group), then this link is defined as: zij. if there is no exchange in direction i→j then: zij=0. the link of trust exchange is valued: zij=1,2,3... . in the case of trust, the value of the link, when existing, is valued from 1 (no trust) to 3 (full trust). in other cases, as for example the information networks, the link was valued either with 0 (no existing link) or 1 (there has been a link).the total number of actors participating is defined as n. 3.2 from density to complexity density (d) is a characteristic of the entire network. it is defined as the proportion of all relations occurring in the matrix to the number of all possible links (n2 -n) [18]: d = n∑ i=1 n∑ j=1 zij n2 −n (1) where i̸=j, zij is the link from actor i to actor j and n is the total number of actors in the network. if the network is visualized as a polygon, its density is the proportion of the existing diagonals to all possible (double-directed) diagonals. d is significant for the intensity of activity and for the extent to which all possible "chances" (contact places) of a network have been exhausted. however, this should not be considered to mena everything for the intensity of activity or the chances, because e.g. a network with n=4 and d=100% is still much simpler than a network of with n=50 and d=30%. thus, complexity (comp) is proposed as a more accurate indicator of the practical difficulties that a lobbyist or policy maker has to be confronted with, if he/she enters the network. comp increases with n and d and can here be defined as the average proportion of the (existent) links to each actor, which is the simplified product of n*d (the denominator is simplified from n-1 to n): comp = n∑ i=1 n∑ j=1 zij n (2) the most complex of sub-networks of this case study is that of trust and not of information. the simplest one seems to be this of scientific information. the complexity is an indicator which implies the intensity of tasks for an organization which participates. thus, in the case of the "loch lomond & the trossachs national park" management, the most challenging task is the development of trustworthiness. second comes the general communication and last the scientific cooperation (fig. 2 and tab. 1). 210 n.d. hasanagas figure 2: visualization of the sub-networks of "loch lomond & the trossachs national park": trust, general information, and scientific information in table 1, the links, the actors’ number, the density and the complexity are presented for the three sub-networks. practically, this means that a chairperson or a campaign officer who is responsible for strengthening the trustworthiness of his/ her organization is mostly stressed in the framework of this issue. table 1: complexity of trust, general information and scientific information in comparison content of exchange trust general information scientific information links 184 98 34 actor number (n) 27 27 16 density (d) 27.25% 14.51% 14.16% complexity (comp) 6.81 3.62 2.12 a secondary role is played by press officers that are responsible for general communication. last come experts (e.g. biologists or environmental and forest scientists) who deal with scientific information, as the network of this kind of information is the simplest one. 3.3 exchange relations functionality trust trust is a basis for prestige and concentration of jurisdictions, authorizations, and competence in decision-making. if e.g. the forestry commission trusts the royal scottish forestry society, which trusts the national trust of scotland and the last two trust the friends of the loch lomond, then the last one proves the most trustworthy as it is able to gain the trust of all previous actors (also from the forestry commission indirectly). thus, trust is a relational value created through successive transfer of reputation. for the reputation of the actor a, it is not merely important how many actors trust a, but also how much reputation these actors gain from other actors, etc [4]. the following formula for calculating the status of an actor in a network has been proposed [17]: digital perception functionality for institutional design: the forest policy issue of "loch lomond & the trossachs national park" of scotland as a case study 211 t = (i −ac)−1 − i (3) where t is a matrix including the status values of all elements, and c is the matrix presenting the real network (of trust exchange). general information importance the indicator closeness centrality (cc) is based on the distance d (i.e. the shortest number of links) between two actors. if i.e. the royal scottish forestry society gives information to the friends of the loch lomond and the latter to the national trust of scotland (and there is no direct information link from the royal scottish forestry society to the last one), then the distance from the royal scottish forestry society to the national trust of scotland is d=2 (links). the sum of all distances from an actor i to any other actor is the closeness of the actor i and then the closeness centrality of i is defined as its inverse closeness: cc(i) = ⌊∑ j d(j, i) ⌋−1 (4) the fewer links are needed to connect i to any other actor, the higher its cc is. if an actor has information that is regarded as important by the other actors, then one can expect this actor to have a high closeness centrality [2]. thus, cc is used as an indicator of the information importance. the information control the index betweenness centrality (cb) quantifies the information control exerted by an actor i and is defined as the sum of the ratios of shortest paths between other actors that the actor i sits on: cb(i) = ∑ |pi(i, j)| |p(i.j)| (5) where p(i,j) and pi(i,j) are the sets of all shortest paths between i and j, and those shortest paths passing through i, respectively. thus, an actor with a high percentage of cb plays the role of the go-between for many other actors in term of shortest paths and, in this way, it can control the distribution of information within the network [20] [21]. it should be clarified that cb shows who is the most crucial "postman" of information in a network, while cc shows who is the most important "sender" who can thereby impose an opinion and function as decision-maker. 4 results and discussion 4.1 meta-data: trust formula 3 defines the position of each actor in comparison to the others. a public forest actor (actor 3) and two public environmental actors (actors 5 and 8) take the top positions in the scale of the trust status (t) (fig. 3). despite the dominant hypothesis that forest policy is outside-controlled (namely by other sectors that may be commercially more important like tourism, water policy etc) [21], in this case a powerful forest actor seems to play a leading role. in figure 3, the trust status is measured on the vertical dimension in %. the horizontal dimension has no meaning. the top three values are: 212 n.d. hasanagas t3 = 8.80%, t5 = 8.35%, t8 = 7.80% the nature conservation agencies have more intensive presence (actors 5 and 8) while the most powerful actor is from the forestry sector (actor 3). these three actors belong to the public and not to the private sector. figure 3: hierarchy of trust status 4.2 meta-data: communication general information importance. the public conservation actor 5, the public exploitation actor 3 and the public conservation actor 8 possess to all appearances the most crucial general information resources in the network (fig.4): cc(5) = 5.50%, cc(3) = 5.24%, cc(8) = 5.00% the closer the actor is to the centre, the higher the closeness centrality of the actor; which means a higher position in the general information hierarchy. the lines surrounding the centre represent different and lower values of closeness centrality, while moving further away from the centre of the circle. the order of the actors around a circle (polar coordinates) has no sociological meaning; the circular order has been carried out automatically by visone software so as to minimize over-crossings. a public agency for nature conservation is regarded as most important in the network (actor 5). a public agency for forest management (actor 3) lies quite close to actor 5. third comes actor 8, which is again a public agency of nature conservation. all other public actors (such as 7, 11, 13 etc) as well as all private actors (environmental ngos such as 26, 25 etc and land owners or forest associations such as 10, 16, 22, 24) lie quite far from the centre. thus, this network is dominated by a few actors of the public sector, while the private sector does not play leading digital perception functionality for institutional design: the forest policy issue of "loch lomond & the trossachs national park" of scotland as a case study 213 role. it is also noticeable that the reputation of the involved scientific institution (actor 23) is very low. the actor which achieves to impose its information as most "important" is not the most powerful one. in this case, the most "important" actor is a conservation agency (actor 5). it seems that the powerful forest actor (actor 3) shares the general information importance with the conservation actors 5 and 8. the conservation actor 5 has achieved a higher influence in general information. this may occur because nature conservation is of interest for a larger part of the public and policy sectors than a forest actor, and this actor has generally a good reputation. figure 4: closeness centrality in general information general information control. figure 5 shows that actor 8 exerts the strongest control on general information distribution. in figure 5, the cb is measured in % and decreases from the centre to the periphery, while the circular order was carried out again by visone software so as to minimize over-crossings and has no sociological meaning. actor 5 is second, and third comes actor 3: cb(8) = 42.24%, cb(5) = 17.04%, cb(3) = 14.15% a practical meaning of cb is that if actor 8 was to leave the network, most information channels would be destroyed and most actors would experience a noticeable lack of information. although actor 5 is considered to be the most "important" one (s. above cc), the largest part (42.24%) of information channels is sustained by actor 8. in other words, the most important "sender" of politico-administrative information is supposed to be actor 5, but the most crucial "postman" of this information is apparently actor 8. without actor 8, actor 5 would lose its influence because the distribution of its information would be hindered. thus, someone who is aware of this network structure is able to differentiate the "sender" from the "postman" and can try to influence the first one in order to achieve a substantial change in the policy content or in the communication of the network. 214 n.d. hasanagas however, although the same three powerful actors hold the top three positions in the general information control, there are noticeable differences between them; it is observed that the most powerful exploitation actor 3, is the weakest of the three in general information control. simultaneously, the conservation actor 8, which occupies the last rank among the three actors in power and general information "importance", exerts the strongest control on the distribution of general information. this difference between power, information importance and information control is the clearest evidence that information is not a resource for developing power. although the most powerful actor is an exploitation actor (actor 3), it has left the communication functions ("importance" and control) to two conservation actors, 8 and 5. although actor 5 has gained a stronger reputation ("importance" in general argumentation), actor 8 seems to have been involved in more communication channels and sub-networks. the same three public actors control the general communication of the network while the rest actors lie at the periphery of the network once again. the scientific institution (actor 23) plays again a very weak role in the control of general communication. figure 5: betweenness centrality in general information 4.3 meta-information: "objectivity" scientific information importance. assuming that scientific information has been distinguished from political and social norms in our empirical research, the importance of the "objective" expertise can be measured in this network. as already shown (fig.1), only 34 out of 98 information links (34.6%) consist of scientific information, while these 34 links are only 18.4% of the total interactions (184 links). in other words, science plays a rather weak role in the network. thus, one can achieve only a weak influence on the policy outcome using "brilliant" expertise. indeed, the public actors 8, 5, 3 monopolize once again the index cc (importance). although actor 8 possesses the lowest trust among the top three actors (fig. 2), it has the highest cc in scientific information (fig.6): digital perception functionality for institutional design: the forest policy issue of "loch lomond & the trossachs national park" of scotland as a case study 215 cc(8) = 21.56%, cc(3) = 18.69%, cc(5) = 17.52% a different ranking between the top three actors is observed. actor 8 is considered to be the least "important" of these three in general information, but it is regarded as the most "important" one by the few science-interested actors. this phenomenon shows that science and general communication (politicoadministrative information) are two quite autonomous worlds with different hierarchies and priorities (what or who is "important" in the one world is not necessarily equally "important" in the other). scientific reputation does not necessarily depend on the formal identity of an actor: the scientific actor 23 does not appear at all in the scientific communication network. figure 6: closeness centrality in scientific information control of scientific information. in our study, the control of scientific information seems to be more relevant to the status of power. actor 3 is the most trustworthy and simultaneously has the highest cb, while actor 8 does not even appear among the top three (fig. 7): cb(3) = 45.74%, cb(7) = 23.26%, cb(5) = 16.28% the forest actor (actor 3) seems to be strongly involved in scientific sub-networks and to control the scientific communication with its forest expertise even in such a cross-sectoral issue like "loch lomond & the trossachs national park". this constitutes evidence that despite its multidisciplinarity [16], forest science can be self-regulated and can offer a sound basis for political negotiation and management even in cross-sectoral issues. although actor 8 was considered to be most "important" in scientific information, if it leaves the network, it will influence the scientific communication between the actors only little; the other actors have alternative access 216 n.d. hasanagas to its "important" information; this practically means that an "important" (crucial) source is not necessarily irreplaceable. actors 3, 7 and 5 are public actors. thus, the control of scientific communication is still dominated by the state while the private sector plays quite a peripheral role. the scientific institution (actor 23) does not appear at all. figure 7: betweenness centrality in scientific information 5 conclusions using software for social network analysis, the politico-administrative reality can be perceived as a system of hierarchies (formal or informal ones). the exact perception output depends on the engineering design of the software, namely of the algorithms included in the program. each algorithm applied to an appropriate type of relation (e.g. trust or information) produce a different output-aspect of the politico-administrative reality. it was observed that the actors 3, 5 and 8 are the top three in trust status. the same ones occupy the first three places in almost all of the four centrality indicators of information exchange (closeness and betweenness in general and scientific information) however, not in the same position. there is a marked differentiation among them: the most trustworthy, actor 3, neither possesses the most important general information (cc of general information) nor exercises the strongest control on its distribution (cb of general information). the least powerful, actor 8, exerts a remarkable control on general and scientific information (cb of general and scientific information). actor 8 may fix up its deficit in trust status through information control. the control of information is not necessarily based on the trust (practically meaning legitimacy or acceptance) but rather on the involvement that an actor has achieved in a network. self-evidently, a network-experienced actor is expected to have more opportunities of becoming digital perception functionality for institutional design: the forest policy issue of "loch lomond & the trossachs national park" of scotland as a case study 217 more strongly involved. the most powerful, actor 3, mainly exerts its control potential on the distribution of scientific information. however, it was also shown that scientific information plays a much weaker role in the network than the general information. therefore, a powerful actor is supposed to implement its power using general information rather than expertise. the following hypotheses are supported by these findings. specifically: 1. "important" information is not a source of trust (as a power component). 2. between actors of relatively high trust status, the most trusted one does not need to control information in order to strengthen its position. a less trusted actor may supplement the lack of power status with information control (which makes this actor irreplaceable). 3. possessing information resources which are regarded by others as "important" does not appear to be very strongly related to the trust position. a practical advice for forest and environment-related actors that derives from this analysis would be to set strengthening of their image as a first priority and not to acquire "important" expertise because it does not seem to be so relevant to their trust status and leading potential. an actor with a strong reputation may even be more influential in science than purely scientific institutions. scientific information does not necessarily play a decisive role in policy making. by gaining trust an actor becomes powerful in a network and thus can define which information is "important". finally, an "important" actor (sender with high closeness centrality) is not necessarily irreplaceable; it has only persuaded many others that it possesses crucial (very useful) information for them. as presented, this information can also be accessed through an actor with high betweenness centrality which acts as a "postman". however, a drastic change in the information content distributed in the network can be achieved only if the sender is influenced. an additional lesson which is of interest specifically for the forestry sector is that the crosssectorality is not always unfavorable for the forest actors, though it makes the power relations uncertain. as shown, a powerful public forest actor in scotland plays a leading role in a quite cross-sectoral issue like that of the "loch lomond & the trossachs national park". moreover, it manages to control the cross-sectoral scientific communication. the quantitative network analysis is an operational form of system theory in policy analysis that can produce meaningful data, if the contents of the exchange relations (giving and receiving of trust, information etc) are clearly operationalized. socio-informatics can open up new ways of describing politico-administrative structures and predicting power in multi-sectoral policies like rural development or forest-environmental policy. particularly, it can help in the description of informal hierarchies like these of trust, information "importance" and communication control which determine the policy output. it is possible that an actor may lose in a policy issue, because it is unaware of its own power (trust) status (trust) or because it confuses the information "importance" (closeness centrality) with the information control potential (betweenness centrality). the "loch lomond & the trossachs national park" is only an example of applying quantitative network analysis to environmental issues. by using larger sample of networks in future research, analyzing further exchange relations (like financial means, pressure etc) and employing complementary qualitative methods, like document analysis and observation, a more accurate and effective policy consulting can be achieved. acknowledgement i would like to thank all of the above anonymous actors for their participation in my research. i would also like to thank profs u. brandes, p.kenis, s. kühnel, dr j. raab and ms despinagreta ceco for the useful discussion and support. 218 n.d. hasanagas bibliography [1] m. baur, m. benkert, u. brandes, s. cornelsen, m. gaertler, b. koepf, j. lerner and d. wagner, visone software for visual social network analysis, proc. 9th int. symp. graph drawing, lecture notes in computer science, 2265, 463-464, springer, 2002. 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[23] s. alexiadis, ch. ladias and n. hasanagas. examining agriculture from a regional perspective: implications for the common agricultural policy, land use policy, 30 (1): 665-669, 2013. international journal of computers communications & control issn 1841-9836, 9(6):664-671, december, 2014. hadoop optimization for massive image processing: case study face detection i̇. demir, a. sayar l̇lginç demir advanced technologies research institute the scientific and technological research council of turkey ilginc.demir@tubitak.gov.tr ahmet sayar* computer engineerig department kocaeli university, turkey *corresponding author: ahmet.sayar@kocaeli.edu.tr abstract: face detection applications are widely used for searching, tagging and classifying people inside very large image databases. this type of applications requires processing of relatively small sized and large number of images. on the other hand, hadoop distributed file system (hdfs) is originally designed for storing and processing largesize files. huge number of small-size images causes slowdown in hdfs by increasing total initialization time of jobs, scheduling overhead of tasks and memory usage of the file system manager (namenode). the study in this paper presents two approaches to improve small image file processing performance of hdfs. these are (1) converting the images into single large-size file by merging and (2) combining many images for a single task without merging. we also introduce novel hadoop file formats and record generation methods (for reading image content) in order to develop these techniques. keywords: hadoop, mapreduce, cloud computing, face detection. 1 introduction in the last decade, multimedia usage has increased very quickly, especially as parallel to high usage rate of the internet. multimedia data, stored by flicker, youtube and social networking sites like facebook, has reached enormous size. today search engines facilitate searching of multimedia content on large data sets. so these servers has to manage storing and processing this much data. distributed systems are generally used to store and process large scale multimedia data in a parallel manner. distributed systems have to be scalable for both adding new nodes and for running different jobs simultaneously. images and videos are the largest set of these multimedia contents. so, image processing jobs are required to run in distributed systems to classify, search and tag the images. there are some distributed systems enabling large scale data storing and processing. hadoop distributed file system (hdfs) [1] is developed as an open-source project to manage storage and parallel processing of large scale data. hdfs parallel processing infrastructure is based on mapreduce [2], [3], [4] programming model that is introduced firstly by google file system (gfs) [5] in 2004. mapreduce is a framework for processing highly distributable problems across huge data sets using a large number of computers (nodes), collectively referred to as a cluster. copyright © 2006-2014 by ccc publications hadoop optimization for massive image processing: case study face detection 665 the work presented in this paper proposes two optimization techniques to hadoop framework for processing/handling massive number of small-sized images. this enables extreme parallel processing power of hadoop to be applied to contents of massive number of image files. however, there are two major problems in doing so. first, image files need to be modeled in an appropriate format as a complete entity, and second, they need to be adapted to the mappers in order to utilize parallelization of hadoop framework. as a solution to the first problem, an input file format called imagefileinputformat is developed. a new record generator class called imagefilerecordreader is also developed in order to read/fetch the content of image and create whole image pixel data as an input record to maptask [6]. regarding the second problem, we develop two approaches. first approach is based on combining multiple small size files into a single hadoop sequencefile. sequencefile is created by imagefilerecordreader class, which is developed as an extension to hadoop. second approach proposes a technique to combine many images as a single input to maptask without merging. this technique does not require special input file format as sequencefile, so that images can be used as in their original format. to achieve this, we introduce novel image input format and an image record reader, which is multiimagerecordreader class, to fetch the image content into image processing library. the architectural details are presented in section 3. these two approaches together with the naive approach are going to be applied on distributed face detection applications on images. the effectiveness of the proposed techniques is proven by the test cases and performance evaluations. remaining of this paper is organized as follows. section 2 presents related works. section 3 explains the proposed architecture. it covers extending hadoops application programming interfaces to effectively manipulate images. section 4 evaluates the performances of techniques on a sample scenario. section 5 gives the summary and conclusion. 2 related work hdfs is specialized in storing and processing large-size files. small-size files storage and processing ends up with performance decrease in hdfs. namenode is the file system manager in hdfs master node which registers file information as metadata. when using massive number of small-size files, the memory usage of namenode increases so leading master node to be unresponsive for file operation requests from client nodes [7]. moreover, number of tasks to process these files increases and hadoop jobtracker and tasktrackers, which initialize and execute tasks, have more tasks to schedule. in that way, total hdfs job execution performance decreases. for these reasons, storing and processing massive number of images require different techniques in hadoop. dong et al. propose two techniques for this problem given in [7] and [8]. in [7] they propose firstly, file merging and perfecting scheme for structurally related small files, and secondly, file grouping and perfecting for logically related small files. their approach is based on categorization of files based on their logical or structural properties. in [8], they propose another similar approach on the same problem. they introduce a two-level perfecting mechanism to improve the efficiency of accessing small files, and use power point files as a use case scenario. on the other hand, we tackle this problem by introducing a new input file format and a new record generator class in order to read the content of images and create whole image data as an input record to maptask. hadoop [3] is based on a parallel programming paradigm mapreduce employing a distributed file system for implementation on very large clusters of low performance processors aimed at text based searching. although it has been mainly utilized for textual data collections such as crawled web documents and web logs, later it has been adopted in various 666 i̇. demir, a. sayar types of applications. for example, it has been used for satellite data processing [4], bioinformatics applications [5], and machine learning applications [6]. there are a few example usages of hadoop in image processing. these are mostly implementations of content-based image searching. golpayegani and halem [9] adopted hadoop in satellite image processing. they propose a parallel text-based content searching. so, each image is annotated with some textual information after fetched by the satellites. similarly krishna et al. [10] proposes a hadoop file system for storage and mapreduce paradigm for processing images crawled from the web. the input to the whole system is a list of image urls and the contextual information aka the metadata of the image. searching is done over the metadata of the images. the only challenge in such application is defining key and value pairs and as well as defining map and reduce functions. the other issues are handled by hadoop core system. the architecture presented in [9] and [10] are called hybrid architectures, because they use text-based annotated data for searching and they access the result images by a url defined in content data. afterwards, they can process the image and redefine their metadata and save it with the current version. there is also another type of use cases of hadoop as in [11]. kocakulak and temizel [11] propose a map reduce solution using hadoop for ballistic image comparison. firearms leave microscopic markings on cartridge cases which are characteristic to each firearm. by comparing these marks, it is possible to decide whether these two cartridge cases are fired from the same firearm or not. the similarity scores returned by the algorithm included similarity score for each part of the cartridge case and their weighted sum. all correlation results were passed by map tasks to the partitioner as key/value (k, v) pair. the key and the value constituted the id of the cartridge case and the similarity score object respectively. the work presented in this paper is different from [9] and [10] because they use hybrid model to search images, i.e., images are annotated with some textual information enabling content-based searching. in our model, we use images in their pure formats, and instead of text search we utilize advanced face detection algorithm by comparing pixel information in the images. in addition, since we are doing face recognition, we cannot cut the images (mapping) into small parts as in [11]. if we cut the images we can degrade the success of the system. in other words, we might possibly cut the image at a place where a face might be located. so, we keep the images as a whole and propose a different approach as given in section 3. 3 architecture: hadoop optimization for massive image processing 3.1 distributed computing with hadoop hdfs is a scalable and reliable distributed file system consisting of many computer nodes. the node running namenode is the master node and nodes running datanode are worker nodes. datanodes manage local data storage and report feedbacks about the state of the locally stored data. hdfs has only one namenode but can have thousands of datanodes. hadoop uses worker nodes as both local storage units of file system and parallel processing nodes. hadoop runs jobs parallel by using mapreduce programming model. this model consists of two stages which are map and reduce whose input and outputs are records as <key, value> pairs. users create jobs by implementing map and reduce functions and by defining the hadoop job execution properties. after having defined, jobs are executed on worker nodes as maptask or reducetask. jobtracker is the main process of hadoop for controlling and scheduling tasks. jobtracker gives roles to the worker nodes as mapper or reducer task by initializing tasktrackhadoop optimization for massive image processing: case study face detection 667 ers in worker nodes. tasktracker runs the mapper or reducer task and reports the progress to jobtracker. hadoop converts the input files into inputsplits and each task processes one inputsplit. inputsplit size should be configured carefully, because inputsplits can be stored more than one block if inputsplit size is chosen to be larger than hdfs block size. in that way, distant data blocks need to be transferred over network to maptask node to create inputsplit. hadoop map function creates output that becomes the input of the reducer. so, the output format of the map function is same with the input format of the reduce function. all hadoop related file input formats derive the fileinputformat class of hadoop. this class holds the data about inputsplit. inputsplit does not become input directly for the map function of the mapper class. initially, inputsplits are converted into input records consisting of <key, value> pairs. for example, in order to process text files as inputsplit, recordreader class makes text lines of the file as an input record in <key, value> format where key is the line number and value is the textual data of each line. the content of the records can be changed by implementing another derived class from recordreader class. in distributed systems, the data to be processed is generally not located at the node that processes that data and this situation causes performance decrease in parallel processing. one of the ideas behind the development of hdfs is making the data processed in the same node where it is stored. this principle is called data locality which increases the parallel data processing speed in hadoop [6]. using massive number of small size files causes shortage of memory in master node due to increasing sizes of namenode?s metadata file. moreover, as the number of files increases, the number of tasks to process these files increases and the system ends up with workload increases in hadoop?s jobtracker and tasktrackers, which are responsible for initialization, execution and scheduling of the tasks. these might lead master node to be unresponsive for file operation requests from client nodes [7]. in brief, storing and processing massive number of images require different techniques in hadoop. we propose a solution to this problem of hadoop by an application of face detection. 3.2 interface design in order to apply face detection algorithm to each image, map function has to get the whole image contents as a single input record. hdfs creates splits from input files according to the configured split-size parameter. these inputsplits become the input to the maptasks. creating splits from files causes some files to be divided into more than one split, if their file size is larger than the split-size. moreover, a set of files can become one inputsplit if the total size of input files is smaller than the split size. in other words, some records may not be represented as the binary content of each file. this explains why new classes for input format and record reader have to be implemented to enable maptask to process each binary file as a whole. in this paper, imagefileinputformat class is developed by deriving the fileinputformat class of hadoop. imagefileinputformat creates filesplit from each image file. because, each image file is not splitted, binary image content is not corrupted. in addition, imagefilerecordreader class is developed to create image records from filesplits for map function by deriving hadoop?s recordreader class. in that way pixel data of images are easily fetched from hadoop input splits into image processing tasks (map tasks). after that point, any image processing algorithm can 668 i̇. demir, a. sayar be applied to image content. in our case, map function of the mapper class applies the face detection algorithm to image records. haar feature-based cascade classifier for object detection algorithm defined in opencv library is used for face detection [12]. java native interface (jni) is used to integrate opencv into interface. implementation of map function is presented below. "faceinfostring" is the variable that contains the information about detection properties such as image name and coordinates where faces are detected. c l a s s : mapper function : map map(text key ( f i l e n a m e ) , b y t e s w r i t a b l e v a l u e ( imgdata ) , o u t p u t c o l l e c t o r ) getimgbinarydata_from_value ; convertbinarydata_to_javaimage ; i n i t i a l i z e o p e n c v _ v i a _ j n i i n t e r f a c e ; runopencv_haarlikefacedetector ; f o r e a c h ( detectedface ) c r e a t e f a c e b u f f e r _ f a c e s i z e ; copyfacepixels_to_buffer ; c r e a t e _ f a c e i n f o s t r i n g ; c o l l e c t o u t p u t : s e t _ k e y _ f a c e i n f o s t r i n g ; set_value_faceimgbuffer ; end_foreach hadoop generates names of output files as strings with job identification numbers (e.g.: part 0000). after face detection, our image processing interface creates output files as detected face images. in order to identify these images easily, the output file names should contain detected image name and detected coordinate information (eg: sourceimagename(100,150).jpg). imagefileoutputformat class is developed to store output files as images with desired naming. reducetask is not used for face extraction because each maptask generates unique outputs to be stored in the hdfs. each task processes only one image, creates output and exits. this approach degrades the system performance seriously. the overhead comes from initialization times of huge number of tasks. in order to decrease the number of tasks, firstly, converting small-size files into single largesize file and process technique is implemented. sequencefile is a hadoop file type which is used for merging many small-size files [13]. sequencefile is the most common solution for small file problem in hdfs. many small files are packed as a single large-size file containing small-size files as indexed elements in <key, value> format. key is file index information and value is the file data. this conversion is done by writing a conversion job that gets small-files as input and sequencefile as output. although general performance is increased with sequencefile usage, input images do not preserve their image formats after merging. preprocessing is also required for each addition of new input image set. small files cannot be directly accessed in sequencefile, whole sequencefile has to be processed to obtain an image data as one element [14]. secondly, combining set of images as one inputsplit technique is implemented to optimize small-size image processing in hdfs. hadoop combinefileinputformat can combine multiple files and create inputsplits from this set of files. in addition to that, combinefileinputformat selects files which are in the same node to be combined as inputsplit. so, amount of data to be transferred from node to node decreases and general performance increases. combinefileinhadoop optimization for massive image processing: case study face detection 669 putformat is an abstract class that does not work with image files directly. we developed combineimageinputformat derived from combinefileinputformat [15] to create combinefilesplit as set of image. multiimagerecordreader class is developed to create records from combinefilesplit. this record reader uses imagefilerecordreader class to make each image content as single record to map function (see technique in fig.1). imagefileoutputformat is used to create output files from detected face images and stored into hdfs. figure 1: combine and process images technique 4 performance evaluations figure 2: performance comparison to test the system and evaluate the results, we have set up an hdfs cluster with 6 nodes. face detection jobs are run on a given set of image files on the cluster. hdfs cluster is set up with 6 nodes to run face detection jobs on image sets. each node has a hadoop framework installed on a virtual machine. although virtualization causes some performance loss in total execution efficiency, installation and management of hadoop become easier by cloning virtual machines. maptasks require large dynamic memory space when map-function for the image 670 i̇. demir, a. sayar processing executes. default java virtual machine (jvm) heap size is not enough for large size images. so, maximum jvm size for hadoop processes is increased to 600 mb. five different small size images are used as input files. distribution of the images according to file sizes are preserved in input folders. the images in the input folders went through the face detection job with the three types of approaches in hdfs. these are (1) one task per image brute-force approach (for comparison only), (2) sequencefile processing approach and (3) combine and process images approach (see performance results in fig.2). 5 conclusion the effectiveness of the proposed technique has been proven by the test cases and performance evaluations. as figure 2 shows, the proposed approach, combine images and then process, has become the most effective method in processing image files in hdfs. the sequencefile processing is slower than the combining technique due to the fact that combineimageinputformat enforces creation of inputsplits by combining images in the same node. additionally, in sequencefile approach, inputsplits to be processed in maptask does not always consist of datablocks in the same node. so some datablocks may be transferred from other storage node to maptask node. extra network transfer causes performance loss in total job execution. on the contrary, sequencefile approach has better performance against the task per image approach, because small number of input files decreases number of created tasks. in that way, job initialization and bookkeeping overheads of tasks are decreased. the slope of the job completion time curve for task per image approach has increased as number of input images increases. but slopes of curves of the other two techniques have slightly decreased by increasing number of input images, because task per image approach causes heavy burden on initialization and bookkeeping by increasing number of tasks. on the contrary, number of tasks is not increased as proportional to the number of images in sequencefile and combine images techniques. a small number of tasks has been able to process more images when the number of input images is increased. consequently, image processing like face detection on massive number of images can be achieved efficiently by using the proposed i/o formats and record generation techniques for reading image content into map tasks are discussed in this paper. we also explained the inner structure of map tasks to read image pixel data and process them. in the future, we plan to enhance and apply the proposed technique on face detection in video streaming data. bibliography [1] http://hadoop.apache.org/. [2] berlinska, j.; m. drozdowski. (2011); scheduling divisible mapreduce computations, journal of parallel and distributed computing, 71(3): 450-459. [3] dean, j.; s. ghemawat. (2010); mapreduce: a flexible data processing tool, communications of the acm, 53(1): 72-77. [4] dean, j.; s. ghemawat. (2008); mapreduce: simplified data processing on large clusters, communications of the acm, 51(1): 1-13. hadoop optimization for massive image processing: case study face detection 671 [5] ghemawat, s.; h. gobioff.; s. t. leung.(2003); the google file system, proceedings of the 19th acm symposium on operating system principles, ny, usa: acm, doi:10.1145/945445.945450. [6] white, t. (2009); the definitive guide. 2009: o’reilly media. [7] dong, b.; et al. (2012); an optimized approach for storing and accessing small files on cloud storage, journal of network and computer applications, 35(6): 1847-1862. [8] dong, b.; et al. (2010); a novel approach to improving the efficiency of storing and accessing small files on hadoop: a case study by powerpoint files, ieee international conference on services computing (scc), florida, usa: ieee, doi:10.1109/scc.2010.72. [9] golpayegani, n.; m. halem. (2009); cloud computing for satellite data processing on high end compute clusters, ieee international conference on cloud computing, bangalore, india: ieee, 88-92, doi:10.1109/cloud.2009.71. [10] krishna, m.; et al. (2010); implementation and performance evaluation of a hybrid distributed system for storing and processing images from the web, 2nd ieee international conference on cloud computing technology and science, indianapolis, usa: ieee, 762-767, doi:10.1109/cloudcom.2010.116. [11] kocakulak, h.; t. t. temizel. (2011); mapreduce: a hadoop solution for ballistic image analysis and recognition, international conference on high performance computing and simulation (hpcs), l̇stanbul, turkey, 836-842, doi:10.1109/hpcsim.2011.5999917. [12] http://opencv.org [13] http://wiki.apache.org/hadoop/sequencefile [14] liu, x.; et al. (2009), implementing webgis on hadoop: a case study of improving small file i/o performance on hdfs, ieee international conference on cluster computing and workshops, louisiana usa: ieee, 1-8, doi:10.1109/clustr.2009.5289196. [15] https://hadoop.apache.org/docs/current/api/org/apache/hadoop/mapred/lib/ combinefileinputformat.html ijcccv11n1.dvi international journal of computers communications & control issn 1841-9836, 11(1):67-76, february 2016. predictive input delay compensation with grey predictor for networked control system a. kuzu, s. bogosyan, m. gokasan ahmet kuzu* 1. istanbul technical university electric-electronics faculty control and automation engineering department turkey, 34469 maslak, istanbul kuzuah@itu.edu.tr 2. tubitak bilgem turkey, 41100 gebze, kocaeli *corresponding author: ahmet.kuzu@tubitak.gov.tr seta bogosyan ece department university of alaska fairbanks, fairbanks, usa sbogosyan@alaska.edu metin gokasan istanbul technical university electric-electronics faculty control and automation engineering department turkey, 34469 maslak, istanbul gokasan@itu.edu.tr abstract: the performance of networked control systems is affected strictly by time delay. most of the literature in the area handle the problem from a stability perspective. however, stability optimized algorithms alone are not sufficient to reduce synchronization problems caused by time delay between the action and reaction in geographically distant places, and the effect and performance of other system components should also be taken into account. in teleoperation applications the reference is often provided by a human, known as the operator, and due to the nature of the human system, references provided by the human operator are of a much lower bandwidth when compared to common control reference inputs. this paper focuses on the operator, and proposes an approach to predict the manipulator’s motion (created by the operator) ahead of time with an aim to reduce the time delay between the master and slave manipulator trajectories. to highlight the improvement offered by the developed approach, hereby called predictive input delay compensator (pidc), we compare the performance with the only other study in the literature that handles this problem using the taylor series approach. the performance of these two approaches is evaluated experimentally for the forward (control) path on a puma robot, manipulated by a human operator and it has been demonstrated that the efficient latency in the forward path is decreased by 100ms, on average, reducing the forward latency from 350ms to 250ms. keywords: communication network delay, delay regulator, grey predictor, taylor series, teleoperation. 1 introduction because of their huge potential to contribute to human life in many different ways, teleoperation and bilateral control systems have been attracting significant interest in control and copyright © 2006-2016 by ccc publications agora university 68 a. kuzu, s. bogosyan, m. gokasan communication communities. telesurgery using remote medical robots, exploration in hazardous environments using teleoperating robots could be good examples of such promising applications. the ultimate aim of a networked control system is the synchronization of the position and/or force between the master and slave in geographically distant motion control systems. currently, the most widely used network communication medium is the internet. however, the internet brings a variable delay between the transmitted channels. this makes control implementations over the internet challenging. the problem of variable can be eliminated via delay regulators [1] resulting in constant, and often relatively long delays. time delay compensation problem in bilateral control systems has been addressed by many different approaches. to name a few of the major methods, scattering variables [2], wave variables [3], smith-predictor (sp) [4], astrom’s modified sp [5], sliding-mode control (smc) [6] and , via the design of communication disturbance observer (cdob) [7]. moreover optimal control methods are used to find an optimal solution in terms of stability and performance constraints of the system [8]. the focus of this study will again be on the performance improvement of master-slave position tracking in networked robot control systems. while the ultimate goal of the networked control is full synchronization, network delay between master and slave is a major obstacle for the desired performance, and network delays happen randomly. in teleoperation applications the reference is often provided by a human, known as the operator. due to the nature of the human system, references provided by the human operator are of a much lower bandwidth when compared to common control reference inputs, and this can sometimes be problematic. all of the above mentioned studies discuss system stabilization under network delay [9], but do not address the operator delay, which also contributes to the delay between master and slave. meanwhile, the prediction of the input delay (in this case, created by the human operator) has the potential to reduce this network latency. however, to the authors’ best knowledge, the only study in the literature addressing this concept is [9], which uses a taylor series based analytical approach to handle this problem. taylor series simply performs the extrapolation of position based on velocity, meanwhile acceleration has significant effect on both velocity and position, and affects the prediction error negatively. in this study, we propose a method based on grey prediction for predictive input delay compensation, and demonstrate experimentally the advantages of the proposed method over the one using taylor series in predicting the operator’s motion. the grey prediction not only performs extrapolation, but unlike the taylor method fits a differential equation to the system dynamics. as a result, grey prediction is more effective in considering the transients, hence, the acceleration. the organization of the paper is as follows. section ii presents the benchmark taylor series based predictor. section iii presents grey prediction. section iv introduce the networking control system and the application of the both two predictor on it. section v introduce the experimental setup and results with conclusions in section vi. 2 taylor based pidc as benchmark system in this section, we will first discuss the benchmark pidc approach based on taylor series. subsystems of the human, such as skeleton, muscle, and neural systems behave similar to massspring-damper like structures, hence result in a high time constant for the operator. this also makes it acceptable to assume the human motion output to be continuously differentiable in time [9]. by accepting this assumption, future signal values can be predicted using simple geometric approaches. the prediction formula is predictive input delay compensation with grey predictor for networked control system 69 lim t→0 qmi(n + h) ≈ (h + 1)qmi(n) −hqmi(n−1) (1) here qmi(n + h) denotes h step further value , qmi(n) denotes current value and finally qmi(n−1) denotes previous value. we must mention that, there are just two error source which is neglected. one is high order terms and the second is discretization. 3 grey based pidc grey system theory [10] is developed for systems characterized by uncertain information. grey prediction is a scientific quantitive prediction method which is based on the theoretical treatment of the original data to determine the future output of the system [11]. basically, it can be defined as a local curve fitting extrapolation method, which requires four data sets only. in grey systems, gm(n,m) denotes a grey model. here n denotes the order of the difference equation, and m is the number of the variables. the commonly used grey model is of the gm(1,1) type. it represents the first order derivative, and one input variable is used for prediction purposes. the process of the grey prediction can be given as below [?]: step 1: collecting the original data sequence, and using generalized coordinate, q (0) mi = { q (0) mi(1),q (0) mi (2), . . . ,q (0) mi(n) } , n ≥ 4 (2) where q (0) mi(n) = qmi(n) (3) q (0) mi (n −1) = qmi(n−1) (4) . . . q (0) mi (1) = qmi(n−n + 1) (5) (6) here n denotes buffer size, q denotes generalized coordinate, m subscript denotes master side not slave side, i subscript denotes ith joint angle. for instance qm3 denotes master manipulators 3rd joint angle. moreover, q (0) mi denotes zero order ago of qmi. step 2: conducting an accumulated generation operation, ago, on the original data sequence in order to diminish the effect of data uncertainty; q (1) mi = { q (1) mi(1),q (1) mi (2), . . . ,q (1) mi(n) } , n ≥ 4 (7) where q (1) mi(k) = k ∑ i=1 q (0) mi(i),k = 1,2, . . . ,n (8) here the q (1) mi denotes first order ago of qmi. step 3: establishing the grey difference equation and then calculating its background values; q (0) mi(k) = −aizi 1(k) + bi (9) zi (1)(k) = 0.5 { q (1) mi(k) + q (1) mi(k −1) } (10) 70 a. kuzu, s. bogosyan, m. gokasan here the ai denotes developing coefficient in grey theory, bi denotes grey input. step 4: constructing data matrix b and data vector y; b =       −zi (1) (2) 1 −zi (1) (3) 1 ... ... −zi (1) (n) 1       (11) yi = [ q (0) mi (2) ,q (0) mi (3) , . . . ,q (0) mi (n) ]t (12) step 5: resolving the matrix; yi = biâi (13) âi = b t i b −1 i b t i yi = [ ai bi ] (14) step 6: deriving the solution to the grey difference equation; q (1) mi (k + 1) = [ q (0) mi (1) − bi ai ] e−aik + bi ai (15) step 7: conducting the inverse accumulated generation operation to obtain a prediction value q (0) mi (k + 1) = [ q (0) mi (1) − bi ai ] e−aik(1 −eai) (16) step 8: by substituting k with n + h −1 q (0) mi (n + h) = [ q (0) mi(1) − bi ai ] e−ai(n+h−2)(1 −eai) (17) step 9: by rearranging formula qmi (n + h) = [ qmi(n−n + 1) − bi ai ] e−ai(n+h−2)(1 −eai) (18) 4 a configuration for the networked control system here we will introduce our proposed networked control system configuration to explain the requirement and the performance measure of the predictive input delay compensator (pidc) algorithms. however first, we will introduce our standard configuration without the pidc. in the standard configuration, the operator forces the master manipulator to a desired posture, which in turn will dictate the slave motion. in order for the slave to track the master motion in the closest possible way, on the master side, an astrom smith predictor (asp) generates the control signal for the model plant. then the control signal generated on the master side, is transmitted to the slave side passing through a delay regulator send unit (drsm)" through the internet to delay regulator receive unit (drrs). on the slave side, a model tracking control (mtc) algorithm inputs the received control to an other model process (same as the model plant at master side) and forces the slave manipulator to track the trajectory of the model plant. predictive input delay compensation with grey predictor for networked control system 71 the angular displacement output of the mtc is fed back to the asp passing through a delay regulator send unit (drss) through the internet to delay regulator receive unit (drrm). [1] here τoq{1,2,3} denote the joint torques generated by the operator, τqm{1,2,3} denote the joint torques applied to the manipulator after the addition of gqm{1,2,3} gravitational compensation terms. τmq{1,2,3} denote the torque signals fed to drsm to be sent to slave side. τ̄mq{1,2,3} denote the delay regulated torque signals coming through the internet from the master to the slave. τcq{1,2,3} denote the joint torques generated by mtc, τqs{1,2,3} denote the joint torques applied to the manipulator after the addition of gqs{1,2,3} gravitational compensation terms. finally qs{1,2,3} denote the slave manipulator’s joint angle (actual) positions. in this study, the predictive input delay compensator (pidc) unit is added to this configuration between the operator and asp . with this addition, it is now possible to predict and compensate the delay caused by the operator, which will allow sending the master side information to the slave side with less delay. this reduced delay increases the synchronization between the master and slave trajectories. to reduce nonlinearities of the master and slave manipulators, namely to increase the compliance on the master side and compensate the gravity on the slave side, gravity compensation blocks gcm and gcs added, respectively. the overall master-slave architecture is given in fig. 1. however the function detail of each block is outside the scope of this study, and will not be discussed. 5 experimental system and results next, experiments have been performed to conduct a comparative performance evaluation for the taylor based and grey prediction approaches. the 6-dof puma560 industrial robot is used for experimentation. the manipulator is operated as a 3-dof system by the operator as can be seen in fig. 2. the well-known euler-lagrange based dynmaic model of the manipulator has the following general form: m(q).q̈ + v (q, q̇).q̇ + g(q) = γ (19) where, q : nx1 position vector m(q) : nxn inertia matrix of the manipulator v (q, q̇) : nx1 vector of centrifugal and coriolis terms g(q) : nx1 vector of gravity terms γ : nx1 vector of torques in this study the system will be taken on the consideration as an independent joint control system. this approach allows each manipulator joint to be controlled independently as a siso system, with the nonlinearities and couplings taken as a disturbance affecting each joint actuator. for our experimental system, since the human speed is considerably low, the main nonlinearities come from the gravity effect g(q). for that reason, in our experiments we apply system gravity compensation g̃(q) ≈ g(q), to cancel and/or reduce the gravity effect on the experimental system to a negligible level. the use of an independent joint control system approach, simplifies the system to be estimated to a one-degree-of-freedom process. 72 a. kuzu, s. bogosyan, m. gokasan figure 1: extended control scheme of networked control experiments are performed for three cases. then, q̃mi is also evaluated for three separated case. these cases and evaluation formulas are; case 1: no pidc q̃mi(n) = qmi(n) (20) case 2: taylor series based benchmark pidc q̃mi(n) = (h + 1)qmi(n) −hqmi(n−1) (21) predictive input delay compensation with grey predictor for networked control system 73 figure 2: experimental setup case 3: grey theory based proposed pidc q̃mi(n) = [ qmi (n −n + 1) − bi ai ] e−ai(n+h−1)(1 −eai) (22) the results of those experiments are seen in fig. 3. here, fig. 3a and fig. 3b depict the results for the first joint , fig. 3c and fig. 3d for the second joint, fig. 3e and fig. fig. 3f for the third joint. the performance of each joint is further demonstrated by also highlighting the zoomed version of the region marked in red. the figures in the right are zoomed versions for the highlighted sections in the diagrams on the left side. for each figure, the grey line represents the operator motion, which is taken as the reference motion to be predicted. the solid line depicts the grey predictor’s output, and finally dashed line demonstrates the output of the benchmark taylor series based predictor. in each figure, it is easily seen that when the angular velocity is low, both algorithms demonstrate similar performance. however when the acceleration increases, the performances show differences. only operation intervals where there is significant operator motion have been selected in the zooms of fig. 3b, fig. 3d and fig. 3f. in all three figures, we see that the grey prediction method achieves a faster prediction of 100ms on average when compared with the taylor based approach. on the other hand, the benchmark method demonstrates a prediction performance that varies between 10ms and 100ms, and demonstrates a poor performance in tracking transients as indicated by the high amplitude oscillation observed in fig. 3b starting at 5.7ms, and in fig. 3d starting at 2.8ms for the benchmark system output. hence, it can be said that the proposed grey based pidc demonstrates a faster and more accurate prediction performance then the taylor based pidc. 74 a. kuzu, s. bogosyan, m. gokasan 0 5 10 15 20 −20 −15 −10 −5 0 5 10 15 20 a n g u la r p o si tio n s( d e g ) time(s) reference grey prediction benchmark prediction (a) 5.5 6 6.5 5.2 5.4 5.6 5.8 6 6.2 6.4 6.6 6.8 7 7.2 a n g u la r p o si tio n s( d e g ) time(s) reference grey prediction benchmark prediction (b) 0 5 10 15 20 −10 0 10 20 30 40 50 a n g u la r p o si tio n s( d e g ) time(s) reference grey prediction benchmark prediction (c) 2.5 2.6 2.7 2.8 2.9 3 15.4 15.45 15.5 15.55 15.6 15.65 15.7 15.75 15.8 15.85 15.9 a n g u la r p o si tio n s( d e g ) time(s) reference grey prediction benchmark prediction (d) 0 5 10 15 20 −30 −20 −10 0 10 20 30 40 50 60 70 a n g u la r p o si tio n s( d e g ) time(s) reference grey prediction benchmark prediction (e) 2.8 3 3.2 3.4 3.6 3.8 13 13.2 13.4 13.6 13.8 14 14.2 14.4 a n g u la r p o si tio n s( d e g ) time(s) reference grey prediction benchmark prediction (f) figure 3: measured and predicted angular positions of each joint and zoomed versions a) joint 1, b) zoomed area of joint 1, c) joint 2, d) zoomed area of joint 2, e) joint 3, f) zoomed area of joint 3 predictive input delay compensation with grey predictor for networked control system 75 6 conclusion in this study, a grey system theory based pidc is developed and implemented for the prediction of the master manipulator motion in order to reduce the transmission latency between the master and slave. our philosophy is to reduce the latency in every way possible within our capability, considering network latency is unavoidable and random. experiments are conducted on a puma 560 manipulator which is just compensated for gravitational force to allow easy manipulation for the operator. the operator randomly manipulates the arm, while both the benchmark and proposed schemes predict the future trajectory of the robot motion created by the operator. the proposed approach outperforms the taylor series based benchmark approach, by predicting the joint motions approximately 100ms ahead on average, while the benchmark’s predictor performance varies between 2.8ms-100ms. based on these results, it can be concluded that grey prediction meets our motion prediction requirements better then the taylor series based approach, which is currently the only other study in the literature to address input delay compensation. acknowledgment this study has been supported by national science foundation-computer & information sciences and engineering (nsf-cise) , and nsf office of international science & engineering (nsf-oise). the authors declare that there is no conflict of interests regarding the publication of this article. bibliography [1] kuzu, a. et al (2011); 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(2006); grey information: theory and practical applications, springer. international journal of computers communications & control issn 1841-9836, 10(2):174-187, april, 2015. web service composition framework using petrinet and web service data cache in manet m. deivamani, s.r. murugaiyan, v. ravisankar, p. victer paul, r. baskaran, p. dhavachelvan m. deivamani*, r. baskaran department of computer science and engineering, anna university, chennai, india. m.deivamani@gmail.com, baaski@annauniv.edu *corresponding author:m.deivamani@gmail.com s.r. murugaiyan department of computer science and engineering, m.s.university, tamil nadu, india. murugaiyansr@gmail.com v. ravisankar department of computer science, bharathiar university, tamilnadu, india. v_ravisankar@yahoo.com p. victer paul department of information technology, sri manakula vinayagar engineering college, puducherry, india. victerpaul@gmail.com p. dhavachelvan department of computer science, pondicherry university, puducherry, india. dhavachelvan@gmail.com abstract: a mobile ad hoc network (manet) is characterized by multi-hop wireless links and frequent node mobility. every neighboring node in the manet is likely to have similar task and interests, several nodes might need to access the similar web service at different times. so, by caching the repeatedly accessed web service data within manet, it is possible to reduce the cost of accessing the same service details from the uddi and also from the external providers. composition of web services leads to a better alternative as, at times a candidate web service may not completely serve the need of the customer. an effective data cache mechanism (dcm) has been proposed in [6] using the distributed spanning tree (dst) as a communication structure in mobile network to improve scalability and lessen network overload. as an enhancement, ant colony optimization (aco) technique has been applied on dst to cope with the fragile nature of the manet and to improve the network fault tolerance [1]. in these perspectives, an efficient web service cache mechanism (wscm) can be modeled to improve the performance of the web service operations in manet. in this paper, a fine grained theoretical model has been formulated to assess the various performance factors such as cooperative cache and mobility handoff. in addition to these, the performance improvement of wscm using dst and aco optimized dst techniques in manet has been proved experimentally using precision and data reliability of the system using appropriate simulation. keywords: data cache, web service, distributed spanning tree, ant colony optimization, manet, omnet++ copyright © 2006-2015 by ccc publications web service composition framework using petrinet and web service data cache in manet 175 1 introduction a mobile ad-hoc network (manet) is an autonomous collection of mobile nodes that communicate over relatively bandwidth constrained wireless links. since the nodes are mobile, the network topology may change rapidly and unpredictably over time. the network is decentralized; where all network activity including discovering the topology and delivering messages must be executed by the nodes itself. i.e., routing functionality will be incorporated into mobile nodes [18]. the nodes in manet would probably work for tasks of similar goal (common interest). so, most of the nodes would try to access the same web service data at different time through their corresponding access point (ap). the access points may be located at the boundaries of the manet, where reaching them could be costly in terms of delay, power consumption, and bandwidth utilization. moreover, the access points would be connected to a highly overloaded resource (e.g., a satellite), or an external network that is susceptible to intrusion plays a vital role in response time, security and availability of the system. for such reasons, it is recommended to cache the frequently accessed service information within the nodes in the manet and the search application should check for the availability of the required service data within the network before requesting the external service registry [2]. caching of service refers to the technique of caching the service method invocation information (wsdl) from the registry or service response from the corresponding service providers. the cached responses of service methods can be utilized only if the future requests use similar arguments as that of cached responses. caching the wsdl information saves significant time and resources because subsequent service requests of similar methods will not be required to download wsdl files in a repetitive fashion. at this juncture, a service item refers to the cached wsdl information description or/and the cached web response of the corresponding service. so, the manet applications should check for the existence of the desired service item within the network before attempting to request the external service source [2, 6]. this scenario can reduce the overload of the system for accessing external source for same service and also avoid the possible intrusion threats. a set of proxy nodes are introduced in the manet to provide information about the mobile nodes in the network and the services invoked by them. the proxy nodes are configured with a domain ontology and petri net modelling. the domain ontology enhance the selection of appropriate web service (by using semantics), from the service registry or peer agent nodes. the petri net modelling aims to provide composite value added service to the service requester. the service data caching within the manet can be discussed in two methods based on the diversity of the cached service information. in most of the works, the decision to cache a service is done locally in the proxies [3, 14, 16, 17], that is, without taking into account the all the peers within the network. in such case, there may situation happens that multiple copies of the same data can be cached in the proxy nodes. this redundancy of same service data cache could reduce the possibility to cache different data that are also of interest, which affect the overall performance of the cache system. on other hand, nodes are made to decide the caching data co-operatively among the proxies [13, 15, 18], which can improve the cache diversity and also the overall data cache performance of the system. various opportunities and challenges, like load balancing and mobility, which arise on caching web data within mobile networks, are theoretically discussed in [12]. lan wang [3] proposed clustering in large-scale manet as a means of achieving scalability through a hierarchical approach in which every node in the cluster is one hop away from every other node, that is, each cluster is a diameter-1 graph. but static cached data item manager may easily become the traffic bottleneck and single point failure of the cluster [4]. hassan artail et al. [2] proposed the minimum distance packet forwarding technique for search applications 176 m. deivamani, s.r. murugaiyan, v. ravisankar, p. victer paul, r. baskaran, p. dhavachelvan within manet that are based on the concept of selecting the nearest node from the designated nodes. the cache techniques in the studied works endure problems such as large hop count, message density and single point failure because of not following some efficient communication structure within the manet. to cope with the problem stated, distributed spanning tree (dst) has been used as a communication structure in manet for effective data cache technique proposed in our previous work [5]. dst is a recent and formally proved communication structure in manet to lessen node isolation problem, to reduce the number of hops required to reach the nodes and makes the network scalable [7, 8, 10]. another important performance factor in manet is finding and maintaining routes since node mobility causes topology change which need to be observed for effective communication. in [1], ant colony optimization has been used to deal with the fragile nature of the manet which dynamically identifies the optimal path between the nodes in the dst on-demand. it is also justified that applying aco on dst, enhance the effective routing of message (at low cost) in the manet which in turn reduces the number of message hops required for communication to achieve excellent efficiency in dcm applications. though, effective wscm in manet using dst and aco techniques has been formally proposed in [1, 6], experimentation analyses of the work has been performed for very few performance factors such as hit ratio and message passes. thus, in this paper, it is intended to conduct an extensive analysis on several other critical performance factors such as cooperative cache model, mobility hand-off, precision and data reliability. 2 background information needed in this section, the discussion on innovative techniques proposed in [1, 6] which are necessary to understand the performance assessments performed in the following sections of the paper. 2.1 distributed spanning tree (dst) distributed spanning tree (dst) [10] is the interconnection formation we follow as in [5, 11, 21] which, improve the routing and reduce the number of message passes required for any communication in manet. dst systematizes manet into a hierarchy of groups of nodes. the dst is an overlay structure designed to be scalable [11]. it supports the growth from a small number of nodes to a large one. a comprehensive algorithm for formulation of dst in manet has been proposed and exemplified in [9]. consequently, the manet can be logically converted into dsts and each dst should have its root node, named as the head node (hn) and the possible leaf nodes (lns). every hn will hold the complete details regarding its lns and vice versa. these hns are to be generated dynamically and should hold the service cache details, which is to be accessed by their corresponding lns and indeed by other hns also. in addition to the cache details, domain ontology and petrinet formalism is included in the hns to deliver prominent service compositions. the dst formulated manet can be represented as gm in equation(1), gm =   dst1 = (hn1, ln11, ln12, .....ln1(j1−1)) dst2 = (hn2, ln21, ln22, .....ln2(j2−1)) . . . dsti = (hni, lni1, lni2, .....lni(ji−1))   (1) web service composition framework using petrinet and web service data cache in manet 177 where, • dstv is the distributed spanning tree and ‘i’ is the total number of dsts formed in the network and 0 < v ≤ i • hnv is the head node (hn) and ‘i’ is the total number of hns in the peer network equal to the number of dsts and 0 < v ≤ i • ln refers to the leaf node(s). in lnvz, refers to the corresponding hnv and 0 < z ≤ jv − 1, where ‘jv − 1’ is the total number of lns in the corresponding dst. ant colony optimization for dst ant colony optimization (aco) [19, 21] is one of the most recent techniques for approximate optimization. the inspiring source of aco algorithms are real ant colonies. more specifically, aco is inspired by the ants’ foraging behavior. by applying the aco over the formulated dst [1], we can obtain the optimal path in terms of reduced number of message passes among the nodes in the network. aco is also capable to reform a new optimal path in case of any problem with the current optimal path. in this paper the ant colony optimization algorithm has been modified and proposed for finding an optimal path in dst of the manet. by optimizing every dst and connection among all the other dsts through their hns, it can be argued that the entire network is optimized with aco technique for improved efficiency. the complexity of aco technique depends on the method it is implemented in the manet. in dst structure, computational complexity for aco technique can be calculated as, o(ndst ∗ nln) + o((ndst )2) (2) where, • ndst is the number of dsts or the number of hns formed in the network • nln is the number of ln under a hn (theoretically taken same number of lns under every hn) 2.2 web service data cache mechanism (wscm) with dst and aco techniques an efficient wscm system in manet with dst and aco techniques has been formulated in [1, 6] with necessary algorithms. the projected system has the capability to cope with fragile and dynamic topology changing manet environment and the system structure can be viewed as a four layered as shown in figure 1. manet network layer is a network level layer consists of wireless and highly dynamic topology network. dst formulation layer is simple and converts the graph structure mp2p network into a collection dsts. this dst structure provides the features that are necessary for a dynamic network like reduced size of routing table, minimizes routing overhead, easy network management, reduced message pass, load balancing and fault tolerance. this layer offers the dynamic node insertion into the network and exit from the network in both normal and abnormal manner. this layer also makes the system highly scalable. 178 m. deivamani, s.r. murugaiyan, v. ravisankar, p. victer paul, r. baskaran, p. dhavachelvan figure 1: the layered architecture of wscm in aco optimized dst manet aco optimization layer provides the system to manage with the dynamic nature of the manet. this layer works in simple, effective and on demand way which makes the system to operate on a fragile system with asymmetric links and constantly changing topology. web service data cache management and service composition is an application level layer with specified protocol for an effective web service information cache management system in manet. using the petrinet formalism, the web services are composed together to enhance the service quality. thus, using this four layer system structure, wscm application can be efficiently performed in the highly fragile manet environment using dst and aco techniques. 3 experimental analyses in this section, an extensive analysis of dcm system using dst and aco techniques in manet has been performed based on critical performance factors such as cooperative cache model, mobility and hand-off, availability, routing technique, cache replacement method, precision and data integrity of the system. 3.1 simulation setup a manet environment with 30 nodes and 70 service items has been simulated using omnet++ tool, which is an object-oriented modular discrete event network simulator. table 1 show the partial view of hns and its neighboring nodes, and hop distance between them. simulation parameters are followed as similar to [1]. in the simulated network, the hns formed are node05, node11, node17, node23 & node29 and other nodes act as ln to any one of these hns. for our simulated, equation (1) can be rewritten as,  dst1 dst2 dst3 dst4 dst5   =   06 03 02 05 17 _ _ 12 01 08 14 20 25 _ 18 04 07 19 10 22 27 24 14 13 16 23 28 _ 30 29 09 15 21 26 _   (3) web service composition framework using petrinet and web service data cache in manet 179 where, • the node number are to be preceded by the term node, for example the dst1 should be interpreted are dst1 = (node06, node03, ..., node17), in which the first node node06 is the hn and all other nodes are lns of dst1. to simulate the web service cache mechanism, we created 50 different service items and stored in the service registry which are accessed by the proxy nodes in the manet through the access point. when a node requires any service, it will send the request to its corresponding hn (proxy node). the hns upon receiving the requests, extracts the keywords and match with the cache entries to identify the service has already executed from that node with in a cut-off time. the cut-off time is a time span a service information in a cache will remain valid, after which the wsdl information will be deleted from the cache (leading to cache miss for the next attempt). this deletion ensures to devour the up to date information in the cache. the hns advertises its presents in the network for the lns to identify and request for a service. s.no hn nearest hns and distance in hop(s) hn 1 hop distance from hn hn 2 hop distance from hn 1 node06 node18 2 node30 4 2 node12 node06 4 node17 2 3 node18 node30 3 node12 2 4 node24 node17 4 node18 3 5 node30 node12 2 node06 5 table 1: hns formulated and its hop distance from the nearest two hns in the simulated manet during the very first time access of any service, the ln saves the copy of the accessed service item and intimates its corresponding hn to save the type of the service item and the details of ln which holds it. when any node request for the web service of similar nature, the requesting node is served with the service item by the ln which holds the cache, identified through its hn. a keyword extracted from the request may or may not match with the cache entries. if it matches, the wsdl information is provided to the corresponding requesting ln using standard message passing. if the keyword does not match with the cache table, the domain ontology is used at the first level to find related services (rather than exact keyword matching) from the cache. at the next stage, if related services also give a cache miss, the peer hns or the external service registry is contacted for availing the service configurations. upon reaching all the hns, it can be found that the service does not exist within the network and obtained from the external uddi/service providers. a service request initiated by a ln can be fulfilled by an atomic service or a composite service. at times, atomic services may not be available for a given service request, where several compatible services are identified, composed and executed. to identify the compatible services for composing a value added service, petrinet formalism is used in the mns. petri net is used to mainly identify the reachability of the compatible web services within the domain. definition 1: the best and worst case analysis for accessing the cached service item based on the message hops can be modeled as follows. best case: the total number of message hops required to access the cached data item is minimum, when service item is being cached in any ln which is under the hn of the requesting node. 180 m. deivamani, s.r. murugaiyan, v. ravisankar, p. victer paul, r. baskaran, p. dhavachelvan this can be expressed as, n(accessmessagehops) + n(dictionarysearch) ≤ 4n (4) where, • n(accessmessagehops) is the total number of message hops required to access the cached service item. • n(dictionarysearch) is the total time to search the semantic of the keyword in the search. • n is the total number of message hops between hn and its ln (consider equal for every lns). worst case: the total number of message hops required to access the cached service item is maximum, when the hn of the node that holds the requested service item is at distance ‘k’ from the hn of requester node, where ‘k’ is the total number of hns in the manet. this can be expressed as, n(accessmessagehops) + n(dictionarysearch) ≥ (kxm) + 4n (5) where, • n(accessmessagehops) is the total number of message passes required to access the cached item • n(dictionarysearch) is the total time to search the semantic of the keyword in the search • m is the total number of message pass between two hns (consider equal for between every hns). • n is the total number of message pass between hn and its ln (consider equal for every lns). an extensive analyses to model the various performance factors such as cooperative web service cache model, mobility and hand-off, availability, routing technique, cache replacement method, precision and consistency for the wscm in dst and aco optimized dst manet have been performed in the following sections. 3.2 cooperative cache model to improve the service information accessibility, mobile nodes should cache different service item that of their neighbor nodes [1]. every ln should cooperatively cache the different service to avoid the replicated caching of same service within the network. caching same service on different lns may reduce the access delay but on considering size of the cache memory in lns it will block caching more frequently accessing services (different). definition 2: let lni is the mobile node which cache the service item. to show that each ln caches different service, ln1(sid) ∩ ln2(sid) ∩ ln3(sid).... ∩ lnk(sid) = ϕ (6) lnm(sid1) ∩ lnm(sid2) ∩ lnm(sid3).... ∩ lnm(sidn) = ϕ (7) web service composition framework using petrinet and web service data cache in manet 181 where, • k be the number of ln which cache data item in manet. • n be the number of service items cached in mth ln and 0 < m < k. • sid is web service index entry in ln_table of a ln. eq. 6 refers that no same service is cached by the different lns and eq. 7 refers to no same service is within a ln. thus, there is no repetition in the web service index entry in ln_table of every ln. so, every service is cached only once in the manet. figure 2: comparison on precision performance of three different schemes figure 3: comparison on service request received and served using three different schemes 3.3 mobility and hand-off model due to the fragile nature, mobile nodes are set to move free in manet and node tracking task added complex. this tracking task can be accomplished in two scenarios, exit or remove from the manet [5] and switching among the dsts within the manet. scenario 1: a node can exit/remove from the network dynamically in the following fashion: the node that wants to get remove from the network should send an inform message to its hn, so that the corresponding hn removes the node details and its cached web service identity from its lns list, which obviously removes from the spanning tree. 182 m. deivamani, s.r. murugaiyan, v. ravisankar, p. victer paul, r. baskaran, p. dhavachelvan figure 4: comparison on % of utilization of consistent service item performance of three different schemes scenario 2: switching of node from one dst to another is configured automatically by the hn by passing some messages with the node. any ln can voluntarily hand-off itself from any hn take hn1, if the condition satisfies that number of hops between ln and hn1 exceeds the number of lns under hn2. and the ln can join to hn2 in which number of hops between ln and hn2 will be less than number of lns under hn2. this hand-off should be intimated to both hn1 and hn2. it is must that every ln should be under any hn. if a request arises in the mean time between handover, the hn1 will transmit a “binding warning” and intimate ln is now under hn2. s.no ln manet manet with dst scheme manet with aco optimized dst scheme no. of data items cached no.of data item request served no. of request received precision (%) no. of data items cached no. of data item request served no. of request received precision (%) no. of data items cached no. of data item request served no. of request received precision (%) 1 node06 11 31 95 32.62 11 60 121 49.68 15 94 124 75.63 2 node12 13 53 127 41.77 20 70 145 47.98 11 91 148 61.80 3 node18 10 28 112 24.81 15 71 130 54.52 15 89 138 64.61 4 node24 17 29 94 30.62 12 62 109 57.13 14 74 115 64.39 5 node30 10 20 79 24.78 12 55 94 58.94 15 64 100 64.20 aggregate performance 60 160 507 30.9 70 318 599 53.7 70 413 625 66.1 table 2: comparison on precision for hns involved in serving the nodes in all three different scenarios definition 3: let vi be a mobile node in manet which is under hni and hnj be another hn in the manet. then, vi can decide to hand-off from hni and to join under hnj, if it satisfies the rule of eq. 9. if, hni, hnj ∈ {hn1, hn2, hn3, ....hnn} (8) then, nhop(vi, hni) > n(lni) & &nhop(vi, hnj) > n(lni) (9) where, web service composition framework using petrinet and web service data cache in manet 183 • n be the total number of hns in manet. • nhops(v, hn) be the number of hops required to reach node v from hn. • n(lni) be the total number of lns under the head node hni. figure 5. comparison on data item cached and served using three different schemes. thus, any ln whose distance from existing hn is lesser than another hn can perform handoff from existing hn and join with new one. the mobility and handoff model works under the assumption that the hns will always be in access range within the manet. this assumption is made as lns under the mn should not be disconnected from the spanning tree and thereby to the network. 3.4 precision the precision refers to the ratio of total number of service request received to the total number of requested service found in the manet. the different performance data observed from the simulation in first 100 seconds are tabulated in table 2; which contain hns created, no. of services cached by lns of each hn and no. of service item requests served by each hn, either at an atomic service or a composition. composition of web services is carried using the petri net modelling by the following phases – identifying the similar web services using domain ontology, classification as compatible and non-compatible, execution of compatible web services pertaining to the goal of the service request. to explain in clear manner, consider the first entry in table 2, the mobile node, node06 is an hn and the total number of data items cached in its lns, the total number of data item request served using the cache and the total number of request received for service item are 11, 31 and 95 respectively. thus precision for hn node06 is 32.62%. table 2 shows that precision percentage of hns in the manet can be improved using dst and aco techniques. the maximum precision value recorded for manet, manet with dst scheme and manet with aco optimized dst scheme is 41.7%, 58.94 and 75.63% respectively which is illustrated in figure 2. this is because the dst and aco techniques reduce the number of message hops required for any operation, such as cache request and cache reply, by discovering an optimal path between the nodes in the manet. so operations performed faster and more requests are served from nodes local cache within the stipulated time period. figure 3 illustrates the comparison on service request received and served using three different schemes. from figures 3 and 4, it can be observed that in aco optimized dst scheme outperforms other two schemes. thus, precision performance of the system can be improved from dst scheme from 30.9% to 53.7% and which is further improved to 66.1% using aco optimization technique. 184 m. deivamani, s.r. murugaiyan, v. ravisankar, p. victer paul, r. baskaran, p. dhavachelvan s.no ln manet manet with dst scheme manet with aco optimized dst scheme no. of service items cached no.of service item request served no. of service items deleted (expired) before use utilization % of consistent service item no.of service items cached no. of service item request served no.of service items deleted (expired) before use utilization % of consistent service item no. of service items cached no. of service item request served no.of service items deleted (expired) before use utilization % of consistent service item 1 node02 4 15 2 50.0 7 26 2 71.4 8 59 0 100.0 2 node08 6 28 3 50.0 8 28 1 87.5 7 47 2 71.4 3 node09 7 10 3 57.1 5 24 3 40.0 6 27 1 83.3 4 node13 5 16 2 60.0 6 32 3 50.0 7 42 0 100.0 5 node18 5 17 2 60.0 7 32 2 71.4 6 33 1 83.3 6 node20 8 26 4 50.9 5 26 3 40.0 7 40 0 100.0 7 node21 7 7 4 42.9 8 23 0 100.0 8 23 0 100.0 8 node24 5 12 3 40.0 7 33 3 57.1 7 27 2 71.4 9 node26 7 6 4 42.9 9 14 2 77.8 6 23 1 83.3 10 node27 6 23 3 50.0 8 70 2 75.0 8 92 1 87.5 aggregate performance 60 160 30 50.3 70 318 21 67.0 70 413 8 88.0 table 3: comparison on % of utilization of consistent service item by lns in all three different scenarios 3.5 service reliability service reliability or consistency refers to the correctness of the cached service at the time of access within the manet. though consistency technique followed is time-based it is not required that every mobile should be synchronized in clock. every hn which stores the service item type also store metadata about the service item si which contain the time at which si is being cached. this information is used to check the service item validity. ‘t ’ is the constant time value in the manet which can be varied based on service item being updated in outside network. the factor used to measure the consistency of the system is percentage of consistent service item usage. table 3 shows the comparison on % of utilization of consistent service item by lns in all three different scenarios for first 100 seconds of simulation run. from these statistics, it can be observed that utilization percentage of consistent service item is much improved dst manet and aco optimized dst manet schemes. the same is illustrated in the figure 4 and 5, which confirms the improved performance of the dst and aco optimized dst schemes over manet scheme. table 4 shows the utilization increases more using petrinets and deliver compositions. as per the assumption, the mobile agents tender similar service request, a service composition held in the cache will be a suitable candidate for most of the request, rather being a single atomic service. without loss of generality, we can say that the service served ratio to the atomic service with respect to the composite service will be less. and it is trivial that if an atomic service can satisfy a customer request, a composite service (which includes compatible atomic services) will also satisfy the request in a more efficiently. 3.6 discussion to summarize, the dst structure offers the capabilities that are necessary for a dynamic network like reduced size of routing table, minimizes routing overhead, easy network management, reduced message hops, load balancing and fault tolerance. aco optimized dst structure provides the system to manage with the highly fragile nature of the manet and to find the optimal route between hns and hn & its lns on demand fashion. this layered approach works in simple, effective and on demand way which makes the system to operate on a fragile environment with asymmetric links and constantly changing topology. thus, the performance of service cache technique for web service composition in manet has been analysed for cooperative cache, mobility hand-off, precision and data reliability method. an extensive experimentaweb service composition framework using petrinet and web service data cache in manet 185 s.no ln manet with dst and aco schemes manet with dst , aco and petri net no. of service items cached no.of service item request served no. of service items deleted (expired) before use utilization % of consistent service item no.of service items cached no. of service item request served no.of service items deleted (expired) before use utilization % of consistent service item 1 node02 8 59 0 100.0 8 63 0 100.0 2 node08 7 47 2 71.4 7 58 1 85.7 3 node09 6 27 1 83.3 6 41 0 100.0 4 node13 7 42 0 100.0 7 48 1 85.7 5 node18 6 33 1 83.3 6 33 0 100.0 6 node20 7 40 0 100.0 7 42 0 100.0 7 node21 8 23 0 100.0 8 29 0 100.0 8 node24 7 27 2 71.4 7 35 2 71.4 9 node26 6 23 1 83.3 6 27 1 83.3 10 node27 8 92 1 87.5 8 95 0 100.0 aggregate performance 70 413 8 88.0 70 471 5 92.6 table 4: comparison on % of utilization of consistent service item vs a composition by lns tion has been performed on precision and service reliability of the system which confirms that the aco optimized dst scheme improves the efficiency of service cache technique in manet environment. 4 conclusion the work presented in this paper described modeling and assessing the various performance factors for the service cache mechanism for composing web services using dst and aco techniques in manet proposed in our previous works. a comprehensive theoretical model has been developed for the performance factors such cooperative cache, mobility hand-off, precision and data reliability methods. in addition to these, the performance improvement of web service cache mechanism (wscm) has been proved experimentally for improved based on precision and service integrity factors using three different schemes such as manet, dst manet and aco optimized dst manet. the simulation results shows that the precision performance of the wscm system is improved using the dst scheme from 30.6% to 53.6% and which is further improved to 68.1% using aco optimization scheme. and the service reliability performance is enhanced from 50.3% to 67.0% and to 88.0% using dst and aco optimization schemes respectively. acknowledgements this work is a part of the research project sponsored under the major project scheme, ugc, india, reference no: f. 41-639/july 2012 (sr). the authors would like to express their thanks for the financial support offered by the sponsored agency. bibliography [1] r. 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[20] a. colorni, m. dorigo, v. maniezzo (1991); distributed optimization by ant colonies, proc. of ecal91 european conference on artificial life, elsevier publishing, amsterdam, the netherlands, 134-142. [21] p. victer paul, t. vengattaraman, p. dhavachelvan and r. baskaran (2010); improved data cache scheme using distributed spanning tree in mobile adhoc network, the international journal of computer science and communication (ijcsc), 1(2): 329-332. gaoijcccv12n6.pdf international journal of computers communications & control issn 1841-9836, 12(6), 813-823, december 2017. a rapid recognition of impassable terrain for mobile robots with low cost range finder based on hypotheses testing theory y. gao, x.y. wu, y. liu, j. m. li, j. h. liu yang gao* school of automobile, chang’an university, xi’an, shaanxi province, 710061,china *corresponding author: nchygy@chd.edu.cn xueyi wu, yu liu, jian ming li, jia hao liu school of automobile, chang’an university, xi’an, shaanxi province, 710061,china abstract: the recognition of impassable terrain plays a vital role in the motion planning of a mobile robot, which generally relies on expensive sensors such as stereo vision cameras. this paper proposes a rapid impassable terrain recognition algorithm based on hypotheses testing theory using low-cost range finders with different diffusion angles. in this algorithm, a slope estimation model using two range finders mounted on different heights is first established, where the influence of the diffusion angle of the range sensor is considered. to deal with inaccurate measuring from low cost range finders, the hypothesis testing theory is then applied to judge whether there is an impassible terrain approaching, where the historical slope estimation results are treated as a sample set of the same slope, and the judgement of impassible terrain is then made based on the sampling set rather than the concurrent slope estimation. so the robot is only required to count the number of slope estimation that support the determination of a terrain as being impassible, and the judgement is confirmed only when that number is larger than a precisely designed threshold value. then the stable recognition for impassable terrain would be acquired while the risk of wrong judgement is limited. the experiments’ results indicate that this algorithm can provide a reliable recognition of impassable terrain using lower cost range finders with different diffusion angles with minimal computation. keywords: impassable terrain, range finder, slope, hypothesis testing. 1 introduction recognition of impassable terrain, which generally includes rapid vertical variations, is a critical and fundamental issue for mobile robots. over the previous few decades, techniques and theories for terrain recognition of intelligent automatic vehicles or robots have achieved many successes. [8] presented a three-dimensional (3-d) vision technique for incrementally building an accurate 3-d representation of rugged terrain using multiple sensors. this technique demonstrated the desired accuracy for self localization tasks but is too complex for rapid recognition of impassable terrain . [17] proposed a method for the retrieval of 3-d surface models of the earth using optical and radar imaging sensors. however, this requires 3-d sensor and is difficult to fulfill the efficiency requirement. [2] proposed a method for building a 3-d world model for a mobile robot through sensory data derived from outdoor images using a camera. [21] proposed a method for terrain recognition using a 3-d reconstruction technique that requires 3-d sensors and shows poor efficiency. [11] proposed an overview of stereo vision, stereo image processing copyright © cc by-nc 814 y. gao, x.y. wu, y. liu, j. m. li, j. h. liu as well as a method of obstacle avoidance and navigation based on stereo vision for a mobile robot. [4] proposed a novel navigation framework for autonomous vehicles where a stereo camera is used for both slope estimation and obstacle detection. [12] proposed an approach for obstacle detection in a greenhouse environment, where the depth data provided by the kinect 3d sensor are used for slope estimation. [16] proposed a novel method for analyzing a mobile robot’s ability to travers rough terrain where the roughness and the slope are calculated using an elevation map built by a laser range finder. [9] proposed a hierarchical, decentralized and networked controled system for mobile robots where the slope of terrain is detected using a laser range finder. this approach shows good robustness for various environments but needs much more computational resources. [15] proposed a terrain recognizer where a database consisting of five terrain classes is generated, then depth images are enhanced using confidence map based filtering technology. this approach classifies the terrain into five types, but expensive depth sensing and much computational resources are required. [10] proposed a survey of the distribution of slope angles in the alishan area using airborne lidar and aerial digital camera. [20] proposed a novel approach for obstacle detection using optical flow without recovering range information. this approach shows good efficiency but is not reliable without recovering range information. [18] proposed a collision-free area detection algorithm for an autonomous mobile robot, using a depth camera. however, depth cameras are expensive and may lead to much computational capacity, while the accuracy would gradually decrease with increasing distance. [7] proposed a multisensory data-fusion system for driving an autonomous earthwork vehicle operating in a sanitary landfill, using ultrasonic sensors, a laser range finder, and several charge-coupled device cameras. [13] proposed a method for recognition of uneven terrain using 3d laser scanner. [23] proposed a novel but complex method for real-time obstacle detection and recognition, where several key obstacle characteristics are identified based on image information and geometric information using support vector machine (svm). [14] proposed a novel algorithm to qualitatively categorize the terrain type in real-time, using high fidelity sensors. [11] proposed an application of an image registration method for a mobile manipulator, where the range image may be interpreted for object recognition using ultrasonic range finders. [5] proposed a real time terrain recognition method for a mobile robot moving on uneven terrain to capture a terrain depth image using a rgb-d sensor, which may result in poor performance as the environment changes. [1] proposed a probabilistic framework in which the visual information and the mechanical supervision interact to learn the terrain types. it may be concluded that most existing research rely on sensors generally including: 2d sensors such as vision sensors, range finders, laser range finders, ultrasonic range finders, etc; 3d sensors such as 3d vision sensors, 3d laser range finders, etc. the 3d sensor is generally expensive but provides abundant information for the task. as to the 2d sensors, the laser range finder can provide precise angle measuring but is much more expensive. the ultrasonic range finder, on the other hand, is widely used due to its lower cost and can acquire distance measuring along the wave propagation direction of the ultrasonic range which causes a diffusion angle and provides only slightly less accurate measuring in respect of both distance and angle. so, for these lower cost 2d range finders which may come up with different diffusion angles, such as the ultrasonic and infrared range finders, it is important to develop an algorithm for the reliable recognition of the impassable terrain, provide a good conditions for path planning [19], [6], [3]. 2 mathematical model of recognition in this paper, descent terrain is not discussed because of the limited sensor ability. we can define the impassable terrain as a terrain with a slope angle larger than a threshold value of θt , which is set according to the travel capability of the robot. so, impassable terrain may be a rapid recognition of impassable terrain for mobile robots with low cost range finder based on hypotheses testing theory 815 recognized according to the slope estimation result. 2.1 slope estimation based on range finder with various diffusion angles as the slope estimation algorithm will be proposed in another paper, a brief introduction is proposed as follows. a robot coordinate system is mounted on the mobile robot as shown in figure 1, where the orientation of the robot is the x axis, the origin point is settled on the floor under the wheels, and the y axis is settled along the height of the robot. assume there are two range finders mounted on the robot at different heights, as shown in figure 1, whose positions are oa(xa, ya) and ob(yb, yb) respectively and the vertical difference is oaob = h. assume the measurement of sensor a is da(mm), whose diffusion angle is φa, the measurement of sensor b is db(mm), whose diffusion angle is φb. sensor a for instance detects a sector such as a spreading beam as shown as figure 1. let points a and b denote the two boundary contact points of the spreading beam and the obstacle, among which the lower one is a, and the upper one is b. for an obstacle whose slope is θ, the beams of the two sensors are shown in figure 2. so, for sensor a, the horizontal and vertical distances between sensor a and contact point a, respectively denoted as dxa(mm) and dya, is expresses as formula (1). dxa = |da|cos ( φa 2 ) dya = |da|sin ( φa 2 ) (1) similarly, sensor b forms the two boundary contact points c and d, where the horizontal and vertical distances between sensor b and the lower contact point c is expressed as formula (2). dxb = |db|cos ( φb 2 ) dyb = |db|sin ( φb 2 ) (2) let ∆x,∆y denote the difference between the two boundary contact points a and c along the x axis and y axis respectively. then, formula (3) can easily be obtained, where xa, xc, ya, yc, are the x and y coordinates of the two boundary contact points a and c respectively. ∆x = xa−xc = xa + dxa−xb−dxb ∆y = ya−yc = ya−dya−yb + dyb (3) accordingly, the slope of the obstacle θ can be obtained through formula (4). θ = arctan ( ∆y ∆x ) (4) 2.2 impassable terrain recognition based on hypothesis testing theory using unreliable slope estimation it is generally the case that sensors can only provide inaccurate measurements, resulting in inaccurate estimations of the slope. to address this problem, gaussian noise is used to describe the inaccurate nature, whose distribution can be expressed by formula (5), where µ and δ denote the expectation and the variance respectively. in this paper, µ is supposed to be the estimation 816 y. gao, x.y. wu, y. liu, j. m. li, j. h. liu figure 1: one sensor diffusion angle figure 2: two sensors diffusion angles of the slope by using the algorithm described above, implying that the estimation results most likely reflect the actual slope, while δ implies the possible distribution of the actual slope. n(µ, σ) = 1√ 2πσ2 e −(x−µ) 2 2σ2 (5) phit(r, r ∗) = { η1n(r, r ∗, δhit) 0 ≤ r ≤ rmax 0 otherwise (6) η1 = ( ∫ rmax 0 n(r, r∗, δhit)dr) −1 (7) n(r, r∗, δhit) = ∫ r 0 1√ 2πδ2 hit e − (r−r ∗) 2 2δ2 hit dr (8) so, the inaccurate sensor information can be modeled by adding a gaussian noise. let phit(r, r ∗) denote the possibility that r is a noisy observation to r∗ (r∗ represents the actual value of the sensor, rmax represents the maximum measured value of the sensor, r∗ and δhit denote the expectation and the variance respectively of the sensor), which can be obtained by formula (6). the normalizer η1 is expressed as formula (7). the gaussian observed noise n(r, r∗, δhit), as expressed by formula (8), shows that the further r is from r∗, the less phit is. let the wave line superscript denote a gaussian distributed random variable, while the subscript k denotes the time step. then, (θ̃k − n(θk, δ)) denotes the possible distribution of the correct slope on time k affected by the gaussian noise, as shown in formula (5), whose expectation is the slope estimation value θk. assume there are n independent historical slope estimations for the same terrain, while the terrain is supposed to be static. these estimations can then be treated as n estimation samples ({θ̃k ∣ ∣ ∣ k = 0...n}) of the same one terrain confronting the robot. for each observation of the same terrain, applying formulas (1) to (4) to estimate its slope can yield only two opposite results; the terrain with a slope that is passable or impassable. let p denote the probability that the estimation result θ̃k indicates an impassable terrain. let m denote the number of estimation results that indicates it is an impassable terrain. then, the probability, that all the n samples are found to indicate an impassable terrain, conforms to a binomial distribution b(n, p). considering the inaccurate nature of the estimation process, if p is smaller than a threshold value pu, a passable terrain can be confirmed. so, formula (9) is applied to judge whether there is an impassable terrain. here the threshold value pu being approximate to 0 implies a low likelihood of misrecognition. the hypothesis testing theory can then be applied for a reliable recognition of the terrain type based on the n estimation samples as follows. p ≤ pu (9) a rapid recognition of impassable terrain for mobile robots with low cost range finder based on hypotheses testing theory 817 figure 3: relationship between k and p0.05(a) let hypothesis h0 be: a passable terrain is detected (p ≤ pu). let event a be: the number of the estimation samples that have proved to indicate an impassable terrain (θ̃k ≤ θt ), is larger than the threshold value k. let a be the opposite event of a. let pp(a) denote the probability of a with p given. then formulas (10) to (12) can be induced where c j n denotes the extraction of j samples from n samples. according to the hypothesis testing theory, pp(a) also denotes the probability of rejecting h0 and a happening while p is given. similarly, pp(a) denotes the probability of accepting h0 and a happening while p is given. pp(a) = n ∑ j=k+1 c j n(1−p)n−jpj (10) pp(a) = k ∑ j=0 c j n(1−p)n−jpj (11) sup p≤pu p p(a) ≤ α (12) there are two types of error that may occur. the first type is h0 may be rejected and a has happened while h0 is in fact true (p ≤ pu). the second type is h0 may be accepted and a has happened while h0 is in fact untrue (p > pu). the hypothesis testing theory has proved that the probability of the both types of error cannot be decreased simultaneously, and the decreasing of the probability of the first type should be guaranteed. thus, we can confine the probability of the first type under a manually selected level α as expressed by formula (12). as pp(a) is in proportion to p, let ppu = α, then formula (12) is fulfilled. let p = pu, then k can be obtained from formula (10). as a result, it can be concluded that, with the given α if m ≤ k, then a passible terrain is confirmed to be detected; otherwise an impassable terrain is confirmed. for instance, if n = 100,pu = 0.05,α = 0.01, then k = 10 would result (figure 3 shows the relationship between k and p0.05(a) . so an impassable terrain could be confirmed only if m is larger than k. 3 experimental study to validate the algorithm, several experiments are described in this section. all these experiments were carried out in our laboratory, which is a typical indoor environment. a pioneer 3-dx mobile robot (figure 4), equipped with six ultrasonic range finders (figure 5) and a hokuyo utm-30lx laser range finder (figure 6), are used in the experiments. the laser range finders are mounted on the top of the robot, which is capable of providing a long maximum measuring 818 y. gao, x.y. wu, y. liu, j. m. li, j. h. liu range (about 30m) with very limited deviation (up to 50mm). meanwhile, the maximum sensing range of the ultrasonic range finder is about 5m. the diffusion angle of the laser range finder is so limited that it could be considered as zero, while that of the ultrasonic range finder is 4.6 â°. in the following experiment, the number 3 ultrasonic range finder, as shown in figure 5, and the measuring data of the laser range finders, along the orientation of that ultrasonic sensor, are used. both of the two range finders are mounted along the y axis with a height difference of 60mm. the principle of laser and ultrasonic ranging is expressed by formula (13). d = νt/2 (13) where d represents the distance value, v represents the speed of light propagation or sound wave propagation, t represent the time of light propagation or sound wave propagation. figure 4: mobile robot figure 5: ultrasonic range finder figure 6: laser range finder 4 results and discussion 4.1 slope estimation experiments figure 7 shows an example of the inaccurate measuring of the ultrasonic range finder where an object is located in front of the robot at a distance about 500mm. the horizontal ordinate of figure 7 denotes the elapsed time, while the vertical ordinate denotes the range measuring. the blue line shows the measuring of the ultrasonic sensor where a large deviation appears. table 1: objects with different slopes object index 1 2 3 4 5 6 7 8 9 slope(o) 6 7 8 9 10 11 12 13 14 table 2: different distance between the robot and the objects distance index 1 2 3 4 5 6 7 8 9 distance (mm) 500 1000 1500 2000 2500 3000 3500 4000 4500 this section describes several groups of experiments that were conducted. in the first group, nine objects with different slopes were placed for estimation (table 1) to show the large deviation of estimation result (figure 8). in the second group, four objects with different slopes were placed to show the performance of the proposed algorithm where 60 historical samples were adopted for each object (figure 9, 10, 11). it is generally the case that the robot has to distinguish the impassable terrain from the passable ones while the θt is set to 10o. so four objects whose slopes are less than 10o were chosen to represent the passable terrain, during which the slope increase a rapid recognition of impassable terrain for mobile robots with low cost range finder based on hypotheses testing theory 819 figure 7: ultrasonic sensor measuring value figure 8: deviation between the estimated slopes and the actual slopes intervals are set to 1o separately. meanwhile, four objects whose slopes are larger than 10o were also chosen to represent the impassable terrain, during which 1o is set to be the slope increase interval. the accuracy of the measuring may be getting worse with the growing distance. so, we set a group of increasing distances for the objects as shown in table 1. the ultrasonic range finder can only operate within 5m, so the largest distance is set to 4.5m while the distance increase interval is set to 500mm, as shown in table 2. the nine objects with different slopes (table 1), were set in front of the robot at different distances (table 2). all the deviations between the estimation results and the actual slopes are shown in figure 8, in which the horizontal ordinate denotes the actual horizontal distance (mm) between the objects and the sensors while the vertical ordinate denotes the deviation (o). the nine lines in figure 8 show the different deviations with different object slopes. overall, figure 8 shows that larger distances result in larger errors due to the larger deviation in the sensor measuring. moreover, a poor performance occurs when the slope of object is less than 10o. this is mainly because of the misreading of the ultrasonic sensor caused by the specular reflection of the ultrasonic beam toward any direction other than toward the sensor. on the contrary, a better performance was observed when the slope is larger than 10o, in which case the ultrasonic sensor is less likely to provide a misreading. however, an even better performance was observed when the distance is less than 2000mm, even if the slope is less than 10o. this is mainly because the diffused reflected ultrasonic beam has been received by the sensor although most of the ultrasonic beam has been reflected to some other direction by the specular reflection. fortunately, obstacles and impassable terrain generally refer to a surface whose slope is larger than 10o. 820 y. gao, x.y. wu, y. liu, j. m. li, j. h. liu figure 9: estimation result for 8o slope figure 10: estimation result for 10o slope figure 11: estimation result for 12o slope 4.2 terrain type recognition experiments figure 9, 10, 11 shows three objects set in front of the robot with different slopes close to the, a situation which is more likely to result in a wrong judgement. in each experiment, 60 historical slope estimation results were acquired for each object from which 50 estimation results were used to apply to the hypothesis testing. so, for each recognition, the number of samples n = 50 , pu = 0.03, α = 0.05 then k = 4 could be obtained. table 3, 4, 5 shows the results of three groups of experiments for three objects where each group consists of three experiments. in table 3, an object with 8o slope is used, and only one result among the sampling sets of each experiment was larger than θt . as a result, this object was confirmed to be passable terrain in all three experiments. in table 4, an object with 10o slope is used instead, which resulted in more than 10 wrong slope estimation in each experiment, where the slope estimation result is less than θt . however, with the help of the hypothesis testing theory, our algorithm successfully made right recognition for the type of the terrain in all three experiments. in table 5, an object with 12o slope is used instead, which resulted in only one wrong slope estimation in both the first and the third experiments, where the slope estimation result is less than θt . thus, the proposed approach confirms that this is an impassable terrain. table 3: the recognition results to objects with actual slope 8o experiment index m result 1 1 passable 1 1 passable 1 1 passable a rapid recognition of impassable terrain for mobile robots with low cost range finder based on hypotheses testing theory 821 table 4: the recognition results to objects with actual slope 10o experiment index m result 1 32 impassable 1 36 impassable 1 34 impassable table 5: the recognition results to objects with actual slope 12o experiment index m result 1 49 impassable 1 50 impassable 1 49 impassable 5 conclusion this paper proposes an impassible terrain recognition algorithm for a mobile robot. based on this algorithm, historical slope estimation results are used to lessen the influence of inaccurate estimation according to the hypothesis testing theory. this allows different types of low cost range finders with different diffusion angles and inaccurate measuring to be used in the task of recognizing impassible terrain. because the simple slope estimation model is employed and only the estimations referring to impassable terrain are counted, this algorithm is rapid and easily implemented. experiments have demonstrated that the proposed algorithm increases the reliability in most cases with an inexpensive mean for robots to recognize impassable terrain. acknowledgments this work is supported by the national natural science fundation of china (61503043); natural science foundation of shaanxi provincial (2015jq6214); shaanxi natural science foundation (2017jm7016); fundamental research funds for the central 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(2010); obstacle detection and recognition in natural terrain for field mobile robot navigation, wcica, jinan, 6567-6572, 2010. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 603-614 fuzzy local trend transform based fuzzy time series forecasting model j. dan, f. dong, k. hirota jingpei dan, fangyan dong, kaoru hirota tokyo institute of technology japan, 226-8502 yokohama, 4259 nagatsuta, midori-ku, e-mail: {dan,tou,hirota}@hrt.dis.titech.ac.jp jingpei dan chongqing university p.r.china, 400044 chongqing, 174 shazhengjie, shapingba e-mail: danjingpei@hotmail.com abstract: a fuzzy local trend transform based fuzzy time series forecasting model is proposed to improve practicability and forecast accuracy by providing forecast of local trend variation based on the linguistic representation of ratios between any two consecutive points in original time series. local trend variation satisfies a wide range of real applications for the forecast, the practicability is thereby improved. specific values based on the forecasted local trend variations that reflect fluctuations in historical data are calculated accordingly to enhance the forecast accuracy. compared with conventional models, the proposed model is validated by about 50% and 60% average improvement in terms of mlte (mean local trend error) and rmse (root mean squared error), respectively, for three typical forecasting applications. the mlte results indicate that the proposed model outperforms conventional models significantly in reflecting fluctuations in historical data, and the improved rmse results confirm an inherent enhancement of reflection of fluctuations in historical data and hence a better forecast accuracy. the potential applications of the proposed fuzzy local trend transform include time series clustering, classification, and indexing. keywords: time series forecasting, fuzzy time series, trend, transform. 1 introduction based on zadeh’s works (see [1] and [2]), the concept of fuzzy time series and its models for forecasting have been proposed to solve the forecasting problems where the historical data are linguistic values (see [3][5]). conventional fuzzy time series forecasting models that are based on fuzzy time series of original data are limited to forecasting specific values that do not reflect fluctuations in historical data. local trend variations, however, are mainly concerned with real applications. for example, forecast of changing direction of stock price are more important for stock investors to make reasonable determinations than specific forecast values of stock price. in addition, the forecasted specific demand values are unreliable since historical data is distorted when it transfers along the supply chain due to the bullwhip effect, so local trend variations of demand that are not suffered from the bullwhip effect are more valuable and practical for supply chain managers. therefore the practicability of conventional fuzzy time series forecasting methods suffers from the limitation of forecasting specific values. this study aims to improve the copyright c⃝ 2006-2011 by ccc publications 604 j. dan, f. dong, k. hirota practicability and forecast accuracy by forecasting local trend variations that reflect fluctuations in historical data. it should be noted that the word "trend" usually refers to the long-term trend in statistics, whereas as used in this paper word "trend" means local trend variation in short term or during one period. recently, some trend involved fuzzy time series models have been proposed to improve forecasting. huarng has proposed heuristic models by integrating problem-specific heuristic knowledge with chen’s model [6] to improve forecasting by reflecting the fluctuations in fuzzy time series [7]. a trend-weighted fuzzy time series model for forecasting taiwan stock exchange capitalization weighted stock index (taiex) has been proposed in [8]. chen and wang have proposed a method to predict taiex based on fuzzy-trend logical relationship groups to improve forecast accuracy [9]. the interval rearranged method has been proposed to reflect fluctuations in historical data and improve forecast accuracy of fuzzy time series in [10]. although all these methods are involved with trends, they are intrinsically conventional fuzzy time series forecasting methods since they are all based on original data and their forecasting targets are specific values. fuzzy local trend transform is proposed to provide a different forecasting basis by transforming original data into a linguistic representation of local trend variations called fuzzy local trend time series. in contrast to conventional fuzzy time series forecasting models, local trend variations are forecasted based on the transformed fuzzy local trend time series in the proposed model. forecast accuracy of specific values is hence enhanced by forecasted local trend variations that reflect fluctuations in historical data. three typical forecasting targets, enrollment forecasting, stock index forecasting, and inventory demand forecasting are used to validate the proposed model. to make an effective evaluation, forecasts are evaluated by two measures from different aspects. root mean squared error (rmse) is used to evaluate forecast accuracy of specific values while mean local trend error (mlte) measure [11] is used to evaluate how accurately forecast reflects fluctuations in historical data. for enrollment forecasting, the proposed model outperforms typical fuzzy time series models in terms of rmse and mlte. especially, comparing to chen’s model [6], huarng’s model [12], and cheng et al.’ model [13], mlte results show an improvement of 73.3%, 55.6%, and 66.7%, respectively. for taiex forecasting, the proposed model gets the smallest rmse result while the second rank in terms of mlte compared to chen’s model [6], yu’s model [14], and cheng’s model [8]. for inventory demand forecasting, compared to huarng and yu’ model [15], cheng et al.’ model [13], and chen and wang’ model [9], the proposed model yields about 50%, 50%, and 33.3% improvement in terms of mlte and 48.3%, 73.3%, and 49.1% improvement in terms of rmse. the mlte results demonstrate that the proposed model outperforms conventional fuzzy time series models in significantly reflecting fluctuations in historical data, and the improved rmse results confirm an inherent enhancement of reflection of fluctuations in historical data and hence a better forecast accuracy. the rest of the paper is organized as follows: in section 2, fuzzy time series and fuzzy c-means clustering are briefly reviewed. the proposed fuzzy local trend transform based fuzzy time series forecasting model is elaborated in section 3. empirical analyses on three forecasting targets to demonstrate the proposed model are illustrated in section 4. 2 fuzzy time series and fuzzy c-means clustering: a brief review the proposed model is based on conventional fuzzy time series forecasting model and the fuzzy c-means clustering method is applied in the proposed fuzzy local trend transform, so fuzzy time series and fuzzy c-means clustering are briefly reviewed by adjusting the notations. fuzzy local trend transform based fuzzy time series forecasting model 605 2.1 fuzzy time series let u be the universe of discourse, where u = {u1, u2, · · · , ub}. a fuzzy set ai of u is defined as ai = ∑b j=1 fai(uj)/uj, where fai is the membership function of the fuzzy set ai; fai : u → [0, 1], ua is a generic element of fuzzy set ai and fai(ua) is the degree of belongingness of ua to ai; fai(ua) ∈ [0, 1] and a ∈ [1, b]. in [3], the general definitions of fuzzy time series are given as follows: definition 1. let a subset of real numbers y (t)(t = · · · , 0, 1, 2, · · · ) be the universe of discourse by which fuzzy sets fj(t) are defined. if f(t) is a collection of f1(t), f2(t), · · · , then f(t) is called a fuzzy time series defined on y (t). definition 2. if fuzzy time series relationships assume that f(t) is caused only by f(t − 1), then the relationship can be expressed as: f(t) = f(t − 1) ∗ r(t, t − 1), which is the fuzzy relationship between f(t) and f(t − 1), where ∗ represents as an operator. (note that the operator can be either max-min [4], min-max [5], or arithmetic operator [6].) to sum up, let f(t − 1) = ai, and f(t) = aj, the fuzzy logical relationship between f(t) and f(t − 1) can be denoted as ai → aj, where ai refers to the left-hand side and aj refers to the right-hand side of the flr. furthermore, these fuzzy logical relationships can be grouped to establish different fuzzy relationship. these groups are called fuzzy logical relationship groups (flrgs). on account of its simplicity, flr method is chosen by most researchers. the procedure for forecasting using conventional fuzzy time series models has four main steps: (1) define universe of discourse and intervals; (2) define fuzzy sets and fuzzify observations in the original time series; (3) establish fuzzy relationships; (4) forecast and defuzzify the outcome. assume that the current state of f(t) is ai, fdef(t + 1) can be forecasted and defuzzified by the following rules: rule 1: if there is ai → aj in fuzzy logical relationship groups, then f(t + 1) = aj and defuzzified as fdef(t+1) = centerj, where centerj is the center of cluster j to which aj belongs. rule 2: if there is ai → # in fuzzy logical relationship groups, then f(t + 1) = ai and defuzzified as fdef(t + 1) = centeri, where # represents null value and centeri is the center of cluster i to which ai belongs. rule 3: if there is aia1, a2, · · · , aj in fuzzy logical relationship groups, then the forecast at t + 1 is calculated as fdef(t + 1) = (center1 + center2 + · · · + centerj)/j, where cj is the center of cluster to which aj belongs. 2.2 fuzzy c-means clustering fuzzy c-means (fcm) clustering is a method of clustering which allows one piece of data to belong to two or more clusters, and is frequently used in pattern recognition [16]. the fcm is based on minimization of the objective function jm = n∑ i=1 c∑ j=1 umij ∥xi − cj∥ 2, 1 ≤ m ≤ ∞, (1) where m is any real number greater than 1, uij is the degree of membership of xi in the cluster j, xi is the i-th of d-dimensional measured data, cj is the d-dimension center of the cluster, and ∥ ∗ ∥ is any norm expressing the similarity between any measured data and the center. fuzzy partitioning is carried out through an iterative optimization of the objective function (1), with the update of membership uij = 1/ ∑c k=1 (∥xi − cj∥/∥xi − ck∥) 2/m−1 and the cluster centers cj = ∑n i=1 u m ij (xi/ ∑n i=1 u m ij ). 606 j. dan, f. dong, k. hirota this iteration will stop when maxij|uk+1ij − u k ij| < ε, where ε is a termination criterion between 0 and 1, whereas k is the iteration step. this procedure converges to a local minimum or a saddle point of jm. 3 proposed fuzzy local trend transform based fuzzy time series forecasting model in literature, trends are widely represented by using absolute variations, slopes, or relative variations between two consecutive points in literature. in section 3.1, trends or local trend variations in study are defined as relative variations, or ratios. the algorithm of fuzzy local trend transform based on local trend variations is then elaborated in section 3.2. the fuzzy time series forecasting model based on fuzzy local trend transform is presented in section 3.3. 3.1 local trend variations to address the limitation of absolute variations [17] and slopes( [18][20])for representing local trend variations, relative variations, which are the ratios between two consecutive data points in a given historical time series, are adopted to indicate local trend variations in this study. assuming that for any given time series p(t), t = 1, 2, · · · , n, n ∈ n, local trend variation between time t and t − 1 is defined as rt = (p(t) − p(t − 1))/p(t − 1), t = 2, 3, · · · , n, (2) then the time series of local trend variations for p(t) is defined as t(t) = rt, t = 2, · · · , n. the reasons for forecasting based on local trend variations instead of original time series data are explained as follows. first, the forecasts based on original time series data may not reflect the fluctuations in historical data properly. in most previous studies, the forecasts are equal at some consecutive points which indicated that forecasting based on original time series are not appropriate for reflecting fluctuations [15]. second, original time series data varies dramatically in different contexts while the time series of local trend variations varies slightly. finally, forecasting based on local trend variations are more suitable for reflecting fluctuations in historical data since directions and variation degrees of local trend variations can be indicated by signs and magnitudes of ratios easily. ratios are preferable in terms of demonstrating the differences in various contexts, ratios-based lengths of intervals are hence adopted to improve fuzzy time series in [15]. forecasting based on local trend variations are therefore considered more suitable for reflecting fluctuations in historical data and forecast accuracy should be further improved inherently. 3.2 fuzzy local trend transform to forecast local trend variations, original time series is represented by linguistic local trend variations of original data firstly, the algorithm of fuzzy local trend transform is hence proposed as follows: step 1 : obtain the local trend variation time series t(t) by calculating ratios between each two consecutive data points in the original time series p(t) in terms of equation (2). step 2: divide t(t) into three basic clusters in terms of local trend changing direction, i.e., decreasing cluster td, unchanged cluster tu, and increasing cluster ti. it is easy to determine td and ti in terms of sign of ratios after tu is determined. assume that the interval for unchanged fuzzy local trend transform based fuzzy time series forecasting model 607 table 1: parameter śá for determining the interval of unchanged cluster max(|t(t)|)(×10−2) max(|t(t)|) ≤ 1 max(|t(t)|) ≤ 10 max(|t(t)|) ≤ 20 · · · α(×10−2) 0.01 0.1 0.2 · · · cluster is [−α, α], α is determined by max(|t(t)|) according to table 1 since it is possible that the definition of unchanged cluster varies from problem to problem. the observations in t(t), of which the values are greater than α, are then assigned to ti, while the observations of which the values less than −α are assigned to td. step 3: divide ti and td into ci and cd clusters by applying fcm, respectively. assume that the number of observations in ti and td are ni and nd, respectively, then the number of clusters for ti, td and t(t) are predefined by users as ci (2 ≤ ci ≤ ni), cd (2 ≤ cd ≤ nd), and c (c = cd + ci + 1), respectively. ti and td are then divided into ci and cd clusters by fcm, respectively, as described in section 2.2. consequently, the cluster centers and memberships with respect to the clusters are obtained. step 4: fuzzify the local trend time series t(t) as fuzzy local trend time series ft (t). first, the clusters of t(t) are achieved by combining the clustering results obtained in step 2 and step 3. then the linguistic terms ai (i = 1, 2, · · · , c) are defined corresponding to the clusters. t(t) is finally fuzzified into ft (t) by assigning ai to t(t) when the maximum membership of t(t) occurs at the cluster to which ai belongs. 3.3 fuzzy local trend transform based fuzzy time series forecasting model theoretically, the proposed fuzzy local trend transform can be integrated with any conventional fuzzy time series forecasting model. because of simplicity, the proposed model is integrated with chen’s model [6] as stated in section 2.1. the proposed model differs from chen’ model in step 1 and step 4 as described in the following: step 1: transform original time series into fuzzy local trend time series by the proposed fuzzy local trend transform as stated in section 3.2. step 2: establish fuzzy logical relationships and fuzzy logical relationship groups based on fuzzy local trend time series obtained in step 1 as described in section 2.1. step 3: forecast and defuzzify the possible outcomes of local trend variations, which is denoted as tdef(t), t = 1, 2, · · · , n − 1, based on fuzzy logical relationship groups as described in section 2.1. step 4: calculate specific values based on forecasted local trend variations obtained in step 3 in terms of equation(3) that is defined as ppre(t) = p(t) × tdef(t), t = 1, 2, · · · , n − 1, (3) where ppre(t) indicates predicted specific values. 4 empirical analyses on forecasting based on fuzzy local trend time series to validate the proposed model, three applications are used in the empirical analyses, including student enrollment forecasting (the enrollments of the university of alabama [4]), stock index forecasting (taiwan stock exchange capitalization weighted stock index (taiex)), and inventory demand forecasting [15]. the first two data sets are algebraic growth data and widely used to validate fuzzy time series models in many relevant studies while the inventory demand data set is exponential growth data and typical in supply chain management application. we 608 j. dan, f. dong, k. hirota compare the proposed model with typical fuzzy time series models in terms of two forecast accuracy measures. one is conventional measure rmse (root mean squared error) that is commonly used to measure forecast accuracy of specific values based on quantitative error in fuzzy time series forecasting, while the other is mlte (mean local trend error), which is proposed to measure how accurately forecasts reflect fluctuations in actual data based on local trend error [11]. it is more effective and proper for comparing the models by evaluating them from two different aspects than using one or more conventional measures that based on quantitative error. for a given time series yt, t = 1, · · · , n, the prediction of yt is ft, t = 1, · · · , n, rmse is defined as rmse = √√√√1 n n∑ t=1 (ft − yt)2, t = 1, 2, · · · , n, n ∈ n, (4) ąą while mlte is defined as mlte= 1 n−1 n−1∑ i=1 ei×100%, ei = { 1 sign(yt+1−yt) ̸=sign(ft+1−ft) 0 sign(yt+1−yt)=sign(ft+1−ft) t=1, 2,· · ·, n, n ∈ n, (5) where ei indicates the number of local trend change errors, sign indicates the operator for outputting the sign of the operand. when the predicted local trend variation is inconsistent with the original one in the same interval, ei is equal to 1, otherwise ei equals to 0 [11]. the experiments are implemented using matlab r2010a. in section 4.1, the forecasting algorithm is illustrated step by step with the example of enrollment forecasting. performance of the proposed model for stock index and inventory demand forecasting are analyzed in section 4.2 and section 4.3, respectively. 4.1 forecasting enrollments the yearly data of student enrollments of the university of alabama from 1971 to 1992 are commonly used in previous studies on fuzzy time series to validate fuzzy time series models. to make proper comparison with other models, the same data set is used in this study to validate the proposed model. the procedure of forecasting enrollments by the proposed model is as follows: step 1: transform original time series into fuzzy local trend time series by the proposed fuzzy local trend transform. (1) obtain the local trend time series t(t) as shown in the third column of table 2 by calculating the local trend variations of the enrollments between each two consecutive years in the original time series p(t) in terms of equation(2) . (2) divide t(t) into decreasing cluster td, unchanged cluster tu, and increasing cluster ti. since none of the observations in t(t) is equal to 0 and maximum absolute value of the observations in t(t) is 7.6675%, α is determined to be 0.1% according to table 1. consequently, td = {−5.8274, −3.1385, −2.3840, −2.2714, −0.9638}, tu = {0.0466}, and ti = {0.1189, 0.4147, 0.6664, 1.6535, 1.8872, 1.9071, 2.2414, 3.8912, 4.5179, 5.1987, 5.4145, 5.4742, 5.9643, 5.9782, 7.6576}. (3) divide ti and td into ci into cd clusters by applying fcm, respectively. for fcm, the predefined number of clusters c = cd + ci + 1. to make fair comparison, the total number of clusters is predefined to be 7 in this study as the same as used in the previous studies. assume that cd = ci = 3, the cluster centers of td and ti and the membership grades of td and ti are shown in table 3 and table 4, respectively. naturally, the center of unchanged cluster ti is 0%. (4) fuzzify the local trend time series t(t) into fuzzy local trend time series ft (t) as shown in the fourth column of table 2. combine the clustering results obtained in step (2) and step fuzzy local trend transform based fuzzy time series forecasting model 609 table 2: forecasting local trend variations and specific values of the enrollments year actual local trend fuzzified local forecasted local trend forecasted enrollment variation trend variation variation (×10−2) enrollment 1971 13055 1972 13563 3.8912 a6 1973 13867 2.2414 a5 0.1233 13580 1974 14696 5.9782 a7 1.1623 14028 1975 15460 5.1987 a6 3.8752 15266 1976 15311 -0.96378 a3 0.1233 15479 1977 15603 1.9071 a5 1.2224 15498 1978 15861 1.6535 a5 1.1623 15784 1979 16807 5.9643 a7 1.1623 16045 1980 16919 0.66639 a5 3.8752 17458 1981 16388 -3.1385 a2 1.1623 17116 1982 15433 -5.8274 a1 -2.3003 16011 1983 15497 0.4147 a5 1.2224 15622 1984 15145 -2.2714 a2 1.1623 15677 1985 15163 0.11885 a5 -2.3003 14797 1986 15984 5.4145 a7 1.1623 15339 1987 16859 5.4742 a7 3.8752 16603 1988 18150 7.6576 a7 3.8752 17512 1989 18970 4.5179 a6 3.8752 18853 1990 19328 1.8572 a5 0.1233 18993 1991 19337 0.046565 a4 1.1623 19553 1992 18876 -2.384 a2 -2.5770 18839 rmse 438.18 mlte 21.0526% (3) to achieve the clusters of t(t), the linguistic variables are then defined as shown in table 5 according to the clusters. each local trend variation in t(t) is fuzzified by the linguistic variable to which the maximum membership belongs in terms of the results in table 3 and table 4. table 3: membership grades of decreasing cluster for each linguistic variable clusters cluster centers linguistic td(1) td(2) td(3) td(4) td(5) variables 1 -2.5770 a2 0.0000018 0.9 0.9785 0.9407 0.000055 2 -5.8231 a1 0.9999974 0.0393 0.0031 0.0070 0.000006 3 -0.9758 a3 0.0000008 0.0607 0.0184 0.0523 0.999939 ft (t) a1 a2 a2 a2 a3 step 2: the flrs of ft (t) are established as shown in table 6 according to definition 3 in 2.1. then, the flrs are rearranged into flrgs as shown in table 7. step 3: the possible outcomes of local trend variations from 1973 to 1992 are forecasted and defuzzified as shown in the fifth column of table 2. table 4: membership grades of increasing cluster for each linguistic variable clusters cluster linguistic ti(1) ti(2) ti(3) ti(4) ti(5) ti(6) ti(7) ti(8) centers variables 1 4.3997 a6 0.0603 0.0387 0.0215 0.0238 0.0639 0.0684 0.1719 0.9139 2 1.2224 a5 0.9077 0.9416 0.9680 0.9667 0.9123 0.9063 0.7715 0.0332 3 6.0036 a7 0.0319 0.0197 0.0105 0.0095 0.0238 0.0253 0.0566 0.0530 ft (t) a5 a5 a5 a5 a5 a5 a5 a6 ti(9) ti(10) ti(11) ti(12) ti(13) ti(14) ti(15) 1 4.3997 a6 0.9924 0.4936 0.2486 0.1929 0.00063 0.00027 0.1947 2 1.2224 a5 0.0013 0.0199 0.0146 0.0123 0.00007 0.00003 0.0499 3 6.0036 a7 0.0063 0.4864 0.7371 0.7948 0.9993 0.9997 0.7554 ft (t) a6 a6 a7 a7 a7 a7 a7 step 4: the enrollment values from the year 1973 to 1992 are calculated based on the forecasted local trend variations obtained in step 3 as shown in the last column of table 2. the number of intervals of the universe of discourse affects forecasting results [21]. when analyzing the sensitivity of c, which varies in {7, 9, 11, 13}, the rmse and mlte results {502.401, 440.3566, 380.8953, 313.2307} 610 j. dan, f. dong, k. hirota table 5: cluster centers of local trend variations of enrollments cluster centers linguistic variables (×10−2) -5.8231 a1(big decrease) -2.5770 a2(decrease) -0.9758 a3(small decrease) 0 a4(almost unchanged) 1.2224 a5(small increase) 4.3997 a6(increase) 6.0036 a7(big increase) and {23.1579%, 24.2105%, 20%, 13.6842%} respectively. the average rmse and mlte results indicate that forecast error decrease as the number of clusters increases, in other words, the bigger the number of clusters the higher the forecast accuracy. table 6: fuzzy logical relationships of local trend variations of enrollments a6→a5 a5→a7 a7→a6 a6→a3 a3→a5 a5→a5 a5→a7 a7→a5 a5→a2 a2→a1 a1→a5 a5→a2 a2→a5 a5→a7 a7→a7 a7→a7 a7→a6 a6→a5 a5→a4 a4→a2 a2→# table 7: fuzzy logical relationship groups of local trend variations of enrollments group 1: a1→a5 group 2: a2→a1,a5,# group 3: a3→a5 group 4: a4→a2 group 5: a5→a2, a4, a5, a7 group 6: a6→a3, a5 group 7: a7→a5, a6, a7 table 8: comparisons for enrollment forecasting under the same conditions year actual chen huarng cheng et al. the proposed enrollment [6] [12] [13] model 1971 13055 -ąą 1972 13563 14000 14000 14242 -ąą 1973 13867 14000 14000 14242 13580 1974 14696 14000 14000 14242 14028 1975 15460 15500 15500 15474.3 15266 1976 15311 16000 15500 15474.3 15479 1977 15603 16000 16000 15474.3 15498 1978 15861 16000 16000 15474.3 15784 1979 16807 16000 16000 16146.5 16045 1980 16919 16833 17500 16988.3 17458 1981 16388 16833 16000 16988.3 17116 1982 15433 16833 16000 16146.5 16011 1983 15497 16000 16000 15474.3 15622 1984 15145 16000 15500 15474.3 15677 1985 15163 16000 16000 15474.3 14797 1986 15984 16000 16000 15474.3 15339 1987 16859 16000 16000 16146.5 16603 1988 18150 16833 17500 16988.3 17512 1989 18970 19000 19000 19144 18853 1990 19328 19000 19000 19144 18993 1991 19337 19000 19500 19144 19553 1992 18876 19000 19000 19144 18839 rmse ąą 646.79 477.91 466.17 438.18 mlte 78.9474% 47.3684% 63.1579% 21.0526% the proposed method is compared with typical models for enrollment forecasting, including chen’s model [6], huarng’s model [12], and cheng et al.’ model [13]. to make fair comparison, seven linguistic variables are defined in all the compared models and the rmses and mltes are computed with reference to the year 1973. as shown in table 8, the comparative results show that the proposed method outperforms the other models in terms of rmse and mlte. fuzzy local trend transform based fuzzy time series forecasting model 611 especially, comparing to chen’s model, huarng’s model, and cheng et al.’ model, the proposed model makes about 73.3%, 55.6%, and 66.7% improvements in terms of mlte, respectively. 4.2 stock index forecasting table 9: comparisons for taiex forecasting date actual actual local chen yu cheng the proposed forecasted local index trend (×10−2) [6] [14] [8] model trend (×10−2) 00/11/02 5,626.08 1.477 5300 5340 5463.85 5524.81 -0.35 00/11/03 5,796.08 2.933 5750 5721.67 5644.8 5611.08 -0.27 00/11/04 5,677.30 -2.0922 5450 5435 5797.8 5815.39 0.33 00/11/06 5,657.48 -0.35033 5750 5721.67 5690.9 5657.47 -0.35 00/11/07 5,877.77 3.7478 5750 5721.67 5673.06 5648.99 -0.15 00/11/08 6,067.94 3.134 5750 5760 5871.32 5897.36 0.33 00/11/09 6,089.55 0.35487 6075 6062.5 6042.47 6088.09 0.33 00/11/10 6,088.74 -0.013303 6075 6062.5 6061.92 6093.84 0.07 00/11/13 5,793.52 -5.0957 6075 6062.5 6061.19 6079.54 -0.15 00/11/14 5,772.51 -0.36397 5450 5435 5795.5 5685.36 -1.87 00/11/15 5,737.02 -0.61861 5450 5435 5776.59 5763.81 -0.15 00/11/16 5,454.13 -5.1867 5450 5435 5744.65 5728.37 -0.15 00/11/17 5,351.36 -1.9204 5300 5340 5409.92 5352.29 -1.87 00/11/18 5,167.35 -3.561 5350 5350 5317.42 5304.12 -0.88 00/11/20 4,845.21 -6.6486 5150 5150 5151.81 5070.94 -1.87 00/11/21 5,103.00 5.0517 4850 4850 4861.89 4953.18 2.23 00/11/22 5,130.61 0.53814 5150 5150 5093.9 5120.92 0.35 00/11/23 5,146.92 0.31689 5150 5150 5118.75 5134.17 0.07 00/11/24 5,419.99 5.0382 5150 5150 5213.56 5150.48 0.07 00/11/27 5,433.78 0.25378 5300 5340 5459.32 5439.04 0.35 00/11/28 5,362.26 -1.3338 5300 5340 5391.6 5425.62 -0.15 00/11/29 5,319.46 -0.80459 5350 5350 5327.23 5314.92 -0.88 00/11/30 5,256.93 -1.1895 5350 5350 5288.71 5311.49 -0.15 00/12/01 5,342.06 1.5936 5250 5250 5232.44 5210.46 -0.88 00/12/02 5,277.35 -1.2262 5350 5350 5309.05 5327.84 -0.27 00/12/04 5,174.02 -1.9971 5250 5250 5250.81 5230.78 -0.88 00/12/05 5,199.20 0.48431 5150 5150 5157.82 5128.29 -0.88 00/12/06 5,170.62 -0.55274 5150 5150 5180.48 5202.82 0.07 00/12/07 5,212.73 0.80783 5150 5150 5154.76 5162.82 -0.15 00/12/08 5,252.83 0.7634 5250 5250 5192.66 5216.33 0.07 00/12/11 5,284.41 0.59761 5250 5250 5228.75 5256.46 0.07 00/12/12 5,380.09 1.7784 5250 5250 5257.17 5288.08 0.07 00/12/13 5,384.36 0.079304 5350 5350 5343.28 5398.00 0.33 00/12/14 5,320.16 -1.2067 5350 5350 5347.12 5376.30 -0.15 00/12/15 5,224.74 -1.8263 5350 5350 5289.34 5273.20 -0.88 00/12/16 5,134.10 -1.7655 5250 5250 5203.46 5178.54 -0.88 00/12/18 5,055.20 -1.5608 5150 5150 5121.89 5088.74 -0.88 00/12/19 5,040.25 -0.29661 5450 5405 5050.88 5010.54 -0.88 00/12/20 4,947.89 -1.8667 5450 5405 5037.42 5032.72 -0.15 00/12/21 4,817.22 -2.7126 4950 4950 4954.3 4904.19 -0.88 00/12/22 4,811.22 -0.12471 4850 4850 4836.7 4800.37 -0.35 00/12/26 4,721.36 -1.9033 4850 4850 4831.3 4803.96 -0.15 00/12/27 4,614.63 -2.3129 4750 4750 4750.42 4679.69 -0.88 00/12/28 4,797.14 3.8046 4650 4650 4654.37 4598.48 -0.35 00/12/29 4,743.94 -1.1214 4750 4750 4818.62 4813.06 0.33 00/12/30 4,739.09 -0.10234 4750 4750 4770.74 4736.76 -0.15 rmse 176.32 170.27 121.47 114.63 mlte 64.44% 62.22% 26.67% 31.11% the daily stock index, taiex (taiwan stock exchange capitalization weighted stock index), which is the other widely used data set in fuzzy time series studies, is used to further validate the proposed model for out-sample forecasting. the taiex data during 2000/01/01 2000/10/31 are used as training data set, and the data during 2000/11/01 2000/12/31 are used as testing data set. 612 j. dan, f. dong, k. hirota the proposed model is compared with chen’s model [6], yu’s model [14], and cheng’s model [8]. the comparison of the forecasting results is shown in table 9. the proposed model gets the smallest rmse result while the second rank in terms of mlte among the compared models. cheng’s model gets the smallest mlte since it incorporates trend-weighting into chen’s model for taiex forecasting. the proposed model, however, improves forecast accuracy of specific value about 6% compared to cheng’s model by reflecting fluctuations in historical data. 4.3 inventory demand forecasting demand forecasting plays a very important role in supply chain management. to further validate the applicability of the proposed method for demand forecasting, an inventory demand data set [15] is used in this study. this data set has been used in several previous studies, so it is proper for fair comparison. inventory demand data from 1 to 19 are used as training set while data from 20 to 24 are used as testing set. table 10: comparisons for inventory demand forecasting time actual huarng and yu cheng et al. chen and wang the proposed inventory demand [15] [13] [9] model 20 227 206 205.5290 209.945 215.5516 21 223 228 216.4187 224.055 231.8985 22 242 228 216.4187 224.055 243.5307 23 239 244 216.4187 234.11 246.5969 24 266 244 216.4187 244.33 261.0037 rmse 15.1 28.7295 14.88 7.6845 mlte 100% 100% 75% 50% the proposed model is compared with huarng and yu’ model [15], cheng et al.’ model [13], and chen and wang’ model [9]. as shown in table 10 comparing with huarng and yu’ model, cheng et al.’ model, and chen and wang’ model, the mlte results are improved by the proposed model about 50%, 50%, and 33.3%, respectively. this indicates that the proposed model outperforms the comparative models significantly in reflecting fluctuations in historical data. consequently, the forecast accuracy of specific value is improved by the proposed model about 48.3%, 73.3%, and 49.1%, respectively, in terms of rmse, which indicates that forecast accuracy is inherently improved by the proposed model by reflecting fluctuations in historical data. 5 conclusion in contrast to conventional fuzzy time series forecasting models that are based on original fuzzy time series, a different forecasting basis, fuzzy local trend time series, which is the linguistic representation of local trend variations of original data, is provided by the proposed fuzzy local trend transform. local trend variations, which are defined as ratios between any two consecutive data points in original time series, are thereby forecasted based on fuzzy local trend time series and the specific values are are calculated accordingly. therefore the practicability and forecast accuracy are improved by reflecting fluctuations in historical data. the proposed model is validated by using three typical forecasting targets. the results are evaluated by two measures from different aspects. compared to conventional fuzzy time series models, the proposed model yields about 50% and 60% average improvement in terms of mlte and rmse, respectively for the three application areas. the mlte results indicate that the proposed model outperforms conventional fuzzy time series models significantly in reflecting fluctuations in historical data, and the improved rmse results confirm an inherent enhancement of reflection of fluctuations in historical data and hence a better forecast accuracy. fuzzy local trend transform based fuzzy time series forecasting model 613 theoretically, the proposed fuzzy local trend transform can be integrated with any fuzzy time series model. optimization of the parameters predefined in the proposed fuzzy local trend transform is being considered. a linguistic representation of time series data based on the proposed fuzzy local trend transform for the application to time series data mining is ongoing. bibliography [1] l. a. zadeh, outline of a new approach to the analysis of complex systems and decision processes, ieee trans. systems, man,and cybernet, vol.3, no.1, pp.28-44, 1973. [2] l.a. zadeh, the concept of a linguistic variable and its application to approximate reasoning, part 1, information sciences, vol.8, no.3, pp.199-249, 1975. [3] q. song, b. s. chissom, fuzzy time series and its model, fuzzy sets and systems, vol.54, no.3, pp.269-277, 1993. [4] q. song, b. s. chissom, forecasting enrollments with fuzzy time series -part i, fuzzy sets and systems, vol.54, no.1, pp.1-9, 1993. [5] q. song, b. s. chissom, forecasting enrollments with fuzzy time series -part ii, fuzzy sets and systems, vol.62, no.1, pp.1-8, 1994. [6] s. m. chen, forecasting enrollments based on fuzzy time series, fuzzy sets and systems, vol.81, no.3, pp.311-319, 1996. [7] k. h. huarng, heuristic models of fuzzy time series for forecasting, fuzzy sets and systems, vol. 123, no.3, pp.369-386, 2001. [8] c. h. cheng et al, trend-weighted fuzzy time series model for taiex forecasing, lecture notes in compter science, part iii, vol.4234, pp.469-477, 2006. [9] s. m. chen, n. y. wang, fuzzy forecasting based on fuzzy-trend logical relationship groups, ieee trans. systems, man, and cybernetics-part b: cybernetics, vol.40, no.5, pp.1343 1358, 2010. [10] h. y. jung et al, fuzzy time series reflecting the fluctuation of historical data, seventh international conference on fuzzy systems and knowledge discovery, pp.473-477, 2010. [11] j. dan et al, mean local trend error and fuzzy-inference-based multicriteria evaluation for supply chain demand forecasting, journal of advanced computational intelligence and intelligent informatics, vol. 15, no. 2, pp.134-144, 2011. [12] j. r. hwang et al, handling forecasting problems using fuzzy time series, fuzzy sets and systems, vol.100, no.1-3, pp.217-228, 1998. [13] c. h. cheng et al, multi-attribute fuzzy time series method based on fuzzy clustering, expert systems with applications, vol.34, no.2, pp.1235-1242, 2008. [14] h. k. yu, a refined fuzzy time series model for forecasting, physica a, vol.346, no.3/4, pp.657-681, 2005. [15] k. h. huarng, h. k. yu, ratio-based lengths of intervals to improve fuzzy time series forecasting, ieee trans. systems, man, and cybernetics-part b: cybernetics, vol.36, no.2, pp.328-340, 2006. 614 j. dan, f. dong, k. hirota [16] j. c. bezdek et al, fcm: the fuzzy c-means clustering algorithm, computers & geosciences, vol.10, no.2-3, pp.191-203, 1984. [17] j. r. hwang et al, handling forecasting problems using fuzzy time series, fuzzy sets and systems, vol.100, no.1-3, pp.217-228, 1998. [18] a. udechukwu et al, discovering all frequent trends in time series, the 2004 winter international symposium on information and communication technologies, pp.1-6, 2004. [19] c. h. chen et al, mining fuzzy frequent trends from time series, expert systems with applications, vol.36, no.2, part 2, pp.4147-4153, 2009. [20] i. z. batyrshin, l. b. sheremetov, perception-based approach to time series data mining, applied soft computing, vol.8, no.3, pp.1211-1221, 2008. [21] k. h. huarng, effective lengths of intervals to impove forecasting in fuzzy time series, fuzzy sets and systems, vol.124, no.3, pp.387-397, 2001. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 581-582 “the disassembly line: balancing and modeling” -book reviewf.g. filip title of book: the disassembly line: balancing and modeling authors: seamus m. mcgovern and surendra m. gupta editing house: mcgraw hill companies inc., new york isbn: 978-0-07-162287-5 pages: xvii+373 this book is about the disassembly of end-of-life products with particular emphasis on methods and techniques for solving the disassembly line balancing problem. disassembly is viewed as "the systematic separation and extracting valuable entities for possible future re-usage". in fact, disassembly is a distinct phase of the product lifecycle. it follows the "before life" phases (such as design and economical evaluation), "useful period" phases (such as manufacturing, distribution, usage and maintenance) and "end of life" phases (such as collecting, sorting). disassembly might represent the essential first phase of the future activities, such as re-use and re-manufacturing and recycle. due to the ever higher public awareness, the more and more strict regulations concerning environment quality preservation and increasing economic effectiveness and attractiveness for industry, the activities of recovering valuable parts and subassemblies have become a desirable alternative to the old fashioned disposal processes of end-of-life products. the authors state, in the preface of the book, that the "disassembly line seems to be the most efficient way to disassemble a product". consequently, the primary concern of the book is "the complete disassembly of [end-of-life] products on a paced disassembly line for component/material recovery purposes". the authors aim at investigating "the qualitative and quantitative aspects of multi-criteria solution sequences using the various combinatorial optimization techniques" (page 16) to solve the disassembly line balancing problem (dlpb). the dlbp consists in finding a disassembly feasible solution sequence which preserves precedence constraints and aims at attaining several objectives, such as minimizing the number of work stations and total idle time, ensuring similar idle time at each work station, while attempting to remove hazardous parts and materials and extracting highly demanded product components at the earliest moments possible and minimizing the number of direction changes required for disassembly (removing parts with similar part removal directions together), (page 102). the book is composed of 29 chapters grouped into three parts entitled "disassembly background", "disassembly-line balancing" and "further disassembly-line considerations" which address general aspects concerning disassembly processes, variations of methods and techniques to solve the dlbp, and other problems related to the disassembly line, respectively. part i comprises six chapters which are meant to set the stage for the subsequent chapters. various information concerning disassembly processes, assembly lines, disassembly lines, other related researches, graphical representations and computational complexity of combinatorial problems are provided. part ii is made up of 20 chapters and addresses the statement and analysis of the dlbp and several specific variations of methods and techniques which were adapted for solving the problem, tested on four application cases exprimental instances and compared. the objectives of this part of the book are: stating the mathematical model of dlbp, establishing the difficulty of the problem by using the complexity theory and determining the data sets and evaluation criteria to be used in analyzing the problem and solving techniques which are selected (page 99). copyright c⃝ 2006-2011 by ccc publications 582 it is demonstrated (in chapter 9) that the dlbp is a complex np complete problem in the strong sense and necessitates specialized solution techniques. accordingly, authors plea for combinatorial optimization approaches and select several algorithms to solve the problem. the techniques to be utilized to solve the dlbp are introduced in chapter 10 and their usage and performances in solving the problem are presented in chapters 12 through 19. there are seven techniques which are adapted, tested and compared. the exhaustive search is used to provide the optimal solution. two metaheuristic approaches (genetic algorithms and ant colony optimization) are next studied. two purely deterministic searches (the greedy algorithm and the "hunter-killer" search) and two 2-phase hybrid methods are adapted and tested. the four experimental instances (the eight-part personal computer, the enhanced 10-part dlbp case, the 25-part cellular instance, and the size independent "a priori" benchmark with a known optimal solution) are described in chapter 11. chapter 20 contains a detailed comparison of the six heuristic and metaheuristic techniques as applied to the dlbp with respect to several performance measures. several complementary research results are reviewed in chapter 21 together with future research directions. disassembly processes interact with other "before life", "useful", and "after life" periods of product usage and recovery. as a result, to make the picture complete, part iii addresses other areas of disassembly research such as product planning, line and facility design, operations scheduling and sequencing, inventory, "just-in-time", revenue and unbalanced lines (chapter 22 through 29). the authors of the book form a team who may be viewed as a fine and synergic combination of two complementary experiences and backgrounds from academia and industry. seamus mcgovern, an electronics engineer at the volpe national transportation system center, holds a commission in the us navy as an aerospace duty engineer as well as a part-time industrial engineering faculty appointment at the northeastern university. surendra m. gupta is a professor of mechanical and industrial engineering and a director of the laboratory for responsible manufacturing at the northeastern university. he has authored/co-authored over 400 technical papers and is a pioneer in the domain of the book. this book represents a very valuable work in a rather young research domain, which may be viewed as opened by the pioneering paper of güngör and gupta entitled "disassembly line in product recovery (international journal of production recovery, 40 (11), 2002). the volume mainly reflects the original studies of authors and their colleagues. it also makes an exhaustive and systematic review of the results which are reported in the domain scientific literature and are due to other scientists. the organization of the document is well thought and the presentation style is rigorous and clear. subsequently, though information content is very dense and diverse, the book is accessible and its study is scientifically rewarding. special remarks can be made to the uniform and coherent notation which is used throughout the book and to graphical illustrations. a final remark of appreciation is to be made to the excellent quality of editing and printing of the book due to the staff of mcgraw-hill companies. in conclusion, the book is a timely work which contains relevant, inspiring and challenging information. therefore, this reviewer warmly recommends it to the readers of academia and industry as well who are interested in modern manufacturing issues and combinatorial optimization methods and software. florin gheorghe filip romanian academy, bucharest, romania e-mail: ffilip@acad.ro http://www.biblacad.ro/florinfilip.html international journal of computers communications & control issn 1841-9836, 9(6):711-720, december, 2014. an algorithm for production planning based on supply chain kpis d. makajić-nikolić, s. babarogić, d. lečić-cvetković, n. atanasov dragana makajić-nikolić*, sladjan babarogić danica lečić-cvetković,nikola atanasov university of belgrade, faculty of organizational sciences serbia, 11000, belgrade, jove ilića 154 e-mail: gis@fon.bg.ac.rs, sladjan@fon.bg.ac.rs danica@fon.bg.ac.rs, atanasovn@fon.bg.ac.rs *corresponding author abstract: this paper observe multi-period multi-product production planning problem in make-to-stock production environment with limited production capacity. such problem is identified in fast moving consumer goods industry. the goal was to develop an algorithm for supporting dynamic production triggering decisions in relation with two supply chain key performance indicators: stock cover and customer service level. the presented approach is applied to a real example in several scenarios based on different decision criteria. keywords: production planning, stock cover, customer service level, heuristic algorithm. 1 introduction manufacturing companies that operate in markets with changing demand are often faced with the problem of insufficient supplies of finished products. constant fluctuations in demand and the required financial investments are influencing the decision concerning the expansion of the production capacity. until the production capacity is actually increased, the manufacturing company has to meet the growing demand of the market with its existing production capacity. as customer orders are received periodically and production capacities are often insufficient, it is necessary to make a choice of products which will be produced in each period. in circumstances of reduced uncertainty, it is possible to use exact methods for planning customer satisfaction by cycle, when trends in demand are predictable over a longer period of time. however, in real life this is not the case, as demand is a weekly phenomenon which requires dynamic decisionmaking. therefore, in this paper we propose an algorithm for production planning in which decision on triggering new production is based on two supply chain key performance indicators (kpi): customer service level and stock cover. this paper is organized in six sections. next section describes related work with conceptual foundations. problem description with relevant notation is presented in section tree. heuristic algorithm for inventory planning is given in fourth section. in fifth section, numerical results with case study are described. last section is dedicated to conclusions of the research. 2 related work according to [5] uncertainty in production companies is categorized into environmental uncertainty based on demand and supply uncertainty and system uncertainty (within the production process) mainly related to production lead time, quality or failure of production process. uncertainty depends on level of information required to perform relevant business activity based on efficient and effective management decision [4]. responsive production planning and control copyright © 2006-2014 by ccc publications 712 d. makajić-nikolić, s. babarogić, d. lečić-cvetković, n. atanasov system according to [7] is the most important factor in achieving good delivery performance and demand satisfaction in supply chain. this fact represents one of important reason to focus more on customer service as external performance, once when internal performance is already achieved on certain level. as recognized by shen and daskin in [13] major cost factors associated with designing and managing a supply chain are the facility location costs, the inventory management costs, and the distribution costs, and always should be considered jointly and integrated with customer service goals. customer service was recognized as key measure of performances within production companies according to [9] and very well described by [11]. overall managerial question in supply chain is to determine a cost-effective customer-service level in correlation with profits and associated costs, what lead to question: which service level will satisfy customers and what level of inventory is required? jeffery et al. identified a range of models for determining service level and the appropriate level of inventory, process was carried out based on logistic regression to understand how performance of delivery are dependent on three independent variables: order lead time, errors in forecast, and variation in demand [6]. further development of service level and customers selection in make-to-stock production environment was evaluated by [8], while authors in [1] evaluated possibilities for maximization of customer service with limited production capacity and customer classification. stock cover is key performance indicator measuring length of time that available finished goods will last if forecasted consumption happens. available finished goods ready to be delivered to customer according to identified demand are in direct correlation with customer service level. dellaert and jeunet [2] evaluated stock cover in relation to behavior of lot-sizing rules in a multilevel context, when forecast demand is subject to changes within the forecast window and relevant lead time. according to [12], supply system needs to ensure adequate stock level to satisfy customers need, despite that additional stock only generates unnecessary costs, which customer has to absorb at the end. managing customer service level and stock cover represents highly complex problem of supply chain, taking into account that these two kpis are leading to opposite directions high stocks assume high customer service level, and, at the same time, stock need to be minimized to deliver working capital reduction and overall company efficiency. working capital reduction coupled with increase of sales and certain service level was evaluated by [14] through results of horizontal collaboration between supply chain members. combined approach of managing in parallel customer service level and stocks cover was done by [3] who evaluated main obstacles in increasing pressure to reduce working capital, growing variety of products and the fulfillment of a demanding service level. petri nets model of production planning system based on supply chain kpis: customer service level and stock cover was presented in [10]. 3 problem description in this paper we observe multi-period multi-product inventory planning problem in make-tostock production environment with limited production capacity. we started from a real example of fast moving consumer goods (fmcg) in serbia. choice of products than will be produced should be made in each period (cycle). this decision is based on two key performance indicators: customer service level (csl) and stock cover (sc). the basic assumptions of the observed problem can be divided into three groups as follows. • customers orders assumptions: – customers place orders in all or almost all of the cycles; – demand for each product is uneven and is known only for one cycle in advance; an algorithm for production planning based on supply chain kpis 713 – demand for each product represents the sum of all customers orders for delivery in given cycle; – decision about fulfillment is done in given cycle when all orders are received; – demand is fulfilled from the stock, entirely or partially, depending on the inventory level; – orders that have not been fully met in the reporting cycle shall not be compensated in the subsequent cycles no reordering policy; • inventory assumptions: – if the incoming customer orders in a single cycle do not exceed the available stock of finished goods, the allocation is complete and all customer orders are fulfilled, while any surplus products are stored for the next cycle; – inventory holding costs are neglected; – total inventory capacity is not limited. therefore, inventory planning problem becomes production planning problem; • production assumptions: – the production capacity is limited and constant in the entire period; – there is no possibility for production extension in medium term planing horizon; – due to specific production technology requirements, outscoring with acceptable costs is not possible;; – lot sizes of products are different and fixed; – production time and costs are neglected due to homogeneity of the products; in order to formulate the algorithm and based on the problem assumptions, the following notation will be used in the remaining of the paper: • n number of products; • m number of periods in the observed time horizon; • mcsl minimally acceptable customer service level; • msc minimally acceptable stock cover; • li lot size of i-th product, i = 1, . . . , n; • c available production capacities in each period; • si inventory level of i-th product at the beginning of the observed time horizon, i=1,. . . ,n; • tij demand for i-th product in j-th period, i=1,. . . ,n, j=1,. . . ,m; • pij forecast for i-th product in j-th period, i=1,. . . ,n, j=1,. . . ,m; forecasts are calculated using k-periods moving average: pij = 1 k k∑ u=1 tij−u (1) 714 d. makajić-nikolić, s. babarogić, d. lečić-cvetković, n. atanasov 4 algorithm the goal of the algorithm (fig.1) is to support two-phased decision making process. the result of the first phase is a list of products that should be produced in observed period, where decision is made based on calculated kpis (csl and sc). in the second phase, the algorithm forms a list of products that will be produced, applying one of four defined criteria: minimal csl, minimal sc, maximal capacity utilization and maximal number of products and taking into account the available capacities. the proposed algorithm has polynomial complexity. figure 1: algorithm represented as uml 2.0 activity diagram using parameters defined in problem description section, the following variables are calculated in each period. • sij is a stock level of i-th product in j-th period, i=1,. . . ,n, j=1,. . . ,m. • qij delivered quantity of i-th product in j-th period, i=1,. . . ,n, j=1,. . . ,m. the delivered an algorithm for production planning based on supply chain kpis 715 quantity of each product depends on demand and stock level as follows: qij = { tij , sij ≥ tij sij, otherwise , i = 1, . . . , n, j = 1, . . . , m • cslij = qij/tij customer service level of i-th product achieved in j-th period, i=1,. . . ,n, j=1,. . . ,m, observed as fill rate, indicates ratio between delivered and ordered quantity; • scij = sij/pij stock cover for i-th product provided in j-th period, i=1,. . . ,n, j=1,. . . ,m. based on variables values, two decisions must be made sequentially for each product in each period. 1. the first decision refers to the request for the production of the i-th product in j-th period (dsij, i = 1, . . . , n, j = 1, . . . , m). request for production is initiated if any of the indicators (cslij or scij) falls below the minimum value, i.e: dsij = { 1 (production requested) , cslij < mcsl or scij < msc 0 (production not requested), otherwise for i = 1, . . . , n, j = 1, . . . , m. as a result, a set of all products that should be produced during j-th period is obtained: dj = {i|dsij = 1}. 2. since the available production capacities are limited without possibilities of extension in short term and often insufficient, the second decision is related to a choice of products that will be produced. this choice can be made using several criteria: minimal csl, maximal number of product and maximal production capacities utilization. let p be a set of chosen product. in the following, each of the above criteria for generating the set p will be described in detail. options 1 and 2 minimal csl and minimal sc according to the first two criteria, the higher priority is given to the products with smaller demand satisfaction or with smaller stock cover in the previous period. these criteria should be used in the case of large variations in the customer service level or stock cover among products. for each time period j, the following procedure is applied. initialization: p = ∅. do find i∗ such that csli∗j = min{kpiij|i ∈ d}. if li∗ ≤ c then i∗→ p , c := c − li∗ endif. dj := dj i∗ until c = 0 or dj = ∅. variable kpiij represents key performance indicator cslij (option 1) or scij (option 2) depending on chosen criteria. the output of the procedure is the set p which contains the indexes of the products that will be produced. 716 d. makajić-nikolić, s. babarogić, d. lečić-cvetković, n. atanasov options 3 and 4 maximal number of products and maximal production capacities utilization maximal number of products can be used as criterion when company wants to cover the market with wide variety of products. when there is a large lack of capacity, maximal production capacities utilization should be used as a criterion. for each period j, these two criteria can be modeled as following knapsack problem: max ∑ u∈dj ai ·xi s.t.∑ u∈dj li ·xi ≤ c where: xi = { 1, if the producti is chosen to be produced 0, otherwise , i = 1, . . . , n ai = { 1, if the criterion is number of products (option 3) li, if the criterion is capacities utilisation (option 4) , i = 1, . . . , n after obtaining the optimal results, set of products that will be produced, p , contains all products i such that xi = 1. 5 computational results and discussion the algorithm has been applied on real data calculation based on 28 weeks observation, made for four products of real, medium sized fast moving consumer goods (fmcg) company. installed production capacity is 290 units per period (week), while lot sizes are 120, 110, 170 and 50 units for products p1, p2, p3 and p4, respectively. customers’ orders are shown in table 1. forecast is calculated based on three-week moving average (equation 1). table 1: customers’ orders for 28 weeks product w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 p1 35 75 29 48 40 52 29 59 67 82 p2 50 122 55 129 40 346 70 102 112 16 p3 68 112 48 94 62 357 75 98 124 18 p4 6 23 27 57 0 30 45 38 56 69 product w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 p1 96 88 45 23 16 31 166 16 34 137 p2 83 28 40 28 53 100 70 36 49 292 p3 11 25 49 34 63 117 112 48 69 325 p4 14 67 171 27 4 66 5 8 15 110 product w21 w22 w23 w24 w25 w26 w27 w28 p1 83 23 110 156 109 102 34 69 p2 95 119 447 56 154 34 112 107 p3 162 128 314 93 156 88 115 128 p4 3 86 153 1 1 1 0 25 an algorithm for production planning based on supply chain kpis 717 based on descriptive statistical analysis for the observed period, it can be concluded that products p2 and p3 had the highest average demand (105.18 and 110.46, respectively) but also the highest standard deviation of demand (99.81 and 87.69, respectively). in addition, these two products had three major demand peaks in the same periods (w6, w20 and w23). these facts lead us to the conclusion that production capacity will remain mean issue in the future. developed algorithm can be used at operational and strategic level. operational level is related to the decision of production requesting and launching. table 2 illustrates the operational decisions. it shows the decisions for the product p1 in the entire period when mincsl criterion is used. the first column represents the weeks of the observed period. the second and third columns give the orders and the forecasts per weeks for product p1. columns labeled as sb and sa shows the stock level before and after product delivery while the next column gives the amount of delivered quantities. columns csl and sc show calculated values for customer service level and stock cover per periods. the last three columns represent the decisions about requested, confirmed and missed production, respectively. in the last row of the table 2, the values of total delivered quantities, average scl and sc, and total number of production request, conformation and missing are given. table 2: production decisions for 28 weeks for p1 and mincsl criterion time order forecast sb sa delivered csl sc pr pc pm w1 35 36 110 75 35 1 2.083 w2 75 66 75 0 75 1 0 1 1 w3 29 95 120 91 29 1 0.958 1 1 w4 48 46 211 163 48 1 3.518 w5 40 51 163 123 40 1 2.428 w6 52 39 123 71 52 1 1.821 1 1 w7 29 47 71 42 29 1 0.9 1 1 w8 59 40 162 103 59 1 2.554 w9 67 47 103 36 67 1 0.771 1 1 w10 82 52 36 0 36 0.439 0 1 1 w11 96 69 120 24 96 1 0.346 1 1 w12 88 82 144 56 88 1 0.686 1 1 w13 45 89 176 131 45 1 1.477 1 1 w14 23 76 251 228 23 1 2.987 w15 16 52 228 212 16 1 4.077 w16 31 28 212 181 31 1 6.464 w17 166 23 181 15 166 1 0.643 1 1 w18 16 71 135 119 16 1 1.676 1 1 w19 34 71 119 85 34 1 1.197 1 1 w20 137 72 205 68 137 1 0.944 1 1 w21 83 62 68 0 68 0.819 0 1 1 w22 23 85 120 97 23 1 1.146 1 1 w23 110 81 97 0 97 0.882 0 1 1 w24 156 72 0 0 0 0 0 1 1 w25 109 96 120 11 109 1 0.114 1 1 w26 102 125 11 0 11 0.1080 0 1 1 w27 34 122 120 86 34 1 0.703 1 1 w28 69 82 86 17 69 1 0.208 1 1 total 1533 0.902 1.346 21 13 8 718 d. makajić-nikolić, s. babarogić, d. lečić-cvetković, n. atanasov table 3 shows requesting for productions and decisions about confirmation or missing the production for all four products in the entire observed period. abbreviation used in the table3 are the same as in the table 2. in the last row total numbers of production request, conformation and missing are given for all the products. production requests for all four products appeared in 9 out of 28 weeks and in 5 of them only two product were produced. table 3: production decisions for 28 weeks for all products and mincsl criterion p1 p2 p3 p4 time pr pc pm pr pc pm pr pc pm pr pc pm w1 1 1 1 1 w2 1 1 1 1 w3 1 1 1 1 1 1 w4 1 1 1 1 w5 1 1 1 1 1 1 w6 1 1 1 1 1 1 w7 1 1 1 1 1 1 1 1 w8 1 1 1 1 1 1 w9 1 1 1 1 1 1 1 1 w10 1 1 1 1 1 1 w11 1 1 1 1 1 1 w12 1 1 1 1 1 1 w13 1 1 1 1 w14 1 1 w15 1 1 1 1 w16 1 1 1 1 1 1 w17 1 1 1 1 1 1 w18 1 1 1 1 1 1 w19 1 1 1 1 w20 1 1 1 1 1 1 1 1 w21 1 1 1 1 1 1 1 1 w22 1 1 1 1 1 1 1 1 w23 1 1 1 1 1 1 1 1 w24 1 1 1 1 1 1 1 1 w25 1 1 1 1 1 1 1 1 w26 1 1 1 1 1 1 1 1 w27 1 1 1 1 1 1 w28 1 1 1 1 1 1 total 21 13 8 25 20 5 20 15 5 19 14 5 at strategic level, a decision about the most appropriate criterion can be made using the proposed algorithm. computational results for all observed products and all four decision criteria are given in table 4. column "delivered" represents quantities which are delivered during entire period per product. columns "csl" and "sc" represents kpis. the last column represents the percent of launched production requests. the percentages of capacity utilizations for options 1, 2, 3 and 4, are 0.863, 0.857, 0.872 and 0.817, respectively. after algorithm results presentation, management of the observed company considers option 1 the most appropriate. applying the first option (min csl) in algorithm, the highest level of csl and the largest quantity of delivered products are provided. as positive side effect, high level of capacity utilization is achieved even that was not included as criterion an algorithm for production planning based on supply chain kpis 719 table 4: summary results delivered csl sc card(p)/card(d) option 1 (min csl) p1 1533 0.90 1.35 0.714 p2 2263 0.91 0.98 0.800 p3 2538 0.89 1.29 0.750 p4 778 0.83 7.36 0.737 total 7112 avg 0.88 avg 2.75 avg 0.753 option 2 (min sc) p1 1533 0.90 1.40 0.619 p2 2240 0.93 1.05 0.833 p3 2488 0.89 1.03 0.682 p4 728 0.77 7.30 0.684 total 6989 avg 0.87 avg 2.70 avg 0.709 option 3 p1 1823 0.99 1.78 1 (max capacity utilization) p2 1687 0.67 0.67 0.600 p3 2940 0.98 2.44 1 p4 575 0.59 2.08 0.591 total 7025 avg 0.81 avg 1.74 avg 0.765 option 4 p1 1823 0.99 1.78 1 (max number of product) p2 1690 0.73 0.76 0.625 p3 2263 0.80 1.32 0.619 p4 879 0.91 11.20 1 total 6655 avg 0.86 avg 3.76 avg 0.782 in option 1. although the average results in the last column indicate the similar percentages of launched production requests for all four options, detailed analysis by products shows that even nearly 40% production requests for some products remain unrealized in options 3 and 4. considering this indicator in the last column, option 1 gives the most balanced values. 6 conclusions the aim of this paper was to develop a heuristic algorithm for multi-period production planning based on supply chain kpis: customer service level and stock cover. analyzing fmcg company with limited production capacities, two important decisions are recognized in each period: which products should be produced (production requesting) and which product can be produced (production launching). the proposed algorithm provides support for both decisions. the first decision is based on two used kpis, while the second decision can be made by using one of four criteria: mincsl, minsc, maximal capacity utilization and maximal number of products. developed algorithm was applied in real fmcg where option 1 was chosen as the most appropriate according to their business policy. however, the main advantage of the algorithm is the fact that it offers a choice among four different decision criteria based on company’s business policies. it can be extended in order to generate demand forecast based on different forecasting techniques and adding new kpi: demand forecast accuracy, which will be used for evaluation of the impact of forecast accuracy on customer service level and stock cover variations. 720 d. makajić-nikolić, s. babarogić, d. lečić-cvetković, n. atanasov bibliography [1] babarogić, s.; makajić-nikolić, d.; lečić-cvetković, d.; atanasov n. (2012); multi-period customer service level maximization under limited production capacity, international journal of computers communications & control, issn 1841-9836, 7(5): 798-806. [2] dellaert n.p., jeunet j. (2003); demand forecast accuracy and performance of inventory policies under multi-level rolling schedule environments, research at international institute of infonomics, heerlen, the netherlands. [3] fernandez, r.; gouveia, j. b.; pinho, c. (2010); overstock a real option approach, journal of operations and supply chain management, issn 1984-3046, 3(1): 98-107. [4] galbraith, j. r. (1973); designing complex organizations, reading, ma: addison-wesley. [5] ho, c. (1989); evaluating the impact of operating environments on mrp system nervousness, int j prod res, issn 0020-7543, 27(7): 1115-1135. [6] jeffery m.m., butler j.r., malone c.l. (2008); determining a cost-effective customer service level, supply chain management: an international journal, 13: 225-232. [7] lane, r.; szwejczewski, m. (2000); the relative importance of planning and control systems in achieving good delivery performance, prod plan control, issn 0953-7287, 11(5): 422433. [8] lečić-cvetković, d.; atanasov, n.; babarogić, s. (2010); an algorithm for customer order fulfillment in a make-to-stock manufacturing system, international journal of computers communications & control, issn 1841-9836, 5(5): 983-791. [9] lin, j.; chen, j.h. (2005); enhance order promising with atp allocation planning considering material and capacity constraints, journal of the chinese institute of industrial engineers, issn 1017-0669, 22(4): 282-292. [10] makajić-nikolić, d.; lečić-cvetković; atanasov, n.; babarogić, s. (2013); an approach to production planning for supply chain performance improvements, proceedings of xi balkan conference on operational research, isbn 978-86-7680-285-2, 357-366. [11] meyr, h. (2009); customer segmentation, allocation planning and order promising in make-to-stock production, or spectrum, issn 0171-6468, 31(1): 229-256. [12] okulewicz, j. (2009); verification of a service level estimation method, total logistics management, issn 1689-5959, 2: 67-78. [13] shen z-y.m., daskin m. (2005); trade-offs between customer service and cost in integrated supply chain design, manufacturing and service operations management, 7(3): 188-207. [14] wadhwa s., kanda a., bhoon k.s. (2006); bibhushan impact of supply chain collaboration on customer service level and working capital, global journal of flexible systems management, 7(1-2): 27-35. international journal of computers communications & control issn 1841-9836, 12(5), 704-714, october 2017. delay tolerant networks over near field communications: the automatic multi-packet communication r.o. schoeneich, p. sadło radosław olgierd schoeneich* institute of telecommunications warsaw university of technology, poland *corresponding author: rschoeneich@tele.pw.edu.pl piotr sadło institute of informatics warsaw university of technology, poland p.sadlo@stud.elka.pw.edu.pl abstract: the near field communication (nfc) is designed for sending small data, which size does not exceed the several dozen bytes, for distance not higher than two inches. the nfc can be used in store-carry-forward scenarios which are bases for disruptive tolerant networks (dtn). the aim of this paper is proposal how to use nfc in transmission of large data in dtn. we propose an algorithm of fragmenting, sending and receiving data. we described software which was used in our prototype and discussed about implementation. finally we showed results of tests performed on our model. obtained results presents well characteristics of our solution. keywords: mobile communication, radio communication, wireless networks, personal communication networks, cooperative communication. 1 introduction nowadays, an attention of scientists working on ad-hoc networks is focused on delay and disruptive tolerant networks (dtn). the dtn is a specific network in which nodes are mobile, therefore the network topology is often changeable what can be reason of lack of end-to-end path between source and destination nodes. the communication of dtn is done by storing-carrying and forwarding messages. applications of dtn are specific: previously it was proposed for interplanetary communication [1], but now it is used in military [14], search and rescue [15], wildlife monitoring [10], body sensing [17] and in many other solutions [21] [13]. the dtn transmission is based on wireless interfaces which are usually long range like radio satellite communications and medium range e.g. wifi and bluetooth standards. one of the possible communication interface, which fulfills dtn requirements of wireless medium and easy applicability of store-carry-forward paradigm is a very short range near field communication (nfc) [24]. nfc is used to transfer small packets on a very short-range. transmission throughput is very low. this standard is widely used for cashless payment and short tag exchange. it is applicable not only on cards and tags but also on small mobile devices e.g. smartphones. unlike other wireless communication solutions, nfc is able to setup sessions between devices automatically. the usage of nfc can be effective implemented in the dtn environment. automatic and secret communication ability is advantage which supports the implementation of dtn objectives. the aim of this paper is to present solution how to use the nfc to communicate dtn devices using store-carry-forward without data size limitation. for this purpose, we proposed a simple way of communication with the automatic exchange data between devices. we designed and implemented solution how to apply nfc without size limit of the transferred data. copyright © 2006-2017 by ccc publications delay tolerant networks over near field communications: the automatic multi-packet communication 705 the rest of this paper looks as follows: in section 2 we presented the state of the art: we discuss nfc technology, basics of dtn, and other telecommunication issues. in section 3 we present the algorithm of automatic establishment communication session and a mechanism for transferring big packets of data. in the section 4 we present results of tests performed on our prototype. chapter 5 contains a conclusions. 2 state of the art the near field communication is a radio communication standard, based on rfid technology (radio-frequency identification). it allows to wirelessly transfer data on a distance up to 20 cm [2]. in practice, the exchange of information over distances is smaller than 4 cm. nfc can operate in two modes: active and passive. in the active mode, device alternatively communicate with each other. both devices need power energy. the device which is at standby state does not emit signals. in passive mode, only one device can initiate the connection. passive devices cannot read data by themselves; however, they can store it. nfc applications are focused on widely used wireless cashless payment. contactless payment [16] is done by cards and smartphones. other area of nfc usage are applications working with nfc tags with web addresses [23], tags for indoor navigation or information and wide aspects of smart homes e.g. office and home secure, body area communications [25]. all of this applications are using very short nfc messages and no one extends the basic communication functionality. delay and disruptive tolerant networks firstly was proposed by k. fall [7] for interplanetary communication. the basic idea is to provide communications in scenarios in which traditional telecommunication approach does not work. the dtn networks evaluate based on wireless adhoc and sensor network (manet and wsn) approach: every node acts as a part of network and can route a message. based on this for manet and wsn there was proposed several routing protocols [20] [19] and algorithms which supports data handling [8] [9] [12] [5] [3] and transmission [22]. the dtn adds the new assumption to the ad-hoc approach: store-carryforward messages. storing and forwarding messages is done by nodes called message ferries or mules. as a message ferry can act mobile device associated with human [4], animal or mobile machine [6]. by analogy to manet, dtn requires specific solutions for routing and data handling [11] [18]. all this solutions are working with popular communication standards like wifi and bluetooth. as authors know none of these solution is designed for very short range solution like nfc. 3 dtn over nfc multipacket communication this section describes algorithms for sending and automatic receiving large data. algorithms are implemented on android environment. we propose automatic solution that splits large data into elementary packets and avoids "touch to send" restriction that is mandatory in android nfc api. 3.1 message fragmentation into nfc packet each message before sending must be adopted to the size of nfc packet. it means, the fragmentation process must be taken. afterwards, nfc session is set up between devices (nodes) and the packets in sequence are sent to the receiver. sequence of steps is shown in figure 1. 706 r.o. schoeneich, p. sadło figure 1: sending process table 1: the detailed description of each record item description unique number unique number for the message bitstream payload number of packets number of parts generated by splitting the message packet number succeeding number of part delay tolerant networks over near field communications: the automatic multi-packet communication 707 figure 2: block diagram of the algorithm responsible for sending the nfc message in the example the message is divided into 8 fragments. each fragment is built from four nfc records, which are saved in nfc data exchange format (ndef). detailed description of each record is presented in table 1. the receiver analyzes each record of the received packet. the unique number is used to check in the local database if exists any received data of the message. the payload is appended to this data in case of existance another data of the message. after this operation the receiver sends the confirmation flag to inform the sender that the packet has been received and processed succesfully. the sender takes the next packet from the stack and sends it to the receiver. this process is repeated until the stack is empty. 3.2 data sending besides specifying a unique part number and payload, the algorithm determines packet number and calculate the total number of packets of the message. the number of packets is calculated by dividing the size of the message by the size of the single packet. the message is passed to the algorithm responsible for sending data as an input. additional parameters are: an unique number for transmission which was generated in the previous step, the number of packets to send, the number of the current being sent packet, and the defined packet size. the message is mapped to the byte stream described as the variable file. after 708 r.o. schoeneich, p. sadło reading each byte, algorithm determines if the examined byte is the last in the stream and/or whether the output buffer is not full. the algorithm stops injecting data into the input buffer if: (a) the amount of stored data reaches the maximum size of the packet, or (b) all data has been read from the file buffer. the packet is send after the completion of this process. algorithm of sending nfc message is presented in figure 2. 3.3 data receiving algorithm describing the behavior of the system during the process of packets receiving is presented in figure 3. in first step the algorithm analyzes received records. it is verified whether the payload is not empty and checks the database to determine whether another packets of the same message were not transmitted. data is appended to the stream in case, when another part of the message is found. if the application determines that the received packet is the last one, it is marked in the database. the algorithm decides whether the packet is the final one by comparing the parameters: the number of the package and the total number of packets. if the number of packet is equal to the number of message packets, the algorithm assumes that the message was sent. parts of the message are sent sequentially. the sending node will retransmit the packet, if a confirming flag is not sent by the receiver. 3.4 the automatic nfc multi-packet transfer the basic mechanism to nfc data communication is android beam. as a standard procedure before sending a message to another device, android beam enforces to tap mobile device screen. this procedure prevents device from unwanted automatic data send. automatic data send is mandatory for multi packet data transfer, therefore we propose the method to handle this problem. automatic tap functionality was implemented using xposed framework. the framework enables dynamic simulation of the tap action. xposed modules allow to perform actions immediately before and after triggering a captured method. in order to capture the system methods we re-implemented methods: handleloadpackage and findandhookmethod. this methods are called with parameters: the package name, the name of the hooked method and object of an xcmethodhook abstract class. the object have the appropriate method responsible for modifying the behavior of the hooked method. 3.5 delay tolerant networks support the nfc multi-packet communication system was proposed as a part of dtn system for a smartphone. the full functionality of dtn device consists of a group of functions responsible for communication and storage. in terms of architecture, this set of functionalities was implemented in an integrated way. the first component includes a group of functions responsible for autonomous sending and receiving nfc multi-packet messages. the functionality of communications in our implementation consists of the use of communication nfc interfaces. dtn paradigm store-carry-forward is implemented in classical way based on simple epidemic routing idea. this means that all messages are replicated and send to every connected device. 4 results the goal of our tests was to compare the effectiveness of the nfc multi-packet data transfer process for approaches with manual and automatic handling. manual handling requires tap to send (tts), the automatic handling is performed without tts. delay tolerant networks over near field communications: the automatic multi-packet communication 709 figure 3: block diagram of the algorithm responsible for receiving the nfc message figure 4: modular architecture for dtn via nfc using android device 710 r.o. schoeneich, p. sadło figure 5: the total mean transfer time for automatic and manual transfer for packets: a) 2kb, b) 4kb, c) 8kb and d) 16k for tests we proposed packets with sizes: 2 kb, 4kb, 8 kb, 16 kb, 32 kb and 64 kb. for each message more than 100 measurements were done. for each measurement the transfer execution time and the total mean throughput was reported. the total transfer time was defined as a sum of: (a) preparation of the nfc message, (b) user interaction (during tests with tts), (c) data transfer. for tts we manually perform screen tapping as soon as it is possible. tests were done on samsung galaxy s3 and lg l65 mobile phones. both devices have got built-in nfc module. samsung galaxy s3 was working on android 4.0 operation system, lg l65 was working on 4.4 android operation system. tests were done in 12 square meters room. at first we perform measurements of packet transfer time (see figure 5). the time increases with the size of the packet as expected for tts and without tts. some transfer attempts were failed during tests. sessions were interrupted by the logical link control protocol (llcp). llcp is a second layer protocol responsible for setting up session between two nfc enabled devices in peer-to-peer communication. llcp was returning errors during setting up session (marked in figure 5 as 0 ms transfer time sample) or setting up session longer than expected. the second circumstance had big influence on the transfer time, which was getting longer. the effectiveness defined as transmission success ratio of both solutions was presented in figure 6. the effectiveness is increasing with the size of the packet. success ratio was higher than 80 % almost in each test which we have done. in most cases approach without tts was more efficient. furth more there was 100% correctly sent samples for 8 kb and 16 kb packets. although, the efficacy of transferring data without the tts was higher, there were more samples with extreme values. transferring time reached tens of seconds. xposed is not officially supported by google. this framework can be unstable. release of the next android version cause that xposed authors has to adopted framework to the new version. we suspect that xposed instability has got influence on measurements without tts tests. the variation and standard deviation were much larger for transmission with bypassed tts. because of that we decided to focus on median value to calculate throughput for tests. figure 7 presents the median time of transferring packets. results presents the better transfer time for delay tolerant networks over near field communications: the automatic multi-packet communication 711 figure 6: the success ratio of multi-packet transfer figure 7: the median of mean transfer time 712 r.o. schoeneich, p. sadło figure 8: analysis of time needed to perform the operation while transferring the file figure 9: throughput in function packet size automatic solution without tts. the second parameter measured was throughput the total speed of data transfer. we determine the throughput as a message size divided by total transfer time. the total transfer time is the sum of preparation message time, user interaction time and data transfer time. data transfer time increases with the size of the packet. creation time is constant for all samples in the packet size group and has huge impact on the results. the throughput increases with the size of the packet. packet creation time is more negligible with larger packet size. this phenomenon is illustrated on figure 8. moreover the automatic mechanism is more effective through the tts mechanism elimination. results was presented in figure 9. 5 conclusions in this paper we have presented the idea of nfc multi packet communication for dtn networks. we decided to create this work due to the lack of data about nfc usage in dtn networks especially for larger message transmission which requires fragmentation. we described in detail algorithms for sending, receiving and automatic handling of fragmented message. the algorithms was implemented on android device and extensively tested. we examined our automatic approach comparing with human-involved standard android tap-to-send method. all test was conducted with different packet sizes. obtained results confirm the good performance of our solution. the automatic approach has lower total transfer time and higher throughput then manual one. delay tolerant networks over near field communications: the automatic multi-packet communication 713 bibliography [1] burleigh s., hooke a., torgerson l., fall k., cerf v., durst b., scott k., weiss h. 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(1996); personal area networks: near-field intrabody communication, ibm syst. j., 35(3-4), 609-617, 1996. doi:10.1147/sj.353.0609 ijcccv8n1.pdf int j comput commun, issn 1841-9836 8(1):30-36, february, 2013. outlier detection with nonlinear projection pursuit m. breaban, h. luchian mihaela breaban, henri luchian "alexandru ioan cuza" university of iasi, romania e-mail: {pmihaela, hluchian}@infoiasi.ro abstract: the current work proposes and investigates a new method to identify outliers in multivariate numerical data, driving its roots in projection pursuit. projection pursuit is basically a method to deliver meaningful linear combinations of attributes. the novelty of our approach resides in introducing nonlinear combinations, able to model more complex interactions among attributes. the exponential increase of the search space with the increase of the polynomial degree is tackled with a genetic algorithm that performs monomial selection. synthetic test cases highlight the benefits of the new approach over classical linear projection pursuit. keywords: outlier detection, nonlinear projections, genetic algorithms 1 introduction mining for outliers is of great importance in many domains: fraud detection, disease identification,intrusion detection, fault diagnosis and so on. outliers or anomalies represent rare observations/events that deviate from the majority of data either in magnitude or with respect to an overall pattern. whatever the data, a statistical model can be attached to it and consequently the data can be considered to be generated by a statistical process. in this view, outliers correspond to very low probabilities under the underlying distribution. outliers may sometimes simply occur due to erroneous recording of data and not due to meaningful anomalous data which would correspond in the theoretical model to changes in the generative process. identifying them must be one of the first steps in data analysis as their presence misleads many algorithms from the machine learning area solving tasks like clustering, classification or regression. this paper proposes and investigates a new framework for outlier detection derived from the classical projection pursuit methodology, aiming at alleviating some of the drawbacks of the standard approach. an analysis of the popular approaches for outlier detection highlight the benefits of the new approach. the material is structured as follows. section 2 surveys existing computational methods for outlier detection. section 3 succinctly describes the framework of projection pursuit. the method we propose is presented in section 4 and is empirically investigated in section 5, while section 6 draws the conclusions. 2 computational methods for outlier detection we consider the unsupervised framework of outlier detection: there is no pre-specified generative model for the data and no labels are available to provide examples from which an algorithm could learn. several surveys exist in literature that provide a state-of-the-art for outlier detection in this framework [1–4]. generally, the literature distinguishes among several classes of methods. copyright c© 2006-2013 by ccc publications outlier detection with nonlinear projection pursuit 31 most statistical methods for outlier detection are parametric methods: given a certain kind of statistical distribution outliers are detected as those points with low probability of being generated. in the univariate case usually the normal distribution is used and outliers are considered those observations that lie at a distance larger than k standard deviations from the mean. for the multi-variate case the mahalanobis distance is considered to compute the distance of the observations from the mean. the main drawback of this approach is that the parameters of the distribution (mean and standard deviations) are computed based on all observations, including the possible outliers, and therefore they may be highly biased. non-parametric approaches based on standard deviation identify the subset of observations that, after exclusion, determines the highest decrease in variance; using any form of the minkowski metric the method can be generalized for the multi-variate case. the drawback of this method is that the size of the search space is exponential w.r.t. the number of observations. the first and the third quartiles are also used to identify outliers in the univariate case. distance-based approaches [10–13] for outlier detection base their decisions on computing the distances between each data point and its neighbors. these are multi-variate approaches. the main advantage of these methods over statistical approaches is that no hypothesis on the type of distribution is made, hence such methods are applicable without distribution-dependent restrictions. these methods are computationally expensive due to the calculations of distances between data points; therefore, various ways of scaling them up for large databases have been proposed. still, one important drawback is present: even if they do not make any assumptions on the type of distribution they are not parametric-free methods. the result is very sensitive to some user-tuned parameters like the radius of the neighborhood, the number of neighbors to be used or the threshold indicating the average distance to the neighbors above which a data is considered as outlier. clustering-based approaches employ an unsupervised clustering algorithm to identify groups in data. in this context outliers are identified as unusually small groups in data. popular algorithms used in this context are hierarchical clustering methods (mostly the single-link variant) and density based clustering methods like dbscan. the methods have the advantage of being generally applicable to any distributions in data. their drawbacks reside in high computational costs requiring distance computations between data items and sensitivity to parameters in case of density-based methods. all the above-mentioned multi-variate techniques take into account the entire attribute space. based on distance computations, a drawback is inherent: in high-dimensional spaces the ratio of the distances of the nearest and farthest neighbors to a given target is almost one making outlier detection an impossible task. projection pursuit is hardly mentioned in existing surveys on outlier detection. when it is, attention is given usually to a particular exponent of this class of methods principal component analysis (pca)which is in fact a dimensionality reduction method that aims at preserving as much as possible the variance in data and not a method dedicated to outlier detection. projection pursuit can be used to identify subspaces of the original attribute space where outliers are present, alleviating the mentioned drawback resulting from high dimensionality. this paper highlights the important role projection pursuit can play for outlier identification and enhances the classical methodology by introducing nonlinear projections. 3 projection pursuit a k dimensional projection of a data set x ∈ rn×d consisting of n items described by d numerical attributes is a linear transformation involving k orthogonal vectors in a d-dimensional space. these vectors form an orthogonal basis a ∈ rk×d. the projection of x into a is 32 m. breaban, h. luchian the product z = x · at resulting in a new representation for each of the n data items in a k-dimensional space. projection pursuit (pp) [5] is a technique aiming at identifying low-dimensional projections of data that reveal interesting structures. the framework of pp is formulated as an optimization problem with the goal of finding projection axes that minimize/maximize a measure of interest called projection index. the projection index can be formulated to identify subspaces where clusters are visible, linear combinations that discriminate between given classes or low-dimensional views of data that reveal the presence of outliers. depending on the formulation of the index under maximization/minimization analytical methods exist (the case of pca), gradient-based methods may be used (if the index is continuously differentiable) or probabilistic heuristics like hill climbing or simulated annealing are employed. the current work is conducted towards identifying single-dimensional views (one-dimensional projections) of data that present outliers. these can be manually inspected, or simple statistical rules (univariate analysis) can be applied to identify and exclude the outliers. a popular index used to derive projections with high chances of containing outliers is kurtosis defined as the fourth moment around the mean divided by the square of the variance: kurt = n ∑ l=1 (y(l) − µ)4 (n − 1)σ4 (1) where µ is the mean and σ is the standard deviation of the single-dimensional projection. a value close to 3 indicates a normal distribution. higher values indicate the presence of extreme deviations while lower values indicate bimodal distributions. in consequence, this index should be maximized to derive projections containing outliers. other indices also exist but are more expensive computationally [6, 7]. one drawback of the classical projection pursuit approach is that linear projections corresponding to linear combinations over the original attributes, are not able to model complex generative models and consequently are not able to detect the outliers in all cases. such a case is illustrated in figure 1. to alleviate this drawback we extend the original framework to allow the derivation of nonlinear projections. to this aim, we extend the data set by introducing new features built as products of the original ones. projection pursuit can be further conducted with classical methods proposed in literature. because the extension we propose increases considerably the search space, we also design and investigate an optimization framework based on multi-modal genetic algorithms, which allows searching simultaneously for the relevant attributes combinations and the coordinates of the projection axis. 4 an algorithm for detecting outliers with nonlinear projections nonlinear projections can be performed after introducing in the analysis new features generated as products of original features. this is a standard approach in other data mining tasks (i.e. regression and classification) to extend standard methods that derive linear models but hardly used in the field of projection pursuit in general and outlier detection in particular. one drawback is inherent to this approach: the exponential increase of the number of new attributes introduced into the analysis with the increase of the degree of the polynomial model. if the number of original attributes in a data set is m, the number of monomials of degree 2 that can be introduced in the analysis is m(m+1) 2 while the number of monomials of degree 3 is m(m+1)(m+2) 6 . in general, the number of monomials of degree i that can be formed on m variables in ( m+i−1 i ) and the number of attributes that can be introduced to derive polynomials up to a certain degree k is nk = ∑ k i=1 ( m+i−1 i ) = (m+k)! m!k! − 1. outlier detection with nonlinear projection pursuit 33 to deal with the large number of features and speed up the projection pursuit algorithm we incorporate a feature/monomial selection mechanism. to this aim a genetic algorithm is designed that simultaneously selects good monomials and searches for good projection axes within the selected monomial subspace.a multi-modal genetic algorithm is used in order to allow for simultaneous exploitation of several good monomial subspaces. a candidate solution corresponding to a chromosome in population consists of two parts: • a boolean string of length equal to the number of monomials to a given degree k: value 1 corresponds to monomials to be included in search of projections; • a vector in the euclidean space playing the role of the projection axis, corresponding in fact to numerical weights in the resulted polynomial transformation such mixed representations are common in feature selection tasks solved with genetic algorithms in the context of clustering [15, 16] and classification [14]. the projection y(l) ∈ r of an item x(l) ∈ rm in the subspace encoded by a chromosome is computed as follows: y(l) = ∑ nk i=1 bi · wi ∗ m (l) i where m (l) i is the ith monomial computed as a product over elements from x(l). using only a subset of the monomials to conduct further the search of axes is equivalent to assigning weight 0 for the rest. for multi-modal search we use the multi niche crowding ga [8], an algorithm able to maintain stable subpopulations within different niches, to maintain diversity throughout the search and to converge to multiple local optima. mnc is a steady state algorithm that implements replacement based on pairwise comparisons. both the selection and replacement operators implement a crowding mechanism. mating and replacement within members of the same niche are encouraged while allowing at the same time some competition for the population slots among the niches. selection for recombination takes place in two steps: one individual is selected randomly from the population; its mate is the most similar individual from a group of size s which consists of randomly chosen individuals from the population. the two chosen individuals are subject to recombination operators and one offspring is created. the individual to be replaced by the offspring is chosen according to a replacement policy called worst among most similar : f groups are created by randomly picking g (crowding group size) individuals per group from the population and one individual from each group that is most similar to the offspring is identified; then, the one with the lowest fitness value among these is replaced. in the original mnc algorithm the replacement is always performed, even if the fitness of the offspring is lower than the fitness of the individual chosen to be replaced. in our implementation we adopt a simulated annealing strategy: lower fitness survival is accepted with a probability that decreases during the run of the algorithm. the similarity between two individuals is computed based on the boolean part of the chromosome that encodes the monomial subspace; the hamming distance is used. recombination between two chosen individuals consists of crossover that generates one offspring which is subsequently mutated. dedicated crossover and mutation operators are designed and applied to each of the two segments of a chromosome. the crossover operator applied to two chromosomes consists in fact of two operations performed independently on the two parts of the chromosomes. uniform crossover is used on the binary segment encoding the monomial subspace. on the numerical segment, crossover generates each gene of the offspring as a convex combination between the corresponding genes of the parents. mutation is also applied in two distinct phases. each gene in the binary segment is flipped with a given probability which we call binary mutation rate. to each weight in the numerical 34 m. breaban, h. luchian segment corresponding to a selected monomial a random value in the interval (-0.25, 0.25) is added with a probability called weights’ mutation rate. the binary mutation rate is lower than the weights’ mutation rate in order to encourage better exploitation of a given subspace for optimal projection axes. before evaluation, the weight vector in the selected subspace of the offspring is normalized to unit length. the evaluation consists in computing the projection of all data items on the axis given by the weight segment of the chromosome in the encoded monomial subspace, followed by the computation of a projection pursuit index dedicated to detecting clusters in data. the index we use is the kurtosis. we choose to maximize this index mainly because its reduced computational complexity compared to other proposed indices for cluster detection. when the projection is normalized to mean 0 and variance 1 the index consists in summing up the values raised to the fourth power, divided by the total number of items. usually, projection pursuit is preceded by a linear transformation on the data called sphering that guarantees that every linear projection is distributed with mean 0 and standard deviation 1, eliminating the need for further normalization. a sphering procedure is also applicable in our case after all monomials to degree k are added to the original data. however, in our experiments we normalize each projection prior to computing the kurtosis. without a multi-modal search scheme, outliers should be identified and eliminated incrementally, based on linear combinations at first, then monomials of degree 2, 3 and of higher order can be introduced iteratively. the multi-modal algorithm allows for several single-dimensional projections maximizing the index to be returned in one run. this brings some advantages: in a standard heuristic only one solution is returned; identifying all outliers requires iteratively the exclusion of the identified outlier and a new execution of the algorithm. the benefits of multi-modal search were recently highlighted in the context of linear pp [9]. 5 experiments figure 1 represents a data set containing 100 observations in a two-dimensional space. it is illustrative for the drawback of classical pp: linear projections of data on one axis are not able to identify the interior outliers. the parameters of the new method are set as follows: s = 0.15 ˇ pop size, g = 0.10 ˇ pop size, f = 0.15 ˇ pop size. the mutation operator is applied at different rates on the two segments of a chromosome: approximately one mutation during 10 iterations is applied on the binary segment while 1 mutation per iteration is applied on the numerical segment; using a steady-state scheme, only one offspring is generated and evaluated at each iteration. the population consists of 50 individuals, randomly initialized: on the binary segment approximately 4 monomials are selected (set to 1) while on the numerical segment the values are generated in the interval [-1,1]. the algorithm was executed at first with monomials of degree one, to simulate one run of a classical pp algorithm: the search is conducted in the original feature space. the black line in figure 1 a) represents the projection axis generating a linear combination of attributes of maximum kurtosis, returned by our method in this step. figure 1 b) represents the histogram of the data under this linear combination of maximum kurtosis: the exterior outlier appears at the left while the interior outliers get mixed with the rest of the data. without excluding the identified outlier the algorithm was executed again including in the search space all monomials of degree 2. figure 1 c) represents the distribution of the nonlinear combination (of maximum kurtosis) of the same data derived with our method: the three interior outliers can be identified at the right, outlier "1" being at the extreme, followed at the left by outliers "2" and "3". as a second test case we are interested in the ability of our method to detect the outliers when noise attributes are introduced. to this aim, 3 uniformly-distributed attributes outlier detection with nonlinear projection pursuit 35 are added to the data set in figure 1, the result consisting in 100 observation in a 5-dimensional space. figure 2 presents the results of two runs of our algorithm: the linear projection (b) is similarly oriented in the original space, with the outlier at the left and the nonlinear projection (b) identified the three interior outliers at the right. figure 1: a synthetic data set containing 4 outliers a) the projection axis detected with classical pp is drawn in black; b) the histogram of the linear projection detected with classical pp: only the exterior outlier can be identified; c) the histogram of the nonlinear projection returned with new method: the three interior outliers appear at the right figure 2: a synthetic data set containing 4 outliers in a 2-dimensional space and 3 more uniformly-distributed attributes a) the projection axis detected with classical pp is drawn in black; b) the histogram of the linear projection detected with classical pp c) the histogram of the nonlinear projection returned by the new method 6 conclusions the paper proposes an extension of the classical linear projection pursuit framework in the context of outlier detection. by creating nonlinear projections of data the new method is capable of modeling diverse generative processes and identify outliers which linear projection pursuit does not detect. the proposed method compares positively to distance-based and density-based approaches: the new method provides results which are not altered by the presence of many uniform/gaussian attributes, as it happens with the other approaches, where distance computations over the entire space of attributes are performed. this is because pp intrinsically performs subspaces/attributes selection and moreover, our method deals with monomial selection explicitly. at the same time, the (non)linear combinations of attributes identified to contain outliers can provide useful explanatory information to the user on the nature/source of outliers. excepting the univariate analysis performed in the last step for outlier exclusion, our method is parameterfree. scaling up for very large databases is favored by the fact that the proposed algorithm can be easily parallelized: as the most demanding step is projecting the entire data set on a given axis, this operation can be executed in parallel for distinct observations. 36 m. breaban, h. luchian acknowledgement this work was supported by posdru/89/1.5/s/63663 commscie grant. bibliography [1] h.-p. kriegel, p. kröger, a. zimek, outlier detection techniques, tutorial at 16th acm sigkdd conference on 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[2] v. hodge, j. austin, a survey of outlier detection methodologies, artif. intell. rev., 22(2):85-126, 2004. [3] irad ben-gal, outlier detection, in: maimon o. and rockach l. (eds.), data mining and knowledge discovery handbook: a complete guide for practitioners and researchers, kluwer academic publishers, 2005. [4] v. chandola, a. banerjee, v. kumar, anomaly detection: a survey, acm comput. surv., 41(3), art. 15, 2009. [5] j. h. friedman and j. w. tukey, a projection pursuit algorithm for exploratory data analysis, ieee trans. comput., c23(9):881-890, 1974. [6] stahel, w. a., breakdown of covariance estimators, research report 31, fachgruppe fur statistik, e.t.h. zuurich, 1981. [7] j.h. friedman, exploratory projection pursuit, j am stat assoc, 82(1):249-266, 1987. [8] v. vemuri and w. cedeńo, multi-niche crowding for multimodal search. practical handbook of genetic algorithms: new frontiers, ed. lance chambers, vol.2, 1995. [9] a. ruiz-gazen, s. l. marie-sainte, and a. berro, detecting multivariate outliers using projection pursuit with particle swarm optimization, proc. of compstat2010, 89-98, 2010. [10] knorr, e.m. and ng, r.t., a unified approach for mining outliers, proc. conf. of the centre for advanced studies on collaborative research (cascon), toronto, canada, 1997. [11] knorr, e.m. and ng, r.t., finding intensional knowledge of distance-based outliers, proc. int. conf. on very large data bases (vldb), edinburgh, scotland, 1999. [12] angiulli, f. and pizzuti, c., fast outlier detection in high dimensional spaces, proc. european conf. on principles of knowledge discovery and data mining, helsinki, finland, 2002. [13] hautamaki, v., karkkainen, i., and franti, p.. outlier detection using k-nearest neighbour graph, proc. ieee int. conf. on pattern recognition (icpr), cambridge, uk, 2004. [14] a. sierra, high-order fisher’s discriminant analysis, pattern recognition, 35(6):1291-1302, 2002. [15] j. handl, j. knowles, feature subset selection in unsupervised learning via multiobjective optimization, int. j. of computational intelligence research, 3:217-238, 2006. [16] m. breaban, h. luchian, a unifying criterion for unsupervised clustering and feature selection, pattern recognition, 44(4):854-865, 2011. international journal of computers communications & control issn 1841-9836, 9(4):389-396, august, 2014. a method to construct approximate fuzzy voronoi diagram for fuzzy numbers of dimension two d. arotaritei dragos arotaritei "grigore t. popa" university of medicine and pharmacy romania, 700115 iasi, str. universitatii, 16 e-mail: dragos.arotaritei@umfiasi.ro abstract: in this paper, we propose an approximate "fuzzy voronoi" diagram (fvd)for fuzzy numbers of dimension two (fndt) by designing an extension of crisp voronoi diagram for fuzzy numbers. the fuzzy voronoi sites are defined as fuzzy numbers of dimension two. in this approach, the fuzzy numbers have a convex continuous differentiable shape. the proposed algorithm has two stages: in the first stage we use the fortune’s algorithm in order to construct a "fuzzy voronoi" diagram for membership values of fndts that are equal to 1. in the second stage, we propose a new algorithm based on the euclidean distance between two fuzzy numbers in order to construct the approximate "fuzzy voronoi" diagram for values of the membership of fndts that are smaller than 1. the experimental results are presented for a particular shape, the fuzzy ellipse numbers. keywords: approximated fuzzy voronoi diagram, fuzzy numbers of dimension two, computational geometry, fuzzy arithmetic, bisector median, path planning. 1 introduction many studies have been made relating to voronoi diagram and its application [1]. usually, the main algorithms deal with exact voronoi diagram. only few of them deal with approximated voronoi diagram. in some of paper, 2d objects has been taken into account in order to construct voronoi diagram. an algorithm to construct voronoi diagram for convex sites is proposed in [2]. the authors start with the postoffice problem where the main question is how to process a set of sites in the plane in order to find the closest site to a query point. the study in [2] treats points as disjoint convex polygons. in [3] the authors proposed voronoi diagrams of ellipses using an incremental algorithm that constructs also the exact delauny graph. a distance from a point to an ellipse is defined to be the minimum euclidean distance to the boundary of the ellipse with negative value in case the point is inside the ellipse. most of the papers treat the problem of disjoint objects. in some papers, the objects can intersect [4] or one object can be inside another object [4]. in the latter case, the circles can intersect each other and circles can contain other circles [4]. in [5] the authors used an incremental algorithm in order to construct voronoi diagrams for convex objects. a polynomial approximation of the curves is suggested. an algorithm that computes an approximate version of voronoi diagram is introduced in [6]. the cells in these approximated diagrams are cubes or difference of two cubes. other papers deal with weighted voronoi diagrams [7] using the euclidean distance functions. each point p from a given set p of points in the plane has an associated weight that expresses the level of influence over this neighborhood. fuzzy objects are an important part in the fuzzy set theory. 1-d fuzzy sets have been extended to fuzzy plane and fuzzy plane geometry (circles and polygons) in [9]. only very few papers deal with fuzzy voronoi diagram. in ( [10][11]) the authors proposed an algorithm that deals with fuzzy objects in fuzzy voronoi diagrams. the "fuzzy voronoi" diagram is an extension of the voronoi diagram based on fortune’s algorithm for crisp cases. the "fuzzy voronoi" diagram copyright © 2006-2014 by ccc publications 390 d. arotaritei has been extended for a set of fuzzy circles, rather than fuzzy points, of the same radius. the authors proved that the boundaries of this diagram are constructed by intersecting a number of hyperbolae and they proposed an algorithm that computes the exact formula [11]. the geometric approach is presented in [12]. the authors define the basic fuzzy geometric shapes (point, line, circle, ellipse and polygon) using the 2-d support space on the level of fuzzy set. a fuzzy measure for trapezoidal fuzzy number is proposed in [13]. optimal path planning for mobile robot navigation is an important area of research in computer science. usually, obstacles are approximated by rectangular or circular cylinders and voronoi diagram can be an effective solution [14]. an interesting approach is optimal path planning in an uncertain (fuzzy) environment and 3d space that conduct to idea of fuzzy voronoi diagram. fuzzy solution can be very effective for various problems [15]. we present a different algorithm that computes an approximated fuzzy voronoi diagram for fuzzy numbers of dimension two. it is possible to extend the proposed algorithm to n-dimensional fuzzy numbers using α-cuts. 2 fuzzy numbers of dimension two before we proceed to the main results described in this paper we make a short review of the crisp voronoi diagram [1] and fndt [16]. some proposal for fuzzy distance are investigated by some authors ( [17][19]). we will use euclidean distance in fuzzy workspace and some of notations adapted from [11] for voronoi cell and voronoi diagram. letp be a discrete subset in the metric space x. the voronoi cell v (p) is defined as the set of all points x in x that have the distance to p lower or equal to the distance from x to other points q in p [11]. v (p) = {x ∈ x|∀q ∈ p, d(x, p) ≤ d(x, q)} (1) the voronoi diagram of p is the set of all voronoi cells denoted by v (p). the points p are named voronoi sites of the set p ( [10][11]). a fuzzy numbers of dimension n can be defined according to ( [9], [16]). let’s consider a fuzzy graph g ⊂ r × r... × r, a functional relation in rn having the membership function µg(x1, x2, ..., xn) ∈ rn. we define an n-dimensional fuzzy number f[a]n as any non-empty convex fuzzy subset of g ∈ rn that meets the following conditions [16]: • a is a normal fuzzy set, i.e. there is an x0 ∈ rn, µg(x0) = 1; • a is a convex set, that is membership function is convex hypersurface, i.e. µa(αx + (1 − α)y) ≥ min(µa(x), µa(y)), α ∈ (0, 1]; • the membership function is a convex hypersurface that for ∀a ∈ r, there is µa(x) = a. in particular, for n=2 we have fuzzy numbers of dimension two (fndt), as they are defined in ( [9], [16]). a general shape is shown in fig. 1a. in fig. b we showed the cut of this surface (top view) for a level α ∈ (0, 1]. the fuzzy voronoi diagram involves computing distances in fuzzy spaces. the results of calculus depend of the metrics used for the fuzzy space.an important point is the selection of the shape of fndt, derivable in all the points or not: convex polygon, rectangular, square, circle, ellipse or closed curves. in our approach, a continuous convex closed curve of fndt is considered. the circle or ellipsis is a particular case. various metrics in f̃(ℜ) have been proposed in the literature, most of them based on the hausdorff distance ( [18][19]). other authors proposed definitions for measures in the euclidean a method to construct approximate fuzzy voronoi diagram for fuzzy numbers of dimension two 391 figure 1: (a) a fuzzy number of dimension 2; (b) top view of α-cut of fndt (general case) space, function that capture more information about vagueness, α-cut that transform fuzzy number into an interval number and distance index based on centroid of fuzzy number. the α-level set aα of a fuzzy number is a non fuzzy set defined by: aα = {x ∈ ℜ|µ(x) ≥ α}, µa(x) = sup a∈(0,1] {α · gaα(x)} (2) where gaα(x) is the characteristic of the function aα. 3 approximated fuzzy voronoi diagram (afvd) for fuzzy numbers of dimension two in the next paragraphs we define the fvd for a set of sites that are fuzzy numbers of dimension two. let p̃ be a subset of ℜ2, which is constructed from fuzzy numbers of dimension two and let p̃ ∈ p̃ be a fndt. a fuzzy voronoi cell of p̃ is defined, in a similar manner as in [11], by ṽ (p̃): ṽ (p̃) = {(x, µp̃(p̃))|∀q̃ ∈ p̃, d(x, p) ≤ d(x, q)} (3) we note p = (px, py) and q = (qx, qy) two crisp points that belong to a fndt at α-cut level. we omitted α from the following formulae in order to simplify the notations. the euclidean distance between two points p and q that belong to a fndt at α-cut level is given by: de(p, q) = √ (px − qx)2 + (py − qy)2 (4) this definition is consistent with the results obtained in the particular degenerated case when fndt become a single point or when α = 1. let us consider two fndt ellipse shapes not necessary equal. each level of the α-cuts level will produce two ellipses in the same plane. the fuzzy voronoi diagram for n fndt is the union of voronoi diagrams of fuzzy α-cuts for all n fndt. each α-cut of fndt become a set of ellipses for which a voronoi diagram is required. it is easy to demonstrate that given two disjoint continuous convex closed curves a and b, for any two points inside of continuous convex closed curves, the bisector median of these two points are bordered by other two bisector median of two points situated on the contour of the curves. a conclusion of this assertion is that to construct the bisector median of all the points on surface of two continuous convex closed curves it is enough to construct the bisector median for all the points on the contour of the curve that is the border of the bisector median for all the points. 392 d. arotaritei figure 2: (a) the construction of the bisector median for two points located on the fndt and the asymptotes of the fvd; (b) the construction of apvd for first curve for each α-cut, we can compute the fuzzy voronoi diagram using only the contour of the curve from fndt obtained by this α-cut. in what follows, we will denote by hk ∈[0,1) the α-cut level of index k. the starting point for our approach is the calculus of bisector median for two points, one of them situated on the first curve and the second one situated on the second curve. let’s denote by x1(x1, y1) and x2(x2, y2) these two points. the equation of the bisector median is given by a line, perpendicular on the middle of the segment x1x2. if the slope of the line x1x2 is m,the slope of the bisector median of x1x2 will be m′ = −1/m = (x2 − x1)/(y2 − y1). the equation bisector median (bm) is a line that pass by the point m(xm, ym), the middle of the segment x1x2 and it has the slope m′. after few manipulations, the bms equation si given by: y = − x2 − x1 y2 − y1 · x + x22 + y 2 2 − x 2 1 − y 2 1 2 · (y2 − y1) (5) the fuzzy voronoi diagram for two fndt consists from all the bisector medians for all the points situated on the two closed curves. the asymptotes for the envelopes of all bisector medians is given by the bisector median of internal tangents of the two closed convex curves. if we apply counterclockwise rotation and translation for any point with φ angle in euclidean space, for any two ellipses we have simplified parametric equations of two ellipses (as in fig. 2(a) and fig. 2(b)). let’s consider two convex closed curve that after eventually rotation and translation looks like in fig. 2(a). the origin of axis is located in the center point of the first unimodal fndt and his center point of the second unimodal fndt is located on the axis 0x (fig 2(a)). we consider two points situated on this curves x1(x1, y1) and x2(x2, y2) as in fig. 2b. the bisector median of these points will be given by formula (5). let’s consider a line that pass in the point c1(0, 0), with m2 incrementally from point tl2 and tl1 arbitrary chosen on the asymptotic limits for fuzzy voronoi diagram s1 and s2. the two lines intersect in the point p(xp, yp) given by the solution of the system:{ yp = m1xp + n1 yp = m2xp + n2 (6) { m1 = −(x2 − x1)/(y2 − y1), n1 = (x22 + y 2 2 − x 2 1 − y 2 1)/(2 · (y2 − y1)) m2 = const., n2 = 0 (7) a method to construct approximate fuzzy voronoi diagram for fuzzy numbers of dimension two 393  xp = x22+y 2 2−x 2 1−y 2 1 2·(x2−x1+m2·(y2−y1)) yp = m2xp (8) if m1 ̸= m2, the two line intersect in the point p(xp, yp), where xp = (n1 − n2)/(m1 − m2) and yp = m1((n1 − n2)/(m1 − m2)) + n1. in the case m1 = m2, the two lines are parallel, so no intersection point exists. in our algorithm we simply skip this situation and we go to the next increment. let’s consider the bisector median for points c1 and c2. this crisp voronoi diagram for two points c1 and c2 split conventionally the plane in two halves, left (where first curve is located) and right (where the curve is located). the envelope of the fuzzy voronoi diagram for left half-plane is given by the minimum of distance between points p and c1. for a given m1, this distance is the intersection of the line r (fig. 2(b)) and one of the bisector median for all the lines given by formula (5). the minimum of distance is given by: dm = d 2(c1, p) = d 2 m = x 2 p + y 2 p = m 2 1 · x 2 p (9) the function dm reach the minimum when x2p reach the minimum, that is we must find (x1, y1) and (x2, y2) for which this minimum is obtained. if the curves are given by parametric equations, this mean that we must find the pair (t1, t2) for which dm = f(x1(t1), y1(t1), x2(t2), y2(t2)) = g(t1, t2), t1 ∈ ∠(t11, a, t12), t2 ∈ ∠(t21, c, t22) reach the minimum for a given m2. for each m2j ∈ [mmin, mmax], j = 1, ...n we will find an pj. the union of all the pj will give the left envelope of the afvd for these two fndt at hk level of α-cut. the envelope is asymptotic limited by the bisector median of the internal tangents. fvleft = n∪ j=1 pj (10) for the right side, the formulas (6)-(9) are a bit changed, taking into account the equation of the line r that must pass in the point c2(xc2, 0). after few calculations, the formulas become: xp = x22+y 2 2−x 2 1−y 2 1−q·(y2−y1) 2·(x2−x1+m2·(y2−y1)) , q = 2 · m2 · xc2 = const. yp = m2xp + n2, n2 = −m2 · xc2 (11) dm = d 2(ca2, p) = d 2 m (12) d2m = (xp − xc2) 2 + y2p = (1 + m 2 2) · x 2 p + 2 · (m2n2 − sc2) · xp + xc2 2 + n22) (13) afvd use numerical methods to find the minimum of function given by formulas (7)-(13). let’s suppose that we have the equation of curves given by parametric formula. we can convert the equation of curves into parametric formulas. the quadratic form (circle, ellipse) can be easily converted to parametric form. we taken into account two options. the first one use a minimization with constrains of the function dm as a function of two parameters in order to find (t1, t2) and so one (xp, yp). mint1,t2f(t1, t2) such that t1,min ≤ t1,max, t2,min ≤ t2 ≤ t2,max (14) it is a problem o constrained nonlinear optimization that can be solved by many methods: interior-point, sqp, active-set, trust-region-reflective. we chosen to use function fmincon implemented in matlab toolbox. 394 d. arotaritei the second option is possible in the case of twice differentiable curves. we can find the extremum of the function f(t1, t2) by calculating the stationary points using first partial derivative and thereafter by select the points that are local minimum using the second derivative. the saddle and the maximum points are eliminated. both numerical solutions have a problem: how to choose the initial point in order find the global minimum. the surface of equations (13)-(11) can have many local minima, so we must start with initial solution close enough to global minimum. in order to overcome this situation, we propose to use a brute-force algorithm (bfi ) to find the initial points. we split the domain of ti, i=1,2 in ni parts. for each combination (tij, tik) we find a bisector median and a intersection points. in total we will have maximum n1 ∗ n2 lines that intersect the line r. using formula (14), the pairs (t10, t20) for which the minimum of f(t1, t2) is obtained is the starting point for minimization in the next refinement stage. the same reasoning is used for the second curve that have the center c2(xc2, 0). after both envelopes have been found, we rotate and translate the curves in order to come in initial position. in our experiments we used the first method and the ’trust-region-reflective’ algorithm. 4 algorithm for approximated fuzzy voronoi diagram and experimental results we can summarize the algorithm for apvd as follow. step 1: construct the crisp voronoi diagram for crisp points that are the centers of convex closed curves (fortune’s algorithm)p. step 2: for each level hk of α-cuts do the two following steps.. step 3: for each voronoi cell that includes a convex closed curve ai, construct the fuzzy border generated by all adjacent crisp voronoi cells. the fuzzy border is generated by afvd described previously. step 4: calculate the intersection of the fuzzy borders and apply the norm max for all fuzzy points. step 5: if hk ̸= 0.0, go to step 2, else stop the algorithm. in our experimental results we used ellipses given in a parametric form. it is easy to transform the parametric form into quadratic form, and reverse. the experimental results, afvd for six ellipses, are presented in fig. 3a and fig. 3b. in fig. 3b, the solid line inside the grey area represents the crisp voronoi diagram for centers ci of closed curves (ellipses). unbounded cells have at infinite the limit determined by internal tangent of adjacent cells corresponding to crisp voronoi cells of the centers of closed curves (ellipses). computational time for crisp voronoi (fortune’s algorithm) is given by the known result o(n log n). our approach considers a less efficient algorithm, based on construction of bisector lines, for which the computational time is o(n2 log n). 5 conclusions and future works in this paper, we introduced approximated fuzzy voronoi diagram for fuzzy numbers of dimension two. we developed an original algorithm for computing fuzzy voronoi diagram for fuzzy numbers of dimension two with closed convex derivable curve shape. we used α-cuts in order to develop the diagram and the boundaries of voronoi edges of fuzzy cells. a method to construct approximate fuzzy voronoi diagram for fuzzy numbers of dimension two 395 figure 3: (a)the maximum norm for all fuzzy points inside a fuzzy voronoi cell (cvde: crisp voronoi diagram-edges); (b)afvd for six ellipses we considered not only the contour of the fuzzy numbers of dimension two at h level of α-cuts but also points inside this area. the same dimension for all the fuzzy object is a particular case of the presented algorithm. the fuzzy numbers have the same size but different sizes are taken into account. the experimental results presented the fuzzy voronoi diagram for fndt not only for two sites but also for six sites with applicability and algorithm extension to n sites. bibliography [1] fortune, s. (1887); a sweep algorithm for voronoi diagrams, algoritmica, 2:153-174. [2] mcallisterm, m.; kirkpatrick, d.; snoeyink, j. (1996); a compact piecewise-linear voronoi diagram for convex sites in the plane, discrete comput. geom., 15-73. [3] karavelas, m.; yvinec, m. (2003); voronoi diagram of convex objects in the plane, in proc. europ. symp. algorithms, lncs springer, 337-348. [4] yap, c. k. (1987); o(n log n) algorithm for the voronoi diagram of a set of simple curve segments, discrete comput. geom., 2:365-393. [5] arya, s.; malamatos, t. (2002); linear-size approximate voronoi diagrams, in: 13th annual acm-siam symp. on discrete algorithms, society for industrial and applied mathematics, 147-155. [6] emiris, i.; hemmer, m.; tsigaridas, e.; tzoumas, g. (2008); voronoi diagram of ellipses: cgal-based implementation, acs-tr-363603-01 technical report, university of groningen. [7] aurenhammer, f.; edelsbrunner, h.; an optimal algorithm for constructing the weighted voronoi diagram in the plane, pattern recognition, 17(2): 251-257. 396 d. arotaritei [8] burnikel, c.; mehlhorn, k.; schirra, s. (1994); how to compute voronoi diagram of line segments: theoretical and experimental results, in proc. 2nd annual symposium, lecture notes of computer science, 855:227-237. [9] buckley, j.j. ; eslami, e. (1997); fuzzy plane geometry ii: circles and polygons,fuzzy sets and systems, 87:79-85. [10] jooyandeh, m.; mohades, a.; mirzakah, m. (2009); uncertain voronoi diagram, information processing letters, 109:709-712. [11] jooyandeh, m.; khorasani, a.m. (2009); fuzzy voronoi diagram, advances in computer science and engineering, 13th international csi computer conference, csicc 2008, 6:82-89. [12] chaudhuri, b. b. (1991); some shape definitions in fuzzy geometry of space, pattern recognition letters, 12: 531-535. [13] goetschel, r.; voxman, w. (1986); elementary fuzzy calculus, fuzzy sets and systems, 18:31-43. [14] takahashi, o.; schilling, r.j. (1989); motion planning in plane using generalized voronoi diagrams, ieee transactions on robotics and automation, 5(2):143-150. [15] razavi, s.h.; h. amoozad, h.; zavadskas, e.k. ; hashemi, s.s. (2013); a fuzzy data envelopment analysis approach based on parametric programming, international journal of computers communications & control, issn 1841-9836, 8(4):594-607. [16] kaufmann, a.; gupta, m.m. (1984); introduction to fuzzy arithmetic: theory and applications, an nostrand reinhold company, ny. [17] chakraborty, c.; chakraborty, d. (2006); a theoretical development on a fuzzy distance measure for fuzzy numbers, mathematical and computer modelling, 43:254-261. [18] grzegorzewski, p. (1998); metrics and orders in space of fuzzy numbers, fuzzy sets and systems, 97:83-94. [19] woxman, w. (1998); some remarks on distance between fuzzy numbers, fuzzy sets and systems, 100:353-365. int j comput commun, issn 1841-9836 vol.7 (2012), no. 2 (june), pp. 325-340 small signal monitoring of power system using subspace system identification a. mohammadi, h. khaloozadeh, r. amjadifard aliakbar mohammadi department of electrical engineering science and research branch islamic azad university tehran, iran. aa.mohammadi@srbiau.ac.ir hamid khaloozadeh k. n. toosi university of technology tehran, iran. h_khaloozadeh@kntu.ac.ir roya amjadifard tarbiat moallem university tehran, iran. amjadifard@tmu.ac.ir abstract: in this paper, small signal analysis of power systems is investigated using subspace system identification (ssi) methods. classical small signal analysis methods for power systems are based on mathematical modeling and linearized model of power system in an especial operating point. there are some difficulties when such a classical method is applied, specially, in the case of large power systems. in this paper, such difficulties and their bases are investigated and in order to avoid them, it is suggested to use ssi algorithms for small signal analysis of power systems. the paper discusses extracting of small signal properties of power systems and presents some new suggestions for application of subspace system identification methods. different types of subspace system identification algorithms were applied to different power system case studies using the presented propositions. the benefits and drawbacks of subspace system identification methods and the presented suggestions are studied for small signal analysis of power systems and power system monitoring. several comparisons were investigated using computer simulations. the results express the usefulness and easiness of proposed methods. keywords: small signal, subspace identification, power system, monitoring. 1 introduction it is not more than 20 years that a new horizon has been opened in system identification. subspace system identification (ssi) has been one of the most attractive methods for system identification since 1990s. there is a large amount of literatures devoted to the algorithms based on subspace system identification [1–3]. factually, we can present a list of renowned type of ssi algorithms as following; numerical subspace state space system identification (n4sid) [4], multivariable output error state space (moesp) [5, 6], past output (po-moesp) [7, 8], canonical correlation analysis (cca) [9], orthogonal decomposition (ort) [2]. although they are similar in some general aspects, but there are several differences which may distinguish them. actually, they don’t perform quite the same as each other since they practice various copyright c⃝ 2006-2012 by ccc publications 326 a. mohammadi, h. khaloozadeh, r. amjadifard mathematical tools in different ways. it is not too far to expect fairly different advantages or disadvantages when using ssi algorithms. a useful review of subspace system identification algorithms is available in [10]. authors in [1] present a unifying theory which may be helpful to understand ssi methods. during the past years, application of ssi methods is being developed increasingly to different areas of industry and engineering sciences. maybe the main reason for such a development hides behind the capability and easiness of application of ssi methods for multi-input/multi-output (mimo) systems. in addition, state space structure of ssi methods is usually considered as an advantage. ssi methods also use robust, fast and consistent mathematical tools for calculations, which provide them with some considerable advantages [2, 3]. numerous investigators have worked on ssi methods and they have used different ssi algorithms for different applications. moreover, mimo specifications and also state space based analysis motivated many investigators to apply ssi methods for different analysis of power systems. however, there are not so many ssi applications in power systems, yet. the first steps of ssi applications in power systems may be seen in [11]. the paper provides low order model of large scale power system using n4sid algorithm. results of paper express that ssi based model is in lower orders, more optimized and more suitable for controller design in comparison with classical system identification and modeling. an application of power transformer identification was developed in [12] using frequency response data and ssi methods. authors in [13] and [14] introduce an algorithm based on ssi and some applications of such algorithm for transformer debugging and parameter estimation. a heffron-phillips model of synchronous generator was identified in [15] using subspace identification algorithms and online measurements. in [16] the parameters of a heffron-phillips model of synchronous generator were extracted from closed loop data using ssi algorithms. it divides identification problem of a closed loop system to two open loop identification and then it uses ssi algorithms to identify each open loop transfer function. using some mathematical processing of the provided transfer function, it provides a transfer function as a generator model. in [17], authors discuss a model predictive controller design for multi-machine power system using ssi algorithms. the design uses a recursive subspace system identification algorithm in order to provide a mimo self-tuning adaptive controller; therefore it can be used for online applications. [18] mentions use of different types of power system signals which are applicable to ssi algorithms. it uses such signals to provide identification data. modal analysis of power system was developed using subspace system identification methods and sampled data. [19] also discusses modal analysis and oscillatory stability study of power systems based on ssi methods. it provides a voltage stability measure using identified critical modes of power system. [20] introduces a power system stabilizer (pss) using n4sid algorithm of ssi methods. it provides a power system model using ssi and then it designs a mimo power system stabilizer using identified model. in [21], authors also discuss a pss using stochastic subspace system identification approaches. they also mention small signal analysis of power systems. this paper aims at small signal analysis of power systems using ssi. it proposes some useful notes on how to extract small signal properties of a power system using ssi algorithms. the paper clarifies the process of small signal monitoring of power system using subspace system identification algorithms. it also compares several ssi based analysis of power systems with the classical methods to distinguish the advantages. the paper structure is as following; in order to highlight drawbacks of classical methods, there would be an introduction to classical small signal analysis of power systems in the next section. subspace system identification algorithms are discussed in section three to high-light the advantages of such methods. application of ssi methods for power system small signal analysis is introduced in section four. there are some new ideas in section four, which are used to extract small signal monitoring of power system using subspace system identification 327 small signal properties of test case power systems in section five. on-line monitoring of power system is discussed in section six. in section seven, the application of different ssi algorithms for several power systems is discussed. finally, the conclusions are presented. 2 drawbacks of analytically small signal analysis of power systems we are usually interested in extraction of modes, participation factors of modes, damping ratios and oscillatory frequencies of power system which all are called small signal properties of power systems. since a power system is naturally a nonlinear system, one should follow the following stages to achieve small signal properties of a power system: 1. finding the details of all included elements (generator constants, transformer and line parameters,) 2. finding nonlinear model of power system using constant, parameters and theoretical relations of variables for different power system elements. 3. solving a load flow problem in order to provide an operating point. 4. linearizing of nonlinear model using the provided operating point. 5. application of modern small signal methods to provide small signal properties. providing an operating point, a nonlinear modeling and linearizing the model are all tough works in application, especially when the system is large. we also know that the parameters of system may change during the normal operation of system. therefore, it is obvious that some of the above stages are not applicable to a real system or the result of such an analysis is not reliable. moreover, there is always a big gap between the analysis done on a piece of paper and the system behavior. such a method is not applicable for monitoring of power system, neither. this is a considerable drawback for a scientific method. it is always a question at the end of theoretical analysis of power systems; to what extent are the results useful and applicable? it is not easy to response to the question unless we make a bridge between real world phenomena and theoretical analysis. what can be suggested at this point is usually application of a system identification method, at least, for estimating a linear model. classical identification methods are useful in many applications. while using a classical system identification method, the biggest difficulty origins from single-input/single-output (siso) structure of such methods. classical system identification methods may fall into whirlpool of over parameterization. coping with such problems is itself a new problem. our suggestion for overcoming such problems is to use subspace system identification (ssi) methods. ssi methods are good solution for multi-input/multi-output (mimo) systems. they can be considered as the bridge for passing over the gap between real world system and theoretical analysis. the next section investigates ssi methods to glorify their useful advantages for small signal analysis of power systems. 3 subspace system identification generally, we can arrange ssi methods into two categories from the measurement view; stochastic and deterministic ssi algorithms. if the ssi algorithm uses exogenous input measurements in its raw identification data set, it is called as deterministic ssi algorithm (dssi). 328 a. mohammadi, h. khaloozadeh, r. amjadifard otherwise, it is called a stochastic subspace system identification (sssi) method [2, 22]. if it is supposed to provide sssi methods in an algorithmic way for further understanding, we can arrange the following notations. further investigations of the following notes are also useful for understanding of dssi algorithms; model: the considered model of system for a typical sssi algorithm is{ x(t + 1) = ax(t) + w(t) y(t) = cx(t) + v(t) ,e {[ w(t) v(t) ] [ wt (s) vt (s) ]} = [ q s st r ] δts (1) where y ∈ rny,x ∈ rnare samples of output and state vectors. vt ∈ rny,wt ∈ rn are stationary, zero average state and output noise vectors, consequently. identification data: sssi methods use only samples of system outputs. however, identification data should usually be provided in the following structure: f(t) , [ y(t) y(t + 1) · · · y(t + k − 1) ]t p(t) , [ y(t − 1) y(t − 2) · · · y(t − k) ]t (2) where f(t) and p(t) are future and past data set. k should be strictly bigger than n. it can be a guess. therefore, this is not a restrictive condition. now, we can formulate sssi problem as below [1, 2, 5, 10, 22]: “there are n samples of output vectors, y (t) , [ y(0) y(1) · · · y(n − 1) ] , from a system of order n. find matrices a, c, q, r, s and n for the structure defined in (1).” block hankel matrices: sssi algorithms begin data processing by forming the following block hankel matrices: hk,k = e{f(t)pt (t)} =   λ(1) λ(2) · · · λ(k) λ(2) λ(3) · · · λ(k + 1) ... ... . . . ... λ(k) λ(k + 1) · · · λ(2k − 1)   (3) where λ(l) = e{y(t + l)yt (l)}, l = 0,1, · · · ,l , 2k − 1 ≤ l , k > n is the correlation of future and past data. actually, sssi uses statistical properties of output samples for further processing. the word stochastic in the expression (stochastic subspace system identification) may arise from this point. system order: sssi uses the following singular value decomposition (svd) of hankel matrix in order to provide system order (n): hk,k = [ usys unoise ] [ ∑ sys 0 0 ∑ noise ] [ v tsys v tnoise ] ≃ usysσsysv tsys (4) we can detect noise singular values by detecting a big gap among the singular values of hankel matrix. thus, noise singular values can be neglected since they are very smaller than system singular values. therefore, system order, n can be defined as n , dim(σsys) (5) estimation of state space matrices: svd of hankel matrix can also provide us with an extended observability matrix; ok , usysς1/2sys (6) small signal monitoring of power system using subspace system identification 329 it can also present controllability matrix: ck , σ1/2sysv t sys (7) we can easily obtain system matrix using extended observability matrices: a = o † k−1ok(p + 1 : kny,1 : n) c = ok(1 : ny,1 : n) (8) estimation of variance matrices: if we define c̄t = ck(1 : n,1 : ny) (9) variance matrices can be evaluated as below; q = π∗ − aπat , s = c̄t − aπ∗ct , r = λ(0) − cπ∗ct (10) where the essentials of the above formulation is provided by solving the following algebraic riccati equation; πk+1 = aπka t +(c̄t − aπkct )(λ(0) − cπkct )−1(c̄ − cπkct ) π∗ = lim πk k→∞ (11) innovation model for state estimation: if we are interested in innovation model for state estimation, we should have an estimation of kalman gain matrix: k = (c̄t − aπ∗ct )(λ(0) − cπ∗ct )−1 (12) therefore, states can be estimated using the following dynamic equations, x(t + 1) = ax(t) + ke(t) y(t) = cx(t) + e(t) cov{e(t)} = r (13) considering above mentioned notations, we can investigate the deterministic version of ssi algorithms using the following model:{ x(t + 1) = ax(t) + bu(t) + w(t) y(t) = cx(t) + du(t) + v(t) ,e {[ w(t) v(t) ] [ wt (s) vt (s) ]} = [ q s st r ] δts (14) where ut ∈ rnu,yt ∈ rny,xt ∈ rnare samples of input, output, state vectors and vt ∈ rny,wt ∈ rn are stationary, zero mean state noise and output noise vectors. in the case of deterministic identification, subspace system identification problem can be formulated as below [2, 4, 22]: “there are n samples of input vectors u = [ u0 u1 u2 ... un−1 ] and output vectors y =[ y0 y1 y2 ... yn−1 ] from a system of order n. find a, b, c, d, q, r, s matrices and n for the structure defined in (14).” there are several different algorithms available for ssi. they usually use some consistence mathematical tools that provide them with pretty useful benefits. there are two well-known subspace system identification algorithms expressed in table 1. they use the same measurements, the same block hankel matrices, different types of projections, svd of different matrices, 330 a. mohammadi, h. khaloozadeh, r. amjadifard the same method for extraction of system order and different extended observability matrices. moesp does not need to estimate future states of system, but n4sid provides future state vectors by using a weighting matrix. moesp uses extended observability matrix to extract system matrices but n4sid uses future states and through a least square problem estimates the system matrices. investigating table 1 expresses the following advantages for subspace system identification algorithms: 1. ssi algorithms are the only system identification methods that can easily and extensively be applied to all mimo and siso systems. 2. estimation of system order is one of the steps of ssi algorithms. this advantage reduces amount of time, cost and calculations. 3. ssi methods can handle big packages of data. 4. on-line operations of ssi methods are easier and can easily be applied to mimo systems. 5. ssi methods use robust mathematical tools such as svd, lq decomposition, least square and qr decomposition. they also don’t need nonlinear optimization. 6. some ssi algorithms only use output data to identify a model. this is a considerable advantage. 4 application of ssi methods for small signal analysis of power systems the ssi advantages expressed in previous section can be used to overcome the difficulties with classical small signal analysis of power systems. using ssi methods reduces above five steps to the following three steps: 1. measuring input/output signals of power system. 2. identification of a linear model for power system using ssi algorithms. 3. application of modern small signal methods to provide small signal properties. as it can be seen, the four first steps vanished and two other steps replaced them. the fifth step left with no change. therefore, one can provide small signal analysis of power systems in an easier and faster way. since application of ssi algorithms are very easy, power system small signal analysis will be provided in very low levels of cost and time. we are usually interested in identification of most oscillatory and least damped modes of power system. such modes are usually related to electro-mechanical parts of power system. therefore, in order to identify most critical modes, the angle and speed of electrical machines should be measured. signal measuring is the starting point of system identification. since measured signals should have enough persistency of excitation, we should use most effective inputs. in attention to differential equations of a single machine power system [23], mechanical torque and field voltage are proper inputs. suppose that input vector u and output vector y of a power system have been measured. the goal is to find small signal properties of power system (modes, damping ratios, oscillation frequencies, participation factors) using several samples of u and y. it is announced that if small signal monitoring of power system using subspace system identification 331 step operation moesp algorithm n4sid algorithm 1 model { xt+1 = axt + but + wt yt = cxt + dut + vt e {[ wt vt ] [ wts v t s ]} = [ q s st r ] δts { xt+1 = axt + but + wt yt = cxt + dut + vt e {[ wt vt ] [ wts v t s ]} = [ q s st r ] δts 2 measured data u = [ u0 u1 u2 ... un−1 ] y = [ y0 y1 y2 ... yn−1 ] u = [ u0 u1 u2 ... un−1 ] y = [ y0 y1 y2 ... yn−1 ] 3 block hankel matrices u0,k−1 =  u0 u1 · · · un−1 u1 u2 · · · un ... ... ... ... uk−1 uk · · · uk+n−2   ∈ rknu×n y0,k−1 =  y0 y1 · · · yn−1 y1 y2 · · · yn ... ... ... ... yk−1 yk · · · yk+n−2   ∈ rkny×n u0,k−1 =  u0 u1 · · · un−1 u1 u2 · · · un ... ... ... ... uk−1 uk · · · uk+n−2   ∈ rknu×n y0,k−1 =  y0 y1 · · · yn−1 y1 y2 · · · yn ... ... ... ... yk−1 yk · · · yk+n−2   ∈ rkny×n 4 extra predefined matrices up , u0,k−1 yp , y0,k−1 up , u0,k−1 , yp , y0,k−1 , uf , uk,2k−1 , yf , yk,2k−1 wp , [ up yp ] , wf , [ uf yf ] 5 lq decomposition [ up yp ] =[ l11 0 l21 l22 ] [ qt1 qt2 ]   ufwp yf   =   l11 0 0l21 l22 0 l31 l32 l33     q t 1 qt2 qt3   6 projection yp/u⊥p = l22q t 2 yf/uf wp = l32l † 22wp 7 singular value decomposition (svd) l22 =[ u1 u2 ] [ ∑ 1 0 0 0 ] [ v t1 v t2 ] l32l†22wp =[ u1 u2 ] [ ∑ 1 0 0 0 ] [ v t1 v t2 ] 8 system order n , dim(σ1) n , dim(σ1) 9 extended observability matrix ok , u1 ∑1/2 1 ok , u1 ∑1/2 1 t , |t | ̸= 0 10 future state estimation ———xf = t −1σ 1/2 1 v t 1 ∈ rn×n xk , xf = [ xk xk+1 · · · xk+n−1 ] ∈ rn×n 11 estimation of state space matrixes c = ok(1 : ny,1 : n) a = o † k(1 : ny(k − 1),1 : n) .ok(ny + 1 : kny,1 : n) solving a least equare problem to estimate b and d [ â b̂ ĉ d̂ ] = [ xk+1 yk,k ] [ xk uk,k ]t  .  [ xk uk,k ] [ xk uk,k ]t −1 table 1: comparison of two well-known ssi algorithms; moesp (multivariable output-error state space) algorithm and n4sid (numerical algorithm for subspace state space system identification) algorithm. 332 a. mohammadi, h. khaloozadeh, r. amjadifard n samples of input/output vectors are availabe, then one can identify the following state space linear model by utilizing a subspace system system identification algorithm: x(t + 1) = ax(t) + bu(t), y(t) = cx(t) + du(t) (15) one can find the system modes and as a result damping factors and damping frequencies by digging the matrixa. however, we may encounter some difficulties when calculating the participation factors of modes in states. the problem arises since the state vector x of the identified model is not correspondent to that of the real power system which we may obtain by analytical methods. therefore, mode in state participation factors can’t be utilized using the identified a. in order to cope with such a problem, it is proposed to use modal canonical realization of (15). using t as a similarity transformation matrix, one can provide the following modal canonical realization: ż = λz + b̄u, y = c̄z + d̄u x = tz, λ = t−1at, b̄ = t−1b,c̄ = ct, d̄ = d (16) generally, λ is in jordan and block diagonal structure. thus, mode in state participation factor (pki) is defined as pki , ∂λi ∂akk (17) where akk is a diagonal element of system matrix. since in (16), the system matrix is diagonal with modes as its diagonal elements, we can write: pki , ∂λi ∂akk = ∂λi ∂λi = 1 (18) therefore, modal canonical realization can maximize (100 %) mode in state participation factor of model. in order to clarify the point, suppose that u is zero, λ is diagonal and z0 is the initial condition vector of modal canonical realization. thus, we can write: ż = λz ⇒ zi = e−λitz0i , i = 1,2, · · · ,n (19) therefore, the only participating mode in state zi is λi, so the participation factor of mode λi in the state zi is 100% and each mode is mapped to a state. considering above point and output equation of (15), one can write: y = c̄z ⇒ yk = ∑n i=1 c̄kizi = ∑n i=1 c̄kiz 0 i e −λit, k = 1, · · · ,ny (20) therefore, output yk is affected by mode λi and mode in output participation factor (pki) is proposed as: pki , c̄kiz0i ,k = 1, · · · ,ny and i = 1, · · · ,n (21) in order to provide participation factors, one may need z0 which can be provided through following relation: z0 = t−1x0 (22) x0 is the initial condition vector of the identified state space model. it is also provided by ssi algorithms. some investigators [24] discuss another kind of participation called state in mode participation factor. in most of literatures state in mode and mode in state participation factors are considered the same as each other and they have been used interchangeably. however,there is a discussion on some differences in [24]. the following proposition challenges the definition of state in mode participation factor. small signal monitoring of power system using subspace system identification 333 proposition 1. "state in mode participation" is a meaning-less expression. proof: a system includes some physical elements (such as resistors, capacitors and inductors in electrical systems or dampers, springs and masses in mechanical systems). each element has a value and a role in topology and configuration of system. configuration and topology of system provides the system with unique set of differential equations which we call it mathematical model. a presentation of system mathematical model is state space structure which has a system matrix called a which is affected by basic elements and configuration of system. systems modes are one of the mathematical properties of a, therefore system modes only rely on system elements and system configuration. system states don’t affect its modes so there is no state in mode participation and system modes are not affected by its states. 2 example 2. for instance, one can provide the series rlc circuit with the following state space model: [ ẋ1 ẋ2 ] = [ λ1 0 0 λ2 ] [ x1 x2 ] + [ 1 1 ] vi, vc = [ λ1λ2 (λ1−λ2) −λ1λ2 (λ1−λ2) ] [ x1 x2 ] (23) where λ1,2 = −ξωn ± jωn √ 1 − ξ2, ωn = 1/ √ lc, ξ = (r/2)/ √ l/c (24) therefore, λ1,2 rely only on l, c and r and the topology of circuit and the states depend on system modesλ1,2. thus, there is no participation of states in modes, and the expression state in mode participation is meaningless. 5 test cases and simulations identification process should be provided with sampled input/output signals. therefore, computer simulations of the following test case power systems have been conducted using matlab/simulink installed on a computer with 2.4 ghz cpu and 4g ram. we study the systems shown in figure 1; a single machine connected to an infinite bus, and a four-machine two-area system. since we may extract the small signal properties of generator angle and speed, it is recommended to use mechanical torque as input and rotor speed and/or its angle as output signals. torque and power are the same in per-unit system. therefore, we use mechanical power as input signal. in order to have enough persistency of excitation in input signals, we added a white noise to input signals. to provide more realistic operating conditions, we added a white noise to output signals, as well. effect of noises will be investigated later. 5.1 single machine three-bus system power system shown in the left side of figure 1 is a three bus single machine power system with no control and exciter. the parameters of the system are those used in [23]. we are supposed to extract all small signal properties of system using ssi algorithms and the methods illustrated in previous sections. we acquired 300 samples of input/output data through a 30 second simulation. using the ssi algorithms presented in table 1 , some linear models were identified and the results are presented in table 2. it is clear that for investigating performance of noises, we cannot manipulate their averages since the operating point may vary. this is not applicable in this study. therefore, each noise variance was altered separately in order to see its effect. in table 2, it is expressed that an increase in input noise variance may lead to a better model from the view of fpe (final prediction error) measure. however, we should be conservative when the estimation of 334 a. mohammadi, h. khaloozadeh, r. amjadifard figure 1: left; single machine three-bus power system. right; two-area four-machine power system. σu σy λ ξ ωn (hz) |p| fpe cm −0.714 ± j6.35 0.112 1.0165 [ 0.503 0.503 0.503 0.503 ] ssim01 0.0001 0 −0.7975 ± j6.2443 0.1276 0.9988 [ 0.5037 0.5037 0.5037 0.5037 ] 7.4e-5 ssim02 0.001 0 −0.7192 ± j6.3203 0.1139 1.0105 [ 0.5028 0.5028 0.5028 0.5028 ] 8.5e-5 ssim03 0.01 0 −0.7727 ± j6.1300 0.1253 0.9825 [ 0.5033 0.5033 0.5033 0.5033 ] 11e-5 ssim04 0.001 0.01 −0.7192 ± j6.3203 0.1139 1.0105 [ 0.5028 0.5028 0.5028 0.5028 ] 0.59 ssim05 0.001 0.01 −0.7192 ± j6.3203 0.1139 1.0105 [ 0.5028 0.5028 0.5028 0.5028 ] 0.60 table 2: small signal analysis of single machine power system using ssi algorithms; abriviations are: classical model (cm), subspace system identified model (ssim), participation matrix (p), variance of input noise (σu), variance of output noise (σy), eigenvalue (λ), damping factor (ξ), natural frequency (ωn), and final prediction error (fpe). small signal properties is under consideration. actually, a large increase in input noise variance may alter the operating point or its absorption area. this may lead to instability. in table 2, if we compare ssim4 and ssim5 with ssim2, we can see that output noise has no effect on subspace system identification. actually, additive output noise does not have considerable effects on the ssi identification, since ssi algorithms use robust linear algebra tools. it is noticeable that the left eigenvector of a wide matrix are not considerably sensitive to additive white noise, [2]. therefore, the identification is not sensitive to additive output noise. what is more, the identification process has no effect on normal operating conditions of power system, since the applied input noise is too weak. 5.2 two-area four-machine system the power system introduced in [25] is used as the second case study (see the right side of figure 1). this power system has four generators and two fully symmetrical areas linked together by a weak line. it was specifically designed [23] to study low frequency electromechanical oscillations in large interconnected power systems. despite its small size, it can thoroughly mimic the behavior of typical systems in actual operation. in the case of no pss (power system stabilizer), when the measured signals are differential speeds and differential angles of all generators, the cva algorithm detected 10 modes shown in figure 2. as illustrated in figure 2, there are 8 oscillatory modes, and two non-oscillatory modes. there are three zero modes, since there is no infinite bus as a reference for rotor angles. small signal monitoring of power system using subspace system identification 335 figure 2: pole-zero map of the identified model foe two-area system (no pss) using cva. figure 3: left: normalized participation factors of modes in differential speeds of generators. right: normalized participation factors of modes in differential angles of generators. (no pss, measurements are differential angles and differential speeds). moreover, the speed governors have not been modeled [23], (λ7,8 and a real pole in the origin). in order to distinguish between local and inter-area modes, the participation factors of modes are illustrated in figure 1. participation factors were calculated using the approach presented in previous section. table 3 illustrates the small signal properties of two-area system extracted from figure 2 and figure 1. in order to damp oscillatory modes, different power system stabilizers have been designed and applied to two-area power system. performances of pss have been investigated using ssi algorithms and the method mentioned in previous section. table 4 summarizes the damping performance of multi-band (mb) pss, ∆ω pss and ∆pa pss [26] when applied to two-area power system. all psss perform very well. they stabilize the naturally unstable system. however, it is clear that the mb-pss is superior to the other two psss, since it provides significantly more damping for all modes. the results presented here using identification method, are identical to those analytically achieved in [26]. mode ωd(hz) ξ(%) participated states λ1,2 1.1904 16.70 δ1,δ2 (local, area 1) λ3,4 0.9871 17.84 δ3,δ4,ω3,ω4 (local, area 2) λ5,6 0.3061 24.35 δ3,δ4,ω1,ω2,ω3,ω4 (inter-area) λ7,8 0.0095 20.78 δ1,δ2,δ3,δ4,ω1,ω2,ω3,ω4 (inter-area) table 3: small signal properties of two area test system (no pss). 336 a. mohammadi, h. khaloozadeh, r. amjadifard pss ωd(hz) ξ(%) mode type no pss 1.1904 16.70 local 0.9871 17.84 local 0.3061 24.35 inter-area mb pss 0.3942 51.91 inter-area ∆ω pss 0.7115 47.71 inter-area 0.1113 67.82 controller pa pss 0.4655 43.02 inter-area table 4: investigating the effects of pss on oscillatory modes of two-area system using ssi. figure 4: monitoring of single machine three-bus system 6 on-line monitoring of power system using ssi on-line monitoring of power system is an open area which attracts tremendous consideration of investigators. using analytic method for on-line monitoring may lead in calculation problems and the results are not also trustfully. since the identification process uses sampled signals of power system, there would not be any gap between identification results and power system behavior. therefore, the results are trustworthy in the case of power system monitoring. ssi algorithms can easily be applied to on-line power system monitoring. they can provide a state space model which is most suitable for on-line monitoring. a single machine three-bus power system with two transmission lines is used to investigate on-line monitoring of power systems (figure 2). some computer simulations were conducted for 60 second. in the first 30 seconds, both lines are in operating condition. as a result of a fault, for the second 30 seconds, line 2 is out of work. some state space models were identified in every 20 seconds using ssi algorithms. there would be three models and the operating point will change for the second model, so one can call it as an operating point transient model. three identified model and their properties are available in table 5. ssim6 represents the system performance in the initial operating point before the fault. ssim8 represents system performance after breaking out of second line. ssim7 is a middle model between ssim6 and ssim8. as it can be seen from the column of modes column in table 5 , modes has moved slightly to the right and as a result stability of system has reduced. it implies that the operating point should be re-tuned in order to recover more stable operating conditions. damping frequency and also damping factor has reduced. it implies the need for some control in order to provide power system with more suitable synchronizing and damping factors. it is considerable that the whole monitoring process expresses a little movement of eigenvalues toward the right half plane. moreover, there is a decrease in damping frequency. therefore, a control should be applied to mechanical torque and field voltage, because it is predictable that a variation in operating conditions of power system may lead to instability and oscillatory behavior. small signal monitoring of power system using subspace system identification 337 model λ ωd (hz) ξ |p| ssim6 −0.6010 ± j8.0143 1.2755 0.0748 [ 0.5016 0.5016 0.5016 0.5016 ] ssim7 −0.5269 ± j7.0677 1.1249 0.0743 [ 0.5012 0.5012 0.5012 0.5012 ] ssim8 −0.4810 ± j6.9452 1.1054 0.0691 [ 0.5018 0.5018 0.4775 0.4775 ] table 5: subspace system identification models (ssim) identified during online monitoring. 7 effect of different ssi algorithms on small signal analysis of power systems there are several kinds of subspace system identification algorithms. they may all use the same mathematical tools. but they are different in applying tools to measurement data. therefore, ssi algorithms may show slightly different results for the same input/output set of data. there are three main algorithms which are usually used in the papers discussing power system analysis; moesp (multi-variable output-error state space), n4sid (numerical state space subspace system identification) and cva (canonical variate analysis). we are supposed to compare performance of such algorithms when they are used for small signal analysis of power systems in this section. figure 1 shows the system under study. small signal analysis of mentioned power system was conducted using four models and different ssi algorithms. the models were investigated; 1) classical model of 3-bus single machine power system which has no control and the effect of field is ignored. 2) single machine power system model containing effects of field voltage. 3) auto-voltage regulated (avr) power system. 4) single machine power system with avr and power system stabilizer (pss). in each case, kd were calculated using the system parameters available in [23]. table 6 contains results of small signal analysis for several models of single machine power system using two different ssi algorithms. identification results are almost the same for two ssi algorithms. they are slightly different in estimation of non-oscillatory modes. table 6 also expresses that ssi algorithms are not capable of identifying non-dominant modes. that is not a draw-back, since non-dominant modes are not so important for system analysis. as a whole, one can use subspace system identification algorithms for sure, when it is needed to estimate most effective modes of power system. therefore, subspace system identification algorithms are convenient solution for extracting small signal stability properties of a power system. 8 conclusions and future works the paper discusses pitfalls of small signal analysis of power systems when using analytic methods. analytic methods use basic theories and the provided parameters in order to extract operating point and linearized model of power system. in this paper, it is expressed that we can avoid such boring, time consuming and cost effective stages, using system identification methods. the most proper method for power system small signal analysis is then, application of subspace system identification (ssi) algorithms. ssi methods have many other advantages which were discussed in the paper. extraction of modes and their participation factors are easy when using ssi methods but it needs some modification in ssi algorithms. in order to fulfill such requirements, some suggestions presented in this paper. using ssi algorithms, some ideas 338 a. mohammadi, h. khaloozadeh, r. amjadifard no. classical moesp cva 1 λ 0 ± j6.39 0 ± j6.4158 0 ± j6.4158 ξ 0.0000 0.0000 0.0000 ωd 1.0200 hz 1.0211 hz 1.0211 hz 2 λ λ1,2 = −0.11 ± j6.41 λ3 = −0.2040 λ1,2 = −0.1095 ± j6.4114 λ3 = −0.2038 λ1,2 = −0.1095 ± j6.4114 λ3 = −0.2038 ξ 0.0170 0.0171 0.0171 ωd 1.0700 hz 1.0204 hz 1.0204 hz 3 λ λ1,2 = +0.5040 ± j7.2300 λ3 = −20.202 λ4 = −31.230 λ1,2 = +0.5045 ± j7.2321 λ3 = −0.0015 λ4 = −15.1521 λ1,2 = +0.5045 ± j7.2321 λ3 = −0.0310 λ4 = −15.1174 ξ -0.0700 -0.0696 -0.0696 ωd 1.1500 hz 1.1510 hz 1.1510 hz 4 λ λ1 = −0.7390 λ2,3 = −1.005 ± j6.607 λ4,5 = −19.797 ± j12.822 λ6 = −39.097 λ1 = 0.0000 λ2 = −4.5770 λ3,4 = −1.0357 ± j6.6150 λ5,6 = −16.857 ± j16.288 λ1 = 0.0000 λ2 = −4.6266 λ3,4 = −0.9946 ± j6.5951 λ5,6 = −16.906 ± j16.265 ξ ξ2,3 = 0.15 ξ4,5 = 0.84 ξ3,4 = 0.1547 ξ5,6 = 0.7191 ξ3,4 = 0.1491 ξ5,6 = 0.7204 ωd ωd2,3 = 1.05hz ωd4,5 = 2.04hz ωd2,3 = 1.0528hz ωd4,5 = 2.5924hz ωd2,3 = 1.0496hz ωd4,5 = 2.5886hz table 6: small signal analysis of single machine power system using different types of subspace system identification algorithms. 1) classical model (no field control, kd=0) . 2) system model containing effects of field voltage, (kd=1.53). 3) auto-voltage regulated (avr) system, (kd = −7.06). 4) system model with avr and power system stabilizer (pss), (kd = 14.08) small signal monitoring of power system using subspace system identification 339 such as power system 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the other object to avoid the coupling effect existing between the mobile sensors for the same target. once tracks are formed and confirmed, it not merely the number of mobile sensors that can be estimated and quantified; rather, the information gathered by the tracking algorithm can also be associated and fused. it is known that the role of multi-sensor data association in wss environments involves acquiring, processing and combining data [1, 2] coming from different sources, including sensors and databases, into a more precise set of data. thus, copyright c⃝ 2006-2012 by ccc publications 40 j.i.-z. chen, y.-n. chung data association consists of the three aforementioned steps. a dfa (data fusion algorithm) is the most important technique for maintaining tracking procedures. mobile-sensor tracking with a dfa is a prerequisite step for mobile-sensor surveillance systems in wss deployments. once tracks are formed and confirmed, the number of targets can be estimated and information, such as the target position and velocity, computed for each track [3]. in the literature, several dfas for mtt (multiple target tracking) have been proposed and discussed. it is well known that the jpda (joint probabilistic data association) dfa technique, discussed in references [4–8] focusing mainly on mtt deployment, is appropriate for a high false-target density environment. however, these techniques for solving mtt problems may cause some unreliability (latency) because in a nearest-neighbor or all-neighbors-based environment, the relationships between sensor measurements and existing target tracks are usually considered independently [9]. thus, currently a traditional hnn (hopfield neural network), which incorporates weighted objective costs and constraints into an overall energy function, is employed to combine with the neural network approaches for achieving good tracking results. [10] then, through minimizing the overall energy function, superior performance results can be obtained. [11] the chnn (competitive hopfield neural network) algorithm has been applied in image processing. [12] moreover, soujeri and bilgekul [13] adopted a conventional hnn to solve the problems of multiuser reception for asynchronous mc-cdma (multi-carrier coded-division multiple-access) systems in multipath fading channels. since this approach is defective in that the weighting values are very difficult to properly determine, frequently the solution obtains an irrational result, as reported by zhou. [14] thereafter, conventional hnn schemes for tracking maneuvering or non-maneuvering targets with mobile sensors over wss deployments are very sparse. however, wang et al. [15], recently, combined an hnn with a genetic algorithm, designated as hnn-ga, for proposing a mobile agent-based strategy utilizing a low network load and cooperation of mobile agents, to dynamically optimize the combination of nodes and deploy tasks among nodes. based on hnn, the selection method investigated by liang et al. [16], in which the sensor node having the lowest cost and satisfying the distance requirement of a mimo (multi-input multi-output) system, is selected to function as the best transmitting and receiving antenna in wsn environments. by extending the idea expressed in [17], wang et al. [18] proposed a new dynamic sensor node selection strategy to implement global searching to reduce the search space of a ga and ensure the validity of each chromosome in wsn applications. deploying with mobile sensors in wss environment is increasingly becoming integral to targets tracking, mainly due to its convenient deployment, small size, real-time characteristic, and flexibility to support integrated applications. the technique of wss is applied to the traffic monitoring and control in [19] by the authors semertzidis et al. the information is fused and used to provide real-time analytical. however, it is necessary for the important issue of energy supply to the operation of sensor nodes over wss. yen et al. in [20] proposed cld (controlled layer deployment) protocol to guarantee coverage and energy efficiency for sensor nodes deployed in a wss. in order to promote the operation efficiency of the data fusion for the sensor nodes deployed in wss environment. multi-layer clustering routing algorithm is presented by liu et al. in [21] where the wss techniques is developed to track the moving vehicles, and the new scheme efficiently overcomes the hot spot problem in wss environment. furthermore, the authors, shi et al., propose a structure that represents the sensor communications with the fusion centre, obtain the optimal estimation algorithm at the fusion centre, and provide a theoretical closed-form for the steady-state error covariance matrix which has low energy consumption and guarantees a desired level of estimation quality at the fusion centre. [22] however, to apply the advantages of the hnn technique in this research the improved chnn method, which can by artfully arranging the updating function and the cost measurement properly eliminate the aforementioned dilemma, is adopted for solving target tracking with mobile sensors in wss deployments. the chnn is an improved hnn wherein a a data fusion methodology for wireless sensor systems 41 cooperative decision is made on the basis of the simultaneous input of a community of neurons. each neuron receives information from other neurons and also conveys information to others [11, 14]. with this collective information, each neuron moves to a stable stage with the lowest value of a predefined energy function. with such a result, the operation of association between mobile sensor measurements and existing tracks can be obtained under global optimal consideration, which in turn can increase the accuracy of mobile sensor tracking systems. furthermore, due to the embedded competitive updating scheme, the chnn concept has the capability of removing the burden of weight setting. it has also been demonstrated that the network is guaranteed to converge into a stable and rational stage during evolution. [13] thus, the dilemma of lapsing into irrational solutions such as those in traditional hnns can be eliminated. to the best of the author knowledge, the proposed mean is an innovative idea for discussion when exploring the field of wsns. the aim of this study is to enhance system performance by means of a dfa for wss environments, the focus being on a chnn to obtain global matching between mobile sensor measurements and existing tracks. as illustrated in fig.1, moving targets tracked by sensor nodes are considered for improving the tracking deployment for mobile-sensor data association with assumed targets. moreover, in an environment where the dense targets are spread out randomly, some targets can be very close to each other. the measurements produced by these close targets can confuse the data association computation algorithm and result in inaccurate relations hips. consequently, the approach for solving the data association problem should be considered globally. the reminder of this report is organized as follows. in section ii the problem formulation includes the gating technique, and the basic concept of the hnn is illustrated; developing a dfa based on the chnn scheme is presented in section iii; the maneuvering compensator algorithm is explicated in section iv; section v reports the simulation results for the proposed algorithm; and section vi both concludes the report and offers recommendations for further research. 2 problem formulation in this sub-section to obtain the information from the mobile sensor belonging to all of the sensors, without loss of generality, the type of sensing methods is considered to be completely established. it is also assumed that the synchronization between various sensor levels under circumstances of multiple levels of data association is finished. several synchronization methods can be adopted for this procedure [18]. moreover, since this study focuses on data association for tracking targets within wsn environments, several issues are reasonably ignored, e.g., the impact of sensing accuracy on the sensing range and node densities of a sensor, as well as the data fusion overhead and sensing lifetime of a network. thus, a dynamic model with a discrete model set for a multi-sensor tracking algorithm for a mobile sensor can be formulated. 2.1 gating technique according to the mobile sensor tracking situation, the model of a moving target can be defined in variable state equations, given as [23] x(k + 1) = f(k)x(k) + g(k)w(k) + u(k) (1) and y (k) = h(k)x(k) + v (k) (2) where x(k) denotes the state vector of the target, y (k), the measurement vector of the target, f(k) and g(k), the transition matrix and the noise-gain matrix of the target, respectively; w(k), 42 j.i.-z. chen, y.-n. chung is the system noise associated with the target, assumed to be normally distributed with a zero mean and variance q(k). moreover, u(k) represents the forced input; v (k), the measurement error associated with the target, assumed to be normally distributed with zero mean and variance, r(k), uncorrelated with w(k); and h(k) the measurement matrix of the target. the initial state of the target is considered to be a gaussian pdf (probability density function) with a known mean vector, x̂ (0 | 0), and a covariance matrix, p (0 | 0). a large number of close measurements observed from the mobile sensors in an actual tracking situation are provided to determine the trajectory estimates for any target that might be presented. it is difficult to precisely determine which target corresponds to which in such closely spaced measurements. i.e., the data association problem in mobile sensor tracking or associating the measurement vector y (k) to the existing track model for each step, k, must first be solved. additionally, consider an environment wherein a dense target is completely deployed. here, determining the gating size is the obligatory first step in solving the problem of associating observations with tracks. additional logic is required when an observation or multiple observations fall within the gates of multiple target tracks or a single track. a typical situation for a gate diagram consisting of three target tracks t1, t2 and t3 is illustrated in fig. 2. in this figure there are three targets, but eight measurements are assumed to have been obtained. first, the gating technique is applied to eliminate the less probable measurements such as m6 to m8. a chnn algorithm is applied to complete the associative relationship between the remaining measurements and the tracked targets. then, the association of the measurements of the remaining m1,m2, · · · ,m5, with the respective targets should be determined. 2.2 two-dimensional binary neural network-hnn consider that a network consists of n×m mutually interconnected neurons. in this research, the hnn specifically adopted to search for the algorithm belongs to a two-dimensional binary neural network wherein the total input-to-neuron, indexed by (g,i) , i.e., λg,i, at time k, can be calculated as [15] λg,i = n∑ h=1 m∑ j=1 tg,j;h,j � vh,j + ig,i (3) where ig,i denotes a bias input, tg,j;h,j, the interconnection intensity between neuron (g,i) and neuron (h,j); vg,i, a binary state of the (g,i)th neuron. moreover, the state of vg,i is determined by the signum function vg,i = { 1, λ > 0 0, otherwise (4) thus, the expression in 1 explicitly describing the hnn dynamics operating in this network receives weighted inputs tg,j;h,j � vh,j from each neuron, e.g., (h,j), and a bias input (g,i) from outside. therefore, one neuron at a time is updated while the rest remain stationary after a random initialization of each neuron with binary values. this method for neuron updating has been shown to decrease the lyapunov function of a two-dimensional hopfield network given by e = − n∑ g=1 n∑ h=1 m∑ i=1 m∑ j=1 tg,j;h,jvg,ivh,j − 2 n∑ g=1 m∑ i=1 ig,ivg,i (5) where vg,i has the constraint defined in (4). a data fusion methodology for wireless sensor systems 43 3 joint data association in a competitive neural network in this subsection the chnn algorithm is applied to the potential-target measurements to formulate a solution for data association problems. the chnn concept is applied to the data association technique presented here. by means of a network for joint data association, the state of vg,i indicates an associative status between the g − th mobile sensor measurement and the i − th target, expressed as vg,i = { 0, data noassociated 1, data associated (6) then the objective function used for obtaining measurements and mobile sensor targets association with the best decision is given by e = α n∑ g=1 m∑ i=1 dg,ivg,i + β n∑ g=1 n∑ h=1 m∑ i=1 m∑ j=1 vg,ivh,jδg,h + γ m∑ i=1   n∑ g=1 vg,i − 1  2 (7) where dg,i is the distance between the i−th true target and the g −th measurement. the factor dg,i here needs a special design for achieving the task of data association as described subsequently. in eq. 7 the first term is the sum of the distances between the associated measurements and the mobile sensor targets. a situation wherein none of the newly obtained measurements is appropriate for certain specific targets in the processing of data association is unavoidably vexing. concurrently, the previous target information should be kept and chosen as the next item of information in such cases. to achieve this end, the m target data is included as part of the measurement. assume that there are m targets and n − m newly obtained mobile sensor measurements and that the m targets are arranged in front of the n−m measurements to obtain a total of m measurements. where g = i, the distance dg,i is defined as dg,i = r where r is the radius of the gate, according to the aforementioned arrangements. hence, if measurements are distributed inside the gate, then one should be chosen. however, if no measurements are there, then the target itself should be chosen. another constraint is that if g ̸= i and 1 ≤ g ≤ m, then dg,i = ∞. this constraint prevents one target from choosing another as its measurement. thus, after the aforementioned calculations, the distance dg,i can be determined as dg,i =   r, g = i ∞, g ̸= iand1 ≤ g ≤ m√ [τt (k)s(k)−1τ(k)], g ̸= iandg > m (8) where s(k) is the covariance matrix of the innovation τ(k), the superscript denotes the transportation of a matrix, and τ(k) = y (k) − hx̂ (k | k − 1) . moreover, in 5 the second term attempts to guarantee that each measurement can be associated with only one target. moreover, the third term forces the condition wherein each target has one, and only one, associated measurement and the three terms α, β, and γ specify important constant factors. since these factors are highly dependent on the number of targets, the target-measurement distances and the radius of the gate, it is very difficult to determine the appropriate values for them. for this reason, irrational solutions have periodically been reported for the use of traditional hopfield approaches [10, 14] when the weighting factors have not been properly determined. however, to reduce the difficulty in determining the values of the weighting factors, a cwta (competitive winner-take-all) updating is proposed and adopted as vg,i = { 1, λg,i = max {u1,i · · · · · ·un,i} 0, otherwise (9) 44 j.i.-z. chen, y.-n. chung with this modified updating rule, the rigid constraint that each target should be associated with one and only one measurement is automatically embedded inside the network evolution results. as such, the third term can naturally be removed from the objective function. thus, the objective function is simplified and obtained as e = α n∑ g=1 m∑ i=1 dg,ivg,i + β n∑ g=1 n∑ h=1 m∑ i=1 m∑ j=1 vg,ivh,jδg,h (10) to avoid being bounded by irrational solutions, a reasonable method for the network described below is adopted. i.e., once the cwta updating has been applied, it is noteworthy that if α is set as a unit, β can be easily considered greater than the radius of gate r, a relatively constant value. by comparing the resultant objective function with the lyapunov function of the twodimensional hopfield network in 5, the parameters of the interconnection intensity and the bias input can be obtained as ig,i = − α · dg,i 2 (11) and tg,i;h,j = −βδg,h (12) respectively. it can be clearly to seen that the chnn is not fully interconnected from, 11 and 12. however, the chnn is locally connected instead of the neurons in the same column. hence, the total input to neuron (g,i) can be applied to these two equations and obtained as λg,i = −β m∑ j=1 vg,j − α 2 dg,j (13) the proposed algorithm was found to be convergent during network evolutions. 4 estimation of maneuvering targets with an adaptive filter in real-world applications, either maneuvering or non-maneuvering targets must be tracked simultaneously with mobile sensor data association tracking techniques. on the basis of the concept of wireless sensor network equipped with different sensing hardware for distinctly specified applications, a viable method for human interfacing with the environment can be provided. therefore, if target maneuvers occur, then the maneuver-detection and acceleration-estimation algorithm should be applied to modify the parameters of the tracking filter. the developed methods allow the maneuvers to be tracked without diverging or severely distorting the estimate. to complete such an adaptive procedure, the kalman filter equations are modified in this subsection. specifically, certain parameters must be changed for fitting the data association and matching the chnn algorithm. now, the measurement innovation, ρ(k), and the innovative covariance matrix, s(k), of the kalman filter should be modified as ρ(k) = y (k) − h(k)x̂ (k | k − 1) (14) and s(k) = δ(k) − r(k) (15) , respectively, where δ(k) = h(k)p (k | k − 1)ht (k). furthermore, two hypotheses, h0 and h1, corresponding to the assumptions that the system behavior is normal and the target is moving a data fusion methodology for wireless sensor systems 45 with the maneuvers, are assumed as generating events. the detection criterion can be defined as f(k) = k∑ j=k−n+1 ρt (j)s−1(j)ρ(j) h1 > < h0 ζ (16) where f(k) is a chi-squared random variable with n × m degrees of freedom, where n is the residual window size and m denotes the dimension of the measurement vector, since the noises v (k) and w(k) are assumed to be zero mean gaussian white noises. in (16) the criterion ζ can be chosen from standard chi-squared tables [24]. moreover, now consider a situation wherein the target initiates and suffers from a severe maneuvering condition. in this case the proposed algorithm will implement the detection and the estimation for the situation on the basis of certain statistical calculations. in this situation, another important algorithm is also applied to increase the estimation accuracy. in such an algorithm, for the i − th component of a vector, on the basis of the detection results, the testing condition with the components which have jumps are expressed as |ρi(k)| ≤ ∣∣∣d√sii(k)∣∣∣ (17) where d represents the rejected innovation, being a constant related to the gaussian pdf. the variance of the rejected innovation can be calculated as var(d2) = ρ2i (k) {ai(k)δii(k) + rii(k)} −1 (18) where var(·) is variance operator; the parameter ai(k) can be computed as ai(k) = [ρii(k)/d] 2 − rii(k) rii(k) (19) once the parameters have been modified and determined by eq. 18 and 19, the innovative values ρ(k) at time k can exist on the boundaries of the acceptable region defined by 17. thus, to keep the target on track, the covariance of the prediction error p (k | k − 1) is modified to [am(k) · p (k | k − 1)], where am(k) is the largest value of all the ai(k) shown in 19. by virtue of the data association algorithm, not only is a better performance obtained, but the tracking filter also has faster responses in server-maneuvering situations. however, under such a procedure, i.e., after the approach is adopted, the kalman gain increases gradually, after which the tracking performance of the wss will become more stable and efficient. 5 simulation results and discussion developing simulation programs (using matlab ) by virtue of the proposed dfa is implemented in this subsection. the developed dfa associating with chnn technique is first validated in an environment wherein three targets are tracked in wsn deployments. next the dfa is also applied to track five targets, including two non-maneuvering and three maneuvering ones. the initial conditions for simulating the tracking of two targets are listed in table i, mentioned here mainly for demonstrating the accuracy and efficiency of the proposed algorithms. the transition matrix f(k) and the noise gain matrix g(k) corresponding to the target for the sampling interval t , which is assumed in the simulation to be two seconds, are given by 46 j.i.-z. chen, y.-n. chung f(k) =   1 t 0 0 0 1 0 0 0 0 1 t 0 0 0 1   and g(k) =   t2/2 0 t 0 0 t2/2 0 t   , respectively. the initial value of the state error covariance is assumed to default as p (0 | 0) =   10000 100 0 0 0 100 100 0 0 0 10000 100 0 0 100 100  . after the assignment of initial conditions is completed, the procedure of simulation for the dfa algorithm is following the steps illustrated in fig. 3. the results from tracking three targets without and with the dfa calculation are graphed in fig. 4 and fig. 5, respectively. in these simulations eighty steps monte carlo are implemented; moreover, 10 estimated tracking (measurements) with dataassociation calculation are sampled for reciprocal comparison for accuracy. it is easy to see that the many more matching situations occur in fig. 5. i.e., all of the tracking paths tightly parallel the true path marked with circles. it should be emphasized that a little difference does exist the paths of the true targets and the results presented in fig. 4 and fig. 5, since the tracking is generated with a random function of the software program. usage of random-number generators for the measurement of noise and clutter points is illustrated in the simulation. furthermore, a kalman filter is utilized to recursively estimate the state vector x̂(k|k) . on the basis of each hypothesis formulated from the measurement data received, the corresponding correlations can be promptly calculated. hence, the position errors caused by the use and non-use of dfa calculation and without dfa calculation are plotted in fig. 6, and fig. 7, respectively. it is reasonable to state that the larger position error, with an order of ten thousands, is generated by the results obtained prior to dfa calculation; whereas, a smaller position error, in an approximate order of hundreds, occurs in the case after dfa calculation. a case with all the noise uncorrelated is assumed in the simulation. thus the values α = 1 and β = r + 1 are chosen in the simulation for five targets and ten to thirty measurements, where r is the radians of the gate. after eighty monte carlo iterations the simulation results from tracking the five targets, two non-maneuvering (target 1 and target 2) and three maneuvering (target 3 to target 5), are graphed in fig. 8. the results of the tracking position errors after dfa calculation are plotted in fig. 9. according to the simulation results based on several different situations, we know that the performance of the proposed algorithm, dfa on chnn-based, is quite well. the proposed chnn-based dfa algorithm might have a hardware complexity (number of neurons) in direct linear proportion to the number of tracked targets and the deployed mobile sensors in wsss. moreover, tracking for targets with mobile sensors by using the dfa algorithm in wsns is constrained by the requirement for training the chnn. however, this chnn can be thoroughly implemented in analog vlsi technology with currently existing methods. therefore, the authors believe that, overall, since a chnn is more appropriate for a neural network based in multi-mobile sensor tracking environments, one may expect to see many such applications in wss constructions in the near future. furthermore, although in this type of environment the size of the optimization problem requiring much attention is considerably larger, the proposed dfa algorithm performs quite well, as in the simulation illustration. this is probably due to a sparse assumption of fewer tracked targets. however, the scale of the chnn that would be required for the implementation of a practical mobile sensor might become a problem when the number of sensors and the measurements become very large. a current issue for development is reduction of the scale of the quadratic optimization problem that the chnn must solve, so that available analog chnn ic (integrated circuit) implementation can be used to build practical mobile sensors. besides, the trends for implementing the wss in large scale network are a data fusion methodology for wireless sensor systems 47 generally to distribute the fused-data in some small area networks separately. the dfa based on chnn proposed in this paper, thus, can be implemented in the various mobile environments both in large scale and small scale networks. finally, since the amount of energy consumed by a mobile sensor during the processing of the dfa is large, such consumption is another important issue. recently, several methods have been proposed for investigating energy-awareness problems in the mobile sensors in wss [25]. the authors are currently working on developing a method for decreasing energy consumption by a mobile sensor so that the lifetime of sensors in wss can be increased. table 1: initial conditions of two targets x(m) ẋ(m/s) y(m) ẏ(m/s) target 1 1500 400 3500 560 target 2 1000 600 4000 440 table 2: initial conditions of five targets x(m) ẋ(m/s) y(m) ẏ(m/s) target 1 100 400 3500 560 target 2 1000 600 400 440 target 3 0 550 8000 80 target 4 20 540 9400 -100 target 5 0 0 12500 -70 figure 1: deployment with two targets and five mobile sensor nodes, with sensing areas covered in circles. 48 j.i.-z. chen, y.-n. chung figure 2: relationships between measurements and predicted targets, based on gating technique. figure 3: the procedure of simulation for the dfa algorithm. figure 4: data association results for tracking three targets prior to dfa calculation. a data fusion methodology for wireless sensor systems 49 figure 5: data association results for tracking three targets after dfa calculation figure 6: position errors in tracking three targets prior to dfa calculation. figure 7: position errors in tracking three targets after dfa calculation. 50 j.i.-z. chen, y.-n. chung figure 8: simulations of tracking of five targets (using chnn algorithm). figure 9: position errors in tracking six targets after dfa calculation (using chnn technique). 6 conclusions an innovative dfa algorithm based on chnn techniques for tracking multiple targets with mobile sensors over wss environments has been proposed in this report. this tracking technique has been investigated for its advantages in choosing an optimal correlation between mobile sensor measurements and existing target tracks. because of the application of the chnn technique, it was discovered that the system is relieved of the burden of determining the proper weighting factors as in a traditional hnn; therefore, the network can always achieve a rational solution. moreover, an adaptive procedure for tracking maneuvering targets is also employed in this algorithm via stochastic process obtained by the dfa. on the basis of the simulation results obtained in this study, it can be claimed that this dfa algorithm is capable of obtaining the optimal correlations between true targets and mobile sensor measurements in wss scenarios. finally, the approach developed in this research has demonstrated not only stable performance for tracking procedures but 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[23] m. s. grewal, a. p. andrew, kalman filtering, theory and practice-using matlab, 2nd ed. john wiley & sons, inc., new york, 2001 [24] a. papoulis, s. u. pillai, probability, random variables, and stochastic processes. 4th ed. mcgraw-hill, comp., inc., new york, 2002 [25] x. wang, d. wang, y. wang, agrawal, d. p., a. mishra, on data fusion and lifetime constrains in wireless sensor networks. in proceeding ieee int. communication. conf., glasgow, scotland, vol. 9, pp. 3942-3947, 2007. int j comput commun, issn 1841-9836 vol.7 (2012), no. 4 (november), pp. 674-687 impact of network infrastructure parameters to the effectiveness of cyber attacks against industrial control systems b. genge, c. siaterlis, m. hohenadel béla genge, christos siaterlis and marc hohenadel institute for the protection and security of the citizen european commission, joint research centre via e. fermi, 21027, ispra (va), italy e-mail: {bela.genge, christos.siaterlis, marc.hohenadel}@jrc.ec.europa.eu abstract: the fact that modern networked industrial control systems (nics) depend on information and communication technologies (ict), is well known. although many studies have focused on the security of scada systems, today we still lack the proper understanding of the effects that cyber attacks have on nics. in this paper we identify the communication and control logic implementation parameters that influence the outcome of attacks against nics and that could be used as effective measures for increasing the resilience of industrial installations. the implemented scenario involves a powerful attacker that is able to send legitimate modbus packets/commands to control hardware in order to bring the physical process into a critical state, i.e. dangerous, or more generally unwanted state of the system. the analysis uses a boiling water power plant to show that the outcome of cyber attacks is influenced by network delays, packet losses, background traffic and control logic scheduling time. the main goal of this paper is to start an exploration of cyber-physical effects in particular scenarios. this study is the first of its kind to analyze cyber-physical systems and provides insight to the way that the cyber realm affects the physical realm. keywords: cyber attacks, industrial control systems, scada, security. 1 introduction modern critical infrastructures (ci), e.g. power plants, water plants and smart grids, rely on information and communication technologies (ict) for their operation since ict can lead to cost optimization as well as greater efficiency, flexibility and interoperability between components. in the past cis were isolated environments and used proprietary hardware and protocols, limiting thus the threats that could affect them. nowadays cis, or more specifically networked industrial control systems (nics), are exposed to significant cyber-threats; a fact that has been highlighted by many studies on the security of supervisory control and data acquisition (scada) systems [1, 2]. the recently reported stuxnet worm [3] is the first malware specifically designed to attack nics. its ability to reprogram the logic of control hardware in order to alter physical processes demonstrated how powerful such threats can be. stuxnet was a concrete proof of a successful cyber-physical attack but by no means a trivial attack. it required a thorough knowledge of the physical system, software and os vulnerabilities. in this paper we consider an adversary with a lower level of sophistication that instead of reprogramming the highly specialized hardware (plcs) as in the stuxnet case, he exploits the ability of control hardware to communicate with remote stations using well-established protocols such as tcp, that are normally used by the operator to read sensor values, e.g. pressure, temperature, and control actuators, e.g. valves. furthermore, we identify the communication and control logic implementation parameters that influence the outcome of cyber attacks against nics and that could be used as effective measures for increasing the resilience of industrial installations. in our study the attacker is located copyright c⃝ 2006-2012 by ccc publications impact of network infrastructure parameters to the effectiveness of cyber attacks against industrial control systems 675 outside the plant, somewhere in the internet, from where he/she exploits the capability of control hardware to communicate with remote stations. although direct connections between control hardware and the internet are usually avoided in nics implementations [5], the attacker might install a malware on a station with an internet connection located within the corporate network that could be used to forward messages to the control hardware. we consider that the control hardware is running legitimate code designed to keep the physical process in a normal operating point, while the attacker tries to bring it to a critical state, i.e. dangerous, or more generally unwanted state of the system [6]. the method employed by the attacker is a repeated transmission of commands to the control hardware that open/close specific valves. the main contribution of this paper is that it is the first of its kind to analyze cyber-physical systems and provides insight to the way that the cyber realm affects the physical realm. the real applicability of the implemented scenario is confirmed not only by stuxnet, but by other past events as well. one example in this sense is a 2002 penetration test done by a security firm for a power company located in california. the testers parked their van outside a remote substation, where they noticed a wireless antenna. without leaving their vehicle they managed to connect to the system and within 20 minutes they have not only mapped the entire network, but also ”they were talking to the business network and had pulled off several business reports” [4]. by taking these events and adapting them to our scenario we can further imagine that the testers are in fact attackers that install a remotely accessible gateway and launch their attack from the anonymity provided by the internet. the attack scenario has been implemented with the help of our previously developed framework [7] that uses simulation for the physical components and an emulation testbed based on emulab [8, 9] to recreate the cyber part of nics, e.g., scada servers, corporate network, etc. in the implemented scenario we have used the model of a boiling water power plant developed by bell and åström [10]. the paper is structured as follows. after an analysis of related work in section 2 we present a brief overview of the experimentation framework in section 3. the methodology and implemented experiments are presented in section 4 and 5, respectively. the paper concludes in section 6. 2 related work an approach where real sensors and actuators, combined with simulated plcs and communication protocols were used to study cyber-physical systems has been proposed by queiroz, et al. [15]. their study showed that while plcs are under a dos attack, operators might take delayed or wrong decisions that could disrupt the operation of the plant. a similar experiment has also been documented by davis, et al. [16] that used the powerworld server to study the effects of communication delays between the physical process and human operators. in the same direction, the work of chabukswar, et al. [17] proved that a ddos attack against communication nodes between controllers and sensors causes the plcs to take wrong decisions based on old sensor values. cárdenas, et al. [18], went further by not only documenting the effects of dos attacks on sensors, but also proposing a new detection mechanism and possible countermeasures. the previously mentioned approaches have demonstrated the effectiveness of dos attacks, but without reaching a sophistication level that would have allowed the attacker to reprogram the low level control logic of the plcs. this fact sets an important barrier in terms of knowledge, skills and efforts required by the attacker, as was the case of stuxnet, where developers had also knowledge of the plc code, os and hardware details. in this category we find the work of nai fovino, et al. [5] that have proposed an experimental platform for studying the effects of cyber attacks against nics. in their paper the authors describe several attack scenarios, including dos attacks and worm infections that send modbus packets to control hardware. although the 676 b. genge, c. siaterlis, m. hohenadel authors provide a wide set of countermeasures, they do not identify communication parameters that affect the outcome of the attacks. moreover, in our analysis we have also identified installation-specific parameters that can directly affect the resilience of physical processes. 3 framework overview after providing a brief description of a typical nics architecture, this section presents a short overview of our previously developed experimentation framework used in our experiments. 3.1 process control architecture overview in modern nics architectures, one can identify two different control layers: (i) the physical layer composed of all the actuators, sensors, and generally speaking hardware devices that physically perform the actions on the system, e.g. open a valve, measure the voltage in a cable; (ii) the cyber layer composed of all the ict devices and software which acquire the data, elaborate low level process strategies and deliver the commands to the physical layer. the cyber layer typically uses scada protocols to control and manage the physical devices within the cyber layer. the ”distributed control system” of the cyber layer is typically split among two networks: the control network and the process network. the process network usually hosts all the scada (also known as scada masters) and hmi (human machine interface) servers. the control network hosts all the devices which, on the one side control the actuators and sensors of the physical layer and on the other side provide the ”control interface” to the process network. a typical control network is composed of a mesh of plcs (programmable logic controller). from an operational point of view, plcs receive data from the physical layer, elaborate a ”local actuation strategy”, and send back commands to the actuators. plcs execute also the commands that they receive from the scada servers (masters) and additionally provide, whenever requested, detailed physical layer data. 3.2 framework architecture the previously developed framework [7] follows a hybrid approach, where the emulab-based testbed recreates the control and process network of nics, including plcs and scada servers, and a software simulation reproduces the physical processes. the architecture, as shown in figure 1, clearly distinguishes 3 layers: the cyber layer, the physical layer and a link layer in between. the cyber layer includes regular ict components used in scada systems, while the physical layer provides the simulation of physical devices. the link layer, i.e. cyber-physical layer, provides the ”glue” between the two layers through the use of a shared memory region. the physical layer is recreated through a soft real-time simulator that runs within the sc (simulation core) unit and executes a model of the physical system. the simulator’s execution time is strongly coupled to the timing service of the underlying operating system (os). throughout the paper the term time step is used to denote the time between two successive executions of the physical model in the simulator. the cyber layer is recreated by an emulation testbed that uses the emulab architecture and software [8, 9] to automatically and dynamically map physical components, e.g. servers, switches to a virtual topology. besides the process network, the cyber layer also includes the control logic code that in the real world is run by plcs. the control code can be run sequentially or in parallel to the physical model. in the sequential case, a tightly coupled code (tcc) is used, i.e. code that is running in the same memory space with the model, within the sc unit. in the parallel case a loosely coupled code (lcc) is used, i.e. code that is running in another address space, possibly on another host, within the r-plc unit (remote impact of network infrastructure parameters to the effectiveness of cyber attacks against industrial control systems 677 plc mem physical models r-plc unit master unit physical layer cyber-physical layer cyber layer sc unit plc code plc code r-plc unit emulab testbed plc mem figure 1: experimentation framework architectural overview plc). the unit that implements global decision algorithms based on the sensor values received from the r-plc units is also present in the proposed framework as the master unit. the cyberphysical layer incorporates the plc memory, seen as a set of registers typical of plcs, and the communication interfaces that ”glue” together the other two layers. prototypes of sc, r-plc and master units have been developed in c# (windows) and have been ported and tested on unix-based systems (freebsd, fedora and ubuntu) with the use of the mono platform. matlab simulink was used as the physical process simulator (physical layer). from simulink models the corresponding ’c’ code is generated using matlab rtw. the communication between sc and r-plc units is handled by .net’s binary implementation of rpc (called remoting) over tcp. for the communication between the r-plc and master units, we used the modbus over tcp protocol. 4 methodology in this section we provide a description of the methodology we used, including a description of the scenario and experimental setup. 4.1 scenario previous security events [3, 4] involving nics showed that attackers can (easily) compromise stations located within an installation’s internal network. these stations can then be used as gateways for downloading malicious code and for remotely controlling other stations, including control hardware such as plcs. the implemented scenario assumes that there is already a compromised station providing access to plcs. this is used by the attacker to send remote modbus commands in order to bring the physical process into a critical state. the physical process used in our experiments is the boiling water power plant (bwpp) model developed by bell and åström [10]. within this context the critical state is given by an increased steam pressure that is more than twice the value of a typical operating point. although to the best of our knowledge the literature does not mention anything about the consequences of running the process with these parameters, we assumed that this might cause physical damages 678 b. genge, c. siaterlis, m. hohenadel and therefore could become a desired target for the attacker. furthermore, we assumed that the attack is conducted for a limited time period of 10 minutes, as immediately after it is started the process experiences significant deviations from its normal operating point. consequently, alarms might be turned on and human operators might intervene by switching off devices or disconnecting equipment. as shown later in this paper, the 10 minute time period is more than enough for the attacker to bring the physical process into a critical state. our experimental results presented in the next section show that the outcome of the attack is affected not only by network delays, packet losses and background traffic, but also by the execution of plc control code and the speed of control valves. these can be used as effective measures to increase the resilience of physical processes confronted with cyber attacks. 4.2 experimental setup the previously described scenario has been implemented in the joint research centre’s (jrc) experimental platform for internet contingencies (epic) laboratory. the emulab testbed included nodes with the following configuration: freebsd os 8, amd athlon dual core cpu at 2.3ghz and 4gb of ram. we have emulated network delays, packet losses and background traffic with the dummynet [11, 12] and iperf [13] software in order to recreate a dynamic and unpredictable environment such as the internet. as shown in figure 2, the experimental setup consisted of 6 hosts: 1 host for running the sc unit, 3 hosts for running the r-plc units, 1 host for running the compromised station and 1 host for the attacker. the attacker uses the compromised station to forward modbus packets to r-plc units and finally to write the plc memory within the sc unit. the control code that is in charge of maintaining the bwpp at a constant operating point has been implemented as tcc code, where tcc1 controls the fuel valve, tcc2 controls the steam valve and tcc3 controls the feed-water valve. the role of the r-plc units is simply to enable access to the physical model using the modbus/tcp protocol. although r-plc units can also run control code, the decision to implement control code as tccs has been made based on the granularity of the process model execution step that needs to be less than 1ms. this is needed in order to emulate plc tasks that can be scheduled to run within milliseconds. consequently, the chosen time step for the physical model was of 0.5ms. network delays, background traffic and packet losses, specific to a dynamic environment such as the internet, have been emulated between the attacker’s station and the compromised station. the limited bandwidth and communication capabilities of plcs have been emulated with 10mbit/s lans (lan2 and lan1, respectively), while using a 100mbit/s lan (lan0 ) between r-plcs and the sc unit in order to maximize the performance of the interaction between r-plc units and the bwpp model. 4.3 boiling water power plant as already mentioned, in this paper we use the boiling water power plant (bwpp) model developed by bell and åström in [10]. it models a 160mw oil-fired electric power plant based on the sydsvenska kraft ab plant in malmö, sweden. the operation of the process is controlled by three valves, i.e. fuel valve, steam valve and feed-water valve, while the operator is able to monitor the process by reading three sensors: steam pressure, water level and generated electricity. the following equations describe the dynamics of the physical process [10]: impact of network infrastructure parameters to the effectiveness of cyber attacks against industrial control systems 679 industrial ict emulation 10.1.1.2 lan0 100 mbit 10.1.1.3 10.1.1.4 10.1.1.5 10.1.3.3 10.1.3.4 10.1.3.5 10.1.3.2 10.1.2.2 10.1.2.3 lan2 10 mbit traffic source traffic sink generator (y2) steam valve (u2)water valve (u3) pressure sensor (y1) level sensor (y3) fuel valve (u1) r-plc unit 2 (steam valve) r-plc unit 1 (fuel valve) r-plc unit 3 (water valve) l a n 1 1 0 m b it internet tcc1 (fuel valve) tcc2 (steam valve) tcc3 (water valve) turbine boiler delay and packet loss emulation sc unit compromised station attacker figure 2: experimental setup ẋ1 = −0.0018u2x 9/8 1 + 0.9u1 −0.15u3, ẋ2 = (0.073u2 −0.016)x 9/8 1 −0.1x2, ẋ3 = (141u3 − (1.1u2 −0.19)x1)/85, y1 = x1, y2 = x2, y3 = 0.05(0.1307x3 + 100sq + er/9−67.975), sq = (1−0.001538x3)(0.8x1−25.6) x3(1.0394−0.0012304x1) , er = (0.85u2 −0.147)x1 + 45.59u1 −2.514u3 −2.096, 0 ≤ ui ≤ 1(i = 1,2,3), u̇1 ≤ 0.007/sec,−1.0 ≤ u̇2 ≤ 0.1/sec, u̇3 ≤ 0.05/sec, (1) where x1,x2 and x3 denote the steam pressure (kg/cm2), electric output (mw), and fluid density (kg/m3), respectively; y1,y2 and y3 denote the outputs of the model, where y3 is the water level deviation (m); u1,u2 and u3 are the valve positions for fuel flow, steam flow and feed-water flow, respectively; sq and er denote the steam quality and evaporation rate (kg/s), respectively. in our experiments we used a normal operating point for the bwpp in which the pressure equals 108 kg/cm2, that was achieved by keeping the three valves in the following normal valve positions (nvp): u1 = 0.34, u2 = 0.69 and u3 = 0.433 [14]. as a consequence, tccs, that implement the control logic code, must maintain constant the position of the three valves in order to provide a constant steam pressure of 108 kg/cm2. for the critical state we consider a pressure of 250 kg/cm2 that is more than twice the value of the steam pressure of the process running in the previously mentioned normal operating point. the attacker is able to bring the 680 b. genge, c. siaterlis, m. hohenadel bwpp into the critical state by continuously sending modbus packets (around 100/sec for each valve) that keep the steam and feed-water valves in the closed position (u2 = 0 and u3 = 0, respectively) and the fuel valve in the opened position (u1 = 1). throughout this paper we use the term attacker’s valve positions (avp) to denote the position of the three valves in the attacker’s setting. 5 attacks and analysis in this section we analyze the influence of several parameters on the cyber attacks launched remotely from the internet, as described in the scenario from the previous section. the analysis is conducted in two phases: in the first phase we analyze the ability of the attacker to maintain the control valves in the avp; in the second phase we analyze the ability of the attacker to increase the steam pressure to 250 kg/cm2, thus actually bringing the plant into the critical state. while the goal of the second phase is clear, the goal of the first one needs further explanation. the actual goal of the first phase is to analyze the reaction of plcs in terms of commands sent to the valves, reaction that might provide assistance in the rationale of the results in the second phase. in both phases we have measured the influence of: plc task scheduling (ts) every 100ms and 1ms; network delays of 0s, 0.5s and 3s; packet losses of 1%, 5% and 10%; and background traffic of 2.5mbit/s, 5mbit/s and 10mbit/s. such extreme values for network delays, i.e. 3s, and packet losses, i.e. 10%, can rarely be measured over the internet (possibly over satellite links or multiple intermediate proxies). nevertheless, we have included them in our analysis in order to justify our statements related to the required magnitude of these parameters for influencing the outcome of the attack. for each configuration setting, representing a combination of plc ts, network delays, packet losses and background traffic we have run one experiment for 10 minutes. in total, we have run 540 experiments in 9 hours. 5.1 the effect of the cyber attack on the position of control valves the implementation of modbus over tcp allows attackers to remotely control the three valves, i.e. fuel valve, steam valve, feed-water valve, thus providing a certain anonymity to the attacker. nevertheless, this status is compensated by fluctuating parameters such as network delays, packet losses and background traffic that can have a major effect on the outcome of the attack. in this sub-section we analyze the effects of these parameters and two different ts, i.e. 100ms and 1ms, on the position of the three control valves. 100ms task scheduling the effect of network delays on the position of control valves for a 100ms ts time is given in figure 3. each sub-figure shows the position of a specific control valve during the 10 minute attack, starting with the nvp shown at t = 0 minutes and following with the changes induced by the attacker that, as shown by most of the figures, bring the valves into the avp. the differences we observe in the behavior of the three valves are caused by the motion speed of each valve that is different in each case (equation 1). thus, the fastest to open and close is the steam valve (0.1/sec for opening and 1/sec for closing) followed by the feed-water valve (0.05/sec) and by the fuel valve (0.007/sec). consequently, the greater the speed, the higher the fluctuations that we see in figure 3 and the higher the ability of plcs to maintain the nvp. one of the main conclusions from our results (figure 3) is that network delays are beneficial to the physical process when confronted with cyber attacks. nevertheless, the attacker is able to bring the valves into the avp even for network delays of 3s. the attacker is most successful with impact of network infrastructure parameters to the effectiveness of cyber attacks against industrial control systems 681 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) fuel valve: 0s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) fuel valve: 0.5s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) fuel valve: 3s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 0s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 3s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 3s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) feed-water valve: 0s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) feed-water valve: 0.5s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) feed-water valve: 3s emulated delay figure 3: effect of network delays on control valve positions for 100ms ts, 1% loss rate and 2.5mbit/s background traffic the fuel valve as this has the lowest speed and the control code is running only once every 100ms, while the attacker sends one modbus command every 10ms, i.e. 100/sec. for the other two valves the plcs are able to produce a slight deviation from the avp; however, these are unable to bring the valves back to the nvp. as we increase the network delays we notice that plcs are causing larger deviations from the avp and in the extreme case of 3s the attacker is able to produce only small deviations from the nvp for the fuel and feed-water valves. nevertheless, the attacker is still able to close the steam valve, as its closing speed (1/sec) exceeds 10 times its opening speed (0.1/sec). based on these results we can conclude that only extreme network delays, e.g. 3s, have a major influence on the outcome of the attack. in addition, an attacker could successfully exploit a different opening and closing speed of valves, that could be interpreted as a slower reaction of the plcs. going further, in figure 4 we show the effect of packet losses on the position of the valves for the same ts of 100ms. by increasing the packet loss from 1% (figure 3) to 5% and 10% (figure 4) we also increase the deviations from avp. nevertheless, even with higher packet losses plcs are unable to maintain the nvp, not even in the case of slower valves such as the fuel and feed-water valves. as shown in the same figure, we have also experimented with extreme packet losses of 10%. however, as the attacker uses a 10 times higher packet rate than the control code scheduling rate, even in this case plcs are unable to keep the valves in the nvp. we have also investigated the effect of background traffic on the three valves, shown in figure 5. by increasing the background traffic from 2.5mbit/s (figure 3) to 5mbit/s (figure 5) we do not notice major effects, as the maximum network capacity is 10mbit/s and the 100 modbus 682 b. genge, c. siaterlis, m. hohenadel 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) fuel valve: 5% emulated packet loss 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) fuel valve: 10% emulated packet loss 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 5% emulated packet loss 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 10% emulated packet loss 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) feed-water valve: 5% emulated packet loss 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) feed-water valve: 10% emulated packet loss figure 4: effect of packet loss on plant valve positions for 100ms ts, 0s emulated delay and 2.5mbit/s background traffic 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) fuel valve: 5mbit/s emulated traffic 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) fuel valve: 10mbit/s emulated traffic 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 5mbit/s emulated traffic 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 10mbit/s emulated traffic 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) feed-water valve: 5mbit/s emulated traffic 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) feed-water valve: 10mbit/s emulated traffic figure 5: effect of background traffic on plant valve positions for 100ms ts, 0s emulated delay and 1% packet loss messages sent every second for each valve generate a traffic of around 140kbit/s, with a total of 420kbit/s for all three valves. nevertheless, when the background traffic reaches the network capacity fewer messages reach the plcs which are able to maintain the nvp with only small deviations. however, even in this later extreme case the steam valve is affected by the attacker, as its closing speed is 10 times higher than its opening speed, and counteracting one single packet received from the attacker requires 10 executions of the control code. 1ms task scheduling the previous results have shown that if plcs have a control code ts of 100ms they are not effective in maintaining the nvp. moreover, the outcome of the attack is affected only by extreme impact of network infrastructure parameters to the effectiveness of cyber attacks against industrial control systems 683 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) fuel valve: 0s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) feed-water valve: 0s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 0s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 0.5s emulated delay 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 3s emulated delay figure 6: effect of network delays on plant valve positions for 1ms ts, 1% loss rate and 2.5mbit/s background traffic cases of network delays and background traffic. for this reason we have also experimented with a ts of 1ms that could significantly improve the reaction of the plcs. for a ts of 1ms, even in the case of 0s emulated delay, the attacker is only able to produce insignificant deviations from nvp for fuel and feed-water valves, as shown in figure 6. nevertheless, the steam valve is still affected by the attack and the attacker is able to bring it to the avp. however, in this case larger network delays show a significant effect on the steam valve, as the attacker is unable to maintain the steam valve in the avp for delays larger than 3s. as the fuel and feed-water valves present only insignificant changes already shown in figure 6, we further focus our attention on the steam valve. we have repeated the experiments for different packet losses and background traffic, with the results shown in figure 7. as in the case of the previous results, larger packet losses do not improve the response of the plcs. nevertheless, the extreme case of 10mbit/s for background traffic ensures small deviations from the nvp for the first 5 minutes of the experiment. in the second half more packets reach the plc that produce larger and larger deviations and finally after 8 minutes are able to bring the valve into the avp. the results from this sub-section have shown that the attacker is able to affect the nvp for all three valves if the plcs use a ts of 100ms. within this setting network delays and background traffic are major factors that influence the attack, as also illustrated in table 1. nevertheless, the attacker is still able to produce major deviations from nvp for valves with an opening and closing speed difference. for this reason, a better solution would be to provide a smaller ts combined with an equal opening and closing speed of valves. as shown by the results, with a smaller ts, the fuel and feed-water valves experience insignificant deviations from nvp with minimum emulated delays and background traffic. in contrast, the speed difference of the steam valve still causes major deviations from nvp, that are reduced by only extreme network delays of 3s and a background traffic of 10mbit/s. lowering the value of the ts below 1ms could be considered a measure for a more resilient control code. however, this is only possible if the control code’s execution time is smaller than this value. 684 b. genge, c. siaterlis, m. hohenadel 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 5% emulated packet loss 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 10% emulated packet loss 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 5mbit/s emulated traffic 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam valve: 10mbit/s emulated traffic figure 7: effect of packet loss and background traffic on the steam valve position for 1ms ts and 0s emulated delay 5.2 the effect of the cyber attack on the steam pressure in the previous sub-section we have shown that the attacker can cause major deviations from the nvp for all three valves. in this sub-section we analyze the effects that the previously presented deviations have on the steam pressure. following the same experimental strategy, we have first recorded the steam pressure for a ts of 100ms followed by a ts of 1ms. 100ms task scheduling the results from figure 8 show that for an emulated network delay of 0s, the attacker is able to increase the steam pressure above 250 kg/cm2 after only 2.5 minutes the attack is started. moreover, the attacker is able to bring the process into a critical state even for a network delay of 0.5s. only extreme network delays of 3s show a major impact and prevent the successful outcome of the attack, although the attacker is still able to produce major deviations from nvp, as previously shown in figure 3. with larger packet losses the process reaches the critical state after 4 minutes for a 5% loss and 6 minutes for 10% loss, as shown in the same figure. the reason for this behavior is that all three valves still experience major deviations from their nvp, as shown in figure 4, which causes the pressure to increase immediately after the attack is started. the background traffic affects the outcome of the attack only in extreme cases when it reaches the maximum network capacity. otherwise, the attacker is able to open and close the valves, as shown in figure 5, and to bring the process into the critical state. 1ms task scheduling by decreasing the ts time to 1ms the attacker is not able to reach his goal in neither of the settings included in this study (figure 9). nevertheless, it is still able to increase the steam pressure to a maximum value of 234 kg/cm2 for minimal network delay, packet loss and background traffic. the reason behind this is that the attacker is still able to completely close the steam valve, although it is able to cause only negligible deviations from the nvp for the other two valves, as shown in figure 6. impact of network infrastructure parameters to the effectiveness of cyber attacks against industrial control systems 685 0 150 300 450 600 750 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 0s emulated delay 0 50 100 150 200 250 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 3s emulated delay 0 100 200 300 400 500 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 5% emulated packet loss 0 100 200 300 400 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 10% emulated packet loss 0 150 300 450 600 750 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 5mbit/s emulated traffic 0 30 60 90 120 150 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 10mbit/s emulated traffic figure 8: effect of network delays, packet loss and background traffic on the steam pressure for 100ms ts table 1: average valve positions and maximum pressure (p) during cyber attacks fuel valve steam valve feed-water valve max p (kg/cm2) (target: 0.34) (target: 0.68) (target: 0.43) (target: 108) parameters 100ms 1ms 100ms 1ms 100ms 1ms 100ms 1ms delay (s) 0 0.93 0.34 0.04 0.05 0.03 0.433 585 234 0.5 0.75 0.34 0.11 0.31 0.21 0.433 459 173 3 0.4 0.34 0.25 0.51 0.39 0.433 198 131 loss 5 0.84 0.34 0.1 0.3 0.14 0.433 407 204 (%) 10 0.61 0.34 0.17 0.46 0.2 0.433 347 145 traffic 5 0.92 0.34 0.04 0.05 0.03 0.433 586 234 (mbit/s) 10 0.34 0.34 0.55 0.67 0.43 0.433 128 110 by increasing the emulated network delays the maximum pressure induced by the attacker reduces gradually from 173 kg/cm2 for 0.5s to 131 kg/cm2 for 3s (table 1). however, larger packet losses and background traffic do not produce major differences in the outcome of the attack, unless extreme values are used. nevertheless, a 5% packet loss is still able to reduce the maximum pressure to 204 kg/cm2, while a 10% packet loss reduces the maximum pressure to 145 kg/cm2. in the extreme case of 10mbit/s background traffic the maximum pressure is reduced to 110 kg/cm2. the results from this section have shown that by decreasing the ts to 1ms the physical process is able to react more efficiently to cyber attacks. network delays, packet losses and background traffic have also shown to have an influence on the attack. nevertheless, by using a ts of 100ms these are able to affect the outcome of the attack only in extreme cases. consequently, designers should consider using a lower ts whenever possible in order to prevent the successful outcome of similar attacks. 686 b. genge, c. siaterlis, m. hohenadel 0 50 100 150 200 250 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 0s emulated delay 0 30 60 90 120 150 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 3s emulated delay 0 50 100 150 200 250 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 5% emulated packet loss 0 30 60 90 120 150 180 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 10% emulated packet loss 0 50 100 150 200 250 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 5mbit/s emulated traffic 0 30 60 90 120 0 1 2 3 4 5 6 7 8 9 1 0 time (m) steam pressure (kg/cm2): 10mbit/s emulated traffic figure 9: effect of network delays, packet loss and background traffic on the steam pressure for 1ms ts 6 conclusion the study presented in this paper showed that cyber attacks exploiting knowledge on nics can use regular modbus packets to bring the physical process into a critical state. within this scenario we evaluated the impact of network and installation-specific parameters on cyber attacks targeting a power plant. the experimental results showed that while communications parameters such as network delays, packet losses and background traffic have a limited effect on the attack, task scheduling and properties of physical processes, i.e. the speed of control valves, can become effective measures for increasing the resilience of physical processes. the main contribution of this paper is that it identifies two key parameters that could be adopted at design-time to increase the resilience of physical processes confronted with cyber attacks. the first one, i.e. control code task scheduling, provides engineers an efficient mechanism to counterbalance disturbances caused by malicious command packets, while the second one, i.e. the speed of control valves, provides insight into the way that an attacker might manipulate knowledge on physical properties to bring the process into a critical state. such properties should be taken into account at process design time, which will lead to a more resilient physical process. bibliography [1] s. east, j. butts, m. papa, s. shenoi, a taxonomy of attacks on the dnp3 protocol, in proceedings of ifip advances in information and communication technology, 311:67–81, 2009. [2] t.c. aseri, n. singla, enhanced security protocol in wireless sensor networks, international journal of computers communications & control, 6(2):214–221, 2011. [3] the symantec stuxnet dossier, 2010, http://www.wired.com /images_blogs/threatlevel/2010/11/w32_stuxnet_dossier.pdf [4] a.s. brown, scada vs. the hackers can freebie software and a can of pringles bring down the u.s. power grid?, mechanical engineering, 124(12), 2002. impact of network infrastructure parameters to the effectiveness of cyber attacks against industrial control systems 687 [5] i. nai fovino, m. masera, l. guidi, g. carpi, an experimental platform for assessing scada vulnerabilities and countermeasures in power plants, in proceedings of human system interactions, pp. 679–686, 2010. 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[18] a. cárdenas, s. amin, z.s. lin, y.l. huang, chi-y. huang, s. sastry, attacks against process control systems: risk assessment, detection, and response, in proceedings of the acm symposium on information, computer and communications security, pp. 355–366, 2011. international journal of computers communications & control issn 1841-9836, 13(1), 117-128, february 2018. modern interfaces for knowledge representation and processing systems based on markup technologies a. a. mohammed saeed, d. dănciulescu ali amer mohammed saeed* doctoral school of informatics university of piteşti 110040 piteşti, 1 târgul din vale str., romania *corresponding author: ali.amer81@gmail.com daniela dănciulescu computer science department university of craiova 200585 craiova, 13 a. i. cuza str., romania danadanciulescu@gmail.com abstract: the usage of markup technologies to specify knowledge to be processed according to a specific field of application is a common technique. representation techniques based on markup language paradigm to describe various types of knowledge including graph based models is considered and details on using knowledge representation and processing (krp) systems in education are presented. xml, and voicexml were selected to implement smart interface for krp systems. keywords: krp systems, markup technologies, intelligent interfaces, vxml 1 introduction this article deals with markup mechanisms for knowledge, but also for voice interfaces. it is based on [13] being an extended version of the previous work of the first author. the coverage of the subject follows. the next section deals with the usability and efficiency of the following approaches to be used in krp context: sgml / xml, rdf extensions, state-based modeling scxml, and voice xml. from the rdf (resource description framework) category, in the context of krp systems, ckml (conceptual knowledge markup language), ontology markup language (oml) and dlml (descriptive logic markup language) are useful. other approaches are based on the ontology interface layer (oil) and the darpa agent markup language (daml). of the ontological development tools, the most commonly used are: duet (uml enhanced tool), ubot, protege, and ontolingua. an example of processing using descriptions in natural language is illustrated using scxml. scxml and voicexml are covered in the third section. interaction of knowledge bases using java technologies is demonstrated in the fourth section. for this purpose, the legacy knowledge model is modeled by a graph that indicates the inheritance relationship of object attributes. the fifth section is dedicated to the usage of krp systems in education. it is shown that, for visually impaired users, the usage of voicexml based technologies to translate various educational resources is feasible. 2 markup models and knowledge representation by models and markup technologies, in the context of this paper, we understand such models and technologies obtained from sgml (standard generalized markup language; iso 8879:1986 copyright ©2018 cc by-nc 118 a. a. mohammed saeed, d. dănciulescu [28]). sgml is a meta-language, i.e. an artificial language which allow us to describe other languages, in general for the formatting of documents [13]. sgml was used initial by the association of american publishers. then it has become a powerful model with applications and multiple influences. for example, coleman and willis (1997) proposed the usage of sgml in the conservation of the publications of the libraries [4]. in the same year, already appeared html (hypertext markup language, 1990 [23]) useful for www, and extensible markup language (xml, 1996) as the language of the description of the structured information [31]. therefore, sgml is known as being the father of both html and xml [13]. however html is a court specifies its dtd of sgml (with markers predefined), and xml is a subset of sgml where users can define their own tags and attributes. an xml document is composed of markers (tags) and data "character" (char, character). a marking is a string of characters bounded by the symbols "<" and ">". an xml file contains three sections: a header (<?xml version="1.0" encoding="utf-8?">), the definition of document type internal or external (example: <!doctype document system "location of its dtd">) and the root (xml information in this part may be set as a tree structure). a xml schema to describe the set of rules used by knowledge representation and processing (krp) systems can be given as below (regula.xsd). <?xml version ="1.0"?> <xs : schema xmlns : xs="http ://www. w3 . org /2001/xmlschema"> <xs : element name="krprule"> <xs : complextype> <xs : sequence> <xs : element name="rlabel" type="xs : s t r i n g "/> <xs : element name="rleft" type="xs : s t r i n g "/> <xs : element name="rright" type="xs : s t r i n g "/> </xs : sequence> </xs : complextype> </xs : element> </xs : schema> xml processors are used to verify whether the xml documents are well formed or not. to access and editing an xml document, initially is loading the xml document in associated task (example with javascript) [13]: parser = new domparser ( ) ; xmldoc = parser . parsefromstring ( text ," text /xml " ) ; then extract the elements of the xml document for processing. in the context of the knowledge representation of the rules used by krp systems we can consider <kr> as a root element which may include one or more elements of the rule type. each item rule has a unique identifier rid. a rule of association (a → b) is formed of "the hypothesis of a" and "b the consequent part". therefore, each hypothesis must have one or more items. each item hypothesis has a name that is represented by a sequence. this model is described below. modern interfaces for knowledge representation and processing systems based on markup technologies 119 xml document: <kr><rule rid="1"> <hyp> <itemhyp> <name>a</name> </itemhyp> </hyp> <infer > <iteminfer > <name>b</name> </iteminfer> </infer > </rule> </kr> the dtd of the model: <!element kr ( rule+)> <!element rule (hyp i n f e r )> <!attlist rule rid cdata> <!element hyp (itemhyp+)> <!element itemhyp (name)> <!element i n f e r ( iteminfer+)> <!element iteminfer (name)> <!element name (#pcdata)> to describe structures useful to outline knowledge in the field of science of the soil for agriculture, the authors of [12] have converted xml declarations in a format useful for application, called kbml (knowledge based markup language). all meta-information is stored in a file kbml, while the actual data may be available in any data source (distributed, etc.). according to [13], kbml is not a markup language, but merely an application of xml. in the context of modeling and knowledge processing many specialized markup notations have been developed, such as: rdf/xml (model supertitles for expressing graphs as rdf documents that xml [27]), ckml (the conceptual knowledge markup language, 2000 [10]), oml (ontology markup language [26]), dlml (logical description markup language [22]). oml is an extension of the shoe and supports the lambda expressions. oml and ckml are based on the conceptual graphs introduced by sowa (2008) in [17]. querying rdf data is possible by specific languages, some in the lines of traditional database query languages, others based on logic and rule language [1]. stratified graphs can be used to automatic generation of queries in formal or natural language [5,6,14]. the kernel of a rdf model is made up of nodes and pairs of attached attributes/values. a description of the rdf syntax is presented in [3] and can be understood on the basis of the following example that describes the creator of the file tey.rdf located in a folder on a windows server: <rdf :rdf> <rdf : description rdf : about=" f i l e :///home/ a l i / tey . rdf"> <xf : creator> <rdf : description rdf : about="http ://www. upit . ro/"> <xf :name>a l i</xf :name> </rdf : description > 120 a. a. mohammed saeed, d. dănciulescu </xf : creator> </rdf : description > </rdf :rdf> for rdf diagrams one shall specify the space of rdfs names. the fundamental rdf classes are: rdfs:resource (class resources), rdf:property (describes the properties of the resources) and rdfs:class (for specifying the type or category). to define a new class of rdf diagram, the corresponding resource class has the property rdfs:type whose value resource is rdfs:class. the resources which belong to the defined class are called courts. an example that describes a collection of resources is: <rdf : bag rdf : id="docs−apply"> <rdf : l i rdf : resource=" f i l e :/// lucru / teza . docx" /> <rdf : l i rdf : resource="https ://www. upit . ro /\_doc/8806/a_27_c_taxe . pdf" /> <rdf : l i rdf : resource=" https ://www. upit . ro /\_doc/11836/proc_mencs . pdf" /> </rdf : bag> new versions ckml have included the ideas and techniques on the informational flow (if information flow) and the design of the logic of the distributed systems. the final version ckml is both a language based on the logic of the information document and a language based on frames. in accordance with kent(2000), "in ckml the specification requires the use of the concept of mathematical lattice or the most practical notion of conceptual space" [10]. the basis of the theoretical portion of the practice based on ckml is the ckp theorem which states the equivalence between data structures of type conceptual lattice and formal context (classification). oml provides three levels of further specify the restrictions [26]: top sequences (corresponding informational flow); the intermediate pipe calculation of binary relations; lower logical expressions (corresponding to concept graphs). expressing an ontology is possible using the languages of specification such as [13]: kif (knowledge interchange format), cl (common logic), oil, daml+oil and allure. kif is based on the logic of the predicates [25], but provides a lisp oriented syntax for this. from the point of view of the semantic, there are four categories of constant in kif: constant of type object, constant of function type, constant of relation and logical constant. oil (ontology inference layer [7]) extends rdf diagram to provide an intuitive syntax and a great power of expression and a semantics more clearly defined with easy to use descriptive logic within the framework of the schemes of reasoning. such oil brings together and unifies three directions: descriptive logic, modeling based on frames and modeling rdf/xml. (daml darpa agent markup language ) + oil has a syntax diagram type rdf, that inherits the primitives of rdf (subclass, domain, range) and primitive added extras like transitivity, cardinality etc. schematic daml+oil is oriented on the objects in which the concepts are abstracted by grades and roles through the properties of the objects. thus, the ontological model daml+oil is based on a lot of the axioms about the classes and properties, as well as a set of builders very useful from the perspective of the rpc systems [13]: intersectionof; unionof; complementof; oneof; toclass; hasclass; hasvalue; mincardinalityq; maxcardinalityq; cardinalityq. the result of the foregoing the evolutionary process is [13]: 1) oil extends rdf; 2) daml extend rdf; 3. daml+oil daml integrates and oil and extends the rdf; 4) allure extends daml+oil and rdf. modern interfaces for knowledge representation and processing systems based on markup technologies 121 the final result of the research on ontological modeling using rdf/xml has led to the specification of the allure, in three versions [13]: allure lite (simple hierarchy, hierarchy of classes with simple constraints), allure dl (maximum expressiveness) and allure full (very expressive). for the processing of meta-data described using specific markup ontologies have been developed a variety of tools for annotation, navigation, utilities (api), edit, view graphics, marking, pan, validation, import, export, compilation, query, search etc. a list of them would be too long. we will be limited to the most important tools, the rest being described in the references indicated: duet (daml uml enhanced tool), ubot, the platform protégé, and ontolingua. ontolingua editor allows for the creation of ontologies, exploration and collaborative editing. using ontolingua, it is possible to export and import formats like: kif, daml + oil, okbc, prologue, the loom, ontolingua and clip. can import data in the protégé format. 3 scxml and voice xml scxml provides a generic state-machine, an execution environment based on ccxml and harel state tables, according to w3c(2015) in [30]. also in [11] it is mentioned that: "using scxml as the representation of the state machine is seen as a benefit". the mentioned authors found that "large portions of the scxml standard are not necessary for it to be useful to our customers and us." ccxml is designed to upgrade vxml dialog systems with advanced telephony functions. an example of the scxml representation is for speech recognition in the natural language. for the implementation of the krp systems, the role of the scxml is active in the framework of the failures, through voice and natural language. according to the above considerations, it was our choice to propose the usage of vxml to create voice-enabled applications [29]. voicexml (vxml) is a markup language for specifying the vocal dialog between a man and a software application, for example a krp system. thus, using voicexml 2.0 one can develop krp applications which provides automatic recognition of speech (asr automated speech recognition) and interactive vocal response (ivr interactive voice response). the main elements of voicexml are: • <vxml> start/close any vxml document; • <var>, <assign>, <clear> used to declare, assign and delete variables; • <grammar> to specify the grammar of the text under recognition; • <catch>, <throw>, <error>, <noinput>, <nomatch> to manage exceptions; • <menu>, <choice>, <enumerate> to deal with menu; • <if>, <else>, <elseif> to describe conditional aspects; • <initial>, <form>, <field>, <filled>, <option> to process forms; • <prompt>, <reprompt>, <value> for input operations; • <prompt>, <audio>, <record>, <reprompt> to deal with multimedia entities; • <block> to describe the code to be executed; • <disconnect>, <exit> for the management of the sessions; • <meta>, <metadata> for metadata management; 122 a. a. mohammed saeed, d. dănciulescu • <noinput>, <nomatch>, <help> to manage events and actions; • <subdialog>, <goto>, <return>, <link> for dialog control; • <object>, <property>, <param>, <script>, <submit>, <transfer>, <log> for server oriented processing of parameterized queries. vxml applications may be of the type uni or many document. an application many document allows us to define a root document which defines all the entities visible in and recovered by the documents son. vxml applications are oriented to the following categories: • queries to retrieve information from web-based infrastructures (like voice portals, web call centers); • transactions to execute specific transactions with a web-based back-end database. figure 1: a knowledge database inheritance and its graph representation to optimize the development of voice based user interfaces, the following facts should be understood: 1. a vxml application is a single voicexml document, or a set of documents which forms a conversational finite state machine (cfsm). the root document is loaded firstly and remains loaded while transitioning over documents belonging to the same application. 2. a session is opened by the user to start the interaction with the vxml interpreter, and is closed by a request from the user, a document, or the interpreter itself. modern interfaces for knowledge representation and processing systems based on markup technologies 123 3. vxml has two types of dialogs: forms and menus. each dialog has associated one or more speech and/or dtmf (dual-tone multi-frequency) grammars. 4. a form defines an interaction that obtains values for a set of variables. 5. a menu provides the user with some alternative options and follows other dialogs depending on the selection. 6. a subdialog is like a function call; after interaction returns to the original form. 7. there exist two types of grammars in vxml: machine directed (the form items are executed in the sequential order) and mixed initiative (the flow has to be directed both by the user and by the application). the example described below is a skeleton query type using the voice-based interface when querying inheritance graphs of knowledge is considered, as [18,19], proposed for text interfaces. this model can be extended for the implementation of the interfaces based on voice within web-based krp systems. to demonstrate the basic principles of voice-based interfaces, a simple knowledge database (described in fig. 1) is considered. based on a client-server implementation in java, a dialog for the computation of the certitude factor is shown: private object [ ] [ ] b3 = { {" s1 " , " s2 33" ," atr (a , 3 ,2 ) atr (b , 1 , 7 ) atr ( c ,8 ,14)"} , {" s2 " , " s4 " ," atr (y ,5 ,15) atr ( e ,8 ,21)"} , {" s3 " ," s4 s5 " ," atr ( f ,40 ,10) atr (g ,6 ,20)"} , {" s4 " ,"" ," atr (h ,20 ,10) atr (x ,50 ,30)"} , {" s5 " ," s6 " ," atr (h ,30 ,20) atr (y ,4 ,3)"} , {" s6 " ,"" ," atr (x ,35 ,40) atr (a ,4 ,1)"} }; dialogue : the f i r s t step f o r c l i e n t 1 >>> welcome . ready f o r you ! >>> select your knowledge base : b3 −−− the knowledge base b3 was s e l e c t e d f o r processing . >>> select the object to i n v e s t i g a t e : s1 −−− the object s1 was s e l e c t e d . >>> select the a t t r i b u t e under i n v e s t i g a t i o n : h −−− your choice was the a t t r i b u t e h . answer : the value of a t t r i b u t e h of object s1 \\ i s 30 and the c e r t i t u d e f a c t o r i s fc =20. vxml code : <prompt>welcome . ready f o r you!</prompt> <prompt count="1"> select your knowledge base : <value expr="knowledgebase"/>? </prompt> // javascript code <prompt> the knowledge base </prompt> // code to print −−knowledgebase−− <prompt> was s e l e c t e d f o r processing . </promt> <prompt count="2"> 124 a. a. mohammed saeed, d. dănciulescu select the object to i n v e s t i g a t e : <value expr="object"/>? </prompt> // javascript code <prompt> the object </prompt> // code to print −− object name−− <prompt> was s e l e c t e d . </promt> <prompt count="3"> select the a t t r i b u t e under i n v e s t i g a t i o n : <value expr="attribute "/>? </prompt> // javascript code <prompt> your choice was the a t t r i b u t e </prompt> // code to print −−attribute−− <prompt>. </promt> 4 markup technologies from the point of view of the process of xml tying java implemented by jaxb, it is noticed the existence of two major components [24]: a generator of diagrams and compiler of diagrams and the process actually involves tying seven actions: the generation of classes; the compilation of classes; unmarshal (xml documents which satisfy the restrictions in the diagram source are processed by the jaxb. also, jaxb lets you transfer xml data from sources other than the files and xml documents such as the nodes dom, paintings rows of characters, sax sources and so on and so forth); the generation of the shaft into which describes the contents of an xml document; validation (unmarshal process involves the validation of the source before generating shaft into which describes the contents. where there is a change in the shaft in the next step can be used the operation jaxb validation to confirm the changes before to transform the contents into a document xml); the client application may change the xml data represented by jaxb shaft using the interfaces generated by the compiler jaxb; marshal (the shaft that describes the contents is converted into the xml document. the content can be validated before the conversion. a process called "marshalling" offers a client applications the ability to convert a java derived from jaxb in data xml.) with a force greater than the programming, can be used jaxp technology (java api for processing xml) based on sax (simple api for parsing xml) and dom (document object model). during the operation of the "parsing" based on sax it generates events that notify the components identified, and java application must deal with the events of the callback methods (for the construction of the structure of the database). the operation of parsing dom build in the memory a representation tree diagram of the data from the xml document. jaxp technology allows the transformation of xml documents using xslt technology (extensible stylesheet language transformation). xmlbeans technology is used to compile the xml layout with obtaining in memory, the classes, and has been developed in the period 2003-2014 by the apache software foundation to enable the processing of large structures. therefore, for the processing of databases structured knowledge which comply with a diagram and are stored in xml files may be used jaxb technologies facing on the diagram xml and jaxp facing on the direct rendering of documents xml. jaxp is a good choice for large knowledge database to be processed in terms of low computational capacity. the following example shows how a xslt stylesheet is used to transform a sample data set into voicexml 2.0 format. modern interfaces for knowledge representation and processing systems based on markup technologies 125 <?xml version ="1.0"?> <phdstud> <stud> <pid >06</pid> <uni>university of pitesti </uni> <phds>ali amer mohameed saeed</phds> <year >2017</year> </stud> <stud> <pid >107</pid> <uni>university of bucharest </uni> <phds>radu mihai</phds> <year >2017</year> </stud> </phdstud> <vxml version ="2.0"> <form id="s t a r t"> <audio>some phd students </audio> <xsl : for−each s e l e c t="phd"> <audio>phd student id i s <xsl : value−of s e l e c t="pid " /></audio> <break time="100ms"/> <audio>comes from phdsch.< xsl : value−of s e l e c t="uni"/></audio> <break time="100ms"/> <audio>the phd name i s <xsl : value−of s e l e c t="phds" /></audio> <break time="100ms"/> <audio>year of defence i s <xsl : value−of s e l e c t="year"/></audio> <break time="100ms"/> </x sl : for−each> </form> </vxml> 5 krp systems in education according to [16], a krp system for artificial education (ae) should take into consideration four elements. in ae, the first element, "knowledge would include knowledge of pedagogy (teaching practices and beliefs), curriculum, and knowledge regarding the individual student’s needs, assessments, evaluating, and more". the second element is connected to problem solving. in this context, the krp system should "look at past successful and unsuccessful pedagogies used with individual student, and it would be able to present instructional material to that specific student in a way the benefited him or her individually". the last two elements are connected to developers and administrators, but the mentioned authors did not conclude on smart interfaces for krp educational systems. however, they emphasize on intelligent tutoring systems (its), but its are "emphasizing those aspects which have relevance to user support, rather than detailed consideration of the merits of pedagogical or student knowledge modelling strategies" as shown by hefley & murray (1993) in [8]. following horvitz(1999), an intelligent user interface should consider imprecision and uncertainty aspects during run-time [9]. this is more important in ae, due to the nature of queries 126 a. a. mohammed saeed, d. dănciulescu formulated by learners. as salih(2014) mentioned [15], the natural user interfaces (nui) will be the next generation of user interfaces to improve user experiences. our proposal is based both on artificial intelligence techniques to deal with imprecision/uncertainty and natural language aspects with speech understanding and knowledge restructuring for fast answering systems. therefore, any krp system for education should consider preliminary requirements to understand the learner’s behaviour, markup models and technologies to implement solutions to queries given in "approximate" natural language by learners. one krp system for education should be able to represent not only pedagogical aspects, but also, different variants of content, and appropriate behaviour according to the learn initiatives. smart interfaces of krp systems for education are based on voice-enabled applications to support e-learning in many ways, making possible the usage of e-learning systems by visually impaired users. in web-based e-learning systems, the output is generated in html format. in order to support voice type output, one step more is required to translate html to vxml. in the following, only a short example for translating a table is given. only the vxml code is shown. <?xml version = ’ ’1.0 ’ ’? > <vxml version = ’ ’2.0 ’ ’ > . . <block> the next structure i s table 1 </block> <block> the table name i s </block> // code . . . <block> row 1 </block> <block> column 1 </block> <block> e [ 1 ] [ 1 ] </block> <block> column 2 </block> <block> e [ 1 ] [ 2 ] </block> <block> row ending 1 </block> <block> row 2 </block> <block> column 1 </block> <block> e [ 2 ] [ 1 ] </block> <block> column 2 </block> <block> e [ 2 ] [ 2 ] </block> <block> row ending 2 </block> <block> this the ending of table 1 </block> </vxml> 6 conclusions this work has analyzed the detailed rules for the description of the information structured used in context of krp systems, using markup languages. some markup technologies based on java are considered. the best choice is a model of the xml, and from the point of view of the java technologies for the processing of xml documents, it is found that for practical application jaxb (object interrogation, processing in memory) and jaxp (linear, facing processing on the fragments identifying and dealing with events) are more appropriate to be used. the effort of jaxb programming is less and object processing is promoted. in addition, by voice xml can be describes the smart interfaces of the krp systems based on voice. modern interfaces for knowledge representation and processing systems based on markup technologies 127 bibliography [1] angles, r.; gutierrez, c. 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[31] [online] xml cover pages; available: http://xml.coverpages.org, accessed on december 15, 2016. international journal of computers communications & control issn 1841-9836, 11(6):819-831, december 2016. delay-sensitive optimization models and distributed routing algorithms for mobile wireless sensor networks g.a. montoya, y. donoso germán a. montoya, yezid donoso* system and computing engineering department universidad de los andes bogotá, colombia, south america ga.montoya44@uniandes.edu.co *corresponding author: ydonoso@uniandes.edu.co abstract: communication disruptions caused by mobility in wireless sensor networks introduce undesired delays which affect the network performance in delay sensitive applications in mwsn. in order to study the negative effects caused by mobility, we propose two mathematical models to find the minimum cost path between a source node and a destination node considering the nodes position changes across time. our mathematical models consider the usage of buffers in the nodes to represent the fact of storing a message if there is not an appropriate forwarding node for transmitting it. in order to contrast our mathematical models results we have designed two kinds of algorithms: the first one takes advantage of the closest neighbours to the destination node in order to reach it as fast as possible from the source node. the second one simply reaches the destination node if a neighbour node is precisely the destination node. finally, we compare the delay performance of these algorithms against our mathematical models to show how efficient they are for reaching a destination node. this paper is an extension of [10].a the mathematical model proposed in [10] is improved by adding two new binary variables with the aim of make it more readable and compact mathematically. this means a post-processing algorithm is added only for evaluating if a solution is at the first network state. keywords: mathematical model, delays, mwsn. areprinted (partial) and extended, with permission based on license number 3961371369962 ©[2016] ieee, from "computers communications and control (icccc), 2016 6th international conference on". 1 introduction the advances of wireless sensor networks (wsn) have allowed attaching sensor devices to entities such as objects, animals or humans, for monitoring a physical variable presented in a particular environment. however, these sensors are equipped with limited batteries whereby it is required to implement energy efficient routing techniques to extend as much as possible the lifetime of these devices [1] [2]. moreover, communication disruptions caused by mobility in wireless sensor networks introduce undesired delays which affect the network performance in delay sensitive applications, such as military or healthcare monitoring applications. for the latter, due to they deal with health states, illness and continuous medical supervision, a base station should no experiment delays from the information collected by the sensors [3] [4]. given the scenario described above, novel routing algorithms are emerging for solving these delays problems [7] [8] [9]. moreover, these algorithms need to be compared against a mathematical model for knowing their delay performance in terms of delay. with the purpose of analyse the negative effects caused by mobility, we propose a mathematical model to find the minimum cost path between a source node and a destination node considering the nodes position changes across time. our mathematical model considers the usage of buffers in the nodes to represent copyright © 2006-2016 by ccc publications 820 g.a. montoya, y. donoso the fact of storing a message if there is not an appropriate forwarding node for transmitting it. this paper is an extension of [10] (doi: 10.1109/icccc.2016.7496736). the mathematical model proposed in [10] is improved by adding two new binary variables with the aim of make it more readable and compact mathematically. this means a post-processing algorithm is added only for evaluating if a solution is at the first network state. in order to contrast our mathematical model results we have designed two algorithms: the first one takes advantage of the closest neighbours to the destination node in order to reach it as fast as possible from the source node. the second one simply reaches the destination node if a neighbour node is precisely the destination node. the remainder of the paper is organized as follows: section 2 describes the problem statement in a general view. section 3 presents the problem formulation, that is, how the problem is described as a modular problem. section 4 shows the mathematical model proposed, the objective function and the constraints. section 5 presents the proposed algorithms for comparing them against the optimal solution given by the mathematical optimization model. finally, sections 6 and 6 show the results and conclusions respectively. 2 general problem statement the figure 1.a presents the problem we want to solve, where a rounded node is a sensor and a square node corresponds to a base station (destination node). suppose we have a mwsn where at time t1 there is a communication path between the source sensor node n1 and the base station. however, at time t2, the node n2 moves away from the node n3, causing a communication disruption for transmitting information from n1 to the base station. once n3 has realized of this problem at time t3, n3 has to perform routing corrections in order to reestablish the communication path between n1 and the base station. this reestablishment can be perfectly performed using routing techniques, but at the expense of introducing undesired delays for building again the communication path between n1 and the base station. in some applications these delays can be omitted because do not affect the application purpose, but in other cases, such as delay sensitive applications like military or health applications, this disadvantage might mean a very low network performance in terms of delay. (a) (b) figure 1: problem definition: (a) problem; (b) solution given the problem above, our proposal consists to design a mathematical model for finding the minimum cost path between a source node and a destination node considering the network is moving across time [5] [6]. this formulation would be very important because it can give us optimal values for contrasting with algorithms results. delay-sensitive optimization models and distributed routing algorithms for mobile wireless sensor networks 821 3 problem formulation in this section our problem is enunciated and described in detail, as well as some assumptions are shown in order to simplify our proposed mathematical models. figure 2: problem scenario based on the figure 2, we will describe our problem: • mobile network: assume we have a mobile network, at which the nodes position changes across time periods. for this reason, the links cost between the network nodes also changes across time periods. this means that at each time period the network has particular links cost, different from the links cost at other time period. given that at each time period the network have different links cost, we could say this reflects the network state in a given time period. for this reason, each network at a given time period will be called network state. for instance, the network state at time period 1 is called network state 1, the network state at time period 2 is called network state 2, and so on. in other words, according to the figure 2.a we have an initial network (network state 1) compound by 4 nodes. as these nodes conform a network, there are interrelations between them that we will call links. these links have a cost, which can be represented, for example, by the distance, the signal to noise ratio or the rssi measurement between the nodes. in the next time period, the network costs at the network state 1 change and then these new interrelations between the nodes are now the network state 2. as the next time period occurs, the network at the network state 2 becomes the network at the network state 3, and this network will be the network at the network state 4, and so on. • nodes: each node is denoted as nit where i is number of the node and t is the network state of the node. depending on the communication range, a node can communicate with another node in the direction described by the figure 2. for example, n11 can communicate with n21 and n31. 822 g.a. montoya, y. donoso • buffer in each node: in telecommunication networks, a router or a sensor (a node) can decide not-sending its message, storing it in a buffer until it would be appropriated to send it. in our model, this situation is represented as a link between n11 and n12, meaning that n11 can store its message in its buffer, that is, the node n12. • costs: as it was mentioned previously, a link has a cost. then, there is a cost for sending a message from n11 to n21 called c21l11 , and denoted as c jul it , that is, this is the cost to carry a message form the node i at the state t to the node j at the state u at the network state l. • directed graph: in this example our goal consists to carry a message from the node 1 to the node 4. then, our source node is the node 1, and our destination node is the node 4. in this sense, a directed graph is constructed from the source to the destination. for this reason, the links direction points to the destination. • goal: our goal consists to carry a message from the source node to the destination node using the neighbours nodes as forwarding nodes for passing a message, and even using the buffers, if it is necessary, for waiting an appropriated situation for sending the message. in this sense, we have to find the minimum cost path between a source node and a destination node considering the network is changing across time, that is, through the network states. additionally, for simplicity we assume only one link can be selected for sending the message per each network state. this means that if a message is at the node n11, this node at this network state 1 can send a message to only one neighbour, n21 or n11, or storing it in its buffer, that is, n12. • example result: according to the example shown in the figure 2.b and based on the links cost, the minimum cost path from the source node, n11, to the destination node, node 4, is the path compounded by the highlighted links: n11 to n31, n32 to n33, n33 to n34 and n34 to n44. as we will describe later in the mathematical formulation, this result can be also expressed in terms of x: x31111 = 1, x 332 32 = 1, x 343 33 = 1 and x 444 34 = 1. the rest of x jul it values are zero. 4 mathematical model solution two mathematical models are proposed, which achieve same optimal solutions since they are equivalents, but the second one is mathematically more complete than the first one. 4.1 first approach in this section is proposed a mathematical model for finding the minimum cost path between a source node and a destination node considering the network is changing across time. the table 1 presents the sets, parameters and decision variables for building the mathematical model. next, our mathematical model is enunciated. min ∑ i∈n ∑ t∈s ∑ j∈n ∑ u∈s ∑ l∈s c jul it x jul it (1) subject to: delay-sensitive optimization models and distributed routing algorithms for mobile wireless sensor networks 823 table 1: sets, parameters and variables description sets and parameters description n nodes set. s states set. o source node. d destination node. st state at which we want to obtain the minimum cost path from the source to the destination. c jul it link cost from the node i at the state t to the node j at the state u at the network state l. variables description x jul it determines if the link at the state l from the node i at the state t to the node j at the state u is selected for building the path towards the destination (binary variable). yi,l determines if the node i at the state l is selected as a forwarding node for building the path towards the destination (binary variable). ∑ j∈n ∑ u∈s x jul it yjl = 1 ∀i ∈ n | i = o, ∀t, l ∈ s | t = 1, l = 1 (2) ∑ i∈n ∑ t∈s ∑ j∈n ∑ u∈s x jul it yimyjl = 1 ∀l,m ∈ s | l < st,m = l− 1 (3) ∑ j∈n yjl = 1 ∀l ∈ s | l ≤ st (4) ∑ i∈n ∑ t∈s x jul it yimyjl = 1 (5) ∀j ∈ n | j = d, ∀u,l,m ∈ s | l < st,u = l = st,m = l− 1 the equation 1 corresponds to the objective function, which attempts to find the xjulit variables at the minimum cost. the expression 2 establishes that once a xjulit is selected considering that i = o, t = st1 and l = networkstate1, we can know the forwarding node j for sending the message. the equation 4.1 determines the predecessor node i at the network state m (previous to the network state l) required for building the path. the expression 3 allows to be coherent the forwarding and predecessor nodes in the intermediate states, that is, the network states different from the network state 1 and the network state indicated by the parameter st. finally, the equation 4 assures that only one xjulit must be selected at each network state from the network state 1 up to the network state indicated by the parameter st. in summary, this mathematical model gives us the minimum cost path by introducing the following parameters: the source node, the destination node and the state (the network state) at which we want to obtain the minimum cost path from the source node to the destination node. however, in this model we cannot prescind from the state parameter, that is, we cannot 824 g.a. montoya, y. donoso know the network state (from all the network states) at which we would obtain the minimum path cost. for this reason, it is required to use this mathematical model in an iterative way, that is, obtaining the minimum path cost for all the network state and then, selecting the lowest of the minimum paths obtained. this process is represented in the algorithm 1. algorithm 1 solution pseudocode 1: o = source; d = destination; k = number of states 2: minlocal = ∞; minstate = 0 3: for i = 1 to k do 4: minsolution = mathmodel(o, d, i) 5: if minsolution < minlocal then 6: minlocal = minsolution 7: minstate = i 8: end if 9: end for in this algorithm, the mathematical model is used for each network state, and once we have analysed all the network states, we finally determine which one obtained the minimum cost path , denoted as minsolution, at which network state, expressed as minstate. in summary, with the mathematical model and this algorithm we can obtain the minimum cost path given a source node and a destination node. 4.2 second approach in this section is presented a second mathematical model proposed for constructing a minimal cost path from a source node to a destination node considering a mobile network. notice that this mathematical model yields same results as the first one, but is mathematically better described. the variables for this new approach are described in the table 2. the sets and parameters are the same from the table 1. table 2: variables description for the second approach variables description x jul it determines if the link at the state l from the node i at the state t to the node j at the state u is selected for building the path towards the destination (binary variable). yi,l determines if the node i at the state l is selected as a forwarding node for building the path towards the destination (binary variable). djl determines if the node j is selected at the destination state l (binary variable). dsl determines if the state l is selected as a destination state (binary variable). next, the second mathematical model is described. min ∑ itjul c jul it x jul it (6) subject to: ∑ l>1 djl = 1 ∀j ∈ n (7) delay-sensitive optimization models and distributed routing algorithms for mobile wireless sensor networks 825 ∑ l djl = 1 ∀j ∈ n | j 6= destination (8) djl ∗dsl = djl ∀j ∈ n; ∀l ∈ s (9) ∑ l dsl = 1 (10) dsl = 0 ∀l ∈ s | l = 1 (11) dsl ∑ i∈n ∑ t∈s ∑ j∈n ∑ u∈s x jul it yimdjl = dsl ∀l,m ∈ s | m = l− 1 (12) dsl ∑ i yim = dsl ∀l,m ∈ s | m 6 l (13) dsl ∑ i yim = 0 ∀l,m ∈ s | m > l (14) ∑ i∈n ∑ t∈s ∑ u∈s ∑ l∈s x jul it = 1 ∀j ∈ n | j = destination (15) dsl ∑ i∈n ∑ t∈s ∑ j∈n ∑ u∈s x jum it yinyjm = dsl ∀l,m,n ∈ s | m > 1 ∧m = l∧n = m− 1 (16) dsl ∑ i∈n ∑ t∈s ∑ j∈n ∑ u∈s x jum it = dsl ∀l,m ∈ s | m 6 l (17) ∑ i∈n ∑ t∈s ∑ j∈n ∑ u∈s x jul it yjl = 1 ∀i ∈ n | i = source ∀l ∈ s | l = 1 (18) the equation 6 corresponds to the objective function, which will try to find the xjulit variables with the less possible cost cjulit . the previous expressions are explained in the following items: • destination state constraints (from 7 to 15 ): the following expressions are referred to the destination state, that is, the state at which the destination node is found at the minimum possible cost. – defining djl: djl allows to obtain the destination state l at which the destination node j is found at the minimum possible cost. the expression 7 avoids that djl will be one at the first state. the equation 8 avoids djl will be one for a node different from the destination node. – defining dsl: dsl allows to extract only the destination state l at which the destination node is found at the minimum possible cost. the expression 9 allows to know the state l at which djl was selected. the equation 10 indicates that only one destination state is possible. in the expression 11 we assume it is not possible that the destination state will be the first state. 826 g.a. montoya, y. donoso – selecting the forwarding node: the forwarding node indicates the node selected at each state for constructing the minimum cost path. the expressions 12 and 13 restricts to one the number of yjl for each state less than the destination state. the equation 14 restricts to zero the number of yjl for each state higher than the destination state. the expression 15 indicates that it is possible only one link to the destination node for all states, that is, only one state is selected, and for the rest of the states, the link must be zero. • intermediate state constraints: these constraints allow selecting the predecessor node yim based on the current forwarding node yjl. in order to understand what these two types of nodes means, let’s see an example. if we have a link between the nodes 1 and 2 in the direction from 1 to 2, the current forwarding node is 2 and the predecessor node is 1. the expression 16 allows to select the predecessor node at the intermediate states, where intermediate states refers to the states between the destination and the source states. the equation 17 restricts to one the number of xjulit for each state equal or less than the destination state. • source state constraint: the source state indicates the state at which the source node starts to construct the minimum cost path. the expression 16 restricts to one the number of xjulit for the source state. • defining the first state solution constraint: all the constraints described above allow to find the minimum cost path between a source node and a destination node through several network states. however, up to now our model does not consider the destination state can be the first network state. for this reason it is necessary to apply the following post-processing pseudocode: algorithm 2 post-processing pseudocode 1: parameters source, destination 2: minsolution = mathmodel(source, destination) 3: costfirststate = cjulit | i = source,j = destination,t = u = l = 1 4: if costfirststate < minsolution then 5: minsolution = costfirststate 6: end if this pseudocode basically indicates that if the cost between the source and the destination node is less than the solution found by the mathematical model, then the solution is at the first state, otherwise the solution is given by the mathematical model. 5 algorithms proposals to contrast the mathematical model results we have designed two kinds of algorithms, which will be described below: 5.1 single path with connection to the sink (sipcos) as we saw previously in the mathematical model, our goal consists to find the minimum cost path between a source node and a destination node. we are going to assume that a link cost represents the delay for sending the information from a node to another one. for simplicity, we assume that each link cost (delay) will be proportional to the distance between two nodes in the delay-sensitive optimization models and distributed routing algorithms for mobile wireless sensor networks 827 network. therefore, our algorithm will try to reach the destination node at the minimum delay possible from the source node. figure 3: problem scenario in the figure 3 is presented the following example. suppose the source node is the node 3, and the destination node is the node 50. our proposal includes two methods. the first one consists to perform a partial broadcast from the destination node to the closest neighbours. now, these neighbours, the blue ones (called connected nodes to the destination) know how to reach quickly the destination node in such case that a message arrives to them. the second method consists to send a message from the source node to forwarding nodes in order to reach a connected node to the destination. this method intends to reach as fast as possible the destination node, trying to obtain similar values respect to the mathematical model values. the pseudocodes of these two methods are described below. algorithm 3 destination node algorithm 1: d = destination 2: r = number of rounds 3: dn = neighbours(d) . dn: destination neighbours 4: while r > 0 do 5: dn = neighbours(dn) 6: sendcontrolmessage(dn) 7: r = r −1 8: end while the algorithm 2 defines a simple technique to select the neighbours nodes connected to the destination node. this technique consists to send from the destination node a control message to the destination neighbours, and these ones send this control messages to its neighbours many times as the parameter round allows it. the algorithm 3 corresponds to the forwarding node algorithm. this algorithm is initially performed in the source node. therefore, this node selects a neighbour node based on its low cost to transmit the message in order to reach a connected node to the destination or the destination itself. once a neighbour node has received the message from the source node, it has to find a new neighbour node for passing the message. this process is performed for the next selected neighbour nodes until a connected node or the destination node has been reached. remember 828 g.a. montoya, y. donoso that a connected node will allow us to reach rapidly the destination node in such case that a message arrives to them. this is possible since the connected node is linked directly to the destination node or to other connected node which possibly will be connected directly to the destination node. once a neighbour node is selected for sending the message, this node is added to the array path. then, when the destination is reached, the array path contains the message’s traceability from the source node to the destination node. algorithm 4 forwarding node algorithm for sipcos 1: parameter s . source node 2: parameter d . destination node 3: parameter dn . destination neighbours nodes 4: array path = [] . the building communication path 5: variable fn = s . fn: forwarding node 6: function fn(i) . fn: forwarding neighbours function 7: mincost = ∞ 8: destinationneighbourfound = 0 9: array path = [path fn] 10: while message /∈ dn ∨ d do 11: forwardingneighbours = neighbours(fn) 12: for i = 1 to forwardingneighbours do 13: if fn(i) ∈ dn then 14: destinationneighbourfound = 1 15: sendmessage(fn(i)) 16: path = [path fn(i)] 17: end if 18: end for 19: if destinationneighbourfound == 0 then 20: for i = 1 to forwardingneighbours do 21: neighbourcost = cost(fn, fn(i)) 22: if neighbourcost < mincost then 23: fn = fn(i) 24: sendmessage(fn) 25: path = [path fn] 26: end if 27: end for 28: end if 29: end while 30: if message ∈ dn then 31: dn = neighbours(dn) 32: if destination ∈ dn then 33: sendmessage(d) 34: path = [path d] 35: else 36: sendmessage(dn) 37: path = [path dn] 38: end if 39: end if 5.2 single path without connection to the sink (sip) in contrast to the sipcos algorithm, the sip algorithm does not take into account the connected nodes to the destination. for this reason, the algorithm must find exactly the destination. therefore, it is too much difficult to find the destination using this method. the pseudocode of this algorithm is described in the algorithm 4. delay-sensitive optimization models and distributed routing algorithms for mobile wireless sensor networks 829 algorithm 5 forwarding node algorithm for sip 1: parameter s 2: parameter d 3: array path = [] 4: variable fn = s 5: function fn(i) 6: mincost = ∞ 7: array path = [path fn] 8: destinationfound = 0 9: while message /∈ d do 10: forwardingneighbours = neighbours(fn) 11: for i = 1 to forwardingneighbours do 12: if forwardingneighbours(i) == d then 13: destinationfound = 1 14: sendmessage(d) 15: path = [path fn(i)] 16: end if 17: end for 18: if destinationfound == 0 then 19: for i = 1 to forwardingneighbours do 20: neighbourcost = cost(forwardingnode, fn(i)) 21: if neighbourcost < mincost then 22: forwardingnode = fn(i) 23: sendmessage(fn) 24: path = [path fn] 25: end if 26: end for 27: end if 28: end while 29: if message ∈ d then 30: path = [path d] 31: end if 6 results the graphs for the three scenarios, at its first network state, are shown in the figure 4. there were chosen 3 basic scenarios in order to test in a basic way the behavior of the mathematical model, the sipcos and sip algorithms. the results are shown in the following tables: table 3: results for the mathematical model parameters and variables scenario 1 scenario 2 scenario 3 nodes 7 10 12 states 14 20 24 source node 1 1 1 destination node 7 10 12 solution path 1,3,7 1,2,8,7,10 1,8,12 delay 2 4 2 according to the first scenario, the sipcos algorithm showed the same delay respect to the optimal value, while the sip algorithm used an extra delay to reach the destination. this extra delay is a low value because this scenario corresponds to a small network. for this reason, if a network has few nodes, there is more probable the sip algorithm reach the sipcos’s performance. once the network has increasing in size, such in case the scenario 2 or 3, the sip’s 830 g.a. montoya, y. donoso (a) (b) figure 4: problem definition: (a) scenario 1; (b) scenario 2 figure 5: scenario 3 table 4: results for sipcos algorithm parameters and variables scenario 1 scenario 2 scenario 3 nodes 7 10 12 states 14 20 24 source node 1 1 1 destination node 7 10 12 solution path 1,3,7 1 2 9 7 10 1,8,2,12 delay 2 4 3 table 5: results for sip algorithm parameters and variables scenario 1 scenario 2 scenario 3 nodes 7 10 12 states 14 20 24 source node 1 1 1 destination node 7 10 12 solution path 1,4,3,7 1,2,8,9,7,10 1,3,5,9,2,12 delay 3 5 5 performance decreases. finally, for the third scenario the sipcos algorithm presented a better performance respect to the sip algorithm, thanks to the connected nodes which allowed reach the destination node as fast as possible. in this scenario the sip algorithm lose too much time delay-sensitive optimization models and distributed routing algorithms for mobile wireless sensor networks 831 in the nodes 3 and 5, because it had not a clear strategy to go out from ending nodes. conclusions in this paper we propose a mathematical model which is able to find the minimum cost path between a source node and a destination node considering a mobile network. additionally, there were proposed two algorithms, the sipcos and sip algorithms, in order to compare their results against the mathematical model. the results showed the sipcos has a better performance compared against the sip algorithm because it has efficient strategies for find quickly the destination node or destination neighbours, which facilitates the fact of building and finding the communication path from the source node to the destination node. for this reason, the sipcos’s performance was very similar to the mathematical model and it could be a good option to be used in mwsn. bibliography [1] i. f. akyildiz and m. c. vuran (2010); wireless sensor networks, wiley, 2010. [2] j. zheng and a. jamalipour (2009); wireless sensor networks: a networking perspective, wiley-ieee press, 2009. [3] a. a. ahmed (2013); an enhanced real-time routing protocol with load distribution for mobile wireless sensor networks, computer networks, 57(6):1459-1473. [4] a.a. ahmed (2007); real-time wireless sensor networks, university of virginia, 2007. [5] b. buchli, f. sutton, j. beutel (2012); gps-equipped wireless sensor network node for high-accuracy positioning applications, wireless sensor networks lecture notes in computer science, springer, 7158:179-195. [6] s. li, x. ma, x. wang, m. tan (2011); energy-efficient multipath routing in wireless sensor network considering wireless interference, journal of control theory and applications, 9(1):127-132. [7] g. m. de araujo, j. kaiser and l. b.becker (2014); genetic machine learning approach for link quality prediction in mobile wireless sensor networks, cooperative robots and sensor networks, 1-14. [8] g. m. de araujo, j. kaiser, l. b.becker (2012); an optimized markov model to predict link quality in mobile wireless sensor networks, procedia computer science, 10:1100-1105. [9] j. a. torkestani young (2012); mobility prediction in mobile wireless networks, journal of network and computer applications, 35(5):1633-1645. [10] g. a. montoya and y. donoso (2016); a delay-sensitive mathematical model approach and a distributed algorithm for mobile wireless sensor networks, computers communications and control (icccc), 2016 6th international conference on, ieee xplore doi: 10.1109/icccc.2016.7496736, 45-50. international journal of computers communications & control issn 1841-9836, 11(2):194-208, april 2016. energy synchronized transmission control for energy-harvesting sensor networks z. fan, x. liu zuzhi fan* department of mathematics, jinan university guangzhou 510632, china *corresponding author: tfanzz@jnu.edu.cn xiaoli liu computing center, jinan university guangzhou 510632, china txlliu@jnu.edu.cn abstract: energy harvesting and recharging techniques have been regarded as a promising solution to ensure sustained operations of wireless sensor networks for longterm applications. to deal with the diversity of energy harvesting and constrained energy storage capability, sensor nodes in such applications usually work in a duty-cycled mode. consequently, the sleep latency brought by duty-cycled operation is becoming the main challenge. in this work, we study the energy synchronization control problem for such sustainable sensor networks. intuitively, energy-rich nodes can increase their transmission power in order to improve network performance, while energy-poor nodes can lower transmission power to conserve its precious energy resource. in particular, we propose an energy synchronized transmission control scheme (estc) by which each node adaptively selects suitable power levels and data forwarders according to its available energy and traffic load. based on the large-scale simulations, we validate that our design can improve system performance under different network settings comparing with common uniform transmission power control strategy. specially, estc can enable the perpetual operations of nodes without sacrificing the network lifetime. keywords: wireless sensor networks, energy harvest, transmission control. 1 introduction the advance of energy harvesting and recharging techniques makes it is feasible to build long-term sensor networks for cyber-physical applications [1, 2]. in such energy-harvesting networks, sensor node with extended functional units can continuously extract energy from ambient environment, such as solar power, wind energy resource, motion and wireless charging. although energy-harvesting sensor networks can obtain renewable energy, they impose several challenges. energy harvesting opportunities and rate are highly environmental-dependent, and usually related to the spatiotemporal distribution of sensor nodes. for example, in solar-powered networks, the harvested energy may vary significantly with node position (e.g. under the sun or shadow) or weather patterns (e.g. cloudy or sunny). moreover, the energy storage units, such as batteries or capacitors are limited in power capacity and have been shown to be leakage-prone [8]. therefore, it is impossible to operate the sensors at full duty-cycle even in such energy-replenishing networks. in consideration of harvested energy, the existing power management solutions have been widely exploited in wireless sensor networks [3, 7, 9, 10]. these approaches can be classified into two categories, one is dynamic duty-cycle scheduling schemes [3, 10], which are based on assumption that the working period of nodes could be scheduled to meet the requirement of data copyright © 2006-2016 by ccc publications energy synchronized transmission control for energy-harvesting sensor networks 195 forwarding. however, the work schedule in many scenarios is often dependent on the application requirements, such as sensing coverage and tracking delay [11]. the other kind is focused on the energy-aware routing [7, 9, 11]. all these schemes can optimize the network performance under their supposed application scenarios. in this paper, we study the efficient utilization of harvested energy from the perspective of transmission power control. our motivation is quite straightforward. we observe that the increasing of transmission power can be beneficial to improving network performance, such as packet delivery ratio and delay. meanwhile, the transmission power is directly associated with energy consumption of data transmission. thus, we introduce the transmission power control to improve network performance while guaranteeing the sustainability in energy-harvesting sensor networks. specifically, we propose energy synchronized transmission control scheme (estc), a middle layer between application and network layer. with estc, each node can adaptively adjust its transmission power based on its energy-harvesting capability and traffic overload. estc can be seamlessly integrated with current routing algorithms so that different optimization objectives can be achieved. we also present a backoff approach to avoid transmission collision among concurrent data forwarding. our major contributions of this work are summarized as follows. we first describes energyharvesting sensor nodes and the duty cycle model. to balance the delivery delay and energy efficiency, we propose a distributed energy synchronized transmission control approach to find the best power level for each forwarder without sacrificing network lifetime. at last, we perform extensive large-scale simulations to validate the proposed scheme. working with different routing policies, the simulation results show that estc can: i) reduce end-to-end delivery delay; ii) synchronize the energy consumption among different sensor nodes; iii) balance the traffic load to increase the node lifetime. the rest of the paper is organized as follows: section 2 briefly presents the related work. the concrete design of our scheme is discussed in section 3 and the simulation results are presented in section 4. section 5 concludes the paper. 2 related work there are two research fields related to our design: energy harvesting techniques and transmission power control. recently, energy harvesting and recharging technologies have been developed to ensure the sustainability of sensor networks. many motes or platforms are designed to collect and storage these energy from environment [8]. to fully utilize the replenished energy, different power management [11] and duty-cycle based schemes [3] have been proposed. kansal et al. [11] have proposed temporal-based approaches to adjust the duty-cycle of sensor node in order to optimize the network performance. in [10], gu et al. first put forward the concept of energy synchronization communication, by which each node adaptively adjusts its own active instances according to the available energy budget so that the cross-delay over node can be minimized. challen et al. [9] present idea, an integrated energy-aware architecture to address the energy dynamic issue of sensor nodes. in particular, they propose a holistic architecture to trade off energy objective function and other application-defined utility, such as low power listening, energy aware routing and distributed localization. in [4], guo et al. study the joint problem of mobile data gathering and wireless charge. differing from above schemes, their motivation is to study the mobility scheduling for efficient energy recharging and data collection. in the direction of low-duty-cycle networks, many recent works have been proposed to reduce the delivery delay for different traffic patterns. gu et al. [17] suggest that the communication delay could be bounded with the duty-cycle adjustment of sensor nodes. besides the delay optimization, liu et al. [12] study the 196 z. fan, x. liu joint routing and sleep-scheduling problem, which has been proved a non-convex problem. they transform it into equivalent sigmoidal program by relaxing the flow constraints and then solve it with iterative geometric programming. transmission power control techniques have been widely used to optimize network performance in wireless networks. in particular, most of them are focused on the topology control [13]. wattenhofer et al. [13] proposed a location-based, distributed topology control algorithm to balance the network connectivity and the network lifetime. at first, each node starts a neighbordiscovery process with a lower transmission range and then gradually increases its transmission radius until either one node is found in each cone of given degree or the maximum transmission power is reached. then, a redundant edge removal process is performed in order to reduce the nodes degree and thus increase network throughput. in [6], cheng et al. study the throughput optimization problem with transmission power control in sensor networks. they propose algorithms in order to minimize the total transmission power and total interference. based on various link models, both computing algorithms and heuristics are discussed for the purpose of throughput maximization. in [14], cotuk et al. analyze the impact of varied transmission power control strategies on network lifetime. specially, they study the effect of power levels discretization on energy consumption, which is significant for practical research because the levels of transmission power are usually discrete in reality. in [15], fan et al. propose a delay-bounded transmission power control scheme for the performance optimization in low-duty-cycle sensor network. under the given delay bound, a cross-layer transmission power control approach is presented so that all data delivery could be achieved with minimum energy cost. in [5], berbakov et al. consider the similar application scenario as our design. however, their goal is to find the optimal power allocation in order to maximize the total throughput within given deadline. they also assume that the storage capacity of sensor nodes is infinite and the leakage effect is negligible. however, none of works have considered the utilization of transmission control strategy to achieve performance improvement in sustainable sensor networks. 3 energy synchronized transmission control with harvested energy in this section, we present the design of energy synchronized transmission control algorithm. 3.1 network model we assume a sensor network with n energy-harvesting nodes, each of them has a fixed number of discrete transmission power levels, i.e., pi,(1 ≤ i ≤ k), where k is the maximum number of adjustable transmission power levels. figure 1 illustrates the configuration of energy-harvesting node, which replenishes energy from surrounding environments, receives data packets and delivers them to the sink at possible transmission power level. also, we suppose that all nodes are scheduled to work in a duty-cycled mode. as shown in figure 2, a sensor node is in either active state or a dormant state. when a node is in the active state, it can transmit or receive packets from neighboring nodes. while a node is in the dormant state, it turns off all function modules except a timer to wake itself up. for successful communication, the sender should be aware of the time slots and have to wait for its receiver to wake up before it can send a packet. we define sleep latency, sij(t) as the time interval from the moment the sender i has a packet ready to be sent at time t to the moment that the receiver j is in the active state. without loss of generality, we suppose t is the common working period of the whole network, which can be further divided into a number of time slots with equal length. to simplify, the length of time slot is appropriate for a round-trip transmission time, τ. based energy synchronized transmission control for energy-harvesting sensor networks 197 on such assumptions, the working schedule, γi for node i can be uniquely represented as a set of active time slots, i.e., γi = {ti1, t i 2, ..., t i k}, where k is the number of time slots that the node is in the active state. for example, the work schedule in figure 2 is {2,6,8}. s p1 p2 pk data queue harvested energy a b c + figure 1: energy-harvesting node. 2 6 81 3 4 5 7 9 10 active dormant t figure 2: working schedule. in our design, it assumes that all node know the schedule of its one-hop neighbors. sleep latency is the main component of delivery delay in such energy-harvesting networks. 3.2 design objectives given k available power levels, the sender have k options to relay the data packets in its data queue. we take energy harvesting into consideration and look into the following issue: what’s the optimal transmission control policy with harvested energy? generally, it is necessary for node to consider three aspects for such transmission decision. • energy budget: in general, the sender can select larger transmission power levels to reduce the sleep latency while it has enough energy budget. on the contrary, it should lower down transmission power to save energy. as shown in figure 1, node a may increase its transmission power in order to reduce sleep latency if it has additional energy supply. • traffic load: obviously, the traffic load has a significant impact on the transmission decision. more energy supply is needed when there are more data packets to be delivered in the data queue. • routing metric: given transmission power, there are usually multiple potential forwarders available for current node. however, different routing metrics are designed for varied objectives. for example, the delay is the main issue to be addressed in duty-cycled sensor networks. if there is no energy constraint, we can select the maximum available transmission power for each node in order to obtain the minimized end-to-end delay. however, such a naive and uniform transmission power control policy can waste precious energy resources and incur more transmission interference and collision. take the dynamics of energy, traffic overload and routing strategies into consideration, our design goals include: • delay optimization. in real world, sensor nodes are deployed to monitor or response emergency surveillance. instead of hard deadline, our protocol provides an adaptive transmission control approach to reduce delivery delay. • energy synchronization. in energy-harvesting sensor network, each node has different energy-gathering and storage capability. taking energy leakage into account, it is important to synchronize the demand with energy supply. in other words, we tend to consume as much energy as possible while providing the sustainability of network. 198 z. fan, x. liu • balancing traffic overload. differing from wired network, the bandwidth and energy are constrained resources in sensor networks. therefore, it is important to perform data delivery over multiple forwarding paths from source nodes to the destination. our protocol dynamically switches forwarding among various potential forwarders according to routing metrics. • localized behavior. it is important to keep the protocol as scalable as possible since the global coordination among hundreds of nodes may incur more energy consumption. therefore, all behavior of our protocol are localized to achieve high scalability and low overhead. • transmission collision avoidance. transmission interference and collision may happen when multiple nodes within transmission range try to send packets simultaneously. it is necessary to introduce the corresponding mechanism to reduce such collision. 3.3 protocol architecture in this section, we propose an energy synchronized transmission control scheme (estc) which adaptively selects the optimal transmission power at transmission layer and diverts traffic overload through different forwarders at network layer in order to improve network performance with the extra harvested energy. in specific, our protocol includes the following components. • estc module. • energy estimation module. • delay estimation module. as shown in figure 3, estc is the kernel module, which is responsible for the selection of approximate transmission power level and next-hop forwarder. the data queue in upper layer is holding all data packets to be relayed. assuming all data packets have the same size, the traffic overload can be represented as the length of data queue. energy and delay estimation are two modules that help estc to make the forwarding decision. in other word, estc will select the transmission power level and corresponding forwarder according to the available energy and feedback from neighboring nodes. the detail of these modules is discussed in the following sections. estc application layer data queue routing algorithm energy estimation delay estimation mac figure 3: estc architecture. d a c p1 b p1 p1 {2mj,2} {2mj,7} {1mj,8} {1.8mj,5} p2 p1 figure 4: example of energy and delay estimation 3.4 energy estimation to carry on energy synchronized communication, it is significant to know the amount of energy that can support data delivery. in sustainable sensor networks, the harvested energy is energy synchronized transmission control for energy-harvesting sensor networks 199 usually unpredictable and changes significantly over time [8]. energy estimation module traces both energy load as well as the harvesting rate on a node. let the battery level for node s at the time t0 be bs(t0), a common model to estimate the available energy at time t1 is: bs(t1) = bs(t0) + ∫ t1 t0 h(t)dt − ∫ t1 t0 l(t)dt (1) here, h(t) and l(t) are the energy harvesting rate and consuming rate at time t. other models, such as online assess model in [16] can also be used, but they are usually focused on specific harvesting platforms, for example, the supercapacitor-powered sensor node. instead, we assume a general in-site model by reading the energy value in the later simulations. notice that, the primary objective of our design is to pursue the network sustainability, which can be represented as the network lifespan. to prolong the network lifetime, it is required to balance the energy consumption among sensor nodes. in short, the issue is how much energy can be used for each node without compromising the network lifetime? assuming the remaining energy of the network is known a prior, we can formally define the energy availability of given node. definition 1. (energy availability). available energy of node s, bas is the extra energy which can be used for data delivery but without reducing the network lifetime. assuming the average energy of the network (b′) is known a prior, we have bas = bi(t) − b′(t), where bi(t) is the remaining energy of node at time t. however, it is usually inefficient to update the average energy level of the whole network from time to time. one possible solution is to approximate the average energy with the energy load of ancestor nodes. as shown in figure 4, node a,b,c can forward their data packets to the sink via node d and their remaining energies are 2mj, 1mj and 1.8mj, respectively. accordingly, the available energy for node d is the difference between its own energy and the average value, i.e., 0.4mj. one may argue that we can use the lowest energy level (1mj of node b) as the baseline. however, we do not assume a fixed transmission power and forwarding path. that is, node a may switch to node c or even increase transmission power to reach node d directly for the energy-efficiency. obviously, it is not accurate to take the minimum residual energy as the reference energy. maintaining the accuracy of energy estimation requires periodical exchange of these information. in practice, piggybacking on the normal data traffic can be used to reduce this control overhead. when a node receives message from its ancestor, it will recalculate the average energy value and then put it into the header of data packet. in such way, each node can trace the available energy of the network. 3.5 fitting routing algorithms with given transmission power pi, there may be multiple available candidates for data forwarding in sensor networks. in this section, we will show how estc works with various routing metrics, such as link-quality-based routing (etx) [18], delay-based routing (dess) [19] and power-aware routing (par) [22]. to be scalable, it assumes that all routing decisions are made based on the local information. link-quality-based routing etx is a link-quality-based routing algorithm, in which the expected transmission count is taken as routing metric. the one-hop etx is the average number of transmission required to 200 z. fan, x. liu send a packet over a link, which is usually described as the reciprocal of link quality. however, wireless link is often extremely unreliable in the real environment. the pair-wise link qualities, described as the packets reception ratio (prr) could be very different under varied transmission power. in practice, link quality is usually evaluated by periodically broadcasting probe messages as that in [10], which is a little energy-consuming in our design due to the adjustable transmission powers. on the other hand, reception signal strength indicator (rssi) has a close relationship with prr according to the empirical results [20]. specifically, there is a clear threshold for rssi to achieve a nearly perfect link quality. to save energy, we use the rssi instead of prr as the metric to filter qualified forwarding candidates. generally, a higher transmission power tends to bring better link quality [20]. in detail, the sender periodically monitors the received signal strength from its one-hop neighbors and then evaluates their rssis. for given transmission power, we can select the candidate with the strongest rssi as next hop. delay-based routing in duty-cycled sensor network, sleep latency is often in the order of seconds, while propagation delay and processing delay (in the order of milliseconds) can be ignored. therefore, it is necessary to estimate the sleep latency. assuming each node is assigned a predefined work schedule, we can calculate the sleep latency by the waiting time from the ready time to the moment that it is sent out. to do that, each node only needs to share its work schedule with their neighboring nodes. in a resource-constrained environment, it is energy-consuming and non-scalable to estimate the end-to-end delay among different sender-receiver pairs. instead, we use one-hop delay value as the evaluation metric. in ideal network with perfect link, one-hop delay can be represented as sij = (tj − ti), where tj, ti are the wake-up time of transmission pair. for example, the sleep latency from node a to b in figure 4 is 8-2=6. notice that, the end-to-end delay is also related to the length of forwarding path. to model such parameter, we present the one-hop relative delay, dij as, dij = sij ∗ dj di . (2) here, dj,di are the distances from the sender and next-hop to the destination, respectively. assuming two candidates wake up at the same time, the nearer one would be selected as next hop. in real scenario with unreliable links, we can evaluate delivery delay according to the model proposed in [21]. energy-based routing in estc, the selection of transmission power is from the view of sending node. however, it is possible to integrate estc with power-aware routing algorithm in order to maximize energy efficiency and network lifetime [22]. to do that, each node can periodically collect energy information from its one-hop neighbors and make the forwarding decision in time. in specific, we select the metric aiming at maximizing the lifetime of all nodes. as a result, the neighboring node with maximum consumed energy for each packet would be selected as the potential forwarder. similar as link-quality-based routing, the sender exchanges the statistical information with its neighbors, including residual energy, queue size so that the optimal forwarder could be chosen. taking figure 4 as an example, if the lengths of their queue are the same, node c instead of node b would be selected since it has more residual energy. energy synchronized transmission control for energy-harvesting sensor networks 201 3.6 collision avoidance though there is low data traffic in duty-cycled sensor networks, it is possible that multiple transmissions among neighboring nodes are collided when transmission power is increased. meanwhile, the concurrent transmission happens only when their receivers wake up at the same time. to resolve the conflicts, we introduce transmission-power-based backoff approach. when a node intends to begin a transmission, it first backs off for a period of time at the begin of a slot. the duration of the backoff depends on the power level used in the transmission. the higher the transmission power, the shorter the back off duration. when multiple nodes within communication range decide to send packets, they back off first before transmission and the one with highest power level starts first. other nodes listen to the channel first after the backing-off time. once catching the ongoing transmission, they will abort their own transmission and insert the data packet with updated timestamp into the data queue. suppose the backoff time bound is tb and the maximum number of concurrent transmissions is c, we can divide tb into c slots for different backoff durations. a sender can compute its backoff duration tb with the following equation. tb = ⌊c(1− i k )⌋ tb c + x,1 ≤ i ≤ k. (3) where i is the number of transmission power level and x is a random number generated from [−tb c ,+tb c ] if i ≤ k and from [0,+tb c ] if i == k. this ensures that the backoff time is positive and within the backoff bound. the random period can reduce the chance of collisions when two or more nodes use the same power level. by using such backoff method, we avoid conflicts but also save energy since the transmission with higher power level starting early can be heard by more potential senders. 3.7 energy synchronized transmission control (estc) scheme based on the energy estimation and routing algorithm, estc protocol can make the approximate forwarding decision. the detailed process of transmission power decision is described in algorithm 5. to efficiently synchronize the harvested energy, estc takes an energy adaptive strategy based on the feedback of energy estimation and real-time traffic load. if there is no additional energy budget, the current node sends the packet with minimum transmission power level. otherwise, the sender can increase its transmission power in order to support the packet delivery in data queue. our first step is to decide the amount of energy that can be used by the data delivery per packet. assuming that each packet has the same length, the energy consumption for data delivery is only dependent on the used transmission power. in other word, the sender can calculate the energy consumption for given transmission power level. given the frame size of data packet (f) and the data rate (r), we can compute the energy consumption of data transmission, ei by given transmission power pi with the following equation. ei = pi ∗ f r . (4) here, pi is the power consumption for given transmission power level pi, which is usually dependent on the wireless radio. next, we can decide the appropriate transmission power level by comparing ei with the available energy (see line 1-9). with the selected transmission power, estc sends packet in the order of data queue according to the given routing policy (see line 10-16). since data packets can only be delivered 202 z. fan, x. liu when the next-hop forwarder wake up in duty-cycled sensor network, the data queue in upper layer is holding data packets generated by current node or received from the other nodes. each data packet includes the following fields, (id, timestamp, transmissiontimes). to prioritize the delay, we suppose all data packets are stored in the order of their generated time. for example, it needs to forward data packets via the earliest wake-up node with dess. if the transmission succeeds, it would update the available energy and fetch the next packet from data queue. otherwise, estc inserts the failed packet into the data queue and waits for the next schedule. the packet would be dropped if it is out of the maximum allowable transmissions (transmissiontimes). notice that, the above algorithm is locally executed at the individual node, which is completely distributed. algorithm 1: energy synchronized transmission control at time t require: the number of packets in data queue, n; require: the average energy of ancestor nodes, b′; require: the amount of remaining energy, bi; require: the maximum number of concurrent transmissions, c; require: the routing algorithm, routingpolicy; require: the backoff time bound, tb; 1: pa ← p1; 2: bs ← bi(t)−b′(t)/n; 3: for all transmission power pi ∈ [p1,pk] do 4: ei ← pi ∗ fr ; 5: if (ei > bas ) then 6: break; 7: end if 8: pa ← pi; 9: end for 10: if (routingpolicy is etx) then 11: select next hop (nh) according to link quality; 12: else if (routingpolicy is dess) then 13: select next hop (nh) according to sleep latency; 14: else if (routingpolicy is par) then 15: select next hop (nh) according to energy level; 16: end if 17: for i = 0 to k do 18: if (i == k) then 19: x ← rand(0,+tb c ); 20: else if (i < k) then 21: x ← rand(−tb c ,+tb c ); 22: end if 23: end for 24: tb ←⌊c(1− ik)⌋ tb c + x; 25: back off the time, tb. 26: fetch and send packet to nh with power level pa. energy synchronized transmission control for energy-harvesting sensor networks 203 4 performance evaluation in this section, we validate the performance of energy synchronized transmission power control scheme. in specific, we assume a data collection scenario consisting of energy-harvesting sensor nodes, which is a common communication pattern. source nodes periodically sense and generate data packets, then deliver them to the sink node through multi-hop forwarding path. due to energy efficiency, all nodes except the sink are presumed to work with low-duty-cycle mode. 4.1 baseline and selection of routing algorithms to verify the effectiveness of our design, we compare estc scheme with those that do not use energy synchronization mechanism, i.e., the uniform transmission power control (termed utpc later). to verify our design, the estc is integrated with the various routing algorithms. • link-quality-based: etx [18] is proposed to minimize the expected transmission count for multi-hop data communication. • delay-based: dess [19] is presented in order to minimize delivery delay for duty-cycled sensor networks. • power-aware-metric: par [22] is proposed to minimize the energy consumption and then prolong the network lifetime. in our experiment, we take the relative energy budget as routing metric. notice that, all routing algorithms can be easily integrated with our design. for given transmission power, each node selects the corresponding candidate according to the above routing metrics. 4.2 simulation setup we assume that all sensor nodes are randomly deployed in a 200m × 200m square field, where 40 nodes are selected as data source and the sink is located in the right corner of sensor area. the average data rate of source node is 2 packets with frame size 64b for each working period. without otherwise specified, we set radio parameters strictly according to the cc2420 radio hardware specification [23]. in detail, we select 8 typical transmission power levels, 25dbm, -15dbm, -10dbm, -7dbm, -5dbm, -3dbm, -1dbm, 0dbm indexed from level 0 to level 7. the energy model is identical to the practical measures of cc2420, i.e., the corresponding transmission power ranges from 29.04mw to 57.42mw. the energy consumption of data reception is 62mw. each experiment is repeated 30 times with different deployments and working schedules generated by random seed. for each experimental setting, the result is averaged over 100 sourceto-sink communications under given network size and density. to emulate the energy-harvesting environment, each node is supposed to increase its energy resource at a stochastic charging rate. in the simulation, three performance metrics are evaluated: i) the e2e delivery delay, defined as the total time spend for the delivery of a packet; ii) the energy efficiency, defined as the standard deviation of remaining energy for all nodes within the network; iii) the network lifetime, defined as the number of time slots from the beginning to the time when first node is running out of its energy. we have to mention that it is a dynamic concept in energy-harvesting network, only representing the current status of network. with the replenishment of energy, the dying nodes can refresh and join the data forwarding again. 204 z. fan, x. liu 4.3 performance evaluation this section evaluates the e2e delivery delay, energy consumption and network lifetime for different schemes. moreover, we compare estc and utpc scheme under varied network settings. working with etx in this section, we first study the delivery delay of both estc and utpc while the number of nodes changes from 200 to 600. as can clearly be seen from figure 5(a), estc has a smaller delay than utpc under all node densities. for example, estc reduces the e2e delay by 47% compared with utpc when the number of nodes is 600. it can also observed that the delay for etx-based schemes increases with the number of nodes. more nodes are deployed in the network, more potential candidates are available. on the other hand, those candidates near to the sender are more likely selected, leading to a longer data forwarding path for etx-based forwarding. to verify, we plot the the forwarding length of both schemes in figure 5(c), which shows the average length of both estc and utpc is increasing with nodes density. more importantly, the average length of estc is much shorter than utpc in all cases. for example, the maximized length for uptc is 103 while the corresponding length for estc is 61. figure 5(b) shows that estc has smaller standard deviation of energy than utpc, representing that nodes with estc has more balanced energy consumption in the process of data collection. the reason is that estc tends to distribute the energy consumption among different nodes by adjusting the transmission power. we plot the transmission power levels (tpls) used in the data forwarding process of estc as figure5(d). it can be clearly seen that nodes with estc can adaptively select transmission power according to the availability of energy resource and link quality. from the figure, it is observed that many nodes select very low transmission power, such as level 0 or 1 in dense deployed area. notice that, utpc tends to select the same transmission power for all data delivery, resulting in higher energy consumption. 200 300 400 500 600 0 1000 2000 3000 4000 5000 6000 number of nodes e 2 e d e liv e ry d e la y (# o f sl o ts ) etx with estc etx with utpc (a) e2e delay 200 300 400 500 600 0 3 6 9 12 15 18 21 24 number of nodes s ta n d a rd d e vi a tio n o f e n e rg y (m j) etx with estc etx with utpc (b) energy std 200 300 400 500 600 0 10 20 30 40 50 60 70 80 90 100 110 number of nodes p a th l e n g th ( # o f h o p s) etx with estc etx with utpc (c) path length 0 10 20 30 40 50 0 1 2 3 4 5 6 7 forwarding path (# of hops) t x p o w e r l e ve l i n d e x (d) tpl diversity figure 6: impact of node density for etx. energy synchronized transmission control for energy-harvesting sensor networks 205 100 200 300 400 0 2000 4000 6000 8000 10000 12000 14000 working period (# of slots) e 2 e d e liv e ry d e la y (# o f sl o ts ) etx with estc etx with utpc (a) e2e delay 100 200 300 400 0 3 6 9 12 15 18 21 24 working period (# of slots) s ta n d a rd d e vi a tio n o f e n e rg y (m j) etx with estc etx with utpc (b) energy std figure 7: impact of duty cycle for etx. figure 6(a) shows the impact of working period on network performance, where the e2e delivery delay increases with the working period. this is because the duty cycle is reduced while the working period increases, leading to the prolong of sleep latency per hop. for energy efficiency, we observe the similar result that the energy dissipation among nodes is more balanced for estc than utpc as figure 6(b). working with dess in this section, we study the network performance of both estc and utpc working with dess. again, estc has a smaller delay than utpc for dess under all node densities. figure 7(a) shows that the delay for dess-based scheme decreases as node density, in which more potential candidates with earlier wake-up schedule are provided. moreover, dess-based schemes have much lower delays compared with those etx-based schemes. for instance, the average e2e delay for dess is around 200 under all node densities. on the contrary, the value for etx is more than 1000 as shown in figure 5(a). the rationale behind is that dess prioritizes the delivery delay since each node always selects the earliest wake-up neighbor to forward data packets. the other reason is that the path length for dess routing is much shorter than etx as shown in figure 7(c). similarly, estc outperforms utpc on energy efficiency due to the adjusting of transmission power levels as shown in figure 7(b). figure 8(a) shows the average delay under different working periods. we can see that the delivery delay increases with the working period of nodes. for example, the average e2e delay increases from 156 to 573 for estc. totally, with energy synchronization, estc can reduce delivery delay by 20% than utpc under all working periods. figure 8(b) shows the energy efficiency under varied duty cycles, which proves the slight superiority of estc over utpc. the main reason is that the schedule of nodes is assumed to be fixed in the whole lifetime so that the same node is usually selected as the forwarder in multiple times. on the other hand, the total energy consumption for dess-based approach is much less than etx-based scheme due to shorter forwarding path. working with par in this section, we study the network performance of proposed schemes with power-aware routing algorithm. in the simulation, we assume that each node can harvest energy within predefined period and then measure the network lifetime. in special, the average charging rate of nodes is set from 0 to 3. figure 9(a) shows that estc can maintain longer lifetime than utpc under all energy charging rates. notice that, the network lifetime with energy-harvesting capability is much longer than that of static sensor network. for example, the network lifetime lasts 3.7 million of slots with average charge rate 3, around 9 times of the lifespan when there is 206 z. fan, x. liu 200 300 400 500 600 0 100 200 300 400 500 600 700 number of nodes e 2 e d e liv e ry d e la y (# o f sl o ts ) dess with estc dess with utpc (a) e2e delay 200 300 400 500 600 0 3 6 9 12 15 18 number of nodes s ta n d a rd d e vi a tio n o f e n e rg y (m j) dess with estc dess with utpc (b) energy std 200 300 400 500 600 0 5 10 15 20 25 number of nodes p a th l e n g th ( # o f h o p s) dess with estc dess with utpc (c) path length figure 8: impact of node density for dess. 100 200 300 400 0 200 400 600 800 1000 1200 1400 1600 1800 working period (# of slots) e 2 e d e liv e ry d e la y (# o f sl o ts ) dess with estc dess with utpc (a) e2e delay 100 200 300 400 0 2 4 6 8 10 12 14 16 working period (# of slots) s ta n d a rd d e vi a tio n o f e n e rg y (m j) dess with estc dess with utpc (b) energy std figure 9: impact of duty cycle for dess. 0 1 2 3 0 0.5 1 1.5 2 2.5 3 3.5 4 x 10 6 average charging rate per node (mj/period) n e tw o rk l ife tim e ( # o f sl o ts ) par with estc par with utpc (a) network lifetime 0 1 2 3 0 400 800 1200 1600 2000 average charging rate per node (mj/period) e 2 e d e liv e ry d e la y (# o f sl o ts ) par with estc par with utpc (b) e2e delay figure 10: impact of charging rate for par. energy synchronized transmission control for energy-harvesting sensor networks 207 no extra harvested energy. also, we deploy the e2e delay for both utpc and estc in figure 9(b), which demonstrates estc can still reduce the delivery delay even working with the power aware routing policy. in fact, when the packet is delivered along with an energy-rich path, it is more likely to be transmitted at high power level, leading to a lower sleep latency. 5 conclusion harvesting energy technique provides opportunities for the substantiality of resource-constrained sensor networks. to efficiently utilize the harvested energy, we propose energy synchronization transmission control scheme which can work together with different routing strategies. in specific, estc adaptively selects the suitable transmission power according to the energy availability and traffic load in order to reduce delay and balance the energy consumption. we verify the effectiveness of our design by conducting large-scale simulations, showing that estc can reduce delivery delay and energy consumption without compromising network lifespan compared with uniform transmission power control design. acknowledgment this work was supported in part by nsfc under grant no. 61373125, 61572233 and "the fundamental research funds for the central universities" under grant no. 21615442. bibliography [1] m. li et al (2009), canopy closure estimates with greenorbs: sustainable sensing in the forest, acm sensys, proceedings of the 7th acm conference on embedded networked sensor systems, 99-112. 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[23] http://www.ti.com/ international journal of computers communications & control issn 1841-9836, 12(1):7-25, february 2017. improved timing attacks against the secret permutation in the mceliece pkc d. bucerzan, p.l. cayrel, v. dragoi, t. richmond dominic bucerzan* aurel vlaicu university of arad department of mathematics and computer science romania, 310330 arad, elena dragoi, 2 corresponding author:dominic@bbcomputer.ro pierre-louis cayrel laboratoire hubert curien, umr cnrs 5516, université de lyon, saint-etienne, france pierre.louis.cayrel@univ-st-etienne.fr vlad dragoi laboratoire litis ea 4108 université de rouen ufr sciences et techniques, 76800 saint etienne du rouvray, france vlad.dragoi1@univ-rouen.fr tania richmond laboratoire imath, ea 2134, avenue de l’université , bp 20132, 83957 la garde cedex, france tania.richmond@univ-tln.fr abstract: in this paper, we detail two side-channel attacks against the mceliece public-key cryptosystem. they are exploiting timing differences on the patterson decoding algorithm in order to reveal one part of the secret key: the support permutation. the first one is improving two existing timing attacks and uses the correlation between two different steps of the decoding algorithm. this improvement can be deployed on all error-vectors with hamming weight smaller than a quarter of the minimum distance of the code. the second attack targets the evaluation of the error locator polynomial and succeeds on several different decoding algorithms. we also give an appropriate countermeasure. keywords: communication systems, theory of error correcting codes, code-based cryptography, mceliece pkc, side-channel attacks, timing attack, extended euclidean algorithm. 1 introduction in the history of cryptography public key schemes are quite recent. in the classic era both encryption and decryption algorithms are symmetric, that is why having access to the keys gives a total control on both encryption and decryption steps. these types of schemes are called symmetric cryptosystem and are still widely used. nonetheless several security properties can hardly be achieved with the use of symmetric cryptography. that is the main reason public key cryptography appeared. the first public key cryptosystem was invented by diffie and hellman [5]. but despite the fact that public key schemes are rather new, they are widely spread in practice. moreover there are few constructions to be used in practice and they are all based on number theory problems, more exactly the hardness of factoring and discrete logarithm problem. but this is rather concerning since there is little theoretical support indicating that these problems are indeed hard. one of the main threats for these schemes is the arrival of the quantum computer. peter shor has shown that both computation of discrete logarithm and factoring problem can be done in polynomial time on a quantum machine [13]. in reaction to this thread, several solutions have been proposed, such as hash-based cryptography, code-based cryptography, lattice-based cryptography, and multivariate cryptography. the new facts concerning the post-quantum cryptography are well discussed in [2]. code-based cryptosystems were introduced in 1978 by robert j. mceliece [8]. but many variants were attacked and partially or totally broken. up to now, none of the proposed variants seemed as copyright © 2006-2017 by ccc publications 8 d. bucerzan, p.l. cayrel, v. dragoi, t. richmond strong and secure as the original mceliece public-key cryptosystem (pkc) using goppa codes. structural attacks managed to reveal the secret key and totally break variants that used the generalized reed-solomon codes [15] or qc-ldpc codes [10] and many other variants. as the goppa codes still resist to structural attacks, they present a real interest in our approach. so we focus our attention on the cryptanalysis of the mceliece pkc using goppa codes. more exactly on the side-channel attacks using time differences between two executions of the same task. the interest of timing attacks is both practical and theoretical: we avoid unsecured implementations and discover new attacks succeeding in a polynomial time. the main purpose of these type of attacks is to reveal a part of the secret key and a breaking point of an algorithm. the authors of such exploits usually end up by giving the necessary countermeasures and the secure variant of the algorithms. in the case of the mceliece pkc using goppa codes, most of the timing attacks were discovered since 2008. falko strenzke’s articles mention several weak points mostly situated in the decoding algorithm [14, 16, 18, 19]. some of these can be repaired by an intelligent and cautious way of the programming manner where countermeasures were proposed in [1, 3, 19]. all of the mentioned attacks were realised on a mceliece pkc implementation using the patterson algorithm (cf. fig. 1) for decoding goppa codes. the number of error corrections in the patterson algorithm is bounded: up to t errors can be corrected, where t is the degree of the goppa polynomial. our contribution is to reveal a new timing attack against the error-locator polynomial (elp) evaluation and to improve two existing attacks. in our new version of the two existing attacks combined, we detail how the relation between the two attacks is crucial in order to avoid eventual errors. the attacks are executed on the extended euclidean algorithm (eea) and exploit the number of iterations. as the authors mentioned, the initial attacks are limited and may not allow the total break of the permutation. this limit is situated in the number of equations detected by their attack. we will use a new relation between the number of iterations in the two steps in order to expand the system and to fully determine the secret permutation. we will also give a single countermeasure, which is efficient to all types of attacks exploiting the eea in this particular manner. the second contribution is in giving a new timing attack against the elp evaluation. the importance of this new attack is that it operates on the polynomial evaluation, applied in several decoding algorithms as the patterson algorithm, berlekamp-massey algorithm or any general decoder for alternant codes. we will show that this attacks succeeds on several variants of the polynomial evaluation. 2 background for all the necessary background on coding theory we address the reader to any book in this field, for example [7]. nevertheless we give here the details concerning binary goppa codes. 2.1 goppa codes definitions and properties we will focus exclusively on binary goppa codes in this paper, but it is easy to generalize our results to q-ary codes: -goppa polynomial: g(x) is a polynomial over f2m[x] with deg(g) = t. -goppa support: l = {α0,α1, ..,αn−1} subset of f2m s.t. g(αi) 6= 0. the syndrome polynomial associated to c ∈ fn2: sc(x) = n∑ i=1 ci x+αi . improved timing attacks against the secret permutation in the mceliece pkc 9 definition 1 (binary goppa code). given g(x),l and sc(x) the binary goppa code is defined as: γ(l,g) = {c ∈ fn2 | sc(x) ≡ 0 mod g(x)}. among the most important properties that a goppa code satisfies we recall the followings: proposition 2. a goppa code γ(l,g) is a linear code over f2. its length is given by n = |l|, its dimension is k ≥ n−mt, where t = deg(g) and its minimun distance d ≥ t + 1. the syndrome polynomial sc(x) satisfies the following property: sc(x) = ω(x) σ(x) mod g(x), where σ(x) = t∏ i=1 (x + ai) is called the error locator polynomial (elp) and the elements ∀i ∈ {1, . . . , t}ai ∈l are the error positions. irreducible binary goppa codes are defined by an irreducible goppa polynomial g and admit the maximum length n = 2m. we use this type of codes in the rest of the paper and adopt the following notations: • for the permutation of the support elements: π(l) = l′ = (π(0), π(1), . . . , π(αi), . . . , π(αn−2)). where π is an element of the symmetric group. • let p(x) be a monic polynomial of degree t over f2m with t roots denoted ai: p(x) = xt + stt−1x t−1 + stt−2x t−2 + . . . + st2x 2 + st1x + s t 0, where the coefficients si ∈ fm2 are the elementary symmetric functions: stt−1 = t∑ i=1 ai, stt−2 = t∑ i=1,j=1 i6=j aiaj, . . ., st1 = t∑ j=1 t∏ i=1 i 6=j ai and st0 = t∏ i=1 ai. alternant decoders for the (irreducible) binary goppa codes, we can use (at least) three different decoding algorithms: 1. the extended euclidean algorithm (eea); 2. the berlekamp-massey algorithm; 3. the patterson algorithm. extended euclidean algorithm (eea). the first decoding algorithm can correct up to t 2 errors. we can increase the error-correction capabcity and correct up to t errors when the syndrome associated to g2 is used. unfortunately, the corresponding parity check matrix has two times more rows and the construction is more complex. the berlekamp-massey algorithm. similarly as the eea, the berlekamp-massey algorithm has to use g2 in order to decode t errors. the advantage of this algorithm is that it isn’t vulnerable to several existing timing attacks and it allows a fast and constant-time computation. some advantages are listed in [3]. the patterson algorithm. the patterson algorithm offers another solution for the syndrome decoding. the decryption described in [12] permits to correct up to t errors by using the syndrome associated to g but not to g2. 10 d. bucerzan, p.l. cayrel, v. dragoi, t. richmond 2.2 the mceliece cryptosystem the mceliece pkc [8] is composed by the three following algorithms. key generation:the first step is to generate the support (the set of n = 2m elements) and the goppa polynomial g of degree t. then, the parity check matrix can be built and brought to a systematic form: [in−k|r] in order to recover a generator matrix g of the goppa code. we randomly choose a non-singular k × k matrix s and a n × n permutation matrix π, and compute the public k ×n generator matrix g = sgπ . the key generation procedure outputs sk = (γ(l,g),s, π) and pk = (n,t,g). message encryption: • inputs: message m ∈ fk2, public key pk = (n,t,rt ). • output: ciphertext z ∈ fn2. 1. randomly choose an n-bit error-vector with weight wt(e) = t; 2. encode z = mg⊕e; 3. return z. message decryption: • inputs: ciphertext z ∈ fn2, secret key sk = (γ(l,g),s, π). • output: message m ∈ fk2. 1. compute z′ = zπ−1; 2. find m′ = ms from z′ ⊕ e using decode(z′) with the secret code; 3. compute m = m′s−1; 4. return m. decode(.) is an alternant decoder (presented in the previous subsection). existing side-channel attacks there are several papers on side-channel attacks against the mceliece pkc and a quick review must be done in order to clear up the reader’s understanding. most of the attacks target the patterson decoding algorithm and exploit several weaknesses. table 1: patterson algorithm: existing timing attacks and countermeasures step ref. countermeasure ¶ z′ = zπ−1 · sz′(x) = h′z′(xt−1, . . . ,x2,x, 1)t ¸ sz′(x) −1 mod g(x) via eea [18] control flow ¹ τ(x) = √ x + sz′(x)−1 º b(x)τ(x) ≡ a(x) mod g(x) [14,16] in eea make sure deg(a) ≤b t 2 c ; deg(b) ≤bt−1 2 c deg(ri) = deg(ri−1) − 1 via eea and deg(τ) = t− 1 » σ(x) = a2(x) + xb2(x) ¼ e = (σ(α0),σ(α1), . . . ,σ(αn−1)) ⊕ (1, . . . , 1) [1,17,19] the non-support or make sure deg(σ) = t ½ e′ = eπ ¾ z = z′ ⊕e′ improved timing attacks against the secret permutation in the mceliece pkc 11 there are mainly two types of attacks classified by their goal: 1. attacks recovering the secret message m [1,17,19]; 2. attacks recovering (fully or partially) the secret key sk [14,16–18]. the attacks on steps ¸ and º are able to determine some relations on the support elements by counting the number of iterations in the eea. we improve it in section 3. the attack on step ¼ reveals error positions using timing differences in the elp evaluation. the attacker is able to find the error-vector with a certain non negligible probability. the basic idea is that two different polynomials, with some different degrees, are not evaluated in the same time. so the timing difference gives some information on the error-vector. we improve this attack in section 4. in the rest of this paper, we assume that an attacker chooses a weight 0 < r < t for the error-vector e and we use the following notations: deg(g) = t and wt(e) = r. in the next sections we will detail the complexity analysis of these attacks as well as adapted parameter values. 3 timing attack against double using of the eea goal: the attacker’s goal is to recover the secret permutation π. identification of a leakage: the leakage is identified at steps ¸ and º of the patterson algorithm. this type of attack was already published in [16,18]. the two steps using the eea are considered as independent parts. in this section, we propose to show the relation existing between both steps and thus attack them. in fact, the main problem of previous attacks is the limited number of cases that can be exploited. they just can be applied on wt(e) ∈ {2, 4} as shown in [16] or wt(e) ∈{2, 4, 6} as presented in [18]. the problem comes from a simple fact: the number of iterations is given by two conditions. one of the condition is that all of the quotients in the eea must be polynomials of degree equal to one. so when this condition is not fulfilled the number of iterations could not be any longer controlled by the attacker. we will use n¸ and nº as notations for the number of iterations in the 3rd step ( respectively 5th step) of the patterson algorithm. motivations of our attack: we will show that using the relation between both steps will allow us to fully control the number of iterations. the other contribution is in finding the relation between both steps and in using it for building a larger set of equations. we will show that we are able to extend the limited equation number of the system up to wt(e) = deg(g) 2 . the main interest is that instead of finding only equations involving the permutation of 2, 4 or maybe 6 elements, we can extend it as much as necessary in order to discover the secret permutation. in terms of complexity, instead of enumerating all possible permutations, i.e. n! permutations, we reduce the complexity to the following expression: p∑ i=3 ( n i ) , where p ≤ deg(g) 2 − 1 hence for small values of p, we have: p∑ i=3 ( n i ) ≤ ( n p ) × (p− 2) ≤ ( en p )p × (p− 2) 12 d. bucerzan, p.l. cayrel, v. dragoi, t. richmond (where e = 2.718281828 . . . is the basis of the natural logarithms). in order to make clear the difference between the naive attack and our attack, we propose to give a lower bound for the complexity of the naive attack:( n e )n ≤ n! finally: p∑ i=3 ( n i ) ≤ ( en p )p × (p− 2) << (n e )n ≤ n! so the naive attack would be exponential in the length of the code as a timing attack would only have a complexity exponential in the maximum of the error-vector’s weight needed for the attack (often extremely small in comparison with the code’s length). scenario: the attacker proceeds in the three following steps: 1. he chooses a random message m and computes c = mg; 2. he randomly chooses an error-vector e of small weight wt(e) < t (t is the correction capacity of the code) and computes z = mg⊕e; 3. he sends z to an oracle (o), which outputs the message m and the number of iterations in steps ¸ and º of the patterson algorithm from fig. 1. main idea: for wt(e) = 2p with p ∈ n, the attacker will find equations having the following form: wt(e)∑ i=1 π(αi) = 0. he will be able to build this type of equations with 0 < wt(e) < deg(g) 2 . we will denote by n¸ the number of iterations in the ¸ step. conditions: the general assumption is that the attacker knows the public key pk, the order of all elements in the support l (l is supposed to be public, for example in the lexicographic order) and has access to an oracle o. these assumptions are the same as in previously mentionned works. we improve the attack in the same context. the oracle o is also able to give some extra informations: the timing for the whole particular algorithm or just one step. we assume that the attacker can violate the procedure by adding wt(e) < t errors. the attacker is able to choose the number and the positions of errors. 3.1 step ¸ in the patterson algorithm it was shown in [18] that the syndrome inversion leaks some information. the attack is based on the number of iterations used in the eea, in order to compute the inverse of the syndrome polynomial s(x) modulo the goppa polynomial g(x). it uses the following properties: n¸ ≤ deg(σ) + deg(σ′), for wt(e) < deg(g) 2 . we will not detail here all conditions, as they are well explained in [18], but we only give some important facts in order to make things clearer and to prepare the attack. let us consider the elp σ(x) = xr + srr−1x r−1 + srr−2x r−2 + · · · + sr2x 2 + sr1x + s r 0, with r ≡ 0 mod 2. then σ′(x) = srr−1x r−2 + · · · + sr3x 2 + sr1. improved timing attacks against the secret permutation in the mceliece pkc 13 in this case the maximum number of iterations is given by the coefficient srr−1 = r∑ i=1 ai. so if srr−1 6= 0, we obtain n¸ = 2r − 2 and all quotients have a degree equal to 1. if s r r−1 = 0, srr−3 6= 0, then n¸ = 2r − 4 and all quotients have a degree equal to 1. 3.2 step º in the patterson algorithm locate the leakage: two observations have to be done in order to understand and to locate the leakage point. the first one is about the number of iterations. it was proven (in [14]) that this number is (with a high probability): nº = nº∑ i=1 deg(qi) = deg(b). in the following paragraph, we will give some relations between τ(x), b(x), a(x) and σ(x) (given in steps ¹, º and » in fig. 1). we will prove some new relations. the new relations between these polynomials allow us to build the attack in such a manner that previous ambiguous cases were eliminated. these relations are crucial for better understanding of the entire decryption algorithm as they influence each step of the process and each particular form of the involved polynomials. there are some useful properties that are going to be used in our approach: proposition 3. 1. if r ≡ 0 mod 2, then deg(a) = r 2 see [14]. 2. if r ≡ 1 mod 2, then deg(b) = r−1 2 see [14]. 3. if deg(τ) ≤br 2 c, then deg(a) = deg(τ) + deg(b). 4. if deg(τ) ≤br 2 c and deg(τ) 6= 0, then wt(e) ≡ 0 mod 2. fact: • when wt(e) is odd: for an error-vector with hamming weight wt(e) = 2k + 1, with k ≤ p− 1, we have the following relations: deg(b) = k, deg(a) ≤ k and deg(τ) ≥ 2p−k. • when wt(e) is even: for an error-vector with hamming weight wt(e) = 2k, with k ≤ p, we have the following relations: deg(a) = k, deg(b) ≤ k − 1 and deg(b) = 0 ⇔ deg(τ) = k. 3.3 number of iterations we saw that the number of iterations in the eea equals deg(b) so we will focus on the form of the polynomial b(x). more exactly, in the case when r is even. let: σ(x) = x2p + s 2p 2p−1x 2p−1 + s 2p 2p−2x 2p−2 + s 2p 2p−3x 2p−3 + . . . + s 2p 2 x 2 + s 2p 1 x + s 2p 0 . we separate odd powers from even ones and get: σ(x) = (x2p + s 2p 2p−2x 2p−2 + . . . + s 2p 2 x 2 + s 2p 0 ) +(s 2p 2p−1x 2p−1 + s 2p 2p−3x 2p−3 + . . . + s 2p 1 x) σ(x) = (xp + √ s 2p 2p−2x p−1 + . . . + √ s 2p 2 x + √ s 2p 0 ) 2 +x( √ s 2p 2p−1x p−1 + . . . + √ s 2p 1︸ ︷︷ ︸ b(x) ) 2 14 d. bucerzan, p.l. cayrel, v. dragoi, t. richmond so deg(b) is given by the coefficients s2p2i−1 with i ∈ {1, 2, . . . ,p}. therefore the number of iterations could be given by the same coefficients under an extra condition: all of the quotients have a degree equal to one. so we can distinguish p−1 possible cases depending on the coefficients, if the degree of the coefficients is equal to 1 in each iteration. therefore:  nº = p− 1 if s 2p 2p−1 6= 0 nº = p− 2 if s 2p 2p−1 = 0 and s 2p 2p−3 6= 0 nº = p− 3 if s 2p 2p−1 = 0 s 2p 2p−3 = 0 and s 2p 2p−5 6= 0 ... in all cases, the same assumption is made: the degree of the quotient equals 1 in each iteration. it means that we might have the number of iterations without any condition on the coefficients. 3.4 attack against the pair (n¸,nº) how it works. in this paragraph, we will explain how our attack works. we start by presenting the general relation for the pair (n¸,nº). using 3.1 and 3.2 we get the following property: proposition 4. let wt(e) = 2p < t/2. (n¸,nº) = (4p− 4,p− 2) ⇒ 2p∑ i=1 ai = 0 with probability psuccess. we will give a snapshot of each step in the attack and give some information on the success probability. 1. find the position of π(0) (see [14]). 2. set wt(e) = 4: fix π(0) ∈{error-vector} and find 3∑ i=1 ai with ai 6= 0. fix π(0) 6∈ {error-vector} and find 4∑ i=1 ai with ai 6= 0. 3. set wt(e) = 6: fix π(0) ∈{error-vector} and find 5∑ i=1 ai with ai 6= 0. fix π(0) 6∈ {error-vector} and find 6∑ i=1 ai with ai 6= 0. 4. . . . 5. set wt(e) = b t 2 c: fix π(0) ∈{error-vector} and find wt(e)−1∑ i=1 ai with ai 6= 0. fix π(0) 6∈ {error-vector} and find wt(e)∑ i=1 ai with ai 6= 0. in appendix 1, a toy example is presented for a better comprehension. improved timing attacks against the secret permutation in the mceliece pkc 15 3.5 success probability the success probability psuccess is described by the following event: {all the quotients have a degree=1}. if we consider all elements of our support as uniformly distributed variables and the independence of each step inside the eea, under the initial assumptions we have: psuccess = p({n¸ = 4p− 4}∩{nº = p− 2}) = p({n¸ = 4p− 4})p({nº = p− 2}) = (1 − 1 n )n¸+nº. experimental results show that for n = 2048 and wt(e) = 4, in order to find equations of the following form: π(α1) + π(α2) + π(α3) + π(α4) = 0 the probability equals 0.998. it means that less that 0.2% of the cases are not exploitable among all possible cases under the condition: n¸ = 4 and nº = 0. in other words, each time this combination is revealed, the probability of having a good equation for our attack equals 0.998 for the given parameters. 3.6 experimental work in order to validate the relations that we presented in the previous paragraph for (n¸,nº), we used a pari/gp implementation of the mceliece cryptosystem (the code will be publicly available). we computed a keypair, then encoded and decoded a given message multiple times, by checking the value of the couple (n¸,nº), searching for the valid combinations described above. we also used different values for m, the extension degree of the finite field. we ran the algorithm until we got the specific combination about hundred times. then, we obtained an average value for the necessary iterations required to get the searched combination. the results are presented in the following table: table 2: number of necessary iterations to get the combination for error-vectors of hamming weight 4, 6 and 8 combination: n¸ 4 8 12 nº 0 1 2 number of iterations for m = 7 127 138 142 number of iterations for m = 8 235 270 273 it means that for m = 7, we need to send to the oracle in average 127 different ciphertexts, in order to get the wanted relation (π(α1) + π(α2) + π(α3) + π(α4) = 0). in the case of the previous equation, the density of such configurations equals in average 1 127 ×psuccess = 1 127 × ( 1 − 127 128 )4 . knowing one relation allows us, by fixing one of the positions, to reduce the number of ciphertexts that has to be sent to the oracle. it means that wanted relations are revealed more often as we progress in the attack. it also gives the first intuition on the structure of the permutation (see appendix 1). attack implementation in order to practically test our attack, we used the same software implementation. in order to reveal timings close to real values, we repeated the attack for more than 106 times. we presented the obtained results are in the following table: 16 d. bucerzan, p.l. cayrel, v. dragoi, t. richmond table 3: timings (in sec.) for decryption in the case of n = 211 and t = 16 wt(e) timings for expected attack equation timings for random type equation 4 30141892 × 10−6 304856 × 10−4 6 3072799 × 10−5 310234 × 10−4 8 31597171 × 10−6 32242382 × 10−6 10 3285724 × 10−6 3345847 × 10−5 remarks: we didn’t give the timings for the odd values as they are constant and independant from the linear combinations between the permutations of the error positions. from figure 3, we observe that there’s a slight difference between the attack on this type combinations and on randomly distributed combinations. as we mentioned before for the random combinations those with the maximum number of iterations are more likely to appear (the case when all coefficients are different from zero). so in this case, we have the timing difference required for our attack to succeed. in section 3.7, we will explain how the patch will work not only on this type of attacks but even on other types as the bit-flipping attacks. 3.7 countermeasures we have seen that it is possible to attack a system by knowing how many times the eea is repeated. the number of iterations can go from 0 to t− 1 in the syndrome inversion and from 0 to t/2 in the elp determination. in order to avoid a correlation-finding from the number of iterations, we propose to introduce extra iterations into the eea. the number of extra iterations should be chosen between 0 and a value that we call extra. the extra value is either t/2 or t−1, for the syndrome inversion ¸ or the elp determination º, respectively. the variable i contains the number of iterations realized in the first part of the secured eea. we chose to use integer values in the extra eea steps, in order to avoid divisions by zero that may occur if we keep the previous terms. the point is to keep computing things that are as computationally expensive as the original eea, so that an attacker can’t make the difference between true steps and extra steps. we present the proposed secured modified eea: input: f(x), g(x), dbreak and t. output: a(x) and b(x) s.t. a(x) ≡ b(x)f(x) mod g(x) 1. d ← dbreak 2. [b−1,b0] ← [0, 1] 3. [r−1,r0] ← [g(x),f(x)] 4. i ← 0 5. while deg(ri) > d do i ← i + 1 ri−2(x) = ri−1(x)qi(x) + ri(x) bi(x) ← bi−2(x) + qi(x)bi−1(x) end while 6. a(x) ← ri(x) 7. b(x) ← bi(x) improved timing attacks against the secret permutation in the mceliece pkc 17 8. extra = f(t,dbreak) 9. while i < extra do i ← i + 1 ri−2(x) = 3qi(x) + 5 bi(x) ← 5 + 6qi(x) end while the new security parameters: we recall the fact that normal security parameters do not take in consideration timing attacks. usually security parameters are given under the assumption of possible naive attacks or known structural attacks as isd [9] for example for the mceliece pkc the usual parameters are: 100-bit security n = 2048, t = 50 128-bit security n = 2960, t = 56 256-bit security n = 6624, t = 115 for the first parameters a timing attack with p = 6 would reveal a complexity less than 261 elementary operations, that is way lower than the original security level proposals. so for timing attacks larger parameters have to be taken in consideration in order to maintain the same level of security. for example in order to same a 100 bit security lever against this type of timing attacks one should propose n = 131072. the usual solution is not to increase the values of the parameters but to propose secure variant of the algorithm, variant that is not vulnerable to the specified attack. our proposal is less faster that the original algorithm, it operates (t− 1) ×o(1) for the syndrome inversion and t 2 ×o(1) for the key equation (where o(1) is the usual complexity for a division). meanwhile it is secure against timing attacks described below. the proof is very simple and it based on the fact that this particular type of timing attacks are based on the number of iterations is the ee algorithm. since our algorithm performs the same number of iterations no matter what relations are hidden between the polynomial coefficients it can’t reveal any of such secret relations. once the countermeasure was applied, we ran the same attack and got the following timings for selected hamming weights (the average timings are presented for more than 107 simulations in fig. 4). table 4: timings (in sec.) for decryption in the case of n = 211 and t = 10 wt(e) timings for attack type equations timings for a random type combination 6 57.99 57.90 7 57.89 8 57.94 58.03 9 58.33 10 57.81 57.89 remark: we observe that the protected implementation is impossible to attack (using the same techniques). we stress that the proposed countermeasure is also efficient in the case when an attacker wants to use previous techniques, like in [14,16,18]. 18 d. bucerzan, p.l. cayrel, v. dragoi, t. richmond 4 timing attack against the elp evaluation goal: the attacker’s goal is to find the secret permutation π. identification of a leakage: a leakage is identified at step ¼ of the patterson algorithm: the elp evaluation. we recall that the elp is denoted σ in subsection 2.1. the attack is based on the fact that the form of the polynomial differs from the element to decode. we will prove that the algorithm’s complexity is strongly related to the coefficients of σ(x). we will then perform a timing attack on the elp evaluation and control the values of the coefficients of σ(x). motivations of our attack: one of the main motivations of our attack is that it can operate on all existing implementations of a general alternant decoder. it operates on the elp evaluation, step that has to be computed in any decoding algorithm. we will give two basic algorithms for the elp evaluation with some improvements and show that even with the published improvements our attack succeeds. we will choose the polynomial evaluation from right to left (the naive algorithm) and from left to right (the ruffini-horner scheme). imagine that our polynomial has a degree equal to an integer t. the first algorithm computes the result within 3t−1 operations (t additions and 2t−1 multiplications). as for the second one it computes the result within 2t operations (t additions and t multiplications). it was proven by v. pan in 1966 [11] that the ruffini-horner’s scheme [6] is optimal in terms of complexity. the main idea of the improvement is to use the fact that some support elements have particular properties (like 0 and 1). knowing the fact that one coefficient equals zero fasten up the algorithm as operations like multiplication or sum have fix values if they take zero as one of the input element. the same thing happens within the multiplication by one. so we will exploit these properties in order to improve our implementation. each time a coefficient equals one or zero it will be store in a special table used afterwards for multiplication or addition. the case where a coefficient equals zero is rare and its probability has been studied in [4]. nevertheless, each time there’s a coefficient equal to zero we will no longer multiply it by the corresponding element as the multiplication equals zero. so we will use the predefined tables to get rid of the useless operations. we will proceed exactly the same way when the multiplication of an element has to be done when a coefficient equals to one. so each time we have one coefficient equal to zero, using our predefined tables, we get rid of two operations (one addition and one multiplication). scenario: the attack scenario is the same as in the previous attack except for the last step. in fact, the attacker gets the running time for the elp evaluation in this section (step ¼ in figure 1). idea: for wt(e) = 2, the attacker will find the positions of π(0) and π(1) the permutation of zero and one. after enough iterations, he will fix those two positions and repeat this attack with wt(e) = 3, he will then find the secret permutation π (using exhaustive search for the remaining positions). conditions: the assumptions are the same as in the previous attack excepted that the attacker does not know the order of the elements in the support l. improved timing attacks against the secret permutation in the mceliece pkc 19 4.1 success probability as we said, in this attack we will only consider polynomials with a degree lower than three. for the case r = 3 we will give the full table of probabilities. we will start with the following general problem: problem: let p(x) be a monic polynomial of degree r with r distinct roots over f2m. what is the probability that all its coefficients are different from zero? this problem was treated in [4] and the results show that the probability can be bounded. for the classical parameters of the mceliece pkc, i.e. n = 2048 and t ≤ 50, the authors obtain: p ≥ 0.95 proposition 5. let p(x) be a monic polynomial of degree 3 with three distinct roots over f2m and m mod 2 = 1. the probability p3 that all its coefficients are different from zero satisfies: p3 = 1 − 5 2m . 4.2 finding the permutation of the support elements zero and one 1. consider the error-vectors ei with wt(ei) = 1. in this case, the error locator polynomial has the following form: σ(x) = x + ai, with ai ∈l = {0, 1,α, . . . ,αn−2}. if ai 6= 0, there is one addition (+) in the σ(x) evaluation. 2. consider the error-vectors ei with wt(ei) = 2. in this case, the error locator polynomial has the following form: σ(x) = x2 + s21x + s 2 0, with s 2 1 = ai + aj and s 2 0 = aiaj. we distinguish two possible cases: (a) σ(x) = x2 + s21x + s 2 0 if aiaj 6= 0 (b) σ(x) = x2 + s21x if aiaj = 0 the case (b) leads to a computation of the polynomial evaluation with one extra addition (+) and the timings reveal all the couples (αi, 0). we can assume now that the position of π(0) is known. 3. we fix this position and we seek for the position of π(1). since the polynomial σ(x) = x2 + s21x, the fastest evaluation is obtained for the couple (π(0), π(1)) as there is only one addition (+) and one square computation. 20 d. bucerzan, p.l. cayrel, v. dragoi, t. richmond 4.3 attack scenario when r = 3 we will consider error-vectors with hamming weight that equals 3. the corresponding σ(x) polynomial has always one of the eight following representations: 1. σ(x) = x3 + s32x 2 + s31x + s 3 0 if s 3 1s 3 2s 3 0 6= 0 2. σ(x) = x3 + s32x 2 + s31x if s 3 0 = 0 and s 3 2s 3 1 6= 0 3. σ(x) = x3 + s32x 2 + s30 if s 3 1 = 0 and s 3 2s 3 0 6= 0 4. σ(x) = x3 + s31x + s 3 0 if s 3 2 = 0 and s 3 1s 3 0 6= 0 5. σ(x) = x3 + s32x 2 if s32 6= 0 and s 3 1 = 0 and s 3 0 = 0 6. σ(x) = x3 + s31x if s 3 1 6= 0 and s 3 2 = 0 and s 3 0 = 0 7. σ(x) = x3 + s30 if s 3 0 6= 0 and s 3 2 = 0 and s 3 1 = 0 8. σ(x) = x3 if s30 = 0 and s 3 2 = 0 and s 3 1 = 0 straightforward we deduce the following cases: (a). σ(x) = x3 + s32x 2 + s31x + s 3 0 if s 3 1s 3 2s 3 0 6= 0 and p = n−5 n (b). σ(x) = x3 + s32x 2 + s31x if s 3 0 = 0 and s 3 1s 3 2 6= 0 and p = 3 n (c). σ(x) = x3 + s32x 2 + s30 if s 3 1 = 0 and s 3 1s 3 0 6= 0 and p = 1 n (d). σ(x) = x3 + s31x + s 3 0 if s 3 2 = 0 and s 3 1s 3 0 6= 0 and p = 1 n several cases can be eliminated by considering the fact that we accomplished the first step and we know the position of π(0). if we consider all the error-vectors where ai 6= 0 ∀i ∈ {1, 2, . . . ,n−1} (i.e. 0 is not a root of p(x)), we reduce the possibilities for σ(x). the new form of the system is the following:  σ(x) = x3 + s32x 2 + s31x + s 3 0 if s 3 1s 3 2s 3 0 6= 0 σ(x) = x3 + s32x 2 + s30 if s 3 1 = 0 and s 3 2s 3 0 6= 0 σ(x) = x3 + s31x + s 3 0 if s 3 2 = 0 and s 3 0s 3 1 6= 0 in all cases, x3 must be computed so we will not consider this part in the timing differences. in the structure that computes the polynomial evaluation the fastest is the last one. but this case is performed only when s32 = 0. 4.4 finding the positions of two elements such that π(αj)π(αk) = 1 in order to increase the number of equations in our system, we exploit the fact that (f2m) ∗ is cyclic. recall: we know the positions of π(0), π(1) and π(α1) + π(α2) + π(α3) = 0. without loss of generality, we choose to fix "π(0)" on the first position and choose two other positions such that the sum is different from 1. we are able to do that because we know the position of "π(1)" and the couples (α1,α2) such that 1 + α1 + α2 = 0. we get two new positions b1 and b2 such that b1 + b2 6= 1. the error locator polynomial is: σ(x) = x3 + s32x 2 + s31x. for b1b2 = 1 we get σ(x) = x3 + s32x 2 + x. this form is the fastest to be computed as there is one less multiplication compare to the other case. improved timing attacks against the secret permutation in the mceliece pkc 21 4.5 system resolution number of equations: we will give the number of linear and quadratic equations obtained by the attacker. finding the positions of π(0) and π(1) reduces the search set to (n− 2) elements. • the first set of linear equations: equation type (1): π(αj)π(αk) = 1 ⇒ ]eq. = n−22 the last equation is determined by all the other ones because for the last couple only one possible solution remains available. for instance, if the attacker finds (n−2 2 − 1) different equations the last equation can be directly determined. • the second set of linear equations: equation type (2): π(αj) + π(αk) = 1 ⇒ ]eq. = n−22 . as the first set, the last one can be determined by all others. this comes from the fact that for the three positions, we fixed the position of π(1) as the first one. so we have (n−2) possibilities on the second position. but there are two repetitions for each (π(1), π(αj), π(αk))-vector. • the third set of quadratic equations: equation type (3): π(αi) + π(αj) + π(αk) = 0 ⇒ ]eq. = (n−2)(n−4) 6 the total number of equations for π(αi) + π(αj) + π(αk) = 0 including the second set equals (n− 1)(n− 2) as the third position is fixed and the two others are free and different. here, the number of repetitions equals six. so we obtain ( (n−2)(n−1) 6 − n−2 2 ) equations. to illustrate how the attack works a toy example is given in appendix 2. conclusion in this article, we focused our attention on the cryptanalysis of the mceliece pkc with the binary goppa codes. we showed the existing weak points in the patterson decoding algorithm and determined the relations between the number of iterations in two different steps of the algorithm and the secret permutation. since those relations were the main connection idea between the two extended euclidean algorithms, we set up a timing attack based on this fact. the advantage of this attack is that it increased the probability of success by avoiding ambiguous cases, undetectable in previous attacks. the other advantage is that it allows higher expansion of the number of equations determined by the attacker in order to find the secret permutation. the second important contribution of our article is a new attack that can be performed on several different decoding algorithms. it reveals that even intelligent variants of some polynomial evaluation algorithms might leak information and need to be patched or replaced. the ideas discovered in the attacks might be reused in any further implementations using the 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(2008), side channels in the mceliece pkc, in j. buchmann and j. ding (eds.), post-quantum cryptography, second intl. workshop, lncs5299: 216–229. improved timing attacks against the secret permutation in the mceliece pkc 23 appendix 1 toy example for the eea attack consider f24 [x] = f2[x] x4+x+1 . the generator matrix g of the goppa code and the support l = {0, 1,α,α2, . . . ,α14} are public. let m ∈ fk2 be the message and o the decoding oracle. we notice that if l is public, one can find g(x) such that l = f2[x] g(x) . the other way is equaly true: if g(x) is public then one can easily find l. suppose that the secret permutation is: π(l) = l′ = {α,α2,α3, . . . ,α14, 0, 1} = {`i |i ∈ (1 . . . 16)} • 1st step: • the attacker asks o to decode all the z = mg⊕e with wt(e) = 1. ? n¸ and nº reveals the position of π(0): `15. ◦ this is mainly due to: σ(x) = x we have τ(x) = 0 and s−1(x) = x • 2nd step: • the attacker asks o to decode all the z = mg ⊕ e with wt(e) = 4 (the positions (`i1,`i2,`i3 ) are the three non-zero positions of e and `i4 = `15). ? the couple ( n¸ nº ) = ( 4 0 ) reveals all (`i1,`i2,`i3 ) such that `i1 + `i2 + `i3 = 0. here (`i1,`i2,`i3 ) ∈{(`1,`4,`16), (`3,`14,`16), . . .}. ◦ deg(σ) = 4 and deg(ω) = { 2 if `i1 + `i2 + `i3 6= 0 0 if `i1 + `i2 + `i3 = 0 ◦ deg(b) = { 1 if `i1 + `i2 + `i3 6= 0 0 if `i1 + `i2 + `i3 = 0 • 3rd step: • the attacker asks o to decode all the z = mg ⊕ e with wt(e) = 4 (the positions (`i1,`i2,`i3,`i4 ) are the four non-zero positions of e). ? the couple ( n¸ nº ) = ( 4 0 ) reveals all (`i1,`i2,`i3,`i4 ) such that `i1 +`i2 +`i3 +`i4 = 0. here (`i1,`i2,`i3,`i4 ) ∈{(`1,`2,`10,`16), (`2,`3,`13,`16), . . .}. ◦ deg(σ) = 4 and deg(ω) = { 2 if `i1 + `i2 + `i3 + `i4 6= 0 0 if `i1 + `i2 + `i3 + `i4 = 0 ◦ deg(b) = { 1 if `i1 + `i2 + `i3 + `i4 6= 0 0 if `i1 + `i2 + `i3 + `i4 = 0 • 4th step: • the attacker asks o to decode all the z = mg ⊕ e with wt(e) = 6 (the positions (`i1,`i2,`i3,`i4,`i5 ) are the five non-zero positions of e and `i6 = `15). ? the couple ( n¸ nº ) = ( 8 1 ) reveals all (`i1,`i2,`i3,`i4,`i5 ) such that `i1 +`i2 +· · ·+`i5 = 0. here (`i1,`i2,`i3,`i4,`i5 ) ∈{(`1,`2,`3,`12,`16), (`3,`4,`8,`12,`16), . . .}. 24 d. bucerzan, p.l. cayrel, v. dragoi, t. richmond ◦ deg(σ) = 4 and deg(ω) = { 4 if `i1 + `i2 + `i3 + `i4 + `i5 6= 0 2 if `i1 + `i2 + `i3 + `i4 + `i5 = 0 ◦ deg(b) = { 2 if `i1 + `i2 + `i3 + `i4 + `i5 6= 0 1 if `i1 + `i2 + `i3 + `i4 + `i5 = 0 • 5th step: • the attacker asks o to decode all the z = mg ⊕ e with wt(e) = 6 (the positions (`i1,`i2,`i3,`i4,`i5,`i6 ) are the six non-zero positions of e). ? the couple ( n¸ nº ) = ( 8 1 ) reveals all (`i1,`i2,`i3, . . . ,`i6 ) such that `i1 +`i2 +· · ·+`i6 = 0. here (`i1,`i2,`i3, . . . ,`i6 ) ∈{(`1,`2,`3,`4,`6,`16), . . .}. ◦ deg(σ) = 4 and deg(ω) = { 4 if `i1 + `i2 + `i3 + · · · + `i6 6= 0 2 if `i1 + `i2 + `i3 + · · · + `i6 = 0 ◦ deg(b) = { 2 if `i1 + `i2 + `i3 + · · · + `i6 6= 0 1 if `i1 + `i2 + `i3 + · · · + `i6 = 0 • . . . • last step: the attacker has to solve the following system of quadratic equations in order to find the secret permutation:  `15 = π(0) ; 1 st step `1 + `4 + `16 = `3 + `14 + `16 = · · · = 0 2nd step `1 + `2 + `10 + `16 = `2 + `3 + `13 + `16 = · · · = 0 3rd step `1 + `2 + `3 + `12 + `16 = `3 + `4 + `8 + `12 + `16 = · · · = 0 4th step `1 + `2 + `3 + `4 + `6 + `16 = · · · = 0 5th step . . . solving the system will allow to fully determine the secret permutation π(l) = l′ = {α,α2,α3,α4, . . . , 0, 1}. 2 toy example for the elp evaluation attack consider f23 [x] = f2[x] x3+x+1 . g and the support l = {0, 1,α,α2,α3,α4,α5,α6} are public, m ∈ fk2 and o is the decoding oracle. we notice that if l is public one can find g(x) such that l = f2[x] g(x) . the other way is equaly true: if g(x) is public then one can easily discover l. suppose that the secret permutation is: π(l) = l′ = {α,α3, 1,α4,α5, 0,α2,α6} = {`i |i ∈{1, . . . , 8}} • 1st step: • the attacker asks o to decode all the z = mg⊕e with wt(e) = 2 (the positions (`j,`k) are the two non-zero positions of e). improved timing attacks against the secret permutation in the mceliece pkc 25 ? the faster step ¼ reveals the position of π(0): `6. • the attacker asks o to decode all the z = mg⊕e with wt(e) = 2 (the positions (`6,`k) are the two non-zero positions of e). ? the faster step ¼ reveals the position of π(1): `3. • 2nd step: • the attacker asks o to decode all the z = mg ⊕ e with wt(e) = 3 (the positions (`3,`j,`k) are the three non-zero positions of e). ? the faster step ¼ reveals all the couples (`j,`k) such that `3 + `j + `k = 0. here (`j,`k) ∈{(`1,`2), (`4,`5), (`7,`8)}. • 3rd step: • the attacker asks o to decode all the z = mg ⊕ e with wt(e) = 3 (the positions (`6,`j,`k) are the three non-zero positions of e). ? the faster step ¼ reveals all the couples (`j,`k) such that `j`k = 1. here (`j,`k) ∈ {(`1,`8), (`2,`4), (`5,`7)}. • 4th step: • the attacker asks o to decode all the z = mg ⊕ e with wt(e) = 3 (the positions (`i,`j,`k) are the three non-zero positions of e). ? the faster step ¼ reveals all the triplets (`i,`j,`k) such that `i + `j + `k = 0. here (`i,`j,`k) ∈{(`1,`4,`7), (`1,`5,`8), (`2,`4,`8), (`2,`5,`7)}. • the attacker has to solve the following system of quadratic equations in order to find the secret permutation:  `6 = π(0) ; `3 = π(1) 1 st step `1 + `2 = `4 + `5 = `7 + `8 = 1 2 nd step `1`8 = `2`4 = `5`7 = 1 3 rd step `1 + `4 + `7 = `1 + `5 + `8 = 0 4 th step `2 + `4 + `8 = `2 + `5 + `7 = 0 4 th step solving the system will allow to fully determine the secret permutation π(l) = {α,α3, 1,α4,α5, 0,α2,α6}. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 701-712 application plugins for distributed simulations on the grid i.l. muntean, a.i. badiu ioan lucian muntean technical university of cluj-napoca department of computer science romania, 400027 cluj-napoca, 28 g. baritiu e-mail: ioan.lucian.muntean@cs.utcluj.ro alexandra ioana badiu technical university of cluj-napoca data communication center “pusztai kalman” romania, 400027 cluj-napoca, 28 g. baritiu e-mail: badiu@net.utcluj.ro abstract: computing grids are today still underexploited by scientific computing communities. the main reasons for this are, on the one hand, the complexity and variety of tools and services existent in the grid middleware ecosystem, and, on the other hand, the complexity of the development of applications capable to exploit the grids. we address in this work the challenge of developing grid applications that keep pace with the rapid evolution of grid middleware. for that, we propose an approach based on plugins for grid applications that encapsulate a set of commonly used type of grid operations. we further propose more complex high-level functionalities, such as the plugins for remote exploration of simulation scenarios and for monitoring of the behavior of end-user applications in grids. we provide an example of a grid application constructed with these software components and evaluate based on it the performance of our approach in the context of the simulation of biological neurons. the results obtained on test and production grids demonstrate the usefulness of the proposed plugins, with a small performance overhead compared to traditional grid tools. keywords: grid computing, application plugins, simulation services, gridsfea. 1 introduction simulation is acknowledged today as the third pillar of modern sciences, in addition to theory and experiments. computational sciences are driven by simulation and provide the widest majority of applications for high-performance computing resources available in grid environments. today, user communities of grid computing, having scientific computing background, are bounded to a large extent to a middleware traditionally employed within the respective community. such examples are the communities formed around the projects: teragrid, supported by the globus toolkit [12], enabling grids for e-science (egee/egee2) supported by the g-lite middleware [8], or nordugrid, where the advanced resource connector [3] has emerged from. a special place in this sense is taken by the user community formed around the project distributed european infrastructure for supercomputing applications (deisa/deisa-2). in deisa, the user has typically the choice between the globus toolkit and unicore [5]. the co-existence of these middleware imposes big challenges to the end-users of grids. basically, these middleware are rather incompatible in many aspects (security, job and resource copyright c⃝ 2006-2011 by ccc publications 702 i.l. muntean, a.i. badiu management, etc). therefore, users have to accommodate with different tools, languages, and operating procedures. this makes the adoption of a middleware other than the one used within his community a challenging task for the computational scientist. a great effort, supervised by the group grid interoperability now of the open grid forum (ogf-gin) [20] is currently invested by the international grid community in the interoperability of these various middleware. such examples in europe are international consortia such as the european grid infrastructure (egi) [11] or the initiative for globus in europe (ige) [12] aim at providing grid middleware components that are capable of operating in an unified grid middleware (umd [9]). in this context, the development of grid applications that keep pace with the continuous evolution of the different middleware still remains a big challenge that complements the approach to the integration of the middleware. in this paper, we address this latter challenge: the development of grid applications. for that, we employ the grid simulation framework for engineering applications (gridsfea) [15], a software package for the gridification of numerical simulation programs. our approach is to offer out-of-the-box high-level functionalities, packed as plugins, commonly needed in such applications. the plugin mechanism available with the eclipse platform has been successfully used in our work. gridsfea plugins cover for example job submission, job monitoring, file transfer, simulation explorer etc. interactions with different middleware are possible due to low-level plugins that pack client-side functionality of these middleware. the major benefit of our work is that new applications requiring grid interactions or extensions to existing software packages can rapidly be developed without any grid-knowledge, by solely incorporating these plugins. furthermore, end-users get a unified way of working with the grid, regardless the underlying middleware in place. in [16] we proposed a first version of the plugin-based architecture of client-side tools of gridsfea. the evaluated prototype included only one high-level plugin, the parameter generator functionality. in the current work, we further refine the proposed architecture with additional lowand high-level plugins. the evaluation is carried out on development and production grids. the remaining of this paper is organized as follows: next section outlines the related work. in sec. 3, the gridsfea framework is introduced. eclipse plugins are briefly introduced in sec.4. section 5 presents the plugins for grid applications available with gridsfea. new highlevel plugins are discussed in sec. 6. the results of this work in the context of a scenario from computational neuroscience are the topic of sec. 7, whereas sec. 8 outlines our conclusions. 2 related work for the development of grid applications, middleware specific client-side libraries and toolkits are available in the grid community. such examples are cog kit [23] for globus toolkit [2], glue [4] and cream [1] for g-lite. the use of these libraries requires a thorough understanding of many technical and architectural details of each middleware stack. a great simplification in this sense is brought by the grid application toolkit (gat) [2] and its successor simple api for grid application (saga) [14]. they provide a common programming interface to different grid operations, hiding the implementation details of each middleware. the grid know-how required for the application developer is rather low. thus, for low-level grid operations, gat and saga are a very good option. nevertheless, they are not meant to provide high-level functionalities. low level plugins of gridsfea are based on community libraries such as gat or cog kit. in addition to these, the framework comes with ready-to-use out-of-the-box application components providing high-level functionalities. thus, both the application developer and the end-user of the grid need almost no knowledge about the grid. more evolved tools for the development of grid applications are the grid frameworks such as g-eclipse [14], gridbus [22] , ws-gaf [19] or gfac [13]. these programs come with their application plugins for distributed simulations on the grid 703 own strengths and weaknesses. gridbus has evolved more in the direction of grid resource broker and cloud, having a rich support for parameter sweep use cases. composition of scientific applications from web services is the main feature of ws-gaf and gfac, being more a serverside development. a tool that focuses on the end-users is g-eclipse. it allows operations on more than one middleware (e.g. g-lite, gria [21]), and, to some extent, the development of new grid applications. the enabling technology of g-eclipse is the plugin mechanism of the eclipse platform. gridsfea considers both server-side and client-side developments. in the approach proposed in this paper, the plugins of gridsfea are designed to be interoperable with the ones of g-eclipse and to complement them with high-level functionalities such as remote exploration of simulation scenarios. furthermore, the gridsfea framework comes with its own support for simulation migration on grid computing environments. the adoption of the grid standards is essential in the realization of grid developments. with this respect, we mention here the jsdl [18] specification of the open grid forum. the end-user requests for jobs and resources can be specified independently of the grid middleware on which the jobs will run. client tools become, to a large extent, responsible for translating the requests to the specifics of the target middleware. at the moment of writing, only a part of the jsdl specification is implemented by gridsfea. the jsdl support complements the existing full support to gt4 resource specification language available with gridsfea. 3 an overview of the gridsfea framework gridsfea is a framework that enables computational scientists and engineers to gridify numerical simulation programs and to run them comfortably on the grid. the framework handles simulation scenarios on the grid and assists the user with typical tasks such as formulation of a computing job, submission of the job to the grid, retrieval of resulted simulation data, investigation of simulation results remotely and locally. two major types of simulation jobs are especially supported by gridsfea on the grid: parameter sweep and long running jobs. the framework is modular and distributed, having components located on the end-user side (client-side applications), on grid nodes (simulation services), and on the grid systems where the user jobs run (the checkpoint migration tool). in fig. 1 is depicted the architecture of gridsfea. simulation scenarios are annotated by the framework; at the beginning of the job execution, computation metadata of the scenario is collected by the application wrappers and provided to the simulation services. the services organize the metadata and provide it to consumer applications. grid data exploration tools or the migration tool are examples of such applications. based on information recorded in the metadata, the consumers can interact with the simulation data located on the grid. figure 1: modules of gridsfea located in the user space (left) and in the grid environment (right) [15]. we briefly introduce below the role of each of the major elements of the framework. more 704 i.l. muntean, a.i. badiu details about gridsfea can be found in [15]. the simulation services are the core of gridsfea. their role is to store, organize, and provide scenario metadata to all modules of the framework. the client application library (cal) acts on the one hand as a client to the simulation services. on the other hand, it handles all the specific grid interactions typically required by client applications such as authentication in grid, file transfer etc. thus, this module acts as the glue that binds the framework components and integrates the third-party grid libraries. on top of cal client-side applications are built, such as shell or gui-based user tools for interacting with the simulations on the grid. such examples are portal applications, data browser programs, and command-line tool when shell interactions are required by the scientist. gathering the metadata of the scenarios and handling of simulation checkpoints (checkpoint transfer, simulation resume, and registration of new checkpoints) are the main responsibilities of the checkpoint migration tool. state-of-the-art grid libraries available in the scientific community such as cog kit, gat (globus toolkit), deshl (unicore), and cream (g-lite) are employed by the framework for the implementation of grid interactions. this way, our framework allows for client-side interoperability between grids based on these middleware. features such as the migration of simulation scenarios or the preview of results in grid make our framework very valuable to computational scientists and engineers and give it a uniqueness touch in the field of grid applications. 4 eclipse plugins in the field of software development, the eclipse platform is widely employed, on the one hand due to its rich set of supportive tools such as compilers, debuggers, editors, and, on the other hand, due to the large number of contributions from the software community that bring many additional features to the platform. the big success of the eclipse platform was possible mainly because of the powerful plugin mechanism it employs, which is a good example of stateof-the-art software engineering [5]. basically, the way that the eclipse ide can be extended by its contributors is specified in terms of extension points. the plugins contribute with appropriate extensions to these points, without that the source code of the development environment gets changed. at runtime, these plugins are located, identified, and loaded mostly only when their functionality is required. in [16] we have presented a detailed list of features that are important to our approach. we summarize below the most relevant ones: • by means of extension points and extensions, plugins interact, without being familiar with each others implementation. • the extension points define the public interface the plugin publishes and that can be implemented by further applications. • any number of extensions can be used for contributing with functionality to an application. • the software life-cycle of these plugins is defined in such a way, that a plugin can be installed, updated, debugged, removed independently of the rest of the platform/hosting application. a further advantage of the eclipse plugin model and mechanisms is that plugins can be easily grouped together, packed and deployed as extensions of the eclipse platform, of an existing plugin, or of a plugin-based application. the last two deployment targets are especially of interest in the context of our work on the gridsfea framework. application plugins for distributed simulations on the grid 705 5 gridsfea plugins for grid computing applications the mechanisms of gridsfea are applicable to a wide range of simulation scenarios and can also be used with relatively small customization effort by further grid applications not necessarily related to computational sciences and engineering. to foster the reuse of the know-how we gathered with gridsfea, we proposed in [16] a new plugin-based organization of the client-side modules of our framework. as enabling technology have been used the eclipse plugins. the plugin mechanism allows for modular organization of software, for clean and rapid integration of new functionalities, for the extension of existing implementations, for the reuse of framework’s features by other applications, all these without altering the existing source code of the software. the plugins available so far in our framework provide functionalities such as: • low-level interactions with the grid (such as authentication, job submission, file transfer); • high-level end-user operations (such as parameter sweep, exploration of results). figure 2: gridsfea plugins for the development of grid applications [16] figure 2 depicts the components of gridsfea realized with plugins, the arrows representing extension point-extension relationship between components. several client-side functionalities of gridsfea such as job submission, job monitoring and exploration of results have been refactored as plugins. in this paper, we further extend this architecture with two groups of plugins: first, we have designed plugins for checking the behavior of applications and services deployed on various grids. secondly, we provide a plugin for the remote exploration of simulation results located on the grid. these new components of our framework allow the user to tackle various cases studies, such as benchmarking a grid site from an application perspective or deciding on the early stage of a long simulation weather to continue or not the respective grid computation. based on the proposed plugins, an application with basic grid capabilities can be rapidly created. in fig. 3 we show the architecture of such an application. gridsfea plugins can make contributions to all layers of classical three-layer software architectures, as long as the integration of the grid components is done using the plugin mechanism discussed in the previous section. this way, the developer can focus entirely on the functionality required by the application domain. in the upcoming sections, we give two usage scenarios for the plugins of gridsfea: the first one is the client-side monitoring of applications and services deployed on grid sites of potential interest for a given end-user and the second one is the exploration of simulation results located on grid. 6 novel high-level plugins this section introduces two novel high-level plugins of gridsfea. both are focused on the end-user, the computational scientist: with the first plugin, the user can formulate job requests 706 i.l. muntean, a.i. badiu figure 3: enhancing a custom application (right) with grid computing capabilities by means of gridsfea plugins (left): formulation of computation tasks, their submission to gt4-based grids, investigation and download of computation results (gray boxes) trimmed to the needs of its favorite simulation applications and submit them to different grid sites. the simulation results are accessed with the second plugin, the simulation explorer, without that the user knows the actual location of the data in grid. 6.1 plugins for monitoring grid applications the goal of these plugins is to provide a simple yet effective mean to check to what extent jobs involving specific applications can run properly one or more grid sites. such functionality is very useful to users that have access to several grid sites and have one or more applications to run on those sites. a set of pre-defined grid jobs is made available to the application hosting the monitoring plugins. these jobs exercise the targeted application, can use grid services such as data transfer, job submission, or information services, and are submitted to the known grid sites. the plugin collects the execution results for each test job and updates the status of the respective application and services of the grid sites. the computational scientist, as an end-user of these plugins, can create test jobs by providing job scripts in the jsdl format. the plugins implement a subset of the jsdl specification that allows for most common operations with jobs on grids. in addition to that, the framework accepts also native ws-gram job scripts. by offering support for the jsdl specification of the open grid forum, the framework is not bound a single grid middleware or user community. furthermore, in order to employ these plugins, the end-users and client application of different grid middleware only need to be able to phrase jsdl requests. the incoming jsld requests are transformed into middleware specific job requests by means of adapter plugins, as depicted in fig. 4. the submission of the adapted jobs to the targeted grid sites is carried out by middleware-specific libraries (e.g. cream for glite, cog kit for globus toolkit 4), that become available to the framework in the form of contributing plugins. the adaptor plugins contribute to the template job plugin by providing implementations of the extension point of the latter one. community libraries such as gat, cog kit are packed as plugins that export high level packages. our approach is lightweight and flexible for several reasons. first, the contributor plugin corresponding to a grid middleware, such as the gram 4, encapsulates properly the client-side specifics of that middleware, here globus toolkit 4. the sole interaction between the plugins is done by means of the defined extension points. second, the monitoring plugin may be used for one or more grid sites without any changes. furthermore, the customized test jobs can be used to benchmark the grid sites from an application perspective. the end-user of the grid can write application plugins for distributed simulations on the grid 707 figure 4: jsdl incoming job requests are adapted on-the-fly to middleware-specific outgoing job requests by means of plugins. test jobs that reflect the way she uses a given application for her scenarios, the response of the monitoring plugins being thus trimmed to the user needs. by providing access to different types of grid middleware, our plugin bundle provides clientside interoperability of grids. in a seamless way, files can be transferred between heterogeneous sites (such as from gt4 to unicore) and jobs can be submitted to grid sites running any of the supported grid middleware. the beneficiaries of our approach are, on the one hand, the endusers with access to multiple grid sites (for operations such as checking the existence of installed programs, running application trimmed test jobs, benchmarking sites from an application perspective). on the other hand, these plugins can be easily integrated in any eclipse-based plugin or rcp (rich client platform) application with the purpose of interacting with grid installations (such as monitoring tools). 6.2 plugin-based exploration of simulation scenarios a second example of usage scenario of the gridsfea plugins contributes to the interaction of end-users with remote data located in grids. for that, a plugin for data exploration was designed based on components of gridsfea. all data belonging to a simulation run i.e. input files, input parameters, result files, post processing results, and checkpoint files are grouped logically in a simulation scenario. scenarios can be downloaded to the end-user space, transferred between grid sites, or used to reset/continue a simulation at the same location or at a different one. the access to the simulation scenarios is made in a way transparent to the end user. he needs not to know the exact location of the remote data nor the way (protocol and service) to access it. with our plugin running on the user system, metadata of scenarios is retrieved from simulation services. the plugin lists all instances of a scenario, instances created during its migration for several times. all the movements and changes of a scenario are recorded with the simulation services and can be displayed by tools such as the simulation explorer plugin (eclipse-based plugin), the gridsfea browser (standalone desktop application), or a thin client for mobile devices. a common feature of these tools is that the scenario data can be explored on the client side in a preview-like mode. excerpts from scenario files can be displayed in the end-user application, as well as results of the post processing steps (images generated by visit, matlab, gnu plot etc.). thus, the preview of scenarios enables the user to comfortably investigate/explore the results of the simulation without downloading the files to the end-user system. this feature gives the user the needed instrument to assist her in deciding if the results worth further investigations 708 i.l. muntean, a.i. badiu or the simulation needs to be recalculated, even with changes in the input configuration of the simulation. complete result files can also be downloaded to the end-user’s machine and further explored locally with scientific visualization tools, e.g. 7 results the results discussed in this section have been obtained on three grid sites: one operated with gt4 (labacal.utcluj.ro, 48 cores), one with gt5 (acalgrid01t.utcluj.ro, 48 cores), and one again with gt4 (a01.hlrb2.lrz-muenchen.de, +8000 cores) as well. the first two sites are for the development and testing of grid applications, the third one is a production site from the deisa infrastructure. the aim of the experiments was to show how the plugins introduced in sec. 6 were employed to build a custom application. this evaluation was done with respect to the requirements of a simulator based on the neocortex program [17] for investigating the dynamic properties of microcircuits of spiking neurons. 7.1 scenario: simulation of neural microcircuits understanding the behavior of biological neurons, in particular of the human one, is a challenging task for the entire neuroscience community. this is an interdisciplinary area, with major contributions from biology, physics, chemistry, psychology. since the experimental and observational means are in this particular area rather limited, the simulation becomes a very valuable instrument for gathering knowledge, along with theory and experiments. neurosim is a spiking neural simulator based on the neocortex tool, at the moment of writing still under development, having the goal to compute on hpc systems realistic models of neural microcircuits based on biological neuron models. the general aim of the investigations where neurosim will be used is the evaluation of the impact the different parallelization strategies on the dynamics of neural microcircuits. in-depth descriptions of this simulation program and of the mathematical models it employs in simulations are beyond the scope of this paper. nevertheless, we outline a few typical computational requirements of neurosim: batch mode (headless operation), support for input/output files and program arguments; post-processing operations with gnuplot; parameterbased investigations; parallel runs using hybrid parallelization techniques (mpi combined with openmp). the list above comprises requirements common to typical applications running on hpc systems. the execution of such applications on computing grids with hpc resources is widely supported by gridsfea. 7.2 custom application based on high-level plugins: gridsfea trotter from the pool of plugins available with gridsfea we have built a rich client application named here gridsfea trotter, having the kern functionalities: incoming requests for simulation jobs are formulated in jsdl, these requests are transformed into gram4 or gram5 specific job descriptions, jobs are submitted to any grid site running the middleware gt4 or gt5, results of the jobs are monitored and measured, computation results are presented to users in terms of simulation scenarios, results and post-processing files are pre-viewed remotely and further processed locally. the application comes with a set of pre-defined job templates that can be used to benchmark an application/service hosted by a grid site. these templates have been customized to the needs of neurosim and have been used for describing simulations of neural microcircuits. the template jobs for neurosim address different ways of working with this program such as performing sequential and parallel runs, staging in input files (network descriptions, initial values etc), staging out results, trigger post-processing operations. gridsfea trotter comprises the application plugins for distributed simulations on the grid 709 high-level plugins for benchmarking grid applications and for remote exploration of simulation scenarios, and a suite of low-level plugins, such as the template job plugin, gram 4 and gram 5 adaptors, file transfer, plugins packing community libraries (cog kit, gt4 client, and gt5 client). by means of extension points, further adaptors can be loaded automatically at runtime by the trotter. 7.3 results and measurements the goal of our experiments was to quantify the overhead introduced by our tool in comparison to the local execution of the simulation programs and to corresponding tools from the grid middleware. the job scripts perform the operations: submission of jobs without file staging, submission of jobs with file staging (in and out), and submission of jobs that require a checkpoint file handled by gridsfea. the computation tasks are described in the jsdl format. the implementations of the jsdl specification and of its posix extension are partial. the suite of templates was executed on different grid sites using both our plugin-based application and the client tools of globus toolkit. in table 1 is displayed the execution time of job scripts for different linux commands contained by the templates. these commands have very small execution times, of the order of mili-seconds. table 1: execution time for the jsdl job templates with gridsfea trotter. the size of the input and of the output file is approximately the same for one measurement. template labacal[s] acalgrid01t[s] hlrb2[s] /bin/ls, /bin/date, /bin/hostname 3.8 3.7 3.9 neurosim (no processing) 6.7 6.9 7.1 batch job 2.5 2.6 2.8 considering the small duration of the execution of the commands employed in these tests, the results indicated in table 1 show actually the overhead introduced by the grid middleware deployed on any of the three sites. this overhead has the order of seconds and clearly dominates the execution time of the grid job. each site has a slightly different response time, depending on its setup. when comparing time results obtained on any of these sites, one should fairly consider the response time characteristic to the respective site. the job scripts for executing neurosim require file staging operations. table 2 shows the results related to such file operations (upload of input and checkpoint files, download of result, standard output/error, and checkpoint files). these experiments have been conducted with the gridsfea trotter application on the grid site labacal. the file dimensions employed here are typical for different scenarios of neurosim, corresponding to neural microcircuits with different physical connections (ranging from o(1) to o (104) synapses for one neuron). the execution time of the simulation program has been subtracted from these time results to allow a fair comparison of the scenarios. the time results listed in the second column of table 2 correspond to neurosim jobs where input and output files have been staged in and staged out, resp. the third column contains execution time of simulations that need to be restarted from a checkpoint and include the transfer time of that file. these values represent the total transfer time, including the contribution of gridsfea trotter and of the grid middleware. results in the last column represent the time needed for the trotter to find the actual location of a checkpoint file, information provided by the simulation services of gridsfea. this information is extracted from the metadata about the scenario collected by the framework. the localization time must be added to the values in the third column to get the entire duration of such a job from the user perspective. the time needed 710 i.l. muntean, a.i. badiu table 2: time measurement for executing neurosim jobs with gridsfea trotter. the size of the input, output and checkpoint file is approximately the same for one measurement. scenario size[mb] stage in/out time [s] checkpoint time[s] localization time[s] 0.01 6.9 5.5 9.92 0.10 5.9 5.4 9.89 1.0 5.3 5.4 9.90 10 10.2 14.1 8.72 100 53.5 98.1 9.93 to get the localization of the checkpoint does not depend on the size of the scenario (given in the first column) and is a characteristic of the framework. the job files in the gram 4 format corresponding to the jsdl jobs used for these measurements have been submitted to the grid with the client tool of the globus toolkit middleware (see table 3). table 3: time measurement for the execution of neurosim jobs with client tools of gt. the size of the input, output and checkpoint file is approximately the same for one measurement. scenario size[mb] stage in/out job time [s] checkpoint job time[s] 0.01 4.4 5.5 0.10 3.9 4.6 1.0 4.2 4.8 10 6.1 9.2 100 31.3 63.8 the localization of the checkpoint has been carried out by the user, as well as the altering of the job file. thus, these have not been considered in the measurements. we can see that the duration of the jobs is shorter than the execution of the same scenarios with gridsfea trotter. this is mainly due to the parallel file transfers implemented in the client tools of the globus toolkit. this feature is currently missing from the cog kit library that is employed by our application. nevertheless, the advantages of our approach are threefold: automatic localization of checkpoint files, automatic generation of the job script that relied on checkpoints, and the use of the middleware independent job description language jsdl. the evaluation of the simulation explorer plugin, hosted by gridsfea trotter, was carried out against the tools of the gt middleware. the results were similar to the ones above. our conclusion reads that for small data size, below 10 mb, the overhead of our approach was small. for larger files, the download with the plugin took longer. the preview data is typically much smaller than the raw data produced by the simulation. thus, it is expected that in real-word usage, gridsfea trotter will have almost the same response time as the middleware specific tools. an advantage of our approach is the integration of the simulation explorer with the rest of the plugins being capable, thus, to automatically obtain form the simulation services localization information about the scenario data, regardless where the jobs have been computed. therefore, the investigation of the scenarios was carried out with the trotter in a seamless way. the performance of this approach was to some extent better than in [15], without plugins. nevertheless, the localization time was higher here due to generally longer responses from the grid servers. the experiments conducted here showed that the plugins we provide are useful to users from the scientific computing area. we have created a sample application, gridsfea application plugins for distributed simulations on the grid 711 trotter, which demonstrates the way our plugins are integrated and are employed for highlevel operations on the grid. a strong limitation of our approach occurs when large files are involved in the operations of the end-user, the time overhead introduced by our approach being significantly high. this could be reduced by exploiting parallel file transfers, for example. a further drawback is that applications need to support the eclipse plugin mechanism in order to host our plugins. nevertheless, our plugin-based approach brings out-of-the-box high-level functionalities very useful to the development of grid applications. 8 conclusions and lessons learnt we have presented in our work on a plugin-based approach for the development of grid applications. with our approach, the realization of both high-level (focused on the grid user) and low level (oriented towards the grid) operations is possible, leading to out-of-the-box functionalities reusable in further applications. this was demonstrated here with gridsfea trotter. comfortable for the grid user, the operations on grid introduce an overhead in some cases, when large data files are involved. in other cases, the performance of our approach is similar to the one of the native grid client tools, such as the ones of the globus toolkit, but enriches their usage within more complex scenarios such as the exploration of simulation data located on grid. specifics of the grid middleware are packed as contributing plugins, thus, making the adoption of a further middleware a straightforward task. moreover, the use of jsdl for formulating job requests opens the use of applications based on our plugins to scientific communities relying of different grid middleware than the globus toolkit. in the global context of the unification of the grid middleware, our work makes a step forward from the grid application development perspective. acknowledgements this work was partly supported by national council of scientific research in high education within the pnii human resources program, project number 10/2009, and by the posdru program, financing contract posdru/89/1.5/s/62557. bibliography [1] c. aiftimiei, p. andreetto, s. bertocco s. dalla fina, a. dorigo, e. frizziero a. gianelle, m. marzolla, m. mazzucato, m. sgaravatto, s. traldi, and l. zangrando. design and 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[22] s. venugopal, r. buyya, and l. winton. a grid service broker for scheduling distributed data-oriented applications on global grids. in mgc ’04: proceedings of the 2nd workshop on middleware for grid computing, pages 75–80, new york, ny, usa, 2004. acm. [23] g. von laszewski and m. hategan. workflow concepts of the java cog kit. j. grid computing, 3(3-4):239–258, 2005. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 72-80 evaluation measures for partitioning based aspect mining techniques g. czibula, g. s. cojocar, i. g. czibula gabriela czibula, grigoreta sofia cojocar, istvan gergely czibula babeş-bolyai university 1, m. kogălniceanu street, 400084, cluj-napoca, romania e-mail: {gabis, grigo, istvanc}@cs.ubbcluj.ro abstract: aspect mining is a research direction that tries to identify crosscutting concerns in already developed software systems, without using aspect oriented programming. the goal is to identify them and then to refactor them to aspects, to achieve a system that can be easily understood, maintained and modified. in this paper we propose two new evaluation measures for evaluating the results of partitioning based aspect mining techniques. a small example on how to compute them is provided. the applicability of these measures to different aspect mining techniques is also discussed. keywords: partitioning, aspect mining, crosscutting concern, evaluation. 1 introduction nowadays, software systems have become more and more complex and large. a software system is usually composed of many core concerns and (some) crosscutting concerns (like logging, exception handling). if core concerns can be cleanly separated and implemented using existing programming paradigms, this is not true for crosscutting concerns, as a crosscutting concern has a more system-wide behaviour that cuts across many of the core concerns implementation modules. the aspect oriented paradigm is one of the approaches proposed, so far, for the design and implementation of crosscutting concerns. aspect oriented techniques allow crosscutting concerns to be implemented in a new kind of module called aspect, by introducing new language constructs like pointcuts and advices [13]. kiczales et al. introduce for the first time aspect oriented programming (aop) in [11]. since 1997 the aspect oriented paradigm has been slowly adopted by the industry, too, leading to the appearance of new research problems like software reverse engineering, reengineering, and refactoring to use the aspect-oriented paradigm in order to benefit from the advantages it brings. aspect mining is a research direction that tries to identify crosscutting concerns in already developed software systems, without using aop. the goal is to identify them and then to refactor them to aspects, to achieve a system that can be easily understood, maintained and modified. the task of crosscutting concerns identification cannot be successfully done using only a manual approach, as it is a difficult and error-prone process due to the complexity of software systems, their size, the lack of documentation, etc. as a consequence researchers have focused on developing tools and techniques that help developers to identify the crosscutting concerns in already developed software systems. the tools and techniques proposed, so far, try to discover the symptoms that an inadequate solution for a crosscutting concern implementation has over a software system: duplicated code, scattering of concerns throughout the entire system and tangling of concern-specific code with that of other concerns. although aspect mining is a relatively new research domain, many aspect mining techniques have been proposed. some use metrics [14], some use formal concept analysis [2, 24, 25], or execution relations [1]. there are also a few approaches that use clone detection techniques copyright c⃝ 2006-2011 by ccc publications evaluation measures for partitioning based aspect mining techniques 73 [3, 22] or natural language processing [18]. a few techniques use clustering in order to identify crosscutting concerns [8, 15, 20, 23]. there are very few comparisons made between the aspect mining techniques proposed so far [4, 10, 16, 17], and even less comparisons based on the obtained results [4, 17]. one important cause is the lack of measures for evaluating the results obtained and the quality of the results (i.e. how well did the technique manage in separating crosscutting concerns from non-crosscutting concerns, and in separating one crosscutting concern from other crosscutting concerns). the main contribution of this paper is to propose two new evaluation measures for comparing partitioning based aspect mining techniques. the paper is structured as follows. the context in which the measures are defined is introduced in section 2. the new evaluation measures are defined in section 3. a small example on how two compute the measure is given in section 4. the applicability of the newly introduced measures is studied in section 5. some conclusions and further work are given in section 6. 2 formal model in [5] cojocar and şerban have proposed a formal model for partitioning based aspect mining. the measures that we proposed in this paper are based on their model. in the following we briefly introduce this model. a software system s is viewed as a set of elements from the system: s = {s1, s2, . . . , sn}, where si, 1 ≤ i ≤ n. an element can be a statement, a method, a class, a module, etc. the number of elements of the system is denoted by n (n = |s|). a crosscutting concern is considered as a set of elements c ⊂ s, c = {c1, c2, ..., cm}, elements that implement this concern. ccc denotes the set of all crosscutting concerns that exist in the system s, ccc = {c1, c2, ..., cq}, and q denotes the number of crosscutting concerns in the system s, q = |ccc|. it is considered that two different crosscutting concerns do not have elements in common, meaning that ci ∩cj = ∅,∀i, j, 1 ≤ i, j ≤ q, i ̸= j. the problem of aspect mining is viewed as the problem of identifying a partition k of the software system s, such that ccc ⊂k. definition 1. optimal partition of a system s. being given a partition k = {k1, k2, ..., kp} of the system s, k is called an optimal partition of the system s with respect to the set ccc = {c1, c2, ..., cq} of all crosscutting concerns, iff: (1) p ≥ q; (2) ∀c ∈ ccc, ∃kc ∈k such that c = kc. from the aspect mining point of view, k is an optimal partition of the system s if and only if the components of each crosscutting concern c ∈ ccc are in the cluster kc and kc contains only the elements of c. 3 evaluation measures in this subsection we propose two measures for evaluating a partition of a software system from the aspect mining point of view. such a partition can be obtained using a partitioning algorithm, such as a clustering algorithm. in the following, let us consider a partition k = {k1, . . . , kp} of a software system s and ccc = {c1, c2, ..., cq} the set of all crosscutting concerns from s. we assume that each crosscutting concern consists of a set of elements, i.e., ci = {ci1, ci2, . . . , cimi}. definitions 2 and 6 introduce evaluation measures for a partition of a software system from the aspect mining point of view. 74 g. czibula, g. s. cojocar, i. g. czibula definition 2. cohesion of recovered crosscutting concerns core. let k be a partition of a software system identified by a partitioning based aspect mining technique. the cohesion of crosscutting concerns ccc recovered in partition k, denoted by core(ccc,k), is defined as: core(ccc,k) = 1 q q∑ i=1 core(ci,k). core(ci,k) is the cohesion of crosscutting concern ci in partition k and is defined as: core(ci,k) = ∑ k∈mci |ci ∩k| |ci ∪k| |mci| , where mci is defined as: mci = {k | k ∈k, ci ∩k ̸= ∅}. for a given crosscutting concern c ∈ ccc, core(c,k) defines the degree to which the components of c belong together. lemma 3. if k is a partition of the software system s and ccc is the set of crosscutting concerns in s, then the following inequality holds: 0 ≤ core(ccc,k) ≤ 1. for lack of space, we will not give the proof of lemma 3. remark 4. larger values for core indicate better partitions with respect to ccc, meaning that core has to be maximized. in the following we give a necessary and sufficient condition for a partition k to be an optimal partition, with respect to the set of crosscutting concerns from the software system s. lemma 5. if k = {k1, k2, . . . , kp} is a partition of the software system s, and ccc is the set of crosscutting concerns in s, then k is an optimal partition iff core(ccc,k) = 1. for lack of space, we will not give the proof of lemma 5. the next measure determines the percentage of elements that must be analyzed in order to discover all the crosscutting concerns from the system. usually, partitioning based aspect mining techniques return the clusters to be analyzed in a specific order. let σ be a permutation of the set {1, 2, . . . p}. σ denotes the order in which the clusters from a partition of the software system are analyzed: kσ(1) is the first analyzed cluster, kσ(2) is the second, etc. the permutation σ is particular to each partitioning based aspect mining technique. definition 6. complexity of crosscutting concerns discovery codi. let σ be a permutation of the set {1, 2, . . . p}. the complexity of crosscutting concerns ccc discovery in partition k, denoted by codi(ccc,k, σ), is defined as: codi(ccc,k, σ) = 1 q q∑ i=1 codi(ci,k, σ). codi(ci,k, σ) is the percentage of the elements that need to be analyzed in the partition k in order to discover the crosscutting concern ci, and it is defined as: codi(ci,k, σ) = 1mi mi∑ j=1 codi(cij,k, σ). codi(cij,k, σ) is the percentage of the elements that need to be analyzed in the partition k in order to discover the element cij of crosscutting concern ci, and it is defined as: codi(cij,k, σ) = 1n r∑ l=1 |kσ(l)|., where r ∈ {1, , 2, . . . , p} and it has the property that cij ∩kσ(r) ̸= ∅. evaluation measures for partitioning based aspect mining techniques 75 in our view codi(ccc,k, σ) gives the complexity of crosscutting concerns discovery, and defines the percentage of the number of elements that need to be analyzed in the partition in order to discover all the crosscutting concerns that are in the system s. we consider that a crosscutting concern was discovered when all the elements that implement it were analyzed. lemma 7. if k is a partition of the software system s, σ is a permutation of k and ccc is the set of crosscutting concerns in s, then the following inequality holds: 0 < codi(ccc,k, σ) ≤ 1. for lack of space, we will not give the proof of lemma 7. remark 8. smaller values for codi indicate shorter time for analysis, meaning that codi has to be minimized. based on the evaluation measures defined above, the comparison of the results obtained by different aspect mining techniques can be made from two different criteria: 1. partitioning. the degree to which each crosscutting concern is well placed in the partition (using measure core ). 2. ordering. how relevant is the order in which the clusters are analyzed (using measure codi ). in order to compare two partitions obtained by partitioning based aspect mining techniques, we introduce definition 9. the definition is based on the properties of the evaluation measures defined above and considers both criteria presented above. definition 9. if k1 and k2 are two partitions of the software system s, ccc is the set of crosscutting concerns in s, and σ1 and σ2 are permutations of k1 and k2 respectively, then k1 is better than k2 iff the following inequalities hold: core(ccc,k1) ≥ core(ccc,k2), codi(ccc,k1, σ1) ≤ codi(ccc,k2, σ2). for the above definition we can remark the following: remark 10. if at least one of the inequalities from definition 9 is not satisfied, we cannot decide which of the partitions k1 or k2 is better from the aspect mining point of view (considering simultaneously both criteria). remark 11. however, the importance of the above mentioned comparison criteria may depend on the user of the aspect mining technique. in our view, the most important criterion is partitioning (how well the crosscutting concerns are grouped) and the last one is ordering (how quickly the crosscutting concerns are discovered). 4 example in the following, a small example showing how to compute core and codi measures is presented. let s = {s1, s2, ..., s17} be a software system with 17 elements, and let c1 = {s2, s3, s17} and c2 = {s1, s4, s8} be the crosscutting concerns that exist in the system s (ccc = {c1, c2}). let k = {k1, k2, k3, k4, k5} be a partition of the software system s, where: k1 = {s2, s16}; k2 = {s3, s7, s8, s9, s17}; k3 = {s1, s4, s5, s12}; k4 = {s6, s10, s13}; k5 = {s11, s14, s15}. 76 g. czibula, g. s. cojocar, i. g. czibula core using definition 2, we have to compute core(c,k) for each c ∈ ccc. c1 first we have to determine the set mc1 . it consists of two elements: mc1 = {k1, k2}. the value of core(c1,k) = 12 ∑ k∈mc1 |c1 ∩k| |c1 ∪k| . we obtain that core(c1,k) = 12( |c1∩k1| |c1∪k1| + |c1∩k2| |c1∪k2| ) = 1 2 (1 4 + 2 6 ) = 7 24 . c2 the set mc2 also consists of two elements: mc2 = {k2, k3}. based on definition 2 core(c2,k) = 12 ∑ k∈mc2 |c2 ∩k| |c2 ∪k| . we obtain that core(c2,k) = 12( |c2∩k2| |c2∪k2| + |c2∩k3| |c2∪k3| ) = 1 2 (1 7 + 2 5 ) = 19 70 based on the definition 2, core(ccc,k) = 1 2 (core(c1,k) + core(c2,k)) = 12( 7 24 + 19 70 ) = 473 1680 . codi we consider the permutation σ in which the partition k is analyzed to be the identity permutation. using definition 6, we have to compute codi(c,k, σ) for each c ∈ ccc. c1 based on the definition, codi(c1,k, σ) = 13[codi(s2,k, σ)+codi(s3,k, σ)+codi(s17,k, σ)] = 1 3 [ |k1| 17 + |k1|+|k2| 17 + |k1|+|k2| 17 ] = 1 3 ( 2 17 + 2+5 17 + 2+5 17 ) = 16 51 c2 based on the definition, codi(c2,k, σ) = 13[codi(s1,k, σ)+codi(s4,k, σ)+codi(s8,k, σ)] = 1 3 [ |k1|+|k2|+|k3| 17 + |k1|+|k2|+|k3| 17 + |k1|+|k2| 17 ] = 1 3 (2+5+4 17 + 2+5+4 17 + 2+5 17 ) = 29 51 based on the definition, codi(ccc,k, σ) = 1 2 [codi(c1,k, σ)+codi(c2,k, σ)] = 12( 16 51 + 29 51 ) = 45 102 . for this example, an optimal partition is: k1 = {s6, s9, s10}; k2 = {s2, s3, s17}; k3 = {s11, s12, s14, s15}; k4 = {s5, s7, s13, s16}; k5 = {s1, s4, s8}. 5 applicability in this section we present some of the existing approaches in aspect mining. for each approach we analyze the applicability of the evaluation measures proposed in this paper. marin et al [14] have proposed an aspect mining technique that uses the fanin metric [9]. their idea is to search for crosscutting concerns among the methods that have the value of the fanin metric greater than a given threshold. the result obtained by this technique can be viewed as a partition of the software system to be mined. the partition contains two clusters: the first one contains the methods that have the fanin greater than the given threshold and the second contains the remaining methods. so, the evaluation measures core and codi can be applied for the result of this technique. a graph based approach in aspect mining is introduced in [19]. the basic idea of this technique is to determine methods that are similar. the approach is to construct a graph between the methods of the software system, to determine the connex components of this graph, called clusters, and then to identify crosscutting concerns in the obtained clusters. as the set of connex components determined by this technique represents a partition of the analyzed software evaluation measures for partitioning based aspect mining techniques 77 system, the evaluation measures core and codi can also be applied for the result of this technique. there are a few aspect mining techniques proposed in the literature that use clustering in order to identify crosscutting concerns [8, 20, 23]. he and bai [8] have proposed an aspect mining technique based on dynamic analysis. they obtain execution traces for each use case, but they apply clustering and association rules to discover aspect candidates. shepherd and pollock [23] have proposed an aspect mining tool based on clustering. they use hierarchical clustering to find methods that have common substrings in their names. the obtained clusters are then manually analyzed to discover crosscutting concerns. a clustering approach for identifying crosscutting concerns is proposed and a partitional clustering algorithm named kam is introduced in [20]. an evolutionary approach in aspect mining is introduced in [21] and two genetic clustering algorithms used to identify crosscutting concerns are proposed. the clustering approach proposed in [21] is based on the use of genetic algorithms [6]. as all the above presented clustering techniques provide a partition of the software system, the applicability of core and codi evaluation measures is assured. there are in the literature some aspect mining techniques, briefly presented in the following, that do not provide a partition of the entire analyzed software system, but a subset of it. core and codi measures cannot be applied for these techniques, as they require a partition of the entire system. however, the proposed measures can be extended in order to also consider these kind of situations. in the future we plan to tackle these particular cases. breu and krinke [1] have proposed an aspect mining technique based on dynamic analysis. the mined software system is run and program traces are generated. from program traces, recurring execution relations that satisfy some constraints are selected. among these recurring execution relations they search for aspect candidates. this approach is adapted to static analysis in [12]. in this approach the recurring execution relations are obtained from the control flow graph of the program. tonella and ceccato [24] have also proposed an aspect mining technique based on dynamic analysis. an instrumented version of the mined software system is run and execution traces for each use case are obtained. formal concept analysis [7] is applied on these execution traces and the concepts that satisfy some constraints are considered as aspect candidates. tourwé and mens [25] have proposed an aspect mining technique based on identifier analysis. the identifiers associated with a method or class are computed by splitting up its name based on where capitals appear in it. they apply formal concept analysis on the identifiers to group entities with the same identifiers. the groups that satisfy some constraints and that contain a number of elements larger than a given threshold are considered as aspect candidates. bruntink et al [3] have studied the effectiveness of clone detection techniques in aspect mining. they did not propose a new aspect mining technique, but they tried to evaluate how useful clone detection techniques are in aspect mining. shepherd et al [22] have proposed an aspect mining technique based on clone detection. they search for code duplication in the source code using the program dependency graph. the obtained results are further analyzed to discover crosscutting concerns. breu and zimmermann [2] have proposed an history based aspect mining technique. they mine cvs repositories for add-call transactions on which they apply formal concept analysis. concepts that satisfy some constraints are considered aspect candidates. sampaio et al [18] have proposed an aspect mining technique to discover aspect candidates early in the development lifecycle. they use natural language processing techniques on different documents (requirements, interviews, etc.) to discover words that are used in many sentences. 78 g. czibula, g. s. cojocar, i. g. czibula the words that have a high frequency and have the same meaning in all the sentences are considered aspect candidates. 6 conclusions and further work in this paper we have proposed two new measures (core and codi) for evaluating the results of partitioning based aspect mining techniques. we have also proved three important lemmas related to the proposed evaluation measures. a small example on how to compute these measures was provided. we have also discussed the applicability of these measures to different aspect mining techniques. further work may be done in the following directions: to adapt the measures to consider the case in which the crosscutting concerns have overlapping elements (the same element belongs to different crosscutting concerns); to evaluate some of the existing aspect mining techniques using the proposed measures; to extend the proposed evaluation measures in order to consider the situation in which the result obtained by an aspect mining technique is not a partition of the entire software system. acknowledgements this work was supported by cncsis -uefiscsu, project number pnii idei 2286/2008. bibliography [1] s. breu and j. krinke. aspect mining using event traces. in proceedings of international conference on automated software engineering (ase), pages 310–315, 2004. [2] s. breu and t. zimmermann. mining aspects from version history. in s. uchitel and s. easterbrook, editors, 21st ieee/acm international conference on automated software engineering (ase 2006). acm press, september 2006. [3] m. bruntink, a. van deursen, r. van engelen, and t. tourwé. on the use of clone detection for identifying crosscutting concern code. ieee transactions on software engineering, 31(10):804–818, 2005. [4] m. ceccato, m. marin, k. mens, l. moonen, p. tonella, and t. tourwé. a qualitative comparison of three aspect mining techniques. in iwpc ’05: proceedings of the 13th international workshop on program comprehension, pages 13–22. ieee computer society, 2005. [5] g. s. cojocar(moldovan) and g. serban. a formal model for partitioning based aspect mining. infocomp journal of computer science, brazil, 6(3):19–26, 2007. [6] c. cubillos, e. urra and n. rodríguez. application of genetic algorithms for the darptw problem. international journal of computers, communication and control, vol. iv, no. 2:127-136, 2009. [7] b. ganter and r. wille. formal concept analysis. springer-verlag, berlin, heidelberg, new york, 1996. [8] l. he and h. bai. aspect mining using clustering and association rule method. international journal of computer science and network security, 6(2):247–251, february 2006. evaluation measures for partitioning based aspect mining techniques 79 [9] b. henderson-sellers. object-oriented metrics: measures of complexity. prentice-hall, inc., upper saddle river, nj, usa, 1996. [10] a. kellens, k. mens, and p. tonella. a survey of automated code-level aspect mining techniques. transactions on aspect-oriented software development, special issue on software evolution, vi(lncs 4640):145–164, 2007. [11] g. kiczales, j. lamping, a. menhdhekar, c. maeda, c. lopes, j.-m. loingtier, and j. irwin. aspect-oriented programming. in proceedings european conference on object-oriented programming, volume lncs 1241, pages 220–242. springer-verlag, 1997. [12] j. krinke. mining control flow graphs for crosscutting concerns. in 13th working conference on reverse engineering: ieee international astrenet aspect analysis (aaa) workshop, pages 334–342, 2006. [13] r. laddad. aspectj in action: practical aspect-oriented programming. manning publications co., 2003. [14] m. marin, a. van, deursen, and l. moonen. identifying aspects using fan-in analysis. in proceedings of the 11th working conference on reverse engineering (wcre2004)., pages 132–141. ieee computer society, 2004. [15] g. s. moldovan and g. serban. aspect mining using a vector-space model based clustering approach. in proceedings of linking aspect technology and evolution (late) workshop, pages 36–40, bonn, germany, march, 20 2006. aosd’06. [16] b. nora, g. said, and a. fadila. a comparative classification of aspect mining approaches. journal of computer science, 2(4):322–325, 2006. [17] c. k. roy, m. g. uddin, b. roy, and t. r. dean. evaluating aspect mining techniques: a case study. in icpc ’07: proceedings of the 15th ieee international conference on program comprehension, pages 167–176, washington, dc, usa, 2007. ieee computer society. [18] a. sampaio, n. loughran, a. rashid, and p. rayson. mining aspects in requirements. in early aspects 2005: aspect-oriented requirements engineering and architecture design workshop (held with aosd 2005), chicago, illinois, usa, 2005. [19] g. serban and g. s. moldovan. a graph algorithm for identification of crosscutting concerns. studia universitatis babes-bolyai, informatica, li(2):53–60, 2006. [20] g. serban and g. s. moldovan. a new k-means based clustering algorithm in aspect mining. in proceedings of 8th international symposium on symbolic and numeric algorithms for scientific computing (synasc’06), pages 69–74, timisoara, romania, september, 26-29 2006. ieee computer society. [21] g. serban and g. s. moldovan. aspect mining using an evolutionary approach. wseas transactions on computers, 6(2):298–305, 2007. [22] d. shepherd, e. gibson, and l. pollock. design and evaluation of an automated aspect mining tool. in 2004 international conference on software engineering and practice, pages 601–607. ieee, june 2004. [23] d. shepherd and l. pollock. interfaces, aspects, and views. in proceedings of linking aspect technology and evolution workshop(late 2005), march 2005. 80 g. czibula, g. s. cojocar, i. g. czibula [24] p. tonella and m. ceccato. aspect mining through the formal concept analysis of execution traces. in proceedings of the ieee eleventh working conference on reverse engineering (wcre 2004), pages 112–121, november 2004. [25] t. tourwé and k. mens. mining aspectual views using formal concept analysis. in scam ’04: proceedings of the source code analysis and manipulation, fourth ieee international workshop on (scam’04), pages 97–106, washington, dc, usa, 2004. ieee computer society. int j comput commun, issn 1841-9836 8(1):146-152, february, 2013. solving method for linear fractional optimization problem with fuzzy coefficients in the objective function b. stanojević, m. stanojević bogdana stanojević mathematical institute of the serbian academy of sciences and arts 36 kneza mihaila, 11001 belgrade, serbia e-mail: bgdnpop@mi.sanu.ac.rs milan stanojević university of belgrade, faculty of organizational science 154 jove ilića, 11000 belgrade, serbia e-mail: milans@fon.bg.ac.rs abstract: the importance of linear fractional programming comes from the fact that many real life problems are based on the ratio of physical or economic values (for example cost/time, cost/volume, profit/cost or any other quantities that measure the efficiency of a system) expressed by linear functions. usually, the coefficients used in mathematical models are subject to errors of measurement or vary with market conditions. dealing with inaccuracy or uncertainty of the input data is made possible by means of the fuzzy set theory. our purpose is to introduce a method of solving a linear fractional programming problem with uncertain coefficients in the objective function. we have applied recent concepts of fuzzy solution based on α-cuts and pareto optimal solutions of a biobjective optimization problem. as far as solving methods are concerned, the linear fractional programming, as an extension of linear programming, is easy enough to be handled by means of linear programming but complicated enough to elude a simple analogy. we follow the construction of the fuzzy solution for the linear case introduced by dempe and ruziyeva (2012), avoid the inconvenience of the classic weighted sum method for determining pareto optimal solutions and generate the set of solutions for a linear fractional program with fuzzy coefficients in the objective function. keywords: fuzzy programming, fractional programming, multi-objective programming. 1 introduction in the present paper the fuzzy linear fractional optimization problem (with fuzzy coefficients in the objective function) is considered (folfp). the importance of linear fractional programming comes from the fact that many real life problems are based on the ratio of physical or economic values (for example cost/time, cost/volume, profit/cost or any other quantities that measure the efficiency of a system) expressed by linear functions. usually, the coefficients used in mathematical models are subject to errors of measurement or vary with market conditions. dealing with inaccuracy or uncertainty of the input data is made possible by means of the fuzzy set theory. in [6] a basic introduction to the main models and methods in fuzzy linear programming is presented and, as a whole, linear programming problems with fuzzy costs, fuzzy constraints and fuzzy coefficients in the constraint matrix are analyzed. [9] presents a brief survey of the existing works on comparing and ranking any two interval numbers on the real line and then, on the basis of this, gives two approaches to compare any two copyright c⃝ 2006-2013 by ccc publications solving method for linear fractional optimization problem with fuzzy coefficients in the objective function 147 interval numbers. [1] generalizes concepts of the solution of the linear programming problem with interval coefficients in the objective function based on preference relations between intervals and it unifies them into one general framework together with their corresponding solution methods. dempe and ruziyeva [2] solved the linear programming problem with triangular fuzzy coefficients in the objective function. they derived explicit formulas for the bounds of the intervals used for defining the membership function of the fuzzy solution of linear optimization and determined all efficient points of a bi-objective linear programming problem by means of weighted sum optimization. their approach is based on chanas and kuchta’s method [1] based on calculating the sum of lengths of certain intervals. the state of the art in the theory, methods and applications of fractional programming is presented in stancu-minasian’s book [8]. multiple-optimization problems are widely discussed in [4]. many mathematical models considers multiple criteria and a large variety of solution methods are introduced in recent literature (see for instance [3, 5]). [7] introduces a linear programming approach to test efficiency in multi-objective linear fractional programming problems. interpreting uncertain coefficients in folfp as fuzzy numbers, we derive formulas for the α-cut intervals that describe the fuzzy fractional objective function. operating with intervals, we construct a bi-objective parametric linear fractional programming problem (bolfp). we use the procedure introduced by lotfi, noora et al. to generate all efficient points of (bolfp). the membership value of a feasible solution is calculated as cardinality of the set of parameters for which the feasible solution is efficient in (bolfp). in this way, the fuzzy optimal solution is obtained as a fuzzy subset of the feasible set of (folfp) and the decision-maker will have the opportunity to choose the most convenient crisp solution among those with the highest membership value. in section 2 we formulate the fuzzy optimization problem folfp, set up notation and terminology, construct equivalent bi-objective linear fractional programming problem bolfp, and describe a new way to generate efficient points for bolfp. a procedure to compute the membership function of each feasible solution is given in section 3. we give an example of folfp and its fuzzy solution obtained by applying the new solving method in section 4. conclusions and future works are inserted in section 5. 2 fuzzy linear fractional optimization problem the linear fractional programming problem with fuzzy coefficients in the objective function is max x∈x c̃t x + c̃0 d̃t x + d̃0 (1) where x = {x ∈ rn|ax ≤ b,x ≥ 0}, a is the m × n matrix of the linear constraints, b ∈ rm is the vector representing the right-hand-side of the constraints, x is the n−dimensional vector of decision variables, c̃, d̃ ∈ rn, c̃0, d̃0 ∈ r represent the fuzzy coefficients of the objective function. 2.1 equivalent problems we replace problem (1) by problem (2) that describes the maximization of the α-cut intervals of the objective function over the initial feasible set. max x∈x [ cl(α) t x + c0l(α) dr(α)t x + d 0 r(α) , cr(α) t x + c0r(α) dl(α)t x + d 0 l(α) ] (2) 148 b. stanojević, m. stanojević where cl(α) (cr(α)), dl(α) (dr(α)), c0l(α) (c 0 r(α)), d 0 l(α) (d 0 r(α)) represent the vectors of the left sides (right sides respectively) of the α-cut intervals of the fuzzy coefficients of the objective function. for a deeper discussion of α-cut intervals we refer the reader to [10] and [11]. according to chanas and kuchta [1], an interval [a,b] is smaller than an interval [c,d] if and only if a ≤ c and b ≤ d with at least one strong inequality. in this way, for each fixed α-cut, problem (2) is equivalent to the bi-objective linear fractional programming problem (3) max cl(α) t x + c0l(α) dr(α)t x + d 0 r(α) , max cr(α) t x + c0r(α) dl(α)t x + d 0 l(α) (3) subject to x ∈ x. 2.2 the special case of triangular fuzzy numbers let us consider now a subclass of fuzzy numbers – the continuous triangular fuzzy that are represented by a triple (cl,ct ,cr) [2]. in this case cl (α) = αct + (1 − α)cl and cr (α) = αct + (1 − α)cr. by simple analogy, similar formulas are derived for dl(α), dr(α), c0l(α), c0r(α), d 0 l(α) and d 0 r(α). using these formulas, problem (4) is constructed in order to be solved instead of problem (3). max (αct + (1 − α)cl) t x + αc0t + (1 − α)c 0 l (αdt + (1 − α)dr) t x + αd0t + (1 − α)d 0 r (4) max (αct + (1 − α)cr) t x + αc0t + (1 − α)c 0 r (αdt + (1 − α)dl) t x + αd0t + (1 − α)d 0 l subject to x ∈ x. so far, the construction is similar to the construction given in [2] for the linear case. the analogy cannot continue due to the fact that the weighted sum of linear fractional objectives is not linear fractional objective any more. that is why we formulate a new method for generating efficient points for problem (4) in next section. 2.3 the generation of efficient points solving a multiple-objective linear fractional problem by optimizing the weighted sum of the objective functions is not impossible but it is quite complicated. instead of that, we will construct the convex combination of the marginal solutions of problem (4), and use each point in the combination to generate an efficient point. by our method all points on the segment that connects the two marginal solutions are mapped to the set of all pareto optimal solutions of problem (4). the generation method is based on a linear procedure (proposed by lotfi, noora et al. [7]) that determine whether a feasible point is efficient for a linear fractional programming problem or not. theorem 1 reformulates the results introduced in [7] by applying them to the bi-objective linear fractional programming problem (3). theorem 1. (adapted from [7]) for arbitrarily fixed α ∈ [0,1], x∗ ∈ x is a weakly efficient solution in (3) if and only if the optimal value of problem (5) below is zero. solving method for linear fractional optimization problem with fuzzy coefficients in the objective function 149 max t s.t. 0 ≤ t ≤ d−1 + d + 1 , 0 ≤ t ≤ d−2 + d + 2 , cl(α) t x + c0l(α) − d + 1 = ( cl(α) t x∗ + c0l(α) ) θ1, dr(α) t x + d0r(α) + d − 1 = ( dr(α) t x∗ + d0r(α) ) θ1, cr(α) t x + c0r(α) − d + 2 = ( cr(α) t x∗ + c0r(α) ) θ2, dl(α) t x + d0l(α) + d − 2 = ( dl(α) t x∗ + d0l(α) ) θ2, x ∈ x, d+1 ,d − 1 ,θ1,d + 2 ,d − 2 ,θ2 ≥ 0. (5) we have obtained the following theoretical result that will be needed in section 3. proposition 2. for an arbitrary fixed α ∈ [0,1] and for any convex combination x∗ ∈ x of the marginal solutions of (3), the components of x in the optimal solution of (5) represent an weakly efficient point of (3). we give only the main ideas of the proof. we have to construct the cone that contains all points that dominate the given point on the segment that connects the two marginal solutions. one particular case is presented in figure 1 but the basic idea of the proof can be drawn from it. let us consider that the feasible set of problem (3) is the quadrilateral abcd and for a given value of α, rotational points of the two objective functions are e and f respectively. marginal solutions are b for the first objective and d for the second one. g is an arbitrary point on the segment bd. the value of the first objective function is constant on the line eg and can be improved by rotating eg anticlockwise toward eb. on eb the maximal value is reached. the value of the second objective function is constant on fg and can be improved by rotating fg clockwise toward fd. on fd the maximal value is reached. hence, all points contained in the cone egf dominate point g. by solving problem (5) with starting point g, the feasible points from the cone egf are analyzed and an efficient point from the cone is selected. a more complete theory may be obtained by deriving conditions under which theorem 1 can be used in generating all efficient points for a multiple-objective linear fractional programming problem. 3 solving method in this section we propose a procedure to compute the membership function of each feasible solution of problem (1). the procedure is essentially based on the new method of generation of efficient points for the bi-objective linear fractional programming problem (3). • for each α ∈ [0,1]: – initialize ψ (α) = ϕ. – find marginal solutions x1α and x 2 α in (3) and construct their convex combination xα (λ) = λx 1 α + (1 − λ)x2α, λ ∈ [0,1]. – for each λ ∈ [0,1], solve problem (5) with x∗ = xα (λ). due to theorem 1, an efficient point xeffαλ for problem (3) is obtained. ψ (α) = ψ (α) ∪ { x eff αλ } . • for each x ∈ x calculate its membership value µ(x) = card{α|x ∈ ψ (α)}. 150 b. stanojević, m. stanojević practically, ψ (α) represents the set of pareto optimal solutions of (3) for corresponding parameter α. each feasible solution of (1), for which at least one α ∈ [0,1] exists so that it is efficient in (3), has non-negative membership value in the fuzzy solution of (1). 4 example to complete the discussion we describe the procedure of finding fuzzy solution for one simple example. max −1̃x1 + 1̃0x2 + 4̃ 2̃x1 + 5̃x2 + 1̃ (6) subject to x1 ≥ 1, −x1 + 2x2 ≤ 1, 2x1 + x2 ≤ 8, 2x2 ≥ 1, x1,x2 ≥ 0. figure 1: feasible set of problem (6) and efficient points for problem (4) for α = 0.2 figure 1 describes the feasible set of the problem as the quadrilateral abcd. first time, fuzzy coefficients were treated as triangular fuzzy numbers with parameters (c − 1,c,c + 5) and ( c0 − 1,c0,c0 + 1 ) for nominator, (d − 1,d,d + 2) and ( d0 − 1,d0,d0 + 10 ) for denominator. two pairs of marginal solutions were found: x1α = (1,1), x 2 α = (1,0.5) for α ∈ [0,0.55], and x1α = x2α = (1,1) for α ∈ [0.56,1]. hence, µ((1,1)) = 1 and µ(x) = 0.55 for each x on the segment line [(1,1) ,(1,0.5)]. all other feasible solutions have membership value equal to 0 in the fuzzy optimal solution. see table 1. α x1α x 2 α ψ (α) x µ(x) [0,0.55] (1,1), (1,0.5) ab x ∈ [ab) 0.61 [0.56,1] (1,1) (1,1) b x = b 1 table 1: computational results for the first set of fuzzy numbers solving method for linear fractional optimization problem with fuzzy coefficients in the objective function 151 second time, fuzzy coefficients were treated as triangular fuzzy numbers with parameters (c − 1,c,c + 5) and ( c0 − 1,c0,c0 + 1 ) for nominator, (d − 2,d,d + 10) and ( d0 − 1,d0,d0 + 10 ) for denominator three pairs of marginal solutions were found: x1α = (1,1), x 2 α = (3.75,0.5) for α ∈ [0,0.23], x1α = (1,1), x2α = (1,0.5) for α ∈ [0.24,0.62], and x1α = x2α = (1,1) for α ∈ [0.63,1]. hence, µ((1,1)) = 1, µ(1,0.5) = 0.62, µ(x) = 0.23 for each x on the segment line [(3.75,0.5) ,(1,0.5)], and µ(x) = 0.62 for each x on the segment line [(1,1) ,(1,0.5)]. all other feasible solutions have membership value equal to 0 in the fuzzy optimal solution. for α = 0.2, the marginal solutions and the rotational points of the two objectives are presented in figure 1 by b and d and e and f respectively. the efficient set is, in this case, ab ∪ ad. see table 2. α x1α x 2 α ψ (α) x µ(x) [0,0.23] (1,1) (3.75,0.5) ab ∪ ad x ∈ (ad] 0.23 [0.24,0.62] (1,1), (1,0.5) ab x ∈ [ab) 0.61 [0.63,1] (1,1) (1,1) b x = b 1 table 2: computational results for the second set of fuzzy numbers 5 conclusions and future works in the present paper the fuzzy linear fractional programming problem with fuzzy coefficients in the objective functions is considered. the calculation of the membership function of the fuzzy solution is described. the initial problem is first transformed into an equivalent α-cut interval problem. further, the problem is transformed into a bi-objective parametric linear fractional programming problem. for each value of the parameter, the bi-objective problem is solved by generating its efficient points from any convex combination of its marginal solutions. the solving method works for any kind of fuzzy numbers but particular formulas were derived for the special case of triangular fuzzy numbers. the decision making process under uncertainty is widely studied nowadays. in our future works we will focus on identifying how the procedure of generation of efficient points for biobjective fractional programming problems can be applied in a more general case. also, we will study other kind of fuzzy optimization problems with fractional objectives. acknowledgements this 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[11] zimmermann h.-j., fuzzy set theory and its applications, kluwer academic publishers, 1996. international journal of computers communications & control issn 1841-9836, 12(1):131-147, february 2017. delay/disruption tolerant networking-based routing for rural internet connectivity (drinc) c. velásquez-villada, y. donoso carlos velásquez-villada*, yezid donoso systems and computing engineering department universidad de los andes bogotá d.c., colombia, south america *corresponding author: ce.velasquez917@uniandes.edu.co ydonoso@uniandes.edu.co abstract: rural networking connectivity is a very dynamic and attractive research field. nowadays big it companies and many governments are working to help connect all these rural, disconnected people to internet. this paper introduces a new routing algorithm that can bring non-real-time internet connectivity to rural users. this solution is based on previously tested ideas, especially on delay/disruption tolerant networking technologies, since they can be used to transmit messages to and from difficult to access sites. it introduces the rural connectivity problem and its context. then, it shows the proposed solution with its mathematical model used to describe the problem, its proposed heuristic, and its results. the advantage of our solution is that it is a low-cost technology that uses locally available infrastructure to reach even the most remote towns. the mathematical model describes the problem of transmitting messages from a rural, usually disconnected user, to an internet connected node, through a non-reliable network using estimated delivery probabilities varying through time. the forwarding algorithm uses local knowledge gathered from interactions with other nodes, and it learns which nodes are more likely to connect in the future, and which nodes are more likely to deliver the messages to the destination. our algorithm achieves an equal or better performance in delivery rate and delay than other well-known routing protocols for the rural scenarios tested. this paper adds more simulation results for the proposed rural scenarios, and it also extends the explanation of the mathematical model and the heuristic algorithm from the conference paper "delay/disruption tolerant networks based message forwarding algorithm for rural internet connectivity applications" [1] (doi: 10.1109/icccc.2016.7496732).a keywords: dtn routing, opportunistic forwarding, rural connectivity, rural internet, non-real-time communications. areprinted (partial) and extended, with permission based on license number 3958950667073 ©[2016] ieee, from "computers communications and control (icccc), 2016 6th international conference on". 1 introduction the united nations declared access to internet as a human right in 2011 [2]; however, around 60% of the world population [3], especially in rural and underdeveloped regions does not have any internet access. access to internet can improve life quality through access to more information, education and applications that will ease the life of citizens in remote areas [2]. giving access to internet to these communities is not an easy task. there are many technical and social challenges, such as a reliable supply of electricity, access to electronic devices, network coverage, education and training, that have to be overcome before a complete solution can be given. copyright © 2006-2017 by ccc publications 132 c. velásquez-villada, y. donoso internet connectivity around the world is estimated at 43.4% (individuals using the internet) for 2015 [3] and it is even lower for developing countries (35.3%) and population in rural areas (see figure 1). many benefits of internet connectivity for rural communities in areas such as education, health-care, agriculture, economics and politics, among others, have already been discussed in one of our previous papers [4]. there are also benefits based on the local infrastructure deployed, since users can create contents and share them with neighbors or other local users, local news can be reported on a local website, and people can share their music, documents, videos or libraries. in some communities, people have created local websites for their businesses and even local radio stations over the infrastructure that also allows them to connect to internet. these applications depend on a strong deployment of a local infrastructure and the presence of people willing to train and educate local users. internet connectivity, then, seems associated with more possibilities of bridging the social divide and reducing the social and economic gaps around the world. figure 1: individuals using the internet. a) developed countries, developing countries and the world. b) statistics by world region. information taken from [3] how to connect world’s rural population to internet is a current and important global issue. many big companies in information technologies, like google and facebook, are testing techniques and methods to help connect these currently disconnected rural users. governments are trying to cover more of their territories, through contracts to deploy the necessary infrastructure and pushing local telecommunications companies to cover more towns. networking equipment providers also expect an increase in coverage through more energy efficient equipment. google has recently launched an initiative to use stratospheric balloons to connect the whole world at low costs, and facebook is developing a model of free restricted internet access in partnership with governments and cellular carriers. these and other initiatives for rural connectivity, will be discussed in more detail in section 2. this paper proposes a routing and forwarding algorithm for a low cost technology that can be used over local user devices for non-real-time communications. this solution will allow people to ask for and retrieve information from internet, for non-real-time applications, using transportation vehicles to carry their requests and replies. figure 2 offers a schematic view of the possible scenario for this connectivity problem. the proposed solution will need an additional infrastructure that can be installed on buses or other mechanical transportation means to remote towns, and a device that can be installed at the main building in the town to serve as a request center. it will also need an application for people to install on their devices so they could be able to send requests to the request center or to the transportation device, and obtain their answers when the transportation returns to town; however, there will be no guarantees about delivery times or even deliveries at all. users will be connected in a non-real-time fashion to internet. this technology uses delay and disruption tolerant networks (dtn) to connect delay/disruption tolerant networking-based routing for rural internet connectivity (drinc) 133 everyone to internet using non real-time communications based on the dtn architecture [5] and inspired on daknet [6]. daknet is a technology used to interconnect rural communities in india using mechanical transportation means. daknet uses prophet [7] as its routing protocol to deliver and receive messages between users and internet. however, as far as we know, there are not publications about its performance on field. we tested the official implementation of prophet in the one (opportunistic network environment) simulator [8] and compared our proposal against it and other well-known dtn routing protocols, in rural connectivity scenarios for developing countries. figure 2: proposed scenario for the rural connectivity problem this paper extends the conference paper [1]. the key additions of this journal version are as follows. first, figure 4, has a small correction. it has an additional device in the internet connected town, to receive user’s requests and internet replies. section 2 includes and describes more related works, and dtn implementations for rural solutions. besides, this paper contains an extended explanation of the mathematical model and of the heuristic algorithm from section 3. finally, this paper contains additional results for the simulated scenarios. this document is divided as follows: section 2 presents a review about rural internet networking. it reviews some connectivity technologies and initiatives that are currently being tested and developed around the world for rural internet connectivity. it also contains a brief description of the most important protocols in the state-of-the-art of dtn routing protocols that can be used for rural connectivity. section 3 gives a detailed description of our proposed mathematical model, solution architecture, and forwarding algorithm developed in this research. section 4 analyses the simulation results, including the comparison with other protocols. finally, section 4 presents general conclusions, highlighting the most important achievements of this research, and it also states some possible directions for future work. 2 delay/disruption tolerant networks and rural networking 2.1 delay/disruption tolerant networks delay/disruption tolerant networks (dtn) are a recent kind of networking technology for environments with difficult conditions. dtn were developed for spatial communications to over134 c. velásquez-villada, y. donoso come the long distances (long delays) and frequent disruptions that are usual for this environment. from this research, the dtn architecture [5] and the bundle protocol [9] were developed. the dtn architecture describes a networking technology that has to be able to work on environments with long delay and frequent disruptions. it relaxes several assumptions of the traditional internet, including the ideas that there exists an end to end path between source and destination, that error correction based on acknowledgements is effective, that packet switching is the most appropriate abstraction for interoperability and performance, and that endpoint-based security mechanisms are enough. the bundle protocol is the technical implementation of the dtn architecture. it proposes a new intermediate layer between the application layer and the transport layer. the bundle protocol is independent of the networking and network access technologies, and it allows dtn nodes to exchange messages between neighbors as a relay for a transmission between distant dtn nodes. figure 3 shows a graphical explanation of the bundle protocol position in the networking layered model. it shows that bundles can communicate between intermediate dtn nodes or between end dtn nodes. it also shows that transmitting a bundle from a dtn node to another is made via lower layer protocols, starting with the transport layer and finishing in the other node’s transport layer, and that this communication can be made over tcp/ip protocols or any other networking stack. it also shows that transmitting a single bundle between two dtn nodes can require the exchange of several segments, datagrams and frames between the inferior layers. figure 3: bundle layer as middleware between dtn applications and traditional networking protocols [10] 2.2 rural internet networking recently, there have been many initiatives to connect the unconnected part of the world. these unconnected citizens are, primarily, located at rural areas in developing countries. main players in the it world, like google, and facebook, are heading these initiatives. the next paragraphs summarize a review of a couple of their projects and a dtn-based, rural connectivity delay/disruption tolerant networking-based routing for rural internet connectivity (drinc) 135 technology, the daknet project. google’s loon [11] and link [12] projects are some of google initiatives to connect people to internet. these projects are focused on rural users in developing countries, but they are part of a google’s initiative to connect more users to internet and expand its business. the loon project uses aero-static balloons that create a mesh network between them and several earthbased stations. these earth-based stations have internet connectivity via satellital internet or wireless links. the loon project allows local users to send request to the balloon network (via lte technology) and obtain a response in almost real time through the connection at the earthbased station. the link project deploys optical fiber networks in metropolitan regions, and it builds wi-fi networks as last-mile solution for potential users. google started the link project in uganda and it is expanding it in ghana. it has installed or is currently installing five fiber optical networks in these two countries. they report that these networks have increased internet connectivity availability and bandwidth. they allow users to access educational information, share medical results, expand the impact of their businesses and build cooperation networks between universities. facebook’s internet.org [13] and aries [14] projects are some of facebook initiatives to connect rural people to internet. the internet.org project, also called free basics, is an initiative in developing countries and rural areas where local users with a mobile phone can access a limited version of internet via a local mobile carrier. facebook, in agreement with local governments and mobile carriers, give this limited internet access free of charge. the aries project is one of the facebook’s initiatives to bring connectivity to areas where there is not networking infrastructure available. aries is an antenna array that uses multiple input multiple output (mimo) technology to transmit up to 24 simultaneous streams over the same spectrum. it is a base station with 96 antennas that tries to improve the spectral efficiency of wireless communications, working at different frequencies and reaching 71 bps/hz. facebook is developing this technology with the idea to reduce deployment costs of networking in urban areas; besides, it is aiming at connecting, through this antenna, rural population living in a radius of 40 km. of urban centers. facebook estimates that 97% of the world’s population live inside these areas. governments are also investing in rural internet connectivity. in colombia, the ict ministry created in 2011 the proyecto nacional de fibra óptica (national optical fiber project) with the aim to connect to internet all colombian municipalities. this governmental contract had a cost of around 230 million usd. it was planned to be executed between 2012 and 2014 [15]. in recent declarations from the colombian ict minister, he stated that the project currently covers around 90% of the colombian municipalities. although this is a big investment, it covers the main urban developments in colombia; rural areas, near these municipalities, are not being covered by this project. daknet [6], [16] is a rural connectivity solution based on dtn. daknet has been in use for several years in india. daknet installs kiosks in rural towns, where users can go and use a computer to communicate with other rural users or with internet servers in a non-real-time fashion. messages are stored in the local computer until a motorcycle or a bus stops by the town, collecting the messages via an access point installed on it. the vehicle will carry the messages to an internet connected spot, where they will be delivered and the desired answers retrieved. daknet uses prophet [7] as its routing protocol. daknet is the main inspiration for the present work. 2.3 dtn routing protocols epidemic routing [17] is the most known routing protocol for opportunistic networks. it replicates the messages it wants to deliver giving copies to every node it meets until the message 136 c. velásquez-villada, y. donoso gets to its destination. epidemic guarantees the delivery of a message if enough resources, including time, are given. spray and wait routing [18] is the evolution of epidemic routing. it also is a replication-based routing protocol. it works in two phases, a replication phase where the node creates copies of the messages it wants to deliver, and give the copies to every node it meets, just like epidemic routing. the second phase is the delivery phase, in this phase the nodes stop the replication of messages and they will only pass the message to the destination node. prophet [7] is a probabilistic routing protocol based on the history of contacts between the nodes and transitivity. prophet estimates a delivery probability for every node it meets and decreases it by a constant factor over a constant time interval. it also can calculate the delivery probability for nodes it has not met, using intermediary nodes to deliver the messages (transitivity). a more detailed classification and comparison of dtn routing protocols can be found in reviews [19], [20] and [21]. the heuristic proposed in this paper uses ideas from some of the protocols described in this section. 3 dtn for rural internet connectivity 3.1 proposed architecture this section presents a rural networking connectivity scenario, where communication requests will be originated from nodes in an usually disconnected rural area, to other nodes or towards internet servers. the originating rural nodes make a mobile, sparsely distributed network. they will be able to communicate with each other and they could serve as relays to deliver messages to a static, always-on node in the nearest town, the access point (ap). the ap can store users’ requests until a mobile access point (map) comes to the town and retrieves them. the map will store the requests from the small, disconnected towns it visits and it will deliver them at the drop point, an internet connected node (ic) in a bigger, internet connected town. the ic is the connection between the dtn for rural users and the usual internet. the ic can deliver the rural users’ messages to the respective internet servers and retrieve their replies. all users’ replies will be stored in the ic until a map comes by and collects the replies for the users in any of the towns in its route. see figure 4, for a general scheme of the proposed architecture. dtn nodes representing rural users are expected to get more sparsely distributed as they are located farther away from the town’s ap. they can use other dtn nodes to convey requests to the ap and to deliver replies from internet to the final user. 3.2 optimization model for the rural internet connectivity problem this subsection introduces a new bidirectional, multiobjective, non-linear mathematical model for a dtn in a rural networking connectivity scenario. this model is extended and corrected from our previous models in [4] and [22]. this mathematical model maximizes the delivery probabilities over the paths that may exist through time, and at the same time it tries to minimize the mean delivery time for each message. this model optimizes through time the routing of messages based on the availability probability of each link in a possible path. a path from a source node to a destination may not exist at a single moment but over various intervals. also, the mathematical model will try to deliver all messages to their destinations, restricted to the links and buffers capacities and nodes’ availability probabilities given by their movements. see figure 5 for a graphical representation of the mathematical abstraction. the mathematical model is presented in equations (1) to (7). delay/disruption tolerant networking-based routing for rural internet connectivity (drinc) 137 figure 4: proposed architecture for the rural internet connectivity solution the mathematical model assumes that there is a set of rural nodes (n), with intermittent connections (e) between a subset of them and the local ap. these connections are modeled through time dependant availability probabilities (aij(t)). if the availability probability at any given time for a couple of nodes is bigger than zero, a path can be formed using them. the set p represents the possible paths that can exist in the network through time. delivery probabilities (dij(t)) are calculated using availability probabilities of neighboring nodes and their knowledge of a path to, or a node with previous contacts with, the destination. nodes and links have capacities that should be respected. we assume that these capacities do not change over time, then, parameters cij and cii represent the link capacity from node i to node j and the node i capacity to store messages through time, respectively. parameter bi(t) is a time based vector with demands and supplies for node i through time. all b vectors are grouped together in matrix b, representing the desired flow of information for the system. the model uses discrete times t. table 1: mathematical notation for the rural internet connectivity problem var./param. definition n set of nodes, i ∈ n e set of links, (i,j) ∈ e t set of discrete time intervals t ∈ t p set of paths, (i,j) ∈ p , aij(t) > 0 b matrix of information demands and supplies, bi(t) ∈ b aij(t) availability probability of (i,j) ∈ e at time t dij(t) delivery probability of node i through node j at time t cij capacity of link (i,j) ∈ e cii storage capacity of node i δt time interval duration xij(t) data flow through link (i,j) ∈ e at time t 138 c. velásquez-villada, y. donoso figure 5: mathematical abstraction for the rural internet connectivity problem each of them represents a moment lasting long enough for a node to reliably transfer a message to a neighbor. the lasting of each time interval is modeled through the parameter δt. forwarding decisions are made based on links’ availability probabilities, the size and the delivery time of the message. these decisions are represented by the positive integer variable xij(t), which represents how much information should flow over a link at any given time. table 1 summarizes all the model’s parameters and variables. objective functions max ∏ (i,j)∈p dij(t)xij(t) (1) min ∑ t∈t δt∑ i∈n |bi(t)| (2) constraints∑ j∈n xij(t) − ∑ j∈n xji(t) + xii(t) −xii(t− 1) = bi(t) ∀(i,j) ∈ e,i 6= j,t ≥ 1 (3) ∑ j∈n xij(t) − ∑ j∈n xji(t) + xii(t) = bi(t) ∀(i,j) ∈ e,i 6= j,t = 0 (4) xij(t) + xji(t) ≤ cijδt ∀(i,j) ∈ e,i 6= j (5) xii(t) ≤ cii ∀i ∈ n (6) xij(t) ∈ z≥0 ∀(i,j) ∈ e,p ∈ p,t ∈ t (7) equations (1) and (2) represent the objective functions intended to maximize the delivery probability of messages over all paths through time, and to minimize the mean time taken to delay/disruption tolerant networking-based routing for rural internet connectivity (drinc) 139 deliver all messages. equations (3) to (7) state the mathematical model constraints. equations (3) and (4) are data flow constraints, requiring all messages to be delivered to their destinations. equations (5) and (6) are capacity constraints, requiring all transmission to be in the limits of buffers and links capacities. finally, equation (7) states that the decision variable is a positive integer variable defined over all available paths through time and representing the data flow over an edge for a time interval. 3.3 heuristic approach of the proposed solution for the rural internet connectivity problem every user in the rural networking connectivity scenario, shown in figure 4, should use a distributed routing algorithm to deliver all its messages to their destinations. the proposed algorithm introduced in this subsection works with limited and local information. a node only knows where it is based on a general location abstraction given by a uniform resource identifier (uri) described in the dtn architecture [5]. based on this general location it can direct its requests to the nearest ap. each node must create two tables that it should save to a non-volatile memory. one table is for saving the name, address, availability probability, last contact time, average time between contacts and centrality of every node it meets (neighbors’ table). the centrality is a measure of how many nodes a neighbor has met, and it can be used to make forwarding decisions. nodes with better centrality metrics will be preferred when no delivery probability to a destination is known. delivery probabilities (d) are calculated from nodes that have a path to the destination. they can give a delivery probability based on previous contacts with the destination, and neighboring nodes of these nodes can have a delivery probability through them. delivery probabilities are kept at a deliveries table. when a node wants to send a message to another node or to internet (through the ap), it will create an entry in its deliveries table with the information about the neighbors with paths to the destination, the average time between contacts for each one of these neighbors, the delivery probability to the destination through the neighbors, and the estimated time to deliver the message given by the neighbor. dtn nodes will store in memory these tables, so they can forward messages in an effective way, minimizing time and maximizing the delivery probability. nodes should have enough capacity to store own’s and neighbors’ messages. since the communication model is not a real time one, this should not be a problem. nodes will communicate using wireless links (ieee 802.11g/n/ac or a similar technology) that will give them at least 50 mbps, so a contact of a few minutes (a map visiting the location of an ap in the rural town) should be enough for a couple of nodes to exchange the messages (requests and replies) that they have for each other. the ap should have a significantly bigger memory capacity, since it is the bottleneck for all messages leaving the rural network and going to internet. the memory size should depend on the frequency of delivery and reception of messages. if maps pass frequently by the ap’s position, the memory could be smaller than the memory requirement for aps where maps pass once a day or even less frequently. figure 6 depicts a flow diagram representing the routing and forwarding algorithm for each dtn node. 4 results for rural internet connectivity scenarios this section presents the results for different rural internet connectivity scenarios. these scenarios have several rural users distributed over a map, one ap, one ic, and one or more mobile aps, connecting the ap to the ic. these proposed scenarios are located around a small and remote town in colombia, called la macarena. it is a small community of about 500 inhabitants, with difficult access conditions. 140 c. velásquez-villada, y. donoso figure 6: flow diagram for the proposed algorithm for the rural internet connectivity solution there are currently three different ways to reach the town, a road to the nearest town (5 hours), two daily flights in small airplanes (for 5 to 10 people), and small canoes by the guayabero river. this town also has a military base, where communication antennas work all day long. rural users, then, have to go to the town in order to communicate to the capital or any other part of the country. we scaled the map around la macarena using openjump [23] to use the mobility models of the one simulator. rural dtn users are scattered around the town and they move around from the town to small farms nearby. there are roads from the town to most farms, also, the river is a very popular way of transportation. for our simulations, dtn users can generate messages at any time and they can receive replies from internet (through the static node at the military base or from a map). messages have to get to the center of the town, at the military base, to be transmitted to internet. there are dtn nodes moving in their farms, neighboring farms and the town, maps going from the farms to the town and viceversa over roads and the river, and the ap in the military base in the center of the town. this simulation scenario can be seen in figure 7. we created several scenarios changing the number of nodes (from 10 to 100 rural users), the size of messages (from 2 kb to 2 mb), and the time between messages (from 1 to 12 hours). these scenarios are described in table 2. messages are generated from rural dtn user nodes and the ap. these messages can be sent from user node to user node, from user node to ap or from ap to user node. maps only serve as relays to carry the messages from one location to another. every scenario was tested for 3 simulated days (72 hours) with 5 different random seeds. the simulation results were obtained by changing the nodes’ buffers and changing the user nodes movement model from random to maproute (a model where nodes follow a predefined path at a constant speed defined randomly at the beginning of the simulation). we changed the nodes’ buffers from 10mb to 2000mb, but the results were always the same, they were not affected by the buffer size. we also changed the number of message replicas that our proposed algorithm uses; however, results were neither affected by replication number changes. at the end, our algorithm does not use replication. results are shown in figures 8 to 15. they show the delivery rate, delay, overhead, and hop count of our drinc algorithm against prophet, epidemic, and sprayandwait routing protocols. it can be seen that drinc achieves the same delivery rate or even a better one in some cases than epidemic routing and prophet for the proposed scenarios, and that all delay/disruption tolerant networking-based routing for rural internet connectivity (drinc) 141 figure 7: scenario for the rural internet connectivity problem based on la macarena town in openjump table 2: simulation scenarios the rural internet connectivity problem based on la macarena town scenario parameters number dtn nodes ap maps message size frequency 1 10 1 3 2 mb 1-2 hours 2 10 1 3 2 kb 1-2 hours 3 10 1 3 2 mb 1-12 hours 4 10 1 3 2 kb 1-12 hours 5 100 1 3 2 mb 1-12 hours 6 100 1 3 2 kb 1-12 hours protocols deliver more messages with the random movement of the user nodes than they do with the maproute movement model of the nodes. this could be due to the size of the scenario and that the random movement model gives more contact opportunities in this scenario. the 95% confidence intervals were calculated for every result and they appear in the figures. figures 8 and 9 show simulation results for epidemic, prophet, and sprayandwait routing protocols against our drinc algorithm. see table 2 for a summary of the scenarios. figure 8 shows delivery results for scenarios 1 to 4, these are scenarios with 10 nodes as rural users, generating messages every 1 to 2 hours or every 1 to 12 hours. figure 9 shows delivery results for scenarios 5 and 6, these scenarios are the big ones, with 100 nodes as rural users, and they are generating new messages every 1 to 12 hours. when nodes have enough opportunities to communicate (random movement of the nodes), almost all messages are delivered by all protocols; however, when nodes are kept apart most of the time, by using maproute movement, all protocols deliver between a 50% to 60% of the messages, with sprayandwait performing a little bit below this percentage. figures 10 and 11 show simulation results for delivery delay for epidemic, prophet, and sprayandwait routing protocols against our drinc algorithm. see table 2 for a summary of 142 c. velásquez-villada, y. donoso figure 8: delivery rate for the rural internet connectivity scenarios with 10 nodes. the scenarios change the message size between 2 mb and 2 kb, and the frequency of message generation from 1 to 2 hours or from 1 to 12 hours figure 9: delivery rate for the rural internet connectivity scenarios with 100 nodes. the scenarios change the message size between 2 mb and 2 kb figure 10: delay in thousands of seconds for the rural internet connectivity scenarios with 10 nodes. the scenarios change the message size between 2 mb and 2 kb, and the frequency of message generation from 1 to 2 hours or from 1 to 12 hours delay/disruption tolerant networking-based routing for rural internet connectivity (drinc) 143 figure 11: delay in thousands of seconds for the rural internet connectivity scenarios with 100 nodes. the scenarios change the message size between 2 mb and 2 kb figure 12: overhead (messages generated/messages delivered) for the rural internet connectivity scenarios with 10 nodes. the scenarios change the message size between 2 mb and 2 kb, and the frequency of message generation from 1 to 2 hours or from 1 to 12 hours figure 13: overhead (messages generated/messages delivered) for the rural internet connectivity scenarios with 100 nodes. the scenarios change the message size between 2 mb and 2 kb 144 c. velásquez-villada, y. donoso figure 14: hop count for the rural internet connectivity scenarios with 10 nodes. the scenarios change the message size between 2 mb and 2 kb, and the frequency of message generation from 1 to 2 hours or from 1 to 12 hours figure 15: hop count for the rural internet connectivity scenarios with 100 nodes. the scenarios change the message size between 2 mb and 2 kb the scenarios. figure 10 shows delay results for scenarios 1 to 4. and, figure 11 shows delay results for scenarios 5 and 6. these results show that all protocols have similar performance regarding delay when the movement model is random. sprayandwait has the best delay and prophetv2 has the worst. drinc is tied in second place with epidemic. figures 12 and 13 show simulation results for messages generated over messages delivered (overhead) for epidemic, prophet, and sprayandwait routing protocols against our drinc algorithm. see table 2 for a summary of the scenarios. figure 12 shows overhead results for scenarios 1 to 4. and, figure 13 shows overhead results for scenarios 5 and 6. these results show that in small sized scenarios (scenarios 1 to 4, with 10 rural nodes) prophetv2 has the best overhead when the movement model is maproute, and sprayandwait has the best overhead when the movement model is random. sprayandwait has also the best performance in overhead when the scenario has 100 nodes. our drinc algorithm has the same overhead as epidemic. figures 14 and 15 show simulation results for the number of hops used to deliver the messages for epidemic, prophet, and sprayandwait routing protocols against our drinc algorithm. see table 2 for a summary of the scenarios. figure 14 shows hop count results for scenarios 1 to 4. and, figure 15 shows hop count results for scenarios 5 and 6. these results show that all protocols have a similar performance regarding hop count for small sized scenarios (scenarios 1 to 4, with 10 rural nodes). sprayandwait has the best performance regarding hop count when delay/disruption tolerant networking-based routing for rural internet connectivity (drinc) 145 the scenario has 100 nodes. our drinc algorithm has the same hop count as epidemic. results suggest that our proposed algorithm uses more messages to find paths and gather knowledge from the network than other protocols. this can be seen in a higher overhead metric for our drinc algorithm than it is for other known protocols. drinc delivers the same or more messages than all protocols used for comparison and it does it in the same time or less, with exception of sprayandwait that has the best delay performance for nodes using the maproute movement model; although, it delivers less messages. conclusions and future work conclusions • delay/disruption tolerant networking is a technically feasible way to bring internet connectivity to remote rural areas in the world. simulation results in section 4 show promising results for several dtn routing protocols. they also show that a new protocol, specifically tailored for rural connectivity scenarios, based on the ideas presented in this paper can enhance the performance of current dtn routing protocols, delivering more messages with less delay. to get to a product solution that could be deployed in rural areas, an implementation on an electronic platform should be done. a development with electronics and software will be very useful for tuning to obtain a robust solution. • the drinc algorithm presented in section 3 combines the best characteristics of some of the most known dtn routing protocols. from results in section 4, it can be seen that our drinc algorithm performs as well as these protocols. simulation results show that our drinc algorithm solution delivers almost all messages when the mobility model given by the simulator allows rural nodes to interact more frequently. when the simulator is configured to use a mobility model that keeps rural nodes apart from each other most of the time, the delivery rate of all protocols drops significantly. this effect is minimized when there are 100 rural nodes, creating more contact opportunities. it can also be seen that in this scenario, all protocols have a very similar performance regarding all metrics. only spray and wait differentiates from the others by delivering less messages with less delay and having less overhead in general. future work this paper provides the basic research to test the feasibility of a technological solution based on dtn technologies for rural internet connectivity for non-real-time applications. technology development, implementation and testing is necessary before a complete solution can be given. simulation results for our drinc algorithm suggest that it improves the delivery rate and delay when compared with well known dtn routing protocols for rural connectivity scenarios; however, more tuning should be done regarding the updating of delivery probabilities, in a way that faster routes or newly discovered routes can be fairly compared with previous ones, reducing the number of hops, and the overhead, in the network. also, it is necessary to perform tests in real implementations and tune the inner working of the drinc algorithm in electronic platforms and in real life conditions, taking into account errors and disruptions given by the physical world, and network access’ technologies. 146 c. velásquez-villada, y. donoso acknowledgment i would like to thank the colombian “departamento administrativo de ciencia, tecnología e innovación colciencias conv. 528/2011” for the funding of the research leading to this paper. it was also partially funded by: • systems and computing engineering department, universidad de los andes in colombia; • european union seventh framework programme (fp7/2007-2013) under grant agreement no. 269985. bibliography [1] velásquez-villada, c.; donoso, y. 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[23] openjump gis (2016) http://www.openjump.org/ international journal of computers communications & control issn 1841-9836, 12(5), 645-660, october 2017. phasing of periodic tasks distributed over real-time fieldbus s.-h. lee, h.-w. jin, k. kim, s. lee sang-hun lee hyundai mobis 17-2, 240 mabuk-ro, giheung-gu, yongin-si, gyeounggi-do 16891, korea sanghun@mobis.co.kr hyun-wook jin* department of computer science and engineering konkuk university 120 neungdong-ro, gwangjin-gu, seoul 05029, korea *corresponding author: jinh@konkuk.ac.kr kanghee kim department of smart systems software soongsil university 369 sangdo-ro, dongjak-gu, seoul 06978, korea khkim@ssu.ac.kr sangil lee agency for defense development songpa p.o.box 132, seoul 05661, korea happyjoy@add.re.kr abstract: in designing a distributed hard real-time system, it is important to reduce the end-to-end delay of each real-time message in order to ensure quick responses to external inputs and a high degree of synchronization among cooperating actuators. in order to provide a real-time guarantee for each message, the related literature has focused on the analysis of end-to-end delays based on worst-case task phasing. however, such analyses are too pessimistic because they do not assume a global clock. with the assumption that task phases can be managed by using a global clock provided by emerging real-time fieldbuses, such as ethercat, we can try to calculate the optimal task phasing that yields the minimal worst-case end-to-end delay. in this study, we propose a heuristic to manage the phase offsets in the distributed tasks to reduce the theoretical end-to-end delay bound. the proposed heuristic reduces the search time for a solution by identifying time intervals where actual communication occurs among inter-dependent tasks. furthermore, to analyze the distribution of endto-end delays in different phases, we implemented a simulation tool. the simulation results showed that the proposed heuristic can reduce worst-case end-to-end delay as well as jitter in end-to-end delays. keywords: task phasing, end-to-end delay, real-time, fieldbus, ethercat. 1 introduction fieldbuses are industrial networks widely used in small-to-large-scale distributed control systems, such as vehicles and factory automation systems. while the fieldbuses define only the physical, the data link, and the application layers, they provide real-time features for deterministic communication latency. emerging real-time fieldbuses provide highly accurate clock synchronization as a key feature to support a global clock in distributed systems. for instance, the distributed clock of ethercat provides accurate clock synchronization with errors smaller than a few tens of nanoseconds [2] [3]. copyright © 2006-2017 by ccc publications 646 s.-h. lee, h.-w. jin, k. kim, s. lee such accurate clock synchronization provides a novel opportunity to adjust task phasing across distributed computing nodes in order to better synchronize dependent tasks (e.g., multiaxis motion control). however, obtaining optimal task phasing across distributed nodes has not been accorded sufficient attention in the literature, whereas worst-case task phasing in each node has been extensively studied [22] [19] [8]. this is due to coarse-grained clock synchronization on legacy networks; moreover, the performance analysis of distributed tasks by considering jitters in task execution time is not straightforward. thus, researchers have focused on the analysis of worst-case end-to-end delays while assuming no global clock. however, once we assume a precise global clock for all nodes, we can investigate the problem of finding an optimal phase combination of tasks in order to reduce worst-case end-to-end delays. though few efforts have been devoted in recent research to enhancing task phasing, these either assume no jitters for tasks [4] or apply phasing to a single node [11] [10]. in this paper, we describe a novel off-line algorithm that heuristically searches for nearoptimal task phasing with respect to the worst-case end-to-end delay. one novelty of the proposed algorithm is that it takes into account the jitters in execution times of distributed tasks while trying to reduce search time to find near-optimal task phasing. an exhaustive search needs to consider all combinations of the variable execution times of tasks. to reduce search time, our algorithm deals with feasible time ranges of communication. to analyze the distribution of end-to-end delays in different phase combinations, we implemented a tool that simulated variable execution times of tasks and the behavior of real-time fieldbus. in the analysis, we assumed a real-time fieldbus, such as ethercat, that comprised a master and multiple slave nodes. we modeled each node as a set of periodic tasks scheduled on a uniprocessor by a fixed-priority scheduling algorithm. the results of analysis showed that our algorithm can efficiently search for a phase combination that can significantly reduce both worst-case end-to-end delay and jitter. the rest of this paper is organized as follows: in sec. 2, we provide an overview of ethercat and discuss related work. in sec. 3, we describe the system model that we assume in this study. we outline our algorithm to find a near-optimal task phase combination and describe our simulation tool for the analysis of end-to-end delays in sec. 4 and sec. 5, respectively. sec. 6 contains details of a case that involves finding an optimal node phase combination for a given industrial task set. finally, in sec. 7, we present conclusions. 2 background 2.1 ethercat in this paper, we consider ethercat as the reference model for real-time fieldbuses. ethercat is an open specification that covers the physical, the data link and the application layers of the communication protocol stack. owing to its low cost, compatibility with the tcp/ip protocol and efficiency, ethercat is becoming popular in many real-time applications such as factory automation systems and motion control systems. an ethercat network consists of a single master node and multiple slave nodes allowing various network topologies such as line, daisy chain, and star. the master node generates ethercat telegrams, each of which is encapsulated in ethernet frames. a telegram may include several datagrams for different slave nodes, where a datagram includes an address to specify the corresponding slave node. the master node transmits the resulting ethernet frames through the standard ethernet interface. each slave node then reads from and writes to the datagram on the fly in a specially designed network controller called the ethercat slave controller. the ethercat slave controller can forward ethernet frames to the next slave node at the hardware phasing of periodic tasks distributed over real-time fieldbus 647 level while performing such read-and-write operations. the propagation delay at the controller is less than 1µs [9]. thus, ethercat provides high-speed message relay between adjacent nodes, and can guarantee deterministic communication delays. ethercat provides a global clock synchronization feature called distributed clock that synchronizes the clocks of the master and slave nodes to the reference clock represented by a selected slave. the distributed clock controller can generate an interrupt called the sync signal as the global reference clock [18]. the signal can operate in either cyclic or single-shot mode, where the period and shift time are specified in nanosecond resolution. thus, we can adjust the release time (i.e., phase) of a task by means of the sync signal. 2.2 related work sung et al. [18] showed the potential of the global clock provided by ethercat for synchronized control processes, but they considered only communication tasks while assuming no preemption by higher-priority tasks. a few researchers recently suggested heuristic approaches that iteratively adjusted task phases for isochronous control on ethercat [11] [10]. although they could improve actuation jitters, the suggested approaches could not be applied at design phase for a system yet to be constructed because those are based on an on-line algorithm. in addition, they targeted only task phasing on the master node by assuming a simple task set in the slave nodes. craciunas et al. [4] tried to decide the optimal offset of tasks in terms of utility, but did not consider jitters in execution times of tasks. in this study, we propose a off-line algorithm that searches for a near-optimal task phase combination on both master and slave nodes while considering jitters in execution times of tasks. traditionally, task phasing was addressed in order to obtain worst-case response times of tasks. under fixed-priority scheduling, tasks with varying inter-release times were analyzed at a worst-case time instant, called a critical instant [13], since the release time of each task was assumed not to be managed. in a distributed real-time system, such an assumption was extended to every node because the unpredictability of the release time of a task was exacerbated by the jitters introduced by varying response times of the preceding task [19] [8]. in addition, it was assumed that the communicating tasks on different nodes were synchronized by messages. there were also studies to analyze the best-case response time [1] [16], but these studies did not target distributed systems. several studies were conducted on the analysis and simulation of real-time industrial networks [17] [6] [7] [22]. these studies, however, did not address task phasing to obtain minimum worst-case end-to-end delays in the design of distributed real-time systems. it is important to reduce analysis time to find an optimal solution. for example, garcia-valls et al. [21] [20] proposed selectively checking for dynamic systems in real-time reconfiguration. in this study, we try to reduce analysis time by considering only feasible communication time intervals. 3 system model we consider a distributed real-time system that interconnects a master node and n −1 slave nodes with a real-time fieldbus, which is common in many industrial applications. for example, in a motion control system, the master node generates position commands to describe motion trajectory and slave nodes serve as motor drives to respond to these commands. in the following, we explain the task and the network models, and define end-to-end delay. 648 s.-h. lee, h.-w. jin, k. kim, s. lee figure 1: system model 3.1 task model we consider a set of periodic tasks for both master and slave nodes and these may have different sets of tasks. we denote a communication task by τn = (cminn ,c max n ,tn,pn, φn), where cminn and c max n are its minimum and maximum execution times, tn is the period, pn is its priority, and φn is its phase with respect to a certain reference time. the jitter in the execution time represented by cmaxn −cminn is caused not only by jitters in system software, but also by different code branches in tasks. we assume that the relative deadline of each task is equal to its period. each node has only one communicating task. the rationale for this is that many industrial network protocols do not support multiplexing and de-multiplexing between tasks [5] [14]. moreover, as the name implies, only the master node is assumed to initiate message transmission. each slave node may only piggyback some data on message passing through the node on the fly, but cannot initiate message transmission. thus, there exists a chain of communicating tasks across nodes (i.e., τ1 → τ2 → ··· → τn) for each communication period as shown in fig. 1. each communication task gives rise to an infinite sequence of jobs. τn,i denotes the ith job of τn. the start time of τn,i is denoted by sn,i and the finish time by fn,i. a sender job on the master node (i.e., τ1,i) sends a message to the network device at the finish time, f1,i, while receiver jobs on the slave nodes (i.e., τn,i, 1 < n ≤ n) look up the message from the message queue at the start time, sn,i. we do not suppose that the receiver jobs are synchronously released by a message; rather, we assume that the jobs are released by a periodic task scheduler as many real systems do. if there is no message arrived, then the receiver job may finish without further processing. the periods of the sender and receiver tasks are identical because both operate on the same message initiated by the master node. the message arrival time of the ith message on node n is represented by an,i. for task scheduling, we assume a fixed-priority scheduling algorithm, such as rate monotonic [13]. since each task is assigned a static priority, the execution of a task assigned a lower priority can be preempted by one assigned a higher priority, which were modeled as the release jitter in several previous studies. due to such preemption and jitters in execution times of tasks, there can be many potential time points at which τn,i starts. thus, we define the start time of τn,i as a set of time points, i.e., sn,i = {sminn,i , · · · ,s max n,i }. similarly, the phasing of periodic tasks distributed over real-time fieldbus 649 finish time of τn,i is defined as a set fn,i = {fminn,i , · · · ,f max n,i }. the one-to-many correspondence r : sn,i → fn,i maps a start time point of τn,i to a set of potential finish time points. that is, r(sn,i) = {f1n,i, · · · ,f m n,i}⊆ fn,i. and r(s min n,i ) ∪·· ·∪r(s max n,i ) = fn,i. we also define the arrival time an,i = {aminn,i , · · · ,a max n,i }. 3.2 network model emerging real-time fieldbuses, such as ethercat, guarantee deterministic communication delays between any two nodes through two design choices. the first involves connecting any two adjacent nodes with a dedicated link. that is, there are no interfering nodes on the link and, thus, no packet collisions. the second design choice is such that each slave node n conducts cut-through switching to relay packets between node n−1 and n+ 1, instead of, store-and-forward switching. this switching scheme when implemented at the hardware level eliminates the likelihood that the internal software operations of each slave contribute to end-to-end communication delay. as a result, ethercat guarantees deterministic communication delays from the master to any slave n, which is analyzed by prytz et al. [15] as follows: dcomm(n) =dt(s) + (n− 1) ×df + dr(s), (1) where dcomm(n) is the communication delay from the master’s memory to slave n’s memory as shown in fig. 1, dt(s) is the total transmission time of a message of size s on the master network device, and df is the total forwarding time of a message on the slave side, which is a constant of 1 µs, irrespective of the message size. dr(s) is the total reception time of a message of size s on the slave network controller. we do not have to consider any delay in piggybacking data on the message at the slave node because this operation is conducted on the fly while the message is being forwarded by the network controller. 3.3 problem statement in this paper, we define end-to-end delay of the ith message as the time from the beginning of the sender job at the master to the completion of the receiver job that consumes the ith message at slave. suppose φ1 + dcomm(n) ≤ φn, the ith message is consumed by either τn,i or τn,i+1 at slave n because periods of sender and receiver tasks are the same as mentioned in sec. 3.1. we define d0e2e(n,i) to be the set of end-to-end delay values observed when τn,i consumes the message on slave, i.e., for all f1,i and sn,i that satisfy f1,i + dcomm(n) ≤ sn,i, (2) d0e2e(n,i) = {fn,i −s1,i | fn,i ∈r(sn,i), s1,i ∈r −1(f1,i)}, (3) where r−1 is the inverse correspondence of r defined in sec. 3.1 (i.e., r−1 : fn,i → sn,i). we also define d+1e2e(n,i) to be the set of end-to-end delays when τn,i+1 receives the message as the message arrives after the beginning of τn,i, i.e., for all f1,i and sn,i that satisfy f1,i + dcomm(n) > sn,i, (4) d+1e2e(n,i) = {fn,i+1 −s1,i | fn,i+1 ∈ fn,i+1, s1,i ∈r −1(f1,i)}. (5) 650 s.-h. lee, h.-w. jin, k. kim, s. lee thus, we define the end-to-end delay de2e(n,i) of the ith message as follows: de2e(n,i) = d 0 e2e(n,i) ∪d +1 e2e(n,i) = {d min n,i , · · · ,d max n,i }. (6) in this study, we want to analyze the worst-case end-to-end delay max(dmaxn,1 , · · · ,d max n,h/t1 ), where dmaxn,i = max(de2e(n,i)), and h is the hyperperiod defined as the least common multiple of the periods of all tasks. then, we aim to focus on finding an optimal combination of task phases leading to the minimal worst-case end-to-end delay: minimize (φ1,φ2,···φn ) max(dmaxn,1 , · · · ,d max n,h/t1 ) subject to un ≤ uupper for 1 ≤ n ≤ n, (7) where un and uupper represent the processor utilization of a given task set on node n and upperbound of the utilization which guarantees schedulability, respectively. this is challenging given that de2e(n,i) has very large number of elements due to variable execution times of tasks and preemption by higher-priority tasks. in the next section, we describe the proposed algorithm that searches for a near-optimal task phase combination without calculating all elements of de2e(n,i). 4 algorithm for optimal task phasing in this section, we propose a heuristic algorithm for optimal task phasing with respect to minimal worst-case end-to-end delay. the algorithm can efficiently consider the jitter in execution times of tasks. if we exhaust all possible combinations of the variable execution times of tasks, it requires a long time to analyze the worst-case end-to-end delay for a given task phase combination. to avoid this, prior to the actual analysis of the worst-case end-to-end delay of a phase combination, we first determine the time intervals where communication can occur and efficiently find the worst-case end-to-end delay by using this information. 4.1 overview fig. 2 shows the steps of our algorithm. in order to find the optimal phases of tasks, we heuristically calculate the worst-case end-to-end delays for different phase combinations. a phase combination is denoted by phasei = {1 + ∆1,i, 2 + ∆2,i}, where n is an initial sequence of task phases on node n, and ∆n,i is the ith sequence of variations to be added to n. if we analyze end-to-end delays for all possible phase combinations, we need to examine hσt cases, where h is the hyperperiod, and σt is the number of all tasks in the system. to reduce the number of search cases, in the first step, we assume that the slave nodes have the same set of tasks, which is common in many industrial control systems [12] [11]. we do this in order to be able to consider only the master and the first slave nodes, while simply calculating the phase combinations for the other slave nodes n (2 < n ≤ n) by adding (n−2)×df + dr(s) to the best phase combination for the first slave node. moreover, we only consider phases that satisfy φ1 + dcomm(2) ≤ φ2, and do not specifically consider the phases for tasks with lower priority than the communication task. we also deal with phases with identical relative phase combinations, such as {1, 2} and {1 + ∆′, 2 + ∆′}, as the same phase combinations; thus, we can omit the latter. the granularity of the task phase is also configurable to reduce analysis time, which is discussed in sec. 6.3. more importantly, we need to deal with the variable execution times of tasks. if we consider every case of variable execution times of all tasks, we need to examine (( (cmax1 −c min 1 )×h/t1× (cmaxm1 −c min m1 )×h/tm1×···×(c max mj −c min mj )×h/tmj ) × ( (cmax2 −c min 2 )×h/t2×(c max s1 −c min s1 )× phasing of periodic tasks distributed over real-time fieldbus 651 figure 2: steps to find optimal phase combination h/ts1×···×(cmaxsk −c min sk )×h/tsk )) cases for each phase combination, where j and k are the number of non-communicating tasks that have a higher priority than τ1 and τ2 on the master and the slave nodes, respectively. we cannot simply reduce the number of these cases by assuming a larger resolution of execution times, because we can omit the case that generates the worst-case end-to-end delay with a large resolution. in order to reduce analysis time while considering variable execution times, the second step of our algorithm analyzes the communication range, rangei, for each phasei by only using the minimum and maximum execution times of tasks. a communication range represents a time interval where actual communication occurs between τ1 and τ2. we describe the second step in finding communication ranges in sec. 4.2. in the third step, we efficiently calculate the maximum end-to-end delay dmax2,i for every message for a given phase. in sec. 4.3, we detail the third step to analyze the maximum endto-end delay for a given communication range. once we determine the maximum end-to-end delay for each phase combination, we choose the phase combination that provides the minimum worst-case end-to-end delay in the last step. 4.2 analysis of communication ranges as we have mentioned in sec. 4.1, in order to analyze the worst-case end-to-end delay for a given phase combination, we deal with the ranges of communication time points instead of considering all possible combinations of the execution times of tasks. we find the communication ranges by using the minimum and maximum execution times of tasks. first, we calculate the start and finish time ranges for all jobs of τ1 on the master node, which are represented by s1 = {s′1,1, · · · ,s ′ 1,h/t1 } and f1 = {f ′1,1, · · · ,f ′ 1,h/t1 }, respectively, where s′n,i = {s min n,i ,s max n,i } and f ′n,i = {f min n,i ,f max n,i }. it should be noted that s ′ n,i and f ′ n,i have only minimum and maximum values, whereas sn,i and fn,i defined in sec. 3.1 include all possible time points. the time range information f1 is then translated into the range information of message arrival time on the first slave node, which is denoted by a2 = {a′2,1, · · · ,a ′ 2,h/t1 }, where a′2,i = {a min 2,i ,a max 2,i }. the start and finish time ranges on the first slave node represented by s2 = {s′2,1, · · · ,s ′ 2,h/t2 } and f2 = {f ′2,1, · · · ,f ′ 2,h/t2 } are calculated as well. for example, fig. 3 shows the communication range for the first message. for each node in the figure, the upper row represents time flow with the maximum execution times of tasks while the lower one shows time flow with the minimum execution times. in the master node of this example, τ1 is preempted by a higher-priority task as shown in the upper row, which results in a larger smax1,1 than s min 1,1 . the difference between f min 1,1 and f max 1,1 becomes even greater due to the jitter in execution time (i.e., cmax1 −c min 1 ). we can calculate a min 2,1 and a max 2,1 by adding dcomm(2) to fmin1,1 and f max 1,1 , respectively. in the slave node, two non-communicating tasks have a higher priority than τ2. although the figure shows that τ2,1 is not preempted by these higher-priority tasks in the case of minimum execution time (i.e., the lower row), τ2,1 is delayed by a higher652 s.-h. lee, h.-w. jin, k. kim, s. lee figure 3: analysis of communication ranges priority task at the start point and preempted by the other one during execution in the case of maximum execution time. consequently, for the given task set and phase combination, the range of message arrival, [amin2,1 , a max 2,1 ], and that of the start time of τ2,1, [s min 2,1 , s max 2,1 ], overlap. by providing such information for the third step, we can efficiently analyze the worst-case end-to-end delay dmax2,1 . 4.3 analysis of worst-case end-to-end delay by using the feasible time ranges of communication provided by the second step, we analyze the worst-case end-to-end delay for a given task phase. as we have mentioned in sec. 3.3, to analyze the worst-case end-to-end delay we need to calculate de2e(2, i) for 1 ≤ i ≤ h/t1 and find max(dmax2,1 , · · · ,d max 2,h/t1 ). since de2e(2, i) = d0e2e(2, i) ∪d +1 e2e(2, i) by eq. 6, we describe how to calculate max(d0e2e(2, i)) and max(d +1 e2e(2, i)) in this section, respectively. as defined by eq. 2 and eq. 3 in sec. 3.3, d0e2e(2, i) is a set of end-to-end delays calculated supposing that the ith receiver job τ2,i consumes the ith message. however, since we have only range information represented by minimum and maximum values, we modify eq. 2 as follows: fmin1,i + dcomm(2) = a min 2,i ≤ s max 2,i , (8) which is satisfied if there is a possibility that the ith receiver job receives the ith message. in this case, we can calculate the maximum end-to-end delay of the ith message as follows: max ( d0e2e(2, i) ) = fmax2,i − min ( r−1 ( amin2,i −dcomm(2) )) = fmax2,i − min ( r−1 ( fmin1,i )) = fmax2,i −s min 1,i , (9) which is derived from eq. 3. in order to calculate the end-to-end delay, we need to know the start time of the sender job (i.e., s1,i) and the finish time of the receiver job (i.e., f2,i). it is obvious that the end-to-end delay becomes maximum if the sender sends the message in earliest phasing of periodic tasks distributed over real-time fieldbus 653 time (i.e., starts at smin1,i ) while the receiver is delayed for as long as possible (i.e., finishes at fmax2,i ). in eq. 9, the start time of the sender job is reversely calculated from the message arrival time amin2,i . on the other hand, if there is a chance that the message arrives after the beginning of the ith receiver job, the message happens to be handled by the next (i.e., (i + 1)th) job. this occurs when the following equation derived from eq. 4 is satisfied: fmax1,i + dcomm(2) = a max 2,i > s min 2,i . (10) then the equation for finding the maximum end-to-end delay of the ith message can be derived from eq. 5 as follows: max ( d+1e2e(2, i) ) = fmax2,i+1 − min ( r−1 ( a∗2,i −dcomm(2) )) . (11) here a∗2,i is the earliest arrival time of the message processed by τ2,i+1. that is, a∗n,i = max(s min n,i + �, a min n,i ), (12) where � represents a very small value. if the message is arrived at smin2,i + � while the receiver job starts at smin2,i , the message is processed by the next receiver job. however, if s min n,i + � < a min n,i , then aminn,i is the earliest time point. it should be noted that min(r −1(a∗2,i − dcomm(2))) in eq. 11 can be neither smin1,i nor s max 1,i . however, we have only minimum and maximum values that present range information to reduce analysis time. thus, it is difficult to find the exact min(r−1(f1,i)) for arbitrary f1,i (i.e., a∗2,i − dcomm(2)), unless we analyze every preemption point of the sender job by considering variable execution times. although we could calculate a converged value by using a recurrence equation for each message, to reduce the time needed for analysis, we pessimistically assume that the worst-case preemption always occurs and estimate min(r−1(fn,i)) as follows: min(r−1(fn,i)) = { fn,i −cmaxn,i −pr max, if fn,i −cmaxn,i −pr max n > s min n,i , sminn,i , otherwise, (13) where prmax is the worst-case preemption time of τn,i. as we calculate smaxn,i and f max n,i assuming the maximum execution time for both communicating task and non-communicating higherpriority tasks, the time range [smaxn,i , f max n,i ] includes the worst-case preemption time. hence, prmax can be expressed as: prmax = fmaxn,i −s max n,i −c max n,i . (14) by using eq. 14, we restate eq. 13 as follows: min(r−1(fn,i)) = { fn,i −fmaxn,i + s max n,i , if fn,i −f max n,i + s max n,i > s min n,i , sminn,i , otherwise, (15) therefore, by eq. 9 and eq. 11, we can find the worst-case end-to-end delay of ith message as follows: dmax2,i = max ( de2e(2, i) ) = max ({ max ( d0e2e(2, i) ) , max ( d+1e2e(2, i) )}) . (16) 654 s.-h. lee, h.-w. jin, k. kim, s. lee figure 4: simulation tool 5 simulation tool in real systems, end-to-end delays vary due to jitters in the execution times of tasks. thus, it is not feasible to evaluate the distribution and the average of end-to-end delays by using a simple mathematical equation. instead, we implemented a simulation tool consisting of four components: configuration manager, node objects, simulation kernel, and log manager. fig. 4 shows the overall design of the proposed simulator. the analysis tool is based on discrete-event simulation to simulate task scheduling, message transmission/forwarding, dma, and i/o event handling. configuration parameters such as the number of nodes, task sets, network bandwidth, message size, and event handling mode are defined in the configuration file in xml format. thus, a user of the tool can easily change the attributes of the system to be analyzed. furthermore, newly defined attributes can be described simply by extending the xml schema. the configuration module reads the configuration file at the initialization phase and sets the environment variables of the simulation kernel with values extracted from the configuration file. the configuration module also creates node objects as much as described in the configuration file and sets attributes of these objects, such as event handling mode. the node objects contains a snapshot of a run-time image of each node, such as the state information of the run queue and the message queue. the run queue stores task control blocks, which saves a given task’s mission as well as the amount of time for which it executes for the given period. the mission is classified into three categories: send, receive, and other. the message queue stores network messages, which are yet to be sent to the network or yet to be consumed by a receiver task. the simulation kernel performs the analysis loops. the simulation kernel consists of a clock emulator, a cpu emulator, and a network emulator. the clock emulator simulates a synchronized global clock and increases its counter for every simulation loop. the cpu emulator run task(s) on each node for every time unit. the scheduler selects a task to run based on the fixed-priority scheduling algorithm. our current implementation only supports the rm scheduling algorithm but our design is general enough to add other scheduling algorithms. the task executer emulates the behavior of the selected task based on its mission information. if the mission is send, the task executer posts a send request to the network device at the finish time of the job. on the contrary, in the receive case, the task executer consumes a message from the message queue at the start time of the job. the task executer simply consumes the time for tasks dedicated to other missions. the runtime generator generates the execution time of a job according to cminn and c max n for every task. in the current implementation, we phasing of periodic tasks distributed over real-time fieldbus 655 table 1: task set on slave nodes task name ti and ci description motoract 250µs, 25∼35µs controls a motor (highest priority) rtmsg 250µs, 10∼15µs receives real-time messages from the network, and shares it with the motoract task nrtmsg 250µs, 7∼10µs handles non-real-time messages (lower priority than rtmsg) healthmon 500µs, 6∼9µs performs health monitoring (lowest priority) table 2: simulation parameters parameters value parameters value network bandwidth 100 mbps network forwarding delay (df) 1 µs event handling mode interrupt interrupt handling time 5 µs packet size 50 bytes dma overhead for a 50byte packet 1 µs support uniform distribution and normal distribution for the type of execution time distribution but other distribution type can also be added. the network emulator simulates operations of the network device (e.g., message transmission, dma, and raising an interrupt). event handling can be carried out in either polling or interrupt mode. if the network device is configured as a polling-based device, the event is handled when a receiver task is released. thus, in this mode, the network emulator silently inserts a received message into the receive queue without any notification when it arrives. when the network device is configured as an interrupt-based device, the network emulator sends an interrupt signal to the cpu emulator so that the event handler is invoked as soon as a message arrives preempting a running task. the network emulator also decides the message transmission speed based on the bandwidth information, and maintains the remaining bytes of a message being sent on each object node. the log manager stores the results of analysis in output files. the final output represents the total running time of the tool, the end-to-end delay for each message, and the average end-to-end delay. the log manager also saves intermediate information generated during analysis. 6 industrial case study in this section, we detail an industrial case to show that our algorithm can efficiently propose enhanced task phasing across distributed nodes for a given task set. 6.1 analysis environments table 1 shows the task set we ran on the slave nodes. we used the task set defined in kim et al. [12] for the motor drive. each slave node scheduled these tasks using the fixed-priority scheduling algorithm. we assume that the execution times of tasks followed a uniform distribution, which was not necessary for our analysis. table 2 shows the configuration parameters for the simulation tool. we borrowed such network parameter values as queue size, bandwidth, and forwarding delay from ethercat specifications. 656 s.-h. lee, h.-w. jin, k. kim, s. lee figure 5: distribution of end-to-end delays of different phasing schemes 6.2 comparisons with other phasing schemes in this section, we compare end-to-end delays of four phase combinations. one of these phase combinations was generated by our algorithm in sec. 4 (suggested-algo). another phase combination was limited to the master node by assuming the critical instant on the slave (masteronly). the third was generated based on the worst-case execution times (wcet) of tasks without considering the jitters in execution time (wcet-only). the other phase combination represented worst-case phasing with respect to end-to-end delay (worst-case). by utilizing the simulator described in sec. 5, we measured the end-to-end delays, and present the cumulative probability of these and the minimum, average, and maximum end-to-end delays. the distribution of end-to-end delays of four phase combinations are compared in fig. 5. it can be seen that the end-to-end delays of suggested-algo were distributed among the lowest values, whereas those of worst-case yielded the highest values. although wcet-only adjusted the phases of tasks, it was largely unable to enhance end-to-end delay in comparison with worstcase, because wcet-only did not consider variable execution times of tasks. it is also evident that master-only significantly reduced end-to-end delays, but still generated higher values than suggested-algo due to the absence of phasing on the slave node. fig. 6 compares the minimum, average and maximum end-to-end delays of the four phase combinations. compared with master-only and wcet-only, suggested-algo yielded end-to-end delays that were shorter by 19% and 86%, respectively. it also reduced jitter by 67% and 98%, respectively. in the case of wcet-only, the minimum end-to-end delay was significantly lower than its average and maximum values. this was because wcet-only shows the lowest end-toend delay when tasks consume their wcet, although its probability is very low, as shown in fig. 5. 6.3 impact of phase granularity as mentioned in sec. 4.1, we can change the granularity of the task phase to reduce the number of phase combinations to be considered. a higher granularity can reduce search time but harms optimality. to analyze the impact of granularity on search time and optimality, we ran our algorithm by varying granularity and analyzed the worst-case end-to-end delays of the suggested phase combinations. fig. 7 shows that the number of search cases can be significantly reduced with a higher phasing granularity. however, fig. 8 shows that the worst-case end-to-end delays increased as phasing of periodic tasks distributed over real-time fieldbus 657 figure 6: minimum, average, and maximum end-to-end delays of different phasing schemes figure 7: number of phase combinations with different phase granularities figure 8: worst-case end-to-end delay with different phase granularities granularity increased, which means that the chances of finding an optimal phase combination also decreased with a higher granularity. 658 s.-h. lee, h.-w. jin, k. kim, s. lee figure 9: impact of the number of simulated periods on the analysis accuracy 6.4 accuracy of delay distribution to analyze the distribution of end-to-end delays, our simulator varied the execution times of tasks for every period during the simulation. therefore, the simulator could provide an accurate distribution when a sufficient number of periods had been simulated. thus, the larger the number of periods simulated, the more accurate the distribution of end-to-end delays. in order to analyze the impact of the number of simulated periods on the accuracy of performance measurement, we ran our simulator by varying the number of simulated communication periods from 10 to 10,000. fig. 9 shows the minimum, average and maximum end-to-end delays analyzed with different numbers of periods. we can see that the average end-to-end delay of the 10-periods case was slightly lower than in other cases, while the minimum and maximum end-to-end delays were the same for all cases. in general, if variations in tasks’ execution time and the number of tasks are large, a small number of simulated periods may produce a less accurate distribution of end-to-end delays. however, we would not need an excessively large number of periods, as shown in our results. 7 conclusions in this study, we proposed a heuristic algorithm that searched for a near-optimal task phase combination on distributed nodes with respect to the minimal worst-case end-to-end delay for emerging real-time fieldbuses, such as ethercat. to reduce search time, the algorithm identifies time intervals where communication occurs and efficiently calculates the worst-case end-to-end delay for a given task phase combination without considering all possible execution times of tasks and their combinations. to analyze the distribution of end-to-end delays of different phasing approaches, we also implemented a simulation tool that considered variable execution times of tasks and the behavior of a real-time fieldbus. we carried out an industrial case study to show that the proposed heuristic can efficiently suggest near-optimal task phasing across distributed nodes for minimum worst-case end-to-end delays. the simulation results clearly showed that our algorithm can reduce end-to-end delay and its jitter by 86% and 98%, respectively, compared with other task phasing approaches. in summary, we need to consider variable execution times of tasks for optimal phasing of distributed tasks, but also need to apply task phasing to both master and slave nodes to achieve minimum worst-case end-to-end delay. phasing of periodic tasks distributed over real-time fieldbus 659 acknowledgment this work was supported by the dual use technology program (um13018rd1). bibliography [1] bril r.j., steffens e.f.m., verhaegh w.f.j. 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(2009); stochastic analysis of can-based real-time automotive systems, ieee trans. industrial informatics, 5(4), 388-401, 2009. international journal of computers communications & control issn 1841-9836, 13(5), 824-836, october 2018. optimization of the latency in networks sdn g.a. keupondjo satchou, n.g. anoh, t. n'takpé, s. oumtanaga gilles armel satchou keupondjo* laboratoire de recherche en informatique et télécommunications (larit) institut national polytechnique félix houphouët boigny de yamoussoukro, côte d'ivoire *corresponding author: armel.keupondjo@inphb.ci nogbou georges anoh 1. laboratoire de recherche en informatique et télécommunications (larit) institut national polytechnique félix houphouët boigny de yamoussoukro, côte d'ivoire 2. unité de recherche et d'expertise numérique (uren) université virtuelle, côte d'ivoire georges.anoh@uvci.edu.ci tchimou n'takpé 1. laboratoire de recherche en informatique et télécommunications (larit) institut national polytechnique félix houphouët boigny de yamoussoukro, côte d'ivoire 2. laboratoire de mathématique et informatique (lmi) université nangui abrogoua , côte d'ivoire tchimou.ntakpe@gmail.com souleymane oumtanaga laboratoire de recherche en informatique et télécommunications (larit) institut national polytechnique félix houphouët boigny de yamoussoukro, côte d'ivoire oumtana@gmail.com abstract: unlike traditional networks, software-de�ned networking (sdn) are characterized by a physical separation of the control and the transfer plan. thus, a centralized controller communicates the functions of control plan to each device via the openflow protocol whenever he is asked or that it deems appropriate. this impact strongly the latency time which is important for new services or multimedia applications. in order to optimize the time of transmission in network data with sdn, the proactive approach based on the algorithm back pressure is usually o�ered. however, we note that the proactive approach while reducing strongly this time, does not account settings such as the failure of a node part of the way to transfer or the breaking of a bond that greatly increases the latency time. in this document, we will propose a joint routing approach based on proactive and reactive routing. this in order to optimize the routing functions by simply placing the tra�c where capacity allows, in order to avoid congestion of highly stressed parts of the network taking into account the failures and signi�cantly reduce the time latency. simulation results show that our proposal allows a considerable reduction of latency even when there are failures in the network. keywords: software-de�ned networking (sdn), routing, multi-path, latency. 1 introduction conceptually, a router is characterized by a control plan, and a transfer plan. as a result, the routers are responsible for the supervision of the topology of the network and the transfer of packages using static, or dynamic routing protocols. what is often at the origin of a load of signi�cant calculation. in networks sdn (software-de�ned networking) this charge is the responsibility of one or several controllers [2,9,12,14]. copyright ©2018 cc by-nc optimization of the latency in networks sdn 825 as a result, a centralized controller communicates control plane functions to each device via the openflow protocol [17] whenever it is requested or deemed appropriate [4, 16]. this has a signi�cant impact on latency, which is important for multimedia applications. in order to optimize the transmission delay in data networks with sdn, two routing approaches are generally proposed; such as the proactive approach and the reactive approach. however, with the reactive approach, resource utilization is optimal and this approach is resilient to outages; which is not the case for the proactive approach which greatly reduces the transmission delay. however, the proactive approach does not take into account parameters such as the failure of a node in the transfer path or the break of a link. but these failures greatly increase the latency. despite the incremental deployment of new routing solutions adapted to modern uses of the internet [5] facilitated by sdn networks. it is in this context that we propose in this article a mixed routing approach that will reduce latency in case of network failures. this makes it possible to optimize the routing functions by simply placing the tra�c where the capacity allows it, in order to avoid the congestions of some parts of the network that are heavily loaded, taking into account faults and considerably reducing the latency. 2 related works the algorithm back pressure [8,11,13] is a well-known optimal �ow algorithm. it refers to an algorithm to dynamically route tra�c over a network to multiple bonds using gradients of congestion. its implementation requires that each node maintains a separate queue for each stream on the network, and a single queue is served at a time. as tra�c in the data network is usually very large, maintaining the data structure of the queues at each node becomes complex. in [1], the authors propose an approach allowing each node on the network to maintain a single queue that implements the fifo (first in first out) approach for each outbound links, instead of keeping a separate queue each stream on the network. they also propose an algorithm that force back pressure, to use the minimum amount of network resources while maintaining maximum throughput. however, for low �ow rates arrived, delays will be much higher. that's why in [15], the authors propose to combine the algorithm of routing of shortest path to back pressure, in order to maintain several queues at each intermediate node for each stream and ensure that packets of a stream will reach the respective after crossing more than destination n nodes. this allows to optimize the delay of package among the variants of back pressure presented. but it overload due to queues of more and larger nodes. in [10] , the authors propose a variant of the algorithm of back pressure based on the lifo (last in first out) approach for minimization of the time. but the main limitation of this method is that some early packages get trapped in the lifo bu�er inde�nitely. with the emergence of sdn and its use of more and more in networks of data in [6], the authors rely on the centralized management of the network with sdn and o�er improved routing back pressure. associated with techniques of shortest paths to the destination routing to optimize transmission times and delays results in looping of packets on the network. this proposed method shows a signi�cant gain in performance in terms of reduction of delays in packets, length of queue average, average length of jump while retaining ownership of optimality of the routing algorithm �ow back pressure base. however, suppose that a node reboots into the network. it has more rules of transfer and so cannot make a decision of transfer if a �ow happens at that moment as long as the controller will again provide transfer rules. this can signi�cantly impact the time of transmission. and also one of the problems in data with sdn network is control of the size of the table of rules of transfers. 826 g.a. keupondjo satchou, n.g. anoh, t. n'takpé, s. oumtanaga the question that arises is how to optimize latency in the face of the problem of power outages on the network. 3 the sdn paradigm in networks sdn, one or several controllers have supported the calculation of roads, thereby routers are reduced to simple devices of transmission. figure 5, shows the routing of a package between a source and a destination in sdn network with open�ow protocol. the controller sends a transfer rule to the router a (1) so that when a package arrives at this router (2), with an ip destination which is included in the transfer rule network ip address, the router will know immediately by what interface transfer the package (proactive approach). router b, having no management rule concerning the package reached his level, will contact the controller (4) to �nd out the attitude compared to the package. the controller sends a transfer rule (5) concerning the package and the router is then able to transfer the package to the next node of the network (6) (reactive method). figure 1: diagram of a simple sdn network the main goal of tra�c engineering is to avoid congestion of some heavily-used parts of the network by controlling and optimizing routing functions (placing tra�c where capacity permits) [3]. the challenge here is to adapt well to the dynamic character of the topology (case of breakdowns) and the demand. 3.1 analysis of reactive and proactive methods for the realization of this work we have adopted a methodology following the goal. it is, to make an assessment of the transfer time of the stream as well as load produced for this transfer of �ow from the source to the destination, while considering the di�erent routing methods. optimization of the latency in networks sdn 827 reactive method in this approach, each time the router or the node receiving a package, reports the event to the controller and receives in return rules in an exchange, in order to decide on the transfer to the appropriate following router. an example on an architecture represented by �gure 2 below. when the node (a), receives a package from pc1 to pc2 (1), it passes the request to the figure 2: descriptive diagram approach reactive controller (2). the controller relies on the current topology of the network and communicates the transfer rule to the node (a) that runs (3). operations (1), (2), (3) are thus repeated in each node until the package arrives at its destination (pc2). we evaluate the deadline for transmission and all the messages exchanged during the transfer of the �ow from the source to the destination. during the application of the rule of transfer to the controller: � it takes a time t = tjc + tcj for (1) and (2) on di�erent nodes requesting the controller. for n nodes in the path from the source s to destination d, the transfer time is de�ned as follows: t(s,d) = n∑ j=1 (tjc + tcj + tj) (1) � c represent the controller � tjc represents the transmission delay of node j to the controller � tcj represents the transmission delay of the controller at node j � tj is the veri�cation time of the transfer rules in order to decide on the transfer 828 g.a. keupondjo satchou, n.g. anoh, t. n'takpé, s. oumtanaga all of the messages of the path with the source s and the destination d is de�ne by : αch(s,d) = n∑ i=1 (αic + αci) + n∑ i=1 n∑ j=1 αij (2) � c represent the controller � αic the number of messages exchanged between a node i and the controller c � αci the number of messages exchanged between the controller c and a node i � αij the number of messages exchanged between a node i and a node j proactive method proactive behavior consists of the transmission of rules by the controller until the router receives the associated packages. several solutions have been developed based on the algorithm of back pressure suitable for the data networks very sensitive to latency and especially the qos [7]. figure 3: descriptive diagram proactive approach figure 3 representing a network architecture to proactive behavior. when the node (a) of the �gure 3 receives a packet from pc1 to pc2 destination (1), he runs the appropriate for this type of package and this transfer rule in its table. operation (1) is thus repeated until the package arrives at its destination. when checking the rules of transfer from the source to the destination: � it takes a time tj which varies according to the size of the transfer of each node table and j belonging to the path between s and d. t(s,d) = n∑ j=1 tj (3) optimization of the latency in networks sdn 829 � messages sent on the path to source s and destination d are de�ned by : αch(s,d) = n∑ i=1 n∑ j=1 αij (4) where αij = { ksilelien(i,j)∈ch(s,d) 0,sinon experimental evaluation of the proactive and reactive approaches in order to value these di�erent approaches, we emulated the topology (�gure 4) wan, on a physical machine. the latter is characterized by an intel core i5 processor 4 cores running at 2.53 ghz and an equal to 8 gb ram. the machine that simulates the virtual network uses mininet in its version 3.2, which allows to create the topology considered sdn. we will evaluate on the architecture �gure 4, the impact that can have these two approaches on latency. we ignore possible failures in the network. figure 4: topology of simulation our tests are carried out with the controller ryu under its 3.2 version. it is based on python and provided apis to communicate between applications and the network infrastructure. in addition, the protocol used for communication between the controller and the switches is openflow1.3, as well as the python3 programming language. in addition, we use ping and cbench tools to measure network performance metrics. the simulation followed an iterative process and the simulation process is repeated ten (10) times. thus we have the results of �gure 5. we see that the result of the proactive approach remains very lower than the reactive approach to each iteration. we note as well that the proactive approach allows to optimize the latency 830 g.a. keupondjo satchou, n.g. anoh, t. n'takpé, s. oumtanaga figure 5: latency analysis compared to the reactive approach assuming that no failure occurs in the network. we suppose issues occurred at the level of some network nodes (the nodes have restarted) and we test the scalability of the proactive approach. assessment of the latency of the proactive approach with failure when a node restart or when a break of link on a path, we see that it takes a time µ which varies according to the time of retransmission of the transfer rules of each node, which restarts on the path between the source s and destination d. we suppose that µ is the time-out period new rules of transfer on the part of the controller. suppose that the controller sends the transfer rules to the nodes each q seconds. so when he is a loss on a transmission path, it becomes obvious that the timeout is less than or equal to the time taken by the controller before sending the new transfer rules. we have : µ = q − te (5) where te is the elapsed time since the controller has transferred the transfer rules. it is obvious that te ≤ q then µ = q ≤ te < q. suppose a node j decides to contact the controller for new transfer rules. time to get the rule tp is estimated by: tp = tjc + tcj + tc (6) where tjc is the time-out for the node j contact the controller c, tc is the time set by the controller to process the request of the node and tcj the time taken to transmit the transfer rule to the node. still using the simulator mininet, ryu under openflow1.3, as well as programming python3 language controller. we have obtained the results in �gure 6 and �gure 7. these results show that when there is a failure in the network, the time the latency of the proactive method increases considerably (�gures 6 and 7). because it is not resilient to failures. if µ > tp in the strictly proactive case, one is obliged to wait for a time µ. however in this case, it is interesting to ask the transfer rules in order to receive them within tp (more quickly). where hybrid our approach, that we present in the following section. 4 proposed approach to realize the potential of programmable networks, we propose in this paper another variant of the algorithm of back pressure with league of nations based on a mixed method. taking into account the set of messages exchanged between the source and destination during the transfer of optimization of the latency in networks sdn 831 figure 6: latency with failure analysis figure 7: average latency time with failure analysis the stream for a better evaluation of the period. this in order to optimize the routing functions by simply placing the tra�c here where capacity allows, to avoid congestion in some parts of the network strongly requested and also control failures in the network. the operation of the joint method is shown in �gure 8. 4.1 analysis of approach when a node receives a packet for a given destination, it checks if there is a transfer for this destination rule since his transfer table and runs it so yes (node 8). otherwise a message (packet_in) (node 2) is sent to the controller and the controller determines the path satis�able, then sends the transfer rules (packet_out) to all nodes in the path selected to avoid situations where the transfer rules are obsolete. if a node on a path restarts, and he contacts the controller as node 2 of the descriptive scheme, it will take time then the set r the path nodes that restart will use a time de�ned by :∑ j∈r tjc + tcj + tc (7) to get the new transfer rules. if a set node restart but do not contact the controller, they will use a time de�ned by: ∑ j∈r µj (8) 832 g.a. keupondjo satchou, n.g. anoh, t. n'takpé, s. oumtanaga figure 8: descriptive diagram approach joint to get the transfer rules. in these analyses, the transmission time of the information on a path to a source s to destination d is de�ned by : t(ch(s,d)) = ∑ j∈r (tjc + tcj + tc) + ∑ j∈r µj + ∑ j∈ch(s,d) (tj + t ′ j) (9) where tj is the transfer time of the node j, t′j is the waiting time in the queue of the node j and µj is the timeout of the new rules of transfer of the node j with : r = {j ∈ ch(s,d) : µj > tjc + tc + tcj} (10) r′ = {j ∈ ch(s,d) : µj < tjc + tc + tcj} (11) the number of messages in the network nodes in the path for the transmission of information between the source to the destination is de�ned by : αch(s,d) = ∑ i=1 (αic + αci) + n∑ i=1 n∑ j=1 αij (12) αic = { 1ifthenodei∈ch(s,d) 0,else αci = { 1ifthenodei∈ch(s,d) 0,else αij = { kifthelink(i,j)∈ch(s,d) 0,else k with all the messages destined for the node j 4.2 proposed algorithm the algorithm that we o�er can be described in the following : we repeat the actions 1 to 3 to the destination. optimization of the latency in networks sdn 833 algorithm 1 : reapro-cm input: network topology output: transfer rules begin step 1 : processing of a node that receives a data transfer to the destination if a packet arrives at a node j then if j is not the recipient node then if j has a transfer rule so j applies this rule to transfer else if µ > tp then j contact the controller. go to step 2. if j is waiting for the new transfer rule endif endif else j receive the packet endif endif step 2 : controller processing determine the assessments of the di�erent links on the network determine the optimal paths using the dijsktra algorithm of the k shortest paths at time t communicate the transfer rule to the node that initiated the request as well as the nodes as a part of the path selected to reduce the tables of transfers of other nodes go to step 3 step 3 : processing of a node that receives a transfer rule the node running the transfer rule and send the package to the next to the destination node end 834 g.a. keupondjo satchou, n.g. anoh, t. n'takpé, s. oumtanaga 5 simulation and results in order to evaluate our algorithm, we emulated the topology (�gure 9) wan, on a physical machine. the latter is characterized by an intel core i5 processor 4 cores running at 2.53 ghz and an equal to 8 gb ram. the machine that simulates the virtual network uses mininet in version 2, a widely used tool in the sdn community and it can simulate topologies classic like bus, ring, hierarchical, or customized, that re�ect the exact con�guration a topology of company. in order to isolate the operating system experiences, we simulate virtual machines (vms) through a named virtualbox virtualization application. reserve at least two vms, one for the emulated network and one for the controller. the performance of the proposed algorithm is presented in �gure 8. figure 9: topology of simulation the simulation followed an iterative process. the simulation process is repeated ten (10) times and the results are presented in �gures 10 and 11. figure 8 shows the latency time of the routing back-pressure trouble free approach is lower compared to the routing back pressure with failure. indeed, a failure of a node or link requires wait for a node controller of new transfer rules. however, the controller sends the transfer rules according to a period. as a result, a node that is a failure of a link or falling down, must wait for a period of time less than or equal to the delay of the controller transfer rules. to minimize the latency time found with the results of �gure 5, we proposed an alternative approach that improves signi�cantly the lag time when a failure occurs in the network as shown in �gure 5. the simulation results show that our approach (reapro-cm) latency time, remains much lower than that of back-pressure with failure (voting-bp with breakdown). indeed, when a failure occurs in the network (link broken) during transmission of the stream, our approach provides a backup path allowing to move this �ow without however wait until the controller for optimization of the latency in networks sdn 835 figure 10: latency with failure and rescue way new transfer rules. when a failure occurs at the level of a node (a node reboot), our approach evaluates the time remaining until the node receives new transfer rules. if this time is very high, the node asks the controller new rules of transfer immediately. these principles implemented in our approach to routing can signi�cantly reduce latency when there is a failure in the network (�gure 11). figure 11: comparison approach mixed with failure and proactive approaches 6 conclusion this article o�ers an approach of mixed multi-path routing in networks of data with sdn to resolve failures coming into the network. these failures have a signi�cant impact on latency where interest to propose a joint routing based on the proactive and reactive approaches. mathematical analysis and simulation results show both the algorithm that we o�er allows to optimize the transmission time in networks of data with league if all the messages exchanged during the transfer of information are taken into account. because the main problem in networks with sdn sdn routing tables cannot contain only a very limited number of rules. bibliography [1] bui, l.; srikant, r.; stolyar, a. 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[17] openflow switch speci�cation [online]. https://www.opennetworking.org/softwarede�ned-standards/speci�cations/, accessed: june 25, 2016. international journal of computers communications & control issn 1841-9836, 12(4), 550-561, august 2017. a solution for interoperability in crisis management f.j. pérez, m. zambrano, m. esteve, c. palau francisco j. pérez*, marcelo zambrano, manuel esteve, carlos palau communications department distributed real time systems and applications lab universitat politècnica de valència 46022 valencia, camino de vera s/n, spain, *corresponding author: frapecar@upvnet.upv.es abstract: to effectively manage a crisis, is necessary the participation of multiple agencies related to protection and public safety, which allow actions in accordance with the demands of environment and requirements of all those affected. interoperability is the key to comprehensive and comprehensive crisis management (cm), that allow to face any type of disaster, at any time or place. for this, it is necessary the permanent exchange of information that enables all the agencies involved, coordinate its operations and collaborate to manage the situation in the best way possible. this article describes the approach to interoperability in cm carried out by secure european common information space for the interoperability of first responders and police (sector) european project, which has one of its main objectives, the development of an interoperability platform that allows the agencies involved in the management of a crisis, to achieve a joint, coordinated and collaborative response. the platform has a core, a common information space (cis), which manage as a single storage entity, all information of the information systems (iss) connected to the platform, regardless of the model of data and computer applications used by each one of them. keywords: interoperability, distributed architectures, command and control, crisis management, data models. 1 introduction a crisis can be defined as an unforeseen situation that endangers the environment, property and / or life of people. the cm refers to those resources and processes required to deal with a crisis in the best possible way. one of its most important characteristics is its multi-agency nature, which allows meet the demands of a critical environment and the requirements of all those affected, through the resources, skills and knowledge coming from different protection and public safety agencies involved [3] [14] [15]. it presents four domains of operation: physical, information, cognitive and social. • the physical domain is related to the environment where the crisis develops (extension and geographical location, environmental conditions, available resources, affected, etc.). • the domain of information refers to all those aspects related to the information needed to describe the operations environment (sensor, measurement, storage, publication, etc.). • the cognitive domain makes use of this information, to create situation awareness from which can be made decisions in line with reality. • the social domain deals with inter-agency interaction, promoting collaboration between them and covering the three domains described above. it allows the effective exchange of information (information domain); the creation of a common situational awareness and copyright © 2006-2017 by ccc publications a solution for interoperability in crisis management 551 consensual decision making (cognitive domain); and the coordinated execution of these decisions, which bring about the desired effects on the environment (physical domain) [16] [2]. the capability of a system to exchange and understand information from other systems is defined as interoperability [9]. in heterogeneous and complex environments such as cm, interoperability is the key to achieve the orchestration of the resources involved, and to allow a coordinated and collaborative response [20] [22] [24]. this article details the solution proposed by the european project sector for interoperability in cm. sector is a part of the seventh framework program of the european union for research and technological development (fp7), and the universitat politècnica de valència (upv) has actively collaborated in its development [21]. one of its objectives is the design and implementation of an interoperability platform that allows the agencies involved integrating within a communications infrastructure to exchange and share information related to the management of a crisis. the platform has as core, a middleware layer that adapts the information coming from different iss, to the model and format data defined in a cis, which manages as a single storage entity, all the information coming from those iss. the feasibility and operability of the platform has been validated by means of functional tests on a prototype implemented based on the architecture described in this article, and within a simulated scenario for a crisis. each of the iss integrated to the platform, shared in the cis, information related to the state of operations environment and the resources deployed both inside and outside himself. this information made it possible to create accurate and real situational awareness, which in turn enabled an effective planning and coordination of response and recovery operations. the paper is divided into five sections: firstly, an introduction to the proposal and methodology used; second, it describes the motivation and the oeuvres that have been taken as main references for the development of sector project; third, it details the propose architecture and the functionalities of each of its components: fourth, it describes the tests of functionality and the obtained results; and finally, presents the conclusions and final notes for this work. 2 motivation and related work one of the most important factors to be considered regarding interoperability in the cm is the heterogeneity of data and the way users access them. many crisis management systems enable inter-agency interoperability through the use of a proprietary set of it tools and a standardized data model, that enabling the transparent exchange of information between applications and agencies (e.g. coordcom [18], atos [1] or destriero [19]. however, this scheme is limited by the usability and scope of those tools designed by the manufacturer, which do not necessarily achieve the particular scope and requirements of all agencies. in general, users are reluctant to use external computer tools, either by affinity with the applications they use regularly or by mistrust and expertise lack with computer applications that they do not know. this work focuses on the cm social domain, and its main contribution lies in the capabilities of the platform, to allow the exchange of information between the agencies involved, regardless of data model, systems and applications computer used in each of them. agencies use their own tools and iss to exchange and share information between them. the architecture detailed in this article has taken as main references for its development to the national information exchange model (niem) and the non-relational data base (db) (no structured query language (nosql) db). niem was created by the united states department of defense (dod) and the department of homeland security (dhs) in order to interconnect 552 f.j. pérez, m. zambrano, m. esteve, c. palau communities with the need to exchange information to fulfill a common goal. it proposes the development of a middleware layer and the adoption of a normalized data model to allow the iss involved to exchange information independently of their data model and proprietary applications (figure 1) [7]. ���������� � ���� ��� ��� � �� � ����������� ���� � � ����������� ��� � �� � ����������� ���� � � ����������� ����������� �� ��� ��� � ��� � figure 1: niem schema on the other hand, the nosql dbs allows to store each object with an independent data schema, solving the deficiencies of relational dbs in how many to the management of heterogeneous data [6] [13]. figure 2 shows a diagram that summarizes the proposal made by sector in terms of data management with nosql dbs. ����������������������������������������������� ����������������������������������������������� ������ ������ ������ ������ ������������� ��� �������� �������� �� ���������������������������������������������� ���������������������������������������������� �������� �������������������� �������������������� �������������������� �������������������� ������������������� ������������������� ������������������� ������������������� ����� �������� ��������� ��������� ��������� �� �� �� ���� ���� ���� ���� figure 2: no relational distributed data base schema 3 architecture the architecture has been designed to support the requirements of availability, scalability and information heterogeneity present in the cm. it is based on a communications distributed infrastructure (cdi) and middleware layer, which enable iss to exchange and share information regardless of their data model, systems and computer applications. the middleware layer adapts the information coming from the iss to the data model defined in the cdi. the information that needs to be shared is stored locally on a nosql db, in order to later distribute it said information on the other nodes dbs that make up the cdi. the sharing and exchange of information are a solution for interoperability in crisis management 553 done through a messaging and notification mechanism (detailed below), under the extensible markup language (xml) data schema defined in niem and a data distribution service (dds) protocol. the architecture has been developed entirely under free software, eliminating dependence with manufacturers (maintenance, upgrades, etc.) and leaving an oppen-door for customization and development of new functionalities. linux has been chosen as the operating system for the communication nodes, mongodb as nosql db manager [23], java as a programming language for the development of the various modules of the platform, and angularjs [13] as framework javascript for the development of the human machine interface (hmi). to facilitate its design, implementation and scalability, the architecture has been divided into three main elements(figure 3): • information systems: devices, tools or computer systems, registered on the platform to contribute and / or obtain information from it. • cdi: responsible for providing connectivity between the nodes that make up the platform. its topology, as well as the technology used in the communication links, are transparent to the middleware layer, which facilitates the implementation and scalability of the platform. • middleware layer fulfills the functions of transceiver between the cdi and the iss. it is responsible for allowing the exchange of information between iss integrated to the platform. it is subdivided into four components: interoperability boxes (ibxs), communications nodes, cis and hmi. figure 3: architecture 3.1 interoperability boxes they are responsible for adapting the information coming from the iss to the xml format defined in the cdi, and vice versa. each ibx has an interface to the is and another to the 554 f.j. pérez, m. zambrano, m. esteve, c. palau comunications node, allowing the flow of information between them through web services (ws) [12]. the requests / responses from and to the iss are made through the wss that have been defined in the is in question (simple object access protocol (soap), representational state transfer (rest), hypertext transfer protocol (http), etc.), while the requests / responses from and to the communications nodes, according to platform design and following the principles of standardization proposed, are made only by soap. among the communication interfaces, there are two sublayers (transformation and communications), which allow the conversion of the data format in a bidirectional way. due to the heterogeneity in the formats and protocols used by iss, each of them needs their own ibx, and there will be as many ibxs as iss will be integrated into the platform. figure 4 (left panel) shows a general block diagram for an ibx. ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 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����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� ��������� ����������� ��������…� �� ��� �� �� �� ���� ��� ����� �� ��� ���� ��� �� �� ��������� ������ �� ��������� �� ����� �� ���� ��� ����������������� ��������� �� �� ����������� ������������ figure 4: interoperabilty box (left panel) and communications node (right panel) 3.2 communications nodes they are the gateway to the platform and among its main responsibilities are the registration of users and systems, as well as the notification of any information changes or relevant event on the platform, to the local iss (integrated to the platform through it) and other communications nodes. figure 4 (right panel) shows a block diagram summarizing the internal architecture of a communications node. four main elements can be observed: a user layer responsible for making available to iss, the platform configuration services (e.g. iss registration, notifications subscription, addressing, etc.) and interaction with the cis (reading, writing, deleting and updating data); a layer of operations responsible for the management and functional processes inside node (e.g. data validation, node-to-node communication, data management, etc.); two groups of transversal services responsible for the security, processes supervision and node access; and a local storage space (lss), in which is it stored a replica of the cis and the users and systems register. a solution for interoperability in crisis management 555 when a new node needs to be integrated into the platform, the first step is to register on the same. once authorized its access, the node becomes part of the cluster that shapes the cis, and initiate communications with the other nodes through the cdi. the information received (data and metadata) is stored in the lss, which is accessible by all iss registered in that node, and will be forwarded to the other nodes that so require it. the platform can be configured with the number of nodes required, and each node can serve more than one iss, depending on the scope of the platform, the processing capacity of the node, and the specific requirements of each is in terms of resources, security, availability and / or reliability. 3.3 common information space the cis is the core of the architecture. it allows the management of all the information coming from the iss registered in the platform, as a unique storage entity, and it is based on a nosql db cluster, to enable the storage of each data object under an independent schema. the cis presents a data model based on niem, with a binary documental structure type json (bson), this promotes the exchange of information and the transparency of the physical objects location. moreover it takes advantage of the architecture distributed character to replicate the information in each of the cluster nodes, balancing the data processing, and providing availability, reliability and scalability to the platform. among its most important features, it is important highlight the decentralization, scalability and adaptability about the data heterogeneity. both the workload as the diffusion level are parameterizable, and they should be configured following the crisis environment needs. sector uses mongodb as db engine, due to its multiplatform character open source and the document-orientation storage [23]. regarding the information management, it has been selected opensplice (open source distribution of dds protocol) for the implementation of publish/subscribe mechanism on data distribution [4]. figure 5 summarizes a block diagram with cis internal architecture. figure 5: common information space. 3.4 communications and data management the information exchange among nodes, and among a node and the local iss, it is performed using a publish/subscribe mechanism, with a xml data schema based on niem. the xml datagrams, contain the data and metadata required to notify, to the iss and the communication nodes, about any change performed in the availability of information and/or the platform status. before the data storage, the datagrams should be validated and transformed to the binary format bson defined in the cis, through validation and transform modules available in each node. 556 f.j. pérez, m. zambrano, m. esteve, c. palau the publish/subscribe mechanism has been developed taking in to account the performance optimization and the use of network resources operating in low capability environments. this is the case for cm situations with low process capacity and storage, limited bandwidth, packet loss etc. [23] [5]. the information publication and the information requests, use the dds protocol to distribute the data and metadata through the cdi. the iss should specify the topics of which they will be providers, and the topics of which they want subscribe as consumers. every time that new information is shared through the cis, the local node notifies to the local iss subscribed to the topic, about the information update via "notification" service. if the information is relevant for a is, the registered ibx can retrieve the information from the cis, transform it into its proprietary data model and make the adapted information accessible to their informatics tools and/or systems. similarly, the local node shares the update with the other nodes in the platform using the "messaging" service, and these notify to their iss if applicable about the new information available. this process is shown in figure 6. figure 6: notification process. 3.5 security and human machine interface the platform access and privacy is managed through the hmi. it is able to create users, manage profiles and assign permissions using the "privacy and security" module. it also allows the maintenance and the audits of the platform via "monitoring and control" module. each node, stores a copy of information related with the users and systems registry, guaranteeing always the accessibility and the security in the platform. regarding to the communications security, it is managed using security protocols as hypertext transfer protocol secure (https) and transport layer security (tls), and using security certificates to enable the cis access. 4 validation and results the platform feasibility and functionality, was validated using a prototype developed following the architecture described in this paper and executing a simulation in a flood scenario in roermond city (netherland). two communication nodes were deployed, and used by the involved agencies in the simulation, for sharing and interchanging information related with the a solution for interoperability in crisis management 557 flood and the environment status. figure 7, shows the final deployment configuration for the scenario validation. ����������� ������� �� � �� � �� ����������� ����������� �������� �� � �� � �� � �� � ���� �� ���� ���� ��� �� ��� �� � �� � ���� ���������� ������ �� � �� � ��� ��� ����������� �� � �� � ���� � � �� � �������� � �� ����� � ��� ��� ��������� � ��� �!������ ���������� � ��"�� ��� �� ������ ��������� ������#�� ����������� ������ �� �� � ��������� �� � ������ ��������� � ���������������� �������� ���� �$ "����" �� ������ %������� ���&#�!���� ��'���� �� �� �������� ������� ��� ��������� �� � �� � (��� ����������� (��� �����'� �����'� ��� ������������� ��� ������ �� � �� � ��� ����������� ��� ������� ��� �� �� ����������� ������ �� �������������� ��� ������ figure 7: platform deployment schema the distributed architecture of sector platform, divides the data processing load among all the cdi nodes and decreases the hardware and software requirements in the communication nodes. a unique host with standard features regarding to the hardware and the software (i7 octa core processor, 32gb of ram, 2tb of disk and vmware vsphere 6.5) was used during the tests for virtualizating the 2 communication nodes and the iss with their respective ibxs. each participating agencies, added and/or got information from the cis, using their own tools and informatics systems. table 1, shows a summary with the participation of the agencies, and the iss used by them during the tests. table 1: scenario actors and information systems coordination type organization actor system fire brigades sgsp eden police psni inasafe local red cross iaemo eden roermond mayor ssv gcm directorate for national safety and security external end-user1 webgis bavarian police pas inasafe international uk red cross iaemo gpa ami external end-user2 gpf+gpa the simulacrum starts with the information sharing related with the environment conditions and the status of the rivers maas and mesue, by the local cm agencies. after the information analysis, and following the obtained forecasts, the protocol of an imminent flooding is started. after the corresponding preventive actions, including reports publication and generation of alerts through the cis (e.g. affectation perimeter, climatic and ambient conditions of the operational environment, hot spots, location and planning of first responders units, etc.). the flooding 558 f.j. pérez, m. zambrano, m. esteve, c. palau overflows the local response capacity, so the emergency state was declared and the help of national and international neighbouring agencies was requested by the local authorities. during the response phase and the situation stabilization, the information related with the actual situation of the environment, the permanent status reports, and location of first responder units, victims and affected people were shared through the cis. following the information shared in the cis, the response and recovery actions were planned, deploying five multi-agency response groups, covering all the affected zone by the flooding. figure 8, summarizes the participation of the different actors in the exercise, and some of the tools used. figure 8: test scenario during the simulacrum, and with the final purpose of validate the platform, it was attended by collaborative personnel, both tactical and strategic, from the participant agencies, as well as representatives of several agencies related with the cm in european union. after the simulacrum, a questionnaire was carried out to the attendant personnel, to have a general impression about the platform functionalities. the obtained results are summarized in appendix a, table 2. the 100% of the respondents were agree, to a greater or less extent, with the platform usability. an 81% would use the platform, either for training or a real environment, and believes that its agency would benefit with the platform functionalities. a 3% would not use the platform, and a 16% would be undecided about its use. in addition, an 87% of the respondents would recommend the use of the platform and agrees with the proposed approach for the inter-agency interoperability. the proposed solution has been validated for this use case, but it is possible to generalize it in any scope for cm (fires, earthquakes, etc.). the only requirement for the integration of new tools into the platform is the ibxs development, which allow the data exchange and the communication with the nodes following the niem format defined in the cdi. 5 final notes and conclusions in complex and diverse environments such as cm, the interoperability is the key for an integrated and comprehensive management, allowing a flexible and effective response to any type of disaster that may arise. for this, it is necessary a continuous exchange of information, allowing all involved agencies, to coordinate their operations and collaborate to manage the situation in the best possible way. this paper describes the approach of the european project sector, for the interoperability between the agencies involved in the cm. it describes the architecture of an a solution for interoperability in crisis management 559 interoperability platform, able to integrate into a communication infrastructure the information from the different systems, used by the involved agencies, sharing and interchanging information. the main contribution of the platform, is found in its capability to manage heterogeneous data, and allow users from different agencies, to obtain and share information, using their own systems and informatics tools. the core of the architecture, is in its cis, which allows manage as unique storage entity, all the information coming from different information systems registered in the platform. it is based on a no relational distributed db, and niem data model, allowing the heterogeneous data management and divide the work process among all the nodes in the platform. the platform was validated using a developed prototype following the architecture described in this paper, and tested in a simulated scenario for a crisis. the platform capabilities were verified to facilitate the exchange of information among agencies, and the shared information was used by the personnel involved to plan and coordinate the response and recovery operations. moreover, the stored information in the cis, may be used as support for the management of future crisis, assisting decision support and command & control systems, with relevant information regarding problems and solutions raised in previous events. regarding to the future work, alternatives for the implementation of the cis are being explored, based on distributed database schemas, that allow the requirement optimization in hardware and software for the storage and the data processing [11] [10]. on the other hand, the human machine interface, currently allows administrators access to the platform configuration and monitoring tools. however, its implementation leaves an open-door for the development of other type of tools for customization or requirements compliance. currently a proprietary tool is being developed, and it is able to exchange audio and video information in real time, regardless of the communication technology used in the data link between the nodes. acknowledgment this work is funded by european commision as part of fp7 (no. 607821). project supported by leonardo company, universitat politècnica de valència, aselsan elektronik sanayi ve ticaret a.s, totalforsvarets forskningsinstitut, itti sp zoo, consorzio interuniversitario nazionale per l’informatica, saadian technologies limited, e-geos spa, universitaet stuttgart, thales sa, police service of nothern ireland, wyzsza szkola policji w szczytnie, health service executive hse, szkola glowna sluzby pozarniczej, stichting studio veiligheid, and eberhard karls universitaet tuebingen. bibliography [1] atos (2014); safeguarding your citizens and assets with 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(2010); reference architecture for collaborative design, international journal of computers communications & control, 5(1), 71–90, 2010. [9] ieee; ieee standard computer dictionary: a compilation of ieee standard computer glossaries, ieee, 1990. [10] li y., manoharan s. (2011); a performance comparison of sql and nosql databases, 2013 ieee pacific rim conference on communications, computers and signal processing (pacrim), victoria, bc, 15-19, 2013. doi: 10.1109/pacrim.2013.6625441 [11] padhy r. p., patra m. r., satapathy s. c. (2011); rdbms to nosql: reviewing some next non-relational database’s, international journal of advanced engineering sciences and technologies, 11(1), 15–30, 2011. [12] pirnau m. (2010); implementing web services using java technology, international journal of computers communications & control, 5(2), 251–260, 2010. [13] tudorica b. g., bucur c. (2011); a comparison between several nosql databases with comments and notes, 2011 roedunet international conference 10th edition: networking in education and research, ieee, 1–5, 2011. [14] unchr (2012); manual for emergency situations, united nations high commisioner for refugees, 2012. [15] wayne b. (2008); guide to emergency management and related terms, definitions, concepts, acronyms,organizations, programs, guidance, executive orders & legislation, fema, 2008. [16] williams a. p. (2010); agility and interoperability for 21st century command and control, vol. 4, ccrp, 2010. [17] [online]. available: https://angularjs.org/, accesed on 31 march 2017. [18] [online]. available: http://www.carmenta.com/en/products/carmenta-coordcom/, accesed on 2 may 2017. [19] [online]. available: http://www.destriero-fp7.eu/, accesed on 2 may 2017. [20] [online]. available: http://www.fema.gov/, accesed on 14 april 2017. [21] [online]. available: http://www.fp7-sector.eu/, accesed on 14 april 2017. 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[24] [online]. available: http://www.unisdr.org/, accesed on 11 april 2017. a solution for interoperability in crisis management 561 appendix a: questionair table 2: questionair for tactical and strategic personnel question (5) (4) (3) (2) (1) the key features of the platform are suitable for dealing with crisis response. 8 18 5 the sector platform offers added value to support faster decision making in response activities. 10 20 1 the sector platform offers added value to support improved decision making regarding resource and activity planning in crisis response. 11 16 4 sector enhances the cop for the current crisis management tools. 8 23 using the sector platform can be effective to speed up response time. 9 20 2 the sector platform combines useful neutral 3rd party applications and information sources for crisis response. 8 18 5 sector boosts interoperability and information exchange among different organizations. 16 14 1 access to the data from the integrated it systems and tools is available quickly and efficiently. 6 20 5 sector provides new capabilities, which enhance the use of the crises management tools. 6 21 4 i would recommend a fully developed sector platform to a colleague. 8 14 9 i would use the sector platform in "real life". 5 16 8 2 i would use the sector platform for field exercises. 9 20 2 i would also use the sector platform during the recovery phase of a crisis. 7 13 10 1 i consider my organization’s efforts in the field could benefit from the coordination facilitated by the sector platform. 6 15 9 1 a tool based on sector would be a valuable asset in crises and emergency management. 6 23 2 notwithstanding budget constraints, my organization would see value in funding its own sector service or becoming a funding partner in a multi-agency sector service. 2 11 17 1 a pay-per-use model is a good way for organizations to fund a sector service. 1 10 15 2 3 a fixed, flat annual fee is a good way for organizations to fund a sector service. 3 15 13 international journal of computers communications & control issn 1841-9836, 13(5), 792-807, october 2018. generalized ordered propositions fusion based on belief entropy y. li, y. deng yangxue li, yong deng* institute of fundamental and frontier science university of electronic science and technology of china, chengdu *corresponding author: dengentropy@uestc.edu.cn abstract: a set of ordered propositions describe the di�erent intensities of a characteristic of an object, the intensities increase or decrease gradually. a basic support function is a set of truth-values of ordered propositions, it includes the determinate part and indeterminate part. the indeterminate part of a basic support function indicates uncertainty about all ordered propositions. in this paper, we propose generalized ordered propositions by extending the basic support function for power set of ordered propositions. we also present the entropy which is a measure of uncertainty of a basic support function based on belief entropy. the fusion method of generalized ordered proposition also be presented. the generalized ordered propositions will be degenerated as the classical ordered propositions in that when the truth-values of non-single subsets of ordered propositions are zero. some numerical examples are used to illustrate the e�ciency of generalized ordered propositions and their fusion. keywords: dempster-shafer evidence theory, ordered proposition, uncertainty measure, belief entropy, information fusion. 1 introduction in resent year, with the intensi�cation of competition in the modern information war, information technology has developed rapidly, and the amount of information has increased explosively. thus, as the critical technologies for information collection, storage and processing, the essentiality of information modeling and fusion has gradually increased. there are many methods to model information, such as probability theory [10], dempstershafer evidence theory [7,23], rough sets [40], fuzzy sets [6,7,9,19,19,22�24,24], z-numbers [17, 37,37], d numbers [1,5,76] and as so on. a specialized fusion algorithm is used for each method. ordered proposition is a new approach to model information which is proposed by liu et al. [35]. a set of ordered propositions describe the di�erent intensities of a characteristic of a objects, the intensities increase or decrease gradually. for example, consumers evaluate the quality of a product on a rank of "wonderful, good, indi�erent, weak". a set of ordered propositions can be expressed as a basic support function (similar to belief function in dempster-shafer evidence theory), whose elements represent the truth-value (belief value) of each proposition. the truthvalues of a basic support function must be convex, because a subject cannot be two degrees in same characteristic. such as, we cannot say the quality of a product is both wonderful and indi�erence simultaneously. a basic support function is divided into determinate part and indeterminate part [35]. the determinate part is the sum of truth-values of each ordered proposition. the sum of indeterminate part and determinate part is one. in the ordered propositions fusion, the indeterminate part is prorated to each proposition and itself. therefore, the indeterminate part can express the uncertainty for all ordered propositions. in this paper, we de�ne the generalized ordered propositions, they extend the indeterminate part to all non-single subsets of ordered propositions. the truth-value of a non-single subset expresses the uncertainty of the propositions in copyright ©2018 cc by-nc generalized ordered propositions fusion based on belief entropy 793 it. for example, the "wonderful, good" express "the quality of this product is wonderful" or "the quality of this product is good". in order to ensure the convex property of a basic support function, the indeterminate part is listed separately. the generalized ordered propositions will be degenerated as the classical ordered propositions in that when the truth-values of non-single subsets of ordered propositions are zero. the ordered propositions fusion is an important and extensive problem [35]. previously, a fusion algorithm based on centroid is proposed [42], which fuse the basic support functions of two sets of ordered propositions and ensure the convexity. however, this approach has a few shortages [35]. in order to address these shortages, a new fusion method based on consistency and uncertainty measurements was presented by liu et al. for the fusion of ordered proposition [35]. they also introduced entropy to measure the uncertainty of the basic support function based on shannon entropy [35]. but this entropy only considered the determinate part of a basic support function, the indeterminate part is ignored. in dempster-shafer evidence theory, an entropy is presented to measure the uncertainty of a belief function, named deng entropy [6]. when we add the groups of propositions in ordered propositions, the basic support function is more similar with the belief function. in this paper, we introduce a new entropy to measure the uncertainty of a basic support function based on belief entropy. it will be degenerated as the entropy which is proposed by liu et al. in that when the indeterminate part of a basic support function is zero. additionally, the fusion method of generalized ordered propositions based on consistency and uncertainty measurements is introduced. when the truth-values of non-single subsets of ordered propositions are zero, the fusion result is same as the fusion result of liu et al.'s method. the rest part of this paper is organized as follows. section 2 brie�y discusses the de�nitions and properties of ordered propositions, dempster-shafer evidence theory and belief entropy. section 3 introduces the de�nition and properties of generalized ordered propositions. section 4 discusses the proposed method for measuring uncertainty of a basic support function. the fusion method of generalized ordered proposition is described in section 5. section 6 presents some numerical examples. finally, this paper is concluded in section 7. 2 preliminaries 2.1 ordered propositions in this section, some background knowledge about ordered propositions is brie�y introduced [35]. de�nition 1 (ordered propositions). for a set of propositions p1,p2, · · · ,pn, the truth-value of pi is denoted as λ(pi). λ(pk) = max{λ(p1), · · · ,λ(pn)}. p1,p2, · · · ,pn are ordered propositions if [35] (1) ∀i = 1, 2, · · · ,n, all subjects described in pi are s; (2) ∀i = 1, 2, · · · ,n, si describes the same characteristics or features of s; (3) ∀i = 1, 2, · · · ,k − 1, λ(pi) ≤ λ(pi+1); and ∀i = k,k + 1, · · · ,n− 1, λ(pi) ≥ λ(pi+1). de�nition 2 (basic support function). for a set of ordered propositions p = {p1,p2, · · · ,pn}, a function λ is called the basic support function of the ordered propositions if [35] (1) λ is de�ned on {p}∪{{pi}|1 ≤ i ≤ n}, where p indicates indeterminacy; (2) λ(pi) ≥ 0, 1 ≤ i ≤ n; 794 y. li, y. deng (3) ∑ 1≤i≤n λ(pi) ≤ 1; (4) λ(p) = 1 − ∑ 1≤i≤n λ(pi). de�nition 3 (determinate part and indeterminate part). for a basic support function λ, the determinate part λ(p) and indeterminate part λ(p) are de�ned as [35] λ(s) = ∑ i=1,··· ,n λ(pi), λ(p) = 1 −λ(p). (1) de�nition 4 (mean value). the mean value of a basic support function λ is de�ned as [35] λ = ∑n i=1 λ(pi) n . (2) de�nition 5 (measure of convexity). the measure of convexity of a basic support function λ is de�ned as [35] convex(λ) = max{λ(p1),λ(p2), · · · ,λ(pn)}−λ. (3) it was clear that the maximum of the measure of convexity is 1 −λ. thus, the normalized convex(λ) as follows: [35] nc(λ) = (max{λ(p1),λ(p2), · · · ,λ(pn)}−λ)/(1 −λ). (4) de�nition 6 (center of a basic support function). for a basic support function λ = (λ(p1),λ(p2), · · · , λ(pn)), the center of λ is de�ned as [35] ci(λ) =   argmaxi=1,··· ,nλ(pi), nc(λ) ≥ θ∑ i=1,··· ,n∧λ(pi)≥τ·λ λ(pi) × i∑ i=1,··· ,n∧λ(pi)≥τ·λ λ(pi) , otherwise, (5) θ is set to 0.55 in [35], 1 < τ ≤ 1.5. in order to model the complex information of interaction, complex networks are proposed [4, 20,21,40,65,69]. the measure of consistency is essential to information, a�ected by the reliability of the information source [9, 11, 17, 32, 46, 64, 66, 74]. the reliability of obtaining data is very important for information fusion [41]. de�nition 7 (measure of consistency). if ci(λ1) and ci(λ2) are the centers of the basic support functions λ1 and λ2.the consistency between λ1 and λ2 is de�ned as [35] ∆g(λ1,λ2) = |ci(λ1) −ci(λ2)|/(n− 1). (6) if ∆g = 1, then λ1 and λ2 are totally con�icting. if ∆g = 0, then λ1 and λ2 are consistent. otherwise, if 0 < ∆g < 1, then λ1 and λ2 are partially con�icting. the consistency between λ1 and λ2 can be divided into 3 degrees [35]. 0 ≤ ∆g ≤ δ1 indicates the consistency between λ1 and λ2 is high. δ1 ≤ ∆g ≤ δ2 indicates the consistency between λ1 and λ2 is medium. δ2 ≤ ∆g ≤ 1 indicates the consistency between λ1 and λ2 is poor. generalized ordered propositions fusion based on belief entropy 795 2.2 dempster-shafer evidence theory evidence theory is widely used in many applications such as target recognition [29,30], decision making [1,11], uncertain processing [3,13,16,16,20,21,26�28,31,35], risk management [18,36], fault diagnosis [4,15,25,56,60] and as so on. the frame of discernment θ is the exhaustive hypotheses of variable, x. θ = {x1,x2, · · · ,xi, · · · ,xn}. the power set of θ is 2θ = {∅,{x1}, · · · ,{xn},{x1,x2}, · · · , {x1,x2, · · · ,xi}, · · · , θ}, where ∅ is an empty set [7,23]. de�nition 8 (basic probability assignment (bpa)). a basic probability assignment function m : 2θ → [0, 1], which satis�es [7,23]: m(θ) = 0 ∑ a∈2θ m(a) = 1 0 ≤ m(a) ≤ 1, (7) the mass m(a) indicates how strongly the evidence supports a. 2.3 belief entropy shannon entropy is widely used to measure the uncertainty of a probability. in addition, a belief entropy named deng entropy is proposed to measure the uncertainty of a bpa [6]. de�nition 9 (belief entropy). for a bpa, m, de�ned on the frame of discernment θ, it's belief entropy is de�ned as [6] ed(m) = − ∑ a⊆θ m(a) ln m(a) 2|a| − 1 , (8) where a is the focal element of m, |a| is the cardinality of a. 3 generalized ordered propositions 3.1 de�nitions de�nition 10 (generalized ordered propositions). for a set of propositions p1,p2, · · · ,pn, it's power set, {∅,{p1},{p2}, · · · ,{pn},{p1,p2}, · · · ,{p1, · · · ,pn}}, let λ(pi,pj · · ·) represent the truth-value of {pi,pj, · · ·} and λ(pk) = max{λ(p1), · · · ,λ(pn)}. p1,p2, · · · ,pn are generalized ordered propositions, if (1)∀i = 1, 2, · · · ,n, all subjects described in pi are s; (2)∀i = 1, 2, · · · ,n, pi describes the same characteristics or features of s; (3)∀i = 1, 2, · · · ,m− 1, λ(pi) ≤ λ(pi+1); and ∀i = m,m + 1, · · · ,n− 1, λ(pi) ≥ λ(pi+1). de�nition 11 (basic support function of the generalized ordered propositions). for a set of generalized ordered propositions p = {p1,p2, · · · ,pn}, it's power set 2p = {∅,{p1},{p2}, · · · ,{pn}, {p1,p2},{p1,p3}, · · · ,{p1,p2, · · · ,pn}} a function λ is called a basic support function of the generalized ordered propositions if (1) λ is de�ned on 2p ; (2) λ(a) ≥ 0,a ⊆ p ; (3) λ(∅) = 0; (4) ∑ 1≤i≤n λ(a) = 1, where a ⊆ p ; 796 y. li, y. deng take the example of "the quality of a product", the basic support function is{(0.1, 0.3, 0.2, 0.0), (λ(p1,p2) = 0.2,λ(p2,p3) = 0.2)}. λ(p1) = 0.1 means the truth-value of 1st proposition "the quality of a product is wonderful" is 0.1; λ(p2) = 0.3 means the truth-value of 2nd proposition "the quality of a product is good" is 0.3; λ(p3) = 0.2 means the truth-value of 3rd proposition "the quality of a product is indi�erence" is 0.2; λ(p4) = 0.0 means the truth-value of 4rd proposition "the quality of a product is weak" is 0.0; λ(p1,p2) = 0.2 means the uncertain truth-value of 1st proposition and 2nd proposition is 0.2; λ(p2,p3) = 0.2 means the uncertain truth-value of 2st proposition and 3rd proposition is 0.2. 3.2 properties de�nition 12 (determinate part and indeterminate part). for a basic support function of generalized ordered proposition, the determinate part and indeterminate part is λ(p) = n∑ i=1 λ(pi), λ(p) = ∑ a⊆p∧a 6={q1},··· ,{qn} λ(a) = 1 −λ(p). (9) de�nition 13 (mean value). the mean value of a basic support function λ of generalized ordered propositions is λ = ∑n i=1 λ(pi)(1 + ∑ pi⊂a λ(a)) n , (10) where a ( {p1,p2, · · · ,pn}. de�nition 14 (degree of convexity). the degree of convexity of a basic support function λ of generalized ordered propositions is: convex(λ) = maxi=1,··· ,n{λ(pi)(1 + ∑ pi⊂a λ(a))}−λ,i = 1, 2, · · · ,n, (11) where a ( {p1,p2, · · · ,pn}. the normalized convex(λ) is nc(λ) = (maxi=1,··· ,n{λ(pi)(1 + ∑ pi⊂a λ(a))}−λ)/(1 −λ), i = 1, 2, · · · ,n. (12) de�nition 15 (center of a basic support function). a basic support function of generalized ordered propositions λ = {(λ(p1),λ(p2), · · · ,λ(pn)), (λ(p1,p2), · · · ,λ(p1,p2, · · · ,pn))}, the center of λ is ci(λ) =   argmaxi=1,··· ,nλ(pi)(1 + ∑ pi⊂a λ(a)), nc(λ) ≥ θ ∑ i=1,··· ,n∧λ(pi)≥τ·λ λ(pi)(1 + ∑ pi⊂a λ(a)) × i∑ i=1,··· ,n∧λ(pi)(1+ ∑ pi⊂a λ(a))≥τ·λ λ(pi)(1 + ∑ pi⊂a λ(a)) , otherwise, (13) where a ( {p1,p2, · · · ,pn}. generalized ordered propositions fusion based on belief entropy 797 4 uncertainty measure uncertainty can evaluate the quality of information [2,3,13,15,16,31,32,39,39,47,48,50,61,71]. the more uncertainty, the less information [7,8]. a method to measure the uncertainty of a basic support function of ordered propositions based on shannon entropy is proposed by liu et al. [35]. de�nition 16 (liu et al.'s entropy). for a basic support function λ = (λ(p1),λ(p2), · · · ,λ(pn)), λ 6= (λ(p1) = 0,λ(p2) = 0, · · · ,λ(pn) = 0) and n ≥ 2. let λ(pk) = max{λ(p1),λ(p2), · · · ,λ(pn)}, 1 ≤ k ≤ n. if βλ(pk) ≤ λ(pj) ≤ λ(pk),β ≥ 0.9 and 1 ≤ j ≤ n, then λ(pj) is quasi-maxima. let n′ is the total number of maxima and quasi-maxima. the liu et al.'s entropy of λ is de�ned as: [35] e(λ) =   − n∑ i=1 λ(pi) ln λ(pi), n ′ = 1, − n∑ i=1 λ(pi) ln λ(pi) + (ln n + n∑ i=1 λ(pi) ln λ(pi)))(n ′/n)α, 2 ≤ n′ ≤ n, (14) where α = 0.1. when indeterminate part of a basic support function is equal to zero, this entropy can accurately measure the uncertainty of a basic support function. for example, given two basic support functions µ1 = (0.005, 0.99, 0.005, 0.0, 0.0), µ2 = (0.0049995, 0.990001, 0.0049995, 0.0, 0.0), we can given e(µ1) = 0.062933 and e(µ2) = 0.062928 using eq. (14). e(µ1) is greater than e(µ2), this means that the uncertainty of µ1 is higher than the uncertainty of µ2. the result is reasonable. when there are multiple maxima of a basic support function, liu et al.'s method can also measure uncertainty accurately. take two basic support functions µ3 = (0.5, 0.5, 0.0, 0.0), µ4 = (0.15, 0.7, 0.1, 0.05), then e(µ3) = 1.34 and e(µ4) = 0.914. it is reasonable that e(µ3) > e(µ4). however, when indetermination part of a basic support function is not equal to zero, this entropy doesn't apply to measure uncertainty of a basic support function. for example, for two basic support functions µ5 = (0.2, 0.3, 0.0, 0.0) and µ6 = (0.7, 0.1, 0.1, 0), then e(µ5) = 0.6831, e(µ6) = 0.7103. e(µ5) < e(µ6), this means that the degree of uncertainty of µ6 is higher. it is obviously counterintuitive. in order to take into considered not only the determinate part but also indeterminate part, we present the a new method to measure uncertainty of a basic support function of generalized ordered proposition based on belief entropy [1,6]. de�nition 17 (the entropy based on belief entropy). for a basic support function of generalized ordered propositions λ = {(λ(p1),λ(p2), · · · ,λ(pn)), (λ(p1,p2),λ(p1,p3), · · · ,λ(p1,p2, · · · ,pn))}, λ 6= (λ(p1) = 0,λ(p2) = 0, · · · ,λ(pn) = 0) and n ≥ 2. let λ(pk) = max{λ(p1),λ(p2), · · · ,λ(pn)}, 1 ≤ k ≤ n. if βλ(pk) ≤ λ(pj) ≤ λ(pk),β ≥ 0.9 and 1 ≤ j ≤ n, then λ(qj) is quasi-maxima. let n′ is the total number of maxima and quasi-maxima. the entropy of λ is de�ned as: ed(λ) =   − n∑ i=1 λ(a) ln( λ(a) 2|a|−1 ), n′ = 1, − n∑ i=1 λ(a) ln( λ(a) 2|a|−1 ) + (ln n + λ(a) ln( λ(a) 2|a|−1 ))(n′/n)α, 2 ≤ n′ ≤ n, (15) where a ⊆{q1, 12, · · · ,qn}, |a| is the number of elements of a, α = 0.1. using eq.( 15) to calculate the uncertainty of µ5 and µ6, the results are ed(µ5) = 2.3837, ed(µ6) = 1.2114. ed(µ5) > ed(µ6), it is reasonable. for two basic support functions of generalized ordered propositions µ7 = {(0.2, 0.5, 0.1, 0.0), (µ7(p1,p2) = 0, 1,µ7(p2,p3) = 0.1)} and µ8 = 798 y. li, y. deng {(0.2, 0.6, 0.1, 0.0), (µ8(p1,p2) = 0.1)}. the results are ed(µ7 = 1.5790 and ed(µ8) = 1.1988 using eq. (12). ed(µ7) > ed(µ8), this means that the degree of uncertainty of µ7 is higher than µ8. 5 fusion of generalized ordered propositions for a set of generalized ordered propositions p = {p1,p2, · · · ,pn}, let λ1 and λ2 are two basic support functions of p . denote the fusion result of λ1 and λ2 is ω. the processes of method for fusion of basic support functions of generalized ordered propositions is shown in fig. 5. the steps of this method can be explained as follows: start λ1, λ2, ω1, ω2 . 1 ={ 1(p1,p2, ,pnyes 2 ={ 2(p1,p2, ,pn no 2 ={ 2(p1,p2, ,pn no yes no calculate the initial fusion result ω ʹ. calculate the center ci(ω ʹ ). calculate the consistency δg( ). ci(ω ʹ ) is integer? yes end calculate the final fusion result with method . calculate the final fusion result with method . no ω = 2. ω = 1.yes figure 1: the processes of proposed method step 1: give two basic support functions λ1, λ2 of a set of generalized ordered propositions p = {p1,p2, · · · ,pn}, and the weights ω1, ω2 of two basic support functions respectively. generalized ordered propositions fusion based on belief entropy 799 table 1: process of calculating ω′ by eq. (16). λ1 to ω′ λ2 to ω′ ω′ truth-value obtained by ω′(p1) 0.025 0.08625 0.11125 truth-value obtained by ω′(p2) 0.345 0.345 0.69 truth-value obtained by ω′(p3) 0.08625 0.025 0.11125 truth-value obtained by ω′(p4) 0.025 0.025 0.05 truth-value obtained by ω′(p1,p2) 0 0.01875 0.01875 truth-value obtained by ω′(p2,p3) 0.01875 0 0.01875 step 2: determine whether λ1 is equal to {λ1(p1,p2, · · · ,pn) = 1} and if λ2 is equal to {λ2(p1,p2, · · · ,pn) = 1}. if λ1 = {λ1(p1,p2, · · · ,pn) = 1} and λ2 = {λ2(p1,p2, · · · ,pn) = 1}, the fusion result ω = (1/n, 1/n, · · · , 1/n). if λ1 = {λ1(p1,p2, · · · ,pn) = 1} but λ2 6= {λ2(p1,p2, · · · ,pn) = 1}, the fusion result ω = λ2. if λ2 = {λ2(p1,p2, · · · ,pn) = 1} but λ1 6= {λ1(p1,p2, · · · ,pn) = 1}, the fusion result ω = λ1. if λ1 6= {λ1(p1,p2, · · · ,pn) = 1} and λ2 6= {λ2(p1,p2, · · · ,pn) = 1}, take the next step. step 3: calculate the initial fusion result. ω′(a) =   ω1 ·λ1(a)(1 + ∑ a⊂b λ1(b)) + ω2 ·λ1(a)(1 + ∑ a⊂c λ1(c)), |a| = 1, ω1 ·λ1(a)(1 − ∑ pi⊂a λ1(pi)) + ω2 ·λ2(a)(1 − ∑ pi⊂a λ1(pi)), 1 < |a| ≤ n, (16) where a,b,c ⊆{p1,p2, · · · ,pn}, i = 1, 2, · · · ,n, ω1 + ω2 = 1. for example, there are two basic support functions λ1 = {(0.05, 0.6, 0.15, 0.05), (λ1(p2,p3) = 0.15} and λ2 = {(0.15, 0.6, 0.05, 0.05), (λ2(p1,p2) = 0.15)}. the weights are ω1 = ω2 = 0.5. the process of calculating initial fusion result ω′ by using eq. (16) is illustrated in table 1. step 4: calculate the center of initial fusion result ω′ with eq. (13), ci(ω′). step 5: calculate the consistency between λ1 and λ2 with eq. (6), ∆g(λ1,λ2). step 6: determine whether the center of initial fusion result ci(ω′) is integer. if ci(ω′) is integer, take the step 7, otherwise take the step 8. step 7: calculate the �nal fusion result ω with method i. step 7.1: positive regulation. ω(pi) =   i∑ k=1 ω′(pk)[1 + ϕ(i−k)]∑ci(ω′)−k j=0 (1 + jϕ) , if i < ci(ω′), ci(ω)∑ k=1 ω′(pk)[1 + ϕ(i−k)]∑ci(ω′)−k j=0 (1 + jϕ) + n∑ k=ci(ω′)+1 ω′(pk)[1 + ϕ(k −ci(ω′))]∑k−ci(ω′) j=0 (1 + jϕ) if i = ci(ω′), n∑ k=i ω′(pk)[1 + ϕ(k − i)]∑k−ci(ω′) j=0 (1 + jϕ) if i > ci(ω′), ω(a) = ω′(a), (17) where ϕ = 0.2, 0.1, 0 when the consistency between two basic support function is high, medium, poor respectively, a is the non-simple subset of p . step 7.2: negative regulation. 800 y. li, y. deng when the consistency between two basic support functions is poor, the measure of uncertainty is used to compress the curve of truth-value of ω vertically until the entropy of ω approximately equals the entropy of ω′, that is |e(ω) −e(ω′)| ≤ �. this process is called negative regulation and outlined in algorithm 1. algorithm 1 the procedure of negative regulation. input: the initial fusion result ω′ and basic support function ω after positive regulation output: the �nal fusion result ω 1: δ ← 1 2: while |e(ω) −e(ω′)| ≤ � do 3: i ← index of maximum truth-value of ω 4: k ← 1 5: for k = i to n− 1 do 6: if ω(pk) > ω(pk+1) then 7: ω(pk) = ω(pk) − δω(pk+1)(ω(pk)−ω(pk+1)) ω(pk)+ω(pk+1) 8: ω(pk+1) = ω(pk+1) + δω(pk+1)(ω(pk)−ω(pk+1)) ω(pk)+ω(pk+1) 9: end if 10: end for 11: for k = i; k > 1; k −− do 12: if ω(pk) > ω(pk−1) then 13: ω(pk) = ω(pk) − δω(pk−1)(ω(pk)−ω(pk−1)) ω(pk)+ω(pk−1) 14: ω(pk−1) = ω(pk−1) + δω(pk−1)(ω(pk)−ω(pk−1)) ω(pk)+ω(pk−1) 15: end if 16: end for 17: if e(ω) < e(ω′) − � then 18: δ ← 1 19: end if 20: if e(ω) > e(ω′) + � then 21: δ ← δ/2 22: end if 23: end while step 8: calculate the �nal fusion result ω with method ii. step 8.1: positive regulation. denote a = dci(ω)e, b = bci(ω′)c for convenience, thus ω(pi) =   i∑ k=1 ω′(pk)[1 + ϕ(i−k)] (( ∑a−k j=0 (1 + jϕ)) −ϕ) , if i < b, ω′(pb) + γ(a−ci(ω′)), if i = b∧ω′(pb) 6= ω′(pa), ω′(pa) + γ(ci(ω ′) − b), if i = a∧ω′(pb) 6= ω′(pa), ω′(pb) + γ/2, if i = b∧ω′(pb) = ω′(pa), ω′(pa) + γ/2, if i = a∧ω′(pb) = ω′(pa), n∑ k=i ω′(pk)[1 + ϕ(k − i)] (( ∑k−b j=0(1 + jϕ)) −ϕ) , if i > a, ω(a) = ω′(a), (18) generalized ordered propositions fusion based on belief entropy 801 table 2: the fusion process and result of example (1) variables values λ1 {(0.1, 0.4, 0.2, 0.1), (λ1(p1,p2) = 0.1,λ1(p2,p3) = 0.1)} λ2 {(0.1, 0.4, 0.2, 0.1), (λ2(p1,p2) = 0.1,λ2(p2,p3) = 0.1)} λ1 = λ2 0.2275 nc(λ1) = nc(λ2) 0.3269 ci(λ1) = ci(λ2) 2.4615 ∆g(λ1,λ2) 0 ω′ {(0.11, 0.48, 0.22, 0.1), (ω′(p1,p2) = 0.05,ω′(p2,p3) = 0.04)} ω′ 0.2419 nc(ω′) 0.3711 ci(ω′) 2.3407 ω {(0.0324, 0.5777, 0.2705, 0.1482), (ω(p1,p2) = 0.05,ω(p2,p3) = 0.04)} where γ = γ1 + γ2, γ1 = b−1∑ k=1 ω′(pk)[1 + ϕ(b−k)] ( ∑a−k j=0 (1 + jϕ)) −ϕ + n∑ a+1 ω′(pk)[1 + ϕ(k −a)] ( ∑k−b j=0(1 + ϕj)) −ϕ , γ2 = b−1∑ k=1 ω′(pk)[1 + ϕ(a−k) −ϕ] ( ∑a−k j=0 (1 + jϕ)) −ϕ + n∑ a+1 ω′(pk)[1 + ϕ(k −a)] ( ∑k−b j=0(1 + ϕj)) −ϕ , ϕ = 0.2, 0.1, 0 when the consistency between two basic support function is high, medium, poor respectively, a is the non-simple subset of p . step 8.2: negative regulation. it is same as step 7.2. 6 numerical examples (1) two basic support functions are λ1 = {(0.1, 0.4, 0.2, 0.1), (λ1(p1,p2) = 0.1,λ1(p2,p3) = 0.1)} and λ2 = {(0.1, 0.4, 0.2, 0.1), (λ2(p1,p2) = 0.1,λ2(p2,p3) = 0.1)}. the weights of λ1 and λ2 are ω1 = ω2 = 0.5. the fusion processes and results are shown in table 2. λ1 and λ2 are consistent, and they all mean that the 2nd proposition is most likely to be correct. so the fusing basic support function should reach the maximum truth-value at the index 2. the results are reasonable. (2) two basic support functions are λ1 = (0, 0.1, 0.2, 0.7) and λ2 = {(0.1, 0.1, 0.1, 0.6), (λ2(p3,p4) = 0.1)}. the weights of λ1 and λ2 are ω1 = ω2 = 0.5. the fusion result is ω = {(0.0096, 0.0394, 0.1172, 0.8188), (ω(p3,p4) = 0.015)}. λ1 and λ2 are not exactly the same, but nc(λ1) = 0.6 > 0.55 and nc(λ2) = 0.5512 > 0.55, so ci(λ1) = ci(λ2) = 4 and ∆g(λ1,λ2) = 0. similar to the previous example, the fusing basic support function should reach the maximum truth-value at the index 4. so the results are reasonable. (3) two basic support functions are λ1 = (0.7, 0.2, 0.1, 0) and λ2 = {(0.1, 0.1, 0.1, 0.6), (λ2(p3,p4) = 0.1)}. the weights of λ1 and λ2 are ω1 = ω2 = 0.5. the fusion results are shown in table 4. λ1 and λ2 are totally con�icting and the fusion result is ω = {(0.1333, 0.4737, 0.2680, 0.11), (ω(p3,p4) = 0.015)}. the result shows that the 2nd proposition is most likely to be true, which is logical. it is reasonable that the uncertainty of the result is high. 802 y. li, y. deng table 3: the fusion process and result of example (3) variables values λ1 (0.7, 0.2, 0.1, 0) λ2 {(0.1, 0.1, 0.1, 0.6), (λ2(p3,p4) = 0.1)} ω′ {(0.4, 0.15, 0.105, 0.33), (ω′(p3,p4) = 0.015} ed(ω ′) 1.5185 ω {(0.1333, 0.4737, 0.2680, 0.11), (ω(p3,p4) = 0.015)} ed(ω) 1.4832 7 conclusion in order to better model the uncertain information of the characteristics of a subject, we proposed the generalized ordered propositions based on classical ordered propositions. the generalized ordered propositions extended the indeterminate part of a basic support function to all groups of propositions, not just the universal set of propositions. then we considered the determinate part, indeterminate part, mean, degree of convexity and center of a basic support function in the situation of generalized ordered propositions. these properties can also be applied in classical ordered propositions. additionally, we found the existing entropy of a basic support function does not apply when the indeterminate part is not zero. to address this shortage, we presented a new entropy based on belief entropy. this entropy measures not only the uncertainty of the determinate part but also indeterminate part of a basic support function. when the indeterminate part equals to zero, this entropy is degenerated into the existing entropy. finally, we instructed the fusion method of basic support functions in generalized ordered propositions based on consistency and uncertainty. the experimental results show that the method is e�ective. acknowledgment the authors greatly appreciate the reviews' suggestions and the editor's encouragement. the work is partially supported by national natural science foundation of china (grant nos. 61573290, 61503237). con�ict of interest the authors declare that there is no con�ict of interests regarding the publication of this paper. bibliography [1] abelln, j. 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(2018); a dematel-based completion method for incomplete pairwise comparison matrix in ahp, annals of operations research, doi:10.1007/s10479-018-2769-3, 2018. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 615-621 floating license management automation using web technologies a. doloca, o. ţănculescu adrian doloca gr.t. popa university of medicine and pharmacy, department of mathematics and informatics romania, universităţii 16, 700115 iaşi e-mail: ad@umfiasi.ro oana ţănculescu gr.t. popa university of medicine and pharmacy, department of fixed prosthodontics romania, universităţii 16, 700115 iaşi e-mail: otanculescu@eprpru.umfiasi.ro abstract: this paper examines the use of distributed computing based on web services with application to floating license management. the main goal is to automate the processes pertaining to the management activities and ensure at the same time that the security and flexibility requirements are met. we present the challenges posed by these requirements, propose a design and some implementation aspects using the latest .net development platform. keywords: floating license management, distributed system, web service, multithreading, .net development. 1 introduction software is a vital component of today’s businesses. the fast development of the software industry has produced applications for automating virtually every task, thus saving time and finances. at the same time protecting software from unauthorized usage became a strong necessity. except the public domain that contains material not owned or controlled by anyone, all other software products are subject to the copyright law which gives the author of an original work exclusive right regarding its distribution, publication and modification. the software license is for that matter a legal instrument governing the usage and redistribution of software applications protected by copyright, but at the same time a technical means which will prevent the application from running when no authorization is available. this allows a better control of the product’s distribution and ensures that illegal use will be kept to a minimum allowing the producer to economically benefit from the investment and to prevent revenue loss [1]. concerning the method of protection, two main categories of software licensing systems are most popular: node-locked and floating licenses. other license types and a method of optimization are presented in [2]. node-locked licenses allow an application to be used on only one machine. this is achieved by making the license machine dependant. therefore the license will include some unique ids of hardware components like hard drives, cpu id, network card id, which will be checked as part of the license verification process. floating licenses (roaming licenses) allow multiple users to share access to a software product. usually the number of users simultaneously running the application is limited, this limit being embedded in the license itself. this provides a higher degree of flexibility since users are not restricted to using the application from a designated workstation. the software can be copyright c⃝ 2006-2011 by ccc publications 616 a. doloca, o. ţănculescu installed on any number of client computers but if the limit is reached a new user whishing to start it is put on hold and will be able to start its session as soon as an existing session is finished. flexnet and the older version flexlm [3] are the de facto standards in electronic licensing being a partner of major software producing companies. these products support a large variety of license management scenarios focusing at maintaining a high degree of flexibility in choosing feature and pricing configurations. using flexnet software producers can generate, transfer and activate licenses and can also protect their products against piracy. [4] and [5] deliver an insight into the challenges that floating license management poses and shows how flexlm finds solutions to these challenges. this paper presents an original software system that provides flexibility in the management of floating licenses in enterprise environments, attaining a high degree of automation by using the latest internet development technologies. it also discusses details of implementation and benefits of this system. the presentation is structured as follows. chapter 2 looks into the features that an advanced floating license management system is required to have. chapter 3 shows the structural aspects and communication between system components. security issues generated by exchanging information over public networks are addressed in chapter 4. some implementation aspects and recommendations can be found in chapter 5. final conclusions are presented in chapter 6. 2 system features and functions the floating license management system should have at least some features to ensure that the terms of the license contract are enforced and that this is done in a secure manner, with minimum effort by automating the necessary tasks. below we present these features in detail. a. limit the number of simultaneously running application instances according to the license contract at startup the application will connect to the license server in order to check if it has the authorization to run. if the maximum number of running instances has already been reached, the requesting application is denied the right to start and will close. when an ongoing working session is finished, the corresponding seat on the license server is freed and another application can start. of course, some requirements exist for this feature: the license information must be centralized on a license server; the license server must be accessible in a network by all client computers which have the application installed. b. ensure security of the license content and prevent fraud attempts the license content resides on the license server usually in the form of a text or a binary file which contains information pertaining to the company that acquired the license and the maximum number of simultaneously running application instances. in the proposed system the following set of parameters are used: a) license-id: uniquely identifies the license in the license database residing at the producer of the software. it contains a random number and possibly the creation date and time. this will block, for example, any attempt to use older licenses that have been issued to the same company, for the same software. b) company-id: short identifier of the company that purchased this license. it prevents license usage across companies by simply copying the license content. floating license management automation using web technologies 617 c) application-id: identifies the application the license refers to. different software can use the same license management system by using different application-ids. it prevents illegal use of licenses issued for other software. d) configuration-id: this piece of information specifies the configuration/version of the software which will be running (demo version, full version, light version, professional version, enterprise version, etc.). it ensures that only the authorized version of the software will be used. e) hardware-id: uses one or a combination of multiple serial numbers of certain hardware pieces to ensure that the license is used only on a certain machine that was authorized for license management purposes. the most popular hardware related serials are: the hard drive id (volume serial number), network card physical address (mac), cpu id. this prevents installing the license on several machines and using them simultaneously for increasing the number of running software instances over the authorized limit. f) mnri (maximum number of simultaneously running application instances): specifies how many instances of the software can run at the same time on client computers that use the license server. if this limit is reached, additional instances are prevented from running until an existing session is terminated. the above listed information is combined into a unique string – the license content, which is encrypted using a strong encryption algorithm and stored in the license file. the encryption protects the license from any attempts to adjust the contained parameters for using it in other environments than the one it was issued for. c. provide flexibility for license configuration and installation for maximum configuration and installation flexibility the license file will be regularly synchronized with a remote server controlled by the software producer. the first time the license server is started it will contact the remote server for the first synchronization. as a result, the license file is created and subsequently used until the next synchronization takes place. during each license synchronization, any modifications in the license parameters on the remote server are automatically transmitted to the license server. in this way no manual updating process of the license is required. this process is depicted in figure 1, in chapter 3. the synchronization ensures that the license used by the client is up-to-date and it hasn’t been tampered with. at the same time all contract amendments that are made at one time are quickly and automatically implemented on the client’s server. d. provide flexibility of working with multiple application types and multiple configurations of a software product due to the application-id and configuration-id parameters the license is linked to a specific software product and a version/configuration. the same license structure can be used with other software products just by adapting these two parameters accordingly. on the client side, one license server will be able to handle multiple licenses for multiple applications and configurations. e. offer flexible administrative functions for the client company as well as for the producer of the software regarding the used licenses, the client company is able to perform the following tasks: install license during the first synchronization; 618 a. doloca, o. ţănculescu update license information during subsequent synchronizations; monitor and manage active users and licenses; configure intranet and internet network settings, etc. the software producer has also an administration tool that enables them to: create new licenses according to specifications given in section b of this chapter; modify existing licenses (upgrade or downgrade); monitor the synchronization state of each client; block or activate a license, etc. 3 system architecture the architecture of the floating license management system is based on a distributed structure (figure 1) comprising three major components: 1) client workstations running the licensed software; 2) in-house license server which authorizes software utilization on client workstations on one hand, and performs license synchronization with the remote server on the other hand; 3) remote license server controlled by the software producer, hosts the license database and performs automatically synchronizations over the internet with the clients, acting as a web service. the distributed structure using an in-house license server and a remote license server ensures centralized administration of the licenses both at the client and at the software manufacturer. also the license installation and update are done automatically with a minimum of intervention from human operators. this accomplishes two main goals: ease of administration and security of the license content. other advantages include use of public networks (internet) and flexibility in case of software upgrade [6]. the client computer sends a message to the in-house license server during startup requesting authorization for software utilization. at the server, a service (e.g. windows service) receives the message and based on the content of the license residing in the license file and on the current number of running application instances sends back a response message authorizing or denying software utilization. using a web service client, the license server checks periodically with the remote license server if there are any modifications and, if necessary it updates the license file. if this communication fails, then a warning is issued on the in-house license server but the functionality is not affected. checking with the remote license server is attempted again and if it fails repeatedly, after a specific amount of time, the license on the in-house license server expires, which results in blocking all software utilization on client workstations. normal functionality is resumed after a successful communication between the in-house and the remote license servers. this mechanism ensures that the license is up-to-date at all times and that fraud attempts are avoided. floating license management automation using web technologies 619 internet ....... client computer 1 client computer 2 client computer n in-house license server (client side) license service license file client for license web service license manager remote license server (software producer side) license database license web service license manager desktop app. web app. client workstations (with licensed software) ssl channel ssl channel license synchronization application authorization figure 1: floating license management system architecture 620 a. doloca, o. ţănculescu 4 security issues being a system that relies on exchanging information over public computer networks and also working with private information (i.e. the license content) in an environment that is not controlled by the software manufacturer, security has an important role. there are two aspects of this problem: securing the data transfer during the synchronization process and protecting the license content. an effective method for dealing with the first aspect is to create an encrypted communication channel using secure socket layer connections (ssl). thus, not only the data sent to the remote license server but also the license content received back, are protected from possible outside attacks. the subject of using ssl for windows web services is covered in [7]. the information contained by the license file (see chapter 2, section b) must also be protected as it contains sensitive information established by the license contract. because the information is combined into a unique string, this can be encrypted using an advanced encryption algorithm like rijndael [8] and stored as a string representation in the license file. the license service will read and decrypt internally this information at startup. 5 implementation aspects the overview architecture of the license server is presented in figure 2. as the application has to be able to communicate at all times, it was implemented as a windows service which works in the background and spawns two additional execution threads. one is the tcp port listener thread which receives all the requests from the local network for application start-up authorization and keeps track of the machines and active users that use the licenses. the other thread is responsible for synchronizing the license file content with the remote license server. this structure allows timely responses through both communication channels by keeping the two activities as independent as possible. license file settings file license service worker object thread 1 tcp listener thread 2 license synchronizer internet user list tcp port figure 2: license server implementation structure we chose for implementation the microsoft .net framework platform using the visual studio .net 2008 development environment. among the reasons: floating license management automation using web technologies 621 development support for a wide range of technologies like web service, windows service, web applications and desktop applications; support for programming of tcp ports; very good documentation. 6 conclusions this paper discusses the aspects of floating license management and proposes a specific system architecture that meets the flexibility requirements and offers at the same time, a high degree of automation for most of the license management activities. using the web service technology, license installation, activation and synchronization can be performed automatically by the system. details of implementation using the latest .net development framework are presented. bibliography [1] r. addy, software license management, effective it service management, pp.263-273, springer berlin heidelberg, 2007. 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[8] advanced encryption standard, http://en.wikipedia.org/wiki/advanced_encryption_standard. international journal of computers communications & control issn 1841-9836, 10(2):188-199, april, 2015. zero-watermarking algorithm for medical volume data based on difference hashing b.r. han, j.b. li, y.j. li baoru han, jingbing li*, yujia li college of information science and technology hainan university haikou, 570228, china email:6183191@163.com, jingbingli2008@hotmail.com, liyujia1219@gmail.com *corresponding author: jingbingli2008@hotmail.com abstract: in order to protect the copyright of medical volume data, a new zerowatermarking algorithm for medical volume data is presented based on legendre chaotic neural network and difference hashing in three-dimensional discrete cosine transform domain. it organically combines the legendre chaotic neural network, three-dimensional discrete cosine transform and difference hashing, and becomes a kind of robust zero-watermarking algorithm. firstly, a new kind of legendre chaotic neural network is used to generate chaotic sequences, which causes the original watermarking image scrambling. secondly, it uses three-dimensional discrete cosine transform to the original medical volume data, and the perception of the low frequency coefficient invariance in the three-dimensional discrete cosine transform domain is utilized to extract the first 4*5*4 coefficient in order to form characteristic matrix (16*5). then, the difference hashing algorithm is used to extract a robust perceptual hashing value which is a binary sequence, with the length being 64-bit. finally, the hashing value serves as the image features to construct the robust zero-watermarking. the results show that the algorithm can resist the attack, with good robustness and high security. keywords: zero-watermarking, medical volume data, difference hashing, legendre chaotic neural network, three-dimensional discrete cosine transform. 1 introduction with the extensive application of digital technology, a large number of digital images are used in our daily life and work. digital images meet the requirements of people’s senses, and also provide convenience for people’s life and work. people pay more and more attention to the copyright issues in digital image. digital watermarking, as a new security measure, is widely used in digital image copyright protection [1, 2]. as an important branch of information security research area, digital watermarking also is an effective way to protect the integrity of digital image [3, 4]. it is an effective complement to traditional encryption techniques. digital watermarking is the meaningful information hidden into the digital works, as a basis for the identification of copyright. at the same time, the watermarking information can be detected and analyzed by detecting algorithm, to determine the copyright holder and to protect the digital products. however, due to the digital watermarking technology research being multidisciplinary, the communication theory, computer science and signal processing, and many other areas involved, for which it is unable to avoid the inherent drawbacks in these fields [5, 6]. this brings certain difficulty and challenge to research work. at present, in the field of digital watermarking, the digital image watermarking has become a branch of the most widely used, the most mature development, and the most fruitful achievements [7]. along with the construction of hospital informatization, digitalization has become more and more deeply into the medical field [8]. medical diagnostic equipment will produce a lot of medical image information every day. these medical images as a basis for medical diagnosis copyright © 2006-2015 by ccc publications zero-watermarking algorithm for medical volume data based on difference hashing 189 have an extremely important position. the establishment of medical digital image transmission standard has promoted the exchange of the digital medical imaging information. however, medical images information during network transmission may also encounter tampering, illegal copying and other information security issues [9,10]. medical image is not only an important basis for doctors to diagnose the disease, but also involving the patient’s privacy. in order to ensure the medical image security, reliability and availability, we must effectively solve the security problem of medical image management. the emergence of medical image digital watermarking can solve this problem [11]. it is the specific meaning digital information or some secret information embedded into the digital medical image, which can realize the information hiding and copyright protection. medical image especially is strict to the requirement of image quality does not allow the distortion and not allowed to do any changes. in this case, people put forward the concept of zero-watermarking [12]. zero-watermarking is a novel digital watermarking technique. it is different from general digital watermarking in its use of the characteristics of the original image to construct the watermarking, and not directly embed watermarking in the image, so it does not break the original image [13]. image hashing is also called the digital fingerprint, it is a summary of multimedia information, which can be widely applied in image authentication, image indexing and retrieval, digital image watermarking, etc [14]. image hashing represents the image itself with a short digital sequence, which is a kind of expression of image compression based on visual content. usually, the image hashing should satisfy the requirements of perceptual robustness, uniqueness and security. image hashing technique can be any image-resolution image data into a binary sequence of hundreds or thousands of bits. for a large database of image retrieval, this means greatly reducing the search time, but also reduces the cost of storage media images. at the same time, its robustness feature ensure that it can resist a variety of different types of attacks. in addition, the characteristics of image hashing technology security make the copyright protection of image become possible. the main image hashing methods now mainly focus on the characteristics of robustness study [15,16]. it is mainly divided into the method based on image statistics, a rough image representation, the relationship and visual feature points extraction. combined the features of above mentioned extraction methods, in order to better satisfy the perceptual image hashing robustness, security, and uniqueness, the discrete cosine transform to extract the feature is considered to be an ideal method. according to the characteristics of medical volume data, this paper presents a zerowatermarking algorithm for medical volume data based on legendre chaotic neural networks and differences hashing. the algorithm is based on three-dimensional discrete cosine transforms perception of the low frequency coefficient invariance and image hashing robust feature, which is a robust zero-watermarking method. the algorithm uses the robust hashing sequence in medical volume data transform domain to construct the zero-watermarking, instead of modifying the features of medical volume data. it can adapt to the characteristics of medical volume data, can resist strong attack, and has very strong robustness. and it uses legendre chaotic neural network scrambling and encryption, which has good security and confidentiality. 2 legendre chaotic neural network the paper uses a new legendre chaotic neural network. the legendre chaotic neural network model is shown in figure 1. the legendre chaotic neural network selects legendre polynomials as the activation function of hidden layer. performance close to the theoretical values of the chaotic sequence is generated by the legendre chaotic neural network weights and the chaos initial value. the chaotic sequence is used for scrambling. a polynomial defined by the following formula is called the legendre polynomial. 190 b.r. han, j.b. li, y.j. li definition 1. p0(x) = 1, pn(x) = 1 2nn! dn dxn (x2 − 1)n, n = 1, 2, 3, · · · (1) pn(x) is known as legendre polynomials. it is known as the weight function n orthogonal polynomials in space [−1, 1]. figure 1: legendre chaotic neural network set the input layer to the hidden layer weight is wj , hidden layer to the output layer weight is cj.the activation function of hidden layer neuron is legendre orthogonal polynomials. the hidden layer neuron input is netj = wjx j = 0, 1, 2, . . . , n (2) hidden layer neurons output are as a set of legendre orthogonal polynomial terms pj(netj), j = 0, 1, 2, . . . , n, which can be obtained by formula (1) recursive. legendre chaotic neural network output is y = n∑ j=0 cjpj(netj) (3) set the training sample is (tt, dt), t = 1, 2, · · · , l. where l is the number of samples tt = (x1t, x2t, · · · , xmt) is legendre chaotic neural network input. dt is legendre chaotic neural network desired output. the network is trained using bp learning algorithm. error formula is as follows: et = dt − yt (4) e = 1 2 l∑ t=1 e2t (5) network weights adjustment formula is as follows. ∆cj = −η ∂e ∂cj = ηetpj(netj) (6) ∆wj = −η ∂e ∂wj = ηetcjp ′ j(netj)xj (7){ wj(k + 1) = wj(k) + ∆wj(k) cj(k + 1) = cj(k) + ∆cj(k) (8) where k is the training epochs t = 1, 2, · · · , l; j = 0, 1, 2, · · · , n. zero-watermarking algorithm for medical volume data based on difference hashing 191 3 three-dimensional discrete cosine transform three-dimensional discrete cosine transform formula is as follows. f(u, v, w) = c(u)c(v)c(w) [m−1∑ x=0 n−1∑ y=0 p−1∑ z=0 f(x, y, z) ∗ cos (2x + 1)uπ 2m cos (2y + 1)vπ 2n cos (2z + 1)wπ 2p ] (9) u = 0, 1, · · · , m − 1;v = 0, 1, · · · , n − 1;w = 0, 1, · · · , p − 1 in the formula, c(u) = {√ 1/m u = 0√ 2/m u = 1, 2, . . . , m − 1 (10) c(u) = {√ 1/n v = 0√ 2/n v = 1, 2, . . . , n − 1 (11) c(u) = {√ 1/p w = 0√ 2/p w = 1, 2, . . . , p − 1 (12) where f(x, y, z) is volume data of the data values in the (x, y, z). f(u, v, w) is the data corresponding to the three-dimensional discrete cosine transform coefficients. three-dimensional inverse discrete cosine inverse transform formula is as follows: f(x, y, z) = [m−1∑ x=0 n−1∑ y=0 p−1∑ z=0 f(u, v, w) ∗ cos (2x + 1)uπ 2m cos (2y + 1)vπ 2n cos (2z + 1)wπ 2p ] (13) u = 0, 1, · · · , m − 1; v = 0, 1, · · · , n − 1; w = 0, 1, · · · , p − 1 three-dimensional discrete cosine transform for medical volume data is shown in figure 2. 4 difference hashing perceptual hashing has become a hot research topic in the field of multimedia signal processing and multimedia security. perceptual feature extraction is the core part of the perceptual hashing structure. the validity and reliability of perceptual feature extraction will directly affect the robustness and uniqueness of image perception hashing sequence. image perceptual hashing study is mainly for image authentication and image retrieval. image features include image color, texture, edge, corner and image transform domain coefficient, etc. compared with perceptual hashing, difference hashing in speed is much faster. compared with the average hashing, the effect of the difference hashing is better in the case of almost the same efficiency. it is based on the gradual implementation. based on the difference hashing algorithm, a difference hashing algorithm in three-dimensional discrete cosine transform domain is presents for medical volume 192 b.r. han, j.b. li, y.j. li (a) the original medical volume data (b) the medical volume data in transform domain figure 2: three-dimensional discrete cosine transform for medical volume data data feature extraction in this paper. figure 3 depicts the algorithm flow. in the algorithm flow, medical volume data is represented by a three-dimensional map into a one-dimensional feature vector. the algorithm is used to extract the robust features of medical volume data, which can increase the robustness of watermarking algorithm. figure 3: the difference hashing algorithm flow 5 zero-watermarking algorithm zero-watermarking embedding figure 4 illustrates zero-watermarking embedding process. zero-watermarking extraction figure 5 illustrates zero-watermarking extraction process. zero-watermarking algorithm for medical volume data based on difference hashing 193 figure 4: embedding process 6 simulation and analysis the legendre chaotic neural network parameters are as follows. the number of hidden neurons is 3,e = 10−4,η = 0.1 ,l = 1000 and the number of training is 1500 epochs. its training error curve is shown in figure 6, in the 69 epoch it has converged to the expected error. scrambling initial value is 0.46. the chaotic sequence for scrambling is shown in figure7. the scrambled watermarking image is shown in figure 8. 1. without attack the medical volume data without attack is as shown in figure 9 (a). the slice is shown in the figure.9 (b). the extracted watermarking image is shown in the figure 9(c). 2. filtering attack medical volume data is filtered attack. using [5 ∗ 5] median filter, repeat 10 times, the corresponding medical volume data is shown in the figure 10 (a). the slice is shown in the figure 10 (b). the extracted watermarking image is shown in the figure 10(c). this shows that the algorithm has a better anti-filter ability. 3. jpeg compression attack the percentage of compression quality is examined medical volume data after jpeg compression for the impact of watermarking. when the compression quality percentage is 8%, the corresponding medical volume data is shown in the figure 11 (a). the slice is shown 194 b.r. han, j.b. li, y.j. li figure 5: extraction process figure 6: training error curve in the figure 11 (b). the extracted watermarking image is shown in the figure 11(c). this shows that the algorithm has better anti-jpeg compression capability. 4. gaussian noise attack gauss noise intensity coefficient is measured the added noise interference size in medical zero-watermarking algorithm for medical volume data based on difference hashing 195 figure 7: chaotic sequence for scrambling figure 8: the scrambled watermarking image (a) (b) (c) figure 9: simulations without attack image. when the noise intensity is 20%, the corresponding medical volume data is shown in the figure 12 (a). the slice is shown in the figure 12 (b). the extracted watermarking is shown in the figure 12(c). this shows that the algorithm has strong robustness against noise attack. 5. zoom attack 196 b.r. han, j.b. li, y.j. li (a) (b) (c) figure 10: simulations under filtering attack (a) jpeg 8% (b) (c) figure 11: simulations under jpeg compression attack (a) 20% (b) (c) figure 12: simulations under filtering attack medical image is zoomed attack. when the zoom factor is 0.2, the corresponding medical volume data is shown in the figure 13 (a). the slice is shown in the figure 13 (b). the extracted watermarking image is shown in the figure.13(c). this shows that the algorithm has strong robustness against zoom attack. 6. shear attack when the medical volume data is shear 10% from the z-axis direction. the corresponding medical volume data is shown in the figure.14 (a). the slice is shown in the figure 14 (b). the extracted watermarking image is shown in the figure 14(c). this shows that the algorithm has a better anti-shear capability. 7. translate attack zero-watermarking algorithm for medical volume data based on difference hashing 197 (a) (b) (c) figure 13: simulations under filtering attack (a) (b) (c) figure 14: simulations under filtering attack medical volume data is translated attack. when the vertical downward is 5%, the corresponding medical volume data is shown in the figure 15 (a). the slice is shown in the figure 15 (b). the extracted watermarking image is shown in the figure 15(c). this shows that the algorithm has a better anti-translate capability. (a) (b) (c) figure 15: simulations under filtering attack 8. distort attack medical volume data is distorted attack. when the distorting factor is 15, the corresponding medical volume data is shown in the figure 16 (a). the slice is as shown in the figure 16 (b). the extracted watermarking is shown in the figure 16(c). this shows that the algorithm has a better anti-distort capability. 9. rotation attack. 198 b.r. han, j.b. li, y.j. li (a) (b) (c) figure 16: simulations under filtering attack medical volume data is rotated attack. when the medical volume data rotated anticlockwise 10 degrees, the corresponding medical volume data is shown in the figure 17 (a). the slice is shown in the figure 17 (b). the extracted watermarking image is shown in the figure 17(c). this shows that the algorithm has a better resistance to rotation attack ability. (a) (b) (c) figure 17: simulations under filtering attack 7 conclusion according to the transform domains perception of the low frequency coefficient invariance and the robustness of image hashing, a new robust watermarking algorithm for medical volume data is presented in this paper. it shows that the algorithm has the following characteristics. 1. the medical volume data has the good transparency without making any changes to the original medical data. 2. it uses the discrete cosine transform coefficients of low frequency stability characteristics and difference hashing algorithm robustness, thereby increasing the robustness of the algorithm. 3. the relationship between the initial values and chaotic sequences is contained in the chaotic neural network, which is inherently unpredictable. therefore, the algorithm is theoretically absolutely security. 4. the watermarking can be extracted without original medical volume data, and it realizes the blind detection. the results show that this algorithm has better robustness and security. zero-watermarking algorithm for medical volume data based on difference hashing 199 8 acknowledgements this work was supported by the national natural science foundation of china (no: 61263033) and the natural science foundation of hainan province (no: 614241). bibliography [1] r.barnett (1999); digital watermarking: applications. techniques and challenges, electronics & communications of engineering journal, 11(4):173-183. 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[16] y. lei, y. wang, and j. huang (2011); robust image hash in radon transform domain for authentication. signal processing: image comm., 26(6): 280-288. international journal of computers communications & control issn 1841-9836, 13(3), 323-336, june 2018. multi-objective binary pso with kernel p system on gpu n. elkhani, r. c. muniyandi, g. zhang naeimeh elkhani*, ravie chandren muniyandi centre for cyber security faculty of information sciene and technology universiti kebangsaan malaysia 43600 bangi, selangor, malaysia *corresponding author: naeimeh.elkhani@siswa.ukm.edu.my ravie@ukm.edu.my gexiang zhang school of electrical engineering southwest jiaotong university chengdu 610031 sichuan, p.r. china zhgxdylan@126.com abstract: computational cost is a big challenge for almost all intelligent algorithms which are run on cpu. in this regard, our proposed kernel p system multi-objective binary particle swarm optimization feature selection and classification method should perform with an efficient time that we aimed to settle via using potentials of membrane computing in parallel processing and nondeterminism. moreover, gpus perform better with latency-tolerant, highly parallel and independent tasks. in this study, to meet all the potentials of a membrane-inspired model particularly parallelism and to improve the time cost, feature selection method implemented on gpu. the time cost of the proposed method on cpu, gpu and multicore indicates a significant improvement via implementing method on gpu. keywords: parallel membrane computing, gpu based membrane computing, kernel p system, parallel multi-objective binary pso, parallel kernel p system-multi objective binary pso. 1 introduction in the literature [1], from one point of view, methods of feature selection for classification can be divided into three families 1) methods for flat features (filter models, wrapper models, embedded models), 2) methods for structured features (graph structure) and 3) methods for streaming features. a main disadvantage of the filter approach despite its lower time consumption is the fact that it does not interact with the classifier, usually leading to worse performance results than those obtained with wrappers. however, the wrapper model comes with an expensive computational cost. an intermediate solution for researchers can be the use of embedded methods that are usually a mix of two or more feature selection methods from different origins which use the core of the classifier to establish a criterion to rank features. from another perspective for the division of feature selection and classification methods, wide range of mixed methods are developed mainly based on evolutionary learning methods such as genetic algorithm (ga), neighborhood search like k nearest neighbor (knn) and swarm intelligence algorithms such as particle swarm optimization (pso). based on our review on ga and knn, the problems of various proposed methods can be categorized to three parts. first; e.g., pure genetic algorithm generally has limitations such as 1) slow convergence, 2) lacks of rank based fitness function and 3) being a time-consuming approach. mixed methods of ga and copyright ©2018 cc by-nc 324 n. elkhani, r. c. muniyandi, g. zhang knn were not capable to tackle with these problems completely. second; in terms of classification accuracy, resulted accuracy in intelligent feature selection and classification algorithms varies greatly either in different datasets, also due to building unstable method overfitting risk increases dramatically when they examine on high density datasets. pso because of first; its ability to match with graph model as genes (nodes) and define relationship between them (edge), second; higher accuracy in compared with flat (filter and wrapper) methods third; reasonable time complexity on cpu is our candidate in proposing a membrane-inspired feature selection method.computational cost is a big challenge for almost all intelligent algorithms which are run on cpu. recently new attempts have been started to develop parallel feature selection and classification methods such as [24] and some efforts are focused on parallelization of intelligent optimization algorithms, such as parallel genetic algorithm on cpus/computers to identify informative genes for classification [16,23], parallel genetic algorithm on gpu [6,15,22], parallel pso on gpu [14,19–21,28] and parallel processing of microarray data [13]. in this regard, our proposed membrane-inspired feature selection method should perform with an efficient time that we aimed to settle via using potentials of membrane computing in parallel processing due to the inherent large-scale parallelism feature of membrane computing, any membrane computing inspired model can fully represent this computation model only in the case of using the parallel platform. from the beginning of introducing this model, it was a big concern in all membrane related studies. for instance, to fully implement parallelism of such membrane computing model and to support an efficient execution [25] used a platform based on reconfigurable hardware. without parallelism, all subsequent studies face a challenge of how to make rules available in all steps of computation. in [2] a sequential computing of membrane computing, they just had an option of using one membrane and made the rules periodically available based on time-varying sequential p system. a sequential kernel p system multi objective binary particle swarm optimization feature selection and classification method proposed in the study of [8,9]. even by using minimal parallelism of using rule; at least a rule from a set of rules in a membrane, e.g., with the active membrane; solving np-complete problems in polynomial time through trading space for time leads to make a more efficient model of membrane computing. the architectural differences between cpus and gpus cause cpus to perform better on latency-sensitive, partially sequential, single sets of tasks. in contrast, gpus performs better with latency-tolerant, highly parallel and independent tasks. recently, several studies attempted to utilize membrane computing to improve intelligent algorithms. for instance, multi-core processing used in the study of [17] utilized a membrane computing inspired genetic algorithm and [18] have highlighted parallelism in membrane computing in the case of solving the n-queen’s problem. as a variant of p system, kernel p system (kp system) introduced for the first time in the study of [11,12]. this variant of p system integrates most of the features of membrane computing which have been successfully used for modelling problems and are applied in various application. generally, there is two types of the rules in kp systems: first type of rules deals with the objects to transfer them between compartments or send the objects from compartment to environment and vice versa; second type of rules deal with the membrane structure to change the topology of the compartments. multi objective optimization refers to the problem of finding a set of values which meet the limitations and are capable of optimizing the set of values to another set of values which are the objective of optimization. pso method itself is divided to two different approaches called single objective and multi-objective. these two approaches meet different requirements. single objective is appropriate for the problems have only one correct solution. in versus, most of the hard problems are often confronted with multi-objective decision problem that their goal is to find the "best" solution which corresponds to the minimum or maximum value of a single objective that lumps all different objectives into one. the combination of membrane computing with optimization algorithm has been used in many studies such as [26,27]. multi-objective binary pso with kernel p system on gpu 325 in this study, all the rules of kp system as rewriting and communication rule, division, input/output, link creation have used to develop the proposed kp-mobpso model. a multicore kernel p system multi objective binary particle swarm optimization feature selection and classification method proposed in the study of [8]. the most important attempts to parallelize membrane computing models are being done via using of graphic processing units (gpus) [3–5, 7,10]. all of these efforts have demonstrated that a parallel architecture is better positioned in performance than traditional cpus to simulate p systems, due to the inherently parallel nature of them, and specifically gpus obtain very good preliminary results simulating p systems. 2 criteria to execute kernel p system multi objective binary pso on gpu the important factor in implementing a p system-based model on gpu is to attention the rate of communication between the threads, which is related to the dependencies between objects [17, 18]. previous approaches of implementing p system on gpu did not consider this factor that exert effect on the performance of executing model on gpu. according to figure 1, every single thread using the local memory and a thread block uses the share memory and a grid of thread blocks use global memory. the main strategy will be assigning dependent objects to the same thread for execution. dependent objects in the proposed model are those objects that should be produced by prior rule and enter the compartment as input object to trigger the next rule. this is the reason rules are following priority for execution means those rules have higher priority should be execute first to generate the objects which are necessary to trigger the execution of other rules. thus, in our model those objects that their existence is dependent to the existence of other objects in the compartment will execute on the same thread along with their parent objects. to design kp-mobpso-svm model on gpu, two important points are concerned, first, the dependency between the objects and rules to decrease the rate of communication, second; access to the lesser cost memories in the execution of threads like local and shared memory. as it is shown in the figure 1, a single thread is used to assign objects and rules which are dependent to each other and they can use the local memory to keep the value for the objects and send the value to another rule to trigger its execution. when the execution of dependent rules is done, and completed in single threads, it will be needed to share the result of the threads and make a decision to choose the best result to continue the execution of model. to do this, a thread block which belongs to the current single threads can exchange the result via shared memory. according to [12], a kp system of degree n is a tuple, kπ = (o,µ,c1, . . . ,cn, i0) where o is a finite set of objects, called an alphabet; µ defines the membrane structure, which is a graph, (v,e), where v represents vertices indicating compartments and belongs to a set of labels l(li, . . . ), and e represents edges; ci = (ti,wi), 1 ≤ i ≤ n, is a compartment of the system consisting of a compartment type from t and an initial multiset, wi, over o; i0 is the output compartment, where the result is obtained (this will not be used in this study). each rule r may have a guard g, in which case r is applicable when g is evaluated to true. its generic form is r{g}. kp systems use a graph-like structure (similar to that of tissue p systems) and two types of rules 1. rules to process objects: these rules used to transform object or to move objects inside compartments or between compartments. these rules are called rewriting, communication and input-output rules: 326 n. elkhani, r. c. muniyandi, g. zhang figure 1: gpu memory a rewriting and communication rule: x → y{g}, where x ∈ a+, y ∈ a?, g ∈ finite regular expressions fe over (a ⋃ ā); y at the right side defines as y = (a1, t1), . . . , (ah, th), where aj ∈ a and tj ∈ l, 1 ≤ j ≤ h, aj is an object and tj is a target, respectively. b the input-output rule: (x/y){g}, where x,y ∈ a?, g ∈ finite regular expressions fe over (a ⋃ ā); means that x can be sent from current compartment to the environment or y can be brought from environment to the target compartment. 2. system structure rules: these rules make a fundamental change in the topology of the membranes for example with division rule on a compartment, dissolution rule on a specific compartment, make a link between compartments or dissolve the link between them. these rules are described as follow: a division rule: []li →lil...lih {g}, where g ∈ finite regular expressions fe over (a ⋃ ā); means compartment li can be replaced with h number of compartments. all newly created compartments inherit objects and links of li; b dissolution rule: []li → λ{g}; means compartment li is not exist anymore as well as all its links with other compartments. multi-objective binary pso with kernel p system on gpu 327 c link-creation rule: []li; []lj → []li − −[]lj{cg}; means a link will be created between compartment li with compartment lj. if there is more than one compartment with the label lj, one of them will have a link with lj non-deterministically. d link-destruction rule: []li −−[]lj → []li; []lj{cg}; means the existence link between li and lj will eliminate and there will not be any link between them anymore. the same as link creation, if there are more than one compartment which have a link with li then one of them will be selected non-deterministically to apply this rule. 3 proposed model the entire proposed model includes two main parts, first part, features selection based on kpmobpso plus classification based on (kernel p system-support vector machine) kp-svm and second part, kp-embedded feature selection/svm classification. the first part defines modelling and implementing previous mobpso based on kp system rules with some improvements which leads to the result consists of different set of marker genes, so called kp-mobpso feature selection. thereafter, an error rate calculator based on kp-svm applied to measure the error rate of marker gene sets. to design the first part of the model, first it is needed to design improved version of kp-mobpso on gpu. to do so, we assume there are 4 compartments each including 6 particles as figure 2 and figure 3. the same process will repeat for each particle as follow: step 1: to assign one thread for each gene of dataset (which is including of 100 genes) first we need to allocate memory in the host for dataset of genes. each gene keeps the values of six samples, therefore an array of threads of at least size 6 is needed. moreover, some other threads are defined to save the values will be generated in the processing steps on the gpu and will return back to the host. step 2: after allocating host memory to each gene in the dataset, genes are needed to transfer to device memory to execute on gpu. step 3: by assigning random number of genes inside each particle, the main process will start by executing kp-mobpso-svm. the kernel defined as "addkernel" will add the genes inside the particles and will execute the rules on each thread. the sets of rules called "subgraph" and "mycost" carry the dependent rules as r1 to r10 (table 1). thus, these rules will execute on each single threads of genes. local memory will keep the value of all variables which are defined as objects and all the rules have access to the same local memory to pull and push the value of objects. after that, the value of object "fit" for each thread and the initial value for the object "pbestscore" need to save in shared memory to execute the function called "compare" according to the rules r11 and r12 to refresh the value of object "pbestscore" with the minimum value of objects "fit". this function will implement on all threads from thread 1 to thread 4. then, the velocity function according to the r13 to r16 will be executed on each thread with utilizing of local memory figure ??. also, two sets of rules including replacing rules and decision rules will be executed on threads to initiate the cycle of particle preparation inside the compartments and restart the first part of model again till predefined iteration (it=100). then after getting the marker genes collected by each thread, kp-svm rules will apply to calculate the error rate for each set of marker genes. these rules are reminded in (table 2). step 4: at the end of first part of the model, the error rates of each set of marker genes will be calculated. according to the table 2, (r1) is flag which is an object with default value zero. r2, flag value will rewrite to 1 if it meets the guard values including an error rate between 0 and 0.3 as well as having at least two cancerous genes indexes in marker genes. r3, q and e are the counters of normal genes and cancerous genes respectively which rewrite to a default value zero, and marker genes 2 keep a backup of marker genes resulted from first part of the model. 328 n. elkhani, r. c. muniyandi, g. zhang table 1: kp-mobpso rules r1:rewriting [[p,max_c]position]0 → [[(position1 . . .positionn)1 . . . (position1 . . . positionn)p]0[[]1[]position]0 r2:communication [[(position1 . . .positionn)1 . . . (position1 . . .positionn)p]position]0 → [(position1 . . .positionn)1 . . . (position1 . . .positionn)p]1]0 r3:communication [(position1 . . .positionn) . . . (position1 . . .positionn)p]1 → [(position1 . . . positionn)]p1 . . . [(position1 . . .positionn)p]]1 rulesinsideeachp : []p1 . . . []pn : r4 > r5 > r6 > r7 r4:rewriting [(position,a,maxc,p)] → [ngenes], [ngenes] → [newngenes,q,c] r5:(communication/rewriting) [(newngenes,c,p,a)] → [c], [c] → [sumdiss], [a] → [snr] [snr] → [sumsnr], [sumdiss,sumsnr] → [fit ] r6:link creation []position−−−−[]master r7:(communication/rewriting) [fit ] → [pbestscoren]master, [qn] → [qn]master r8:division [[pn][pbestscoren,qn]master]1]0 → [p ]1 . . . [p ]n[pbestscoren,qn,gbestscore]master]1[[]p1 . . . [p ]n [fitness,pbest,gbest,v elocity,c1,c2,w,v max,s]master]1]0 r9: membrane dissolution [[[]p1 . . . []pn[]master]1]θ → λ r10: link creation [[[]p1 . . . []pn[pbestscoren]master]1]0, [[[]p1 . . . []pn[fitnessn]master]1]0 → [[]p1 . . . []pn[pbestscoren]master]1]0 −−−−− [[[]p1 . . . []pn[fitnessn]master]1]0 r11: communication/rewriting [[[pbestscoren]master]1]0 → [[fitnessn]master]1]0, [[[pbestn]master]2]0 → [[[fitnessn]master]2]0 < [[[pbestscoren]master]1]0, 1 ≤ n ≤ p [[[gbestscore]master]1]0 → [[pbestscoren]master]1]0, [[[gbestn]master]2]0 → [[[pbestscoren]master]1]0 < [[[gbestscore]master]1]0, 1 ≤ n ≤ p [[[converge]master]1]0 → in[[[[pbestscore]]master]1]0 r12:communication [[[position]]1]0 → [[[position]master]1]0 r13: communication/rewriting [[[positionn,c1,c2,c,w,pbest,gbest,p,maxc,rand]master]1]0 → [[[v elocity]master]2]0 [[[v elocity]master]2]0 → [v max]master]1,v elocity > v max [[[v elocity]master]1]0 → [−v max]master]2,v elocity < −v max [[[[position]]master]1]0 →,rand <= 1/(1 + exp(−2 ∗v elocity) [[[[position]]master]2]0 → 0,rand > 1/(1 + exp(−2 ∗v elocity) r14:output/link creation/communication & rewrite [[[[position]]master]1]0 → [[position]1]0[[]1]0, [[]position]0 → []1]0 −−−− [[]position]0[[[position]]1]0 → [[position]position]0 r15: division rule [[]p1 . . . []pn[]master]12[[]p1 . . . []pn[]master]121[[]p1 . . . []pn[]master]122 r16: membrane dissolution [[]p1 . . . []pn[]master]12 → λ multi-objective binary pso with kernel p system on gpu 329 table 2: kp-svm rules r1:rewriting (s) → find(m(:) == i), table(i) → lenght(s), 1 ≤ i ≤ 100 r2:rewriting markergenes(i, it + 1) → i,table(i) > it, 1 ≤ i ≤ 100, 1 ≤ it ≤ n r3:rewriting y(i, 1) → +1,max_j → max_j + 1, 1 ≤ i ≤ 50 r4:rewriting y(i, 1) →−1,max_f → max_f + 1, 51 ≤ i ≤ 100 r5:rewriting badgenes(it + 1, 1) → max_f r6:rewriting y (j, 1) → +1, 1 ≤ j ≤ max_j ∗ 3 y (j, 1) →−1,max_j ∗ 3 ≤ j ≤ max_j ∗ 3 + max_f ∗ 3 r7:input wholedata(k, 1) → a(i,j), 1 ≤ i ≤ 100, table(i) > it,j = 1,k → k + 1 wholedata(k, 1) → a(i,j), 1 ≤ i ≤ 100, table(i) > it,j = 3or5 wholedata(k, 2) → a(i,j)1 ≤ i ≤ 100, table(i) > it,j = 2or4 wholedata(k, 2) → a(i,j),k → k + 11 ≤ i ≤ 100, table(i) > it,j = 6 r8:rewriting wholedata(k, 1),wholedata(k, 2) → xy,holdout = 0.10 → pcvpartition x(p.training),y (p.training) → sv mstructsvmtrain sv mstruct,x(p.test) → csvmclassifysum(y (p.test)c)/p.testsize errrate y (p.test),c → conmat err(1, 1) → errratefirst 100 times iteration, gbestscore = inf err(it + 1, 1) → errrate constant → err(it + 1, 1)not first 100 times iteration, gbestscore 6= qinf r9:rewriting particle2 → markergenes(:, it + 1)1 ≤ it ≤ n, 0 ≤ err(it + 1, 1) ≤ 0.3 r10:division a(i,j) → (particle2, it = 1), (particle2, it = 2), . . . , (particle2, it = n) 330 n. elkhani, r. c. muniyandi, g. zhang figure 2: designing on gpu step 5: elapsed time so far for feature selection executed on one particle is 5.914 sec. step 6: let assume compartment number 1 is chosen because of meeting the criteria of having better error rate. marker genes 2 object will be used for further procedure on gpu. to implement embed feature selection method, another kernel defined as "second kernel". for each gene number from 1 to 100, rules number r4-r10 will apply to see whether entering a new gene can improve the error rate of that particle or no. r4 and r5, gives a flag to index of genes based on the type of genes whether they are belonged to normal genes or cancerous genes as +1 and -1, respectively. in parallel, q and e which are the counters of normal genes and cancerous genes will be update. r6, the object maxj and maxf updates the number of normal and cancerous genes. r7, clear the value of q and e. r8 reserve a place for the samples of gene indexes are selected as marker genes and r9, inputs the real value of reserved samples inside a compartment called wholedata. wholedata compartment keeps real data samples for gene indexes are already highlighted as multi-objective binary pso with kernel p system on gpu 331 figure 3: continue designing on gpu marker genes. r10, applies an svm package with rewriting rules to evaluate error rates. to decide whether adding a new gene can improve the error rate or no, rule number 11 compares error rates after adding each gene (from 1 to 100) with the constant error rate object resulted in the first part of modelling for each set of marker genes. if adding a new can already can improve constant error rate of that set of marker genes, the r11 will add this gene index to the set of marker genes. otherwise if a gene value already enter to the particle cannot improve the error rate, it should be exit from the particle. rules, r12-15 apply output and rewriting rules to eliminate gene values inside the particle. after executing the computation model for n number of iterations which each iteration represents one particle from it=1 to it=n, particles of marker genes will update by new genes and result a new set of particles as (particle 3, it=1/ particle 3, it=2/. . ./ particle 3, it=n). it means by the end of embedded feature selection and classification; real data set will divide to a new compartment. every time one gene will be added to the set of genes and error rate will calculate to decide adding the gene to the set is suitable or no. if 332 n. elkhani, r. c. muniyandi, g. zhang table 3: benchmark kp-mobpso-svm maxc =100 maximum number of genes p=25 number of particles maxiteration=100 maximum number of generating fitness, gbestscore, pbestscore objects to save the results hardware cpu core(tm)i5-6200u cpu @2.30 ghz (ram): 12.0 gb hardware gpu nvida geforce 680 adding the gene decrease the error rate means the gene will be kept and if increase the error rate means the gene will be removed of the chosen gene set. new value for "sum", "mean" value, "snr" (signal to noise) value, "dissimilarity" value, "fit" (fitness) value, "velocity" value, "std" (standard deviation) value, "gbestvalue" value. total elapsed time for all processes of 2 particles is 13.12 sec. 4 evaluation and result the objective of this study was to improve the time efficiency in implementing a membranebased optimization algorithm. in this regard, two models as kp-mobpso and kp-mobpsosvm are implemented on cpu, multicore and gpu to compare their time complexity. the comparison of the time cost between two models indicates gpu based executions can increase time efficiency of the models drastically. the benchmark of evaluation in terms of the maximum number of the genes, number of particles, maximum number of iterations, and hardware specification of cpu and gpu are explained in the table 3. according ot table 4 and table 5 with the same example of 25 particles executed on multi-core and gpu with 4 workers and independent iteration respectively, time cost dropped significantly from 5.5 min to 73.87 sec in kp-mobpso feature selection and from 15 min to 164 sec in kp-mobpso feature selection and classification. while execution on cpu was taken 3.68 min in kp-mobpso feature selection and 8 min in embed kp-mobpso-svm feature selection and classification. comparing cpu and multicore indicates, due to the frequent interaction between clients and workers, it takes longer time and does not lead to improvement in timely execution of multicore kp-mobpso. therefore, as it is shown by example, a gpu based kp-mobpso and kp-mobpso-svm are more time efficient than cpu based models. regardless of taking example, table 3 indicates the time complexity of the parallel kp-mobpso-svm on gpu according to the big o calculated as o(nm) + o(n) + o(n/p ∗m) + o(n/p) + o(m/2p)) + o(m/p ) +o(m/(p ∗q)) +o(m2/p 2) +o(m2/2p 2) +o(q2/p 2) +o(2q/p) +o(m∗q2/p 2) where (n=max number of particles, m=max number of genes, i=max number of iteration, q: max number of samples, p=max number of processors). the time complexity of sequential kp-mobpso-svm is o(nm) + o(inm)) + o(in) + o(m/2) + o(m2/2) + o(m/2) + o(mq) + o(m) + o(m2) + o(q2) + o(q) + o(mq2) + o(n) where (n=max number of particles, m=max number of genes, i=max number of iteration, q: max number of samples). o(m2) +o(mq2)ando(m2/p 2) +o(q2/p 2) are the highest time cost for sequential and parallel kp-mobpso-svm respectively. it is shown how the number of processors decrease the time cost of implementing kp-mobpso-svm on gpu and leads to more time efficient method. multi-objective binary pso with kernel p system on gpu 333 table 4: kp-mobpso kp-mobpso-svm cpu 25 particles: 3.68 min (10 times of 100 iteration) multi-core 25 particles: 5.5 min (4 workers) gpu 2 particles: 5.91 sec 25 particles: 73.87 sec (independent iteration) table 5: kp-mobpso-svm kp-mobpso-svm cpu 25 particles: 8 min (10 times of 100 iteration) multi-core 25 particles: 15 min (4 workers) gpu 2 particles: 13.12 sec 25 particles: 164 sec (independent iteration) 334 n. elkhani, r. c. muniyandi, g. zhang 5 conclusion to design kp-mobpso-svm model on gpu, two important points are concerned, first, the dependency between the objects and rules to decrease the rate of communication, second; access to the lesser cost memories in the execution of threads like local and shared memory. thus, according to the first criteria, those objects that their existence is dependent to the existence of other objects in the compartment will execute on the same thread along with their parent objects. based on the second criteria, objects and rules which are dependent to each other will use the local memory to keep the value for the objects and will send the value to another rule to trigger its execution. when the execution of dependent rules is done, and completed in single threads, it will be needed to share the result of the threads and make a decision to choose the best result to continue the execution of model. to do this, a thread block which belongs to the current single threads can exchange the result via shared memory. the time cost of kp-mobpso and embedded kp-mobpso-svm on gpu, cpu and multicore are compared in the table 3 which indicates a significant improvement in time cost via executing both kpmobpso and embedded kp-mobpso-svm on gpu. in 25 particles, kp-mobpso takes 3.68 min to complete a set of 100 iteration. while the multicore due to frequent visiting of client to exchange the result was not capable of improving time complexity, gpu does better. in gpubased execution of kp-mobpso, 25 particles take 73.87 sec to complete. therefore, a four times improvement has happened in time efficiency. in terms of kp-mobpso-svm, 8 min execution time have not improved by multicore while gpu has improved the efficiency to 5-fold. the big o calculation indicates the time efficiency of the proposed kp-mobpso-svm improved from o(m2)+o(mq2) in sequential method to o(m2/p2)+o(q2/p2) in parallel execution. acknowledgment the work of n. elkhani and r. c. muniyandi has been supported by frgs/1/2015/ict04/ukm /02/3, national university of malaysia, ministry of higher education, malaysia. the work of g. zhang was supported by national natural science foundation of china (61672437, 61702428) and by sichuan science and technology program (18zdyf2877, 18zdyf1985, 2017gz0159). bibliography [1] alelyani, s.; 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(2009); gpu-based parallel particle swarm optimization, evolutionary computation, 2009, cec’09. ieee congress on, 1493-1500, 2009. hayijcccv12n6.pdf international journal of computers communications & control issn 1841-9836, 12(6), 824-838, december 2017. minimizing uplink cellular outage probability in interference limited rayleigh and nakagami wireless fading channels m. hayajneh mohammad hayajneh computer and network engineering department, college of information technology, united arab emirates university, uae mhayajneh@uaeu.ac.ae abstract: we propose a game theoretic non-cooperative algorithm to optimize the induced outage probability in an uplink cellular interference limited wireless rayleigh and nakagami fading channels. we achieve this target by maximizing the certainty equivalent margin (cem). we derive a closed-form formula of the outage probability in nakagami flat-fading channels, then we show that minimizing the induced outage fading probability for both rayleigh and nakagami channels is equivalent to maximizing cem. we present a non-cooperative power control algorithm using the game theory framework. through this non-cooperative game, we argue that the best decision in such an environment is for all users to transmit at the minimum power in their corresponding strategy profiles. this finding considerably simplifies the implementation of the proposed game. keywords: cellular network, code-division-multiple-access (cdma), outage probability, power control, non-cooperative game (npg). 1 introduction game theory was first brought to the literature by john von neumann and oskar morgenstern in 1944 [7], and by the end of 1970’s game theory became a framework of great value in the researcher’s resources whenever there is a situation in which an agent’s action depends on the other agents’ actions. the basic aim of game theory is the method where deliberate dealings between intelligent agents, produces outcomes according to the agents’ desires [2], [10]. in a non-cooperative game, an agent counters the actions of other agents by selecting a strategy from his strategy space in aiming to optimize a utility function that quantifies its quality of service level (qos). in an uplink cellular data communications systems, cellular users (agents) prefer to have a high signal-to-interference ratio (sir) at the base station (bs) while sending at the lowest possible power, this helps to have longer battery life and helps cope with interference problem in cdma systems [8]. also, it is important to have a high sir, as this means having a low error rate, a more reliable system, and high channel capacity, which results in higher attainable bit rates [14], [9]. in game theoretic power control algorithms for cellular systems, each cellular user maximizes his utility functionby transmitting at a feasible power level to reciprocate the other cellular users’ aggregate transmitted powers. this action generates a series of power vectors that converges to an operating point that is called nash equilibrium (ne) point. however, due to nature of the non-cooperative algorithms that lack cooperation between the users, it is not guaranteed that this operating point will be efficient, in the sense that it may not be the most preferred operating point by all cellular users [11]. game theoretic framework was first exploited in [13] to provide solutions and insights for the power control problem for cdma communication systems, then was more exploited in [11] copyright © cc by-nc minimizing uplink cellular outage probability in interference limited rayleigh and nakagami wireless fading channels 825 and [12]. the research work in [5] presented a bound that relates the induced rayleigh channel fading outage probability to the sir margin. where sir margin is just the sir with the signal power and the interference noise power are replaced by their statistical means. then they used perron-frobenius eigenvalue theory to allocate the cellular users’ transmit powers in a manner that minimizes the system outage probability. for more feasible case, that is, when the transmitted powers are constrained along with other constraints, they modeled the optimization problem as a geometric programming problem to find a global solution. however, the global solution was based on a centralized algorithm that run on the bs to collect data about all path gains of the links between transmitters and their corresponding receivers in the cellular communications system. in this paper the problem addressed in [5] to minimize the outage probability and the certainty-equivalent margin for rayleigh wireless fading channels, is solved in a non-centralized manner under both rayleigh and nakagami fading channels. we also provide a simpler proof of the strong relationship between the two problems, namely: minimizing the fading induced outage probability, and maximizing the cem. we then present a closed-form formula of the outage probability in a nakagami flat-fading channel. using the outage probability formula, we prove that both problems mentioned above remain equivalent in nakagami flat fading channels. what is left from the paper is presented as follows: the system model we studied in this paper is described in section 2. deriving a closed-form formula of the induced outage probability in an interference limited nakagami flat fading channels is performed in section 3. the strong correlation between the minimization of the outage probability and the maximization of the cem is presented in 4. in section 5, our proposed non-cooperative power control game (npg) is discussed. simulation results are presented and explained in section 6, and our conclusions are casted in section 7. 2 system model the system under investigation in this paper is similar to that studied in [3]. in [3], the solutions for optimizing the system cem and the system uplink outage probability under rayleigh flat-fading channels were introduced. in this paper however, we extend our study to figure out a non-centralized power control game theoretic-algorithm for also nakagami flat-fading channels. thus, the system under study is as follows: assume we have one cell with n cellular users communicating with bs in the uplink direction. which can be seen as n transmitter-receiver pairs, where the ith transmitter transmits messages at a power level pi from his action profile pi to the corresponding ith receiver (the bs). the power profile pi set is assumed to be convex set. the received power level at receiver i from transmitter k is given by: hi,kχi,kpk (1) in which hi,k > 0 is the link gain between transmitter k to receiver i. hi,k captures the effect of spreading gain and/or cross correlation between codes in cdma (code division multiple access ) systems. χi,k, i, k = 1,2, ..., n are exponentially independent distributed (id) random variables with mean equal to 1 in a wireless rayleigh flat-fading channel. on the other hand in a nakagami flat-fading channel, χi,k, i, k = 1,2, ..., n are gamma id random variables with mean also equal to 1. hence, the power received from user k at receiver i is modeled as exponential (gamma) random variable with expected value expressed as: e{hi,kχi,kpk} = hi,kpk (2) 826 m. hayajneh inan interference limited fadingchannels, thebackgroundadditivewhitegaussiannoise (awgn) effect is considered minor compared to the power interference from the other active users. therefore, the signal-to-interference ratio of the ith user at the corresponding receiver is given by: siri = hi,iχi,ipi ∑n k 6=i hi,kχi,kpk . (3) it is clear from (3) that siri is a random variable, which is a ratio of an exponentially (gamma) distributed random variable to a summation of independent exponentially (gamma) distributed random variables with different means under rayleigh (nakagami) fading link. the outage probability oi, defined as the probability that the sir of an active user i will be less than a threshold sirth, then for user i: oi = pr{siri ≤ sirth} = pr{hi,iχi,ipi ≤ sirth n ∑ k 6=i hi,kχi,kpk} (4) this probability was derived in [5] for a rayleigh flat fading channel: oi = 1 − n ∏ k 6=i 1 1 + sirth hi,kpk hi,ipi (5) the system outage probability, o is described as: o = max1≤i≤n oi (6) where o may be considered as on of the performance metrics of the cellular system and the adopted power control algorithm. the cem of user i is given as the ratio of his certaintyequivalent sir to the corresponding threshold sir. mathematically, cemi = sircei sirth (7) where, the certainty-equivalent sir (sircei ) of user i is described as the ratio of the mean of his received power at the corresponding receiver to the mean of the interference from the active other users in the system, that is, sircei = hi,ipi ∑n k 6=i hi,kpk (8) while, the system certainty-equivalent sir, sirce is given as: sirce = min1≤i≤n sir ce i (9) thus, the system cem is given by: cem = min1≤i≤n cemi (10) the cem is yet another cellular system performance metric for the deployed power control algorithm. minimizing uplink cellular outage probability in interference limited rayleigh and nakagami wireless fading channels 827 3 interference limited outage probability under nakagami flat fading channel in this section we present our extension to the previous work in [3]. here, we derive a closedform formula of the outage probability in an interference limited cellular communications system under nakagami flat fading channels. having said that, let αi,k be the fading coefficient of the link connecting transmitter k with receiver i, then αi,k has a nakagami distribution: fαi,k(ω) = 2mm γ(m)ωm ω2m−1 exp ( − m ω ω2 ) . (11) where ω is defined as [9] ω = e{α2i,k} it controls the spread of nakagami distribution. the fading figure denoted by m is given as [9] m = ω2 e{(α2i,k − ω) 2} ≥ 1 2 , it stands as a measure of the harshness of the fading channel, for example when m = ∞, it corresponds to a nonfading channel, and if m = 1/2 it corresponds to the half-normal which is the most severe fading channel. the coming lemma presents a closed-form of the outage probability in a flat-fading nakagami channels. lemma 1. under nakagami flat fading channel with ω = 1 and m = 2, the outage probability oi is formulated as: oi = 1 −  n − n ∑ k 6=i   1 1 + sirth hi,k pk hi,i pi     n ∏ k 6=i   1 1 + sirth hi,k pk hi,i pi   2 (12) proof: for simplicity, define yi,k , hi,kχi,kpk, where χi,k = α 2 i,k, and hence yi,k is a gamma distributed random variable, that is: fyi,k(ω) = ξmi,k γ(m) ωm−1 exp(−ξi,k ω) (13) where ξi,k = m ω hi,k pk . in the following calculations we set m = 2 and ω = 1. henceforth, the probability oi of user i can be expressed as: oi = pr{yi,i ≤ sirth n ∑ k 6=i yi,k} = ∫ xi,k 0 fyi,i(ω)dω (14) where xi,k , sirth ∑n k 6=i yi,k. it is clear that xi,k is a summation of independent gamma distributed random variables with different means. for the sake of simplicity, we first find a conditional outage probability in (14) oci(xi,k) = pr(oi|xi,k = xi,k), with xi,k is a realization of the random variable xi,k. therefore, we have the following: oci(xi,k) = ξ 2 i,i ∫ xi,k 0 ω exp(−ξi,i ω)dω = 1 − (1 + ξi,i xi,k) exp(−ξi,i xi,k) (15) 828 m. hayajneh and hence, outage probability oi can be found as follows: oi = ∫ ∞ 0 oci(ω) f xi,k(ω)dω = e{oci(xi,k)} = e{1 − (1 + ξi,i xi,k) exp(−ξi,i xi,k)} (16) using the following intermediate equations: ∫ ∞ 0 ωn exp(−a ω)dω = γ(n + 1) an+1 , (17) e{exp(−ξi,i sirth yi,k)} =   1 1 + sirth hi,k pk hi,i pi   2 , (18) and e{yi,k exp(−ξi,i sirth yi,k)} = hi,k pk   1 1 + sirth hi,k pk hi,i pi   3 (19) we end up with the following formula of the outage probability oi, oi = 1 − n ∏ k 6=i   1 1 + sirth hi,k pk hi,i pi   2 − n ∑ k 6=i sirth hi,k pk hi,i pi   1 1 + sirth hi,k pk hi,i pi   3 × n ∏ l 6=i,l 6=k   1 1 + sirth hi,l pl hi,i pi   2 (20) to make (20) look like (12), we just need to substitute the following equations in (20): n ∏ l 6=i,l 6=k   1 1 + sirth hi,l pl hi,i pi   2 = ( 1 + sirth hi,k pk hi,i pi )2 n ∏ l 6=i   1 1 + sirth hi,l pl hi,i pi   2 (21) and n ∑ k 6=i sirth hi,k pk hi,i pi   1 1 + sirth hi,k pk hi,i pi   = n ∑ k 6=i  1 −   1 1 + sirth hi,k pk hi,i pi     (22) =  n − 1 − n ∑ k 6=i   1 1 + sirth hi,k pk hi,i pi     which concludes the proof. ✷ minimizing uplink cellular outage probability in interference limited rayleigh and nakagami wireless fading channels 829 4 relation between outage probability and cem 4.1 rayleigh flat fading channel we present the following proposition which was first introduced as a remark in [5], and then we deliver our own simpler argument. theorem 2. minimization of the fading induced outage probability oi and maximization of cemi of user i in a rayleigh fading channel are equivalent in terms of power allocation. proof: here, we need to keep in mind that the problem under investigation is to minimize the system outage probability using non-centralized algorithm, that is, every cellular user allocates his transmit power level such that his outage probability is minimized. mathematically speaking, min pi∈pi oi = min pi∈pi 1 − n ∏ k 6=i 1 1 + sirth hi,kpk hi,ipi (23) and this of course is equivalent to the following: max pi∈pi n ∏ k 6=i 1 1 + sirth hi,kpk hi,ipi (24) or min pi∈pi n ∏ k 6=i ( 1 + sirth hi,kpk hi,ipi ) (25) using the fact that the logarithmic function is a monotonic function, therefore minimizing (25) is identical to minimizing the following: min pi∈pi n ∑ k 6=i log ( 1 + sirth hi,kpk hi,ipi ) (26) using the logarithmic inequality log(x) ≤ x − 1, an upper bound of (26) can be given as: min pi∈pi ∑n k 6=i log ( 1 + sirth hi,kpk hi,ipi ) ≤ minpi∈pi n ∑ k 6=i sirth hi,kpk hi,ipi (27) it is intuitive to notice that the right-hand side of the inequality in (27) is equivalent to maximizing the cem, that is max pi∈pi hi,ipi sirth ∑n k 6=i hi,kpk = maxpi∈pi cemi (28) ✷ 830 m. hayajneh 4.2 nakagami flat fading channel similar to rayleigh flat fading channels we have the following proposition for nakagami flat fading channels. theorem 3. minimization of the fading induced outage probability oi and maximization of cemi of user i under a nakagami fading channel are equivalent in terms of power allocation. proof: for simplicity, let us define ti,k , sirth hi,k pk hi,i pi . minimizing the outage probability oi given in (12) is equivalent to the following problem: max pi∈pi     n − n ∑ k 6=i [ 1 1 + ti,k ]   n ∏ k 6=i [ 1 1 + ti,k ]2    (29) or max pi∈pi    ln  n − n ∑ k 6=i [ 1 1 + ti,k ]   + 2 n ∑ k 6=i ln ( 1 1 + ti,k )    = maxpi∈pi    ln  1 + n ∑ k 6=i ( 1 − 1 1 + ti,k )   + 2 n ∑ k 6=i ln ( 1 − ti,k 1 + ti,k )    ≤ maxpi∈pi    n ∑ k 6=i (1 − 1 1 + ti,k ) − 2 n ∑ k 6=i ti,k 1 + ti,k    = maxpi∈pi    n ∑ k 6=i − ti,k 1 + ti,k    (30) where we used the inequality ln(x) ≤ x − 1 again and the assumption that ti,k << 1 (sir is high) to get the inequality in (30). now one can see that the problem in (30) is equivalent to: max pi∈pi    n ∑ k 6=i 1 + ti,k ti,k    =maxpi∈pi    n − 1 + n ∑ k 6=i hi,i pi sirth ti,k pk    (31) and since n − 1 is a constant common for all users, therefore (31) is equivalent to: min pi∈pi { ∑n k 6=i sirth hi,k pk hi,i pi } (32) finally, this problem is clearly equivalent to the problem of maximizing cemi. ✷ in the next section we present a simple non-cooperative game g2, and we show that g2 results in an optimal power allocation to maximize the system cem, minimize the system outage probability o, and minimize the total transmitted power. 5 power control algorithm to optimize the outage probability in this section we introduce a non-cooperative power control game as defined in (34), in which user i attempts to find the optimal transmit power level pi from his strategy space pi minimizing uplink cellular outage probability in interference limited rayleigh and nakagami wireless fading channels 831 that enables him to obtain a maximum possible certainty-equivalent-margin cem∗, instead of maximizing cemi directly as given below: g1 : maxpi∈pi cemi, = maxpi∈pi hi,ipi sirth ∑n k 6=i hi,kpk ,∀i = 1,2, ..., n (33) the reason for avoiding maximizing cemi directly is that the game g1 in (33) has an objective function cemi which is linear in pi given the power vector p−i of all users except for the ith user. this will lead user i to send at the maximum power in his strategy space, as will all users. this selfish act will result in all users having very small cems. due to this reason we propose the following non-cooperative power control game g2 defined as follows: g2 : maxpi∈pi cem ∗ i (n), n = 1,2, · · · subject to pi(n) = min ( pi−max, cem∗i (n) sirth ∑n k 6=i hi,kpk hi,i ) (34) seemingly, game g2 is a multistage game where in the nth stage, user i transmits at a power level pi(n) that enables him to attain a constant cem ∗ i (n) (e.g. cem ∗ i (n) = 1), then if transmit power level pi(n) is feasible, i.e., pi(n) < pi−max, user i seeks a higher value of cem ∗ i at the (n+1)th stage of the game. mathematically speaking, user i sets cem∗i (n+1) > cem ∗ i (n) and finds pi(n + 1) such that cem ∗ i (n + 1) = hi,ipi(n+1) sirth ∑ n k 6=i hi,kpk and so forth until he/she is satisfied with the value of cem∗i (n). however, it turns out that game g2 is a one shot game, that is, it has a nash equilibrium point with all users able to attain their maximum cem∗i in the first stage (n = 1) as we show in the following lemma. the lemma also guarantees the existence, uniqueness, and optimality of the nash equilibrium point: lemma 4. the non-cooperative game g2 with strategy space pi = [pi−min, pi−max] for user i and with pi−min > 0, has a unique nash equilibrium operating point. also, this nash equilibrium point is optimal in the sense that it corresponds to the minimum total transmitted power of all users. users in game g2 will attain their maximum possible cem∗i in the first trial. proof: at each time instance, user i updates his transmit power pi in order to satisfy the following equation: cem∗i = hi,ipi sirth ∑n k 6=i hi,kpk , (35) for a target cem∗i . exploiting the linearity of equation (35), we rewrite it in a matrix form as follows: a p = p , (36) where the entries of the matrix a are given by: a(i, j) = { sirth cem ∗ i hi,j hi,i , if i 6= j 0, if i = j and p = (p1, p2, ..., pn) is the vector of transmit powers of all users. since hi,j > 0, matrix a is a nonnegative irreducible matrix. by the perron-frobenius theorem, the largest eigenvalue in magnitude of matrix a is real and positive [6], [1], and [4]. therefore, if it happens that 1 is 832 m. hayajneh this eigenvalue of a, then the solution p will be the eigenvector that corresponds to eigenvalue 1. otherwise, the only solution of equation (36) is the trivial solution p = 0. since p = 0 is not a feasible solution, each user will transmit at the lowest power level in his strategy space. therefore, the nash equilibrium point of game g2 is unique and corresponds to an optimal point that minimizes the total transmitted power of all users. equation (36) holds true for any cem∗i ≥ 1 including the maximum value that users can attain at the equilibrium point. this implies that the cellular users will attain their maximum possible cem∗i in their first trial, i.e., g2 is a one-shot game. ✷ it is easy to notice that if user i increases his power unilaterally to improve hisher cemi and oi, at least one other user in the network will be harmed. lemma 4 implies that in an interference-limited wireless rayleigh and nakagamai flat fading channels the best policy is for all users to transmit at the minimum power in their corresponding strategy spaces. the question that may arise is: how do we guarantee the quality of service (qos), e.g. sir at the bs? the following lemma answers this question: lemma 5. if users in an interference-limited wireless fading channels are not able to attain a satisfactory qos (sir) by transmitting at their minimum power vector pmin = (p1−min, p2−min , ..., pn−min) in their corresponding strategy spaces, they will not be able to attain a satisfactory qos by transmitting at any other power vector pl = (pl1, p l 2, ..., p l n) larger than pmin, p l > pmin component wise. proof: let n= (1,2, ..., n) be the indexing set of all active users in the cell. suppose cemmini is the cem value user i attained by assuming that all users are transmitting at the minimum powers in their corresponding strategy spaces, that is, ∀i ∈ n : cemmini = hi,i pi−min sirth ∑n k 6=i hi,k pk−min , (37) and suppose that cemli is the value of cem the ith user attains assuming all users transmitting at pl, henceforth, ∀i ∈ n we have: cemli = hi,i p l i sirth ∑n k 6=i hi,k p l k (38) to prove lemma 5, it is enough to prove that there is a subset of users kn ⊂ n , such that: cemln ≤ cem min n ,∀n ∈ kn (39) suppose this is not true, therefore cemli > cem min i ,∀i ∈ n (40) define d l,min i as: d l,min i = cem l i − cem min i = 1 sirth [ hi,i p l i ∑n k 6=i hi,k p l k − hi,i pi−min ∑n k 6=i hi,k pk−min ] ,∀i ∈ n (41) without loss of generality, ∀i ∈ n let pli = δi pi−min, where δi > 1. then, (41) can be written as: d l,min i = hi,i pi−min sirth [ δi ∑n k 6=i δk hi,k pk−min − 1 ∑n k 6=i hi,k pk−min ] ,∀i ∈ n (42) minimizing uplink cellular outage probability in interference limited rayleigh and nakagami wireless fading channels 833 table 1: equilibrium values of cemi and oi for the first 10 users in a rayleigh flat fading channel using perron-frobenius theorem and our npg game g2 at sirth = 3 results using perron-frobenius theorem [5] results of game g2 i pi cemi oi pi cemi oi 1 0.1292 13.7107 0.0703 0.0100 14.9809 0.0645 2 0.1162 13.7107 0.0703 0.0100 16.6629 0.0582 3 0.1476 13.7107 0.0703 0.0100 13.0747 0.0736 4 0.1482 13.7107 0.0703 0.0100 12.9549 0.0742 5 0.1290 13.7107 0.0703 0.0100 14.9275 0.0647 6 0.1192 13.7107 0.0703 0.0100 16.3274 0.0594 7 0.1297 13.7107 0.0703 0.0100 15.0442 0.0643 8 0.1312 13.7107 0.0703 0.0100 14.6970 0.0657 9 0.1327 13.7107 0.0703 0.0100 14.4091 0.0670 10 0.1361 13.7107 0.0703 0.0100 14.2906 0.0675 average 13.7107 0.0703 average 13.7823 0.0704 the inequality in (40) implies that d l,min i > 0 for all i ∈ n , therefore δi n ∑ k 6=i hi,k pk−min > n ∑ k 6=i δk hi,k pk−min,∀i ∈ n (43) for simplicity define xi,k = hi,k pk−min, then (43) can be expressed as: δi n ∑ k 6=i xi,k > n ∑ k 6=i δk xi,k,∀i ∈ n (44) if we set i = 1 in the above equation we get the following result: δ1 > δm, m = 2,3, ..., n, (45) while if we set i = 2, we get the following: δ2 > δm, m = 1,3, ..., n, (46) from equation (45), we have δ1 > δ2, while from (46) we find δ2 > δ1, so we have a contradiction. this proves the statement in equation (39), and this concludes the proof of lemma 5. ✷ 6 simulation results in our simulations, we considered one cell with n = 50 receiver and transmitter pairs in the uplink mode. the path gains hi,k were generated according to a uniform distribution on the interval [0,0.001] for all i 6= k ∈ n and hi,i = 1 ∀i ∈ n . game g2 was run for different values of the threshold signal-to-interference ratios (sirth) in the interval [3,10]. in fig.1 we depict the system certainty-equivalent margin values (cem) resulting from game g2 versus the threshold signal-to-interference ratios, sirth under both rayleigh and nakagami flat fading channels. while in fig.2, we present the resulting system outage probability, o 834 m. hayajneh table 2: equilibrium values of cemi and oi for the first 10 users in a rayleigh flat fading channel using perron-frobenius theorem and our npg game g2 at sirth = 10 results using perron-frobenius theorem [5] results of game g2 i pi cemi oi pi cemi oi 1 0.1439 4.0985 0.2159 0.0100 4.0243 0.2194 2 0.1541 4.0985 0.2159 0.0100 3.7254 0.2347 3 0.1266 4.0985 0.2159 0.0100 4.5414 0.1971 4 0.1497 4.0985 0.2159 0.0100 3.8603 0.2275 5 0.1375 4.0985 0.2159 0.0100 4.1930 0.2116 6 0.1378 4.0985 0.2159 0.0100 4.1877 0.2118 7 0.1096 4.0985 0.2159 0.0100 5.3148 0.1711 8 0.1514 4.0985 0.2159 0.0100 3.8264 0.2293 9 0.1398 4.0985 0.2159 0.0100 4.1414 0.2139 10 0.1384 4.0985 0.2159 0.0100 4.1889 0.2118 average 4.0985 0.2159 average 4.1255 0.2156 table 3: equilibrium values of cemi and oi for the first 10 users in a nakagami flat fading channel using perron-frobenius theorem and our npg game g2 at sirth = 3 results using perron-frobenius theorem results of game g2 i pi cemi oi pi cemi oi 1 0.1368 13.7415 0.0580 0.0100 14.2272 0.0561 2 0.1501 13.7415 0.0580 0.0100 12.9034 0.0618 3 0.1305 13.7415 0.0580 0.0100 14.6840 0.0543 4 0.1360 13.7415 0.0580 0.0100 14.2485 0.0560 5 0.1270 13.7415 0.0580 0.0100 15.0958 0.0528 6 0.1539 13.7415 0.0580 0.0100 12.5887 0.0633 7 0.1499 13.7415 0.0580 0.0100 12.8494 0.0620 8 0.1402 13.7415 0.0580 0.0100 13.7151 0.0581 9 0.1577 13.7415 0.0580 0.0100 12.2655 0.0649 10 0.1552 13.7415 0.0580 0.0100 12.5932 0.0633 average 13.7415 0.0580 average 13.8343 0.0581 minimizing uplink cellular outage probability in interference limited rayleigh and nakagami wireless fading channels 835 3 4 5 6 7 8 9 10 10 0 10 1 10 2 threshold signal−to−interference ratio, sir th m in im u m e q u il ib ri u m c e rt a in ty − e q u iv a le n t− m a rg in , c e m figure 1: minimum equilibrium cem in rayleigh and nakagami fading channels versus the threshold sir ratio. of a rayleigh fading channel(∗) and a nakagami fading channel (◦) versus the threshold sir (sirth) compared to the minimum bound 1/(1 + cem) ( solid line) and the upper bound 1 − e−1/cem (dashed line) derived in [5]. in this figure, as one can see, in the rayleigh case, the upper bound overlaps with the equilibrium outage probability which is the output of game g2. the results shown in fig.1 and fig.2 happened to be very close to the results obtained using perron-frobenius theorem [5] (in rayleigh fading channel) but at a lower power allocation. this is clear in tables 2 4. in these tables we are casting cemi and oi for the first 10 users as evaluated by the perron-frobenius theorem in [5] and as equilibrium outcomes of the npg game g2 of both channel models: rayleigh flat fading channel and nakagami flat fading channel. the averages of cemi and oi presented in the tables are calculated for all the 50 users in the system. we noticed that the average value of cem obtained through npg game g2 was higher than that obtained by the perron-frobenius theorem based algorithm for all values of sirth. the average value of o achieved through npg game g2 was sometimes lower and sometimes higher than that achieved by perron-frobenius theorem. by examining tables 2 4, one can see that by transmitting at perron-frobenius power eigenvector many users attain lower values of cem than they obtained when all users transmitted at the minimum power vector. this exactly agrees with what was proved in lemma 5. finally, notice that in fig.2 results show that users can achieve better performance in a nakagami fading channel with (m = 2) than in a rayleigh channel. this is expected since a nakagami fading channel with fading figure m = 2 represents a less severe fading channel than a rayleigh fading channel which is a nakagami fading channel with fading figure m = 1. 836 m. hayajneh 3 4 5 6 7 8 9 10 10 −2 10 −1 10 0 threshold signal−to−interference ratio, sir th m a x im u m e q u il ib ri u m o u ta g e p ro b a b il it ie s , o figure 2: maximum equilibrium rayleigh fading induced outage probability (∗), the lower bound of the outage probability 1 1+cem (solid line), the upper bound 1 − e−1/cem (dashed line) and the maximum outage probability in a nakagami channel (◦) versus the threshold sir ratio. table 4: equilibrium values of cemi and oi for the first 10 users in a nakagami flat fading channel using perron-frobenius theorem and our npg game g2 at sirth = 10 results using perron-frobenius theorem results of game g2 i pi cemi oi pi cemi oi 1 0.1539 4.0590 0.1906 0.0100 3.7011 0.2078 2 0.1432 4.0590 0.1906 0.0100 3.9928 0.1936 3 0.1384 4.0590 0.1906 0.0100 4.1278 0.1876 4 0.1553 4.0590 0.1906 0.0100 3.6718 0.2094 5 0.1378 4.0590 0.1906 0.0100 4.1369 0.1872 6 0.1305 4.0590 0.1906 0.0100 4.3997 0.1766 7 0.1593 4.0590 0.1906 0.0100 3.5804 0.2143 8 0.1553 4.0590 0.1906 0.0100 3.6847 0.2087 9 0.1456 4.0590 0.1906 0.0100 3.9305 0.1965 10 0.1483 4.0590 0.1906 0.0100 3.8569 0.2000 average 4.0590 0.1906 average 4.0948 0.1903 minimizing uplink cellular outage probability in interference limited rayleigh and nakagami wireless fading channels 837 7 conclusion we proved the tight relationship between the two optimization problems: minimizing the system outage probability and maximizing the system cem under rayleigh and nakagami flat fading wireless channels and. a closed-form of the outage probability under nakagami flat fading channelwasalsoprovided. wethenproposedanasynchronousdistributednon-cooperativepower control game-theoretic algorithmtooptimize the systemcemand the systemoutageprobability. using the proposed non-cooperative game g2, we proved that the best power allocation in an interference limited rayleigh and nakagami fading wireless channels is the minimum power vector in the total strategy spaces of active users in the system. power was more effectively and more simply allocated according to this proposed non-centralized algorithm than the centralized algorithm in [5]. bibliography [1] farrokhi h., rezayi m.(2012); an improved distributed power-control scheme for cellular mobile systems, turkish journal of electrical engineering & computer sciences, 20(1), 1-8, 2012. [2] fudenberg d., tirole j. (1991); game theory, the mit press, 1991. [3] hayajneh m., abdallah c. (2003); performance of game theoretic power control algorithms in interference limited wireless fading channels, proc. of sixth baiona workshop on signal processing in communications, 1-8, 2003. [4] hayajneh m., abdallah c. (2015); game theoretic distributed power control algorithms for uplink wireless data in flat fading channels, international journal of computers communications & control, 10(4), 520-538, 2015. [5] kandukuri s., boyd s. (2002); optimal power control in interfernce limited fading wireless channels with outage probability specifications, ieee trans. wireless comm., 1(1), 46-55, 2002. [6] mitra d. (1993); an asynchronous distributed algorithm for power control in cellular radio systems, proc. 4th winlab workshop on 3rd generation wireless information networks, 177-186, 1993. [7] von neumann j., morgenstern o. (1944); theory of games and economic behavior, princeton university press, princeton, 1944. [8] peterson r., ziemer r., borth d. (1995); introduction to spread spectrum communications, prentice hall, upper saddle river, nj, 1995. [9] proakis j.g. (2000); digital communications, the mcgraw hill press 1221 avenue of the americas, new york, ny, 2000. [10] ross d. (1999); what people want: the concept of utility from bentham to game theory, university of cape town press, south africa, 1999. [11] saraydar c.u., mandayam n.b., goodman d.j. (2001); pricing and power control in multicell wireless data network, ieee jsac, 19(9), 1883-1892, 2001. 838 m. hayajneh [12] saraydar c.u., mandayam n.b., goodman d.j. (2002); efficient power control via pricing in wireless data networks, ieee tras. comm., 50(2), 291303, 2002. [13] shah v., mandayam n.b., goodman d.j. (1998); power control for wireless data based on utility and pricing, proceedings of pimrc, 1427-1432, 1998. [14] zander j. (1992); distributed cochannel interference control in cellular radio systems, ieee tran. veh. tchnol., 41, 305311, 1992. int j comput commun, issn 1841-9836 8(2):263-274, april, 2013. an hybrid text-image based authentication for cloud services d.e. popescu, a.m. lonea daniela elena popescu, alina madalina lonea faculty of electrical engineering and information technology, university of oradea romania, 410087 oradea, 1, armatei romane str. e-mail: depopescu@uoradea.ro, madalina_lonea@yahoo.com abstract: the problem of securing access to the online information is acute today when access to bank accounts, health records, intellectual property and business or politically sensitive information are made by only a few clicks, regardless of geographic location. at the same time, more and more of these accesses are made from handsets. cloud computing is eminently suitable for addressing problems related to limited client resources, as it offloads computation from clients and offers dynamic provisioning of compute resources. authentication of the companys users to the cloud service is mandatory because in this way it is eliminated the attacks risks to enter into the cloud services. a suitable authentication is required for organizations that want to access the cloud services. our solution regards increasing security at the security access point level of cloud computing and it is in fact a strong hybrid user authentication solution based on using image combined with text in order to avoid the weakness of simple user and password solution for authentication. a two factor password image based authentication method is proposed in this paper for cloud services. this authentication approach is used without additional hardware involved and presents the advantages of utilization in terms of security and usability. every time when the user will be asked to provide his/her identity, a form for each image included in the photo will be listed. the user will have to remember the secret code for each image and to carefully introduce them in the forms. the global cloud access solution will be based on our hybrid proposed text-image based solution, and will be completed by the x.509 certificates. keywords: authentication, multi factor password authentification, strong authentification, image based, cloud services, iaas, paas, saas. 1 introduction as cloud computing (cc) model seems to be the best solution for solving the online access to services that became ubiquitous, authentication is becoming a focal point for security professionals [1]. the problem of securing access to the online information is acute today when access to bank accounts, health records, intellectual property and business or politically sensitive information are made by only a few clicks, regardless of geographic location. at the same time, more and more of these accesses are made from handsets. this introduces security vulnerabilities and complications, because handsets have computational, and power limitations compared with traditional computers and they are constrained in terms of text input being more prone to theft than traditional computers. it is also important to point out that mobile devices input constraints make difficult for users to input complex passwords. cloud computing is eminently suitable for addressing problems related to limited client resources, as it offloads computation from clients and offers dynamic provisioning of compute resources. so, cc emerges as a new computing paradigm which aims to provide on-demand scalable services over the internet via cloud vendors to multi-tenant organizations. enterprises are interested to move their on-premises infrastructure into cloud computing. however they are still concerned about the security risks implied by the act of embedding their resources within the cloud computing environment. copyright c⃝ 2006-2013 by ccc publications 264 d.e. popescu, a.m. lonea authentication of the companys users to the cloud service is mandatory because in this way it is eliminated the attacks risks to enter into the cloud services. a suitable authentication is required for organizations that want to access the cloud services. therefore, credential management, strong authentication, delegated authentication are leveraged across the cloud delivery models. implementing authentication is very important, but organizations should be carefully at the attack implications. attacks (like: impersonation, phishing, brute force dictionary based password) could occur on the credential details. thus, authentication must be secured using the best techniques. decreasing the risks in the cloud environment should be the priority for the cloud providers and the organizations that adopt the cloud services. they also should select the appropriate solution in terms of cost [2]. csa (2010) provides different recommendations for each type of the cloud services used. thus: • software as a service (saas) and platform as a service (paas) cloud environment provide several authentication options for their customers. in the case of enterprises, the identity provider (idp) authenticates users and a trust relationship should be realized between the organizations and the cloud services by federation. besides the enterprises could exist individual users that will want to authenticate at the cloud services. they could do it using the user-centric authentication (like: google, yahoo id, openid, live id etc.). hence, those individual users will access multiple sites using a single set of credentials [2]. • infrastructure as a service (iaas) cloud environment disposes by two categories of users: the enterprise it personnel and the application users. the enterprise it personnel are the ones that develop and manage applications in the iaas cloud model. for this type of users the solution that is recommended is to use a dedicated vpn with the iaas environment, in order to apply the existing enterprise authentication systems (e.g. single sign-on solution or ldap-based authentication) into the cloud environment. if the vpn tunnel is not realized for feasibility reason, then authentication assertions (saml, ws-federation) are applied together with standard web encryption (ssl), which will determine the expanding of the enterprises sso capabilities to the cloud service. another solution that could be implemented in order to obtain the credentials authentication of users is to use the openid outside of the enterprise and to control the access of the users by specifying the appropriate privileges. furthermore, also the oath-compliant solution (open authentication) could be implemented in the cloud systems for authenticating the users. these compliant solutions uses strong authentication [2]. our solution regards increasing security at the security access point level of cc and it is in fact a strong hybrid user authentication solution based on using image combined with text in order to avoid the weakness of simple user and password solution for authentication. beyond this introductory section, our paper contains other 5 sections. section 2 points some background related with identity and access management in cc, section 3 emphasizes some background and related work concerning authentication based on image and text, section 4 presents our proposed solution and section 5 contains our concluding remarks. 2 identity and access management in cloud computing the general cloud computing architecture is composed by a massive network of "cloud servers" [3] that uses virtualization to maximize the utilization of the computing power available/per server (figure 1). according to tianfield (2011) [4] the cloud architecture consists of an hybrid text-image based authentication for cloud services 265 cloud platform architecture (cpa) and cloud application architecture (caa). clouds users interact with cpa and caa using the cloud portal, which allows the user to select a service from a service catalogue. further, the system management will find the correct resources that will be allocated in the cloud by the provisioning service. the optional monitoring and metering component tracks the usage of the cloud, so the resources used can be attributed to a certain user. figure 1: cloud computing architecture [3] cc offers a lot of advantages such as: it is an efficient way to store and maintain databases, being an helpful tool for business, the services offered by cc are in cloud as saas, cloud computing solutions are in general less expensive than their software counterparts (pricing being offered on a per-user basis), an efficient use of cc reduce energy consumption significantly, the costumers are freed of problems related to the technological issues of installing and maintaining the it. in [3] are identified as threats in cc: • abusive and flagrant use of cc solution: use of stringent registration and validation process, improving the monitoring and coordination throughput the cc, analysing the customer traffic, monitoring network blocks • serious breach in interface and api solution: the use of security model analysis of cloud apis, the implementation of a strong authentication and access controls and the evaluation of the api chain dependency • insider threats and attacks solution: securing overall information, efficient compliance reporting, efficient breach notification processing it is important to note that all the mitigation techniques proposed by [3] are related with the authentication process. in conclusion, cloud providers should have established a secure access and technical solutions for doing it, in order to ensure that the right people access the right services. the data that will be stored in the cloud could be accessed only by authorized users, which are specified by the provider. the solution is to integrate the data and services accesses in the identity and access management (iam) infrastructure, whose requirements are [2] [5]: 1. identity provisioning/de-provisioning 2. authentication 3. identity federation 4. access control 266 d.e. popescu, a.m. lonea authentication and authorization aspects of cloud computing are related with various forms of identity federation and claims-based authentication that facilitate transactions between cloud entities. in cc the servers are not accessed direct through network connections, they are accessed by the services they provide, ensuring a high degree of transparency to the cloud. users in fact access certain cloud components (request brokers) and those cloud components distribute requests to individual servers, as appropriate. this important functioning aspect of cc was use as a basis for the security components and architecture solution for cc environments given in [6]. in order to preserve the transparency character for cc, security components and services must be transparent and also generic adjustable to individual users, requirements, applications, and required services. further, figure 2 was introduced and discussed, with the purpose to emphasize the security components for cc environment. because our paper is based on the authentication solution, our contribution will be on the security access point (sap) component. hence, the security components of figure 2 are: • the application access point (aap) server is the service that distributes based on types of requests, or other parameters cloud service requests to individual application servers. it is related and use the services publishing and dispatching (spd) server. the spd server is based on the uddi standard for discovering application services available in the cloud and it is used for publishing and discovering of cloud applications services [6]. • the communication access point (cap) is in fact the communication services provider, which is able to accept requests coming through different communications protocols [6]. • the security access point (sap) is the cloud server that provides front-end security services and is responsible with the authentication of users. it must be based on open standards and applicable in an open environment [6]. • certification authority (ca) server provides certification services in the cloud by issuing certificate to the client and to the sap [6]. • the identity management system (idms) x.500 compliant directory, is another server that provides registration and identification services in the cloud [6]. figure 2: security components and architecture for cloud computing environments [6] we have to point out that, in order to ensure the cc security, we can implement our proposed solution at the client level, at the sap level and at the aap level, but this paper consider only the authentication at sap level. an hybrid text-image based authentication for cloud services 267 3 background and related work there are three main techniques for user authentication: knowledge based techniques, tokenbased techniques and techniques based on biometrics. the problem with the biometrics systems is the difficult trade-off between impostor pass rate and false alarm rate and the fact that they often require specialized devices unpleasant to use. they eliminate the limitations of the human brain [7] recall problems are eliminated, and so are security problems concerning users writing down or choosing simple passwords. this eliminates nearly all of the problems with security. knowledge-based systems are the most frequently used for user authentication in our days. most token-based authentication systems are also using knowledge based authentication to prevent impersonation through theft or loss of the token. but, the fundamental weakness of knowledge-based authentication schemes based on recall-based authentication, is the human limitation to remember secure text passwords. text-based authentication is vulnerable to more complex attacks, such as brute force attacks and packet sniffing. with brute force attacks, an intruder tries to guess the users password, or uses a password hash file. alternatively, an intruder can use easily downloaded packet sniffing technologies such as ethereal (akula). although a random, nonsensical password offers good security, the human brain finds them almost impossible to remember. an alternative for these knowledge-based authentication systems is to orient to the recognitionbased systems [8]. the passwords have evolved from a simple dictionary or personal piece of text, to a nonsense mixture of different types of characters. this new approach of text based passwords is in conflict with the human brains ability to remember strings. all studies made on human memory patterns show that the brain is more adept at remembering images. in [9] is examined the requirements of a recognition-based authentication system and proposed deja vu, which authenticates a user through his ability to recognize previously seen images. the proposed authentication system is more reliable and easier to use than traditional recall-based schemes based on user passwords or pins and it has the advantage that it prevents users from choosing weak passwords and makes difficult to write down and share passwords with others. this is demonstrated by dhamija et al (2000) in their user study where 90% of all participants succeeded in the authentication tests using deja vu, and only 70% succeeded using passwords and pins. another important study in this field was made by jackson (2006) [10], where the author considered from the beginning that brute force attacks can still be a problem for an image-based system, and it is important to identify the right number of combinations available that does not compromise the system to this type of attack, and does not overload the user with images. furthermore, jackson (2006)also proposed a solution against shoulder surfing, which is a grid based image authentication system that randomize the position of different images each session (in situations where the intruder was not able to get a clear view of the image clicked, only an area view). the prototype designed by jackson (2006) was used to evaluate the possibility of image-based authentication (iba) method to be the main security method. he made some experimental studies and found that images, faces and text mixed with images seemed to offer good results concerning human memory. five experiments were made for testing security, usability, recall, methodology undertaken and whether user were able to remember passwords based on multiple image-based interfaces. the results of these experiments indicates that the recall levels over the three interfaces was about 90% and the best performers were obtained by the text mixed with images (story-based interface) and images (picture based-interface). the results experiments have been encouraging for iba method; they showed that users were quite able to remember 268 d.e. popescu, a.m. lonea figure 3: different image-based interfaces on security systems [10] passwords contained on different image-based interfaces, and the human brain is able to hold successfully passwords on three completely different interfaces. furthermore, they demonstrated that the method of combining text with images is the most effective which is the basis for our approach, together with the idea of randomizing the images as solution against shoulder surfing. nitin et al.(2008), in [11] described the new facility for authentication added to juit-iba system which is running within the jaypee university and information technology (juit) and is globally accessible through the website: www.juit-iba.org. being an iba system, it is userfriendly and it uses kerberos protocol in order to strengthen the security during authentication process. the sign in seal advanced security feature was introduced to this system in order to make it more secure. a sign-in seal is a secret between the computer that is setting up and iba. your sign-in seal is saved to your computer and it is associated with your computer. if you log in from multiple computers, you will need to create a separate sign-in seal for each one. it is convenient to instantly recognize a genuine iba sign-in page that ensure you that you are not on a page created by hackers attempting to steal your iba id and password the seal can be customized by creating a text seal or by uploading an image. from the security point of view it is important that even if the hacker knows or guesses the id on your personal information, he cannot use it to discover your sign in seal. even yahoo has implemented the sign in seal method, with a seal that can be a text or an uploaded image (figure 4) and it must be used in combination with our proposed method in order to increase the security. this will be subject for our future work. figure 4: sign in seal at yahoo an hybrid text-image based authentication for cloud services 269 newman et al. (2005) presents and analyses in [12] a user authentication techniques using images that can be used in local or in remote authentication. the system consists of an authentication server (as), and an authentication user agent (aua) and it requires that the user have assigned a subset of images (as passwords) from a larger set. the set of all images used by iba system, named image set, contains images that are distinctive to the human eye, they are not easily describable and they differ in structure. the as has the authentication database of images and associations of users with their individual image sets. it is part of the trusted computing base, being never compromised. when the authentication is made remote, the channel is encrypted using diffie-hellman. for the attack scenarios, they considered four locations of vulnerabilities: information stored on the as, information sent between as and the aua, the output of the aua and the input of the aua. for security analysis purposes, they considered the situations: keystroke logging: aua input, shoulder surfing: aua output logging, tempest attack: aua output, brute force attack, frequency correlation attack: presentation sets, leaking image set size. for storing the individual image set for each individual user, only the indices into the image space will be stored. if the encryption is required, a good proposed solution is to send the images in clear over the channel with the permutation encrypted. the hidden permutation is applied by aua to the images in order to display them, record the users selections and sent these back to the as. their study was important for our proposed solution and we apply our solution within the as and aua components, which are called in this paper as sap (security access point) authentication solution. in the past few years, an important research was made regarding the usability and security of challenge questions, that are commonly used as a backup when users forgot their main" authentication secret. most challenge questions rely on a user’s knowledge of their early life, something static over time. this kind of information can be discovered by a determined attacker. so, the standard mechanism based on textual questions can be replaced by the challenge protocol developed by renaud et al (2010) in [14]; it uses a set of pictorial elements to prompt answers. the prompts solicit associative memories and serve as a stronger cue to aid the recall. all the pictures serve as an additional recall aid, while the use of an indirect question (the direct answer is not in a database or a public source) helps to reduce the exposure of the user to targeted observation attacks [15]. by using this more usable picture-based system it is maintained the same level of security as traditional questions as long as multiple questions are used in serial order. another approach for authentication was proposed by micallef, et al. (2009) and uses an authentication avatar which represents the identity, including personality, of a fictional person that is generated almost randomly from a minimal user input [16]. an avatar profile (ap) contains information about the avatar, and a subset of the ap information is used by the user to respond to challenge questions regarding the avatar. in this way the security is improved since, unlike the users own information, the avatar information is not as easily determined by an attacker. since such fiction information is likely to be more challenging for a user to recall (than their own, personal information), the proposed approach uses techniques such as repeated exposure to graphical imagery (related to the avatar) of users at every login in order to improve the memory association. such images can be associated with the avatar itself, and also with elements of the ap (a picture of the avatars pet). this recovering password solution was used as basis in our authentication solution, for increasing the security level in authentication. the proposed solutions from [14] and [16] can be integrated into the global authentication process. our proposed approach share with them the idea of combining text with images and the idea of using a randomize process in the authentication procedure. another work in context of iba was realized by confident technologies. they provides image-based, multifactor authentication solutions for enterprise companies, websites, web and 270 d.e. popescu, a.m. lonea mobile applications, and mobile devices [13]. it encrypts one-time authentication codes within an image-based challenge, being easy to use and highly secure. users simply identify which pictures match their previously-chosen, secret categories to authenticate. image-based authentication solves the traditional trade-off between security and usability by providing strong authentication that is easy for people to use. as users simply tap a few pictures to authenticate, it is ideally suited for use on mobile devices. confident technology can be used as a standalone multifactor authentication solution, or as an additional layer of authentication. 4 the proposed authentication solution the system consists of an authentication service server (as), and an authentication user agent (aua) and it requires that the user have assigned a subset of images (as passwords) from a larger set. the set of all images used by the image based authentication (iba) system, named image set, contains images that are distinctive to the human eye, they are not easily describable and they differ in structure. the as has access to the authentication database of images and associations of users with their individual image sets. the current solution for authenticating in cc is given in figure 5 and concerns the access of the user to the security access point (sap). figure 5: the current cc sap authentification solution in order to perform stronger authentication for accessing the cloud services, we propose to use an authentication scheme (figure 6) that perform the following steps: 1. make a text-based authentication based on the user id and a text password to have access to the cloud services 2. make an hybrid text-image based authentication that uses our own proposed solution for authentication; it combines the images with text and is a good solution for avoiding the brute force attacks and to ensure a strong authentication scheme 3. use the x.509 standards for obtaining the user credentials step 2 from the authentication scheme of figure 6 suppose that when the user will register into the cloud service, he/she will receive a randomly grid of images. each grid of images will contain 3 images and each image will have a corresponding number (e.g. 1, 2, 3) (figure 7). the user will have to provide a secret code for each image. in this sense, the user will receive a registration form, where it is asking to introduce secret characters for each corresponding number like in figure 8. lets suppose that there were introduced the following codes like it is emplasized in figure 8. after the user introduced their specific secret code for each corresponding image, the registration will be realized. the user should remember which code he/she had provided for each an hybrid text-image based authentication for cloud services 271 figure 6: our cc sap authentification solution figure 7: grid of images figure 8: the registration form 272 d.e. popescu, a.m. lonea type of image (e.g. in the above example the user choose for the house image the hoho code, for the apple image the apap code and for the flower image the flfl code), because the proposed authentication method requires entering these codes, but each time the images will be associated with different numbers (from 1 to 3), because the numbers are generated using a permutation algorithm. therefore, for authentication procedure there will be the same images each time, but with another corresponding numbers (figure 9). our hybrid text-image solution can be applied not only for accessing the cloud, but also as a authentication method at cloud client level (especially for mobile clients) and also at the application level. figure 9: relationship between the randomly grid of images and the secret code provided by user. 5 conclusions and further work a suitable authentication is required for organizations that want to access the cloud services. our solution regards increasing security at the security access point level of cloud computing and it is in fact a strong hybrid user authentication solution based on using image combined with text in order to avoid the weakness of simple user and password solution for authentication. all authentication methods have drawbacks and currently there is not a system that cannot be attacked. it is reasonable to assume that there is never likely to be a 100% secure system of authentication. the biometric concept is extremely secure, but the biometric systems have the disadvantage that require additional authentication periphery. this adds an additional cost that the standard user is not willing to pay. token-based authentication has seen a massive expansion in recent years, especially in the banking sector. adoption of smart card technology in the banking world and access based on smartcards for access to companies and organizations have increased the degree of usability of chip and pin-based authentication. image-based authentication appears to offer the best solution. it provides increased security; is very versatile and does not require significant organizational changes in the enterprise. in terms of cost, image-based authentication is convenient as an alternative to text-based, because it require no significant extra costs. the main advantage of text-based authentication means that all approaches of this kind of authentication are similar. users are familiar with these authentication systems. in contrast, image-based approaches are likely to have different interfaces, which is likely an hybrid text-image based authentication for cloud services 273 to be different from a security system of an organization to another one. a two factor password image based authentication method is proposed in this paper for cloud services. this authentication approach is used without additional hardware involved and presents the advantages of utilization in terms of security and usability. every time when the user will be asked to provide his/her identity, a form for each image included in the photo will be listed. the user will have to remember the secret code for each image and to carefully introduce them in the forms. as a future work, we want to develop our approach for these randomizing appearing of images that will increase the security level of the authetication system in cloud environment, being an effective solution against shoulder surfing attacks. acknowledgement this work was partially supported by the strategic grant posdru/88/1.5/s/50783, project id50783 (2009), co-financed by the european social fund investing in people, within the sectoral operational programme human resources development 2007-2013. bibliography [1] parc,r.c., et al.,authentication in the clouds: a framework and its application to mobile users, acm cloud computing security workshop (ccsw); 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[11] nitin, vivek kumar sehgal, et al., image based authentication system with sign-in seal, proc. of the world congress on engineering and computer science, wcecs 2008, san francisco, usa, 2008. [12] newman r.e. harshp., and jayaraman p, security analysis of and proposal for image based authentication, ieee carnahan, 2005. [13] confident technologies inc., confident imageshieldtm, available at: http://www.confidenttechnologies.com/products/confident-imageshield, 2011. [14] renaud k., just m., pictures or questions? examining user responses to associationbased authentication, acm proceedings of the british hci conference 2010, dundee, scotland, 6-10 september 2010. [15] just m. and aspinall d., personal choice and challenge questions: a security and usability assessment. in l. cranor, editor, soups, acm international conference proceeding series. acm, 2009. [16] micallef n., just m., using avatars for improved authentication with challenge questions, proc. of the the fifth international conference on emerging security information, systems and technologies (securware 2011), august 2011. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 1, month: february, year: 2020 article number: 1007, https://doi.org/10.15837/ijccc.2020.1.3712 ccc publications a stochastic max pooling strategy for convolutional neural network trained by noisy samples s. sun, b. hu, z. yu, x.n. song shuai sun department of electrical engineering, zhengzhou electric power college, zhengzhou 450000, china sunshuai@126.com bin hu department of electrical engineering, zhengzhou electric power college, zhengzhou 450000, china 13949101656@163.com zhou yu department of electrical engineering, zhengzhou electric power college, zhengzhou 450000, china 232689536@163.com xiaona song* college of mechanical engineering, north china university of water resources and electric power, zhengzhou 450000, china *corresponding author: songxiaona1@126.com abstract the deep convolutional neural network (cnn) has made remarkable progress in image classification. however, this network performs poorly and even cannot converge in many actual applications, where the training and test samples contain lots of noises. to solve the problems, this paper puts forward a network training strategy based on stochastic max pooling. unlike the traditional max pooling, the proposed strategy first ranks all the values in each receptive field, and then selects a random value from the top-n values as the pooling result. compared with common pooling methods, stochastic max pooling can limit the pooling selection to a larger value that represents the main information of the pooling area which reduces the chance of introducing noises into the network, and enhances the robustness of extracting noisy image features. experimental results show that the cnn used stochastic max pooling strategy can converge better than traditional cnn and classified noisy images much more accurately than traditional pooling methods. keywords: image classification, deep learning, convolutional neural network (cnn), stochastic max pooling. https://doi.org/10.15837/ijccc.2020.1.3712 2 1 introduction in recent years, deep learning has become a research hotspot of artificial intelligence (ai). this technology has been successfully applied in various field, such as image classification [1, 2, 9], object detection [3, 7, 10, 11], and image segmentation [4, 5]. one of the most important deep learning models is the deep convolutional neural network (cnn) [6]. with special structural elements (e.g. pooling, convolution and receptive field), the deep cnn enjoys immense popularity in image processing. many scholars have attempted to further improve the excellent performance of the deep cnn in terms of function and structure. the functional improvement either modifies or increases network functions, enhancing the network ability to achieve specific functions. for instance, nair [8] replaced the traditional sigmoid function with rectified linear unit (relu) function, leading to better network convergence. srivastava [13] put forward the dropout function, which greatly suppresses network overfitting than traditional methods. wen [14] designed and implemented the intermediate loss function, and realized more accurate classification based on sample correlations than the traditional softmax loss function. the structural improvement aims to express features in a better way through structural adjustment to the network. currently, many scholars strive to obtain richer feature information by increasing the depth and width of the network. for example, krizhevsky [6] proposed an eight-layer cnn called the alexnet, which won the large scale visual recognition challenge (ilsvrc) 2012. simonyan [12]further increased the number of network layers to 19, and achieved 92.7% top-5 test accuracy in imagenet, 55% higher than that of alexnet. in ilsvrc 2015, the 152-layer resnet [5] further improved the top–5 test accuracy to 95.7%. however, the deep cnn provides no specific solution to problems like noisy images. in actual engineering, both the training and test samples contain lots of noises under the effects of lighting or imaging device. despite being capable of processing images, the special structure of the cnn cannot eliminate the effects of noises on the network. if the deep cnn is directly applied to feature extraction, it will be inevitable for the extracted features to contain noises. in the deep cnn, the key to feature extraction is the pooling layer. this layer extracts features for the second time, making them invariant to displacement, scaling and deformation. the most popular pooling method is max pooling, which takes the maximum value in each receptive field. many experiments have found that this method can easily retain the noise (usually the maximum value) of the original image, and amplify the noise effects. this phenomenon is particularly obvious in max pooling of a highly noisy image. unable to differentiate between noises and normal signals, max pooling does not apply to processing noisy images. to solve the above problem, this paper puts forward a deep cnn based on stochastic max pooling. unlike max pooling, the stochastic max pooling ranks the values in each receptive field and selects one from the top-n values as the pooling result. this approach reduces the chance of taking a noise as the feature. like dropout structure, the network structure based on stochastic max pooling can be viewed as a set of n sub-models, and thus have a high accuracy. the rest of the paper is organized as follows. section 2 reviews recent pooling methods. section 3 introduces our method of stochastic max pooling. experiments are discussed and evaluated in section 4. a summary in section 5 concludes this paper. 2 literature review in the deep cnn, the pooling layer plays a critical role in feature extraction. in this layer, the convoluted features are further extracted and reduced dimensionally. meanwhile, their invariance to displacement, scaling and deformation is enhanced. there are three commonly used pooling methods, including average pooling, max pooling and stochastic pooling [15]. the average pooling outputs the average of all values in each receptive field. this pooling method reflects the overall features of the receptive field. the max pooling takes the maximum value in the respective field as its result, highlighting the most salient feature of each receptive field. many experiments have confirmed the superiority of max https://doi.org/10.15837/ijccc.2020.1.3712 3 pooling over average pooling. however, max pooling only considers the value with the strongest response, ignoring all the other values. thus, the effects of the low-response values on the network are neglected in max pooling. to solve the problem, zeiler [15] presented the stochastic pooling on international conference on learning representations (iclr) 2013. this pooling method randomly selects a value from each receptive field at a certain probability. with such randomness, the network structure based on stochastic pooling is equivalent to a set of multiple sub-models, similar to that of dropout [13]. each sub-model is a network that outputs a value of the corresponding receptive field. nevertheless, a stochastic pooling network may treat a small value in the receptive field as the pooling result. such a small value cannot reflect the typical features of the receptive field. what is worse, the small value could become a noise in feature transmission, and suppress the network performance. in practical applications, the images often contain a lot of noises. when the image contains a lot of noise, noise pixels may appear in the pooled area, and these pixels are often very large. therefore, noise may be introduced into the network through maximum pooling, thus reducing the performance of the network. in this case, the stochastic pooling result might be a small value, which is not a good reflection of the essential characteristics of this region. to solve the problem, this paper combines the max pooling and stochastic pooling into a hybrid strategy called the stochastic max pooling. if applied to a noisy image, the proposed strategy can extract the primary features by selecting the value with high response and filtering out the small values, and enhance the stochasticity of the network, turning the network into a set of sub-models. in this way, the network performance can be enhanced despite the noises in the training samples. 3 design of stochastic max pooling 3.1 deep cnn the cnn is a deep learning model that can automatically learn and extract features from data. the model greatly outshines the traditional methods in generalization. it has been successfully applied in the fields of pattern classification, object detection and object recognition. being a multi-layer supervised learning network, the cnn mainly consists of input layer, hidden layers and output layer. the network structure is optimized through error backpropagation to solve unknown parameters. the hidden layers, including alternating layers of convolution and down-sampling modules, are the cornerstone of the cnn. during the operation, the original image is imported to the cnn via the input layer, subjected to convolution and pooling, and then outputted via the output layer. in each convolution layer, every neuron in the feature map is connected to a local receptive field on the previous layer, and the local features are extracted through convolution. in general, the convolution operation can be expressed as: xlj = f( ∑ i∈mj xl−1i × k l ij + b l j ) (1) where, l is the serial number of the layer; k is the convolutional kernel; mj is a receptive field of the input layer; b is bias. in each pooling layer, the pooled feature map has the same number of neurons as the inputted feature map, but its size is only 1/n of the former (n is the pooling size). the pooling operation mainly reduces the resolution and feature dimensions of the feature map, while enhancing the network robustness to displacement, scaling and distortion. the pooling operation can be defined as: xlj = f(β l j down(x l−1 j ) + b l j ) (2) where down()is the pooling function, β is the weight coefficient. 3.2 deep cnn based on stochastic max pooling the proposed stochastic max pooling first ranks all the values in each receptive field, and then randomly selects one of the top-n values as the pooling result. this strategy is different from average https://doi.org/10.15837/ijccc.2020.1.3712 4 figure 1: a pooling example pooling, max pooling and stochastic pooling. however, it can be viewed as the integration between max pooling and stochastic pooling. to describe our strategy clearly, the first step is to define the three common pooling methods. the max pooling, which outputs the maximum value in each receptive field, can be defined as: ymax = max(r) (3) where ymax is the pooled maximum value, r is all the values in each receptive field. in the pooling area (i.e. receptive field) of figure 1, the maximum value in that area is 9, that is, ymax = 9. the average pooling, which takes the average of all the values in each receptive field, can be defined as: ymean = mean(r) (4) where ymean is the result of average pooling. in figure 1, the average of all the values in the pooling area is 3.1. in this case, ymean = 3.1. the stochastic pooling selects a value from each receptive field at a certain probability. the probability pi for the value ai at position i in the receptive field can be defined as: pi = ai∑ k∈r ak (5) then, the stochastic pooling operation can be defined as: ystochastic = al where l ∼ p(p1, ...,pl) (6) where ystochastic is the result of stochastic pooling. in the proposed strategy, i.e. the stochastic max pooling, the pooling result is selected from the top-n values of each receptive field. thus, all the values in the receptive field should be ranked to determine the top-n values. then, a value is randomly taken from the n values. hence, the stochastic max pooling can be defined as: yrestricted = random(sn) (7) where random is a function that randomly selects a value; sn is the top-n values in the receptive field. if n=3, then the gray values in figure 1 are the top-3 values in the receptive field. then, one of the three values will be selected as the result of stochastic max pooling. 3.3 discussion the pooling layers are of great significance to the deep cnn. these layers both transmit and reduce the dimensions of features, making the network invariant to displacement, scaling and deformation. the value selection from each receptive field directly bears on the effects of feature extraction and transmission in the pooling layers. among the three common pooling methods, the max pooling often outperforms the average pooling, and enjoys greater popularity than the other two methods. in general, the maximum value in a https://doi.org/10.15837/ijccc.2020.1.3712 5 figure 2: a pooling example figure 3: an example of stochastic maximum pooling method process receptive field can represent the features of that field. however, max pooling was found not suitable for two cases. the first case is that the original image contains a lot of noises. in this case, the maximum value in a receptive field might be noise. through max pooling, the noise will be mistaken for feature and introduced to the network. as shown in figure 2, the value of 50 is possibly a noise, and will be selected by max pooling as the result. however, the pixel value represented by 50 is just noise, which obviously does not conform to the principle of feature extraction. in the second case, there are many values in the receptive field that are close to the maximum value. as shown in figure 1, the value 9 will be selected by max pooling. however, the other values (e.g. 8.7 and 8.5) are close to 9 and also reflect the features of the field. the slight differences between these values could be magnified through the layer-by-layer transmission and the processing by thousands of neurons in the network, exerting a huge impact on the network performance. in this case, max pooling eliminates the chance for other values to be selected, which might otherwise enhance network performance. in stochastic pooling, any value in a receptive field has a chance to be selected. compared to max pooling, stochastic pooling allows a great diversity in pooling result. however, some small values from pooling areas may be introduced to the network, and disturb the feature extraction. as shown in figure 1, the value 0.1 might be selected as the result of stochastic pooling. this value does not reflect the typical features of the field and will affect network accuracy. our strategy of stochastic max pooling overcomes the above defects. figure 3 shows the process of stochastic maximum pooling method. we can see that stochastic max pooling can select a representative value from each receptive field and randomize the pooling result. similar to stochastic pooling, the randomized network can be considered as a set of multiple sub-models, each of which operates based on a unique pooling result. the set of sub-models outperforms any individual sub-model, which is proved by the research of the dropout mechanism. in addition, stochastic max pooling also enjoys a high robustness in processing highly noisy images. https://doi.org/10.15837/ijccc.2020.1.3712 6 figure 4: structure of the deep cnn model figure 5: errors at different n values. 4 experiments and results analysis 4.1 experimental verification of stochastic max pooling this subsection aims to verify the effectiveness and superiority of the deep cnn based on stochastic max pooling. the deep cnn model was extended from lightnet [1], a versatile, standalone matlab-based environment for deep learning. the extension only adjusts the parameters of the pooling layers. as shown in figure 4, the pooling size is 3x3. for comparison, four models were developed, respectively based on max pooling (model a), average pooling (model b), stochastic pooling (model c) and stochastic max pooling (model d). two datasets were selected for our experiments, including the street view house numbers (svhn) and cifar-10 dataset (canadian institute for advanced research). the svhn dataset was divided into a training set of 73,257 images and a test set of 26,032 images. the cifar-10 dataset contains 60,000 images in 10 categories. here, 50,000 images are used for training and 10,000 for test. all the images in the two datasets are of the size 32x32, and not preprocessed. the four models were applied to the two datasets. the number of top-values n was set to 3 and the number of network trainings to 40. the final results are displayed in table 1 below. table 1: errors of the four models on the two datasets (%) model a b c d svhn 6.5 6.4 5.7 4.9 cifar-10 23.1 23.5 22.3 21.1 as shown in table 1, model d (stochastic max pooling) achieved the minimum errors. the accuracies of model d were as high as 95.1% and 78.9%, respectively, on svhn and cifar-10. model a (max pooling) and model b (average pooling) had basically the same accuracies. model c (stochastic pooling) realized a lower error than models a and b, but the error was higher than that of model d. the experiment shows that the n value has a great impact on the final results. to find the best n value, a series of experiments were conducted on model d by adjusting the n value from 1 to 9. according to the experimental results in figure 5, the error gradually increased with the growth in https://doi.org/10.15837/ijccc.2020.1.3712 7 figure 6: several images from the head dataset. figure 7: convergence curves of the four models. n value. when n increased to 9, the model acted the same as model c (stochastic pooling) and introduced noises into the network, pushing up the error. 4.2 noisy image experiments based on stochastic max pooling this subsection attempts to verify if stochastic max pooling is robust in processing noisy images. for this purpose, the heads images were extracted from surveillance videos on public places. these highly noisy images, all of which are 32x32 in size, were collected into a head dataset, including 10,000 training samples and 2,000 test samples. several images from the head dataset are given in figure 6. for comparison, gaussian noises were added to svhn and cifar-10. then, the four models in subsection 4.1 were applied to the head dataset, the noisy svhn and the noisy cifar-10. the results (table 2) show that model d (stochastic max pooling) outperformed the other models in processing noisy images [17, 18]. table 2: errors of the four models on noisy images (%) model a b c d svhn 12.1 12.5 11.2 9.1 cifar-10 29.3 29.5 28.7 26.1 head dataset 3.4 3.4 3.0 2.1 next, the intensity of the additive gaussian noise was increased, and the resulting noisy datasets were processed by the four models again. figure 7 displays the convergence curves of the four models on the cifar-10 dataset added with high-intensity gaussian noise (mean: 0; variance: 0.1). with the growing intensity of the additive gaussian noise, models a, b and c could not converge to the optimal solution (the curve a, b, c are coincident in figure 7), while model d (stochastic max pooling) continued to converge to that solution. it can be seen from the figure 7 that stochastic max pooling can continue to converge at this time, but the error rate is high. this kind of limit experiment on the other hand shows the processing ability of the stochastic max pooling proposed in this paper. the comparison further verified the robustness of stochastic max pooling to noises. https://doi.org/10.15837/ijccc.2020.1.3712 8 5 conclusions the deep cnn has achieved remarkable results in image processing, thanks to its special structure. to further improve its performance, this paper optimizes the deep cnn both in function and structure. the stochastic max pooling was proposed to solve the problem that many training and test samples contain a lot of noises. stochastic max pooling limits the selection range of pooling to a larger value that can represent the main information of the pooling area, reduces the possibility of introducing features, increases the randomness of the network, and enhances the generalization ability of the network. the proposed method can extract representative features through the pooling layer, and enhance the network randomness. the randomized network has a better performance in processing noisy images. the proposed method was verified on a self-designed head dataset and two public datasets (svhn and cifar-10), and proved to be effective, superior and robust to noises. acknowledgments this work is supported by the national natural science foundation of china (no.61472444, 61671470). this work is also supported by the henan province science and technology research project of china (no.182102210065), the key scientific research project in henan colleges and universities of china (no.17b520019) and the 13th five-year plan for education science in henan province (no.2019-jkghyb-0461). references [1] benkaddour, m.k.; bounoua, a. 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(2013). stochastic pooling for regularization of deep convolutional neural networks, arxiv. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: sun, s.; hu, b.; yu, z.; song, x.n. (2020). a stochastic max pooling strategy for convolutional neural network trained by noisy samples, international journal of computers communications & control, 15(1), 1007, 2020. https://doi.org/10.15837/ijccc.2020.1.3712 international journal of computers communications & control issn 1841-9836, 11(2):248-258, april 2016. sparse online learning for collaborative filtering f. lin, x. zhou, w.h. zeng fan lin* software school, xiamen university china, 308b of general office at haiyun campus, xiamen university, xiamen, 361009 *corresponding author: iamafan@xmu.edu.cn xiuze zhou department of automation, xiamen university china, general office at haiyun campus, xiamen university, xiamen, 361009 zhouxiuze@foxmail.com wenhua zeng software school, xiamen university china, 502 of general office at haiyun campus, xiamen university, xiamen, 361009 whzeng@xmu.edu.cn abstract: with the rapid growth of internet information, our individual processing capacity has become over-whelming. thus, we really need recommender systems to provide us with items online in real time. in reality, a user’s interest and an item’s popularity are always changing over time. therefore, recommendation approaches should take such changes into consideration. in this paper, we propose two approaches, i.e., first order sparse collaborative filtering (socfi) and second order sparse online collaborative filtering (socfii), to deal with the user-item ratings for online collaborative filtering. we conduct some experiments on such real data sets as movielens100k and movielens1m, to evaluate our proposed methods. the results show that, our proposed approach is able to effectively online update the recommendation model from a sequence of rating observation. and in terms of rmse, our proposed approach outperforms other baseline methods. keywords: recommender systems, collaborative filtering, online learning, socfi, socfii 1 introduction with the prosperity of such large-scale online commercial websites and online shopping websites as amazon [1], barnes, netflix, ebay, etc, users are continuously exposed into increasing amount of items. consequently, the information flow which is increasingly complex and huge makes the user lost, and thereby be tired of the inefficient search. in order to deal with this problem, and also to predict the user’s unknown preferences based on some user’s preferences we have studied [2,3], a modern technique named collaborative filtering(cf) is put forward.cf has become widely used as one of the most successful learning techniques to build real-world recommendation systems. consider online e-commerce applications where a user wishes to watch a movie or buy a product, the system offers recommendations using cf techniques in exploiting one’s previous preference and that of others. a good recommendation system is extremely beneficial to users in accurately predicting their preferences and providing satisfactory recommendations, and consequently benefiting the company [1]. the fundamental assumption of cf is that if two users rate many items similarly, they will be likely to rate other items similarly [2]. copyright © 2006-2016 by ccc publications sparse online learning for collaborative filtering 249 despite the successful application in such many fields as book [6], music [7], news [8], etc, all these traditional cf approaches share a common but critical drawback that these approaches have to be re-trained completely from scratch whenever new training data arrives, which is clearly non-scalable for large real recommendation systems in which user’s rating data often arrives sequentially and frequently [5, 9]. it takes quite a long time to learn a new model when large numbers of parameters need to be estimated. the popularity of items and the interests of users are always changing over time [10, 11]. therefore, recommendation approaches shall take these changes into consideration. traditional cf approaches work well on the user-item rating data when the latent factors number k is very small. however, when the k become large, these approaches fail to well deal with the user-item rating data, since the user-item rating data is sparse and of large scale, and many approaches need a large k to get accurate results. although the batch algorithm of matrix factorization has a high accuracy, we can’t stand the high memory cost and time complexity in the real-world recommender system. nowadays, the data recommendation system which has a enormous amount of data is characterized as followed [12]: (1) high volume, system need to deal with huge amount of training data; (2) high velocity, new data often arrives very rapidly and sequentially; (3) high dimensionality, the data from users has a large number of features; (4) high sparsity, many feature elements are zero. to tackle the above challenges, recent years have witnessed some studies for online collaborative filtering [3, 4]. the state-of-the-art online collaborative filtering (ocf) approach avoids the highly expensive re-training cost of traditional batch matrix factorization algorithms by applying the simple online gradient descent (ogd) algorithms to solve the matrix factorization task [3]. muqeet ali et al. proposed a parallel collaborative filtering for streaming data by using distributed stochastic gradient descent algorithm [4]. unfortunately, these methods are generally based on the first order optimization framework (e.g., online gradient descent) to find the optimal solutions of low-rank matrix factorization. the ignorance of second order information results in the slow convergence of these approaches. besides, the latent factors number is actually quite likely to be very large which is a difficulty for the first order optimization framework even the framework which has already taken the second order information. to tackle this issue, we propose to solve the following sparse collaborative filtering problem. to address the weakness of these first order or second order online cf approaches and reduce data storage space and increase computing speed, we propose such sparse online collaborative filtering (socf) as first order sparse collaborative filtering (socfi) and second order sparse online collaborative filtering (socfii). our proposed approach is able to effectively online update the recommendation model from a sequence of rating observation. the sparse online collaborative filtering (socf) takes consider the latent factors of the low rank matrix and online second order optimization method. the key idea of socf is to not only update the user and item weight vectors at each round, but also estimate their distribution and take full account of large latent factors. because of full account of this case, socf converges significantly faster and thus achieves much lower values of rmse and mae than those of the regular first order algorithms when receiving the same amount of rating observations. the rest of the paper is organized as follows. section 2 introduces the background information and presents the problem formulation. section 3 exhibits the proposed sparse collaborative filtering algorithm, which takes first order and second order information into consideration. section 4 presents our experimental results and analysis. section 5 draws conclusions and discusses the future work. 250 f. lin, x. zhou, w.h. zeng 2 background information and related work in this section, we introduce some background information, related works and the problem we’re going to solve. collaborative filter (cf) and content-based filtering are two strategies widely used in recommendation systems for recommending items for users. cf makes prediction by using only the user-item interaction indormation without additional information or domain knowledge, so it has a wider application. the key idea of the cf is that users who have similar preferences in the past are likely to have similar preferences in the future. memory-based algorithms show good performance on accuracy, but they cannot handle scalability and sparsity problems of data. all these cf algorithms achieved good results without using additional information. in order to solve the data sparsity problems, many model-based cf methods have been proposed. model-based cf techniques aim at building a model to represent user rating data, and use that model to predict user preference for a specific item. for example, the singular value decompositon (svd) obtains the main factors to reduce dimensimality. hofmann converts the latent semantic model from information retrieval to collaborative filtering. these models not only reduce the dimensions of the user-item matrix and smooth out the noise information, but also help the algorithm to alleviate scalability of data. recommendation systems provide an effective way for information filtering to discover useful information according to the historical preferences expressed by users [15]. at present, cf approaches, as the most widerly used method in recommendation system, can be generally grouped into two types: model-based cf and memory-based cf. model-based cf approaches provide item with recommendation by first developing a model of user ratings, and then predicting user’s preference for a specific item through reained model [13–16]. while memory-based approaches predict rating of users according to all user ratings [1, 7, 20]. generally, the model-based cf approaches is more accurate than memory-based approaches [21]. matrix factorization is one of the most popular and the state-of-the-art methods of modelbased cf approaches, which was used by the winner of the netflix prize [22–25]. svd puts the items that are highly relevant and apparent together as a singular factor, and breaks up the vector into a small order approximation matrix. one user with one item represents a vector in this space and the rating that a user assigns to an item is the dot product of their feature vectors [26]. the key idea of latent factor model assumes that the similarity between users and items is discovered by the lower-dimension data. the system minimizes the regularized squared error on the set of known ratings to learn the factor vectors [27]. low rank matrix factorization is considered to be a very effective method and achieves good results in practice [20, 28]. we will base our matrix factorization study on the collaborative filtering, which is traditionally defined as: ∑ (a,b)∈a l(ra,b,ua,vb) + λ 2 ( m∑ a=1 ∥ua∥2 + n∑ b=1 ∥vb∥2 ) where a ⊆ {(a,b)|ra,b is known}, l(r,u,v ) = (r −ut v )2, ua,vb ∈ rk and λ is a regularization parameter. a is the user-item rating set we have known. although the matrix factorization technique can obtain high accuracy, we cannot stand with it for a long time to run. stochastic gradient descent algorithm requires an iterative many times until convergence. in practical, the model-based approaches have to be retrained completely for new records when new users’ rating data arrives sequentially and frequently [6]. in contrast to traditional collaborative filtering algorithms, online learning promptly update the predictive model and able to avoid expensive re-training cost when a new instance appears [7, 8]. in online algorithms, these models need to be retrained when each new data arrives. the online sparse online learning for collaborative filtering 251 algorithms only need a single iteration which processes the events in the order of time over the training data [30]. although the online learning algorithm loses some accuracy, it avoids high time complexity and memory cost. 3 sparse collaborative filtering algorithm in this section, we introduce our proposed sparse collaborative filtering algorithms, including first sparse order collaborative filtering (socfi) algorithm and second order online sparse collaborative filtering (socfii) algorithm. 3.1 first online sparse collaborative filtering algorithm with n users and m items in the user-item rating matrix is broken up into two low rank matrixes. the user-item rating matrix r ∈ rn∗m is broken down into two low rank matrixes. ua is the a-th row from the user matrix u ∈ rn∗k, and vb is the b-th row form item matrix v ∈ rm∗k. the rank of k ≪ min{n,m}. ra,b is the movie b rated by user a. the predicted score is the dot product of ua and vb, i.e., r̂a,b = uta vb. |c| represents the number of observed ratings. in general, one can define different type of loss function for different purposes. for example, for the root mean square error (rmse), i.e., , we define the loss by the square error function as: rmse = √√√√ 1 |c| ∑ (a,b)∈c (ra,b − r̂a,b)2 we define the loss by the square error function as: l(ua,vb,ra,b) = (ra,b −uav tb ) 2 and for the mean absolute error (mae), i.e., mae = 1|c| ∑ (a,b)∈c |ra,b − r̂a,b| , we define the absolute loss function as: l(ua,vb,ra,b) = |ra,b −uav tb | traditionally, k is treated as the latent factors number. when k is set as a small value, the traditional algorithm will work well. however, when k is set as a large number, the algorithm will fail, which implies the traditional algorithm cannot tackle tasks with large factors number. however, the latent factors number is quite likely to be very large in reality. to tackle this issue, we propose to solve the following sparse collaborative filtering problem, ∑ (a,b)∈a l(ra,b,ua,vb) + λ 2 ( m∑ a=1 ∥ua∥2 + n∑ b=1 ∥vb∥2 ) + τ ( m∑ a=1 ∥ua∥1 + n∑ b=1 ∥vb∥1 ) furthermore, since the data usually comes one by one, we propose to solve the sparse collaborative filtering problems through online learning techniques. specifically, we will update the two vectors as follows: ua ← arg min u ⟨∂ul(ra,b,ua,vb) + λua,u⟩+ τ∥u∥1 + 1 2ηt ∥u−ua∥2 and vb ← arg min v ⟨∂vl(ra,b,ua,vb) + λvb,v⟩+ τ∥v∥1 + 1 2ηt ∥v −vb∥2 252 f. lin, x. zhou, w.h. zeng these two updates enjoys closed-form solutions: ua ← stτηt[(1−ηtλ)ua −ηt∂ul(ra,b,ua,vb)] (1) and vb ← stτηt[(1−ηtλ)vb −ηt∂vl(ra,b,ua,vb)] (2) where stv(w) = sign(w)⊙ [|w|−v]+ and ⊙ denotes element-wise product. 3.2 second online sparse collaborative filtering algorithm in order to improve the convergence speed, we propose a second order online collaborative filtering algorithm. the key idea of socfii is to not only update the user and item weight vectors at each round, but also estimate their distribution, i.e., mean and covariance matrix. in the second order online collaborative filtering, where ua and vb are assumed satisfy gaussian distributions. the objective functions are dkl ( n(µua, ∑ ua )∥n(µua,t, ∑ ua,t ) ) + ηl(ra,b,ua,vb) + λ 2 vtb ∑ ua vb and dkl ( n(µv, ∑ vb )∥n(µva,t, ∑ vb,t ) ) + ηl(ra,b,ua,vb) + λ 2 uta ∑ vb ua where kkl is kl divergence. in this way, the algorithm significantly outperform first order algorithm. however, the latent number k has to be set as a small value. to solve this issue we proposed the following two online objective functions: cua(µua, ∑ ua ) = dkl ( n(µua, ∑ ua )∥n(µua,t, ∑ ua,t ) ) + η⟨∂ul(ra,b,µua,t,vb),µua⟩ + λ 2 vtb ∑ ua vb + ητ∥µua∥1 and cvb(µvb, ∑ vb ) = dkl ( n(µvb, ∑ vb )∥n(µvb,t, ∑ va,t ) ) + η⟨∂vl(ra,b,µvb,t,vb),µvb⟩ + λ 2 uta ∑ vb ua + ητ∥µvb∥1 these objectives linearize the loss functions and introduce sparsity regularization. we can solve these two objectives in two steps: µua,t+1 = arg min µua cua(µua, ∑ ua ) = arg min µua 1 2 (µua −µua, t) t ∑−1 µua,t (µua −µua,t) + η⟨∂ul(ra,b,µua,t,vb),µua⟩+ ητ∥µua∥1 ∑ ua,t+1 = arg min∑ ua cua(µua, ∑ ua ) i.e., ∑ ua,t+1 = ∑ ua,t − ∑ ua,t vbv t b ∑ ua,t 1/λ + vtb ∑ ua,t vb (3) sparse online learning for collaborative filtering 253 and µvb,t+1 = arg minµvb cvb(µvb, ∑ vb ) = arg min µvb 1 2 (µvb −µvb,t) t ∑−1 µvb,t (µvb −µvb,t) = + η⟨∂vl(ra,b,µvb,t,vb),µvb⟩+ ητ∥µvb∥1 ∑ vb,t+1 = arg min∑ vb cvb(µvb, ∑ vb ) i.e., ∑ vb,t+1 = ∑ vb,t − ∑ vb,t uau t a ∑ vb,t 1/λ + vtb ∑ vb,t vb (4) in practice, it is computationally expensive to get µua,t+1, µvb,t+1 and update ∑ ua,t+1 ,∑ vb,t+1 . to solve this issue, we can set the covariance matrices as diagonal, which will produce the following closed-form solutions for the two mean vectors: (µua,t+1)i = stητ( ∑ ua,t )i,i [ (µua,t)i −η( ∑ ua,t )i,i(∂ul(ra,b,µua,t,vb))i ] (5) and (µvb,t+1)i = stητ( ∑ vb,t )i,i [ (µvb,t)i −η( ∑ vb,t )i,i(∂vl(ra,b,µvb,t,vb))i ] (6) algorithm 1 first order sparse online collaborative filtering (socfi) require: a sequence of rating pairs {(at,bt,rab), t = 1, · · · ,t} 1: initialization: initialize a random matrix for user matrix u ∈ rn×k and item matrix v ∈ rm×k 2: for t = 1,2 · · · ,t do 3: receive rating prediction request of user at on item bt 4: make prediction r̂atbt = uatv t bt 5: the true rating ratbt is revealed 6: the algorithm suffers a loss l(ua,vb,ra,b) 7: update uat and vbt by (1), (2), respectively 8: end for 4 experiment in this section, we present the experimental results to evaluate the performance of our proposed methods by using online the root square error (rmse) on the data set. our experiments are performed on two real data sets: movielens100k and movielens1m. these two data sets are classic movie rating data sets collected by the movielens web site (www.movielens.umn.edu). movielens is publicly available data set, and it is widely used to study recommendation systems. the movielens100k consists of 100,000 ratings from 943 users on 1,682 movies and the movielens1m consists of 1,000,209 ratings from 6,040 users on 3,900 movies. 254 f. lin, x. zhou, w.h. zeng algorithm 2 second order sparse online collaborative filtering (socfii) require: a sequence of rating pairs {(at,bt,rab), t = 1, · · · ,t} 1: initialization: initialize a random matrix for user matrix u ∈ rn×k and item matrix v ∈ rm×k, and covariance matrix ∑ u, ∑ v to be item i 2: for t = 1,2 · · · ,t do 3: receive rating prediction request of user at on item bt 4: make prediction r̂atbt = uatv t bt 5: the true rating ratbt is revealed 6: the algorithm suffers a loss l(ua,vb,ra,b) 7: update uat and vbt by (3), (4), respectively 8: update ∑ ua and ∑ vb by (5), (6), respectively 9: end for 4.1 compared algorithm we compare our methods with other two online algorithm cf algorithms as follow: (1) ocf: online collaborative filtering for learning a rank-k matrix factorization by using online gradient descent [3]; (2) da-ocf: dual-averaging method of probabilistic matrix factorization for online collaborative filtering by absorbing previous rating information in an approximate average gradient of the loss [9]; (3) socfi: first order sparse online collaborative filtering; (4) socfii: second order sparse online collaborative filtering by setting covariance matrices as diagonal to simplify the calculation. our experiments are conducted on two real data sets, i.e. movielens100k and movielens1m, which are classic movie rating data sets collected by the movielens web site (www.movielens.umn.edu). movielens, as a publicly available data set, is widely used to study recommendation systems. the movielens100k consists of 100,000 ratings from 943 users on 1,682 movies while the movielens1m consists of 1,000,209 ratings from 6,040 users on 3,900 movies. the rank parameter k of matrix u and v is set to four cases: 5, 10 and 50, respectively. after all the parameters are set, all the experiments are conducted 10 times randomly for each data set. to make a fair comparison, the learn arte r of all algorithms is set to 0.01, and λ parameter in daocf is set to 0.006, which was suggested to achieve the best performance according to reference [31]. 4.2 experiment and analysis table 1: the results of movielens100k movielens100k k=5 k=10 k=50 rmse rmse rmse ocf 1.1218 0.9904 1.5007 daocf 1.1038 1.0882 1.2654 socfi 1.4083 1.2502 1.2886 socfii 1.0355 0.9859 1.3191 for performance metric, we evaluate the performance of online collaborative filtering algorithms by measuring their scores of online root square error (rmse) on the test set. the sparse online learning for collaborative filtering 255 (a) movielens100k, k=5 (b) movielens1m, k=5 (c) movielens100k, k=10 (d) movielens1m, k=10 average performance of all approaches is shown in table 1. table 2: the results of movielens1m movielens1m k=5 k=10 k=50 rmse rmse rmse ocf 1.1147 0.9769 1.2345 daocf 1.0136 1.0077 1.1286 socfi 1.4201 1.2702 1.2577 socfii 1.0573 0.9486 1.1179 by comparison with ocf and daocf approaches, we can find from table 1 that the socfii always achieves best results on these data sets, while has smaller rsme values in all cases. when k is set to a small value, such as 5 or 10, both of the traditional algorithm daocf and the socfii we proposed perform very well in small scale data sets and large scale data sets. in small scale data sets in which k is set to a large value, such as 50, the daocf works better than socfii. however, when the data sets become large ones, socfii achieves best results. due to the socfi algorithm’s lost on second order information, the socfi may not perform very well in the case of a very small k on both small scale data sets and large scale data sets. however, in reality, the latent factors number k stands a good chance to be very large. when we turn the k into a large value, adapting to the real situation, the socfi achieves better results on these 256 f. lin, x. zhou, w.h. zeng (e) movielens100k, k=50 (f) movielens1m, k=50 figure 1: performance of online collaborative filtering approaches on the movielens100k and movielens1m data sets. data sets. this shows that our proposed method which exploits the confidence information can better handle data online and is suitable to large-scale dynamic collaborative filtering scenario. to further evaluate our approaches for online learning, figure 1 shows more details. we observe that the curve of the daocf is always fluctuant instead of smooth. this is disadvantageous for stable output, because sometimes users will be recommended with item which involve severe and numerous errors. the curves of socfii and ocf are very similar. however, the former is slightly better than the latter. 5 conclusion and future work in this paper, we propose two approaches to deal with the user-item ratings for online collaborative filtering. we focus on the online matrix factorization problem which consists of learning the basis set of users in order to adapt it to online cf recommender systems. a user’s interest and an item’s popularity are always changing over a long period of time. so, recommendation approaches should take such changes into consideration. then, an empirical study has been conducted on two benchmark data sets, namely, movielens100k and movielens1m. these experimental results demonstrate that our online algorithm achieves more accuracy performance than other online algorithms while dramatically boosting efficiency. our approaches are suitable to large-scale dynamic collaborative filtering scenario. now, many collaborative filtering algorithms are unable to capture the latest change of user preferences over time. in the future, we will focus our works on the improving 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[31] g. ling, h. yang, i. king, and m. r. lyu (2012), online learning for collaborative filtering, ieee world congress on computational intelligence, brisbane, australia, 1 – 8. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(3), 359-374, june 2019. effect of soft errors in iterative learning control and compensation using cross-layer approach g.-m. jeong, k. lee, s.-i. choi, s.-h. ji, n. dutt gu-min jeong school of electrical engineering kookmin university, seoul, korea gm1004@kookmin.ac.kr kyoungwoo lee* department of computer science yonsei university, seoul, korea *corresponding author: kyoungwoo.lee@gmail.com sang-il choi department applied computer engineering dankook university, korea choisi@dankook.ac.kr sang-hoon ji advanced robotics group kitech, ansan, korea robot91@kitehc.re.kr nikil dutt department of computer science uc irvine, ca, usa dutt@ics.uci.edu abstract: in this paper, we study the effects of radiation-induced soft errors in iterative learning control(ilc) and present the compensation techniques to make the ilc systems robust against soft errors. soft errors are transient faults, which occur temporarily in memories where the energetic particles strike the sensitive region in the transistors mainly under abnormal conditions such as high radiation, high temperature, and high pressure. these soft errors can cause bit value changes without any notification to the controller, affect the stability of the system, and result in catastrophic consequences. first, we investigate and analyze the effects of soft errors in the ilc systems. our analytical study shows that when a single soft error occurs in the output data from the ilc, the performance of the learning control is significantly degraded. second, we propose novel learning methods by incorporating the existing techniques across the system abstraction levels in the ilc to compensate for soft-error-induced incorrect output. the occurrence of soft errors is estimated by using a monotonic convergence of the erroneous outputs in a cross-layer manner, and our proposed methods can significantly reduce these negative impacts on the system performance. under the assumption of soft error occurrence, our analytic study has proved the convergence of the proposed methods in the ilc systems and our simulation results show the effectiveness of the proposed methods to efficiently reduce the impacts of soft-error-induced outputs in the ilc systems. keywords: soft error, iterative learning control, compensation. 1 introduction with the advances in the highly integrated semiconductor and microelectronic manufacturing techniques, the variability of electronic devices has drastically increased in real time. on the copyright ©2019 cc by-nc 360 g.-m. jeong, k. lee, s.-i. choi, s.-h. ji, n. dutt other hand, soft errors or transient faults induced by various sources (e.g., heat and cosmic radiation) have become one of the critical threats to the system stability [4]. soft errors, different from hard errors, which are permanent faults in the system and cannot be corrected without replacement, are difficult to detect and can be as critical as hard errors to the system reliability in certain application areas [4,10,14,15,17,19,22]. the probability of soft error occurrence can be extremely low because these errors generally occur under abnormal situations. however, a single soft error can result in a bit value change, that is, from 0 to 1, or vice versa, in the memory without notifications to the application if there is no error detection or correction mechanism. thus, depending on the bit location of the soft error, it can be critical to the stability of the controller. until now, there have been several researches for fault tolerant techniques to reduce the negative impacts of the soft errors on embedded systems. in particular, cross-layer-based approaches have been considered as promising techniques to analyze the trade-off among the performance, power, and reliability and have been presented to maximize the reliability with minimal overheads of the power and performance in various embedded systems [3,8,12,13,21]. iterative learning control(ilc) systems have been widely deployed and used for various conventional and emerging control applications [1,2,5,7,9,16,20]. a single error or an incorrect output can cause a catastrophic consequence because these control systems can be used for missionand safety-critical systems. however, no research has been conducted to protect the ilc systems from soft errors, to the best of our knowledge, while the soft error rate is exponentially increasing with technology scaling. to this end, we analyze the effects of soft errors in the ilc and propose compensation methods to increase the reliability of the ilc systems against soft errors, in particular, techniques in a cross-layer manner. first, we discuss the impacts of a single soft error on the output in the ilc systems and show that the soft error significantly affects the performance of the learning control. for an intensive analysis, we have selected an example with 4-byte floating point number [6] and have shown that a soft error in the ilc systems can lead to a large number of errors in the value and incorrect outputs eventually as threats to the system stability and reliability. indeed, a soft error can significantly reduce the speed of the learning curve in a linear discrete-time system. therefore, it is required to design and develop mitigation techniques to protect the ilc systems from soft errors. second, we propose novel compensation methods against soft errors for the ilc systems. we have made a few assumptions about the soft errors in the ilc systems and investigated the new detection and compensation methods. in particular, from a monotonic convergence of the output error, we have estimated the occurrence of the critical soft errors that significantly affect the system output and result in catastrophic consequences. we propose four compensation methods: (i) exact rollback method, (ii) recovery with the previous iteration value, (iii) recovery with the adjacent value, and (iv) recovery with the desired value. in addition, the methodology and effectiveness of these four compensation techniques have been evaluated on the basis of their advantages and disadvantages. based on the assumptions that we have made for the soft errors in this study, the convergence of each method has been presented. our extensive simulation results show that the negative impacts of the soft errors can be successfully canceled with the proposed compensation methods. the remainder of this paper is organized as follows. in section 2, related works are briefly summarized for the soft-error related problems and the ilc for discrete-time systems. in section 3 and 4, the effect of the soft error in the ilc is discussed and a new learning method to compensate for the effect of the soft error is introduced, respectively. an illustrative example is described in section 5 and the conclusion follows in section 6. effect of soft errors in iterative learning control and compensation using cross-layer approach 361 2 related works in this section, the preliminary results for the researches on the soft-errors and the ilc are briefly discussed. 2.1 soft errors several decades of technology scaling have brought us to a point where transistors have become extremely susceptible to even small fluctuations in the voltage levels, slight noise in the power supply, signal interference, and cosmic particle strikes [4]. any of these effects can temporarily toggle the logic value of a transistor, and therefore, it is called a transient fault or soft error. these soft errors are not permanent and nondestructive, that is, resetting of the device restores the normal behavior. an energetic particle such as an alpha particle, a neutron, or a free proton, can form a diffusion region of a cmos transistor and produce a charge that can result in toggling of the logic value of the gates or flip-flops. this phenomenon of change in the logic state is called a transient fault or a soft error. soft errors can result in erroneous program states, incorrect outputs, and, eventually, system crashes. soft errors have already been proved to cause significant fiscal damages [22]. for example, sun blamed the soft errors for the crash of their million-dollar line sun flagship servers [14]. more recently, hewlett packard acknowledged that a large installed base of a 1024-cpu server system in the los alamos national laboratory has been frequently crashing owing to soft errors [15]. in another incident, the soft errors brought a billion-dollar automotive factory to halt every month [10], [17]. further, highly integrated reliability-sensitive embedded devices such as mobile health-care systems and anti-lock braking systems (abss) in the automotive engine control units (ecus) are significantly threatened by the exponentially increasing soft error rates with technology scaling. thus, it is necessary to combat the soft errors in embedded systems in both emerging and traditional computing environments. recently, several selective protection techniques have been proposed in order to combat soft errors in a cost-efficient manner by protecting the failure critical data only [11, 13, 18, 21]. for instance, partially protected caches(ppc) [11] utilizes the knowledge of the content and device hardware capabilities to selectively store the critical data in a more reliable hardware (e.g., protected cache) and non-critical data in a less reliable one (e.g., non-protected cache). in [3], a novel memory cell design method considering robustness to soft-errors has been proposed for a deep learning accelerator. cross-layer-based techniques have been presented to maximize protection at a minimal cost by coupling and cooperating different error control schemes across the system abstraction layers from the hardware layer to the application layer in mobile embedded systems [12]. in [8], a cross-layer approach has been introduced to robust face recognition against soft errors. however, these techniques have been proposed to mitigate the impacts of the soft errors on domain specific embedded systems or general computer systems, and not control systems, in particular. 2.2 ilc for linear discrete time system some of the preliminary results for the ilc are briefly discussed, considering the monotonic convergence of the output errors [5][9]. we consider a linear time invariant(lti) system described by x(i + 1) = ax(i) + bu(i) y(i) = cx(i) (1) 362 g.-m. jeong, k. lee, s.-i. choi, s.-h. ji, n. dutt where, u ∈ r1, x = [x1, · · · ,xn]t ∈ rn, and y ∈ r1 are the input, the state, and the output of the system, respectively. a, b and c are matrices of appropriate dimensions. let ud(i), xd(i), and yd(i) represent the input, the state, and the output corresponding to the desired trajectory. also let the desired output yd(i), i ∈ [σ,n + σ − 1], be given and u[i,j] := [u(i), · · · ,u(j)]t ,y[i,j] := [y(i), · · · ,y(j)]t . here σ denotes the relative degree, which means that caσ−1b 6= 0 and caib = 0, i < σ − 1. the transfer function of the system is represented as g(z) = β1z n−1 + · · · + βn zn + α1zn−1 + · · · + αn . (2) here, we assume that the relative degree, σ, is known as a priori (i.e., β1 = · · · = βσ−1 = 0). based on the relative degree, we can obtain the input-to-output relation as y(i + σ) = caσx(i) + caσ−1bu(i). (3) in addition, the following input-to-output mapping is used for ilc: y[σ,n+σ−1] = hx(0) + ju[0,n−1], (4) h = [ (h0) t , · · · , (hn−1)t ]t , jd0 =   j0 0 · · · 0 j1 j0 · · · 0 ... ... ... ... jn−1 jn−2 · · · j0   , where hl = caσ and jl = caσ+l−1b. the time interval for the output of interest is [σ,n +σ−1] for minimum phase systems. also, at every iteration, we set xk(0) = xd(0). considering (4), an input update law is given as uk+1 [0,n−1] = u k [0,n−1] + se k [σ,n+σ−1], (5) where ek [l,m] = yd [l,m] −yk [l,m] and s ∈ rn×n is the learning gain matrix. the monotonic convergence of the above ilc method is shown in the following lemma. lemma 1. for the uncertain system (1), if the condition ||i −js|| ≤ ρ < 1, (6) holds for all k, the output error ek [σ,n+σ−1] monotonically converges to 0 as k →∞. that is, ||ek+1 [σ,n+σ−1]|| ≤ ρ||e k [σ,n+σ−1]||, lim k→∞ ||ek[σ,n+σ−1]|| = 0. (7) proof: from (4) and (5), we can obtain ek+1 [σ,n+σ−1] = ju d [0,n−1] −ju k+1 [0,n−1] = jud[0,n−1] −j(u k [0,n−1] + se k [σ,n+σ−1]) = [i −js]ek[σ,n+σ−1]. taking the norms on the both sides and from (6), we can obtain (7). 2 effect of soft errors in iterative learning control and compensation using cross-layer approach 363 3 effect of soft error in iterative learning control in this section, we analyze the effect of soft errors on the ilc. it is shown that the learning performance is significantly affected by the soft errors in a linear discrete time system. 3.1 illustrative example of ilc figure 1 shows the basic structure of the ilc [1]. as shown in figure 1 and equation (5), the input of the next iteration is determined from the output error and input of the current iteration. since a large amount of memory should be used for the ilc, it can be easily affected by the soft errors in the memory. figure 1: basic ilc configuration for discrete time systems (modified from the figure in [1]) for the analysis of the effect of soft errors, let us consider an example of a linear discrete-time single-input and single-output(siso) systems. let us consider the following linear discrete-time system: x(i + 1) =   0 0 −0.11 0 0 0 1 0  x(i) +   10 0  u(i) y(i) = [ 1 0.5 0.0625 ] x(i). (8) this system has two minimum-phase zeros (z = −0.25) and the relative degree of the system is 1. the desired trajectory is given as follows: yd(i) = { 0, i = 0 0.2 sin(0.1π(i− 1)), 1 ≤ i ≤ 21. (9) here, we set n = 21, u(21) = 0 and s = αi21×21 = 0.1i21×21. since the system (8) and the learning gain α satisfy the conditions ||j0|| > ∑n−1 i=1 ||ji|| with 1 > 0.7361 and |1−αj0| < 1 with 0.9 < 1, which are sufficient conditions for the convergence in [16], the convergence condition ||i − 0.1j|| < 1 is satisfied. the input update law is expressed as uk+1 [0,20] = uk[0,20] + 0.1e k [1,21]. (10) figure 2 shows the outputs and inputs when there is no soft error. as shown in figure 2(c), the root mean square(rms) error for the output error approaches 0 as k →∞. 364 g.-m. jeong, k. lee, s.-i. choi, s.-h. ji, n. dutt -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0 5 10 15 20 in p u ts i yy1 yy2 yy3 (a) inputs for different values of k -0.5 -0.25 0 0.25 0.5 0 5 10 15 20 o u tp u ts i yyd yy1 yy2 yy3 (b) desired output and the outputs for different values of k 0 0.4 0.8 1.2 1.6 0 5 10 15 20 25 30 t h e r m s e r r o r k (c) rms error for different values of k figure 2: outputs and inputs when there is no soft error 3.2 effect of soft errors to ilc let us assume that a soft error occurs in the memory. if we assume that there is single bit error in the 4-byte floating point variable, the effect of the soft error is different, depending on the bit location among the 32 bits where a single soft error occurs. table 1 summarized the structure of the 4-byte floating point variable [6]. table 1: binary format for floating point variable with single precision (32bits) [6] parts sign exponent fraction number of bits 1 8 23 table 2 presents an example of the effect of the single soft error on the 4-byte floating point variable with a value of 0.2. as summarized in tables 1 and 2, the soft errors in the sign bit, exponent part, and the upper part of the fraction lead to a large number of errors introduced and, eventually, can be critical to the control system. in real embedded systems, 1-bit error can be overcome by ecc(error correction code). however when there are 3 or more bit errors, ecc cannot check those errors. also the error values can be larger than that of 1-bit error. assuming 1-bit error in msb can show the effect of soft-errors in ilc. in order to show the effect of soft-errors, we assume just 1-bit error for the simulation without loss of generality. by considering above mentioned aspects, let us discuss the effects of the soft error on the effect of soft errors in iterative learning control and compensation using cross-layer approach 365 table 2: effect of soft error on the 4-byte floating point variable with a value of 0.2 part error bit approximate vaule (≈) sign 1(msb) -0.200000 exponent 2 6.805647 × 1035 3 0.000000 4 0.000000 5 0.000003 6 0.000781 7 0.012500 8 0.800000 9 0.400000 fraction 10 0.137500 11 0.231250 12 0.215625 13 0.192188 14 0.196094 15 0.201953 16 0.200977 17 0.199512 18 0.199776 19 0.200122 20 0.200061 21 0.199969 22 0.199985 23 0.200008 24 0.200004 25 0.199998 26 0.199999 27-32 0.200000 ilc of the previous example for system (8). for the previous example, let us assume that there are soft errors in the memory. to analyze the effect of soft errors, we also assume the followings: • 4-byte floating point variables are used for the storage and processing of all the data. • 1-bit soft error occurs in yk(4) in the most significant bit(msb) of the 4-byte memory. • a soft error occurs at every 4-th iteration corresponding to the iteration number k. with this setting as an example, since the msb of the 4-byte floating number variable is the sign bit, the sign of yk(4) changes at every 4-th iteration. the learning is performed under these assumptions for the soft errors. figure 3 shows the effects of a soft error for the previous example. as shown in figure 3, a soft error can significantly degrade the performance of the learning control. in other words, the soft errors reduce the learning speed and affect the convergence of the output error in the ilc. 366 g.-m. jeong, k. lee, s.-i. choi, s.-h. ji, n. dutt -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0 5 10 15 20 in p u ts i yy1 yy2 yy3 (a) inputs for different values of k -0.5 -0.25 0 0.25 0.5 0 5 10 15 20 o u tp u ts i yyd yy1 yy2 yy3 (b) desired output and the outputs for different values of k 0 0.4 0.8 1.2 1.6 0 5 10 15 20 25 30 t h e r m s e r r o r k (c) rms error for different values of k figure 3: outputs and inputs when there are soft errors induced 4 cross-layer approach to compensate for the soft error in this section, we propose new detection and compensation methods for the soft errors in the ilc by obtaining the monotonic convergence with the cross-layer approach, as described in figure 4. as summarized in table 2, depending on the location of the soft error, its effect on the control system varies. the errors in the upper part of the 4-byte data format are critical to the control systems, while the other errors are negligible. similarly, with regard to the monotonic convergence of the output error, it is not required to detect all the soft errors. in this study, we detect the soft error that prohibits the monotonic convergence, and further, we compensate for the soft error with other relevant data available. figure 4: compensation of soft error in a cross-layer manner for further analysis, some assumptions are made by considering the soft error imposed on the system as follows: effect of soft errors in iterative learning control and compensation using cross-layer approach 367 • (a1) a soft error occurs in the system output, that is, yk. • (a2) at iteration k, only one soft error occurs. hence soft-error occurs once at iteration k. • (a3) there is no contiguous soft error corresponding to iteration k. in other words, when there is a soft error at iteration k, there is no soft error at k + 1 and there may be a soft error at k + 2. with regard to (a1), soft errors can occur in the system, restored memories of the ilc, or the learning controller. different from other variables, the system output yk can have soft errors within all the components such as the system, restored memory, and learning controller. by considering these aspects, we make an assumption (a1). using (a2), we can detect the location of the soft error in the 4-byte floating point variable. further, using (a3), we can derive the monotonic convergence with the output error of the previous iteration. it should be noted that depending on the compensation method used, we can neglect or alleviate the related assumptions. 4.1 detection of critical soft errors using cross-layer approach if condition (6) is satisfied, ||ek+1 [σ,n+σ−1]|| ≤ ρ||e k [σ,n+σ−1]|| should be guaranteed for some ρ < 1. however, as shown in figure 3, and tables 1 and 2, depending on the location of the soft error, the monotonic convergence cannot be guaranteed. among all the soft errors, we only detect those errors affecting the monotonic convergence. therefore, when ||ek+1 [σ,n+σ−1]|| ≥ ||e k [σ,n+σ−1]||, we can conclude that a critical soft error has occurred. after detecting a soft error, we can find the location and compensate for the soft error. considering these facts, we propose a soft error detection algorithm to find the location of error j and iteration k for the ilc as follows : detection of critical soft errors in a cross-layered manner • step 1: for the learning curve, validate whether ||ek||∞ ≥ ||ek−1||∞. • step 2: if the error ||ek||∞ is larger than or equal to ||ek−1||∞, it is determined that a critical soft error occurs at iteration k. • step 3: determine j, which makes ||ek(j)|| = ||ek||∞, that is, the location of the largest value at iteration k. the following lemma shows that we can detect the location of the soft error with the assumptions (a1)-(a3). lemma 2. (determining time j and iteration k, where soft error occurs. ) let us assume that a soft error satisfying (a1)-(a3) occurs in the process of the ilc in the uncertain system (1). using the above algorithm, the location of the soft error, that is, time j and iteration k can be determined. 368 g.-m. jeong, k. lee, s.-i. choi, s.-h. ji, n. dutt proof: if there is no soft error, (7) holds for ek and ek−1. however, if there is a critical soft error, we can obtain ||ek|| ≥ ||ek−1||. thus, we can conclude that a critical soft error occurs at iteration k. 2 when there is a critical soft error at iteration k, let us assume that the soft error occurs in j, that is, ek(j) contains the soft error. let us denote ẽk(j) as the real output error when there is no soft error, and ẽk by replacing ek(j) with ẽk(j) in ek. from the definition of the ∞-norm, we can obtain the following equations: ||ek||∞ = max{||ek(σ)||, · · · , ||ek(j)||, · · · , ||ek(n + σ − 1)||} (11) ||ẽk||∞ = max{||ek(σ)||, · · · , ||ẽk(j)||, · · · , ||ek(n + σ − 1)||} (12) further, according to the relationship among ek, ẽk, and ek−1, the following equations hold: ||ek||∞ ≥ ||ek−1||∞, ||ẽk||∞ < ||ek−1||∞. (13) therefore, to satisfy (13), the following equation should be guaranteed by using (11) and (12). ||ek||∞ = ||ek(j)|| (14) therefore, if we select the maximum value among the output errors at iteration k, we can determine j. 4.2 compensation of soft errors after detecting a soft error in yk(j), we can compensate for the soft error in several ways. for instance, as shown in figure 5, we can use the stored data for yk(j), the value of the previous iteration yk−1(j), the adjacent value yk(j − 1), or the desired output yd(j). figure 5: recovery of the soft error induced yk(j) with related data in general, for the resilient design of a soft error, the exact rollback method has been widely used. after periodically copying the value of yk to the buffers, we can restore the value when an error is detected. although we need additional buffer spaces for the rollback, it is a simple effect of soft errors in iterative learning control and compensation using cross-layer approach 369 and effective technique to reduce the memory errors. if advanced error-detection methods can be supported, it can be used without assumptions (a2) and (a3). however, with regard to (a1), this method cannot be used when the soft error occurs within the system, that is, before copying the data to the buffer. for the soft errors in the memory or learning controller, the rollback method can be used irrespective of the variables. the value of the previous iteration yk−1(j) can be used to compensate for yk(j). since the learning is performed according to the iteration number k, it is easy to prove the monotonic convergence. in addition, it can be used when there is a soft error within the system or before copying the data to the buffer. further, we can use the adjacent value yk(j−1). for real-time control, this approach can be used for the compensation of soft errors. in this example, we do not need additional buffers. if more advanced error-detection methods can be supported, it can be used, relaxing assumptions (a2) and (a3). otherwise, the desired value yd(j) can be applied for the compensation. since the error ek(j) becomes zero, monotonic convergence can be derived. based on these facts, we can summarize the compensation method as follows: compensation method 0 exact rollback • step 1: restore the measured output value yk to an additional buffer at iteration k. • step 2: when a soft error is detected at iteration k and time j, recover it with yk(j) from the buffer. compensation method 1using the value of the previous iteration yk−1(j) • step 1: restore the output value yk−1 to the memory at iteration k − 1. • step 2: when a soft error is detected at iteration k and time j, replace yk(j) with yk−1(j) . compensation method 2using the adjacent value yk(j − 1) • step 1: when a soft error is detected at iteration k and time j, replace yk(j) with yk(j−1) . compensation method 3using the desired value yd(j) • step 1: when a soft error is detected at iteration k and time j, replace yk(j) with yd(j) . with the presented compensation methods, monotonic convergence is proved in the theorems below. for compensation methods 1 and 3, convergence is given based on (a1)-(a3), whereas, for the compensation method 2, we need another condition for the magnitude of the compensated error. theorem 3. (convergence of the ilc when using compensation method 1) let us assume that a soft error satisfying (a1)-(a3) occurs at the process of the ilc in the uncertain system (1). let us apply the input update law (5), the critical soft error detection method, and compensation method 1 to the uncertain system (1). if the condition (6) holds for all k, the output error ||ek [σ,n+σ−1]||∞ converges to 0 as k →∞. 370 g.-m. jeong, k. lee, s.-i. choi, s.-h. ji, n. dutt proof: let us consider that a soft error occurs in yk(j). that is, ||ek||∞ ≥ ||ek−1||∞ and ||ek||∞ = ||ek(j)||. from lemma 2, we can detect j and k. further, we compensate for the data using yk−1(j) instead of yk(j). let us denote the compensating value yk−1(j) as ŷk(j) and the compensated k-th iteration data as êk, respectively. from these values, we obtain êk = [ek(σ), · · · , êk(j), · · · ,ek(n+ σ − 1)], where êk(j) = yd(j) − ŷk(j) = yd(j) −yk−1(j) = ek−1(j). since ||êk(j)|| = ||ek−1(j)||, from (13) and (14), we can obtain ||êk||∞ ≤ ||ek−1||∞. (15) further, since there is no soft error at iteration k + 1 from (a3), the following equation holds: ||ek+1||∞ < ρ||êk||∞,ρ < 1. (16) from (15) and (16), the output error approaches 0 as k →∞. 2 theorem 4. (convergence of the ilc when using compensation method 2) let us assume that a soft error satisfying (a1)-(a3) occurs at the process of ilc in the uncertain system (1). let us apply the input update law (5), the critical soft error detection method and compensation method 2 to the uncertain system (1). if the compensated error satisfies ||yd(j)−yk(j−1)|| ≤ ||ek−1||∞ and condition (6) holds for all k, the output error ||ek[σ,n+σ−1]||∞ converges to 0 as k →∞. proof: we can detect j and k with the detection method. let us compensate for the data using yk(j − 1) instead of yk(j). let us denote the compensating value yk(j − 1) as ŷk(j) and the compensated k-th iteration error as êk, respectively. from these values, we obtain êk = [ek(σ), · · · , êk(j), · · · ,ek(n + σ − 1)], where êk(j) = yd(j) − ŷk(j) = yd(j) −yk(j − 1). if ||êk(j)|| = ||yd(j) −yk(j − 1)|| ≤ ||ek−1||∞, from (13) and (14), we can obtain ||êk||∞ ≤ ||ek−1||∞. (17) further, since there is no soft error at iteration k + 1 from (a3), the following equation holds: ||ek+1||∞ < ρ||êk||∞,ρ < 1. (18) from (17) and (18), the output error approaches 0 as k →∞. 2 theorem 5. (convergence of the ilc when using compensation method 3) let us assume that a soft error satisfying (a1)-(a3) occurs in the process of the ilc in the uncertain system (1). let us apply the input update law (5), the critical soft error detection method and compensation method 3 to the uncertain system (1). if the condition (6) holds for all k, the output error ||ek [σ,n+σ−1]||∞ converges to 0 as k →∞. proof: we can detect j and k with the detection method. let us compensate for the data using yd(j) instead of yk(j). then, for the compensated data at iteration k, we obtain êk = [ek(σ), · · · , êk(j) = 0, · · · ,ek(n + σ − 1)]. hence, from (13) and êk(j) = 0, we can obtain ||êk||∞ ≤ ||ẽk||∞ < ||ek−1||∞. (19) effect of soft errors in iterative learning control and compensation using cross-layer approach 371 further, since there is no soft error at iteration k + 1 from (a3), the following equation holds: ||ek+1||∞ < ρ||êk||∞,ρ < 1. (20) from (19) and (20), the output error approaches 0 as k →∞. 2 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0 5 10 15 20 in p u ts i yy1 yy2 yy3 (a) inputs for different values of k -0.5 -0.25 0 0.25 0.5 0 5 10 15 20 o u tp u ts i yyd yy1 yy2 yy3 (b) desired output and the outputs for different values of k 0 0.4 0.8 1.2 1.6 0 5 10 15 20 25 30 t h e r m s e r r o r k (c) rms error for different values of k -0.012 -0.01 -0.008 -0.006 -0.004 -0.002 0 0.002 0 5 10 15 20 25 30 d if f. b e t. f ig . 2 (c ) a n d f ig . 6 (c ) k (d) differences between the output errors when there is no soft error and when it is compensated using method 1 figure 6: outputs and inputs when soft error compensation method 1 is used 5 simulation results let us apply the proposed methods to the case in section 3.2. the soft error condition in the previous example satisfies assumptions (a1)-(a3) and we can apply the detection and compensation methods to this example. figure 6 shows the results of compensation method 1. as shown in figure 6, we can observe that the output error converges to 0 as k →∞ and the compensation method works well although there is a slight performance degradation as compared with the case without soft errors. figure 6(d) shows the differences between the output errors when there is no soft error and when the soft error is compensated using compensation method 1. figure 7 shows ||ek||∞ trajectory when there is no soft error and when the induced soft error is compensated using the compensation methods. for compensation methods 1 and 3, we can observe that the error approaches 0 as k → ∞, whereas for compensation methods 2, ||yd(j)−yk(j−1)|| ≤ ||ek−1||∞ cannot always be satisfied, and the monotonic convergence cannot be guaranteed. 372 g.-m. jeong, k. lee, s.-i. choi, s.-h. ji, n. dutt in this example, when using compensation method 3, as compared to compensation method 1, êk(j) becomes 0, and it reduces the speed of convergence. as shown in figure 7, compensation method 1 is the best method in this example, among compensation methods 1, 2, and 3, considering the monotonic convergence in the ilc. further, depending on the location and characteristics of the soft errors, we can combine the presented four compensation methods for achieving a better performance. 0 0.1 0.2 0.3 0.4 0.5 0 5 10 15 20 25 30 y y 1 k method 1 method 2 method 3 figure 7: ||ek||∞ according to k for the different compensation methods 6 conclusion in this paper, we have studied the effects of soft errors in the ilc and proposed novel compensation methods for the soft errors in the ilc. first, we have analyzed the effects of soft errors in the ilc. it has been shown that a soft error can affect the speed of the learning curve and make the controller lose the learning history. second, based on the detection algorithm for the critical soft error that affects the monotonic convergence, we have proposed compensation methods for the ilc. the convergence of the proposed method has been proved under the assumptions for soft errors. from the experimental results, we have demonstrated the effectiveness of our proposed methods. furthermore, it is expected that several directions of researches could be developed. we could make more complicated assumptions such as multiple soft errors and extend the results to the various control applications. as an example in the ilc, depending on the location and characteristics of the soft errors, we could combine the four presented compensation methods for achieving better performance. these remain as future work. funding this research was supported by the basic science research program through the national research foundation of korea (nrf) funded by the ministry of education (nrf-2018r1d1a1a09083894), and also supported by the national research foundation of korea (nrf) funded by the korean government (msip) (nrf-2016r1a5a1012966). author contributions the authors contributed equally to this work. effect of soft errors in iterative learning control and compensation using cross-layer approach 373 conflict of interest the authors declare no conflict of interest. bibliography [1] ahn, h.-s.; chen, y.; moore, k. l. 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(2004). characterizing the effects of transient faults on a high-performance processor pipeline, 2004 intl. conf. on dependable systems and networks, 61-70, 2004. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(2), 220-232, april 2019. extended todim method for madm problem under trapezoidal intuitionistic fuzzy environment h.p. ren, m.f. liu, h. zhou haiping ren* teaching department of basic subjects jiangxi university of science and technology,330013 nanchang, china *corresponding author: 9520060004@jxust.edu.cn manfeng liu the collaborative innovation center jiangxi university of finance and economics,330013 nanchang, china liumanfeng@sina.com hui zhou school of mathematics and computer science yichun university,336000 yichun, china huihui7978@126.com abstract: in actual decision making process, the final decision result is often affected by decision maker’s psychological behavior, however, for the multiple attribute decision making (madm) problem in which attributes values are expressed with trapezoidal intuitionistic fuzzy numbers, there are few literatures considering the decision maker’s behavior factors in decision making process. for this case, this paper first proposes a new distance measure of tifns and a new ranking method which considers decision maker’s attitude behavior, and then develops an extended todim decision making method. finally an example is given to illustrate the validity and practicability of the proposed method. keywords: todim method, trapezoidal intuitionistic fuzzy number, multiple attribute decision making, ranking method. 1 introduction in recent years, with the increasing complexity of the managerial decision making environment, many managerial decision-making problems contain qualitative properties which are difficult to quantify. zadeh’s fuzzy sets have been greatly successful in dealing with fuzzy management decision making problems [3,4,18,20,22,35]. zadeh’s fuzzy set is characterized by a single scale (membership), which can only characterize the support and opposition of the two aspects of the evidence. but some decision making problems have ambiguous hesitant phenomenon with respect to evaluation of information, and zadeh’s fuzzy set is hard or difficult to depict these situations. therefore, many scholars developed zadeh’s fuzzy set, and intuitionistic fuzzy (if) set is one of the most famous fuzzy sets among them. originally proposed by atanassov in 1986 [1], if sets can well describe the hesitation and uncertainty of judgment through the addition of a non-membership parameters, which can describe the vague characters of things comprehensively. then if sets have become a powerful and effective tool in dealing with uncertain or vague information in actual applications. in dealing with ambiguity and uncertainty, if sets are more flexible and practical than fuzzy sets, and thus they have been applied widely in decision making. because of the complexity and uncertainty of objective things and the limitation of decision maker’s knowledge, membership and non-membership functions are sometimes difficult to represent by using the precise numbers. but interval number can be very useful to describe this kind copyright ©2019 cc by-nc extended todim method for madm problem under trapezoidal intuitionistic fuzzy environment 221 of case, so atanassov and gargov [2] extended if sets to interval-valued if sets. some scholars put forward the concept of continuous if numbers to describe an uncertain quantity or a difficult quantification number on the basis of the concept of if set. grzegrorzewski [10] extended if sets to the continuous case of if numbers . nehi and maleki [21] put forward trapezoidal intuitionistic fuzzy number (tifn), and defined the corresponding operation rules, which caused great concern in the academic community. shu [27] proposed the definition of triangular intuitionistic fuzzy number, which is a special example of tifn, and they put it to the application of fault tree analysis, base on these research, wang and zhang [31] further expanded it and gave the definition of a generalized tifn. different from the definitions of if sets, tifn is added to a trapezoidal fuzzy number, which makes the membership degree and the non-membership degree no longer be only a fuzzy concept good or excellent; then, the assessment information given by the decision makers can be expressed exactly. comparing with if sets, they have more attractive explanation, and are easy to be quantified and executed by the decision maker, and thus they have more theoretical value in the field of decision science [8,14,16,29]. at present, the theory and application of fuzzy numbers, such as triangular intuitionistic fuzzy number, trapezoidal intuitionistic fuzzy number, have been received great attention. but most of the existing decision-making methods do not consider the influence of the behavior of the decision-makers in the decision process, because they are assumed that the decision maker is completely rational. however, the actual decision-making process is often accompanied by the different psychological behavior of the decision-makers and the attitude of the risk and other factors of behavior. kahneman and tversky [13] proposed the prospect theory, which can describe the decision maker’s psychological behavior. based on prospect theory, gomes and lima ( [6,7]) developed a new madm method named todim method, which has made many successful applications, such as material evaluation [34], green supplier selection [26], logistics outsourcing [30] etc. fan et al. [5] proposed an extension of todim (h-todim) to solve the hybrid madm problems in which attribute values have three forms: crisp numbers, interval numbers and fuzzy numbers. qin [23] proposed a generalization of the todim method under triangular intuitionistic fuzzy environment. ren et al. [25] extended the todim method to deal with the madm problem in which attribute values are expressed with pythagorean fuzzy numbers. zhang et al. [33] developed the todim method to solve the madm problem in which the attribute values are expressed with neutrosophic numbers. in this paper, we will develop a new extension of todim method to solve the madm problem in which attribute values are expressed with tifns, and an application example is used to illustrate the validity and practicability of the proposed method. 2 preliminary knowledge 2.1 definitions of tifns firstly, we recall the definition of the tifn and the related theory. in order to use the concept of if sets to define an uncertain number or difficult to quantify the amount, grzegrorzewski [10] extended the if sets to the continuous case of the if sets, and gave the following definition: definition 8. let r be the set of real numbers, a is called an if number in r , if its membership function µa(x) and non-membership function νa(x) are respectively defined as follows ( [10]) : 222 h.p. ren, m.f. liu, h. zhou µa(x) =   0, x < a1 fa(x), a1 ≤ x ≤ a2 1, a2 ≤ x ≤ a3 ga(x), a3 ≤ x ≤ a4 0, a4 < x and νa(x) =   1, x < b1 ha(x), b1 ≤ x ≤ b2 0, b2 ≤ x ≤ b3 ka(x), b3 ≤ x ≤ b4 1, ab < x where 0 ≤ µa(x) + νa(x) ≤ b2,ai,bi ∈ r,i = 1,2,3,4, and they satisfy b1 ≤ a1 ≤ b2 ≤ a2 ≤ b3 ≤ a3 ≤ b4 ≤ a4 . the four functions fa(x),ga(x),ha(x) and ka(x) are real value aunctions defined in interval [0,1]. the functions fa(x) , ka(x) are non-decreasing coctinuous functions and ga(x) , ha(x) are non-increasing continuous functions. on the basis of grzegrorzewski’s if numbers, nehi [21] developed the tifn in 2005, which is given in definition 2. definition 9. let b1 ≤ a1 ≤ b2 ≤ a2 ≤ b3 ≤ a3 ≤ b4 ≤ a4 ∈ r. a fuzzy number a is called a tifn, if its membership function µa(x) and non-membership function νa(x) are respectively defined as follows( [21]): µa(x) =   0, x < a1 x−a1 a2−a1 , a1 ≤ x ≤ a2 1, a2 ≤ x ≤ a3 a4−x a4−a3 , a3 ≤ x ≤ a4 0, a4 < x and νa(x) =   1, x < b1 x−b1 b2−b1 , b1 ≤ x ≤ b2 0, b2 ≤ x ≤ b3 b4−x b4−b3 , b3 ≤ x ≤ b4 1, ab < x we denote a by a =< (a1,a2,a3,a4), (b1,b2,b3,b4) >. definition 10. for two tifns a1 =< (a11,a12,a13,a14), (b11,b12,b13,b14) > and a2 =< (a21,a22,a23,a24), (b21,b22,b23,b24) > , the operational laws are defined as follows [2]: (1) a1+a2 =< (a11+a21,a12+a22,a13+a23,a14+a24), (b11+b21,b12+b22,b13+b23,b14+b24) > (2) ka1 =< (ka11,ka12,ka13,ka14), (kb11,kb12,kb13,kb14) >, for k > 0 usually, α−cut set is a very effective tool for describing the number of fuzzy numbers [11], and tifns have two classes α−cut sets: (a+)α and (a−)α. definition 11. let a =< (a1,a2,a3,a4), (b1,b2,b3,b4) > be a tifn, then the two classes α−cut sets (a+)α and (a−)α are defined as follows, respectively: (a+)α = {x ∈ r|µa(x) ≥ α} (a−)α = {x ∈ r|1 −νa(x) ≥ α} extended todim method for madm problem under trapezoidal intuitionistic fuzzy environment 223 according to definition 4, each α−cut set is a closed interval,thus they can be denoted as (a+)α = [(a + l )α, (a + u )α] and (a −)α = [(a − l )α, (a − u )α], where (a+l )α = inf{x ∈ r|µa(x) ≥ α}, (a+u )α = sup{x ∈ r|µa(x) ≥ α}, (a−l )α = inf{x ∈ r|1 −νa(x) ≥ α}, (a−u )α = sup{x ∈ r|1 −νa(x) ≥ α}. then, for a tifn a =< (a1,a2,a3,a4), (b1,b2,b3,b4) > , we can easily derive the following results: (a+)α = [(a + l )α, (a + u )α] = [a1 + (a2 −a1)α,a4 − (a4 −a3)α], (a−)α = [(a − l )α, (a − u )α] = [b1 + (b2 − b1)(1 −α),b4 − (b4 − b3)(1 −α)], 2.2 a novel distance measure of tifns in the follow we will develop a novel distance of tifns based on the following distance measure between two fuzzy numbers proposed by grzegorzewski in 1998. lemma 2.1. for two any fuzzy numbers a and b, and the corresponding α−cut sets are respectively [(a+l )α, (a + u )α] and [(a − l )α, (a − u )α], then the distance measure between them is defined as [9]. d(a,b) = ( ∫ 1 0 ((al)α − (bl)α) 2 dα + ∫ 1 0 ((au )α − (bu )α) 2 dα)1/2. inspired by the lemma 1, we define the following distance measure for two arbitrary tifns and as follows: d(a,b) = 1 2 ( ∫ 1 0 ((a+l )α − (b + l )α) 2 + ((a+u )α − (b + u )α) 2 dα)1/2 + 1 2 ( ∫ 1 0 ((a−l )α − (b − l )α) 2 + ((a−u )α − (b − u )α) 2 dα)1/2 it is easy to prove that the new distance measure can satisfy the non negativity, symmetry and triangle inequality. by straightforward calculation, we can get theorem 1. theorem 1. let a1 =< (a11,a12,a13,a14), (b11,b12,b13,b14) > and a2 =< (a21,a22,a23,a24), (b21,b22,b23,b24) > be two tifns,then the distance measure between a1 and a2 is defined as follows: d(a1,a2) = 1 2 [ (i1 + i2) 1/2 + (i3 + i4) 1/2 ] (1) proof: here i1 = ∫ 1 0 ((a+1l)α − (a + 2l)α) 2 dα = ∫ 1 0 [(a21 −a11) + (a22 −a21 −a12 + a11)α]2dα = ∫ 1 0 [x + (y −x)α]2dα = x2 + x(y −x) + 1 3 (y −x)2 = 1 3 (x2 + xy + y2) = 1 3 ((a21 −a11)2 + (a21 −a11)(a22 −a12) + (a22 −a12)2) where x = a21 −a11,y = a22 −a12. similarly, we have i2 = ∫ 1 0 ((a+1u )α − (a + 2u )α) 2 dα = 1 3 ((a23 −a13)2 + (a23 −a13)(a24 −a14) + (a24 −a14)2) 224 h.p. ren, m.f. liu, h. zhou i3 = ∫ 1 0 ((a−1l)α − (a − 2l)α) 2 dα = 1 3 ((b21 − b11)2 + (b21 − b11)(b22 − b12) + (b22 − b12)2) i4 = ∫ 1 0 ((a−1u )α − (a − 2u )α) 2 dα = 1 3 ((b23 − b13)2 + (b23 − b13)(b24 − b14) + (b24 − b14)2) then by lemma 1, we can derive the conclusion (1). 2 3 a new ranking function of tifns in actual decision making process, the final decision result is often affected by the different attitudes of decision-makers, although many scholars have already considered the influence of different attitude index for the madm problems in which attributes values are expressed with interval number and triangular fuzzy number [17, 24, 28]. however, for the madm problem in which attributes values are expressed with tifns, and there is no literature considering the decision maker’s attitude in decision making process. thus, we take the decision maker’s mentality into the decision-making process, and put forward a new tifn ranking method. definition 12. let ã = [al,au ] be an interval fuzzy number, mã = 0.5(al + au ) and dã = 0.5(au −al). fã(λ) : [0, 1] → ã is a function of parameter with the following form: fã(λ) = mã + (2λ− 1)dã = (1 −λ)al + λau. here, the parameter λ is called attitude index of interval number ã. remark 3.1. apparently, fã(λ) is a monotonic increasing function on interval[0, 1]. when an attribute is a benefit type attribute, i.e. the value of it is the-larger-the-better. when λ = 0,then fã(0) = al = [al,al] is smaller than the fuzzy number ã = [al,au ],thus for benefit type attribute, the parameter λ = 0 demonstrates a pessimistic attitude. similarly, λ = 1 demonstrates an optimistic attitude and λ = 0.5 demonstrates a moderate attitude. lemma 3.1. let ã = (al,am,au ) be a triangular fuzzy number, and for any real number α ∈ [0, 1], α− cut set of ã can be easily derived as follows [15]: ãα = [a l(α),au (α)] = [al + (am −al)α,au − (au −am )α] remark 3.2. for two arbitrary triangular fuzzy number ã and b̃, α − cut sets are often used to compare them. considering the α − cut sets of triangular fuzzy numbers are still interval numbers, and α is an arbitrary value in interval [0, 1],to eliminate the arbitrariness and reflect the decision maker’s attitude behavior, ren and liu [24]developed a new ranking function of triangular fuzzy number considering with attitude of decision maker(s) motivated by definition 5. definition 13. let ã = (al,am,au ) be a triangular fuzzy number, then for any parameter λ ∈ [0, 1], the function f(ã,λ) is a new ranking function of triangular fuzzy number considering attitude of decision maker(s) with the following formula [24]: f(ã,λ) = ∫ 1 0 (1 −λ)al(α) + λau (α)dα obviously, f(ã,λ) can be rewritten as the following form: f(ã,λ) = ∫ 1 0 (1 −λ)(al + (am −al)α) + λ(au − (au −am )α)dα = [(1 −λ)al + am + λau ]/2 extended todim method for madm problem under trapezoidal intuitionistic fuzzy environment 225 let r ∈ [0, 1], then according to definition 6, ren and liu [28] gave the following rule for comparing two triangular fuzzy numbers ã = (al,am,au ) and b̃ = (bl,bm,bu ): (i) for arbitrary λ ∈ [0, 1], if f(ã,λ) ≤ f(b̃,λ),then ã is smaller than b̃, and noted ã ≤ b̃; (ii)for arbitrary λ ∈ [0, 1], if f(ã,λ) = f(b̃,λ),then ã is equal to b̃, and noted ã = b̃; (iii)for arbitrary λ ∈ [0,r],if f(ã,λ) ≤ f(b̃,λ),while when λ ∈ [r, 1], f(ã,λ) ≥ f(b̃,λ); then for the decision maker whose attitude is pessimistic, the ranking result is ã ≤ b̃, which for the decision maker whose attitude is optimistic, the ranking result is ã ≥ b̃. motivated by definition 6, we will develop a new ranking function of trapezoidal intuitionistic fuzzy number defined in definition 7. definition 14. let a =< (a1,a2,a3,a4), (b1,b2,b3,b4) > be a tifn, and p(α) is a real function defined on [0, 1] , then a new ranking function f(a,λ) including the attitude behavior of decision maker is defined as follows: f(a,λ) = 1 2 ∫ 1 0 (1 −λ)(a+l )α + λ(a + u )αdp(α) + 1 2 ∫ 1 0 (1 −λ)(a−l )α + λ(a − u )αdp(α) particularly, if p(α) = αr+1, then we can get f(a,λ) = 1 2(r+2) [(1 −λ)(a1 + b1) + (r + 1)(1 −λ)(a2 + b2) +(r + 1)λ(a3 + b3) + λ(a4 + b4)] remark 3.3. if r = 0,λ = 1/2, then f(a,λ) is as same as that ranking function of ye [32]. similar discussion with remark 1, the parameter λ is the attitude index. then the ranking function f(a,λ) can reflect the attitude behavior, and thus it can better depict the actual decision process with the help of different values of λ than that ranking function of ye [32]. definition 15. for two given tifns a1 and a2 , and r ∈ [0, 1], the relationship of a1 and a2 can be defined as follows: (i) for any λ ∈ [0, 1], if f(a1,λ) ≤ f(a2,λ),then a1 is smaller than a2, and noted a1 ≤ a2; (ii)for any λ ∈ [0, 1], if f(a1,λ) = f(a2,λ),then a1 is equal to a2, and noted a1 = a2; (iii)for any λ ∈ [0,r],if f(a1,λ) ≤ f(a2,λ),while when λ ∈ [r, 1], f(a1,λ) ≥ f(a2,λ); then for the decision maker whose attitude is pessimistic, the ranking result is a1 ≤ a2, while for the decision maker whose attitude is optimistic, the ranking result is a1 ≥ a2. 4 extended todim method for madm under tifn environment for a given madm problem, let x = {x1,x2, · · · ,xm} be a possible alternatives set, and o = {o1,o2, · · · ,on} be the evaluation attribute set.d = {d1,d2, · · · ,ds} is the expert set. suppose the rating of xi (i = 1, 2, · · · ,m)with respect to oj(j = 1,2, · · · ,n)given by expert dk (k = 1,2, · · · ,s)is a linguistic term noted by s̃kij , which belongs to the linguistic terms set { absolutely low, low, fairly low, fairly high, high, absolutely high }. then the madm problem can be expressed with matrices s̃k = (skij)m×n,k = 1,2, ...,s. let w = (w1,w2, ...,wn)t be the attribute weight vector, and each element wj represents the degree of importance of attribute, which can be given by decision maker or determined by some weighting methods, such as ahp method or entropy weighting method. 226 h.p. ren, m.f. liu, h. zhou the calculation steps of the extended todim method considering the decision maker’s attitude are given as follows: step 1. according to table 1 [32],s̃kij can be transformed with tifns ãij(k),i = 1,2, ...,m,j = 1,2, ...,n and k = 1,2, ...,s. table 1: linguistic terms and corresponding tifns linguistic terms tifns absolutely low (al) < (0.001, 0.001, 0.001, 0.001), (0.001, 0.001, 0.001, 0.001) > low (l) < (0.0, 0.1, 0.2, 0.3), (0.0, 0.1, 0.2, 0.3) > fairly low (fl) < (0.1, 0.2, 0.3, 0.4), (0.0, 0.2, 0.3, 0.5) > medium (m) < (0.3, 0.4, 0.5, 0.6), (0.2, 0.4, 0.5, 0.7) > fairly high (fh) < (0.5, 0.6, 0.7, 0.8), (0.4, 0.6, 0.7, 0.9) > high (h) < (0.7, 0.8, 0.9, 1.0), (0.7, 0.8, 0.9, 1.0) > absolutely high (ah) < (1.0, 1.0, 1.0, 1.0), (1.0, 1.0, 1.0, 1.0) > the linguistic terms decision matrices s̃k = (skij)m×n are transformed into trapezoidal intuitionistic fuzzy decision matrices ãk = (ãkij)m×n (k = 1,2, ...,s). step 2. let s̃ij be the total score of alternative xi with respect to attribute oj given by all decision makers, and it is defined as ãij = 1 s s∑ k=1 ãkij. (2) step 3. determine the weights of evaluation attributes. using the definition 7 and p(α) = αr+1, we can get the intuitionistic fuzzy sorting function matrix f(λ) = (f(ãij,λ))m×n,where f(ãij,λ) is the ranking function of fuzzy number ãij considering with the attitude of decision maker. for the maximum tifn ã∗ =< (1,1,1,1) ,(1,1,1,1) >, f(ã∗,λ) = 1. now, we will propose a new weighting method by means of the proposed ranking function. the reasonable weight should be the minimum of the total deviation of the alternative xi(i = 1,2, · · · ,m) and the positive ideal solution ã∗. therefore, we can establish the following optimization model: ming(w) = n∑ j=1 m∑ i=1 wj(1−f(ãij)) s.t.   w ∈ h n∑ j=1 wj = 1 wj ≥ 0, j = 1,2, ...,n (3) by solving the eq. (3), the optimal solution w∗ = arg maxs is chosen as the optimal attribute weights. step 4. calculate todim score as follows: (i) calculate wrc = wcwr ,where the value wrc represents the weight value of criteria r divided by the weight of the reference point c,and wr = max 1≤c≤n {wc}. extended todim method for madm problem under trapezoidal intuitionistic fuzzy environment 227 (ii) for given value of attitude index λ ,calculate φc(xi,xj) =   √ d(xi,xj ) wrc , f(xic,λ)−f(xjc,λ) > 0 0 f(xic,λ)−f(xjc,λ) = 0 −1 θ √ d(xi,xj ) wrc ,f(xic,λ)−f(xjc,λ) < 0 here d(xi,xj) = n∑ c=1 wrcd(ãic, ãjc). here the parameter θ is an important parameter in prospect theory, and θ > 1 shows that the individual is losses aversion, and θ < 1 shows the individuals are attenuated when facing the losses [19]. here we set θ = 2.25 , which is the most often used value of θ in prospect theory. (iii) let δ(ai,aj) = n∑ c=1 φc(ai,aj), i,j = 1,2, ...,m, calculate the comprehensive evaluation index value: ξi = m∑ j=1 δ(ai,aj)− min 1≤i≤m m∑ j=1 δ(ai,aj) max 1≤i≤m m∑ j=1 δ(ai,aj)− min 1≤i≤m m∑ j=1 δ(ai,aj) , i = 1,2, · · · ,m. step 5. rank the alternatives according to ξi(i = 1,2, · · · ,m) in decreasing order. 5 applied example suppose that a company wants to invest a large amount of money in the best options(herrera and herrera-viedma [12]; [32]). there are four parallel alternatives: x1 (a car company), x2 (a food company), x3 (a computer company), x4 (an arms company) and three evaluation attributes o1(the risk analysis), o2 (the growth analysis), and o3 (the environmental impact analysis). the risk investment company now employs four experts to evaluate these four alternative enterprises. the evaluation values are expressed with linguistic terms, and the corresponding trapezoidal intuitionistic fuzzy evaluation decision matrices are listed in table 2 to table 4. our task is to choose the best investment plan by the method presented in this paper. table 2: linguistic evaluation values given by expert 1 alternatives o1 o2 o3 x1 m m fl x2 fh fh m x3 m fh m x4 h m fl the specific calculation steps of the proposed decision making method considering with the psychological behavior of the decision makers are given below: step 1. the linguistic terms decision matrices s̃k = (skij)m×n are transformed into ãk = (ãkij)m×n (k = 1,2, ...,s) and given in table 5 to table 7. step 2. the evaluation information of the expert group is gathered and expressed with decision matrix ã = (ãij)m×n , which is shown in table 8. 228 h.p. ren, m.f. liu, h. zhou table 3: linguistic evaluation values given by expert 2 alternatives o1 o2 o3 x1 fl m l x2 fh h m x3 m fh fl x4 h fh fl table 4: linguistic evaluation values given by expert 3 alternatives o1 o2 o3 x1 m fh fl x2 m fh m x3 fh fh m x4 h h m table 5: trapezoidal intuitionistic fuzzy decision matrix given by expert 1 alternatives o1 o2 o3 x1 <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> <(0.1,0.2,0.3,0.4), (0.0,0.2,0.3,0.5)> x2 <(0.5,0.6,0.7,0.8), (0.4,0.6,0.7,0.9)> <(0.5,0.6,0.7,0.8), (0.4,0.6,0.7,0.9)> <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> x3 <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> <(0.5,0.6,0.7,0.8), (0.4,0.6,0.7,0.9)> <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> x4 <(0.7,0.8,0.9,1.0), (0.7,0.8,0.9,1.0)> <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> <(0.1,0.2,0.3,0.4), (0.0,0.2,0.3,0.5)> table 6: trapezoidal intuitionistic fuzzy decision matrix given by expert 2 alternatives o1 o2 o3 x1 <(0.1,0.2,0.3,0.4), (0.0,0.2,0.3,0.5)> <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> <(0.0,0.1,0.2,0.3), (0.0,0.1,0.2,0.3)> x2 <(0.5,0.6,0.7,0.8), (0.4,0.6,0.7,0.9)> <(0.7,0.8,0.9,1.0), (0.7,0.8,0.9,1.0)> <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> x3 <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> <(0.5,0.6,0.7,0.8), (0.4,0.6,0.7,0.9)> <(0.1,0.2,0.3,0.4), (0.0,0.2,0.3,0.5)> x4 <(0.7,0.8,0.9,1.0), (0.7,0.8,0.9,1.0)> <(0.5,0.6,0.7,0.8), (0.4,0.6,0.7,0.9)> <(0.1,0.2,0.3,0.4), (0.0,0.2,0.3,0.5)> step 3. in order to facilitate the comparison with the results of ye [32], here we also assume that the attribute weights are known with w1 = 0.3490, w2 = 0.3020 and w3 = 0.3490. step 4. for given attitude index value λ = 1/2 and r = 0 , the comprehensive evaluation values of the extended todim method are calculated as ξ1 = 0,ξ2 = 1.0000,ξ3 = 0.7421,ξ4 = 0.4370, extended todim method for madm problem under trapezoidal intuitionistic fuzzy environment 229 table 7: trapezoidal intuitionistic fuzzy decision matrix given by expert 3 alternatives o1 o2 o3 x1 <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> <(0.5,0.6,0.7,0.8), (0.4,0.6,0.7,0.9)> <(0.1,0.2,0.3,0.4), (0.0,0.2,0.3,0.5)> x2 <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> <(0.5,0.6,0.7,0.8), (0.4,0.6,0.7,0.9)> <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> x3 <(0.5,0.6,0.7,0.8), (0.4,0.6,0.7,0.9)> <(0.5,0.6,0.7,0.8), (0.4,0.6,0.7,0.9)> <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> x4 <(0.7,0.8,0.9,1.0), (0.7,0.8,0.9,1.0)> <(0.7,0.8,0.9,1.0), (0.7,0.8,0.9,1.0)> <(0.3,0.4,0.5,0.6), (0.2,0.4,0.5,0.7)> table 8: evaluation information of the expert group xi o1 o2 o3 x1 <(0.2333,0.3333,0.4333, 0.5333),(0.1333,0.3333, 0.4333,0.6333) > <(0.3667,0.4667,0.5667, 0.6667),(0.2667,0.4667, 0.5667,0.7667) > <(0.0667,0.1667,0.2667, 0.3667),(0.0000,0.1667, 0.2667,0.4333) > x2 <(0.4333,0.5333,0.6333, 0.7333),(0.3333,0.5333, 0.6333, 0.8333)> <(0.5667,0.6667,0.7667, 0.5667),(0.5000,0.6667, 0.7667, 0.9333)> <(0.3000,0.4000,0.5000, 0.6000),(0.2000,0.4000, 0.5000,0.7000)> x3 <(0.3667,0.4667,0.5667, 0.6667),(0.2667,0.4667, 0.5667,0.7667) > <(0.5000,0.6000,0.7000, 0.8000),(0.4000,0.6000, 0.7000,0.9000)> <(0.2333,0.3333,0.4333, 0.5333),(0.1333,0.3333, 0.4333,0.6333)> x4 <(0.7000,0.8000,0.9000, 1.0000),(0.7000,0.8000, 0.9000,1.0000)> <(0.5000,0.6000,0.7000, 0.8000),(0.4333,0.6000, 0.7000,0.8667)> <(0.1667,0.2667,0.3667, 0.4667),(0.0667,0.2667, 0.3667,0.5667)> step 5. based on the values of ξi(i = 1, 2, 3, 4), the ranking order of the alternatives is obtained as x2>x3>x4>x1, and x2 is the best alternative.this result is in agreement with the one obtained in (ye [32]). 6 conclusion interactive multiple criteria (todim) decision method is developed on the basis of the prospect theory, which can describe the psychological behavior of human under uncertain environment, and has been successfully applied to many madm problems. todim method is easier than prospect theory in processing fuzzy numbers, and some authors have already developed it to solve madm problems in which the attributes values are expressed with crisp numbers, triangular fuzzy numbers, intuitionistic fuzzy numbers, pythagorean fuzzy and neutrosophic numbers. however, there is no research on the trapezoidal intuitionistic fuzzy environment, and the main work of this paper is to extend todim method to solve madm problems under tifn environment. first, the article proposes a new class of distance measure of tifns, the distance measure can better measure the difference between two tifns. then a new ranking function of tifns is introduced, which can take into account the decision-makers’ attitude with an attitude index. finally, the extended todim method is put forward to solve the madm problem in which the attribute evaluation values are expressed with tifns. the advantage of this method lies 230 h.p. ren, m.f. liu, h. zhou in the decision making process which can take into account the decision maker’s mentality and the decision maker’s perceived value of the gain and loss, so that the decision-making process is more consistent with the objective reality. the proposed distance measure and ranking function can also be used to other madm methods when the attribute values are expressed with tifns. funding the authors would like to thank the support of the national natural science foundation of china (no.71661012) and science & technology research project of jiangxi educational committee (no. 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(2014). a fuzzy linear programming method for group decision making with additive reciprocal fuzzy preference relations, fuzzy sets and systems, 246, 19–33, 2014. international journal of computers communications & control issn 1841-9836, 14(1), 7-20, february 2019. sentiment analysis method based on piecewise convolutional neural network and generative adversarial network c. du, l. huang changshun du*, lei huang school of economics and management beijing jiaotong university beijing 100044, china *corresponding author: summer2015@bjtu.edu.cn abstract: text sentiment analysis is one of the most important tasks in the field of public opinion monitoring, service evaluation and satisfaction analysis under network environments. compared with the traditional natural language processing analysis tools, convolution neural networks can automatically learn useful features from sentences and improve the performance of the affective analysis model. however, the original convolution neural network model ignores sentence structure information which is very important for text sentiment analysis. in this paper, we add piecewise pooling to the convolution neural network, which allows the model to obtain the sentence structure. and the main features of different sentences are extracted to analyze the emotional tendencies of the text. at the same time, the user’s feedback involves many different fields, and there is less labeled data. in order to alleviate the sparsity of the data, this paper also uses the generative adversarial network to make common feature extractions, so that the model can obtain the common features associated with emotions in different fields, and improves the model’s generalization ability with less training data. experiments on different datasets demonstrate the effectiveness of this method. keywords: sentiment analysis, piecewise convolution neural network, generative adversarial network. 1 introduction as one of the most important tasks in the field of public opinion monitoring, service evaluation and satisfaction analysis in network environments, text sentiment analysis needs to determine the opinions and preferences of customers in the text. in this article, these subjective texts with emotional polarity mainly refer to users’ comments on products or services, which can provide the decision-making bases of potential consumers when purchasing products and services. analysis of these comments is extremely helpful in mining the potential needs of users and improving products and services, but these comments are growing in large numbers every day. the analysis by humans is not only costly, but also lagging in time. therefore, it is necessary to analyze the emotional polarity of these texts using appropriate intelligent algorithms. although there have been many studies on text sentiment analysis, it is still a huge challenge. at present, text sentiment orientation analysis can be mainly divided into two methods: the first category is the sentiment orientation analysis method based on an emotion dictionary, and the second category is the statistical machine learning based analysis method. the first type of dictionary-based approach first requires the construction of an emotional dictionary. the chinese emotional dictionary primarily includes two emotional dictionaries, hownet and ntusd. hownet has been released by china knowledge network and ntusd was released by taiwan university. such methods include chen xiaodong’s [1] application of a sentiment lexicon to analyze the emotional tendencies of microblog texts; li chun et al [4] chose hownet’s strong tendencies as seed words, combined with contextual influence. it calculates the emotional copyright ©2019 cc by-nc 8 c. du, l. huang propensity score of a word by calculating the similarity between the common word and the emotional seed word, and then determines the emotional tendency of the sentence. this kind of sentiment analysis method is used to analyze the emotional tendency of sentences by examining the emotional polarity of words. it is a shallow analysis method that doesn’t analyze or model the overall semantics of sentences. in the machine learning-based method, traditional methods usually use adjective features, word frequency features and n-gram features as the characteristics of the sentiment orientation analysis of the text. these features only consider the meaning of the words themselves, or model adjacent words. the words are coded as vectors (word vectors) in vector space. with the development of internet applications, the number of corresponding network data increases sharply, and text data is analyzed simply by artificial design features or traditional natural language processing grammar analysis tools. this method is not only noneffective but also inefficient. the rise of neural networks enables them to be used on emotional analysis of texts for semantic synthesis to automatically extract the features of sentences. finally, classifiers are used to classify their emotional polarity. there are many works of this kind of methods, such as socher et al. [5–7,9,28] using a cyclic neural network to extract the semantic features of sentences; zeng daojian et al. [12] used convolutional neural networks to extract sentence features of specified tasks. the two neural network structures, recurrent neural networks and convolutional neural networks, are the two most effective methods for automatically learning sentence features in deep learning. this method of self-learning features has also made great progress in other fields of artificial intelligence. in terms of natural language processing tasks, it does not need to rely on tools such as traditional grammar analysis. it can automatically learn features from sentences, and thus it has received extensive attention from scholars. note that except of text classification, in the field of pattern classification, classification and application of other methods also have a wide range of trying. [33,34] the research by hang cui et al. [2] shows that the sentiment analysis of texts has domain dependence. in different fields, traditional sentiment analysis methods are difficult to maintain at optimal levels at all times. different methods have different adaptabilities in different fields. in a neural network approach, a convolutional neural network is capable of modeling the combination of word features with a small number of parameters. at the same time, convolutional neural networks have the following advantages over traditional natural language processing analysis tools:(1) as an effective method for automatically acquiring sentence features in deep learning, it is possible to automatically learn from the sentences the features that are most relevant to a sentiment analysis task. this improves the performance of the sentiment analysis model by extracting important features related to it in different fields.(2) emotional analysis using convolutional neural network does not require the use of additional resources such as a sentiment lexicon, avoiding the problem of constructing an emotional lexicon and low emotional dictionary coverage. however, the original convolutional neural network model ignores the sentence structure information that is important for text sentiment analysis, and it is easy to overfit. in view of the above deficiencies, this paper adopts the piecewise pooling strategy, which enables the deep learning-based convolutional neural network model to model sentence structures and segment the main features of different structures. the segmented convolutional neural network (pcnn) analyzes the emotional propensity of the text by combining the structural information and the domain information of the text; and uses the dropout algorithm to enhance the generic ability of the model. at the same time, the user’s feedback on the service involves many different fields, and there is less labeled data in each field. the lack of data makes the training of the convolutional neural network more difficult. the parameters cannot be fully optimized and cause the model to be under-fitting. therefore, in the case that data volume expansion is difficult, in order to alleviate the sparseness of the data, this paper uses the generated adversarial network to extract the sentiment analysis method based on piecewise convolutional neural network and generative adversarial network 9 common features of texts in different fields, so that the model can acquire the common features related to emotions in the feedback of different fields and enhance the generalization ability of the model in cases where there is less training data. experiments on different data demonstrate the effectiveness of this method. 2 an emotional analysis model that combines pcnn and gan convolutional neural networks are widely used in natural language processing tasks such as relation extraction, information retrieval, and sentiment analysis, and have achieved remarkable results. in this section, this paper will use a convolutional neural network to extract the features of the text, and automatically select the important features of the sentiment tendency that are suitable for the text field to be classified from the input text, and alleviate the dependence of the sentiment analysis model on the text field. furthermore, the deficiencies of the convolutional neural network ignoring the structural information of text sentences are improved. the pooling layer is used to extract the structural features of the text and improve the performance of the sentiment analyzer. we also introduce the generative adversarial network to model, which enables the model to extract common features related to sentiment analysis between different domains of text, and improve the effectiveness of the model in the case of sparse data. 2.1 emotional analysis model based on convolutional neural network the model mainly consists of two parts. the first part is the feature extraction operation, which consists of two steps: convolution and pooling. the second part is the emotion classifier. the following is a description of each component separately. convolution operation a standard convolutional neural network usually consists of a convolution layer and a pooling layer. unlike the input of image processing, which is composed of image pixels, in natural language processing tasks, a matrix is usually used to represent a sentence or a paragraph as an input. each line of the matrix represents a language token, which can be a character or a word. in the model presented in this paper, each row of the input matrix is a vectorized representation of a linguistic symbol. in image processing, the convolution kernel of the convolutional layer usually slides over all parts of the image, while in natural language processing the convolution kernel slides only in the direction of text expansion. that is, the width of the input matrix (the dimension of the word vector) coincides with the width of the convolution kernel. assuming that the height of the convolution kernel is w , and the width and word vector dimensions are both d , the convolution kernel can be represented as a matrix w ∈ rw×d . let the vectorization of the i th language symbol in the input be represented as si , and the input text can be represented by the matrix formula s = (st1 ,s t 2 , · · · ,s t |s|) . then the convolution operation can be expressed as follows: cj = w ⊗ sj:j+w−1 (1) 1 ≤ j ≤ |s|− w + 1 , cj is a feature value extracted by the convolution operation between the word j and the word in the window of convolution kernel. the effective features obtained by feature extraction of text by a convolution kernel are not comprehensive. in order to be able to extract more abundant information from text, a number of different convolution kernels are usually used, which can be expressed as a three-dimensional 10 c. du, l. huang tensor ŵ = {w1,w2, · · · ,wn} . the convolution operation of the convolutional layer can be expressed as follows: ci,j = wi ⊗ sj:j+w−1 (2) 1 ≤ i ≤ n , the input text is subjected to the i−th convolution kernel convolution operation to obtain the feature vector ci = {ci,1,ci,2, · · · ,ci,|s|−w+1} , then all convolution kernels can get a total of n feature vectors, also known as feature map. sentiment classifier based on dropout algorithm and softmax classifier the feature vector extracted by the convolution kernel still has many eigenvalues. if the feature analyzer is used directly to train the sentiment analyzer, the number of parameters that need to be optimized is still very large, and the model training is difficult and easily susceptible to overfitting. the pooling operation can further select features, effectively reduce parameters, and select some features that are most suitable for sentiment analysis as the final features of the sentiment analyzer. the pooling layer compresses the input feature map, which can simplify the network computation complexity; on the other hand, the pooling operation can extract the main features. the essence of the pooling operation is sampling; which extracts features of the text from a sentence-level or higher-level text representation consisting of a vector representation of words. for different input texts, even when some of the expressions associated with the sentiment analysis of the current domain change, the output after the pooling operation is constant, which can enhance the robustness of the convolutional neural network. there is a certain anti-disturbance effect. because the model uses a maximum pooling operation in the local neighborhood, the model can obtain the maximum degree of translational invariance of the text features. this feature is very important for sentiment analysis models because the model can effectively extract strong emotional features from the text. these features are in different positions in the text, and the pooling layer can select the most relevant features from the feature map through the pooling operation [10] . for the feature vector ci obtained by the input text s through the i th convolution kernel, the maximum pooling operation can be expressed by the following formula: pi = max(ci) (3) before the pooling layer, the feature vector obtained by the convolution kernel is connected to the pooling layer through a nonlinear mapping layer. then formula 3 can be rewritten as follows: ĉl = f(ci) pi = max(ĉl) (4) f represents a nonlinear mapping function. at this time, the neural network of the feature extraction operation section can be represented as shown by the convolution and pooling layers in figure 1 . the resulting feature vector is ps = [p1,p2, · · · ,pn] . sentiment analysis method based on piecewise convolutional neural network and generative adversarial network 11 figure 1: sentiment analysis model based on convolution neural network pooling operations the output obtained by the convolution operation and the pooling operation represents the advanced features of the original input text. in order to get the final sentiment analysis result, these features need to be input into the classifier through the fully connected layer for emotional tendency classification. as shown in figure 1 , after the features of the text are extracted by the convolutional neural network, the feature vectors are input to the softmax classifier for classification. this article uses the dropout method to connect feature vectors with the softmax classifier. the input data of the neural network is randomly connected to the neurons of the next layer according to a preset proportional parameter ρ . in the convolutional neural network of this paper, the pooled feature vector is set to 0. therefore, in the subsequent calculation process, only other elements that are not set to 0 participate in the operation to obtain the output of the network. the specific process of parameter update is as follows: first, the pooled feature vector is set to 0 according to the ratio ρ , and the element without 0 participates in the operation of the softmax classifier and obtains the gradient-optimized neural network outputting the neural network parameters. then, the input vectors of all the samples in the sample set are sequentially accepted, and the connection of the input elements is randomly set to 0 in the same manner, and the elements participating in the training are selected until all the samples are used by the neural network to train the model parameters. each time a sample is entered, the probability that the input element is set to 0 is ρ , so each time the input element of the participating operation is connected differently, the updated network weight parameters are also different. when using neural networks for prediction, it is necessary to multiply the parameters of the entire network by 1 −ρ to obtain the final classifier network parameters. assuming that the eigenvector obtained by the convolution and pooling operations of the input text s is ps , the way the dropout algorithm sets its element to 0 can be represented by the bernoulli distribution b . first, use the bernoulli distribution to generate a binary vector of the same dimension as c (the element takes only 0 or 1) r , ie r ∼ b(ρ) , and then do the hadamard product with the eigenvector. the vector that is ultimately entered into the classifier can be expressed as: cd = ps ×r (5) set the network parameter we of the softmax classification layer, and the bias term is be ,then the neural network output can be expressed as: 12 c. du, l. huang o = f(wecd + be) (6) where f is the activation function. then the probability that the entered text sentiment tends to i is: p(i|θ) = eoi∑n j=1 e oj (7) where θ represents all parameters of the neural network, oi represents the value of the i -th item of the output vector, and n represents the number of categories of the text. let the sample set be expressed as ω , then the model’s optimization objective function can be calculated by the following formula. lsen = |ω|∑ i=1 −logp(yi|si,θ) + λ ‖ θ ‖22 (8) where λ is the parameter of the regularization term. in the actual experiment, the traditional convolutional neural network uses the stochastic gradient descent method to optimize the objective function. the update method of the parameter θ is: θ = θ −α ∂l ∂θ (9) α is the learning rate. 2.2 piecewise pooling based convolutional neural network sentiment classification model there is a deficiency in the process of extracting text features using the sentiment analysis model based on a traditional convolutional neural network. regardless of whether or not the texts or chinese, english or other natural language texts, the sentences have a certain structure, and traditional convolutional neural networks ignore the structural features of these sentences. as shown in figure 2 , both chinese and english sentences can contain grammatical structures such as a subject, predicate and object. but watching huppert, a great actress tearing into landmark role, is riveting. subject attributive adjunct object predicate 房间 环境 都是 很不错的 objectpredicatesubject figure 2: structures in sentences while existing deep learning method has difficulty parsing sentences, modeling the sentence structures will make the calculation process more complicated. however, if the simulation of grammatical structures can be added to network structures, the learning of sentence features will be significantly improved. traditional maximum pooling extracts a maximum value from the features of a convolution kernel convolution, and does not make any distinguish between the grammatical elements of sentences. in response to this problem, this paper proposes the use of sentiment analysis method based on piecewise convolutional neural network and generative adversarial network 13 piecewise pooling strategy for sentiment analysis. the piecewise pooling strategy divides the feature vector of a sentence into segments and performs maximum pooling operations on each segment. by segmenting the different grammatical structural components in a sentence, the segmented pooled feature vector can extract the features of the corresponding components in the sentence. the difference between segmented pooling and non-segmented pooling operations can be seen from the comparison in figure 3 . figure 3: piecewise pooling and non-piecewise pooling the rest of the model is identical to the traditional convolutional neural network model described above, with the main difference being the pooling part. the feature vector ci obtained after the convolution kernel convolution operation is different from the original pooled layer pair by single max-pooling. the model in this section uses piecewise max-pooling. the convoluted feature vector is divided into m segments, that is, ci = (ci,1,ci,2, · · · ,ci,m) . for different segments, the maximum pooling operation is performed separately, and the piecewise pooling operation may be expressed as equation 10 . pi = [max(ci,1), max(ci,2), · · · , max(ci,m)] (10) the piecewise pooling operation is performed on the feature vectors obtained by all convolution kernels, and then the feature vectors are nonlinearly transformed by the fully connected layer to obtain the feature vector of the input text, namely: ps = [f(p1),f(p2), · · · ,f(pi)] 1 ≤ i ≤ n , f represents a nonlinear activation function. 2.3 multi-domain common sentiment feature extraction based on generative adversarial networks although the piecewise convolution neural network can effectively analyze the emotional tendency of the text, in order to make the neural network fully trained, the generalization of the network parameters is good enough, and more annotation data is needed. however, in each segment, there is relatively little data to be labeled, and data sparseness makes network training more difficult. therefore, this paper proposes to use the generated adversarial network to extract common features related to emotions in different fields to alleviate the above problems. the shared private model separates the feature space into shared and private spaces, but there is no guarantee that shared features cannot exist in the private feature space, and vice 14 c. du, l. huang versa. therefore, some useful sharing features can be ignored in the shared private model, and the shared feature space is also vulnerable to certain task-specific information. therefore, a simple principle can be applied to multitasking learning. a good shared feature space should contain more public information and no task-specific information. to solve this problem, we introduce adversarial training into the multitasking framework. introduction for generative adversarial network first, we give a brief introduction to the generative adversarial network (gan), generated by the goodfellow et al. [3] was proposed in 2014, and quickly received extensive attention from the academic and industrial circles. the goal of gan is to learn a generator distribution pg(x) that is fully matched to the real data distribution pdata(x) . specifically, the gan learns pg(x) by training a generation network g and a discrimination network d, wherein g is a sample generated from the distribution pg(x) to generate forged samples, and attempts to make the discrimination network unable to distinguish the forged samples from the real samples. ;d determines whether the sample is from pg(x) or pdata(x) , trying to distinguish the forged sample from the real sample. the two games are mutually optimized and alternately optimized. when the discriminator reaches the nash equilibrium, that is, the discriminator cannot distinguish the forged sample from the real sample, the obtained pg(x) is consistent with pdata(x) . this minimum-maximum game can be optimized by the following risks: max d min g v (d,g) = ex∼pdata [logd(x)] + ez∼p(z)[log(1 −d(g(x))] (11) due to the role of the discriminator, the sample generated by the generator is forced to approach the true distribution of the data progressively and unbiased. if the generator produces a sample distribution that perfectly matches the real data distribution, the discriminator will not be able to tell whether the input is from real data or a sample generated by the generator network, giving a probability value of 0.5 for all inputs, which is the nash equilibrium. although gan was originally proposed for the generation of random samples, gan can be used as a general tool for measuring the equivalence between distributions [11] , which can be used for feature extraction. multi-domain common sentiment feature extraction gan figure 4: multi-domain common sentiment feature extraction with generative adversarial network inspired by gan, we propose a adversarial-sharing-private model for multi-domain common sentiment feature extraction. the shared convolutional neural layer first, and then the private sentiment analysis method based on piecewise convolutional neural network and generative adversarial network 15 convolutional neural network extract features from the input text. the features extracted by the shared convolutional neural network are input into the discriminator network, trying to make the discriminator unable to distinguish which subdivision from which the feature comes from, and the discriminator tries to distinguish the features of different domains. then, the extracted common features and the private features extracted by the piecewise domain are spliced as a feature to the accurate prediction of the text sentiment tendency in the sentiment orientation analyzer. this kind of adversarial training encourages the sharing space to be purer, ensures that the shared representation is not polluted, and can well preserve the common emotional features of different fields and eliminate the private emotional characteristics of the segment. the domain discriminator network is used to map the shared representation of the sentence into a probability distribution and estimate which fields the encoded sentence comes from. d(ps,θd) = softmax(b + ups) (12) u is a learnable parameter and b is the bias. in order to train the discriminator network, the parametric loss ladv is used to optimize the parameters of the network to prevent the private sentiment features related to the emotional tendency of the specific domain from spreading to the shared sentiment orientation feature space. the counter-loss is used to train the model to extract common sentiment features in different domain texts so that the discriminator network cannot reliably predict which category of the currently entered text belongs to base on these features. the original form discriminator of gan can only be used in the case of two classifications. in order to overcome this problem, this paper extends its s to multiple types of forms, so that the model of this paper can train sentiment analyzers in multiple fields together. the objective function against the network can be expressed as follows: ladv = min θs (λ max θd ( k∑ k=1 n∑ i=1 dki log[d(e(s k))])) (13) dki indicates the domain tag to which the currently input text belongs. the basic idea is that, given a sentence, the shared convolutional neural network extracts a representation of an eigenvector misleading discriminator network. at the same time, the discriminator tries to classify the field of the input text correctly. after training, the shared sentiment feature extractor network and the discriminator network reach the nash balance, and the discriminator cannot distinguish which domain these common features come from. the common feature extractor model can represent figure 4. finally, the loss function of the sentiment analysis method model of pcnn and gan proposed in this paper can be expressed as follows: l = lsen + λladv (14) λ is a hyperparameter for balance sentiment analysis loss and adversarial loss. 3 experiment settings 3.1 data sets three data sets were used primarily in the experiment. the first is the chinese hotel data collection (ctrip hotel for short), which was compiled by tan songbo from the insti16 c. du, l. huang tute of computing technology of the chinese academy of sciences and is a commentary on the service of large-scale customers. the corpus was automatically collected from the ctrip (http://hotels.ctrip.com/) and then organized. the corpus size is 10,000, including 7000 positive evaluation samples and 3,000 negative evaluation samples. the second data set is the english data set, from stanford university’s emotional tree library, which labels the emotional polarity of each phrase in the sentence and the entire sentence. in this paper, only the emotional polarity of the sentence is extracted and classified. the data set contains a total of 11,855 sentences, including training sets, validation sets, and test sets containing 8544, 1101, and 2210 samples, respectively. the emotional polarity of each sentence is in the range of [0,1]. the smaller the score, the more the emotion tends to be negative. otherwise, the emotion tends to be positive. the emotional scores of all sentences in the data set are manually labeled and then averaged. it has a good reliability. according to the distribution of the emotional scores of the dataset and its data description, the sample with the sample score between [0, 0.5) is first divided into negative samples, and the sample score is divided into positive emotions between [0.5, 1]. this is a coarser-grained sentiment analysis. further divided into five levels of sentiment, including negative [0, 0.2), negative [0.2, 0.4), neutral [0.4, 0.6), positive [0.6, 0.8), positive [0.8, 1].in order to make the experiment closer to a real production environment, and to verify the effectiveness of the sentiment analysis method of pcnn and gan proposed in this paper, this paper uses the third data set. it is the review text (dianping for short) from different fields that we crawled from the public service website (http://www.dianping.com/). its data includes six segments of food, hotels, movies, entertainment, marriage, home improvement, and the comments are divided into different emotional tendencies based on the scoring information in the comments. this paper selects 30,000 reviews as the training set and 10,000 as the test set. 3.2 data pre-processing for the chinese data set, the chinese word piecewise package nlpir developed by the chinese academy of sciences is first used for chinese word piecewise. since the experiment in this article has a chinese data set, you need to call the package participle. the english data itself contains independent words, so there is no need for word piecewise. since the minibatch training model is used during training (multiple samples are learned at a time, and the text length of multiple samples may be different), so the length of the text needs to be fixed-length. since the length of the natural language text is inconsistent, the longest sentence length l _ max is first calculated. for sentences with a sentence length less than l _ max, the text is uniformly filled with the < \s > symbol to the length l _ max (the vector of < \s > is always set to 0), this will unify the text length. the purpose of this approach is to improve computational efficiency, and when the length of the data is uniform, the computational time overhead can be effectively reduced. at the same time, in order to ensure the features are extracted at the beginning and the end of the text during the convolution process, a certain number of < \s > corresponding to the convolution kernel should be added at the beginning and end of the longest text as padding. 3.3 pre-training of word embedding word embedding is required before training of the model. word embedding acts as a distributed representation of words as an input suitable for neural networks. many current studies have shown that executing word embedding pre-training on a large-scale corpus, and then applying the obtained word embedding to subsequent training, can speed up the convergence of neural network models and achieve a better local optimal solution. in this paper, the word2vec algorithm is used to pre-train word embedding. the word embedding of this algorithm shows better performance in many natural language processing tasks, and has higher efficiency. this sentiment analysis method based on piecewise convolutional neural network and generative adversarial network 17 paper chooses the skip-gram model and the negative sampling model to pre-train the word embedding of chinese and english words. the pre-training of chinese word embedding uses the text content crawled on baidu encyclopedia. the english word vector is pre-trained using new york times. 3.4 setting of experimental parameters in the training optimization process of the model, we use the adam optimizer to train and optimize the model. the parameter settings of the optimizer adopt the recommended parameter values. in this paper, the model mainly has the following hyperparameters: the dimension d of the word vector, the number n of convolution kernels, the ratio ρ of the dropout algorithm, and the piecewise number t of the piecewise pooling in the piecewise convolutional neural network. in order to obtain the optimal hyperparameter setting, this paper uses grid-search to determine the value of some hyperparameters. the word embedding dimension n selects a value from { 50, 100, 200, 300} ; the number n of convolution kernels takes a value in { 100, 150, 200 } . the ratio ρ of the dropout algorithm is 0.5 based on experience. in the experiments in this paper, multiple experiments were performed using these parameters, and then the average of the results was obtained. the number of segments of the piecewise pooling was performed in this paper. the results were given in the analysis. 4 results and analysis in order to verify the effectiveness of the sentiment analysis method of the fusion piecewise convolution neural network and the generated adversarial network, the method of this paper is compared with some mainstream emotional analysis baseline methods. the parameter sensitivity experiment is carried out on the piecewise pooling segment number hyperparameters which are important to the performance of the model in the hyperparameters proposed in this paper, so as to find the more suitable parameter settings in the application scenario. 4.1 comparison method in order to verify the validity and correctness of the proposed model, this paper selects a model based on traditional methods and a neural network method such as rntn proposed by richard socher et al. as a baseline method. the first method of comparison is the naive bayesian method (nb for short) using the word bag feature. the second method is to use the word bag feature as input to perform emotion classification using a support vector machine (svm for short) classifier. the third method is the naive bayesian method (binb for short) of the bag feature obtained using the binary grammar language model. the fourth method is to use the average word vector of the sentence as the input feature and use the fully connected network as the classifier (vecavg for short). the fifth method is the recurrent neural network (rnn). the sixth method is a recurrent neural network (mv-rnn) with a semantic transformation matrix [7] . the seventh method is based on a tensor-based cyclic neural network (rntn). the last comparison method is the traditional convolutional neural network. 4.2 analysis of experimental results first, because there is no multi-domain information on the ctrip hotel dataset and the stanford english emotional tree database dataset, the performance of the segmented convolutional neural network model and the baseline model is first compared. it can be seen from the results in table 1 that the neural network method generally has higher performance than 18 c. du, l. huang table 1: text classification precision on different data sets(%) model stanford ctrip two levels sentiment five levels sentiment nb 81.8 41.0 80.2 svm 79.4 40.7 86.7 binb 83.1 41.9 85.9 vecavg 80.1 32.7 82.1 rnn 82.4 43.2 87.8 mv-rnn 82.9 44.4 87.6 rntn 85.4 45.7 89.3 cnn 81.9 45.6 88.5 pcnn 85.4 45.9 89.7 table 2: text classification recall on different data sets(%) model two levels sentiment five levels sentiment two levels sentiment(reduced) nb 72.1 35.7 66.1 svm 72.6 33.2 61.2 binb 75.2 36.6 62.4 vecavg 74.1 36.3 62.3 rnn 77.3 40.2 69.7 mv-rnn 78.0 42.2 73.1 rntn 80.3 42.6 75.7 cnn 79.1 41.5 73.5 pcnn 80.8 43.2 74.9 pcnn+gan 83.3 47.9 80.1 the conventional method. especially in the five-level sentiment analysis with finer granularity, the neural network method can obtain the key features of the text well. an important reason for the binb method to achieve better results is that the binary grammar model considers a certain combination of semantics, but with it is a large computational overhead. at the same time, unlike the method of cyclic neural networks, the use of maximum pooling in convolutional neural networks can automatically extract the features most relevant to sentiment analysis tasks, and has positive significance for text sentiment analysis tasks, so it has achieved good results. traditional methods such as binb can only extract combined features from adjacent words, and traditional convolutional neural networks cannot model grammatical structures. the segmented convolutional neural network method has the best effect on both datasets because it simulates the grammatical structure information of the text, supplements the original emotional words, and extracts the combined semantic features of different positions. in the dianping data, the data comes from different fields. we compare the performance of different methods in this data to fully demonstrate the effectiveness of the proposed pcnn and gan methods. it can be seen from table 2 that the fusion segmented convolutional neural network and the sentiment analysis method of the generated adversarial network proposed in this paper have achieved the best results. the comments crawled from the dianping contain different subdivisions. the comment text in each field is relatively small, and the comment text itself is relatively short. therefore, samples in different fields have a certain degree of sample sparseness. sentiment analysis method based on piecewise convolutional neural network and generative adversarial network 19 this paper also reduces the number of samples in each field, making sparse data a more serious side-effect. it can be seen that all methods have performance degradation problems to varying degrees, but the method proposed in this paper still achieves good results. the stability of the proposed method in the case of multi-domain data sparseness is fully explained. in the piecewise convolutional neural network, a very important parameter is the piecewise pooling segment number. since the piecewise is a simulation of the text’s grammatical structure, the piecewise number determines the validity of the text structure information extraction. in this paper, the parameter sensitivity test is carried out on the three data sets for the number of segments. the results are shown in figure 5. it can be seen that a reasonable piecewise can better obtain the grammatical structure information of the text, and an excessively large number of segments destroys the structure of the sentence itself. the pooling operation cannot effectively extract the features most relevant to sentiment analysis, making the sentiment analysis effect worse. therefore, it is very important to select the appropriate segment number to simulate the grammatical structure of the sentence. ����� �������� �������� figure 5: segments number of pooling parameter sensitive experiment 5 conclusion in this paper, we show the difficulty of maintaining optimality in different fields for traditional text sentiment analysis methods. different methods have different applicability problems in different fields. the original convolutional neural network model ignores the sentence structure information that is very important for text sentiment analysis, and it is susceptible to overfitting. in this paper, the piecewise pooling strategy is adopted to enable the deep learningbased convolutional neural network model to model the sentence structure and segment the main features of different structures. it combines the structural information and domain information of the text to analyze the emotional tendency of the text; and uses the dropout algorithm to enhance the generic ability of the model. at the same time, user feedback on services involves many different areas, with less data in each field. less data makes the training of convolutional neural networks more difficult, and the parameters cannot be fully optimized, resulting in under-fitting of the model. therefore, in the case that data volume expansion is more difficult, in order to alleviate the sparseness of data, this paper uses the generated adversarial network to extract common features of texts in different 20 c. du, l. huang fields. this enables the model to acquire common features related to emotions in feedback from different fields, and to enhance the generalization ability of the model with less training data. experiments on different data demonstrate the effectiveness of this method. bibliography [1] chen, x. (2012); research on sentiment dictionary based emotional tendency analysls of chinese microblog, huazhong university of science & technology, 2012.(in chinese) [2] cui, h.; mittal, v.; datar, m. (2006); comparative experiments on sentiment classification for online product reviews, aaai, 6, 1265-1270, 2006. [3] goodfellow, i.j.; pouget-abadie, j.; mirza, m. et al. (2014); generative adversarial nets, intl conf. on neural information processing systems, mit press, 2672-2680, 2014. [4] li, d.; qiao, b.; yuanda, y.; et al. (2008); word orientation recognition based on semantic analysis, pattern recognition & artificial intelligence, 2008.(in chinese) [5] luong, t.; socher, r.; manning, c.(2013); better word representations with recursive neural networks for morphology, proc. of the seventeenth conf. on computational natural language learning, 104-113,2013. [6] socher, r. (2014); recursive deep learning for natural language processing and computer vision, stanford university, 2014. [7] socher, r.; huval, b.; manning, c.d. et al.(2012); semantic compositionality through recursive matrix-vector spaces, proc. of the 2012 joint conf. on empirical methods in natural language processing and computational natural language learning, association for computational linguistics, 1201-1211, 2012. [8] socher, r.; chen, d.; manning, c.d. et al (2013); reasoning with neural tensor networks for knowledge base completion, advances in neural information processing systems, 926-934, 2013. [9] socher, r.; perelygin, a.; wu, j. et al.(2013); recursive deep models for semantic compositionality over a sentiment treebank, proc. of the 2013 conf. on empirical methods in natural language processing, 1631-1642, 2013. [10] srinivas, s.; sarvadevabhatla, r.k.; mopuri, k.r. et al. (2016); a taxonomy of deep convolutional neural nets for computer vision, frontiers in robotics and ai, 2:36, 2016. [11] taigman, y.; polyak, a.; wolf, l. (2016); unsupervised cross-domain image generation, arxiv preprint arxiv, 1611.02200, 2016. [12] zeng, d.; liu. k.; lai, s. et al. (2014); relation classification via convolutional deep neural network, proc, of coling 2014, the 25th intl conf. on computational linguistics: technical papers, 2335-2344, 2014. [13] zhang, w., zhang, z., chao, h.c. et al. (2018); kernel mixture model for probability density estimation in bayesian classifiers, data mining and knowledge discovery, 32(3), 675-707, 2018. [14] zhang, w.; zhang, z.; qi, d. et al. (2014); automatic crack detection and classification method for subway tunnel safety monitoring, sensors, 14(10), 19307-19328, 2018. international journal of computers communications & control issn 1841-9836, 13(6), 1032-1046, december 2018. a new hybrid method in global dynamic path planning of mobile robot x.r. song, s. gao, c.b. chen, k. cao, j.r. huang xiaoru song*, song gao, kai cao, jiaoru huang school of electronic and information engineering, xi’an technological university no.2 of xuefu middle road, xi’an, shaanxi, china *corresponding author: masha0422@163.com 545313193@qq.com, 602507619@qq.com, 674330497@qq.com abstract: path planning and real-time obstacle avoidance is the key technologies of mobile robot intelligence. but the efficiency of the global path planning is not very high. it is not easy to avoid obstacles in real time. aiming at these shortcomings it is proposed that a global dynamic path planning method based on improved a* algorithm and dynamic window method. at first the improved a* algorithm is put forward based on the traditional a* algorithm in the paper. its optimized heuristic search function is designed. they can be eliminated that the redundant path points and unnecessary turning points. simulation experiment 1 results show that the planned path length is reduced greatly. and the path transition points are less, too. and then it is focused on the global dynamic path planning of fusion improved a* algorithm and dynamic window method. the evaluation function is constructed taking into account the global optimal path. the real time dynamic path is planning. on the basis of ensuring the optimal global optimization of the planning path, it is improved that the smoothness of the planning path and the local real-time obstacle avoidance ability. the simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional path planning algorithms with the fusion algorithm in the paper. it is more fit to the dynamics of the robot control. and when a dynamic obstacle is added, the new path can be gained. the barrier can be bypass and the robot is to reach the target point. it can be guaranteed the global optimality of the path. finally the turtlebot mobile robot was used to experiment. the experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. and the planned global path is smoother. when the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. it can re-plan the path. it can bypass the random obstacle to reach the original target point. the outputting control parameters are more conducive to the robot’s automatic control. the fusion method is used for global dynamic path planning of mobile robots in this paper. in summary the experimental results show that the method is good efficiency and real-time performance. it has great reference value for the dynamic path planning application of mobile robot. keywords: mobile robot, path planning, dwm algorithm. 1 introduction path planning is one of the most basic and important issues of mobile robots [2]. it is one of the independent acts to complete the task. the mobile robot path planning is a more difficult to solve the problem especially in the dynamic environment. it requires the robot process as far as possible not to deviate from the specified path, unless the obstacles encountered when the movement had to bypass the walk. but after avoiding the obstacle, the mobile robot must return to the original path until it reaches the destination [3,8,15,18,21]. copyright ©2018 cc by-nc a new hybrid method in global dynamic path planning of mobile robot 1033 aim at this problem the dynamic window method is used in this paper. it has a good obstacle avoidance capability in the dynamic environment [6]. but this method does not satisfy the global optimal path planning. the node-based a* algorithm is suitable for global path planning. it has the advantages of simple data, small calculation compared to the biological inspired ga, pso, etc [7] [9]. but when the environmental space increases, it needs large storage space, low efficiency, and poor real-time decision-making [11]. so the path curvature of the traditional a* algorithm is discontinuous. it will cause the movement parameters jump at the turning point. these are not conducive to the robot control problems [1]. aim at these problems the algorithm is improved. it makes the path smoother on the basis of the overall optimization of the planning path [12]. so a global dynamic path planning method is proposed that it is combining improved a* algorithm and dynamic window method in the paper. it is proposed the evaluation function taking into account the global optimal path. and the real-time path planning is carried out by using dynamic window method based on the evaluation function. it would make the path smoother on the basis of ensuring the overall optimization of the planning path. at the same time it improves the local obstacle avoidance. 2 the construction of advanced a* algorithm in mobile robot global path planning the traditional a* algorithm is a kind of global path planning algorithm [9]. it is a typical heuristic search algorithm in artificial intelligence. aim at solving the problem of a large number of invalid search paths often generated in path planning [24]; the heuristic function is used to improve the search efficiency. a* algorithm design core is its evaluation function: f(n) = g(n) + h(n) (1) where,f(n) is the evaluation value of each node. it may be searched in the path plan [14]. the cost evaluation value of the starting point goes through the node n to the target point. g(n) is the actual evaluation value from the starting node to the current node. h(n) is the evaluation value from the current node to the target node (heuristic function). the h(n) can directly affect the performance of the algorithm. so it tries to approach the actual assessment h∗(n) of the path planning problem. it is needed [20]. but it is also less than h∗(n). 2.1 the design of the optimization heuristic function assuming that the h(n) is actual value of the current node to the target node. when h(n) < h(n), more nodes is low efficiency, but it can search for the optimal path. when h(n) = h(n), it is ideal, searching along the shortest path and being the most efficient. when h(n) > h(n), less nodes, high efficiency, but it is often difficult to search for the optimal path [23]. the traditional a* algorithm uses the manhattan distance or euclidean distance to define the heuristic function [16], namely hm (n) = |nx −gx| + |ny −gy| (2) he(n) = √ (nx −gx)2 + (ny −gy)2 (3) where, nx, ny, gx, gy is the current node and target point. 1034 x.r. song, s. gao, c.b. chen, k. cao, j.r. huang a* algorithm searches the 8-neighborhood grid of the current node each time [25]. so these two heuristic function designs are not the best choice taking into account the two-wheeled robot control. therefore, a heuristic function that is closer to the actual value h(n) is designed with manhattan distance and euclidean distance in the paper. h(n) = { √ 2dx(n) + dy(n) −dx(n) dy(n) > dx(n)√ 2dy(n) + dx(n) −dy(n) dx(n) > dy(n) (4) where, dx(n) = |nx −gx| dy(n) = |ny −gy| , the rest parameters are the same as in the above formulas (2) and (3). 2.2 the proposition of key point selection strategy a* algorithm planning optimal path contains a lot of redundant and unnecessary turning points. it is not conducive to the control of the robot [25]. in order to solve this problem, a key node selection strategy is proposed in the paper. (1) extract key turning points. starting from the second node of the planning path, if the current node is on the same line as the previous node and its parent node, the previous node is redundant. the redundant point is deleted. the path would update. all the path nodes would traverse. a path sequence will eventually get that contains only the starting point, the turning point, and the end point. (2) delete the redundant turning point. after extracting the turning point there may still be redundant points in the trajectory. assuming that path point after extracting the key turning point is {pk|k = 1, 2,l,n}, connect p1p3, if p1p3 do not pass the obstacle, namely the line distance p1p3 from the obstacle is greater than the preset threshold, then continue to connect p1p4, until the p1pk(k = 3, 4,l,n) through obstacles, then connect pk−1 and p1, while delete the middle redundant points, update the path; repeat the above procedure from the node p2 until there is no redundant turning point in the path. the experimental results of the key point selection strategy are shown in fig.1. it can be seen that the application of key selection strategy algorithm is better than traditional a* algorithm. fig.1 (a) is the original path sequence of the traditional a* algorithm. fig.1 (b) is the path sequence obtained by advanced a* algorithm to extract the critical turning point.fig.1 (c) is the final path sequence by advanced a * algorithm removing the redundancy turning point. simulation experiment 1 results show that the planned path length is reduced greatly. the path length is from 11.455m to 10.946m. and the path transition points are less greatly. 3 the establishment of mobile robot motion model in dynamic window method (dwa) the dynamic window method has the dynamic obstacle avoidance capability of the robot in the dynamic environment [17]. the multi-group velocity can be sampled in the velocity space (linear velocity v and angular velocity w) [26]. the trajectory of the robot is simulated at these speeds for fixed time interval.after acquiring multiple sets of trajectories the corresponding speed of the optimal trajectory is selected. it is driving the robot movement according to the evaluation index. a new hybrid method in global dynamic path planning of mobile robot 1035 figure 1: the experimental result of the key point selection strategy (where "-" means the planning path, "***" means the obstacle. "◦" means the node.) figure 2: the robot motion diagram 1036 x.r. song, s. gao, c.b. chen, k. cao, j.r. huang 3.1 the establishment of the robot movement model in the dynamic window algorithm it is need that simulates the movement of the robot trajectory. so it is necessary that the robot movement model is known. it is assumed that the trajectory of the move robot is a circular arc. the arc trajectory is represented by (vt,wt). when the robot trajectory is calculated, consider two adjacent moments first. due to the short distance of motion between adjacent moments (general chute sampling periods ms), the trajectories of the two adjacent points can be viewed as a straight line. that is, the robot moves along the axis. the motion of the robot is shown in fig. 2. therefore, when the robot moves in the omni directional direction, the trajectory motion model of the two adjacent moments t + 1 relative to t is always as follows equation (5):  x = x + vx∆t cos(θt) −vy∆t sin(θt) y = y + vx∆t sin(θt) −vy∆t cos(θt) θt = θt + wt∆t (5) where, x and y is the robot projection distance of axis x and y. vx and vy is the robot moving speed of axis x and y. θ is the angle between the robot and the axis x. ∆t is the two adjacent moment. 3.2 speed sampling based on the above-mentioned robot’s trajectory motion model, the trajectory can be deduced according to the speed. so sample a lot of speed, calculate the trajectory, and evaluate the track is good or not is only needed [25]. how to sample speed is also a core of dwa. in the twodimensional speed space (v,w) there exist endless groups of speed. according to the limitations of the robot itself and the environment, the robot sampling speed is controlled in a certain dynamic range. the speed limit of mobile robot between its own maximum and minimum speed: vm = {v ∈ [vmin,vmax],w ∈ [wmin,wmax]} (6) the impact of mobile robot performance by the motor: there is a maximum acceleration and deceleration limit due to the limited motor torque. so there is a dynamic window in the period of sim-period of the mobile robot trajectory forward simulation. the speed is the robot can actually reach: vd = {(v,w) ∣∣∣∣∣v ∈ [vc − v̇b∆t,vc + v̇a∆t]∧w ∈ [wc − ẇb∆t,wc + ẇa∆t] (7) where vc and wc are the current speed of the robot. the other speeds are corresponding to the maximum acceleration and the maximum deceleration. (3) about mobile robot security considerations in order to allow the robot to stop before the obstacles encountered, so speed range is needed under the maximum deceleration: va = {(v,w) ∣∣∣v <= √2 ·dist(v,w) · v̇b ∧√2 ·dist(v,w) · ẇb} (8) where dist(v,w) is the closest distance from the obstacle according to velocity (v,w). a new hybrid method in global dynamic path planning of mobile robot 1037 3.3 design the evaluation function selection in the velocity space, there are several groups of sampling speed is feasible, so design evaluation function selects the optimal trajectory. the criteria of the design evaluation function are the robot as far as possible to avoid obstacles during local navigation, and towards the target quickly forward. the design evaluation function is: g(v,w) = σ  α ·heading(v,w)+β ·dist(v,w)+ γ ·velocity(v,w)   (9) where heading(v,w) is the azimuth angle evaluation function, it indicates the azimuth deviation (angular deviation) between the end direction of the simulated trajectory and the target at the current set sampling speed (v,w). dist(v,w) is the closest distance of the robot itself to the obstacle at speed (v,w). if there is no obstacle on the track, it is set as a constant. velocity(v,w)is the evaluation of the current speed size; σ(·) is the smoothing function, α,β,γ is the weight of the three factors. the deviation is smaller, the evaluation is higher value. the distance is greater, it is the higher evaluation value. the higher speed, and the higher evaluation value. these three indicators are normalized and then weighted. 4 the proposition of advanced a* algorithm fusion dwa algorithm path planning method dynamic window planning path method is online real-time, with good local dynamic barrier capability. however, the requirements of global optimal path planning are missed. it is a fatal problem; it will fall into the local optimum. so the advanced a * algorithm for global path planning, fusion of dynamic window method for local obstacle avoidance, it ensures the global optimization of the dynamic planning path. therefore, in order to avoid the dynamic window method into the local optimal, a dynamic window evaluation function to estimate the global optimal path is proposed: g(v,w) = σ  α ·pheading(v,w)+β ·dist(v,w)+ γ ·velocity(v,w)   (10) where pheading(v,w) is the azimuth deviation between the simulated trajectory end point and the current target point at the current speed (v,w). the current target point is the global optimal path sequence point; it is nearest to the current point in the robot forward direction. this evaluation function allows the local path planning to follow the global optimal path contour, thus ensuring the global optimal. this algorithm is shown in fig. 3. 5 algorithm simulation verification in order to verify the validity of the fusion algorithm designed in this paper, two experiments were carried out in the matlab simulation environment, the grid map scene (10m × 10m, grid spacing 0.5m),and the coverage of the obstacle was 23%. in the simulation verification experiment, the starting point coordinates are (1.25 m, 0.75 m), the target point coordinates are (5.75 m, 9.75 m). simulation experiment 2 adds dynamic obstacles based on the experiment 1 to verify the dynamic obstacle avoidance performance of this algorithm. 1038 x.r. song, s. gao, c.b. chen, k. cao, j.r. huang figure 3: fusion algorithm for mobile robot global dynamic path planning simulation 1: in the above mentioned simulation environment, the mobile robot uses the traditional a* algorithm, the advanced a* algorithm adding a key extraction strategy, the dynamic window algorithm and the fusion algorithm proposed in this paper are used to validate the path planning. the simulation results of the algorithm are shown in fig.4. in fig.4, the blue box is the static obstacle, the black origin is the experimental mobile robot, and green solid line is the robot simulation path. it can be seen from fig. 4 (a) that the traditional a* algorithm can plan a global optimal path. the length is 11.455m. in fig. 4 (b), the advanced a* algorithm of key selection strategy can improve the path obviously, not only the redundant points in the traditional a* algorithm path are deleted, but the path length is better than the traditional a* algorithm. the path length is 10.946m. in fig. 4 (c), the dynamic path method is more smooth and suitable for automatic control of the robot than the a* algorithm, but the path of the dynamic window method is non-global and its path length is about 14.439 m. in fig. 4 (d), the fusion algorithm uses the evaluation function, the global optimal path is considered, so it can guarantee global optimization compared with the dynamic window algorithm. compared with the traditional a* algorithm, the fusion algorithm makes the path length greatly optimized, the path length is about 11.126m.the planning path is smoother, so the curvature change of the path is continuous .these are more in line with the dynamic control of the robot. simulation 2: based on the simulation experiment 1, the dynamic obstacle (5.75, 6.0) is added, the real-time dynamic obstacle avoidance simulation results of the mobile robot are shown in fig. 5. in fig. 5, the red point is the artificial dynamic obstacle, the purple solid line is the simulation a new hybrid method in global dynamic path planning of mobile robot 1039 path of the robot, and the rest is consistent with the simulation in figure 4.form fig. 5 (a) and fig. 5 (b), it can be seen that the traditional a* algorithm and the advanced a* algorithm cannot avoid the sudden dynamic obstacle. in fig. 5 (c), the path delimited by the dynamic window method, it can avoid the dynamic obstacle, but it is not global optimal. in fig.5 (d), the fusion algorithm in this paper can avoid the dynamic obstacle, bypassing the obstacle to the target point, and the planned path to maintain global optimality; it achieves the expected purpose of global dynamic path planning for mobile robots. figure 4: various algorithmic path planning results 6 implementation and experimental results the experimental platform used in this article is the turtlebot 2 two-wheel-drive robot produced by irobot. the robot is equipped with the kinect depth sensor developed by microsoft as the visual sensor, korea’s yujin kobuki mobile base, it is shown in fig. 6. in this experiment, we choose our school international science and technology cooperation base in shaanxi province, autonomous system and intelligent control international joint research center of unmanned system laboratory. the experimental environment of this project was about 10mx10m. the experimental environment was randomly, it is shown in fig.7. robot uses kinect sensor to obtain the actual scene of the three-dimensional point cloud data, combined with the odometer and other information through the slam (...) algorithm, it can obtain grid map in the interior scene. after the robot receives map of slam constructed, the navigation algorithm is invoked, that is, the advanced a* path planning algorithm for global path walking. in the process of obstacle avoidance, when the robot encounters the dynamic obstacle, calls the local dwa algorithm, the real-time dynamic obstacle avoidance is performed, 1040 x.r. song, s. gao, c.b. chen, k. cao, j.r. huang figure 5: the simulation results of robot dynamic obstacle avoidance figure 6: the turtlebot 2 mobile robot used in the experiment the mobile robot global dynamic path plan is realized. fig.8 shows a block diagram of the data processing for robot path planning. in the experiment the environment local map in the movement is shown in fig. 9. it is a new hybrid method in global dynamic path planning of mobile robot 1041 figure 7: laboratory to build mobile robot navigation and obstacle avoidance experimental figure 8: the block diagram of data processing for robot path planning established by the kinect visual sensor of the robot. in the experiment, the initial scan was done using a speed of 20cm/s forward and rotational speed of 20cm/s, this is considered an average speed, as the robot is capable of 50cm/s forward and rotational speeds. the speed of the robot in the course of the road is appropriately slow, it ensures that the environmental data is fully analyzed and the environmental maps are established. as shown in fig. 9, the surrounding dark area is kinect has not yet obtained the environmental information, the blue-green area is the obstacle obtained by the expansion operation during the movement, the black part is known as an obstacle after detecting and recognizing. if a dynamic obstacle is encountered (such as a dynamic pedestrian or man-made obstacle) during the course of the process, an optimal line is re-planed and the robot smoothly bypasses the obstacle to the target point. this experiment was carried out in unmanned system laboratory, about 10m * 10m labo1042 x.r. song, s. gao, c.b. chen, k. cao, j.r. huang figure 9: the map of the robot in the indoor scene figure 10: indoor automatic dynamic obstacle avoidance path planning ratory, a number of robot autonomous global navigation obstacle avoidance experiment is performed, the experimental results shown in fig. 10 and fig. 11. in fig. 10, the sequence diagram of the global path planning experiment results is shown, it carried out by the mobile robot in the artificially set static obstacle experiment environment. fig. (a) is the initial entry of the test a new hybrid method in global dynamic path planning of mobile robot 1043 figure 11: dynamic obstacle figure 12: dynamic obstacle avoidance experiment results area, fig. (b) and fig. (c) are normal operations along a pre-defined path, fig. (d) is near the end of the path. fig. 10 shows the normal operation of the mobile robot in the static obstacle of the experimental environment, the sudden emergence of human obstacle , as shown in fig. 11 (a), mobile robot is running along the specified path, suddenly appeared in a red clothes (set the dynamic obstacle), the pre-planned path is blocked. turble robot detects a fault, re-planning the path is re-planned; the final fig. (b) shows the path of the robot is re-planned, bypassing the obstacle, and returning to the original set end. in fig. 12, the blue is a static obstacle, the black circle is the mobile robot in the experiment, and the red is the dynamic obstacle (this experiment selects the person as a dynamic obstacle).in fig.(a), the green solid line indicates the path planning of the robot when the dynamic obstacle person is initially immobile. in fig. (b), the purple line indicates the re-planned path; it is obtained after the dynamic obstacle person randomly sets the obstacle point in the robot running. the results of the final experiment show that in the relatively complex environment, the robot can perform the global dynamic path planning more accurately, and it can dynamically bypass the obstacle and reach the target point in real time to realize the global path planning of real-time obstacle avoidance. 1044 x.r. song, s. gao, c.b. chen, k. cao, j.r. huang 7 conclusion aim at the problem of mobile robot path planning, a global dynamic path planning method based on advanced a* algorithm fusion dynamic window method is proposed. based on the redundancy of the traditional a* algorithm, this paper designs a key selection strategy, the redundant points in the traditional a* algorithm can be removed, the performance of the global path planning is improved. based on the dynamic window algorithm, an evaluation function considering the global optimal path is constructed; it can avoid the local optimal optimization of the dynamic window algorithm and ensure the global optimality of the path planning. therefore, compared with the traditional a* algorithm, the path of this algorithm is more smooth, the curvature change is continuous, and the motion control parameters can be output. these are more conducive to the feedback control of the mobile robot and can be obstructed in real time. compared with the dynamic window algorithm, the global optimization of the planned path can be ensured. finally, the simulation and experimental results show that the improved a* algorithm and dwa combined method have good stability and real-time performance for the global real-time dynamic path planning of mobile robots, which provides a reference for the motion control of the mobile robot in the global dynamic environment. acknowledgment this paper is grateful to the following projects: national key research and development program (2016yfe0111900), shaanxi international science and technology cooperation project (2018kw-022 and 2017kw-009), shaanxi provincial natural science fund (2017jm6041 and 2014 jm2-6093), also thanks to xi’an industrial university of international science and technology cooperation base independent system and intelligent control international joint research center of the unmanned system laboratory and autonomously intelligent control research and innovation team support for this subject. conflict of interest the authors declare no conflict of interest. bibliography [1] abd, m.e.; ahmed, m.y.; amgad, m.b.; yehia, z.e. 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(2015). algorithm for auv’s 3-dpath planning and safe obstacle avoidance based on biological inspired model, control and decision, 30(5), 798–806, 2015. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 428-441 applications of fuzzy technology in business intelligence a. meyer, h.-j. zimmermann andreas meyer inform institut f. operations research und management gmbh risk & fraud division pascalstr. 23 52076 aachen, germany e-mail: andreas.meyer@inform-software.com hans-jürgen zimmermann rwth, aachen institute of technology templer graben 55 52062 aachen, germany e-mail: zi@or.rwth-aachen.de abstract: fuzzy set theory has been developed during the last decades to a demanding mathematical theory. there exist more than 50,000 publications in this area by now. unluckily the number of reports on applications of fuzzy technology has become very scarce. the reasons for that are manifold: real applications are normally not single-method-applications but rather complex combinations of different techniques, which are not suited for a publication in a journal. sometimes considerations of competition my play a role, and sometimes the theoretical core of an application is not suited for publication. in this paper we shall focus on applications of fuzzy technology on real problems in business management. two versions of fuzzy technology will be used: fuzzy knowledge based systems and fuzzy clustering. it is assumed that the reader is familiar with basic fuzzy set theory and the goal of the paper is, to show that the potential of applying fuzzy technology in management is still very large and hardly exploited so far. keywords: fuzzy technology in business intelligence, fraud detection, risk assessment, intelligent data mining, fuzzy expert systems 1 introduction in his first paper on fuzzy sets l.a. zadeh [20] already mentioned as one motivation of his theory of fuzzy sets: "the fact remains that such imprecisely defined ’classes’ play an important role in human thinking, particularly in the domains of pattern recognition, communication of information, and abstraction." this statement has become even more true in the meantime since we have moved from a time of scarce data into the period of data warehouses etc., i.e., into a world of abundance of data, in which people try hard to extract useful information from masses of data ( [24]). from the point of view of applications we still consider as some of the most important goals of fuzzy set theory to extract information from data and model it visually or otherwise in such a way, that people can understand it, communicate it and to model problems adequately. they can use it to solve their problems better than by a purely dichotomous modeling language. in doing this professionally, they can combine the high computing power of edp with human experience and creativity. extremely successful fuzzy models were first used in engineering intelligence in areas such as fuzzy control ( [13, 24, 26, 30]). controlling cranes, cement kilns, video cameras, washing machines, abs, and even subway systems by fuzzy control turned out to be almost sensational. most copyright c⃝ 2006-2011 by ccc publications applications of fuzzy technology in business intelligence 429 of these applications had one feature in common: these were manmade systems, the control of which was often nonlinear (and therefore difficult to model traditionally). however the controls could be decomposed into linear systems by modeling human experience by fuzzy technology ( [27]) and one could then determine the adequate parameters, operators ( [6]) and membership functions, as well as the defuzzification models, by trial and error. practitioners loved these controllers because they used predominantly rather basic fuzzy set theory and operators as well as membership functions could be defined rather than determined on the basis of human knowledge. many of these models have become regular teaching material in control engineering courses. in the meantime fuzzy set theory was further developed, it became more powerful mathematically and it became more strenuous to learn and understand it. when one started to apply it to business intelligence and to human decision making another problem became visible: many applications do not permit a trial and error calibration as in fuzzy control because the results of a fuzzy model cannot easily be compared with the results or the behavior of the real system. think of strategic decisions, of evaluations of long term vulnerability of companies or persons, the determination of the creditworthiness ( [29]) of persons or institutions. here the human knowledge that goes into the fuzzy model has to be modeled properly in advance. that means, that operators ( [17]), membership functions, inference methods ( [27]) etc. have to properly map the counterparts in the human mind, in which they are very often very context dependent. this is no longer only a mathematical problem but predominantly a problem of psycho-linguistics or similar disciplines ( [7, 25]). unluckily this part of science is much less developed than the mathematics of fuzzy set theory. hence, in applications one often has to rely again on assumptions rather than on scientific results when modeling operators ( [19, 28]), membership functions ( [5, 9, 16]) and other parts of fuzzy models. the justification of assumptions, of course, also depends on whether one wants to build descriptive or prescriptive models. in the following sections we want to show, that, never-the-less, practical applications of fuzzy models in the area of business intelligence are also possible and very useful. 2 riskshield a software solution relying on fuzzy technology this chapter describes the real world software application riskshield [11] which has predominantly been developed in the years 1999 to 2010. riskshield is one of the leading tools in the european payments/banking and insurance sector to fight fraud. well-known air cargo organizations currently consider its possible use to enhance air freight security at acceptable costs. we will report on the practical use of riskshield in the mentioned industries in chapter 3. to briefly describe the solution independently of its purpose and industry in which it is used (payments/banking, insurance, air cargo) we will describe the solution rather in the context of banking, i.e. the term ’transaction’ may refer to a financial transaction in the payments sector, to a claim in the insurance sector or to a set of information concerning a parcel in the air cargo sector. 2.1 software description the basics riskshield assesses the risk of incoming transactions individually in real-time within milliseconds. the software works for the human user invisibly in the background. an it application of the client (e.g. the bank or insurance company) sends every transaction to riskshield. the software’s multi channel approach includes real-time evaluation of additional statistical data and patterns. it builds up internal histories for all relevant entities in the process. typically 430 a. meyer, h.-j. zimmermann riskshield installations include 10 to 20 profiles which are constantly updated with any incoming event. predefined groups can be established to detect any untypical behavior. fuzzy technology is used to evaluate all different aspects in many cases the combination of current information with dynamic patterns and histories is needed to identify critical events. for every incoming transaction riskshield returns a risk score which triggers the possible decision for (manual) special treatment of the transaction. depending on configuration the software product can further create a case (alarm) to be further manually investigated and returns the main indicators for the suspicion to bring the human investigators in a more comfortable position to start the special treatment of the transaction (see figure 1). every transaction is scored against the same riskshield decision system to assure consistent results. to evaluate all information provided to the system several rules and patterns of the decision system may contribute to the assessment of an individual transaction. the type of fuzzy inference used in riskshield corresponds to the inference described for fuzzytech in [15], [30, pp. 255 ff.]. other logics and techniques of riskshield riskshield utilizes several complementary techniques like fuzzy technology [10], matching of patterns against dynamic profiles, recognition of networks (see below) and external lists to ensure a good risk assessment quality. a combination of those techniques forms the decision system within the software solution to assess the risk of a single transaction, i.e. the threat potential which may be caused by the respective transaction. matching of patterns against dynamic profiles: the decision making system can use several profiles which can be defined according to the requirements. based on these profiles riskshield recognizes defined patterns in real-time which can comfortably edited by in-house risk and fraud experts without internal it support or support by the vendor inform ( [11]). recognition of networks and external lists: through riskshield it is possible to detect social and historical relationships based on historical data as well as to use external lists. the meaning of an external list can be defined within the decision system. therefore these external lists can be used in different ways, e.g. to define positive/white lists or negative/black lists. an example for a white list in internet banking systems might be a list of account numbers or a list of beneficiary accounts; in credit card issuing systems a black list might consist of bin-ranges (first x digits of the credit card number); in the air freight area a black list of addressees could be derived from a list of pep (politically exposed persons) which also receive a special treatment in riskshield applications for anti money laundering. external lists can not only form white or black lists but also any other list which is relevant for the decision making system. the entries of each list can be associated to a valid-until timestamp, if required. the combination of these complementary techniques offers the riskshield user exhaustive possibilities to build a complete safety net dealing with multi-channel fraud attacks and threats. response and alert management the risk score riskshield has returned is used for a decision and an automated workflow control (possibly special treatment of a transaction). this score represents the decision which applications of fuzzy technology in business intelligence 431 has been made regarding the current transaction (approval, referral, decline, hold movement/ hold payment). additional scores and information can be defined to be returned by the software solution, e.g. • case classes: groupings of alerts regarding the class of the transaction or the assumed type of fraud. • importance: alerts can be rated regarding their importance so that the most important alerts can be worked with first. • hints: a list of rules which had a main impact to the score of the transaction. • actions: criteria can be defined to trigger consecutive actions, e.g. send email/fax/sms, update master data, provide information to crm, get address scoring. riskshield offers the possibility to document the investigation process of the respective alert through a web front end. additionally, the system offers further analysis functionality to monitor and trace each assessment in detail (drill-down functionality). riskshield’s use of fuzzy inference combined with matching of patterns against dynamic profiles, recognition of social and historical relations and external lists provide superb fraud prevention qualities for the payments, insurance and probably also for the air cargo security sector. as a result users will obtain almost only relevant hits due to the low false positives output. research has shown that the automated identification of suspicious transactions for further investigation matches senior analyst evaluations to a very high percentage of cases. other key features of riskshield • real-time decision making – response and other actions within milliseconds – up to 4,100 single assessments per second • processing – automatic threat hit matching – analysis and simulation – analysis and complex simulation with production data (chart, table, and record filtering) – all decisions and decision criteria are transparent to the threat analyst • implementation – standard interfaces (xml, csv, jms) – preand post-processing plug-ins for quick implementation – software updates within minutes – external support not needed – backward compatibility – supports a high secure pci-dss compliant implementation (relevant to credit card business) 432 a. meyer, h.-j. zimmermann figure 1: scheme of riskshield (input data exemplarily from payments sector) 2.2 benefits of fuzzy logic from real world users’ perspective real world business users from the industries banking/payments, insurance and air cargo perceive that fuzzy technology as a basis of automated software solutions gives a competitive edge for the following reasons: • decision systems using fuzzy logic represent experience of experts adequately: fuzzy logic lets you rely on the experience of people who already understand your system. • fuzzy logic is flexible: with any given system, it is easy to layer on more functionality without starting again from scratch. • fuzzy rules consider fraud/threat and non-fraud/non-threat indicators: like a human expert all aspects of decision making can be integrated into a decision system using fuzzy rules. • decision systems using fuzzy logic are easier and more cost-effectively to maintain: before changing an existing rule or adding a new one the impact of the changes to the overall decision system has to be analyzed. as fuzzy logic systems require much less rules than conventional rule based systems the impact of possible changes can be anticipated much easier. decision systems using fuzzy logic result in shorter reaction times to new threat patterns as explained new rules can be set into productive use faster. applications of fuzzy technology in business intelligence 433 • fuzzy logic helps minimize false-positives: the concept of fuzzy logic represents fuzzy bounds. thus, the usage of hard bounds which typically result in false-positives is obsolete. • decision systems using fuzzy logic are more reliable: conventional rule bases systems have to consider a lot of special cases. one missing case may lead to completely unexpected behavior of the system. within fuzzy systems the differentiation between special cases is not needed so much. 3 fraud prevention and risk assessment in this chapter we report on the practical use of fuzzy logic through the software product riskshield (see the description in chapter 2) to prevent fraud in the payments industry (section 3.1), to detect insurance fraud (section 3.2). furthermore we describe its possible use to enhance air freight security at acceptable costs (section 3.3). 3.1 payments industry the challenge in the last few years, fraudsters have become well organized and well equipped to work professionally to attack the payments industry. they prefer to attack payment systems with a low ratio of effort/success. this is in particular the case for online banking platforms where it is possible to use automated scripts like trojans and viruses that capture the customer online banking session to communicate directly with the attacking server platform (man-in-the-middle and man-in-the-browser attack). furthermore, phishing attacks are popular as they require not much manual effort. in the past hardware based it security measurements have been sufficient for many years to protect internet banking transactions against fraud, as fraud attacks followed relatively simple patterns. this has changed completely in the last few years. hardware based it security products used by end-customers are static regarding its security functionality. that means once they have been issued they won’t change their behavior. so when it turns out in practice that the hardware products are not resistant to some online attack banks had to implement an additional security measurement or to replace at high overall costs the hardware products for all customers. today even banks with the best equipped platforms realize, that relying solely on hardware based protection is too risky (as reaction to new fraud patterns takes too much time in practice) and too expensive. the patterns different data is required to identify a fraud pattern and protect the account. we need to combine all information we have available in the environment as there are many questions to be answered. what is the usual type and nature of the customer’s transactions? which beneficiary accounts is he using and what do we know about it? does the amount of the current transaction exceed the usual payments done by this customer? are there other account holders for this account and does this transaction fit to one of their profiles? the transaction might be not typical for the account holder, but may be it is typical for the beneficiary account? what other products does the customer have and how does he use them? is he a share holder? how many cards does he have? what do we know concerning the ip address? is the ip address well known in combination with 434 a. meyer, h.-j. zimmermann this customer? or is a foreign ip address unexpected for this customer that is always using one of the domestic providers? what happens during the internet session? does the ip address change suddenly? is the usage of the online banking web application as expected? the solution the approach to effectively fight against the current sophisticated attacks should be a combination of expert knowledge ( [26]) and a generic software solution. the technical expert must have knowledge of the design, workflow, and architecture of the existing online banking platform and the business expert must have knowledge about the bank’s products and customer profiles. fortunately, fraud patterns have fingerprints that help to identify them. once the pattern is identified, a software solution is required that is capable to process all required data sources, to combine the information streams, to identify the sequence of events, to be enhanced on-the-fly, and that is connected to the existing it environment to stop the payment and to generate alerts. fuzzy technology ( [21–23]) can easily model expert knowledge about fraud patterns and put it into a software solution. for example, a rule like the following could cross the mind of a human expert and could easily be entered into a fuzzy logic based solution like riskshield: in case of a payment from a customer account with an amount of untypical high value, an ip address from a risky region instead of the well known ip address for this account, stop payment for manual intervention. an effective ant-fraud solution has to address further important issues: noise reduction: an online banking fraud prevention software solution would be useless, if the quality of the generated alerts would be so bad that it led to an inflation of alerts which hide the single valid fraud alarm. so "noise" reduction is required to get the most important alerts only. as riskshield implementations have shown, this is possible by a most powerful anti fraud pattern definition that filters out undesired alerts down to an acceptable false-positive rate. when evaluating measurable input data riskshield uses fuzzy bounds instead of hard bounds to minimize false-positives. particularly in this regard, fuzzy logic is an important underlying concept for the system. alerts: once the payment has been put to hold by riskshield the case investigation user has time to access the alert data, to verify all the data displayed, to switch the view from the account layer into the customer or beneficiary layer, and to review the profiles and statistical data. this helps to get an overview about the situation in total. actions: after investigating the case, the user has the possibility to initiate further actions like releasing or finally stopping the payment by one mouse click. other actions can be attached to the workflow as well, like an online update of the riskshield-server to have this information available for all further decisions. once all necessary actions have been initiated, the user closes the case which is then sent to the riskshield reports to check the efficiency of the case queues and users. regarding the use of fuzzy technology riskshield supports its users e.g. through graphical editors to define membership functions, to determine operators, to automatically perform the inference process and to determine a suitable defuzzyfication process. in the payments industry riskshield is also in use to detect fraud in other areas ( [4, 8]) (so-called channels) including: debit cards and credit cards from the perspective of the issuing bank or from the merchant’s or acquirer’s perspective, atm skimming, loan application and others. the software product’s multi-channel approach combines findings about a customer across different channels to better assess whether a current transaction from a specific channel applications of fuzzy technology in business intelligence 435 has indeed been authorized by the supposed customer. 3.2 insurance industry due to insurance fraud insurance companies across the globe lose significant amounts of money. the sum of total payments to fraudsters may even by far exceed the profits generated by an insurance company. insurance experts assess that, depending on the line of business and country, more than 10 percent of all claims notifications have a fraudulent background. insurance companies are nowadays battling strongly to keep customer’s affections. however, they perceive too high fraud losses to be unacceptable. regarding their claims handling process insurance companies are typically faced with the challenge to reduce settlement and handling costs of claims on the one hand and to increase customer satisfaction on the other hand. increase of customer satisfaction includes low insurance premiums and fast, unbureaucratic settlement of legitimate claims. reduction of settlement and handling costs seems to be a contradictory objective. because due to the typically high percentage of fraudulent claims, it seems insurance companies have to manually review each single claim very carefully. but this would increase the claims handling costs and would annoy the honest customers awaiting fast settlement of their legitimate claim. recently we have recognized the trend among the rather big insurance companies to look for the introduction of some kind of intelligent software which ensures that simultaneously legitimate claims are assigned to a fast settlement track and suspicious claims are assigned to senior fraud experts of the company for further manual review. to do so, the software should automatically assess each single claim and make a decision on how to proceed with the claim. the outputs of the software have to be transparent and traceable. claims handlers and fraud experts of the insurance company may rather accept a fraud detection software in case they feel confident the software assesses the claims in the same way they would do. that is why expert systems based on fuzzy technology, e.g. riskshield from inform, seem to be well suited to assess insurance claims. in doing so, riskshield takes both incriminating and exonerating indicators into account. the system balances these indicators as a human expert would do and finally consolidates them to an overall risk score. expert rules are formulated in a linguistic if-then context, similar to the human language. furthermore, riskshield gives additional outputs to indicate the reason of the assessment or to advice what to do next in terms of claims handling. moreover, riskshield checks, as a human claims handler would do, for each incoming claim whether the claim related information is already sufficient for a qualified evaluation. the process of gauging like a human expert when evaluating a claim is illustrated in the following example: • a new claim, let’s see . . . • a new contract . . . (incriminating) • a 2,000 claim, thus a minor loss of property . . . (exonerating) • a brand new mercedes . . . (incriminating) • no police on scene . . . (incriminating) • the car will be repaired at a partner workshop . . . (exonerating) • a minor damage with no injuries . . . (exonerating) • the policy holder has had only few previous claims . . . (exonerating) 436 a. meyer, h.-j. zimmermann • it is a long time customer re other lines of business . . . (exonerating) • conclusion: i think we should pay this claim. inform typically delivers its product riskshield accompanied by consulting services. these services include the provision of customized fraud detection and claims assessment logics which turned out to combine superb fraud detection quality with minimum false positives: claims marked by riskshield as suspicious for further investigation match senior analyst evaluations typically in more than 90riskshield’s fraud detection rules consider different types of information like the policy holder, the claims history, the policy itself, the policy history, the loss, and others. fuzzy technology is useful in the given context to reflect human thinking and to use soft boundaries instead of crisp boundaries which in general will be easily explored and misused by fraudsters. an insurance claim automatically classified by riskshield as inconspicuous can be settled faster and more cost-effectively and so strengthening customer loyalty. the resulting saving in time at claims handlers and senior fraud experts for a detailed investigation of claims which have been classified as unusual leads to the reduction of fraud losses which can save a insurance company millions every year. the fuzzy technology based solution riskshield is successfully used in more than 35 insurance portfolios across europe and north america for automatic claim evaluation and fraud risk assessment. recently the software solution has also been applied to detect medical billing fraud. in a pilot scheme for one of the leading us car insurance companies inform analyzed through riskshield four years of data of all medical billing claims submitted to the insurance company in one us state. inform figured out that approximately 18% of all medical bills are to a significant extent incorrect resp. fraudulent. 3.3 air freight security the latest terror attack attempts using explosive parcels in air freight result in the need to increase security through extended security controls of the parcels at affordable additional costs. a manual reliable security check of every parcel to be delivered cannot be implemented in practice from both organizational and costs perspectives. this subsection describes the idea of applying software-based monitoring solutions which have been proven in the payments/banking sector to fight fraud and to detect irregular patterns, to the air freight sector to identify in realtime suspicious parcels to be delivered. it is reasonable to assume that an air cargo organization already has the required information like sender, addressee, place of posting, freight declaration and other specifications electronically available and possibly stored in one central or a few central applications or data bases. the combination of such pieces of information plus some specific knowledge of the typical behavior of geographical regions as senders (typical previous movements) and the parcels an addressee typically receives should be sufficient from our view to identify suspicious items of freight. to fully automate this process air cargo organizations may consider the use of inform’s software riskshield which relies on fuzzy logic as an underlying core technology to keep false alarms acceptably low. items which have been identified by riskshield as suspicious will in the air cargo scenario be referred for manual investigation. air cargo companies currently consider the use of the software solution as it allows economically justifiable close checks to increase airfreight security to a much higher level. in case an air cargo organization already have implemented comprehensive x-ray checks, possibly for every single parcel due to regulatory provisions like from luftfahrtbundesamt in germany, a software like riskshield would applications of fuzzy technology in business intelligence 437 nevertheless increase the security level as in practice one possibly cannot trust in the reliability of a single x-ray check, especially if such x-ray checks have been taken abroad in a "risky country". 4 customer segmentation in financial services 4.1 crisp vs. fuzzy clustering banks have different products to offer to their customers ( [14, pp. 3-9]), they have (very) different customers for which the products are differently suited, and they have a lot of information (data) of their customers. an efficient marketing of these products requires, as in other markets, a segmentation of the customers such that only suitable products are offered to the appropriate class of customers. even to day often this segmentation is performed by using single criteria such as age, property, income etc. this obviously does not lead to adequate classes of customers. a better way to classify customers is to use classical clustering, in which a number of attributes can be used to describe a customer. this, however, leaves the problem of objects (e.g. customers) that can not be assigned unequivocally to any particular cluster. with fuzzy cluster analysis ( [18]), such as the fuzzy c-means algorithm ( [2, 3]), each customer is not assigned to one and only one cluster but rather with different degrees of membership to each class or segment. for outlyers the degrees of membership might not be meaningful, but the use of possibilistic clustering ( [12]) cures even this weakness by normalizing the sum of all assigned degrees of membership for each element (customer) to be 1. 4.2 segmenting customers of banks let us consider a (smaller) data base of 120,000 records of customers, from which 300 records were samples randomly ( [14, p. 3]). the sample records contain all the data fields which are available via the bank’s customer information system, such as: age, sex, marital status, number of children, income, assets, information on other banking activities and also the bank’s profit margin for each individual customer. the next step would be to select those features from all the characteristic describing a customer, which are relevant for customer segmentation. after discussions with experts the following features may be relevant: • age (in years) • net monthly income • financial assets deposited with the bank • loans taken out from the bank • annual marginal gain for the bank. this value denotes the sum which the bank earns or loses from the individual customer per year. now the optimal number of clusters has to be determined. a number of criteria have to be satisfied: it should be possible to interpret the different clusters in terms of the expert’s vocabulary; the classification should make sense in terms of the different products the bank wants to sell, etc. technically two measures vary with the number of clusters: the partition coefficient (increasing with the number of clusters) and the partition entropy (falling with the number of clusters). both tend towards monotone behavior depending on the number of clusters. as the optimal number of clusters can be considered the number where the entropy lies below the rising trend and where the value of the partition coefficient lies above the falling trend. the "optimal" number of clusters is determined by simulation. in our case the best number of clusters is 7 and 438 a. meyer, h.-j. zimmermann the cluster centers are as shown in figure 2 (from [14, p. 16]). in this table the clusters have already been "labeled" by experts. figure 3 (from [14, p. 17]) shows the membership values of the cluster centers. it is obvious, that a crisp clustering would never have arrived at such a classification. the rest of the customers can now be classified according to the 7 classes. this can be done directly by using dataengine or normally by the respective c-means algorithm in the software system of the bank. the goal of such a classification run can either be to segment customers in a databank or to assign a new customer to any of these classes. customers are not exclusively assigned to one cluster, but they may belong to several clusters to different degrees. this corresponds more to reality, even though the labels of the clusters may suggest that the clusters are highly homogeneous with respect to the elements in them. since the segmentation process does not lead to a once and for all segmentation ( customers become older, richer or change their position) a repetition of the process may lead to other clusters and to other customers in existing clusters. this dynamic segmentation ( [1]) may also indicate changes in the structure of the customers which otherwise could not be recognized. to avoid misunderstandings it should be mentioned, that the customer segmentation described above is only the core of a larger "financial suite" that is used in banks. there is an extensive preprocessing of customer data before segmentation and the results of the segmentation are then used for several purposes, which will be sketched in the next section. figure 2: centers of the 7 clusters 4.3 extending customer segmentation to other areas of banking one of uses could be the allocation of recourses (sales persons, advisors, etc.) to different classes of customers. after they have been assigned to the different segments, profit potentials can be determined for customer classes or single customers. on the basis of these potentials targets for salespersons can be defined, which can then be used to judge the performance of sales persons in different segments. at the same time sales campaigns can be designed and planned for different products or different segments. so far the analysis (segmentation) and the measures derived from them were static, i.e. they investigated structures at a certain point in time. the management of banks is, however, not only interested in static structures but also in the dynamic behavior of customers. they might want to know, for instance, when a customer will leave the bank, long before that happens. only then counter measure can be taken. this leads to one major complication: customers are now no longer defined by numerical or other features (at a certain point in time), but customer behavior is defined by vectors or functions over time. classical fuzzy cluster methods, however, cluster points and not functions. hence, either those methods have to be modified or new methods have to be developed. it would exceed the scope of this paper to describe in detail these methods and their application to dynamic problems of applications of fuzzy technology in business intelligence 439 figure 3: bar chart of the cluster centers banks. the interested reader is referred to angstenberger ( [1]) or zimmermann ( [30]) for more detailed descriptions and examples of applications. 5 conclusions mathematically fuzzy set theory has been developed very far. the application of fuzzy control in engineering problems is also quite accepted and wide spread. in business intelligence, however, applications of fuzzy technology are still rather scarce, even though the original motivation and justification of fuzzy set theory was to model and improve human communication. one reason for this might be, that the appropriate modeling of human e is still difficult and context dependent rather than generic. more contributions of psycho-linguistics in this direction are still desirable. another reason may be, that the successful application of fuzzy technology into management systems requires the acceptance of this technology by managers. this in turn is strengthened by considering management needs, views, and attitudes already when developing applications in this area. successful pilot applications my also be useful. in this paper such applications are described in the area of financial management. technologically fuzzy expert systems as well as fuzzy cluster approaches are used. it would have exceeded the scope of this paper to describe the theoretical bases on which the applications rest in detail. we have rather tried to stress those features which are considered to be important from a manager’s point of view. all the applications described are real world applications which have shown to be very successful. we hope to have presented attractive problem solutions which can be used as pilot applications and, hence, increase the application of fuzzy technology in the management area. 440 a. meyer, h.-j. zimmermann bibliography [1] l. angstenberger, dynamic fuzzy pattern recognition with applications to finance and engineering, kluwer academic publishers, boston, dodrecht, london, 2001 [2] j.c. bezdek, pattern recognition with fuzzy objective function algorithms, new york, london, 1981 [3] j.c. bezdek, j.d. harris, fuzzy partitions and relations, fuzzy sets and systems 1, 111-127, 1978 [4] b.bouchon-meunier, r.r.yager, l.a.zadeh (edtrs.) uncertainty in intelligent and information systems, world scientific, singapore, 2000 [5] h. dishkant, about membership function estimation, fuzzy sets and systems 5, pp.141-147, 1981 [6] d. dubois, h. prade, a review of 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zimmermann, fuzzy set theory and inference mechanism, in: g.mitra (edr.) mathematical models for decision support, berlin , heidelberg 1988 [28] h.-j. zimmermann, p.zysno, latent connectives in human decision making, fuzzy sets and systems 4, pp.37-51, 1980 [29] h.-j. zimmermann, p. zysno, decisions and evaluations by hierarchical aggregation of information, fuzzy sets and systems 10, pp.243-266, 1983 [30] h.-j. zimmermann, fuzzy set theory and its applications, fourth edition, boston, dodrecht, london, 2001 international journal of computers communications & control issn 1841-9836, 12(5), 631-644, october 2017. an uncertainty measure for interval-valued evidences w. jiang, s. wang wen jiang*, shiyu wang school of electronics and information, northwestern polytechnical university xi’an, shaanxi province, 710072, china *corresponding author: jiangwen@nwpu.edu.cn jiangwenpaper@hotmail.com, wangshiyu@mail.nwpu.edu.cn abstract: interval-valued belief structure (ibs), as an extension of single-valued belief structures in dempster-shafer evidence theory, is gradually applied in many fields. an ibs assigns belief degrees to interval numbers rather than precise numbers, thereby it can handle more complex uncertain information. however, how to measure the uncertainty of an ibs is still an open issue. in this paper, a new method based on deng entropy denoted as uiv is proposed to measure the uncertainty of the ibs. moreover, it is proved that uiv meets some desirable axiomatic requirements. numerical examples are shown in the paper to demonstrate the efficiency of uiv by comparing the proposed uiv with existing approaches. keywords: dempster-shafer theory, interval-valued belief structure, interval evidence, uncertainty measure, deng entropy. 1 introduction dempster-shafer evidence theory, also known as d-s theory was proposed by dempster [8] and extended by shafer [45], it has received widespread attention and application in information processing [18,25,40,43,46,52]. as compared with classic probability theory, d-s theory allocates the belief to multi-subset proposition and does not require a priori information. accordingly, d-s theory is used to process the uncertain information in many fields such as risk assessment [16,24,39,60], decision making [4,7,11,36,38,58], fault diagnosis [20,26,27,41,48,51], information fusion [2,9,12,19,35] and pattern classification [3,42,44,55]. although the application of d-s theory has made considerable progress, there are still some common issues in urgent need to be solved. for instance, conflict processing should be taken into consideration when the obtained evidence is highly conflicting with each other [28,30,37,53], for we may get the count-intuitive results [29, 59]. in view of this, many scholars have carried out extensive and profound research. denœux [15] considered the evidence expressed by fuzzy-valued which acquire lots of application [57]. moreover, the classic d-s theory demands precise belief degrees, yet it is not always available in some cases. for instance, in the decision making, the experts sometimes cannot provide an accurate assessment because of the lack of information. at this time, an interval-valued belief structure (ibs) [56] is more suitable for dealing with the uncertainty problem. about extending the d-s theory to ibs, many scholars have carried out some research such as denœux [14] put forward a set of concepts about interval-valued belief structure and initially explored the combination and the uncertainty of it. lee & zhu [34] proposed the combination of two interval evidence. wang [54] proposed the approach to combine and standardize the interval evidence in one step. however, it must be noted that there are still many unresolved issues about interval-valued belief structure. one of the crucial issues is uncertainty measurement [10,50]. from the perspective of information theory, klir elaborated the inner relationship between uncertainty and information [33]. copyright © 2006-2017 by ccc publications 632 w. jiang, s. wang bronevich [5,6] discussed some of the issues and applications of the measurement of the uncertainty for imprecise probabilities. however, even how to measure the uncertainty of the mass function in d-s theory is still a considerable issue [21,23]. dubois & prade presented weighted hartley entropy [17] to express the non-specificity of bpa. klir & wierman [32] explored five axiomatic requirements for the uncertainty measures including range, probabilistic consistency, set consistency, additivity and subadditivity, respectively. abellán & masegosa [1] have extended the axiomatic approach by appending new monotonicity requirement. among existing uncertainty measures, aggregated uncertainty (au) [22] and ambiguity measure (am) [31] are two representative measures, yet they have their own shortcomings, such as low sensitivity and high computing complexity. deng entropy [13] divided the belief for each focal element into all potential subsets. on the other hand, there is not many approaches about the uncertainty measure for interval-valued belief structure. denoeux [14] proposed a rudiment to measure the uncertainty, yet it was immature and lacked the mathematical proof. song [49] defined the axiomatic requirements for uncertainty measure and presented a new method iu to measure the uncertainty. but iu lost part of the information and may cause the counter-intuitive result because of the transformation from belief structures to probability distributions. accordingly, how to effectively measure the uncertainty of interval-valued belief structure is still an open issue. in this paper, a new method based on deng entropy to measure the uncertainty of the interval-valued belief structure and its axiomatic proof is presented as well. several examples are shown to illustrated the rationality and effectiveness of the method. the remainder of this paper is organized as follows. section 2 starts with a brief presentation of d-s evidence theory and some other indispensable related concepts. in section 3, we present a new method to measure the uncertainty of the interval-valued belief structure. some numerical examples are given to demonstrate the validity of our new method in section 4. conclusions are summarized in section 5. 2 preliminaries 2.1 dempster-shafer evidence theory dempster-shafer evidence theory, as introduced by demster [8] and expanded later by shafer [45], has been widely used in dealing with uncertainty. some basic concepts in d-s theory are introduced as follows. let θ be a finite set of worlds, which is called a frame of discernment (fod). θ consists of some propositions, which are mutually exclusive and exhaustive, and indicated by θ = {θ1,θ2, . . . ,θi, . . . ,θn}. (1) let 2θ be the power set of θ, namely 2θ = {∅,θ1,θ2, . . . ,θn,{θ1 ∪θ2}, . . . ,{θ1 ∪θ2 ∪·· ·∪θi}, . . . , θ}. (2) for a fod θ, a mass function is a mapping m : 2θ → [0, 1], it is also called the basic probability assignment (bpa) or the belief structure. bpa must satisfy the following condition{ ∑ a∈2θ m(a) = 1, m(∅) = 0. (3) for a bpa, its belief function bel : 2θ → [0, 1] is defined as bel(a) = ∑ b⊆a m(b), (4) an uncertainty measure for interval-valued evidences 633 the plausibility function pl : 2θ → [0, 1] is defined as pl(a) = 1 −bel(ā) = ∑ b∩a6=∅ m(b). (5) assume there are two bpas m1 and m2 with the same fod, it can be combined by dempster’s combination rule. m(a) = 1 1 −k ∑ b∩c=a m1(b)m2(c), (6) where k = ∑ b∩c=∅ m1(b)m2(c). (7) k is between [0,1], which is called the coefficient of conflict. when k = 1, dempster’s combination rule will be invalid. 2.2 interval-valued belief structure uncertainty is sometimes no longer described by a unique belief structure, but by a convex set of belief structures verifying certain constraints. a set of concepts of interval-valued belief structure (ibs) is given as follows [14]. let θ be the frame of discernment, f1,f2, . . . ,fn be n subsets of θ and [ai,bi] be n intervals with 0 6 ai 6 bi 6 1, (i = 1, 2, . . . ,n). an interval-valued belief structure (ibs) m is a belief structure on θ such that ai 6 m(fi) 6 bi, (8) where 0 6 ai 6 bi 6 1, i = 1, 2, . . . ,n, (9) n∑ i=1 ai 6 1 and n∑ i=1 bi > 1, (10) m(a) = 0 ∀a /∈{f1,f2, . . . ,fn}. (11) obviously, m are non-empty imposes certain constraints on the ai and bi. if the singleton m is an ibs with ai = bi = m(fi) for ∀fi, m degenerates to a precise belief structure (bs). an ibs means the interval associated to each subset of θ is [0,1]. it may be interpreted as reflecting “second-order” ignorance, that is, ignorance of what the state of belief of an agent may be. let m be an interval-valued belief structure, namely ai 6 m(fi) 6 bi for i = 1, 2, . . . ,n. if ∀k ∈{1, 2, . . . ,n}, ai and bi satisfy n∑ i=1 ai + (bk −ak) 6 1, (12) n∑ i=1 bi − (bk −ak) > 1. (13) then, m is called a normalized interval-valued belief structure (nibs) [54]. for a non-normalized interval-valued belief structure m, which violates eq. (10), it can be normalized by following equations. âi = ai ai + ∑n j=1,j 6=i bj , i = 1, 2, . . . ,n, (14) 634 w. jiang, s. wang b̂i = bi bi + ∑n j=1,j 6=i aj , i = 1, 2, . . . ,n. (15) on the other side, if m has already satisfied eq. (10), but not eqs. (12) and (13), it can be normalized by following two equations. âi = max  ai, 1 − n∑ j=1,j 6=i bj   , i = 1, 2, . . . ,n, (16) b̂i = min  bi, 1 − n∑ j=1,j 6=i aj   , i = 1, 2, . . . ,n. (17) the concepts of belief function and plausibility function may easily be generalized to an interval-valued belief structure. since these quantities are linear combinations of belief masses constrained in closed intervals, their ranges are both closed intervals. let m be a normalized interval-valued belief structure on θ. for ∀a ∈ θ, its belief function and plausibility function are defined respectively as bel(a) =  min ∑ fi⊆a m(fi), max ∑ fi⊆a m(fi)   , (18) pl(a) =  min ∑ fi∩a 6=∅ m(fi), max ∑ fi∩a 6=∅ m(fi)   , (19) where min ∑ fi⊆a m(fi) = max   ∑ fi⊆a ai,  1 − ∑ fi 6⊂a bi     , (20) max ∑ fi⊆a m(fi) = min   ∑ fi⊆a bi,  1 − ∑ fi 6⊂a ai     , (21) min ∑ fi∩a 6=∅ m(fi) = max   ∑ fi∩a 6=∅ ai,  1 − ∑ fi∩a=∅ bi     , (22) max ∑ fi∩a 6=∅ m(fi) = min   ∑ fi∩a 6=∅ bi,  1 − ∑ fi∩a=∅ ai     . (23) 2.3 deng entropy since shannon entropy [47] was proposed to quantify the expected value of the information volume contained in a message, it has became a significant approach to measure the uncertainty. however, for a mass function in d-s theory, shannon entropy cannot calculate its uncertainty because the mass function includes multiple subset elements. to measure the uncertainty of the mass function, deng [13] proposed deng entropy as follows ed(m) = − ∑ a⊆θ m(a) log2 m(a) 2|a| − 1 , (24) an uncertainty measure for interval-valued evidences 635 where m is a bpa defined on the frame of discernment θ, a is the focal element of m, and |a| is the cardinality of a. deng entropy is analogous with the classical shannon entropy, but the belief for each focal element a is divided by (2|a| − 1) which indicates the potential supports in a. 3 proposed uncertainty measure for interval-valued belief structures in an interval-valued belief structure, the belief degree for each subset is not a precise value but an interval. so contrasted with single-valued belief structures, an interval-valued belief structure is more vague and more uncertain, since an ibs has the “second-order” ignorance. thus, how to measure the uncertainty of the ibs is an essential issue. in this paper, a new method to measure the uncertainty of ibs is proposed. definition 1. let m be a normalized interval-valued belief structure on the frame of discernment θ = {f1,f2, . . . ,fn}, and it satisfies ai 6 m(fi) 6 bi, which means the accurate belief m(fi) ∈ [ai,bi]. then the uncertainty measure of the ibs m is as follows uiv (m) = 2n∑ i=1 [ min m(fi)∈[ai,bi] ẽd(fi), max m(fi)∈[ai,bi] ẽd(fi)], (25) where ẽd(fi) = −m(fi) log2 m(fi) 2|fi|−1 , (26) and |fi| is the cardinality of fi. the new measurement method we proposed is based on deng entropy, not shannon entropy, so our method is more suitable to handle the proposition of multi-subsets. for deng entropy, the belief of the focal element m(fi) is divided by the number of potential subsets 2|fi| − 1 that demonstrates the non-specificity of the evidence. the more single elements are contained in focal elements, it is obvious that the greater the uncertainty. the term −m(fi) log2 m(fi) is analogous to shannon entropy and is the measure of discord of the evidence. thereby, it is also appropriate to quantify the uncertainty of interval-valued belief structure. obviously, uiv is an interval number. its value embodies the belief distribution of different proposition in ibs, and its interval length reflects the ambiguity generated by the belief expressed in intervals. song [49] proposed the axiomatic requirements for a measure of uncertainty for a normalized interval-valued belief structure m. theorem 2. let u be a measure of uncertainty for a normalized interval-valued belief structure m on the fod θ = {θ1,θ2, . . . ,θn}, then u must content the following condition. 1. whenever the nibs defines a precise probability distribution, u degenerates to shannon entropy. 2. when the nibs assigned to all subsets of θ are completely unknown, its uncertainty is maximum. thus, u reaches its maximum value. 3. if the nibs assigns to a certain singleton of θ is 1, the uncertainty of it is 0. therefore, u gets its minimum value 0. it will be shown that our new method satisfies the above-mentioned axiomatic requirement. proof: 636 w. jiang, s. wang 1. if the nibs m defines a precise probability distribution on θ = {f1,f2, . . . ,fn}, uiv (m) = 2n∑ i=1 [ẽd(fi), ẽd(fi)] = n∑ i=1 −m(fi) log2 m(fi) 2|1| − 1 = − n∑ i=1 m(fi) log2 m(fi). from the above equation, we can see that when m defines a precise belief structure on θ, uiv degenerates to deng entropy. moreover, when m defines a precise probability distribution, uiv degenerates to shannon entropy. 2. when the nibs assigned to all subsets of θ are completely unknown, that is for ∀fi ∈ 2θ, [ai,bi] = [0, 1]. it is apparent that uiv (m) = 2n∑ i=1 [ min m(fi)∈[0,1] ẽd(fi), max m(fi)∈[0,1] ẽd(fi) where ẽd(fi) = −m(fi) log2 m(fi) 2|fi|−1 , and it can be seen as a function of fi, now the independent variable fi is ∈ [0, 1]. therefore, the minimum value of ẽd(fi) is 0 and the maximum value may be mutative with the change of |fi| yet it can always get its maximum value for any fi, that is max m(fi)∈[0,1] ẽd(fi) = max ẽd(fi) so, uiv (m) = [0, 2n∑ i=1 max ẽd(fi)]. in this case, the value and the interval length of uiv are both the maximum value, which indicates that m is totally uncertain, that is, its uncertainty is maximum. 3. if the nibs assigns to a certain singleton of θ is 1, there is no harm in supposing that for singleton fk, m(fk) = 1, and the belief degree of all the rest subsets is 0. then uiv (m) = 2n∑ i=1 [ min m(fi)∈[ai,bi] ẽd(fi), max m(fi)∈[ai,bi] ẽd(fi)] = [ min m(fi)∈[1,1] ẽd(fk), max m(fi)∈[1,1] ẽd(fk)] + 2n∑ i=1 i 6=k [ min m(fi)∈[0,0] ẽd(fi), max m(fi)∈[0,0] ẽd(fi)] = −1 × log2 1 21 − 1 − 2n∑ i=1 i 6=k (0 × log2 0 2|fi| − 1 ) = 0 in fact, the uiv at this time is not 0, but [0,0]. this result thoroughly explains the m under this circumstance is totally definite, and it is also in line with intuition. an uncertainty measure for interval-valued evidences 637 table 1: nibss in example 3 {f1} {f2} {f3} {f1,f3} m1 [0.2,0.3] [0.1,0.35] [0.4,0.6] [0,0] m2 [0.2,0.3] [0.1,0.35] [0.35,0.7] [0,0] m3 [0.2,0.3] [0.1,0.35] [0,0] [0.4,0.6] m4 [0.2,0.3] [0.1,0.35] [0.2,0.3] [0.2,0.3] the number of the nibs 1 2 3 4 u iv 0 0.5 1 1.5 2 2.5 3 figure 1: the uiv of each nibs in example 3 2 4 numerical examples in this section, several examples are given to demonstrate the effectiveness of uiv . example 3. assume a frame of discernment θ = {f1,f2,f3}, and consider four nibss defined as shown in table 1. we can calculate the uiv of the nibss as follows uiv (m1) = [1.239, 1.580] uiv (m2) = [1.157, 1.583] uiv (m3) = [1.959, 2.444] uiv (m4) = [2.042, 2.569] and they are also graphically shown in fig. 1. the yellow portion represents the endpoint of the interval of the uiv . the range of uiv (m2) is larger than uiv (m1) from the figure, since m2(f3) is more uncertain than m1(f3). however, the value of uiv (m2) is close to uiv (m1) because the belief distribution in m1 and m2 are about the same. considering uiv (m3) and uiv (m1), it is obvious that both the length and the value of uiv (m3) are bigger since the 638 w. jiang, s. wang table 2: uiv in example 4 cases uiv a={1} [2.080,3.803] a={1,2} [3.216,4.886] a={1,2,3} [3.949,5.864] a={1,2,. . . ,4} [4.609,6.743] a={1,2,. . . ,5} [5.238,7.581] a={1,2,. . . ,6} [5.851,8.400] a={1,2,. . . ,7} [6.458,9.209] a={1,2,. . . ,8} [7.062,10.013] a={1,2,. . . ,9} [7.663,10.816] multi-element can take along more uncertainty than single element even though in the same interval. it is worth noting that compared with uiv (m3), uiv (m4) is close but slightly larger. although a great deal of belief are assigned on the multi-element in m3 and it conveys illegibility, the allocation form which distributes the belief to more subsets is more excursive and this result is we take for granted. example 4. suppose that we have a frame of discernment θ = {1, 2, . . . , 10}. a nibs m is shown as follows. m(2, 3) = [0.1, 0.25], m(a) = [0.6, 0.8], m(θ) = [0.1, 0.2] where a is a varying subset of θ. a starts at a = {1}, increases one more element every time and ending with a = {1, 2, . . . , 9}. the uiv of m are shown in table 2 and fig. 2. the yellow portion represents the endpoint of the interval of the uiv . from fig. 2, the result shows that uiv increases monotonically with the number of elements in a. this is rational because the more elements contained in a subset, the more uncertain it is. from the example it can be seen that uiv is capable of reflecting such a feature. in the first two examples, some superior properties are demonstrated. then an example from song [49] are used to illustrate our proposed uiv and contrast it with song’s uncertainty measure iu. the formula of song’s measurement are shown as follows. definition 5. let m be a normalized interval-valued belief structure on the fod θ = {f1,f2, . . . ,fn}, and it satisfies ai 6 m(fi) 6 bi. then iu of the ibs m is as follows iu(m) = n∑ i=1 (− ai + bi 2 log2 ai + bi 2 + bi −ai 2 ) (27) example 6. the example song used in the paper is shown in table 3, and to make a comparison with song’s method, the consequents of iu and our new method uiv are both demonstrated in table 4. for the nibss from m1 to m5, we can see their belief intervals are completely consistent, merely the corresponding subsets are disparate. the uncertainty degree iu proposed by song, are so similar that it is difficult to measure the uncertainty accurately. moreover, the belief assignment of m1 and m5 are entirely different, yet their iu are almost identical. for uiv , m5 with more belief assigned to multiple elements has a higher uncertainty, m2 and m3 take an uncertainty measure for interval-valued evidences 639 the size of a 1 2 3 4 5 6 7 8 9 u iv 0 2 4 6 8 10 12 figure 2: uiv in example 4 table 3: nibss in song’s example (θ = {f1,f2,f3}) {f1} {f2} {f3} {f1,f2} {f1,f3} {f2,f3} {f1,f2,f3} m1 [0.2,0.4] [0.1,0.3] [0.3,0.6] [0,0.1] [0,0] [0,0] [0,0] m2 [0.2,0.4] [0,0] [0,0] [0,0.1] [0.3,0.6] [0,0] [0.1,0.3] m3 [0,0] [0.1,0.3] [0,0] [0,0.1] [0.2,0.4] [0.3,0.6] [0,0] m4 [0,0] [0,0] [0.3,0.6] [0,0.1] [0.3,0.6] [0.1,0.3] [0.2,0.4] m5 [0,0] [0,0] [0,0] [0,0.1] [0.3,0.6] [0.1,0.3] [0.2,0.4] m6 [0,1] [0,1] [0,1] [0,0] [0,0] [0,0] [0,0] m7 [0,0] [0,0] [0,0] [0,0] [0,0] [0,0] [0,1] table 4: iu and uiv of the nibss iu uiv m1 1.930 [1.239,2.073] m2 1.609 [2.074,3.778] m3 1.889 [2.110,3.568] m4 1.575 [1.714,3.181] m5 1.939 [2.513,4.532] m6 3 [0,1.592] m7 1.793 2.807 640 w. jiang, s. wang table 5: nibss and their iu and uiv in example 7 {f1} {f2} {f1,f2} iu uiv m1 [0.1,0.25] [0.3,0.4] [0.4,0.5] 1.163 [2.016,2.323] m2 [0.2,0.3] [0.4,0.45] [0.2,0.4] 1.163 [1.764,2.213] second place, as well as m1 is the most precise of these nibss. furthermore, uiv is suitable for measurement for the reason that the difference in calculated values is significant and thus has a degree of discrimination. another detail of concern is m6 and m7. the uncertainty of m7 is low, while the maximum uncertainty degree occurs on m6. the cause of this consequence as song said in [49], “this is caused by the transformation from belief structures to bayesian belief structures, which will cause information loss.” uiv (m6) is comparatively small because m6 only distribute the belief to singleton. in addition, m7 actually is not a normalized interval-valued belief structure. it turns into a nibs m7({f1,f2,f3}) = 1 by eqs. (16) and (17). after standardization, uiv (m7) is a precise number and its uncertainty can be effectively measured. example 7. let a frame of discernment be θ = {f1,f2}. two nibss, their iu and uiv are shown in table 5. we can calculate that both two bayesian belief structures of m1 and m2 are m(a) = [0.3, 0.5], m(b) = [0.5, 0.65], and iu is not competent to measure the uncertainty in this situation. because for two unrelated nibss with significant differences in the degree of uncertainty, their iu are equivalent. through the above analysis, it is found that uiv is more reasonable to measure the uncertainty of the interval-valued belief structures. 5 conclusion d-s theory has been widely used in information processing and information fusion. in many applications, we can only obtain an interval-valued belief structure instead of a basic probability assignment defined on single values, due to lack of information and some other reasons. it is indispensable to measure the uncertainty of the ibs, there is still an open issue. the main contribution of this paper is a new method based on deng entropy, uiv is proposed to measure the uncertainty of an ibs. it is proved that uiv meets some axiomatic properties. numerical examples are illustrated to show the effectiveness of uiv and discuss its characteristic. moreover, it is found that uiv is more reasonable and sensitive in comparison with existing methods. acknowledgments the work is partially supported by national natural science foundation of china (grant no. 61671384), natural science basic research plan in shaanxi province of china (program no. 2016jm6018), aviation science foundation (program no. 20165553036), fundamental research funds for the central universities (program no. 3102017oqd020). an uncertainty measure for interval-valued evidences 641 bibliography [1] abellán j., masegosa a. 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[60] zhang x., mahadevan s., deng x.(2017); reliability analysis with linguistic data: an evidential network approach, reliability engineering & system safety, 162, 111–121, 2017. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(3), 419-436, june 2019. reliability assessment model for industrial control system based on belief rule base y.h. wang, p.l. qiao, z.y. luo, g.l. sun, g.z. wang yuhe wang, peili qiao*, zhiyong luo, guanglu sun, guangze wang school of computer science and technology harbin university of science and technology harbin 150080, china *corresponding author: qiaopl@hrbust.edu.cn guangze wang library harbin university of science and technology harbin 150080, china abstract: this paper establishes a novel reliability assessment method for industrial control system (ics). firstly, the qualitative and quantitative information were integrated by evidential reasoning(er) rule. then, an ics reliability assessment model was constructed based on belief rule base (brb). in this way, both expert experience and historical data were fully utilized in the assessment. the model consists of two parts, a fault assessment model and a security assessment model. in addition, the initial parameters were optimized by covariance matrix adaptation evolution strategy (cma-es) algorithm, making the proposed model in line with the actual situation. finally, the proposed model was compared with two other popular prediction methods through case study. the results show that the proposed method is reliable, efficient and accurate, laying a solid basis for reliability assessment of complex icss. keywords: belief rule base (brb), industrial control system (ics), evidential reasoning (er), reliability assessment, covariance matrix adaptation evolution strategy (cma-es) algorithm. 1 introduction the industrial control system (ics) integrates computer technology, communication and the control theory [9]. it is widely used in infrastructure industries like power transmission, water supply, oil and gas transportation, etc. in the ics, each controller regulates multiple components. the fault or intrusion of one component will threaten the security of the entire system. once the ics fails, the user will not only lose monitoring and control, but also suffer from facility damage, economic losses and even casualties. therefore, it is of great significance to accurately assess the reliability of the ics, especially in complex applications [12]. the existing ics reliability assessment methods are mainly based on knowledge, model or data [18]. the knowledge-based methods include fault mode, effects, and criticality analysis (fmeca) [10], fault tree [8], decision tree [1], and risk analysis [15]. these approaches mainly rely on qualitative or quantitative knowledge. the model-based methods, namely, open-switch fault diagnosis [20], signal-based coding [11] and processing fault diagnosis [12], can diagnose the faults of different systems according to the actual industrial processes. however, it is difficult to build effective models for large-scale icss. the data-based methods emerge due to the proliferation of industrial data collection techniques, such as distributed control system (dcs) and supervisory control and data acquisition (scada). relying on historic process data, many databased methods are suitable for ics fault detection, e.g. shewhart individuals control chart [5], hotelling’s t-squared (t 2) control chart [2], quality control chart [17], principal component analysis (pca) [16], and knowledge discovery in databases (kdd) [19]. copyright ©2019 cc by-nc 420 y.h. wang, p.l. qiao, z.y. luo, g.l. sun, g.z. wang each type of the above methods has its defects. for knowledge-based methods, the experts are often unable to obtain the accurate qualitative knowledge, owing to the complexity of the ics and the numerous factors [13]. the model-based methods depend heavily on specific samples and cannot be extended easily to general cases. neither can they use qualitative or quantitative data. the data-driven methods work well in reliability assessment of accurate data samples, but perform poorly in differentiating normal data from abnormal data [14]. what is worse, the ics data cannot be acquired under certain conditions. to sum up, the existing methods cannot effectively utilize all the various uncertain information in the ics, including expert knowledge and historical data. it is imperative to develop an approach to assess ics reliability against these semi-quantitative data. to solve the above problems, this paper integrates qualitative and quantitative information by evidential reasoning (er) rule, and then establishes an ics reliability assessment model based on belief rule base (brb), which is a powerful nonlinear strategy for uncertainty problems [6] [22]. in this way, both expert experience and historical data were fully utilized in the assessment. finally, the initial parameters were optimized by covariance matrix adaptation evolution strategy (cma-es) algorithm, making the proposed model in line with the actual situation [7]. the remainder of this paper is organized as follows: section 2 analyzes the reliability of the ics; section 3 sets up a brb-based model and applies it to assess the ics reliability; section 4 compares the proposed model with two other methods through a case study on a tobacco factory; section 5 wraps up this paper with several meaningful conclusions. 2 problem overview 2.1 ics structure hmi engineering workstations data historian control server or mtu communications routers control center power line communication radio, microwave or cellular communication satellite communicatio n wide area network field site 1 field site 2 field site 3 modem modem pl c ie d rtu wan interface card figure 1: typical scada structure consider scada as a typical ics, the structure of scada is illustrated in figure 1 [21]. in the control center, there are a human-machine interface (hmi), an engineering workstation, a data historian, a master terminal unit (mtu) and a communication router. all these components are connected via a local area network (lan). among them, the hmi mainly displays the data collected from the site, while the mtu stores and processes input and output data. in the wide area network, the information is transmitted between the control center and the field sites via power line, radio, microwave, cellular or satellite. on the field sites, there are multiple distributed remote terminal units (rtus) or programmable logic controllers (plcs), which control local reliability assessment model for industrial control system based on belief rule base 421 processes. the ics can collect various data, analyze the trend and issue alarms when parameters exceed the allowable range. according to the struture of scada, the main components of ics can be divided into three types as shown in table 1 [3]. table 1: the type of main components in ics first level second level third level ics components hardware components plc rtu hmi mtu other hardware software components os(operation system) db(data base) software of scada system software of plc other software communication profinet communication routers modem other communication components 2.2 the framework for ics reliability assessment the accuracy of ics reliability assessment hinges on the clear identification of various influencing factors. in fact, these factors can be divided into internal factors and external factors. as shown in table 1, the internal factors are the faults of the system components, including software fault, hardware fault and communication fault, while the external factors include network attacks and human errors, both of which directly bears on the system security. therefore, the ics reliability can be assessed from both the fault and security of the system. on this basis, the framework of ics reliability assessment was constructed. as shown in figure 3, the framework consists of three layers: the target layer, the criteria layer and the plan layer. the target layer is the reliability assessment; the criteria layer encompasses fault assessment and security assessment; the plan layer contains various antecedent attributes that affect the criteria layer. 2.3 process of ics reliability assessment firstly, the fault and security attributes of the ics were integrated by the er rule for fault and security analyses. based on the analysis results, the brb was adopted to establish a model and cma-es for training. finally, the ics reliability assessment model was obtained. the specific steps are presented in figure 4 below. 2.4 mathematical description let fa, sc and fr be the fault assessment result, security assessment result and final result of ics reliability assessment, respectively. the set of antecedent attributes c1 and that c2 for fault assessment and security assessment can be respectively expressed as: c1 = { c11,c 1 2, · · ·c 1 n } (1) 422 y.h. wang, p.l. qiao, z.y. luo, g.l. sun, g.z. wang internal factors external factors software fault hardware fault communication fault network attacks human errors ics fault ics security influencing factors ics reliability figure 2: relationship between influencing factors and ics reliability antecedent attribute 1 antecedent attribute n antecedent attribute 1 antecedent attribute n ics fault assessment ics security assessment ics reliability assessment plan layer criteria layer target layer figure 3: framework of ics reliability assessment c2 = { c21,c 2 2, · · ·c 2 m } (2) where c1i and c 2 i are the i-th antecedent attribute for fault assessment and security assessment, respectively. then, the fault assessment result and security assessment result can be respectively obtained by: fa = er1 ( c1, t1 ) (3) sc = er2 ( c2, t2 ) (4) where er1(•) is the relationship between the leading attribute and the fault assessment result; t1 is the set of parameters for er1(•) ; er2(•) is the relationship between the leading attribute and the security assessment result; t2 is the parameters of er2(•). on this basis, the brb-based ics reliability assessment result can be obtained by combining the fault and security assessment results: fr = brb(fa,sc,t) (5) where brb(•) is the conversion from fault and security assessment results to reliability assessment result; t is a set of parameters. 3 construction of brb-based ics reliability assessment model as mentioned before, the first step of ics reliability assessment is to clearly identify all the faults in the system components. after all, the fault of one component will threaten the security of the entire system. moreover, the ics reliability assessment must consider both quantitative data (e.g. system duration, fault frequency and plc fault tolerance) and qualitative data (e.g. reliability assessment model for industrial control system based on belief rule base 423 antecedent attribute antecedent attribute er ics fault assessment result er ics security assessment result brb model final result cma-es figure 4: the process of ics reliability assessment terminal controllability, communication stability). the former can be collected by data monitoring system, while the latter require expert interpretation. if the two types of data are uncertain, it is impossible to assess the ics reliability in an accurate manner. considering the excellent performance of the er and the brb for uncertainty problems, the author decided to set up a multi-layer ics reliability assessment model, optimize the model with the historical data of the brb, and integrate multiple attributes with the er rule. the proposed model contains a fault assessment model and a security assessment model. besides, the parameters was trained by the cma-es algorithm, such that the assessment indices are in line with the actual situation. 3.1 fault assessment attributes reliability is defined as the ability or possibility to perform a specified function without failure within a specified time and under specified conditions. the reliability of the system can be evaluated by mean time to repair, failure rate and mean time to failure [22]. in our framework of ics reliability assessment, the fault assessment covers three aspects. for each attribute, the weight t is the most important parameter in the er process. the antecedent attributes of fault assessment are listed in table 2. 3.2 security assessment attributes the ics security is partially affected by network attacks. the common attacks include virus and trojan attack, dos attack, detection attack, u2r attacks and r2l attack. specifically, the virus and trojan attack destroys the ics or steals system data with a malicious program; the dos consumes an excessive amount of ics resources, making services unavailable; the detection attack scans the ics network and unlocks the hidden security dangers; the u2r attack acquires the rights of the ics superuser through manipulation of the ics vulnerabilities; the r2l attack remotely gains unauthorized access to the ics. the different attacks pose varied levels of threats and brings diverse damages to the ics. therefore, the attack type, continuous attack time, attack frequency and attack severity were selected as the antecedent attributes of the security assessment. the ics security is also affected by human errors like mis-operation, unauthorized entry, etc. hence, the occurrence frequency and severity of such errors were also taken as the antecedent attributes of the security assessment. table 3 lists all the antecedent attributes of the security assessment. 424 y.h. wang, p.l. qiao, z.y. luo, g.l. sun, g.z. wang table 2: the antecedent attributes of fault assessment first level second level third level the fault reliability of ics hardware reliability of ics the rate of failure (c111)(t 1 11 = 0.15) mttf( mean time to failure (c112)(t 1 12 = 0.2) mttr(mean time to repair) (c113)(t 1 13 = 0.15) mpmt(mean preventive maintenance time) (c114)(t 1 14 = 0.1) mpbf(meantime between failure) (c115)(t 1 15 = 0.1) the failure severity (c116)(t 1 16 = 0.15) the failure tolerance (c117)(t 1 17 = 0.15) software reliability of ics(c12)(t2 = 0.15) mttf( mean time to failure ) (c121)(t 1 2 = 0.2) mttr( mean time to repair ) (c122)(t 1 22 = 0.2) the fluency of software (c123)(t 1 23 = 0.3) mpbf(mean time between failure) (c124)(t 1 24 = 0.15) the rate of failure (c125)(t 1 25 = 0.15) communication reliability of ics the rate of lost (c131)(t 1 31 = 0.2) the packet loss rate (c132)(t 1 32 = 0.3) mttf( mean time to failure ) (c133)(t 1 33 = 0.2) the failure severity (c134)(t 1 34 = 0.2) delay rate (c135)(t 1 35 = 0.15) 3.3 er rule process er rule is developed as a multi-criteria decision analysis (mcda) approach on the basis of belief decision and d-s theory [22]. compared to the d-s theory of evidence, the calculation process of the er rule is linear. the er rule can be implemented in the following steps: assume that there are p basic attributes {c1, · · ·ci, · · ·cp} of a general attribute c in a two-level hierarchy, and {t1, · · · , ti, · · ·tp} denotes the weights of the basic attributes, where 0 ≤ ti ≤ 1 . there are m assessment grades. step 1. convert the belief of each assessment level into belief. the conversion is shown in reliability assessment model for industrial control system based on belief rule base 425 table 3: the antecedent attributes of security assessment first level second level third level forth level the security reliability of ics(cs) security event (c21)(t 2 1 = 0.5) the type of attacks (c211)c 2 11 = 0.2) virus and trojan attack (c2111)(t 2 111 = 0.3) u2r attacks (c2112)(c 2 112 = 0.2) r2l attack (c2113)(c 2 113 = 0.2) dos attack (c2114)(c 2 114 = 0.3) continuous attack time (c212)(t 2 12 = 0.2) the frequcy of attacks (c213)(t 2 13 = 0.3) the severity of attacks (c214)(t 2 14 = 0.3) error operatio (c22)(t 2 2 = 0.5) the rate of events (c221)(t 2 21 = 0.5) the severity of attacks (c222)(t 2 22 = 0.5) figure 5, where qi,j represents the basic probabilistic set relative to the j-th assessment level qi,θ is the rest of probabilistic set of the unassigned to any resultaccording to the i-th attribute, qi,θ denotes unallocated basic probability mass relative to the in significant degree of the i-th basic attribute, qi,θ represents the unassigned basic probability mass with respect to the incompleteness of the i-th basic attribute. figure 5: the conversion process step 2. combine the first i-th attributes through the er, and the detailed process can be describe as figure 6. where qi(i+1),j represents the probability set of the j-th evaluation grade after the combi426 y.h. wang, p.l. qiao, z.y. luo, g.l. sun, g.z. wang figure 6: the combination process nation of the first i-th basic attributes, and ki(i+1) can be calculated by: ki(i+1) = 1 1 − ∑n k=1 ∑n j=1 j 6=k pi(i),kpi+1,j (6) step 3. determine the belief of the j-th assessment level and the belief of the unassigned attributes by: β̂j = qi(m),j 1 −qi(m),θ (j = 1, 2, · · · ,n) (7) β̂ = q̃i(m),θ 1 −qi(m)θ (8) compute the belief of the assessment results after determining all attributes: βi,j = ci,j+1−u(ci) ci,j+1−ci,j (ci,j+1 ≤ u (ci) ≤ ci,j+1) βi,j+1 = 1 −βi,j βi,k = 0(k = 1, · · · ,n,k 6= j,j + 1) (9) where u (ci) is the value of attribute ci and ci,j is the j-th reference value of ci . the er rule can be described as an attribute fusion process. based on the original data on the ics, the various attributes should be mixed and then partially allocated to the third layer. then, the assessment level of second layer attributes should be determined according to the third layer attributes, and that of first layer attributes according to the second layer attributes. finally, the attributes of the three layers were integrated again by the er rule, yielding to final ics assessment result. 3.4 the reliability model based on brb the assessment results fused by er rule should be graded according to the safety assessment result and fault assessment result, because the ics reliability is an integration of fault and security. then, the fused results will be taken as the input of the brb model. as shown in table 4, there are five reliability levels in fa and sc : c1 ∈{tw,b,g,e,tb} (10) reliability assessment model for industrial control system based on belief rule base 427 table 4: reliability level for ics reliability level the level of fault assessment (fa) the level of security assessment (sc) the level of final result (fr) 1(the worst) tw tw tw 2(bad) b b b 3(good) g g g 4(excellent) e e e 5(the best) tb tb tb c2 ∈{tw,b,g,e,tb} (11) where c1 is the reliability level of fa ; c2 is the reliability level for of sc . the final result fr can be described as: fr = (fr1,fr2,fr3,fr4,fr5) = ({tw,b,g,e,tb}) (12) the reliability of rk can be expressed as: if fa = c1k and sc = c 2 k, then fr = {(sc1, t1,k) , · · · , (sc5, t5,k)} with a rule weight θk and attribute weights ρc1 and ρc2 . the brb model have 25 rules in the initial case. the initial belief of the rules was determined by reliability assessment. if the fault assessment or security assessment are the worst ( tw ), it is impossible to guarantee the data accuracy in the ics, i.e. the assessment result must the worst ( tw ). for example, the reliabilities of r5, r10 , r15 , r20 , r25 can be expressed as: iffaistwandscistw,thefris{(tw, 1), (b, 0), (g, 0), (e, 0), (tb, 0)} iffaisbandscistw,thefris{(tw, 1), (b, 0), (g, 0), (e, 0), (tb, 0)} iffaisgandscistw,thefris{(tw, 1), (b, 0), (g, 0), (e, 0), (tb, 0)} iffaiseandscistw,thefris{(tw, 1), (b, 0), (g, 0), (e, 0), (tb, 0)} iffaistbandscistw,thefris{(tw, 1), (b, 0), (g, 0), (e, 0), (tb, 0)} (13) 3.5 the reasoning process of brb model to obtain the stability of the ics, the brb model was derived by the er method through the following steps: step 1. calculate the matching degree of the antecedent attribute of the training sample to a rule: aki =   al+1i −ai(t) al+1i −a l i k = l ai(t) −ali al+1 −ali k = l + 1 0 k = 1, · · · ,k(k 6= l, l + 1) (14) where aki is the i-thantecedent rule; ai(t) is the i-th attribute in the data; a l i and a l+1 i are the reference value of the l-th and ( l + 1 ) thantecedent attributes, respectively; k represents the number of belief rules. step 2. compute the weight of the k-th rule from the matching degree tki : tk = θk ∏m i=1 ( aki )δi∑k l=1 θl ∏m i=1 ( ali )δi (15) 428 y.h. wang, p.l. qiao, z.y. luo, g.l. sun, g.z. wang where ρi is the i-thantecedent attribute. step 3. integrate the rules and generate belief of different outputs by the analytic synthesis algorithm of the er which was described in chapter 3.3. step 4. generate the final output according to the belief of different outputs: faactual = n∑ n=1 cntn (16) 3.6 cma-es optimization of the brb the brb was optimized under following three constraints: rule weight, attribute weight and rule. the rule weight constraint, reflecting the relative importance of the rule, can be expressed as: 0 ≤ θk ≤ 1, k = 1, 2, . . .l (17) the attribute weight must be normalized to [0, 1]: 0 ≤ ρn ≤ 1,n = 1, . . . ,tk (18) similarly, the rule must be normalized to [0, 1]: 0 ≤ ρn ≤ 1,n = 1, . . . ,tk (19) where ti,k is the belief of the k-th rule. the value of ti,k=1 if the modified rule can be fully executed and smaller than one if otherwise: n∑ i=1 ti,k ≤ 1, k = 1, 2, . . . ,l (20) outputestimated = n∑ n=1 p (sci) ti (21) next, the brb parameters should be trained by the objective function to reduce the error between the assessed and actual outputs. here, the error is measured by the mean square error (mse): mse (θk, ti,k,ρn) = 1 t t∑ i=1 ( output estimated − output actual) 2 (22) the objective function and constraints of the brb parameter training can be expressed as: min mse (θk, ti,k,ρn) 0 ≤ θk ≤ 1 0 ≤ ρn ≤ 1,n = 1, . . . ,tk 0 ≤ ti,k ≤ 1, i = 1, . . . ,n, k = 1, 2, . . .l∑n i=1 ti,k ≤ 1 (23) the parameter training with the l-cma-es consists of four steps: step 1. parameter initialization. the initial mean0 is equal to the initial parameter ω0,and ω is: ω = {θ1, . . . ,θl,β1,···, . . . ,βn,,δ1, . . . ,δtk} (24) reliability assessment model for industrial control system based on belief rule base 429 where the initial value of each parameter is given by the expert. step 2. population sampling. taking the central solution as the central expectation of the solution space, a normal distribution population is generated as: ωg+1q ∼ mean g + ηgn (0,cg) (q = 1, · · · ,n) (25) where ω0 is the first solution in the solution space; ωg+1q is the q-th solution in the solution space, g is the number of iterations; mean is the mean distribution, i.e. the central expectation; η is the step size; c is the covariance matrix of the population. step 3. selection and recombination. the candidate solution in the solution space are screened by leaky bucket mechanism. after the candidate solutions satisfying the constraints are obtained, the solutions of the child populations will be selected in the population according to the adaptive function. then, the central expectation in the population moves towards the local optimal solution, and guides the evolution of the population. when the previous optimal solution population ε is obtained, the population’s expectations can be updated by: meang+1 = ε∑ i=1 γiω g+1 i,µ ( ε∑ i=1 γi = 1 ) (26) where ε is the number of child populations; γ is individual weight (the total weight is 1); µ is the size of the parent population; ωi:µ means the i-th candidate solution is obtained from the parent population µ in the (g + 1)-th iteration according to the fitness value. after recombination, the central region of the population will move towards the child populations, such that the candidate solution is more accurate than the parent population. step 4. update of covariance matrix. in the next iteration, the optimal solution needs to be found based on the covariance matrix. during the iteration process, the transformation of the covariance matrix varies with the length and orientation of the long axis of the elliptical distribution of the population. the change in orientation reflects the trend and direction of evolution, while the change in length represents the scope of the population search. the covariance matrix should be updated by: cg+1 = (1 −a1 −aε) cg+a1pg+1 ( pg+1 )t +aε ε∑ i=1 γi   ( ω g+1 i,µ −mean g ) ηg     ( ω g+1 i,µ −mean g ) ηg   t (27) where ai and aε is the total learning rate; p is the evolution path (initial value=0). then, the evolution path should be updated as: pg+1 = (1 −ap) pg + √√√√ap (2 −ap) ( s∑ i=1 γ2i )−1 meang+1 −meang ηg (28) where ap ≤ 1 is the retrospective period of the evolution path. the step η should be updated as: ηg+1 = ηg exp  aη dη   ∣∣∣|pg+1η ∣∣∣ | e||n(0,i)|| − 1     (29) where dη is the damping coefficient; e||n(0, i)|| is the expectation of the euclidean paradigm ||n(0,i)||; i is the vector of the unit matrix; aηis the look back window; pη is the conjugate evolution path (initial value=0). the conjugate evolution path should be updated as: 430 y.h. wang, p.l. qiao, z.y. luo, g.l. sun, g.z. wang pg+1η = (1 −aη) p g η + √√√√aη (2 −aη) ( ε∑ i=1 γ2i )−1 c(g)− 1 2 mg+1 −mg ηg (30) the above formula(27-30) should be executed repeatedly until reaching the accuracy requirement. then, the optimal solution should be outputted, and serve as the model inputs after training. 4 case study 4.1 the assessment grades of the attributes since the ics reliability is affected by external and internal factors, this section designs simulation experiments involving both internal faults and external security incidents. the experiments were carried out in the actual industrial control environment. the faults and incidents were selected and rated empirically by experts. some of them are quantitative information, and some are qualitative. both internal faults and external security incidents were divided into four levels according to the actual situation (table 5). the dataset was derived from the log events of a chinese tobacco factory, which were recorded over 100 days by a plc-controlled device in scada system. table 5: the levels of internal faults worst bad good best c111(times/day) 15 10 5 0 c112(hours) 3 12 21 30 c113(minutes) 60 40 20 10 c114(minutes) (minutes) 60 40 20 c115(hours) 3 12 21 30 c116 given by experts c117 given by experts c121(hour) 3 12 21 30 c122(minutes) 1 40min 20min 10 c123 given by experts c124(hour) 3 12 21 30 c131(times/ day) 5 3 1 0 c132(%) 10 5 3 0% c133(hour) 10 20 30 40 c134 given by experts c135(seconds) 1s 0.3s 0.1s 0.01s 4.2 internal and external reliabilities based on er rule the original data were fused by the er algorithm, using the attributes and reference levels in table 5-6. the fusion follows the process detailed in section 3. according to the er algorithm, the author obtained quantitative observation data for 30 days, which reflect the internal reliability reliability assessment model for industrial control system based on belief rule base 431 of the ics. the daily observation data are shown in figure 7. it can be seen from figure 7 that the internal reliability of the ics fluctuated violently, indicating a high frequency of system faults, while the external reliability changed less significantly, which reflects the moderate frequency of external attacks. table 6: the levels of external security incidents worst bad good best c2111 given by experts c2112 given by experts c2113 given by experts c2114 given by experts c212(minutes) 20 10 5 0 c213(times/day) 5 3 1 0 c214 given by experts c221(times/day) 5 3 1 0 c222 given by experts figure 7: internal reliabilities for 30 days 4.3 model construction the proposed method (brb) was contrasted with two prediction models, i.e. back propagation neural network (bp) and markov prediction model (mm). for consistency, the initial parameters of all three methods were trained by the cma-es algorithm. the bp is a popular data-based prediction model. this model takes gaussian functions as training neurons, and adopts radial basis function kernels in the middle layer for nonlinear transform of input parameters. compared with traditional neural networks, the bp enjoys a small scale and fast operation, thanks to the limited number of intermediate layers. the mm is a typical semi-quantitative assessment model based on bayesian decision theory. it bears high resemblance to the proposed brb model. the high accuracy and efficiency have earned it immense popularity. the initial weights and levels of belief rules are listed in table 7. the initial parameters of the two contrastive algorithms are given in table 8. the input parameters of the cma-es 432 y.h. wang, p.l. qiao, z.y. luo, g.l. sun, g.z. wang figure 8: external reliabilities for 30 days table 7: initial weights and levels of belief rules rule rule weight sc(t) sc1, sc2, sc3, sc4, sc5=tw, b, g, e, tb 1 1 tw (sc1, 1), (sc2, 0), (sc3, 0), (sc4, 0), (sc5, 0), (sc, 0) 2 1 b (sc1, 0), (sc2, 1), (sc3, 0), (sc4, 0), (sc5, 0), (s, 0) 3 1 g (sc1, 0), (sc2, 0), (sc3, 1), (sc4, 0), (sc5, 0), (sc, 0) 4 1 e (sc1, 0), (sc2, 0), (sc3, 0), (sc4, 1), (sc5, 0), (sc, 0) 5 1 tb (sc1, 0), (sc2, 0), (sc3, 0), (sc4, 0), (sc5, 1), (s, 0) table 8: the initial parameters of the two contrastive methods comparison initial parameters mm initial probability vector = [0.2,0.2,0.2,0.2,0.2]; initial probability transition matrix = [1,0,0,0,0;0,1,0,0,0;0,0,1,0,0;0,0,0,1,0;0,0,0,0,1]; bp input neuron: 3;output neuron: 1; sliding window size: 3 table 9: the input parameters of the cma-es algorithm the semantic initial parameters value m0 = o0; σ0 = 0.5; λ = 13; τ = 6; a1 = 0.0031; aτ = 0.0066; p0ψ = 0; aψ = 0.147; p 0 σ = 0; aσ = 0.1813; dσ = 1.1813; e = 0.6; loop = 100; algorithm are displayed in table 9. 4.4 simulation and result analysis ten rounds of validations were performed to verify the effectiveness of our method. the exact range of the result was determined through interval estimation.the 100 reliability values reliability assessment model for industrial control system based on belief rule base 433 1 2 3 4 5 6 7 8 9 10 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 days r e li a b il it y mm bp actual brb figure 9: the results of round 1 41 42 43 44 45 46 47 48 49 50 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 days r e li a b il it y mm bp actual brb figure 10: the results of round 5 91 92 93 94 95 96 97 98 99 100 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 days r e li a b il it y mm bp actual brb figure 11: the results of round 10 were divided into 10 groups. the first 9 groups were adopted as the training data, and the rest as the test data. the results of the first, fifth and tenth round of training are presented in figure 9, 10 and 11, respectively. table 10: the mses after round 10 rounds 1 2 3 4 5 brb 4.676e−5 3.097e−5 1.345e−4 0.987e−4 1.362e−4 mm 0.963e−2 6.698e−3 2.322e−2 6.342e−2 3.987e−2 bp 2.547e−3 3.147e−3 6.357e−3 7.214e−3 1.365e−2 rounds 6 7 8 9 10 brb 7.321e−4 8.541e−5 1.247e−4 6.218e−5 1.361e−4 mm 0.897e−2 3.657e−3 3.465e−2 7.645e−2 1.644e−2 bp 1.247e−2 4.365e−3 4.514e−3 5.987e−3 7.365e−3 table 10 lists the mses between the predicted results and the actual results of the three methods after round 10. table 11 provides the overall mses and the interval estimation results of the three methods after round 10. the interval estimation results refer to the mean estimation interval at the confidence level of 95%. 434 y.h. wang, p.l. qiao, z.y. luo, g.l. sun, g.z. wang table 11: the results of overall mses and the interval estimation after round 10 mse interval estimation (95%) brb 2.36 e−4 [2.12 e−4, 3.87 e−4] mm 2.16 e−4 [1.37 e−2, 3.98 e−2] bp 6.37 e−3 [3.780 e−3, 8.98 e−3] the above results show that the proposed brb model, the mm and the bp respectively controlled the mse on the order of 1,000th, 100th and 1,000th. thus, our model reduced the error by 100 times from the level of the mm. moreover, our model achieved a small and acceptable error range. thus, the proposed method is reliable and efficient, in addition to its good prediction accuracy. 5 conclusions considering the internal and external influencing factors of ics reliability, this paper puts forward a new ics reliability assessment model based on the brb. the ics reliability assessment was divided into internal fault assessment and external security assessment, making the assessment more objective. the brb can process semi-quantitative information in our samples, which contain both quantitative data and qualitative knowledge. in this way, the model can be trained well with sufficient samples. the initial training parameters were optimized by the cma-es algorithm, aiming to improve the accuracy of model inputs. finally, the proposed model was compared with two other popular prediction methods through case study. the results show that the proposed method is reliable, efficient and accurate, laying a solid basis for reliability assessment of complex icss. funding this work was supported in part by the scientific research starting foundation for returned overseas of heilongjiang province under grants lc2018030. author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] alambeigi, f.; wang, z.; hegeman, r. 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(2017). a model for hidden behavior prediction of complex systems based on belief rule base and power set, ieee transactions on systems, man, and cybernetics: systems, pp(99), 1-7, 2017. international journal of computers communications & control issn 1841-9836, 10(3):428-440, june, 2015. group pattern mining algorithm of moving objects’ uncertain trajectories s. wang, l. wu, f. zhou, c. zheng, h. wang shuang wang, lina wu, fuchai zhou, cuicui zheng software college northeastern university shenyang, china wangsh@mail.neu.edu.cn haibo wang h. john heinz iii college carnegie mellon university pittsburgh, usa haibowang@cmu.edu abstract: uncertain is inherent in moving object trajectories due to measurement errors or time-discretized sampling. unfortunately, most previous research on trajectory pattern mining did not consider the uncertainty of trajectory data. this paper focuses on the uncertain group pattern mining, which is to find the moving objects that travel together. a novel concept, uncertain group pattern, is proposed, and then a two-step approach is introduced to deal with it. in the first step, the uncertain objects’ similarities are computed according to their expected distances at each timestamp, and then the objects are clustered according to their spatial proximity. in the second step, a new algorithm to efficiently mining the uncertain group patterns is designed which captures the moving objects that move within the same clusters for certain timestamps that are possibly nonconsecutive. however the search space of group pattern is huge. in order to improve the mining efficiency, some pruning strategies are proposed to greatly reduce the search space. finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on both real and synthetic trajectory datasets. keywords: probabilistic frequent group pattern, uncertain data, trajectory pattern mining, moving object. 1 introduction in recent years, with the development of various location-aware devices, such as rfid tags, cell phones, gps navigation systems, and point of sale terminals, trajectory data has become ubiquitous in various domains [1]. such data provides the opportunity of discovering usable knowledge about movement behavior, which fosters ranges of novel applications in human mobility understanding [2], smart transportation, urban planning [3], biological studies [4], environmental and sustain ability studies [5]. ideally, a trajectory is represented as a sequence of locations, and each is being associated with a corresponding time stamp. however, this is a simplification that does not take into account the inherent uncertainties in such trajectories. for example, a position reported in a gps signal usually implies a location point with an error range rather than an exact location. moreover, as location privacy has become increasingly a concern, many locations are blurred when they are made public. though the importance of mining uncertain data has been recognized [6–8], to the best of our knowledge there is little work in the trajectory pattern mining literature that studies its effect in the knowledge discovery process. a useful data analysis task in movement is to find a group of moving objects that are traveling together for a certain time period. this concept, what we refer to as group patterns, can copyright © 2006-2015 by ccc publications group pattern mining algorithm of moving objects’ uncertain trajectories 429 be defined in both spatial and temporal dimensions: (1) a group of moving objects should be geometrically close to each other; (2) they should be together for at least some minimum time duration. although mining group patterns has been extensively studied on certain trajectory database, such as flock [9, 10], convoy [11, 12], swarm [1], traveling companion [13, 14], and gathering [15], none of these work deal with the inherent uncertainty in trajectory database. in this paper, we consider the problem of mining group patterns in the context of uncertain trajectory database. in contrast to previous work that did not model the trajectory with uncertainty, we represent a trajectory of a moving object as a sequence of reported locations at corresponding time stamps and a probability distribution function. the probability distribution function represents the probability distribution of possible locations of the trajectory at a given time instant. in particular, the probability distribution function can be discrete or continuous. however, discovering the group patterns from uncertain trajectories is not an easy task because of adding a new dimension-probability. the challenges are two-fold: (1) the first challenge is how to design an appropriate distance function to measure the (dis)similarity between two uncertain trajectories. particularly, an effective similarity metric should be able to conduct measurements in terms of different probability distributions, taking into account spatial distances, temporal intervals, as well as relevant probabilities. (2) another challenge is how to find group patterns efficiently. due to adding the probability in uncertain trajectory database, judging frequent group patterns requires to compute the frequent probabilities, and this is not a trivial task. additionally, group patterns mining solutions may lead to an exponential number of results due to the downward closure property. so it is important to propose some effective pruning rules to reduce the massive search space and the number of probability computations. given the afore mentioned challenges, existing works for group patterns mining on exact trajectory data do not tackle the data uncertainty problem well. for instance, due to adding the probability, the concept and similarity distance metric function are not suitable for the uncertain environment. and also the traditional mining algorithms cannot be used directly to solve the uncertain group patterns mining. our research makes the following contributions: (1) we propose a new problem, mining group patterns over uncertain trajectory data. (2) we introduce a novel and adaptive metric to measure the dissimilarity between two uncertain trajectories. (3) we design an efficient algorithm to discover all uncertain group patterns. in addition, we propose several pruning techniques to reduce search space and avoid redundant computation. (4) extensive experiments demonstrate that the effectiveness and efficiency of our algorithm. the remaining of the paper is organized as follows. section 2 discusses the related work. we introduce the distance similarity function of two uncertain trajectories in sections 3. in section 4, we give the definitions of uncertain group patterns, and then introduce our efficient algorithm based on breath-first search strategy. experiments testing effectiveness and efficiency are shown in section 5. finally, our study is concluded in section 6. 2 related work group pattern mining over exact trajectory data.group pattern mining, which is to discover a group of objects that move together for a certain time period, is an important data analysis task for moving object trajectories. the research mainly included flock, convoy, and swarm pattern mining. the concepts of group patterns can be distinguished based on how the group is defined and whether they require the time periods to be consecutive. specifically, a flock [9, 10] is a group of objects that travel together within a disc of some user-specified size for at least k consecutive time stamps. a major drawback is that a circular shape may not reflect the natural group in reality, which may result in the so-called lossy-flock problem [11]. to avoid rigid restrictions on the sizes and shapes of the group patterns, the convoy is proposed to capture 430 s. wang, l. wu, f. zhou, c. zheng, h. wang generic trajectory pattern of any shape and extent by employing the density-based clustering. instead of using a disc, a convoy requires a group of objects to be density-connected to each other during k consecutive time points. while both flock and convoy have strict requirement on consecutive time period, the rigid definition of flock and convoy sometimes makes it not practical to find potentially interesting patterns. in contrast to flock, convoy, li et al [1] proposed a more general type of trajectory pattern, called swarm, which is a cluster of objects lasting for at least k (possibly non-consecutive) timestamps. because this is more realistic as different people may temporarily leave the cluster at some snapshots, in this paper our uncertain group pattern definition is based on swarm and is extended to uncertain data model. although there are a lot of works on mining group patterns, all these algorithms are designed for exact trajectory data and cannot be extended to uncertain trajectory data directly. pattern mining on uncertain data.another set of researches related with our work are pattern mining over uncertain data. existing work on mining frequent itemsets from uncertain databases falls into two categories based on the definition of a frequent itemset: expected support-based frequent itemset and probabilistic frequent itemset. both definitions consider the frequency (support) of an itemset as a discrete random variable. the former employs the expectation of the support as the measurement. that is, an itemset is frequent only if the expected support of the itemset is no less than a specified minimum expected support. the latter uses the frequentness probability as the measurement, which is the probability that an itemset appears no less than a specified minimum support times. then, an itemset is frequent only if its frequentness probability is no less than a specified minimum probability threshold. however, the use of expected support may lead to the loss of important patterns. thus, the use of a probabilistic frequentness measure has been more popular recently. a recent survey for comparing these two measures and analyzing their relationships is given in [16].for the problem of uncertain sequence pattern mining, some initial research has been undertaken. for example, the expected support-based frequent sequential pattern mining has been studied in [17]. in contrast, zhao et al. [18] proposed to mine probabilistic frequent sequential patterns according to the frequentness probability. however, all of these works only considered the simple value-based data type, and are not suitable for the complex data type-trajectory data, which contains both spatial and temporal information. our paper is the first work to solve the group patterns mining problem on uncertain trajectory data. 3 similarity metric of two uncertain locations in this section, we present our method of computing the spatial proximity of objects with uncertain locations. we formalize the model of uncertain trajectories in section 3.1 and give the similarity distance function in section 3.2. frequently used symbols throughout this paper are summarized in table 1. 3.1 uncertain trajectory model let tdb = {t1, t2, · · · tn} be a linearly ordered list of n timestamps. let odb={o1, o2, · · · , om} be a collection of m moving objects that appear in tdb. the locations of objects o observed at timestamps tdb are uncertain. we first give the definition of a certain trajectory. definition 1. (certain trajectory). a certain trajectory tr is represented as a sequence of points {(x1, y1, t1), (x2, y2, t2), · · · , (xn, yn, tn)} (t1 < t2 < · · · < tn), where n is the number of points in the trajectory and (xi, yi) are the coordinates of the ith point at timestamp ti. group pattern mining algorithm of moving objects’ uncertain trajectories 431 in some uncertain trajectories’ studies [6,7], they commonly used probability density function (pdf) to represent the uncertainty. unfortunately, the exact probability distribution is not easily computed. so in this paper, we adopt the expectation and variance to model the uncertain trajectory. we can use the evolving density estimator [8] to compute the mean value u and standard deviation v of an object’s position at each time. based on the definition of certain trajectories, we have the definition of an uncertain trajectory as follows: definition 2. (uncertain trajectory). an uncertain trajectory utr is a sequence of random variables, and all the random variables at different timestamps are assumed to be independent. an utr is represented as {(x1, y1, u1, v1, t1), (x2, y2, u2, v2, t2), ..., (xn, yn, un, vn, tn)} (t1 < t2 < ... < tn) , where ui and vi are the expectation and variance of utr at timestamp ti. table 1: summary of the use of notations symbol illustration odb moving object set, odb = {o1, o2, o3, ..., om} oj the jth object o objects subset,o ⊆ odb tdb timestamp set, tdb = {t1, t2, ..., tn} ti timestamp ti t time subset,t ⊆ tdb utri the ith object’s uncertain trajectory utri(j) the jth sample point of the ith uncertain trajectory disti(utra, utrb) the distance of the two objects at time ti cdb the database of clusters using fcm algorithm cti the clusters at time ti ctij the jth cluster of cti cti(oj) the set of clusters that object oj is in at timestamp ti p(oj ∈ ctij) the probability of object oj belonging to an cluster ctij p(o ∈ ctij) the probability of objects o belonging to an cluster ctij p(o ∈ cti) the probability of objects o belonging to the clusters cti mino the minimum objects threshold mint the minimum timestamps threshold minprob the minimum probability threshold pr(support(o, t) ≥ mint) the probability that objects of o are in the same cluster for at least mint timestamps 3.2 expected distance function before giving the definition of the uncertain group pattern, we introduce a novel and adaptive metric ee-distance (expected euclidean distance) for measuring the similarity between two uncertain trajectories. trajectory similarity is commonly estimated using trajectory distance measures, such as the euclidean distance, the dynamic time warping (dtw) distance, the principal component analysis (pca) distance, the edit distance with real penalty (erp), and the longest common subsequence (lcss) distance. however, there is no trajectory similarity measure that can beat all the others in every circumstance as introduced in [19]. in this paper we adopt the euclidean distance due to its simplicity in implementation and low computation complexity. next, we will show how to 432 s. wang, l. wu, f. zhou, c. zheng, h. wang compute the uncertain instant distance between two trajectories based on the expected euclidean distance. definition 3. (uncertain instant distance). we can treat the distance between two uncertain trajectories at timestamp ti as a square sum of the sample points, as shown in equation(1), utra(i) is the sample point of uncertain trajectory utra at timestamp ti, the same as utrb(i). disti(utra, utrb) = (utra(i) − utrb(i))2 (1) utra(i) and utrb(i) are the independent random variables, so disti(utra, utrb) is also the random variable. the expectation of the random variable disti(utra, utrb) can be computed in equation (2). e((utra(i) − utrb(i))2) = e(utra(i))2 + var(utra(i)) +e(utrb(i)) 2 + var(utrb(i)) − 2e(utra(i)) · e(utrb(i)) (2) according to the equation (2), we can easily compute the expected distance of two uncertain locations in o(1) time complexity. unlike other works requiring exact probability distribution function, our distance formulation is statistically sound and only requires knowledge of the general characteristics of the data distribution, namely, its mean and variance. 4 mining probabilistic frequent group patterns in this section, we first provide definitions of our probabilistic frequent group patterns, and then show how to find all probabilistic frequent group patterns in an uncertain trajectory database. 4.1 probabilistic frequent group patterns definition definition 4. (frequent group pattern)[1]. a pair (o, t) is said to be a group pattern if all objects in o are in the same cluster at any timestamp in t . specifically, given two minimum thresholds mino and mint, for (o, t) to be a frequent group pattern, where o = {oi1, oi2, ..., oil} ⊆ odb and t ⊆ tdb, it needs to satisfy two requirements: (1) there should be at least mino objects; (2) objects in o are in the same cluster for at least mint timestamps. t1 t2 t3 cluster1 cluster2 cluster1 o2 o1 o3 o2 o3 o1 cluster2 cluster1 o2 o1 o3 figure 1: object clusters at each timestamp in certain database t1 t2 t3 cluster1cluster1 cluster2 cluster3 cluster3 cluster1 cluster2 figure 2: object clusters at each timestamp in uncertain database fig.1 shows an example. there are 3 objects and 3 timestamps, odb = {o1, o2, o3}, tdb = {t1, t2, t3}. each sub-figure is a snapshot of object clusters at each timestamp. it is easy to see that o1 and o2 travel together for most of the time. given mino=2 and mint = 2, there are only one frequent group pattern:({o1, o2}, {t1, t2, t3}). group pattern mining algorithm of moving objects’ uncertain trajectories 433 as shown in fig.2, in the uncertain scenario, the object’s location is not an exact position, but an uncertain range. thus, it is more realistic to cluster the object in different clusters than to a single cluster. based on this point, we can apply a fuzzy clustering algorithm (e.g.fcm [20,21]) to create the cluster at timestamp ti. fuzzy clustering would assign each object a ą°degree of belongingnessąą(belongingness probability) for each cluster. a simple example of clusters is given in table 2. table 2: an example of uncertain clusters. time uncertain clusters 1 c11 = {o1 : 0.3; o2 : 0.5; o3 : 1.0}, c12 = {o1 : 0.5; o2 : 0.5}, c13 = {o1 : 0.2} 2 c21 = {o1 : 0.3, o2 : 0.8}, c22 = {o1 : 0.7, o3 : 0.2}, c23 = {o2 : 0.2, o3 : 0.8} 3 c31 = {o1 : 1.0, o2 : 1.0, o3 : 1.0} an object oj ∈ odb with an uncertain location at timestamp t ∈ tdb has a belongingness probability of cluster c ∈ cdb, the probability is denoted as p(oj ∈ c), where p(oj ∈ c) ∈ [0, 1]. at timestamp ti, an object could belong to more than one cluster, we use cti(oj) to denote the set of clusters that object oj is in, the total belongingness probability of object oj in different clusters is one p(cti(oj)) = ∑ c∈cti p(oj ∈ c) = 1. in addition, for a given objectset o, we write cti(o) = ∩ oj∈o cti(oj), which means o occurs at timestamp ti in the same cluster. we assume that different objects and different timestamps are mutually independent, i.e., the belongingness probability of an object has no effect on those of other objects. so, the probability of the object set o belonging to a cluster c could be computed in equation (3), the probability of object set o in the same clusters at timestamp ti could be computed in equation (4). to make our framework more general, we take clustering as a preprocessing step. the details are given in example 1. p(o ∈ ctij) = ∏ oj∈o p(oj ∈ ctij) (3) p(cti(o)) = ∑ ctij∈cti p(ctij ∈cti) (4) example 1: for tdb = {t1, t2}, odb = {o1, o2, o3} in table 2, at timestamp t1 : c11 = {o1 : 0.3; o2 : 0.5; o3 : 0.8}; c12 = {o1 : 0.5; o2 : 0.5; o3 : 0.2}; c13 = {o1 : 0.2}, o = {o1; o2}, then p(o1 ∈ c11) = 0.3, p(o2 ∈ c11) = 0.5, p(o ∈ c11) = 0.3 ∗ 0.5 = 0.15,and p(ct1(o)) = p(o ∈ c11) + p(o ∈ c12)=0.15 + 0.25 = 0.4. in the same way, at timestamp t2 and t3, p(ct2(o))=p(o ∈ c21)=0.24, p(ct3(o)) = 1.0. in the uncertain scenario, the number of timestamps that objects o in the same cluster at the timestamps t , denoted as support(o, t), is no longer certain. instead co-occurrence is described by a discrete probability distribution function. as shown in example 1, the probability of the objects o = {o1; o2} occurring in the same cluster at timestamp t1 , t2 and t3 is 0.4, 0.24 and 1.0 respectively. the frequency distribution is described in table 3. for example, the probability that o occurring in the same cluster at least two timestamps is 0.054. the definition of probabilistic frequent group pattern should consider this uncertainty. table 3: frequency distribution of o = {o1; o2} occurring at timestamps t = {t1, t2, t3} frequency of timestamp ≥ 0 ≥ 1 ≥ 2 ≥ 3 probability 1.0 1.0 0.054 0.096 definition 5. (probabilistic frequent group pattern). a pair (o, t) is an probabilistic frequent group pattern iff(o, t) is a frequent group pattern and pr(support(o, t) ≥ mint) ≥ minprob, minprob is the probability threshold. 434 s. wang, l. wu, f. zhou, c. zheng, h. wang for a given group pattern (o, t), the frequentness probability, pr(support(o, t) ≥ mint), which is interpreted as the probability that objects of o are in the same cluster for at least mint timestamps. under the definition of the probabilistic frequent group pattern, it is critical to compute the frequent probability of a group pattern efficiently. the frequentness probability pr(support(o, t) ≥ mint), could be computed by means of the paradigm of dynamic programming shown in equation(5) [22]. p (o,t) ≥i,j =   p (o,t) ≥i−1,j−1 ∗ p(o ∈ ctj ) + p (o,t) ≥i,j−1 ∗ (1 − p(o ∈ ctj )) o ∈ ctj p (o,t) ≥i,j−1 o /∈ ctj (5) for the sake of the following discussion, we define that p (o,t)≥i,j denotes the probability that objects o appears at least i timestamps among the first j timestamps in the given time set t . the dynamic programming approach is to split the problem of computing p (o,t)≥i,j at the first j timestamps into sub-problems of computing the frequentness probabilities at the first j − 1 timestamps. our goal is to find all probabilistic frequent group patterns. note that even though our problem is defined in the similar form of uncertain frequent pattern mining [22], none of previous work in uncertain frequent pattern mining area can solve exactly our problem. because fp mining problem takes transactions as input, group pattern discovery takes clusters at each timestamp as input. if we treat each timestamp as one transaction, each "transaction" is a collection of "itemsets"rather than just one itemset.therefore, there is no trivial transformation of fp mining problem to group pattern mining problem. the difference demands new techniques to specifically solve our problem. 4.2 uncertain trajectory data preprocessing when mining probabilistic frequent group patterns, we assume that each moving object has a reported location at each timestamp. however, in most real cases, the raw data collected is not as ideal as we expected. the sampling timestamps for different moving objects are usually not synchronized. even though many complicated interpolation methods could be used to fill in the missing data with higher precision, any interpolation is only a guessing of real positions. in this paper, we use linear interpolation to obtain possible position and statistical value at an arbitrary time between two consecutive sample times. we define the o(t) = {xt, yt, ut, vt} as an uncertain object between two consecutive sample timestamp ti and ti+1 , the value of o(t) can be computed in equation(6). xt = xi + (xi+1 − xi) · t − ti ti+1 − ti , yt = yi + (yi+1 − yi) · t − ti ti+1 − ti ut = ui + (ui+1 − ui) · t − ti ti+1 − ti , vt = vi + (vi+1 − vi) · t − ti ti+1 − ti (6) in order to make our approach has extensibility, we treat the computation of the spatial proximity and construction the clusters of objects with uncertain locations as a preprocessing step. in this way, spatial proximity of objects and clustering methods can be flexible and applicationdependent. we only require a set of clusters as input for each timestamp, where each object is associated with a belongingness probability that specifies the confidence the object is in a cluster at a given timestamp. the preprocessing algorithm is as follows. group pattern mining algorithm of moving objects’ uncertain trajectories 435 algorithm preprocessing.preprocessing algorithm input: uncertain trajectory database utd output: uncertain clusters database cdb 1.for each object oi 2. for each timestamp tj 3. if (no sample value) 4. interpolate the value; 5. compute the expected distance distj(utri, utrk) for each object ok in odb; 6.use fcm cluster algorithm based on the expected similarity; 7.output uncertain clusters database cdb; for each object oi, we compute the expected distance of every object ok at time tj (line 1-5), then we uses the fcm mining algorithm to cluster the uncertain objects at each timestamps(line 6). finally, we get the uncertain clusters database cdb(line 7). 4.3 pruning techniques although we use the dynamic programming to compute the frequent probability of a group pattern, the cost of computation is still high, the time complexity is o(n2), n is the number of timestamps in t . fortunately, we find two efficient pruning rules based on properties of the frequent probability. pruning rule 1(count prune): given a group pattern (o, t), if cnt(o, t) < mint, then (o, t) is not a probabilistic frequent group pattern,cnt(o, t) is the numbers of timestamps in t that o in the same clusters. pruning rule 2 (expected prune): given a group pattern (o, t), e sup(o, t) is the expected support of a group pattern (o, t), is defined as the sum of the probabilities that objects o occurring in the same cluster in each of the timestamps in t , e sup(o, t) = ∑ tj∈t p(o ∈ cti). if λ− < minprob, then (o, t) is not a probabilistic frequent group pattern. λ− =   −(mint −1 − e sup(o, t)2) 4e sup(o, t) 0 < mint −1 − e sup(o, t) e sup(o, t) < 2e − 1 21+e sup(o,t)−mint mint −1 − e sup(o, t) e sup(o, t) ≥ 2e − 1 e sup(o, t) mint other (7) pruning rules could be used to prune infrequent group pattern before computed the exact frequent group probability. the running time of computing the cnt(o, t) and e sup(o, t) is o(n), but o(n2) for the exact probability pr(support(o, t) ≥ mint). so pruning rules can significantly improve the running time of mining algorithm. 4.4 mining algorithm of probabilistic frequent group pattern traditional frequent itemset mining is based on support pruning by exploiting the antimonotonic property of support. in uncertain databases, recall that support is defined by a probability distribution and that we mine group patterns according to their frequentness probability. it turns out that the frequentness probability is also anti-monotonic. theorem 6. ∀o ⊆ o(prime), pr(support(o, t) ≥ mint) ≥ pr(support(o(prime), t) ≥ mint), all subsets of a probabilistic frequent group patterns are also probabilistic frequent group patterns. 436 s. wang, l. wu, f. zhou, c. zheng, h. wang we can use the theorem 1 to prune the search space for probabilistic frequent group pattern. that is, if a group pattern (o, t) is not a probabilistic frequent group pattern,i.e.pr(support(o, t) ≥ mint) < minprob, then all patterns o(prime) ⊃ o cannot be probabilistic frequent group patterns. we propose a probabilistic frequent group patterns mining approach based on the apriori algorithm. like apriori, our method iteratively generates the probabilistic frequent group patterns using a bottom-up strategy. each iteration is performed in two steps, a join step for generating new candidates and a pruning step for calculating the frequentness probabilities and extracting the probabilistic frequent group patterns from the candidates. the pruned candidates are, in turn, used to generate candidates in the next iteration. theorem 1 is exploited in the join step to limit the candidates generated and in the pruning step to remove group patterns that need not be expanded. in the join step, our algorithm adopts the breadth-first implementation. because the depth-first strategy does not fully use the downward closure of the probabilistic support,this is due to the fact that the depth-first implementation does not know all frequent k-objectset before considering the (k + 1)-objectset. this may lead to a bigger search space. the detailed steps of our algorithm to compute the probabilistic frequent group pattern is listed in algorithm pfgpm. we first select frequent 1-object sets (line 1), and then recursively generate candidate (k + 1)-objectset from k-objectset (line 2-9). at each iteration, only the frequent k-objectsets are extended (apriori property, line 3-4). we first scan the database to calculate the expected support of each candidate, and use the pruning rules to prune candidate (line 5), if not be pruned, compute the frequentness probability(line 6-8). next, we output those patterns satisfying |o| ≥ mino and probabilistic support (line 9). algorithm pfgpm. probabilistic frequent group pattern mining algorithm input: uncertain clusters database cdb output: probabilistic frequent group patterns 1.apply the pruning rules first, then calculate the frequentness probability for each 1-objectset,find all the frequent 1-objectset called cand, and sort them in alphabetic order; 2.k = 2 3.for each o in cand 4. extend o using a breadth-first search like strategy to its supersets with o as prefix, denoted o(prime); 5. using pruning rule 1 and 2 for o(prime); 6. if(o(prime) not pruned) 7. compute the frequent group probability(fgp) as shown in equation(5); 8. if(fgp ≥ minprob) 9. output (o(prime), fgp), if|o(prime)| ≥ mino 10.k = k + 1; 11.go to 3; 5 experiments 5.1 datasets real data. a truck dataset (http://www.chorochronos.org/) recording 50 trucks delivering concrete to construction sites around athens over 33 days and consisting of 276 trajectories. to increase the size of moving objects, we considered each distinct trajectory as the id of an object, yielding 276 trucks with 2449 timestamps. in our experiments, we consider only the first 128 group pattern mining algorithm of moving objects’ uncertain trajectories 437 positions of each trajectory. we normalize the positions of trajectories into a unit space. the probabilities of each trajectory were assigned according to two different distributions: (1) each certain position p was assigned a probability according to a uniform distribution in the range of (0.5, 1.0]. (2) each position was assigned a normal distribution n(0.5,0.2) in the range of [0, 1.0]. synthetic data. we first generate two certain trajectories based on a custom gstd data generator [23]. each dataset contains 10,000 uncertain trajectories with the same length 128. we then convert these certain trajectories to uncertain trajectories in the way described above. the fuzzy clusters of teach timestamp are obtained by the fuzzy c-means clustering algorithm [20] with m = 2 and eps = 0.01, where m is the weighting exponent and eps is the termination criterion. each object is assigned a belongingness probability by the fuzzy clustering algorithm. the default values of mino=10, mint=0.5,(half of the overlapping time span), minprob=0.3. 5.2 performance evaluation no previous technique addresses probabilistic frequent group patterns mining for uncertain trajectories. we compare our pfgpm approach with an alternative, pfgpm-np, that does not use any pruning rules.we present the experimental results in this section. all the experiments are run on a desktop pc with a 2.66ghz cpu and 4gb ram. effect of punning rules: we use two dataset with varying parameter settings to test the performance of the pruning rules. (1) synthetic dataset. we extract the first 274 trajectories of synthetic dataset; (2) truck datasets. each position p in two datasets was assigned a probability according to a uniform distribution approach.as shown in fig.3, the pruning works well for skewed dataset (truck dataset). the reason is straightforward: the more skewed the data, the higher the number of objects is infrequent and, thus, cannot be pruned. regarding the effect of parameters, the larger mint and minprob are, the more objects will be pruned. however,mint has a more significant influence than minprob, that is, pruning rule 1 has more strong power than pruning rule 2. min t n u m b e r o f o b je c t to b e e n p ru n e d truck sythetic (a) varying mint minprob n u m b e r o f o b je c t to b e e n p ru n e d truck sythetic (b) varying minprob figure 3: pruning power effect of |odb| and |tdb|. we ran out our algorithm in synthetic data set. fig.4 and fig.5 depict the running time when varying |odb| and |tdb| respectively. in both figures, pfgpm-np is much slower than pfgpm. furthermore, pfgpm-np is usually 5 times slower than pfgpm. comparing fig.4 and fig.5, we can see that pfgpm is more sensitive to the change of |odb|. this is because its search space is enlarged with larger |odb|, whereas the change of |tdb| increases the computing time of frequent probability, which does not directly affect the running time of pfgpm. effect of mino. fig.6 shows the running time w.r.t. mino. with the increasing of mino, the running time will decrease because less group patterns would meet the requirement. besides, it is 438 s. wang, l. wu, f. zhou, c. zheng, h. wang |odb |(*100) r u n n in g t im e ( se c o n d ) figure 4: running time with varying |odb| |t db |(*100) r u n n in g t im e ( se c n o d ) figure 5: running time with varying |tdb| obvious that pfgpm-np takes much longer time than pfgpm. the reason is that pfgpm-np does not use any pruning rules to find the frequent group patterns, this leads to more redundant computation for frequent probability and larger search space. 0 200 400 600 800 1000 1200 1400 1600 3 10 15 20 25 30 min o ru n n in g t im e (s e c o n d s) pfgpm-np pfgpm (a) synthetic data set 0 100 200 300 400 500 600 700 3 10 15 20 25 30 min o ru n n in g t im e (s e c o n d s) pfgpm-np pfgpm (b) truck data set figure 6: object cluster at each timestamp in certain trajectory database effect of mint. fig.7 shows the influence of mint on the runtime when using different data sets. when mint decreases, we observe that the running time of all the algorithms goes up due to the number of probabilistic frequent group patterns increases. however, we find that the growth speed of pfgpm is low due to all pruning methods it employed. min t (*10) r u n n in g t im e ( se c o n d ) (a) synthetic data set 0 400 800 1200 1600 2000 1 2 3 4 5 6 7 8 9 10 min t (*10) r u n n in g t im e ( se c o n d ) pfgpm-np pfgpm (b) truck data set figure 7: running time with varying mint effect of minprob. finally, we test the running time of two compared algorithms with varying the probabilistic frequent threshold, minprob in two different datasets. in fig.8, we can obverse that pfgpm is always faster than pfgpm-np algorithm. with regards to the change of minprob, the running time of all algorithms remains approximately the same. thus, we can discover that the influence of probabilistic frequent threshold will be smaller than that of mint to the total running time. group pattern mining algorithm of moving objects’ uncertain trajectories 439 minprob ru n n in g t im e (s e c o n d ) (a) synthetic data set minprob ru n n in g t im e (s e c o n d ) (b) truck data set figure 8: running time with varying minprob 6 conclusion in this paper, we have formulated and studied the problem of mining probabilistic frequent group patterns in uncertain trajectory database. we introduce a novel notion expected distance to measure the dissimilarity between uncertain locations. in order to mining such group patterns efficiently, we proposed several pruning techniques to reduce search space and to avoid many complicated computations. we further designed a apriori-based algorithms using breadth-first implementations for efficient enumeration of all probabilistic frequent group patterns from uncertain data. extensive experimental results show the effectiveness and efficiency of the mining algorithm. in our further study, we aim to extend our current approach to be able to handle more complex patterns for the trajectory data. acknowledgement this research is partially supported by fundamental research funds for the central universities (grant no. 130317003), the national natural science foundation of china (grant no. 61440014, grant no. 61300196), the natural science and technology major project (grant no. 2103zx03002006), and the liaoning province science and technology project (grant no. 2013217004). bibliography [1] z. li, b. ding, et al, swarm: mining relaxed temporal 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[23] y. theodoridis, j. r. o. silva, et al, on the generation of spatiotemporal datasets, the 6th international symposium on advances in spatial databases, 147-164, (1999). international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(5), 615-628, october 2019. a force/position hybrid controller for rehabilitation robot j. hua, l.l. he, z.q. kang, k.d. yan jin hua* xi’an university of architecture and technology, school of mechanical and electrical engineering xian 710055, china xi’an technological university, college of electronic information engineering xi’an 710021, china *corresponding author: huahua_dz@aliyun.com lile he xi’an university of architecture and technology, school of mechanical and electrical engineering xi’an 710055, china zhiqiang kang xi’an university of architecture and technology, school of mechanical and electrical engineering xi’an 710055, china keding yan xi’an technological university, college of electronic information engineering xi’an710021, china abstract: the growing ageing population in china poses a huge demand for rehabilitation care, which can be satisfied by the latest robot technology. focusing on the motion system of a six degrees-of-freedom (dof) robot, this paper explores the relationship between the force, torque, velocity and the postures of the end effector and joint. drawing on robot control theories, the existing manipulator force/position hybrid controllers were reviewed, and a force/position hybrid controller was designed for path planning of rehabilitation robot. then, the robot was modelled on the robot operating system (ros), using the unified robot description format (urdf) file and the moveit! setup assistant. finally, our controller was tested in the ros virtual simulation environment. the results show that our controller can facilitate and optimize the design of the path of rehabilitation robot. keywords: rehabilitation robot, compliance control, bezier curve, robot operating system (ros). 1 introduction the ageing problem has become increasingly serious in china since the 1990s. from 1990 to 2014, the elderly population, defined as people aged 65 and over, increased from 62.99 million (5.57%) to 88.11 million (6.96%). the elderly population is expected to account for over 20% copyright ©2019 cc by-nc 616 j. hua, l.l. he, z.q. kang, k.d. yan in the total population of china by 2040. currently, tens of millions of elderlies in china need rehabilitation care, posing a huge pressure on the medical and insurance systems. many industrial robots have been modified to provide rehabilitation care to the elderlies. for example, li and ye [8] created a motion relationship mapping model for upper limb joints, established several rehabilitation robots based on the relationship, and used joint torque signals to control the motion of the robots. wang [18] developed a highly flexible pneumatic upper limb rehabilitation robot, which effectively prevents secondary damage to the affected limb. pan and song [12] designed an upper limb rehabilitation robot using impedance control strategy. the existing rehabilitation robots mainly adopt the structure of a multi-degrees of freedom (dof) exoskeleton with a single manipulator. the motions of the manipulator are controlled based on the signals of muscle force on the affected limb. this control mode cannot obtain the user’s movement intention easily, or achieve good real-time response and accuracy. drawing on robot control theories, the existing manipulator force/position hybrid controllers were reviewed, and a force/position hybrid controller was designed for path planning of rehabilitation robot. then, the robot was modelled on the robot operating system (ros), using the unified robot description format (urdf) file and the moveit! setup assistant. finally, our controller was tested in the ros virtual simulation environment. the results show that our controller can facilitate and optimize the design of the path of rehabilitation robot. 2 literature review robot technology is highly interdisciplinary and integrated. it covers many different fields, such as automatic control, computer technology, advanced sensing and mechanism. in recent years, more and more robots have been used to provide special services. the compliance control is the key and difficult point in the research of robot technology. the compliance means the manipulator can adapt to the changes in the environment, keeping its contact force with the environment at a desired level. over the years, many methods have been developed for compliance control of robot, including impedance control (converting feedback force into position or speed for dynamic adjustment), force/position mixed control, stiffness control [15], and implicit force control [9]. being the hybrid of force control and position control, the compliance control can be achieved either actively or passively. under active compliance control, the robot perceives and regulates the contact force, completing the mixed control of its force and position. considering the uncertainty of contact stiffness, hogan [3] designs a force feedforward controller by inverse dynamics approach, and realizes parallel control of robot force and position. sun and chen [17] proposes a robot control method coupling motion prediction and impedance control, which ensures that the manipulator is flexible to react with unknown restricted mechanisms. xu [22] puts forward a discrete-time sliding mode impedance controller with adaptive gain, and applies the controller to accurately control the force and position of a micro-gripper using piezoelectric bimorphs under uncertainties and interferences. based on force/position hybrid control, chang et al. [2] develops a fuzzy control method for redundant manipulator, which guarantees the accuracy of the end effector without sacrificing autonomy and flexibility. singh and sukavanam [16] creates a robust adaptive force/position hybrid control strategy based on neural network (nn). under this strategy, the robot dynamics with uncertainties and disturbances are described by feedforward neural network (fnn), laying the basis for effective control of the force and position of the end effector. hu et al. [4] conducts orthogonal separation of force control and position control in the time domain, and realizes grinding and polishing of the aspherical surface through mixed control of both force and position. a force/position hybrid controller for rehabilitation robot 617 under passive compliance control, the robot position is controlled by the rigid robot, while the contact force is controlled by a flexible joint added to the end of the robot. compared with the active mode, passive compliance control is easy to implement, rich in economic benefits and widely applicable in industries. nonetheless, the high demand on precision limits the use of passive compliance control in fast-responding applications. the most famous passive compliance controller is the remote center compliance (rcc) device developed by whitney and rourke [21]. the rcc device has been adopted for assembly operations to achieve compliance with any compliance center. however, this device cannot adapt well to operations with different stiffness requirements, due to its inflexible compliance center and fixed stiffness. beijing university of technology [14] designed a pneumatically-driven passive-compliance robotic hand that can control the contract force at a low level and complete precision hole and shaft assemblage. the robotic hand consists of an air bag and an air bearing. the former reduces the angular stiffness and the latter lowers the lateral stiffness. harbin engineering university [20] presented a passive-compliance flexible gripper for underwater search and object grabbing. the gripper has two fingers, a removable wrist, easily replaceable end effectors, a support mechanism, an active locking mechanism and a passive compliance mechanism. despite the lack of a single dof, the gripper can grab objects in different postures. huang et al. [5] introduces a compliance mechanism to the abrasive belt of a robotic polishing system. the mechanism can adapt to the changing contact force of the system, eliminating the need of manual grinding of turbine blades. to cope with dynamic changes of the working environment and the uncertainties of human-computer interaction (hci), zhang et al. [23] designs a manipulator with passive compliance structure and active compliance control, and adds several elastic drive modules between the joint motor and connecting rod. baksys et al. [1] set up an electric vibration table with a compliance device, examines the stress of the vibration table, and discloses the relationship between vibration frequency and assembly time. kilikevicius and baksys [7] analyzes the effects of dynamic systems and excitation parameters on passive compliance assembly by vibrating the casing in the axial direction on the platform basis. huang et al. [6] puts forward a passive compliance control strategy for the position and force of a robotic polishing system, and installs a compliance device at the end effector for force control. from the above, because of the manipulator dynamics model contains the uncertainty structures and parameters, even more, the external disturbance, the method based on the model control will be resulted in the system control quality decline, more over instability from different degrees affection. therefore, the adaptive control method which is selected in this paper, is proposed to deal with these uncertainties and external disturbances. the application of compliant control technology in service robot becomes hotter, but more difficult in the process of interacting with human. in this work, the force/position hybrid control method is adopted to solve the operation task of assisted rehabilitation movement. combined with self-adaptation control, it structured the model which is contains the robot manipulator and the contact environment as a whole. 3 design of force/position control algorithm for robotic manipulator mechanical impedance is the ratio of the increment of dynamic force in any dof and the resulting increment of dynamic displacement. this ratio is generally a nonlinear coefficient. therefore, when the manipulator is in contact with the working environment, both the position of the end effector and the dynamic relationship between the position and the contact force should be controlled to adjust the input torque of the manipulator.currently, the two control 618 j. hua, l.l. he, z.q. kang, k.d. yan tasks are mainly implemented by two methods. the first method is the transform of the force signal into position or velocity, followed by the dynamic adjustment of the stiffness and damping part. the second method is the hybrid control of force/position. the hybrid position/force control decomposes the task space into two orthogonal subspaces for position control and force control. the position is controlled in the tangent subspace under environmental constraints, while the force is controlled in the normal subspace under environmental constraints. in this way, the position/force control is decoupled. let k and p be the kinetic energy and potential energy of the manipulator, respectively. then, the lagrange equation of the manipulator can be expressed as l = k − p . thus, the following equation can be derived: τ = d dt dl dq̇ − dl dq (1) therefore, the dynamic equation of the manipulator can be defined as: τ = mi (qi) q̈i + hi (qi, q̇i) + gi (qi) (2) where m(q), h(q, q̇), g(q) are the inertial matrix, centrifugal force and gravity vector of the manipulator, respectively. in the force space, the force acting on the end effector can be mapped to the equivalent joint space moment by the transposition jt of the jacobian matrix of the manipulator. when the end of the manipulator contacted with the external environment, there will be an fe interaction between the manipulator and the external environment. hence, in order to maintain the manipulator balance state, the output of torque controlling is expressed as follows: τ = m(q)q̈ + h(q, q̇) + gq −jtfe (3) figure 1: design of our force / position hybrid controller a force/position hybrid controller for rehabilitation robot 619 the design of our force/position hybrid controller is shown in figure 1. the output torque of the controller is the sum of the torque computed by the position control information via the inverse jacobian matrixj−1 and that computed by the force control information via jt . the inverse jacobian matrix stands for the mapping relationship between the change of the coordinate velocity of each member in the cartesian coordinate system and the change of the joint angular velocity of that member.considering that if only basic impedance control is used to maintain the dynamic relationship between the effector of the robot and the response contact force of the rehabilitation patients’ limbs, although smooth contact is achieved to reduce the impact in contact, it cannot achieve good control of the rehabilitation intensity.so the controller is designed by adding artificial neural network, so as to correct the expected output impact using the error between the output of the reference model and the actual output. in the traditional force/position control algorithm, the adaptive part of environmental parameters is improved. the contact force at the robot effector is measured by the six-dimensional force/torque sensor, which is used as the control part of the force feedback to the force/position hybrid controller. after learning the adaptive modified nn, the position will be changed.so, we select the parameter model of the environment asmds2 + bds + k . when, md is the target inertia of the impedance model, bd is the target damping, and kd is the target stiffness matrix. in the controller, s is a diagonal matrix. if i = 6, then the matrix is a unit matrix of 6 ∗ 6. therefore, s̄ can be obtained as: s̄ = i −s (4) the essence of the hybrid control is to set the dof for the desired path of the end effector, that is, to select the control component of the controller through s and s̄ . the cartesian force, which on the end of the manipulator, maps the equivalent joint torque according to the transpose jacobian matrix jt . when the manipulator performs the action task, it is inevitable that it will have physical contact with the external environment or the operating object. as the task executing, manipulator will inevitably contact with the external environment or the operating object. that leads to the results of positional constraints in the positional space. in addition to considering the dynamic model of the robot arm itself, the model of the interaction between the robot arm and the external environment should be established when modeling the control object, so as to realize the flexible control of the task action process by the dynamic model. considering the dynamic model of the arm, while the model of the interaction between the robot and environment will be confirmed, so as to achieve the flexible control process by the whole dynamic model. the force sensor is mounted between the end gripper and the joint arm.the data, which acquired from the experiment in a certain rehabilitation teaching movement, are the position and the six-axis force obtain from the force sensor. from that measurement data, the interaction force of this kind rehabilitation exercise between the manipulator and the environment is obtained. in the diagram, the τ is the total driving torque of the six joints (i = 6). the joint position q and joint velocity can be measured by the photoelectric encoder. acquired by six-axis torque sensor, hf is converted to the effective vector cf in cartesian coordinate system by using the coordinate transformation. so suppose this is a s selection matrix, which elements in the diagonal matrix are set to 1, corresponding to its degree space are controlled by the applied force loop. for example, when: s = diag[1, 0, 1, 0, 1, 1] (5) so xd = [xd, 0,zd, 0,βd,γd] , as known from the formula, the expected displacement of the y-axis and the rotation around the y axis should be ignored. as the same as in the position control ring. in addition, the original six-degree-of-freedom manipulator become a redundant mechanical manipulator. meanwhile, the change of the expected force is controlled by the force closed loop. 620 j. hua, l.l. he, z.q. kang, k.d. yan the motion differential equation is: ẋe = jθ̇ (6) so as to, ẋe is the m × l dimensional vector, that is composed of linear velocity and angular velocity of the end effector, θ̇ is the n× l dimensional vector, composed of angular velocity of each joint in manipulator, while, j is m × n matrix. when jis singular matrix, that means m < n, the joint angular velocity of redundant manipulator is: θ̇ = j+ẋe + (i −j+j)ϕ̇ (7) in this formula, j+ is the pseudo-inverse of j, if j is full rank, then j+ = jt (jjt )−1. i is the identity matrix, (i −j+j) is the null space projection matrix, and is any joint angular velocity vector. when φ̇ = 0, θ̇ can be solved with the minimum norm solution. in other words, the motion of the end effector can be guaranteed under the control of the minimum joint velocity in joint space, or use k∇h(θ) instead ofθ̇ : θ̇ = j+ẋe + k(i −j+j)∇h(θ) (8) where the h(θ) can be optimized by the gradient projection method. 4 robot trajectory planning based on bezier curve the bezier curve can be defined as: b(u) = m∑ j=0 bmj (u)pj, 0 ≤ u ≤ 1 (9) wherepj is the control point; bmj (u)is the m-order bernstein polynomial: bmj (u) = m! j!(m− j)! uj(1 −u)m−j (10) the polynomial form of the bezier curve can be expressed as: b(u) = m∑ i=0 aju i (11) whereajis a coefficient determined by control pointpj: aj = m! (m− i)! i∑ j=0 (−1)j+i j!(i− j)! pj (12) according to the principle of bezier curve principle, a planned path for the rehabilitation robot was obtained (figure 2). as shown in figure 4, the planned path consists of a straight segment qaqb and an arc segment qbqc ; the origin and radius of the arc segment are oa and r, respectively; the fifthorder bezier transition segment is q′bq ′′ b . the straight segment reaches the front of the target slag, which is at the center of the arc segment, while the arc segment bypasses the target slag to complete the slagging action. a force/position hybrid controller for rehabilitation robot 621 figure 2: trajectory planning for the rehabilitation robot let δ be the tolerance of the fifth-order bezier transition segment. then, the qbq′′b of center angle γ can be obtained by triangulation: γ = 2arctan(δ/2r) (13) let t be the transformation matrix from the global coordinate system o − zy x to the arc segment coordinate system oa − xayaza . then, the two ends of the fifth-order bezier transition segment ( qb0 and qb5 ) can be respectively expressed as: q′b = qb − qb −qa | qb −qa | δ (14) q′′b = o + t(rcosγ rsinγ 0) t (15) the six points of the fifth-order bezier transition segment can be obtained as:  qb0 = q ′ b qb1 = qb0 + (αb/5) · tb0 qb2 = 2qb1 −qb0 qb5 = q ′′ b qb4 = qb5 − (αb/5) · tb5 qb3 = 2qb4 −qb5 + 0.05( db du )2|u=1 · ~r r2 (16) where u ∈ [0, 1] is the fifth-order displacement parameter of bezier curve b = b(u)(u = u(t)); αb is the first-order differential vector; ~r is the vector pointing from the new start point of the arc segment to the center of the circle. the straight segment and bezier curve contain the same unit tangential vector at the transition pointt and curvature vector n in q′b . the unit vector along the direction of q′bqb can be expressed as: tb0 = nb0 = qb −q′b δ (17) the velocity at the intersection of the arc segment and the bezier curve is the same as the direction: tb5 = oa (18) to constraint the start and end of the straight segment, the continuous velocity and acceleration, straight segment displacement, acceleration, maximum velocity, maximum acceleration and 622 j. hua, l.l. he, z.q. kang, k.d. yan maximum jerk must satisfy: u0 = 0,u1 = 1, ü0 = ü1 = 0, u̇max = vmax/lab, ümax = amax/lab andümax = jmax/lab. to satisfy the velocity requirement, the straight segment and the fifth-order bezier transition segment must satisfy: dlab(u) dt |u=1 = db(u) dt |u=0 (19) since the straight segment precedes the fifth-order bezier transition segment, the velocity must satisfy: { u̇0 = 0 u̇1 =| ḃ(u)|u=0 | ·λ/lab (20) wherelab is the path function for straight segment; λ is the scale factor of the fifth-order bezier transition segment. to constraint the start and end of the arc segment, the continuous velocity and acceleration, straight segment displacement, acceleration, maximum velocity, maximum acceleration and maximum jerk must satisfy:u0 = 0,u1 = 1, ü0 = ü1 = 0, u̇max = vmax/rbc, ümax = amax/rbc andümax = jmax/rbc. in the path, the arc segment is at the end segment. then, the velocity must satisfy:{ u̇0 =| dotb(u)|u=1 | ·λ/rbc u̇1 = 0 (21) where rbc is the path function for the arc segment. 5 experimental verification 5.1 ros-based modelling of rehabilitation manipulator the urdf is a standard ros xml format in the ros for robot modelling [24]. the ros also provides a c + + parser for urdf files. this parser helps to implement motion planning or model-based computing. the model-based computing is easy to complete, for the information of the model is directly called in the algorithm code [11]. after urdf modelling, the moveit! setup assistant was employed to configure the robot model. firstly, the semantic robot description format (srdf) file was generated based on the user-imported robot model, creating a feature pack for the moveit! configuration. then, the robot was configured, visualized and simulated. the resulting configuration files were stored in the "config" folder. the srdf file saves all the information of the visual configuration, the urdf file stores the compiled information, the "kinematic.yaml" file retains the configuration of the kinematic solver kdl_kinmatics_plugin. in the "launch" folder, a series of launch files were generated automatically for the launch of the manipulator. the move_group is the core of the moveit! architecture [13]. both kinematics solution and motion planning need the launch file move_group to start the manipulator. the moveit! setup assistant offers various algorithms for robot motion planning. each of these algorithms is a plugin with a unique focus. moveit! system configuration frameworkas shown in the figure 3, the move_group [10] integrates various functions to the moveit! environment through plugins. thus, all external information, including their interactions with the internal data must be implemented by moveit!. in this paper, the open motion planning library (ompl) in moveit! is adopted to realize the robot functions [19]. the ompl, as an open-source library, contains various cutting-edge algorithms. it is used by moveit! to plan the robot motion from start to destination. a force/position hybrid controller for rehabilitation robot 623 figure 3: moveit! system configuration framework 5.2 experimental design based on impedance control in the ros, the built-in rqt_plot provides the drawing functions. however, this function is too simple to visualize a huge amount of data. hence, the matlab was selected for data analysis. the whole operation flow chart as shown in the figure 4. to avoid controlling the manipulator each time, the information about the topic joint_states was saved as a bag file. the command order as follows: 1). rosbag record -o manipulater1.bag /joint_states. "create a trajectory motion simulation namedjoint_states. o is the indicator of the output file; manipulater1 is the name of the output file". 2). rosbagplay youbot_action2.bag -clock."a terminal was turned on to play back the data, clock is the simulation time". 3). rostopic echo /joint_states>record2.txt. "the output text was saved to store the data". finally, the text was copied to windows, imported to excel and analyzed via matlab simulation. figure 5 shows the modelled rehabilitation manipulator in virtual environment. when the robot performs the auxiliary operation of basic rehabilitation movement, it is necessary to perform the basic rehabilitation actions such as the circular motion of the joint on a fixed track and the linear motion of push, pull and drag on the muscle or joint of the recovered person. 5.3 results analysis when the robot performs the basic auxiliary rehab operation, it is necessary to imitate people like action such as the joint circular motioning, and a fixed plan tracking,and the linear motion like pushing, pulling from muscle. then, the first-order and second-order differential vectors of the trajectory should be computed, that is reached there habilitation exercise positions. 624 j. hua, l.l. he, z.q. kang, k.d. yan figure 4: experimental flowchart figure 5: the modelled rehabilitation manipulator a force/position hybrid controller for rehabilitation robot 625 figure 6: the fifth-time bezier curve of rehabilitative exercise (a) (b) figure 7: the acceleration(a), velocity (b) variations of the end effector in one control cycle figure 8: position, velocity and acceleration of the end effector 626 j. hua, l.l. he, z.q. kang, k.d. yan the straight segment and arc segment was linked up by the fifth-time bezier curve. we defined variable δ was 10mm . the six points were calculated as the following information in the three-dimensional location: qb0 = (467.5932, 0, 325.3327),qb1 = (477.5448, 0, 324.2365), qb2 = (473.2109, 0, 323.7652),qb4 = (475.5001, 0, 324.5602), qb5 = (478.0034, 0, 325.8482)andqb6 = (480.3521, 0, 328.0214). the result of planning trajectory based on the fifth-time bezier curve is shown in figure 6. the position, velocity and acceleration variations were plotted (figure 7). it can be seen that the acceleration of the end effector changed smoothly, which is conducive to the rehabilitation efficiency and comfort. figure 8 presents the position, velocity and acceleration of end effector in the x-z plane. the acceleration curve at the end of the manipulator shows that our method slows down the abrupt changes in acceleration caused by rapid velocity change, when the end effector suddenly changes in the x-z plane. 6 conclusions the hybrid control of position and force is essentially to select the dof according to the desired path of the end effector, that is, to select the control component of the controller through s and s̄. in this paper, a force/position hybrid controller is designed based on the principle of impedance control. the control dofs were selected through reconstruction of the desired path, and then the s and s̄ of the controller were identified. next, our controller was tested in the ros virtual simulation environment. the results show that our controller can facilitate and optimize the design of the path of rehabilitation robot. acknowledgement this paper was supported by national sonar key laboratory open fund(6142109kf2018); national fund youth project(11804263). bibliography [1] baksys, b.; kilikevicius, s.; chadarovicius, a. 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(2013). adaptive discrete-time sliding mode impedance control of a piezoelectric microgripper, robotics ieee transactions on, 29(3), 663-673, 2013. [23] zhang, x.l.; gu, x.x.; zhao, h.f.; wang, k.(2016). design of a compliant robotic arm based on series elastic actuator,robot, 38(4), 385-394, 2016. [24] zuo, x.r.; han, l.l.; zhuang, j.; shi, q.q.; huang, y.(2015). design of human-robot interaction system for space robot using robot operating system, computer engineering and design, 36(12), 3370-3374, 2015. international journal of computers communications & control issn 1841-9836, 12(2):166-182, april 2017. domain/mapping model: a novel data warehouse data mode i. bojičić, z. marjanović, n. turajlić, m. petrović, m. vučković, v. jovanović ivan bojičić*, zoran marjanović, nina turajlić, marko petrović, milica vučković faculty of organizational sciences, university of belgrade jove ilića 154, 11000 belgrade, serbia ivan.bojicic, marjanovic.zoran, turajlic.nina, petrovic.marko, vuckovic.milica@fon.bg.ac.rs *corresponding author: ivan.bojicic@fon.bg.ac.rs vladan jovanović allen e. paulson college of engineering and information technology, georgia southern university statesboro, usa vladan@georgiasouthern.edu abstract: in order for a data warehouse to be able to adequately fulfill its integrative and historical purpose, its data model must enable the appropriate and consistent representation of the different states of a system. in effect, a dw data model, representing the physical structure of the dw, must be general enough, to be able to consume data from heterogeneous data sources and reconcile the semantic differences of the data source models, and, at the same time, be resilient to the constant changes in the structure of the data sources. one of the main problems related to dw development is the absence of a standardized dw data model. in this paper a comparative analysis of the four most prominent dw data models (namely the relational/normalized model, data vault model, anchor model and dimensional model) will be given. on the basis of the results of [1]a, the new dw data model (the domain/mapping modeldmm) which would more adequately fulfill the posed requirements is presented. keywords: data warehouse, data models, relational/normalized model, data vault model, anchor model, dimensional model, domain/mapping model areprinted (partial) and extended, with permission based on license number 4057540167908 [2016] ©ieee, from "computers communications and control (icccc), 2016 6th international conference on". 1 introduction a data warehouse can be defined as a model of a concrete business system representing a set of all of the states of that system during a given interval of time. the constant changes (organizational, legislative, functional, etc.) that a business system faces also reflect on the supporting data warehouse. hence, one of the main issues, related to dw development and maintenance, is the inconsistency, between the actual system and its supporting data warehouse, which increases over time. overcoming this discrepancy requires a flexible dw data model i.e. a data model which could be easily adaptable to the frequent changes in the business system. an additional issue in the field of dw development is the absence of a standardized model for representing the structure of a data warehouse (i.e. a standardized dw data model). existing approaches propose that the data should be organized in compliance with the third normal form (3nf) [2] or the multi-dimensional model [3]. both approaches exhibit some limitations related to the difficulty in maintaining the data warehouse when the structure of the data sources changes. on the other hand both approaches are standardized by means of corresponding metamodels defined in the common warehouse metamodel (cwm) [4]. two additional approaches, aimed at addressing these limitations, have emerged in recent years, namely the anchor model [5], based on data that has been normalized into the sixth normal form (6nf), and the data vault copyright © 2006-2017 by ccc publications domain/mapping model: a novel data warehouse data mode 167 table 1: fundamental concepts of the the data models object relationship attribute identifier normalized model relation foreign key domain primary key data vault model hub link satellite business / primary key anchor model anchor / knot tie attribute primary key dimensional model dimension fact attribute business / primary key model [6] which can (but need not) also store data normalized into the 6nf. it should be noted that the anchor and data vault models are not standard extensions of cwm. by identifying the strengths and weaknesses of each of these models it is possible to establish the foundation for a new dw data model which would more adequately fulfill the posed requirements. the comparative analysis of these models is given in [1] (doi: 10.1109/icccc.2016.7496754) and based on those results and as an extension, this paper proposes a novel data warehouse data model: the domain/mapping model (dmm). the remainder of the paper is organized as follows: first the fundamental concepts of the dw data models will be identified and elaborated. sections 2 is devoted to the analysis of the four most prominent dw data models. section 3 details the groundwork for the proposed model, which is introduced in section 4. several examples illustrating the usage of the proposed dmm are given in section 5. the final section gives a brief summary of the work. 2 comparative analysis of the data models data models are intellectual instruments for specifying the static characteristics of systems, i.e. for describing the objects, their attributes and their relationships in a stationary state [7]. as a data warehouse is defined as a model of a concrete business system representing a set of all of the states of that system during a given period of time, it is imperative that the underlying data model, be able to, not only support the specification of the system as it transitions through states, but also withstand changes in the business system or data sources. the four most prominent models are analyzed in this paper: the normalized model [2,8,9], the data vault model [6,10–12], the anchor model [5,13] and the dimensional model [3,14,15]. at the highest level of abstraction, all of the described models are based on several fundamental concepts, as depicted in table 1. 2.1 built-in semantics the main point of difference among the models is the level of built-in semantics they provide. the normalized model does not presume any semantic constraints and, as such is extremely general, as the development of any given business model is based on mappings between sets. furthermore, it does not provide any implicit concepts which would enable maintaining the history of changes of an object nor the values of its attributes. the data vault model assumes that business objects have a stable identifier and somewhat alters the structure of the source by allowing for an object to be arbitrarily structured (i.e. its structure can be split into several satellites). the data vault model is suited for tracing changes in the values of attributes, except for the hub identifier (i.e. the business key). the anchor model is highly normalized. it provides two concepts, the anchor and the knot, for representing business objects. in addition, it enables the tracing of the history of all concepts, save for knots. 168 i. bojičić, z. marjanović, n. turajlić, m. petrović, m. vučković, v. jovanović the dimensional model is based on the events that take place within a business system and the dimensions which define them. furthermore, it is possible to define numerical properties for expressing the quantitative aspects of the events. the tracking of the history of changes is based on the complex rules pertaining to changes in dimensions. all of the models provide a single representation of an object (or entity) except for the anchor model in which concepts of a concrete system can be represented by anchor or knot concepts. the main difference is that the normalized, data vault and anchor models are normalized, while the dimensional model is denormalized. a relationship between objects is represented through the foreign key concept in the normalized model, which establishes a "tight relationship". the data vault, anchor and dimensional models define the relationship between objects through the link, tie and fact concepts, respectively, wherein the relationship is realized as a table which stores the primary keys of the objects in the relationship. in addition, in dimensional model it is customary to store additional derived or aggregated attributes in the structure of a fact. with regard to attributes, it should be noted that the data vault and anchor models separate the structure of an object from the object itself, by using satellites and attributes, respectively, which reference the object via a foreign key. the difference between these two models is that, in the anchor model a separate table is created for each attribute, while, in the data vault model, the attributes can be grouped according to various criteria into multiple satellites of one object. the normalized and dimensional models keep the attributes within the structure of the object. in all of the models each concept, which is used for representing an object type, has an identifier. the data vault model assumes that the identifier is the actual business key. somewhat similarly, the dimensional model, when using type 2 scds, also expects the existence of a business key on the basis of which the dimension values will be grouped. 2.2 resilience to change as previously mentioned, the constant changes that a business system faces also reflect on the supporting data warehouse. hence, one of the primary requirements related to data warehouse development is to provide the ability of absorbing changes in the structure of the data sources, without changing the structure of the underlying data model, in order to facilitate future maintenance and extensions of the data warehouse. in this section the data models will be analyzed from the viewpoint of their adaptability and extensibility. more precisely, the evaluation of this aspect will be focused on establishing whether changes in the structure of the data sources can be handled simply through the addition of new concepts, without requiring modifications of the dw physical layer. the normalized model has proven to be the optimal data model for transactional systems since, on the one hand, it guarantees minimal data redundancy and, on the other, decomposes the structure of the system down to the level of its fundamental objects. however, in the data warehouse domain it demonstrates certain weaknesses, as data warehouse requirements differ considerably from transactional system requirements. the main weakness of the normalized model lies in the fact that the attributes and relationships are built into the structure of the system’s objects, which leads to a number of maintenance-related issues. namely, any change in the structure of the source (attributes or relationships) will require changes in the structure of the normalized model, as depicted in fig.1, where the cardinality of the relationship between the employee and organizational unit concepts is adjusted in order to allow for an employee to be assigned to more than one organizational unit. in the depicted example it is necessary to create the position table, transfer the existing data (pertaining to the relationship between the employee and organizational unit entities) into the position table, and delete the foreign key, domain/mapping model: a novel data warehouse data mode 169 referencing the organizational unit, from the employee table. figure 1: cardinality change in the normalized model in addition, the normalized model has some shortcomings when it comes to the reconciliation of source models since, due to the potential semantic heterogeneity of the source models, it may not be possible to design a single reconciled model, as demonstrated by golfarelli in [15]. the data vault model exhibits much greater flexibility with regard to changes in the structure of the sources. its flexibility derives from the underlying language, as the structure of an object is decoupled from the object itself, and placed in a physically separate concept: a satellite. furthermore, every relationship (i.e. link), regardless of its cardinality, is always created as a table representing an aggregation of the related objects. figure 2: flexibility of the data vault model as depicted in fig.2(a), in the initial structure of the source system an employee could be assigned to one and only one organizational unit. consequently, the corresponding dv model contains the h_employee and h_organizationalunit hubs with the associated satellites. the hubs are related through the l_position link. if the structure of the source changes to allow for an employee to be assigned to more than one organizational unit, the target dv model will not undergo any changes, as shown in fig.2(b). 170 i. bojičić, z. marjanović, n. turajlić, m. petrović, m. vučković, v. jovanović furthermore, if the position concept is enhanced with an additional property, e.g. startdate, the dv model once again proves its flexibility, given that it is only necessary to extend (not modify) the existing model by adding an s_position satellite (containing the introduced property) to the l_position link, as illustrated in fig.2(c). moreover, further modifications, e.g. relating the position concept to other concepts of the source model, will not require changing the existing structure of the dv model, given that it allows for two links to be related. however, it should be noted that, even though in the previous examples the dv model did not undergo any changes at the physical level (i.e. it was only extended), the semantics of the source system have implicitly been changed by representing a strong entity (i.e. the position aggregation) as a link (i.e. event or relationship) in the dv model. an additional drawback of the dv model is the fact that, the transformation process is irreversible, i.e. obtaining the structure of the original source model from an existing dv model is not possible. because the dv model is semantically much poorer than the source models, certain information contained in the source models will inherently be lost in the transformation. for instance, in the previous examples (figs.2(b) and 2(c)) it would be impossible to determine into which of the two source models the dv model would be reversibly transformed. in keeping with the same example, the adaptability of the anchor model, to changes in the structure of the sources, will be examined. all of the models have been created using the original anchor modeling tool [16]. initial model (fig.3(a)) consists of two anchors: ou_organizationalunit and em_employee. the tie between the two anchors (ou_engages_em_assignedto), wherein an employee can be assigned to at most one organizational unit, is implicitly named on the basis of the roles of the related anchors. following the transformation the tie becomes a table whose primary key is the identifier of the concept that participates in the relationship with a maximum cardinality of 1. figure 3: flexibility of the anchor model if the cardinality changes, in order to allow for an employee to hold more than one position domain/mapping model: a novel data warehouse data mode 171 (as depicted in fig.3(b)), the only alteration of the model is extension of the primary key of the organizational unit with the primary key of the table representing the tie. however, if the structure of the source system changes so that a startdate is added to the relationship (ou_engages_em_assignedto), the anchor model does not support this feature. namely, the transformation of a relationship into an object, or the addition of structural features to a relationship, is not allowed. instead, the tie can reference a knot representing a set of all of the start dates pertaining to the given position, as depicted in fig.3(c). consequently, at the physical level the table, representing the tie, is modified by adding attribute (the primary key of the knot) which becomes part of the composite key of the tie. the dimensional model exhibits the same weaknesses as the normalized model, as the addition of new attributes in the source model requires changing the structure of corresponding data warehouse dimension. yet the relationships in the dimensional model are more stable, given that this model is process-oriented, i.e. it is designed on the basis of the core business processes of an enterprise. nonetheless, the introduction of additional concepts in the source model (which become dimensions in the data warehouse model) also requires the modification of the corresponding fact in order to include a foreign key referencing the newly formed dimension. 2.3 temporal aspects by defining a data warehouse as a collection of time-variant data [2], inmon established one of the main requirements for data warehouses, namely that they must enable the tracking of past, current and future states of objects and events, as well as the points in time at which the changes occurred [17]. the notion that a transactional system should support more than one time dimension was first put forward in [18] when snodgrass proposed the bitemporal concept, splitting the time aspect into two dimensions: valid time and transaction time. nowadays, as stated in [19], it is customary for the time aspect to be treated through three dimensions valid time, transaction time and user-defined time. it can be said that a data model supports the modeling of temporal aspects if it provides built-in mechanisms for capturing both the valid time and the transaction time. the aim of this section is to explore whether the data models support the tracking of the entire history of business system objects, and if so, through which mechanisms. the normalized model does not provide built-in mechanisms for capturing temporal aspects, rather it is left to the data warehouse designer to decide which temporal concepts will be included and how they will be modeled [20]. the data vault model implicitly incorporates the transaction time into each concept (hub, link and satellite) via the load date and load end date concepts. as this model assumes that the structure of objects will change over time, satellites can be used to capture the changes in values which occur during the life-cycle of an object. hence the valid time concept can only be applied to the structure of an object, not the object itself nor its relationships. consequently, and in light of the underlying premise that business keys are immutable, the data vault model does not provide a built-in mechanism for tracking the transaction time for business keys. nevertheless, such cases can be handled by using the same-as link whereby two hubs, with different business keys, are asserted to be identical [6]. the anchor model provides the historized option for representing time-variant attributes and ties which, however, only allows for capturing their valid time. when this option is used the structure of each of the chosen concepts is supplemented with a validfrom attribute [21] which captures the beginning of the validity period of the value (e.g. anchor modeling tool generates a changedat attribute for capturing the valid time). it is implicitly assumed that the end of the validity period of a previous value corresponds to the beginning of the validity period of the 172 i. bojičić, z. marjanović, n. turajlić, m. petrović, m. vučković, v. jovanović new value. however, such an assumption may not always be valid. in addition, if the static option is used when creating the model, the possibility of tracking the history of value changes for such attributes, or ties, will be lost. the stated reason is that this option is to be used for values which are considered to be stable, e.g. the date of birth. yet, this does not take into account the possibility that the value of an attribute may be incorrectly recorded in the source, or the possibility that several sources, storing different values of the same attribute, may exist. anchors and knots are considered to be immutable, which may not always be the case. the transaction time (i.e. recordedat) is not incorporated into the structure of the concepts representing business system objects, but is stored as metadata in separate tables instead [13]. the dimensional model enables the tracking of valid time through the various scd types it offers (excluding type 1 and type 3 scds which do not allow for tracking the valid time of an object). however, one of the main issues, related to tracking changes in dimensions, is the choice of the scd type, as switching to a different scd type at a later point in time inevitably leads to changes in the structure of the dimension. in addition, if the type 2 scd is chosen, changes is in the business key of an object cannot be tracked, as it is the basis for tracking changes in the other attributes. in the dimensional model the transaction time is not incorporated into the structure of the concepts representing business objects, instead it is recorded in separate log tables. 2.4 completeness and traceability of data in accordance with inmon’s definition [2] the second crucial characteristic of data warehouses is the non-volatility of data, which assumes that all of the data that has been integrated into a data warehouse must be retained in the data warehouse, unmodified, in order to ensure that a given query, executed at any point in time, will always produce the same, consistent result [22]. the completeness and traceability requirements are in direct collision with the single version of the truth concept which assumes that the data, that is to be stored in the data warehouse, is prepared and filtered in advance. the single version of the truth is, thus, the result of integrating data from multiple sources with the aim of providing a uniform basis for analysis and avoiding redundancy [23]. however, in order to achieve this aim, it is customary when designing a data warehouse, to choose, out of all of the data relating to a single concept and extracted from various sources, which one will represent the "truth" and, as such, be stored in the data warehouse. all other occurrences (i.e. those which do not conform to the "truth") are discarded and will not be loaded into the data warehouse. as a result, given that the data warehouse stores only part of the source data, it is impossible to conduct analyses on all of the values which exist in the source systems. consequently, the single version of the facts concept emerged which assumes that the data warehouse stores all of the source data [24], thereby making it possible to provide different views for different users according to their specific needs. in essence, this approach promotes an elt (extract-load-transform) process in which the transformation of data takes place after the data warehouse has been loaded, as opposed to the traditional etl approach in which the data is transformed prior to its being loaded into the data warehouse. as a result, the data warehouse will store all of the available data from all of the sources spanning the entire history of the business system. therefore, the analytical processing of the data will be performed on-demand depending on the business users needs [24]. the aim of this section is to explore whether the data models provide mechanisms for, on the one hand, ensuring the completeness of the data which is transferred from the sources and, on the other, for tracing the stored data back to the sources from which it originated. the normalized model, as was the case with the temporal aspect, does not implicitly provide domain/mapping model: a novel data warehouse data mode 173 table 2: comparison of dw data models normalized data vault anchor dimensional built/in semantics no yes yes yes resilience to change no partially partially no temporal aspect no partially partially yes completness no yes partially no traceability no yes yes no concepts to support the traceability of data or the recording of data originating from multiple sources. traceability can, for example, be accomplished by recording additional metadata for each n-tuple, if the single version of the truth approach is adopted. however, if the single version of the facts approach is adopted the normalized model demonstrates certain weaknesses. namely, storing data from several sources in the same model would require storing one n-tuple per source in which the same business concept is present. this leads to the issue of relating these n-tuples, i.e. it requires using additional concepts for relating n-tuples representing the same business object. furthermore, it introduces redundancy (as the n-tuples representing the same business object contain a number of attributes with the same values) thereby eliminating one of the good traits of the normalized model. the data vault model supports the traceability of data by requiring that the source, from which the hub was initially loaded, be recorded. likewise, the satellite and link concepts include built-in attributes for recording the source from which the loaded values originated (i.e. the recordsource attribute) [11]. however, the main shortcoming of this model lies in the fact that the structure of a hub contains the business key, the value of which is assumed to be stable or rarely changes. moreover, the structure of the business key might also change. these situations are resolved by introducing a new hub which will be related to the original hub via a "same-as"link. consequently, more than one hub, with the same attributes and the same relationships with other model elements, will exist. in addition, the data vault model enables tracing data from multiple sources, by recording the data source in a satellite. the anchor model supports the traceability of data, via the metadata concept, as all timevariant concepts (thus excluding anchors and knots) can reference the metadata capturing the source of the data. in effect, this also means that it is not possible to record that, for a single object two identical values, originating from two different sources, are both valid at the same point it time. reason for this is that, at the implementation level, there exists a unique constraint over a group of attributes: identifier, valid time and the value of the attribute. the dimensional model does not provide built-in mechanisms for tracing the stored data back to the sources. furthermore, it does not allow for recording multiple values (originating from multiple sources) for a single object, during the same period of time. 3 groundwork in light of the previous discussion it can be concluded that none of the analyzed models completely fulfill all of the necessary requirements pertaining to dw data models (as summarized in table 2). by defining and studying the issues, recognized in the previous section, a number of conclusions will be made which will set the grounds for designing a new dw data model. extensibility and adaptability of the data model as previously demonstrated, data models with a higher degree of integration among objects and attributes or objects and relationships (i.e. the normalized and dimensional models) exhibit 174 i. bojičić, z. marjanović, n. turajlić, m. petrović, m. vučković, v. jovanović less flexibility and adaptability to changes in the structure of the data sources in comparison to models with a lower degree of integration (i.e. anchor and data vault models). example 1: the illustrative examples given in section 2.2. conclusion 1: the data model should reinforce a "loose" coupling between the objects and their attributes and relationships, and support the automated transformation of source model concepts into the 6nf. resilience to changes in the values of object identifiers although this issue was not explicitly described in the previous analysis of data models (because the identifier is in fact part of the structure of an object), it is relevant because the analyzed models differ in the way that they handle object identifiers. an object identifier is defined as an attribute with a unique value where the inverse domain→object mapping also has a (0 or 1:1) cardinality. while such a viewpoint is justified in transactional systems, it cannot be applied to the business identifiers of objects when it comes to temporal systems with, potentially, several data sources. example 2.1: the pension and disability insurance (pio) fund of the republic of serbia, established by the law on pension and disability insurance has two, in the most part, independently maintained information systems in its belgrade and novi sad branches. consequently, there are a number of cases in which the two branches assigned two different valid personal numbers (representing business identifiers) to the same person. given that the integration of these two information systems requires the storing of both identifiers, the same object will have two different, yet simultaneously valid, values for the same identifier. on the other hand, in several cases a single personal number was assigned to more than one person. example 2.2: the ministry of interior of the republic of serbia is responsible for assigning unique master citizen numbers (in serbian: jedinstveni matični broj gradjanajmbg) to all citizens. however, there are some cases in which a citizen was mistakenly assigned two different jmbg numbers which are both valid at the same time. in addition there are a few cases of duplicate jmbg numbers, i.e. the same jmbg was assigned to different citizens. conclusion 2: the data model should provide for defining an object identifier, but also allow for a 1:m cardinality not only for the object→domain mapping, but for the inverse domain→object mapping as well. in other words, object identifiers should be attributes with multiple, possibly shared, values. the same holds for all other attributes of an object. resilience to changes in the structure of object identifiers the structure of an object can, over time, undergo changes with regard to the attributes (or group of attributes) representing the business identifier. this issue is related to those models which presume the existence of a business identifier (the data vault and dimensional models). example 3.1: up till 1982, the business identifier of an insured person object in the pio fund of the republic of serbia was the personal number. with changes in legislation, jmbg were introduced as object identifiers. example 3.2: up till 2003, the business identifier of a contributor object in the pio fund was the registration code. with changes in legislation, a group of attributes was introduced as a composite object identifier: the tax identification number (in serbian: poreski identifikacioni broj pib), municipality and street. conclusion 3: the data warehouse data model should enable storing semantically different business identifiers for a single object in different periods of time. data redundancy even though most of the analyzed data models (save for the dimensional model) dedicate special attention to this issue, data redundancy will inevitably occur as new data sources are added to the data warehouse at various points in time. this issues stems from the fact that all of the models structurally relate the attributes to their corresponding objects. domain/mapping model: a novel data warehouse data mode 175 example 4: the information system of the ministry of interior of the republic of serbia has, among many others, two subsystems: register of births (responsible for assigning and processing unique master citizen numbers) and human resources. when the first subsystem data is loaded, which ever one that may be, the jmbg will, as an attribute, become part of the object it belongs to (e.g. an individual). the loading of the second subsystem data (e.g. human resources and the employee object which also contains a jmbg attribute) will lead to the storing of the same jmbg values in two different places, independent of one another, in the same dw. conclusion 4: the domains, i.e. the sets from which the attributes of an object take their values, should be structurally independent, so as to allow for the same values to be used by the attributes of different objects in order to keep the data redundancy as minimal as possible. capturing the temporal aspect as discussed in section 2.3, the capturing of temporal aspects is only partly supported in most of the analyzed data models. example 5: the information system of the customs administration, which is a part of the ministry of finance of the republic of serbia, has, among others, two subsystems: the transit system (new computerized transit system ncts) and reference data rd. the ncts subsystem regularly relies on the rd codelists. the rd codelists have a validity period, and new codelists are introduced into the system at least one month before the beginning of their validity period. it is obvious that all of the system’s objects are subject to the temporal aspect. conclusion 5: the data model should allow for capturing the temporal dimension, i.e. the valid time and transaction time for every object, attribute and relationship within the system. furthermore, the capturing of the temporal aspect should be implicit, i.e. it should not depend on the expertise of the designer nor the degree of knowledge about the real system. capturing the temporal aspect this issue was described in section 2.4. while none of the analyzed data models were, for the most part, initially designed to satisfy this requirement, the data vault and anchor models do exhibit certain flexibility due to the fact that attributes and relationships are not structurally integrated into the objects. however, the shortcomings of these models are exposed when the temporal dimension is introduced. namely, when the source model is replaced by another model version the mechanisms provided by the two data models do not allow for tracking the continuity of the two sources, i.e. they cannot be perceived as two versions of the same model. example 6: it has been illustrated, through several examples, that it may be expected for the data warehouse to store several different, yet simultaneously valid, values for the same attribute. conclusion 6: the data model should implicitly allow for simultaneously storing several different values for a single object characteristic (relationship or attribute) while also maintaining a reference to the version of the source (model) from which the value was retrieved. all data is equal example 7: two of the analyzed models, namely the data vault and anchor models, introduce novel concepts which differ semantically from the concepts which are customarily used for describing business objects. the data vault model provides the reference data concept, while the anchor model offers the knot concept. both concepts play a role in representing static, immutable objects. the ministry of health of the republic of serbia maintains various sets of objects related, for example, to the organizational structure, human resources or medical equipment of the health facilities within its jurisdiction. the processing of such objects relies on the reference data which belongs to a separate classifications submodel, which represents a unique codelist system accessible by all business objects. such codelists would be represented as reference data in data vault models or knots in 176 i. bojičić, z. marjanović, n. turajlić, m. petrović, m. vučković, v. jovanović anchor models. the main limitation of such an approach is that codelists are regarded as immutable, even though they are managed by another business function, i.e. they are managed, like any other object in the system. conclusion 7: the data model should give equal importance to each type of data, regardless of whether it represents metadata, whether it originates from within the business system or the frequency with which it changes. 4 the domain/mapping model taking into account the issues and conclusions described in the previous section, a new model the domain/mapping model (dmm) is proposed. the proposed dmm, depicted in fig.4 is a general model which has been specifically designed to reconcile the semantic differences of existing data warehouse conceptual models, eliminate redundancy, be resilient to changes, allow for the capturing of temporal aspects, enable traceability, extensibility and adaptability, as well as to maintain a single version of the facts. figure 4: the domain/mapping model conceptual model the core concept of the proposed model is the domain which represents a set of values or objects. a set of values represents a value domain which is universal and does not depend on temporal and spatial aspects. it is a set of a finite number of atomic elements, from which the attributes of an object take their values. each such set is predefined to include an additional element: the unknown (i.e. null) value. definition 1. a value domain dv = (i,v ) is an ordered pair, where: • i is a finite set of identifiers and • v is a finite set of values. a value domain is represented by an ellipse symbol with the name of the value domain inside the ellipse. an object domain represents a set of real-world objects which are influenced by temporal and spatial aspects, i.e. the objects are mutable in time and space. definition 2. a object domain do = (i,t,s) is an ordered triplet, where: • i is a finite set of identifiers, domain/mapping model: a novel data warehouse data mode 177 • t is a temporal indicator and • s is a spatial indicator. an object domain is represented by a double-lined ellipse symbol with the name of the object domain inside. a mapping is a concept which allows for forming the structure of the objects and their relationships. when an attribute is defined, then the mapping is between an object domain and a value domain. if a relationship between objects is defined, then the mapping is between the object domains. the forming of the structure of the objects and their relationships is influenced by the temporal aspects. furthermore, the data warehouse structure is defined in accordance with a particular model version (different sources or versions of the same model). definition 3. a mapping m = (f,t,v ) is an ordered triplet, where: • m is the mapping between two domains, • t is a temporal indicator and • v is the version of the model in which the mapping is defined. a mapping is represented by a solid undirected line with the name of the mapping on it. definition 4. f is a pair consisting of a mapping function i.e. a mapping f(x) = dd → dr and its inverse mapping f′(x) = dr → dd, where: • dd is the domain of the mapping and, • dr is the codomain (i.e. range) of the mapping. definition 5. mappings between two value domains are forbidden. definition 6. a temporal aspect t = (ttt,tvf,tvt) is an ordered triplet of temporal dimensions, where: • ttt is the transaction time, • tvf is the beginning of the validity period and • tvt is the end of the validity period. definition 7. a spatial aspect s = (lt,lg) is an ordered triplet of temporal dimensions, where: • lt is the latitude and • lg is the longitude. definition 8. a latitude lt = (gd,gm,gs,gdw) is an ordered quadruple, where: • gd = the degree ranging from 0-90, • gm = the minutes, • gs = the seconds and • gdw = the direction (north/south). definition 9. a longitude lg = (gd,gm,gs,gdh) is an ordered quadruple, where: 178 i. bojičić, z. marjanović, n. turajlić, m. petrović, m. vučković, v. jovanović • gd = the degree ranging from 0-180, • gm = the minutes, • gs = the seconds and • gdh = the direction (west/east). 5 dmm examples in this section several examples, demonstrating the utilization of the proposed dmm model, will be given. the depicted examples also illustrate the aptness of the proposed model with regard to the issues and the fulfillment of the requirements postulated in section 3. two simplified submodels of the pio fund model, depicting changes in the structure of the insured person concept during a given period of time, are shown in fig.5. as depicted, the initial insured person concept was later modified by introducing the jmbg, which then also became the business identifier of the object due to changes in legislation. up till then the business identifier was the personal number (pn). the fulfillment of conclusions 1-3 will be demonstrated through this example. figure 5: business identifier replacement the following fig.6(a) depicts the dmm model with three domains: personal numbers, first names and surnames. it also contains the insured person concept, whose concrete instances are related to the defined domains, via the mapping concept. figure 6: the initial (a) and modified (b) insured person dmm model all mappings have an m:m cardinality (i.e. all attributes are multi-valued) which satisfies the requirement that several values of the same attribute can be stored at the same time, across two dimensions: the temporal and source dimensions. namely, if several different systems store different values of a single attribute, and if the value of the attribute may change over time, the proposed model would be capable of upholding these changes. this is possible due to the domain/mapping model: a novel data warehouse data mode 179 fact that the mapping of domains is accomplished through the mapping concept which implicitly stores both the valid time and the version of the source metamodel. consequently, the data warehouse stores all values of all data source model versions, without loss of information. fig.6(b) illustrates this scenario, when a new attribute, which also becomes a new object identifier, is added. at the physical level, all of the introduced concepts are implemented as tables. as depicted in fig.6(b), the initial model was modified by simply adding new structures, without altering or deleting existing ones, i.e. by mapping corresponding domains. the next example demonstrates how the elimination of data redundancy is achieved by keeping the domains (from which the attributes take their values) structurally independent, thereby allowing for several different attributes to use the defined domains at the same time. the depicted example shows that different business functions from different data sources use the same value domains. fig.7 depicts a simplified human resources submodel (of the model of the ministry of interior of the republic of serbia) which contains an employee concept consisting of the jmbg, first name, last name and birth date attributes. the first three attributes are mappings onto value domains, while the birth date is a mapping onto an object domain date, which is itself composed of three value domains: days, months and years. if a new data source is introduced, it is possible to reuse the existing domains, in order to eliminate redundancy, without altering the existing data warehouse structure. the following fig.7 depicts a register of births submodel of the same business system, which describes the part of the system related to the jmbg of citizens. this submodel maintains the jmbg numbers of all citizens and includes all of the jmbg numbers from the human resources department, i.e. those belonging to the employees of the ministry of interior. it is customary for those jmbg numbers to be stored redundantly, which leads to data anomalies. the redundancy is eliminated by mapping both concepts (employee and individual) to the value domain unique master citizen numbers. figure 7: the absence of data redundancy in the dmm model furthermore, the individual concept also reuses the days, months and years value domains, as the concrete values of these domains are utilized for constructing the unique master citizen numbers (the first 7 digits of the jmbg correspond to the date of birth of an individual: two digits for the day, two for the month and the last three digits of the year of birth). by building a net of domains, which can be used for various concepts, data redundancy is kept minimal, which addresses issue 4 described in section 3. in addition, the dmm fulfills the requirement postulated in conclusion 5, by implicitly 180 i. bojičić, z. marjanović, n. turajlić, m. petrović, m. vučković, v. jovanović capturing the valid time and transaction time for all objects stored in the data warehouse, as well as for the mappings they participate in. the fulfillment of the requirement postulated in conclusion 6, is accomplished by using an implicit m:m cardinality for object-attribute or object-object mappings. consequently, it is possible for a single object to have several different values for the same attribute or to be simultaneously related to several objects of the same type. finally, the fulfillment of the requirement postulated in conclusion 7, is depicted by the following two submodels (fig.8). the first shows a simplified medical equipment submodel (which is part of the ministry of health model). if the equipment codelist is modeled without the capability of tracing changes in its values through time, it would not be possible to maintain the classification model without changing its structure. the following submodel classifications, shows how a new source can be easily introduced into the data warehouse without requiring changes in the existing model, even if it the two models are not at the same level of abstraction. figure 8: medical equipment as demonstrated by the previous examples, a dmm consists of two fundamental concepts, domains and mappings, which provides complete flexibility at the physical level while overcoming the weaknesses of the presented data warehouse data models. conclusion the proposed dmm is capable of absorbing the changes which may occur in the source 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[24] r. damhof (2008); the next generation edw, available at: http://prudenza.typepad. com/files/ international journal of computers communications & control issn 1841-9836, 13(2), 205-220, april 2018. new failure mode and effects analysis based on d numbers downscaling method b. liu, y. hu, y. deng baoyu liu college of infomation science and technology jinan university, guangzhou, china baoyu.l@hotmail.com yong hu big data decision institute, jinan university, tianhe, guangzhou, 510632, china henryhu200211@163.com yong deng* 1. big data decision institute, jinan university, tianhe, guangzhou 510632, china 2. institute of fundamental and frontier science, university of electronic science and technology of china chengdu, 610054, china *corresponding author: prof.deng@hotmail.com abstract: failure mode and effects analysis (fmea) is extensively applied to process potential faults in systems, designs, and products. nevertheless, traditional fmea, classical risk priority number (rpn), acquired by multiplying the ratings of occurrence, detection, and severity, risk assessment, is not effective to process the uncertainty in fmea. many methods have been proposed to solve the issue but deficiencies exist, such as huge computing quality and the mutual exclusivity of propositions. in fact, because of the subjectivity of experts, the boundary of two adjacent evaluation ratings is fuzzy so that the propositions are not mutually exclusive. to address the issues, in this paper, a new method to evaluate risk in fmea based on d numbers and evidential downscaling method, named as d numbers downscaling method, is proposed. in the proposed method, d numbers based on the data are constructed to process uncertain information and aggregate the assessments of risk factors, for they permit propositions to be not exclusive mutually. evidential downscaling method decreases the number of ratings from 10 to 3, and the frame of discernment from 210 to 23 , which greatly reduce the computational complexity. besides, a numerical example is illustrated to validate the high efficiency and feasibility of the proposed method. keywords: failure mode and effects analysis; dempster-shafer evidence theory; basic belief assignment; belief function; risk priority number; d numbers; evidential downscaling; aggregate assessment 1 introduction failure mode and effects analysis (fmea) was first developed to assess known and potential faults and prevent them from happening in the 1960s [5]. it is an efficient and powerful technology in engineering and management fields, including defining, identifying, and removing known and potential faults, errors, and risk from the system, process, design as well as service. [1, 51]. besides, in other fields, such as risk management, healthcare management and engineer design, fmea plays an important role [2, 8, 10, 11, 15, 24, 36]. furthermore, fmea not only identifies factors that induce fault but also classifies the likelihood and severity of such fault [11]. copyright © 2018 by cc by-nc 206 b. liu, y. hu, y. deng a traditional method in fmea is risk priority number (rpn), which is acquired by multiplying the grades of occurrence, severity, and detection. thus, how to aggregate the assessments of three risk factors is a key issue, especially when the evaluated information given by experts is uncertain. these years, a large number of approaches have been proposed to improve fmea methodology [5, 11, 23, 26, 37, 43]. it’s inevitable to deal with uncertainty in real world [20–22,29,44]. due to the efficiency to handle linguistic information of fuzzy sets [13,18], an intuitionistic fuzzy approach for fmea is proposed in [15], which offers some advantages over earlier models as it accounts for degrees of uncertainty in relationships among various criteria or options, specifically when relations cannot be expressed in definite numbers. in addition, an integrating hesitant 2-tuple linguistic term sets and an extended qualiflex approach is proposed by liu h c et al. in [13]. furthermore, as is known to all, d numbers [9] and grey theory are two popular methods to process the uncertain information. therefore, based on the two theories, plenty of methods are presented [23]. classical rpn is criticized due to its several shortcomings. for example, the importance weights of three risk factors are taken into consideration. besides, classical rpn can do nothing about the uncertain information, etc. aiming at these problems, a method which is effective to dispose the uncertainty of assessment is proposed by chin et al. [4]. however, the algorithm is too complex computationally, the reason is that the numeric ratings of every risk factor are from 1 to 10 so that the number of frame of discernment is 210, which greatly increase the computational load [11]. in other to reduce the computational complexity, an evidential downscaling method is proposed in [11]. nevertheless, the d-s combination theory it uses requests that propositions are exclusive mutually. as a matter of fact, traditional fmea ratings are obtained by subjective judgment of the experts. therefore, the boundary of two adjacent ratings is fuzzy, hence the propositions are not actually mutually exclusive. for purpose of solving this problem, in this paper, a new method to evaluate risk in fmea based on d numbers and evidential downscaling method, named as d numbers downscaling method, is proposed. in the proposed method, on the one hand, the evidential downscaling is utilized to decrease the frame of discernment so that greatly reduces the computational complexity. on the other hand, according to the data, d number is constructed to processing uncertain information and aggregate the assessment of risk factors because d numbers permit propositions that are not exclusive mutually. the rest of the paper is organized as follows. key concepts and previous theories are briefly reviewed in section 2. a new method to evaluate risk based on d numbers downscaling method is proposed in section 3. a numerical example is illustrated to show the feasible of the proposed method in section 4. a brief conclusion is drawn in section 5. 2 preliminaries 2.1 risk priority number the real systems are very complicated to be modelled [14,27,46]. risk priority number (rpn) is a traditional and typical method to model and evaluate risk in fmea of the complicated systems, which is acquired by multiplying the grades of occurrence assessment (o), severity assessment (s), and detection assessment (d). that is rpn = osd, where o stands for the probability of occurrence of failure mode, s refers to the severity of failure mode and d refers to the probability of failure being detected. the three risk factors are evaluated by fmea experts using a 1 to 10 numeric scale, as expressed in tables 1 to 3 [11]. new failure mode and effects analysis based on d numbers downscaling method 207 the larger rpn is, the more important degree it is supposed to be assigned, referring to the failure mode should be more priority to be corrected. however, as a traditional method for fmea, classical rpn has been controversial all the time for many reasons, and the most important ones are listed as follows: • the three risk factors have the same importance in rpn, but in practical applications of fmea, they might be treated with different weight. • other factors are not taken into consideration, such as economy factor. • different combination of risk factors might obtain the same rpn, nevertheless, the potential risk might be totally different. • the mathematical formula for calculating rpn lacks the scientific basis. there is not any proof to clarify the reason why o, s and d should be multiplied to obtain rpn. • in fact, the scores of the three factors are difficult to be determined accurately. therefore, fmea experts usually provide different types of assessment information, some of which are uncertain and incomplete data. 2.2 dempster-shafer evidence theory dempster-shafer evidence theory (d-s evidence theory) constructs a basic probability assignment(bpa) in the frame of discernment. through combining bpas the imprecise and uncertain information can be fused [5]. with its rapid development, it is regarded as an important method that is extensively applied in many fields such as complex networks and systems [8, 10, 12, 19, 45, 50, 52], multisource information fusion [6, 7, 17, 28, 41, 42, 49], uncertainty modelling [1,2,16,48,53], pattern recognition [30,31] and imprecise payoff [34,35]. definition 1. let θ = {h1,h2, · · · ,hn} be a finite nonempty set, which is composed of n mutually exclusive and exhaustive elements. denote p(θ) as the power set composed of 2n elements of θ. the bbas function is defined as a mapping of the power set p(θ) to a number between 0 and 1, that is, m : p(θ)− > [0, 1], and which satisfies the following conditions: m(∅) = 0; (1)∑ a⊆p(θ) m(a) = 1. (2) the mass m(a) represents how strongly the evidence supports a. definition 2. two bodies of evidence a and b of θ can be used to calculate the belief level for some new hypothesis c shown as follows: the measure of conflict k that is also called as the conflict coefficient between a and b is given as: k = ∑ a∩b=∅,∀a,b⊆θ mi(a)mi′(b); (3) and the mass function after combination is: m(c) =   ∑ a∩b=c,∀a,b⊆θ mi(a)mi′(b) 1 −k , when a 6= ∅ 0, when a = ∅, (4) 208 b. liu, y. hu, y. deng where k reflects the conflict between two bodies of evidence. absolutely, 0 ≤ k ≤ 1. k = 0 shows the absence of conflict between two bodies of evidence. k = 1 shows complete conflict between mi and mi′. when k = 1, the dempster ’s rule of combination is no longer applicable. 2.3 the evidential downscaling method an evidential downscaling method is proposed in [11], which is based on euclidean distance from multi-scale to construct the bbas. the new method , decreasing the frame of discernment from 210 to 23, is shown as follows: step 1. calculate the group assessment into a crisp number with weighted average method. step 2. construct the bbas by definition 2 with the euclidean distances between group assessment and multi-scale ratings. when calculating the distance, an evidential downscaling method is presented to divide the rating into three scales: ‘10’ for ‘bad’, ‘1’ for ‘good’, and ‘5’ for ‘bad or good’ because rating 10 expresses the most serious degree, rating 1 expresses that failure is almost impossible and rating 5 expresses the moderate degree, which decreases the number of frame of discernment from 210 to 23. the bbas can be constructed as follows: m({b}) = d(g) d(g) + d(b,g) + d(g) , (5) m({g}) = d(b) (d(g) + d(b,g) + d(g) , (6) m({b,g}) = 1 −m({b}) −m({g}). (7) step 3. combine the bbas of risk factors by dempster’s combination rule. step 4. acquire the risk priority according to the aggregation of ‘bad’. let m({b})osd, m({g})osd and m({b,g})osd be the aggregation of three risk factors for ‘bad’, ‘good’, and ‘bad or good’. the final assessment of ‘bad’ and ‘good’ can be calculated as follows: m({b})′osd = m({b})osd + m({b,g})osd 2 , (8) m({g})′osd = m({g})osd + m({b,g})osd 2 . (9) 2.4 d numbers [9] d numbers is an effective method to deal with uncertain information, which overcomes the shortcomings of dempster-shafer (d-s) theory. nowadays, d number is extensively used in many fields such as dependence assessment, emergency management, and aggregating operator [33,54,55]. it can be defined as follows: definition 3. let ω be a finite nonempty set, d number is a mapping d : ω → [0, 1], such that∑ b⊆ω d(b) ≤ 1 and d(∅) = 0, (10) new failure mode and effects analysis based on d numbers downscaling method 209 where ∅ is an empty set and b is a subset of ω. more importantly, different from the concept of frame of discernment in d-s theory, the elements of set ω do not require to be mutually exclusive in d numbers. if ∑ b⊆ω d(b) = 1, the information is considered to be complete; otherwise, the information is regarded to be incomplete. for a set ω = {b1,b2, · · · ,bi, · · · ,bn}, where bi 6= bj, if i 6= j. then, a special d number can be expressed by d({b1}) = v1, d({b2}) = v2, · · · · · · , d({bi}) = vi, · · · · · · , d({bn}) = vn or d = {(b1,v1), (b2,v2), · · · , (bi,vi), · · · , (bn,vn)}, where vi > 1 and ∑n i=1 vi ≤ 1. for the detailed information about d numbers combination rule, please refer [9]. a combination rule, a kind of add operation, is proposed to combine two d numbers. 3 the proposed method as a traditional method for fmea, classical rpn is acquired by multiplying the rating of three risk factors. nevertheless, classical rpn is criticized for several disadvantages mentioned in 2.1. with the aim of overcoming these shortcomings, a large number of methods to evaluate risk for fmea are proposed. however, existing methods either have a huge computing quantity, such as the chinś method, or require the propositions to be mutually exclusive, like the method proposed by du y et al.. as a matter of fact, traditional ratings are divided by the subjective judgments of the experts which are based on the individual experience. therefore, the boundary of two adjacent ratings is fuzzy, which means that two propositions in fmea are not mutually exclusive. hence, it is obviously unreasonable that dempster-shafer combination theory is used to aggregate assessment in the evidential downscaling method proposed in [11]. aiming at these problems, a new method to evaluate risk in fmea based on d numbers and evidential downscaling method is proposed. based on the three risk factors as well, the proposed method, for one thing, decreases the number of the frame of discernment from 210 to 23 by making use of the evidential downscaling method, which greatly reduce the computational complexity. for another, d numbers are utilized to manage the uncertain information. because d numbers allow the propositions to not be exclusive, it is reasonable that d numbers are used to process uncertain information and aggregate the assessments. furthermore, in consideration of the fuzzy information given by the experts on different failure modes, weighted averages are calculated to substitute them in the proposed method. suppose there are n failure modes and m experts, the specific steps are shown as follows: the failures with higher values of ‘bad’ are assumed to be more important and should be given higher priorities. step 1. calculate the mathematical expectation of the score given by experts for evaluating each risk factor by eq. (11). 210 b. liu, y. hu, y. deng definition 4. let si jl be the mathematical expectation of the score s of failure mode i given by expert j in the assessment of risk factor l, it is calculated as follows: s i j l =   s, if s is integer a× c% + b×d%, if s is (a : c% , b : d%) with c% + d% = 100% a× c% + 1 9 × (55 −a) × (1 − c%), if s is (a : c%) but 0 < c% < 100% 5.5, if s is missing (11) where i = 1, · · · ,n, j = 1, · · · ,m, l = o,s,d. step 2. calculate the euclidean distances between group assessment and multi-scale ratings. consistent with the evidential downscaling method mention in 2.3, then the new score sgi jl of each factor of each failure mode after downscaling can be obtained as follows: d(b) i j l = |s i j l − 10|, (12) d(g) i j l = |s i j l − 1|, (13) d(b,g) i j l = |s i j l − 5|, (14) sg i j l = d(b) i j l (d(g) i j l + d(b,g) i j l + d(g) i j l ) , (15) where i = 1, · · · ,n, j = 1, · · · ,m, l = o,s,d. step 3. construct d numbers on the basis of sgi jl in the light of definition 5. definition 5. let dejfi be the d number of failure mode i of experts j, which stands for the three assessments of experts j towards failure mode i, is modeled as follows: d ej fi = {(bijo,vo), (b ij s ,vs), (b ij d,vd)} (16) with b i j l = λj ×sg i j l (17) where λj is the weight for expert j, and vl (l = o,s,d) is the weight of the risk factor standing for the importance of it in fmea. in addition, i = 1, · · · ,n, j = 1, · · · ,m, l = o,s,d. step 4. according to the processes of dfi numbers mentioned in [9] and definition 6, for each failure mode, the combination of d numbers can be calculated and the i(d) of each failure mode can be obtained as well. most importantly, the failure modes with the lower values of i(d) are assumed to be more important and should be given higher priorities. definition 6. let dfi be the d number of failure mode i (i = 1, · · · ,n) aggregate the assessment of m experts, thus, it can be calculate as follows: dfi = d e1 fi ⊕de2fi ⊕···⊕d ej fi ⊕···⊕demfi (18) figure 1 is the sub-flowsheet of the proposed method, that is, the procedure of downscaling and constructing d number. figure 2 is the general flow-chart. new failure mode and effects analysis based on d numbers downscaling method 211 figure 1: the procedure of downscaling and constructing d number figure 2: the general flow-chart of the proposed method table 1: occurrence assessment by fmea team members failure mode expert1 expert2 expert3 expert4 expert5 1 1 1 1 1 2 1 : 50%, 2 : 50% 1 1 1 1 3 2 2 : 90% 2 2 2 4 8 8 8 : 80%, 9 : 20% 8 8 5 6 6 6 6 6 6 2 13 2 2 2-3 7 2 2 2 9 2 8 1 1 : 75%, 2 : 25% 1 1 1 9 3 3 3 3 3 10 1 : 80%, 2 : 20% 1 1 1 1−2 : 85%, 3 : 15% 11 4 4 4 3−4 : 75%, 5 : 25% 4 12 9 9 9 9 9 13 8 8 : 80% 8 8 8 14 3 3 4 3 3 15 3 3 3 3 3 : 70%, 4 : 30% 16 1 1 1 1 1 17 3−5 : 90%, 6 : 10% 4 4 4 4 18 2 2 2 : 90% 2 2 19 7 7 7 7 7:80% 20 9 9 9 7 : 30%, 8−9 : 70% 9 21 9 8 − 9 9 9 9 212 b. liu, y. hu, y. deng table 2: severity assessment by fmea team members failure mode expert1 expert2 expert3 expert4 expert5 1 7 : 20%, 8 : 80% 8 8 6−7 : 50%, 8−9 : 50% 8 2 8 8 8 8 3 7 − 9 : 90% 8 6-8 8 8 4 8 8 8 8 7 − 9 : 80% 5 8 7 − 9 : 90% 8 8 8 6 8 8 8 6-8 8 7 9 : 75%, 8 : 25% 9 9 9 9 8 4 4 4 4 : 50%, 5 : 50% 3−5 : 75%, 6−7 : 25% 9 2 2 2 2 2 10 2 2 1−2 : 60%, 3−4 : 60% 2 2 11 2 2-3 2 2 2 12 3 3 3 : 60%, 4 : 40% 3 3 13 2−3 : 80%, 3−4 : 20% 3 3 3 3 14 7 8 7 7 7 15 3 3 3 3 3 16 8 8 8 8 17 8 4 8 8 8 18 7 7 7 7 7 19 2 1−2 : 75%, 2−3 : 25% 2 2 2 20 8 8 8 8 21 3 3 3 3 3 4 numerical example suppose that there are 5 experts evaluating risk in fmea, who give their assessments on the three risk factors of 21 failure modes as shown in table 1 to 3. the weights for the five experts are assumed to be 0.3, 0.3, 0.2, 0.1 and 0.1. take failure mode 1 of expert 1 for example. as shown in table 1, three ratings the expert 1 gives are 1, (7:20%, 8:80%), and 3. thus, according to eq. (11), the mathematical expectation of score are 1, 7.8, and 3. then, the euclidean distances between it and ‘bad’, ‘good’ and ‘bad or good’ are calculating as in table 4. therefore, the new scores can be obtained as follows: sg1 1o = d(b)1 1o d(g)1 1o + d(b,g) 1 1 o + d(g) 1 1 o = 9 9 + 0 + 4 ≈ 0.6923. table 3: detection assessment by fmea team members failure mode expert1 expert2 expert3 expert4 expert5 1 3 3 3 3 3 : 90% 2 3 3 3 3 3 3 4 4 4 4 3−4 : 80%, 5−6 : 20% 4 5 5 5 5 5 5 6 6 6 : 85%, 7 : 15% 6 6 6 1 1 : 85%, 2 : 15% 2 1 7 3 2 2 1−2 : 75%, 3−4 : 25% 2 8 3 3 3 : 80%, 4 : 20% 3 3 9 3 3 3 − 4 : 60%, 5 : 40% 3 3 10 4 4 4 4 4 11 3 : 70%, 5 : 30% 3 3 3 12 7 7 7 7 7 13 6 6 6 5-7 6 14 4 4 4 4 4 15 4 4 : 95% 4 4 4 16 3 3 3 3 3 17 5 5 5 5 5 18 7 6-8 7 7 7 19 4 4 4 8 − 9 : 90% 4 20 4 : 60% 9 9 9 9 21 6 6 4-6 6 4 : 25%, 5 − 7 : 75% new failure mode and effects analysis based on d numbers downscaling method 213 table 4: euclidean distances between it and "bad", "good" and "bad or good" bad good bad or good d(b)1 1o = |1−10| = 9, d(g) 1 1 o = |1−1| = 0, d(b, g) 1 1 o = |1−5| = 4; d(b)1 1s = |7.8−10| = 2.2, d(g) 1 1 s = |7.8−1| = 6.8, d(b, g) 1 1 s = |7.8−5| = 2.8; d(b)1 1d = |3−10| = 7, d(g) 1 1 d = |3−1| = 2, d(b, g) 1 1 d = |3−5| = 2; 0 5 10 15 20 25 failure mode 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 th e s g o f th e o cc u rr e n ce a ss e ss m e n t expert1 expert2 expert3 expert4 expert5 figure 3: the new score of occurrence assessment sg1 1s = d(b)1 1s d(g)1 1s + d(b,g) 1 1 s + d(g) 1 1 s = 2.2 2.2 + 6.8 + 2.8 ≈ 0.1864. sg1 1d = d(b)1 1d d(g)1 1d + d(b,g) 1 1 d + d(g) 1 1 d = 7 7 + 2 + 2 ≈ 0.6364. the data of the other three assessments is treated in the same way, among which the result of occurrence assessment with the new scores are shown in figure 3. as shown in figure 3, the higher initial rating tends to the lower new score while the lower rating tends to the higher one. then, the first element b can be calculated by multiply the weight of the expert 1 and the new score: b1 1o = λ1 ×sg 1 1 o = 0.3 × 0.6923 = 0.2077. 214 b. liu, y. hu, y. deng table 5: the representation of d numbers for failure mode 1 (f1) f1 d numbers expert 1 de1f1 = {(0.2077, 1 3 ), (0.0559, 1 3 ), (0.1909, 1 3 )} expert 2 de2f1 = {(0.2077, 1 3 ), (0.0500, 1 3 ), (0.1909, 1 3 )} expert 3 de3f1 = {(0.0947, 1 3 ), (0.0333, 1 3 ), (0.1273, 1 3 )} expert 4 de4f1 = {(0.0692, 1 3 ), (0.0217, 1 3 ), (0.0636, 1 3 )} expert 5 de5f1 = {(0.0692, 1 3 ), (0.0167, 1 3 ), (0.0627, 1 3 )} b1 1s = λ1 ×sg 1 1 s = 0.3 × 0.6923 = 0.0559. b1 1d = λ1 ×sg 1 1 d = 0.3 × 0.6923 = 0.1909. with the same process, the b of other d numbers can be obtained. next, the data is treated using the d numbers. it needs to be emphasized that the weight of each rich factor is treated as the second parameter v. it is worth mentioning that the judgments for the relative importance of each criterion are various from different expert. in order to compare with other methods which do not take it into consideration and prove the feasibility of the proposed method as well, the importance of three factors are assumed to be same. that is, v is supposed to be identically equal to 1 3 . therefore, the assessment result being disposed through above process are expressed in the forms of d numbers. according to the data of expert 1, a d number de1f1 is constructed, where de1f1 = {(0.2077, 1 3 ), (0.0559, 1 3 ), (0.1909, 1 3 )}. similarity to de1f1 , according to the data of expert 2, expert 3, expert 4 and expert 5, four d numbers de2f1 , d e3 f1 , de4f1 and d e5 f1 are generated. table 4 shows these d numbers. in the same method, for failure mode fi, and expert ej, each d number dejfi can be constructed, too. for each failure mode, all experts’ data represented by d numbers are combined according to the combination rule of d numbers mentioned in [9]. take the failure mode 1 (f1) for example, the integrated assessment of three experts is the aggregation of de1f1 , d e2 f1 , de3f1 , d e4 f1 and de5f1 , df1 = d e1 f1 ⊕de2f1 ⊕d e3 f1 ⊕de4f1 ⊕d e5 f1 . in accordance with the processes of dfi numbers mentioned in [9], df1 is calculated. therefore, the i(df1 ) of df1 can be obtained. using the same method, dfi and i(dfi) are acquired, which are shown in table 5. in the meantime, according to i(dfi)(i = 1, 2, · · · , 21), the ranking of 21 failure modes are obtained, where the failure modes with the lower values of i(d) are given higher priorities. in table 6, the risk priority rankings of the evidential downscaling method are shown. compared with the ranking of the evidential downscaling method, the proposed method’s is similar. as seen from table 6, failure mode 3, failure mode 9, failure mode 12, failure mode 13, failure mode 15, failure mode 18, failure mode 20 and failure mode 21 have the same risk priority ranking in both methods. besides, it is indicated that the five of highest risk priority rankings are new failure mode and effects analysis based on d numbers downscaling method 215 0 5 10 15 20 25 failure mode 0 5 10 15 20 25 th e r a n k o f tw o m e th o d s the proposed method the evidential downscaling method figure 4: the comparison of risk priority ranking by two method figure 5: the occupation of ratings between two method failure mode 20, 4, 5, 12 and 21, which means that these 5 faults are most likely to occur in both two methods. moreover, in both two methods, the five of the lowest priorities are failure mode 8, 9, 10, 11 and 15, indicating that these 5 failures are supposed to be. figure 4 shows the comparison of risk priority rankings by the proposed method and the 216 b. liu, y. hu, y. deng table 6: the comparison of risk priority ranking by two methods failure mode result the rank of proposed method the evidential downscaling method 1 0.06634 12 10 2 0.07464 16 15 3 0.06597 11 11 4 0.04524 3 2 5 0.04210 2 3 6 0.06309 9 14 7 0.05789 7 9 8 0.08779 20 21 9 0.08743 19 19 10 0.08616 18 20 11 0.08824 21 18 12 0.04596 4 4 13 0.05568 6 6 14 0.06670 13 12 15 0.08518 17 17 16 0.06896 15 16 17 0.0633 10 7 18 0.06064 8 8 19 0.06781 14 13 20 0.02350 1 1 21 0.05345 5 5 evidential downscaling method, in which the x-axis shows the rankings and y-axis shows the failure modes. as is shown in figure 4, it indicates that the rank result generated by proposed method has similar trend with the evidential downscaling method, which proves the proposed method is valid. however, in consideration that it is out of reality to request the propositions to be exclusive mutually, the proposed method is more reasonable. in 5, the occupation of ratings between two methods for each failure mode is shown. from figure 5, the edge of the area around with different color is almost near the midline of the graph, which proves that the proposed method similarly has a good effect for risk assessment. 5 conclusion the conventional fmea method has been criticized for its deficiencies especially in the evaluation of risks of failure mods and computation of classical rpn. a large number of new methods for fmea have been proposed. nevertheless, some deficiencies still exist in these methods, such as huge computing quality. although the evidential downscaling method presented by du y et al. is capable to greatly reduce the amount of calculation, it is not reasonable that the propositions are requested to be exclusive mutually. therefore, a new method to evaluate risk in fmea based on d numbers downscaling method is proposed with the purpose of solving this problem. obviously, the proposed method can handle the uncertain information well in fmea. compared with the traditional rpn, the proposed method not only can dispose the scores of three risk factors even if they are uncertain, but also takes into consideration the relative importance weigths of risk factors. therefore, the result of the proposed method is more reliable. in comparison to the existing method, the proposed has more succinct calculation formulas, which has a smaller amount of calculation so 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[55] zhou, x., shi, y., deng, x., deng, y., (2017); d-dematel: a new method to identify critical success factors in emergency management, safety science, 91, 93–104, 2017. international journal of computers communications & control issn 1841-9836, 13(1), 83-98, february 2018. factors space and its relationship with formal conceptual analysis: a general view h. liu, i. dzitac, s. guo haitao liu* 1. institute of intelligence engineering and mathematics liaoning technical university, fuxin 123000, china 2. college of science liaoning technical university, fuxin 123000, china *corresponding author: liuhaitao@lntu.edu.cn ioan dzitac 1. aurel vlaicu university of arad 310330 arad, elena dragoi, 2, romania ioan.dzitac@uav.ro 2. agora university of oradea 410526 oradea, p-ta tineretului 8, romania, idzitac@univagora.ro sicong guo 1. institute of intelligence engineering and mathematics liaoning technical university, fuxin 123000, china 2. college of science liaoning technical university, fuxin 123000, china guosizong@tom.com abstract: conceptual generation is a key point and basic problem in artificial intelligence, which has been probed in the formal concept analysis (fca) established by g. wille. factors space (fs) is also a branch of cognition math initiated by p.z. wang at the end of last century, which has been applied in information processing with fuzzy concepts effectively. this paper briefly introduces the historic background of fs and its relationship with fca. fs can be seen as a good partner of fca on conceptual description and structure extraction; combining fca with fs, we can get more clear and simple statements and more fast algorithms on conceptual generation. keywords: factors space (fs), formal concept analysis (fca), rough sets, conceptual generation, basic concept semi-lattice, fuzzy logic. 1 introduction there were three mathematical branches describing cognition and thought of human beings simultaneously: formal concept analysis [32] (wille 1982), rough sets [14] (pawlak, 1982) and factors space [29] (wang, 1982). we call them the branches of cognition math since there may be not any mathematical branch clearly declaring its object is to describe cognition process before. even though many mathematical branches, such as mathematical logic, probability, optimization have been applied widely in artificial intelligence, those branches just do ai jobs spontaneously by their own natural characteristics, while cognition math conscientiously does ai jobs, fitting the needs of the era. relying on the involution between intension and extension, r. wille gave a mathematical definition to the concept and gave algorithms for the generation of conceptual lattice, and since then, the computer has done conceptual generation automatically. he had opened the door of copyright ©2018 cc by-nc 84 h. liu, i. dzitac, s. guo cognition math. similarly, z. pawlak brought in the rough sets to do knowledge discovering for databases, and p. z. wang brought in the factors space to represent things and thinking. the articles of wille are very serious. unfortunately, he focuses on the attribute value rather than the attribute name. the number of columns in his formal background table exponentially increases, and the algorithms on concept lattice in fca encounters the trap of n-hard complexity. z. pawlak emphasizes attribute name rather than attribute value, making the number of columns of formal background table greatly reduced and turned into the table of information system in rough sets, which plays the theoretical foundation for relational database. unfortunately, his theory is not deep enough to avoid n-hard trap in attribute induction. pawlak school emphasizes attribute name, but is not really aware of the significance of the attribute name. what is the difference between attribute value and attribute name? attribute values, such as tall, middle and short, are a group of varying states under the name height. from the viewpoint of mathematics, an attribute value is a constant, while the name is a variable that represents each one of its states. height is an important factor for basketball player selection since the selection limits the varying of height. for the same reason, it is important for weightlifter selection, while it is not important for student examination since there is no limit to its varying. to manifest the influence of height with respect to the talents selection, we would like to say that height is a factor. in this paper, the word factor stands for a name, which influences or causes something. people engaged in database know fca and rs very well, but they are not familiar with factors space when fs was presented by p. z. wang at 1982, which was used to do ontology and cognition rather than database. to make mathematical discussion on randomness vs fuzziness, wang presented fs to represent the basic space in a probability field and represent the discussion universe in fuzzy field respectively, and discovered a dual relationship: a fuzzy set defined on universe u can be represented by a probability field in the power of u; fuzzy measures can be determined by the falling shadow of a random set. this is the core of falling shadow theory [18]. wang has proved the existence and uniqueness theorem around the probability distribution in the power p(u), which lays the foundation for the applications of fuzzy sets and other subjective non-additive measures. fs plays a very important role in the development of fuzzy mathematics in china. since 2012, wang has turned his target to data science and finds that by means of the supports of fs, we can unify and deepen the theories of fca and rs. all subjects in fca and rs can be briefly and clearly stated, and the mentioned n-hard traps can be resolved by faster algorithms. fs provides natural base for cognation, information and data science. in this paper, we will introduce and develop the theory of fs briefly. we will introduce the historic background of factors space theory in section 2. what is factors and factors space? we will answer the questions in section 3. the core idea of background relation of fs will be given in section 4. in section 5, we will deepen the conceptual generation and its ai applications based on fs. section 6 will draw a brief conclusion. 2 the historic background of factors space theory in 1982, wang presented the first paper of factors space to explore and compare two kinds of uncertainty: fuzziness and randomness. he used factors space to represent basic space in probability and the discussion universe in fuzzy sets respectively. he clearly answered questions about the sources, and distinguished and relationship of the two kinds of uncertainties. we will introduce the historic background of factors space in following sub-sections. factors space and its relationship with formal conceptual analysis: a general view 85 2.1 basic space in probability what is randomness? why and when does it occur? tossing a coin, we can get two outcomes, head or back randomly. to know why randomness occurs, we need to find out those factors that influence the tossing process, such as shape of coin, actions of fingers, condition of desktop, environmental influence and so on. they will impact the outcomes. we may make such a determinism hypothesis: when the states of all factors are fixed, the outcome will be determinate. if not, there must be some influential factors that have not been considered, and then the hypothesis should be held considering all missing factors. each factor f has a state space x(f) as a dimension; let fo be the set of all factors, and they form the high-dimensional space x(fo), which is what we call the factors space. this hypothesis tells us that, if the considered set fo of factors is sufficient and if the state of factors can be observed and controlled precisely, then there is no randomness; randomness occurs if the considered factors are not complete or even if complete, they cannot be observed and controlled precisely. randomness is the uncertainty on the occurrence of event caused by the lack of conditional factors with their observation and controlling. if we cannot observe a state x precisely into a single point but a subset c in x = x(fo), c is called the condition of experiment. under the condition, we only know x is in c but don’t know where it is. when c is not small enough and crosses the border of the opposite sides with respect to an event, the randomness occurs. even though conditional factors are not sufficient, there exists causality rule between condition and result. probability is generalized causality, which is the essential scale of the frequency of an event. the basic model of probability statistics is: fix the circle, move the point, where circle stands for the condition c, and point stands for the point x in x. kolmogorov gave an axiomatic definition on probability field (ω,a,p), where ω is the basic space, a is an σ-algebra on ω, and the probability p is an additive measure defined on a. wang admires him so much that he defines the random variable as a mapping ξ : ω → r; based on the definition, classical probabilities are carried by ξ and become probability distribution functions or distribution densities on real line r, and then probability becomes a modern mathematical branch. however, how can we define a random variable ξ, a mapping from ω to r? only factors space can describe a random variable by a determinate mapping, and the basic space ω must be a factors space [17]. as a great mathematician, kolmogorov has had the idea of factors space already. viewing the basic space ω as a factors space, we can study probability from a new aspect: focusing on the transformation between randomness and certainty. the core problem is how to decrease the randomness so that it is transferred into certainty? we can divide the set fo of factors definition into three subset fo = g1 + g2 + gc, where g1 +g2 is the set of considered factors, and gc is the set of unconsidered factors, g1 is the set of considered factors whose states can be observed and controlled with accuracy c, which is small enough so that it does not cross the borders of opposite side of an events. g2 is the factors in g but not in g1. then we can have a formula ξ = g1(x) + g2(x) + g c (1) where g1 is a function, g2 is a probability density under the condition c, and gc is a gaussian distribution of white noise when all unconsidered factors are neglect-able. let ξ be the hitting score of a shooter. we need to calculate its mean a = mξ and the mean square error σ = (ξ −mξ) 1/2. passing through special trains, we can reduce the error σ, and then move the parameter a to be the real target. an important task of probability is how 86 h. liu, i. dzitac, s. guo to promote the hitting probability of a shooter throughout special trains on improving the two parameters a and σ respectively. 2.2 discussion universe of a fuzzy set l. a. zadeh (1921-2017) was a great world-renowned computer scientist in the history [4]. to open a path to develop artificial intelligence from the mathematical base, he presented fuzzy sets in 1965 [36], deeply influencing the progress of ai applications in the world. a fuzzy subset of the universe u is defined by a mapping µa : u → [0, 1], called membership function of a. he encouraged wang to make a study on the discussion universe u; while the selection of u is crucial for the fuzzy description. for example, "young" is a fuzzy concept. to see whether a man is young, it is very difficult to measure the membership under the factor age, but is much easier if we consider more factors such as "face", "action", "vigor". p.z. wang treated the discussion universe u as a factors space and found that fuzziness is the uncertainty on the conceptual extension caused by the lack of cognitive factors [18]. even though cognitive factors are not sufficient, there exists the law of excluded middle in recognition still. membership degree reflects the generalized law of excluded middle, which essentially scales the frequency of covering. lacked factors are essentially the objective factors in randomness but essentially the subjective factors in fuzziness. the basic model of probability statistics is: fix the circle, move the point; the basic model in fuzzy statistics is: fix the point, move the circle. where the point stands for a state in factors space x, the circle stands for a subset in x. this is a duality. a circle of u is a point in the power p(u); while a point x in u can be transferred a circle x∗ = {c|c � x} in p(u). therefore, we get a dual principle between fuzziness and randomness: a fuzzy experience model in the ground u can be transferred to a probability experience model in the sky p(u). according to the principle of duality, wang presented the falling shadow theory, which defines the membership degree of a as the covering probability of the related random set ξ: µa(x) = p(ξ � x) (2) falling shadow theory is the population theory for fuzzy statistics (interval statistics, set-valued statistics), which is a special contribution of factors space for fuzzy sets theory praised by the founder l. a. zadeh [36]. factors space theory gave subjective measures, including fuzzy measure, a united statement and gave a deep theorem: for any subset a of u, set a∗ = {c|c ⊇ a}, ∗a = {c|c ⊆ a} and called the idea and filter of a respectively. set (a∗)c = {c|cc∩a 6= ∅}, (∗a)c = {c|c∩ac 6= ∅}. let (x,t) be a topological space, denote t1 = {a∗|a ∈ t}, t2 = {∗a|a ∈ t}, t3 = {(a∗)c|a ∈ t}, t4 = {(∗a)c|a ∈ t}, and let ti(i = 1, 2, 3, 4) be the hyper-topologies generated by t1, t2, t3 and t4 respectively. (p(t),ti)(i = 1, 2, 3, 4) are hyper-topological spaces on the power of i = 1, 2, 3, 4, let ai be the σ-algebra generated from ti respectively, (p(t),ai)(i = 1, 2, 3, 4) are called four basic hyper-measurable spaces on the power p(t) respectively. let p be a probability defined on (p(t),ai), for any a ∈ t set b(a) = p(a∗),n(a) = p(∗a),an(a) = p((∗a)c),ab(a) = p((a∗)c) (3) where b, n, an and ab are known as the belief, plausibility, anti-plausibility and anti-belief measures respectively. they are non-additive measures. theorem 1. (extension and uniqueness theorem) given any one of the four non-additive measure on a, there exists one and only one probability p on (p(t),ai) such that eq.(2) is held. proof is given in [18]. factors space and its relationship with formal conceptual analysis: a general view 87 why did wang use factors space to study on randomness vs randomness? he wants to develop cognition math. fuzzy sets theory brings fuzzy concepts into math so that the programs can do smart recognition and control like brain of human being; fuzzy sets and fuzzy logic open a new door of mathematics toward ai applications. factors space can deepen the researches of fuzzy sets. treating the discussion universe u as a factors space, we can select factors to make a concept clearer and decrease its fuzziness, and we can calculate membership degree more reasonably. fuzzy sets represent cognition and thinking from the aspect of extension only, but lack the aspect of intension. factors space is a plate to combine extension and intension, which can represent things and thinking. h. x li and others use factors space to do fuzzy computer [25] and fuzzy controller with 4 stages inverse pendulum and factor space has been successfully applied to ai in china. 3 factors and factors spaces based on the ideological background from history, we introduce and develop the factors theory. a lot of theoretical papers before 2012 can be found in the reference [3,6–11,13,16,19, 20,24,26,31,34,35]. since 2012, fs has turned to the data science [1,2,12,21–23,27,28,30,37,38]. we have mentioned that a factor causes something. but according to the interpretation of dictionary, factor is the reason causing something or the essence of thing, which tells us that a factor essences the very thing. what is the thing? human being has accumulated a lot of knowledge on the generation of things, and the perfect model occurs in biology. gene is the key to opening the door of biology, which induces the discovery of dna, the mystery of life. gene was called the factor by g. mendel; factor is the generalization of gene, which is the key to opening the door of ontology and cognition, said wang, who gives special emphasize from philosophy: anything is united in the opposite of quality and quantity; factor is the root of quality, as gene is the root of biological attributes. without the string of factors, attributes are like broken strings of pearls sprinkled over a floor [28]. by means of factors, things are organized and concepts are generated. the group of states bunched by factor f forms a dimension x(f) called the state space of f. any factor must be defined on a discussion universe u. a factor f can be mathematically defined as a mapping f : u → x(f). factor is the aspect we observe and focus on; factors can be operated from rough to fine or from fine to rough, reflecting the analysis process and the synthesis process in thinking respectively. consider a group of simple factors f1, · · · ,fn defined on u, and they combine to a complex factor denoted as fo = {f1, · · · ,fn} = f1 ∨ ·· · ∨ fn. the state space of fo can be defined as the cartesian product space x(fo) = ∏ i x(fi). each simple factor fi performs analysis, which maps an object u in u to fi(u) in x(fi); the complex factor fo performs synthesis and get the coordinate of u in x(fo). for example, consider height(f1), weight(f2), age(f3) and sex(f4) defined on a group of people u. suppose that u=john in u, f1(john)=1.75m, f2(john)=75kg, f3(john)=25years, f4(john)=male, then, we get that fo(john)=(1.75m, 75kg, 25years, male). john can be represented as a point in the coordinate system. factors space is a generalized coordinate system of things. definition 2. let f = (f;∨∧,0) be a lattice with minimum 0, xf = {x(f)}(f∈f) is a family of sets satisfying (1) x(0) = {∅}; (2) for any irreducible subset t of f (s,t ∈ t implies s∧ t = 0), we have that 88 h. liu, i. dzitac, s. guo x(t) = ∏ f∈t x(f) (4) where ∏ stands for cartesian product. ψ = (u,xf ) is called a factors space on u, if for any u ∈ u and f ∈ f , there exists an unique f(u) ∈ x(f). (i.e., each f ∈ f is a mapping f : u → x(f)). a member f in f is called a factor, and 0 is called empty factor. the operations ∨ and ∧ are called the synthesis and decomposition of factors respectively. we often limit the definition that fo = {f1, · · · ,fn} = f1 ∨ ·· · ∨ fn, and f = p(fo), f is the power of fo. in this case, the operations ∨ and ∧ become to ∪ and ∩ respectively. this definition is a relaxed vision given here. originally, the index set was defined as a boolean algebra f = (f;∨∧,¬,0,1) [26], which is too strong for our study. of course, we often treat f as a boolean algebra if it is needed. remark 3. about the name "factors space", we need to emphasize here: our definition is different from the factor space x/∼ representing the set of classes classified by the equivalent relation ∼. from now on, we use the word factors, not factor, in writings; but, we keep using the word in factor analysis initiated by l. l. thurston [15]. as the great psychologist in 1931, even though he did not take high mathematical description into cognitive psychological measurement, he raised the banner of factors more than half a century earlier as our pioneer. today, factors space inherits his target forward. factors space is a generalization of cartesian coordinate space. any quantitative cartesian space can be viewed as a factors space since every variable is a factor; factors space extends those coordinate spaces from fixed dimension to variable dimension. the main breakthrough of factors space is that the states of factor can be changed from quantitative to qualitative. there are two types of states of a factor; one is quantitative; for example, the state of height can be measured as a real number in the interval [10, 250] (cm); the other type is qualitative; the state is taken as a word in natural language, tall, middle or short. in most cases, the two types of states can be employed simultaneously, and form a pair of state space x(f) and x(f), the former one stands for qualitative and the latter one the quantitative state space. in the ai applications, we employ qualitative on semantic description and quantitative on formulae calculation. the most important point is how to transfer each other. fuzzy sets theory provides technique to do such a kind of transformations, which enables fs to build its coordinate system by available axes. 4 background set and correlation distribution in the section, we need to introduce the core concept of factors space. definition 4. given a factors space ψ = (u,xf ) with f = p(fo) and fo = f1∨·· ·∨fn, denote r = fo(u) = {(x1, · · · ,xn)|∃u ∈ u; x1 = f1(u), · · · ,xn = fn(u)}. (5) which is called the background set of ψ. the background set r is the image of universe u in x(fo), which can be understood the real cartesian product space of f1, · · · ,fn. outside of r, the state-configuration of the n factors is not possible to be realized, so that r can be seen as the state space of the combined factor fo. when f1, · · · ,fn are independent, we have that r = x(f1) ×···×x(fn); otherwise, there are correlations existing between those factors. the background relation r is also called the correlation of factors f1, · · · ,fn . factors space and its relationship with formal conceptual analysis: a general view 89 correlation of factors is the source of factors space theory, which generates causality, logic, concept generation and decision making. correlation of factors is the foundation of cognition math. a basic theorem was a proposal in paper [23]. we give an intuitive proof as follows: theorem 5. let r be the correlation of f and g, set x = x(f) and y = x(g). for any e ⊆ x and e′ ⊆ y , the inference "if x is in e then y is in e′" is identically true if and only (e ×y ) ∩r ⊆ (x ×e′) ∩r. (6) figure 1: background relation determines inference proof: the proposition "x is in e" is equivalent to "x is in e and y is in y " and "(x,y) is in e × y ". since (x,y) is in r always, the proposition "x is in e" is equivalent to "(x,y) is in (e×y )∩r". similarly, the proposition "y is in e′" is equivalent to "(x,y) is in (x ×e′)∩r". the inference "if x is in e then y is in e′" is true if and only (e ×y ) ∩r ⊆ (x ×e′) ∩r 2 since the factors f and g can be complex factors in high dimension space, theorem 5 can be used in general cases, which tells us that the correlation of factors determines all inferences between those factors. it is obvious that the background set is indeed the formal background in fca. fs theory further underscoring the significance of the formal background emphasized by wille. so far, we get background set as a theoretical base with determinacy. but, it has to be extended to handle uncertainty from the following two reasons: 1) an object u in u is not a point but a granule, which may be a complex system. according to the viewpoint of granular computing [33], the size of granule can be varying in hierarchical model. for example, u was a man, the factor f = height maps u to a determinate state f(u) = tall in x(f), but, when the size of granule u is changed from a man to a group of men, what is the state f(u)? here comes the uncertainty. 2) u is a theoretical term, which is not available in practice. for example, consider that u consists of all people. where to identify a person? in the east or the west? when to identify a person? include those who have been died or will be born? in practice, the universe is a sampling of u with randomness. from the two reasons, we need to treat u as random variable and extend background set to background distribution relatedly. definition 6. given a factors space ψ = (u,xf ) with f = p(fo) and fo = f1 ∨·· ·∨fn, and suppose that x(fi) = {ai1, · · · ,aik(i)}(i = 1, · · · ,n) are qualitative state spaces both. let p be the probability carried by factors from random variable u. denote r(x1, · · · ,xn) = p(f1(u) = x1, · · · ,fn(u) = xn) ( ∑ {b(x1, · · · ,xn)|xi = ai1, · · · ,aik(i) (i = 1, · · · ,n)} = 1) . (7) 90 h. liu, i. dzitac, s. guo where r is called the background distribution of ψ, or the correlation distribution between factors f1, · · · ,fn. the definition can be extended to quantitative state spaces. 5 factors space in concept generation concept generation is a key point and a basic topic in cognation math. we only introduce the topic on concept generation in the paper. 5.1 general target of ai suppose that there are some intelligent problems about bodies of water treated by hydrologists. there are 21 objects as follows: 1. tarn, 2. trickle, 3. rill, 4. beck, 5. rivulet, 6. runnel, 7. brook, 8. bum, 9. stream, 10. torrent, 11. river, 12. canal, 13. lagoon, 14. lake, 15. mere, 16. plash, 17. pond, 18. pool, 19. puddle, 20. reservoir, 21. sea. we take those terms as objects to form a set u. the tasks of ai are treating objects according to a general target: picking up information and transferring to the knowledge. even though there are only 21 objects, each of them is not a piece of body in the land, but has been classified as a word in hydrology, and they are rambling too. the first task of ai is organizing objects into a conceptual system: from tousle to intellect. 5.2 using factor to distinguish and classify objects concepts come from comparing and distinguish objects; it could not be done without factors. definition 7. we call object u and v in u are different if there is a factor f defined on u such that f(u) 6= f(v). (8) otherwise, they are called the same with respect to factor f. any two things in the world have the same points and different points simultaneously, without factors, we couldn’t judge if two things are same or different. a factor f is a mapping defined on the universe u, which determines an equivalent relation ∼ on u: u ∼ v if and only if f(u) = f(v). the equivalent relation determines a classification on u; if we consider only qualitative state space here, then the classified factorial space can be denoted as u/f = {ck = (uk1, · · · ,ukn(k))}k=1,··· ,k (9) where the number of states of x(f) is m = n(1) + · · · + n(k), n(j) is the number of objects in kth subclass. definition 8. [38] denote cf = n(1)(n(1) − 1) + · · · + n(k)(n(k) − 1)/m(m− 1) (10) which is called the distinguish degree of factor f with respect to u. the bigger the distinguish degree, the finer the division taken by f on u. factors space and its relationship with formal conceptual analysis: a general view 91 table 1: factorial table for bodies of water u 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 f1 h n n n n n n n n n n a n n a n a n n a n f2 m r r r r r r r r r r r m m m m m m m m m f3 s s s s s s s s s s s s i s s s s s s s i f4 c c c c c c c c c c c c c c c t c c t c c 5.3 atomic concepts as for the bodies of water, to pick up information and organize objects into a conceptual system, we need to consider some factors: is the body natural or artificial? how is its manner, running or muddy? is the water body in sea or in land? is it constant or temporary? and so on. those factors are often the names of attributes, but factors do not play the role of passive descriptors but initiative leaders. in the bodies of water, we can define that f1 = natural?, x(f1) = {natural,artificial}; f2 = running?, x(f2) = {running,muddy}; f3 = sea − inland, x(f3) = {sea,inland}; f4 = timeliness, x(f4) = {constant,temporary}. then we can get a factor space ψ = (u,xf ) with fo = f1 ∨f2 ∨f3 ∨f4, the factor tableau can be taken as the sampling in x = x(f1)×x(f2)×x(f3)×x(f4). citing the data from paper [5], we get table 1. how to analyze on the tableau? given a factors space ψ = (u,xf ) with fo = f1 ∨·· ·∨fn, for any [a] ∈ x(fo), denote [a] = f−1o (a) = {u|fo(u) = (f1(u), · · · ,fn(u)) ∈ a} (11) if [a] ∈ r, i.e., [a] 6= ∅, we say that a is an atomic intension, or say a is a real configuration. the collection of all the atom intensions form the division u/fo = {[a]}, which includes all subclasses divided by factors f1, · · · ,fn. it is clear that fo is an isomorphic mapping from u/fo to r. definition 9. given factors space ψ = (u,xf ) with f = p(fo) and fo = f1 ∨·· ·∨fn, for any a ∈ r, the pair α = (a, [a]) is called an atomic concept and [a], a are called the extension and intension of α respectively. return to the bodies of water, the cartesian product space x(f1) ×x(f2) ×x(f3) ×x(f4) concludes 2 × 2 × 2 × 2 = 16 elements, but, the real product xf , i.e., the background set r includes 6 elements only r={nmsc, nrsc, arsc, nmic, amsc, nmst} according to our definition, there are six atomic concepts: α1=(nmsc, {1,14,18}), α2=(nrsc, {2,3,4,5,6,7,8,9,10,11}), α3=(arsc, {12}), α4=(nmic, {13,21}), α5=(amsc, {15,17,20}), α6=(nmst, {16,19}). 5.4 how important are the atomic concepts? atomic concepts have 4 basic functions in ai: 1. atomic concepts provides the finest classification of u under factors f1, · · · ,fn. theorem 10. given factors space ψ = (u,xf ) with f = p(fo) and fo = f1 ∨ ·· ·∨ fn, each atomic concept α = (a, [a]) is not able to be refined (i.e.,[a] is not able to be divided ) by the factors f1 ∨·· ·∨fn. 92 h. liu, i. dzitac, s. guo proof: for any atomic extension [a] and any u,v ∈ [a], we have that fi(u) = [a] = fi(v) for i = 1, . . . ,n, so that u ∼ v for i = 1, . . . ,n. 2 the smaller the number of atomic concepts with respect to the number of objects in u, the simpler and clearer the structure of knowledge is but the rougher the classification is. to break the limit of finest division, we need to find out more factors, and this is a deepening process and will be discussed in the future. 2. the finest classification provides the universal code for knowledge organization, retrieval and inquiring. of course, the code is edited under the guidance of factors. 3. an answer system can answer following questions based on atomic concepts: is rill α1? no, since α1=(nmsc, {1,14,18}) does not includes rill (3). does α2 include rill? yes, since α2=(nrsc, {2,3,4,5,6,7,8,9,10,11}) includes rill. does rill have the attribute a? no, since fi(rill) = a 6= n. what is the concept an object with attributes n and s belonging to? it may α1, α2 or α4 be. 4. all atomic concepts form the background relation or the correlation r. what is the shape of r? what is the causality generated from r? there comes a lot of functions applied in ai, we will discuss in the future. 5.5 basic concepts semi-lattice definition 11. given a factors space ψ = (u,xf ) with f = p(fo) and fo = f1 ∨·· ·∨fn, for any a ⊆ r, denote [a] = ∪{[a]|a ∈ a}, for any a ⊆ r, γ = (a, [a]) is called a concept with the intension a and extension [a]. set γ = {γ = (a, [a])|a ⊆ r}, γ = (γ, or, and) is called the conceptual lattice on ψ = (u,xf ), where γ orγ′ = (aora′, [a] ∪ [a′]) and γ andγ′ = (aanda′, [a] ∩ [a′]); even γ = (γ, or, and, not) is called the conceptual boolean algebra on ψ = (u,xf ), where not γ = (not a, [a]c). do not be afraid that the generated concepts are too little; we are afraid that too much. if there are k atomic concepts, then 2k new concepts will be generated. to avoid the information overflow, we generate basic concepts only. definition 12. a concept is called basic if its intension can be stated in a conjunctive normal form. intuitively speaking, the extension of a basic concept can be mapped to a hyper-rectangle within r in x(fo). in bodies of water, each atomic concept is a basic concept. the concept γ=(nmc, {1,14,18,13,21}) is a basic concept since its intension can be written to the conjunctive normal form n∧m∧(s∨ i) ∧ c = n ∧ m ∧x(f3) ∧ c = n ∧ m ∧ c. how to get the basic concept semi-lattice? we introduce wang’s algorithm [21] by the example of bodies of water. 1) calculating distinguish degree cf of each factor and then changing the order of columns to do f-classification on u, where f holds the maximal distinguish degree cf. m = 22 f1 : n(n) = 16,n(a) = 5,cf 1 = 1 − (16 × 15 + 5 × 4)/21 × 20 = 0.4; f2 : n(r) = 11,n(m) = 10,cf 2 = 1 − (11 × 10 + 10 × 9)/21 × 20 = 0.5; f3 : n(s) = 19,n(i) = 2,cf 3 = 1 − (19 × 18 + 2 × 1)/21 × 20 = 0.2; f4 : n(c) = 19,n(t) = 2,cf 4 = 0.2; cf 2 > cf 1 > cf 3 = cf 4 . factors space and its relationship with formal conceptual analysis: a general view 93 u 12 2 3 4 5 6 7 8 9 10 11 1 13 14 15 16 17 18 19 20 21 f1 a n n n n n n n n n n h n n a n a n n a n f2 r r r r r r r r r r r m m m m m m m m m m f3 s s s s s s s s s s s s i s s s s s s s i f4 c c c c c c c c c c c c c c c t c c t c c we get the f2-classification u = u1{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}+u2{1, 13, 14, 15, 16, 17, 18, 19, 20, 21}. repeatedly, do classification on u1 and return to step 1): u 12 2 3 4 5 6 7 8 9 10 11 f1 a n n n n n n n n n n f2 r r r r r r r r r r r f3 s s s s s s s s s s s f4 c c c c c c c c c c c m=11 f1 : n(n) = 10,n(a) = 1,cf 1 = 1 − (10 × 9 + 1 × 0)/11 × 10 = 9/11; f2 : n(r) = 11,n(m) = 0,cf 2 = 1 − (11 × 10)/11 × 10 = 0; f3 : n(s) = 11,n(i) = 0,cf 3 = 1 − (11 × 10)/11 × 10 = 0; f4 : n(c) = 11,n(t) = 0,cf 4 = 1 − (11 × 10)/11 × 10 = 0. cf 1 > cf 2 = cf 3 = cf 4 we get the f1-classification u1 = u11{12} + u12{2, 3, 4, 5, 6, 7, 8, 9, 10, 11}. similarly, we can repeat the step 1) on other sub-classes. finally, we get a tree with following branches u2 = u21{1, 13, 14, 16, 18, 19, 21} + u22{15, 17, 20}; u21 = u211{13, 21} + u212{1, 14, 16, 18, 19}; u212 = u2121{1, 14, 18} + u2122{16, 19}. the figure of the tree is drawn in fig.2. the complexity of basic concept semi-lattice is o(m2n), where m and n are the number of rows and columns of factorial tableau respectively. in fig.2, each node is a basic concept, and the node at the top is the original concept with extension u. when a baby born, the origin is zero concept (empty,u), no intension statement, the extension is the chaotic universe. from top to bottom, the intension increases and the extension shrinks. the undermost node is not a concept but an ideal extreme with empty extension. we add it in the fig.2 to match the original figure drawn by ganter and wille. figure 2: semi-lattice of basic concepts 94 h. liu, i. dzitac, s. guo theorem 13. the operation or may not be closed for two basic concepts except that the intension of two concepts are different at only one factor. proof is obvious, we only hint that γ1 or γ4 = (nmscornmic,{1, 14, 18}∪{13, 21}) = (nmc,{1, 14, 18, 13, 21}), which is the basic concept γ mentioned above. performing the operation or on two basic concepts, we get a basic concept. what is the condition of the result? the intensions of the two basic concepts are nmsc and nmic, and their difference is on the factor f3 only. without the condition, the operation of or is not closed in basic concepts. be careful, all basic concepts form a semi-lattice, but may not be a lattice. 5.6 the consistence with formal concept analysis concept generation was established by wille, who defined the formal background as k = (g,m,i), where g is a group of objects, m a group of attribute values, i a relation from g to m : (g,m) ∈ i implies that object g holds attribute value m. for any a ⊆ g, define s(a) = {m ∈ m|∀g ∈ a; (g,m) ∈ i}, and for any b ⊆ m, define t(b) = {g ∈ g|∀m ∈ b; (g,m) ∈ i}. α = (b,a) is called a concept if the fallowing convolution principle is holds: s(a) = b, t(b) = a (12) theorem 14. given a factors space ψ = (u,xf ) with f = p(fo) and fo = f1 ∨ ·· ·∨fn, for any basic concept α = (a, [a]), [a] and a satisfy the convolution principle. proof: let α = (a, [a]) be an atomic concept. taking g = u, m = x(fo) and i = {(u,a)|a = fo(u)}, we have s([a]) = {m ∈ m|∀g ∈ [a]; (g,m) ∈ i} = {fo(u)|u ∈ [a]} = a; t([a]) = {u ∈ u|(u,a) ∈ i} = {u ∈ u|fo(u) = a} = [a]. hence [a] and a satisfy convolution eq.(12). let α = (a, [a]) be a basic concept. we have s ([a]) = {a ⊆ x (fo) |∀u ∈ [a] ; fo (u) ∈ a = a}; t (a) = {u ∈ u |fo (u) ∈ a} = [a]. hence [a] and a satisfy convolution eq.(12). 2 the proposition proves that the definition of basic concepts in fs is consistent with the original definition in fca. it does also hint that general concepts may not satisfy the convolution principle. what is the relationship between table 1 and the original table of wille? the original table shows a formal background with 22 objects and 8 attribute values. table 1 replaces the 8 attribute values by 4 factors, and since the object channel has no attribute value in the original table, it has been taken out of the table. as shown in fig.2, apart from the top and bottom nodes, there are 8 red atomic and 4 blue non-atomic basic concepts. to be easy to match to fig.3, we number the nodes as follows: node (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) [a] u1 u2 u11 u12 u21 u22 u211 u212 u2121 u2122 it is obvious that nodes (3),(4),(6),(7),(9),(10) are atomic, color red, nodes (1),(2),(5),(8) are non-atomic basic concepts, color blue. we can see that fig.2 is consistent with fig.3. indeed, the semi-lattice drawn in fig.2 is a sub-lattice drawn in fig.3. factors space and its relationship with formal conceptual analysis: a general view 95 figure 3: conceptual lattice(ganter & wille [5]). in practical applications, the generated basic concepts are too much, we need to review those concepts by experts, rarely select those concepts who can be understood by human being, and then rename them into a special database. fig.2 decreases the number of non-atomic basic concepts from 15 in origin to 4 in fig.2. however, we can change the semi-lattice by changing the order of factor-divisions, and the union of that semi lattice can completely full the original ’concept lattice’: theorem 15. given a factors space ψ = (u,xf ) with f = p(fo) and fo = f1 ∨ ·· ·∨fn, for any basic concept γ = (a, [a]), there is a semi-lattice s generated by wang’s algorithm taking γ as a member in it. proof: let the intension a is written in the conjunction normal form: a = ai(1) ∧ ·· · ∧ ai(k) (k ≤ n), where ai(j) = ai(j),j(1) ∨ ·· ·∨ai(j),j(r) is a disjunction of atomic intensions with respect to the factor fi(j)(j = 1, ,k). according to wang’s algorithm, doing classifications by factors fi(1), · · · ,fi(k) successively, the basic concept must be occurred in the semi-lattice of basic concept. 2 basic concepts have 2 basic functions in ai: 1. a set of inference rules can be shown in the graph of semi-lattice. let s be the graph of a semi=lattice. from bottom to up, any node γ = (a, [a]) links at least one node γ′ = (a′, [a′]) . it is obvious that the inference arrow γ ⇒ γ′ is constant true. 2. an answer system can answer the following questions based on basic concepts: is pool in u1? no, since pool is α1 numbered red 9, which links to blue 8, then links to blue 5, then links to blue 2, no way to link to blue 1. it does not belong to u1. is drill in u1? yes, since drill is α2 numbered red 4, which links to the node numbered blue 1, having extension u1. 96 h. liu, i. dzitac, s. guo 6 conclusion factor space provides a new view point toward formal concept analysis, which promotes the signification of formal background. by means of the support of fs, all problems in fca or rs can get more clear and simple statement, and the n-hard trap fca and rs encountered will be overcome by faster algorithms. concept generation is a key point and basic problem in artificial intelligence and factor space has got good progress on concept generation, which will be an important tool for cognition, information and data science. acknowledgement the authors specially thank professor p.z. wang, was a good friend and collaborator with the father of fuzzy sets: lotfi a. zadeh (1921-2017), for his guidance and valuable suggestions. this study was partially supported by the grants (grant nos. 61350003, 11401284, 70621001, 70531040) from the natural science foundation of china, and the grant (grant nos. l2014133) from the department of education of liaoning province. bibliography [1] cheng, q.f.; wang, t.t.; guo, s.c.; zhang, d.y.; jing, k.; feng, l.; wang, p.z. 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(2017); an improved decision tree algorithm based on factor space theory, journal of liaoning technical university (natural science), 36(3), 109–112, 2017. int j comput commun, issn 1841-9836 8(1):153-160, february, 2013. pso for graph-based segmentation of wrist bones in bone age assessment p. thangam, k. thanushkodi, t.v. mahendiran p. thangam assistant professor, cse department, coimbatore institute of engineering and technology, coimbatore 641109, tamilnadu, india. e-mail: saithangam@gmail.com k. thanushkodi director, akshaya college of engineering and technology, coimbatore -642109, tamilnadu, india. email: thanush12@gmail.com t.v. mahendiran assistant professor, eee department, coimbatore institute of engineering and technology, coimbatore 641109, tamilnadu, india. e-mail: saimahegobi@gmail.com abstract: skeletal maturity is a reliable indicator of growth and skeletal bone age assessment (baa) is used in the management and diagnosis of endocrine disorders. bone age can be estimated from the left-hand wrist radiograph of the subject. the work presented in this paper proposes the development of an efficient technique for segmentation of hand-wrist radiographs and identifying the bones specially used as regions of interest (rois) for the bone age estimation process. the segmentation method is based on the concept of particle swarm optimization (pso) and it consists of graph-based segmentation procedure. the system provides an option of either segmenting all the bones totally or segmenting only the specific rois under consideration. the system is validated with a data set of 100 images with 50 radiographs of female subjects and 50 of male subjects. the time taken for segmenting each bone is calculated and the results are discussed. keywords: skeletal maturity, bone age assessment (baa), particle swarm optimization (pso), graph-based segmentation, left-hand wrist radiograph. 1 introduction the chronological situations of humans are described by certain indices such as height, dental age, and bone maturity. of these, bone age measurement plays a significant role because of its reliability and practicability in diagnosing hereditary diseases and growth disorders. bone age assessment using a hand radiograph is an important clinical tool in the area of pediatrics, especially in relation to endocrinological problems and growth disorders. a single reading of skeletal age informs the clinician of the relative maturity of a patient at a particular time in his or her life and integrated with other clinical finding, separates the normal from the relatively advanced or retarded. [1] the bone age of children is apparently influenced by gender, race, nutrition status, living environments and social resources, etc. based on a radiological examination of skeletal development of the left-hand wrist, bone age is assessed and compared with the chronological age. a discrepancy between these two values indicates abnormalities in skeletal development. this is applied in the management and diagnosis of endocrine disorders. copyright c⃝ 2006-2013 by ccc publications 154 p. thangam, k. thanushkodi, t.v. mahendiran 2 background of baa the main clinical methods for skeletal bone age estimation are the greulich & pyle (gp) method and the tanner & whitehouse (tw) method. gp is an atlas matching method while tw is a score assigning method. [2] gp method is faster and easier to use than the tw method. bull et al performed a large scale comparison of the gp and tw method and concluded that tw method is the more reproducible of the two and potentially more accurate. [3] in gp method, a left-hand wrist radiograph is compared with a series of radiographs grouped in the atlas according to age and sex. the atlas pattern which superficially appears to resemble the clinical image is selected. tw method uses a detailed analysis of each individual bone, assigning it to one of eight classes reflecting its developmental stage (in terms of scores). the sum of all scores assesses the bone age. this method yields the most reliable results. in detail, in the tw method twenty regions of interest (rois) located in the main bones are considered for the bone age evaluation. each roi is divided into three parts: epiphysis, metaphysis and diaphysis; it is possible to identify these different ossification centers in the phalanx proximity. the development of each roi is divided into discrete stages, as shown in figure 1, and each stage is given a letter (a,b,c,d, . . . i), reflecting the development stage as: • stage a – absent • stage b – single deposit of calcium • stage c – center is distinct in appearance • stage d – maximum diameter is half or more the width of metaphysis • stage e – border of the epiphysis is concave • stage f – epiphysis is as wide as metaphysis • stage g – epiphysis caps the metaphysis • stage h – fusion of epiphysis and metaphysis has begun • stage i – epiphyseal fusion completed. a b c d e f g h i figure 1: tw stages for phalanx bone. by adding the scores of all rois, an overall maturity score is obtained. this score is correlated with the bone age differently for males and females. [4] hence for accurate estimation of bone age, the rois are to be properly extracted and analyzed. so baa requires efficient segmentation schemes for further processing. this paper proposes an efficient segmentation technique using graphs for segmenting the bones in the radiograph. we have done a thorough survey of literature on baa methods in our previous work [5], explaining in detail the various work done in baa and providing directions for future research. our previous work [6] describes a computerized baa method for carpal bones, by extracting features from the convex hull of each carpal bone, named as the convex hull approach. we have also proposed an automated baa method to estimate bone age from the feature ratios extracted from carpal and radius bones, named as the feature ratio approach. [7] our decision tree approach utilizes features from the radius and ulna bones and their epiphyses for baa. [8] we have also exploited the epiphysis/ metaphysis region of interest (emroi) in baa using our hausdorff distance approach. [9] a comparative study of pso for graph-based segmentation of wrist bones in bone age assessment 155 the above four baa approaches has been conducted using partitioning technique. [10] we have also proposed an efficient method for feature analysis of radiographs using principal component analysis (pca) based on pso. [11] the work presented in this paper is a novel method for segmenting the wrist bones from the input radiographs using pso, applying the graph-based segmentation. 3 system design the system consists of three modules, namely: image preprocessing, edge detection and graph-based segmentation. 3.1 image preprocessing image preprocessing is performed in two steps, image smoothing and grayscale conversion. image smoothing is done to reduce the noise within the image or to produce a less pixilated image. most smoothing methods are based on low pass filters. in our system, we have done smoothing to reduce noise by using a gaussian filter. gaussian filter reduces the magnitude of higher frequencies proportional to the lower frequencies, but at the cost of more computation time. but the speeding up of smoothing is achieved by splitting 2d gaussian g(x,y) into two 1d gaussians g(x)g(y) and carrying out filtering in 1d, first row by row and then column by column. grayscale conversion is done as follows. colors in an image are converted to a shade of gray by calculating the effective brightness or luminance of the color and using this to create a shade of gray that matches the desired brightness. 3.2 edge detection edge occurs where there is a discontinuity in the intensity function or a very steep intensity gradient in the image. using this assumption, if one take the derivative of the intensity value across the image and find points where the derivative is maximum, then the edge could be located. [12] we have made use of sobel edge detector to detect the edges. the sobel operator performs a 2-d spatial gradient measurement on an image. typically it is used to find the approximate absolute gradient magnitude at each point in an input grayscale image. the sobel edge detector uses a pair of 3 × 3 convolution masks, one estimating the gradient in the x-direction (columns) and the other estimating the gradient in the y-direction (rows). a convolution mask is usually much smaller than the actual image. as a result, the mask is slid over the image, manipulating a square of pixels at a time. the actual sobel masks [13] are given below: the magnitude of the gradient is then calculated using the formula: |g| = √ gx2 + gy2 (1) an approximate magnitude can be calculated using: |g| = |gx| + |gy| (2) 156 p. thangam, k. thanushkodi, t.v. mahendiran 3.3 graph-based segmentation graph-based segmentation method [14] measures the evidence for a boundary between two regions by comparing two quantities: • intensity differences across the boundary and • intensity differences between neighboring pixels within each region. intuitively, the intensity differences across the boundary of two regions are perceptually important if they are large relative to the intensity differences inside at least one of the regions. graph-based image segmentation techniques generally represent the problem in terms of a graph g = (v,e) where each node viϵv corresponds to a pixel in the image, and the edges in e connect certain pairs of neighboring pixels. a weight is associated with each edge based on some property of the pixels that it connects, such as their image intensities. depending on the method, there may or may not be an edge connecting each pair of vertices. let g = (v,e) be an undirected graph with vertices viϵv , the set of elements to be segmented, and edges (vi,vj)ϵe corresponding to pairs of neighboring vertices. each edge (vi,vj)ϵe has a corresponding weight w((vi,vj)), which is a non-negative measure of the dissimilarity between neighboring elements vi and vj. the segmentation algorithm defines the boundaries between regions by comparing two quantities. the internal difference of a component c in an image is given by: int(c) = max e ϵmst(c,e) w(e) (3) where w(e) is the largest weight in the minimum spanning tree (mst) of the component. the difference between two components c1, c2 which are vertices of the graph is defined to be the minimum weight edge connecting the two components, given by: dif (c1,c2) = min vi ϵ c1,vj ϵ c2,(vi,vj) ϵ e w((vi,vj)) (4) if there is no edge connecting c1 and c2, we let dif (c1,c2) = ∞. the region comparison predicate evaluates if there is evidence for a boundary between a pair or components by checking if the difference between the components, dif(c1,c2) is large relative to the internal difference within at least one of the components, int(c1) and int(c2). 3.4 overview of pso particle swarm optimization (pso) is an algorithm for finding optimal regions of complex search space through interaction of individuals in a population of particles. pso algorithm, originally introduced in terms of social and cognitive behavior by eberhart and kennedy in 1995 [15] has been proven to be a powerful competitor to other evolutionary algorithms such as genetic algorithms. pso algorithm simulates social behavior among individuals (particles) flying through multidimensional search space, each particle representing a single intersection of all search dimensions. [16]–[19] the particles evaluate their positions relative to a global fitness at every iteration, and companion particles share memories of their best positions, and then use those memories to adjust their own velocities and positions. at each generation, the velocity of each particle is updated, being pulled in the direction of its own previous best solution (local) and the best of all positions (global). computation of optimal threshold is handled here with particle swarm optimization (pso). implementation of pso algorithm analyzed here to find out the optimal threshold for segmentation. the population size of particles refers the number pso for graph-based segmentation of wrist bones in bone age assessment 157 of particles in iterative process, thus denoting components in the image here. a population of particles is initialized with random positions and velocities in d-dimensional space. a fitness function, f is evaluated, using the particle’s positional coordinates as input values. positions and velocities are adjusted, and the function is evaluated with the new coordinates at each time-step. 3.5 implementation of pso for graph-based segmentation the implementation of the segmentation algorithm consists of the following steps. step 1: swarm formation: for a population size p, the particles are randomly generated between the minimum and the maximum limits of the threshold values. step 2: objective function evaluation: the objective functions of the particles are evaluated. step 3: ‘pbest’ and ‘gbest’ initialization: the objective values obtained above for the initial particles of the swarm are set as the initial pbest values of the particles. the best value among all the pbest values is identified as gbest. step 4: velocity computation: the new velocity for each particle is computed using equation (5). v[i] = v[i] + c1 ∗rand(i)∗(pbest[i] − present[i]) + c2∗rand(i)∗(gbest[i] − present[i]) (5) step 5: position computation: the new position for each particle is computed using equation (6). present[i] = present[i] + v[i] (6) where, v[i] is the particle velocity, present[i] is the current particle (solution), pbest[i] and gbest[i] are defined as stated before, rand(i) is a random number between (0,1), c1, c2 are learning factors. usually c1 = c2 = 2. step 5: swarm updation: the values of the objective function are calculated for the updated positions of the particles. if the new value is better than the previous pbest, the new value is set to pbest. similarly, gbest value is also updated as the best pbest. step 6: termination: if the stopping criteria are met, the positions of particles represented by gbest are the optimal threshold values. otherwise, the procedure is repeated from step 4. 4 results and discussion the use of image pre-processing techniques such as image smoothing and gray scale conversion improves the quality of the digitized radiograph. the noise caused due to radiation and other external factors are eliminated. application of sobel edge detector identifies the boundary of the bones or the regions of interest. this facilitates in better segmentation. finally, the pso algorithm for graph-based image segmentation is used to individually segment each bone in the left-hand wrist radiograph. the algorithm provides an option of whether to segment the entire radiograph (to identify all the bones in the radiograph) or to segment selected rois alone (some individual bone only). figure 2 (a) depicts the input radiograph image, figure 2 (b) denotes the image after smoothening, figure 2 (c) provides a snapshot of edge detection using sobel operator, figure 2 (d) provides the snapshot of segmenting all the wrist bones in the left hand radiograph image, while figure 2 (e) shows segmentation of selective rois (in this case the proximal, middle, and distal phalangeal bones). figure 3 shows the graph depicting the performance assessment of the segmentation. the time taken to segment each bone using the pso technique is calculated 158 p. thangam, k. thanushkodi, t.v. mahendiran (a) (d) (e) (b) (c) figure 2: (a) input radiograph image, (b) smoothened image, (c) edge detected image, (d) segmenting all the bones in the image, (e) segmenting selective (phalangeal) rois performance assessment of segmentation 0 0.5 1 1.5 2 2.5 c ap ita te h am at e lu na te tr iq ue tr al p is ifo rm s ca p ho id tr ap ez iu m tr ap ez oi d m et ac ar p al s d is ta l p ha la ng es m id dl e p h al an ge s p ro xi m al p ha la ng es r ad iu s u ln a roi bones t im e ( s e c o n d s ) graph pso-graph figure 3: performance assessment of segmentation and is compared with the plain graph-based technique and the results are tabulated in table 1. the segmentation was regarded as accurate if the sum of over selected and under selected pixels were less than 25. the segmentation process was accurate by 0.94 for males and 0.96 for females, as tabulated in table 2. the pso algorithm is implemented with the following parameters, population size: 50, max min w = 0.6, w = 0.1, c1 = c2 = 1.5, iteration: 50. pso for graph-based segmentation of wrist bones in bone age assessment 159 5 conclusion pso algorithm was used for graph-based segmentation of left hand wrist radiograph images, which can be further used for skeletal bone age assessment. the input image was first preprocessed to remove noise and was grayscale converted to improve image quality. sobel edge detector was used for edge detection and then pso combined graph-based segmentation was performed. the segmentation procedure provided two options, whether to segment all the wrist bones as a whole or to segment selective roi bones. the time taken to segment each bone was calculated and the results were tabulated. the system was tested with 100 left hand wrist images (50 males and 50 females). the quality of the segmentation was influenced by the image quality. for radiographs over exposed to radiation, further preprocessing was required, to achieve good 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(2001); swarm intelligence, morgan kaufmann publishers. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(5), 753-771, october 2019. dcm: d number extended cognitive map. application on location selection in scm l. zhao, f. xiao lian zhou computer and information science southwest university, chongqing, china fuyuan xiao* computer and information science southwest university, chongqing, china *corresponding author: xiaofuyuan@swu.edu.cn, doctorxiaofy@hotmail.com abstract: offshore outsourcing is a widely used management technique for performing business functions with the aim of reducing labor and transportation costs. the selection of locations has a significant influence on the supply chain’s resilience and qualities, but the influence of multiple external factors on the supply chain’s performance in local places in a complex and uncertain environment has not been examined. in this study, we investigated the influence of external factors in a highly uncertain and complicated situation in which relationships between external factors and supply chain resilience are complicated. furthermore, we proposed a novel model to select locations from a comprehensive perspective. specifically, the fuzzy cognitive map (fcm) is utilized to simulate the dynamic influence process where the adjacency is aggregated by d numbers. the weights of different resilience capabilities are considered from the perspective of maximizing benefits by using the decision-making trial and evaluation laboratory-analytic network processes (dematel-anp) model. by comparing the distance to the ideal solutions, we selected the best alternative location. our results differ from the general case, which reveals that the weights of different capabilities influence selections. keywords: offshore outsourcing, supply chain resilience, location selection; fcm, d number, dematel-anp, multicriteria decision making (mdm). 1 introduction in recent years, the globalization of the economy and competition has led to low consumption, and thus, the offshore outsourcing process has attracted extensive attention as one of the most adaptive strategies [9, 13]. offshoring allows for firms to focus on core competencies to improve their productivity, efficiency, and flexibility. the core competencies release a range of expertise that crosses traditional functions horizontally and adapts to changes in the long-term demands of clients, thus continuing domination over competitors [21]. a comparatively successful offshore outsourcing strategy can significantly assist a firm in improving its production efficiency, productivity, and flexibility to improve total profits and tackle emergency situations [22]. however, factors limiting the extensive application of offshore outsourcing remain, and outsourcing may elicit some potential threats to client firms by extending their supply chains (scs) [6]. offshore outsourcing also has the potential to transfer specific ownership of the business copyright ©2019 cc by-nc 754 l. zhao, f. xiao activities and resources to low-cost providers overseas. comparing the keyword offshore outsourcing to a similar keyword, outsourcing, we usually consider offshore outsourcing to involve a vendor located in a country other than that of the buyer. therefore, only the location of the seller differentiates the above two keywords. the core of offshore outsourcing is the practice of using a supplier rather than in-house employees to perform a function [2]. the following question to be solved is where to outsource [7]. the selected provider will participate in increasing integration of the sc. previous research has shown that the supply chain network is vulnerable to disruptions, and the failure of elements within the sc may cause the failure of the whole network. we assumed that an ideal offshore outsourcing location would satisfy the requirements of employers in terms of both economic and political aspects to enhance the performance of the whole sc. however, an erroneous choice may cause a significant negative effect on the entire sc network [20]. to realize competitive advantages of supply chains, the priority is to develop ’agility’, ’selfalignment’, and ’adaptation’ (triple a’s). supply chain ’agility’ indicates an instant response to short-term perturbations caused by uncertainties in the upstream and downstream supply chains. with respect to offshore outsourcing supply chains, agility is related to the capabilities of the supply chain to deal with unexpected changes in market demand, which can transform from challenges into opportunities and lead the supply chain to gain competitive advantages in a volatile and turbulent environment [33]. supply chain ’alignment’ refers to the integration process of several members in the supply chain to achieve better performance [25]. the necessity of the attribute has been highlighted in many studies and requires further investigation [35]. finally, the adaptability of the supply chain enables the supply chain to evolve based on market changes in strategies [15]. recent researches have shown the importance of supply chain adaptability as a dynamic capability. in summary, the reviewed capabilities could be denoted by the keyword ’resilience’, and more specifically, the best way to improve a supply chain’s competitiveness in an offshore outsourcing scenario is to maximize its resilience. most client firms are offshoring core competencies with global suppliers, with a focus on the selection of key value-creating competencies. this situation increases sc complexity and interdependency, leading to a risk of sc vulnerability. therefore, sc resilience is very crucial to minimize property losses in disruptions. recent research has revealed that the tohoku earthquake affected supply chains in the philippines in 2017. specifically, due to the lack of resilience in supply chains, many properties and client firms suffered from destructive natural disasters [1].the above event suggests that a resilient supply chain is important for minimizing the negative effects of disruptions and allowing for the sc to recover normal activity in a short time [9].if a corporation has a responsive sc, sc resilience can return to a normal or even better state [10]. furthermore, an offshore outsourcing process also involves unexpected outer disruptions and accidental events, and incoming potential risks would cause damage to sc systems. moreover, due to the complexity and interdependence of entities in sc, a minor failure within the sc may cause a failure cascade in the whole system. therefore, sc resilience is considered an important indicator in the offshore outsourcing process. despite these challenges, previous research has not made progress in investigating the relationship between locational decisions in offshore outsourcing and sc resilience. few studies have employed sc resilience as an indicator when selecting offshore outsourcing locations. thus, we assumed that an ideal offshore outsourcing location has positive effects on sc resilience to prevent accidental disruptions and ensure sc robustness. to bridge the gap between offshore outsourcing and sc resilience, this study proposed a combined method based on the analytic network process (anp) [12], decision-making trial and evaluation laboratory (dematel) method [11], fuzzy cognitive map (fcm) [12], and technique for order preference by similarity to an ideal solution (topsis) [16]. specifically, fcm can represent all connections with the considered dcm: d number extended cognitive map. application on location selection in scm 755 abilities to evaluate alternative locations and address the imprecise and fuzzy weights of links. dematel and anp are combined to determine the weights of fcm, and topsis is implemented in alternative locations specific to outcomes gathered from fcm. in this study, fcm can predict the impacts of alternative locations on sc resilience by simulating scenarios over time, and the executive functions can select an optimal offshore outsourcing location. furthermore, anp-dematel is used to determine the weights of links by inputting evaluations from experts. in addition, topsis is considered an efficient tool for ranking alternative offshore outsourcing locations with respect to sc resilience. the aim is to maximize sc resilience by choosing an ideal offshore outsourcing location. in addition, to avoid subjectivity and fuzziness of experts assessments, multiple experts are invited to evaluate the project, and their opinions are aggregated by d numbers the rest of this paper is organized as follows. the background of our study is overviewed in section 2. in section 3, some related preliminaries related to our study are introduced. in section 4, we present our hybrid integration approach for selecting the best alternative outsourcing locations with respect to supply chain resilience.we present a dcm based integration decision model for selecting the best alternative outsourcing locations with respect to supply chain resilience. section 5 presents an empirical application of the proposed model. the verification and discussion of our approach’s rationality and superiority are described in section 6. section 7 conducts a sensitivity analysis. in the final section, we present the conclusions of our study. 2 preliminaries 2.1 d number theory in the real world, uncertainty modeling and optimization are difficult to quantify because knowledge and information are imprecise and incomplete [14]. dempster-shafer evidence theory (also known as d-s theory or evidence theory) is an efficient tool for dealing with the information fusion issue [3,4,17,28]. however, some inherent drawbacks limit the broader application of the d-s theory. first, d-s theory requires a strong hypothesis that elements in the discerning frame are mutually exclusive, which is very difficult to satisfy, especially in linguistic assessments. second, a norm basic probability assignment (bpa) must follow a completeness constraint in d-s theory, which means that the sum of all focal elements in a bpa must be equal to 1 [6]. however, the experts do not have access to the overall knowledge, and the assessment is based only on partial information, potentially resulting in an incomplete bpa [32,33]. to address this issue, d numbers are presented [8,18–20,29]. 2.2 fuzzy cognitive maps political scientist robert axelrod introduced cognitive maps in the 1970s to represent social scientific knowledge [23]. the fuzzy cognitive map (fcm), an extension of the cognitive map, is a causal description of a model of the behavior of a system [24]. fcm is an interactive structure of concepts, each of which interacts with the rest, and reveals the dynamics and different behaviors of the system. each concept is described by a number ai that represents its value and results from the transformation of the fuzzy real value of the system’s variable, for which this concept stands, in the interval [0,1]. there are three types of causal interactions between concepts that represent the type of influence from concepts to the others. 756 l. zhao, f. xiao 2.3 dematel the methodology of the decision making trial and evaluation laboratory (dematel) was developed initially by the battelle memorial association in geneva [5] and is an effective method for analyzing the direct and indirect relationships between components in the system with respect to severity and type [34]. dematel is widely applied in supply chain management and service quality evaluation. by utilizing this method, we can extract a better understanding of the structural relationships, and thus, this method is an ideal way to solve complicated system problems. the procedure for implementing dematel to solve dependent evidence consists of four steps namely 1) define the quality feature and establish the measurement scale 2) extract the drm of influential factors 3) normalized drm 4) calculate trm. 2.4 anp network analysis is a very useful tool to model real application [31, 36, 37]. the analytic network process (anp) is capable of tackling dependence among components in a system. it is a generation of the analytic hierarchy process (ahp) and allows for more complicated interrelationships among decision elements by replacing the hierarchy in the ahp [26] with a network. the network structure of anp includes the control level and network level. the control level consists of goal and independent criteria whose weights can be obtained by ahp. there is at least one goal at the control level. at the network control level, the network spreads out in all directions and involves arrows between clusters or loops within the same cluster. these arrows and loops indicate the relations among clusters or within a cluster. anp is also applied to prioritize factors or criteria in the decision-making problem. in this paper, we only consider one goal, and criteria are omitted at the control level. 2.5 topsis the technique for order preference by similarity to ideal solution (topsis), which was developed by hwang and yoon in 1981 [38], is a method for ranking alternatives in applications and concepts [30, 39]. the core ideal of topsis is to choose alternatives that simultaneously have the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution. the positive ideal solution maximizes the benefit criteria and minimizes the cost criteria, whereas the negative ideal solution maximizes the cost criteria and minimizes the benefit criteria. 3 selecting offshore outsourcing location considering supply chain resilience as shown above, supply chain resilience (scre) is a crucial factor in offshore outsourcing and should receive close attention when selecting an outsourcing location. the main thrust of this paper is to rank some alternative locations and select the most suitable one from the perspective of scre. however, in the real world, multiple factors influence scre, and these factors interact with scre under changing conditions in foreign locations. thus, the interacting behaviors among scre and these external factors should be regarded as a dynamic mechanism in a highly complex and uncertain manner. in addition, the scre could be divided into various capabilities (e.g., flexibility, visibility, anticipation, etc.). these different capabilities have weights corresponding to different criteria under one goal of maximizing total benefits. all of these issues should be taken into consideration via a comprehensive perspective. in summary, the issue dcm: d number extended cognitive map. application on location selection in scm 757 of selecting the best offshore outsourcing location is a typical multi-criteria decision-making (mcdm) problem. since most existing research has researches have regarded the issue as a static problem or focused on one aspect of it, we propose a comprehensive perspective to tackle this complicated problem. first, we use a fuzzy cognitive map extended by the d number to identify the influencing mechanism of outer factors on scre in an offshore outsourcing location. then, an anp-dematel model is utilized to assign weights to different capabilities of scre in the benefit perspective. finally, we rank alternative locations with disparate outer factors by using the topsis method. the details are introduced below. start define dimensions, sub-dimens ions and alternatives to build framework step1 establish interdependences between dimens ions step2 construct initial direct relation matri x within dimens ions using d ematel step2.1 normalize direct relation matri x step2.2 construct total relation matri x step2.3 compute dispatcher and receiver step2.4 obtain inner dependence matrix and observe causal relationship expert evaluations step3 construct network structure of anp step3.1 construct pair-wise comparis on matrixes using anp step3.2 cal culate relative important weights of matrixes step3.3 check consis tency of matrixes cr<0.1 no step3.3 check consis tency of matrixes step3.3 check consis tency of matrixes step3.3 check consis tency of matrixes step3.3 check consis tency of matrixes finish integration of dematel and anp d e m a t e l p ro c e s s a n p p ro c e s s figure 1: an overview of the weight assignment framework 3.1 identify the impacts of outer factors on scre to determine the impact of outer factors on scre in offshore outsourcing, the fcm is introduced to simulate the process in a complicated and uncertain environment. the procedure is presented below. 1) expert evaluation: some authorities on outsourcing will be invited to define crucial outer factors that are closely related to scre. they may use their experience, knowledge and some interactive techniques such as the delphi method or affinity diagrams to identify these factors for further research. similarly, the different aspects of scre capability are addressed in this step. these outer factors and the scre capabilities will be regarded as the nodes in the fcm that describe the real problem or general system. 2) identify the causal connections between nodes (wi → wj). the type of relation (positive or negative) and its intensity should be taken into consideration and defined. the preliminary means of conducting this procedure is to define intensity scales. all evaluations should be mapped on the interval [0, 1]. we use ahp to quantify the degree of intensity. the scientific evaluations 758 l. zhao, f. xiao are transformed into a quantitative range by implementing the ahp method. 3) aggregate the multiple experts’ opinions in different groups. in the real world, the interactions between external factors and scre capabilities are very uncertain due to their complexity. to address this problem, multiple experts with different backgrounds will be invited to decrease uncertainty and obtain a relatively precise result. d number theory is introduced to fuse these evaluations due to its superiority in representing incomplete and imprecise information. 4) fcm inference. fcm can not only represent causal relations but also predict future implications through dynamic simulations. at the initial stage, each fcm node is assigned a value [0, 1], and the initial state vector v 0si = (v 0 1 v 0 2 · · · v 0 n−1 v 0 n) is obtained. then, using eq. (7), the behavior of the influencing mechanism will be revealed. in this stage, the impacts of external factors on scre can be identified, and the evolutionary process of fcm can quantify these impacts. 3.2 weight different resilience capability an overview of the integrated dematel and anp approach for assigning different weights to scre capabilities is given in fig. 1. briefly, the values are gathered from a computer database and questionnaires. the dematel method is employed to deal with the inner dependency by gathering pairwise comparisons. the inner dependency is then structured, and anp is implemented to perform further calculations. the details are listed as follows: step 1: define the criteria, objective, sub-criteria, and alternatives of the anp model. the criteria and sub-criteria are acquired from experts’ knowledge, experience, and other appropriate approaches. in this paper, the objective is to maximize the offshore outsourcing practitioners’ benefits, and the alternatives are different scre capabilities. step 2: construct a direct-relation matrix using dematel: decision makers are required to compare the criteria pairwise with respect to influence and direction. the comparison scale is defined at 5 levels: (0) no influence, (1) low influence, (2) medium influence, (3) high influence, and (4) extreme influence. step 2.1: normalize the direct-relation matrix. the direct-relation matrix is transformed to the normalized matrix. step 2.2: calculate the total relation matrix. the total relation matrix could be obtained via the normalized direct-relation matrix. step 2.3: to visualize the relations among factors, we calculate the dispatcher and receiver groups. the dispatcher is estimated from d−r, which has positive values and a greater influence on other factors. they are assumed to exhibit higher priority and are called dispatcher groups, where r is the sum of the columns and d is the sum of rows in the matrix t. the other values with negative values of d − r receive more influence from one another and are considered to have lower priority; these are called receiver groupers. the value d + r here shows the relation degree of each factor with others. those factors with higher d + r have more of a relationship with each other, and those with lower d + r have less of a relationship with each other. step 2.4: obtain the inner dependence matrix: the sum of each column is 1 in the total relationship matrix. step 3: use anp to construct the network for the problem and access the different scre capabilities. step 3.1: calculate the relative weights of different criteria in offshore outsourcing benefits. the ahp method is utilized to calculate their weight. a scale of 9 from equal importance to extreme importance is used to measure the relative importance of criteria. a decision maker would judge the relative dominance between each pair of criteria, and the results will be turned dcm: d number extended cognitive map. application on location selection in scm 759 into values of 1, 3, 5, 7, and 9, which represent equal importance, moderate importance, strong importance, demonstrated importance, and extreme importance, respectively, whereas values of 2, 4, 6, and 8 indicate intermediate importance. through the process of ahp, the relative weights of criteria are obtained. step 3.2: the supermatrix can be acquired by entering the vectors derived from dematel, and then, the normalized weighted matrix is transferred from the supermatrix by multiplying the relative weights of criteria from step 3.1., in which every column’s sum is 1. the weighted matrix then converges to a stable value after it is raised by its limiting power. step 4 determine the relative weights of the scre capabilities: the final weights are found in the corresponding row in the limit of the supermatrix; these weights should be taken into consideration when we select optimal offshore outsourcing locations. 3.3 rank alternative locations and select optimal one in scre perspective some alternative locations for offshore outsourcing are discussed and compared at this stage. since these alternatives have different outer factors, they have different values for each outer factor. we input these initial vectors into the fcm, and after the fcm reaches an equilibrium stage, various scre capabilities will be activated. the topsis method is then used to quantify the distance of alternative locations to the ideal solution considering the relative weights of each scre capability. the location with the greatest value according to eq. (18) will be chosen as the suitable location for offshore outsourcing. 4 empirical case study in this section, we present an empirical case to illustrate the effectiveness of our method for location selection considering scre. four alternative locations are considered in our study. this study pursues a generalized finding and proves the proposed method’s effectiveness. in this study, we have been required to select the best offshore outsourcing location considering scre capability in the background of a single and explanatory case study. the chosen case is highly representative and valuable because it evaluates the global supply chain of spirit drinks with geographically remote locations worldwide. the supply chain is so complicated that only the focal producer has more than 30 production facilities. we will use the proposed method to select the most suitable locations step by step. 4.1 identify the impacts of outer factors on rscre 1) expert evaluation: some experts are invited to describe outer factors that influence the resilience of the supply chain as well as the detailed capabilities of scre. the results are shown in the appendix. more specifically, the nodes in the fcm will be acquired. 2) in this step, the experts judge the causal relations among the nodes as well as their intensity. ahp is implemented to make pairwise comparisons, and quantitative intensities are obtained. all experts have the same weights; thus, the outcome will be the average of all experts. the consistency ratio should be less than 0.1, and all numbers map into [0, 1]. the intensity levels are shown in table 1. 3) aggregate experts’ opinions from different groups. since the causal relations among nodes are highly complicated and uncertain in practice, multiple experts are invited to assess the causal relations among those external factors and capabilities. in this case, three groups of experts are invited to evaluate the causal relations between them. these three groups have different backgrounds, and they will give their opinions from different perspectives. the impact 760 l. zhao, f. xiao table 1: intensity levels and their associated quantities from ahp intensity level quantity(expert1) quantity(expert2) quantity(expert3) average none 0 0 0 0 very weak 0.03 0.055 0.026 0.037 weak 0.06 0.062 0.076 0.066 moderate 0.117 0.143 0.121 0.127 strong 0.225 0.264 0.256 0.248 very strong 0.568 0.477 0.521 0.522 consistency ratio 0.07 0.07 0.07 0.045 of f12 (technological infrastructure) on cap9 (collaboration) can be considered as an example. in the first group, seven experts conclude that technological infrastructure has a strong positive relationship with collaboration; two experts believe the relationship is solid, and only one thinks it is moderate. in the second group, six experts assign a strong relation between f12 and cap9; one holds the view of a moderate relationship. and the results are shown in table 2 and table 3. see also in appendix the interactions between the criteria and sc resilience capabilities. table 2: the adjacency matrix (part a) id f1 f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 f14 f15 f16 f1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 f2 0 0 0 0 -0.533 0 0 0 0.251 0 0 0 0 0 0 0 f3 0 0 0 0 0 0 0 0 0 0 0 0 0 0.523 0 0.248 f4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 f5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 f6 0 0 0.254 0 0 0 0 0 0 0 0 0 0 0 0 0 f7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 f8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 f9 0 0 0 0 0 0 0 0.067 0 0.248 0 0 0 0.251 0 0 f10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 f11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 f12 0 0 0 0 0 0 0 0 0 0 -0.067 0 0 0 0 0 f13 0 0 0 0 0 0 0 0 0 0.071 0 0 0 0.251 0 0 f14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 f15 0 0 0 0 0 0 0 0 0 0 -0.067 0 0 0 0 0 f16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cap1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cap2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cap3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cap4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cap5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cap6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cap7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cap8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cap9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 dcm: d number extended cognitive map. application on location selection in scm 761 table 3: the adjacency matrix (part b) id cap1 cap2 cap3 cap4 cap5 cap6 cap7 cap8 cap9 f1 0 0 0 0 0 0 0 0.531 0 f2 0 0.251 0 0.133 0 0 0 0 0 f3 0 0 0 0 0 0 0 0 0 f4 0 0 0 0 0 0 0 0 0 f5 -0.127 0 0 -0.253 0 0 0 -0.131 0 f6 0 0 0 0 0 0 0 0 0 f7 0 0 0.252 0 0.526 0 0 0 0 f8 0 0 0 0 0 0 -0.071 0 0 f9 0 0 0 0 0 0 0 0 0 f10 -0.388 0 0 -0.251 0 0 -0.131 0 -0.04 f11 0 -0.066 -0.127 0 0 0 0 0 0 f12 0.248 0.248 0.248 0 0.129 0 0 0 0.279 f13 0 0 0 0 0 0 0 0 0 f14 0 0 0 -0.068 0 0 -0.131 0 0 f15 0 0 0 0 0 0.068 0 0 0.131 f16 -0.525 0 0 0 0 0 0 0 0 cap1 0 0 0 0 0 0 0 0 0 cap2 0 0 0 0 0 0 0 0 0 cap3 0 0 0 0 0 0 0 0 0 cap4 0 0 0 0 0 0 0 0 0 cap5 0 0 0 0 0 0 0 0 0 cap6 0 0 0 0 0 0 0 0 0 cap7 0 0 0 0 0 0 0 0 0 cap8 0 0 0 0 0 0 0 0 0 cap9 0 0 0 0 0 0 0 0 0 4) fcm inference. the fcm nodes’ values are assigned to [0, 1] to simulate the impacts of outer factors on scre capabilities. in this study, we set the initial state vector v 0si = (0 0 · · · 1 · · · 0) (n = 1, 2, · · · , 16). the number in the initial state vector represents the external factors, and our one factor is given a value of 1; the outer factors are assigned to 0. the activation value of the scre capabilities can be obtained. in this way, a better perspective of the impact of each outer factor on the scre capabilities is provided. in appendix can be see the impact of each outer factor on the 9 scre capabilities and the influence of each outer factor on the scre capabilities. f12 (technological infrastructure) has the most positive effects on scre, whereas f9 (tax rate) significantly impairs the whole resilience of the supply chain. the conclusion drawn from the table is that the offshore outsourcing practitioner should select locations with great technical infrastructure and a lower tax rate. 4.2 weight different resilience capability step 1: the objective, criteria, sub-criteria, and alternatives of the decision-making problem are introduced in section 3.2. finally, 5 criteria, as well as 21 of their sub-criteria, are identified: technical aspects: (c1) efficiency, (c2) security, (3) energy sustainability, (4) circulation of capital, (5) advanced technology, and (6) technical innovation economic aspects: (c7) operation cost, (c8) investment cost, (c9) production cost, (c10) return on investment, and (c11) maintenance cost political aspects: (c12) foreign dependency, (13) capability with respect to the national political and legislative situation, (14) capability with respect to national energy policy objectives, and (15) public policy and financial support social aspects: (c16) social benefits, (17) social acceptability, and (18) job creation environmental aspects: (c19) greenhouse emissions, (20) land requirements, and (21) impact on the ecosystem step 2: construct direct relation matrixes according to the 4-level scale, which features inner dependence. taking c7-c11 as an example, the initial relations among them are listed in 762 l. zhao, f. xiao the appendix. not all direct relation matrixes are listed here due to space limitations. steps 2.1-2.2: the direct relation matrixes can be transformed into total relation matrixes to reveal their comprehensive relations. still taking c7-c11 as an example, the overall relation matrix is shown in the appendix. step 2.3: the impact diagram is constructed as introduced in section 3. the horizontal axis refers to r+d, and the vertical axis is d-r. the impact diagram visualizes the direct and indirect relations, where d+r is the sum of relations among the elements and shows the importance of each element. d-r indicates the causal relations; d −r > 0 indicates that the element affects other elements to a greater extent than it is affected by them. the impact diagram of c7-c11 is shown in appendix. step 2.4: the results reveal a strong inner dependence among the sub-criteria of the economic aspect. thus, the inner dependence matrix is calculated. table 8 in the appendix shows the inner dependence matrix of the economic aspect. taking c7 −c9 as an example, the total relation of c7-c9 is 0.101, and rc7 = 0.101 + 0.243 + 0.088 + 0.111 + 0.119 = 0.661. therefore, the inner dependence of c7-c9 is 0.101/0.661 = 0.152 step 3: now, dematel and anp are integrated to assign weights to different scre capabilities. step 3.1: first, we calculate the values of different criteria with respect to offshore outsourcing benefits. this step is processed by the ahp method using the 5-level scale. the following results are obtained: technical aspects: 0.41, economical aspects: 0.18, social aspects: 0.18, environmental aspects: 0.11, and political aspects: 0.12 step 3.2: the initial unweighted supermatrix of anp can be acquired by entering the vectors derived from dematel and step 2. the results are shown in the appendix. multiplying the unweighted supermatrix by the criteria relative weights from step 3.1 produces the normalized supermatrix. the supermatrix is then increased to a sufficiently large power until convergence occurs to obtain the limited supermatrix. step 4: the relative weights of the scre capabilities are determined with respect to benefits. the final priority values are found in the corresponding columns of the limited supermatrix. the results are shown in the appendix. 4.3 rank alternative locations and select optimal one in scre perspective in this section, the optimal alternative locations for offshore outsourcing are selected by using topsis based on the existing experts’ evaluations. first, a group discussion is conducted in which the experts evaluate 4 potential alternative locations: l1, l2, l3, and l4. in this way, scores are acquired that represent how a location fulfills outer factors. a five-point scale is developed in the appendix. the scores of each outer factor for the locations consist of an initial vector, which is then input into the fcm to determine the adjacency as in section 4.1. after several iterations, the fcm presents an equilibrium state that reflects the level of scre capabilities. the concrete process of the evolutionary process is shown in appendix. in this study, the equilibrium state emerges after more than 4800 iterations. the equilibrium values generally reflect the final scre capability: the higher the value, the more elastic the supply chain will be. the sign (+,−) indicates that the supply chain resilience will be enforced or damaged. for example, capability 8 (market position) reaches -0.6990 in location 1, which indicates that cap8 will be impaired in location 1 with a degree of 0.699. cap3 (anticipation) is 0.4296 in location 3 after many iterations. therefore, cap3 will be activated to some extent in location 3. the detailed results dcm: d number extended cognitive map. application on location selection in scm 763 are shown in the appendix. in this way, the mechanism of evolutionary processes is revealed, and the scre capabilities in each location are presented for further decision-making. once the equilibrium values of the different capabilities in the 4 locations are obtained, the topsis method is utilized to analyze the consistency of each alternative location with respect to the ideal solution from the resilience perspective. the weights of each capability from section 4.2 will be considered. first, we construct a decision matrix d = (xmm) from table 13. then, we normalize the decision matrix by eq. (12). the weights of each capability are considered in this step, and using eqs. (14)-(18), the results are calculated and listed in appendix. the most suitable location considering supply chain resilience is location 3, whose value is 0.6866. the different values of the scre capabilities in the four locations are shown in fig. 2. 1 2 3 4 5 6 7 8 9 l1 -0.6863 0.3906 0.479 -0.6843 0.3721 0.1781 -0.699 0.4838 -0.3012 l2 -0.7795 -0.1778 0.2174 -0.5065 0.3473 0 -0.5927 0 -0.1471 l3 0 0.321 0.4296 -0.5263 0.3474 0.1781 -0.5927 -0.2205 0.1971 l4 -0.7096 0 0 -0.5843 0 0 -0.699 0.5276 -0.3825 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 capability l1 l2 l3 l4 figure 2: comparison of the 4 locations 5 sensitivity analysis to investigate the impact of the scre’s weights (developed by wcapi for capability capi, where i = 1, 2, · · · , 9) on the selection of offshore outsourcing locations with the best benefits, we conduct a sensitivity analysis. eleven experiments were conducted. in the appendix can bee see the details of these experiments. as shown in the appendix, in the first nine experiments, the weight of each scre capability is set as the highest one by one, and the others are set at low and equal values. for example, in experiment 1, the weight of cap1 is 0.5, and the weights of the remaining scre capabilities (cap2-cap9) are 0.0625. the highest weight is computed as 0.5 as follows. since there are a total of 9 capabilities, 8 are assumed to be of equal importance and are allocated equal weights of 0.0625. this leaves the weight of cap1 as 0.5, which ensures that the sum of all weights of the capabilities is 1. similarly, in the next 8 experiments, we set each capability as the most important one by giving it a weight of 0.5 and leaving equal weights of 0.0625 for the remaining 764 l. zhao, f. xiao capabilities. in experiment 10, the weights of each scre capability are equal to 0.111. in experiment 11, the real case in our study is shown. as shown in the appendix, location 4 has the highest score in 7/11 experiments (including the case study). therefore, based on the assessments obtained, our offshore outsourcing location is comparatively insensitive to weights, with location 4 emerging as the winner in the majority of cases (7/11). however, in this study, the best option for practitioners is l3. this could be regarded as a special case that reveals that the selection in the real situation may be different than that in the theoretical analysis. 6 results of the simulation of four alternative locations in this section, we further investigate the acquired results and discuss some implications of the analyzed data. the supply chain resilience values are presented in table 13. the results are gathered from the fcm dynamic process, which reveals the future trends of supply chain resilience. although the optimal solution is identified by comparing its distance to the ideal solution, we explore some special cases by using the results of our proposed model. 6.1 results of simulation of location 1 the results show that 5 resilience capabilities will be enforced and 4 resilience capabilities will be damaged. the final values range from -0.6990 to 0.4838. visibility (cap2), anticipation (cap3) and adaptability (cap6) are the highest in this location among the four alternatives, which indicates that these three capabilities will be increased to different extents. however, recovery capability (cap4) and financial strength (cap7) are significantly impaired compared with the other three alternatives. 6.2 results of simulation of location 2 in location 2, the supply chain’s adaptability (cap4) and market position (cap8) will not be influenced and thus will remain at a stable level. the anticipation capability (cap3) and security (cap5) will increase slightly (+0.2174 and +0.3473, respectively) in location 2. in addition, other capabilities will be damaged to different extends extents. among these damaged capabilities, flexibility (cap1) is greatly impaired, even compared with other places. companies seeking flexibility in their supply chain should avoid choosing this location. 6.3 results of simulation of location 3 in location 3, the flexibility of the supply chain will be stable and will not be influenced. five capabilities (cap2, cap3, cap5, cap6, and cap9) will be improved. among those capabilities, adaptability (cap6) and collaboration (cap9) have their highest values at this location among the four alternatives, whereas the market position of this location has the lowest value compared with the other alternatives. companies that are concerned with adaptability and collaboration should pay more attention to this location. 6.4 results of simulation of location 4 the supply chain’s capabilities of visibility (cap2), anticipation (cap3), security (cap5), and adaptability (cap6) are not influenced in location. it has a comparatively good market position among the four alternatives (the value is 0.5276). however, other resilience capabilities will be impaired. dcm: d number extended cognitive map. application on location selection in scm 765 since offshore outsourcing refers to the transfer of some specific ownership business activities or resources to low-cost providers outside of the client company’s country of origin, a new provider will be a new player in the sc network. the offshore outsourcing process can exert some impacts on sc resilience compared to in-home production. many capabilities of sc resilience are enhanced, while some capabilities are weakened. in this part, we discuss the differences between offshore outsourcing and in-home production with respect to sc resilience. hence, if a manager seeks to preserve or even improve some capabilities of sc resilience, they should consider whether or not to select offshore outsourcing; if they choose offshore outsourcing, they should also consider which location to select. 7 conclusions and future works offshore outsourcing is a heated issue that has received extensive attention. however, the impacts of complicated and diverse environments on supply chain resilience may minimize the ability of supply chains to defend against risks. to overcome this problem, we propose to select a suitable location where the supply chain’s resilience will be maximized based on an integrated mcdm method. the main contributions and innovations of this research paper could be summarized as follows. firstly, this paper combines fcm and d number theory to the concept development if dcm that not only remains the abilities to represent uncertainty but also contributes to aggregating knowledge from different sources (experts/commanders). since uncertain information fusion has been studied for many years, indicating that d number theory is an effective framework to represent and fuse uncertain information. the combination of d number theory and fcm is shown to be valuable approaches through illustration. secondly, dematelanp is implemented to quantify the weight in context of offshore outsourcing problem. from sensitivity analysis, we conclude that a concise result could be obtained. overall, the proposed method can support practitioners while evaluating alternative outsourcing locations according to their impacts on the sc resilience. experts perceived the main advantage of the proposed method in the ability to predict effects due to indirect implications, which are otherwise very difficult to predict, especially for large models. in fact, in our case, one location would improve resilient capabilities and two locations would rather damage it. such location behavior could not been predicted without our simulation. for academics, this paper provides a groundwork for further studies because it is the first time that a research shows how offshore outsourcing location decision-making can improve, preserve or damage sc resilience. looking to the future, empirical works would validate the influences detected. in addition, the developed hybrid method is generic, flexible and easily adaptable. therefore, it could be applied easily to other sectors to represent messy problems with causalities and predict future outcomes. our future work involves the validation of the proposed model results by comparison with other techniques available in the literature for the selection of outsourcing locations. funding this research is supported by the fundamental research funds for the central universities(no. xdjk2019c085) and chongqing overseas scholars innovation program(no. cx2018077). author contributions l.z designed, performed experiments and wrote the paper. f.x developed the method, conceived the experiments and revised the paper. 766 l. zhao, f. xiao conflict of interests the authors declare no conflict of interest. bibliography [1] ali, s.; hongqi, l.; khan, s.u.; zhongguo, y.; liping, z. 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[39] yoon, k.p.; kim, w.k.(2017). the behavioral topsis. expert systems with applications, 89, 266–272, 2017. international journal of computers communications & control issn 1841-9836, 14(1), 21-38, february 2019. resp: relay suitability-based routing protocol for video streaming in vehicular ad hoc networks o.a. hammood, n. nizam, m. nafaa, w.a. hammood omar a. hammood*, mohd nizam, muamer nafaa, waleed a. hammood department of computer system and software engineering university malaysia pahang (ump) , malaysia kuantan-26300, pahang, malaysia *corresponding author: omer_almajeed@yahoo.com mnizam@ump.edu.my, muamer.scis@gmail.com, engwaleed54@yahoo.com abstract: video streaming in vehicular ad hoc networks (vanets) is a fundamental requirement for a roadside emergency and smart video surveillance services. however, vehicles moving at a high speed usually create unstable wireless links that drop video frames qualities. in a high-density network, network collision between vehicles is another obstacle in improving the scalability of unicast routing protocols. in this paper, the relay suitability-based routing protocol (resp) which makes a routing decision based on the link stability measurement was proposed for an uninterrupted video streaming. the resp estimates the geographic advancement and link stability of a vehicle towards its destination only in the small region. to ensure the reliability while extending the scalability of routing, the relay suitability metric integrates the packet delay, collision dropping, link stability, and the expected transmission count (etx) in the weighted division algorithm, and selects a high-quality forwarding node for video streaming. the experimental results demonstrated that the proposed resp outperformed the link lifetime-aware beacon-less routing protocol (lbrp) and other traditional geographical streaming protocols in providing a high packet delivery ratio and less packet delay with various network densities, and proved the scalability support of resp for video streaming. keywords: video streaming, scalability, geographic unicast routing, link stability, reliability, qos. 1 introduction vehicular ad hoc networks (vanets) are widely applied in information and communication technology to ease transportation problems [8,23]. video streaming in vanets is a central part towards the realization of intelligent transportation systems [2,31], and it has many promising potential applications, ranging from safety to entertainment. however, video streaming in vanets faces several challenges due to the stringent video quality level requirements [7,9] . the major problems associated with scalable video streaming over vanets are frequent link failures [33] due to greedy vehicle mobility, lack of link quality measurement, and link lifetime measurement in terms of the vehicle velocity rather than the relative velocity of vehicles. the network condition is dynamic with respect to vehicle densities and the distance between the source and the destination vehicle [22]. the existing geographic routing protocols based on greedy or improved greedy routing determine the shortest routing paths in such a way that it can be scaled up to a large vanet environment; however, it degrades the performance of the routing approach under high mobility scenarios. the integration of different routing aspects such as relative velocity, densitybased collision probability, distance to reach the destination, and expected transmission count (etx) [15] in the measurement of routing relay suitability can improve the performance of video streaming over the dynamic vanet environment. an improper integration of these parameters copyright ©2019 cc by-nc 22 o.a. hammood, n. nizam, m. nafaa, w.a. hammood tends to compensate each other for their influence on the scalable and efficient router selection for video streaming [4]. to cope with these challenges, the geographic unicast routing protocols have been employed as a suitable solution for the distribution of video flows in vanets [24]. recently, several geographic unicast routing protocols have evolved to achieve scalability in video streaming, however, a scalable video streaming that meets the stringent video quality requirements without video distortions under a highly dynamic network topology is a challenging task in unicast routing. greedy perimeter stateless routing (gpsr) is a commonly used geographic unicast routing protocol in wireless networks. the conventional location-based routing protocols depend on the distance metric in which the nearest vehicle is selected as a forwarder. in highly dynamic networks, the greedy node selection can lead to frequent link failures and retransmissions. beside the dynamic network topology, video streaming solution has to comply with the quality of service (qos) requirements [25]. to decide the reliable and qos forwarding vehicle in the network, the recent geographic routing protocols must accumulate multiple routing factors. geographic unicast routing is more effective for a wireless communication [10,14], however, there are many issues in implementing a scalable and reliable geographic unicast routing over vanets. for example, unicast geographic routing selects a greedy node in the communication range with the aim of advancing the packet forwarding [3]. despite distance information, a common aspect of the geographic routing protocol is that it must select and relay data packet through a stable node. two key facts that limit greedy routing performance in video streaming are node mobility and collision due to the unreliability of the greedy node. when a packet is dropped due to node mobility or collision, the sender node either retransmit the packet to the same forwarder or through the currently available greedy node. consequently, it triggers unnecessary retransmissions, an end-to-end packet delay, and even video buffering and distortion. it is essential to focus on the trade-off between the suitability of a node in advancing packets toward the destination and the stability of its connectivity to the sender node. in this paper, the relay suitability-based unicast routing protocol (resp) for an uninterrupted video streaming in the vanet topology is proposed with the aim of extending the scalability of geographic unicast routing protocols. the proposed resp builds a small region at the boundary of the communication range using an offset angle and integrates the compensating factors using a weighted division algorithm to improve the performance of a vanet router selection process. it significantly reduces packet retransmission and propagation delay in video streaming. the main contributions of the proposed resp protocol are summarized as follows: • the proposed resp protocol considers multiple routing aspects for an efficient unicast routing and scalable video delivery with minimized distortions and buffering. • node density-aware data collision and relative velocity-aware link stability measurements facilitate the scaling up of resp to a larger vehicular network. • the geographical advancement of a near-by vehicle towards the destination is considered in the router selection to avoid the involvement of several intermediate vehicles in the routing. further, the consideration of etx avoids packet loss, resulting in a successful timely video frame delivery. • the weighted division algorithm integrates multiple routing aspects and ensures an efficient and stable link selection for video delivery. • the performance of the resp routing protocol was evaluated using a network simulator and compared to the routing policy centralized in terms of packet delivery ratio (pdr), resp: relay suitability-based routing protocol for video streaming in vehicular ad hoc networks 23 delay, overhead, and frame setback ratio (fsr). the remaining part of this paper is organized as follows: section 2 presents a discussion of the existing related works while section 3 presents the idea of the proposed system model, including the positive forwarding region formation, the degree of mobility, different routing aspects of relay suitability factor, forwarding probability and delay metrics, and scalable and qos data forwarding by applying weighted division algorithm. in section 4, the performance of the proposed system was evaluated and presented while section 5 presents a conclusion and a brief introduction to the intended future work. 2 related works several unicast routing protocols have been proposed for video streaming in vanets [20]. the geographic unicast routing protocols are classified into three types, which are greedy routing, opportunistic routing, and trajectory-based routing protocols [5, 19]. in vanets, a commonly used routing strategy is the greedy forwarding strategy in which a node selects a neighbor close to the destination for data forwarding [26]. the opportunistic routing scheme follows the storecarry-and-forward technique and avoids unnecessary packet dropping especially when no forwarding node is available. however, it is prone to long delays and inadequate for video-streaming. the trajectory-based strategy provides more importance to a vehicle to act as a forwarder when it is moving towards the destination vehicle. the gpsr is a greedy-based unicast routing protocol in vanets which assumes that the nodes are aware of their own position and the location of the destination [18]. the gpsr follow two different modes for data routing, which are the greedy mode and the perimeter mode. the perimeter mode is applied when there is no greedy router in the communication range. however, the greedy mode can fail since there is a high chance of the greedy router to move out of the communication range. conventionally, several works have been presented on improving the basic gpsr [5, 28]. the movement prediction routing (mopr) considers the link stability concept and attempts to solve the issues in greedy routing mode while the improvement gpsr (i-gpsr) consolidates different metrics such as the distance to the destination, vehicle density, moving direction, and vehicle speed to make routing decisions. likewise, the multimedia multimetric map-aware routing protocol (3mrp) [17] exploits the distance to the destination, vehicle density, trajectory, and available bandwidth to select the next hop for routing while the available bandwidth estimation (abe) [1] calculates the available bandwidth on a link between two nodes. the mmmr exploits the roadmap considering the possible obstacles; however, it assigns an equal importance or weight to all the metrics in the score estimation. a better scheme must provide variable weights to the routing metrics depending on the current network conditions. however, the acquisition of motion prediction information is a difficult task. most of the conventional routing techniques focus only on better packet delivery rates with low data delivery delays. however, they do not address the quality of video delivery when users are watching video sequences. video dissemination techniques have not considered video qualities [6,20] which is a crucial concept for the success of safety and traffic-warning video streaming in vanets [18]. thus, the multi-hop routing service is essential to the awareness of quality of experience (qoe) requirements and network conditions. it assists the video streaming approach to avoid the loss of video sequences and quality. a real-time video dissemination with a qoe-supportive solution in vanet [6] imposes the use of a distortion threshold. qoe represents the experience of the end users on a delivered service and their satisfaction rate about it. it considers the average delay in routing decisions, however, average delay reduction is not equivalent to qoe maximization [25]. the network conditions include collision warning, traffic congestion, and sharing parking information. when a collision or network congestion occurs, 24 o.a. hammood, n. nizam, m. nafaa, w.a. hammood video streaming might be affected. the main reason behind network congestions and collision is a high routing overhead and to solve this issue, the beaconless routing protocols have been proposed [20,21] rather than a continuous information exchange with the neighboring vehicles, each vehicle considers the speed and position information before making any forwarding decision [11]. from the information, the receivers set a timer and the vehicle to first reach count zero will be selected as the next hop relay. for a high traffic collision environment, taking a routing decision based on the distance and vehicle stability is insufficient. the reactive routing protocols have a better mean opinion score in a vanet environment [27] whereas the location-based vanet routing protocols have a good result due to location accuracy [12, 24]. however, they face a degradation in average mean opinion score [32] and affect the quality of video streaming over vanets. several works have focused on the performance evaluation of the position-based routing protocols for video streaming in vanets based on the peak signal-to-noise ratio, video quality metric, and qoe measures [25,32]. to avoid the issue of retransmission, etx has been employed in vanet protocols, however, etx alone tends to present a long routing path and high delays. thus, neither the location-based distance measure nor the etx metric alone is efficient for video streaming over vanets. to accomplish this, the proposed resp protocol exploits relay suitability as a metric of communication quality and considered different routing aspects in relay suitability estimation. 3 the proposed system model idea the proposed work considered the proximity of the vehicles to the destination. expected transmission count (etx), collision probability, and relative velocity based link stability in relay suitability measurement. the weighted division algorithm is used for an efficient integration of factors in the scalable router selection for unicast geographic routing. there are two different routing aspects considered in relay suitability measurement data forwarding probability and data forwarding delay. the first aspect includes the link stability and collision probability. figure 1 demonstrates the resp architecture of the proposed work. considering the vehicle velocity alone is not feasible for scalable router selection because the velocity difference between two vehicles is dependent on the proximity of the vehicle. a considerable number of neighbors increase the possibility of packet losses due to collisions. a high traffic, to a greedy vehicle, results in collision and packet loss and limit the quality of a greedy link since it increases video distortion. thus, the proposed work considered the collision probability in a scalable router selection. the second routing aspect handles the problem of data forwarding delay with the support of geographical advancement towards the destination and etx. the proposed work only considered a few vehicles located in a preferable forwarding region (near to the destination vehicle). it avoids the selection of the number of hops to reach the destination and a high computational time. the sending vehicle applies the relay suitability measurement only on the vehicles in a preferable forwarding region. the distance routing metric alone enables the routing protocol to select the shortest routing path, however, it is prone to transmission failures due to poor link quality since the transmission capability of a node is not considered in the decision-making process. fthe selection of routing vehicles with low etx increases the reliability of a network. however, etx alone is prone to long routing paths and high latency on routing video frames. thus, the distance or etx metric alone is inefficient for video streaming over vanets. to accomplish this, the proposed work takes into account different routing aspects in relay suitability estimation. moreover, simply combining the routing factors is not an adequate way, since these are compensating with each other. thus, the proposed work designs an efficient weighted division algorithm and efficiently delivers the video stream over selected scalable routing vehicles. resp: relay suitability-based routing protocol for video streaming in vehicular ad hoc networks 25 figure 1: resp architecture 4 proposed routing mechanism this section is intended to explain the mechanism of the proposed routing protocol and how this new mechanism can enhance the performance of the proposed protocol. 4.1 positive forwarding region formation the proposed work focused on the consolidation of multi-objective metrics which combine delay and stability with a successful forwarding probability. delay and packet loss should not exceed their predefined limits since both are the deciding factors of video quality. the transmission of a video demands the use of high-quality links within a short time, thus, high-quality positive forwarding nodes must be selected from the neighbor list. every node must broadcast a neighbor’s discovery message to identify its neighboring nodes (nn) in the communication range r. a node which receives the request message must respond by sending its identification number (id) and the information relevant to the multi-objective metric. every node receives several messages from its neighbors and builds its routing table. the nodes follow the periodic alive message propagation to maintain their presence in the communication range as a neighbor. if a node does not receive an alive message from the registered neighboring node until a predefined period, it erases the entry related to such neighbor from its routing table. before selecting the router node, the source node (sn) executes the multi-objective function. the proposed work employs no execution control, the sender node would be executed for the neighboring nodes located in all the directions ad this results in an imperfect router selection and affects the video quality. since there are compensating factors for distance metric, the sender selects and transmits the packet to a node in the backward direction to the destination. therefore, to minimize the unnecessary transmission hop increment and quality degradation, each node that transmits the data packet must generate a "forwarders list" based on the distance information and apply the link quality 26 o.a. hammood, n. nizam, m. nafaa, w.a. hammood measurement on them using a weighted division algorithm. the notations used in the work are listed in table 4. table 1: notations used in resp protocol notations description nn neighboring node r communication range sn source node lsx, lsy location coordinates of node sn dn destination node distnd distance between the destination and neighboring node distsd distance between the source and destination node posfn positive forwarding region etx expected transmission count ∆ vj relative velocity between node j and i cpj collision probability of node j nnm the neighbors which are added in the mpfn of a node link qualityi,j quality of a link i,j α and β weighting factors pi,j and pj,i weighting the packet delivery ratio of links i,j and j,i every forwarder of the data packets must generate a forwarders list. note that the sn and intermediate nodes exploit the same process. to route towards the destination nodes (dn), the sn obtains the location information of the dn using the external location server. by applying equation 1, the sender node estimates the distance between the source (lsx, lsy) and destination with the location coordinates (ldx , ldy). the distnd represents the distance between the destination and the neighboring nodes while distsd represents the distance between the source and the destination nodes. from equation 2, the sender node generates the set of forwarding nodes (fn), which include the nodes that satisfy the condition of distsd > distnd. dist_sd = [(l_dx − l_sx )2 + (l_dy − l_sy )2 ](1/2) (1) psofn = {fn|nn ∈ distsd > distnd} (2) the process of forwarding node selection from the positive fn (posfn) set provides the direction to deliver the data packets to the destination dn. in this way, although the data packets can approach the dn, the reachability cannot be guaranteed due to the negligence of the communication quality between the nodes. the proposed resp needs to identify the quality routers that forward the data packets by considering the offset angle-based degree of mobility. 4.2 degree of mobility the proposed resp balances the router suitability to forward the video packets between the node geographic advancement towards the destination and the link stability. to do this, the proposed resp protocol considered a small region at the boundary of the communication range formed by the offset angle and the source node as the center of the circle. the restricted small region has been referred to as the "most preferable forwarding area" (mpfn) (figure 2). since the vehicles present at the edge of the communication range can move out of the communication range at any time, the vehicles present in the mpfn area were selected for the measurement resp: relay suitability-based routing protocol for video streaming in vehicular ad hoc networks 27 of relay suitability. the preferable routers have a small degree of mobility. also, there is a chance of experiencing a bad signal reception which can negatively impact the video quality. the forwarding region (fn) and the mpfn are shown in figure 2 the area of the segment areatuv in a communication range of a node is named as the forwarding region, and the nodes located within the areatuv are denoted as a set of fn. the restricted segment area of areatuv is denoted as mpfn (areaopqr) because the nodes within the segment of the areav op can move out of the communication range of a sender node at any time. this can increase the uncertainty of a forwarding node, thus, the resp restricts the forwarding region segment area and selects a preferable forwarding region for routing. notably, the nodes within the segments ofareasqr, areasto, and areasup increased the delay due to the long traveling path of a packet towards the destination. theareaopqr is estimated using the following equations: areaopqr = areasop − areasqr (3) the points ’o’ and ’p’ lie on the circle of communication range, with an angle of α. initially, the value of α is assigned as 450. if there are no nodes within the mpfn, all the nodes in the fn that satisfy the conditions of equation 2 are involved in the relay suitability measurement. moreover, points ’q’ and ’z’ lie at the distance of half of the communication range with an angle of α. areasop = ( distop 4 ) x(4r2 − distop2)1/2 (4) areasqr = ( distqr 4 ) x(4r2 − distqr2)1/2 (5) the areaopqr is estimated by substituting equations 4 and 4 in 3. from the measured area, the nodes present inside the area are called the preferable forwarding nodes. the sender node applies the relay suitability measurement to these nodes and improves the efficiency of routing. the proper selection of routers facilitates the scaling of resp routing to a large network without reducing its routing reliability. 4.3 different routing aspects of relay suitability factor the main aim of the proposed methodology is to design a scalable and an efficient geographic routing protocol by considering different aspects of routing strategy and efficiently integrating the compensating routing factors. the source selects the neighbors from the preferable forwarding region before sending the data packet. to select the router, the resp protocol exploits the relay suitability (res) as a metric of communication quality. a sender node selects a neighbor node ∈ mpfn with high res to enhance the reliability. if the sender node simply selects a neighbor with a low per as per the distance metric, the routing delay increases because of the increase in the number of hops to the dn. it limits the scalability of the network to support video streaming in vanets. hence, the proposed resp applies the delay, collision impact, stability, and etx metrics in weighted division algorithm and selects a high-quality router for video streaming. 4.4 forwarding probability and delay metrics the most deciding factors of forwarding probability are collision-free network and link stability. the proposed resp protocol suggests the estimation of link stability according to the 28 o.a. hammood, n. nizam, m. nafaa, w.a. hammood figure 2: most preferable forwarding regions relative velocity of two vehicles and data collision probability using node density. the current works considered node velocity but the use of node velocity alone cannot guarantee the estimation of the future network topology from the relative velocity of two vehicles since their velocity does not depend on their proximity. based on this, the relative velocity of a node with the neighboring nodes located in the restricted forwarding region was considered in the proposed protocol. if the vehicles move in the same direction at the same speed, their relative velocity is considered as zero, but if they are moving in the same direction with a significant difference in their speed, the relative velocity is considered high. the relative velocity (∆v) is used as a primary factor in the determination of relay suitability. ∆ vi = vsn − vi , wherei varies from 1 to |mpfn| (6) moreover, the packet collision increases with the number of active nodes (the nodes transmitting the packets simultaneously), even as a high traffic to a selected intermediate vehicle can cause both collision and packet loss. thus, the resp estimates the probability of packet dropping due to collisions. the packet level lossy network collision model for each link ni,j is taken as the input number of nodes that add the node j to its corresponding mpfn (nnm) over the total neighbors. equation 7 showed the estimation of collision probability of node j. cpj = ( areanetwork xnnm ) (totalnodesx 3.14 xr2) (7) it considered the network area, total number of nodes, and the communication range of the nodes. assume that all the nodes know the network area and the number of nodes; when a node is added in the mpfn of most of its neighboring nodes, there is a chance for packet collision. the delay and etx are essential parameters for video streaming over vanets and to deliver video files to their destination on time, it is essential to select efficient routing nodes. considering etx alone expose the routing protocol to long routing path determination and high latency on routing video frames. etxi,j = 1 (pi,j xpj,i) (8) by applying equation 8, the etx value for a communication link ij can be estimated, where pij and pji represent the packet delivery ratio of links ij and ji, respectively. by using all resp: relay suitability-based routing protocol for video streaming in vehicular ad hoc networks 29 the factors, the proposed resp estimates the res value for all the nodes ∈ mpfn. moreover, the distance metric-based selection router or the greedy node is not much reliables since the can move out of the communication range anytime. thus, the distance or etx metric alone is inefficient for video streaming over vanets and in the resp, both metrics were considered as forwarding delay factors. 4.5 scalable and qos data forwarding by applying weighted division algorithm the proposed resp considered multiple routing aspects such as the probability of packet dropping due to the collision, etx, relative velocity, and distance factor. however, an improper integration of these factors tends to compensate each other for their influence on the scalable and efficient router selection for video streaming since the greedy node drops the packet due to mobility and enables the sender node to retransmit the same packet frequently. moreover, the selection of the shortest link for routing increases video frame delivery delay and video buffering. thus, the proposed resp integrates the compensating factors using a weighted division algorithm to improve the performance of a vanet router selection process. link qualityi,j = { [(1 −α) (1 − ∆vi,j r ) ] + (α) (1 −cpj) (1 −β) (etx) + (β) (1 − [disti,j r ] } (9) in equations 8 and 9, α and β are the weighting factors, and moreover, both values are in the range of 0 to 1. the stability and collision probability is the forwarding probability relevant metrics. if the ratio of ∆vi,j r is nearly 1, the link will be disconnected. the factor cpj also creates a negative impact. the weighted addition of factors 1( ∆vi,j r ) and (1-cpj) are taken in the denominator whereas the weighted summation of etx and geographic advancement are taken in the numerator. the result of a weighted division algorithm represents the quality of the communication link. the node with a high link quality in the mpfn is selected in the packet forwarding. the collision-free links with a better stability significantly mitigate the packet delay and dropping. thus, the selection of high-quality links ensure a successful delivery of video frames and a significant reduction in video distortion and buffering. the algorithm 1 illustrates the process of resp protocol in detail. the processes in resp protocol are divided into three procedures, such as most preferable forwarding region selection, relay suitability (link quality) measurement, and scalable router selection. every node that has data packets to forward to the destination executes the aforesaid three procedures to select the scalable router for video streaming. the following flow chart in figure 3 provides a detailed picture of processes in resp protocol. 5 performance evaluation the performance of the proposed resp was evaluated in an ns2 simulator on a 2000 m road scenario where the vehicles move in two independent directions. the simulation model imports the roadmap from the sumo. the maximum velocity of vehicles in each lane was 130 km/h. in this scenario, the vehicles follow move mobility model for movement. to evaluate the performance of the resp, it was compared to lbrp [11], lsgr [13] and gpsr [18] under various scenarios by varying the number of vehicles. the agents such as mytraffictrace, myudp, and myudpsink were built inside the ns2 to generate video traffic. the sender forwards the video format of yuv qcif or yuv cif to the destination. to support the digitized video transfer, this work utilized the mpeg4 codec to compress the file. the mytraffictrace agent fragments 30 o.a. hammood, n. nizam, m. nafaa, w.a. hammood figure 3: flow chart of process of resp protocol resp: relay suitability-based routing protocol for video streaming in vehicular ad hoc networks 31 algorithm 1 scalable router selection for video streaming 1: input: neighbor list of a sender node 2: output: scalable video delivery 3: procedure 1: most preferable forwarding region selection 4: procedure 2: relay suitability (link quality) measurement 5: procedure 3: scalable router selection 6: every sender node () 7: { 8: procedure 1 () 9: if distsd > distsd then 10: the node is added in the forwarding region, fn; area of mpfn is measured; the nodes in the mpfn are identified; 11: else 12: the node is in the neighbor list only; 13: end if 14: } 15: procedure 2 () 16: { 17: if ∆v > 0 then 18: the node is added in the forwarding region, fn; cpj and etxi,j is measured; link quality is measured using the equation (9); 19: else 20: the node is removed from the mpfn; 21: end if 22: } 23: procedure 3 () 24: { 25: for all i = 0; i + +; i < |mpfn| do 26: { 27: if link quality (i) > max then 28: max = link quality (i) 29: } 30: the node in the max is selected as a router; 31: end if 32: end for 33: } 34: } 32 o.a. hammood, n. nizam, m. nafaa, w.a. hammood each of the video frames in the compressed file into several segments. it was transmitted using myudp and received using myudpsink on the receiver side. the sender trace file retains the timestamp, the packet id, and the packet payload size for every udp packet transmission, and the video trace file holds information about each frame. by utilizing the tcl script, the network objects were configured for video streams. the video clip used in the simulation was 5.7 mb in size, 30 fps frame rate, with a pixel resolution of 720 x 576 in mpeg format. the simulation parameters are listed in table 4. table 2: simulation parameters simulator network simulator 2 average total vehicle 50-100 density (veh/km) 130 vehicle max speed (km/h) randomly area 2000 x 100 m2 communication range 250 m interface type phy/wirelessphy mac type ieee 802.11p queue type droptail/priority queue queue length 50 packets antenna type omni antenna routing protocol resp, lbrp, lsgr, and gpsr transport agent myudp, and myudpsink application agent qmytraffictrace frame rate 30 fps input video format yuv qcif simulation time 150s 6 performance metric the following performance metrics were used to evaluate the performance of scalable and reliable vanet routing under different density scenarios. • packet delivery ratio (pdr): it is the ratio between the total received packets and the generated • delay: it is the time taken to deliver the data from the source to the destination vehicle. • overhead : it refers to the total number of control packets involved in the scalable and reliable video transmission process. • frame setback ratio: the frame setback ratio is the ratio of the number of delayed frames and the number of forwarded frames per second. these were analyzed to show the impact of the routing schemes on the user perception in scenarios with different end-to-end distances and node densities. resp: relay suitability-based routing protocol for video streaming in vehicular ad hoc networks 33 7 results and discussion two key performance metrics are shown in figure 4, where the performance of the proposed protocol was compared to the performance of three other protocols lbrp, lsgr, and gpsr. in this figure, the validated influence of vehicular traffic density on performance metrics was on the path lifetime and the overall transmission delay for the target content. in figure 3a, the performance of the constructed path lifetime was compared. recall that the path lifetime is determined by the link with the minimum lifetime. as can be seen from the figure, the proposed resp produced the longest path lifetime since it preferred the neighboring vehicle that can maintain a high connectivity performance. next in importance is that the lsgr showed the second-best path lifetime as a result of the consideration of both link lifetime and one-hop transmission distance information on the protocol design. on the contrary, without the consideration of the link connectivity property, the path tends to be fragile as illustrated in the gpsr and lbrp. especially for gpsr, the constructed path is the most easily broken path with the largest inter-relay distance. figure 4(b) showed the observed delay of the resp, lbrp, lsgr, and gpsr when the number of nodes was varied from 50 to 100. resp achieved a very low packet delivery delay for varying nodes. there is a minimal variation in the packet delay between resp and lbrp. most of the delivered packets by resp reached their destination on time, and this leads to a considerable delay in lsgr and gpsr. however, for the resp, efficient packet transmissions were performed with a highly stable and less congested communication links. the influencing parameters in the resp were the node stability and network collision. it minimized the comfigure 4: the three-performance metrics (a) path lifetime; (b) transmission delay. link lifetime-aware beacon-less routing protocol (lbrp); link state-aware geographic routing (lsgr); greedy perimeter stateless routing ( gpsr) 34 o.a. hammood, n. nizam, m. nafaa, w.a. hammood munication delay without degrading the packet delivery ratio. both resp and gpsr limits the packet delay in the range of 0.01 to 0.03 seconds. however, lbrp delivers the packets in 0.04 seconds in the low-density network. when the number of nodes increased to 100, the data packets of lbrp were delivered with a delay of 0.02 seconds which was closer to the delay of resp and gpsr at the same scenario. even though resp showed a better performance in terms of delay, the packet delivery ratio was less compared to those of lbrb, lsgr, and gpsr. the path lifetime metrics increases with vehicle speed when the energy factor must be considered in the holding time computation to increase network lifetime. in figure 5, the other three performance metrics were shown, where the performance of the proposed resp was compared to the performance of lbrp, lsgr, and gpsr. in this figure, the validated influence of vehicular traffic density on the performance metrics was in the packet delivery ratio, overhead, and the overall frame setback ratio. figure 5 (a) displayed the packet delivery ratio of resp, lbrp, lsgr, and gpsr when the number of nodes was varied from 50 to 100. the performance of these solutions was compared for scalability by increasing the vehicle density with the same data traffic. the number of vehicles in the network showed a huge impact on the packet delivery ratio of the 4 protocols. for instance, resp improved the packet delivery ratio from 81.5% to 97.8% when the number of nodes increased from 50 to 100 compared to lbrp, lsgr, and gpsr. resp achieved the highest delivery ratios because it does not have to select the intermediary vehicles in advance or wait to forward the data packets. initially, lbrp attained a packet delivery ratio closer to the resp. with 70 to 80 nodes, lbrp improved the packet delivery ratio by 33% more than lsgr. the minimum node density reduced the probability of lbrp failing the communication links between the nodes. however, lsgr required more time to establish the entire path to the destination under a low-density environment and this increased its link breakage probability which resulted in a weaker packet delivery ratio. beyond 60 nodes, lsgr drastically increased the packet delivery ratio compared to gpsr. the high dense scenario made the construction of the lsgr path faster. with a sufficient number of nodes, resp improved the packet delivery ratio by 8.4% and 17.5% compared to lbrp, lsgr, and gpsr, respectively. figure 5(b) showed the overhead of the four protocols under various dense scenarios. as expected, increasing the number of nodes leads to a high overhead due to the increment of control packets. due to the data transmission interruption in gpsr, the overhead fluctuates with various node density. for instance, lsgr spent 10980 packets to deliver data packets at a low dense scenario while decreasing the overhead to 30205 packets at a high dense scenario. resp achieved a better result when using the hello packets to generate its neighbor’s list and share the velocity information. compared to the other protocols, resp attained low overhead at a lower node number, but an increased overhead (from 8960 to 21600 packets) when the node number was increased from 50 to 100. lbrp conducted a receiver-based beaconless routing, however, it transmitted the content request and response packets. the number of these packets are increased compared to the number of packets carrying video contents under a highly dynamic network topology. for instance, lbrp increased the overhead from 15983 to 39875 packets when the number of nodes was increased from 50 to 100 in a high-density scenario but gpsr had the highest overhead rate (starting from 39008 and ending at 79502). figure 5(c) showed the result of the frame setback ratio. the delayed data packet is a primary reason behind the fsr value decrement. as expected, the fsr value was increased by all the protocols when the number of nodes was increased from 50 to 100. the resp considers the relative velocity in link stability measurement rather than the node velocity. it improves the accuracy of node stability measurement and the performance of the resp. for instance, resp achieved 0.0014 of fsr when the number of nodes was increased from 50 to 100. moreover, the number of neighboring nodes having the node identity in their corresponding mpfn list decides resp: relay suitability-based routing protocol for video streaming in vehicular ad hoc networks 35 figure 5: the three-performance metrics (a) packet delivery ratio; (b) overhead; (c) frame setback ratio. link lifetime-aware beacon-less routing protocol ( lbrp); link state-aware geographic routing (lsgr); greedy perimeter stateless routing (gpsr) 36 o.a. hammood, n. nizam, m. nafaa, w.a. hammood the packet collision at a given node. considering this scenario, resp made routing decisions and delivered data packets on time, thereby significantly reducing the frame setback ratio. compared to lbrp, resp reduced the frame setback ratio by 0.358 under a high dense scenario. 8 conclusion this work presented a scalable unicast geographic routing protocol for video streaming in vanets. the building of a small region at the boundary of the communication range using an offset angle and applying the relay suitability measurement only on those nodes can reduce computational complexities without degrading the performance. in vanet scenarios, vehicle density, vehicle mobility, and destination mobility also reduce the scalability of protocols. the relay suitability measurement includes the forwarding probability and the delay measurement, and this ensures the trade-off between the node geographic advancement towards the destination and the link stability. this work conducted extensive experiments over vanet scenarios where the performance of the proposed and existing unicast routing protocols was compared in terms of scalability by increasing the vehicle density. the simulation results showed improvements in the resp compared to 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(2015); energy-efficient context-aware matching for resource allocation in ultra-dense small cells, ieee access, 3(9), 1849-1860, 2015. international journal of computers communications & control issn 1841-9836, 13(3), 383-390, june 2018. reduction of conditional factors in causal analysis h. liu, i. dzitac, s. guo haitao liu*, sicong guo 1. institute of intelligence engineering and mathematics liaoning technical university, fuxin 123000, china 2. college of science liaoning technical university, fuxin 123000, china *corresponding author: liuhaitao@lntu.edu.cn ioan dzitac 1. aurel vlaicu university of arad 310330 arad, elena dragoi, 2, romania ioan.dzitac@uav.ro 2. agora university of oradea 410526 oradea, p-ta tineretului 8, romania, idzitac@univagora.ro abstract: faced with a great number of conditional factors in big data causal analysis, the reduction algorithm put forward in this paper can reasonably reduce the number of conditional factors. compared with the previous reduction methods, we take into consideration the influence of conditional factors on resulted factors, as well as the relationship among conditional factors themselves. the basic idea of the algorithm proposed in this paper is to establish the matrix of mutual deterministic degrees in between conditional factors. if a conditional factor f has a greater deterministic degree with respect to another conditional factor h, we will delete the factor h unless factor h has a greater deterministic degree with respect to f, then delete factor f in this case. with this reduction, we can ensure that the conditional factors participating in causal analysis are as irrelevant as possible. this is a reasonable requirement for causal analysis. keywords: factors space, causal analysis, reduction of factors, fuzzy logic. 1 introduction causal analysis in factors space [18] is proposed in the paper [22], which extracts causal rules from the background distribution in between a group of factors. this is the original methodology provided by the factor space for the machine learning, classification and decision-making and so on. the paper [13] applies those causal rules to causal reasoning, and the paper [17] improves the inductive algorithm introduced in paper [22]. the paper [1] puts forward that the slip-differential algorithm, improving the precision of causal reasoning. the paper [15] gives the rule extraction with respect to multi-result factors, which connects multi-label learning theory [5]. the paper [2] presents a reasonable statement on logistic regression based on fuzzy sets and the factor space theory. the paper [14] introduces the historic background of factors space and its relationship with formal concept analysis [6]. a lot of theoretical papers about factors space can be found in the reference [3, 4, 7–12, 16, 19–21, 23, 24]. all this lays a complete foundation for the unified depiction of causal induction and reasoning in artificial intelligence. however, in the face of the impact of big data, the number of factors to be processed by causal analysis is surprisingly large. we will discuss how to simplify and merge the large number of conditional factors in this paper. the idea of the article [22] is that the factor which has the strongest influence on the resulted factor will be used first. by using it, we can have a causal rule and delete some data. repeating copyright ©2018 cc by-nc 384 h. liu, i. dzitac, s. guo the process, when all the data are deleted all unused conditional factors were reduced. this reduction method is determined by the deterministic degrees of conditional factors with respect to the resulted factor. this paper makes a supplement to the idea of reduction. not only do we consider the influence of the conditional factors on the result factor, but also the relationship between the condition factors is taken into consideration. the deterministic degrees of a condition factor with respect to other factors should be considered. the conditional factors are reduced or merged according to the degree of mutual determination, and the last is a set of conditional factors that are as not related to each other as possible, which is the best condition for causal analysis. the structure of this paper is that section 2 introduces the mutual relationship in between conditional factors, and section 3 introduces the reduction algorithm of conditional factors. section 4 is a short conclusion. this paper is a mathematical method without specific examples. 2 mutual relationship in between conditional factors the factor is the quality root, each factor in command of a string of attributes. for example, the color is a factor, which commands the red, orange, yellow, green, blue, blue, purple and so on. it mathematically is defined as a mapping f : u → x(f). f is color, for example, u are a group of cars, x(f)={red, orange, yellow, green, green, blue, purple}, which draws our attention from the group of cars to their colors. x(f) is called the state space of the factor f , where the states are described by natural language words, called the qualitative states; of course, factors also can have quantitative state space, then back to the variable. the factor is the promotion of variables. factor f is regular if there are at least two objects u and v such that f(u) 6= f(v). considering a set of basic factors f∗ = {f1, · · · ,fn}, we can define a synthetic factor by any subset f = {f(1), · · · ,f(k)} of f∗ with the state space x = x(f(1)) ×···× x(f(k)) ( × stands for cartesian product). denote the synthetic factor as f = {f(1) ∪·· ·∪f(k)}. it is easy to prove that p(f∗) = (p(f∗),∪,∩,c) forms a factorial boolean algebra, where the operations ∪ and ∩ are called the synthesis and separation of factors respectively. denote xf∗ = {x(f)}(f∈p(f∗)), and φ = (u,xf∗) is called the factor space defined on u. a factor f defines an equivalence relation ∼ in the domain u: for any u,v ∈ u, u ∼ v if and only if f(u) = f(v). denote the subclass of u containing u as [u]f = {v ∈ u|f(v) = f(u)}. we call that h(f,u) = {[u]f|u ∈ u} the division of u by f. we call that f is more specific than h, denoted as h(f,u)〉h(h,u), if for any u, there is an v in u such that [v]f ⊆ [u]h, and for any u, there is an v in u such that [u]f ⊆ [v]h. it is obvious that h(f,u)〉h(h,u) if and only if h(f ∪ h,u) = h(f,u), in this case, for any a ∈ x(h), there are a1, · · · ,at ∈ x(f), such that [a]h = [a1]f + · · · + [at]f. we call that f and h are equivalent if h(f,u)〉h(h,u) and h(h,u)〉h(f,u). suppose that the numbers of subclasses in the divisions of f and h are s and t respectively, and suppose that s,t > 1. if h(f ∪ h,u) is the roughest common more specific division of h(f,u) and h(h,u), then we call that f and h are independent in division. in this case, for any b ∈ x(h), there are a1, · · · ,as ∈ x(f) such that [b]h = [a1]f + · · · + [as]f ; and for any a ∈ x(f), there are b1, · · · ,bt ∈ x(h) such that [a]f = [b1]h + + [bt]h. given a factors space φ = (u,xf∗), selecting f1, · · · ,fk and g from xf∗, called a set of conditional factors and a result factor respectively, and extracting m objects from u to form a sample domain u ′, we obtain the combined states data of these objects with respect to the k + 1 factors. causal analysis aims to extract causal rules from conditions to the result based on the sample distribution of u ′. one of the key concepts is the deterministic degree of factor fi with respect to g. reduction of conditional factors in causal analysis 385 definition 1. (wang2015) if there is an object u ∈ u ′ such that [u]fi ⊆ [u]g, then we say that [u]fi is a deterministic class of fi with respect to g. the ratio d of the number of objects in all deterministic classes of fi with respect to g and the number of objects in u ′ is called the determination degree of fi with respect to g. when fj(u) = fj(v),we have that [u]fi = [v]fi. to avoid repetition, denote [u]fi = [v]fi = [a]fi, then we have d(fj,g) = ∑ {|[a]fi||[a]fi is a deterministic class of fi on g}/m (1) where |a| stands for the number of elements in a. in this section, we will consider the deterministic degree d(f,h) of a conditional factor f with another conditional factor h. the whole theory is applied on a sampling u ′ ⊆ u. theorem 2. let f, h be two conditional factors on sample u ′. factor f is more specific than h on u ′ if and only if d(f,h) = 1. proof: suppose that f is more specific than h on u ′. for any u ∈ u ′, there is v ∈ u ′ such that [v]f ⊆ [u]h, it means that [v]f is a deterministic subclass of f with respect to h. therefore, all the elements of u ′ is covered by deterministic subclasses of f with respect to h, then, we have that d(f,h) = 1. inversely, suppose that d(f,h) = 1. for any a ∈ x(h), let [a] be the subclass that has the state a under h, there must be an element u ∈ u ′ such that h(u) = a, and then [a] = [u]h. since d(f,h) = 1, we have that [u]f ⊆ [u]h, i.e., [u]f ⊆ [a]; for any a ∈ x(f), let [a] be the subclass has the state a under f, there must be an element u ∈ u ′ such that f(u) = a. since d(f,h) = 1, we have that [u]f ⊆ [u]h, i.e., [u] ⊆ [u]f. therefore, the factor f is more specific than h. 2 theorem 3. if f is more specific than h on u ′, and the two factors h and f are not equivalent, then d(h,f) < 1. proof: suppose that d(h,f) = 1. according to proposition 2, h is more specific than f, and then f and h are equivalent u ′. this is a contradiction. 2 theorem 4. if f is more specific than h on u ′, then d(f,g) ≥ d(h,g). proof: suppose that [a] is a deterministic subclass of h (a ∈ x(h)). there is u ∈ [a]h such that h(u) = a. since that f is more specific than h on u ′, we have that h(f ∪h,u) = h(f,u), and then [a]h = [a1]f + · · · + [at]f, where [a1]f, · · · , [at]f are deterministic degrees of f on u ′ with respect to g both. it is obvious that d(f,g) ≥ d(h,g). 2 theorem 5. if f and h are two regular factors mutual independent in division on u ′, then d(f,h) = d(h,f) = 0. proof: since f and h are two regular factors mutual independent in division on u ′ for any b ∈ x(h), there are a1, · · · ,as ∈ x(f) such that [b]h = [a1]f + · · ·+ [as]f; and for any a ∈ x(f), there are b1, · · · ,bt ∈ x(h) such that [a]f = [b1]h + · · ·+ [at]h, and it ensures that [a]f \ [b]h 6= φ and [b]h \ [a]f 6= φ hold for any a ∈ x(f) and any b ∈ x(h). then d(f,h) = d(h,f) = 0. 2 there are three kinds of relationship between conditional factors: 1. d(f,h) is rather larger and d(h,f) is rather smaller; 2. d(f,h) and d(h,f) are rather larger both; 3. d(f,h) and d(h,f) are rather smaller both. according to the statements mentioned above, in case 1, if d(f,g) is larger, then we need not the factor h when f is taken part in with respect to the result g; in case 2, factors f, h are related to each other closely, and they are not suitable to be conditional simultaneously, and need to do reduction; in the case 3, factors f, h are rather independent, so they don’t need to be deleted provided they have important influence to the resulted factor. 386 h. liu, i. dzitac, s. guo table 1: conditional factors factor name state space f1 age x(f1)={old, middle, young} f2 income x(f2)={high, average, low} f3 student x(f3)={y, n} f4 credit x(f4)={very-good, good, un-recorded} f5 education x(f5)={primary, junior, university, graduated} f6 civil x(f6)={civil, private} f7 housing x(f7)={rent, narrow, mansion} f8 car x(f8)={car, bike} f9 health x(f9)={healthy, sickness} f10 residence x(f10)={town, rural} table 2: causal data u1 u2 u3 u4 u5 u6 u7 u8 u9 u10 u11 u12 u13 u14 f1 o o m o o m m m m y y y y y f2 l a h a h l a h l a h a l l f3 n n n n n n n n n y n y y y f4 u u v g v u g v g v v v g u f5 p j g u u j u j p g u u u g f6 c c c c c p c p p p c p p p f7 n n m m m n n m n n m r r r f8 b b c c b b c c b c c b b b f9 h s h h s h s s h h h s h s f10 r t t t t r t r r t t t t t g 0 1 2 1 2 0 1 2 0 2 2 2 1 0 3 reduction of conditional factors causal analysis aims to extract the rules form conditional to resulted factors; the more independent the better the conditional factors. the reduction of conditions factors obeys such a principle: for a pair of the factors with higher deterministic degrees with respect to the resulted factor g both, delete one of them except their mutual deterministic degrees are smaller both (i.e., in the case 3). this principle aims to take conditional factors into causal analysis as independent as possible. algorithm step 1. rank the conditional factors according to their deterministic degrees with respect to the resulted factor g from high to low; step 2. write the matrix of deterministic degree between conditional factors; step 3. for any i and j, if (d(fi,fj) > 0.5 or d(fj,fi) > 0.5) and d(fi,fj) > d(fj,fi), then delete fj. the remaining factor sequence is the conditional sequence that is required by the reduction. if causal analysis is performed according to this sequence, the sequence will be terminated when the causal tree is formed, and all unused conditional factors are deleted at all. example. in customer analysis, the goal is to open the market. the utility factor is the purchasing power of the customer, and the form factor is the information of the customers. take the form factors as the conditionals; the benefit factor should be the result to do the causal analysis. the conditional factors considered are listed in table 1. selecting 14 customers to form a sampling universe u ′ = {u1 u2 u3 u4 u5 u6 u7 u8 u9 u10 u11 u12 u13 u14}, table 2 presents causal data type. reduction of conditional factors in causal analysis 387 table 3: frequencies of factors f1 f2 f3 f4 f5 f6 f7 f8 f9 f10 0 4 0 6 2 0 0 0 0 0 table 4: the matrix of mutual deterministic degrees between conditional factors f4 f2 f5 f1 f3 f6 f7 f8 f9 f10 f4 0 0 0 0 0 0 4 0 0 f2 4 0 0 5 0 4 9 0 5 f5 2 2 0 2 0 2 4 4 8 f1 0 0 0 9 4 0 0 0 5 f3 0 2 0 2 4 0 0 0 4 f6 0 0 0 0 0 0 0 0 0 f7 0 0 0 3 3 3 3 0 0 f8 0 0 0 0 0 0 0 0 0 f9 0 0 0 0 0 0 0 0 0 f10 0 0 0 0 4 0 0 0 0 the steps of reduction of conditional factors are shown as follows: step 1. reordering conditional factors according to their deterministic degrees with respect to g. remember that m=14, to be more simple, we write all frequencies by 14 times. the results are given in tables 3. the new order is shown as: f4, f2, f5, f1, f3, f6, f7, f8, f9, f10. step 2. the matrix of mutual deterministic degrees between conditional factors is listed in table 4. step 3. when i = 2, j = 8, d(fi,fj) = 9/14 > 0.5 and d(fi,fj) > d(fj,fi) = 0, delete f8; when i = 5, j = 10, d(fi,fj) = 8/14 > 0.5 and d(fi,fj) > d(fj,fi) = 0, delete f10; when i = 1, j = 3, d(fi,fj) = 9/14 > 0.5 and d(fi,fj) > d(fj,fi) = 0, delte f3. after deleting the three conditional factors, the new causal analysis data style is presented in table 5 according to the causal analysis [22], do rule extraction by f4 to get that rule 1: if credit is very good, then the purchasing power is #2 taking out those customers having very good credit from u ′, table 6 presents newer causal analysis data style. do rule extraction by f4 and f2 to get that rule 2: if credit is unrecorded and income is low, then the purchasing power is #0; rule 3: if credit is unrecorded and income is average, then the purchasing power is #1; rule 4: if credit is good and income is average, then the purchasing power is #1; table 5: new causal data u1 u2 u3 u4 u5 u6 u7 u8 u9 u10 u11 u12 u13 u14 f4 u u v g v u g v g v v v g u f2 l a h a h l a h l a h a l l f5 p j g u u j u j p g u u u g f1 o o m o o m m m m y y y y y f6 c c c c c p c p p p c p p p f7 n n m m m n n m n n m r r r f9 h s h h s h s s h h h s h s g 0 1 2 1 2 0 1 2 0 2 2 2 1 0 388 h. liu, i. dzitac, s. guo table 6: new causal data (with 8 factors) u1 u2 u4 u6 u7 u9 u13 u14 f4 u u g u g g g u f2 l a a l a l l l f5 p j u j u p u g f1 o o o m m m y y f6 c c c p c p p p f7 n n m n n n r r f9 h s h h s h h s g 0 1 1 0 1 0 1 0 table 7: new causal data (with 2 factors) u′ u9 u13 f4 g g f2 l l f5 p u f1 m y f6 p p f7 n r f9 h h g 0 1 taking out those customers having been contributed to rule extraction from u ′, the newer causal analysis data style is given in table 7. do rule extraction by f4, f2 and f5 to get that rule 5: if credit is good, income is low, and education is univ., then the purchasing is #1; rule 6: if credit is good, income is low, and education is prim., then the purchasing is #0; now, the universe u ′ has been empty, the rule extraction has finished. we just select three factors in use, all of others have been deleted at all. what are the relationship in between the three factors? d(f4,f2) = 0 < 0.5, d(f2,f4) = 4/14 < 0.5, d(f4,f5) = 0 < 0.5, d(f5,f4) = 2/14 < 0.5, d(f2,f5) = 0 < 0.5, d(f5,f2) = 2/14 < 0.5. all of mutual deterministic degrees between them are small, which satisfy the requirement of causal analysis. 4 conclusion in the face of the challenge of big data, the number of conditional factors in causal analysis is very large, so the reduction of conditional factors is an important task. the proposed reduction algorithm can reasonably reduce the number of conditional factors. compared with the previous reduction methods, we take into consideration the influence of conditional factors on resulted factors, as well as the relationship among conditional factors themselves. in this paper we consider the mutual deterministic degrees in between conditional factors. such reduction ensures the conditional factors are selected as independent as possible, causal analysis requires such selection, and this improvement is of great importance in practice. reduction of conditional factors in causal analysis 389 acknowledgement the authors specially thank professor p. z. wang for his guidance and modification. this study was partially supported by the grants (grant nos. 61350003, 11401284, 70621001, 70531040) from the natural science foundation of china, and the grant (grant nos. l2014133) from the department of education of liaoning province. bibliography [1] bao, y. k.; 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(1994); factor rattans, category fr (y), and factor space, journal of mathematical analysis and applications, 186(1), 254–264, 1994. int j comput commun, issn 1841-9836 8(2):312-319, april, 2013. smallest number of sensors for k-covering t. tabirca, l.t. yang, s. tabirca tatiana tabirca, sabin tabirca school of computer science, misl and 4c centre university college cork, college road, cork, ireland e-mail: tabirca1@cs.ucc.ie, tabirca@cs.ucc.ie laurence t. yang department of computer science st. francis xavier university, antigonish, ns, b2g 2w5, canada e-mail: ltyang@stfx.ca abstract: this paper presents some theoretical results on the smaller number nk(a, b) of sensors to achieve k coverage for the rectangular area [0, a]× [0, b]. the first properties show the numbers nk(a, b) are sub-additive and increasing on each variable. based on these results, some lower and upper bounds for nk(a, b) are introduced. the main result of the article proves that the minimal density of sensors to achieve k-coverage is λ(k) ≤ k/2 improving a previous result of ammari and das [2]. finally, the numbers n1(a, b) are tabled for some small values of a, b. keywords: wsn networks, coverage, range. 1 introduction wireless sensor networks (wsn) consist of a large number numbers of sensors distributed uniformly in a target area, which monitor in a cooperative manner the physical word. sensors are in fact small devices capable of sensing variations in temperature, light, gas, motion etc, computing and storing information and communicating with the neighbour sensors. the technology has nowadays made it possible to produce these devices at a cheap price so that wsn networks are now involved in various applications from agriculture, surveillance, asset tracking, health care to building safety and evacuation. an important aspect in wsn applications is the coverage problem, which investigates how well the target area is monitored by sensors. if each point of the area is covered by at least k sensors then the wsn network is said to be k-covered, where k is the degree of the coverage. for example, tracking wsn networks are at least 3-covered as they use triangulation. moreover, most wsn networks must be at least 2-covered to assure the robustness property. it is clear that the bigger the coverage degree is, the more sensors must be used in wsn networks. so far, all research works on the wsn coverage problem have assumed that a large number of sensors is distributed in the target area to assure the network is k-covered. however, no information has been given about the number of sensors to use for the target area or equivalently about the sensor density. this article comes to investigate some properties of the minimal number of sensors and to provide un upper bound for the minimal density of sensors to achieve k-coverage in a rectangular area. 1.1 problem statement consider a set of sensors s = {s1, s2, s3, ..., sn} in the 2d plane with the same sensing range r. the position of each sensor si is known and given by the coordinates (xi, yi). the target area a to monitor can be of any shape but for simplicity it is considered to be rectangular a = [0, w]× [0, h] of width w and height h. copyright c⃝ 2006-2013 by ccc publications smallest number of sensors for k-covering 313 definition 1. a point (x, y) ∈ a is covered by a sensor si if √ (xi −x)2 + (yi −y)2 ≤ r. the target area a is k-covered by the sensors s if each point (x, y) ∈ a is covered by at least k different sensors. it is clear that the number of sensors n to achieve k-coverage increases directly with k. hence, the bigger is k the more sensors are needed for k-coverage. so far, the main assumption has been that sensors are cheap devices and the numbers of them to deploy is not important. consequently, it has been considered that the number of sensors to deploy is big enough to achieve k-coverage on the target area. 1.2 related works investigating 1-coverage or circle packing has been researched as geometrical combinatorial since 1939. researchers have tried to find mathematical equations for the optimal 1-covering or even to prove that some configurations are optimal. for example kershner [4] investigated the problem of covering any 2d set of points with similar circles based on some geometrical combinatorial techniques. this early work proved that the minimal number of circles n(ε) of radius ε to cover a close set of point m satisfies lim ε→0 n(ε) = 2 √ 3 9 ·ε2 · |m|, where |m| denotes the area of closed by m. the result was proven by using a double inequality for the quantity πε2n(ε) representing the total area covered by the circles. an important consequence of this result is that the proportion of unavoidable overlapping can be approximated by 2π √ 3 9 ≃ 1.209. we can also mention the early work of verblunsky [10] who proved that the minimum number n(l) of circles of radius 1 to cover a square of length l should satisfy n(l) ≥ 2l2 + l 3 √ 3 . these two results come to suggest that the sensor density for 1-coverage can be estimated by 2 √ 3 9 ≈ 0.384. however, these early works [4], [10] do not provide any information about the pattern of circles used to achieve the minimal coverage. recently, several articles on circle packing problems investigated efficient ways to cover a rectangle with similar circles (see [6], [8] or [9] amongst others). these geometrical combinatorics researches confirmed that optimal packing is difficult to be achieve even for small number of circles. furthermore, no pattern was detected for the packing configuration that gives optimality. recently, several papers have dealt with the k-coverage problems in the context of sensor networks studying conditions when this is achieved or algorithms to detected when this happens. some of these contributions have made marginal reference to the minimum number or equivalently to the minimum density of sensors that assures k-covering of a given area. generally, all these works have considered that the number of sensors to use is big enough to k-cover the target area. adlakha and srivasyava [1] developed an exposure-based model to find the sensor density required to achieve full coverage of a given area. they proved that the number of sensors to achieve 1-covering is in the order of o(a/r2), where a is the area to cover however they did not provide any constant for the magnitude of a/r2. ammari and das [2] investigated the problem of k-coverage proposing a condition to achieve it. they considered the target area divided in "reuleaux" triangles which are formed by the intersection of 3 circles. the main result of their work states that the target area is k-covered if and only if each "reuleaux" triangle contains at 314 t. tabirca, l.t. yang, s. tabirca least k-active sensors. another important results proposed by ammari and das gives that the minimal density of sensors to guarantee k-coverage is λ(r, k) = 2k (π− √ 3)·r2 = 1.4188·k r2 , where r is the radius of the sensing disks. most covering problems present huge difficulties to solve or to derive a polynomial algorithm even in particular cases like regular or simple shapes and lower dimensional space. the 2d problem of covering a bounded domain with arbitrary shaped objects was proven to be exponential on the size of the packing space [7]. the particular case of covering any polygon with n similar disks is known to be np-hard [5]. consequently, the problem of finding the least number of disks to k-cover a rectangle is np-hard. all these works have shown that calculating the minimal number of circles to pack a rectangle is a hard problem and there is no patter associated with this covering. moreover, the results concerning the minimal number of circles for k-coverage are all either asymptotic based on some limits or approximative based on some inequalities. 2 minimal number of sensors consider the following problem "find the smallest number of sensors nk(a, b) that should be used to achieve k-coverage for a rectangular area of sizes a and b with sensors of the same radius". we can suppose that all the sensors have the coverage radius of 1 unit. by convention nk(a, b) = 0 when a ≤ 0 or b ≤ 0. it is clear that a k-coverage with n sensors satisfies n ≥ nk(a, b). (1) the following results can be directly obtained based on equation 1 and on the definition of nk(a, b). lemma 1. the function nk(a, b) is symmetrical on a, b nk(a, b) = nk(b, a), ∀a, b > 0. lemma 2. the function nk(a, b) is monotonically on each variable: a1 ≤ a2 ⇒ nk(a1, b) ≤ nk(a2, b). b1 ≤ b2 ⇒ nk(a, b1) ≤ nk(a, b2). k1 ≤ k2 ⇒ nk1(a, b) ≤ nk2(a, b). lemma 3. the function nk(a, b) is sub-additive on each variable: nk(a1 + a2, b) ≤ nk(a1, b) + nk(a2, b). nk(a, b1 + b2) ≤ nk(a, b1) + nk(a, b2). nk1+k2(a, b) ≤ nk1(a, b) + nk2(a, b). proposition 1. n1( √ 2 ·n, √ 2 ·m) ≤ n ·m when n, m ∈ n. proof: consider that the rectangular area of sizes a = √ 2 ·n, b = √ 2 ·m is divided into a grid of n×m squares of size √ 2. each square can be 1-covered by its circle as figure 2 shows. hence, n1( √ 2 ·n, √ 2 ·m) ≤ m ·m since there is a 1-coverage with n ·m circles. 2 evidence shows that n1( √ 2 ·n, √ 2 ·m) = m ·m for many values of m, n. however, we have not been able to produce a coherent proof of the fact that n1( √ 2 · n, √ 2 · m) ≥ n · m nor a counterexample to show that n1( √ 2 ·n, √ 2 ·m) < n ·m for some values of m, n. smallest number of sensors for k-covering 315 figure 1: 1-covering of the rectangle w = √ 2 ·n, h = √ 2 ·m. proposition 2. the numbers n1(a, b) satisfy the following inequality n1(a, b) ≤⌈ a √ 2 ⌉ · ⌈ b √ 2 ⌉, ∀a, b ∈ r (2) where ⌈x⌉ is the ceiling function. proof: consider n = ⌈ a√ 2 ⌉∈ n so that we have a√ 2 ≤ n or a ≤ n· √ 2. similarly, if m = ⌈ b√ 2 ⌉∈ n we obtain b ≤ m · √ 2. now, the following inequality can be derived based on lemma 1 n1(a, b) ≤ n1 ( n · √ 2, m · √ 2 ) ⇒ n1(a, b) ≤ n ·m ⇒ n1(a, b) ≤⌈ a √ 2 ⌉ · ⌈ b √ 2 ⌉, which it proves the theorem. 2 the result above gives only an upper bound of values in which the number n1(a, b) can be located. theorem 1. for k-coverage problem, the numbers nk(a, b) satisfy the following inequality k ·a · b π ≤ nk(a, b) ≤ k · ⌈ a √ 2 ⌉ · ⌈ b √ 2 ⌉, ∀a, b ∈ r (3) proof: the sub-additivity property is used as follows nk(a, b) = n1+...+1(a, b) ≤ n1(a, b) + ... + n1(a, b) = = k ·n1(a, b) ≤ k · ⌈ a √ 2 ⌉ · ⌈ b √ 2 ⌉, which proves the right hand side inequality. for the left hand side we considered that each point of the rectangle is covered by at least k circles. hence, the nk(a, b) circles cover the whole rectangle surface by k times. hence, the area of the circles is greater than k times the area of the rectangle. nk(a, b) ·π ·12 ≥ k ·a · b ⇒ nk(a, b) ≥ k ·a · b π . 316 t. tabirca, l.t. yang, s. tabirca 2 assume that the minimal density of sensors nk(a,b) a·b does not depend on a, b for large values of a, b, so that we can write λ(k) ≃ nk(a,b) a·b ,∀a, b. in this case the minimal density of sensors can be evaluated by the following result. theorem 2. the minimum density of sensors to achieve k-covering for rectangular areas satisfies k π ≤ λ(k) ≤ k 2 . (4) proof: each member of equation 3 is divided by a · b to obtain k π ≤ nk(a, b) a · b ≤ k · ⌈ a√ 2 ⌉ · ⌈ b√ 2 ⌉ a · b ⇒ k π ≤ nk(a, b) a · b ≤ k · ⌈ a√ 2 ⌉ a · ⌈ b√ 2 ⌉ b . consider that the minimum density to achieve k-covering is independent of the area to cover hence it can be denoted by λ(k). so that we have k π ≤ λ(k) ≤ k · ⌈ a√ 2 ⌉ a · ⌈ b√ 2 ⌉ b , ∀a, b > 0. if a, b →∞ are big then the fractions become lima→∞ ⌈ a√ 2 ⌉ a = 1√ 2 and limb→∞ ⌈ b√ 2 ⌉ b = 1√ 2 so that k π ≤ λ(k) ≤ k 2 . 2 theorem 2 shows that the minimal density to achieve k-coverage with sensors of radius 1 is between 0.318109 · k and 0.5 · k. the first conclusion we can extract is that this number is far smaller than the density proposed in [2] which is 1.4188 · k. this huge difference would raise serious question marks on the "reuleaux" triangulation approach developed by ammari and das and hence on the results they proposed. the second conclusion is that, in particular for k = 1, this result states that the density for 1-covering is between 0.318 and 0.5, which is in concordance with the early results of kershner and verblunsky. on the other hand, the minimal density of sensors nk(a,b) a·b can also have the following upper bound for any a, b ≥ 2. nk(a, b) a · b ≤ k · ⌈ a√ 2 ⌉ a · ⌈ b√ 2 ⌉ b ≤ k · a√ 2 + 1 a · b√ 2 + 1 b ⇒ nk(a, b) a · b ≤ k · ( 1 √ 2 + 1 a ) · ( 1 √ 2 + 1 b ) = k · [ 1 2 + 1 √ 2 · ( 1 a + 1 b ) + 1 a · b ] ⇒ nk(a, b) a · b ≤ k 2 · [ 1 + p + 2 √ 2 a ] , where p and a are the perimeter and the area of the target region respectively. this provides an upper bound for the density based on the perimeter and the surface of the target area. 3 some computational results this section is to find directly or using some computation some of the numbers nk(a, b). firstly, we start with the numbers n1(a, b), which can be calculated for several small values of a, b. for example, n1( √ 2, √ 2) = 1 and furthermore n1(a, b) = 1, ∀a, b ≤ √ 2. this simple case can be extended to the situation where we have a row of sensors to achieve minimal 1-coverage (see figure 3). smallest number of sensors for k-covering 317 figure 2: optimal 1-covering of the rectangle with a ≤ √ 2. theorem 3. the following two results can apply for the situation when the rectangle has either the width or the height less than √ 2 (see figure 3): n1(a, √ 4−a2 ·m) = m,∀m ∈ n, a ≤ √ 2 n1( √ 4− b2 ·n, b) = n,∀n ∈ n, b ≤ √ 2. proof: the proof only considers the case when a ≤ √ 2 as the second one is similar. figure 3) shows a 1-covering of the rectangle with m sensors so that n1(a, √ 4−a2 · m) ≤ m. lets start from an 1-covering with n1(a, √ 4−a2 · m) sensors of a target area with the sizes w = a, h = m · √ 4−a2. consider the rectangle is divided into m small rectangles r1, r2, ..., rm each of sizes w = a, h = √ 4−a2. the focus is now on r1 which is fully covered with some circles from which there is one with the smallest x coordinate for the centre. this circle is then translated so that it will fit into the whole rectangle. it is clear that the area of r1, previously covered by some circles, is now covered by one circle. hence, this new configuration is still a 1-coverage with the same number of sensors. now, r2 must have at least one circle to cover the nodes in common with r1 so that we can use it to repeat the same type of transformation. after m steps we find that there are m circles amongst the n1(a, √ 4−a2 ·m) circles that can be positioned as in figure 3. hence, n1(a, √ 4−a2 ·m) ≥ m. 2 this theorem provides directly the following two consequences. remark 3.1. n1(1, m) = ⌈ m√ 3 ⌉,∀m ∈ n. remark 3.2. n1(2, m) ≤⌈ m√ 2 ⌉+⌈ m 4 √ 2−2 ⌉,∀m ∈ n. for the second remark it is clear that n1(2, m) = n1( √ 2 + 2 − √ 2, m) ≤ n1( √ 2, m) + n1(2− √ 2, m) = ⌈ m√ 2 ⌉+⌈ m 4 √ 2−2 ⌉. it seems that ⌈ m√ 2 ⌉+⌈ m 4 √ 2−2 ⌉ represents the value of n1(2, m) for several small values of m = 1, 2, 3, 4. however, there is no proof to show that n1(2, m) = ⌈ m√ 2 ⌉+⌈ m 4 √ 2−2 ⌉. these simple results help in tabling the numbers n1(n, m) for some small values of n, m ∈ n when one of the indices is 1 or 2 (see table 1). n,m m=1 m=2 m=3 m=4 m=5 n=1 1 2 2 3 3 n=2 2 4 4 6 7 n=3 2 4 6 7 11 n=4 3 6 7 9 11 n=5 3 7 11 11 14 table 1: table with the values n1(n, m), n, m = 1, 2, 3, 4, 5 318 t. tabirca, l.t. yang, s. tabirca the values of nk(n, m) for big n, m are not simple to calculate since generating a k-coverage is np-complete. a simple generic computation for nk(n, m) has to go firstly through all the values between nr = n · m · k/π, n · m · k/2 in order to generate all the possible configurations of nr circles. secondly, the k-coverage property should be tested for each configuration of nr circles. if the property holds for a particular configuration of nr circles then nk(n, m) = nr (see algorithm 1). testing whether a set of sensors or configuration of circles achieves k-coverage is a well studied problem with few polynomial solutions (see [3] for an o(nr log nr) solution). however, the problem of generating all the configurations with nr circles within the target area [0, n]× [0, m] is computationally hard and it can be solved only by using searching methods like backtracking. the running time to search exhaustively for the optimal configuration can be in this case very big but the algorithm provides the correct value for nk(n, m). figure 3: execution times for deterministic and probabilistic approaches. algorithm 1 generic scheme to calculate nk(n, m). function(n,m,k) for nr= n·m·k π to n·m·k 2 do // generate all the possible nr circles repeat generate a new configuration with nr circles test k-coverage for the circles if k-coverage holds then return nr; end if until possible end for the alternative to this approach is to generate a very large number of random configurations hoping that the k-coverage with nk(n, m) circles is reached by one of them. in this case the number nk(n, m) is not accurately computed but the execution time can substantially be reduced. the following simulations give a good illustration of the trade off between accuracy and running time. figure 4 presents the execution times for the deterministic algorithm against the probabilistic approach with 50000 and 100000 iterations. one can see that the execution times of the deterministic algorithm grew at an exponential rate when w · h increases. the execution for the small area of w = 5, h = 5 took more than 18 minutes. on the other hand, the execution times for the probabilistic approaches have a slow increasing rate far smaller than in the smallest number of sensors for k-covering 319 deterministic case. moreover, the probabilistic version with 100000 iterations provided the same results as the deterministic solution. 4 conclusions this article has investigated some theoretical properties related with the minimum number of sensors nk(a, b) to achieve k-covering of a rectangular area. firstly, the numbers nk(a, b) have been proven to be sub-additive on each variable. secondly, we have found a interval of possible values for nk(a, b) numbers between k·a·bπ and k ·⌈ a√ 2 ⌉·⌈ b√ 2 ⌉. based on that the minimal density of sensors to achieve k-coverage has been proven to be less than k/2 improving a result of [2]. some computation has been used to generate the numbers n1(a, b) for small values of a, b. acknowledgement the first author would like to acknowledge the financial support provided by the misl and 4c research centres and to thank to prof cormac sreenan and dr ken brown for the useful discussions on the wsn topics. bibliography [1] adlakha, s., srivastava m., critical density threshold for coverage in wireless sensor networks, proc. 2003 ieee wcnc, 1615-1629, 2003. [2] ammari, h.m., das, s.k, clustering-based minimum energy wireless m-connected kcovered sensor networks, proc. of 2008 ewsn sensor, lncs 4913, 1-6, 2008. [3] huang, c.f., tseng, y.c. the coverage problem in a wireless sensor network, proc.of the 2nd acm int. conf. wsna, 115-121, 2003. [4] kershner, r. the number of circles covering a set, american j. of math., 61: 665-671, 1939. [5] fowler, r.j., paterson, m.s., tanimoto, s.l., optimal packing and covering in the plane are np-complete, information processing letters, 12: 133-137, 1981. [6] melisen, j.b.m., shuur, p.c., covering a rectangle with 6 and 7 circles, discrete applied mathematics, 99: 146-156, 2000. [7] milenkivic, v.j., translational polygon containment and minimal enclosure using linear programming based restriction, proc. of the acm symposium on theory of computing, 109-118, 1996. [8] nurmela, k.j., ostergard, p.r.j., covering a square with up to 36 equal circles, research report hut-tcs-a64, laboratory of theoretical computer science, helsinky university of technology, 2000. [9] tarnai, t., gasper, z., covering a square by equal circles, elementary math., 167-170, 1995. [10] verblunsky, s., on the least number of unit circles which can cover a square, journal of london math society, 24: 164-170, 1949. ijcccv11n4.dvi international journal of computers communications & control issn 1841-9836, 11(4):480-492, august 2016. modeling mobile cellular networks based on social characteristics j. ma, w. ni, j. yin, r.p. liu, y. yuan, b. fang ji ma* 1. beijing university of posts and telecommunications beijing, china, 100876 2. digital productivity flagship, csiro, australia, 2122 *corresponding author: maji@bupt.edu.cn wei ni, jie yin, ren ping liu digital productivity flagship, csiro, australia, 2122 wei.ni@csiro.au, jie.yin@csiro.au, ren.liu@csiro.au yuyu yuan, binxing fang beijing university of posts and telecommunications beijing, china, 100876 yuanyuyu@bupt.edu.cn, fangbx@bupt.edu.cn abstract: social characteristics have become an important aspect of cellular systems, particularly in next generation networks where cells are miniaturised and social effects can have considerable impacts on network operations. traffic load demonstrates strong spatial and temporal fluctuations caused by users social activities. in this article, we introduce a new modelling method which integrates the social aspects of individual cells in modelling cellular networks. in the new method, entropy based social characteristics and time sequences of traffic fluctuations are defined as key measures, and jointly evaluated. spectral clustering techniques can be extended and applied to categorise cells based on these key parameters. based on the social characteristics respectively, we implement multi-dimensional clustering technologies, and categorize the base stations. experimental studies are carried out to validate our proposed model, and the effectiveness of the model is confirmed through the consistency between measurements and model. in practice, our modelling method can be used for network planning and parameter dimensioning to facilitate cellular network design, deployments and operations. keywords: social characteristics, mobile networks, spectral clustering, energy efficiency, traffic model. 1 introduction due to the increasing popularity of smart phones, mobile data has been exponentially increasing [1]. the radio base stations (rbs), e.g., node b, act an important role in cellular networks. when mobile users move between radio base stations and access data network, those activities cause rbs to present significant social features. first, the spatial resources are limited since a certain space has its maximum capacity of people. second, and temporal resources are restricted because of human physiology reasons, using mobile is only a portion during users’ day life-cycle. third, users’ behaviors lead a social pattern of radio base stations. understanding those characteristics is important for data traffic forecasting, network optimization, energy saving and delivering of service. previous works has explored that data traffic has significant spatial-temporal pattern in access network. paper [6] characterize users’ activity patterns and find significant temporal and spatial variations in different parts of the network. traffic spatial distribution has been studied copyright © 2006-2016 by ccc publications modeling mobile cellular networks based on social characteristics 481 in paper [7], which propose a spatial model of traffic density to simulate log-normal distribution. the reason of the traffic phenomena is mobility of mobile users, while people move between rbss and use their equipment to access data network. gonzález et al. [8] have studied the human mobility patterns on mobile call and sms transactions with a six-month period data. however, human mobility study on mobile data has its limits, since only traffic data are captured while users using their mobile equipments. song et al. [9] pointed out a 93% potential predictability in user mobility across the whole user base, by measuring the entropy of each individual’s trajectory. in [21], the authors proposed a feedback simulation model to analyse the interaction between urban densities and travel mode split. human activities has inherent social characteristics, thus recent papers pay more attentions to the traffic social patterns and its impact to cellular networks. in [23], colored petri net (cpn) models are proposed to specify citizen’s preferences and affinities in front of an urban contextual change, while multiple agent system is proposed to evaluate the social dynamics by translating the cpn semantic rules into agent’s rules. the authors of papers [10,11] use the gini coefficient to measure user social pattern of a certain area or period, and apply it to spectrum and energy efficiency in heterogeneous cellular networks. in [22], a social similarity-aware multicast routing protocol is proposed for delay tolerant networks. the social similarity is quantified as the probability of the encounter of two nodes in the future based on the number of common neighbors in the history. social informations can also be used to detect community structure [12] and to match interests [13] in heterogeneous networks. however, the above works were focused only on certain parts of social characteristics, and are unable to characterize the social behaviours of rbss in a comprehensive way. in this paper, we develop a new model which categorizes base stations based on multiple important social characteristics of the base stations (more specifically, the traffic at the base stations). four key properties are taken into account to indicate the social characteristics of rbss, namely, traffic fluctuation, user nondeterminacy, temporal homogeneity and usage diversity. based on these, we apply multi-dimensional spectral clustering techniques and categorize rbss with similar social patterns into groups. for each of the categories, we establish a temporal traffic model and derive important model parameters. our proposed model is able to characterize different cellular scenarios and terrains with an emphasis on the diversity of user mobility and traffic fluctuation. this is of practical value. specifically, the model can refine network simulation and network planning parameters for a range of practical network environments. users social behaviours and mobility patterns can be captured in those parameters. in this sense, our proposed model can facilitate designing, simulating, and evaluating network deployment. the rest of the paper is organized as follows. in section 2, we describe the actual mobile data, based on which our modelling study was conducted. in section 3, the four key characteristics, i.e., traffic fluctuation, user nondeterminacy, temporal homogeneity and usage diversity, are defined. in section 4, we elaborate on the clustering based social behaviour modelling method. the application scenarios on energy efficiency are given in section 5, followed by a conclusion in section 6. 2 data preliminary our study in this work is based on a real-world mobile data set comprised of user transaction logs, which are collected from the biggest city, chongqing, in china. this data set records 1.6 million anonymous mobile users’ data traffic on a saturday, which has 38,000 rbss within the city over 2,000 square kilometres. each time a user initiates a package data request, the location of the associated rbs and traffic summary are recorded. we extract the data schema as a map of points of interest (poi), while each rbs is a poi. mobile users generate data traffics when 482 j. ma, w. ni, j. yin, r.p. liu, y. yuan, b. fang they check these points. each record of our data set includes a list of attributes, including subscriber identity (sid), local area code (lac), cell identity (ci), coordinate, total traffic volume, time-stamp, type and etc. sid is the anonymous mobile number hashed by sha-1 algorithm. a rbs can be identified by the composite of lac and ci. coordinate includes the latitude and longitude of a rbs. we consider the total traffic to calculate measurements in general instead of up-link and down-link traffic. the attribute type presents the protocol codes of transactions. for example, http requests are recorded as code 204 of post and 205 of get; smtp, pop3, and imap are represented by 500, 501 and 502 respectively. to make this attribute more representative, we categorize those 51 codes into eight types, namely web, stream media, instant messaging (im), file sharing, e-mail, multimedia messaging service (mms), voice over internet phone (voip), and miscellaneous traffic. these traffic data show nonuniform patterns in spatial-temporal dimensions. in temporal dimension, total traffic presents dual peak like fluctuation, and peak hours are about 8:00 am and 19:00 pm. there are also little burst around noon and 14:00 pm. web surfing, which contributes 73% traffic, has the similar peak hours. other types have little differences in usage hour. the second contribution is a bundle of miscellaneous traffics, gives a portion of 11%, which includes several signaling and unrecognized traffics such as dns query, apple push, multi-cast, session initiation, and etc. stream media and im generate volumes of 7% and 6%. the others share the rest about 3% of total. 3 social characteristics extracted from traffic data we study the characteristics of cellular networks from perspectives of individual rbs, with an emphasis on user activity patterns and social behaviour. we consider two types of characteristics. one is traffic fluctuation, and the others are entropy based characteristics, including user nondeterminacy, temporal homogeneity, and usage diversity. 3.1 traffic fluctuation traffic fluctuation indicates the fluctuation of traffic volume at a rbs. considering one day, the traffic fluctuation can be expressed as a time sequence of 24 hours. we use spearman’s rank correlation coefficient to measure the similarity between two sequences. it is defined as the pearson correlation coefficient between the ranked variables. the value is between −1 and 1. a perfect correlation of +1 or −1 occurs when each of the variables is a perfect monotone function of the other, and 0 indicates there is no correlation. we rank the hourly traffic volume of each rbs as descending order, and each hour has a rank number in a day. for given two rank sequences p and q, the fluctuation similarity can be calculated by: ρ(p,q) = ∑n i=1(pi − p̄)(qi − q̄) √ ∑n i=1(pi − p̄)2 √ ∑n i=1(qi − q̄)2 ,n = 24, where pi or qi is the traffic rank at hour i = 1,2, · · · ,24. 3.2 entropy based characteristics rbss can have non-uniform traffic density in spatial-temporal dimensions [6] or application usage [14]. we introduce shannon entropy to generalize nonuniform traffic characteristics from rbss’ angle. modeling mobile cellular networks based on social characteristics 483 user nondeterminacy user nondeterminacy indicates human mobility within each cell, which further indicates the social function of the cell. for instance, a residential zone has a stable group of users, while users at a transport junction are fast changing. this property shows the uncertainty of user traffic volumes. for each rbs, we define the user nondeterminacy as: su = − ∑ i u(i) log u(i), where u(i) is the proportion of traffic generated by user i. a greater value means rbs serves more users, and the users generate traffics more evenly. su can be as small as 0, if only one user uses this rbs in a whole day. in our work, we use sid as an aggregate group to calculate user nondeterminacy. temporal homogeneity another important aspect of rbss is temporal homogeneity, which is the distribution of time intervals between traffic generations. usually, a single function area has a sharp temporal pattern of traffic, while a mixed function area has more stable traffic in temporal dimension. to capture such patterns, we aggregate the traffic volume on an hourly basis. the temporal homogeneity of each rbs can be defined as: sh = − ∑ i h(i) log h(i), where h is the proportion of traffic generated within an hour i. a lower value of sh shows a concentration on a few hours. this property has the maximum value of log 24 ≈ 4.585, under the condition that traffic is evenly divided into 24 hours. the attribute timestamp is divided into 24 hours for producing temporal homogeneity. usage diversity users’ usage behaviour is another key indicator of the traffic pattern, which reflects different application preferences and access habits of users. we use traffic protocols as service identification. to extract those protocols, we categorize traffics into eight major types. usage diversity of data services can be described by the type entropy, which is defined as: sv = − ∑ i v(i) log v(i), where v(i) is the proportion of traffic by the above categories. this property shows the diversity of traffic volume of the eight types, which would have a larger value if a rbs resource is more evenly distributed among different services. our analysis shows that there is no obvious correlation between usage diversity and user nondeterminacy, or temporal homogeneity. the pearson’s correlation coefficients are only 0.117 and 0.182 respectively. 4 clustering based social modeling and analysis the characteristics of rbss can identify the function and the population density of the coverage areas of the rbss, since these characteristics are strongly related to human mobility and social activities. modelling the realistic rbs characteristics can be useful for network designing and simulating with social behaviours. clustering analysis is an unsupervised technique to study laws without prior knowledge. in this section, we propose to model rbs social characteristics using spectral clustering [15]. spectral clustering has many advantages compared with traditional 484 j. ma, w. ni, j. yin, r.p. liu, y. yuan, b. fang clustering algorithms. affinities between every pair of rbss can be used. and there is no strong assumption on the statistics of clusters, such as the number of clusters or feature selection. we would like to highlight that our key contribution is the social characteristics modeling for mobile networks. we extract four properties to examine social characteristics of rbss, and all these characteristics are strongly related to human mobility and social activities. our model is able to be applied to characterize different cellular scenarios and terrains. this application is enabled by our new designed spectral clustering method. our new design of spectral clustering method is able to implement the area functions distinguish. by inspecting the gap of an eigenvalue and the average with the previous value we can identify how many clusters needed to be grouped. it is worth pointing out that the application of the spectral clustering to areas function discovery is new, although the concept itself is not. our new design of spectral clustering method is also necessary to implement the area functions distinguish. in fact, we cannot exactly know how many cellular scenarios and terrains existed in current network before we really find them out. none of the existing works are able to address the issue, though cells clustering have been extensively studied. 4.1 spectral clustering the main idea of spectral clustering is using the spectrum of the similarity matrix of the data to perform dimensionality reduction before clustering. a typical spectral clustering process has these following steps: constructing affinity matrix we construct a n dimensional distance matrix m, where n is the number of rbss. each element mij represents the distance between rbs i and rbs j. distance matrix m can be transformed to affinity matrix w. w is weighted adjacency matrix, and each edge is weighted by pairwise vertex affinity. w(i,j) is equal to 0 if i and j are not connected, and w(i,j) gives positive value if i,j are connected. a greater value indicates two rbss are closer in characteristic space. we use gaussian similarity function w(xi,xj) = exp(−m(i,j)2/(2σ2)) to form matrix w, where the parameter σ controls the width of neighbourhood. building laplacian given a graph with affinity matrix w, the way to construct k partitions is to solve min-cut problem. to avoid over cutting, the normalized cut is introduced [16]. for a given number k of subsets, the min-cut approach simply consists in choosing a partition a1, ...,ak which minimizes: ncut(a1, . . . ,ak) = 1 2 k ∑ i=1 w(ai,āi) vol(ai) where āi is the complement of ai ,vol(ai) is the weights of edges of ai. according to the normalized cut problem, the normalized graph laplacian [17] can be used as given by: l norm = d−1/2(d−w)d−1/2 where d = diag(d1, . . . ,dn), and di = ∑ j wij. modeling mobile cellular networks based on social characteristics 485 clustering with reduced dimensions after laplacian matrix l is constructed, we find the first k generalized eigenvectors u1,u2, . . . ,uk, corresponding to the k smallest eigenvalues of l, and form a matrix u = [u1,u2, . . . ,uk] ∈ rn×k by stacking the eigenvalues in columns. we normalize each row i of matrix u so that they have unit euclidean norm by: tij = uij √ ∑k g=1 u 2 ig ,uij ∈ u, where tij is the element of matrix t. we then treat each row of matrix t as a point in rn×k and cluster them into k clusters via the k-means clustering algorithm [18]. analyzing and characterizing to characterize each cluster, we first use kurtosis and skewness to estimate the data distribution of each feature. kurtosis is a descriptor of the shape of a probability distribution. a higher kurtosis means more of the variance is the result of infrequent extreme deviations. skewness is a measure of the asymmetry of the probability distribution. negative skew indicates that the tail on the left side of the probability density function is longer than the right side. conversely, positive skew has the longer right tail. for example, normal distribution has kurtosis of 3 and skewness of 0; kurtosis of log-normal is greater than 3, and skewness is positive. after the function of probability distribution is determined, we apply maximum likelihood estimation (mle) to estimate the corresponding parameters. 4.2 clustering with traffic fluctuation we first study on clustering traffic fluctuation similarity. however, traffic fluctuation similarity is a relative property, but not an absolute coordinate. in order to keep coherence with other characteristics, we transform the correlation coefficient to euclidean distance. according to alternative definition of ρx,y: ρx,y = ∑ i xiyi −nµxµy nσxσy where µx and µy are the means of x and y respectively, and σx and σy are the standard deviations of x and y , if x and y are standardized, they will each have a mean of 0 and a standard deviation of 1, we can get the distance expression from correlation coefficient [19]: d(x‡,y ‡) = √ 2n(1 −ρ(x‡,y ‡)) (1) the codomain of function 1 is from 0 to 2 √ n. the normalized distance is shown as: dnorm(x‡,y ‡) = √ 1 −ρ(x‡,y ‡) 2 (2) we use this normalized formula and standardized traffic ranks to calculate the distance on traffic fluctuation similarity. fig.1 shows the smallest 20 eigenvalues of the laplacian matrix with 2-phase simple moving average. we can identify gaps by inspecting the difference between an eigenvalue and the average with the previous value. we can see that from the figure, the major gaps appear between the first three values. there are minor gaps after the fourth and fifth eigenvalue. according to those gaps, it is reasonable to group the data into 2, 4, 6 or 7 clusters. 486 j. ma, w. ni, j. yin, r.p. liu, y. yuan, b. fang 0 5 10 15 20 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 rank v a lu e eigenvalues sma 2 figure 1: the smallest 20 eigenvalues of the laplacian generated from traffic fluctuation. we proceed to show the results of grouping our data into four clusters. the generated clusters exhibit obvious patterns of distinct traffic fluctuations as shown in fig.2, in which there is a mixed cluster (cluster 1), one busy afternoon cluster (cluster 2), a evening time cluster (cluster 3), and cluster 4 has two peak time at 1:00 am and 10:00 am. by clustering rbss with traffic fluctuation, the results can be used to operate the complementary cells to improve spectrum and energy efficiency. 0 5 10 15 20 25 0 0.5 1 1.5 2 2.5 3 3.5 4 x 10 7 hour of day a ve ra g e t ra ff ic cluster #1 (34.6%) cluster #2 (28.5%) cluster #3 (19.9%) cluster #4 (17.0%) figure 2: the average traffic of four clusters generated from traffic fluctuation similarity. 4.3 clustering on full dimensions we inspect the distribution of user nondeterminacy, temporal homogeneity, and usage diversity within the traffic data. user nondeterminacy follows normal distribution in this city (µ = 3.69,σ = 1.63). the expectation is 3.69 and standard deviation is 1.63. the density histogram of temporal homogeneity has negative skew and peaks are greater then 4. it follow truncated laplace distribution (µ = 4.11,σ = 0.79), where µ is a location parameter and σ is a modeling mobile cellular networks based on social characteristics 487 scale parameter. usage diversity is also well fitted by normal distribution. a city range have a expectation of 1.08 and a standard deviation of 0.38. we combine all characteristics, including user nondeterminacy, temporal homogeneity, usage diversity, and traffic fluctuation, to investigate the traffic patterns of individual rbs. the full dimensional distance between rbs i and rbs j can be written as: mi,j = √ (sui −suj )2 + (shi −shj )2 + (svi −svj )2 + 1 −ρi,j 2 entropy based properties are normalized in distance calculation. we proceed to show the results of grouping our data into three clusters. fig. 3 illustrates the characteristics of the three clusters that are generated using our spectral clustering method. fig. 3(a) shows the three entropy based social aspects of the three clusters, i.e., user instability, temporal homogeneity, and usage diversity. we can see that each cluster has an ellipse-like distribution of points. the clusters are visually separable in the three-dimensional space. figs. 3(b), 3(c), and 3(d) show the traffic fluctuation of the three clusters along with the time. the three clusters exhibit distinct patterns of their average traffic fluctuation, as highlighted in the figures, whereas cells of the same clusters show strong similarity of the patterns, as respectively shown in each of the three figures. table 1: statistical results of three clusters cluster user nondeterminacy temporal homogeneity usage diversity mean std mean std mean std 1 4.705 1.104 3.923 0.371 1.227 0.344 2 6.089 0.741 4.090 0.197 1.259 0.230 3 3.422 1.012 3.206 0.618 0.944 0.298 table 1 further gives the mean and standard deviation of each property for each cluster. since the entropy based properties have no obvious groups, a clustering with only these properties will cause an average separation. in other words, traffic fluctuation represents human mobility and social activity patterns. the other three characteristics indicate the community variety. full dimensional clustering can recognize different function areas and communities. further more, we have analyzed nine clusters result in detail. fig. 4 shows the average traffic fluctuation of the generated nine clusters and table 2 reports the characteristics of the other three properties (user nondeterminacy, temporal homogeneity and usage diversity). table 2: statistical analysis of nine clusters no. prop. user nondeterminacy temporal homogeneity usage diversity mean std. kurt. skew. mean std. kurt. skew. mean std. kurt. skew. 1 25.7 % 4.31 1.20 3.45 -0.67 3.87 0.43 12.52 -2.28 1.20 0.33 2.97 0.02 2 16.3 % 5.51 0.72 3.01 -0.30 4.09 0.24 3.91 -1.03 1.20 0.19 2.85 -0.19 3 15.0 % 6.69 0.57 3.42 0.65 4.19 0.14 4.11 -0.94 1.23 0.22 3.040 0.07 4 14.2 % 4.79 0.93 3.12 -0.34 3.85 0.32 3.50 -0.79 1.55 0.21 2.74 0.31 5 13.5 % 4.94 0.88 2.57 -0.31 3.82 0.29 2.52 -0.26 0.83 0.19 2.23 -0.01 6 8.1 % 5.33 0.71 3.18 -0.41 3.93 0.28 3.77 -0.94 1.22 0.18 2.80 0.06 7 5.3 % 2.78 0.77 2.50 -0.03 2.90 0.40 2.63 0.13 0.96 0.30 2.35 0.05 8 1.4 % 1.68 0.60 1.87 -0.09 1.78 0.55 3.27 -0.90 0.95 0.45 1.93 -0.02 9 0.5 % 5.85 0.57 2.08 -0.36 4.15 0.14 2.92 -0.86 0.98 0.27 1.96 -0.33 488 j. ma, w. ni, j. yin, r.p. liu, y. yuan, b. fang 0 1 2 3 0 2 4 6 0 2 4 6 8 10 usage diversitytemporal homogeneity u se r n o n d e te rm in a cy cluster #1 cluster #2 cluster #3 (a) 0 5 10 15 20 25 10 4 10 6 10 8 10 10 hour of day t ra ff ic v o lu m e average traffic of cluster #1 samples of cluster #1 (b) 0 5 10 15 20 25 10 4 10 6 10 8 10 10 hour of day t ra ff ic v o lu m e average traffic of cluster #2 samples of cluster #2 (c) 0 5 10 15 20 25 10 4 10 6 10 8 10 10 hour of day t ra ff ic v o lu m e average traffic of cluster #3 samples of cluster #3 (d) figure 3: the characteristics of three clusters: (a) the clustering result of the entropy based social characteristics, user instability, temporal homogeneity, and usage diversity, where the three clusters are produced using our spectral clustering method. (b), (c), and (d) the traffic fluctuation of the three clusters. modeling mobile cellular networks based on social characteristics 489 0 5 10 15 20 25 0 2 4 6 8 10 12 x 10 7 hour of day a ve ra g e t ra ff ic cluster #1 (25.7%) cluster #2 (16.3%) cluster #3 (15.0%) cluster #4 (14.2%) cluster #5 (13.5%) cluster #6 (8.1%) cluster #7 (5.3%) cluster #8 (1.4%) cluster #9 (0.5%) figure 4: average of the measured traffic for the nine clusters that we generate using our multidimensional spectral clustering method. distributions of the other three entropy based social characteristics are provided in table 2. using the table, we can derive models for each social aspect of a cluster. specifically, we can use 1. a normal distribution or its truncated variations to model the social characteristics with kurtosis of around 3 and skewness of around 0. examples include the user instability of cluster 2. 2. weibull, gamma and log-normal distributions to model the social characteristics with kurtosis of between 3.3 and 3.5. examples include the truncated weibull distribution for the user instability of cluster 1. 3. truncated laplace distributions to model the social characteristics with kurtosis of greater than 3.5 and skewness of less than −0.7. examples include the temporal homogeneity of clusters 1 and 2. in addition, the weibull distribution can also be used to model the social characteristics with kurtosis of less than 2.7. examples include the three entropy based characteristics of cluster 7. 5 applications to energy-efficient wireless networks mechanisms that adapt the cellular layout to the traffic density distribution can be used to improve the energy efficiency of a cellular network, including heterogeneous cellular deployment, cell breathing and relay network [20]. the social characteristics of rbs can be used as adjusting factors or constraints in these approaches to wireless networks. in heterogeneous cellular networks, base stations can be switched off if a decrease of traffic is predicted. the combination of multi-layer rbss is designed to meet non-uniform traffic demand. the social characteristics are concise properties to indicate human activity pattern in rbss’ effective coverage. in heterogeneous rbs deployment, user nondeterminacy of overlapped cells illustrates user mobility pattern in a certain area. in low user nondeterminacy area, micro cells tend to keep active since they are more energy efficient at the same traffic level. and to avoid frequent switching, the rbss with high temporal homogeneity have high potential to keep active. cell breathing is a coverage optimization mechanism to reduce energy consumption. the rbss 490 j. ma, w. ni, j. yin, r.p. liu, y. yuan, b. fang are switched off to reduce their energy consumption and their neighboring cells are zoomed in to meet the traffic demand. clustering rbss with the combination of traffic fluctuation correlation and geographic distance can be used to identify the cells that are to zoom out, and the cells that are to zoom in or can be switched off. fig.5 shows cells are clustered to four groups according traffic fluctuation within a district of chongqing. rbss of different groups, which have complementary traffics, can be operated together to improve energy efficiency. another approach of cell layout adaptation is relay network. relaying can be performed by using repeater stations or mobile devices as relays. a relay network with low user nondeterminacy is stable and more efficient. 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 longitude span (km) l at it ud e sp an ( km ) figure 5: cells are clustered to four groups according traffic fluctuation within a district of chongqing social characteristics are interactive with multi-layer rbss operations. the dynamics of rbss’ operation (e.g., switch on/off, zoom in/out) can affect social characteristics in spatialtemporal dimensions, which in turn improve the accuracy of modeling wireless network and refine the energy efficient designs of the networks. conclusions in this paper, we proposed a new model to characterize the social features of individual rbss. key measures of traffic fluctuation, user nondeterminacy, temporal homogeneity and usage diversity, are defined. multi-dimensional clustering can be carried out based on these social measures to categorize cellular base stations. we also established social models for each of the categories and derived important model parameters. the applications to energy efficient wireless networks were studied. the proposed models are of practical value and can facilitate designing, simulating, and evaluating network deployment. acknowledgment this work was supported by the national basic research program of china under the grant no.2013cb329606 and the china scholarship council. modeling mobile cellular networks based on social characteristics 491 bibliography [1] index, cisco visual networking (2014), global mobile data traffic forecast update, white paper, february, 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[23] m. a. piera, r. buil, m. m. mota (2014), specification of cpn models into mas platform for the modelling of social policy issues: fupol project, international journal of simulation and process modelling, 9(3):195-203. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 81-89 architecting robotics and automation societies over reusable software frameworks: the case of the g++ agent platform f. guidi-polanco, c. cubillos franco guidi-polanco, claudio cubillos pontificia universidad católica de valparaíso av. brasil 2241, valparaíso, chile. e-mail: {franco.guidi,claudio.cubillos}@ucv.cl abstract: this work presents a software framework that allows the implementation of societies made-up by autonomous collaborative devices. the framework is structured as a multi-layer reusable architecture, adopting the software agent as the design paradigm. in this paper, an overview of its main features is offered, and an example of its adoption in the design of a robot colony is provided. keywords: global automation, robots colony, agent architecture, agentbased framework. 1 introduction in de last decades, robotics and automation systems have evolved both in “body” and in “soul”: physiological functions of robots (like sensing, walking, etc.) have reached a degree of maturity, and control systems allow programming robots as entities that can perform autonomous and proactive behaviours. today, the research is focused in architecting models of societies made-up by autonomous mobile cooperating devices, performing common goal-seeking tasks. building software for such kind of systems, however, encompasses several challenges, such as how to increase efficiency in the development of control modules (avoid building always from scratch or unnecessary replicating code), how to the deploy software updates in geographically distributed devices, or how to provide data interaction between mobile robots over unreliable communication channels, among other common development problems. in our vision, the agent paradigm is envisioned as the software abstraction for development of societies of distributed autonomous collaborative systems. under such paradigm we have designed an agent-based framework providing a reusable architecture that addresses common issues in robotics and automation systems. this paper offers a description of the state of the art in software architectures for robotics and automation systems, and describes our agent-based framework architecture (the g++ agent platform). 2 related work the multi-agent system (mas) paradigm is being adopted to implement control and communication in distributed automation and robotic societies. in such systems, the modelling paradigm is centred in the concept of agent. an agent is a software entity capable to perceive its environment, to evaluate these perceptions against some given design objectives, and to perform some activity in order to reach them, interacting with other similar entities, and acting over its environment. agents should be designed to exhibit robust operation, even if they are immersed in an open or unpredictably changing environment [18]. copyright c⃝ 2006-2011 by ccc publications 82 f. guidi-polanco, c. cubillos in recent years the literature offers several examples of multi-agent architectures and organizations created for domain-specific applications (see [8], [11], [15] for some examples). these architectures accent the identification of agent’s roles and responsibilities, and the description of their interactions and communications. as expected, due to their ad-hoc nature, these architectures are hardly reusable outside their original domains. in order to improve the reuse of design, some studies establish the convenience of identifying and separating domain-specific aspects from those generic aspects that are common in families of systems. one example is the orientation followed in [16] that proposes the reuse of organizational coordination mechanisms across different problem domains and environmental situations. nevertheless, their work just emphasizes organization and distribution of tasks and goals, while the system’s structure is not deeply treated. an important contribution, in accordance with the latter approach, is the holonic paradigm [17]. this approach, offers an organizational model highly reusable, which can be applied at diverse abstraction levels and replicable in different domains [10]. however, it is a conceptual model that does not specify implementation of concrete services that can be required and reused when developing such systems. on the other hand, models of agent societies and agent platforms implementations play insufficient attention to the agent’s environment, which is an essential part in robotic system’s structure. in practice agent architectures fail to adequately identify and consider its role. as indicated in [19], popular frameworks minimize the environment reducing it just to a message transport system or to a brokering infrastructure. in terms of structure and services, the development of generic agent platforms (e.g. jade [2]) presents concrete architectures with high degree of reusability, but made-up by low-granularity components (commonly, basic communication and directory services), that implement commonly agreed abstract models (e.g. fipa). also, these platforms are not designed to satisfy security, connectivity, and scalability requirements originated in the robotic and automation domain [7]. the adoption of agent systems as enabling technologies for the development of distributed organizations’ infrastructures is currently matter of research. in particular, the agent technology seems not only to satisfy the demand for high flexibility requested by enterprise-wide integration, but also to provide approaches to support autonomous self-configuration and self-adaptability of their activities in their operational environment. 3 an abstract model for agent-based robotics societies we envision the agent paradigm as the software engineering approach to model entities (control modules) in robotic architectures. the arguments in favour of an agent-oriented approach in software engineering for modelling a system can be summarized in the three ideas indicated in [9]: (1) agent oriented decompositions are an effective way of partitioning the problem space of a complex system; (2) the key abstractions of the agent-oriented mindset are natural means of modelling complex systems; and (3) the agent-oriented philosophy for modelling and managing organizational relationships is appropriate for dealing with the dependencies and interactions that exist in complex systems. our approach introduces a layered model that identifies and classifies system’s components (i.e. agents and services) accordingly with different granularities. those components and services that share similar levels of reuse from both, the structural and the organizational point of view, are grouped together. the model is build recognizing at its basis the physical environment, which is virtualized in superior levels, making explicit the way in which agents will interact with it. this vision is constructed as the abstract model depicted in figure 1. the abstract model is divided by the following five layers: a) environment: it is composed by physical objects pertaining or observed in the real world (e.g. objects in mobile robot’s environment, wired or wireless communication networks, computaarchitecting robotics and automation societies over reusable software frameworks: the case of the g++ agent platform 83 figure 1: the layers in a robotic society tional systems in organizations, human operators, etc.), and concepts conventionally adopted for its characterization (e.g. geographical coordinates obtained from a gps service, temperatures, data transmission latency, water flows measurements, etc.). the physical world is conceptualized as a multidimensional space surrounding agents accomplishing physical-related tasks. b) autonomous equipments: represent computing-enabled platforms, such as mobile robots, automated factory machines, or computing devices, which has to be programmed in order to act proactively in the robotic community. such systems, can offer a wide range of capabilities expressed in terms of cpu, runtime memory, data storage, data communication, or operating system. these equipments are usually provided with sensors that allow the perception of surrounding relevant variables, actuators to interact with the environment (changing their own position, taking objects, etc.), and communications devices to interchange messages with other equipments. also, the autonomous equipments provide the runtime environment for the agents, so they must satisfy a set of minimum hardware/software requirements imposed by the agent platform’s software (or in an opposite point of view, the agent platforms must be designed to be executed in specific categories of devices). c) agent platform: corresponds to the software that offers the base classes to build agents, and to virtualize environment-dependent services (e.g. interfaces to peripheral devices, motors, databases, network communication, etc.). it also offers the execution environment that controls the entire agent’s life-cycle, and regulates its interactions with other agents and resources. a known example of agent platform is jade [2]. a comprehensive list of agent platforms can be found in [1]. d) agent-based architecture: represents a reusable architecture to support the development of different kinds of agent-based systems. the architecture specifies a set of common services (e.g. directory facilitator, yellow pages, etc.), and a framework of communication/content languages (e.g. acl [6], kqml [4], etc.) and interaction protocols, necessary to achieve interoperability among agents. an example of a particular agent-based architecture is specified by fipa standards, which was conceived to obtain interoperability between different and generic agent systems (e.g. fipa request interaction protocol [5]). e) domain-specific multi agent system (dsmas): corresponds to a concrete instance of a multi-agent system, where domain-dependent agents are designed to represent real-world services and systems, and interactions among them are well defined. at this level, agents are often abstractions of real entities pertaining to the application domain. dsmas architectures can be reused within the scope of the context they were created for. examples of dsmas could be a colony of exploration robots, an automated work cell, or a domotic network of devices. 84 f. guidi-polanco, c. cubillos 4 the g++ agent platform the g++ agent platform runs over a java virtual machine (jvm) hosted in a computational device. the execution environment of the g++ agent platform provides connectivity services, being responsible for the interactions among all agents. it is also responsible for the virtualization of the physical environment, through the implementation of sensors and actuators interfaces that agents can access. the platform provides a container, which is the environment for the execution of agents. a container runs over a java runtime environment that allows the access to the resources offered by the host. the container presents to the contained agents common services, such as messaging transport, local event communication, and support for access to external data repositories. containers implement connectivity services among them for message interchange, and for agent and services migration. they also provide connectivity and state monitoring of external agents, and they instantiate proxies to make transparent the communication between external and internal agents. 4.1 the communication infrastructure since early stages of the design, this agent platform has been envisioned as the cornerstone of the distributed architecture for automation systems. in particular, under our conception this environment not only corresponds to the space where agents can perform their duties (as all platforms do), it is also aimed to provide a reliable communication infrastructure that agents can (and should) exploit to interact among themselves in a distributed application. as result, the g++ agent platform is able to offer an implementation of a robotic and automation system that will delegate to the own agent’s container the conduction of the major communication traffic. so agents can communicate among themselves asking their own container to deliver the message to its destination. messages are delivered following the best effort policy (i.e. no unnecessary delays are introduced in their expedition), but it is not guaranteed their reception in the right order. this can happen for two main reasons: (1) the latency of the internet, plus costs incurred in retransmissions of packets naturally tends to increase the time required to transmit a message over long distances, and (2) the interconnections between containers define the paths that messages have to follow from the source to the target, each node acting as a router (the processing time on each container has to be added to the network delays described above). the platform, however, can guarantee the delivery of messages, detecting and informing the sender when they are not arrived within the pre-established time. a time window and a timestamp message field are used in the message for this scope. the time window value can also be infinite, which means no time window is specified. the message timestamp can also be useful to the message receiver, to determine the exact sequence of messages. 4.2 virtual mobility virtual mobility allows agents to be suspended, transported and restored in diverse containers. mobility can be decided autonomously by the agent, in terms of the moment and destination in which it will be done, or can be enforced by the agent’s owner, or by another agent. mobility is implemented through the serialization of the state of the agent, the transport together with the code (if necessary), and the de-serialization at the destination container. the platform does not provides support for the serialization of the stack of calling methods, so when this procedure is activated, the agent has to be suspended. when an agent is moved from its home container to a foreign container, its original agent management system together with the mobility service is responsible to keep trace of the new position of the agent. in such a way, it is possible to implement automatic roaming in the communication to the agent. architecting robotics and automation societies over reusable software frameworks: the case of the g++ agent platform 85 4.3 interaction with the environment the structure of an agent considers a subsystem responsible for achieving information from its environment, where the environment can be virtual, composed by software processes or systems running in a computing device, or physical, as the real world is. for example, a virtual agent can be able to listen to keystrokes, listen to messages sent by other agents, receive network information, or perceive events from the operating system; a robotic agent can be enabled with sonars, infrared range sensors, accelerometers or gyroscopes to perceive the physical environment and its own relationship with it. on the other hand, agents must be able to act over its environment in order to achieve their goals. the actions can result in a virtual effect, such as the creation of files, the communication of messages to other agents, or physical, as commands over the engine in a wheel-enabled robot. sensors and actuators are closely related to the environment because their functionality depends directly on the aspects that they have to detect. in this way, sensors and actuators are device-dependent. however, enabling software agents with specific sensors and actuators can limit their mobility in virtual spaces. the g++ agent platform manages sensors and actuators through interface objects that can be attached to agents in runtime. this allows a migrating agent to get access to the specific sensors and actuators offered in each container/platform. this flexibility is obtained providing a common interface for all sensors and actuators that the agent must use to interact with. also is supported the definition of descriptors to recognize sensors and actuators that the agent could access. the independence between the agent implementation and its environment makes it possible to follow an evolutionary approach in the development of software agents. portability of agents allows new agents to be tested in simulation environments before they are deployed in the real world (e.g. a mobile robot controller). on the other hand, the model well suits for agents based on learning architectures or requiring initial training (such as those based on neural networks), because they can be conditioned for final execution in a simulated environment. 5 the agent-based architecture for automation and robotics this agent-based architecture introduces a communication standard and a set of services to build global automation systems in different domains. the former defines the languages that will be used for exchange of information between entities participating in automation systems. the latter, the set of services that are available for supporting their activity. three services are offered at this level: a) messaging: it provides persistence and reliability in direct messaging between senders and well-defined receivers. it is based on based on persistent messages queues, which allows time-decoupled communications among participants b) event distribution: it implements the asynchronous publish/subscribe communication model. each container provides local event publication and notification services. the architecture for global automation systems includes agents for the management of distributed subscriptions and notifications. c) service brokering: it supports dynamic reconfiguration of the relationships between service providers and consumers. each container provides local event publication and notification services. the architecture for global automation systems includes agents for the management of distributed subscriptions and notifications (that is, among different service points). the design of the services has explicitly considered the problem of distribution, particularly the unreliability of network connections, which makes indistinguishable crashed components from slow components. this problem, common to all implemented services, was addresses through a mechanism of registration and renewal of the registration with the service provider, that interested users must perform during their lifecycle. 86 f. guidi-polanco, c. cubillos 6 an example of domain-specific multi agent system for the domain-specific application example, let us consider a colony of mobile robots that have to explore a surface, and cooperate synchronously collecting certain objects, that must be carried to the robots’ base. this colony requires the participation of different kinds of autonomous devices: explorers, which are autonomous mobile robots provided with telecameras and grippers, that recognize objects to be collected, and carry them to the nearest transport robot. transport robots: they are autonomous mobile robots that receive the objects collected by explorers and transport them in batches to the colony nest. coordinator: is the autonomous system that receives communication from carriers and coordinates the operation of explorer and transport robots. it operates in a fixed computing device, which is located outside the exploring area, and connected through satellite to the communicator robot. communicator: is a mobile device that acts as a gateway between the coordinator and the robots located in the surface to explore. colony nest: is the computing device that runs messaging and brokering services of the nest. figure 2: a colony of explorers. in this system all the members run a container where operates the agent that implements the own control module. at startup, the containers pertaining to all devices in the surface to explore establish communication among themselves, using a peer-to-peer discovering protocol. then they register their service capabilities in the servicebroker running in the colony nest device. the coordinator, remotely located, creates a data channel with the colony nest, using a known ip direction to locate it. thus, the coordinator asks the service broker located in the colony nest for the suitable robotics devices to accomplish the task. from the answer provided by the service broker, the coordinator selects a communicator device, some explorers and transport robots. the coordinator transmits the mission to each robotic device. the explorers move across the surface detecting objects to be collected, and transmit their positions to the coordinator. with this information, the coordinator assigns routes to the transport robots, in order to pick up the objects collected by the explorers. if an explorer or transport robot goes out of the communication range, it starts a “turn back home” procedure in order to reestablish communication. if messages had been sent to that robot during the “blackout”, they are kept in the messages queue of the messagingservice and transmitted when the device is “visible” again. the coordinator could be subscribed in the event broker to listen for certain events, such as mechanical failures. thus, if a failure message is received, the coordinator can take measures like a replacement for the defective robot. in the following subsections, the system is described using the model presented in this chapter. architecting robotics and automation societies over reusable software frameworks: the case of the g++ agent platform 87 6.1 environment and autonomous equipments (levels 1 and 2) the environment is mainly composed by the physical surface that have to be explored, the objects that have to be collected, the obstacles in the route of each robot, etc. also, available communication networks or global positioning systems that can be accessed by devices are considered part of the environment. in terms of autonomous equipments we have all the devices participating in the colony, including the coordinator and the colony nest devices. robots are mobile vehicles enabled with sensors (cameras, gps, encoders, etc.), actuators (engines, grippers, etc.) and communication interfaces (wi-fi, satellite, etc.) that must be available for local control modules (robot controllers). 6.2 the agent platform (level 3) the g++ agent platform is used to provide the underlying software infrastructure for the implementation control modules. each device must provide a java runtime environment where a g++ container will run. also, in each robotic device the agent platform must have access to the api (application programming interface) of the available sensors and actuators, in order to build the access to physical components of the robots. the access to the communication stack is obtained, in general cases, through the standard tcp/ip interface provided by the device’s operating system. 6.3 agent-based architecture (level 4) the agent based-architecture is made-up by all the components required to achieve interoperability among the different participants. for example, the servicebroker agent, which is responsible for maintaining the network location (ip address) of each robotic device, and the list of services that them are capable to provide. another agent that participates in the architecture is the messengeragent that supports message queues for reliable delivery of message to mobile robots. 6.4 the domain-specific multi-agent system (level 5) each participant in the colony is conceptualized as a software agent, programmed to accomplish its own mission. the implementation requires providing every one of the devices with an agent/control. the control modules can be implemented using different artificial intelligence model. at this level must be also programmed the standard interfaces to the sensors and actuators, and registered locally as service objects in the device’s container. 7 conclusion in this work we have described our framework for the implementation of distributed robotics and automation systems. its design was driven by the interest to obtain a decoupled and scalable infrastructure in different application scenarios. this approach emphasizes software engineering aspects of agency, which is a differentiating point when comparing it with other architectures, whose functionalities are more focused in distributed artificial intelligence. currently we are developing some case of studies that can help us to test the framework in systems offering different complexity levels. 88 f. guidi-polanco, c. cubillos 8 acknowledgement this work has been partially funded by conicyt through fondecyt project no. 11080284 and the pontificia universidad católica de valparaíso (www.pucv.cl), through nucleus project no. 037.115/2008 "collaborative systems". bibliography [1] agentlink. software products for multiagent systems. technical report. europe’s network of excellence for agent-based computing, 2002. [2] f. bellifemine, a. poggi, g. rimassa. jade, a fipa-compliant agent framework. proceedings of the 4th int. conference on practical applications of intelligent agents and multiagent technology, 1999. [3] p. eugster, p. felber, r. guerraoui, a. kermarrec. the many faces of publish/subscribe. acm computing surveys, vol. 35, no. 2 (june 2003), p. 114-131. [4] t. finin, d. mckay, r. fritzson, r. mcentire. kqml: an information and knowledge exchange protocol.proceedings of the int. conference on building and sharing of very largescale kniowledge bases, december 1993. [5] fipa (2002). fipa acl message structure specification. standard n. sc00061g, december 2002. [6] m. genesereth, s. ketchpel. software agents. communications of the acm, vol 37, no. 7, 1994, p. 48-53. 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[15] t. rogers, a. sekmen, j. peng. attention mechanisms for social engagements of robots with multiple people. the 15th ieee international symposium on robot and human interactive communication, roman 2006, pp.605-610, sept. 2006. [16] m. sims, d. corkill, v. lesser. separating domain and coordination in multi-agent organizational design and instantiation, proceedings of the international conference on intelligent agent technology, iat 2004. [17] p. valckenaers, h. van brussel, t. holvoet. fundamentals of holonic systems and their implications for self-adaptive and self-organizing systems. proceedings of the second ieee international conference on self-adaptive and self-organizing systems workshops, sasow 2008., pp.168-173, oct. 2008. [18] g. weiss. multiagent systems: a modern approach to distributed artificial intelligence. mit press, cambridge, massachusetts, 1999 [19] d. weyns, m. schumacher, a. ricci, m. viroli, t. holvoet. environments for multiagent systems, state-of-the-art and research challenges. in lecture notes in computer science, vol 3374 (2005), berlin, heidelberg, germany. international journal of computers communications & control issn 1841-9836, 11(6):832-844, december 2016. general model for adequate cloud service selection using decision making methods o. pantelić, a. pajić, a. nikolić ognjen pantelić*, ana pajić department of information systems faculty of organizational sciences, university of belgrade jove ilica 154, 11000 belgrade, serbia *corresponding author: pantelico@fon.bg.ac.rs ana.pajic@fon.bg.ac.rs ana nikolić novomatic lottery solutions djordja stanojevica 12, 11070 novi beograd, serbia anikolic@novomaticls.com abstract: cloud computing (cc) is a technology that surely brings innovations in today’s business world, more and more companies around the world are widely incorporating this technology into their businesses. from a technical, as well as organizational point of view transferring enterprise it to the cloud is a complex task. various factors have to be taken into consideration in order to make a right choice when moving it services to the cloud. the goal of this paper is to identify and to discuss in detail all factors that influence organization’s decision to adopt cloud. general model for cloud adoption, introduced in pantelić et al. [13]a, consists of the key factors driving the organizational benefits when moving to the cloud. the purpose of the model is to support decision makers in evaluating the benefits, risks and costs of using cloud computing. in this paper the general model is extended with two new aggregation methods for harmonization of alternatives rankings in a group decision process. we present the results of two new methods using the method results from previous research [13], as rank inputs, into an aggregate (group) preference. the idea is to find consensus ranking that minimizes disagreement among previous methods results. there were no strong differences between the results of performed methods. the results have shown that software as a service model and storage as a service model dominated according to not just arithmetic-mean method, but also to geometric-mean method. keywords: cloud adoption, general model, it investments, cloud services, multicriteria methods. areprinted (partial) and extended, with permission based on license number 3958430574606 ©[2016] ieee, from "computers communications and control (icccc), 2016 6th international conference on". 1 introduction during the past decade, many enterprises have shifted their focus towards the use of cloud computing (cc) as a viable option to reduce costs and to improve it and business agility. the cloud computing model provides almost unlimited computing resources for users which are taken on demand and pay per use. it represents an extension of distributed computing and grid computing technology, which aims to deliver a variety of services over the internet via cloud providers. cc offers a lot of advantages to the businesses of all sizes. by moving business to the cloud, it decision makers can take advantage of highly efficient cloud infrastructure copyright © 2006-2016 by ccc publications general model for adequate cloud service selection using decision making methods 833 which reduces energy consumption considerably and they are freed of problems related to the technological issues of installing and maintaining the it. however, there are many uncertainties with regard to the actual realization of these benefits, particularly around the cost savings and the data security. despite many cloud applications are currently widely and successfully used, the growth of cc has slowed down. according to [6], the technical aspect of security in the cloud is one of the main reasons for the slowing down in the growth of cc. reference [15] underlined security, interoperability, and portability as major obstacles to broader cloud adoption. this research is an extension of [13] (doi: 10.1109/icccc.2016.7496751). the general model for cloud adoption introduced in [13] is extended with two new methods for group decision making. goal of the paper is to present approaches to creating the preference structure of alternatives by assigning ordinal ranks to the alternatives. paper presents results of two new methods using the method results from our previous research, as rank inputs, into an aggregate (group) preference. the idea is to find consensus ranking that minimizes disagreement among previous methods results. in addition, we discussed in detail key factors driving cloud adoption. the paper is organized as follows. section 2 points some background related to cloud computing and presents in detail advantages and disadvantages of different cloud services. in section 3, we introduce a general model of cloud adoption decision process with multi-criteria decision making methods as one of the key factors. detailed analysis of relevant factors is reported. the purpose of the model is to support decision makers in evaluating the benefits, risks and costs of using cc. the model can be used as the guideline for enterprises that are considering moving their it services to the cloud. our results from implementation of multi-criteria decision making methods, in particular analytics hierarchy process (ahp), iterative compromise ranking (icor) and multiattribute utility theory (maut), are given in section 4.1, and the results of new preference aggregation methods are given in section 4.2. the last section contains our concluding remarks and ideas for future work. 2 cloud computing service models there are many definitions of cc. a definition of cc by nist (national institute of standards and technology) is "a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction" [10]. reference [3] characterize cc as a pay per use model which offering computing, storage and software "as a service". an important aim of this technology is delivering computing as a utility. cc has always been divided into three broad service categories: infrastructure as a service (iaas), offers virtualized computation, storage and communication resources to the customers on demand, platform as a service (paas), provides an environment for developing applications using languages, services and tools supported by provider, and finally software as a service (saas), provides to the consumer applications and services that run on the providers infrastructure. as new cloud providers enter the market, even more cloud service models are available today. many additional models can be found in literature [16]. these service models are network as a service, storage as a service, database as a service, security as a service, integration as a service, management as a service, testing as a service, information as a service, communication as a service and monitoring as a service. they share many similarities but have their own distinct differences as well. we will analyze in more detail below the characteristics of these models. cloud service models describe how cloud services are made available to clients. the most widely used cloud service model is saas that offers an on demand online software subscription. saas provides significant efficiencies in cost and delivery by removing the need for technical staff 834 o. pantelić, a. pajić, a. nikolić to install, manage and upgrade software. moreover, it reduces the cost of licensing software. the consumer does not control the underlying cloud infrastructure and it operational functions, which cause important concern related to security, performance and service availability. some software customization is often available, but this is more limited than with paas and iaas models. with paas, consumers have control over the deployed applications while the service provider delivers the computing platform at predictable service level and cost. the platform consists of required software tools and it is hosted on the hardware infrastructure of the service provider. in this model, it is not necessary to hire people to maintain the platform and hardware infrastructure. benefits for using paas include the ability to eliminate cost of upkeep for hardware and the consumers can concentrate on building software. the downside is that the developers must work within the constraints of the platform which may cause data portability and application interoperability issues. on the other side, consumers have more real control over their infrastructure with iaas than with paas or saas model. iaas model allows automated deployment of servers, processing power, networking and storage. consumers have direct access to their infrastructure resources, just as they would with traditional infrastructure but optimize infrastructure spend. the downside is that some applications may not run on available infrastructure of service provider due to their very specific hardware requirements. "compared to iaas offerings, applications riding on paas deliver better performance due to the intrinsic cloud support for the programming platform" highlighted buyya et al. in [3]. core datacenter infrastructure consists of storage, servers, the network, and management tool for infrastructure maintenance and monitoring. each of these components has created a separate market niche. it enables the design of efficient in-network services. network as a service is not a new concept, but a new business model for delivery efficient in-network services [5]. it enables the design of efficient in-network services. however, the same concern about the cloud provider’s ability to guarantee high service availability appears. storage as a service allows consumers to manage data storage with no need for it staff and to align it spends with data consumption. with storage as a service, disaster recovery of data in the cloud is offered. the drawbacks are long latencies and limited bandwidth [14]. database as a service delivers database functionality as a service and consumers can deploy new databases quickly, securely and cheaply. the downside is a lack of control over network performance issues [16]. on the other side, gartner is predicting a growth of the cloud-based security service market [11]. security as a service is data and application protection solution, which is usually included in saas model. businesses employ integration as a service in order to share data between systems as well as with one or more external applications in real-time. the ultimate goal is the process improvement, to gain agility and automate business processes. besides, management as a service model offers a suit of saas based applications to ensure cost-effective it management operations. consumers can easily analyze, monitor and evaluate performance metrics to ensure the optimization of it resources [18]. the monitoring as a service offerings are focused on monitoring the insights of it infrastructure assets. it provides the complete overview of the infrastructure, servers, networks, storage as well as the applications. privacy concerns are still the main obstacles in adapting this model. communication as a service is known as a collection of different vendors’ services that facilitate enterprise communications. the core concept of this model is that using this service is very convenient. furthermore, information as a service integrates the information to provide real-time and high quality information about a business entity [17]. the last model is testing as a service which delivers the on-demand test execution. it is most suitable for test executions that do not require in-depth knowledge of the system. general model for adequate cloud service selection using decision making methods 835 3 general model elements there are many factors that determine which cloud service model is right for the specific organizational needs. a review of the literature reveals that factors impacting cloud adoption tend to be psychological as well as technical. the decision on migrating it services to the cloud should take into consideration a variety of socio-technical factors. it is not sufficient to examine simply the cost of deploying their it systems on the cloud. in [13] the general model for cloud adoption was briefly presented, aiming to support the cloud adoption decision process. the model consists of seven key factors driving the organizational benefits when moving to the cloud. decision makers can use the model to evaluate the benefits, risks and costs of using cloud. it is important that decision makers view cloud adoption project from different perspectives in order to fully understand cloud services and their various pros and cons. as most of the pros and cons are unquantifiable, making the decision about cloud services today’s biggest concern. it is important to notice a list of the major factors that weight into the decision. once decision makers understand the key factors driving cloud adoption, the next challenge is to select a most suitable cloud service to migrate their it services. therefore, in this section we thoroughly evaluate key factors that influence on organization decision to adopt cloud computing. the starting point must be to determine a company’s objectives. it is important to consider what the company wants to achieve by migrating it services to the cloud. ideally any company wants to increase profit, while decreasing cost. cloud computing has the ability not only to decrease costs, but also to run the business more effectively. it can reduce the cost of equipment, energy, and infrastructure administration and management, needed to run the same business. moreover, the business can remain current on technological advances. the company will be able to invest in new projects for direct business benefits, which eventually increases business value. although cloud computing has many advantages over traditional techniques, it does not necessarily mean that cloud will improve the business. cloud migration can result in a considerable amount of organizational changes. for example, system administrators will no longer have complete control of it infrastructure and services. in order to take advantages of the cloud, the company should look to align cloud service with business requirements and objectives. from an enterprise perspective, flexibility, scalability, and business continuity are also important besides cost. one of the most significant barriers to adoption is security, mainly the migration of critical applications and sensitive data to public cloud environment [8]. perhaps organizations will not completely outsource their back-end computing resources and activities to cloud provider. rather, they will engage in a partnership with more than one cloud provider and will establish heterogeneous computing environments. the influence of cloud services on global economy and environment is also significant. cloud computing seems to be a new model for doing business with a potential to change global industries’ business models. investing in cloud globally will create new jobs and economic opportunities. it will allow developing economics to become more competitive driving more investments. additionally, there is in fact a risk of it sector job loss from investing in cloud. for example, companies are more likely to move jobs related to maintaining infrastructure to the cloud. it departments will not need server engineers, systems administrators, or data center managers, however, they will still need good network engineers, it managers and business analysts to support applications. traditional roles will change and expand in development flexible application that suits cloud environment. there will be new opportunities for it staffs if they adjust their skills and focus more on systems integration, system security and data privacy. the organization will be more collaborative as employees can work from any location, any time. furthermore, adopting cloud technologies requires changes in organizational culture and people’s perception of new technologies. employees may be fear on how new technologies will affect their work, which 836 o. pantelić, a. pajić, a. nikolić can result in employees resistance. thus, employees’ perception of cloud benefits is possible barrier to cloud adoption. cloud services also reducing it sector’s carbon footprint. cloud providers operate in a massive scale, so energy consumption, the number of harmful substances and waste is significantly lower compared to the traditional in-house business. since cloud computing paradigm is based on sharing configurable computer resources by multiple users, the number of equipment, facilities, and resources for business can be greatly reduced or completely eliminated. this way of doing business can certainly lead to a more sustainable it infrastructure and processes. in july 2011, the carbon disclosure project commissioned a study that examined financial and carbon benefits of cloud [4]. a total of eleven organizations in the united states that have been using cloud computing for at least two years participated in the study. detailed case studies revealed a 50 percentage reduction of carbon in 2020 with adopting cloud technologies versus no cloud at all. the potential carbon reductions of 85.7 million tons per year by 2020 for multi billion dollars united states companies is comparable to the annual emissions from 16.8 million passenger vehicles. the report shows that organizations can achieve an annual energy use reduction of 12.3 billion dollars by 2020. the next step is cc evaluation based on financial metrics. as already mentioned, cc saves money but it might lead to financial loss due to security risks, possible data access problems, etc. researchers recommend using several financial metrics to evaluate how well cloud would be beneficial for enterprises. decision makers should be able to use these metrics to assess what cloud offering should best meet business and technical requirements. metrics are used to understand the factors needed for clear distinction between two and more different cloud offerings. these metrics are used for measurement of the business value of it. there are direct and indirect financial metrics. direct metrics refers to indicators that measure financial gain or loss at limited or no distance from the production functions, such as a metric used to measure investment performance. by contrast, indirect methods measure general business and it performance. most commonly used metrics are cost benefit analysis (cba), return on investment (roi), and total cost of ownership (tco) [9]. cost benefit analysis is widely used direct financial metric which simply calculate the value of the benefits, and subtracts the financial cost associated with it. the payback method is built into the analysis. it measures the length of time required to regain the benefits of investment to repay its cost. the period of time could be one month, one year, or three years mostly. it lacks of the time value of money (tvm) function, which is considered reliable for investments whose benefits expected to be received over longer periods of time. on the other side, net present value (npv) analysis considers the time value of money through the use of discount rate. discount rate is here the most critical decision variable used to define the present value of an investment. the present value of an investment is always less than or equal to the future value at the end of determined period of time. academics’ and practitioners’ recommendations regarding discount rate are ranged from 1 to 15 percent. net benefits are simple calculated by the sum of benefits discounted at the discount rate minus cost. if cloud computing project has a positive nvp value then it is expected to be a good decision. according to the investopedia, return on investments (roi) is a cash flow metric used to compare the efficiency of a number of different investments. when risks and other factors between the choices are equal, the investment with the higher roi is considered the better decision. most forms of roi analysis compare the benefits of investment with the cost of the investment. the result is represented as a ratio or percentage. a positive roi result means that investment returns is higher than cost. if cloud business case scenario has a positive roi, it can be considered as investment with net gain. a simple roi equation is not adjusted for the time value of money. therefore, it cannot be trusted to measure returns over long periods of time. investing in cloud computing can deliver general model for adequate cloud service selection using decision making methods 837 significant long term benefits. it is good practice to use roi metric discounted for present value. the result will be considerably less than the unadjusted roi. cloud computing investment will be still worthwhile, if the discounted roi result is higher than company’s minimum acceptable rate of return. up to this point, we have discussed the three most common direct metrics. in the following, we introduced total cost of ownership (tco) and time to market as indirect metrics. measuring the business impact of it functions begins with comprehensive tco methodology. tco tries to quantify all tangible expenses, as well as some intangible ones that can be assigned a monetary value. total costs are a critical component of the it value equation, and tco analysis includes the costs of deploying an it product across the product’s whole life cycle. for cloud technologies, expenses may depend on selected cloud service and selected cloud provider. there are costs associated with cloud license and maintenance fees, integration process, and employee training. at the same time, cloud can reduce cost for software and hardware upgrades, infrastructure cost, energy cost, labour costs, etc. many cloud providers will reveal a tco analysis to show the overall costs and benefits associated with migrating to their cloud platform. hence, cloud service evaluation can be done by comparing tco of cloud solution with tco of traditional it departments within enterprise [9]. for it department, the agility to deploy new applications and enter new markets is important key performance indicator. it can be measured by time to market (ttm) metrics which measures the length of time to lunched new product or service. lower ttm leads to an increase in the company’s competitive advantage. as ttm increases, the risk of losing customers also increases. with cloud computing, speed to market can be reduced while significantly reducing the cost. however, it is no longer sufficient to measure only financial performance [12]. instead, it is necessary to use multi-criteria decision analysis methods to determine the value of intangible assets. by using these methods, decision makers can rate and apply weights to quality attributes of different cloud service models, based on their preferences. the trend is to combine two or more methods to make up for shortcomings in any single particular method. analytic hierarchy process (ahp), iterative compromise ranking (icor) and multiattribute utility theory (maut) are a well-known multi-criteria decision making methods [7]. maut approach uses a function called utility. decision maker assigns a utility value to each alternative. this utility reflects the alternatives importance to the decision. the utility values can range from 1 to 10, where 1 represents the lowest importance and 10 the highest. decision maker gives a score to each of alternatives for each attribute. the final ranking value of the alternative is calculated as the sum of each alternative’s score for each attribute multiplied by the weight of that attribute. the alternative with the best value of the aggregated function is considered as the best of the alternatives. maut method is applied in all types of problems which have significant amount of uncertainty, such as financial, energy management problems, etc. reference [1] underlined ahp method as simpler form of maut. the utility function is calculated using the technique based on pairwise comparisons. the weights for the criteria and the relative performance scores for each of the alternatives on each criterion are determined by this technique. the relative scale from 1 to 9 is used to measure importance between two criteria. the higher number means the chosen criterion is considered more important than other criterion being compared with. the scores are assigned for each alternative option for each criterion using normalized pairwise comparisons matrix of identified criteria. icor is the third method described in this paper. it defines a narrow set of possible solutions closest to the ideal solution. the weight is assigned to each criterion and represents its relative importance. the compromise solution can be established by different strategies. the example is the strategy of "the majority of criteria". this strategy is demonstrated in our previous research [13]. we now propose to use two different group decision making methods to rank a finite set of 838 o. pantelić, a. pajić, a. nikolić alternatives. arithmetic-mean and geometric-mean as aggregation methods are introduced. we want to present some approaches to creating the preference structure of alternatives by simply assigning ordinal ranks to the different alternatives. these methods will be further discussed throughout the following sections. choosing a cloud provider is also a multiple attributes decision making problem. an increasing number of companies are offering variety of cloud computing services to enterprises. amazon web services and salesforce.com are regarded as the pioneers of cloud computing. google, microsoft, ibm, gogrid, and rackspace have also entered the cloud market. so many cloud providers create healthy competition among providers to meet the service level agreement (sla) requirements. the provider is obligated to meet performance objectives defined by the sla metrics. despite different protecting strategies and technologies for solving security and privacy problems, the service agreement signed with service provider becomes the only guarantee. it is necessary to assess security risks implied by the act of embedding resources within the cc environment. a major consideration should be data security and data availability of company’s critical data in the cloud. the location of the data centers and servers must be known to ensure the security regulations are followed. cloud provider should have very limited access to sensitive and critical data. these added security features can result in higher providers’ prices. the extended general model for cloud adoption is presented in figure 1. the extended model is consist of two new aggregation methods. the figure 1 shows seven key factors and brief explanation of each, with the aim to support appropriate analysis of different aspects. company’s strategic goal global economy financial metrics multicriteria decision methods cc provider’s offer sensitivity of the data personnel capability cost reduction green computing cost benefit analysis ahp services data privacy in the cloud methods to improve data security in the cloud the number of it experts the huge number of applications energy saving return on investment icor prices employee training doing business on the internet co2 emissions total cost of ownership maut reliability employees’ ability to accept changes payback method arithmetic-mean method business policy total cost of ownership geometric-mean method time to market analysis results deciding on the cloud service model characteristics of the company cc characteristics of the cc service model figure 1: extended general model for cloud adoption general model for adequate cloud service selection using decision making methods 839 4 methods and results 4.1 decisionmaking using ahp, maut and icor methods in this section, we briefly present the results from methods that previously have been applied to ranking different cloud services [13]. experts from the academic and the business world provide criteria weights and take part in ranking different alternatives. table 1 summarized the criteria considered in our research, while table 2 summarized the alternatives. criteria data security data availability the monthly average price ease of use flexibility consumer scope of control the level of customization the level of cloud provider support the risk of changing cloud provider overall business growth company size number of it experts table 1: defined set of criteria alternatives software as a service platform as a service infrastructure as a service network as a service storage as a service database as a service security as a service integration as a service management as a service testing as a service information as a service communication as a service monitoring as a service table 2: defined set of alternatives the methods results are presented in table 3. from these results, we can conclude that fifth alternative, storage as a service model, is the alternative with the largest utility values. the flexibility and ease of use may be the reason for the highest score. on the other hand, it has a low impact on business’s growth. the alternative with the lowest utility values is platform as a service. with paas, consumer needs to put in development effort to create and test applications 840 o. pantelić, a. pajić, a. nikolić and to have control over the deployed applications. the problem appears in understanding of who is responsible for which areas of cc architecture. the resulting weights of the ahp method show software as a service model as the alternative with the highest weighted preference. software as a service model is the most suitable model because it provides significant efficiencies in cost and cloud provider is fully responsible for all components of cc architecture. however, decision makers cannot neglect existing security challenges. the lowest weighted preference has platform as a service model. using ikor method decision makers get the solution that is the closest to the ideal. the alternative option that is the closest to the ideal is software as a service model, while on the last position in ranking list is network as a service model. the disadvantage of creating the specific virtual network is the need to control the devices from the cloud. alternatives maut method ahp method icor method software as a service 8.105 0.110 1 platform as a service 6.393 0.057 11 infrastructure as a service 6.662 0.062 10 network as a service 6.424 0.071 13 storage as a service 8.447 0.106 2 database as a service 7.328 0.065 6 security as a service 6.982 0.058 9 integration as a service 7.648 0.082 3 management as a service 6.994 0.058 7 testing as a service 8.152 0.088 4 information as a service 7.351 0.071 18 communication as a service 7.129 0.069 12 monitoring as a service 7.204 0.092 5 table 3: the final ranking of the alternative options using maut, ahp and icor method 4.2 group decision making using aggregation methods in many group decision making problems, individual preferences on a given set of alternatives are obtained from several decision makers and then these individual preferences are aggregated to a single collective preference. for group problem solving, the individuals participating in making a group decision assign ranks to the same finite set of alternatives. the idea is to harmonize opinions of the participants in the group in order to select the most acceptable alternative for the whole group. we will use in our approach two types of preference aggregation methods for harmonization of alternatives rankings [2]. these methods require, as the input data, individual preferences on a same set of alternatives and derive the consensus ranking that minimized disagreement among a group. they are considered as a single criterion analysis, since the individuals compare alternatives directly. here we present the approach to implement these methods using alternatives rankings obtained by the previous demonstrated decision making methods in [13]. each method’s ranking is combined to produce an overall assessment. in this way we critically evaluate results of previous research. moreover, it reduces subjectivity of assigning individual preference values to alternatives by decision makers. first method is arithmetic-mean method based on arithmetic mean of the ranking made by all decision makers. the overall preference of an alternative is estimated by arithmetic mean. general model for adequate cloud service selection using decision making methods 841 the result is an aggregated value that is between maximum and minimum value. the most acceptable alternative has the lowest average overall rate. geometric-mean method is similar to arithmetic-mean method. it uses the geometric mean of individual ranks instead of the simple arithmetic mean to determine the average rank. the alternative with the lowest value is the most acceptable. a geometric mean is always less than or equal to an arithmetic mean. it tends to reduce the effect of very high and very low values, which helps in eliminating extreme values (outliers). first we define individual ordering matrix of alternatives. rows of a matrix relate to the alternatives, columns relate to methods results. elements of individual ordering matrix represent alternative assigned rank for a method. the elements are shown in table 4. alternatives ahp method ranking maut method ranking icor method ranking software as a service 1 3 1 platform as a service 13 13 11 infrastructure as a service 10 11 10 network as a service 7 12 13 storage as a service 2 1 2 database as a service 9 6 6 security as a service 12 10 9 integration as a service 5 4 3 management as a service 11 9 7 testing as a service 4 2 4 information as a service 6 5 8 communication as a service 8 8 12 monitoring as a service 3 7 5 table 4: individual ordering matrix of alternatives there were no strong differences between the results of arithmetic-mean and geometricmean methods. consensus is achieved for the most acceptable alternative and the next ranked alternative. software as a service model and storage as a service model is ranked at first and second place respectively. these alternatives dominated according to all three decision making methods. maut method ranked the software as service alternative in third place, while ahp and icor method elected this alternative as the most favorable cloud service model. the reason for this result might be the significant reduction in it cost and ultimately the provider is responsible for maintaining the software. we want to underline the slight difference in geometric mean value of these two alternatives. moreover, two alternatives have the same average value of rankings. table 5 reports arithmetic mean and geometric mean value of an alternative. furthermore, there is significant disagreement over the alternatives which are ranked the lowest. in these methods, platform as a service is ranked as the worst solution. the rank order of security as a service, network as a service, and infrastructure as a service, alternatives differs. it shows higher or lower score level of these alternatives in one of the previous decision making methods. infrastructure as a service alternative is valued considerably higher by maut method, while security as a service is valued considerably lower by ahp method. 842 o. pantelić, a. pajić, a. nikolić alternatives arithmetic mean value geometric mean value software as a service 1.667 1.442 platform as a service 12.333 12.296 infrastructure as a service 10.333 10.323 network as a service 10.667 10.298 storage as a service 1.667 1.587 database as a service 7.000 6.868 security as a service 10.333 10.260 integration as a service 4.000 3.915 management as a service 9.000 8.849 testing as a service 3.333 3.175 information as a service 6.333 6.214 communication as a service 9.333 9.1588 monitoring as a service 5.000 4.718 table 5: arithmetic-mean method and geometric-mean method results conclusion cloud computing is a technology that surely transforms the way corporate it services are delivered and managed. decision to migrate existing services to the cloud can be complicated as selecting the most favorable cloud service model is is a rather complex process. the paper described the characteristics of different cloud services with the aim of helping decision makers to fully understand organizational benefits and drawbacks of cc. the focus was on developing general model for cloud adoption with the aim to support decision makers to investigate cloud adoption decisions and to make the right choice. the paper is an extension of [13] where different cloud service models are evaluated using three multi criteria decision making methods, in particular ahp, icor and maut methods. the criteria preference values have been assigned by the authors after discussions with experts from different stakeholder groups. the final ranking of the alternative options for all three methods were reported. in this paper we thoroughly evaluated seven key factors of general model for cloud adoption. because most of the pros and cons of cloud are unquantifiable, it is important that decision makers view cloud adoption project from different perspectives in order to fully understand cloud services. furthermore, decision makers have to compare the cost of different cloud providers, deployment options and usage scenarios. by using decision making methods, decision makers can rate and apply weights to quality attributes of different cloud service models, based on their preferences. in the present paper we have introduced some other decision making methods to show another view of ranking cloud services. the general model for cloud adoption is extended with two new aggregation ranking methods for harmonization of alternatives ranking for group problem solving. the feasibility and usefulness of the application of arithmetic-mean method, and geometric-mean method, have been demonstrated. the new approach used alternatives rankings obtained by the previous demonstrated decision making methods. the results have shown that software as a service model and storage as a service model dominated according to not just arithmetic-mean method, but also geometric-mean method confirmed this. furthermore, results proved platform as a service model as alternative with the worst preferences. therefore, we can highlight that consumers prefer cloud service models with low risk solution implementation. in all demonstrated methods, platform as a service is ranked as one of the worst general model for adequate cloud service selection using decision making methods 843 solution. this is due to the fact that the developers must work within the constraints of the platform. we can conclude that factors like: cost, ease of use and disclosed scope of controls between provider and consumer, are the main factors which determine the prioritization of cloud service models. data security and privacy protection issues did not play a key role in selecting right cloud service model as we expected. the findings of this study are a base for a future work consisting in the concrete case study implementations. certainly there may be additional factors which may have not been considered within this paper. it is also possible to evaluate other group decision making methods and types of preference aggregation methods for harmonization of alternatives rankings. bibliography [1] alexander, m. 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(2004), it management-as-a-service, ca technologies, whitepaper, no. 451. international journal of computers communications & control issn 1841-9836, 12(2):254-264, april 2017. a multiple attribute group decision making method based on 2-d uncertain linguistic weighted heronian mean aggregation operator w.h liu, h.b. liu, l.l. li weihua liu* school of management science and engineering shandong university of finance and economics, jinan 250014, china the second ring road no. 7366 lixia district of jinan, shandong province,china *corresponding author: weihualiu@sdufe.edu.cn haibo liu school of chemistry and chemical engineering qufu normal university, qufu 273165, china qflhb@163.com lingling li school of management science and engineering shandong university of finance and economics, jinan 250014, china lilingling@sdufe.edu.cn abstract: 2-dimension uncertain linguistic variables can describe both subjective evaluation result of attributes and reliability of the evaluation results in multiple attribute decision making problems. however, it is difficult to aggregate these evaluation information and give comprehensive results. heronian mean (hm) has the characteristic of capturing the correlations between aggregated arguments and is extended to solve this problem. the 2-dimension uncertain linguistic weighted hm aggregation(2dulwhma) operator is employed in this paper. firstly, the definition, properties, expectations and the operational laws the 2-dimension uncertain linguistic variables are investigated. furthermore, the properties of the 2dulwhma operators, such as commutativity, idempotency and monotonicity, etc. are studied. some special cases of the generalized parameters in these operators are analyzed. finally, an example is given to demonstrate the effectiveness and feasibility of the proposed method. keywords: 2-dimension (2-d) uncertain linguistic variables; heronian mean; aggregation operator. 1 introduction multiple attribute decision making (madm) refers to ranking and selecting the best alternatives by utilizing the known information. it has been widely employed in economic, science and technology etc. since churchman et al. [1] introduced the multi-attribute decision making and employed it in enterprise investment decisions, the classical multi-attribute decision making has attracted more and more research attention. however, owing to the complexity and uncertainty of objective things and the fuzziness of human thought, a large number of madm problems are uncertain, which are called uncertain multiple attribute decision making problems. those uncertain multiple attribute decision making problems are difficult to evaluate alternatives for decision makers using real numbers in many cases [25].it is more reasonable and natural expressed by combining linguistic information (good, fair, poor) with fuzzy term (slightly, very, mightily, copyright © 2006-2017 by ccc publications a multiple attribute group decision making method based on 2-d uncertain linguistic weighted heronian mean aggregation operator 255 extremely, obviously) . the researches on the madm based on linguistic variables have made the fruitful achievements[614]. however, there exist a kind of the linguistic fuzzy madm problems in practical decisionmaking, such as review of the science technology project, blind evaluate of economic industry system, etc. in these decision-making problems, decision makers not only assess all the indicators of evaluation objects, but also estimate the familiarity with the given results. therefore, zhu et al. [15] presented the concept of 2-dimension linguistic assessment information to solve this kind of decision making problems. liu et al. [16]extended 2-dimension linguistic information to deal with the multiple attribute group decision making problems with unknown weight. 2dimension linguistic employs two class linguistic information to describe the judgment on the object representing evaluation result and reliability of evaluation respectively. this can easily distinguish indetermination between decision making problems and subjective understanding, which is helpful to express opinions more accurately for decision makers. when 2-dimension linguistic assessment information is described by uncertain linguistic variables, it is called as 2-dimension uncertain linguistic variables. at present, research based on 2-dimension linguistic information mainly focus on the following. aggregation operators is a hot topic. 2-dimension uncertain linguistic power generalized weighted aggregation operator is proposed and some properties are discussed[15]. 2-dimension uncertain linguistic generalized hybrid weighted average operator is proposed and employed in technological innovation ability evaluation[17]. liu and qi[18] proposed some generalized dependent aggregation operators for 2-dimension linguistic information and developed a group decision making method based on these operators. 2-dimension uncertain linguistic density geometric aggregation operators and 2-dimension uncertain linguistic density generalized aggregation operators is proposed and used in an example[19]. another topic is operational rules, the operational rules of 2-dimension uncertain linguistic variables are improved by transferring it into intuitionistic fuzzy numbers to make the operations more accurate[20]. yu et al. [21] transformed 2-dimension linguistic information into generalized triangular fuzzy number, and proposed 2-dimension linguistic weighted average (2dlwa) operator and 2-dimension linguistic ordered weighted average (2dlowa) operator. although the 2-dimension linguistic variables can reflect the evaluation of decision makers on objects, some information to be aggregated can still be omitted easily, which makes the integrated result cannot be better to express alternatives synthetically. however heronian mean operator can capture the correlations of the aggregated arguments, so we combine 2-dimension linguistic variables with heronian mean and propose some 2-dimension uncertain linguistic heronian mean aggregation operators and weighted heronian mean aggregation operators, further apply them to solve the madm problems. in order to do so, the remainder of this paper is shown as follows. in sect. 2, we briefly introduce the operational rules of 2-dimension linguistic variables and the heronian mean. in sect. 3, some heronian mean aggregation operators based on 2-dimension linguistic variables are proposed and commutativity, idempotency and monotonicity are studied.in sect.4 detailed calculating steps are given to solve the group decision making problem with 2-dimension uncertain linguistic information. in sect. 5, we use an illustrate example to verify the efficiency of the proposed method, and some special cases are also discussed. in the last section, the conclusions are given. 256 w.h liu, h.b. liu, l.l. li 2 2-dimension uncertain linguistic variable and heronian mean 2.1 uncertain linguistic variable when decision makers need to express the qualitative information, generally it is necessary to set an appropriate linguistic assessment set in advance. let the linguistic assessment set be s = {sl |l = 1, 2, · · · ,l− 1} , where sl represents a linguistic variable, l is odd number. when l = 7, it is represented as = (extremely poor, very poor, poor, medium, good, very good, extremely good). 2.2 2-dimension uncertain linguistic variable in many cases, decision makers should give both evaluation conclusion of evaluation objects and other similar the reliability of their evaluation. in order to reliably describe decision makers judgment to the evaluated object, 2-dimension uncertain linguistic variable s̃ = (sf ,st ) has been proposed[22]. sf = {ṡi |i = 1, 2, · · · ,n− 1 }represents decision makers judgment to an evaluated object and st = {s̈i |i = 1, 2, · · · ,m− 1 } represents the subjective evaluation on the reliability of their given results. definition 1. let s̃ = ([ṡa, ṡb] , [s̈c, s̈d]) where ṡa, ṡb ∈ sf is i class uncertain linguistic information, s̈c, s̈d ∈ st is ii class uncertain linguistic information [21]. in order to minimize the loss of linguistic information, the discrete linguistic assessment sets of 2-dimension uncertain linguistic information are extended to continuous linguistic assessment sets, such that ṡa, ṡb ∈ sf = {si |i ∈ [0, t]} and let s̈c, s̈d ∈ st = {sj |j ∈ [0, t]} be the set of all 2-dimension uncertain linguistic variables. consider any three uncertain linguistic variables s̃ = ([ṡa, ṡb][s̈c, s̈d]),s̃i = ([ṡai, ṡbi][s̈ci, s̈di]) and s̃j = ([ṡaj, ṡbj ][s̈cj, s̈dj ]).let λ ≥ 0,then the operational rules are defined as follows[15][23˜24]: s̃i ⊕ s̃j = ([ṡai, ṡbi][s̈ci, s̈di]) ⊕ ([ṡaj, ṡbj ][s̈cj, s̈dj ]) = ([ṡai+aj, ṡbi+bj ][s̈min(ci,cj), s̈min(di,dj)]) (1) s̃i ⊗ s̃j = ([ṡai, ṡbi][s̈ci, s̈di]) ⊗ ([ṡaj, ṡbj ][s̈cj, s̈dj ]) = ([ṡai×aj, ṡbi×bj ][s̈min(ci,cj), s̈min(di,dj)]) (2) λs̃ = λ([ṡa, ṡb][s̈c, s̈d]) = ([ṡλa, ṡλb][s̈c, s̈d]) (3) s̃λ = ([ṡa, ṡb][s̈c, s̈d]) λ = ([ṡaλ, ṡbλ][s̈c, s̈d]) (4) according to above equation from (1) to (4),following relationship can be easily proved: s̃i ⊕ s̃j = s̃j ⊕ s̃i (5) s̃i ⊗ s̃j = s̃j ⊗ s̃i (6) λ(s̃i ⊕ s̃j) = λs̃i ⊕λs̃j (7) λ1s̃⊕λ2s̃ = (λ1 + λ2)s̃ (8) s̃λ1 ⊕ s̃λ2 = s̃(λ1+λ2) (9) definition 2. let s̃ = ([ṡa, ṡb][s̈c, s̈d]) be a 2-dimension uncertain linguistic variable, m is the length of sf and n is the length of st ,then the expectation e(s̃) of s̃ is defined as e(s̃) = a + b 2(m− 1) × c + d 2(n− 1) (10) for any 2-dimension uncertain linguistic variable s̃i and s̃j, if e(s̃i) ≥ e(s̃j) then s̃i ≥ s̃j ,or vice versa. a multiple attribute group decision making method based on 2-d uncertain linguistic weighted heronian mean aggregation operator 257 2.3 heronian mean heronian mean is one of aggregation methods, which can reflect the interrelationship of the input arguments[25][26]. definition 3. let si(i = 1, 2, · · · ,n) be a collection of nonnegative numbers, then hm is defined as hm(s1,s2, · · · ,sn) = 2 n(n + 1) n∑ i=1 n∑ j=i √ aiaj (11) definition 4. let p,q ≥ 0 and p , q do not take the value 0 simultaneously, then generalized heronian mean(ghm) is defined ghm(s1,s2, · · · ,sn) =   2 n(n + 1) n∑ i=1 n∑ j=i a p ia q j   1 p+q (12) 3 the 2-dimension uncertain linguistic weighted heronian mean aggregation operators definition 5. let p,q ≥ 0 and p,q do not take the value 0 simultaneously. s̃i = ([ṡai, ṡbi][s̈ci, s̈di]) be a collection of 2-dimension uncertain linguistic variables. 2-dimension uncertain linguistic generalized heronian mean aggregation (2dulghma) operators :ωn → ω is defined as 2dulghma(s̃1, s̃2, · · · , s̃n) =   2 n(n + 1) n∑ i=1,j=i s̃ p i s̃ q j   1 p+q (13) according to the operational rules of 2-dimension uncertain linguistic variables, formula(13) can be transformed into the following formłş 2dulghma(s̃ 1 , s̃ 2 , · · · , s̃n) = ( 2 n(n+1) n∑ i=1,j=i s̃ p i s̃ q j ) 1 p+q =    ṡ ( 2 n(n+1) n∑ i=1,j=i a p i a q j) 1 p+q , ṡ ( 2 n(n+1) n∑ i=1,j=i b p i b q j) 1 p+q   ,[min i s̈ci, min i s̈di ] (14) theorem 6. (commutativity). let (s̃′1, s̃ ′ 2, · · · , s̃′n) is any permutation of (s̃1, s̃2, · · · , s̃n) then 2dulghma(s̃1, s̃2, · · · , s̃n) = 2dulghma(s̃′1, s̃′2, · · · , s̃′n) proof: since (s̃′1, s̃ ′ 2, · · · , s̃′n) is any permutation of (s̃1, s̃2, · · · , s̃n) ,then 2dulghma(s̃1, s̃2, · · · , s̃n) = ( 2 n(n+1) n∑ i=1,j=i s̃ p i s̃ q j ) 1 p+q = ( 2 n(n+1) n∑ i=1,j=i s̃i ′ps̃j ′q ) 1 p+q = 2dulghma(s̃′1, s̃ ′ 2, · · · , s̃′n) (15) 2 258 w.h liu, h.b. liu, l.l. li theorem 7. (idempotency). let s̃i = ([ṡai, ṡbi][s̈ci, s̈di])(i = 1, 2, · · · ,n) be a collection of uncertainty linguistic variables. if all s̃i are equal, for all i ,s̃i = s̃ = ([ṡa, ṡb][s̈c, s̈d]) then 2dulghma(s̃ 1 , s̃ 2 , · · · , s̃n) = s̃ proof: 2dulghma(s̃ 1 , s̃ 2 , · · · , s̃n) = 2dulghma(s̃, s̃, · · · , s̃) = ( 2 n(n+1) n∑ i=1,j=i s̃ps̃q ) 1 p+q = ( 2 n(n+1) (n + (n− 1) + · · ·1)s̃p+q ) 1 p+q = s̃ (16) 2 theorem 8. (monotonicity). let s̃i = ([ṡai, ṡbi][s̈ci, s̈di]) and s̃ ′ i = ([ṡ ′ ai , ṡ′bi][s̈ ′ ci , s̈′di])(i = 1, 2, · · · ,n) be a collection of uncertainty linguistic variables. if all s̃i are 2-dimension uncertain linguistic variables. for all i,if s̃i ≤ s̃i′ ,then 2dulghma(s̃1, s̃2, · · · , s̃n) ≤ 2dulghma(s̃′1, s̃′2, · · · , s̃′n) proof: 2dulghma(s̃1, s̃2, · · · , s̃n) = ( 2 n(n+1) n∑ i=1,j=i s̃ p i s̃ q j ) 1 p+q ≤ ( 2 n(n+1) n∑ i=1,j=i s̃i ′ps̃i ′q ) 1 p+q = 2dulghma(s̃′1, s̃ ′ 2, · · · , s̃′n) (17) 2 theorem 9. (boundedness).let 2dulghma operator s̃i = ([ṡai, ṡbi][s̈ci, s̈di])(i = 1, 2, · · · ,n) ,then min i (s̃ i ) ≤ 2dulghma(s̃ 1 , s̃ 2 , · · · , s̃n) ≤ max i (s̃ i ) proof: let s̃min = min(s̃1, s̃2, · · · , s̃n) s̃max = max(s̃1, s̃2, · · · , s̃n) ,since s̃min ≤ s̃i ≤ s̃max,then ( 2 n(n+1) n∑ i=1,j=i s̃ p mins̃ q min ) 1 p+q ≤ ( 2 n(n+1) n∑ i=1,j=i s̃ p i s̃ q j ) 1 p+q ≤ ( 2 n(n+1) n∑ i=1,j=i s̃ p maxs̃ q max ) 1 p+q (18) 2 eq.(14) assumes that all of arguments being aggregated are of equal importance. however, in many real cases, the importance degrees are not equal. thus, we need to assign different weights for different arguments, and further define a new aggregation operator to process this case. because unfair evaluation information may be provided by some decision makers, the weight of evaluation information are larger when they are consistent with other evaluation information given by other decision makers. otherwise, smaller weight will be set. as result, the influence of outlier values can be reduced, and the decision results based on these operators will be more reasonable and reliable. definition 10. let p,q ≥ 0 and p,q do not take the value 0 simultaneously. s̃i = ([ṡai, ṡbi][s̈ci, s̈di]) be a collection of 2-dimension uncertain linguistic variables whose weight vector is ω = (ω1,ω2, · · · ,ωn)t satisfying ωi ∈ [0, 1] and n∑ i=1 ωj = 1 , then 2dulwhma is called the 2-dimension uncertain linguistic weighted hm aggregation (2dulwhma) operator: ωn → ω . a multiple attribute group decision making method based on 2-d uncertain linguistic weighted heronian mean aggregation operator 259 if 2dulwhma(s̃1, s̃2, · · · , s̃n) = ( 2 n(n+1) n∑ i=1,j=i (ωis̃i) p (ωjs̃j) q ) 1 p+q =    ṡ( 2 n(n+1) n∑ i=1,j=i (ωiai) p(ωjaj) q ) 1p+q , ṡ( 2 n(n+1) n∑ i=1,j=i (ωibi) p(ωjbj) q ) 1 p+q   , [ min i s̈ci, mini s̈ di ] (19) similar to theorems 69,we can prove that the 2dulwhma operator with the following properties: theorem 11. (commutativity) let (s̃′1, s̃ ′ 2, · · · , s̃′2) is any permutation of (s̃1, s̃2, · · · , s̃n) then 2dulwhma(s̃1, s̃2, · · · , s̃n) = 2dulwhma(s̃′1, s̃′2, · · · , s̃′2) theorem 12. (idempotency) let s̃i = s̃(i = 1, 2, · · · ,n) then 2dulwhma(s̃1, s̃2, · · · , s̃n) = s̃ theorem 13. (monotonicity) let s̃i and s̃′i (i = 1, 2, · · · ,n) are 2-dimension uncertain linguistic variables. for all i, if s̃i ≤ s̃′i ,then 2dulwhma(s̃1, s̃2, · · · , s̃n) ≤ 2dulwhma(s̃′1, s̃′2, · · · , s̃′n) theorem 14. (boundedness) the operator lies between the max and min operators: min(s̃1, s̃2, · · · , s̃n) ≤ 2dulwhma(s̃1, s̃2, · · · , s̃n) ≤ max(s̃1, s̃2, · · · , s̃n) 4 a method for group decision making based on weighted hm under 2-dimension uncertain linguistic environment in this section, we consider a group decision making problem with 2-dimension uncertain linguistic information. 2dulwhma proposed in section 3 will be used to solve a group decision making problem. let a = {a1,a2, · · · ,am} be a discrete set of alternatives and c = {c1,c2, · · · ,cn} be the set of attributes, whose weighting vector is ω = {ω1,ω2, · · · ,ωn}t such that ωi ≥ 0 and n∑ i=1 ωi = 1 . let d = {d1,d, · · · ,dt} be the set of decision makers, and λ = (λ1,λ2, · · · ,λt) is the expert weight vector such that λk ≥ 0 and t∑ k=1 λk = 1 ,the decision matrix dk = [d̃kij]m×n is 2-dimension uncertain linguistic variable about the attribute cj for the alternative ai and d̃kij = ([ṡak ij , ṡak ij ][s̈ck ij , s̈dk ij ]) where ṡa, ṡb ∈ sf and s̈c, s̈d ∈ st is uncertain linguistic information. then the ranking of alternatives is required. the method involves the following steps: step 1: utilize the 2dulwhma to aggregate the evaluation values of each expert (j = 1, 2, · · · ,n) 260 w.h liu, h.b. liu, l.l. li d̃ij = 2dulwhma(d̃ 1 ij , d̃2 ij , · · · , d̃t ij ) = ( 2 t(t+1) t∑ k=1,l=k (λk ij d̃k ij ) p (λl ij d̃l ij )q ) 1 p+q =    [ṡ( 2 t(t+1) t∑ k=1,l=k (λkija k ij ) p (λl ij al ij )q ) 1 p+q , ṡ  2 t(t+1) t∑ k=1,l=k (λk ij bk ij ) p (λl ij bl ij )q   1 p+q   , [ min k s̈cik , mink s̈ dik ] (20) step 2: aggregate the evaluation information of each attribute by 2dulwhma operator based on the following formula (i = 1, 2, · · · ,m) d̃i = 2dulwhma(d̃i1, d̃i2, · · · , d̃im) = ( 2 m(m+1) m∑ j=1,k=j (ωjd̃ij) p (ωkd̃ik) q ) 1 p+q =    ṡ 2 m(m+1) m∑ j=1,k=j (ωjaij) p(ωkaik) q   1 p+q , ṡ  2 m(m+1) n∑ j=1,k=j (ωjbij) p(ωkbik) q   1 p+q   , [ min j s̈cij, min j s̈dij ] (21) step 3: calculate the expectation e(d̃i) of 2-dimension uncertain linguistic variable according to equation(10). step 4: rank all the alternatives and select the best ones in accordance with the ranking of e(s̃) . 5 examples illustration and discussion in this section, the method are illustrated through an application case of evaluation in extraefficient economic industry system of shandong province. from this example, we explains the actual application and the effectiveness of the proposed method. in order to evaluate industry ecological level, four typical industries are selected and expressed by {a1,a2,a3,a4}. a1 is for ecological agriculture,a2 is for environmentally friendly industry, a3 is for energy saving industry, and a4 is for circular economy. three experts {e1,e2,e3} were invited to evaluate these projects by following indexes expressed by { c1,c2,c3,c4}. c1 expressed industrial efficiency reflectting the quantity and quality of industry economic growth, and it could be evaluated by the energy consumption, material consumption and water consumption, and etc. c2 expressed industrial structure coordination reflecting industry coordination among the economic benefits, environmental benefits and social benefits, and it could be evaluated by industrial spatial distribution harmony, industrial structure harmony and r&d to investment industrial gross output etc. c3 expressed environment benefits reflecting impacts on the industry environmental over entire product life cycle ,it could be evaluated by cleaner production, pollution control, reclamation of wastes and condition of work safety etc. c4 expressed performance indicator measuring the industry development contribution for social progress ,it could be evaluate by total tax and profit payment, total assets contribution and green gdp per person etc. supposed that λ = (0.243, 0.514, 0.243) is the weight vector of the five experts, and ω = (0.25, 0.27, 0.25, 0.23) is the index weight. the index values given by the experts take the form of 2dimension uncertain linguistic variables, and they are shown in tables 1-3. the experts adopted i class linguistic set and the ii class linguistic set. a multiple attribute group decision making method based on 2-d uncertain linguistic weighted heronian mean aggregation operator 261 according to step 1step 4, we got the following result as table 4. the 2dulwhma were utilized to aggregate the arguments, some different overall attribute value d̃i of the alternatives ai (i = 1, 2, 3, 4) were list in table 4. from this table, we could find that the overall attribute values of each alternative depended on the choice of the parameters p and q,but the ranking was kept unchanged. according to table 4,a1 has higher ecological level. conclusions and future works the multiple attribute decision making problems based on 2-dimension uncertain linguistic variables have been applied to a wide range of areas. compared with the traditional uncertain linguistic variables, 2-dimension uncertain linguistic variables add a subjective evaluation on the reliability of the evaluation results given by decision makers, so they can better express fuzzy information. in addition, the hm can take all the decision arguments and their relationships into account. based on hm and 2-dimension uncertain linguistic variables, 2dulwhma operators are developed in this paper. some desirable properties, such as commutativity, idempotency, monotonicity and boundedness are discussed. moreover, different p,q cannot affect ranking of alternatives , which proved that it is a flexible multiple attribute decision making method in that the decision makers, and they can choose different values of the parameters p and q according to their actual needs. finally, to demonstrate the effectiveness and feasibility of the developed method, an example about industry ecological level is given. in further research, the proposed operators and methods can be extended to other fuzzy information. table 1: the index values of industry given by expert e1 industry attribute(c1) attribute (c2) attribute (c3) attribute (c4) a1 ([ṡ4, ṡ5], [s̈2, s̈3]) ([ṡ2, ṡ3], [s̈3, s̈3]) ([ṡ3, ṡ5], [s̈4, s̈4]) ([ṡ4, ṡ5], [s̈1, s̈2]) a2 ([ṡ3, ṡ4], [s̈2, s̈3]) ([ṡ3, ṡ5], [s̈3, s̈3]) ([ṡ4, ṡ4], [s̈4, s̈4]) ([ṡ4, ṡ4], [s̈1, s̈2]) a3 ([ṡ2, ṡ3], [s̈2, s̈3]) ([ṡ3, ṡ4], [s̈3, s̈3]) ([ṡ3, ṡ4], [s̈4, s̈4]) ([ṡ4, ṡ5], [s̈1, s̈2]) a4 ([ṡ5, ṡ6], [s̈3, s̈4]) ([ṡ1, ṡ2], [s̈3, s̈3]) ([ṡ3, ṡ4], [s̈4, s̈4]) ([ṡ3, ṡ4], [s̈1, s̈2]) table 2: the index values of industry given by expert e2 industry attribute(c1) attribute (c2) attribute (c3) attribute (c4) a1 ([ṡ4, ṡ4], [s̈3, s̈4]) ([ṡ3, ṡ4], [s̈2, s̈3]) ([ṡ4, ṡ4], [s̈3, s̈3]) ([ṡ5, ṡ6], [s̈3, s̈4]) a2 ([ṡ4, ṡ5], [s̈2, s̈3]) ([ṡ3, ṡ3], [s̈2, s̈3]) ([ṡ4, ṡ5], [s̈3, s̈3]) ([ṡ4, ṡ4], [s̈3, s̈3]) a3 ([ṡ3, ṡ4], [s̈3, s̈3]) ([ṡ4, ṡ5], [s̈2, s̈3]) ([ṡ2, ṡ3], [s̈3, s̈3]) ([ṡ3, ṡ4], [s̈3, s̈4]) a4 ([ṡ5, ṡ5], [s̈3, s̈4]) ([ṡ4, ṡ5], [s̈2, s̈3]) ([ṡ2, ṡ2], [s̈3, s̈3]) ([ṡ3, ṡ4], [s̈3, s̈4]) table 3: the index values of industry given by expert e3 industry attribute(c1) attribute (c2) attribute (c3) attribute (c4) a1 ([ṡ5, ṡ5], [s̈2, s̈3]) ([ṡ3, ṡ3], [s̈2, s̈2]) ([ṡ4, ṡ4], [s̈3, s̈4]) ([ṡ4, ṡ5], [s̈1, s̈1]) a2 ([ṡ4, ṡ4], [s̈2, s̈3]) ([ṡ4, ṡ5], [s̈2, s̈2]) ([ṡ1, ṡ2], [s̈3, s̈4]) ([ṡ3, ṡ3], [s̈1, s̈1]) a3 ([ṡ3, ṡ4], [s̈2, s̈3]) ([ṡ5, ṡ5], [s̈2, s̈2]) ([ṡ2, ṡ3], [s̈3, s̈4]) ([ṡ4, ṡ4], [s̈1, s̈1]) a4 ([ṡ3, ṡ4], [s̈2, s̈3]) ([ṡ2, ṡ3], [s̈2, s̈2]) ([ṡ4, ṡ5], [s̈3, s̈4]) ([ṡ3, ṡ5], [s̈1, s̈1]) 262 w.h liu, h.b. liu, l.l. li table 4: aggregate attribute values by the 2dulwhma and the rankings of the industry p,q industry d̃i(i = 1, 2, 3, 4) e(d̃) ranking p=0.5,q=0.5 a1 ([ṡ0.188, ṡ0.336], [s̈1, s̈1]) 0.01091 a1 > a2 > a4 > a3a2 ([ṡ0.131, ṡ0.240], [s̈1, s̈1]) 0.007745 a3 ([ṡ0.091, ṡ0.229], [s̈1, s̈1]) 0.006672 a4 ([ṡ0.115, ṡ0.248], [s̈1, s̈1]) 0.007554 p=1,q=1 a1 ([ṡ0.324, ṡ0.375], [s̈1, s̈1]) 0.014547 a1 > a2 > a4 > a3a2 ([ṡ0.300, ṡ0.349], [s̈1, s̈1]) 0.013526 a3 ([ṡ0.267, ṡ0.342], [s̈1, s̈1]) 0.012687 a4 ([ṡ0.282, ṡ0.347], [s̈1, s̈1]) 0.013122 p=5,q=5 a1 ([ṡ0.765, ṡ0.770], [s̈1, s̈1]) 0.0319827 a1 > a2 > a4 > a3a2 ([ṡ0.763, ṡ0.768], [s̈1, s̈1]) 0.031904 a3 ([ṡ0.759, ṡ0.767], [s̈1, s̈1]) 0.031798 a4 ([ṡ0.761, ṡ0.768], [s̈1, s̈1]) 0.031843 p=10,q=10 a1 ([ṡ0.873, ṡ0.874], [s̈1, s̈1]) 0.036389 a1 > a2 > a4 > a3a2 ([ṡ0.872, ṡ0.873], [s̈1, s̈1]) 0.036367 a3 ([ṡ0.871, ṡ0.873], [s̈1, s̈1]) 0.036336 a4 ([ṡ0.872, ṡ0.873], [s̈1, s̈1]) 0.036349 p=1,q=0.5 a1 ([ṡ0.252, ṡ0.326], [s̈1, s̈1]) 0.012046 a1 > a2 > a4 > a3a2 ([ṡ0.218, ṡ0.286], [s̈1, s̈1]) 0.010498 a3 ([ṡ0.180, ṡ0.277], [s̈1, s̈1]) 0.009517 a4 ([ṡ0.199, ṡ0.285], [s̈1, s̈1]) 0.010091 p=1,q=2 a1 ([ṡ0.444, ṡ0.474], [s̈1, s̈1]) 0.019121 a1 > a2 > a4 > a3a2 ([ṡ0.430, ṡ0.460], [s̈1, s̈1]) 0.018551 a3 ([ṡ0.407, ṡ0.455], [s̈1, s̈1]) 0.017963 a4 ([ṡ0.417, ṡ0.458], [s̈1, s̈1]) 0.01824 p=1,q=5 a1 ([ṡ0.647, ṡ0.658], [s̈1, s̈1]) 0.0272 a1 > a2 > a4 > a3a2 ([ṡ0.643, ṡ0.654], [s̈1, s̈1]) 0.02701 a3 ([ṡ0.633, ṡ0.652], [s̈1, s̈1]) 0.02677 a4 ([ṡ0.637, ṡ0.653], [s̈1, s̈1]) 0.026875 p=1,q=10 a1 ([ṡ0.783, ṡ0.787], [s̈1, s̈1]) 0.032726 a1 > a2 > a4 > a3a2 ([ṡ0.782, ṡ0.786], [s̈1, s̈1]) 0.03266 a3 ([ṡ0.778, ṡ0.785], [s̈1, s̈1]) 0.03257 a4 ([ṡ0.780, ṡ0.785], [s̈1, s̈1]) 0.032608 p=0.5,q=1 a1 ([ṡ0.252, ṡ0.786], [s̈1, s̈1]) 0.012046 a1 > a2 > a4 > a3a2 ([ṡ0.218, ṡ0.286], [s̈1, s̈1]) 0.010498 a3 ([ṡ0.180, ṡ0.277], [s̈1, s̈1]) 0.009517 a4 ([ṡ0.199, ṡ0.285], [s̈1, s̈1]) 0.010091 p=2,q=1 a1 ([ṡ1.233, ṡ1.234], [s̈1, s̈1]) 0.051394 a1 > a2 > a4 > a3a2 ([ṡ1.106, ṡ1.160], [s̈1, s̈1]) 0.047219 a3 ([ṡ0.917, ṡ1.106], [s̈1, s̈1]) 0.042142 a4 ([ṡ1.039, ṡ1.164], [s̈1, s̈1]) 0.045891 p=5,q=1 a1 ([ṡ1.108, ṡ1.108], [s̈1, s̈1]) 0.046167 a1 > a2 > a4 > a3a2 ([ṡ1.050, ṡ1.074], [s̈1, s̈1]) 0.044247 a3 ([ṡ0.958, ṡ1.050], [s̈1, s̈1]) 0.041829 a4 ([ṡ1.016, ṡ1.075], [s̈1, s̈1]) 0.043543 p=10,q=1 a1 ([ṡ1.057, ṡ1.057], [s̈1, s̈1]) 0.044041 a1 > a2 > a4 > a3a2 ([ṡ1.027, ṡ1.039], [s̈1, s̈1]) 0.043031 a3 ([ṡ0.977, 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(2013); intuitionistic fuzzy geometric heronian mean aggregation operators, applied soft computing, 13(2): 1235-1246, doi: 10.1016/j.asoc.2012.09.021 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 1, month: february, year: 2020 article number: 1006, https://doi.org/10.15837/3735/ijccc.2020.1.3735 ccc publications the pseudo-pascal triangle of maximum deng entropy x. gao, y. deng xiaozhuan gao 1 institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 611731 yong deng12* 1. institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 611731 2. medical center, vanderbilt university, nashville, tn. usa, 37205 *corresponding author: dengentropy@uestc.edu.cn, prof.deng@hotmail.com abstract pascal triangle (known as yang hui triangle in chinese) is an important model in mathematics while the entropy has been heavily studied in physics or as uncertainty measure in information science. how to construct the the connection between pascal triangle and uncertainty measure is an interesting topic. one of the most used entropy, tasllis entropy, has been modelled with pascal triangle. but the relationship of the other entropy functions with pascal triangle is still an open issue. dempster-shafer evidence theory takes the advantage to deal with uncertainty than probability theory since the probability distribution is generalized as basic probability assignment, which is more efficient to model and handle uncertain information. given a basic probability assignment, its corresponding uncertainty measure can be determined by deng entropy, which is the generalization of shannon entropy. in this paper, a pseudo-pascal triangle based the maximum deng entropy is constructed. similar to the pascal triangle modelling of tasllis entropy, this work provides the a possible way of deng entropy in physics and information theory. keywords: deng entropy, maximum deng entropy, pascal triangle, dempster-shafer evidence theory, basic probability assignment. 1 introduction pascal triangle (known as the yang hui triangle in china) is a triangular arrangement of the binomial coefficients and that it contains many remarkable numerical relations [19, 64]. pascal-triangle has also become a suitable model system for the exploration of statistical mechanical structures [53, 64]. pascal triangle has attracted many people attention and been used in many fields [45, 46], such as network [28], cellular automata[79] and so on[5, 6, 43]. however, how to set connection between pascal triangle and uncertainty is an interesting issue. in uncertainty, entropy plays an important role [8, 82] and has been used in many fields [9, 22, 58, 75]. there are various entropies, such as shannon entropy, renyi entropy [51], kolmogorov entropy and so on. hence, setting the connection between pascal triangle and entropy is an issue with considering. https://doi.org/10.15837/3735/ijccc.2020.1.3735 2 tsallis proposed the tsallis entropy based on the probability theory as generalization of boltzmanngibbs statistics [61] and analyzed the connection between tsallis entropy and pascal-triangle [62]. as the extension of probability theory, dempster-shafer evidence theory (d-s evidence theory) has attracted many people attention. about uncertainty in d-s evidence theory, there are also various measurement. deng proposed deng entropy to measure the uncertainty of d-s evidence theory [10, 57]) [14], which is compatible with shannon entropy. deng entropy has attracted many people attention since proposed [3, 15, 25, 26, 27, 54, 66], which has been used in many fields, such as information fusion [35, 47], quantum information [20], uncertainty measurement [41] and so on [63, 73]. recently, kang and deng proposed the maximum deng entropy, which bpa satisfies some conditions with the changes of frame of discernment [24]. based on the maximum deng entropy, the paper proposed the pseudo-pascal triangle to further explore deng entropy. the paper is organized as follows. the preliminaries dempster-shafer evidence theory and deng entropy are briefly introduced in section 2. section 3 introduced the maximum deng entropy and discussed the some properties of bpa, besides, analysed the semi-pascal-triangle of bpa. finally, this paper is concluded in section 4. 2 preliminaries in this section, the preliminaries of d-s theory [10, 57] and deng entropy [14] will be briefly introduced. 2.1 dempster-shafer evidence theory d-s evidence theory offers a useful fusion tool for uncertain information [18, 77]. d-s evidence theory needs weaker conditions than the bayesian theory of probability, d-s evidence theory assigns the probability into the power set of events[16]. d-s evidence theory has been used to many applications, such as data fusion [1, 49, 60], conflict management [33, 55], pattern recognition [44, 52, 56], evidential reasoning [17, 80, 81] and so on [2, 21, 34, 74]. some preliminaries in d-s theory are introduced as follows. for additional details about d-s theory, refer to [10, 57]. definition 1. ( frame of discernment) let θ be the set of mutually exclusive and collectively exhaustive events ai, namely θ = {a1,a2, · · · ,an} (1) the power set of θ composed of 2n elements of is indicated by 2θ, namely: 2θ = {φ,{a1} ,{a2} , · · · ,{a1,a2} , · · · , θ} (2) definition 2. ( mass function) for a frame of discernment θ = {a1,a2, · · · ,an}, the mass function m is defined as a mapping of m from 0 to 1, namely: m : 2θ → [0, 1] (3) which satifies m (φ) = 0 (4)∑ a⊆θ m (a) = 1 (5) in d-s theory, a mass function is also called a basic probability assignment (bpa) or a piece of evidence or belief structure. the m(a) measures the belief exactly assigned to a and represents how strongly the piece of evidence supports a. if m (a) > 0, a is called a focal element, and the union of all focal elements is called the core of a mass function. there are some studies about bpa, such as association [48], negation [40, 76], divergence [75] and information quality [7, 29, 30]. https://doi.org/10.15837/3735/ijccc.2020.1.3735 3 2.2 deng entropy uncertainty modelling is still an open issue [32, 38, 39, 50, 59, 65]. many models are presented to model the complexity such as network models [31, 68, 69, 70, 71], bayesian [23, 42, 78] and d numbers [12, 13, 36, 37]. among many math tools and models, entropy function plays an important role in real applications [11, 72]. deng proposed an deng entropy, which is an generalization of shannon entropy [14]. definition 3. (deng entropy) given a bpa, deng entropy can be defined as: hd = − ∑ a⊆θ m(a)log2 m(a)) 2|a| − 1 (6) through a simple transformation, deng entropy can be rewrite as follows: hd = ∑ a⊆θ m(a)log2(2|a| − 1) − ∑ a⊆θ m(a)log2m(a) (7) where m is a bpa defined on the frame of discernment θ, and a is the focal element of m, |a| is the cardinality of a. besides, the term ∑ m(a) × log2(2|a| − 1) could be interpreted as a measure of total nonspecificity in the mass function m, and the term −m(a) × log2m(a) is the measure of discord of the mass function among various focal elements. definition 4. (the maximum deng entropy) given a bpa, the maximum deng entropy is as follows: hm−d = − ∑ m(a) × log2 m(a) 2|a| − 1 (8) if and only if m(a) = 2|a| − 1∑ 2|a| − 1 (9) the more details about maximum deng entropy refer to [24]. 3 the pseudo-pascal triangle of maximum deng entropy 3.1 the example of maximum deng entropy the specific bpa of having maximum deng entropy was showed as follows. example 1. given the frame of discernment θ = {a}, there is only one phenomenon, as follows: m(a) = 1 hd = 0 from the above, it can be easily found that only having one element means no uncertainty, that is to say, the event is certain. example 2. given the frame of discernment θ = {a,b}, the bpa and maximum deng entropy are shown below: m(a) = 1 5 ,m(b) = 1 5 ,m(a,b) = 3 5 hm−d = 2.3219 from the above, the bpa of having maximum deng entropy has the phenomenon that m(a) and m(b) has the same distribution. https://doi.org/10.15837/3735/ijccc.2020.1.3735 4 example 3. given the frame of discernment θ = {a,b,c}, the bpa and maximum deng entropy are as follows: m(a) = 1 19 ,m(b) = 1 19 ,m(c) = 1 19 m(a,b) = 3 19 ,m(b,c) = 3 19 ,m(a,c) = 3 19 ,m(a,b,c) = 7 19 hm−d = 4.2474 it can known that the mass function having the same cardinality has same distribution. besides, from the above example 1, example 2 and example 3, it can be seen that the bpa follow a certain rules with changes of frame of discernment. more details, it can be discussed in the next. 3.2 distribution of maximum deng entropy frame of discrement |x|= 1 |x|= 2 |x|= 3 |x|= 4 θ = {a} 1 θ = {a,b} (1/5,1/5) 3/5 θ = {a,b,c} (1/19,1/19,1/19) (3/19,3/19,3/19) 7/19 θ = {a,b,c,d} (1/65,1/65, 1/65,1/65) (3/65,3/65,3/65, 3/65,3/65,3/65) (7/65,7/65, 7/65,7/65)15/65 table 1: the bpa of having maximum deng entropy table 1 shows bpa of having maximum deng entropy in various frame of discernment. in table 1, |x| represents the cardinality of focal element, θ represents the frame of discernment, the other information represents the bpa. such as, for θ = {a,b}, in the second column, ( 15, 1 5 ) means the m(a) = 15 and m(b) = 1 5 , ( 3 5 ) means the m(a,b) = 3 5 . from the table 1, it can be known focal element having the same cardinality has same bpa in the case of maximum deng entropy. besides, the bpa can be rewrite the fig .1. in fig .1, the first column, n corresponds to the number of frame of discernments. besides, the second column corresponds to the bpa with having maximum deng entropy. for example, in (2, 3/5), 2 represents that the cardinality of focal element, 3/5 represents the bpa. figure 1: the bpa of having maximum deng entropy 3.3 pseudo-pascal triangle of maximum deng entropy based the above discussion, it can be seen that the bpa has some regularity. fig, 2 shows the connection between pseudo-pascal triangle and the maximum deng entropy. in fig. 2, n represents the number of frame of discernment. |m| represents the cardinality of bpa, the others represents the number of having the 2|m| − 1. for better understanding, it can be considered that a system can be divided with some subsystems. that is to say, a system is evenly distributed, every subsystem occupies a certain proportion. when the frame of discernment is n, the system can be divided into∑m=n m=1 (2|m| − 1) ×cmn parts, the focal elements whose cardinality is |m| has 2 |m| − 1 parts. from the fig. 2, when the n = 2, the system can be divided 5 parts. in this system, the number having 2|1| − 1 = 1 parts is 2, the number having 2|2| − 1 = 3 parts is 1. through the above analyse, it can be known that the distribution of the maximum deng entropy is similarly with pascal triangle, https://doi.org/10.15837/3735/ijccc.2020.1.3735 5 figure 2: the pseudo-pascal triangle called pseudo-pascal triangle. hence, the paper proposed the connection between pseudo-pascal triangle and the maximum deng entropy. 4 conclusions pascal triangle is an important tool in mathematics to deal with some issue. uncertainty plays an essential role in modern lief. hence, how to generate the connection between pascal triangle and uncertainty is an interesting issue. deng entropy can provide a method to measure the uncertainty in d-s evidence theory, which is compatible with shannon entropy. the paper discussed the bpa of the maximum deng entropy, which changes with n according to evolutionary rules. by discussion, it can be found that the bpa of the maximum deng entropy can construct pseudo-pascal triangle. the paper analysis the bpa of maximum deng entropy, which is useful for uncertainty measurement. the paper have set the relationship between pseudo-pascal triangle and maximum deng entropy. however, the relationship between maximum deng entropy and pascal triangle still needs further exploration. acknowledgment the work is partially supported by national natural science foundation of china (grant nos. 61973332). references [1] abellán, j.; 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(2018). complexity, entropy and the physics of information, crc press, 2018. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: gao, x.; deng, y.(2020). the pseudo-pascal triangle of maximum deng entropy, international journal of computers communications & control, 15(1), 1006, 2020. https://doi.org/10.15837/ijccc.2020.1.3735 international journal of computers communications & control issn 1841-9836, 12(4), 577-591, august 2017. high-speed train control system big data analysis based on fuzzy rdf model and uncertain reasoning d. zhang dalin zhang national research center of railway safety assessment beijing jiaotong university beijing, 100044, china dalin@bjtu.edu.cn abstract: china high-speed train control system is a combination of computer, communication and control. its events are diverse, including sensor data stream, gps signal, gsm-r transmission data, real-time video monitoring data, train control software data, etc. these data have the typical characteristics of big data. if these data are well applied, this will be of great help to operations, maintenance, safety, passenger services, etc. this paper presents an efficient analysis method based on the fuzzy rdf model and uncertain reasoning for high-speed train control system big data. we have used the method proposed in this paper to analyze the data of the high-speed train control system. the experiment results show that the method proposed in this paper has good efficiency and scalability for the analysis of big data with different structures, types and context sensitive from high-speed train control system. keywords: high-speed train control system, fuzzy rdf, d-s theory, uncertain reasoning. 1 introduction railway transportation system is a complex system with many factors. it has been unable to meet the high transport capacity, high efficiency, high safety, high quality of service requirements relying on traditional theory and technical methods. the intelligent rail transport system has become a trend, such as the european rail traffic management system, the japanese cyber rail, the us irs (intelligent railway system), etc. in recent years, the ibm smarter railroad [3], cisco smart + connected railway [11], simens intelligent train [20], they have promoted the development of the intelligent railway. high-speed train control system makes use of the information provided by the ground, the distance between the target and the route, and generated the control curve automatically on the train control equipment. china has developed the standard of chinese train control system(ctcs) in 2008, which have 4 level totally [17]. the ctcs-3 uses the gsm-r wireless communication for bidirectional transmission of information between the train and the ground. it uses the wireless blocking center(rbc) to generate traffic permits, uses the track circuit to achieve the train occupancy check, and uses a transponder to achieve train positioning. at the same time, it has the function of ctcs-2. high-speed train control system is a combination of computer, communication and control. its events are diverse, including sensor generated data stream, gps signal, gsm-r transmission data, real-time video monitoring data, train control software data, etc. these data have the typical characteristics of big data, including massive data, distributed, complex, context-sensitive, etc. if these data are well applied, this will be of great help to high-speed rail operations, maintenance, safety, passenger services, etc. the dynamic monitoring system(dms) is a comprehensive system for dynamic monitoring of the use status of the relevant equipment of the train control system. it can analyze the data, guide copyright © 2006-2017 by ccc publications 578 d. zhang the maintenance, and provide the basis for the decision. dms consists of vehicle information collection, ground data center and query terminal. a variety of complex data analysis beyond our current ability range to use dms. how to extract the useful knowledge from dms’s massive data effectively is an urgent need for the current situation. because these excavated knowledges can help the maintenance department to achieve rapid and accurate fault diagnosis, and provide reasonable preventive maintenance decision support. for big data, the current distributed database technology nosql and hadoop can handle it well. if the semantic technology is used to analyze, the main challenge lies in the distributed query and reasoning. such as streaming media, sensors, the current distributed computing technology can also meet this demand. the challenge of semantic technology is the flow reasoning, that is in the process of continuous arrival of the data and the incomplete information should be used for reasoning. such as distributed reasoning can use hadoop / storm and the flow reasoning can use continuous(dynamic) queries [2]. there are many types of data in the dms system, and the structure is very complex and the speed of change is very fast. it not only needs high performance but also needs management and real-time analysis. if it should be needed to meet the above three, there is no good solution. in view of the above problems, this paper proposes a distributed real-time context-aware data processing method based on the fuzzy linked data and uncertain reasoning. this method extends the rdf model to a fuzzy rdf model, and propose real-time distributed context reasoning based d-s theory. 2 ctcs-3 data context modeling 2.1 ctcs-3 data the system structure of ctcs-3 is shown in the following figure 1. the real-time data of ctcs-3 can be summarized as follows: (1) rbc. generating the traffic permit according to the track circuit, interlocking and other information; accepting vehicle equipment data through gsm-r. (2) gsm-r network. two-way communication between vehicle equipment and ground equipment. (3) transponder. sending positioning, level conversion information, line parameters and temporary speed-limited to the vehicle equipment. (4) secure computer. generating the dynamic speed curve and monitoring the safe operation of the train. (5) track circuit. checking the train occupancy and sending traffic permit information. (6) ctc system. realization of centralized control for the station signal equipment. 2.2 linked data at present, more and more big data applications began to introduce semantic technology, which makes the description of the data more standardized and rich in machine comprehensible semantics. rich semantic links make the system more open and inter-operable, and make big data analysis deep into the knowledge level, which requires big data technology can provide a wealth of related functions and simple reasoning ability. big data technology can effectively solve the distributed environment of the web scale of unstructured information management and utilization issues. and the associated data brings the rich formalized semantics, which is a tool for cross-domain integration and intelligent analysis. high-speed train control system big data analysis based on fuzzy rdf model and uncertain reasoning 579 figure 1: ctcs-3 system structure resource description framework(rdf). rdf [13] is a triplet 〈subject,predicate,object〉, which describes the generic model of the resource description, where the subject is a resource with a uniform identifier(uri), or a blank node with no name space, such as doi, isbn etc.; object could be a resource with a uri, a blank node, or a string value; and a predicate that represents the relationship between the subject and the object. more and more big data applications are described and encoded using the rdf model. the meta-data and ontology make the data semantically recognizable by the machine, enrich the semantics of big data, and make the data have better interoperability. since the linked data [7] is semantically described, it is no longer a piece of information. when we combine linked data with big data mining, which provides a powerful tool for big data analysis, which implements semantic-based integration in the data. when using the linked data technology in big data applications, we can call is as a linked big data application. linked data. linked data is a data application form, which uses the uri as a data identifier, and rdf triplet structure as a data model, and based on http, which is a simplified realization of the semantic web, and the intention is to build data web. the linked data is based on the existing web technology (http, url and html), and the web specification is further standardized and defined with basic principles. depending on the principle of the linked data, the elements of the triad of the linked data should be encoded as rdf as much as possible. especially the subject, must be able to access in an open http way, and the rdf description should contain other more data link. the data in the linked data is not an independent, context-free abstract data, but a clear knowledge unit with a uri identifier and an rdf description (including cross-domain links), which is the basic semantic unit managed in the web. any one thing, people, institutions, places, events, concepts can be described as a linked data, so the linked data to big data development is inevitable. 2.3 fuzzy rdf model the purpose of context modeling is to describe the data and its environment, which plays an extremely important role in building a context-aware system. at present, the context modeling methods are key-value model, tag configuration model, graph model, object-oriented model, based on the logical model, ontology model. ontology model has the advantages of knowledge sharing, logical reasoning, easy knowledge reuse, and so on. in this paper, the use of extended rdf modeling method is also a kind of ontology. this study used drupal [12] to convert ctcs-3 data into linked data. the rdf data model 580 d. zhang is the basis of linked data, but in specific applications, will inevitably use some domain ontology, such as foaf, dublin core, skos, owl, sioc and so on. drupal also supports the import of external ontologies, which can define the mapping between these external and local content models. drupal provides a way for mapping content types, fields, and nodes to classes, attributes, and objects in the rdf triplet model, namely subject, predicate, object. in the field of ctcs-3 data analysis, some concepts are not deterministic, such as speed, security, etc., so the rdf model needs to be extended. the context representation of this paper is based on the fuzzy rdf model, and it is optimized for ctcs-3 data processing. fuzzy rdf model(frdf). the frdf model fr is expressed as fr = 〈fs,fp,fo〉, where the fs is the fuzzy concept, and the fp is the set of fuzzy attributes for the subject, the fo is a set of fuzzy concept instances. the attributes fp is defined as fp = {p1,p2, · · · ,pn}, the pi is an attribute that represents the relationship between the subject and the object; fo = {o1,o2, · · · ,on}, the oi represents a fuzzy instance. the function of the fuzzy degree in the frdf model is µ : fo → [0, 1]. if µoi = 1, we call oi concrete instance. for example, the "the no g115 train" is a fuzzy concept, meanwhile, it is an instance of another concept "train". "the speed of no g115 train is very fast" is an example of ctcs-3 data, which can be described by the frdf model. the "no g115", "train", "speed", "very", "fast" can total be described by the frdf model as an instance. the "train" is a fuzzy concept, and it has the fuzzy attribute "driving speed". the value of speed can be expressed as a group of instances {s(slow), m(middle), f(fast)}. fuzzy contexts. according to the frdf model, fuzzy context fc is defined as fc = 〈fs,fp,fo〉. the fs is a set of fuzzy concepts, fp is a fuzzy attribute set that represents the relationship between the subject set fs and the object set fo, and fo is a set of fuzzy objects. 2.4 frdf model based on d-s theory the d-s theory [10] is a generalization of the bayesian reasoning method, which mainly takes advantage of the bayesian conditional probability in probability theory, and requires a priori probability. the d-s theory does not require a priori probability, can describe uncertainty, is widely used to deal with uncertain data. the d-s theory classifies the information that people are interested in some identification frameworks. basic probability assignment(bpa). for any of the recognition frames θ and its subset a, if the following conditions are met: (1) m(φ) = 0; (2) ∑ a⊆θ = 1; then, m : 2 θ yields−→ [0, 1] is called the bpa function. m(a) = 0 is the basic probability assignment of a, which indicates the degree of belief in a. the bpa is also called the mass function. belief function. to express the degree of trust in the proposition, d-s theory introduces the concept of belief function bel, and its relationship with bpa satisfies bel : 2θ yields −→ [0, 1] ,bel(a) = ∑ b⊂a m(b) = 1(∀a ⊂ θ). d-s rule. for n limited mass functions m1, m2, · · · , mn on the one recognition frame θ, ∀a ⊆ θ, a = a1 ∩a2 ∩·· ·∩an, then the synthesis of n belief functions is as follows: m(a) = (m1 ⊕m2 ⊕···⊕mn)(a) = 1 k ∑ a m1(a1) ·m2(a2) · · ·mn(an) (1) k = ∑ a 6=∅ m1(a1) ·m2(a2) · · ·mn(an) (2) the k is called the normalization factor, which reflects the degree of conflict among the evidence. when k = 0, it is called incomplete conflict; when 0 < k < 1, it is called incomplete conflict; when k = 1, it is called full conflict. high-speed train control system big data analysis based on fuzzy rdf model and uncertain reasoning 581 d-s theory does not well reflect the structure of contextual information and the relationship between them, so its application is limited. to play its superiority, it must use a valid context information representation. the d-s theory uses the evidence recognition framework e and the conclusion recognition framework θ to divide the information. the frdf model divides the context into two layers: the context of the low-level acquisition and the context of the high-level reasoning, which can naturally be combined to establish the frdf model based on the d-s theory. the frdf model has a very good scalability. the frdf model can be expanded based on the d-s theory easily. in the frdf model based on the d-s theory(frdfds), the fs is still the fuzzy concept. the fp is the set of attributes for the subject which been expanded and contains some special evidence attributes and conclusion attributes, such as hasconclusions, hasevidences, hasbpavalue and so on. the fo is a set of instances of concept which also been expanded and contains some special evidence and conclusion instances. at present, there are five attributes which are added to the basic frdf model: the attribute hasconclusions, the attribute hasevidences, the bpa numeric attribute hasbpav alue, the bal value attribute hasbal, and the time attribute hastimetag. in the context of the frdfds model, all data is represented as a unified frdfds model, which is the advantage of the linked data, and each data can be considered as a node of the frdfds model instance. the hastimetag attribute is used to get the context information for the time. the hasconclusions attribute may map a single instance or multiple instance instances. likewise, the attribute hasevidences corresponds to all evidence instance collections. in the pervasive computing environment, a conclusion context information may be another conclusion of the evidence context information. in order to adapt to the context of the dynamic environment, diversity, uncertainty, we add data attribute hasbel and hasbpav alue for the conclusion and evidence, which are used to describe the probability of the values. the above frdfds model is universal, just as the basic rdf model, the relevant attributes and instances can be added to express the uncertainty information of the context. belief structure. given a recognition frame θ, the evidence space e, the mapping f : e → θ and the basic belief distribution value m, where θ and e are finite sets, the quaternion d = 〈θ,e,f,m〉 is called a belief structure. based on the basic frdfds model given in this paper, we can construct the belief structure d , and then create the instance of the uncertainty context information, and finally, use the evidence combination rule step by step reasoning process. the m is the distribution of the basic belief, which is a quantitative evaluation based on evidence. we can make a synthesis based on different sources of evidence through visiting their value of m correspondingly. 3 multi-layer real-time uncertain context reasoning method context reasoning is the key to solve the problem of uncertainty reasoning, and which often depends on the context model. according to the frdfds model proposed in this paper, three kinds of reasoning mechanisms can be used in context reasoning [1], namely, ontology reasoning, rule reasoning and evidence theory reasoning. the rule-based reasoning is to generate new knowledge by matching existing facts with predefined rules. for example, when the sensor information is collected: "john is currently in the bedroom, the indoor curtains closed, the light intensity is dark", you can infer that “john is sleeping". the reasoning process of the ontologybased is like the rule-based reasoning process, except that the rules used are defined by the owl language itself to obtain knowledge that is implicit in explicit definitions and declarations, such as the symmetrical property symmetricproperty, the transitive property transitiveproperty, etc. for the reasoning based on d-s theory, the evidence set and the conclusion set are expressed 582 d. zhang by multiple discriminant frameworks respectively. the synthetic method provides the hierarchical reasoning model of evidence. uncertainty is transferred layer by layer based on the synthesis rules of d-s theory. the hierarchical model can add the evidence based on the original synthesis, greatly improving the efficiency of evidence synthesis. ctcs-3 is the complex application of the internet of things. the logic-based reasoning method will become more complex. to solve the problem of large data, this paper proposes a distributed multi-layer real-time uncertain context reasoning framework based on frdfds model. in this framework, the first layer generates the context state according to the frdfds model instance, in which the frdfds instance is generated by the complex event processing engine. since the event processing agent may be multiple, the frdfds instance is distributed. ctcs-3 data is collected in real-time, so the context state is continually updated with updated data in real-time. the context state of the (i + 1)th layer can be inferred from the context of the ith layer. the reasoning process combines the traditional rule-based reasoning and the uncertainty reasoning based on the d-s theory. the overall algorithm framework is proposed by this paper in algorithm 1. the algorithm 1 combines traditional rule-based reasoning and the uncertainty reasoning based on the d-s theory. from the algorithm 1, we can see that all context can be expressed by a frdfds model instance, and this framework is multi-layer and real-time and has a good adaptability. in algorithm 1, the fuzzy degree is described by the function µ. when all reasoning is finished, the framework will trigger an update_context() procedure. the procedure ds_reasoning() and update_context() will be present in algorithm 2 and algorithm 3. algorithm 1 multi-layer real-time uncertain context reasoning framework require: input context[layers][instances] = context[leveal0][instances0];//layers represent the level of context; instance is a frdfds model instance set; ensure: output context[layers][instances]; // the context at time t; 1: initialize context[layers][instances]; 2: initialize context update = false; 3: for (all layers ∈ n and layers > 0) do 4: for (all frdfds model instance in context[layers][instances]) do 5: if instance is an uncertain then 6: call d-s theory reasoning procedure ds_reasoning(); 7: else 8: call rule-based reasoning interface; 9: end if 10: end for 11: end for 12: if update= false then 13: call update_context(context[layers][instances]); 14: update= true; 15: end if the algorithm 2 gives a complete uncertainty reasoning process based on d-s theory. the whole process is based on the belief structure which can map the frdfds model perfectly. the classical evidence combination formula requires that all evidence of participation in the synthesis have the same degree of importance. however, in the pervasive computing environment, the evidence obtained from different sources of evidence may differ in importance and belief, so that the basic credibility of the evidence needs to be corrected before the evidence is synthesized to reflect the different importance of the evidence and belief. the formula for correcting the belief high-speed train control system big data analysis based on fuzzy rdf model and uncertain reasoning 583 is as follows: m′i(ai) = { bel(e)mi(ai) when ai 6= θ 1 − ∑ m′i(ai) when ai = θ (3) for different context applications, the bel function is different, but the choice of bel generally follows the following rules: (1) the more important evidence, the higher belief, the corresponding correction factor is great, that is, we assigned a big weight for the important evidence distribution. (2) the higher evidence conflict, the lower belief, the smaller corresponding correction factor, that is, there will be a larger amendment in the future. algorithm 2 uncertainty reasoning based on the d-s theory require: input a context instance in instance set; //an instance in one layer context ensure: output a context instance in instance set; //the context instance after running a reasoning process; 1: procedure ds_reasoning(instance) { 2: initialize conflict factor threshold value = k; 3: for (all conclusions in instance) do 4: // conclusion is also a frdfds model instance. 5: get its evidence set and 6: calculate the bpa numeric and the bal value; 7: calculate the conflict factor; 8: if conflict_factor 6 k then 9: calculate the bal value for this conclusion; 10: else 11: calculate the modified bal value for evidence set; 12: calculate the bal value for this conclusion; 13: end if 14: end for 15: } the weighted combination rule is proposed to fuse the evidence, which solves the shortcomings of the evidence theory in the case of high degree of conflict between the evidence. however, the reasoning model of the method is not adaptive. therefore, this paper improves the combination rule with the following modified belief formula: m′(a) = (m′1 ⊕m ′ 2 ⊕···⊕m ′ n)(a) = ∑ a ω1m ′ 1(a1) ·ω2m ′ 2(a2) · · ·ωnm ′ n(an)∑ a 6=∅ ω1m ′ 1(a1) ·ω2m ′ 2(a2) · · ·ωnm′n(an) (4) ωi = ∑n j=1,j 6=i(1 −dbpa(mi,mj))∑q i=1 ∑n j=1,j 6=i(1 −dbpa(mi,mj)) (5) the dbpa(mi,mj) is the distance of mi, and mj. the formula (5) reflects the different importance and belief of the evidence which resolves the one-vote veto problem and the robustness problem in the conflict evidence combination. it makes full use of the conflict evidence information and avoids the valid information loss. in order to improve the speed of analysis, we do not look back at historical data, but only analyze and update the current node data based on rules, d-s theory. this paper uses the transitive property transitiveproperty to update the state attribute information in drupal module instead of using based a finite state machine method. as shown in algorithm 3, this paper uses fuzzy similarity to update the fuzzy data on the same data node. in the distributed, multi-layer, 584 d. zhang real-time uncertain context updating based on similarity, the former context in the different node is fused by the d-s evidence theory. based on the similarity of fuzzy sets, the new context is fused to the existing context in the context update processing. the fuzzy similarity calculation rules is as follows. fuzzy set similarity. the recognition framework is θ = {a1,a2, · · · ,an} . the fuzzy set m and n are two random fuzzy subsets, and the similarity between them is ψ(m,n) = 1 − 1 |θ| n∑ i |µm (ai) −µn (ai)| (6) algorithm 3 real-time context update based on similarity require: input context[layers][instances] at time t and t-1;// the context at time t; layers represent the level of context; instance is a frdfds model instance set; ensure: output the updated context[layers][instances]; 1: procedure update_context(context[layers][instances]) { 2: for (all all layers ∈ n) do 3: for (all frdfds model instance in context[layers][instances] ) do 4: initialize similarity threshold value = s; 5: get the previous t-1 instance p_instance; 6: calculate the index of similarity between p_instance and instance; 7: if index 6 s then 8: make an additional with previous 9: end if 10: end for 11: end for 12: } 4 experiment and evaluation 4.1 dms as shown in figure 2, to monitor real-timely the operation of ctcs-3 equipment, china highspeed train equipped with dms which consists of on-board information detecting equipment, ground data center and inquiry terminal. the on-board information detecting equipment collect data from atp, transponder, track circuit and rbc, and then transmits them to the ground center through gprs/gsm-r/wlan network. through this remote monitoring method, the ground center can monitor and deal with the working states and faults of on-board signal equipment. the business logic of dms includes data acquisition, storage and analysis, and command. the main function of data acquisition part is data sampling and aggregation. the storage and analysis part mines and analyses the running status data of all related systems comprehensively. it can realize the early warning analysis and fault diagnosis. based on the storage and analysis, the command part provides data sharing service for daily production and emergency dispatching of the electrical department, and it can improve the efficiency of the electrical department daily work. 4.2 data analysis system framework at present, the capability of analysis needs to be further improved in dms. the work of this paper can be seen as an extension of dms which is integrated with dms in the way as shown high-speed train control system big data analysis based on fuzzy rdf model and uncertain reasoning 585 figure 2: dms network structure figure 3: data analysis integration framework in figure 3. in the figure 3, the middle part of the figure is the work of this paper and which is marked with a dotted line; the top part is the dms ui, which is a set of functions that can be used in all aspects of railway operations; the base part is the dms data pool. it is easy to see from the left of the middle of the figure 3 that our analysis method is based on drupal module [12]. the rdf is the standard framework for semantic web and also is a recommended framework for linking data. the linked data technology enables data to be interoperability, reusable, and easier to use. drupal is the first mainstream content management system to support semantic web technology in its core, which can publish linked data by exposing content with rdf. our main work in this paper is layout on the right of the middle of the figure. we extend the rdf to a frdfds model based on the d-s theory firstly. all data in the dms data pool will be expressed as a frdfds data. we also customize the data reasoning rule on the nodes by our reasoning rule module. accordingly, the query plug-in also was expanded and programmatically filters and displays content. we implemented the algorithms presented in this paper as two functional plug-ins deployed to drupal modules. 4.3 experiment analysis as shown in figure 2, there are two types of dms, one is deployed in the railway bureau and the other in the ministry of railways. our experiment was deployed dms data center in beijing communication and signaling section of beijing railway bureau. we selected beijingtianjin inter-city train control system data as the research object. specifically, we selected two 586 d. zhang work area’s real-time data from 6 am to 6 pm in one day as experimental data. we collect the performance of the algorithms every half hour and analyze it. our experiment is detailed, we try to find the various problems of our algorithm. the experiments were divided into 3 groups simultaneously on 3 ibm x3650 m4 servers respectively. the first group analyzes the data of a work area. its purpose is to analyze the influence of the number of layers on the performance of the reasoning algorithms. the second group analyzes the data of two work areas. its purpose is to analyze the effect of the increase of the number of nodes on the performance of the algorithm. the third group also analyzes one work area data, but its analysis algorithms were replaced by proposed in paper [10]. we have implemented the method in paper [10] and compared it with our reasoning method. these three ibm x3650 m4 servers are connected with dms data center through a highspeed network, each with 2 intel xeon e5-2600 processors and 4gb memory, and the operating system of them are ubuntu 14.04. the drupal 8.0 with our algorithm was deployed in every server respectively which can automatically generate a variety of frdfds nodes. the first group experiment tested the distributed multi-level real-time uncertain context reasoning method. the results are shown in figure 4(bar graph). according to figure 4, we recorded the time of the four-tier query respectively, where the first layer is drupal based data query time, it reflects the performance of drupal; the second layer, the third layer, the fourth layer were a higher level of knowledge reasoning inquire. we summed up the 24 knowledge which needs to reason through the algorithm in the preliminaries. this knowledge includes certainty and uncertainty, which are distributed in these three layers. as can be seen from the figure, the results showed that the algorithm performance is not significantly decreased when the amount of data is increased to a certain value from the level0 to level2; the level3 layer calculation has increased significantly. this is because we deployed several uncertain reasoning, for example, safety, flexibility and so on. the second set of experiments analyzes the data of two work areas, which doubled the amount of experimental data and focus on the efficiency of the algorithm in a distributed data environment. as shown in the figure 4(line graph), we compare the results of the first set of experiments with the experimental results of the second group (dense data and sparse data), when the number of data increases, the performance of the methods is decreased, because the increase of the number leads to more complexity of the state rule and uncertain calculation. the column chart in the figure represents the one work area sparse data, and the line chart represents the two areas dense data. but our algorithm does not increase much with the increase of experimental data, this is because the method is based on the current node, and we will update the current node based on the fuzzy similarity at the end of each calculation, as shown in algorithm 3. this shows that our algorithm has good scalability. to more intuitive statistics of our algorithm efficiency, we counted the average process time of each layer by our algorithms with the increase in the number of layers in different types of data. as shown in the following figure 5, when the algorithm handles the data(data1) of one work area, the average time of level 0 is 34.0 ms; the average time of level1 is 46.9 ms; the average time of level2 is 57.1 ms; the average time of level3 is 98.3 ms. when the algorithm handles the data of two work areas(data2), the average time of level 0 is 47.5 ms; the average time of level1 is 65.0 ms; the average time of level2 is 86.2 ms; the average time of level3 is 110.3 ms. with the increase in data, level3 processing time increased by 12 ms. this also shows that it has a better performance, because our method uses a similarity-based method, avoiding the calculation of many context nodes of the top level. we have implemented jousselme’s method [10] and compared it with the reasoning method in this paper. the purpose of experiment 3 is to evaluate the correctness of the proposed method and jousselme’s in 24 point-in-time, and the results are shown in figure 6. in the initial period, high-speed train control system big data analysis based on fuzzy rdf model and uncertain reasoning 587 figure 4: performance comparison on different data sets figure 5: performance comparison on different data density the correctness of this algorithm is slightly lower than the paper 12 method; at time point 9, the correctness of this method begins to be higher than the paper 12 method; then, the correctness of the method is improved, and at time point 15, the correctness of this method reaches a steady state. due to the existence of conflicting evidence, the use of classical evidence theory for reasoning may result in incorrect results, and improved combinatorial rules can improve this situation. the paper [10] method proposed a weighted combination of rules for evidence fusion, to solve the shortcomings of high conflict between the evidence, but the lack of self-adaptability of the method. on the basis of this idea, this paper first gives the belief distribution table by the expert and then adjusts it by human intervention in the concrete calculation. if there is no human intervention, the original credit distribution table is calculated. in the first eight time points, we have a corresponding fine-tuning of the credit rating table and achieved good results. in general, as can be seen from the above experiments, the research method in this paper is effective in dealing with the distributed context sensitive complex event of the internet of things. what’s more, when in large-scale networking applications, it has better performance than the 588 d. zhang figure 6: evaluate the correctness of the proposed method and method in paper [10] general method and scalability. in addition, this method has high correctness in combination with expert dynamic intervention, and the experimental results show that it has attractive usability in the field of dynamic control. 5 related research 5.1 railway intelligent transportation system the railway management department has been using the "failure repair" work mode. however, efficient railway operations require maintenance and repair equipment timely, so as to avoid the occurrence of a failure, by analyzing the evolution of equipment status [25]. the distributed inspection and monitoring system continues to collect infrastructure status data and train operating status data, which generate relevant information through conversion. the evolutionary trend of this information can be analyzed to gain knowledge of the evolution of the infrastructure state, which provides decision support for preventive maintenance of the infrastructure. in recent years, with the development of railway information construction and intelligent transportation management, china’s railway transport system [18] has gradually built a number of application-oriented management information systems. at present, these information management systems deal with different aspects of vehicle, infrastructure operation and maintenance management, which have independent organizational structure and produce different forms of data. if we want to use the data of each information system to provide support for the upper application, you must first solve the problem of data sharing. based on the above, we can realize the intelligent processing of data, using data fusion analysis, expert system, data mining, knowledge reasoning and other technologies, in the field of warning, traffic control, integrated scheduling, resource management, operation management, fault analysis. this is one railway intelligent transportation system(rits), which is proposed according to the actual situation of china’s railway and makes each information system work together. in order to realize the rits, many scholars have carried out many researches, such as meta-data sharing based on meta-data [5], general data mode based on xml [15], iot [24], cloud computing technology [8], knowledge reasoning [4] and so on. in summary, the scholars have begun to study the use of cloud computing and distributed systems to collect and analysis related information for rail transport, infrastructure management, maintenance departments [21] . however, the development of these work is mainly focused on transport planning and operation and management. in the communication, signal system, there is no relevant research and applications are reported. the train control system and the gsm-r network used in the high-speed train are large and complex systems. the application of new technologies makes the workload and the difficulty of high-speed train control system big data analysis based on fuzzy rdf model and uncertain reasoning 589 work greatly increased in the infrastructure maintenance department. a variety of complex data analysis process greatly exceeded our current maintenance capacity. in addition, our maintenance personnel do not have enough time and energy to analyze the data, which leads to a lot of data idle, and has not been fully utilized. how to effectively analyze the massive data produced by various detection and monitoring devices, and get useful rules and knowledge that is an urgent task for high-speed rail. it helps the maintenance department to achieve rapid and accurate fault diagnosis and provide reasonable support for preventive maintenance and maintenance decisions. 5.2 context sensitive event processing the context model plays an important role in the development and application of the data analysis system in heterogeneous environments. there are various context representation models were presented [19], including key-value model, object oriented model and ontology-based model. the ontology is the best model of event context representation, but the traditional ontology cannot handle uncertain knowledge which limits its application in uncertain event processing, so in recent years there have been some fuzzy ontology model and reasoning research: the logic based fuzzy model [6] attempts to integrate fuzzy logic into ontology design structure [16]; the distributed fuzzy reasoning petri net and fuzzy ontology [22] were used for distributed fuzzy reasoning extensively [14]. the challenge of the context sensitive system is mainly to make the right decision for the user’s context in real-time. the processing context data in an intelligent way is called context reasoning. in recent years, there has been some research work on the processing of context sensitive events. zhou et al. proposed a similarity based on context reasoning, which defines the similarity between the context models [26]. this method does not require the initial context information, thus reduces the complexity of the reasoning process. helmer et al. described the framework of context event processing, and summarized the current context support the commonly used event processing systems [9]. ashish et al. proposed a context sensitive and complex event processing method based on ontology [23]. however, the most of current articles discuss the idea and framework of context sensitive complex event processing, which lack the details of processing algorithms. 6 conclusion china high-speed train control system is a combination of computer, communication and control. its data is diverse, including sensor generated data stream, gps signal, gsm-r transmission data, real-time video monitoring data, train control software data, etc. this paper presents an efficient analysis method based on the fuzzy linked data and uncertain reasoning for high-speed train control system big data. we have used the method proposed in this paper to analyze the data of the high-speed train control system. the experiment results show that the method proposed in this paper has good efficiency and scalability for the analysis of large data with different structures, types and context sensitive from train control system. the work of this paper is based on the real practical application. in the future, there is still a lot of work to be done, such as the adaptive distribution of belief value, rdf representation of expert knowledge, the architecture of reasoning system based on drupal. acknowledgment this work is sponsored by the national natural science foundation of china under grant no. 61502029. 590 d. zhang bibliography [1] anicic d. et al. 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[23] taylor k., leidinger l.(2011), ontology-driven complex event processing in heterogeneous sensor networks, eswc’11 proceedings of the 8th extended semantic web conference on the semanic web: research and applications , part ii, 285-299, 2011. [24] zhang n. et al. optimization scheme of forming linear wsn for safety monitoring in railway transportation, international journal of computers communications & control, 9(6), 800810, 2014. [25] zheng w., hu n. (2015), automated test sequence optimization based on the maze algorithm and ant colony algorithm, international journal of computers communications & control, 10(4): 593-606, 2015. [26] zhou h., wang y., cao k. (2013), fuzzy ds theory based fuzzy ontology context modeling and similarity based reasoning, computational intelligence and security (cis), 2013 9th international conference on, ieee, 707-711, 2013. international journal of computers communications & control issn 1841-9836, 12(5), 612-630, october 2017. benchmarking of recommendation trust computation for trust/trustworthiness estimation in hdns h. el-sayed, s. sankar, h. yu, g. thandavarayan hesham el-sayed*, heng yu, gokulnath thandavarayan college of information technology, uae university, al ain-15551, united arab emirates *corresponding author: helsayed@uaeu.ac.ae heng.yu@uaeu.ac.ae, gokul@uaeu.ac.ae sharmi sankar department of information technology, ibri college of applied sciences (mohe), sultanate of oman sharmisankar41@gmail.com. abstract: in the recent years, heterogeneous distributed networks (hdns) is a predominant technology implemented to enable various application in different fields like transportation, medicine, war zone, etc. due to its arbitrary self-organizing nature and temporary topologies in the spatial-temporal region, distributed systems are vulnerable with a few security issues and demands high security countermeasures. unlike other static networks, the unique characteristics of hdns demands cutting edge security policies. numerous cryptographic techniques have been proposed by different researchers to address the security issues in hdns. these techniques utilize too many resources, resulting in higher network overheads. this being classified under light weight security scheme, the trust management system (tms) tends to be one of the most promising technology, featured with more efficiency in terms of availability, scalability and simplicity. it advocates both the node level validation and data level verification enhancing trust between the attributes. further, it thwarts a wide range of security attacks by incorporating various statistical techniques and integrated security services. in this paper, we present a literature survey on different tms that highlights reliable techniques adapted across the entire hdns. we then comprehensively study the existing distributed trust computations and benchmark them in accordance to their effectiveness. further, performance analysis is applied on the existing computation techniques and the benchmarked outcome delivered by recommendation trust computations (rtc) is discussed. a receiver operating characteristics (roc) curve illustrates better accuracy for recommendation trust computations (rtc), in comparison with direct trust computations (dtc) and hybrid trust computations (htc). finally, we propose the future directions for research and highlight reliable techniques to build an efficient tms in hdns. keywords: direct trust computations, hybrid trust computations (htc), heterogeneous distributed networks (hdns), receiver operating characteristics, recommendation trust computations (rtc). 1 introduction heterogeneous distributed networks have become increasingly popular in the recent years, expanding its contribution across different computing fields with promising results. in wireless networks, the nodes are equipped with on board units (obu) that creates a dynamic communication between different agents without the need for any network infra-structure. based on copyright © 2006-2017 by ccc publications benchmarking of recommendation trust computation for trust/trustworthiness estimation in hdns 613 the user demands and network behavior, the hdns can be classified into mobile ad-hoc network (manet), vehicular ad-hoc network (vanet) and wireless sensor network (wsn), featured with unique characteristics to serve a particular environment. many researchers have rendered incredible contribution in all these domains and have developed standards and protocols to serve the domain [3, 13]. certain vital operations like sensing [16], routing [14, 17, 23] and event monitoring [22] are some of the major research topics carried out by the researchers, rendering prodigious contributions and problem-solving techniques in every domain. nowadays, the networks are loaded with built-in reliable standards and protocols, despite this the security and privacy concerns are still some of the major issues [15], degrading the efficiency and reliability of the application. owing to the openness of the wireless network and uncontrollability of the nodes without a central authority (ca), the availability of compromising nodes and unauthorized access becomes inevitable in the network. many researchers have worked on this issue and proposed various schemes that can be broadly classified as hard and soft security schemes [29,30]. in the hard security scheme, cryptographic algorithms are implemented to ensure confidentiality on information exchange and authentication of participation nodes. it demands more resource utilization to perform these actions and consumes more power and processing capabilities that are scarcely available in these networks. moreover, the reputation of the participating nodes and the trustworthiness of the received message cannot be verified using these schemes/techniques. therefore, establishing a behavior analysis on these nodes and the verification and validation of the message exchange is implemented using the soft security scheme called trust management systems (tms). using trust systems, the behavior analysis is performed for the participating nodes and the selfish acts are reported by computing the trust metrics. in general, attributes like trustworthiness, reputation, belief and confidence impose uncertain behavior in hdns. imprecise information floating around hdns is unavoidable due to their limitations in wireless communications [8]. hdns demands an efficient technique to calibrate the opinions and recommendations derived from direct and indirect communications. for this purpose, trust computation (tc) is an important element in tms, which will enable to compute a trust metric between the trustor and trustee with the available information. computing and verifying the trust metric in unsupervised environment is challenging and demands an efficient technique to handle it. also during trust computations, the probability level of acceptance rate in trust metric will change periodically due to the spatial-temporal factors. the probability levels on trustworthiness can be estimated dynamically by monitoring the risk factors involved. risk analysis (ra) [11] is an important framework to measure trust metric in hdn and will be used to adjust the acceptance rate of the trust metric based on the current requirement as shown in figure 1. many technologies have been evolved for ra which primarily focuses on data driven and node driven risk analysis. decision making in hdns is adapted to refine the obtained trust metric using aggregated information. due to the presence of uncertainty and imprecisions in the trust computation logic, a reliable aggregation technique will help the requestor to estimate a reliable trust metric of the enquiring node. implementing tms for a fixed network is simple and direct, easily computing the trustworthiness of a participating node. but, establishing a tms in hdns is highly challenging due to the uncertain behavior in the environment. the following key points are some of the major issues in establishing tms in distributed networks. • the spatial and temporal dimensions of the participating nodes are ever changing in the dynamic networks making the node observation more challenging. decisions made from interactions may further provide futile results as the location and time cannot be referenced in the environment which may increase the selfish behavior [10]. 614 h. el-sayed, s. sankar, h. yu, g. thandavarayan figure 1: a general view on trust supported models • as every network follows a particular mobility model [25], handshaking between nodes increases overhead due to the limitations in wireless network. • also the network does not follow a master-slave relationship, hence the decision making on a recommendation message from a neighbor node necessitates further verification to strengthen the result. it also limits the trust chain to be intact which cannot be forwarded further. in the recent years, tms has contributed numerous models for the hdn and enormous contributions from researchers has led to the development of an efficient model. however, a detailed survey and benchmarking of these models is required for the entire hdn. some researchers have published survey papers on tms which highlights the trust computing techniques only for a particular network domain [9, 33]. also, benchmarking the existing techniques and comparing the techniques for an effective study, is still a major shortfall in these survey papers. therefore, a cohesive survey is still in high demand for the entire distributed networks to understand the accomplishment of the existing tms models and requires benchmarking techniques to explore the best models for the entire domain of hdn. in this paper, we have made a thorough survey on tms on a broader spectrum, highlighting the best practicing techniques in every hdn domain. further, the existing techniques are tested with statistical models and the effectiveness is measured. the outcome of the performance measure presented in the paper highlights the best techniques for tms which addresses the security and privacy issues effectively. also, the paper conclusively recommends future research trends for a holistic tms in hdn. 2 trust dimensions trust is a relationship between two or more agents demonstrating the belief with one another, with more reliability and trustworthiness. in hdn, the agent can be a vehicle node, road-side unit (rsu), mobile node or sensor node. trust relationships are formed between agents to benchmarking of recommendation trust computation for trust/trustworthiness estimation in hdns 615 compute the trust relationship between trusting agents and trusted agents. in this section, we introduce the trust dimensions which help to understand the basic entities of trust. 2.1 trust environments a node can analyze the trust relationship and compute the trust metrics based on the network environment in which the participant nodes exist. the trust computation in the environment can be broadly classified as physical and virtual trust. in physical trust, the trust is established between nodes based on direct interactions. in this environment, the trust evaluation is restricted to single hop interaction; whereas, in the virtual trust a trust metric can be computed from nodes which are connected by intermediate nodes. in this environment, the nodes are virtually connected and share recommendations to derive a trust metric. 2.2 trust definition in literature the importance of trust is widely accepted and acknowledged across different arenas in a multifaceted vision and connotations. in the field of sociology, trust is referred to as a basic fact of social life that involves both emotional and cognitive dimensions, sprouted from the past as well as from the present activity. in philosophy, trust is determined as plausible that concedes to form relationships among people. it attributes to be an inevitable trait of our societal life and determines our relationship with the environment, despite the fact that it always carries the risk of being unwarranted. likewise, in the economics domain, trust can be conceptualized by bounded rational and subjective probabilities. it make decisions on the business activities to reduce the cost price and thereby discerned as an economic lubricant. in the field of computer science and technology, trust is employed as a medium to provide a high-end security, privacy, reliability and integrity in different computing group that ranges from network application to internal system computations. in computing, trust is the firm vision and belief to authorize an entity to act dependably secure and reliable within a specific context. it is to be kept in mind that both the trustor and trustee should be validated equally to enhance the contextual factor of expectancy. 2.3 trust attributes the three main properties of trust on a network are asymmetry, transitivity and composability. asymmetry trust is assured as two nodes trust each other on the same level. transitivity trust is an inferred trust. composability trust, receives information from all available sources and compose an opinion value. 2.4 trust metrics trust evaluation metrics are classified into three categories: trust on scale, trust by facets, and trust upon logic. trust on scale scheme measures the level of trust using discrete or continuous series. trust through facets measure rely on three attributes; the belief, the disbelief and uncertainty. the trust is represented as a triplet phase. trust upon logic uses a probability approach to determine the trust. the ratio upon the number of packets forwarded and received is one trust measure. 616 h. el-sayed, s. sankar, h. yu, g. thandavarayan figure 2: taxonomy of trust computations 3 trust computing approaches : an overview as the hdns is utilized by life-saving applications, the verification and validation of both data and node is necessary to ensure the authenticity of the incoming data. therefore, trust management systems have an immense contribution in establishing the trustworthiness in the self-organized hdn. tms ensures the network to be safe from selfish and malicious nodes. in order to enable trustworthiness between entities, measuring the trustworthy is an essential task. with the uncertainties existing in the environment, the trustworthiness of a node or message has to be ascertained. additionally, a security system has to be in place to thwart any attacks and to detect vulnerabilities. adapting a reliable tms model in hdn is the most effective way to have a secure and reliable service in the environment. enormous efforts using different statistical and learning techniques have been built into tms enabling seamless service. trust on a node can be ensured with subjective assessment on reliability and accuracy, as they traverse along the node in a given context. trust computations are measured upon experience, recommendation and knowledge. the experience is measured at regular intervals in a trust table using direct trust strategy. recommendation on trust sets its path as the values in the trust table are propagated to the other nodes. knowledge of the total trust is evaluated based on the previous trust computations. the strategies used to measure the trust at various situations differ based on the environment and applicable mode of trust, the taxonomy is illustrated in figure 2. based on the tms schemes, trust computations are considered as an essential module to verify and validate both node and data. in all the trust computation techniques, a trust metric will be derived from the information shared by the peers. the reliability and credibility of the trust metric is based on the completeness of the chosen factors that are taken into account. in general, trust computations can be broadly classified into two categories based on the network compatibility, (i) centralized trust computations and (ii) distributed trust computations. in centralized trust computations, a central authority (ca) will manage the behavior of the participating nodes and the trustworthiness is derived from the recommendations of ca. the benchmarking of recommendation trust computation for trust/trustworthiness estimation in hdns 617 tms based on this scheme will follow a master-slave property and the scheme will provide a reliable service to the nodes. however, there are limitations in these schemes, the entire system access can be denied when the ca is under attack or compromised. also, having centralized servers in the distributed environment is not highly recommended as it could impact the dynamicity of the nodes. summarizing, in order to have a reliable service in the hdn environment, the node has to follow the peer-to-peer communication and has to be equally distributed in terms of receiving, maintaining and distributing the trust metric. for this purpose, a distributed trust computation offers a reliable service to the node and dynamically manages the trust metric in the hdn environment. in distributed trust schemes, the nodes are capable of managing themselves and the trust metric is computed based on the input from the participating nodes alone. this paper discusses only the schemes related to the distributed trusted models. further, trust computation in the distributed trust models follow three different strategies to compute the trust metric; (i) direct trust, (ii) indirect trust and (iii) hybrid trust. in direct trust computations (dtc), the trustworthiness of a node will be computed from the direct experience. based on the interactions with a single-hop node, the evidences are collected and stored from the direct input relay node. when a node wants to participate in the network for data exchange, a trust score for the node is computed from the direct interactions with the trustee node and the experiences are shared from the single-hop nodes. in indirect trust computations (itc), the node trustworthiness will be computed from the recommendations provided from the surrounding nodes. the nodes will constantly collect evidence and monitor the neighboring nodes behavior. the opinions collected are based on the past and present interaction with the node. if a trustee node wants to know about the trustworthiness of a participating node, it can request other nodes to share their opinions and the other nodes which have opinion on the participating node will share its information as recommendations based on their previous experience. using this information, the trustee node can compute the trust metric for the participating node and can evaluate the nodes trustworthiness. in hybrid trust computations (htc), the results from dtc and itc are utilized. the trust metric is computed using both the direct experience and recommendations from surrounding nodes are aggregated together. the basic idea in this scheme is to evaluate the trust metric using direct experience and verify the same using the opinions obtained from the itc. using this scheme, the selfish behavior nodes can be easily determined from the opinions provided by the surrounding nodes. the existing methodologies in distributed trust computations with maximum reliability and efficiency are taken into consideration and a detailed comparison on different trust computations are discussed in table 1-3. in this tables, the selected schemes are evaluated based on the system context, trust metrics, system merits, complexity and limitations of the system. based on the comparison, a brief discussion on the classifications of different networks with incorporated trust schemes are highlighted in table 4. heterogeneous networks are incorporated with different trust schemes to fulfill and improve the performance. the schemes are listed in table 4 with a brief explanation provided for vanets, manets and wireless sensor networks (wsn). the schemes in direct trust computations (dtc) category computes the trustworthiness of a node based on its own experience, as well as on the opinions obtained from the single-hop neighbor nodes. initially, every single node monitors the performance of other directly connected nodes and maintains a precise information table pertaining to the success ratio of the message-forwarding rate, message-strength, message-veracity and transaction rates. subsequently, when a new target node requests for a participation with a trustee node in the network, the trustee node probes an opinion about the target node from its trustable single-hop neighbor nodes. 618 h. el-sayed, s. sankar, h. yu, g. thandavarayan table 1: direct trust computations direct (experience) trust computations ref. approach context trust metrics merits complexity limitations [32] bayesian approach packet forwarding based trust metric [0,1] *windowing scheme is used*trustworthy route computation * route computation * opinion calculation vulnerable to colluding and badmouth attack [33] role and experience sensing cluster based aggregation trust value [0,1] *identitybased aggregation *effective local action decision *cluster head computation nonresistivity to selfish cluster-heads [19] a multifaceted trust computing approach roles, experience, priority, and majority opinion based trust interval [-1,1] *limits consulting advisors *majority consensus *computational complexity vulnerable to selfish and spoofing attacks [20] bayesian probabilistic approach opinion analytics based trust value [0,1] *beta and gaussian reputation system *expert opinion theory *computational complexity vulnerable to sybil attack [6] markov chain approach trust value based trust value [0,1] *secure authentication for group management *computational complexity trust decay factor not considered [28] recommen -dation exchange approach neighbor trust based trust value [0,1] *resistance to false *recommendation attack *computational complexity not suitable for more complex scenarios [5] perronfrobenius (pf) theorem message behavior analysis trust value [0,1] *generate trust values full or partial data *computational complexity vulnerable to on-off and collusion attack benchmarking of recommendation trust computation for trust/trustworthiness estimation in hdns 619 table 2: indirect trust computations indirect (recommendation) trust computations ref. approach context trust metrics merits complexity limitations [12] analytical trust model node behavior model trust value [0,1] *entity and data approach with markov chain *restrict selfish behavior *space complexity *computation complexity demands constant monitoring and updating [7] stochastic petri net (spn) technique trust chain optimization trust value [0,1] *precise trust metric from social and qos trust *computation complexity vulnerable to selfish behavior [24] geographic hash table consensus technique based trust opinion [0,1] *identify storage nodes *decrease storage cost *computation complexity susceptible to sophisticated attacks [18] dempstershafer theory(dst) attack resistant model trust value [0,1] *trust evaluation for both data and node *space complexity *computation complexity vulnerable to sybil and collusion attacks [21] dempstershafer theory (dst) trust path model belief map [0,1] *detection of malicious nodes and benign nodes *computation complexity demands constant monitoring and updating 620 h. el-sayed, s. sankar, h. yu, g. thandavarayan table 3: hybrid trust computations hybrid trust computations ref. approach context trust metrics merits complexity limitations [1] geometric mean-based node trust model trust metric [0,1] *estimate node trust level *reliable routing *computation complexity requires constant monitoring and updating [2] stochastic petri nets technique subjective trust validation trust value [0,1] *resilient to black/sink hole, slandering and badmouthing attacks *space complexity *computation complexity system overhead and cluster head failure [4] dempstershafer theory (dst) user centric privacy based trust value [0,1] *estimate trust event messages *computation complexity vulnerable to selfish behavior [26] bayesian statistical approach dynamic cluster based trust value [0,1] *filter dishonest recommendations *computation complexity nonresistance to time and location dependent attacks [27] fuzzy logic and graph theory node trust model trust value [0,1] *filtering algorithm for node *decay method for routing *computation complexity vulnerable to trust based attacks benchmarking of recommendation trust computation for trust/trustworthiness estimation in hdns 621 table 4: trust in heterogeneous distributed networks heterogeneous distributed networks trust schemes vanet manet wsn trust agent based scheme *effective decision making using agents on routing *manage reputation score for agents *estimate node trust level using agents and provides reliable routing hybrid trust scheme *estimate trust event messages *helps to extricate malicious and selfish nodes *detect inside attackers *distinguish trust nodes decay method for routing *identify malicious sensor data recommendation trust scheme *enhance trustworthiness estimation *detect fraudulent servers *precise trust metric from social and qos trust *resistance to false attacks on sensor nodes direct trust scheme *generate trust values with full or partial data *secure authentication for group management *behavior analysis of nodes and computes trust score the neighbor nodes which has previous experience with the target node, share its opinion about the target node. after collecting the opinions, the trustee node computes the trust metric for the target node and evaluates its trustworthiness. in order to serve this purpose, techniques like bayesian approach, markov chain modelling and perron-frobenius (pf) models are employed in computing the trust metric in dtc. as the trustworthiness are computed only from the direct nodes, the derived trust metric is instantaneous and may not be accurate. additionally, due to the limitations like mobility and connectivity issues that already exists in the hdn environment, the communication between two nodes are short lived and the opinions obtained from the neighbor nodes will be significantly minimal. these lead the trust metric in dtc provide a futile and biased results. the recommendation trust computations (rtc) category utilizes techniques and computes the trust value from the recommendations relayed from various indirect nodes. every node follows a transitive path to derive the trustworthiness of other nodes. in order to find the trustworthiness of a target node, the trustee node broadcasts a message to the neighboring nodes, requesting an opinion about the target node. the nodes holding previous handshaking experience with the target node will share their opinions to the trustee node in a multi-hop fashion. once the trustee node receives the opinions, it validates the received data based on the signal strength, distance, time lapse, similarity, energy level and closeness of the node. for this purpose, techniques like dempster-shafer theory (dst), stochastic petri net (spn) and geographic hash tables are employed to verify the opinions and compute the trust metric for the target node. due to fact that the recommendations are obtained from unknown and indirect nodes, the recommendations exhibit selfishness that are too hard to identify and validate. moreover, attacks like sybil and on-off attacks would easily compromise the network and malicious nodes remain undetectable due to the indirect connections. consequently, in such above mentioned cases, the hybrid trust computations (htc) can be employed as a reliable technique that involves both the functionalities of direct and recommenda622 h. el-sayed, s. sankar, h. yu, g. thandavarayan figure 3: taxonomy of trust computations tion trust computation schemes. along the lines of htc technique, the trustee node computes the trustworthiness of a target node, procures opinions from the neighbor node without any limitations. both direct referrals and indirect recommendations are utilized to derive the trust metric for the target node. using this technique, the trustworthiness of both data and node could be verified. generally, the trustee node combines the recommendations from indirect vehicles and aggregates it to a single opinion. this aggregated metric will be further validated by the direct referrals that are in close proximity to the trustee node. in this technique, the derived trust metric for the target node is verified and validated by both the direct and the indirect nodes. thereby, this multifaceted computation strategy efficiently computes and validates the trust metric, providing more resistance to the selfish behavior and thwarts the trust related attacks in the hdn environment. 4 estimation of trust computations trust is computed based on two varieties of trust between the trustor and trustee nodes. they are direct trust and recommendation trust. in direct trust, the trust value is authenticated by a directed path between the trustor and the trustee. whereas, in recommendation trust, the trust value is authenticated by third parties. in hybrid computation, both directed path trust value and third party recommended trust values are considered and the trust computation models are shown in figure 3. 4.1 direct trust computation (dtc) direct-trust computations are estimated with trust values ranging between +1 and -1. a tangent-hyperbolic function is used to estimate the trust value (a) built on the experience of trustor node (b), as shown in equation (1). the equation is generalized, as a trustor node enbenchmarking of recommendation trust computation for trust/trustworthiness estimation in hdns 623 table 5: trust elements and their properties element property pi path directed trust value wi prioritizes the level of importance on trust τ i node directed trust value; +1 for positive experience; -1 for negative experience n unlimited, varies with environment ri recommended trust value range between +1 and -1 counters many trustee nodes and affect the evaluation of trust. the trust value will be computed using equation (2), where pi represents the direct-path experience with ith trustee node, n is the total number of trustee nodes that participate with the trustor node, wi represents the weight for the directed path prioritizing the experience and τ i is +1 for a positive trust with the ith trustee node and -1 for distrust. the trust element properties are listed in table 5. a = sinh(b) cosh(b) = tanh(b) (1) tdtc = tanh( n∑ i=1 τi ×piwi) (2) the reason behind the choice of tangent hyperbolic function in comparison to the other identity activation function and logistic sigmoidal function being, the identity function maps output values range between −∞ to ∞, logistic sigmoidal function maps output range lies between 0 and 1, whereas tangent hyperbolic function maps output values range from −1 to 1. summarizing these, the trust values calculated in dtc use hyperbolic tangent function, to return trust values between −1 and +1. the corresponding hyperbolic tangents values ratio over hyperbolic sine represented as sinh() function and hyperbolic cosine represented as cosh() function. 4.2 recommendation trust computation (rtc) the trustor node has no directed path of experience with the trustee, therefore, it enquires a third node otherwise called the recommender node. when nodes do not encounter directed path experiences, they rely on third party recommendations. in this situation, to justify reasonable recommendation, the trustor always initiates multiple recommendation path experiences. the path experiences are not limited. the recommended trust values depend on the trustworthiness of the third party nodes with the trustor itself. multiple trust recommendations from multiple third party nodes to the trustor widens the vision of the trustor in fixing the trust value for the trustee. tr = n∑ i=1 (ri ×tdtci ) (3) the recommender node when enquired delivers a trust value ri on the ith trustee node. the trustor holds tdtc the direct trust value for the recommender node, as shown in equation (3). the recommender nodes trust values reasonability is crosschecked by the trustor by inquiring/investigating several third party recommendation nodes. 624 h. el-sayed, s. sankar, h. yu, g. thandavarayan table 6: trust computation (tc) summary trustee-id trustor-id wi pi τ i tc (expected) actual dtc rtc htc trust 166497924 725979012 4 1 1 0.999329 0.999329 0.999329 +1 166497924 258110 5 1 1 0.999909 0.999909 0.999909 +1 166497924 334400 5 1 1 0.999909 0.999909 0.999909 +1 166497924 492685 4 1 1 0.999329 0.999329 0.999329 +1 166497924 396581 4 1 1 0.999329 0.999329 0.999329 +1 166497924 328049 4 0.5 -1 0.995055 -0.99505 -0.59703 -1 166497924 534929 4 1 -1 0.995055 -0.99505 -0.59703 -1 166497924 768446340 4 1 -1 0.999329 -0.99933 -0.59960 -1 166497924 223418 3 0.5 -1 0.964028 -0.96403 -0.57842 -1 166563460 379568 5 1 1 0.999909 0.999909 0.999909 +1 4.3 hybrid trust computation (htc) the trustor acquires direct trust values and recommendation trust values for the trustee node. the equations (3) and (2) are combined together in equation (4) to arrive at hybrid computation trust value th. if tdtc value is high and tr value is low, then considerations on tdtc is more when compared with tr, or vice versa. th = {tdtc = +}, (1 ×|tdtc|×tr)(−1 ≤ tdtc ≤ 1)(−1 ≤ tr ≤ 1) (4) in particular instances, when tdtc = 1 and the trustor is assured complete certainty on the trust value, then the trustor will no longer consider the third party nodes recommendations. otherwise, as tdtc = 0 and the trustor is not assured complete certainty on the trust value, then the trustor will rely on the recommendations that are relayed from the third party nodes. in addition, the consideration of recommendations trust depends on the percentage of certainty values of direct trust. 5 empirical results the dataset from www.trustlet.org is explored and the trust values are computed using all the three computation strategies dtc, rtc, and htc which are listed in table 6. the expected/actual trust values are computed and plotted for visual illustration as shown in figure 4. 6 receiver operating characteristics (roc) benchmarking trust computations the operating characteristics are classified for the actual and estimated trust values with true positive (tp), true negative (tn) along with false positive (fp) and false negative (fn) accordingly in table 7. the dataset from www.trustlet.org is being used to plot the roc for 1000 instances, with equally distributed trust and distrust values (500 instances each). the characteristic of true positive denotes the proportion of trusted trust values correctly classified as +1, true negative is the proportion of distrust trust values correctly classified as -1, false positive benchmarking of recommendation trust computation for trust/trustworthiness estimation in hdns 625 figure 4: trust computation summary (direct, recommendation, and hybrid) table 7: actual vs estimated trust values actual trust value estimated trust value 1 / -1 trust distrust trust distrust trust tp fp tp fn distrust fn tn fp tn shows the proportion of distrust trust values incorrectly classified as +1 and false negative as the proportion of trusted trust values incorrectly as -1. the sensitivity or true positive rate (tpr) on trust values are calculated by dividing the true positive values by the summed up value of true positive and false negative. later, specificity or true negative rate (tnr) on trust values are calculated by dividing the true negative values by the summed up value of true negative and false positive. the estimations on true positive/negative and false positive/negative for expected and actual trust/distrust values as shown in figure 5(a) moving ahead further graph is plotted on sensitivity and specificity as shown in figure 5(b). the values estimated for sensitivity and specificity are presented in table 8. the true positive rates will be plotted along the y axis and false positive rates will be plotted along the x axis. hence, an roc is defined by trp and fpr. the roc curve shown in figure 6, illustrates no false negatives and no false positives on the left upper coordinate (0, 1). a probability threshold has been used to classify the trust/distrust values. the values are classified based on probability. when the probability shoots the threshold it gets into class 1 (trust) and as it becomes low, class 0 (distrust). the group discrimination can also point some errors in certain cases. a roc graph is constructed for all the three trust computation techniques described above. the computations are done for many varying thresholds. the roc curve in figure 6 showcases the area under curve (auc) for all dtc, rtc and htc. the graph with the highest auc portion is the best recommended trust computation model. hence, rtc 626 h. el-sayed, s. sankar, h. yu, g. thandavarayan figure 5: benchmarking results figure 6: roc curve benchmarked on trust computations benchmarking of recommendation trust computation for trust/trustworthiness estimation in hdns 627 table 8: roc analysis (sensitivity vs specificity) trust/distrust sensitivity low (95%) up (95%) specificity low (95%) up (95%) -0.999 1.000 0.789 1.000 0.083 0.000 0.379 -0.995 1.000 0.789 1.000 0.250 0.085 0.540 -0.964 1.000 0.789 1.000 0.333 0.138 0.612 -0.600 1.000 0.789 1.000 0.417 0.194 0.681 -0.597 1.000 0.789 1.000 0.583 0.319 0.806 -0.578 1.000 0.789 1.000 0.667 0.388 0.862 0.964 1.000 0.789 1.000 0.750 0.460 0.915 0.995 1.000 0.789 1.000 0.917 0.621 1.000 0.999 0.500 0.291 0.709 1.000 0.713 1.000 1.000 0.167 0.052 0.402 1.000 0.713 1.000 is pronounced as the best recommended trust computation to be adopted for trust management and is benchmarked with experimental results. the complete roc analysis is highlighted in table 8, where the upper bound and lower bound of the sensitivity, specificity are specified. the negative/positive trust values are mapped towards both fpr and tpr. the upper/lower bounds ensure the trustworthiness for the received trust values between the nodes. 7 conclusion and future enhancements in this research, we have quantified the trust metrics between nodes in heterogeneous distributed network. we have defined three possible distributed trust computation strategies, we make an effort to zero-in on the best one and describe its performance by substantiating with highly accurate trustworthiness. the nodes in hdns demonstrate trustworthiness among the participating nodes using dtc, rtc or htc. the trust evaluation is performed and trust metrics are shared and recommended among the participating nodes. the trust values are measured using discrete/continuous series. the dataset from www.trustlet.org when computed with dtc, htc and rtc delivers appropriate values for tp, tn, fp and fn. when the computed values of tpr and fpr are plotted on a roc, the auc presents itself. the auc (area under curve) values are 0.938, 0.979 and 1.000 for dtc, htc and rtc respectively. interpreting the roc graph with respective auc regions, we conclude that the recommendation trust computation (rtc) delivers a highly accurate trustworthiness score when compared with the other two trust computations. hence, the rtc techniques trust computation performance is benchmarked with appropriate experimental results and we declare that rtc delivers promising trustworthiness and is more reliable over dtc and htc. in future, we plan to extend recommendation trust computation (rtc) in vanets to eliminate malicious entries and secure the vehicular network appropriately. the next step is to extend our research further ahead to include more efficient and dynamic approaches for trust computing, ensuring a high degree of trustworthiness. acknowledgments this research was supported by the roadway, transportation, and traffic safety research center (rttsrc) of the united arab emirates university (grant number 31r058). data for this study comes from www.trustlet.org. the dataset holds 841,372 trust observations 628 h. el-sayed, s. sankar, h. yu, g. thandavarayan with 717,667 trusts and 123,705 distrusts, which is archived at http : //www.trustlet.org/datasets/extendedepinions/userrating.txt.gz. the trustlet, open research datasets on trust metrics has been cited by some researchers. reference: p. massa, k. souren, m. salvetti, d. tomasoni. scalable computing: practice and experience. bibliography [1] babu s.s., raha a., naskar m.k. 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(2013); trust modeling for message relay control and local action decision making in vanets, security and communication networks, doi: 10.1002/sec.519, 6(1), 1-4, 2013. gang108-122-pb.pdf international journal of computers communications & control issn 1841-9836, 12(1):26-40, february 2017. a similarity measure-based optimization model for group decision making with multiplicative and fuzzy preference relations x.r. chao, g. kou, y. peng xiangrui chao, yi peng* school of management and economics university of electronic science and technology of china chengdu, china, 610054 *corresponding author: pengyi@uestc.edu.cn gang kou school of business administration southwestern university of finance and economics chengdu, china, 610074 ⋆ authors are alphabetically ordered and contribute equally to this work. abstract: group decision making (gdm) problem based on different preference relations aims to obtain a collective opinion based on various preference structures provided by a group of decision makers (dms) or experts, those who have varying backgrounds and interests in real world. the decision process in proposed question includes three steps: integrating varying preference structures, reaching consensus opinion, selecting the best alternative. two major approaches: preference transformation and optimization methods have been developed to deal with the issue in first step. however, the transformation processes causes information lose and existing optimization methods are so computationally complex that it is not easy to be used by management practice. this study proposes a new consistency-based method to integrate multiplicative and fuzzy preference relations, which is based on a cosine similarity measure to derive a collective priority vector. the basic idea is that a collective priority vector should be as similar per column as possible to a pairwise comparative matrix (pcm) in order to assure the group preference has highest consistency for each decision makers. the model is computationally simple, because it can be solved using a lagrangian approach and obtain a collective priority vector following four simple steps. the proposed method can further used to derive priority vector of fuzzy ahp. using three illustrative examples, the effectiveness and simpleness of the proposed model is demonstrated by comparison with other methods. the results show that the proposed model achieves the largest cosine values in all three examples, indicating the solution is the nearest theoretical perfectly consistent opinion for each decision makers. keywords:group decision making; multiplicative preference relations; fuzzy preference relations; similarity measure; optimization model. 1 introduction group decision making (gdm) aims to obtain a solution alternative(s) to a given question based on the opinions provided by a set of experts. when comparing alternatives in real word problem, experts may use any of the following preference structures: preference orderings, utility functions, multiplicative preference relations, and fuzzy preference relations (herrera et al.[1], herrera-viedma et al.[2]). the process is composed of the three parts. 1) integrating the different preference structures provided by varying decision makers [1,2,3,4,5,6,7,8,9]. 2) reaching consensus based on modified the preference information when the collective opinion cannot be copyright © 2006-2017 by ccc publications a similarity measure-based optimization model for group decision making with multiplicative and fuzzy preference relations 27 accepted by non-cooperative decision makers [10,11,12,13,14]. 3) selecting the best alternative from the collective opinion. in first step of previous question, two main groups of techniques have been developed for integrate different preference relations in gdm problem. the first class of techniques (chiclana et al.[3,5]; delgado et al.[6]; herrera et al.[1]; herrera-viedma[2]) transforms different preference structures into uniform formats, after which a selection operator is implemented to rank alternatives on the basis of a fuzzy majority. the second class of techniques (fan et al.[7]; ma et al.[8]; xu et al.[ 9]) uses optimization models to directly obtain a collective priority vector instead of using transform functions to obtain uniform preference information. the limitations of these two classes of approaches are: 1) the transformation functions may cause information loss during the conversions, and 2) existing optimization methods are so complex to obtain solution that it is not easy to be used to management practice. the goal of this study is to propose a cosine similarity measure-based optimization method for deriving a collective priority vector in decision making problem with multiplicative preference relations and fuzzy preference relations. the basic idea of the model is that the collective priority vector should be as similar per column as possible to a pairwise comparative matrix (pcm) from each decision maker so that the solution is the nearest theoretical perfectly consistent opinion for each decision makers. the proposed model can be implemented in four steps with simple computation for integrating multiplicative preference relations and fuzzy preference relations. moreover, the proposed model can also be used to derive priority vector from pcm in fuzzy ahp. furthermore, the model can be extended to other preference structures by transforming other preference formats into a mixture of multiplicative preference relations and fuzzy preference relations, and used the consensus reaching methods by iterative modification based on our proposed priority vector obtaining method to improve consensus degree (such as chen et al.[15];wu and xu[16]; dong et al[17]; xu et al[18,19], yu et al.[20], etc.). the remainder of this study is organized as follows. section 2 reviews related work and provides some preliminaries. section 3 describes the proposed cosine similarity measure-based optimization model. section 4 uses three numeric examples to illustrate the implementation of the proposed model and compare with some well-known methods. section 5 concludes the study. 2 related work and preliminaries 2.1 related work the group decision making problem with different preference structures, which was also called multi-person decision making in their papers, has a long research history and been defined as a decision-making process by different dms or experts with different decision information presented in terms of utility functions, preference orderings, or preference relations with multiplicative or fuzzy formats (arrow [21]; sen[22]; kickert et al.[23]; saaty[24]; kacprzyk and fedrizzi [25]; chiclana et al.[3,5]; herrera et al.[1], etc.). a utility function comprises real numbers for alternatives to represent the utility evaluations provided by dms. a preference ordering is a ranking of alternatives in best-to-worst order, with no additional information. a preference relation is based on pairwise comparison. it is a simple process in which experts provide ratios that compare one alternative to another. for a multiplicative preference relation, the ratio can be a real number in the set {1/9, 1/8, ..., 1/2, 1, 2,...,8 ,9} . for a fuzzy preference relation, the ratio is provided as a fuzzy number. an important issue in this problem is the derivation of a collective opinion on the basis of different preference formats provided by each decision maker (chiclana et al.[3,5]; delgado et al.[6]; herrera et al.[1]; herrera-viedma[2]). various approaches have been developed to solve 28 x.r. chao, g. kou, y. peng above problems in following steps: (1) convert different preference information into uniform structures, (2) aggregate uniform preference structures using selection operators, and (3) rank the alternatives and make a decision. delgado et al. [6] developed a fusion operator for integrating numerical and linguistic information using transformation functions between the numerical and linguistic values. chiclana et al. [3] proposed a general model for integrating different preference formats using fuzzy preference relations. they aggregate uniform preference information using an ordered weighted averaging (owa) geometric operator with a fuzzy quantifier. chiclana et al.[5] discussed the properties of different preference formats and multiplicative preference relations and proposed transformation functions that can convert varying preference information into uniform representations and then used an owa geometric operator to rank alternatives. the operator, which includes two degree choices, is a multi-criteria decision-making method (mcdm). the transformation functions proposed in chiclana et al.[26] have been used in many studies as a benchmark for comparing different methods. herrera et al.[1] defined a transformation function between the multiplicative and fuzzy preference relations. they also adopted fuzzy preference relations as a uniform preference format. the owa geometric operator was also used to rank alternatives. chiclana et al. [26] developed a fuzzy mcdm model to derive a collective opinion when preferences were presented using preference orderings, utility functions, and fuzzy preference relations and studied the internal consistency of the model. herrera-viedma et al. [2] proposed a consensus model for varying preference information in gdm questions. their model can automatically obtain a consensus. xu [27] studied different interval preference relations and proposed a model for deriving overall weights in multi-attribute decision making. another class of methods for obtaining a collective opinion is optimization-based. one strength of this kind of approach is that it can avoid information loss during the conversion of preference relations because there is no need to transform different preference formats into a uniform structure. fan et al. [7] constructed a goal-programming model to reduce differences in collective opinions and each decision maker’s pcms, thereby demonstrating that the collective opinion must be close to perfectly consistent. ma et al. [8] proposed an optimization model for integrating four different preference formats without unifying them into one structure. they analyzed perfectly consistent conditions in four preference relations and constructed an objective function on the basis of the idea that the derived priority vector has the least distance from each decision maker’s preference. xu et al.[9] introduced a nonlinear programming method for handling decision makers coming from different areas and providing varying preference information, including utility values, preference orderings, and multiplicative, incomplete multiplicative, fuzzy, and incomplete fuzzy preference relations. wang et al. [28] proposed a chi-square model to derive priority methods wherein the goal is to develop an optimal priority vector as close to each decision maker’s opinion as possible. 2.2 preliminaries the gdm problem with different preference formats can be divided into two stages; first aggregating the varying preference information and determining a collective group preference and then selecting a ranking of alternatives using a previous collective group preference. assume that ω = {a1, a2, ..., an} is a finite set containing n alternatives and π = {dm1, dm2, ..., dmn} is a finite set including k decision makers.λ = {σ1, σ2, ..., σn} is a finite set containing the degrees of importance of decision makers. in general, ∑n i=1 σi = 1. with each σi being prespecified by decision makers. the collective group preference w = {w1, w2, ..., wn} indicates the degree of importance and best-to-worst selection order ranking the alternatives. the preference relations can be represented in different formats with orderings, utility values, and multiplicative a similarity measure-based optimization model for group decision making with multiplicative and fuzzy preference relations 29 and fuzzy preference relations. herein, we discuss the aggregation of multiplicative and fuzzy preference relations, and other preferences can be transformed into these two types of preference relations (chiclana et al.[26]). multiplicative preference relations are the most widely used in decision-making situations. a multiplicative preference relation is essentially a binary relation f = ω×ω → {1/9, 1/8, · · · , 8, 9} represented as a pcm matrix (aij)n×n, i.e. f(ai, aj) 7→ aij.the entry aijof the pcm denotes the relative degree of importance of alternative ai with respect toaj. ai and ajare equally important if aij = 1, and a higher aij value indicates a greater weight for the alternative.when aij = 9, ai is preferred absolutely to aj, and ai is inferior to every aj if aij = 1/9.it is evident that the pcm of a multiplicative preference relation is positive reciprocal, i.e. aij × aji = 1 for arbitrary i, j ∈ {1, 2, · · · , n}. a pcm is called perfectly consistent if aijajk = aikfor arbitrary i, j, k ∈ {1, 2, · · · , n}.in practice, the pcm is not always perfectly consistent. saaty [24][29] proposed the consistency ratio index, which must be less than 0.1 when consistency can be accepted. aguarón et al. [30] and escobar et al. [31] proposed a geometric consistency index for ahp. many other methods for measuring consistency have also been proposed ergu et al. [32]; lin et al.[33,34]; lin and kou[35].etc). if consistency is present, there are many derivation methods for priority vectors, including the eigenvector method (saaty [36]), weighted least squares method (chu, kalaba, and spingarn [37]), logarithmic least squares method (crawford and williams [38]), a heuristic approach (lin et al.[39]), and the cosine maximization method (kou and lin[40], etc). moreover, gomez-ruiz [41] proposed a estimation method using neural network. fuzzy preference relations have been extensively studied (tanino [42]; chiclana et al.[3]; herrera-viedma et al.[43]) and have many practical applications in the decision-making process owing to the difficulty of comparing two alternatives using a crisp real number. a fuzzy preference relation is a binary fuzzy membership function g : ω×ω → [0, 1], i.e., g(ai, aj) 7→ pij, where pij is the fuzzy degree of importance of ai with respect to aj. the fuzzy pairwise comparative matrix (pij)n×n is constructed as a real number from 0 to 1. if pij = 0.5, ai has the same preference as those of aj, pij = 1 and pji = 0 indicates that ai is unanimously preferred to aj. pij ∈ (0.5, 1) and pji ∈ (0, 0.5) indicate that ai is preferred over aj. there is an additive reciprocal relation in a fuzzy pairwise comparative matrix (pij)n×n, i.e., pij + pji = 1 for arbitrary i, j ∈ {1, 2, · · · , n}. the definition of perfectly consistent for a fuzzy pair-wise comparative matrix is different from that for a multiplicative preference relation: (pij)n×n is considered to be perfectly consistent, if pijpjkpki = pjipkjpik for arbitrary i, j, k ∈ {1, 2, · · · , n}, herrera-viedma et al.[43]summarized eight consistency properties of fuzzy preference relations and proposed a method for testing and improving the consistency of a fuzzy preference relation. xu et al. [18] studied the ordinal consistency of a fuzzy preference relation in terms of three cycles in a directed graph. zhang et al.[44] discussed consistency issues in group decision making with fuzzy preference relations. mikhailov l. [45] introduced linear fuzzy preference programming for deriving a priority vector for a fuzzy preference relation. xu and da [46] proposed a least-deviation method for obtaining a priority vector of a fuzzy preference relation. wang et al.[28] used a chi-square method for deriving a priority vector on the basis of fuzzy and multiplicative preference relations. other methods for priority derivation of fuzzy ahp can be found in kacprzyk[47], roubens[48], and chiclana et al.[3]. a similarity index in mathematics is used to measure the similarity of two vectors. many similarity measurement indices, including similarity coefficient and distance function methods, have been introduced and employed in many domains, such as machine learning and decision science. the most widely used similarity measure is the cosine similarity index. for two vectors ~ri = {ri1, ri2, · · · , rin} and ~rj = {rj1, rj2, · · · , rjn} the cosine similarity measure is a binary 30 x.r. chao, g. kou, y. peng function defined as 〈~ri, ~rj〉 = ~ri · ~rj ‖~ri‖‖~rj‖ = ∑n k=1 rikrjk √ ∑n k=1 r 2 ik √ ∑n k=1 r 2 jk ∈ [0, 1] (1) kou and lin [40] used this similarity measure for ahp and constructed a cosine maximization model to derive a priority vector in ahp. the idea is that the derived priority vector is to be most similar to each column of a pcm according to the cosine similarity measure. inspired by the work of kou and lin [40], this study extends the similarity measure method to group decision-making by applying the cosine similarity measure to the gdm problem with multiplicative and fuzzy preference relations. 3 cosine similarity measure maximization model for gdm problem with multiplicative and fuzzy preference relations assume there are two classes of preference structures: multiplicative preference relations and fuzzy preference relations. let (a (k) ij )n×n, k = 1, 2, · · · , km and (p (k) ij )n×n, k = km+1, km+2, · · · , k be multiplicative and fuzzy preference relations, respectively. existing results show that the pcm is perfectly consistent when it satisfies the following conditions: a (k) ij = wki wkj , i = 1, 2, · · · , n; j = 1, 2, · · · , n. (2) p (k) ij = wki wki + w k j , i = 1, 2, · · · , n; j = 1, 2, · · · , n. (3) let ~aj (k) = (aij), a2j, · · · , anj) t be the column vector of (a (k) ij ) and ~w = (w1, w2, · · · , wn) t . kou and lin [40] proved that the existing similarity relation between each column of a pcm and a derived priority vector-particularly, the cosine similarity measure-is equal to one if and only if the pcm is perfectly consistent. the higher the degree of consensus, the closer the cosine similarity measure is to 1. that is 〈~aj (k), ~wj〉 = ∑n k=1 akjwk √ ∑n k=1 a 2 kj √ ∑n k=1 w 2 k = ∑n k=1 wk wj wk √ ∑n k=1 wk wj 2 √ ∑n k=1 w 2 k = ∑n k=1 w 2 k √ ∑n k=1 w 2 k √ ∑n k=1 w 2 k = 1 (4) for fuzzy preference relations, we can show that there exists a similarity relation between a derived priority vector and each column of the pcm after the transformation: b (k) ij = p (k) ij 1 − p (k) ij (5) let ~bj = (b1j, b2j, · · · , bnj) t ,j = 1, 2, · · · , n. when the pcm is perfectly consistent, i.e., bij = wi wj , the following relation can be obtained: 〈~bj (k) , ~wj〉 = ∑n k=1 bkjwk √ ∑n k=1 b 2 kj √ ∑n k=1 w 2 k = ∑n k=1 pkj 1−pkj wk √ ∑n k=1 pkj 1−pkj 2 √ ∑n k=1 w 2 k = ∑n k=1 wk wj wk √ ∑n k=1 wk wj 2 √ ∑n k=1 w 2 k = 1 (6) a similarity measure-based optimization model for group decision making with multiplicative and fuzzy preference relations 31 therefore, it is evident that the cosine similarity measure is equal to 1 if the multiplicative and fuzzy preference relations of decision makers are perfectly consistent. if an entry in a pcm is 1 or 0 when a preference relation is fuzzy, one alternative is unanimously preferred over the others. in this case,bijis 0 or does not exist. now, bij is defined as the limit of pijclose to one. if we assume, without loss of generality, that pkj = 1, then 〈~bj (k) , ~wj〉 = b1jw1 + bijw2 + · · · + bkjwk + · · · + bnjwn √ b21j + b 2 2j + · · · + b 2 kj + · · · + b 2 nj √ ∑ i w 2 i = b1j bkj w1 + · · · + wk + · · · + bnj bkj wn √ b21j b2 kj + · · · + 1 + · · · + b2 nj b2 kj √ ∑ i w 2 i (7) since bij = limx→1− pij 1−pij → +∞, the cosine similarity measure yields lim bkj→+∞ 〈~bj (k) , ~wj〉 = lim bkj→+∞ b1j bkj w1 + · · · + wk + · · · + bnj bkj wn √ b21j b2 kj + · · · + 1 + · · · + b2 nj b2 kj √ ∑ i w 2 i = wk √ ∑ i w 2 i (8) letting limbkj→+∞〈 ~bj (k) , ~wj〉 = 1, we obtain wk = 1 and wi = 0, i = 1, 2, · · · , k − 1, k + 1, · · · , n, which is nonsensical in terms of economic and management theories and practices. this shows that the pcm is not perfectly consistent in this case. therefore, in practice, we assume that p (k) ij ≈ 1(e.g.p k ij = 0.9999 ) and p (k) ij ≈ 0(e.g.p k ij = 0.0001 )when dms apply the cosine similarity measure to gdm problem. including one in each column and row of a fuzzy pcm directly yields a preference ordering from the fuzzy pcm. in the gdm problem, the derived collective priority vector should be most largely consistent for each decision makers. therefore, the group preference should have the highest similarity measure between the derived collective priority and each column of decision makers’ pcms. inspired by this idea, we construct a cosine similarity measure maximization optimization model as follows to optimize the maximization similarity measure: max c = k ∑ k=1 n ∑ j=1 σkc (k) j = km ∑ k=1 n ∑ j=1 σk ~w ~aj (k) ‖~w‖‖~aj (k)‖ + k ∑ k=km+1 n ∑ j=1 σk ~w~bj (k) ‖~w‖‖~bj (k) ‖ s.t. { ∑n i=1 wi = 1 wi > 0, i = 1, 2, · · · , n (9) where c is the total similarity measure and c(k) is the similarity measure between the collective priority vector and the pcm of the kth decision maker. to simplify the computing process, we normalize the vectors and denote ~w′ , ~aj ′(k) and ~bj ′(k) as follows: ~w′ = (w′1, w ′ 2, · · · , w ′ n) t = ~w ‖~w‖ = ( w1 ‖~w‖ , w2 ‖~w‖ , · · · , wn ‖~w‖ )t (10) ~aj ′(k) = (a ′(k) 1j , a ′(k) 2j , · · · , a ′(k) nj ) t = ~aj (k) ‖~aj (k)‖ = ( a (k) 1j ‖~aj (k)‖ , a (k) 2j ‖~aj (k)‖ , · · · , a (k) nj ‖~aj (k)‖ )t (11) ~bj ′(k) = (b ′(k) 1j , b ′(k) 2j , · · · , b ′(k) nj ) t = ~bj (k) ‖~bj (k) ‖ = ( b (k) 1j ‖~bj (k) ‖ , b (k) 2j ‖~bj (k) ‖ , · · · , b (k) nj ‖~bj (k) ‖ )t (12) 32 x.r. chao, g. kou, y. peng this transforms the optimization model into the following model: max c = km ∑ k=1 n ∑ j=1 σk ~w ′ ~aj ′(k) + k ∑ k=km+1 n ∑ j=1 σk ~w ′~bj ′(k) s.t. { ∑n i=1 w ′2 i = 1 wi′ > 0, i = 1, 2, · · · , n (13) for the sake of simplicity, the above optimal model naturally can be denoted by vector in space analytic geometry. the objective function can be rewritten as follows: c = ~w′ · ( km ∑ k=1 n ∑ j=1 σk ~aj ′(k) + k ∑ k=km+1 n ∑ j=1 σk ~bj ′(k) ) (14) denoting the ~v = (v1, v2, · · · , vn) t = ( ∑km k=1 ∑n j=1 σk ~aj ′(k) + ∑k k=km+1 ∑n j=1 σk ~bj ′(k) )t . it is evident that the ~v must exist and be uniqueness. therefore, the objective function is max c = ~w′ ·~v (15) since the ~w′ and ~v are normalized vectors, the c essentially is cosine similarity value of two vectors. therefore, we can draw the conclusion that the maximum value of (15) must be 1. in this time, the solution ~w′ must be ~v or −~v and the latter is rounded off in real life questions. from the constraint condition ∑n i=1 w ′2 i = 1, it follows that ~w ′ should be normalize and the solution is (16). ~w′ = ~v ‖~v‖ (16) now, it is evident that following condition exists by (9) and (13): n ∑ i=1 wi = n ∑ i=1 w′i‖~w‖ = ∑n i=1 vi ‖~v‖ ‖~w‖ = 1 (17) this means that ‖~w‖ = ‖~v‖ ∑n i=1 vi (18) we can obtain the unique solution ~w = ~w′‖~w‖ = ~v ‖~v‖ ‖~w‖ = ~v ‖~v‖ ‖~v‖ ∑n i=1 vi = ~v ∑n i=1 vi (19) remark1: it is evident that total cosine value c = nk is optimal if the decision makers’ pcms are all perfectly consistent. in this case, the priority vector has highest cosine value to each column vectors of pcms. for single pcm, the cosine value c = n which is order of the pcm if the priority vector has perfectly consistency, owing to the cosine value is 1 between each column vector of the pcm and priority vector in this case. in order to use the calculation process, the proposed model can be implemented using the following algorithm in four steps with simple mathematical operations. a similarity measure-based optimization model for group decision making with multiplicative and fuzzy preference relations 33 algorithm: step 1: transform fuzzy pcms (pij), k = km+1, km+2, · · · , k into (bij), k = km+1, km+2, · · · , k by (5); step 2: calculate ~aj ′(k), k = 1, 2, · · · , km and ~bj ′(k) ,k = km+1, km+2, · · · , k by (11) and (12); step 3: calculate transformed weights coefficients vector ~w′ by (16); step 4: calculate the collective priority vector ~w by (19) remark2:in step 2, a column including 1 in the fuzzy pcm is not considered if bij ≤ 10 4, in the calculation. letting pkhl = 1, without loss of generality, σl〈~w, ~bl (k) 〉 in optimization model (13) cannot be considered, and in this case, the transformed weights coefficient vector ~v is ~v = ( k ∑ k=1 n ∑ j=1 σk ~aj ′(k) + k ∑ k=km+1 n ∑ j=1,j 6=l σk ~bj ′ (k))t (20) since thecolumn including bhl = 1, approximate to (0, 0, · · · , 1h, · · · , 0) t and lim pkl → 1bkl = 0, k 6= h cannot provide any effective preference information. in this case, this column will be removed to avoid a large deviation in the priority vector. 4 illustrative examples this sectiondescribes the implementationof theproposedmodel andpresents threenumerical examples to compare the proposed model with some existing methods in same problems. example 1 consider the following multiplicative and fuzzy preference structures, which comes from chiclana et al.[5]; fan et al.[7]; wang et al.[28]: an investment company wishes to obtain the best option for investing a sum of money among four alternatives car company, food company, computer company, and arms company. the decision makers come from four consultancy departments of the investment company, and they have the same degrees of importance. each department provides its preference information, with dm1 and dm2 providing the following multiplicative preference relations: dm1 =     1 1/7 1/3 1/5 7 1 3 2 3 1/3 1 1/2 5 1/2 2 1     and dm2 =     1 3 1/4 5 1/3 1 2 1/3 4 1/2 1 2 1/5 3 1/2 1     dm3 and dm4 express their preference information in terms of fuzzy preference relations as dm3 =     0.5 0.1 0.6 0.7 0.9 0.5 0.8 0.4 0.4 0.2 0.5 0.9 0.3 0.6 0.1 0.5     and dm4 =     0.5 0.5 0.7 0.1 0.5 0.5 0.8 0.6 0.3 0.2 0.5 0.8 0 0.4 0.2 0.5     it is evident that p (4) 14 = 1 and limp(4)14 →+∞ b (4) 14 = 1. in this case,b (4) 24 = 0.15 × 10 −9 ≤ 10−4 ,b (4) 34 = 0.40 × 10 −9 ≤ 10−4 and b (4) 44 = 0.1 × 10 −9 ≤ 10−4 ; moreover, we can calculate ~v by (20). the collective priority vector by the cosine similarity measure optimization model is found to be (w1, w2.w3, w4) t = (0.2075, 0.3882, 0.2124, 0.1919)t , with the ranking of alternatives a2 ≻ a3 ≻ a1 ≻ a4, which is the same as the results provided by chiclana et al. [5]. chiclana et al. [26] select the best alternative using the weighted average operator on the basis of uniform preference formats using the transformation function pkij = 1 2 (1 + log9 a k ij), i = 1, 2, · · · , n; j = 1, 2, · · · , n (21) 34 x.r. chao, g. kou, y. peng then, they aggregate uniform fuzzy information into a collective opinion using the owa operator φq as p∗ij = φq(p 1 ij, p 2 ij, · · · , p n ij) = n ∑ h=1 vhd h ij, i = 1, 2, · · · , n; j = 1, 2, · · · , n (22) where dhij = is the hth largest value among the collection of p 1 ij, p 2 ij, ·, p n ij and v1, v2, · · · , vnis a weight vector with vh = q( h n ) − q(h−1 n ), h = 1, 2, · · · , n. q(·)is a fuzzy quantifier with the membership function q(r) =      0 r < γ, r−γ µ−γ γ < r < µ 1 r < µ. (23) in practice, the fuzzy quantifier (γ, µ) is always selected as (0.3,0.8) ,(0,0.5) and (0.5,1), representing "most", "at least half", and "as much as possible, respectively. the owa operator φq is used to rank the alternatives or select the most desirable alternative(s) through the same calculation as that in the previous process. fan et al. [7] established a goal-programming model for gdm problem with two preference information based on the idea that the priority vector must be closest to each column of the pcms provided by the dms. their model does not need to transform different preference formats into a uniform structure. it is constructed as max km ∑ k=1 n ∑ i=1 n ∑ j=1 σk|wi − a (k) ij wj| + k ∑ k=km+1 n ∑ i=1 n ∑ j=1 σk|wi − p (k) ij (wi + wj)| s.t. { ∑n i=1 wi = 1 wi > 0, i = 1, 2, · · · , n (24) wang et al. [28] constructed a chi-square optimization model on the basis of the same idea as that of fan et al. [7]. they differentiate the distance using chi-square optimization max km ∑ k=1 n ∑ i=1 n ∑ j=1 σk [ (a (k) ij − wi/wj) 2 wi/wj ] + k ∑ k=km+1 n ∑ i=1 n ∑ j=1 [ (a (k) ij − wi/(wi + wj)) 2 wi/(wi + wj) ] s.t. { ∑n i=1 wi = 1 wi > 0, i = 1, 2, · · · , n (25) table 1 shows the results as compared with these three existing methods. the model proposed in this paper achieves the greatest cosine similarity measure. there is a small difference in the ranking of alternatives. all four methods provide the same best and second-best alternatives, but they disagree on the last two alternatives. example 2 consider single fuzzy pcm to derive priority vector, which is used in xu and da[46]; wang et al.[28]: this example was used by xu and da [46] and wang et al. [28] to derive a priority vector in a fuzzy preference relation. the following matrix is the fuzzy pcm in their example. dm =     0.5 0.7 0.6 0.8 0.3 0.5 0.4 0.6 0.4 0.6 0.5 0.7 0.2 0.4 0.3 0.5     a similarity measure-based optimization model for group decision making with multiplicative and fuzzy preference relations 35 table 1: comparative results with existing methods for gdm with multiplicative and fuzzy preference relations approaches w1 w2 w3 w4 ranking of alternatives cosine value(c) chiclana et al. [5] 0.2452 0.2972 0.2487 0.2092 a2 ≻ a3 ≻ a1 ≻ a4 12.6642 fan et al. [7] 0.1280 0.4301 0.2515 0.1903 a2 ≻ a3 ≻ a4 ≻ a1 12.4711 wang et al.[28] 0.1697 0.3376 0.2741 0.2184 a2 ≻ a3 ≻ a4 ≻ a1 12.5692 our model 0.2075 0.3882 0.2124 0.1919 a2 ≻ a3 ≻ a1 ≻ a4 12.8284 xu and da[46] constructed a least-deviation method for priority vector derivation in fuzzy ahp. a transformation function and rank transitivity property were used in the iteration algorithm. min n ∑ i=1 n ∑ j=1 [ 92pij−1(wi/wj) + 9 2pij−1(wj/wi) − 2 ] s.t. { ∑n i=1 wi = 1 wi > 0, i = 1, 2, · · · , n (26) table 2 summarizes the results of the two above mentioned methods and the proposed model. the rankings of alternatives by these three methods are the same. the cosine similarity measure value generated by our model is slightly greater than those by the other approaches. table 2: comparative results with existing methods for gdm with multiplicative and fuzzy preference relations approaches w1 w2 w3 w4 ranking of alternatives cosine value(c) xu and da[46] 0.4297 0.1784 0.2769 0.1150 a1 ≻ a3 ≻ a2 ≻ a4 3.998186 wang et al. [28] 0.4284 0.1286 0.2755 0.1159 a1 ≻ a3 ≻ a2 ≻ a4 3.998209 our model 0.4300 0.1800 0.2479 0.1151 a1 ≻ a3 ≻ a2 ≻ a4 3.998231 example 3 consider four different preference formats by transformation functions, which is investigated by ma et al. [8] and xu et al.[9]: this example includes four different preference formats, i.e., preference ordering, utility values, multiplicative preference relations, and fuzzy preference relations. dm1 =     1 1/7 1/3 1/5 7 1 3 2 3 1/3 1 1/2 5 1/2 2 1     and dm2 =     1 3 1/4 5 1/3 1 2 1/3 4 1/2 1 2 1/5 3 1/2 1     dm3 and dm4 express their preference information in terms of fuzzy preference relations as dm3 =     0.5 0.1 0.6 0.7 0.9 0.5 0.8 0.4 0.4 0.2 0.5 0.9 0.3 0.6 0.1 0.5     and dm4 =     0.5 0.5 0.7 0.1 0.5 0.5 0.8 0.6 0.3 0.2 0.5 0.8 0 0.4 0.2 0.5     dm5 = {ui|i = 1, 2, 3, 4} = {3, 1, 4, 2}, dm6 = {ui|i = 1, 2, 3, 4} = {2, 3, 1, 4} dm7 = {0i|i = 1, 2, 3, 4} = {0.5, 0.7, 1.0, 0.1} and dm8 = {oi|i = 1, 2, 3, 4} = {0.7, 0.9, 0.6, 0.3}. where dm1 and dm2 provide multiplicative preference relations, dm3 and dm4 express their preference information in terms of fuzzy preference relations, dm5 and dm6 utilize preference orderings, and dm7 and dm8 use utility values. 36 x.r. chao, g. kou, y. peng we can convert preference orderings and utility values to fuzzy preference relations using the following transformation functions (chiclana et al.[5]; herrera et al.[1]; ma et al.[8]): pij = 0.5(1 + oj n − 1 − oi n − 1 ) (27) pij =    u2 i u2 i +u2 j (ui, uj) 6= (0, 0), 0.5 (ui, uj) = (0, 0) (28) by (27) and (28), the multiplicative and fuzzy preference relations can be obtained as follows: dm5 =     0.5 0.167 0.667 0.333 0.833 0.5 0.1 0.667 0.333 0 0.5 0.167 0.667 0.333 0.833 0.5     , dm6 =     0.5 0.667 0.333 0.833 0.333 0.5 0.167 0.667 0.667 0.833 0.5 1 0.167 0.833 0 0.5     dm7 =     0.5 0.338 0.200 0.962 0.833 0.5 0.329 0.980 0.800 0.671 0.5 0.990 0.038 0.020 0.010 0.5     and dm8 =     0.5 0.377 0.576 0.845 0.623 0.5 0.692 0.900 0.424 0.308 0.5 0.800 0.155 0.100 0 0.5     then, we can derive a priority vector (w1, w2.w3.w4) t = (0.2006, 0.3811, 0.2886, 0.1297)t using the cosine similarity measure optimization model, with the ranking of alternatives a2 ≻ a3 ≻ a1 ≻ a4 . from table 3, we observe that the four methods generate the same ranking (chiclana et al.[3]; ma et al.[8]; xu et al.[9]), and that our model again achieves the highest cosine value, indicating that the similarity is most similar to each column of the pcms of the decision makers. table 3: comparative results with existing methods for four different preference formats in gdm approaches w1 w2 w3 w4 ranking of alternatives cosine value(c) chiclana et al. [3] 0.5651 0.7826 0.6619 0.4973 a2 ≻ a3 ≻ a1 ≻ a4 ma et al. [8] 0.2210 0.3426 0.2755 0.1159 a2 ≻ a3 ≻ a4 ≻ a1 26.0116 xu et al.[9] 0.2210 0.3426 0.2827 0.1537 a2 ≻ a3 ≻ a4 ≻ a1 25.9763 our model 0.2006 0.3811 0.2886 0.1297 a2 ≻ a3 ≻ a1 ≻ a4 26.0279 furthermore, though the models developed by ma et al.[8] and xu et al.[9] are optimizationbased, which are similar to the model proposed in this study, their computational complexities are higher than our model. the former obtain results by transforming preference into certain formats which are suitable to calculate solution of programming, the latter need to solve nonlinear programming using genetic algorithm. their models are presented as follows: ma et al. [8] proposed an optimization model for different formats. they optimize the minimum distance between the priority vector and a perfectly consistent pcm. their model is given by min m1 ∑ k=1 n ∑ i=1 n ∑ j=1 (wiu (k) j − wju (k) i ) 2 + m2 ∑ k=m1+1 n ∑ i=1 n ∑ j=1 [ wi(n − o (k) j ) − wj(n − o (k) i ) ]2 + m3 ∑ k=m2+1 n ∑ i=1 n ∑ j=1 (wi − wja (k) ij ) 2 + m4 ∑ k=m3+1 n ∑ i=1 n ∑ j=1 [ wi − (wi + wj)p (k) ij ]2 s.t. { ∑n i=1 wi = 1 wi > 0, i = 1, 2, · · · , n (29) a similarity measure-based optimization model for group decision making with multiplicative and fuzzy preference relations 37 xu et al.[9] constructed a nonlinear optimization model to handle a gdm problem with missing values in the pcm. their model is given as min [ m1 ∑ k=1 n ∑ i=1 n ∑ j=1 ∣ ∣ ∣ ∣ ∣ ∣ wi − u (k) i / n ∑ j=1 u (k) j ∣ ∣ ∣ ∣ ∣ ∣ p + m2 ∑ k=m1+1 n ∑ i=1 n ∑ j=1 ∣ ∣ ∣ ∣ ∣ ∣ wi − n − o (k) i n − 1 / n ∑ j=1 n − o (k) j n − 1 ∣ ∣ ∣ ∣ ∣ ∣ p + m3 ∑ m2+1 n ∑ i=1 n ∑ j=1 ∣ ∣ ∣ a (k) ij − wi/wj ∣ ∣ ∣ p + m4 ∑ m3+1 n ∑ i=1 n ∑ j=1 ∣ ∣ ∣ p (k) ij − wi/(wi + wj) ∣ ∣ ∣ p ]1/p s.t. { ∑n i=1 wi = 1 wi > 0, i = 1, 2, · · · , n (30) both models (ma et al.[8] and xu et al. [9]) needed to solve complex optimization models. particularly, model (30) uses a genetic algorithm to obtain a solution and involves high computational complexity. however, the model proposed herein can be solved using a lagrangian approach and can be directly used to obtain a collective priority vector by following four simple steps. 5 discussion and conclusion how to aggregate different preference formats is an important question in group decision making problem because it’s natural for dms with different backgrounds to represent their preferences using different formats. in this paper, an optimization model was developed on the basis of the cosine similarity measure to deal with multiplicative preference relations and fuzzy preference relations. the basic idea of the model is that the collective priority vector should be as similar per column as possible to a pairwise comparative matrix (pcm) from each decision maker in order to guarantee the priority vector is nearest to perfectly consistency for each decision makers. compared with existing optimization-based methods, the proposed model is computationally simple, because it can be solved using a lagrangian approach and obtain a collective priority vector by following four simple steps. the proposed method can also be used to derive priority vector of fuzzy ahp and provided to reach consensus by mans of existing iterative modification methods. three previously published examples were used to compare the proposed model with some existing approaches. the results show that the rankings generated by different approaches are similar and that the proposed model achieves the greatest cosine values in all three examples, indicating that the proposed model achieves the nearest theoretical perfectly consistent opinion for each decision makers. furthermore, since the proposed model can be adapted to gdm problems with various preferences formats using transformation functions, it provides an efficient and simple way to handle different preference structures. acknowledgment this research was supported in part by grants from the national natural science foundation of china (#71222108, #71325001 and #71471149) and major project of the national social science foundation of china (# 15zdb153). 38 x.r. chao, g. kou, y. peng bibliography [1] f. herrera, e. herrera-viedma, f. chiclana (2001), multiperson decision making based on multiplicative preference relations, european journal of operational research, 129(2):372385. 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[48] m. roubens (1989), some properties of choice functions based on valued binary relatios,european journal of operational research ,40:309-321. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 622-634 network analysis functionality in environmental policy: combining abstract software engineering with field empiricism n.d. hasanagas nikolaos d. hasanagas university of kavala institute of technology greece e-mail:nikolaos.hasanagas@gmail.com abstract: an empirical application of abstract network analysis software is presented in this paper. environmental policy networks are used as a case study. the visualization of the real network hierarchy and activity (formal and informal) is feasible only by using special software. a system of "actors" (e.g. public institutions, interest groups, enterprises) interacting with each other and dealing with a particular environmental issue constitutes a policy network, which influences the environmental policy functionality. the impacts of policy content ambiguity on network characteristics have been analyzed by using network analysis software as an example of combining algorithms with empiricism. recommendations are made to software engineers about possible combination of algorithms with statistics and enrichment of the network analysis software with more visual analytic functions. stronger familiarization of software engineers with policy analysis discourse and of policy analysts with positivism becomes more imperative for this purpose. on the basis of the quantitative results, environmental policy-makers are advised to invest more in trust development than in pressure and to instrumentalize more scientific information under conditions of ambiguity. keywords: environmental policy, rural development, ambiguity, network analysis software, network density. 1 introduction 1.1 aims and orientation this research is presenting the results of an application of the software visone [3, 4] in the analysis and visualization of hierarchies and policy impacts. the software alone is not able to be useful for analyzing practical policy issues, if the links of the network are not well defined and functionalized. this paper will show in details how software of social network analysis can be integrated into theoretical frameworks of policy analysis and in general will point out the importance of implementing software in the classical policy research in order to visualize and analyze the formal and informal hierarchies developed in policy making. the software engineers will improve the functionality of the software they develop, if they achieve to integrate it into the empirical implementation. for this purpose, they should cooperate with positivist social scientists and policy researchers. the cases presented in this paper concern the impacts of legal acts which are characterized by ambiguous formulation. this paper is expected to interest policy makers and policy addressees (like associations of forest and land owners, environmental groups, enterprises, public agencies), as it provides information about obstacles they are going to confront with during their involvement in ambiguous environmental policy issues. suggestions will be made for the improvement of their strategy. copyright c⃝ 2006-2011 by ccc publications network analysis functionality in environmental policy: combining abstract software engineering with field empiricism 623 basic features of the networks will also be presented. methodological and theoretical points will be discussed. 1.2 literature review the need of abstracting social phenomena in a form of algorithm-based ontology has already been pointed out on numerous research occasions [1, 2, 5, 6, 16]. such an abstract approach is a software tool for social network analysis. it is based on matrix algebra and can serve as an interdisciplinary tool for in-depth comparative exploration of different policy fields and issue networks in quantitative terms. in other words, it is a functionalization of the system theory in the analysis of policy fields [7–11, 32]. through such a tool, the knowledge can be accessible for various learning communities [15, 17, 23, 25–30]. however, an appropriate functionalization of the links of a network on an empirical basis is necessary. otherwise, the abstract tool will remain interesting only for mathematicians or software engineers and it will not be applicable on particular policy fields. the necessity for cooperation between software engineers and experts of particular fields, in order to integrate applicable algorithms in the software, has been acknowledged [19–21, 33]. however, such joint projects could be many more than they are until now. a policy network is a system of relations (exchange of information, trust, pressure) between actors (interest groups, enterprises and agencies), which deal with a particular policy issue (e.g. the certification of forestry sustainability in denmark). what keep them together are the power relations between the actors [8, 13] and of course the hope of everyone -even of the weak onesto gain something from their networking. the power is nothing but the ability of someone to get someone else doing something, even if this may be unpleasant for the latter. the political power can be distinguished in two main dimensions: a) trust and b) pressure. pressure may be official or unofficial and is based on the institutional dependence which may be created in transparent or intransparent framework. the dependence on material support is a weak dimension in the networks discussed in this paper [34], it presented no substantial correlation and will not be extensively examined on this occasion. trust usually causes the longest-term effects because it assures legitimization to the trusteeactor and keeps the behaviour of the trustor under control even when the trustee-actor is absent and cannot supervise the trustor (e.g. the client follows the advice of the attorney even outside his office). it is understandable that the power more precisely its unequal distributionbuilds formal and informal hierarchies in a network. the sharper the oligarchy is within the network, the fewer the conflicts are, because normally none starts a conflict, when it is evident who is going to win. under these conditions, the network is supposed to become more sustainable [24]. however, our results allow us discussing this hypothesis more critically; it is going to be shown that though high legal ambiguity decreases oligarchy and thus lets conflicts proliferate, the network is not dissolved. in contrast, the contacts between the actors proliferate and the more policy sectors are integrated within the network (e.g. tourism, forestry, agriculture, industry, urbanization and new relationship between urban and rural areas etc). another usual hypothesis which is here critically examined is that the trust between the actors is restricted, when the cross-sectorality of the network increases and the contacts between the actors proliferate [13, 16, 31]. our finding supports that though sectors and contacts proliferate under conditions of high legal ambiguity, the trust relations also proliferate and are not deconstructed. in contrast, the relations based on pressure seem to be deconstructed. it has been found that the use of general information (political arguments) is avoided as a 624 n.d. hasanagas mean of over-bridging sectors in networks which are characterized by high legal ambiguity ("low formalization degree") [12–14]. the hypothesis supported by our findings is that in case of high legal ambiguity the scientific information is more intensively used than the general one. the findings will also support the hypothesis that the more non-regulative instruments (namely informative or financial means) are included in environmental policy content, the more ambiguous this content becomes [14]. 1.3 paper’s innovation even a software like visone which has been tested and proven to be appropriate for social network analysis, cannot be useful, if it is not integrated in an appropriate field-related theoretical and empirical framework. in this paper, the software visone is applied to forest environment policy networks by using theory of organized interests. the "new relationship" between urban and rural areas can also be depicted on the interactions between interest groups and public institutions. the examination of the real (or not) "partnership" character of this relationship as provided by the european spatial development perspective is also enabled. it is an example of supporting abstract software with concrete empirical concepts. the results can be used as a feedback to software engineers as well as to policy analysts and power theorists (how network analysis software can be used for understanding policy arena hierarchies and their interaction with characteristics of policy content such as ambiguity). the innovation lies in the combination of empiricism and abstract software (fig. 1). figure 1: supporting abstract software engineering with field research empiricism 2 the empirical basis 2.1 design of the empirical research based on concepts of policy analysis such as power (trust and pressure), information, ambiguity, real policy networks are opened up in various countries and an empirical database is created. then, the data are processed through visone software and statistics software spss network analysis functionality in environmental policy: combining abstract software engineering with field empiricism 625 considering concepts of policy analysis. qualitative interviews serve as a corrective tool during the theoretical concept building and the interpretation or the quantitative findings (fig. 1). the data were selected from 12 networks of environmental policy throughout 8 countries of the european union (they will be described below). standardized questionnaires were used. the interviewees were chairpersons, general secretaries, lobbyists or managing directors of the actors. the first actor was randomly selected from a specific list and asked to mention an environmental issue (e.g. certification of sustainability in forestry), in which he was involved in the last two years and thought that he managed to be "successful". then, he was asked to mention the actors with whom he came in contact, within the frame of the particular issue. next, it was asked from these actors to report those actors that they came in contact with, within the frame of the same issue, as well, and so on. through this snowball sampling, the whole network was structured step by step till it stopped getting expanded. this is the method of the complete network analysis. then, it can be turned into a matrix and be processed algebraically and statistically. by using matrix algebra, it is possible to retrace the flow of information, of trust and of the pressure, and, thus, to find the individuals that can control most of the actors, revealing the true hierarchies (formal or informal). the variables of visone were processed through cross-sectional analysis (pearson test) at significance level of 5% after normality test. pearson test is more flexible and functional, as it offers a clearer overview of correlations among variables, in contrast to other tests such as multivariate regression which may become more rigid and restrictive regarding variable relations. the network sampling which is applied is by definition a non-random sample. however, this is no methodological weakness as long as only analytic statistics (correlations between variables) is conducted and no generalization of descriptive statistical results (e.g. mean, variance) on a larger population is aimed. qualitative interviews with field experts (lobbyists, directors etc) have confirmed the reliability of the quantitative results and help a deeper understanding of their perception and their causal relations, as will be discussed below (section 3). 2.2 functionality & variables at first step, the details of the actor’s identity are recorded so as to know whether he was a public or private one and the policy sector he is active in (agriculture, forestry, tourism, research, urbanization, etc.). the hierarchy analysis of the network was based on the concept of oligarchy. the fewer the actors, which concentrate the power (pressure, trust), the higher the oligarchy becomes. the hierarchies are practically depicted in the form of chain control relationships: if the actor a gained actor’s b trust, b gained c’s trust and c gained d’s trust and so on, then a could influence all: b, c and d, with just one call. similar chains are formed in the case of pressure. an actor being on the top of numerous and longest chains, he has the highest power status in the network and he acquires the highest position in the hierarchy of the network. the previous power chains are calculated by a specific algorithm, the katz status formula (formula 1): t = ac + a2c2 + ... + akck (1) where t is a matrix including the status values of all actors as elements, c is the matrix presenting the real network (e.g. of trust links). for that purpose a specific software (visone) is used, which visualizes this hierarchy, giving it the shape of a pyramid (fig. 2). in this way, the actors are examined as objects comparable with each other and the networks can be clearly perceived as systems [23]. thereby, the networks become also comparable with each other, independent of the different qualitative nature of the policy issues they concern. the position (status) of each actor in the vertical y-axis of the 626 n.d. hasanagas pyramid is measured as power share (%) the actor possesses. actors that are found at the same level in the pyramid hold the same percentage. the more actors at the lowest level are, the larger is the bottom x of the pyramid, and, thus, a pyramid with a large base is shaped, which shows the existence of a high oligarchy (the horizontal layout of the actors at the various levels of the pyramid has nothing to do with their power). figure 2: examples of environmental policy networks by visone the oligarchy of every network was set as the difference between the maximum (max) and the minimum (min) power value (status) that appeared in each network divided by the average power of all actors of the network and it may vary from 0 till infinity (formula 2) [21]. oligarchy is a meta-data derived from power and it is not measured any more as % but as a pure number. thus, it is a mono-dimensional (single-number) indicator of the politico-administrative structure. more precisely, the links of trust and the links of pressure are primary data. the power calculated with katz status formula is 1st class meta-data and the oligarchy which calculated using power status is a 2nd class meta-data [27–30]. oligarchy = status max − status min status average (2) another variable is the density of the links (general contacts, conflicts, trust, pressure, exchange of general or scientific information). this variable becomes visually clear in the polygonal form of the networks in fig. 2. if every relation is regarded as a diagonal, then the density exnetwork analysis functionality in environmental policy: combining abstract software engineering with field empiricism 627 presses the percentage of the existing diagonals divided by the total number of all possible diagonals of the polygon (formula 3). thus, density is a 1st class meta-data based on the links number (primary data) of a particular relation type (trust, conflicts etc). in fact, high density practically means that every chance of cooperation (or conflict) within the network has been explored and used at highest possible level. density of relation a = number of links of relation a/(n2 − n) (3) where n is the number of actors participating in the network. in general, katz status and oligarchy provides insights about the real structure (formal and informal) of the network, about the power concentration and thereby about the coordination potential, while the density is suitable for the diagnosis of the intensity of the activity and the importance of each type of relation (general contacts, conflicts, trust, pressure, general or scientific information) for the network cohesion as well as about the saturation of policy activity (e.g. lobbying) within a network (fig.3). figure 3: data classification apart from the general contacts that are detected, more specialized relations were measured by means of the following questions: trust: to what extent would you let each of the following actors, with whom you came in contact with, to make a decision on behalf of you or you would follow their suggestions without examining them critically? 1: there is no such possibility, 2: partly, 3: to the hilt institutional pressure: tell us for each actor, that you came in contact with, whether he was irreplaceable for you, because of a specific function he had, in the particular issue. 0: no, 1: yes general information: tell us from what actor you received information (of any kind). 0: if no information was received, 1: if there is information flow. scientific information: name three actors from whom you received the most important scientific information. this relation was scaled with 0 and 1, as in the case of the general information. conflict: name the actors you were in conflict with. this was scaled with 0 and 1. ambiguity of policy content: 628 n.d. hasanagas tell us how often you needed interpretation of the laws and other rules concerning the particular issue, you were involved in, from a solicitor or other experts. 1: not at all, 2: sometimes, 3: very often. the average of the grades that the actors noted in a network showed the general ambiguity of the policy content of network issue (fig. 3). interventionism: it has been calculated as the inverse of the ambiguity. since the ambiguity varies from 1 to 3, the legal interventionism varies respectively from 1 to 0,33. it just constitutes a subsidiary indicator for technical-statistical reasons, which presents a significant correlation with crosssectorality and is presented as an alternative indicator. 3 the case study: meta-information of functionality 3.1 networks analysis the 12 environmental policy networks are the following ones: i) in denmark a network of 18 "actors", concerning the certification of sustainable management of natural recourses was explored. this issue treated the enactment of specifications and control processes about the management of natural resources, so that their production potential will not be degraded. network of the same issue (sustainability of natural resources) was opened up in ii) finland (24 actors) and in (iii) spain (38 actors). iv) in bavaria, a network of 14 actors was opened up, which was concerning "the eco-account", namely the evaluation and the counterbalance of environmental consequences. v) moreover, in bavaria another network of 16 actors was opened up, which dealt with the mapping and description of forest biotopes. vi) in sweden, a network related to the protection of biotopes (11 actors) and one more vii) related to the national forest policy (14 actors) were explored. viii) in greece, a network of 13 actors concerning the article 24 of the constitution was opened up. ix) in scotland, the network was related to the forest policy (23 actors) and x) to the issue of the national park loch lomond and trossachs (27 actors). xi) in ireland, a network related to the provisional marketing services in forestry was examined (25 actors) and xii) in spain, a network concerning research on castanea genetics (21 actors) was analyzed. all 12 networks involved 234 actors in total: 30% public agencies and 70% private actors. according to their function they can be categorized as follows: a) 43% interest groups which deal with the economic utilization of natural resources (unions of enterprises, forest industries and landowners), b) 42% actors aiming at nature conservation (e.g. institutions of environmental protection, environmental organizations), c) 6% individual enterprises (not unions of enterprises), d) 9% scientific actors (universities, research centers). in all these issue networks forest policy and rural development matters are strongly interconnected and are included in the wider field of the environmental policy discourse. 3.2 consequences of ambiguity hypothesis 1 as the ambiguity increases, and the hierarchy (oligarchy) dissolves, and consequently the conflicts increase, the networks not only do not dissolve but in contrast, the contacts between the actors and the cross-sectorality increase. in table 1, there is a significant positive correlation between ambiguity, density of conflicts and density of general contacts. the cross-sectorality has a reciprocal relation with the legal interventionism, so practically it could be assumed that it increases with the ambiguity [12,18,31]. network analysis functionality in environmental policy: combining abstract software engineering with field empiricism 629 the more ambiguous the policy content is, the more disagreements appear about the obligations and rights of the actors and about which measures or specifications should be further institutionalized: who will pay compensations for any restrictions imposed on natural resource utilization? what authorizations should the interest groups have? which "risk" should be considered to be "acceptable"? etc. such questions are steadily posed in case of ambiguous lawmaking. under these conditions, the probability of conflict becomes higher. the only antidote would be a clear law. if the law is explicit enough from the beginning, it becomes clear who can participate in the network, and what he is expected or allowed to do. as mentioned above, the fact that the networks dissolve, when the conflicts increase, is frequently supported. however, in table 1, it is observed that apart from the conflicts that become denser to ambiguity (0,168), the general contacts among the actors become much denser (0,557). it is thus understandable that in truculent and uncertain political-administrative conditions, the actors try to broaden their coalitions in order to outbalance the chances which are lost or new threats created because of conflicts. therefore, more and more opportunities of collaboration are used and the network is sustained, despite the increasing conflicts. thus, the software proves that the policy networks have retroaction mechanisms. in table 1, it is also evident that the cross-sectorality is further developed, as the state intervensionism (inverse of ambiguity) decreases (-0,132). actors from many different sectors can interpret the abstract aims and restrictions of a policy content, in such a way that they seem to be in accordance with their interests and capacities. an ambiguous law about the "protection of mountain water resources" makes possible the involvement of a wide spectrum of actors: from environmental organizations up to unions of construction firms, but also fresh water enterprises or angling tourism business. of course, this may cause a proliferation of conflicts but without putting the network at risk. in table 1, the oligarchy decreases as the ambiguity increases (-0,441) (namely, the form of the pyramid becomes unclear, see fig. 1). the decrease of oligarchy gives a free scope to conflicts because it is less evident who is going to win. thus, anyone -even the weak onesfind the courage to risk a controversy. the dissolution of hierarchy is the result of the fact that it is not clear who controls whom. table 1. consequences of ambiguity in conflicts, in the density of general contacts, in cross-sectorality and in oligarchy ambiguity density of conflict-relations pearson coefficient .168 level of significance .010 density of general contacts pearson coefficient .557 level of significance .000 oligarchy (official and unofficial hierarchies) pearson coefficient -.441 level of significance .000 interventionism cross-sectorality (number of involved political sectors) pearson coefficient -.132 level of significance .043 hypothesis 2 under high ambiguity conditions (even if the general contacts get denser and there is high crosssectorality and uncertainty in the network, as supported in hypothesis 1), the trust relations proliferate, while the relations of pressure decrease. the common belief is that under high ambiguity, the contacts proliferate in a network, and consequently the trust is reduced, as the potential sources of difference and threats increase and 630 n.d. hasanagas it becomes more difficult for a larger number of participants to become familiar enough with each other and to build clear and well coordinated coalitions. however, as seen in table 2, when the ambiguity increases, the trust seems also to increase (0,242). simultaneously, there seems to be a minimization of the usage of pressure (-0,407). this happens because it is not clear when, how and who can activate a regulative coercive tool. therefore, trust becomes a more preferable politico-administrative power form than pressure. when some actors provide convincing interpretations, then they gain the trust of the rest actors. inversely, actors, who have already gained the trust of others, thanks to their multidisciplinary staff and their stable and lawful behavior have better chances to present their interpretations as more convincing [11, 13, 18, 22, 24, 31]. apart from that, in a network of high ambiguity a kind of "natural selection" also takes place; from the very beginning the only actors who survive are rather the reliable actors. thus, in such a network it is more crucial for an actor to gain the trust of the others and satisfy his interest than to try to exert pressure "in the name of" an ambiguous rule with unknown results. it could be assumed that under conditions of high ambiguity, the anxiety derived from the uncertainty of the interpretation surpasses the anxiety of a new coalition. for this reason, trust can be developed. table 2. consequences of ambiguity in the composition of power (trust and institutional pressure) ambiguity density of trust relation pearson coefficient .242 level of significance .000 density of relations of pressure pearson coefficient -.407 level of significance .000 hypothesis 3 as the ambiguity increases, the use of scientific information increases, while the use of general information is restricted. as seen in table 3, the ambiguity creates a "friendly" or even attractive environment for the experts (lawyers, planers etc), since the need of using scientific information increases (0,250) at the expense of general information (-0,329) which can be used by any lobbyist, even by those who are not considered to be "specialized" in the particular policy issue. because of the need of actors to be adjustable to the steadily developing "new technologies" and to the socio-political and economic changes, both expert ("scientific") knowledge on legal matters and non-legal rules (e.g. norms of nature, technology, and marketing) is demanded. therefore, the contribution of a wide range of scientists (solicitors, archeologists, biologists etc) is indispensable, and information which is regarded as "scientific" proliferates. as the ambiguity increases, the use of general information (matters of organizing, politics and administration) is reduced. in ambiguous issues, one expects from the scientists to play more important and intensive role in comparison with the director of public relations and the administration staff. in contrast, administrative staff can solve problems only by using formalized procedures expressed through general information and not by developing "scientific" creativeness. table 3. policy impacts of ambiguity in general and scientific information ambiguity density of scientific information pearson coefficient .250 level of significance .000 density of general information pearson coefficient -.329 level of significance .000 network analysis functionality in environmental policy: combining abstract software engineering with field empiricism 631 hypothesis 4 when a policy measure is not a direct (regulative) instrument (threat of penalty) but an indirect (economic or informational) instrument (motivating or convincing), then it is more ambiguous. in table 4, it is evident that the pressure from the public actors towards the private ones, which is the basis for implementing regulative tools that necessitate clear rules, tends to decrease under conditions of high ambiguity (-0,140). ambiguity tends to appear in policy contents which include indirect instruments. example of indirect instrument is a law which encourages the use of anti-pollution technology via the award of an ecological badge. the ambiguity of the indirect instrument is derived from the wide range of possible interpretations concerning the forms of "environment-friendly" attitude, the standards, the form, the procedure and the source of the reward (e.g. environmental tax, compensation for restrictions on the exploitation of natural resources, award of an ecological badge) and the procedure of performance assessment. in table 4, an increase of the number of contacts from the part of private actors towards the public ones (chances of lobbying) is also observed (0,146) under conditions of high ambiguity. this can be explained by the need of private actors to receive clear and reliable information about the law implementation. thus, ambiguity can become a basis for new obstacles but also for new lobbying and collaborations but also between public and private actors. table 4. consequences of ambiguity in the relations of public-private sector and in the institutional pressure of public actors ambiguity number of relations of pressure exercised by public pearson coefficient -.140 actors to private actors level of significance .032 average of general contacts from the private actors pearson coefficient .146 towards the public ones level of significance .026 4 conclusion & discussion a software toolbox which is engineered for social network analysis can be useful for analyzing policy issues and politico-administrative systems as multi-objective entities (different types of links, hierarchies and actors). it may be applicable in improving power status, policy implementation and lobbying as well as in policy e-learning systems for lobbyists and administrators (e.g. using networks as role play scenarios). such a software product is purposeful for detecting the real hierarchies and activities (not only the formal but also the informal ones). basic points for software engineers and policy analysts are the following ones: the visualization is especially useful for this purpose. the algorithm of katz status seems to be a useful tool, as it has been proven not only to be useful for visualizing hierarchy but also to visualize political oligarchy, at least according to the definition employed in this paper. the oligarchy indicates the potential of politico-administrative coordination. apart from that, the oligarchy concept which is based on the power calculation and the density of trust, pressure, information and conflicts presented field-related properties such as statistical correlation with policy content ambiguity. another useful algorithm which presented properties interesting for policy-makers and policy analysts is this of density of various types of links, which practically indicates intensity of activity. as these properties are detected through statistical analysis, software designed for social network analysis can become more efficient, if it includes statistical functions, particularly tests, or if it provides the possibility of exporting output data to statistical software or to convert them in other 632 n.d. hasanagas types of files which should be importable to statistical sheets. in any case, statistical analysis can hardly be isolated from social network analysis, if useful properties of social network are expected to be found. therefore, social network analysis software should offer combined functions of matrix algebra and statistics. an additional technical advantage could be to offer the possibility of appearance of each type of links in different color within the same network file. in this way, it would be possible to visually distinguish trust from pressure links and to comparatively examine the hierarchy of trust and this of pressure within the same pyramid. similarly, a visual comparison of different densities (trust, scientific information, general information etc) would be possible. before starting primary data collection and meta-data definition, the software should be integrated in theoretical frameworks of policy analysis and empirical research design. without a precise definition and measurement of each type of links (e.g. trust, pressure), the quantitative metadata of such software toolbox will have no meaning for the policy field they are applied to and will not be convincing researchers and experts of empirical disciplines. thus, a systematic collaboration between software engineers, policy analysts and policy field experts is necessary. for this purpose, software engineers should maintain narrow contact to policy analysis discourse and policy analysts should adopt more positivism. basic conclusions for policy-makers could be the following ones: although ambiguity increases the conflicts among the actors, it does not dissolve the network but increase the cross-sectorality and it lets the contacts proliferate. moreover, it prevents the development of strict hierarchies based on pressure and favors the development of trust. in this way, it may contribute to the development of more sustainable collaborations, as trust is effective even without surveillance. in contrast, the use of pressure does not seem to be effective under conditions of ambiguity. using information which is regarded as "scientific" is also an effective strategy. interest groups of forest and landowners etc should pay extra attention to this point because they are normally characterized by restricted multidisciplinarity in comparison with the environmental organizations and thus, they have a more restricted persuasion potential in comparison with the environmentalists. actors involved in ambiguous issues must be especially well prepared for a lot of disagreements and conflicts. the role of "peacemaker" can be adopted by anyone who can gain the trust of the others and will not try to implement a forced "peace" that "suits" his interests through pressure. the successful lobbying of a private actor to the public actors is of major importance in order to assure access to explicit instructions. ambiguous policy contents do not only cause difficulties but can also provide opportunities of satisfying interests and building new coalitions. the need for an apt and long-term lobbying becomes more imperative in the case of indirect (economic and informational) instruments, which tend to be characterized by higher ambiguity than the direct (regulative) instruments. acknowledgements the research initiative proposed by this paper has been supported by the department of landscape architecture, kavala institute of technology, drama, greece), and by the institute of forest policy and nature conservation of goettingen university (germany). bibliography [1] d. arotaritei, genetic algorithm for fuzzy neural networks using locally crossover. int. j. of computers communications & control, issn 1841-9836, 6(1): 8-20, 2011. 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[34] n.d. hasanagas, power factor typology through organizational and network analysis. using environmental policy networks as an illustration. publisher ibidem. stuttgart, 2004. international journal of computers communications & control issn 1841-9836, 11(5):645-656, october 2016. statistical automaton for verifying temporal properties and computing information on traces a. ferlin, v. wiels, p. bon antoine ferlin* 1. ifsttar, 20 rue élisée reclus, bp 70317, 59666 villeneuve d’ascq cedex, france 2. onera/dtim, 2 avenue e. belin bp74025, 31055 toulouse, france 3. airbus operations s.a.s., 316 route de bayonne, 31060 toulouse cedex 09, france *corresponding author: antoine.ferlin@ifsttar.fr virginie wiels onera/dtim, 2 avenue e. belin bp74025, 31055 toulouse, france virginie.wiels@onera.fr philippe bon ifsttar, 20 rue élisée reclus, bp 70317, 59666 villeneuve d’ascq cedex, france philippe.bon@ifsttar.fr abstract: verification is decisive for embedded software. the goal of this work is to verify temporal properties on industrial applications, with the help of formal dynamic analysis. the approach presented in this paper is composed of three steps: formalization of temporal properties using an adequate language, generation of execution traces from a given property and verification of this property on execution traces. this paper focuses on the verification step. use of a new kind of büchi automaton has been proposed to provide an efficient verification taking into account the industrial needs and constraints. a prototype has been developed and used to carry out experiments on different anonymous real industrial applications. keywords: statistical büchi automaton, information computation, runtime verification, dynamic analysis, linear temporal logic. 1 introduction development of critical software is constrained by certification standards. more precisely, do178 concerns avionics software. it defines objectives for each step of the software development process and, in particular, for the verification phase. airbus has investigated the use of several kinds of verification methods. these methods can be classified using two criteria: formal or not, static or dynamic. classic verification means are typically not formal: test is dynamic, review is static. formal techniques are typically static, but there has been a lot of work recently on run-time verification which can be classified as formal and dynamic. the origin of this work is the verification of temporal properties in an industrial context. these properties cannot easily be verified using the current airbus verification framework based on static analysis [18]. another way consists in using dynamic analysis to perform the verification of temporal properties. testing can be difficult for properties involving timing aspects. simulation, which consists in testing a software on a simulated hardware, has been experimented copyright © 2006-2016 by ccc publications 646 a. ferlin, v. wiels, p. bon and allows the analysis of the program at each step of its execution. however, even if commandability and observations are easier with simulation than with testing, considered executions are very long and manual verification of a property is difficult and costly. this paper presents a dynamic analysis approach to formally verify temporal properties on execution traces generated by simulation. the context of the industrial process restricts the dynamic analysis approach to off-line analysis techniques, i.e. a posteriori verification on execution traces. constraints also exist on the generation of traces: values of variables at given instants are obtained by positioning explicit observation points in the program, and the number of such observation points must be minimized for efficiency reasons. execution traces are generated using existing test cases. test strategy is not considered in this paper. it is focused on the use of patterns of temporal properties to compute statistical information. the aim is double. the first goal is to conciliate the temporal properties which classically have a semantics on infinite traces, with finite traces. secondly, when a property is violated, this method can provide additional information to the client instead of the basic "ok/failure" response. when bugs occurs, this method limits the necessary investigations, hence reduces the debugging cost. an overview of the proposed approach is detailed in section 2. section 3 positions our work according to related work. section 4 recalls classic definitions and details the verification phase using automata that compute statistical information. section 5 provides experiment results in terms of verification time and statistical information use. finally, we conclude about our work and provide future work elements in conclusions. 2 overview of the approach we are working within the airbus simulation framework. this framework simulates hardware of the program to be verified. the simulated hardware is instrumented, so the program executed on this framework is the embedded software. extraction of execution data consists in generating a trace with the help of observation points. they can only be defined using a debuggin interface. our goal is to verify a temporal property on an execution trace of a given program. the first step consists in defining an adapted language to formalize a temporal property. the second step is trace generation. verification of the property on the trace is the last step (figure 1). in this paper, we focus on the last step. however, we give some essential elements of the first two steps hereafter. the first step consists in identifying properties to be verified. currently, the industrial methodology verifies temporal properties using non-formal static analysis. in other words, the verification phase is done by engineers using code review. hence, the goal of this approach is typically focused on industrial needs. actually, verification cost and time have to be reduced as much as possible and safety has to be at least identical with the present verification approach. in addition, the new equipped approach must be as simple as possible for operators. formalisation of temporal properties consists of four steps. firstly, some properties are gathered from critical software. secondly, properties are classified. then, a dedicated language is defined from gathered classified properties. this language is based on a combination between a subset of ltl and regular expressions. some operators on variables had been defined in order to have access to some information such as the time of the last modification of a variable. the language definition is not the purpose of this article and can be found in [9]. use of a dedicated language is a consequence of the desire for simplification for operators. finally, the properties studied are formalized using our dedicated language. one issue with a posteriori dynamic analysis is gathering traces. indeed, trace size quickly increases with execution time and with the number of collected variables. we use two ways to minimize traces. firstly, we only collect, at each step of the program (a step is a c instruction), statistical automaton for verifying temporal properties and computing information on traces 647 program property observation points execution trace result static analysis simulation framework trace verification figure 1: overview of the approach variables which are necessary to verify a given property. secondly, we only collect these variables if their value changes. more details on trace generation can be found in [8]. this last step consists in verifying a formalized property on an execution trace. this step needs as input an execution trace given by the previous steps. properties are formalized using language defined in the first steps and are then transformed into a büchi automaton which is executed on the trace to be verified. 3 related work existing work on run-time verification [1] can be classified in two categories: on-line or a posteriori. an "on-line" approach means that the verification is done during the execution of the programme, whereas an "a posteriori" approach is done on execution traces. on-line verification consists either in adding assertions to the programme to express the properties to be monitored during the execution, or in executing, parallel to the programme, an automaton representing the property and taking as inputs data from the programme execution. a lot of work exists for on-line verification, especially for java programmes and in the aspect-oriented programming community [7,12,13,16,19]. we work on c programmes which are either sequential hard-realtime programmes, or multitask realtime programmes (arinc 653, posix platforms) and for certification reasons, we are restricted to an "a posteriori" verification. existing works are fewer and differentiated by the following criteria. the first criterion is the nature of the considered execution traces. in several existing works, complete execution traces can be obtained by listening to all the variables of the programme [3, 17]. this is not the case in our context: traces are obtained by positioning observation points in given places of the programme and we use static analysis to compute a minimized number of observation points. indeed, the bigger the number of observation points, the bigger the size of the execution trace. the second issue is the implementation of an efficient verification technique on the traces obtained that can be very large. existing works are based on rewriting techniques [12] or specific techniques, such as translation of ltl formula into state machines [11] [6]. we use an existing 648 a. ferlin, v. wiels, p. bon tool for ltl properties and propose specific techniques for regular expressions and parametric properties. the last criterion is the way to handle finite traces. classic models in temporal logic are infinite, while we want to verify properties on finite traces. existing work proposes different solutions to this problem. [2] proposes an adaptation of the temporal language semantics. [15] and [4] propose a multi-valued logic to handle the cases where it is not possible to conclude on the satisfaction of a formula. in particular, [4] needs to generate two büchi automata (one for the ltl3 property φ and one for ¬φ). this method requires time when the property depth is high. [15] also gives an interesting account of different work dealing with the finite trace issue and especially work that explores means to decide whether all possible infinite completion of a finite trace verifies a given property. a simple solution can also be to loop on the last state in order to render a finite trace infinite, but it modifies the satisfiability of some formulae [13]. we propose a simple pragmatic approach to this problem in section 4.2. 4 verification of properties on execution traces the last step of our approach consists in verifying a formalised property on an execution trace. this step needs as input the formalised property and a corresponding trace. the property is formalized and the trace is generated according to the method proposed in 2. the temporal property is translated into a büchi automaton using ltl2ba [10]. hence, it is executed on the trace to perform the evaluation of the mapping formula. in order to explain our approach, let us recall the definition of a büchi automaton, which is based on the definition of the classic automaton. 4.1 definitions [14] defines an automaton as follows: definition 1 (automaton). an automaton is a 5-uplet a = (q, σ,→,q0,f such that: • q is a set of states • σ is an alphabet • →∈ q× σ ×q is a transition relation • q0 is an initial state • f ⊆ q a set of final states. definition 2 (accepting condition). a word w ∈ σ∗ n length sized is a word of l(a) [14] where a is an automaton, if and only if there is a sequence (q)i,i∈[|0,n]| which begins at q0, such that ∀i ∈ [|0,n− 1]|, (qi,wi,qi+1) ∈→ and q −n ∈ f . an automaton a recognises the regular language l(a) defined on the σ alphabet. all words of l(a) are finites words. the büchi automaton definition can now be expressed using the automaton definition [5]: definition 3 (büchi automaton). a büchi automaton is an automaton such that the accepting condition of a word is modified, in order to accept infinite words. definition 4 (accepting condition). a word w ∈ σω is a word of l(b), where b is a büchi automaton, if and only if there is a sequence (q)i,i∈n which begins at q0 and such that ∀i ∈ [|0,n− 1]|, (qi,wi,qi+1) ∈→ and ∀j ∈ n,∃k ∈ n,k > j and qk ∈ f . statistical automaton for verifying temporal properties and computing information on traces 649 after the recall of these definitions, the next step consists in handling finite trace with a büchi automaton which only accepts infinite words or traces. 4.2 the finite trace problem let us assume that φ is a temporal property and σ∞ an infinite trace. the property is transformed into a büchi automaton bφ. hence, σ∞ � φ if and only if σ∞ is recognized by bφ. we deal with finite trace, whereas the semantics of ltl is on infinite ones. a classic solution consists in transforming the finite trace into an infinite one by looping over the last element. but this solution introduces border effects which have to be controlled. hence, if we have a result on the trace σ∞, what will we able to conclude about the finite trace σ which derives from σ∞? this is how we propose to respond to this question. a specific algorithm has then been defined to handle the execution of the büchi automaton at the end of the trace. this algorithm answers the following question: is there an infinitely often accessible final state? this algorithm performs: 1. computing the strongly connected components of the büchi automaton, by only taking into account transitions where the formula is true. 2. defining a direct acyclic graph (dag), equivalent to the büchi automaton, from the strongly connected components computation. 3. browsing the direct acyclic graph for each element of the current state. 4. determining for each state of the dag accessed from an element of the current state if it is a final state of the büchi automaton. we have thus implemented a verification algorithm for the property on the trace. but the transformation of a finite trace into an infinite one modifies the satisfiability of some formulae. the satisfiability of a property may change according to the nature of a trace, as presented in the following instances. example 5. the formula 3(2a ∨ 2¬a) (which means: eventually we always have a or we always have not a) is true with a finite trace which loops at the end, because the last state is a or non-a (figure 2,b)). with an infinite trace, this formula may be false (figure 2,a)). for instance, if the trace alternates a and non-a at each state, the formula will be false. a) a ¬ a a ¬ a a ¬ a a ¬ a a b) a ¬ a a ¬ a a ¬ a a ¬ a a figure 2: two traces in the considered context, the satisfaction of a formula may depend on the place where the execution is stopped in the program. example 6. in the following example, the first trace is a theoretical trace corresponding to an infinite execution of the program. the other two traces are prefixes of the infinite trace where execution has been stopped at different execution times. the property 2(p ⇒ 3q) is false on the first prefix, but the same property is true on the second prefix. 650 a. ferlin, v. wiels, p. bon p q p p q p q p p q p p q figure 3: satisfiability depends on the end of trace in order to evaluate the interpretation of a property, we propose the following algorithmic method. φ is a property to be verified on an execution trace σ. aφ refers to the büchi automaton associated to φ. σi is a trace state which is not the last trace state. hence, there are two possible cases: • if the set of reachable states of aφ is the empty set, then the property will be violated; • when the last state of σ is reached, there is a loop over this one. aφ is then considered as a graph. transitions with the formula evaluated to false are deleted. the mapping direct acyclic graph is computed and is browsed in order to determine if an accepting state is reachable infinitely often. two sub-cases are possible: – an accepting state of aφ is reachable infinitely often. hence the property is satisfied until the end of trace. however, it could be violated after this point of the programme execution. this is the case of safety and liveness properties. – any accepting state of aφ is infinitely often reachable. the property is not satisfied. however, it would be satisfied after this point of the programme execution. this is the case of liveness property. 4.3 statistical büchi automaton, a pragmatic approach we propose a pragmatic approach to handle "end of trace". we provide the user with statistical information on the satisfaction of the property. two kinds of information are provided: • additional information for all properties which have not been verified. when the property is not verified, the last state where the property is true is provided. this information helps targeting where the potential problem is inside the code. • additional information for properties which comply with a pattern. this second aspect allows the giving of a set of information which is richer than the other one, because information is given even if the property is evaluated to true. statistical information depends on the pattern and is computed using an automaton which is executed instead of the büchi automaton. the definition of the statistical büchi automaton is split into two parts hereafter. the first part consists of the statistical counters. the second part consists of their integration into a classic büchi automaton. definition 7. c is a set of integer variables called counters. λc : (c → z) → (c → z) is a set of actions on c, depending on current value of all variables. operations are characterized by the following grammar: 〈 operations 〉 ::= | 〈 operations 〉 〈 operation 〉 statistical automaton for verifying temporal properties and computing information on traces 651 this rule defines a list of operations. the list can be empty or not. 〈 operation 〉 ::= 〈 counter 〉 = 〈 expression 〉 this rule defines an operation. an operation always modifies a counter. 〈 expression 〉 ::= 〈 zconst 〉 | 〈 counter 〉 | 〈 expression 〉 + 〈 expression 〉 | 〈 expression 〉 | 〈 expression 〉 × 〈 expression 〉 | 〈 expression 〉 ÷ 〈 expression 〉 | 〈 expression 〉 % 〈 expression 〉 | min(〈 expressions 〉 ) | max(〈 expressions 〉 ) this rule defines an expression. integer constants (zconst) and counter can be used inside operations. authorized operations are addition, opposite, *plication, euclidean division, modulo, minimum and maximum. 〈 expressions 〉 ::= 〈 expression 〉,〈 expression 〉 | 〈 expression 〉,〈 expressions 〉 this rule is used for min and max operations. the list has at least two expressions. hence, a statistical büchi automaton is : definition 8. a statistical automaton is a five-uplet a = (q, σ,→,q0,f,c0) where: • q is a set of states • σ an alphabet • →⊆ q× σ × λc ×q a transition relation • q0 ∈ q is the initial state; • f ⊆ q a set of accepting states; • c0 : c → z¿, is a function which returns the initial value of each element of c statistical information depends on the pattern and is computed using an automaton which is executed in parallel with the büchi automaton. example 9. the automaton of the pattern 23p allows the computing of the number of states where p is true (figure 4). each time p is true, the nb_p counter is incremented. at the end of trace execution, we know how many times p was true. in this pragmatic approach, the statistical automaton replaces the classic büchi automaton. each time a transition is true, the mapping list of operations is applied to the counters. currently, three patterns are recognized (these patterns have often been encountered in the studied industrial applications). the past version of each pattern is recognized to 652 a. ferlin, v. wiels, p. bon start ¬ p p{nb p + +} figure 4: automaton of the pattern 23p • 23p • 2(p ⇒ q) • 2(p ⇒ 3q) adding a new pattern with this approach is facilitated. actually, each pattern is described inside a file (formula, statistical automaton, printing system of statistical information). hence, if an agent wants to collect additional information for a new pattern, he can define the automaton attached to that pattern to compute his own information. the next step of this work consists in testing our approach on real industrial cases. 5 implementation and experiments in this section, we detail some elements of the implementation of the prototype antares, and propose experiments on real industrial cases. let us recall, antares is an industrial proprietary prototype. 5.1 antares, a tools for analyse of trace of execution of software antares implements our approach from the transformation of the temporal property into a büchi automaton to the verification of this property on an execution trace. antares uses ltl2ba for the transformation. the major steps are summarized in figure 1 antares is written in ocaml with around 16, 300 lines of code. the verifier is split into three applications: • a reader module, which reads the trace. this task can be done by several readers dispatched on several networked computers. this fact allows the distribution of the calculus load on available resources. in addition, two trace formats are currently handled by the tool: the standard vcd format (value change dump ascii based format for dump files), and a data base format. because these trace formats are transformed into a generic format which is used for verification, adding a new format is easy. the stored trace σ is a sequence of states σj. a state is a list of pairs (variable, value); • a central server module, which will be able to ask the appropriate reader module, if the reading task is dispatched on several computers; • a client module, which does the verification. in order to evaluate our prototype, experiments have been done on traces which come from different avionics software. the goal of these experiments was to check that identified requirements can be formalized using our specification language and to assess the efficiency of our approach, for all kinds of properties. as a reminder, verification of temporal properties with non-formal static analysis (code reviewing) needs several days. we aim to decrease the verification time. statistical automaton for verifying temporal properties and computing information on traces 653 our approach has been tested on programs in order to evaluate the different aspects of this one. the first program is an embedded software, whereas the last one is a hardware model of the dynamic-analysis framework we use to generate execution trace for the first program. the experiments conditions are identical for all experiments : the used machine is an octo-core machine with a 2ghz-and-512ko-cache processor. 5.2 experiments on a first software this experiment evaluates three aspects of our approach: reverse reading, parametric properties, and computation of additional information for a specific pattern. this software is based on the arinc 653 standard. the verified part of this software is around 550 lines of c code. the arinc 653 library is around 2,600 lines of c code. it is a multi-task software which is stopped after a finite number of loops. the software communicates with other applications using dynamically created ports. the property consists in ensuring that each port has been initialised before being used. the size of the trace is relatively small (about 129 states). the property is verified for all possible values of used_port which are 1,2, and 5 in the trace. the formalized properties are the shape of �(used_port = x ⇒ 3·(created_port = x)), where x ∈ {1, 2, 5}. the recognized pattern is �(p ⇒ 3·q). then, the mapping statistical automaton computes the number of occurrences of p (used_port = 1 for example) before q occurs (created_port = 1). table 1: additional information: occurrence of used port port occurrence 1 107 2 2 5 20 table 2: computation time module time (s) reader 0.015 server 0.024 client 0.07 execution times of each module are displayed in table 2. because of the small size of the trace, only one reader module is used here. this is in charge of the trace reading, the server makes the link between the reader and the client, and the client is responsible for the verification itself. verification time of the client includes transformation of the ltl formula into a büchi automaton, duplication of the büchi automaton and verification of the formula. verification of this property on a trace with 129 states is efficient, considering that four properties are simultaneously verified: one for each value of used_port. according to table 1, which gather the results of the verification of the properties on the trace, we can see the usage of each port. relative to the number of elements, port 1 is used for 82, 9% of the cases, whereas port 2 is used for 1, 6% of the cases. this experiment shows that statistical information can be used to check orthogonal properties, such as the number of uses of a port. indeed, with this statistical information, the used port and their frequency of use are known. 5.3 experiments on a second software this experiment has been done on a model of the airbus simulation framework. this model consists of a processor, an interruption control system, an eeprom, an emitter, and a watchdog. this model represents 675 lines of c. this is a mono-task software which loops infinitely. the aim of this study consists in verifying the behaviour of the watchdog. it checks time between two writing actions on the eeprom. if the difference between the two writing actions is more than t0, then an interruption signal is sent to the calculator and the emitter. in this experiment, we want to verify that a variable must be written in the epprom at lest every t0 seconds. if time is more than t0, a watchdog raises a signal to the controller, otherwise 654 a. ferlin, v. wiels, p. bon nothing happens. the property can be split into two sub-properties which are: • 2((τ − last_write_time > t0) ⇒ signal), means when the delta between the last write time and the current time (τ) is upper than t0, then signal will be raised (overflow case). • 2((τ − last_write_time 6 t0) ⇒ ¬(signal)) means when the delta is lower or equal to t0, then signal will not be raised (nominal case) . the properties have been verified on a trace with 1, 848, 633 states. in addition, the equations follow the pattern 2(p ⇒ q). hence, we have information about how much the overflow case and the nominal case happen. we cut trace reading in 5 reader modules. table 3: second software, time results module nominal time(s) overflow time(s) reader(min-max) 9.21-10.09 9.21-10.09 server 3.04 3.04 client 25.84 35.84 pattern information says that the nominal case happens 1, 848, 629 times (more than 99, 99% of the cases) and overflow case happens only 4 times (2.16 × 10−4%). this information is essential, because it shows that overflow is not frequent. if overflow appears more frequently, then the software will probably have a bug. the acceptable maximal level of appearance of this second property depends on the size of the trace and on the software. this kind of problem could not be detected without statistical information. finally, this experiment shows that verification of temporal properties on big traces including computation of statistical information is efficient (less than 50 seconds, trace reading included). in addition, use of patterns can help to detect bugs which are complex to find. 5.4 discussions around the experiments the experiments have been processed on embedded avionics software and on models of the dynamic-analysis framework. it shows that the defined approach is efficient on big traces. indeed, the order of magnitude of time verification is several hundred seconds in comparison with several days with a non-formal verification method (code reviewing). use of patterns to compute statistical information is useful to help decide if a property is true or false when the entire trace has been analysed. in addition, this statistical information allows the treatment of orthogonal properties such as "how much time a port has been used?" (first software). conclusions this paper has presented an approach for the verification of temporal properties on execution traces of avionics software. this approach was needed because these kinds of properties are difficult to verify using existing techniques. the definition of the solution was guided by industrial needs and constraints. this approach proceeds in three steps: definition of a dedicated property language, generation of trace executions using static analysis, and verification of the property on the trace. the verification step is built in order to handle finite traces, whereas temporal properties have a semantics on infinite traces. to do this, additional statistical information about the trace are provided to help the verifier to conclude about the verification result. this statistical information depends on the property pattern. frequently encountered patterns are implemented in antares. other patterns can easily be added with the definition of a new statistical automaton. statistical automaton for verifying temporal properties and computing information on traces 655 a prototype antares has been developed (18, 500 lines of ocaml code) and integrated into the airbus simulation framework. experiments conducted on industrial applications allowed us to assess the efficiency of our approach on different kinds of properties and different sizes of trace. an industrial deployment of the tool would require adding a graphical user interface to display results and to provide user-friendly ways to write properties. in addition, in order to simply the specification of properties we aim to improve the language with parametric variable handling. we hope to improve the handling of past formulae by reverse reading the trace during the automaton execution. these are parts of future work. this approach is a contribution to the overall industrial strategy, which consists in reducing the part of testing in the verification process of avionics software by developing and integrating static and dynamic analyses techniques [18]. future work will target means to better combine static and dynamic analysis. acknowledgment the work is funded by the french national research agency (anr) in the context of the perfect and re(h)strain projects. bibliography [1] runtime verification, 2001-2009. 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[19] volker stolz and eric bodden (2006), temporal assertions using aspectj, proceedings of the fifth workshop on runtime verification (rv 2005), electronic notes in theoretical computer science, 144(4):109 –124. international journal of computers communications & control issn 1841-9836, 14(1), 78-89, february 2019. a chi-square distance-based similarity measure of single-valued neutrosophic set and applications h.p. ren, s.x. xiao, h. zhou haiping ren teaching department of basic subjects jiangxi university of science and technology,330013 nanchang, china 9520060004@jxust.edu.cn shixiao xiao* chengyi university college jimei university,361021 xiamen, china *corresponding author:xiaoshixiao@jmu.edu.cn hui zhou school of mathematics and computer science yichun university,336000 yichun, china huihui7978@126.com abstract: the aim of this paper is to propose a new similarity measure of singlevalued neutrosophic sets (svnss). the idea of the construction of the new similarity measure comes from chi-square distance measure, which is an important measure in the applications of image analysis and statistical inference. numerical examples are provided to show the superiority of the proposed similarity measure comparing with the existing similarity measures of svnss. a weighted similarity is also put forward based on the proposed similarity. some examples are given to show the effectiveness and practicality of the proposed similarity in pattern recognition, medical diagnosis and multi-attribute decision making problems under single-valued neutrosophic environment. keywords: chi-square distance measure, similarity measure, multi-attribute decision making, single-valued neutrosophic set. 1 introduction since fuzzy set was first proposed by zadeh and has achieved a great success in various fields due to its capability of handling uncertainty [4, 6, 9, 14, 21, 29]. over the last decades, some extended fuzzy sets have been introduced by researchers, such as intuitionistic fuzzy set [1], vague set [10], interval-valued intuitionistic fuzzy set [2] and hesitant fuzzy set [28]. as an extension of zadeh’s fuzzy set, intuitionistic fuzzy set can better describe the situation when decision making process exists decision makers’ hesitation than zadeh’s fuzzy set by adding a non-membership degree parameter [32]. in recent years, intuitionistic fuzzy set has received a lot attention and been applied to many fields, such as management decision, pattern recognition and medical diagnosis [5, 7, 18, 20, 27]. in practice, indeterminate and inconsistent information may occur, then intuitionstic fuzzy set cannot deal with these situations well because it only contains the true membership degree and the false membership degree (non-membership degree). for example, when an authority wants to choose the best candidate, ten experts are invited to take part in the decision. for one candidate he gained 10 votes from the experts. there are 3 votes yes", 2 votes "no", 2 "gave up"" and 3 "undecided". in this case, intuitionistic fuzzy set cannot describe it well. to overcome this shortcoming, smarandache [26] introduced a concept of neutrosophic set, which is an extension of intuitionistic fuzzy set from philosophical point of copyright ©2019 cc by-nc a chi-square distance-based similarity measure of single-valued neutrosophic set and applications 79 view. neutrosophic set is defined as a set containing the degree of truth, indeterminacy, and falsity. for afore-mentioned example, the vote result can be expressed by a neutrosophic set. however, the original neutrosophic set is difficult to apply in practical problems. to applied it easily in science and engineering fields, wang et al. [30] introduced the concept of single-valued neutrosophic set (svns), which is a subclass of smarandache’s neutrosophic set. because svns is easy to express, it has been a useful mathematical tool for handling various practical problems involving imprecise, indeterminacy, and inconsistent data [11,15,19,23,25]. in recent years, the study and applications of information measures of fuzzy sets have received a lot attention. similarity measure is one of the most important measurement tools for comparing the degree of similarity between two objects. since li and chen [17] introduced the definition of the similarity measure between two intuitionistic fuzzy sets. since then intuitionistic fuzzy similarity measures have received great attention. from a different point of view, many similarity measures are proposed and applied to solve various practical problems of madm, pattern recognition and medical diagnosis, etc. [12,13,16,22,31,34]. as an extension of intuitionistic fuzzy set, some similarity measures of neutrosophic set is developed from those of intuitionistic fuzzy sets, and some new similarity measures are also proposed, but the references are still rare ( [35]). based on vector similarity functions, some similarity measures between simplified neutrosophic sets are put forwards, such as similarity measures based on jaccard, dice, and cosine functions [33–35]. we find that the existing similarity measures have shortcomings, and the detail analysis can be found in example 1. then the aim of this paper is to develop a new similarity measure of svnss based on chi-square distance measure, which is an important measure in statistical theory. we will show the advantage of the proposed similarity measure with existing similarity measures of svnss through comparison with some numerical examples. three examples are provided to demonstrate the effectiveness and practicality of the proposed similarity in the application of pattern recognition, medical diagnosis and multi-attribute decision making. the remains of this article are organized as follows: section 2 will recall some basic concepts and properties of svnss and similarity measure. section 3 introduces a new similarity measure between svnss based on ordinary chi-square distance measure, and put forward a weighted similarity for further applications. section 4 develops the applications of the proposed similarity measure with some examples. finally, conclusions are provided in section 5. 2 preliminary knowledge in this section, some basic concepts and properties of svnss and similarity measure are presented. smarandache [26] originally introduced a concept of neutrosophic set from philosophical point of view. definition 1 let x be a universal set. a set is called a neutrosophic set, if it is characterized by three parameters: truth-membership function ta(x), indeterminacy-membership function ia(x) and falsity-membership function fa(x) .that is a has the following form: a = {< x,ta(x),ia(x),fa(x) > |x ∈ x}. hereta(x),ia(x),fa(x) : x →]−0, 1+[, ]−0, 1+[ is non-standard interval, and they satisfy −0 ≤ sup ta(x) + sup ia(x) + sup fa(x) ≤ 3+ a neutrosophic is defined as a set containing the degree of truth, indeterminacy, and falsity. however, the original neutrosophic set is difficult to apply in practical problems. to apply it 80 h.p. ren, s.x. xiao, h. zhou easily in science and engineering fields, wang et al. [30] introduced single-valued neutrosophic set (svns), which is a subclass of smarandacheążs neutrosophic set. definition 2 let x be a universal set. a set a is called a svns, if it is characterized by three parameters: truth-membership function ta(x), indeterminacy-membership function ia(x) and falsity-membership function fa(x). that is, a has the following form: a = {< x,ta(x),ia(x),fa(x) > |x ∈ x}. hereta(x),ia(x),fa(x) : x → [0, 1], and they satisfy 0 ≤ ta(x) + ia(x) + fa(x) ≤ 3. for convenience, when x = {x}, we briefly denote the element < x,ta(x),ia(x),fa(x) > of a by < ta,ia,fa >.element < ta,ia,fa > is often named as a single-valued neutrosophic value (svnv). definition 3 [30] let x be a universal set, and a = {< x,ta(x),ia(x),fa(x) > |x ∈ x}and b = {< x,tb(x),ib(x),fb(x) > |x ∈ x} are two svnss in x, then (i) the complement of a svns a is ac = {< x,fa(x), 1 − ia(x),ta(x) > |x ∈ x} (ii)a ⊆ b if and only if ta(x) ≤ tb(x),ia(x) ≥ ib(x),fa(x) ≥ fb(x), for all x in x. (iii) a = b if and only if a ⊆ b and b ⊆ a. in the following discussion, we always use sv nss(x) to denote the set of all svnss in x. definition 4 will introduce the definition of a similarity measure between two svnss a and b. definition 4 [35] let a and b be two svnss, and s is a mapping s : sv nss(x) × sv nss(x) → [0, 1]. we call s (a,b) the similarity measure between a and b if it satisfies the following properties: (i) 0 ≤ s (a,b) ≤ 1; (ii)s (a,b) = 1 if and only if a = b; (iii)s (a,b) = s(b,a); (iv) if a ⊆ b ⊆ c , then s(a,c) ≤ min{s(a,b),s(b,c)}. 3 a new chi-square distance-based similarity this section contains two subsections. the first subsection will propose a new similarity measure between two svnss based on chi-square distance measure. the second subsection will compare the proposed similarity measure with existing similarity measures of svnss. 3.1 a new proposed similarity based on chi-square distance this section will propose a new similarity measure between two svnss based on chi-square distance measure. the name of the chi-square distance measure is derived from pearson’s chisquared test statistic χ2(x,y) = n∑ i=1 (xi−yi)2 xi , which is used to compare two discrete probability distributions. however, as a distance measure, the function d(x,y) should be symmetric for two objects x and y .then chi-square distance measure of two real vectors x = (x1,x2, ...,xn) and y = (y1,y2, ...,yn) is proposed as the following formula [24]: d(x,y) = n∑ i=1 (xi −yi)2 xi + yi (1) a chi-square distance-based similarity measure of single-valued neutrosophic set and applications 81 chi-square distance measure is one of most important distance measure used in face recognition [24]. to avoid the fact that the denominator is zero, the numerical value is meaningless. for later use, we proposed a revised version of chi-square distance measure formula as follows: d(x,y) = n∑ i=1 (xi −yi)2 2 + xi + yi (2) note that the constant 2 can be changed as any other positive number. let x = {x1,x2, ...,xn} be a universe set. then for two given svnss a = {< xi,ta(xi),ia(xi),fa(xi) > |xi ∈ x} and b = {< xi,tb(xi),ib(xi),fb(xi) > |xi ∈ x}, the new neutrosophic fuzzy information measure based on chi-square distance s ∆= s (a,b) is constructed as follows: s = 1 − 1 2n n∑ i=1 [ (ta(xi)−tb(xi))2 2+ta(xi)+tb(xi) + (ia(xi)−ib(xi))2 2+ia(xi)+ib(xi) + (fa(xi)−fb(xi))2 2+fa(xi)+fb(xi) + |ma(xi) −mb(xi)| ] (3) where mj(xi) = 1+tj (xi)−fj (xi) 2 , j = 1,n. to prove the information measure (3) is a valid similarity measure, we need the following lemma which can be easily proved by straightforward calculation. lemma 1 let a,b,c be three non-negative real numbers, and 0 ≤ a ≤ b ≤ c. then (i) (a−c) 2 2+a+c ≥ (a−b) 2 2+a+b (ii) (a−c) 2 2+a+c ≥ (b−c) 2 2+b+c theorem 1 let x = {x1,x2, ...,xn} be a universe set. a = {< xi,ta(xi),ia(xi),fa(xi) > |xi ∈ x} and b = {< xi,tb(xi),ib(xi),fb(xi) > |xi ∈ x} are two svnss. then information measure s (a,b) given by (2) is a valid similarity measure between svnss a and b.that is, s (a,b) satisfies the properties (i)-(iv) of definition 4. proof (i) it is obvious that 0 ≤ s (a,b) ≤ 1. (ii) when a = b, i.e. ta(xi) = tb(x),ia(xi) = ib(xi),fa(xi) = fb(xi), for all xi in x. then ma(xi) = 1 + ta(xi) −fa(xi) 2 = 1 + tb(xi) −fb(xi) 2 = mb(xi) hence we have s (a,b) = 1. (iii) the result is obvious. (iv)if a ⊆ b ⊆ c , i.e. ta(xi) ≤ tb(x) ≤ tc(x),ia(xi) ≥ ib(xi) ≥ ic(xi),fa(xi) ≥ fb(xi) ≥ fc(xi), then 0 ≤ ma(xi) ≤ mb(xi) ≤ mc(xi),for all xi in x. consequently, we can get |ma(xi) −mc(xi)| ≥ |ma(xi) −mb(xi)|, |ma(xi) −mc(xi)| ≥ |mb(xi) −mb(xi)| thus by (i) of lemma 1, we have (ta(xi) −tc(xi)) 2 2 + ta(xi) + tc(xi) ≥ (ta(xi) −tb(xi)) 2 2 + ta(xi) + tb(xi) , (ia(xi) − ic(xi)) 2 2 + ia(xi) + ic(xi) ≥ (ia(xi) − ib(xi)) 2 2 + ia(xi) + ib(xi) , 82 h.p. ren, s.x. xiao, h. zhou (fa(xi) −fc(xi)) 2 2 + fa(xi) + fc(xi) ≥ (fa(xi) −fb(xi)) 2 2 + fa(xi) + fb(xi) . then we can easily conclude that s(a,c) ≤ s (a,b). by (ii) of lemma 2, we have (ia(xi) − ic(xi)) 2 2 + ia(xi) + ic(xi) ≥ (ib(xi) − ic(xi)) 2 2 + ib(xi) + ic(xi) , (fa(xi) −fc(xi)) 2 2 + fa(xi) + fc(xi) ≥ (fb(xi) −fc(xi)) 2 2 + fb(xi) + fc(xi) . (ta(xi) −tc(xi)) 2 2 + ta(xi) + tc(xi) ≥ (tb(xi) −tc(xi)) 2 2 + tb(xi) + tc(xi) , then we can easily conclude that s (a,c) ≤ s (b,c). hence s(a,c) ≤ min{s(a,b),s(b,c)}. this completes the proof of theorem 1. if we consider the important degree of xi ∈ x = {x1,x2, ...,xn}, then we can establish a weighted similarity measure sw ∆ = s w (a,b) between snnss a and b as follows: sw = 1 − 12 n∑ i=1 wi [ (ta(xi)−tb(xi))2 2+ta(xi)+tb(xi) + (ia(xi)−ib(xi))2 2+ia(xi)+ib(xi) + (fa(xi)−fb(xi))2 2+fa(xi)+fb(xi) + |ma(xi) −mb(xi)| ] (4) where wi (i = 1, 2, ...,n) is the important degree of the element xi, they satisfy wi ∈ [0, 1] and n∑ i=1 wi = 1. if we set wi = 1n (i = 1, 2, ...,n), then sw (a,b) = s(a,b). similar to the proof process of sr(a,b)in theorem 1, we can easily prove that the weighted similarity measure sw (a,b) is also a valid similarity between two snvss a and b . that is sw (a,b) satisfies the properties (i)-(iv) of definition 4. 3.2 comparison of various similarity measures to demonstrate the validness and performance of the new proposed similarity measure, some numerical examples are used to compare it with existing similarity measures: jaccard similarity sj(a,b), dice similarity sd(a,b), cosine similarity sc(a,b) , improved cosine similarity c1(a,b)and c2(a,b), tangent function-based similarity t1(a,b), t2(a,b) , and cotangent function-based similarity cot1(a,b), cot2(a,b) . these similarity measures are given as follows( [33], [34], [35]): sj(a,b) = 1 n n∑ i=1 sj1 sj2 , (5) where sj1 = ta(xi)tb(xi)+ia(xi)ib(xi)+fa(xi)fb(xi) and sj2 = (t 2a(xi)+i 2 a(xi)+f 2 a(xi))+ (t 2b(xi) + i 2 b(xi) + f 2 b(xi)) −sj1 sd(a,b) = 1 n n∑ i=1 2(ta(xi)tb(xi) + ia(xi)ib(xi) + fa(xi)fb(xi)) (t 2a(xi) + i 2 a(xi) + f 2 a(xi)) + (t 2 b(xi) + i 2 b(xi) + f 2 b(xi)) , (6) sc(a,b) = 1 n n∑ i=1 ta(xi)tb(xi) + ia(xi)ib(xi) + fa(xi)fb(xi)√ t 2a(xi) + i 2 a(xi) + f 2 a(xi) √ t 2b(xi) + i 2 b(xi) + f 2 b(xi) , (7) a chi-square distance-based similarity measure of single-valued neutrosophic set and applications 83 c1(a,b) = 1 n n∑ i=1 cos [π 2 max (|ta(xi) −tb(xi)|, |ia(xi) − ib(xi)|, |fa(xi) −fb(xi)|) ] , (8) c2(a,b) = 1 n n∑ i=1 cos [π 6 (|ta(xi) −tb(xi)| + |ia(xi) − ib(xi)| + |fa(xi) −fb(xi)|) ] , (9) t1(a,b) = 1 − 1 n n∑ i=1 tan [π 4 max (|ta(xi) −tb(xi)|, |ia(xi) − ib(xi)|, |fa(xi) −fb(xi)|) ] , (10) t2(a,b) = 1 − 1 n n∑ i=1 tan [ π 12 (|ta(xi) −tb(xi)| + |ia(xi) − ib(xi)| + |fa(xi) −fb(xi)|) ] , (11) cot1(a,b) = 1 n n∑ i=1 cot [π 4 max (|ta(xi) −tb(xi)|, |ia(xi) − ib(xi)|, |fa(xi) −fb(xi)|) ] , (12) cot2(a,b) = 1 n n∑ i=1 cot [π 4 + π 12 (|ta(xi) −tb(xi)| + |ia(xi) − ib(xi)| + |fa(xi) −fb(xi)|) ] , (13) example 1 suppose that x = {x} , we consider pattern recognition problems with six pairs of svnss shown in table 1. the calculated numerical values of these 9 existing similarity measures and proposed similarity measure are shown in table 1. table 1: values of the different similarity measures under different pairs of (a,b) case 1 case 2 case 3 case 4 case 5 case 6 a <0.3,0.3,0.4> <0.3,0.3,0.4> <1,0,0> <0.4,0.2,0.6> <0.4,0.4,0.2> <0.4,0.4,0.2> b <0.4,0.3,0.4> <0.4,0.3,0.3> <0,1,1> <0.2,0.2,0.3> <0.5,0.2,0.3> <0.5,0.3,0.2> sj(a,b) 0.9737 0.9429 0 0 0.8500 0.9474 sd(a,b) 0.9867 0.9706 0 0 0.9189 0.9730 sc(a,b) 0.9910 0.9706 0 null 0.9193 0.9733 c1(a,b) 0.9877 0.9877 0 0 0.9511 0.9877 c2(a,b) 0.9986 0.9945 0 0.8660 0.9781 0.9945 t1(a,b) 0.9213 0.9213 0 0 0.8416 0.9213 t2(a,b) 0.9738 0.9476 0 0.7321 0.8949 0.9476 cot1(a,b) 0.8541 0.8541 0 0 0.7265 0.8541 cot2(a,b) 0.9490 0.9004 0 0.5774 0.8098 0.9004 s(a,b) 0.9713 0.9463 0 0.5833 0.9886 0.9714 from table 1, we can see that the similarity measures c1(a,b) and t1(a,b) cannot carry out the recognition between case 1 and case 2. for case 4, there are only four reasonable similarity 84 h.p. ren, s.x. xiao, h. zhou measures c2(a,b) , t2(a,b), cot2(a,b) and s(a,b) intuitively consistent. an interesting counter-intuitive case occurs when three snvss a =< 0.4, 0.4, 0.2 >, b =< 0.5, 0.2, 0.3 > and c =< 0.5, 0.3, 0.2 >. they can be written in forms of intuitionistic fuzzy values as: a =< 0.4, 0.2 >, b =< 0.5, 0.3 > and c =< 0.5, 0.2 >, respectively. in this case, boran and akay [3] pointed out that it is expected that the similarity degree between a and b should not be less than the similarity degree between a and c since they are ordered as a ≤ b ≤ c according to score function and accuracy function. however, the similarity degree between a and c is greater than the similarity degree between a and b when the existing similarity measures are used (the results can be found in table 1), which does not seem to be reasonable. table 1 shows that our proposed similarity measure is in agreement with this analysis. according to the above analysis, the proposed similarity measure is the most reasonable similarity measure. 4 applications in the following discussion, we will give two examples in pattern recognition and medical diagnosis to demonstrate the effectiveness and practicability of the proposed similarity measure. example 2 assume that there are two patterns in x = {x1,x2} . the two patterns are expressed by svnss, which are shown as follows: a1 = {< x1, 0.2, 0.0, 0.2 >,< x2, 0.2, 0.0, 0.2 >,< x3, 0.2, 0.0, 0.2 >}, a2 = {< x1, 0.4, 0.0, 0.4 >,< x2, 0.4, 0.0, 0.4 >,< x3, 0.4, 0.0, 0.4 >}. assume that there is an object b = {< x1, 0.3, 0.0, 0.3 >,< x2, 0.3, 0.0, 0.3 >,< x3, 0.2, 0.0, 0.3 >} our task is to classify the object b in a1 or a2 . according to the recognition principle of maximum similarity measure between svnss, the process of assigning the object b to a1 or a2 is described by k = arg max 1≤i≤2 {sr(ai,b)} (14) by eq. (3), we can get the similarity measures between a1,a2 with b : s(a1,b) = 0.9700,s(a2,b) = 0.9415. then the pattern b is classified in a1 according to the recognition rule given by eq. (14). this result is consistent with our intuition. example 3 we consider the following pattern recognition problem: there are three patterns a1, a2 and a3 , which are represented by svnss in universe set x = {x1,x2,x3}, as follows: a1 = {< x1, 1.0, 0.2, 0.0 >,< x2, 0.8, 0.3, 0.0 >,< x3, 0.7, 0.1, 0.1 >} a2 = {< x1, 0.8, 0.1, 0.1 >,< x2, 1.0, 0.1, 0.2 >,< x3, 0.9, 0.2, 0.1 >} a3 = {< x1, 0.6, 0.3, 0.2 >,< x2, 0.8, 0.2, 0.3 >,< x3, 0.6, 0.3, 0.2 >} given an unknown pattern b, which is represented by the svns: b = {< x1, 0.5, 0.3, 0.2 >,< x2, 0.6, 0.3, 0.2 >,< x3, 0.8, 0.2, 0.1 >} our task is to classify the pattern b in one of the classes a1,a2 and a3 . according to the recognition principle of maximum similarity measure between svnss, the process of assigning the pattern b to ak (k = 1, 2, 3) is described by k = arg max 1≤i≤3 {sr(ai,b)} (15) a chi-square distance-based similarity measure of single-valued neutrosophic set and applications 85 by eq.(3), we can get the similarity measures between b with ai (i = 1, 2, 3) : s(a1,b) = 0.9115,s(a2,b) = 0.8813,s(a3,b) = 0.9345. then the pattern b is classified in a3 according to the recognition rule given by eq. (15). some medical diagnosis problems are very complex. physicians need to use modern medical technologies to obtain a lot of information available to physicians for the help of decision, but the information is often incomplete, indeterminate and inconsistent. the svnss proposed by wang et al. [30] can be better choice to express this kind of information than zadehążs fuzzy sets and intuitionistic fuzzy sets. now in example 4 we will utilize the proposed similarity measure to solve a class of medical diagnosis problems. example 4 the medical diagnosis problem is adapted from de et al. [8]. let q = q1 (viral fever), q2 (malaria), q3 (typhoid), q4 (stomach problem), q5 (chest problem) be a set of diagnoses (diseases) and s= s1(temperature), s2(headache), s3 (stomach pain), s4 (cough), s5(chest pain) be a set of symptoms. each diagnosis qi (i = 1, 2, 3, 4, 5) can be represented by svnss as follows: q1 = {< s1, 0.4, 0.6, 0.0 >,< s2, 0.3, 0.2, 0.5 >,< s3, 0.1, 0.2, 0.7 >,< s4, 0.4, 0.3, 0.3 >,< s5, 0.1, 0.2, 0.7 >} q2 = {< s1, 0.7, 0.3, 0.0 >,< s2, 0.2, 0.2, 0.6 >,< s3, 0.0, 0.1, 0.9 >,< s4, 0.7, 0.3, 0.0 >,< s5, 0.1, 0.1, 0.8 >} q3 = {< s1, 0.3, 0.4, 0.3 >,< s2, 0.6, 0.3, 0.1 >,< s3, 0.2, 0.1, 0.7 >,< s4, 0.2, 0.2, 0.6 >,< s5, 0.1, 0.0, 0.9 >} q4 = {< s1, 0.1, 0.2, 0.7 >,< s2, 0.2, 0.4, 0.4 >,< s3, 0.8, 0.2, 0.0 >,< s4, 0.2, 0.1, 0.7 >,< s5, 0.2, 0.1, 0.7 >} q5 = {< s1, 0.1, 0.1, 0.8 >,< s2, 0.0, 0.2, 0.8 >,< s3, 0.2, 0.0, 0.8 >,< s4, 0.2, 0.0, 0.8 >,< s5, 0.8, 0.1, 0.1 >} suppose there are two patients p1 and p2 , with respect to all the symptoms, can be represented by the following svnss: p1 = {< s1, 0.8, 0.1, 0.1 >,< s2, 0.6, 0.3, 0.1 >,< s3, 0.2, 0.0, 0.8 >,< s4, 0.6, 0.3, 0.1 >,< s5, 0.1, 0.3, 0.6 >} p2 = {< s1, 0.0, 0.2, 0.8 >,< s2, 0.4, 0.4, 0.2 >,< s3, 0.6, 0.3, 0.1 >,< s4, 0.1, 0.7, 0.2 >,< s5, 0.1, 0.8, 0.1 >} our aim is to determine the patients p1 and p2 belong to which diagnosis of qj (j = 1, 2, 3, 4, 5) , respectively. because the medical diagnosis problem is actually a pattern recognition problem, then we can use the diagnosis rule as follows: if k = arg max 1≤j≤5 {sr(qj,pi)}, then we assign the patient p1 and p2 to the diagnosis qk. s(q1,p1) = 0.9160,s(q2,p1) = 0.9360, s(q3,p1) = 0.9000,s(q4,p1) = 0.7220 and s(q5,p1) = 0.6640, s(q1,p2) = 0.8103,s(q2,p2) = 0.7259, s(q3,p2) = 0.8043,s(q4,p2) = 0.8435 and s(q4,p2) = 0.7553 then, by the above diagnosis rule, we can assign the patient p1 to the diagnosis q2 (malaria), and p2 to the diagnosis q4 (stomach problem). this result is in agreement with the one obtained in de et al. [8]. 86 h.p. ren, s.x. xiao, h. zhou example 5 (multi-attribute decision making) we consider a madm problem adopted from ye [33]. a manufacturing company wants to select the best global supplier form a set of four suppliers a = {a1,a2,a3,a4} whose core competencies are evaluated according to the four attributes o = {o1,o2,o3,o4}: o1 (the level of technology innovation) , o2 (the control ability of flow), o3 (the ability of management) , o4 (the level of service). the attributes are all benefit attributes. the weight vector for the four attributes determined by decision maker is w = (w1,w2,w3,w4) t = (0.30, 0.25, 0.25, 0.20)t suppose that the evaluation value of the alternative ai(i = 1, 2, 3, 4) with respect to oj(j = 1, 2, 3, 4) is a snvn aij =< tij,iij,fij > , which is obtained from a questionnaire of a domain expert. for example, when we ask the opinion of an expert about an alternative a1 with respect to an attribute o1 , he/she may say that the possibility in which the good statement is 0.5 and the poor statement is 0.3 and the degree in which he/she is not sure is 0.1. for the neutrosophic notation, it can be expressed as a11 =< t11,i11,f11 > . the evaluation values are listed in table 2. table 2: evaluation values of each alternative with respect to each attribute1 alternatives o1 o2 o3 o4 a1 <0.75,0.2,0.3> <0.7,0.2,0.3> <0.65,0.2,0.25> <0.75,0.2,0.1> a2 <0.8,0.1,0.2> <0.75,0.2,0.1> <0.75,0.2,0.1> <0.85,0.1,0.2> a3 <0.7,0.2,0.2> <0.78,0.2,0.1> <0.85,0.15,0.1> <0.76,0.2,0.2> a4 <0.8,0.2,0.1> <0.85,0.2,0.2> <0.7,0.2,0.2> <0.86,0.1,0.2> now, we will propose a decision making method based on the proposed similarity measure to solve this problem and the detail steps is given as follows: step 1 determine the ideal solution a∗ as follows: a∗ = (< t∗j ,i ∗ j ,f ∗ j >)1×4 = (< max 1≤i≤4 (tij), min 1≤i≤4 (iij), max 1≤i≤4 (fij) >)1×4 step 2 according to eq. (4), calculate similarity measures between each alternative ai(i = 1, 2, 3, 4) and the ideal solution a∗ as follows: s(a1,a ∗) = 0.9859,s(a2,a ∗) = 0.9955,s(a3,a ∗) = 0.9919 and s(a4,a∗) = 0.9942. step 3 according to the similarity measure values, the ranking order of the four suppliers is a2 � a4 � a3 � a1. hence, the best supplier is a2 , which is in agreement with the result obtained by using weighted projection similarity and weighted dice similarity methods( ye [33]). 5 conclusion neutrosophic sets are suitable to model the indeterminate and inconsistent information occurred in many practical problems. in this paper, we have proposed a new chi-square distancebased similarity measure of svnss. the new proposed similarity measure is a valid similarity measure and it can also overcome the counter-intuitive cases of the existing similarity measures by using some numerical examples. we have given the applications of the proposed similarity measure in pattern recognition and medical diagnosis. furthermore, a multi-attribute decision making method is proposed through an example in which attribute values are expressed with svnvs. a chi-square distance-based similarity measure of single-valued neutrosophic set and applications 87 as a prospect, the madm method proposed in this paper could be applied to other madm problems, such as the risk evaluation, credit evaluation. in the future work, we shall extend the proposed similarity to clustering analysis and image processing. funding this paper was supported by the national natural science foundation of china (no.71661012) and foundation of jiangxi educational committee (no. gjj170496). author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. bibliography [1] atanassov, k. t.(1986); intuitionistic fuzzy sets, fuzzy sets and systems, 20(1), 87–96, 1986. 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(2017); single-valued neutrosophic similarity measures based on cotangent function and their application in the fault diagnosis of steam turbine, soft computing, 21(3), 1–9, 2017. international journal of computers communications & control issn 1841-9836, 10(2):200-210, april, 2015. hapa: harvester and pedagogical agents in e-learning environments m. ivanović, d. mitrović, z. budimac, l. jerinić, c. bădică mirjana ivanović*, dejan mitrović, zoran budimac, ljubomir jerinić department of mathematics and informatics faculty of sciences, university of novi sad, serbia mira@dmi.uns.ac.rs, dejan@dmi.uns.ac.rs, zjb@dmi.uns.ac.rs, jerinic@dmi.uns.ac.rs *corresponding author: mira@dmi.uns.ac.rs costin bădică computer and information technology department faculty of automatics, computers and electronics, university of craiova, romania cbadica@software.ucv.ro abstract: in the field of e-learning and tutoring systems two categories of software agents are of the special interest: harvester and pedagogical agents. this paper proposes a novel e-learning system that successfully combines both of these agent categories and introduces two distinct sub-types of pedagogical agents helpful and misleading. whereas helpful agents provide the correct guidance for the given problem, misleading agents try to guide the learning process in the wrong direction by offering false hints and inadequate solutions. the rationale behind this approach is to motivate students not to trust the agent’s instructions blindly, but to employ critical thinking. consequently, students will be put in a "softly stressed" environment in order to prepare them for real working environments in their future work in companies. nevertheless students themselves will decide on the correct solution to the problem in question. keywords: e-learning, adaptability, personalization, intelligent agent, harvester agents, pedagogical agents. 1 introduction software agents (or simply agents), can be defined as autonomous software entities with various degrees of intelligence, capable of exhibiting both reactive and pro-active behavior in order to satisfy their design goals. from the point of e-learning and tutoring systems, two types of agents are of special research interest: harvester and pedagogical. harvester agents collect learning material from online, heterogeneous repositories. the core properties of the agent technology (e.g. parallel and distributed execution, mobility, and inter-agent communication) can bring significant benefits to the harvesting process [16]. pedagogical agents can be defined as "lifelike characters presented on a computer screen that guide users through multimedia learning environments" [6]. their main goals are to motivate and guide students through the learning process [7]. this paper presents a stand-alone e-learning architecture named harvester and pedagogical agent-based e-learning system (hapa), and designed to help learners during solving programming tasks. hapa consists of three main components: harvester agents, classifier module, and pedagogical agents. the harvester agents collect the appropriate learning material from the web. their results are fed into the classifier module, which performs automatic classification of individual learning objects. finally, a pair of specially designed pedagogical agents one helpful and one misleading is used to interact with students and guide them to comprehend the learning material. the helpful pedagogical agent provides useful hints for the problem in copyright © 2006-2015 by ccc publications hapa: harvester and pedagogical agents in e-learning environments 201 question. the misleading pedagogical agent guides the learning process in the wrong direction. because the student is never sure with which agent (s)he is interacting, this novel approach encourages students not to follow the agent’s/tutor’s instructions blindly, but rather to employ critical thinking. we believe that this "softly stressed" environment could help learners to face stressful and competitive real working environments. to the best of our knowledge, none of the existing e-learning systems employs this combination of pedagogical agents in conjunction with harvester agents for collecting additional learning material. this is the main idea and contribution behind the work presented in this paper. the initial ideas of using harvester and two types of pedagogical agents were presented in [8, 9]. this paper concentrates on improvements and a concrete implementation of initial ideas, with a welldefined set of functional components. harvester agents are now defined as web crawlers [11], specialized for collecting java source code examples. the new classifier module has also been defined, and a set of tools for preparing helpful and misleading hints, and visual representation of pedagogical agents has been implemented. early evaluation results are presented as well. the rest of this paper is organized as follows. section 2 provides an overview of the existing work related to the employment of harvester and pedagogical agents. in section 3, a detailed insight into the proposed system is presented. section 4 brings implementation details and early evaluation results. overall conclusions are given in section 5. 2 related work there are many interesting approaches to using software agents in e-learning and tutoring environments. for example, abits [2], mathtuthor [3], and educ-mas [4] incorporate intelligent agents in order to improve the students’ learning outcomes. however, none of these systems use harvester and pedagogical agents in the same environment. it was shown in [17] that inherited properties of the agent technology – parallel and distributed execution – can be used to optimize the web crawling process of harvesting agents. the same approach has been taken in hapa, except that our agents are highly specialized to search for syntactically correct java source code examples. agent based search system (abss) relies on harvester agents to improve the quality of search query results [15]. the system is capable of not only harvesting heterogeneous remote learning object repositories, but also tracking changes in them. similarly, agcat represents an agentbased federated catalog of learning objects [1]. the harvesting process is delegated between two agents librarian and interlibrarian that, respectively, maintain the local repository of learning objects, and perform the federated search and retrieval on remote repositories. both of these systems use sophisticated harvesting agents to retrieve the best-suited learning objects. the difference between hapa and both abss and agcat is in the approach used to deliver the harvested content. abss and agcat are sophisticated search engines; they enable their users to pull the data using search queries. on the other hand, our system monitors and evaluates the student’s progress through a course. if a decline in student’s performance is detected, hapa can harvest additional appropriate learning material and then push it to the student. an interesting analysis of 39 studies related to the effects of pedagogical agents onto the learning outcome has been presented in [6]. the initial conclusion is that only 5 studies have detected positive effects of using pedagogical agents. however after a more detailed analysis it was observed that only 15 of the 39 studies used a control group without an agent, while actual motivational approaches were implemented in only 4 of these 15 studies. our first intention was not to implement visual representations of pedagogical agents in hapa environment. however since several studies [5,6] discuss that un-appealing visual representations 202 m. ivanović, d. mitrović, z. budimac, l. jerinić, c. bădică of an agent can have a negative impact on the student’s willingness to interact with the agent, we decided to visualize our pedagogical agent(s). we implemented a simple character (see figure 2) and let students decide to use it or not during learning and assessment activities. both versions of our pedagogical agents are represented with the same visual character. smartegg is a web-based pedagogical agent that assists students in learning sql [13]. it is integrated into an intelligent e-learning system sql-tutor. the agent includes a visual representation with animated gestures, and can express different behaviors: introductory, explanatory, and congratulatory. smartegg is relatively simple, and is employed just as a more pleasant way of presenting the learning material. as shown, there is a lot of ongoing research related to the usage of harvester and pedagogical agents in e-learning environments. however, although many existing systems incorporate either type of agents, there have been no previous attempts to efficiently integrate both harvesting and motivational-level agents. an additional, and more important contribution of hapa is the concept of helpful and misleading pedagogical agents. 3 hapa system overview hapa is currently a stand alone e-learning system which helps students in learning and especially in solving programming problems. at a later stage hapa could be included as a component in other tutoring systems devoted to learning programming languages. our intention is to incorporate it in protus, a tutoring system we designed to help learners in learning essentials of java programming language [10, 19]. a high-level overview of the system architecture is outlined in figure 1. hapa includes several important components: harvester agents, the classifier module, repositories of helpful and misleading hints, and pedagogical agents. the functioning of each component and their mutual interactions are described in more details in the following sub-sections. 3.1 harvesting and classifying the learning material hapa is mainly focused on the code completion type of tasks, in which students are expected to fill-in missing parts of the program. the student is given a code snippet, and then requested to complete the source code to meet the program specification. code completion tasks are wellsuited for both testing and improving the student’s programming skills, because they require a thorough understanding of the underlying programming concepts. as the initial step of constructing the code completion tasks, the additional learning material is collected by the harvester agents. the learning material consists of java source code examples. with the abundance of these examples available on the web, harvester agents have been implemented as web crawlers [11]. the system administrator (e.g. the teacher) specifies starting web pages. harvester agents scan these pages in search of syntactically valid java source code examples. first, a simple search for some more important java keywords, such as class, is performed. a block of text that contains keywords is then processed by a syntax analyzer to determine whether it is a valid java program. the text can either be directly embedded in the web page or attached to the page as an external file (e.g. a zip archive). after it processes the current page, the agent continues the harvesting process on all pages linked from the current one, and so on. many agents can be deployed on a computer cluster and perform the harvesting in parallel. a centralized repository of visited pages is maintained in order to avoid duplicate work. the harvested learning material is fed into the classifier module (see figure 1), which automatically associates each java source code example with a concrete lecture topic. the classification is performed via the static source code analysis. the classifier module hapa: harvester and pedagogical agents in e-learning environments 203 figure 1: a high-level overview of the hapa system. constructs an abstract syntax tree for the given java example, and then inspects programming constructs that appear in the tree. as a result, each example is assigned to the appropriate lecture topic. in return, the teacher is able to analyze and focus on examples of a particular interest, i.e. those that are directly attached to the lecture topic in question. currently, the classifier’s decision on which example belongs to which topic is a suggestion to the teacher. in the end, the teacher makes the final selection and filter the obtained source code examples. in order to improve the classifier’s performance, more intelligent source code classification techniques will be implemented in the future (e.g. [12]). once the harvested learning material has been classified, the teacher can use them select most appropriate solutions for learning topic and prepare the code completion tasks. this step is performed manually, using a specially designed gui tool. the tool enables teacher to quickly scroll through the classified java source code examples, select the ones to be actually used, and process them by removing parts of the code and constructing useful and misleading hints which will be offered to students. the hints are incorporated in pedagogical agent and used in learning. 3.2 pedagogical agents the significant novelty of this work is incorporation in a learning environment two different types of pedagogical agents – helpful, and misleading. both agents are hidden from the student behind the same interface and visual representation (see figure 2), and take turns in interacting with the student at random time intervals. therefore, the student is never sure with which agent he/she is interacting. the rationale behind this approach is to motivate students not to trust the agent’s hints blindly. instead, they should critically analyze the problem and the proposed hint, and independently decide on the proper solution. in the scientific literature and the actual software products, it is common to represent pedagogical agents as lifelike, animated characters. although we feel that there is no real value in this approach we nevertheless decided to implement simple visual pedagogical hapa agent and let students decide if they will activate it or not, using the on/off switch button. but, although maybe "fun" to look at in the beginning, over the time the visual character stands in the way 204 m. ivanović, d. mitrović, z. budimac, l. jerinić, c. bădică figure 2: visual representation of a pedagogical hapa agent. of solving the assignment. this was somehow confirmed during the experimental phase with students. they distract the student from concentrating on the problem in question, and in the extreme case, may negatively affect his/her willingness to use the system. both pedagogical agents are capable of adapting to each individual student. agents track a set of information about the student, including his/her personal data (such as class and age), the ratio of correct and incorrect solutions to each code completion problem, and the student’s grade for each lecture topic. based on the accumulated data agents can intervene if the student’s success rate becomes unsatisfactory. for example, if the student gives to many wrong answers to questions regarding for loops, the pedagogical agent will recommend additional learning material. additionally, it will repeat the appropriate code completion tasks until a certain success threshold is reached. we believe that if students do not know if agent gives correct or wrong directions and hints, it will additionally motivate students to critically think and assess their knowledge. also in future real working environments they will face different helpful but also malicious colleagues who will maybe suggest them wrong procedures and steps. so we would like to put students in unexpected situations and motivate them to reassess their knowledge and skills. 4 hapa implementation and evaluation previously described functionalities of hapa were used to guide the implementation process. for example, harvesting is a process that can and should be distributed and executed in parallel. then, students should be able to interact with and use hapa through a web interface. and, like all web-based systems, hapa should be resilient to hardware and software failures, malicious attacks, etc. given these implementation requirements, and its popularity in developing software agents and multi-agent systems, java has been chosen as the implementation platform for hapa. 4.1 helpful and misleading hints in order to provide the reader with a better insight into the evaluation of hapa, some examples of the prepared code completion tasks are presented here. the two given tasks are tailored to topics on for loops and classes in java, respectively. helpful and misleading hints assigned to each task are also presented and discussed. the task tailored to the topic on for loops in java requires the student to complete a program for calculating the first 10 members of the fibonacci sequence. the skeleton program presented to students is shown in listing 1. hapa: harvester and pedagogical agents in e-learning environments 205 listing 1: code completion task related to for loops c l a s s fig { p u b l i c s t a t i c v o i d main ( s t r i n g [ ] a r g s ) { i n t [ ] f = new i n t [ 1 0 ] ; // todo : implement t h e f o r l o o p h e r e p r i n t ( f ) ; } } based on this skeleton, the following set of helpful and misleading hints for pedagogical agents have been prepared [8]. 1. for (int i = ?; i < 10; i++){} “what should be the starting index? remember that the first element of the fibonacci sequence has the index 0, while the expression for calculating other elements is fi = fi−1 + fi−2” 2. for (int i = 0; i ≤ ?; i++){} “what should be the ending index? although you need 10 numbers, remember that the index of the first element is 0.” 3. for (int i = 0; i < 10; ?){} “should you use ++i or i++ to modify the value of i? remember that this modification is always executed at the end of the for loop.” 4. for (int i = ?; i < 10; i++){} “what should be the starting index? hint: the first element of the fibonacci sequence is often denoted as f0.” 5. for (int i = 0; i ≤ ?; i++){} “what should be the ending index? hint: look at the initialization of the array f – how many elements does it have?” 6. for (int i = 0; i < 10; ?){} “should you use ++i or i++ to modify the value of i? remember that ++i first increases the value of i, and then uses the new value.” by suggesting that f0 is the first element of the fibonacci sequence in hint 4, the misleading agent tries to suggest the improper usage of 0 for the initial value of i. in the expression fi = fi−1 + fi−2, this decision would cause the index to go out of the array bounds. similarly, in hint 5, the agent suggests that the student should use 10 as the final value of i (note the expression i ≤ ? ), disregarding the fact that java array indexes are 0-based. the final hint 6 is there to confuse the student, since both ++i and i++ are correct. listing 2 shows the skeleton for a more complex task tailored to classes, fields and methods. the given class represents a rectangle, defined by its upper-left point (x, y), width, and height. the student’s task is to write a method that calculates the rectangle area. the focus is on the proper definition of the method’s input parameter and the return value. listing 2: code completion task related to classes. c l a s s rect { p r i v a t e f l o a t x , y , width , h e i g h t ; p u b l i c rect ( f l o a t x , f l o a t y , f l o a t width , f l o a t h e i g h t ) { // t h e p a r a m e t e r s a r e saved i n t o c o r r e s p o n d i n g f i e l d s } // todo : c a l c u l a t e t h e r e c t a n g l e a r e a h e r e } for this example the following set of hints are defined. hints 7 and 8 are used by the helpful, while hints 9 and 10 are used by the misleading agent. 206 m. ivanović, d. mitrović, z. budimac, l. jerinić, c. bădică 7. “to calculate the area, you need width and height, both of which are available as fields. remember that a method can access its object’s fields without limitations.” 8. “while writing the method, remember that somebody needs to use the result; outputting it on the screen won’t be of much use to anybody!” 9. “to calculate the area, you need both width and height. make sure your method has access to these values!” 10. “remember that long command for outputting values on screen? here’s a hint: it starts with ’system’.” given misleading hints are based on our long-term experience in conducting exercises for the introductory java programming course. it has been observed that initially, a relatively large portion of students has the problem of grasping the concept of fields, methods, and method arguments. that is, they tend to specify fields as method arguments, rather than to use them directly. this is what the misleading hint 9 tries to suggest. the given constructor implementation that receives both width and height also works in favor to this suggestion. similarly, when asked to calculate some value (in this case, the rectangle area), beginner students often tend to just print the value on screen, rather than to return it from the method. this is what hint 10 tries to lead them to. after preparing these and many other code completion tasks and hints, the system has been evaluated in practice. we expected that evaluation results would obtain adequate feedback necessary to continue our efforts and improve system’s functionalities. 4.2 evaluation results following the implementation of hapa, an evaluation of the system was conducted during school year 2013/14. the main goal of the evaluation was to examine the effects of helpful and, more importantly, misleading pedagogical agents. because the current implementation of hapa is the first prototype of the system, the evaluation was performed on a small group of self-motivated students. 24 second-year students were selected, on the basis of their previous programming experience, as well as their scores in previous programming-related course at our department of mathematics and informatics, faculty of sciences (dmi) and 18 students from department of information technology at the higher school of professional business studies (dmt). during the semester, from time to time the students were given a number of java code completion tasks to solve. once it presents a task, the system waits for the student’s input for a certain amount of time. if no input is detected, a pedagogical agent provides a hint for solving the task. as noted earlier, helpful and misleading agents take turns at random time intervals, and the student is never sure with which agent he/she is interacting. moreover, students were not even aware of the fact that there are two types of pedagogical agents. students’ responses and actions were logged by the system. after the testing an analysis of the log was conducted in order to determine whether the hints have had any influence onto the students’ though processes. as they used the system partially in blended learning style, for some lessons to obtain their opinion we did not use classical form of questionnaire. we just interview them and ask them about their opinion. we expected that in such friendly atmosphere they would be completely honest. the summary results are presented below. students from dmi showed better results and are were more eager in using hapa system. students from dmt made a lot of mistakes and were in majority of cases frustrated by using the system. more detailed explanations are given in the rest of the section. hapa: harvester and pedagogical agents in e-learning environments 207 group dmi_g1 consists of eighteen students from dmi, they have had previous programming experience, have achieved great scores in a previous programming course and were among the best ones. group dmi_g2 consists of six other students that have previous programming experiences but did not pass previous programming course. 12 students from dmi_g1 were very cautious and thought critically most of the time, and recognized and ignored hints from misleading agent. other 6 students also employed critical thinking and in majority of cases recognized misleading hints. but in several cases when they were no sure about the proper choice they accepted hints form misleading agent. 4 students from dmi_g2 blindly followed all hints and did not think whether they are correct or not. 2 other students were a little bit confused, they thought that some hints were wrong, but as they were not absolutely sure about that they decided to follow all hints. this is probably the result of their inappropriate knowledge. in the next several cases we will illustrate some specific students’ behaviors. one student among the best students had no problem in solving the tasks, and, according to his own account, rarely considered hints. this is because he had already learned java programming in high-school, and so could understand and solve the problems without any assistance. another student has considered the hints, but was also able to solve majority of the tasks without any assistance. however, he indicated that hint 7 helped him to solve the problem. he has also correctly observed that some of the presented hints were wrong. a more interesting situation was with the two students who had no previous java programming experience and have low scores on previous programming course. one of them took hint 4 for granted and received the array-index-out-of-bounds exception. on the second attempt, she was given hint 1 and was able to complete the task successfully. a similar scenario has been observed with the fourth student. by accepting hint 9, he initially wrote a method that accepts both width and height as input parameters. then, after receiving hint 7, he was able to complete the task successfully. the system was also tested and used among 18 students of the dmt. involved students were programming beginners and they used system after completion of the first programming course. the results of the experiments showed that the students of dmt had more trust in agents, and made more mistakes based on the hints of misleading pedagogical agents. majority of them (even 13) blindly followed suggestions and believed that hints were well-intentioned. other were more or less confused and do not know if can trust or not to obtained hints. several interesting conclusions can be drawn from these results. if students are unable to solve the problem on the first try, they have the tendency to trust the agent’s/tutor’s hints. this is because they have never encountered a misleading agent/tutor before and do not anticipate such behavior. the student who had noticed the wrong hint was confused but assumed that it was an implementation error. for some future work, it could be beneficial if students were informed that some hints might be intentionally misleading. secondly, students who are not confident in their java skills find the presence of a virtual agent/tutor “reassuring.” additionally, the misleading behavior and the attempt of the agent to “trick them” transform hapa into a kind of a game with the goal of beating the system. both of these effects have a significant positive impact onto the students’ motivation to use the system and employ critical thinking. these are the exact goals set for our proposed system. finally, results suggest that students from dmt were more misled by pedagogical agents than students from dmi. a reasonable explanation could be that students from dmi are better (achieved better results in secondary school, passed with high marks first programming course) and they are generally highly motivated (usually students from dmi are more ambitious about their future career and jobs) to master their programming knowledge and skills. both groups of students have the similar attitude to visual representation of pedagogical 208 m. ivanović, d. mitrović, z. budimac, l. jerinić, c. bădică agent(s) and their use in the system. as we mentioned, students were allowed to explicitly (de)activate visual forms of agents. in the beginning of using system it was attractive to them to see agents and communicate with them so they intensively used them. lately, especially when they did not know how to solve task and as they were getting more tired and nervous about hints, visual agents irritated them and they decided to switch off visual forms of agents. 5 conclusions and future work the agent technology has been recognized as a useful tool in a wide variety of domains. for e-learning and tutoring systems, harvester and pedagogical agents are of the special interest. the main contribution of this paper is the proposal of a new e-learning system named hapa that incorporates both harvester and pedagogical agents. harvester agents, along with the classifier module, are designed to collect the best-suited java source code examples from the web, and tailor them to particular lessons within a course. a more important functionality, however, is achieved by defining two new sub-types of pedagogical agents – helpful and misleading. as noted, the helpful pedagogical agent provides correct suggestions and hints for the problem in question. on the other hand, the misleading agent tries to guide the problem solving process in a wrong direction, by offering false suggestions and hints. the main aim for this approach is to motivate students not to follow the agent’s directions blindly, but instead to analyze both the problem and the suggestions thoroughly and employ critical thinking. according to our knowledge, none of the existing e-learning systems use this kind of helpful and misleading pedagogical agents in combination with harvester agents. hapa is currently realized as a prototype. future improvements will be concentrated on integrating it into our existing, fully-featured web-based e-learning architecture protus [10, 19], but it can also be integrated in some other types of available learning systems [14,18]. in order to achieve adaptability and personalization, protus incorporates several models, including: domain model, which serves as a storage of the learning material, student model for maintaining both static and dynamic information about each individual student, application model, which applies different strategies on the input received from the learner model in order to ensure efficient personalization, and the adaptation model that follows the instructional directions provided by the application module in order to organize learning resources into a navigational sequence tailored to the particular learner. obviously, modules that comprise hapa fit nicely into the organizational models of protus. harvester agents, along with the classifier module, can be used to obtain and generate learning material for the domain model. the learning material stored in the domain model consists of individual lessons, which are further decomposed into tutorials, accompanying examples, and tests. therefore, harvester agents and the classifier module can be used to collect examples and tests. proposed helpful and misleading pedagogical agents can be integrated into the adaptation model. this integration will harness the benefits of both architectures. the resulting system will be capable of providing high-quality tasks and examples, and exhibiting adaptive and personalized behavior. it will offer motivational pedagogical agents that guide students through the learning process and encourage critical thinking. having in mind that usual way of employing pedagogical agents is to help students to systematically test their self-confidence and knowledge and somehow keep them less active and expecting constant positive help we decided to apply the opposite way. we believe that if students do not know if agent gives correct or wrong directions and hints, will additionally motivate students to critically think. also in future real working environments they will face 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(2013); ontology-based architecture with recommendation strategy in java tutoring system. computer science and information systems 10, 1: 273-261. international journal of computers communications & control issn 1841-9836, 13(5), 895-907, october 2018. service innovation decision analysis based on in�uence diagrams l. tan liang tan school of information management jiangxi university of finance and economics nanchang 33013, china ghosticy@foxmail.com abstract: the in�uence diagram is a probabilistic model for presenting decision problems as a directed graph. in this study, the dynamic in�uence diagram and the interactive dynamic in�uence diagram are used to model the three parties to service innovation: customers, suppliers, and service enterprises. the models analyze the decisions of these di�erent parties and describe the process by which service enterprises should consider their own innovation conditions as well as those of the other parties, that is, customers and suppliers. moreover, during the process of service innovation, service enterprises should be in constant communication with customers and suppliers. after the customers and suppliers respond, service enterprises can modify their innovation decision-making, and improve service innovation quality and income. keywords: in�uence diagram, service innovation, decision-making. 1 introduction traditional decision-making tools such as the decision tree and decision table are limited and cannot describe the conditional independence and time sequence relations between uncertain quantities. by contrast, the in�uence diagram (id)a probabilistic model for presenting decision problems as a directed graph-uses these relations to simplify the solution, thereby quickly becoming a research hotspot and the key point of decision analysis in uncertain environments. koller in stanford university et al. propose a multi-agent id (maid), which uses a structure that a�ects the characteristics of the game, combined with game theory and the graph model [12]. maid can e�ectively represent the static structure between the multi-agent relationship, and deal with complex multi-agent countermeasures. compared with game theory, maid increases the conditional independence of uncertain variables, thereby simplifying the model and making it easier to solve. in order to express the temporal relation, this paper applies dynamic id (did) or interactive did (i-did) to solve dynamic decision-making problems with single or multiple agents [14]. service innovation refers to new ideas, new technical methods, and new or improved service, to ensure that potential users have a service experience that is di�erent from past experiences [4]. service innovation is not restricted to service industry innovation alone; all service-related innovations can be classi�ed under the concept of service innovation [17]. bilderbeek, hertog et al. developed the four-dimension model of service innovation, consisting of new service concepts, service delivery systems, customer interfaces, and non-technical factors [3]. hertog classi�ed the innovation of service enterprises into �ve modes-internal innovation, service process innovation, comprehensive innovation, customer-driven innovation, and supplier-dominated innovation and discussed the positive e�ect of knowledge-intensive services on the role of innovation. aranda, molina�fernández, who created the service innovation degree model, emphasized that organization members' knowledge and their ability to exchange such knowledge e�ectively, is the copyright ©2018 cc by-nc 896 l. tan foundation of successful innovation. therefore, knowledge management has become a decisive factor in service innovation [2]. galluj believes that service innovation has two main interaction features: (1) external interaction, which refers to employees interacting with external customers and (2) internal interaction, which refers to the interaction between internal sta� and the senior leadership. the two parties will consciously or subconsciously participate in driving innovation consciousness [9]. 2 participants in service innovation service innovation mainly consists of three participants-service enterprises, customers, and suppliers. enterprise leaders, top-level sta� members, and innovation teams together implement service innovation through the internal structure of the enterprise. the participation of customers and suppliers in service innovation is an important channel for innovation and supports service innovation through the external structure of the service enterprise. as is shown in figure 1, service innovation is a process in which managers, employees, innovation teams, customers, and suppliers jointly participate. figure 1: participants in service innovation service innovation is often created through the cooperation of service enterprises, customers, and suppliers. the overall process of service innovation refers to participation in the transformation of ability from an internal point of view. when de�ning innovation, service enterprises, customers (existing customers, planned customers, potential customers), and suppliers often convey their opinions to the senior sta�, leaders, and innovation groups of service companies. they cooperate with the company to implement innovative ideas more e�ectively and facilitate the emergence of new service de�nitions. service enterprises need customers and suppliers to participate in the research and development (r&d) and planning of new services. this cooperative process allows each participant to shares their ability. after new service plans are developed, it is necessary to implement these plans. all sta�, right from the top to the bottom, need to learn the new service concepts to facilitate new service delivery, marketing management, sales service, and so on. in the process of following reviews and innovation safeguards, the content transformation between companies and their customers is critical. the service company may obtain market evaluations of the new service from customers and implement the relevant innovation guarantee scheme in the new service, which cannot spread the knowledge between the customer and the company. therefore, service innovation is a comprehensive process that involves many important participants. there are three main aspects to service innovation: (1) knowledge transfer service innovation decision analysis based on in�uence diagrams 897 from customers; (2) knowledge integration of service enterprises; and (3) knowledge sharing of suppliers. in general, service innovation is an activity that many subjects participate in together. the service enterprise designs and develops the innovation. the customer is the motivation behind the innovation and accepts the results. the supplier is a partner in service innovation [20,21]. in this process, customers, suppliers, and service enterprises are all agents. the three-party decisionmaking system is, thus, a multi-agent decision making model. there is considerable literature that uses game and evolution game theory to analyze the decision-making relationship. so, i-did is used to analyze the evolutionary relationship among the three parties [13], and the simpli�ed discriminative model updates (dmu) model is used to solve the decision problems of service innovation [18]. 3 id of single agent first, the customer id is modeled as shown in figure 2. suppose the customer is agent j ; the customer's possible actions are participation and non-participation; the possible observation values are that the innovation conditions are mature or that they are immature. figure 2: a customer id model in the �gure, the rectangle is the decision node; the state and observation are random nodes, represented by an ellipse and associated with a conditional probability table; rj is the value node, represented by a rhombus and associated with a value function. suppose that the probability of customers' interest is x1. the conditional probability of the state transfer function of the customer is shown in table 1, the conditional probability of the observation function is shown in table 2, and the conditional probability of the value function is shown in table 3. table 1: conditional probability of customers' state transfer function interested disinterested x1 1-x1 secondly, the supplier's id is modeled as shown in figure 3. suppose the supplier is agent k; the supplier's possible actions are cooperation and non-cooperation; the possible observations are that the conditions of innovation are mature or that they are immature. suppose that the probability of the supplier's interest is x2. the conditional probability of the state transfer function of the supplier is shown in table 4, the conditional probability of the 898 l. tan table 2: conditional probability of customers' observation function state mature innovation conditions immature innovation conditions interested y1 1-y1 disinterested 1-y1 y1 table 3: conditional probability of customers' value function action interested disinterested participation z11 z12 non-participation z13 z14 observation function is shown in table 5, and the conditional probability of the value function is shown in table 6. finally, the id model of the service enterprise is as shown in figure 4. suppose that the service enterprise is the deciding agent i. there are two possible actions-innovation, non-innovation; the possible observations are that the conditions of innovation are mature or that they are immature; moreover, there are two states of service enterprises-interested and disinterested. suppose that the probability of service enterprise interest is x3. the conditional probability of the state transfer function of the supplier is shown in table 7, the conditional probability of the observation function is shown in table 8, and the conditional probability of the value function is shown in table 9. 4 id of multiple agents in order to research the multi-agent decision-making problem in a dynamic uncertain environment, maid, interactive id, and i-did are proposed. i-did is a research method to solve the dynamic decision process. it is based on the interactive partially observable markov decision process (i-pomdp), which is an extension of the partially observable markov decision process (pomdp) [11]. in i-pomdp, the deciding agent considers other agents as single decision-making subjects, rather than simply treating them as noise from the environment, as game theory does. this better matches reality. but i-pomdp also cannot express the decision process directly, and cannot solve the complex computation problem in the state space. therefore, we choose i-did figure 3: a supplier id model service innovation decision analysis based on in�uence diagrams 899 table 4: conditional probability of supplier's state transfer function interested disinterested x2 1-x2 table 5: conditional probability of supplier's observation function state mature innovation conditions immature innovation conditions interested y2 1-y2 disinterested 1-y2 y2 table 6: conditional probability of supplier's value function action interested disinterested cooperation z21 z22 non-cooperation z23 z24 table 7: conditional probability of service enterprise's state transfer function interested disinterested x3 1-x3 table 8: conditional probability of service enterprise's observation function state mature innovation conditions immature innovation conditions interested y3 1-y3 disinterested 1-y3 y3 table 9: conditional probability of service enterprise's value function action interested disinterested innovation z31 z32 non-innovation z33 z34 900 l. tan figure 4: a service enterprise id model to express the multi-agent decision-making process. 4.1 interactive id considering the service enterprise as the deciding agent, we generate an interactive id as shown in figure 5 [8]. panel (a) shows the interactive id of the l level, in which the model nodes mj,l−1 and mk,l−1 are represented by hexagons. in addition to the model nodes, there are also random nodes such as "action j", "action k" and dashed lines between them (called policy chains). the nodes "action j", "action k" are used to denote the probability distribution of the actions of j and k. the future state is the three-way interaction state, in which the observation i node represents the observation value of agent i, and the ri node represents the value function of agent i. figure 5: interactive id panel (b) shows the internal structure of the model nodes mj,l−1. the model node mj,l−1 contains the model of j on the l-1 level, denoted as m1j,l−1, m 2 j,l−1. to solve each model, we obtain the corresponding optimal action set and its probability distribution, represented as "participation1j" and "non-participation2j" in panel (b). assume that the optimal action set obtained by solving m1j,l−1 is opt, then the probability distribution of the action pr(aj∈action 1 j)=1/|opt|(aj∈opt), otherwise 0. therefore, the action nodes of l-1 level interactive id are transformed into the random nodes ("participation1j" and "non-participation 2 j" in the �gure), and the probability distribution of the action aj corresponding to the optimal policy is generated as 1/|opt|(aj∈opt). in figure 4, the corresponding action nodes ("participation1j" and "non-participation 2 j") and the node mod[mj] are the parent nodes of the node "action j". since each action node corresponds to a model, the number of action nodes in the model nodes mj,l−1 is the same as the number of modservice innovation decision analysis based on in�uence diagrams 901 els in the model node. the conditional probability distribution of the random node "action j" uses the probability distribution of the action node "participation1j" or "non-participation 2 j". furthermore, the value of mod[mj] selects the distribution of which node. use the value of mod[mj] to distinguish between the di�erent models of j. for example, when the value of mod[mj] is m1j,l−1, the random node "action j" adopts the distribution of node "participation 1 j"; when the value of mod[mj] is m2j,l−1,the node "action j" adopts "non-participation 2 j". the probability distribution of the node mod[mj] is the belief of the agent i to the j model. panel (b) clari�es the concept of the strategy chain and we �nd that the strategy chain can be represented by the traditional id arc (or edge). panel (c) is similar to (b). 4.2 i-did the interactive id is expanded in time to obtain i-did, as shown in figure 5 [8]. the dashed dotted arrow between model nodes is called the model update chain. the conditional probability distribution on random nodes "interaction statet+1" and "observet+1" correspond to the state transfer function ti and the observation function oi in i-pomdp [10]. figure 6: an i-did of service innovation figure 6 [8] shows the implementation of the model update chain in i-did with customer agent agent j as an example. assuming that both models m1j,l−1 and m 2 j,l−1 of agent j at time t in level l produce only one optimal action, and assuming that the optimal actions are "participation" and "non-participation" respectively, then j may obtain two di�erent observations: "innovation condition mature1j" and "innovation condition immature 2 j," then the model node contains four new models at time t+1 (mt+1,1j,l−1 , m t+1,2 j,l−1 , m t+1,3 j,l−1 , m t+1,4 j,l−1 in the �gure).since j updates its belief through its actions and observations, the belief of these models is di�erent. these four models represent the i-dids or dids on the l-1 level, whose action nodes correspond to the random nodes "action1j", "action 2 j", "action 3 j" and "action 4 j" respectively. the supplier agent k is similar to the customer agent j. 4.3 dmu method the solution of i-did is very complicated. this is mainly because the number of candidate models in i-did increases exponentially with time (as can be seen from figure 7). the state space of s is the interactive state between agents. it is very large, and increases rapidly with the increase in the modeling nested layer number among agents. the existing solution algorithm is based on the principle of equivalent behavior (be). the basic idea is that the behaviors of the two candidate models on agent j predicted by agent i are exactly the same, so we consider that the behaviors of the two models are equivalent. we can delete one model and assign the 902 l. tan belief of the deleted model to the other model. the policy tree is used to represent the solution model, as seen in figure 8. the left is the did of a single agent j in two time slices, and the right is the corresponding policy tree. the policy tree of t time slices is represented by πtmj,l−1; therefore, opt(mj,l−1),π t mj,l−1 , in which opt(.) is the solution of the model. in the policy tree, each branch extends from the root node to the leaf node and is an action-observation sequence, represented by ht−1j ={a t j,o t+1 j } t−1 t=0 . for example, there are two branches in figure 7 [8]: "participation→ innovation condition mature→ participation" and "participation→ innovation condition immature→ non-participation". figure 7: realization of model update chain in i-did figure 8: the policy tree and the branch of id the bottom-up approach is used to solve i-did, and the interactive id to obtain the policy tree. then we contrast the policy tree from top to bottom to check whether the models are behaviorally equivalent. the solution of the level 0 model is used by the traditional did method; it provides a probability distribution for the corresponding action node of the level 1 i-did. one of the most e�ective methods based on the behavioral equivalence principle is the dmu method, which was proposed by doshi and zeng (2009) [7]. it obtains the minimum model set by merging the policy tree. the minimum model set: m̂j,l−1 is the minimum model set of mj,l−1, that is, for any mj,l−1∈m̂j,l−1, there is no other model m'j,l−1∈m̂j,l−1/mj,l−1 and opt(m'j,l−1)=opt(mj,l−1). the key of the dmu method is to get the minimum model set m̂j,l−1 and m̂k,l−1, that is, merge service innovation decision analysis based on in�uence diagrams 903 the policy tree from top to bottom, and obtain the policy graph [19]. formally, let m̂j,l−1∈m̂j,l−1, then: b̂i(m̂j,l−1|s) = ∑ mj,l−1∈mj,l−1 bi(mj,l−1|s) (1) where bj,l−1 is the level l-1 belief, and mj,l−1 ⊆ mj,l−1 is the build extended model set in which the representative m̂j,l−1 is included. correspondingly, let m̂k,l−1∈m̂k,l−1, then: b̂i(m̂k,l−1|s) = ∑ mk,l−1∈mk,l−1 bi(mk,l−1|s) (2) according to the dmu method solution, the multi-agent i-did is modeled and solved in this paper. assume that the 0-level models of agent j and k are both the did, and x1=0.5, x2=-0.5, x3=0.5, y1=0.7, y2=-0.7, y3=0.7, z11=8, z12=-5, z13=2, z14=0, z21=8, z22=-5, z23=2, z24=0, z31=8, z32=-5, z33=2, z34=0, r11=8, r12=5, r13=2, r14=5, r15=3, r16=2, r17=2, r18=1, r21=-1, r22=-3, r23=-5, r24=-3, r25=1, r26=0, r27=0, r28=-1, m=0.85, n=0.85, and x=0.5, in which the interactive state number is 2, the action number of agent i, j and k are all 2, the observation number of agent i is 8, and the observation number of agent j and k are both 2. the initial model numbers n=25 and n=50 are used to solve the model respectively [16]. table 10: dmu method's speed level 1 n agent number t dmu times solve the 0-level model 25 2 3 124 50 2 3 215 25 2 5 652 50 2 5 1247 25 3 3 96 50 3 3 160 25 3 5 552 50 3 5 987 be model 25 2 3 24 50 2 3 24 25 2 5 52 50 2 5 52 25 3 3 132 50 3 3 132 25 3 5 9087 50 3 5 9087 in table 10, n represents the initial model number, t represents the number of time slices, and the dmu time unit is seconds. it can be seen that the time required to solve and extend the multi-agent i-did model of three agents is much larger than that of two agents. in other words, if we only build a model only between the service enterprise and customer or between the service enterprise and supplier, the process is much faster and the length of time slice is much longer. however, the service enterprise must consider the combined e�ect of both the supplier 904 l. tan and customer; it must establish three agent models. in the 0-level model, the number of agents has no relationship with the speed; this is because we assume that the three agents i-did of the 0-level model is the did. so it is the same as the two agent model; only, we need to consider another agent, such as customer or supplier. the solution of the model is figure 9, in which (a) is the result of three time slices; (b) is the result of �ve time slices; dmu 25 is the result of the dmu method, where the initial model is 25; dmu 50 is also considered. the abscissa represents the value of , and the ordinate represents the average value obtained by the service enterprise. when t=3 in the �gure, each average is the average of 10 one thousand game income values. when t=5, each average is the average of 10 one hundred game income values. it can be seen from the �gure that the average value increases with decreasing, until it approaches the exact algorithm. when the initial model n=50, the average value is generally higher than n=25, that is to say, the more models there are, the greater the gain. furthermore, the average value increases as the time slice increases, and the accuracy of decisions improves. but with the increase of the time slice and the initial model, the solution speed becomes progressively slower until it cannot be solved. therefore, we need to choose the appropriate time slice and initial model number. figure 9: comparison of model solving results it can be seen from the results of the above model that the quality of decisions made by service enterprises, customers, and suppliers in service innovation is related to the number of time slices and the initial model. because i-did is an individual decision-making method, it considers a problem from a single agent perspective, unlike the nash equilibrium as a whole, such as game theory, since the service enterprise is the main agent in this case. of course, the customer or supplier can also be considered the main agent. in the process of model solving, suppose the environment state is "interested", the customer decision action is "participation" and the supplier decision action is "cooperation", the service enterprise's decision-making action should be "innovation;" in such a case, making the decision will obtain the maximum return value of 8. suppose the environment state is "disinterested", the customers' decision action is "non-participation" and the supplier's decision action is "non-cooperation", then the service enterprise's decision-making action should be "non-innovation"; "innovation" will be the worst action as the probability of innovation failure is great, and we obtain the maximum penalty value of -5. the setting of the data is the hope of the enterprise to innovate; this is mainly because there is no way out without innovation [13]. there could be mistakes in the innovation process, but it also allows enterprises to accumulate experience. consequently, the reward of innovation is greater than the punishment of innovation failure. setting x=0.5 means that customer, service enterprise, and supplier initially have no preference for the maturity of the innovation condition. this is an a priori bayesian probability that becomes gradually more ine�ective as the time slice increases. when m=0.85, it means that the customer wants to participate with an accuracy of 0.85, and the corresponding value of n indicates the accuracy of the observation of supplier cooperation. at each time slice, the service enterprise will make a decision about whether to service innovation decision analysis based on in�uence diagrams 905 innovate or not, and will then obtain a corresponding reward or penalty value. with a longer time slice, the communication between the service enterprise and the customer or supplier increases the gradual understanding of the environment and the opponent increases the probability of making the right decision, and the return value increases. the more models for each agent, the bigger the return value and the ability to solve the problem is greatly tested. so the number of initial candidate models is the key to the quality of the decision-making. 5 conclusion first, service innovation is not an end in itself, but a means to an end, because the enterprise needs the bene�t. the enterprise should decide whether or not to innovate for its own bene�t. second, the initial degree of interest from the customer and supplier is only an a priori probability. after many decisions, it tends to a stable value, which means there is no direct relationship between the initial interest of the customer and supplier and the �nal innovation results. di�erent initial values only a�ect the speed of the solution. third, if the service enterprise takes more time to make the decision, the e�ect of the innovation will be more ideal. as time progresses, multiple decisions will make the e�ect of the innovation more desirable. lastly, the more customer and supplier factors that the service enterprise considers, the more reasonable its decision will be. at the same time, we should not blindly consider too many factors for the sake of rationality, since this may slow down the decision-making speed of the enterprise, thus a�ecting the e�ciency of service innovation. in summary, a service enterprise should consider its own innovation conditions and then continue communicating with the customer and supplier, predicting possible reactions between them, correcting the innovation decision, and improving the quality and bene�t of service innovation. funding this research was supported by the national natural science foundation of china (no. 61562033); 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(2010); customer-firm interaction and service innovation performance: a perspective of organizational learning from customers, chinese journal of management, 7(2), 218-224, 2010. international journal of computers communications & control issn 1841-9836, 13(5), 837-852, october 2018. exploring analytical models for proactive resource management in highly mobile environments y. kirsal, v.v. paranthaman, g. mapp yonal kirsal* european university of lefke faculty of engineering department of electronics and communication engineering lefke, north cyprus tr-10 mersin, turkey *corresponding author: ykirsal@eul.edu.tr vishnu vardhan paranthaman, glenford mapp school of science and technology middlesex university london, nw4 4bt v.paranthaman@mdx.ac.uk, g.mapp@mdx.ac.uk abstract: in order to provide ubiquitous communication, seamless connectivity is now required in all environments including highly mobile networks. by using vertical handover techniques it is possible to provide uninterrupted communication as connections are dynamically switched between wireless networks as users move around. however, in a highly mobile environment, traditional reactive approaches to handover are inadequate. therefore, proactive handover techniques, in which mobile nodes attempt to determine the best time and place to handover to local networks, are actively being investigated in the context of next generation mobile networks. using this approach, it is possible to enhance channel allocation and resource management by using probabilistic mechanisms; because, it is possible to explicitly detect contention for resources. this paper presents a proactive approach for resource allocation in highly mobile networks and analysed the user contention for common resources such as radio channels in highly mobile wireless networks. the proposed approach uses an analytical modelling approach to model the contention and results are obtained showing enhanced system performance. based on these results an operational space has been explored and are shown to be useful for emerging future networks such as 5g by allowing base stations to calculate the probability of contention based on the demand for network resources. this study indicates that the proactive model enhances handover and resource allocation for highly mobile networks. this paper analysed the e�ects of β and α, in e�ect how these parameters a�ect the proactive resource allocation requests in the contention queue has been modelled for any given scenario from the conference paper �exploring analytical models to maintain quality-of-service for resource management using a proactive approach in highly mobile environments" [9] (doi:10.1109/icccc.2018.8390456).a keywords: analytical modelling, proactive resource management, contention, time before handover, time to handover, highly mobile environments. areprinted (partial) and extended, with permission based on license number 4386390665938 ©[2018] ieee 7th international conference on computers communications and control (icccc) 1 introduction with the rapid development of mobile communication technologies such as wifi, femto-cells, long-term evolution advanced (lte-a), vehicular ad-hoc network (vanet), integration copyright ©2018 cc by-nc 838 y. kirsal, v.v. paranthaman, g. mapp of various other wireless networks known as heterogeneous networking (hetnet) has become necessary to provide the mobile node (mn) with ubiquitous communication. for instance, where, a mn can connect to a nearby lte, the calls rejected by lte networks due to lack of radio access can over�ow to overlaying wifi networks, thus reducing the call blocking probability and improving bandwidth utilization in cellular overlay networks. however, this may result in frequent forced handover in those areas covered by small cells and leads to extra signalling overheads [8]. in these environments, traditional handover techniques, which depend on a reactive approach, have been found to be inadequate because of high speeds as resources must be quickly allocated and deallocated as the mobile user moves around. hence, good resource management must be considered as a key enabling functionality to allow seamless connectivity in highly mobile heterogeneous environments. the advent of latest wireless technologies, high data rate multimedia services and heterogeneous user equipments, necessitate the development of e�cient techniques for resource management in highly mobile heterogeneous environments [15]. in addition, the high mobility between hetnets may lead to a high number of unnecessary handovers as well as handover failures due to the user's velocity [6]. unnecessary handover occurs when the mobile user's dwell time within the network coverage is less than the handover process from the neighbouring networks to the system. thus, the mobile user leaves the network coverage area before the handover process is executed [20]. this causes network connection interruption, thus quality of service (qos) of the system degrades. on the other hand, handover failures may lead to data loss, long delays, and even communication interruptions, which are not tolerable for safety life critical applications such as accident prevention, emergency and disaster management as required in intelligent transport systems (its) [1]. proactive behaviour by systems refers to anticipatory, self-initiated and change-oriented behaviour in situations. proactive behaviour involves acting in advance of a future situation, rather than just reacting. it means taking control and making things happen rather than just adjusting to a situation or waiting for something to happen. therefore, this proactive approach helps any system to perform better than a reactive approach [17]. it is very important to provide a seamless service to all the users in a network and therefore, these users and their contention for resources have to be looked in detail. in this context, client-based handover can be more e�cient, since the metrics for the handover decision mechanism can be monitored by the user equipment (ue) from its wireless interfaces and can be used to decide to trigger the handover. in addition to the need for new handover decision mechanisms, there is also a need for better resource reservation mechanisms due to varying tra�c characteristics, qos application requirements and wireless channel conditions at the access point (ap). e�cient resource management is needed to optimize the performance of a wireless network because only a limited number of simultaneous calls can be hosted by a wireless cell. therefore, incoming handover calls and new calls should not compromise the quality of the ongoing calls in the cell. traditionally, user contention has been used to analyse the need for speci�c resources such as radio channels by mobile users. however, it is possible to use this contention to proactively manage these resources. implementing the appropriate pre-planned resource allocation when the system state changes are known as proactive resource management. it provides the ability to specify end-to-end qos quickly and to determine whether the speci�ed qos can be provided in advance of when these resources will be required [3]. y-comm is an architecture that has been designed to build future mobile networks by integrating communications, mobility, qos and security [13]. the researchers of y-comm have explored new parameteres such as time before handover (tbh) and network dwell time (ndt) as shown in figure 1 [18]. these two parameters was introduced in [16] and are used to provide a new proactive approach for handover and resource allocation in heterogeneous environments. exploring analytical models for proactive resource management in highly mobile environments 839 mn wireless network ndt tbh figure 1: illustrating time before handover (tbh) and network dwell time (ndt) thus, in this paper, possible contention probabilities are considered and operational areas are explored for certain scenarios using an analytical modelling approach. no contention, partial contention and full contention are three possible contentions that are considered based on two key parameters i.e., time to get resource (t) and resource hold time (n). t is the time when actual resource requested is available for use i.e., even after entering the network's coverage range with a successful soft handover, the resource required by the mn might not be available, for example, other users might be holding the resource. n is the resource usage time or when actual exchange of data is taking place. thus, in order to achieve seamless handover in highly mobile environments, it is important to predict the resource availability considering t and n. this paper extends the conference paper [9]. the key additions of this journal version are as follows. it adds more on methods to formulate β and α more accurately for any given scenarios. a proactive approach used in [9] is analysed for resource allocation to formulate β and α by investigating all contention probabilities for channel access to a wireless network, results are obtained and veri�ed using analytical models. the proposed model and results presented improve the capacity and services delivered by mobile networks. the rest of the paper is organized as follows: section 2 includes and describes more related works. section 3 gives details of resource allocation and contention in highly mobile environments. section 4 gives a detailed description of our proposed model with the derived markov queuing model for the new proactive approach and section 5 shows the results and e�ects of β and α. section 6 concludes and gives the future direction of the work. 2 related work bandwidth is an extremely valuable and scarce resource in mobile networks. therefore, e�cient mobility-aware bandwidth reservation is necessary to support multimedia applications (e.g., video streaming) that require qos. several research e�orts were carried out looking at routing, security and applications for highly mobile environment but very few addressed handover and resource allocation issues.recent works in [2] and [11] clearly show that researchers are interested in proactive handover or predictive handover mechanisms, however these e�orts considered parameters like user preferences, user location and application requirements. several studies of the vertical handover procedure, mobility management and common radio resource management schemes in heterogeneous environments have been reported in the literature [5,21,24]. these studies show that there are several dynamic factors that must be considered in vertical handover decisions for e�ective network usage including policies to determine whether or not to handover should occur as well as mechanisms to determine the best network to handover. the work in [4] has proposed a model for enhancing the modelling of vertical handover in integrated cellular and wireless local area network environments. in addition, in [10] an intelli840 y. kirsal, v.v. paranthaman, g. mapp gent handover decision approach is proposed to minimize the handover failures and unnecessary handovers whilst maximizing the usage of resources in highly mobile environments. on the other hand, qos and carrier to interference-and-noise ratio (cinr) are some common metrics that must also be considered in this context. any proposed solution must also be scalable because in the future the mn may have the ability to handover to hundreds of possible target networks. in this context, client-based handover can be more e�cient, since the metrics for the handover decision mechanism can be monitored by the ue from its wireless interfaces and can be used to decide to trigger the handover [23]. knowing the velocity and current position of an mn could help to estimate where the mn is heading, thus the next position of mn where handover might be performed can be predicted. proactive handover in which the mn actively attempts to decide when and where to handover has been shown to be an e�cient handover policy mechanism to minimize packet loss and service disruption as an impending handover can be signalled to the higher layers of the network protocol stack [12]. a mobility prediction scheme for mns was proposed in [14]. probability and dempster-shafer processes were applied to predict the likelihood of the next destination based on the user's habits (e.g., frequently visited locations). in addition, second-order markov chain process was applied at each road junction for predicting the likelihood of the next road segment transition, given the direction to the destination and the path from the trip origin to that speci�c road junction. a proactive unnecessary handover avoidance scheme was proposed in [22] for the lte-a small cells. unnecessary handover was avoided by calculating the probability of the active time during the dwell time in one cell and comparing the same to �nd if pre-de�ned threshold exceeds certain value. here, a model to estimate the dwell time in the small cell was not considered. in [7], fernandez et al. highlighted the need for a decision mechanism for choosing an appropriate point of attachment for high mobility nodes. all studies above suggested resource management as well as handover guidelines in order to achieve seamless communication in high mobility environments. however, in this paper the work in [9] has been extended and analysed the request not being a�ected and staying in queue considering all possible contention probabilities in highly mobile environments based on the time the mobile user needs to acquire network resources before the handover. 3 resource allocation and contention in mobile networks in the era of increasing connectivity, wireless networks are gaining importance in several applications. one of their obvious bene�ts is the support of mobile users. in complex network infrastructures consisting of many aps, seamless connectivity becomes an important aspect because many applications rely on real-time communication and therefore need seamless handover between aps. if there is a decrease in quality the connection below a minimum threshold then the connection is lost, and the client must scan the wireless channels and look for a new ap in order to establish a new connection. the scanning procedure typically takes a long time and thus prohibits seamless connectivity [6]. most of the analysis on handover and resource allocation was service oriented and it is important to look into the impact on individual users based on their mobility and dwell times in a network. in mobile environments it is also necessary to understand why contention should be considered and this can be done by looking at classical approach to analyse the performance of mobile networks. classical handover occurs when a mn changes its points of attachments (poa) from the current wireless network to another network using a reactive approach i.e., the handover is initiated only after the mn is within the network coverage of the next wireless network. classical handover uses a reactive approach i.e., when the mn is moving away from the area covered by one cell and entering the area covered by another cell the call is transferred to the second cell in order exploring analytical models for proactive resource management in highly mobile environments 841 to avoid call termination when the mn gets outside the range of the �rst cell. in this approach in order to start the handover, the mn should be in the coverage range of the second cell and has to exchange the relative information to start the handover and complete the handover before exiting the �rst cell. there are a number of parameters that need to be known to determine whether a handover is required. the signal strength of the base station (bs) with which communication is being made, along with the signal strengths of the surrounding stations. additionally the availability of channels also needs to be known. the mn is obviously best suited to monitor the strength of the bss at its location, but only the cellular network knows the status of channel availability and the network makes the decision about when the handover is to take place and to which channel of which cell [24]. accordingly, the mn continually monitors the signal strengths of the bss it can hear, including the one it is currently using, and it feeds this information back to the bs. when the strength of the signal from the bs that the mn is using starts to fall to a level where action needs to be taken then the cellular network looks at the reported strength of the signals from other cells reported by the mn. it then checks for channel availability, and if one is available it informs this new cell to reserve a channel for the incoming mn. when ready, the current bs passes the relevant information for the new channel to the mn, which then makes the change. once there the mn sends a message on the new channel to inform that it is now within the coverage of the new network. if this message is successfully sent and received then the network shuts down communication with the mn on the old channel, freeing it up for other users, and all communication takes place on the new channel. under some circumstances such as when one base transceiver station is nearing its capacity, the network may decide to hand some mns o� to another base transceiver station they are receiving that has more capacity, and in this way reduces the load on very busy the base transceiver station. hence, access can be opened to the maximum number of users. in fact channel usage and capacity are very important factors in the design of a cellular network. in [10] a service oriented approach for mns is presented, considering that the services will be resumed as soon as the mn moves to a di�erent network. but the work did not focus on the resources available at the ap or bs and the e�ects of mobility in acquiring this resource. understanding this e�ect is very important as the mns would be waiting to acquire a channel and might move out of the current network due to mobility without service. the problem with the classical handover approach is that the ap/bs does not know in advance the network requirements of mns heading towards it. due to this the mn even after entering the network coverage, it will have to wait until the resource becomes free. in a highly mobile environments, mns will have less time to spend in a network coverage therefore, the ap/bs has to anticipate the network conditions much before based on the mns about to reach its network in order to have an e�ective resource utilization. 3.1 network coverage parameters for mobile networks in this work we are further exploring and rede�ning the communication range segments [15] as shown in figure 2 which can be put into e�ective use for achieving both proactive handover and resource allocation for a highly mobile environment. figure 2 shows a more advanced scenario in which three consecutive overlapping wireless networks are segmented based on various key time variables which can be used to enhance handover and resource allocation. time before handover (υ) is the time after which the handover process should start and time to handover (~) is the time before which the handover to next coverage range has to be completed, if not it will result in a hard handover. network dwell 842 y. kirsal, v.v. paranthaman, g. mapp time to handover interference region time to get resource time before handover resource hold time handover prepare time network dwell time exit threshold figure 2: communication range segmentation time (ℵ) as de�ned in y-comm framework is the time mn will spend in the coverage i.e., the network dwell distance (ndd) of new network. time to get resource, t is the time when actual resource requested is available to the requested user i.e., even after entering the network's coverage range with a successful soft handover, the resource required by the mn might not be available, for example, other users might be holding the resource. resource hold time, n is the resource usage time or when actual exchange of data is taking place. handover prepare time (%) is the time taken to prepare for handover during which the resource usage or data transmission will be paused and will be resumed after successful handover to the new network. usually ~ and % are very small compare to the values of other segments and therefore, t can be approximately equal to υ if there are resources available in the new network, i.e., if there is no contention. with the knowledge of these coverage parameters, it is possible to enhance the resource management based on the user contention for resources in a mobile network with proactive handover. a new proactive resource allocation based on the user contention to acquire a wireless channel resource is presented in the following section. 4 the proposed analytical model for proactive resource allocation this section presents the proposed model to maintain qos for using proactive resource management approach in highly mobile environments. in this paper, all the possibilities of contention interaction i.e., no contention, partial contention and full contention have been explored to show the practicability as well as the operational spaces of the overall system for highly mobile users in cellular system. these interactions may result in a request leaving the contention queue due to full contention with a subsequent request or the request in queue may be rearranged due to partial contention. the contention queue only queues requests before the mns reach the next network. once the mn reaches the relevant network, its channel request in the contention queue will be placed in the channel allocation queue. the proposed proactive resource allocation model is shown in figure 3. in the proposed approach, the decision algorithm decides whether the mn's request will be admitted to the system based on values of t and n of all the mns requesting a channel. there are three possible contention possibilities that a�ect mobile users in this scenario. the probability of full contention before entering the queue is given as α. in addition, the probability of full contention occurring in the contention queue can be expressed as β. finally, partial contention exploring analytical models for proactive resource management in highly mobile environments 843 + ... full contention no/partial contention no/partial contention adjustment/swapping ( ) full contention in queue d (1) (1) 1 2 3 i 1 2 ... s µs channel allocation queue proactive resource request system with contention queue (1)(1) = eff µm figure 3: proactive resource allocation queuing system with β and α in the contention queue can be expressed as θ and this may result in modi�cation of the request and/or re-ordering of request in the contention queue. since θ does not involve in leaving the contention queue therefore, it will have no overall e�ect with respect to the rate of transfer to the channel allocation queue in this paper. the formulation of θ and validation is still in progress and will be considered as a future work. 4.1 the proposed proactive markov queuing model the proposed proactive resource allocation queuing model considers s number of channels and can allow i requests at time t. q is the queueing capacity of the proposed system. the arriving requests may be sent from di�erent mn to the system. it is assumed that the originating calls can join the system with an arrival rate of λo. similarly, the handover calls can join the system with an arrival rate of λh. hence, the total arrival rate is λ=λh+λo. according to [25], for a two-dimensional �uid model, the arrival rate of handover calls can be obtained as follows: λh ≈ µm µs λo (1) hence, the inter-arrival time of consecutive task follows the poisson process which can be distributed as an exponential distribution with arrival rate λ=λh+λo. λ = s∑ i=1 λi (2) if there is no full contention in the contention queue, the mobile calls can join the system with an arrival rate of λ(1 − α). thus, scheduling and arrangement of requests take place and the total e�ective arrival rate can be calculated as: λeff = λ(1 −α) ∗ (1 −β) (3) figure 4 shows the state diagram of the proposed model. let's de�ne the states i (i=0,1,2,· · · ,s+q) as the number of requests in the system at time t. p0 p1 psp2 ps+q…... …... µs+µm 2(µs+µm) s(µs+µm) sµs+(s+1)µm sµs+(s+q)µm eff eff eff eff eff eff 3(µs+µm) figure 4: state diagram of the proposed approach in proposed model, t and n are exponentially distributed with a mean rate of µs and µm, respectively. µm can be calculated based on the literature in [9,10,25]. equation 4 is used in 844 y. kirsal, v.v. paranthaman, g. mapp the literature for the dwell time in wireless and mobile systems [10,19,25] for handover queuing models. thus, µm can be calculated and described as follows: µm = e[ν] ·l π ·a (4) where, e[ν] is the average velocity (ν) of mn, l is the length of the perimeter of cell (a cell with an arbitrary shape is assumed), and a is the area of the cell. hence, the total channel holding time of a call is exponentially distributed with mean 1/(µs + µm). if there are fewer than s requests in the system, i < s, only i of the s channels are busy and the combined service rate for the system is i(µs + µm) or sµs + iµm if s ≤ i ≤ s + q as shown in figure 4. µi =   i(µs + µm) 0 ≤ i < s sµs + iµm s ≤ i ≤ s + q (5) ρ is the tra�c intensity in the system, where ρ = λeff/(µs + µm). as the requests are rejected from entering the system (α) and requests in the contention queue can also be removed (β) due to full contention with subsequent requests. assuming a system in a steady state, the state probabilities, pi's, can be obtained as in equation 6. pi is the probability that there are i calls in the system. pi =   ρi i! ·p0 0 ≤ i ≤ s ρs s! ·λi−s eff ·p0 i∏ j=s+1 [sµs+(j−s)µm] s < i ≤ s + q (6) in equation 6, pi is the probability that there are i calls in the system. p0 can be de�ned as follows: p0 =   s∑ i=0 ρi i! + s+q∑ i=s+1 ρs s! ·λi−seff i∏ j=s+1 [sµs + (j −s) µm]   −1 (7) the mean queue length (mql) i.e., the average number of requests in the system can then be calculated as mql = s+q∑ i=0 i ·pi. mql =   s∑ i=0 iρi i! + s+q∑ i=s+1 i · ρ s s! ·λi−seff i∏ j=s+1 [sµs + (j −s)µm]  p0 (8) similarly, the blocking probability (pb), throughput (γ) and mean response time (mrt) of the exploring analytical models for proactive resource management in highly mobile environments 845 system can be calculated as follows: pb = p(s + q) = ρi s! ·λqeff ·p0 s+q∏ j=s+1 [sµs + (j −s) µm] (9) γ = s+q∑ i=0 i ·µipi (10) mrt = mql γ (11) 4.2 request management in the contention queue an example of the proactive resource allocation in queue is shown in figure 5. let us consider a simple queue and requests with (t,n) arrive to the queue as shown in figure 5. reqa arrives with (10,10) in seconds to the contention queue, the decision algorithm checks the queue and reqa is queued at the front as the queue is empty. dreqa(10,10) d d d reqb(15,10) reqc(22,2) reqd(3,3) reqa (10,10) reqa (10,10) reqa (10,10) reqd (3,3) reqb (20,5) reqb (20,5) reqa (10,10) reqb (20,5) dreqe(7,14) reqd (3,3) reqb (21,4) reqe (7,14) drop reqc drop reqa figure 5: an example of proactive resource allocation management in queue now reqb arrives with (15,10) and the time when it needs the channel and is when reqa will still be using the channel resulting in partial contention for reqb. because, reqa release the channel at the end of 20. therefore, reqb is modi�ed to tb = 20 and nb is modi�ed to (15 + 10) − 20 hence, the modi�ed request for reqb is (20,5). reqc now arrives with (22,2) however reqb will release the channel at 25 which means that reqc will never get the channel and therefore it is not admitted to the contention queue due to full contention. a rejection reply is sent to node c causing it to do an immediate handover to another network. now reqd arrives with (3,3), there is no contention with reqa or reqb and therefore, it is placed at the head of the queue since tc + nc < ta i.e., 6 < 10. reqe arrives with (7,14) this results in no contention with reqd, full contention with reqa and hence reqa is ejected from the contention queue because te < ta and te + ne > ta + na. reqb experiences partial contention and hence the request is modi�ed accordingly. 4.3 useful service vs mobile service since the mn can leave the queuing system due to service (µs) or due to mobility (µm), it is necessary to distinguish these two events to properly re�ect the performance of the system. we 846 y. kirsal, v.v. paranthaman, g. mapp therefore de�ne two concepts which are important in a mobile environment. the �rst is useful service in classical handover, usc, where the mn leaves the system after using the channel. when the mn leaves the system due to mobility and is not served by the channel, this is called mobile departure or service, umc. for the classical case, we can represent these parameters as follows: usc = sµs sµs + qµm , umc = qµm sµs + qµm (12) we say that a system in which usc > 0.5 represents that 50% of the overall service rate is due to the channel being used, while usc < 0.5 represents under utilization because most of the overall service rate is due to mns leaving the system due to mobility. these concepts are applied to give a better understanding of the e�ects of mobility. the system parameters used are taken from [9] for consistency. the system has a �xed number of identical channels: s = 12. q is the queuing capacity, which represents the number of requests waiting for service and is limited to 100. the service rate of the system µs is 0.01 requests/sec. the average speed of the mn and the radius of the network are taken as 10km/h to 80km/h and 1000m for all calculations, respectively. the rates are translated into requests per second in order to use consistent values. 10 20 30 40 50 60 70 80 velocity (km/h) 0.75 0.8 0.85 0.9 0.95 1 u se r fu l s e r v ic e (a) useful service 10 20 30 40 50 60 70 80 velocity (km/h) 0 0.05 0.1 0.15 0.2 0.25 m o b il e s e r v ic e (b) mobile service figure 6: useful service vs mobile service we can observe from figure 6 that as the velocity of the mn increases, the useful service of the system is signi�cantly reduced and the mobile service is increasing. this means that the number of mns which are moving out of the network due to mobility without being served is increasing. hence, in mobile networks it is necessary to take into account the mobility aspects to ensure e�ective resource management, so that we can optimize the useful service of the channels. in these environments most users will leave the system without being served resulting in poor user experience as well as poor system performance. the key observation is that because of the reactive approach in the classical model, mns can queue for a channel with no hope of getting the channel while they are in the coverage area of the network. as a result this is useless waiting and if this inability to obtain a channel can be signalled to the mn before queueing for the channel then it would allow the user to immediately look for an alternative network and hence would improve the quality of experience (qoe) as well as the overall system performance. this means that the analysis of contention between di�erent mns has to be analysed in detail so that only mns that have a chance of getting the channel should be queued for service. exploring analytical models for proactive resource management in highly mobile environments 847 5 results and discussion this section presents numerical results in order to show the accuracy and e�ectiveness of the proposed analytical model of the proactive approach to improve resource allocation in highly mobile environments. in addition, the results obtained are analysed and formulated for β and α by investigating all contention probabilities. the system parameters used are mainly taken from [9] and relevant literature [10, 19]. the system has a �xed number of identical channels: s=12 and the queue capacity is 100. it is assumed that the moving direction of the mobile users can be detected by the bs/ap using a control channel. in addition, a mixed tra�c pattern is also assumed, as in [9] where on average a minimum of 2 slots are 0.5 ms. hence, the rates are translated into request per second in order to use consistent values. the service rate of the mobile users µm is calculated using equation 4. the requests are entering the system due to the proposed contention system with arrival rate λeff. 5.1 e�ects of α and β the focus of the paper is to understand the e�ects of β and α by investigating all contention probabilities together with the operative area of various parameters to maintain qos for resource management. in [9] all possible contention probabilities are considered with di�erent scenarios. however, more accurate results of a request not being a�ected by β and α are analysed and presented. in addition, 3d graphs are also generated in order to see a complete picture for such systems. the parameters taken for the rest of the �gures are; s=12, q=100, µs=0,02(reqs/sec), e[ν]=50km/s and r=1000m, unless stated otherwise. figure 7: mql results vs α with di�erent β the �gure 7 shows mql results as a function of α considering all full contention probabilities occurring in the queue (β) for a light tra�c loads (e.g, λ=0.5). the �gure clearly shows the importance of β. even though, the system has a light tra�c loads, the mql increases without considering β. however, the mql decreases as β increases. this is due to the full contention occurring in the queue. mns are not allowed to the system since they will not have enough time to get service. mns carry on their journey without entering the system. 848 y. kirsal, v.v. paranthaman, g. mapp (a) mean queue length (b) throughput figure 8: 3d representation of all contention probabilities for λ=1.7 hence, the operational area of the mns in acquiring a resource is shown in figure 8 for high tra�c loads (e.g., λ=1.7) in 3d. for instance, the system is almost full (mql=110.98 reqs/sec) when there is no contention (β=α=0). however, considering both contention probabilities the decision of acquiring a resource for mns can be e�ciently achieved by analysing the results in figure 8. the figure 8a shows that, the mns start to line up when β=0.6 and α=0.7 for high tra�c loads (e.g., λ=1.7). however, the mns can get a channel even when the contention queue is fully loaded. in addition, when the full contention probability in the queue is high in the system, it a�ects the overall performance signi�cantly. for instance, the mns can get a channel when β=0.7 regardless of α. on the other hand, in order to obtain optimum qos, the system performance depends also on α values for low values of β. the throughput results are shown in 3d in figure 8b considering all possible contention probabilities. the results indicate the drop in the throughput of the given both tra�c loads since more requests are removed from the system due to contention. in other words, the throughput decreases as mns leave the system considering the contention probabilities. however, this improves the overall network performance as these requests can be dealt with by other networks. (a) mrt vs α (b) 3d representation of mrt for λ=1.7 figure 9: mean response time results for all contention probabilities the figure 9a shows mrt results as a function of α considering various full contention probabilities occurring in the queue (β) for di�erent tra�c loads (e.g., λ=1, 1.7). as clearly seen from the �gure, the best mrt results can be obtained for high value of β. when β=0.9, the all mns can get a channel regardless of α. even though the system has moderate tra�c loads (e.g., λ=1) the full contention probability in the queue is important parameter of getting a resource in such system. this can be clearly seen in figure 9b. 5.2 probability of a request staying in the contention queue in the queueing analysis presented in previous section, α and β values are assumed. in order to utilize the proposed proactive approach, a model has to be developed to obtain the probability of a request not been a�ected by α and β. it is known from the standard queueing analysis for exploring analytical models for proactive resource management in highly mobile environments 849 a system of k nodes: pn = ρ n 1 −ρ 1 −ρk+1 (13) where, ρ is the tra�c intensity (λ/µ), k is the size of queue and n is the number of request in the system. arrival to contention queue full contention in queue (1)(1) = eff (1) (1) figure 10: proactive contention queue it can be assumed that the proactive contention queue as a simple m/m/1/k queue. here, we are interested only to �nd out the probability of a given request staying in the contention queue and not be a�ected due to β. here the arrival rate to the contention queue is λ(1−α) and service rate is β as we are only interested in the requests a�ected by β as shown in figure 10. therefore, ρ can be represented as: ρ = λ(1 −α) β (14) therefore, pn−1 = ρ n−1 1 −ρ 1 −ρk+1 (15) here, the number of requests in the system becomes n− 1 as there are no entries in the server. in order to �nd the probability of a given request staying in the contention queue without being a�ected by β, a request needs to satisfy three conditions: � the new incoming request occupied the nth position in the queue, i.e., last position of the queue. � new incoming request is having a full contention with an entry at the nth position and pushing that entry our of the queue. � new request after occupying a place in the queue is not a�ected by the subsequent new incoming request. here, let us assume that pafull is the probability of full contention for incoming request a. pbfull is the probability of full contention for the requests in contention queue and here an average velocity is considered. pcfull is the probability of full contention for a new subsequent incoming request c after a. � (1−pbfull) n−1 is the probability that request a occupies the nth position in the contention queue and it is not colliding with any other request before it. � (1 − pafull) n−1pafull is the probability that request a do not collide with the request in front of nth position but does collide with the request already occupying the nth position. � 1 − [(1 −pcfull) n−1pcfull] is the probability of the request a not getting booted out by a new subsequent request entering the queue i,e c. 850 y. kirsal, v.v. paranthaman, g. mapp therefore, all these three conditions can be represented as shown in equation (16) to calculate the probability of a request staying in the queue and not been a�ected by β. n=k∑ n=1 {[((ρn−1)(1 −ρ) 1 −ρk+1 (1 −pbfull) n−1 ) + (m=k∑ m=n ρm(1 −ρ) 1 −ρk+1 (1 −pafull) m−1pafull )] × ( 1 − (1 −pcfull) n−1pcfull )} × (1 −pbfull) (16) for example, let us consider t for mna is 10s and mnb is 60s. n for mna is 20s and mnb is 30s. the resulting probability of full contention based on our two node model: pafull = 0.085714, pbfull = 0.064286 and let us assume p c full = 0.2. probability of a request not a�ected by β for k = 1 is 0.68635, k = 20 is 0.45405 and n = 80 is 0.75405. calculating β accurately is very important so that the requests leaving contention queue due to full contention can be served by an alternative network. since, β is dependent on the values of n and t of each request in the queue and new incoming request, therefore, a detailed analysis is required to accurately model β. by using this approach, it is possible to achieve seamless communication. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.25 0.3 0.35 0.4 0.45 0.5 0.55 p ro b a b il it y k = 5 k = 10 k = 20 k = 40 k = 80 (a) ρ = 0.1 to 0.9 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 p ro b a b il it y k = 5 k = 10 k = 20 k = 40 k = 80 (b) ρ = 1.1 to 2.0 figure 11: probability of a request not being a�ected by β let us consider ta and tb are both 20s. mna is moving at a velocity of 30mph and average velocity of the requests in the contention queue as 10mph. therefore, for network the pafull is 0.094055 and pbfull which is the probability of full contention for the requests with average velocity in the queue is 0.346165. let us assume pcfull as 0.2. therefore, we can �nd the probability of request a staying in the queue using equation (16) and the resulting graphs for di�erent ρ values from 0.1 to 0.9 and ρ values from 1.1 to 2 are shown in figure 11a and 11b, respectively. from the result we can observe that as ρ increases the probability of a request being a�ected by β, resulting in not reaching the channel queue increases. in other words, probability of a request not being a�ected by β deceases as shown in figure 11. in addition, it is clearly seen from the figures 11a and 11b that k a�ects the system performance signi�cantly especially for highly loaded system. 6 conclusion and future work analytical models for a proactive system where the users contend for resources with a thorough analysis has been presented. these models have yielded two key parameters, β and α, that have to be calculated to e�ectively use the proposed proactive approach. it has been shown that if these two parameters are identi�ed then we can ensure that each users in the system will be e�ectively served by at least one of the available network. this can be achieved by using vertical handover to alternative network if an user is about to experience a full contention in the target network. to the best of our knowledge there is no study that has modelled a proactive model in exploring analytical models for proactive resource management in highly mobile environments 851 the context of users contending for resources based on their mobility. with the results showing that the proposed approach is outperforming the classical model. the application of the work and approach in a middlesex vanet testbed is the future work. bibliography [1] alhabo, m.; li, z.; naveed, n. 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(2001); modeling of hando�s and performance analysis of wireless data networks, international conference on parallel processing workshops, 491-496, 2001. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(2), 272-285, april 2019. ensemble sentiment analysis method based on r-cnn and c-rnn with fusion gate f. yang, c. du, l. huang fushen yang beijing university of chemical technology beijing 100029, china changshun du* center for information technology beijing university of chemical technology beijing 100029, china *corresponding author: ducs@mail.buct.edu.cn lei huang school of economics and management beijing jiaotong university beijing 100044, china abstract: text sentiment analysis is one of the most important tasks in the field of public opinion monitoring, service evaluation and satisfaction analysis in the current network environment. at present, the sentiment analysis algorithms with good effects are all based on statistical learning methods. the performance of this method depends on the quality of feature extraction, while good feature engineering requires a high degree of expertise and is also time-consuming, laborious, and affords poor opportunities for mobility. neural networks can reduce dependence on feature engineering. recurrent neural networks can obtain context information but the order of words will lead to bias; the text analysis method based on convolutional neural network can obtain important features of text through pooling but it is difficult to obtain contextual information. aiming at the above problems, this paper proposes a sentiment analysis method based on the combination of r-cnn and c-rnn based on a fusion gate. firstly, rnn and cnn are combined in different ways to alleviate the shortcomings of the two, and the sub-analysis network r-cnn and c-rnn finally combine the two networks through the gating unit to form the final analysis model. we performed experiments on different data sets to verify the effectiveness of the method. keywords: sentiment analysis, convolutional neural network, recurrent neural network, fusing gate. 1 introduction the basic task of sentiment analysis is to classify the polarity of a given text at the document, sentence or feature/aspect level and to determine whether the opinions expressed in the document, sentence or entity feature/aspect are positive, negative or neutral. the complexity of textual information leading to the detection of emotions in plain text makes for a challenging task. at present, the sentiment analysis algorithms with good results are all based on statistical learning methods. the performance of this method depends on the quality of feature extractions, while good feature engineering requires a high degree of expertise and is time-consuming, laborious, and offers poor mobility. the neural network approach can reduce the dependency of feature engineering. copyright ©2019 cc by-nc ensemble sentiment analysis method based on r-cnn and c-rnn with fusion gate 273 currently, some neural network-based methods have been used for sentiment classification tasks and socher et al. [8–12] proposed modeling using the recursive neural networks. these methods have proven to be effective in constructing sentence representations. however, recursive neural networks need to construct text as a data structure representation of a tree structure in order to capture the semantics of the sentence. therefore, to a large extent, the rationality and validity of the text tree structure determines the performance of this type of method. at the same time, the construction of this text tree representation has a time complexity of at least o(n 2), so when such a model is applied to a long sentence or document, significant time overhead is required. in addition, it is difficult to express the relationship between two sentences using a tree structure. therefore, recursion is not suitable for long text modeling. another neural network model commonly used for natural language processing tasks, the recurrent neural network (rnn), has a time complexity of o(n). the model uses the linguistic symbolic order input model in the text to model the text and store all of the text semantics in a fixed-size hidden state. compared to other methods, rnn has a good ability to capture context information, which is useful for modeling long text and obtaining the semantics of long text. however, rnn is a biased model, and the closer to the end of the expanded word, the more information is retained than the previous word. it is well known that key features related to sentiment analysis may appear anywhere in the document, not just at the end of the text. therefore, when the rnn is used to capture the semantic features of the entire input text, the change of the position of the key features will result in different degrees of effectiveness reduction, and may even completely ignore the important information, resulting in greatly degraded performance of the model. at the same time, other work also uses the convolutional neural network (cnn) for sentiment classification [6] . one reason why cnn was introduced into natural language processing tasks is that cnn can solve the problem of sequential deviation of features such as words. this is because it has translation invariance when using pooling operations such as maximum pooling, that is, the semantic features of different positions in the text are unbiased, regardless of the location of the feature. therefore, compared with recursive or recurrent neural networks, cnn is more conducive to capturing semantic features that are independent of position and order in text. the time complexity of cnn is o(n). however, previous research work on cnn tends to use a simple convolution kernel such as a fixed window size [1,4] . when using such a kernel, it is difficult to determine the size of the window. small windows can cause important information to be lost, while large windows lead to huge parameter spaces, making network training extremely difficult. at the same time, text analysis methods based on cnn can obtain the important features of text through pooling, but it is difficult to obtain the context information. the piecewise pooling strategy utilized by the sentiment analysis model in du et al’s work can partially alleviate the shortage of cnn [3] . however, modeling for long-distance dependence is still poor. mathieu cliche [2] uses cnn and long/short-term memory networks to model sentences separately. although this method can improve the experimental results, it still cannot overcome the defects of cnn and rnn. in order to solve the limitations of the above-mentioned recurrent neural network, recurrent neural networks and convolutional neural network models, some work has been attempted to integrate the recurrent neural network and the convolutional neural network. for example, these works [5,13] use convolutional cyclic neural networks to extract sequence features. unlike other works, this paper proposes a sentiment analysis based fusion recurrent-convolutional neural network (r-cnn) [7] and convolutional-recurrent neural network (c-rnn) sentiment analysis method. firstly, rnn and cnn are combined in different ways to alleviate the shortcomings of the two. the sub-analysis networks r-cnn and c-rnn are constructed respectively. lastly, the final analysis and analysis model is composed by combining the two networks with a fusion 274 f. yang, c. du, l. huang gate. we performed experiments on different data sets to verify the effectiveness of the method. 2 sentiment analysis method based on r-cnn and c-rnn with fusion gate 2.1 r-cnn-based text sentiment feature extraction model firstly, this paper proposes a deep neural model based on the r-cnn to capture the semantics of the text, and uses the obtained semantic features as the characteristics of the sentiment analyzer to analyze the sentiment orientation. figure 1 shows the network structure of the model in this section. the input to the network is the text s, which consists of the word sequence w1,w2, · · · ,wn of the text. figure 1: structure of recurrent-convolution neural network semantic feature extraction of words in the text, the meaning of each linguistic symbol is related to the context in which it is located. the contextual location of the word can help the model to obtain a more precise meaning of the word in a particular scene. in order to enable the semantic features extracted by the neural network to fuse the context of the word, this paper uses the r-cnn model to extract features from the customer’s comment text. the model first uses a bidirectional recurrent neural network to capture the contextual information of the word and combine it with the word embedding representation of the word itself to obtain an enhanced representation of the word. the definitions cl(wi) and cr(wi) represent the left context information (the loop is expanded from the front to the back) and the right context information (expanded from the back), respectively, of the word wi. cl(wi) and cr(wi) are realvalued dense vectors of dimension c, and the calculation processes are expressed as equation 1 and equation 2, respectively. cl(wi) = f(w lcl(wi−1) + w sle(wi−1)) (1) cr(wi) = f(w rcr(wi+1) + w sre(wi+1)) (2) e(wi) represents the word embedding representation of the i-th word wi in the input text sequence. cl(wi − 1) represents the left context information of the previous word wi−1. w l is a parameter matrix that converts the forward-expanded hidden layer state (left context information feature) of the bidirectional cyclic neural network into the next hidden layer state. wsl is a parameter matrix that embeds the information of the current word into the left context ensemble sentiment analysis method based on r-cnn and c-rnn with fusion gate 275 information feature added to the next word. f is a nonlinear activation function. the right context information feature cr(wi) is calculated in a similar manner, as shown in equation 2. in order to conveniently calculate the context information of the first and last words of the input text, a rightmost context feature cr(wn) of all text sharing is set to estimate the leftmost context information feature cl(w1). after computing the text sequence using equations 1 and 2, the left context information feature of the current word captures all semantic information that is expanded from front to back to the word. the right context information feature captures all the semantics that are expanded from back to front to the word. for example, in figure 1, cl(w5) retains the semantics of the context "...this hotel bathroom comparison" made up of all the previous words on the left side of the word “clean." cr(w1) retains the semantics of the phrase “the hotel bathroom is clean..." on the right and end of the word “this home". after obtaining the contextual semantic features of the current word, the contextual semantic feature is merged with the word embedded representation of the word itself to obtain the final representation of the current word, and the formal representation is as follows: xi = [cl(wi); e(wi); cr(wi)] (3) as shown in equation 3, the final representation xi of the word wi consists of a splicing of the left context vector cl(wi), the word embedding e(wi), and the right context vector cr(wi). in this way, the word representations learned by the model contain rich word context information. compared to traditional language models or neural network models that use only fixed window sizes, these models use only part of the textual information around the word when processing each language symbol. the rcnn model can model the entire text sequence and preserve the structural information such as the order of the words. the context information can better eliminate the ambiguity of the meaning of the word wi. at the same time, the rnn structure forward scan obtains all left context semantic features cl, and the backward scan obtains all right context features cr, with a total time complexity of o(n), which has high efficiency. after obtaining the representation xi of the word wi the linear activation function tanh transforms the xi and outputs it to the next layer, namely: y (2) i = tanh(w (2)xi + b (2)) (4) 1 ≤ i ≤ |s|. y(2)i is a potential semantic vector that integrates word and context information, so each dimension can be thought of as an element that contains a certain type of semantic information. these semantic elements are useful for extracting features related to sentimental tendencies throughout the input text. obtaining the semantic features of the full text from the semantic features of words directly using the representation of a single word in the text to analyze the sentiment orientation of the entire comment is still very difficult, so it is also necessary to use the representation of the words to calculate the representation of the entire comment text. from the perspective of cnn, the aforementioned rnn structure can be considered as the convolutional layer of cnn. when the representation of all words is calculated, the maximum value pooling operation is used to obtain the semantic features of the full text from all word representations of the comment text sequence, as shown in equation 5: 276 f. yang, c. du, l. huang y(3) = |s| max i=1 y (2) i (5) the maximum value among the features obtained by extracting each convolution kernel convolution operation is different from the maximum pooling operation employed by other work. in the r-cnn model, the maximum pooling operation extracts the maximum value for all word representations of the entire comment text. that is, the maximum value of the k-th dimension of all the word semantic features y(2)i is selected as the value of the full-text semantic feature y(3) of the k-th semantic element. after the maximum pooling operation, the comment texts with different lengths are converted into fixed-length full-text semantic vectors, which can better preserve the important semantic information of the entire text. there are many types of pooling operations in convolutional neural networks, such as average pooling, minimum pooling, and the like. as mentioned above, in the whole comment, some emotion-related words and their combined semantics are the most relevant features of the sentiment analysis task, and are very effective features for judging the emotional tendency of comments. extracting these important features can improve the performance of the sentiment analysis model. therefore, in the model of this section, the maximum pooling operation is still used. the pooling layer uses the output of the rnn as input, and the time complexity of the pooling layer is also o(n). therefore, the time complexity of the cascaded rcnn sentiment feature extraction model is still o(n), which maintains high efficiency. 2.2 text sentiment feature extraction model based on c-rnn in the previous section, this paper proposes the use of rcnn for feature sentiment feature extraction of comment texts. in this section, this article will examine another combination of convolution and recurrent neural networks. that is to say, the convolution operation is performed first, and then the combination of the loop structure is used to propose a feature extraction model based on c-rnn. convolution operation segmentation extraction of local emotional features of review text a standard convolutional neural network usually consists of a convolutional layer and a pooled layer. as mentioned above, the standard convolutional neural network can extract the combined features of words and has translation invariance. these combined features can be extracted anywhere in the text, and convolution operations can be extracted. in the model presented in this section, the input is a matrix of words embedded in the text, and each line of the matrix is a vectorized representation of a linguistic symbol. the convolution kernel slides in the direction in which the text is expanded, that is, the width of the input matrix (the dimension of the word vector) coincides with the width of the convolution kernel. assuming that the height of the convolution kernel is w, and the width and word vector dimensions are both d, the convolution kernel can be represented as a matrix w ∈ rw×d. let the vectorization of the i-th language symbol in the input be represented as si, and the input text can be represented by the matrix s = (st1 ,s t 2 , · · · ,s t |s|). then the convolution operation can be expressed as follows: cj = w ⊗ sj:j+w−1 (6) 1 ≤ j ≤ |s| − w + 1, cj is the eigenvalue extracted by the convolution operation between the convolution kernel starting from the word j and the convolution kernel height being the window. ensemble sentiment analysis method based on r-cnn and c-rnn with fusion gate 277 figure 2: local feature stitch and pooling the feature extraction of text by relying solely on a convolution kernel is not comprehensive. in order to extract more abundant information from the text, multiple different convolution kernels are usually used. these convolution kernels can be expressed as a three-dimensional tensor ŵ = {w1,w2, · · · ,wn}, and the convolution operation of the convolutional layer can be expressed as follows: c j i = wi ⊗ sj:j+w−1 (7) 1 ≤ i ≤ n, the input text is subjected to the ith convolution kernel convolution operation to obtain the feature vector ci = {c1i ,c 2 i , · · · ,c |s|−w+1 i }. then all convolution kernels can get a total of n feature vectors. in the model proposed in this section, all convolution kernels are of the same size, so the same comment text after convolution operation will get n vectors with the same dimension and containing the local semantic features of the comment text. the same dimension of these feature vectors can be regarded as different types of features of the same part in the comment, that is, each local semantic feature êi is composed of n features. different local semantic features may have different n feature strengths. unlike the traditional convolutional neural network model, the convolutional neural network model in this section no longer uses the pooling layer, but splices the feature vector after the convolution operation into the sequence feature output to the next layer, as shown in figure 2. rnn fusion text sentence structure features after obtaining the local features of the comment text by using the convolutional layer, the local feature is regarded as a text sequence input to the cyclic neural network, and the longdistance dependence of the comment text can be modeled, and the structural features of the sentence are incorporated into the local features of the text. in this paper, the bidirectional rnn based on lstm computing nodes is used to perform text representation learning on the stitched feature sequences, and then the feature vectors learned in the two directions are stitched together as a vector representation of the text. thus, the semantics represented by the feature vector are more comprehensive and rich than rnn. the t-th local feature obtained after splicing is expressed as êt, and the one-way calculation process of incorporating the structural features of the text sentence through rnn can be expressed as follows: 278 f. yang, c. du, l. huang   i = σ(uiêt + w iht−1 + bi) f = σ(uf êt + w fht−1 + bf ) o = (uoêt + w oht−1 + bo) c̃ = tanh(ucêt + w cht−1 + bc) ct = ct−1 ⊗ f + c̃ ⊗ i ht = o• tanhct (8) ht is the hidden state of the computing node. c̃ indicates that the candidate cell transition is calculated from the previous time hidden state ht and the current input xt. ct indicates the cell state, which is obtained by the forgetting gate and input gate weighting calculation by the cell state ct−1 of the t−1 step and the cell-selective c̃. ht is the current output of the computing node, and is also the current hidden state. it is the amount of information that the cell state ct is finally output through the output gate selection. the local feature of the review text s is forwardly expanded through the rnn to obtain the hidden layer state −→ hs of the forward sentence structure feature. the backward expansion results in the hidden layer state ←− hs of the backward sentence structure feature. what is obtained after splicing is the final feature vector hs representing the input comment text, that is, hs = [ −→ hs, ←− hs]. therefore, the overall structure of the c-rnn-based text sentiment feature extraction model proposed in this paper can be represented as shown in figure 3. the computational time complexity of the convolutional layer and the cyclic layer is o(n), so the total time complexity of the model proposed in this section is still maintained at o(n), which maintains the efficiency of the model. figure 3: text sentiment feature extraction model based on convolution-recurrent neural network 2.3 sentiment analysis model based on r-cnn and c-rnn of with fusion gate feature fusion based on gating unit after r-cnn and c-rnn respectively extract the emotional features of the review text, the features extracted by the two networks need to be integrated and input into the sentiment orientation analyzer to obtain the final sentiment analysis result. for comments in different segments, there is a difference in the characteristics associated with emotional orientation, that is, emotional tendencies have domain dependence. the comment texts in the same segment have different styles, so there are some differences in the sentiment orientation characteristics. the two combined models of rnn and cnn proposed in this paper ensemble sentiment analysis method based on r-cnn and c-rnn with fusion gate 279 are rich in features. however, in different fields, the same feature has different effects on the discrimination of sentiment orientation. too many features will introduce noise into the sentiment orientation, which degrades the performance of the analyzer. however, relying on manual screening of these features is very difficult. therefore, an automatic method is needed to select these features to make domain, text adaptability, remove redundant information, and ensure the effectiveness of the sentiment orientation analyzer. this paper proposes to use the fusion gate to automatically filter and fuse the two features extracted by r-cnn and c-rnn. the process of fusion can be represented in figure 4. figure 4: r-cnn combines c-rnn extract features based on gate unit the fusion gating unit calculates, based on the two characteristics currently input, how many features can be passed through the fusion gating unit, that is, how many original features need to be retained for the two features of the output features. for convenience of representation, the features extracted by the r-cnn described above are denoted as erc, and the features extracted by the c-rnn are denoted as ecr . the calculation process of the control ratio g of the fusion gating unit to the feature passing is as follows: g = σ(ugerc + w gecr + b g) (9) ug and wg are the weight parameter matrix of the fusion gating unit connected to erc and ecr, respectively, and bg represents the offset. in order to simplify the calculation, the fusion gating unit uses the same control ratio for feature extraction of the two extracted features, and the feature fusion calculation process can be expressed as follows: ed = g ⊗ erc + (1 −g) ⊗ ecr (10) ed is a feature vector that characterizes the input commentary sentiment feature obtained by fusing the two features of the fusion gating unit. softmax classifier gets comments emotional sentiment after obtaining the emotional feature ed of the review text, the softmax classifier is used to classify the emotional sentiment of the review based on the feature vector, and obtain the emotional tendency of the review. set the network parameter we of the softmax classification layer, and the bias term is be, then the neural network output can be expressed as: o = f(weed + be) (11) 280 f. yang, c. du, l. huang f represents the activation function. then the probability that the entered text sentiment tends to i is: p(i|θ) = eoi∑n j=1 e oj (12) θ represents all parameters of the neural network, oi represents the value of the i-th item of the output vector, and n represents the number of categories of the text. let the sample set be expressed as ω, then the model’s optimization objective function can be calculated by the following formula. lsen = |ω|∑ i=1 −logp(yi|si,θ) + λ ‖ θ ‖22 (13) λ is the parameter of the regular term. in the actual experiment, we use the stochastic gradient descent method to optimize the objective function, then the parameter θ is updated in the way: θ = θ −α ∂l ∂θ (14) α is the learning rate. 3 experiment settings 3.1 data set three data sets were used primarily in the experiment. the first is the chinese hotel data collection (ctrip hotel), with a corpus size of 10,000, including 7,000 positive evaluation samples and 3,000 negative evaluation samples. the second dataset is the english dataset, a film review dataset released by pang and lee in 2005. the dataset contains a total of 10,662 commentaries on the film, with emotional sentiment being half positive and half negative emotionally biased samples. the sentiment orientation tab of the review reflects the overall emotional sentiment of the reviewer’s comments. this paper divides the data set into a training set, a validation set, and a test set, which contain 8530, 1066, and 1066 samples, respectively, with half having positive and half having negative emotional tendencies. the emotional polarity of each sentence is in the range of [0,1]. the smaller the score, the more the emotion tends to be negative. otherwise, the emotion tends to be positive. the emotional scores of all sentences in the data set are manually labeled and then averaged. it has good reliability. in order to make the experiment closer to the real production environment, and to verify the effectiveness of the sentiment analysis method based on the r-cnn and c-rnn proposed in this paper, the third data set is used in this paper. it is the review text (dianping for short) in different fields that we crawled from the public service website (http://www.dianping.com/), including data on six segments of food, hotel, movie, entertainment, marriage, and home improvement. and based on the scoring information in the comments, the comments are divided into different emotional tendencies. this paper selects 30,000 reviews as the training set and 10,000 as the test set. ensemble sentiment analysis method based on r-cnn and c-rnn with fusion gate 281 3.2 data pre-processing for the chinese data set, the chinese word segmentation package nlpir developed by the chinese academy of sciences is first used for chinese word segmentation. the english data itself is an independent word, so there is no need for word segmentation. since the minibatch training model is used during training (multiple samples are learned at a time, the text length of multiple samples may not be the same). at the same time, the use of c-rnn to extract features of different texts needs to ensure the uniformity of dimensions. after convolution, the splicing can guarantee the feature dimension and therefore needs to complete the operation for the length of the text. since the length of the text of the comment is inconsistent, the longest sentence length l_max is calculated first. for sentences with a sentence length less than l_max, the text must be unified with the < \s > symbol to the length l_max (the vector of < \s > is always set to 0), thus unifying the text length. the length of the unified text can improve the calculation efficiency, and when the length of the data is uniform, the calculation time overhead can be effectively reduced. at the same time, in order to ensure the feature extracted at the beginning and the end of the text during the convolution process, a certain number of < \s > corresponding to the convolution kernel is added at the beginning and end of the longest text as padding. 3.3 pre-training of word embedding word embedding is required before formal training of the model. word embedding acts as a distributed representation of words as an input suitable for neural networks. many current studies have shown that executing word embedding pre-training on a large-scale corpus, and then applying the obtained word embedding to subsequent training, can speed up the convergence of neural network models and achieve a better local optimal solution. in this paper, the word2vec algorithm is used to pre-train word embedding. the word embedding of this algorithm shows better performance in many natural language processing tasks, and it has higher efficiency. this paper chooses the skip-gram model and the negative sampling model to pre-train the word embedding of chinese and english words. the pre-training of chinese word embedding uses the text content crawled on baidu encyclopedia, and the pre-training of english word vectors is performed on the new york times corpus. 3.4 setting of experimental parameters in the training optimization process of the model, the adam optimizer is used to train and optimize the parameters of the model. the parameters of the adam optimizer are set by the author. in this paper, the model mainly has the following hyperparameters: the dimension d of the word vector, the number n of convolution kernels in the c-rnn, the dimension nrc of the hidden state in the loop structure in the r-cnn, and the dimension ncr of the hidden state in the c-rnn. in order to obtain the optimal hyperparameter setting, this paper uses grid-search to determine the value of some hyperparameters. the dimension nrc of the hidden state in the loop structure in the r-cnn and the dimension ncr of the hidden state in the c-rnn are selected from { 50, 100, 200, 300 }. the number n of convolution kernels takes values in { 100, 150, 200 }. in the experiments in this paper, multiple experiments were performed using these parameters, and then the average of the results was obtained. 282 f. yang, c. du, l. huang 4 results and analysis in order to verify the effectiveness of the proposed r-cnn and c-rnn integrated sentiment analysis method based on the fusion gating unit, the method of this paper is compared with some mainstream sentiment analysis baseline methods. 4.1 comparison method in order to verify the validity and correctness of the proposed model, this paper selects a model based on traditional methods and a neural network method such as rntn proposed by richard socher et al. as a baseline method. the first method of comparison is the naive bayesian method (abbreviated as nb) using the word bag feature. the second method is to use the word bag feature as input to perform emotion classification using a support vector machine (abbreviated as svm) classifier. the third method is the naive bayesian method by using the bag feature obtained from the binary grammar language model (abbreviated as binb). the fourth method is to use the average word vector of the sentence as the input feature and use the fully connected network as the classifier (abbreviated as vecavg). the fifth method is the recurrent neural network (rnn). the sixth method is a recurrent neural network (mv-rnn) with a semantic transformation matrix [1] . the seventh method is based on a tensor-based cyclic neural network (rntn). the last comparison method is the traditional convolutional neural network. 4.2 analysis of experimental results it can be seen from the results in table 1 that the neural network method generally has higher performance than the conventional method. especially in the five-level sentiment analysis with finer granularity, the neural network method can obtain the key features of the text well. an important reason for the binb method to achieve better results is that the binary grammar model considers a certain combination of semantics, but with it comes significant computational overhead. at the same time, compared with the cyclic neural network method, the use of maximum pooling in convolutional neural networks can automatically extract the features most relevant to sentiment analysis tasks, and has positive significance for text sentiment analysis tasks, so it has achieved good results. traditional methods such as binb can only extract combined features from adjacent words, and traditional convolutional neural networks cannot model grammatical structures. the segmented convolutional neural network method has the best effect on both datasets because it simulates the grammatical structure information of the text, supplements the original emotional words, and extracts the combined semantic features of different positions. in the dianping data, the data comes from different fields. we compare the performance of different methods in this data to fully demonstrate the effectiveness of the proposed r-cnn and c-rnn integrated sentiment analysis method based on the fusion gating unit. it can be seen from table 2 that the method proposed in this paper achieves the best results. the comments crawled from the dianping contain different subdivisions. the comment text in each field is relatively small, and the comment text itself is relatively short. therefore, samples in different fields have a certain degree of sample sparseness. this need model can effectively extract emotion-related features from the text in order to correctly judge the emotional tendency of the text. this paper also reduces the number of samples in each field, making the data sparse more serious. it can be seen that all methods have performance degradation problems to varying degrees, but the method proposed in this paper still achieves good results. the stability of the proposed method in the case of multi-domain data sparseness is fully explained. ensemble sentiment analysis method based on r-cnn and c-rnn with fusion gate 283 table 1: sentiment analysis precision on different data sets(%) model stanford ctrip two levels sentiment five levels sentiment nb 81.8 41.0 80.2 svm 79.4 40.7 86.7 binb 83.1 41.9 85.9 vecavg 80.1 32.7 82.1 rnn 82.4 43.2 87.8 mv-rnn 82.9 44.4 87.6 rntn 85.4 45.7 89.3 cnn 81.9 45.6 88.5 r-cnn&c-rnn 85.8 46.1 89.8 table 2: sentiment analysis precision on dianping data sets(%) model two levels sentiment five levels sentiment two levels sentiment(reduced) nb 72.1 35.7 66.1 svm 72.6 33.2 61.2 binb 75.2 36.6 62.4 vecavg 74.1 36.3 62.3 rnn 77.3 40.2 69.7 mv-rnn 78.0 42.2 73.1 rntn 80.3 42.6 75.7 cnn 79.1 41.5 73.5 r-cnn&c-rnn 83.5 47.6 80.6 284 f. yang, c. du, l. huang table 3: sentiment analysis precision after adding negative words on the dataset(%) model negated positive negated negative binb 39.0 27.6 vecavg 16.5 17.9 rnn 43.3 44.2 mv-rnn 62.4 56.2 rntn 81.4 72.6 r-cnn&c-rnn 81.7 77.2 in order to further illustrate the effectiveness of the integrated analysis method of r-cnn and c-rnn based on the fusion gate, this paper selects some samples from the dianping data set. adding negative words to the sample converts the emotional tendency of the sample into opposing emotions, called negative positive and negative negative. for example, the “food in this restaurant is delicious" is changed from positive to negative as “the food in this restaurant is not delicious". turning “this film is ugly" from negative to positive by adding a negative to the original negative (negative negative), “this movie is not ugly." it can be seen that such a change is very subtle for the change of the review text. when the negative word is added to the positive sample, the emotional tendency changed to negative absolutely. however, when the negative word is added to the negative sample, the change of the emotional tendency is tiny. these samples can test the effectiveness and stability of sentiment analysis methods for emotion extraction, and test whether the model can capture emotional details in the text. the results of the test are shown in table 3. we can see that the proposed method is superior to all baseline methods. the experimental results show that our model can better capture the emotional features of the review text from the details. 5 conclusion in this paper, the performance of traditional text sentiment analysis methods depends on the quality of feature extraction, while good feature engineering requires a high degree of expertise, and is time-consuming, laborious, and affords poor mobility. the neural network approach can reduce the dependency on feature engineering. rnn can obtain context information, but the order in which the features appear in the text will cause the model modeling to be biased. the text sentiment analysis method based on cnn can obtain important features of text through pooling, but it is difficult to obtain context information, and the problem of poor modeling ability for long distance dependence is poor. in this paper, a sentiment analysis method based on fusion gating unit r-cnn and c-rnn is proposed. firstly, cnn and rnn are combined in different ways to alleviate the shortcomings of the two, and then the sub-analysis networks r-cnn and c-rnn are constructed respectively. lastly, the final analysis model is composed by combining two networks through the fusion gating unit. the model proposed in this paper has carried out more detailed experiments on different data, which proves that the proposed method has strong adaptability in different fields. it is more able to extract emotion-related features from text, and maintains good validity in shorter texts and fewer samples. at the same time, the experimental results of adding negative words in the comment text show that the r-cnn and c-rnn integrated sentiment analysis methods based on the fusion gating unit can better capture the emotional features of the review text from the details and that it has good stability. ensemble sentiment analysis method based on r-cnn and c-rnn with fusion gate 285 author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] collobert, r.; weston, j.; bottou, l.; et al. 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(2017). an end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition, ieee transactions on pattern analysis and machine intelligence, 39(11), 2298-2304, 2017. international journal of computers communications & control issn 1841-9836, 13(3), 337-352, june 2018. a multi-criteria decision-making model for evaluating suppliers in green scm w. jiang, c. huang wen jiang*, chan huang school of electronics and information, northwestern polytechnical university xi’an, shaanxi province, 710072, china *corresponding author: jiangwen@nwpu.edu.cn huangchan@mail.nwpu.edu.cn abstract: in order to develop recycle economy and friendly saving environment, many business enterprises have deployed green supply chain management (gscm) practices. by employing related theorise of gscm, organizations expect to minimize the environment impact caused by their commercial and industrial activities in supply chain. different suppliers may provide different gscm practices, so evaluating their gscm performance to rank the green suppliers is an important aspect in practice. in this paper, a novel decision method named fuzzy generalized regret decision-making method is proposed. the fuzzy generalized regret decision-making method is based on ordered weighted averaging (owa) operator, which is used to effectively aggregate individual regrets related to all stats of nature for an alternative under fuzzy decision-making environment. by combing the proposed method with the application background of gscm practices, a novel fuzzy decision model for evaluating gscm performance is further proposed. in the proposed model, the regret of decision maker is taken into consideration with an aim of minimizing the dissatisfaction when choosing the best green supplier. individual regrets related to all criteria for a green supplier are aggregated to obtain effective regret. finally, the green suppliers can be ranked according to the effective regrets. a numerical example is used to illustrate the effectiveness of the proposed method. keywords: generalized regret decision making; green supply chain; multi-criteria decision making; fuzzy set theory. 1 introduction as the awareness of environment protection is increasing and the concerning regulations from government become more strict, green supply chain management (gscm) plays more and more important role nowadays [2, 7, 17]. gscm is widely used particularly in commercial and industrial applications all over the world [1,20,37]. the main purpose of using gscm is to avoid the negative effects on the environment caused by the commercial and industrial activities [31,59]. a lot of companies have adopted concerning theory of gscm in order to reduce the environmental and legal risk during the supply chain and enhance international competitiveness [6]. when applying gscm practices, it is very necessary for companies to evaluate their own gscm performance. besides, some companies also need to green their supply chains by selecting the better supplier from the existing green suppliers [25, 26]. therefore, an effective tool for evaluating gscm performance is vital in practice [22,30,40]. many factors should be taken into consideration during the green supply chain, such as production, material, transformation, storage, purchasing, after-sales service and so on [3,5,28,50,60]. it is common that information is related to multiple aspects in many applications [9,23,51]. actually, evaluating and selecting suppliers is a multi-criteria decision making problem [18,48]. it is usually inevitable that information contains some uncertainty when the suppliers are evaluated by human judge. therefore the copyright ©2018 cc by-nc 338 w. jiang, c. huang environment is fuzzy for majority of multi-criteria decision making problems in practice. many multi-criteria decision making methods have developed in fuzzy environment [21,24,39]. among these methods, ahp(analytical hierarchy process) and topsis (technique for order of preference by similarity to ideal solution) method are the most popular [21,38]. besides, many other theories are developed and applied to supplier evaluation and selection, such as anp(analytic network process), vikor(vlsekriterijumska optimizacija i kompromisno resenje), dematel(d ecision-making trial and evaluation laboratory), promethee(preference ranking organisation method for enrichment of evaluations), copras(complex proportional assessment) and so on. for instance, in [20] when rating and selecting of potential suppliers, economics (cost), operational factors (quality and delivery), and environmental criteria, recycle capability and ghg emission control are considered by using fuzzy topsis method. later the promethee method is proposed to rank the suppliers according to each decision maker’s preferences in [20]. in [40] the multiple criteria evaluation method for green supply programs is based on integrating rough set theory elements and fuzzy topsis. literature [49] introduces a new decision framework to evaluate gscm practices by combining monte carlo simulation, ahp and vikor methods under fuzzy environment. however, few work pay attention to apply regret theory to solve the problem of evaluating green supplier’s performance in green supply chain. the regret decision theory is developed by loomes and sugden [42], bell [4] and fishburn [19]. for a basic regret decision-making model, there are several different regrets for an alternative under different states of nature. the received payoffs exist some difference when decision makers choosing different alternatives under each state of nature. the regret value is defined as a reflection of the difference between the payoff from the choice of alternative and the best payoff from another choice of alternative under the same state of nature. different from the classic decision making theory, the basic point of regret decision making is trying to minimize the dissatisfaction when not making the best decision. by applying the regret decision theory, the decision maker can choose the alternative that bringing the minimum regret. therefore the dissatisfaction of decision maker is minimum if the alternative with the minimum regret is carried out. in this sense, the alternative with the minimum regret is the best choice, which is the most reliable one and can bringing the best payoff compared other alternatives. naturally, the aim is to find the alternative with the minimum regret in regret decision-making model. so it is a key step to find an exact regret associated with all states of nature for each alternative. however, the regret aggregation method has some limitations in basic regret decision-making theory. for this reason, yager proposed the generalized regret decision-making method based on owa operator [55]. this method provides a parameterized family of operations, which can be used to more effectively aggregate an alternative’s individual regrets than the basic regret decision theory [55]. however yager’s method is designed for certain decision environment and cannot settle the problem in fuzzy environment. in practice, the information obtained often is uncertain [10, 13, 19]. many decision-making problems are under uncertain environment [8, 10, 36, 39]. in these cases, the method proposed by yager is not applicable even though this method is an effective tool to handle multi-decision making problem. the main contribution of this paper is summarized as follows. first, a new fuzzy multiple criteria decision-making method based on regret theory is proposed. extended the yager’s generalized decision-making method, the proposed method, called fuzzy generalized decision-making method, can effectively handle the decision making problems in fuzzy environment. second, the proposed method is combined with the application background of gscm practices, a novel multi-criteria decision-making model for evaluating the green supplier’s performance is further proposed. by aggregating the individual regrets associated to a supplier, the effective regret can be obtained. the effective regret of a green supplier is smaller, and the decision makers a multi-criteria decision-making model for evaluating suppliers in green scm 339 are more satisfied with the decision of choosing this supplier. our aim is to select the supplier with the minimum effective regret, which is the best supplier. according to all effective regrets obtained, suppliers can be ranked. a numerical example is used to illustrate the effectiveness of the proposed decision model. the rest of this paper is organized as follows. section 2 shows the basic concepts, including fuzzy set theory, owa operator and presented regret type decision making. in section 3, the proposed decision-making method and decision model for suppliers evaluation are introduced in detail. section 4 presents a numerical application to illustrate the effectiveness of the proposed model. in the end, conclusion and the future works are shown in section 5. 2 preliminaries 2.1 fuzzy set theory the fuzzy set theory proposed by zadeh [58] is widely applied in many fields. nowadays many studies are related to the fuzzy set theory, such as intelligent event process [43,52,57], evidence theory [7,9,33,34], aggregation operator [49,56,74], decision making [27,76] and so on [16,63]. let x be the universe of discourse, x = {x1,x2, · · · ,xn}, a fuzzy set ã defined on x is characterized by a membership function µ ã (x), which can be denoted as: ã = { 〈 x,µ ã (x) 〉 |x ∈ x}, µ ã (x) → [0, 1]. µ ã (x) indicates the degree of x ∈ x in ã. triangular fuzzy number is a special type of fuzzy sets, µa(x) for a triangular fuzzy number a = (a,b,c) is defined as following: µa(x) =   0, x < a x−a b−a , x < a,x < c,a ≤ x ≤ b c−x c−b , b ≤ x ≤ c 0, x > c (1) let a = (a1,b1,c1) and b = (a2,b2,c2) be two triangular fuzzy numbers. some basic arithmetic operations for triangular fuzzy numbers are given as follows [41]: a+b = (a1,b1,c1) + (a2,b2,c2)=(a1 + a2,b1 + b2,c1 + c2) (2) a−b = (a1,b1,c1) + (a2,b2,c2)=(a1 −a2,b1 − b2,c1 − c2) (3) λa = (λa1,λb1,λc1) (4) where λ is a real number. the distance between two triangular fuzzy numbers is a basic concept for triangular fuzzy number. there are many methods for calculating the distance. one popular and classical distance function is defined as follows [15]: d(a,b) = √ [(a1 −a2)2 + (b1 − b2)2 + (c1 − c2)2] (5) the similarity of two triangular fuzzy numbers a and b can be defined as [29]: s(a,b) = 1 − |a1 −a2| + |b1 − b2| + |c1 − c2| 3 (6) obviously, the value of s(a,b) is larger, the two triangular fuzzy numbers a and b are more similar. 340 w. jiang, c. huang 2.2 ordered weighted averaging (owa) operator averaging operator proposed by yager is a important tool for information fusion [46, 57]. an owa operator of dimension m is a mapping owa: rm → r that has an associated m dimensional weighting vector w = (w1,w2, · · ·wm). if j = 1, · · · ,m the yj are a collection of numeric values. then the owa aggregation of these value is owa(y1,y2, · · · ,ym) = ∑m k=1 wkyρ(k) (7) where wk ∈ [0, 1] and ∑m k=1 wk = 1. ρ(k) is the index of the kth largest of yj. a number of approaches have been introduced to obtain the owa weights [47]. in [56] yager introduced a functional method of obtaining wk from function f : [0, 1] → [0, 1]: wk = f( k n ) −f( k − 1 n ), k = 1, 2, · · · ,n (8) where function f satisfies f(x) ≥ f(y) if x > y, and f(0) = 0 and f(1) = 1. 2.3 generalized regret decision-making theory for a decision-making problem, assume there are m possible states of nature and n alternatives, then this problem can be shown as following in a matrix s = [sij]m×n, where sij is the payoff received from the result of decision about jth alternative under ith state of nature. according to the basic regret decision making theory, the decision of selecting an alternative should meet the desire that the regret of this choice is minimum [4,42]. for this point, the regret matrix v is r = [rij]m×n, where rij = si max − sij represents the difference between the payoff si max received from the jth alternative and the maximal payoff sij received from another alternative under ith state of nature. the regret rj = aggi=1 to m [rij] for each alternative is calculated and then select the alternative with minimum regret r′ = minnj=1 to n [rj]. in the framework of generalized regret type decision-making based on owa, the equation of calculating aggregated regret rj is defined as [55]: rj = owa(r1j,r2j, · · · ,rmj) = ∑m k=1 wkrjpj(k) (9) one condition is that a smaller regret is assigned no more weight than a bigger regret. that is if riρi(k1) > riρi(k2) then wk1 > wk2, which ensures a greater regret to dominate among all individual regrets for an alternative. the owa weights of regrets can be obtained by eq. (8), in which one feasible function f is f(x) = xr, and r ∈ (0, 1) [55]. so the equal for obtaining owa weights of regrets is shown as follows [55]: rj = ∑m k=1 wkrjpj(k) = ∑m k=1 (( k m ) r − ( k − 1 m ) r )rjpj(k) (10) 3 the proposed method and model for gscm practices 3.1 the proposed fuzzy generalized regret decision-making method the generalized regret decision-making method proposed by yager is an effective decision making-method and overcomes the limitations of the basic regret decision making method. however this method is designed for exact number and can not be applied for fuzzy environment. as we all know many decision-making problems are presented in the fuzzy environment in practice. for this issue, a fuzzy generalized regret decision making method is proposed in this paper. the a multi-criteria decision-making model for evaluating suppliers in green scm 341 step 1:make up decision making team step 2: determine the relevant evaluation criteria and green suppliers determine the weights of criteria step 3:set linguistic sacle and assess all suppliers for each criterion using linguistic variables step 4:calculate weighted fuzzy decision -making matrix step 5:compute regret decision-making matrix step 6:calculate owa weight and aggregate regrets for each supplier under all criteria step 7:rank all suppliers according to the effective regrets figure 1: the proposed model for ranking green suppliers in gscm practice 342 w. jiang, c. huang proposed method extends yager’s generalized regret decision making method to the fuzzy environment and this can be used to effectively handle fuzzy decision making problem. the proposed fuzzy generalized regret decision-making method consists of the following steps: step 1: for a decision making problem assume ci for i = 1 to m is the state of nature ,the existing alternative is aj for i = 1 to n. and the weight of the states of nature should be taken into consideration, which is expressed by triangular fuzzy number and is called the fuzzy weight in this paper. then matrix can be indicated as: s =   s11 s12 · · · s1n s21 s22 · · · s2n ... ... ... ... sm1 sm2 · · · smn   (11) where sij = (aij,bij,cij), which is a triangular fuzzy number, represents the payoff for alternative j under state of nature i. step 2: the fuzzy weight of each states of nature is transformed to crisp number, then the matrix multiplied by defuzzified weights of the decision criteria is transformed to weighted fuzzy-decision matrix: v =   v11 v12 · · · v1n v21 v22 · · · v2n ... ... ... ... vm1 vm2 · · · vmn   (12) where vij = vij ·wi and wi is the defuzzified weight of the ith state of nature. step 3: in this step the regret matrix is expected to get from the weighted fuzzy-decision matrix. assume vi max is the maximum fuzzy number of ith row matrix. first the maximum vi max under the state of nature ci is obtained by sorting the triangular fuzzy numbers of ith row of matrix v . according to the regret theory, the regret value rij is represented by the difference between matrix element vij and the maximum vi max. a difference measure dif is given based on the similarity of two triangular fuzzy numbers in this paper and is defined as: dif(vij,vi max) = 1 −s(vij,vi max) (13) obviously the value of dif(vij,vj max) is larger, the difference degree between two triangular fuzzy numbers vij and vi max is bigger. based on the difference measure dif, the regret matrix r is obtained as: r = [rij]m·n , i = 1, 2, · · · ,m; j = 1, , 2, · · · ,n where rij = dif(vij,vj max). step 4: since the regret matrix elements obtained though the last step are crisp numbers, thus the effective regret of each alternative can be calculated by eq. (9). step 5: finally all the alternatives should be ranked according to the effective regrets obtained in the step 4. the effective regret value is smaller indicating the corresponding alternative is better. among all alternatives the best one is the one with the minimum effective regret. 3.2 the decision model for gscm practice the aim of using gscm practices is developing friendly environment and reduce the adverse effects on the environment. it is important to evaluate suppliers according to their comprehensive performance about gscm’s criteria. in this part, based on the proposed fuzzy generalized regret decision-making method, a new decision model is proposed to evaluate the green supplier’s a multi-criteria decision-making model for evaluating suppliers in green scm 343 performance in gscm. when using the proposed model evaluates the performance of suppliers, different criteria can be seen as different states of nature and green suppliers are equivalent to alternatives. assessments made by experts reflect the performance of green suppliers under different criteria. the assessment matrix is the decision-making fuzzy matrix in the proposed method. the process of ranking green suppliers with the proposed model is shown in figure 1 and indicated as follows. step 1: the environmental experts are selected to form a decision-making team. step 2: experts determine the relevant criteria for selecting and evaluating green suppliers. then the weights of all criteria are given by experts according to the importance of each criterion. step 3: set the appropriate linguistic scale for green suppliers related to criteria for alternatives. those linguistic scale is used to evaluate all green suppliers under each criterion by environmental experts. then those linguistic scale are transformed to scale with fuzzy numbers, which is used to transform the linguistic assessments to the assessment matrix expressed by fuzzy number. step 4: according to the weights of each criterion, the assessment matrix are disposed to get the weighted fuzzy assessment matrix. first assessments from different experts for each criterion should be fused into one fuzzy number so that fused assessment matrix is obtained. in addition, the weights of criteria with fuzzy number is transformed to crisp number. then the weighted fuzzy assessment matrix v is obtained by multiplying the defuzzified weight and fused assessment matrix. step 5: the regret decision-making matrix is calculated according to weighted fuzzy assessment matrix v . first by ranking the triangular fuzzy numbers, the maximal fuzzy number vi max is selected from ith row of weighted fuzzy assessment matrix v . then based on matrix v and vi max for i = 1 to i = m, the regret decision-making matrix r is obtained by eq. (6). step 6: owa weight is calculated by eq. (10) and individual regrets under each criteria for each green suppliers are aggregated in this step. in order to aggregate the regrets based on owa operator, each column elements in matrix r is sorted in descending order to get rjpj(k). then eq. (7) is used to aggregate all individual regrets under each criteria for each supplier to obtain effective regrets. step 7: alternative suppliers can be ranked according to the effective regrets. the alternative with smaller effective regret, then it ranks higher. 4 an illustrative application and discussing in this section, a numerical example from [49] is used to show the proposed decision model in detail and illustrate its effectiveness. it is considered that a manufacture employs gscm practices and needs to evaluate four green suppliers according to their performance in gscm. and the managers of this manufacture make use of our proposed decision model to make this evaluation. first, the basic example data in [49] is given. the criteria for evaluating four suppliers is shown in table 1. those criteria are divided into four groups, which is inbound,operations,production operations, outbound operations, and reverse logistics and four suppliers. each criterion group consists of several sub-criteria. globe fuzzy weights of all criteria based on the importance of each criterion and linguistic assessments of three experts are presented in table 3. in addition, linguistic scale is shown in table 4. then the assessment matrix is disposed to get the weighted fuzzy assessment matrix. assessments of three experts can be fussed by equal because there is no weight information about these experts. let a = (a1,b1,c1), b = (a2,b2,c2), c = (a3,b3,c3) be the assessments given by three experts, then those three assessments can be fused by d = 1 3 × (a + b + c) . the fused 344 w. jiang, c. huang assessments matrix s is shown in table 4. the fused assessments matrix is weighted to ensure that can reflect the importance degree of criteria. the weights of criteria can be transformed to the crisp number first. the weight w̃ = (w1,w2,w3) of each criterion is defuzzified by using following function: w = (w1 + 4w2 + w3) 6 (14) after defuzzfying, the fuzzy weights are transformed to crisp numbers. by multiplying the defuzzified weight and fussing assessment matrix, the crisp weights and a weighted fuzzy assessment matrix v are shown in table 5. the next step is to calculate the regret decision-making matrix according to weighted fuzzy assessment matrix v . first, all triangular fuzzy numbers need to be ranked to find the the maximal fuzzy number vi max. it can be seen that any two triangular fuzzy numbers v1 = (a1,b1,c1) and v2 = (a2,b2,c2) of case in [49] always satisfy b1 > b2 and c1 > c2 if a1 > a2. distance between two triangular fuzzy numbers defined in section 2, which is commonly applied to rank triangular fuzzy number. the value of distance can be calculated by eq. (5 in this example. it is noted that the best assessment given by expert is vg with triangular fuzzy scale (7, 9, 10). let l = (7, 9, 10), vi max = max[vij]. thus the distance between l and vi max is minimum among all elements in row i. it can be seen that any two triangular fuzzy numbers v1 = (a1,b1,c1) and v2 = (a2,b2,c2) of case in [49] always satisfy b1 > b2 and c1 > c2 if a1 > a2. eq. (13) is used to calculate distance between l and vij so that we can find vi max according to the minimum value of distance. the result of largest fuzzy numbers of each row is showed in table 5. based on the data in table 5, the fusing regret matrix r shown in table 6 is obtained by eq. (6). at this stage, each column elements in matrix r are sorted in descending in order to get rjpj(k). the effective regrets for four suppliers can be obtained by eq. (10). same as the case for calculating effective regrets in literature [55], r in eq. (10) takes 0.5 in this case. and there are 18 criteria, so m in eq.(10) is 18. owa weights wk and rjpj(k) is shown in table 7. after obtaining the effective regrets, the alternative suppliers can be ranked. the greater regret for a supplier, the higher the ranking. finally, alternative suppliers can be ranked according to the effective regrets. the result of effective regrets and the both ranking result obtained by proposed model and by the method in [49] are presented in table 8. according to the ranking result shown in table 8, supplier d ranks number 1, which shows that the performance of supplier d is the best among four suppliers. therefore, the decision of this gscm problem is choosing supplier d to support gscm practice for this manufacture. by comparing two ranking results shown in table 8, it can be seen that both two results support that: (1) supplier a and supplier d rank higher than supplier b and supplier c, that is to say, the performance of supplier a and supplier d are better than supplier b and supplier c; (2) supplier c rank higher than supplier d, that is, supplier c is better than supplier b. the difference of two ranking results is the ordering of supplier a and supplier d. the ranking result obtained by the proposed method shows the supplier d rank higher than supplier a. however, the result obtained by method in [49] shows that supplier a and supplier d both rank number one. in [49] supplier a and supplier d both rank number one, which means there is no difference between good or bad for supplier a and supplier d. so in practice it is difficult for managers to make a choice between supplier a and supplier d. however, the proposed method overcomes this shortcoming. according to table 8, it is known that the effective regrets of supplier a and supplier d are much lager than supplier b and supplier c and the regret of supplier a is lager than supplier d. this result indicates that supplier a and supplier d are better alternatives for a multi-criteria decision-making model for evaluating suppliers in green scm 345 table 1: criteria for evaluating gscm [49] the group of the criteria criteria concrete content of criteria inbound operations c1.1 choosing suppliers by environmental criteria c1.2 guiding suppliers to establish their own environmental programs c1.3 urging/pressuring suppliers to take environmental actions c1.4 purchasing environment friendly items production operations c2.1 design products for recycling c2.2 using cleaner technology c2.3 improving capacity utilization c2.4 promoting remanufacturing outbound operations c3.1 enhancing vehicle operating efficiency c3.2 encouraging eco-driving c3.3 using environmental friendly packaging c3.4 reducing empty running c3.5 improving vehicle routing using gps (global positioning system) and other systems c3.6 increasing vehicle payload capacity reverse logistics c4.1 re-use of products and components c4.2 recycling of materials c4.3 waste management c4.4 taking back packaging table 2: linguistic assessments of suppliers and weights of criteria [49] criteria supplier a supplier b supplier c supplier d weight c1.1 vg, g, g f, g, p f, g, g g, vg, f (0.03, 0.05, 0.08) c1.2 g, f, p vg, g, g g, f, f vg, vg, g (0.03, 0.05, 0.08) c1.3 p, f, f f, p, g p, f, vp g, f, vg (0.04, 0.07, 0.12) c1.4 f, g, g f, g, p g, f, vg f, vg, f (0.01, 0.02, 0.04) c2.1 f, p, p f, g, f f, g, f vg, g, g (0.03, 0.05, 0.09) c2.2 f, g, f g, vg, vg f, f, g vg, g, vg (0.03, 0.06, 0.10) c2.3 vg, g, vg p, f, p f, g, g vg, g, vg (0.05, 0.09, 0.16) c2.4 g, vg, vg p, p, f f, p, p p, g, f (0.02, 0.04, 0.07) c3.1 vg, g, g g, p, g f, g, f vg, f, g (0.03, 0.06, 0.10) c3.2 vg, g, g f, p, g g, vg, vg f, g, g (0.04, 0.07, 0.11) c3.3 vg, g, vg g, f, p f, g, f vg, g, g (0.05, 0.11, 0.20) c3.4 g, f, vg p, f, f f, g, g vg, vg, vg (0.03, 0.05, 0.09) c3.5 g, f, f p, vp, vp f, g, f p, vp, f (0.04, 0.06, 0.10) c3.6 vg, g, g f, p, f p, vp, vp g, vg, g (0.04, 0.07, 0.12) c4.1 g, vg, vg f, g, g g, vg, f f, p, g (0.02, 0.03, 0.06) c4.2 vg, g, vg p, f, vp f, f, g vg, g, g (0.02, 0.04, 0.08) c4.3 f, g, g f, p, g g, vg, f f, p, f (0.03, 0.06, 0.11) c4.4 vp, p, p f, f, g f, g, g vg, g, g (0.02, 0.03, 0.05) table 3: linguistic scale for evaluating gscm performance of suppliers [49] linguistic scale for evaluating suppliers triangular fuzzy scale very poor (vp) (0, 1, 3) poor (p) (1, 3, 5) fair (f) (3, 5, 7) good (g) (5, 7, 9) very good (vg) (7, 9, 10) 346 w. jiang, c. huang table 4: the fused assessments matrix [49] criteria supplier a supplier b supplier c supplier d c1.1 (5.667, 7.667, 9.333) (2.333, 4.333, 6.333) (4.333 ,6.333, 8.333) (5.000 , 7.000, 8.667) c1.2 (3, 5, 7) (5.667, 7.667, 9.333) (3.667, 5.667, 7.667) (6.333, 8.333, 9.667) c1.3 (2.333 ,4.333, 6.333) (3, 5, 7) (1.333, 3.000, 5.000) (5.000, 7.000, 8.667) c1.4 (4.333, 6.333, 8.333) (3, 5, 7) (5.000, 7.000, 8.667) (4.3333, 6.3333, 8.0000) c2.1 (1.667, 3.667, 5.667) (3.667, 5.667, 7.667) (3.667, 5.667, 7.667) (5.667, 7.667, 9.333) c2.2 (3.667, 5.667, 7.667) (6.333, 8.333, 9.667) (3.667, 5.667, 7.667) (6.333, 8.333, 9.667) c2.3 (6.333, 8.333, 9.667) (1.667, 3.667, 5.667) (4.333, 6.333, 8.333) (6.333,8.333,9.667) c2.4 (6.333, 8.333, 9.6667) (1.667, 3.667, 5.667) (1.667, 3.667, 5.667) (3, 5, 7) c3.1 (5.667, 7.667, 9.333) (3.667, 5.667, 7.667) (3.667, 5.667, 7.667) (5.000, 7.000, 8.667) c3.2 (5.667, 7.667, 9.333) (3, 5, 7) (6.333, 8.333, 9.667) (4.3333, 6.3333, 8.3333) c3.3 (6.333, 8.333, 9.667) (3, 5, 7) (3.667, 5.667, 7.667) (5.667,7.667,9.333) c3.4 (5.000, 7.000, 8.667) (2.333, 4.333, 6.333) (4.333, 6.333, 8.333) (7, 9, 10) c3.5 (3.667, 5.667, 7.667) (0.333, 1.667, 3.667) (3.667, 5.667, 7.667) (1.333, 3.000, 5.000) c3.6 (5.667, 7.667, 9.333) (2.333,4.333,6.333) (0.333,1.667,3.667) (5.6667, 7.6667, 9.3333) c4.1 (6.333, 8.333, 9.667) (4.333, 6.333, 8.333) (5.000, 7.000, 8.667) (3, 5, 7) c4.2 (6.333, 8.333, 9.667) (1.333, 3.000, 5.000) (3.667, 5.667, 7.667) (5.667, 7.667, 9.333) c4.3 (4.333, 6.333, 8.333) (3, 5, 7) (5.000, 7.000, 8.667) (2.333, 4.333, 6.333) c4.4 (0.667, 2.333, 4.333) (3.667, 5.667, 7.667) (4.333, 6.333, 8.333) (5.667, 7.667, 9.333) table 5: the weighted fuzzy assessment matrix and the best assessment on each criteria criteria defuzzified weight supplier a supplier b supplier c supplier d the largest fuzzy number c1.1 0.052 (0.293, 0.396, 0.483) (0.121, 0.224, 0.327) (0.224, 0.327, 0.431) (0.259, 0.362, 0.448) (0.293, 0.396, 0.483) c1.2 0.052 (0.155, 0.259, 0.362) (0.293, 0.396, 0.483) (0.190, 0.293, 0.396) (0.327, 0.431, 0.500) (0.327, 0.431, 0.500) c1.3 0.073 (0.175, 0.325, 0.475) (0.225, 0.375, 0.525) (0.100, 0.225, 0.375) (0.375, 0.525, 0.650) (0.375, 0.525, 0.650) c1.4 0.022 (0.094, 0.137, 0.181) (0.065, 0.109, 0.152) (0.109, 0.152, 0.188) (0.094, 0.137, 0.174) (0.109, 0.152, 0.188) c2.1 0.053 (0.089, 0.195, 0.302) (0.195, 0.302, 0.409) (0.195, 0.302, 0.409) (0.302, 0.409, 0.498) (0.302, 0.409, 0.498) c2.2 0.062 (0.226, 0.350, 0.473) (0.391, 0.514, 0.596) (0.226, 0.345, 0.473) (0.391, 0.514, 0.596) (0.391, 0.514, 0.596) c2.3 0.095 (0.602, 0.792, 0.918) (0.158, 0.348, 0.538) (0.412, 0.602, 0.792) (0.602, 0.792, 0.918) (0.602, 0.792, 0.918) c2.4 0.042 (0.264, 0.348, 0.403) (0.070, 0.153, 0.236) (0.070, 0.153, 0.236) (0.125, 0.209, 0.292) (0.264, 0.348, 0.403) c3.1 0.062 (0.347, 0.473, 0.576) (0.226, 0.350, 0.473) (0.226, 0.350, 0.473) (0.309, 0.432, 0.535) (0.350, 0.473, 0.576) c3.2 0.072 (0.406, 0.550, 0.669) (0.215, 0.359, 0.502) (0.454, 0.598, 0.693) (0.311, 0.454, 0.598) (0.454, 0.598, 0.693) c3.3 0.115 (0.728, 0.958, 1.112) (0.345, 0.575, 0.805) (0.422, 0.652, 0.882) (0.652, 0.882, 1.073) (0.728, 0.958, 1.112) c3.4 0.053 (0.267, 0.373, 0.462) (0.124, 0.231, 0.338) (0.231, 0.338, 0.444) (0.373, 0.480, 0.533) (0.373, 0.480, 0.533) c3.5 0.063 (0.401, 0.528, 0.612) (0.106, 0.232, 0.359) (0.274, 0.401, 0.528) (0.401, 0.528, 0.612) (0.401, 0.528, 0.612) c3.6 0.073 (0.415, 0.562, 0.684) (0.171, 0.318, 0.464) (0.024, 0.122, 0.269) (0.415, 0.562, 0.684) (0.415, 0.562, 0.684) c4.1 0.033 (0.211, 0.278, 0.322) (0.144, 0.211, 0.278) (0.167, 0.233, 0.289) (0.100, 0.167, 0.233) (0.211, 0.278, 0.322) c4.2 0.043 (0.274, 0.361, 0.419) (0.058, 0.130, 0.217) (0.159, 0.245, 0.332) (0.245, 0.332, 0.404) (0.274, 0.361, 0.419) c4.3 0.063 (0.274, 0.401, 0.528) (0.190, 0.317, 0.443) (0.317, 0.443, 0.549) (0.148, 0.274, 0.401) (0.317, 0.443, 0.549) c4.4 0.032 (0.021, 0.074, 0.137) (0.116, 0.180, 0.243) (0.137, 0.201, 0.264) (0.180, 0.243, 0.296) (0.180, 0.243, 0.296) a multi-criteria decision-making model for evaluating suppliers in green scm 347 table 6: regret matrix criteria supplier a supplier b supplier c supplier d c1.1 0 0.167 0.063 0.035 c1.2 0.161 0.029 0.126 0 c1.3 0.192 0.142 0.283 0 c1.4 0.012 0.041 0 0.015 c2.1 0.207 0.101 0.101 0 c2.2 0.151 0 0.151 0 c2.3 0 0.422 0.169 0 c2.4 0 0.185 0.185 0.130 c3.1 0 0.117 0.117 0.041 c3.2 0.040 0.223 0 0.128 c3.3 0 0.358 0.281 0.064 c3.4 0.095 0.231 0.124 0 c3.5 0 0.281 0.113 0 c3.6 0 0.236 0.415 0 c4.1 0 0.059 0.041 0.104 c4.2 0 0.217 0.106 0.024 c4.3 0.035 0.120 0 0.162 c4.4 0.162 0.060 0.039 0 table 7: sorted regret values and owa weights ρk supplier a supplier b supplier c supplier d owa weight ρ1 0.207 0.422 0.415 0.162 0.236 ρ2 0.192 0.358 0.283 0.130 0.098 ρ3 0.162 0.281 0.281 0.128 0.075 ρ4 0.161 0.236 0.185 0.104 0.063 ρ5 0.151 0.231 0.169 0.064 0.056 ρ6 0.095 0.223 0.151 0.041 0.050 ρ7 0.040 0.217 0.126 0.035 0.046 ρ8 0.035 0.185 0.124 0.024 0.043 ρ9 0.012 0.167 0.117 0.015 0.040 ρ10 0 0.142 0.113 0 0.038 ρ11 0 0.120 0.106 0 0.036 ρ12 0 0.117 0.101 0 0.035 ρ13 0 0.101 0.063 0 0.033 ρ14 0 0.060 0.041 0 0.032 ρ15 0 0.059 0.039 0 0.031 ρ16 0 0.041 0 0 0.030 ρ17 0 0.029 0 0 0.029 ρ18 0 0 0 0 0.028 table 8: the effective regrets and the ranking results for four suppliers regret the proposed model the method in [49] supplier a 0.107 2 1 supplier b 0.242 4 3 supplier c 0.210 3 2 supplier d 0.076 1 1 348 w. jiang, c. huang decision makers, and the best choice is supplier d which has the best performance. from table 8, the effective regrets of supplier d and supplier a is 0.076, and 0.107 respectively. according to these two regret values, the manager can easily make a clear choice to choose supplier d. 5 conclusion a good gscm is important for reducing the environmental harm cased by industrial activities through the supply chain. the companies need to evaluate their own gscm performance and select the best supplier. therefore a good evaluation model plays an important part. in this paper, a novel fuzzy multi-criteria decision making method for gscm is proposed. in the proposed decision-making method, the regret sense of decision-maker is taken into consideration, which improves the accuracy and reliability of decision making. besides, instead of using the basic regret decision-making method, the fuzzy generalized regret decision-making method is proposed to obtain effective regret. the generalized regret decision making method based on owa is used for certain decision-making environment in [55]. on this basic, the proposed method can be used to settle problems under fuzzy decision-making environment. then the proposed method is implemented to the case about gscm practices introduced in [49], as a result of which, a more accurate and clearer ranking result about green suppliers is obtained. a comparative analysis of the result verifies the effectiveness and feasibility of the proposed method in this paper. in the future research, the proposed method is expected to extend its applications. more multi-decision making problems in any other application background can be considered to resolve with this method. acknowledgments the work is partially supported by national natural science foundation of china (program no. 61671384,61703338), the seed foundation of innovation and creation for graduate students in northwestern polytechnical university (program no. zz2017126). conflict of interests the authors declare that there is no 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(2017); fuzzy evidential influence diagram and its evaluation algorithm, knowledge-based systems, 131, 28–45, 2017. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 442-457 decision support system for evaluating existing apartment buildings based on fuzzy signatures g.i. molnárka, l.t. kóczy gergely i. molnárka, lászló t. kóczy széchenyi istván university, györ, hungary, h-9026 györ, egyetem tér 1. e-mail: mgergo@sze.hu, koczy@sze.hu abstract: in historical district at european cities it is a major problem how to take decision on renovating or replacing existing buildings. this problem is imminent in budapest (hungary) in many traditional districts such as the ferencváros district where we selected a compound area for further examination. by financial aid for the renovation of these buildings which awarded by municipal assembly of this district in question there is much uncertainty and confusion concerning how to decide whether or not and how to reconstruct a building where new private owners apply for support. in this paper we propose a formal evaluation method based on fuzzy signature rule bases (the formal being a special case of l-fuzzy object). using the available expert knowledge we propose a fuzzy signature model including relevance weights and weighted aggregations for each node and parent node, respectively, so that as a result a single membership value may be calculated for each building in question. linguistic labels for decision (such as worthless, average, highly valuable, etc.) are generated from the values thus obtained. such linguistic calculations might be of help for the municipal assembly awarding financial support. a complete example wit 26 buildings is presented. keywords: building condition evaluation, fuzzy signatures, weighted relevance aggregation operators. 1 introduction in the last twenty years due to the transition to democracy changes have happened in the property structure of building stocks, among others, in the ferencváros ("francis town") district of budapest (capital of hungary) [1], [2]. the tendency of enhancing the role of private ownership resulted in difficulties of the maintenance process, since the expenses of a general intervention were multiples of the owners’ capitals available. in practice, numerous external financial corporations are involved nowadays in supporting renovation and rehabilitation. this solution, however, increases the citizens’ level of indebtedness and, consequently, influences the national debt harmfully. for supporting renovation municipalities offer non-repayable direct financial assistance to the communities of flat owners. this financial aid is achievable annually by application: awarding is decided by the municipal assembly. the (non-financial) background of the decision on awarding consists mainly of urban aspects (façade and roof renovation), and is based at present exclusively on the data given by the applicant in the application form. however these aspects are important and conform to public interest, numerous factors are disregarded and therefore the efficiency of the municipal financial assistance is challengeable. from the point of public view it may also be important to include other attributes of the given building, e.g. physical (static condition, energetic efficiency, etc.) and psychological (technical copyright c⃝ 2006-2011 by ccc publications decision support system for evaluating existing apartment buildings based on fuzzy signatures 443 ageing, architectural, etc.) values. based on further information an objective ranking of buildings may be created, which can help consider the condition of the applicants’ buildings during the process of awarding. in the present article a proposal for a decision-support tool is outlined that is based on a collection of technical information of the involved buildings. the attributes of buildings can be arranged in a predefined structure, where all information on the buildings can be evaluated systematically. without any detailed explanation it is obvious that the goal (decision-support tool) and the circumstances (classified data structure, involvement of experts) strongly recommend the application of an expert system. issuing from the character of data on hand and the observation methods applied, the expert system may well be based on fuzzy set theory [3], especially on the approach of fuzzy signatures which is an efficient tool for describing and characterizing objects with multiple and vague uncertain attributes. 2 professional overview of the examined building stock the ferencváros district has four main residential areas. the mid-part is located close to the city centre (see figure 1). figure 1: overall view of the examined area (middle ferencváros) the urban structure of this area was formed in the most effective period of urbanization in budapest (1875-1920). the majority of the still existing apartment buildings were built in the first decades of the twentieth century or in the interwar period. until the mid-nineties this district represented the slum area in the urban texture of the capital. in the period of the ’90s this area came into the attention of international urban designers and politicians by the expressed rehabilitation process. among others the project manager sem ix municipal company won the fiabci prize (a notable award of international real estate federation) in the category of public sector in 1998. beside the attractive intervention for renovating series of valuable buildings and public areas these changes caused serious relocation of citizens. the main axis of the rehabilitated urban structure is the tompa street that links a busy traffic route (the middle ring street) with a cozy and silent square in the centre of the district. at this semi-intensive area 26 apartment houses can be found on both sides of the 400 m long street. the overview of the area can be seen in figure 2. for introducing the apartment houses in the examined area it is worthwhile to describe the buildings by the age of construction. 444 g.i. molnárka, l.t. kóczy figure 2: tompa street as the axis of a rehabilitation project two buildings (8%) were built in the age of historicism (1875-1905, a period using various historical styles), although their constructions had been changed in the next periods. therefore the global description of these buildings is given together with the next set of buildings. the larger part of the buildings (14 pieces; 54%) was constructed in the age of academic style (1905-1920). the main attributes of these apartment houses (formerly rental houses) are collected in table 1. three buildings were built in the interwar period (12%) those reflect to the age of modern with their attributes (see table 2). the rest (seven buildings; 26%) was constructed during the rehabilitation period (1990-2004). these apartment houses represent the attributes of this age (see table 3). the date of construction can be estimated by an overall look at the external architectural elements applied (cornices, decorations, etc.) and on the shape of the building. with the knowledge of the assortments of each period, a global description of buildings can be done. however the collected attributes may represent the entire building stock in the examined area, it has to be remarked that every case is different. in figure 3 a collection of different buildings represents the motley stock. in spite of the rehabilitation project, some buildings remained in quite bad state with evidences of lack of maintenance (fig.3a). in case of the larger part of the historic buildings only the façade was renovated (fig. 3b). the poor energetic performance (see table 4) and the natural ageing of associated building constructions (roof constructions, chimneys, waterproofing, etc.) may cause serious problems in the near future for the owners. beside this, the setting of buildings, the application of narrow courtyards produces apartments with bad attributes (lack of sunlight in rear side apartments, badly ventilated courts, lack of vegetation, etc.). fig.3.c. represents a good example for good quality renovation and modernization at the same time: the two-storey building was enlarged with two more storeys and a penthouse with retaining the aesthetic values of the original building. in this case the transformation of the building resulted in good life circumstances (it received a common garden with the recently built adjoined block; only the well-orientated apartments were kept). the buildings from the modern era (fig 3e, 3f) are supposed to be in fairly good condition. however, although it is invisible outside, the reinforced concrete beams and pillars cause serious geometric thermal bridges (resulting in bad energetic attributes; even because of accumulation of vapor the appearance of mould on the interior surface of external walls is not excludable). it is also remarkable that the reinforced bauxite concrete load-bearing constructions need to be revised periodically (decennially). the recently built apartment houses (fig 3g, 3h) correspond to all regulations; the building decision support system for evaluating existing apartment buildings based on fuzzy signatures 445 type of construction (estimated) footing made of mixed materials (stones, bricks and concrete) traditional brickwork vertical load bearing system (clay brick with lime-mortar) prussian vault type slab over the cellar (steel beams with clay brick fields) reinforced concrete slabs in intermediate storey (different solutions of early era of reinforced concrete) dowelled beam floor as loft slab (made of timber) traditional timber roof framing with ceramic tiles or zinc plates coupled openings made of timber horizontal waterproof layer under load-bearing walls (paper-based bitumen layer) natural stone skirting and simple rendering external drainage system façade ornaments geometric ornaments, cornices that articulate the façade simple statues (in low artistic quality) type of installation inner court ventilated cellar ventilated loft light well differentiated arrangement of apartments by social status former reconstructions local strengthening of floors, replacement in some cases façade renovation (drainage system, rendering, skirting) table 1: global attributes of rental houses built in period of 1875-1920 type of construction (estimated) pad footing made of concrete and stones post and beam framework made of reinforced concrete with infilling brickwork wall reinforced concrete slabs (in one case: utilization of bauxite as accelerating admixture) flat roof with slag heat insulation and paper-based bitumen layer covering coupled openings made of timber waterproof layer under the entire building (paper-based bitumen layer) natural stone skirting and simple rendering or natural stone cladding and brick façade internal drainage system façade ornaments type of installation integrated arrangement to an enclosed stairway modern spatial structure with bright apartments former reconstructions flat roof reconstructions façade renovation (broken stone replacements, rendering) periodic revision of reinforced bauxite concrete structures table 2: global attributes of apartment houses built in the interwar era 446 g.i. molnárka, l.t. kóczy type of construction (estimated) footing made of concrete post and beam framework made of reinforced concrete with infilling masonry unit reinforced concrete slabs heat insulated pitched roof with concrete tiles plastic openings with insulated glazing waterproof layer under the entire building (reinforced modified bitumen layer) simple rendering external drainage system façade ornaments simple and valueless decorations type of installation integrated arrangement to an enclosed stairway modern spatial structure with bright apartments former reconstructions table 3: global attributes of apartment houses built in the ’90s heat transmission coefficient (u[w/m2k]) mean values current regulation (estimated) external walls 1.44 0.45 loft slabs 0.80 0.30 openings 2.80 1.60 table 4: present energetic performance of former rental houses constructions and the associated elements are supposed to be acceptable in quality. in spite of these facts, it has to be pointed out that the public interest could not be achieved here. from the aspect of cityscape these buildings are negligible. 3 application of fuzzy signatures in the case of the planned decision support tool the generalization of fuzzy sets first introduced by zadeh [4] has to be mentioned; zadeh’s early student, goguen proposed the concept of l-fuzzy sets [5]. l-fuzzy membership grades are elements of an arbitrary lattice l: a : x → l,∀x ∈ x (1) vector-valued fuzzy sets are introduced in [6]. they are special l-fuzzy sets, where l is the lattice of n-dimensional fuzzy vectors, l=[0,1]n in (1). vector valued fuzzy sets assign to each element of x a set of quantitative features rather than a single degree this way providing additional information about the specific element. fuzzy signatures [7] are generalized vector valued fuzzy sets, where each vector component is possibly another nested vector. this generalization can be continued recursively to any finite depth, thus forming a signature with depth m (2). decision support system for evaluating existing apartment buildings based on fuzzy signatures 447 as : x → [ai]ki=1,ai = { [0,1] [aij] k j=1 ,aij = { [0,1] [aijl] k l=1 ,∀x ∈ x (2) figure 3: representative buildings of the examined building stock the structure of fuzzy signatures can be represented both in vector form and also as a tree graph (figure 4 represents both the vector form and the tree graph of the proposed structure). fuzzy signatures can be considered as special, multidimensional constructions that are applicable for storing structured fuzzy data. in this structure the dimensions are interrelated in the meaning that a sub-group of variables determines a character on a higher level. therefore, complex and interdependent data components can be described and evaluated in a compact way. in many applications, the obtained information of experts can be described in different ways, even the structure of observation can be different; nevertheless decisions have to be taken by these data. with the assistance of signatures these alterations in structures can be handled. the 448 g.i. molnárka, l.t. kóczy figure 4: tree structure and vector form of fuzzy signature main advantage of the application of fuzzy signatures is that they can handle situations with uneven data structures and information. furthermore, the model created for the given task can be arranged hierarchically [8]; this feature is very similar to the way of thinking of human experts. this fact underlines the argument that fuzzy signatures are deployable on the area of decision making. the advantage of fuzzy signatures is that they organize the available data components into hierarchical structures. this hierarchy determines the basic structure of fuzzy signature-based observations. it may occur that some elements are missing at several observations. therefore, it is necessary to have a kind of structure modifier operator for comparing signatures with quite different structures. it is advisable to apply aggregation operators for reducing sub-trees to their parent node. in case of a multileveled hierarchy, a recursive process leads to obtaining the aggregated value of the parent node. in our case, as the most important question the aggregation operators had to be defined. the structure of the fuzzy signature supports the use of different aggregation operators for each node. 4 the proposed new approach as it was described in section 2, due to several different aspects it is difficult to compare the elements of inhomogeneous building stock. this fact makes the judgements professionally unfounded in the awarding procedure presented in the introduction. in the following a formal system will be proposed that may support the decisions of the municipal assembly in the awarding procedure. similarly to [9], the proposed tool offers an effective solution for applying the experts’ knowledge in responsible decision making. this system is based on fuzzy signatures described in section 3. in the recent decades the importance of visual examination on site increased in comparison decision support system for evaluating existing apartment buildings based on fuzzy signatures 449 with destructive analyses among building diagnostic experts. the shapes of the deteriorations, the information collected about checked building constructions and circumstances may offer enough evidence for experienced specialist to esteem the causes and consequences of building failures. taking advantage of this, some new diagnostic tools were created (cf. koppány, [10]). as a comparable study, a comprehensive assessment of rental houses in lisbon was compiled; furthermore, an objective evaluation of rental houses was done with a method based on visual examination on site [11]. the application of fuzzy set theory was proposed in bridge maintenance systems as well recently [12]. as agárdy implies [13], soft computing techniques may help differentiating sets of objects of built environment by several aspects. as a decision support tool the fuzzy expert system was also proposed for determining the importance of intervention in case of building failures [14]. the circumstances of the evaluation of building stocks discussed above are quite different from the core problem of the above approaches. the methods of the assessments that were implied in the mentioned papers are applicable for examining the physical condition of the buildings and their elements without considering the narrower and wider context in their place. the aim of the present paper is to obtain a comprehensive and comparable data set for each building; therefore it was not possible to narrow the aspects to the physical and measurable conditions without losing important information. this intent resulted in a large scale set of examination aspects that is difficult to handle without any classification abilities. this reason also explains why the adaptation of hierarchical system of fuzzy signature makes the support tool effective. the model. as the first step, the basic structure of the fuzzy signature developed from the available data and the experts’ knowledge; then the adequate fuzzy sets of data elements must be determined. the next step is the identification of fuzzy signature based rules applying the experts’ knowledge and the available input-output pairs. when the rule base is ready, the fuzzy signature based observation can be directly evaluated, thus generating a suggested decision. let us overview the concrete structure of the fuzzy signature suitable for representing the set of attributes and their respective relations used in the building evaluation approach. in the context of this study the accessible information on a building may be arranged in five main groups, which groups constitute the first level of the fuzzy signature structure (nodes x1; x2; x3; x4 and x5). for aggregating sub-trees within the fuzzy signatures the wrao operator (weighted relevance aggregation operator, introduced by mendis et al. [15]) was applied. with the application of weighted aggregations more expert knowledge can be involved in the examination. the initiated relevance weight determines the relevancy of a child node on a higher level. for determining the relevance weights by observation mendis et al. [16] propose a method. let us denote the value and relevant weight of l child of n pieces of aggregating nodes with xl and wl. the wrao function is denoted with @ in (3). @(x1,x2, ...,xn;w1,w2, ...,w3) = ( 1 n n∑ l=1 (wl · xl)p )1 p (3) where the p is the aggregator factor p ∈ r, p ̸= 0. for describing different components of the structure linguistic variables may be applied. the next step is to define these linguistic variables and their membership functions. the global analysis of apartment houses is a complex task. a large number of factors has to be considered while a building is evaluated. the goal of the discussed study was to obtain 450 g.i. molnárka, l.t. kóczy a comprehensive character of each building. therefore, the parent nodes in the fuzzy signature structure are the five important segments of building sciences: urban planning (cityscape attributes); art and architecture (architectural quality); engineering (constructional quality); urban sociology (life quality) and protection of monuments (aesthetic attributes). in the following the parent and child nodes are described in groups: their relevance weights are denoted with w. for the proper application of fuzzy set signatures it has to be confirmed that at the leaves membership functions shall be applied over the [0,1] interval: for this reason the basic sets of the determined attributes have to be normalized to [0,1]. the closer the value of observation is to 1 the better is the quality in each respect. at the nodes and at the leaves of the fuzzy signature structure partitions of triangular or trapezoidal sets are applied forming ruspini partitions [17]( ∑ i ai(x0) = 1 for every x0 ∈ x[0,1]). a. cityscape attributes (x1;w1=0.4). the urban values of buildings are collected in the group of cityscape attributes. the matching of the examined building into the urban texture can be described by the following three child nodes (their membership functions are presented on figure 5): matching to the character of the street (x11;w11=0.8) this attribute represents how the examined building harmonizes with the surroundings in shape, color, etc. matching to the position (x12;w12=0.4) this attribute represents how the relation is between the examined building and its position in the street (corner or other prevailing position). matching of the height of the façade (x13;w13=0.2) this attribute represents how the examined building differs from the surroundings vertically. figure 5: membership functions of the cityscape attribute and of its child nodes b. architectural quality (x2;w2=0.75). the broadly defined architectural quality represents the value of the building itself. as a product a building may represent extra meanings: it is more than a simple apartment house, it reflects the era of its design and construction. in this respect, four different attributes were established. the membership functions are presented in figure 6. reproducibility (x21;w21=0.8) from the point of view of the reproducibility the buildings can be examined to what degree their construction could be realized with up-to-date technologies; considering available techniques and current regulations. in this case, mutatis mutandis, the lower value means that the given building is easier replaceable. decision support system for evaluating existing apartment buildings based on fuzzy signatures 451 authenticity (x22;w22=0.3) this feature represents that how the examined building corresponds to the atmosphere of the age of its construction. value of in-built materials (x23;w22=0.4) the proper application of fine materials (e.g. natural stone, wrought iron, hand-manufactured bricks as claddings, etc.) increases the quality of the buildings. this feature represents in what rate the examined building contains additional attributes. body shaping (x24;w24=0.7) although the architectural tools applied in design had changed during the ages, the plasticity of buildings was always important, even in the age of modern. beside the plasticity it is also important whether the body shaping is in harmony with the surroundings. figure 6: membership functions of architectural quality and of its child nodes c. constructional quality (x3;w3=0.85). the condition and the energetic performance of building constructions determine heavily the overall quality of the buildings. the exploring of the in-built constructions and their qualifying are done here only by visual examination on site. the discussed study does not contain a detailed and objective survey. the two components of this quality descriptor are as follows: consistence of building constructions (x31;w31=0.8) although this fuzzy sub-signature can be divided into more sub-trees, here only single merged values are applied. energetic attributes (x32;w32=0.4) the comparative analysis of external building constructions is based on estimated values for homogenous unit portions of the external wall, the openings and the loft slab. as a standard here the national regulations have to be also taken into account. the values that represent the observations are not equal to the index number applied for determining the energetic quality of buildings. d. life quality (x4;w4=0.45). this aspect has three components. figure 8 represents the child nodes and the life quality membership functions. spatial arrangement of buildings (x41;w41=0.6) the spatial arrangement of flats may be concluded from the shape and the layout of the buildings. although there can be a great variation of spatial arrangements of flats in a house itself, the type of the flats, the accommodation density 452 g.i. molnárka, l.t. kóczy figure 7: the membership functions of constructional quality and of its child nodes together determine the life quality of the examined building. existence of connection with private parks, gardens (x42;w42=0.4). a part of the buildings have direct connection with private parks or gardens that influence the value of the building positively. the index that represents the existence of connection is binary (yes or no, 0/1). recreational quality (x43;w43=0.6) the distance of the examined buildings from the traffic route or the busy intersections influence the noise and pollution level of the interior spaces of the buildings. the recreational quality index represents this aspect as an accumulated value. figure 8: membership functions of life quality and of its child nodes e. aesthetic attributes (x5;w5=0.5). although the national heritage office has its professional tool for appreciating the historic value of buildings, in the present analysis only the external marks are taken into account. this attribute and its leaves can be seen in figure 9. professional judgement (x51;w51=0.8) the professional attributes of aesthetic evaluation are obtained from the analysis of the proportion of the buildings and the application of architectural elements. this analysis is founded on the qualification of the harmony between the design and the realization. public judgement (x52;w52=0.3) for a comprehensive opinion about the examined buildings a public poll was done. 29 non-professionals filled questionnaires for representing the public opinion decision support system for evaluating existing apartment buildings based on fuzzy signatures 453 about the examined buildings. for evaluating the questionnaires a linguistic approximation according to [18] was applied. some answers differed from the typical radically; the peaks and "needles" originating from this phenomenon were cut off ("noise cutoff"). figure 9: membership functions of aesthetic attributes and of its child nodes the rule base. at the first level the number of available input variables is five. their partitions (3;3;5;3;6 partitions) result that the total number of rules in the rule base is 810. at the level of child nodes a different (much higher) number of rules can be identified from the partitions of input variables. the detailed description of these input variables and their partitions can be above. the hierarchical structure of the signatures presents dealing with a rule base of very high complexity. inference method. basing on mamdani-type inference [19], tamás [20] introduced the generalized method that operates on signature based rule bases. in this method the alteration is only in the first step, where the degree of matching between observation and the antecedents of rules is determined. in the discussed procedure this method is applied, where the minimum conjunction is taken as the aggregation operator for reducing the signature structure. figure 10 represents the outline of a general fuzzy inference system. figure 10: outline of fuzzy inference systems 5 results the examined buildings were evaluated with the method discussed above. the partial results are collected in table 5. as an example the values obtained for building 17 in fig 3c are presented here: the result of fuzzy signature inference is a fuzzy set that can be defuzzified for determining the consequence value. in the present examination the cog (center of gravity) method was 454 g.i. molnárka, l.t. kóczy figure 11: fuzzy signature and aggregated values for building 17 (fig. 3c) table 5: evaluation of the examined buildings decision support system for evaluating existing apartment buildings based on fuzzy signatures 455 applied. the applied categories for the assessment of buildings at the side of consequence are visible in table 6 where the categories and corresponding ranges of values for the ranked buildings are presented. categories values buildings (nr.) worthless 0-19% below average 20-39% 6;8;9;14;15;20;21;23;24;25;26;27;28;29;30 average 40-74% 5;7;10,12;13;16;17;18;19;22 valuable 75-89% highly valuable 90-100% table 6: categories of overall value of the examined buildings the result of the evaluation demonstrates that the building stock has a balanced value. in spite the fact that each building has different conditions (age, aesthetical, constructional quality), the deviation among their category indices is low. among other conclusions this result may confirm the perception that the buildings constructed recently did not increase the overall value of the examined building stocks. 6 conclusions and future work as a summary it can be stated that the results of calculations correlate to the former assumptions. the utilization of linguistic variables supported the evaluation of non-measurable (e.g. aesthetic) and uncertain (e.g. energetic performance) values. the application of weighted relevance aggregators in the reducing phase also maintained the professional aspects in examination. the unique hierarchical construction of fuzzy signature structure enhanced the adequacy of the overall evaluation. these statements certify the previously expressed supposition that a complex and well defined decision support tool based on fuzzy signature structure may help in the mentioned awarding procedure. therefore, after several refinements of the tool, it is going to be presented to the municipal assembly. these refinements may take place in a further research project in the following directions. the complexity of the building (different structural and sub-structural components, spatial object with complicated relation to the users and the surroundings, etc.) requires more complex analyses. that is why it is necessary to increase the number of attributes and to create more precise linguistic variables. for avoiding the confusion in the system in the process of the enlargement of the structure it is advisable to create further sub-trees (sub-sub-trees). it is also worth considering that a confidence index can be involved for the process of determination of the matching the rules that may support differentiating the observations. the developed decision support tool may take the intermittently determined aspects of the assembly. therefore the application of controllable relevance weight on the parent nodes has to be examined. acknowledgement the authors wish to record their gratitude to attila koppány for his advices and professional assistance during each phase of the project. 456 g.i. molnárka, l.t. kóczy bibliography [1] g. locsmándi, large-scale restructuring processes in the urban space of budapest, in r. dalla d. longa (ed.) urban models and public-private partnership, berlin, springer berlin heidelberg, 2011 [2] s. lowe, a tale of two cities rental housing in budapest and sofia in the 1990s, journal of housing and the built environment, kluwer academic publishers, pp. 249-266, 2000 [3] l. t. kóczy and d. tikk, fuzzy systems (in hungarian), budapest, typotex, 2000 [4] l. a. zadeh. fuzzy sets, information and control, 8, pp. 338-353, 1965 [5] j. a.goguen, l-fuzzy sets, journal of mathematical analysis and applications, 18, pp. 154-174, 1967 [6] l. t. kóczy, vector valued fuzzy sets, busefal, tolouse, pp. 41-57, 1980 [7] l. t. kóczy, t. vámos, g. biró, fuzzy signatures, 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approach to clustering, information control, 15, pp. 22-32, 1969 [18] a. mehler, k. reinhard, g. klir, k. sentz, on the issue of linguistic approximation. aspects of automatic text analysis, studies in fuzziness and soft computing, berlin, springer berlin, heidelberg, 209, pp. 61-78, 2007 [19] e.h.mamdani, s.assilian, an experiment in linguistic variables with a fuzzy logic controller, international journal of man machine studies, 7, pp. 1-13, 1975 [20] k. tamás, l. t. kóczy, mamdani-type inference in fuzzy signature based rule bases, 8th international symposium of hungarian researchers on computational intelligence and informatics, budapest, pp. 513-525, 2007 international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(2), 154-169, april 2019. weighted random search for hyperparameter optimization a.c. florea, r. andonie adrian-cătălin florea* department of electronics and computers transilvania university of braşov, romania *corresponding author: acflorea@unitbv.ro răzvan andonie department of computer science central washington university, usa andonie@cwu.edu abstract: we introduce an improved version of random search (rs), used here for hyperparameter optimization of machine learning algorithms. unlike the standard rs, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change. the intuition behind our approach is that a value that already triggered a good result is a good candidate for the next step, and should be tested in new combinations of hyperparameter values. within the same computational budget, our method yields better results than the standard rs. our theoretical results prove this statement. we test our method on a variation of one of the most commonly used objective function for this class of problems (the grievank function) and for the hyperparameter optimization of a deep learning cnn architecture. our results can be generalized to any optimization problem defined on a discrete domain. keywords:hyperparameter optimization, random search, deep learning, convolutional neural network. 1 introduction the vast majority of machine learning algorithms involve two different sets of parameters: the training parameters and the meta-parameters (also known as hyperparameters). while the training parameters are learned during the training phase, the values of the hyperparameters have to be specified before the learning phase. for instance, the hyperparameters of neural networks typically specify the architecture of the network (number and type of layers, number and type of nodes, etc). determining the optimal combination of hyperparameter values leading to the best generalization performance can be done through repeated training and evaluation sessions, trying different combinations of hyperparameter values. we call each training + evaluation process for one combination of hyperparameter values a trial. each trial is computationally expensive, since it involves re-training the model. in addition, the number of trials increases generally exponential with the number of hyperparameters. therefore, it is important to reduce the number or trials [9]. this can be done by both reducing the number of hyperparameters and reducing the value range of each hyperparameter, while still maximizing the probability to hit the optimal combination [2,3]. various hyperparameter optimization methods were developed during the years, ranging from very simple ones, such as grid search (gs) and manual tuning [14, 20, 28]1, to highly 1https://github.com/jaak-s/nips2014-survey 82 out of 86 optimization related papers presented at the nips 2014 conference used gs. copyright ©2019 cc by-nc weighted random search for hyperparameter optimization 155 elaborated techniques: nelder-mead [1, 24], simulated annealing [17], evolutionary algorithms [12], bayesian methods [32], etc. recently, there has been significant interest in the area of hyperparameter optimization, especially since the rise of deep learning which puts a lot of pressure on the existing techniques due to the very large number of hyperparameters involved and the significant training time needed for such architectures. the focus in hyperparameter optimizations presently oscillates between introducing more sophisticated techniques (sequential model-based global optimization [2], reinforcement learning [34,35], etc) and various attempts to optimize existing simple techniques. rs falls into the category of simple algorithms [2, 3]. making use of the same computational budget, rs generally yields better results than gs or more complicated hyperparameter optimization methods [2]. especially in higher dimensional spaces, the computation resources required by rs methods are significantly lower than for gs [21]. rs consists in drawing samples from the parameter space following a particular distribution for each of the parameters. each trial is drawn and evaluated independently from the others, which makes rs a very good candidate for parallel implementations. some recent attempts to optimize the rs algorithm are: li’s et al. hyperband [22], which speeds up rs through adaptive resource allocation and early-stopping; domhan et al. [8], which have developed a probabilistic model to mimic early termination of sub-optimal candidate; and florea et al. [9], where we introduced a dynamically computed stopping criterion for rs, reducing the number of trials without reducing the generalization performance. there are various software libraries implementing hyperparameter optimization methods. hyperopt [4] and optunity [7] are currently two of the most advanced standalone packages. bayesian techniques are implemented by packages like bayesianoptimization [29] and pygpgo [27]. some of the best known general purpose machine learning software libraries also provide hyperparameter optimization: libsvm [5] and scikit-learn [26] come with their own implementation of gs, with scikit-learn also offering support for rs. auto-weka [18], built on top of weka [11] is able to perform gs, rs, and bayesian optimization. lately, commercial cloud-based services started to offer hyperparameter optimization capabilities. among them we count google hypertune [38], bigml’s optiml [36], and sigopt [40]. all of them support mixed search domains, sigopt being able to handle multi-objective, multisolution, constraint (linear and black-box), and parallel optimization. in this context, our contribution is an improved version of the rs method, the weighted random search (wrs) method. unlike the standard rs, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change p and we use the best value found so far for that particular hyperparameter with probability 1 − p, where p is proportional to the hyperparameter’s relative importance in the variation of the objective function. the intuition behind our approach is that a value that already triggered a good result is a good candidate for a new trial and should be tested in new combinations of hyperparameter values. for the same number of trials, the wrs algorithm produces significantly better results than rs. we obtained theoretical results which prove this statement. we tested our algorithm on a slightly modified version of one of the most commonly used objective function for this class of problems the grievank [10] function, as well as for the hyperparameter optimization of a deep learning cnn architecture using the cifar-10 [37] dataset. unlike our previous work on rs optimization [9], where our focus was on the dynamic reduction of the number of trials, the focus of the wrs method is the optimization of the classification (prediction) performance within the same computational budget. the two approaches make use of different optimization techniques. the paper proceeds as follows. section 2 is a general presentation of our wrs algorithm. 156 a.c. florea, r. andonie section 3 describes theoretical results and the convergence of wrs. sections 4 and 5 contain experimental results. the paper is concluded with section 6. 2 the wrs method we first present the generic intuitive description of the wrs algorithm, which is the core of our contribution. technical details will be provided later. the standard rs technique [2] generates a new multi-dimensional sample at each step k, with new random values for each of the sample’s dimensions features, in our case xk = {xki}, i = 1, . . . ,d, where xi is generated according to a probability distribution pi(x), i = 1, . . . ,d, and d is the number of dimensions. wrs is an improved version of rs, designed for hyperparameter optimization. it assigns probabilities of change pi, i = 1, . . . ,d to each dimension. for each dimension i, after a certain number of steps ki, instead of always generating a new value, we generate it with probability pi and use the best value known so far with probability 1 −pi. the intuition behind the proposed algorithm is that after already fixing d0 (1 < d0 < d) values, each d-dimensional optimization problem reduces itself to a d−d0 dimensional one. in the context of this d−d0 dimensional problem, choosing a set of values that already performed well for the remaining dimensions might prove more fruitful than choosing some d−d0 random values. in order to avoid getting stuck in a local optimum, instead of setting a hard boundary between choosing the best combination of values found so far or generating new random samples, we assign probabilities of change for each dimension of the search space. wrs has two phases. in the first phase, it runs the rs for a predefined number of trials and allows: a) to identify the best combination of values so far; and b) to give enough input on the importance of each dimension in the optimization process. the second phase considers the probabilities of change and generates the candidate values according to them. between these two phases, we run one instance of fanova [15], in order to determine the importance of each dimension with respect to the objective function. intuitively, the most important dimension (the dimension that yields the largest variation of the objective function) is the one that should change most frequently, to cover as much of the variation range as possible. for a dimension with small variation of the objective function, it might be more efficient to keep a certain temporary optimum value once this has been identified. a step of the wrs algorithm applied to function maximization is described by algorithm 1, whereas the entire method is detailed in algorithm 2. f is the objective function, the value f(x) has to be computed for each argument, xk is the best argument at iteration k, whereas n is the total number of iterations. at each step of algorithm 2, at least one dimension will change, hence we always choose at least one of the pi probabilities to be equal to one. for the other probabilities, any value in (0, 1] is valid. if all values are one, then we are in the case of rs. besides a way to compute the objective function, algorithm 1 requires only the combination of values that yields the best f(x) value obtained so far and the probability of change for each dimension. the current optimal value of the objective function can be made optional, since the comparison can be done outside of algorithm 1. algorithm 2 coordinates the sequence of the described steps and calls algorithm 1 in a loop, until the maximum number of trials n is reached. weighted random search for hyperparameter optimization 157 algorithm 1 a wrs step objective function maximization input: f ; (xk, f(xk)); pi,ki,pi(x), i = 1, . . . ,d output: (xk+1,f(xk+1)) 1: randomly generate p, uniform in (0,1) 2: for i = 1 to d do 3: if (pi ≥ p or k ≤ ki) then 4: // either the probability condition is met or more samples are needed 5: generate xk+1i according to pi(x) 6: else 7: xk+1i = x k i 8: end if 9: end for 10: // usually this is the most time consuming step 11: compute f(xk+1) 12: if f(xk+1) ≥ f(xk) then 13: return (xk+1,f(xk+1)) 14: else 15: return (xk,f(xk)) 16: end if algorithm 2 wrs objective function maximization input: f ; n; pi(x), i = 1, . . . ,d output: (xn,f(xn )) 1: // phase 1 run rs 2: for k = 1 to n0 < n do 3: perform rs step, compute (xk,f(xk)) 4: end for 5: // intermediate phase, determine input for wrs 6: determine the probability of change pi, i = 1, . . . ,d 7: determine the minimum number of required values ki, i = 1, . . . ,d 8: // phase 2 run wrs 9: for k = n0 + 1 to n do 10: perform wrs step described in algorithm 1, compute (xk,f(xk)) 11: end for 12: return (xn,f(xn )) 158 a.c. florea, r. andonie 3 theoretical aspects and convergence we aim to analyze the theoretical convergence of algorithm 2 and compare it to the rs method. similar to gs and rs, we make the assumption that there is no statistical correlation between the variables of the objective function (hyperparameters). to make explanations more intuitive, we first discuss the two-dimensional case, and then generalize for the multi-dimensional case. we will also define what we consider "a set of good candidate values" for pi and ki, i = 1, . . . ,d (used in steps 6 & 7, algorithm 2). we denote by n ≥ 1 the number of iterations (steps) for both rs and wrs. 3.1 two-dimensional case in the two-dimensional case (d = 2), we aim to maximize a function f : s1 ×s2 → r, where s1 and s2 are countable sets. we define as global optimum the point x∗(x∗1,x ∗ 2), with x ∗ 1 ∈ s1 and x∗2 ∈ s2, so that f(x ∗) ≥ f(x),∀x ∈ s1 × s2. pi,ki, (i = 1, 2) are the probabilities of change, respectively, the required number of distinct values for xi, as previously defined. |si| is the cardinality of si, i = 1, 2. we denote by prs:n and pwrs:n the probability for rs, respectively wrs, to reach the global optimum after n steps. the following theorem establishes that, in the two-dimensional case we can choose k2 so that pwrs:n ≥ prs:n (1) theorem 1. for any function f : s1 ×s2 → r there exists k2, so that pwrs:n ≥ prs:n. proof: we consider the case of maximizing function f , and choose the arguments in the decreasing order of their probabilities of change. since the value for one dimension always changes, we have p1 = 1,p2 ≤ 1. having p1 = 1, the value of k1 can be ignored: the condition at step 3 in algorithm 1 will be always true for i = 1. at each step k, k ≤ k2, wrs is identical with rs and we have pwrs:k = prs:k. at step k + 1 > k2, rs generates new values for x k+1 1 and x k+1 2 , and computes f(x k+1 1 ,x k+1 2 ). for wrs, xk+11 is generated with probability one, but x k+1 2 is generated with p2 ≤ 1. with probability 1−p2, the best value known so far for x2 is used, instead of generating a new one. xk+1 can be written as: xk+1 = { (xk+11 ,x k+1 2 ), with probability p2 (xk+11 ,x k 2 ), with probability 1 −p2 (2) with probability p2, each step in wrs is identical to the same step in rs, and all points in s1 ×s2 are accessible to wrs. therefore, rs and wrs have the same search space and both converge probabilistically to the global optimum. ignoring the statistical correlation between the two variables, the probability of rs to hit the optimum after one iteration (the best case) is: prs = 1 |s1| 1 |s2| (3) for wrs, this probability is: pwrs = 1 |s1| ( p2 1 |s2| + (1 −p2) 1 |s2|−m2 + 1 ) (4) where m2 is the number of distinct values already generated for x2. using (3) and (4), (1) becomes: weighted random search for hyperparameter optimization 159 1 − (1 −pwrs)n ≥ 1 − (1 −prs)n (5) which is equivalent to 1 |s1| ( p2 1 |s2| + (1 −p2) 1 |s2|−m2 + 1 ) ≥ 1 |s1| 1 |s2| (6) after multiplying both sides by |s1|, (6) can be rewritten as p2 1 |s2| + (1 −p2) 1 |s2|−m2 + 1 ≥ 1 |s2| (7) which reduces to p2(1 −m2) ≥ 1 −m2 (8) because p2 ≤ 1, (8) is true for m2 > 1. relation (1) is therefore satisfied if we choose k2 so that at least two distinct values are generated for x2. 2 3.2 multi-dimensional case for the general case of optimizing a function f : s1 × s2. . .×sd → r, with si, i = 1, . . . ,d countable sets and under the same assumption that the variables are not statistically correlated, prs and pwrs are defined as: prs = d∏ i=1 1 |si| (9) pwrs = 1 |s1| d∏ i=2 ( pi 1 |si| + (1 −pi) 1 |si|−mi + 1 ) (10) where mi is the number of distinct values already generated for xi. following the rationale from section 3.1, we have the following theorem: theorem 2. for any function f : s1 × s2. . .×sd → r there exist ki, i = 1, . . . ,d, so that pwrs:n ≥ prs:n. proof: we consider again the maximization of function f . given ki, i = 1, . . . ,d the minimum number of values required for each of the dimensions xi with ki ≤ ki+1, i = 1, . . . ,d− 1 and k ≥ kd, xk+1 is given by:  (xk+11 ,x k+1 2 , . . . ,x k+1 d−1,x k+1 d ), with probability pd (xk+11 ,x k+1 2 , . . . ,x k+1 d−1,x k d), with probability pd−1 −pd . . . (xk+11 ,x k+1 2 , . . . ,x k d−1,x k d), with probability p2 −p3 (xk+11 ,x k 2, . . . ,x k d−1,x k d), with probability 1 −p2 (11) starting from (9) and (10), we can express pwrs:n as: 1 − ( 1 − 1 |s1| d∏ i=2 ( pi 1 |si| + (1 −pi) 1 |si|−mi + 1 ))n (12) 160 a.c. florea, r. andonie and prs:n as: 1 − ( 1 − d∏ i=1 1 |si| )n (13) since all elements of the products from (12) and (13) are positive (1−pi ≥ 0, and mi cannot be greater than |si|), a sufficient condition to satisfy (1) is:( 1 |si| + (1 −pi) 1 |si|−mi + 1 ) ≥ 1 |si| (14) for each i ≥ 2), which reduces to pi(1 −mi) ≥ 1 −mi (15) and, since pi ≤ 1, is equivalent with mi ≥ 2, for i = 2, . . . ,d (16) relation (1) is satisfied if we choose ki so that at least two distinct values are generated for each dimension. 2 according to these results, for a well chosen set of ki, i = 1, . . . ,d, at any step n, wrs has a greater probability than rs to find the global optimum. therefore, given the same number of iterations, on average, wrs finds the global optimum faster than rs. in other words, on average, wrs converges faster than rs. moreover, for wrs, the number of generated values for xi, i = 1, . . . ,d, follows a binomial distribution with probability pi. after n steps, the expected value for this distribution is npi. therefore, mi has, on average, an upper bound of npi. the number of distinct generated values depends on the cardinality of si and the probability distribution used to generate xi. for example, in the case of the uniform distribution, the expected value for mi is: e[mi] = |si|∑ 1 ( 1 − ( |si|− 1 |si| )npi) (17) and mi > 1 when npi > 1. hence, for any number of steps n, with n ≥ 1/pi, (1) is true. by choosing ki so that ki > 1/pi, (1) is true for all values of n. it can be also observed that the difference between pwrs:n and prs:n increases with an increasing value of n. 3.3 choosing pi and ki regardless of the distribution used for generating xi, by choosing for ki (step 6, algorithm 2) a value that can guarantee the generation of at least two distinct samples, (1) is true and wrs has a higher probability to find the optimum than rs. we decide to sort the function variables depending on their importance (weight) and assign their probabilities pi accordingly: the smaller the weight of a parameter, the smaller it’s probability of change. therefore, the most important parameter is the one that will always change (p1 = 1). in order to compute the weight of each parameter, we run rs for a predefined number of steps, n0 < n. on the obtained values, we apply fanova [15] to estimate the importance of the hyperparameters. if wi is the weight of the i-th parameter and w1 is the weight of the most important one, then pi = wi/w1, i = 1, . . . ,d. by assigning higher probabilities of change to the most important parameters and running rs for n0 steps, we make sure that (16) is satisfied for these parameters. for simplicity, we set ki = n0 for all parameters, but these values can be adjusted depending on the objective function. weighted random search for hyperparameter optimization 161 4 an example: griewank function optimization to illustrate the concept behind wrs, we consider a simple function with a known analytic form. since the function is very fast to compute, we can test the performance of our algorithm on a very large number of runs. this will allows us to perform an unpaired t-test on the results and rule out the random factor when assessing its performance. the grievank [10] function is widely used to test the convergence of optimization algorithms. it’s analytic form is given by: gd = 1 + 1 4000 d∑ i=1 x2i − d∏ i=1 cos xi√ i (18) the function poses a lot of stress on optimization algorithms due to its very large number of local minimums. we use a slightly modified version of g6, given by: g∗6 = 1 + i− 1 4000 6∑ i=1 x2i − 6∏ i=1 cos xi√ i (19) and maximize −g∗6. the function has a global maximum at 0, for xi = 0, i = 1, . . . , 6. the term i− 1 is introduced in order to alter the parameters’ importance(weight) which, otherwise, would have been the same across all dimensions. we use s = [−600, 600] for all six parameters and run the optimizer for 1000 trials, with an initial rs phase of 1000/e = 368 steps [9]. after the first rs phase, we run fanova and obtain the weights of the parameters, listed along with their probabilities of change in table 1. table 1: parameter weights and probabilities for g∗6 parameter x1 x2 x3 x4 x5 x6 weight 0.07 0.18 1.24 7.77 23.52 43.96 probability 0.002 0.004 0.028 0.177 0.535 1.00 we compare our results against rs, on the same search space, performing 1000 trials on 10000 runs. table 2 shows the best result achieved by both rs and wrs across all 10000 runs, as well as the average value and the standard deviation of the achieved results across all runs. the standard error for the t-test is 0.176, df = 19998 and p-value ≤ 0.001. table 2: wrs vs. rs results for g∗6 values for 1000 runs optimizer best found value average value sd rs -1.50 -33.10 14.06 wrs -1.28 -14.58 10.63 the results obtained by wrs are clearly better than the ones achieved by rs, as also depicted in fig. 1. fig. 2 shows the results obtained for one optimization session with 1000 trials. it can be observed that the algorithm tends to achieve improving results as the number of trials increases. 162 a.c. florea, r. andonie figure 1: performance of wrs vs. rs for the g∗6 optimization figure 2: convergence of wrs for the g∗6 function 5 cnn hyperparameter optimization our next application of the wrs is for the optimization of a cnn architecture. currently cnn in one of the best and most used tools for image recognition and machine vision [25] and there has been a lot if interest in developing optimal cnn architectures [13, 19, 31, 33]. current cnn architectures are complex, with a high number of hyperparameters. in addition, the training sets for cnns are large and this increases training times. hence, we have a high number of trials, each trail with significant execution time. decreasing the number of trials is critical. weighted random search for hyperparameter optimization 163 when applying wrs to our cnn optimization problem we consider the following hyperparameters: • the number of convolution layers an integer value in the set {3, 4, 5, 6}; • the number of fully-connected layers an integer value in the set {1, 2, 3, 4}; • the number of output filters in each convolution layer an integer value in the range [100, 1024]; • the number of neurons in each fully connected layer an integer value in the range [1024, 2048]. we generate each hyperparameter according to the uniform distribution and assess the performance of the model solely by the classification accuracy. we use keras [6] to train and test the cnn for 300 trials ten epochs each on the cifar10 [37] dataset. we run our test on an ibm s822lc cluster with ibm power8 nodes, nvlink and nvidia tesla p100 gpus2. the cifar-10 dataset consists of 60000 32 × 32 color images in 10 classes, with 6000 images per class. the data is split into 50000 training images and 10000 test images. we do not use data augmentation. the base architecture of the network is represented in fig. 3. the model has between three and six 3 × 3 convolutional layers and between one and four fully connected layers. both the convolutional and fully connected layers use relu [23] activation and the output layer uses softmax. we add one 2 × 2 max pooling layer with a dropout [25] of 0.25 for every two convolutional layers and use a dropout of 0.5 for the fully connected layers. we compare the results obtained by our wrs algorithm against the ones obtained by the rs, nelder-mead (nm), particle swarm (ps) [16] and sobol sequences (ss) [30] implementations provided by optunity [39]. figure 3: the cnn architecture after the first phase of the algorithm, which consists in running rs for 300/e = 110 trials, we obtain the weights for each parameter. these values, along with the probabilities of change, are listed in table 3. after running fanova, the resulted most important three parameters are (in decreasing order of their weights): the number of neurons in the first fully connected layer, 2http://www.cwu.edu/faculty/turing-cwu-supercomputer 164 a.c. florea, r. andonie the number of fully connected layers, and the number of convolutional layers. the weights of the other parameters are more than an order of magnitude smaller. therefore, the second phase of wrs clearly favors the change in the first three most important parameters. table 3: parameter weights and probabilities for cnn convolutional fully connected layers layers conv 1 conv 2 conv 3 conv 4 conv 5 conv 6 full 1 full 2 full 3 full 4 7.4 11.85 0.51 0.79 1.62 0.73 2.26 1.26 26.28 0.87 3.22 1.75 0.28 0.45 0.02 0.03 0.06 0.03 0.09 0.05 1.00 0.03 0.12 0.07 fig. 4 shows the least squares five degree polynomial fit on the accuracy results obtained for each of the 300 trials using: wrs the solid line, rs, nm, ps, ss the dashed lines. the trend of the wrs performance is similar to the one from fig. 1. the plot considers the actual values, reported at each iteration, instead of the local best in order to better reveal the variation of those values. figure 4: least squares five degree polynomial fit on rs, nm, ps, ss vs. wrs accuracy for cifar-10 on 300 trials. the plot considers the values reported at each iteration the best accuracy, as well as the average and standard deviation, across all 300 trials for all algorithms, are depicted in table 4. wrs method outperforms all other considered methods (see table 4 and fig. 5). table 4: algorithms’ results for cnn accuracy on cifar-10 optimizer best result average sd wrs 0.85 0.79 0.09 rs 0.81 0.75 0.04 nm 0.81 0.77 0.03 ps 0.83 0.78 0.03 ss 0.82 0.75 0.05 table 5 shows the best found architecture by each algorithm. we observe that for the weighted random search for hyperparameter optimization 165 figure 5: performance of wrs, rs, nm, ps and ss for cnn optimization wrs and rs methods, the resulted architectures have only one fully connected layer and several convolutional layers (five for rs, six for wrs). table 5: best identified cnn architectures on cifar-10 optimizer convolutional fully connected layers layers conv 1 conv 2 conv 3 conv 4 conv 5 conv 6 full 1 full 2 full 3 full 4 wrs 6 1 736 508 664 916 186 352 1229 rs 5 1 876 114 892 696 617 1828 nm 5 3 564 564 564 560 563 1529 1542 1542 ps 5 1 479 792 584 411 593 1379 ss 5 2 402 933 750 997 777 1545 1268 table 6: wrs accuracy average and standard deviation. row headings are numbers of fully connected layers while column headings are numbers of convolutional layers fc /c 1 2 3 4 3 0.74 (0.02) 0.70 (0.03) 0.74 (0.01) 0.69 (0.03) 4 0.78 (0.01) 0.74 (0.03) 0.74 (0.03) 0.63 (0.07) 5 0.81 (0.02) 0.80 (0.02) 0.74 (0.07) 0.65 (0.06) 6 0.82 (0.01) 0.76 (0.04) 0.72 (0.09) 0.39 (0.21) table 6 details the results obtained by wrs, showing the accuracy average and the standard deviation values for each combination: (number of fully connected layers, number of convolutional layers). table 7 shows the number of trials performed by wrs for each of these combinations. we notice that the wrs algorithm favors one of the combinations, namely {1, 6}, and uses it for almost two thirds of the number of trials. it is important to mention that within the best 200 trials, only 10 sets of values contain a different combination than {1, 6}. this is either {1, 5} seven times, or {2, 5} three times. the first different combination than {1, 6} is at the 136-th position. in table 6, we observe that this combination also triggers the best results. this, together with the fact that wrs performs on average better than rs, validates our 166 a.c. florea, r. andonie table 7: wrs number of trials. row headings are numbers of fully connected layers while column headings are numbers of convolutional layers fc /c 1 2 3 4 3 4 4 4 7 4 8 3 8 9 5 9 7 9 4 6 199 6 10 9 hypothesis that the probability that this combination of hyperparameters corresponds to the global optimum is higher than for any other combination. 6 conclusions we have introduced an improved version of rs, the wrs method. within the same computational budget (i.e., for the same number of iterations), wrs converges on average faster than rs. the wrs algorithm yields better results both for the optimization of a well known difficult mathematical function and for a cnn hyperparameter optimization problem. there is little information required to be transferred between the consecutive steps of the algorithm, as pointed out in the description of algorithm 1. this implies that the wrs algorithm can be easily implemented in parallel. since we made no assumptions on the objective function, our results can be generalized to other optimization problems defined on a discrete domain. we plan to test out algorithm on other classes of optimization problems, in particular on the optimization of various machine 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[40] [online]. sigopt, sigopt. https://sigopt.com/ sigopt. accessed: 2019-01-10. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 713-730 quality of service control for wlan-based converged personal network service e.-c. park, i.-h. kim, g.-m. jeong, b. moon eun-chan park, bongkyo moon department of information and communication engineering, dongguk university-seoul, korea e-mail: ecpark@dongguk.edu, bkmoon@dongguk.edu in-hwan kim mobile device development team, sk telecom, seoul, korea e-mail: ihkim@sktelecom.com gu-min jeong school of electrical engineering, kookmin university, seoul, korea e-mail: gm1004@kookmin.ac.kr abstract: this paper proposes a framework for quality of service (qos) control in wlan-based converged personal network service (cpns). first, we show that the cpns devices in wlans occupy the shared wireless channel in an unfair manner; and thus, qos is degraded. the reasons of such problem are analyzed from two viewpoints of (i) channel access mechanism according to carrier sensing multiple access protocol of wlan and (ii) tcp congestion control mechanism in response to packet loss. to improve qos and assure fair channel sharing, we propose an integrated qos control framework consisting of admission control and rate control. based on the available capacity, the admission control determines whether or not a specific qos service can be admitted. the rate control is implemented using congestion window control or token bucket algorithm. the proposed mechanism differentiates qos service from best-effort (be) service such that the qos service is preferentially served to satisfy its qos requirements and the be service is served to share the remaining resource in a fair manner. the extensive simulation results confirm that the proposed scheme assures qos and fair channel sharing for wlan-based cpns. keywords: cpns, qos, fairness, wlan, tcp 1 introduction recently, many new portable devices equipped with wi-fi interface, e.g., smart phone, tablet pc, mobile internet device (mid), portable media player, and digital camera/camcorder, have been introduced. they are connected to the internet or with each other and enable many new emerging services to come into wide use, such as real-time multimedia streaming, video telephony, on-line game, image/video sharing. in order to provide converged service for the portable devices in short-range personal networks such as home networks or in-car networks, open mobile alliance (oma) makes a standard for converged personal network service (cpns) [1], [2] by identifying the overall service architecture, service scenario, and functional requirements. the cpns system consists of cpns server, personal network gateway (pn gw), and personal network element (pne); the pne accesses to the pn gw via wireless link and is provided with copyright c⃝ 2006-2011 by ccc publications 714 e.-c. park, i.-h. kim, g.-m. jeong, b. moon various services from the cpns server. we consider in this paper that the cpns system is implemented with ieee 802.11 wireless local area networks (wlans) [3]. wlan is widely deployed in home networks and installed in many devices, i.e., wlan is a de facto standard for wireless networking for portable devices in small area, and it is the most suitable wireless access technology considering the service scenario, coverage, and data rate for cpns. this paper deals with the issue of qos assurance in wlan-based cpns. the various services of cpns (e.g., real-time multimedia streaming, internet telephony, file uploading/downloading, remote surveillance) have different qos requirements in terms of delay, loss, or throughput; however it is a challenging problem to satisfy these diverse qos requirements because the mac protocol of wlan works in a distributed contention-based manner and the quality and capacity of wireless channel are time-varying and location-dependent. first, we show that the pnes1 access the channel in an unfair way, i.e., the uploading (ul) pnes dominate the use of shared channel while the downloading (dl) pnes cannot occupy their fair shares; and thus, qos can be severely degraded especially for dl pnes. we analyze the reasons of this problem from two viewpoints; (i) unfair channel access mechanism of mac and (ii) asymmetric congestion control mechanism of tcp. to resolve this problem, we propose an integrated qos control framework, which consists of admission control and rate control. we classify the service into two categories; service with and without qos requirements, each of which is referred to as qos service and best-effort (be) service, respectively. the proposed scheme differentiates the qos service from the be service. the admission control is applied to the qos service in order to prevent an excessive request of qos service from being allowed. the admission control estimates the available channel capacity and converts the required data rate of a qos service to the equivalent channel capacity. only if the requirement is less than the available capacity, the qos service can be admitted and the required capacity is virtually reserved. on the other hand, the rate control is applied to both qos service and be service so that the required data rate of qos service can be assured and the be services are enforced to share the channel in a fair manner. the rate control is implemented by employing tcp congestion window control mechanism or token bucket mechanism. furthermore, the proposed framework can be extended to minimize the delay of qos service and to support weighted fairness for be services. consequently, qos can be assured for qos services and fairness can be attained among be services, which is confirmed through ns-2 [4] simulations. there have been many proposals for assuring qos and fairness in ieee 802.11 wlans in the literature. the enhanced distributed channel access (edca) mechanism has been standardized in ieee 802.11e [5] to improve qos by differentiating the channel access delay between different access categories. similar qos enhancement mechanisms have also been proposed for prioritized service differentiation [6–8]; they modify the basic mac protocol and use differentiated mac parameters, e.g., inter-frame space, contention window, or transmission opportunity, depending on the service priority. moreover, the fairness issue of wlan has been actively studied. many mac protocols have been proposed to assure mac-layer fair access opportunity between competing stations or between dl and ul stations by employing a proper backoff mechanism or by controlling contention window size [9–12]. recently, the issue of tcp fairness in wlan has been addressed in [13–15]. they aim to assure fairness between tcp flows in wlan by limiting tcp congestion window size [13], by employing per-flow scheduling in the access point (ap) [14], or by differentiating channel access delay based on ieee 802.11e [15]. compared to these previous studies, this paper makes the following contributions; • this paper proposes a generalized framework to assure qos and fairness simultaneously, it is applicable regardless of packet size, transmission rate, service direction (dl or ul), and 1in this paper, we interchangeably use the terms, pne, device, and station. quality of service control for wlan-based converged personal network service 715 transport-layer protocol (tcp or udp). • the proposed mechanism provides qos service with the absolute assurance in terms of data rate. at the same time, it provides be service with the weighted fairness without decreasing the overall channel utilization. • the proposed mechanism is practical and simple for cpns; it defines a service establishment signaling but does not modify the standard ieee 802.11 mac protocol, and the major function of the proposed scheme needs to be implemented in the pn gw while the pnes require a minor modification. the rest of this paper is organized as follows. in section 2, we identify the problem of unfair channel sharing and qos degradation arising in wlan-based cpns and analyze its reasons. the qos control framework and algorithm are proposed in section 3, and the performance is evaluated through extensive simulations in section 4. the conclusion follows in section 5. 2 problem statement in this section, the problem of qos degradation and unfairness is shown via the preliminary simulation, and the reasons of such problem are analyzed. pn gw (access point) mobile phone surveillance camera camcorder digital camera iptv mid internet wlan uplink downlink figure 1: service model of wlan-based cpns. 2.1 qos degradation and unfairness in wlan-based cpns we consider the service model of wlan-based cpns, especially focusing on home network, as shown in fig. 1. the pn gw plays a role of ap in wlan and it is connected to the internet or cpns server through a wired link and communicates with the pnes. we consider the following typical service scenario for cpns; • the digital camera and camcorder upload their photos and moving pictures to the pn gw so that the contents can be played in the iptv, smart tv, or digital photo frame. also, they upload the contents to the pc or remote storage server to free up its built-in memory, or they upload the contents to social community sites (e.g., youtube or flickr) to share them with others; • the surveillance camera uploads the real-time streaming video to the server for the purpose of security; 716 e.-c. park, i.-h. kim, g.-m. jeong, b. moon table 1: service scenario used in the simulation. device service direction transmission required start protocol data rate time iptv streaming dl tcp 1 mb/s 0 sec mobile phone file transfer dl tcp n/a 0 sec camcorder file transfer ul tcp n/a 50 sec surveillance camera security ul udp 1 mb/s 100 sec • the mid and mobile phone download multimedia contents from the cpns server or the internet to store the contents into the built-in memory and play them later. moreover, they can play the multimedia contents in the pc or remote storage server as streaming. we perform preliminary ns-2 simulation with the specific service scenario given in tab. 1. note that the service without the requirement of data rate, i.e., file transfer of mobile phone and camcorder, is considered as be service. the mac and phy parameters are set according to ieee 802.11b specification, the phy transmission rate and packet size are set to 11 mb/s and 1 kbyte, respectively, the buffer size in the pn gw is set to 100 kbyte 2, and the tcp window size is set to 64 kbyte (default value in microsoft windows xp). the file size is assumed to be large enough for the service to last till the end of simulation. the wireless channel error is modeled as a two-state markov-modulated process, where the packet error rate in the wireless channel is 1%. we observe per-device throughput to evaluate the performance in terms of qos and fairness from fig. 2. before the camcorder uploads a file at t = 50 sec, the required data rate for iptv is satisfied and the remaining capacity is utilized by the mobile phone, and the throughputs of both devices are stable. when t = [50 100] sec, however, the average throughput of iptv is about 770 kb/s, i.e., the required data rate cannot be assured, moreover its throughput fluctuates severely, which results in the significant degradation of qos due to the increase of delay and jitter. also, the throughput of mobile phone is decreased suddenly because of the ul file transfer of camcorder. the wireless channel capacity is not allocated in a fair way; during the time interval between 50 sec and 100 sec, the average throughput of camcorder is higher than that of iptv and mobile phone by about 2.10 and 1.87 times, respectively. after t = 100 sec, the throughput of surveillance camera is stable and satisfies its required data rate of 1mb/s and it is little affected by the other services. however, the throughputs of iptv and mobile phone are further decreased. even in this case, the throughput of camcorder is higher than those of iptv and mobile phone by about 3.10 and 2.22 times, respectively. these simulation results confirm that (i) the qos of dl service is highly vulnerable to the ul service while the qos of ul service is little affected by the dl service, (ii) the channel capacity is unfairly shared among be services; the ul service is favored over the dl service. 2.2 causes of the problem mac: unfair channel access in infrastructure wlans, all the devices are associated with the ap. the ul devices transmit packets to the ap while the dl devices receive packets from the ap. the mac-layer contention for channel access takes place among transmitters, ap and ul devices, according to the carrier sensing multiple access (csma) mechanism, i.e., ieee 802.11 distributed coordination function 2the buffer size is determined for bounded queuing delay to support the multimedia streaming services whose qos is sensitive to delay and/or delay variation. quality of service control for wlan-based converged personal network service 717 0 0.5 1 1.5 2 2.5 3 0 25 50 75 100 125 150 th ro u g h p u t (m b /s ) time (sec) iptv (dl) mob. phone (dl) camcorder (ul) surv. cam (ul) figure 2: per-device throughput in the wlan-based cpns without qos control. (dcf) [3]. if the ap wins the contention, the dl devices can be served, otherwise if the ul device wins, it gets its own transmission opportunity. defining the number of ul and dl devices as nu and nd, respectively, the number of contending devices are nu + 1, independent of nd. the dcf mechanism provides all the contending devices with the equitable chances of channel access in the long term. assume that all the ul devices always make attempt to send packets at the same phy-layer transmission rate and the data generation rates for all the dl devices are same. then, the steady-state throughputs of ul and dl devices, denoted as thu and thd respectively, become thu = c nu + 1 , thd = c (nu + 1)nd . (1) here, c denotes the effective capacity of wlan. defining a fairness index, γ, as the ratio of the average throughput achieved by the dl devices to that by the ul devices, it becomes γ , thd thu = 1 nd (2) from (1). it is important to note that γ depends on nd, but it is irrespective of nu. we perform ns-2 simulation to validate this analysis. figure 3(a) shows the average perdevice throughput with various values of nd(= nu) when devices send/receive udp traffic. the discrepancy between thu and thd increases, i.e., γ decreases, as nd increases. moreover, we observe that the average per-device throughput of an individual ul device is nearly equal to the sum of per-device throughputs of all dl devices, which agrees with (1). tcp: asymmetric congestion control the problem of unfair channel access becomes exacerbated when devices send/receive tcp traffic. as shown in fig. 3(b), even when only one dl device coexists with one ul device (i.e., nd = nu = 1), the throughput of dl device is lower than that of ul device by about 26%, i.e., γ = 0.74. as nu increases from 2 to 8, the value of γ remarkably decreases from 0.19 to 0.03. the aggravation of unfair channel sharing results from the asymmetric congestion control mechanism of tcp in response to packet loss. in general, the capacity of wlan is smaller than that of wired link connected to the ap. according to tcp congestion control, tcp sender increases its transmission rate until it detects a packet loss. consequently, the downlink buffer of 718 e.-c. park, i.-h. kim, g.-m. jeong, b. moon ap easily becomes congested and packets are dropped due to buffer overflow. it is important to note that two different types of packets are buffered in the ap’s downlink buffer; tcp data packet for dl device and tcp ack packet for ul device. when tcp data packet for dl device is dropped in the buffer, the tcp sender recognizes this as an indication of congestion and decreases the congestion window by half to relieve the congestion; and thus the throughput of dl device is halved. on the other hand, the tcp in the ul device can be unresponsive to the tcp ack packet loss in the ap’s downlink buffer. due to the cumulative ack mechanism of tcp, the loss of an ack packet for ul device does not necessarily decrease the congestion window as long as the next ack packet with a higher sequence number is delivered before the retransmission timer expires. therefore, the cumulative ack mechanism lets the ul device tolerate the loss of ack packets, and the tcp in the ul device increases the congestion window gradually until it reaches the maximum size, increasing the throughput of ul device. consequently, the throughput of ul device becomes higher than that of dl device, and the unfairness problem occurs more severely, compared to the case of udp traffic. 0 0.5 1 1.5 2 2.5 3 1 2 4 8 th ro u g h p u t (m b /s ) number of ul/dl pnes (nu = nd) ul: per-pne dl: sum(pne) dl: per-pne 0 0.5 1 1.5 2 1 2 4 8 th ro u g h p u t (m b /s ) number of ul/dl pnes (nu = nd) ul: per-pne dl: sum(pne) dl: per-pne (a) udp (b) tcp figure 3: per-device throughput with different numbers of ul/dl devices. 3 qos control mechanism we propose qos control mechanism that provides qos service and be service with the quantitative assurance for qos and the relative fairness in terms of throughput, respectively. we describe the overall structure and signaling of the proposed mechanism in the first subsection, and the rate control and admission control, two main components of the proposed mechanism, in the subsequent subsections. lastly, we discuss several issues to elaborate and extend the proposed mechanism. 3.1 overall structure and signaling figure 4 shows the operational flow chart of the proposed qos control mechanism. to establish a service flow (sf) in advance of service start, a pne transmits a service request (s-req) message to the pn gw, where several properties of the service (e.g., required data rate, packet size, and protocol) are described. if the required data rate is not specified in the s-req message, we consider such a service as be service, otherwise the service is considered as qos service. on receiving the service request, the pn gw classifies the service as qos service or be service. for qos service, the admission control is applied so that the service can be admitted only if its required data rate can be assured. for be service, the service is admitted without admission quality of service control for wlan-based converged personal network service 719 estimate available capacity service request qos service requirement < availability calculate target congestion window and token rate conestion window control tcp yes yes no reject service no no yes rate shaping by token bucket accept service rate control admission control figure 4: operational flow chart of the proposed qos control mechanism. control and the remaining resource is evenly allocated to be services under the rationale that the priority of qos service is strictly higher than that of be service. once the service is admitted, the pn gw broadcasts a service response (s-rsp) message to the pnes, to acknowledge the resource reservation for the qos service and to inform the pnes with the on-going be services of the change in the available resource. the s-rsp message includes target values of tcp congestion window (cgw) and token generation rate for both qos and be services, which are calculated by the pn gw based on the qos requirement, available channel capacity, and the number of be sfs. the rate control is implemented by congestion window control for tcp flows or token bucket algorithm for udp flows. receiving s-rsp message, the pne sets the advertised window size (i.e., the maximum congestion window size) for the tcp flow, as the value in the s-rsp message, to control its transmission rate. for the udp flow, pn gw or pne sets the token generation rate for the flow as the value in the s-rsp message, so that its transmission rate can be regulated around the target rate. when a service is terminated, a service termination (s-end) message is delivered to the pn gw so as to release the resource occupied by the terminating service and redistribute it to the pnes with existing be services. the pn gw recalculates the target values of congestion window and token generation rate and informs pnes of the updated values by broadcasting the s-rsp message. 3.2 rate control we propose two mechanisms for rate control; congestion window control for tcp flows and rate shaping for udp flows. first, we focus on the rate control for tcp flows. it is shown from [13] that by controlling the size of congestion window, the packet loss due to buffer-overflow in the ap’s buffer can be avoided and the unfair channel sharing among tcp flows can be alleviated. we extend this idea to qos service and be service delivered by tcp flows. to assure the resource reservation for qos service and fair resource allocation among be services, we determine the value of the maximum congestion window for service i, denoted as cgwmax,i, as; cgwmax,i = wib, (3) 720 e.-c. park, i.-h. kim, g.-m. jeong, b. moon where wi =   rqos,i c̃ for qos service,( 1 − ∑nqos j rqos,j c̃ ) 1 nbe for be service. (4) here, nqos and nbe are the number of qos and be service flows, respectively, rqos,i denotes the required data rate for the ith qos service, and c̃ is the estimated capacity of wlan. note that the size of cgw limits the number of on-the-fly packets, i.e., the number of packets that can be transmitted before receiving tcp ack. from (3) and (4), we can see that a dedicated portion of buffer is reserved for each qos service for assuring its desired data rate and the remaining portion of buffer is evenly distributed to be services for fairness. proposition 1. the cgw control in (3) and (4) prevents packet loss due to buffer-overflow in the ap. proof: from (3) and (4), it can be shown that nqos+nbe∑ i cgwmax,i = b. (5) since the number of on-the-fly packets is limited by the cgw and the packets may be stored in the buffer except the ap, they are not dropped in the ap’s downlink buffer due to bufferoverflow. therefore, the proposed cgw control can remove one cause of unfair channel sharing, i.e., asymmetric congestion control in response of packet loss. 2 proposition 2. assume that the wireless link is a bottleneck link so that the queuing delay in the downlink buffer of ap is a dominant factor of round trip time (rtt) and that the packet loss rate in the wireless link is negligible due to link-layer collision avoidance and retransmission mechanisms. then, the cgw control in (3) and (4) assures the desired data rate for qos service and achieves fair and full channel utilization for be service. proof: from proposition 1 and the assumptions of proposition 2, we can consider that the packet loss, which may occur due to buffer-overflow, collision among contending stations, and the poor quality of wireless channel, is insignificant. then, the size of congestion window increases up to its maximum value according to tcp congestion control, and the steady-state throughput for the ith service flow, th∗i , becomes th∗i = cgwi t ∗rtt,i , (6) where t ∗rtt,i is the value of rtt in steady state for the ith flow. decomposing t ∗ rtt,i as queueing delay in the ap’s downlink buffer (tq,i) and the other delay (to,i) (e.g., processing delay at a node and link propagation delay), trtt,i can be represented as t ∗rtt,i = tq,i + to,i ≈ b c̃ , (7) from the assumptions of proposition 2, i.e., tq,i ≈ b/c̃ ≫ to,i, where c̃ is the effective capacity of wlan in steady state. from (3) and (7), we can rewrite (6) as th∗i ≈ wib b / c̃ = wic̃ = { rqos,i for qos service,( c̃ − ∑nqos j rqos,j ) 1 nbe for be service. (8) quality of service control for wlan-based converged personal network service 721 also, the total throughput in steady state becomes from (8) nqos+nbe∑ i th∗i = c̃. (9) consequently, it is shown from (8) and (9) that the proposed cgw control mechanism assures the desired data rate for qos service and allows be services to fully share the channel in a fair manner. 2 next, we focus on the rate shaping for udp flows. whenever a new service is requested or terminated, the pn gw calculates the token rate (rtoken) for each service as the value in the rightmost of (8) and broadcasts this value to the pnes via s-rsp message. receiving the s-rsp message, the ul pne sets the token generation rate for ul udp flow as the value received. similarly, the pn gw sets the token generation rate for dl udp flows. when a packet arrives, the corresponding amount of tokens are removed from the bucket and the packet is served. if the remaining token is less than the size of a new arriving packet, its service is deferred until the amount of tokens becomes larger than the packet size. in this way, the transmission rate of udp flow can be regulated around the target rate and the proposed mechanism still works even when tcp and udp flows coexist. 3.3 admission control consider that a new qos service requests the data rate of rqos,i and there are nqos existing qos services. the new qos service will be admitted only if the following condition is satisfied, rqos,i <  αc̃ − nqos∑ j rqos,j   (10) where α(0 < α < 1) is a control parameter. the key point of the proposed admission control is estimating the capacity accurately, which is described below. consider that a pne sends or receives l-byte (excluding tcp/ip header) tcp data packet to or from the pn gw at the phy rate of r mb/s according to the channel access mechanism of ieee 802.11 dcf. let us define tdata(r, l) and tack(r) as the average time in the unit of µs required to successfully serve the tcp data packet and tcp ack packet, respectively; they can be represented as tdata(r, l) = tdifs + tbo + tmsdu(r, l) + tsifs + tmack tack(r) = tdifs + tbo + tmsdu(r, 0) + tsifs + tmack. (11) in (11), tdifs and tsifs are dcf inter frame space (difs) and short inter frame space (sifs), respectively, tmack is the time required to send mac-layer ack frame, which is constant, tbo is the average backoff time before sending a mac frame. moreover, tmsdu(r, l) is the time required to send a mac service data unit (msdu) at the rate of r mb/s, which can be given as; tmsdu(r, l) = tphy + (l + lh)8 r , (12) where tphy is the time required to send phy layer preamble and header and lh is the size of tcp/ip/mac header in byte. note that r is selected among pre-defined rates (e.g., 11, 5.5, 2, and 1 mb/s for 802.11b) depending the channel quality and the link adaptation mechanism adopted. 722 e.-c. park, i.-h. kim, g.-m. jeong, b. moon next, we calculate tbo. let us define pc as the probability of packet collision in steady state, and ctw(n) as the size of contention window under the condition that the transmission succeeds after n consecutive failures. on the detection of transmission failure, the contention window size is doubled according to the binary exponential backoff (beb) mechanism of ieee 802.11, it can increase up to the maximum value of ctwmax and returns to the minimum value of ctwmin if the transmission succeeds, i.e., ctw(n) = { 2nctwmin for n ≤ m, ctwmax for n > m. (13) where m = log2 (ctwmax/ctwmin). then, ctw becomes3 ctw = m∑ n=0 pnc (1 − pc)2 nctwmin + ∞∑ n=m+1 pnc (1 − p)ctwmax = ctwmin 1 − pc − pc(2pc)m 1 − 2pc . (14) on the other hand, the probability of packet collision, pc can be represented with ctw . according to the p-persistent model [17], which closely approximates the standard ieee 802.11 protocol, each node makes a transmission attempt with the probability of pa = 2/ctw in steady state. then, the probability that a station collides with any of the other stations becomes pc = 1 − (1 − pa)m−1 = 1 − ( 1 − 2/ctw )m−1 , (15) where m is the number of active contending stations. we can get pc and ctw by solving two equations (14) and (15) numerically 4. a station selects a random backoff counter uniformly distributed between zero and ctw -1. if a station decrements its backoff counter in the backoff stage, the other (m-1) active contending stations also decrement their own backoff counters. therefore, the effective backoff time can be approximated as tbo = ctw − 1 2 1 m tslot, (16) where tslot is the slot time. from (11) – (16), tdata(r, l) and tack(r) are obtained. finally, we can get the capacity of wlan, c(r, l) (mb/s), when stations send/receive l-byte tcp data packet at the rate of r mb/s; c(r, l) = 8l tdata(r, l) + tack(r) . (17) table 2 lists several parameters of ieee 802.11b used in the analysis model. we can extend the analytical capacity model to the case where the link adaptation mechanism is applied so that the phy-layer transmission rate changes depending on the channel quality. when the auto rate fallback (arf) algorithm [18], which is the most common link adaptation algorithm, is used, the distribution of phy-layer transmission rate can be obtained from the analysis result in [19]. the rate distribution can also be obtained by measuring signal to noise ratio (snr) experimentally when a close-loop snr-based link adaption algorithm is used. let 3here, we assume that the number of retransmissions is infinite even though it is limited to a certain value in the ieee 802.11 standard. however, this assumption is not invalid since the probability that the number of retransmissions reaches the maximum value is very low. 4the simulation study in section 4.1 shows that the actual capacity under the proposed mechanism is little deviated from the analytical capacity derived with the condition of m = 1, i.e., pc = 0 from (15). quality of service control for wlan-based converged personal network service 723 table 2: parameters of ieee 802.11 in the analysis model. parameter value slot time (tslot) 20 µs sifs (tsif s) 10 µs difs (tdif s) 50 µs ack (tmack) 248 µs phy header (tp hy ) 192 µs tcp/ip/mac header (lh) 74 byte minimum contention window (ctwmin) 32 maximum contention window (ctwmax) 1024 us define p(ri) as the probability that the phy-layer rate is ri ∈ r. the effective capacity can be obtained as c̃(l) = ∑ ri∈r c(ri, l)p(ri). (18) 3.4 discussion adjustment of the required data for admission control the proposed admission control makes the decision on the admission of qos service by comparing the required data rate and the available capacity, as shown in (10). however, the total capacity, c̃, is not constant but depends on several factors such as packet size, transportlayer protocol, number of contending nodes, and channel quality. for example, the capacity of wlan decreases as the packet size decreases, because the mac/phy overhead required to transmit a frame becomes relatively large as the packet size decreases. recall that the analytical capacity in section 3.3 is derived under the condition that the stations send/receive l-byte tcp packets. also note that the maximum size of ethernet frame is 1500 bytes and the size of voip packet is typically a few hundreds bytes, and that the data service is usually served with tcp while some real-time service may be served with udp. we need to cope with the variation of capacity due to difference in the packet size, transport-layer protocol, and channel quality. for this purpose, we propose an approach of adjusting the required data rate of qos service, instead of adjusting the total capacity. consider that a qos service is requested with the required data rate of rqos,i and the average tcp packet size of l byte, which is different from the standard packet size of lo used in the capacity estimation. also, consider that the transmission rate for the service is r mb/s 5. in order to deal with the capacity variation due to different packet size, the required data rate for the qos service with the packet size of l is adjusted to the equivalent data rate, r′qos,i; r′qos,i = c̃(lo) c(r, l) rqos,i. (19) from (19), r′qos,i increases as the packet size decreases to compensate for the decrease of capacity. similarly, r′qos,i decreases if the channel quality for the service is relatively good because the time required to send a packet with the higher transmission rate is smaller than that with the lower transmission rate, i.e., a station in good channel quality consumes less resource to serve a 5the transmission rate may change during service time due to varying channel quality. however, the channel quality is mostly depends on the path-loss, which is determined by the distance between transmitter and receiver; and thus the transmission rate does not change severely as long as the station does not move. 724 e.-c. park, i.-h. kim, g.-m. jeong, b. moon packet. note that the adjusted rate requirement, r′qos,i is used not only in the admission control (10), but also in the tcp window control (4). in the same way, the required data rate for a qos service with udp can be adjusted. in contrast to tcp, udp does not require transport-layer ack transmission from the receiver, resulting in less overhead and higher capacity. the capacity with udp traffic, denoted as cudp(r, l) can be estimated by replacing (17) with (20); cudp(r, l) = 8l tdata(r, l) . (20) then, the equivalent required data rate for a udp qos service, r′qos,i, becomes r′qos,i = c̃(lo) cudp(r, l) rqos,i. (21) extension to support weighted fairness for be services the proposed scheme can be easily extended to support weighted fairness for be services. consider that a be service has the corresponding service weight wbe(≥ 1), whose value is integer and determined by a service operator of cpns depending on the relative service priority. also, we consider the service weight is delivered to the pn gw in the s-req message. then, the pn gw calculates the maximum congestion window (cgwmax) for tcp be service and the token generation rate (rtoken) for udp be service as cgwmax = ( 1 − ∑nqos j rqos,j c̃ ) b∑nbe k wbe,k , rtoken =  c̃ − nqos∑ j rqos,j   1∑nbe k wbe,k . (22) these values of cgwmax and rtoken are delivered to pnes in the s-rsp message. receiving the s-rsp message, each pne sets the congestion window size and token generation rate for its be service that has the service weight of wbe,k, each of which is denoted as cgw ′max,k and r ′ token,k, respectively, as; cgw ′max,k = wbe,kcgwmax, r′token,k = wbe,krtoken. (23) note that cgw ′max,k = cgwmax and r ′ token,k = rtoken for a normal be service with wbe,k = 1. from (22) and (23), we observe that be services share the buffer and capacity in a weighted manner and this extension for weighted service does not degrade qos service since nbe∑ k cgw ′max,k = ( 1 − ∑nqos j rqos,j c̃ ) b < b, nbe∑ k r′token,k = c̃ − nqos∑ j rqos,j < c̃. (24) quality of service control for wlan-based converged personal network service 725 further service differentiation for qos the proposed mechanism can further differentiate service to enhance qos. the 802.11e edca can be straightforwardly incorporated with the proposed mechanism so that the channel access for the qos service can be made preferentially over the be service. moreover, such service differentiation can be simply realized without employing 802.11e edca. there is an interface queue between ip layer and mac layer, which typically operates in a first-come-first-serve (fcfs) manner. by implementing dual queue, one for qos service and the other for be service, and adopting the priority scheduling algorithm for the dual queue, the packets of be service is not served as long as there is the packets of qos service waiting for the service. in this way, the delay for qos service can be minimized. 4 performance evaluation in this section, we perform the extensive ns-2 simulations to evaluate the performance of the proposed qos control mechanism in terms of qos assurance and fair channel sharing. the simulation configuration is same to that in section 2 and 802.11 mac/phy parameters used in the simulations are listed in tab. 2. 4.1 validation for capacity estimation first, we validate the methodology for capacity estimation by comparing the simulation results with the analysis results. the capacity is affected by the packet size or transport-layer protocol as described in section 3.3, and the estimated capacity is used in the admission control and rate control; therefore the accurate estimation of capacity plays a key role in the proposed mechanism. we consider the following two scenarios; • tcp-only: we observe the network capacity with different tcp packet size (l = 500 and 1500 bytes) and different number of ul and dl pnes (nu = nd = 1 ∼ 5). • mixed: in this scenario, each two pnes upload and download 1 kbyte tcp packets, while each nudp pnes upload and download udp packets, respectively. the size and interval of udp packet are set to 1500 bytes and 30 ms (i.e., 360 kb/s), and 200 bytes and 20 ms (i.e., 32 kb/s), each of which is set to consider the real-time video and audio service for mobile device, respectively. fig. 5 compares the capacity obtained from simulation with that obtained from analysis, for the above two cases. from fig. 5(a), we observe that the simulation result is almost immune to the number of pnes. this result seems not to agree with the well-known wlan capacity model [17], [20], where the capacity mostly decreases as the number of stations increases due to the increased collision probability. these previous models make an assumption of saturated traffic, i.e., every station always has packets to send and it always participates in the mac-layer contention. however, this explanation does not hold in our mechanism because the admission control along with the rate control assures the sum of transmission rates for all the service is regulated not to exceed the available capacity so that every station cannot always take part in the mac-layer contention. note that the analysis result shown in fig. 5 is derived under the condition that the number of active contending stations is one, i.e., m = 1 and pc = 0 accordingly from (15), resulting in the constant capacity, regardless of nd and nu. on the other hand, as was expected, the capacity decreases with the smaller size of packet. however, fig. 5(b) shows that the capacity linearly increases or decreases with respect to the number of pnes with udp traffic in the mixed traffic case. from the analysis in section 3.3, the estimated capacity in the 726 e.-c. park, i.-h. kim, g.-m. jeong, b. moon case of 1500-byte and 200-byte udp packet is 5.00 and 1.57 mb/s, respectively, and that in the case of 1-kbyte tcp packet is 3.22 mb/s. 0 1 2 3 4 5 1 2 3 4 5 ca p a ci ty ( m b /s ) number of tcp pnes (nd = nu) sim: l=500 sim: l=1500 anal: l=500 anal: l=1500 2 2.5 3 3.5 4 1 2 3 4 5 ca p a ci ty ( m b /s ) number of udp pnes (nd = nu) sim: l=200 sim: l=1500 anal: l=200 anal: l=1500 (a) tcp-only (b) mixed figure 5: comparison of capacity obtained from simulation and capacity derived from analysis. fig. 5 shows that there is little difference in the simulation results and analysis results for both cases. it is important to note that the capacity (c̃) with the standard packet size of 1kb is used as decision criteria of admission control (see (10)), and the required data rate is scaled if the packet size or transport-layer protocol is different from those used to estimate the capacity as shown in (19) and (21). the results in fig. 5 confirm that the estimated capacity is accurate, regardless of packet size and/or transport-layer protocol, and that the capacity can be easily estimated under the assumption of negligible collision probability, regardless of the number of stations. hereafter, we set the packet size of qos and be service as 1 kbyte to concentrate on evaluating performance without the effect of packet size. 4.2 qos assurance and fair channel sharing the objective of this simulation is to evaluate the performance of the proposed mechanism in terms of qos assurance and fair channel sharing. figure 6(a) shows per-pne throughput under the simulation scenario in section 2.1 (see tab. 1). by comparing the case without qos control (see fig. 2), we observe that the proposed mechanism strictly assures the desired data rate for qos services, e.g, 1mb/s for iptv (dl) and 1mb/s for surveillance camera (ul), and the assurance is hardly affected by other services. the proposed mechanism completely removes the unfairness between dl and ul be services; the throughputs of the dl mobile phone and ul camcorder are almost equal during the whole simulation time when both coexist. moreover, the severe fluctuation in the throughput is significantly reduced, compared to the case without qos control. table 3 compares the per-pne throughput with the proposed qos control and that without qos control. during t = [50-100]s and [100-150]s, the fairness index is increased from 0.53 and 0.45 to 1.01 and 1.00, respectively, due to the proposed qos control mechanism. also, the total throughput of the proposed mechanism is not decreased to assure qos and fairness, rather it is slightly increased in some cases, compared to the case without qos control. next, we focus on the delay of streaming service (iptv) for three cases; • base: no qos control mechanism is implemented. • qos: the proposed qos control mechanism is implemented and the pn gw serves packets with the fcfs scheduling discipline. quality of service control for wlan-based converged personal network service 727 0 0.5 1 1.5 2 2.5 3 0 25 50 75 100 125 150 th ro u g h p u t (m b /s ) time (sec) iptv (dl) mob. phone (dl) camcorder (ul) surv. cam (ul) 0 100 200 300 400 0 25 50 75 100 125 150 d e la y (m se c) time (sec) base qos qos+ base qos qos+ (a) per-pne throughput (b) delay of streaming service (iptv) figure 6: throughput and delay with the proposed qos control mechanism for the simulation scenario in tab. 1. table 3: comparison of per-pne throughput in each time interval. interval device direction throughput (mb/s) (sec) w/o control with control 0 ∼ 50 iptv dl 1.00 1.00 mob. phone dl 2.25 2.24 50 ∼ 100 iptv dl 0.78 1.00 mob. phone dl 0.87 1.18 camcorder ul 1.63 1.17 100 ∼ 150 iptv dl 0.51 1.00 mob. phone dl 0.71 0.91 camcorder ul 1.58 0.91 surv. cam ul 1.00 1.00 • qos+: this enhances the proposed qos control mechanism by implementing dual queue in the pn gw and applying the strict priority scheduling discipline to the dual queue, as discussed in section 3.4. as shown in fig. 6(b), the delay of base is higher than that of qos+ up to 7.5 times and it oscillates highly after a ul service starts at t = 50 s, while the delay of qos+ is maintained at the smallest value (about 45 ms) and it does not increase due to other services. in the case of qos, the variation of delay is small as qos+, but the delay increases once other service starts; the average delay is about 45, 125, and 170 ms, in the time interval of [0-50]s, [50-100]s, and [100150]s, respectively. we also measure the jitter of streaming service, defined as standard deviation of delay 6. in the case of base, the jitter increases from 16 ms to 77 ms in the time interval of [0-50]s and [100-150]s, respectively. the jitter of qos during t = [100-150]s increases from 3.64 ms to 7.13m by about 2 times, compared to the time interval of t = [0-50]s. however, as can be expected from 6(b), the jitter of qos+ is not affected by other service and is maintained below 3.9 ms for the whole simulation time, reconfirming that the proposed mechanism can minimize delay of qos service, as well as assuring its desired data rate. 6the jitter can be defined in several ways to quantify the variation in the delay of successive packets. it is an important performance index for streaming service, as well as delay. 728 e.-c. park, i.-h. kim, g.-m. jeong, b. moon table 4: fairness and capacity with different number of devices. (nu, nd) (1,1) (1,2) (1,4) (2,1) (2,2) (2,4) (4,1) (4,2) (4,4) fairness index 0.987 0.988 1.004 1.000 1.014 1.000 0.992 0.999 0.982 capacity (mb/s) 3.27 3.35 3.34 3.26 3.32 3.34 3.31 3.26 3.27 4.3 fairness with different number of devices, weights, and transmission rates here, we focus on evaluating the performance of the proposed mechanism from the viewpoints of fairness and capacity for tcp be services. table 4 shows fairness index and total throughput with different number of ul/dl devices, nu and nd. the fairness index is not much deviated from the ideal value of one, it ranges between 0.982 and 1.014 in all the cases of nd and nu. also, the capacity is little affected by the number of devices, it ranges between 3.26 mb/s and 3.35 mb/s and close to the analysis result of 3.23 mb/s. these results validate that the proposed algorithm assures fair channel sharing among be services without debasing the total throughput, regardless of the number of devices. the next simulation evaluates how the proposed mechanism can support the weighted fairness for be services, which is discussed in section 3.4. figure 7 shows throughput for six pnes (three dl pnes and another three ul pnes) that have different service weights of 1, 2, or 4. we observe that the throughput is almost in proportion to the service weight, the throughput of pne5/pne6 is higher than that of pne1/pne2 by 3.96 times and that of pne3/pne4 is higher than that of pne1/pne2 by 1.97 times. moreover, there is no notable difference between throughput of dl pne and ul pne that have the same service weight. since the proposed mechanism assures weighted fairness, the cpns operator can assign different service weight based on the service priority, service fee, or operational policy, to increase its revenue. 0 0.25 0.5 0.75 1 1.25 1.5 0 25 50 75 100 th ro u g h p u t (m b /s ) time (sec) pne1: dl, w=1 pne2: ul, w=1 pne3: dl, w=2 pne4: ul, w=2 pne5: dl, w=4 pne6: ul, w=4 figure 7: per-pne throughput with different service weights. finally, we observe the performance of fairness in multi-rate wlan configuration. table 5 shows per-pne throughput, total throughput, and fairness index, as well as indicating the direction and transmission rate (hi and lo denote the transmission rate of 11 mb/s and 2 mb/s, respectively.). the difference in the per-pne throughput is insignificant, i.e., the fairness index is almost one, regardless of transmission direction and transmission rate. however, the total throughput decreases as the number of low-rate pnes increases, because the low-rate pne occupies the channel longer than the high-rate pne to transmit a packet. the proposed mechanism assures throughput-fairness, regardless of transmission rate of pnes. moreover, it is expected from the results of fig. 7 and tab. 5 that the proposed mechanism can be extended quality of service control for wlan-based converged personal network service 729 table 5: fairness and capacity with different transmission rates. per-pne throughput (mb/s) capacity fairness dl: hi ul: hi dl: lo ul: lo (mb/s) index 1.626 1.645 3.271 0.988 0.863 0.880 0.871 2.614 0.985 0.574 0.582 0.561 0.558 2.275 0.996 to support time-fairness, i.e., allocate channel occupation time fairly, by assigning high weight to the high-rate pne, to improve the total throughput without impairing fairness. 5 conclusion in this paper, we proposed a generalized framework to assure qos and fairness for wlanbased cpns. we identified the problem of qos degradation and unfair channel sharing that occurs in the existing wlan-based cpns, and analyzed its cause from the viewpoints of unfair channel access of mac and asymmetric congestion control of tcp. by employing admission control and rate control, the proposed mechanism provides absolute guarantee for qos service in terms of data rate, at the same time, it provides weighted fairness for be service without decreasing the efficiency of channel sharing. moreover, the assurance of qos and fairness is effective, regardless of service direction, packet size, transmission rate, and transport-layer protocol. through analysis and simulation, it was confirmed that the proposed mechanism effectively achieve qos assurance for qos service and fair channel access for be service. acknowledgement this work was supported in part by basic science research program through the national research foundation of korea (krf), funded by the ministry of education, science, and technology [grant no. 2010-0008711], and by information technology research center (itrc) support program through the national it industry promotion agency (nipa), funded by the ministry of knowledge economy, korea [grant no. nipa-2011-c1090-1121-0005]. corresponding author is g.-m. jeong. bibliography [1] open mobile alliance, converged personal network service architecture candidate version 1.0, oma-ad-cpns-v1_0-20100615-c, jun 2010. 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[13] s. pilosof, r. ramjee, d. raz, y. shavitt, and p. sinha, understanding tcp fairness over wireless lan, in proc. of ieee infocom, pp. 863-872, 2003. [14] y. wu, z. niu, and j. zheng, study of the tcp upstream/downstream unfairness issue with per-flow queuing over infrastructure-mode wlans, wireless comm. and mobile computing, vol. 5, no. 4, pp. 459-471, 2005. [15] d.j. leith, p. clifford, d. malone, and a. ng, tcp fairness in 802.11e wlans, ieee communications letters, vol. 9, no. 11, pp. 964-966, 2005. [16] s. choi, k. park, and c. kim, performance impact of inter-layer dependence in infrastructure wlans, ieee trans. mobile computing, vol. 5, no. 7, pp. 829-845, 2006. [17] f. calì, m. conti, e. gregori, dynamic tuning of the ieee 802.11 protocol to achieve a theoretical throughput limit, ieee trans. on networking, vol. 8, no. 6, pp. 785-799, dec. 2000. [18] a. kamerman and l. monteban, wavelan-ii: a high-performance wireless lan for the unlicensed band, bell lab technical journal, pp. 118-133, 1997. [19] j. choi, k. park, and c.-k. kim, analysis of cross-layer interaction in multirate 802.11 wlans, ieee trans. on mobile computing, vol. 8, no. 5, pp. 682-693, may 2009. [20] g. bianchi, performance analysis of the ieee 802.11 distributed coordination function, ieee journal on selected areas in communications, vol. 18, no. 3, pp. 535-547, march 2000. international journal of computers communications & control issn 1841-9836, 12(4), 449-460, august 2017. contributions of florin gheorghe filip in information science and technology i. dzitac, m.j. manolescu acad. florin gheorghe filip at 70 years (born on july 25, 1947, bucharest, romania) ioan dzitac 1,2 ; misu-jan manolescu 2,∗ 1. aurel vlaicu university of arad 310330 arad, elena dragoi, 2, romania ioan.dzitac@uav.ro, mmj@univagora.ro *corresponding author: mmj@univagora.ro 2. agora university of oradea 410526 oradea, p-ta tineretului 8, romania, abstract: romanian scientist florin gheorghe filip was born in 1947 and this year he turns 70. f.g. filip is an engineer and ph.d. in control engineering and computer science. still very young, he became corresponding member of the romanian academy in 1991 (when he was only 44 years old), and, at 52 years old (1999), become full member in the highest learned society of romania. from 1970 to 2000, he worked at the national r&d institute in informatics bucharest (ici). for 10 years, during 2000-2010, he was vice president of the romanian academy. in 2010, he was elected president of the "information science and technology" section of romanian academy. at present, he is the director of the romanian academy library. his fields of scientific interest have been: decision support systems (dss), large-scale systems control and optimization, technology management and foresight and it application to cultural domain . he has authored/co-authored over 300 technical papers, 13 monographs, and edited/co-edited 24 contributed volumes. keywords: decision support systems (dss), hierarchical large-scale systems, managerial activity, recognition, scientific publications. copyright © 2006-2017 by ccc publications 450 i. dzitac, m.j. manolescu 1 introduction "to whom it may concern ... ... dr. filip has shown himself to be a very innovative and productive researcher whose papers are equivalent in quality and cutting-edge findings to those from the best research organizations of the world. witness his publications in automatica, computers in industry and other leading journals in the field. the establishment of dr. filip’s stature as an internationally recognized researcher in his field and the acceptance of his work are shown by the large number of international conferences where he has served as session chairman and/or program committee member and many invited seminars he has presented in other countries ..." (t.j. williams). this is what theodore j. williams, professor of engineering and director, purdue lab. for applied industrial control, purdue university, wrote about f.g. filip in august 1994. in the sequel we will highlight several aspects of acad. filip’s professional life. similar works within this paper has been published in 2007, with occasion of 60 years from birth of acad. filip, in the computer science journal of moldova [1] and in the international journal of computers communications & control [8]. 2 a short biographical note florin gheorghe filip was born on 25 of july 1947 in bucharest, romania. he graduated in control engineering from politehnica university bucharest (pub) in 1970 and received ph.d. from the same university in 1982. in 1974 , he was a guest researcher in swedish universities (chalmers th in gothenburg, teknikum uppsala, university of lund, kth, stockholm). in 1993 and 1996 he was a visitor of german fraunhoffer institutes: first berlin and iitb karlsruhe, respectively. in 1983, prof. filip was invited to deliver a 10-day course on hierarchical control systems at institute of automation of the chinese academy of sciences in shenyang, china. from 2006 to 2011, he spent one month each years as a chercheur-enseignant at école centrale de lille (france). he was elected as a corresponding member of the romanian academy (ra) in 1991 and become full member of the ra in 1999. during 2000-2010, he was vice-president of the academy (elected in 2000, and re-elected in 2002 and 2006). in 2010 was elected as a president of the 14th section "information science and technology" of the ra (re-elected in 2015). also he was elected as a honorary member of academy of sciences of moldova and romanian academy of technical sciences in 2007. prof. filip is one of the founding members of the international academy for it and quantitative management (iaitqm), founded in 2012 at university of nebraska in omaha, usa. he was president of ifac tc 5.4 sml large scale systems (2002-2008) and a member of two other ifac technical committees: tc 4.5 (hms), tc 5.2 (mmc) and one ifip wg 5.12 (enterprise integration). prof filip was the managing director of icinational institute for r&d in informatics (19911997), and the president of scientific council of ici (1995-2003). presently, he is the director of the romanian academy library. f.g. filip gave master courses on computer applications, expert systems, computer supported decision-making, and enterprise engineering at pub and other romanian universities such as agora university oradea, university of bucharest, lucian blaga university of sibiu, valahia university of targoviste, academy of economic studies (ase) bucharest. he has supervised doctoral studies in pub and institute for artificial intelligence (icia) of the romanian academy. four of his former phd students (a. alexandru, m. cioca, l. duţă, contributions of florin gheorghe filip in information science and technology 451 and c.b zamfirescu) are at present professors in various universities and supervise their phd students, and c. cândea is the leader of ropardo, a successful software company. f. g. filip was an adjunct professor at the university of the chinese academy of sciences (2013-2016). he delivered invited lectures and seminars at the invitation of universities and research centers in england (city university, london, 1985), austria (insbruck university, 1996), brazil (espirito santo university, vitoria, 1996), czechoslovakia (cas utia, 1983), china (university of cas school of management and university of finance and economics, chengdu, both in 2013; cas 2016), france (laas toulouse, 1994; ec lille, 2008), poland (warsaw polytechnic university, 1980), sweden (chalmers th, göteborg, 1974 ; uppsala university, 2011), germany (th ilmenau, 1983; zki berlin, 1991; fraunhofer first, 1995). his main personal scientific interests include: hierarchical optimization and control of largescale systems, decision support systems (dss), applications of it in the cultural sector, technology management and foresight. from the very beginning of his carrier he was interested to find effective solutions to real-life problems. in 1972, as a young engineer, he was appointed to implement a teleprocessing information system in the largest steelworks in romania, in galaţi city (250 km away from bucharest). he noticed the very high length of the lines which were to connect the central computer to the terminals placed in various production sections and decided to devise an algorithm based on graph theory to minimize the total length and, consequently, the costs. while as a guest researcher in the royal technical university (kth) stockholm, in 1974, devised an efficient heuristic algorithm. then, he approached many practical process and production control problems he identified in various industries (mainly refineries, chemical plants, water systems, discrete part manufacturing), tried to find adequate scientific methods to solve them and then came back to applications with a view to deploying the corresponding computerized devised algorithms. prof. filip was an ipc member of more than 50 international conferences held in europe, usa, south america, asia and africa. since 2008 he has been the ipc chair of international conference on computers communications and control. also chaired the ipc of several other conferences held in chile (2008), france (2010), romania (2006, 2007, 2008, 2009, 2010, 2012, 2014, 2016), portugal (2010), germany (2015). he is one of the honorary chairs of the itqm (information technology and quantitative management). he was invited to deliver plenary talk at scientific conferences held in england (liss2013, reading), chile (ifac mcpl2004, university of the americas, santiago, ifac workshop on logistics, santiago, ieee cesa2012, santiago), china (cesa2005, university tzinghua beijing, itqm2013, suzhou), france (cscs2013, villeneuve d’asq), portugal (ifac mcpl2010, coimbra), spain (ifac basys2010, valencia; cio2012, vigo), tunisia (cesa1997, hammamet). 2.1 several professional highlights 1. creating of one of the first romanian cad (computer aided design) packages, containing original optimization algorithms in graphs called optconf designing equipment configurations (1974); 2. designing the first romanian experimental decision support systems for a steady and discreet production management, with a demonstration of their operation in teleprocessing mode at the world cybernetics conference in bucharest (1976) (on the felix256 computer placed in ici connected to the ibm vt 52 terminal and cii mitra minicomputer, located at the sala palatului (palace hall); 452 i. dzitac, m.j. manolescu d. tufis, i. dzitac, l.a. zadeh m.j. manolescu, f.g. filip (icccc2008, oradea) 3. coordination of teams who built dispatcher dss family (containing original optimization algorithms for discrete time systems with hard constraints, sparse matrices, and slowly variable in time parameters) implemented in refineries, chemical plants and retention and distribution water systems (1980-2000). 4. refining, adapting, and using control theory methods to model the interacting entities of the cultural sector and creative industries (since 1996). 2.2 recognitions and awards prof. filip is a honorary magister of dunărea de jos university of galaţi (since 2006), a honorary member of the agora university senate (since 2007) and has received doctor honoris causa title from lucian blaga university of sibiu (november 2000), valahia university, târgovişte (2007), ovidius university, constanta (2007), ecole centrale de lille, france (2007), politehnica university of timişoara (2009), agora university of oradea, academy of economic studies of bucharest (2014), university petrol-gaze of ploieşti (2017), and university of piteşti (2017). prof. filip received the national orders "serviciu credincios" (contentious service), at rank of "great cross" and "steaua romaniei" (star of romania) at rank of "knight" from the presidents of romania in 2000 and 2017 , respectively, and "marin drimov" medal from the bulgarian academy of sciences. he is the recipient of several prizes such as: copy ro prize for the best book in it for the book "computer aided decision-making" (in 2002), the grigore moisil prize, and medal "man of the year 1999" from the association for economic informatics, "w. scott jr. in it" award from the iaitqm for "remarkable record in the theory, algorithm, education and applications of information technology" (2013), "stefan odobleja" award in information science and technology from the academy of scientists for the book optimization dans les sciences de l’ingenieur (2015). contributions of florin gheorghe filip in information science and technology 453 "walter scott jr. in it award" for f.g. filip by the iaitqm at itqm2013, suzhou, china (anonymous sponsor), cheng-few lee, cheng siwei, florin gheorghe filip, daniel berg, yong shi 3 publications 3.1 published books (monographs and volumes) f.g. filip is author/co-author of 13 monographs, and edited/co-edited 24 contributed volumes, such as: [2], [17], [18], [20], [34], [35], [43], etc. the summary of the monograph computer-supported collaborative decision-making [34] is presented at springer web page [45]: addresses specific concepts, technologies, and systems for collaborative activities with particular emphasis on decision-making; balanced presentation of well consolidated and modern methodologies, together with pacing information and communications; includes various industrial, financial and "culture economy" applications. "this is a book about how management and control decisions are made by persons who collaborate and possibly use the support of an information system. the decision is the result of human conscious activities aiming at choosing a course of action for attaining a certain objective (or a set of objectives). the act of collaboration implies that several entities who work together and share responsibilities to jointly plan, implement and evaluate a program of activities to achieve the common goals. the book is intended to present a balanced view of the domain to include both well-established concepts and a selection of new results in the domains of methods and key technologies. it is meant to answer several questions, such as: a) how are evolving the business models towards the ever more collaborative schemes?; b) what is the role of the decision-maker in the new context?; c) what are the basic attributes and trends in the domain of decision-supporting information systems?; 454 i. dzitac, m.j. manolescu volume co-edited with l.a. zadeh, d. tufis and i. dzitac (2008, romanian academy) [43] monograph co-authored with c.b. zamfirescu and c. ciurea (2016, springer) [34] d) which are the basic methods to aggregate the individual preferences?; e) what is the impact of modern information and communication technologies on the design and usage of decision support systems for groups of people?" 3.2 most cited papers florin gheorghe filip has authored/co-authored over 300 scientific papers. for example, some selected papers authored by f.g. filip are [9] [16] or co-authored with other international scientists [3][7], [19], [21][25], [27] [38], [40], [41], etc. only in web of science core collection, the papers authored/co-authored by f.g. filip has been cited over 200 times. the most cited paper authored by f.g. filip is decision support and control for large-scale complex systems [15]. this paper has been cited in a lot important international journals, indexed in web of science, such as: robotics and autonomous systems, information sciences, international journal of systems science, technological and economic development of economy, ieee transactions on industrial electronics, studies in informatics and control, romanian journal of information science and technology, quality and reliability engineering international, biometric and intelligent decision making support, international journal of computers communications & control, simulation modelling practice and theory, and others. many citations in web of science has also other papers such as: a decision-making perspective for designing and building information systems [16], job shop scheduling optimization in real-time production control [29], dss in numbers [33], multilevel optimization algorithms in computer-aided production control in process industry [23], etc. 4 science and research management 1. management of ici (national institute of informatics) during 1991-1997, as a directorgeneral. at the time, ici obtained about 50% of the it research projects funded by the european commission and it was placed (in 1997) first among the most famous 10 research institutes in romania in the report of the coopers & lybrand company consortium, submitted to the european commission. in 1992, ici team introduced internet into the country through the rnc network 1992; 2. he was part of the first team of the five evaluators from romania for the project proposals for the eu research program called ec-peco (european commission pays de l’europe centrale et orientale) in 1992; 3. he introduced the romanian research funding system through competition in the field of "informatics" (1992) and co-founder and deputy chair (1996-2003) of the council for the romanian academy grants; 4. he was the secretary general of the romanian national advisory council for researchdevelopment, and innovation (1997-2010); 6. member in the "ist prize" jury, organized by eurocase (federation of academies of applied and engineering sciences in europe) for 10 years (1997-2007); 7. the first romanian scientist in the it advisory group (istag) of the european commission (2001), and nato science for peace subcommittee for "computer networks" (2000). 5 editorial activities acad. filip is a member in the editorial staff of several scientific journals, such as: 1. systems analysis, modeling and simulation (taylor and francis, 1993-2004); 2. international j. of critical infrastructures (inderscience publishers, since 2004); 3. computer journal of moldova (chisinau, since 1993); 4. information technologies and control (sofia, since 1998); 5. romanian journal of information science and technology (romanian academy); 6. studies in informatics and control (ici, founder and chief-editor, from 1989); 7. international journal of computers communications & control (agora univ., chief-editor from 2006); 8. control engineering and applied informatics (srait); 9. romanian journal of informatics and automatics (ici, founder; 10. romanian journal of automatics; 11. technological and economic development of economy (taylor & francis); 12. international journal of information technology & decision making (science direct); 13. advances in electrical and computer engineering (stefan cel mare univ.); 14. financial innovation (springer, since 2016); 15. journal of system and management sciences (beijing jiatong univ., china). f.g.filip is the founder and editor-in-chief of studies in informatics and control (1991), and co-founder and editor-in-chief of international journal of computers communications & control (2006). international journal of computers communications & control (founded in 2006 at agora university by i. dzitac, f.g. filip, m.j. manolescu) 6 conclusion florin gheorghe filip authored/co-authored over 300 papers published in international journals (ifac j. automatica, ifac j. control engineering practice, annual reviews in control, computers in industry, large scale systems etc.) and contributed volumes released by international publishing houses (pergamon press, north holland, elsevier, kluwer, chapman & hall and so on). he is also the author/co-author of 13 monographs (published by editura tehnică bucharest, hermes-lavoisier paris, j. wiley & sons london, springer) and editor/co-editor of 24 volumes of contributions (published by editing house of the romanian academy, pergamon press, elsevier, institute of physics melville usa, ieee computer society los alamitos usa). his main scientific contributions include: hierarchical optimization and control of largescale systems, decision support systems (dss), applications of it in the cultural sector, technology management and foresight. in our bibliographic list are presented 42 selected works (papers, monographs, volumes) authored/co-authored by f.g. filip [2-7], [9-44]. acad. filip have a good international impact and many collaborators, from countries such as: brazil (l.f. autran monteiro gomes), bulgaria (k. boyanov, b. sendov), chile (f. cordova, c. lagos, g. lefranc), china (g. kou, y. peng, c. siwei, y. shi, m. tang), czech republic (k. bakule, j. zavorka), finland (k. leiviskä), france (j. bernussou, p. borne, a. dolgui, a. el kamel, h. panetto, a. titli, p. zarate), germany (r. popescu-zeletin, k. reinisch, a. sydow), greece (p. groumpos, g. metakides, p. spirakis), hungary (g. kovacs, l. monostori), lithuania (g. dzemyda, a. kaklauskas, z. turskis, e.k. zavadskas), mexic (a. molina), poland (w. findeisen. m.a. brdys, k. malinowski), portugal (a.d. correia, l.m. camarinha matos), republic of moldova (g. căpăţănă, i. cojocaru, s. cojocaru, g. duca, c. gaindric), romania (b. bărbat, v. balas, m. bizoi, g. bologa, i. buciu, c. ciurea, h. dragomirescu, m. guran, d. donciulescu, i. dumitrache, i. dzitac, e.a. iancu, a. ionita, i. ivan, r. lile, a. manolescu, m.j. manolescu, i. moisil, g. neagu, s. nicoară, s.i. niţchi, h. oros, n. paraschiv, d. popescu, r.e. precup, o proştean, d. ştefănoiu, a.m. suduc, h.n. teodorescu, d. tufiş, c.b. zamfirescu), russia (f. aleskerov), spain (e. herrera -viedma, a. ortiz), sweden (a. hedin), uk (p.d. roberts), and from usa (r. andonie, d. berg, s.m. gupta, s.y. nof, d.j. power, g. tecuci, t.j. williams, a.b. whinston, l.a. zadeh). we cannot come to an end without mentioning the man florin gheorghe filip. all of us that know him, if asked which are the first three words coming in our minds we shall not hesitate to tell: intuitive, generous and catalyst. he is able to intuit a young researcher, a valuable research team or an academic institution with prospects. full of modesty, he does not know envy and is always eager to support those needing his advices, with a generosity specific to great spirits. his behavior is always of a catalyst, precipitating many beneficial reactions, support without waiting for a reward. acknowledgment we are honoured and we grateful to prof. filip for his contributions, in his capacity of editorin-chief, of our international journal of computers communications & control (co-founded in 2006 with i. dzitac and m.j. manolescu at agora university in oradea, romania). bibliography [1] bărbat b.e. (2007), filip the catalyst, the computer science journal of moldova, 15(2), 115-119, 2007. [2] borne p., popescu d., filip f.g., ştefanoiu d. (2012), optimisation en sciences de l’ingenieur, hermes-lavoisier, paris, 2012 (in french). [3] brândaş c., panzaru c., filip f. g. (2016), data driven decision support systems: an application case in labour market analysis, romanian journal of information science and technology, 19(1-2), 65-77, 2016. [4] cândea c., filip f.g. (2016), towards intelligent collaborative decision support platforms, studies in informatics and control, 25(2), 143-152, 2016. [5] duţă l., filip f.g., henrioud j.m.(2003), a method for dealing with multi-objective optimization problem of disassembly processes, assembly and task planning, 2003. proceedings of the ieee international symposium, 163-168, 2003. [6] duţă l; filip f.g., (2008), control and decision-making process in disassembling used electronic products, studies in informatics and control, 17(1), 17-26, 2008. [7] duţă l., filip f.g., popescu c. (2008), evolutionary programming in disassembly decision making international journal of computers communications & control, 3(supll.), 282-286, 2008. [8] dzitac i., manolescu m.j., oros h., văleanu e. (2007); 60 years from birth of academician f.g. filip, international journal of computers communications & control, 2(3), 209-216 , 2007. [9] filip f.g. (1989), creativity and decision support systems, studies and researches in computer and informatics, 1(1), 41-49, 1989. [10] filip f.g. (1991), system analysis and expert systems techniques for operative decision making, systems analysis modeling simulation, gordon and breach science publishers, 8(3), 213-219, 1991. [11] filip f.g. (1994), evolutions in systems analysis, modelling and simulation in computer based industrial automation, systems analysis, modelling and simulation, 15, 135-149, 1994. [12] filip f.g. (1995), it culture dissemination in romania: experiments and achievements, in a. inzelt and r. coenen (eds.), knowledge technology transfer and forecasting, kluwer academic, dordrecht, 149-160, 1995. [13] filip f.g. (1995), towards more humanized dss, in: l.m. camarinha matos and h. afsarmanesh (eds.), balanced automation systems: architectures and design methods, chapmann & hall, london, 230-240, 1995. [14] filip f.g. (1998), optimization models with sparse matrices and relatively constant parameters, systems analysis modelling and simulation, 33, 407-430, 1998. [15] filip f.g. (2008), decision support and control for large-scale complex systems, annual reviews in control, 32(1), 61-70, 2008. [16] filip f.g. (2012), a decision-making perspective for designing and building information systems, international journal of computers communications & control, 7(2), 264-272, 2012. [17] filip f.g. (2002), decizie asistată de calculator decizii, decidenţi, metode şi instrumente de bază, editura tehnică, bucureşti, 2002 (in romanian). [18] filip. f.g. (2007), sisteme suport pentru decizii, editura tehnică, bucureşti, 2007 (in romanian). [19] filip f.g., alexandru a., socol i. (1997), technology management and international cooperation: several success stories, j. human systems management, 16, 223-229, 1997. [20] filip f.g., bărbat b. (1999), informatică industrială: noi paradigme şi aplicaţii, editura tehnică, bucureşti, 1999 (in romanian). [21] filip f.g., ciurea c., dragomirescu h., ivan i. (2015), cultural heritage and modern information and communication technologies, technological and economic development of economy, 21(3), 441-459, 2015. [22] filip f.g., donciulescu d. a. , filip c.i. (2001), a cybernetic model of computerization of the cultural heritage, computer science journal of moldova, 9(2), 101 -112, 2001. [23] filip f.g. , donciulescu d.a., gaşpar r., muratcea m., orăşanu l. (1985), multilevel optimization algorithms in computer-aided production control in process industry, computers in industry, 6(1), 47-57, 1985. [24] filip f.g., donciulescu d.a. (1983), on on-line direct dynamic coordination method in process industry, ifac j. automatica, pergamon press, oxford, 19(1), 317 320, 1983. [25] filip f.g., dragomirescu h., predescu r., ilie r. (2004), it tools for foresight studies, studies in informatics and control, 13(3), 161-168, 2004. [26] filip f.g., dumitrache i., iliescu s.s. (eds.) (2002), large scale systems: theory and applications, elsevier science ltd. (pergamon), oxford, 2002. [27] filip f.g., herrera-viedma e. (2014), big data in the european union, the bridge, 44(4), 33-37, 2014. [28] filip f.g., leiviskä k. (2009), large-scale complex systems, in: nof s.y. (ed.), springer handbook of automation, springer, berlin heidelberg, 619-638, 2009. [29] filip f.g., neagu g., donciulescu d. (1983), job shop scheduling optimisation in real-time production control, computers in industry, 4(4), 395-403, 1983. [30] filip f.g., neagu g., donciulescu d. (1996), dssfm: from technology to decision making, proceedings 14th ifac world congress, san francisco, vol b, 367-372, 199 [31] filip f.g., neagu g. (1993), cim in continuous and discrete manufacturing: object-oriented generic modeling, ifac j.control eng. practice, 1(5), 815-825, 1993. [32] filip f.g., popescu d., mateescu m. (2008), optimal decisions for complex systems-software packages, mathematics and computers in simulation, 76(5-6), 422-429, 2008. [33] filip f.g., suduc a.m., bizoi m. (2014), dss in numbers, technological and economic development of economy, 20(1), 154-164, 2014. [34] filip f.g., zamfirescu c.b., ciurea c. (2016), computer-supported collaborative decisionmaking, springer, 2016. [35] guran m., filip f.g. (1986), sisteme ierarhizate şi în timp real cu prelucrarea distribuită a datelor, editura tehnică, bucureşti, 1986 (in romanian). [36] guran m., filip f.g., donciulescu d., orăşanu l. (1985), hierarchical optimization in computer-aided dispatcher systems in the process industry, large scale systems, 8(2), 157167, 1985. [37] nicoară e.s., filip f.g., paraschiv n. (2011), simulation-based optimization using genetic algorithms for multi-objective flexible jssp, studies in informatics and control, 20(4), 333-344, 2011. [38] nof s.y., morel g., monostori l., molina a., filip f.g. (2006), from plant and logistics control to multi-enterprise collaboration, annual reviews in control, elsevier, 30(1), 55-68, 2006. [39] pascu c., filip f.g. ( eds.) (2005); visions on information society future in an enlarged europe, editura academiei , bucureşti, 2005. [40] seok h., nof s.y., filip f.g. (2012), sustainability decision support system based on collaborative control theory annual reviews in control, 36(1), 85-100, 2012. [41] reşteanu c., filip f.g., ionescu c., somodi m. (1996), on optimal choice problem solving, in: systems, man, and cybernetics, 1996, ieee international conference, 3, 1864-1869, 1996. [42] ştefanoiu d., borne p., popescu d., filip f.g., el kamel a. (2014), optimization in engineering sciences: approximate and metaheuristic methods, john wiley & sons, london, 2014. [43] zadeh l.a., tufiş d., filip f.g., dzitac i. (eds.) (2008), from natural language to soft computing: new paradigms in artificial intelligence, editing house of the romanian academy, 2008. [44] zamfirescu c.b., filip f.g. (2010), swarming models for facilitating collaborative decisions international journal of computers communications & control, 5(1), 125-137, 2010. [45] http://www.springer.com/gp/book/9783319472195 international journal of computers communications & control issn 1841-9836, 13(1), 99-116, february 2018. dynamic multi-hop routing protocol based on fuzzy-firefly algorithm for data similarity aware node clustering in wsns m. misbahuddin, a.a. putri ratna, r.f. sari misbahuddin misbahuddin* department of electrical engineering, faculty of engineering, universitas indonesia, depok, 16424, indonesia *corresponding author: misbahuddin@ui.ac.id anak agung putri ratna department of electrical engineering, faculty of engineering, universitas indonesia, depok, 16424, indonesia ratna@eng.ui.ac.id riri fitri sari department of electrical engineering, faculty of engineering, universitas indonesia, depok, 16424, indonesia riri@ui.ac.id abstract: in multi-hop routing, cluster heads close to the base station functionaries as intermediate nodes for father cluster heads to relay the data packet from regular nodes to base station. the cluster heads that act as relays will experience energy depletion quicker that causes hot spot problem. this paper proposes a dynamic multihop routing algorithm named data similarity aware for dynamic multi-hop routing protocol (dsa-dmrp) to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. the dsa-dmrp uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters. in this mechanism, each node can recognize and note its similar neighbor nodes. next, k-hop clustering algorithm (khopca) that is modified by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra-cluster and inter-cluster transmission. the dsa-dmrp was compared against the khopca to justify the performance. simulation results show that, the dsa dmrp can improve the network lifetime longer than the khopca and can satisfy the requirement of the dynamic multi-hop routing protocol. keywords: clustering, data similarity, multi-hop routing, fuzzy system, firefly algorithm, wireless sensor networks (wsns). 1 introduction wireless sensor networks (wsns) rapidly grow in various applications for many domains. besides, wsns is also an integral part of the internet of things (iot) that can share data for improving the human capability in monitoring a local environmental condition and process automation. it consists of a set of sensor nodes that are deployed in an appropriate environment in the ad hoc model to observer and interact with the physical world or biological system remotely. therefore, wsns should be able to adapt dynamically with the charged environment. recently, wsns play important role in various necessaries of human such as flood monitoring [14], weather monitoring, earthquake detection [22], tracking [19], volcanic eruption, military necessity [10], healthcare observation [3] agriculture automation [26], and manufacturing automation [17]. each node is composed of sensor, low ability processor, limited capacity storage and power supply, and transceiver. therefore, the efficient usage of energy that is supplied by battery is copyright ©2018 cc by-nc 100 m. misbahuddin, a.a. putri ratna, r.f. sari important issue in order to prolong the network lifetime. topologically, the clustering techniques have been commonly used to improve the network performance such as prolonging the network lifetime, enhancing the network scalability, increasing the bandwidth efficiency, and increasing fault tolerance [1]. clustering technique divides the nodes on a network into many logical or physical groups termed as clusters. each cluster is composed of a node selected as cluster head (ch) and many regular nodes called cluster members. each regular node senses data of the environmental condition and forwards to its ch. meanwhile, the ch functions to sense data, aggregate data, and relay them to the other ch or base station (bs). clustering techniques consist of two fashions, equal sized clustering and unequal sized clustering. in equal sized clustering, all clusters have the same size number of cluster members. the chs closer to bs have an additional function, not only sensing data, aggregating data, and sending the aggregated data to bs but also forwarding data from the other chs to bs. these chs have a heavier load than the chs farther from bs, so that they consume more energy and deplete energy more quickly than the other chs. thus, the network connectivity is disrupted in relaying data to bs. this event is termed as a hot spot problem. to overcome the hot spot issue in the network, the topology of unequal sized clustering can be used to organize the load balancing among the chs [?]. architecture of the unequal sized clustering is to reduce the clusters size closer to bs and increase the clusters size as the distance between ch and bs. in our work, load of clusters can not be arranged through such way because the cluster size is determined according to the clustering technique based on the data similarity referred spatial and temporal correlation. therefore, such clustering technique requires a specific routing protocol to increase the energy efficiency in transmitting the sensed data by the regular nodes to bs via either a ch with a single-hop or some ch with multi-hop. furthermore, this technique is also a dynamically changed clustering in each round. the topology of the network changes in each round because each cluster is established based on the data similarity of the adjacent nodes. because such clustering technique generates unequal sized clusters, the selection of some nodes as chs is a crucial problem for improving the energy saving in order to prolong the network lifetime. this paper proposed a dynamic multi-hop routing protocol designed specially for a data similarity aware node clustering that is a topology of the unequal sized cluster to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. generally, this protocol runs in two main steps. the first step is the dynamic node clustering based on the data similarity using fuzzy aggregation technique. the second step is a routing algorithm using the modified k-hop clustering algorithm (khopca) [6] by adding a priority factor that is obtained by a hybrid approach of fuzzy system and firefly algorithm. there are two variables that are considered to obtain the priority factor, i.e., the residual energy and distances between chs and bs. the remainder of this paper is organized as follows: section 2 presents literature review related with the routing protocols in wireless sensor networks. section 3 describes our approach used for dynamic multi-hop routing protocol. section 4 presents the simulation results to show the performance evaluation. finally, section 5 concludes this paper. 2 routing protocol in wsns routing is the best path to transmit a data packet from a source node to a destination node. the clustering-based routing in wsns, there are two types of path, i.e. data traffic within a cluster termed as intra-cluster, and data traffic between clusters called inter-cluster. in the intra-cluster, each regular node senses a local environmental condition and transmits it to dynamic multi-hop routing protocol based on fuzzy-firefly algorithm for data similarity aware node clustering in wsns 101 corresponding ch. meanwhile, the ch senses, receives, and aggregates data. then it transmits the aggregated data packet to either bs directly or via intermediate chs. one of highly important issues in many literatures of the clustering-based routing techniques is the use of more energy efficient methods in order to prolong the network lifetime. there are two main steps that needs the appropriate technique in order to achieve better network performance in term of lifetime. both steps are the clustering technique and the chs election method. the clustering techniques are classified into two major categories, i.e. unequal sized clustering and equal sized clustering techniques. similarly, the chs election approaches are categorized into three major groups, i.e. preset, random, and attribute based method [1]. in preset approach, all nodes that are selected as chs are adjusted before they were deployed in the environment. in random methods, the chs are selected among of the nodes randomly in the field. on the other hand, attribute-based approaches select the chs among of the nodes based on some of their characteristics, such as the residual energy and distance to the bs. there are several equal clustering based routing protocols that select chs randomly. among other is the low energy adaptive clustering hierarchy (leach) [12] that is a hierarchical clustering-based routing protocol which has been used widely as a benchmark. there are many leach-based routing protocols that have been proposed to improve the energy efficiency in order to prolong the network lifetime such as leach-centralized (leach-c) [13], leachbased energy (leach-e) [15], and leach with distance-based threshold (leach-dt) [16]. the disadvantage of the leach-based routing protocol is that chs are close to the bs that consumes more energy than the chs farther from bs. consequently, the chs near the bs died earlier. this causes a disruption of the network connectivity termed as a hot spot problem. single-hop routing protocol can overcome the hot spot problem because it does not require the intermediate bs to relay the data packet to bs. however, this approach has a limitation of transmission coverage, so that the scalability of the network cannot be achieved. in order to overcome the hot spot problem on the network, some unequal sized clustering approaches using single-hop routing have been proposed. however, these approaches are a waste of energy and the transmission coverage are limited [18]; [4]; [7]. therefore, several multi-hop routing protocols using unequal sized clustering technique [2]; [21]; [24] have been proposed to overcome the hot spot problem, improve the network scalability and optimize the energy-saving in order to prolong the network lifetime. in fact, some of specific applications in wsns not only satisfy the three purposes, but also require a clustering technique based the data similarity readings of adjacent nodes to obtain the data pattern of the observed environment to make decision or prediction. therefore, to fulfill the requirements of the applications, this paper proposes a new dynamic multi-hop routing protocol using the unequal size clustering technique based on the data similarity. this protocol proposes an incorporation the k-hop clustering algorithm (khopca) rules [6] and fuzzy system-firefly algorithm [25]. our proposed routing protocol is called data similarity aware for dynamic multihop routing protocol (dsa-dmrp). the dsa-dmrp starts to establish the node clustering based on data similarity among all nodes in the network using the fuzzy aggregation technique as a dynamic unequal sized clustering mechanism. finally, the chs election and establishment of the best route path uses the khopca rules and a priority factor in the chs election. the priority factor considers the residual energy and distance to the bs using integration of fuzzy system (fs) and firefly algorithm (fa) to improve the network lifetime. 102 m. misbahuddin, a.a. putri ratna, r.f. sari 3 the proposed model of routing protocol 3.1 network model data similarity aware node clustering in wsns is an unequal sized clustering-based wsns. a node will merge or leave its cluster depend on its data similarity degree to those of neighbor nodes. therefore, perhaps there are some nodes that are an intersection in some clusters. due to an unequal sized clustering, there are some clusters that have member nodes fewer than those of other clusters. moreover, there are some individual nodes which does not belong to any cluster. the characteristic of such a clustering-based model requires a properly specific routing algorithm. hence, a multi-hop clustering-based routing protocol was developed to overcome the problem. all sensor nodes that are randomly deployed on the network are stationary. they are homogeneous in their capabilities of sensing, processing, and communicating. the bs is assumed as a stationary site, and it has no energy constraint. not all nodes can communicate directly with other nodes. only nodes that can communicate directly to the sink will be considered to become the cluster heads. the node clustering and the data gathering are run into rounds. in each round, there are four steps, (i) all nodes sensed the local data, (ii) the nodes divided themselves into several clusters, (iii) the nodes created a multi-hop routing, (iv) the member nodes sent their data to the corresponding chs, and the chs aggregates the data in order to forward them to the bs. 3.2 energy consumption model energy consumption in wsns is an urgent issue to be considered in order to increase the longer network lifetime. the energy consumption models have been developed in several literatures. figure 1 shows the consumption model of radio energy. tx electronic tx amplifier rx electronic l-bits message l-bits message etx-elec(l,d) etx-amp(l,d) erx-elec(l) etx(l,d) erx(l) figure 1: transmission energy dissipation model [24] the dissipated energy to transmit l-bits message with distance d and receive l-bits can be computed respectively using the following equations [7]. etx(l,d) = etx−elec(l) + etx−amp(l,d) = l(eelec + eampd p) = { l(eelec + εfsd 2, if d ≤ d0 l(eelec + εmpd 4, if d > d0 (1) erx(l) = erx−elec(l) = l×eelec (2) where eelec is the consumed energy in either transmitter or receiver circuit, is for amplifier radio model, and p is the path loss. the path loss is adjusted to p = 2 and the free space model is used by assigning eamp = εfs if the distance d is less than or equal to the threshold distance. on the contrary, the path loss is assigned p = 4 and the multi-path fading model is employed by setting eamp = εmp if distance d more than the distance threshold d0. besides, the sensing data by sensor also consumes amount of energy significantly as follows [24]. dynamic multi-hop routing protocol based on fuzzy-firefly algorithm for data similarity aware node clustering in wsns 103 esensing(l) = l×esens (3) the clustering model based on data similarity has a high correlation between the data read by each node in any cluster. therefore, ch can use data aggregation ways to bundle all highly related data into a single length-fixed packet. the consumed energy to aggregate n packet of l-bits by the cluster head is calculated by [24]: eaggre(l) = n× l×eda (4) in the network, there are k clusters in which each cluster contains of si (i = 1, 2, ..., k) regular node. in each round, a node senses l-bit packet data and transmits it to the corresponding cluster head once a frame. thus, the consumed energy by a regular node in the intra-cluster communication between the regular node i and its cluster head j is calculated by [24]: ereg−j(i, l,d) = esensing(l) + etx(l,d) (5) where d(i, j) is the distance between regular node i and ch j. on the other hand, the consumed energy by the ch in both intra-cluster and inter-cluster communication consist of five activities i.e. ch senses data, ch receives data packets, ch aggregate data packets, ch receives and transmits data packets from the other ch, and ch transmits the aggregated packets and the received packets of other ch to the bs [24]. thus, the consumed energy by a ch can be obtained using the following equation [24]. ech(i, l,d) = l(esensing(l) + eelecsi + eda (si + 1) + eelecrelay(j) + (relay(j) + 1) (eelec + eampd(i,nh))p) (6) where d(i, nh) is the distance between ch j and next hop (nh) in which nh may be ch or base station. moreover, relay(j) is the number of forwarded packets from other chs. 3.3 dynamic node clustering based on data similarity in wsns, there are several applications that require a data similarity based node clustering approach. such approach is highly related to two important issues, i.e. the spatial and temporal correlation. in the spatial correlation, the data that is sensed by adjacent nodes tend to have a high data similarity degree. meanwhile, in the temporal correlation, the data that is sensed by each node consecutively at an observed location tend to have a high correlation of its data readings. the fuzzy aggregation technique in equation 7 [9] can measure the data similarity degree considering the spatial correlation of two data a and b that is sensed by two adjacent nodes. sim(a,b) = exp −||a− b||2 2 ×η2 , sim(a,b) =∈ [0, 1] (7) where η is the constant gaussian kernel by setting η = 1.74. the data similarity degree of 1 is a highest level and 0 is a lowest level. to establish the unbalanced clusters based on the data similarity, the dsa-dmrp use two main algorithms to identify the neighbor nodes that satisfy the data similarity degree. both algorithms are embedded in each node in order to broadcast and receive the beacon message as shown in algorithm 1 and 2 [20] : 104 m. misbahuddin, a.a. putri ratna, r.f. sari 1. in each node, two simple data structures are required: (i) the spatneighbor is a vector data structure to store the information of a spatially closed neighbor nodes; and (ii) simneighbor is a vector data structure to store the information of the similar neighbor nodes. the information contains all of the three local variables, i.e., address (add), current data readings (cdr), and the number of similar neighbors (nsn ). 2. algorithm 1: beacon message, the broadcasting (line 1-3) is a main function to broadcast a beacon message periodically. this function consists of three sub functions: (i) the send sub function (line 1) transmits the information about the identifier address (add), the current data reading (cdr), and the number of similar neighbors (nsn ). (ii) the delay sub function prevents the transmissions simultaneously. (iii) the timeexpire sub function limits the broadcasting time of the nodes. 3. algorithm 2: the receiving main function is employed by the node to receive a beacon message. the message is used by any node receiver to identify whether the transmitter node includes as a similar neighbor node. 4. the beacon message of add, cdr, and nsn is utilized to identify the similar neighbor candidates (line 1-2). the data similarity degree is measured by the fuzzy aggregation technique (line 7). if its similarity degree is more than or equal to sidegree and also the similar neighbor candidate is not existed within the data structure, it is added into the data structure as one of the members of the similar neighbor (line 10). the number of similar neighbor (nsn ) is incremented (line 11). in contrast, if the similar neighbor candidate existed within the data structure, it is removed from the data structure (line 13), and the number of similar neighbor (nsn ) is decremented (line 14). algorithm 1 beacon message broadcasting() 1: send(add, cdr, nsn) 2: delay(interval + rand()) 3: timeexpire() 3.4 cluster head election and multi-hop routing dsa-dmrp forms routes that are started through selecting several cluster heads. establishing routes and selecting chs utilize k-hop clustering algorithm (khopca) [6] that consists of four rules. however, the rules proposed by the khopca do not consider how to prolong the network lifetime and overcome the hot spot problem. to address both problems, our dsa-dmrp proposed modified khopca’s rules by adding a priority factor (pf) to select the prospective common nodes as the chs. the pf is calculated via an incorporation between fuzzy model and firefly algorithm (fa) that consider two input variables, i.e. residual energy and distance to the base station. the khopca constructs the network routes using a set rules that were inspired by game of life [8]. the khopca is implemented over the networks that have been clustered. a route from a regular node to the base station is established based on a set of rules that define transition of previous state to current state of a node depending on the previous state of its similar neighbors. the state of a node is represented by a weight w ∈ [wmin,wmax]. minimum distance to a cluster head is defined as the minimum weight wmin, whilst the maximum weight wmax is the dynamic multi-hop routing protocol based on fuzzy-firefly algorithm for data similarity aware node clustering in wsns 105 algorithm 2 receiving data for node clustering receiving(add,cdr,nsn) 1: spatneighbor 2: simneighbor 3: simneighbor ← createsimneighbor(add, cdr, nsn) 4: sigma ← 1.74 5: n ← cdr 6: m ← datareading() 7: s ← exp( || n-m || 2/(2 * sigma2)) eq. 7 8: if (s ≥ sidegree) then 9: if (!isexistsimneighbor(simneighbor) then 10: addsimilarneighbor(simneighbor) 11: increnumsimilarneighbor() 12: else if ( thenisexistsimneighbor(simneighbor) 13: removesimneighbor(simneighbor) 14: decrnumsimneighbor() 15: end if 16: end if maximum distance to a cluster head. the khopca has four rules to establish the network route as follows [6]: 1. if node i with wight wi has a neighbor node that has the highest weight wn where wn > wi, ∀n ∈ ln(i) of its all neighbor nodes, and ln(i) is the list of i similar neighbors, the node i changes its weight to wi = wn 1. 2. if node i has no a similar neighbor with a higher weight, where wi 6= wmax, wi ≥ wn and ∀wn ∈ w(ls(i)), the node i decreases its weight to wi = wi 1 3. if weight of the node i is wi = wmin and also wi = wn where wn ∈ w(ls(i)), the node i adjusts its weight to wi ← wmax and states itself as ch. in this case, none of its similar neighbors has a higher weight than wmin. 4. if weight of node i is wi = wmax and weight of one of its neighbor nodes has also weight wn = wmax, where ∃ wn ∈ w(ls(i)), the node n decreases its weight to wn = wn 1. in this case, there are two chs in the same cluster. although those four rules are simple, they can construct all nodes in the network to create a multi-hop routing. the first rule aims to form a top-down hierarchical structure through adjusting its weight with a difference-one of the highest weight node existing in the list of similar neighbors. the second rule intends to avoid the less weighted nodes that are most likely to quit from a cluster. thus, the higher weighted nodes that the ch attract at the surrounding nodes will merge into its cluster in order to a fragmented cluster. the third rule describes that a node declares itself as a ch if all similar neighbors have a minimum weight. this situation shows that the isolated nodes are chosen as ch on the minimum weight level. the fourth rule overcomes a situation where there are two chs in the same cluster. therefore, one of them must survive as a ch, while other node must be a follower node of the ch. the weakness of the rules is that it has not considered how to prolong the network lifetime. therefore, the third rule determines a node to be a ch. the fourth rule defined itself as a ch. 106 m. misbahuddin, a.a. putri ratna, r.f. sari both rules are modified by adding a priority factor that considers the residual energy and the distance to the bs as follows. 3. if weight of the node i is wi = wmin, wi = wn where wn ∈ w(ls(i)), and also its ph is highest in the same cluster, the node i adjusts its weight to wi ← wmax and states itself as ch. in this case, none of its similar neighbors has a higher weight than wmin. 4. if weight of node i is wi = wmax, and weight of one of its neighbor nodes has also weight wn = wmax, where ∃ wn ∈ w(ls(i)), as well as its ph is highest in the same cluster, the node n decreases its weight to wn = wn 1. in this case, there are two chs in the same cluster. priority factor of cluster head selection one of criteria to select prospective regular node as ch is the priority factor as shown in third and fourth rule of modified khopca’s rules. the priority factor (pf) is calculated using a incorporation between fuzzy logic and firefly algorithm (fa). the pf is obtained through a procedure that consists of four steps: normalization, fuzzification, inference engine, and defuzzification as shown in figure 2. to obtain the proper selection of fuzzy rule in inference process, firefly algorithm is used to optimize the the tsukamoto fuzzy rule base table. as input variable of the fuzzy logic, our proposed dsa-dmrp considers two variables, i.e. the residual energy er(n) and the distance to the bs dbs(n). fuzzification defuzzification inference engine optimized tsukamoto fuzzy rules firefly algorithmnetwork simulation for dsa-dmrp normalization er(n) dbs(n) pf(n) figure 2: procedure of priority factor to select cluster head before fuzzification that maps crisp input to membership degree via membership function, the first step normalizes both input variables er(n) and dbs (n) into range of [0, 1]. this step is performed to avoid the difference of range value in each cluster. normalization of both input variables use a general formula as follows: dn(n) = d(n) −min(d) max(d) −min(d) (8) where dn(n) is normalized value and d(n) is real values of input variable d for node n. the input variable d can be applied for both input variables er and dbs. the maximum real value of d is defined as max(d) and the minimum real value of d is represented as min(d). in the second step, the fuzzifier maps normalized crisp values d to the membership functions to convert to be the linguistic fuzzy values. in this study, our proposed fuzzy model uses five membership functions that are symbolized with very low (vlow), low, medium, and high for both input variables as shown in equation 9 up to 13 respectively. dynamic multi-hop routing protocol based on fuzzy-firefly algorithm for data similarity aware node clustering in wsns 107 yv low =   1 if x ≤ 0.1 0.3−x 0.3−0.1 if 0.1 ≥ x ≤ 0.3 0 if x ≥ 0.3 (9) ylow =   0 if x ≤ 0.1 or x ≥ 0.5 x−0.3 0.3−0.1 if 0.1 ≥ x ≤ 0.3 0.5−x 0.5−0.3 if 0.3 ≥ x ≤ 0.5 (10) ymadium =   0 if x ≤ 0.3 or x ≥ 0.7 x−0.5 0.5−0.3 if 0.3 ≥ x ≤ 0.5 0.7−x 0.7−0.5 if 0.5 ≥ x ≤ 0.7 (11) yhigh =   0 if x ≤ 0.5 or x ≥ 0.9 x−0.7 0.7−0.5 if 0.5 ≥ x ≤ 0.7 0.9−x 0.9−0.7 if 0.7 ≥ x ≤ 0.9 (12) yhigh =   0 if x ≤ 0.7 0.9−x 0.9−0.7 if 0.7 ≥ x ≤ 0.9 1 if x ≥ 0.9 (13) likewise, the defuzzifier use seven membership functions i.e. very small (vsmall), small, rather small (rsmall), medium, rather large (rlarge), large, and very large (vlarge) as shown in equation 14 up to 20 respectively to obtain the fuzzy output. pfv small =   1 ifx ≤ 0.05 0.2−x 0.2−0.05 if 0.05 ≥ x ≤ 0.2 0 if x ≥ 0.2 (14) pfsmall =   0 if x ≤ 0.05 or x ≥ 0.35 x−0.2 0.2−0.05 if 0.05 ≥ x ≤ 0.2 0.35−x 0.35−0.2 if 0.2 ≥ x ≤ 0.35 (15) pfrsmall =   0 if x ≤ 0.05 or x ≥ 0.35 x−0.35 0.35−0.2 if 0.2 ≥ x ≤ 0.35 0.5−x 0.5−0.35 if 0.35 ≥ x ≤ 0.5 (16) pfmadium =   0 if x ≤ 0.35 or x ≥ 0.65 x−0.5 0.5−0.35 if 0.35 ≥ x ≤ 0.5 0.65−x 0.65−0.5 if 0.5 ≥ x ≤ 0.65 (17) pfrlarge =   0 if x ≤ 0.5 or x ≥ 0.8 x−0.65 0.65−0.5 if 0.5 ≥ x ≤ 0.65 0.8−x 0.8−0.65 if 0.65 ≥ x ≤ 0.8 (18) pflarge =   0 if x ≤ 0.65 or x ≥ 0.95 x−0.8 0.8−0.65 if 0.65 ≥ x ≤ 0.8 0.95−x 0.95−0.8 if 0.8 ≥ x ≤ 0.95 (19) 108 m. misbahuddin, a.a. putri ratna, r.f. sari pfv large =   0 if x ≤ 0.8 0.95−x 0.95−0.8 if 0.8 ≥ x ≤ 0.95 1 if x ≥ 0.95 (20) in third step, the inference engine performs a fuzzy reasoning against the crisp input in the fuzzy rule base table containing n rules. in our study, we use tsukamoto fuzzy system [23] with two inputs and an output. the typical rules of tsukamoto uses and-based fuzzy rule base table that are represented as if-then as shown in equation (21) as follows: if in1 = a and in2 = b then out = c (21) where a and b are the membership degree of the corresponding input membership functions and c is min(a, b). in final step, all outputs of the fired rules are aggregated and converted to be a single-crisp output value. to obtain the single-scrip output value as value of the priority factor, our fuzzy model uses the average-weighted tsukamoto defuzzification model [23]. the priority factor pf(n) can be formulated in equation (22) as follows: pf(n) = σ25i=1 µi×ci σ25i=1 µi (22) where µi is min(µer, µdbs ) to corresponding membership functions within the fuzzy rule i. also, c i is the output of corresponding membership function in rule i. optimization of and-based fuzzy rule via firefly algorithm the inference engine of fuzzy system has usually many rules. the selection of fuzzy rule requires a proper method to obtain the best performance of the fuzzy system. in our fuzzy model, there are two input variabels in which each input variable has five membership functions. these mean that the number of rules is 5 x 5 = 25. because the output fuzzy has seven member functions, the number of output alternatives of the 25 rules is 725. the output alternatives of 725 become an np-hard problem to find the best solution in turning of the fuzzy rule base table. the np-hard problem can be addressed a fuzzy system that uses firefly algorithm (fa) to optimize the tsukamoto fuzzy rule base table in order to prolong the network lifetime based data similarity aware node clustering. fa is a population-based swarm intelligent search algorithm [11]. each individual firefly in population has a role as a candidate solution in the search space. each firefly moves toward a new position. the new position represents a better candidate solution. finally, they find the best solution. the movement is represented by their attractiveness. the attractiveness is proportional to the intensity of the emitted light by adjacent fireflies. the better solution is usually measured by the fitness value. let xi be the ith firefly in the population, where i = 1, 2, ..., n and n is the population size. the attractiveness β with the euclidian distance rij between two adjacent fireflies xi and xj can be computed using the equation (23) as follows [27]: β(rij) = βmin + (β0 −βmin)e−γr 2 ij (23) rij = ||xi −xj|| = √√√√ d∑ k=1 (rik − rjk)2 (24) dynamic multi-hop routing protocol based on fuzzy-firefly algorithm for data similarity aware node clustering in wsns 109 where d is the problem dimension with k = 1, 2, . . . , d. the parameter β0 indicates the fireflies’ attractiveness at the distance r = 0, and γ is the light absorption coefficient. βmin is the minimum value of β as shown in equation 23. the attractiveness of β is limited in the range of [βmin, β0]. each firefly x i is compared with all other fireflies xj , where j = 1 , 2 , . . . , n and . if xj is brighter than xi, xi will be attracted to and move toward xj. the movement of the firefly xi that is attracted toward the firefly xj can be calculated by [27]: xik(t + 1) = xik(t) + β −γr2ij + (xik −xjk + α(t)siεi (25) α(t + 1) = α(t)(1/9000) 1 t (26) where εi is a uniformly distributed random value in the range of [-0.5, 0.5] and parameter α is the dynamically updated step factor using equation (26) and si is the length of scale of each designed variable. in algorithm 3 [28], the fa starts to optimize the and-based fuzzy rules through generating randomly the population of n fireflies. since there are 25 controllable parameters in fuzzy rules as mentioned previously, the length of feasible solution is a string of 25. each value of feasible solution contains number 1 to 7 representing seven output member functions. algorithm 3 fa to optimize the and-based fuzzy rules input: population: n ; dimension d; iterator time: t output: global best firefly’s brightness x(t) t ← 1 (initialization) initialize all fireflies brightness xik while t ≤ t do update the parameter α according to eq. 26 for i=1:n do for j=1:n do if j 6= i then compute the attractiveness of β according to eq. 23 if f(xj(t) < f(xi(t)) then move xi(t) toward xj(t) according to eq. 25 f(xi(t)) ← evaluate fitness of firefly according to eq. 27 t ← t + 1 end if end if end for end for end while before conducting the iteration process, the fitness value of each feasible solution is computed using f(xi) fitness function. the optimal solution will be obtained when the iteration reaches the maximum iteration time m. in each iteration, the parameter is updated firstly using equation (26). next, the attractiveness β between two fireflies xi and xj is calculated according to equation (23), where j 6= i. the movement of the xi firefly towards the xj firefly using equation (25) is processed if the fitness value of xj firefly is better than that of xi firefly. 110 m. misbahuddin, a.a. putri ratna, r.f. sari to evaluate each feasible solution, the fa requires a fitness function f(xi). the feasible solutions are taken from the corresponding tsukamoto fuzzy rules to be extracted and simulated in the network using network simulation of dsa-dmrp. the best solution will be obatined if all fitness within same approaching value. the fitness is computed using three parameters, i.e. the number of rounds when the first node dies (fnd), the number of rounds in which half of nodes are dead (hnd), and the number of rounds until the last node dies (lnd). the network lifetime is measured using the three parameters. the equation (27) is the fitness function and its constrains, which are used to maximized the network lifetime formulated as follows [29]: maximize: fitness = w1fnd + w2hnd + w3lnd (27) address to 0 ≤ wj ≤ 1, 3∑ j=1 wj = 1 (28) where wj with j = (1, 2, 3) are the weights to determine the important objective of parameters of fnd, hnd, and lnd. 4 performance evaluation this study is a experimental research using a a ns-3 network simulator version 3.25. the performance of our proposed dsa-dmrp was evaluated using term of the network lifetime of fnd, hnd, and lnd, as well as the round history of alive nodes in various data similarity degrees of the cluster. furthermore, the dsa-dmrp was compared agianst the khopca with exactly the same scenario. 4.1 simulation scenario the experimental data used to simulate the both protocols utilized the humidity readings gathered by the intel berkeley research lab [5]. the data was collected from 54 sensor nodes deployed in a 640m x 480m sized network. before the simulation is executed, there are some the network parameters and setting parameters of firefly algorithm that need to be set as shown in table 1 and table 2 respectively. table 1: simulation parameters of networks parameter value data similarity degree (sidegree) 0.7 to 0.9 gaussian kernel constant η 1.74 initial energy 0.5 joule eelec 50 nj/bit ε fs 100 pj/bitt/m2 ε amp 0.03 pj/bit/m2 data packet size 4000 bit dynamic multi-hop routing protocol based on fuzzy-firefly algorithm for data similarity aware node clustering in wsns 111 table 3: comparison between khopca and proposed dsa-dmrp in term of the network lifetime and in various data similarity degrees (sidegree) sidegree khopca dsa-dmrp fnd hnd lnd fnd hnd lnd 0.7 206 1393 1421 543 1489 1575 0.8 134 1368 1391 316 1420 1477 0.9 240 1380 1452 316 1368 1470 table 2: setting parameters of firefly algorithm parameter value maximum iterations time (t) 100 the number firefly in population (n) 30 β 0 1 β min 0.2 ε amp 0.005 dimension of population (d) 25 4.2 experimental results table 3 shows the results obtained in the comparison between khopca and our proposed dsa-dmrp in term of fnd, hnd, and lnd. in this experiment, there are three weights to set the fitness function i.e. w1=0.8, w2=0.2 and w3=0. moreover, the node clustering based on data similarity was established through three experiments with the data similarity degree (sidegree) 0.7, 0.8, and 0.9. the dsa-dmrp was compared against the khopca to justify the performance. it shows that the dsa-dmrp can reach a longer network lifetime than the khopca in all conditions due to an addition of the priority factor in the khopca’s rules. however, the difference between fnd and hnd is highly significant both in the khopca and the dsa-dmrp because the unequal sized cluster is not designed specially to overcome the hot spot problem for chs close to bs but actually, it is designed in other to satisfy the requirement of the multi-hop routing protocol for the dynamic node clustering based on the data similarity of their neighbors. figure 3 shows three round histories of alive nodes versus rounds for each routing protocol with sidegree 0.7, 0.8 and 0.9 respectively. it clearly shows that the dsa-dmrp and the khopca have an approximately same stability of alive nodes. both protocols show that most of nodes died simultaneously in term of lnd. however, the dsa-dmrp is longer in all terms fnd, hnd, and lnd. therefore, the dsa-dmrp can extend the network lifetime in a relatively significant manner. the stability of alive nodes in both protocols are caused by the stability of khopca’s rules in selecting the chs. meanwhile, the network lifetime in dsa-dmrp that longer than the khopca is caused by adding the factor priority in selecting chs. in order to justify the stability of alive nodes in each protocol for three data similarity degrees, we compare them in variable the data similarity degree for each protocol. figure 4 shows the round history of alive nodes for the khopca and the dsa-dmrp. the khopca clearly shows the data similarity degree of 0.7 and 0.8 are more stable than at of 0.9. whereas, the dsa-dmrp indicates that the data similarity degree of 0.8 and 0.9 is more stable than those of 0.7. however, figure 4 shows an inverse phenomenon between both the alive node stability graphs. the phenomenon of the khopca protocol shows the graphs of similarity degrees of 0.7 and 0.8 that are more stabile than the similarity degree of 0.9. on the other hand, in the phenomenon 112 m. misbahuddin, a.a. putri ratna, r.f. sari 0 500 1000 1500 0 1 0 2 0 3 0 4 0 5 0 rounds n u m b e r o f a li v e n o d e s dsa−dmrp, sidegree=0.7 khopca, sidegree=0.7 0 500 1000 1500 0 1 0 2 0 3 0 4 0 5 0 rounds n u m b e r o f a li v e n o d e s dsa−dmrp, sidegree=0.8 khopca, sidegree=0.8 0 500 1000 1500 0 1 0 2 0 3 0 4 0 5 0 rounds n u m b e r o f a li v e n o d e s dsa−dmrp, sidegree=0.9 khopca, sidegree=0.9 figure 3: relation between the number of alive nodes and rounds in sidegree = 0.7, 0.8, and 0.9 dynamic multi-hop routing protocol based on fuzzy-firefly algorithm for data similarity aware node clustering in wsns 113 0 500 1000 1500 0 10 20 30 40 50 rounds n u m b e r o f a li v e n o d e s sidegree=0.7 sidegree=0.8 sidegree=0.9 0 500 1000 1500 0 10 20 30 40 50 rounds n u m b e r o f a li v e n o d e s sidegree=0.7 sidegree=0.8 sidegree=0.9 figure 4: comparation between the data similarity degree in the alive nodes vs the rounds for the khopca and the dsa-dmrp of dsa-dmrp protocol, the similarity degrees of 0.8 and 0.9 are more stable than the similarity degree of 0.7. moreover, in the rounds before reaching 1000 of the khopca protocol, the number of formed clusters in the similarity degrees of 0.7, 0.8, and 0.9 are almost same. thus, more and more cluster heads in the similarity degree of 0.9, more and more the probabilities of the nodes will be dead because the cluster heads more consume the energy than those of the member nodes. on the contrary, the rounds before reaching 1000 of the dsa-dmrp, the number of formed clusters is almost same the number of formed clusters in the khopca protocol. however, before reaching 1000 rounds, the dsa-dmrp has less the number of cluster heads in the similarity degrees of 0.8 and 0.9 than those of the similarity degree of 0.7. finally, the problems in this case are caused by the elected cluster heads that are not only effected by the similarity degree but also affected by the residual energy and the distance to the base station. 5 conclusions our proposed dsa-dmrp is a dynamic multi-hop routing protocol using unequal sized clustering approach. this protocol is based on the modified khopca rules by adding a priority factor. the priority factor is a parameter for selecting the chs in the network that consider the residual energy and distance to the bs. the fuzzy aggregation technique is used to measure the data similarity degree of adjacent nodes. the dsa-dmrp was compared against the khopca to justify the performance. the dsadmrp and the khopca have an approximately same stability of alive nodes. however, the dsa-dmrp can reach a longer the network lifetime than the khopca in all terms of fnd, hnd, and lnd. therefore, the dsa-dmrp can extend the network lifetime in a relatively significant manner and can satisfy the requirement of multi-hop routing protocol for dynamic node clustering based on the data similarity of their neighbors. 114 m. misbahuddin, a.a. putri ratna, r.f. sari acknowledgement the authors gratefully acknowledge the ministry of research, technology and higher education of indonesia for supporting this work through dikti’s research grant under grant no. 1173/un2.r12/hkp.05.00/2016 bibliography [1] afsar, m. m.; tayarani, m. h. 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(2016); swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks, expert systems with applications, 55, 313-328, 2016. international journal of computers communications & control issn 1841-9836, 13(1), 62-70, february 2018. elder monitoring workflow system for independent living s. jecan, d. benta, l. rusu, r. arba sergiu jecan computer science for economics department babes-bolyai university of cluj-napoca romania, 400591 cluj-napoca, m. kogălniceanu, 1 sergiu.jecan@econ.ubbcluj.ro dan benta 1. beck et al. services cluj-napoca dan.benta@bea-services.com 2. agora university of oradea romania, 410526 oradea, piata tineretului, 8 lucia rusu* computer science for economics department babes-bolyai university of cluj-napoca romania, 400591 cluj-napoca, m. kogălniceanu, 1 *corresponding author: lucia.rusu@econ.ubbcluj.ro raluca arba dsega-german babes-bolyai university of cluj-napoca romania, 400591 cluj-napoca, m. kogălniceanu, 1 raluca.arba@econ.ubbcluj.ro abstract: this paper presents an automatic workflow framed in a gerontechnology solution, as part of the active and assisted living (aal) platform in mobile@old project. our solution aims to increase or preserve cognitive functions, to track medication and coordinate physical activity through an exercising game (exergame). the exergame is customized according to each elderly person’s reactions and specificities. the workflow involves doctors, physiotherapists, the elderly person and their caregivers, in an ecosystem designed to ensure well-being and independence. keywords: workflow management systems, gerontechnology, personalized medicine, control. 1 introduction elderly population is increasing around the world and technologies for this area should be more and more common. eu statistics show that by 2025 more than 20% of population will be over 65 years old, and a significant percent of them over 80. the european commission has committed to offer healthy and dignified ageing for older people, in order to allow them to enjoy a good quality of life and their independence, as well as to remain active in society and their families. healthy life years (hly) or "disability-free life expectancy" indicator is the number of years a person of a certain age can expect to live without disability [11,12]. hly indicator is part of the core set of european structural indicators, recognized in the lisbon strategy [11,12]. in this area we can mention several prominent research projects for elders’ well-being and independent living: aspa (activating senior potential in ageing europe); demhow (demographic change and housing wealth); lepas (long-run economic perspectives of an ageing society); maggie (major ageing and gender issues in europe); multilinks how demographic changes copyright ©2018 cc by-nc elder monitoring workflow system for independent living 63 shape intergenerational solidarity, well-being and social integration: a multilinks framework ; sharelife employment and health at 50+: a life history approach to european welfare state interventions; sprew generational approach to the social patterns of relation to work; recwowe reconciling work and welfare in europe [6]. following standard assumptions, we take an elderly person to be defined as being at least 65 years old. most elderly people are retired and are affected by several chronical conditions, most commonly related heart problems, mobility and mental deficiencies. clinical studies show that the elderly’s cognitive abilities are affected by various degrees, ranging from simple forms of amnesia, mild cognitive impairment (mci) or mild to severe age-associated memory impairment (aami), to mild or severe dementia and alzheimer [1, 7]. for these reasons, the active and assisted living joint program (aal jp) encourages several research programs for assistive technology and ubiquitous computing in order to offer several e-heath or m-heath solutions for independent living [6]. in recent years, gerontechnology has developed a symbiosis between gerontology and technology, an interdisciplinary research into technology for an ageing society, for improving the quality of life of elderly individuals [1]. elderly monitoring activities can be assimilated with a workflow due to the repetitive character of the steps to be followed as well as the daily activities specific to the elderly. for this reason we offer a gerontechnology solutions based on an automatic, as a module of aal platform mobile@old. workflow based on to elderly personalized treatment, track medication and feedback for increasing or preserving cognitive function, physical activity focused on personalized exercises and wellbeing. section 2 describes software and aal solutions for elderly people, used as assistive technologies in the rehabilitation domain. workflow description and business process analysis is given in section 3. section 4 presents a usability elders’ test of the proposed workflow. conclusions and future work are discussed in the last section. 2 related work ambient assistive living technology (alt) improves the elderly’s lives, especially those with numerous conditions as a barrier to quality of life. the two major barriers are cognitive and physical in nature. physical barriers consist of loss of physical function (lack of mobility, hemiparesis, paresis or failing eyesight and hearing) and cognitive barriers consist of loss of cognitive function, starting with amnesia, aami, mci, evolving to severe mci, dementia or alzheimer [7,8]. for these reasons, for most elderly people, drug therapy is part of their everyday life. many important mobile applications are focused on medication management and reminders of daily activities or medical tests and appointments in order to offer personalized medicine based on companion diagnostic, monitoring and disease surveillance [9]. mymeds is an application available for android and ios devices, as well as on the web. it manages medication by sending daily reminders by text, email or push notification. the application shows medication that should be taken and its use, tracks the elderly’s medication, saves and analyzes personal history. mymeds has notifications on refilling prescriptions and suggests the best price for each upcoming prescription. care4today is another mobile health manager for seniors in both assisted living and independent living. it manages medication with reminders, tracking and connectivity. through a user-friendly interface, it offers medication reminder tiles with information on timing, dosage, adherence tracking and dates for prescription refills. each drug can be selected from a medication database built into the application. the tracking of medication schedule provides reports for every type of medication. care4today can share these records with doctors directly from the screen and can connect with healthcare providers and caregivers [10]. 64 s. jecan, d. benta, l. rusu, r. arba there are also several applications for failing eyesight and hearing. dragon dictation is a free application that offers the possibility to dictate text and then send it as an email message. in the same manner, caregivers or seniors can dictate reminders to them and post on facebook and twitter. vizwiz is designed for partially sighted elderly people, to help them use their phone for taking photos, ask questions and get spoken answers. read2go provides a choice of font size and settings and offers several functions as an e-book reader: browse, search and download books [10]. if the elderly accept a physical activity based on exergames they get a relaxing daily fun and self-motivation [4]. exergames use remote hand held controllers, motion sensors for capturing and monitoring body movements. some of the exergames that we could mention: nintendo wii, playstation move, dance dance revolution and xbox kinect [14]. 3 workflow automations for elders monitoring 3.1 business process in exergame elders monitoring physiotherapy for the elderly is an essential component of their well-being. its main goal is to preserve balance while walking and maintain core physical gestures that allow them to perform basic daily activities on their own eating, drinking, getting (un)dressed, washing, combing, shaving, walking etc. the loss of any one of these abilities leads to loss of independence. most common causes for loss of physical abilities are neurological conditions (paraplegia, hemiplegia, parkinson), psychopathy (aami, mdi, alzheimer, dementia), conditions affecting the locomotors system. in many of these cases, as well as in the case of seniors who do not suffer from any serious conditions, it is recommended to have occupational therapy, which focuses on daily activities to increase mobility and muscle tonus, as well as improve state of mind and reduce any form of psychopathy [5]. the main participants involved as users are: the elder defined as a person who has over 65 years and sometimes carers as supervisors that receive notifications from application. carers cooperate with elderly and will be alerted when health conditions decrease or new health worrying symptoms appear. carers can be husband, wife, children, other relatives or friends. another participant is the doctor, with a key role in the elderly’s examination and monitoring. all messages, alerts, health monitoring parameters, daily habits, and time schedule for medication are set by the doctor and all physical exercises or exergames are set by the physiotherapist (kinetotherapist). both are main actors in mobile@old platform and coordinate automations of the elderly person’s communication, in order to ensure their well-being [2,3]. since most elderly suffer from various diseases and health conditions, a preliminary evaluation is necessary, carried out by one or several medical specialists (step 1, figure 1). the physicians fill the module recommendation with information on the patient’s health, chronic conditions, necessary medication, recommended behavior and lifestyle, as well as the module restrictions, if applicable. they also determine the frequency of periodical evaluations. in this case the senior person will repeat step 1 through step 4 whenever necessary (figure 1). all the data is handled in senior database. the data concerning the patient’s health is updated daily/weekly/periodically according to the relevant parameters. as a result of the reservations expressed by our participants, the module physical activity trainer (pat) was conceived in two different versions. for each exercise in exercise database we store several specific fields like: exercise category, difficulty level, exercise name, relevant joints for the exercise, repeating time, exercise description [2,3]. if the elders refuse to join exergame program, they must follow medical recommendations. in this case, the person is not a user in physical activity trainer (pat), but if she changes her mind later all medical examinations can be used by the physiotherapist elder monitoring workflow system for independent living 65 figure 1: elder monitoring workflow to prepare a personalized exergame. if the elderly person does not use kinect and interaction with physiotherapist is done only by tablet and smart mobile, the physiotherapist records personalized exercises as a videogame in reference exercises [2,5]. for elderly person who accepts kinect, pat implementation offers several exergames as video serious game, designed in a customized manner, depending on the restrictions imposed by the chronic elderly. every exergame has two avatars: one for user and one for trainer. physiotherapist’s recommendation takes into account personal elder profile, which includes diseases information, medical recommendation, sex, age, aso. all type of physical exercises is personalized, depending on the restrictions imposed by the chronic disease of the elderly. physiotherapists will register a set of exercises with gradually levels of difficulty. all monitoring processes and elders’ performance are directed by procedures associated to kinect parameters which are recorded in exercise database [3]. 3.2 elders monitoring workflow this workflow ensures automatic monitoring for pat and vital sign monitoring (vsm) module as a part of mobile@old platform. daily analyzes and health parameters (systolic and diastolic blood pressure, pulse, glucose value, breathing rate) are repetitive activities that can be easily coordinated by a workflow. caregiver intervention is justified only when they exceed normal limits, in which case the designed workflow alerts the physician and caregiver. to monitor physical exercise for the elderly who does not accept kinect as an automated solution, the proposed workflow automates this process. daily analyzes and health parameters (systolic and diastolic blood pressure, pulse, glucose value, breathing rate) are repetitive activities that can be easily coordinated by a workflow. caregiver intervention is warranted only when they exceed normal limits, in which case the projected workflow alerts the doctor and caregiver. technical tools for workflow development was provided by jobrouter ag, consist on cloud storage with jobtable, jobsub, jobpdf, jobselect and jobarchive modules. the designed workflow was tested in several browsers like: firefox 47.0.1, google chrome version 51.0.2704.106 66 s. jecan, d. benta, l. rusu, r. arba m (64-bit), safari 5.1.7 (7534.57.2), opera 38.0 version 38.0.2220.41 and internet explorer 11 version 11.0.9600.17416. all tests passed with success. designed workflow involves the following steps (figure 1): (1) examination and registration medical tests are a start step. the designated doctor (doctor) fills the form with basic information on the patient, common medical measures (blood pressure, heart rate), dates for various medical tests, information on whether these have been taken before or after meals, and optionally, details on other conditions or restrictions due to any chronic disease. files documenting additional tests can be added. the patient can decide whether he wants to start exergame or not. doctor, a specialized professional, has the rights to determine the senior patient’s examination, to indicate or change medication, to access for add, modify or delete the elderly’s records and/or fields in several tables, such as: daily_habit, elder_disease, disease_type, generic_drug, drug, normal_analysis, and schedule for lab analyse. doctor decides time management for the medication: before or after eating, how long before lunch, 3/2 times per day (morning, lunch, evening), weekdays or even afternoon table, indication and contraindication for exercise and daily activities at home, and also follows and decides prescriptions based on analysis/ medication administration history table. this information serves as a guide for physical therapist in the exergame individual plan [3,13]. (2) generate pdf test results is a system step processed by jobpdf module. based on a previously designed template, a pdf version of the form is generated. (3) archiving results analysis is a system step processed by jobarchive module, archive action. the previously mentioned pdf is archived. it can be accessed by doctors or elderly patients for monitoring and disease surveillance. (4) attach documents to test results is a system step processed by jobarchive module, clip action. it allows the addition of test results to the form archived in step 3. (5) send email with test results is next system step processed by send e-mail automatic procedure, with a document that contains the results of medical tests. if the patient does not wish to continue the exergame course of treatment, he receives an email confirmation of this decision. (6) generate exergame plan is a user step directed by the physical therapist. if the patient accepts the exergame program, the designated physiotherapist receives the test results and a request to design an appropriate course of treatment. he must then fill the corresponding form with the following information: name of the exercise, number of repetitions (5/10/20 ...), and frequency (once, twice or three times a day). optionally, a video demonstrating the exercises can be added. it should be mentioned that one and the same video may come with different indications (for example, different number of repetitions), depending on the elderly’s patients physical abilities and health condition [2,3,5]. (7) generate pdf exergame plan is a system step processed by jobpdf module. based on a previously designed template, a pdf corresponding to the course of treatment is generated. (8) archiving exergame plan is a system step processed by jobarchive module, archive action. the pdf is archived. (9) attach videos to exergame plan is a system step processed by jobarchive module, clip action. videos corresponding to each exercise are attached to the form archived in step 3. elder monitoring workflow system for independent living 67 (10) send e-mail with exergame plan is a system step processed by send e-mail automatic procedure. after the completion of the treatment plan, the patient and/or the caregiver receive an e-mail with the treatment plan and corresponding test results. system steps of all types (jobpdf, jobarchive or send e-mail) are automatically processed by the system. if further needs will arise and multiple doctors and physiotherapist will be involved for a single elder, we can implement parallelization steps as a beginning and ending sub-process. for this case we used only user steps (directed by doctor, physiotherapist and elder) and system steps (7-10). the physician or physical therapist (as workflow users) can access periodic reports detailing the evolution of the medical condition of the elderly included in the aal program, the accepted or refused personalized exercise program as well as the performance and evolution of each elderly person. monitoring exergame program, tests, performance and evolution are directed by the pat module which involves kinetics and other wearable technologies and is detailed in other papers [5,13]. moreover, if a patient is transferred from one doctor to another, the last one will have access to a full patient medical history (figure 2). figure 2: gui for personal information, examination and exercise plan 4 experimental results we offer the doctor same level of importance as the patient on the platform, based on the analysis of response related to health issues. needs analysis started with a questionnaire with 61 items, that was applied to 69 persons, elderly people between 60 and 87 years (median 67,4), with chronic or severe diseases, and several of them (12% 8) with disabilities. these results (figure 3) showed that most of the elders suffer from at least one chronic disease (95%), or two 68 s. jecan, d. benta, l. rusu, r. arba chronic illnesses (62%) even 3 chronic diseases (47%) and some of them have impairments or disabilities [3,5]. figure 3: feedback from elders out of the elderly that participated in our study, only 8 out of 69 (12%) accepted to use the proposed workflow as an automatic solution to monitor their health parameters and exergames, and accepted to use the mobile application made available to them. only 4 out of 69 (6%) accepted monitoring exergames via kinect. the target audience has coronary heart disease (7), myopia (6), hypertension (5), hypermetropia (4), aami (4), osteoporosis (4), diabetes (3), mci (3), thyroid disorders (2), overweight (3) or angina (2). only two of them have disabilities: hemiparesis. all of them are computer literate users (clus) and have a healthy life style. elders accepted our proposal about workflow and usability evaluation [3,5,13]. on the other site, we invited several clus: doctors (5) physiotherapists (3) and caregivers (6) to use our workflow and evaluate usability based on a questionnaire with 15 items. they interacted with workflow as users and with elders as patients. in fact, everyone has a user role in our proposed workflow. we presented several relevant results related to the elderly because all the others (doctors, physiotherapists and caregivers) gave only answers that were strongly agree or agree. seniors find the system useful (q4), learn to use it quickly (q3) and like it (q5). only few of them dislike the interface (q1), not recommended it to others and are neutral to performing tasks with the system (q2). figure 3 shows details about 6 of 15 answers, the rest included open questions (4) related to clu (3) and wearable technologies (2). 5 conclusions this paper presents an automatic workflow for monitoring of the elderly. we focused on two major problems of third age: monitoring health parameters and chronically disease and monitoring exercise plans. we discussed three major participants in the workflow system: (i) the doctor, who coordinates periodic medical examinations and personalized treatment, (ii) the physiotherapist, who follow doctor’s indications and contraindications and coordinate exercise plans and daily activities at home and (iii) the elder person, who is the core beneficiary. all steps are only user steps conducted by the doctor, physiotherapist and the senior user or system steps. system steps use several database tables managed by mobile@old platform and generate several documents, such as medical examination reports, medication schedule or exergame plan. all actors involved in the workflow testing are computer literate users (clus) and have accepted our questionnaire related to system usability. moreover, the consulted seniors have a healthy life style and find our solution useful and easy to learn. a real benefit seems to be elder monitoring workflow system for independent living 69 for doctors, physiotherapists and caregivers, who appreciated asynchronous communication and message automation. as future improvement, we intend to develop new functionalities and to adapt the application to new requirements in order to best fit elders’ needs in this area. acknowledgment this research was supported by the executive unit for financing higher education, research and development and innovation through the partnership program, the project "mobility pattern assistant for elderly people", project number pn-ii-pt-pcca-2013-4-2241. we gratefully acknowledge the contribution of colleagues from beck et al. services srl cluj-napoca (ro) and jobrouterž ag mannheim (de). bibliography [1] hyry, j. (2015); designing projected user interfaces as assistive technology for the elderly, acta universitatis ouluensis, a scientiae rerum naturalium, 664, 2015. [2] lohan, e.s.; cramariuc, o.; malicki, l.; brencic, n.s.; cramariuc, b. (2015); analytic hierarchy process for assessing e-health technologies for elderly indoor mobility analysis, mobihealth’15 proceedings of the 5th eai international conference on wireless mobile communication and healthcare, great britain october 14 16, 2015, pp. 54-57. [3] rusu, l.; mocanu, i.; jecanm s., sitar, d. (2016); monitoring adaptive exergame for seniors, journal of information systems & operations management, 10(2), 2016. [4] ying-yu, c. (2015); exergaming: therapeutic benefits in older adults, http://lermagazine.com/article/exergaming-therapeutic-benefits-in-older-adults, accessed january 2016. [5] zdrenghea, d.; ile, m.; zdrenghea, m.; sitar-taut, a-v., pop, d. (2014); the effects of maximal and submaximal exercise testing on nt-probnp levels in patients with systolic heart failure, romanian review of laboratory medicine, 22(1), 25-33, doi: 10.2478/rrlm2014-0008, march 2014, http://www.degruyter.com/view/j/rrlm.2014.22.issue-1/rrlm-20140008/rrlm-2014-0008.xml?format=int. [6] ambient assisted living programme; http://www.aal-europe.eu, accessed august 2017. [7] alzheimer’s & dementia association; http://www.alz.org/ dementia /mild-cognitiveimpairment-mci.asp, accessed august 2017. [8] the active and assisted living joint programme (aal jp); https://ec.europa.eu/digitalsingle-market/en/active-and-assisted-living-joint-programme-aal-jp, accessed august 2017. [9] president’s council of advisors on science and technology (2008); priorities for personalized medicine, 2008. [10] top app for the elderly (2014); https://myageingparent.com/technology/communication/topipad-apps-for-the-elderly/, accessed august 2017. 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[14] xbox.com; xbox kinect ;www.xbox.com/kinect, accessed march 2016. international journal of computers communications & control issn 1841-9836, 12(2):201-216, april 2017. a singleton type-1 fuzzy logic controller for on-line error compensation during robotic welding i. davila-rios, i. lopez-juarez, g.m. mendez, r. osorio-comparan, g. lefranc, c. cubillos ignacio davila comimsa posgrado interinstitucional en ciencia y tecnologia (picyt) ciencia y tecnologia no 790 col. saltillo 400, c.p. 25290 saltillo, coahuila. mexico. idavila@comimsa.com ismael lopez-juarez* robotics and advanced manufacturing group centro de investigacion y de estudios avanzados del ipn (cinvestav) av. ind. metalurgica 1062, p. ind. saltillo-ramos arizpe, c.p. 25900 ramos arizpe, coahuila, mexico *corresponding author: ismael.lopez@cinvestav.mx gerardo maximiliano mendez instituto tecnologico de nuevo leon (itnl) av. eloy cavazos 2001 col. tolteca, c.p. 67175 guadalupe, nuevo leon, mexico gerardo.maximiliano.mendez@gmail.com roman osorio-comparan instituto de investigaciones en matematicas aplicadas y sistemas universidad nacional autonoma de mexico (unam) circuito escolar s/n, ciudad universitaria, coyoacán, c.p. 04510, mexico city roman@unam.mx gaston lefranc escuela de ingenieria electrica pontificia universidad catolica de valparaiso avda brasil 2950, valparaíso, chile 2430000 gaston.lefranc@pucv.cl claudio cubillos escuela ingenieria informatica pontificia universidad catolica de valparaiso avda brasil 2950, valparaíso, chile 2430000 claudio.cubillos@pucv.cl abstract: during robot welding operations in the manufacturing industry there is a need to modify on-line the welding path due to a mismatch in the position of the components to be welded. these positioning errors are due to multiple factors such as ageing of the components in the conveyor system, clamp fixtures, disturbances, etc. therefore, robot reprogramming is needed which requires a stop in the production line and consequently an increment in production costs. this article is an extension of [1]a and presents an alternative solution to this problem that involves the use of structured lighting using a low-cost laser beam, a cmos camera and a gaussian singleton fuzzy logic controller. to validate the proposed control system, a robotic cell was designed using an industrial kuka kr16 robot for welding metallic plates. the method was evaluated experimentally under lateral and vertical positioning errors. keywords: gas metal arc welding (gmaw), industrial robotics, artificial vision, robot path control, fuzzy logic. areprinted (partial) and extended, with permission based on license number 3947080516854 [2016] ©ieee, from "computers communications and control (icccc), 2016 6th international conference on". 1 introduction the welding process establishes an electrical arc between a continuously fed electrode and the weld pool; which is protected by a gas administered externally, hence its name gas metal arc welding (gmaw) or commonly known as metal inert gas (mig). during the process, the molten electrode is transferred to the workpiece through the electric arc and serves as the filler metal (weld bead) which is deposited accurately by and automated mechanism, e.g. by copyright © 2006-2017 by ccc publications 202 i. davila-rios, i. lopez-juarez, g.m. mendez, r. osorio-comparan, g. lefranc, c. cubillos an industrial robot. during robot welding manufacturing operations, misalignments are likely to occur. there are several reasons; it can be due to disturbances, positioning sensing errors, ageing of welding fixtures and in general errors in positioning and conveying mechanisms. it is a common practice to make some robot reprogramming to solve the problem that requires the stop of the production line with an increment in production costs. there are currently commercial solutions that can be adapted to solve these common errors, however the costs can be very high, and in the order of the price of the industrial robot itself, which may not be affordable for some small companies. in this article, we present an alternative solution to this problem that involves the use of structured lighting employing a low-cost laser beam, a cmos camera and a pc-based fuzzy controller. 1.1 related work and objective the work presented in this paper is an extended version of the originally published article in the ijccc [1]. similar work on robot control for seam tracking has been presented by graf et al. [2] who developed a trajectory-based control for seam tracking by modelling the trajectory as a continuous curve in 3d. they showed good results; however, the technique has some drawbacks in cases where the solution does not exist for correcting the orientation of the seam location or if the current robot location is close to the last location. santti et al. [3] have recently tested high performance processors to extract the dominant line from the segmented data of the trajectory. the system can achieve a line extraction speed of more than 1000 fps, which enables real-time visual seam tracking and robot control. some approaches that only use an optical sensor have been reported. liu et al. [4] have proposed to use optical filtering and the modification of the camera’s exposure time to extract the geometrical profile of the seam. some other approaches also have appeared in the literature that addresses neuro-fuzzy controllers that can be applied to robot positioning control [5]. the objective of the research presented in this paper is to correct the robot’s end effector position online to eliminate the need of reprogramming the robot. in this manner, errors due to disturbances, positioning sensing errors, ageing of welding fixtures and in general errors in positioning and conveying mechanisms are eliminated. our proposal is based on previous approaches using structure lighting (i.e. laser sensor) and image processing to quantify the misalignment and to react before the actual beam is formed by sending robot commands to reposition the arm robot so that initial offset cannot affect the next piece to be weld in a production line. the performance of the fuzzy controller is verified experimentally. the paper is structured as follows. in section 2, the test bed is explained. the image processing for detecting the part misalignment is described in section 3, whereas the design of the controller and its experimental results are explained in sections 4 and 5, respectively. finally, conclusions, current and envisaged work are given at the end of the paper in section 6. 2 test bed the following equipment composes the test bed: a kuka robot arm manipulator, gmaw welding station, wire feeder that controls the wire supply to the torch, a pc-based data acquisition system and an inert gas tank as it is depicted in figure 1 the test bed also includes a video camera and a laser sensor oppositely positioned to each other. by using this configuration, occlusions by the torch itself can be avoided. however, a perspective error occurs which is compensated by a homography matrix when using triangular singleton type-1 (t1) fuzzy logic controller (flc) and it is directly compensated without the homography when a gaussian singleton t1 flc controller is used. with our proposal using the a singleton type-1 fuzzy logic controller for on-line error compensation during robotic welding 203 figure 1: welding robotised cell. gaussian singleton t1 flc, the use of algorithms for perspective error correction and image processing of high computational costs are avoided. the welding cell is controlled by a pc-based controller. the computer has an intel xeon @ 1.86 ghz processor with 3gb ram that hosts the application interface and which also includes the following functions: serial communication. for on-line modification of the welding path. the communications are carried out at low-level between the pc and the krc2 controller using the 3964 protocol. image processing. filtering and segmentation are carried out using a cmos basler a602fc camera as input device with a spatial resolution of 656x490 pixels. voltage and current modification. the arch current and voltage is modified by daq sensoray 626 using its i/o port. different i/o signals are used to control the on/off of the welding station, the wire feeding system (including the open/close state of the gas valve), the laser sensor on/off condition, the welding travel speed and the distance from the tip of the torch to the workpiece. the robot manipulator is used in slave mode. during the operations, a robot positioning program is run in the krc2 controller that continuously search for motion commands from the pc controller in order to start an incremental motion of the robot arm. this program also controls the selection between tool and world coordinates, and the speed and motion step size during incremental motions. the positioning fuzzy controller resides in the pc controller sending the path modification commands to the robot controller during welding operations. 3 image processing during image processing some distortion can occur due to the alignment of the camera with respect to the image plane as it is shown in figure 2. a homography relates two images in perspective, where plane points from one scene are related to the second image. this relationship is valid if the scene is flat or the displacement small [6] as it is illustrated in figure 3. in this manner the homography helps to eliminate the perspective from one image and also 204 i. davila-rios, i. lopez-juarez, g.m. mendez, r. osorio-comparan, g. lefranc, c. cubillos figure 2: camera alignment figure 3: image perspective a singleton type-1 fuzzy logic controller for on-line error compensation during robotic welding 205 to facilitate the calibration. the homography map points (equation 1) from the work plane (z = 0) to the points in the image plane (equation 2). the homography components depend on the intrinsic camera parameters and its location in the space (extrinsic parameters), which are constants. x = [x,y, 1]t (1) x′ = [x′,y ′, 1]t (2) the equation that represents the homography transformation is given by equation (3), where the homography matrix h can be obtained by using four points in the calibrated image and the image in perspective [6]. |x′| = |h||x| (3) in the test bed, typical errors occur when two metallic plates to be welded are misaligned. the first step to correct the situation is to measure this misalignment using image processing tools. once the positional misalignment is quantified, the information is sent to the robot controller for compensation. the methodology consists of using a laser beam (50 mw with λ = 656nm) aimed to both plates as it is shown in figure 4. figure 4: welding methodology when the laser beam is projected onto both plates, a discontinuous line appears and it is captured by the camera sensor as a reference signal. misalignments can occur in two directions, either they can be lateral misalignments or height misalignments. in both cases, the misalignment is captured in the image. in order to accurately process this information, the image pre-processing is carried out first by smoothing the image applying a mean filter and max/min filtering to reduce noise. after the pre-processing stage, the image is segmented in the region of interest (roi) where the necessary information is found (pixel ii,j in figure 5). the main idea is to take an image of dimension kxl from the original i image with dimension ixj, so that i(kxl) < i(ixj). having a reduced image in size is also useful to speed up the processing time. the segmented roi is shown in figure 6. the gap between plate a and plate b can be observed within the rectangle formed by the dashed lines. once the gap is determined, the next step is to determine discontinuity1 and discontinuity2 from the laser pattern and captured from each plate as showed in figure 7. during normal welding operation, the robot’s torch should be located in the middle point between the plates as it is indicated (robot) in figure 8. in the case of any offset either lateral 206 i. davila-rios, i. lopez-juarez, g.m. mendez, r. osorio-comparan, g. lefranc, c. cubillos figure 5: image segmentation figure 6: roi with segmented image figure 7: discontinuity between plates a singleton type-1 fuzzy logic controller for on-line error compensation during robotic welding 207 misalignment or misalignment in the z axis direction, the point will move accordingly, hence requiring a repositioning strategy. figure 8: point without offset position in situations where the robot’s torch is not in the correct position, the situation has to be assessed first by measuring the misalignment. a corrective motion will be a distance between points union (x,y) and robot (x,y) as depicted in figure 9. the corrective motion has to be in any direction within 3d space volume, so that we proposed a fuzzy algorithm to correct it. figure 9: torch offset position 4 design of the controller in our experiments two controllers were tested. the first controller is based on the type-1 fuzzy logic control which uses triangular and trapezoidal membership functions. the second controller uses gaussian membership functions and is referred to as singleton fuzzy logic controller. according to mendel [7] membership functions characterize fuzzy sets. a type-1 (t1) fuzzy set, a which is in terms of a single variable x ∈ x, is a generalization of a crisp set. it is defined on a universe of discourse x and is characterized by membership function µa(x) that takes on values in the interval [0,1]. a membership function provides a measure of the degree of similarity of an element in x to the fuzzy set. such a set may be represented as a = {(x,µa(x))|∀x ∈ x} (4) t − 1 membership function, µa(x) is constrained to be between 0 and 1 for all x ∈ x, a gaussian membership function is specified by two parameters m,σ as follows: 208 i. davila-rios, i. lopez-juarez, g.m. mendez, r. osorio-comparan, g. lefranc, c. cubillos gaussian(x : m,σ) = exp(−(x−m) 2 σ2 ) (5) where m and σ denote the center and width of the function, respectively. the shape of the function can be controlled by adjusting the parameter σ. a small value of σ will generate “thin" membership function, while a big σ will lead to a “flat" membership function. fuzzy logic controllers (flc’s) are useful control schemes for plants having difficulties in deriving mathematical models or having performance limitations with conventional control schemes. error e and change of error e′ are the most used fuzzy input variables in most fuzzy control works, regardless of the complexity of controlled plants. also, either control input u(pd− type) or incremental control input ∆u(pi − type) is typically used as fuzzy output variable. t1 flc’s are both intuitive and numerical systems that map crisp inputs to a crisp output. every flc is associated with a set of rules with meaningful linguistic interpretations, obtained from either numerical data, or experts. based on this kind of statement, actions are combined with rules in an antecedent-consequent format, and then aggregated according to approximate reasoning theory, to produce a nonlinear mapping from input space x1 ×x2 to the output space y . 4.1 input-output relationship a fuzzy control in the general case is based on linguistic variables to handle imprecise and vague information. the information is embedded in fuzzy sets that are combined in rules to define actions to be taken as it is indicated in [8] and [9]. in our case, we need to define a set of input-output relationships in order to quantify the positional error of the robot arm with respect to the plate’s welding position. in other words, the input information for the robot is the misalignment of the robot’s end effector and the output is a robot position command to correct such misalignment. the design of the tracking system is focused on the offset compensation in automatic production lines, where the parts to be welded are moved by pallets to other welding stations and where typical positioning errors are in the range of few millimetres. these errors are likely to occur between the current workpiece and the next due to several reasons as mentioned earlier. during these circumstances, the user would normally correct the robot path by reprogramming the robot which is time consuming. our proposal is to carry out the correction on-line, using the same welding program. the idea is to modify incrementally the robot’s offset path at the starting of the welding operation should a misalignment is detected so that not reprogramming is needed. in order to modify the path a deviation measurement has to be compared to the original path, so that the robot "knows" where to move to and for how much using linguistic variables such as “right", “left", “far left", “decrease slightly", “increase greatly", etc. the linguistic variables are translated into fuzzy sets with a membership function that considers a value in the range [0, 1]. 4.2 fuzzy design the steps to be considered in the design are normalisation, fuzzification, determination of fuzzy rules, defuzzyfication, and denormalisation. the first step is accomplished by using the information given from a sensing system, which in this case is obtained from the laser beam pattern as detected by the camera. this value is read and must be normalised to the range [0,1]. the next step is to fuzzify this information to assess its membership within the fuzzy sets. in our case the variable depends on the workpiece misalignment and we should find its membership function within the following fuzzy sets: far left, left, near left, near, near right, right, and far right. a singleton type-1 fuzzy logic controller for on-line error compensation during robotic welding 209 once the input data has been normalised and fuzzified, then this is used to build up the fuzzy rules what is meant to be the control antecedent. these rules are built depending on the requirement of the system and after its evaluation their membership function is determined in relation with the output set (consequent). in order to have useful singleton real values, the data set is defuzzified to obtain a new robot coordinate which is in turn sent to the robot controller to ultimately correct the welding trajectory. if we consider the x axis to be the welding direction as depicted in figure 4, it is clear that the misalignment would come only in the y or z axis direction. considering this assumption, the control system is based on two input variables and two output variables. the operation range is defined in the interval [−10, 10] mm in the y axis and [−5, 5] mm in the z axis. the zero value is considered to be the reference value for the welding path. figure 10 shows a set of input data in the y and z axes during the evaluation tests of the controller. these values comprise the whole set of values likely to be encountered during operations. for instance, if the algorithm detects a misalignment point (10, -5) it means that the workpiece had an offset of 10mm to the right and -5mm downwards that needs to be compensated. figure 10: robotic motion range for error compensation the trapezoidal and triangular membership functions are used to design the input and output fuzzy sets as shown in the figure 11. figure 11: fuzzy sets after the degree of membership of the input values from the input fuzzy sets is defined, the antecedent is created using fuzzy rules as follows: 210 i. davila-rios, i. lopez-juarez, g.m. mendez, r. osorio-comparan, g. lefranc, c. cubillos if yref is far left then decrease greatly posrobot if yref is left then decrease posrobot if yref is near left then decrease slightly posrobot etc. ... ... ... ... ... ... during the defuzzyfication stage the required non-fuzzy real values are obtained using the centre of area (coa) method as a fuzzy conclusion “y is a". this can be determined by equation 6 in the discrete domain. y = ∑ i µa(yi) ×yi∑ i µa(yi) (6) where µa(yi) is the membership function. having defuzzified the data, their values are denormalised obtaining a real value which is used as a new robot coordinate. the fuzzy robot controller will also consider other errors related to other variables such as ageing of the positioning mechanisms or disturbances. the correction is on-line, during the welding process avoiding the need of stopping the production line. the operating working range is ±10mm in y axis and ±5mm in z axis. the fuzzy controller was developed in c++ using the visual studio compiler. 4.3 performance assessment in order to assess the statistical performance of the controller a set of experiments were carried out. the robot path tracking ability during the operation range was evaluated ±10mm in y axis and ±5mm in z axis. the evaluation helps to analyse its behaviour against any variation of the experimental factors. the factors are considered either combined or in its individual form so that the interaction can be identified as indicated in [7]. care was taken to consider two important aspects during the experimental design that are replicate and aleatorisation. the use of replicates is very important to determine the experimental error. the aleatorisation allows confirming that the random probability variables refer to independent probability distributions. considering the above assumption, the input variables to the system are the position values that are sent to the robot in the y and z axis direction (disty , distz) and as output, the real distance observed in both axes (disty real and distz real). experimental design considers variables with two or three levels and k factors referred as to 2k or 3k, respectively. in our experiments we decided to use 32 with 2 replicates. the decision to use three levels was based on the interest of using the central point within the robot’s range motion in both, the y axis and the z axis. 5 results 5.1 results with the t1 flc the experimental procedure can be observed in figure 12. the experimental set up and the coordinate frame are shown. the offset value (disty, distz) that the fuzzy controller has to compensate is showed in the dashed rectangle. the output value is considered as the real value measured along the main axes (disty real, distz real). it is important to note that the welding seam is applied along the x axis. table 1 contains the results from 18 experimental runs. the first 9 corresponds to the first replicate and runs 10 to 18 correspond to the second replicate. figure 13 shows results obtained during the correction of lateral misalignment and the measured absolute error. similarly, figure 14 shows the obtained results during the correction in the vertical distance and its absolute a singleton type-1 fuzzy logic controller for on-line error compensation during robotic welding 211 figure 12: experimental set up error. the measured error demonstrates that the robot fuzzy controller compensates the misalignment in all cases. the maximum observed error is 1.6mm in y axis, which is considered to be appropriate for practical purposes in industrial manufacturing processes. figure 13: lateral misalignment and absolute error in comparison with the triangular t1 flc, the gaussian t1 flc uses a bigger area of exploration. it was decided to duplicate the area 40mm x 20mm. the lateral misalignment range is +/− 20 mm in the y axis whereas the vertical misalignment is +/− 10 mm in the z axis. this can be observed in figure 15. the gaussian t1 flc uses more information as it considers mid points between the initial and final points. every point within the exploration area forms a fuzzy rule. if we consider that the points are spaced 2mm then we will have 21 points in y axis by 11 in the z axis making a total of 231 points or fuzzy sets for the singleton flc. the gaussian type of fuzzy sets are illustrated in figure 16. the gaussian controller has the advantage of not requiring the homography since the input information is given directly from the camera in pixels. for the reference point (0,0) which is supposed to be the point where the welding torch should be aligned with is located in the point 212 i. davila-rios, i. lopez-juarez, g.m. mendez, r. osorio-comparan, g. lefranc, c. cubillos table 1: experimental results and error evaluation (mm) disty disty real error y distz distz real error z absy error absz error -10 -10,2 0.2 -5 -5 0 0.2 0 -10 -10.4 0.4 0 -0.4 0.4 0.4 0.4 -10 -10.4 0.4 5 4.4 0.6 0.4 0.6 0 0.47 -0.47 -5 -5.26 0.26 0.47 0.26 0 -0.43 0.43 0 -0.42 0.42 0.43 0.42 0 -1.3 1.3 5 5.28 -0.28 1.3 0.28 10 10.6 -0.6 -5 -5.2 0.2 0.6 0.2 10 10.6 -0.6 0 0.6 -0.6 0.6 0.6 10 9.6 0.4 5 5.2 -0.2 0.4 0.2 -10 -9.5 -0.5 -5 -4.95 -0.05 0.5 0.05 -10 -10.2 0.2 0 -0.2 0.2 0.2 0.2 -10 -11.6 1.6 5 4.9 0.1 1.6 0.1 0 -0.53 0.53 -5 -5.3 0.3 0.56 0.3 0 0 0 0 -0.1 0.1 0 0.1 0 -0.33 0.33 5 5.27 -0.27 0.33 0.27 10 10.5 -0.5 -5 -5 0 0.5 0 10 9.6 0.4 0 -0.4 0.4 0.4 0.4 10 9.6 0.4 5 5 0 0.4 0 figure 14: vertical misalignment and absolute error a singleton type-1 fuzzy logic controller for on-line error compensation during robotic welding 213 figure 15: exploration area in the y and z axis figure 16: gaussian type fuzzy sets 214 i. davila-rios, i. lopez-juarez, g.m. mendez, r. osorio-comparan, g. lefranc, c. cubillos table 2: results from the singleton flc no. y axis z axis posroboty posrobotz error y axis error z axis 1 -20 10 19.59255 -9.99768 0.40745 0.00232 2 -20 0 19.98974 0.87296 0.01026 0.87296 3 -20 -10 19.8391 10 0.1609 0 4 0 10 0.02595 -10 0.02595 0 5 0 0 -0.02274 0.00005 0.02274 0.00005 6 0 -10 0.02708 9.99652 0.02708 0.00348 7 20 10 -19.90655 -10 0.09345 0 8 20 0 -19.96467 0.0156 0.03533 0.0156 9 20 -10 -19.99986 10 0.00014 0 10 -19 9 19.79273 -9.99535 0.79273 0.99535 11 -19 10 17.99499 -10.04568 1.00501 0.04568 12 -17 10 18.09578 -10 1.09578 0 13 -10 5 9.16485 -5.54219 0.83515 0.54219 14 -10 0 10.45749 -0.00265 0.45749 0.00265 15 -10 -5 10.87992 4.61136 0.87992 0.38864 16 0 5 0.197392 -5.95809 0.197392 0.95809 17 0 0 -0.0322 0.05833 0.0322 0.05833 18 0 -5 0.07501 5.80521 0.07501 0.80521 19 10 5 -10.03783 -5.96332 0.03783 0.96332 20 10 0 -9.96102 0 0.03898 0 21 10 -5 -9.15139 4.04127 0.84861 0.95873 (3.34598, -3.07248) pixels and so on with the other 230 points located within the total area. once all points are related within the image, then the next step is to form the fuzzy sets by using the gaussian function given by equation (5). since the information comes from the process and does not require normalisation, the number of fuzzy sets are 231 and its membership will be given by the mean and standard deviation from each point. in order to generate the fuzzy sets it is required to carry out at least 10 tests in each point, so the total number of tests are 2,310 points to generate the fuzzy sets. to validate the performance of the gaussian t1 flc several tests were carried out within the exploration area. the position error of the torch in the y and z axis are the input variables (given in pixels), while the output variables are the robot’s end effector coordinates given by posroboty and posrobotz. the table 2 shows the results obtained after 21 trials. it can be observed that errors are lower compared to the case of the triangular t1 flc and having a maximum value of approximately 1mm. figures 17 and 18 show the corresponding error graphs where it is clear that the misalignment of the plates was corrected and in all cases the repositioning of the arm had an error lower than 1mm, which is better that in the case of the triangular t1 flc [1]. conclusions this article presented an alternative solution to this problem that involves the use of structured lighting using a low-cost laser beam, a cmos camera and a singleton type-1 fuzzy logic controller. to validate the proposed control system, a robotic cell was designed using an industrial kuka kr16 robot for welding metallic plates. the method was evaluated experimentally a singleton type-1 fuzzy logic controller for on-line error compensation during robotic welding 215 figure 17: relationship between offset error and correction in the y axis figure 18: relationship between offset error and correction in the z axis under lateral and vertical positioning errors. the methodology presented is for the correction of welding trajectories due to positioning errors using structured lighting and gaussian t1 flc. the implementation results demonstrated the feasibility of the proposed gaussian t1 flc. the controller was validated experimentally with errors within the working range ±20mm for the y axis and ±10mm in the z axis. during experiments, the obtained results showed a maximum error of 1 mm, which is considered appropriate in practical manufacturing tasks. the statistical analysis of the results indicated that there is statistical evidence to consider a mean of zero value for average error in both axes with α = 0.01 for the triangular t1 flc, while it shows that the gaussian t1 flc is more efficient since it works under a type system exploring the area more exhaustive using intermediate points. in addition, due to a greater number of diffuse rules and the use of gaussian functions that include the value of the media and of the deviation standard, the model includes the uncertainties (noise) of the process sensors, i.e., the number of pixels detected during the processing of the image, which vary with the different levels of illumination. future work is looking into two areas: inspection of welding seam and type-2 fuzzy logic controllers. in terms of inspection is intended to expand the scope of the project by including the verification of width and height of the seam using a camera with greater dynamic range (approximately 120 db), and the use of logarithmic algorithms that reduce high luminance that are present during the welding process. acknowledgment authors want to acknowledge the consejo nacional de ciencia y tecnología (conacyt) for the scholarship provided to ignacio dávila-ríos during his phd studies, as well as the grant support from conacyt no. 61373. 216 i. davila-rios, i. lopez-juarez, g.m. mendez, r. osorio-comparan, g. lefranc, c. cubillos bibliography [1] ignacio davila-rios, ismael lopez-juarez, gerardo m. mendez, roman osorio-comparan, gaston lefranc and claudio cubillos (2016); a fuzzy approach for on-line error compensation during robotic welding, computers communications and control (icccc), 2016 6th international conference on, isbn 978-1-5090-1735-5, ieee xplore, doi:10.1109/icccc.2016.7496766, 264270. 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(2007); design and analysis of experiments, john wiley sons, 6th edition, isbn-10: 047148735x, 2007. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 1, month: february, year: 2020 article number: 1009, https://doi.org/10.15837/ijccc.2020.1.3779 ccc publications a venture capital recommendation algorithm based on heterogeneous information network s. wu, h. li, l. liu sen wu* donlinks school of economics and management university of science and technology beijing, china 30 xueyuan road, haidian district, beijing 100083, china *corresponding author: wusen@manage.ustb.edu.cn huifei li donlinks school of economics and management university of science and technology beijing, china 30 xueyuan road, haidian district, beijing 100083, china hunterli_et@qq.com lu liu donlinks school of economics and management university of science and technology beijing, china 30 xueyuan road, haidian district, beijing 100083, china liulu1891@163.com abstract according to its characteristics, venture capital can be described as a typical heterogeneous information network, which includes multiple kinds of nodes and various relations. getting hints from pathrank algorithm, this paper proposes vc-recom, a recommendation algorithm based on heterogeneous information network, which helps investment companies find suitable startup projects. besides, the experimental results show that the proposed algorithm can produce more effective recommendation results for investment firms compared with other methods. keywords: pathrank, venture capital recommendation, heterogeneous information network, meta path. 1 introduction recently, the prosperity of venture capital industry has caught a lot of attention. venture capital (vc) is a form of private equity, a medium to long-term form of finance provided in return for an equity stake in potentially high growth companies [13]. it can bring high returns to investment companies when they get a promising startup. but it is hard for investment trusts to find suitable startups. while, the recommendation algorithm can recommend suitable items to users based on their https://doi.org/10.15837/ijccc.2020.1.3779 2 preferences. we try to apply recommendation algorithms to venture capital to help investment companies find the right startups. currently, there are some commonly used recommendation algorithms, such as the recommendation based on collaborative filtering, content-based recommendation algorithm, model-based recommendation algorithm and hybrid recommendation [3] [17]. however, these recommendation algorithms are based on the traditional homogeneous information network, while venture capital network is a typical heterogeneous information network. a heterogeneous information network is a special type of information network with the underneath data structure as a directed graph [12], which either contains multiple types of objects or multiple types of links [15].when we use the homogeneous information network to analyze the realistic venture capital behavior, it can only describe the relationship between the same type of objects. and it ignores the attribute information of investment companies and startup projects, such as the location of the venture project, the field and so on, which are included in the heterogeneous information network. therefore, this paper tries to establish a heterogeneous information network model for venture capital recommendation to get a more accurate result. at present, there are several classic heterogeneous information network recommendation algorithms, such as heterecom [11], pathrank [8] and so on. based on the thought of pathrank algorithm, this paper puts forward a venture capital recommendation algorithm to solve the problem of venture capital recommendation. 2 related work venture capital plays an irreplaceable role in the transformation of science and technology into actual productive forces [14] [18]. at present, how to choose a startup for an investment company is still a key issue for venture capital research. jiang l discussed how to make investment decisions based on the point of view of vpn rules [5]. dicks d l et al. explained the impact of disgust on investment in innovative projects from the perspective of investor distaste for knighttian’s uncertainty [2]. li m et al. solved the investment decision-making problem by constructing a mathematic optimization model of the integrated gray correlation coefficient that maximizes the decision value, and applied it to choose the startup project for investment companies [9]. xu et al. adopted the design science approach and developed a prediction model to support corporate venture capital(cvc) investment decisions by identifying a list of potential investees from a large pool of portfolio companies for a cvc investor [16]. justin chircop et al. studied whether religiosity influences vc investment decisions. and the result shows that vc located in more religious countries are more risk averse and their investments are of better quality [1]. however, there are few researches on solving the choice of venture project from the perspective of venture capital recommendation. this paper applies the recommendation algorithm to venture capital and helps investors choose the right investment projects. at present, some recommendation algorithms are based on homogeneous information networks. when venture capital behavior is abstracted as homogeneous information networks, some attribute information and relationships between different objects are discarded. in order to express venture capital behavior more completely, this paper sets up a heterogeneous information network model for venture capital, which is the foundation of heterogeneous information network recommendation algorithms. current heterogeneous information network recommendation algorithms often ignore the structural information of heterogeneous information networks. in order to solve the problem that heterogeneous networks neglect the structure information of network, many scholars seek help from pagerank algorithm [10], integrating pagerank algorithm and heterogeneous information networks to the recommendation system to achieve the node ranking in the network and improve the effectiveness of the recommendation system. both jeh et al. and kleinberg have made great strides in node ranking algorithm based on random walk [4] [7], but sangkeun lee et al. realized that it was difficult to make a breakthrough in the results if only the structural information of heterogeneous information networks was used to rank the nodes. so they tried to construct a heterogeneous information network model that contained both structural information and attribute information and proposed the pathrank algorithm [6] [8]. the algorithm is a flexible recommendation system that simulates recommendation based on collaborative filtering, content-based recommendation and context-based recommendation. based on the pathrank algorithm, this paper proposes the venture capital recommendation alhttps://doi.org/10.15837/ijccc.2020.1.3779 3 gorithm of heterogeneous information networks. this algorithm aims at ranking the startups and recommending startups to investment companies. 3 the model of venture capital recommendation algorithm 3.1 venture capital network definition 1 (heterogeneous venture capital network): venture capital network is a typical heterogeneous information network g = (v, e),where v is a finite set of venture capital nodes, e ⊆ v ×v is a finite multi-set of venture capital edges. venture capital nodes are vc firms, startups and the attributes of startups in heterogeneous venture capital network. every venture capital edge links two venture capital nodes that belong to different venture capital node types. figure 1: an example of heterogeneous venture capital network fig.1 is an example of heterogeneous venture capital network. investment firm1, investment firm2, startup1, startup2, field1, field2, field3, round1, round2 and round3 are all venture capital nodes. investment f irm1 investing→ startup1, startup1 invested by→ investment f irm1, startup1 → f ield1, startup1 → round1, f ield1 → startup1, round1 → startup1 and so on are venture capital edges. definition 2 (venture capital network schema): venture capital network schema, denoted as t g = (a, r), is a meta template for heterogeneous venture capital network g = (v, e) with the object type mapping φv = v → a and the link type mapping φe = e → r, which is a directed graph defined over object types a, with edge types as relations from r. where a is a finite set of venture capital node types, and r is a finite set of venture capital edge types. venture capital node type is abstracted from venture capital nodes and venture capital edge type is abstracted from venture capital edges. fig.2 is a venture capital network schema. it is abstracted from fig.1. for example, investment firm1 and investment firm2 are abstracted as the venture capital node type of investment firms, startup1and startup2 are abstracted as the venture capital node type of startups, field1, field2 and field3 are abstracted as the venture capital node type of fields, round1, round2 and round3 are abstracted as the venture capital node type of rounds. investment f irm1 investing→ startup1 is abstracted as the venture capital edge type of investment f irm investing→ startup. different attributes of startup projects contribute to different venture capital network schemas. for example, if the attribute node type of a startup project’s network model contains the entrepreneurial https://doi.org/10.15837/ijccc.2020.1.3779 4 figure 2: an example of venture capital network schema location, another network model does not include the entrepreneurial location, the two network models are different network models. definition 3 (venture capital meta path): a venture capital meta path on the venture capital network schema t g = (a, r), is defined as a1 r1→ a2 r2→···an rn→ ··· rl→ al+1, representing the flexible relationships from a1 to al+1, where a = {a1, a2, · · · , an, · · · , al, a1+1}, r = {r1, r2, · · · , rn, · · · , rl}, a1 is a vc firm, a2 and al+1 are startups, a3, · · · , al are not limited to startups or vc firms. because our work is to recommend startups to vc firms, the first node of the venture capital meta path must be a vc firm, the second and the last one must be startups. definition 4 (adjacency matrix): in heterogeneous venture capital network g = (v, e), r is the set of all venture capital edge types, adjacency matrix pa is a |v |× |v | matrix, where pa (i, j) = { 1 if nodei → nodej can be abstracted as an edge type belonging to r 0 otherwise . (1) definition 5 (transition matrix): transition matrix pr is a |v |× |v | matrix, where pr (i, j) =   1∑ j pa(i,j) if nodei → nodej can be abstracted as an edge type belonging to r 0 otherwise . (2) definition 6 (edge adjacency matrix): in heterogeneous venture capital network g = (v, e), edge adjacency matrix ern is a |v |× |v | matrix under the venture capital edge type rn, where ern (i, j) = { 1 if nodei → nodej can be abstracted as rn 0 otherwise . (3) definition 7 (edge transition matrix): edge transition matrix prn is a |v |× |v | matrix under the venture capital edge type rn, where prn (i, j) =   1∑ j ern (i,j) if nodei → nodej can be abstracted as rn 0 otherwise . (4) definition 8 (path transition matrix): in heterogeneous venture capital network g = (v, e), path transition matrix pmp under meta path a1 r1→ a2 r2→···an rn→ ··· rl→ al+1 is a |v |× |v | matrix, where pmp = pr1 × pr2 ×···× prn ×···× prl. https://doi.org/10.15837/ijccc.2020.1.3779 5 3.2 the specific introduction of vc-recom in this section, we explain how to compute the scores of startups for every vc firm under metapaths. like pathrank, vc-recom is the stationary probability of random walker’s being at each node after enough number of random walk iterations. but the pathrank ranks all the nodes of all node types, vc-recom recommends the startups to vc firms. so we need to rank all the nodes of startups. in each iteration of vc-recom, the random walker has three options: transition: with probability wtrans, move to one of the adjacent nodes. restart: with probability wre, restart the random walk from the vc firms that we are going to recommend to. path following: with probability wpath, follow one of venture capital meta paths. transition and restart are inherited from personalized page-rank, and they fulfill the roles to reflect global authorities of the invested startups and relevance to other startups. path following is to reflect the semantics of the given venture capital meta paths in the ranking score. formally, vc-recom vector is defined as follows under venture capital meta paths of mp1, · · · , mpc. ~r = wtrans × pr ×~r + wpath × ( w1p mp1 t + · · · + wcp mpc t ) ×~r + wre ×~t. (5) let v be the set of all the venture capital nodes in heterogeneous venture capital network, ~r is a |v |× 1 vector,pr is a |v |× |v | transition matrix describing the relationship among all the venture capital nodes and pmp1 , · · · , pmpc are |v | × |v |path transition matrices describing the relationship between nodes under venture capital meta path mp1, · · · , mpc. ~t is a |v |× 1 restart vector, where ti = { 1 if the nodei is a v c f irm that we will recomment to 0 otherwise . (6) besides, w1 + w2 + · · · + wc = 1. for example, if we want to recommend startups to investment firm2 in fig.1 under the venture capital meta path ’investment f irm investing→ startup project has the attriute→ round the attribute of→ startup project’, denoted as mp1, we need to calculate transition matrix pr, path transition matrix pmp1 and ~t. let r1,r2,r3 represent ’investment f irm investing→ startup project’, ’startup project has the attriute→ round’, ’round the attribute of→ startup project’. pr is described in tab.1, pmp1 is equal to pr1 × pr2 ×pr3, where pr1 is the edge transition matrix of venture capital edge ’investment f irm investing→ startup project’, pr2 is the edge transition matrix of venture capital edge ’startup project has the attriute→ round’, pr3 is the edge transition matrix of the venture capital edge ’round the attribute of→ startup project’, pr1, pr2, pr3 are described in tab.2, tab.3, tab.4. ~t = (0, 1, 0, 0, 0, 0, 0, 0, 0, 0). i1, i2, s1, s2, r1, r2, r3, f1, f2 and f3 represent investment firm1, investment firm2, startup1, startup2, round1, round2, round3, field1,field2 and field3. the specific steps of vc-recom: input: wtrans, wre, wpath, ~t, w1, · · ·, wc transition matrix pmp1, · · ·, pmpc and pr,the number of recommended startups k initialize ~r = ones (|v | , 1) /|v |, ~r1 = ~0; while |~r − ~r1| > 0.0001 do ~r1 = ~r, ~r = wtrans × pr ×~r + wpath × ( w1p mp1 t + · · · + wcp mpct ) ×~r + wre ×~t end while return topk startup projects; output: topk startup projects. https://doi.org/10.15837/ijccc.2020.1.3779 6 table 1: the description of pr i1 i2 s1 s2 r1 r2 r3 f1 f2 f3 i1 0 0 1/2 1/2 0 0 0 0 0 0 i2 0 0 0 1 0 0 0 0 0 0 s1 1/5 0 0 0 1/5 0 1/5 1/5 1/5 0 s2 1/6 1/6 0 0 0 1/6 1/6 1/6 0 1/6 r1 0 0 1 0 0 0 0 0 0 0 r2 0 0 0 1 0 0 0 0 0 0 r3 0 0 1/2 1/2 0 0 0 0 0 0 f1 0 0 1/2 1/2 0 0 0 0 0 0 f2 0 0 1 0 0 0 0 0 0 0 f3 0 0 0 1 0 0 0 0 0 0 table 2: the description of pr1 i1 i2 s1 s2 r1 r2 r3 f1 f2 f3 i1 0 0 1/2 1/2 0 0 0 0 0 0 i2 0 0 0 1 0 0 0 0 0 0 s1 0 0 0 0 0 0 0 0 0 0 s2 0 0 0 0 0 0 0 0 0 0 r1 0 0 0 0 0 0 0 0 0 0 r2 0 0 0 0 0 0 0 0 0 0 r3 0 0 0 0 0 0 0 0 0 0 f1 0 0 0 0 0 0 0 0 0 0 f2 0 0 0 0 0 0 0 0 0 0 f3 0 0 0 0 0 0 0 0 0 0 table 3: the description of pr2 i1 i2 s1 s2 r1 r2 r3 f1 f2 f3 i1 0 0 0 0 0 0 0 0 0 0 i2 0 0 0 0 0 0 0 0 0 0 s1 0 0 0 0 1/2 0 1/2 0 0 0 s2 0 0 0 0 0 1/2 1/2 0 0 0 r1 0 0 0 0 0 0 0 0 0 0 r2 0 0 0 0 0 0 0 0 0 0 r3 0 0 0 0 0 0 0 0 0 0 f1 0 0 0 0 0 0 0 0 0 0 f2 0 0 0 0 0 0 0 0 0 0 f3 0 0 0 0 0 0 0 0 0 0 4 experiment this section describes the experimental results on the evaluation of vc-recom. with the development of recommendation systems, how to evaluate the advantages of a recommendation system has gradually become an important issue, which has become an independent research subject. at present, the commonly used evaluation indicators are: accuracy and recall. this paper mainly uses both accuracy and recall to evaluate the vc-recom. 4.1 experimental setup we conducted several experiments to validate the proposed vc-recom method using the dataset in tab.5. according to the dataset, we built the heterogeneous venture capital network schema (fig.2). https://doi.org/10.15837/ijccc.2020.1.3779 7 table 4: the description of pr3 i1 i2 s1 s2 r1 r2 r3 f1 f2 f3 i1 0 0 0 0 0 0 0 0 0 0 i2 0 0 0 0 0 0 0 0 0 0 s1 0 0 0 0 0 0 0 0 0 0 s2 0 0 0 0 0 0 0 0 0 0 r1 0 0 1 0 0 0 0 0 0 0 r2 0 0 0 1 0 0 0 0 0 0 r3 0 0 1/2 1/2 0 0 0 0 0 0 f1 0 0 0 0 0 0 0 0 0 0 f2 0 0 0 0 0 0 0 0 0 0 f3 0 0 0 0 0 0 0 0 0 0 table 5: data description of venture capital data field data description investment firm total 1119 investment companies startup project total 8739 startup projects investment event total 17985 investment events field total 31 kinds of fields round total 18 financing rounds in the experiments, we wanted to recommend the startups to the vc firms, so we exploited several different venture capital meta paths in tab.6. we use the capital of the first letter of each node type name as its abbreviation. like pathrank, not only can vc-recom exploit the semantics behind the different types of nodes and edges in a heterogeneous graph, but also it can emulate various recommendation semantics such as collaborative filtering, content based filtering, and their combinations. for example, isxs is a mixed meta path of isrs and isfs. isxs in this paper is equal to 0.5isfs+0.5isrs. table 6: meta paths and semantic analysis meta path semantic analysis same method isis recommends startup projects with the same investment firms as the projects that the investment firm has invested in user-based collaborative filtering isfs recommends startup projects that are similar to the field of the projects that the investment firm has invested in content(field)-based filtering isrs recommends startup projects with the same investment rounds as the project that the investment firm has invested in content(round)based filtering isxs recommends startup projects that are similar to startup projects that the investment firm has invested in based on fields and rounds mixed recommendation 4.2 experimental results for studying the recommendation performance of the vc-recom algorithm, we need to choose some venture capital meta paths first, and then study the influence of the number of recommended startups k to accuracy and recall of the recommendation under the guide of venture capital meta paths. furthermore, we compare the result of vc-recom to the user-based collaborative filtering algorithm and heterecom. https://doi.org/10.15837/ijccc.2020.1.3779 8 each experimental result is the average of 5 runs with the dataset divided into two parts, training set (80% of the dataset) and test set (20% of the dataset). and we set wtrans = 0.35, wre = 0.05, wpath = 0.6 to conduct experiments to get the best result after optimizing the parameters wtrans, wre and wpath. 4.2.1 the effect of recommendation number k fig.3 and fig.4 study the effect of recommendation number k on the accuracy and the recall rate of each path. figure 3: the influence on the accuracy of each path when k changes from fig.3, we can see that as the value of k increases continuously in the venture capital algorithm based on heterogeneous information networks, the recommendation accuracy under each path gradually decreases. among all the meta-paths isfs, isrs and isis, the accuracy result of isrs is best, showing that the venture capital node type round reflects the investment companies’ preference mostly. isfs has the lowest recommendation accuracy rate, indicating the venture capital node type field reflects the investment companies’ preference less than other node types. the isxs integrates the information of isrs and isfs, and has a high recommendation accuracy rate. figure 4: the influence on the recall of each path when k changes as we can see from fig.4, with the increase of k, the recall rate under each path gradually increases. the recommendation recall rate of meta path isrs and isxs is generally higher than that of other paths because of the larger association between the node type round and the investment companies in meta path isrs. however, isis and isfs have lower recall rates, but isis has a higher recall rate than isfs because of the venture capital node type field having less association with investment companies. 4.2.2 comparison of vc-recomand other algorithms this part mainly compares vc-recom with user-based collaborative filtering algorithm and heterecom algorithm under the meta path. because the venture capital meta path isis has the same https://doi.org/10.15837/ijccc.2020.1.3779 9 semantic to user-based collaborative filtering algorithm (usercf), we choose the venture capital meta path isis to make comparisons. (1) comparison of recommended results figure 5: a heterogeneous venture capital network fig.5 is a heterogeneous venture capital network. now we want to recommend a startup to investment firm2 by using vc-recom, heterecom and usercf according to fig.5. and the result is in tab.7. table 7: recommendation result analysis vc-recom heterecom usercf startup3 startup1 startup1 from tab.7, we can see that if we use heterecom and usercf algorithms, the recommended startup is startup1, but vc-recom is startup3. what makes the difference is that heterecom and usercf algorithms only consider the relationship between startups and investment firms, while the vcrecom algorithm can use the information of any nodes in the heterogeneous information network by random walking. startup3 has the same attributes with startup2, which are much closer to investment firm2’s preferences. so, recommending startup3 to investment firm2 is more reasonable than startup1. that is to say, vc-recom can recommend startups that are much more accordant with investment firms’ preferences. (2) comparison in accuracy and recall figure 6: comparison of the accuracy of the three algorithms fig.6 studies the difference of the accuracy among vc-recom, heterecom and user-based colhttps://doi.org/10.15837/ijccc.2020.1.3779 10 laborative filtering algorithm. it can be seen that the recommendation accuracy of vc-recom and heterecomare decreasing as the recommendation number k increases. and their accuracy rates are higher than usercf. the accuracy rate of the vc-recom algorithm is higher than that of heterecom. that is to say, vc-recom is more effective to recommend startups preferred by investment companies. figure 7: comparison of the recall of the three algorithms fig.7 studies the difference of the recall rate among vc-recom and the other two algorithms. it can be seen that the recall rate is increasing with the increase of the recommendation number k. vc-recom’s recall rate is higher than those of heterecom and usercf. usercf has the lowest recall rate. 5 conclusions this paper establishes a heterogeneous venture capital network model, applying the recommendation algorithm to the field of venture capital analysis, in which the venture capital entities and their attributes are transferred into network nodes and incorporated into the heterogeneous information network model. then it proposes the venture capital recommendation algorithm vc-recom based on the established heterogeneous information network model, and compares the proposed algorithm with user-based collaborative filtering algorithm and heterecom algorithm. the experiment results show that vc-recom is more effective to recommend startups preferred by investment companies. funding this work is supported by national natural science foundation of china (nsfc) under grant no. 71971025, 71271027. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] chircop, j.; johan, s.; tarsalewska, m. 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[18] zuo, z.; shi, f.; tan, g.; accounting, s. o. (2017). does state-owned venture capital play a "crowding-in" role? an empirical research based on certification mechanism, journal of finance & economics, 43(12), 17–29, 2017. https://doi.org/10.15837/ijccc.2020.1.3779 12 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: wu, s.; li, h.; liu, l. (2020). a venture capital recommendation algorithm based on heterogeneous information network, international journal of computers communications & control, 15(1), 1009, 2020. https://doi.org/10.15837/ijccc.2020.1.3779 paun_ijcccv10n5.pdf international journal of computers communications & control issn 1841-9836, 10(5):617-626, october, 2015. impact of membrane computing and p systems in isi wos. celebrating the 65th birthday of gheorghe păun i. dzitac ioan dzitac 1. aurel vlaicu university of arad romania, 310330 arad, elena dragoi, 2 ioan.dzitac@uav.ro 2. agora university of oradea romania, 410526 oradea, piata tineretului, 8 rector@univagora.ro "congratulations, g. paun! since 2000, you have been cited ... times for your article... this means that the number of citations your article received places it in the top 0.5% within its field according to esi. your work is highly influential, and is making a significant impact among your colleagues in your field of study. congratulations on your extraordinary career accomplishment!" essential science indicators (esi), isi thomson, 2009. gheorghe păun (b. dec. 6, 1950) abstract: membrane computing is a branch of computer science initiated by gheorghe păun in 1998, in a technical report of turku centre for computer science published as a journal paper ("computing with membranes" in journal of computer and system sciences) in 2000. membrane systems, as gheorghe păun called the models he has introduced, are known nowadays as "p systems" (with the letter p coming from the initial of the name of this research area "father"). this note is an overview of the impact in isi wos of gheorghe păun’s works, focused on membrane computing and p systems field, on the occasion of his 65th birthday anniversary. keywords: membrane computing, p systems, isi web of science(wos), scientific impact, h-index. 1 introduction: brief bio-sketch of gheorghe păun "gheorghe păun is an example of a person affirming his own existence by writing. he is a prolific writer with a huge number of papers: tens of scientific books, hundreds of articles, several novels, poems, and books on games." (g. ciobanu, [1]). copyright © 2006-2015 by ccc publications 618 i. dzitac gheorghe păun (b. december 6, 1950, romania) is a romanian mathematician and theoretical computer scientist. he is also a writer and promoter of culture (editor-in-chief of a monthly cultural magazine "curtea de la argeş") [9]. g. păun graduated the faculty of mathematics of the bucharest university in 1974 and got his phd at the same faculty in 1977 (under the supervision of solomon marcus). he has won many scholarships, in germany, finland, the netherlands, spain, etc. presently he is a senior researcher at the institute of mathematics of the romanian academy, bucharest, and visiting researcher at sevilla university, spain. since 1997 he is a corresponding member, and since 2012 is a full member of the romanian academy. since 2006 he is a member of academia europaea. his main research fields are formal language theory (regulated rewriting, contextual grammars, grammar systems), automata theory, combinatorics on words, computational linguistics, dna computing, membrane computing. he has (co)authored and (co)edited more than 60 books in these areas, and he has (co)authored more than 500 research papers. in the last two decades he has visited many universities from europe, usa, canada, japan, china, also participating to many international conferences, several times as an invited/keynote speaker. he is a member of the editorial board of numerous computer science journals and professional associations. 2 g. păun and membrane computing gheorghe păun is the founder of the membrane computing area of computer science, a research field inspired from the bio-chemistry of the cell (a more suitable name can be "cell computing"). the membrane systems, introduced in 1998 in [3] and disseminated in 2000 in [4], are usually called "p systems". most cited of all g. păun’s works is "computing with membranes" published in 2000, [4], having more than 850 citations (the second position of all cited papers of journal of computer and system sciences). these 850 citing papers have also in turn over 4,900 citations, [7]. in february 2003, gheorghe păun answered a few questions on the occasion of isi nomination of [4] as a fast breaking paper in the field of computer science, [8]: "st: why do you think your paper is highly cited? it is both based on several "classic" theoretical computer science tools/techniques (grammars, automata, lindenmayer systems, regulated rewriting, grammar systems), and related to rather "modern" branches of computer science (molecular computing in general, dna computing in particular); it is highly interdisciplinary (it proposes computation models based on the structure and the functioning of the living cell). the model is very versatile; many variants were proposed, biologically or mathematically motivated. most of these variants have attractive computer science features: universality and/or computational efficiency; they also promise to have relevance from a biological point of view, as algorithmic models of the cell as a whole (or of other cell-like systems). st: does it describe a new discovery or a new methodology that’s useful to others? it proposes a new branch of natural computing: a distributed, parallel, and nondeterministic computing model, processing multisets in a cell-like or tissue-like compartmental structure, thus extending the field of molecular computing. topics such as communication, synchronization, (maximal) parallelism, space-time trade-off, complexity, localization, etc. can find a natural framework to be dealt with. this is also a (reductionistic) global algorithmic model of the living cell, a kind of model the biologists are waiting for in the near future. impact of membrane computing and p systems in isi wos. celebrating the 65th birthday of gheorghe păun 619 st: what were some of the circumstances that led you to do this research? the research was done in the prolongation of several years of work in dna computing, which, in their turn, have continued many years of work in formal language theory. very specifically, the paper was written in the framework of a project supported by the academy of finland, during a stage in turku centre for computer science, in the scientifically very "hot" group of arto salomaa. st: could you summarize the significance of your paper in layman’s terms? the goal is to abstract a computability model from the structure and the functioning of the living cell. in short, in the compartments determined by a "membrane structure" (a celllike hierarchical arrangement of membranes) one places multisets of "objects" (corresponding to the chemicals swimming in solutions) and "evolution rules" (corresponding to chemical reactions). the rules are applied in a maximally parallel (each object which can evolve by a rule from the same compartment should evolve) nondeterministic (the objects and the rules are randomly chosen) manner. the objects can also pass through membranes (one can say that they are "communicated" from one compartment to another one), the membranes can be dissolved, divided, created. the application of rules and the communication of objects can be controlled in various ways, either biologically or computer science motivated. a sequence of "transitions" from a configuration of the system to another configuration is called a "computation" and with a halting computation one associates a result, e.g., in the form of the multiset of objects present in a specified output membrane at the end of the computation. several variants were considered. most of them are computationally universal (they can compute all turing computable vectors of natural numbers); this holds true even in certain cases where the computation is done by communication only (no object evolves, but only changes the compartments where it is placed). when an enhanced parallelism is available (e.g., via membrane division) polynomial solutions to np-complete problems can be obtained by a space-time trade-off. no biological implementation was reported, but there are several software implementations, as well as several (preliminary) applications, mainly in simulating biological phenomena. details can be found at the web address http://ppage.psystems.eu, as well as in the monograph gheorghe păun, membrane computing. an introduction, springer-verlag, berlin, 2002." 3 collaborators and followers of gheorghe păun publications of g. păun have been cited in more than 15,000 international works (co)authored by more than 1700 computer scientists as: y. matyiasevich, a. salomaa, g. rozenberg, a. ehrenfeucht, m. hagyia, j. kral, j. berstel, j. beauquier, b. rozoy, j. dassow, m. novotny, r. freund, p.r.j. asveld, i.m. havel, r. siromoney, k.g. subramanian, f. urbanek, e. csuhajvarju, j. kelemen, a. kelemenova, j. hromkovic, m. latteux, m. clerbout, e. makinen, n. nirmal, h.c.m. kleijn, al. meduna, cc. squier, z. tuza, x.m. nguyen, f.j. brandenburg, j. kari, v. niemi, h. fernau, j. shallit, d. watjen, a. lepisto, a. carpi, t. harju, m. jantzen, h. bordihn, d. raz, j. honkala, t. yokomori, g. mauri, m. katsura, v. manca, k. krithivasan, j. reif, m. margenstern, j. goldstine, y. rogozhin, h. tanaka, m. conrad, s. crespi reghizzi, m.j. pérez-jiménez, d. wotschke, a. obtulowicz, m. holcombe, o. ibarra, o. ecegioglu, c. teuscher, j. karhumaki, l. cardelli, e. shapiro, j. wiedermann, e. moriya, c. rossello, k. ueda, s.g. akl etc. g. păun is an very influential author, a founder of several research groups in membrane computing. many researchers were attracted to investigate molecular computation models by g. păun, from countries as: romania, republic of moldova, hungary, austria, spain, the netherlands, finland, china etc. 620 i. dzitac many well know researchers were involved in studying open problems proposed g. păun: j. berstel, l. boasson, j. beauquier, b. rozoy, p.r.j. asveld, f. urbanek, j. dassow, m. latteux, m. clerbout, c.c. squier, z. tuza, j. cassaigne, s. schwer, p. seebold, e. makinen, f.j. brandenburg, a. lepisto, a. carpi, j. kari, v. niemi, d. hauschildt, m. jantzen, d. raz, d. pixton, m.j. pérez-jiménez, g. mauri, cl. ferretti, k. khrithivasan, r. freund, m. margenstern, y. rogozhin, h. j. hoogeboom, a. obtulowicz, etc. books (co)authored by gheorghe păun: 1. grammars for economic processes, the technical publ. house, bucharest, 1980 (in romanian). 2. matrix grammars, the scientific and enciclopaedic publ. house, bucharest, 1981 (in romanian). 3. contextual grammars,the publ. house of the romanian academy, bucharest,1982 (in romanian). 4. recent results and problems in formal language theory, the scientific and enciclopaedic publ. house, bucharest, 1984 (in romanian). 5. the paradoxes of hierarchies, the scientific and enciclopaedic publ. house, bucharest, 1987 (in romanian). 6. (in collaboration with j. dassow, germany) regulated rewriting in formal language theory, akademie-verlag, berlin, 1989, springer-verlag, berlin, 1989 (nr. 18 in series monograph on theoretical computer science). 7. (in collaboration with e. csuhaj-varju, hungary; j. dassow, germany; j. kelemen, czechoslovakia) grammar systems. a grammatical approach to distribution and cooperation, gordon and breach, series topics in computer mathematics, london, 1994. 8. marcus contextual grammars, kluwer academic publ., dordrecht, boston, london, 1997. 9. (in collaboration with g. rozenberg, a. salomaa) dna computing. new computing paradigms, springer-verlag, heidelberg, 1998 (translated in japanese, in 1999, in chinese and russian in 2004). 10. (in collaboration with c. calude) computing with cells and atoms, taylor and francis, london, 2000. 11. membrane computing. an introduction, springer-verlag, berlin, 2002 (translated in chinese, in 2012). collective volumes (co)edited by gheorghe păun: 1. mathematical aspects of natural and formal languages, world scientific publishing, singapore, 1994. 2. mathematical linguistics and related topics, editura academiei romane, bucharest, 1995. 3. artificial life: grammatical models, the black sea university press, bucharest, 1995. impact of membrane computing and p systems in isi wos. celebrating the 65th birthday of gheorghe păun 621 4. (with a. salomaa) new trends in formal languages: control, cooperation, combinatorics., lecture notes in computer science 1218, springer-verlag, berlin, 1997. 5. computing with bio-molecules. theory and experiments, springer-verlag, singapore, 1998. 6. (with a. salomaa) grammatical models of multi-agent systems, gordon and breach, london, 1999. 7. 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(with r. gutierrez-escudero et al.) proceedings of the seventh brainstorming week on membrane computing, fenix editora, sevilla, volume 1-2, 2009. 47. (with m.j. perez-jimenez, a. riscos-nunez) pre-proceedings of tenth workshop on membrane computing, wmc10, curtea de argeş, august 2009. 48. (with m.j. perez-jimenez, a. riscos-nunez, g. rozenberg, a. salomaa) membrane computing, tenth international workshop, wmc 2009, curtea de argeş romania, august 2009, selected and invited papers, lncs 5957, springer-verlag, berlin, 2009. 49. (with m.a. gutierrez-naranjo et al.) proceedings of the eighth brainstorming week on membrane computing, fenix editora, sevilla, 2010. 50. (with m. gheorghe, t. hinze) pre-proceedings of the 11th conference on membrane computing, cmc11, jena, germany, 2010. 51. (with m. gheorghe, t. hinze, g. rozenberg, a. salomaa) membrane computing. 11th international conference, cmc11, jena, germany, august 24-27, 2010. revised, selected, and invited papers, lncs 6501, springer-verlag, berlin, 2010. 624 i. dzitac 52. (with m.a. martinez-del-amor, i. perez-hurtado, f.j. romero-campero, l. valenciacabrera) proceedings of the ninth brainstorming week on membrane computing, sevilla, 2011, fenix editora, sevilla, 2011. 53. (with m. gheorghe, s. verlan) pre-proceedings of twelfth international conference on membrane computing, cmc12, fontainebleau, paris, august 2011. 54. (with m. gheorghe, g. rozenberg, a. salomaa, s. verlan) membrane computing. 12th international conference, cmc12, fontainebleau, france, august 2011. revised, selected, invited papers, lncs 7184, springer-verlag, berlin, 2012. 55. (with m.a. martinez-del-amor et al.) proceedings of the tenth brainstorming week on membrane computing, fenix editora, sevilla, vol. 1-2, 2012. 56. (with l. valencia-cabrera et al.) proceedings of the eleventh brainstorming week on membrane computing, fenix editora, sevilla, 2013. 57. (with l. pan, m.j.pérez-jiménez, t. song) proceedings of the 9th international symposium bic-ta 2014, wuhan, china, comm. in computer and information sciences, vol. 472, springer, 2014. 58. (with g. rozenberg, a. salomaa) discrete mathematics and computer science, ed. academiei, bucureşti, 2014. v (cu l.f. macias-ramos et al.) proceedings of the twelfth brainstorming week on membrane computing, fenix editora, sevilla, 2014. 59. (with l.f. macias-ramos et al.) proceedings of the 13th brainstorming week on membrane computing, fenix editora, sevilla, 2015. 4 impact of g. păun’s works the publications of g. păun have been cited in more that 15,000 international works, (co)authored by more than 1700 computer scientists. more than 200 papers (co)authored by g. păun are indexed/abstracted in isi wos, having over 3,000 citations and h-index = 28 [7]. the most cited paper is "computing with membranes", [4], published in 2000, having more than 850 citations (the second position of all cited papers from journal of computer and system sciences. [7]). in 2009, g. păun was included by isi (thompson-reuters) in the highly cited researchers category, which means he was at that time among the most cited 0.5% computer scientists in the world. the most cited paper published in international journal of computers communications & control (ijccc) is "spiking neural p systems with anti-spikes", coauthored by g. păun (with l. pan), [2], [7]. also the papers [6] and [5] are in top 10 of ijccc. nowadays there exist over 2500 works and over 60 phd thesis in the field of membrane computing and p systems (and about 50 collective volumes), with over 500 (co)authors from romania, austria, the netherlands, germany, finland, japan, uk, canada, hungary, india, italy, spain, czech republic, usa, poland, france, republic of moldova, china, switzerland, australia, new zealand, filipine, malaesia, slovakia etc. a webpage dedicated to p systems is hosted in vienna [10]. every year three meetings dedicated to p systems are organized: conference of membrane computing (former workshop on membrane computing, initiated in 2000), brainstorming week on membrane computing (since impact of membrane computing and p systems in isi wos. celebrating the 65th birthday of gheorghe păun 625 2003), and asian conference on membrane computing (since 2012). several conferences in natural computing or unconventional computing explicitly have membrane computing in their scopes. international recognition of g. păun consist in: • invitations as visiting researcher received from many universities and research institutes from hungary, czech republic, slovakia, germany, finland, france, japan, holland, austria, spain, usa, canada, poland, italy, new zealand, greece, china, singapore, etc., and finished with many collaborations with local researchers. • over 100 invited lectures to universities from germany (magdeburg, frankfurt, hamburg, tubingen), hungary (budapesta, gyor), czech republic (brno, prague, opava), slovakia (bratislava), spain (tarragona, barcelona, madrid, sevilla), finland (turku, laapeenranta), holland (leiden), japan (kyoto, tokyo-chiba, tokyo-waseda, tokyodendai, hiroshima), france (paris, lille), uk (london), canada (ontario), poland (warsaw), us (greenvile nc, binghamton), italy (milano, roma, brescia, pisa, l’aquila, siena, palermo, verona), greece (xanthi), china (beijing, wuhan), etc; • visiting professor at technical university of vienna (austria), turku centre for computer science (finland), rovira i virgili university of tarragona (spain) and politechnica university of madrid (spain), banach center of poland academy of sciences (poland), university of singapore, university of malaysia, hungarian academy etc. • humboldt scholarship in germany at university of magdeburg (may 1, 1992 -july 31, 1993; july-august 1999); many scholarhips in france, finland, spain, the netherlands; • research scholarship "ramon y cajal" in spain (tarragona and sevilla) for five years (2001-2006) and leader of a project of excellency in spania (sevilla, 2009-2014). • member of the program committees of more than 120 international conferences, organizer of the symposiums artificial life: grammatical models (mangalia romania, 1994), molecular computing (mangalia romania, 1997), multiset processing (curtea de argeş romania, 2000), membrane computing (curtea de argeş 2001, 2002, 2009, tarragona 2003, milano 2004, viena 2005, leiden 2006, salonic 2007, edinburgh 2008, jena 2010, fontainebleau 2011, budapesta 2012, chisinau 2013, praga 2014, valencia 2015); • founder and main organizer of series "brainstorming week on membrane computing" (tarragona 2003, sevilla 2004-2015); member of steering committee of conferences developments in language theory, universal machines and computations and dna based computing, and of workshops grammar systems and descriptional complexity in formal systems. g. păun is/was member in editorial board of many journals, as: journal of universal computer science (springer-verlag), journal of computing and informatics (computers and artificial intelligence), acta cybernetica, journal of automata, languages, and combinatorics, grammars, fundamenta informaticae, romanian journal of information science and technology, computer science journal of moldova, international journal of foundations of computer science (world scientific), international journal of computer mathematics, natural computing: an international journal, soft computing (springer)area editor (dna and membrane computing), biosystems (elsevier); theoretical computer science. natural computing series (elsevier); international journal of unconventional computing; new generation computing (springer and omsha-japonia), progress in natural science (elsevier and science in china press), economic 626 i. dzitac computation and economic cybernetics studies and research, international journal of computers communication & control, etc. he also received several scientific honors and awards, in romania and abroad, among others, the doctor honoris causa title from: 1. university of opava – the czech republic (2008), 2. university of piteşti romania (2010) and 3. agora university of oradea romania (2015). 5 summary a quick overview of gheorghe păun computer science activity and, especially, of the impact of his activity is provided, focusing on the data from isi wos and on the developments in membrane computing, a branch of natural computing initiated by him in 1998. acknowledgment celebrating gheorghe păun’s 65th birthday, we wish him a good health, long life, and new scientific achievements! bibliography [1] g. ciobanu (2010); writing as a form of freedom and happiness celebrating the 60th birthday of gheorghe păun, international journal of computers communications & control, 5(5): 613-615. [2] l. pan, g. păun (2009); spiking neural p systems with anti-spikes, international journal of computers communications & control, 4(3):273-282. [3] gheorghe păun (1998); computing with membranes, issue 208 of tucs technical report, turku centre for computer science, 1998. [4] g. păun (2000); computing with membranes, journal of computer and system sciences, 61(1):108-143. [5] g. păun; m.j. pérez-jiménez (2010); solving problems in a distributed way in membrane computing: dp systems, international journal of computers communications & control, 5(2):238-250. [6] g. păun; m.j. pérez-jiménez, a. riscos-nunez (2008); tissue p systems with cell division, international journal of computers communications & control, 3(3):295-303. [7] http://isiwebofknowledge.com (retrieved on july 3, 2015). [8] http://www.esi-topics.com/fbp/2003/february03-gheorghepaun.html (retrieved on may 15, 2015). [9] http://www.imar.ro/gpaun/ (retrieved on april 10, 2015). [10] http://ppage.psystems.eu/ (retrieved on april 10, 2015). int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 90-100 speed control of a permanent magnet synchronous machine powered by an inverter voltage moment approach j. khedri, m. chaabane, m. souissi jamel khedri, mohamed chaabane (corresponding author), mansour souissi research unit of industrial processes control national school of engineers of sfax enis, route soukra, km 3.5 bp.w, 3038, sfax, tunisia e-mail: {khedrijamel,chaabane_uca}@yahoo.fr, mansour.souissi@ipeis.rnu.tn abstract: in this paper, the method of moments is presented in order to synthesize controllers for current and speed of a magnet permanent synchronous machine (pmsm). the controller’s dynamics are chosen in respect with the time domain constraints, which have equality relationships between the moments of the closed-loop system and those of the reference model. the linear matrix inequality (lmi) formalism is used for the controller synthesis. the proposed controller is applied for the speed tracking of a pmsm to illustrate the performances of the method. keywords: times specifications, method of moments, reference model, lmi, pmsm. 1 introduction new industrial applications require variable speed drives with high dynamic performance. in recent years several techniques of control have been developed, allowing pmsm with variable speed to achieve these performances. however, vector control, which allows decoupling between control variables remains the most widely used [1–3]. the main advantages of this configuration are that the regulatory cascade is a very industrial responded [4, 5]. it gives high dynamic performance for a wide range of applications. the complexity of the dynamic model of the permanent magnet synchronous variable speed and the presence of external disturbances and parametric variations limit the performance of the control law [6]. thus, our main contribution in this paper consist in introducing a time constraint with the help of a reference model, simple to operate and which characterizes the dynamics of the closed loop system. this time constraint is formulated by the equality of the first time moments of the closed-loop system and those of the reference model [7–11]. practically, a first lmi is used to insure closed-loop stability, a second one permits, by minimizing the norm 2 of time moments cost, to identify time specifications (overshoot, response time ...). without loss of generality, the methodology has been restricted in this paper to the case of square, invertible, mimo (multi inputs, multi outputs) systems. the paper is organised as follows: in section 2, we define the state space representations of the systems and those of the controller; moreover, the problem of the design of a controller verifying stability and time performances is stated. in section 3, we propose a solution of this problem, owing to the demonstration of a specific theorem. experimental results applied to a permanent magnet synchronous machine (pmsm) are presented in section 4. copyright c⃝ 2006-2011 by ccc publications speed control of a permanent magnet synchronous machine powered by an inverter voltage moment approach 91 2 problem statement consider a linear time invariant (lti) square invertible system h(s) with m inputs and m outputs: { ẋsys(t) = asysxc(t) + bsysu(t) y(t) = csysx(t) (1) the controller is modeled in the state space by:{ ẋc(t) = acxc(t) + bce(t) u(t) = ccxc(t) + dce(t) (2) with e(t) = r(t)−y(t) (3) where: xsys(t) ∈ rl is the state vector of the system,u(t) ∈ rm is the control input,y(t) ∈ rm is the output,r(t) ∈ rm is the reference of closed-loop system, figure 1. matrices asys, bsys, csys are supposed to be known with appropriate dimensions. the controller model matrices ac, bc, cc and dc are to be computed. figure 1: closed-loop system the synthesis of controller requires the construction of the augmented model which regroups the system and the controller model. consider the augmented state vector z(t) defined as: z(t) = [ xsys(t) xc(t) ] . (4) then, the closed-loop system, figure 1, can be represented by the following model:{ ż(t) = abfz(t) + bbfr(t) y(t) = cbfz(t) (5) with: abf = [ asys −bsysdccsys bsyscc −bccsys ac ] ; bbf = [ bsysdc bc ] ; cbf = [ csys 0 ] (6) let’s note that the closed-loop stability is ensured if and only if there exist a positive definite matrix p with p = p t , such as the lmi pabf + a t bfp < 0 (7) 92 j. khedri, m. chaabane, m. souissi is satisfied. to facilitate the study, our work is restricted to the case where the controller c(s) is to be synthesised. the controller design can be considered as a static feedback gain k for the augmented system. the controller synthesis is ensured by the fact that matrices ac and bc are defined a priori and only the matrices cc and dc are to be computed. for this purpose, we consider the following hypothesis: the matrix abf of augmented system can be written as follows: abf = ã + b̃kc̃ (8) where: ã = [ asys 0 −bccsys ac ] ; b̃ = [ bsys 0 ] ; c̃ = [ −csys 0 0 i ] (9) it is to notice that the gain k is defined as: k = [ dc cc ] (10) the extended system is then characterized by (ã, b̃, c̃). the objective of this work is to ensure closed-loop stability and to specify transient performances which are characterized by a reference model tref(s) . the dynamics of the reference model are determined by the use of the time moments approach. in this case, the time moments of transfer w(s), which are represented in state space by (aw , bw , cw , dw ) (see [4]), are given by the following relations: mw,0 = −cw a−1w bw + dw mw,j = (−1)j+1cw a −(j+1) w bw + dw (11) (j = 1...∞) mw,0: is the 0th order moment. the exposing j refers to the order of moments. for applying the proposed moment approach, we will define briefly the first three time moments. the 0th order moment m0 is equal to the static gain of the system and represents the area of its impulse response. the first order moment m1 characterizes the average time tm , the same as the average value of a probability density: tm = m1(g) m0(g) g(t) is the impulse response of the system, represented by figure 2. for a first order system, we have m1 = τm0 where represents the time constant of the system. more generally, m1 is used to characterize the time response of the considered system. the second order moment m2, characterizes the variance σ (or dispersion ∆τ) of the impulse response around its mean time tm. in practice, the time moments m0, m1, m2 carry sufficient information on the impulse response: static gain, mean time and dispersion. speed control of a permanent magnet synchronous machine powered by an inverter voltage moment approach 93 figure 2: characterization of an impulse response the controller has to be designed such that the closed-loop transfer t(s) is equal to the reference model tref(s) . (i + h(s)c(s))−1h(s)c(s) = tref(s) (12) or c(s)(i −tref(s)) = h−1(s)tref(s) (13) it is known that such control law u(t) is in the form: u(t) = ke(t) using state space representation of c(s), we can write: u(s) = [cc(si −ac)−1bc + dc]e(s) (14) while referring to considered hypothesis given by relation (9), equation (13) can be written as: k [ i (si −ac)−1bc ] (i −tref(s)) = h−1(s)tref(s) (15) if we set: f(s) = h−1(s)tref(s) (16) w(s) = [ i (si −ac)−1bc ] (i −tref(s)) (17) then, the time constraint (15) can be expressed by: kw(s) = f(s) (18) notice that the last relation (18) is linear, then the formalism lmi can be applied. in fact, an ideal equality in the form (18) between the closed-loop t(s) and tref(s) is not possible to obtain. 94 j. khedri, m. chaabane, m. souissi therefore, it is recommended to replace an ideal equality by an approximation between t(s) and tref(s). in order to achieve this condition, we use the time moments approach, as mentioned previously. therefore, when the objective is to characterize the time response, the equality (18) is transformed to a minimization of the quadratic distance between the first (n + 1) time moments of the closed-loop system and its reference model, where the minimization criterion is defined as: (kmw,j −mf,j)t (kmw,j −mf,j) < γi (19) j = 0, 1, ..., n. using this criterion, we can notice that the transient responses of t(s) and tref(s) become approximately similar (they have approximately the same overshoot, the same response time, etc.) the overall objective is achieved if there exist a symmetric and positive defined matrix p and a gain k given by relation (10). in such case, the closed-loop stability condition (7) and the minimization criterion condition (19) are satisfied. in the next section, we present a solution to this problem, which can be formulated by the theorem 1. 3 mains results the objective of this section is to give solution of the previous problem. 3.1 theorem 1. consider a lti system (asys, bsys, csys) (1) and the corresponding augmented system (ã, b̃, c̃) defined in (9). consider a static state feedback ke stabilizing the pair (ã, b̃). the stability of the closed-loop system and the time constraints imposed by the reference model tref(s) are guaranteed, if the optimisation problem: minimum γ (l, g, p) with the constraints p > 0 [ sym{p(ã + b̃ke)} (lc̃ −gke)t + pb̃ (lc̃ −gke) + b̃t p −(g + gt ) ] < 0 (20) and [ −i (lmw,j −gmf,j) (lmw,j −gmf,j)t −γi ] < 0j = 0, 1, ..., n. (21) has a solution, then the gain of dynamic controller c(s) is given by : k = g−1optlopt (22) notice that lopt, gopt correspond to the minimal value of γ denoted γopt. before demonstration of theorem 1, let us recall the following result [6, 7]. 3.2 lemma 1. consider the augmented system (5), where the state matrix abf is defined by (8). the following statements are equivalent: speed control of a permanent magnet synchronous machine powered by an inverter voltage moment approach 95 1) there exist a symmetric and positive defined matrix p = p t > 0 and two matrices ke and k such that: p(ã + b̃ke) + (ã + b̃ke) t p < 0 (23) p(ã + b̃kc̃) + (ã + b̃kc̃)t p < 0 (24) 2) there exist a symmetric and positive defined matrix p = p t > 0 , a non singular matrix g and two matrices ke and k such that:[ sym{(ã + b̃ke)t p} pb̃ b̃t p 0 ] + sym{ [ 0 i ] g[(kc̃ −ke)− i]} (25) 3.3 proof of lemma 1 first of all, notice that the orthogonal complements of the second term of (25) are: v = [ i 0 ] , the orthogonal complement of v , such that v v ⊥ = 0, is v ⊥ = [ 0 i ] ; and u = [ i (kc̃ −ke)t ] , the orthogonal complement of u, such that uu⊥ = 0, is: u⊥ = [ (kc̃ −ke)t −i ] applications of elimination lemma [9] lead to left and right multiplication of (25) by v and v ⊥ respectively, which gives inequality (23): [ i 0 ] sym{ [ 0 i ] g [ (kc̃ −ke)− i ] } [ i 0 ] = p(ã + b̃ke) + (ã + b̃ke) t p < 0 even, if (25) is left and right multiplied respectively by u and u⊥, we get inequality (24). 3.4 proof of theorem 1 by using shur complement, the condition (21) can be expressed as: (lmw,j −gmf,j)(lmw,j −gmf,j)t < γi (26) j = 0, 1, ..., n. besides, if one multiplies equation (18) by g: lw(s) = gf(s) (27) equation (19) becomes: (lmw,j −gmf,j)(lmw,j −gmf,j)t < γi (28) j = 0, 1, ..., n. one demonstrates that (26) and then the condition (21) correspond to the minimization of the 2 norm of the error between the (n+1) first moments of the closed-loop and those of the reference model. if the minimization of γopt is satisfied, then the two transfers t(s) and tref(s) are very close in low frequencies, which implies the desired transients for the closed-loop system. the stability is also insured due to the fact that condition (20) implies condition (24), equivalent to condition (7) which expresses closed-loop stability. 96 j. khedri, m. chaabane, m. souissi 4 application to the permanent magnet synchronous machine (pmsm) our permanent magnet synchronous machine (pmsm) is powered by a two levels voltage inverter. to obtain non reciprocating quantities, our model is done in the park landmark [10]. thus we have the following equations in the synchronously d-q reference frame [11, 12]:  i̇d = −rs ld id + lq ld wriq + 1 ld vd i̇q = −rs lq iq − ldlq wrid − ϕ lq wr + 1 lq vq ẇr = p (ce−cr) j − fc j wr ce = p[(ld −lq)id + ϕ]iq − fc j wr − fcj wr ẇr = p2 j [(ld −lq)id + ϕ]iq − fc j wr − pj cr (29) where vd and vq are the stator voltages, id and iq are the stator currents, ldand lq are the inductances, rs is the stator winding resistance, ϕ is the flux linkage of the permanent magnets, wr is the angular velocity of the motor shaft, fc is the friction coefficient relating to the rotor speed; j is the moment of inertia of the rotor , ce is the electromagnetic torque, cr is the load torque and p is the number of pole. in an equivalent manner, these equations can be written in the form of a general transfer function h(s) who contains the open-loop transfer of variables to control namely hid(s) and hwr(s). h(s) = [ hid(s) 0 0 hwr(s) ] (30) where: hid(s) = 1 rs 1 + τid(s) , hwr(s) = hbfiq p2ϕ fc 1 + τ(s) (31) where τid = ld rs ; τ = j fc let hbfiq(s),is the closed-loop transfer of the current iq: hbfiq = 11+τbfiq(s) τbfiq is chosen equal to 7.0175 10-4 s in order to perform a time response of the closed-loop system ten times faster than open-loop. notice that: τiq = lq rs ; hiq(s) = 1 rs 1+τiq(s) the objective is to reduce the time response of the system, to keep the overshoot of the closedloop inferior to 5% while decoupling the two outputs. these specifications can be carried out by a second order reference model with static unity gain for the speed, and a first order one for the current id: tref(s) = [ 1 1+τidref s) 0 0 1 1+ 2ζ w (s)+ s 2 w2 ] (32) the design choices correspond to τidref = 0.1τid in order to perform a time response of the closed-loop current ten times faster than the open-loop. ζ = 7.6205, w = 93.906rds−1 in order to obtain a closed-loop speed 3.3 times faster than the open-loop one. let’s note that hwr has two constants time: τ = j fc = 0.5333s, τ1 = 7.047210−4s−1 the two constants time of the closed-loop speed are τbf = 0.1618s, τ1bf = 7.017510−4s notice that the decoupling specification is insured by a diagonal reference model. the d-axis current id is regulated to follow a zero setpoint. the static speed control of a permanent magnet synchronous machine powered by an inverter voltage moment approach 97 state feedback ke stabilizing the pair (ã, b̃) such as (23) feasible has been computed: ke = [ −79.1667 0 0 722 0 0 0 −2.1433 0 0.0939 ] (33) solving lmis (20) and (21) by lmi optimization algorithm, the matrices p , g and l can be obtained: g = 108 [ 0.0065 0 0 4.5881 ] ; γopt = 6.609610 −14 (34) the obtained static gain is: k = [ 5.7 0 722 0 0 0.5006 0 0.09386 ] (35) 5 experimental results and interpretations the implementation of our model was performed on a test benchmark consisting of two permanent magnet synchronous machines (figure 3) fitted by the same encoder and powered by a voltage inverter; one was used as a motor and the other one as a load. the proposed control algorithm was executed by the use of matlab/ simulink software. then it was compiled and implemented on dspace 1104. the digital sampling period was taken equal to 0.1ms. figure 3: photo of the test bench. 98 j. khedri, m. chaabane, m. souissi figures (4)-(6) illustrate the experimental results of the dynamic behaviour of pmsm for speed operating without charge. 0 5 10 15 −8 −6 −4 −2 0 2 4 6 time in s id a n d id r e fe re n ce in a id measured id reference figure 4: d-axis current and its reference 0 5 10 15 −8 −6 −4 −2 0 2 4 6 time in s iq a n d iq r e fe re n ce in a iq measured iq reference figure 5: q-axis current and its reference 0 5 10 15 −250 −200 −150 −100 −50 0 50 100 150 200 250 time in s s p e e d a n d s p e e d r e fe re n ce in r d /s wref w meas figure 6: speed and its reference whose data are listed in the appendix. the results show that the closed loop system with speed control of a permanent magnet synchronous machine powered by an inverter voltage moment approach 99 the synthesized controller have a good behaviour: indeed, the measured speed and dq-axis currents track well the trajectory of reference one with good accuracy over the whole speed range, moreover, the time constraint imposed with the help of time moments has permitted to keep the closed loop outputs very close to those of the reference model. 6 conclusion in this paper, a controller design, for current and speed tracking of a pmsm has been developed by using the method of moments. the controller’s dynamics are chosen in respect with the time domain constraints. these constraints have equality relationships between the moments of the closed-loop system and those of the reference model. the proposed controller has been achieved in order to ensure the closed-loop stability and to specify transient performances in regard with a reference model. in this study, the lmi formalism has been used for the controller synthesis. finally, we have applied the proposed controller for the speed tracking of a pmsm which has given satisfactory results. 7 acknowledgements the authors wish to thank all the team of the laboratory of automatic and industrial informatics of the university of poitiers france and special thanks to pr. gerard champenois for the aid he afforded us for the realization of our experimental work validation. 8 appendix : motor parameters motor rated power 1 kw rated current 6.5 a pole pair number (p) 2 d-axis inductance ld 4.5mh stator resistance 0.56 ω motor inertia j 2.08.10−3kg.m2 friction coefficient fc 3.9.10−3nm.s.rad−1 magnet flux constant ϕ 0.064 wb bibliography [1] vas, p., sensorless vector and direct torque control, oxford, u. k, oxford univ. press, 1998. [2] bose, b. k., power electronics and ac drives, englewood cliffs, nj, prentice-hall,1986. [3] bose, b. k., power electronics and variable frequency drives, technology and appliquation,ieee press 1997. [4] flaus, j. m, industrial control, hermes, paris 1994. [5] maret, l., automatic control, polytechnic press romandes, 1987. 100 j. khedri, m. chaabane, m. souissi [6] lin, f-j, real time position controller design with torque feedforward control for a pm synchronous motor , ieee trans. on industrial electronics, vol.44, n◦.3, june 1997, pp. 398-407. [7] bentayeb, a., n. maamri and j-c trigeassou, the moments in control: a tool for analysis, reduction and design,international journal of computers, communications & control (ijccc). vol. 2, num. 1, pp. 82-103, 2007. [8] maamri, n. and trigeassou, j. c, pid design for time delayed systems by the method of moments ,european control conference, groningen holland (1993). [9] maamri, n. and trigeassou, j. c, controllers design by moments placement ,european control conference, rome italy (1995). [10] maamri, n., bentayeb, a. and trigeassou, j. c, design and iterative optimization of reduced robust controllers with equality constraints , recond-milan (2003). [11] trigeassou, j. c., method of moments in automatic . conférence internationale francophone d’automatique, cifa, lille (2000). [12] mehdi, d., control theory some insights . report automatic control uca-enis sfax tunisia avril (2004). [13] peaucelle, d. and arzelier, d., an efficient numerical solution for h2 static output feedback synthesis . european control conference, 2001, pp.3800-3805. [14] skelton, r. e., iwasaki, t. and grigoriadis, k., a unified algebraic approach to linear control design , taylor & francis, (1998). [15] bose, b. k., power electronics and ac drive . new york, prince hall, first edition, (1986), chapter 2, page 95. [16] sturtzer, g. and sanigiel, e., modeling and control of three-phase motors . [17] hassaine, s., applications of new technology command to permanent magnet synchronous machine. thesis, esip, poitiers, march 2008. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(5), 672-691, october 2019. risk evaluation in failure mode and effects analysis based on d numbers theory b. liu, y. deng baoyu liu 1. institute of fundamental and frontier science university of electronic science and technology of china chengdu, 610054, china 2. school of data and computer science sun yat-sen university, china guangzhou, china baoyu.l@hotmail.com yong deng* institute of fundamental and frontier science university of electronic science and technology of china chengdu, 610054, china *corresponding author: prof.deng@hotmail.com abstract: failure mode and effects analysis (fmea) is a useful technology for identifying the potential faults or errors in system, and simultaneously preventing them from occurring. in fmea, risk evaluation is a vital procedure. many methods are proposed to address this issue but they have some deficiencies, such as the complex calculation and two adjacent evaluation ratings being considered to be mutually exclusive. aiming at these problems, in this paper, a novel method to risk evaluation based on d numbers theory is proposed. in the proposed method, for one thing, the assessments of each failure mode are aggregated through d numbers theory. for another, the combination usage of risk priority number (rpn) and the risk coefficient newly defined not only achieve less computation complexity compared with other methods, but also overcome the shortcomings of classical rpn. furthermore, a numerical example is illustrated to demonstrate the effectiveness and superiority of the proposed method. keywords: failure mode and effects analysis, dempster-shafer evidence theory, d numbers, risk evaluation, aggregate assessment. 1 introduction failure mode and effects analysis (fmea) is an efficient technology for identifying potential faults, problems, risk, and errors from the system, procedure, and service. it improves the reliability by preventing these faults, problems, risks, and errors from occurring. risk evaluation is a pivotal procedure in fmea. nowadays, fmea is developed so fast that it is extensively applied in many fields, such as medical care [3,29,32,49,60], society [51], environmental protection [1,8], financial service [65], industry [2,24,69,78], and so on. a traditional method for risk evaluation in fmea is the classical risk priority number (rpn), which is obtained by multiplying the grades of the occurrence assessment, severity assessment, and detection assessment. therefore, how to aggregate the assessment information of copyright ©2019 cc by-nc risk evaluation in failure mode and effects analysis based on d numbers theory 673 these three risk factors is a significant issue, especially when it comes to the information with uncertainty. focusing on this problem, many math models such as fuzzy sets [14, 16, 17, 79], r numbers [53], d numbers [20], z numbers [21,26,27,39,76] and evidence theory [10,55], have been applied to the real applications [37, 50]. in [28], kim et al. present a general model to explain the functional relationship among the three factors, and use the model to discuss the unique role of each factor for comparing the risk of different failure modes. in [77], a new method to risk evaluation based on dempster-shafer evidence theory is proposed. some other evidential fema are presented recently [5]. nevertheless, although classical rpn is easy to use because of its succinct form, it is still criticized for its weaknesses. for example, the experts usually give the assessment with uncertainty or fuzzy information, but classical rpn is not appropriated to treat the fuzzy assessment. furthermore, different combination of risk factors might acquire the same rpn, however, the potential risk might be totally different so that they might have different priorities. with the aim of overcoming these weaknesses, many methods, such as chin’s method [9], are proposed. however, existing methods almost is not only too complex to calculation, but also do not take the non-exclusiveness between two adjacent rankings into account. actually, because of the subjectivity of the experts, two adjacent estimation scales are supposed to be not exclusive mutually. in order to solve the problems, a novel method based on d number theory [13] is proposed in this paper. on the one hand, the assessments for each failure mode are aggregated through constructing and combining d numbers because two propositions are allowed to be non-exclusive in d numbers theory. on the other hand, rpn is applied in the proposed method so as to reduce the computation complexity, simultaneously, novel compute mode to rpn and risk coefficient present in this paper is capable to get rid of the weaknesses of the classical rpn. last but not the least, an illustrative example is used to show the effectiveness and superiority of the proposed method. the remainder of this paper is organized as follows. key concepts and previous theories are reviewed in short in section 2. in section 3, a novel risk evaluation in failure mode and effects analysis based on d numbers theory is proposed. to demonstrate the effectiveness and superiority of the proposed method, a numerical example is illustrated in section 4. last but not the least, a brief conclusion is drawn in section 5. 2 preliminaries 2.1 dempster-shafer evidence theory the real application is inevitable to deal with uncertainty [4, 6, 45–47]. dempster-shafer evidence theory (d-s theory) [10, 55], is a significant theory to handle uncertainty information [11]. compared with bayesian theory, it needs weaker conditions so that it is often deemed as an extension of the bayesian theory. d-s theory is widely applicated in many fields, such as decision making [6,22,30,44,71], pattern recognition [40,41,43,73], evidential reasoning [15,42,74,83–85], risk and reliability [52,54], information fusion [59,61,75], uncertainty modelling [19,25,58] and conflict management [36,68,81]. definition 1. let θ = {h1, h2, · · · , hn} be a finite nonempty set, which consist of n mutually exclusive elements. let p(θ) be the power set of θ, which is composed of 2n elements. the basic probability assignment (bpa) function is defined as a mapping of the power set p(θ) to a number between 0 to 1, that is m : p (θ) → [0, 1], and which satisfies the following conditions: m (∅) = 0; (1) 674 b. liu, y. deng ∑ a∈p(θ) m (a) = 1. (2) the mass m(a) represents how strongly the evidence supports to a. definition 2. let m1, m2 be two bpas defined on the frame of discernment θ. the dempster’s combination rule, denoted by m = m1 ⊕ m2, is defined as follows: m (a) = { 1 k−1 ∑ b∩c=a m1(b)m2(c), a 6= ∅ 0, a = ∅ (3) with k = ∑ b∩c=∅ m1(b)m2(c) (4) where k is a normalization constant which reflects the conflict of two bodies of evidence. actually, 0 ≤ k ≤ 1. k = 0 shows the absence of conflict between two bodies of evidence. while k = 1 shows complete conflict between m1 and m2. besides, when k = 1, the dempster’s combination rule is not any longer applicable, a possible explanation is open world assumption [32, 33]. in order to make decision in terms of the bpa, a method called pignistic probability transformation is present in [57], which derive a distribution of probabilities from the bpa. the pignistic probability transformation function is defined as follows: definition 3. let m be a bpa on the frame of discernment θ, a pignistic probability transformation function betpm : θ −→ [0, 1] associated to m is defined by betpm (x) = ∑ x∈a, a∈θ 1 |a| m(a) 1 −m(∅) (5) where m (∅) 6= 1 and |a| is the cardinality of proposition a. 2.2 fuzzy set theory fuzzy sets were proposed independently by zadeh [79] in 1965 as an extension of the classical notion of set. fuzzy set theory is widely applied in many fields [23, 33, 72]. it reflects the stay of the object and its fuzzy concept as a fuzzy set. then, it sets up the appropriate membership functions through fuzzy set about operation and transform, and analyzes the fuzzy object based on the fuzzy mathematics. in the objective world, there are many fuzzy phenomena. for example, when evaluating a person’s appearance, people usually use linguistic variables whose values are represented by words or sentences in a natural or artificial language, such as "very pretty", "pretty", "general", "ugly", and "very ugly". definition 4. denote l as the universe of discourse, a fuzzy set a is described by a membership function µa satisfying µa : l −→ [0, 1] where µa(x) is called the membership degree of x ∈ l belonging to fuzzy set a. for l = {x1, . . . ,xi, . . . , xn}, the fuzzy set (a,µa) is represent by µa (x1) x1 , . . . , µa (xi) xi , . . . , µa (xn) xn . risk evaluation in failure mode and effects analysis based on d numbers theory 675 1 ! " #0 !(") x figure 1: graphical presentation of the triangular fuzzy number it is obvious that a fuzzy set is characterized entirely by its membership function. when µa(x) get value from {0, 1}, fuzzy set a degrade into a classical set. a is a fuzzy subset of the real number r, and its membership function satisfies µa (x) : r −→ [0, 1] where x is real number and there exists an element x0 such that µa = 1. triangular fuzzy numbers are the most extensively applied fuzzy numbers. a triangular fuzzy number is usually expressed as a = (a1,a2, a3), as graphically shown in figure 1, which has the following membership function µa (x) =   0, x < a1 x−a1 a2−a1 , a1 ≤ x ≤ a2 a3−x a3−a2, a2 ≤ x ≤ a3 0, x > a3 (6) where a1 < a2 < a3. in practice, fuzzy numbers are bound up with linguistic variables to describe the fuzzy evolution to objects. 2.3 risk priority number the real systems are too complicated to be modelled. risk priority number (rpn) is a traditional and typical method to model and evaluate risk in fmea. rpn is calculated by multiplying the grades of occurrence assessment (o), severity assessment (s), and detection assessment (d). that is rpn = o×s×d (7) where o stands for the probability of occurrence of failure mode, s refers to the severity of failure mode and d refers to the probability of failure being detected. the three risk factors are evaluated by fmea experts using a 1 to 10 numeric scale. besides, occurrence assessment is expressed in table 1. the larger rpn is, the more important degree it is supposed to be assigned, referring to the failure mode should be more priority to be corrected. although this method is easy to use because of its sententious form, traditional rpn is still criticized for its weaknesses. for example, traditional rpn does not take the weights of three risk factors into consideration. besides, different combination of risk factors might acquire the same rpn, however, the potential risk might be totally different so that they might have different priorities. 676 b. liu, y. deng 2.4 d numbers d number, is a useful tool to model uncertain information, which overcomes the shortcomings of dempster-sharfer theory [20]. nowadays, d number is widely used in many fields such as decision making [7,19,31], risk assessment [18,29,35,67], reliability analysis [70,80], data fusion [8,62,66]. it is defined as follows: definition 5. let ω be a finite nonempty set, d number is a mapping d : ω −→ [0, 1], such that ∑ b⊆ω d (b) ≤ 1 and d (∅) = 0 (8) where ∅ is the empty set and b is a subset of ω. if d (b) = 1, the information is regarded to be complete while d (b) < 1, it is considered to be incomplete. most importantly, different from d-s theory, d number does not request the elements of set ω to be mutually exclusive. in order to express the non-exclusiveness in ω, a fuzzy membership function is used to measure the exclusive/non-exclusive degree [82]. definition 6. let ai and aj be two non-empty elements in 2ω, the non-exclusive degree between ai and aj is characterized by a fuzzy membership function u¬e as follows: u¬e : 2 ω × 2ω −→ [0, 1] (9) with u¬e (ai,aj) = { 1, ai ∩aj 6= ∅ p, p ∈ [0, 1], ai ∩aj = ∅ (10) besides, u¬e (ai,aj) = max x∈ai, y∈aj {u¬e(x,y)} (11) let ue be the exclusive degree between ai and aj, then ue = 1 −u¬e. an illustrative example is given as follows to express the calculation of the non-exclusive degree. table 1: assessment rankings for occurrence in fmea ranking probability of occurrence possible failure rate 10 extremely high: failure almost inevitable ≥ 1/2 9 very high 1/3 8 repeated failures 1/8 7 high 1/20 6 moderately high 1/80 5 moderate 1/400 4 relatively low 1/2000 3 low 1/15000 2 remote 1/150000 1 nearly impossible ≤ 1/150000 risk evaluation in failure mode and effects analysis based on d numbers theory 677 1 2 3 4 5 1 0 a b c figure 2: graphically presentation of fuzzy variables in table 2 example 7. suppose there is a non-empty set ω = {a,b,c}, where a,b,c are three fuzzy variables represented by triangular fuzzy numbers given in table 2 and shown in figure 2. the nonexclusiveness degree can be calculated as follows [12]: u¬e (a,b) = areaa∩b areaa + areab −areaa∩b (12) where the areas of fuzzy numbers a and b are characterized by areaa and areab, and the area of the overlap of a and b is areaa∩b. thus, according to eq.12, we have u¬e (a,b) = areaa∩b areaa + areab −areaa∩b = 0.25 1 + 1 − 0.25 ≈ 0.1429 u¬e (b,c) = areab∩c areab + areac −areab∩c = 0.25 1 + 1 − 0.25 ≈ 0.1429 u¬e (a,b,c) = max{u¬e (a,b) ,u¬e (b,c)} = max{0.1429, 0.1429} = 0.1429 u¬e (a,{a,b}) = max{u¬e (a,a) ,u¬e (a,b)} = max{1, 0.1429} = 1 similar with dempster combination rule, the combination rule is discussed under two cases: complete information and incomplete information. definition 8. (complete information). let d1 and d2 be two numbers over ω with∑ a⊆ω d1 (a) = 1 and ∑ a⊆ω d2 (a) = 1, the combination of d1 and d2, indicated by d = d1 ⊙ d2, is defined by d (a) =   0, a = ∅ 1 1−kd ( ∑ b∩c=a u¬e (b,c) d1 (b) d2 (c) +∑ b∪c=a,b∩c=∅ u¬e (b,c) d1 (b) d2 (c)), a 6= ∅ (13) with kd = ∑ b∩c=∅ (1 −u¬e (b,c) )d1 (b) d2 (c) (14) table 2: the fuzzy number of fuzzy variables fuzzy variables fuzzy numbers a (1, 2, 3) b (2, 3, 4) c (3, 4, 5) 678 b. liu, y. deng it is worth mentioning that this combination rule can be degenerated to the classical dempster’s rule if u¬e = 0 for any b∩c = ∅. a numerical example is used to illustrate the combination of two d numbers under the complete information situation as follows: example 9. there are two d numbers over ω = {a,b}: d1 (a) = 0.4, d1 (b) = 0.6 d2 (a) = 0.9, d2 (b) = 0.1 and assume u¬e (a,b) = 0.1429. thus, we have d′ = d1 ⊙ d2 that kd′ = (1 − 0.1429) × [0.4 × 0.1 + 0.6 × 0.9] = 0.4971 d′(a) = 1 1 − 0.4971 × 1 × 0.4 × 0.9 = 0.7159 d′(b) = 1 1 − 0.4971 × 1 × 0.6 × 0.1 = 0.1193 d′(a,b) = 1 1 − 0.4971 × 0.1429 × [0.4 × 0.1 + 0.6 × 0.9] = 0.1648 definition 10. (incomplete information). let d1 and d2 be two numbers over ω with∑ a⊆ω d1 (a) < 1 and∑ a⊆ω d2 (a) < 1, the combination of d1 and d2, defined by d = d1 ⊙ d2 and calculated as follows: d(a) = { 0, a = ∅ f (q1,q2) dt(a)∑ b⊆ω dt(b) , a 6= ∅ (15) with dt (a) = ∑ b∩c=a u¬e (b,c) d1 (b) d2 (c) + ∑ b∪c=a,b∩c=∅ u¬e (b,c) d1 (b) d2 (c) ,∀ a ∈ ω and q1 = ∑ a⊆ω d1(a), q2 = ∑ a⊆ω d2(a) (16) where f (q1,q2) is a function satisfying 0 ≤ f (q1,q2) ≤ max{q1,q2}, f (q1,q2) = 1 if q1 = 1 and q2 = 1. next, a simple example is used to illustrate the combination process of d numbers according to definition 10. example 11. there are two d numbers over ω = {a,b}: d1 (a) = 0.7, d1 (b) = 0.2 d2 (a) = 0.5, d2 (b) = 0.3 risk evaluation in failure mode and effects analysis based on d numbers theory 679 assume u¬e (a,b) = 0.1429, and let f (q1,q2) = q1 ×q2. thus, we have d = d1 ⊙ d2 that f (q1,q2) = (0.7 + 0.2) × (0.5 + 0.3) = 0.72 dt (a) = 0.7 × 0.5 = 0.35 dt (b) = 0.2 × 0.3 = 0.06 dt (a,b) = 0.1429 × (0.7 × 0.3 + 0.2 × 0.5) ≈ 0.044 thus, ∑ b⊆ω d1(b) = 0.35 + 0.06 + 0.044 = 0.454 d (a) = f (q1,q2) dt (a)∑ b⊆ω d1(b) ≈ 0.555 d (b) = f (q1,q2) dt (b)∑ b⊆ω d1(b) ≈ 0.095 d (a,b) = f (q1,q2) dt (a,b)∑ b⊆ω d1(b) ≈ 0.070 if there are n d numbers expressed as d1, d2, · · · , dn, whose weights are w1,w2, · · · , wn, satisfying ∑n i=1 wi = 1. at first, the average d number among d1,d2, · · · ,dn is defined as d̄ (a) = n∑ i=1 widi(a),∀a ⊆ ω. (17) then, the result of combination d1,d2, · · · ,dn is acquired by combining average d number d̄ with itself n− 1 times. d = d̄ ⊙ d̄ ⊙ · · · ⊙ d̄ (18) where ⊙ is the combination rule given in definition 8 and definition 10. 3 proposed method failure mode and analysis (fmea) is an efficient technology to identify and remove potential faults, errors and risk from systems. in fmea, risk evaluation is a significant procession. traditionally, risk priority number (rpr) is used to evaluate risk, which is calculated by multiplying the grades of occurrence assessment (o), severity assessment (s), and detection assessment (d). although classical rpn is easy to use for its concise form, it is still criticized for its weaknesses. for example, traditional rpn does not take the weights of three risk factors into consideration. besides, different combination of risk factors might acquire the same rpn, however, the potential risk might be totally different so that they might have different priorities. so far, many methods are presented. nevertheless, most of them not only have rather complex algorithm, but also do not consider the non-exclusiveness between two rankings in assessment. with the aim of solving these problems, in this paper, a novel method to risk evaluation based on d numbers theory is proposed. first of all, each assessment rating is treated as a fuzzy 680 b. liu, y. deng variable which is represented as a fuzzy number. then, non-exclusive degree between two ratings can be calculated. next, for each failure mode, the assessments of experts are aggregated by d numbers theory. the assessments are treated as d numbers and then are combined by using d numbers combination rule. furthermore, rpns are calculated for ranking the failure mode. last but not the least, when coming to the same rpn of some failure modes, a variable, named risk coefficient, is defined to rank the failure modes with the same rpn. the large risk coefficient is, the more important degree it is supposed to be assigned, referring to the failure mode should be more priority to be corrected. step 1. make sure the triangular fuzzy numbers for each rankings in assessment and calculate the non-exclusive degrees by 12. step 2. the assessments of experts are regarded as d numbers, that is, for each failure mode, the assessments of each expert is constructed as a d number. therefore, the aggregation of experts’ assessment of each failure mode by combining the corresponding d numbers. if information is complete, the equations in definition 8 are used to the combination. if the information is incomplete, the equations in definition 10 are applied to the combination. what is more, if different experts have different weights, it is supposed to put the 17 into use. step 3. for the results of step 2, use 5 to calculate pignistic probability transformation (ppt). step 4. calculate rpn. firstly, calculate the mathematical expectation of each assessment. then, use eq. (7) to calculate the rpn of each failure mode. step 5. calculate risk coefficient. definition 12. let s be the standard deviation of the ratings of three assessments, defined as risk coefficient. as mentioned above, different combination of risk factors might acquire the same rpn, however, the potential risk might be totally different. in this paper, risk coefficient, the standard deviation of the ratings of three assessments, is used to evaluate such kinds of failure modes. as a matter of fact, this method is reasonable. for example, there are two failure modes, the assessments of which are shown in table 3. table 3: the assessments of three risk factors occurrence assessment severity assessment detection assessment fm1 1 10 6 fm2 2 6 5 apparently, two failure modes have the same rpn. however, as is shown in table 3, compared with fm2, fm1 has the better grade in occurrence assessment but performs worse in severity and detection assessment. therefore, fm1 should be more priority to be corrected. when observing and analyzing the data, it is not difficult to find that the distribution of fm2’s data is more concentrated, which has smaller standard deviation. besides, it is worth mentioning that standard deviation is usually used to measure the risk in financial field. thus, it is reasonable that the standard deviation of the ratings of three assessments is applied to measure the risk in risk evaluation. step 6. rank the failure modes though rpn. the larger rpn is, the more priority failure mode is supposed to be corrected. if the failure modes have the same rpn, their rankings depend on risk coefficient defined in definition 12. the failure modes with the lager risk coefficient are assumed to be more significant and should be given higher priorities. risk evaluation in failure mode and effects analysis based on d numbers theory 681 table 4: the results of the risk evaluation item rating of risk factor expert 1 expert 2 expert 3 o s d o s d o s d 1 3:40% 7 2 3:90% 7 2 3:80% 7 2 4:60% 4:10% 4:20% 2 2 8 4 2 8:70% 4 2 8 4 9:30% 3 1 10 3 1 10 3 1 10 3 4 1 6:80% 3 1 6 3:70% 1 6 3 7:20% 2:30% 5 1 3 2:50% 1 3 1:70% 1 3:60% 1 1:50% 2:30% 2:40% 6 2 6 5 2 6 5 2 6 5 7 1 7 3 1 7 3 1 7 3 8 3 5:60% 1 3 5:80% 1 3 5:80% 1 6:40% 6:20% 7:20% 9 2:90% 10:60% 4 2:75% 10:90% 4 2:80% 10:90% 4 1:10% 9:40% 1:25% 9:10% 1:20% 9:10% 10 1 10 6 1 10 6 1 10 6 11 1 10 5 1 10 5 1 10 5 12 1 10 6:60% 1 10 5:80% 1 10 6:70% 5:40% 4:20% 5:30% 13 1 10 5:80% 1 10 5 1 10 5 4:20% 14 1 10 6 1 10 6:80% 1 10 6 7:20% 15 2 7:95% 3 2 7 3 2 7 3:70% 6:5% 4:30% 16 2:90% 4 3 2:75% 4 3 2:80% 4 3:80% 1:10% 1:25% 1:20% 2:20% 17 2 5:90% 3 2 5:90% 3 2 5:60% 3 6:10% 6:10% 6:40% 4 numerical example in order to demonstrate the effectiveness and superiority of the proposed method, a numerical example in [77] is solved in this section. supposing there are three experts who evaluate 17 failure modes and identify the ratings of the three risk factors. the assessment results are expressed in table 4. in this illustrative example, the weights of experts and risk factors are supposed to be equal to 1. taking the failure mode 1 for example, the detailed computing is expressed as follows: step 1. the ratings are represented by triangular fuzzy numbers listed in table 5 and shown in figure 3. from table 4, the occurrence assessments of three experts are (3 : 40%, 4 : 60%), (3 : 90%, 4 : 10%) and (3 : 80%, 4 : 20%). therefore, according to the eq. (12), the non682 b. liu, y. deng 0 1 2 3 4 5 6 7 8 9 10 assessment ranking 0 1 f u z z y m e m b e r sh ip d e g r e e figure 3: graphically presentation of fuzzy variables in table 5 table 5: the fuzzy numbers of the assessment ratings assessment ratings fuzzy numbers 1 (0, 1, 2) 2 (1, 2, 3) 3 (2, 3, 4) 4 (3, 4, 5) 5 (4, 5, 6) 6 (5, 6, 7) 7 (6, 7, 8) 8 (7, 8, 9) 9 (8, 9, 10) 10 (9, 10, 10) exclusiveness degrees are calculated as follows: u¬e (3, 3) = 1, u¬e (4, 4) = 1, u¬e (3,{3, 4}) = 1 u¬e (4,{3, 4}) = 1, u¬e (3, 4) = 0.25 1 + 1 − 0.25 ≈ 0.1429 step 2. construct d numbers and combine d numbers. according to section 3, corresponding to the evaluations of three experts, three d numbers are constructed as follows: d1 (3) = 0.4, d1 (4) = 0.6 d2 (3) = 0.9, d2 (4) = 0.1 d3 (3) = 0.8, d3 (4) = 0.2 then, because these d numbers have complete information, the combination among d1, d2 and d3 is calculated by 10-14, that is d1 ⊙ d2 ⊙ d3. at first, calculate d′ = d1 ⊙ d2: kd′ = (1 − 0.1429) × [0.4 × 0.1 + 0.6 × 0.9] = 0.4971 d′ (3) = 1 1 − 0.4971 × 1 × 0.4 × 0.9 = 0.7159 d′ (4) = 1 1 − 0.4971 × 1 × 0.6 × 0.1 = 0.1193 d′ (3, 4) = 1 1 − 0.4971 × 0.1429 × [0.4 × 0.1 + 0.6 × 0.9] = 0.1648 risk evaluation in failure mode and effects analysis based on d numbers theory 683 0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18 the proposed method the method of j. yang et al. figure 4: the risk priority of the failure modes using two methods next, calculate d = d′ ⊙ d3: kd = (1 − 0.1429) × [0.7159 × 0.2 + 0.1193 × 0.8] = 0.2045 d (3) = 1 1 − 0.2045 × [1 × 0.7159 × 0.8 + 1 × 0.1648 × 0.8] = 0.8857 d (4) = 1 1 − 0.2045 × [1 × 0.1193 × 0.2 + 1 × 0.1648 × 0.2] = 0.0714 d (3, 4) = 1 1 − 0.2045 × 0.1429 × [0.7159 × 0.2 + 0.1193 × 0.8] = 0.0429 step 3. calculate pignistic probability transformation (ppt) of the result of step 2 by 5 betp (3) = 0.8857 + 0.0429 2 ≈ 0.9071betp (4) = 0.8857 + 0.0429 2 ≈ 0.0929 step 4. the mathematical expectation of the occurrence assessment is 3 × 0.9071 + 4 × 0.0929 = 3.0929. besides, three experts have the same evaluation to the severity assessment (s) and detection assessment (d) of failure mode 1, what is more, the evaluation are real numbers, 7 and 2, without any uncertainty. thus, in these two assessments, the results through step 1 to 4 are still 7 and 2. step 5. calculate the rpn of failure mode 1 (rpn1) by 7. rpn1 = o1 ×s1 ×d1 = 3.0929 × 7 × 2 = 43.3006 step 6. calculate the risk coefficient of the failure mode 1. x̄1 = 3.0929 + 7 + 2 3 ≈ 4.0310 s1 = √ 1 3 · [(3.0929 − 4.0310)2 + (7 − 4.0310)2 + (2 − 4.0310)2] ≈ 2.6287 the data of other failure modes are treated through step 1 to 6 as mentioned above. then, according to the rpns and risk coefficients, the risk priorities of the failure modes are obtained, which are shown in table 6. meanwhile, in order to demonstrate the effectiveness of the proposed method, the results are compared with that of the j. yang et al.’s method [77]. as shown in table 6, the results of two methods are similar. apart from failure mode 6, 11 and 13, other failure modes have the same risk priority rankings in both two methods. in addition, it is indicated that the five of highest risk priority rankings are failure mode 9, 2, 10, 684 b. liu, y. deng table 6: the results of the risk evaluation failure mode rpn risk coefficient the rankings of the proposed method the rankings of the j. yang et al.’s method 1 43.3006 2.6287 9 9 2 64 3.0551 2 2 3 30 4.7258 12 12 4 18 2.5166 15 15 5 3.0726 1.1478 17 17 6 60 2.0817 5 3 7 21 3.0551 14 14 8 15.0657 2.0110 16 16 9 78.10103376 4.1593 1 1 10 60 4.5092 3 3 11 50 4.5092 7 6 12 51.452 4.5047 6 6 13 50 4.5092 7 6 14 60 4.5092 3 3 15 42 2.6458 10 10 16 23.5152 1.0203 13 13 17 30.4038 1.5643 11 11 14, and 6, which refers that these 5 faults are most likely to occur. furthermore, in both two methods, failure mode 16, 7, 4, 8, and 5 have the five of lowest priorities, indicating that these 5 failures are almost impossible to happen. figure 4 shows the comparison of risk priorities of two methods, in which the ranking is on the abscissa axis while the failure mode is on the vertical axis. as shown in figure 4, two curves have the similar trend, which indicates that the proposed method is as effective as j. yang et al.’s method. nevertheless, the results also reflect the different evaluations using two methods, which precisely demonstrates the superiority of the proposed method. as seen in figure 4, using j. yang et al.’s method, failure mode 11, 12, 13 have the same risk priority, while failure mode 12 has the larger risk priority compared with failure mode 11 and failure mode 13 in the proposed method. because the highest rating of failure mode 12 in detection assessment is 6, which is higher than that of failure mode 11, 13 in detection assessment. thus, failure mode 12 is obviously supposed to have higher risk priority compare with failure mode 11 and failure mode 13. what is more, in j. yang et al.’s method, failure mode 6, 10, 14 have the same risk priority, but in the proposed method, failure mode 6 has the lower risk priority compared with failure mode 10 and failure mode 14. although these failure modes have the same rpn, compared with failure mode 10 and failure mode 14, failure mode 6 has two lower risk ratings in severity assessment and detection assessment and merely a lager rating in occurrence assessment. therefore, failure mode 6 is supposed to have the lower risk priority compared with failure mode 10 and failure mode 14. risk evaluation in failure mode and effects analysis based on d numbers theory 685 5 conclusion in this paper, a novel method to risk evaluation in failure mode and effects analysis based on d numbers theory is proposed. in the proposed method, the application of the d numbers not only aggregates the fuzzy assessment in risk evaluation, but also takes the non-exclusiveness into account. besides, the shortcomings of rpn are overcome successfully. furthermore, the numerical example 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(2019). evidential reasoning approach with multiple kinds of attributes and entropy-based weight assignment, knowledgebased systems, 163, 358–375, 2019. int j comput commun, issn 1841-9836 8(2):220-234, april, 2013. improving offline handwritten digit recognition using concavity-based features m. karic, g. martinovic miran karic, goran martinovic j. j. strossmayer university of osijek croatia, 31000 osijek, kneza trpimira 2b e-mail: miran.karic@etfos.hr, goran.martinovic@etfos.hr abstract: this paper examines benefits of using concavity-based structural features in recognition of handwritten digits. an overview of existing concavity features is presented and a new method is introduced. these features are used as complementary features to gradient and chaincode features, both among the best performing features in handwritten digit recognition. two support vector classifiers (svcs) are chosen for classification task as the top performers in previous works; svc with radial basis function (rbf) kernel and the svc with polynomial kernel. for reference, we also used the k-nearest neighbor (k-nn) classifier. results are obtained on mnist, usps and digits datasets. we also tested dataset independency of various feature vectors by combining different datasets. the introduced feature extraction method gives the best results in majority of tests. keywords: complementary features, concavity features, digit recognition, feature extraction, handwritten character recognition, off-line recognition. 1 introduction handwritten digit recognition is an important area of optical character recognition research. common applications are bank check processing, postal code recognition for mail sorting and recognition of various forms for automated data entry. these applications require high recognition accuracy and speed. handwritten digit recognition is also often used as a platform for testing performance of classification algorithms. typical character recognition process consists of preprocessing, segmentation, feature extraction and classification [1]. this paper deals only with feature extraction and classification. selection of features and classifiers is vital for performance of a recognition system. in [2] [3] it was concluded that feature extraction is of primary importance in character recognition tasks. even simple classifiers can give very high recognition accuracy when a well-chosen feature extraction method is used. a better classifier can still be used to improve the recognition accuracy. combining classifiers is another method used to improve the accuracy; however, we will only discuss single classifier recognition. feature extraction is a process for capturing relevant characteristics of a target object (in this case a digit) from an image with a fixed number of feature variables that make a feature vector. it is preferable that the size of a feature vector be as small as possible [4]. process is sometimes skipped and the classification is performed directly on the raw image data. numerous types of features for offline handwritten digit recognition exist, ranging from structural, which are based on geometric and topological properties of a digit, to statistical, which are based on digit image statistical properties. good features should maximize the between-class variance [2] [5]. classification is a process of assigning new data to a category based on training data in known categories. in this paper, we use a number of human identified digit images split into training and test set. a classifier learns on training images and labels and produces output based on copyright c⃝ 2006-2013 by ccc publications improving offline handwritten digit recognition using concavity-based features 221 test images. output is then compared to test labels to evaluate the classification performance. a good classifier should be able to learn on the training data but maintain the generalization property to be accurate when identifying the test set. in [2] performance of a range of features and classifiers was tested in handwritten digit recognition tasks. it was concluded that gradient feature [6] and chaincode feature [7], both types of direction features, overall performed best in all tests. furthermore, adding complementary structural features may improve the accuracy as in [2] [8]. support vector classifier (svc) with radial basis function (rbf) kernel gave the highest accuracy. this paper further investigates how complementary structural features affect overall recognition accuracy. complementary features incorporate character properties that supplement primary feature vector. one type of studied complementary features in [2] is concavity features. we expand this research with an introduced variation of concavity based complementary features and another variation described in [9]. gradient and chaincode features are used as primary features. classification is performed using the support vector classifiers and k-nearest neighbor (k-nn) classifier. new data is also used to test recognition performance. datasets used for the experiments are mnist [10], usps [11] and digits [12]. the first two are well known datasets in evaluation of handwritten digit recognition and classification algorithms [2] [13] [14], while the last one is relatively unknown [15]. all are divided into standard training and test sets to ensure fair comparison of different classification meethods. transferring expertise from one dataset to another is an unsolved problem [12] [15] so we also tested how various features perform on data obtained by combining different datasets. to make this research easy to reproduce and extend, source codes in matlab m-file format used to extract features and links to used datasets are available online [16]. the remainder of the paper is organized as follows. section 2 explains primary and complementary feature extraction methods in detail. section 3 brings a summary of classification methods. section 4 presents datasets used to test recognition performance and some previous results on these datasets. section 5 shows the experimental setup. recognition results are shown and discussed in section 6. conclusion is given in section 7. 2 feature extraction this section presents feature extraction methods used in handwritten digit recognition experiments. first, the primary features are shown, namely gradient and chaincode features. following are complementary features based on digit image concavity. two existing and an introduced concavity feature extraction methods are explained. for every method, first a brief explanation of the method is given, and then the setup we used in the experiments. source images for feature extraction are binary images. size of all images matches the size of mnist dataset images. other datasets are converted into the format of mnist dataset. feature vectors are scaled to values in range 0 – 1 so that all feature variables would contribute to classification process to the same extent. scaling is performed separately by feature extraction method, meaning if we use gradient and concavity feature vector, first a maximum value fmax1 of gradient feature vector is found. gradient feature vector is divided by fmax1. concavity feature vector is divided by its maximum value fmax2. feature vectors are then merged. 2.1 gradient gradient features, as in [1] [2] [6], are calculated by using the sobel operator masks (figure 1) on character image to compute the gradient components on two axes. grayscale images should be used, but if source images are binary they can simply be converted into pseudogray images, 222 m. karic, g. martinovic as in [1] [2] [6]. gradient strength and direction is computed from gradient components in every character image pixel. figure 1: sobel masks gradient vectors are then mapped to, most often, four or eight standard directions. every vector is decomposed into two components on two nearest standard directions. figure 2 shows eight standard directions and gradient vector decomposition. character image is divided into zones by a grid, usually 4 × 4 or 5 × 5. the total sum of the component vectors is calculated for each standard direction in a zone. standard direction intensities for all zones make a feature vector. different but similar methods for gradient feature extraction can also be found in the literature, as in [9]. figure 2: gradient standard directions and vector decomposition since the digit recognition in our experiments is performed on binary images, first an image is converted into a pseudo-gray image. a 3 × 3 gaussian lowpass filter with σ = 0.5 is used to blur binary image. gradient features are then extracted using eight standard directions and a 5×5 grid. this makes a feature vector of size 5×5×8 = 200. a transformation on the feature vector, y = x0.5, known as box-cox transform [17], is carried out to make its distribution closer to the normal distribution. 2.2 chaincode chaincode features, as in [2] [7] are calculated based on a character contour. every pixel on a contour is assigned a direction code, based on its succeeding pixel’s relative position, as in figure 3. there are eight possible directions. character image is then divided into zones by a grid, usually 4 × 4 or 5 × 5. for every zone, number of direction codes for each direction is counted, making a feature vector. number of features can be halved by summing codes in opposite directions, as in [2]. chaincode feature extraction is performed directly on binary digit images. 5 × 5 grid and information in eight directions was used to form a vector of 5 × 5 × 8 = 200 feature variables. box-cox transformation y = x0.5, as in [17], is used on the feature vector to make its distribution closer to the normal distribution. 2.3 concavity concavity features are used primarily as complementary features since they contain only a limited amount of information but can improve recognition accuracies of other feature extraction improving offline handwritten digit recognition using concavity-based features 223 (a) (b) figure 3: example of a contour (a) and its chaincode values (b) methods. they are structural features based on measurements of character concavities. this paper considers three different concavity feature extraction methods; two existing and one new. the first method by favata et al. is explained in [9], we will label it conc1 for future reference. the second method by liu et al. is shown in [2] and is labeled conc2. the introduced method is labeled conc3. all concavity features are extracted from binary images. below is a short overview of existing methods and a more extensive explanation of the new method. method conc1 uses a star-like operator that shoots rays in eight directions. it is observed what each ray hits, a character or image border. this operator is applied to every pixel on a digit image. unfortunately, there is no detailed explanation of the method that would allow identical reproduction, however we believe that our reproduction should produce similar results as it utilizes the same idea. authors themselves say that they presented one particular implementation of their philosophy and that others are possible. image is divided into zones by a 5×5 grid, and for every zone total number of border hits for each direction is counted. similar procedure is used in gradient feature extraction. to reduce the number of features, diagonal hits are divided among neighboring horizontal and vertical direction counts. total number of features is 5×5×4 = 100. figure 4a shows conc1 feature extraction. method conc2 measures the distance from character convex hull to character pixels. only horizontal distances are calculated, both from the left and from the right. figure 4b shows conc2 feature extraction. in [2] it is used in conjunction with crossings (crs) feature extraction method. this method counts the number of transitions from black to white pixels on a binary image for every row. total number of features depends on image size, and for conc2 it is equal to two times the image height in pixels, while for crs it is equal to image height. (a) (b) figure 4: conc1 (a) and conc2 (b) feature extraction we tested by using only conc2, and by using both conc2 and crs features. since all digit images we used areactually 20×20 pixels centered by mass in a 28×28 pixel frame, we performed conc2 feature extraction on these 20 × 20 pixels. this gave us a feature vector of 40 variables. crs features were extracted from the whole 28×28 image, giving additional 28 features. the proposed concavity feature extraction method, conc3, is based on measurements of char224 m. karic, g. martinovic acter concavity regions in a binary image. first the convex hull of a character is calculated using a method described in [18]. by subtracting character image from the hull, concavity regions are obtained. position of the center of mass, width, height, and the area of each outer region, i.e. on the convex hull border, are calculated. for the inner regions, the center of mass position and the area are calculated plus one feature variable denoting that an inner concavity exists. there are 5 feature variables per outer region and 4 feature variables per inner region. number of observed outer and inner regions must be defined in advance. since we use these features for handwritten digit recognition, only two inner regions are possible and occur for number eight, thus we observe a maximum of two inner regions. after some experimenting we concluded that a maximum of five outer regions give best results. this gives a feature vector of 2 × 4 + 5 × 5 = 33 variables. feature variables are sorted by area size, separately for inner and outer regions. four measurements for an inner region with the largest area are the first four variables of a feature vector. measurements of the next inner region by area make following four variables. measurements of the outer regions follow, again starting from a region with the largest area. if any of the regions does not exist, feature variables are filled with zeros, only position is filled with values 0.5, 0.5 indicating the character center. to minimize impact of noise and errors incurred during image retrieval, regions under a certain threshold are cut off. figure 5 shows the conc3 feature vector variables, while figure 6 illustrates the feature extraction process. figure 5: conc3 feature vector. cx and cy are region center of mass coordinates, w and h are region width and height. it can be observed that conc2 features will not take into account the whole concavity of a character. this can be seen on figure 6c. conc3 features have the advantage of using the whole concavity, which is the basis for the assumption that our method will give better results. (a) (b) (c) figure 6: conc3 feature extraction: (a) determining inner and outer regions, (b) extracted regions, (c) comparison of conc2 (left) and conc3 (right) features improving offline handwritten digit recognition using concavity-based features 225 3 classification support vector machines with radial basis function (svm-rbf) kernel and polynomial kernel (svm-poly) are selected for classification. svms often give best results in digit recognition, as in [2]. k-nn classifier is included as a simple classifier for comparison with svms. more detail on classifiers is presented below. 3.1 support vector classifiers in general, support vector machines (svms) solve binary classification problems. multi class classification is accomplished by combining multiple binary svms [2]. for svms, a solution to an optimization problem is required, defined as follows [8] [13] [19]: min ω,b,ξ { 1 2 ωt ω + c l∑ i=1 ξi } , (1) yi ( ωt ϕ (xi) + b ) ≥ 1− ξi, ξi ≥ 0, i = 1, ..., l, where (xi, yi) are training set instance-label pairs, c is the regularization parameter, w is the vector of coefficients, b a constant, ξi are parameters for handling nonseparable data (inputs) and ϕ maps input into higher-dimensional space. usually the following problem is solved: min α { 1 2 αt qα −et α } , (2) yti α = 0, 0 ≤ αi ≤ c, i = 1, ..., l, where e is the vector of all ones, q is an l by l positive semidefinite matrix, qij ≡ yiyjk (xi, xj) are training set instance-label pairs and k (xi, xj) ≡ ϕ (xi)t ϕ (xj) is the kernel function. we used two kernels, radial basis function (rbf) (3) and polynomial (4) kernel: k (xi, xj) ≡ exp ( −γ∥xi −xj∥2 ) , (3) k (xi, xj) ≡ ( xti xj )d . (4) to find optimal values of the variance parameter (γ) of the rbf kernel and the cost parameter c of the svm we used a simple grid search with values 2i where i is in range -15 to 3 for γ, and -3 to 15 for c, with the minimum step value of 0.1. similar procedure is used for the polynomial kernel of the second degree. for all svm classification tasks we used the libsvm library [19]. 3.2 k-nn classifier the k-nearest neighbor algorithm (k-nn) is a method for classification based on the nearest training objects in the feature space. when k=1, the class of the nearest training objects becomes the class of the test object. when k>1 the class of the test object is determined by the majority vote of its neighbors. a weighted version of the algorithm is used in this paper. each of the neighbors are assigned a weight equal to inverse of distance to test object. this ensures that closer neighbours contribute more to the decision than distant neighbours. 4 datasets this section presents the tested datasets. all used datasets contain grayscale images of handwritten digits. since binary images are required, we converted images from all datasets to binary form using otsu’s global threshold method [20] [21]. 226 m. karic, g. martinovic 4.1 mnist mnist dataset is a well known dataset for handwritten digit recognition, created by yann lecun [10]. it has 60000 training samples and 10000 test samples. all images are size 28 × 28 pixels with 256 levels of gray. actual size of digit images is 20 × 20 pixels, centered in a larger image using center of mass. the datasets are available online, along with a list of best performing methods and their recognition accuracies [22]. several binarized samples from the mnist dataset are shown in figure 7a. 4.2 usps usps dataset is a us postal handwritten digit dataset with images obtained from envelopes [11]. it has 7291 training samples and 2007 test samples. digits are scaled to fit in 16 × 16pixel images with 256 levels of gray. some experimental results on usps dataset are available online [23]. several binarized samples from the usps dataset are shown in figure 7b. 4.3 usps-r a modified version of usps dataset, labeled usps-r, is also used, since the distribution of original usps is uneven on training and test sets [24]. the modified version is created by merging both sets, reshuffling and then randomly dividing the full set to a new training and test set of equal size. each set has 4649 samples. the same dataset as in [24], available online, was used in experiments. 4.4 digits digits dataset is a less known dataset for handwritten digit recognition, described in [12]. it has 1893 training samples and 1796 test samples. digits are prepared in a similar format to usps dataset, with16 × 16-pixel images and 256 levels of gray. experimental results on this dataset can be found in [12]. several binarized sample images from the digits dataset are shown in figure 7c. (a) (b) (c) figure 7: samples from mnist (a), usps (b) and digits (c) datasets 4.5 combined another dataset is used, derived by combining mnist, usps-r and digits datasets. its training set actually consists of the equal number of instances from the three datasets. digits training set has the smallest number of instances therefore the equal amount of instances is randomly taken from training sets of each of the other two datasets, creating two shorter training sets, labelled mnist-s and usps-s. combined dataset consists of mnist-s, usps-s and digits training sets. 1893 instances are taken from each dataset, giving 5679 instances total. improving offline handwritten digit recognition using concavity-based features 227 experiments are conducted by training on combined, mnist-s or usps-s sets and testing on the test set of mnist, usps-r or digits datasets. this allows us to compare recognition accuracy on datasets with equal training set size, when using mnist-s and usps-s. using combined set allows us to verify whether recognition accuracy can be increased by learning on instances from another dataset, and what type of features are performing best. a similar method is used in [15], where training on training sets of two datasets and testing on the test set of the third was performed. this gave very poor recognition accuracies, often an order of magnitude lower than when training on the training set of the same dataset. this might produce unreliable results therefore we decided to experiment by adding instances. 5 experimental setup handwritten digit recognition performance was tested on four different datasets (mnist, two versions of usps and digits) using four classifiers and ten feature vectors. this gives a total of 40 recognition accuracies for each dataset. support vector classifiers with rbf kernel and second degree polynomial kernel are used for classification as described earlier. k-nn classifier is also used for comparison. recognition accuracy is shown for the case k = 1, i.e. 1-nn, and for the best result for all values of k = 1, 2 . . . 11, marked k-nn, using the method described earlier. all datasets are first converted to binary form because the features used in experiments are extracted from binary images. images are then converted to mnist format. image area containing a digit is scaled to size 20×20 pixels, keeping the aspect ratio. this scaled image is then placed on a frame of 28×28 pixels, positioning the digit center of mass in the center of the frame. feature vectors are defined to contain one primary feature which is combined with complementary features. five vectors use gradient and five chaincode as primary feature extraction method. in addition, four out of each of the five vectors contain complementary features, conc1, conc2, conc2 with crs and conc3, while one has no complementary features. these are explained earlier. precise description of the feature vectors is given in table 1. feature vectors e-grg, e-blr and e-mul exist in [2] and have the same labels. feature vector features extraction methods size e-grg gradient 200 e-grc1 gradient + conc1 300 e-grc2 gradient + conc2 240 e-grc2c gradient + conc2 + crs 268 e-grc3 gradient + conc3 233 e-blr chaincode 200 e-blc1 chaincode + conc1 300 e-blc2 chaincode + conc2 240 e-mul chaincode + conc2 + crs 268 e-blc3 chaincode + conc3 233 table 1: feature vectors following experiments on the four datasets, additional experiments with reduced training sets are executed. this allowed us to investigate the impact of training set size on the recognition accuracy depending on the feature vector. also we can compare recognition accuracies on datasets when the training set sizes are equal. in addition, merging these reduced training sets allowed us to verify how the recognition accuracy is affected for different feature vectors when 228 m. karic, g. martinovic we add training instances which are obtained using a different image acquisition process, in this case belonging to another dataset. as defined earlier, combined, mnist-s and usps-s sets are used for these experiments. results when training on mnist-s set and testing on mnist test set, and also when training on usps-s and testing on usps-r test set are obtained. combined set is used for training in combination with test sets of mnist, usps-r and digits datasets. results obtained using combined training set can be compared to results when using mnist-s, usps-s and digits training sets to verify how additional instances from other datasets affect recognition performance. 6 experimental results and discussion the experimental results in form of error rates are presented in this section, followed by our observations and discussion. dataset used for an experiment is given in the title of every table. in the table rows the names of the feature vectors described earlier are listed, while the columns contain the used classifiers. shaded results are obtained using the introduced method, while the best results by category are marked with bold letters. by different category we consider a different primary feature, or a different classifier. more importance is given to analysis of svm classifier results, while the 1-nn and k-nn results are presented as an indication of svm classifier superiority. 6.1 results on the individual datasets table 2 shows the results obtained on the mnist dataset. the best recognition accuracy, i.e. lowest error rate (0.67%), is achieved using the e-grc3 vector and the svm-rbf classifier. it is also the best attained recognition accuracy in the article. immediately behind is the accuracy of the vector e-grg (error rate 0.68%) containing only the primary feature, gradient, and no complementary features. other vectors in the category achieve lower accuracies. relationships among the vectors when using the svm-poly classifier remained approximately the same, with slightly lower accuracies, only the vector e-grc3 has an even greater advantage over the other vectors. vectors that use chaincode primary feature achieved lower recognition accuracies. vector e-mul has the advantage over the other vectors in its category for both svm classifiers (error rate 1.00 for svm-rbf). vector e-blc1 is second and e-blc3 third by accuracy. improving offline handwritten digit recognition using concavity-based features 229 table 3 shows the results obtained on the usps dataset. the best recognition accuracy is once again achieved using the e-grc3 vector and the svm-rbf classifier. error rate of 2.39% is the lowest among results reported in [23] using a single classifier. on top of that, we used binary images for training and testing which puts our method in a disadvantage. using the svm-poly classifier, e-grc3 and e-grg (2.64%) have equal error rates, while other vectors have higher error rates, i.e. decreased performance of the primary feature. vectors with chaincode primary feature give higher error rates with all classifiers, with vector e-blc3 performing best. table 4 shows the results obtained on the usps-r dataset. feature vector e-grc3 and svm-rbf classifier give the best recognition accuracy on this dataset as well (error rate 1.33%). results are similar to results on usps dataset, but with significantly higher recognition accuracies. when using the svm-poly classifier, vectors with complementary features do not increase the error rate of the primary feature. the vectors e-grc3 and e-blc3 still give the highest recognition accuracies. table 5 shows the results obtained on the digits dataset. error rates on this dataset are significantly higher than on other tested datasets. complementary features increase recognition accuracy in all tests. for vectors with gradient primary feature, e-grc1 gives the best performance overall (error rate 4.73%), e-grc2c is second (4.79%) and e-grc2 and e-grc3 with equal error rate third (4.90%). among vectors with chaincode primary feature, e-blc1 gives the lowest error 230 m. karic, g. martinovic rate when svm-poly classifier is used (4.90%). with svm-rbf classifier e-mul vector achieves the lowest error rate (5.23%). vector e-blc3 has the lowest average error rate when both svm classifiers are taken into account. overall, according to expectations, svm-rbf classifier gives the best recognition performance, followed by svm-poly, while 1-nn and k-nn achieve significantly lower accuracy. the vector e-grc3 gives the highest accuracy in the majority of the tests, except for the digits dataset where vector e-grc1 achieved higher accuracy. in addition, e-grc3 is the only vector improving on the results of the primary feature vector e-grg in all tests, while e-grc1, e-grc2 i e-grc2c achieved higher error rates than e-grg on several occasions. figure 8 gives error rates on four datasets using the svm-rbf classifier for vectors containing gradient primary feature. it can be seen that concavity features improve recognition accuracy, with vector e-grc3, containing proposed concavity features, giving best recognition performance. vectors using the chaincode primary feature achieve lower recognition accuracies. among these vectors, the best results are achieved using the proposed vector e-blc3 and vector e-mul. figure 8: error rates on four datasets for five feature vectors using svm-rbf classifier 6.2 results on combined datasets the results of recognition on the mnist test set, when training on the reduced training set mnist-s, are shown in table 6. compared to the results obtained by training on the full mnist training set, recognition accuracies are lower. the use of complementary features greatly improves the recognition accuracy, with feature vector e-grc3 performing best (error rate 1.66% using svm-rbf). vector e-blc3 achieves lower error rate than e-mul when reduced training set improving offline handwritten digit recognition using concavity-based features 231 is used. when training on the combined dataset, best results are still obtained by using the vector e-grc3, as shown in table 7. additional training images improved the recognition accuracy of e-grc3 (error rate 1.54% using svm-rbf). vector e-mul now has higher recognition accuracy than vector e-blc3, however it has not increased. table 8 shows the results obtained on the usps-r test set, when training on the reduced training set usps-s. feature vectors e-grc3 and e-blc3 achieve best recognition accuracies in their categories in all tests. accuracy of the vector e-blc3 is only matched by e-mul when using the svm-rbf classifier. the lowest error rate is achieved by the vector e-grc3 (2.06%). vectors with complementary features e-grc1, e-grc2 and e-grc2c fail to decrease the error rate of the primary feature vector e-grg. introducing additional training images overall reduced the error rates considerably, as shown in table 9. feature vector e-grc3 is still unmatched (error rate 1.74%), while other vectors with complementary features again fail to decrease the error rate of e-grg in their categories. vector e-blc3 is unmatched among vectors using chaincode primary feature, decreasing error rates more then other vectors when additional training images are added. 232 m. karic, g. martinovic since all images of the digits dataset training set are included in the combined training set, error rates on the digits dataset can be compared to error rates on the digits test set, trained on the combined training set, shown in table 10. introducing additional images to training set increased error rates on this test set. possible explanation for this outcome is a different methodology used for retrieving data of digits dataset and the other two datasets. significantly higher error rates on the digits dataset than on the other two datasets also indicate difference in the data format. vectors with complementary features are not successful in increasing the recognition accuracy of the gradient primary feature vector e-grg. among vectors with chaincode primary feature, complementary features increase the recognition accuracy, with the vector e-mul giving the best recognition accuracy. best performance of the svm-rbf classifier in combination with the vector e-grc3 was shown in this series of experiments as well. for every vector, best recognition accuracy is given when using the svm-rbf classifier. the vector e-grc3 gives the best recognition accuracy in all experiments except on the digits dataset. taking into account experiments on individual and combined datasets, the vector e-grc3 gives the highest recognition accuracy on seven out of nine different experimental setups. graphical representation of the results obtained when training on reduced training sets is given in figure 9a and when training on combined training sets in figure 9b. it can be concluded that the vector e-grc3 gives the best results overall. vectors using the chaincode primary feature achieve lower recognition accuracies than vectors using the gradient primary feature. the vector e-blc3 and vector e-mul give lowest error rates in their categories. 7 conclusion experimental results showed that complementary features can significantly improve recognition performance. the proposed concavity feature extraction method in conjunction with gradient features gave the highest recognition accuracy in majority of experiments. the method worked well with chaincode features as well, being one out of two top performers. it also has the improving offline handwritten digit recognition using concavity-based features 233 (a) (b) figure 9: error rates for five feature vectors using svm-rbf classifier (a) on three datasets with equal (reduced) training set size and (b) on datasets with training on combined set lowest feature count among observed complementary features, which lowers computational cost of classification. experiments using reduced training sets showed that the proposed concavity method outperforms other observed approaches making it useful for applications requiring use of a small training set. adding training instances from another dataset reflected on the recognition accuracy differently for different datasets. accuracy was increased on two datasets and decreased on one, indicating that learning process is sensitive to small differences in image retrieval and preprocessing. overall, the proposed method achieved the best performance. acknowledgements this work was supported by research project grant no. 165-0362980-2002 from the ministry of science, education and sports of the republic of croatia. bibliography [1] c.-l. liu, k. nakashima, h. sako, h. fujisawa, handwritten digit recognition: investigation of normalization and feature extraction techniques, pattern recognition, 37(2):265-279, 2004. 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[24] c. e. rasmussen, c. k. i. williams, gaussian processes for machine learning, 2nd ed., the mit press, 2006. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 2, month: april, year: 2020 article number: 3659, doi.org/10.15837/ijccc.2020.2.3659 ccc publications sensor coverage strategy in underwater wireless sensor networks l. yao, x. du lu yao computer department qinghai normal university, xining 810008, china 401516575@qq.com xiujuan du* 1. computer department qinghai normal university, xining 810008, china 2.academy of plateau science and sustainability xining 810008, china *corresponding author: dxj@qhnu.edu.cn abstract this paper mainly describes studies hydrophone placement strategy in a complex underwater environment model to compute a set of "good" locations where data sampling will be most effective. throughout this paper it is assumed that a 3 −d underwater topographic map of a workspace is given as input.since the negative gradient direction is the fastest descent direction, we fit a complex underwater terrain to a differentiable function and find the minimum value of the function to determine the low-lying area of the underwater terrain.the hydrophone placement strategy relies on gradient direction algorithm that solves a problem of maximize underwater coverage: find the maximize coverage set of hydrophone inside a 3−d workspace. after finding the maximize underwater coverage set, to better take into account the optimal solution to the problem of data sampling, the finite vc-dimension algorithm computes a set of hydrophone that satisfies hydroacoustic signal energy loss constraints. we use the principle of the maximize splitting subset of the coverage set and the ”dual” set of the coverage covering set, so as to find the hitting set, and finally find the suboptimal set (i.e., the sensor suboptimal coverage set).compared with the random deployment algorithm, although the computed set of hydrophone is not guaranteed to have minimum size, the algorithm does compute with high network coverage quality. keywords: underwater wireless sensor networks(uwsns), finite vc-dimension, gradient direction, sensor deployment. 1 introduction in recent years, with the rapid development of underwater sensor network research and applications and the improvement in hardware technology, sensor nodes have the ability to collect various resources and information underwater [5],[19],[15]. sensor nodes suitable for underwater environments can be cooperatively combined. an underwater wireless sensor networks (uwsns) is formed via acoustic doi.org/10.15837/ijccc.2020.2.3659 2 communication. regardless of the application of the uwsns, the deployment problem of the sensor node is first solved because it directly affects the accuracy and comprehensiveness of the monitoring results[11]. a reasonable and effective sensor deployment solution can greatly reduce the network construction time, quickly cover the underwater range, and extend the network life[28]. precise uwsns sensor node deployment is the foundation of building a sensor network, and it is a prominent issue of research. a good node deployment strategy has an important impact on network performance and network survivability [3]. the underwater sensor node deployment problem is the organic combination of the front-end information collection and back-end data processing of the uwsns system and is the fundamental challenge of the sensor network. at the application site, node deployment needs to be performed through appropriate scheduling algorithms. at present, the research on underwater network coverage mostly adopts an ideal coverage environment model [8],[25]. the perceived area of the node is considered to be a shape-formed prototype area, and the perceived radius of the node is the same in all directions; the effects of irregular shapes in the area of sensor coverage are not considered. precise sensor coverage in a relatively harsh environment, such as the detection and sampling of underwater objects, can affect underwater warfare and early warning and water pollution prevention systems as well as other applications [24],[30]. although the node layout can adopt the dense sensor random deployment strategy (which carries high labor and material costs), sensor signals are susceptible to environmental and material obstructions. the node perception range is rendered irregular, and monitoring blind zones are prone to occur. based on this perspective, this paper presents a scheduling algorithm for randomly deploying sensors in complex underwater models. the complex underwater environment is modeled by measuring terrain data. terrains, basins, continental shelves, continental slopes, isolated islands, and mid-ocean ridges in underwater terrain are already included in fig.1(a)(considering computational complexity, 3−d models have been scaled down).at present, sonar technology has been used to better measure the seabed topographic map. to facilitate calculation and demonstration, the cross-sectional view required by this model, such as the 2 − d workspace map see [14], is shown in fig.1(b). the model takes the underwater terrain into account, thus verifying the reasonability and effectiveness of the scheduling algorithm. -4 2 -2 2 0z 1 three-dimensional scale model maps of subsurface trenches,basins,continental shelves,etc. 2 y 1 x 4 0 0 -1 -1 (a) three-dimensional scale model maps of subsurface trenches, basins, continental shelves, etc. (b) cross-sectional pattern containing the trench. figure 1: underwater topographic map and cross section. the basic principle of using a scheduling algorithm to complete underwater environment deployment is that the greater the probability p of effective sensor deployment, the better the algorithm a . here,p(si|r3,a) represents the probability of effective deployment by the a algorithm in the case of r3, r3 represents complex underwater terrain, s is the sensor node, and a represents the scheduling algorithm. the a algorithm consists of a two-part algorithm; one part is able to achieve maximum range coverage in the 3 − d environment(see algorithm1), and the other part optimizes the hydrophone node set under the maximum range coverage condition(see algorithm2). in section 3, the iterative algorithm is used to obtain the gradient vector direction of each part of either the trench or mid-ocean ridge. the hydrophone adjusts the sinking position according to the gradient direction symbol to ensure the maximum underwater area. in section 4, the finite vc-dimension algorithm doi.org/10.15837/ijccc.2020.2.3659 3 is used to obtain an approximate optimal hydrophone node coverage set, thereby reducing redundant hydrophone nodes. 2 related work in recent years, many researchers have begun to develop algorithms for underwater sensor deployment. different deployment algorithms have been proposed according to different application scenarios and requirements. the common feature of these algorithms is that the sensor nodes use either static deployment algorithms or dynamic random deployment algorithms. 2.1 creation of static deployment algorithm static deployment is widely used in underwater sensor networks. when either the underwater environment is calm or the network state is relatively stable, the hydrophone nodes are generally calculated and deployed at predetermined locations according to the application requirements. for the problem of node deployment in underwater 3 − d environments, he, ming et al. proposed an adaptive algorithm for 2 − d to 3 − d space deployment strategies. see [23], a series of rules for effectively constructing 3 −d network topologies are proposed to resolve node deployment problems and planning in 3−d space. the problem of 3−d space node deployment is approached by conversion to a 2−d problem. in [26], the author obtains the number of active nodes required for basic network coverage and connectivity under the grid deployment model. however, the algorithm is applicable only to 2 −d environments and cannot meet the application requirements of a 3 −d environment. see [20], the use of multiple connected underwater sensors was suggested. combined with the deployment characteristics of 3 − d space, a deployment algorithm based on network connectivity performance was proposed.regarding the dense deployment of sensor nodes, a deployment method with nodes having the longest lifetime is recommended to optimize the coverage set. static deployment algorithms are typically used in applications that either take place in uncomplicated environments or have a relatively stable network state. hydrophone nodes are placed at locations that are determined and calculated based on application requirements [27]. in this case, the hydrophone node deployment problem is usually abstracted into a linear programming problem, and the proposed solution is optimized for either network performance or cost. however, in a natural underwater acoustic environment, the location of hydrophone node deployment is often undetermined due to either the uncertainty of the underwater environment or the undetectable nature of the monitoring environment. in this case, the dynamic feature of the underwater sensor node is applied, although the coverage performance of the sensor cannot be guaranteed. 2.2 dynamic random deployment algorithm in [18], the nodes have mobility. after being deployed in the target area, each node uses its repulsive force to move in the opposite direction of the neighboring nodes until the repulsive forces from the various directions are balanced by the nodes. this method reduces the overlap area between nodes. the vfa algorithm proposed by zou and chakrabarty is also based on the addition of node mobility functional modules [31]. the vfa first performs the mobility simulation. after determining the locations of the mobile nodes, all of the nodes move to the specified positions at one time. algorithm deployment is highly efficient, and the algorithm complexity varies according to the number of nodes and the size of the target area. the algorithm complexity of deploying k nodes in a given area divided into n×m grids is o (nmk). see [7], a new sensor network structure is introduced, and an underwater sensor network node deployment algorithm based on a random surface configuration is proposed. during the initial configuration of the network, several nodes are randomly deployed on the horizontal plane, and then the depth of neighboring nodes in the spatial adjustment area is arranged according to each node so that the underwater 3 − d space is sufficiently covered.arvind et al. proposed an energy modelbased method designed to extend the life of the network by balancing motion and energy loss in the network,experiments have proved the effectiveness of the method[12]. doi.org/10.15837/ijccc.2020.2.3659 4 3 3 − d sensor space coverage problem the 3 −d underwater environment hydrophone deployment problem is a leading issue in sensor deployment research [17]. although many scholars have developed high-coverage and low-complexity solutions, many algorithms are theoretical because they are based on an ideal environment; the practicality of these algorithms in complex underwater terrain environments remains to be confirmed [21]. in this paper, we roughly abstract the actual terrain, such as underwater trenches and basins, into curved surfaces from the actual underwater terrain data provided(in the actual calculation process, the deployment position of the sensor does not need to be accurately calculated, and this does not affect the calculation result).the direction of bumps in the complex terrain is calculated by the gradient direction method (called the trench, terrain such as the basin is concave terrain, and the topography of the mid-ocean ridge is convex terrain)[10],[29]. according to the direction of the bump, the range in which the hydrophone node needs to be deployed with high probability can be calculated. problem 1. (complex terrain hydrophone node coverage):for a given range of 3 − d complex environments r3, effectively calculate the maximize underwater coverage set of hydrophone s = {s1,s2,s3, ...,sn}; thus, in the environment r3, each complex terrain is guaranteed to have hydrophone node coverage. definition 2. assume that the slope, including the slope and the approximately smooth surface, is referred to as a continuous surfacefor which m ∈ r3, m = {m1,m2, . . . ,mn}, and mi(1 < i < n) divides the surface m into a smaller surface. h(m) is a smooth surface fitted by the section mi in 3 −d space. then, loss function j(θ) can be expressed as in equation (1) : j(θ) = 1 n n∑ i=1 [hθ (mi) −mi] 2 (1) where θ represents the gradient direction. in order to minimize the value of j(θ), the partial derivative of j(θ) can be obtained from (1): ∂j(θ) ∂θ = − 1 n2 n∑ i=1 [hθ (mi) −mi] 2 ·mi (2) since the hydrophone s is adjusted in the gradient direction, the parameter θ must be updated in the negative gradient direction: θ ← θ − 1 n2 n∑ i=1 [hθ(mi) −mi] 2 ·mi (3) according to equation (3), the topographic trend of irregular underwater terrain can be conveniently calculated. if θ is either a decreasing function or a negative gradient direction, then the surface area is concave terrain, which commonly causes blind spots for the hydrophone. when deploying a hydrophone, the position of the hydrophone should be adjusted to increase the probability that the collection area covers the blind spot,as shown in fig.2(a). restrictions 3. a hydrophone,si ∈ s, is effectively deployed in the designated area if si meets the following conditions: • energy loss restrictions: in the 3 − d underwater environment r3, the the acoustic signal propagation loss tl is mainly composed of two parts: expansion consumption and attenuation consumption.i.e.tl = 10k lg x + α(f)x.where x is the acoustic signal propagation distance, k is the acoustic signal propagation factor (the general value is 1.5), α(f) is the absorption factor. • perceptual range restrictions: the sensor node si in the 3 − d space r3 has a coordinate of gsi (xi,yi,zi).the perceptual range is a ball bi centered on gsi and radius rsi.at this time, in the space r3 ,the probability that any point p(x,y,z) can be detected by node si : p(si,p) = { 1,d(si,p) ≤ rsi 0,d(si,p) > rsi (4) doi.org/10.15837/ijccc.2020.2.3659 5 (a) in 3 −d environment, the gradient algorithm is used to get the θ value, and the hydrophone adjusts the underwater depth according to the θ value. (b) in the gradient pattern of the depressed terrain in the 3−d graph, the gradient vector in the range of 0 to −2 in the figure is the negative direction, and the terrain can be determined as the depressed terrain. figure 2: the gradient direction algorithm calculates the 3−d underwater topographic map and the range of values of θ . where d(si,p) = √ (x−xi)2 + (y −yi)2 + (z −zi)2 is european distance from p to si. • terrain restrictions: m can be a smooth or nearly smooth terrain surface so that it can be divided into small enough mi to make m≈ hθ(mi); in a complex environment, the hydrophone is susceptible to obstacles, such as occlusion and there flection of sound off unsmooth objects; therefore, the above points need to be considered during the algorithm experiment. the above mentioned concentration restrictions also provide practical theoretical requirements for the feasibility of the algorithm. submarine terrain that adhere to the characteristics of mathematical functions rarely occur in actual underwater environments. however, the various terrains on the seabed generally have a very large volume or area, and uneven terrain is abstracted into mathematical conformity by fitting methods. the surface of the model does not affect the results of the entire algorithm. because the algorithm only needs to calculate the gradient vector of the fitted surface of the terrain and adjust the hydrophone to the negative region of the gradient vector, effective coverage in the current environment can be achieved. not all terrain can be covered by a hydrophone, such as in the case of fig.3(a). in this case, underwater terrain data cannot be accurately obtained, which makes it impossible to calculate the direction of the gradient vector by definition 2 because the terrain area has been completely separated from the outside world and the hydrophone cannot pass through the obstacle. the object communicates with an external hydrophone. ultimately, the hydrophone task cannot be completed. we refer to fig.3(b). as the sensing dead zone for a hydrophone node in a random deployment scenario[16]. such blind areas mainly denote underwater concave terrain areas (including trenches, basins, etc.). 3.1 gradient direction algorithm based on complex terrain the gradient direction algorithm process is as follows algorithm 1: step1:because the negative gradient direction is the fastest falling direction of the function, the gradient direction of a certain point underwater can be calculated quickly. the gradient direction symbol of θ or j(θ) can be calculated by the given terrain data surface m to determine whether the area of the current data is concave terrain. table 1 shows the coordinates of underwater terrain data example.by calculating the gradient of the coordinates of the point, the gradient direction of the specific region in fig.2(b) is obtained.the gradient direction is calculated from this data, where "-" represents concave terrain. step2:position the hydrophone node set s = {s1,s2,s3, ...,sn} to ensure that sufficient hydrophones are available to cover all the complex underwater terrainand establish a node model for the waters to be tested.initialize the content of restrictions 3 and establish the constantp(si|r3,a→ 1) constant established. doi.org/10.15837/ijccc.2020.2.3659 6 algorithm 1 gradient direction algorithm 1: input: simulating underwater terrain dataset. 2: initialization parameters such as si,mi, θ,and hθ (mi) etc. 3: find partial derivatives for j(θ): ∂j(θ) ∂θ = − 1 n2 n∑ i=1 [hθ (mi) −mi] 2 ·mi 4: continuously update θ: θ ← θ − 1 n2 n∑ i=1 [hθ(mi) −mi] 2 ·mi 5: obtain the θ array and judge the underwater terrain: 6: if θ > 0 then 7: θ = max (θ, 0) 8: else 9: θ = min (θ, 0) 10: end if 11: output: gradient direction value θ as shown in table 1 and coordinate position p(x,y,z) of hydrophone s table 1: partial underwater environment data model based on gradient direction algorithms. no. m1 m2 m3 m4 m5 m6 m7 m8 θ 1 0.02 0.0371 0.0428 0.0207 0.0954 0.0986 0.1539 0.1601 -0.3109 2 0.0453 0.0523 0.0843 0.0689 0.1183 0.2583 0.2156 0.3481 0.3337 3 0.0262 0.0582 0.1099 0.1083 0.0974 0.228 0.2431 0.3771 -0.5598 4 0.01 0.0171 0.0623 0.0205 0.0205 0.0368 0.1098 0.1276 0.0598 5 0.0762 0.0666 0.0481 0.0394 0.059 0.0649 0.1209 0.2467 -0.3564 6 0.0286 0.0453 0.0277 0.0174 0.0384 0.099 0.1201 0.1833 0.2105 7 0.0317 0.0956 0.1321 0.1408 0.1674 0.171 0.0731 0.1401 0.2083 8 0.0519 0.0548 0.0842 0.0319 0.1158 0.0922 0.1027 0.0613 -0.1465 9 0.0223 0.0375 0.0484 0.0475 0.0647 0.0591 0.0753 0.0098 0.0684 10 0.0164 0.0173 0.0347 0.007 0.0187 0.0671 0.1056 0.0697 -0.0962 11 0.0039 0.0063 0.0152 0.0336 0.031 0.0284 0.0396 0.0272 0.0323 12 0.0123 0.0309 0.0169 0.0313 0.0358 0.0102 0.0182 0.0579 0.1122 13 0.0079 0.0086 0.0055 0.025 0.0344 0.0546 0.0528 0.0958 -0.1009 14 0.009 0.0062 0.0253 0.0489 0.1197 0.1589 0.1392 0.0987 0.0955 15 0.0124 0.0433 0.0604 0.0449 0.0597 0.0355 0.0531 0.0343 0.1052 16 0.0298 0.0615 0.065 0.0921 0.1615 0.2294 0.2176 0.2033 0.1459 17 0.0352 0.0116 0.0191 0.0469 0.0737 0.1185 0.1683 0.1541 -0.1466 18 0.0192 0.0607 0.0378 0.0774 0.1388 0.0809 0.0568 0.0219 0.1037 19 0.027 0.0092 0.0145 0.0278 0.0412 0.0757 0.1026 0.1138 0.0794 20 0.0126 0.0149 0.0641 0.1732 0.2565 0.2559 0.2947 0.411 -0.4983 21 0.0473 0.0509 0.0819 0.1252 0.1783 0.307 0.3008 0.2362 0.383 22 0.0664 0.0575 0.0842 0.0372 0.0458 0.0771 0.0771 0.113 0.2353 23 0.0099 0.0484 0.0299 0.0297 0.0652 0.1077 0.2363 0.2385 0.0075 24 0.0115 0.015 0.0136 0.0076 0.0211 0.1058 0.1023 0.044 -0.0931 25 0.0293 0.0644 0.039 0.0173 0.0476 0.0816 0.0993 0.0315 0.0736 26 0.0201 0.0026 0.0138 0.0062 0.0133 0.0151 0.0541 0.021 0.0505 27 0.0151 0.032 0.0599 0.105 0.1163 0.1734 0.1679 0.1119 0.0889 28 0.0177 0.03 0.0288 0.0394 0.063 0.0526 0.0688 0.0633 0.0624 29 0.01 0.0275 0.019 0.0371 0.0416 0.0201 0.0314 0.0651 0.1896 30 0.0189 0.0308 0.0197 0.0622 0.008 0.0789 0.144 0.1451 -0.1789 doi.org/10.15837/ijccc.2020.2.3659 7 (a) under such terrain conditions, the sensor is unable to perform sensing work, and even if it can work, information communication cannot be achieved. (b) in the case of random deployment, the presence of blind spots in the hydrophone nodes is a high probability event. figure 3: underwater blind zone and hydrophone unreachable zone. step3:calculate the magnitude and direction of the θ value of each hydrophone node. if the direction is negative, then the hydrophone is in a recessed area. room is still available for the drop, and the node position is adjusted to be in the blind spot of the hydrophone node. the node is automatically added, and the adjusted node set is output as s. 3.2 experimental setup for sensor deployment this paper uses matlab to simulate the gradient direction algorithm. mainly consider the indicators that maximize the coverage of underwater terrain and maximize the sensing data.according to the restrictions 3,the simulation parameters in the experiment are set as follows: the monitored water area is 2000meter × 2000meter,and the depth is 4000meter; the underwater sensor is mainly hydrophone.set the maximum sensing radius rsi = 100meter, the minimum sensing radius rsi = 10meter, the operating frequency is 35khz, and the initial energy is 20joule.the average of the results was obtained by multiple calculations, and the random deployment results were used as a reference for the algorithm during the experiment. the advantages of the algorithm can be directly deduced from fig.4(a), and the hydrophone node in the depressed terrain can be significantly increased, thereby increasing the probability of node deployment in the terrain region. the coverage of the number of nodes distributed under the depressed terrain is calculated and presented in fig.4(b). fig.4(c) shows that calculating the gradient direction results in node deployment that is more distributed in the complex space compared with that of randomly deployed nodes. although the gradient direction algorithm can ensure that the hydrophone nodes are deployed with a high probability in the hydrophone blind spots, the calculation result is based on the following assumptions: • the underwater topographical coordinates have been obtained by underwater sonar detection; • each hydrophone node can calculate its position and judge the value of θ; • each hydrophone node can overlap with the sensing area of the adjacent node, which does not interfere with the detection; according to the gradient descent algorithm, section 3 describes the ability to deploy hydrophone nodes in a complex underwater environment. this ability can quickly form an underwater sensor network to calculate the sensor set with the largest coverage. according to greedy algorithm theory, the hydrophone coverage set s obtained in the gradient direction algorithm is not necessarily the doi.org/10.15837/ijccc.2020.2.3659 8 -4 2 -2 2 0 s im u la te d w a te r d e p th (k ilo m e tr e ) 1 random deployment algorithm hydrophone distribution 2 simulated underwater area length(kilometre) 1 simulated underwater area width(kilometre) 4 0 0 -1 -1 (a) random deployment algorithm hydrophone distribution. -4 2 -2 2 0 s im u la te d w a te r d e p th (k ilo m e tr e ) 1 gradient direction algorithm hydrophone distribution 2 simulated underwater area length(kilometre) 1 simulated underwater area width(kilometre) 4 0 0 -1 -1 (b) gradient direction algorithm hydrophone distribution. 0 20 40 60 80 100 120 140 160 180 200 number of sensors 0 10 20 30 40 50 60 70 80 90 100 c o ve ra g e p e rc e n ta g e in c o n ca ve (% ) hydrophone coverage calculated under the same number of hydrophone nodes random algorithm gradient direction (c) hydrophone coverage calculated under the same number of hydrophone nodes. 0 5 10 15 20 25 30 35 40 45 50 generations -0.5 0 0.5 1 1.5 2 2.5 3 3.5 j( ) optimal, average function value change trend (d) optimization and average of j(θ) after 50 iterations. figure 4: hydrophone placed in a complex underwater environment. optimal solution in the complex underwater environment.fig.4(d) shows that algorithm 1 optimizes the j(θ) process, from lower to higher numerical changes after 50 iterations. according to greedy algorithm theory, each hydrophone node can calculate the regional optimization solution belonging to the local node, but all hydrophone sets are not necessarily the optimal coverage set s∗. the locally optimal results calculated by each hydrophone node do not affect the local optimum calculated by subsequent sensor nodes. 4 calculate the approximate optimal hydrophone node coverage set an underwater sensor network that relies entirely on the node random deployment strategy cannot meet actual application requirements regardless of its performance and cost. the hydrological device adopts a completely random deployment strategy, which cannot guarantee network coverage and connectivity efficiency. in section 3, the problem 1 of maximizing the coverage of nodes in complex areas was well resolved.however, if the data sampling is poor (e.g., it contains too few sampled data and/or has an incorrect sampling),the optimal coverage has measure 100%,but the underwater data sampling quality is not high. in addition, the excess nodes (it obtained by algorithm 1) will increase the access collision rate of the acoustic channel and reduce the quality of network communication. this section will be based on the above coverage algorithm 1 through the finite vc-dimension, which brönnimann and goodrich proposed to find the approximate optimal coverage set of the hydrophone collection system and calculate the suboptimal solution of the coverage set [13]. the main result of [4],[1] is that for hydrophone set systems with finite vc-dimension d, the hypothesis set hitting set h of size c can be calculated in polynomial time o (dc• log (dc)). to apply this result, we must first introduce h and convert the hydrophone set problem into an instance of the h problem by calculating ∑ .then, we define a set system of finite vc-dimensions and use a ”dual” coverage set with finite vc-dimension d. doi.org/10.15837/ijccc.2020.2.3659 9 4.1 finite vapnik-červonenkis-dimension and hitting set h let ∑ = {x,s} be the collection system,see fig.5(a).the ”dual” set system is defined by ∑d = {xd,sd} is defined by xd = s and sd = {sx|x ∈ x}, where sx consists of all sets s ∈s covering the complex underwater region set x [1].the ”dual” set system is shown in fig.5(b). notice that the set of hydrophone set s candidates now becomes the ground set xd. the hitting set for ∑d = {xd,sd} is a set h d ⊆ xd such that h d ∩sd 6= ∅ for each set sd in sd (i.e., h d contains members from all sets of sd).finding the best coverage set problem for ∑ is equivalent to finding the problem of smallest hitting set of ∑ , letting c be the size of the minimum hit set (the size of the minimum set cover)[9]. (a) x represents a set of areas that need to be covered in a complex underwater environment. s covers a set of hydrophone nodes in a complex underwater environment. each hydrophone node sensing interval is projected onto the boundary of a complex underwater environment to form set x. (b) the ”dual” configuration specific subset a ⊆ xd creation subset and region arrangement shown in (a).the hydrophone cover set is grouped under the mark xd, and each set s ⊆ s is a set that covers the underwater complex terrain in the boundary decomposition. figure 5: finite vc-dimension and a ”dual” coverage set with finite vc-dimension d. definition 4. (finite vapnik-červonenkis dimension):let ∑d = {xd,sd} denote a set system.a set a ⊆ xd is said to be shattered by sd ,if for any subset b ⊆ a there exists some s ∈sd such that b = a∩sd, then the set a ⊆ x is shattered by s. the finite vc-dimension of ∑d is the cardinality of the largest shattered subset of xd. in other words, a is shattered if each of its subsets can be induced by intersecting a with some set in sd. although it may not be possible to shatter all sets of size d, as long as there exists one such set we say that the vc-dimension is at least d. to state that a set system has vc-dimension d we must prove that no set of size larger than d can be shattered. the vc-dimension of our ”dual” set system ∑d = {xd,sd} is upper bounded by the following lemma [4]: lemma 1. (vc-dimension for sampled data) the vc-dimension of the ”dual” of the set system representing the sampled instance of problem 1 is bounded by o log(n),where n is the size of complex terrain data, see definition 2. proof 5. (proof of lemma1) the coverage area under m for each underwater complex environment consists of up to k = n−1 parts.thus, xd can be thought of as an arrangement of k−intervals, which are sets composed of at most k disjointed regular intervals. select a ⊆ xd, where d = |a|. the set a defines a sub-arrangement of at most 2kd subdivisions in m, and any two members of sd within the same subdivisions induce the same subset of a (i.e., sdi ∩ a = s d j ∩ a, if s d i and s d j are in the same subdivisions). to shatter a each of its subsets must be induced, but no sdi can induce more than one subset of a.therefore, to induce all subsets in a there must be at least 2d subdivisions in the sub-arrangement. it is impossible to shatter a if: 2kd < 2d ⇔ 2k < 2d d (5) for d > 4, log(2k) < d/2 ⇒ 2k < 2d/d. this observation and the conditional limit of k = n− 1, mean that the upper limit of the finite vc-dimension is 2 log (2n). doi.org/10.15837/ijccc.2020.2.3659 10 4.2 find the suboptimal sensor coverage set the algorithm proposed by brönnimann and goodrich is based on finding an ε-net that that approximates the optimal hitting set h [22]. let ∑ = {x,s} denote a set system. a set h ⊆ x is said to be an εnet if it intersects each set s ∈ s of cardinality larger than ε |x| of ∑ . we can generalize this defnition by including an additive weight function ω on every subset of x. in the generalized case, an ε−net is required to hit every s with weight at least εω (x). let a net − finder of size t be an algorithm z that given a weight function ω and work on set x,returns a (1/r)−net of size t (r) for a set system ∑ . also,a verifier is an algorithm y that given a subset h ⊆ x either confirms that h is hitting set or returns to the hydrophone coverage set s ∈s such that s ∩ h = ∅ when h is not the hitting set of x. the main result of [6] is that given the algorithms z and y , we can find the hitting set of size t (2c) (where c is the optimal size) at most by executing the following algorithm: the termination algorithm 2 hitting set h (x,s): 1: select c = 1; 2: given the net−finderz and the verifier y , confirm if there is a hitting set h with a size at most t (2c): a. set the weights of all elements in x equal to 1 ,set k = 1; b. uses z to find a (1/2c)−net of size t (2c) (called net−n).use y to verify that n is hitting set: c. if n is not hitting set then d. y return the set s ∈s such that s ∩n = ∅; e. else if h is hitting set then f. exit step 2 with true. g. if k > 2c log(|x|/c) then, h. step 2 exits with false i. else j. set k = k + 1, double all the weights of the element of s, k. return to step b. l. end if m. end if 3: 4: if step 2 return true (i.e., there is a hitting set h of size c) then 5: the program terminates and h is a near-optimal hitting set. 6: else 7: set c = 2c 8: repeat 9: step 2 10: until 11: h is hitting set. 12: end if condition in step b is a remarkable result from [6]. indeed, you can prove that step 2 always returns a hitting set within o(2c log(|x|/c)) iterations if it exists. since step 2 returns a set of size t (2c), and because of the double condition in step 3, the size of hitting set h is, at most,t (2c). the overall cost of this algorithm is o(c log(|x|/c))(tx(|x|, |s|,c) + ty (|x|, |s|,c)) where tz and ty are the running times of the net finder and validation algorithms, respectively. under the above algorithm 2, hitting set h of size c can be returned within the polynomial time o (dc• log (dc)), (i.e., the hydrophone suboptimal coverage set). we now only need to verify that doi.org/10.15837/ijccc.2020.2.3659 11 the suboptimal solution calculated using the finite vc-dimension algorithm can achieve the maximum underwater coverage s∗. in the next section, an analysis of the overall performance will be given. 4.3 performance analysis of optimal coverage set when studying the characteristics of complex environmental models, combined with the characteristics of network coverage performance and application requirements, the following three indicators are mainly considered: • effective coverage: the ratio between the area covered by the nodes in the network and the total size of the target area is defined as the effective coverage. to accurately analyze the effective coverage, it is necessary to calculate the area of the coverage; • network lifetime:the period from the start of the network to the death of the first node in the network is called the network lifetime, and its unit is the number of rounds.fig.6(b) shows the relationship between the network lifetime and the remaining nodes of the algorithm 1,2; • network energy consumption: the remaining energy of the network reflects the energy consumption of the whole network;fig.6(c) shows the residual energy of the nodes in the algorithm 1,2 survival on the network energy consumption during the period. 0 20 40 60 80 100 120 140 160 180 200 number of hydrophone nodes 0 10 20 30 40 50 60 70 80 90 100 c o ve ra g e r a te ( % ) comparison of coverage rates under the same number of nodes between different algorithms random algorithm gradient direction algorithm gradient direction+vc-dimension algorithm (a) comparison of coverage rates under the same number of nodes between different algorithms. 0 50 100 150 200 250 300 number of rounds 0 20 40 60 80 100 120 140 160 180 200 n u m b e r o f re m a in in g h yd ro p h o n e n o d e s network survival time varies with hydrophone random algorithm gradient direction algorithm gradient direction+vc-dimension algorithm (b) network survival time varies with hydrophone. 0 50 100 150 200 250 300 number of rounds 0 2 4 6 8 10 12 14 16 18 20 n e tw o rk r e si d u a l e n e rg y( j) the residual energy of a single hydrophone in the uwsns changes with time random algorithm gradient direction algorithm gradient direction+vc-dimension algorithm (c) the residual energy of a single hydrophone in the uwsns changes with time. figure 6: network performance analysis of random deployment algorithm, gradient direction algorithm and vc-dimension optimization. see section 3.2 for the experimental environment and parameters.fig.6 shows the relationship between the number of hydrophone nodes and the network coverage. note that with the same number of hydrophone nodes, different coverage rates can be obtained from different algorithms. the random algorithm can be used to measure a coverage rate of approximately 70% with the same number of nodes. the gradient vector algorithm can increase the coverage of hydrophone nodes by more than 95%, which mainly occurs after random deployment see fig.6(a). the hydrophone node is adjusted to the blind zone of the complex sensor node caused by random deployment; therefore, without increasing the number of hydrophone nodes, sensor coverage in complex areas is effectively improved [2] . however, in this process, a large amount of hydrophone node redundancy will occur, which not only wastes resources but also places hydrophone nodes excessively, which affects the working efficiency of the nodes [29]. the combination of finite vc-dimension and gradient direction optimizes the hydrophone coverage set, and the coverage rate can reach 95%, which effectively reduces the number of hydrophones. although the finite vc-dimension algorithm is only an approximate optimal coverage set, sensor coverage has been significantly improved compared to the random deployment algorithm. 5 conclusion this paper presents a novel approach to reduce the number of hydrophone during the construction of a complex underwater sensor network model. specifically, we incorporated sensor limitations into doi.org/10.15837/ijccc.2020.2.3659 12 a practical sensor placement algorithm.this algorithm is based on a gradient direction strategy that transforms the sensor placement problem into an instance of the set cover problem. however, the calculation process revealed that the gradient direction algorithm only considers the local optimal problem, which results in a hydrophone coverage set generated by the calculation that is too concentrated in the sensor blind zone, resulting in node deployment redundancy.the hydrophone coverage set is optimized by the finite vc-dimension algorithm. uwsns deployment focuses not only on the geometric relationship between the physical location and spatial location of the node but also has a direct impact on the network topology, perceived data quality, and network robustness. of course, the study of complex underwater sensor deployment issues should consider the aspects of coverage, connectivity and energy consumption, and good network connectivity ensures that the accuracy of the information collected by the underwater sensor nodes is delivered to the user terminal in a timely manner. deployment issues also depend on support technologies, such as time synchronization and node location technology. an interesting topic for future research is to study placement strategies without underwater terrain data.due to the limitations of the content of the article, we will focus on this in future research. funding this work was supported in part by the national natural science foundation of china under grant 61962052 and grant 61902273, in part by the innovation team foundation of qinghai offifice of science and technology under grant 2020-zj-903, in part by the key laboratory of iot of qinghai under grant 2020-zj-y16, in part by the hebei iot monitoring center under grant 3142016020. author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. references [1] bartlett, p.l.; 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(2020). sensor coverage strategy in underwater wireless sensor networks, international journal of computers communications & control, 15(2), 3659, 2020. https://doi.org/10.15837/ijccc.2020.2.3659 international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(3), 329-343, june 2019. combination of evidential sensor reports with distance function and belief entropy in fault diagnosis y. dong, j. zhang, z. li, y. hu, y. deng yukun dong institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 610054, china jiantao zhang college of information science and technology jinan university, tianhe, guangzhou, 510632, china zhen li college of information science and technology jinan university, tianhe, guangzhou, 510632, china yong hu big data decision institute jinan university, tianhe, guangzhou, 510632, china yong deng* 1. institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 610054, china 2. big data decision institute jinan university, tianhe, guangzhou, 510632, china *corresponding author: dengentropy@uestc.edu.cn; prof.deng@hotmail.com abstract: although evidence theory has been applied in sensor data fusion, it will have unreasonable results when handling highly conflicting sensor reports. to address the issue, an improved fusing method with evidence distance and belief entropy is proposed. generally, the goal is to obtain the appropriate weights assigning to different reports. specifically, the distribution difference between two sensor reports is measured by belief entropy. the diversity degree is presented by the combination of evidence distance and the distribution difference. then, the weight of each sensor report is determined based on the proposed diversity degree. finally, we can use dempster combination rule to make the decision. a real application in fault diagnosis and an example show the efficiency of the proposed method. compared with the existing methods, the method not only has a better performance of convergence, but also less uncertainty. keywords: dempster-shafer evidence theory, sensor data fusion, fault diagnosis, evidence distance, belief entropy, information volume. 1 introduction in mechanical engineering, some systems are very complex, which might have many components, reflecting with each other [11,38,44,71]. it is likely that something happens unexpectedly in the systems and causes serious problems due to a variety of reasons, such as unfavorable weather, bad environment or a long time of working. as a result, making full use of sensor reports information is extremely significant to make a reasonable decision in fault diagnosis [58,81]. in order to make a rational decision when using sensor data fusion technology, some works have been proposed to handle uncertainty [39, 45, 59, 77], such as fuzzy set theory [15, 63, 66, 74, 76, 83], z numbers [30, 31], d numbers [8, 9, 41, 64, 65], r numbers [47, 48] and so on. one copyright ©2019 cc by-nc 330 y. dong, j. zhang, z. li, y. hu, y. deng of the most used math tools in sensor data fusion is evidence theory [4, 49]. this theory can efficiently model uncertain information with basic probability assignment (bpa), or called as belief function [21]. in addition, the dempster rule can efficiently combine the sensor reports from different sources [79]. due to the desirable properties, evidence theory has been accepted as de facto standard in decision making [10,28,78], risk and reliability analysis [13,27,40], system optimization [67] and pattern recognition [23,24,34,46,57]. however, an open issue of evidential sensor data fusion is that the illogical results will be obtained when sensor reports conflict with each other in a high degree [20, 56, 75]. many methods were presented to address this issue [60, 80]. for example, yager [70] removed the process of normalizing in d-s combination rule, smets [50, 51] proposed the conjunctive and disjunctive rules, murphy [42] combined the conflicting evidence with average operation, fan and zuo [16] presented a combination method to fuse conflicting evidence in fault diagnosis, dubois and prades method [12], lefevre et al. [33] and so on. recently, jiang et al. [25] applied belief entropy into sensor data fusion and received the best performance. although these methods have some advantages, one of the disadvantages is that some information is not fully used. for example, the distance information and the difference of information volume are not considered in [25]. in this paper, we proposed an improved evidential method, which is conceptually simple, and yet is able to provide much better accuracy and less uncertainty. the basic idea is to obtain the appropriate weights for different reports. the distribution difference between two bpas is first measured by belief entropy [6]. then the diversity degree among bpas can be obtained by combining distribution difference and evidence distance [29]. according to it, the weight of each bpa can be determined. finally, we can make a decision for fault diagnosis by using dempster combination rule. an application in fault diagnosis and an example show that our proposed approach can not only increase the accuracy of fault diagnosis but also decrease the uncertain information volume, which is more reasonable. the remainder of this paper is organized as follows. section 2 introduces some backgrounds, including dempster-shafer evidence theory, evidence distance and belief entropy. section 3 formulates the proposed method for evidential sensor data fusion. a real application in fault diagnosis and an example are given in section 4 to show the efficiency of the method. the conclusions are in section 5. 2 preliminaries 2.1 dempster-shafer evidence theory the application in data fusion needs efficient math tools [38]. dempster-shafer evidence theory, proposed by dempster [4] and shafer [49], is effective to handle uncertain information. definition 1. let x be a set of mutually exclusive and collectively exhaustive events, shown as follow [4,49]: x = {θ1,θ2, · · · ,θi, · · · ,θ|x|} (1) where set x is called a frame of discernment, whose power set is: 2x = {∅,{θ1}, · · · ,{θ|x|},{θ1,θ2}, · · · ,{θ1,θ2, · · · ,θi}, · · · ,x} (2) definition 2. for a frame of discernment x = {θ1,θ2, · · · ,θ|x|}, a mass function is a mapping m from 2θ to [0,1]. m : 2θ → [0, 1] (3) combination of evidential sensor reports with distance function and belief entropy in fault diagnosis 331 which satisfies the following condition: m(∅) = 0, ∑ a∈2x m(a) = 1 (4) a, a member of the power set, is called a focal element of the mass function, or named as basic probability assignment (bpa). bpa is the key issue in evidence theory and many relative processing are presented such as negation [19,73], correlation [26] and divergence measure [17,52]. definition 3. given two bpas, m1 and m2, they can be combined by, m(a) = { 0, a = ∅ 1 1−k ∑ b∩c=a m1(b)m2(c), a 6= ∅ (5) with k = ∑ b∩c=∅ m1(b)m2(c) (6) where k is a parameter that reflects the conflict between m1 and m2. if k = 0, m1 and m2 have no contradiction. if k = 1, they are totally conflict. many open issues about conflict management are still not well addressed [2]. some alternatives are proposed to modify the combination rule [53,54,61,68], others are presented to modify the data models [80] or handle this problem under open world assumption [55,56]. 2.2 evidence distance definition 4. let m1 and m2 be two bpas on the same frame of discernment of x, which contains n mutually exclusive and exhaustive hypotheses. the distance between m1 and m2 is [29]: dbpa(m1,m2) = √ 1 2 (−→m1 −−→m2)td(−→m1 −−→m2) (7) where d is a 2n × 2n matrix whose elements are d(a,b) = |a∩b| |a∪b| , a,b ∈ p(x). 2.3 belief entropy it should be pointed out that uncertainty measurement, decision making and optimization under uncertainty is still an open issue [14,22,37]. entropy is an efficient tool to model uncertainty [7, 32, 72]. recently, a new belief entropy, named as deng entropy was proposed [6]. it has a good performance in measuring uncertainty. also, it has a backward compatibility, which means when the uncertain information is represented by probability distribution, belief entropy will degenerate to shannon entropy [1,3,35,43]. definition 5. let a be a proposition of bpa, |a| is the cardinality of a. then, belief entropy is defined as [6]: ed = − ∑ a⊆x m(a)log m(a) 2|a| − 1 (8) 332 y. dong, j. zhang, z. li, y. hu, y. deng when the bpa has only one element, which means |a| = 1, then it can be written as shannon entropy [6]. ed = − ∑ a⊆x m(a)log m(a) 2|a| − 1 = − ∑ a⊆x m(a)log m(a) (9) 3 evidential sensor data fusion this section formulates a new weighted average approach for evidential sensor data fusion. the proposed method considered both evidence distance and belief entropy to obtain the appropriate weights assigning to different data reports. using the weight to pre-treat the multi-source reports, and making the final decision by dempster combination rule. the flow chart is shown in figure 1. figure 1: structure of the proposed sensor data fusing method definition 6. let edi,edj be the belief entropy of m1,m2, then the distribution difference is defined: αij = e |edi−edj| (10) definition 7. let dbpa(mi,mj) be the evidence distance of m1,m2, the diversity degree between combination of evidential sensor reports with distance function and belief entropy in fault diagnosis 333 two bpas is defined: dij = 1 αij ×dbpa(mi,mj) (11) definition 8. a diversity of distribution and distance matrix (ddd) is also defined: ddd =   1 d12 · · · d1i · · · d1n d21 1 · · · d2i · · · d2n ... ... ... ... ... ... di1 di2 · · · 1 · · · din ... ... ... ... ... ... dn1 dn2 · · · dni · · · 1   (12) the computation step is shown as following. • step 1, collect sensor information and transform into bpas. • step 2, use equation (7) to calculate the evidence distance between two bpas, which shows the difference. • step 3, use equation (8) to calculate the belief entropy of bpa, which shows the distributive characteristic. • step 4, calculate the diversity degree between two bpas. • step 5, the support of the bpas is given as: sup(mi) = n∑ j=1, j 6=i dij (13) • step 6, the credibility degree of the bpas is obtained. crdi = sup(mi) n∑ i=1 sup(mi) (14) • step 7, it is easy to see that ∑n i=1 crdi = 1. as a result, crdi can be the weight of each bpa. (ω1,ω2, · · · ,ωn) = (crd1,crd2, · · · ,crdn). thus, a new weighted evidence can be obtained, which is: m(a) = ω1 ×m1(a) + ω2 ×m2(a) + · · · + ωn ×mn(a) (15) • step 8, use dempster combination rule to combine the new weighted evidence to get the result. furthermore, if the number of original evidence is n, then the new evidence should be combined for (n− 1) times [25]. it should be noticed that if dbpa(mi,mj) = 0, which means that there is no conflict between mi and mj. in this situation, the value of diversity degree dij cannot be determined. so we proposed our own solution, which can be discussed further. first, if there are only three bpas m1,m2,m3 and m1 = m2, the two bpas can be regarded as the same one, then use murphy’s method, which is assigning the weight equally to each bpa. 334 y. dong, j. zhang, z. li, y. hu, y. deng second, if there are more than three evidences. supposing there are n bpas, which can be divided into m groups according to their dbpa. if there are only two groups, it will just be the same as the first situation. if there are more than two groups, and each group has k bpas, noted as k1,k2, · · · ,km, and ∑m i=1 ki = n. {m11 m12 · · · m1k1} {m21 m22 · · · m2k2} ... ... ... ... {mm1 m12 · · · mmkm} (16) then, select one bpa from each group. suppose they are m11,m21, · · · ,mm1, and use the proposed method to obtain the weight, which is {w(m11),w(m21), · · · ,w(mm1)} = {ω1,ω2, · · · ,ωm} (17) since w = ∑m i=1 kiωi, and the finally weight can be gained. {ω11,ω12, · · · ,ω1k1,ω21,ω22, · · · ,ω2k2, · · · ,ωm1,ωm2, · · · ,ωmkm} = { ω1 w , ω1 w , · · · , ω1 w︸ ︷︷ ︸ k1 , ω2 w , ω2 w , · · · , ω2 w︸ ︷︷ ︸ k2 , · · · , ωm w , ωm w , · · · , ωm w︸ ︷︷ ︸ km } (18) two examples are given to illustrate how it works. example 1. suppose the frame of discernment is x = {a1, a2, a3}, and there are three bpas. m1(a1) = 0.3, m1(a2,a3) = 0.7 m2(a1) = 0.3, m2(a2,a3) = 0.7 m3(a1) = 0.8, m3(a2,a3) = 0.2 then the weight should be obtained as w(1/4, 1/4, 1/2). example 2. suppose the frame of discernment is also x = {a1, a2, a3}, and there are four bpas. m1(a1) = 0.3, m1(a2,a3) = 0.7, m2(a1) = 0.3, m2(a2,a3) = 0.7, m3(a1) = 0.8, m3(a2,a3) = 0.2. m4(a1) = 0.6, m4(a2,a3) = 0.4. in this example, we can just consider m2,m3 and m4, then use the proposed method to gain the weights, which are ω2 = 0.2585,ω3 = 0.3072,ω4 = 0.4343 finally, re-assign the weights to get the result: w = (0.2054, 0.2054, 0.2441, 0.3451). combination of evidential sensor reports with distance function and belief entropy in fault diagnosis 335 4 experiments 4.1 application in fault diagnosis the complex systems is very complicated since each factor in the system interacting with each other in a very complicated way. to address this issue, network analysis [18,36,62,69,82] and data fusion based technology are presented to deal with complexity and guarantee the reliability of the complex system. [25] gave a case of motor rotor fault diagnosis, where the vibration signal is collected by acceleration sensor (m1), velocity sensor (m2), and displacement sensor (m3). the possible faults including normal operation (f1), unbalance (f2), misalignment (f3), pedestal looseness (f4). the collected data report is shown in table 1, where x is a frame of discernment, and x = {f1,f2,f3,f4}. table 1: output of the multi-senors [25] mi f1 f2 f3 f4 x m1 0.06 0.68 0.02 0.04 0.20 m2 0.02 0 0.79 0.05 0.14 m3 0.02 0.58 0.16 0.04 0.20 the computation steps are as following. by equation (7), the evidence distance between two bpas, dbpa(m1,m2) = 0.7276, dbpa(m1,m3) = 0.1249, dbpa(m2,m3) = 0.6063 using equation (8) to get the belief entropy of each bpa. ed1 = 2.1663, ed2 = 1.5417, ed3 = 2.4232 the distribution difference is obtained by equation (10). α12 = 1.8674, α13 = 1.2930, α23 = 2.4145 and the diversity degree calculated by equation (11) are: d(m1,m2) = 0.7360, d(m1,m3) = 6.1923, d(m2,m3) = 0.6831 finally, the weights by equation (14): ω1 = 0.4551, ω2 = 0.0932, ω3 = 0.4517 according to the weights, a new set of data can be got by calculating: m(f1) = 0.06 × 0.4551 + 0.02 × 0.0932 + 0.02 × 0.4517 = 0.0382 in the same way, we can calculate m(f2) = 0.5714,m(f3) = 0.1550,m(f4) = 0.0409,m(x) = 0.1944. therefore, the decision can be made by fusing {0.0382, 0.5714, 0.1550, 0.0409, 0.1944} with dempster combination rule for 2 times, which are shown in table 2. the results of other methods are also listed as a comparison. following [25], we will use ∆ = 0.7 as the threshold. since the fault f2 has a belief degree of 89.18%, it can be told that unbalance is the fault of the equipment. and compared with other methods, the new method performs much better. the belief entropy in figure 2 is the smallest, which means that the proposed method has the smallest uncertain information volume. 336 y. dong, j. zhang, z. li, y. hu, y. deng table 2: comparison of the result of several existing methods combination rule f1 f2 f3 f4 x diagnosis result dempster [4,49] 0.0205 0.5229 0.3933 0.0309 0.0324 uncertainty murphy [42] 0.0112 0.6059 0.3508 0.0153 0.0168 uncertainty jiang [25] 0.0111 0.7265 0.2313 0.0144 0.0168 unbalance deng [5] 0.0111 0.7728 0.1851 0.0139 0.0165 unbalance proposed method 0.0106 0.8918 0.0713 0.0115 0.0148 unbalance figure 2: belief entropy comparison. 4.2 example the proposed method is not only efficient in dealing with uncertainty, but also be falseevidence resilient. suppose the system has collected five evidences from five different sensors [5], which are shown as follows: m1 :m1(a) = 0.5, m1(b) = 0.2, m1(c) = 0.3; m2 :m2(a) = 0, m2(b) = 0.9, m2(c) = 0.1; m3 :m3(a) = 0.55, m3(b) = 0.1, m3(c) = 0.35; m4 :m4(a) = 0.55, m4(b) = 0.1, m4(c) = 0.35; m5 :m5(a) = 0.55, m5(b) = 0.1, m5(c) = 0.35; the results are shown in table 3, obviously, the second evidence m2 is conflicting with the others. although there are more and more evidences that support a, dempster’s method cannot reach a reasonable result. m(a) will always be zero as long as one evidence does not support it. while other methods revise the disadvantage. apparently, the proposed method can have a probability 0.7663 to support a when the third evidence is fused, and it always has the highest support. in previous work, some information is not fully used. in our proposed method, not only the distance information between bpa, but also the difference of information volume of each bpa is considered. as a result, our method can efficiently measure the support degree of each sensor report. that is, if one report is more reliable, more weights will be assigned to this report. therefore, the result of our improved method is more reasonable and more desirable. despite the advantages, the proposed method might not be very suitable in some situations. combination of evidential sensor reports with distance function and belief entropy in fault diagnosis 337 for example, the calculation process will be extremely complex if the data of evidence is huge. [25] considered belief entropy, and considered evidence distance, while our method considered both of them. therefore, the calculation complexity might be twice as those methods. in addition, the proposed method might reach an unreasonable result if a bad evidence repeats many times. this is because the bad evidence cannot be identified, and they could have a mutual confirmation, leading to an illogical fusing result. table 3: results of different method combination rule m1, m2 m1, m2, m3 m1, m2, m3, m4 m1, m2, m3, m4, m5 dempster [4,49] m(a)=0 m(a)=0 m(a)=0 m(a)=0 m(b)=0.8571 m(b)=0.6316 m(b)=0.3288 m(b)=0.1228 m(c)=0.1429 m(c)=0.3684 m(c)=0.6712 m(c)=0.8772 murphy [42] m(a)=0.1543 m(a)=0.3500 m(a)=0.6027 m(a)=0.7958 m(b)=0.7469 m(b)=0.5524 m(b)=0.2627 m(b)=0.0932 m(c)=0.0988 m(c)=0.1276 m(c)=0.1346 m(c)=0.1110 jiang [25] m(a)=0.4206 m(a)=0.6819 m(a)=0.8244 m(a)=0.8945 m(b)=0.3944 m(b)=0.1310 m(b)=0.0326 m(b)=0.0071 m(c)=0.1850 m(c)=0.1871 m(c)=0.1430 m(c)=0.0984 deng [5] m(a)=0.1543 m(a)=0.5816 m(a)=0.8061 m(a)=0.8909 m(b)=0.7469 m(b)=0.2439 m(b)=0.0481 m(b)=0.0086 m(c)=0.0988 m(c)=0.1745 m(c)=0.1457 m(c)=0.1005 proposed method m(a)=0.1543 m(a)=0.7663 m(a)=0.8554 m(a)=0.9063 m(b)=0.7469 m(b)=0.0424 m(b)=0.0082 m(b)=0.0015 m(c)=0.0988 m(c)=0.1913 m(c)=0.1364 m(c)=0.0922 5 conclusions in fault diagnosis and other sensor data fusion systems, the reports of different sensors may be influenced by some complex environments, leading them less reliable. therefore, how to efficiently determine the reliability of each report, or to say, the weight of each report is very important. to address this issue, we propose an improved method based on the belief entropy and the evidence distance. the method considers both the degree of conflict and the difference of information volume among evidences. an application and an example illustrate the efficiency of the method in evidential sensor fusion. it shows that the proposed method is more efficient for highly conflicting evidences with better performance of convergence and less uncertainty, compared with the existing methods. some related advantages and disadvantages of different methods are discussed as follows. • dempster’s method, which can well deal with imprecise and uncertain information, is widely used in fusing information. however, when it comes to highly conflicting evidences, the method will always lead to some illogical results. • murphy’s method, which uses an average operation to combine the conflicting evidence, is able to deal with highly conflicting evidences to some extent. however, the difference and relationship of evidences is neglected. • in jiang’s method, belief entropy is used to calculate the weight of each evidence. it considers the difference of evidence, which makes it more reasonable than murphy’s method. 338 y. dong, j. zhang, z. li, y. hu, y. deng • in deng’s method, which is different from jiang’s method, evidence distance is used to calculate the weight rather than the belief entropy. and the similarity between two evidences is proposed. • the proposed method, considering both belief entropy and evidence distance, has a better result. although it might be not very suitable in some situations, it leverages the advantages of jiang’s method and deng’s method. funding the work is partially supported by national natural science foundation of china (grant nos. 61573290, 61503237). author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] abellan, j. 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(2017). a method for recognizing fatigue driving based on dempster-shafer theory and fuzzy neural network, mathematical problems in engineering, 10, 2017. international journal of computers communications & control issn 1841-9836, 13(4), 574-589, august 2018. arithmetic operations with spiking neural p systems with rules and weights on synapses h.f. wang, k. zhou, g.x. zhang huifang wang, kang zhou* department of math and computer wuhan polytechnic university, 430023 wuhan, hubei, china *corresponding author: zhoukang65@whpu.edu.cn 13667286986@139.com gexiang zhang 1. school of electrical engineering southwest jiaotong university, chengdu, china 2. robotics research center xihua university, chengdu, china zhgxdylan@126.com abstract: the application of spiking neural p systems with rules and weights on synapses to arithmetic operations is discussed in this paper. we design specific spiking neural p systems with rules and weights on synapses for successfully performing addition, multiplication and the greatest common divisor. this is the first attempt to discuss the application of the new variant of spiking neural p systems, spiking neural p systems with rules and weights on synapses, and especially the use of spiking neural p systems to perform the greatest common divisor. comparing with the results reported in the literature, smaller number of neurons are required to fulfill the arithmetic operations. keywords: sn p systems, rules and weights on synapses, addition, multiplication, the greatest common divisor. 1 introduction membrane computing was initiated in [12]. the distributed and parallel computing devices in membrane computing are called p systems [13]. some applications of p systems were reported in [2, 28, 29]. especially, applications of p systems to arithmetic operations were discussed in [1, 2, 4, 31]. some views on membrane computing were given in [3, 14]. for more information about membrane computing, one can refer to [15, 33]. spiking neural p systems (in short, sn p systems) are a variant of p systems, which were introduced in [5] as a class of parallel and distributed computing models. in recent years, sn p systems have been widely investigated in theory and applications. for instance, various variants of sn p systems were constructed by considering different biological sources [9,11,17,19,20,22,27] and the computing power/computational efficiency of some variants was discussed [10,18,26]. a wide range of applications of sn p systems was also presented, such as optimization [30], knowledge representation [23], fault diagnosis [16,24], and logical gates and circuits [6, 21]. sn p systems have been also used to perform arithmetic operations [8, 25, 32]. in order to answer the open problem in [8], sn p systems with a single input neuron for the addition and multiplication were constructed in [32]. these studies indicate that arithmetic/logic operations are promising applications of sn p systems. in this paper, the application of sn p systems with rules and weights on synapses (rwsn p systems) is discussed to perform arithmetic operations. smaller number of neurons are required to copyright ©2018 cc by-nc arithmetic operations with spiking neural p systems with rules and weights on synapses 575 construct the sn p systems for addition of n natural numbers and multiplication of two arbitrary natural numbers compared with the ones in [32]. this is the first attempt to discuss the use of spiking neural p systems to perform the greatest common divisor. we divide the solution for the greatest common divisor into several individual modules, and the binary representation method in [34] is used here. the inputs and outouts of these systems are natural numbers expressed in binary form, which are encoded as appropriate sequences of spikes. this paper is organized as follows. section 2 briefly introduces the sn p systems with rules and weights on synapses. in section 3, sn p systems with rules and weights on synapses are used to perform arithmetic operations. conclusions and future work are presented in section 4. 2 spiking neural p systems with rules and weights on synapses in this section, sn p systems with rules and weights on synapses are briefly described. more details can be referred to [7]. such a system of degree m ≥ 1 is a construct of the form: ∏ = (o,σ1,σ2, . . . ,σm,syn,σin,σout), where: • o = {a} is the singleton alphabet (a is called spike); • σ1,σ2, . . . ,σm are neurons of the form σi = (ni), with 1 ≤ i ≤ m, where ni is initial number of spikes in neuron σi; • syn is the set of synapses; each element in syn is a pair of the form ((i,j),w, r(i,j)), where (i,j) indicates that there is a synapse connecting neurons σi and σj, with i,j ∈ {1, 2, . . . ,m}, i 6= j; w ∈ n(w 6= 0) is the weight on synapse (i,j); r(i,j) is a finite set of rules of the following two forms: (1) e/ac → a; d, where e is a regular expression over o, c ≥ 1 and d ≥ 0; (2) as → λ, for some s ≥ 1, with the restriction that as /∈ l(e) for any rule e/ac → a; d from any r(i,j); • σin and σout indicates the input neuron and the output neuron, respectively. a rule e/ac → a; d is called a standard firing rule; if l(e) = {ac}, then the rule can be written in the simplified form ac → a; d. a rule of the form as → λ is called a forgetting rule. the firing rules are applied as follows. if e/ac → a; d ∈ r(i,j), and neuron σi contains k spikes such that ak ∈ l(e), k ≥ c, then the rule is enabled. this means consuming (removing) c spikes (thus only k−c remain in σi) from neuron σi. neuron σi is fired, and it produces one spike. this one spike is multiplied w times by the weight w of the synapse in the process of transmission, and then reaches neuron σj after d time units (that is, w spikes arrive at neuron σj). when neuron σi contains exactly s spikes, then forgetting rule as → λ ∈ r(i,j) is enabled. by using it, s ≥ 1 spikes are removed from the neuron σi. in order to express the synapse between the output neuron and the environment, the environment is represented by the letter e, and the synapse between the output neuron and the environment can be expressed as (out,e). 576 h.f. wang, k. zhou, g.x. zhang 3 arithmetic operations with sn p systems with rules and weights on synapses in this section, sn p systems with rules and weights on synapses are used to design specific systems to perform three types of arithmetic operations, addition, multiplication and the greatest common divisor. 3.1 addition in this subsection, we describe an sn p system with rules and weights on synapses that performs the addition of n natural numbers. such a system is called the sn p system with rules and weights on synapses for n − addition. the system has only one input neuron and can be used to perform the sum of arbitrary n natural numbers with k binary bits, where n ≥ 2, k ≥ 1. compared with the standard sn p system, this type of sn p systems can reduce the number of neurons. as shown in fig. 1, we construct an sn p system ∏ add(n,k) with rules and weights on synapses to solve the n−addition problem. its formal definition is omitted. figure 1: the sn p system ∏ add(n,k) with rules and weights on synapses for addition the sn p system with rules and weights on synapses for n − addition outputs the sum in binary form of n natural numbers with k binary bits, provided that the neuron σin is in binary form. let t denote the current time step. in the initial configuration (t = 0), only the neuron σa0 in the system contains 2 spikes and the neuron σc contains k spikes. the computing procedure of the sn p system with rules and weights on synapses in fig. 1 is composed of the following steps: arithmetic operations with spiking neural p systems with rules and weights on synapses 577 1. preprocessing of inputs. at time t = 0, with two spikes in the auxiliary neuron σa0, the rules on synapses (a0, in) and (a0,a1) can be applied. one of the two spikes is consumed by the firing rule a2/a → a on synapse (a0,a1), and one new spike is produced. then this new spike is multiplied by the weight 2 on synapse (a0,a1) during the transfer. that is, at the next time, two spikes will arrive at neuron σa1, so that the rules on the two synapses emitted by neuron σa1 will be enabled and applied. the other spike is consumed by the forgetting rule a 2/a → λ on synapse (a0, in), so the number of spikes that neuron σin receives from the auxiliary neuron σa0 is 0 at t = 1. similarly, the above process is repeated and then we can get that when time t = 2, neuron σin receives two spikes from the auxiliary neuron σa1(the weight on synapse (a1, in) is 2); until time t = k, neuron σin receives 2(k − 1) spikes from neuron σak−1 (the weight on synapse (ak−1, in) is 2(k − 1)). then the auxiliary neurons send spikes to σin again, from σa0 to σak−1, until the computation ends. 2. input and store the first n-1 numbers. at time t = 1, the digit which is associated with the power 20 in the binary representation of the first natural number is provided to the input neuron σin, while the auxiliary neuron σa0 sends 0 spikes to σin. at this time, the number of spikes that neuron σin contains may be 0 or 1. if there is no spike and no rule is activated, then 0 spike will be sent to neuron σb0 at the next time. if there is one spike, then the firing rule a → a on synapse (in,b0) is triggered. this means that neuron σb0 can receive one spike at the next time. in this way, we can store the lowest bit of the first natural number into the neuron σb0. similarly, we can store the digits which are associated with the power 21, 22, . . . , 2k−1 of the first natural number into σb1, σb2 , . . . , σbk−1, respectively. at the next time, the second natural number will begin to be input to the system, and the input process is same as that of the first number. 3. input the last natural number and calculate the sum of each bit. at t = (n − 1)k + 2, the spikes of the lowest bit of the last natural number arrive in σb0, and at the same time, n spikes transmitted by synapse (c,b0) also arrive at σb0 after (n− 1)k + 1 steps of delay. the rules on synapses (b0,out) and (b0,b1) can be applied when the number of spikes in σb0 is at least n and n + 2, so the system starts to calculate the sum and binary carry at this step, which corresponds to the power 20 of the n natural numbers. then the sum is sent to the output neuron σout, and the carry is sent to the next neuron σb1. by the same token, the binary digits corresponding to the power 2i of the last natural number and the n spikes transmitted by synapse (c,bi) arrive at σbi at the same time. differing from σb0, the carry from σbi−1 also arrives at σbi at this time, where i = 1, 2, . . . ,k−1, and the neuron σbk only receives the carry from σbk−1. in neuron σbi, we assume that the number of spikes receiving from the input neuron σin is p, and the number of spikes that receives from the previous neuron σbi−1 is q, then the neuron σbi contains n + p + q spikes. the rules on the synapse (bi,out) can be divided into two cases, where i = 0, 1, . . . ,k − 1. • if the value of p + q is odd, that is p + q = 2j + 1, where j = 0, 1, . . . ,n − 1, the rule an+2j+1/an+1 → a on synapse (bi,out) is applied and consumes n + 1 spikes in neuron σbi, then one spike is sent to the output neuron σout. the output neuron will send this one spike to the environment at next time, that is, the corresponding binary bit of sum is 1. • if the value of p + q is even, that is p + q = 2j, where j = 0, 1, . . . ,n− 1, then n spikes in neuron σbi are forgotten by the rule a n+2j/an → λ on synapse (bi,out), and no new spikes are produced. that is, the corresponding binary bit of sum is 0. 578 h.f. wang, k. zhou, g.x. zhang at the same time of producing each bit of the sum, it is possible to generate carry. when j 6= 0 (without 0 or 1 spike), the rule (an+2j,an+2j+1)/a2j → a on synapse (bi,bi+1) is applied and produces one spike. then j spikes are sent to the next neuron (the weight on synapse (bi,bi+1) is j), where j = 1, 2, . . . ,n − 1. it should be noted that the neuron σbk only receives the carry spikes from the previous neuron σbk−1. 4. output the result. each of the binary bits of the sum can be sent to the output neuron σout at different steps, and σout uses the rule a → a to output the result to the environment based on the number 0 or 1 of spikes in σout. based on the above description, the system shown in fig. 1 can calculate the sum of the n natural numbers with k binary bits, where n ≥ 2, k ≥ 1, and send the result to the environment. as an example, let us consider the addition 1102 + 0112 = 10012, that is, n = 2, k = 3. fig. 2 depicts the sn p system ∏ add(2, 3) with rules and weights on synapses. table 1 reports the spikes contained in each neuron of ∏ add(2, 3), as well as the number of spikes sent to the environment, at each step during the computation. the input and the output sequences are written in bold. note that the first instance of time for which the output is valid is t = 7. figure 2: the sn p system ∏ add(2, 3) with rules and weights on synapses for addition compared with the example in [32], when k = 3, the number of neurons required to solve this problem by using the standard sn p system is 14; as shown in fig. 2, the number of neurons required in sn p system with rules and weights on synapses is 10, which effectively reduces the number of neurons. arithmetic operations with spiking neural p systems with rules and weights on synapses 579 table 1: the configurations and outputs of ∏ add(2, 3) at each time step during the computation of the addition 1102 + 0112 = 10012 t in a0 a1 a2 c b0 b1 b2 b3 out output 0 0 2 0 0 3 0 0 0 0 0 0 1 0 0 2 0 o(4)o(5)o(6) 0 0 0 0 0 0 2 1+2 0 0 2 o(3)o(4)o(5) 0 0 0 0 0 0 3 1+4 2 0 0 o(2)o(3)o(4) 0 1 0 0 0 0 4 1 0 2 0 o(1)o(2)o(3) 0 1 1 0 0 0 5 1+2 0 0 2 o(1)o(2) 3 1 1 0 0 0 6 0+4 2 0 0 o(1) 0 4 1 0 1 0 7 0 0 2 0 0 0 0 4 0 0 1 8 2 0 0 2 0 0 0 0 1 0 0 9 4 2 0 0 0 0 0 0 0 1 0 10 0 0 2 0 0 0 0 0 0 0 1 3.2 multiplication multiplication of two arbitrary natural numbers is also called the general binary multiplication, which has been performed on an sn p system in [32]. in order to reduce the number of neurons in the system, for two binary numbers m and n with k binary bits, we construct the sn p system ∏ multiple(k) with rules and weights on synapses as shown in fig. 3 to perform the general binary multiplication, where k ≥ 1, m ≥ 0, n ≥ 0. the sn p system with rules and weights on synapses for the general binary multiplication of fig. 3 outputs the product m×n in binary form of two natural numbers m and n with k binary bits, provided that the neuron σin is in binary form. assuming that m and n are two natural numbers with k binary bits, and m and n are rewritten as follows: m = k−1∑ i=0 mi2 i,n = k−1∑ j=0 nj2 j,then: m×n = n0m020 + (n0m1 + n1m0)21 + · · · + (n1mk−1 + n2mk−2 + · · ·+ nk−1m1)2 k + · · · + nk−1mk−122k−2 from the above expression we can see that the product of m and n can be decomposed into the sum of 2k − 1 terms. let t denote the current time step. in the initial configuration (t = 0), the neuron σa0 contains 2 spikes and σgi contains k spikes, where i = 2, 3, . . . ,k. fig. 3 shows the sn p system with rules and weights on synapses for the general binary multiplication, and the operation process consists of the following steps: 1. preprocessing of input. it is similar to the input preprocessing part of the system∏ add(n,k), so we only describe the differences. the auxiliary neuron σai only acts on the natural number m, which is the first input to the system, where i = 0, 1, . . . ,k − 1. when each of the corresponding binary bits of m is input to 580 h.f. wang, k. zhou, g.x. zhang figure 3: the sn p system ∏ multiple(k) with rules and weights on synapses for multiplication σin, the auxiliary neurons σa0, σa1, . . . , σak−1 send 2, 4 ,. . . , 2k spikes to σin, respectively. 2. input and store the binary bits of the first natural number m. at t = 1, the digit which is associated with the power 20 in the binary representation of m has been provided to σin, while the auxiliary neuron σa0 sends 2 spikes to σin. so the number of spikes that neuron σin contains may be 2 or 3. if there are 2 spikes, then the rule a2 → λ on synapse (in,b0) can be applied, so that the 2 spikes are consumed and no spike is sent out. at t = 2, the neuron σb0 receives only one spike from synapse (c,b0) (after one step delay), and this spike will be forgotten by rule a → λ on synapse (b0,d0) at the next step. if there are 3 spikes, where two of them come from σa0 and the other one from the environment, then the rule a 3 → a on synapse (in,b0) is triggered; as a consequence, one spike is sent out. then at t = 2, there are two spikes placed in neuron σb0, where one of them comes from synapse (in,b0) and the other one from synapse (c,b0). these two spikes remain in σb0 for the next steps. thus, k binary bits of m can be stored in the neurons σb0, σb1, . . . , σbk−1 by repeating the above operations. if there are 2 spikes, the i− th binary bit of m is 1; if there is no spike, the i− th binary bit of m is 0, where i = 0, 1, . . . ,k − 1. 3. each binary number of the natural number m multiplies all binary numbers of n, which can produce all possible products of binary numbers of m and n. at t = k + 1, the digit which is associated with the power 20 in the binary representation of n has been provided to the input neuron σin. if the digit is 1, then each rule a → a on synapses (in,b0), (in,b1), . . . , (in,bk−1) is triggered at the same time, and each of neurons σb0, σb1, . . . , σbk−1 will receive one spike at next step. otherwise, no spike will be sent to neurons σb0, σb1, . . . , σbk−1. now let us focus on the neuron σbi, where i = 0, 1, . . . ,k − 1. at t = k + 2, the number of spikes in σbi may be 0,1,2,3, and we can thus consider the following four cases. • if σbi contains 0 spikes, then no rule can be applied, thus no spike is sent out. this encodes the operation n0mi = 0 × 0 = 0. arithmetic operations with spiking neural p systems with rules and weights on synapses 581 • if σbi contains 1 spike, then it comes from σin. the rule a → λ on synapse (bi,di) is triggered, so that one spike is consumed and no spike is sent out. this encodes the operation n0mi = 1 × 0 = 0. • if σbi contains 2 spikes, then they are all remained from the previous step. no rule can be applied. this encodes the operation n0mi = 0 × 1 = 0. • if σbi contains 3 spikes, then two of them are remained from the previous step and the other one comes from σin at this step. the rule a3/a → a; i on synapse (bi,di) is triggered; as a consequence, one spike reaches the neuron σdi after i steps of delay and one spike is consumed, thus two spikes in σbi are left for the next step. this encodes the operation n0mi = 1 × 1 = 1. when n1 reaches neuron σbi, σbi also receives four spikes from neuron σg2 (after k + 2 steps delay). therefore, the number of spikes contained in neuron σbi at this time may be 4, 5, 6, and 7, respectively, corresponding to the four cases 0, 1, 2, and 3 mentioned above, and the rules on synapses (bi,di+1) can be applied and send the result n1mi to neuron σdi+1, where i = 0, 1, . . . ,k − 1. similarly, we can get n2mi, . . . ,nk−1mi in the same way, and njmi is sent to neuron σdi+j, where 0 ≤ i ≤ k − 1, 0 ≤ j ≤ k − 1. 4. the sum of terms. at t = k + 3, n0m0 arrives at neuron σd0 through synapse (b0,d0). . . . at t = 3k + 1, nk−1mk−1 arrive at σd2k−2. the number of spikes contained in neuron σdi above corresponds to the binary bit of m×n, where i = 0, 1, . . . , 2k − 2. 5. output the result. the spikes in neurons σd0, σd1, . . . , σd2k−2 above are sent to the output neuron σout. according to the number of spikes in the output neuron σout, we consider the following two cases. • if the number of spikes is odd, the rule a2j+1/aj+1 → a on synapse (out,e) is applied, so that j + 1 spikes are consumed and one spike is sent to the environment, and j spikes remain in σout for the next step. • if the number of spikes is even, the rule a2j/aj → λ on synapse (out,e) is applied, so that j spikes are consumed and no spike is sent to the environment, and j spikes remain in σout for the next step. from time t = k + 5, the system begins to have output. therefore, the system shown in fig. 3 can perform the general binary multiplication, and output the result to the environment. as an example, let us consider the multiplication 112 × 112 = 10012, that is k = 2. fig. 4 depicts the sn p system ∏ multiple(2) with rules and weights on synapses. table 2 reports the spikes contained in each neuron of ∏ multiple(2), as well as the number of spikes sent to the environment, at each step during the computation. note that the time for the first instance that the output is valid is t = 7. when k = 2, the number of neurons required in the multiplication sn p system is 17 in [32]; as shown in fig. 4, the number of neurons required in sn p system with rules and weights on synapses is 11, which effectively reduces 6 neurons. 3.3 the greatest common divisor in this subsection, the greatest common divisor of two natural numbers is performed on sn p systems with rules and weights on synapses. we divide the solution for the greatest common divisor into several individual modules, and then call the different modules according to the 582 h.f. wang, k. zhou, g.x. zhang figure 4: the sn p system ∏ multiple(2) with rules and weights on synapses for multiplication method steps. the method for solving the greatest common divisor that we use is the binary method. please refer to [34] to learn more details on the binary method. each of the modules required for the above method is given below. these modules are achieved by sn p systems with rules and weights on synapses. module 1: to determine whether the natural number is even if a number is even, then the digit which is associated with the power 20 in the binary representation is 0; otherwise, this digit is 1. so we can only use the digit which is associated with the power 20 in the binary representation of the number to judge whether it is even or odd. for a natural number, we construct the sn p system ∏ even with rules and weights on synapses as shown in fig. 5 to determine whether it is even or not. if the input is even, the number of spikes that output to the environment is 0, otherwise, the output of this system is not 0. the time that the output is valid for the first instance is t = 3. figure 5: the sn p system ∏ even with rules and weights on synapses module 2: to divide the number by 2 (to shift one bit to the right) the binary even number is divided by 2, which means that the last bit 0 of this binary number is removed. so if the length of binary bits of the input is k, then the length of binary arithmetic operations with spiking neural p systems with rules and weights on synapses 583 table 2: the configurations and outputs of ∏ multiple(2) at each time step during the computation of the multiplication 112 × 112 = 10012 t in a0 a1 c g2 b0 b1 d0 d1 d2 out output 0 0 2 0 2 2 0 0 0 0 0 0 0 1 1+2 0 1 o(1)o(2) o(4) 0 0 0 0 0 0 0 2 1+4 0 0 o(1) o(3) 2 0 0 0 0 0 0 3 1 0 0 0 o(2) 2 2 0 0 0 0 0 4 1 0 0 0 o(1) 3 3 0 0 0 0 0 5 0 0 0 0 0 7 7 1 0 0 0 0 6 0 0 0 0 0 2 2 0 2 0 1 0 7 0 0 0 0 0 2 2 0 0 1 2 1 8 0 0 0 0 0 2 2 0 0 0 2 0 9 0 0 0 0 0 2 2 0 0 0 1 0 10 0 0 0 0 0 2 2 0 0 0 0 1 bits of the output is k − 1. for an even number, we construct the sn p system ∏ divide2 with rules and weights on synapses as shown in fig. 6 to divide it by 2. the time that the output is valid for the first instance is t = 4. figure 6: the sn p system ∏ divide2 with rules and weights on synapses module 3: to add 1 to the number for a natural number, we construct the sn p system ∏ add1 with rules and weights on synapses as shown in fig. 7 to add 1 to it. the first time step that the output starts to be emitted by the system is t = 3. module 4: to compare the value of two natural numbers we construct the sn p system ∏ comp with rules and weights on synapses as shown in fig. 8 to compare two natural numbers. the inputs of this system are two appropriate sequences of spikes of two natural numbers in binary form, but what is different from the other modules is that the input here starts from the highest bit to the lower. for two natural numbers in binary form with the same length of binary bits, we compare 584 h.f. wang, k. zhou, g.x. zhang figure 7: the sn p system ∏ add1 with rules and weights on synapses them from the highest bit of each number to the lower order, until the system outputs 2 or 1. the result is the last bit of the output. if the result is 0, then in1 = in2; if the result is 1, then in1 < in2; if the result is 2, then in1 > in2. figure 8: the sn p system ∏ comp with rules and weights on synapses module 5: subtraction the design principle of the subtraction module is referred to [8]. for two natural numbers, we construct the sn p system ∏ sub with rules and weights on synapses as shown in fig. 9 to perform subtraction. the time that the output is valid for the first instance is t = 4. figure 9: the sn p system ∏ sub with rules and weights on synapses module 6: to calculate the result (to multiply by 2 with given times) by calling the above five modules, the numbers d and g are obtained. this module uses d and g to calculate the greatest common divisor g × 2d. that is, move g to the left by d bits, arithmetic operations with spiking neural p systems with rules and weights on synapses 585 table 3: the configurations and outputs of ∏ even at each time step during the process of judging whether a = 011002 and b = 100102 is even t in(a) in(b) aux out output 0 0 0 1 0 0 1 0+2 0+2 0 0 0 2 0 1 0 0 0 3 1 0 0 0 0 4 1 0 0 0 0 5 0 1 0 0 0 6 0 0 0 0 0 7 0 0 0 0 0 where d represents the number of times that all the two numbers are even. we construct the sn p system ∏ multiply2 with rules and weights on synapses as shown in fig. 10 to calculate the result. the input is the number g in binary form. the first time step that the output starts to be emitted by the system is t = 3. figure 10: the sn p system ∏ multiply2 with rules and weights on synapses by calling above modules, we can get the greatest common divisor of two natural numbers. in what follows, an example is provided to detail the process. as an example, let us consider the greatest common divisor of the numbers a = 011002 and b = 100102. the calling process of modules to calculate gcd(011002, 100102) is as follows. in the initial time, we assume that g = 0 and d = 0. step 1. call the module 1 to determine whether a and b are even when the input of system ∏ even is a = 011002 or b = 100102, the spikes contained in each neuron of ∏ even, as well as the number of spikes sent to the environment at each step during the process that judge whether a or b is even, are reported in table 3. from table 3, we can know that the output of ∏ even is 0, so a = 011002 is an even number, and b=100102 is an even number, too. step 2. call the module 2 to divide a and b by 2 when the input of system ∏ divide2 is a = 011002, the output is 0110. when the input of system ∏ divide2 is b = 100102, the output is 1001. so, after dividing a and b by 2, we can get a = 01102 and b = 10012. step 3. call the module 3 to add 1 to d in the initial time, d = 0, so the input of system ∏ add1 is 0. after adding 1 to d, we can get d = 1. step 4. call the module 1 to determine whether a and b are even 586 h.f. wang, k. zhou, g.x. zhang table 4: the configurations and outputs of ∏ comp at each time step during the process of comparing the value of a = 00112 and b = 10012 t in1 in2 out output 0 0 0 0 0 1 0 1 0 0 2 0 0 1 0 3 1 0 0 0 4 1 1 2 1 when the input of system ∏ even is a = 01102, the output is 0, so a = 01102 is an even number. when the input of system ∏ even is b = 10012, the output is 1, so b = 10012 is odd. step 5. call the module 2 to divide a by 2 the input of system ∏ divide2 is a = 01102. after dividing a by 2, we get a = 0112. step 6. call the module 1 to determine whether a is even the input of system ∏ even is a = 0112, and then the output of ∏ even is 1, so a = 0112 is an odd number. step 7. call the module 4 to compare the value of a and b in the system ∏ comp, in1 = a = 00112 and in2 = b = 10012. table 4 reports the spikes contained in each neuron of ∏ comp, as well as the number of spikes sent to the environment at each step during the process of comparing the value of a = 00112 and b = 10012. the output of the system is 1, so a < b. step 8. call the module 5 and module 2 to calculate (b−a)/2 because a < b, we can get in1 = b = 10012 and in2 = a = 00112 in the system ∏ sub. after computing in ∏ sub, we get b−a = 10012 − 00112 = 01102. then the input of system ∏ divide2 is 01102. after dividing it by 2, we can get b = (b−a)/2 = 01102 ÷ 102 = 0112. step 9. call the module 4 to compare the value of a and b in the system ∏ comp, in1 = a = 0112 and in2 = b = 0112. the output of the system is 0, so a = b. step 10. call the module 6 to calculate the result from the above steps, we can get g = a = b = 0112 and d = 1. so the input of system∏ multiply2 is g = 0112, and the spikes contained in each neuron of ∏ multiply2, as well as the number of spikes sent to the environment at each step during computation of the multiplication 0112 × 21 = 1102 are reported in table 5. so the greatest common divisor of 011002 and 100102 is g × 2d = 0112 × 21 = 1102. through the above analysis we can see that the greatest common divisor of two arbitrary natural numbers is available by calling the corresponding modules of method. 3.4 discussion for addition and multiplication, both the systems constructed in [32] and the ones in this paper have only one input neuron, and the integers encoded in binary form should be input in order. the system in [32] to perform the addition of n natural numbers with 3 binary bits arithmetic operations with spiking neural p systems with rules and weights on synapses 587 table 5: the configurations and outputs of ∏ multiply2 at each time step during the computation of the multiplication 0112 × 21 = 1102 t in aux1 out output 0 0 0 0 0 1 1 0 0 0 2 1 1 0 0 3 0 1 1 0 4 0 0 1 1 5 0 0 0 1 6 0 0 0 0 requires 14 neurons, while 10 neurons are enough by using sn p systems with rules and weights on synapses. when the multiplication of two natural numbers with 2 binary bits is performed, the system designed in [32] requires 17 neurons, while in this paper, we use only 11 neurons. thus, we can conclude that the systems with a single input neuron for addition and multiplication in [32] are improved by using the sn p systems with rules and weights on synapses. it is worth pointing out that the designed sn p systems in this paper use larger number of synapses and delays than the systems in [32]. for addition, due to the constraints of input conditions, the systems need to calculate the sum of each bit until the last number is input, which makes the addition system relatively complicated. in addition, this study is the first attempt to design an sn p system to calculate the greatest common divisor of two natural numbers. 4 conclusions and future work the use of the sn p systems with rules and weights on synapses to design specific systems for fulfilling the arithmetic operations is focused in this paper, including addition, multiplication and the greatest common divisor. comparing with the results reported in [32], smaller number of neurons are required to perform the addition and multiplication. calculating the greatest common divisor of two natural numbers is not an easy task. this paper achieved a good design by using several modules. following this work, we will consider how to use the sn p system with rules and weights on synapses to perform the division operation. acknowledgments this work was supported by national natural science foundation of china (61179032, 61672437, 61702428), the special scientific research fund of food public welfare profession of china (201513004-3), the guiding scientific research project of hubei provincial education department (2017078), the humanities and social sciences fund project of hubei provincial education department (17y071) and sichuan science and technology program (2018gz0185, 2018gz0085, 2017gz0159). 588 h.f. wang, k. zhou, g.x. zhang bibliography [1] atanasiu, a.; martinvide, c. 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[34] [online]. available: en.wikipedia.org/wiki/greatest_common_divisor. international journal of computers communications & control issn 1841-9836, 13(2), 280-293, april 2018. outlook of coordinated transmission control in 5g networks for iots u. tariq, a. aldaej usman tariq* department of information systems college of computer science and engineering prince sattam bin abdulaziz university, saudi arabia *corresponding author: u.tariq@psau.edu.sa abdulaziz aldaej department of information systems college of computer science and engineering prince sattam bin abdulaziz university, saudi arabia a.aldaej@psau.edu.sa abstract: the sum of wireless nodes are forecasted to ascent sharply from about ten billion in year 2018 to twenty-five billion by 2020. consequently, the data packet capacity response is anticipated to increase in future. the projected 5th generation (5g) of cellular grids is anticipated to be a blend of grid mechanisms with diverse magnitudes, transfer energy, backhaul networks and wireless access equipment. although there are several fascinating complications comprised by the 5g context, we highlighted the problems of communication mechanism with the potential of internet of things (iot). we offer a charter to discover required method constraints strategy that deal with the preeminent advances in terms of possible output for device-todevice inspired mobile grids. we explore the working and breakdown of immediate data and energy handover in iot and assess individually about communication outage likelihood and energy outage possibility. keywords: wireless communication, receiving antennas, 5g, internet of things (iot) 1 introduction to assess the payload communication response on the mobile grid, in entities of gigabit/second/ km square, the method implemented in reference [5] initiates with identified inhabitants concentrations in year 2010 for dissimilar setting types [1] and supposing a ratio of 1percent per annum for upsurge in subscriptions [2], then analyzes and marks supplementary forecasting of demand to year 2025 and beyond. in the present day, with the current long-term evolution-advanced criteria, the 3gpp provision’s a distinct-hop transmission tool in which the wireless admittance association between the base station (bs) and operator is transmitted by only one spread station. with the support of multi-hop transmitter, the wireless association between the base station and operators can be stretched to more than two nodes to expand the handling and grid capability. several nodes communicate payload to and from the resultant base station effects in the decline of route loss. nevertheless, practicing a multi-node transmitter method entails additional wireless resources to spread data from end to end nodes. additional intrusion is also formed due to a larger sum of synchronized communications in the system. novel agility and broadcast controlling systems are thus significant for accomplishing an extraordinary quality of service, though growing the entire cellular grid’s ability. over the preceding years, cooperative cellular data exchange [3] have achieved noteworthy devotion in the research communal. the key notion is the attainment of spatial uniformity devoid of consuming as a criterion the presence of several transceivers in distinct stations. more precisely, the respective cellular device turns out to be a fragment of copyright © 2018 by cc by-nc outlook of coordinated transmission control in 5g networks for iots 281 an enormous disseminated group, sharing its distinct aerial to promote the transaction among devices, using either amplify-and-forward or decode-and-forward schemes. as an outcome, the concluding endpoint can collect several reproductions of the similar data, which can be pooled to advance the consistency of the communication. consequently, disseminated collaborative activities can successfully transmit the wireless data, by possibly provisioning: (i) complex spatial range and output; (ii) lesser battery intake and condensed intrusion; and (iii) compliance to system settings. 1.1 the internet-of-things (iot) the internet has discarded substantial barriers in linking inhabitants, organizations, and neighborhoods universally by focusing diverse areas of interest. transceiver motes produce information, information generates understanding, and understanding impels accomplishment. as a result, establishing logic of the information that individual nodes produce does have the authentic significance for inhabitant. the creation of context aware cellular devices has created an ample recognition and acceptance by industry, user and common residents. at the moment, we observe soaring escalation in cellular traffic than traditional phone lines. enhanced energy capacity, adaptation of smart devices and expansion of network for cellular functions will affect a steady downfall in the augmentation of personal computers. with the mounting implementation of context aware devices and group medium, society event tracking and consequently inhabitant activity information are available for further processing at an enlarged scale. applications the idea of the internet of things is not novel. to hand, numerous premature iot samples are available in the arena of engineering mechanism, development device, and cellular networks. we are presently coming into contact with the iot, where masses of innovative nodes are frequently being coupled to the network. since this phenomenon augment abilities such as environment cognizance, amplified energy/battery handling, and power objectivity, and as new individuals and novel categories of data are associated, we will rapidly come into the internet of everything, where gears that were quiet will have an expression. well-being and robustness:ieee 802.15.4 based tools are permitting us to observe without difficulty the acute life-symbols of people with special needs. formerly, the absence of proper feelers, shared with an unfeasible mesh of cables, has rigorously restricted healing applications. at the present, patients can be examined successfully for grave disorders such as slumber apnea, even though in couch napping. for observing serious constraints such as pulse frequency, even though a sick is experiencing vigorous bodily workout, the linkage of cables is unworkable. this has advanced where individuals can now practice iot over body area based connected nodes such as handheld devices that not only observe interval and achieve messages but also screen the diabetic level in a user’s blood devoid of the discomfort and distress of a spike puncture. the ieee p2413 based applications have achieved milestones in medicine and health. agile inhabitants: prospect homes will have segments of linked nodes into a principal structure that can empower the user with a computing device to govern everything in functioning a smart-home. with a dash of a control, the handler can use device over any application in the connected environment such as safekeeping, temperature device, or smart theatre. try on the homegrown multimedia system, forming spontaneously the seamless environment, acoustic arrangement, and others can all be functioned from a particular application. industry: as per cisco, currently manufacturing network grid is worth thirty two trillion dollars. by the year 2030, it could range to eight five trillion dollars of yield. at production 282 u. tariq, a. aldaej facility, the iot benefits the enterprise to accumulate information about progressions going on daily bases. with the help of thousands of feeler devices on the assemblage stripe and the devices in each sequence, administrators can promptly discover the position of assembly. they are capable to exchange that statistic with collaborators in additional subdivisions. devices at cisco has stemmed in modernizations never previously comprehended. [4] smart environment architectures and applications the alteration of the current system to the requirements of iot unquestionably has a lot of benefits because of its natural capacity to collaborate with the current link node grid structure. the advancement of iot which is built on presently adopted internet protocol, uniform resource identifier and domain name server tools, can be directed on the foundation of previously learned awareness and understanding, with the practice of the prevailing, normally adopted systems and methods. iotsys [8] delivers an adaptation level to empower the incorporation of entities in the iot design. it is dedicated on enabling data exchange for rooting nodes, which are exhausting ipv6. the emphasis on the adaptation framework is on improving the compatibility of nifty entities. the adaptation framework exploits the internet protocol v6 based low-power wireless personal area networks (lowpan), constrained application protocol (coap) and extensible markup language substitution over the internet service area to relate with transceiver nodes. the iotsys adaptation framework targets at administering an access theory for current mote structures, set up currently in structure computerization, a mass which can be installed openly on rooted 6lowpan nodes. with transportable networks taking noteworthy depends on the collaboration of the discrete resource sourcing devices that form network associations, the operator controlling a node may adopt to the supportive assignation. consequently a precise supporting module is the nonmethodological operational support, which resides on current wireless systems and innovative standards, such as grid programming. intermediary function software methodologies, also measured as link level cumulating methods, perform as arbitrator executor both on a cellular node and inside the mobility aware network. technical challenges even though the right to use delay presented by long-term evolution is satisfactory for supreme cellular broadband users, it possibly will not be enough for delay critical use cases, such as traffic wellbeing, structural safety, or developing built-up internet systems. to guarantee provision for such operation-oriented cellular systems, subsequent peers of wireless right to use must permit for latencies on the demand of one millisecond or less. configuration and scalability to empower the anticipated enormous traffic escalation, supplementary band will have to be allocated to cellular wireless infrastructures. in subsequent group equipment, the motivation will be on guaranteeing additional band beneath 6.5 gigahertz. nevertheless, to accomplish extended traffic difficulties and capability empowerment of the very wide-ranging broadcast bandwidths, required for multi-gigabit per second payload rates, succeeding wireless right to use will outspread the choice of process to rates above ten gigahertz. outlook of coordinated transmission control in 5g networks for iots 283 figure 1: data transfer procedures of internet of things vs. customary system mobility next generation feelers with several aerial essentials can advance exposure for extraordinarydata-ratio communications as well as considerably escalate global structure capability. beamforming, where numerous feeler features are adopted to practice constricted rays, is a resourceful system for enhancing both information ratios and capability. three-dimensional multiplexing, where broadcast features are utilized to deliver numerous information relays separately to solitary or supplementary terminals, is an alternative illustration of a vital multi-aerial procedure. figure 2: beamforming for long-term evolution and ieee 802.16 standard devices 284 u. tariq, a. aldaej 1.2 network architecture the evolution to a schmoozed civilization will empower to a substantial quantity of linked nodes, which spread trivial sums of information occasionally. such nodes will regularly be unassuming and unnoticeably rooted into the inhabitant surroundings. this necessitates trivial wireless-component strategy and broadcast methods modernized to the undisturbed data exchange needs. nodes have to be capable to function for longer sessions on minute batteries. it is required to accomplish nil-overhead data exchanges by streamlining linkage situations for nodes and granting rights to use network with nominal beckoning. exploiting the node’s snooze prospects can diminish battery utilization, resulting to extended operational uses. the all-purpose consent on the necessities for 5g are: • procedure upsurge in zone capability with veneration to lte-advanced. • one millisecond round trip time delay. • procedure upsurge in battery productivity in relations of joules per bit. • procedure decline in rate of setting out. • movement provision and continually-on the linkage of handlers that have extraordinary data requests. protocols reflexive sub-interference conquest is illustrated as the indicator-power reduction executed by the route cost because of the physical parting among spread and collect feelers of the identical node. classic reflexive sub-interference conquest methods consist of: guiding sub-interference rout: in this method, the key energy sections of the spread/collect feelers of a full duplex node have nominal connection, empowering the sub-interference to be moderately suppressed preceding to the collector’s radio frequency visible-edge. correspondent withdrawal: in correspondent withdrawal, the set of interval-field withdrawal systems such as preparation-centered procedures can be hired together by "distinct-response distinct-yield" and "numerous-response numerous-yield" oriented procedures, where the former may execute sub-interference destruction by misusing the three-dimensional diversity accomplished by the linked numerous spread and collect feelers. feeler parting and sub-interference elimination: aggregating the route cost amid the transfer/collect feelers set up an effective method to weaken the sub-interference control, in this technique a sophisticated feeler parting indicates improved sub-interference suppression routine. while depending on feeler parting, the regular inaccessibility may also abuse the adjacent structures or the valuable presence of a protective plate, only if that harsh limitations forced on the node magnitude can be gratified. in conclusion, the unusual design will also entail the improvement of an integrated mechanism and administration strategy (grid prototype and edge evolution) to streamline grid procedures through diverse systems. it is assumed that the design/technique should be capable to harmonize with traditional/throwback framework and should agree to the handlers horizontal hegira. necessities for operators’ data pooling technique novel elastic multiplexing and accumulation techniques, for instance, built in elastic-network and elastic-inactive optical grid. cardinal frequency handling empowered bandwidth-adaptable outlook of coordinated transmission control in 5g networks for iots 285 figure 3: a conception of the general 5g design optical signal detection devices will be measured to edge admission and grids, established on multi-transferor "ofdm" systems for varied inflection and multiplexing, will lead to improved represent-perceptivity detailed dataset at the control-focused level. this will outcome in the abundant formulation of the robust optical signal detection devices that can accomplish peak transmission provisions. 2 related work innovative and interconnected environment necessitates not only over-the-top-fast integration for ingenious devices, and minicomputers, but also making accessible a gigantic sum of contextaware, resourceful, habitually stumpy-cost and squat-complexity nodes and making them linked even under very difficult settings. we believe iot will be the ultimate system for 5th generation frameworks. what presently outlook as an obstacle of the iot are incoherent devices. for instance, we partake radio-frequency identification, we use diminutive-array data relay methods, ultra-wideband and near-field communication, and this possibly will be a tricky in the forthcoming systems, especially in context of a smart metropolitan, where an integrated structure for 286 u. tariq, a. aldaej continuous node linking is essential. 5th generation networking is a worthy prospect to offer this integrated context. 5g will improve the signal route communications to guarantee broad scope for these nodes. for the up-interconnection communication, 5g can optionally provide the procedure for nonquadratic diverse connection methodologies for illustration such as random up-interconnection data relay from the battery-oriented iot transceiver sensing nodes. 5g nodes will also provision route oriented data connections, achieved by the grid. this will tolerate communications to be spread in anticipation that uninterrupted coverage is possible, which permits these nodes to consume lesser control communications. 2.1 multimeter wave channel in the preceding few years, the 28 gigahertz, 38 gigahertz and 80 gigahertz rate of recurrence bands have converted as an essential component for millimeter waves cellular and adhoc data relays [6] [7] [9]. lately, millimeter waves broadcast depth operations have been examined in enclosed and compact metropolitan open-air surroundings [10] [11]. millimeter waves broadcasting-methods are likely to function in substantial guided communications and it should respond to the routine of electrically-navigated multi-portion feeler groups to justify for the degree surge in unrestricted space route loss. expected omni functional route loss in compact metropolitan millimeter wave’s network is important for method policy and for approximating exposure and the ability of developing revolutionary extensive band cellular grids. academics’ practice interval graphs and three-dimensional sections to perfect the omni-indicator "committed information rate" and analogous joint angle-of-departure and angle of arrival control ranges, which have been adopted effectively in forming millimeter wave frequencies [15]. qodm multi-bearer variation in the practice of quadratic occurrence disunion multiplexing (qodm) has succeeded in telecommunications due to its extraordinary frequency spread ability with great bandwidth effectiveness and its strength with respect to multi-route dwindling and extended intervals [16]. core purpose that qodm as an explicit set of multicarrier inflection has been broadly adopted for extraordinary-data-ratio broadcasts in interval-dispersive settings which has substantial gains over interval-field comparison. qodm separates an extraordinary-ratio data flow into n corresponding flows, which are then spread by controlling n discrete transferors. in case, the creditor wants to be capable to detach signals passed by diverse transferors, they have to be squarish. frequency approximation is an additional vital fragment of a logical qodm system. with frequency approximation, qodm methods can use logical exposure to gain a three decibel "signal to noise" proportion improvement over variance discovery. for qodm methods with several spread and beneficiary feelers for system ability or routine enhancement, frequency data is indispensable to range merging, intrusion dominance, and frequency discovery. in practice, the precision of frequency state data significantly impacts the general scheme routine. fbmc a substitute to qodm has been greatly endorsed: filter bank centered multicarrier (fbmc) [14], [13]. it depends on sharing the band into numerous orthogonal substitute-bands, moreover, fbmc put on a filtering method to every substitute carrier in divergence to qodm. a method adopting fbmc is much more appropriate to a prospective 5g structure. whereas in principle, outlook of coordinated transmission control in 5g networks for iots 287 fbmc has several agreeable integrant, applied method arrangements reduces most of idle channels. as a multicarrier system, fbmc can value from multi-feeler channels. "multiple input, multiple output" systems can also be useful. 3 requirements for 5g the pervasiveness of cellular correspondence, attached with a normalization on internet protocol (ip) as the data procedure, the accessibility of squat-cost and prevailing computing devices, service and data in the cloud, and growing data bandwidths "anticipated and conveyed" are approaching to add an idea of 5g. simple necessities are illustrated as follows: • provision for stationary, movable, wireless and satellite connection tools; • an accessible and elastic device grid that can be modified to the necessities from various amenities and horizontal markets (e.g., grid sharing, system utility virtualization); • resource effectiveness for amenities extending from squat data iot facilities to extraordinary bit ratio software facilities; • battery productivity and energy control accession; • subsidiary integration from a distant user equipment (ue) through a spread ue to the system, and provision link between subsidiary networks and uninterrupted networks. 3.1 perceptive wireless networks and platforms 5g and cognitive broadcasting are the two developing protocols/systems to encounter the substantial cellular data flow of forthcoming wireless grids. current developments in cognitive broadcasting tools have stressed the requirement for innovative and upgradeable feeler systems with improved output. one improvement of feelers when functioned for 5g is the infinitesimal proportions as 5g is concentrated to practice the millimeter wave frequencies. cognitive broadcastings are fully programmable cellular nodes and has a widespread revision provision for accomplishing improved system and operational performance. system should be context aware and must vigorously achieve adaptation in the grid procedures, band operation systems, frequency access techniques and broadcast waveform used. its prospect will currently be determined by the organized research institutes that are functioning on novel methodologies with physical testbed oriented experimentation of cognitive broadcasting grids 3.2 current standards and application scenarios making 5g a genuine cutting edge futuristic industry standard demands a profound familiarity of what it requires to motivate novel portable protocols from regulation to industry and in what way these nodes will relate with other connectivity systems such as wi-fi (ieee 802.11ac) and bluetooth (ieee 802.15.1). • keeping in focus for the mainstream requirements, the subsequent set of 5g provision is gaining industry recognition. • 1 to 10 gigabits per second links to edge points in the inhabitant environments; • 1 millisecond completion (i.e. transmitter-receivertransmitter) round trip latency; • ten to thousands of coupled nodes; 288 u. tariq, a. aldaej • 1000x transmission capacity per entity zone; • requirement of 99.99 percent accessibility; • a 90 percent decrease in system battery usage; • requirement of hundred percent coverage; • up to five year energy availability for stumpy battery oriented nodes such as ieee 802.15.4 standardized devices 4 prospective work link layer clarifications built on the ieee 802.19 wireless concurrence operational set regulation have three simple necessities: (a) the system edge program need to be harmonious with the transmission capacity accumulation procedure; (b) devices with dualistic or additional grid edges must fit into the identical decisive field; and (c) associations must be standardized. in an anticipated system, the quality of service of cognizant broadcast regulator practices a cross layer methodology to robustly react to the variations in the scheme constraints. it practices automatic repeat request to boost the wireless association devoid of initiating a regular timeout. furthermore, it upholds the end-to-end exposition of transmission control protocol (tcp) since it does not send tcp acknowledgement to the contributor before the tcp sector is acknowledged effectively at the destination. 4.1 synchronized multipath transmission (smt) in smt, there is no dissimilarity concerning the main and minor routes. all routes are alike, and are adopted separately in data broadcast. smt utilizes the identical method of continuance numbers as stream control transmission protocol. smt practices the round-robin procedure to drive the packets along the access routes. the distribution of payload to the aerial is supervised by the magnitude of the bottleneck window for every route. smt transmits the data end to end to a route as soon as the overcrowding window for required route becomes accessible. when quite a lot of routes can be adopted for the broadcast, the route is nominated by the round-robin method. smt plugs the transmission overcrowding window of every route before hopping on to the subsequent. agility between mobile terminals and wlan lacks proportionality. there are two categories of mobile-wlan horizontal context aware node data transfers: voluntary handover where a cellular node is enclosed by both wireless grid and agrees to handover form solitary grid to alternative, and obligatory handover where a cellular node is departing the cluster of one system while exposed by alternative grid. in the course of a wlan to mobile grid required horizontal handover, "stream control transmission protocol" routine is influenced by: the sinking of sequential packets on the wlan connection for the reason that of the interruption in the wireless spectrum coverage, denoted as handover anomaly, and arbitrary data drain over wireless connection due to noise and intrusion, denoted to as a fault failure. we witnessed, regardless of the gains of exhausting multi-route broadcast, smt can perform de-grouping, which lowers the routines, considering the basis that the routes in smt may have diverse features in terms of transmission capacity and latency. smt suggestions the method "postponed acknowledgement (pac) for smt" to diminish ack data transmissions. pac empowers the smt to postpone the ack, even if the payloads reach as disarray. as the transporting of ack is repeatedly observed, the receiver should diligently evaluate every ack expected to distinguish missed payloads as swiftly as promising. in outlook of coordinated transmission control in 5g networks for iots 289 table 1: payload rates, mandatory for data set transfer in an assumed time intermission note. comparative 5g data rate: 15bps/hz (uplink) and 30 bps/hz (downlink) top spectral productivity five minutes one hour ten hours 100 gigabits 03 gbps 240 mbps 30mbps 10 gigabits 300 mbps 25 kbps 3.1 mbps 01 gigabits 30 mbps 2.5 kbps 300 kbps 100 megabits 03 mbps 250 kbps 30 kbps table 2: interval to completely recover from bottleneck loss and data transfer during recovery session handover speed repossesion time data transfer in the course of recovery 500 mbps 08 minutes 14 gigabits 01 gbps 15 minutes 55 gigabits 10 gbps 125 minutes 500 gbps every ack, the recipient updates the transmitter of the sum of payload packets it has acknowledged since the distribution of the last notifications. this suggestion necessitates adjustments to be made to arial (in both source and fetcher). support of device mobility between networks we recommend to embrace backhaul systems form the transitional grids between the entree systems to which the users are associated and the central grid. such backhaul systems are suitable and ever more vital, because the trivial-cell mechanism entails an outsized number of links with all entry points, as a substitute of fixing the networks on a lesser number of large aerials. we can assume that backhaul grids principally comprised of associations between "digital subscriber line access multiplexer" and the central system. normally these links use gigabit ethernet or "multiprotocol label switching" mechanism. in both cases, the guidelines for stream control transmission protocol packet broadcast and rebroadcast can be illustrated as follows: (a) as a definite rule, every edge node should constantly spread original data via the main route; and (b) a source should attempt to re-spread a chunk (which is a stream control transmission protocol data component) to a substitute active endpoint id over the alternate route if broadcast over the crucial route has been unsuccessful. low latency communication delay continually outlooks at the forefront each and every time when mobile transmission is taken into the consideration. overlook qos, bit inaccuracy ratio, accessibility, consistency, circuit rapidity, terrestrial analysis or cost-value of a grid, the latency linked with communicating data over a mobile network continually escalate to the top, getting a reasonable portion of responsiveness. but delay in a mobile grid can differ significantly and researchers must be cognizant of all the diverse schemes to handle with this coefficient. we have acknowledged multiple adjustments to a futuristic device grid that would be required: • diminish communication time interludes, e.g., decrease it to "90 microseconds", and squatter qodm representative periods empowering firm and well-organized data communication; 290 u. tariq, a. aldaej • reform of phy networks permitting primary frequency assessment; • practice of standard codes (e.g., for data networks) and segment ciphers (e.g., for device frequencies) which can initiate fast and consistent deciphering; • adaptation of extraordinary uniformity intensities enhancement of the consistency of frequency recognition and deciphering, as well as accessibility. it is suggested to adopt convolutional ciphering in the "accelerative inaccuracy rectification" schema for progression computerization. 4.2 payload-collection (synthesization and reproduction) payload-collection (pc) is a renowned tool in cellular dissemination, particularly in batterycontrolled systems. pc entails in relating accumulation methods to review the grid node communications that streams over the routes. by decreasing the volume of grid communication, the devices lessen the battery usage events (e.g. communication, response, congestion impact, and eavesdropping). nevertheless, payload-collection carries a data aggregation drawback, subsequently it losses the level of information-precision and escalates the transmission weakness in information loss malfunctions. some procedures necessitate extraordinary transmission consistency and greater information precision. accordingly, in procedures with high importance, rather than decreasing the grid communication, the system must repeat the packets in order to intensify the consistency and guarantee enhanced information correctness. packet-reproduction is a method generally adopted in latency and interference-forbearing systems. in these systems/grids, integration is not certain, so the packet-reproduction can rise the consistency of data distribution. one of the prominent packet-reproduction method is projected by spyropoulos et al. [12] and it is termed spray-and-wait. in this scheme, an individual payload-creator device can reproduce its packets up to a determined number of copies. when the payload-creator device encounters a legitimate neighbor device, it transmits a copy to that nearby connect node. once it dispenses all information copies, the payload-creator delays for the receiver endorsement notifying that the packet has been acknowledged. in case this verification is not received, the payload-creator device delays for the instance when it encounters the receiver and handover itself the packet. on the subject of transference, which is precisely a key prerequisite for outsized grid node deployment, the interrelated tasks rely on neighbor devices, which are selected as device custodians. the anticipated context is aimed to be entirely disseminated, the synchronization of the roles does not rely on a neighboring or crucial entity. as an alternative, the devices accomplish discrete role assessments and modify these choices using data collected from a nearby cluster/zone. 4.3 experimental validation a number of occurrence-oriented applications in enormous scale and extremely compact iot settings are associated with metropolitan systems where a device is allied to an outsized device network, and every device is capable to identify a set of occurrences. for instance, in a dense device grid, the devices function seamlessly and can give-and-take data linked to heat/smoke/fire recognition, automobile mishap alarm, and disaster and salvage procedures. the validation scenario has been simulated using the "network simulator 3.28" [12] which was modified to accommodate as per intended scenarios. the features engaged are: the capacity feature in figure 5 is the constraint, which is adopted to regulate the communication burden of the system; comprehensive explanation of the communication capacity aspect outlook of coordinated transmission control in 5g networks for iots 291 table 3: emulation parameters factors assessment packet size 1024 bits medium access control header 300 bits physical layer specific preamble and header 256 bits acknowledgement 224 bits + physical layer header channel bit rate 1024 bits slot time 65 microseconds short interferframe space 30 microseconds distributed coordination function interframe space 112 microseconds acknowledgement timeout 250 microseconds contention window (cwmax) 1 megabyte transmit power 2 walts receiving power 1.8 walts sensing power 1 walts table 4: packet layout of code division multiple access based internet of things payload communication: packet transmission code preface code source user equipament identification packet lenght data size cyclic redundancy check 56 bits 05 bits 05 bits 0 14 bits 15 bits figure 4: illustration of diverse and similar grids for high payload communication 292 u. tariq, a. aldaej is in [17]. in dissimilar systems, the various pico-clusters establish supplementary zones, and the intersection of function and pico-clusters with diverse communication frequencies expands the interfering clusters. thus, additional user equipment is authorized to use coordinated control in dissimilar systems. the normal cluster output in the dissimilar grid state appears to be worse than for the non-coordinated case, because diverse scheduler procedures were applied and evaluated. the performance outcomes cannot be accomplished by altering the scheduler limitations in distinct cluster processes. 5 conclusion 5g systems/grids/nets should simplify the synthesis of numerous cellular and adhoc grids, which should tolerate for all standard applications and device of forthcoming smart cities. the practice of millimeter wave band for 5g iot operation is observed. in broad-spectrum, missing routes of diverse broadcast techniques is amplified in the millimeter wave spectrum. although qodm is widely and consistently adopted in present communication schemes, inflection methods with enhanced spectral effectiveness are desirable to encounter the data ratio and grid capability necessities of application/hardware. millimeter wave frequency coding is a dynamic study area that does not presently reveal a universally chosen resolution for 5g. the concept of multiple input, multiple output systems is systematically studied; mimo operations were detached. application of millimeter wave frequency is obligatory for well-organized iot systems within 5g cellular grids, and respectively, the study determinations in the area stay to progressively increase. acknowledgement this project was supported by the deanship of scientific research at prince sattam bin abdulaziz university, alkharj, kingdom of saudi arabia under the research project 2017/01/7170 bibliography [1] bellanger, m.(2010); physical layer for future broadband radio systems, radio and wireless symposium (rws), 2010 ieee, 436439, 2010. 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[17] vahid, s. spectrum floor for future mobile communications systems, internal 5gic study, 2014 international journal of computers communications & control issn 1841-9836, 12(3), 347-364, june 2017. system selection and performance evaluation for manufacturing company’s erp adoption b.z. niu, k.l. chen, h.z. huang, y. li, l. chen baozhuang niu, kanglin chen, lei chen* school of business administration, south china university of technology guangzhou, 510640, p.r.china bmniubz@scut.edu.cn, kanglin_chen@qq.com *corresponding author: jayden_business@foxmail.com huizhong huang groupe danone, guangzhou, 510620, p.r.china 290092790@qq.com yuan li lingnan college, sun yat-sen university guangzhou, 510275, p.r.china jerry_yuanli@163.com abstract: enterprise resource planning (erp) system is an important investment for manufacturing companies that can affect their competitive advantages and operational performance. however, the implementation of erp can be a complicated process, where many strategic decisions have to be made. we focus on two critical decisions in erp implementation: (1) erp system selection, and (2) erp operational performance evaluation. for the former, we use analytic hierarchy process (ahp) to design the key performance indicator (kpi) system. for the later, we combine ahp and fuzzy integrated evaluation (fie) methods to effectively evaluate the implementation of erp. we use a typical industrial example and data analysis to illustrate our framework. keywords: erp system selection, erp performance evaluation, analytic hierarchy process, fuzzy integrated evaluation, manufacturing companies. 1 introduction nowadays, severe market competition has dramatically transformed the business environment. for manufacturing companies, whose competitive advantages are mainly low cost operations and quick-response management, the implementation of information systems becomes critical. it is widely accepted that enterprise resource planning (erp) has the ability to integrate the flow of material, finance, and information and to support organizational strategies [10]. however, the implementation of erp system can be a highly complicated process, especially for those contract manufacturing companies who have multiple businesses such as self-branded business, manufacturing business, and design business [4]. according to an independent research report [3], in 2014, 42% of the surveyed companies consider their erp projects as a “neutral", or “not clear" , or “failed" project. an important reason for the failure of erp system implementation is that the erp systems on the market do not fit the company’s operations properties. for the successful implementation of erp system, the adjustment of business process, the selection of suitable erp system and it tools, and the effective performance evaluation are the most critical decisions [15], [8], although all of them are hard to make. being aware of these, managers in the manufacturing industry turn to consulting companies (e.g., ibm and accenture) to find erp solutions. copyright © 2006-2017 by ccc publications 348 b.z. niu, k.l. chen, h.z. huang, y. li, l. chen meanwhile, many it service companies identify this demand and build online service platform to help manufacturing companies to implement erp systems. for example, tectec (http://www.technologyevaluation.com) proposes a erp selection and assessment approach for its customers, and this approach is proven to be effective in their application cases for manufacturing companies that produce pharmaceutical and botanical products, industrial machinery products, and electronics and high-tech products [9], [10]. recently, we have consulting interactions with a multinational manufacturing company which is austria-headquartered. they turned to us for suggestions to implement erp system to manage their supply chain. we conducted surveys and found that the standard approach proposed by tectec need be detailed. thus we develop a framework to help the company to select the erp system based on analytic hierarchy process (ahp) and use fuzzy integrated evaluation (fie) method to measure the performance of their erp implementation. our work is summarized in this paper. all the data that is used to illustrate our framework comes from this consulting project. we combine the objectives of choosing the most appropriate erp system and vendor with different criteria. we also compare the alternatives based on evaluators’ opinions and identify the most appropriate erp system. 2 literature selecting the suitable erp system for enterprise can help avoid the failure of erp system implementation, so it is important to select the appropriate erp system. there are several common methods to choose appropriate erp system or the other management information system ( [10], [15], [8]). the scoring method is one of the most popular methods, which is simple and intuitive, but does not guarantee the feasibility of resources. for example, [12] uses 10 criteria to evaluate the erp system and develop a framework based on nominal group technique (ngt) and analytic hierarchy process (ahp) to select the erp system. some other methods are developed to improve the efficiency of erp system implementation procedures, for example, [13], [14], and [4]. in practice, many companies use some financial indicators to select erp systems. since financial indicators are reported by professional institutions, they can be viewed as a trustable data resource, and can be used to index the implementers of erp system [7]. useful information includes the market size, the vendors and the overall system performance, etc. industry and academia also pay attention to the evaluation of erp system. companies want to use timely, accurate and objective performance evaluation to continuously adjust and improve the erp project. academia also try to identify the factors affecting the performance of erp system through empirical study, and then construct the evaluation system to evaluate the performance of erp system implementation ( [5], [15]). [13] points out that it takes companies a long time to see the effect of erp system on the performance. as a result, in the study on the comparison of performance between companies adopting erp system and companies without erp system, researchers couldn’t find a significant difference [5]. when the time window is large enough that can eliminate this effect, there is a significant difference on the performance between companies using erp system and companies without it [15]. 3 selection of erp system 3.1 the criteria for selection before we discuss the criteria for selection of erp system, determining the strategic objectives of erp project is very necessary. strategic objectives guide the team and indirectly coordinate system selection and performance evaluation for manufacturing company’s erp adoption 349 the interests of different departments inside the company. the implementation of the erp system including software and vendors. the quality of the system itself decides the influence of erp system to the company. erp vendors are responsible for erp system development, implementation and maintenance services. without vendors, the companies is unable to successfully implement erp project. these two aspects are essential to the success of an erp project. therefore, we defined two objectives: selecting the most appropriate erp system and selecting the most appropriate erp system vendor. after determining the strategic objectives, we need to find the specific attributes of criteria according to two objectives. the following will discuss the attributes of criteria for erp system selection and erp system vendor selection. erp system most of the enterprisers have gradually understood the benefits from erp system. according to the report of panorama in 2014 [11], the most popular reason enterprisers implement erp project is to improve the business (15%). the reason followed by is to better integrate the cross-regional and cross-department system (14%), and to get better service to customers (12%). figure 1: reasoning for implementing erp we can analyze the attributes of criteria to select erp system from the reasons for implementing erp system: corresponding to the reason to improve business performance, erp system should have complete functionality and help improve the company’s performance by integrate business process through the complete module and fit function. meanwhile, user-friendly interface and operations can help the internal and external personnel operate and understand the system, which can also help improve the operation efficiency and improve business performance. corresponding to the reason to better integrate the cross-regional and cross-department system, erp system should have excellent system flexibility, providing the ease of in-house development and the ease of integration. the compatibility is particularly important to integrating the cross-regional and cross-department system. corresponding to better customer service, erp system should have high system reliability, high system stability. recovery ability can help avoid the loss of customers in the face of mistakes. 350 b.z. niu, k.l. chen, h.z. huang, y. li, l. chen at the same time, long-term maintenance can also improve customers’ satisfaction. in addition, the total cost of the erp system implementation is a factor that company must take into consideration. the total cost including system purchase price, consultant cost expenses, system maintenance cost and infrastructure cost. according to the report of panorama in 2014, it shows that more than 54% of project will exceed the budget for unexpected technical or organizational issues. considering a long-time implementation of erp system, the implementation time is also an important attributes. according to the report of panorama, 63% of the erp system implementation will take more time than expected. erp vendor according to the report of panorama in 2014 [11], among the global famous vendors of erp system, oracle (34%) is the most popular, followed by microsoft dynamics (20%) and sap (16%). companies will pay great attention on the reputation of erp system vendors. the financial condition, scale of vendor and market share will be taken into consideration. figure 2: factors to help select erp systems in addition, the service that vendors provide matters a lot to the companies. in the service that vendors provide, the implementation of erp is the most common (21%), followed by related training (19%), organizational change management (14%), software selection (11%).the service is very important because the vendors have professional knowledge and the development and maintenance of system largely depends on the vendors. with the service of vendors, the system selection and performance evaluation for manufacturing company’s erp adoption 351 companies can integrate internal resources and external knowledge and play an important role on the development, implementation and maintenance of erp system project. therefore, in order to choose suitable system vendors, companies need to consider the vendors’ technical capability, including r&d capability, technical support capability and implementation ability; also, ongoing service needs considering, which includes warranties, consultant service, training service and service speed. based on the attributes of criteria above for the objective of erp system and erp system vendor, we sums up the attributes affecting the selection of erp system: choosing the most appropriate erp system: minimizing total cost (price, maintenance costs, consultant expenses, infrastructure costs), minimizing implementation time, having complete functionality (module completion, function-fitness, security), having user-friendly interface and operations (ease of operation, ease of learning), having excellent system flexibility (upgrade ability, ease of integration, ease of in-house development), having high system reliability(stability, recovery ability). choosing the most appropriate erp vendor: having good reputation (financial condition, scale of vendor, market share), providing good technical capability (r&d capability, technical support capability, implementation ability), and supplying ongoing service (warranties, consultant service, and training service, service speed). 3.2 ahp-based approach to select erp system introduction of ahp analytic hierarchy process (ahp) was developed by thomas saaty in 1971, mainly used in decision-making problems with uncertain circumstances and many criteria [16]. the main property of ahp is that it can turn qualitative problem quantitative. it gives a quantitative importance of each level and uses mathematical method to determine the weights of all elements [5]. basic steps are as follows: (a) determine the objectives and criteria p attributes u = {u1,u2, ...up}, (b) pairwise comparison and judgment matrix the pairwise comparison show the importance of one attributes to another. this subjective judgment can be convert to a numerical value using a scale of 1-9. we can draw the judgment matrix from pairwise comparison. (c) weights calculation and aggregation calculate the greatest characteristic root and characteristic vector of the judgment matrix s. the characteristic vector is the importance of each evaluation attributes or alternatives and also is the distribution of weight coefficient. (d) check the consistency. we need to check the consistency of the judgment matrix with ci = λmax−n n−1 . if the consistency index ci of judgment matrix is less than 0.10, we believe the results of the analytic hierarchy sort have satisfactory consistency and the weights is reasonable; otherwise, the pairwise comparison matrix need to adjust and redistribute the weights. an example of company a company a is a large multinational manufacturing corporation. company a has its own production workshop and assembly workshop in mainland china. its product is involved in seven industries and there are thousands of different types of products. after ten years of development, company a has rapidly expanded business and grown fast. the staff team has 352 b.z. niu, k.l. chen, h.z. huang, y. li, l. chen grown from dozens to more than 1500 and the annual sales expand to millions of dollars from only six hundred million. with the growing of business, the difficulty of company’s management is also appearing, which makes the implement of erp system become necessary. within the company, the sales department is only responsible for the order fulfillment. the lack of a standard process makes the sales department low-efficient. besides, there is contradiction between purchasing department and project department. project department, as the service department of the purchasing groups, makes the final decision in the procurement process. the purchasing department can only give suggestions and fulfill the order. this mismatching between right and duty during the procurement process in two departments induces many conflicts. besides, the financial department uses an independent financial system which only manages the cash flow in that department but not the whole company. outside the company, purchasing department does not collaborate well with the suppliers. most of the suppliers are small and medium-sized companies and the information construction remains to be improved. the company cannot timely access to the useful information and the low standardization level of business operation process brings lots of troubles to company a. erp system may help company a to integrate the departments and manage the information within and outside the company. the leaders of company a are thinking whether they should purchase the professional erp system and form a project team. they have already selected three erp systems from different vendors, denoted as system 1, system 2, system 3. the required function for erp system of different industries has a huge difference. therefore, enterprisers need to know its industry characteristics and function requirements, when choosing the appropriate erp system. in addition, the enterpriser need consider the scale of company. we ask three leaders as evaluators for a questionnaire survey and propose erp system selection framework as follows. a) identify the erp system characteristics for the erp system selection, we collect the opinions through the purchasing department, project department, finance department, human resources department and marketing department. it is decided that the system selection is considered from two aspects: one is the erp system itself; the other is the erp system vendor. b) organize the hierarchy structure of objectives, criteria, and alternatives. objectives are the target of the problem. criteria is to extract the attributes for evaluating erp systems. alternatives are the feasible solutions of the problem. in the case of company a, there are two objectives with different criteria. the first objective is screening out the most appropriate erp system. there are six attributes for evaluating the erp system, including minimizing total cost (c1), minimizing implementation time (c2), having complete functionality (c3), having user-friendly interface and operations (c4), having excellent system flexibility (c5), having high system reliability(c6). there are three alternatives, called as system 1,system 2 and system 3. the second objective is choosing the most appropriate erp vendor. there are three attributes for evaluating the erp vendor, including having good reputation (d1), providing good technical capability (d2), supplying ongoing service (d3). the alternatives are same, called as system 1, system 2 and system3. c) the comparison of attributes among criteria (for example) c1:minimizing total cost; c2:minimizing implementation time. if the ratio is 3:1, the evaluator think that minimizing total cost is more important than minimizing the implementation time. the importance degree of former is 3 compared to the later. if the ratio is 1:5, the evaluator think that minimizing the implementation time is more important. its importance degree is 5 compared to minimizing total cost. system selection and performance evaluation for manufacturing company’s erp adoption 353 figure 3: ahp-based erp system selection framework figure 4: ahp-based erp vendor selection framework table 1: criteria questionnaire 9:1 8:1 7:1 6:1 5:1 4:1 3:1 2:1 1:1 1:2 1:3 1:4 1:5 1:6 1:7 1:8 1:9 c1 c2 354 b.z. niu, k.l. chen, h.z. huang, y. li, l. chen table 2: alternatives questionnaire 9:1 8:1 7:1 6:1 5:1 4:1 3:1 2:1 1:1 1:2 1:3 1:4 1:5 1:6 1:7 1:8 1:9 s1 s2 table 3: judgment matrix of attributes for erp system evaluator 1 c1 c2 c3 c4 c5 c6 wi(weight) c1 minimizing total cost 1 1/3 1/3 1 1/3 3 0.0766 c2 minimizing implementation time 3 1 1/4 3 1/3 5 0.1500 c3 having complete functionality 3 4 1 5 3 7 0.3954 c4 having user-friendly interface and operations 1 1/3 1/5 1 1/5 5 0.0766 c5 having excellent system flexibility 3 3 1/3 5 1 5 0.2690 c6 having high system reliability 1/3 1/3 1/7 1/5 1/5 1 0.3240 λmax: 65.222; consistency: 0.0829 the comparison of alternatives: known from the ahp selection framework, each attributes correspond three alternatives which need to take account of the project, so decision-makers need to compare the alternatives for each attributes. d) select erp system we can obtain the corresponding judgment matrix through the pairwise comparison after we collect the questionnaire of the evaluators. we need to check the consistency of the judgment matrix with ci = λmax−n n−1 . we find that the consistency index ci of judgment matrix is all less than 0.10.the following only show the judgment matrix for erp system and each alternative of evaluator 1. comparing the importance of attributes to each evaluator, we can find that three evaluators tend to share the same opinion: for the attributes of erp system, “having complete functionality " is considered as a very important attributes for three evaluators, of which the relative weight comes to be the 1st for evaluator 1 and evaluator 2, 2nd for evaluator 3. “having excellent system flexibility " is also of great importance, respectively to be the 2nd,2nd and 1st for evaluator1, 2 and 3. while, “having high system reliability "is considered as the least important attribute for all three evaluators. it is necessary to analyze the result with the situation of company. company a, a large manufacturing enterpriser, is an integrated supplier providing complete sets of production lines, equipment and services. it has its own production workshop and assembly workshop with complete functional departments. more specifically, the number of project team is large and the organizational structure is loose. there are too many types of equipment and spare parts to procure. there is too much communication between project teams and different functional departments, so are the purchasing department and suppliers of company a. moreover, the difficulty of purchasing has worsen the information distortion and then deepen the contradictions between project teams and functional departments. thus, an information system covered multi-department is particularly important, which can reduce the conflicts between different detable 4: judgment matrix of alternatives for c1 and importance of attributes the pairwise comparison of alternatives for c1 system 1 system 2 system 3 wi(weight) system 1 1 3 5 0.6370 system 2 1/3 1 3 0.2583 system 3 1/5 1/3 1 0.1047 λmax: 3.0385; consistency: 0.0370 system selection and performance evaluation for manufacturing company’s erp adoption 355 table 5: judgment matrix of alternatives for c2 and importance of attributes the pairwise comparison of alternatives for c2 system 1 system 2 system 3 wi(weight) system 1 1 5 1/3 0.2790 system 2 1/5 1 1/7 0.0719 system 3 3 7 1 0.6491 λmax: 3.0649; consistency: 0.0624 table 6: judgment matrix of alternatives for c3 and importance of attributes the pairwise comparison of alternatives for c3 system 1 system 2 system 3 wi(weight) system 1 1 7 3 0.6694 system 2 1/7 1 1/3 0.0879 system 3 1/3 3 1 0.2426 λmax: 3.0070; consistency: 0.0068 table 7: judgment matrix of alternatives for c4 and importance of attributes the pairwise comparison of alternatives for c4 system 1 system 2 system 3 wi(weight) system 1 1 1/3 1/7 0.0879 system 2 3 1 1/3 0.2426 system 3 7 3 1 0.6694 λmax: 3.0070; consistency: 0.0068 table 8: judgment matrix of alternatives for c5 and importance of attributes the pairwise comparison of alternatives for c5 system 1 system 2 system 3 wi(weight) system 1 1 5 1 0.4806 system 2 1/5 1 1/3 0.1400 system 3 1 3 1 0.4054 λmax: 3.0291; consistency: 0.0279 table 9: judgment matrix of alternatives for c6 and importance of attributes the pairwise comparison of alternatives for c6 system 1 system 2 system 3 wi(weight) system 1 1 1 1/5 0.1336 system 2 1 1 1/7 0.1194 system 3 5 7 1 0.7471 λmax: 3.0126; consistency: 0.0121 356 b.z. niu, k.l. chen, h.z. huang, y. li, l. chen table 10: judgment of importance of attributes attributes evaluator 1 evaluator 2 evaluator 3 erp system minimizing total cost 0.0766(4) 0.1500(3) 0.0378(5) minimizing implementation time 0.1500(3) 0.1500(3) 0.1790(3) having complete functionality 0.3954(1) 0.3910(1) 0.3356(2) having user-friendly interface and operations 0.0766(4) 0.0565(5) 0.0566(4) having excellent system flexibility 0.2690(2) 0.2085(2) 0.3710(1) having high system reliability 0.0324(6) 0.0420(6) 0.0200(6) erp vendor having good reputation 0.0719(3) 0.0554(3) 0.0995(3) providing good technical capability 0.2790(2) 0.5990(1) 0.3355(2) supplying ongoing service 0.6491(1) 0.3456(2) 0.5650(1) partments and project teams by sharing the information effectively. to sum up, we can find the great importance of “having complete functionality". at the same time, company a is a foreign multinational enterprisers. it has different branches in 44 countries around the world and five business areas. there are many different brands and products in each business area. while in the implementation of procurement, the boundaries between different departments is clear and they independently do different works in the business. there is no communication and no collaboration which generates a lot of repetitive work and additional costs such as assessment, repeated negotiation, travel cost, quality control cost and so on. it is necessary for company a share information and resources cross areas and departments, so the system flexibility is very valued. for the attributes of erp vendor, “providing good technical capability" and “supplying ongoing service" are of great importance while “having good reputation" is not considered as an important attribute. “providing good technical capability" includes r&d capability, technical support capability and implementation ability. besides the initial system development, maintenance and upgrade stage of erp project also need excellent technical support. in the adjustment stage, erp system need continuous operation maintenance, and even need a new version or new functions. the system’s maintenance and upgrade require a long-term technical support. “supplying on-going service" includes the most basic warranty service, consultant service and training services. the users’ feedback is an important criterion to see whether erp system is running smoothly. it is necessary that users approve and understand the system. the implementation of erp project requires users to master the complicated operation skills. if the employee do not understand how the system works, it will ultimately affect the entire erp system. the success of erp system must be based on reasonable operation. in the erp system implementation of company a, training objects include the suppliers of a company besides the employees. there are more than 500 suppliers and quite of them are small and medium-sized companies, which adopt the traditional manufacturing management mode and are lack advanced management philosophy. the information cannot be inputted and processed timely and the information management system is incomplete and imperfect. to help the suppliers to adapt the erp system is necessary and challenging. therefore, ongoing training service is valued. after three evaluators give a weight to all attributes, we can get the evaluation score of three erp systems through the two judgment matrix (including criteria judgment matrix and alternative judgment matrix). considering that we develop two objectives (erp system and erp vendor), we give equal weights to them. finally we get the evaluation score of three erp systems. following is the result of evaluator 1: system selection and performance evaluation for manufacturing company’s erp adoption 357 table 11: evaluation score of erp systems (1) evaluator 1 erp system erp vendor final score system 1 0.4957 0.3129 0.4043 system 2 0.1185 0.323 0.22075 system 3 0.3858 0.3641 0.37495 (1) equal weights to erp system and erp vendor table 12: evaluation score of erp systems (2) evaluator 1 evaluator 2 evaluator 3 final score system 1 0.40430(1) 0.37155(2) 0.27362(2) 0.34982(2) system 2 0.22075(3) 0.18145(3) 0.23123(3) 0.21114(3) system 3 0.37495(2) 0.44700(1) 0.49515(1) 0.43904(1) (2) equal weights to each evaluator e) get the final result. evaluator 2 and evaluator 3 prefer to choose system 3 while evaluator 1 prefers to choose system 1. we can find that the score of evaluator 1 to system 1 and system 3 is close. moreover, system 3 gets the highest final score. the result show that system 3 is the most appropriate erp system for company a. 4 evaluation of erp system company a started to promote the erp project after selecting the appropriate erp system. in order to implement erp project successfully, company a set up a team to take charge of the entire implement. in the preparatory stage, company a focused on the training and helped employee understand the erp system. then, company a started to research and analyze, even the specific operation of each departments, in order to adapt the erp system to match the company. after this, company a formally set up erp system. they built a complete system framework taking full consideration of opinions from each departments and vendors. company a fully combined the original function with the business process. in addition, erp project team also optimized the mismatch between erp system and company’s business. next is to import massive data. company a successfully imported the internal and external data before changing the system and checked the accuracy of data. in november 2013, company a officially started using erp system. after cautious consideration and selection, the new system still bring impact to the company on business. with time goes by, employees have been familiar with erp system and it has run methodically. reviewing the erp project of company a, it went well during the implementation, but we need to see whether it brings significant benefits to company a. the evaluation of implementation of erp system is of great importance, which involved the influence on company’s strategy, the impact on performance of management and the business process. the following will show the evaluation for the performance of the erp system implementation. 4.1 the framework of evaluation the success of erp project is far more than that system goes live. how to judge or define the success of erp project is also different for different companies or different industries. according 358 b.z. niu, k.l. chen, h.z. huang, y. li, l. chen to company a’s business and structure, we listened the opinion of managers and sort out the following performance evaluation structure. figure 5: structure of performance evaluation the description of each factors in the structure of evaluation above is as follows. it’s worth mentioning that “functionality" and “implementation result" are positive statements, “matching" and “attitude of users" is reverse. this is to reduce the respondents’ deflection and is also helpful to remove those regardless of content. as a result, we need to adjust correspondingly when scoring. in the following results, “average score" is not adjusted and the “real score" is adjusted. functionality: erp system has user-friendly interface and operations; erp system has high intelligence; erp system has excellent flexibility and compatibility; erp system has high reliability. matching: erp system does not match the company’s operation process; erp system does not adapt to the mismatch; the data entry and processing of erp system does not match with the original model; erp system does not match company’s organizational structure and strategy. implementation results: erp system helps improve the efficiency and communication cross-department; erp system promotes the collaboration with suppliers; erp system help the company with demand forecasting and capacity management; erp system help the company improve the quality of the products and arrange the production reasonably. attitude of users: the users of erp system do not get the corresponding training and do not understand erp system; the management does not know the implementation of erp system implementation and give no support to it; the users of erp system think that it does not improve the performance. the performance get even worse than before; erp system lacks flexibility and makes the company lose advantages. 4.2 the analysis based on fie 30 questionnaires were distributed within the company, we recycled 30 questionnaires and the 23 of them were valid. the results are as follows: from the data we can see that the mean of real average score is 3.3894 and the total real score is 54.2308, higher than the total real average score 48, under normal distribution assumption. if system selection and performance evaluation for manufacturing company’s erp adoption 359 table 13: evaluation score strongly disagree disagree neutral agree strongly agree average score real score standard deviation functionality 1 0.0000 0.0769 0.2308 0.6923 0.0000 3.6154 3.6154 0.62 2 0.0000 0.3846 0.3077 0.3077 0.0000 2.9231 2.9231 0.83 3 0.0000 0.3077 0.4615 0.2308 0.0000 2.9231 2.9231 0.73 4 0.0000 0.0769 0.3846 0.4615 0.0769 3.5385 3.5385 0.75 matching 5 0.0000 0.3077 0.5385 0.1538 0.0000 2.8462 3.1538 0.73 6 0.0000 0.5385 0.3846 0.0769 0.0000 2.5385 3.4615 1.12 7 0.0000 0.3846 0.3077 0.3077 0.0000 2.9231 3.0769 0.84 8 0.1538 0.3846 0.3077 0.1538 0.0000 2.4615 3.5385 1.42 implementation result 9 0.0000 0.2308 0.3077 0.4615 0.0000 3.2308 3.2308 0.80 10 0.0000 0.0000 0.3077 0.6154 0.0769 3.7692 3.7692 0.58 11 0.0000 0.1538 0.3846 0.4615 0.0000 3.3077 3.3077 0.72 12 0.0000 0.0769 0.0769 0.7692 0.0769 3.8462 3.8462 0.66 attitude of users 13 0.0769 0.4615 0.4615 0.0000 0.0000 2.3846 3.6154 1.38 14 0.2308 0.3846 0.3846 0.1538 0.0000 2.7692 3.2308 1.26 15 0.2308 0.5385 0.1538 0.0769 0.0000 2.0769 3.9231 2.02 16 0.0000 0.3077 0.4615 0.2308 0.0000 2.9231 3.0769 0.75 total 54.2308 only judging from this data, we can say that company a thinks the erp project help improve the performance. there are some shortcomings in the classical statistical analysis. it cannot show the overall attitude intuitively and cannot directly show the proportion of different order of evaluation. given the order of evaluation in questionnaires is fuzzy, we analyze the data using the fuzzy integrated evaluation (fie) method. when determining the weights of each sub-factor, we use the ahp method. fie fie is a method to evaluate after fuzzy transform according to the criteria and measured values. the process of fie: assume the evaluation target as a fuzzy set composed of a number of factors; then set order of evaluation to these factors and make up a fuzzy set; next calculate the membership degree of each factors to the order of evaluation; and then according to the weights of factors in the evaluation, calculate the quantitative value [18]. the evaluation of erp implementation of company a a) the factors set ut: u = {u1,u2,u3,u4}= {functionality, matching, implementation results, attitude of users} u1 = {u11,u12,u13,u14}= {friendly operation, intelligence, flexibility, reliability} u2 = {u21,u22,u23,u24}= {match up, changeability, data entry, organizational structure} u3 = {u31,u32,u33,u34}= {cross-department communication, external communication, demand forecasting, production arrangement} u4 = {u41,u42,u43,u44}= {operational training, management training ,performance, rigidity} b) the evaluation set for factors: evaluation set is a collection of all results of the evaluation by evaluators. v= strongly disagree, disagree, neutral, agree, strongly agree c) the fuzzy relationship matrix r: according to the questionnaire statistics, we can get the proportion of different order of evaluation .the statistical records are as follows: 360 b.z. niu, k.l. chen, h.z. huang, y. li, l. chen table 14: factor set u1 factor set u1 strongly disagree disagree neutral agree strongly agree friendly operation 0.00% 7.69% 23.08% 69.23% 0.00% intelligence 0.00% 38.46% 30.77% 30.77% 0.00% flexibility 0.00% 30.77% 46.15% 23.08% 0.00% reliability 0.00% 7.69% 38.46% 46.15% 7.69% table 15: fuzzy relationship matrix r1 =   0.00% 7.69% 23.08% 69.23% 0.00% 0.00% 38.46% 30.77% 30.77% 0.00% 0.00% 30.77% 46.15% 23.08% 0.00% 0.00% 7.69% 38.46% 46.15% 7.69%   r2 =   0.00% 15.38% 53.85% 30.77% 0.00% 0.00% 7.69% 38.46% 53.85% 0.00% 0.00% 30.77% 30.77% 38.46% 0.00% 0.00% 15.38% 30.77% 38.46% 15.38%   r3 =   0.00% 23.08% 30.77% 46.15% 0.00% 0.00% 0.00% 30.77% 61.54% 7.69% 0.00% 15.38% 38.46% 46.15% 0.00% 0.00% 7.69% 7.69% 76.92% 7.69%   r4 =   0.00% 0.00% 46.15% 46.15% 7.69% 0.00% 15.38% 38.46% 38.46% 23.08% 0.00% 7.69% 15.38% 53.85% 23.08% 0.00% 23.08% 46.15% 30.77% 0.00%   we can get the fuzzy relationship matrix r1 from u1. similarly, we can get fuzzy relationship matrix r2 from u2. while this subset is disjunctive, we should reverse the arrangement. d) the weight of each factor: in the evaluation system, the importance of each factor to realize the goal of system is different. the weight of each factor show the different importance. set the weights reasonably and appropriately is important for evaluation. here we use ahp to get the weights. the supervisors of company score the four factors: functionality, matching, implementation results, and attitude of users. we get the following results: calculate the greatest characteristic root and characteristic vector of the judgment matrix. the characteristic vector is the importance of each evaluation factors and also is the distribution of weight coefficient. u = [0.2477, 0.1259, 0.5538, 0.0727]. similarly, calculate the weight of each factor under the four dimensions according to the experts’ scoring: each weight of factors passes the consistency check. e) get the evaluation results. b = u ∗ r: b1 = u1 ∗ r1= (0.0000 0.2299 0.3975 0.3571 0.0154); b2 = u2 ∗ r2= (0.0000 0.1679 0.4360 0.3590 0.0370); b3 = u3 ∗ r3= (0.0000 0.0927 0.2966 0.5657 0.0451); b4 = u4 ∗ r4= (0.0000 0.0799 0.3147 0.4684 0.1618). d = u ∗ r= (0 system selection and performance evaluation for manufacturing company’s erp adoption 361 table 16: comparison matrix of factors comparison matrix of factors u1 u2 u3 u4 wi(weight) u1 functionality 1 3 1/3 3 0.2477 u2 matching 1/3 1 1/5 1/3 0.1259 u3 implementation result 3 3 1 5 0.5538 u4 attitude of users 1/3 5 1/5 1 0.0727 λmax: 4.1975; consistency: 0.0740 table 17: comparison matrix of sub-factors in u1 comparison matrix of sub-factors in u1 u11 u12 u13 u14 wi(weight) u11 functionality 1 3 1/3 1/2 0.1612 u12 matching 1/3 1 1/5 1/3 0.0740 u13 implementation result 3 5 1 5 0.5641 u14 attitude of users 2 3 1/5 1 0.2006 λmax: 4.2219; consistency: 0.0831 table 18: comparison matrix of sub-factors in u2 comparison matrix of sub-factors in u2 u21 u22 u23 u24 wi(weight) u21 match up 1 5 3 3 0.5244 u22 change ability 1/5 1 1/2 1/2 0.0957 u23 data entry 1/3 2 1 1/3 0.1390 u24 organizational structure 1/3 2 3 1 0.2408 λmax: 4.1575; consistency: 0.0590 table 19: comparison matrix of sub-factors in u3 comparison matrix of sub-factors in u3 u31 u32 u33 u34 wi(weight) u31 cross-department communication 1 1/3 4 3 0.2854 u32 external communication 3 1 3 4 0.4944 u33 demand forecasting 1/4 1/3 1 2 0.1290 u34 production arrangement 1/3 1/4 1/2 1 0.0912 λmax: 4.2367; consistency: 0.0886 table 20: comparison matrix of sub-factors in u4 comparison matrix of sub-factors in u4 u41 u42 u43 u44 wi(weight) u41 operational training 1 3 1/2 3 0.3089 u42 management training 1/3 1 1/3 3 0.1612 u43 performance 2 3 1 3 0.4369 u44 rigidity 1/3 1/3 1/3 1 0.0930 λmax: 4.2148; consistency: 0.0805 362 b.z. niu, k.l. chen, h.z. huang, y. li, l. chen 0.1352 0.3405 0.4810 0.0452). f) analyze the results. the results show 1.64% of evaluators strongly disagree that erp project bring positive effect; 18.75% of them disagree; 34.05% remain neutral; 42.87% agree the positive effects of erp project and 2.88% strongly agreed with it. according to the maximum membership degree principle, the conclusion is "agree". multiply the raw score (1-5) in likert scale by the number of sub-factors, 16. then we get the level parameters in evaluation set and the column vector is: p = de = 016 + 0.135232 + 0.340548 + 0.481064 + 0.045280 = 55.0677. the result is close to the statistical analysis result, 54.2308. it shows that the result based on fie is consistent with the result based on classical statistical analysis. company a recognizes erp project as a beneficial project. figure 6: 6 s assessment scale we can see the factor “attitude of users" get the highest score (60.5846). factor “implementation results" follows (57.0085). the other two factors “matching degree "(52.2384) and “functionality" (50.5253) is not ideal, which are lower than the average score (55.0677). this result is meaningful for company a’s management. they should focus on improving the matching degree and the system’s functionality in the future. regarding the factor “matching degree", we can see that in most cases, company’s organizational structure and process mismatch the erp system’s functionality. when there exist mismatches between erp system and company’s business process, what to do depends on different situations. on the one hand, if the operation of erp system is inefficient, company can ask the vendors to adjust the system to adopt company. on the other hand, if the erp system can improve more efficient performance, then company can make appropriate changes on the operation process to adapt to erp system. regarding the factor “functionality", although company a pays much attention to system’s functionality during the selection, we still find that the respondents is not very satisfactory with the functionality. it reflects that there exists difference between the effects after t implementation and expectations. company a should fully understand the erp is a long-term project and it is system selection and performance evaluation for manufacturing company’s erp adoption 363 ongoing to look for problems and put forward the solution. the project team needs to stay close to the erp vendors and solve the problems together. 5 conclusions it is necessary to use the proper process control method during the implementation of a successful erp system project. this research focuses on the selection and performance evaluation of erp system. we study the criteria of the erp system selection and develop a framework to select erp system based on ahp method. we combine the objective and criteria, then compare the importance of attributes among criteria and alternatives, which represents the opinion of different evaluators from different departments. finally, we select the most appropriate erp system. the selection based on ahp helps the erp system match with the strategies of the company. with the help of ahp, we can divide the goal of company into simple ones. this help the goal be put into practice. the selection framework based on ahp could be adjusted according to the development of company and has a high degree of flexibility. after the selection of erp system, we study the evaluation of erp implementation in company a. we combine the likert scale, ahp and fie methods, from four dimensions (the system’s functionality, the matching degree, implementation results and attitude of users), to evaluate the implementation of erp project. we find that objective and accurate evaluation of the erp implementation can help company allocate resources. we develop an evaluation framework based on fie and also use ahp to determine the weights, which reducing the subjectivity of evaluation. regarding the future work, creating a practical decision support software package could serve. this can be valuable for companies facing similar decision-making problems as company a, which we have extensively studied in this paper. acknowledgement this paper is supported in part by national natural science foundation of china (no. 71571194, 71301032, 71201175), and excellent young teachers program of guangdong universities and colleges (yq2015014). bibliography [1] borne p., popescu d., filip f. g., stefanoiu d., dubuisson b.(2013); optimization in 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(1999); fuzzy logic and the calculi of fuzzy rules, fuzzy graphs, and fuzzy probabilities. computers & mathematics with applications, 37(11-12), 35, 1999. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 458-461 remembering the beginnings c.v. negoita constantin virgil negoita computer science, hunter college, city university of new york http://www.hunter.cuny.edu/cs/faculty/negoita e-mail: cnegoita@hunter.cuny.edu abstract: this paper wants to provide a personal view of the beginnings of fuzzy logic which was first introduced in the 1960’s by lotfi zadeh, when fuzzy sets were presented as models of any vague concept, making possible the computation with words. keywords: fuzzy sets, fuzzy systems, vagueness 1 introduction we know zadeh as we know no other eminent man who have made the last half of the last century memorable in science history. a mere glance at the materials to which we have access still suffice to show that our information regarding him is of such a kind as to leave scarcely anything to be desired. in the first place we have his papers. they are written with talent. he has not only left a minute record of his research during a space of nearly five decades but he found real pleasure in communicating on paper. as a scientist, he was consistent. his memoir of himself remains unfortunately a fragment, but enough was completed to illustrate that portion of his career during which fuzzy sets were promoted. but if we owe much to the communicativeness of zadeh himself, we owe much also to the communicativeness of his peers. the admiration of his followers is well known, but the picture which the critics drew of him was not always a pleasing one. they saw him not as he presented himself to the fascinated eye of friendship. it should be remembered that they knew him only from a vivid immediacy that derives from the speech of witnesses, full of slang of the moment yet extremely serious because the fuzzy set seemed of the utmost cultural significance. what they painted was what they understood, and what they understood was very little. 2 fuzzy sets and generalized sets it is common for logicians to give truth conditions for predicates in terms of set theory. "john is tall" is defined to be true just in case the individual john is in the set of tall men. zadeh used the same path and said that "tall men" is a fuzzy set when the membership is a matter of degree. in presenting the theory of fuzzy sets, we hoped to break through the bars of the prison of set theory. to understand a fuzzy set, imagine a two-dimensional world called flatland. each flatlander is incarcerated in a flat set. we can peel him off and place him back somewhere else. if we fling a flatlander into our three-dimensional world, he can see only two-dimensional cross sections of our world, a family of crisp sets. simply put, by adding another dimension, we can capture more features. this is what a fuzzy set does. it adds a new dimension: our evaluation of the membership. using classical flat mathematics, a fuzzy set can be represented by a family of crisp sets, projected on the flatland. copyright c⃝ 2006-2011 by ccc publications remembering the beginnings 459 the classical rules of logic are represented by operations on the set with only two elements: true and false (0 and 1). in the universe of all sets call it the category of sets this important set is called classifier. when we tried to investigate the category of fuzzy sets, it was impossible to find a similar classifier. in fact in fuzzy set theory there is no fuzzy set of fuzzy subsets of a fuzzy set. the point is that there are two predicates in set theory: membership and equality. in the category of generalized sets, both can be fuzzy, but in fuzzy set theory only membership is allowed to be. this fact puzzled a lot of people. some critics bordered on the vituperative, and the tenets of fuzzy logic were dismissed as comical. its arguments were declared frivolous and idle exercises in irrelevance and blasphemy. in 1977 arbib wrote bad reviews for the periodicals, and in 1984 zeleny published a paper on the (ir)relevancy of fuzzy set theory. that, no doubt, explains why herbert toth, in 1987, in his phd thesis at the university of vienna suspected that probably something has gone wrong in the development and interpretation of the theory. toth didn’t deny zadeh’s original definition to be natural, immediate and elegant. this assertion was sufficiently justified by the vast amount of literature in an epidemically growing number of papers and books. 3 fuzzy systems and the postmodern times perhaps to deter us fuzzy people from further abuse, or perhaps only to improve our connection, jim bezdeck established our annual meetings. these are the origins of the international meetings that have today become pilgrimages, where all controversis were clarified when fuzzy sets were represented as families of crisp sets, and the japanese started to implement fuzzy comptrollers, and, later, fuzzy systems were implemented as neural networks. but, still then, some mathematicians never understood fuzziness, because, for them, the precise specification of a set could be given only by binary logic. the anatomy of a boom is simple. over time, most ideas will rise in value. as this happens, people are attracted to them and this causes the ideas to rise more. this further gain attracts more people and gradually, perhaps over a period of a century, the number of people looking for this increase in value comes to determine what ideas are worth. the knowledgeable man, as he unwisely considers himself, is now concerned with the way an idea is attracting interest. that, rightly for the moment, determines its value. the binary logic, beautiful, useful, and promising, determined the modern era, obsessed with mathematical models. scientific truths became the pillars of progress. zadeh, speaking of degrees of truth, shocked the foundation of modernity, and became the postmodern of information sciences. more than that, he defined postmodernity as a return to premodernity. why do postmodern scientists, in their advancing years, when love for precision survives, but only barely, cleave to the fluid vagueness as though it were cable for rappelling and not a tightrope any longer? to embrace the whole from one point of view. the remarks made are less detailed, but more sure. you perceive each object less distinctly, but you describe the facts with more certainty. the details of the immense picture are lost in the shade, but you conceive a clear idea of the entire object. in the philosophy of science this fact has been known for a long time. in the sixth century, leontius from bizantium observed that our impression of the world is vague, not revealing the details. if we attempt to particularize by division into species and individuals, the general view is lost: we are heading not towards truth but towards an infinite regress. in 1906, in france, pierre duhem, in a book about physics, its object and structure, distinguished between practical facts 460 c.v. negoita expressed in vague, ordinary language and theoretical facts expressed in precise, quantitative language. he argued that confidence in the truth of a vague assertion may be justified just because of its vagueness, which makes the assertion compatible with a whole range of observed facts. there is a balance between precision and certainty: one is increased only to the detriment of the other. in fuzzy systems [1], a book i published in england, in 1981, i noted that one can build an infinite number of functions with values in an unit interval. in other words, we can say that a set never becomes exhausted in its evaluations. the set is massive, complete in itself, totally and wholly given, equivalent to the inert world of objects and things. as a result of an evaluation, a fuzzy set corresponds to the human consciousness. the set is both logically and ontologically prior to the fuzzy set. the latter is dependent upon the former. the fuzzy set is inconceivable without the set and is derived from it trough ordering. the fuzzy set is a borrowed being, an ordered set. it is through man that fuzziness enters the world. what is about the being of man that occasions fuzziness? the need of synthesis, the freedom to move up in the universe of concepts in order to find out stability. the reason that vague descriptions are used is to achieve stability. although awareness of the vagueness in our descriptions has existed for a very long time, only zadeh’s inquiries, which come under the heading "fuzzy", have brought matters to a head. it was zadeh who coined the term when he spoke about fuzzy sets, blurred, indistinct in shape or outline, frayed, fluffy number of things of the same kind, together because they are similar, with different degrees of togetherness. the description is fuzzy, not the things. people in the business of making exact descriptions concluded that an exact description is virtually impossible. this is a fact we have to accept and adjust to. inexactness is not a liability. on the contrary, it is a blessing in the sufficient information it can convey with less effort. the vague description is easier to remember. that is inexactness makes for greater efficiency. goguen in 1969 and belman in 1970, agreed with zadeh, when he said that precise quantitative analyses are not likely to have much relevance to the real-world problems which involve humans either as individuals or in gropus. he saw that as the complexity of a system increases, our ability to make precise and yet significant statements about its behavior diminishes. he saw all these things. he said also that precision and certainty are incompatible. the 1960s were an age of innovation. at every corner critical thought and change were eating away the foundations of the traditional modern era. in 1969 i defended a phd thesis on information retrieval. to retrieve means to specify descriptors and rules for combining them. immediately the question of vagueness came up. in my dissertation, i have questioned the meaning of vagueness in linguistic description with obsessive insistence. as my research went on, i began first to suspect, then to dislike, and finally to detest everything connected with boundaries between classes because a continuum exists that makes it impossible to do so satisfactorily. in 1971 i published information storage and retrieval which sounded another note of the famous war waged by common sense against the abuse of binary logic, a war in which so many were to engage. out of this book grew fuzzy sets and their applications, written together with dan ralescu in 1974, where we introduced the theorem of representation of a fuzzy set as a family of crisp sets. 4 some final remarks the history of fuzzy systems is marked by different visions. on the one hand a fuzzy set is a function. on the other hand a fuzzy set is a family of sets. the first vision took logic as its remembering the beginnings 461 paradigm. the second vision made possible the recent embracing of connectionism. for the idea of a model of vagueness, i was indebted to zadeh whose paper on "fuzzy sets" appeared in 1965. it was he who first proved, it would seem, conclusively, that the meaning of words could be represented by numbers. to him we owe the first full and satisfactory representation of a vague word as a function. berkeley was the place where zadeh was preaching his gospel, but his onslaughts were mercy compared with those terrible philippics in which, at vanderbilt university in nashville, georgescu roegen gave vent to his rage against arithmomorphism, the worship of numbers. in 1971, in an extraordinary book, the entropy law and the economic process, among the thesis that he defends is the claim that concepts are not arithmomorphic. they do not overlap. concepts like "good" or "tall" have no boundaries. instead they are surrounded by a penumbra within which they overlap with their opposites. at a particular historical moment, he notes, a nation may be both a democracy and a dictatorship just as there is an age when a man is both young and old. to the category of concepts we cannot apply the fundamental law of the binary logic, the principle of excluded middle (x cannot be both a and non-a). this axiom has so deeply imbued the modern era that it was felt to be natural and selfevident. its denial seemed to be nonsensical. in opposition to the binary logic is what one might call premodern dogmatic logic, which assumes that a and non-a do not exclude each other as predicates. this logic was predominant in the first centuries. for the premodern church fathers, the opposition was a category of man’s mind, not in itself an element of realty. the idea was that thought can only perceive in contradictions. the only way in which the world can be grasped ultimately lies, not in thought, but in the experience of oneness. (three divine persons were grasped as one god). basically, fuzzy logic is based on the same feeling, and its applications allow engineers to create machines that approach human responses to stimuli, working with incomplete and unclear data to generate positive actions. using fuzzy logic, japanese washing machines are able to decide how dirty clothes are, how much water and soap should be used to wash them, and how long it should take to get them clean, all things an experienced launderer would know how to do instinctively. to me, the most impressive accomplishment was a fuzzy system built by michio sugeno, in 1985, at the tokyo institute of technology, when he stabilized a helicopter that lost a rotor blade. no human pilot can manage that, and no mathematical model either. fuzzy systems, linguistically inspired, are a direct consequence of the seminal papers of zadeh, published in the 1970s. in 1975 classical fuzzy set theory had reached its apogee, since solutions of its basic problems were now at hand. classical fuzzy set theory then changed from an heroic phase, in which we addressed ourselves to hitherto unfathomable questions, to an academic phase, in which a wealth of detail, albeit most important detail, was worked on by an army of competent scholars and technologists following well-established lines. the period of trail blazing was over, though most of the practical benefits were yet to be reaped. bibliography [1] negoita, c.v., fuzzy systems, abacus press, tunbridge wells, uk, 1981 [2] negoita, c.v., expert systems and fuzzy systems, benjamin cummings, menlopark, ca, 1985 [3] negoita, c.v., fuzzy sets, newfalcon publications, tempe, az, 2000 international journal of computers communications & control issn 1841-9836, 13(3), 303-320, june 2018. on distributed solution to sat by membrane computing h.n. adorna, l. pan, b. song henry n. adorna algorithms and complexity lab. department of computer science university of the philippines diliman diliman 1101 quezon city, philippines hnadorna@up.edu.ph linqiang pan* 1. key laboratory of image information processing and intelligent control of education ministry of china school of automation huazhong university of science and technology wuhan 430074, hubei, china 2. school of electrical and information engineering zhengzhou university of light industry zhengzhou 450002, henan, china *corresponding author: lqpan@mail.hust.edu.cn bosheng song key laboratory of image information processing and intelligent control of education ministry of china school of automation huazhong university of science and technology wuhan 430074, hubei, china boshengsong@hust.edu.cn abstract: tissue p systems with evolutional communication rules and cell division (tpec, for short) are a class of bio-inspired parallel computational models, which can solve np-complete problems in a feasible time. in this work, a variant of tpec, called k-distributed tissue p systems with evolutional communication and cell division (k-∆t pec, for short) is proposed. a uniform solution to the sat problem by k-∆t pec under balanced fixed-partition is presented. the solution provides not only the precise satisfying truth assignments for all boolean formulas, but also a precise amount of possible such satisfying truth assignments. it is shown that the communication resource for one-way and two-way uniform k-p protocols are increased with respect to k; while a single communication is shown to be possible for bi-directional uniform k-p protocols for any k. we further show that if the number of clauses is at least equal to the square of the number of variables of the given boolean formula, then k-∆t pec for solving the sat problem are more efficient than tpec as show in [39]; if the number of clauses is equal to the number of variables, then k-∆t pec for solving the sat problem work no much faster than tpec. keywords: membrane computing, distributed p system, sat, communication complexity 1 introduction since the research area of membrane computing was proposed in 1998 [8,19], the research lines about computation power of various variants of p systems [25, 26, 26, 33, 37], their applications [8,24,29,36] and implementation issues [15,16,32] have been investigated widely. several solution copyright ©2018 cc by-nc 304 h.n. adorna, l. pan, b. song approaches and techniques have been presented in the literature for solving satisfiability problem (sat) using many variants of p systems [11, 12, 27, 30]. each of these variants of p systems solving sat provided better solutions than the conventional model in terms of time efficiency or computational time complexity [18,31,38]. most of them are benefited from the nondeterministic maximal parallelism of p systems and its ability to produce exponentially many cells or regions (in linear time) in a computation [35]. evolution and communication are the core operations in the solutions offered by the variants of p systems, where communication allows objects to be transmitted to the other regions/cells for further processing. in [14], hernandez, et al. provided a solution to 3-sat, where the amount of communications is measured from a dynamic communication complexity perspective [1]. several results might have been reported using communication as a measure of complexity [2,5,6], but the analyses would be dissimilar to that of [1]. in order to capture the concept of communication complexity as introduced by a.c. yao in [34], gh. păun, et al. [21] introduced and defined a so-called dp scheme, where the input of a k-dp scheme is partitioned and these parts are distributed to the participating components. necessarily, these components need to communicate to solve a problem, and the so-called intercomponent communication rules are introduced as a new kind of set of communication rules. computation done by distributed model in this work depends on the agreed upon partition of the input among the participating p systems. a partition is called balanced partition if the number of objects or the length of the part of the input assigned to each participating component p systems is almost equal. let w be an input for a distributed system with two components. the partition w1 and w2 of w, where wi is assigned to πi, i = 1, 2 is balanced if and only if ||w1|− |w2|| ≤ 1. in particular, it is a balanced fixed-partition, if w = w1w2; otherwise, we have unbalanced (fixed-)partition. the distributed p systems halt if and only if all participating component p systems halt. if a distributed system halts in a specified accepting configuration, then it accepts/decides/solves a problem. since k-dp scheme was introduced, some of the results on this variant were reported in [7,10,23]. in [4], buño, et al. introduced a distributed solution for n-queens problem as presented in naranjo, et al. [13]. indeed, in [4], the reduction of the n-queens problem to sat of a dp scheme were used, where the components are p systems with active membranes. recently, buño, et al. [3] capitalized the power of tissue p systems (tpec) with evolutional communication rules and cell division introduced in [39], in proposing a distributed solution to sat. in particular, they defined a so-called 2-dtpec. the paper claimed a decent advantage of distributed solution compared to the non-distributed one with respect to the results reported in [39]. also, only two participating components in systems were considered working on a balanced partition. in this work, the solution presented in [3] will be revisited and some other insights into the consequences of our results are provided. some other relevant issues related to communication complexity in distributed tissue p systems with evolutional communication and cell division are proposed. contributions of the present work are summarized as follows: (a) a variant of tissue p systems with evolutional communication and cell division, called kdistributed tissue p systems with evolutional communication and cell division (k-∆tpec, for short) and the corresponding recognizer version are proposed. (b) a uniform solution to the sat problem by k-∆tpec under balanced fixed-partition is presented. the solution provides not only the precise satisfying truth assignments for all boolean formulas, but also a precise amount of possible such satisfying truth assignments. on distributed solution to sat by membrane computing 305 (c) the communication resource for one-way and two-way uniform k-p protocols is increased with respect to k; while a single communication is shown to be possible for bi-directional uniform k-p protocols for any k. (d) we further show that if the number of clauses is at least equal to the square of the number of variables of the given boolean formula, then k-∆tpec for solving the sat problem are more efficient than tpec as show in [39]; if the number of clauses is equal to the number of variables, then k-∆tpec for solving the sat problem works no much faster than tpec. the rest of this work is organized as follows. section 2 provides the preliminaries of a dp scheme, introducing the concepts of p protocols and balanced (fixed) partition and the communication resources used in the analyses of the solution to sat. in section 3, distributed tissue p systems with evolutional communication and cell division or k-∆tpec are defined. solution to sat using 2-∆tpec is presented in section 3, while solution to sat using 3-∆tpec is given in section 5. remarks on the relative efficiency of distributed solutions are provided in section 4. finally, conclusions and discussions are given in section 5. 2 preliminaries in this section, the notions of k-dp scheme and communication complexity of p systems are presented, then tissue p systems with cell division and evolutional communication rules are introduced [39]. definition 1. a k-dp scheme (k ≥ 2) is a tuple k-∆π = (γ, 0, π1, π2, . . . , πk,r∆), where • π is a fixed variant of p system; • γ is an alphabet of objects in the whole system ∆; • 0 is the common/shared environment of πi; • πi for i = 1, 2, . . . ,k are p systems of the fixd variant π with γ as working alphabet, skin membranes or local environments of each p system will be labelled injectively as s1,s2, . . . ,sk; • r∆ is a finite set of rules of the form (si,u/v,sj), where 1 ≤ i,j ≤ k, i 6= j, and u,v ∈ γ∗, such that uv 6= λ. we denote by |uv| the weight of the rule (si,u/v,sj). this antiport-like communication rule is called inter-component communication rule. the mechanism by which a k-dp scheme performs its computation could be found in [21]. in particular, an input for a k-dp scheme is partitioned into k parts and distributed one part to each of the k components of the dp scheme. thus, communication to solve the problem is inevitable. in this paper, definition of balanced and unbalanced partition of an input is provided. definition 2. a partition {p1,p2, . . . ,pk} is called balanced partition if and only if for all i, pi have the same size or at most a difference of 1. otherwise, we call it an unbalanced partition. definition 3. we call a k-balanced (unbalanced) partition {p1,p2, . . . ,pk} (resp., a k-balanced (unbalanced) fixed-partition) if and only if the k-partition of input is done from left to right with respect to the (resp., fixed) ordering of the input. 306 h.n. adorna, l. pan, b. song cooperation between component p systems of a dp scheme is defined in the set r∆ of intercomponent communications. r∆ specifies the mode of communication protocol of a dp scheme. in what follows, k-p protocols for a k-dp scheme are defined. definition 4. let k-∆π be a k-dp scheme. • k-∆π is called 1-way k-p protocol if and only if r contains only rules of the form (si,u/λ,sj). • k-∆π is called 2-way k-p protocol if and only if r contains rules of the form (si,u/λ,sj) and (si,λ/v,sj). • k-∆π is called bi-directional k-p protocol if and only if r contains rules of the form (si,u/v,sj), (si,u/λ,sj) and (si,λ/v,sj). a k-dp scheme computes as follows. all component p systems of a k-dp scheme are aware of the problem that they are solving. each component p system knows only the part of the input assigned to them. we allow each component p system to perform computation to the input part known to them. to solve the problem, component p systems must communicate with respect to a particular protocol. definition 5. a configuration δj of k-∆π is a vector in c = 0×m〈1,0〉×m〈2,0〉× . . .×m〈k,0〉, where 0 is the common environment, and m〈i,0〉 are sets of multisets of objects in 〈i, 0〉, i = 1, 2, . . . ,k. in particular, δj = (m0j,m1j,m2j, . . . ,mkj ) ∈c indicates that at time j, m0 ∈ 0 and mi ∈m〈i,0〉, i = 1, 2, . . . ,k. definition 6. a computation of k-∆π is the transition of configurations represented by a sequence δ : δ0 ⇒ δ1 ⇒ ··· ⇒ δh, where δ0 is the initial configuration, and δh is the final or halting configuration. the initial configuration δ0 is a vector of initial multisets contained in 0 (local environment of component p systems). δ is a halting computation if and only if δh is a configuration, where one of the objects yes or no is contained in some (specified) membrane of the system. k-∆π has an accepting configuration if and only if δ is a halting computation and at configuration δh, object yes appeared in a specified membrane in the system. δ is a rejecting computation if and only if at δh object no appeared. definition 7. a language l is decided by k-∆π, l = l(k-∆π) if and only if for every input w over some alphabet, there is a halting computation δ of k-∆π that decides on w ∈ l. in this work, the existence of at least an object yes in 0 implies an affirmative decision on a problem, while the appearance of at least an object no in 0 connotes a negative decision. all component p systems with the same and uniform procedure in processing input part, that is to say, component p systems of a k-dp scheme would be all the same, is called a uniform k-dp scheme. if we have a uniform k-∆π, then k-p protocol is called uniform k-p protocol. this work focuses on the amount of communications used by component p systems in deciding the satisfiability of some formula ϕ in conjunctive normal form (cnf). thus, the following notions are used [1]. definition 8. [21] let ∆ be a dp scheme, δ : δ0 ⇒ δ1 ⇒···⇒ δh is a halting computation in ∆, where δ0 is the initial configuration. then for each i = 0, 1, · · · ,h− 1, we have the following parameters: • comn(δi ⇒ δi+1) = { 1, if a communication rule is used in this transition, 0, otherwise, on distributed solution to sat by membrane computing 307 • comr(δi ⇒ δi+1) denotes the number of communication rules used in this transition, • comw(δi ⇒ δi+1) denotes the total weight of the communication rules used in this transition. the above mentioned parameters can also be used to measure computations, results of computations, systems and languages (problems). definition 9. let l(∆) be the set of strings accepted by ∆. for x ∈{n,r,w}, we define: comx(δ) = ∑h−1 i=0 comx(δi ⇒ δi+1), δ is a halting computation, comx(w, ∆) = min{comx(δ) | δ is an accepting computation of ∆ for w}, comx(∆) = max{comx(w, ∆) | w ∈ l(∆)}. 3 distributed recognizer tissue p systems in this section, we introduce the notions of (recognizer) tissue p systems with evolutional symport/antiport rules and cell division and k-distributed tissue p systems with evolutional symport/antiport rules and cell division. definition 10. a tissue p system (of degree q ≥ 1) with evolutional symport/antiport rules and cell division (tpec) is a tuple π = (γ,e,m1, . . . ,mq,r,iout), where 1. γ is a working alphabet of objects; 2. e ⊆ γ is the set of objects initially located in the environment; 3. mi, 1 ≤ i ≤ q, are finite multisets over γ; 4. r is a finite set of rules of the following forms: (a) evolutional communication rules: i. evolutional symport rules: [u]i[ ]j → [ ]i[u′]j, for 1 < i ≤ q, 0 < j ≤ q,i 6= j; u ∈ γ+,u′ ∈ γ∗ or i = 0, 1 < j ≤ q; u ∈ γ+,u′ ∈ γ∗, and there exists at least one object a ∈ γ\e, which is in cell i = 0. the length of an evolutional symport rule is defined as |u| + |u′|. ii. evolutional antiport rule: [u]i[v]j → [v′]i[u′]j, where 0 ≤ i 6= j ≤ q, u,v ∈ γ+,u′,v′ ∈ γ∗. the length of an evolutional antiport rule is defined as |u|+ |u′|+ |v| + |v′|. (b) division rules: [a]i → [b]i[c]i, where i ∈{1, 2, . . . ,q}, i 6= iout,a,b,c ∈ γ. 5. iout ∈{1, 2, , . . . ,q}. the details of the mechanism on how tpec works can be found in [39]. in what follows, we introduce the notion of recognizer tpec systems. definition 11. a recognizer tissue p system with evolutional symport/antiport rules and cell division of degree q ≥ 1 is a tuple π = (γ, σ,e,m1, · · · ,mq,r,iin, iout), where 308 h.n. adorna, l. pan, b. song • γ has two distinguished objects yes and no; • σ ⊆ γ is the input alphabet; • m1, · · · ,mq are finite multisets over γ \ σ; • iin ∈{1, · · · ,q} is the label of the input cell, and iout = 0; • all computations halt; • either an object yes or an object no is released into the environment at the last step of any computation. theorem 12. [39] let pmctdec(k) be the set of all decision problems solvable in a uniform way and polynomial time by means of recognizer tissue p systems with cell division and evolutional communication rules of length at most k. then, sat ∈ pmctdec(4). in this work, at least two recognizer tissue p systems with evolutional symport/antiport rules and cell division of degree q ≥ 1 are used to solve the sat problem, where the input multiset is partitioned with respect to the number of component recognizer tissue p systems. consequently, a so-called k-distributed tissue p system with evolutional symport/antiport rules and cell division rules (k-dtpec system) is defined. definition 13. a k-distributed tissue p system with evolutional symport/antiport rules and cell division or k-dtpec system is defined as follows: k-∆tpec = (γ, 0, π1, π2, . . . , πk,r∆, iout), where 1. γ is a set of alphabet of objects in the whole system ∆; 2. 0 is the common or shared environment of all πi, i = 1, 2, . . . ,k; 3. πi, i = 1, 2, . . . ,k are recognizer tissue p systems with evolutional symport/antiport rules and cell division. each πi has the alphabet of objects in γ. each cell of πi will be labelled 〈i,j〉, where j = 1, 2, . . . ,di, and di denotes the number of cells of πi. the external region or the environment is different for each component. we will refer to the environment of component i as the local environment of i and is denoted by the label 〈i, 0〉; 4. r∆ is a set of finite inter-component communication rules of the form: (〈i, 0〉,u/v,〈j, 0〉), where 〈i, 0〉 and 〈j, 0〉 are the local environments of components i and j, respectively, u,v ∈ γ∗, and |uv| ≥ 1; 5. iout is the component of the dtpec designated as the output component. only the objects produced in the output region of the system are considered as output in a halting computation of the dtpec. note that the k-dp tissue p system used in the next section is actually a uniform k-distributed recognizer tissue p system with evolutional symport/antiport rules. on distributed solution to sat by membrane computing 309 4 solving sat by 2-∆tpec in this section, a 2-∆tpec is presented that solves the satisfiability of any instance ϕ of the sat problem. in particular, a uniform 2-p protocols is constructed which is based on the construction in [39]. theorem 14. let ϕ be any instance of the sat problem in cnf with m clauses and n variables. then there exists 2-∆tpec deciding on satisfiability of ϕ under a balanced fixed-partition in 3n + 3dm 2 e + 4 steps using antiport-like inter-component communication rules. proof: a 2-∆tpec for the sat problem will be constructed such that component p systems are quite the same with that presented in [39] but with some additional rules. let 2-∆tpec be defined as follows: 2-∆tpec = (γ, 0, π1, π2,r∆), where • γ = γ1 ∪ γ2, • 0 is the common shared environment of the π1 and π2, • πk, k = 1, 2 are recognizer tissue p systems defined as: πk(〈n,m〉) = (γk, σk,ek,mk,1,mk,2,mk,3,mk,x,rk, ikin = 2, ikout = 0), where: – γk = γ′k ∪v , γ′k = σh ∪{ai, ti,fi, t ′ i,f ′ i,βi,β ′ i | 1 ≤ i ≤ n} ∪ {ei,j,e′i,j,ei,j,e′i,j,ei,j,ei,j | 1 ≤ i ≤ n, 1 ≤ j ≤ m} ∪ {di,j,h,d′i,j,h,di,j,h,d′i,j,h | 1 ≤ i ≤ n, 1 ≤ j ≤ m, 1 ≤ h ≤ n− 1} ∪ {bjcj,cj,c′j,ej | 1 ≤ j ≤ m} ∪ {bj,h,b′j,h | 1 ≤ j ≤ m, 1 ≤ h ≤ n− 1} ∪ {α,α′i | 0 ≤ i ≤ 3n + 3mk} ∪ {d,emk+1,α3n+3mk+1,yes,no,y,y ′}, v = {vl | 1 ≤ l ≤ 2n, where n is the number of variables}. – σk = {xi,j,xi,j | 1 ≤ i ≤ n, 1 ≤ j ≤ m}, – ek = {α′i | 0 ≤ i ≤ 3n + 3mk}, – mk,1 = {a1, . . . ,an,e1}, mk,2 = {b1, . . . ,bn,d,α0}, mk,3 = {a1, . . . ,an}, andmk,x = ∅, – rk is the set of rules of each k = 1, 2 component. the set of rules we will use are those set of rules r1,i until r26,i, from [39] only with the following modifications and addition: (a) we split r1,i into r1,i;c, where c indicates in which cell r1,i will be applied on variable i : r1,i;3 ≡ [ a1 ]3 → [ β1 ]3 [ β′1 ]3, 1 ≤ i ≤ n, r1,i;1 ≡ [ ai ]1 → [ t′i ]1 [ f ′ i ]1, 1 ≤ i ≤ n; (b) we replace r27 with the following: r27 ≡ [ vl emk+1 ] 1[ vl ]3 → [ y ]1 [ vl yes ]3; 310 h.n. adorna, l. pan, b. song (c) we replace r28 with the following: r28 ≡ [ vl yes ]3 [ ]0 → [ ]3 [ vl yes ]0; (d) we replace r29 with the following: r29 ≡ [ α3n+3mk+1 d ]2 [ ]0 → [ ]2 [ no ]0; (e) cell-labeling rules: rp;c denotes rp is used to label cell c, r30;1 ≡ [ β1 ]3 [ t′1 ]1 → [ v1 ]3 [ v1 t1 ]1, r31;1 ≡ [ β′1 ]3 [ f ′ 1 ]1 → [ v2 ]3 [ v2 f1 ]1. r32;1 ≡ [ βi ]3 [ t′ivl ]1 → [ v2l ]3 [ ti v2l ]1, l ∈{1, . . . , 2 n}, 2 ≤ i ≤ n, r33;1 ≡ [ β′i ]3 [ f ′ ivl ]1 → [ v2l−1 ]3 [ fi v2l−1 ]1, l ∈{1, . . . , 2 n}, 2 ≤ i ≤ n; (f) cleaning rules, unused objects (during computation) are dump to cell x r34 ≡ [ yesvl ]0 [ ]x → [ ]0 [ yesvl ]x, r35 ≡ [ ]3 [ y ]1 → [ y′ ]3 [ ]1, r36 ≡ [ y′ ]3 [ ] → [ ]3 [ y ]0, r37 ≡ [ y ]0 [ ]x → [ ]0 [ y ]x. • r∆ is the set of inter-component communication rules: rbi-∆2 = {r′1 ≡ (〈1, 0〉,yesvl/yesvl,〈2, 0〉),r ′ 2 ≡ (〈1, 0〉,no/no,〈2, 0〉), r′3 ≡ (〈1, 0〉,λ/no,〈2, 0〉),r ′ 4 ≡ (〈1, 0〉,no/λ,〈2, 0〉), r′5 ≡ (〈1, 0〉,yes/λ, 0),r ′ 6 ≡ (〈1, 0〉,no/λ, 0), r ′ 7 ≡ (〈2, 0〉,yes/λ, 0), r′8 ≡ (〈2, 0〉,no/λ, 0)}. note that each (uniform) recognizer tissue p system πk(〈n,m〉) in a dp scheme will process all boolean formulas ϕ, which are in conjunctive normal form (cnf) with n variables and m clauses, where 〈n,m〉 = (n+m)(n+m+1) 2 + n, as long as appropriate input multiset cod(ϕ) is supplied to each component system [39]. furthermore, we will use non-deterministic maximal parallelism in the application of rules of the system. thus, the correctness of the computations made by the component p systems of 2-∆tpec is done [39]. in the construction of 2-∆tpec, labelling rules are introduced. initially, both cells 1 and 3 contain a1,a2, . . . ,an, which represent the variables in ϕ. after applying rules r1,i;1 and r1,i;3, we would have 2n copies of cell 1 and 2n copies of cell 3 in both π1 and π2 in 2-∆tpec. each cell 1 contains a unique truth assignments of the n variables to be evaluated. each cell 3 contains the corresponding sequence of βi and β′i, 1 ≤ i ≤ n. note that the number of t ′ i and f ′ i equals to the number of βi and β′i, respectively. we need to show that our labelling of all cells 1 is unique to guarantee a consistent truth assignments by both component p systems in 2-∆tpec for each variable in ϕ before the intercomponent communication is done. the set of labelling rules is composed of r30;1 to r33;1 of 2-∆tpec. the existence of βi and β ′ i in each of cell 3 is assured after applying r1,i;3, 1 ≤ i ≤ n. also t′is and f ′ is are in each cell 1 after applying r1,i;1. labelling rules can be expressed as follows. given initial labels obtained by using r30;1 and r31;1. let r32;1 be the mapping gt′ : l 7→ 2l, and r33;1 be the mapping gf′ : l 7→ 2l − 1. these mapping are bijections. thus the unique labelling vl of each cell 1 is obtained. furthermore, each cell 1 labelled vl contains distinct thruth assignments that makes true the formula ϕ. the labelling procedure is done in o(n) steps. each component p system of ∆tpec performs its evaluation individually in 3n + 3mk + 2 steps, where mk = dm2 e. in particular, after 3n + 3mk steps, emk+1 and vl are found in each cell 1, which means the truth assignment for ϕ is satisfied. rule r27 collects all pairs vl and yes in cell 3 at step 3n+ 3mk + 1, then r28 releases pairs of vl and yes to the local environment of each component p system in the dp scheme. the communication rule r′1 can be applied at the same time unused pairs of vl and yes during the communication will be dumped to cell x. on distributed solution to sat by membrane computing 311 finally, the object yes will be at the common environment after step 3n + 3mk + 4 or the object no will be at the common environment after step 3n+ 3mk + 3. note that at δh the object yes is in 0. 2 the succeeding results will measure the amount of communications in each component p system. theorem 15. there exists a bi-directional p protocol ∆tpec for solving the sat problem under a balanced fixed-partition such that comn(∆tpec) = 1, comr(∆tpec) = s, comw(∆tpec) = 4s, where s is the number of satisfying truth assignment to the sat problem. proof: the dp scheme 2-∆tpec from theorem 14 decides ϕ using bi-directional p protocol. after the component p systems of ∆tpec individually decide on their parts of the input, they would need to communicate their decisions to the other components for consistency of truth assignments. since 2-∆tpec is using an antiport-like inter-component communication rules, this requires only one communication. each of the component p systems, if ϕ is satisfiable, then the system will certainly have some vl. in particular, if (〈1, 0〉,vlyes/vlyes,〈2, 0〉) is used by both component p systems π1 and π2, which implies both of them obtained at least a satisfying truth assignment for ϕ. let tk be the set of satisfying truth assignments obtained by πk, (k = 1, 2), then t1 ∩ t2 is the set of satisfying truth assignments for ϕ. let |t1 ∩ t2| = s, then comn(∆tpec) = 1, comr(∆tpec) = s, comw(∆tpec) = 4s. 2 theorem 16. let ϕ be any instance of sat in cnf with m clauses and n variables. then under a balanced fixed-partition, there is a two-way 2-p protocol ∆tpec for solving the sat problem such that comn(∆tpec) = 2, comr(∆tpec) = s + t, comw(∆tpec) = 2(s + t), where s is the number of satisfying truth assignment to ϕ1, and t is the number of satisfying truth assignments of the sat problem. proof: the same 2-∆tpec in theorem 15 is used but rules in r∆ will be as follows. r2-∆2 = {r′1 ≡ (〈1, 0〉,vl yes/λ,〈2, 0〉),r ′ 2 ≡ (〈1, 0〉,λ/v ′ l yes〈2, 0〉), r′3 ≡ (〈1, 0〉,no/λ, 0),r ′ 4 ≡ (〈2, 0〉,y ′ yes/λ, 0),r′5 ≡ (〈2, 0〉,no/λ, 0)}. furthermore, we add cell 4 to each component p system of ∆tpec. then we have ∆tpec = (γ, 0, π1, π2,r∆), where πk(〈n,m〉) = (γk, σk,ek,mk,1,mk,2,mk,3,mk,4,mk,x,rk, ikin = 2, ikout = 0), such that mk,4 = {ai | 1 ≤ i ≤ n}, and γk = γ′ ∪v ∪v ′ ∪{�i,�′i,κi,κ ′ i γi,γ ′ i}. similarly, we add the following rules in rk : rule applied to cell 3 with objects βi,β′i,κi and κ ′ i r1,i;3 ≡ [ a1 ]3 → [ β1 κ1 ]3 [ β′1 κ ′ 1 ]3, 1 ≤ i ≤ n. rule applied to generate copies of cell 4 with objects γ′i and � ′ i r1,i;4 ≡ [ ai ]4 → [ γ′i ]4 [ � ′ i ]4, 1 ≤ i ≤ n. cell-labeling rules for cell 4: r30;4 ≡ [ κ1 ]3 [ γ′1 ]4 → [ v1 ]3 [ v1 γ1 ]4, r31;4 ≡ [ κ′1 ]3 [ � ′ 1 ]4 → [ v2 ]3 [ v2 �1 ]4, r32;4 ≡ [ κi ]3 [ γ′ivl ]4 → [ v2l ]3 [ γi v2l ]4, l ∈{1, . . . , 2 n}, 2 ≤ i ≤ n, r33;4 ≡ [ κ′i ]3 [ � ′ ivl ]4 → [ v2l−1 ]3 [ �i v2l−1 ]4, l ∈{1, . . . , 2 n}, 2 ≤ i ≤ n. 312 h.n. adorna, l. pan, b. song additional rules r38 ≡ [ v2l y ]0 [ vl ]4 → [ vl ]0 [ v ′ l y ′ ]4, r39 ≡ [ ]0 [ v′l y ′ ]4 → [ v′l y ′ ]0 [ ]4. let ϕ = ϕ1 ∧ϕ2, where ϕ1 is assigned to π1 and ϕ2 is assigned to π2. the inputs are placed in the appropriate cells of the component p systems of ∆tpec in an encoded form. solution must be made known to both component p systems of ∆tpec. thus, the decision has been made known to both component p systems if object yes appeared in 0 or the common shared environment of π1 and π2. note that communications start from left to right, then from right to left. when both components of ∆tpec have already been produced all labels vl in cell 1, and if object emk+1 appears at least in a cell 1 (it means ϕ is satisfiable). emk+1 together with vl will be evolved to yes, vl in 〈k, 0〉,k = 1, 2. at this time, communication for the system commences. π1 will communicate to π2, all labels vl of cell 1 that appear in its local environment use the rule (〈1, 0〉,vl yes/λ,〈2, 0〉). suppose there are s copies of vl in 〈1, 0〉, hence 2s copies of objects have been communicated to π2 in one communication using s symport-like inter-component rules. let t1 and t2 denote sets of satisfying assignments obtained by π1 and π2, respectively, then t1 ∩t2 is the set of satisfying assignments for the sat problem. after performing rules r38 and r39, π2 will send all v′l and yes to π1 to inform the solution on the satisfiability of the sat problem. at the same time, π2 sends objects yes, y′ to 0. finally, π1 is informed with the satisfying assignments for the sat problem. therefore, comn(∆tpec) = 2, comr(∆tpec) = s + t, comw(∆tpec) = 2(s + t). 2 theorem 17. let ϕ be any instance of the sat problem in cnf with m clauses and n variables. then under a balanced fixed-partition, there is a one-way 2-p protocol ∆tpec for the sat problem such that comn(∆tpec) = 1, comr(∆tpec) = s, comw(∆tpec) = 2s, where s is the number of satisfying truth assignment of the sat problem. proof: the same 2-∆tpec in theorem 16 is used. communications between π1 and π2 end after π1 sends its pairs vl, yes to π2. after using r39, π2 sends copies of yes to 0 to declare satisfiability of the sat problem. at the end of computation/communications, both π1 and π2 know the labels of the satisfying truth assignments for the sat problem, which requires only a single communication using s number of communication rules with a total of 2s objects, where s is the number of satisfying truth assignment of the sat problem. therefore, comn(∆tpec) = 1, comr(∆tpec) = s, comw(∆tpec) = 2s. 2 remark 18. suppose we consider an unbalanced fixed-partition for the input of the sat problem. let |p1| = m1, and |p2| = m − m1 such that |m1 − m2| ≥ 2. then 2-p protocol ∆tpec would need 3n + 3m′ + 3 steps to provide a decision for the sat problem, where m′ = max{m1,m2}. eventually, results on communication complexity (theorems 15, 16 and 17) can be re-stated for the case of unbalanced fixed-partition. 5 solving sat by 3-∆tpec in this section, solution to the sat problem will be presented using 3 components recognizer tissue p systems. theorem 19. let ϕ be any instance of the sat problem in cnf with m clauses and n variables. then under a balanced fixed-partition, there is a one-way 3-p protocol 3-∆tpec for sat such that comn(∆tpec) = 2, comr(∆tpec) = v ′′ + v ′, comw(∆tpec) = 2(v ′′ + v ′), where v ′′ is the number of satisfying truth assignments to ϕ2, and v ′ is the number of satisfying truth assignments of π1 for ϕ1. on distributed solution to sat by membrane computing 313 proof: let 3-∆tpec = (γ, 0, π1, π2, π3,r∆) be a dp scheme, where each πk (k = 1, 2, 3) is the same as those in theorem 16. each πk has almost the same set of rules presented in theorem 16 in processing input instance ϕ of sat. in this model, the following rule is added: r40 ≡ [ v′l vl y ]0 [ vl ]4 → [ vl ]0 [ v ′ l y ′ ]4. consequently, the following inter-component communication rules for 3-∆tpec will be used. r1-∆3 = {r′1 ≡ (〈1, 0〉,vl yes/λ,〈2, 0〉),r ′ 2 ≡ (〈2, 0〉,v ′ l,yes/λ〈3, 0〉), r′3 ≡ (〈3, 0〉,y ′ yes/λ, 0),r′4 ≡ (〈3, 0〉,no/λ, 0), r′5 ≡ (〈2, 0〉,no/λ, 0),r ′ 6 ≡ (〈1, 0〉,no/λ, 0)}. communication between components of 3-∆tpec is done successively from π1 to π2, then from π2 to π3. after each component processed their part of the input, π1 starts communication with π2 by sending all labels of cell 1. π2 obtained all these vl, which are labels of cell 1 that provide satisfying truth assignments for ϕ from π1. let t1 be the set of all labels vl of cell 1, if |t1| = v ′, then π1 sent 2v ′ copies of object to π2 in one step. now π3 obtained from π2 copies of object v′l and yes after 3n + 3mk + 6 steps. each v ′ l is a label of a satisfying truth assignment made by π1 and π2, hence all copies of objects v′l,vl,vl′, yes are contained in 〈3, 0〉 after π2 sent objects v′l, yes to π3. π3 uses r40 and r39 to prepare using (〈3, 0〉,y′ yes/λ, 0) to declare satisfiability of ϕ. the number of y′ is equal to the number of satisfying truth assignments of ϕ. in particular, the number of y′ is equal to |t1 ∩t2 ∩t3|, where t1,t2, and t3 are sets of satisfying truth assignments evaluated by π1, π2, and π3, respectively. let |t1| = v ′, and |t2| = v ′′. finally, we have comn(3-∆tpec) = 2, comr(3-∆tpec) = v ′′ + v ′, and comw(3-∆tpec) = 2v ′′ + 2v ′. 2 theorem 20. let ϕ be any instance of the sat problem in cnf with m clauses and n variables. then under a balanced fixed-partition, there is a two-way 3-p protocol 3-∆tpec for the sat problem such that comn(3-∆tpec) = 4, comr(3-∆tpec) = 2s + v ′ + v ′′, comw(∆3-tpec) = 2(s + v ′ + v ′′), where s is the number of satisfying truth assignment to ϕ, v ′ is the number of satisfying assignments of π1 for ϕ1, and v ′′ = |t1 ∩ t2|, where t1 and t2 are satisfying truth assignments of ϕ1 and ϕ2, respectively. proof: the 3-∆tpec in theorem 19 is used but the set of inter-component communication rules r∆ = r2-∆3 uses only the symport-like communication rules. specifically, r2-∆3 = r1-∆3 ∪{(〈2, 0〉,λ/v ′ l,〈3, 0〉), (〈1, 0〉,λ/v ′ l,〈2, 0〉)}. from π1, it is easy to know that 2v ′ copies of object are sent to π2, where v ′ = |t1|, t1 being the set of satisfying truth assignments for ϕ1 evaluated by π1. using r40 and r39, π2 will eventually send objects v′l and yes to π3. the total amount of objects is equal to 2v ′′ in a single communication. π3 realizes s labels that give satisfying truth assignments for ϕ, where s is the total number of labels that are common to all component p systems. after 3n + 3mk + 10 steps, y′ and yes will be sent by π3 to 0, simultaneously, objects v′l and yes are sent to π2. hence π2 sends the same copies of objects to π1. the communication going back from π3 to π1 requires 2s copies of objects using 2s rules in two communications. therefore, comn(3-∆tpec) = 4, comr(3-∆tpec) = s + v ′ + v ′′, and comw(3-∆tpec) = s + 2v ′ + 2v ′′. 2 theorem 21. let ϕ be any instance of the sat problem in cnf with m clauses and n variables. then under a balanced fixed-partition, there is a bi-directional 3-p protocol 3-∆tpec for the sat problem such that comn(3-∆tpec) = 1, comr(3-∆tpec) = s, comw(∆3-tpec) = 6s, where s is the number of satisfying truth assignment to ϕ. 314 h.n. adorna, l. pan, b. song proof: the 3-p protocol 3-∆tpec used in this proof will have component p systems similar to that in theorem 15, but we use the following additional rules: r41 ≡ [ vl yes ]3 [ vl ]4 → [ ]3 [ v′3l yes 3 ]4, and r42 ≡ [ ]0 [ v′l yes ]4 → [ vl yes ]0 [ ]4, and the set of inter-component communication rules r∆ as follows: rbi-∆3 = {r′1 ≡ (〈1, 0〉,yesvl/yesvl,〈2, 0〉),r ′ 2 ≡ (〈1, 0〉,yesvl/yesvl,〈3, 0〉), r′3 ≡ (〈2, 0〉,yesvl/yesvl,〈3, 0〉), r′4 ≡ (〈1, 0〉,y yes/λ, 0),r ′ 5 ≡ (〈2, 0〉,y yes/λ, 0),r ′ 6 ≡ (〈3, 0〉,y yes/λ, 0), r′7 ≡ (〈1, 0〉,no/λ, 0),r ′ 8 ≡ (〈2, 0〉,no/λ, 0),r ′ 9 ≡ (〈3, 0〉,no/λ, 0)}. furthermore, each component of 3-∆tpec is as follows: πk(〈n,m〉) = (γk, σk,ek,mk,1,mk,2,mk,3,mk,4,mk,x,rk, ikin = 2, ikout = 0), such that mk,4 = {ai | 1 ≤ i ≤ n} and γk = γ′ ∪v ∪v ′ ∪{�i,�′i,κi,κ ′ i γi,γ ′ i},r34 6∈ rk. in this modified 3-∆tpec, the additional rules allow each component p system to triple its vl and yes in order to prepare for a simultaneous antiport-like communications. if ϕ is satisfiable, then rules r′1,r ′ 2, and r ′ 3 could be used. simultaneously, by all component p systems send y, yes to 0. since the communication is bi-directional, this is done in one step, using s rules and total of 6 objects. note that s is the number of satisfying assignments for ϕ. therefore, comn(3-∆tpec) = 1, comr(3-∆tpec) = s, comw(∆3-tpec) = 6s, where s is the number of satisfying truth assignment to ϕ. 2 6 relative performance of k-∆tpec the relative performance and parallelizability of k-∆tpec is considered in this section. the concept of weak parallelizability introduced in [21] is also considered. a problem l is said to be (k,m)-weakly comx parallelizable, for some k ≥ 2,m ≥ 1 and x ∈{,n,r,w}, if there is a dp scheme ∆ with k components and there is a finite f∆ ⊆ l such that each string x ∈ l−f∆ can be written as x = x1x2 · · ·xk, such that ||xi|− |xj|| ≤ 1 for all 1 ≤ i,j ≤ k, each component πi takes as input the string xi, 1 ≥ i ≤ k and string x is accepted by ∆ in a halting computation δ such that comx ≤ m. a problem l is called weakly comx parallelizable if it is (k,m)-weakly comx parallelizable for some k ≥ 2,m ≥ 1. in the case of k-∆tpec, k = 2, 3 deciding on the sat problem, the following results on parallelizability are obtained. in particular, results presented in section 3 implies the following. theorem 22. let sat= {ϕ | ϕ has n variables and m clauses}. 1. let 2-∆tpec be a uniform bi-directional 2-p protocol for sat under balanced fixed-partition, then sat is (2,r)-weakly comx parallelizable, where (r,comx) ∈ {(1,comn), (s,comr), (4s,comw)}, s is the number of satisfying truth assignments for ϕ. 2. let 2-∆tpec be a uniform two-way 2-p protocol for sat under balanced fixed-partition, then sat is (2,r)-weakly comx parallelizable, where (r,comx) ∈ {(2,comn), (s + t,comr), (2(s + t),comw)}, where s is the number of satisfying truth assignment to ϕ1, and t is the number of satisfying truth assignments for ϕ. 3. let 2-∆tpec be a uniform one-way 2-p protocol for sat under balanced fixed-partition, then sat is (2,r)-weakly comx parallelizable, where (r,comx) ∈{(1,comn), (s,comr), (2s,comw)}, where s is the number of satisfying truth assignment of ϕ1. on distributed solution to sat by membrane computing 315 in the case of 3-∆tpec for solving the sat problem under balanced fixed-partition, the results in section 5 implies the following. theorem 23. let sat= {ϕ | ϕ has n variables and m clauses}. 1. let -∆tpec be a uniform bi-directional 3-p protocol for sat under balanced fixed-partition, then sat is (3,r)-weakly comx parallelizable, where (r,comx) ∈{(1,comn), (s,comr), (6s,comw)}, where s is the number of satisfying truth assignments for ϕ. 2. let 2-∆tpec be a uniform two-way 3-p protocol for sat under balanced fixed-partition, then sat is (3,r)-weakly comx parallelizable, where (r,comx) ∈{(4,comn), (2s + v ′ + v ′′,comr), (2(s + v ′ + v ′′),comw)}, where s is the number of satisfying truth assignment to ϕ, v ′ is the number of satisfying assignments of π1 for ϕ1, and v ′′ = |t1 ∩t2|, where t1,and t2 are satisfying truth assignments of ϕ1, and ϕ2, respectively. 3. let 2-∆tpec be a uniform one-way 3-p protocol for sat under balanced fixed-partition, then sat is (3,r)-weakly comx parallelizable, where (r,comx) ∈ {(2,comn), (v ′′ + v ′,comr), (2(v ′′ + v ′),comw)}, where v ′′ is the number of satisfying truth assignment to ϕ2, and v ′ is the number of satisfying assignments of π1 for ϕ1. the relative efficiency of performance of k-∆tpec (k = 2, 3) can also be viewed with respect to its computation time spent solving a problem. in this respect, k-∆tpec will be compared to the efficient solution presented in [39]. let timeπ(〈n,m〉)(n,m) be the running time of π(〈n,m〉), and time∆tpec (n,m) denotes the running time of ∆tpec. in [39], timeπ(〈n,m〉)(n,m) = 2n + 3m + 2, while theorem 14 gives time2-∆tpec (n,m) = 3n+3m k +4. the following limit represents the speed up ratio between π(〈n,m〉) and 2-∆tpec(n,m). lim n→∞ timeπ(〈n,m〉)(n,m) time2-∆tpec (n,m) = lim n→∞ 2n + 3m + 2 3n + 3m k + 4 . the value of this limit is required to be at least 2, to imply improvements of the computation by 2-∆tpec(n,m) compared with that by π(〈n,m〉)(n,m) solving the same problem. let k = 2, and m = n, the speed-up ratio is: lim n→∞ timeπ(〈n,m〉)(n,m) time2-∆tpec (n,m) = lim n→∞ 5n + 2 4.5n + 4 ≈ 1.11. this suggests that for any k ≥ 2, as long as m ≤ n, k-∆tpec could not do significant advantage compared with π(〈n,m〉) for solving sat. it can also be observed that if for any k, n = m2, we would have lim n→∞ timeπ(〈n,m〉)(n,m) time∆tpec (n,m) = lim m→∞ 2m2 + 3m + 2 3m2 + 3m k + 4 < 2 3 . this would mean no parallelism. if we let m = n2, then speed-up ratio becomes lim n→∞ timeπ(〈n,m〉)(n,m) time∆tpec (n,m) = lim n→∞ 2n + 3n2 + 2 3n + 3 k n2 + 4 = k ≥ 2. 316 h.n. adorna, l. pan, b. song this shows that k-∆tpec computes in at least half the required number of steps by π(〈n,m〉), if m ≥ n2, for any k. the uniform recognizer tissue p systems in [39] may not be the optimal uniform recognizer tissue p systems for solving the sat problem, that is, deciding sat with the smallest possible steps, but it is efficient enough to compare the relative performance of k-∆tpec for solving sat. 7 conclusions and discussions in this work, a distributed p scheme that solves instances ϕ of sat is presented. the power of the recognizer tissue p systems with evolutional communication rules and cell division from [39] is capitalized in a dp scheme. labelling of all cells 1 after cell division is suggested to give precise and exact decision on the satisfiability of ϕ. moreover, ∆tpec requires that whatever is the decision for ϕ, all component p systems know the decision. two possible types of communication that ∆tpec could be performed, namely, antiport-like inter-component communication and symport-like inter-component communication. thus, the concept of a p protocol is introduced. taking into account the types of inter-component communications on dp scheme, one-way p protocol, two-way p protocol and bi-directional p protocol are defined. the concept of a uniform p protocol is also mentioned. the idea of balanced and unbalanced partitions are also presented and, in particular, a so-called (un)balanced fixed-partition is considered in distributing parts of the input to component p systems of dp scheme. it is shown that under a balanced fixed-partition k-∆tpec, could be able to decide on the satisfiability of any instance ϕ of sat using only one communication under a bi-directional k-p protocol. the number of inter-component rules is the number of satisfying truth assignments for ϕ. but the number of objects sent by the k component p systems increases with respect to k. in the case k = 2, 3, we obtained comn(2-∆tpec) = 1, comr(2-∆tpec) = s, comw(2-∆tpec) = 4s, and comn(3-∆tpec) = 1, comr(3-∆tpec) = s, comw(∆3-tpec) = 6s, where s is the number of satisfying truth assignment to ϕ. notice that k-∆tpec is a uniform dp scheme, that is, each component p system πk has (almost) the same set of rules being implemented during every computation. it is also assume that each cell in tissue p systems is connected to every other cells in the system and can communicate directly with each other. the only trade-off is extra steps for each component to reproduce the objects to be communicated using bi-directional mode of communication. this is polynomial with respect to the number of component p systems. this implies that under k-∆tpec, sat is (k, 1)weakly comn parallelizable, for all k. note that this invariance with respect to weakly comn is obtained under a balanced fixed-partition of input, fixed encoding and with a bi-directional k-p protocol. the same invariance could be observed in the case of (k,s)weakly comr parallelizability of k-∆tpec under a balanced fixed k-partition using bi-directional communication mode, for any k. notice that the k components p systems in k-∆tpec will have to produce k copies of the labels of cell with satisfying truth assignments of their respective part of the input. eventually, k-∆tpec uses the antiport-like inter component communication rule that matches these labels together with yes. finally every cell in k-∆tpec sends object yes to 0 to signal the end of the computation and decided the satisfiability of ϕ. note that in remark 18, it was stated that theorem 15 and theorem 16 could be re-stated in the case of unbalanced partition. then at least for k = 2, 3, sat is (k, 1)-weakly comn parallelizable and (k,s)-weakly comr parallelizable under an unbalanced fixed-partition. it is believe that sat is (k, 1)-weakly comn parallelizable and is also (k,s)-weakly comr parallelizable for any k under an unbalanced k fixed-partition. on distributed solution to sat by membrane computing 317 statement 1 of both theorem 22 and theorem 23 shows that sat belongs to the class of problems that could be solved by uniform k-∆tpec, k = 2, 3 with comw(2-∆tpec) = 4s, and comw(3-∆tpec) = 6s, where s is the number of satisfying truth assignment to ϕ, which implies that the objects needed to be communicated by the system increases with the number components. note that a uniform 3-∆tpec for sat needs more 2s objects to decide the satisfiability of ϕ compared with 2-∆tpec. using the uniform k-∆tpec in this paper, it might be reasonable to believe that sat may be (k,s)-weakly comw parallelizable, but it is not (k + 1,s)-weakly comr parallelizable, for any k and for some s. in the case of one-way and two-way uniform k-p protocols under balanced fixed-partition (k = 2, 3), it was demonstrated that the total amount of objects to be communicated and the total number of inter-component rules are increased with respect to the number of component p systems of k-∆tpec. these results suggest that comx, x ∈{n,r,w} is directly proportional to k. in particular, sat belongs to the class of problems that is (2,r)-weakly comx parallelizable, which do not belong to the class of problems that are (3,r)-weakly comx parallelizable, where (r,comx) ∈ {(r,comn), (r,comr), (r,comw)}. it is of interest to know if these observed relations between 2-∆ and 3-∆ could be extended to k-∆ and (k + 1)-∆ with one-way and two-way uniform k p protocols under balanced fixed-partition. it is also realized that the amount of clauses related to the number of variables is quite necessary in order to obtain efficiency in using k-∆tpec to solve sat. in particular, if m ≤ n, the relative efficiency of k-∆tpec cannot be equal to 2. this is regardless if we increase the number k of component p systems. but at m = n2, we obtain a reasonable relative efficiency k, for any k ∈ o(n). notice here that this efficiency is an upper bound of the precise efficiency we wanted to obtain, since k-∆tpec is compared only to a particular π(〈n,m〉) for solving sat. in [21], a problem l is said to be (k,r,s)-efficiently comx parallelizable, for some k ≥ 2,r ≥ 1,s ≥ 2, and x ∈{n,r,w}, if it is (k,r)-weakly comx parallelizable, and there is a dp scheme ∆ such that lim n→∞ timeπ(x) time∆(x) ≥ s, for all p systems π such that l = l(π). moreover, timeπ(x) is the smallest number of steps need for π to accept string x should be estimated with respect to all π for l, while time∆(x) is just given by means of a construction of a suitable dp scheme ∆. it might be reasonable to believe that sat is (k,r,s) efficiently comx parallelizable, where (rcomx) ∈ {(r1,comn), (r2,comr), (r3,comw)}, for some real numbers ri, i = 1, 2, 3; s ≤ k is the speed up ratio and k is the number of components in the uniform dp scheme under bi-directional, one-way and two-way uniform k-p protocol. notice that in order to minimize the amount of objects to be communicated proper, encoding of objects is necessary. we need not to communicate the whole multiset of objects, but an encoded version of them. this encoding add-up to the time and number of cells to be used by component p systems in the systems. in the case of this paper, cell labelling is proposed to encode the truth assignment uniquely to maintain consistency of assigning truth values to variable being evaluated by the whole systems. in order to keep the use of rules efficiently, we have to expect to produce at most exponential amount of cells. finally, we suggest that one of possible path to take in this line of research is to minimize the amount of objects to be communicated by component p systems in solving problems, keeping the performance of component p systems within reasonable efficiency. uniform p protocols under balanced fixed-partition are the ones considered, and remarked on the unbalanced fixed-partition for solving sat is provided. it would be nice to consider what may be called optimal-partition, where we design partition of the objects of the input and see how it fared with fixed-partition with respect to communication measures. non-uniform k-p 318 h.n. adorna, l. pan, b. song protocol solving hard problems might also be a nice direction to pursue. by non-uniform, means allowing each component p system to perform what it thinks necessary with respect to the input part. furthermore, it is of interest to consider communication resources with respect to some communication p protocols or dp schemes for solving other hard problems. acknowledgements the work was supported by national natural science foundation of china (61320106005, 61602192, and 61772214), china postdoctoral science foundation (2016m600592 and 2017t100554), and the innovating scientists and technicians troop construction projects of henan province (154200510012). h. adorna was also supported by semirara mining corporation, inc. professorial chair of the college of engineering, u.p. diliman, dost-engineering research and development for technology program grant, and an ovcrd rlc grant 2017-2018. bibliography [1] adorna, h., păun, gh. pérez-jiménez, m.j. 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(2015): on the universality of axon p systems, ieee t. neur. net. lea., 26(11), 2816-2829, 2015. appendix: some tables for inter-component communications the following tables below show how communication between component p systems transfer. the table of communications starts when the systems already obtained a satisfying truth assignments of their respective input parts. notice that table 6 which is continued in table 7 starts at step 4. this initially started when emk+1 appeared in any of the cell 1, which will be the step 1 of the table. below are tables for cases where there are at most three component recognizer tissue p systems with evolutional communication rules and cell division. one-way, two-way and bi-directional p protocol are considered below. notice that the set of communication rules differs per kind of p protocol model. the set of rules used by each component p systems are mostly based on the results in [39]. variations on rules rk for each k depends on the p communication mode required of the systems. the tables provide labels of each column and each row provides information on specific action transferred with respect to the preceding row. one would notice that the set of inter-component rules of other p communication mode are similar as stated in the proof of some of the theorems. on distributed solution to sat by membrane computing 321 table 1: expanded communication configuration for k-∆. rejecting communication (1). step rule cell 1 cell 2 cell 3 cell x 〈1, 0〉 0 〈2, 0〉 cell x cell 3 cell 2 cell 1 1 r26 vl αp,d vl vl αp vl 2 r29 y αp+1,d vl, vl, yes αp+1,d y 3 r′6 no vl, yes y ′ αp+2,d 4 no vl, yes y′ αp+3,d table 2: expanded communication configuration for k-∆. rejecting communication (2). step rule cell 1 cell 2 cell 3 cell x 〈1, 0〉 0 〈2, 0〉 cell x cell 3 cell 2 cell 1 1 r26 vl αp,d vl vl αp vl 2 r29 y αp+1,d vl, vl αp+1,d y 3 r′2 y no no y 4 y no y table 3: expanded communication configuration for bi-∆2. accepting communication. step rule cell 1 cell 2 cell 3 cell x 〈1, 0〉 0 〈2, 0〉 cell x cell 3 cell 2 cell 1 1 r26,r27 emk+1, vl αp,d vl vl αp emk+1 vl 2 r28,r35 y αp+1,d vl, yes vl, yes αp+1,d y 3 r′1,r34,r36 αp+2,d y ′ vl, yes vl, yes y′ αp+2,d 4 r′7,r ′ 5,r37 αp+3,d yes vl yes vl yes yes αp+3,d vl 6∈ t1 vl 6∈ t2 5 y,vl yes yes y,vl yes table 4: expanded communication configuration for 2∆2 and 1-∆2. accepting communication. note that vl′ found in cell x are those which are elements of t1 ∩t2. step rule cell 1 cell 2 cell 3 cell 4 cell x 〈1, 0〉 0 〈2, 0〉 cell x cell 4 cell 3 cell 2 cell 1 1 r26 emk+1 αp vl �i �i vl αp emk+1 r27 vl d γi γi d vl vl vl 2 r26 y αp+1 vl yes �i �i vl yes αp+1 y r28 d γi γi d r35 vl vl 3 r26 αp+2 y ′ �i vl yes vlyes �i y′ αp+2 r36 d γi γi d r′1 vl vl 4 r26,r34, αp+3,d vl,�i,γi y v 2 l yes 2 vl,�i,γi αp+3,d r38,r37 y vl′ 6∈ t1 yes y vl,�i,γi αp+3,d 5 r26,r39 αp+4,d vl,�i,γi y vl yes2 yes y v′l y ′ �i αp+6,d vl′ γi vl′′, l′′ 6= l 6 r26,r ′ 2 αp+7,d vl,�i,γi y vl v ′ l yes y v ′ l y ′ �i αp+7,d y′ yes2 vl′ γi vl′′, l′′ 6= l 7 αp+8,d vl,�i,γi y v ′ l yes y ′ yes vl yes y v′l y ′ �i αp+7,d vl′ γi vl′′, l′′ 6= l 322 h.n. adorna, l. pan, b. song table 5: expanded communication configuration for 1∆3. accepting communication. step rule cell 1 cell 2 cell 3 cell 4 cell x 〈2, 0〉 0 〈3, 0〉 cell x cell 4 cell 3 cell 2 cell 1 4 r26 αp+3 �i v 2 l y vl �i αp+3 r37 d γi yes2 yes γi d r38 vl vl′, yes y vl 5 r26 αp+4 �i vl vl y �i αp+4 r39 d γi yes2 yes γi d v′l vl′ vl vl, yes y 6 r26 αp+5 �i v ′ l vl y �i αp+5 r′ d γi y ′ yes γi d vl′ yes2 vl 7 r26 αp+6 �i y ′ v′l yes �i αp+6 r40 d γi yes vl yes γl d vl′ vl′ yes vl 8 r26 αp+7 �i y ′ vl yes �i v′l αp+7 r39 d γi yes vl′ yes γi y′ d vl′ vl 9 r26 αp+8 �i y ′ v′l y ′ y �i αp+8 r′ d γi yes vl yes γi d vl vl′ yes vl 10 αp+8 �i y ′ v′l y �i αp+8 d γi yes vl γi d vl vl′ yes vl international journal of computers communications & control issn 1841-9836, 12(1):41-52, february 2017. lagrangian formulation for energy-efficient warehouse design i. derpich, j. sepulveda ivan derpich*, juan sepulveda departamento de ingeniería industrial universidad de santiago de chile, usach ave. ecuador 3769, estacion central, santiago, chile (ivan.derpich,juan.sepulveda)@usach.cl *corresponding author:ivan.derpich@usach.cl abstract: energy consumption in modern warehouses is today an important issue which has not received much attention in the scientific community. in this paper it is addressed the problem of warehouse design considering the energy costs incurred by vehicles and equipment in a fully or partially automated facility. closed-form solutions are obtained by a formulating the lagrangian of an operational cost function with equality constraints. the contribution of the paper is to develop formulas for reduced energy consumption and pollution, both relevant aspects in sustainable engineering systems. an example applied to a distributor of mro items is presented. in this version the energy cost is integrated into the formula, modifying the method presented in [1].a keywords: energy optimization, integrated facility design, green supply chains. areprinted and extended, with permission based on license number 3954930120977 ©[2016] ieee, from "computers communications and control (icccc), 2016 6th international conference on" 1 introduction this paper is an extension of [1], which considers separately the cost of travel in the plane and the cost of energy for moving the items along the vertical axis. in this present paper both costs are integrated and formulas are obtained for designing facilities in an automated facility. the design of a warehouse or a distribution center (dc) has become a fundamental decision for the optimization of a supply chain. even though there exist several articles in the academic literature in which efficient layout models have been studied, it has not been formulated a simple mathematical model in order to obtain the basic variables for the construction of a dc in three dimensions (3d). in warehousing facilities the movement of goods is performed by various types of equipment such as agv (automated guided vehicle), fork lifters, stackers, vertical reach trucks, conveyors or by automated robotics equipment of cartesian movement adjacent to the storage racks, as in as/r (automated storage/retrieval) systems. in the literature there exist works with formulas to design shelves in two and three dimensions buy they do not adequately consider the problem of movement in the z-axis, thereby giving inefficient results with very high shelves that ignore energy consumption and sub-optimize costs. this occurs because the known approaches consider the movement only on the x-y plane, and such horizontal movement as expensive as in height, which in general does not hold in actual facilities. the problem is exacerbated for heavy materials due to greater energy waste. in this paper the above problem is solved by a cost model which considers in addition the cost of movement in height. with the model and derived formulas, more efficient shelves design with less energy consumption is generated. the formulas developed are optimal with respect to travel distances and they are obtained from solving a nonlinear optimization problem with linear constraints through a lagrange transformation. copyright © 2006-2017 by ccc publications 42 i. derpich, j. sepulveda the formulas found in the literature mainly address the problem in two dimensions; for instance, bassan et al. (1980) [2], consider a rectangular warehouse and racks which are parallel or perpendicular to the wall. they discuss also the optimal location of the warehouse door, and the optimal design when the storage area is divided into different zones. all of their work is developed on the horizontal plane; they fix a priori a given number of vertical racks and that value is used as a parameter. some formulas for three dimensions that have been developed, consider implicit equations whose resolution must be made with an iterative method, this makes it difficult to use, as for example in onut et al. (2008) [3]. in this paper it is considered that supplies are received from several suppliers and dispatched to many customers; consequently the study is complemented with the design of a multi -level warehouse (according to product flow) by using an abc methodology. baker and canessa (2009) [4] state that a structured approach for the design of a storage system in the company is not currently available and they make a compilation of different approaches to finalize with the proposition of a new design methodology. another classic work is gu et al. (2010) [5] in which a detailed revision of the warehouse design research is presented by considering practical case studies and computational support tools. in addition, they present a framework for a systematic classification. gu et al. (2010) [5] also conclude that even though there are a large number of papers focusing on details of the different systems, there are few addressing the decision making concerning storage systems. the work by rouwenhorst el al. (2000) [6] presents a framework of references and classification of problems regarding design and control of storage facilities. to the review of the literature on storage systems is added the need for studies on isolated design rather than sub-problems. a central idea in [6] is dividing the functions of planning and design of warehouses at three levels: strategic, tactical and operational. at the strategic level, the number of warehouses, size and location of each, the equipment for handling materials, the functional areas, the process flow and warehouse layout, are determined. at the tactical level, manpower needed to operate the warehouse, the location of the products in the functional areas, replacement and order picking policies, are determined. at the operational level, the concern relates to the routing of the products, batch determination, daily and weekly staff allocation and control tasks. in this framework of analysis it can be seen that the design of the warehouse, and in particular the number of shelves, is a strategic decision. moreover, the determination of the sizes of product areas according to categories a, b and c is a tactical decision strongly involved with the decisions of the operational level. thus the determination of the optimal design of a warehouse or distribution center is essential to minimize the transfer of products and therefore to minimize energy expenditure. in a relatively recent work, heragu et al. (2005) [7] propose a mathematical model of mixed integer linear programming to decide which section to assign each product (dispatch, reservation or cross-docking), and also decide what process flow to associate, in addition to other decisions of operational and tactical level . a recent article by zhou et al. in 2016 [8], addresses the question of how to design facilities, in a finite horizon taking into account various requirements of demand with respect to size, price, location, security, among others. they study the impact of re-design and methods to modify the design of the facility and present a mixed integer programming model and solve it by column-generation and branch and bound algorithms. roodbergen et al. 2006 [9], they published a work on picking area layout in a warehouse, so that the average travel distance is minimized. they provide formulas that can be used to calculate the average length that an order runs under different routing policies. the optimal layout is determined using a model of nonlinear programming. the optimal number of aisles depends on the discipline of picking and the size of the picking list. lagrangian formulation for energy-efficient warehouse design 43 another approach that is used is dynamic storage allocation (dsap or dynamic storage assignment problem). in the paper by li et al. [10] is presented an integrated mechanism for the purpose of optimization which is based on the abc classification and the mutual affinity of the products. the mechanism is developed by using a data mining technique and the authors show that the dsap is a np-hard problem. the results show that it is an advantageous approach to the classic abc method, but requires the formulation of a complex problem and its resolution through a simulation of a metaheuristic method, which is not easy. in the paper already cited, zhou et al. [8] present a new approach called self-managed warehouses is shown, whose use has rapidly grown in the world and are a universal trend. the problem in such a warehouse is the need for re-doing the layout frequently, so it is necessary to have direct ways to reconfigure the space continuously. in this work, the developed mathematical formulas are explicit and easy to use for the design of a warehouse or dc; it is assumed that a rectangular space is available, with a single main entrance gate in front in the center of the longest wall and an exit door located similarly at the rear wall. the formulas give the number of double racks to be used, the number of pallet spaces in each frame and its height also measured in pallets. the objective function is to optimize the total cost of movement of goods in the dc. in the first chapter a review of the literature on the use of mathematical methods in the design of distribution centers is presented. we conclude that not available explicit formulas for the design , so there are models applied to special cases .in the second chapter the formulas proposed in the paper has been much development, a rectangular supposed cellar with a single door in the center and a cost function nonlinear type is used with linear constraints , which are solved using lagrange system to obtain the sought formulas. in the third chapter an experiment with a test case taken from the literature, one of the few papers found with similar studies is presented . the results are comparable , obtaining wineries lower height, which also reduces the total cost of transportation of the articles.finally in chapter four conclusions and future work are presented . this is related to the development of formulas that can be used where space is restricted. 2 distribution center configuration in the literature different configurations for the layout of a distribution center reportedl. francis (1967) [11] studied architecture design theoretically and proved that the best configuration is an area that both measurements are the same , that is a perfect square. however in most cases, a rectangular area , is assumed. in this paper we will assume the same, however eventually develop formulas to tell whether the area is square or rectangular. 2.1 assumptions some assumptions to be made in developing the layout of the distribution center are: • goods are stored in double racks, except for the rack adjacent the wall, which has only one side. • the shape of the store is rectangular (see figure 1). • there should be wide aisles between the racks, and along the walls the width of these aisles should be the same. • goods enter through a door located at one of the walls of the store and leave through the same door. 44 i. derpich, j. sepulveda the first study of bassan et al. (1980) [2] conducted on the design of a distribution center provided two possible distributions of the shelves. in this paper, only the distribution shown in figure 1 is analyzed, where the shelves are located in the sense of the narrower direction (v). figure 1: rectangular distribution center the notation used in this study is as follows: • n = total storage capacity in storage spaces (slots). • a = width of an aisle, where it is assumed that all the aisles have the same width. • w = width of double shelf. • w= average weight of a unit storage in a container. • l = length of a storage unit in the shelf. • m = total number of storage spaces along a shelf. • h = number of vertical storage spaces. • n = number of double shelves. • u = length of the store. • v = width of the warehouse. • d = annual movement (demand) of the warehouse, in storage units. it is assumed that a unit of stored article occupies a unit of space (in items / year). • ch =material handling cost of moving a unit of article per unit of length in the x-y surface (in us$/m). • ce = energy cost (in us$/unit of energy). • tv = average travel distance on the v-direction in meter. lagrangian formulation for energy-efficient warehouse design 45 • tu = average travel distance on the u-direction in meter. • th = average travel distance in height in meter. • ve = velocity of moving a unit of weight w in a distance in height (mt/sec) 2.2 cost function this paper considers only the cost of moving and handling of goods inside the warehouse. for this, we use figure 1 as a reference and hence the measures considered. first the length and width of the distribution center will be determined, which are determined by: width u = n(w + a) (1) length v = 2a + ml (2) then the area of the distribution center is as follows: uv = n(w + a)(2a + ml) (3) in order to calculate the cost of moving goods, the average travel distance of each stored unit is required. it will be assumed that the doors are located at the centers of the walls and that all goods are equally likely to be used. to determine the cost function of the dc, we must first set the probabilities of carrying a good in the horizontal plane and in height. in the horizontal plane, two axes of movement are defined: one for lateral movement (u direction) and one towards the bottom of the warehouse (v direction), as shown in figure 1. thus, let tv , tu and th be the distances in each direction. as the probability of taking a good to the left or right from the door of the cd store is the same, the average travel distance on the horizontal axis is (4): tu = u 4 = n(w + a) 4 (4) for the distance on direction v, the length of an average trip depends on the category of the item, that is, a, b or c. let pa ,pb and pc be the probability that the item to be moved belongs to category a, b and c, respectively. in addition, let ma, mb and mc be the corresponding number of reserved spaces in the direction v for products in categories a, b, c, respectively. with this formulation the average length of a trip in direction v is given by: tv = a + pa mal 2 + pb(mal + mbl 2 ) + pc(mal + mbl + mcl 2 ) (5) by property of probabilities, the following holds: pa + pb + pc = 1 (6) 46 i. derpich, j. sepulveda by factorizing in function of l and arranging in (5) the equation (7) is obtained tv = a + l[pa ma 2 + pb(ma + mb 2 ) + pc(ma + mb + mc 2 )] (7) the term pa ma 2 corresponds to the average number of slots to be traveled for storing a product of type a; the term pb(ma + mb 2 ) corresponds to the average number of slots to be traveled for storing a product of type b, while the term pc(ma + mb + mc 2 ) corresponds to the average number of slots to be traveled for storing a product of type c. the total capacity of category a, b and c within the dc to store goods can be expressed as follows: na = 2manh (8) nb = 2mbnh (9) nc = 2mcnh (10) now, let’s get the cost of consumption of energy expended in the movement of goods in height h. considering an agv to move the articles in height, we have the following expression for power consumption. th = wht (11) where: • w is the average mass of conveyed articles. • t is the average time spent on moving the load a unit distance h.t es el tiempo medio gastado en mover la carga una unidad de distancia h. by common expression of the average speed v = lh t then t = lh ve . let e be the consumption of energy: e = cew hlh ve (12) in (12) ce is the cost of energy expended in moving a load of w kilos in height at a constant speed ve, taking a time lh. if other equipment is used to move materials in height, as it could be drones or an automated robot, expression (12) will change. for an agv equipment, the cost equation for a dc with the abc method is (13): c = 4dch[a+l(pa ma 2 +pb(ma+ mb 2 )+pc(ma+mb + mc 2 ))+ n(w + a) 4 ]+2dce[ wlh2 ve ] (13) the model can be expressed as in (14): lagrangian formulation for energy-efficient warehouse design 47 c = 4dch[a+l(pa ma 2 +pb(ma + mb 2 ) +pc(ma +mb + mc 2 ) + n(w + a) 4 ] + 2dce[ wlh2 ve ] (14) subject to na = 2manh (15) nb = 2mbnh (16) nc = 2mcnh (17) by transforming the system by converting to the lagrangian function see the reference luenberger (1989) [12] , we have: c = 4dch[a + l(pa ma 2 + pb(ma + mb 2 ) + pc(ma + mb + mc 2 ) + n(w + a) 4 ] + 2dce[ wlh2 ve ] + (18) λa[na − 2manh] + λb[nb − 2mbnh] + λc[nc − 2mcnh](19) by partial differentiation on ma , mb , mc , n, h, λa , λb y λc we obtain. δlg δma = 4chdl( pa 2 + pb + pc) − 2λanh = 0 (20) δlg δmb = 4chdl( pb 2 + pc) − 2λbnh = 0 (21) δlg δmc = 4chdl( pc 2 ) − 2λcnh = 0 (22) δlg δn = 4chd (w + a) 4 − 2λamah− 2λbmbh− 2λcmch = 0 (23) δlg δh = 4dcewlh ve − 2λaman− 2λbmbn− 2λcmcn = 0 (24) δlg λa = na − 2manh = 0 (25) δlg λb = nb − 2mbnh = 0 (26) δlg λc = nc − 2mcnh = 0 (27) lete k1 and k2 be respectivelly: k1 = pa 2 + pb + pc (28) k2 = pb 2 + pc (29) 48 i. derpich, j. sepulveda as λa, λb and λc represent the shadow price of the corresponding resource, that is , for a, b or c, respectively, values of λa, λb and λc are obtained as shown in (25), (26), (27): λa = 2chdlk1 nh (30) λb = 2chdlk2 nh (31) λc = chdlpc nh (32) finally we obtain: n = 5 √ 16chl 3exp21cew (w + a)3chve (33) h = 5 √ (w + a)c2hexp1v 2 e 8lc2ew 2 (34) ma = na 2 5 √ (w + a)2cew 2l2exp31chve (35) mb = nb 2 5 √ (w + a)2cew 2l2exp31chve (36) mc = nc 5 √ (w + a)2cew 2l2exp31chve (37) where : exp1 = k1na + k2nb + pcnc 2 (38) theorem 1. for the values of the lagrangian multipliers, it holds that: λa > λb > λc (39) proof: the only different term between the expressions of λa and λb is k1 and k2. let us recall the corresponding expressions: k1 = pa 2 + pb + pc (40) k2 = pb 2 + pc (41) therefore if pa > 0 and pb > 0 then k1 > k2 and λa > λb. (42) lagrangian formulation for energy-efficient warehouse design 49 on the other hand, it can be seen that if pb > 0 then λb > λc. (43) 2 this theorem has the following implication, it shows that the shadow price of the space resource intended for items type a are greater than those of items type b and in turn both are greater than than type c’s. the shadow price of a resource is a measure of its value or scarcity, in this case it represents how much it is saved when an extra slot unit is allocated to each of the types of dedicated storage areas a, b or c. 3 experimenting with a test case in order to validate the developed formulas, various calculations of warehouse distribution were performed using the data of (onut et al, 2008) [2] for mro (maintenance, repair, and operational) items showing abc categories. note that this is the only reference found in the literature with formulas for the case in three dimensions. in that document, the developed formulas are implicit and the problem was solved by the method of particle swarm optimization (pso). the store used was programmed to handlefive families of products, including personal cleaning, home cleaning, chemical raw materials, electrical spare parts, and ceramics objects including 10, 14, 62, 11 and 7 types of articles, respectively. before applying the formulas developed in this paper, a procedure is applied for determining which group according to the abc method is assigned to a given product. therefore, the goods in class a are closer to the door, while class b at a distance of midrange and class c at a greater distance. after completing this process, 14 elements in class a, 24 class b elements and 12 elements of the class c were found. the probabilities of membership in each class were taken as 0.6, 0.3 and 0.1 respectively. the total flow of the warehouse is 120,000 pallets of products per year and the capacity of the warehouse is 6,000 pallets. the capacity of the warehouse is divided into size classes 3,000 (na), 2000 (nb) and 1000 (nc). with respect to the dimensions of the warehouse, there are available racks with loading by both sides of a total width (w) of 2.2 mt., a length of 0.9 mt. wide and 1 mt. height. the width of the aisles is 2 mt. and the width of doors 4 mt. the cost factors considered are two. first we considered the cost of use the agv moving in the horizontal plane ch, it is obtained per meter transported. second we considered the energy cost generated by the movement in 3d of the agv (ce, it is obtained per meter transported per kilo and per the spended time in seconds. with this information, the summary data are: • l=0.9 meters (length of storage unit or slot) • n=6000 (total storage capacity in slots) • a=2.0 meters (aisle width) • d=120,000 (anual demand for pallets) • ch=us$ 1.13*10-3 (material handling cost in us$/mt) • ce= us$ 7.91*10-6 (energy cost us$/mt-kg-seg) • na=3,000 (number of pallets in category a) • nb=2,000 (number of palletes in category b) 50 i. derpich, j. sepulveda table 1: results obtained using the developed formulas values n h ma mb mc m obtained 15.06 5.01 19.89 13.26 6.63 39.77 rounded 15 5 20 13 7 40 table 2: shadow prices for each type of space a,b, c. λa λb λc 7.18 2.56 0.51 • nc=1,000 (number of pallets in category c) • pa=0.6 (probability of product is category a) • pb=0.3 (probability of product is category b) • pc=0.1 (probability of product is category c) • w=20 (mass,kg) • w=2.2 (meters) • ve=0.2 (meters/second) with these values the dimensions that give the formulas for the number of shelves (n), number of containers in height (h), number of shelves type a (ma), number of shelves type b (mb) and number shelves type c (mc) are shown in table 1 below: it is noted that the number of double shelves is 15 which gives 30 racks in total, with a number 40 slots, which added to the space occupied by the aisles gives an area of 2,520 square meters. the shadow price values are shown in table 2 below it is appreciated that the space dedicated items type a is more expensive than type b, and type c; and that the space dedicated to type b items is more expensive than the articles dedicated to type c. thus each space unit type a contributes a value of 7.18 us$/slot-year to the cost of moving materials in a year, while each unit type b space contributes 2.56 us$/slot-year while the c type contributes 0.51 us$/slot-year. thus, if there was additional space for warehousing this should go to space for items type a first and then items type b and c. the total average cost of warehousing separated by movement in the horizontal plane on the one hand and vertical movement for another, it is shown in table 3. table 3: average annual costs in us$ cost of movement in the horizontal plane 21,971 cost of movement in the vertical axis 5,222 total cost 27,192 lagrangian formulation for energy-efficient warehouse design 51 this is the total cost corresponding to the storage and picking operations for the total annual demand, this gives an average cost of 4.53 us$/slot and it is consistent with the shadow prices obtained. conclusions and future works in this paper new approach to the design of distribution centers was presented; particularly for a rectangular design with the heightwise movement. explicit formulas to calculate the number of pallets, the number of double shelves, the number of unit spaces for each shelf and number of unit spaces in height were developed. the formulas obtained are easy to use. these have been obtained by a lagrangian function obtained from a quadratic minimization problem with equality constraints. in the literature there are no explicit formulas in three dimensions by which this work is a valuable contribution to the design of distribution centers in three dimensions. future works developing closed-form solutions as to those found in this paper should be continued, but conforming to a given area, since this is a typical situation in the company, in which the available surface poses constraints to the warehouse design problem. acknowledgment the authors are very grateful to dicyt (scientific and technological research office), project number 061317dc and the industrial engineering (ie) department, both of the university of santiago of chile for their support in this work. also to ie graduates ren’e ibacache and nicole muńoz who helped in the model implementation. bibliography [1] i. derpich and j. sepulveda (2016), a model for storage facility design with energy costs, computers communications and control (icccc), 2016 6th international conference on, ieee xplore, e-issn 978-1-5090-1735-5, doi: 10.1109/icccc.2016.7496753, 147 150. [2] y. bassan, y. roll and m.j. rosenblatt (1980), internal layout design of a warehouse, iie transactions, 12(4): 317-322. [3] s. onut, u.r.tuzkaya, d. bilgehan (2008), a particle swarm optimization algorithm for the multiple level warehouse layout design problem, computers & industrial engineering, 54: 783-799. [4] p. baker and m. canessa (2009), warehouse design: a structured approach , european journal of operational research, 193: 425-436. [5] j. gu, m.goetschalckx and l.f. mcginnis (2010), research on warehouse design and performance evaluation: a comprehensive review, european journal of operational research, 203:539-549. [6] b. rouwenhorst, b. , b. reuterb, v. stockrahmb, g.j. van houtumc, r.j. mantela and w.h.m. zijmc (2000), warehouse design and control: framework and literature review, european journal of operational research , 122(3): 515-533. [7] s.s. heragu, l. du, r.j. mantel and p.c. schuur (2005), mathematical model for warehouse design and product allocation international journal of production research, 43(2):327-338. 52 i. derpich, j. sepulveda [8] s. zhou, y. gong and r. de koster(2016), designing self-storage warehouses with customer choice, international journal of production research, 54(10): 3080-3104. [9] k.j. roodbergen and f.a. vis (2006), a model for warehouse layout, iie transactions, 38:799-811. [10] j. li, m. moghaddam and s.y. nof (2016), dynamic storage assignment with product affinity and abc classification a case study, int j adv manuf technol, 84:2179-2194. [11] r.l. francis (1967), on some problems of rectangular warehouse design and layout, journal of industrial engineering, 18: 595-604. [12] d. g. luenberger (1989), linear and non linear programming, chapter 10, addison wesley iberoamericana, 299-305. international journal of computers communications & control issn 1841-9836, 12(3), 429-441, june 2017. a new adaptive elastic net method for cluster analysis j. yi, p. zhao, l. zhang, g. yang junyan yi beijing key laboratory of robot bionics and function research, department of computer science and technology beijing university of civil engineering and architecture 1 zhanlanguan road beijing, 100044, china yijunyan@bucea.edu.cn peixin zhao* school of management, shandong university, jinan, china 27 shanda nan road jinan, 250100, china *corresponding author: pxzhao@sdu.edu.cn lei zhang department of computer science and technology beijing university of civil engineering and architecture 1 zhanlanguan road beijing, 100044, china lei.zhang@bucea.edu.cn gang yang school of information renmin university of china 59 zhongguancun street beijing, 100872, china yanggang@ruc.edu.cn abstract: clustering is inherently a highly challenging research problem. the elastic net algorithm is designed to solve the traveling salesman problem initially, now is verified to be an efficient tool for data clustering in n-dimensional space. in this paper, by introducing a nearest neighbor learning method and a local search preferred strategy, we proposed a new self-organizing nn approach, called the adaptive clustering elastic net (acen) to solve the cluster analysis problems. acen consists of the adaptive clustering elastic net phase and a local search preferred phase. the first phase is used to find a cyclic permutation of the points as to minimize the total distances of the adjacent points, and adopts the euclidean distance as the criteria to assign each point. the local search preferred phase aims to minimize the total dissimilarity within each clusters. simulations were made on a large number of homogeneous and nonhomogeneous artificial clusters in n dimensions and a set of publicly standard problems available from uci. simulation results show that compared with classical partitional clustering methods, acen can provide better clustering solutions and do more efficiently. keywords: self-organizing neural network, elastic net, adaptive, cluster analysis. 1 introduction cluster analysis is useful in exploratory data analysis. it is an important but challenging task in unsupervised learning. the essence of the task is to partition a set of objects into a number of copyright © 2006-2017 by ccc publications 430 j. yi, p. zhao, l. zhang, g. yang clusters, minimizing the within-cluster variability and maximizing the between-cluster variability [10]. data clustering is a common technique for statistical data analysis. it can be widely used in a variety of scientific and engineering disciplines such as biology [2, 19, 22], computer vision [6, 8, 9, 17] and optimizations [18, 24]. many clustering algorithms have been proposed. generally they can be divided into the following categories: hierarchical clustering method, partitional clustering method, density based clustering method, grid based clustering method, and neural network based clustering method and so on [23]. in the last 20 years also, neural networks have been shown to be powerful tools for solving clustering problems. at present, neural networks-based clustering has been dominated by sofm (self-organization feature map) and art (adaptive resonance theory). sofm is a powerful tool for data analysis, and used widely in data mining, pattern recognition, clustering analysis [1, 11, 12, 16]. but when applied in real practice a number of user-dependent parameters cause problems. like the k-means algorithm, somf need to predefine the size of the lattice, i.e., the number of the clusters, which is unknown for most circumstances. in addition, trained somf may be suffering from input space density misrepresentation, where areas of low pattern density may be over-represented, and areas of high density under-represented. some researchers have proposed modifications of somf algorithm [4, 14]. in this work we propose a new self-organizing nn clustering method based on the elastic net, and attempt to alleviate the problems mentioned above. elastic net is evolved from sofm, proposed by r. durbin and d. willshaw [5] as a heuristic method. like sofm, the elastic net tries to find a topology preserving map between two spaces. it was originally proposed to solve the traveling salesman problem (tsp) [20]. later it was given a mechanical statistic foundation [15]. then elastic net can be used as a tool for data clustering in n-dimensional space and for diverse of problems in pattern research [3, 7, 13, 21]. the advantages of the elastic net method for cluster analysis are as follows: firstly, elastic net is an unsupervised learning network, and the manual training is not required. secondly, elastic net does not need the prior knowledge about the number of clusters, and its effectiveness is not effected by the input sequence of data and isolated data. thirdly, elastic net’s geometry corresponds well to the problem definition, which makes it suitable for solving high-dimensional clustering problems. and its convergence is governed by well-established physics theories leading to a sensible solution. lastly, when solving the clustering problems, the number of neurons needed in the elastic net is linearly related to the number of spatial data points. that is, the complexity of the elastic network algorithm is o(n), where n is the number of spatial data points [20]. the performance of the proposed method is evaluated by simulations on a number of homogeneous and nonhomogeneous synthetic clustering problems in n dimensions and some sets of publicly standard test problems available from uci (accessible via the web at http://archive.ics.uci.edu/ml/datasets.html). simulation results show that the proposed method can consistently and efficiently provide better clustering solutions compared with the classical partitional clustering algorithms. 2 the new adaptive clustering elastic net method in the section, a new self-organizing nn approach for clustering acen is presented. acen consists of two phases: the adaptive clustering elastic net phase and local search preferred phase. let xi(i = 1, 2, ...,n) be a data set x with n objects; kłş the number of clusters; mjłş the centroid of cluster cj(j = 1, 2, ...,k). then acen tries to minimize the cost function sum of a new adaptive elastic net method for cluster analysis 431 euclidean distance (sed): sed = n∑ i=1 k∑ j=1, xi∈cj d(xi,mj) (1) the details of two phases are described below. 2.1 the adaptive clustering elastic net phase given m unit coordinates y = {y1,y2, ...,ym} defining the rubber band of the elastic net, n data points x = {x1,x2, ...,xn} defining the clustering data set. the new energy function of the adaptive clustering elastic net is defined as below: e = −α(t)k n∑ i=1 ln m∑ j=1 e−|xi−yj|/2k 2 + β(t) m∑ j=1 |yj −yj+1| (2) the positions of the points defining the rubber band are updated according to the following formula: ∆yj = α(t) n∑ i=1 wij(xi −yj) + β(t)k(yj+1 − 2yj + yj−1) (3) { α(t + 1) = (1 −ψ)α(t) if α(t) > αmin, α(t + 1) = αmin otherwise. (4) { β(t + 1) = (1 + ϑ)β(t) if β(t) < βmax, β(t + 1) = βmax otherwise. (5) where ψ = damping factor of α(t) (0 ≤ ψ < 1). ϑ = promoting factor of β(t) (0 ≤ ϑ < 1). here wij is the same as the original elastic net. the parameters ψ and ϑ in eqn.(4) and (5) are small positive parameters, which are selected empirically. this phase helps the elastic net to find a cyclic permutation of the points so as to minimize the total distance of adjacent points in shorter time. after the computation of the adaptive clustering elastic net phase, each element of the data set is matched with the points of the elastic band. therefore the permutation of data points can be obtained from the sequence of the elastic net points. then the distances of adjacent elements of the data set can be used to divide the clusters. each point dotted in fig.1 represents the distances between the adjacent elements of the permutation. the value of the x-axis is the sequence of elements of the data set. the value of the y-axis is the distance between the adjacent elements of the data set. therefore, each pick in fig.1 represents a larger distance between the adjacent elements, meaning a cluster change. for large data sets, some elements may be lost in the final permutation. these elements are regarded as noise elements in our algorithm. we proposed the nearest neighbor learning method to handle the noise elements. the learning method is described as below. for each noise element, find its nearest data xi in all of the matched elements of the data set, and calculate the distances between the noise element and the two neighbors xi−1 and xi+1 in the path. compare the two distances and insert the noise element between xi and the nearer neighbor element xi−1 or xi+1. then update the sequence of the data set. 432 j. yi, p. zhao, l. zhang, g. yang figure 1: the distances between the adjacent elements of the data set. 2.2 the local search preferred phase in the first stage, we could achieve initial clusters according to the distances between the adjacent points. for most of the clustering problems, the first phase could efficiently identify high quality solutions. according to the requirements of different practical applications, the clustering solutions sometimes still need to be further optimized. then the local search preferred phase is needed to help the network to search for the better solutions. mathematically the steps of the second phase can be stated as below. step 1. select the number of the nearest neighbors p and get the centroids mj of each cluster cj in the first phase. step 2. assign each element in the data set x to the clusters cj with the closest centroid mj using the euclidean distance as the criteria. step 3. update k medoids. for j = 1 to the number of clusters k do (a) find a subset csubset in the cluster cj. csubset corresponds to mj and its p nearest neighbors that have not been evaluated before current iteration. (b) calculate the new medoid q = arg min xi∈csubset ∑ xi∈cj d(xk,xi) (6) after that the old medoid mj is replaced by q if it is different from mj. (c) repeat steps (a) and (b) until the medoid does not change any more. step 4. repeat steps 2 and 3 until the k medoid does not change any more. the local search preferred phase uses the euclidean distance as the criteria to assign each point like many other partitional clustering algorithms. the algorithm chooses the objects in the data set as the medoids of the clusters. the selection of the initial centroids in the first phase helps the algorithm to overcome the disadvantage of the instability of the other partitional algorithms, and could achieve better solutions. 3 algorithm for the clustering problem, the proposed algorithm acen is presented as below: a new adaptive elastic net method for cluster analysis 433 step 1. given the initial number m of dynamic nodes y = {y1,y2, ...,ym} and establish the nodes in a small circle with the center of the circle at the centroid of the clustered points. m = 2.5n. step 2. decide the sequence of the nodes. step 3. for each data points xi(i = 1, 2, ...,n), choose a node and move the node and its neighbors on the circle towards the points. step 4. repeat until all the points xi(i = 1, 2, ...,n) have been matched with the nodes. step 5. get the sequence of the xi(i = 1, 2, ...,n) according to the sequence of the nodes. step 6. use the nearest neighbor learning method to update the sequence of data set. step 7. di(i = 1, 2, ...,n) (the distance of the adjacent data points) is used to divide the groups cj(j = 1, 2, ...,k), where larger distances di mean cluster changes. step 8. assign each point in the data set x to the clusters cj with the closest centroid mj under euclidean distance metric. step 9. update k medoids. for j = 1 to the number of clusters k do (a) find a subset csubset in the cluster cj. csubset corresponds to mj and its p nearest neighbors that have not been evaluated before current iteration. (b) calculate the new medoid q = arg min xi∈csubset ∑ xi∈cj d(xk,xi) (7) after that the old medoid mj is replaced by q if it is different from mj. (c) repeat steps (a) and (b) until the medoid does not change any more. step 10. repeat steps 8 and 9 until k medoid does not change any more. our algorithm improves the original elastic net method by introducing the adaptive clustering parameter strategy and the nearest neighbor learning method. combining the new elastic net method with the local search preferred strategy, the proposed algorithm could obtain better solutions for cluster analysis problems. the advantages of acen mainly reflected in the following aspects: • acen does not need the prior knowledge about the number of clusters. • algorithm’s effectiveness is not effected by the input sequence of data and isolated data. • given n values of the number of clusters k, acen could obtain the different solutions for different numbers of clusters just running the network one time, which effectively improve the computation speed. on the contrary, given n values of the number of clusters k, the traditional partitional clustering algorithms such as k-means and k-medoids need to run n times. • due to the geometrical and physical characteristics of the elastic net, acen could express and calculate high-dimensional data set simply and efficiently, and display the data intuitively. • by introducing the adaptive parameter strategy and the nearest neighbor learning method, combined with the local search preferred method, acen could consistently and efficiently identify high quality clustering solutions. • the algorithm is geometric in nature, and the evolution of the algorithm can be tracked visually. 434 j. yi, p. zhao, l. zhang, g. yang it has been pointed out that rearrangement clustering is equivalent to the tsp. and it can be solved to optimality by solving the tsp [3]. but there is a serious defect in previous approaches when applied to data which falls into natural clusters as shown in fig.2. objects in fig.2 only have two features: horizontal and vertical coordinates. when these objects are clustered by the method of rearrangement clustering, although x and y are very close the objects x and y will be separated by 16 objects in two different clusters. it is clear that generally clusters may be broken into pieces so as to minimize the dissimilarity to adjacent clusters. using the acen method these problems could be solved effectively. in acen the partition is taken based on the larger distances between the adjacent elements in the permutation, which is more suitable for the definition of the natural cluster problem, which could help the algorithm to achieve high quality clustering solutions. (a) (b) figure 2: (a) three clusters; (b) the tsp path specifying the optimal rearrangement. although x and y are very close, their placement in the rearrangement is far apart. 4 simulation results extensive simulations were implemented on a large number of homogeneous and nonhomogeneous synthetic clusters in n dimensions. and the value of sed shown in eqn. (1) was adopted to judge the quality of the different methods. parameters were set as follows: α(0) = 0.6; β(0) = 1.5; ψ = 0.001; ϑ = 0.002; k = 0.5. we tested the acen on some nonhomogeneous clusters with different density regions. first we compared the acen with k-means and k-medoids methods to solve the problem shown in fig.2. these data falls into natural clusters. acen identified high quality clustering solution for this simple problem. on the contrary the other two partitional clustering methods did not cluster the data well as predicted. the typical results produced by three methods were shown in fig.3. in the following figures, the different shapes and colors of points represent different clusters. the second of the comparative computational tests we report used the nonhomogeneous cluster points in 2d and 3d respectively. the problems both involved 200 points. to compare the performance of the three methods, we recorded the average sed values obtained from 20 simulations with different initial cluster centroids for k-means and k-medoids. because the results of acen were not effected by the initial cluster centroids, we only took one trial for acen. the comparisons of acen with k-means and k-medoids were shown in fig.4 and fig.5. from fig.4 and fig.5 we can see clearly: • acen could identify high-quality solutions consistently for all of the tested problems. the a new adaptive elastic net method for cluster analysis 435 other two partitional clustering methods both get poor solutions. • the values of sed produced by acen were 29.58% and 41.74% less than the average values of sed produced by k-means over 20 trials, and 28.18% and 45.54% less than the average values of sed produced by k-medoids over 20 trials respectively for the tested 2d and 3d clustering problems. the proposed method consists of two phases: the adaptive clustering elastic net phase and the local search preferred phase. the first phase can identify clusters according to the distance between the adjacent points. the aim of the second phase is to minimize the cost function sum of euclidean distance (sed) in eqn.(1). for most of the cases, solutions produced by the first phase are good enough, then the second phase is not necessary. in the following, we clustered 500 points into 4 clusters in 2d using the proposed algorithm. the result of the first phase and the optimized result produced by the second phase were shown in fig.6. from fig.6 we can see clearly that the solution produced by the first phase was the same as the optimized result produced by the second phase, and acen could achieve high quality solution for the clustering problem without using the local search preferred phase. to meet the requirements of different practical applications, the local search preferred phase is used to improve the solution quality. we tested the proposed method on a homogeneous artificial clustering problem involve 150 data points in 2d. fig.7 shows the evolutions of clustering process using the proposed approach for this homogeneous artificial problem. fig.7(a) shows the result of the adaptive clustering elastic net phase. fig.7(b) and fig.7(c) display the intermediate processes of clustering. fig.7(d) shows the final solution produced by the local search preferred phase of the acen for the clustering problem. from fig.7 we can see clearly that the proposed algorithm could identify high quality solutions of giving clustering problem, and the decrease of the value of sed showed that the local search preferred phase could optimize the result effectively. in the third of our experimental tests we selected a large number of instances of 50, 100, 300, 500 and 1000 homogeneous and nonhomogeneous simulated cluster points in 2d, 3d, 4d, 7d 10d and 13d to show the solution qualities of acen, k-means and k-medoids. in the following experiments, to compare performance of the three methods, we recorded the average sed values obtained from 20 simulations with different initial cluster centroids for k-means and k-medoids. as described before, acen was performed only once. in the proposed method we use another set of parameter values as follows: α(0) = 0.6; β(0) = 1.5; ψ = 0.0025; ϑ = 0.0025. comparisons of the proposed method with the k-means and k-medoids methods were shown in table 1 and table 2. (a) typical clustering result of kmeans (b) typical clustering result of kmedoids (c) clustering result of the acen figure 3: the typical clustering results of three methods for the natural cluster problem described in fig. 2: (a) k-means method; (b) k-medoids method; (c) the proposed method. 436 j. yi, p. zhao, l. zhang, g. yang (a) sed = 189.92 (b) sed = 186.21 (c) sed = 133.74 figure 4: the typical solutions and the average values of sed obtained from 20 simulations produced by three methods for the nonhomogeneous data in 2d: (a) k-means method; (b) kmedoids method; (c) the proposed method. (a) sed=525.56 (b) sed=562.17 (c) sed=306.15 figure 5: the typical solutions and the average values of sed obtained from 20 simulations produced by three methods for the nonhomogeneous data in 3d: (a) k-means method; (b) kmedoids method; (c) the proposed method. figure 6: the result produced by the first phase and the optimized result produced by the second phase of acen. the two results were the same. a new adaptive elastic net method for cluster analysis 437 (a) the result of the adaptive clustering elastic net phase: sed=88.16 (b) the intermediate processes of clustering: sed=84.46 (c) the intermediate processes of clustering: sed=82.98 (d) the final result of the local search preferred phase: sed=82.86 figure 7: evolutions of acen and the values of sed of the clustering process. 438 j. yi, p. zhao, l. zhang, g. yang table 1: the average values of sed over 20 trials produced by k-means and k-medoids and the value of sed produced by acen on sets of 50, 100, 300, 500 and 1000 synthetic data in 2d, 3d and 4d. data number dimension 2 dimension 3 dimension 4 of kmeans kmedoids acen kmeans kmedoids acen kmeans kmedoids acen size clusters sed sed sed sed sed sed sed sed sed 50 3 32.33 29.8 20.85 46.82 42.19 29.66 44.28 44.27 36.83 5 31.87 29.37 15.59 26.17 25.72 19.47 37.48 38.04 29 10 19.35 16.85 7.29 23.16 21.4 11.39 24.54 24.7 17.4 100 3 76.85 73.98 45.21 75.41 74.14 67.7 109.27 109.38 109.27 5 68.76 64.38 39.71 104.37 96.42 83.05 69.8 68.77 54.09 10 37.72 31.45 20.88 67.02 55.91 35.95 62.36 59.54 44.91 300 3 316.14 288.72 156.74 252.86 253.44 252.32 381.8 351.98 247.73 5 186.23 178.6 99.63 249.96 234.22 191.74 271.27 269.27 233.84 10 114.38 109.69 80.17 146 14.25 127.76 184.75 183.44 173.1 500 3 645.76 505.98 221.87 564.6 595.15 388.7 654.73 698.28 448.91 5 473.01 346.35 208.93 433.16 410.04 336.76 474.99 488.12 426.57 10 263.33 225.86 158.97 301.64 288.96 261.16 346.28 365.4 309.63 1000 3 739.43 714.95 430.77 984.59 809.36 797.51 1216.3 1331.27 916.36 5 639.8 616.95 396.37 781.41 724.79 693.75 969.57 926.82 869.2 10 419.75 405.18 345.37 646.31 655.78 628.33 806.21 811.8 692.55 an examination of table 1 and table 2 yields the following observations: • for all of the 30 homogeneous and nonhomogeneous clustering challenges, acen could obtain better clustering solutions than k-means and k-medoids. • the values of sed calculated by the proposed method around 50, 100, 300, 500 and 1000 points for different dimensions were 35.03%, 31.2%, 30.25%, 30.19% and 29.53% less than the average values of sed calculated by the k-means over 20 trials; and 33.89%, 29.33%, 27.34%, 27.23% and 25.87% less than the average values of sed calculated by the kmedoids over 20 trials respectively. • like many partitional clustering algorithms, the results of k-means and k-medoids depended on the selection of the initial cluster centroids. on the contrary, the results of acen were stable and were not influenced by the selection of initial centroids. • acen’s effectiveness was not effected by the input sequence of data and isolated data, and could produce stable clustering solutions . for the last of our experiments we took some sets of standard test problems available from uci (accessible via the web at http://archive.ics.uci.edu/ml/datasets.html). the standard problems we solved include iris data set and zoo data set. comparisons of acen with k-means and k-medoids on standard problems were summarized in table 3. for this comparison, the value of sed and the accuracy were adopted to judge the quality of the clustering solutions. in table 3, we also performed 20 simulations for k-means and k-medoids, and the average values of sed and accuracy were calculated for each standard problem. because the results of acen were stable and were not effected by the initial centroids, it was performed only once. the iris plants database is the best known database found in the literature of cluster analysis and referred frequently in cluster analysis texts. the data set contains 3 classes of iris plant with a new adaptive elastic net method for cluster analysis 439 table 2: the average values of sed over 20 trials produced by k-means and k-medoids and the value of sed produced by acen on sets of 50, 100, 300, 500 and 1000 synthetic data in 7d, 10d and 13d. data number dimension 7 dimension 10 dimension 13 of kmeans kmedoids acen kmeans kmedoids acen kmeans kmedoids acen size clusters sed sed sed sed sed sed sed sed sed 50 3 60.17 55.51 41.5 67.22 69.71 55.42 78.22 78.66 61.4 5 57.21 67.12 37.61 58.7 60.28 45.58 72.84 69.75 52.91 10 38.55 30.03 27.15 44.34 47.28 29.03 57.13 56.53 30.42 100 3 129.37 110.65 107.62 145.55 138.62 123.93 150.66 137.54 141.61 5 119.03 118.4 98.99 147.81 133.84 128.27 157.99 135.34 133.59 10 88 8740 63.89 108.39 108.49 75.16 121.19 116.77 91.71 300 3 468.58 492.8 376.95 594.06 539.17 444.53 707.93 621.61 513.57 5 386.18 377.22 331.98 498.24 452.06 443.21 504.93 472.72 423.52 10 335.11 332.33 292.95 420.15 383.47 352.41 421.29 419.52 376.32 500 3 925.75 887.49 726.5 918.19 864.07 759.74 1071.6 1035.17 945.52 5 691.72 676.57 614.96 865.06 859.41 716.71 862.08 876.33 707.35 10 618.28 586.41 530.67 774.71 660.53 615.03 834.16 761.27 694.91 1000 3 1603.92 1547.7 1327.82 2082.18 1812.84 1563.24 1945.86 1822.22 1713.09 5 1374.88 1306.2 1166.27 1638.95 1599.03 1391.72 1981.69 1743.76 1507.95 10 1289.32 1287.17 956.41 1565.89 1404.95 1135.8 1657.39 1520 1371.93 table 3: computational resultsstandard uci clustering problems. algorithm iris zoo sed accuracy sed accuracy k-means 125.74 69% 133.63 66% k-medoids 123.66 71% 127.52 68% acen 98.95 91% 102.38 90% 50 instances each. the zoo data set contains 7 classes and each animal is made up of 16 properties, 15 of them as boolean and 1 numeric property. in the tests we set the parameter values as described before. considering table 3 we can make the point: acen was much more efficient than the other two partitional clustering methods. the accuracies of the acen algorithm for standard uci clustering problems iris and zoo were 22% and 24% higher than k-means , and 20% and 22% higher than k-medoids respectively. our experiments clearly illustrate the improvement of the clustering solution quality using the acen compared with the traditional partitional clustering methods. moreover the results produced by acen are not effected by the input sequence of data and isolated data, which ensure it consistently and efficiently identify high quality solutions for different problems. 5 conclusions in this paper, we proposed a new self-organizing neural network approach-the adaptive clustering elastic net (acen) for cluster analysis. our approach has two phases, the adaptive clustering elastic net phase and the local search preferred phase. the first phase is proposed by 440 j. yi, p. zhao, l. zhang, g. yang introducing an adaptive clustering parameter strategy and the nearest neighbor learning method into the original elastic net. this phase is used to find a cyclic permutation of the points so as to minimize the total distances of the adjacent points, and assign each point to different clusters using the euclidean distance as the criteria. the local search preferred phase aims to minimize the total dissimilarity within each clusters. we tested the acen method and some classical partitional clustering algorithms on the same data sets include homogeneous and nonhomogeneous synthetic clusters in n dimensions and some sets of publicly standard problems available from uci. experiments show that compared with the other traditional partitional clustering algorithms, acen could provide better and stable clustering solutions and do more efficiently for all of the tested problems. acen algorithm’s effectiveness is not effected by the input sequence of data and isolated data. in addition acen does not need the prior knowledge about the number of clusters, which makes it more suitable for solving clustering problems. acknowledgment this work is funded by national natural science foundation of china (no. 61402032), beijing municipal natural science foundation (no. 4144072), project supported by the beijing excellent talent (no. 2013d005017000017), scientific research foundation of beijing university of civil engineering and architecture (no. 00331613002 and 00331616023, y13-23), open research fund program of beijing key laboratory of robot bionics and function research,the independent innovation foundation of shandong university, iifsdu(ifw12109), the key project of philosophy and social science plan of jinan(2014), shandong province natural science foundation(zr2015gm012),the china postdoctoral science foundation (no.2014t70624),the national statistical science research programm(2013lz38) and beijing municipal commission of education (no. km201410016007 and 21271413117). bibliography [1] alahakoon d., halgamuge s.k. 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(2015); a variables clustering based differential evolution algorithm to solve production planning problem, international journal of simulation modelling, 14(3), 525538, 2015. international journal of computers communications & control issn 1841-9836, 10(2):230-237, april, 2015. implementing bpmn 2.0 scenarios for aal@home solution l. rusu, b. cramariuc, d. benta, m. mailat lucia rusu babeş bolyai university of cluj-napoca romania, 400591 cluj-napoca, m. kogălniceanu, 1 lucia.rusu@econ.ubbcluj.ro bogdan cramariuc it center for science and technology bucharest romania, 011702 bucharest, av. radu beller, 25, sector 1 bogdan.cramariuc@ieee.org dan benţa* agora university of oradea romania, 410526 oradea, piaţa tineretului, 8 *corresponding author: dan.benta@univagora.ro marinela mailat babeş bolyai university of cluj-napoca romania, 400591 cluj-napoca, m. kogălniceanu, 1 marinelamailat@yahoo.com abstract: aging tendency of european population and live longer and independently desire requires aal solution for particular elders (chronic diseases, disabilities, aso). nitics project aim is to develop advanced itc solutions including monitoring and navigational support for indoor to support elderly in their daily activities. this paper offers a bpmn implementation for indoor assistance based on iot (sensor monitoring) and activity workflow implementation. our solution offers an intelligent care center solution for caregivers monitoring and elders support. keywords: iot, aal@home, bpa, bpm, bpmn, workflow. 1 introduction the european population is aging and tends to live longer and independently. this requires for a good quality of life staying at home. european commission (ec) ambient assisted living joint program (aal jp) offers an opportunity to incorporate the technological progress in communications, internet-of-things (iot) and artificial intelligence (ai). moreover, advances in medical and assistance or caring sciences are increasingly making people with disabilities autonomous and self-sufficient. advanced ict services including monitoring and navigational support are needed to support the mobility of elderly and disabled persons in their home during their daily activities [1]. iot has many definitions, focused on infrastructure, object interconnection, context awareness, communication interoperability, security, privacy or other particular features. iot means "things having identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social, environmental, and user contexts" [2]. sap definition offered by stephan haller, sap ag, focuses on business processes "a world where physical objects are seamlessly integrated into the information network and where the physical objects can become active participants in business processes". services are available to interact with these ’smart objects’ over the internet, query and change their state and any information associated with them, taking into account security and privacy issues. copyright © 2006-2015 by ccc publications implementing bpmn 2.0 scenarios for aal@home solution 231 based on iot features for addressing elements such as convergence, content, collections (repositories), computing, communication, and connectivity from the context, this concept could be applied for homecare solutions which needs interconnection between people and things. ambient assisted living (aal) focused on several areas aal4persons: aal@home, aalonthe-move, aal@work and aal@community. other authors talk about aal as home care systems (hcs) which are focused on the support of living assistance for people with special needs (elderly, disabled) in their own homes. hcs domain can be divided into three parts: emergency treatment services, autonomy enhancement services, and comfort services. emergency treatment covers assistance, detection, prediction, prevention services. these services offer early prediction and recovery from critical conditions, emergency and safe detection and alert propagation of emergencies (sudden falls, heart attacks, strokes, panics, aso) .autonomy enhancement is focused on eating, drinking, dress, medication, cooking, cleaning, shopping services. comfort services cover all areas that do not fall into the previous categories: logistic, finding things, home automation, social contacts, infotainment, safety [3]. this paper offers a solution for aal@home using smarthome concept as an assisted living technologies (alt), by offering a bpmn 2.0 implementation which helps to assist elder people. after an introduction, in the second section we deal with business process analysis (objectives and specific features, actors and roles in our project), and then we develop several workflow scenarios using aal@home features, based on end-user (elderly and care-givers) requirements, which include alt and telecare features. last section presents conclusions and future work. 2 related results there were previously mentioned some aal solutions for particular elders. robocare, is an italian aal@home research project focuses on the development of distributed systems with software and robotic agents for generating active services in environments for humans may need assistance and guidance. this solution allowing vulnerable elderly people to lead an independent lifestyle in their own homes. robocare has two scenarios: the robocare domestic environment (rde) and the health-care institution (hci) scenario. it covers several fields of research: robotic platforms, sensory systems, activity supervision in complex environments, and human-technology interaction [4]. robocare investigate the integration of robotic, sensory and automated reasoning components into rde and hci scenarios. this project offers main aspects related to technology for elderly care: development of the enabling domotic components (intelligent sensors and robotic platforms) for deployment the target scenarios and development functionalities of domotic components (activity supervision and diagnostics) using service-providing software infrastructure [4]. smart-home architecture was described by bregman and korman using interaction of four modules: central management units (cmu), user interface (ui), home equipment and appliances interface (heai), external communication interface (eci). the cmu has several components: operating system (shos smart home operating system), the smart-home database (shdb), ai (artificial intelligence) engine or home intelligence (hi) and application services (as) [5]. an implementation of this model was done in www.liorzehome.com using objectoriented approach. each event is defined as a set of properties: event name: a description of the event name, input devices with devices inside the ehome that trigger an event, and functions: event triggers: that return true when a trigger is invoked, output operation with the desired output operation, output alert, the ehome main alerts defines the ui for main alerts. if an event requires an alert, output alert describes the alert. the design follows object-process methodology (opm), opcad, opds (object-process diagrams) [6]. 232 l. rusu, b. cramariuc, d. benta, m. mailat another solution for ambient intelligence based home care systems (ahcs) was offered by amica (ami care and assistance) prototype for monitoring monitoring drinking, monitoring food quality, location tracking and fall detection, using different amica services interconnected by service platform which was developed in belami project (bilateral german-hungarian collaboration project on ambient intelligence system), based on soa paradigm. coaalas (companion for ambient assisted living on alive-shareit platforms) aim is to model the sensor network around disabled users as societies, with the expected behavioral patterns, for supporting smart assistive tools [7]. from a huge hcs we mention aaloa (aal open association) which is supported by several aal projects: braid (fp7), monami (fp6), oasis (fp7), osami-commons (itea2), persona (fp6), soprano (fp6), universaal (fp7) and wasp (fp6) [8]. 3 nitics business process analyze (bpa) nitics project within the aal program (network infrastructure for innovative home care solutions) aim is to develop an advanced ict solution which includes monitoring and navigational support for indoor and to support elderly people in their daily activities (nutrition, personal hygiene, home care) in the context in which an integrated solution is still missing [9]. main operational objectives are [1]: • a) defining and designing a flexible service platform allowing and facilitating the integration and consolidation of existing elements, leading to a continuous improvement and extension of end-user services. nitics also provides opportunities to design new customized services to better take care about the end-user; • b) improving the quality of life of elderly and disabled persons by allowing them to be mobile in a safe way inside the house and sustain them in their daily life activities; • c) improving self-sufficiency of elderly and disabled persons, by self-caring at home (selfcheck of health conditions and life-style, medication reminder, nutrition status monitoring and alerting), in order to avoid excessive workload and cost from the involved caregivers • d) improving the response in terms of efficiency (quality and speed) from the care providers and from the individual’s family in emergency situations, by an alarming system, by sensors/cameras feedback to carers, by remotely controlling devices and by video conversations with carers. providing care in an efficient way, by making use of reliable information on the condition of the elderly, which will indicate whether an informal carer is needed for aid or if a more specialized formal carer has to intervene. in our project we have identified four categories of actors: end-users, caregivers, it specialists, application and service providers. end-users are elderly and people with diseases and disabilities. because of agile methodology for system development application and service providers should be able to develop and implement applications and offer maintenance and upgrade services, according to end-users or caregivers demand. first step in our system development was to analyze the end-user requirements. we have applied two questionnaires for elderly people (over 65 years) in order to identify the main activities in which they need help and also to identify the type of help needed. after first questionnaire we have identified two scenarios to address the needs of the users: implementing bpmn 2.0 scenarios for aal@home solution 233 • activities that require physical help in which case a platform is needed that triggers a request for help either automatically (e.g. vital parameters are out of range, falling alarm, etc) or through the interaction with the user (e.g. button pressing). the request is sent to a caregiver which can be a professional, family, neighbor or friend (aal@home features). • needs that can be addressed fully by an itc platform alone: reminders, day organizers, virtual entertainment and interaction, etc (aal@home and aal@comunity features). based on our results, we re-define siciliano classifications for nitics project, ordering need items by importance, as obtained from the interviews and we update challenges and electronic support according to end-users requirements. in hierarchical classification we used three clusters: needs, support and challenges and we obtained connections between these clusters linked with basic needs for elders living [1]. second questionnaire has 61 items (q1-q61), and was applied on 59 persons, elder people between 65 and 83 years, with chronically or severe diseases, several of them (12%) have also disabilities. end-users hierarchically needs and electronically support was classified hierachicaly from very important need (first row) to less important need (last row). another needs classification is focused on caregivers help to solve their needs. needs that require help from a caregiver prevail and needs that can be addressed without direct help form caregiver. we focused on first category of needs for the third survey regarding the caregiver perspective on the elderly needs and their perception towards various dimensions of daily life (i.e., health, interaction with technology, health care etc.). the interviews were based on a questionnaire specifically developed for this purpose which comprised 32 items (q1-q32) divided in seven sections: demographic data and other primary measures, specificity of caregiving services, the client related impact, evaluation the opinion of the caregivers, according to their own experience, on the importance of the functionalities of an ict platform towards facilitating the delivery of caregiving services to the elderly, technology acceptance and the users’ perception in relation with the project goals (interest in future participation in surveys and system testing). 4 nitics business activity monitoring (bam) nitics has developed using rde and hci scenarios detailed by cesta and pecora based on several domotic components and functionalities of domotic components using service-providing software infrastructure [4]. this section offers only hci scenarios for emergency treatment of elders with special needs and implementation was developed based on agile methodology, using domotic components, soa and intelligent agents. if we consider a private care center that can be assimilated as an organization, which has its own business process (bp). care center activity can be a business process, which is defined as a collection of related tasks that produce a specific service or product (to serve a particular goal, in our case elders care) for a particular customer: elderly people [10]. bpmn 2.0 specification defines three models for different aspects of processes: process model, choreography model and collaboration model. to achieve the planned objectives of the project, of the center as an organization we use the process model that describes how operations are carried out [11]. because our target was a care center like an entity, we model the process at a private level of abstraction, as an internal business processes. in future work-package we shall develop public levels as collaborative b2b processes. we used the software activiti 5.13 as a platform for bpmn 2.0, released under the apache open source, written in java, which can run in any java application, on a server, in the cluster or in the cloud. it is an alternative implementation of bpmn 2.0 jbpm (jboss bpm). activiti 234 l. rusu, b. cramariuc, d. benta, m. mailat modeler offers a solution modeling analyst bpmn 2.0 business process that can be compiled in a web browser. this process can be easily distributed because it requires no client software before one starts modeling. activiti designer is an eclipse plug-in (editing application framework) that allows developers to improve its process modeling activity in a process modeler bpmn 2.0, which can be executed by the processing engine. activiti explorer provides an overview of the processes implemented, management, interacting with them and viewing tables in the database managed processing engine [12]. the control of this care center is done through a web portal in which access is allowed to both health care professionals, patients, and their relatives. recording and monitoring equipment in patients’ rooms uses xmpp server, which are scripts that monitor and take the equipment alarms and sending the data to the interpretation workflows pubsub method (publish and subscribe). workflows are managed by active framework, which runs each process. sending data is ensured by bosh transport protocol and for identifying each user equipment and network, jabber employs a unique identifier (jid), consisting of the user name @ name of the server that houses the respective users. equipment registered is required to be monitored by assigning a jid unique for each. portal scripts require monitoring a particular device. when it receives an alarm from one of the sites, it creates a device notification and follows the chain of tasks assigned for each type of alarm process diagrams, from active to complete execution of that process. scripts are responsible for announcing persons assigned a specific task through the portal by notification by sms or email. client control section presents the apartment of a patient with all the equipment found in him. in the living room are the following sensors: • main entrance access point: represented by the apartment alarm system, it indicates that the system is armed or not; • entrance sensor: a sensor that transmits if the apartment front door is opened or closed; • living room siren: it is a siren that can start in case of serious alarms; • window sensor: a sensor that transmits if the window is open or not; • living room heating: a sensor attached to a radiator that indicates whether heater is switched into the chamber; • living room thermostat: a thermostat that can show several indicators, such as whether the heater is switched on, room temperature or desired temperature. in the bedroom are some of the sensors that are in the room • presence bedroom: a motion sensor that indicates whether someone is in the room or when he or she has moved last time. • bedroom light: indicates whether the bedroom light is on / off, and brightness level. • monitoring camera: camera that monitors the patient’s room. • bedroom alarm button: is a button next to the bedside alarm that can be pressed in an emergency. • bedroom floor sensor: in the floor in front of the bed there is a sensor to track whether this is down from the bed and cannot get up. implementing bpmn 2.0 scenarios for aal@home solution 235 figure 1: bpmn implementation for bed alarm workflow • bed sensor (bed occupancy sensor) indicates whether the bed is occupied or not, specifically whether or not it is the patient bed. in addition, there are other sensors as required for patient safety: presence bedroom, bedroom light, monitoring camera, bedroom alarm button, bedroom floor sensor, bed occupancy sensor. all sensors have the option to get the history of the events that have triggered, and the ability to perform certain tasks directly accessing the portal, without having displacement chamber. these tasks can be reinforcement safety system, turning heat, light switch, changing luminosity, opening and closing the door or window. 236 l. rusu, b. cramariuc, d. benta, m. mailat we exemplify bpmn implementation for bed alarm workflow (fig. 1). equipment that governs the workflow is the sensor bed bed occupancy sensor. this sensor triggers an alarm if the patient exceeds a certain number of hours without getting up from bed. for example, if the patient does not rise over 10 hours suggesting that the patient may have an ailment. from here the patient is monitored bed alarm workflow is started by setting specific parameters. pubsub technique requires scripting to monitor the equipment. from the moment the script start monitoring waiting sensor bed alarms. when an alarm is received, it is recorded in history, and then starts a sub alarm to those who need to take alarm and to execute the task. depending on the parameters set to start workflow are determined people who will receive this alert. for the default parameters in parallel performs the following tasks: obtain assistance one email address, it sends an e-mail that is announced by this alarm, it creates a portal notification for this nurse to obtain his phone number and to send an sms to this notice. at the same time it is announced and nurse 2, following the previous steps (similar to nurse 1). once these tasks have been completed, it sends a signal that the first two nurses were announced and the first sub-ending. after the time set to start workflow, if neither of the two nurses did not take the alarm then goes ahead and announces the following persons. patient’s relatives are notified only by sms and notification portal, and the response time is greater than of the nurses. when one of them takes the alarm notification, then the script is disabled. the historical record sends permission to perform the task, and the script goes back to the state of waiting for a new alarm monitoring. demonstration parameters that can start this workflow are the following: o maximum occupancy: 8:00 p.m. o alarm priority: high o type of alarm : sms, email and notification portal o the receiver alarm 1: nurse 1 and nurse 2 if they do not take the alarm in 5 minutes; o alarm receiver 2: two nurse ( who has already been notified by the portal, but not via email or sms) unless you take alarm in 5 minutes, o alarm receiver 3: relative 1 and relative 2 (will be notified through the portal and sms), if those do not accept within 30 minutes; o alarm receiver 4: audible alarm emergency room. 5 conclusions and future works our solution offers a bpm approach for an intelligent home care solution. elderly care in a specialized institution may be considered a business process. the existence of repetitive tasks enables automation of these processes. each process is modeled and it is presented as a set of individual activities or by compound several sub-processes. the modeling process shows the activities in the order in which they occur, the actors performing the activities, inputs and outputs for each activity, information flows input/output during the process, the rules used in the process. all these are made with the activiti framework, which may possess all components to develop complex workflows. bpmn 2.0 standard underpinning this framework and it facilitates the work of business analysts and developers, providing the right set of graphics required modeling process. process automation is a necessity because it allows labor efficiency for nurses and specialized caregivers. indeed, they are not forced to make regular visits to patients, just only when the visits are needed. in addition the health, elderly people can be easily traced. the proposed model is safe for patients being monitored continuously and it also ensures that their relatives or friends are well cared for. by comparison with other existent solution, we focused on process automation and modeling workflows using iot elements. acknowledgment the work presented has been founded by the research grant fp7 aal joint programme, networked infrastructure for innovative home care solutions, and was financed by center it implementing bpmn 2.0 scenarios for aal@home solution 237 for science and technology, romania. bibliography [1] nitics (network infrastructure for innovative home care solutions) (2013), aal joint program © nitics (4 of 33) call aal-2012-5, http://pl.ssw.org.pl/portofolioview/dolore-magna-aliqua [2] botterman, m., (2009); internet of things: an early reality of the future internet. workshop report, european commission information society and media. [3] nehmer, j., karshmer, a., becker, m., lamm, r.( 2006); living assistance systems an ambient intelligence approach. proceedings of the international conference on software engineering (icse), doi: 10.1145/1134285.1134293, 43-50. [4] cesta, a., pecora, f., (2005); the robocare project: intelligent systems for elder care, american association for artificial intelligence, www.aaai.org [5] bregman d.; korman, c. (2009); a universal implementation model for the smart home, international journal of smart home, 3(3): 15-30. [6] zoref, l.; bregman, d.; dori, d. (2009); networking mobile devices and computers in an intelligent home, international journal of smart home, 3(4): 15-22.. [7] becker, m.; werkman, e.; anastasopoulos, m.; kleinberger, t. (2007); approaching ambient intelligent home care systems, pervasive health conference and workshops, 2006, doi: 10.1109/pcthealth.2006.361656, 1-10. [8] universal open platform and reference specification for ambient assisted living, http://www.universaal.org/index.php/en [9] serbanati, a.; medaglia, c.m.; ceipidor, u.b. (2011); emphbuilding blocks of the internet of things: state of the art and beyond, isbn 978-953-307-380-4, 382 pages, intech publisher. [10] white, s.a.; miers, d. (2008); bpmn modeling and reference guide: understanding and using bpmn, future strategies inc., lighthouse point, florida, usa. [11] omg, ’business process model and notation (bpmn): version 2.0 specification’, technical report formal/2011-01-03, object management group, (january 2011). [12] http://www.activiti.org/userguide/, last access: 12.07.2013. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 1, month: february, year: 2020 article number: 1008, https://doi.org/10.15837/ijccc.2020.1.3739 ccc publications a neural network classification model based on covering algorithm and immune clustering algorithm f. zhao fang zhao school of information science and technology, zhejiang shuren university hangzhou 310015, china fly_sylvia@163.com abstract inspired by the information processing mechanism of the human brain, the artificial neural network (ann) is a classic data mining method and a powerful soft computing technique. the ann provides a valuable tool for information processing and pattern recognition, thanks to its advantages in distributed storage, parallel processing, fast problem-solving and adaptive learning. the constructive neural network (cnn) is a popular emerging neural network model suitable for processing largescale data. in this paper, a novel neural network classification model was established based on the covering algorithm (ca) and the immune clustering algorithm (ica). the ca is highly comprehensible, and enjoys fast computing speed, and high recognition rate. however, the learning effect of this algorithm is rather poor, because the training set is randomly selected from the original data, and the number of nodes (covering number) and area being covered are greatly affected by the learning sequence. to solve the problem, the ica was introduced to preprocess the data samples, and calculate the cluster centers based on the antibody-antigen affinity. the ca and the ica work together to determine the covering center and radius automatically, and convert them into the weights and thresholds of the hidden layer of neural network. the number of hidden layer neurons equals the number of covering. in addition, the mcculloch-pitts (m-p) neurons were adopted for the output layer. based on the input feature of the hidden layer, the output feature completes the mapping from input to output, creating the final classes of the original data. the introduction of the ica fully solves the defect of the ca. finally, our neural network classification model was verified through experiments on real-world datasets. keywords: artificial neural network (ann), covering algorithm (ca), immune clustering algorithm (ica), constructive neural network (cnn) 1 introduction the artificial neural network (ann) is an intelligent computer system mimicking the biological neural network of animal brains. being a soft computing technique, the ann enjoys various advantages, ranging from distributed storage, parallel processing, fast problem-solving and adaptive learning. as a result, the ann has been widely applied in many fields, especially in information processing and pattern recognition [1, 2, 3, 4, 11, 15, 17, 21]. https://doi.org/10.15837/ijccc.2020.1.3739 2 proposed by zhang ling and zhang bo, the constructive neural network (cnn) is grounded on the geometrical meaning of mcculloch-pitts (m-p) neurons. the main ideal of the cnn is to transform the neural network design into a problem of domain coverage [24].based on feedforward cnn, the covering algorithm (ca) is highly comprehensible and easy to construct, without no false covering center and a few hidden network nodes. in addition, the algorithm also boasts a fast computing speed, a high recognition rate, and a quick identification of the covering center. however, the learning effect of this algorithm is rather poor. this is because the training set is randomly selected from the original data, and the number of nodes (covering number) and area being covered are greatly affected by the learning sequence. the immune clustering algorithm (ica) is an excellent way to process non-convex datasets, without needing the number of clusters [13]. the algorithm aims to build a memory set that can identify and represent the structure of the target dataset. when it is applied to 2d and 3d datasets, the ica first obtains the memory network, then visualize the distribution structure of the dataset, and finally output desirable clustering results [5, 8]. in the light of the above, this paper designs a novel neural network classification model. the ica was taken as the frontend processor of the neural network to cluster the data samples. besides, the ica was combined with the ca, eliminating the negative impact of learning sequence on efficiency. in this model, the center and radius of covering are determined automatically, and converted into the weights and thresholds of the hidden layer of the neural network. the number of hidden layer neurons equals the covering number, while the m-p neurons were selected for the output layer. the inputoutput mapping was completed by evaluating the input feature of the hidden layer. the effectiveness of our model was proved through experiments. 2 covering algorithm and immune clustering algorithm 2.1 covering algorithm (1) constructive neural networks (cnn) the cnn was developed by zhang ling and zhang bo, drawing on the geometrical meaning of m-p neurons. the network mainly projects an n-th dimensional vector onto an (n+ 1)-th dimensional hypersphere, turning the design of a neural network into a problem of domain covering [24]. the mapping process can be explained as follows: first, the bounded set d is transformed in n-th dimensional space and mapped to sn, t : d → sn, where sn is a hypersphere in the sn-th dimensional space; through the transform, d is mapped one by one to a hypersphere with a radius of d, facilitating the data analysis and processing on the hypersphere. for a given sample set, setting up a network is equivalent to covering a given sample set with a set of domains. in this way, the neural network construction is converted into a domain covering process. with clear geometrical meaning, the cnn can process large data problems intuitively with a small amount of calculation. hence, this technique has attracted much attention in the academia [7, 9, 18, 19, 23]. (2) covering algorithm an m-p neuron is a processing unit that contains multiple inputs and a single output. the input-output relationship of m-p neurons can be expressed as a symbolic function: y = sgn(< ω,x > −θ) (1) where x = (x1,x2, · · · ,xn)t is an n-th dimensional input vector; ω = (ω1,ω2, · · · ,ωn)t is a weight vector; θ is a threshold. the classification boundary of m-p neurons is a hyperplane in n-th dimensional space: < ω,x > −θ > 0 (2) where ω is a unit vector and the normal vector of the hyperplane; |θ| is the distance between the origin of the coordinates and the hyperplane. in n-th dimensional space, eq. (1) consists of two functions with < ω,x > −θ > 0 represents the hyperplane p . if < ω,x > −θ > 0, then the corresponding point https://doi.org/10.15837/ijccc.2020.1.3739 3 falls in the positive half space of the hyperplane and sgn(< ω,x > −θ) = 1; if < ω,x > −θ < 0, then the corresponding point falls in the negative half space of the hyperplane and sgn(< ω,x > −θ) = −1. the m-p neurons can be seen as recognizers that can identify the spatial locations partitioned by hyperplanes. however, the problem will become too complex to be represented intuitively by the hyperplane of the neurons, when there are too many dimensions and hyperplanes. to solve the problem, some improvements were made by zhang and zhang [23]. it is assumed that the input vectors are of equal length, and limited to a sphere in the n-th dimensional space. then, < ω,x > −θ > 0 represents the part of the sphere p in the positive half of the space. this part is called a spherical field (fig.1) on the sphere. if ω and x are equal, then ω is the center of this spherical field, and r(θ) is the radius. the radius decreases monotonically with the increase of θ. taking eq. (2) as the activation function sgn(< ω,x > −θ), the activation function is the characteristic function of the spherical domain on the corresponding sphere. in this way, the complex problem can be solved easily in the sphere: sgn(x) = { 1, x > 0 0, x ≤ 0 (3) figure 1: the spherical field through the above transform, the neural network architecture can be constructed by finding the optimal covering. let the definition domain h of the input be the bounded set d in the n-th dimensional space, and sn be the n-th dimensional hypersphere in the n + 1-th dimensional space. if the inputs are unequal, the sample points can be mapped to the spherical surface s. t : d → sn,t(x) = (x, √ r2 −‖x‖2) (4) eq. (4) can be understood as follows: located inside s, d is an n-th dimensional hyperplane passing through the origin in the (n + 1)-th dimensional space; then, each point on d is projected perpendicularly to s through transform t. the original points have a one-to-one correspondence with the projections on the hemispherical surface (fig.2). to sum up, each neuron (ω,θ) is a characteristic function of the spherical domain with ω as the center on the hypersphere sn and the radius of r(θ) = r(cos(θ/r)). figure 2: the transform d → sn https://doi.org/10.15837/ijccc.2020.1.3739 4 if the samples to be classified are unknown, then each sample is judged whether it belongs to a certain area, according to the class of the sample and the covering cnn, i.e. whether the domain < ω,x > −θ > 0 exists. it is necessary to check if any field contains an unknown sample in the spherical sphere. once such a field is determined, the unknown sample can be categorized correctly. it is assumed that the learning sample has a total of n classes, x = {x1,x2, · · · ,xn}. the basic idea of the ca can be described as: setting up a spherical sphere for each sample point in each class, such that the final constructed field covers all the initial sample points. for the k-th learning sample xk, the spherical domain can be learned and constructed by selecting the point ai not covered in xk, and computing d(ω) by: d1(ω) = max x/∈xk {< ai,x >} d2(ω) = min x∈xk { < ai,x > |< ai,x >> d1(ω) } d(ω) = 1 2 (d1(ω) + d2(ω)) (5) in addition, a covering c(ai), < ω,x > −θ > 0 should be built with ai as the center and θ = d(ω) as the threshold. algorithm 1 ca for sample set x step 1: initialize the class number i = 1, find the maximum modulus r of all samples in sample set x, and then project the point in x to the corresponding sphere. the center of the sphere is at the origin and the radius is r(r > r). step 2: construct a covering c(i). if no point in xi is covered, go to step 7; otherwise, take any point ai in xi has not been covered. step 3: perform calculation by eq. (5), and make the covering c(ai) with ai as the center and θ as the threshold. step 4: find the center of gravity of all points covered by c(ai), map it to the sphere, and denote the projection as a′i. continue to calculate the threshold θ ′ by eq. (5), and find the spherical sphere c(a′i). step 5: if c(a′i) is greater than the number of points covered by c(ai), such that a ′ i → ai and θ′ → θ, go back to step 4; otherwise, go to step 6. step 6: calculate the translation point a′′i of ai, and set up c(a ′′ i ). if c(a ′′ i ) is greater than the number of points covered by c(ai), go to step 4; otherwise, obtain a coverage c(i). step 7: if class label i does not reach the maximum number of classes, then go back to step 3; otherwise, terminate the algorithm. 2.2 immune clustering algorithm immune algorithms are designed based on the immune process of the natural immune system [14]. among them, the ica designed by de castro, named artificial immune network algorithm (ainet) [13], has been widely used for data compression and clustering [6, 10, 12, 22]. the pseudo code of this ica is presented below. in the above algorithm, the differentiation between classes in affinity relies on affinity measurement. let x1 = (x11,x12, · · · ,x1m) and x2 = (x21,x22, · · · ,x2m) be two sample points. then, the affinity between them can be measured by the euclidian distance d1,2: d1,2 = √√√√ m∑ j=1 (x1j −x2j )2 (6) so far, the basic ideas of the ca and ica have been introduced, paving the way for model construction. https://doi.org/10.15837/ijccc.2020.1.3739 5 algorithm 2 the ainet 1: initialization: randomly initialize the antibody ab; 2: set the main parameters: σd (death threshold), σs (suppression threshold), n (clone multiplier), ξ (affinity maturation rate), n (number of clones); 3: while the termination condition is not satisfied do 4: for an antigen xi ∈ ag do 5: affinity calculation: calculate the affinity between xi and each ab in the antibody network; 6: cloning: reorder the ab based on the affinity value, and select the n antibodies with the highest affinity to xi for cloning. note that the number of cloned antibodies is positively correlated with the affinity; 7: mutation: perform mutation on the set of clones by c = c −a∗ (c −xi); 8: selection: calculate the affinity between population c and xi after mutation, and add the antibody with the highest affinity ξ% into memory cell m; 9: cloning inhibition: remove the antibody node with the antibody-antibody affinity less than σs in m; remove the antibody node with the antibody-antigen affinity less than threshold σd in m; 10: end for 11: consolidate antibodies and memory cells: ab = [ab; m]; 12: network suppression: remove antibody nodes whose affinity between ab antibodies is less than the threshold σs; 13: optimization: randomly generate some new antibodies to replace the r% antibodies with the lowest affinity in the antibody network; 14: end while 3 model construction 3.1 ica-based determination of covering center and radius after the run of the ainet algorithm, a simplified structure can be obtained to represent the original dataset, namely, the memory cell population [20] (fig.3 (a)). on this basis, the minimal spanning tree (mst) of the antibody was obtained by prim’s algorithm (fig.3 (b)). then, the side length of the mst was depicted in a bar chart (fig.3 (c)). for each edge, if the sum of its standard deviation and mean connection strength is smaller than the connection strength in the current iteration, then the edge should be considered an inconsistent edge. this ensures that the edges connected together can represent the same class [1, 16]. the process looks like this: figure 3: the mst and bar chart of the dataset to be clustered as shown in fig.3, three sides were much longer than the other sides. four cluster classes and their center points can be obtained by cutting from the three sides. next, the number, center and https://doi.org/10.15837/ijccc.2020.1.3739 6 radius of covering can be derived from the clustering results, and a covering set c(a1,a2, ...,ap) can be obtained through the ica, where p is equal to the cluster class. 3.2 neural network model let e = { rt = (xt,y t), t = 0, 1, · · · ,p } be a sample set. then, the memory of the neural network can be described as follows: the neural network remembers the memory relationship between input xi and output y i. the association of the neural network can be expressed as: if the input of the neural network is xi + ∆i, where ∆i is interference or noise, the output of the neural network will remain as y i. using a neuron to represent the space division of the sphere, the memory-association or classification function of the traditional neural network can be achieved through the space division. thus, neural network classification model can be defined by the following elements: (1) feature vector of sample point a(a1,a2, · · · ,an). this vector carries the features of the neuron. each neuron has a unique feature vector, and determines if the class output should be activated based on this feature vector. (2) radius r. the radius defines the scope of influence of a neuron. (3) neuron class or function. this element defines the function of each neuron in the neural network, and marks the class of the sample. the output of the neuron is either activated or disabled. (4) input x. the input defines the input vector (x1,x2, · · · ,xn) of the data sample. if the input is within the scope of influence of a neuron, then the neuron output will be activated in the current iteration; otherwise, the neuron output will be disabled. (5) activation function: y = sgn( i∑ ‖xi −ai‖2 −r2) (7) sgn(x) = { 1, x > 0 0, x ≤ 0 (8) so far, the authors have defined the neural network classification model, and introduced its parameters. 4 model architecture and its algorithm unlike the backpropagation (bp) model, a neural network does not need weight adjustment in its construction. instead, the structure is integrated into the learning process of the neural network. in this way, the established network can tolerate a high fault and recognize each sample. 4.1 model architecture the architecture of classification neural network model is shown in fig.4. as shown in figure 4, the neural network consists of three layers, namely, the input layer, the classification layer and the m-p layer. the input layer mainly defines the input vector (x1,x2, · · · ,xn) of the data sample. the classification layer, containing m neurons, divides the sample space into m subspaces. the scope of influence of the m neurons equal the size of the covering, which covers all samples. each neuron is mapped to the output layer, corresponding to a class. the m-p layer maps the neurons to the output layer based on their relevance to the input features, and outputs the classes of the neurons. the total number of classes of the original sample data is denoted as k. 4.2 algorithm design by the algorithm of our model, the test sample data inputted into the model are clustered and then converted into the hidden layer. finally, the classification results of the inputs were outputted. (1) determination of classification neurons (2) determination of m-p neurons https://doi.org/10.15837/ijccc.2020.1.3739 7 figure 4: the architecture of neural network algorithm 3 constructing classification neurons require: sample set r = { (x1,y1), (x2,y2), · · · , (xp,yp) } ensure: classification neurons step 1: initialize the number of classification layer neurons m = 0. step 2: process the sample set by the ca and the ica to obtain the covering set: c(a1,a2, · · · ,ap). step 3: for each covering in c, generate new classification neurons. m = m + 1,am = xk the weights of m-p neurons can be determined based on the components of neuron clustering samples: wmk = −(y(am))k,m = 1, 2, · · · ,m,k = 1, 2, · · · ,k (9) θk = − ∑ m wmk (10) this section fully explains the construction and algorithm of the neural network classification model. 5 experiments and results analysis to verify its effectiveness, the proposed neural network classification model was applied to two experiments on the data from three uci standard databases, in comparison to the k-means clustering (kmc) and the ca. the classes, number of samples and number of conditional attributes of iris, wine, and zoo databases are listed in table 1 below. table 1: parameters of the three datasets dataset number of classes number of samples number of conditional attributes iris 3 150 4 wine 3 178 13 zoo 7 214 15 the original data were divided into a training set and a testing set. the former was used to train the classification neural network, and the latter to test the accuracy of this network. the ica was employed to determine the number and center of clusters, calculate the radius of the clusters, and convert them to the network parameters. table 2 lists the classification accuracies of our model, https://doi.org/10.15837/ijccc.2020.1.3739 8 table 2: the classification accuracy rate of our method method classification accuracy (%) our method k-means covering iris 95.80 88.67 5.58 wine 92.26 86.52 91.49 zoo 88.14 84.31 89.87 the kmc and the ca. the accuracy is the mean of 50 tests. overall, our model achieved better classification accuracy than the two contrastive methods. the superiority of our model can be explained as follows: our model consists of both classification layer neurons and m-p neurons. from the perspective of covering, the results of immune clustering are adopted to partition the sample space, giving a clear meaning to the hidden layer neurons and making it easy to extract the rule knowledge. the whole sample space is completely divided by all hidden layer neurons. the number of neurons equals the number of immune cluster centers. each classification neuron is described by two parameters (a,r) and its class, where a and r are the feature vector and radius of hidden layer neurons, respectively. furthermore, the m-p layer serves as the output layer, and the hidden layer neurons are mapped to the output layer according to the relationship between them and the input features. in this way, our model outputs the classes of the original data. 6 conclusion to improve the classification results of neural networks, this paper puts forward a novel neural network classification model and its algorithm based on the ca and the ica. the data samples were preprocessed by the ica to determine the covering center according to the antibody-antigen affinity. then, the ca was introduced to automatically determine the center and radius of the covering. then, these parameters were converted into the weights and thresholds of the hidden layer of the neural network. the number of hidden layer neurons equals the number of clusters. the introduction of the ica solves the problem of the ca in the random selection of initial covering center. experimental results show that the proposed neural network classification model outperformed classic classification methods. further research will integrate the ca and individual neural networks, aiming to further enhance the learning and recognition accuracy of our model in the context of big data. references [1] al-enezi, j. r., abbod, m. f., alsharhan, s. 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(1999); a geometrical representation of mccullochpitts neural model and its applications, ieee transactions on neural networks, 10, 925-929, 1999. https://doi.org/10.15837/ijccc.2020.1.3739 10 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: zhao, f. (2020). a neural network classification model based on covering algorithm and immune clustering algorithm, international journal of computers communications & control, 15(1), 1008, 2020. https://doi.org/10.15837/ijccc.2020.1.3739 international journal of computers communications & control issn 1841-9836, 12(4), 492-502, august 2017. the logistic regression from the viewpoint of the factor space theory q.f. cheng, t.t. wang, s.c. guo, d.y. zhang, k. jing, l. feng, z.f. zhao, p.z. wang qifeng cheng research and development center, china academy of launch vehicle technology, fengtai district, beijing, 100076, p. r. china tiantian wang school of science liaoning technical university, fuxin, 123000, p. r. china sicong guo, peizhuang wang institute of intelligent engineering and mathematics liaoning technical university, fuxin, 123000, p. r. china dayi zhang*, kai jing, liang feng, zhifeng zhao general hospital of coal mining industry group fuxin, fuxin, 123000, p. r. china *corresponding author:18242875460@163.com abstract: logistic regression plays an important role in machine learning. people excitingly use it in conceptual matching yet with some details to be understood further. this paper aims to present a reasonable statement on logistic regression based on fuzzy sets and the factor space theory. an example about breast cancer diagnosis is displayed to show how the factor space theory can be incorporated into the understanding and use of logistic regression. keywords: logistic regression, factor space theory, fuzzy sets, logistic membership function 1 introduction in 1965, zadeh put forward the concept of fuzzy subset which made ordinary subset generalize that the range of grade of membership has been relaxed from binary variables {0,1} to continuous variables [0,1] [23]. today, we are facing the tide of big data, the essential meaning of fuzzy set is shined by its original definition still. along the way, the factor space theory has been established to provide a deeper mathematical foundation for artificial intelligence [13–15]. the factor space theory builds a bridge connecting certainty and uncertainty and the two sides can be transferred to each other by changing the dimension of related factor space [16]. the falling shadow theory [17] based on factor space was developed to compare fuzziness with randomness. no matter randomness or fuzziness, they are both caused by a lack of factors. randomness is a kind of uncertainty, which is caused by the lack of conditional factors for predicating. randomness breaks the law of causality, while probability theory replaces it by a generalized causality law: even though insufficient conditions can not determine whether an event will occur or not, they determine the occurrence of an event with a certain probability. fuzziness is another kind of uncertainty, which is caused by the lack of identifying factors for recognition. fuzziness breaks the law of excluded middle, while fuzzy theory replaces it by a generalized law copyright © 2006-2017 by ccc publications the logistic regression from the viewpoint of the factor space theory 493 of excluded middle: even though insufficient identifying factors can not determine if an object conforms a concept, they identify an object to a concept with a certain membership degree. an important relationship between fuzziness and randomness is emerged from the similarity: the fuzziness phenomenon on the ground, the universe u, can be described as a randomness phenomenon on the sky, the power of u [22]. subjective reliability is the non-additive measure since they are the fallen of probability from sky to ground, this is the core idea of the falling shadow theory. logistic regression analysis (lra) is a simple but effective method which is widely used in classification, extending the techniques of multiple regression analysis to research situations in which the outcome variable is categorical [3]. in stratification research, demographic research and social medicine, the use of logistic regression is routine [8]. some related works have been done on logistic regression. a forward stepwise logistic regression analysis is conducted and the results show that the peer ratings of collaboration can predict you belong to learning problem group or not learning problem group [10]. split-sample, cross-validation and bootstrapping methods for estimation of internal validity of a logistic regression model are compared, which ends up with the recommendation of bootstrapping [12]. the number of events per variable (epv) in lra has influences on the validity of the model, and it turns out that low epv plays a major role [9]. yet these works only focus on the strategic level of logistic regression, deep understanding hidden behind this common model should be mined. in artificial intelligence, the regressed curves can be regarded as membership functions. the selection of the types of regressed curves decides the quality of curve fitting partially. logistic regression is fitted by the logistic membership function. in this article, we will expand the discussion about logistic regression based on factor space theory and fuzzy sets which can present a relatively different state from before. the paper is proceeded as follows. we introduce the types of membership functions in section 2. the logistic membership function is put forward in section 3. in section 4, logistic regression based on the factor space theory is discussed further and an algorithm is proposed accordingly. in section 5, an example is used to identify the effectiveness of the proposed algorithm. finally, a brief conclusion is made in section 6. 2 type of membership functions definition 1. a fuzzy subset a defined on universe of discussion u is a mapping µa: u→[0,1], µa(u) is called the membership degree of u with respect to a [23]. from definition 1, two fundamental meanings are revealed: firstly, a fuzzy subset stands for the extension of a fuzzy concept, it is a milestone of intelligence mathematics; secondly, a fuzzy subset builds a bridge to step over the gap between quantitative and qualitative phenomena, it was the main bottleneck of information revolution. while the falling shadow theory can provide a deeper mathematical foundation for the revolutionary change [5,6,18]. definitions 2, 3 and 4 are listed to show how the falling shadow of a random subset is generated. definition 2. let (u,b) be a measurable space. (p(u),b − ) is called a super-measurable space defined on (u,b) if (p(u),b − ) is a measurable space. definition 3. given a probabilistic field (ω,f,p) and a super-measurable space (p(u),b − ) on (u,b). a mapping ξ: ω → p(u) is called a random set if ξ−1(a) = {ω ∈ ω | ξ(ω) ∈ a} ∈ f whenever a ∈ b − . 494q.f. cheng, t.t. wang, s.c. guo, d.y. zhang, k. jing, l. feng, z.f. zhao, p.z. wang a probability distribution can be induced on b − from p on f through the mapping ξ, p(a) = p(ξ−1(a)), a ∈ b − , which makes (p(u),b − ) a super probabilistic field [19]. definition 4. given a super probabilistic field (p(u),b − ) and a random set ξ : ω → p(u), we call a fuzzy subset aξ on u the falling shadow of ξ if µaξ(u) = p ( ω | u ∈ ξ(ω) ) for all u ∈ u. it is revealed in definition 4 that µaξ can be viewed as the covering function of a random set ξ on u. the random set is called clouds, and the fuzzy set is called the falling shadow of the clouds. the thicker the cloud, the higher the darkness of the shadow of the cloud [19]. meanwhile, according to definition 1, µaξ is also the membership function with respect to fuzzy subset aξ. definition 5. a membership function µaξ is also called the possibility distribution of a concept aξ on u . possibility varies from probability. according to definition 5, possibility stands for the covering chance of ξ to u and it does not hold exclusiveness; while probability holds the exclusiveness and stands for the chance of monopolization [20]. theorem 1. let u = (−∞, +∞) be the one dimensional state space. given a random interval r with falling shadow µa, let ζ be the left extreme point of random interval r, then ζ is a random variable defined on (u,b). and the possibility distribution of the concept a is the same as the distribution function f(u) of ζ : µa(u) = f(u) = p(ζ ≤ u). proof: given probabilistic field (ω,f,p), measurable space (u,b) and super-measurable space (p(u),b − ). since r is a random set, then r−1(a) = {ω ∈ ω | r(ω) ∈ a}∈ f whenever a ∈ b − . because ζ is the left extreme point of r, then for each ω ∈ ω, there exists β ∈ b, which makes r(ω) ∈ a =⇒ ζ(ω) ∈ β. since ζ(ω) is a real number on the interval (−∞, +∞), according to the definition of random variable [21], it is obvious that ζ is a random variable. and µa(u) = p { ω | u ∈ r(ω) } = p { ω | ζ(ω) ≤ u } = p(ζ ≤ u). 2 from theorem 1, we can distinguish possibility distribution from probability distribution and combine the two terminologies by means of density function and distributed function respectively. the membership function µa determines the extent that u belongs to a concept a. for one concept, different types of membership functions exhibit different membership degree for a certain point u, making it necessary to clarify which type of membership functions should be chosen accordingly. definition 6. a is a fuzzy subset defined on universe of u whose membership function is µa. if µa(x) > min{µa(a),µa(b)} for any a < x < b, then a is a convex fuzzy subset [4]. the logistic regression from the viewpoint of the factor space theory 495 a convex fuzzy set divides a into five parts with four points l−, l+,u−,u+ (l− ≤ l+ ≤ u− ≤ u+): k = (−∞, l−], l = (l−, l+], t = (l+,u−], u = (u−,u+], v = (u+, +∞). µa ≡ 0 when x ∈ k or x ∈ v, µa ≡ 1 when x ∈ t. (l−,µa(l−)) and (u+,µa(u+)) are the inflection points. definition 7. we call | l | and | u | the lower and upper interim length respectively. even though the shape of membership functions is variable with countless changes, the essential variations focus on the two interim periods. the length of interim reflects the degree of fuzziness with respect to a membership function, the narrower the length of interim, the more precise the representation of a concept. for ease of simplicity, we only discuss the membership function on the left fuzzy interval l formed by the distribution of left extreme point ζ on the interval. there are three common types of probability density functions of ζ: uniform type, cosine type and normal type. due to space limitation, only uniform distribution of ζ is displayed here to show how the membership function is generated. for example, ζ is uniformly distributed on fuzzy segment (l−, l+], the probability density function of ζ is f1(x) = 1 l+ − l− , x ∈ (l−, l+]. according to theorem 1, the membership curve of the concept on this interval is µ1(x) = p1(ζ ≤ x) = x− l− l+ − l− . (1) it is a straight line ranging from 0 to 1 on fuzzy segment (l−, l+]. curve fitting is the foundation of optimization, the quality of fitting depends on whether the curve chosen is appropriate or not. in this article, we introduce another type of membership function which is frequently used in classification but lacking deeper understanding, as well as expanding the discussion on it from the viewpoint of factor space. 3 logistic membership function let γ be the random variable defined on u, indicating attribute x. and let y be the indication variable of concept α with the extension a defined on u, which takes value 1 when u ∈ a and 0 else. denote px = p{y = 1|γ = x}, which is the possibility distribution of the concept a with respect to the variable x. to estimate the possibility, use the maximal likelihood principle. consider a series of sampling points (x1,y1), · · · , (xm,ym), the likelihood function is as follows: l = m∏ i=1 pyixi (1 −pxi) 1−yi. (2) it is not easy to calculate the derivative, put logarithm on it and get the new likelihood 496q.f. cheng, t.t. wang, s.c. guo, d.y. zhang, k. jing, l. feng, z.f. zhao, p.z. wang function: l = ln m∏ i=1 pyixi (1 −pxi) 1−yi = m∑ i=1 ( yilnpxi + (1 −yi)ln(1 −pxi) ) = m∑ i=1 ( yi(lnpxi − ln(1 −pxi)) ) + m∑ i=1 ln(1 −pxi) = m∑ i=1 (yiln pxi 1 −pxi ) + m∑ i=1 ln(1 −pxi). (3) since ln px 1−px varies on the interval [0, +∞), also being related to the attribute x, we define ln px 1−px = ax + b, then px = 1 1+e−(ax+b) . and equation (3) can be transferred into l = m∑ i=1 (yi(axi + b)) − m∑ i=1 ln(1 + eaxi+b). (4) maximize l so that a and b can be achieved. this is an optimization problem and some strategies solving this type of problems such as gradient descent [?] can be applied. definition 8. the logistic regression function is defined as: φ(x; a,b) = 1 1 + e−(a >x+b) x,a ∈ rn, a>x = a1x1 + · · · + anxn. when n = 1, φ(x; a,b) = 11+e−(ax+b) ; φ(x; 1, 0) = 1 1+e−x . it is obvious that this method of estimating a and b is kind of complex, while simplicity is what we pursue eventually. another method which can transfer the problem into a linear problem will be introduced in the next section. since pa(x) = 11+e−(ax+b) is central symmetric with respect to the point (− b a , 1 2 ), we can get p¬a(x) = pa(− 2b a −x) = 1 1 + e−(a(− 2b a −x)+b) = 1 1 + eax+b . and it is easy to know pa(x) + p¬a(x) = 1 1 + e−(ax+b) + 1 1 + eax+b = 1. with this property, the logistic regression function p(x) = 1 1+e−(ax+b) can be seen as the logistic membership function. and this property is available among another three types of membership functions referred in section 2, the details of which are omitted here. definition 9. we call p(x) = 1 1+e−a(x−x) the logistic interim function. 3.1 logistic regression in risk attributable factor space the factor space theory paves the way for logistic regression since some concepts find their footholds in the factor space, which contributes to the better understanding of logistic regression. the logistic regression from the viewpoint of the factor space theory 497 definition 10. a factor space defined on universe of discussion u is a family of set ψ =( {x(f)}(f∈f); u ) satisfying: (1) f = (f, ∨ , ∧ ,c ,1,0) is a complete boolean algebra; (2) x(0) = {∅}; (3) for any t ⊆ f , if {f|f ∈ t} are irreducible (i.e.,s 6= t ⇒ s ∧ t = 0 (s,t ∈ t)), then x({f|f ∈ t}) = ∏ f∈t x(f) ( ∏ stands for cartesian product) (4) ∀f ∈ f , there is a mapping with same symbol f : u → x(f). f is called the set of factors, f ∈ f is called a factor on u. x(f) is called the state space of factor f [22]. denote ij = x(fj) and i = i1 ×···× in. definition 11. given an attribute space o = {oi1···in|ij ∈ ij,j = 1, · · · ,n}) in factor space ψ = ({x(f)}(f∈f={f1,··· ,fn}); u). for each ij ∈ ij, oi1···in is called a granule of x(f) [5]. denote that qi1···in = p { u|f(u) = oi1···in } , p = {qi1···in|ij ∈ ij,j = 1, · · · ,n} is called the probability distribution of attributes. assumption 1. x(f1),x(f2) · · · ,x(fn) are all partial ordered sets, it means for one attribute fi whose range of values is {ij1, ij2, · · · , ijr}, ij1 ≤ ij2 ≤ ··· ≤ ijr is always correct. it should be clear that if there exists two granules o1···1···1 and o1···3···1, granule o1···2···1 is anyhow certain to appear. then we naturally suppose that p is convex. a convex probability distribution of attributes p is called the background distribution with respect to universe u [7]. definition 12. a factor space ψ̃ = ( {x(f)} (f∈f̃); u ) is called a risk attribute factor space if f̃ = {f1, · · · ,fn; fn+1}, where f1, · · · ,fn stand for attribute factors and fn+1 stands for a risk factor with binary attribute space x(fn+1) = {1, 0}. given a group of sampling points on i ×{1, 0}: s = {(x1i, · · · ,xni; yi)}(i=1,··· ,m). for ( i1, · · · , in; in+1 ) ∈ i ×{1, 0}, denote qi1···inin+1 = | {t | x1t = i1, · · · ,xnt = in; yt = in+1} | m . (5) it is obvious that ∑ i1 · · · ∑ in ∑ in+1 qi1···inin+1 = 1. definition 13. q̃ = {i1, · · · , in; in+1 ∈ i ×{1, 0} | qi1···inin+1 > 0} is the support set of q = {qi1···inin+1}(i1,··· ,in;in+1)∈i×{1,0}. 498q.f. cheng, t.t. wang, s.c. guo, d.y. zhang, k. jing, l. feng, z.f. zhao, p.z. wang in the risk attribute factor space ψ̃, logistic regression is mainly based on the support set q̃. while the condition that qi1···inin+1 = 0 still exists and transformation used to handle it will be discussed in the final part of this section. also, the definition of the logistic membership function fitted out through logistic regression get updated in ψ̃. definition 14. the membership function of illness α: p(x) = 1 1 + e−(θ0+θ1x1+···+θnxn) = 1 1 + e−θ >x , where θ = (θ0,θ1, · · · ,θn)>, x = (1,x1, · · · ,xn)>. the parameters θ1, · · · ,θn are called risk attribute coefficients of factors. the bigger the coefficient θj, the more important the factor fj on risk-increasing. definition 15. let α be a concept with extension a ⊆ u and intension o ⊆ x(f), we call conditional probability p{u ∈ a|f(u) = oi1···in} the possibility of concept α under oi1···in. remark 2. the difference between possibility and probability is clear again. since possibility is equal to the membership degree of the factorial configuration with respect to concept α, the logistic membership function of illness α is pi1···in = p{u ∈ a|f(u) = ai1···in} = | {t | x1t = i1, · · · ,xnt = in; yt = 1} | | {t | x1t = i1, · · · ,xnt = in} | . (6) also, the probability corresponding to possibility pi1···in is qi1···in,in+1=1 = p{u ∈ a|f(u) = oi1···in}p{f(u) = oi1···in} = | {t | x1t = i1, · · · ,xnt = in; yt = 1} | m . (7) it is obvious that ∑ pi1···in 6= ∑ qi1···in,in+1=1. (8) then the approach to estimate θ through logistic regression is developed as algorithm 1, and the membership degree for the unknown samples can be calculated. algorithm 1 logistic regression algorithm 1: given sample points s = {(x1i, · · · ,xni; yi)} (i = 1, · · · ,m). 2: for | {t | x1t = i1, · · · ,xnt = in} | > 0 (i1 ∈ i1, · · · , in ∈ in) : 3: if | {t | yt = 1} |> 0 and | {t | yt = 0} |> 0, then 4: calculate pi1···in 5: let yi1···in = ln pi1···in 1−pi1···in 6: else 7: yi1···in = ln |{t|yt=1}|+0.5 |{t|yt=0}|+0.5 [1] 8: end if 9: set yi1···in = θ0 + θ1i1 + · · · + θnin, do linear regression to get the coefficients θ0,θ1, · · · ,θn. the logistic regression from the viewpoint of the factor space theory 499 4 example to show how the factor space theory is imbedded into logistic regression, in this section, an example calculating the membership degree of breast cancer using algorithm 1 is considered. the data extracted from [2] are shown in table 1 and table 2. there are 1896 samples in total which form the universe of discussion u. four attribute factors f1,f2,f3,f4 are considered and each factor is a categorical variable whose set of value is i1 = {0, 1, 2},i2 = {0, 1, 2, 3},i3 = {0, 1},i4 = {0, 1, 2} respectively. the meaning of these coded numbers are shown in table 4. then the factor space ψ = ( {x(f)}(f∈f=f1,f2,f3,f4); u ) involving 72 different granules oi1···i4 of x(f) can be established. the risk attribute factor space ψ̃ = ( {x(f)} (f∈f̃=f1,f2,f3,f4;f5); u ) is also confirmed, where x(f5) = {1, 0}. the data must be processed before using algorithm 1. for the background distribution p, there are some granules whose qi1···i4 = 0 which means no samples are included when f1 = i1, · · · ,f4 = i4, then those granules should be deleted. for some granules, the samples are very small and there is little meaning to consider them, so the granules whose total samples are less than 5 are deleted. hence 33 granules remain and the samples decrease to 1837. algorithm 1 is applied to the 1837 samples and we can get the membership function of breast cancer as follows: p(x) = 1 1 + e−(0.19x1+0.24x1+0.36x3+0.80x4−0.75) . (9) equation (9) represents a hyperplane in five-dimensional space. although the interim still exists, we ignore it on account of the complexity. the estimated membership degree for each granule can be calculated. table 5 depicts the real possibility pi1···i4, the calculated possibility p(xi) and probability qi1···i4,i5=1. then equation (8) can be verified easily:∑ pi1···i4 = 18.5227, ∑ qi1···i4,i5=1 = 0.4899. in table 5, the rows that the difference between pi1···i4 and p(xi) are over 0.2 are marked in red, only four granules 13th, 16th, 21st and 32nd are included. since membership degree is estimated through linear regression, it is necessary to compare yi1···i4 and ln p(xi) 1−p(xi) , figure 1 shows the differences of their values. the circles represent the real differences between yi1···i4 and ln p(xi) 1−p(xi) . since least-square estimation is applied here, it is necessary to do parameter test. the vertical line segments represent the 95% confidence intervals of random residuals. it is obvious that the confidence intervals of the 16th, 21st and 32nd cases don’t cover 0, yet not far away from 0. errors from different angles indicate that logistic regression is applicable in this example. from equation (9), we know that θi (i = 1, 2, 3, 4) is 0.19, 0.24, 0.36, 0.80 accordingly. θi (i = 1, 2, 3, 4) are all positive numbers which means the risk of having breast cancer will grow when the coded number of factor i gets larger. this is in accordance with the existing knowledge: the risk of having breast cancer will become larger if girls get their first period at a younger age; nulliparous women and those who give the first birth when they are older will have higher risks of having breast cancer; doing previous breast biopsies1 means you are suspected of having breast cancer, the more you do, the larger the chance of being suspected will be; the risk of having breast cancer will grow if the number of people with breast cancer in near relations goes up. this conclusion verifies the efficiency of the logistic regression from another perspective. 1a biopsy is a medical test commonly performed by a surgeon, interventional radiologist, or an interventional cardiologist involving extraction of sample cells or tissues for examination to determine the presence or extent of a disease. 500q.f. cheng, t.t. wang, s.c. guo, d.y. zhang, k. jing, l. feng, z.f. zhao, p.z. wang 5 conclusions the possibility in logistic regression is equal to the membership degree in factor space. connection between the two sides is established through the membership function. the paper shows that the factor space theory gives logistic regression a relatively different state. meanwhile, logistic regression forms another foothold of the factor space theory in the big data era. this is of great meaning since it gives us the reason and motivation to explore the factor space theory deeply. acknowledgements this work is supported in part by natural science foundation of liaoning province, china [2015020570] and national natural science foundation (nnsf) of china under grant [61304090]. bibliography [1] bao y.k (2011); data analysis, tsinghua university press, 2011. 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(1993); win-win strategy for probability and fuzzy mathematics, the journal of fuzzy mathematics, 1(1), 223-231, 1993. [21] wang p.z. (2013); fuzzy mathematics and optimization,beijing normal university press, 2013. [22] wang p.z, liu z.l, shi y, guo s.c. (2014); factor space, the theoretical base of data science, annals of data science, 1(2), 233-251, 2014. [23] zadeh l.a. (1965); fuzzy subsets, information and control, 8, 338-353, 1965. 502q.f. cheng, t.t. wang, s.c. guo, d.y. zhang, k. jing, l. feng, z.f. zhao, p.z. wang appendix table 1: detailed distribution of cases and their matched controls in all strata defined by crossclassifying the four risk factors risk factor category age mothers age at at first previous plus sisters no. no. menarche live breast with breast of of birth biopsies cancer cases controls 0 0 0 0 14 27 0 0 0 1 3 1 0 0 0 2 0 0 0 0 1 0 3 1 0 0 1 1 1 0 0 0 1 2 0 0 0 1 0 0 54 85 0 1 0 1 20 12 0 1 0 2 1 1 0 1 1 0 5 5 0 1 1 1 2 0 0 1 1 2 0 0 0 2 0 0 81 100 0 2 0 1 18 20 0 2 0 2 3 0 0 2 1 0 7 12 0 2 1 1 4 2 0 2 1 2 0 0 0 3 0 0 27 14 0 3 0 1 12 7 0 3 0 2 1 0 0 3 1 0 0 2 0 3 1 1 1 0 0 3 1 2 1 0 1 0 0 0 27 56 1 0 0 1 8 7 1 0 0 2 1 0 1 0 1 0 1 4 1 0 1 1 0 0 1 0 1 2 0 0 1 1 0 0 112 173 1 1 0 1 27 12 the logistic regression from the viewpoint of the factor space theory 503 table 2: table 1 continued risk factor category age mothers age at at first previous plus sisters no. no. menarche live breast with breast of of birth biopsies cancer cases controls 1 1 0 2 4 0 1 1 1 0 14 4 1 1 1 1 1 2 1 1 1 2 0 0 1 2 0 0 187 174 1 2 0 1 41 20 1 2 0 2 10 1 1 2 1 0 11 10 1 2 1 1 5 0 1 2 1 2 0 1 1 3 0 0 41 47 1 3 0 1 15 5 1 3 0 2 4 0 1 3 1 0 4 5 1 3 1 1 1 0 1 3 1 2 1 0 2 0 0 0 9 15 2 0 0 1 3 2 2 0 0 2 2 0 2 0 1 0 1 1 2 0 1 1 0 0 2 0 1 2 0 0 2 1 0 0 43 44 2 1 0 1 14 5 2 1 0 2 1 0 2 1 1 0 3 2 2 1 1 1 2 0 2 1 1 2 0 0 2 2 0 0 53 52 2 2 0 1 9 8 2 2 0 2 2 0 504q.f. cheng, t.t. wang, s.c. guo, d.y. zhang, k. jing, l. feng, z.f. zhao, p.z. wang table 3: table 2 continued risk factor category age mothers age at at first previous plus sisters no. no. menarche live breast with breast of of birth biopsies cancer cases controls 2 2 1 0 3 1 2 2 1 1 2 1 2 2 1 2 0 0 2 3 0 0 17 4 2 3 0 1 4 3 2 3 0 2 1 0 2 3 1 0 3 0 2 3 1 1 3 0 2 3 1 2 0 0 table 4: levels of the risk factors risk factor range coding age at menarche < 12 2 12-13 1 ≥ 14 0 age at first live birth < 20 0 20-24 1 25-29(or nulliparous) 2 ≥ 30 3 no. of previous 0 or 1 0 breast biopsies ≥ 1 1 no. of mothers 0 0 plus sisters 1 1 with breast cancer ≥ 2 2 the logistic regression from the viewpoint of the factor space theory 505 table 5: pi1···i4, p(xi) and qi1···i4,i5=1 for 33 granules risk factor category age mothers case age at at first previous plus sisters pi1···i4 p(xi) qi1···i4i5=1 number menarche live breast with breast birth biopsies cancer 1 0 0 0 0 0.3415 0.3207 0.0076 2 0 1 0 0 0.3885 0.3739 0.0294 3 0 1 0 1 0.6250 0.5711 0.0109 4 0 1 1 0 0.5000 0.4624 0.0027 5 0 2 0 0 0.4475 0.4304 0.0441 6 0 2 0 1 0.4737 0.6275 0.0098 7 0 2 1 0 0.3684 0.5211 0.0038 8 0 2 1 1 0.6667 0.7081 0.0022 9 0 3 0 0 0.6585 0.4887 0.0147 10 0 3 0 1 0.6316 0.6806 0.0065 11 1 0 0 0 0.3253 0.3640 0.0147 12 1 0 0 1 0.5333 0.5606 0.0044 13 1 0 1 0 0.2000 0.4519 0.0005 14 1 1 0 0 0.3930 0.4200 0.0610 15 1 1 0 1 0.6923 0.6175 0.0147 16 1 1 1 0 0.7778 0.5105 0.0076 17 1 2 0 0 0.5180 0.4781 0.1018 18 1 2 0 1 0.6721 0.6713 0.0223 19 1 2 0 2 0.9091 0.8199 0.0054 20 1 2 1 0 0.5238 0.5689 0.0060 21 1 2 1 1 1.0000 0.7463 0.0027 22 1 3 0 0 0.4659 0.5368 0.0223 23 1 3 0 1 0.7500 0.7210 0.0082 24 1 3 1 0 0.4444 0.6254 0.0022 25 2 0 0 0 0.3750 0.4097 0.0049 26 2 0 0 1 0.6000 0.6074 0.0016 27 2 1 0 0 0.4943 0.4675 0.0234 28 2 1 0 1 0.7368 0.6619 0.0076 29 2 1 1 0 0.6000 0.5585 0.0016 30 2 2 0 0 0.5048 0.5263 0.0289 31 2 2 0 1 0.5294 0.7124 0.0049 32 2 3 0 0 0.8095 0.5843 0.0093 33 2 3 0 1 0.5714 0.7580 0.0022 506q.f. cheng, t.t. wang, s.c. guo, d.y. zhang, k. jing, l. feng, z.f. zhao, p.z. wang 5 10 15 20 25 30 −1.5 −1 −0.5 0 0.5 1 1.5 2 residual case order plot r e si d u a ls case number figure 1: the differences between yi1···i4 and ln p(xi) 1−p(xi) 1yager.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):771-771, december, 2015. foreword r.r. yager ronald r. yager1 iona college, new rochelle, ny 10801, usa, e-mail: yager@panix.com. here we are celebrating the fiftieth anniversary of lotfi zadeh’s pioneering paper fuzzy sets that appeared in information and control in 1965. while the paper was clear, direct and easy to understand the ideas presented were revolutionary and ground breaking. this article now has close to sixty thousand citations as noted in google scholar. clarity and simplicity are the hallmark of the writing of zadeh. this has always reminded me of the writings of sigmund freud. one rarely needs to draw on complex mathematics to read zadeh’s papers. the capacity of fuzzy sets to represent and manage imprecise linguistic concepts has proven to be of great use in the modern technological world where there is now a great interest in building intelligent systems that can model human reasoning but take advantage of the vast amount of information available on the internet. if the idea of fuzzy sets was not introduced in zadeh’s ground breaking paper in the 1960’s it would have naturally arisen in early 2000’s as we moved into intelligent systems. however the early reception of fuzzy sets was not very promising both the artificial intelligence community and the probabilistic community were very dubious of the worth of this new field. interestingly a number of researchers from romania, the home of this journal, were among the early supporters of ideas presented by zadeh. zadeh persevered in the face of adversity, describing himself as thick skinned, until mid 1980’s when the japanese engineers provided significant applications of fuzzy sets in control systems. particularly notable among these applications was the use of fuzzy control to the sendai subway. these applications brought a new appreciation to the possibilities of fuzzy sets and clearly changed its history. the editors of this special issue of the international journal of computers communications & control dedicated to the 50th anniversary of the publication of lotfi zadeh’s pioneering paper fuzzy sets have provided a collection of papers representative of the current state of the field of fuzzy sets. included in this issue are papers investigating some current theoretical issues and applied papers in domains in which fuzzy sets has introduced some benefits. the editorial team is to be congratulated for providing a wonderful anniversary gift to professor zadeh and a useful collection of articles for the community. ronald r. yager new york, october 2015 1 editor’s note: "ronald r. yager is professor of information systems and director of the machine intelligence institute at iona college. he is among the world’s most highly cited researchers with over 47,000 citations (updated by editor) to his work in google scholar. he is editor and chief of the international journal of intelligent systems and serves on the editorial board of numerous journals. he has published over 500 papers and edited over 30 books in areas related to fuzzy sets, human behavioral modeling, decision-making under uncertainty and the fusion of information. he was the recipient of the ieee computational intelligence society pioneer award in fuzzy systems. he received the special honorary medal of the 50-th anniversary of the polish academy of sciences. he received the lifetime outstanding achievement award from international the fuzzy systems association. he received honorary doctorate degrees, honoris causa, from the azerbaijan technical university and the state university of information technologies, sofia bulgaria. dr. yager is a fellow of the ieee, the new york academy of sciences and the fuzzy systems association. he has served at the national science foundation as program director in the information sciences program. he was a nasa/stanford visiting fellow and a research associate at the university of california, berkeley. he has been a lecturer at nato advanced study institutes. he received his undergraduate degree from the city college of new york and his ph. d. from the polytechnic institute new york university." source: http://www.iona.edu retrieved on 2th october 2015. copyright © 2006-2015 by ccc publications international journal of computers communications & control issn 1841-9836, 12(4), 562-576, august 2017. coverage hole recovery algorithm based on molecule model in heterogeneous wsns x.l. song, y.z. gong, d.h. jin, q.y. li, h.c. jing xiaoli song* 1. state key laboratory of networking and switching technology, beijing university of posts and telecommunications, beijing 100876, china 2. henan university of science and technology, luoyang 471023, china *corresponding author: song_xiaoli@163.com yunzhan gong, dahai jin state key laboratory of networking and switching technology, beijing university of posts and telecommunications, beijing 100876, china qiangyi li, hengchang jing henan university of science and technology, luoyang 471023, china abstract: in diverse application fields, the increasing requisitions of wireless sensor networks (wsns) have more and more research dedicated to the question of sensor nodes’ deployment in recent years. for deployment of sensor nodes, some key points that should be taken into consideration are the coverage area to be monitored, energy consumed of nodes, connectivity, amount of deployed sensors and lifetime of the wsns. this paper analyzes the wireless sensor network nodes deployment optimization problem. wireless sensor nodes deployment determines the nodes’ capability and lifetime. for node deployment in heterogeneous sensor networks based on different probability sensing models of heterogeneous nodes, the author refers to the organic small molecule model and proposes a molecule sensing model of heterogeneous nodes in this paper. dsmt is an extension of the classical theory of evidence, which can combine with any type of trust function of an independent source, mainly concentrating on combined uncertainty, high conflict, and inaccurate source of evidence. referring to the data fusion model, the changes in the network coverage ratio after using the new sensing model and data fusion algorithm are studied. according to the research results, the nodes deployment scheme of heterogeneous sensor networks based on the organic small molecule model is proposed in this paper. the simulation model is established by matlab software. the simulation results show that the effectiveness of the algorithm, the network coverage, and detection efficiency of nodes are improved, the lifetime of the network is prolonged, energy consumption and the number of deployment nodes are reduced, and the scope of perceiving is expanded. as a result, the coverage hole recovery algorithm can improve the detection performance of the network in the initial deployment phase and coverage hole recovery phase. keywords: coverage hole recovery algorithm, molecule model, data fusion, heterogeneous wireless sensor network 1 introduction the past few years have witnessed the development of wireless sensor technology and manufacturing microelectronic technology. wireless sensor networks consist of a large number of micro sensor nodes which have perception, calculation, and communication abilities that are applied to the military or civilian areas, such as environmental monitoring, industrial control, battlefield surveillance, detection of high-risk environments, biological medicine, intelligent household, health monitoring and forecast systems [24,25]. copyright © 2006-2017 by ccc publications coverage hole recovery algorithm based on molecule model in heterogeneous wsns 563 wsns are composed of a large number of autonomous nodes which are densely deployed in the target region using a multi-hop, wireless communication mode, and self-organizing, largescale high density wireless sensor network system organization. the effective deployment of nodes in a wireless sensor network is an important prerequisite for the normal operation of the wsns [1,16]. underwater wireless sensor network (uwsn) is widely applied to information and data collection in many fields such as marine pollution monitoring, marine disaster prevention, underwater aided navigation, marine resources exploration, battlefield surveillance, mine detection, and underwater target detection, tracking and positioning. thus uwsn causes intensive attention and has been one of the current research hotspots [1, 24, 25]. a large amount of research has been conducted in many applications, for example, goals locating and tracking et al, referring to uwsns and communication protocols [15–17]. however, in view of deployment optimization research on the underwater wireless sensor nodes, the progress is still slow [9,21,22]. compared with onshore wireless sensor nodes deployment, deployment researches on underwater wireless sensor nodes have more particularities, as the underwater environment is relatively complicated and the mobile wireless sensor nodes can move with water flows frequently. therefore, it’s essential to enhance the studies on how to adjust the position of the underwater wireless sensor nodes to improve the monitoring effect, according to the changeable underwater environment and considering the mobility of monitored targets [8,12,30]. the coverage problem is an essential problem in nodes deployment of wireless sensor network; under the condition that the residual energy and ability of perception, communication, and computing of sensor node are limited, using a certain strategy combination of software and hardware, which can ensure the coverage area, network lifetime and good connectivity, thus realize effective awareness and monitoring, is an important indicator of network perception service quality, and is also a hot topic in sensor network research [15–17,21,22]. in many applications, there are usually a variety of monitoring objects in the surveillant area. for example, the sensor nodes need to monitor water temperature and salinity in water environmental monitoring, or the sensor nodes need to surveil a variety of chemical diffusions to provide early warnings about factory pollution [3,9,10,12,30]. since the hardware cost of abundant sensor nodes is higher currently, in multi-object monitoring applications, each node assembles a variety of different types of sensors; these nodes are heterogeneous. for the limitation of node energy, the more sensors are assembled on a node, the shorter lifetime of the node. two important problems should be considered in multiple object monitoring network coverage, namely how to use less costs to obtain the ideal network coverage performance, and how to weigh the importance of network monitoring of different objects according to different objectives [8,14,23,29]. heterogeneous characteristics of heterogeneous wireless sensor networks give expression to three aspects: node heterogeneity, link heterogeneity, and network protocol heterogeneity [13,19, 20]. node heterogeneity includes perception ability, calculation ability, communication ability, energy, etc. the aspects of communication ability, perception ability, and residual energy have the greatest influence on the coverage. there has been little research to date on the coverage problem of the random deployment of heterogeneous wireless sensor networks. a coverage optimization algorithm of wireless sensor network is presented to perceive the radius of the heterogeneous networks, which can effectively improve the coverage ratio in [26]. a multi-objective evolutionary algorithm based on integer vector planning is proposed in [2], which can effectively solve the problem of multiple target monitoring. both of the two perception models which are used in [5] and [31] are relatively simple. a routing protocol-based evolutionary algorithm is proposed for the heterogeneous cluster node of wsns in literature [11], which can effectively reduce the errors of cluster nodes while 564 x.l. song, y.z. gong, d.h. jin, q.y. li, h.c. jing handling data combination and separation, and prolong the survival time of a network, but does not give the algorithm of non-cluster nodes. an efficient dynamic clustering strategy (edcs) is put forward in literature [4], which can effectively solve the selection problem of the multilevel heterogeneous network cluster node, effectively improve the performance of a network and prolong the survival time of a network, without giving a specific node deployment algorithm, however. a new idea is proposed by increasing heterogeneous nodes to prolong the survival of wireless sensor network in literature [18], but the heterogeneous node perception problem is not considered. to solve the sensing radius of the heterogeneous network node deployment problem, the expanding-virtual force algorithm (ex-vfa) algorithm is put forward in [28], but the connectivity and data fusion problem is barely considered in ex-vfa. because of the wireless sensor network nodes’ energy depletion or other reasons, the area which is not covered by any node forms “empty holes” in the target field [6]. how to repair the holes is also a hot research topic [7]. to solve the problem of covering blind areas, a node deployment coverage algorithm of heterogeneous sensor networks based on organic small molecules model is proposed in this paper. based on the organic small molecule structure model, a heterogeneous node detection model is established. in order to improve the network coverage, extend the life of the network, and achieve the optimal deployment of sensor nodes to the monitored area, a data fusion model is proposed that combines data fusion and decision rules. randomly deployed mobile nodes will move according to the principle of the “virtual potential field.” the effectiveness of the coverage algorithm is verified by simulation experiments, which can effectively reduce the number of deployment nodes and the node energy consumption, improve the efficiency of the network coverage, prolong the network lifetime, expand the detection range, and improve the detection performance of the network. the energy consumption of the wireless sensor network determines the lifetime of the wireless sensor network directly. reasonably deploying the wireless sensor nodes can improve the coverage effect of the wireless sensor network and reduce the movement distance of the wireless sensor nodes. a nodes deployment algorithm is designed in this article based on perceived probability model of underwater wireless sensor network within the monitored area. the simulation results show that the algorithm in this article is superior to the virtual force algorithm on improving the coverage effect of underwater wireless sensor network and reducing the movement distance of the wireless sensor nodes, which can achieve the goal of wireless sensor network nodes’ reasonable distribution to reduce energy consumption in the initial deployment phase and coverage holes recovery phase. the rest of this paper is organized as follows: the background literatures are introduced in section 1. the assumption and mathematical models for node coverage and perceived are given in section 2. the virtual force algorithm is explained in section 3. our algorithm in this article is presented in section 4. the results are analyzed in section 5. the conclusion and some future perspectives are brought in section 6. 2 related work the sensing range of each wireless sensor node is limited in the monitored area. this requires the reasonable deployment of wireless sensor nodes according to certain algorithms in order to ensure that the entire area can be monitored within the sensing range in the monitored field. the position of wireless sensor nodes after being randomly deployed is uncertain in the coverage problem of wireless sensor network, regarding mobile wireless sensor nodes which can’t satisfy the requirements in many cases. coverage hole recovery algorithm based on molecule model in heterogeneous wsns 565 the mobile wireless sensor nodes move according to targets in order to improve the coverage effect of wireless sensor networks. the movement processes consume a lot of energy, so some reasonable adjustment of the location of wireless sensor nodes and reduction of the movement distance of wireless sensor nodes thereby reduce the consumption energy of wireless sensor networks. 2.1 assumption to simplify the calculation, a quantity n of the same mobile nodes deploy randomly in the monitored region. the mobile wireless sensor node sj owns wireless sensor network id number j. the same wireless sensor nodes in the network own the same sensing radius rs, the same communication radius rc, and rc = 3rs. the wireless sensor node can obtain the location information of itself and its neighbor nodes. the mobile node owns eini initial energy which is sufficient to support the completion of the mobile node position migration process. the mobile node sending 1 byte data consumes es energy and receiving 1 byte data consumes er energy. the mobile node migration of 1 meter consumes em energy. the distance between the node sa and the node sb is d(sa,sb). the time length of the number k movement process is tk. the balance distance between the two wireless sensor nodes of the number k movement process is dk. among them, d1 < d2 < d3 < · · · < dk < · · · < dm. 2.2 coverage model the monitored area is a×b×c pixels, which means that the size of each pixel is ∆x×∆y×∆z. the perceived probability of the i-th pixel perceived by the wireless sensor network is p(pi), when p(pi) ≥ pth (pth is the minimum allowable perceived probability for the wireless sensor network), the pixels can be regarded as perceived by the wireless sensor network. the value of pcov(pi) is used to measure whether the i-th pixel is perceived by the wireless senor node, i.e. pcov(pi) = { 0, if p(pi) < pth 1, if p(pi) ≥ pth (1) the coverage rate rarea is the ratio of the perceived area to the sum of the monitoring area, which is defined in this article, i.e. rarea = parea sarea = ∆x× ∆y × ∆z × ∑a x=1 ∑b y=1 ∑c z=1 pcov(pi) ∆x× ∆y × ∆z ×a×b ×c (2) among them, parea is the perceived area while sarea is the sum of the monitoring area. 2.3 perceived model this article defines the event rij as the point whereby the i-th pixel pi is perceived by the wireless sensor node with id number j. the probability of occurrence of the event is p(rij), 566 x.l. song, y.z. gong, d.h. jin, q.y. li, h.c. jing which is equal to the perceived probability p(pi,sj), namely, the pixel pi is perceived by the wireless sensor node sj, i.e. p(pi,sj) =   1, if d(pi,sj) ≤ rs −re ln{1 − e− 1 2re [d(pi,sj) −rs −re]}, if rs −re < d(pi,sj) < rs + re 0, if d(pi,sj) ≥ rs + re (3) where: d(pi,sj) is the distance between the i-th pixel pi and the j-th wireless sensor node sj; rs is the sensing radius of the k-th type wireless sensor node; and re is the perceived error range of the k-th type wireless sensor node. the cooperative sensing monitoring method between lots of wireless sensor nodes is used in this research and the collaborate perceived probability of pixel pi being perceived by any of underwater wireless sensor nodes is shown as: p(pi) = 1 − n∏ j=1 [1 −p(pi,sj)] (4) 3 virtual force algorithm 3.1 virtual force algorithm assume that the wireless sensor nodes are the particulates in the electric field and that an electric force between the wireless sensor nodes exists and moves the wireless sensor nodes under the action of the electric force as evenly as possible in order to achieve a reasonable distribution of underwater wireless sensor networks, thus improving the coverage effect of the targets in the monitoring area. if the distance between the two wireless sensor nodes is too far, the attractive force will play a major role and bring the two nodes close to each other. if the distance between the two wireless sensor nodes is too close, the repulsion force will play a major role and make the two nodes separate from each other. calculating the distance d(si,sj) between the two nodes si and sj, which determines how the mobile wireless sensor nodes should move. the repulsion f(pi,sj) of wireless sensor node si to wireless sensor node sj can be represented as: f(si,sj) =   k1 d(si,sj)α1 , 0 < d(si,sj) < r 0, d(si,sj) ≥ r (5) where: k1, α1 is the gain coefficient, d(si,sj) is the distance between the two nodes si and sj, and r is the effective distance. the direction of the force is composed of wireless sensor node si to the wireless sensor node sj. after decomposition, the component along the x-axis direction can resolve to fx(si,sj), the component along the y-axis direction can obtain fy(si,sj), and the component along the z-axis direction is found to be fz(si,sj). so the total value of the force along the x-axis direction is fx(sj) = n∑ i=1 fx(si,sj), the sum of the force along the y-axis direction is fy(sj) = n∑ i=1 fy(pi,sj), and the sum of the force along the coverage hole recovery algorithm based on molecule model in heterogeneous wsns 567 z-axis direction is fz(sj) = n∑ i=1 fz(pi,sj). thus the resulting force from the circular monitoring area whose center is located in the k-th type wireless sensor nodes sj with the radius rk is: fxyz(sj) = √ f 2x (sj) + f 2 y (sj) + f 2 z (sj) (6) after force calculations according to the sum of the force, the wireless sensor node sj moves to the new location (xnew,ynew,znew) from the original location (xold,yold,zold): xnew =   xold, |fxyz(sj)| ≤ fth xold + fx(sj) fxyz(sj) ×maxstep×e−f −1 xyz(sj), |fxyz(sj)| > fth (7) ynew =   yold, |fxyz(sj)| ≤ fth yold + fy(sj) fxyz(sj) ×maxstep×e−f −1 xyz(sj), |fxyz(sj)| > fth (8) znew =   zold, |fxyz(sj)| ≤ fth zold + fz(sj) fxyz(sj) ×maxstep×e−f −1 xyz(sj), |fxyz(sj)| > fth (9) where, fth is the virtual force threshold. the wireless sensor node need not move when the virtual force which the node received is less than the value. maxstep is the maximum allowable movement distance. the virtual force algorithm is as follows: step 1: each wireless sensor node in the monitoring area broadcasts the information which includes the node id and location information. step 2: the wireless sensor node updates the neighbor table information if the wireless sensor node receives the broadcast information of the neighbor nodes. step 3: formulas (5) and (6) are used to calculate the resultant force f(sj) of wireless sensor node sj. step 4: formulas (7) to (9) are used to calculate the new location where the wireless sensor nodes need to move to. step 5: the movement process won’t proceed if the new location where the wireless sensor node needs to move to is located outside of the monitoring region in which case the wireless sensor node needn’t move, so proceed to step 6; otherwise it is moved to a new location, then go to step 6. step 6: the algorithm stops when it reaches a pre-set cycle number t ; otherwise begin the next movement process, then go to step 1. 3.2 problems in the virtual force algorithm the problem in the virtual force algorithm is the following: randomly deploy 16 wireless sensor nodes in the two-dimensional monitoring area and the result is shown in figure 1 (the nodes are located in center of the circles). according to the virtual force algorithm, all of the circles have to move to the new location; but in fact the red circles don’t need to move because moving the red circles won’t improve the percentage of coverage, but consumes the energy of the wireless sensor nodes. the same problem also exists when the wireless sensor nodes are randomly deployed in threedimensional monitoring areas. so how to solve the problem in the nodes’ density area is discussed in the next section. 568 x.l. song, y.z. gong, d.h. jin, q.y. li, h.c. jing figure 1: sixteen randomly deployed wireless sensor nodes in the monitoring area 4 perception model using molecular structure we assume that the monitoring area h is rectangular, and that n kinds of heterogeneous perception abilities of wireless sensor nodes are randomly distributed within the rectangle h, and also assume that the wireless sensor network has the following properties: (1) heterogeneous perception ability sensor. sensors have different perception abilities; the perception of the radius is different, and the node model perception is the probability perception model. the position of the sensor node i is (xi,yi), the perception distance is ri, and the perceived probability is pi according to the function pi(ri). the typical perception probability model is as follows: p(si,q) =   0, r + re ≤ dip, eir ei e(−λα β+αδ), r −re ≤ dip ≤ r + re, 1, r −re ≥ dip . (10) where p(si,q) is the perception probability of the sensor node si to target node q, dip is euclidean distance between sensor node si and the target node q, re(re < r) is an uncertainty perception measure of the sensors, ei is the initial energy of the sensor nodes, eir is the remaining energy, α = dip − (r − re), λ and β are the attenuation coefficient of perception quality for the things within the scope of sensor nodes monitoring. δ is random number meeting normal distribution, which shows the reality of various interference in the influence of the perceived probability; (2) all sensor nodes have the ability of data fusion, and underwater communications have enough energy to finish self-positioning and can move to the specified location freely; (3) before the deployment algorithm is executed, all nodes have finished self-positioning accurately, and the nodes’ coordinates are known; (4) there are three kinds of working state in node life: dormancy, detection, and communication. the node energy consumption in the communication state is the largest, and is the smallest in a dormant state. the relationship of the energy consumption and the time of the nodes in the communication state is positive. meanwhile, the larger the communication distance, the more energy is consumed. coverage hole recovery algorithm based on molecule model in heterogeneous wsns 569 dsmt is put forward by the french scholar dezert in literature [27], and is later developed jointly by dezert and smarandache as well as other scholars. dsmt is an extension of the classical theory of evidence, but different from basic d-s theory. dsmt can combine with any type of trust function of an independent source, but is mainly concentrated in a combined uncertainty, high conflict, and inaccurate source of evidence. especially when the conflict between sources becomes bigger or the elements are vague or relatively imprecise, dsmt can go beyond the limitations of the d-s theoretical framework to solve complex fusion problems of static or dynamic status. on the basis of the above assumptions and dsmt related theory, we give the following definitions: definition 1: p-reliability coverage: assume that point q exists in the monitoring area h, and that the credibility of the target node at point q in sensor networks, namely, test results p(q) meets p(q) ≥ p (0 < p ≤ 1) (11) definition 2: effective coverage proportion: if the area h that needs to be monitored is two-dimensional, the acreage of h is s(h), the acreage meeting p-reliability coverage is sp(h), and the ratio of sp(h) and s(h) is η = sp(h) s(h) × 100% (12) where η is the effective coverage proportion of the two-dimensional monitoring area h. if the monitoring area h is three-dimensional, its volume is v (h), the volume meeting preliability coverage is vp(h), and the ratio of vp(h) and v (h) is η = vp(h) v (h) × 100% (13) where η is the effective coverage proportion of the three-dimensional monitoring area h. definition 3: effective detection rate: if there are n(h) target nodes in the monitoring area h, in which the na(h) target nodes are effectively monitored by the sensor network, then ϕ = na(h) n(h) × 100% (14) where ϕ is the effective detection rate of the monitoring area h in the sensor network. organic small molecules are the organic compound whose molecular weight is under 1000, such as methane (ch4), ethane (c2h6), ethanol (c2h5oh), benzene (c6h6), etc. the main atoms of small organic molecules are carbon atoms, hydrogen atoms, oxygen atoms, or nitrogen atoms, among others et al. they are grouped together according to certain structures as shown in figure 2. inspired by this, the author combined different structures’ sensor nodes into one sensing unit according to certain rules. the compound nodes having different complementary sensing nodes can effectively expand the scope of perception, improve the efficiency of a single node’s perception, and increase the effective coverage of sensor networks by an appropriate data fusion model. definition 4: perception atoms: assuming there are n sensor nodes in the monitoring area h, including m nodes which have the ability to perceive target nodes alone, the nodes that can monitor the target and transmit data to the cluster head nodes individually are called perception atoms. definition 5: perception molecules: assume that n kinds of sensor nodes are deployed in the monitoring area, and the different kinds of sensor nodes are combined into sensing units in 570 x.l. song, y.z. gong, d.h. jin, q.y. li, h.c. jing figure 2: structure model of methane accordance with the appropriate data fusion model. these are called sensing unit perception molecules. perception molecules are the smallest perception and communication unit. assume that there are n perception atoms in the molecule, each perception atomic data is θ1,θ2, . . . ,θn respectively, and then the perception molecular fusion data source is u = {θ1,θ2, · · · ,θn} (15) considering the perceiving unit model and data fusion model, we put forward a sensor network node deployment algorithm based on a small molecule model as follows: step 1: build an appropriate perception model, and determine the unit (perception molecule) perception range and the position of the atom in every perception unit according to the monitoring and coverage requirements for monitoring area of h. compute the optimal distance between the molecules, and the number of all kinds of sensing nodes. step 2: randomly deploy sensing nodes in the monitoring area h according to the number of all kinds of sensing nodes in step 1. step 3: select a starting node, which usually is a node in vertex position of the monitoring area h. then select the corresponding node closest to the starting node in order to build a sensor molecule, and set its id to 1consequently. step 4: choose a perception node closest to perception molecule 1, and build the second perception molecule according to step 3, and set its id to 2. according to the "molecular force" principle, the force between molecules is associated with the distance between the molecules. assume that the distance between the two perception molecules is d′, the best distance for molecules is di, t is the threshold value; when the distance d′ between perception molecules meets d′ < di − t (16) the intermolecular force is repulsion, and node 2 moves far away from a unit. when the distance d′ between perception molecules meets d′ > di + t (17) the intermolecular force is attraction, and node 2 moves closely to a unit. until the perception molecular distance meets di − t ≤ d′ ≤ di + t (18) node 2 moves completely. step 5: using the greedy algorithm, repeat steps 3 and 4, and adjust the molecular position until the entire molecules are adjusted. coverage hole recovery algorithm based on molecule model in heterogeneous wsns 571 step 6: each perception molecule completes the initialization for data fusion task allocation and data fusion node rotation sequence. consequently, the perception molecular sensor nodes act as data fusion nodes and communication nodes in turn. step 7: the data fusion nodes collect the data of other sensors for data fusion at regular intervals. step 8: the nodes for data fusion send the data to sink nodes, and then specify a data fusion node and broadcast the messages to other nodes according to the data fusion node rotation order. step 9: sensor nodes repeat steps 7 and 8 after receiving the data fusion command. 5 results analysis we simulate our algorithm by matlab and assume that the monitoring area h is square whose side length is 3000m. the aim is to obtain p-reliability coverage in the monitoring area, and p = 0.9. the effects of target distribution and other environmental factors are not considered. we assume that two types of sensors are used in underwater sensor networks: passive sonar sensors and giant magnetic resistance sensors. for passive sonar sensors the effective radius of perception is r = 50m under the premise of probability coverage. according to the character of giant magneto resistance sensors and the magnetic properties of the target node, the perception range of giant magneto resistance sensors is irregular, and can be assumed to be elliptic, with an elliptical long radius ra = 60m and short radius rb = 30m under the condition of meeting the probability coverage. the numbers of two kinds of sensor are deployed by a ratio of 1:4 and the total number is n. the simulation experiments are carried out using the random deployment algorithm and based on the “virtual force” deployment algorithm respectively. these two kinds of algorithm simulation results are compared with the performance of the deployment algorithm in this paper. the higher the network coverage rate and the more effective the detection rate of the target node, the better the performance of network monitoring consequently. in the deployment phase simulate 100 times when n = 100,n = 120,n = 140,n = 160,n = 180,n = 200. the simulation results are averaged and shown in figures 3 to 5. figure 3: percentage of coverage after deployment in different algorithms 572 x.l. song, y.z. gong, d.h. jin, q.y. li, h.c. jing figure 4: sum of nodes movement distance after deployment in different algorithms figure 5: sum of net energy consumption after deployment in different algorithms randomly lose 10 nodes after initial deployment and use their algorithms in wireless sensor networks to recover coverage holes. the results of 100 simulations are averaged and shown in figures 6 to 8. figure 6: percentage of network coverage after recovery coverage holes in different algorithms coverage hole recovery algorithm based on molecule model in heterogeneous wsns 573 figure 7: sum of nodes’ movement distance after recovery coverage holes in different algorithms figure 8: sum of net energy consumption after recovery of coverage holes in different algorithms compared with initial nodes randomly deployed and virtual force algorithm, figure 3 and figure 6 show our method achieves more effective coverage rate, a single node perception is more efficient as well, which is a key target of node deployment. the algorithm of this article reduces the blind area during perception and the scope of overlapping sense of node perception by adopting data fusion algorithm, compared with virtual force algorithm which is better than the initial randomly nodes deployed, thus obtains the best effect within the three methods mentioned above. in our algorithm, both the number of moving perceptual nodes and the energy consumption are greater than those of the virtual force algorithm at the initial stage of deployment, nevertheless, the less energy consumption as a whole and the longer lifetime of wsn are superior to those of the rest compared methods after the complete deployment due to dormancy mechanism of redundancy node as showed in figures 3 to 8. the monitoring area coverage percentage increases. the sum of nodes’ movement distance increases and the sum of nodes’ energy consumption increases with the growing number of wireless sensor nodes as shown in figures 3 to 8. the nodes’ movement distance is larger and the coverage effect is worse when using the virtual force algorithm in areas that are dense with wireless sensor nodes, because the virtual force algorithm cannot reasonably move the wireless sensor nodes in the initial deployment phase and coverage hole recovery phase. 574 x.l. song, y.z. gong, d.h. jin, q.y. li, h.c. jing 6 conclusions in this paper, a heterogeneous sensor network node deployment algorithm is presented based on the organic small molecule model and used to reduce the deployment node number as well as improve the efficiency of network coverage. we describe the details of our algorithm and compare it with other proposed schemes. the comparison and theory analysis show that the proposed scheme outperforms most of the existing schemes. further work needs to be done in order to improve and optimize the model presented in this paper and try other fusion strategies to optimize the heterogeneous sensor network node deployment algorithm and make it more efficient. acknowledgements this work was partially supported by the national high-tech research and development program of china (863 program) under grant no. 2012aa011201, in part by the major research plan of the national natural science foundation of china under grant no. 91318301, and in part by the national natural science foundation of china (nsfc) under grant no. 61202080.the authors also acknowledge the helpful comments and suggestions of the editors and reviewers gratefully, which have improved the presentation. bibliography [1] aggarwal a., kirchner f. 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(2013); efficient and dynamic clustering scheme for heterogeneous multi-level wireless sensor networks, acta automatica sinica, 39(4), 454-460. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 635-646 the research of qos approach in web servers y. hu, d. mu, a. gao, g. dai yansu hu, dejun mu, ang gao, guanzhong dai school of automation northwest polytechnical university xi’an 710072, china e-mail: huyansu@gmail.com, mudejun@nwpu.edu.cn, snailgao@gmail.com, daiguanzhong@nwpu.edu.cn abstract: proportional delay guarantee has been widely used in the web qos service, and the most basic methods are the feedback of control theory and the predictive control of queuing theory. while the former belonging to passive control has a long setting time and imperfect real-time, the latter can not simulate the web server queuing system well because of the model limitations. after the experimental verification and shortages analysis of the two methods, an improved approach is proposed in this paper. based on the queuing feature of web server and the http 1.1 persistent connection, the improved approach predicts the delay by calculating the queue length and service rate and achieves the relative delay guarantee of different classes by adjusting their quota of worker threads. the experimental results demonstrate that the approach could maintain the relative delay guarantees well even in poor network environment and performs a much better superior compared with the traditional methods. keywords: web server, proportional delay guarantee, feedback control, predictive control 1 introduction the increasing diversity of web applications the last decade has witnessed an increasing demand for provisioning of different levels of quality of service (qos) to meet changing system configuration and satisfy different client requirements. proportional delay differentiation (pdd) service aims to ensure the qos parameters between data flow of different classes to meet the specified proportions, so the requests with higher priority will receive the quality of service which is "at least not lower than" the low priority. there are many previous studies worked on the web qos and two different methods are used to achieve the differentiated service: the feedback of control theory (see [1], [2], [3], [4] and [5]) and predictive control of queuing theory (see [2], [6] and [7]). although the algorithms are different, the qos architecture of web server they used is non-distinctive. the qos of web application is usually related to the network layer transmission and the operating system kernel. the latter that needs to replace the operating system on all the terminals is difficult to deploy, so more and more researchers tend to the differentiation service on the application layer. it ensures the pdd service by changing the original web fifo mechanism and the modified apache mpm (multi-processing modules) architecture is illustrated in figure 1. 1. the single connection queue is improved to a multi-queue structure in accordance with the classified strategy. the listener monitors the network port, accepts the client tcp connections, classifies the requests based on some classified strategy, and then puts them into the appropriate waiting queue. copyright c⃝ 2006-2011 by ccc publications 636 y. hu, d. mu, a. gao, g. dai 2. any class can not consume the server resource unlimitedly, so the requests of different classes must be isolated. the thread per connection structure of mpm allows all the worker threads in pool to be the resource to be allocated. so we divide the pool into several sub-pools which are isolated with each other. the number of threads in each subpool is known as the thread quota, and the requests are serviced in the corresponding sub-pool. 3. for n kinds of classes, let ci(i = 1, ..., n) be the thread quota allocated to the class i. when load varies, the size of sub-pool is dynamically adjusted to ensure the proportion between classes constant. and is calculated by feedback control used the delay observer to get the real delay (figure 1 ¬method), or by predictive control used the queue length observer to get the request arrival rate(figure 1 ­method). ` worker pool 1 worker 1 worker m worker pool n waiting queue n waiting queue 1 requests classified queue length observer quota schedule delay observer feedback control http response data flow control flow predictive control performance isolation ( )y k figure 1: the web server architecture for pdd service but there are some imperfections and limitations in both the methods. for example, the feedback control is essentially a passive approach which works only after the deviation appeared. the delay in response is significant since the server response time is particularly slow. although the predictive control based on queuing theory adjusts the controller output in advance, it can not get the precise plant model because of limitations itself. so an improved predictive approach is proposed to overcome their shortages and the contributions in this paper can be summarized as follows: 1) a general architecture for proportional delay service in web server is summed up. 2) the two methods referred above are verified and compared by experiments. 3) implement and evaluate an improved approach to get a better performance. 2 the comparison of two pdd methods 2.1 the feedback of control theory assuming c worker threads are concurrent on the web server. for n kinds of classes, let δi be the constant weighting factor of class i where the higher priority has a smaller parameter value, and di be the expectation of class i’s average measured delay, then the proportional delay guarantees can be described as follows: di/dj = δi/δj, 1 ≤ i ≤ n, 1 ≤ j ≤ n, (1) the research of qos approach in web servers 637 ∑n i=1 ci = c (2) quota schedule feedback control d y ! delay observer i xie plant i y i c jc figure 2: the feedback control model where ci is the thread quota of class i. so the improved mpm is equivalent to a control model as figure 2. the system desired output is the inherent priority parameter ratio between class i and i + 1 , which is ydesire = [y1desire, y2desire, ..., yn−1disire] t. (3) yidesire = δi/δi+1, i = 1, 2, ..., n − 1. (4) similarly, the controller output is the thread quota ratio and y(k) is the measured output ratio between the class i and i + 1, which is x(k) = [x1(k), x2(k), ..., xn−1(k)] t. (5) xi(k) = ci(k)/ci+1(k), i = 1, 2, ..., n − 1. (6) y(k) = [y1(k), y2(k), ..., yn−1(k)] t. (7) yi(k) = di(k)/di+1(k), i = 1, 2, ..., n − 1. (8) obviously, the deviation is e(k) = y(k) − ydesire. on the web server, all classes share a single host resource. so the feedback controller��class-per-loop��adjusts x(k) to satisfy equation (1) by the measured e(k) . the web server is modeled as a linear system, i.e. a r-order difference equation is y(k) = ∑r l=1 [aly(k − l) + blx(k − l)]. (9) where al and bl are the unknown parameters. the transfer function in z-domain is g(z) = y(z) x(z) = ∑r l=1 blz r−l zr − ∑r l=1 alz r−1 . (10) the plant’s mathematical model (order and parameters) is obtained by system identification which is presented by the authors in [8] in detail. then we can design the controller based on the classical control theory. take the pi control for example, the controller transfer function in z-domain is d(z) = kp + kit(z + 1) 2(z − 1) , (11) and the closed-loop system transfer function is gc(z) = d(z)g(z) 1 + d(z)g(z) . (12) 638 y. hu, d. mu, a. gao, g. dai 2.2 the predictive control of queuing theory although the feedback maintains the system at the balance, it takes an imperfect real-time for the lagging output. so [2, 6, 7] try to correct the deviation before the system output being affected with the help of queuing theory. in the predictive control, the queue length observer (see figure 1­) measures the request arrival rate λi and service rate µi. then the predictive controller reallocates the thread quota ci for respective class. m/m/1/∞ queuing system the web server performance mainly subjects to the system bottlenecks(see [7]), so each class in apache can be considered as a m/m/1/∞ queuing model. define ρ = λ/µ be the system traffic intensity. and the residence time is the sum of queuing time (connecting time) and service time (transfer time), which is l = ω + χ (13) where ω and χ are independent from each other. according to paper [2], the mean residence time is calculated by equation (14). l = ω + e[χ] = ρ µ(1 − ρ) + 1 µ = 1 µ − λ , (ρ < 1) (14) to meet the proportion relationship specified by equation (1), we can get µj(k) − λj(k) µi(k) − λi(k) = δi δj (15) while normalized the thread quota si = ci/ci, 1 ≤ i ≤ n, ∑n i=1 si = 1, (16) hence, for class i, the service rate can be rewritten as µi(k) = µsi(k) (17) where µ is the total service capacity. then equation (14) becomes li = 1µsi−λi . combined with equation (15),   µs2(k) − λ2(k) µs1(k) − λ1(k) = δ2 δ1 µs3(k) − λ3(k) µs2(k) − λ2(k) = δ3 δ2 ... µsn(k) − λn(k) µsn−1(k) − λn−1(k) = δn δn−1 ∑n i=1 si = 1 (18) solving equations at each sampling time, we can get the thread quota for different classes (s1, s2, ..., sn). the research of qos approach in web servers 639 m/g/1/∞ queuing system in m/m/1/∞ model, the service time obeys the memoryless exponential distribution. but when it is general distribution, the correlation between the current and several previous service time must be considered (see [9]). the paper [6, 10] described the "heavy tail" features of web service time series {χn, n ≥ 1}, and gave a more compatible queuing model m/g/1/∞ for the web server. according to the pollaczek-kinchin(pk) formula and lemma, if si is the normalized thread quota for class i, service time χi obeys the bounded pareto distribution, the mean queuing time is m1,i = e[χi] = mi/si, m2,i = e[χi] = m2/si 2 (19) hence the mean residence time is li = ω + e[χi] = m2,iλi 2(1 − λie[χ]) + m1 si = m2,iλi 2(1 − λie[χ]) + m1 si (20) = m2λi 2si(si − m1λi) + m1 si where m1 ,m2 are the constants related to shape parameter of bounded pareto distribution, λi is the requests arrival rate which is obtained by the queue length observer. combined equation(20) with equation (1), si(si − m1λi) si+1(si+1 − m1λi+1) 2m1si+1 + λi+1(m2 − 2m21) 2m1si + λi(m2 − 2m21) = δi+1 δi (21) solve the equations similar to equation (18) and get the results (s1, s2, ..., sn). 2.3 experimental results the test-bed consists of a web sever and two client machines, each with a 3.0ghz pentium 4 professor and 521mb ram connected with 100 mbps ethernet. the web server is apache 2(httpd-ver2.0.53) running on windows nt and the total number of server processes is configured to 100. the two client machines run liunx-2.6.27 with surge (see [11]) (ver 1.00a) as the workload generator and each operates 120 concurrent ue. requests are classified according to their source ip address. all the experiments are under http1.1 pipeline and the number of maximum concurrent clients in surge is 1. set δ1/δ1 = 1/2,which means the delay of high class is half of the low one. the sampling period is set to 10 sec, and all the experiments last 3000 sec. 300 valid data is measured and the controller works after 750 sec. the results under pi controller and predictive controller m/g/1/∞ are respectively shown in figure 3. obviously, before the controllers works there is no differentiated service in different classes. but after 750 sec, the thread quota for high class increases until the expectation is satisfied. and all the methods achieve the proportional delay guarantees well. in order to compare the features of the two methods, the mean value of the measured delay ratio l1/l2 and the variance relative to δ1/δ2 = 0.5 are defined as follows: ee = ∑300 k=75 l1(k)/l2(k) 300 − 75 (22) de = ∑300 k=75 [l1(k)/l2(k) − δ1(k)/δ2(k)]2 (23) compute equation (22) and (23), we get ee = 0.5198,de = 4.56 under the pi controller,while ee = 0.4136 ,de = 5.5376 under the m/g/1/∞ predictive controller.now we can see 640 y. hu, d. mu, a. gao, g. dai 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 500 1000 1500 2000 2500 3000 20 30 40 50 60 70 80 d e la y r a ti o th re a d q u o ta delay ratio high class thread low class thread (a) pi control 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 500 1000 1500 2000 2500 3000 20 30 40 50 60 70 80 d e la y r a ti o th re a d q u o ta delay ratio high class thread low class thread (b) m/g/1/∞ queuing predictive control figure 3: the change of delay ratio and thread quota under different controllers 1. as a passive control, the pi controller works until the deviation appeared. although the output is stabilized in the vicinity of the expectation value, there is a long setting time ts ≈ 300s. 2. in contrast, the latter with a much better setting time ts ≈ 40s only maintains the delay proportion at 0.4. this is because the queuing theory based on pk formula asks for the statistics balance which means the requests arrival rate is equal to service rate. so the model limitation leads to the ineffaceable deviation. 3 an improved predictive control from section 2, the queuing theory is invalid when the web loads bursts and fluctuates dramatically. meanwhile, for the web queuing process, the number of requests in the front of request j can be measured. on this issue, an improved predictive control is proposed in this paper to achieve the relative delay guarantees. 3.1 design the relationship of the requests arrival rate, service rate and the queue length of class i are demonstrated in figure 4(see [12]).the arrival curve arrviali(k) presents the rate of tcp connections, and the service curve servicei(k) presents the capacity of web server for class i. ∆arrivali(k) ∆k = λi(k) (24) ∆servicei(k) ∆k = µi(k) (25) the research of qos approach in web servers 641 when classi is overload, the arrival rate is always greater than the web service rate,i.e. λi(k) > µi(k).so the vertical distance between the two curves is the actual length ni(k) of waiting queue i at the kth sampling time, which is ni(k) = arrivali(k) − servicei(k). from the view of the tail, the waiting time wi,ni−1is the sum of the processing time of the top (ni − 1) requests. considered the processing time χi,ni itself, the mean residence time is li,ni = wi,ni−1 + χi,ni = ∑ni j=1 χi,j (26) if the mean residence time li(k+1) can be predicted as l̃i(k) based on the queue length ni(k), we can adjust the thread quota to satisfy equation (1). q u e u e l e n g th time ( ) i l k ( ) i l k service curve arrival curve ( ) i n k k 1k 1k figure 4: the arrival curve and service curve 3.2 dealy prediction the web load is actually the superposition of lots of on/off process(see [11]). just like figure 5, web pages are transferred during object, and each page is composed of several embedded files which are transferred in a single tcp persistence connection under http 1.1. the intervals between every embedded url are active off time corresponding to the processing time spent by browse parsing web page. the inactive off time is a longer pause corresponding to clients’ "thinking procedure". therefore, the request of a web page consists a sequence of http requests with self-similarity, which is embedded url on object inactive off active offurl time figure 5: http1.1 on/off model si,n = ∑yi,n j=1 xi,jn = 1, 2, ...ni(k) (27) where si,n means the size of the nth web page in waiting queue i, xi,j is the size of embedded files, and yi,n is the number of its embedded files. the previous research indicates that the heavytailed distribution is more accuracy for web page (see [13] and [14]),and the probability mass function is: 642 y. hu, d. mu, a. gao, g. dai f(x) = αkα 1 − (k/p)α x−α−1, k < x < p (28) where k and p are the size of minimum and maximal file,α(0 < α < 2) is the sharp parameter which determine the variability of distribution. generally set 1 < α < 2to correspond to the actual communications. the number of embedded files yi,n follows pareto distribution (see [11]).yi,n and xi,j,xi,j itself are all independent from each other. the mathematical expectation of page sizesiis e[si] = e[yi]e[xi], i = 1, 2, ...n (29) let {si,1, si,2, ..., si,ni(k)}be the size of http requests on a tcp connection at the k thsample. then the total size of the page in waiting queue i to be transferred is ∆i(k) = ∑ni(k) n=1 si,n = ∑ni(k) n=1 ∑yi,n j=1 xi,j (30) this means that the forecasting delay is the time spent by ci(k) threads concurrently processing the files of size ∆i(k). the paper [15] illustrates that the processing time of a static web page is approximately linear to the size of request files. combined the condition that the unit of the operation system is 64kb, the total size of the page to be transferred is χ(s, ci) = b × (s/64) + d × ci (31) where b is the transmission coefficient, d is the overhead of threads switching and synchronization. they can be obtained by linear regression. so the total delay predicted is: li(ni(k), ci(k)) = ∑ni(k) n=1 χi(si,n, ci(k))/ci(k) (32) l̃i(k) = e[li(ni(k), ci(k))] = ni(k) 64 ( be[si] ni(k) + 64d) (33) 3.3 threads dispatch combined equation (33) with equation (1): l̃i(k) l̃i+1(k) = δi δi+1 = ni(k)ci+1(k) ni+1(k)ci(k) be[si] + 64dci(k) be[si+1] + 64dci+1(k) (34) regardless the priority, web pages stored in server is identical. so we have e[xi] = e[xi+1]. if zi(k) = ni(k)/ni+1(k),e = be[si],f = 64d,equation (34) can be rewritten as δi δi+1 = zi(k) fci(k)ci+1(k) + eci+1(k) fci(k)ci+1(k)+eci(k) (35) where zi(k) is obtained by the queue length observer, and equation (35) can be written into the form of ci+1 = fi(ci(k), zi(k)). take class 1 be the reference (δ1 = 1).considered the equation (2), the number of threads for each class is solved. the research of qos approach in web servers 643 3.4 parameter regression as described in section 3.2, the parameters b and din equation (31) are calculated by the least square algorithm. the sampling period is set to 10 sec and the parameter regression experiments totally obtain 61 group valid data. figure 6 presents the pages size, the predict delay and the measured delay when the thread quota varies. the identification results are b = 9.7,d = 55.7 under 0.05 significant level. 20 40 60 80 100 120 0 20 40 60 80 100 20 40 60 80 d e la y ( m s ) / fi le s iz e ( k b ) th re a d q u o ta time(/sec) estimate processing time file size thread figure 6: parameter regression 3.5 experimental results the test-bed has been shown in the section 3.3. request rate of class1 varies from 6/s to 15/s every 300 seconds and class 2 keeps 12/s. the sampling time is t = 10s and all the experiments last 2000s. the reference value is 0.5, i.e. δ1/δ1 = 0.5. the thread quota is equivalent for each class in the initial state. the results under different controllers are shown in figure 7. each sub-picture has two insets. the upper is the request arrival rate of two classes and their delay proportion. the lower presents the change of their thread quota. compared the controllers, the results are in accordance with what we analyzed before: 1. whatever using feedback, m/g/1/∞ predictive control or improved predictive control, they all achieve pdd in web server. the experiments also did at sampling time t = 8s and t = 15s which are similar. so in order to save space, we only give the best effect figure at t = 10s. 2. define the variance ξ be the stability evaluation index ,which is ξ = √∑n k=1 (yi(k) − y1d)2 / n (36) compute it under the different methods respectively, we find that the variance of proportional delay to 0.5 in improved predictive controller and m/g/1/∞ predictive controller is 40.18% and 57.4% of which in pi controller. this means the predictive controller is more stable when the load changes. 3. compared the setting time in figure 7a,7b and 7c. for the feedback, the setting time is nearly 100s at rising edge and 150s at the falling edge. the m/g/1/∞ predictive controller is almost the same situation but a little shorter. but those of the improved predictive controller are less than 1s at both edges, which presents a much better dynamic 644 y. hu, d. mu, a. gao, g. dai 6 8 10 12 14 16 0 500 1000 1500 2000 0 0.2 0.4 0.6 0.8 1 re q u e s t ra te p e r s e c o n d s (1) delay ratio class1 request rate class2 request rate service time ratio 30 35 40 45 50 55 60 65 70 0 500 1000 1500 2000 q u o ta (2) quota variation class1 thread class2 thread (a) pi control 6 8 10 12 14 16 0 500 1000 1500 2000 0 0.2 0.4 0.6 0.8 1 re q u e s t ra te p e r s e c o n d s (1) delay ratio class1 request rate class2 request rate service time ratio 30 35 40 45 50 55 60 65 70 0 500 1000 1500 2000 q u o ta (2) quota variation class1 thread class2 thread (b) m/g/1/∞ queuing predictive control 6 8 10 12 14 16 0 500 1000 1500 2000 0 0.2 0.4 0.6 0.8 1 re q u e s t ra te p e r s e c o n d s (1) delay ratio class1 request rate class2 request rate service time ratio 30 35 40 45 50 55 60 65 70 0 500 1000 1500 2000 q u o ta (2) quota variation class1 thread class2 thread (c) improved predictive control figure 7: the change of delay ratio and thread quota under different controllers performance. there are two reasons for this. one is the load changes influence waiting queue length first, and then this leads to the delay changes observed which exists delay. the other is the refreshed controller output works in the next sample time, while both the predictive controllers adjust the quota early. 4. finally, compared how they adjust the thread quota when load changes in figure 7b and 7c. because of the limitation of pk formula, it takes a long time to adjust the quota to the steady state in figure 7b. but the improved predictive controller can complete the the research of qos approach in web servers 645 procedure in just "one step". 4 conclusion and future work the paper first presents a general web qos architecture, and then analyzed the defects of two traditional differentiated methods by experimental results. finally, based on the queue length observer and parameter regression, improved predictive controller is proposed and produces a much better performance in real-time and stability. but there are still some problems. for example, if the parameters b and d obtained online, it will be more accurate to the web model. meanwhile, the paper only talked about the static requests and is not compatible to the dynamic loads. in the future, we should also break the local area network, 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[16] a. gao, d. mu, y. hu, w. pan, proportional delay guarantee in web qos based on predictive control, proceedings of icise 2009, nanjing,china,pp.173-176,2009. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(4), 475-488, august 2019. facial expression decoding based on fmri brain signal b. cao, y. liang, s. yoshida, r. guan benchun cao 1. zhuhai laboratory of key laboratory of symbol computation and knowledge engineering of ministry of education, zhuhai college of jilin university zhuhai, 519041, china 2. key laboratory of symbol computation and knowledge engineering of ministry of education, college of computer science and technology, jilin university, changchun, 130012, china caobc15@mails.jlu.edu.cn yanchun liang 1. zhuhai laboratory of key laboratory of symbol computation and knowledge engineering of ministry of education, zhuhai college of jilin university zhuhai, 519041, china 2. key laboratory of symbol computation and knowledge engineering of ministry of education, college of computer science and technology, jilin university, changchun, 130012, china ycliang@jlu.edu.cn shinichi yoshida school of information, kochi university of technology, kochi 782-8502, japan yoshida.shinichi@kochi-tech.ac.jp renchu guan* 1. zhuhai laboratory of key laboratory of symbol computation and knowledge engineering of ministry of education, zhuhai college of jilin university zhuhai, 519041, china 2. key laboratory of symbol computation and knowledge engineering of ministry of education, college of computer science and technology, jilin university, changchun, 130012, china *corresponding author: guanrenchu@jlu.edu.cn abstract: the analysis of facial expressions is a hot topic in brain-computer interface research. to determine the facial expressions of the subjects under the corresponding stimulation, we analyze the fmri images acquired by the magnetic resonance. there are six kinds of facial expressions: "anger", "disgust", "sadness", "happiness", "joy" and "surprise". we demonstrate that brain decoding is achievable through the parsing of two facial expressions ("anger" and "joy"). support vector machine and extreme learning machine are selected to classify these expressions based on time series features. experimental results show that the classification performance of the extreme learning machine algorithm is better than support vector machine. among the eight participants in the trials, the classification accuracy of three subjects reached 70-80%, and the remaining five subjects also achieved accuracy of 50-60%. therefore, we can conclude that the brain decoding can be used to help analyzing human facial expressions. keywords: brain-computer interface, machine learning, extreme learning machine, fmri, image processing. copyright ©2019 cc by-nc 476 b. cao, y. liang, s. yoshida, r. guan 1 introduction brain is the control center of the entire body. it is of great importance and is an indispensable part of us. the brain-computer interface as one of the state-of-the-art issues in the field of science has attracted more and more attentions. if we can judge people’s specific behaviors by analyzing the brain, we can understand the brain better [11]. functional magnetic resonance imaging (fmri) is a popular technique for studying brain in recent years [11,18]. it can scan the brain to obtain images and shows the area of the voxel that is activated in the brain when the subject is stimulated by outside stimulus. through the voxels we can determine which behaviors that the subject was doing are activated in the brain. medical image processing is a hot issue in the image recognition field. more and more researchers have already invested into the study of medical image processing [12]. using computers to help people identify medical images can reduce people’s errors caused by overwork and improve the accuracy of judgment [12]. recently, the problem of brain decoding has attracted broad attention. the facial expressions decoding is the main research direction for brain decoding. in daily human interactions, facial expression is one of the most intuitive feelings for humans to communicate. it is crucial for us to understand others’ emotions. if the brain activity corresponding to the facial expression can be retrieved, this information can be used to analyze facial expressions. this can be achieved by brain computer interface (bci), which is a technique to obtain the internal signal of the brain through an external device connected to the brain [16]. at present, electroencephalogram (eeg) and functional magnetic resonance imaging (fmri) have successfully completed the acquisition of non-invasive signals of brain-computer interface systems [14]. the imaging principle of fmri is through the magnetic field inside of the machine to affects the activity of the human neuron. the activity of the neuron changes the blood flow and blood oxygen, and fmri is imaged by measuring these changes. because of its non-invasive and reproducible features, fmri is a hot topic for medical researchers and experts. studies [7] have pointed that fmri not only can interpret simple movement instructions, but also can interpret complex advanced thinking activities that are related to certain regions of the brain. therefore, fmri technology is more suitable for decoding human facial expressions [6]. in particular, the research on the location of brain regions has been widely used. the brain information decoding technology refers to using fmri and machine learning algorithms to estimate the subject’s facial expressions under the stimulation of expression images according to the acquired brain signals. however, low recognition accuracy is a major problem in brain information decoding, which has limited this technology’s fast spreading. the main task of this paper is to extract the brain image features that have been stimulated to show different states through the fmri brain images analysis with higher classification accuracy. comparing with the time series, we find out different stimulus labels corresponding to different brain image features. the remainder of this paper is organized as follows. section 2 introduces the fundamental concepts of related algorithms. section 3 elaborates the data parsing process of fmri data. section 4 presents the specific implementation process of the experiment and the analysis of related experimental results. finally, the conclusion is drawn in section 5. 2 related theory to better understand our model, this section gives a detailed introduction and explanation about all the related algorithms including cross validation, support vector machine algorithm, extreme learning machine algorithm and brain decoding. for each algorithm, this section specifies its principles and implementation steps. facial expression decoding based on fmri brain signal 477 2.1 cross validation algorithm cross-validation is an algorithm used to evaluate the performance of classifiers [1]. its main idea is to divide data sets into training sets and verification sets. training sets are mainly used to train classifiers and verification sets are used to verify the classification performance. according to the data partitioning, cross-validation methods are divided into three categories hold-out method [2], k-fold cross validation method [15] and leave-one-out cross validation method [10]. the k-fold cross-validation method divides the original data set into k parts, which is divided into k parts. each of the k parts is used as verification set; the other k-1 parts are used as the training set. we get k models and take the arithmetic average as the accuracy of the model. the advantage of the k-fold cross-validation method is that the segmentation is reasonable so that the data set can be fully utilized, the probability of occurrence of the under-fitting problem is reduced and the training data is greatly increased, and the trained model can be more optimized. because k-fold cross-validation can effectively avoid over learning and less learning and the result is convincing, we used k-fold cross-validation method. 2.2 support vector machine support vector machine (svm) is a very popular machine learning algorithm [17]. the original support vector machine was specifically designed to solve the linear divisibility problem. introducing kernel functions, svm can map low-dimensional data to high dimensions and solve the linear indivisibility problems. the purpose of svm is to generate a hyperplane that divides the linearly separable data into two classes and make the point closest to this line as far away as possible from the hyperplane. the interval which is called the geometric interval should be as large as possible so that the data can be completely separated. we assume that the formula for the linear segmentation plane is wt + b = 0. the geometric interval can be obtained by solving the following optimization problem: f(x) = maxw,b 1 2 ‖w‖2. (1) subject to : yi(wxi + b) ≥ 1 where the weight vector w and the offset value b represent the parameters of the classification plane. for nonlinear classification problems, svm cannot classify them in the low-dimensional space and the kernel function solves this problem well. the kernel function can map the data from the low dimension to high dimension. with kernel functions, svm can be used to classify nonlinear problems. there are different types of kernel functions such as sigmoid kernel, gaussian kernel and tanh kernel. 2.3 extreme learning machine extreme learning machine (elm) is a neural network with a single hidden layer which randomly initializes weights and do not need to adjust parameters during training [9]. the elm consists of three layers: the input layer, hidden layer, and output layer. the learning speed of elm is fast because elm has only one single hidden layer. elm initializes weights randomly and there is no need to adjust offsets and weights in the training process. figure 1 shows a neural network with single hidden layer. if there are n groups input data for the neural network, it can be expressed as (xj, tj) (2) 478 b. cao, y. liang, s. yoshida, r. guan where xj = [xj1,xj2, · · · ,xjn] t ∈ rn tj = [tj1,tj2, · · · ,tjm] t ∈ rm we assume that the number of hidden nodes in the neural network is l. figure 1: single hidden layer neural network then the neural network can be expressed as l∑ i=1 βig(ωixj + bi) = oj,j = 1, 2, ...,n. (3) where g(x) denotes the activation function of the hidden layer node, ωi = [ωi1,ωi2, · · · ,ωin] t denotes the input weight of the i-th node on the hidden layer, and bi denotes the offset of the i-th node on the hidden layer, βi = [βi1,βi2, · · · ,βim] t represents the output weight of the i-th node of the hidden layer, ωi ·ωj indicates the inner product of the weights ωi and ωj. the goal of training single hidden layer neural network is to minimize the error between the output and the expected value of the network output layer min( ∑n j=1‖oj − tj‖). hereinto, l∑ i=1 βig(ωixj + bi) = tj,j = 1, 2, ...,n. it can be expressed in the form of matrix h ·β = t (4) where h represents the output matrix of the hidden layer node, β represents the weight of the output of the hidden layer, t represents the target output of the output layer: h(ω1, ...,ωl,b1, ...,bl,x1, ...,xn ) =  g(ω1 ·x1 + b1) · · · g(ωl ·x1 + bl)... ... ... g(ω1 ·xn + b1) · · · g(ωl ·xn + bl)   (5) β = [β1 · · ·βl] t (6) facial expression decoding based on fmri brain signal 479 t = [t1 · · ·tn] t . (7) in order to optimize the neural network, we expect to obtain ŵl, b̂l and β̂l , making ‖h(ŵl, b̂l) ·β −t‖ = minw,b,β‖h(ŵi, b̂i) ·β −t‖ (8) where i = 1, 2, · · · ,l. this is equivalent to minimizing the loss function: e = n∑ j=1 ‖ l∑ i=1 βig(ωixj + bi) − tj‖2 (9) for solving this kind of problem, the gradient descent method is often used to find the optimal solution. however, using the gradient descent algorithm, we need to adjust the parameters of all hidden layers based on expert experience or optimization algorithms. to avoid this problem, we introduce extreme learning machine (elm). for elm, it is no need to adjust the parameters and the offsets as long as the weights are initialized. the training process can be summarized as: h·β = t, and we can get output weight β̂ β̂ = h+ ·t (10) where h+ represents the generalized inverse matrix of matrix h. huang et al. have proved that the norm of the solution β̂ is not only smallest but unique [8]. 2.4 brain decoding the flowchart of brain information decoding is shown in figure 2. first, by giving stimulus images and measuring brain activity data, the facial expressions of the subjects are recorded according to the time series. then, we extract the feature vectors under this stimulus according to activated voxels. finally, the obtained feature vectors are corresponding to the facial expressions through the time series to form a feature vector and label input classifier for counting the recognition accuracy. figure 2: brain information decoding processes 480 b. cao, y. liang, s. yoshida, r. guan 3 fmri data analysis for the preprocessing procedure, we use statistical parametric mapping (spm) [13] to analyze the obtained fmri images, personal parsing process and group parsing process. the process of fmri data analysis is shown in figure 3. figure 3: fmri data analysis process 3.1 data preprocessing before analyzing the data of the brain image, we need to preprocess the brain data and use the spm software to complete the process. it mainly consists of four steps: slice timing, realignment, normalize, and smooth. slice timing. during the collection of brain images, because we cannot scan the entire brain image at a time, we scan brain in slice. in the process of scanning, there will be slight differences in the acquisition time of each slice; therefore we need to correct time difference in the acquisition slice to ensure that the acquisition time is the same for each slice. slice timing is used to correct the difference in time between the slice and the slice in the brain image. realignment. although we fix the head of the subjects, some subjects still had slight head shake during the experiment. the process of realignment is to unify all the head portraits of the subjects in the entire test process, and the purpose is to correct head shake. assuming that subject’s brain shaking category is within an acceptable category, then we correct it by some methods to make it close to the exact value, but if it is not in this category, we need to delete the portrait. in general, the range of head movements we examined was that the translation does not exceed 2.0 mm and the rotation does not exceed 2.0 degrees. normalization. because different subjects have different brain sizes and shapes, we standardize the brain images of the subjects before carrying out the experiment in order to deal with them in a unified way. we place the brain images of the subjects with differences in a common space and use the same norms to describe the specific sites. for example, when there are multiple experimental participants with different head shapes, if we want to make an experiment for them facial expression decoding based on fmri brain signal 481 to select an area of the brain, we should develop a standard spatial brain template to achieve accurate positioning. smooth. since fmri contains a large amount of noise, to minimize the impact of noise and obtain images with less difference from the original data, we smooth the acquired image. in spm software, smoothing is the process of deconvoluting the acquired brain image using a gaussian function. 3.2 statistical analysis process directly using the preprocessed data to data analysis will bring a lot of unnecessary problems. to enable the entire experiment to be proceed smoothly, we divide the preprocessed data into two areas, the interest region and other regions. in data analysis process, we extract feature vectors directly from the regions of interest (roi) [4]. in the statistical analysis process, a very important statistical analysis model is used, namely the generalized linear model (glm) [19]. glm is an extension of the linear model. the independent variables should be continuous values rather than discrete integers or data, and the relationship between the expected values of the independent and dependent variables are linear. the independent variables of the generalized linear model are not only limited to continuous numerical data, but discrete integers are also satisfactory. the other difference is that we need to determine the connection function when modeling glm. in simple terms, glm is based on the assumption that the test result (represented by y) at each pixel is a linear combination of certain parameters x. these parameters are not only related to the specific brain regions, but are related to the experimental task and the experimental time. the matrix composed of these parameters is called the design matrix. it can be expressed as y = βx + ε where ε represents the error, we have changed the parameters of the solution after the transformation through the glm. we originally asked for a statistical analysis of the variable y, and now we are solving the parameter β. we can get brain activation map by fitting statistics to β. the statistical inference procedure for parameter β is as follows: step 1: confirm the accuracy of design matrix x. step 2: the least squares method is used to fit the parameter β, and make the error sum∑n i=1 ε 2 reach the minimum. step 3: perform t-test or f-test on parameter β. step 4: according to t test or f test statistical results to infer parameters β. 4 experiment and discussion in our experiment, the fmri image of the subject under stimulation was first obtained by magnetic resonance. then the fmri image analysis software spm was used to analyze the fmri images of different subjects stimulated by different expression images (anger and joy) respectively. the activated brain voxel area was obtained under the corresponding stimulus. this experiment allows participants to maintain a consistent facial expression and the stimulus images they see. the voxel area that is activated by the time series corresponds to the stimuli portrait. the obtained active voxel area was used as the feature vector and the facial expressions of the subjects were used as labels, which are input into the classifier to evaluate the performance of the algorithm. paul ekman claims that there are six basic emotions in human emotion [5]. they are "anger", "disgust", "sadness", "happiness", "joy", and "surprise". in our case, anger and joy will be used to predict. 482 b. cao, y. liang, s. yoshida, r. guan 4.1 experimental data there are 8 subjects participated in the experiment, where 5 were males and 3 were females. all the subjects were from 21 to 22 years old and in good health to avoid the effects of gender and age on the experimental results. the safety of the fmri equipment was explained to the subjects before the experiment and a confidentiality agreement on the experimental results was signed. the stimuli used in this study are anger and joy images. to emphasize facial features, the clothing and background of the stimuli are filled with gray to reduce the impact on the experimental results. in the experimental design, 216 seconds brain activity was imaged for each subject. eight brain scans were performed for each subject and 72 brain activity slices were obtained each time during this process. to reduce bold signal, we show 3 black background scans between the stimuli to stabilize the brain. the stimulating images randomly presented 64 facial portraits, including 32 "joy" facial expressions and 32 "angry" facial expressions. the specific form is shown in figure 4. figure 4: brain scan time this following step is mainly carried out through three steps. first, different facial expression images were randomly presented to stimulate the subjects, and the subjects were asked to make corresponding facial expressions with the images, mainly including two expression images of anger and joy. then the brain data of subjects was scanned by mri to images. the obtained brain images are analyzed using spm to obtain useful information. finally, the data obtained by the experiment was input to the classifier. 4.2 data preprocessing and analysis the original image taken from the fmri device is a dicom format image. to use spm for data analysis, we need to convert the image to the desired analysis format. this experiment uses the mriconvert software to convert the image format. in the process of analyzing the brain images of the subjects, we used spm software to identify the brain activation voxels. the personal analysis process is as follows: 1. input preprocessed brain activity data into generalized linear model (glm). 2. use the design matrix created to calculate the regression coefficient β of the glm model 3. perform t-test on the subject’s brain image to determine which brain voxels are activated 4. analyze the brain regions of activated voxels belongs from the display image (reference brodmann region system [20]). when the stimulus image is joy or anger, the analysis results for each subject’s fmri image are presented in table 1 and table 2, respectively. the pulse repetition time (tr) of the subjects a, b, c, d was 3090ms, and e, f, g, h was 3480ms. the pulse repetition time refers to the interval where the brain is scanned using an mri. the group analysis is to analyze the information collected by all subjects as a whole. in the group analysis, the contrast file obtained by personal analysis of the subjects was used to analyze the whole group of subjects. the results of eight subjects group analysis are shown in figure 5. when the stimulus image is "joy", the activated voxels are located in the lower parietal regions facial expression decoding based on fmri brain signal 483 table 1: brain image analysis results on the stimulus of joy subject p value t value activated broadmann region a 0.01 2.89 17-19,37,39 b 0.001 4.05 17-19,7 c 0.001 5.11 9-19,23-40,44-47 d 0.001 4.15 9-12 e 0.001 4.82 9-12 f 0.0001 9.51 17-19,37 g 0.001 3.70 9,46 h 0.001 4.00 9-12,23-27 table 2: brain image analysis results on the stimulus of anger subject p value t value activated broadmann region a 0.01 3.02 9-12,17-19 b 0.001 4.11 17-19 c 0.001 4.04 37,20,21 d 0.001 4.34 7-16,20-22,39-47 e 0.001 5.92 9-12 f 0.0001 3.72 lateral geniculate body, posterolateral nucleus g 0.001 3.59 9,11-16.25-33,34-36,46,47 h 0.001 4.02 9-12,45-47 of the 40 and 41 regions of the brodmann region, mainly related to perception, vision, reading and language-functional area. when the stimulus image is "angry", the significance level is set to p < 0.01. at this time, the activated voxels are located in the inner and outer occipital regions of the 19 region brodmann area. this area is mainly visual, color vision, and movement–related areas. it can be seen from the figure 5 that amygdala is not completely activated. according to the relevant analysis, as the amygdala is the brain part on the facial expression recognition [3] , the amygdala of the test was only activated a little. furthermore, we can see that the visual and other areas are more fully activated. figure 5: brain activity state (joy, anger) of group analysis 484 b. cao, y. liang, s. yoshida, r. guan 4.3 classification results comparison according to the above discussion, we have already analyzed the fmri image of the subject through spm software and obtained the feature vector. according to the time series record in the experiment, we can associate the feature vector with the facial expression. thus, a data containing feature vector and feature label is generated. to achieve high recognition accuracy, the selection of the classifiers is of great importance. we classify the obtained data using svm and elm, respectively. in this comparison experiment, the number of elm hidden layer nodes is 1000 nodes and the sigmoid function is the activation function. and the c value of svm is 0.5 and the sigmoid function is selected as the kernel function, we use the grid search algorithm to find the optimal super parameters for the sigmoid function k(x,y) = tanh(γ · xt · y + g) in order to get the optimized svm model. in the parameter selection, we respectively let γ = (1, 2, 3, 4) and g = (0.2, 0.4, 0.6, 0.8). according to the experimental results, we get that the function is optimal when γ = 2andg = 0.6. from the results it can be found that no matter what the data obtained in the stimulation of the joy images or anger images, the classification result of the elm is better than svm. figure 6 shows the comparison of the classification accuracy under the stimulation of the joy images. figure 7 shows the comparison of the classification accuracy under the stimulation of the anger image. therefore, we use elm to classify the obtained fmri images. figure 6: performance comparison of svm and elm for joy images stimulation because the number of hidden layer nodes and the selection of activation function in the elm directly affect the classification performance of the entire elm, to pursue the highest accuracy, we need to determine the optimize number of hidden layer nodes and activation function. figure 8 shows the effect of the active functions and the number of hidden nodes on the classification accuracy of elm for the joy images. from figure 8 we can see that the overall classification performance of the sigmoid function is higher than the tanh function. although using 2500 hidden nodes, the former function is not good as tanh, it does not affect the overall trend. therefore, we use the sigmoid function as the activation function of the classifier elm. from the curve of the sigmoid function, we can see that the number of nodes in the hidden layer ranges from 500 to 4000 and every 500 nodes are located in one interval. we can see that the classification performance reaches the best when the number of nodes is 1000. therefore, the number of hidden layer nodes of the elm classifier is chosen as 1000 nodes. in summary, facial expression decoding based on fmri brain signal 485 figure 7: performance comparison of svm and elm for anger images stimulation the amount of hidden layer nodes is 1000 and the sigmoid function is selected as the activation function. 4.4 discussion we use cross-validation to evaluate the classification performance of elm. it not only enables elm to make more effective predictions, but also reduces the chance of overfitting. the k fold cross-validation is used to evaluate the performance of the elm. in our experiment, let k be 10, that is, we divide the data into 10 groups and use 9 groups each time as the training set to train the elm, the other group as test set. the average accuracy of the ten groups of classification is the classification accuracy of the classifier. in this experiment, the classification accuracy of each subject is shown in table 3. table 3: the accuracy of elm on each subject data subject classification accuracy(joy) classification accuracy (anger) a 55% 55% b 80% 55% c 60% 62% d 53% 70% e 88% 80% f 60% 58% g 68% 70% h 55% 60% table 3 lists the classification accuracy of the brain images acquired by fmri equipment from the 8 subjects under the effect of cross-validation and elm classifiers. it can be seen that the accuracy of 3 subjects (subjects b, e, g) reached 70-80% and the remaining 5 subjects reached 50-60%. according to these results, it can be found that facial expressions can be analyzed by analyzing fmri brain images. among these subject’s results, the accuracy of subject a is low, and the reason for the low accuracy rate may be due to noise. it may also be caused by the 486 b. cao, y. liang, s. yoshida, r. guan figure 8: effect of the activation functions and the amount of hidden layer nodes insensitivity of the subject to the stimulating image. 5 conclusion in this paper, the machine learning algorithm elm is applied to the classification of fmri data. from the results of personal analysis, we can see that the accuracy of the recognition of 3 subjects (b, e, and g) in the 8 subjects reached 70-80%, and the accuracy of the remaining 5 subjects also reached 50-60%. it can be seen that using the brain image obtained by fmri we can analyze people’s facial expressions. from the results we can see that the activated voxels are usually located in the brain regions with visual, ideological, and cognitive functions. this article also provides the proof that we can analyze the facial expressions of the subjects by analyzing the fmri images. it should be pointed out that in the near future, the stimulatory images may not be limited to still images. it is possible to study by using video to stimulate subjects and then observe the experimental results. 6 acknowledgement this work was supported in part by the national natural science foundation of china (61572228, 61602207), the jilin provincial key laboratory of big data intelligent computing (20180622002jc), the key technological research projects in jilin province under grant 20190302107gx, the special research and development of industrial technology of jilin province under grant 2019c053-7, guangdong premier key-discipline enhancement scheme (2016gdyszdxk036), and guangdong key-project for applied fundamental research (2018kzdxm076). bibliography [1] arlot, s.; 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[20] zilles, k; amunts, k.(2010). centenary of brodmann’s map–conception and fate, nature reviews neuroscience, 11(2), 139-145, 2010. int j comput commun, issn 1841-9836 8(5):673-680, october, 2013. estimation of the text skew in the old printed documents d. brodić, č.a. maluckov, l. peng darko brodić, čedomir a. maluckov technical faculty in bor, university of belgrade v. j. 12, 19210 bor, serbia dbrodic@tf.bor.ac.rs, cmaluckov@tf.bor.ac.rs liangrui peng department of electronic engineering, tsinghua university beijing 100084, p.r. china penglr@tsinghua.edu.cn abstract: old printed documents represent the significant part of our heritage. in order to preserve them, the digitalization is indispensable. the paper proposed a robust skew estimation method for old printed document. it is based on the connected components made by filled convex hulls around text element. the connected components are enlarged by oriented morphological operation. then, the longest connected component is extracted. the global orientation of the document is detected by its orientation. accordingly, document image was globally de-skewed. the algorithm is tested on synthetic and real datasets. obtained results proved the algorithms correctness. keywords: document image analysis, moment methods, optical character recognition, skew adjustment. 1 introduction old documents represent the part of great cultural and scientific importance. due to age, it is quite common for such documents to suffer from degradation. examples of degradations include shadows and variable background intensity, smudges, ink seeping, smear and strains. these degradations make image preprocessing particularly difficult and produce recognition errors. in document automatic recognition systems, the quality of the input image is crucial to final performance. there are a variety of interfering effects such as noise and skewing that appear during the scanning process. these components disturb the proceeding and decrease the performance of the recognizer. skew correction plays an important role in the image preprocessing. a small inclination in document image can interfere in the layout analysis and consequently in the rest of the process. that’s why, the identification of the object skew in the image is one of the most important tasks in digital image processing and document image analysis. it is so due to optical character recognition (ocr) system sensitivity to any skew appearance in the text. in this paper, we deal with old printed documents like letters, technical notes, etc. they are characterized with the shape regularity as any other printed text, [1] which contain letters with similar sizes. the distance between text lines is adequate, which facilitates separation of text lines. the orientation of the text lines is similar. that considers pretty the same skew, which represents the global text skew. a large amount of techniques has been developed in order to identify text skew. they are classified as: [1] projection profiles methods, k-nearest neighbor clustering methods, hough transforms methods, fourier transformation methods, cross-correlation methods, and other methods. many of these methods have strong points as well as weaknesses. projection profile method copyright c⃝ 2006-2013 by ccc publications 674 d. brodić, č.a. maluckov, l. peng is a straightforward method, which is suitable for text with uniform skew only. [2] k-nearest neighbor clustering method cannot handle incorporation of noisy subparts in text, which leads to reduced accuracy. [3] the hough transforms method needs preprocessing stage, which defines candidate mapping points. [4] the method is complex and computer time intensive. the fourier transforms method is even more complex. [5] the cross-correlation method is limited only to small skew angles up to 10◦. [6] interesting extension of those methods represent the incorporation of log-polar transformation. [7] however, sometimes it is unstable in application. the techniques classified as other methods are based mostly on combination techniques. they have been reputed as the most efficient ones. however, they are multistage and computer time intensive. such methods are proposed in [8][9]. preprocessing of document image is made by complex decision making. it is performed with complex geometrical filtering. the text skew is identified with the cross-correlation method applied to remain connected components. at the end, local text skew is calculated with the least square method. this technique performs local skew estimation and reliable text localization without restriction of the skew angle value. the main contribution of this paper is the algorithm suitable for the recognition of the text skew in the old printed documents characterized with dominant skew. organization of the paper is as follows. section 2 describes the algorithm. section 3 defines text experiments. section 4 gives the results and discusses them. section 5 makes conclusions. 2 proposed algorithm the proposed algorithm identifies the skew, which represents the dominant skew of the whole printed document. it consists of the steps that follows: 1) uneven illumination reduction with binarization, 2) convex hulls extraction, 3) joining text objects with oriented binary morphology, 4) extraction of the longest object, 5) skew estimation of the longest object by the moments, and 6) global de-skewing of the original document. 2.1 uneven illumination reduction with binarization the binarization method adopts both global and local adaptive thresholds. [10][11] first, multiple candidate thresholds are computed via histogram of the original gray image. those pixels which are definitely background and foreground pixels are recorded, and the remained gray pixels will be binarized by the adaptive thresholding method. second, we get the binarized results of the remained gray pixels by multiple candidate thresholds respectively, the statistical parameters such as run-length are calculated for each binarized images. finally, the optimal threshold is selected when the statistical parameters are stable. if the statistical parameter analysis fails, a global threshold will be calculated by histogram analysis. after the binarization, document image is transformed into a binary matrix b featuring m rows and n columns. it has two intensity levels, i.e. b(i,j) = {0,1}. figure 1 shows the document image before and after binarization process. 2.2 convex hull extraction instead of using bounding boxes, [12] the proposed algorithm exploits the convex hulls over text. convex hull creates a smaller region around the text compared to bounding box. hence, the probability of touching the neighbor text fragments has been reduced because of smaller contour. upon the extraction of convex hulls, they are filled with white pixels due to complementary image. such a text image is given with matrix c. figure 2(a) shows convex hulls extraction. estimation of the text skew in the old printed documents 675 (a) (b) figure 1: document text image: (a) before binarization, (b) after binarization 2.3 joining text objects with oriented binary morphology currently, some of filled convex hulls are joined. they create short connected components (cc). the longest of them ccisr accompanies the attribute of the orientation intention. it is called initial skew rate (isr). ccisr is extracted by the application of the longest common subsequence (lcs). [13] ccisr = max︸︷︷︸ i,j ( k∩ m=1 ccm ) , (1) where k = 1 is the total number of cc. ccisr is shown in figure 2(b). its orientation is calculated by the moments (see eq. (7) for reference). (a) (b) (c) (d) figure 2: document text image: (a) cc extraction (b) ccisr extraction, (c) extended cc extraction (b) the longest cc extraction in order to correctly estimate the text skew, connected components should be extended. hence, morphological erosion is applied to c. this way, the adjacent cc’s are merged establishing parts of the text line. structuring element s representing a variable width line is used. in order not to touch or join separate neighbor text lines, the width of the line should be chosen with caution. it heavily depends on each cc’s height. empirically, it is used as 30% of the connected component’s height, which means that width of structuring element s applied to each cc’s is different. furthermore, its variability is a function of isr, because structuring element s is skewed according to isr orientation. morphological operation is given as: y = c ⊕ s(∠isr). (2) 676 d. brodić, č.a. maluckov, l. peng figure 2(c) shows extended cc made by oriented morphology. 2.4 extraction of the longest object currently, extended ccs are created. they represent partial text lines. it is clear that the longest of them cclng incorporates the orientation which is similar to text skew. hence, it is mandatory to extract cclng from y. again, it is performed with lcs method: [13] cclng = max︸︷︷︸ i,j ( l∩ n=1 ccn ) , (3) where l is the total number of extended cc. cclng is shown in figure 2(d). document text skew can be estimated by identifying the orientation of cclng. 2.5 skew estimation of the longest object by the moments in order to estimate the skew orientation of cclng, the moment based technique is used. moment defines the measure of the pixel distribution in the image. it identifies global image information that depends on its contour. moments of the binary image y featuring m rows and n columns are: [14] mpq = m∑ i=1 n∑ j=1 ipjq, (4) where p and q = 0, 1, 2, 3, . . . , r, and r represents the order of the moment. the central moment’s µpq of the binary image y can be calculated as: µpq = m∑ i=1 n∑ j=1 (i − x)p(j − y)q. (5) the image feature which represents the object orientation θ is obtained from the moments. it illustrates the angle between the object and the horizontal axis. it is given as: [14] θ = 1 2 arctan ( 2µ11 µ20 − µ02 ) , (6) hence, the orientation θ of the longest object cclng estimates the global text skew. 2.6 global de-skewing of the original document according to the orientation of the longest object θ, the initial document image is de-skewed. figure 3 shows the document image before and after de-skewing process. 3 experiments main goal of the experiment is the evaluation of the algorithm’s ability to estimate text skew. it is performed on real and synthetic datasets. in this case, synthetic dataset consist of the samples that include single-line printed text. the samples are given in the resolution of 300 dpi. they are rotated for the angle θ, from 0◦ to 10◦ by 1◦ and from 10◦ to 40◦ by 5◦ steps around x-axis in the positive direction. it is shown in figure 4(a). estimation of the text skew in the old printed documents 677 (a) (b) figure 3: document text image: (a) with skew (b) after de-skew (a) (b) figure 4: dataset rotation: (a) synthetic dataset, (b) real dataset real dataset consists of document image samples given in the resolution of 150 and 300 dpi. they are rotated for the angle θ, from 0◦ to 10◦ by 1◦ and from 10◦ to 40◦ by 5◦ steps around x-axis. figure 5 shows document samples of the real dataset in latin, serbian cyrillic, chinese and greek cyrillic (excerpt from icdar 2013 text skew test). (a) (b) (c) (d) figure 5: samples from real dataset: (a) latin document, (b) serbian cyrillic document, (c) chinese document, (d) greek cyrillic document after the algorithm’s application to dataset obtained result represents the estimated text skew. this result is compared to the reference text skew of the document samples from dataset. the evaluation of the algorithm’s result is made by the absolute deviation: ∆θa = |θref − θa|, (7) where θref is the reference skew of the input text sample and θa is text skew estimated by the algorithm. 678 d. brodić, č.a. maluckov, l. peng 4 results and discussion table 1 shows the absolute deviation of global text skew for synthetic and real dataset. these result are given for the full range of rotation angles (0◦ − 40◦). table 1: absolute deviation for synthetic and real dataset resolution 300 dpi 150 dpi dataset synthetic real synthetic real θref ∆θ isr ∆θ ∆θ isr ∆θ 0 0.0734 1.0362 0.0376 0.0676 -0.6384 0.2490 1 0.0408 2.2297 0.0147 0.0481 0.8453 0.0329 2 0.0355 1.6194 0.1067 0.0141 1.0806 0.0005 3 0.0569 -0.0450 0.0020 0.0078 2.4496 0.2352 4 0.0001 0.7877 0.0209 0.0468 3.3863 0.0086 5 0.0188 2.2632 0.2458 0.0001 1.7279 0.2227 6 0.0118 2.9291 0.0221 0.0492 5.6370 0.1314 7 0.0265 3.9265 0.2889 0.0107 6.0385 0.0679 8 0.0740 4.9008 0.0504 0.0532 6.9414 0.0769 9 0.0604 7.0503 0.0987 0.0398 8.5425 0.0045 10 0.0470 5.5578 0.2775 0.0493 8.4272 0.9760 15 0.0586 11.9536 0.3697 0.2334 15.0123 0.0648 20 0.0680 15.9705 0.3497 0.2476 19.5475 0.1556 25 0.0793 21.9282 0.2223 0.2942 26.5648 0.1537 30 0.0797 27.0247 0.2245 0.3048 28.7789 0.2365 35 0.3266 31.9050 0.1856 0.3770 34.5256 0.2569 40 0.3553 36.8890 0.2028 0.4086 38.9246 4.6753 average 0.0831 0.1600 0.1325 0.4440 from table 1 results are as follows: for synthetic dataset given in the resolution of 300 dpi the absolute deviation is below 0.08◦ for the angles up to 30◦ and below 0.35◦ for the angles between 30◦ and 40◦ with the average value of 0.08◦, for real dataset given in the resolution of 300 dpi the absolute deviation is below 0.37◦ for the angles up to 40◦ with the average value of 0.16◦, for synthetic dataset given in the resolution of 150 dpi the absolute deviation is below 0.05◦ for the angles up to 10◦ and up to 0.415◦ for the angles between 10◦ and 40◦ with the average value of 0.13◦, for real dataset given in the resolution of 150 dpi the absolute deviation is below 0.97◦ for the angles up to 35◦ with the average value of 0.44◦. the proposed text skew algorithm has the average absolute deviation of 0.16◦ for the text skew angle θ up to 40◦. compared obtained result with the result of the algorithm without using oriented morphology, [16] the average value of absolute deviation is lower approx. 0.2◦. it has quite acceptable values of the absolute deviation in the wide range of angles. furthermore, the algorithm has been applied to different types of documents (including few examples from document image skew estimation contest icdar 2013) and different types of letters. it can be estimation of the text skew in the old printed documents 679 used for documents like letters, technical articles, journals, dictionary, etc. furthermore, above results are quite acceptable because geometrical filtering in preprocessing stage was excluded. however, proposed algorithm doesn’t have the versatility of the multi-stage method proposed in [8][9]. this complex methods include complicated steps of geometrical filtering in preprocessing stages in order to exclude some redundant elements. however, such methods are much more computer time intensive. in further development, proposed method should be expanded with the inclusion of some additional geometrical filtering steps. this step will contribute to lower dispersion of absolute error value (up to 0.1◦). 5 conclusions the paper proposed robust method for the estimation of global text skew. the method shows good results of global skew estimation for different resolution of test images. it is a merit of the moments exploration. furthermore, the algorithm is suitable for text skew identification of document types like letters, technical articles, journals, dictionary, etc. due to the exclusion of the preprocessing elements, some of redundant data were included in the process of text skew identification. hence, further development of the algorithm should include geometrical filtering, which will lead to lower dispersion of estimated skew value. acknowledgment this work was partially supported by the grant of the ministry of science from republic of serbia, as a part of the project tr33037 and iii43011 within the framework of technological development program and the national natural science foundation of china under grant no. 61261130590. bibliography [1] amin, a.; wu, s. (2005); robust skew detection in mixed text/graphics documents, proc.of 8th icdar, seoul, korea, 247-251. [2] manmatha, r.; srimal, n. (1999); scale space technique for word segmentation in handwritten manuscripts, proc. of 2nd icsstcv, lncs 1682, london, great britain, 22-33. [3] o’gorman, l. (1993); the document spectrum for page layout analysis, ieee trans pattern anal mach intell, issn 0162-8828, 15(11): 1162-1173. [4] louloudis, g.; gatos, b.; pratikakis, i.; halatsis, c. (2008); text line detection in handwritten documents, pattern recognition, issn 0031-3203, 41(12): 3758-3772. [5] postl,w. (1986); detection of linear oblique structures and skew scan in digitized documents, proc. of 8th icpr, paris, france, 687-689. [6] yan, h. (1993); skew correction of document images using interline cross-correlation, cvgip: graphical models and image processing, issn 1049-9652, 55(6): 538-543. [7] brodić, d.; milivojević, z.n. (2013); log-polar transformation as a tool for text skew estimation, elektronika ir elektrotechnika issn 1392-1215, 19(2): 61-64. [8] saragiotis, p.; papamarkos, n. (2008); local skew correction in documents, int j pattern recognit artif intell issn 0218-0014, 22(4): 691-710. 680 d. brodić, č.a. maluckov, l. peng [9] makridis, m.; nikolau, n.; papamarkos, n. (2010); an adaptive technique for global and local skew correction in color documents, expert syst appl, issn 0957-4174, 37(10): 6832-6843. [10] otsu, n. (1979); a threshold selection method from gray-level histograms, ieee trans sys, man, cyber, issn 0018-9472, 9(1): 62-66. [11] chen, kuo-nan; chen, chin-hao; chang, chin-chen (2012); efficient illumination compensation techniques for text images, digit signal prog issn 0165-1684, 22(5): 726-733. [12] brodić, d.; milivojević, d.r. (2012); an algorithm for the estimation of the initial text skew, inf technol control, issn 1392-124x, 41(3): 211-219. [13] brodić, d. (2011); the evaluation of the initial skew rate for printed text, j electr eng, issn 1335-3632, 62(3): 142-148. [14] kapogiannopoulos, g.; kalouptsidis, n. (2002); a fast high precision algorithm for the estimation of skew angle using moments, proc. of sppra, crete, greece, 275-279. [15] zramdini, a.; ingold, r. (1993); optical font recognition from projection profiles, electronic publishing, issn 0194-4851, 6(3): 249-260. [16] brodić, d.; milivojević, d.; tasić, v.; milivojević, z. (2013); identification of the global text skew based on the convex hulls, proc. of mipro, opatija, croatia, 1282-1286. sensitive ants in solving the generalized vehicle routing problem c.-m.pintea, c.chira, d.dumitrescu, p.c. pop camelia-m. pintea g. cosbuc n.college romania, a. iancu, 400083, cluj-napoca e-mail: cmpintea@yahoo.com camelia chira, d.dumitrescu “babes-bolyai” university romania, m. kogalniceanu, 400084, cluj-napoca e-mail: cchira@cs.ubbcluj.ro, ddumitr@cs.ubbcluj.ro petrică c. pop north university of baia mare romania, v. babeş , 430083, baia mare e-mail: pop_petrica@yahoo.com abstract: the idea of sensitivity in ant colony systems has been exploited in hybrid ant-based models with promising results for many combinatorial optimization problems. heterogeneity is induced in the ant population by endowing individual ants with a certain level of sensitivity to the pheromone trail. the variable pheromone sensitivity within the same population of ants can potentially intensify the search while in the same time inducing diversity for the exploration of the environment. the performance of sensitive ant models is investigated for solving the generalized vehicle routing problem. numerical results and comparisons are discussed and analysed with a focus on emphasizing any particular aspects and potential benefits related to hybrid ant-based models. keywords: ant-based models, optimization, sensitivity, complex problems 1 introduction the potential of ant-based models [2, 3, 9] in solving difficult optimization problems has been well emphasized by successful results obtained in many and varied fields including transportation optimization, quadratic assignment, scheduling, vehicle routing and protein folding. inspired by the real-world collective behaviour of social insects, ant colony system (acs) algorithms [2] rely on the stigmergic interactions between many identical artificial ants to find solutions to a given problem. each ant generates a complete tour (associated to a problem solution) by probabilistically choosing the next node at each path intersection based on the cost and the amount of pheromone on the connecting edge. stronger pheromone trails are preferred and the most promising tours build up higher amounts of pheromone in time. inducing heterogeneity in the population by enabling each artificial ant to react in a different way to the same environment [10] represents a promising approach to the application of antbased models for solving complex real-world problems possibly with a dynamic character. each individual ant can be endowed with a certain level of sensitivity to the pheromone trail triggering various types of reactions to a changing environment. the variable pheromone sensitivity within the same population of ants can potentially intensify the search (normally through high sensitivity levels) while in the same time inducing diversity for the exploration of the environment. the int. j. of computers, communication and control (date of submission: november 24, 2008) decision of a low-level sensitive ant regarding the action to be performed crucially contributes to the quality of the search process and solutions. the generalized vehicle routing problem (gvrp) is an extension of the vehicle routing problem (vrp) and was introduced by ghiani and improta [6]. the gvrp is the problem of designing optimal delivery or collection routes, subject to capacity restrictions, from a given depot to a number of predefined, mutually exclusive and exhaustive node-sets (clusters). the gvrp belongs to the class of generalized combinatorial optimization problems, which are natural extensions of combinatorial optimization problems by considering a related problem relative to a given partition of the nodes of the graph into node sets, while the feasibility constraints are expressed in terms of the clusters. in the literature we can find several generalized problems such as the generalized minimum spanning tree problem (see [13]), the generalized traveling salesman problem, the generalized vehicle routing problem, the generalized (subset) assignment problem, etc. these generalized problems belong to the class of np-complete problems, are harder than the classical ones and nowadays are intensively studied due to the interesting properties and applications in the real world, even though many practitioners are reluctant to use them for practical modeling problems because of the complexity of finding optimal or nearoptimal solutions. ghiani and improta [6] showed that the problem can be transformed into a capacitated arc routing problem (carp) and baldacci et al. [1] proved that the reverse transformation is valid. recently, pop [12] provided a new efficient transformation of the gvrp into the classical vehicle routing problem (vrp). as far as we know, the only specific algorithm for solving the gvrp was developed by pop et al. [11] and was based on ant colony optimization. the aim of this paper is to investigate the performance of the sensitive ant model (sam) [10] in solving the generalized vehicle routing problem (gvrp) [6]. we report numerical results of the sam model for several gvrp benchmark problems and discuss the performance of sam compared to the standard acs technique. 2 definition and complexity of the gvrp let g = (v, a) be a directed graph with v = {0, 1, 2, ...., n} as the set of vertices and the set of arcs a = {(i, j) | i, j ∈ v, i ̸= j}. a nonnegative cost cij associated with each arc (i, j) ∈ a. the set of vertices (nodes) is partitioned into k + 1 mutually exclusive nonempty subsets, called clusters, v0, v1, ..., vk (i.e. v = v0 ∪ v1 ∪ ... ∪ vk and vl ∩ vp = ∅ for all l, p ∈ {0, 1, ..., k} and l ̸= p). the cluster v0 has only one vertex 0, which represents the depot, and remaining n nodes belonging to the remaining k clusters represent geographically dispersed customers. each customer has a certain amount of demand and the total demand of each cluster can be satisfied via any of its nodes. there exist m identical vehicles, each with a capacity q. the generalized vehicle routing problem (gvrp) consists in finding the minimum total cost tours of starting and ending at the depot, such that each cluster should be visited exactly once, the entering and leaving nodes of each cluster is the same and the sum of all the demands of any tour (route) does not exceed the capacity of the vehicle q. an illustrative scheme of the gvrp and a feasible tour is shown in the figure 1. the gvrp reduces to the classical vehicle routing problem (vrp) when all the clusters are singletons and to the generalized traveling salesman problem (gtsp) when m = 1 and q = ∞. the gvrp is np -hard because it includes the generalized traveling salesman problem as a special case when m = 1 and q = ∞. several real-world situations can be modeled as a gvrp. the post-box collection problem 2 3 1 0 7 6 4 5 10 9 8 v 1 v 0 v 2 v 3 v 4 d1=3 d2=5 d3=4 d0=0 d10=3 d9=4 d8=2 d7=2 d4=5 d5=4 d6=5 q1=12 q2=9 q0=0 q4=11 q3=5 11 v 5 d11=7 q5=7 m=2 and q=25 figure 1: an example of a feasible solution of the gvrp described in laporte et al. [7] becomes an asymmetric gvrp if more than one vehicle is required. furthermore, the gvrp is able to model the distribution of goods by sea to a number of customers situated in an archipelago as in philippines, new zeeland, indonesia, italy, greece and croatia. in this application, a number of potential harbours is selected for every island and a fleet of ships is required to visit exactly one harbour for every island. several applications of the gtsp (laporte et al. [8]) may be extended naturally to gvrp. in addition, several other situations can be modeled as a gvrp, these include: • the traveling salesman problem (tsp) with profits (feillet et al. [4]); • a number of vehicle routing problem (vrp) extensions: the vrp with selective backhauls, the covering vrp, the periodic vrp, the capacitated general windy routing problem, etc.; • the design of tandem configurations for automated guided vehicles (baldacci et al. [1]). 3 the acs algorithm for solving gvrp the acs-based algorithm for gvrp [11] uses artificial ants in order to construct vehicle routes by successively choosing exactly one node from each cluster. this task continues until each cluster has been visited. whenever the choice of another node from a cluster would lead to an infeasible solution because of vehicles capacity, the depot is chosen and a new route is started. initially, the nearest neighbor (nn) algorithm with the rule always go to the nearest asyet-unvisited location is considered. the best solution of nearest neighbor (nn) algorithm (l+) is used for acs-based algorithm start. the number of ants corresponds to the number of gvrp customers m. at the beginning of an iteration, an ant is placed at each node (customer). after initializing the basic ant system int. j. of computers, communication and control (date of submission: november 24, 2008) algorithm, the two steps: (i) construction of vehicle routes and (ii) trail update are repeated for a given number of iterations. to favor the selection of an edge with a high pheromone level and high visibility, a probability function pijk is defined as follows: p ij k (t) = τkij(t)[η k ij(t)] β∑ o∈jki τkio(t)[η k io(t)] β (1) where jki is the set of unvisited neighbors of node i by ant k, j ∈ j k i and β is a parameter used for tuning the relative importance of visibility. after an artificial ant has constructed a feasible solution, the pheromone trails are laid depending on the objective value lk. for each edge that was used by ant k, the pheromone trail is updated according to the following rule: τij(t + 1) = (1 − ρ)τij(t) + ρ 1 lk (2) where ρ ∈ (0, 1) is an evaporation rate parameter. a tabu list prevents ants visiting clusters they have previously visited. the ant tabu list is cleared after each completed tour. the global update rule, applied by the elitist ants, as in acs [3] is: τij(t + 1) = (1 − ρ)τij(t) + ρ 1 l+ , (3) where l+ is the so far best solution. 4 sensitive ant-based model for gvrp the sensitive ant model (sam) technique proposed in [10] is engaged for solving the gvrp. the general approach to solving gvrp using sam is the same with the acs approach presented in the previous section except that the transition probabilities defined by sam are used. the initialization of the algorithm, the update rules and the maintainance of a tabu list are kept the same in sam for gvrp. the sam algorithm involves several ants able to communicate in a stigmergic manner (influenced by pheromone trails) for solving complex search problems. within the sam model, each ant is characterized by a pheromone sensitivity level (psl). the psl value is expressed by a real number in the unit interval [0, 1]. when psl is null the ant completely ignores stigmergic information and when psl is one the agent has maximum pheromone sensitivity. the ants with a low psl value are more independent and are considered environment explorers. they have the potential to autonomously discover new promising regions of the solution space. the ants with high psl values are very sensitive to pheromone traces. they are influenced by stigmergic information and therefore intensively exploit the promising search regions already identified. sam introduces a measure of randomness proportional to the level of individual psl in the decisions of ants regarding the path to follow. this is achieved by modifying the transition probabilities using the psl values in a renormalization process [10]. the sam renormalized transition probability for ant k (influenced by psl) is denoted by spijk (t) and is given by the following equation: sp ij k (t) = p ij k (t) · pslk(t), (4) where pijk (t) is the probability for ant k to choose the next node j from current node i (as given in acs see equation 1) and pslk(t) represents the psl value of ant k at time t. table 1: problem characteristics for the ant-based algorithms for gvrp problem vr q q’ no.vehicles no.routes 11eil51 2 160 320 6 3 16eil76a 2 140 280 10 5 16eil76b 3 100 300 15 5 16eil76c 2 180 360 8 4 16eil76d 2 220 440 6 3 21eil101a 2 200 400 8 4 21eil101b 2 112 224 14 7 it can be noticed that the sam probability of selecting the next node is the same with the acs one when psl value is one. in order to associate a standard probability distribution to the system, the sam virtual state corresponding to the ’lost’ probability of (1 − pslk(t)) has to be defined. the associated virtual state decision rule specifies the action to be taken when the virtual state is selected using the renormalized transition mechanism. the following rule is used in the current paper: the ant randomly chooses an available node with uniform probability if the virtual state is selected. this approach favors the increasing of randomness in the selection process with the decreasing of sensitivity level to pheromone. 5 computational results the performance of the sam and acs for solving gvrp is investigated. numerical experiments focus on seven benchmark problems from the tsplib library [14]. these problems contain between 51 and 101 customers (nodes), which are partitioned into a given number of clusters, and in addition the depot. originally the set of nodes in these problems is not divided into clusters. the clustering procedure proposed by fischetti et al. [5] is used to divide data into node-sets. this procedure sets the number of clusters m = [n 5 ], identifies the m farthest nodes from each other and assigns each remaining node to its nearest center. table 1 contains the description of the gvrp instances addressed in this paper. the meaning associated with the columns in table 1 is as follows: • problem: the name of the test problem contains the number of clusters (first digits in the problem name) and the number of nodes (last digits in the problem name). • vr: the minimal number of vehicles needed for a route in order to cover even the largest capacity of a cluster (vr=vehicles/route) • q’: the capacity q · v r, where q is the capacity of a vehicle available at the depot. the same parameter setting was used in both sam and acs algorithms in order to allow a meaningful direct comparison: τ0 = 0.1 (the initial value of all pheromone trails), α = 1, β = 5, ρ = 0.0001 and q0 = 0.5. in the sam algorithm, the psl value is randomly generated between 0 and 1 for each ant. numerical results indicate a competitive performance of the sam algorithm. figure 2 presents sam results from 20 succesive runs for the considered problem instances. int. j. of computers, communication and control (date of submission: november 24, 2008) figure 2: sam results from 20 runs for the considered problem instances [14] table 2: best values and times acs and sam algorithms for solving gvrp problem acs time acs sam time sam 11eil51 418.85 212 418.21 297 16eil76a 668.78 18 651.98 25 16eil76b 625.83 64 599.23 166 16eil76c 553.21 215.00 577.49 88 16eil76d 508.81 177.00 515.64 120 21eil101a 634.74 72 634.74 111 21eil101b 875.58 8.00 966.17 52 tables 2 and 3 present comparative numerical results obtained in 20 runs. the performance of sam in solving gvrp is compared to that of acs [11]. the following information is contained in tables 2 and 3: • best length: the minimal length of collection routes; • best time: the time of the minimal collection routes; • avg. length: the average length for 20 runs; • avg. time: the average time (in seconds) for 20 runs. the computational values are the result of the average of 20 successively runs of both algorithms. termination criterion is either the maximum number of iterations, niter = 250000 or the maximum running time (five minutes) on a amd 2600, 1.9ghz and 1024 mb. when comparing the best solution reported in 20 runs, the performance of the acs and sam algorithms are similar. sam reports better values for three out seven problems while acs does better for other three problems. it should be noticed that whenever a method is able to obtain a better solution, it also reports a longer running time compared to the other one. the average solutions obtained by sam are clearly improved compared to acs although the running time has slightly increased for some of the gvrp instances. sam detects a better average solution (calculated based on the 25 runs) for six out of the seven benchmark problems. table 3: average values and times acs and sam algorithms for solving gvrp problem acs time acs sam time sam 11eil51 429.85 210.20 424.05 96.40 16eil76a 706.09 109.20 677.85 187.60 16eil76b 684.04 50.7 608.62 173.7 16eil76c 625.87 73.00 602.06 42.25 16eil76d 566.56 93.20 533.12 223.45 21eil101a 699.46 29.00 690.39 124.30 21eil101b 996.41 25.95 998.71 27.90 overall better average sam results are facilitated by a better exploration of the search space and exploitation of new solutions. this is due to the variable sensitivity induced in sam via random individual psl values. 6 conclusions sensitive heterogeneous ant-based models facilitate a balanced search process by endowing ants with different pheromone senitivity levels translated into different search strategies. an effective exploration of the search space is performed particularly by ants having low pheromone sensitivity while the exploitation of intermediary solutions is facilitated by highly-sensitive ants. the performance of hybrid ant-based models is investigated with succesful results for solving the np-hard generalized vehicle routing problem. variable pheromone sensitivity in ant-based models proves to be benefic to the search process leading to better results compared to the ant colony system algorithm. numerical results encourage the exploration of new ways to induce heterogeneity in ant-based models. acknowledgments. this work was supported by cncsis-uefiscsu, project number pniiidei 508/2007 new computational paradigmes for dynamic complex problems. bibliography [1] r. baldacci, e. bartolini and g. laporte, some applications of the generalized vehicle routing problem, le cahiers du gerad, g-2008-82, 2008. 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[14] http://www.iwr.uni-heidelberg.de/groups/comopt/software/ tsplib95/vrp/ international journal of computers communications & control issn 1841-9836, 12(2):217-226, april 2017. detecting bridge anaphora d. gîfu, l.i. cioca daniela gîfu* 1. romanian academy iaşi branch codrescu, 2 iaşi, 700481, romania 2. "alexandru ioan cuza" university of iaşi general berthelot st., 16, iaşi, 700483, românia "alexandru ioan cuza" university of iaşi, românia *corresponding author: daniela.gifu@info.uaic.ro lucian-ionel cioca "lucian blaga" university of sibiu 10, victoriei bd., sibiu, 550024, românia lucian.cioca@ulbsibiu.ro abstract: the paper presents one of most important issues in natural language processing (nlp), namely the automated recognition of semantic relations (in this case, bridge anaphora). in this sense, we propose to recognize automatically, as accurately as possible, this type of relations in a literary corpus (the novel quo vadis), knowing that the diversity and complexity of relations between entities is impressive. furthermore, we defined and classified the bridge anaphora type relations based on annotation conventions. in order to achieve the main goal, we developed a computational instrument, bat (bridge anaphora tool), currently still in a test (and implicitly an improvable) version. this study is intended to help especially specialists and researchers in the field of natural language processing, linguists, but not only. keywords: bridge anaphora, annotated novel, bridge anaphora tool, testing corpus, corpus-driven, statistics. 1 introduction the novelty of this study consists in the development of a web application for the automated identification of bridge anaphora type relations in a corpus from the literary area. in this case, the target is the romanian version of the novel quo vadis, authored by the nobel laureate henryk sienkiewicz [24]. initially, a similar study carried out by the same team consisted in the supervised extraction of bridge anaphora type relations, using weka statistics [8]. moreover, there was defined a set of annotation conventions for 11 bridge relations as a result of manual annotations made by a team of trained students in computational linguistics. the hypothesis of this paper is that the triggers have a fundamental role in the automated recognition of semantic relations generally and particularly of bridge anaphora relations. the paper is structured in 5 sections. after a brief introduction about the importance of this study, section 2 mentions some important works focused on bridging anaphora. section 3 describes bridge anaphora relations in the context of semantic relations and section 4 describes a new tool functionality, called bat (bridge anpahora tool). the last section highlights conclusions and mentions the future intentions, one of the main projects of romanian researchers in nlp. copyright © 2006-2017 by ccc publications 218 d. gîfu, l.i. cioca 2 state of the art in our context, the semantic relations play a fundamental role in the information extraction process [1], regardless of the nature of the corpus [2, 9, 10]. up to now, researchers in the nlp area have allocated a lot of time to identifying the best annotation conventions of semantic relations for various literary types [3, 4, 12] based on which the process of automated recognition of semantic relations was not only simplified, but the accuracy of results increased as well, for example in their unsupervised extraction [7]. one of the best known studies on the "bridging" concept originates with h.c. herbert [11]. he starts from several scenarios in which an inference step is needed in order to understand the sense intended by the speaker and he states that the text itself does not offer the solution for solving the inference relation; the reader (or the computational instrument/machine) must use his/its knowledge on the anaphora and the antecedent in order to make a correct text interpretation. in the automated recognition of semantic relations, a special attention is granted to the anaphora resolution [23, 25], using statistic models [19, 22], something that we too exploited. nlp uses for recognizing entities and identifying relations in the text (bridging) systems based on manually created rules (see hobb’s algorithm) [15], but also systems using statistical models that are in turn based on automated learning techniques in order to lessen the workload, models such as conditional random fields (crf) [26]. 3 bridge anaphora in semantic relations context in order to better understand a content, we need thinking instruments, necessary for discovering new ideas or for clarifying the existing ones, illustrating the link between them. the semantic relations [16] describe these interactions, that are indispensable for interpreting texts. the properties of semantic relations were described in [17], this marking the relations between two entities (called poles) as open class. the application describes 10 types of bridge anaphora1. 3.1. a short introduction about semantic relations the semantic relations are represented as being distributions over several paragraphs [18]. in processing the natural language, the semantic relations play a fundamental role in the field of information extraction (ie), that targets the automated extraction of structured information referring to entities such as person names, localities etc. from semi-structured or unstructured texts. the ability to identify and understand these relations in a text can b useful in very many directions, such as: machine translation mt; computer assisted assessment caa; clustering and so on. in order to create an instrument that can carry out, for example the automated translation, the interpretation of anaphora is also very important, especially in cases in which the translation is from a language in which the pronouns have different forms for each gender, into a language in which the pronoun has the same form regardless of gender [15, 22]. 1class-of relation between person-class & person; has-as-member relation between persongroup & person; has-as-part relation between person & person-part; has-as-subgroup relation between person-group & person-group; has-name relation between person & person-name; isa relation between person & person-class; member-of relation between person & persongroup; name-of relation between person-name & person; part-of relation between person-part & person; subgroup-of relation between person-group & person-group. detecting bridge anaphora 219 3.2. about bridge anaphora bridge anaphora [8] are referential semantic relations (beneath the co-referential or anaphoral ones) [5, 6] that includ linguistic expressions that give meaning to the analysed text (here, the narrative "thread" of the novel). our documentation shows that the analysis of semantic relations is focused on structured corpuses such as: online newspapers, blogs, wikipedia texts etc. [1]. a bridge anafora or "bridging" is a semantic relation that represents a link between the anaphora and the antecedent [11, 12]. these two elements will be mentioned in the following also as poles of a bridge-type semantic relation. in the next section we present the 10 types of bridge anaphora relations based on which the bat was developed. an example of bridge-type semantic relation: andrei este numit în diferite cercuri micuţul, din cauza înălţimii. —>(en.) andrei is called in different circles the little guy, because his height. where: • andrei is an antecedent; • micuţul —>(en.) the little guy is an anafor. 3.3. bridge anaphora vs. anaphora a bridge anaphora type relation differs from an anaphorical relation firstly by the fact that it can be identified in the text using a trigger. this trigger can be a word or a group of words that has the property of indicating the presence in the text of the bridge anaphora relation, helping to identify it. in the following, we will exemplify the anaphorical relation and the bridge anaphora type relation in oder to clarify the difference between the two relations, both being referential type relations: anaphorical relation (coreferential) 1:[marcus] era foarte supărat pentru toate cele întâmplate în ultima perioadă, însă 2:[el] nu avea de gând să renunţe. —>(en.) 1:[marcus] was very upset about what happened lately, but 2:[he] was not going to give up. =>[2] anaphorical relation [1]; bridge anaphora type relation (below, the type class-of2) cândva, 1:[petronius] fusese guvernator în 2:[bitinia]... —>(en.) sometime 1:[petronius] was governor in 2:[bithynia]... =>[1] bridge anaphora type relation [2], while governor in is the trigger for this relation. this is a segmentation annotation in xml standoff format: <w lemma="cândva" msd="rg" pos="adverb" id="1" offset="0">cândva</w> <w lemma="," msd="comma" id="2" offset="6">,</w> <clause continue="27" id="clause31"> <entity id="e000900036" type="person"> 2class-of is a bridge anaphora type relation linking a person-class type concept to a person type instance. 220 d. gîfu, l.i. cioca <referential from="e000900036" id="ref000900582" to="e000700030" type="coref"> <w case="oblique" definiteness="no" extra="notindict" gender="feminine" lemma="petronius" msd="npfpon" number="plural" pos="noun" type="proper" id="3" offset="8">petronius</w> </referential> </entity> </clause> <w extra="intranzitiv" lemma="fi" msd="vmil3s" mood="indicative" number="singular" pos="verb" person="third" tense="long" type="predicative" id="4" offset="18">fusese</w> <entity id="e000900037" type="person"> <w case="direct" definiteness="no" gender="masculine" lemma="guvernator" msd="ncmsrn" number="singular" pos="noun" type="common" id="5" offset="25">guvernator</w> </entity> <w lemma="în" msd="sp" pos="adposition" id="6" offset="36">în</w> <clause continue="31" id="clause32"> <entity id="e000900038" type="location"> <referential from="e000900038" id="ref000900584" to="e000900036" type="class-of"> <referential from="e000900038" id="ref000900584" to="e000800035" type="coref"> <w extra="notindict" lemma="bitinia" msd="np" pos="noun" type="proper" id="7" offset="39">bitinia</w> </referential> </referential> </entity> </clause> the anaphorical relations are a widely debated subject [12, 13, 14], proven by numerous specialty papers that present computational instruments for the automated identification of these relations, especially for the pronominal anaphora [15, 22]. this type of relation is much easier to identify in the text, as opposed to a bridge anaphora type semantic relation, because both poles of the the relation, the anaphora and the antecedent refer to the same entity [20]. in order to be able to automatically identify bridge type anaphorical relations, there is necessary a more complex mechanism, that would carry aut in a first phase a preprocessing of the text for its de-ambiguization that consists in segmentation, tokenization, lemmatization, part-of-speech tagging, name entity recognition, and anaphora resolution. 4 bat description bridge anaphora tool is a computational instrument implemented in java language, on the framework java server faces and uses a series of libraries3. bat is created for the automated recognition of bridge type semantic relations, more precisely of the 10 types of referential relations for which annotation conventions have been determined. the output xml file was used in the process of training and testing. we chose the novel quo vadis [24], given that it is a corpus translated into more than 40 de languages, having an impressive number of entities and semantic relations. using the instrument palinka [21] 3see http://primefaces.org/ detecting bridge anaphora 221 the entities and semantic relations were annotated manually. the annotator was already used successfully for annotating the novel quo vadis, a work presented in [3]. this web application (fig. 1) executes in a first phase the training process after which the automated recognition can be initiated. figure 1: the interface of the computational tool in the following, we describe briefly the work methodology. for the trening process, following steps were conceived: the option "yes" is selected for the relations that will be included in the training; figure 2: bat selecting relations for training the xml file is loaded from the application, using the button "train relations", the xml is selected (the manually annotated corpus) after which the button "process file" is pressed. 222 d. gîfu, l.i. cioca figure 3: bat menu for introducing the corpus and for trening relations when the bat identifies the tag <referential>, it carries out four steps: it saves in the mysql database the type of relation (it can be one of the 10 "class-of", "has-as-member", etc.) in the table "referential_type"; figure 4: bat the table "referential_type" generated after the training it saves the type of entity from the identified relation (in the example above we have type="person") in the table "entity_type"; figure 5: bat the table "entity_type" generated after the training it saves the word/words from the tag <entity>specific to the relation in the table "entity_words"; if there are two or more words, they are concatenated with the symbol "|"; detecting bridge anaphora 223 figure 6: bat the table "entity_words" generated after training it saves the actual structure of a bridge type semantic relation, i.e. the "type" and the words forming it, in xml they being identifiable with the elements "id", "from" and "to". figure 7: bat the table "referential_entity" generated after training the processing of the xml file in the training phase of the bat for one or more bridge type semantic relations can take from one minute to several hours, function of the number of relations existing in the annotated corpus. for the "part-of" relation, the training took 2.67 hours, being the most often encountered in the xml file, with a number of 1612 relations. 5 statistics and interpretations bridge anaphora tool used for training 66% of the corpus of quo vadis. mitkov (1998) suggested, for measuring the performance of a computational instrument aiming at identifying anaphorical relations in the text, an equation that defines its success rate. the definition of the success rate is as follows: bat success rate = 534 correctly identified relations / 1035 total existing relations = 61.5%. so at this moment, the bat recognized correctly over 61% of the bridge anaphora type semantic relations that should have been identifying in the text, thus fulfilling the set goal. 224 d. gîfu, l.i. cioca table 1: the results of recognizing the bridge type semantic relations with bat bridge bridge anaphora bridge anaphora bridge anaphora anaphora number identified number identified number identified types with bat in automatically in manually in driven corpus testing corpus testing corpus class-of 189 58 28 has-as-member 347 115 82 has-as-part 109 31 12 has-as-subgroup 176 55 22 has-name 28 9 7 isa 81 25 14 member-of 169 51 38 name-of 158 53 29 part-of 1612 530 249 subgroup-of 337 108 53 total 3206 1035 534 we think that the variations of the values in the column "number of relations identified automatically by bat in the testing corpus" are due also to the fact that the instrument searches "mechanically" in the preprocessed text rigid definitions of the relations. for example: entity of the type person-name + person =>relation "name-of". moreover, there exist two relations that have the same definition, namely the relations: hasas-subgroup and subgroup-of being given by the entities of type person-group+persongroup, the only difference between them being made by the triggers, during testing. conclusions and future work this paper presents a methodology for the automated recognizing of 10 bridge anaphora (or bridging) type semantic relations, each having several particularities. the achieved results are promising, offering a base for future researches. we suggest using in parallel of machine learning models (naïve bayes and support vector machines). the bat instrument, developed for the automated recognition of bridge anaphora relations, will be improved through the addition of several triggers to the existing list, or in the situation in which there would be available even more data for training. bat is far from being a perfect instrument, but it can be improved since it showed to be efficient at least for an experimental purpose for various applications in the nlp area. acknowledgments we thank alexandru iliescu for developing the bat instrument. we are also grateful to all colleagues from nlp-group@uaic-fii who developed the tools for natural language processing used in this research. detecting bridge anaphora 225 bibliography [1] banko, m., cafarella, m. j., soderland, s., broadhead, m., etzioni, o. 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[26] z̆itnik, s, s̆ubelj, l, bajec, m (2014); skipcor: skip-mention coreference resolution using linear-chain conditional random fields, plos one, 9(6): e100101. doi:10.1371/journal.pone.0100101. international journal of computers communications & control issn 1841-9836, 11(6):860-876, december 2016. a new method for colour image segmentation g. schleyer, g. lefranc, c. cubillos, g. millán, r. osorio-comparán gustavo schleyer, gastón lefranc*, claudio cubillos pontificia universidad católica de valpaíso escuela de ingeniería eléctrica avda. brasil #2147 valparaíso, chile *corresponding author: glefranc@pucv.cl ginno millán universidad católica del norte escuela de ingeniería larrondo #1281 coquimbo, chile e-mail: gmillan@ucn.cl román osorio-comparán universidad nacional autónoma de méxico instituto de investigaciones en matemáticas aplicadas y en sistemas ciudad universitaria coyoacán, méxico d.f., méxico e-mail: roman@unam.mx abstract: this paper presents an unsupervised algorithm of colour image segmentation and is an extension of [1]a . this method combines the advantages of the approaches based on split and merge and region growing, and the use of the rgb and hsv colour representation models. the effectiveness of the proposed method has been verified by the implementation of the algorithm using three different testing images with homogeneous regions, spatially compact and continuous. it was observed that the proposed algorithm outperforms the other analysed techniques requiring shorter processing time when compared with the other methods. keywords: computer vision, image processing, image segmentation. areprinted (partial) and extended, with permission based on license number 3930961102612 [2016] ©ieee, from "computers communications and control (icccc), 2016 6th international conference on". 1 introduction image segmentation involves the identification of regions of interest, which generally are an object or a part in a digital image. each region must maximise the homogeneity of its pixels features (colour, texture) and simultaneously maximise the differences with neighbouring regions; moreover, each region must be spatially compact. in general, image segmentation considers an important initial step in most image processing applications, which use the segmentation information in order to perform upper level tasks, such as object tracking or identification, and scenes interpretation. this paper is an extension of paper presented in icccc2016 [1]. two approaches widely used in colour image segmentation are split and merge [2]-[4] and region growing [5]-[9]. in general, the split and merge method begins with an initial and no homogeneous image partition, then keeps on splitting it until homogeneous partitions are obtained. a common data structure used in the implementation of this procedure is the quad tree representation [10]. after a division copyright © 2006-2016 by ccc publications a new method for colour image segmentation 861 step, usually, many small and fragmented regions appear connected in some way during the merging phase. on the other hand, the method based on region growing consists in obtaining a homogeneous image region through a growing process that, beginning from a preselected seed, progressively crowd pixels around it fulfilling a determined homogeneity criterion. the growing process stops when it is not possible to add new pixels to the region. a common post-processing step consists in a union phase that eliminates small regions or neighbouring regions with similar attributes, making larger regions. in this paper proposes an unsupervised colour image segmentation algorithm that combines the advantages of the split and merge and region growing approach using model features in the rgb and hsv colour representation. the effectiveness of the proposed method is compared with a parameter less quadrilateral-based image segmentation method [10] with a fuzzy segmentation hci [11], and with a image segmentation using situational descriptors dct [12]. the proposed algorithm performs the best segmentation and requires the least processing time compared with other methods used in its evaluation. it resulted to be the best approach for real applications that provides regions of homogeneous color, spatially compact and continuous, in a shorter processing time and more robustly against over-segmentation. 2 colour images models a digital image is modelled as three monochrome images, where each group is related to different colour. the rgb model is one of the most used methods where the resulting colour is formed by combining red, green and blue. the rgb model representation is a cube, using rgb coordinates instead of (x, y, z). the rgb complex algorithm establishes a different colour in a cube vertex. it is not easy to determine if these coordinates represent a particular tonality. in order to alleviate this problem a cylindrical coordinate model (hsv) is used. in the hsv model (hue saturation value), the colour represent by three fundamental quantities: tonality, saturation and the intensity value. the tonality identifies the colour, the saturation establishes the colour quantity represented in each pixel and finally its value. when a pixel has low saturation it is grey, and a pixel total saturated is a pure colour. the pixel intensity corresponds to the light quantity of the pixel. hsv model permits colour selection in a better way than rgb model. colour space in hsv model is represented as a cone, where an angle in the circumference base is the tonality, the saturation is measured outwards from the origin and the vertical axis is the intensity value. 3 colour image segmentation method 3.1 technical methods of image segmentation the technical methods of image segmentation are classified in image segmentation techniques based colour image features, techniques based colour image, and image segmentation techniques based on physics [13]-[17]. segmentation techniques based on colour image features can classified in clustering, adaptive clustering and thresholding histogram. image segmentation techniques based on colour image are: split and merge, techniques based on growing regions, graph theory, contours based, and neural networks. • split & merge technique considers spatial information and it has good results in images with homogeneous regions. however, the definition of uniformity of color can be difficult, and the tree can generate quadrangles not present objects in the image. 862 g. schleyer, g. lefranc, c. cubillos, g. millán, r. osorio-comparán • growing regions technique generates compact and spatially connected regions and some approaches produce very fast algorithms. nevertheless, they are expensive in computational time and memory, and they have difficulties in establishing the seeds and the appropriate standard of homogeneity, because the algorithms are sequential. image segmentation techniques based on physics are ssupposedly robust against excessive lighting, and segmented surfaces depending on the composition of the material. the technique is restricted to one or two types of material. it has difficulties in identifying real images when are excessive lighting and shadows. the segmentation method proposed in this paper can be classify into image-based approaches and as a mixture between two categories of segmentation techniques: split & merge and growth regions. the proposed approach divides the image in many regions (split), some of which subsequently merges based on a predetermined criterion (merge), and then algorithm can be classified into the first category. however, if the initial regions generated by the proposed algorithm considers the seeds of a region growing process, then the proposed method can be classify in the second category. the fact that the synergy produced between the two approaches can avoid disadvantages that typically find in the independent performance. 3.2 proposed colour image segmentation method as the original image i0 is 24 bits/pixel rgb format and is very large, the segmentation reduces information. then, the characteristics of the pixels of the image segment is analysed to decide whether to segment them based on their hue, saturation or intensity, using representation in the hsv model, or the segmentation must be done based on the general characteristics of colour using rgb model. the proposed image segmentation method is in fig. 1. this method consists on several algorithm to have an efficient and better segmentation of an image that it can use in real time applications. first step is the rgb in hsv image conversion, reducing to 26 values. it is following with tonality segmentation including tonality label algorithm and extreme value of intensity mask application. after this segmentation, applies a merge of tonality of small and great group algorithm. then, it continues a pixel segmentation with low saturation algorithm, with mask application low saturation and union of low saturation neighbors groups. the next two algorithm are pixel segmentation of extreme values of intensity algorithm and remaining pixels segmentation algorithm. to finish, applies the final merge of small groups algorithm. because of an excessive amount of information produces in excessive processing times a reduction, gives the possibility of practical application of the algorithm, and it is much simpler to identify a colour from hsv model than rgb, the algorithm is to convert rgb image to a hsv image. the reduction is taking 26 different values for each band, which represents by 5 bits/pixel, and as there are, three bands need 3 x 5 = 15 bits per pixel. special pixels those image pixels having extreme values of intensity or low saturation values. the segmentation of pixels with extreme intensity values are white dot (high intensity value) or a black dot (low intensity value) in the digital image. segmentation pixel io with low saturation values is more complex, they appear as grey dots on the image and it is not possible segmentation by hue (tonality), so it is necessary to identify them in the first instance and subsequently dedicate a different processing to receive those pixels segmented by colour. a new method for colour image segmentation 863 figure 1: the proposed image segmentation method reduction of possible combinations first step is the rgb in hsv image conversion, reducing to 26 values, to have an acceptable detail level on image segmentation, reducing the information in the image, reducing processing time. it is following with tonality segmentation including tonality label algorithm and extreme value of intensity mask application. after this segmentation, applies a merge of tonality of small and great group algorithm. then, it continues a pixel segmentation with low saturation algorithm, with mask application low saturation and union of low saturation neighbors groups. this step diminishes the quantity of information provided by the hsv model, so they can adopt integer values between 1 and 26, reducing the number of possible combinations to the inferior integer. segmentation by tonality in the segmentation by tonality, special pixels (image pixels having extreme values of intensity or low saturation values) are separated from the image. once identified the pixels to segment according to their tonality, the neighbouring pixels that 864 g. schleyer, g. lefranc, c. cubillos, g. millán, r. osorio-comparán have the same tonality are merged and those ones with a different tonality are split. then, 26 matrixes are created, one for each possible tonality value, which have been called h matrixes. the segmentation of the no neighbouring pixels of the same tonality is carried out labelling each h matrix. the labelling consists in assigning a number to each component found in the image. so, the image is scanned from left to right and from top to bottom searching for cell sets with value "1", the first set found keeps its original value, but in the next set, the values “1" are replaced by “2", and so on, until the value n is reached, where n is the number of components in the scene. a result of the application of the previous procedure to an image is presented in fig. 2. figure 2: labelling of components the h matrixes store information of tonality, which is lost when they are merged in order to form hseg. from this point, the information of tonality of each region is stored in a column vector, called color_reg. the label value of each region, designated by the num_etiqueta variable, indicates the row of color_reg in, which must be, stored the tonality information. tonality small groups merging analyzing hseg matrix, it shows an over segmentation image, because within the images are small groups of pixels that can correspond to shadows, grooves, reliefs, etc. these details do not represent relevant information, with less than 25 pixels. if h matrices are filtered to remove these small groups, tonality information could lost. instead of filtering these arrays, it creates an algorithm capable of merging these small groups small with larger groups. then, all the groups that have less than a certain quantity of pixels, determined by an algorithm parameter called par_area must be merged with some neighbouring group. the par_area parameter, establishes a pixel minimum area of each segmented group. to find the group with which each component has the biggest quantity of neighbouring pixels, it must be counted each kind of elements, inside the vector of the vecinos structure associated, with this component and to identify which one appears more times. then, in order to identify small groups the areas vector is analysed searching values less than par_area, more formally a small group. the group to merge with gp, with its neighbouring group gfusion_p with which exists the least tonality difference. in the case that there are more than one group with such difference, that is, if the set has more than one element, the group choose is a set, which has the bigger quantity of element. tonality big groups merging a big group gg is defined as all those groups which pixels quantity is bigger than par_area. a new method for colour image segmentation 865 the big groups gg are merged with those neighbouring groups with which it have a tonality difference less than or equal to one. the condition of the areas establishes that the prevailing label value is the one belonging to the group that have the biggest quantity of pixels, considering this, the label value of all the pixels belonging to gg must be replaced by the label value of gfusion_g. mask application low saturation. re-labeling of groups the merger of groups leads to the elimination of some regions and increased the size of others. the formation of groups is constantly changing when the segmentation algorithm iterates. this information must be recorded in the matrix which contains the location and hseg tag value of all groups. however, update this matrix every time a group merges with another would require much processing time, thus new label values are only updated once known all new groups in hseg. as the hseg matrix is scanned, each time that a pair of group, which must be merged, is found, the label values of both groups are stored in a table called tfusion. an algorithm that searches for all the groups of the table tfusion, which are related with each other, is re-labelling of the c matrix. as a result, k groups that cannot be merged are obtained, which are stored in the e(k) variable, finally, the hseg values are refreshed. with this step, it concludes the first iteration of the algorithm of segmentation by tonality. in the next iterations the definition of color_reg cannot be used, in the future it will be considered that color_reg for each group; will correspond to the mean of the i0 values belonging to the respective group. the iterations of the algorithm of segmentation by tonality continue until no new groups can be merged, that is, when tfusion = φ. once concluded this part of the segmentation process, it continue with that described in the next sections. low saturation pixels segmentation. pixel segmentation of extreme values of intensity the low saturation pixel segmentation algorithm is in charge for segmenting these pixels that have not been segmented by tonality segmentation algorithm. the first thing to consider is exist pixels with low saturation, but at the same time presenting intensity extreme values. this type of pixels will not be segmented by the segmentation algorithm pixels with low saturation. therefore, first is to remove in the matrix those pixels with extreme values of intensity. during the design segmentation algorithm pixels with low saturation various criteria such as segmentation were tested for strength value gray, hsv values and rgb values, obtaining better results with rgb. to identify the rgb value of each group present in the matrix, considers the average rgb values of each band. however, the tonality segmentation algorithm, has not identified pixels with low saturation were, they separated only those with extreme values of intensity, which has been done intentionally. as a result some groups of the matrix may be formed in part by low saturation pixel by pixel and partly medium or high saturation. it obtains two averages of the rgb bands, one for pixels with low saturation called psat, named and defined for each group and band rgb with other pixels with medium and high saturation called pno_sat. the euclidean distance between the vectors that contains the mean values of the rgb bands coordinates, corresponding to the low saturation pixels of a group, and the vector that contains the same information in relation with the medium and high saturation pixels of the same group. each element of the vector dsat_no_sat is analysed in order to confirm if they are greater than the δ parameter, if the condition is fulfilled the group associated with this element must be divided, if the area of the two resultant groups is bigger than par_area. δ and par_area are 866 g. schleyer, g. lefranc, c. cubillos, g. millán, r. osorio-comparán algorithm parameters entered by the user. each time a group that must be divided is identified, a register stored in the variable separa. once all the groups that must be divided are identified, the hseg matrix is labelled again in order to register these changes. in this case, the labelling of the hseg matrix is performed using the separa vector. low saturation neighboring groups merging neighbouring groups with low saturation merge. first, the areas of all the groups are calculated, refreshing the areas vector, then the areas of the low saturation groups are obtained refreshing the areassat vector, through the multiplication of t per ms_bajo(fil,col). second, the neighbouring pixels of the low saturation groups are identified, considering that a low saturation group is the one in which more than 50% of its pixels have low saturation values. the neighbouring pixels of each one of these groups are found. another important quantity that must be calculated is the mean value of each one of the coordinates of the rgb model, called pr, pg, and pb, for each segmented group, not considering their saturation level. in order to decide which low saturation groups will be merged, the euclidean distance between the mean of each rgb band corresponding to each group, and all their neighbouring groups is calculated. if this distance is lesser than a certain value established by a new parameter called δ2, then the groups are merged. in other words, each group will be merged with all its neighbouring groups with which the euclidean distance between their mean rgb values is lesser than δ2. the temporal information of the group that must be merged is stored in the agrupa variable. once again, only when all the groups that will be merged are known, the labelling of the hseg matrix proceeds. if there are more than a value stored in agrupa(k), the one that has the minimum euclidean distance with the rgb mean values of the k group is chosen, and the remaining values are eliminated. during the new hseg labelling the merge and labelling algorithm is utilized, but replacing the tfusion variable, by agrupa. once the building of the e matrix is finished, the labelling of the hseg matrix. with this step the low saturation pixels segmentation concludes. segmentation of pixels with extreme brightness values so far they have been segmented groups of pixels with high saturation, which allow good segmentation, then the pixel groups with low saturation have been segmented using averages of the rgb coordinates. it yet to analyze what happens with pixels with intensity extreme values, corresponding to high and low intensity values. the pixels with high intensity values appears as a white dot on the image regardless of the value of hue and saturation, so does the pixels with low intensity values, with the difference that they appear as black dots in the digital image. pixels with intensity high and low brightness have been identified by mv _alto and mv _bajo matrixes, respectively. the first step of the algorithm is to label these matrices as described by mv _alto and mv _bajo matrixes. but due to the way they were built, in each pixels are black in color which indicates the presence points with extreme brightness intensities, so that are these pixels should re labels. as the labeling was defined to identify color pixel. to finish the segmentation algorithm with pixels intensity extreme values, each label value of hseg corresponds to a different region in the image. it adds color_reg, pr, pg, pg and areas variables, and the values for the new groups. a new method for colour image segmentation 867 segmenting remaining pixels when finish all segmentation procedures, and still exist pixels unsorted, means there are groups consisting of fewer pixels than the established by the par_area parameter. during arrays mask filters applied tto mv _alto and mv _bajo matrixes eliminate such groups. pixels belonging to the edge of the image have not analysed by windows 3x3 and therefore not classified or incorrect. this corresponds to 0.43% of the total number of pixels of the image. to finish segmentation algorithm. to identify all remaining pixels, hseg is traversed looking for pixels with value 0. the information regarding the position of these pixels within is stored in a new array called r. this demands significant processing time compared with the rest of the algorithm. final merge of small groups all pixels in the digital image i0 are been classified. the next step is merge of small groups or eliminate these groups. as previously, small group has fewer pixels than the parameter set by the par_area. small groups merge with the most appropriate neighbouring group. merger criterion is the smaller euclidean distance between rgb averages of two neighbouring groups. before deleting or merging small groups identified within the hseg matrix, it is not necessary to traverse the array again. simply analysing the vector and find the smaller elements that par_area, the position vector, of these elements indicates the value of the label of these groups within the matrix hseg, stored in vector unir. the next step is to find neighbouring groups of each small group, with more par_area pixels. using the vecinos expression of all neighbouring groups of small groups of the image, with the exception of those whose pixels are on the edge. in this case, it uses the w 3x3 window to search for group neighbours. to find neighbouring groups of edge pixels of the image must modified according to w window edge where they are looking for these groups. thus, the pixel position to which is seeking its neighbouring groups determines the w window. the next task is then to traverse hseg the edges of the matrix and complete the neighbours variable using the w window corresponding pixel according to the position analysed. once the above process, in the event that any neighbouring row of elements having varying yet, it must filled with a zero. then, calculate the euclidean distance between the rgb averages for each group belonging to the group and all its neighbours groups. for each k element of unir vector, it chooses the minimum distance, after the computation of euclidean distances between the rgb average and all its neighbours. the neighbouring group that has minimum distance is the group that merge k group. this information is stored in a second column of the unir vector created especially for this purpose. the small groups are identified and what group is merged, the changes is put in the hseg label matrix. labelling of the hseg matrix is the last step of image segmentation algorithm: the result are image region, identified with a different label value in the hseg matrix. segmentation has been used hsv and rgb colour model, identifying areas by tonality, saturation and intensity brightness level, as well as the average of the rgb pixel coordinates of each region. 3.3 algorithm implementation it utilizes the filtering images method included in the matlab platform. the goal is to designate the minimum image size that can be each targeted group, so that all groups with a smaller size are included in a larger group. the par_area parameter of the algorithm implies a minimum group size. the default value of this parameter is set to 25, that is to say, once the image segmentation no groups that contain less than 25 pixels over. this prevents over 868 g. schleyer, g. lefranc, c. cubillos, g. millán, r. osorio-comparán segmentation, which means that many groups do not constitute an object or a major part, these groups could have a digital area (defined as the number of pixels) as small as a single pixel is created. the par_area parameter, establishes a pixel minimum area of each segmented group. a small value means a high resolution in the segmentation algorithm, but it could have an over segmented image. if the algorithm estimates that par_area parameter is greater, the algorithmprocessing time is high. a recommendation to adjust this parameter depends on the expected object size in the image. in an extreme case, the image may be segmented into as many groups as the form pixels, which makes no sense. the filtering process is performed automatically by the "bwareaopen" function of matlab, which eliminates an original binary image all the components that are under par_area pixels with value 0, producing a new binary image without deleted items. to remove pixels with value 1 must be obtained negative image, filtering and then get the negative of the filtered image. the bwareaopen function has three steps: determine the connected components; compute the area of each component and eliminate small objects. it has been used connectivity 4 to set the connected components, considering that a couple of pixels with value 0 are neighbours if they are attached horizontally or vertically, at this level of connectivity if only they are attached diagonally, they are neighbours. see fig. 3. when shapes with holes exist, they are removed if less than the minimum number of pixels required to be considered as a region within the segmentation. (a) (b) figure 3: (a) original image; (b) filtered image once identified the pixels, they are segmented according to their tonality. the neighbouring pixels with the same tonality are merged and those with a different tonality are split. it creates 26 h matrixes, one for each possible tonality value. the segmentation of the no neighbouring pixels of the same tonality is carried out labelling each h matrix. the labelling consists in assigning a number to each component found in the image. so, the image is scanned from left to right and from top to bottom searching for cell sets with value 1, the first set found keeps its original value, but in the next set, the values “1" are replaced by “2", and so on, until the value “n" is reached, where "n" is the number of components in the scene. a result of the application of the previous procedure to an image is represented in fig. 4. the results shows that there is an over segmentation image, because there are small groups of pixels may correspond to shadows, grooves, reliefs, etc. constituting the image details but alone do not represent relevant information. it observes many groups with less than 25 pixels. if they had leaked the h matrices would have removed these small groups, but had lost the information tonality of these pixels. it creates an algorithm capable of merging these small groups with larger groups. this merging algorithm determines the neighbours groups for each component. a new method for colour image segmentation 869 the merger of groups lead to the elimination of some regions and increased the size of others, the formation of groups is constantly changing as the segmentation algorithm iterates. once merging clusters and updating values are done, the first iteration of the tonality segmentation algorithm concludes until no new groups can be fused. pixels with low saturation, that have not been segmented by the tonality segmentation algorithm, are segmented by other algorithm using rgb intensity value grey segmentation, which gives better results. after the segmentation of the groups of pixels with low saturation, there are two possible scenarios for each group. one is that no segmentation is performed and the group remains united and the other is that the pixels with low saturation of the group are separated from their pixels with medium or high saturation, dividing the group into two. to decide if the group remains united or divided the δ parameter is used, with a default value of this parameter 25. the criteria states that for a group to be divided, the euclidean distance between two vectors, one containing the coordinates of the mean values of the bands rgb pixels with low saturation of a group, and with other similar pixels with medium and high saturation of the same group must be greater than . in addition the area of the two groups resulting after the division must be greater than the par_area division parameter to be performed. the euclidean distance between two vectors is described by dsat_no_sat(k) = 0.2264 √ r2 −g2 −b2, (1) where r = psat_r(k) − pno_sat_r(k), g = psat_g(k) − pno_sat_g(k), b = psat_b(k) − pno_sat_b(k), and k is the label number. each element of the dsat_no_sat is vector check if greater than δ parameter. if it is greater than δ means that the group associated with this element has to always be divided. when the area of the resulting two groups is greater than par_area parameter, the group to divide, a record is stored in the separa variable is created, which is defined by the expression separa(k) = { max(separa) + 1, if dsat_no_sat(k) > δ ∧areasat(k) > par_area 0 , if dsat_no_sat(k) ≤ δ ∨areasat(k) ≤ par_area , (2) where k is the label number an the initial value of separa is zero. (a) (b) (c) figure 4: (a) original matrix binary; (b): binary matrix image; (c): matrix labeled once the labelling, all new groups are identified. the δ parameter is the maximum euclidean distance between the mean of each low saturation pixels rgb average in each group, and all their neighbouring groups. to decide which groups with low saturation will be merged, the euclidean distance between the averages of each rgb band of each group with respect to all its neighbouring groups is calculated, if the distance is less than a certain set δ2 value, then it proceeds to the merger 870 g. schleyer, g. lefranc, c. cubillos, g. millán, r. osorio-comparán groups. in other words, each group will be merged with its neighbours groups with which euclidean difference between the averages of rgb values is less than the parameter δ2. the newly described euclidean distance is defined by following expression with its mixture of pixels with different levels of saturation, dp_rgb(k)i = 0.2264 √ r2v −g2v −b2v, (3) where rv = pr(k) −vecinos(k), gv = pg(k) −vecinos(k), bv = pb(k) −vecinos(k), k = 1, 2, ..., max(hseg), and i = 1,2,..., quantity of neighbours groups to k group. the temporal information groups to be merged is stored in the agrupa variable, expression agrupa(k) = agrupa(k) ∪v(k), (4) where v(k) = { vecinos(k)i|dp_rgb(k)i ≤ δ2 } . then, only once all the groups will be merged known proceed to label matrix hseg. in the case, that there is more than one stored in agrupa(k) value, the minimum distance of rgb average with group k is chosen, and the remaining values are removed. a new labelling of hseg and labelling groups, is used the fusion algorithm. once all kinds of segmentation described above, it is possible that still exist unclassified pixels. these correspond to those groups formed by a smaller number of pixels than that set by the par_area parameter, which is set to 25. to identify all the remaining pixels, it has to traverse the array in searching of pixels with value 0. the information regarding the position of these pixels within hseg is stored in a new array called r. when all pixels in the digital image are classified, the next step is to remove all small groups. as previously stated, a small group is one that is composed of a smaller number of pixels than that set by the parameter par_area. small groups merge with the most appropriate neighbouring group. once the small groups are identified and known to be merged with that group, you must register the respective label changes in the matrix. for this, we have created an algorithm. summarizing, the image segmentation method has three parameters: par_area, δ and δ2. the par_area parameter, establishes a pixel minimum area of each segmented group. a small value means a high resolution in the segmentation algorithm, but it could have an over segmented image. if the algorithm estimates that par_area parameter is greater, the algorithmprocessing time is high. a recommendation to adjust this parameter depends on the expected object size in the image. the δ parameter is the maximum euclidean distance between the mean of each low saturation pixels rgb average in each group, and all their neighbouring groups. if this distance is less than a certain established value, the parameter is called δ2; in this case, the groups are merged. in other words, each group will be merged with all neighbouring groups with which the euclidean distance between their mean rgb values is less than δ2. the δ and δ2 work with low saturation pixels, controlling the pixel tendencies to be in a higher saturation group or to merge to neighbouring groups with low saturation pixels. the image segmented groups probably grows when these parameters values diminishes. their configuration has to consider the different tonalities. for example, in the image with several objects of similar tonalities, it has to separate groups with low saturation and to avoid those neighbouring groups with the same saturation that merge. this is achieved reducing δ and δ2 values, changing the variable value in the proposed algorithm. the par_area, δ, and δ2 parameters should have to adjust to allow the proposed segmentation algorithm to adapt to images with different colour characteristic. during evaluations of this a new method for colour image segmentation 871 proposed point of view, the parameters values remain constant in all image tested, obtaining good segmentation. however, the results estimated can be improved choosing optimal parameters values in each digital image to be segmented. in the algorithm, the initials parameters are par_area = 25, δ = 5 y δ2 = 2. the hsv possible values are reduced from 256 to 26, in an arbitrary way, to have good results in images with small tonality, instead of reducing all tonalities spectrum presented. 4 evaluations of proposed segmentation method 4.1 proposed segmentation method test to test the proposed segmentation method, it utilize a house image and lego image. figure 5a shows the original house test image, having neighbouring regions with similar tonal values inside; different objects with complex shapes and different textures; and shadows in some regions of the image. figures 5b and 5c show a good performance segmentation, despite some shadow areas segmented as a region. objects with complex shapes are segmented, without an over-segmentation or excessive union of regions. (a) (b) (c) figure 5: (a) original matriz binary; (b): segmented image; (c): segmented regions the house image test has a resolution of 449x322. proposed algorithm obtains a segmented image with 120 groups and it lasts 35.19 seconds. proposed segmentation method has good performance. figure 6: original images for the evaluation to calculate the processing time, it is necessary to consider that the algorithm has been programmed in version 7.0 (release 14) matlab and has been run on a computer with intel centrino processor only 1.86 ghz and 1.5 gb of ram. the processing time is linked to the number of regions in the image. an image so large and few regions may require less processing time than and image of smaller size and more regions to segment. furthermore, for images with the same number of regions and different resolution, it requires a longer processing time. in summary, the processing time required by the algorithm depends on both the image size and the number of regions present therein, the latter being unknown amount before execution algorithm. 872 g. schleyer, g. lefranc, c. cubillos, g. millán, r. osorio-comparán to compare the results with other methods, it is used the objective evaluation method proposed in [18], since it does not require the setting of parameters or a reference image. the l(i) evaluation function is defined through equation (5). l(i) = √ nr[1000(mn)] −1 ∑nr i=1 (e2ia −1/2 i ), (5) where i is the original image to be segmented nr is the number of segmented regions in i, ai is the number of pixels in the region ri, m and n are the height and the width of i, and e2i is the colour error in the ri region, which is defined as the sum of the euclidean distance of the rgb colour vector between i and the corresponding segmented image for each pixel in the region. smaller l value means a better performance. the evaluation is performed comparing the obtained results by means of the proposed algorithm with results obtained by other three methods: hci fuzzy segmentation [11], segmentation using situational dct (discrete cosine transform) descriptors [12] and parameterless quadrilateral-based image segmentation method [10]. figures 7, 8, and 9 show image segmentation using the different methods. where (a) is the original image, (b) the hci fuzzy segmentation, (c) the segmentation using situational dct descriptors, (d) is the parameter less quadrilateral-based image segmentation, and (e) the proposed image segmentation. (a) original (b): hci (c): dct (d): less quad-based (e): proposed figure 7 (a) original (b): hci (c): dct (d): less quad-based (e): proposed figure 8 (a) original (b): hci (c): dct (d): less quad-based (e): proposed figure 9 parameter less quadrilateral-based image segmentation is constructed from a map edge (generated from a segmentation algorithm edges), neighbours quadrangles which have similar characteristics are fused to generate quadrilaterals regions allow local variations eliminating unnecessary a new method for colour image segmentation 873 details, so each region is full and accurately described by a set of quadrilaterals having neighbouring pixels between them. these areas have been separated into different regions, generating more groups. the amount of resulting groups has been called actual or real number of groups, while the original amount of groups calculated by the respective method is called simply number of groups. since (1) penalizes the creation of new groups, which increase the error in those segmentation methods that tend to do over segmentation. the l(i) error is always less than the real l(i) error. when the hci fuzzy and the situational dct descriptors segmentation methods are compared with the proposed method, the real l(i) error must be considered. the objective analysis of the results summarized in the table 1 demonstrates that the proposed method presents a smaller error and it requires less processing time than the methods used during its evaluation when they are applied to the three test images presented in figs. 6, 7, 8. table 1: comparison of results with other methods image stachybotrys chartarum parrot tree resolution 300x300 256x340 450x315 method 1: hci fuzzy segmentation number of groups 6 8 5 l(i) error 0.0731 0.0869 0.0275 real number of groups 1731 1029 1990 real error l(i) 1.3967 1.5517 0.8052 time (sec) 381.47 604.66 641.58 method 2: parameterless quadrilateral-based image segmentation number of groups 62 106 105 l(i) error 0.3511 0.8985 0.41 real number of groups 252 448 390 real error l(i) 0.9105 2.54 1.0628 time (sec) 1706.20 1622.06 4287.42 method 3: segmentation using situational dct descriptors number of groups 347 357 446 l(i) error 4.1938 10.282 12.3995 time (sec) 4742.25 7551.27 12071.94 method 4: proposed segmentation method number of groups 62 106 105 l(i) error 0.199 0.4654 0.2781 time (sec) 24.75 30.11 36.08 the region quantity is also smaller than in the other methods. it is compact and contents precise regions. hence, the proposed method outperforms the other segmentation algorithms especially for real time applications. all experiments are carried out using the same computer. the hci fuzzy segmentation method resulted to have a good segmentation in high value zones and small intensity, but it can over segment in other zones. its processing times are large. in comparison, the dct method has good segmentation in some regions, but over segmentation in others. the parameter less quadrilateral-based image segmentation method has the same problem. in this latter case, the processing time was very large. the obtained results indicate that the proposed method is fast without over segmentation. it has good segmentation capability compared to the other methods. few regions were not 874 g. schleyer, g. lefranc, c. cubillos, g. millán, r. osorio-comparán segmented, but with overall better performance delivering more homogeneous regions of colour, spatially compact and continuous. the processing time required by the proposed algorithm in order to perform the segmentation mainly depends of the image size as much as the initial quantity of regions contained in the image. the evaluation of the proposed method indicates that this requires less than 7% of the processing time required by the approaches involved in the comparison in order to perform the segmentation over the test images. therefore, it is estimated that the proposed approach is suitable in order to be implemented in real applications with small processing times. the parameters par_area, δ, and δ2 must be adjusted to allow the segmentation with different colour features. during the evaluation, the value of these parameters was maintained constant for all testing images, obtaining good results in the segmentation. in the proposed algorithm, the possible values of each hsv channel that the pixel image can take have been arbitrarily reduced from 256 to 26. this reduction has provided good results. it was considered that a scarce variation of tonality was preferable to reduce the complete hsv spectrum. all corresponding algorithms to each method run on the same computer (intel centrino processor 1.86 ghz, 4 gb of ram). as noted in table 1, the proposed method requires less than 7% of the processing time involved employing approaches in comparison to the segmentation of the test images. however, the computer processing time required can decrease further proposed implemented in c++ programming language. conclusions a new unsupervised colour image segmentation algorithm is presented. this method combines the advantages of the approaches based on split and merge and region growing, and the use of the rgb and hsv colour representation. the effectiveness of the proposed method has been checked by means of its implementation using an algorithm, which supplies homogeneous regions, spatially compacts and continuous. it has been observed that the proposed algorithm outperforms other segmentation algorithms and requires shorter processing time. the combinations of both segmentation techniques results in an increment of the rate of the division in the split and merge method. the determining the initial number in the growing region is solved. the proposed algorithm is compared with the results obtained by other three methods: hci fuzzy segmentation, segmentation using situational dct (discrete cosine transform) descriptors and parameter less quadrilateral-based image segmentation method. the obtained result indicates that the proposed method is fast, does not have over segmentation, it has good segmentation and is better than the other methods. a few regions are not segmented. the method has better performance, delivering more homogeneous regions of colour, spatially compact and continuous. the processing time required by the proposed algorithm in order to perform the segmentation mainly depends on the image size and the amount of regions contained within the image. the evaluation of the presented method indicates that this requires less than 7% of the processing time required by the compared approaches making it a good candidate where fast image processing is required. the proposed method can make good segmentation without previous information. it is estimated that it can perform better if par_area, δ, and δ2 parameters are adjusted automatically and to design a method to reduce the hsv space automatically according to the images colour 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(1994); multiresolution color image segmentation. ieee transactions on pattern analysis and machine intelligence, 16(7): 689-700. int j comput commun, issn 1841-9836 8(5):760-768, october, 2013. scalable architecture for cps: a case study of small autonomous helicopter j. yao, j. an, f. hu jianguo yao*, jie an, fei hu school of software shanghai jiao tong university, shanghai, china 800 dongchuan road, minhang, shanghai 200240, china *corresponding author jianguo.yao@sjtu.edu.cn anjie@sjtu.edu.cn, hufei@sjtu.edu.cn abstract: building a scalable and highly integrated systems is an important research direction and one of key technologies in cyber-physical systems (cps). autonomous helicopter is a typical cps application and its flight presents challenges in flight control system design with scalability. in this paper, we present the integration architecture of hardware and software for the flight control system based trex 600 helicopter. in order to enhance scalability, the flight control system uses the pc104 and the arm which is exerted to process the measurement data, including the position, attitude, height etc. the flight control is developed based multi-loop decoupling pi control which is easy to be implemented. finally, the flight control system is successfully verified in the actual autonomous flight control experiment. keywords: cyber-physical systems (cps); autonomous helicopter; component. 1 introduction cyber-physical systems (cps) consider both cyber parts (e.g., computing and communication) and physical parts (e.g., hardware), as well as their interactions [1]. autonomous helicopter is one of typical applications in cps and requires dynamic configuration of system-level performance. helicopter has the advantage of being able to vertically take-off/landing, hover and lateral flight. because of its flexible flight maneuver, even in a restricted region helicopters can fly free and hover in the air for a long time. these characteristics make unmanned aerial vehicle (uav) applicable for many military and civil applications. for example, geographic map detection and aerial photography, security patrols, combat assault, resources exploration, forest fire prevention, film shooting, pesticide spraying etc. uav has become a popular research topic for many military and university research institutions recently. the uav flight control system developed by stanford university airborne module includes two gps receiver, one tachometer for wing feedback, two sets of video camera and transmitter; one pentium-486 processing board, which can realize the automatic hovering and landing [2]. the american georgia tech university uav airborne module includes one dgps receiver, one heading magnetometer, three axis inertial attitude angular velocity measuring unit, one set of sonar and radar altimeter gets height information, one set of cameras, one piipc processing board and wireless card, which not only can manipulate the autonomous flight, can also identify the building and its entrance [3]. american berkeley university research of uav, now can be done independently of the take-off and landing, obstacle avoidance in the complex environment based vision technology, on specific target tracking and anti-tracking, collision detection and multiple machine coordinated flight. meanwhile, there are researches on uav flight control system in china, such as zhejiang university has implemented uav airborne system which is based dsp-cpld, the main research of it is in the hardware realization and software interface; nanjing university of aeronautics & astronautics has designed a uav flight control system which is based 586-engine micro control module. copyright c⃝ 2006-2013 by ccc publications scalable architecture for cps: a case study of small autonomous helicopter 761 figure 1: implemented rotorcraft uav. figure 2: the avionics control system box. based on the listed components for a uav airborne system, we design and assemble a simple prototype uav helicopter. a trex 600 radio-controlled helicopter is chosen as the basic flying vehicle. a simple avionic system will be designed. the miniature pc104 [4] [5] computer will be taken as the airborne computer system. arm will be taken as the sensor data collection controller [6] [7]. the arm data collection controller not only reduce the burden on the pc104, but also make the airborne system scalable. the full avionic system has been tested successfully on controlling the trex 600 helicopter model, and the helicopter can realize fixed point hover and autonomous trajectory plan flight. the outline of this paper is as follows. the components of avionic system are introduced in section 2 in details. a flight control law which is based multi channel decoupling pid control has been designed and implemented in section 3. section 4 show the effect of the overall actual flight control experiment. finally we summarize some conclusions in section 5. 2 trex 600 helicopter model description we choose a small size model helicopter (i.e., trex 600) which is available in the market as the basic aircraft of uav system. trex 600 is a low cost model helicopter and it can easily to be upgraded. we can buy the individual parts of the model helicopter in market easily. trex 600 model helicopter is a high quality toy helicopter in the hobby industry. the control way of main rotor blades is ccpm, three servos are linked to the swashplate to control collective and cyclic angles of attack of the main rotor blades. we upgraded the model to be a uav helicopter with a complete auto-pilot system, sensors and a necessary communication modem. the manual and autonomous control model is switched by the 7 channel of the receiver. the helicopter itself weights about 1770g, and it can provide an effective load up to 1200g, which is far above our budget on the weight of the avionic system and other components to be upgraded. the fuselage of helicopter is constructed with carbon fiber board, which can well protect the flight control equipment. the protected principle is the helicopter body will break open at the crash moment, and the disintegration of aircraft can reduce the impact of crash. the main rotor blades are replaced with heavy-duty carbon fiber ones, which has the quality of high weight and high hardness. the new rotor blades can accommodate extra payloads. the helicopter is powered by a 600xl brushless electric motor which generate 2.68hp at about 14000rpm.the full length of fuselage is 1200mm,the height of helicopter is 405mm.the main rotor is about 1350mm and the tail rotor is 240mm. the implemented rotorcraft uav is shown in fig. 1. designing and installing the flight control system is the first step to implement of the uav helicopter system. it is necessary to take into account the particularity of environment during the design process, strong magnetic field as well as intense vibration interference environment. so we must adopt the shielding and shock-absorption measures. in addition, the weight and 762 j. yao, j. an, f. hu the size of the avionics box are strict limited because of the limited payload capacity. on the other hand, helicopter’s center of gravity position has a great influence on the flight. the general center of gravity position is in front of the main spindle 10mm. the standard must be fulfilled both before and after the installation of flight control system, so as not to destroy the balance of the helicopter. we adopt the design pattern of integrated avionic system [8], which is shown in fig.2. all the flight control system equipment installed in a 26×16×7cm aluminum box, which can play the role of electromagnetic shielding. to try not change the aircraft center of gravity, aluminum box frame mounted on the bottom of aircraft’s landing gear. because of there is no enough room under the original landing gear to install the designed avionics system. while we re-design a landing gear with aluminum alloy and make a larger room under the gear for the control box, the dimensions of it is 30×29×43cm. soft connection is adopted between the new and old landing gear, which can protect the flight control equipment when the helicopter land on the ground. to avoid the disciplinary vibration about 30hz caused by characteristic of the helicopter, silicon rubber are mounted between the aluminum box and the new land gear. experiments show that the measures we take can effectively isolate vibration source. 3 scalable architecture for helicopter system the overall structure of the flight control system shown in fig.3. using a pc104 as a flight control computer, it is the core of the system. the input data of the pc104 are flight sensor data and control commands, output data of it is servo control signal pwm signal. one arm board is used as the flight data collection module, it collect data of each sensor and then send them to pc104 flight control system via ethernet. ground station pc send pc104 control commands through wireless module. the control commands contain the helicopter’s flight path. a servo controller board which is to control the switch between the automatic mode and manual mode. it not only guarantee flight safety but also ensure that the control algorithm to be tested and validated [9]. figure 3: the logical connection diagram of rotorcraft uav system. 3.1 flight control computer component the computer system installed on the helicopter is pcm-3586, a typical industrial embedded computer system. pcm-3586 is a low power consumption (4w@800mhz), x86 embedded motherboard which is designed specifically for pc104 field. the cpu type of pcm-3586 is soc vortex86dx, it has serial/parallel port, high-speed usb2.0, 10m/100m ethernet, 256mb ddr2 system memory, 4g hard disk, 16 channel pwm output. the installed operating system in pc104 is fedora11, which integrates the gcc compiler and eclipse development environment. the main tasks of pc104 include: scalable architecture for cps: a case study of small autonomous helicopter 763 • to maintain wireless communication with the ground station computer, the exchange data consist of control commands and monitoring information. • receiving sensor flight data, including flight attitude, altitude, heading, latitude and longitude information. • to execute flight control laws with a main frequency of 50hz, and generate pwm signal to drive actuators. 3.2 flight data collector component the arm9 industrial embedded computer system is used as flight data collector. it has a samsung s3c2440a processor, fully compatible with linux os, with a main frequency 400mhz and a 64mb sdram. one 256mb compact nand flash disk is taken as the storage media of the linux os, which kernel version is 2.6.32.2 and integrated the arm-linux-gcc cross compiler environment.100m ethernet rj-45 interface (using dm9000 network chip). the flight data collector receive the sensors data includes: height(altimeter), flight heading(threeaxis compass), the aircraft attitude(gyro), position (gps), and then use the tracking differentiator filter way to isolate the biases. the output sensor data is transmitted to the flight control computer through the ethernet which is based on udp protocol. 3.3 position sensing component hc12 oem can tracks gps l2 and sbas satellite signal produced by a canada company.gps provides three dimensional coordinate, which difference accuracy less than 0.5 meters. the gps provides position estimates at 10hz. the oem board should connect with the gps antenna to get the position information. 3.4 attitude sensing component the cs-vg-02 vertical gyroscope is used as the aircraft attitude sensor, which is inertial sensor based mems technology. the gyro is used to measure inclination of the helicopter relative to the horizontal plane, it has two sensitive axes respectively detect roll and pitch angle change, can output angle and angular rate signal simultaneously. the gyro angle static accuracy precision of less than 0.6 and dynamic accuracy of less than 2.5 degrees. it provides fast response time up to 200hz and communication interface is rs422. 3.5 heading sensing component flight heading sensor use the mwc3000l high-precision digital three-axis compass, which integrated three-axis magnetic resistance sensor and two-axis tilt sensor on-chip. it provides roll angle, pitch angle, heading angle and magnetic state, orientation repeatability of 0.5 degrees and inclination repeat of 0.1 degrees. the information update rate is 20hz. but we found that the heading angle signal generated by compass under the condition of vibration can not be used. because of the roll and pitch angle in vibration condition is unstable, which leads to the heading angle instability. accurate yaw angle can be calculated by the original data of three axes compass. under the premise of making the compass’s installation position parallel to the earth’s surface, yaw angle can be calculated from the magnetic field in the x, y direction components.the yaw angle ϕ calculation formula is shown as follows: 764 j. yao, j. an, f. hu ϕ =   90 + arctan(hx/hy) × 180/π, hy > 0 270 + arctan(hx/hy) × 180/π, hy < 0 180, hy = 0,hx < 0 0, hy = 0,hx > 0 (1) where hx is the x-axis direction of the magnetic component,and hy is the y-axis direction of the magnetic component. direction is the heading signal of helicopter. 3.6 low height sensing component in order to ensure that the aircraft can be controlled stability during the takeoff and landing state, the vertical height should be guarantee to be accuracy. in the take-off and landing conditions, height of the helicopter generally less than 1 meter in height control. based on the above considerations, we selected the ultrasonic ranging module to complete the height measurement. ultrasonic ranging module uses the voltage dc5v, with less than 15 degrees sensitive angle, detecting distance is 2cm-450cm, accuracy is 0.3mm+1%, and the data update rate is 40hz. 0 10 20 30 40 50 60 70 0 0.5 1 1.5 2 time(s) h e ig h t( m ) actual filter figure 4: height before and after td. the aircraft itself will generate about 30hz vibration in the actual flight, which will affect the data of ultrasonic range module. the vibration can make the data we collected would emerge burr. we use tracking differentiator filter to filtering and tracking the signal. fig.4 shows the effect before and after the filtering. 3.7 actuation component the servo controller board has the function that switch the helicopter control between manual mode and automatic mode. channel 7 in the radio control system is in charge of the mode of switch. the servo controller board shares a power supply with the servos. such a design can ensure that the manual operation can run independently. the uav helicopter can be operated in either the automatic mode or manual mode. all of the servos are driven by a pulse width modulated(pwm) signal. so the input and output signal of the servo controller board are pwm signal. we use 74ls157 as the core selection chip, which is a type of either-or chip. the analysis of pwm signal is accomplished by a micro control unit (mcu) c8051f330.pwm signal is resolved into high-low level, and then drive the selection chip. each signal which in and out the servos controller board would be processed with light-coupled isolation, which can effectively prevent the signal interference. the update rate of all sensors is ranging from 10-200hz, which is enough for implementation for advanced control algorithms. scalable architecture for cps: a case study of small autonomous helicopter 765 0 5 10 15 20 25 30 −1 −0.5 0 0.5 1 time(s) l a te ra l v e lo ci ty (m /s ) figure 5: lateral velocity. 0 5 10 15 20 25 30 −2 −1.5 −1 −0.5 0 0.5 1 1.5 time(s) l o n g itu d in a l v e lo ci ty (m /s ) figure 6: forward velocity. 4 cyber algorithm design 4.1 online flight data processing gps provides world geographic coordinate information, which can be applied to the position control algorithm. gyroscope provides aircraft attitude information, which can be applied to the attitude control algorithm. but there is no sensor to acquire the velocity information of helicopter which need to be used in the velocity control algorithm. in order to get the velocity information, we adopt the tracking differentiator to tracking the location information of the gps. tracking differentiator can extract reasonable differential signal of the measuring data with the predicted and tracked effect [10]. the general problem describe as a mechanical system that initial with the signal of position and velocity at t moment and output the signal of position and velocity at t+1 moment. the second order discrete forms of tracking differentiator is shown as follows: x1(t + 1) = x1(t) + h × x2(t) x2(t + 1) = x2(t) + h × fhan(x1(t) − x(t),x2(t),r,h0) (2) where x1 describing the motion of the target’s position and x2 is the velocity information of the target. h is the sampling step. the function fhan() is shown as follows: fhan = { −r × a1/d, ∥a1∥ ≤ d −r × sign(a1), ∥a1∥ > d (3) with d = rh0 d0 = dh0 y = x1 − x + h0x2 a0 = √ d2 + 8r∥y∥ a1 = { x2 + y/h0, ∥y∥ ≤ d0 x2 + sign(y)(a0 − d)/2, ∥y∥ > d0 (4) where r is speed factor and h0 is the filter factor, speed factor determines the tracking velocity, filter factor determines the filter effect. the filter effect is proportional to the h0, and the level of phase lose is inversely proportional to the h0. the input signal of the tracking differentiator is velocity of helicopter which is calculated by the gps information. and its output signal is the predicted velocity of helicopter. the effect of forward and lateral velocities is given in fig.5 and fig.6. 766 j. yao, j. an, f. hu figure 7: position controller scheme. 0 5 10 15 20 25 30 −1 0 1 2 3 4 5 time(s) r o ll a n g le (d e g ) figure 8: roll angle during the flight 4.2 control algorithm design the flight control system algorithm uses the body coordinates as the navigation coordinate. the position information which gps provides is in the wgs-84 coordinates. first of all, the information of gps (longitude, latitude, altitude) need to be converted into world geographic coordinates (ned coordinates), and use the position data when helicopter take-off as the original point. then we must transform the ned coordinates into the body coordinates during the navigation control process [11]. simulation studies have shown that a better strategy for the control of a small-scaled helicopter is to use the flight controller as consisting of three cascaded controllers: an inner loop attitude control, a middle loop velocity control and an outer loop position control [12]. the overall flight control scheme is shown in fig.7. height control the height control is a one loop scheme which is a pi controller using feedback from ultrasonic range finder generates collective pitch demands. in order to improve the control stability, we use the vertical velocity signal as the compensation for the collective pitch demands. the calculated formula is shown as follows: ze = zcurrent − zc vzc = k zv p ze vzc = [vzc] v limz −v limz zs = (vz − vzc) × kzvp ac = k z pze + k z i ∫ t 0 (ze + zs)dt (5) where zcurrent is the actual height of helicopter and zc is the target height. ze is the height error and it can be used to calculate the zs which is the compensation for the collective pitch control signal. ac is the collective pitch control signal. v limz is the limitation of the speed of hight. p zv and kzp are the proportional control parameters. k z i is the integral control parameter. yaw control the yaw control is a proportional controller using the feedback from the digital compass generates the rudder channel control demands. gy401 is used to increase yaw control stability. according to body sway velocity, gy401 will make reverse compensation signal to the actuator and suppress the helicopter tail rotor’s swing which just as a tail rotor damper. scalable architecture for cps: a case study of small autonomous helicopter 767 0 5 10 15 20 25 30 −2 0 2 4 6 time(s) p itc h a n g le (d e g ) figure 9: pitch angle during the flight. 0 5 10 15 20 25 30 160 180 200 220 240 260 280 time(s) h e a d in g a n g le (d e g ) figure 10: heading angle during the flight. 0 5 10 15 20 25 30 0 2 4 6 8 time(s) h e ig h t( m ) actual reference figure 11: height control during the flight. −20 −15 −10 −5 0 5 10 15 20 −20 −10 0 10 20 east direction(m) n o rt h d ir e ct io n (m ) actual reference figure 12: position control during the flight. position control the position control is a three-cascade scheme which includes an inner loop, a middle loop and an outer loop. the outer loop takes target position as desired position as inputs and generates desired velocity to the middle loop which using the feedback from the gps. the middle loop is designed as velocity controller which takes desired velocity as inputs and generates the desired attitude angles to the inner loop. the inner loop is designed as the attitude controller which takes desired attitude angles as inputs and generates the actuator commands that drive the helicopter reach the desired attitude. finally the helicopter will reach to the target position through the control demands. 5 real-life autonomous flight experiments a three-loop control scheme for rotorcraft uav system was design and implemented using trex 600 platform. we use the fixed-point hovering experiment to show the controlled effect. the target point is at longitude 12126.31838 and latitude 3101.44161,which change into the ned coordinates is xyz(-7.0658, 2.4706,6).x stand for the west-east coordinate, y stand for the south-north coordinate, and z is the height coordinate. fig.8-fig.10 show three-axes angles during the flight. fig.11 and fig.12 show that position which controlled by the outer loop is get a stable response. the control accuracy is of a radius of two meters. 6 conclusion this paper describes the cyber-physical system implementation of the rotorcraft uav and control scheme based on trex600 aeromodelling helicopter. the rotorcraft is a small helicopter model, which will be changed to adapt to the heavy load in future. the rotorcraft uav system has been tested successfully for automatic flight, including take-off and landing. 768 j. yao, j. an, f. hu the system also has some deficiencies, such as poor wind-resistant performance. there is an influence on the control precision when the wind speed is 5m/s. the flight path is not stable enough, and the altitude of helicopter would be effected when the helicopter flight forward. the next step is to integrate the wind model into the existing control model so as to improve the flight stability, and add new application tasks. acknowledgement this work was supported in part by national natural science foundation of china (no.61303013), shanghai natural science foundation (no.12zr1445700) and research fund for the doctoral program of higher education of china (rfdp) (no.20120073120039). note: the authors contribute equally for the work in this paper. bibliography [1] yao, j. et al; netsimplex: controller fault tolerance architecture in networked control systems, ieee transactions on industrial informatics, 9(1), 346-356, 2013. [2] nonami k; prospect and recent research & development for civil use autonomous unmanned aircraft as uav and mav, journal of system design and dynamics, 1(2),120-128, 2007. [3] johnson, e. et al; the georgia tech unmanned aerial research vehicle gtmax, proc. of aiaa guidance, navigation, and control conference, austin, 2003. [4] qi, j. et al; the servoheli-20 rotorcraft uav project, proc. of the 15th int. conf. on mechatronics and machine vision in practice (m2vip 08), auckland, new-zealand, 2008. [5] cai, g. et al; an overview on development of miniature unmanned rotorcraft systems, frontiers of electrical and electronic engineering in china, 5(1), 1-14, 2010. [6] zhou, w. et al; research on uav flight control computer based on arm, computer measurement & control, 17(7),1286-1288, 2009. [7] cai, g. et al; construction, modeling and control of a mini autonomous uav helicopter, proc. of ieee int. conf. on automation and logistics (ical 08),qingdao, china, 2008. [8] gu, y. et al; integrated avionics system for research uavs, proc. of aiaa guidance navigation and controls conference and exhibit, honolulu, hawaii, 2008. [9] cai, g. et al; development of fully functional miniature unmanned rotorcraft systems, proc. of the 29th chinese control conference (ccc 10). beijing, china, 2010. [10] su, x. et al; the research on single-antenna dgps determination attitude method based on tracking-differentiator, j. of projectiles, rockets, missiles and guidance, 31(2), 199-204, 2011. [11] peng,k. et al; design and implementation of a fully autonomous flight control system for a uav helicopter, proc. of the 26th chinese control conference, zhangjiajie, china, 2007. [12] shim,d. et al; hierarchical control system synthesis for rotorcraft-based unmanned aerial vehicles, proc. of aiaa guidance navigation and controls conference and exhibit, denver, co, aug, 2000. international journal of computers communications & control issn 1841-9836, 12(4), 461-474, august 2017. obfuscation-based malware update: a comparison of manual and automated methods c. barría, d. cordero, c. cubillos, m. palma, d. cabrera cristian barría, claudio cubillos* pontificia universidad católica de valparaíso valparaíso, chile cristian.barria@udp.cl *corresponding author:claudio.cubillos@pucv.cl david cordero universidad andrés bello santiago, chile d.cordero.v@gmail.com miguel palma universidad tecnológica de chile santiago, chile rmiguel.palma06@inacapmail.cl daniel cabrera universidad de valparaíso valparaíso, chile daniel.cabrera@uv.cl abstract: this research presents a proposal of malware classification and its update based on capacity and obfuscation. this article is an extension of [4]a, and describes the procedure for malware updating, that is, to take obsolete malware that is already detectable by antiviruses, update it through obfuscation techniques and thus making it undetectable again. as the updating of malware is generally performed manually, an automatic solution is presented together with a comparison from the standpoint of cost and processing time. the automated method proved to be more reliable, fast and less intensive in the use of resources, specially in terms of antivirus analysis and malware functionality checking times. keywords: security, malware, obfuscation techniques, cyberspace, antivirus. areprinted (partial) and extended, with permission based on license number 3954200696590 [2016] © ieee, from "computers communications and control (icccc), 2016 6th international conference on". 1 introduction malware is a malicious program that provides access to a system without user’s authorization and executes undesirable actions. the terms malware and virus are usually used as the same concept, although they are, in fact, different [4] [19]. initially, the use of malware was strictly associated with the investigation and protection of software developers for intellectual property (using cryptography), but throughout time its objective changed, and therefore, its importance. currently, the aim of malware is mainly related to profitmaking [8] [15]. the evolution of malware began in 1949 when von neumann established the idea of stored program and introduced the automata theory, which is the possibility of developing small replicates of a program that were capable of taking control of other programs with similar structures [13]. while the concept has many applications in science, it is quite easy to apply it to copyright © 2006-2017 by ccc publications 462 c. barría, d. cordero, c. cubillos, m. palma, d. cabrera viruses, considering that, at that time, the two concepts (i.e., virus and malware) were thought as equivalent. in 1971, bob thomas created the first malicious code, named creeper, that was capable of infecting ibm 360 machines on the arpanet network by delivering an on-screen message that said, "i’m the creeper, catch me if you can!" to find and eliminate the creeper another code, called reaper, was created. from this, it originated the current antivirus [3]. primarily, malware affects the critical information infrastructure (cii), which is of prime importance to both, the attackers and the defenders. it is important to highlight that a successful virus leaves the cii defenseless. this is why for both, the defenders and the offenders, there is a necessity of implementing a security information management system (simi) with the objective of preserving the reliability, integrity and availability of the information in order to act upon these threats [18]. in the context of this research, obfuscation will be defined as the execution of transformations in a malware code (source or binary) that changes its appearance through the realization of a series of steps. this maintains the malware functionality and allows it to take control of the systems [6]. it is fundamental to continue carrying out research regarding obfuscation, in order to contribute to its analysis from a malware updating perspective. as the malware updating process is generally performed manually, an automatic solution is to be introduced so to compare both in terms of time and costs. this research, an extended version of a previous work [4], provides a proposal of malware classification based on obfuscation. the novelty of the present work relies on; first, providing a summary of the obfuscation-based malware classification tackled in the previous work [4]; second, formalizing the malware update process and making an empirical demonstration on how a malware, that is detectable by antiviruses, could be reused by updating it and making it undetectable through obfuscation; third, comparing the manual and the automated methods for malware updating though an example. in section 2, this paper presents a summary of malware classification, while in section 3 the use of obfuscation through a crypter for updating the detected malware is explained. in section 4, a chart of the malware updating and its classification is proposed. in section 5, the proposed malware updating process is formalized and explained through an example. in section 6, a software prototype of an automated version of the obfuscation process is presented. section 7, provides an evaluation and comparison of the manual and automated solutions. finally, in section 8, the conclusions of this research are presented and future work. 2 related work in this section, a brief overview of the different malware classification is provided. as it is known, there are different malware classifications stemming from different perspectives. for example, classifications regarding malware infection mechanisms, and others regarding means of malware spread. although these are used in different investigations, there is no given classification from the perspective of obfuscation, as a common vision has not been agreed yet [17]. as it is shown in figure 1, a structure that facilitates the malware classification (species) could be established according to the class, type and generation. this classification will enable to succinctly present a part of the essential information about the malware non-detectable capacities. obfuscation-based malware update: a comparison of manual and automated methods 463 figure 1: malware classification 3 malware updating based on obfuscation according to the malware classification provided in previous investigations, first-generation malware were not considered part of the inverted pyramid shown in figure 1, as they did not present any modifications in their code and they are commonly denominated no-stealth [12] because of their obsolescence. although no-stealth malware are detected by an antivirus, currently they are capable of being transformed into second-generation malware using crypters. crypters are easy to obtain from specialized information security websites, and these could apply obfuscation techniques to any type of file. however, in this research, crypters will be applied to malware, yet not alter their functionality in any of their states [6] [9] [20]. 3.1 update malware updating requires obfuscation to give rise a second generation. this can be performed by using one of the following two methods: a) the manual method. in this, the operation is performed by a person and is applied to the encryptor and oligomorphic malware types. b) the automated method. this refers to a malware that can automatically undergo the process of obfuscation without requiring a human operator and is applied to polymorphic and metamorphic malware [14], as it is shown in table 1. table 1: obfuscation update malware manual automated encryption x oligomorphic x polymorphic x metamorphic x 464 c. barría, d. cordero, c. cubillos, m. palma, d. cabrera 3.2 procedures among the procedures that are highlighted in various sources of information related to computer security, the following can be identified: avfucker, dsplit, rit, hexing and xor. these aim at generating a binary of the original code, producing a modification of a relevant hexadecimal value to evade the antivirus. figure 2 allows us to perform an updating to the malware through the obfuscation process, which will depend on malware status, method, technique and procedure employed. figure 2: malware obfuscation update. in the case of an antivirus detection, a decryptor or stub is resorted to apply the obfuscation method, in order to re-apply the crypter to malware so to achieve its renovation and, thereby its ability of evasion. the report provided by the antivirus detection is directly given to the stub, if this gets detected it directly compromises the encrypted malware. this is put into practice through the following tests: • malware construction (no-stealth) the construction of a malicious code has the objective of obtaining documents from a third party, including certain directories and various extensions from a computer. a part of the code is presented (for confidentiality reasons) in figure 3. figure 3: no-stealth malware code portion • antivirus tests the analysis of a malicious code is performed by a specific antivirus (nod 32) signature, which has the ability to incorporate other antivirus engines. as it is shown in figure 4, this code is detected and reported as an element of risk. obfuscation-based malware update: a comparison of manual and automated methods 465 figure 4: antivirus test • obfuscation application after the malicious code has been detected, obfuscation techniques are applied in order to subject the malware to a crypter (encryption / decryption), as shown in figure 5. once the process has been completed, a detectable code may become undetectable. figure 5: crypter application • screening once the malicious code has undergone obfuscation, the malware is subjected to the antivirus (nod 32) again, under the same conditions described above (same house and built motors). as it is shown in figure 6, the antivirus always generates a reporting message in case the malware is either detected or not. in case the malware goes undetected, the antivirus logs does not consider this file a threat, as shown in figure 7, and therefore, the malware may fulfill its malicious intent. figure 6: antivirus test. as shown in figure 8, it is evident that a first-generation malware could be subjected to obfuscation techniques. in order to do so, a crypter tool is applied to transform this firstgeneration malware into a second-generation, and thereby, rising its hierarchical criteria. 4 obfuscation process and malware classification proposal in accordance with the malware classification that was previously described, a first-generation malware is not considered because it has not undergone the obfuscation techniques yet. however, it is possible to prove that despite the obsolescence of this no-stealth malware, this is still present in the cyberspace and need to be considered. furthermore, this no-sealth malware could be transformed into a threat only by applying an obfuscation procedure, resulting in this becoming undetectable by an antivirus. taking into account the points previously exposed, a flow chart is proposed for the updating of malware, as shown in figure 9. 466 c. barría, d. cordero, c. cubillos, m. palma, d. cabrera figure 7: antivirus logs figure 8: malware transformation thus, in the first inverted pyramid previously described the no stealth malware should be taken into consideration because this could be related to any other malware either belonging to a class (virus, worms, botnet, and trojan horses), or belonging to the first generation. as it is shown in figure 10, a revised inverted pyramid is proposed in order to include a complete structure that classifies malware. 5 current malware updating process the process of detecting malware through the recognition of their signatures, that these are later saved into the antivirus databases. however, through an updating process, these malware could be transformed and become undetectable without losing its functionality as it is described by barría et al [4]. when considering the diagram in figure 11 [11], it could be observed that the updating process of a malware starts in its binary state, and then a crypter is required in order to apply the method of encryption. after this, the crypter output must undergo the antivirus detection (signature-based analysis). in case the encrypted malware goes undetected, it means the updating process has been successful. in the specific case of this work, the obfuscation process employs the dead-code insertion technique [1]. for all aforementioned, the modders defines this as the avfucker [11]. this process involves a series of procedures in which the malware is encrypted by a crypter figure 9: proposed obfuscation procedure. obfuscation-based malware update: a comparison of manual and automated methods 467 figure 10: proposed malware classification and is then reviewed by one or more anti-malware systems. if it is detected, an insertion of a dead code, based on the process of obfuscation, followed by a re-encryption of the resulting code with a crypter is performed. finally, the test is performed against the anti-malware or systems, and the cycle is repeated until a program is obtained with a signature that is no longer detected by these systems. a graphical explanation of this process is illustrated below in figure 11 [10]. when the crypter output is contrasted through detection techniques based on signatures [2] and is detected as malware, the procedure is applied as a whole. this means that the stub, the key and the enciphered malware are included, although only the stub area is detected. this procedure consists of the malware copy creation (crypter output), whose quantity is given by the file size, according to the following equation: nc = (st + k + mc) where: nc= copy number. st= stub bytes size. k = key bytes size. mc= enciphered malware bytes size. each one of these copies, is created for the dead code insertion (00, 90), from byte to byte, from the initial to the final offset, correlatively [7], as observed in figure 12. once all copies are made, they are analyzed by the antivirus. in this way, only files detected as non-malicious are obtained, and the rest are removed when matching with the antivirus registered signature. a group of files is then obtained that is able to avoid the antivirus, but its hexadecimal code has been modified, so it is necessary to verify that the functionality is not affected. what was previously presented is not viable if a file of 1 mb output is used as an example: nc = (st + k + mc) = 1.048.576 copies nc2 = space for the copies on the hard disk 468 c. barría, d. cordero, c. cubillos, m. palma, d. cabrera figure 11: diagram of the malware update process. nc2 = (1.048.576)2 bytes = 1 terabyte the previous analysis implies the following problems: a) capacity, since the number of files created could be higher than the number of information terabytes directly affecting the hardware capacity, which also affects equipment investment; b) test, as a function of the time in which the antivirus performs the analysis on each of the copies; c) functionality, a revision that must be made on an individual basis to the group of undetected files by the antivirus and then prove that the malware functionality is not affected. this is how the procedure seeks to be optimized by increasing the dead code insertion. if the 1 mb output file is used, it is possible to create copies every 1000 bytes (offset with 0.1 percent of the total file size), and the proper analysis is made, considering only the portion of 1000 bytes that is undetected. copies are then created every 100 bytes over it, and this procedure is repeated until reaching the portion of 1 byte [5], considering that the ranges are not necessarily consecutive. thus, hardware use increases, and the number of created files decreases, so the antivirus testing time also decreases. nevertheless, the problem of the malware operation test persists because is needed a controlled environment that allows typical functions of this type of malicious code to be proved, such as persistence, connectivity and spreading. this takes significant time and a great amount of resources for the modder [11]. thus, a tool called an "annotator" is used [16], which permits the functions described above to be verified by replacing input malware on the crypter. in addition, another characteristic is its tiny size, further allowing optimization of the creation of copies and reducing the antivirus analysis and detection time. currently, the iteration of this model is carried out with the support of various tools, including crypters, hex editors, and anti-malware engine systems, among others. it also usually uses a tool to generate a number n copies of the original file, modifying each copy in a different range of offsets and replacing those locations with a dead code. while the use of such tools represents obfuscation-based malware update: a comparison of manual and automated methods 469 figure 12: malware copy generation a significant reduction in the time required compared to the performance of the same process manually (e.g., editing each range of offsets using an editor), this part of the process adds more time to the total iteration, increasing the final cost of the process. having identified this difficulty, the ability to automate this iteration emerges as a natural choice in regard to reducing time and monetary costs. 6 software prototype validation an algorithm was programmed in python version 2, with the addition of pywinauto library to automate the process under study, exposed in figure 13. the automatic process and its algorithm provides the advantage of reducing the execution time of each task, while leaving the structure of the process itself intact, thus allowing an objective performance comparison between the original manual process and the automated one. avoiding human factors on the execution of each task reduces the risk of certain kind of errors, mostly related to distraction, fatigue, among others. this can be more critical in certain tasks, compared to others. for example, selecting the malware sample and using the crypter tool on it (the first task), can be considered a fairly simple and straightforward task. but replicating the file an undetermined amount of times, with different ranges, can eventually involve the repetition of an action hundreds of times, and each one requiring the exact selection of specific ranges, and offsets which is error prone. in the automated method, the algorithm itself will not suffer from fatigue after 5 hours of uninterrupted work, for example, while humans surely will, being a more reliable solution. the general process involves five phases: malware encryption, offsets identification, obfuscation through dead code insertion, anti-malware analysis, and functionality analysis. the encryption phase, as it name sais, involves the encryption of the malware code through the use of 470 c. barría, d. cordero, c. cubillos, m. palma, d. cabrera a crypter software. the offset identification regards the activity of splitting the file for making an exhaustive search looking for the point (offset) in which changing that bit provides a change in the overall malware signature recognized by anti-malware software, thus turning it in undetectable. the anti-malware analysis refers to antivirus check to be performed to the file sample with the modified offset in order to check if it is detected with the virus. please consider that this step does not involve only a single antivirus software but a hole set of them, four or five different at least in order to ensure undetectability. the phase of functionality analysis, comes after the anti malware analysis for each of the files with their diverse offsets and pursues to check that the dead code insertion has not altered or braked the functionality to the malware within the file. the algorithm automatically goes through each iteration, changes the values for each range and advances until the end of the whole process. the only human interaction required is starting the script, since the automated process replaces every task originally executed by human resources. figure 13: malware update automated process 7 experimentation once the prototype was prepared, we compared the time required to complete an instance of the process, from beginning to end, between the automated case and the original case then, it was used simulation software, to evaluate the time required in 50 instances. to standardize the experimental conditions in both cases, the same tools and base file containing the signature of the malware were used, as detailed in the following list: • base file • crypter tool • hex editing tool and file replication "offset locator" obfuscation-based malware update: a comparison of manual and automated methods 471 • anti-malware engine "nod32" both processes were executed on the same computer with the same operating system (windows 7). finally, the economic dimension of each process was evaluated, and each hour of work was assigned the value of clp 1100. 7.1 experimental evaluation the manual process execution required 420 minutes (7 hours), while the prototype software process was completed in 5 minutes. in both cases, it was possible to obtain a series of files that successfully evaded the anti-malware detection engine. considering the value assigned to each hour of work, we observe the following, in table 2. table 2: time and cost comparative table evaluated experimentally. manual process automated process time (hours) 7 0,083 cost (clp) 7.700 1.100 7.2 simulated evaluation fifty process execution cycles in the simulation software generated the following results, in table 3: table 3: time and cost comparative table evaluated by simulation manual process automated process time (hours) 350 4,2 cost (clp) 385.000 5.500 due to the cost being calculated per hour, the automated process value was still considered as a full worked hour, regardless of having completed the entire set of tasks in much less time than that. it is also important to note, that the human executed tasks may differ in terms of time needed, because of the possibility of mistakes during the execution of any action, thus causing an increase of the time needed and, in consequence, an increase in its cost. during this experimental evaluation, however, there were neither difficulties nor failures during the execution of the entire process, so the values provided are as close as possible to an ideal execution. although these values already provide clear evidence of the cost reductions obtained thanks to this automation, this difference may be even higher, because the human resources simulated on the manual process were considered with ideal conditions, such as 100 percent correct execution of tasks (no possibility of mistakes) and continuous, uninterrupted work hours during the whole process. as can be expected, actual human resources will not present such performance, meaning a higher cost for the manual process. 7.3 cost and time comparison figure 14 and figure 15 present the same data, but from a different perspective, which helps to easily perceive the cost reduction obtained with this automation. 472 c. barría, d. cordero, c. cubillos, m. palma, d. cabrera figure 14: cost comparison between manual process and automated process under simulated evaluation figure 15: time comparison between manual process and automated process under simulated evaluation the numbers speak by themselves, as can be easily noticed that the same result can be achieved with way less time and money, thus exposing the advantages of automating the process using the algorithm. as mentioned before, ths structure of the whole process was left untouched, meaning that this comparison is not affected by neither adding, removing nor modifying tasks. each task remains the same, but its execution is improved via automation. 8 conclusions and future work previous researches have provided a malware classifications that has been generated from multiple perspectives; however, there is no classification generated on the basis of the obfuscation perspective. consequently, the first contribution of the present work is to provide a classification that takes into account the obfuscation procedure. this revised classification allows us to hierarchically structure the malware based on their capacities. furthermore, considering that these malware are still present in the cyberspace paves the way to create countermeasures to preserve the security of the information systems. because of the rapid evolution of second-generation malware, those that are considered to be part of the first-generation have been described as obsolete, and therefore unimportant. however, this research has proved that through the application of methods, techniques, procedures and tools of obfuscation it is possible that no-sealth malware could be updated to the point of being obfuscation-based malware update: a comparison of manual and automated methods 473 transformed into second-generation malware, and therefore, go undetectable by the antivirus systems. similarly, the present work incorporates this updating procedure through a flow chart that considers this classification proposal of the revised inverted pyramid previously presented. this work also explains that this updating procedure could be put into practice from a manual method to an automated method. it is evident that in the case of the automated method, the resources of time and costs are more efficiently employed. this represents a relevant challenge for the malware developers, who day-to-day have to deal with between those who protect and those who evade the system of data protection. future work may consider the improvement of this malware updating process in order to employ new techniques with the aim of searching matched patterns and making this process more efficient. acknowledgment the present work has been partially supported by pontifícia universidad católica de valparaíso through scholarship inf-pucv-2016. bibliography [1] balakrishnan a., schulze c. 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(2007), metaaware: identifying metamorphic malware, computer security applications conference, acsac 2007, 411-420, 2007. international journal of computers communications & control issn 1841-9836, 10(5):667-677, october, 2015. advisory, negotiation and intelligent decision support system for leadership analysis r. gudauskas, a. kaklauskas, s. jokubauskiene v. targamadze, l. budryte, j. cerkauskas, a. kuzminske renaldas gudauskas, loreta budryte martynas mazvydas national library of lithuania gedimino pr. 51, lt-01504 vilnius, lithuania r.gudauskas@lnb.lt, l.budryte@lnb.lt arturas kaklauskas*, justas cerkauskas, agne kuzminske vilnius gediminas technical university sauletekio al. 11, lt-10223 vilnius, lithuania arturas.kaklauskas@vgtu.lt, justas.cerkauskas@vgtu.lt, agne.kuzminske@vgtu.lt *corresponding author: arturas.kaklauskas@vgtu.lt saule jokubauskiene, vilija targamadze vilnius university universiteto g. 3, lt-01513 vilnius, lithuania saule.jokubauskiene@kf.vu.lt, vilija.targamadze@fsf.vu.lt abstract: the development of the leader model for quantitative and qualitative analyses began with the goal of integrating managerial, organizational, technical, technological, economic, legal/regulatory, innovative, social, cultural, ethical, psychological, religious, ethnic and other aspects involved in the process of a leaders life cycle. the need to determine the most efficient life cycle of a leader led to the development of the advisory, negotiation and intelligent decision support system for leadership analysis (andes). the objective of the authors of this work for integrating text analytics, advisory, negotiation and decision support systems is to improve the quality and efficiency of intelligent decision-making regarding a leaders life cycle. this andes consists of an intelligent database, database management system, model-base, model-base management system and user interface. keywords: leader, model, intelligent systems, integration. 1 introduction leadership, according to uhl-bien and russ marion [1], is multi-level, processable, contextual, and interactive. today’s organizational leaders are faced with unprecedented complexity in the wake of increasing globalization [2]. various systems are being developed globally for leadership analysis. these systems include information [3, 4], intelligent [5, 6], knowledge [7, 8], expert [9, 10] and decision support [11–17]. the aforementioned systems analyze their managerial, organizational, technical, technological, economic, legal/regulatory, innovative and similar aspects. however, neither the integrated economic, legal/regulatory, technical, technological, organizational, managerial, quality of life, social, cultural, political, ethical nor psychological aspects are generally paid hardly any attention at all. the structure of this paper is as follows: section 2, which follows this introduction, analyses the advisory, negotiation and intelligent decision support system for leadership analysis. certain concluding remarks appear in section 3. copyright © 2006-2015 by ccc publications 668 r. gudauskas, a. kaklauskas, s. jokubauskiene v. targamadze, l. budryte, j. cerkauskas, a. kuzminske 2 advisory, negotiation and intelligent decision support system for leadership analysis first, the existing different intelligent systems were analyzed. it included i information [3, 4], intelligent [5, 6], knowledge [7, 8], expert [9, 10] and decision support [15, 16, 18–21] systems, plus developed multiple criteria methods (codec, copras, duma and dam [18, 19]. the purpose was to determine the most efficient advisory, negotiation and intelligent decision support system for leadership analysis (andes) to analyze a leader’s life cycle. this developed support system consists of a database, database management system, model-base, model-base management system and user interface (figure 1). figure 1: andes components andes is an information system that accumulates data and information from various sources and processes them by extensive use of artificial intelligence techniques. it utilizes various multiple-criteria and artificial intelligence models and provides a decision-maker with data, information and knowledge needed for analyzing, compiling and assessing possible alternative resolutions. it can make a decision, derive the received results and safeguard them. thus the andes can be based on data from various sources, allow users to transform a huge amount of unprocessed data, information and knowledge into an analysis of a problem under consideration. development of the initial version of the advisory, negotiation and intelligent decision support system for leadership analysis was in 2004. the testing of the system has been ongoing since then. a total of 176 distance-learning students tested this system. the continuous testing results resulted in improvements to the system. the testing of the system was the subject of some 18 final master theses. 2.1 database presently the structure of relational databases is the most appropriate in light of the requirements raised by andes. a relational database stores information in tables. every table is given a name for storing it in the external memory of the computer as a separate file. the indexes common for this table are logically interconnected. thereby the entirety of the logically interrelated table comprises the model. the data play very meaningful roles. decisions are made using these as a basis. the more comprehensive the accumulated data are about an object under consideration, the more effective advisory, negotiation and intelligent decision support system for leadership analysis 669 the decision made can be. for example, various economic, social, legal, technical, technological and other factors from the external environment impact knowledge management. the possible operations of an organization objectively change for better or for worse, as external conditions change. usually an organization can organize its operations for more than one market. therefore it is very important to understand and evaluate the constantly changing external micro, meso and macro environment and its impact on an organization’s operations in different markets. the external and internal environments of an organization’s operations can be described for each time period by basing it on specific data, information and knowledge. organizations must react to the fluctuating external environment by making adequate strategic, tactical and operative decisions on the basis of such specific information. since decision-making is an informational process, all of its stages, from the time of setting objectives to the ending of their implementation and evaluating their consequences, must be substantiated by searching for, visualizing, processing and analyzing necessary data, information and knowledge. a leader interacts with a number of interested parties, all of whom are pursuing various goals and all of whom have different potentials, educational levels and backgrounds of experience. therefore all the aforementioned parties in this field approach decision-making in various ways. it is often necessary to define these players in terms of their economic, legal/regulatory, technical, technological, organizational, managerial, quality of life, social, cultural, political, ethical and psychological aspects along with other types of information (see figure 1). this is done to analyze the available alternatives thoroughly and to obtain an efficient compromise solution. such information needs to appear in as much of a user-oriented manner as possible. the presentation of information in andes, which is needed for decision-making, may be in conceptual (digital, numerical), textual, graphic (diagrams, graphs, drawing, etc), photographic, audio (sound), visual (video) and quantitative forms. the presentation of quantitative information involves criteria systems and models, units of measurement, values and initial weights, which fully define the variants provided. conceptual information conceptually describes alternative solutions, criteria and ways used to determine the values and weights of the criteria and the like. upon demand, the andes provides conceptual information (images, audio, video, and so on), which aids the user to get a better understanding of the alternatives in question and their defining criteria. this way andes provides a decision-maker with different conceptual and quantitative information about a leader from a database and a model-base. it allows the decision-maker to analyze the above factors and determine an efficient solution. an analysis of database structures in decision support systems by the type of problem they resolve reveals their various utilities. there are three basic types of database structures: hierarchical, network and relational. andes has a relational database structure, when the information is stored in the form of tables. these tables contain quantitative and conceptual information. each table has a given name by which the computer’s external memory saves it, as a separate file. logically linked parts of the table constitute a relational model. the andes database consists of the following tables: • initial data tables. the data covers general facts about the leader under consideration. the leader’s requirements and their significances as well as an intended salary are included. • leader assessment tables. quantitative and conceptual information about alternative leaders on the andes website [22] show the input data for the multiple criteria analysis in andes. • multivariant design tables. these contain quantitative and conceptual information on the interconnecting elements in the life cycle of a leader in the organization, their compatibilities 670 r. gudauskas, a. kaklauskas, s. jokubauskiene v. targamadze, l. budryte, j. cerkauskas, a. kuzminske and possible combinations, as well as data for the complex multivariant design of the elements previously described herein. • e-self-assessment questionnaire and self-assessment tables. an analysis of the available alternatives is necessary for designing and realizing an effective leader’s life cycle. a computer-aided multivariant design requires the availability of tables containing data on the interconnecting elements of the life cycle of a leader in the organization, along with their compatibilities, possible combinations and a multivariant design. the development of possible variants is possible using the aforementioned tables as the basis for a multivariant design of a leader’s life cycle. the development of millions of alternatives of a leader’s life cycle (including the project on the life cycle of the leader in the organization) is possible by using a multivariant design method. the capacity of these versions to meet various requirements is checked. alternative versions that are unsatisfactory in terms of the requirements raised are excluded from further consideration. a problem involving the significance of criteria compatibilities arises in the process of designing a number of variants of a leader’s life cycle. thereby the performance of a complex evaluation of the alternatives determines that the value of a criterion weight is dependent on the overall criteria under assessment, as well as on their values and initial weights. numerous studies have been conducted worldwide analyzing the reliability of self-assessments. this is quite a controversial issue. a great many researchers attained reliable results proving that self-assessments are sufficiently reliable. our investigations also demonstrate that selfassessments are sufficiently reliable. the basis for the e-self-assessment questionnaire and selfassessment tables is the presumption that it is possible to determine a leader’s level of stress rather accurately by assigning questions for leaders according to some certain methodology and then processing them in accordance with a certain algorithm. 2.2 model-base andes models are subdivided as quantitative and qualitative by their presentations. qualitative models (multicriteria, based on expertise) are based on subjective opinions, experiences and assessments of experts. however, when different experts assess the same characteristics of the object, the derived results are often different. this occurs due to the different experiences, educational levels, purposes, available opportunities and the like of different experts. the derived data can be made more objective by applying the expert methods. quantitative models (i. e. text analytics) reflect the objective features of the objects under consideration, independently of the subjective assessments by experts. such features of an object can be expressed directly by physical units of measurement (monetary units, degrees, percents, ratios and such). qualitative models have as many positive and negative features and quantitative models have. objects being considered by quantitative models are objectively but often not comprehensively reflected. contrariwise, andes qualitative models reflect reality subjectively and comprehensively. therefore the rationality of applying quantitative and qualitative methods often depends on specific, decision-making situations. frequently decision-making requires a comprehensive application of quantitative and qualitative models. for example, it is best to apply qualitative research methods when analyzing the qualitative leadership characteristics (emotions, culture, religious, traditions, ethical leader behaviors, psychological capital). however, when analyzing how much money will be spent over the entire process of an office’s life cycle, such as the costs of its purchase or construction, exploitation, maintenance upkeep, insurance expenses, taxes and the like, the application of quantitative methods is better. advisory, negotiation and intelligent decision support system for leadership analysis 671 a determination regarding the efficiency of alternative leaders often takes into account economic, legal/regulatory, technical, technological, organizational, managerial, quality of life, social, cultural, political, ethical, psychological and other factors. therefore the model-base of the andes should include models enabling a decision-maker to analyze the available variants comprehensively and arrive at a suitable choice. the intention for the following models of the model-base is to perform this function: • codec model (complex determination of criteria weight method including quantitative and qualitative characteristics) • copras model (complex proportional assessment method) • recommender model • duma model (defining a project’s utility degree and market value method) • dam model (multiple criteria, multivariant design of alternatives method) • ne-dss model (negotiation decision support) • self-assessment model • stress management advisory model • text analytics development of the codec model (complex determination of criteria weight method including quantitative and qualitative characteristics) allows for the calculation and coordination of the significances of the described quantitative and qualitative characteristics of the criteria. development of the multiple criteria copras model (complex proportional assessment method) enables a user to obtain a reduced criterion for determining the complex (overall) efficiency of alternatives. this generalized criterion is directly proportional to the relative effect of the values and weights of the criteria under consideration regarding the efficiency of the alternatives. the duma model determines the utility degree and market salary of leaders. it establishes the competitive salary for a leader on the market. the basis for this model is a complex analysis of all the benefits and drawbacks of a leader. a leader’s utility degree and the estimated market salary of a leader are directly proportional to the system of the criteria, which adequately describe them, and the values and weights of those criteria, according to this model. development of the dam model of a multiple criteria, multivariant design of a leader’s life cycle enables a user to design, with the aid of a computer, up to 100,000 alternative versions. quantitative and conceptual information are the bases for any life cycle variant obtained in this way. the bases for the andes are the models described above. andes can generate millions of alternative versions of leader life cycles (including a project for determining the life cycle of the current leader in the organization). andes performs a multiple criteria analysis of a leader’s life cycle, determines a leader’s utility degree and selects the most beneficial variant for a leader. a model base, management system can provide various models according to a user’s needs. the results of its obtained calculations become the initial data for some other models when using some certain model. such a model could be for determining the initial weights of the criteria, for designing a leader’s life cycle in a multivariant method (including a project on the life cycle of the current leader in the organization), for analyzing by multiple criteria and for setting priorities. meanwhile the results of the latter, in turn, are acceptable as the initial data for some other 672 r. gudauskas, a. kaklauskas, s. jokubauskiene v. targamadze, l. budryte, j. cerkauskas, a. kuzminske models (such as for determining the leader utility degree, for providing recommendations and for other undertakings). the ne-dss negotiation decision support model is for use by leaders engaged in different negotiation circumstances. one such example could be the purchase of office premises. a leader performing a multi-criteria analysis of all real estate alternatives selects the objects for starting the negotiations. the leader marks (ticks a box with a mouse) the desirable negotiation objects. the letter writing model generates a negotiations e-mail after the selection of the desired objects. it then sends the e-mail to all real estate sellers once the user clicks "send". the buyer and the seller may perform real calculations (the utility degree, market salary and purchase priorities) of the real estate with the help of andes during negotiations. the bases for these calculations are the characteristics describing the real estate’s alternatives, obtained during negotiations (explicit and tacit criteria system and criteria values and weights). development of the final comparative table is in accordance with the received results. use of andes permits the performance of the multiple criteria analysis and selection of the best real estate for purchase version, following the development of the final comparative table. there are two main categories of rules and procedures in the ne-dss model: • it compiles suggestions for leaders to employ along with the reasons for the recommended further negotiations with a particular leader. • it composes a comprehensive, negotiation e-mail for each of the selected broker leaders. the ne-dss uses information inherited from the previous andes calculations and predefined rules and procedures and composes a negotiation e-mail for each of the selected broker leaders. the e-mail includes a reasonable suggestion for a decrease in the price of the real estate. the e-mail also references the calculations performed by andes. development of the self-assessment model took place while analyzing similar studies that were conducted worldwide. areas of special attention were the criteria systems used, integration of such criteria into one general assessment, use of aggregation methods, reliability level of the results and tendencies of the results. the authors of this work based their work on the aforementioned and other studies. a brief analysis of the above copras model (complex proportional assessment method) and duma model (defining a project’s utility degree and market value method) follows, as an example. copras model (complex proportional assessment method) the determination of the utility degree and value of the alternative under investigation and establishment of the priority order for its implementation does not present much difficulty if the criteria numerical values and weights have been obtained and the multiple criteria decision making methods are used. the results of the comparative analysis of the alternatives are presented as a grouped decision making matrix where columns contain n alternatives being considered, while all quantitative and conceptual information pertaining to them is found in lines. this method assumes direct and proportional dependence of significance and priority of investigated versions on a system of criteria adequately describing the alternatives and on values and weights of the criteria. the system of criteria is determined and the values and initial weights of criteria are calculated by experts. all this information can be corrected by interested parties taking into consideration their pursued goals and existing capabilities. hence, the assessment results of alternatives fully reflect the initial data jointly submitted by experts and interested parties. advisory, negotiation and intelligent decision support system for leadership analysis 673 the determination of significance and priority of alternatives is carried out in four stages. stage 1. the weighted normalized decision making matrix d is formed. the purpose of this stage is to receive dimensionless weighted values from the comparative indexes. when the dimensionless values of the indexes are known, all criteria, originally having different dimensions, can be compared. the following formula is used for this purpose: dij = xij ·qi n∑ j=1 xij , i = 1,m;j = 1,n. (1) where xij the value of the i-th criterion in the j-th alternative of a solution; m the number of criteria; n the number of the alternatives compared; qi weight of i-th criterion. the sum of dimensionless weighted index values dij of each criterion xi is always equal to the weight qi of this criterion: qi = n∑ j=1 dij, i = 1,m;j = 1,n. (2) in other words, the value of weight qi of the investigated criterion is proportionally distributed among all alternative versions aj according to their values xij. stage 2. the sums of weighted normalized indexes describing the j-th version are calculated. the versions are described by minimizing indexes s−j and maximizing indexes s+j. the lower value of minimizing indexes (salary, costs of lifelong learning, etc.) is better. the greater value of maximizing indexes is better (innovation, labour productivity, etc.). the sums are calculated according to the formula: s+j = m∑ i=1 d+ij;s−j = m∑ i=1 d−ij, i = 1,m;j = 1,n. (3) in this case, the values s+j (the greater is this value (alternative ’pluses’), the more satisfied are the interested parties) and s−j (the lower is this value (alternative ’minuses’), the better is goal attainment by the interested parties) express the degree of goals attained by the interested parties in each alternative. in any case the sums of ’pluses’ s+j and ’minuses’ s−j of all alternatives is always respectively equal to all sums of weights of maximizing and minimizing criteria: s+ = n∑ j=1 s+j = m∑ i=1 n∑ j=1 d+ij, s− = n∑ j=1 s−j = m∑ i=1 n∑ j=1 d−ij, i = 1,m;j = 1,n. (4) in this way, the calculations made may be additionally checked. stage 3. the significance (efficiency) of comparative versions is determined on the basis of describing positive alternatives (’pluses’) and negative alternatives (’minuses’) characteristics. relative significance qj of each alternative aj is found according to the formula: 674 r. gudauskas, a. kaklauskas, s. jokubauskiene v. targamadze, l. budryte, j. cerkauskas, a. kuzminske qj = s+j + s−min · n∑ j=1 s−j s−j · n∑ j=i s−min s−j ,j = 1,n (5) stage 4. priority determination of alternatives. the greater is the qj the higher is the efficiency (priority) of the alternative. the analysis of the method presented makes it possible to state that it may be easily applied to evaluating the alternatives and selecting most efficient of them, being fully aware of a physical meaning of the decision-making process. moreover, it allowed to formulate a reduced criterion qj which is directly proportional to the relative effect of the compared criteria values xij and weights qi on the end result. duma model (defining a project’s utility degree and market value method) the leader’s utility degree and market salary are determined in seven stages. stage 1. the formula used for the calculation of leader’s aj utility degree nj is given below: nj = (qj : qmax)100%, (6) here qj and qmax are the significances of the leader obtained from the equation 5. the degree of utility nj of leader aj indicates the level of satisfying the needs of the stakeholders interested in the leader. the more goals are achieved and the more important they are, the higher is the degree of the leader utility. a degree of leader utility reflects the extent to which the goals pursued by the interested parties are attained. therefore, it may be used as a basis for determining leader market salary. the more objectives are attained and the more significant they are the higher will be leader degree of utility and its market salary. stage 2. the efficiency degree eji of money (salary, etc.) invested into leader aj is calculated. it shows by how many percent it is better (worse) to invest money into leader aj compared with leader ai. eji is obtained by comparing the degrees of utility of the leaders considered: eij = nj −ni. (7) the received results are presented as a matrix clearly showing utility differences of the leaders. stage 3. the average deviation kj of the utility degree nj of the leader aj from the same index of other leaders (n−1) is being calculated. kj = n∑ i=1 eji : (n−1). (8) stage 4. the development of a grouped decision making matrix for leader multiple criteria analysis. the market salary of a leader being valuated is calculated according to a block-diagram presented in figure 2. at the beginning, a grouped decision making matrix for leader multiple criteria analysis is developed, the first criterion of which is based on the actual salary of the leader compared and the value of a leader being valuated. the initial value of a leader being valuated is obtained from the following equation: advisory, negotiation and intelligent decision support system for leadership analysis 675 x11 = n∑ j=2 x1j : (n−1). (9) figure 2: block-diagram of leader market salary estimation in this matrix, a leader a1 to be valuated should be assigned the market salary (x11−r). other leaders (a2 − an) salaries (x12 − x1n) known. all the values and weights of the criteria relating to other leaders are also known. the problem may be stated as follows: what market salary x11−r of a valuated leadera1 will make it equally competitive on the market with comparison standard leaders (a2 − an)? this may be determined if a complex analysis of the benefits and drawbacks of the leader is made. using a grouped decision making matrix and the equations 1-9 the calculations are made stage 5. the corrected value x11−p of a leader to be valuated a1 is calculated: x11−p = x11 ∗ (1 + k1 : 100). (10) stage6. it is determined whether the corrected value x11−r of a leader being valuated a1 had been calculated accurately enough: |k1| < s, (11) where s is the accuracy, %, to be achieved in calculating the market salary x11−p of a leader a1. for example, given s = 0,5%, the number of approximations in calculation will be lower than at s = 0,1%. stage 7. the market salary x11−r of a leader a1 to be valuated is determined. if inequality 11 is satisfied the market salary of a leader a1 may be found as follows: x11−r = x11−p (12) 676 r. gudauskas, a. kaklauskas, s. jokubauskiene v. targamadze, l. budryte, j. cerkauskas, a. kuzminske if inequality 11 is not satisfied this means that the value of a leader being valuated had not been calculated accurately enough and the approximation cycle should be repeated. in this case, the corrected valuex11 = x11−p of a leader being valuated is substituted into a grouped decision making matrix of leader multiple criteria analysis and the calculations according to the formulae 1-12 should be repeated until the inequation 2.20 is satisfied. solving the problem of determining the market salary x11−r of a leader a1 being valuated, which would make it equally competitive on the market compared with the leaders (a2 − an), a particular method of defining the utility degree and market salary of a leader was suggested. this was based on a complex analysis of all the benefits and drawbacks of the leader considered. according to this method the leader utility degree and the market salary of a leader being estimated are directly proportional to the system of the criteria adequately describing them and the values and weights of these criteria. the case study [22] presented illustrates the efficiency of the andes in practice. 3 conclusions an advisory, negotiation and intelligent decision support system for leadership analysis (andes) was offered as an example for demonstrating the integration of advisory, negotiation and decision support systems. in the future, there are plans to extend the use of the andes in martynas mazvydas national library of lithuania. the plans for the next stage in the development of the andes involve integrating this system with biometrics (computer mouse advisory system), recommended biometric stress management system and pupil analysis system [18] systems, which the authors herein have also developed. such an integration of intelligent and biometrics systems would provide even better assessments of the emotional states of leaders and the submissions of personalized recommendations to them. bibliography [1] m. uhl-bien, r. marion (2009); complexity leadership in bureaucratic forms of organizing: a meso model, the leadership quarterly, 20(4): 631–650. 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[18] a. kaklauskas (2015); biometric and intelligent decision making support, series: intelligent systems reference library, vol. 81, xii. springer-verlag, berlin. [19] a. kaklauskas (1999); multiple criteria decision support of building life cycle, research report presented for habilitation. technika, vilnius. [20] l. kanapeckiene, a. kaklauskas, e.k. zavadskas, m. seniut (2010); integrated knowledge management model and system for construction projects. engineering applications of artificial intelligence, 23(7): 1200–1215. [21] n. lepkova, a. kaklauskas, e.k. zavadskas (2008); modelling of facilities management alternatives, international journal of environment and pollution, 35(2–4): 185–204. [22] http://iti.vgtu.lt/imitacijosmain/simpletable.aspx?sistemid=518 changjcccv12n6.pdf international journal of computers communications & control issn 1841-9836, 12(6), 790-802, december 2017. time series clustering based on singularity d. chang, y.f. ma, x.l. ding dan chang, yunfang ma*, xueling ding school of economic and management, beijing jiaotong university, beijing, 100044, china dchang@bjtu.edu.cn *corresponding author: yunfangm@bjtu.edu.cn dingxueli@boe.com.cn abstract: with relevant theories on time series clustering, the thesis makes research into similarity clustering process of time series from the perspective of singularity and proposes the time series clustering based on singularity applying k-means and dbscan clustering algorithms according to the shortage of traditional clustering algorithm. in accordance with the general clustering process of time series, time series clustering based on singularity and k-means are made respectively to get different clustering results and make a comparison, thus proving that similarity clustering research of time series from the perspective of singularity can better find out people’s concern on time series. keywords: time series, clustering, singularity, dbscan, kmeans. 1 introduction time series is a high dimensional data type, a random variable which is strictly arranged by time order and correlated with each other. as an important data type in the economic field, it plays a significant role in people’s analysis on market trend and their decision-making. as some time series data increase at the magnitude of several billion per day or even per minute, how to find the data correlation in time series and analyze such huge data with timely and fast response, thus figuring out similar or regular changing pattern, tendency, mutation with obvious change and distribution of outliers, has become an increasingly important and challenging hot topic. during the last decades, a societal focus on the work of university faculty as a measure of returnonthepublic’s investment inhigher education stimulateda reevaluationofhowfacultyperformance ought to be measured and assessed. the development of workable assessment systems is difficult largely due to the fact that the value of assessment is often controversial:clustering analysis is one of the important tasks in data mining. it is a process in which data set is divided into several groups or classes, making the data objects in the same group or class have high similarity and those in different groups or classes different. differing from traditional clustering analysis, data processed in the clustering analysis of time series are changing over time with features of high-dimension, complexity, dynamism and high noise, which are easy to reach largescale. variable clustering approach usually is used to handle the high-dimensional variable [12]. time series clustering refers to cluster time series with similar change into one class, and time series in different classes have obvious different changes [6]. how to cluster the mass time series which is closely related to daily life has aroused concern among many scholars in the field of data mining. most of current classic similarity analysis is based on mathematical model, which considering the overall features of time series. however, for the mass data and the special time series with high-dimension, high noise and high complexity, traditional similarity searching based on mathematical model does not consider that user requirement and concern vary in different situations in real life, which means the importance of singularity in time series differs for different copyright © cc by-nc time series clustering based on singularity 791 users, in spite of the shortening processing time caused by relative technology and more accurate results. there are many definitions for singularity without a formal and standard one that is generally accepted. the definition in the thesis is the data that is obviously diverged from most sample data, namely data points different from most sample data which do not meet the general model of sample data. that is to say the data points taking up very small data volume in the clustering result. the thesis aims to analyze through time series data mining algorithm with theories related to clustering such as dbscan and kmeans, figuring out the existing problems in traditional time series data mining clustering algorithm, confirming the factors which should be comprehensively analyzed during the time series clustering analysis such as the influence of major events or accidental events on time series, and finishing the time series clustering research based on singularity. the case study at the end of the thesis demonstrates the application of time series clustering proposed by the thesis in detailed container port transportation. 2 literature review 2.1 time series basic concept of time series time series is a series composed of data that change over time, which is also called dynamic series. time series metrics or features that can be used for time series classification or regression analysis [7]. time series are used in statistics, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting [14], container shipping freight rate forecasting [8], etc. different from static data, time series is a data object of high complexity and high noise that describes the changing process of things over time. time series exists widely in various fields of daily life. for example, the grain yield of a certain place changes every year; stock price keeps fluctuating over time; the traffic flow of a certain road changes in different time periods. data of time series change over time. according to this feature, every data unit of time series is abstracted to a binary array (t, x) composed of time and corresponding time value, in which t represents time variable and x represents data variable. similarity of time series suppose that there are time series q and time series c, q = {q1, q2, ...qn}, c = {c1, c2, ...cn}, if the distance between q and c satisfies dist(q, c) ≤ ǫ (given similar threshold value which is used to adjust the level of similarity), q and c is similar. besides, dist(q,c) is a distance function, and the most typical one is euclidean distance. however, euclidean distance is not sensitive to noise data and can hardly recognize time series with displacement or stretch. similarity based on euclidean distance regards time series as points in multi-dimensional space and measure the similarity of time series with the distance between points. because of the advantages of fast calculation and low complexity, euclidean distance becomes the most commonly used measurement method. for example, for time series q and time series c of the same length, q = {q1, q2, ...qn}, c = {c1, c2, ...cn}, then q, c ∈ rn; q and c are regarded as two points qi and ci in n-dimensional space, and the distance between them is: d = √ √ √ √ n ∑ k=1 (q2 − ck) 2 792 d. chang, y.f. ma, x.l. ding 2.2 time series clustering algorithm data in real life have features such as huge amount, high-dimension and high noise etc., especially for time series data. and data are expanding and becoming more complicated [1]. so far there is no clustering algorithm that can be applied to data of any type. many scholars have built many clustering algorithms in order to solve the clustering for multi-type data. k-means algorithm k-means has been successfully used in various areas, such as market segmentation, computer vision, geostatistics, astronomy and agriculture [4]. k-means by default considers that cluster comprises all objects within close distance and the ultimate goal is to obtain a compact and independent cluster. in a complete iteration, k-means will calculate the distance between the cluster center and every remaining object and distribute the object to the nearest cluster. iteration is finished after all data objects are detected, and then new clustering center is figured out. description of detailed algorithm is as follows: given data set x = {x1, x2, x3, ...xn}, in which data object xi has m-dimension variable, xi={xi1, xi2, xi3, ...xim}. k represents the number of needed cluster; data set x is divided into k classes; according to the requirement of high similarity inside the cluster and low similarity between clusters, square-sum-of-error criteria function e should be minimum in order to obtain the optimal clustering effect. e represents the sum of distance between each object and its located cluster center [5]. it can be seen from the experimental study of square-sum-of-error function e that when data objects in a data set are intensive and the differentiation between types is obvious, the criteria function of square sum of error is more effective, thus enabling k-means to get a better clustering effect. the algorithm has converged when the assignments no longer change. there is no guarantee that the optimum is found using this algorithm [3]. dbscan algorithm dbscan (density-based spatial clustering of applications with noise) is a spatial clustering algorithm based on density [2]. it requires two parameters: distance (radius of the detection region) and the minimum sample points (minpts). dbscan can be used with any distance function [2] [10] (as well as similarity functions or other predicates) [9]. description of detailed algorithm [10]: (1)search for object p that is not detected in the data set. if p is not processed, then the number of object in the field is no less than minpts. build new cluster c, and add all objects in c to candidate set n; (2)detect object q that is not processed in candidate set n. if the number of object in its neighborhood is greater than or equal to minpts, then add these objects to n; if q does not belong to any cluster, add q to c; (3)repeat 2); keep detecting objects remaining unprocessed in n until n is empty. repeat 1) 3) until all objects are processed. clustering process is demonstrated in (fig. 1). dbscan defines cluster as the biggest set of points that are density-reachable, which means dividing areas of high density into clusters, so it can recognize the noise point, which is different from partition clustering and hierarchical clustering. compared with k-means clustering, it does not need to know k in advance, and can find out cluster of any shape. time series clustering based on singularity 793 figure 1: dbscan clustering process diagram advantages and disadvantages of the algorithm as a mountain-climbing search algorithm, k-means algorithm has certain disadvantages: (1) k should be provided externally and the accuracy of k is closely related to the clustering result, but it is not easy to confirm k, which forms one of the disadvantages of k-means. (2) the clustering result is related to the initial center. if the choice of initial center fails, then it is impossible to get ideal clustering result. (3) k-means should keep iterating on classification adjustment and calculating new cluster until meeting the condition. therefore, the time cost of k-means algorithm is huge for mass data. in spite of many disadvantages, k-means algorithm become one of the most commonly used algorithm in clustering research because of the features of simplicity, high intelligibility, high convergence rate and high scalability. obviously there are many clustering algorithms. according to the brief introduction and analysis of the two algorithms above we can obtain the comparison result of the two clustering algorithms as showed in (tab. 1). table 1: comparison of the dbscan and k-means algorithms feature / algorithm k-means dbscan data type symbolic type and numeric type numeric type high dimension normal normal sensitivity to data order normal sensitive sensitivity to noise sensitive insensitive scalability good good efficiency normal normal algorithm implementation feature mathematical description; easy to understand and implement; repeated scan for the optimal result; but sensitive to the initial condition; number of clusters confirmed by man; all data put in internal storage. one-time scan; able to recognize noise; clustering result independent of parameter; not very ideal clustering effect for data object with uneven density; not high degree of support for high-dimension data. 794 d. chang, y.f. ma, x.l. ding the above two algorithms for similarity measurement figure out the series which is similar to given series through various relative technology at certain target efficiency. target efficiency means that the implement of similarity analysis algorithm is of high efficiency and low complexity. most of current classic similarity analysis is based on mathematical model, which considers the overall features of time series. however, for the mass data and the special time series with high-dimension, high noise and high complexity, traditional similarity searching based on mathematical model does not consider that user requirement and concern vary in different situations in real life, in spite of the shortening processing time caused by relative technology and more accurate results. for example, people pay more attention to the place, time and probability of earthquake in the earthquake prediction; investors put special emphasis on the fluctuation of stock price in the stock market forecast. therefore, it is essential to introduce the influence of key events such as major events and accidental events on time series during the time series analysis. 3 clustering algorithm based on singularity in light of the shortageof traditional algorithmthat it doesnot consider thatuser requirement and concern vary in different situations in real life, the thesis made further optimization of the traditional algorithm by taking different concerns, namely singularity, of users in real life into consideration. 3.1 basic thought of singularity clustering based on the distance-based k-means algorithm, cluster total sample data n to get k class; then with the density-based dbscan clustering algorithm, successively get the data volume mi in every class of ki, and then figure out ρi = mi/n, given threshold value ǫ (defined according to user requirement): when ρi ≥ ǫ,i type is density-reachable at ǫ; when ρ_i <ǫ, i type is not density-reachable at ǫ. then i is regarded as the data class diverged from most data sample, namely singular class, which refers to the data class including singularity. 3.2 clustering based on singularity current time series clustering is made with every series as a whole. for example, if we cluster n time series with current time series clustering algorithm, n time series are regarded as data objects while clustering. what we consider is the whole similarity of n time series, but ignoring the possible similarity of data objects at certain time periods. therefore, the thesis takes the data of n time series at the whole time period (ti −tj, i ∈ n, j ∈ n) as data object, which means making similarity clustering with n∗(j−i) data objects, thus not only considering the similarity showed by different ports at the same year, but also comprehensively considering the similarity demonstrated by different ports at different years and that by the same port at different years. description of detailed process: (1) data labeling. label data after preprocessing. data of n at t is expressed as (t, n). (2) dbscan clustering. choose radius r as density in dbscan algorithm and get k class results. (3) decide discrete class, namely singular class. figure out data volume mi in every ki successively and then figure out ρi = mi/n; given threshold value ǫ. when ρi ≥ ǫ, i class is time series clustering based on singularity 795 density reachable at ǫ; when ρi<ǫ, i class is density unreachable, then it is called the data class that deviates from most data sample, namely singular class. (4) combine with expert advice and accidental events to get reasonable clustering result. 3.3 singularity detection the important feature of singularity in time series carries important information. for example, according to the data from census and statistics department of hong kong, several scholars summarized some irregular events through text mining technology based on webpage, and then integrated these events through delphi technique combining expert advice, figured out the influence direction of these events on the throughput respectively, and searched on international maritime information website for the events that influenced throughput of container in recent years, thus drawing the influence direction of irregular events on port throughput as showed in (tab. 2), which can be used to judge the influence on throughput when irregular events happen. of course, data in the table should be updated and adjusted according to the actual situation in order to keep flexibility. table 2: effect of irregular events on port throughput class important event direction industry factor container cost reduction up container vacancy rate decline up barge cost reduction up economic factor economic crisis down foreign exchange rate uncertain crude oil price rise/fall down/up related policy pallet carrier cross-border license fee reduction up wharf occupation rate decline up natural disaster typhoon/tsunami/earthquake down regional competition port merger up operating efficiency rise of other ports down political factor port labor strike down direct flight for cross-strait trade down terrorist attack down other factor wharf construction up berth number increase up shipping company and wharf company reach related agreement uncertain 4 experimental comparison-taking china’s container port clustering as an example ports have become a significant strategic resource for the development of national economy and regional economy in china. they act as the nodes connecting water transport with land transport and play pivotal roles in logistics network [13]. as the development of port cities and hinterland economy relies on the development of ports, a series of research on ports is of great significance for not only the development of hinterland cities, but also the development of ports themselves [11]. 796 d. chang, y.f. ma, x.l. ding 4.1 choice of representative ports thethesis takes theclustering researchofchina’s containerportsasanexample, duringwhich the choice of container ports data is the basis for research. coastal ports mainly spread at coastal areas such as yangtze river delta, pearl river delta, shandong peninsula and west coast of bohai sea etc., which are the optimal objects of clustering research. ports at yangtze river delta mainly include shanghai port and ningbo-zhoushan port, froming a container transportation systemconsistingof ports suchaslianyungangport; withanemphasis onningbo-zhoushanport and lianyungang port, imported mineral, handling of crude oil and transfer system of ports of shanghai, suzhou, zhenjiang and nanjing etc. should be developed correspondingly; shanghai port and ningbo-zhoushan port form the major coal transfer system. therefore, the thesis chooses shanghai port, ningbo-zhoushan port and liangyungang port as the representatives of ports at yangtze river delta. 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000 dalian port yingkou port qinhuangdao port tianjin port yantai port qingdao port rizhao port shanghai port lianyungang port ningbo-zhoushan port shantou port guangzhou port zhanjiang port haikou port basuo port figure 2: comparison of the dbscan and k-means algorithms 19952014 throughput of coastal container port above designated size in china first of all, we choose throughput of 15 coastal container ports above designated size in china from 19952014 as the time series data to analyze different variation trend demonstrated by different ports. without any process on the original data, it can be seen from (fig. 2) that although all ports largely demonstrate an upward trend as time goes by, some ports show different changing trend at certain stages. for example, from 2008 to 2009, shanghai port and qinhuangdao port showed downward trend while other ports went upward. therefore, we assume that whether there was a big event in 2008 which had great influence on only shanghai port and qinhuangdao port while having no influence or little influence on other ports. in order to better describe various features of different time series at different stages, indicators representing tendency of every stage are clustered as attributes. therefore, the thesis takes throughput growth rate as the attribute of clustering analysis calculation. time series clustering based on singularity 797 4.2 ports clustering based on singularity the time series clustering algorithm that can recognize singularity in 3.3 is adopted in this part in order to figure out the influence of accidental events on every port in port market. current time series clustering is all made with each series as a whole. for example, if we use current time series algorithm to cluster ports, it is made with 15 time series as data objects. therefore, what is considered is the overall similarity of 15 time series, but ignoring the possible similarity of data objects at certain stage. the thesis takes the throughput data of every port from 1995 to 2014 as data objects. taking the port data in each year as one time series data set, so total 19 years between 1995 to 2014 with 15 ports lead to 19 ∗ 15 = 285 data sets. to make similarity clustering, not only considering the similarity showed by different ports at the same year, but also comprehensively considering the similarity demonstrated by different ports at different years and that by the same port at different years. ports clustering based on singularity clustering process of throughput time series figure 3: cluster distribution of coastal container ports above designated size in china according to the detailed process in 3.2, the discrete class results figured out are as follows (please note that the number for this year demonstrates the situation of last year): [1.474297, 4.726063, 100, "1996-yingkou port"], [0.482091, 4.467265, 100, "1998yingkou port"], [3.906951, 3.579445, 100, "2007-yantai port"], [0.530989, 3.517523, 100, "1998-lianyungang port"], [-0.693107, -4.709108, 100, "1998-zhanjiang port"], [5.322229, 4.122826, 100, "2009-zhanjiang port"], [4.670751, 4.580626, 100, "2009-haikou port"], [2.764757, 3.911730, 100, "1997-basuo port"]. clustering effect picture is showed in (fig. 3). 798 d. chang, y.f. ma, x.l. ding singularity detection of throughput time series take ningbo-zhoushan port as an example, as showed in the table. it demonstrated a continuous upward trend on the whole, but changed greatly in 2006. we have found that the reason is the merger of ningbo port and zhoushan port, and based on the exploration of related records and expert interviews we have also found that it is the period of financial crisis. (tab. 3) demonstrates the final turning point formed according to singularity detection, expert experience and major events taking place during the period. as mentioned before, throughput of different ports have different responses to a given event. we can see that in 1998 and 2009, some ports showed distinct turning trend because of the financial crisis, while others kept steady tendency. table 3: turning point of chinese port market time event or major change in policy 1997 national key construction project: finishing the infrastructure of coastal ports latter half of 1997-1998 asian financial crisis: leading to financial crisis in southeast asia 2006 ningbo port and zhoushan port merged as ningbo-zhoushan port 2008 shanghai financial crisis 2009 administrative regulations of port operation 2013 official establishment of china (shanghai) free trade pilot zone according to the irregular influential factors and the clustering bubble diagram, singularity is figured out through comprehensive analysis of social events or expert experience. (1) 1996-yingkou port. yingkou zhongyuan international container wharf limited liability company, a container wharf company jointly operated and managed by yingkou port office and china ocean shipping group company, was set up. the company owns a wharf coastline of 309 meters, a container yard of 150,000 square meters, 2 container unloading bridges, a cfs warehouse of 3000 square meters and other related infrastructure. it can berth one 15,000tons container ship or two 5000-tons container ships, enabling yingkou port achieved container quantity of 14,900 standard containers in 1996. (2) 1997-basuo port. major coastal ports construction and operation, a national key construction project was finished in 1997 with 22 berths were completed and put into use, among which there are one in 200,000-tones mineral transfer wharf second-stage project in beilun harbor district of ningbo port, five in the second-stage project in bayuquan harbor district of yingkou port, six in the second-stage project in west basin of yantai port, three in the second-stage project in front bay of qingdao port, and one in the first-stage project in xiahai of zhanjiang port. throughput of most ports showed an obvious growing trend in this year. (3) because of the financial crisis in southeast asia from the latter half of 1997 to 1998, most ports showed a distinct deviation, especially zhanjiang port, yingkou port, lianyungang port, which experienced dramatic decline in terms of growth rate, indicating the relatively great influence of the financial crisis on the three ports compared with other ports. (4) china’s container transportation kept high-speed increase in 1999 with an obvious trend of concentrating on major hub ports, thus forming yangtze river delta regional ports with shanghai port being the center, pearl river delta regional ports represented by shenzhen including time series clustering based on singularity 799 shenzhen port, shantou port, guangzhou port and haikou port, as well as circum-bohai-sea regional ports including dalian port, qingdao port and yantai port. (5) 2007-yantai port. based on repeated port integration, 30 new port projects were constructed in yantai port in 2007 with four already existing harbor districts including west district, penglai district and longkou district, leading to a throughput of over 100 million tons in 2007. (6) the international financial crisis resulted in an obvious decline in terms of growth rate of ports, which was closely related to the insufficient supply of goods caused by the financial crisis. (7) in 2009, ports largely stayed in the condition of insufficient supply of goods because of the international financial crisis in 2008. although the throughput of most ports kept increasing, the growth rate showed a downward trend. facing such situation, related administrative department in zhanjiang and port companies jointly kept going forward by striving for supply of goods and took them to the port, thus leading to a throughput of over 100 million tons again in 2009, exceeding the expected target. in addition, some opinions concerning promoting the construction and development of hainan international tourism island put forward by the state council clearly proposed "implementing supporting policy for business related to international shipping, improving supporting policy for the development of modern logistics industry, forging a shipping hub and logistics center facing southeast asia while relying on the hinterland of southern part of china". therefore, haikou port smoothly went through the crisis in 2009 caused by the financial crisis in 2008 and showed a distinct upward trend. (8) in addition, it can be seen from the growth curve of throughput that the throughput of shanghai port reached its max at 2014. throughput transferred from shanghai port to taicang port actually indicated the fact that the throughput growth space of shainghai port was increasingly narrow. 4.3 port clustering based on k-means in order to verify that the port clustering based on singularity can better detect the influence of accidental events on different port throughput time series, and to contrast the deficiency of current frequently-used k-means clustering algorithm, k-means clustering algorithm is adopted to cluster the port throughput time series again, thus making a comparison with the time series based on singularity. after standardization of original data, it can be seen in (fig. 4) that the throughput changing trend of the fifteen ports can be largely divided into three classes: fluctuate increase, steady increase, and accelerated increase. to be more specific, it can be divided into five classes. therefore, we assume it is divided into five classes and choose k-means clustering with the max k = 5 in the spss statistical analysis software. (tab. 4) demonstrates the cluster class and the members in every class. according to the table, the first class: ningbo-zhoushan port; the second class: shanghai port; the third class: yingkou port, qinghuangdao port, rizhao port; the forth class: guangzhou port, tianjin port, qingdao port, dalian port; the fifth class: shantou port, lianyungang port, zhanjiang port, yantai port, haikou port, basuo port. 4.4 contrastive analysis of clustering results it can be seen from the two clustering results in 4.2 and 4.3 that the clustering results obtained through two clustering algorithms are quite different. 800 d. chang, y.f. ma, x.l. ding figure 4: trends of throughput of major ports along the coast of china in 1995-2014 (after standardization) table 4: cluster members no. throughput of port (unit: ten thousand tons) cluster distance 1 dalian port 4 18180.566 2 yingkou port 3 11999.398 3 qinhuangdao port 3 22396.025 4 tianjin port 4 14222.241 5 yantai port 5 22071.565 6 qingdao port 4 6685.459 7 rizhao port 3 10612.377 8 shanghai port 2 .000 9 lianyungang port 5 15698.608 10 ningbo-zhoushan port 1 .000 11 shantou port 5 16305.687 12 guangzhou port 4 12746.441 13 zhanjiang port 5 14322.937 14 haikou port 5 10183.355 15 basuo port 5 25108.685 time series clustering based on singularity 801 the clustering result based on singularity is as follows: "1996-yingkou port", "1998-yingkou port", "2007-yantai port", "1998-lianyungang port", "1998-zhanjiang port", "2009-zhanjiang port", "2009-haikou port", "1997-basuo port". k-means clustering results are as follows: the first class: ningbo-zhoushan port; the second class: shanghai port; the third class: yingkou port, qinhuangdao port, rizhao port; the forth class: guangzhou port, tianjin port, qingdao port, dalian port; the fifth class: shantou port, lianyungang port, zhanjiang port, yantai port, haikou port, basuo port. it can be seen that k-means algorithm can detect the similarity of overall feature variation trend of time series, but is not good at detecting different features of every time series at different stages and does not take into account different demands and concerns of users in real life under different situations. times series clustering based on singularity just complement such aspect, which not only considers the similarity of different time series at the same time, but also fully considers the possible similarity of different time series at different times and that of a certain time series at different times; that is to say to take different demands and concerns of users in real life under different situations into consideration. 5 conclusion the thesis proposes the time series clustering based on singularity according to the shortage of traditional clustering algorithm. as users have different demands and concerns in real life under different situations, researches into similarity clustering process of time series are carried forward from the perspective of singularity, which make choice among trend indicators of time series at every stage and optimize the original data. different clustering results are obtained through time series clustering based on singularity and k-means respectively. by the comparison of the clustering results, it can be figured out that time series similarity clustering research from the perspective of singularity can better find out the important point of time series. however, during the singularity detection part, there are some subjective factors to some extent in only the event exploration in relative records and expert interview. if such factors could be quantified, it would be more convincing. finally, as for the time series that are not involved in the research except ports, the clustering algorithm in the thesis can be adopted to figure out the sensitivity of time series to different major events, thus making precautions to the predicted events with the discrimination theory and prediction model. acknowledgment this work was partially supported by beijing logistics informatics research base. bibliography [1] barlas p., heavey c., dagkakis g. 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(2015); real-time vessel behavior prediction, evolving systems, 7, 1-12, 2015. int j comput commun, issn 1841-9836 9(3):313-324, june, 2014. numerical prediction of time series based on fcms with information granules w. lu, j. yang, x. liu wei lu*, jianhua yang, xiaodong liu school of control science and engineering dalian university of technology dalian 116023, china *corresponding author: luwei@dlut.edu.cn abstract: the prediction of time series has been widely applied to many fields such as enrollments, stocks, weather and so on. in this paper, a new prediction method based on fuzzy cognitive map with information granules is proposed, in which fuzzy cmeans clustering algorithm is used to automatically abstract information granules and transform the original time series into granular time series, and subsequently fuzzy cognitive map is used to describe these granular time series and perform prediction. two benchmark time series are used to validate feasibility and effectiveness of proposed method. the experimental results show that the proposed prediction method can reach better prediction accuracy. additionally, the proposed method is also able to precess the modeling and prediction of large-scale time series. keywords: fuzzy cognitive maps (fcms), time series, prediction, , information granules. 1 introduction time series is a sequence of real-data, with each element in this sequence representing a value recorded at some time moment. as a classic issue, time series prediction has been used in diverse fields, which utilizes prediction model describing some useful temporal relationship that is developed by observing a past certain variable or a past family of variables to extrapolate future values. how to construct prediction model of time series is core for prediction of time series. many researcher early focus on how to construct predictive model of time series with the aid of the linear system theory [1], the stochastic process theory [2] and the black-box methodology [3], and dynamical system analysis [4]. however, the constructed prediction model by using these methods cannot solve prediction problem in which the historical data are missing or uncertain. fuzzy sets theory can be used to make semantics and represent the data themselves and fuzzy reasoning offers a viable alternative to ensure robustness to the inherent uncertainty, which has involved into modeling and prediction of time series. song and chissom [5–7] defined the concept of fuzzy time series and developed two fuzzy time series prediction models — the time-invariant model [6] and the time-variant model [7]. following the work of song and chissom, many the improved prediction models associated with fuzzy time series are emerged such as the markov model [8], chens model [9], hwangs model [10], the heuristic model [11], the high order model [12], the local trend model [13] and so on. these models have been used to predict enrollment of university, stock index, temperature etc., which also shows better prediction performance. as data, time series is inherently associated with large size, high dimensionality and a stream-like nature. whereas construction of the fuzzy logic relationships which has important impact on performance of models is a tedious work in the development process of existing fuzzy time series models, which is difficult and complicated for modeling of the large-scale fuzzy time series. to overcome the deficiency, fuzzy cognitive map (fcm for short) with information granules seem to can become an alternative. copyright © 2006-2014 by ccc publications 314 w. lu, j. yang, x. liu fuzzy cognitive map as a soft computing technology for modeling complex systems was proposed by kosko in 1986 [14], which are treated as an alternative way for knowledge-based representation and inference process for its easy of usage and numeric matrix operation for complex system. based on the nature, fcm has capable to handle prediction problem of time series [15, 16]. further, information granules and information granulation [17, 25] play key roles when dealing with large data. as expression of meaningful the abstract entities, information granules come with information granulation. the role of information granulation is to organize detailed numerical data into some meaningful, semantically sound entities (information granules). in particular, granulate information are able to achieve a high level of interpretability and manage phenomena which are complex and consequently the data are overwhelming. the formalism of information granules includes intervals, fuzzy sets, rough sets, shadow sets or alike. there are many methods supporting design of information granules, one of which is fuzzy c-means clustering. it can provide an ability abstracting fuzzy information granules from data with multivariate attribute. in this paper, starting with global view of information granules, we attempt to construct fuzzy cognitive map with information granules to realize prediction of time series. our proposed prediction method of time series includes two stage: the first stage is to construct fuzzy cognitive map prediction model on basis of information granules, and the second stage is to perform prediction by exploiting the fuzzy cognitive map with information granules based model which is constructed by the first stage. the remainder of this paper is organized as follows. section 2 briefly introduces some concepts related to fcm, and then focus on learning method of fcm. section 3 presents a method of constructing fcm with information granules by using fuzzy c-means clustering algorithm.the proposed prediction method of time series based on fcm with information granules is detailed in section 4. in section 5, two benchmark time series are used to validate the feasibility and effectiveness of proposed method, and experimental results is also discussed. finally, some conclusions is provided in section 6. 2 fuzzy cognitive map and its learning method in this section, some concept associated with fuzzy cognitive map is first recalled, and then the learning method based on particle swarms optimization (pso) technology, an effective learning method of fcm, is presented to learn weights of fcm. 2.1 fuzzy cognitive map fcm is simple, very powerful tool for representation of human knowledge and performing reasoning. fcm can describe a given system by concepts and mutual relationships among them, which play an important role for time series modeling and prediction in this paper. fcm is composed of a collection of nodes and directed links (edges) between nodes. in the fcm, nodes is used to represent concepts, say c1, c2, · · · , cn. these concepts can be envisioned as status, variables etc. which is used to describe main dynamic characteristic of problem/process/system. values of nodes (concepts) are fuzzy and change with time. the directed edges represent casuality between nodes or more precisely a way in which one node affects another one. the connections between nodes could be asymmetric. the strength of connections (it is also called weights of fcm) from node cj to node ci, denoted by ωij, is quantified to be range from −1 to 1. the value of ωij reflects different casuality between ci and cj, viz., • ωij > 0, which indicates positive causality between cj and ci, i.e., an increase of value of cj leads to an increase of value of ci (and vice versa). numerical prediction of time series based on fcms with information granules 315 • ωij = 0, which indicates neutral causality between cj and ci, i.e., no relationship between cj and ci. in this case, the connection from ci to cj can be removed. • ωij < 0, which indicates negative causality between cj and ci, i.e., an increase of value of cj leads to an decrease of value of ci (and vice versa). the fuzzy cognitive map can be represented as not only the directed graphic but also a square matrix. the square matrix, which is also called relationship matrix, stores all values of weights of fcm. fig.1 shows an example of fcm model and its relationship matrix that concerns public city health issues. (a) fcm model of public city health issues (b) the relationship matrix of fcm figure 1: fcm model of public city health issues and its relationship matrix. behind fcm, there is mathematical mechanism which is described in forms of (1): ci(t + 1) = f( n∑ j=1 ω ij cj(t) + ω0i) (1) where cj(t) is the the active level (value) of jth node at the tth time moment, ωij ∈ [1, 1] is the value of weight from the concept cj to the concept ci and ω0i ∈ [0, 1] is the bias associated with the ith node. besides, n is the number of nodes of fcm, and f is the transformation function that is generally selected as sigmoid function with steepness parameter σ — f(u) = 1/(1 + e−σu))(u ∈ r, σ > 0), where the steepness parameter σ is associated with the individual node of the fcm. the role of this parameter is to provide some additional calibration of the value of the node [16]. let w i = [ω i1 ω i2 · · · ω in ]t be the weight vector which includes all values of weights from all other nodes to ith node. likewise the vector of active level of nodes is described as c = [c1 c2 · · · cn]t , which includes all values of nodes of fcm. thus we can rewrite (1) to obtain a more concise vector notation as follows. ci(t + 1) = f(w t i c(t) + ω 0i ) (2) once nodes of fcm and weights between these nodes are determined, fcm starts with a given initial state vector c(t) = [c1(t), c2(t), · · · , cn(t)]t to perform iteration computation according to (1). after finite iteration, fcm can reach an equilibrium point or a limit cycle. it is worthy noting that at t + 1 time moment the active level of ith node depends on all the value of node of fcm at t time moment. 316 w. lu, j. yang, x. liu 2.2 the learning of fuzzy cognitive map the sound values of weights can ensure fcm accurately describing dynamic behavior of system to research. several methods such as the hebbian learning [14] on basis of the scheme of unsupervised learning, the numeric data-based pso algorithm [16] and evolutionary optimization [19] have been proposed. as an optimization method, pso algorithm offers possibilities of global, population-based optimization yet not imposing a very heavy computational overload. the method allows for determining parameters of fcm from original time series data without human input. in this paper, we take pso algorithm to learn all parameters of fcm. in brief, pso involves a population of particles whose dynamics is guided by the mechanisms of social interactions and personal experience. the details of pso and its enhancement version are documented in the literature [20] and [21]. the objective of utilizing pso algorithm to learn parameters of fcm is to develop candidate fcm and enable it to mimic the given input data. this optimization problem requires to establish n × (n + 2) parameters. consequently, the particles structure is defined as ŵ = [ ω 11 , ω 12 , · · · , ω 1n , ω 21 , ω 22 , · · · , ω 2n , · · · , ω n1 , ω n2 , · · · , ωnn, ω 01 , ω 02 , · · · , ω 0n , σ 1 , σ 2 , · · · , σn] (3) where w ij ∈ [1, 1] is the weight from node j to node it, w 0i ∈ [0, 1] is the bias associated with the ith node, and σ i > 0 is the steepness parameters of transformation function f. objective function (4) is used to evaluate quality of particles in population, and is defined by taking advantage of an inherent property of fcm model. min : f = 1 (l − 1) n l−1∑ t=1 n∑ i=1 ∥ ĉi(t + 1) − ci(t + 1) ∥2 (4) where c(t + 1) = [c1(t + 1) c2(t + 1) · · · cn(t + 1)]t is a actual response for initial state vector c(t) = [c1(t) c2(t) · · · cn(t)]t , ĉ(t) = [ĉ1(t + 1) ĉ2(t + 1) · · · ĉn(t + 1)] is a response of the condidate fcm for initial state vector c(t), l is a number of input data points, n is a number of nodes of fcm. the objection function can realize comparison between the single-step response of the candidate fcm and the actual response for the same initial state vector. 3 the design of fuzzy cognitive map with information granules one fundamental problem when fcm is used in time series modeling is how to design structure of fcm for a given time series, i.e., how to map the given time series onto the structure of fcm, viz. expressing a meaning of the nodes of the graph and specifying possible causal linkages between pairs of nodes. in general, if the concepts of system to research are easily identifiable, these concepts can be directly cast into fcm. however, the clearly and understandable concepts are hardly obtained from time series including a series of numeric data, which need to be discovered or mined. here the idea of information granulation is adopted. on a basis of numeric data formed are information granules and they can be treated as nodes of fcm. fuzzy c-means clustering [22] can serve as a convenient vehicle to construct information granules from data. in what follows, an illustrative example is presented how to design structure of fcm for time series. let us consider a certain time series as shown in fig.2 (a). the numeric data included in the time series is clustered by using the standard version of fuzzy c-means clustering algorithm with euclid distance, where the number of clusters c is set into 3 and the fuzzification coefficient m is set into 2 as well. the results of clustering is reported in table 1 in where g1, g2, g3 numerical prediction of time series based on fcms with information granules 317 (a) the original time series plot. (b) the expression of fcm with information granules figure 2: fcm expression of a certain time series. are prototypes. these prototypes can be viewed as information granulations represented by the following semantics: • g1 — the amplitude of time series is negative high where locates nearby −1.9019. • g2 — the amplitude of time series is zero where locates nearby 0. • g3 — the amplitude of time series is positive high where locates nearby 1.7832. from perspective of information granules, the dynamic behaviour of timer series can be described through establishing relationships among these granules. table 1: the prototypes and granular description of time series showed in fig.2 (a) cluster no. 1 2 3 prototypes -1.9019 0.025 1.7832 description of prototypes information granules g1 g2 g3 from table 1, if the number of clusters is envisioned as the total number of node of fcm, and the fuzzy semantics associated with prototypes, say g1, g2 and g3, are regarded as the concepts of corresponding nodes of fcm, fcm is constructed to express time series showed in fig.2 (a), which is shown in fig.2 (b). fcm with sound weights can represent dynamic characters of the time series, which can become realization by pso algorithm to minimize objective function (4) (refer to section 2.2). 4 the proposed prediction method of time series based on fuzzy cognitive map with information granules in this section, the proposed prediction method of time series based on fcm with information granules is detailed. fcm is exploited to realize prediction of time series, which is based on the idea that the structure of it can store the values of weights between nodes that describe dynamic behaviour of time series at each iteration step. suppose that x = {x 1 , x 2 , · · · , x l } is a time series. the outline of proposed method is presented in fig.3, which consists of two stages — the first stage (from step 1) to step 5)) is to construct fcm model to express time series and the second stage (from step 6) to step 8)) is to use the constructed fcm model to perform prediction. in what follows, the two stages are detailed respectively. 318 w. lu, j. yang, x. liu the training subset x (1),x (2),…x (k ) g1 x (1), x (2) …, x ( ) g2 g3 the testing subset x (k +1),x (k +2),…x ( ) the input time series reconstructing the numerical data the predicted partition the raw data c (k +1),c (k +2) …c ( ) a fully learned fcm prototypes granulation timer series c (1),c (2) …c (k ) rr 0 50 100 150 200 250 300 350 400 -5 -4 -3 -2 -1 0 1 2 3 4 time a m p li tu d e 0 50 100 150 200 250 300 350 400 0 0.2 0.4 0.6 0.8 1 time 0 50 100 150 200 250 300 350 400 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 50 100 150 200 250 300 350 400 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 prototypes figure 3: the outline of proposed prediction method. step 1) partition the raw time series x into the training set consisted of k observations and the testing set consisted of lk observations. the former is used to develop fcm model, whereas the latter one is separate and is used to carry out prediction of the non-observed data. step 2) generate information granules by fuzzy c-means clustering algorithm. its object is to capture the linguistic and numerical characters of the training set. the stand version of fuzzy c-means clustering algorithm is adopted in this paper, which accepts the all observations from the training set and the predefined number of clustering c, and by the end of clustering algorithm, a prototype vectors p = [p 1 p 2 · · · pc] can be obtained. each prototype p i (i = 1, 2, · · · , c) is assigned into semantics, which result in the formation of information granules, say g1, g2, · · · , gc (see section 3), i.e., prototypes can be make semantics, which form information granules. step 3) ) build the structure of candidate fcm via the information granules generated by step 2). the step plays an important role. as mentioned previously in section 3, the predefined number of clusters c is regarded as the total number of nodes of fcm and information granules are directly taken as the concept of corresponding nodes of fcm. subsequently, the structure of fuzzy cognitive maps is formed. step 4) granulate each observation in the training set by granular interface, which creates ordered data points by the well-known (5) according to prototypes p obtained previously and the each observation in the training set. note that u i can become the active level of the ith node of fcm related to ith granules gi which is formed by assigning semantics into prototype p i . u i (p i , x(t)) = 1 c∑ j=1 ∥ x(t)−pi x(t)−p j ∥ 2 m−1 , i = 1, 2, · · · , c; t = 1, 2, · · · , k (5) where x(t) is an observation at t time moment from the training set, c is the number of cluster, p j (j = 1, 2, · · · , c) is the jth prototype obtained previously, m is fuzzification coefficient, u i (p i , x(t)) is the membership value that x(t) belonged to prototype p i and t is numerical prediction of time series based on fcms with information granules 319 time label. f =   g1 g2 ... gc   =   c(1) c(2) ... c(l)   t =   c1 (1) c1 (2) · · · c1 (l) c2 (1) c2 (2) · · · c2 (l) ... ... ... ... cc (1) cc (2) · · · cc (l)   =   u1 (p1, x (1)) u1 (p1, x (2)) · · · u1 (p1, x (l)) u2 (p2, x (1)) u2 (p2, x (2)) · · · u2 (p2, x (l)) ... ... ... ... uc (pc, x (1)) uc (pc, x (2)) · · · uc (pc, x (l))   (6) according to (5), each observations x i (t) (i = 1, 2, · · · , l − k) from the training subset can be transformed along with time variable t, and become available in the form of their membership values corresponding to each granules gj, which means original time series is converted to granular time series. we unfold (5) along with time label t, (6) can be obtained. let us observe (6), g1, g2, · · · , gc are defined as granular time series which includes c subsequence. note that each row vector of f, say g1, g2, · · · , gc, expresses the level that the given time series can be characterized by corresponding fuzzy semantics p i , whereas each column vector of f, say c(1), c(2), · · · , c(l), expresses the level that an observation of time series x(t) at t time moment can be characterized by all semantics. step 5) learn weights of fcm by pso algorithm according to the transformed granular time series data. its role is to establish a fully learned fcm model. the pso algorithm described in section 2.2 is used to learn all parameters vector ŵ of fcm constructed by step 3) on basis of the granulation time series data c(1), c(2), · · · , c(l) which is formed by step 4). step 6) granulate datum from the testing set by granulation interface, which is regarded as input of the fully learned fcm formed by step 5). a point x(k + s) at k + s time moment within the testing set, which is transformed into a tuples initial state vector c(k + s) according to (5), which indicate the activation level of all nodes in fcm model. step 7) perform iteration computation on basis of (1). fcm model perform one step iteration from initial state vector c(k+s) and generates response vector ĉ(k+s+1). results of iteration computation of fcm indicate the state of granular time series in the next time moment, in other words, the iteration process of fcm is actually the prediction process on the level of information granules. step 8) reconstruction the numerical values according to the activation level of all nodes of fcm at a certain time moment. the numerical prediction is carried out according to (7) which reconstructs numerical values on basis of the activation value among all nodes of fcm at a certain time moment and prototypes formed by fuzzy c-means clustering algorithm prior. x̂(t) = c∑ j=1 ĉj(t) mp j c∑ j=1 ĉj(t)m (7) where c is the number of cluster, p j (j = 1, 2, · · · , c) is prototypes, m is fuzzification coefficient, ĉ j (t) is an activation value of jth node of fcm model at t time moment, and x̂(t) is the predicted numerical value at t time moment. 320 w. lu, j. yang, x. liu 5 experimental study in this section, two benchmark time series — the enrollment of university and taiwan stock exchange capitalization weighted stock index (taiex) time series, are used to carry out profound experiment for validating feasibility and effectiveness of proposed predicted method. the goal is to assess quality of the proposed method and compare with other method based on fuzzy sets theory. 5.1 experimental setup the proposed prediction method concern only one adjustable parameter,say the predefined number of clustering c. the all experiments for the two time series include the two tasks. one is that the quantification of the impact on the prediction accuracy of the proposed method being brought by the number of clusters c which implies the level of granularity. the other is that the predicted results are obtained by using our proposed method are compared with other prediction methods. considering a given time series and a predefined value of c, we first divide the original time series into the training set and the testing set. the training subset is used to develop hfcm model and the testing subset is used to perform prediction. in the sequence, the experiments are carried out by systematically sweeping through the values of the number of clusters c which start with 3 and increment it until the value of 8 is reached. to assure high confidence in the produced prediction results, for each of c, the experiments were repeated 50 times, the average root mean squared error (rmse) are reported whose goal is to evaluate prediction accuracy and compare with other methods. besides, the other one parameters of fuzzy c-means clustering algorithm — fuzzification coefficient m is set into 2 for each of experiments. the standard version of pso algorithm is used to learn parameters of all candidate fcm models of the two time series. parameters of pso algorithm used in all experiments is shown in table 2. table 2: parameters of pso algorithm used in all experiments description value population size 50 acceleration constant ϕ 1 2 acceleration constant ϕ 2 2 inertial weight ζ 0.9 initial positions random number maximum number of iterations 1000 minimum objection function value 105 5.2 experimental results and analysis there are two benchmark time series are applied to validate and analyze the proposed prediction method. the first is the enrollment of university of alabama during 1971–1992, which includes 2 time series which includes 22 observations. the time series is often used by many researchers [6]– [13]. the second time series involves the daily values of taiex from january 1, 2000 to december 30, 2000, which consists of 242 observations. the time series has been used in the related literature [9, 15]. numerical prediction of time series based on fcms with information granules 321 in order to directly compare with other methods, for the first time series, its all observations are taken as not only the training data for constructing fcm model but also the testing data for validating prediction accuracy. whereas for the second time series, data from 2000/1/4 to 2000/10/31 are used to construct fcm model and data from 2000/11/1 to 2000/12/30 are used to perform prediction. in this case, the comprehensive tests that involve the two data sets are carried out respectively according to the description in section 5.1, whose results are reported in fig.4, fig.5, fig.6, table 3 and table 4. figure 4: the plot of the average rmse versus the number of clustering for the enrollment and taiex time series. table 3: the comparison result for enrollment time series. method rmse song’s time-invariant method [6] 677 chen’s method [9] 663 markov method [8] 638 hwang’s method [10] 567 huarng’s method [11] 489 dan’s method [13] 438 proposed method (c=5) 346 1) experiments with respective to parameter c of proposed prediction method. fig.4 represents a plot of the average rmse versus the number of clustering c. several interesting conclusions can be drawn when analyzing fig.4. first, the prediction accuracy is highly sensitive to the choice of the number of clusters. the value of average rmse get lower dramatically with the increasing number of clusters however the reduction of the average rmse becomes less visible when going beyond a certain number of clusters i.e. 5 or 6. in other words, the prediction accuracy is not continuously increase with the increasing number of clusters. the significant increase in the prediction accuracy happens when moving from very low values of clusters to some higher values. the prediction accuracy changes unobviously with the change of clusters number when the value of cluster number c greater than 5 or 6. for example, as to enrollment time series, the average rmse is 593.3 for c=3, whereas it is 356.9 for c=5 and 364.4 for c=8. 322 w. lu, j. yang, x. liu table 4: the comparison result for taiex time series. method rmse chen’s method [9] 176 yu’s method [23] 170 huarng’s method [24] 139 proposed method (c=6) 132 there is smaller difference in rmse at the higher number of cluster just 7.5, but there is larger difference in rmse at the lower number of clusters reaching 236.4. for taiex time series, the situation is also similar and optimal values of c are located at the value of 6. the change of the average rmse is slightly when the value of c over 6. the explanation for this is that when the number of clusters is low, very few granules are generated which is not sufficient to capture the character of time series and gives rise to the larger prediction error. but the number of clusters is larger, the granules may be more minute, which drastically reduces their interpretability. 2) comparison with other prediction methods based on fuzzy sets theory. table 3 and table 4 reports comparison with other methods based on fuzzy sets theory respectively. the results in table 3 show that the proposed prediction method with the best parameters (c=5) can obtain the better accuracy for enrollment time series. the rmse of the proposed methods can achieve 346, while the best result dans method [13] scored 438. at the same time, results for the taiex time series also show superiority of huarngs method [24] . the raw time series and the selected best predicted results for enrollment and taiex time series are illustrated in fig.5 and fig.6 respectively. 1973 1976 1979 1982 1985 1988 1991 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 x 10 4 year e n r o l l m e n t the actual enrollment the predicted enrollment (a) the plot of the actual and predicted enrollment. 0 50 100 150 200 250 4000 5000 6000 7000 8000 9000 10000 11000 time t a i e x the actual taiex the predicted taiex (b) the plot of the actual and predicted taiex. figure 5: the experimental results of using our proposal prediction method 6 conclusions in this paper, a time series prediction method based on fuzzy cognitive with information granules was proposed. it includes two important components — fuzzy clustering algorithm and numerical prediction of time series based on fcms with information granules 323 fuzzy cognitive maps. the former extracts information granules from original time series data and then transform the original time series into the granular time series, while the latter is used to describe these granular time series and perform prediction. comprehensive experiments have been carried out for two benchmark time series to validate feasibility and effectiveness of our proposed method. the results of experiment show predicted accuracy is related to the number of cluster. the proposed prediction method can obtain satisfying prediction accuracy in the case of the sound number of cluster. additional, our proposed prediction method can automatically perform modeling and prediction of time series without more human intervention, its potential advantage is capability of handling modeling and prediction of large-scale time series. acknowledgements the research is supported by the natural science foundation of china under grant 61175041 and the major national science and technology programs of china in the "twelfth five year" plan period (no. 2011zx05039-3-3). bibliography [1] kailath, t. 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(2014); the modeling of time series based on fuzzy information granules, expert systems with applications, 41: 3799-3808. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(3), 375-387, june 2019. wavelet design for automatic real-time eye blink detection and recognition in eeg signals m. miranda, r. salinas, u. raff, o. magna michael miranda* department of informatic engineering metropolitan university of technology, chile jose pedro alessandri 1242, nunoa, santiago, chile *corresponding author: michael.miranda@utem.cl renato salinas department of mechanical engineering university of santiago, chile av. libertador bernardo o’higgins 3363, santiago, chile. renato.salinas@usach.cl ulrich raff department of physics university of santiago, chile av. libertador bernardo o’higgins 3363, santiago, chile. ulrich.raff@usach.cl oscar magna department of informatic engineering metropolitan university of technology, chile jose pedro alessandri 1242, nunoa, santiago, chile omagna@utem.cl abstract: the blinking of an eye can be detected in electroencephalographic (eeg) recordings and can be understood as a useful control signal in some information processing tasks. the detection of a specific pattern associated with the blinking of an eye in real time using eeg signals of a single channel has been analyzed. this study considers both theoretical and practical principles enabling the design and implementation of a system capable of precise real-time detection of eye blinks within the eeg signal. this signal or pattern is subject to considerable scale changes and multiple incidences. in our proposed approach, a new wavelet was designed to improve the detection and localization of the eye blinking signal. the detection of multiple occurrences of the blinking perturbation in the recordings performed in real-time operation is achieved with a window giving a time-limited projection of an ongoing analysis of the sampled eeg signal. keywords: biological signals, electroencephalogram, brain computer interface, eye blink detection, pattern recognition, wavelet design. 1 introduction the electroencephalogram was designed to record the brain activity of a living being, specifically by sensors, with which electrical signals from the brain are captured. any activity that does not come from the brain, that is, noise, is called an artifact. depending on the origin, they can be divided into physiological and extra-physiological artifacts. the first ones are generated by the patient, on the other hand, extra-physiological artifacts arise from sources external to the individual, that is to say from the environment. blinking of eyes is considered an ocular artifact, which is captured in the frontal zones fp1-fp2 of the international system 10-20 [10]. copyright ©2019 cc by-nc 376 m. miranda, r. salinas, u. raff, o. magna the eyeball acts as a dipole, with a positive pole oriented anteriorly (cornea) and a negative pole oriented posteriorly (retina). a blinking of eyes causes the positive pole to approach the fronto-polar electrodes fp1-fp2, producing symmetrical descending deflections [15]. the eeg records are manifested with periodic and unpredictable oscillations, having a greater spectral amplitude in certain frequency bands, which are divided into: 0.5 − 4 hertz (delta band), 4−8 hertz (theta band), 8−12 hertz (alpha band), 12−30 hertz (beta band) and greater than 30 hertz (gamma band) [4]. the extraction of characteristics is an important process in the classification of eeg signals, and additionally, it is also possible to relate patterns with a defined intentionality on the part of the individual. however, the relationship between the electrical signal and the intention is diffuse, besides it is necessary to filter the artifacts, to obtain a signal without noise, so as not to disturb the results. one of the artifacts that must be filtered is the eye blinking, but before filtering it must be detected. the blinking of eyes could be considered, in itself, a control signal, since in general an individual can generate voluntary blinks, being able to increase the frequency and / or amplitude, which in turn can be associated with machine instructions for a computer. from the previous conceptualization, it is possible to devise a brain-machine interface or bci, which uses eye blinking as support data. a brain-machine interface or bci, is defined as the technology that allows capturing brain waves to be processed by a computer with the intention of obtaining information about a state or cognitive process of the person. for example, in dzitac et al. [7] changes in amplitude are studied in the different frequency bands, specifically the events of desynchronization (or event-related erd) and their application to bci. the work that will be presented in this publication, shows the research results related to the automatic detection of eye blinking by designing an ad-hoc mother wavelet, which allows the analysis in the time domain. 2 materials and methods 2.1 creation of an eeg signal database the design of a mother wavelet requires experimental data and validation, from which it is possible to extract the representative pattern present in the study signal. for this reason, a free access database of eeg signals was consulted to obtain experimental data. subsequently, a software based on matlab and java was developed for obtaining in real time experimental validation samples, recording the eeg signal and the video of the face of the individual with the same time-base, this way to correlate the video with the eeg signal and clearly define the occurrence of the blinks. 2.2 obtaining experimental data to verify the main hypotheses of this work, the available data is located in www.physionet.org, specifically the physiobank database. this is a database of high-growth physiological digital signals, with good foundation in data related to the biomedical research community. polysomnographic record signals, of multiple parameters, are included in this database including cardiopulmonary, neural and other fields of biomedicine. the records correspond to both healthy individuals and patients with a wide variety of conditions that concern different pathological implications, such as sudden cardiac death, failure congestive heart, epilepsy, motor disorders, sleep apnea and senility. this database is freely accessible via web and tends to cooperative activities, that is providing data for research and requesting the submission of results for its feedback. the physiobank collections are organized in more than 50 databases, each with a number of records, and wavelet design for automatic real-time eye blink detection and recognition in eeg signals 377 each record contains information collected from a single subject. 2.3 obtaining validation data validation data were obtained from a continuous eeg measurement with the biosensor consisting of a band designed by neurosky mindwave tm, being one of the first to enter the market of eeg amplifiers for use in non-invasive bci. mindwave is very economical due to the simplicity that characterizes it, having one electrode in position fp1 and has the option of being compatible with both ios and android [13]. figure 1: video recording module, status indicator module and real-time software-oscilloscope module the state of the eye blink was detected through a video camera during the eeg measurement, subsequently and then manually added to the file after analyzing the video frames. where "1" indicates the closed eye state and "0" the state of the eye open. all the values are in chronological order with the first value measured in the top of the data. the platform developed is called "eeg studio" and is composed of three modules: 1. oscilloscope that works in real time. 2. video recording: records the face of the individual in real time. 3. status indicator: indicates the correct functioning of the different modules. 3 wavelet transform wavelets are functions that satisfy certain mathematical requirements and are used for the representation of data or other functions. wavelets are very suitable for data approximation of signals with abrupt discontinuities. the fundamental idea behind wavelets is to analyze functions according to scales. in wavelet analysis, the scale used to analyze the data plays a special role. algorithms that use wavelets process data at different scales or resolutions. if a signal or function is observed using a wide "window", the small details are not observed; on the other hand, if the "window" used is narrow, then they can be observed. in wavelet analysis, 378 m. miranda, r. salinas, u. raff, o. magna these windows are automatically adjusted when changing resolution, usually referred to as multiresolution analysis. this makes wavelets a useful and interesting tool. the general procedure of the analysis using wavelets is to adopt a "prototype" function, generally called mother wavelet. the temporal analysis is then carried out using dilatations and translations of said function. the original signal can then be represented as a linear combination of the original function and its translated and dilated ones. this procedure is called a wavelet expansion. the choice of the mother wavelet [13] (and thus the base or the wavelet frame) is not unique and depends on the type of functions or data to analyze. the multi-resolution analysis of the wavelets, makes it a very powerful tool for the study of eeg signals. an example of the eeg artifact and pattern of blinking of eyes is seen in fig. 2. figure 2: eeg signal with two artifacts produced by blinking eyes. the artifacts are indicated with two light gray time intervals 3.1 continuous wavelet transform the continuous wavelet transform allows the analysis of a signal in a segment located in it and consists in expressing a continuous signal as an expansion of terms or coefficients of the internal product between the signal and a mother wavelet function. a mother wavelet is a localized function, belonging to space l2(r), that contains all functions with finite energy and square integrable functions defined: f ∈ l2 ⇒ ∫ |f(t)|2 dt = e < ∞ (1) in this way we have a single modulated window and from this a complete family of elementary functions is generated by dilatations or contractions and translations in time ψa,b(t), called wavelet daughters that meet all the conditions of the form: ψa,b(t) = 1 √ a ψ ( t− b a ) (a,b) � r, a 6= 0. (2) the mother wavelet must comply with the eligibility condition: cψ(t) = ∫ ∞ 0 |ψ(ω)|2 ω dω < ∞ (3) which means that the function is well localized in time, that is, the function oscillates around an axis and its average is zero and that the fourier transform is a continuous band-pass filter, with rapid decrease towards infinity and toward ω = 0. the wavelet transform of a function at a given scale and position, is calculated by the correlation of the form: wavelet design for automatic real-time eye blink detection and recognition in eeg signals 379 cwt (a,b) = 1 √ a ∫ ∞ −∞ x(t)ψ ( t− b a ) dt (4) 3.2 wavelet pattern design applied in cwt when searching a defined pattern it is possible to apply two different techniques to perform multiresolution analysis. the first technique includes the use of an existent wavelet family trying to find whatever fits best the detection of the pattern, i.e. trial and error technique [16]. in reference [16] the biorthogonal wavelet was used. the other alternative consists in designing or building a wavelet starting with the pattern that is asked to be detected. this process can obviously decrease the time to approach the desired goal while improving the detection of the blinking artifact. this approach will work as long as the characteristics of the designed wavelet are close enough to the characteristics of the recorded pattern. based on a given function f with compact support and finite energy, we may consider the construction of a wavelet usable with cwt while approximating this function in the least square sense. various construction methods are possible, and since we have numerical sampling of our pattern over a given interval [a,b], we used the method described by misiti [14]. let us consider a finite set of values: (tk,yk)k=1, ... , k , such that : a ≤ tk ≤ b and yk ≈ f(tk) (5) consider a family f = ρini=1 of linearly independent functions in l 2(a,b), where l2 is the space of square integrable functions over r, and denote by v the vector space spanning f . formulated for this finite set of pairs, the problem consists of seeking coefficients α = αini=1 in r n, where: ψ = n∑ i=1 αiρi (6) such that: k∑ k=1 [ψ (tk) −yk] 2 = min β�rn { k∑ k=1 [vβ (tk) −yk] 2 } (7) such that: ∫ b a vβ (t) dt = 0 (8) where for β in rn: vβ = n∑ i=1 βiρi (9) it is thus a problem of least squares sense minimization with a constraint. the vector α and the lagrange multiplier λ associated with the constraint are obtained by solving the linear system: [ g mt m 0 ][ α λ ] = [ b 0 ] (10) with g, m and b defined by: gi,j = k∑ k=1 ρi (tk) ·ρj (tk) (11) 380 m. miranda, r. salinas, u. raff, o. magna mi = ∫ b a ρi (t) dt (12) bi = k∑ k=1 yk ·ρj (tk) (13) the wavelet ψ(t), must satisfy conditions of admissibility and regularity. regularity is defined as the capability of a given wavelet to reconstruct a signal from the coefficients computed during the transformation process [11]. in other words, a function is regular if it can be locally approximated by a polynomial. according to lipschitz’s definition of regularity [12], a function f is pointwise lipschitz α ≥ 0 at υ, if there exist c > 0, and a polynomial ρυ of degree m = [α], such that: ∀t � r , |f (t) −ρυ(t)| ≤ c · |t−υ|α (14) a function f is uniformely lipschitz α over [a,b] if it satisfies for all υ � [a,b], with a constant c that is independent of υ. the lipschitz regularity of f at υ or over [a,b] is the supremum of the α such that f is lipschitz α. on the other hand, admissibility [11] is defined by the following conditions: ∫ ∞ −∞ ψ (t) dt = 0 (15) ∫ ∞ −∞ |ψ (t) |2dt < ∞ (16) that is, function ψ(t) must be localized in a bounded time interval, having oscillations around time axis, so its average be zero. 3.3 characteristic pattern detection given the waveform pattern associated with eye blinking shown in figure 2, it is necessary to design a mother wavelet as close as possible to the recorded pattern. this new wavelet will be labeled "blinkwave". this designed wavelet can then be used in the cwt analysis because it fulfills the required wavelet properties using the cwt technique [6]. 4 design and use of the mother wavelet to design the new wavelet ("blinkwave") it becomes necessary to isolate the eeg eye blinking pattern. using this procedure, we obtain a vector or a set of finite number of values in equation (6). this vector is utilized in the process described by misiti [14]. designing the wavelet process with polynomials of variable grades, we obtain a mother wavelet with best approximation to the recorded pattern with a polynomial of grade 6, which can be observed in figure 3. in this case, the family f = ρini=1 used in equations (5) to (13), is the polynomial family of grade n ≥ 6. wavelet design for automatic real-time eye blink detection and recognition in eeg signals 381 figure 3: overlay of the waveform produced by eye blinking and the wavelet designed, both normalized in the range [−1, 1] 4.1 design of the mother wavelet according to the method hinted in [18], it is necessary to construct matrices g, m and b, to fulfill equation (10), and to resolve the following linear system:( g mt m 0 )( α λ ) = ( b 0 ) ⇒ a ·u = b (17) i.e., solve u as a function of a: u = a−1 ·b (18) we take as base the pattern ("blinkwave"), which is defined as an n-component vector: {yi}mi=1 = y1, y2, . . . , ym (19) the base pattern is obtained digitizing the continuous signal with a constant sampling frequency fs which defines the time interval ∆t between samples. the matrix or file vector m, is built using the formula in equation (12), considering the admissibility restrictions given by equations (15) and (16), resulting in the following series: mi=[1,m] = ∫ b a ρi (t) dt = { 1 i (bi −ai) } i=[1,m] = = [ (b−a) , ( b2 2 − a 2 2 ) , . . . , ( bn+1 n+1 − a n+1 n+1 )] (20) if one chooses initiating the analysis at the origin, a = 0, then all computations become simpler, then, recomputing m we obtain: m = [ b, ( b2 2 ) , . . . , ( bn+1 n+1 )] 1×(n+1) (21) given m, we must apply the regularity condition indicated in equation (14), to get a new matrix m, as follows: 382 m. miranda, r. salinas, u. raff, o. magna m =  b, ( b2 2 ) , . . . , ( bn+1 n+1 ) 1 0 . . . 0 1 b . . . bn   3×(n+1) (22) afterwards, the matrix g is built using equation (11): g =   b ( b2 2 ) · · · ( bn+1 n+1 ) ( b2 2 ) ( b3 3 ) ... (bn+2 n+2 ) ... ... · · · ...( bn+1 n+1 ) ( bn+2 n+2 ) · · · ( b2n+1 n+2 )   (n+1)×(n+1) (23) prior to solving the linear system for u, we must generate the column vector b, as described in equation (13): b = ( 1 2 ) ∑m i=1 (xi+1 −xi) ( xi+1·yi+1 + xi ·yi )∑m i=1 (xi+1 −xi)(xi+1 2·yi+1 + xi2 ·yi) ...∑m i=1 (xi+1 −xi) ( xi+1 n·yi+1 + xin ·yi )   (n+1)×1 (24) once matrices g and m are generated, it is possible assembling matrix a and proceed solving equation (17): u = a−1 ·b ⇒   α1 α2 ... λ1   = [ g mt m 0 ].1 · [b] (25) mother wavelet ψ(t), is generated using formula in equation (6) replacing the values obtained for αi ψ = n∑ i=1 αi·ρi ⇒ ψ (t) = n+1∑ i=1 αi·xin+1−i (26) thus, for the numerical pattern shown in figure 3 (solid line), represented by vector: y = [0.0, 1.7, 2.0, 1.6, 0.8, 0.0,−0.6,−0.9,−1.1,−1.0,−0.9,−0.7,−0.5,−0.4,−0.3, 0.0] (27) we obtain the polynomial mother wavelet ψ (t) as follow: ψ (t) = α1 ·xn + α2 ·xn−1 + . . . + αn ·xn + αn+1 (28) i.e., the first n numerical coefficients αi are employed, obtained from solving equation (25) and discarding the lagrange multipliers λi. these coefficients for the mother wavelet ψ (t), for the pattern given in equation (27), are shown in equation (29) as follows: p = [380, −1.2 · 103, 1.3 · 103, −640, 110, 1.2, −2.5 · 10−14] (29) wavelet design for automatic real-time eye blink detection and recognition in eeg signals 383 finally, the polynomial equation for the new mother wavelet ψ (t), is shown graphically in figure 3 (dashed line) and it is represented algebraically by equation (30), as follows: ψ (t) = 380 ·x6 − 1200 ·x5 + 1300 ·x4 − 640·x3 + 110 ·x2 + 1.2 ·x− 02.5 · 10−14 (30) 4.2 use of the mother wavelet the new wavelet-blinkwave is used to obtain the coefficients, which will subsequently be processed to obtain the location of each artifact. using the signal shown in figure 3 or the "blinkwave" mother wavelet for analysis and applying the cwt , we obtain a coefficient matrix c(a,b) using equation (31), where: c(a,b) = cwt(”inputsignal”, ”blinkwave”) (31) c(a,b) is the coefficients matrix and cwt is the continuous wavelet transform, applied to input signal. the next step includes the detection and duration of the eye blinking from c(a,b). to improve the analysis, a threshold must be applied to the matrix of wavelet coefficients, as defined in equation (32). t (a,b) = { 0 if c(a,b) < thresholdfixed c(a,b) , otherwise (32) the dimensions of the matrix depend on the number of samples of the analyzed signal and the level or number of scales in the decomposition performed by the wavelet transform. figure 4: graphical representation or scalogram to the left image and the thresholded wavelet coefficient matrix t(a,b) to the right as can be observed in figure 4 there is a defined relation between the coefficients and the searched pattern. in our case, the graphical representations displayed in figure 4 are used to support the detection of two blinking artifacts in the eeg recording of figure 2. a threshold has been applied to the matrix of wavelet coefficients, as defined in equation (14). with this numerical relation, it is possible to determine the temporal location and duration of the pattern in the signal analysis process. in the case studied in this work, the number of samples b is 384 m. miranda, r. salinas, u. raff, o. magna 256 (analysis window size) and 4 levels of decomposition, which involves obtaining a coefficient matrix [c] of 4x256 elements. to improve the location of the blinking patterns, the columns of the coefficient matrix t(a,b) were added as shown in equation (33). v (1, 2, . . . , n) = ∑ a t (a,b) , b = 1, 2, . . . ,n (33) the vector v obtained gives us the actual location in time of the artifact produced by the eye blinking. to achieve a tradeoff between data smoothing and real time analysis, a moving average of a small number of samples with a range of 10[ms] was applied. applying the system shown in figure 1 to the recorded signal displayed in figure 2 allows overlying the dotted signal shown in figure 5, which detects the occurrence of eye blinking and their duration. figure 5: overlay of original eeg signal with the dotted signal highlighting the detected artifacts and their duration produced by eye blinking figure 6: examples of real-time detection of eye blinking with several analysis windows running over a 1000[ms] time interval. a) top: three eye blinks; b) bottom: six eye blinks wavelet design for automatic real-time eye blink detection and recognition in eeg signals 385 5 results and discussion results from this work have been tested with real-time eeg recordings introducing eyeblinking artifacts with different frequencies and amplitudes. figure 6 shows two recordings with three and six eye blinking incidences of variable occurrences within 1000[ms] or one second time intervals respectively, showing variable amplitudes and duration. this figure proves that the algorithm is robust enough to filter artifacts produced by other sources that do not share similarity in shape, as well as detecting a target pattern in its many versions independent of the number of occurrences regardless of the extent and the level of symmetry in the study window. compared to our previous results [16] we have made some major improvements which allowed robust real-time detection in a given time interval including amplitude and duration of the eye blinking occurrences. a new mother wavelet has been designed according to the recorded patterns of the eeg waves displaying eye blinking and the new wavelet decomposition was achieved with four levels that allowed increasing the number of eye blinking artifacts within a given time analysis window demonstrated in figures 6(a) and 6(b). the use of a designed mother wavelet improved the global detection process of the searched pattern detection, using different observation periods for analysis, length of patterns, amplitudes and repetitions. as the relative location was obtained using the "blinkwave" transform wavelet, the thresholding and the sum of coefficients were simple and allowed quick obtaining of precise real time results. 6 conclusions the use of wavelet transforms in analyzing non-stationary signals, such as electroencephalograms, shows comparative advantages with respect to a conventional fourier analysis. the design of a mother wavelet was the main contribution to locate the blinking artifacts in the recorded eeg signals. the paper shows in detail how to design a mother wavelet for a given case. among the advantages of using wavelet signal processing is the multi-resolution analysis, which involves looking at multiple versions of the blinking patterns. the design of a mother wavelet used in the context of wavelet analysis incorporates features of shape, amplitude, and phase shift for detecting patterns using one-dimensional wavelet transform thus ensuring a robust detection and recognition technique of eye blinking phenomena in eeg signals. this work also contributes to bioengineering with respect to brain computer interfaces or bci, allowing real-time detection of eye blinking. two major applications can be visualized: improvement of current bci devices and awareness detection systems. in addition, these eye blinking patterns, generated voluntarily by the user, can translate into simple commands for helping people with disabilities, e.g., commanding a wheelchair in forward or reverse mode. from the results of our work, we conclude that detection of the eye-blinking artifacts in eeg signals can be very helpful now that the timing information of occurrence is well defined. it should be noted that although there are alternative analyses for eye blinking detection from eeg data, those approaches operate off-line, and our method operates in real time. this approach improves respect previous work, in that there are fewer processing steps now and increased precision regarding the timing of the eye blinking events. acknowledgment all authors like to thank the continuous support of the vrac of the technologic metropolitan university of chile and vridei of the university of santiago of chile. 386 m. miranda, r. salinas, u. raff, o. magna author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. bibliography [1] aeschbach, d.; 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(2016). bci: entertainment’s brain hacking tool for control & monitoring, http://neurosky.com/wp-content/uploads/2016/06/control-vs-monitor.pdf wavelet design for automatic real-time eye blink detection and recognition in eeg signals 387 [14] misiti, m.; misiti, y.; oppenheim, g.; poggi, j.m. (2007); wavelets and their applications, iste ltd., 115-131, 2007. [15] polkko j. (2007). a method for detecting eye blinks from single-channel biopotential signal in the intensive care, unit. master’s thesis, 2007. [16] salinas r., e. schachter and m. miranda. (2012). recognition and real-time detection of blinking eyes on electroencephalographic signals using wavelet transform, lecture notes in computer science, 7441, 682-690, 2012. [17] senthil kumar, p.; arumuganathan, r.; sivakumar, k.; vimal, c. (2008). removal of ocular artifacts in the eeg through wavelet transform without using an eog reference channel, international journal of open problems in computer science and mathematics, 1, 188-200, 2008. [18] sharbroug, f.; chatrian, g.e.; lesser,r.p.; luders, h.; nuwer, m.; picton, t.w. (1991). american electroencephalographic society guidelines for standard electrode position nomenclature. journal of clinical neurophysiology, 8, 200-202, 1991. international journal of computers communications & control issn 1841-9836, 11(5):631-644, october 2016. extended collaborative filtering technique for mitigating the sparsity problem k. choi, y. suh, d. yoo keunho choi korea workers’ compensation and welfare service 8, beodeunaru-ro 2-gil, yeongdeungpo-gu, seoul, republic of korea ckh0515@hanmail.net yongmoo suh korea university business school anam-ro 145, seongbuk-gu, seoul, republic of korea ymsuh@korea.ac.kr donghee yoo* department of management information systems, gyeongsang national university, beri 501 jinju-daero, jinju, republic of korea *corresponding author: dhyoo@gnu.ac.kr abstract: many online shopping malls have implemented personalized recommendation systems to improve customer retention in the age of high competition and information overload. sellers make use of these recommendation systems to survive high competition and buyers utilize them to find proper product information for their own needs. however, transaction data of most online shopping malls prevent us from using collaborative filtering (cf) technique to recommend products, for the following two reasons: 1) explicit rating information is rarely available in the transaction data; 2) the sparsity problem usually occurs in the data, which makes it difficult to identify reliable neighbors, resulting in less effective recommendations. therefore, this paper first suggests a means to derive implicit rating information from the transaction data of an online shopping mall and then proposes a new user similarity function to mitigate the sparsity problem. the new user similarity function computes the user similarity of two users if they rated similar items, while the user similarity function of traditional cf technique computes it only if they rated common items. results from several experiments using an online shopping mall dataset in korea demonstrate that our approach significantly outperforms the traditional cf technique. keywords: recommendation system, collaborative filtering, sparsity problem, similarity function. 1 introduction in the age of information overload, information about products and services on the internet is growing explosively; consequently, people have difficulty in processing the overwhelming amount of information that is available. to address this problem, a number of personalized recommendation techniques have been introduced by many studies, so that sellers of online shopping malls can survive high competition and buyers can locate the best product information for their own needs [1–3]. personalized recommendations are usually seen as a specific kind of information filtering that enables people to filter out unnecessary and uninteresting information [4]. among the many recommendation techniques that have been suggested, collaborative filtering (cf) has been widely adopted in many practical applications due to its simplicity and effectiveness and has proven to be useful [2]. copyright © 2006-2016 by ccc publications 632 k. choi, y. suh, d. yoo however, it is still not easy for most online shopping malls to make use of the collaborative filtering technique for recommendation, because explicit rating information required by the technique is rarely available in online shopping malls and/or because there is a high chance of sparsity in the transaction data of online shopping malls. thus, there is a need to devise a way to derive implicit rating information that can play the role of explicit rating information and extend the collaborative filtering technique so that it can be used effectively even when there is a sparsity problem in the transaction data of online shopping malls. it is known that personalized recommendation and improved customer retention forms a virtuous cycle in which good quality of recommendation leads to improved customer retention which in turn leads to the better quality of recommendation through more customer input into the recommendation system [2]. as such, the recommendation system that we propose in this paper will contribute to the higher level of customer retention of online shopping malls, which will help them to survive today’s high competition. in this paper, therefore, we first suggest a means to derive implicit rating information from the transaction data available on an online shopping mall. we assumed that the number of purchasing the same item represents the preference toward the item and thus can be used as implicit rating information on the item. then we propose a new user similarity function that can mitigate the sparsity problem. the traditional collaborative filtering technique calculates the user similarity of two users only when they have rated common items. in that case, we come across bad recommendation quality due to the sparsity problem. instead, our new user similarity function computes user similarity if they rated similar items which include common items. so, we define an item similarity function as a function that computes the item similarity of all pairs of items purchased by two users whose similarity is to be computed and use the item similarity to define the user similarity function. we have implemented both a recommendation system which uses the extended collaborative filtering technique with our new user similarity function and a benchmark system which adopts the traditional collaborative filtering technique. results from a series of experiments using the transaction data of an online shopping mall in korea clearly demonstrate that our approach significantly improves the quality of recommendation systems, compared with those obtained from a benchmark system. the rest of this paper is organized as follows. section 2 reviews the previous studies on recommendation systems, especially those which have attempted to solve the sparsity problem of the collaborative filtering technique. section 3 describes our proposed similarity function in detail, and section 4 provides the details of the several experiments conducted to verify our approach and the results from those experiments. the last section concludes our paper with summary, implications, and limitations. 2 previous works this section reviews general recommendation techniques with a focus on the collaborative filtering technique related to our study. 2.1 general recommendation techniques the techniques used in most recent recommendation systems can generally be classified into one of the following four types: content-based filtering (cbf); collaborative filtering (cf); rulebased approach; hybrid approach. cbf typically 1) constructs a content-based item profile by extracting a set of features from each item in the item set; 2) builds a content-based user profile from a set of features of the extended collaborative filtering technique for mitigating the sparsity problem 633 items that each user purchased; 3) calculates the similarity between the user profiles and the item profiles; and 4) recommends the top n items with the highest similarity scores. in other words, cbf mainly recommends items based on the similarity between items to recommend and items already purchased [5, 6]. however, cbf has several limitations: 1) it is not easy to obtain a sufficient number of features for item profiles and user profiles (insufficient features problem) [7]; 2) items that can be recommended are limited to those similar to the items that a target user previously purchased (over-specialization problem) [8]; and 3) new users who have not yet purchased items cannot get appropriate recommendations (new user problem) [9]. cf typically 1) builds a rating-based user profile from the rating information of each user on items; 2) identifies neighbors (also called like-minded users) who rated items similarly as the target user; 3) predicts ratings of the target user on target items purchased not by the target user, but by the neighbors; and 4) recommends the top n items with the highest predicted ratings. however, cf also has some limitations: 1) it is difficult to recommend items for users who have not yet rated items (new user problem) [10,11]; 2) it is difficult to recommend items that have never been rated by users before (new item problem) [5, 12]; and 3) it makes poor recommendations when rating information is insufficient (sparsity problem) [4,13]. rule-based approach typically derives rules among items in the item set from a large transaction dataset collected over time, using data mining techniques. the rules could be either association rules among items purchased together [14] or sequential patterns among items purchased in sequence over time [15,16]. however, the rule-based approach to recommending items has limitations in that it is difficult to recommend items that do not appear in association rules or sequential patterns; moreover, it does not take into account users’ preference (or rating information) on items. hybrid approach has been developed to overcome – or at least reduce – the weaknesses of cbf, cf, and the rule-based approach [4, 5]. in general, the hybrid approach makes recommendations by combining results from each recommendation technique, selecting one of recommendation techniques to be applied according to specific criteria, or embedding one or more recommendation techniques in applying other recommendation techniques. 2.2 collaborative filtering thus far, many recommendation systems using the cf technique have been developed and used in many practical applications, such as tapestry for recommending news articles [17], grouplens for net news [18], and ringo for music [7]. the cf technique utilizes users’ rating information on items to represent their preference on corresponding items and predicts a target user’s ratings of items based on the user’s similarity in ratings [19,20]. the cf technique can be further classified into model-based and memory-based cf techniques. in the model-based cf technique, a model such as a probabilistic model or a machine learning model is built from a large collection of ratings in order to predict a target user’s ratings on target items [21–23]. koren [23] suggested the new neighbor model, in which neighbor relations were modeled by minimizing the regularized squared error function. in addition, he extended the model to utilize both explicit (i.e., rating information) and implicit feedback (i.e., binary information [rated vs. not rated]) from users. salakhutdinov and srebro [24] introduced a weighted version of trace-norm regularization. the trace-norm regularization is a popular method for completing the user-item rating matrix in cf. however, the method does not perform well when entries of the user-item rating matrix are sampled non-uniformly. in order to solve the problem, they proposed a trace-norm weighted by the frequency of users and items as a regularizer. in the memory-based cf technique, items are recommended mainly based on the similarity 634 k. choi, y. suh, d. yoo between users as described in section 2.1 [25,26]. balabanovic and shoham [5] developed the fab system, which combines cf with cbf to improve the accuracy of recommendation systems by mitigating new item, insufficient features, and over-specialization problems inherent to cf and cbf. in the fab system, items are recommended to a target user if and only if each item is highly similar to the target user’s profile and each item is rated by the neighbors of the target user. yang et al. [27] attempted to propose a new similarity function to be used in cf techniques in order to deal with the weaknesses of cf technique, namely, 1) cf is sometimes overly confident, 2) cf tends to discard some useful information in user profiles, and 3) cf often derives some untrustworthy inferences when making a prediction. to this end, they took into account the similarity between a target item and each of the co-rated items in order to determine whether the two items belong to the same genre of interest or not. in addition, they calculated the similarity between two users by giving different weights to the co-rated items classified into three classes according to the differences between the ratings of the two users on the items. in order to alleviate the sparsity problem, liu et al. [28] proposed a hybrid recommendation system. they first filled a blank in the user-item rating matrix with a weighted average rating of items already rated by the user, where the weights of the rated items were calculated by the similarity between an unrated item and the rated items based on feature values. cf is then applied to the user-item rating matrix. our approach is different from their approach in that our approach uses only users’ ratings on items to mitigate the sparsity problem, whereas their approach needs additional information about features of items, which may cause additional problems (e.g., insufficient features), thereby reducing the application area. shambour and lu [13] proposed a hybrid trust-enhanced cf recommendation approach (tecf), which integrates both an implicit trust-filtering recommendation approach and an enhanced user-based cf recommendation approach. by incorporating trust propagation, they attempted to relax the sparsity problem. although this approach can extend the number of potential neighbors, the reliability of the similarity between potential neighbors still needs to be improved. our study focuses primarily on improving the reliability of the similarity between users in order to mitigate the sparsity problem in a memory-based cf. formoso et al. [10] proposed an approach called profile expansion based on the query expansion techniques used in information retrieval to mitigate the new user problem which can cause the sparsity problem. in their study, the size of a user’s original profile increased by adding a set of item-rating pairs to the profile based on item-global, item-local, or user-global profile expansion technique. item-global profile expansion technique finds items similar to the items already exist in the user profile and adds the found items to the user profile, while item-local profile expansion technique finds items to be added to the user profile based on the items recommended to the user. user-local profile expansion technique finds the user’s neighbors and adds items rated by them to the user profile. the difference between their approach and our approach is that they calculate a user similarity between two users based on the expanded user profile, while we compute the user similarity by taking into account item similarity of all pairs of items rated by these users without expanding the user profile. 3 proposed approach this section provides the explanation on the notations used in the equations that define a new similarity function and explains how we extended ratings on only commonly rated items to ratings on all pairs of items to mitigate the sparsity problem using the new user similarity function. extended collaborative filtering technique for mitigating the sparsity problem 635 table 1: notations notations descriptions u the number of total users i the number of total items ap(a,i) absolute preference of user a on item i rp(a,i) relative preference of user a on item i ra,i rating of user a on item i m the number of items commonly rated by both users uij the number of users who rated both items i and j cosine(a,b) cosine similarity between users a and b ppredicteda,i the predicted preference of target user a on target item i isim (i,j) the similarity between items i and j usim (a,b) the similarity between users a and b k the number of neighbors selected n the number of items recommended 3.1 notations table 1 shows the description of notations used in the equations that define a new user similarity function. however, the equations will be provided in section 3.2, section 3.3, and section 3.4 again with explanation on each notation for the better readability of the paper. 3.2 deriving implicit ratings of users on items in many online shopping malls, it is usually difficult to obtain explicit rating information on items. in order to apply the cf technique to such circumstance, this study derived implicit ratings of users on items from transaction data as an alternative to explicit ratings. first, the absolute preference of user a on item i, ap(a,i), is calculated from following equation. ap (a,i) = ln ( the number of transactions of user a including item i the number of transactions of user a + 1 ) (1) since it only takes into account the frequency of purchase, the absolute preference of user a on item i increases as the number of transactions of user a including that item increases. this value, however, may not represent the preference of user a on item i exactly because the frequency of purchase is quite different depending on the item price, item lifetime, and so on. for instance, since expensive items or items with long lifespan are usually purchased infrequently, the preferences of users on them cannot be higher than those on cheap items or on items with short lifespan. thus, it is necessary to define relative preference which is comparable among items using the absolute preferences of other users on item i. the relative preference of user a on item i, rp(a,i) is thus defined as in equation (2). rp (a,i) = ap(a,i) max.ap(u,i) (2) , where u denotes every user who purchased item i. the reason for using max function as a denominator in equation (2) is to make rp(a,i) range from 0.0 to 1.0 (i.e., normalization). finally, rp(a,i) is multiplied by 5 and rounded up so that implicit rating ranges from 1 to 5, as is mostly used in current recommendation systems, which is explained by equation (3). 636 k. choi, y. suh, d. yoo table 2: example of user-item implicit rating matrix user item 1 item 2 item 3 item 4 item 5 a 4 2 3 b 3 4 5 c 2 2 3 d 3 3 3 e 5 3 2 implicit rating (a,i) = round up (5 × rp (a,i)) (3) 3.3 new user similarity function for mitigating the sparsity problem with the implicit ratings of users on items derived in section 3.2, the similarity between a target user and every other user is calculated. as mentioned earlier, traditional cf-based recommendation systems have calculated the similarity between two users from the rating information of co-rated items by both users, as shown in equation (4). the similarity function defined in equation (4) is cosine similarity which is one of widely used similarity functions in cf. cosine (a,b) = ∑m i=1 (ra,i) (rb,i)√∑m i=1 (ra,i) 2 √∑m i=1 (rb,i) 2 (4) , where ra,i and rb,i denote the ratings of users a and b on item i, respectively and m denotes the number of items commonly rated by both users. for example, in traditional cf approach, the similarity between users a and b is calculated from the rating information of item 3 which is commonly rated by both users (see table 2). however, this may result in untrustworthy inference in calculating the similarity between them since insufficient rating information (i.e., rating information of users a and b only on item 3) is used to calculate the similarity, which is known as the sparsity problem. in addition, some potentially useful information (i.e., rating information of user a on items 2 and 4, and those of b on items 1 and 5) may be discarded in traditional cf approach. in order to mitigate the above problems, this paper adapted the cosine similarity as equation (5) by assuming that two items i and j can be regarded as commonly rated by two users a and b if they are similar to each other and rated by user a or user b. using the equation (5), we can calculate the user similarity between users a and b based on the item similarity, computed by considering rating information of all pairs of commonly rated items (i.e., nia ×nib , where nia and nib denote the number of items rated by users a and b, respectively). in the example of table 2, item similarity is defined to include all pairs of commonly rated items by users a and b when the user similarity between users a and b is to be computed (i.e., nine item pairs: (item 2, item 1), (item 2, item 3), (item 2, item 5), (item 3, item 1), (item 3, item 3), (item 3, item 5), (item 4, item 1), (item 4, item 3), and (item 4, item 5)). our approach, therefore, improves the reliability of the similarity between two users by utilizing rating information of all similar item pairs, thereby mitigating the sparsity problem caused by insufficient rating information. user similarity between two users a and b is defined as follows: usim (a,b) = ∑ i∈ia ∑ j∈ib { isim (i,j)2 × ( ra,i ) × ( rb,j )} √∑ i∈ia ∑ j∈ib { isim (i,j) × ( ra,i )}2 ×√∑i∈ia ∑j∈ib {isim (i,j) ×(rb,j)}2 (5) extended collaborative filtering technique for mitigating the sparsity problem 637 , where ia and ib denote a set of items rated by user a and user b, respectively, and isim (i, j), the similarity between item i and item j, is calculated similarly to usim (a, b)1, using the cosine similarity (i.e., equation (4)), as defined in equation (6). isim (i,j) = ∑uij a=1 (ra,i) × (ra,j)√∑uij a=1 (ra,i) 2 × √∑uij a=1 (ra,j) 2 (6) , where uij denotes the number of users who rated both items i and j. 3.4 predicting preference of a target user on target items after calculating the user similarity between a target user and every other user, the top k users with the highest similarity are selected as neighbors of the target user. then, rating information of the neighbors is used to predict the preference of the target user on target items, as shown in equation (7). ppredicteda,i = 1∑k b=1 |usim (a,b)| × k∑ b=1 usim (a,b) ×rb,i (7) , where ppredicteda,i denotes the predicted preference of target user a on item i, k the number of user a’s neighbors, and usim (a,b) the similarity between the target user a and a’s neighbor user b calculated using equation (5). finally, the top n items with the highest preference are recommended for the target user, where items already purchased by the target user may be included in recommendation list since this study assumes that users may repurchase the same item, differently from usual recommendation systems. 4 experiments this section describes the experimental design for evaluating the effect of our ideas proposed in section 3 on the accuracy of recommendation, and explains the implication of the results from the experiments. 4.1 experimental design the data used in our experiment were provided by one of the biggest online shopping malls in korea from august 16, 2008 to august 15, 2009 (12 months), which consists of 15,860 transactions of 234 users on 1,097 items2, and shows high sparsity rate of 98.59%3. prior to conducting our experiments, we divided our dataset into four parts, as shown in fig 1. firstly, it was divided by time into part a and b, and secondly by random sampling of users into part c and d. part a consists of transaction data collected during the first 6 months and part b during the second 6 months. part c consists of transaction data from 70% of the users, randomly chosen, and part d the transaction data of the remaining users. 1usim (a, b) takes a value between 0 and 1. 2since it is difficult to recommend items to users who have purchased a small number of transactions as in most recommendation systems (i.e., new user problem), this study focused on the users who are involved in more than 30 transactions among total 1,000 users. 33,626 distinct transactions among 256,698 (= 234×1, 097) possible transactions. 638 k. choi, y. suh, d. yoo �������������������������������������������������������������������������������������������������������� �������������������������������������������������������������������������������������������������������� �������������������������������������������������������������������������������������������������������� �������������������������������������������������������������������������������������������������������� �������������������������������������������������������������������������������������������������������� a b c d figure 1: four parts of our dataset when making recommendations, we used part a*(c+d) in order to calculate the similarity between each target user and every other user, and recommended items for the target users in part b*d. in our experiment, as a means to measure the quality of recommendation, we also used precision, recall, and f1, as used elsewhere to evaluate and compare the quality of recommendations. f1 is the harmonic average of precision and recall. in order to ensure that our proposed approach actually improves the quality of recommendation system, we implemented both our recommendation system and a benchmark system using transact-sql on microsoft sql server 2008. 4.2 experimental results and analysis in order to compare our proposed recommendation system with the benchmark system (i.e., traditional cf approach which calculates the similarity between users using equation (4)), we conducted several experiments. in the first experiment, we evaluated the effects of both the number of recommendations and the number of neighbors on the accuracy of the benchmark system. as shown in fig. 2 (a), when the number of neighbors was low (i.e., k ≤ 40), the precision of benchmark system tends to increase as the number of recommendations increases. on the contrary, however, when the number of neighbors was high (i.e., k ≥ 60), the precision of benchmark system tends to increase as the number of recommendations decreases. the best precision was achieved when the number of neighbors was 120 and the number of recommendations was 10. generally, as shown in fig. 2 (b) and (c), the recall and f1 of benchmark system tend to increase as the number of recommendations increases, regardless of the number of neighbors. however, as the number of neighbors increases, the effects of the number of recommendations on the recall and f1 of benchmark system decrease. the best recall was achieved when the number of neighbors was 10 and the number of recommendations was 50, and the best f1 when the number of neighbors was 40 and the number of recommendations was 50. similar experiment was conducted to evaluate the effects of both the number of recommendations and the number of neighbors on the accuracy of our proposed system. as shown in fig. 3 (a), when the number of neighbors was low (i.e., k ≤ 20), the precision of proposed system tends to increase as the number of recommendations decreases. in addition, when the number of neighbors was high (i.e., k ≥ 60), the precision of proposed system also tends to increase as the number of recommendations decreases. the best precision was achieved when both the number of neighbors and the number of recommendations were 10. generally, as shown in fig. 3 (b) and (c), the recall and f1 of proposed system tend to increase as the number of recommendations increases, regardless of the number of neighbors. however, as the number of neighbors increases, the effects of the number of recommendations on the recall and f1 of proposed system decrease. the best recall was achieved when the number of neighbors was 10 and the number of recommendations was 50, and the f1 when the number of neighbors 10 and the number of recommendations was 30. in the final experiment, we compared the best precision, recall, and f1 of benchmark system extended collaborative filtering technique for mitigating the sparsity problem 639 � ����� ���� ����� ���� ����� �� �� �� �� �� ��� ��� ��� p re ci si o n number of neighbors �� �� �� �� �� (a) precision � ����� ���� ����� ���� ����� ���� ����� �� �� �� �� � ��� ��� ��� r ec a ll number of neighbors ��� ��� ��� ��� ��� (b) recall � ����� ����� ����� ����� ���� ����� ����� ����� ����� ���� �� �� �� �� �� ��� ��� ��� f 1 number of neighbors � �� � �� � � � �� � �� (c) f1 figure 2: the effect of both the number of recommendations and the number of neighbors on the accuracy of benchmark system (n in r_n represents the number of recommendations) 640 k. choi, y. suh, d. yoo � ���� ���� ���� ���� ���� ���� �� �� �� �� � ��� ��� ��� p re ci si on number of neighbors ��� ��� ��� ��� ��� (a) precision � ���� ���� ���� ���� ���� ���� ��� ��� ���� �� �� �� �� � ��� ��� ��� r ec al l number of neighbors � �� � �� � �� � �� � �� (b) recall � ���� ���� ���� ���� ���� ���� �� �� �� �� � ��� ��� ��� f 1 number of neighbors ��� ��� ��� ��� ��� (c) f1 figure 3: the effect of both the number of recommendations and the number of neighbors on the accuracy of proposed system extended collaborative filtering technique for mitigating the sparsity problem 641 � ���� ���� ���� ���� ���� ���� �� �� �� �� �� p re ci si on number of recommendations benchma rk proposed (a) precision � ���� ���� ���� ���� ���� ���� ��� ��� ���� �� �� �� �� �� r ec a ll number of recommendations benchma rk proposed (b) recall � ���� ���� ���� ���� ���� ���� �� �� �� �� �� f 1 number of recommendations benchma rk proposed (c) f1 figure 4: a comparison between the benchmark system and the proposed system 642 k. choi, y. suh, d. yoo (i.e., precision at k = 120 (n = 10) and 40 (n = 20, 30, 40, and 50), recall at k = 120 (n = 10 and 20), 40 (n = 30), and 10 (n = 40, 50), and f1 at k =120 (n = 10, 20), 40 (n = 30 and 50), and 60 (n = 40)) with those of our proposed system (i.e., precision at k = 10 (n = 10, 20, 30, 40) and 20 (n = 50), recall at k = 10 (n = 10, 20, 30, 40, and 50), and f1 at k = 10 (n = 10, 20, 30, and 40) and 20 (n = 50)). as shown in fig. 4 (a), (b), and (c), our proposed system considerably outperformed the benchmark system in precision, recall, and f1, regardless of the number of recommendations. the results from our experiments proved that our approach to extending collaborative filtering technique to consider all pairs of similar items when computing the user similarity is effective on mitigating the sparsity problem, thereby enhancing the accuracy of recommendation systems. 5 conclusions the collaborative filtering technique has been suggested as one of the best methods for making recommendations and has proven to be useful in many applications, but that technique is not easy to use for recommendations in online shopping malls, because explicit rating information is rarely available. in addition, one of the problems of the technique, the sparsity problem, occurs due to the low level of customer input into the recommendation system in online shopping malls. therefore, online shopping malls which have to survive high competition must resolve these two problems to be able to make effective recommendations which in turn improve their customer retention rate. with an objective to provide online shopping malls with a way to make better recommendations this paper first explains how to derive implicit rating information from the transaction data that will replace the explicit rating information. it then defines a new user similarity function which computes a user similarity between two users by taking into account item similarity of all pairs of similar items, rated by these users. in order to compare our proposed recommendation system with a traditional recommendation system, we implemented both systems and conducted several experiments. the results obtained from these experiments indicate that our approach considerably outperformed the traditional collaborative filtering approach in precision, recall, and f1. this study, however, leaves something to be desired. more reliable and interesting results could be obtained if we have 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[28] z. liu, w. qu, h. li, c. xie (2010); a hybrid collaborative filtering recommendation mechanism for p2p networks, future generation computer systems, 26(8): 1409-1417. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(2), 183-198, april 2019. eodc: an energy optimized dynamic clustering protocol for wireless sensor networks using pso approach c. jothikumar, r. venkataraman c. jothikumar* department of computer science srm institute of science and technology chennai, india *corresponding author: jothikumar.c@ktr.srmuniv.ac.in revathi venkataraman department of computer science srm institute of science and technology chennai, india revathi.n@ktr.srmuniv.ac.in abstract: a wireless sensor network comprises many small wireless nodes that helps to sense, gather, process and communicate. one of its primary concerns is to optimize the consumption of energy and extend the network lifespan. the sensor nodes can be clustered to increase its lifespan further and this can be accomplished by choosing cluster head for every cluster and by performing data fusion on the cluster head (ch). the proposed system is using an energy efficient hierarchical routing protocol named energy optimized dynamic clustering (eodc) for clustering large ad-hoc wsns and direct the data to reach the sink. the collected data is received by the sink from the set of cluster heads after every round. the cluster head is selected using the particle swarm optimization (pso) approach; the allocation of cluster members is based on the manhattan distance. the metrics used to find the fitness function are location, link quality, energy of the active and inactive nodes. the system employs the shortest path approach to communicate between the cluster heads till it reaches the base station. by this, the energy efficiency and network lifetime have been increased. the analysis and outcomes show that the eodc was found to outperform the existing protocol which compares with this algorithm. keywords: clustering, wireless sensor network (wsn), energy efficiency, routing, particle swarm optimization (pso). 1 introduction in recent times, wireless sensor networks (wsn) are procuring research attention. the nodes communicate with their neighbors to transmit the data till it reaches the destination [2,3]. the potential utilization of sensor networks is in various fields such as target monitoring, environment tracking, healthcare and industries. a wsn generally consists of sensor nodes, used for sensing, processing and communicating to reach the sink. these small sized sensor nodes make it easier to collect the data about the environment. a wsn has various characteristics, including less battery life, power constraints, coping with node failures, and dynamic network topology, etc. flooding is a flat based routing protocol which allows to flood the sensed data in the network through the neighbor nodes [8–10,12]. since the data are flooded throughout the network, the system drains due to duplication of data, overlapping and resource blindness. in direct diffusion the sink initiates the request to the source nodes. the source node saves the path of the sink, once the message is received. the routing consists of interest propagation and gradient setup. interest propagation refers to the query from the sink where the gradient setup transmits the data copyright ©2019 cc by-nc 184 c. jothikumar, r. venkataraman to reach the sink. an adaptive routing protocol called as the sensor protocol for information via navigation (spin) assigns high-level name to describe the data. the spin performs better by saving energy than the flooding approach. leach makes use a stochastic algorithm for selection of ch. the data gathered from the individual cluster member is fused by the ch and forwarded to reach the sink through single-hop communication. to avoid interference between the clusters while transmitting the data, a direct sequence spread spectrum is used. the ch is randomly chosen from the available nodes in the respective cluster to ensure that all the nodes should become a cluster head once. this algorithm can hence reduce the volume of data forwarded directly to a base station (bs). these cluster heads are unevenly distributed in the network. if they happen to be selected randomly only from a dense area, this may lead to increased energy consumption during data transmission. since the chs are elected based on probability, it may lead to overhead and increase in the network load and it is not scalable. the level of energy consumed is a prime concern in draining the network. in order to improve the efficiency, we developed an energy optimized dynamic clustering (eodc) protocol that uses location, link quality, energy of active and inactive nodes to find the fitness function for selecting the ch. the proposed system reduces the energy consumption by selecting the optimized ch and routes the packet through the near optimal path in the network. the paper is structured as follows. the related work of the system is described in section 2. the section 3 describes the eodc protocol. the results of the simulation and discussion are shown in section 4. at last, in section 5, the conclusion segment is covered. 2 related work the design of energy efficient routing protocol is such that it improves the performance and network lifespan. in this segment five hierarchical routing protocols are briefly discussed. in a wireless sensor network, low-energy adaptive clustering hierarchy-centralized (leachc) is the effective clustering approach for data communication. for the first round, the clusters are created and the selection of ch is based on a stochastic algorithm [11]. the bs receives the message about energy as well as the location of nodes. the bs computes the ch as per the received data. the nodes which have the energy more than the average of that set of nodes are considered as chs from the second round. the chs fuse the data gathered from the individual cluster member and forward it towards the bs. for saving energy, each member uses the time division multiple access (tdma) scheduling for transmitting data to reach the ch and once the time is out, the node goes to sleep. the ch must be active to receive the data continuously and further pass the collected data to the base station. the leach-c being a one-hop communication protocol helps in direct transmission from chs to bs. the system performs better than the flat based routing approach by employing a clustering technique. inside the base station controlled dynamic clustering protocol (bcdcp), the clusters are formed with an even number of nodes to balance the energy level [16, 20, 26]. the energy of overall nodes is computed by the bs. the nodes having the energy above the average are chosen as the ch for the next round. the selections of the ch are uniformly placed throughout the sensor field to maximize the distance between the ch in each splitting step. the received data are transmitted to the required ch using tdma scheduling. the ch aggregates the received data before forwarding it. the protocol performs the multi-hop routing technique among the chs for data forwarding using the minimum spanning tree approach. a single ch communicates the information to the bs; the involved ch communicating the data will have high energy for transmission. inside the general self-organized tree-based energy-balanced routing protocol (gsteb), the root node is assigned by the bs depending on the remaining energy and its associated id eodc: an energy optimized dynamic clustering protocol for wireless sensor networks using pso approach 185 is broadcasted to every node [28, 29]. similarly, the nodes in the sensor region, select their parents based on the energy of the node by coordinating with their neighbor and every child node transmits the data to their parents within the given tdma slot. the parent node communicates with the neighbor parents in the form of a tree structure till the data reaches the root node. here, the data is fused and forwarded by the root node to the bs. the root node is dynamic to balance the load in the communication region. the energy of the sensor nodes in each round is known by the bs and this message favors in creating the topology for the round coming next. the hybrid hierarchical clustering approach (hhca) is a clustering hierarchy approach that performs the operation of the dual clustering mechanism with distributed clustering and centralized grid [14, 23, 25, 27]. distributed clustering is a clustering approach where the nodes are distributed randomly and divided into sub clusters and the cluster members pass the data to the cluster head in the sub cluster. the ch in the sub cluster converses the shared data to the centralized grid. the bs computes the fuzzy cmeans approach for the centralized grid and broadcasts this message in the network. if the id of the node matches with the broadcast id that node will act as the centralized grid. distributed clustering uses the leach approach for clustering and ch selection. the cluster members utilize the tdma schedule to share the data to the sub cluster. the sub cluster (distributed clustering) transmits the data to the centralized cluster (grid) through csma or ca mechanism. the centralized grid forwards the data to the sink directly without correlation. heuristic algorithm for clustering hierarchy (hach) is a clustering technique that performs the operation of the clustering mechanism with inactive nodes by selecting the low energy nodes to be in the sleep state based on a stochastic process that should not affect the network coverage [5,6,13,18,21]. the maximum coverage effect is employed in the network to select the inactive nodes to maintain the coverage area. the sink computes the euclidean distance between the nodes and to itself. the computations are based on the coordinates and energy values received by the sink from all the nodes. the ch selection was broadcast to every node present in the network. if the node id and the id received by the node are the same, then that node will be the ch. the election of the ch deals with a heuristic crossover approach that is spatially distributed in the detecting range, and the selected ch has high energy. as far as the proposed system is concerned, we consider leach-c, hhca and the hach protocol for comparison. 3 energy optimized dynamic clustering (eodc) protocol 3.1 network model the nodes are thrown randomly into a square region to observe the environment continuously. n represents the total number of nodes, where n = {n1,n2, ...,nn}. assumptions carried out in a sensing region are • the bs is fixed at the top of the sensing region • all nodes are stationary in the deployment region • being homogeneous, the nodes have the same energy • the adjustment of transmission power is made according to the received signal strength indicator (rssi) value of the nodes 186 c. jothikumar, r. venkataraman 3.2 energy consumption model the energy of the data transit includes transmission circuitry and the volume of the data being transmitted. similarly, even the energy of reception includes the volume of data reception. the energy consumption equation is given as: the energy required to transmit a unit of data is, etr(m,d) = ec(m) + eamp(m,d) ⇒ etr(m,d) = mec + m�fsd2; d < d0 ⇒ etr(m,d) = mec + m�mpd4; d ≥ d0 the energy required to receive a unit of data is err(l) = ec(m) = mec where, etr and err are the transmission and reception energy respectively, ec(m) is the energy consumption per bit of transceiver circuitry, m is the message transmitted, and d indicates the level of distance measured between the nodes. depending on the transmission range, free space (d2) or multipath (d4) propagation is used. figure 1: system architecture of eodc 3.3 operation of the eodc algorithm in eodc, the nodes form the cluster using the pso approach. the fitness function used is computed by the bs for the selection of ch and the cluster members (cm) are joined using the manhattan distance. the purpose of the eodc is to reduce energy utilization in clustering eodc: an energy optimized dynamic clustering protocol for wireless sensor networks using pso approach 187 and data transmission [1,4,7,15,17,19,22,24,30]. the system employs the hach approach for the selection of the inactive nodes in the clusters. the stochastic selection of inactive nodes (ssin) protocol introduces the sleep scheduling mechanism to choose inactive cluster members. the coverage effect (ce) is the effect of identifying the active nodes by putting some nodes to sleep according to the coverage. the number of active nodes present in the network is known which depends on the accumulated coverage effect (ace) of all the clusters. the received data are fused by the ch and forwarded to the nearest ch to form a multihop chain till the data reaches the bs. the ch in the communication region forwards the data to bs. a shortest path approach was employed to achieve the near optimal path to the bs. the algorithm consists of two phases, namely: 1. formation of cluster and ch selection using pso; 2. data transmission and forwarding. formation of cluster and ch selection using pso in the proposed system, the selection of ch adopting the pso approach includes the location of the node, link quality between the nodes, and the energy of the active and inactive nodes. the ch should remain awake always for data reception and transmission. the fitness function of the ch is calculated using these metrics. fch, fitness function of ch is given as an optimization technique and is expressed as, fch = γ ×loc + (1 −γ) ×lq + (1 −γ) ×eckn + (1 −γ) ×e ck m (1) location(loc) gives the identity of the nodes in the network. the computation gives the distance between the neighbor nodes, as well the distance staring from nodes until the ch of the cluster,ck. loc = min k=1,2,...,k { ∑ ni�ck d(ni,chk) | ck | / d(ni,bs) | ck | } (2) where, d(ni,chk) gives the distance from ni to chk,d(ni,bs) represents the distance from ni to bs, and | ck | is the length of the cluster link quality (lq) is the quality of the signal between nodes, ni and the ch in the cluster. the nodes in the ch receive the advertisement message. the strength of the signal is known by the nodes in message reception. the rssi or etx (expected transmission count) is used to calculate the quality of the link, lq. here, etx is a measure of transmission for the successful delivery of a packet from the sender to the receiver. the computation of paths along the links refers to ett (expected transmission time) and is inversely proportional to link quality. therefore, rssiαetxα( 1 ett ) lq = min k=1,2,...,k { ∑ ni�ck etx(ni,ck) | ck | } (3) where, etx(ni,ck) is the maximum successful transmission of data from ni to ck, and | ck | is the length of the cluster. the energy of the active nodes (en(ck)) inside the network is the set of non-ch nodes which address the ch through tdma scheduling and the current ch. the energy consumption of the 188 c. jothikumar, r. venkataraman cluster head (ech) includes the transmission energy of the advertisement message and tdma schedule. the reception energy includes joint_req message and data message. the total energy dissipation of the ch is represented as, ech = etr(ch_adv,ch_tdma) + err(joint_req_ch,data_ch) similarly, the energy dissipation of the non ch (active nodes) belonging to the cluster is represented as: enon−ch = etr(joint_req_ch,data_ch) + err(ch_adv,ch_tdma) the nodes which communicate with the ch in the current round are called as active nodes. the energy requirement of the active nodes belonging to the network is given as the sum of all the optimum clusters. eckn = k∑ k=1,ni�ck e(nki − 1) e(chk) (4) where, e(chk) is the active cluster heads energy. the energy of the inactive nodes (eckm ) is the set of nodes in the clusters which remain in sleep mode in the current round and on further iteration, these nodes will wake and another set of nodes become inactive.the energy of the inactive nodes is calculated as, eckm = k∑ k=1,mj�ck e(mkj ) mine(mkj ) (5) where, min(eckm ) is the minimum energy of the inactive nodes. after computing the metrics, the fitness function that needs to minimize is given as fch = 0.25 ×loc + 0.25 ×lq + 0.25 ×eckn + 0.25 ×e ck m (6) where, γ = 0.25 the fitness function is computed by the bs which broadcasts the message throughout the network. if the node id matches with the id of the node sent by the bs, then the node will act as a ch. the energy consumed by the ch includes the energy of data reception, fusion, and transit. the node will act as ch till the fitness function value is lesser than the threshold value. the energy of data communication includes the length of the data and energy required for the transmitter circuitry in addition to the energy required for amplification to forward the data through free space propagation, if d < do. the consumption of energy in the ch for data transmission is given as, ech = mec(u c n − 1) + meda(u c n ) + mec + m�fsd 2 (7) where ucn represents the number of unique nodes in each cluster. similarly, the energy of data reception includes the length of the data and energy required for receiver circuitry. the energy consumed by the non-ch for data transmission is given as, enon−ch = mec + m�fsd 2 (8) eodc: an energy optimized dynamic clustering protocol for wireless sensor networks using pso approach 189 the total consumption of energy for the single cluster includes the consumption of energy for the non-ch, of the cluster in intra cluster communication in addition to the energy consumption of the ch in the given cluster. the total energy consumed by the clusters is calculated as, eclust = (u c n − 1)enon−ch + ech (9) since, n represents the number of nodes and m indicates the sensor region, the selection of optimal clusters in the system is given using the equation, kopt = √ n √ 2n √ �fs �mp × m d2 (10) the energy consumed by the entire network is the product of consumption of energy for the single cluster and the optimal number of clusters, kopt present in the sensing region. the total energy consumption by the network is given as, substituting (9) and (10) in (11) enetwork = kopt ×eclust (11) the assumption is being carried out as uniform deployment of nodes in the detecting region. the distance from the nodes to the ch is given as e[d] = ∫ ∫ √ x−x2ch + (y −ych)2ρ(x,y)dxdy e[d] = ∫ ∫ √ x2 + y2ρ(x,y)dxdy e[d] = ∫ ∫ r2ρ(r,θ)drdθ (12) here, ρ(r,θ) becomes constant, when the sensors are distributed uniformly in the environment. pso approach: the optimization algorithm deals with the particles’ velocity and position in a swarm. the movement of the particles in the swarm is referred to using the equation given. movement of the particle to the next position from the current position is given as pk+1 = pk + vk+1 (13) the particle’s velocity is given as vk+1 = vk + x1r1(p k p −p k) + x2r2(s k p −p k) (14) where, pkp is the particle’s best position, s k p is the swarm’s best position, p is the particle position, v is the path direction, x1,x2 are the learning coefficients commonly set to 2.0, and r1,r2 are the random numbers with the range of (0,1). 190 c. jothikumar, r. venkataraman data transmission using shortest path approach in this phase the data is transmitted from ch to ch till it reaches the base station. the ch in the communication region forwards the data to the sink since it needs high transmission power. a near optimal path is identified based on the route selection using the energy of the route. the energy of the route in the network is represented as e(rk) = k∑ i=1 (echi −e ch i_tx) (15) where e(rk) is the energy of the route k, echi indicates the energy of the ch, and e ch i_tx denotes the transmission energy of the ch. the route selection is based on inter cluster communication till the data reaches the sink. the route which has the maximum energy to carry the data to the bs is taken as route assignment, ra. figure 2: flow chart of the cluster setup and data transmission ra = max { e(rk); rk�p } (16) eodc: an energy optimized dynamic clustering protocol for wireless sensor networks using pso approach 191 where p is a set of all possible routes, k is a ch that follows the route reaching the sink, and ra specifies the route assignment by condition. the cluster setup and data transmission flowchart is given in figure 2. 4 simulation results the simulation was done using the math-lab simulator. the eodc performance is compared with the hhca, hach and leach-c protocol. in our simulation work, about 500 sensor nodes are placed randomly in a 500m x 500m region. the initial energy of the node is calculated as 0.5j, the value of gamma is set to 0.25 and the size of the data was 500 bytes long. the simulation results are plotted in the graph and shown in figures 3 to 8. the values of the simulation parameters for the scheme are given below, table 1: simulation parameters no. parameter specification 1 senor region 500 x 500 m2 2 number of sensors 500 3 energy of each node 0.5j 4 data size 500 byte 5 ec 50 nj/bit 6 �fs 10 pj/bit/m2 7 �mp 0.0013 pj/bit/m4 8 γ 0.25 9 minimum threshold energy 10−4j the number of communication rounds increases when compared with other protocols such as leach-c, hhca and hach. the network lifespan is proportional to the number of communication rounds. the average energy dissipation at the end of 1000 rounds in eodc is 0.39nj less than hhca which gives 0.48nj and in hach, leach-c the energy of the nodes drains before 1000 rounds. figure 3 illustrates the average energy dissipation for all the nodes. the eodc performs much better than the existing protocol. figure 3: comparison of average energy dissipation 192 c. jothikumar, r. venkataraman in the proposed system, the number of live nodes in the network is high when compared with the existing protocol with respect to the number of rounds. at the end of 1000 rounds in eodc, the number of alive nodes is 95; whereas in hhca, they are 25 and in hach, leach-c, the nodes are dead before 1000 rounds. figure 4 plots the number of live nodes in each iteration. figure 4: number of alive nodes in each round figure 5 and table 2 plot the clusters formed in the network in each round and the improvement achieved with eodc. in the proposed system, the count of clusters created at the end of 1000 rounds is 142, which is higher when compared with the existing protocols like hhca, hach and leachc which give 117, 92, 79 respectively. therefore, the improvements achieved in eodc over hhca, hach and leachc are 21.37%, 54.35% and 79.75% respectively. figure 5: clusters formed in each round eodc: an energy optimized dynamic clustering protocol for wireless sensor networks using pso approach 193 table 2: cluster formation in each round of communication rounds number of clusters formed improvement achieved in % by eodc over eodc hhca hach leach-c hhca hach leach-c 100 86 64 53 45 34.38 62.26 91.11 200 90 68 57 48 32.35 57.89 87.50 300 101 78 60 51 29.49 68.33 98.04 400 110 84 67 57 30.95 64.18 92.98 500 115 89 69 60 29.21 66.67 91.67 600 124 92 74 66 34.78 67.57 87.88 700 129 98 79 68 31.63 63.29 89.71 800 133 102 83 72 30.39 60.24 84.72 900 137 110 88 75 24.55 55.68 82.67 1000 142 117 92 79 21.37 54.35 79.75 figure 6 plots the energy dissipation of the ch with the given existing protocol. the energy consumed by the ch in eodc is 0.12nj for 200 iterations which is less than those of the existing systems like hhca, hach and the leach-c protocol which give 0.18nj, 0.21nj and 0.29nj respectively. figure 6: energy dissipation of the ch under each round figure 7 and table 3 show the sum of residual energy during each round and the improvement achieved by eodc. in the proposed system, the residual energy of the sum of all nodes is evaluated and in comparison, the sum of network’s residual energy is higher than those of the existing systems like leach-c, hhca and the hach protocol. the improvements achieved in eodc over hhca, hach and leach-c are 100%, 211% and 357.14% respectively. 194 c. jothikumar, r. venkataraman figure 7: sum of residual energy during each round table 3: sum of residual energy during each round of communication rounds sum of residual energy improvement achieved in % by eodc over eodc hhca hach leach-c hhca hach leach-c 0 250 250 250 250 0.00 0.00 0.00 100 240 230 213 197 4.35 12.68 21.83 200 230 210 180 160 9.52 27.78 43.75 300 218 185 165 140 17.84 32.12 55.71 400 210 160 140 120 31.25 50.00 75.00 500 195 140 115 95 39.29 69.57 105.26 600 180 122 101 70 47.54 78.22 157.14 700 160 104 75 35 53.85 113.33 357.14 800 140 85 45 0 64.71 211.00 900 120 63 0 0 90.48 1000 96 48 0 0 100.00 figure 8 and table 4 plot the network lifetime comparison with leach-c, hhca, and hach with the eodc protocol. the lifetime gradually increases for the eodc protocol with the total number of nodes, which ranges from 250 to 500. as per the percentage of nodes dying in every round, we calculate the lifespan of the network. the improvements achieved in eodc over hhac, hach and leach-c are 12.85%, 44.77% and 82.18% respectively. the proposed protocol gives better performance in comparison eodc: an energy optimized dynamic clustering protocol for wireless sensor networks using pso approach 195 figure 8: lifetime comparison of the protocol table 4: lifetime comparison of the protocols no. of lifetime comparison improvement achieved in % by eodc over nodes eodc hhca hach leach-c hhca hach leach-c 250 510 431 328 212 18.33 55.49 140.57 300 635 530 364 256 19.81 79.38 148.05 350 792 602 404 306 31.56 96.04 158.82 400 916 671 484 388 36.51 89.26 136.08 450 1014 793 604 477 27.87 67.88 112.58 500 1135 1006 784 623 12.85 44.77 82.18 from the projected graphs of the simulation results, it is concluded that eodc has a lower energy dissipation, increase in the generation of clusters and the sum of residual energy consumed is low when compared with the preceded leach-c, hhca and hach protocols, thus increase the lifespan of the nodes and efficiency of the data transmission across the plane. the performance of eodc is compared with the above mentioned protocols with respect to energy and network lifespan. network lifespan is characterized as the count of rounds at the point until every one of the sensor nodes comes up short on energy. it should be observed that our proposed model still has some outstanding energy beyond 1100 rounds, whereas only slight energy is remaining beyond 1000 rounds with the hach protocols. 5 conclusion energy efficiency is an essential criterion for the lifespan of the sensor network. clustering the nodes together is the potent way to promote the energy efficiency of the network. the eodc approach is a type of hierarchical routing protocol like leach-c, hhca and hach which perform clustering of the sensor nodes. leach, hach and the hach protocol randomly choose the chs, which in turn, increases the re-clustering process; thus more energy is being used and the network lifespan gets affected. in eodc, the cluster heads are chosen based on the pso approach. the metrics of the proposed system are compared with the pso model. one of 196 c. jothikumar, r. venkataraman the primary concerns is an effective ch selection which has been given in the proposed system. the simulation results report that in eodc, the number of clusters in the network is high in comparison. the inter cluster communication is performed using the shortest path algorithm in this scenario. the results show that by doing so the network lifetime is more in our approach when compared with the other hierarchical routing protocols. our simulation results have proven that eodc produces better results than leach-c, hhca and the hach hierarchical routing protocol with reference to the total energy consumption and the lifetime of the network. acknowledgments the authors wish to acknowledge the support extended by department of computer science and engineering , srm institute of science and technology, chennai, india, to carry out this research work. author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] abdul latiff, n.m.; tsimenidis, c.c.; sharif, b.s. 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(2016). clustering hierarchy protocol in wireless sensor networks using an improved pso algorithm, ieee access, 5, 2241-2253, 2016. int j comput commun, issn 1841-9836 8(2):275-293, april, 2013. enhanced dark block extraction method performed automatically to determine the number of clusters in unlabeled data sets p. prabhu, k. duraiswamy puniethaa prabhu, k. duraiswamy department of master of computer application k.s. rangasamy college of technology tamil nadu, india. email: spunitha156@yahoo.co.in, drkduraiswamy@yahoo.co.in abstract: one of the major issues in data cluster analysis is to decide the number of clusters or groups from a set of unlabeled data. in addition, the presentation of cluster should be analyzed to provide the accuracy of clustering objects. this paper propose a new method called enhanced-dark block extraction (e-dbe), which automatically identifies the number of objects groups in unlabeled datasets. the proposed algorithm relies on the available algorithm for visual assessment of cluster tendency of a dataset, by using several common signal and image processing techniques. the method includes the following steps: 1.generating an enhanced visual assessment tendency (e-vat) image from a dissimilarity matrix which is the input for e-dbe algorithm. 2. processing image segmentation on e-vat image to obtain a binary image then performs filter techniques. 3. performing distance transformation to the filtered binary image and projecting the pixels in the main diagonal alignment of the image to figure a projection signal. 4. smoothing the outcrop signal, computing its first-order derivative and then detecting major peaks and valleys in the resulting signal to acquire the number of clusters. e-dbe is a parameter-free algorithm to perform cluster analysis. experiments of the method are presented on several uci, synthetic and real world datasets. keywords: enhanced dbe, automatic clustering, cluster tendency, visual assessment, reordered dissimilarity image. 1 introduction the major concern in data mining is to outline the observed data into knowledge structures. clustering aims at classifying objects of a related class into their relevant categories. partitioning the set of objects o = (o1, o2, ..., on) into c self-related objects is the major process of cluster analysis. various clustering algorithms are reported in the literature [1] and [2]. the general problems involved in clustering of unlabeled data sets are: a) assessing cluster tendency, i.e., value of c. b) grouping the data into c meaningful sets and c) evaluating the discovered clusters c. this paper addresses the problem of determining whether the clusters are present by assessing of clustering tendency of clustering tendency as a prior process before clustering. majority of the clustering algorithms need the number of clusters c as a key factor, so the quality of the resultant clusters mainly depends on the assessment of c. jain and dubes [3] had discussed several statistically based informal techniques for cluster tendency assessment. ling [4] proposed a clustering algorithm based on estimated distribution model. cattell [5] formerly depicted pairwise dissimilarity information about a data set including n objects as an n×n image, where the objects are suitably reordered so that the resultant image is improved and is capable to emphasize the possible cluster structure in the data. the major papers in the visual representation of data dissimilarity include the contribution of [6], [7], [8] and [9]. the universal denominator in all this methodology is reordered dissimilarity image (rdi). the intensity of each pixel in the rdi represents the dissimilarity between the pair of objects denoted copyright c⃝ 2006-2013 by ccc publications 276 p. prabhu, k. duraiswamy by the row and column of the pixel. an observer can merely calculate approximately the number of clusters c (i.e., count the number of dark blocks along the diagonal) of an rdi where the dark blocks posse’s image lucidity (see figure 1c). figure 1: an example for e-vat image. (a) scatter plot of a 3,000 point’s data set with five clusters (b) unordered image (c) reordered e-vat image i(d̄). generating rdis could be done from any of the schemes anticipated in [6], [7], [8] [9] and [11]. this paper develops a novel method to estimate automatically the number of dark blocks (seemingly also the number of possible clusters) in rdis of unlabeled data sets. the proposed enhanced dark block extraction (e-dbe) process combines several common images, signal processing techniques [10] and for the compactness, rdis are generated using enhanced visual assessment of cluster tendency (e-vat) algorithm [11]. later sequential image processing operations (region, segmentation, directional morphological filtering, and distance transformation) are performed to fragment the regions of interest in the rdi and then translate the filtered image into a distancetransformed image. lastly, the altered image is projected on the diagonal axis of the rdi, which yields an one-dimensional signal from which the (potential) number of clusters can be extracted from the dataset using signal processing operations. the rest of this paper is structured as follows: in section 2 we present the literature description of visual approach. section 3 reviews the enhanced vat algorithm and section 4 explains the procedure for cluster count extraction (cce) [12]. section 5 analyses the dark block extraction algorithm. section 6 describes the proposed enhanced dbe approach. section 7 provides results on uci, synthetic and real world data sets for the proposed algorithm. the final section contains a short discussion on results and for future study. enhanced dark block extraction method performed automatically to determine the number of clusters in unlabeled data sets 277 2 literature review some existing approaches of the post clustering cluster validity problem are reviewed before reciting the visual methods for cluster tendency assessment. index-based methods for cluster validity usually underline the intracluster density, intercluster division and additional factors such as geometric or statistical properties of the data are proposed in [13], [14], [15], [16], [17], [18], [19], [20] and [24]. for instance, milligan and cooper [13] compared 30 indices over a sequence of synthetic data sets. above all these, calinski and harabasz [14] index seems to be the best which performs the ratio between the traces of the between-cluster and within-cluster scatter matrix. it is a significant noting that the validity indices are completely dependent on the data and algorithm used to find partitions. probabilistic indices of cluster validity attempt to validate the number of clusters found by probabilistic clustering algorithms. guo [21] proposed a cluster number choice method for a small set of samples using a bayesian ying-yang (byy) model. comparative studies such as [17] and [22] provided experimental comparisons of many criteria such as akaike’s information criterion (aic), minimum description length (mdl), and (byy) for determining the number of clusters based on a gaussian mixture model. a variety of statistical techniques for tendency assessment are discussed in the work of jain and dubes [3]. visual methods for cluster tendency assessment for a range of data analysis problems have been extensively studied in [23]. cattell [5] used single-linkage heuristics to rearrange the elements of small dissimilarity matrices, which were consequently hand-rendered for viewing. floodgate and hayes [7] offered hand-rendered pictures like sneath’s, but reordering was done computationally using single-linkage clustering. majority of the clustering algorithm builds rdis prior to clustering and the rdi is viewed as a visual aid to tendency assessment. this is the problem addressed by the new e-dbe algorithm, which uses the dbe algorithm of liang [25] and e-vat algorithm [11] to find rdis and the number of clusters automatically. a number of significant advantages of e-dbe over index-based or probabilistic methods are summarized as follows: • e-dbe is a preclustering technique, i.e., it does not need the data to be clustered, nor does it locate clusters in the data. on the other hand, the consistency (and weakness) of postclustering index-based methods is entirely dependent on the clustering algorithms used to identify the partitions. • index-based post clustering methods regularly need clustering to be performed several times using a variety of cluster numbers and often find the top partition according to some predefined criteria. repetitive clustering can be computationally expensive, particularly when the range of possibe values of c remains uncertain. e-dbe has no such constraint and is performed just once 3 review of enhanced visual assessment tendency of the many achievable ways to obtain an rdi, apply e-vat to generate rdis of unlabeled data, i.e., to secure inputs to e-dbe algorithm. let o = (o1, o2, o3...on) represent n objects in the data. vectorial data have the type f = (f1, f2, f3...fn), fi ⊂ rh, where every coordinate of the vector fi provides an attribute value of each of h features (i.e., aj, j = 1, 2, 3...h) corresponding to an entity oi. constantly translate f into dissimilarities d = [dij = ||fi − fj||], 1 ≥ dij ≥ 0; dij = dji; dii = 0, for 1 ≤ i, j ≤ n. to make the paper self-sufficient, review of reordering 278 p. prabhu, k. duraiswamy method e-vat is shown in table 1 which is proposed by [11] and an instance is shown in figure 1. table 1 enhanced-visual assessment tendency algorithm input consider the dataset as n x n dissimilarity matrix. d = [dij]where1 ≥ dij ≥ 0; dij = dji; dii = 0, for1 ≤ i, j ≤ n process step (1): transform d to a new dissimilarity matrix r with dij = 1 − exp(−dij/σ), where σ is a scale parameter determined from d using the algorithm of otsu [26] automatically. step (2): form an rdi image i(1) corresponding to r using the vat algorithm [9]. step (2.1): let i = φ, j = 1, 2, ...n and p = (0, .....0). choose (i, j) ∈ argpjandq ∈j max{dpq} place p(1) = i, i ← i and j ← j −{i} step (2.2): iterate for t = 2...n select (i, j) ∈ argpiandq ∈j min{dpq} set p(t) = j, revise i ← i ∪{j} and j ← j −{j} step (2.3): figure the dissimilarity template or matrix r = [dij] = [dp(i)p(j)] where 1 ≤ i, j ≤ n step (3): display the reordered matrix r̃ as the odi ĩ using the conventions given above. output gray scale image i(d), which denotes maximum (dij) to white and minimum (dij) to black figure 1a shows the scatter plot of n = 3, 000 records points in r2, which are created from a combination of c = 5 bivariate normal distributions. these data points are transformed to a 3, 000 × 3, 000 dissimilarity matrix d by using distance measures for calculating distance between each pair of points. the five visually obvious clusters in figure 1a are reflected by the five separate dark blocks along the main diagonal in figure 1c, which is the e-vat image of the records after reordering. on comparing with figure 1b, which is the image of dissimilarities d in original input order, reordering is essential to expose the fundamental cluster structure of the data. the following are some points about e-vat: • e-vat algorithm is performed to determine the number of clusters prior to clustering. even if the estimated result does not match with the true value, it provides a basis for setting the range. • e-vat depends merely on the input d, so a good quality d is decisive when d is a derivative of object vectors. if the input dataset is of high dimensionality nonlinearly separable, it may be improved by performing feature extraction. enhanced dark block extraction method performed automatically to determine the number of clusters in unlabeled data sets 279 4 cluster count extraction (cce) for cluster tendency performance in the following sections, the performance of e-dbe is compared with other preclustering assessment of cluster tendency techniques like dbe [25] and cce algorithm [12]. cce also counts dark blocks in rdis using image transformation techniques. the major steps for this algorithm are summarized in table 2. table 2 the cluster count extraction (cce) algorithm input n×n scaled matrix of dissimilarities d = [dij] and its vat image image(d′) scaled so that max = white and min = black. step (1): threshold image(d′) with otsu’s algorithm [26]. step (2): create a correlation filter ratio of size s′. step (3): apply the fast fourier transform (fft) to both the segmented rdi and the filter. step (4): proliferate tranformed vat image with the composite conjugate of the transformed filter. step (5): compute inverse fft for the filtered image. step (6): acquire the off-diagonal pixel values (e.g., pth off-diagonal) of the back-transformed image and calculate its histogram. step (7): cut the histogram at an arbitrary horizontal line f = w and calculate the numeral of spikes. output the number of dark blocks along the diagonal of image(d′) called as c (cluster interger). the cce algorithm is applicable to built rdis by any of the methods obtainable in the literature. in this algorithm vat [9] was used to obtain rdis from d, but e-vat [11] is used in the proposed e-dbe algorithm. cce algorithm is performed based on the parameter settings suggested in [12], i.e., s′ = 20, p = 1 and w = 0. section 7 analyzes the results of cce with dbe and proposed e-dbe on various synthetic, uci repository and real-world datasets. the result in table 5 shows that e-dbe is more consistent than cce because cce algorithm performs on off-diagnal pixels values of the images which show the poor performance of the method. 5 review of dark block extraction (dbe) liang [25] proposed the dark block extraction algorithm to estimate the cluster number in unlabeled data sets. dbe algorithm counts the dark blocks along the diagonal of an rdi using basic image processing techniques. the method is summarized in table 3. table 3 the dark block extraction algorithm 280 p. prabhu, k. duraiswamy figure 2: dbe results on five-cluster data sets. (a) scatterplot of a data set of 3,000 points. (b) segmented vat image. (c) morphological filtering of vat image. (d) tranformed image (e) signal histogram input n × n scaled matrix of dissimilarities d = [dij], the proportion of the allowed minimum cluster size of the data size n. step (1): transform d to a new dissimilarity matrixd′ using σ scale parameter determined using otsu [26] automatically. step (2): form an rdi image using vat algorithm proposed by [9]. step (3): filter the image using morphological operators with directional line structuring elements. step (4): perform a distance transform on the image to obtain a new gray-scale image. step (5): project the pixel values of the image onto the main diagonal axis of the image to form a projection signal. step (6): smooth the obtained signal to get the filtered signal using filter techniques. step (7): find peak positions pi and valley positions vj in the signal. step (8): select major peaks and valley by removing minor ones using filters. output the numbers of dark blocks (i.e., the number of major peaks) are in the rdi. the results of dark block extraction algorithm are displayed in figure 2. figure 2a shows the scatter plot of 3,000 points. these points are converted to a dissimilarity matrix and perform global threshold which makes the image pixel belong to one of two classes, i.e., background or foreground. enhanced dark block extraction method performed automatically to determine the number of clusters in unlabeled data sets 281 the dissimilarity new matrix is transformed to an rdi using vat algorithm, which is shown in figure 2b. then to compose the segmented image comprehensible morphological operators [25] are applied to perform binary image filtering using line structural element, as shown in figure 2c. later perform the distance transformation to the binary image to acquire a new gray-scale image, smooth filter techniques are applied to obtain peak and valley positions, the results are shown in figure 2d and 2e respectively. as per liang [25] suggestions and future developments, dbe uses simple euclidean space to calculate pairwise dissimilarities when the input records are feature vectors. the euclidean distance may not be appropriate for high dimensional or composite data. the results of dbe are not clear and the cluster extraction is not performed accurately due to vat, datasets and thresholding. enhanced algorithms propose a good quality dissimilarity measures for diverse types of given data sets. 6 enhanced dark block extraction (e-dbe) the proposed algorithm extends a nearly parameter-free technique, called e-dbe, to estimate the cluster number in unlabeled data sets. e-dbe is an algorithm that counts the dark blocks along the diagonal of the rdi. the proposed method relies on e-vat [11], dark block extraction [25] and distance measures for diverse type of attributes, basic image and signal processing techniques [10] later the algorithm is summarized in table 4. table 4 the enhanced dark block extraction algorithm input n × n scaled matrix of dissimilarities d = [dij] and a parameter α, the proportion of the allowed minimum cluster size of the data size n. step (1): transform d to a new dissimilarity matrixd′ = dij = 1 − exp(−dij/σ). σ scale parameter determined d using otsu [26] automatically. step (2): form an rdi image(1) corresponding to d′ using e-vat algorithm. step (3): threshold the image(1) to obtain binary image(2) using the adaptive threshold [27] algorithm. step (4): perform a distance transform on image(2) to obtain a new gray-scale image(3), and scale the pixel values to [0, 1]. step (5): project the pixel values of the image(3) onto the main diagonal axis of the image to form a projection signal histogram(1). step (6): filtering the projected signal is performed by savitzky-golay filter design [29]. step (7): compute the first order derivative of the histogram(1) to obtain signal histogram(2). step (8): select major peaks and valley by removing minor ones using a filter with size α as an optimal one. output the number of dark blocks (i.e., count the number of major peaks) presented in the rdi. 282 p. prabhu, k. duraiswamy prior to step-1 the datasets are preprocessed (normalized) based on the feature characteristics. later the normalized datasets are tranformed to a dissimilarity matrix d of n × n size and the distance measure used here is the city-block distance. the matrix d is the input for constraint free algorithm e-dbe. histogram of the original dissimilarity matrix d related to the scatter plot data set is shown in figure 3a. dissimilarity transformation and image segmentation (step-1): first tranform the original matrix d to a new dissimilarity matrix d′ using a monotonic exponential function f(v) = 1−exp(−v/σ) (parameter σ may be merely selected as the threshold significance obtained by otsu’s algorithm [26]), shown in figure 3b. the resultant histogram of the dissimilarity matrix d′ is shown in figure 3c. figure 3: sample results of e-dbe algorithm. (a) histogram of the original dissimilarity matrix d related to the data set in fig. 1a. (b) monotonic transformation function, i.e., f(v) = 1 − exp(−v/σ) (c) histogram of d′. (d) e-vat image (image(1)) (e) segmentation of e-vat image (image(2)) is obtained from binary image (image(1)) (f) distance transformation on segmentated image (image(2)) to get a gray-scale image (image(3)), (g) diagonal projection signal histogram(1) from image(3) and its equivalent smoothed signal histogram(2). formation of rdi image (step-2): in this step, e-vat algorithm is applied to reorder the new dissimilarity matrix d′ to form an rdi image image(1). e-vat algorithm is performed to find out the figure of clusters previous to clustering. the intensity of every pixel in the rdi represents the dissimilarity among the couple of items denoted by the row and column of the pixel. rdi highlights the achievable clusters as a place darkblocks beside the diagonal of the image, corresponding to sets of objects with small dissimilarity. the rdi of the new dissimilarity matrix d′ is displayed in the figure 3d and shows the formation of 5 clusters in the dataset. formation of binary image using adaptive threshold algorithm (step-3): adaptive thresholding [27] normally accepts a gray scale as input and, in the simplest execution, outputs a binary image representing the segmentation. for every pixel in the image, a threshold has to be calculated. if the pixel significance is lower than the threshold it is set to the background; otherwise it assumes the foreground value. in the proposed e-dbe adaptive or dynamic thresholding algorithm is performed which acquire best result when compared with the previous algorithm. enhanced dark block extraction method performed automatically to determine the number of clusters in unlabeled data sets 283 adaptive threshold algorithm is used to obtain a new threshold σ′ to convert the binary image by image(2)ij = 1 if image (1) ij > σ′and image (2) ij = 0 otherwise. the resultant binary image image(2) is shown in the figure 3e. distance transform of binary image to a new gray-scale image (step-4): to organize the filtered image into an informative one showing the dark block structure information, the values of pixels that are beside or off the main diagonal axis of the image must be considered. first, execute a dt of the binary image (image(2)) to obtain a new gray-scale image image(3) as shown in figure 3f. a dt is a form of depiction of a digital image, which converts a binary image to a gray-scale image in which the value of each pixel is the distance from the pixel to the adjacent non-zero pixel in the binary image(2). there are numerous diverse dts depending upon which distance measures is being used to decide the distance between pixels. euclidean distance measure is applied in this proposed algorithm. after the distance transformation, all pixel values of the dt image(3) are projected onto the main diagonal axis to obtain a projection signal histogram(1), as shown in figure 3g. from the figure, c can be simply calculated because of the quite clear separation between major peaks in the signal histogram(1). detection of major peaks and valleys in the projected signal (steps 5-8): the amount of dark blocks in any rdi is equal to the number of majorpeaks in the projection signal histogram(1). based on the first − order derivative of the projection signal the cluster number c is calculated from the detection of peaks and valleys. although the projection signal histogram(1) is available, need further smoothing to reduce possible false detections due to noise in the signal. here savitzky-golay smoothing filters [29] (also called digital smoothing polynomial filters or least-squares smoothing filters) are typically used to smooth out a noisy signal whose frequency span is large. in this algorithm, savitzky-golay smoothing filters perform much better than typical averaging fir filters performed in [25], which tend to filter out a significant portion of the signal’s high frequency content along with the noise. savitzky-golay filters are optimal in the sense that they minimize the least-squares error in fitting a polynomial to frames of noisy data. it is well recognized that the peaks and valleys of a signal usually correspond to zero− crossing points in its first-order derivative, as shown in figure 3g. remark a significant issue for the e-dbe algorithm is how to successfully set the filter size α for the savitzky-golay filter. actually, α is very simple to set because it reflects the minimum support threshold for the smallest cluster of importance in the data. the novel in this algorithm is • after preprocessing the dissimilarity matrix is transformed to a monotonic exponential function. • distance measure used in this procedure is cityblock distance which gives better results • for better performance the proposed methods uses adaptive threshold for segmentation • first order derivatives are computed • for better projection of the signals the algorithm performs smooth, moving and savitztygolay filters. 284 p. prabhu, k. duraiswamy 7 experiment results of synthetic, uci and real world data sets to assess the e-dbe algorithm with its prior measures, a number of experiments on several synthetically generated data sets, uci machine learning repository [30] as well as real-world data set are carried. the data sets’ characteristics and the results of cce, dbe and enhanced dbe are accomplished in table 5. table 5 summary of synthetic, uci and real datasets’ distinctiveness and the results using cce, dbe and enhanced-dbe data set # instances #clusters#each cluster attribute type # attributes cce dbe edbe synthetic datasets synthetic dataset -1 1000 2 [500,500] integer 2 2 2 2 synthetic dataset -2 1000 3 [500,250,250] integer 2 2 2 3 synthetic dataset -3 1800 3 [300,600,900] integer 2 2 2 3 uci datasets dermatology 357 6 [110,59,70, 48,51,19] integer 34 1 3 6 heart 270 2 [150,120] integer/ real 13 3 1 2 hepatisis 72 2 [12,60] integer/ real 20 1 1 2 iris 150 3 [50,50,50] integer/ real 5 1 2 3 wine 178 3 [59,71,48] integer/ real 13 2 2 3 real world datasets hiv 400 6 [221,144, 11,17,5,1] integer/ real 19 1 3 6 7.1 numerical examples with synthetic datasets observe the results on several synthetic datasets with multifaceted structures, in which an apparent cluster centroid for every cluster is not automatically available. selections of synthetic datasets are based on the sets proposed in [25]. synthetic dataset (s − 1) is composed of two half-moon like patterns (c = 2). the dimension of the dataset is n = 1000, with 500 points in each group. the upper half-moon is generated by fu(ϕ) = 2sin(ϕ) + 0.5 randn for ϕ = [π/500 : π/500 : π], while the lower half moon is produced by f1(ϕ) = 2sin(ϕ + 0.6π) + 0.5 rand for ϕ = [0.4π + π/500 : π/500 : 1.4π], where randn is a probability number drawn from a standard distribution with a zero mean and a standard deviation of one. synthetic dataset (s −2) is generated from a grouping of two bivariate standard distributions and one half-moon enhanced dark block extraction method performed automatically to determine the number of clusters in unlabeled data sets 285 like model (c = 3). the magnitude of the data set is n = 1000, including 500 points for the half-moon pattern and 250 points for each of the two gaussian shapes. the upper half-moon is generated by f(ϕ) = 2sin(ϕ) + 0.3 randn for ϕ = [π/500 : π/500 : π], where rand is a arbitrary number drawn from a regular distribution on the part interval. the two gaussian shapes are generated by the subsequent constituent parameters: the integration proporations are mean1 = 0.5 and mean2 = 0.5; the mean values µ1 = (0.9, 0.5)t and µ2 = (2.1, 0.5)t ; and the covariance matrices ∑ 1 = ∑ 2 = [10; 00.1]. synthetic dataset(s − 3) is generated from a permutation of three circles with the identical centroid but diverse radii (c = 3). for every circle, generate synthetic data points by a1(ϕ) = rsin(ϕ)+brandnsin(ϕ) and a2(ϕ) = rcos(ϕ)+brandncos(ϕ) where b is a constraint that controls the degree of overlap linking different circles, r is the radius of every circle, ϕ = [(2π)/p : (2π)/p : 2π], and p is the size of each cluster. the synthetic datasets (s−1, s−2 and s−3) outcomes of e-dbe algorithm using image processing techniques are depicted in figure 4. the e-vat images are shown in figure 4a, binary e-vat images in 4b and the first order derivative projection signal obtained using smooth, moving and sgolay are presented in figure 4c. 7.2 numerical examples with uci machine learning repository next, consider some uci datasets which are evaluated for the performance of proposed edbe method. the five datasets are dermatology, heart, hepatisis, iris and wine of uci machine learning repository [30]. for each dataset, the enhanced dbe with class attribute and dimensionality reduction [28] are performed. the uci data sets’ characteristics and the consequences of e-dbe are accomplished in table 5. dermatology: the main intend of this database is to determine the category of eryhematosquamous disease. they all allocate the clinical features of erythema and scaling, with very modest differences. the diseases in this group are psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis, and pityriasis rubra pilaris. the dataset include 357 occurrences with 34 features including class attribute. i.e., 110 for class 1, 59 for class 2, 70 for class 3, 48 for class 4, 51 for class 5 and 19 for class 6. starting with 34-dimensional feature vectors, dataset are subjected to preprocessing, normalization and pairwise dissimilarities using the distance measures to get relational data. later the dissimilarity matrix d is submitted to e-dbe algorithm for automatic clustering and the results are shown in figure 5. heart: this dataset encloses the results of the prediction of heart attack. the dataset contains 72 instances and 13 attributes they are age, sex, chest pain type (4 values), resting blood pressure, serum cholesterol and fasting blood sugar etc. the entire number of illustration in this data set is n=270, i.e., 150 represent absence and 120 the occurrence of heart attack. initially with 13-dimensional feature vectors, the dataset preprocessing, normalization and pairwise dissimilarities by the distance measures are performed to acquire relational records. afterwards, the dissimilarity matrix d is proposed to e-dbe algorithm for automatic grouping and the outcome is depticted in figure 6. three clusters are shown as a result of cce algorithm and one cluster is displayed as an outcome of dbe and two clusters by e-dbe (c=2). hepatisis: hepatisis is an irritation of the liver characterized by the occurrence of inflammatory cells in the tissue of the organs. this dataset contains the facts of the patient from which we evaluate whether they are alive or not. the data set holds 72 cases with 20 features (including class) they are age, sex, steroid, antiviral, fatigue and malaise etc. the total integer of instances in this data set is n=72, i.e., 12 are scrutinized as dead, and 60 are alive. the dissimilarity matrix d is submitted to e-dbe algorithm for robotic clustering. the hepatisis dataset is submitted to analyse the concertness of cce, dbe and e-dbe algorithms results are 286 p. prabhu, k. duraiswamy figure 4: results of the e-dbe algorithm on synthetic datasets (s-1,s-2 and s-3) (a) e-vat images synthetic data sets (b) binary e-vat images (c) first order derivative projection signal obtained using smooth,moving and sgolay. enhanced dark block extraction method performed automatically to determine the number of clusters in unlabeled data sets 287 figure 5: results of the e-dbe algorithm on dermatology dataset (a) e-vat image of dermatology dataset, (b) binary e-vat image of dermatology dataset (c) distance transformed image (d)first order derivative projection signal obtained using smooth (e) first order derivative projection signal obtained using moving (f) first order derivative projection signal obtained using sgolay. figure 6: results of the e-dbe algorithm on heart dataset (a) e-vat image of heart dataset, (b) binary e-vat image of heart dataset (c) distance transformed image (d) first order derivative projection signal obtained using smooth (e) first order derivative projection signal obtained using moving (f) first order derivative projection signal obtained using sgolay. 288 p. prabhu, k. duraiswamy shown in the figure 7. figure 7: results of the e-dbe algorithm on hepatisis dataset; (a) e-vat image of hepatisis dataset; (b) binary e-vat image of hepatisis dataset; (c) distance transformed image; (d) first order derivative projection signal obtained using smooth; (e) first order derivative projection signal obtained using moving; (f) first order derivative projection signal obtained using sgolay. iris: this is conceivably one of the best-known databases to be found in the pattern recognition literature. the data set have 3 physical classes, 50 instances each (n=150), where each one class refers to a category of iris plant. the features of each instance consist of 4 numeric standards, consequent to sepal length, sepal width, petal length and petal width respectively. the dissimilarity matrix d is submitted to e-dbe algorithm for computerized clustering and the outcomes are shown in the figure 8. wine: this data set contains the results of chemical analysis of wines grown in the same region in italy but derived from three different cultivars. the investigation determines the quantities of 13 constituents found in each of the three brands of wines. the characteristic are respectively alcohol, malic acid, ash, magnesium, etc. the complete numeral of instances in this items are n =178, i.e., 59 for class 1, 71 for class 2 and 48 for class 3. the e-dbe results for wine data sets are shown in figure 9. 7.3 numerical example with real-word data set the proposed method is tested on the hiv patient datasets collected from various integrated counseling and testing center (ictc) and antiretroviral (art) centers of tamilnadu and pondicherry. the preprocessing techniques are executed and then cce, dbe and e-dbe enhanced dark block extraction method performed automatically to determine the number of clusters in unlabeled data sets 289 figure 8: results of the e-dbe algorithm on iris dataset; (a) e-vat image of iris dataset; (b) binary e-vat image of iris dataset; (c) distance transformed image; (d) first order derivative projection signal obtained using smooth; (e) first order derivative projection signal obtained using moving; (f) first order derivative projection signal obtained using sgolay. 290 p. prabhu, k. duraiswamy figure 9: results of the e-dbe algorithm on wine dataset (a) e-vat image of wine dataset, (b) binary e-vat image of wine dataset (c) distance transformed image (d) first order derivative projection signal obtained using smooth (e) first order derivative projection signal obtained using moving (f) first order derivative projection signal obtained using sgolay. algorithms are applied to the hiv/aids diagnosis dataset containing 400 objects. table 6 shows the structure of the dataset with preprocessing depends upon the feature nature. the attributes are respectively age, sex, wt, hb, treat drug, pill count, initial drug, occupation, marital status, cd4, cd8, ratio, wbc, rbc, pcv, platelet, tlc, sgpt, sgop and drug regimenclass attribute (ca). the complete numeral of items in this data set is n=400, i.e., 221 for class 1, 144 for class 2, 11 for class 3, 17 for class 4, 5 for class 5 and 1 for class 6. we computed pair wise dissimilarities using the euclidean, hamming, mahalanobis distance to get relational table. the e-dbe results shows the cluster count as five (c=6) which is shown in figure 10 a better result when compared with its prior algorithm cce and dbe. obj # ca age sex hb wt treatdrug (regimen) . . . cd4 count wbc sgpt tlc 1 1 25 1 14 60 1 : 500 4600 46.0 4.0 2 2 35 1 11 48 2 : 100 6400 47.0 5.0 : 1 : : : : : : : : : : : 1 : : : : : : : : : : 400 2 45 0 13.5 58 1 . . . 150 3500 40.0 3.0 from the current study, the qualities of clusters are confirmed with the dark blocks on the diagonals and first order derivatives are achieved as peaks and valleys on the enhanced dbe creation. it makes certain impact of objects related to the clusters in the reversed format. 8 discussion and conclusion this paper examines an almost parameter-free method for automatically estimating the number of clusters in unlabeled data sets. the enhanced version of dbe algorithm works for unspecified data objects of n x n dissimilarity matrix and to estimate the feature of cluster being determined. the only user-defined constraint that must be selected ? controls the filter size for applying filtering techniques. it is comparatively easy to make a pragmatic and functional choice for ?, since it effectively specifies the smallest cardinality of a cluster relative to the number of objects in the data. the cluster number extracted by e-dbe appears to be increasingly reliable. e-dbe will perhaps reach its useful limit when the rdi created by any reordering of d is not from a well ordered dissimilarity matrix. in the proposed method distance metrics enhanced dark block extraction method performed automatically to determine the number of clusters in unlabeled data sets 291 figure 10: results of the e-dbe algorithm on hivdrug dataset (a) e-vat image of hivdrug dataset, (b) binary e-vat image of hivdrug dataset (c) distance transformed image (d) first order derivative projection signal obtained using smooth (e) first order derivative projection signal obtained using moving (f) first order derivative projection signal obtained using sgolay. 292 p. prabhu, k. duraiswamy are explored for diverse types of given data sets which yield a better cluster visualization. an achievable extension of this effort concerns the initialization of the c-means clustering algorithm for object data clustering.future work is proposed to obtain a visual clustering algorithm based on 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[30] uci repository of machine learning databases, http://www.ics.uci.edu/ mlearn /mlrepository.html. international journal of computers communications & control issn 1841-9836, 13(3), 353-364, june 2018. implementation of arithmetic operations by sn p systems with communication on request y. jiang, y. kong, c. zhu yun jiang*, chaoping zhu 1. detection and control of integrated systems engineering laboratory 2. school of computer science and information engineering chongqing technology and business university chongqing 400067, china *corresponding author: jiangyun@email.ctbu.edu.cn jsjzcp@163.com yuan kong college of mathematics and system science shandong university of science and technology qingdao 266590, china kongyuan1122@126.com abstract: spiking neural p systems (sn p systems, for short) are a class of distributed and parallel computing devices inspired from the way neurons communicate by means of spikes. in most of the sn p systems investigated so far, the system communicates on command, and the application of evolution rules depends on the contents of a neuron. however, inspired from the parallel-cooperating grammar systems, it is natural to consider the opposite strategy: the system communicates on request, which means spikes are requested from neighboring neurons, depending on the contents of the neuron. therefore, sn p systems with communication on request were proposed, where the spikes should be moved from a neuron to another one when the receiving neuron requests that. in this paper, we consider implementing arithmetical operations by means of sn p systems with communication on request. specifically, adder, subtracter and multiplier are constructed by using sn p systems with communication on request. keywords: membrane computing, spiking neural p system, communication on request, arithmetic operation. 1 introduction since gh. păun first circulated his idea of membrane computing in 1998 [3] [22] (first circulated as a turku center for computer science (tucs) report 208, 1998), membrane computing has developed rapidly for almost two decades. as a branch of natural computing, membrane computing aims on abstract computing ideas from the structure and the functioning of a single cell, and also from complexes of cells, such as tissues and organs (including the brain) [23]. the computational devices in membrane computing are known as membrane systems (p systems, for short). till now, three main classes of p systems have been investigated: cell-like p systems [22], tissue-like p systems [13,50] and neural-like p systems [9]. the present paper deals with a class of neural-like p system, called spiking neural p systems (sn p systems, for short) [9]. sn p systems are a class of distributed parallel computing devices inspired from the way the neurons communicate by sending spikes to each other. in sn p systems, neurons (in the form of membranes) are placed in the nodes of a directed graph, with the edges representing synapses. each neuron contains a number of identical objects, denoted by a and called spikes. each neuron may also contain a number of firing rules and forgetting rules. when the contents copyright ©2018 cc by-nc 354 y. jiang, y. kong, c. zhu of a neuron satisfy some regular expression, a firing rule allows a neuron to send information to other neurons in the form of spikes. on the other hand, forgetting rule removes from the neuron a specified number of spikes. the system evolves by means of firing rules and forgetting rules. and it evolves synchronously, in each time unit, each neuron which can use a rule, no matter firing or forgetting, should use one. when the computation halts, no further rule can be used, and a result is obtained, e.g., in the form of the distance between the first two spikes of the output neuron, or the number of spikes present in a specified neuron in the halting configuration. since 2006 there have been quite a few research efforts put forward to sn p systems. many variants of sn p systems have been proposed, such as asynchronous sn p systems [3], sequential sn p systems [8], sn p systems with anti-spikes [16], homogenous sn p systems [47], sn p systems with astrocytes [19], sn p systems with weighted synapses [21], sn p systems with rules on synapses [32], sn p systems with weights [36], sn p systems with a generalized use of rules [52], sn p systems with white hole neurons [27], sn p systems with request rules [30], cell-like sn p systems [43], extended sn p systems [1], sn p systems with scheduled synapses [2], sn p systems with polarizations [19]. most of the classes of sn p systems obtained are computationally universal, equivalent in power to turing machines [4, 11, 15, 31, 33, 35, 40, 42, 46, 51, 53]. an interesting topic is to find small universal sn p systems [14,20,25,28,29,44,54]. in certain cases, polynomial solutions to computationally hard problems can also be obtained in this framework [10, 17]. moreover, sn p systems have been applied to solve real-life problems [24] [21], for example, to design logic gates, logic circuits [34] and databases [5], to represent knowledge [38], to diagnose faults [26,37,39], or to approximately solve combinatorial optimization problems [49]. sn p systems can also be applied in a very different way, where they are viewed as components of a restricted arithmetic logic unit. some sn p systems were constructed for dealing with basic arithmetic operations. these systems apply different encoding method. in [7], the binary number is encoded as a sequence of spikes: at each time unit, zero or one spike will be provided to the input neuron, depending on the corresponding bit being 0 or 1. the numbers used in [45] are encoded as the interval of time elapsed between two spikes. under the third encoding mechanism, natural numbers are encoded in the form of spike train and introduced in the system through the input neurons, while the results of arithmetic operations are encoded in the form of the number of spikes emitted to the environment [13]. these sn p systems mentioned above perform communication on command, that is the initiative for communication belongs to the emitting neuron. specifically speaking, the application of evolution rules depends on the contents of a neuron, (as mentioned above, checked by a regular expression), a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each neurons linked to the evolving neuron by a synapse. inspired from parallel-cooperating grammar systems, it is natural to consider the opposite strategy – communication on request. in this case, spikes are requested from neighboring neurons, depending on the contents of the neighboring neuron (also checked by a regular expression). on the other hand, no spike is consumed or created, they are only moved from a neuron to another one along synapses when the receiving neuron requests that. this request-response communication is an important concept in software engineering, and computers use it as a basic method to communicate with each other. recently, communication on request was introduced into sn p systems by pan et al., and this variant of sn p systems is shortly called snq p systems [18]. communication on request is a powerful feature in sn p systems: snq p systems using two types of spikes are proved to be universal, equivalent with turing machines, and it is reported that 49 neurons are sufficient for snq p systems to achieve turing universality. in this work, sn p systems with communication on request for performing the arithmetic operations are introduced. the arithmetic operations we will consider are addition, subtraction and multiplication. natural numbers can be encoded in the form of the number of spikes and implementation of arithmetic operations by sn p systems with communication on request 355 introduced in the system through input neurons. and then by performing the computation of the system, a number of spikes are present in the output neuron when the system halts. by analyzing the number of specific spikes in the output neuron, we can obtain the result of this arithmetic operation. the paper is organized as follows. in the next section we recall some preliminaries that will be used in the following, including the formal definition of sn p systems with communication on request. in section 3.1 we present an sn p system with communication on request that is used to add two natural numbers. a subtracter based on sn p system with communication on request is given in section 3.2. an sn p system with communication on request for multiplication is constructed in section 3.3. conclusions and some open problems for future works are present in section 4. 2 spiking neural p systems with communication by request formally, a spiking neural p system with communication on request (shortly, snq p system), with k types of spikes, is a construct of the form (this form is almost the same as the one appearing in [18], except one reasonable change by introducing input neurons) π = (o,σ1,σ2, . . . ,σm,aiin,aiout, in,out), where: 1. o = {a1,a2, . . . ,ak} is an alphabet (ai is a type of spikes), k ≥ 1; 2. σ1,σ2, . . . ,σm are neurons, of the form σi = (a n1 1 a n2 2 . . .a nk k ,ri), 1 ≤ i ≤ m,nj ≥ 0, 1 ≤ j ≤ k, where: a) nj ≥ 0 is the initial number of spikes of type aj contained in the neuron σj, 1 ≤ j ≤ k; b) ri is a finite set of rules of the form e/qw, with w a finite non-empty list of queries of the forms (aps,j) and (a∞s ,j), 1 ≤ s ≤ k, p ≥ 0, 1 ≤ j ≤ m, or j = env; 3. aiin, aiout, 1 ≤ iin, iout ≤ k, are the types of input spikes and output spikes, 4. in ⊆{1, 2, . . . ,m} indicate the input neurons, and out ∈{1, 2, . . . ,m} indicates the output neurons, respectively. a query (aps,j) means that neuron σi requests p copies of as from neuron σj, while the meaning of (a∞s ,j) is that all spikes of type as from σj, no matter how many they are, are requested by σi. specifically, a query of the form (a,env) is allowed to be used, which means that one copy of a is requested from the environment – with the environment supposed to contain arbitrarily many copies of a. this kind of rules can be removed [18], so it will not effect the arithmetic operations. a rule of the form e/qw can be used if both of the following conditions are satisfied: (1) the contents of the neuron are described by the regular expression e; (2) all queries formulated in w are satisfied (for example, if σj contains strictly less than p spikes, then the query (a p s,j) is not satisfiable). specifically, there is a situation called the conflicting queries, where two different neurons σi1, σi2 ask different numbers of occurrences of the same spike as from the same neuron σj (namely, two queries of the forms (a p s,j), (ars,j) with p 6= r, or of the forms (a p s,j), (a∞s ,j) for 356 y. jiang, y. kong, c. zhu p a given number). in the case of conflicting queries, the two rules cannot be used simultaneously, but one of them, non-deterministically chosen, can be used. in snq p systems, the definition of a computational step is quite delicate because of the interplay of the queries. a computational step is described in terms of three sub-steps: (1) in each neuron, a rule is chosen to be applied, and its applicability is checked; (2) the requested spikes are removed from the neurons where they were present. (3) the queries are satisfied, the requested spikes are moved to the requesting neuron. the three sub-steps together form a step, which lasts one time unit. an snq p system starts from the initial configuration, which is described by the number of spikes of each type present in each neuron in the beginning of the computation. then it proceeds by applying the rules synchronously, which means that in each neuron if a rule can be used, then it is applied according to the procedure described above. after a computation step, we can define transitions configurations. any sequence of transitions starting from the initial configuration is called a computation. a computation halts if reaches a configuration where no rule can be used. the result of a halting computation is the number of copies of spikes aiout present in neuron σout in the halting configuration. in order to perform arithmetic operations, it is necessary to introduce the numbers to be computed into the system, which may be encoded in many different ways. here, we use the way discussed in [7]. a positive integer number is given as input to a specified input neuron. the number is specified as the number of input spikes initially contained in the input neuron. the result of the operation is encoded as the number of output spikes present in the output neuron when a computation halts. in the next sections snq p systems are represented graphically, which is easy to understand. an oval with the initial number of spikes and rules inside is used to represent a neuron. the input neurons have incoming arrows and the output neuron have outgoing arrows, suggesting their communication with other devices (or the environment). 3 performing arithmetic operations by sn p systems with communication on request 3.1 an snq p system for addition in this section we present an sn p system with communication on request, as shown in fig. 1, for dealing with the addition of two arbitrary natural numbers. system πadd is composed of 5 neurons: two specified neurons are used as input neurons, where the summand and addend are introduced, and one neuron is used for giving the obtained result. theorem 1. for two arbitrary natural numbers x and y, sn p system with communication on request πadd computes the addition of x and y. proof: we construct a system πadd of the form πadd = ({a,b,c1},σ1,σ2,σ3,σ4,σ5,{a,b},{a},{1, 2}, 5), where: σ1 = (a x,∅), σ2 = (b y,∅), σ3 = (λ,{λ/q(c1,env),c1/q(a∞, 1)}), σ4 = (λ,{λ/q(c1,env),c1/q(b, 2),bc1/q(a,env)}), σ5 = (λ,{λ/q(c1,env),a∗b∗c1/q(a∞, 3)(ab, 4)}). implementation of arithmetic operations by sn p systems with communication on request 357 ax 1/in1 λ/q(c1, env) c1/q(a ∞, 1) 3 by 2/in2 λ/q(c1, env) c1/q(b, 2) bc1/q(a, env) 4 λ/q(c1, env) a∗b∗c1/q(a ∞, 3)(ab, 4) 5/out figure 1: the structure of adder πadd snq p system πadd functions as follows. in the initial configuration of πadd, all neurons are empty. the summand x and the addend y are encoded as spikes ax and by, and are provided to neuron σin1 and neuron σin2, respectively. since neurons σ3, σ4 and σout are empty, all of them can use the rule λ/q(c1,env), the spikes c1 arrives into them, and these neurons become active. with spike c1 inside, neuron σ3 can absorb from neuron σin1 all spikes a , which will be requested by neuron σout in a later step. in the meantime, neuron σ4 absorb spike b, one by one, from neuron σin2. in the next step, the spike b in neuron σ4 will absorb from the environment one spike a, and then both the spike a and spike b are requested by neuron σout together. in this way, through neuron σ3, the spikes a in σin1 move to σout at one time, and through neuron σ4, the spikes b in σin2 move to σout one by one, together with a spike a every time. when the last spike b in σin2 is requested by neuron σ4, and then moves to σout together with a spike a, all the spikes a and b in neurons σin1, σin2, σ3 and σ4 are exhausted. the computation halts, because there is no rule that can be used in the system. at this time, the spikes present in neuron σout are ax+yby (ax absorbed from neuron σ3 and (ab)y absorbed from neuron σ4). the number of spikes a from the output neuron is x + y, which means that the result computed by the system is x + y. table 1: spikes in each neuron of πadd at each step during the computation of the addition 5 + 2 = 7 step 1/in1 2/in2 3 4 5/out 0 a5 b2 λ λ λ 1 a5 b2 c1 c1 c1 2 λ b a5c1 bc1 c1 3 λ b a5c1 abc1 c1 4 λ b c1 c1 a6bc1 5 λ λ c1 bc1 a6bc1 6 λ λ c1 abc1 a6bc1 7 λ λ c1 c1 a7b2c1 with the explanation above, readers can check that, for given x,y > 0, system πadd can correctly compute the addition of x and y, which completes the proof. 2 as an example, let us consider the addition 5 + 2 = 7. table 1 reports the spikes contained in each neuron of πadd at each step during the computation. the input and output spikes are written in bold. 358 y. jiang, y. kong, c. zhu 3.2 an snq p system for subtraction we now describe an sn p system with communication on request πsub used as subtracter, which is shown in fig. 2. system πsub is composed of 6 neurons, where two specified neurons are used to introduce the minuend and subtrahend into the system, and one neuron is used for giving the obtained result. ax 1/in1 λ/q(a∞, 1) 3 by 2/in2 λ/q(b∞, 2) 4 λ/q(c2, env) a∗b∗c2/q(a ∞, 3)(b, 4) 5/out (ab)∗/q(ab, 5) 6 figure 2: the structure of subtracter πsub theorem 2. for two arbitrary natural numbers x and y, where x > y > 0, sn p system with communication on request πsub computes the subtraction of x and y. proof: we construct a system πsub of the form πsub = ({a,b,c2},σ1,σ2,σ3,σ4,σ5,σ6,{a,b},{a},{1, 2}, 5), where: σ1 = (a x,∅), σ2 = (b y,∅), σ3 = (λ,{λ/q(a∞, 1)}), σ4 = (λ,{λ/q(b∞, 2)}), σ5 = (λ,{λ/q(c2,env),a∗b∗c2/q(a∞, 3)(b, 4)}), σ6 = (λ,{(ab)∗/q(ab, 5)}). snq p system πsub functions as follows. in the initial configuration of πsub, all neurons are empty. the minuend x and the subtrahend y are encoded as spikes ax and by, and are provided to neuron σin1 and neuron σin2, respectively. since neurons σ3, σ4 are empty, the spikes a x will be absorbed by σ3, and the spikes by by σ4, respectively. in the meantime, neuron σ5 can use the rule λ/q(c2,env), and spike c2 arrives in it. with spike c2 inside, the neuron σ5 will absorb one spike a from neuron σ3 and one spike b from σ4. in the next step, this pair of spikes a and b will move to the neuron σ6, and spike c2 remains in the neuron σ5. so the absorbability of pairs of spikes a and b continues. when the spikes b in σ2 get exhausted, the last spike b will be absorbed by σ4, moves to σ5 together with one spike a from σ3, and this last pair of spikes of a and b moves to σ6 at last. at this time, there is no rule can be used in the system, so the computation halts. during the computation there are y pairs of a and b moves to σ6, the spikes b get exhausted in neuron σ4, and there are x − y spikes of a left in neuron σ3. at the end of computation, the number of spikes a present in the output neuron is x − y, which means that the result computed by the system is x−y. implementation of arithmetic operations by sn p systems with communication on request 359 table 2: spikes in each neuron of πsub at each step during the computation of the subtraction 5 − 2 = 3 step 1/in1 2/in2 3 4 5/out 6 0 a5 b2 λ λ λ λ 1 λ λ a5 b2 c2 λ 2 λ λ λ b a5bc2 λ 3 λ λ λ λ a4bc2 ab 4 λ λ λ λ a3c2 a2b2 with the explanation above, readers can check that, for given x > y > 0, system πsub can correctly compute the subtraction of x and y, which completes the proof. 2 as an example let us calculate 5−2 = 3. table 2 reports the spikes that occur in each neuron of πsub at each step during the computation. also, the input and output spikes are written in bold. 3.3 an snq p system for multiplication in this section, we present an sn p system with communication on request πmul, as shown in fig. 3 with 6 neurons, for implementing the multiplication of two arbitrary natural numbers. ax 1/in1 λ/q(c3, env) c3/q(a ∞, 1)(b, 5)(a∞, 4) 3 λ/q(c3, env) c3/q(a ∞, 3) 4 by 2/in2 λ/q(c3, env) c3/q(b, 2) 5 λ/q(c3, env) a∗b∗c3/q(a ∞, 3)(b, 3) 6/out figure 3: the structure of multiplier πmul theorem 3. for two arbitrary natural numbers x and y, where x,y > 0, sn p system with communication on request πmul computes the multiplication of x and y. proof: we construct a system πmul of the form πmul = ({a,b,c3},σ1,σ2,σ3,σ4,σ5,σ6,{a,b},{a},{1, 2}, 6), where: σ1 = (a x,∅), σ2 = (b y,∅), σ3 = (λ,{λ/q(c3,env),c3/q(a∞, 1)(b, 5)(a∞, 4)}), σ4 = (λ,{λ/q(c3,env),c3/q(a∞, 3)}), σ5 = (λ,{λ/q(c3,env),c3/q(b, 2)}), σ6 = (λ,{λ/q(c3,env),a∗b∗c3/q(a∞, 3)(b, 3)}). 360 y. jiang, y. kong, c. zhu snq p system πmul functions as follows. in the initial configuration of πmul, all neurons are empty. the multiplicand x and the multiplier y are encoded as spikes ax and by, and are provided to neuron σin1 and neuron σin2, respectively. since neurons σ3, σ4, σ5 and σout are empty, all of them can use the rule λ/q(c3,env), the spikes c3 arrive in them, and these neurons become active. with spike c3 inside, neuron σ5 can absorb from neuron σin2 one spike b. now spike b is present in σ5, so the rule c3/q(a∞, 1)(b, 5)(a∞, 4) in neuron σ3 can be used: all spikes a in neuron σin1 and the one spike b in neuron σ5 are requested by neuron σ3 (there is no spike a in neuron σ4, so no spike a requested). after this rule is used, neuron σin1 becomes empty, and spike c3 is left in neuron σ5, which means neuron σ5 can use again the rule c3/q(b, 2). in the next step, the spikes ax and b are requested by the neuron σout, and the spike c3 is left in neuron σ5. this is the first time the spikes ax arrive in the output neuron. in the meantime, with spike c3 present in neuron σ4 and σ5, neuron σ5 absorb the second spike b from neuron σin2, neuron σ4 absorb the spikes ax from neuron σ3, also with the present of spike c3, these spikes ax and b will be requested by neuron σ3 in the next step, and then moves to neuron σout, which is the second time the spikes ax arrive in the output neuron. table 3: spikes in each neuron of πmul at each step during the computation of the subtraction 5 × 2 = 10 step 1/in1 2/in2 3 4 5 6/out 0 a5 b2 λ λ λ λ 1 a5 b2 c3 c3 c3 c3 2 a5 b c3 c3 bc3 c3 3 λ b a5bc3 c3 c3 c3 4 λ λ c3 a5c3 bc3 a5bc3 5 λ λ a5bc3 c3 c3 a5bc3 6 λ λ c3 a5c3 c3 a10b2c3 with the above explanation, readers can check that, the spikes ax in neuron σin1 finally arrive y times at the output neuron, and at the end of the computation, the number of spikes a present in the output neuron is xy, which means that the result computed by the system is xy. so for given x,y > 0, system πmul can correctly compute the product of x and y, which completes the proof. 2 for example let us consider 5×2 = 10. table 3 reports the spikes that occur in each neuron of πmul at each step during the computation. also, the input and output spikes are written in bold. 4 conclusions and future work using the sn p systems with communication on request instead of the traditional sn p systems communicating on command, we have restudied the problem of considering sn p systems as components of an arithmetic logic unit. specifically speaking, we have proposed three sn p systems with communication on request to implement addition, subtraction and multiplication of two arbitrary natural numbers, respectively. in these systems, natural numbers are introduced into the system as the number of some spike in input neuron, while the result of an arithmetic operation is the number of a specified spike present in output neuron at the end of computation. implementation of arithmetic operations by sn p systems with communication on request 361 first of all, it is an urgent task to propose an snq p system to compute the division between two natural numbers, and this one is probably the most difficult to design. in this work, the adder, subtracter and multiplier contain 5 neurons, 6 neurons and 6 neurons, respectively. the number of neurons is less 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(2008); smaller universal spiking neural p systems, fundamenta informaticae, 87, 117–136, 2008. international journal of computers communications & control issn 1841-9836, 13(1), 129-142, february 2018. tracing public opinion propagation and emotional evolution based on public emergencies in social networks h. wei-dong, w. qian, c. jie huang wei-dong* nanjing university of posts and telecommunications nanjing, jiangsu, peoples r china *corresponding author:huangwd@njupt.edu.cn wang qian nanjing university of posts and telecommunications nanjing, jiangsu, peoples r china 18262621679@163.com cao jie nanjing university of information science and technology nanjing, jiangsu, peoples r china. cj@amss.ac.cn abstract: social network has become the main communication platform for public emergencies, and it has also made the public opinion influence spread more widely. how to effectively obtain public opinions from it to guide the healthy development of the society is an important issue that the government and other functional departments are concerned about. however, the interaction and evolution mechanism between the subject and the environment in the public opinion propagation is complicated, and the public and media attention and reaction to the incident are closely linked with the progress of the incident disposal. and public mining corpus has some shortcomings in the distribution of emotional classification. only the timely update of artificial rules and emotional dictionary resources, it can handle new text data well. in fact, from the perspective of public opinion propagation, this paper built the network matrix between internet users through the forwarding relationship, and used the social network analysis method and the emotion mining analysis technology to study the interaction and evolution mechanism between the subject and the environment in the public opinion propagation, and it studied the role of users in the emotional propagation of social networks. this paper proposed a sentiment analysis method on the micro-blog platform, which expanded the emotional dictionary and took sentence and emoticon and sentence patterns into account, which improved the accuracy of positive and negative classifications and emotional polarity analysis of the micro-blog. keywords: opinion mining, the public opinion propagation, semantic classes, machine learning. 1 introduction people gather on social networking sitesdue to the similar values and habits, and they become the realistic social digital projection. based on modern information dissemination of digital social network, on the one hand, information dissemination speed and efficiency of social network have been greatly enhanced; on the other, information sharing based on social relations provides an effective filtering mechanism to spread a lot of information on the internet. thus, social networking sites become the most important platform for information sharing and dissemination [3]. copyright ©2018 cc by-nc 130 h. wei-dong, w. qian, c. jie public opinion phenomenon on social network and its development laws are very rich and complicated. especially, the subject of social network is the people who are with highly intelligence and adaptive capacity, and its cognition and decision-making behavior is the adaptive process of continuous evolutionary learning, and they changed their behavior through interaction with other subjects as well as the environment [19]. this complex adaptive and learning mechanism is projected to digital social network, and its propagation mechanism has the dynamic characteristics of the time-varying dynamic evolution, complexity and adaptability in the subject created more profound complexity of the system [18]. therefore, it is necessary to explore an effective method to elaborate its run and propagation mechanism. and netizens are always generating massive amounts of data. with the characteristics of rapid growth, structural diversity [21], dynamic updating and wide ranges, these data contains public emotional information of all levels of society. mining the public emotional information makes sense for the research of information retrieval, electronic commerce and public opinion supervision. in recent years, scholars have carried out relevant research on the public opinion propagation mechanism. feixiong [16] studied the diffusion of micro-blog information based on forwarding mechanism and proposed the information diffusion model and verified the correctness of the model by micro-blog hot event. lee [5] applied density-based online clustering method to mine text flow to assess the influence of public opinion events to achieve the goal of situational awareness and risk management; zhao haiqing [22] analyzed the nature of the complex network of public opinion propagation, the study found that: the key node in the core position has a high degree of activity and participation, which should be highly valued by network public opinion monitors and leaders; su xiaoping [9] put forward a local central measurement method, and the measurement results show that in the social network, the importance evaluation of nodes depends on both the centrality of the nodes in the community and "bridging" nature between the community. at present, the researches on emotional analysis mainly focus on the emotion excavation and calculation of the information released by internet users in the internet [12], [13], [14], as well as the analysis the related factors of emotion, such as the stock market volatility the political election results and movie box office, which are predicted by internet emotions. riloff [8] proposed to construct a emotion dictionary based on a large number of corpus statistical analysis in order to classify emotion; wiebe [15] added part-of-speech analysis based on the emotion dictionary and completed the subjective and objective classification of corpus based on bootstrapping algorithm; xu lin-hong [17], based on primary school textbooks, screenplays, literary periodicals and fairy tales, annotated a large amount of chinese corpus, and formed a "emotional vocabulary ontology library". although it has been widely used in the field of emotion analysis of chinese texts, its ontology has some shortcomings in the distribution of corpus and genre only the timely update of artificial rules and emotional dictionary resources, it can handle new text data well. so, this paper extended the emotion dictionary and considered the impact of sentence patterns on sentence sentiment in order to achieve more accurate results. this paper attempted to focus on specific social hot spots and measured the network structure of the public opinion, mined the characteristics of the network structure, and researched the interaction and evolvement mechanism between the main body and the environment in the public opinion propagation. and it introduced the emotion analysis into the evolution of public opinion events, explored the reasons for the sharp fluctuations in emotion throughout the evolutionary life cycle of public opinion, and then provided the decision-making basis for the control of internet public opinion and the dynamic grasp of public opinion. tracing public opinion propagation and emotional evolution based on public emergencies in social networks 131 2 research on the propagation mechanism of public opinion in social network the emergence of social networks provides people with a new information media, but also greatly increases the possibility of the outbreak of public opinion. public opinion propagation on social networks is influenced by many factors. this paper mainly studied the three aspects of the subject elements of public opinion propagation in social networks, propagation networks and emotional tendencies. 2.1 the subject elements of public opinion propagation in social network netizens group is the main body of public opinion in the internet. they have also provided most of the impetus, and they are the main generating force and affected objects of network public opinion. representative of netizens are mainly divided into the following four groups: grassroots, net-a-porter, network opinion leaders and web hyper. grassroots: the grassroots is the most active basic crowd of netizens in china. when grassroots gathered together due to a topic or event that causes a resonance or mood fluctuations, the group leaders assert, repeat, and infect constantly to act on the individual in the way of implication and cross-infected, and they will turn to a common direction, and will immediately turn this concept or belief into action tendencies, so that emotions are greatly vented. net-a-porter: net-a-porter is the netizen who is engaged in forwarding and spreading network information. the existence of the net-a-porter makes network voices amplified infinitely, and the information gets fully widely spread and exchange [10]. network opinion leaders: the behaviors of network opinion leaders are very active and they have excellent ability of expression. for the majority of netizens, they have a strong influence and tractive effort, and the network has decided to focus on the focus and direction of opinion to a certain extent. web hyper: the web hyper is a network planner who popularizes enterprise products, customer brands, events and individuals with the help of the network medium. this purely commercial purpose makes the internet more complex, and they render it through the secondary reports of the traditional media, so that more netizens who are unknown truth are generally involved, and the topic or the event is very easy to be hype, and even become the network killers that hit businesses and slander the individuals [23]. 2.2 individual contact process of propagation because of the initiative and interaction of the individual participation, network propagation has special advantages in the aspects of information acquisition and propagation. individuals in the interpersonal communication are not always passive acceptance of information, and it is not like the real life of the traction of the mass media "agenda setting", and they also play multiple roles, such as publishers and communicators. because of the strong concern, in fact, some individuals have developed into similar network medias that have the extraordinary influence. the gap between the network propagation media and the network space is very weak, and they together constitute the diverse individuals in the network space. the interactive propagation among diverse individuals has become the main way to spread the information of network public opinion. in fact, the macro level of diffusion process of network public opinion information is the diverse individuals contact process through the micro level. specifically, in the emerging social media that represented by social network and micro-blog, emergency network public opinion information always released firstly by a few individuals in the network, and then they were 132 h. wei-dong, w. qian, c. jie forwarded or shared by other individuals in order to communicate one relatively stable group of receiving information. through a virtual social network, "infected group" scale of network public opinion has been expanded, and then they have the social power that cannot be ignored [1]. 2.3 the design of analytic framework to analyze the evolution of public opinion of the social network, it needs to analyze the characteristics of the whole network structure and the characteristics of the key nodes from the two levels. considering the whole development evolutionary characteristics of public opinion events, we need the evolutionary analysis of time series that is aimed at network event scale, density, average degree, clustering coefficient and so on [2], [4], and we focused on the evolutionary process of public opinion of rapid growth stage and the mature stage and calculated the network structure and the network properties of the important nodes in order to explore the evolution rules of public opinion of the whole network. to understand the emotional state held by users in the process of event information, the user’s emotions in the propagation network need to be judged. based on the above research, this paper first crawled and texted the specific events studied, and divided the text into clauses. in the second step, to segment the text, we needed to create a dictionary of special-purpose emotion words that belongs to the event, based on the existing definition of thesaurus, and marked the sentiment classification of the emotion words, polarity value and its intensity. the third step, combined with the sentence, expression and emotional words of this paper gave the emotional tendency of the text and emotional polarity values. 3 construction and analysis of public opinion propagation network in social network the development of public opinion events is always carried out in the dimension of time. after the formation, proliferation, explosion and termination of public opinion, the social network of public opinion presents the communication of user emotion, attitude, intention and view in turn. on the basis of the research on the overall development of public opinion event of the "shandong illegal vaccine", in this paper, the social network analysis method based on time series was used to segment the daily relational data in the process of public opinion social network and form the public opinion social network of daily data. then we analyzed the overall network structure, the evolution of network characteristics and the evolution of key nodes in these different stages. 3.1 data source collection and overall structure analysis on march 18, 2016, an article entitled "billions of yuan of vaccine without refrigeration flows into 18 provinces" caused widespread comments and forwarding from users. micro-blog users expressed their opinions, including celebrities, media, government and other users. the shandong illegal vaccine case caused public opinion to continue to pay attention. in this incident, what was the attitude of different types of users to events? what was the direction of public opinion? what role did the different users play? based on this, a study was conducted on the "illegal vaccine". in this paper, the "illegal vaccine" as the keywords for micro-blog search, we collected data from march 18, 2016 to april 4, 2016, and used web crawler software for micro-blog data crawl, and obtained the data through the preparation of crawling rules, a total of 10 035 data collection. data were cleaned and pre-processed by excel. duplicate data, garbled data, incomplete data, and data without records were deleted. after cleaning, 8,222 data were obtained, and we tracing public opinion propagation and emotional evolution based on public emergencies in social networks 133 figure 1: the forwarding network visualization teased out the micro-blog’s forwarding relationship through sina micro-blog’s forwarding rules: if a forwards b, the value of a <-> b is 1; if the two are not forwarded, the value is 0; and the main diagonal value of the matrix is defined as 1. we constructed the public opinion network matrix of "illegal vaccine in shandong". based on the constructed matrix, visualization software [20] was used to process the forwarding network of the event, and the visualization result was shown in figure 1. 3.2 whole characteristics evolution analysis to analyze the evolution of the overall characteristics of the selected cases, the index of public opinion network of daily data of the event needed to be analyzed, according to the trend and regularity of daily public opinion network, and the law of the overall network topological index with event changes was explored. (1) the evolution of network density the network density is used to describe the close degree of node interaction. the greater the density of the whole network is, the more frequent the interaction between nodes is. the blue curve in fig.2 showed the close degree of interaction of each user daily posting and replies in the public opinion event, and the green curve indicated the change of delta value of each variable in two days. it can be found from the curve trend: from march 19 to the end of march, network density had remained flat changing trend. but in april, this topic again appeared fluctuations. (2) the evolution of network average degree the blue curve in figure 3 showed daily users’ network average degree of the public opinion event, and the green curve indicated the change of delta value of each variable in two days. from the point of curve trend, the development trend of the network average degree was fluctuating. from march 19 to the end of march, the public opinion event was gradually come to an end from the outbreak of public opinion, the value of network average degree appeared larger fluctuation, especially in the period of hot topic. the average degree of the whole network became larger and each node had more surrounding nodes for forwarding the interaction, and the communication was more extensive. namely between march 22-24, the network average degree was higher, it indicated that during this period, 134 h. wei-dong, w. qian, c. jie figure 2: the diagram of network density evolution figure 3: the diagram of the evolution of network average degree figure 4: the diagram of the evolution of network clustering coefficient tracing public opinion propagation and emotional evolution based on public emergencies in social networks 135 many users on the network were repeatedly posting and replying, it may be for a topic or a post and caused a lot of participants to discuss, we can judge, there may be potential opinion leaders. (3) the evolution of network clustering coefficient if a node j in the network has nj neighbor nodes, then there may be a maximum of nj(nj− 1)/2 edges between the nj nodes. we define the ratio of actual number of edges e(j) between nodes nj and the possible number of edges nj(nj − 1)/2 as the clustering coefficient of nodes cc(j), that is: cc(j) = 2e(j)/[nj(nj − 1)] the clustering coefficient cc of the whole network is the average of the clustering coefficients of all nodesj: cc = n∑ j=1 cc(j) n network clustering coefficient describes the degree of network collectivization [6], that is, it is used to identify whether there is a central tendency in the network. whole network clustering coefficient is bigger, the network of centralized trend is higher, and the users of public opinion tend to the interaction with small groups [7]. the blue curve in fig.4 showed the daily change of clustering coefficient of the network public opinion, and the green curve indicated the change of delta value of each variable in two days. from the point of curve trend, after the outbreak of public opinion from march 19, it was the first time to achieve the highest value of network clustering coefficient on march 20, and then it entered into a state of smooth fluctuation and maintained between [0.6, 1]. after march 27, the development of public opinion was into a smooth state, the number of people involved in public opinion and the interaction frequency dropped. in the small batch of node communication, the network clustering coefficient was the same as the network density, which will show a large fluctuation phenomenon. 3.3 key nodes characteristics evolution analysis in the research of the key nodes in the social public opinion network, scholars often used the static structure characteristics of social network, such as the degree centrality and the structural holes, to analyze the differences in grades and advantages between the key nodes and other nodes in the network. therefore, based on the public opinion event of "shandong illegal vaccine",combined with the life cycle theory, we analyzed the characteristics of key nodes in different stages, so as to study the characteristics evolution of network public opinion. according to the event of "shandong illegal vaccine", we focused on the characteristics of the key nodes in the rapid growth stage and the mature stage. statistics showed that the number of people involved existed obvious periodic: after the outbreak of public opinion from march 19, it reached the climax of the public opinion firstly on march 22, and then the growth rate of public opinion had been reduced. it reached the biggest outbreak of the whole public opinion event on march 24, and then it began to subside slowly. based on this, we set the stage of rapid growth from march 19th to march 22nd, the mature stage, from march 23rd to march 24th. degree centrality it can be seen from the diagram of the propagation network, the propagation of public opinion presented divergent trend around the individual points, in the propagation network of public opinion, and it showed that there was a strong interaction space around some topics or some 136 h. wei-dong, w. qian, c. jie users. through measuring degree centrality of this nodes, it can be seen that "the concept of tide", "friendly 88", "swineherd is officer","bing-lv" and other users’ centrality degrees are bigger, and their participation was relatively high, and it showed some characteristics of opinion leaders. in the two stages of the topic development, different nodes played an important role in the network. in other words, the node was only active before a certain stage, which was not coherent. therefore, the evolution of a single node cannot be analyzed. therefore, this section focused on the study of the network structure changes in different stages and the performance of nodes in this stage. structural holes structural hole refers to a non-repeating relationship between two nodes, which is equivalent to a buffer in a network.the value of the contribution to the network of the two related people who exist structural holes between nodes can be accumulated. by using ucinet software, we analyzed structural holes in public opinion propagation network of "shandong illegal vaccine". it can be seen from the table 1, the public opinion propagation network of "shandong illegal vaccine" existeda large number of structural holes. we sorted out the effective scale, and "golden orchard fruit", "qfy180" and "black feeling" ranked the top three. the value of effective scale reflected the position of nodes in the propagation network, and the bigger the value is, the more core the position becomes. the global constrain of these three nodes was relatively small, they were less than 0.2, and it also reflected that the three nodes were not easy to be controlled by other nodes and easier to access and spread public opinion. as can be seen from the results of the structural hole in the mature stage, there were still a large number of structural holes in the network of public opinion of "shandong illegal vaccine". we sorted out the effective scale, and "happy stock886644", "the lord of peach blossom island-6" and "dawn is now tomorrow" ranked the top three. by comparing with the rapid growth stage, it reflected the similar characteristics of the network in different stages of the topic. table 1: the structural holes of rapid growth stage node degree constrain density golden orchard fruit 25.000 0.178 0.850 qfy180 25.000 0.178 0.850 black feeling 37.000 0.117 0.857 open the treasure to eat 31.000 0.131 0.677 friendly 88 36.000 0.122 0.943 i don’t know at all 22.000 0.184 1.390 haohuanlea 11.000 0.350 0.764 pour out words 37.000 0.117 0.857 the analysis showed that in the evolution of network density and network clustering coefficient, the events of rapid growth and maturity showed a relatively steady state, and the trend of its delta curve was basically the same as the original curve. by comparing the network attributes of rapid growth stage and mature stage, the density of networks in mature stage was larger, which indicated that in the mature stage, the overall participation and interaction of network users were more frequent, the propagation distance was shorter and the communication ability was the strongest. in the two stages of topic development, different nodes played an important role in the network. nodes were more active before a certain stage. such activities are not coherent. therefore, it was harder to dismiss the opinion leaders as the whole process of the incident, it needed to identify potential opinion leaders at a finer level of granularity and tracing public opinion propagation and emotional evolution based on public emergencies in social networks 137 table 2: the structural holes of mature stage node degree constrain density happy stock 886644 111.000 0.039 1.175 the lord of peach blossom island_6 21.000 0.217 0.543 dawn is now tomorrow 102.000 0.042 1.351 love the dog 100.000 0.043 1.404 beijing united hospital of china and america 28.000 0.158 0.860 put quality before quantity 40.000 0.111 0.801 love wolf 11.000 0.358 0.655 ren398540882 107.000 0.040 1.256 conducted early warning of incident developments and public opinion regulation. 4 the incident text emotional calculation as the most popular social network platform, micro-blog users express their opinions and feelings through words and expressions. these messages obviously include emotional information. these sections will statistics emotions and intensity in the whole text from three aspects: emoticons, emotion words and sentence patterns. we analyzed the data of "shandong illegal vaccine" from march 18, 2016 to march 28, 2016. 4.1 emotional dictionary construction however, due to the colloquy of micro-blog, a large number of emoticons and related terms for specific events, we constructed an event-specific emotion dictionary. on the basis of comprehensive comparison of existing emotion dictionary, this paper referred to dalian polytechnic chinese emotional vocabulary ontology database, which divides the words into two categories: positive emotion ("happy", "good") and negative emotion ("angry", "sad", "evil", "fear", "shock"). use the word segmentation software to segment the collected text, according to the word frequency from high to low, and then we manually added words that did not come from dalian polytechnic dictionary, but also gave emotional classification and emotional strength to these words. the words included not only adjectives, nouns, but also modal word. part of the expansion of the emotional dictionary is shown. table 3: expansion of emotional dictionary (part) words emotional classification emotional strength emotional tendency bully evil 5 negative affording general satisfaction good 9 positive bend the law for the benefit of relatives or friends evil 7 negative beyond description angry 7 negative get out angry 5 negative barbarian evil 5 negative in order to reduce the influence ofthe collocation of positive emotion words or negative emo138 h. wei-dong, w. qian, c. jie tion words and negative words, in general, the emotional tendency of positive emotion and negative word tends to be negative emotion, and the emotional tendency of negative emotion and negative word tends to be weak emotion or not emotions [11]. therefore, we first determined whether there are negative words adjacent to or similar to the emotional words, in this paper, negative emotions that are negative words modified by negative words, positive emotion words modified by negative words are regarded as negative emotions, negative emotion words modified by negative words are considered as neutral emotions. the negative words include "no", "not", "none", "never", and so on. emoticons are also commonly used to express emotions in micro-blog. this paper still classified emoticons appearing in texts based on the seven emotions categories in dalian polysemous emotional dictionary, and the corresponding relationship between emotion classification and expressions is shown in the table 4. table 4: the corresponding table of emotions and emotional categories (part) emoticonal text emotional classification polarity value emotional tendency [too happy] happy 7 1 [haha] happy 9 1 [humph] angry 3 2 [angry] angry 7 2 [retched] sad 3 2 [disappointed] sad 7 2 [surprised] shock 7 2 [smiles] happy 5 1 4.2 micro-blog emotional polarity calculation the polarity value of each microblog is determined by the emotion word, the sentence pattern and the emoticons in the microblog text. specific model algorithm steps are as follows: (1) for each word in the established emotional dictionary, calculate its tf-idf value with all the text as the total corpus, and mark it as then the emotional polarity of the ith word in this event is:pwi = fi∗pwi; (2) the emotional polarity value of each sentence is determined by the emotion words, emoticons and sentence pattern the process of calculating the emotional polarity of a sentence is to calculate the sum of the emotional polarity values of all the emotional words and emoticons in the sentence firstly, and then adding the sentence rules to determine the polarity of the entire sentence. let p(si) be the emotional polarity of the sentence before considering the sentence pattern, that is: p(si) = ∑ se + ∑ p(w), where ∑ se is the sum of the emotional intensities of all the emoticons in the sentence, and ∑ p(w) is the sum of all the emotion words in the sentence. (3) let p ′(si) be the sentence emotion value after considering the sentence pattern characteristics, and table 5 is the sentence emotional polarity weighted rule of six sentence patterns. (4) the emotional polarity value of a single micro-blog sij is the sum of the emotional polarity values of each sentence, that is, p(sij) = j∑ k=i p ′(sk) tracing public opinion propagation and emotional evolution based on public emergencies in social networks 139 table 5: special sentence calculation instructions sentence pattern p ′(si) rhetorical questions p ′(si) ∗ (−0.6) + (−0.05) interrogative sentence p ′(si) ∗ (−0.2) + (−0.05) exclamation sentence p ′(si) ∗ (1.5) assumptions sentence p ′(si) ∗ (−0.2) declarative sentence p(si) ironically −p(si) 4.3 statistical results emotional calculation was carried out on the collected micro-blog texts of this event, and the emotion word, the sentence pattern and the emoticons were taken into account in the calculation. emotional values of the event micro-blog texts were calculated through a calculation rule of a single micro-blog text. statistics on the intensity of micro-blog texts for a single micro-blog text as a unit were conducted. table 6: positive and negative polarity text statistics negative texts number positive texts number 6434(85%) 1113(14.7%) as can be seen from table 6, negative micro-blog texts occupied most of share, about 0.3% of the texts did not have emotional values, and 85% of the texts were negative texts. negative texts were very unfavorable to the development of the event and the subject of the event, so it was especially important to pay attention to the negative micro-blog in the event. this paper drew the negative polarity intensity figure according to the time (figure 5). combined with the release of time-critical information of vaccine events, the dynamic process of emotion trend can be initially determined. the highest value of first-pass negative emotional polarity was reported on march 18 after the outbreak of illegal vaccine, culminating on the 19th. the second round of the highest negative emotions appeared at 23rd-24th, which was due to the spread of emotions caused by the widespread spread of "the sorrow of vaccine". figure 5: emotional polarity of shandong illegal vaccine the public had a strong emotional response as the incident had many risk characteristics such as affecting children and influencing the population. state food and drug administration, the state council, the national health and family planning commission quickly intervened, 140 h. wei-dong, w. qian, c. jie and issued and responded several times, and communicated with the public. from the abovementioned incident, the public opinion and emotion tendency of internet public opinion showed that the public and the media paid close attention to the incident and responded to the progress of the incident handling. the key nodes of incident handling coincided with the peaks of the network public opinion. it was suggested that government departments plaid an important role in the handling of incidents and the release of key information. timely release of authoritative and transparent objective information was conducive to dredge network public opinion, and it can reduce the negative influence brought by internet public opinion. in this incident, the government sector will definitely not make a clear statement of its impatience and will effectively cut the responsibility so that public emotions will be released and it will be easy to establish and restore trust. 5 conclusion based on the theory of network public opinion evolution, this paper took the example analysis of microblog public opinion events, and used the ways of the data crawler, segmentation technology, the construction of emotional dictionary and annotation patterns to get the emotional tendency and emotional polarity of events then, we explored the causes of the peaks and emotional evolutions that had occurred throughout the evolutionary life cycle of public opinion. the analysis of vaccine events showed that risk perception of the public and society in the media environment tended to deviate easily, and it was difficult to form a self-correcting mechanism. to standardize the processing of media information and to monitor and guide public sentiment is the key to risk communication in crisis and it is a need to speed up the formulation and construction of a system of rules governing the expression of public opinion. the investigation of government and relevant departments’ intervened investigation and its result has played an important role in the development of public opinion. the timely announcement of the government and the factual release of the truth in real time were conducive to resolving the contradictions and quelling the public opinion. in the information era, the government and relevant departments should not expect to reduce the spread by covering up with negative news and openly and fairly dealing with them according to law, which is the basic requirement of a society ruled by law. although this paper is a single case study of recent typical risk events, the illegal vaccine event is a microcosm of the outbreak of public opinion in recent years. lack of rigorous media information and the extensive emotional evolution in the network have a direct impact on risk perception. the changes of network structure and emotional polarity exhibited during the amplification of the risk of illegal vaccines have empirical value in coping with other similar social focus events. acknowledgment work described in this paper was funded by the national natural science foundation of china under grant no. 71671093 and the national natural social foundation of china under grant no. 16zda054. the authors would like to thank other researchers at nanjing university of posts and telecommunications. bibliography [1] chen y.j. 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(2017); effect of tourism building investments on tourist revenues in china: a spatial panel econometric analysis, emerging markets finance and trade, 53(9), 1973-1987, 2017. int j comput commun, issn 1841-9836 8(2):334-345, april, 2013. disaster response project scheduling problem: a resolution method based on a game-theoretical model o.c. vásquez, j.m. sepúlveda, m.d. alfaro, l. valenzuela-osorio óscar c. vásquez 1. department of industrial engineering, university of santiago of chile 3769 ecuador avenue. santiago, chile. 2. computer science laboratory (lip6), pierre and marie curie university 4, place jussieu, paris, france. e-mail: oscar.vasquez@usach.cl juan m. sepúlveda, miguel d. alfaro department of industrial engineering, university of santiago of chile 3769 ecuador avenue, santiago, chile. e-mail: juan.sepulveda@usach.cl, miguel.alfaro@usach.cl luis osorio-valenzuela department of electrical engineering, university of santiago of chile 3519, ecuador avenue, santiago, chile. e-mail: luis.osoriov@usach.cl abstract: we present a particular disaster response project scheduling problem (drpsp) motivated by fukushima’s nuclear accident of japan in 2011. we describe the problem as mps; r, n|prec, dn| ∑ ckf(rk(s)) by using hartmann and briskorn scheme and formulate a mixed integer linear programming (milp) model. due to the np-hardness of the problem, we propose a resolution method based on game theory. this method associates the drpsp to a non-cooperative game model, such that game solution is a feasible solution of the problem. in order to explore the potential of the proposed model and the performance of the resolution method, computational experiments are carried out. the results of resolution method show on average, that the method finds a feasible solution with a difference of 15.44% with respect to optimal solution within one percent of the time required by the milp over gams 22.7.2/cplex 11.0. keywords: disaster response, project scheduling problem, resolution method, gametheoretical model. 1 introduction worldwide, disasters still continue to cause human casualties, damage and destruction of infrastructure, instability in the economy and the order of the community. nowadays, the problems of disaster operation management (dom) are considered an important factor into policies of national security [1]; thus, it is relevant to understand how one can face them effectively and efficiently. in particular, there is increasing recognition of the need of applying of operations research (or) methods in dom, because it develops a scientific approach to help decision making before, during and after a disaster [2]. indeed, dom problems are considered as the opportunity to re-visit and re-define or, being a likely and worthwhile growing area in the next 50 years [3]. dom problems arise in four phases: mitigation, preparedness, response and recovery [4]. an issue of particular interest in the disaster response phase is the project scheduling problem, because it addresses a double dilemma: on the one hand, the urgent need for project completion in the minimum possible time in order to prevent casualties and to reduce the impact on the social, economic, and political order; on the other hand, the scarcity in the number of resources with limited availability to carry it out. some recent works of this area developed in the literature copyright c⃝ 2006-2013 by ccc publications disaster response project scheduling problem: a resolution method based on a game-theoretical model 335 have been proposed by de angelis et al. [5], fukura et al. [6], yan and shih [7], yan et al. [7], zhang et al. [9], córdova and yanine [10]. in general, a disaster response project scheduling problem (drpsp) is considered as a complex variation of the multi-mode resource-constrained project scheduling problem (mrcpsp), simultaneously addressing both scheduling and assignment [11]. in this work, we study a particular drpsp motivated by fukushima’s nuclear accident of japan in 2011, where the shortest completion time was needed in order to avoid fatalities due to radiation exposition, besides of minimizing the number of rescue workers exposed to radiation. the paper is broken down as follows: in section two, the statement of the problem is exposed; in section three, a mixed integer linear programming (milp) model is formulated; in section four, the resolution method is presented based on a game-theoretical model for the problem; in section five, the performance analysis of resolution method is made by computational experiments; and finally, in section six, the conclusions and directions for further research are given. 2 statement of the problem our problem concerns a project scheduling with a completion time equal to the critical path length in order to prevent loss of lives and to reduce the impact on the social, economic, and political order. the processing time of activities is a function of the allocated resources, which have an available workload as stipulated by a technical constraint or some other condition. the goal is to determine a schedule of the project, where the number of resources is minimized. formally, the problem can be understood as a resource-constrained project scheduling problem (rcpsp) and denoted as mps; r, n|prec, dn| ∑ ckf(rk(s)) by using hartmann and briskorn scheme [12]. mps; r, n denotes the case of multi-mode problem with doubly constrained resources [13], which means that the “consumed resource” will be at most to its available workload and the “used resource ” will be one or zero in each time unit. prec and dn denote precedence relations among the activities and the project deadline constraint, respectively. ∑ ckf(rk(s)) denotes the problem objective as cost minimization of the resources in order to meet the project deadline, where ck is the cost per unit of resource k and f(rk(s)) is a function of the consumption rk of resource k in scheduling s. note that minimization of resource costs can be understood as a general goal, since it may consider others objectives such as the the minimization of the resources or the minimization of any non-monetary characteristic associated to them (e.g., utilized energy, generated pollution, etc.). our problem is closely related to the multi-mode double resource-constrained time/cost tradeoffs problem in the deadline version [14]. however, our problem adds a new set of constraints on each double-constrained resource due to the use of dedicated resources [15]. in next section, we formulate a milp model for the drpsp described above. 3 milp model the milp model is defined as follows. a disaster response project processes a set of n activities with a corresponding precedence directed acyclic graph g(v, a), where v is the set of nodes, and a ⊂ v ×v is the set of immediate precedence constraints on the activities. it is noteworthy that g also includes two dummy “start" and “end" nodes, indexed by 0 and n+1. each identical resource k ∈ r = {1, ..., m} has an available workload w , a monetary or non-monetary cost rate ck = c; and it can only perform one activity at a time. without loss of generality, we adopt c = 1 and define f(rk(s)) equal to 1 if the resource k is used and 0 otherwise. each project activity 336 o.c. vásquez, j.m. sepúlveda, m.d. alfaro, l. valenzuela-osorio i ∈ v has a processing time pi, without pre-emption. by definition p0 and pn+1 are zero. the objective is to find a scheduling s of the project, which minimizes the resources and finishes it in a given completion time equal to the critical path length d, thus preventing casualties and reducing the social, economic, and political impacts. decision variables: xk = { 1 if resource k is used 0 otherwise yik = { 1 if activity i is processed by resource k 0 otherwise uij = { 1 if activity i can be processed before activity j ̸= i, by the same resource 0 otherwise cti = completion time of activity i objective function: min m∑ k=1 xk (1) subject to: cti ≤ ctj −pj ∀(i, j) ∈ a (2) ct0 = 0 (3) ctn+1 = d (4) n∑ i=1 yikpi ≤ wxk ∀k (5) m∑ k=1 yik = 1 ∀i (6) [(ctj −pj)−cti] + ε ≤ n(uij) ∀i, j ̸= i, (i, j), (j, i) /∈ a (7) [cti − (ctj −pj)] ≤ n(1−uij) ∀i, j ̸= i, (i, j), (j, i) /∈ a, (8) yik + yjk ≤ 1 + (uij + uji) ∀i, j < i,∀k (9) where ε is a small number and n is a large number. the objective function (1) minimizes the number of resources used for the project. constraint (2) defines the finish-start type of precedence relations between the activities. thus, if for starting activity j it is required that activity i have been completed, then the processing time of activity j minus the completion time of activity j should be greater than or equal to the completion time of activity i. constraints (3) and (4) allow the fictitious activities of the project to be incorporated so that they generate the scheduling of the real activities. thus, constraint (3) determines the project start time, while constraint (4) imposes a duration deadline for the project. constraint (5) indicates that if a disaster response project scheduling problem: a resolution method based on a game-theoretical model 337 machine is used, then the machine usage should be less than or equal to its workload available. constraint (6) establishes that an activity can be processed by only one machine. constraints (7), (8) and (9) are of the on-off type and ensure that if a resource is processing a given activity, then it does not process another one at the same time. in the case of constraint (7), an ε is incorporated that allows approaching the case in which an activity completion time is equal to the start time of another one. it is important to point out that for all uij it is true that uij + uji ≤ 1. this inequality avoids duplicity in the processing of the real activities, because if activities i and j can only be processed by machine k, they can be only processed in one sequence, i.e. i and j or j and i. it is easy to see that the existence conditions for a feasible solution are: the available workload of resources w is greater than or equal to the maximum processing time of activities and the number of resources available m is equal to the number of activities n. in what follows, we assume these feasibility conditions. 4 resolution method the development of a resolution method is based on the computational complexity of the problem. in fact, our problem is np-hard by using a trivial reduction from the multi-mode resource-constrained discrete time-cost tradeoff problem [16]. unfortunately, this np-hard property implies that even modest instances of the problem become intractable in practice, which is specially expensive in disaster situations, where the decisions must be made quickly for preventing casualties and reducing the social, economic, and political impacts. our resolution method is based on a game-theoretical model. this method associates the proposed drpsp to a non-cooperative game model, such that game solution is a feasible solution of drpsp. in the game, the activity or a set of them are the players with a utility, which minimizes the remaining workload of the resource where it is performed; and the strategies are to associate or not with other player in order to execute them in a specified schedule on the same resource. in order to guarantee the precedence and workload constraints, a price mechanism is defined. the solution concept will be a nash equilibrium in pure strategies (pne), which always exists by definition of the game. the resolution method generates new players, that is, a new set of activities from the pne. these new players are integrated to other new game. this process is repeated until new players can not be generated. the description of the game, the algorithms and convergence of the method are presented next. 4.1 a game-theoretical model. players players are activities or a set of them. each player is characterized by α/ω, where α = {1, 2, 3} corresponds to the "type of player" and ω is the set of activities. by definition a player type α = 1 represents a project activity (e.g., activity 1 is defined by 1/{1}). players of type 2 and 3 are formed by two players, a “predecessor" of features α′/ω′ and “successor" of features α′′/ω′′; and a set of activities ω = ω′ ∪∗ ω′′, where ∪∗ is the ordered union, i.e., if ω′ = {i, .., l} and ω′′ = {j, ..., s}, then ω = ω′ ∪∗ ω′′ = {i, .., l, j, ..., s}. both players type 1 and type 2 are differentiated by the time between the completion time of all activities of the “predecessor" player and the start time of all activities of the “successor" player. figure 1 illustrates the difference between a player of type 2 and type 3. in the case of type 2 player, it shows that the time of completion of all activities of the predecessor player with 1/{1} is equal to the start time of 338 o.c. vásquez, j.m. sepúlveda, m.d. alfaro, l. valenzuela-osorio figure 1: difference between players of type 2 and 3. all activities of the successor player with 1/{5}. whereas in the case of the player type 3, it has a time equal to ∆t > 0 between each activity. notice that the players of type 1 and 2 are determined only by the completion time of all activities of the predecessor player and the start time of all the activities of the successor player. therefore, a player of type 2 could have ∆t > 0 between some of its activities, because the predecessor and/or successor could be a player of type 3. figure 2 shows an example of the situation. figure 2: player of type 2 conformed by a player of type 3. strategies consider a game with m players. each player i ∈ {1, ..., m} will have a set of strategies si = {1, ..., i − 1, i, i + 1, ...m, s}. the strategy si = {1, ..., i − 1, i + 1, ...m} means that the player i want to be after player si in a schedule on a resource, shortly we say that the player i "buys" to player si. the strategy si = i indicates that player i does not want to bind with any player in a schedule on a resource, shortly we say that the player i does not want to "negotiate" with any player. the strategy si = s means that player i is available to be before any player in a schedule on a resource, shortly we say that the player is "placed on sale". if the player i decides to "buy"‘, then a "buying price" equal to maximum time to start all the activities of player i is calculated. on the other hand, if the player i decides to "sell", a "selling price" equal to the time of completion of all activities of the player i is calculated. for the calculation of prices, the free slack (fsi) and the earliest start time (esi) of each activity i is needed, being easily obtainable through the critical path method (cpm). table 1 presents the definition of the buying and selling price for each of type of players. player bp (buying price) sp (selling price) 1/{i} esi + fsi esi + pi 2/{ω = ω′ ∪∗ ω′′} esi + min(bpα′/ω′ −esi, bpα′′/ω′′ −spα′/ω′) spα′/ω′ + (spα′′/ω′′ −esj) 3/{ω = ω′ ∪∗ ω′′} esi + min(bpα′/ω′ −esi, bpα′′/ω′′ −esj) spα′′/ω′′ for the player type 2 and 3, i and j are the index of the first activity in set ω′ and ω′′, respectively. table 1: prices definition for types of players. note that if the “buying price" of player a is greater than or equal to the “selling price" of player b, then the set of activities of player a can start after the set of activities of player b. in practice, these prices allow to bind a set of activities, respecting the precedence constraints and disaster response project scheduling problem: a resolution method based on a game-theoretical model 339 deadline of the project. figure 3 presents a illustration of the buying and selling price for each of type of players. figure 3: price illustration of types of players. payoffs the payoff of each player i ∈ {1, ..., m} depends on his strategy and the strategies of other players. the notation is ri(si, s−i) where si ∈ si and s−i ∈ s−i = ⊗ j ̸=i sj. the definition of payoff for player i of feature α/ω is presented in (10) ri(si, s−i) =   ∑ w∈ω pw if ∀si = i, s−i ∈ s−i∑ w∈ω ∪∗ ω′ pw if ∑w∈ω ∪∗ ω′ pw ≤ w, ω∩ω′ = ∅ and a)si = j, sj = s, bpi ≥ spj,∀sk ̸= j(k ̸= i, j) or b)si = s, sj = i, bpj ≥ spi,∀sk ̸= i(k ̸= i, j) where ω′ is the set of activities of the player j −∞ otherwise (10) note that the payoff will be different to minus infinity if and only if the precedence constraints, critical path time as deadline project and the available workload of the resource are satisfied. 340 o.c. vásquez, j.m. sepúlveda, m.d. alfaro, l. valenzuela-osorio in fact, the relation of prices guarantee the precedence and deadline constraints; the relation of the sum of processing time guarantees satisfaction of workload availability of resources, and the relations between the activity set of player avoids the duplicity of activities. in the case si = i, the payoff value will always be different to minus infinity by definition. solution concept in this game, the interaction among players is modeled as non-cooperative and the solution concept is a nash equilibrium in pure strategies (pne). the set of pne of any game is always nonempty, because the strategy profile where all players use the strategy "to negotiate" with no player is a pne by definition. thus, any defined game has at least a pne. 4.2 algorithms we define three algorithms, called algo 1, 2 and 3 respectively, which define our resolution method. algo 1 finds a pure strategies profile for any game. here, we prove that this strategies profile is always a pne. algo 2 generates new players as a new set of activities from pne generated by algo 1. finally, algo 3 integrates algo 1 and algo 2 in a cyclic process, until new players cannot be generated. in practice, the final solution yielded by algo 3 will be the solution of the method. we assume a game with m players and consider a matrix γm×m . let γij be the payoff of player i and player j. the case γij, i ̸= j means that the strategy of row player i is to buy to column player j, the strategy of player j is placed on sale, and the strategies of all player k ̸= i ̸= j is not to buy to player j. note that by definition, both the payoff of player i and the player j are equal. the case of γii means the payoff of player i if his strategy is not “to negotiate" with any player. this matrix γ has two properties: a) it contains all payoffs of each player that could be different to −∞ by definition; and b) at least, γii for all i is different to −∞ by definition. now, we define the algo 1 to find a strategies profile of the game. algorithm 2 algo 1 data: instance data of problem result: strategies profile step 0: generate matrix γ step 1: select the maximum value of matrix γ. step 2: a) assign the strategies of row and columns players of maximum value. b) fix the strategies to component players of the row-column players found. step 3: redefine γ such that a) delete the row and column equal to index row-column players found. b) delete the row and column equal to index of component players of row-column players found. c) delete the row and column equal to index of player, whose component players are some row-column players found. step 4: if γ ̸= ∅ then return step 1 end if step 5: generate the strategies profile. theorem 1. algo 1 finds a pne for the proposed game. disaster response project scheduling problem: a resolution method based on a game-theoretical model 341 proof: we consider a game with m players and a strategies profile found by algo 1. without loss of generality, we assume that 2l players have a one-to-one correspondence between buy-sale strategies and m − 2l players are not no correspondence. let {sin = jn, sjn = s}ln=1 be the notation for a correspondence between the "buyer" player in and the on "sale" player jn. let {skn = kn} m n=2l+1 be the notation for the case with no correspondence. in addition, we define the strategy index p ∈{1, 2...m}. we show that the strategies profile found by algo 1 is pne by definition, i.e., a unilateral deviation in strategy by any single player is not profitable for that player. formally, we prove: 1. rin(sin = jn, s−in) ≥ rin(sin = in, s−in) ∀in 2. rin(sin = jn, s−in) ≥ rin(sin = s, s−in) ∀in 3. rin(sin = jn, s−in) ≥ rin(sin = p, s−in),∀p ̸= jn, in, s ∀in 4. rjn(sjn = s, s−jn) ≥ rjn(sjn = jn, s−jn) ∀jn 5. rjn(sjn = s, s−jn) ≥ rjn(sjn = p, s−jn),∀p ̸= jn, s ∀jn 6. rkn(skn = kn, s−kn) ≥ rkn(skn = s, s−kn) ∀kn 7. rkn(skn = kn, s−kn) ≥ rkn(skn = p, s−kn),∀p ∈{1, ..., m}\kn ∀kn for the conditions 1 and 4, we have a proof by a contradiction. without loss of generality, suppose that the condition 1 and 4 are false for some players pair in and jn with correspondence between buy-sale strategies and that algo 1 selected this correspondence in the step k. now, we note that algo 1 in the step k fixed the "buy" and "on sale" strategies for the players, when they obtain a payoff value greater than the obtained payoff value for them with the "alone" strategy. therefore, we have a contradiction with the assumption case. for conditions 2, 3, 5, 6 and 7, we have a proof by the definition of payoff. in fact, for the case 2 given that the “buyer" player p ̸= in with sp = in does not exist, then rin(sin = s, s−in) is equal to −∞. for the case 6, a similar argument is hold. for the case 3, if the player p ̸= jn, in, s is “on sale", it is bought by another player, then rin(sin = p, s−in) is equal to −∞; else the player p is not "on sale", then the player p is a “buyer" player or a "alone" player, therefore rin(sin = p, s−in) is equal to −∞. for the case 5 and 7, we have a similar argument. 2 the input of algo 2 considers a pne of the game. this pne can have one-to-one correspondences between buy-sale strategies of players. if there are one-to-one correspondences, then a new player is generated by each of them. these new players will have the above definition of prices, considering the "buyer" and "on sale" players. algorithm 3 algo 2 data: pne of game result: new players step 0: while one-to-one correspondences exists in pne do a) generate a new player and compute sale and buy prices, considering the “buyer" and “on sale" players. b) extract the players with one-to-one correspondences of pne. end while finally, we present the algo 3. we consider a sequence of games sg. let pnek be the nash equilibrium of the game k ∈ sg. let npk the new players generated in the game in the position k of sequence sg, shortly game k. algo 3 generates a new game k + 1, integrating each player of npk into the game k + 1, if and only if this player is not in the game k. the game k will be the last game of sg if and only if no new player is integrated. 342 o.c. vásquez, j.m. sepúlveda, m.d. alfaro, l. valenzuela-osorio algorithm 4 algo 3 data: instance data of problem result: solution of instance step 0: call to algo 1. step 1: call to algo 2. step 2: if new players generated are not in set of players k then incorporate the new players generated to game. k := k + 1 return step 0 end if step 3: generate the solution from pne of game k. 5 computational experiments we analyze the resolution method performance by using computational experiments. for this, we generate four projects by the following process: • we define four intervals for the number of activities of the project: 1-10, 11-20, 21-40 and 41-80. for each interval i we define a project pi with a number of activity ni by random way. • we determine ⌈ni 2 ⌉ as maximum value for the processing time of activities of project pi. for each activity of a project pi, a integer value from the interval [1,⌈ni2 ⌉] is assigned as processing time by random way. • we fix three predecessor activities as maximum per activity. for each activity of a project, a number of predecessor activities is defined, and later, the predecessor activity(ies) is (are) assigned. both processes by random way. • for each project, we compute the number of feasible instances by changing the workload availability of resources within the feasible interval (i.e., w between the maximum processing time pmax and the critical path di of project pi). each instance is resolved in optimal way by using the milp proposed. this milp is programmed and run in gams 22.7.2/cplex 11.0 (intel core i3, 3.10 ghz, 4.00 gb ram) for each instance into time window of two hours. finally, we consider a total of 118 instances, in which the optimal value is obtained. table 2 shows the features of generated projects. project i 1 2 3 4 activities 10 18 30 75 pmax 4 8 12 27 d 14 30 47 99 instances 11 23 36 48 table 2: principal features of generated projects. once the instances are defined, the resolution method is programmed in c++ in the same hardware. in case of ties in the maximum value of the matrix, we adopt the priority rule: first row, first column. the metric of analysis considers: the differences between the solution given by the proposed method resolution and the optimal solution (∆s) and, between the computation time in seconds disaster response project scheduling problem: a resolution method based on a game-theoretical model 343 required by the optimal solution and proposed method (∆t). also, we compute the percentages obtained by the ratios between the ∆s and the optimal solution (% ∆s), and between the computation time required by the optimal solution and proposed method (% ∆t). the obtained results are shown in table 3 and figure 4. ∆s % ∆s ∆t %∆t average 0.85 15.44 744.46 0.25 stand.dev. 0.94 23.36 1,123.59 0.27 minimum 0.00 0.00 0.22 0.00 maximum 4.00 100.00 3,402.86 0.88 median 0.00 3.13 2.08 0.15 hits 54 (45.76 %) table 3: results of analysis. figure 4: obtained results for projects. 6 conclusions in this paper we presented a particular disaster response project scheduling problem (drpsp) motivated by fukushima’s nuclear accident of japan in 2011. we described this problem by using hartmann and briskorn scheme [12] and represented it by a mixed integer linear programming (milp). by considering the np-hardness of the problem, we developed a new resolution method based on game theory. this method ensures feasibility of the solution and convergence. the computational experiments yielded good results in comparison with the optimal solution, specially the computational times. on average, the method finds a feasible solution with a difference of 15.44% with respect to optimal solution within one percent of the time required by the milp over gams 22.7.2/cplex 11.0. this computational speed is worth in disaster situations where decisions must be made quickly. further research is proposed in order to improve the resolution method results. in particular, analysis of initial data and generation of subroutines for diversification. the first subject is very important because it can give a best lower bound for the problem, whereas the latter fact allows to jump the local solutions and to improve the final result. 344 o.c. vásquez, j.m. sepúlveda, m.d. alfaro, l. valenzuela-osorio acknowledgments this research has been supported by dicyt ( scientific and technological research bureau) of the university of santiago of chile (usach), project 061117ss and department of industrial engineering. the authors wish to thank dr. nicolás figueroa (puc) and christoph dürr (upmc) for their valuable comments for improving the game definition, to dr. pedro palominos (usach) and dr. richard weber (uch) for their valuable comments on the generation of the testing instances. bibliography [1] jackson b.a., k.s faith., evaluating the reliability of emergency response systems for largescale incident operations, rand corporation monograph 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[16] de p., dunne j., ghosh j.b., wells c.e., complexity of discrete time-cost tradeoff problem for project networks, oper res, 45(2):302-305, 1997. international journal of computers communications & control issn 1841-9836, 10(2):238-247, april, 2015. multicriteria supplier classification for dss: comparative analysis of two methods j.m. sepulveda, i.s. derpich juan m. sepulveda*, ivan s. derpich university of santiago 3769 ecuador ave., santiago, chile, cp 7254758 *corresponding author: juan.sepulveda@usach.cl ivan.derpich@usach.cl abstract: in this paper the analysis of two multicriteria decision making (mcdm) methods for sorting suppliers in industrial environments is presented. the mcdm methods correspond to electre and flowsort and both are applied to the classification of providers in an actual case of the local softdrink bottling industry in chile. the results show that electre as an outranking method it may well classify suppliers in a similar manner as flowsort does. nevertheless, due to the intrinsic underlying fuzzy multicriteria nature of the problem, flowsort is found to be more suitable method for building a rule-based system based on preference functions for automating the process of suppliers clustering when developing strategies of relationship management in the sense of the kraljic categories in supply chain management. keywords: decision support systems, supply management, electre, flowsort. 1 introduction supplier management is crucial in order to improve the benefits that a company can have at the operational, functional, economic, and financial levels and in terms of the supply chain it is embedded. supplier evaluation is a regular process of operational management in many companies; the need arises from finding suppliers for new products, parts, or materials, or for assessing performance of the current supplier base in order to decide continuation of their services. however, in supplier management a more strategic task is classifying suppliers into categories for deciding the relational approach to be followed. kraljic [1] in his pioneering work established categories of suppliers according to the economic impact for the purchasing company and the risks the suppliers may experiment in their respective market. as recently reviewed by monczka et. al. [2] kraljic’s categories are defined by a matrix of four quadrants, namely: routine, leverage, bottleneck and critical quadrant. each category means different strategies ranging from automated transactions by the use of erp and/or edi of commodity items of low total purchasing expenses, i.e. routine category, up to the critical category in which for example the expenses are very high and the suppliers situation in its market is risky due to uncertainties, uniqueness or high dependence. see figure 1 for an example of the kraljic matrix. the number in parenthesis is a level in a scale from 1 to 4, as explained below. level 1: many alternative products and processes; abundant sources of supply; low valuesmall individual transactions; routinary use, unspecified items; no specialized purchasing knowledge required. level 2: complex specifications requiring complex manufacturing or service processes; few alternate production/sources of supply; big impact on operations / maintenance; new technology or untested processes. level 3: high expenditures, commodity items; large marketplace capacity, big inventories; many alternate products and services; many qualified providers; market/price sensitive demand. level 4: critical to profitability and operations; few qualified sources of supply; large expenditures; design and quality critical; complex and/or rigid specifications. copyright © 2006-2015 by ccc publications multicriteria supplier classification for dss: comparative analysis of two methods 239 figure 1: kraljic matrix 1 for strategic positioning in this work the classification or categorization of suppliers in the case study, defines three categories: (a) transactional, (b) collaborative, and (c) integrated. these categories are specific for the company dealt with; however the method is general enough as to be applied to other similar situations. a transactional supplier is understood as the profile of routine suppliers combining low purchasing expenses, easy-to-obtain commodity-type products or services, and with low mutual attraction in the relationship. a collaborative supplier is one of importance to the company due to high purchasing expenses. for this reason, close monitoring of performance is needed in order to assure the service level agreements and to control the costs. the logistics complexity is of medium range as well as the mutual attraction. an integrated supplier is a critical one, of such an importance that a strategic alliance is needed, such as the case of a third party fully manufacturing a component or a product for a given customer company’s brand (oem strategy), as it occurs in the food, pharmaceutical, or car industries. it combines criticality in product positioning with high logistics complexity and high mutual attraction of the relationship. clearly, the three categories above can be understood as fuzzy sets where the implementation process of the method allows the identification of strengths and weaknesses of using formalized supplier selection models to tackle the supplier sorting problem. this highlights potential barriers preventing the adoption of these types of methods. for this purpose, the paper presents a number of relevant issues arising from the application and managerial implications for both customer and suppliers, concerning differentiated management practices according to their classification. besides matrix 1, two other matrices are used to classify suppliers. matrix 2 values the complexity of logistics, and matrix 3 the mutual attraction of the srm (supplier relationship management). in matrix 2 (lead time and stock rotation) the lowest level of logistics complexity is for supplies of fast replenishment and slow moving items. it the follows the case of suppliers of items with short lead time (hours, days, within a week) but of high consumption or inventory turnover. the third level is for slow moving items and long lead times (e.g. imported products); finally, the most complex logistics is for suppliers of items with high rotation and long lead time due the risk of inventory breakdown. the numbers assigned as well as the criteria are depending on the specific application and are 240 j.m. sepulveda, i.s. derpich figure 2: matrices 2 and 3 for logistics complexity and srm attraction level provided by experienced purchasing executives, without loss of generality. in matrix 3 (supplier relationship attraction) the lowest level is assigned when both parties have little mutual interest. it follows when the purchaser is important to the supplier (level 2). for the specific case of the application, the managers gave the highest importance to suppliers with superior power in the relationship (generally due to size). 2 strategies by category with the suppliers being categorized, the organization can apply differentiated approaches to srm. next, a brief description of each category is given. 2.1 transactional category the transactional category has as main characteristics the existence of many alternative products and services, many sources of supply, low value small individual transactions, commoditytype items, requiring little or no specialization of the purchasing executive. hence, this category is non-critical and besides the logistics aspects are simple because lead times are short and rotation is low. the mutual relationship is of low level. the general strategy for this category is the simplification of the acquisition process. the tactics relate to increase the role of computer based systems and reducing the buying effort. specific actions include rationalization of the supplier base, the automation of the purchasing process by the use of automated requisitioning, electronic data interchange, stockless procurement, minimization of administrative costs. little negotiation is needed. multicriteria supplier classification for dss: comparative analysis of two methods 241 2.2 collaborative category the collaborative category has as main characteristics the existence of many alternative products and services, many sources of supply, commodity-type items, requiring little or no specialization of the purchasing executive. however, the purchasing expenses and volumes are higher. hence, the optimization of operations serve as leverage for obtaining commercial advantage. logistics may be more complex due to longer lead times and higher rotation of items. the type of relationship is of purchaser’s power. the general strategy for this category is maximizing the commercial advantage. the tactics involve concentration of purchasing to increase power and also to maintain competition. specific actions involve the promotion of competitive bidding, procurement coordination, adoption of industry standards to reduce dependency. 2.3 integrated category this category has as main characteristics a heavy control of the providers performance, partnerships in the form of a strategic outsourcing, cooperation to optimize mutual benefits. for instance, a strategic supplier performing outsourced implant critical tasks of production and operations. the complexity of operations demands close control and information exchange. the importance of the purchaser and the criticality of the supplier calls for relations of mutual benefits. the attraction force of both parties is high. 3 basics of the two methods the electre methodology is based on the concordance and discordance indices. the simplest method of the electre family is electre-i. see figueira et al. [3]. this method starts from the data in the decision matrix, and assumes that the sum of the weights of all criteria is equal to one. for an ordered pair of alternatives (aj, ak), the concordance index cjk is the sum of all the weights for those criteria where the performance score of aj is least as high as that of ak, as in formula (1). it can be seen that cjk lies between 0 and 1. cjk = ∑ i:aij≥aik wi j, k = 1, .., n, j ̸= k (1) the computation of the discordance index djk is more elaborated: djk = 0 if aij > aik , i = 1, ..., m, that is, the discordance index is zero if aj performs better than ak on all of the criteria. otherwise, djk is calculated as in (2). that is, for each criterion where ak outperforms aj, the ratio is calculated as the difference in performance level between ak and aj and the maximum difference in score on the criterion concerned between any pair of alternatives. djk = maxi=1,..,m aik − aij maxj=1,..,naij − minj=1,...,naij j, k = 1, .., n, j ̸= k (2) the maximum of these ratios (which must lie between 0 and 1) is the discordance index. a concordance threshold c∗ and discordance threshold d∗ are then defined such that 0 < d∗ < c∗ < 1. then, aj outranks ak if the cjk > c∗ and djk < d∗, i.e. the concordance index is above and the discordance index is below its threshold, respectively. flowsort is based on the promethee ranking methodology (figueira et al., 2005) [3], this new sorting method by nemery and lamboray [4] is utilized for assigning actions to completely ordered categories; these categories are defined either by limiting profiles (i.e., min and max values) or by central profiles (or centroids). this method has also been applied by (sepulveda 242 j.m. sepulveda, i.s. derpich et al., 2010) [5] in another management decision problem in the innovation field for diagnosing capabilities in small enterprises. in what follows, limiting profiles will be used. the assignment of an action (i.e., an object to be sorted) into a category is based on the relative position of this action with respect to the defined reference profiles in terms or incoming or outgoing net flows. let a = (a1, a2, ..., an) be the set of n actions or alternatives to be sorted. these actions are evaluated on q criteria gj(j = 1, ..., q); all criteria are supposed to be maximized in the decision making problem. the categories to which the actions must be assigned are denoted by c1, c2, ..., ck. let r = (ri, ..., rk+1) be the set of limiting profiles in the case when a category is defined by an upper and lower limit. let π(x, y) be the preference of an action x over an action y, as in the promethee method. figure 3 shows typical shapes of preference functions where the x-axis is the degree of difference between actions x and y. thus, the positive, negative and net flows ϕ of each action x in r, are computed by equations (4) (5) (6) where ṙi = r u {ai} is the extended set of profiles either for the limiting profile case or the central profiles. the rules for assigning actions ai to a category ch are given by equations (7) and (8) in the case of limiting profiles. π(x, y) = q∑ j=1 wjp(x, y) (3) ϕ+ ṙi = 1∣∣∣ṙi∣∣∣ − 1 ∑ y∈ṙi + π(x, y) (4) ϕ− ṙi = 1∣∣∣ṙi∣∣∣ − 1 ∑ y∈ṙi − π(x, y) (5) ϕ ṙi = ϕ+ ṙi − ϕ− ṙi (6) cϕ+(ai) = ch, if ϕ + ṙi (rh) ≥ ϕ+ṙi(a1) > ϕ + ṙi (7) cϕ−(ai) = ch, if ϕ − ṙi (rh) < ϕ − ṙi (a1) ≤ ϕ− ṙi (rh+1) (8) 4 results of the two methods first, the sorting will be made by the electre method. table 1 shows the score combination for six randomly chosen suppliers and the resulting category according to the values predefined for the combination by using electre. the six cases are for illustrative purposes and show the type of results without loss of generality. table 2 shows the limiting profiles for the flowsort method. the scores for each criteria in flowsort are the same as the combination values in table 1. table 3 shows the results for flowsort. in the table, the six extended sets r correspond to suppliers a,b,c,d,e,f, respectively. the category is obtained by applying the rules defined by equations (7) and (8). it can be observed that for the six suppliers, with the exception of c, the assigned categories are the same. this is encouraging since the basis of each method is very different. it can be said that in case c this provider was better classified by flowsort. the difference obtained in one of the suppliers (supplier c), is mainly because of the assessment data that show that this provider multicriteria supplier classification for dss: comparative analysis of two methods 243 figure 3: types of preference functions table 1: results by electre supplier score combination category a "4-3-3" integrated b "3-2-2" collaborative c "1-2-3" transactional d "4-3-4" integrated e "4-3-4" integrated f "4-3-2" integrated table 2: limiting profiles chosen for flowsort profile c1 c2 c3 r1 4.5 4.5 4.5 r2 3.0 3.0 3.0 r3 1.5 3.0 3.0 r4 0 0 0 244 j.m. sepulveda, i.s. derpich figure 4: graphical representation of flowsort results for two suppliers multicriteria supplier classification for dss: comparative analysis of two methods 245 table 3: results for flowsort flow sort results r1 r2 r3 r4 a(i) category ϕ+ 1.25 0.75 0.5 0 0.83 integrated ṙ1 ϕ− 0 0.33 0.75 1 0.25 integrated ϕnet 1.25 0.42 -0.25 -1 0.58 integrated ϕ+ 1.25 0.92 0.5 0 0.75 collaborative ṙ2 ϕ− 0 0.25 0.75 1 0.42 collaborative ϕnet 1.25 0.67 -0.25 -1 0.33 collaborative ϕ+ 1.25 0.92 0.58 0 0.67 collaborative ṙ3 ϕ− 0 0.25 0.67 1 0.5 collaborative ϕnet 1.25 0.67 -0.08 -1 0.17 collaborative ϕ+ 1.25 0.75 0.5 0 0.92 integrated ṙ4 ϕ− 0 0.42 0.75 1 0.25 integrated ϕnet 1.25 0.33 -0.25 -1 0.67 integrated ϕ+ 1.25 0.75 0.5 0 0.92 integrated ṙ5 ϕ− 0 0.42 0.75 1 0.25 integrated ϕnet 1.25 0.33 -0.25 -1 0.67 integrated ϕ+ 1.25 0.83 0.5 0 0.83 integrated ṙ6 ϕ− 0 0.33 0.75 1 0.33 integrated ϕnet 1.25 0.5 -0.25 -1 0.5 integrated has some lower scores in some criteria (matrixcriterion 1) and high in other ones ( 2 and 3 ) and the final class depends on the decision maker. however, note that flowsort is a method that reflects the in and out flows generated by the alternatives, while the classification made in the electre is arbitrary at some extent by the threshold values chosen for each category. nevertheless, both methods solve the problem and are suitable for automating the classification process as part of a decision support system for supply management. figure 4 shows a graphical representation of the sorting for suppliers 1 and 2, as given by table 3. in table 1 the category is chosen according to the values in table 4. this shows the results of the electre method for the 64 possible combinations of the three matrices with the aggregated dominance index (concordance minus discordance). the score values of table 4 in columns score matrix 1,2,3 (indicated as sm1, sm2, sm3) for each supplier score combination (sc) were given by experienced supply managers same as in the flowsort method. current work is addressing the obtention of these numbers from the key performance indicators in the database of a business intelligence module connected to the erp system. the categories are obtained by sorting in increasing order of the aggregated dominance index (a.d.i.) and by defining thresholds for each category. the weights were chosen as 1/3 each matrix (w.l.g). for instance, as shown in table 1, supplier a with scores "4-3-3" corresponds to the combination "a60"in the appendix, supplier b with scores "3-2-2" corresponds to "a35", and so on. in this case, the same cases were used for both the electre and flowsort methods. 246 j.m. sepulveda, i.s. derpich table 4: categories by electre sc sm1 sm2 sm3 a.d.i. category sc sm1 sm2 sm3 a.d.i. category a1 1 1 1 0 transactional a34 3 1 4 17 collab. a2 1 1 2 1 transactional a37 4 1 3 17 collab. a3 1 2 1 1 transactional a38 4 3 1 17 collab. a4 2 1 1 1 transactional a30 1 3 4 18 collab. a5 1 1 3 2 transactional a31 1 4 3 18 collab. a6 1 3 1 2 transactional a36 3 4 1 18 collab. a7 3 1 1 2 transactional a40 2 2 4 20 integrated a8 1 1 4 3 transactional a43 4 2 2 20 integrated a9 1 2 2 3 transactional a41 2 4 2 21 integrated a10 1 4 1 3 transactional a46 3 2 3 21 integrated a11 2 1 2 3 transactional a47 3 3 2 21 integrated a12 2 2 1 3 transactional a45 2 3 3 22 integrated a13 4 1 1 3 transactional a44 4 4 1 27 integrated a14 1 2 3 6 transactional a39 1 4 4 28 integrated a15 1 3 2 6 transactional a42 4 1 4 28 integrated a16 2 1 3 6 transactional a50 3 2 4 31 integrated a17 2 3 1 6 transactional a53 4 3 2 31 integrated a18 3 1 2 6 transactional a48 2 3 4 32 integrated a19 3 2 1 6 transactional a49 2 4 3 32 integrated a23 2 2 2 7 collaborative a51 3 4 2 32 integrated a20 1 2 4 10 collaborative a52 4 2 3 32 integrated a21 1 4 2 10 collaborative a54 3 3 3 35 integrated a22 2 1 4 10 collaborative a56 4 2 4 43 integrated a24 2 4 1 10 collaborative a57 4 4 2 43 integrated a25 4 1 2 10 collaborative a55 2 4 4 44 integrated a26 4 2 1 10 collaborative a58 3 3 4 45 integrated a27 1 3 3 11 collaborative a59 3 4 3 45 integrated a28 3 1 3 11 collaborative a60 4 3 3 45 integrated a29 3 3 1 11 collaborative a61 3 4 4 55 integrated a32 2 2 3 12 collaborative a62 4 3 4 55 integrated a33 2 3 2 12 collaborative a63 4 4 3 55 integrated a35 3 2 2 13 collaborative a64 4 4 4 63 integrated 5 conclusions and future works in this article, the comparison of two methods for supplier sorting for determining the supplier management strategy in large organizations has been presented. assigning a category to a supplier is an important task within supply chain management since many types of suppliers are commonly in place and differentiated management approaches are needed in order to accomplish the efficiency and service objectives. the sorted categories combined three main dimensions: strategic positioning (matrix 1), logistics complexity (matrix 2), and attraction of the mutual relation (matrix 3). while the original kraljic’s matrix is concerned only with strategic positioning, giving four categories of suppliers: non-critical or routine, leverage, bottleneck, and critical, the contribution in this work is that the analysis has been extended to other dimensions, such as logistics complexity (matrix 2) and the attraction of mutual relation (matrix 3). by using the extended kraljic’s matrix concept developed in this work, a portfolio of strategies may be identified according to the defined categories: transactional, collaborative, and integrated suppliers. commonly, the analysis of suppliers is performed based on experienced managers over a restricted number of cases. in large organizations, however, because of the high number of suppliers, such manual method becomes difficult and prone to error. with multicriteria sorting multicriteria supplier classification for dss: comparative analysis of two methods 247 models it is possible to overcome this weakness and even automate this task. flowsort was compared to electre. a good coincidence was obtained between these two methods. however electre requires previously defined categories by using explicit enumeration and threshold values entered manually by experts. in this sense, flowsort requires less human input being more adaptable for automated processing. as direction for future works, further research is needed in order to examine the robustness of the results and the effects of the scales used in the assessment. also, in order to minimize human data input, the scores in the matrices ideally should be obtained in a direct manner from the key performance indicators (kpi) obtained from the erp system of the organization, or from a business intelligence (bi) module, among other aspects. ongoing work of the authors is addressing these issues. acknowledgments the authors are very grateful to dicyt (scientific and tecnological research office), project number 061117ss and the industrial engineering (ie) department, both of the university of santiago of chile for their support in this work. also to ie graduates marcos melin and stephanie sepulveda who helped in the data collection and model implementation. bibliography [1] kraljic, p. (1983); purchasing must become supply management, harvard business review, september-october, 1983. [2] monczka, r.m., handfield, r.b., giunipero, k.c., patterson, j.l. (2011); purchasing and supply chain management, 5th edition, cengage learning. [3] figueira, j., greco s., ehrgott m. (2005); multiple criteria decision analysis: state of the art surveys, springer-verlag. [4] nemery p.; lamboray c. (2008); flowsort: a flow-based sorting method with limiting or central profiles, top, 16, 90-113, springer-verlag. [5] sepulveda, j., gonzalez, j., alfaro, m. (2010); a metrics-based diagnosis tool for enhancing innovation capabilities in smes, international journal of computers communications & control, 5(5):919-928. international journal of computers communications & control issn 1841-9836, 13(5), 772-791, october 2018. how reliable are compositions of series and parallel networks compared with hammocks? v. dr goi, s.r. cowell, v. beiu, s. hoar , p. ga³par vlad dr goi* 1. faculty of exact sciences aurel vlaicu university of arad elena dragoi st., 310330, arad, romania 2. laboratoire litis ea 4108 université de rouen ufr sciences et techniques avenue de l'université, 76800 saint etienne du rouvray, france *corresponding author: vlad.dragoi@uav.ro simon r. cowell, valeriu beiu, sorin hoar , p storel ga³par faculty of exact sciences aurel vlaicu university of arad elena dragoi st., 310330, arad, romania {simon.cowell, valeriu.beiu, sorin.hoara, pastorel.gaspar}@uav.ro abstract: a classical problem in computer/network reliability is that of identifying simple, regular and repetitive building blocks (motifs) which yield reliability enhancements at the system-level. over time, this apparently simple problem has been addressed by various increasingly complex methods. the earliest and simplest solutions are series and parallel structures. these were followed by majority voting and related schemes. for the most recent solutions, which are also the most involved (e.g., those based on harary and circulant graphs), optimal reliability has been proven under particular conditions. here, we propose an alternate approach for designing reliable systems as repetitive compositions of the simplest possible structures. more precisely, our two motifs (basic building blocks) are: two devices in series, and two devices in parallel. therefore, for a given number of devices (which is a power of two) we build all the possible compositions of series and parallel networks of two devices. for all of the resulting twoterminal networks, we compute exactly the reliability polynomials, and then compare them with those of size-equivalent hammock networks. the results show that compositions of the two simplest motifs are not able to surpass size-equivalent hammock networks in terms of reliability. still, the algorithm for computing the reliability polynomials of such compositions is linear (extremely e�cient), as opposed to the one for the size-equivalent hammock networks, which is exponential. interestingly, a few of the compositions come extremely close to size-equivalent hammock networks with respect to reliability, while having fewer wires.a keywords: two-terminal network, series and parallel network, composition, reliability polynomial. athis paper is partially reprinted and extended, with permission based on licence number 4395350722700 © ieee, from "2018 7th international conference on computers communications and control (icccc)." 1 introduction one well-known problem in information processing is that of identifying schemes (for a particular given technology) that maximize reliability. reliability is an attribute of a system, a reliable system being one which works error-free for extended periods of time�originally an issue only copyright ©2018 cc by-nc how reliable are compositions of series and parallel networks compared with hammocks? 773 for safety-critical applications. the most common interpretation of network reliability [3], [4], [5] is connectivity-based. in [5], the author emphasizes a variety of interpretations for the reliability of networks, such as all-terminal, k-terminal or two-terminal networks: �it comes as no surprise that hundreds of seemingly natural de�nitions arise by examining the plethora of di�erent types of networks, causes and types of failures, and levels and types of operation. one should not expect to �nd a single de�nition for reliability that accommodates the many real situations of importance.� (charles j. colbourn) obviously, the design-for-reliability problem becomes more challenging as the system grows larger (more complex) and is required to function without interruptions for longer times. another aspect of interest is that enhancing/maximizing reliability should be done with a limited number of additional (redundant) components. the number of components is the simplest and most obvious cost function, but other cost functions (also known as �gures-of-merit, or fom) have been proposed and used, such as, e.g., area, power, or energy. it follows that design-for-reliability is a constraint optimization problem: maximize system reliability given limited resources (keeping costs as small as possible). this problem permeates way beyond computers into most man-made systems. nature also seems to rely on reliability principles/schemes at di�erent levels (the most well-known example here being the human brain, having 1011 neurons interconnected by 1015 axons and dendrites, working over many years). in the following we shall �rst of all restrict the scope of our discussions to computers. in this context, reliability was established through �ve lectures given at caltech by john von neumann in january 1952, which were published four years later [22]. the focus was on how to design reliable circuits/computers using unreliable logic gates. the answer was to replicate gates and combine their e�ects by voting and/or multiplexing. another take on this topic was advanced four years later by edward f. moore and claude e. shannon [20], [21]. the major di�erence was that instead of starting from gates, moore and shannon decided to pursue their analysis starting from relays (switching devices). their results were much more encouraging than [22]. in particular, their device-level scheme: � can be used with arbitrarily poor devices (i.e., absolutely random switching devices); � requires redundancy factors which are 10(2...3) (2 to 3 orders of magnitude) less than those needed by gate-level schemes. clearly, logic gates are made out of switching devices (transistors), hence device-level approaches, such as [20] should be used to enhance the reliability of the gates, before applying gate-level schemes such as those suggested in [22]. still, this approach was not taken, as over the last few decades, the cmos transistors have always been reliable enough. with novel nanoscale switching devices and nanoarchitectures under investigation [23], [26] the story is starting to look di�erent [14], [13]. this prospect has triggered our interest in revisiting the work of moore and shannon [7]. their scheme for improving on an unreliable switching device was to replace the device itself by a two-terminal network of identical unreliable switching devices. that is why we further narrow down the focus of this paper to two-terminal networks. in particular, moore and shannon have introduced in [20] and argued in [21] for a particular type of two-terminal networks: hammock networks. a thorough comparison of hammock networks with other highly e�ective specialized networks is called for. for instance, a comparison of hammocks with circulant and harary graphs (for which optimal reliability has been proven under particular conditions [19], [9], [25]). in any case, regular networks bode well with novel 774 v. dr goi, s.r. cowell, v. beiu, s. hoar , p. ga³par array-based designs including vertical fet, finfets [12], gate-all-around fet, and arrays of beyond cmos devices [6]. our fresh analyses of small hammock networks [7] (as well as possible extensions [8], [2]) are exact. they have con�rmed once again how challenging is to compute the associated reliability polynomials. these suggest that the design-for-reliability process using hammock networks will turn out to be quite involved. an alternate design option (also mentioned in [20]) advocates for growing larger networks by combining two smaller networks. these can be connected in series, in parallel, or by "composing" them, i.e., replacing each and every element of a network with the other network (translates into composing their associated reliability polynomials). compositions of hammock networks are mentioned in [20] and [21]. still, series and parallel networks are easier to evaluate (as their reliability polynomials are simpler [17]), while compositions of series and parallel networks inherit this bene�t. it means that one clear advantage of an approach that relies on composing series and parallel networks is a simpler design procedure. the fundamental question is how such composed networks perform versus other two-terminal networks of the same size. in particular, in this paper we compare compositions of small series and parallel networks versus hammock networks. related work. first of all we mention that this article is an extended version of a conference paper [10]. we bring here new results concerning combinatorial properties of compositions and a more detailed analysis of the various foms as functions of some speci�c design requirement, such as number-of-devices and wires. the same technique of composing networks was also used in [1]. there the authors compared hammock networks with compositions of smaller hammock networks. their results emphasize the merit of composing smaller networks, as they come really close to hammocks while having more e�cient algorithms for computing their reliability polynomials. our contribution. the main results that we prove in this article can be summarized as follows: � compositions of series and parallel networks are planar matchstick minimal networks (see proposition 4); � their width w and length l, as well as number-of-devices n and wires ω are related to the hamming weight of the corresponding binary vector (see proposition 5 and theorem 11); � there is an algorithm that determines whether a matchstick minimal network is a composition of series and parallel. our solution is a symmetric binary tree decomposition of depth m = log2 n; � the reliability polynomial of compositions of series and parallel networks can be computed very e�ciently (see theorem 16). the article also provides essential simulations, by detailing the reliability polynomials for all compositions of series and parallel networks with n = 64 as well as for the two 8-by-8 hammocks. by means of several foms we give arguments which prove that, in this particular case, hammocks are more reliable than compositions of series and parallel networks. however, the advantage of compositions is undeniable since they come close to hammocks, they have fewer wires for the same number of devices, and more signi�cantly in our opinion, their reliability can be computed e�ciently. organisation of the paper. the paper is structured as follows. we introduce two-terminal and hammock networks in section 2. compositions of series and parallel networks are discussed in section 3. section 4 starts by introducing reliability polynomials and several foms that we are going to use. afterwards, we determine (exactly) the reliability polynomials for the hammock how reliable are compositions of series and parallel networks compared with hammocks? 775 and composition networks under investigation, and use these for comparative analyses. the paper ends with some conclusions and further directions for research. 2 two-terminal networks 2.1 de�nitions and properties de�nition 1. let n be a strictly positive integer. we say that n is a two-terminal network of size n, or an n-network if n is a circuit, made of n identical devices, that has two distinguished contacts/terminals: an input or source s, and an output or terminus t . with any two-terminal network we associate three parameters: width w, length l, and size n. the width w of n is the size of a �minimal cut" separating s from t . the length l of n is the size of a �minimal path" from s to t . the size of a two-terminal network n is related to l and w by: n ≥ wl (1) (see theorem 3 in [20]). when the equality in eq. (1) holds, we say that n is a minimal network. even though there are several types of minimal networks, we will study here only matchstick minimal networks n. we will denote a matchstick minimal network of width w and length l by nw,l. the set of all matchstick minimal networks of size n = wl will be denoted nn, and we have: n = ⋃ n nn and nn = ⋃ w|n nw,n/w. (2) notice that the set n1,1 has cardinality 1, since there is only one two-terminal network with w = l = 1, that is the single device network n1,1. in the sequel, we will distinguish two subsets of nn, namely the set of hammocks and the set of compositions of series and parallel networks. 2.2 hammock networks matchstick minimal networks with the well-known �brick-wall� pattern are known as hammocks [20] (see fig. 1). they can be generated starting from a parallel-of-series pos network(see fig. 1) by connecting vertically adjacent pairs of wires by short vertical �matchsticks� (red vertical lines in fig. 1). if w and l are both even there are two solutions hw,l and h + w,l (see fig. 1), while otherwise we are left only with hw,l (see [7] for more details). s t p p p p p p p p p p p p p p p p 4-by-4 pos s t p p p p p p p p p p p p p p p p h4,4 s t p p p p p p p p p p p p p p p p h+4,4 s t p p p p p p p p p p p p p p p p 4-by-4 sop figure 1: square 4-by-4 parallel-of-series, hammocks and series-of-parallel. 776 v. dr goi, s.r. cowell, v. beiu, s. hoar , p. ga³par 3 compositions of series and parallel 3.1 de�nitions and properties de�nition 2. let c represent a composition of networks. if we start from the device itself the simplest possible compositions are: two devices in series c(0), and two devices in parallel c(1). at the to the next level, a composition of c(0) with c(1) is c = c(0) • c(1) which is obtained by replacing each device in c(0) by c(1), with the convention that the nodes s and t in c(1) where unlabeled. this composition will be abbreviated as cu, where u = (0, 1). notation 3. more generally, given u = (u0, . . . ,um−1) ∈ {0, 1}m, we will denote by cu the composition c(u0) •· · ·•c(um−1), and the set of all such compositions by c2m (as an example see c23 in fig. 2). s tpp pp pp pp c(0,0,0) s t p p p p p p p p c(1,0,0) s t p p p p p p p p c(0,1,0) s t p p p p p p p p c(0,0,1) s t p p p p p p p p c(1,1,0) s t p p p p p p p p c(1,0,1) s t p p p p p p p p c(0,1,1) s t p p p p p p p p c(1,1,1) figure 2: all the elements of the set c23. theorem 4. let m be a strictly positive integer. then any element in c2m is a matchstick minimal network of size 2m. moreover, we have #c2m = 2m. proof: the fact that compositions of c(0) and c(1) are matchstick minimal networks follows from theorem 3 in [20]. the set of compositions of c(0) and c(1) is: c2m = {cu|u ∈{0, 1}m}, (3) from which it follows immediately that #c2m = 2m. 2 notice that proposition 4 provides an e�cient method for generating matchstick minimal networks of size n = 2m. further we will detail how to compute w and l for any network c ∈ c2m. to achieve this goal, we introduce the well-known concept of hamming weight from coding theory. for any binary vector u ∈ {0, 1}m, its hamming weight |u| is the number of non-zero components of u. theorem 5. let m be a strictly positive integer and cu ∈ c2m. then cu is a matchstick minimal network of size 2m, length l = 2m−|u| and width w = 2|u|. how reliable are compositions of series and parallel networks compared with hammocks? 777 proof: for a binary m-tuple u = (u0, . . . ,um−1) ∈ {0, 1}m the weight |u| gives the number of times c(1) is present in the composition, i.e., the number of times we compose in parallel. by induction it can be deduced that w = 2|u|. since cu has n = 2m devices, it follows that l = 2m−|u|. 2 in conclusion, writing c2i,2m−i = { c2|u|,2m−|u||u ∈{0, 1} m,|u| = i } (4) we have c2m = m⋃ i=0 c2i,2m−i (5) 3.2 combinatorial properties since compositions of c(0) and c(1) o�er an e�cient way of creating matchstick minimal networks, the �rst question that we raise here is to determine the proportion of compositions. in other words, if one randomly picks a matchstick minimal network n ∈ n2m ,with respect to the uniform distribution over the set of all matchstick minimal networks, then �what is the probability that n ∈c2m?� theorem 6. let n be a matchstick minimal network of size 2m. then we have pr(n ∈c2m) ∼ 2−(2 bm/2c−1)(2dm/2e−1)+m. proof: from [7] we have that for a �xed l and w such that n = wl the number of matchstick minimal networks of length l and width w equals 2(l−1)(w−1). hence the total number of matchstick minimal networks of size 2m is equal to m∑ i=0 2(2 i−1)(2m−i−1) ∼ 2(2 bm/2c−1)(2dm/2e−1), (6) which ends our proof. 2 so, if we randomly pick a matchstick minimal network n, the probability that n is a composition of c(0) and c(1) is rapidly decreasing while m is increasing. however, the question now is how to determine whether n is an element of c2m. to answer this question we de�ne the following two operations de�nition 7 (vertical/horizontal cut). let n,w,l be strictly positive integers and n be a matchstick minimal network of width w and length l. we say that n admits a vertical cut if there exists a vertical complete matchstick, and we write n = (nl|nr). we say that n admits a horizontal cut if there is a horizontal free band, i.e. with no matchsticks,and we write n = ( nu nd ) . lemma 8. let w and l be strictly positive integers and n be a wl matchstick minimal network. then n can not admit both a horizontal and a vertical cut. proof: the result is straightforward from the de�nition of a horizontal and vertical cuts (de�nition 7). 2 778 v. dr goi, s.r. cowell, v. beiu, s. hoar , p. ga³par s t p p p p p p p p p p p p p p p p n = ( nu nd ) s t s t p p p p p p p p p p p p p p p p nu and nd (a) s t p p p p p p p p p p p p p p p p n = (nl|nr) s t s t p p p p p p p p p p p p p p p p nl nr (b) figure 3: matchstick minimal networks that admit: (a) horizontal cut; (b) vertical cut. in this article we only consider those vertical and horizontal cuts that bisect the network into two identical halves. in other words a wl -network n can be cut vertically or horizontally and we write n = (nl|nr) with nr = nl, or n = ( nu nd ) with nu = nd, where nl is a w × l/2 network and nu is a w/2 × l network. theorem 9 (decomposable networks). let m be a strictly positive integer and n be a matchstick minimal network of size n = 2m. � if n = (nl|nl) (i.e., admits a vertical cut in half), then n = c(0) •nl; � if n = ( nu nu ) (i.e., admits a horizontal cut in half), then n = c(1) •nu. moreover, n ∈c2m if and only if n admits a binary tree decomposition, with respect to �|� and �−�, of depth m, where the leaves of the tree are n1,1. the proof of this proposition is based on the previous lemma. remark 10. notice that hw,l is not decomposable as a compositions of c (0) and c(1) unless w = 1, l = 1, or w = l = 2, as it does not admit either a vertical or horizontal cut. algorithm 1 decomposition of matchstick minimal networks into composition of c(0) and c(1) input:a matchstick minimal network n of size w × l = 2m output:the corresponding binary vector u if n is decomposable 1: u = [ ] 2: while n 6= n1,1 do 3: if n = (nl|nl) then 4: n = nl 5: append 0 to u 6: else if n = ( nu nu ) then 7: n = nu 8: append 1 to u 9: else 10: break; 11: end if 12: end while how reliable are compositions of series and parallel networks compared with hammocks? 779 3.3 representations in order to compare compositions with hammocks we consider the parameters of the circuits, that is the number of devices n, as well as the number of wires, ω in the circuits . the �rst representation of the brick-wall pattern, which is from moore and shannon [20], uses vertical matchsticks as in fig. 1. the second possibility also suggested by moore and shannon [20] is to use the graph representation. here, we will adopt the third representation from [21], which gave the name to these networks: hammocks. these three representations can all be seen in fig. 4. when counting the number of wires ω we will consider that there are w wires that connect s to the circuit, and w wires that connect t to the circuit. we also count 4 wires whenever we have an �x� shape matchstick. with this convention at hand we can now count ω for a matchstick minimal network, in particular a composition or a hammock. s t p p p p p p p p p p p p p p p p brick-wall representation ( [20]) s t p p p p p p p p p p p p p p p p hammock representation ( [21]) s t graph representation ( [20]) figure 4: three di�erent representations of h4,4. theorem 11. let m be a strictly positive integer and cu ∈ c2m. then the number of wires of the circuit cu is ω = 2m + 2i+1, where i is the position of the most signi�cant bit of the corresponding binary vector u equal to 1. when u is the zero vector the number of wires is ω = 2m + 1. proof: the �rst case u = (0, . . . , 0) ∈ {0, 1}m can be easily deduced from the de�nition of the composition. next consider u = (1, 0, 0, . . . , 0) ∈ {0, 1}m. this circuit cu is a parallel of two identical circuits cv where v = (0, . . . , 0) ∈ {0, 1}m−1. but we know that the number of wires for cv is ω = 2m−1 + 1 and the number of devices for cv equals 2m−1. we also deduce that there are 2m/2m−1 = 2 identical blocks in the composition of cu. notice that these two blocks do not share any wire in common. hence, we obtain the total number of wires for cu, that equals the number of blocks times the number of wires in each block. more exactly the number of wires for cu is ω = 2 × ( 2m−1 + 1 ) = 2m + 2. now we can prove our theorem for any u ∈ {0, 1}m. let u = (u0, . . . ,um−1) be a binary vector such that ui = 1 and uj = 0 for any j > i. denote ui,m−1 = (ui, . . . ,um−1) , which equals ui,m−1 = (1, 0, . . . , 0). notice that cui,m−1 is composed of 2m−i devices and 2m−i + 2 wires. we also know that there are 2m/2m−i identical blocks, all equal to cui,m−1, that do not share any wire in common such that cu is the composition of these blocks. then the number of wires of cu is ω = 2i × ( 2m−i + 2 ) = 2m + 2i+1. 2 theorem 12. let w and l be two strictly positive integers. then ω =   2wl− l for hw,l with w and l even 2wl− l + 1 for hw,l with w or l odd 2wl− l + 2 for h+w,l with w and l even (7) 780 v. dr goi, s.r. cowell, v. beiu, s. hoar , p. ga³par proof: we give here only the proof for one of the cases. for the remaining two cases the arguments are exactly the same. so, let l and w be two odd strictly positive integers. this implies that we have l− 1 columns of "x" shape matchsticks and horizontal wires. on each one of these columns we count (w − 1)/2 matchsticks and one horizontal wire. hence, each column has 4×((w−1)/2) + 1 wires. and there are l−1 such columns, which makes the total number of "interior" wires equal to (l− 1) × (2w− 1). by "interior" wire we mean wires that connect only devices and not s or t with any of the devices. finally, we have to add the number of "exterior" wires, namely those connecting to s and t , which are 2w. thus, we obtain ω = 2wl− l + 1. 2 corollary 13. for square hammocks we obtain ω =   8k2 − 2k for h2k,2k 8k2 − 2k + 2 for h+2k,2k 8k2 + 6k + 2 for h2k+1,2k+1 (8) remark 14. from eq. (8) taking k = 2m/2 it follows that h2m/2,2m/2 has 8× ( 2m/2−1 )2 −2m/2 = 2m+(2m−2m/2) wires. also notice that from theorem 11 there are ( m m/2 ) /2 elements in c2m/2,2m/2 having at most 2m + 2m−1 wires, which is smaller than 2m + (2m − 2m/2). 4 evaluating reliability 4.1 reliability polynomials we will use a classical convention for the reliability polynomial r(p), where p ∈ [0, 1] is the probability that a device is closed. since the polynomial is associated with a network n (either h or c), we shall use the notation r(n; p). this gives r(c; p) and r(h; p) for compositions of c(0) and c(1) and respectively hammocks. lemma 15. r(c(0); p) = p2 and r(c(1); p) = 1 − (1 −p)2. this is well-known [22], [20]. we can now determine the reliability r(c; p) for any c. theorem 16. let m be a strictly positive integer and u = (u0, . . . ,um−1) ∈{0, 1}m. then: r(cu; p) = r(c(u0)) ◦ · · · ◦r(c(um−1); p), (9) where r(c(0); p) and r(c(1); p) are given by lemma 15. the proof of theorem 16 follows directly from de�nition 2 and lemma 15. remark 17. notice that compositions of c(0) and c(1) are by de�nition series and parallel networks. hence, they inherit all the nice properties of this big family of networks. series and parallel networks were extensively studied [24], [18], [11] and e�cient algorithms exist for computing their reliability polynomials (the complexity of these algorithms is linear in n). however, as we have shown in theorem 16, compositions of c(0) and c(1) admit a closed form formula of complexity log2(n) for computing their reliability polynomials. how reliable are compositions of series and parallel networks compared with hammocks? 781 table 1: reliability polynomials for cu. n r(n; p) c(1,1,1,0,0,0) 8p8 − 28p16 + 56p24 − 70p32 + 56p40 − 28p48 + 8p56 −p64 c(1,1,0,1,0,0) 16p8 − 16p12 − 92p16 + 192p20 + 112p24 − 720p28 + 698p32 + 384p36 −1552p40 + 1744p44 − 1116p48 + 448p52 − 112p56 + 16p60 −p64 c(1,0,1,1,0,0) 32p8 − 96p12 − 120p16 + 1424p20 − 4424p24 + 8304p28 − 10894p32 + 10560p36 −7744p40 + 4320p44 − 1816p48 + 560p52 − 120p56 + 16p60 −p64 c(0,1,1,1,0,0) 64p8 − 448p12 + 1680p16 − 4256p20 + 7952p24 − 11424p28 + 12868p32 − 11440p36 +8008p40 − 4368p44 + 1820p48 − 560p52 + 120p56 − 16p60 + p64 c(1,1,0,0,1,0) 64p8 − 128p10 + 96p12 − 32p14 − 1532p16 + 6144p18 − 10752p20 + 10752p22 + 9664p24 − 95616p26 +269664p28 − 450464p30 + 441338p32 + 118784p34 − 1729536p36 + 4486144p38 − 7423040p40 +8938624p42 − 8199136p44 + 5857184p46 − 3294716p48 + 1464320p50− 512512p52 + 139776p54 − 29120p56 + 4480p58 − 480p60 + 32p62 −p64 c(1,0,1,0,1,0) 128p8 − 256p10 − 320p12 + 1472p14 − 5496p16 + 15616p18 + 7200p20 − 138656p22 + 254648p24 + 104576p26− 1062432p28 + 1528032p30 − 17422p32 − 3037184p34 + 4820608p36 − 3005056p38 − 1494624p40 +5473536p42 − 6668992p44 + 5345344p46 − 3166616p48 + 1441024p50 −509600p52 + 139552p54 − 29112p56 + 4480p58 − 480p60 + 32p62 −p64 c(0,1,1,0,1,0) 256p8 − 512p10 − 2688p12 + 9088p14 + 5904p16 − 61952p18 + 61632p20 + 165440p22 − 454320p24 + 141568p26 +1016256p28 − 1785920p30 + 443716p32 + 2654720p34 − 4588384p36 + 2904160p38 + 1526280p40 −5480576p42 + 6670048p44 − 5345440p46 + 3166620p48 − 1441024p50 +509600p52 − 139552p54 + 29112p56 − 4480p58 + 480p60 − 32p62 + p64 c(1,0,0,1,1,0) 512p8 − 3072p10 + 8960p12 − 16640p14 − 43744p16 + 765312p18 − 4637568p20 + 18013760p22 − 51204560p24 +113425312p26 − 203255568p28 + 301928416p30 − 378028286p32 + 403556352p34 − 370208768p36 +293307392p38 − 201225472p40 + 119608832p42 − 61506048p44 + 27263232p46 − 10354528p48 +3339648p50 − 903168p52 + 201152p54 − 35952p56 + 4960p58 − 496p60 + 32p62 −p64 1024p8 − 6144p10 + 1536p12 + 114176p14 − 542144p16 + 1039104p18 + 797952p20 − 11825024p22 c(0,1,0,1,1,0) +43312992p24 − 105270976p26 + 196334304p28 − 297069632p30 + 375202628p32 − 402199296p34 +369674944p36 − 293137856p38 + 201182992p40 − 119600736p42 + 61504944p44 − 27263136p46 +10354524p48 − 3339648p50 + 903168p52 − 201152p54 + 35952p56 − 4960p58 + 496p60 − 32p62 + p64 c(0,0,1,1,1,0) 4096p8 − 57344p10 + 415744p12 − 2050048p14 + 7653632p16 − 22887424p18 + 56715264p20 −119066112p22 + 214987136p24 − 337392384p26 + 463591296p28 − 560492800p30 + 598138512p32 −564338304p34 + 470897216p36 − 347203584p38 + 225750336p40 − 129016384p42 +64511136p44 − 28048704p46 + 10518296p48 − 3365856p50 + 906192p52 − 201376p54 +35960p56 − 4960p58 + 496p60 − 32p62 + p64 782 v. dr goi, s.r. cowell, v. beiu, s. hoar , p. ga³par for hammocks we rely on the results just published in [7] as well on the ones for h8,8 and h+8,8 recently reported in [10], [1]. the associated reliability polynomials were computed using our own recursive depth-�rst traversal of a binary tree algorithm, and are reported in table 2. table 2: reliability polynomials for h+8,8 and h8,8. n r(n; p) h8,8 650p8 − 580p9 + 908p10 − 6880p11 + 4628p12 − 12104p13 + 31618p14 + 372p15 + 10594p16 + 196688p17 − 404536p18 +915388p19 − 5608084p20 + 7645892p21 − 12887466p22 + 56185408p23 − 61734474p24 + 83601572p25 −412397124p26 + 272424760p27 + 274694424p28 + 1746408000p29 − 221980272p30 − 12868843904p31 +11123958002p32 − 11120041788p33 + 156260690872p34 − 378857360436p35 + 264833158482p36 −60539595908p37 + 345161573768p38 + 1581294699620p39 − 10357633700988p40 + 19594821559752p41 −7205288635438p42 − 36413539831436p43 + 75842387925382p44 − 55098726855452p45 − 30641343744796p46 +111186328020944p47 − 111483252211446p48 + 33001245825824p49 + 53388841078258p50 − 85170175686428p51 +59759032847258p52 − 15870886733412p53 − 12944378218252p54 + 19685718553176p55 − 14268363534224p56 +7162471625508p57 − 2694331712884p58 + 775005119032p59 − 169487849178p60 + 27440435336p61 −3113881376p62 + 221751056p63 − 7474305p64 h+8,8 720p8 − 720p9 + 1052p10 − 7864p11 + 6482p12 − 16012p13 + 43042p14 − 16492p15 + 35378p16 + 202080p17 − 418416p18 +840000p19 − 6142350p20 + 7346188p21 − 11370674p22 + 74129792p23 − 100005860p24 + 118520824p25 −656753496p26 + 1014391664p27 − 1060302334p28 + 5318496368p29 − 8451329352p30 + 2451624096p31 −37298482094p32 + 119852403404p33 − 23621628548p34 − 197506250928p35 − 337635320852p36 +1438498163768p37 − 67797908976p38 − 4198255335740p39 + 3015682674902p40 + 8881103035456p41 −15082194064786p42 − 3506508481748p43 + 18452358491432p44 + 39640921395644p45 − 181496618059380p46 +286828005143040p47 − 191038611524520p48 − 138468526731136p49 + 542912067034010p50 − 803232038481876p51 +814719587176720p52 − 634769854840740p53 + 396340290321940p54 − 202017905414696p55 + 84634369170678p56 −29121695246028p57 + 8171460088944p58 − 1843848199008p59 + 327008804562p60 − 43949841128p61 +4211763728p62 − 256604464p63 + 7474305p64 4.2 figures-of-merit to compare compositions with hammocks we will rely on several foms: 1. the well-known reliability improvement index (rii) introduced by klaschka in [15], [16]; 2. the steepness of r(n; p) mentioned by moore and shannon [20]; 3. the variation of r(n; p), which was mentioned in [10]. reliability improvement index the reliability improvement index is de�ned [15], [16] for any network n as rii(n) = log(p) log (r(n; p)) . (10) the rii is a measure of the reliability increase produced by a network n and was used in [7] to estimate how much matchstick minimal networks improve on a single device. steepness of the reliability polynomials the ideal reliability function proposed by moore and shannon is the staircase function: χ(p) = { 0 0 ≤ p ≤ 0.5 1 0.5 < p ≤ 1 how reliable are compositions of series and parallel networks compared with hammocks? 783 one of the goal of [20] was to identify networks having reliability polynomials exhibiting steep 0 → 1 transitions. we de�ne fom1 as: fom1 = max p∈[0,1] r′(n; p). (11) since the transition point might be important, we give a �ner fom for the steepness of the polynomials. this is an enhancement over fom1 which measures how steep is the reliability polynomial as well as how far from 0.5 is the threshold. so, in general fom∗1 is equal to fom1 weighted by a function of the distance between p0 and 0.5. here, we choose a very simple function, that is fom∗1 = max p∈[0,1] r′(n; p) · 1 |0.5 −p0| , (12) where p0 is the point where the maximum is achieved. notice that in our case this is well-de�ned since no network studied in this paper has p0 = 0.5. but this is no longer the case for self-dual networks where p0 = 0.5, and a modi�ed fom1 should be proposed. variation of the reliability polynomial another fom is introduced in this paper. it is related to the variation achieved by a reliability polynomial in a given interval. we shall use the area under r′(n; p) in a given symmetric interval (with respect to 0.5). this is exactly the variation of r(n; p) on that interval, hence for any n: fom2(p0) = r(n; 1 −p0) −r(n; p0) = 1−p0∫ p0 r′(n; p)dp. (13) this fom2 is well-de�ned for the staircase function since χ may be written as the integral of the delta dirac function over the sub-domain [0, 1]. therefore, fom2 is the area under the delta dirac function, between two symmetric points t and 1 − t, with 0 ≤ t < 0.5. 4.3 numerical results reliability improvement index the �rst set of simulations was performed over the whole set c26 as well as for the two hammocks under investigations, h8,8 and h + 8,8 (see table 2). using eq. (10) we have calculated all the riis. these can be seen in fig. 5,6,7. in fig. 5 the scale is linear to get a clear picture of the very large rii values for p close to 1. in includes only the square networks, more exactly n ∈ c8,8 in blue and n = h8,8,h+8,8 in red. a zoom in on the region of interest is shown in fig. 6, where the yellow horizontal line at rii = 1 represents the border between networks that improve reliability and networks which do not. finally, the complete picture (fig. 7), in log scale, includes all networks n ∈c26, the non-square ones being plotted in orange. this �gure shows a wide range of variation for riis. among these, those which go below rii = 1 are not improving over a single device, which means they should not be used. this is in support of selecting square networks which tend to stick together close to rii = 1 when p = 0.5. 784 v. dr goi, s.r. cowell, v. beiu, s. hoar , p. ga³par figure 5: rii(n) for n ∈c8,8 (blue) and n = h8,8,h+8,8 (red). figure 6: zoom on rii(n) for n ∈c8,8 (blue) and n = h8,8,h+8,8 (red). how reliable are compositions of series and parallel networks compared with hammocks? 785 figure 7: rii(n) for n ∈c8,8 (blue), n = h8,8,h+8,8 (red) and n ∈c26 \c8,8 (orange) in log scale. steepness of the reliability polynomials in fig. 8 we plot r′(n; p) for all n ∈ c8,8, as well as n = h8,8 and n = h+8,8. we notice that max p∈[0,1] r′(h8,8; p) = max p∈[0,1] r′(h+8,8; p) = 3.75252 and is reached at p0 = 0.501745, respectively p0 = 0.498255. for compositions we have: max c∈c8,8 ( max p∈[0,1] r′(c; p) ) = 4.1035, which is achieved by u = (1, 1, 1, 0, 0, 0) at p0 = 0.760. 786 v. dr goi, s.r. cowell, v. beiu, s. hoar , p. ga³par figure 8: r′(n; p) this result shows the limitation of this fom, as it does not take into account how far the threshold point is from the desired point, that is 0.5. that is why the enhanced version of this fom, namely fom∗1 gives better results in comparing reliability. this fact is illustrated in fig. 9 and 10. figure 9: r′(n; p) |p0 − 0.5| how reliable are compositions of series and parallel networks compared with hammocks? 787 figure 10: log ( r′(n; p) |p0 − 0.5| ) we observe from fig. 9 and 10 that hammocks are �better� than compositions with respect to fom∗1 . indeed, for hammocks we obtain fom ∗ 1 (h8,8) = 1876.25 and for compositions we have max c∈c8,8   maxp∈[0,1] r′(c; p) |p0 − 0.5|   = 587.56, which is achieved by u = (0, 1, 0, 1, 1, 0) and u = (1, 0, 1, 0, 0, 1). variation of the reliability polynomials in fig. 11 we plot r(n; 1−p0)−r(n; p0) as a function of 0 ≤ p0 < 0.5 for h8,8 and h+8,8, and c ∈c8,8. figure 11: r(n; 1−p0)−r(n; p0) as a function of p0 (0 ≤ p0 < 0.5);n = h8,8 and h+8,8 (red), n = c ∈c8,8 (blue), and χ(1 −p0) −χ(p0) (green). 788 v. dr goi, s.r. cowell, v. beiu, s. hoar , p. ga³par we notice a di�erence between the curves starting to develop from p0 = 0.25 onwards. in table 3 we report the exact values for p0 = 0.25, which correspond to r(0.75) −r(0.25). the two hammocks we have considered here achieve the same value r(h8,8; 0.75) −r(h8,8; 0.25) = 0.985173. for compositions the best value max u∈{0,1}6 r(cu; 0.75) −r(cu; 0.25) = 0.979507, is achieved for u = (0, 1, 0, 1, 1, 0) and u = (1, 0, 1, 0, 0, 1). it should be mentioned that the same two compositions achieve the best values for fom∗1 . in fact, fom2(0.25) correlates perfectly with fom∗1 . indeed, if we totally order compositions and hammocks with respect to fom ∗ 1 , then the same order holds for fom2(0.25). and as expected, the two foms point out to the same network as being the most reliable, namely the hammock. recall that one of the leading arguments when comparing networks was the restriction of the number of devices, which in our case study is always n = 64. now, if we plot fom2(0.25) as a function of the number of wires ω, we see that one of the best compositions, namely u = (0, 1, 0, 1, 1, 0) has fewer wires than the hammocks (see fig. 12). figure 12: r(n; 0.75) − r(n; 0.25) as a function of the number of wires ω. n = h8,8 and n = h + 8,8 (red), and n = c with c ∈c8,8 (blue) figure 13: zoom on the values of r(n; 0.75)−r(n; 0.25) in the vicinity of the maximum values how reliable are compositions of series and parallel networks compared with hammocks? 789 table 3: figure-of-merit for c ∈c8,8, h8,8, and h+8,8. ω n p0 max p∈[0,1] r′(n; p) max p∈[0,1] r′(n; p) |p0 − 0.5| r(n; 0.75) −r(n; 0.25) 120 h8,8 0.498 3.7525 1876.25 0.985173 122 h+8,8 0.502 3.7525 1876.25 0.985173 72 c(1,1,1,0,0,0) 0.760 4.1035 15.78 0.569843 c(1,1,0,1,0,0) 0.710 3.9273 18.70 0.736601 80 c(1,0,1,1,0,0) 0.665 3.7709 22.85 0.848154 c(0,1,1,1,0,0) 0.626 3.5995 28.56 0.905953 96 c(1,1,0,0,1,0) 0.643 3.6861 25.77 0.891705 c(1,0,1,0,1,0) 0.596 3.6409 37.92 0.947539 c(0,1,1,0,1,0) 0.555 3.5568 64.66 0.968192 c(1,0,0,1,1,0) 0.536 3.5354 98.20 0.975413 c(0,1,0,1,1,0) 0.494 3.5254 587.56 0.979507 c(0,0,1,1,1,0) 0.445 3.4606 62.92 0.968192 128 c(1,1,0,0,0,1) 0.555 3.4606 62.92 0.968192 c(1,0,1,0,0,1) 0.506 3.5254 587.56 0.979507 c(0,1,1,0,0,1) 0.464 3.5354 98.20 0.975413 c(1,0,0,1,0,1) 0.445 3.5568 64.66 0.968192 c(0,1,0,1,0,1) 0.404 3.6409 37.92 0.947538 c(0,0,1,1,0,1) 0.357 3.6861 25.77 0.891705 c(1,0,0,0,1,1) 0.374 3.5995 28.56 0.905953 c(0,1,0,0,1,1) 0.335 3.7709 22.85 0.848154 c(0,0,1,0,1,1) 0.290 3.9273 18.70 0.736601 c(0,0,0,1,1,1) 0.240 4.1035 15.78 0.569843 strong points of compositions notice that for u = (0, 1, 0, 1, 1, 0) the corresponding composition comes really close to h8,8 and has an advantage over h8,8, in that c(0,1,0,1,1,0) has only 96 wires, while h8,8 has 120. from a computational point of view, r(cu; p) has several advantages over r(h8,8; p). � firstly, notice that the order of magnitude of the largest coe�cient is 109 for cu compared with 1014 for h8,8. � secondly, the reliability polynomials for compositions are sparser than r(h8,8; p). this is due to the fact that r(cu; p) have non-zero coe�cients only for even powers of p, i.e., 29 non-zero coe�cients versus 57 for r(h8,8; p). � thirdly the absolute values of the coe�cients of r(h8,8; p) are larger on average than the coe�cients of r(cu; p). for the case m = 6, the average value of a coe�cient is of the order 1.3 × 1013 for hammocks, compared with 8.6 × 107 for compositions. from the computational point of view all these arguments favor compositions over hammocks. 790 v. dr goi, s.r. cowell, v. beiu, s. hoar , p. ga³par 5 conclusions in this article we have proposed and analyzed two-terminal networks generated through the repeated composition of the simplest series and parallel networks. we have detailed several structural properties of such networks and have presented an e�cient method for computing their associated reliability polynomials. compositions were compared with hammocks according to three foms: rii, the slope of the reliability polynomials and their variations. for the particular cases considered here we have observed that compositions come very close to hammocks, without surpassing them. still, compositions of series and parallel present several advantages. there are compositions performing almost as well as hammocks, while having fewer wires than hammocks, for the same number of devices. we also noticed that there is a signi�cant computational gap, the reliability polynomials of compositions being much simpler/easier to compute and analyze exactly. acknowledgement research supported in part by the european union (eu) through the european research development fund (erdf) under the competitiveness operational program (cop): biocellnanoart = novel bio-inspired cellular nano-architectures, poc-a1-a1.1.4-e nr. 30/01.09.2016. bibliography [1] beiu, v.; cowell, s.r.; dr goi, v.; hoar , s.; ga³par, p. 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(2017); what's next?, comput. sci. & eng., 19(2), 7�13, 2017. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(4), 557-573, august 2019. hyperparameter importance analysis based on n-rrelieff algorithm y. sun, h. gong, y. li, d. zhang yunlei sun* college of computer & communication engineering china university of petroleum(east china), china no.66, west changjiang road, huangdao district, qingdao 266580, china *corresponding author: sunyunlei@upc.edu.cn huiquan gong faculty of information technology beijing university of technology, china no.100, pingleyuan, chaoyang district, beijing, 100124, china xinel_ghq@126.com yucong li college of computer & communication engineering china university of petroleum(east china), china no.66, west changjiang road, huangdao district, qingdao 266580, china 291731931@qq.com dalin zhang national research center of railway safety assessment beijing jiaotong university, china no.3, shangyuancun, haidian district, beijing, 100044, china dalin@bjtu.edu.cn abstract: hyperparameter selection has always been the key to machine learning. the bayesian optimization algorithm has recently achieved great success, but it has certain constraints and limitations in selecting hyperparameters. in response to these constraints and limitations, this paper proposed the n-rrelieff algorithm, which can evaluate the importance of hyperparameters and the importance weights between hyperparameters. the n-rrelieff algorithm estimates the contribution of a single hyperparameter to the performance according to the influence degree of each hyperparameter on the performance and calculates the weight of importance between the hyperparameters according to the improved normalization formula. the n-rrelieff algorithm analyses the hyperparameter configuration and performance set generated by bayesian optimization, and obtains the important hyperparameters in random forest algorithm and svm algorithm. the experimental results verify the effectiveness of the n-rrelieff algorithm. keywords: hyperparameter optimization, bayesian optimization, rrelieff algorithm. 1 introduction in the process of machine learning, the performance of the algorithm highly depends on the selection of hyperparameters, which has always been a crucial step in the process of machine learning. automated machine learning, represented by bayesian optimization algorithm, has recently achieved great success in hyperparameter optimization, which exceeds the performance of human experts in some cases. copyright ©2019 cc by-nc 558 y. sun, h. gong, y. li, d. zhang however, there are some constraints and limitations in the selection of hyperparameters for bayesian optimization algorithm. researchers and users can only get the hyperparameter configuration after the operation of bayesian optimization algorithm and cannot get the importance analysis of the hyperparameter configuration. therefore, it is necessary to study the algorithm of hyperparameter importance analysis based on a wide range of data sets, so that researchers and users can understand which hyperparameter adjustment will significantly improve the performance of the algorithm. this paper introduces the basic principle of bayesian optimization algorithm and optimized random forest and svm based on openml100 data set. by comparing with grid search and random search algorithm, it could be seen that the hyperparameter performance of bayesian optimization algorithm is high and time-consuming. therefore, we used the hyperparameter configuration and performance data generated by bayesian optimization algorithm to analyze hyperparameter importance. hyperparameter configuration based on the experimental data, we used n-rrelieff algorithm to evaluate the importance of interaction between hyperparameter, so as to determine the important hyperparameter in random forest algorithm and svm algorithm. 2 related works hyperparameter selection is a key step in machine learning process [12]. from the initial manual selection to automatic optimization using algorithms later, the evolving optimization algorithms have made great contributions to improving performance. hyperparameter selection algorithms can be roughly divided into four categories: traditional algorithms (e.g., grid search algorithm [26], random search algorithm [2]), heuristic optimization algorithm [4,18,30], metalearning algorithm [13,22], bayesian optimization algorithm [14,28], etc. the disadvantage of these hyperparameter selection algorithms is that they cannot provide researchers and users with information about the importance analysis of the selected hyperparameters and cannot understand the impact of different hyperparameters and their interactions on performance. scientists have proposed a method for evaluating the importance of hyperparameter machine learning algorithms. sequential parameter optimization (spo) [1] is a model-based parameter optimization approach. spo starts by running the target algorithm with parameter configurations. it then builds a response surface model based on gaussian process regression and uses the models predictions and predictive uncertainties to determine the next parameter cofiguration to evaluate. in 2007, nannen et al. [20] proposed an evolutionary algorithm for parameter correlation estimation. in 2009, chiarandini et al. [6] used a linear mixed effect model to design and analyze the optimization algorithm. with a mixed-effects multi-linear regression they [8] assessed the individual and joint effect of problem features on the performance of both algorithms, within and across the instance classes defined by benchmark parameters. in [21] probst formalized the problem of tuning from a statistical point of view, define data-based defaults and suggest general measures quantifying the tunability of hyperparameters of algorithms. falkner proposed a new hyperparameter optimization method-bohb [10], which combines the benefits of both bayesian optimization and bandit-based methods, consistently outperforms both bayesian optimization and hyperband on a wide range of problem types. breiman [29] uses random forests to assess the importance of attributes: if attributes are deleted from the data set, performance will be degraded, indicating that this attribute is important. based on this principle, forward selection [15] predicts the performance of machine learning algorithms using a subset of hyperparameters, which is initialized to be empty and greedily chooses the next most important hyperparameter. ablation analysis [15] requires default settings and optimization settings, and calculates the ablation trajectory, which reflects the contribution of hyperparameters to the performance difference between the two settings. hyperparameter importance analysis based on n-rrelieff algorithm 559 3 n-rrelieff algorithm design this paper introduces the basic principle of hyperparameter optimization of bayesian optimization algorithm based on gauss process modeling. to hyperparameter configuration and performance data generated by optimization process, we used n-rrelieff algorithm to evaluate the importance of hyperparameter, so that users can understand the important hyperparameter. as shown in fig. 1, the input of hyperparameter optimization includes: algorithm a with configuration space, instance set and cost matrix c. the optimal hyperparameter configuration can be obtained by modeling and optimization of bayesian optimization algorithm. at the same time, the trajectory of searching for the optimal hyperparameter configuration, as well as the hyperparameter configuration and its performance data can be obtained. based on the output data, n-rrelieff algorithm is used to evaluate the importance of hyper-parameters and the effect of interaction between them on performance. next, the two parts are explained in detail. figure 1: algorithm configuration and analysis workflows 3.1 bayesian optimization algorithm question definition the hyperparameter selection problem of machine learning model is regarded as an unknown black box function optimization problem reflecting generalization performance. let θ1, ...,θn represent n hyperparameters of machine learning algorithm, whose domain space is expressed as θ1, ..., θn. the configuration space of the algorithm is defined as θ = θ1 × ... × θn. the hyperparameters θ ∈ θ are trained on the training data set dtrain, and the loss function l(θ,dtrain,dvalid) of machine learning algorithm is obtained on the verification set. the objective function of hyperparametric combinations in optimization problems is defined as follows [11]: f(θ) = 1 k k∑ i=1 l(θ,d (i) train,d (i) valid) (1) θ∗ = argθ∈θminf(θ) for an unknown objective function f(θ), bayesian optimization algorithm searches for a hyperparameter configuration that minimizes the function f(θ) on a bounded set θ. the basic idea of bayesian optimization algorithm is to construct a probability model for functionf(θ), establish evaluation criteria based on this model, determine the next hyperparameter for evaluation in configuration space θ, and at the same time, all the information available in previous evaluation 560 y. sun, h. gong, y. li, d. zhang can be reused for learning the shape of objective function [9]. the use of historical data enables bayesian optimization to find the minimum value of complex non-convex functions through fewer evaluations, but the corresponding cost is to perform more calculations to determine the next sampling point. therefore, the key of bayesian optimization algorithm can be summarized as the following two parts [7]: • establishing a probabilistic model to evaluate the objective function instead of the original complex objective function, which is expensive to evaluate. • the acquisition function is constructed by using the posterior information of the probability model to determine the next sampling point. question definition there are many models to model the objective function, among which the gauss process has been proved to be a convenient and powerful model optimization algorithm. gauss process is a set of random variables. if these random variables obey gauss distribution, then these random variables are gauss process. a gauss process consists of a mean function m : θ → r(m(θ) = 0) and a covariance function (kernelfunction)m : θ → r(m(θ) = 0). its concrete form is [25]: f(θ) ∼ gp(m(θ),k(θ,θ′)) (2) mean function m(θ) = e[f(θ)], covariance function k(θ,θ′) = e[(f(θ) − m(θ)(f(θ′) − m(θ′)))], for simplicity, usually set mean function m(θ) = 0. covariance function is a function of calculating the similarity between two data points in gauss process, which specifies the smoothness and amplitude of the unknown objective function. the selection of covariance function is very important, which affects the matching degree between gauss process and data properties. this paper chooses matérn 5 2 kernel function [15]. compared with other popular gauss kernel functions, it has fewer constrained smoothness assumptions and is very helpful to the optimization settings. the formulas are as follows: k2 5 (θ,θ′) = θ0(1k2 5 (θ,θ′) = θ0(1+ √ 5dλ(θ,θ ′)+ 3 5 d2λ(θ,θ ′))e− √ 5dλ(θ,θ ′)+ √ 5dλ(θ,θ ′)+ 3 5 d2λ(θ,θ ′))e− √ 5dλ(θ,θ ′) (3) among them, θ0 and λ denote the covariance amplitude and length dimensions respectively, and dλ(θ,θ′) = (θ,θ′)tdiag(λ)(θ −θ′) denotes the mahalanobis distance. given the input set g = θ1, ..,θt and the output y = f(θ1),f(θ2), ...,f(θt) of the observation set, the gauss process gp(m,k) is adjusted. due to the mixing of noise, the observed value is likely to be affected, and there is a certain deviation from the actual output value. in order to approach the actual situation, noise should be added to the probability distribution in the experiment. the formula is as follows: y = f(θ) + ε (4) the noise ε satisfies the independent and identically distributed gauss distribution: p(ε) ∼ n(0,σ2). the prior distribution of y is y ∼ n(0,s + σ2i), i is n-dimensional unit matrix, and s represents the covariance matrix k(θ,θ′). the joint prior distribution of the observed value y and predicted value f(θ∗) is as follows: hyperparameter importance analysis based on n-rrelieff algorithm 561 [ y f(θ∗) ] n ( 0, [ s + σ2i k∗ kt∗ k∗∗ ]) (5) in the formula, θ∗ represents the predictive input, kt∗ = k(θ1,θ∗),k(θ2,θ∗), ...,k(θt,θ∗),k∗∗ = k(θ∗,θ∗). according to the estimation of posterior probability of input value by gauss distribution, the predicted distribution at a given test point θ∗ is expressed as [7]: p(f(θ∗)|g,y,θ∗) = n(f(θ∗),cov(f(θ∗))) (6) among, f(θ∗) = k t ∗ [s + σ 2i] − 1y,cov(f(θ∗)) = k ∗∗−k ∗t [s + σ2i] − 1k∗ (7) gp evaluates f(θ∗) with all historical observation points as conditions, and then uses the posterior mean and variance of prediction to select the next set of superparameters on the basis of balanced development and exploration acquisition functions. acquisition function this section introduces the active strategy of selecting the next evaluation point in bayesian optimization: acquisition function, which is a functionα : χ× θ → r mapped from input space χ, observation space r and hyperparameter space θ to real space. the function is constructed from a posterior distribution obtained from known observation data sets d1:t, and the next evaluation point θt+1 is selected by maximizing its guidance: θt+1 ∈ maxx∈χαt(θ; d1:t) (8) this paper uses the promotion-based (expected improvement, ei) strategy [19], which performs better. ei strategy has been proved to be effective in the evaluation of global optimization of many black box functions. the closed form of ei strategy in gauss process is as follows: aei(θ; d1:t) = e[max(fmin −f(θ), 0)] (9) fmin is the optimal solution based on observation set so far. formula below describes the balance between the development and exploration of new sampling points. if the standard deviation of the prediction point is large, it means that the understanding of the point is small, and it is worth exploring; if the mean value is large, it means that the point may be the maximum point, which is worth developing. because the initial sampling data is very few, the algorithm will sample the points with large standard deviation; when the sampling points increase, the standard deviation decreases, and the algorithm tends to the points with large sampling mean, and eventually converges to the global optimal value. 3.2 importance assessment of hyperparameter bayesian optimization algorithm obtains the optimal hyperparameter configuration of machine learning algorithm through two important steps: gp process and iteration of acquisition function. however, due to the abstraction and black-box nature of its internal process, it is impossible to analyze the importance of hyperparameters. in order to increase the interpretability of the hyperparameters selected by bayesian optimization algorithm and to understand the importance ranking of the hyper-parameters of the algorithm itself, an n-rrelieff algorithm is proposed to evaluate the importance of the hyperparameters. 562 y. sun, h. gong, y. li, d. zhang relief algorithm the relief algorithm [17] was originally used in the field of feature selection. the main idea of the relief algorithm is to estimate the ability of this feature to distinguish adjacent samples according to the degree of discrimination of each attribute to an instance. relief’s process is to randomly select an instance i in the training set, search for k instances ij which are similar to instance i, and the samples which belong to the same category with instance i in ij are called h, and the samples of different categories are called m [9]. the weight w [a] of attributes a are estimated according to the values of example i and h and m in ij, and the approximate values of the probabilistic difference shown in formula below are obtained: w [a] = p (diff.valueofa|nearestinst.fromdiff.class)−p (diff.valueofa|nearestinst.fromsameclass) (10) if instances i and h have different attribute values a, then attribute a separates two instances from the same kind of instances, and the formula is expressed as reducing the weight estimation w [a]. if instances i and m have different attribute values a, then attribute values a separates two instances from different instances in a formula that correspondingly increases the weight estimation w [a]. the bigger the weight of the feature is, the stronger the classification ability of the feature is; on the contrary, the weaker the classification ability of the feature is [27]. among them, the difference of attribute value a between different instances i1 and i2 in w [a] is defined as [27]: dif(a,i1,i2) = 0,value(a,i1) = value(a,i2)1,value(a,i1) 6= value(a,i2) (11) the above formulas can be further calculated as follows: dif(a,i1,i2) = |value(a,i1) −value(a,i2) max(a) −min(a) (12) n-rrelieff algorithm the importance of hyperparameters of machine learning algorithm is evaluated. the input data used are the hyperparameter configuration and performance data of machine learning algorithm. on such data sets, performance data are continuous values, and can not be calculated using the latest samples of the same or different types presented in relief. in order to solve this problem, rrelieff [23] introduces the probability of two different instances to determine whether two instances belong to the same class. the probability definition can simulate and predict the relative distance between two instances. rrelieff is currently mainly used in the field of feature selection. this section improves and fuses the rrelieff algorithm and proposes an n-rrelieff algorithm to evaluate the importance of the interaction between hyperparameters. the main idea of the n-rrelieff algorithm for evaluating the importance of hyperparameters is to estimate the contribution of each hyperparameter to performance according to the degree of influence of each hyperparameter on performance. the n-rrelieff algorithm consists of two parts. the first part is to evaluate the importance of a single hyperparameter. in the training set, we randomly select a hyperparameter configuration instance i and select k instances ij which are similar to the instance i. in order to judge whether the instance ij and i belong to the same class, we introduce probability simulation and prediction of the relative distance between the two instances [24], as shown in formula below. among them, θ denotes the probability of different hyperparameter values, pdifa denotes the probability of different categories in similar instances, pdifc denotes the probability of different categories in similar instances, and pdifc|difa denotes hyperparameter importance analysis based on n-rrelieff algorithm 563 the probability of different categories in similar instances with different hyperparameter values. pdifa = p (difvalue (θ) |similarinstance) pdifc = p(difprediction|similar instance) (13) according to conditional probability: pdifc|difa = p(difprediction|difvalue(θ) similar instance) (14) combined formula (11) is available: w [θ] = pdifc|difa×pdifapdifc − (1 −pdifc|difa) ×pdifa1 −pdifc (15) repeat the above process k times to get w [θ], and evaluate the importance of a single hyperparameter according to the weight w [θ]. the second part is to measure the importance of hyper-parameters and understand the influence of the interaction between hyperparameters on the performance of machine learning algorithm. the n-rrelieff algorithm divide the contribution of the hyperparameters by the sum of all the contributions of the hyperparameters to normalization to calculates the importance of the hyperparameters. the formula is defined as (17): w [θm&θn] = ew [θm]+w [θn] e ∑ w[θ] (16) θm and θn denote two different hyperparameters, and ∑ w [θ] denotes the sum of the importance weights of all hyperparameters. this formula is a distortion of the normalization formula, which can more stably evaluate the influence of the interaction between hyperparameters on the performance. the whole process of the n-rrelieff algorithm is as follows: firstly, the contribution (weight) vector of each hyperparameter to the performance is calculated, the elements in the vector are accumulated, the importance of each hyperparameter is sorted by the accumulated value, and t significant hyperparameters are selected to enter the candidate subset of the hyperparameter, thus the iteration process begins. in the iteration process, the importance weights between the significant hyperparameters are calculated, and the importance weights between all the hyperparameters and the hyperparameters are finally output. the flow chart of the algorithm is shown in algorithm 1. in the algorithm, ndc, nda[θ] and ndc&da[θ] represent weight vectors of different predicted values (line 8), weight vectors of different attributes (line 10), and weight vectors of different predicted values and attributes (line 11). the algorithm calculates the importance weight w [θ] of each hyperparameter in line 16. hlist denotes the most important first t hyperparameters. variables d(i,j) (lines 8, 10, 11) are used to measure the distance between two instances ri and ij. the basic principle is that closer instances should have greater impact: d(i,j) = d1(i,j)∑k l=1 d1(i, l) d1(i,j) = e −( rank(ri,ij) σ )2 (17) rank(ri,ij) is the ranking of distance between instance ij and instance ri. σ is used to control distance, which is customized by users. because the expected results can be interpreted by probability, divide the contribution of each instance in k-nearest neighbor instance by the sum of all k contributions to normalization. the reason for using rankings instead of actual distances is that actual distances are related to specific issues, and by using rankings, we ensure that recent instances always have the same impact on weights. 564 y. sun, h. gong, y. li, d. zhang algorithm 1 n-rrelief algorithm 1: input: a training set dtrain consisting of a hyperparameters set g(θ1, ..,θn) and performance set c(c1, ..,cn), dtrain = ((θ1,c1), (θ2,c2), ..., (θn,cn)); 2: output: weight evaluation vector w of interaction between hyperparameters. 3: initialize ndc, nda[θ], ndc&da[θ], w[θ], hlist to 0. 4: for i = 1 to m do 5: random selection example ri; 6: selecting k neighbors ij; 7: for j = 1 to k do 8: ndc = ndc + diff(c,ri,ij) ·d(i,j); 9: for θ = 1 to n do 10: nda[θ] = nda[θ] + diff(θ,ri,ij) ·d(i,j); 11: ndc&da[θ] = ndc&da[θ] + diff(c,ri,ij) ·diff(θ,ri,ij) ·d(i,j); 12: end for 13: end for 14: end for 15: for θ = 1 to t do 16: w [θ] = ndc&da[θ]/ndc − (nda[θ] −ndc&da[θ])/(m−ndc); 17: according to the importance weights of hyperparameters from high to low, and taking the first t into the set of hyperparameters, calculate the interactive importance of hyperparameters in hlist: 18: for a = 1 to t do 19: for b = a + 1 to t do 20: w [θm&θn] = ew[θm]+w[θn] e ∑ w[θ] ; 21: end for 22: end for 23: end for 24: return w [θ] and w [θm&θn] hyperparameter importance analysis based on n-rrelieff algorithm 565 the n-rrelieff algorithm evaluates the importance measure of the interaction between hyperparameters, assigns a weight value to each hyperparameter, and evaluates how the weight is affected by the hyperparameters, so as to determine a series of the most important algorithm hyperparameters. hyperparameters with large weight and hyperparameters combination indicate that the adjustment of these hyperparameters is very important to the performance of machine learning algorithm, while other hyperparameters with small weight mean that even if the hyperparameters are adjusted repeatedly, the influence on the performance of machine learning algorithm is not great. when the computational resources are limited, we can focus on adjusting the hyperparameters with large weights. for the hyperparameters with small weights, we can use their default values in machine learning algorithm. when sufficient computing resources are available, it is still recommended to adjust all hyperparameters. n-rrelieff algorithm can identify important hyperparameters in machine learning algorithm. the results can guide bayesian optimization algorithm to optimize the hyperparameters and improve the performance and efficiency of the algorithm. 4 experiment the experiment in this section is divided into two parts. the first part is to analyze the performance of several hyperparameter optimization algorithms in machine learning hyperparameter optimization process through experiments, and select the best performance hyperparameter optimization algorithm, using its hyperparameter configuration history data to evaluate the importance of hyperparameter experiments; the second part is to use the n-rrelieff algorithm based on the hyperparameter configuration history data obtained from experiments. evaluate the importance of super parameters and combinations of machine learning algorithms and obtain a series of super parameters which have a significant impact on the performance of the algorithms. bayesian optimization algorithm is used to validate the effectiveness of the results obtained by n-rrelieff algorithm, which further ensures that the importance ranking of the machine learning algorithm is accurate. 4.1 hyperparameter tuning experimental data and methods in order to fully verify the superiority of bayesian optimization algorithm, all experiments are carried out on the data set from openml100 [3] this section. openml100 is a benchmark suite that contains 100 data sets from different domains and 500 to 1000 data points with balanced distribution. two classification methods were analyzed on data sets from openml100: svms [5] and random forest [15]. for svms [5], two types of kernels are analyzed: radial basis function and sigmoid kernels. all algorithms use the same data pretreatment steps, including interpolation of missing data and coding of discrete features by one-hot-encoding. support vector machine (svm) is sensitive to the proportion of input variables, so it is necessary to standardize the input variables. after data pretreatment, each classification method is optimized by using grid search, random search and bayesian optimization algorithm. in order to ensure that the hyperparameter optimization method does not produce any deviation because of the configuration space of the hyperparameter, this section uses the same type and range of hyperparameter for the three hyperparameter optimization methods. the hyperparameter types and ranges of the two algorithms are shown in tables 1 and 2. 566 y. sun, h. gong, y. li, d. zhang table 1: hyperparameter configuration space in svm algorithm svm hyperparameters types configuration space default value complexity float [0.001, 1000.0] [1.0] coef0 integer [0.0, 10.0] [0.0] gamma float [2−15, 23] [2−15] shrinking categorical true, false [true] tolerance float [10−5, 10−1] [10−2] imputation categorical mean, median, mode [mean] table 2: hyperparameter configuration space in random forest algorithm random forest hyperparameter types configuration space default value split criterion categorical entropy, gini [entropy] bootstrap categorical true, false [true] max.features float [0.1, 0.9] [0.1] min.samples leaf integer [1, 20] [1] min.samples split integer [2, 20] [2] imputation categorical mean, median, mode [mean] experimental results table 3 and 4 show the svms performance results of different hyperparameter tuning methods under the rbf kernel function and sigmoid kernel function, respectively. table 5 shows the performance results of random forest algorithm under different hyperparameter optimization methods. in order to increase the credibility of the results, this section uses 100 data sets from different domains to verify the average results of each index of svms and random forest algorithm using default parameters, grid search parameters, random search parameters and bayesian optimization algorithm parameters. the experimental results show that bayesian optimization algorithm can obtain the optimal performance and running time in the process of svms and random forest algorithm optimization. table 3: average performance results of the svm (rbf) algorithms under different hyperparameter tuning algorithms parameter adjustment method precision f1 −score recall runtime no (using the default value) 0.22 0.30 0.47 1.8s grid search algorithm 0.78 0.80 0.78 40.3s random search algorithm 0.82 0.82 0.82 28s bayesian optimization algorithm 0.94 0.92 0.94 18.2s table 4: average performance results of the svm (sigmoid) algorithms under different hyperparameter tuning algorithms parameter adjustment method precision f1 −score recall runtime no (using the default value) 0.54 0.37 0.49 2s grid search algorithm 0.80 0.82 0.81 42.9s random search algorithm 0.85 0.83 0.81 30s bayesian optimization algorithm 0.95 0.94 0.94 20.1s hyperparameter importance analysis based on n-rrelieff algorithm 567 table 5: average performance results of the random forest algorithms under different hyperparameter tuning algorithms parameter adjustment method precision f1 −score recall runtime no (using the default value) 0.87 0.89 0.87 2.5s grid search algorithm 0.93 0.93 0.90 45.2s random search algorithm 0.94 0.93 0.93 35.3s bayesian optimization algorithm 0.98 0.97 0.97 25.5s 4.2 importance assessment of hyperparameter in section 4.1, two classifiers use hyperparameter optimization n-rrelieff algorithm to generate a large number of hyperparameter configuration and performance data (including the optimal hyperparameter configuration and performance) during the operation process. these data are used to evaluate the importance of hyperparameter in the two classifiers. each classifier is shown by a figure and a table. fig. 2 shows the average hyperparameter importance of hyperparameter and hyperparameter combination by bar graph. the x axis represents the hyperparameter and hyperparameter combination name, and the y axis represents the hyperparameter importance weight. the higher the weight, the greater the impact of hyperparameters or combinations on performance. if it can not be adjusted to the appropriate value, the accuracy of the algorithm will be reduced. figure 2: n-rrelieff algorithm evaluate hyperparameter and combination importancesvm(rbf kernel) table 6 uses the bayesian optimization algorithm to fix the two most important hyperparameters (using their default values) and adjust the other hyperparameters according to the most important hyperparameters selected by the n-rrelieff algorithm. the performance and running time of hyperparameter optimization are compared when all hyperparameters are adjusted 568 y. sun, h. gong, y. li, d. zhang and only the first three hyperparameters with great importance are adjusted according to the algorithm. table 6: bayesian optimization algorithm to adjust the performance of different hyperparameters-svm(rbf kernel) hyperparameters precision f1 −score recall runtime fixed gamma 0.35 0.32 0.35 16.3s fixed complexity 0.75 0.70 0.72 15.8s adjust all hyperparameters 0.94 0.92 0.94 18.2s adjust n-rrelieff and select top three hyperparameters 0.92 0.92 0.90 9.4s svm results: fig. 2 and fig. 3 analyze two kinds of kernel functions of svms, rbf kernel function and sigmoid kernel function, respectively. the experimental results clearly show that the most important hyperparameter in both cases are gamma, and the second important is complexity. this conclusion is validated by the experiments of different hyperparameter adjustment by bayesian optimization algorithm: the hyperparameter gamma without optimizing, even if all other hyperparameters are adjusted, the classifier will get the worst performance, so it is the most important hyperparameter, complexity is the second. in addition, the performance of bayesian optimization algorithm adjusting the first three important hyperparameters according to n-rrelieff algorithm is not different from that of adjusting all the super-parameters, and the optimization time is greatly accelerated. fig. 3 shows that the interaction between hyperparameters gamma and complexity is more important than that of complexity itself when using the sigmoid kernel. experience shows that gamma and complexity are important hyperparameters in svm (see table.7).this paper uses a wide range of data sets to provide systematic validation for traditional experience. the least important hyperparameter for svm’s accuracy is whether to use shrinkage heuristic algorithm. the purpose of this hyperparameter is to reduce computing resources rather than improve prediction performance. according to the criteria for calculating the impact of hyperparameters on performance, its importance weight accords with the actual results. table 7: bayesian optimization algorithm to adjust the performance of different hyperparameters-svm(sigmoid) hyperparameters precision f1 −score recall runtime fixed gamma 0.60 0.62 0.66 15.3s fixed complexity 0.75 0.75 0.73 16.2s adjust all hyperparameters 0.95 0.94 0.94 20.1s adjust n-rrelieff and select top three hyperparameters 0.93 0.92 0.93 10.2s results of the random forest algorithm: fig. 4 shows the experimental results of the random forest algorithm. the performance of the random forest algorithm is contributed by a small number of hyperparameters. min. sample leaf and max. features are the most important hyperparameters (see table.8). in the experimental process, bootstrap is the most important hyperparameter on only a few data sets. the split criterion is the most important parameter in the data set ’scene’. similarly, the experimental results are consistent with the bayesian optimization algorithm validation experiment and manual parameter adjustment experience. the final conclusion: for all classifiers, the performance changes in most cases depend hyperparameter importance analysis based on n-rrelieff algorithm 569 figure 3: n-rrelieff algorithm evaluate hyperparameter and combination importancesvm(sigmoid) table 8: bayesian optimization algorithm to adjust the performance of different hyperparameters-random forest hyperparameters precision f1 −score recall runtime fixed gamma 0.92 0.90 0.90 20.3s fixed complexity 0.93 0.92 0.92 18.9s adjust all hyperparameters 0.98 0.97 0.97 25.5s adjust n-rrelieff and select top three hyperparameters 0.96 0.95 0.95 13.5s on a small number of hyperparameters. in many cases, the same set of hyperparameters can be applied to different data sets that have the same domain and similar data characteristics. therefore, it is necessary to understand the importance of hyperparameters in many cases, such as setting the default value of the algorithm, analyzing the automated hyperparameter optimization program and so on. in addition, understanding the importance of hyperparameters is a scientific attempt in itself, and can also provide guidance for algorithmic developers. more interestingly, the experimental results show that the hyperparametric interpolation strategy has little effect on the performance of the classifier. in people’s experience, the interpolation strategy is important, but this experiment shows that for interpolation, which strategy has little effect on the results. it should be noted that the results provided in this section do not mean that only adjusting the most important hyperparameters and combinations is sufficient. although hutter [16] and others have shown that this can indeed lead to faster improvements, they also say that when there are enough computing resources, it is still recommended to adjust all hyperparameters. 570 y. sun, h. gong, y. li, d. zhang figure 4: n-rrelieff algorithm evaluate hyperparameter and combination importance-random forest 5 conclusion in this paper, we compare the performance of bayesian optimization algorithm, grid search and random search on several datasets. the results show that bayesian optimization algorithm is superior to other two algorithms in classifier performance and running time. at the same time, n-rrelieff algorithm is used to determine the importance of the interaction between hyperparameters and hyperparameters on 100 data sets. the results show that the same hyperparameters have similar importance on different data sets. for svms, the hyperparameters gamma and complexity are the most important, and for random forest, min. sample leaf and max features are the most important. in order to verify the experimental results, bayesian optimization algorithm optimizes different hyperparameters for each classifier. the results of this experiment are consistent with those of n-rrelieff, and to a large extent with popular views. a surprising result of this analysis is that data interpolation strategy has little impact on performance, which may be limited to the interpolation strategy used in this experiment. it is further proved that this conclusion needs to be studied as a whole and other interpolation strategies are added. future work will analyze which values of important hyperparameters are important, and provide users with the range and information of hyperparameters that can achieve better performance. in addition, it can be extended to regression and clustering algorithms to provide useful experimental support for the field of machine learning algorithm hyperparameter tuning optimization. in addition, the algorithm will be extended to the field of in-depth learning to optimize various network models (e.g. cnn, rnn models) and determine the importance of hyperparameters. hyperparameter importance analysis based on n-rrelieff algorithm 571 funding this work was funded by the fundamental research funds for the central universities (grant no.18cx02019a). author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] bartz-beielstein, t. 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(2017). large scale software test data generation based on collective constraint and weighted combination method, tehnicki vjesnik, 24(4), 1041–1050, 2017. international journal of computers communications & control issn 1841-9836, 12(1):53-60, february 2017. automated 2d segmentation of prostate in t2-weighted mri scans j. jucevičius, p. treigys, j. bernatavičienė, r. briedienė, i. naruševičiūtė, g. dzemyda, v. medvedev justinas jucevičius*, povilas treigys, jolita bernatavičienė, gintautas dzemyda, viktor medvedev vilnius university, institute of mathematics and informatics lithuania, 08663 vilnius, akademijos str. 4 justinas.jucevicius@mii.vu.lt, povilas.treigys@mii.vu.lt, jolita.bernataviciene@mii.vu.lt, gintautas.dzemyda@mii.vu.lt, viktor.medvedev@mii.vu.lt *corresponding author: justinas.jucevicius@mii.vu.lt rūta briedienė, ieva naruševičiūtė vilnius university, national cancer institute lithuania, 08660 vilnius, santariškiu̧ str. 1 ruta.briediene@nvi.lt, ieva.naruseviciute@gmail.com abstract: the prostate cancer is the second most frequent tumor amongst men. statistics shows that biopsy reveals only 70-80% clinical cancer cases. multiparametric magnetic resonance imaging (mri) technique comes to play and is used to help to determine the location to perform a biopsy. with the aim to automating the biopsy localization, prostate segmentation has to be performed in magnetic resonance images. computer image analysis methods play the key role here. the problem of automated prostate magnetic resonance (mr) image segmentation is burdened by the fact that mri signal intensity is not standardized: field of view and image appearance is for a large part determined by acquisition protocol, field strength, coil profile and scanner type. authors overview the most recent prostate mr image segmentation challenge results and provide insights on t2-weighted mri scan images automated prostate segmentation problem by comparing the best obtained automatic segmentation algorithms and applying them to 2d prostate segmentation case. the most important benefit of this research will have medical doctors involved in the management of the cancer. keywords: computer image processing, 2d prostate segmentation, magnetic resonance imaging (mri), t2-weighted scan. 1 introduction various data mining methods find application in medicine and health care. large studies are presented in [9], [22], there are a lot of recent applications, eg. [7], [8], [25], [27]. this paper deals with medical image analysis, particularly with magnetic resonance images and prostate cancer. world cancer research fund international states that prostate cancer is the second most frequent tumor among men and fourth most common among both genders. lithuanian cancer registry data from 2012 shows that prostate cancer prevalence reaches 34% amongst men aging 55 to 74 years. the mortality from the prostate cancer is the second most common after the lung cancer amongst men and the third most common among both genders after lung and stomach cancers [23]. according to european association of urology guidelines, from 10 to 12 core biopsy is recommended in case of prostate-specific antigen level elevation and/or suspicious digital rectal examination findings [1]. contemporary, random systematic prostate biopsy strategy includes failure to detect clinically significant cancer. undersampling in up to 30% of cases copyright © 2006-2017 by ccc publications 54 j. jucevičius, p. treigys, j. bernatavičienė, r. briedienė, i. naruševičiūtė, g. dzemyda, v. medvedev with clinically significant tumors being missed on initial biopsy. this diagnostic uncertainty can lead to repeat biopsy, delayed detection of significant disease and disease overtreatment [20]. despite inaccuracy, biopsy remains the main way that can unambiguously detect prostate cancer if performed on the right location. the latest recommendations in prostate cancer care include multiparametric magnetic resonance imaging (mpmri) as the tool for prostate cancer diagnosis, characterization, staging as well as risk stratification among men, who need active surveillance. today’s computer-aided detection programs are associated with european society of urogenital recommendations for prostate evaluation by mpmri are the attractive subject for research that incorporates the development of new image analysis [4], [16], [26] and data mining algorithms. usually prostate localization and segmentation in magnetic resonance images is done by hand; however, it takes a lot of time and can be inaccurate. this causes the need for software to aid in automated prostate segmentation [13] in a standardized manner. thus the main objective of this study was to overview the current situation of the field and to adopt today’s best methods developed to a procedure named prostate template biopsy [30]. 2 research motivation and experiment setup the problem of automated prostate mr image segmentation is burdened by the fact that most researchers cannot compare the effectiveness of different algorithms due to either troublesome implementation without the help of the original author or algorithm being closed source. what further aggravates the problem is that mri signal intensity is not standardized and image appearance is for a large part determined by acquisition protocol, field strength, coil profile, and scanner type [18]. it is challenging task to identify and segment objects within images due to high object and background variability. in computer vision image segmentation can be described as procedure of finding group of image pixels that shares the same feature and describe homogeneous image region. analysis may take into account object texture, intensities shape and etc [3]. however, the problem arises when investigative object is compound of several regions, object edges are blurred or object’s shape is varying. here the segmentation techniques are applied that makes further processing easier: to each group of pixels that describes the region the unique region label is assigned. researchers [10] have put a big effort to summarize prostate segmentation methods. study reveals four different groups of segmentation algorithms that fall into: • contour and shape based methods exploit contour and shape information to accomplish segmentation task. • region-based methods analyses predominant prostate intensity distribution in different modalities. • supervised and unsupervised classification methods aim at obtaining a partition of the feature space into a set of labels for different regions. for this task classifier and/or clustering techniques are used. • hybrid methods combine a priori boundary, shape, region, and feature information of the prostate gland. methods of this group are robust to noise and produce superior results in presence of shape and texture variations. several successful grand challenges in medical imaging have been organized in recent years to deal with similar issues in the fields of coronary image analysis, retinal image analysis, liver automated 2d segmentation of prostate in t2-weighted mri scans 55 segmentation on computed tomography (ct) scan, lung registration on ct scan, brain segmentation on mri and prostate segmentation on mri. prostate mr image segmentation challenge (promise12) [18] was designed to allow comparison of segmentation algorithms on the basis of robustness and performance by providing hundred t2-weighted mri scans gathered from four different institutions. t2-weighted mr images were used because they contain most anatomical detail and most current researchers focus on them for segmentation. with the aim to evaluate segmentation results the promise12 challenge introduce those widely used metrics: • dice similarity coefficient (dsc) [15], • absolute relative volume difference [12], • average boundary distance [12], • 95% hausdorff distance (hd) [5]. the score for each metric was mapped to a relative value between 0 and 100. the scores for all metrics were then averaged to obtain a score per case, and the average of score over all cases was calculated and used to rank algorithms. table 1 presents prostate detection algorithms and depicts obtained results of the challenge. here, a stands for an automatic method that requires no user interaction; s semi-automatic method that requires some initial user interaction; i interactive method that requires full user interaction from the beginning of segmentation until the end. table 1: prostate detection algorithms [2] team name score type the idea of the algorithm imorphics 84.36 a apply active appearance models on images with increasing resolution by refining results scrautoprostate 83.49 a intensity normalization, marginal space learning, boundary refinement cba 80.66 i smart paint algorithm segments prostate by sweeping the mouse cursor in the object or background sbia 78.34 a multi-atlas based segmentation, zooming into vicinity grisles 77.56 s detect location of the prostate, use probabilistic active shape model for boundary detection robarts 77.32 s contour evolution with the integration of the generic star shapes prior icprostateseg 76.06 a multi-atlas based segmentation using local appearance-specific atlases utwente 75.23 s active appearance models based segmentation cimalab 74.68 a atlas-based segmentation that selects the most similar templates using multi-scale surf analysis and applies linear combination diag 73.30 a multi-atlas based segmentation using selective and iterative method for performance level estimation algorithm for merging atlas labels ethz 72.38 a graph cut based segmentation using image features, context information, semantic knowledge ubudg 70.44 s random decision-based forest for classification and the propagation of region for segmentation rutgers 65.97 a segmentation based on active appearance models two best fully automated algorithms from the promise12 challenge named imorphics [28], [11] and scrautoprostate [2] having scores of 84.36 and 83.49 respectively were chosen for further analysis. the pipelines of the imorphic and scrautoprostate methods are presented in figure 1. 56 j. jucevičius, p. treigys, j. bernatavičienė, r. briedienė, i. naruševičiūtė, g. dzemyda, v. medvedev figure 1: pipelines of the imorphics (on the left side) and scrautoprostate (on the right side) prostate segmentation methods both algorithms were adapted to be suitable for prostate segmentation in 2d space instead of the 3d space originally used in the challenge. all adaptations were performed by choosing the corresponding algorithm designed for 2d space without any modifications. prostate segmentation in 2d space was chosen because of the biopsy procedure named prostate template biopsy [30]: • it has an accuracy of 95%. • it is becoming more and more prevalent. • it uses only two slices out of 10-40 used in 3d space, thus reduces complexity and computational time needed for segmentation. the imorphics method [11], [28] belongs to a group of hybrid prostate segmentation methods. the method converts voxel-based segmentation to surface by using the marching-cube algorithm [19]. segmentation allows indicating whether surface belongs to the prostate gland or not. next, to construct statistical prostate appearance model, so called mean image, a set of possible deformations are introduced and registered together [6]. this step allows finding the minimum information needed to code the mean reference image with the deformations that map mean image to each example image. finally, to obtain features of the image authors use active appearance model that control shape and texture. the model computes the closest match of the prostate gland shape in sample image using the least squares sum of residuals. for the efficiency jacobian matrix describing the average change in residuals with respect to changes in model parameters on a training set is pre-computed and for the initial estimate of the model, authors introduce grid of starting search points across the image, typically 20 mm apart in all directions. at the initial stage the scrautoprostate method [2] applies region based brightness and contrast poison editing technique [21]. then, with the view to constructing statistical shape model, training segmentation mask (represented as a mesh) is constructed the same way as it was presented in the imorphics method by using the marching cube technique. then, orientation and scale variations in those statistical shapes are removed after application of procrustes analysis [14]. the remaining shape variability is finally represented with a point distribution model, and the strongest shape models are extracted through principal component analysis. while testing algorithm with the unseen image, initial segmentation is obtained by applying teh marginal space learning [29] algorithm that computes unknown pose and shape coefficients. the previous step aligns shape model to the image and gives good initial segmentation. at the final step, mesh surface is refined by using non-rigid, hierarchical boundary deformation [17]. introduced refinement iteratively displaces mesh vertices along the mesh surface normal. automated 2d segmentation of prostate in t2-weighted mri scans 57 3 experiments and results in this research, we have used images from 50 cases provided by the promise12 [18] challenge. initial three-dimensional mri data set used by the challenge was split into separate images representing every slice. the number of slices per case varied from 15 to 54. images that did not contain prostate were removed. remaining images were split into two groups representing prostate apex and base parts. from each group, middle image was chosen to represent the apex and base part of the prostate respectively having 100 images in total. all images were gathered from four different institutions and varied in resolution: • 256 x 256 pixels; • 320 x 320 pixels; • 512 x 512 pixels. to test the performance of the 2d prostate segmentation algorithms we have used leave-oneout cross-validation [24], where each image is segmented using a model, built from the training set with this image removed, i.e. image is "excluded" from the set of images and used for validation. the result was then compared against the reference segmentation. results, presented in table 2, show that transition from 3d to 2d space prostate gland segmentation can be accomplished with the minimum loss of accuracy. dsc measure when compared imorphic and scrautoprostate algorithms decreased by 0.04 and 0.01 points respectively. it results that 2d segmentation is successful and opens the possibility to apply prostate template biopsy procedure that unifies biopsy of the prostate gland procedure. figure 2: segmentation results in 2d space of both imorphics and scrautoprostate methods on the left and right respectively. in dashed line reference segmentation, in white line automated segmentation. dsc and 95% hausdorff distance statistics were selected to match those reported in original algorithms for comparison. the dsc measures the amount of overlap between the reference segmentation and the automated segmentation. dsc can range from zero to one, where zero represents no overlap and one corresponds to identical segmentations. the directed hd identifies 58 j. jucevičius, p. treigys, j. bernatavičienė, r. briedienė, i. naruševičiūtė, g. dzemyda, v. medvedev table 2: comparison of prostate segmentation results between 2d and 3d space by analyzing t2-weighted scan data algorithm/measures imorphics scrautoprostate 2d 3d 2d 3d dsc 0.84 0.88 0.83 0.82 hd 6.26 4.17 9.73 not specified execution time (s) 51.11 480 0.24 2.30 the point on the reference segmentation that is the farthest from any point on the model segmentation and measures the distance from this point to the nearest point on the model segmentation. the 95% hd is less sensitive to outliers than hd since it considers the point representing the 95th percentile of the distances instead of the farthest. figure 2 shows sample segmentation results. visually, both algorithms produces quite similar segmentation results. metrics measures are as follows: imorphics dsc and hd values were 0.93 and 4.12; scrautoprostate dsc and hd values were 0.93 and 6.99. conclusions 3d prostate gland segmentation cannot be directly adapted to today’s best methods developed to a procedure named prostate template biopsy. that leads to an investigation whether 3d prostate gland segmentation can be transferred to 2d segmentation while keeping the same segmentation accuracy with the possibility to speed up algorithms execution time. 2d images representing slices are starting point for the analysis and further conclusions. the investigation presented in this paper has shown that there is a minor loss in algorithms accuracy when moving prostate gland segmentation from 3d space to 2d space. dice similarity coefficient when compared imorphics and scrautoprostate algorithms have changed by 0.04 and 0.01 points, respectively. however, imorphics performed slightly better at the cost of execution time. as expected, both algorithms improved execution time by almost 10 times in 2d in comparison to 3d. despite both algorithms perform quite well, the development is necessary for practical usage where automated prostate segmentation in mri is needed. the most important benefit of this 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[30] prostate template biopsy. essexurology.co.uk. retrieved 2016 july 30, 2016. from http://www.essexurology.co.uk/prostate_template_biopsy.php. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(6), 743-758, december 2019. automated expert system knowledge base development method for information security risk analysis d. vitkus, z. steckevicius, n. goranin, d. kalibatiene, a. cenys donatas vitkus* vilnius gediminas technical university, vilnius, lithuania *corresponding author: d.vitkus@vgtu.lt zilvinas steckevicius vilnius gediminas technical university, vilnius, lithuania zilvinas.steckevicius@vgtu.lt nikolaj goranin vilnius gediminas technical university, vilnius, lithuania nikolaj.goranin@vgtu.lt diana kalibatiene vilnius gediminas technical university, vilnius, lithuania diana.kalibatiene@vgtu.lt antanas cenys vilnius gediminas technical university, vilnius, lithuania antanas.cenys@vgtu.lt abstract: information security risk analysis is a compulsory requirement both from the side of regulating documents and information security management decision making process. some researchers propose using expert systems (es) for process automation, but this approach requires the creation of a high-quality knowledge base. a knowledge base can be formed both from expert knowledge or information collected from other sources of information. the problem of such approach is that experts or good quality knowledge sources are expensive. in this paper we propose the problem solution by providing an automated es knowledge base development method. the method proposed is novel since unlike other methods it does not integrate ontology directly but utilizes automated transformation of existing information security ontology elements into es rules: the web ontology rule language (owl rl) subset of ontology is segregated into resource description framework (rdf) triplets, that are transformed into rule interchange format (rif); rif rules are converted into java expert system shell (jess) knowledge base rules. the experiments performed have shown the principal method applicability. the created knowledge base was later verified by performing comparative risk analysis in a sample company. keywords: information security risk analysis, ontology, knowledge base, expert system, transformation, rif, jess. copyright ©2019 cc by-nc 744 d. vitkus, z. steckevicius, n. goranin, d. kalibatiene, a. cenys 1 introduction many authors agree that today information is the critical business part despite the size of the enterprise. companies are adapting to the time changes: growing speed of change and complexity, globalisation [16]. even the smallest company has any information and information system, which are needed to be secured [35]. however, because of enterprises becoming more complex, integrated and connected to third parties, the security and controls budget quickly reaches its limitations [12]. the protection of information resources from the complex and rapidly evolving security threat landscape is a significant challenge to the modern organisation [46]. the reasons stated above motivate for searching of effective ways to increase information security level in organizations. according to the importance of assets, it is necessity to analyse the potential risks to do not allow these risks to be converted into events [48]. as [10] said, risk management has proved to be efficient in the frame of the governance system due to its capacity to reduce costs associated with the management of different risks. according to europe’s general data protection regulation, a risk-based approach to data protection is embraced [30]. so, all companies, including small and medium-sized enterprises (sme), have to protect their information systems. enterprises have two ways of ensuring their information security: to employ a specialist or to outsource the risk analysis service. the problem is that the price of both choices is rather high. therefore, there is a need to automate the security risk analysis process by introducing expert systems or decision support systems, which could help enterprises to perform an information security risk analysis without any special knowledge and without hiring security experts and making security risk analysis process faster and cheaper [47]. there were some attempts to use expert systems for automating a particular part of security risk analysis [4, 5], cybersecurity incident prediction [42] and solving other real-worl problems also [29]. the main reason of such attempts is to make risk analysis process faster and cheaper [47]. also, using expert systems helps to optimise asset management and their life cycle according to risk assessment, especially in specific sectors, like electric power transmission [41], power plant projecting [19]. however, it is necessary to mention that the development of an expert system knowledge base is an expensive and complicated process [24], [15]. so, the motivation of the paper is related to the need to automate the security risk analysis knowledge base development to minimize the expenses. in this paper we propose a novel approach that allows the automatic transformation of existing information security ontologies into the expert system knowledge base rule set. currently, a lot of information, including security information, like standards, best practices, is collected and presented in the ontologies [7]. ontology comparing with usual not semantic database has particular advantages that can be used for developing expert systems [14]. ontology can be expressed in several ways, for example, using graphical software, which makes ontology formation, maintenance, and usage easier [40]. therefore, ontology presented in a particular software, could be used for its automatic transformation into the expert system knowledge base. however, a deep understanding of ontology is needed, since any change in the ontology may require a change in the software’s source code [27]. nevertheless, these disadvantages cannot reproduce the expressiveness of ontology, and expert systems have their inference engine which is separate from the knowledge base. the main advantages of the work is that we provide a method for automated knowledge base development for an expert system, that can be used for risk analysis. it can help to provide an information security risk assessment for non-it specialist cheaper and in a more effective way. the methods of information security risk analysis and the development of reasoning engines for expert systems are out of the scope of this paper. the paper is organized as follows. section 1, current section, is an introduction. section 2 automated expert system knowledge base development method for information security risk analysis 745 presents related works in security risk analysis and ontology usage for the development of expert systems knowledge base. section 3 presents an approach of automatic transformation of existing security ontologies into the expert system knowledge base rule set and presents the implementation of the proposed approach of the information security standards ontology transformation into the jess es rules which is implemented in several steps with the help of a developed tool. owl rl subset of ontology elements are segregated into rdf triplets, that are later transformed into rif format. then, rif rules are converted into jess knowledge bases rules with the help of an external tool. conversion results are presented in section 4. finally, section 5 concludes the paper. 2 related work expert system is an computer-based system which has several parts. the basic architecture of an expert system is shown in figure 1. figure 1: the basic architecture of an expert system [22] the basic architecture of an expert system includes three main parts: knowledge base, inference engine and user interface. one of the biggest problems in expert system usage is to get knowledge base updated [3]. nevertheless, expert systems are used in nowadays for solving actual problems. for example, it can be successfully used as first-line in it help-desk [13]. the majority of expert systems are developed using specialized software called a shell, especially when it comes to the rule-based expert systems. shell allows the user just to create the knowledge base in the form of simple rules and not paying attention to knowledge interpretation engine development, thus minimizing the es development time [2]. nevertheless, forming the knowledge base is a complicated process that has a number of unsolved issues and illustrates the problems of this work. one of them is knowledge base integrity that is hard to maintain [24]. adding new data can destroy existing rules, cause conflicts or endless cycles [25]. in result, it can make expert system work incorrectly. there are some methods to solve knowledge base integrity problem. one of them is kads (and its extension commonkads) methodology [44]. the main idea is to design everything with uml diagrams [28]. the main disadvantage of that method is that it needs a lot of time resources, and the user can still make a mistake if a knowledge base is very complicated. the use of information collected in the form of ontologies for es knowledge base formation seems useful by many experts, but in practice, current research focus on ontologies and expert systems is separated. the main idea of ontology is to identify specific object classes and relations between them [8]. ontology can be used in various business areas, but these areas can be divided into two parts: ontology usage as a vocabulary and ontology usage as a content [9]. ontology usage as vocabulary enabled to develop the second generation of the web [18]. but ontology can be much more than a set of concepts or vocabulary they have a lot of 746 d. vitkus, z. steckevicius, n. goranin, d. kalibatiene, a. cenys applications in artificial intelligence. in this context, ontology can be used as information sources [23]. ontology provides integrity and can be displayed by various graphical tools, has a common format and can be apprehensible in other systems, which designed to work with ontologies [31]. for example, xp.k (extreme programming of knowledge-based systems) methodology is based on agile principles: to start from the simplest model of expert system and grow it with time [26]. model should include just that what is really needed to solve existing problems. knowledge base is created from ontology which is created by experts [38]. the main problem of this approach is that it requires a lot of time to develop the ontology from scratch on iteration basis and does not make use of already existing knowledge collected, while it could be valuable to use already existing information security-related ontologies. some attempts [34] for ontology use as a source for knowledge base formation, but the approach proposed destroys the idea of es that knowledge base and inference engine should be separate. damljesskb [21] software was developed to transform daml (darpa agent markup language) ontology to the jess expert system’s rules, but it can work only with specific daml ontology and jess expert system. another sample is dlejena the software, which transforms the owl 2 rl profile, compatible with pd semantic, to jena expert system platform [32]. the main disadvantage of these programs is that they transform only specific ontologies to the specific expert systems format. on the other hand, there exist a number of information security ontologies that can be used in knowledge base formation of risk-analysis es. currently, most of them are used for other purposes. for example, ontology proposed in [39] can be used as a tool to identify the level of system vulnerabilities according to the internal users’ accounts configuration and system configuration [33]. as the authors state, all illegal activity in the system is done by human resources and internal users have more privileges than external. this tool is based on taxonomy with users’ settings and includes different behavioural motivation, for example, intentional and unintentional activity. rope methodology and the related ontology is used for enterprise it security evaluation, focuses on business processes and risk management [33]. ontology encapsulates well-known information security concepts, such as assets, vulnerabilities, threats, and controls. there is a number of ontologies based on external security standards, like iso 27001/2 that can be used to align external standards with internal procedures [17]. one more security ontology is ontosec [33] that can be used by security engineers can to set configuration effectively, according to the existing security events. in [36] the new exhaustive ontology was proposed, which increased coverage of security standards compared to the existing ontologies and has better branching and depth properties for ontology visualization purposes. it was used for security standards mapping task and was linked with 4 security standards: iso 27001, pci dss, issa 5173 and nistir 7621. summarasing it can be said that there are a lot of information security ontologies and our work allows transforming the selected ontology into expert system rules set in an automated and novel way. 3 automated approach of ontology transformation into es knowledge base in order not to spend time resources for developing knowledge bases from scratch and to solve the limitations specific to earlier research in ontology transformation to knowledge base rule-sets we propose new method that allows transformation of existing information security ontologies into expert system rule-set via universal rif format, that is later converted to the automated expert system knowledge base development method for information security risk analysis 747 format of a specific expert system. in fact, the method can be applied for any ontology type transformation to any es knowledge base with some additional modifications, related to the syntax of es rule defining language. the method proposed is that it uses the rif (rule interchange format) format, which was presented by the w3c consortium in 2010. the primary purpose of this format is to transfer rules from one system to another [49]. unlike other standards of semantic web (rdf, owl, sparql), rif was developed not for defining rules, but as a standard for rule transfer from one system to another, which became extremely important with the increase of standards for rule definition [43]. rif has two dialects: based on logic and based on rules. dialects based on logic include logical languages, like horn logic and others. dialects based on rules include productive rules. the same rules are used in expert systems [37]. rif prd (production rule dialect) is a dialect based on production rules. production rules semantic have format "if condition then activity". figure 2 shows an example of rif prd. figure 2: example of rif prd rif was created in a way to be compatible with other w3c standards. that means that it can be compatible with rdf and owl ontology languages [1]. transferring these ontology languages into rif can be done not only in the form rule-sets, but also in rdf triplets (subject, predicate, object) and owl axioms with rif rules, e.g. rdf data: s(subject), p(predicate) and o(object) are transferred to the rif format in a form: [(p->o]. for example: ex:peter ex:isbrother ex:john; ex:john ex:isfather ex:paul; states that peter is the brother of john, and john is the father of paul. in the rif transferring to: ex:peter [ex:isbrother -> ex:john]; ex:john [ex:isfather -> ex:paul]; rif rule, that uncle is the brother of father: forall ?x ?y ?z (?x[ex:isuncle -> ?z] :and(?x[ex:isbrother -> ?y] ?y[ex:isfather -> ?z])) states, that if x is the brother of y and y is the father of z, then x is the uncle of z. in our experiments we were using owl rl profile as a source. owl rl rules can be divided into four categories (which may overlap): 748 d. vitkus, z. steckevicius, n. goranin, d. kalibatiene, a. cenys triplet structure rules: rdf triplets. transformation to rif is trivial. listing rules: rdf lists. transforming in two ways transforming into a recursive set of rules or transforming into triple structure rules. inconsistent rules: rdf graph inconsistencies, which are expressed in first-order rules. there are several ways how to perform transformation. data type rules: type’s comparison and verification. owl and rif supported types are transformed directly. i.e. owl rl can be transformed into rif rules. however, some limitations still exist. these limitations were introduced by owl founders to provide maximum flexibility without sacrificing the reliability of the calculations. so it can be said that the violation of at least one limitation affects the results of the expert system. the method proposed in figure 3 was implemented using c# language. during the first step, the tool developed converts the information security ontology into rif standard. at the second step (rif transforming into expert system production rules) the rif prd profile was used, which is already included in the rif core profile. the obtained rif rules can later be transformed into the syntax supported by the es or used directly if es supports the rif format, like xsb, jess, and asp. figure 3: general view of the method proposed the detailed method activity diagram is shown in figure 4. the yellow border depicts actions that were proposed and implemented by the paper authors, while actions outside the yellow border are performed with the help of already existing methods and tools. model consists of primary data ontology (owl). transformation of rif rules is performed in several steps: scan ontology. owl\xml ontology is scanned and checked, if the ontology is in owl\xml format, since the developed tool supports only the most popular owl\xml format. if ontology is stored in other formats, external tools should be used first to convert it into owl\xml. check if the element is owl rl. only owl rl subset is converted, but it covers the big part of owl full. elements not belonging to owl rl will be skipped and should be converted into es rules if needed manually or using other methods. collect non-owl rl subset & collect owl rl subset. after successful scanning of owl\xml ontology owl rl elements are extracted for further processing. non-owl rl elements are separated for manual processing transform to rdf triplets. the identified owl rl elements are saved in the form of automated expert system knowledge base development method for information security risk analysis 749 figure 4: the detailed method activity diagram rdf triplet format (subject, predicate, object). check if the element is a simple rdf triplet or composite rdf triplet. evaluation is performed if rdf triplet can be unlinked from other rdf triplets. save element to simple rdf triplet storage. rdf storage stores rds triplets that can be unlinked from other rdf triplets, e.g. attribute type "symmetrical" is saved in the form of rdf triplet (?p rdf:type owl:functionalproperty), where ?p is attribute name. such kind of data is stored in the form of text registry, having 3 fields. save element to composite rdf triplet storage. elements that cannot be unlinked are stored in a storage for composite rdf triplets. for example the ontology statement that all attributes ?p of object ?x are of ?y type: (?x owl:allvaluesfrom ?y) (?x owl:onproperty ?p). up to 3 linked rdf triplets can be stored. analyze rdf triplets & analyze composite rdf triplets. in this step, rdf triplets are transformed according to the conversion table, i.e. rdf triplet conversion templates. the main part of the rdf triplet is predicate, which can acquire values from a finite set of possible 750 d. vitkus, z. steckevicius, n. goranin, d. kalibatiene, a. cenys values because of ontology formality feature. the remaining two triplet elements subject and object are specific concepts or attributes, defined in the ontology, that are used to fill in the conversion template. in several specific cases (e.g.owl:maxcardinality) the template is modified. make rif rules. templates are applied for rdf triplet conversion into rif rules. the formal algorithm in the form of pseudocode of owl rl transformation into rif rules is provided in fig. 5 figure 5: pseudocode for owl rl transformation into rif rules while analyzing xml nodes, rdf triplets are segregated. rdf triples are created according to the predefined patterns: if then t (s1, p1, o1) . . . t (sn, pn,on) t (sr1, pr1, or1) . . . t (srm, prm,orm,) where each argument to the t predicate may be a variable or literal value. rdf triplets created in such a way are later transformed into rif rules. group ( forall ?v11 ... ?v1o ( sr1[pr1->or1] :and( s1[p1->o1] ... sn[pn->on] )) ... forall ?vm1 ... ?vmo ( srm[prm->orm] :and( s1[p1->o1] ... sn[pn->on] )) ) where ?vi1 ... ?vio are the variables which occur in the rule. below the sample transformation is provided. two following rdf triplets are extracted from the ontology: atributte_1 rdf:type owl:symmetricproperty atributte_2 rdf:type owl:symmetricproperty, the template of symmetrical attributes is applied: automated expert system knowledge base development method for information security risk analysis 751 forall ?x ?y ( ?y[?p->?x] :and( ?x[?p->?y] )) the following rif rules are obtained: forall ?x ?y ( ?y[atributte_1->?x] :and( ?x[atributte_1->?y] )) forall ?x ?y ( ?y[atributte_2->?x] :and( ?x[atributte_2->?y] )) the transformation process is composed of two parts: transformation of fixed rules and ontology rules, obtained from its t-box axioms. in owl 2 rl profile both ontology axioms and additional logical rules can be stored [45], but in this research, only the subset of axioms is used as a suitable source for es knowledge base rules. although ontology typically has a very sophisticated structure, it can be easily represented in the form of rdf triplets as was shown earlier. another issue of ontology transformation correctness of ontology that cannot be evaluated by the conversion program. evaluation of ontology composion correctness if out of scope of this paper. it is assumed that ontologies used for experiments were correct and verified by other methods during their development process. some rif rules (fixed rif rules) are obtained without analysis of ontology, since their perception is a part of es. such rules are transformed into es production rules only once and are loaded into the knowledge base. the sample of a fixed rule: forall ?c1 ?c2 ( ?c1[rdfs:subclassof->?c2] :?c1[owl:equivalentclass->?c2]) forall ?x ?z ?y ( ?x[owl:sameas->?z] :and( ?x[owl:sameas->?y] ?y[owl:sameas->?z] )) forall ?x ?y ( rif:error() :and( ?x[owl:sameas->?y] ?x[owl:differentfrom->?y] )) es knowledge base rules are obtained by conversion of rif rules, that are automatically generated from the ontology as described earlier. as also stated earlier, some ontology elements, that were not converted automatically, can be converted manually if they store some valuable information. the final set of rules can be expressed by equation: r(rdf(o)) = fixed_rules ∪ ontology_rules (rdf(o)), where o is ontology, rdf(o) rdf triplets of ontology o, r(rdf(o)) rif is a set of rules, imported into es knowledge base. 4 results and discussion the created program has five main parts: user interface; ontology scanning engine; analysis engine; ontology alignment to rdf triplet engine; transformation (into rif rules) engine. the program class diagram is shown in figure 6. program is composed of 6 main classes: 752 d. vitkus, z. steckevicius, n. goranin, d. kalibatiene, a. cenys figure 6: program class diagram gui form. program management. user can call ontology scanning, conversions into rif rules, error review and rules saving functions. ontology analysis class. responsible for xml/owl ontology scanning, identification of extractable elements and their saving in the form of rdf triplets. rdf triplets storage. stores rdf triplets that can be unlinked from other rdf triplets. composite rdf triplets storage. stores up to 3 linked together complex rdf triplets that can not be unlinked. rif rules class. converts rdf triplets into rif rules as defined. rif rules storage. stores rif rules in the form of presentational syntax that is later used for transformation into es knowledge base rules. all functionality is available from the main program window (figure 7). the user has to press the "scan ontology" button and specify the path to the ontology file. figure 7: the program user interface ontology scanning engine imports (figure 6) the ontology from the external .xml file of owl\xml format. the program later performs the automatic scanning of elements, extraction automated expert system knowledge base development method for information security risk analysis 753 of owl rl subset and performs conversion to rdf triplets. the results are shown in the main window. user, after reviewing the intermedium data and the list of errors, can press the "transform" button. after that, rdf triplets are transformed to rif rules and presented for user review. user can copy them or to save via program interface into the specified file. user interaction is needed to initiate ontology imports and transformation processes. program was tested with several ontologies: w3c consortium test ontologies [51], cloud security ontology [20] which is used in practice and information security standards ontology [36]. while w3c and cloud security ontologies were developed by external parties, the information security standards ontology was developed at vilnius gediminas technical university in accordance with the formal owl 2 rl rules defined in [11] and owl 2 full defined in [50]. its presentation and in-depth description with verification was provided in [36]. all tested ontologies were relatively small (up to 1000 elements). the proportional size of the rl subset in ontologies used in tests was equal to 100% in case of w3c testing ontologies, and 65% in case of information security standards ontology. the proportion of the owl rl subset in the case of cloud security ontology was not analyzed. execution time has not exceeded 50 ms (test platform: intel core i7-4710hq 2.50 ghz, 8 cpu, 16gb ram, windows 8.1 os). there were 15 ontologies at all. results are shown in table 1. table 1: transformation results ontology the part of successfully transf. rules w3c testing ontologies, compatible owl rl 90% cloud computing security ontology 64% information security standards ontology 60% as can be seen from the transformation results, not all ontology elements were transformed. some elements cannot be transformed into separate rules because they are just a part of other element or additional information. the best transformation result was achieved with test ontologies, that are based on the owl rl profile. specific security ontologies had shown worse results. in the case of the information security standards ontology data types xsd.real and xsd.rational have caused 15% of unsuccessful conversion, 20% were caused by subclassof elements and 5% by other xsd data types. general method applicability was approved, since the transformation of even 60% can drastically decrease the knowledge base creation time. for further verification purposes, the generated rules were integrated into the jess-based prototype risk analysis es, adapted for smes (enterprise size definition is adopted by eu recommendation 2003\361). this part of es knowledge base was developed using traditional knowledge base development methods and included rules for identification of appropriate assets, calculating impact and probabilities based on the environment of a specific company (infrastructure, maturity level, environment, sector, etc.), while rules generated automatically from the ontology mainly included information on appropriate security controls. according to iso 27001, information security risk is defined as a potential that some threat exploits an asset or assets group vulnerability and thus undermine the enterprise. risks management process’s purpose is to identify such risks, assess the likelihood of their occurrence and then take actions to reduce them to the acceptable level. almost all risk analysis processes use the same method: identifying assets; identifying problems, threats, vulnerabilities; assessing risk likelihood and impact for assets [6]. after the risk analysis process, the user has to decide how to reduce risks; therefore, es should not only evaluate risks, but also give some recommendations based on the acceptable risk level. 754 d. vitkus, z. steckevicius, n. goranin, d. kalibatiene, a. cenys 5 conclusions and future work the increasing demand for information security compliance and overall understanding of information security management importance leads a modern company to a need of a systematic risk management process. the use of expert systems for risk analysis is seen by many authors as a possible solution. still, the development of expert systems is also a complicated and expensive task, and we indicate it as unsolved problem. advantage of our method is that development of knowledge base can be partially simplified by using already available knowledge sources. some earlier attempts by other authors were made to perform direct integration of security ontologies, that can be seen as a valuable source of information for expert systems knowledge base, with expert systems, but they lacked flexibility. in this paper the new method was proposed that allows automatic transformation of existing information security ontologies into the expert system knowledge base ruleset. information security standards ontology transformation into the jess es was implemented in several steps with the help of a developed tool: owl rl subset of ontology elements are segregated into rdf triplets, that are later transformed into rif format that can be easily converted into jess knowledge bases rules with the help of external tool. the method supports the most popular owl/xml format. both simple (unlined) and complex (linked) owl rl elements can be transformed. the biggest method advantage is that although ontology typically has a very sophisticated structure, it can be easily represented in the form of rdf triplets . the conversion test with different ontologies (w3c testing ontologies, "cloud computing security ontology" and "information security standards ontology" [36]) have shown 60-90% conversion success rate, that proves general method applicability, since automatic generation of even part of expert system knowledge base can decrease the general expert system development price. verification of generated rules was performed by their integration with the knowledge base of a developed jess-based expert system for risk analysis in sme. the obtained results have shown high correlation rate, but what is more important is that the generated rules were successfully integrated into the es knowledge base. later research should be concentrated on tuning the conversion templates for achieving higher conversion success rates of ontologies and finding other sources for automatic filling of the es knowledge base. currently, we see the cve (common vulnerabilities and exposures) and attack trees as the next perspective sources for technical risk evaluation. combination of these two structured sources should provide information on relevant threats as well as probabilities of different attack scenarios. funding this research received no external funding. author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] abbas, a.; privat, g. 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[51] w3c owl 2 web ontology language conformance (second edition), [online]. available online: https://www.w3.org/tr/owl2-conformance/#test_cases, accessed on 03 march 2019. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 462-472 distance based triggering and dynamic sampling rate estimation for fuzzy systems in communication networks c.n. nyirenda, f. dong, k. hirota clement n. nyirenda, fangyan dong, kaoru hirota department of computational intelligence and systems science tokyo institute of technology g3-49, 4259 nagatsuta, midori-ku, yokohama 226-8502, japan. e-mail: {nyirendac, tou, hirota}@hrt.dis.titech.ac.jp abstract: to reduce computational cost in fuzzy systems in communication networks, distance based triggering and sampling rate adaptation probabilities are proposed based on the concept of probability via expectation. the triggering probability, which is calculated by using the square of distance between subsequent input vectors, governs the rate at which the fuzzy system is triggered. the dynamic sampling rate probability, which governs the adaptation of the sampling rate, is computed by using the exponentially weighted moving average (ewma) of the triggering probability. a stopping criterion, based on convergence tests, is also proposed to ensure that the mechanism switches off when the sampling period has converged. the triggering mechanism reduces the number of computations in the fuzzy logic congestion detection (flcd) in wireless local area networks (wlans) by more than 45%. performance, in terms of packet loss rate, delay, jitter, and throughput, however, remains virtually the same. on the other hand, the dynamic sampling rate mechanism leads to more than 150% improvement in sampling rate and more than 70% reduction in fuzzy computations while performance in the other key metrics remains virtually the same. as part of future work, the proposed mechanism will be tested in fuzzy systems in wireless sensor/actuator networks. keywords: communication networks, fuzzy systems, sampling rate. 1 introduction the number of fuzzy logic based applications in communication networks is increasing rapidly. this development is motivated [1] by the difficulties experienced when modeling communication networks by using conventional analytical methods. some of the fuzzy applications include power control [2] in cellular systems; congestion control in ip netwoks [3], [4]; routing [5] and data fusion [6] in wireless sensor networks; and quality of service management in wireless sensor and actuator networks [7]. input parameters are, generally, sampled at a fixed rate and the fuzzy system is triggered accordingly. in some cases, an external signal is used in order to trigger the systems. the fuzzy computations are invoked even when there are no significant differences between the subsequent input parameters, at the expense of precious cpu and memory resources. furthermore, for systems that employ a sampling rate, the rate is chosen by trial and error such that it is difficult to tell if it is optimal. this work proposes a distance based triggering mechanism for fuzzy systems in communication networks. this generic framework can be applied to any fuzzy system with minor customization. a preliminary version of this work was presented in [8]. in this work, the concept of probability via expectation [9] is used to calculate the triggering probability by using the square of the distance between two subsequent input vectors. if the new input parameter vector is very copyright c⃝ 2006-2011 by ccc publications distance based triggering and dynamic sampling rate estimation for fuzzy systems in communication networks 463 far from the previous one, the triggering probability is 1. if subsequent input vector parameters are deemed to be very close to each other, the triggering probability is 0. a sampling rate adaptation probability, which is based on the transformed lorentzian function [10] of the exponentially weighted moving average (ewma) of the triggering probability, is also proposed. when the ewma is very high, it implies that the system’s input vector is under-sampled. as a result, the sampling rate is increased. on the other hand, when the ewma is low, it implies that the system’s input vector is oversampled. the sampling rate is, therefore, decreased. a stopping criterion, based on convergence tests [11], [12] of subsequences of the sequence of the sampling period, is also proposed in order to ensure that the mechanism switches off when the sampling period has converged. these mechanisms are tested on the fuzzy logic congestion detection (flcd) mechanism [13] in the wireless local area network (wlan) environment using simulations on the ns2 platform [14] running on the ubuntu 9.10 os. the computing hardware is composed of a 4gb ram and an intel core i7 860 2.80ghz cpu. the impact of the reduction in fuzzy system evaluations on packet loss rate, delay, jitter and throughput is also evaluated. the rest of the paper is organized as follows. the proposed distance based triggering and the dynamic sampling rate mechanisms are presented in 2. an overview of the flcd approach in wlans is presented in 3. the evaluation of the proposed mechanisms is presented in 4. 2 the distance based triggering and dynamic sampling rate mechanisms the distance based triggering and the dynamic sampling rate mechanisms are incorporated in the input mechanism of the generic fuzzy logic control framework. every τ seconds, the new crisp inputs x1(t), ...,xn(t) are normalized to the range [0,1]. the triggering probability p(tr), which governs the system’s triggering rate, is calculated by using the distance between the new normalized crisp input vector (x∗1(t), ...,x ∗ n(t)) and the previous normalized crisp input vector (x∗1(t − τ), ...,x ∗ n(t − τ)). if the system is not triggered, the previous crisp outputs are used for decision making or control action. when the system has been triggered, fuzzy computations [15] [16] are carried out to generate new output(s). the dynamic sampling rate mechanism uses p(tr) to compute the sampling rate adaptation probability p(ra), based on which the value of τ is adapted to an optimal level. both p(tr) and p(ra) are developed by using the concept of probability via expectation [9]. according to this concept, an event a, which, in a given case, either occurs or does not, corresponds to a set of realizations ω. this set, also denoted by a, is a subset of ω, which denotes the sample space of all possible realizations. the probability of a, the expected proportion of cases in which event a actually occurs, is defined as p(a) = e(i(a,ω)), where i(a,ω) is the indicator function of a, defined by i(a,ω) =   1 (ω ∈ a) 0 (ω /∈ a). (1) 2.1 distance based triggering probability the event of interest is the triggering of the fuzzy system, denoted by tr. a realization for which the event tr takes place is denoted by ω1, while ω1 denotes the sample space of all possible realizations for which triggering is considered. 464 c.n. nyirenda, f. dong, k. hirota to reduce computational overhead when calculating the distance based triggering probability p(tr), the square of distance d2(t) between (x∗1(t), ...,x ∗ n(t)) and (x ∗ 1(t−τ), ...,x ∗ n(t−τ)) is used, where d2(t) = (x∗1(t) − x ∗ 1(t − τ)) 2 + ... + (x∗n(t) − x ∗ n(t − τ)) 2. let v1 : [0,1]n −→ [0,1] denote the distance parameter that defines the variation of ω1 with respect to ω1. this parameter is defined by using v1 =   d2(t) φ if d2(t) < φ 1.0 otherwise. (2) where φ is a normalizing constant. the triggering probability p(tr) is defined as p(tr) = e(i(tr,ω1)); the indicator function i(tr,ω1) is defined by using i(tr,ω1) =   1 (v1 > r1) 0 otherwise. (3) where r1 ∈ [0,1] is a random number. a high p(tr) implies that the distance between the new input vector and the previous one is large. therefore, there is sufficient new information such that the fuzzy system has to be triggered. on the other hand, a low p(tr) implies that the change in the input vector is not significant. therefore, the system should use the previous crisp outputs thereby preserving cpu and memory resources. 2.2 sampling rate adaptation mechanism apart from governing the triggering rate, p(tr) also gives information on whether the sampling period τ is optimal or not. if p(tr) is very high for long periods, it implies that the system is predominantly under-sampled such that there is a need to increase the sampling rate. conversely, if p(tr) is very low for long periods, it implies that the inputs are more or less static. the sampling rate must be reduced because the frequent processing of the inputs is just a waste of computing resources. an inverted bell shaped function must, therefore, be employed in order to ensure that there are little or no changes to τ when p(tr) is close to 0.5, which signifies an optimal sampling rate. on the other hand, in extreme regions, τ must either be decreased or increased by an adaptation factor α. a transformed lorentzian function is employed to generate a bell shaped function depicting this behavior. the traditional three-parameter lorentzian function [10], from which it is derived, is defined by l(z) = ap [ γ2 (z − z0)2 + γ2 ] , (4) where z0 is the centre; γ is the width parameter; and ap determines the peak. sampling rate adaptation probability to track the variations of p(tr) at time t, the exponentially weighted moving average (ewma) of p(tr,t), denoted by p(tr,t), is determined by using p(tr,t) = w ∗ p(tr,(t − τ)) + (1 − w) ∗ p(tr,t), (5) where w ∈ [0,1] is a weighting factor. to ensure that previous values of p(tr,t) are discounted at a medium rate, w = 0.5 is used in this study. distance based triggering and dynamic sampling rate estimation for fuzzy systems in communication networks 465 the event depicting the adaptation of the sampling rate of the fuzzy system is denoted by ra. a realization for which this event takes place is denoted by ω2, while ω2 denotes the sample space of all possible realizations for which the adaptation of the sampling rate is considered. in a similar approach to 2.1, let v2 : [0,1] −→ [0,1] denote the rate adaptation parameter that defines the variation of ω2 with respect to ω2 by using the transformed lorentzian function. the relationship between v2 and p(tr,t) is, therefore, defined by v2 = l ∗(p(tr,t)) = [ (p(tr,t) − 0.5)2 (p(tr,t) − 0.5)2 + γ2 ] . (6) where γ is defined by γ = f(p(tr,t)) =   p(tr,t) if p(tr,t) ≤ 0.5 1 − p(tr,t) if p(tr,t) > 0.5 (7) the sampling rate adaptation probability is defined as p(ra) = e(i(ra,ω2)); the indicator function i(ra,ω2) is defined by i(ra,ω2) =   1 (v2 > r2) 0 otherwise, (8) where r2 ∈ [0,1] is a random number. if p(ra) = 1, τ is adjusted by using τ =   (1 − α)τ if p(tr,t) ≥ 0.5 (1 + α)τ otherwise. (9) this mechanism will help to ensure that p(tr,t) → 0.5. the sampling period can be initialized randomly within a particular range. this mechanism will optimize it on-line based on the variations in the inputs. this characteristic is essential for new links and in situations where new nodes or traffic patterns have been introduced on the already existing links. stopping criterion for sampling rate adaptation once the dynamic sampling rate mechanism starts running, there is a need for a stopping criterion; otherwise, this mechanism will end up consuming precious cpu and memory resources even when the sampling period has converged. the evolution of the sampling rate τ can be presented as a sequence (τk) based on the concept of sequences of real numbers discussed in [11], [12]. when convergence tests show that the sequence has converged, it implies that an optimal sampling period has been realized. therefore, the sampling rate estimation mechanism must be stopped. definition 1. a sequence (τk) converges to τ if for every ϵ > 0, there exists a k ∈ n such that |τk − τ| < ϵ for all k ≥ k. the point τ is called the limit of (τk). if the terms of the sequence get arbitrarily close together, a sequence is said to be cauchy. definition 2. a sequence (τk) is said to be a cauchy sequence if for every ϵ > 0, there exists a k ∈ n such that |τk − τm| < ϵ for all k,m ≥ k. 466 c.n. nyirenda, f. dong, k. hirota every convergent sequence is a cauchy sequence. for real numbers, the converse is also true; every cauchy sequence is convergent. in addition, a sequence is convergent if and only if all of its subsequences converge toward the same limit. a subsequence of the sequence (τk) is a sequence of the form (τkj ), where for each j ∈ n, there is kj ∈ n, and kj < kj+1 for all j. from these observations, we have the following theorem theorem 3. if the first term of a convergent subsequence, τk1 = τk, then for the first p terms, 1 p − 1 p−1∑ j=1 ∣∣τkj+1 − τkj∣∣ < ϵ. (10) proof: this follows from definition 1 and definition 2. because the subsequence (τkj ) is convergent, it is also a cauchy sequence. therefore for every ϵ > 0, there exists a k ∈ n such that ∣∣τkj − τkn∣∣ < ϵ for all kj,kn ≥ k. this implies that the average of the absolute values of the differences of subsequent p terms is also less than ϵ. � by comparing the average of the absolute values of the differences of subsequent terms in a subsequence (τkj ) with ϵ, it is possible to determine whether the original sequence (τk) has converged or not. the terms of the subsequence are extracted from (τk) every l ∗ τ(t) seconds, where l ∈ n. to reduce computational overhead and memory requirements, p is set to 4; on the other hand, ϵ = 1 × 10−5. while the focus is on detecting convergence of (τk), it must be pointed out that cases where this sequence does not converge should also be anticipated. in such cases, the sampling rate estimation mechanism, once activated, will remain on until the system administrator decides to stop it. the proposed mechanisms are tested on the zero-order takagi-sugeno [16] inference based flcd approach [13] in wlans. next, an overview of the flcd algorithm is described. 3 an overview of the flcd approach in wlans the flcd mechanism is a 2-input 1-output system. it is composed of the fuzzy logic control unit (flcu), the congestion notification unit (cnu), and the choke [17] activator (ca) as shown in figure 1. the flcu uses the backlog (queue size) factor x1 and the packet arrival factor x2 to generate the packet marking probability pb. the queue on the outgoing link is sampled at a period τ = 2 msec in order to obtain the two inputs. the cnu either marks (if ecn is enabled) or drops packets with a probability pb. responsive flows such as tcp react to these events by reducing their sending rates thereby reducing congestion at the bottleneck link. pb ca cnux1 x2 pb φ flcu figure 1: the flcd mechanism. for purposes of fairness, in light of non-responsive flows and network anomalies such as denial of service (dos) attacks and routing loops, which may flood the network as the responsive distance based triggering and dynamic sampling rate estimation for fuzzy systems in communication networks 467 flows back off, the ca uses pb to generate a parameter ϕ ∈ [0,1], where ϕ = p3b. the ca probabilistically picks an arriving packet picked based on the value of ϕ. this packet is compared with a randomly chosen packet from the buffer. if they have the same flow id, they are both dropped. otherwise the randomly chosen packet is left unchanged and the arriving packet is queued if the buffer is not full; otherwise it is dropped. as a result, more packets from nonresponsive and tcp-unfriendly flows are dropped at the bottleneck link. 4 evaluation of the distance based triggering and the dynamic sampling mechanisms the distance based triggering and the dynamic sampling rate mechanisms are implemented at the input of the flcd mechanism which is used for congestion control in the access point (ap) of the wlan topology shown in figure 2. the objective is to reduce congestion for traffic flowing from the servers on the high-speed wired/cabled network to the nodes in the bandwidth constrained wireless network. simulations are implemented on the ns2 simulation platform [14]. in figure 2, servers s1, s2 are connected to the gateway which is connected to ap. bw1 and figure 2: the used wlan topology. prop1 denote the bandwidth and propagation delay between the servers and the gateway and between the gateway and the ap while bw2 denotes the wireless channel capacity. the wireless nodes are equidistant from the ap. for brevity, only 5 nodes are shown in figure 2 but more than 5 nodes are used in the simulations. half of the nodes in the wireless network are fixed while the other half consists of mobile nodes. to depict a high-speed topology, bw1 and prop1 are set to 10gbps and 1ms respectively while the capacity, bw2, is set to 144 mbps. two sets of experiments are conducted. the first one evaluates the impact of the distance based triggering mechanism on the flcd algorithm. it is aimed at comparing the number of fuzzy system evaluations in a flcd algorithm with and without the distance based triggering mechanism. the other objective is to find out the impact of the distance based triggering mechanism on system performance. metrics for system performance include packet loss rate, link utilization, packet delay, and jitter. the second experiment evaluates the efficiency of the dynamic sampling rate mechanism. the objective is to find out if it really manages to guide the system toward the optimal sampling rate. convergence times are also captured. simulations are configured as follows. each simulation run takes 100 seconds. in all runs, one ftp flow and one web traffic flow are configured to flow from server 1 to each of the nodes 468 c.n. nyirenda, f. dong, k. hirota in the wlan between 5 seconds and 95 seconds leaving enough time for the simulation to start up and also to shut down gracefully. the standard web traffic generator included in the ns2 platform is used with the following parameter settings: an average of 30 web pages per session, an inter-page parameter of 0.8, an average page size of 10 objects, an average object size of 400 packets and a paretoii shape parameter of 1.002. each web traffic flow has 4 sessions. udp traffic is also configured to flow from server 2 to 10 nodes in the intervals [25s-30s] and [80s-90s]. parameters of udp traffic are as follows: packet size of 1500bytes, packet interval of 12.5ms and a flow rate of 120kbytes/sec. tcp type is new reno with a data packet size of 1000 bytes and ack packet size of 40 bytes. the buffer size is set to 200 bytes. 4.1 experiment 1 testing the impact of the distance based triggering mechanism only the distance based triggering mechanism is activated. the number of wlan nodes is varied by using 10, 20, 30 up to 100 nodes. the value of the normalizing constant φ in (2) is set to 2 × 10−2 based on several trial runs. after that, simulations are carried out. the overall results are average values over 20 independent runs, which are conducted at each and every testing point. these values are plotted in the graphs along with the error bars representing the 99% confidence intervals of the averages. the flcd mechanism that employs the proposed triggering mechanism is labeled flcd+d while the normal one is labeled flcd. figure 3 figure 7 show the results. 10 20 30 40 50 60 70 80 90 100 0 5 10 15 20 25 30 35 40 45 50 55 number of wlan nodes n o . o f f u z z y c o m p u t a t io n s ( x1 0 0 0 ) flcd+d flcd figure 3: number of fuzzy computations as congestion level increases 10 20 30 40 50 60 70 80 90 100 0 1 2 3 4 5 6 7 8 9 10 number of wlan nodes p a c k e t l o s s r a t e (x 0 .0 1 ) (% ) flcd+d flcd figure 4: packet loss rate as congestion level increases in figure 3, the distance based triggering mechanism reduces the number of fuzzy computations by more than 45% while in figure 4 figure 7, packet loss rate, delay, jitter, and throughput remain virtually the same when the distance based triggering mechanism is employed. these results confirm the fact that a great deal of computing power is lost due to redundant computations in fuzzy systems in communication networks. distance based triggering and dynamic sampling rate estimation for fuzzy systems in communication networks 469 10 20 30 40 50 60 70 80 90 100 0 0.5 1 1.5 2 2.5 number of wlan nodes ji t t e r ( m s) flcd+d flcd figure 5: packet jitter as congestion level increases 10 20 30 40 50 60 70 80 90 100 40 45 50 55 60 65 70 number of wlan nodes d e l a y ( m s) flcd+d flcd figure 6: packet delay as congestion level increases 10 20 30 40 50 60 70 80 90 100 9.5 10 10.5 11 11.5 12 12.5 number of wlan nodes t h r o u g h p u t ( m b p s) flcd+d flcd figure 7: throughput as congestion level increases 470 c.n. nyirenda, f. dong, k. hirota 4.2 experiment 2 efficiency of the dynamic sampling rate mechanism both the distance based triggering and the dynamic sampling rate mechanisms are activated. the flcd mechanism that employs the proposed distance based triggering mechanism is labeled flcd+ds while the normal one is labeled flcd. the following parameter values are used: l = 10, α = 1 × 10−3. these values are determined after several trial runs. simulations are carried out with 50 nodes on the wireless network on every run. the overall results are average values over 20 independent runs, which are conducted at each and every testing point. in flcd+ds, the sampling period τ is initialized randomly within (0,1] seconds. based on the existing congestion control literature [3], τ = 2 msec in flcd. apart from the performance metrics used in experiment 1, the averages of the converged sampling periods τ and the convergence times are also presented. to statistically establish if two corresponding averages are significantly different from each other, the one-way anova test, at a significance level of 1%, is applied. table 1 shows the results. columns 2 and 3 denote the corresponding average and standard deviation values for flcd+ds and the flcd respectively. column 4 denotes the percentage improvement that flcd+ds exhibits over flcd. statistically significant percentage improvements are shown in bold. table 1: dynamic sampling rate mechanism vs the conventional approach for 50 nodes. metric flcd+ds flcd improv. loss rate (%) 0.055 ± 0.0047 0.057 ± 0.0051 3.5% delay (sec) 54.59 ± 4.26 52.91 ± 5.54 −3.01% jitter (sec) 1.576 ± 0.136 1.59 ± 0.123 0.88% throughput (mbps) 11.47 ± 0.372 11.49 ± 0.415 0.17% evaluations 12687 ± 1149 50000 74% τ (msec) 5.05 ± 1.634 2 152.5% conv. time (msec) 15.69 ± 5.37 − − the proposed mechanisms reduce the number of evaluations by more than 70%. the converged sampling period in flcd+ds is also increased by more than 150%. again, there is no significant difference between the two approaches in terms of the other key performance metrics; the sampling period converges within the first 20 msec. the dynamic sampling rate mechanism in flcd+ds performs on-line optimization of τ from random values in (0,1] sec to 5.05 ± 1.634 msec. this leads to drastic reductions in fuzzy system evaluations while overall system performance remains virtually the same as in flcd. 5 conclusions and future works to reduce computational cost in fuzzy systems in communication networks, a distance based triggering probability, which governs the triggering rate of the fuzzy system, is proposed. when the system has not been triggered, the previous output(s) are used. a dynamic sampling rate probability, based on the transformed lorentzian function of the exponentially weighted moving average (ewma) of the triggering probability, is also proposed. when the ewma is tending to the extremes, the sampling period is updated in two ways. if the ewma is higher than 0.5, it implies that the system is under-sampled such that the sampling period is decreased probabilistically. conversely, if the ewma is lower than 0.5, it implies that the system is oversampled such that the sampling period is increased probabilistically. a stopping criterion is also distance based triggering and dynamic sampling rate estimation for fuzzy systems in communication networks 471 proposed to ensure that this mechanism is switched off when the sampling period has converged. the triggering probability reduces the number of fuzzy computations in the fuzzy logic congestion detection (flcd) in wlans by more than 45%. performance, in terms of packet loss rate, delay, jitter, and throughput, however, remains virtually the same. on the other hand, the dynamic sampling rate mechanism leads to more than 150% improvement in sampling rate while the number of fuzzy computations in the flcd mechanisn in wlans is reduced by more than 70%. again, performance in the other key metrics remains virtually the same. this work shows that the triggering probability can help to alleviate redundant computations in fuzzy systems in communication networks while preserving the performance levels. it also shows that the system can optimize the sampling period just by using the distance information. as part of future work, the proposed mechanism will be implemented in fuzzy systems in wireless sensor/actuator networks. bibliography [1] m. spott, k. leiviska, and t. martin: roadmap contribution iba c applications in telecommunications, multimedia and services, european network on intelligent technologies (eunite) for smart adaptive systems (sas), july 2004. [2] y.l. chen, j.w. wang, y.s. lin, and j.h. wen: combined fuzzy-based power control with window-based transmission rate management in multimedia cdma cellular systems, international journal of electronics and communications, doi:10.1016/j.aeue.2010.04.009, 2 june 2010. [3] c. chrysostomou, a. pitsillides, a. sekercioglu: fuzzy explicit marking: a unified congestion controller for best-effort and diff-serv networks, computer networks journal (comnet), vol. 53, no. 5, pp.650-667, 9 april 2009. [4] c.n. nyirenda and d.s. dawoud: multi-objective particle swarm optimization for fuzzy logic based active queue management, in proc. of the ieee international conference in fuzzy systems, vancouver, canada, pp. 2231-2238, july 2006. [5] m. balakrishnan and e. e. johnson: fuzzy diffusion analysis: decision significance and applicable scenarios, proc. of ieee military communications conference, no. 1, pp. 21752181, october 2006. 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[11] j.k. hunter and b. nachtergaele: applied analysis, world scientific, 2001. 472 c.n. nyirenda, f. dong, k. hirota [12] b. richmond and t. richmond, a discrete transition to advanced mathematics, ams bookstore, 2009. [13] c.n. nyirenda, d.s. dawoud, f. dong, m. negnevitsky, and k. hirota: a fuzzy multiobjective particle swarm optimized ts fuzzy logic congestion controller for wireless local area networks, journal of advanced computational intelligence and intelligent informatics (jaciii), vol.15, no.1, pp. 41-54, january 2011. [14] ns2 network simulator, http://www.isi.edu/nsnam/ns/, accessed on 28 june, 2010. [15] e.h. mamdani and s. assilian, an experiment in linguistic synthesis with a fuzzy logic controller, international journal of man-machine studies, vol. 7, no. 1, pp. 1-13, 1975. [16] t. takagi and m. sugeno, fuzzy identification of systems and its applications to modeling and control, ieee trans. on systems,man and cybernetics, vol.15, 116-132, 1985. [17] r. pan, b. prabhakar, and k. psounis: choke a stateless active queue management scheme for approximating fair bandwidth, proc. of infocom, pp. 942-951, march 2000. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 647-655 spiking neural p systems with several types of spikes m. ionescu, g. păun, m. j. pérez-jiménez, a. rodríguez-patón mihai ionescu university of piteşti str. târgu din vale, nr. 1, 110040 piteşti, romania e-mail: armandmihai.ionescu@gmail.com gheorghe păun institute of mathematics of the romanian academy po box 1-764, 014700 bucharest, romania, and research group on natural computing department of computer science and ai university of sevilla avda reina mercedes s/n, 41012 sevilla, spain e-mail: george.paun@imar.ro, gpaun@us.es mario j. pérez-jiménez research group on natural computing department of computer science and ai university of sevilla avda reina mercedes s/n, 41012 sevilla, spain e-mail: marper@us.es alfonso rodríguez-patón department of artificial intelligence, faculty of computer science polytechnical university of madrid, campus de montegancedo boadilla del monte 28660, madrid, spain e-mail: arpaton@fi.upm.es abstract: with a motivation related to gene expression, where enzymes act in series, somewhat similar to the train spikes traveling along the axons of neurons, we consider an extension of spiking neural p systems, where several types of “spikes" are allowed. the power of the obtained spiking neural p systems is investigated. some further extensions are mentioned, such as considering a process of decay in time of the spikes. keywords: natural computing, membrane computing, p system, turing computability 1 introduction the present note lies at the intersection of two active research branches of bioinformatics/natural computing, namely, gene expression and membrane computing. specifically, an extension of so-called spiking neural p systems (in short, sn p systems) is considered, with motivations related to gene expression processes. for the reader’s convenience, we shortly recall that an sn p system consists of a set of neurons placed in the nodes of a graph and sending signals (spikes) along synapses (edges of the graph), under the control of firing rules. such a rule has the general form e/ac → ap; d, where e is a regular expression (equivalently, we can consider it a regular language) and a denotes the spike; if the contents of the neuron is described by an element of the regular language (identified by) e, then the rule is enabled, c spikes are consumed and p are produced, and sent, after a time copyright c⃝ 2006-2011 by ccc publications 648 m. ionescu, g. păun, m. j. pérez-jiménez, a. rodríguez-patón delay of d steps, along the synapses leaving the neuron. there also are forgetting rules of the form ac → λ, with the meaning that, if the neuron contains exactly c spikes, then they can be removed (forgotten). one neuron is designated as the output neuron of the system and its spikes can exit into the environment, thus producing a spike train. two main kinds of outputs can be associated with a computation in an sn p system: a set of numbers, obtained by considering the number of steps elapsed between consecutive spikes which exit the output neuron, and the string corresponding to the sequence of spikes which exit the output neuron. these computing devices were introduced in [7] and then investigated in a large number of papers; we refer to the corresponding chapter from [13] and to the membrane computing website [16] for details. in turn, gene expression is an important research area where various transcription factors appears and, important for their activity, their frequency matters – see, for instance, [1], [10], [12]. this means that a spiking like process is encountered, but with several “spikes", the regulator proteins which bind to a promotor depending on their concentration. in some sense we have here a communication process in which a signal encoded in a concentration (the transcription factor) is transduced to a frequency signal (the bursts of mrna associated to the bindings of the transcription factor with the promotor) and again transduced back to a concentration (the level of protein produced). thus, conceptually, we can approach this process in terms of theoretical machineries developed for spiking neurons – with the necessity of considering a variety of spikes, not only one as in the neural case. this is also suggested in [1]: "...we anticipate that frequencymodulated regulation may represent a general principle by which cells coordinate their response to signals." starting from these observations, we relate here the two research areas, introducing sn p systems with several types of spikes. such a possibility was somehow forecasted already from the way the definition in [7] is given, with an alphabet, o, for the set of spikes, but with only one symbol in o; up to now, only a second type of spikes was considered, in [11], namely antispikes, which, when introduced, are immediately annihilated, in pairs with usual spikes. this extension to several types of spikes is natural also in view of the fact that all classes of p systems investigated in membrane computing work with arbitrary alphabets of objects. as expected, having several types of spikes helps in proofs; in particular, we obtain the universality of the sn p systems with several types of spikes for systems with a very reduced number of neurons – remember that for systems with only one type of spikes the proofs do not bound the number of neurons (but such a bound can be found due to the existence of universal sn p systems, hence with a fixed number of neurons, but used in the computing mode, having both an input and an output). three ways to define the result of a computation are considered: as the number of objects inside a specified neuron, as the number of objects sent out by the output neuron, and as the distance in time between the first two spikes sent out during the computation. what is not investigated is the case of generating strings, in the sense of [3], [4], or even in the distributed case of [8]. other open problems are mentioned in the rest of the paper and in the final section of it. 2 formal language theory prerequisites we assume the reader to be familiar with basic language and automata theory, e.g., from [14] and [15], so that we introduce here only some notations and notions used later in the paper. for an alphabet v , v ∗ denotes the set of all finite strings of symbols from v ; the empty string is denoted by λ, and the set of all nonempty strings over v is denoted by v +. when spiking neural p systems with several types of spikes 649 v = {a} is a singleton, then we write simply a∗ and a+ instead of {a}∗, {a}+. for a language l, we denote by sub(l) the set of all substrings of strings in l. as usual in membrane computing, the multisets over a finite universe set u are represented by strings in u∗ (two strings equal modulo a permutation represent the same multiset). if u, v ∈ u∗, we write the fact that u is a submultiset of v in the form u ⊆ v, with the understanding that there is a permutation of v having u as a substring (this can be formally formulated also in terms of parikh mapping, but we do not enter into details). similarly, we write u ∈ sub(l) for a multiset u and a set l of multisets, meaning that u is a submultiset of a multiset in l. a register machine (in the non-deterministic version) is a construct m = (m, h, l0, lh, i), where m is the number of registers, h is the set of instruction labels, l0 is the start label (labeling an add instruction), lh is the halt label (assigned to instruction halt), and i is the set of instructions; each label from h labels only one instruction from i, thus precisely identifying it. the instructions are of the following forms: • li : (add(r), lj, lk) (add 1 to register r and then go to one of the instructions with labels lj, lk non-deterministically chosen), • li : (sub(r), lj, lk) (if register r is non-empty, then subtract 1 from it and go to the instruction with label lj, otherwise go to the instruction with label lk), • lh : halt (the halt instruction). a register machine m generates a set n(m) of numbers in the following way: we start with all registers empty (i.e., storing the number zero), we apply the instruction with label l0 and we continue to apply instructions as indicated by the labels (and made possible by the contents of registers); if we reach the halt instruction, then the number n present in register 1 at that time is said to be generated by m. without loss of generality we may assume that in the halting configuration all other registers are empty. it is known that register machines generate all sets of numbers which are turing computable – we denote this family with nre (re stands for “recursively enumerable"). by nfin we denote the family of finite sets of natural numbers. in the following sections, when comparing the power of two computing devices, number 0 is ignored (this corresponds to the fact that when comparing the power of language generating or accepting devices, the empty string λ is ignored). 3 spiking neural p systems with several types of spikes we directly introduce the type of sn p systems we investigate in this paper; although somewhat far from the idea of a spike from the neural area, we still call the objects processed in our devices spikes. a spiking neural p system with several types of spikes (abbreviated as sn+ p system, of degree m ≥ 1, is a construct of the form π = (o, σ1, . . . , σm, syn, i0), where: 1. o is the alphabet of spikes (we also say objects); 2. σ1, . . . , σm are neurons, of the form σi = (wi, ri), 1 ≤ i ≤ m, where: a) wi ∈ o∗ is the initial multiset of spikes contained in σi; b) ri is a finite set of rules of the forms 650 m. ionescu, g. păun, m. j. pérez-jiménez, a. rodríguez-patón (i) e/u → a, where e is a regular language over o, u ∈ o+, and a ∈ o (spiking rules); (ii) v → λ, with v ∈ o+ (forgetting rules) such that there is no rule e/u → a of type (i) with v ∈ e; 3. syn ⊆ {1, 2, . . . , m} × {1, 2, . . . , m} with i ̸= j for each (i, j) ∈ syn, 1 ≤ i, j ≤ m (synapses between neurons); 4. i0 ∈ {1, 2, . . . , m} indicates the output neuron (σi0 ) of the system. a rule e/u → a is applied as follows. if the neuron σi contains a multiset w of spikes such that w ∈ l(e) and u ∈ sub(w), then the rule can fire, and its application means consuming (removing) the spikes identified by u and producing the spike a, which will exit immediately the neuron. in turn, a rule v → λ is used if the neuron contains exactly the spikes identified by v, which are removed (“forgotten"). a global clock is assumed, marking the time for the whole system, hence the functioning of the system is synchronized. if a rule e/u → a has e = {u}, then we will write it in the simplified form u → a. the spike emitted by a neuron σi go to all neurons σj such that (i, j) ∈ syn. if several rules can be used at the same time in a neuron, then the one to be applied is chosen non-deterministically. using the rules as described above, one can define transitions among configurations. any sequence of transitions starting in the initial configuration is called a computation. a computation halts if it reaches a configuration where no rule can be used. there are many possibilities to associate a result with a computation, in the form of a number. three possibilities are considered here: the number of objects in the output neuron in the halting configuration, the number of spikes sent to the environment by the output neuron, and the number of steps elapsed between the first two steps when the output neuron spikes. in the first two cases only halting computations provide an output, in the last case we can define the output also for ever going computations – but in what follows we only work with halting computations also for this case. we denote by nα(π) the set of numbers generated as above by an sn+ p system π with the result defined in the mode α ∈ {i, o, d}, where i indicate the internal output, o the external one (as the number of spikes), and d the fact that we count the distance between the first two spikes which exit the system. then, nαsn+pm is the family of sets of numbers nα(π), for sn+ p systems with at most m ≥ 1 neurons. as usual, the subscript m is replaced by ∗ if the number of neurons does not matter. before passing to investigate the power of the previously defined systems, let us mention that we have introduced here sn p systems of the standard type in what concerns the rules, i.e., producing only one spike, and without delay; extended rules are natural (e/u → v, with both u and v multisets), but this is a too general case from a computability point of view, corresponding to cooperating p systems. it is important also to note that the rules we use have both additional powerful features – context sensitivity induced by the existence of the control regular language e, and strong restrictions – the produced spike (only one) should leave immediately the neuron, it cannot be further used in the same place without being sent back by the neighboring neurons. these features are essentially present in the proofs from the next section. 4 the power of sn+ p systems we start by considering the case when the result is counted inside the system (like in general p systems, hence somewhat far from the style of sn p systems). spiking neural p systems with several types of spikes 651 lemma 1. nre ⊆ nisn+p3. proof: let us consider a register machine m = (n, h, l0, lh, i). we construct the following sn+ p system π = (o, σ1, σ2, σ3, syn, 1), with: o = {ai | 1 ≤ i ≤ n} ∪ {l, l′, l′′ | l ∈ h}, σ1 = (l0, r1), r1 = {o∗/li → l′i | (li : add(r), lj, lk) ∈ i} ∪ {o∗aro∗/liar → l′′j, (o∗ − o∗aro∗)/li → l′′k | (li : sub(r), lj, lk) ∈ i} ∪ {lh → λ}, σ2 = (λ, r2), r2 = {l′i → lj, l′i → lk | (li : add(r), lj, lk) ∈ i} ∪ {l′′ → l | l ∈ h}, σ3 = (λ, r3), r3 = {l′i → ar | (li : add(r), lj, lk) ∈ i} ∪ {l′′ → λ | l ∈ h}, syn = {(1, 2), (1, 3), (2, 1), (3, 1)}. the functioning of this system can be easily followed. the contents of register r is represented by the number of copies of object ar present in the system. there are also objects associated with the labels of m. initially, we have only the object l0 in neuron σ1. in general, in the presence of a label li of an instruction in i, the instruction is simulated by the system π. for the add instructions, the change of labels is done with the help of neuron σ2 and the addition of a further object ar is done in neuron σ3. for the sub instructions, the check for zero is performed by means of the regular language associated with the rules in r1. the computation continues as long as the work of the machine m continues. when the label lh is introduced – by the neuron σ2 – the computation stops after one further step, when this object is removed from the output neuron, σ1. thus, in the end, this neuron only contains copies of object a1, hence their number represents the value present in the first register of m in the end of the computation. thus, n(m) = li(π). 2 theorem 2. nfin = nisn+p1 = nisn+p2 ⊂ nisn+p3 = nre. proof: the inclusions nisn+p1 ⊆ nisn+p2 ⊆ nisn+p3 are obvious from the definitions. the inclusion nisn+p3 ⊆ nre is straightforward (we can also invoke for it the turing-church thesis). in an sn+ p system with two components, the number of spikes present inside the two neurons cannot be increased (each spiking rule consumes at least one spike and produces only one spike, while there is no duplication of spikes because of multiple synapses which exit a neuron), hence we have nisn+p2 ⊆ nfin. on the other hand, nfin ⊆ nisn+p1. indeed, consider a finite set of numbers, f = {n1, n2, . . . , nk}; assume that 1 ≤ n1 < n2 < · · · < nk (remember that we ignore the number 0). we construct the system π = ({a}, (ank+1, {ank+1/ank+1−ni → a | 1 ≤ i ≤ k}), ∅, 1). we have li(π) = f : each computation has only one step, which non-deterministically uses one of the rules in r1. each such rule just consumes a number of spikes, passing from the initial nk + 1 spikes to any number ni ∈ f , which cannot be further processed. together with lemma 1, this concludes the proof of the theorem. 2 652 m. ionescu, g. păun, m. j. pérez-jiménez, a. rodríguez-patón let us note in the construction from the proof of lemma 1 that all neurons spike a large number of times (related to the length of the computation), not directly related to the number computed in the first register of m. this makes difficult to imagine a system with only three neurons which is universal when the result is defined as the number of spikes sent out. however, one additional neuron suffices in such a case. theorem 3. nfin = nosn+p1 ⊂ nosn+p2 ⊆ nosn+p3 ⊆ nosn+p4 = nre. proof: again, the inclusions nosn+p1 ⊆ nosn+p2 ⊆ nosn+p3 ⊆ nosn+p4 ⊆ nre are obvious from the definitions. the inclusion nre ⊆ nosn+p4 can be obtained by a slight extension of the construction in the proof of lemma 1: we replace the rule lh → λ from r1 with the rule a+1 lh/lha1 → lh. we also add a neuron σ4, considered as output neuron, linked by synapses (1, 4), (4, 1) to the neuron σ1 and containing the unique rule lh → lh. when the computation of m stops, hence lh is introduced in σ1, this object removes one by one the objects a1 and moves to the output neuron. this neuron both sends lh out and back to σ1, hence the number of copies of lh sent out is equal with the number stored in the first register of m. this time, an sn+ p system with two components can compute an arbitrarily large number, by sending out an arbitrarily large number of spikes. for instance, π = ({a}, (a, {a → a}), (aa, {aa/a → a, aa → a}), {(1, 2), (2, 1)}, 2), has lo(π) = {1, 2, . . . } (neuron σ2 spikes step by step, until using the rule aa → a, when only one spike remains in the system and the computation halts). if we have only one neuron, the computation can last as may steps as many spikes are initially inside, hence nosn+p1 ⊆ nfin. on the other hand, nfin ⊆ nisn+p1. indeed, consider again a finite set of numbers, f = {n1, n2, . . . , nk} such that 1 ≤ n1 < n2 < · · · < nk and construct the system π = ({a}, σ1, ∅, 1), with σ1 = (a nk+1, r1), r1 = {ank+1/ank+1−ni+1 → a | 1 ≤ i ≤ k} ∪ {ar/a → a | 1 ≤ r ≤ nk − 1}. we have li(π) = f : each computation starts with a step which uses non-deterministically a rule ank+1/ank+1−ni+1 → a, which decreases the number of spikes from the initial nk +1 to some ni − 1; at this time, one spike was sent out. from now on, we use deterministically rules of the form ar/a → a, for all r = 1, 2, . . . , ni − 1, hence for ni − 1 steps, always sending out one spike. thus, in total, we send out ni spikes, for each ni ∈ f . combining all these remarks, we have the theorem. 2 it is an open problem whether or not the inclusions nosn+p2 ⊆ nosn+p3 ⊆ nosn+p4 are proper. theorem 4. nfin = ndsn+p1 = ndsn+p2 ⊂ ndsn+p3 ⊆ ndsn+p4 = nre. spiking neural p systems with several types of spikes 653 proof: as above, the inclusions ndsn+p1 ⊆ ndsn+p2 ⊆ ndsn+p3 ⊆ ndsn+p4 ⊆ nre are obvious from the definitions. the inclusion nfin ⊆ ndsn+p1 is already proved for sn p systems with only one type of spikes. like in that case, we also obtain the inclusion ndsn+p2 ⊆ nfin: in order to generate an arbitrarily large number, the output neuron should not spike for an arbitrarily large number of steps, but this is not possible in a system with only two neurons, because if only one neuron is working, it can perform only a number of steps bounded by the number of spikes initially present in it. the fact that ndsn+p3 contains infinite sets of numbers is also known for standard sn p systems. what remains to prove is the inclusion nre ⊆ ndsn+p4 and this can again be obtained by an extension of the construction in the proof of lemma 1; because this extension is not immediate, we give the construction in full details. we consider a register machine m = (n, h, l0, lh, i) and construct the sn+ p system π as indicated in figure 1 – this time we do not give the system formally, but we represent it graphically, in the way usual in the sn p systems area. 6 ? 6 ? 6 � 6 1 l0 o∗/li → l′i, for (li : add(r), lj, lk) ∈ i o∗aro∗/liar → l′′j, (o∗ − o∗aro∗)/li → l′′k, for (li : sub(r), lj, lk) ∈ i) a+1 lh/a1lh → lh 2 l′i → lj, l′i → lk, for (li : add(r), lj, lk) ∈ i l′′ → l, for l ∈ h 3 l′i → ar, for (li : add(r), lj, lk) ∈ i) l′′ → λ, for l ∈ h lh → lh 4 c cl3h → lh l2h → λ lh → lh figure 1: the sn+ p system from the proof of theorem 4 the work of this system is identical to that in the proof of lemma 1, until producing the object lh (the objects which arrive in the output neuron σ4 from all other neurons remain here unused). when σ2 introduces the object lh, it is sent to all other neurons. it waits unused in σ4, but in 654 m. ionescu, g. păun, m. j. pérez-jiménez, a. rodríguez-patón σ1 and σ3 it is reproduced in each step, hence these two neurons feed repeatedly each other with one copy of lh. in σ1, each use of the rule a + 1 lh/a1lh → lh removes one copy of a1. in the end of step 1 (we count here only the steps after having lh in the system, hence for the phase when the output is produced), neuron σ4 contains three copies of lh. thus, in step 2, this neuron spikes. from now on, neurons σ1, σ3 spike repeatedly, exchanging copies of lh, σ4 always forgets the two copies of lh received from σ1, σ3 (while σ2 just accumulates copies of lh, which cannot be processed here). when the last copy of a1 is removed from σ1 (if m copies of a1 were present here when lh was introduced, then this happens in step m, after having lh in the system), this is the last step when σ4 receives two spikes. in the next step (m + 1) it receives only the spike produced by σ3, which is used (in step m + 2) by the rule lh → lh in σ4. the computation stops. the number of steps between the two spikes sent out by the output neuron is (m + 2) − 2 = m, hence the number computed by the register machine in its first register. the proof of the theorem is now complete. 2 it is an open problem whether or not the inclusion ngsn+p3 ⊆ ngsn+p4 is proper. 5 final remarks in gene expression it is also the case that the enzymes have a time dependency of their reactivity, which can be captured in terms of sn p systems by considering decaying spikes, in the sense of [6]. for instance, we can associate an age with each produced spike, by using rules of the form e/u → (a, t), where t ≥ 1 is the “duration of life" of this spike. if the spike is not used in a step, then its life is decreased by one unit (this is like having rewriting rules (a, s) → (a, s − 1), used in parallel for all spikes not used in spiking or forgetting rules), until reaching the state (a, 0), when a rule (a, 0) → λ is assumed to be applied. this feature remains to be further investigated. let us close by recalling the fact that besides the synchronized (sequential in each neuron) mode of evolution, there were also introduced other modes, such as the exhaustive one, [9], and the non-synchronized one, [2], which also deserve to be considered for sn p systems with several types of spikes. acknowledgements the work of m. ionescu was possible due to cncsis grant rp-4 12/01.07.2009. the work of gh. păun was supported by proyecto de excelencia con investigador de reconocida valía, de la junta de andalucía, grant p08 – tic 04200. the work of a. rodriguez-paton has been supported by spanish ministry of science and innovation under project tin2009 14421. the careful reading of the paper by two anonymous referees is gratefully acknowledged. bibliography [1] l. cai, c.k. dalal, m.b. elowitz: frequency-modulated nuclear localization bursts coordinate gene regulation. nature, 455 (25 september 2008). 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[16] the p systems website: http://ppage.psystems.eu. international journal of computers communications & control issn 1841-9836, 13(4), 465-476, august 2018. trajectory tracking control for seafloor tracked vehicle by adaptive neural-fuzzy inference system algorithm y. dai, x. zhu, h. zhou, z. mao, w. wu yu dai* 1. college of mechanical and electrical engineering central south university changsha 410083, china 2. state key laboratory of ocean engineering shanghai jiao tong university shanghai 200240, china *corresponding author: daiyu_6@aliyun.com xiang zhu, haibo zhou, zuoli mao, wei wu 1. college of mechanical and electrical engineering central south university changsha 410083, china zhuxiang_csu@163.com, 1584269607@qq.com zhouhaibo@csu.edu.cn, 819964861@qq.com abstract: trajectory tracking control strategy and algorithm for the tracked vehicle moving on the seafloor has aroused much concerns due to the commonly occurred serious slip and trajectory deviation caused by the seafloor extremely soft and cohesive sediment. an improved multi-body dynamic model of a seafloor tracked vehicle (stv) has been established in a simulation code recurdyn/track. a particular terramechanics model with a dynamic shear displacement expression for the vehicle-sediment interaction has been built and integrated into the multi-body dynamic model. the collaborative simulation between the mechanical multi-body dynamic model in recurdyn/track and the control model in matlab/simulink has been achieved. different control algorithms performances including a pid control, a fuzzy control and a neural control, have been compared and proved the traditional or individual intelligent controls are not particularly suitable for the tracked vehicle on the seafloor. consequently, an adaptive neural-fuzzy inference system (anfis) control algorithm with hybrid learning method for parameter learning which is an integrated control method combined with the fuzzy and neural control, has been adopted and designed. a series of collaborative simulations have been performed and proved the anfis algorithm can achieve a better trajectory tracking control performance for the stv as its trajectory deviation can be maintained within a permissible range. keywords: seafloor tracked vehicle, multi-body dynamic model, adaptive neuralfuzzy inference system (anfis), collaborative simulation, trajectory tracking control. 1 introduction tracked vehicles are widely used in the deep seafloor engineering fields, such as seafloor exploration, seafloor cable laying and installation, seafloor dredging, seafloor mineral resources exploitation, etc. the deep seafloor extremely soft and cohesive sediment is completely different from land-surface soils, which makes the tracked vehicle more likely to be involved in serious slip, large sinkage and motion trajectory deviation. its locomotion performance and control characteristics directly affect the continuous operation performance and operation safety for the tracked vehicle on the seafloor. influenced by the seafloor complex and changeable environmental loads, it is particularly difficult to master and evaluate the mobility and locomotion of the seafloor tracked vehicle, copyright ©2018 cc by-nc 466 y. dai, x. zhu, h. zhou, z. mao, w. wu sup et al. adopted a new technology based on euler parameters for evaluating the dynamic properties of a tracked vehicle on seafloor [13] and further put forward a subsystem synthesis method to analyse a multi-body model of a tracked vehicle [15]. kim et al. researched the complicated dynamics of an articulated tracked vehicle crawling on the seafloor inclined and undulating terrain [14]; furthermore the effects of the buoyancy position layout on the dynamics of the vehicle were analysed [16]. li et al. built a virtual prototype of a seafloor tracked mining vehicle and conducted simulations to estimate the vehicle’s locomotion and trafficability [17]. li et al. studied the effect of the grouser height of a seafloor tracked mining vehicle on its tractive performance through an established relationship between the total driving force and the slip of the mining vehicle [18]. dai et al. developed new multi-body dynamic models for three types of seafloor tracked vehicles and performed simulations to evaluate their locomotion and trafficability performances [5–7]; besides, the complex integrated dynamic performances for seafloor tracked vehicles connecting to pipeline systems and surface ships were investigated and evaluated [8,9]. to control the locomotion state and trajectory of the tracked vehicle on the seafloor, herzog et al. established an automatic hydraulic drive mode with slip control of the driving track for a seafloor tracked vehicle; meanwhile an experimental system for the slip control development along with the logic of the automatic driving model was presented [12]. yeu et al. proposed a path tracking method for tracked vehicle on the seafloor with a vector pursuit algorithm to make the vehicle’s motion following the specified path [28]. yeu et al. further based on the kinematics of the seafloor tracked mining vehicle to propose two navigation algorithms, known as dead-reckoning and extended kalman filter [29]. yoon et al. used the indirect kalman filter method with the inner measuring sensors to underwater localization of a seafloor tracked mining vehicle [30]. zhang et al. presented a control method for a straight-line path tracking of a seabed mining vehicle based on the anfis control; however only a single straight-line path was tracked without comparisons to other control methods [19]. han et al. proposed a pid control algorithm and achieved an anticipated goal for a seafloor tracked miner moving along a desired path [10, 11]. wang et al. presented a fuzzy and a predictive controller, further the efficiency of the control method was verified by the computer simulation and experimental results [24]. li et al. built a hydraulic system model of a seafloor self-propelled tracked mining vehicle, and its kinematic control by a fuzzy algorithm was discussed [20]. besides, various path tracking control method researches for vehicles or robots have been conducted. cui et al. investigated a trajectory tracking problem for a fully actuated autonomous underwater vehicle (auv), and two neural networks (nns) were integrated into an adaptive control design, the robustness and effectiveness of the proposed control method were tested and validated through extensive numerical simulation results [4]. sokolov et al. presented a neuro-evolution approach for a crawler robot motion that can autonomously solve the sequences of the navigation and flipper control tasks to overcome obstacles [23]. bozic et al. based on a combination of neural networks and genetic algorithm to intelligent modelling and optimization of energy usage for a wheel-legged (wheg) robot running; simulation of neuro-fuzzy control system was developed for minimization of energy usage during the wheg’s running [2]. chen et al. proposed a robust adaptive position/force control algorithm to track the desired posture and force in opening a door for a mobile robot manipulator, and co-simulation between matlab and recurdyn were performed to verify the dynamic model and control method [3]. barai et al. proposed a two-degree-of-freedom fuzzy controller for foot trajectory tracking control of a hydraulically actuated hexapod robot, and the fuzzy pre-filter was designed by a genetic algorithm (ga) based optimization [1]. wang et al. developed an adaptive position tracking system and a force control strategy for a non-holonomic mobile manipulator robot, which combined the merits of recurrent fuzzy wavelet neural networks (rfwnns), and the simulation and experimental results verified the effectiveness and robustness of the proposed method [25]. widyotriatmo et al. proposed a control method for a team of multiple mobile trajectory tracking control for seafloor tracked vehicle by adaptive neural-fuzzy inference system algorithm 467 robots, the individual mobile robots tracked the assigned trajectories and also should to collision among the mobile robots by the artificial potential field algorithm [26]. ngo et al. developed a robust adaptive self-organizing control system based on a novel wavelet fuzzy cerebellar model articulation controller for a robot manipulator, and verified its effectiveness through simulation and experimental results [21,22]. however, until now, it still remains typical problems need to be further resolved, an optimized trajectory tracking control strategy for the tracked vehicle on the seafloor has not yet been designed; furthermore, a collaborative model with real-time information interactions between the mechanical-control systems has not yet been achieved, so the trajectory tracking control performance and accuracy can not be evaluated and optimized. as it is costly and extremely difficult to perform seafloor in-situ tests, the collaborative simulation for the combined mechanical-control systems is an effective way. an optimized trajectory tracking control strategy and a mechanicalcontrol systems collaborative simulation research were conducted in the paper. 2 multi-body dynamic model of a stv the dynamic simulation code recurdyn/track, based on a relative coordinate system and a recursive algorithm relative, was adopted to establish an improved multi-body dynamic model of a stv as shown in fig. 1. table 1 gives its main structural parameters. figure 1: a 3d improved multi-body dynamic model of a stv a user-written subroutine for characterizing the particular terramechanics model of the seafloor sediment was developed in the c language in the visual studio.net environment and then integrated into the recurdyn/track environment. meanwhile the dynamic processes between the track-sediment interactions were taken into account. through laboratory simulant experiments as shown in fig. 2, a pressure-sinkage relationship and a shear stress-shear displacement relationship between the track-sediment interactions have been obtained. the normal force fni acting on each track link ith element can computed by multiplying the pressure with area of each track link as [27]: fni = pxi · ∆ai = [( kc b + kϕ ) · (∆zi)n ] · ∆ai (1) 468 y. dai, x. zhu, h. zhou, z. mao, w. wu table 1: structural parameters of the stv parameters values total weight underwater (tons) 2.35 overall dimension (m): length × width × height 2.3×1.6×1.2 track contact length (m) 1.6 track width (m) 0.36 distance between centre lines of tracks (m) 1.2 track pitch (m) 0.15 grouser height (m) 0.15 diameter of road wheel (m) 0.04 number of road wheel per track 7 diameter of support roller (m) 0.04 number of support roller per track 5 figure 2: laboratory experimental system for simulant track-sediment interaction mechanics where pxi is the normal pressure, ∆ai is the area of each tack link, b is the width of track link, kc is the sediment cohesion deformation modulus, kϕ is the sediment friction deformation modulus, ∆zi is the sinkage, n is the sediment deformation exponent. the longitudinal shear force flongi is computed by multiplying shear stress with area of each track link: flongi = sgn (jxi) τxi · ∆a = sgn (jxi) τmax ·kr · { 1 + [ 1 kr(1 −e−1) − 1 ] e1−jxi/kω }( 1 −e−jxi/kω ) · ∆a (2) where "sgn" is the signum function, jxi is the dynamic longitudinal shear displacement, τmax is the maximum shear stress, kr is the ratio of the residual shear stress τres to τmax, and kω is the shear displacement when τmax occurs. the dynamic longitudinal shear displacement can be expressed as a differential equation: d dt jxi (xi, t) + rsωs (t) xi · jxi (xi, t) = rsωs (t) −vx (t) (3) where rs and ωs (t) are the radius and angular velocity of the vehicle’s sprocket, xi and vx(t) represent the distance and actual velocity of the centre of each track link. similarly, the lateral shear force flati acting on each track link is computed as: trajectory tracking control for seafloor tracked vehicle by adaptive neural-fuzzy inference system algorithm 469 flati = −sgn (jyi) τxi · ∆a = −sgn (jyi) τmax ·kr · { 1 + [ 1 kr(1 −e−1) − 1 ] e1−jyi/kω }( 1 −e−jyi/kω ) · ∆a (4) where jyi is the dynamic lateral shear displacement. while the dynamic lateral shear displacement as be expressed as: d dt jyi (xi, t) + rsωs (t) xi jyi (xi, t) = vyi (t) (5) where vyi represent actual lateral velocity of the centre of each track link. fig. 3 presents a group of turning simulations trajectories of the stv with different turning velocity ratios (tvrs). the input velocity for the inner track was set to 0.5 m/s, while, the input velocities for the outer track were set to 0.6 m/s, 0.65 m/s and 0.7 m/s, respectively.      max 1 / 1 /1 1 sgn sgn 1 1 (1 ) j kxi xix xxlong i iii r j kj j ef a k e ar k e                        (2) where ‘sgn’ is the signum function, jxi is the dynamic longitudinal shear displacement, τmax is the maximum shear stress, kr is the ratio of the residual shear stress τres to τmax, and kω is the shear displacement when τmax occurs. the dynamic longitudinal shear displacement can be expressed as a differential equation:          , ,s sx i x i s s xi i i r td j x t j x t r t v t dt x      (3) where rs and ωs(t) are the radius and angular velocity of the vehicle’s sprocket, xi and vx(t ) represent the distance and actual velocity of the centre of each track link. similarly, the lateral shear force flati acting on each track link is computed as:      1 1 / max /1 1 sgn sgn 1 1 (1 ) yi yiy yxlat i iii r j ke j k f j a j k e ar k e                          (4) where jyi is the dynamic lateral shear displacement. while, the dynamic lateral shear displacement as be expressed as:        , ,s sy i y i yii i i r td j x t j x t v t dt x    (5) where vyi represent actual lateral velocity of the centre of each track link. fig.3 presents a group of turning simulations trajectories of the stv with different turning velocity ratios (tvrs). the input velocity for the inner track was set to 0.5 m/s, meanwhile, the input velocities for the outer track were set to 0.6 m/s, 0.65 m/s and 0.7 m/s, respectively. -20 -10 0 10 20 0 10 20 30 40 tvr=1.3 tvr=1.4 tvr=1.2l at er al p o si ti on ( m ) longitudinal position (m) figure 3: simulation trajectories of the stv with different turning velocity ratios it can be seen with the increase although a small value of the turning velocity ratio, the turning radius will increase obviously. according to the requirement of the turning velocity ratio for the stv that should not exceed 1.4, the minimum turning radius for the stv is about 12 m, which is much figure 3: simulation trajectories of the stv with different turning velocity ratios it can be seen with the increase although a small value of the turning velocity ratio, the turning radius will increase obviously. according to the requirement of the turning velocity ratio for the stv that should not exceed 1.4, the minimum turning radius for the stv is about 12 m, which is much larger than that on land-surface soft soil and also much larger than the theoretical computational turning radius. fig.4 presents the slips of the inner and outer tracks when the tvr is only 1.2. it can be observed a serious slip condition occurred for the outer track of the stv in spite of a low tvr, and further exhibited a serious slip commonly occurred on the seafloor will result in a much larger turning radius for the tracked vehicle compared to move on the land-surface soft soils. 3 trajectory tracking collaborative control simulations for a stv a control design code matlab/simulink was adopted to establish different control models for the stv. an interface toolkit recurdyn/control was designed to realize the information 470 y. dai, x. zhu, h. zhou, z. mao, w. wu larger than that on land-surface soft soil and also much larger than the theoretical computational turning radius. fig.4 presents the slips of the inner and outer tracks when the tvr is only 1.2. 0 20 40 60 80 100 120 140 0% 10% 20% 30% s li p r at io time (s) 0 20 40 60 80 100 120 140 0% 10% 20% 30% s li p r at io time (s) figure 4: slips of the left and right tracks of the stv it can be observed a serious slip condition occurred for the outer track of the stv in spite of a low tvr, and further exhibited a serious slip commonly occurred on the seafloor will result in a much larger turning radius for the tracked vehicle compared to move on the land-surface soft soils. 3 trajectory tracking collaborative control simulations for a stv a control design code matlab/simulink was adopted to establish different control models for the stv. an interface toolkit recurdyn/control was designed to realize the information communication between the control system model and mechanical dynamic model. the matlab/simulink was taken as the main interface. the plant input and plant output of the mechanical dynamic model were set, meanwhile, a m file for exporting the mechanical dynamic model was compiled. 3.1 comparisons of different control algorithms a recurdyn/track plant block representing a mechanical dynamic model of the stv was created in the matlab/simulink; then the mechanical-control collaborative simulation can be achieved. the theoretical input velocity for the stv is 0.6 m/s in the collaborative simulation. several different control methods including a pid control, a fuzzy logic control and a neural network control were performed and compared. fig.5 shows the co-simulation model interface of a pid control with mechanical dynamic model, and corresponding simulation results with and without external jamming signal for the input velocity also compared. it can be seen the pid control is insensitive to the external disturbance. figure 4: slips of the left and right tracks of the stv communication between the control system model and mechanical dynamic model. the matlab/simulink was taken as the main interface. the plant input and plant output of the mechanical dynamic model were set, while, a m file for exporting the mechanical dynamic model was compiled. 3.1 comparisons of different control algorithms a recurdyn/track plant block representing a mechanical dynamic model of the stv was created in the matlab/simulink; then the mechanical-control collaborative simulation can be achieved. the theoretical input velocity for the stv is 0.6 m/s in the collaborative simulation. several different control methods including a pid control, a fuzzy logic control and a neural network control were performed and compared. fig.5 shows the co-simulation model interface of a pid control with mechanical dynamic model, and corresponding simulation results with and without external jamming signal for the input velocity also compared. it can be seen the pid control is insensitive to the external disturbance. 0 5 10 15 20 -0.2 0.0 0.2 0.4 0.6 0.8 with ejs v el o ci ty ( m /s ) time (s) without ejs figure 5: co-simulation model interface and results of pid control with dynamic model fig. 6 shows a co-simulation model interface of an adaptive fuzzy logic control model with mechanical dynamic model, and the simulation results with and without external jamming signal compared. the inputs for the fuzzy logic controller were velocity error (e) and velocity error derivative (ec), which were the difference between the target velocity and ideal velocity. the output was the velocity compensation (u). compared with the pid control algorithm, the fuzzy logic algorithm has a stronger anti-disturbance ability and better robustness, which was more suitable for the stv motion control. nevertheless, the control precision of the conventional fuzzy logic is not high. step scope recurdyn client block1 -kgain du/dt derivative saturation1 saturation2 fuzzy logic controller band-limited white noise -kgain3 -kgain1 -kgain2 0 5 10 15 20 -0.2 0.0 0.2 0.4 0.6 0.8 without ejsv el o ci ty ( m /s ) time (s) with ejs figure 6: co-simulation model interface and results of fuzzy logic control with dynamic model fig.7 shows a co-simulation model interface of a bp neural network algorithm control model with dynamic model, and simulation results with and without external jamming signal compared. the neural network has the features of self-adapting and self-learning; however, it is weak in expressing the rule knowledge. it can be seen the neural network algorithm has a weak anti-disturbance ability compared to a fuzzy control method. figure 5: co-simulation model interface and results of pid control with dynamic model fig. 6 shows a co-simulation model interface of an adaptive fuzzy logic control model with mechanical dynamic model, and the simulation results with and without external jamming signal compared. the inputs for the fuzzy logic controller were velocity error (e) and velocity error derivative (ec), which were the difference between the target velocity and ideal velocity. the output was the velocity compensation (u). compared with the pid control algorithm, the fuzzy logic algorithm has a stronger anti-disturbance ability and better robustness, which was more suitable for the stv motion control. nevertheless, the control precision of the conventional fuzzy logic is not high. trajectory tracking control for seafloor tracked vehicle by adaptive neural-fuzzy inference system algorithm 471 0 5 10 15 20 -0.2 0.0 0.2 0.4 0.6 0.8 with ejs v el o ci ty ( m /s ) time (s) without ejs figure 5: co-simulation model interface and results of pid control with dynamic model fig. 6 shows a co-simulation model interface of an adaptive fuzzy logic control model with mechanical dynamic model, and the simulation results with and without external jamming signal compared. the inputs for the fuzzy logic controller were velocity error (e) and velocity error derivative (ec), which were the difference between the target velocity and ideal velocity. the output was the velocity compensation (u). compared with the pid control algorithm, the fuzzy logic algorithm has a stronger anti-disturbance ability and better robustness, which was more suitable for the stv motion control. nevertheless, the control precision of the conventional fuzzy logic is not high. step scope recurdyn client block1 -kgain du/dt derivative saturation1 saturation2 fuzzy logic controller band-limited white noise -kgain3 -kgain1 -kgain2 0 5 10 15 20 -0.2 0.0 0.2 0.4 0.6 0.8 without ejsv el o ci ty ( m /s ) time (s) with ejs figure 6: co-simulation model interface and results of fuzzy logic control with dynamic model fig.7 shows a co-simulation model interface of a bp neural network algorithm control model with dynamic model, and simulation results with and without external jamming signal compared. the neural network has the features of self-adapting and self-learning; however, it is weak in expressing the rule knowledge. it can be seen the neural network algorithm has a weak anti-disturbance ability compared to a fuzzy control method. figure 6: co-simulation model interface and results of fuzzy logic control with dynamic model fig.7 shows a co-simulation model interface of a bp neural network algorithm control model with dynamic model, and simulation results with and without external jamming signal compared. the neural network has the features of self-adapting and self-learning; however, it is weak in expressing the rule knowledge. as it can be seen the neural network algorithm has a weak anti-disturbance ability compared to a fuzzy control method. step scope recurdyn client block1 -kgain du/dt derivative saturation1 saturation2 nnetinput output custom neural network band-limited white noise -kgain1 -kgain2 -kgain3 0 5 10 15 20 -0.2 0.0 0.2 0.4 0.6 0.8 without ejsv el o ci ty ( m /s ) time (s) with ejs figure 7: co-simulation model interface and results of neural network control with dynamic model in order to overcome the shortcomings of a single fuzzy control or a single neural control, a fuzzy-neural control method that incorporates the fuzzy control and the neural control was adopted and designed. the fuzzy takagi-sugeno (t-s) model that is more simple for calculation and better for mathematical analysis, was combined with an adaptive neural control systems, then an adaptive neural-fuzzy inference system (anfis) was presented for controlling the stv motion state in the paper. the critical step in this anfis architecture is to realize the self-learning and adaptive of the control parameter. hybrid learning algorithm, namely, a combination of least-squares estimation and back-propagation, was developed for the parameter learning of the membership function. the simulation model interface of the anfis collaborated with the dynamic model for the stv was presented in fig.8. it can be seen a desirable control effect can be obtained by using this control scheme. however, this hybrid algorithm method required more computation and analysis. step scope recurdyn client block1 -kgain du/dt derivative saturation1 saturation2 band-limited white noise anfistrack s-function -kgain4 -kgain1 -kgain2 0 5 10 15 20 -0.2 0.0 0.2 0.4 0.6 0.8 without ejsv el o ci ty ( m /s ) time (s) with ejs figure 8: co-simulation model interface and results of neural-fuzzy control with dynamic model 3.2 trajectory tracking collaborative control simulations and comparisons actually, mechanics properties of the seafloor sediment are always uneven distribution, which will cause the differences of the shear forces and traction forces under two tracks. as a result, a turning moment will be generated, and make the stv move deviate from its predetermined straight-line and circle trajectory. the shear strengths under the left track were set to 3.0 kpa and 4.0 kpa respectively, while the shear strength under the right track was set to a constant value 5 kpa. fig.9 shows the simulation trajectories of the stv under these two conditions. while the green dashed lines both in fig.9 and fig.10 represent the predetermined straight-line paths. it can be seen the actual motion trajectories will deviate from its predetermined straight-line trajectory due to the figure 7: co-simulation model interface and results of neural network control with dynamic model in order to overcome the shortcomings of a single fuzzy control or a single neural control, a fuzzy-neural control method that incorporates the fuzzy control and the neural control was adopted and designed. the fuzzy takagi-sugeno (t-s) model that is more simple for calculation and better for mathematical analysis, was combined with an adaptive neural control systems, then an adaptive neural-fuzzy inference system (anfis) was presented for controlling the stv motion state in the paper. the critical step in this anfis architecture is to realize the self-learning and adaptive of the control parameter. hybrid learning algorithm, namely, a combination of least-squares estimation and back-propagation, was developed for the parameter learning of the membership function. the simulation model interface of the anfis collaborated with the dynamic model for the stv was presented in fig. 8. it can be seen a desirable control effect can be obtained by using this control scheme. however, this hybrid algorithm method required more computation and analysis. 3.2 trajectory tracking collaborative control simulations and comparisons actually, mechanics properties of the seafloor sediment are always uneven distribution, which will cause the differences of the shear forces and traction forces under two tracks. as a result, a turning moment will be generated, and make the stv move deviate from its predetermined 472 y. dai, x. zhu, h. zhou, z. mao, w. wu step scope recurdyn client block1 -kgain du/dt derivative saturation1 saturation2 nnetinput output custom neural network band-limited white noise -kgain1 -kgain2 -kgain3 0 5 10 15 20 -0.2 0.0 0.2 0.4 0.6 0.8 without ejsv el o ci ty ( m /s ) time (s) with ejs figure 7: co-simulation model interface and results of neural network control with dynamic model in order to overcome the shortcomings of a single fuzzy control or a single neural control, a fuzzy-neural control method that incorporates the fuzzy control and the neural control was adopted and designed. the fuzzy takagi-sugeno (t-s) model that is more simple for calculation and better for mathematical analysis, was combined with an adaptive neural control systems, then an adaptive neural-fuzzy inference system (anfis) was presented for controlling the stv motion state in the paper. the critical step in this anfis architecture is to realize the self-learning and adaptive of the control parameter. hybrid learning algorithm, namely, a combination of least-squares estimation and back-propagation, was developed for the parameter learning of the membership function. the simulation model interface of the anfis collaborated with the dynamic model for the stv was presented in fig.8. it can be seen a desirable control effect can be obtained by using this control scheme. however, this hybrid algorithm method required more computation and analysis. step scope recurdyn client block1 -kgain du/dt derivative saturation1 saturation2 band-limited white noise anfistrack s-function -kgain4 -kgain1 -kgain2 0 5 10 15 20 -0.2 0.0 0.2 0.4 0.6 0.8 without ejsv el o ci ty ( m /s ) time (s) with ejs figure 8: co-simulation model interface and results of neural-fuzzy control with dynamic model 3.2 trajectory tracking collaborative control simulations and comparisons actually, mechanics properties of the seafloor sediment are always uneven distribution, which will cause the differences of the shear forces and traction forces under two tracks. as a result, a turning moment will be generated, and make the stv move deviate from its predetermined straight-line and circle trajectory. the shear strengths under the left track were set to 3.0 kpa and 4.0 kpa respectively, while the shear strength under the right track was set to a constant value 5 kpa. fig.9 shows the simulation trajectories of the stv under these two conditions. while the green dashed lines both in fig.9 and fig.10 represent the predetermined straight-line paths. it can be seen the actual motion trajectories will deviate from its predetermined straight-line trajectory due to the figure 8: co-simulation model interface and results of neural-fuzzy control with dynamic model straight-line and circle trajectory. the shear strengths under the left track were set to 3.0 kpa and 4.0 kpa respectively, while the shear strength under the right track was set to a constant value 5 kpa. fig.9 shows the simulation trajectories of the stv under these two conditions. while the green dashed lines both in fig.9 and fig.10 represent the predetermined straight-line paths. it can be seen the actual motion trajectories will deviate from its predetermined straightline trajectory due to the uneven distribution of the sediment mechanics properties. with the longitudinal displacement increases, the deviations will continuously increase. according to the trajectory deviation control requirement for the stv, the deviation should within the allowable range of −1m to 1m. so, it is necessary to perform an effective control for the stv to keep its actual motion trajectory to tracking a predetermined path. uneven distribution of the sediment mechanics properties. with the longitudinal displacement increases, the deviations will continuously increase. according to the trajectory deviation control requirement for the stv, the deviation should within the allowable range of -1 m to 1 m. so, it is necessary to perform an effective control for the stv to keep its actual motion trajectory to tracking a predetermined path. 0 5 10 15 -1.0 -0.5 0.0 0.5 1.0 1.5 (left: 3.0 kpa; right: 5 kpa) (left: 4.0 kpa; right: 5 kpa) l at er al p o si ti o n ( m ) longitudinal position (m) figure 9: straight-line motion trajectory deviation simulations of the stv the above used fuzzy logic control, neural control and anfis control were carried out respectively for the trajectory control as shown in fig.11. it can be seen obviously that with the anfis control, the trajectory deviation is the minimum; when the longitudinal displacement is about 16 m, its lateral trajectory deviation is just about 0.01 m, which indicate the anfis control has a better effect for the stv compared to other control methods. if without an optimized or proper control for the stv, its lateral trajectory deviation will continuously enlarge along with its longitudinal motion. 0 4 8 12 16 0.605 0.610 0.615 0.620 0.625 fuzzy logic control longitudinal position (m) anfis control l at er al p o si ti o n ( m ) nueral network control figure 10: straight-line motion trajectory control simulations comparisons between different controls an anfis model has been built for a predetermined path control for the stv and collaborated figure 9: straight-line motion trajectory deviation simulations of the stv straight-line motion trajectory control simulations comparisons between different controls the above used fuzzy logic control, neural control and anfis control were carried out respectively for the trajectory control as shown in fig. 11. it can be seen obviously that with the anfis control, the trajectory deviation is the minimum; when the longitudinal displacement is about 16 m, its lateral trajectory deviation is just about 0.01 m, which indicate the anfis control has a better effect for the stv compared to other control methods. if without an optimized or proper control for the stv, its lateral trajectory deviation will continuously enlarge along with its longitudinal motion. trajectory tracking control for seafloor tracked vehicle by adaptive neural-fuzzy inference system algorithm 473 uneven distribution of the sediment mechanics properties. with the longitudinal displacement increases, the deviations will continuously increase. according to the trajectory deviation control requirement for the stv, the deviation should within the allowable range of -1 m to 1 m. so, it is necessary to perform an effective control for the stv to keep its actual motion trajectory to tracking a predetermined path. 0 5 10 15 -1.0 -0.5 0.0 0.5 1.0 1.5 (left: 3.0 kpa; right: 5 kpa) (left: 4.0 kpa; right: 5 kpa) l at er al p o si ti o n ( m ) longitudinal position (m) figure 9: straight-line motion trajectory deviation simulations of the stv the above used fuzzy logic control, neural control and anfis control were carried out respectively for the trajectory control as shown in fig.11. it can be seen obviously that with the anfis control, the trajectory deviation is the minimum; when the longitudinal displacement is about 16 m, its lateral trajectory deviation is just about 0.01 m, which indicate the anfis control has a better effect for the stv compared to other control methods. if without an optimized or proper control for the stv, its lateral trajectory deviation will continuously enlarge along with its longitudinal motion. 0 4 8 12 16 0.605 0.610 0.615 0.620 0.625 fuzzy logic control longitudinal position (m) anfis control l at er al p o si ti o n ( m ) nueral network control figure 10: straight-line motion trajectory control simulations comparisons between different controls an anfis model has been built for a predetermined path control for the stv and collaborated figure 10: straight-line motion trajectory control simulations comparisons between different controls figure 11: combined path co-simulation model interface of the anfis control model with dynamic model with the mechanical multi-body dynamic model. fig. 11 shows the collaborative simulation model. var val vt ex ey θt xr yr θr -kgain1 -kgain2 in1 in2 out1 out2 out3 out4 path comparisonin1 in2 in3 out1 path deviation in1 in2 out1 out2 speed distribution -kgain5 -kgain6 saturation saturation1 data scope saturation2 step4 step2 -kgain8 recurdyn client block1 anfisvehicle s-function -kgain3 figure 11: combined path co-simulation model interface of the anfis control model with dynamic model a predetermined combined path including straight-line and turning paths was set for tracking simulation. the input velocity of the stv was set to 0.5 m/s; the turning velocity ratio was set to 1.2. the straight-line and turning motions time were set to 20 s and 90 s, respectively. the motion trajectories simulations between the fuzzy logic control, neural network control and anfis, were conducted and compared relative to the predetermined path in fig.12. 0 10 20 30 0 10 20 30 40 trajectory of fuzzy l at er al p o si ti o n ( m ) longitudinal position (m) trajectory of anfis predetermined trajectory trajectory of nueral figure 12: simulation motion trajectories of the stv with different controls it can be seen that with the anfis control, the trajectory deviation for the stv relative to its predetermined path was the minimum compared to other control methods. for above one round of the predetermined path tracking, the maximum trajectory deviation under anfis control can be maintained around 0.5 m within the allowable range of -1 m to 1m. it can be predicted that with the continuous motions of the stv along with more rounds of above predetermined combined paths, the control effect of the anfis will be more obvious and efficient. 4 conclusions figure 12: simulation motion trajectories of the stv with different controls 474 y. dai, x. zhu, h. zhou, z. mao, w. wu a predetermined combined path including straight-line and turning paths was set for tracking simulation. the input velocity of the stv was set to 0.5 m/s; the turning velocity ratio was set to 1.2. the straight-line and turning motions time were set to 20 s and 90 s, respectively. the motion trajectories simulations between the fuzzy logic control, neural network control and anfis, were conducted and compared relative to the predetermined path in fig. 12. it can be seen that with the anfis control, the trajectory deviation for the stv relative to its predetermined path was the minimum compared to other control methods. for above one round of the predetermined path tracking, the maximum trajectory deviation under anfis control can be maintained around 0.5 m within the allowable range of -1 m to 1m. it can be predicted that with the continuous motions of the stv along with more rounds of above predetermined combined paths, the control effect of the anfis will be more obvious and efficient. 4 conclusions the major conclusions as follows can be drawn from this work. (1) an improved multi-body dynamic model of a stv with integration of a dynamic terramechanics model of the particular sediment has been established and verified. a new collaborative simulation model of the stv integrating a mechanical multi-body dynamic model in recurdyn/track and a control model in matlab/simulink has been developed and co-simulations were achieved. different control algorithms performances including a pid control, a fuzzy control and a neural control, have been compared and proved that the traditional or individual intelligent controls are not particularly suitable for the stv motion control. (2) an adaptive neural-fuzzy inference system (anfis) control algorithm incorporating the fuzzy control and neural network control has been adopted and designed for the stv motion control. a straight-line path tracking controls for the stv by the fuzzy, neural network and anfis controls have performed and proved the anfis control algorithm can achieve a desired control performance for the stv compared to the other controls. (3) a predetermined combined path, including the straight-line and turning paths, has been tracked for the stv by an anfis control algorithm compared to a fuzzy logic and neural network controls. the collaborative simulations have proved the anfis control method can achieve a better control effect among these control algorithms, with its actual maximum trajectory deviation can be maintained around 0.5 m within the permissible range. acknowledgment this research is supported by the national natural science foundation of china (grand no. 51774324 and no. 51105386), the national key research and development program of china (grand no. sq2016yf010109). the authors would like to thank the comments from the anonymous reviewers that improved the quality of the paper. bibliography [1] barai, r.k.; nonami, k. (2007); optimal two-degree-of-freedom fuzzy control for locomotion control of a hydraulically actuated hexapod robot, information sciences, 177(8), 1892-1915, 2007. 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(2014); path tracking control test of underwater mining robot, oceans, 1–4, 2014. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 3, month: june, year: 2022 article number: 3744, https://doi.org/10.15837/ijccc.2022.3.3744 ccc publications optimization of three-dimensional face recognition algorithms in financial identity authentication cong luo, xiangbo fan, ying yan, han jin, xuan wang cong luo 1. school of mathematics and statistics guizhou university of finance and economics, guiyang, 550025, guizhou, china 2. guizhou key laboratory of big data statistical analysis guizhou university of finance and economics, guiyang, 550025, guizhou, china xiangbo fan*, ying yan, han jin, xuan wang 3. big data application and economics guizhou university of finance and economics, guiyang, 550025, guizhou, china 4. new structure financial research center guizhou university of finance and economics, guiyang, 550025, guizhou, china *corresponding author: 1257846629@qq.com abstract identity authentication is one of the most basic components in the computer network world. it is the key technology of information security. it plays an important role in the protection of system and data security. biometric recognition technology provides a reliable and convenient way for identity authentication. compared with other biometric recognition technologies, face recognition has become a hot research topic because of its convenience, friendliness and easy acceptance. with the maturity and progress of face recognition technology, its commercial application has become more and more widespread. internet finance, e-commerce and other asset-related areas have begun to try to use face recognition technology as a means of authentication, so people’s security needs for face recognition systems are also increasing. however, as a biometric recognition system, face recognition system still has inherent security vulnerabilities and faces security threats such as template attack and counterfeit attack. in view of this, this paper studies the application of threedimensional face recognition algorithm in the field of financial identity authentication. on the basis of feature extraction of face information using neural network algorithm, k-l transform is applied to image high-dimensional vector mapping to make face recognition clearer. thus, the image loss can be reduced. keywords: financial identity authentication, feature extraction, three-dimensional face recognition algorithms, optimization. 1 introduction in recent decades, the internet has been developing at an alarming speed. people’s demand for the network is getting higher and higher, enjoying the convenience and speed brought by the network. https://doi.org/10.15837/ijccc.2022.3.3744 2 however, the development speed of network applications is far faster than that of corresponding security facilities. nowadays, the harm caused by various security problems is more and more serious, so the network security problem has been paid more and more attention by the leaders and people of various countries. therefore, various encryption, access control, identity authentication technologies are proposed to improve the security of the network. among them, identity authentication is the basis of mutual communication and e-commerce activities on the network, as well as the basis of network security system. at present, the technology of identity authentication has been widely used in various network services that people enjoy. wire equipment and wireless equipment are widely used, which makes the demand of network security access control and network application in finance, trade and e-commerce grow rapidly. nowadays, biometric recognition methods have been widely used, and face recognition technology is also one of the most widely used. because of its non-contact, convenient collection, easy to accept and other characteristics, it has become an important research topic at home and abroad. face recognition has its irreplaceable technical advantages in the application of electronic transactions, remote video conferencing, entrance guard attendance, entry and exit security, and it is one of the mainstream technologies of authentication and recognition in the future. by looking at the face to distinguish different individuals, recognition and verification of face and human identity is also in line with people’s daily understanding of identity, more easily accepted by the public. to sum up, the research on network authentication based on security face recognition has certain scientific research value and practical value. this paper mainly explores the application of face recognition in network identity authentication by studying the security and practicability of face recognition scheme. in the field of internet-based financial services, the process of user identity authentication usually includes three links: identity registration, token/certificate issuance and management, and identity authentication. first, the user needs to complete the identity registration. in the registration process, the registration authority collects and verifies the user’s identity information to confirm the user’s identity, that is, identity verification. when the user’s identification is successful, the certificate service provider can apply for issuing token/certificate to the user. a valid token/certificate can be used in the subsequent identification process, that is, the certificate service provider confirms the user’s identity according to the token/certificate held by the user to complete the user’s identification. with the deepening of research, great progress has been made in financial identity authentication. nair k. et al. [1] studied and analyzed the background, principle and corresponding algorithm process of fingerprint identification technology in biometrics, including fingerprint image capture, fingerprint image analysis and feature extraction, fingerprint matching and other related technologies. this paper introduces the smart card (smart card), its internal structure, the setting of relevant parameters, and the information security of digital fingerprint stored in the smart card. encryption has become an important part of the network security information system in order to ensure the safe transmission of various information in the network. in this paper, we discuss the steps and flow of des and rsa, which are two typical encryption algorithms in symmetrical and asymmetrical encryption systems, and the cryptographic technology applied in ic cards. and as a well-known hash function in the information summary function md5 specific implementation process. this paper presents a secure network communication platform based on digital fingerprint identity authentication and ic card, which integrates symmetrical and asymmetrical encryption algorithms and uses information digest function to ensure the security and reliability of the transmitted information. in 2003, rila l et al. [2] introduced the basic concept, current situation and application of smart cards closely related to identity authentication, analyzed several types of identity authentication and their advantages and disadvantages, and obtained the reason and significance of adopting two-factor identity authentication based on smart cards in financial system. and the importance and necessity of identity authentication in financial transactions. secondly, the security requirements of emv2000 standard and identity authentication are studied in detail. especially, the security requirements of emv standard are analyzed in detail. key management, application key distribution method in smart cards, security mechanism of smart cards and security requirements of terminals are studied in detail. the transmission of secure messages and external authentication (internal authentication) have been discussed for a long time. secondly, the authentication and encryption algorithms related to identity authentication are systematically studied. the security of symmetric and asymmetric encryption algorithms is analyzed, and https://doi.org/10.15837/ijccc.2022.3.3744 3 some optimizations and improvements are made. therefore, the architecture of pki is also studied. in 2008, nair s k et al. [3] analyzed the needs of financial security, expounded the importance of security construction, proposed the development of security information system based on pki system in order to solve the problem of security control, and then divided security information system into security service system and security application system. through identity authentication and digital signature technology, the security information system is successfully constructed. finally, the operation of the security information system is described. as the basic application technology system in the framework of banking financial service application software, security information system solves the security control problem of various banking business application systems by using pki technology and related products. by establishing a unified authentication system, a unified authority system, a security authentication management system for counter business and a security application system, the bank has effectively protected the secrecy and integrity of data in the process of transmission and storage, and effectively identified the identity of the objects accessing the bank information system. effectively controls access rights to access objects. the system mainly solves two problems: first, using digital certificate to authenticate the identity of the teller to solve the fragile protection of the password and password of the teller, and the problem that the transaction identity cannot be confirmed, so as to ensure that no one can imitate the identity of others to carry out relevant operations. secondly, digital signature is used as cryptography to solve the problems of easy leakage of cryptography, low operation efficiency and the risk of forgery transaction, so as to meet the needs of centralized accounting. in 2016, shi junpeng studied the digital watermarking technology and fingerprint identification technology in copyright protection and identity authentication. the main work is as follows: firstly, a dual watermarking algorithm based on support vector machine (svm) and krawtchouk moment in complex contourlet domain is proposed. the robust watermarking is constructed by krawtchouk moment invariant on the blue component of rgb host image. the green component is decomposed by complex contourlet decomposition, and the digital watermarking is embedded and extracted by svm prediction model. the experimental results show that the algorithm has better robustness to conventional image processing than the digital watermarking algorithm based on wavelet domain and the digital watermarking algorithm based on contourlet domain. with the continuous development of computer technology, two-dimensional face recognition technology has also made considerable achievements in the past 30 years, but compared with the huge prospects of face recognition applications and market demand, most of the current two-dimensional face recognition technology cannot fully meet the needs of practical applications. according to the research, information, illumination, posture, expression, make-up and age are the main factors affecting the performance of two-dimensional face recognition system. in 2002, frvt assessed the factors that could affect the two-dimensional face recognition system. the evaluation results show that the following are the main factors affecting the two-dimensional face recognition technology. (1) information: two-dimensional face image is the result of projection of three-dimensional face plane, which will inevitably lead to the loss of some information and cannot effectively reflect the face identification information. the lack of information is an inherent defect of two-dimensional face recognition. (2) illumination change: illumination change is an important factor affecting two-dimensional face recognition system, especially the face system exposed to the outside world is seriously affected by illumination, and the recognition rate needs to be improved urgently. (3) changes in facial expressions: changes in facial expressions can cause changes in areas such as facial muscle contraction, eye size, eyebrow beating, mouth opening and closing. these changes are the main factors affecting face recognition, which will significantly reduce the recognition rate. (4) attitude change: attitude change can directly affect the amount of information acquired from human face. side face obviously cannot fully reflect the information of human face, which will seriously affect the performance of face recognition system. (5) too many people: when there are too many people in the face database, the recognition algorithm and computer resolution are limited, and the recognition rate will decrease logarithmically with the increase of the number of people. in addition, makeup and age are also the main factors affecting face recognition. research shows that when facial makeup is applied, face recognition will also be significantly affected. older people are easier to recognize than younger people. because of these unalterable defects in the two-dimensional face recognition system, researchers have begun https://doi.org/10.15837/ijccc.2022.3.3744 4 to develop methods to improve the performance of face recognition system or turn directly to other face recognition methods. three-dimensional face recognition is proposed in this case. li ye, wang yinghui, liu jing and others [5] proposed ridge extraction and valley extraction to represent human face in view of the huge volume of three-dimensional face data and low recognition efficiency. ridges and valleys, as extreme points of principal curvature changing along principal direction in local area of surface, can well represent three-dimensional face features. for three-dimensional face extraction ridge point model and valley point model, rough face matching is realized by generating corresponding spatial distribution density histogram after rasterization, and accurate face matching is achieved by calculating lts-hausdorff distance. the experimental results in gavabdb three-dimensional face database show that the method has a high recognition rate. as’ari m a and others [6] apply the depth camera of kinect, a high performance-cost somatosensory device launched by microsoft, to three-dimensional face data acquisition, to a certain extent, to solve the problem of expensive and complex use of previous three-dimensional data acquisition equipment. at the same time, we can use its complete function and simple and easy-to-use program interface to pre-process the collected threedimensional data such as noise reduction. in the aspect of three-dimensional face feature extraction, the method of kernel principal component analysis (kpca) is used to project the three-dimensional face data from high-dimensional space into low-dimensional space under the principle of minimizing the loss of original image information, which further improves the shortcomings of principal component analysis (pca) in dealing with non-linear data. based on the strong classification ability of support vector machine (svm) and the advantage of solving the problem of small sample data, the classification and recognition results of reduced dimension three-dimensional face data are output by svm. in the overall implementation of the system, this paper uses the depth camera of kinect, a microsoft somatosensory device, as the hardware device of face three-dimensional data acquisition, to achieve the functions of acquisition and preprocessing of three-dimensional face data, extraction of three-dimensional face features and three-dimensional face recognition. the experimental results show that the method adopted in this paper can achieve the acquisition and processing of three-dimensional face data, and has a good recognition effect. the method adopted in this paper has a certain significance for the popularization and application of three-dimensional face recognition technology. zhan shu, zhang qixiang, jiang jianguo et al. [7] in order to overcome the recognition difficulties caused by the changes of illumination, expression and posture in three-dimensional face recognition, a nuclear cooperative expression three-dimensional face recognition algorithm based on gabor feature is proposed. collaborative expression classification algorithm uses similar face data to co-represent test faces. sparse coefficients are solved by 2 norms, and test faces are classified correctly according to reconstruction error. in this method, gabor filters are used to extract 40 gabor features of different scales and directions of 3d face depth maps, and then appropriate kernels are selected to map them into high-dimensional kernelspace for non-linear dimensionality reduction and feature selection. finally, three-dimensional face recognition is completed by combining the cooperative expression classification algorithm. experiments on kinect face database and texas three-dimensional face database show that this method can achieve good recognition results with fewer training samples. in order to enhance the robustness of three-dimensional face recognition system to facial expression changes, daniyal f et al. [8], proposed a face recognition system that combines depth data with facial rigid regions. firstly, the nose tips are located according to the geometric features of the face, and the effective facial regions are cut from the point as the center to rectify the faces with various postures to the positive postures. then, the depth map is used as the whole feature to match the nose tips using the two-dimensional principal component analysis (2dpca) algorithm. an improved iterative nearest point (icp) algorithm is used to match the face rigid region as the local feature. finally, the extracted global feature matching results and local feature matching results are fused. the experimental results on casia 3d face database show that the recognition rate of the constructed system is higher than that of the system using a single feature, and it is robust to facial expression changes. lv shiwen, da feipeng, deng xing and others [9] proposed a region-based improved local binary pattern (lbp) based three-dimensional face recognition algorithm for facial expression problems in three-dimensional face recognition. firstly, the pre-processed three-dimensional point clouds are transformed into depth maps and normalized. secondly, according to the influence of facial expressions on the face, the rigid, https://doi.org/10.15837/ijccc.2022.3.3744 5 semi-rigid and non-rigid regions of the face are extracted by using binary mask. for each local region, the improved lbp features are calculated and characterized by equivalent patterns. finally, a single local region is identified by using sparse representation classifier (src), and a weighted sparse representation classifier (w-src) is used to fuse the rigid and semi-rigid regions at decision level, and the final recognition results are given. the experimental results on frgc v2.0 face database show that the method has good robustness and high recognition accuracy. 2 proposed method method 2.1 principal component analysis the basic idea of pca face recognition is to find the most representative feature space from the face image and remove some irrelevant features. a face image is mapped into this feature space to obtain a set of coefficients, which represent the features of the face image. if the coefficients of two face images mapped to this feature space are similar, it means that the two faces correspond to the same person. principal component analysis (pca) mainly uses k-l transform to map high-dimensional vectors representing face images into subspaces formed by several feature vectors, thus completing the optimal analysis and reconstruction of face images. for a face image of m × n dimension size, the principal component analysis algorithm joins its columns to form a column vector of m×n dimension size,m×n is the dimension of the face image and the dimension of the image space is m×n. let m be the number of training samples.xj represents the face image vector formed from the jth face image. then the covariance matrix of all face image training samples is ∑ t = m∑ i=1 (xi −µ)(xi −µ)t among them,µ is the average image vector of the training sample is used. µ = 1 m m∑ i=1 xi let a =[x1 −µ,x2 −µ,...,xm −µ],then ∑ t = aat . according to the principle of singular value decomposition, the eigenvalues and eigenvectors to be solved of aat can be obtained by solving the eigenvalues and eigenvectors of the matrix aat .if the r-th non-zero eigenvalue of the matrix aat is λi(i = 1, 2, ...,r),the eigenvector νi of the aat corresponding eigenvalue, then the aat orthogonal normalized eigenvector is µi = (1/ √ λi)νi it is the eigenvector of aat .if the eigenvalues are arranged from large to small λ1 ≥ λ2 ≥ ...λr ≥ 0,the corresponding eigenvector is {µi}ti=1the eigenvector. in this way, each face image can be projected into the subspace formed by the µ1,µ2, ...,µr.considering the dimension, the eigenvectors corresponding to the first p larger eigenvalues can be selected as the subspace accordingly. p∑ i=1 xi/ r∑ i=1 xi ≥ α among them, the value of is a value derived from experiments. because the extracted eigenvector images are very similar to human faces, they are called "feature faces", so the face recognition method realized by pca transformation algorithm is also called "feature face" method. 2.2 support vector machine algorithms the central idea of support vector machine (svm) algorithm [13] is to find an optimal hyperplane for classification. through learning and training a large number of face training sample data, svm algorithm can judge whether an input belongs to one of the two categories. the algorithm is divided https://doi.org/10.15837/ijccc.2022.3.3744 6 into two stages: learning stage and recognition stage. in the learning stage of svm, the algorithm follows the principle of structural risk minimization to learn the set of face images with labels, so as to determine the optimal parameters of hyperregressive plane. the first stage, learning stage: (1) establishing training sample set {xi,yj},yi ∈{−1, +1},i = 1, ..., l. (2)kernel functions k{xi,xj} and coefficients are selected to transform low-dimensional space to highdimensional space. (3)normalizing the input sample,xi will be stipulated in {−1, +1}. (4)constructing the kernel matrix h {l, l}. (5)solution of lagrange coefficient αi. the constraint condition is, ∑i i=1 αiyi = 0,0 < αi < c. the objective function used is: maxq(α) = ∑1 i=1 αi − 1 2 ∑1 i,j=1 αiαjyiyjk{xi,xj}. (6) the corresponding support vectors sν are searched and the classification hyperplane coefficient vector b is solved. (7) solving all kinds of parameters, constructing the corresponding optimal classification hyperplane, and completing the training. the second stage, identification stage: (1) enter the standard template data that has been trained in the learning stage. (2) utilization f(x) = ∑ sν yiαik(xi,x ′ ) − b calculate the output value of data feature to be classified. (3) according to the classification function f(x′), the data to be classified are classified as {−1, +1} and the classification results are obtained. svm has many unique advantages in solving small sample and non-linear face pattern recognition problems. its performance is superior to many other learning methods and shows good learning ability. at the same time, svm can get the corresponding decision rules from the limited training samples, and it can still get smaller errors for independent test sets. 2.3 neural network algorithms neural network system [14, 15, 16] is a non-linear dynamic system with distributed information storage. the system processes information in parallel and collaboratively, and has a high recognition rate for images. neural networks consist of many simple neurons with parallel operations, as shown in figure 1. neuron is a multi-input, single-output element. the input signals are divided into excitation and inhibition. the output response of a neuron depends on the result of input signal superposition synthesis, and when a certain synthetic input value exceeds a certain threshold, the neuron is activated, otherwise, the neuron will be in an inhibitory state. in the application of face recognition algorithm, most of the neural network models choose error back-propagation algorithm, that is, bp neural network algorithm. the main idea is that the error of the output layer is propagated from the latter layer to the forward layer, so that the error of the hidden layer can be calculated indirectly. the algorithm is divided into two stages: the first stage is the forward process, in which the input information starts from the input layer and calculates the output value of each unit through each hidden layer; the second stage is the back propagation process, in which the error of each unit in the hidden middle layer is calculated from the output error layer to the forward layer. at the same time, this error is used to correct the front weights. the algorithm steps are as follows: step 1: the initial value of the selected coefficient. step 2: calculate each face image sample in turn, repeat the following process until convergence: (1) calculate the actual output oj of each unit in each layer from front to back, (among them,wij is the weights from the front layer to the bottom layer). netj = ∑ i ωijoi,oj = 1 1 −e−netj https://doi.org/10.15837/ijccc.2022.3.3744 7 figure 1: bp neural network (2) calculate the local gradient δj for the output layer (where y is the ideal output value): δj = (y −oj)oj(1 −oj) (3) calculating the hidden layer from the back to the front: δj : δj−1 = oj(1 −oj) ∑ k ωjkδk (4) calculate and save the weight corrections: 4ωjk : 4ωjk(t) = α4ωjk(t− 1) + ηδjoi (5) modified weights: ωij(t + 1) = ωij + 4ωij(t) bp neural network [17] has the following advantages: good non-linear mapping ability, self-learning and self-adaptive ability, generalization ability, fault-tolerant ability and so on. but it also has some shortcomings such as slow convergence speed and local minimization. using momentum rule and adaptive adjustment learning, improved bp neural algorithm has been well applied in face recognition. 3 experiments more and more research organizations, institutions and institutions of higher learning have begun to establish a three-dimensional face database, bosphorus db face database is one of them. the bosphorus db face database consists of 105 people, numbered from bs000 to bs104. numbering method is personnel number + expression abbreviation + expression number. everyone has different postures, expressions and occlusions. eighteen people had beards and fifteen people had data on hair. the age of the subjects ranged from 25 to 35. the database includes 60 men and 45 women, most of whom are caucasians. in addition, 27 professional actors are included in the database. each person has a maximum of 54 face data, 34 people only have 31 face data, a total of 4652 faces. each person has different postures, of which three head postures correspond to seven left and right rotations, four front and back rotations and two samples containing both left and right front and back rotations. each face is stored in a’.bnt’file, which contains the spatial coordinates of each point on the face and the twodimensional image of the face, as well as the pixel values of the image length and width. this paper uses the bosphorus db face database to verify the effectiveness of the proposed three-dimensional face recognition algorithm. the experiment is based on intel (r) core (tm) i5-2330m cpu@2.20ghz and 4g memory configuration machine. based on the bosphorus db standard face image database, the validity of the algorithm is validated by face recognition experiment simulation with the application program of matlab 2014b. https://doi.org/10.15837/ijccc.2022.3.3744 8 figure 2: bosphorusdb face database example table 1: the recognition result of different k parameters on bosphorusdb incentive function parameter 0.1 0.5 1 5 10 20 convergence time (ms) 314 423 589 812 1165 1387 recognition rate (%) 82.3 86.9 93.6 82.7 76.3 74.1 4 discussion 4.1 excitation function experiment in this paper, the s-type excitation function [18]:f(x) = 11+e−kx as shown in figure 3, the smaller the parameter k, the faster the convergence efficiency of the training process of the network, and the disadvantage will cause oscillation; the larger the parameter k, the longer the convergence time of the training process of the network. the difference of parameter k of excitation function will lead to different training time and recognition results. the bosphorus db library is used to test the relationship between different parameters and convergence speed [19] and recognition rate. the results are shown in table 1 below. figure 3: incentive function the experimental results in table 1 above show that when the excitation function remains unchanged, the convergence time increases with the increase of the parameters, and the recognition rate https://doi.org/10.15837/ijccc.2022.3.3744 9 table 2: relations between different values of increment and decrement factors and convergence time and system recognition rate increment factor α 1.8 1.5 1.4 1.3 1.1 1.6 increment factor β 0.9 0.4 0.5 0.7 0.6 0.8 convergence time (ms) 124 562 764 462 617 189 recognition rate (%) 81.5 88.9 91.7 86.8 93.7 83.9 table 3: the relationship between weight adjustment mode, convergence time and system recognition rate arrange mode additional momentum method gradient descent method elastic momentum method convergence time (ms) 827 334 613 recognition rate (%) 88.2 85.7 94.2 reaches its maximum when the parameter k is 1. 4.2 weight adjustment method experiments the training and learning process of bp neural network is mainly the process of adjusting weights. the commonly used methods are the additional momentum method and the gradient descent method [20]. on the basis of studying the above two methods, this paper puts forward the method of adjusting the weights of elastic momentum. the different values of parameter increment and subtraction factor will directly affect the training time and recognition rate of the network. test 1: test the relationship between different values of parameter increment factor α and reduction factor β and convergence time and recognition rate with the help of bosphorus db library. test 2: test the relationship between convergence time and recognition rate with the help of bosphorus db library using three weight adjustment methods. the results are shown in table 2 and table 3, respectively. table 2 shows that the recognition rate of the system is the highest when the incremental factor α and the decrement factor β are 1.1 and 0.6 respectively. as can be seen from table 3, the recognition rate of the additional momentum method is higher than that of the gradient descent method, but the convergence time is longer. it can be inferred that the gradient descent method is used to trap the local minimum in the training process of the neural network. although the convergence time of the improved elastic momentum weight adjustment method is longer than that of the gradient descent method, the recognition rate is higher than that of the additional momentum method and the gradient descent method, which proves that the adjustment method is feasible. 4.3 performance of pca + bp neural network the purpose of this experiment is to compare and analyze the pca+bp algorithm based on category pattern with the commonly used classification algorithm, and set the excitation function of bp network as follows: f(x) = 11+e−x using elastic momentum weight adjustment method, the number of nodes in input layer is 30, and the number of nodes in hidden layer is 90 in bosphorus db library. the output layers are 40. the results are shown in table 4. comparing table 4, pca + bp classifier has more advantages than ordinary algorithm. in the result record of bosphorus db library, the recognition rate is up to 98.7%. thus, the pca + bp classifier proposed in this paper is superior to the nearest neighbor classifier and svm. 5 conclusions with the rapid development of artificial intelligence, face recognition technology has been widely used in identity recognition because of its direct friends and other characteristics, and e-commerce, banking and other fields involving people’s capital and property are also increasingly trying to use face recognition as a means of authentication. however, face recognition system is vulnerable to deception such as photographs, videos and human skin masks. the security threats it faces have become an important factor hindering its application and development. how to make people feel safe in using https://doi.org/10.15837/ijccc.2022.3.3744 10 face recognition system will also become the trend of future face recognition research. in this paper, from the point of view of security, the research work on face recognition algorithm is carried out. (1) based on the analysis of the traditional face recognition system based on network identity authentication, according to the threats faced by the development of face recognition applications. on the basis of the traditional algorithm, we optimize it. the principal component analysis method is used to extract the features of the image. a feature extraction method of bp neural network algorithm and k-l method are defined for the optimal analysis and reconstruction of the image. the results show that the method of three-dimensional face recognition can achieve information recognition very well. (2) feature extraction of pre-processed face images. the classical principal component analysis (pca) method is used to analyze the traditional face features. (3) the improved bp neural network is used to realize face recognition. the experimental results show that the method is robust to illumination noise. although this algorithm realizes face recognition, due to the limited personal energy and ability, there are still some shortcomings to be improved. the following is the outlook of the content to be improved. (1) this algorithm is mainly aimed at single person’s face detection, but does not take into account the multi-face situation in the same image, and fails to achieve accurate location. therefore, how to use effective algorithms for multi-face detection will be the direction of future research. (2) in this paper, we use the existing face detection methods for location detection. in the follow-up work, we will study the algorithm of face location. funding this work was supported by guizhou key laboratory of big data statistics analysis (no.: platform talent plan[2019] 5103); guangdong philosophy and social science planning project (gd21cyj31); characteristic innovation project of general higher education in guangdong province (2021wtscx206). references [1] nair, k. k.; helberg, a.; johannes, vdm (2016). an approach to improve the match-on-card fingerprint authentication system security, sixth international conference on digital information & communication technology & its applications, 119–125, 2016. [2] rila, l.; mitchell, c. j. (2003). security protocols for biometrics-based cardholder authentication in smartcards, international conference on applied cryptography and network security,springer, 254–264,2003. [3] nair, s.k.; dashti, m.t.; crispo, b.; et al. 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[20] soudry, d.; di castro, d.; gal, a.; kolodny, a.; kvatinsky, s.(2017). memristor-based multilayer neural networks with online gradient descent training, ieee transactions on neural networks & learning systems, 26(10), 2408-2421, 2017. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control https://doi.org/10.15837/ijccc.2022.3.3744 12 cite this paper as: luo, c.; fan, x.; yan, y.; jin h.; wang x. (2022). optimization of three-dimensional face recognition algorithms in financial identity authentication, international journal of computers communications & control, 17(3), 3744, 2022. https://doi.org/10.15837/ijccc.2022.3.3744 introduction proposed method method principal component analysis support vector machine algorithms neural network algorithms experiments discussion excitation function experiment weight adjustment method experiments performance of pca + bp neural network conclusions chenijcccv12n6.pdf international journal of computers communications & control issn 1841-9836, 12(6), 803-812, december 2017. intelligent decision support algorithm based on self-adaption reasoning g. chen, y. jin, h. wang, s. cao guomin chen, yingwei jin, huili wang*, shuo cao dalian university of technology dr.chen@vip.126.com, jinyw67@dlut.edu.cn *corresponding author: huiliw@dlut.edu.cn caoshuo@dlut.edu.cn abstract: this paper analyzes the logic deduction and reasoning techniques used in several intelligent decision support algorithms, and proposes a flexible planning method gariv using fuzzy descriptive logic in media enterprise management. combined with experiments, the above methods are illustrated in terms of effectiveness and feasibility. in the end, the challenges and possible solutions of intelligent decision support algorithms with self-adaption reasoning are discussed. keywords: intelligent decision support, propositional logic, non-monotonic logic, descriptive logic, fuzzy logic, automatic reasoning. 1 introduction intelligent decision support (artificial intelligence planning), also known as automated planning (automated planning), is an important area of artificial intelligence research, and covers a knowledge representation, automatic reasoning, non-monotonic logic, human-computer interaction and cognition science and other areas of cross-disciplinary [6]. the first intelligent decision support research, which originated from the automatic reasoning and knowledge representation, in the 20th century, 90 years ago, has been the use of logical deduction method to be solved, with the focus on classical logic of reasoning. mahmood [9]solves the problem of exponential space explosion in the process of knowledge representation, which draws the field of intelligent decision support more and more attention from researchers. intelligent decision support technology has been widely used in aerospace, robot control, logistics scheduling, game character design and system modeling. the results are obvious [8]. the us military forces in the gulf war dynamic analysis and re-planning tools dart is used in automated logistics planning and transportation scheduling, so that the scheduling work, which used to take a few weeks, now can be completed within a few hours. intelligent decision support technology in the field of production scheduling and mars rover, hubble space telescope and other aerospace fields. the application also shows its huge application prospects [11]. especially in recent years, this huge application has also been demonstrated in the field of on-line education, such as dalian dragon stone internet edu service co, who employs the knowledge-based system for learning performance analysis and evaluation [7]. the international planning competition (ipc) has made great progress in the field of intelligent decision support technology, for which 5 sessions have been held continuously since 1998 [12]. the planning system of the previous three competitions mainly deals with the system performance, with the handling time of planning problems being the only criterion to measure the pros and cons of the system. in ipc-4 the consideration of complexity of the problem processing capacity is added to the probabilistic programming problem. in ipc-5 the conformant planning problem is added. in the ipc development process, the scale of problem which needs to dealt with is gradually increased, so that the difficulty is gradually increased. the standard copyright © cc by-nc 804 g. chen, y. jin, h. wang, s. cao problem can describe the complicated and objective world more closely, enabling the intelligent decision support research to deal with complex practical problems. the existing planning methods are divided into two main categories: fusion planning and fusion state space heuristic search planning [14]. by convergence transformation planning method, planning problems are converted into a number of classic problems (such as propositional satisfiability problem, model checking problem, constraint satisfying problem, linear programming problem, theorem proving problem of non-classical logic, etc.), and solving the original programming problem indirectly through solving the transformed objective problem efficiently. in various fusion transformations planning, the logical deduction and reasoning techniques are used, more or less. the planning of the first class predicate logic is the source of these phenomena. it is very important to improve the efficiency of the planning method by using logical deduction and reasoning techniques. this paper mainly studies the planning method and planning system of fusion logical deduction and reasoning technology. firstly, the relevant concepts and problem description of intelligent decision support are introduced. then the planning methods, which combine fusion logic deduction and reasoning technology are described, with a focus on the famous international planning system: flexible programming method of fusion fuzzy description logic. finally, based on fuzzy descriptive logic, a flexible planning method is proposed and experimentally proved effective and feasible in the operation and management of media enterprises. 1.1 intelligent decision support and fusion self-adaptive reasoning programming method at present, intelligent decision support has not yet been defined uniformly, but it is generally believed that intelligent decision support is the process of finding the sequence of actions from the initial state to the target. according to aleksander [1], intelligent decision support is defined as the "understanding and analysis of the surrounding environment. the goal of the realization of a number of alternative actions and the resources provided by the reasoning to develop a comprehensive action to achieve the goal sequence. most of the media companies have witnessed an increasing online application, despite the combination of online and paper, which therefore leads to the wider use of computer-based adaptive technology. 1.2 intelligent decision support problem the description of the planning problem, usually based on the common planning domain description language (pddl) [5], is similar to the representation of the first-order predicate logic, which adds conditional effects to the requirements of ipc, numerical effects, probability effects, etc., leading to more detailed descriptions of the planning problem to be handled. now the most common is pddl3.2, which contains a description of the predicate and soft constraints and the description of the various logical relations. and the abstract description of the planning problem is restricted to classical programming problems and non-classical programming problems. non-classical programming problems mainly refer to conformant programming problems and flexible programming problems as related to this paper. the problem of programming includes the domain definition and problem definition, which define the operationof theplanningproblemand the initial / target state respectively. aclassical programming problem p is a triad (i, o, g), where i is the initial state, o is the operation set, and g is the target state set. the preconditions and the effects of each operation o ∈ o are deterministic. fusing the closed world hypothesis, all the propositions that i describe a state are explicitly depicted, and the true value of the proposition that is not present in the statement intelligent decision support algorithm based on self-adaption reasoning 805 i is false. that is the condition to be satisfied to describe the target state g. example 1 is a partial description of a classical programming problem that fuses pddl. example 1. (logistics planning problem): the logistics planning problem describes a logistical problem in which multiple parcels are transported to a designated location by loading, shipping, and unloading for multiple trucks and packages in several cities for a logistics package that contains two packages a, b , two locations l, p , and a truck r planning problem, the domain description and problem description is as follows: domain description: load(x, y, z) : pre : at(x, z), at(y, z);add : in(x, y ) : del : at(x, z) unload(x, y, z) : pre : in(x, y ), at(y, z);add : at(x, z);del : in(x, y ) move(x, y, z) : pre : at(x, y ), fuel(x);add : at(x, z);del : at(x, y ), fuel(x) (1) problem description: init : at(a, l), at(b, l), at(r, l), fuel(r) goal : at(a, p), at(b, p) (2) conformant programming problem is a triad , in which is the initial state set, is the operation set, and is the target state set. here it is no longer a state, but a state set consisting of all possible initial states. the effect of each operation is uncertain. conformant planning problem to solve is the initial state and the effect of action is uncertain circumstances, to find a certain plan to reach the target solution. flexible planning problem is a triad , in which is the initial state set, the set of operations, the target state set. here, each operation for a specific action with a different degree of satisfaction. some researchers define it as a four-tuple, contains a measure of satisfaction. flexible planning problem is to solve is the satisfaction of different actions (or preferences) measure, choose one of the most satisfactory or satisfactory enough efficient planning solution. the above two kinds of non-classical planning problems, as the expansion of the classical planning problem from the uncertainty of the initial state and the action effect, are more in line with the needs of practical problems and suit the cognitive needs more consistently . 1.3 convergence transformation planning method and fusion self-adaptive reasoning planning method the convergent-transform programming method transforms the planning problem into other types of knowledge representations and is solved accordingly. the planning problem of the fusion logic representation only relies on the logical deductive method to be solved before 1992. logical deductions need to be implemented under conditions that ensure reliability and completeness, which is not efficient. in 1992, neumann [10] proposed the transformation of the planning problem to the propositional satisfiablility (referred to as sat) to solve the problem of fusion to meet the satisfaction of the planning method. ever since then, there have been many researches which have attempted to convert planning problem into model detection problem and linear programming problem. the corresponding planning systems alt [2], mips [3], sgplan [13] have displayed good performances. in the narrow sense, it relies on the logical semantics of the planning problem itself, transforming it into some theorem proving problem of some logical representation, which calls for the corresponding adaptive reasoning programming method. logic theorem proves that the system is to be solved, and the solution of the transformed problem can be reduced to the original solution to the original planning problem. the generallyaccepted explanation is: logical deduction and reasoning technology are used, directly or indirectly, to verify the planning process; the former 806 g. chen, y. jin, h. wang, s. cao focuses on the transformation of knowledge representation, that is, the coding process, while the latter focuses on the use of reasoning technique to prove the process. the transformation of planning problems to other types of knowledge representation is called encoding, and the new knowledge representation is called encode. coding may be a specific kind of storage structure, relational structure, clause set, axiom or axioms, etc. for the convenience of description, the coding here only takes into account the representation of the axiomatic hierarchy and has not yet been converted into a conjunctive normal form (cnf) or a set of clauses. definition 2. (coding of the planning problem). for a particular coding scheme, the coding of a programming problem is a knowledge representation that can be characterized, either explicitly or implicitly, with respect to a given programming problem: (1) prerequisites of operation (the coding of a planning problem) , the intrinsic relationship between effects; (2) the relationship between the operation; (3) the initial state and target conditions; 4) (optional) on the action and state constraints. definition 3. (coding combination of planning problems) for a particular coding scheme, the coding combination of planning problems is a knowledge representation that can explicitly or implicitly describe the following characteristics about a given planning problem: (1) the relationship between the operations; (2) the initial state and target conditions; (3) (optional) on the action and state constraints. the coding combination is a kind of coding scheme which is converted into a logical representation. coding and coding combination are two different concepts, with the former being the planning problem, which corresponds to a suitable overall knowledge representation, and does not distinguish between coding and coding combinations without causing confusion. cresswell [4] transformed the planning problem into model detection problem. 2 flexible programming method based on fusion fuzzy description logic the flexible planning problem (also known as flexible programming problem) introduced introduces a measure of satisfaction to the operation in the planning problem, which is a relaxation of the classical programming. the flexible graph plan system is also the first planning system which addresses such problems. since the description of such problems has been difficult to unify, it is necessary to impose a fuzzy characterization on the operation, or a measure of the state preference, which is worth considering and measuring. the description logic alc* was extended, and a fusion of fuzzy description logic (fuzzy description logic), fuzzy description of the state of the fuzzy description logic, falc* (fuzzy alc*) is proposed, which uses falc* to express the state attributes, and the actions with the relation, which makes the attributes closer to the reality and the actions unique in preference. in addition, the problem definition is transformed into the inclusive relation of the fuzzy concept, and the action definition is transformed into the implicating relation of the fuzzy concept. 2.1 fuzzy description logic problem representation according to badea’s design method of fusion description logic frame, falc* is used to code the planning problem as follows: according to the standard semantics of fuzzy explanation i, it is interpreted as mapping c(s) a truth value between [0,1] them. then we call the subjective c truth degree of the attribute in intelligent decision support algorithm based on self-adaption reasoning 807 the state to be n;r(s, s′) mapped to a truth value between [0,1], and then we call it from state s to state s′, this action has preference l. with the implication of fuzzy concept to express the dynamic axiom da : 〈c, n〉 → ∃〈r, l〉.{s′} ∧ ∀〈r, l〉.〈d, m〉 which, c and d represent that the concept in falc *, r is falc * in the relationship. the formula represents: the state s to meet the premise c, and the truth degree of c is c(s) , trigger preferences r(s, s′) for the action, so that the state s transferred to another state s′, and the state s′ to meet the true degree d(s′) of the property d. for the knowledge base that needs to be described, the following type descriptions are included: pkg2 < pkg etc; status description: connectcitywithmainr < mainroad ∧∃forwardcity. t ∧∃nextcity. t ∧∀forwardcity. city ∧∃nextcity. city; action description: attruckcity ∧ atpkgcity ∧¬onguardtruck ∧〈v aluablep, k2〉 → ∃(loadtruck3, l2). {x(¬atpkgcity ∧ onpkgtruck)}∧∀(loadtruck3, l2). (¬atpkgcity ∧ onpkgtruck) coding etc. 2.2 reasoning ability of fuzzy description logic the post-transformation theory needs to assert the goal of deriving satisfaction degree n from the fuzzy knowledge base, ∑ | =〈goal, n〉, i.e. {γs ∪ γd,〈s(init), n〉}| = ((∃〈plan, l〉.{xgoal}) ∧ (∀〈plan, l〉.〈goal, m〉))(init) (3) among them, γs is the static axiom set, it γd is the dynamic axiom set; init for the initial state, it s(init), n〉 means satisfying the attribute s, and its subjective truth degree is n .(∃〈plan, l〉.{xgoal}) ∧ (∀〈plan, l〉.〈goal, m〉) , indicating that there is a satisfaction degree l satisfying the target state, and satisfying the satisfaction degree m. since the pruning strategy is not adopted, the fpexp algorithm designed to find the optimal solution satisfies the requirements, but it needs to consider the appropriate pruning strategy to compress the search scale. for planning solutions with satisfaction below a given limit, even if the length of the solution is very small, it is not considered as an effective solution. for the trade-off between length and satisfaction, the length of the solution is given priority. 2.3 flexible programming method based on fusion fuzzy description logic we design a flexible programming method which integrates fuzzy descriptive logic and solve the flexible programming problem by transforming the flexible programming problem into the knowledge representation problem of fuzzy description logic. this is a new attempt which corresponds to the complex planning problem to the non-classical logic problem, and use the inference 808 g. chen, y. jin, h. wang, s. cao technology of non-classical logic to deal with the transformed problem. despite the far-fromexpectation result , there is much to be developed with this method, such as adding pruning strategies and other heuristic search strategies. 3 experiment and analysis currently, the enterprise decision-making method for the media enterprise data run features are not fully considered which led to no data on the steady-state decision to determine the enterprise decision-making. in this case, there is not enough clarity of case partitioning, resulting in poor comparability between the final information mining result and the actual data. in this paper, combined with the main features of the production mode of the media enterprise, the steady state judgment is carried out on the data. on this basis, a classification model is constructed to ensure the accuracy of the classification results and evaluate the effectiveness of the algorithm. 3.1 experimental data in this study, a media enterprise reasoning data was used, and the data of the first half of 2012 was selected as the research object. there are 427 attributes of the integrated data, besides the set key number and the data retention time automatically generated by the system, there are 425 condition attributes in total, all of which maintain the numerical characteristics. the steady-state, unsteady-state annotation of the operating data is obtained by referring to the steady-state operating parameters, and the decision-making attributes are obtained. and in order to better test the algorithm 3 (gariv) and maintain the original data of each system unchanged, the original database of the same decision attributes is added. in addition, in order to evaluate the performance of the algorithm synthetically, a variety of partitioning methods are employed to design the data interval. if there is a decision class corresponding to a certain interval in the interval division, the data belonging to the same decision class are divided; it is classified into the same small interval, starting from the next interval starting from the different decision classes in turn. the experiment is implemented on the workstation intel xeon(r) processor (four core, 2.5ghz, 16gram). the algorithm is mainly realized by java writing. in the calculation of the gariv algorithm, two virtual machines are constructed to form two sites. in the experiment, the accuracy of classification can be confirmed, which can effectively guarantee the fairness of the experiment. 3.2 experimental comparison first, the experiment data of algorithm 1, algorithm 2 and algorithm 3 are combined with the selected experimental data. the time of the algorithm is determined by the interval length of the algorithm. fig.1 (a) to fig.1 (d) show λ = 0.7 , the running time chart when the interval length is 10minutes, 20minutes, ..., 90minutes, respectively, when the number of attributes is different. fig. (a) to fig. 2 (b), represents when λ = 0.5,0.7 the running time required for selecting two attributes under different section lengths. intelligent decision support algorithm based on self-adaption reasoning 809 (a) select 1 attribute (b) select 2 attributes figure 1: λ = 0.7 , the running time of different decision algorithms as can be seen from figure 1: • the length of the interval is increasing, the data object will be reduced by a factor of two, and the time required for the algorithm to run will decrease; • when the length of the interval increases, the coincidence degree between the intervals will increase, which will increase the number of λ− compatible class elements. if an attribute is added, the computation amount will increase. therefore, the running time of the algorithm shows a nonlinear trend. the number of objects decreases, but the running time does not decrease with the interval length, but it shows an increasing state. analysis of this situation may be due to it that although the length of the interval is increasing, but based on the number of λ− compatible elements in an increasing number of state, thereby extending the running time; • compared with the algorithm rivd and algorithm rivmi, the two maintain relatively similar running times for a relatively small number of attributes, but the similarity decreases with the increase of the number of attributes, and the running time of the former will be longer. the reason is that the number of compatible classes increases with the increase of the number of attributes. however, it is necessary to judge whether the compatibility class is classified as positive domain when calculating the domain. it leads the algorithm rivd to take longer time for the calculation, and for this algorithm rivmi, the number of compatible class will increase with the property; however, its new conditional entropy, the combination of the original conditional entropy expansion can be achieved, and, therefore, the latter requires a shorter time. 810 g. chen, y. jin, h. wang, s. cao section length (min) o p e ra ti o n t im e ( s ) o p 1000 e ra t 2000 io n io n 3000 m e 4000 5000 6000 0 10 30 50 70 90 (a) rivd select 3 attribute section length (min) o p e ra ti o n t im e ( s ) 1000 2000 3000 4000 0 10 30 50 70 90 (b) gariv select 3 attributes figure 2: the running time required of the two algorithms when λ is different analysis of figure 1, the required minimum run time is the gariv algorithm, which is, theroretically, the algorithm running time for the basic half of the algorithm rivmi. however, because the compatibility class is calculated, it is related to the transmission of partially compatible classes, and overall, the configuration of the virtual machine is difficult to match with the workstation, so that the actual time required for the algorithm gariv exceeds its theoretical time; however, in terms of overall time, the algorithm is relatively low, and with the increase in the number of sites, this time advantage will be more prominent. as shown in fig.2, because the λ values are more irregular and more consistent, λ increases continuously. their compatible classes are more refined and the number of compatible elements decreases. for the algorithm gariv, the amount of compatible class traffic that needs to be done is larger, which results in a longer run time. experimental average classification accuracy and the traditional average classification accuracy were compared to evaluate the effectiveness of the three algorithms. the results are as follows: table 1: the average accuracies of classification algorithm section length (unit:minutes) 10 20 30 40 50 60 70 80 90 rivd λ = 0.5 78.8% 80.3% 81.4% 80.2% 80.4% 80.3% 71.2% 70.9% 69.5% λ = 0.7 78.8% 84.4% 81.3% 82.4% 81.2% 79.4% 76.3% 70.5% 67.3% λ = 0.9 80.1% 81.5% 85.0% 81.9% 78.5% 79.3% 70.3% 66.9% 69.9% rivmi λ = 0.5 85.2% 84.6% 86.6% 85.6% 82.6% 88.3% 79.6% 72.6% 67.9% λ = 0.7 84.5% 94.6% 89.6% 90.3% 92.3% 92.6% 79.2% 73.6% 62.6% λ = 0.9 83.5% 90.2% 88.3% 92.6% 88.3% 92.3% 84.2% 79.3% 72.5% gariv λ = 0.5 77.3% 91.6% 86.3% 89.6% 86.3% 86.3% 80.3% 72.3% 67.5% λ = 0.7 81.2% 93.4% 84.3% 82.5% 85.5% 92.2% 82.5% 81.0% 72.9% λ = 0.9 78.2% 91.1% 88.3% 83.5% 82.7% 84.5% 75.2% 80.5% 71.2% after analysis of the table above, in many traditional analysis methods, the classification effect of knn is the best; and rbf, reptree have a relatively low accuracy, because the multisource information in the steady-state judgment calls for a certain interval of data to judge, which is difficult to achieve through only one stream of data. the reason why the knn accuracy in the classification is better is that it takes the data value of the same sub-section for the same paragraph of its decision-making class. thus, when a neighbor k is computed, the corresponding decision class is as likely as the adjacent test result, and if the value k keeps increasing, a crossclass phenomenon is most likely to occur. this approach effectively reduces the information dimension and enhances the accuracy and efficiency of multi-source information decision making. intelligent decision support algorithm based on self-adaption reasoning 811 the three algorithms proposed in this paper are implemented on the basis of interval division, and are easier to satisfy the requirements of the decision-making stability, and thus have better accuracy than the traditional methods. 4 conclusion and outlook with the demand of problem representation, the programming method of the deductive logic and reasoning technology has been paid more and more attention to by the researchers of the related fields. a variety of non-classical logic calls for the need of the fusion knowledge representation, adding the description of modal, time and ambiguity etc. many researchers use various techniques such as satisfiability judgment technique, first-order predicate lifting technique, fuzzy descriptive logic to express satisfaction degree of planning, and fusion modal logic representation and deduction reasoning technology to deal with various complicated planning problems, which has achieved a recognized outstanding recognition, promoting the intelligent decision support and self-adaptive reasoning of mutual promotion and integration. the uniqueness of media enterprises does not allow the one-sided decision-making system to fully meet the needs of its development, which means that in the future research the features of media enterprises should be taken into consideration so that an exclusive decision-making system can be devised and developed to meet the requirements of this industry in terms of operation and management. bibliography [1] aleksander i., farreny h., ghallab m. 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(2015); multi-model stability control method of underactuated biped robots based on imbalance degrees, international journal of simulation modelling, 14(4), 647-657, 2015. international journal of computers communications & control issn 1841-9836, 13(2), 192-204, april 2018. decoupling 5g network control: centralised coordination and distributed adaptation s.j. lin, j.g. yu, w. ni, r.p. liu shangjing lin*, jianguo yu beijing university of posts and telecommunications beijing, china, 100876 linshangjing@bupt.edu.cn, yujg@bupt.edu.cn *corresponding author: linshangjing@bupt.edu.cn wei ni data 61, csiro australia, 2122 wei.ni@data61.csiro.au ren ping liu university of technology, sydney australia, 2122 ren.liu@uts.au abstract: fifth generation mobile networks (5g) will be featured by miniaturised cells and massive dense deployment. traditional centralised network control cannot adapt to high signalling delay, and is therefore not scalable for future 5g networks.to address this issue, we adopt the software-defined networking (sdn) approach of decoupled network control and data transmission. in particular, delay-sensitive interference suppression for data transmission is decoupled from delay-tolerant topology control and base station coordination. this substantially alleviates the requirement of network control on delay and complexity, hence simplifying 5g control plane design, reducing signalling overhead, and enhancing network scalability. case studies show that our decoupled network control is effective for timely interference mitigation and reliable topology management. the stability and scalability of our approach are also demonstrated. keywords: fifth generation mobile network (5g); network control; protocol stack; standardisation. 1 introduction fifth generation mobile networks (5g) will be featured by miniaturised cells and massive dense deployment [3]. the features are driven by the goal of a thousand times increased network capacity, compared to the current 4g systems [13]. they are further triggering a paradigm shift of network architecture and operation. privately owned/installed plug-and-play base stations and individually subscribed backhaul connections are promising in future 5g deployments. fig. 1 illustrates the promising future 5g architecture, where plug-and-play 5g base stations are connected to the servicing gateways (s-gws) of the 5g core network for interference coordination and mobility control. the backhaul connections between the base stations and the s-gw can be heterogeneous, subscribed to different internet service providers (isps) (like existing household wifi). such architecture is able to support the massive dense deployment of 5g base stations, using existing network infrastructure and saving deployment cost. meanwhile, critical challenges are arising in the control of the heterogeneous 5g networks. particularly, high and substantially unbalanced delay of hundreds of milliseconds may occur on the subscribed backhaul connections between the base stations and the s-gw [10]. close and copyright © 2018 by cc by-nc decoupling 5g network control: centralised coordination and distributed adaptation 193 timely network control that has been necessary in existing cellular systems (with dedicated transport networks) for interference mitigation [1] becomes impossible in the heterogeneous 5g systems. however, timely control will remain crucial in drastically changing wireless environments where interference levels can change instantly. fast power control at intervals of milliseconds will still be necessary to mitigate interference in future 5g systems. figure 1: an illustration on the heterogeneous 5g architecture, where some base stations are connected to the s-gw via cellular infrastructure, as highlighted by the red path, while most base stations are connected to the s-gw through various isp networks, as indicated by the green and blue paths. centralised control signalling travels via these colour-coded paths. earlier, the concept “software-defined networking (sdn)" was proposed to split control and data in wired networks, which can simplify network control and reduce cost [5]. the sdn concept was experimentally demonstrated effective for its application to wired ip/ethernet transport networks of mobile systems [11]. recently, the sdn idea of centralised control and distributed data delivery has been extended for the control of radio access networks (rans) [4, 6–9, 11]. nevertheless, all the extensions would experience the above issue of delayed control if applied to the heterogeneous 5g networks, and fail to suppress interference in a timely manner. one of the sdn extensions to wireless applications was big base station abstraction [4], where a controller models base stations as a dimension of resources, and assigns them and powers to traffic flows. unfortunately, the abstraction was based on an oversimplified assumption of independence between base stations and frequencies. as a consequence, interference, which can substantially differ in practice when different pairs of base stations are allocated with the same frequency, was treated as static. other extensions of the sdn centralised network control were on software-defined real-time pairing of antennas and base stations, adapting to traffic demand and interference [7–9]. they are promising for a local 5g deployment with a dedicated (operator controlled) transport network. 194 s.j. lin, j.g. yu, w. ni, r.p. liu however, the extensions are unsuitable for the heterogeneous 5g architecture involving isp transport networks and incurring delayed control. they are also unsuitable for large and densely deployed systems, where centralised optimisations are computationally prohibitive and limit the network scalability. in this article, we propose to decouple 5g network control and distribute control computations, so that the critical issues of delayed control and limited scalability can be tackled. in particular, delay-sensitive interference suppression for data transmission is decoupled from delay-tolerant topology control and base station coordination. the interference suppression can be achieved quickly through distributed adaptation of base stations. the topology and frequencies are progressively coordinated through a centralised sdn controller. as a result, the requirement of network control on delay and complexity can be substantially alleviated, simplifying 5g control plane design, reducing signalling overhead, and enhancing network scalability. case studies show that our decoupled network control is effective for timely interference mitigation and reliable topology management. the stability and scalability of our approach are also demonstrated. 2 evolutionary view of 5g network control as discussed in section 1, centralised network control faces critical challenges of severely delayed control and limited scalability in a heterogeneous 5g network depicted in fig. 1. decentralisation of network control is the way to eliminate the challenges, and therefore of practical value. we note that a 5g base station will not only be a signal generation and processing entity, but it will also have self-organising capabilities of running a variety of operations specified by software. in this sense, 5g base stations will be equipped with a certain level of intelligence, and capable of automation and local decision-makings. the decisions that a base station can make can include switching from severely interfered (crowed) wireless channels to less interfered channels, as well as adjusting its own transmit power. given the intelligence of individual base stations, the entire network can be visualised as an evolution process driven by changing traffic demand, wireless channel conditions and interference levels. in particular, the network topology and the radio resources assigned with the topology evolve along with those changes. the network topology here describes clusters of cells in the 5g network, where cells belonging to a cluster reuse the same frequency assigned to the cluster. each individual of the base stations drives the evolution of the topology by forming clusters and switching between the clusters (i.e., from severely interfered clusters to less interfered clusters). the entire population of the base stations evolves towards stable topology and balanced interference. fig. 2 shows an example of the network evolution, where only four base stations and two clusters are considered for illustration convenience. the evolution consists of two stages. in the first stage, the base stations within each cluster quickly and interactively adjust their transmit power through distributed automation to balance interference. in the second stage, the base stations choose to stay in the current clusters or switch out to less interfered clusters. this is a speciation stage in the evolution. it requires a brief idea of the entire base station population, and therefore needs centralised assistance. the two stages take place in an alternating manner, until no base stations want to switch clusters and the system is stabilised. such an evolutionary view of network control allows for the decomposition of the network control into centralised and distributed parts. in light of this, a decoupled, hybrid protocol stack for 5g network control is developed, as will be described in section 3. decoupling 5g network control: centralised coordination and distributed adaptation 195 figure 2: an illustration on the evolution modelling of 5g networks, where colour-coded arrow lines represent different base stations. it is worth pointing out that ieee 802.11 access point (ap) can also be treated as rational with self-organising capabilities. however, the aps cannot interact with each other, and therefore are unable to suppress interference. what they can do is to detect and select radio channels with negligible interference on the installation and initialisation. 3 decoupled, hybrid protocol stack for 5g network control the new protocol stack that we propose for 5g network control consists of two layers, namely, upper layer and lower layer. the two layers are designed to decouple the delay-tolerant and delaysensitive aspects of network control, driven by the evolutionary view of 5g network established in the last section. a. the upper layer of the protocol stack is a centralised, delay-tolerant network control, which resides in both base stations and the network controller, tolerates hundreds of milliseconds delay over transport networks, and takes the responsibility of slow-changing topology control and resource management. b. the lower layer of the protocol stack is a distributed, delay-sensitive network control, which resides in the base stations, and is implemented by the automation of 5g devices to combat fast-changing wireless channels and interference at an interval of milliseconds. the two layers are interactive, distributing computations and balancing the complexity. as a result, signalling overhead can be reduced and network scalability can be enhanced. 3.1 upper layer: centralised coordination the upper layer of the new 5g protocol stack is to manage the network topology and frequency, so as to balance interference and satisfy traffic demands. the upper layer is interconnected in a star topology from the centralised network controller to individual base stations. the network topology can be described by the clusters of the cells in the network, denoted by cn (n = 1, · · · ,n). n is the number of clusters. base stations belonging to each of the clusters reuse the same frequency. the base stations may need to switching between clusters (i.e., from the severely interfered clusters to the less interfered) to alleviate interference and meet traffic demand. this causes the dynamics of the network. the upper layer of the new protocol stack models the network dynamics as an evolutionary (game) process. the recent sdn concept can be extended as such that the centralised network 196 s.j. lin, j.g. yu, w. ni, r.p. liu controller provides guidance to the base stations, assisting them to make local cluster-switching decisions. the network controller can also steer the network evolution towards stabilised topology and balanced traffic. the reason for allowing local (distributed) decision-makings is that the globally optimal cluster-switching decisions are computationally and practically impossible in densely deployed 5g small cells, as discussed in section 1. the centralised network controller can be designed to distribute the available 5g spectrum between the clusters c1, · · · ,cn in such a fair fashion that the total achievable data rate of each cluster is equally proportional to the request. methods, such as shapely value of cooperative games [12], can be used to distribute the frequencies. the centralised controller can then evaluate the current clustering by measuring the probability distribution of the achievable data rate ri of every cell i in a cluster cn (i ∈ cn). the distribution function is denoted by fn. the controller further evaluates the probability distribution with regards to all the clusters. the distribution function is denoted by f . for the clusters whose distribution functions fn are left to f (i.e., the clusters have lower achievable data rates), the centralised network controller specifies probabilities for their base stations to switch to other less interfered clusters whose distribution functions are right to f . the probabilities can be specified, depending on the ranking of the base stations within their current clusters and the distances between the distributions of the current clusters and the target clusters. the base stations will have higher probabilities to switch out of a cluster ci, if fi is further away from left to f (i.e., ci is crowded). they will have higher probabilities to switch into cj, if fj is further away from right to f . i 6= j. within the cluster ci, the lower data rate that a base station can achieve, the probability will be higher for the base station to switch out of the cluster, since the base station suffers more severe interference under the current clustering. the first step of the clustering review is to specify the ratio of the femtocells that need to switch clusters. this is an evolutionary process. the ratio of the femtocells that need to switch out of dm can be specified by ṗm =pm ×‖fm(x) − f̄(x)‖d = pm∑ ∀m qm sign ( em(x) −e(x) ) × ( 1 − ∫ ∞ 0 min ( fm(x), f̄(x) ) dx︸ ︷︷ ︸ ) qm , (1) where pm is the ratio of femtocells in dm; ‖ · ‖d denotes the statistical difference between two clusters, which can be calculated by qm/ ∑ ∀m qm; sign(·) is the sign function. qm denotes the under-braced part, in which the integration calculates the area of fm(·) that is not overlapped with f(·). em(·) and e(·) are the means associated with the two pdfs. the sign function indicates that the calculated non-overlapping area is to the left of f(·), if the sign is negative; or to the right of f(·), otherwise. qm is normalized by 1/ ∑ ∀m qm. such design of ṗm allows the evolution process of clustering towards reducing the distributional differences between the clusters. clusters that are below average (i.e., their pdfs are to the left of f(·)) will have their pdfs adjusted rightwards by having some of their femtocells switch to other clusters. specifically, ṗm < 0 means that −ṗm of |dm| femtocells should switch out of dm, and ṗm > 0 means that ṗm of |dm| femtocells should switch into dm. for a femtocell that is to switch out of dm (ṗm < 0), it switches into dj at the probability of ṗj/ ∑ ∀i:ṗi>0 p̄i (j 6= m). next, the second step of the clustering review and adjustment is for every femtocell i ∈dm to specify its own probability to switch clusters, denoted by ṗm,i. this is based on ṗm and the decoupling 5g network control: centralised coordination and distributed adaptation 197 satisfaction level of femtocell i. this step differentiates the femtocells within the same cluster in terms of switching probability. to do this, we develop the following design. ṗm,i = ṗm λme λm eλm − 1 exp ( −λmfm ( xi )) (2) where xi = r gnt i /r req i , and fm(·) is the cumulative distribution function (cdf) of the satisfaction levels within dm. λm is a parameter that can be adjusted so that ṗm,i ≤ 1. the granted data rate rgnti = φmωc log2(1 + γ ∗ i ), i ∈dm. the rationale for this design is that (2) does not change the overall clusterwise switching probability of the cluster, i.e., ṗm. specifically, we have lim |dm|→∞ 1 |dm| ∑ i∈dm ṗm,i = ∫ ∞ 0 ṗm,ifm(x)dx =ṗm λeλm eλm − 1 ∫ ∞ 0 exp ( −λmfm(x) ) fm(x)dx =ṗm eλm eλm − 1 ∫ λm 0 e−udu = ṗm. (3) meanwhile, individual femtocells in the cluster can have different switching probabilities, depending on their individual satisfaction levels. as such, femtocells that receive excessive interference in crowded clusters get high possibilities to switch to other less interfering clusters. note that the second step of creating differential switching probabilities in a cluster is new. individual femtocells’ statuses are leveraged without invalidating the evolution of the entire population (which was specified in the first step). in contrast, conventional evolutionary games are focused on the entire population, ignoring the differences between individuals. as an effect of the two-step design, the satisfaction distributions of different clusters converge through the evolution of clustering. the average satisfaction level of all the femtocells gradually grows until the convergence. after the second step, each femtocell switches its cluster based on the probability specified in (2). the new m clusters, {d1, · · · ,dm}, will be considered in the next round of distributed power adaptation (as described in section 3.2). 3.2 lower layer: distributed adaptation the lower layer of the new 5g protocol stack describes a distributed automation process of every base station in terms of transmit parameters, adapting to the network topology change driven by the upper layer. specifically, every base station, say base station i in the nth cluster (i ∈ cn), collects interference measurement reported by its mobile terminal and independently updates its transmit power pi. the base station does so iteratively, until the transmit power is stabilised. the cluster also becomes stabilised. a well designed utility function, with which the transmit power is independently updated in response to the measured interference, is the key to the lower layer of the protocol stack. the function will lead individual base stations to stabilise and converge in a distributed manner to the stable point of an entire cluster. this requires the function to have a convex/concave structure with respect to all the transmit powers of the cluster. meanwhile, the data rate of each base station should not be compromised at the stable point for the convexity/concavity. an example of such function is given by ui (pi, p−i) = arctan (γi (pi, p−i) γi αn ) −θnpi, i ∈cn (4) 198 s.j. lin, j.g. yu, w. ni, r.p. liu where p−i collects pj for j ∈ cn and j 6= i; γi(pi, p−i) is the signal-to-interference-plus-noise ratio (sinr) of the terminal served by cell i; γi is the target sinr that is required to meet the traffic demand of the terminal; αn is a predefined coefficient to adjust the speed of the stabilisation of pi in cluster cn; θn is a predefined coefficient to adjust the weights of the two terms in the function. ui (pi, p−i) is designed to be maximized at each base station i ∈ cn. the first term at the right-hand side of the function defines the utility which drives the achieve sinr towards the target. this is through increasing the transmit power pi. the second term at the right-hand side of the function is the cost/penalty to achieve the utility. it keeps the growth of pi under control and prevents generating excessive interference to other cells. the function can ensure the stabilisation of each transmit power, e.g., pi. this is due to the concave structure of the function with respect to all the pk’s with k ∈cn [2]. the concavity can be rigorously proved by evaluating the hessian matrix of the function and confirming that the hessian matrix is negative definite with carefully selected αn and θn. as a result, the function has a unique stable point that is the global optimum of the function, as given by p∗i = min ( i−i(p−i)γi αngi √ 1 −θn θn ,pmax ) , (5) where i−i(p−i) is the total interference from other cells in cluster cn to cell i, gi is the channel gain of base station i to its terminal, and pmax is the maximum transmit power of the base station. all the cells will be stabilised at the point. the concavity is also crucial to ensure all the cells to be stabilised at the stable point through distributed automation [12]. this is due to the fact that, on each plane cutting through the concave structure, the intersecting curve is concave. in other words, ui(pi, p−i) is concave with respect to pi for any given p−i. the base station i is able to independently update the transmit power pi towards increasing the utility, based on measured interference. let ĩ−i(k) denote the k-th measurement result of interference (k = 1, 2, · · ·). it can be measured by the mobile terminal on the air interface, and reported to the base station through the virtual interface “i_d". ĩ−i(k) can replace i−i(p−i) in (5) and update p∗i (k + 1) in practical distributed implementations, pushing all the base stations to converge to and stay at the unique stable point of (5). as a result, the requirement of signalling exchange between the cells can be eliminated, as well as the centralised coordination of the upper layer of the protocol stack. other utility functions with convex/concave structures can also be designed. stable points with increased convergent sinrs are expected with proper designs of the functions. 3.3 interactions between the two layers the two layers of the new 5g protocol stack are interactive, as illustrated in fig. 3. on one hand, the distributed automation of the lower layer depends on the network topology and resource management outcome of the centralised control of the upper layer. on the other hand, the centralised network control of the upper layer needs to operate on stabilised systems after the distributed automation of the lower layer, so that the network topology and resources can be adapted and updated. the interaction between the two layers is facilitated by the interface “i_c/d" at each base station. in the downstream direction, the evolving topology that was updated at the upper layer and carried by “i_c" is passed down to the lower layer through “i_c/d" to activate distributed power control at the lower layer. in the reverse direction, transmit powers stabilised at the lower layer are handed over to the upper layer through “i_c/d", and will gather at the centralised network controller through “i_c" to trigger a new round of topology management. decoupling 5g network control: centralised coordination and distributed adaptation 199 figure 3: an illustration on the decoupled 5g control protocol stack, where the upper layer of the protocol stack is interconnected in a centralised manner through the interface (i/f) "i_c" between base stations and the centralised network controller, the lower layer of the protocol stack behaves in a distributed manner through a virtual interface "i_d", and the two layers are interactive through the interface "i_c/d" with every base station the star topology of the upper layer also provides signalling bearers via the interface “i_c" to carry the probabilities to the base stations. on the receipt of the probabilities through the upper layer, each base station independently decides whether to switch clusters and which cluster to switch to. as a result of the centralised upper layer, cells migrate from crowded clusters to less interfered clusters. the distributions of data rate converge across the clusters, as well as getting less spanned. fairness is improved between the clusters, and also within a cluster. moreover, the evolution design of the upper layer eliminates the requirement of signalling exchange between clusters, and tolerates the great level of uncertainty in large practical wireless networks. the design also distributes the complexity of clustering decision-makings between the centralised controller and individual base stations. the scalability of the network is therefore enhanced. 4 performance studies extensive matlab simulations have been conducted to evaluate the hybrid protocol stack for 5g network control. the case where the evaluation is carried out is a heterogeneous 5g network covering a geographical area of 5002πm2, where there are 30 available frequency channels and each channel has a bandwidth of 180 khz. consider that the privately owned/installed plug-and-play base stations are most likely to be indoors and operate at a low power level. we assume that the maximum transmit power of each base station is 20 dbm. we also assume a wall penetration loss of 10 db when the radio signals of a 5g cell leak out from indoors to outdoors and become interference to other cells. the path loss exponent is 3.7. the receive noise at the mobile terminals is set to be −174 dbm/hz. the traffic demand is assumed to be 5.0 mb/s per mobile terminal. 200 s.j. lin, j.g. yu, w. ni, r.p. liu fig. 4 shows the overall throughput of the 5g network with the increasing number of cells, where the number of clusters grows from one to five. apart from our hybrid approach, we also plot the pure centralised sdn network control proposed in [6], where clusters are constructed using graph theory to maximize the distance between the clusters, and the base stations transmit a fixed power of 20 dbm since fast power control at milliseconds intervals is impossible due to centralised and severely delayed control. the figure demonstrates that, in general, the hybrid network control provides higher throughput than the pure centralised control. when the evolutionary clustering is on, the hybrid network control adaptively decides the number of clusters. the throughput under the hybrid network control operates as the topmost of all the five solid curves, which is far beyond the throughput of the centralised approach. a close look at each of the five solid curves shows that for every given number of clusters, the throughput starts by growing, due to the increased traffic demand resulting from the increased cell density. after reaching a peak, the throughput rapidly drops. the reason is because the network is so dense, the clusters are becoming very crowded, and the interference becomes excessively large. distributed automation of power control or even switching between clusters cannot help to suppress the interference. the excessive interference not only offsets the increased throughput resulting from increased demand, but also adversely affects and further reduces the throughput. in this sense, the peak corresponds to the case where the network density and interference are balanced and the throughput is maximized given the number of clusters. a joint look across all the five solid curves in fig. 4 shows that increasing the cluster number is able to increase the peak throughput, as well as the number of cells achieving the peak. of course, clustering can help alleviate interference by reducing the number of cells using the same frequency, as shown in the figure where the number of cells that can be supported increases with the number of clusters. on the other hand, clustering also decreases the bandwidth that every cell can use, as depicted in the figure where the throughput decreases with the increasing number of clusters in the presence of a small number of cells (i.e., n ≤ 50). the conclusion that we draw is that the significantly reduced interference due to the evolutionary clustering is able to compensate for the reduced bandwidth, when the cell density and interference are balanced. as a result of this, the peak throughput keeps growing with the cluster number. fig. 5 plots the average transmit power required to achieve the throughput performance in the previous figure, with respect to the increasing number of cells. the transmit power is the average of all the base stations’ after they are stabilised from the distributed power control automation. we can see that, in general, the stabilised transmit power decreases with the increase of the cluster numbers. this is because clustering helps alleviate interference, which in turn reduces the requirement of transmit power. when the evolutionary clustering of our hybrid network control is on, the average transmit power is as shown as the highlighted black zigzag curve in the figure. this is because the number of clusters is adaptively adjusted to maximize the throughput, when the evolutionary clustering is on. the number of clusters may increase, as the number of active cells grows. this can lead to a sudden drop of the transmit power, partly because of the alleviated interference and partly because of the reduced bandwidth per cell. it is also interesting to notice that the peaks on the highlighted black curve grows slowly with the number of cells. this is consistent with fig. 4, indicating that increased transmit powers are necessary to improve the achievable throughput. the sudden drop of the transmit power from the peaks also indicates that when interference becomes intensive, releasing part of the frequencies for a new cluster can substantially relieve the interference. as a result, much lower transmit powers are required to achieve the same throughput. in other words, the peaks are where the base stations should trade out their bandwidth for relieved interference. fig. 6 shows the convergence of the average throughput of each cluster along with evolution over time, where a single evolution process of 5 clusters is recorded. it demonstrates the converdecoupling 5g network control: centralised coordination and distributed adaptation 201 100 200 300 400 500 50 100 150 200 250 number of cells s ys te m t hr ou gh pu t (m bp s) centralised control proposed hybrid control m = 5 m = 4 m = 3 m = 2 m = 1 figure 4: overall system throughput versus the number of cells, where the number of clusters, denoted by n, grows from 1 to 5. 50 100 150 200 250 300 350 400 450 500 4 6 8 10 12 14 16 18 20 number of cells a ve ra ge t ra ns m it p ow er ( db m ) proposed hybrid control centralised control m = 5 m = 4m = 3 m = 2 m = 1 figure 5: average transmit power per base station versus the number of cells, where the black curve is the actual required average transmit power when the evolutionary clustering of our hybrid network control is on, and is plotted by picking the crossing points of any two adjacent throughput curves in fig. 4. gence of the clusters in terms of throughput. the largest difference among the clusters decreases from 0.8 mbps to 0.1 mbps after 320 evolution stages (i.e., 6.4 seconds), and the difference diminishes by 500 evolution stages (i.e., 10 seconds). we also see that most of the clusters can 202 s.j. lin, j.g. yu, w. ni, r.p. liu fast converge to balancing their throughput. specifically, clusters 1 to 4 converge by 200 stages. cluster 5 does not converge as fast as the others do, because of its small initial size (as will be shown in fig. 7). however, the convergent tendency of the cluster is clear, as shown in the figure. 0 100 200 300 400 500 0 0.5 1 1.5 evolutionary stage a ve ra ge t hr ou gh pu t (m bp s) d1 d2 d3 d4 d50.1 mbps 0.8 mbps figure 6: convergence of the throughput per cell with the growth of evolution stages, where n = 150 and m = 5. fig. 7 shows the stabilization process of the size of each cluster along with evolution over time, corresponding to fig. 6. we can see the difference of the cluster sizes decreases fast with the evolutionary clustering. however, the cluster sizes do not converge to the same size, as opposed to the throughput curves of the clusters which evolve to converge, as shown in fig. 6. the reason for this lies in the network geometry of this particular simulation, where the femtocells are geographically unevenly distributed. as a result, the cluster sizes vary so as to balance interference in each cluster. another interesting finding in fig. 7 is that after the cluster sizes stabilize, the throughput of the clusters continues converging. specifically, by 320 evolution stages, the cluster sizes start to stabilize, while the maximum throughput difference is still about 0.1 mbps. after that, local adjustment of clustering is happening without significantly changing the cluster sizes. the clustering evolution enables the clusters to locally swap their femtocells (e.g., between clusters 4 and 5 in fig. 7) and adjust their geometry, until each of the clusters is most widely spanned in space and intra-cluster interference is alleviated. figs. 6 and 7 also confirm the effectiveness of our proposed framework, as they evidence that the convergence of throughput among the clusters unnecessarily requires the convergence of the sizes of the clusters. our proposed framework, especially the cluster conformation design of (1), targets to balance the throughput (or in other words, balance the interference), rather than the cluster sizes. decoupling 5g network control: centralised coordination and distributed adaptation 203 0 100 200 300 400 500 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 evolutinary stage p er ce nt ag e of f em to ce ll p er c lu st er ( 10 0% ) d1 d2 d3 d4 d5 figure 7: convergence of the cluster size per cluster with the growth of evolution stages, where n = 150 and m = 5. 5 prospective standardisations given the superior performance, the new hybrid network control is promising for future 5g systems. standardisations will be important, focusing on the three interfaces of i_c, i_d, and i_c/d. the cycle that the centralised speciation takes place needs to be specified, which has strong impact on the convergence speed of the network evolution, as described in section 3.1. standardisations will also be required on the utility functions described in section 3.2, which are critical for the convergence of the distributed power control automation, as well as the effectiveness of interference suppression. other standardisation efforts will be on identifying typical probability distribution functions for the centralised controller to describe the distribution of data rate in each cluster, as described in section 3.1. this will be important to leverage the complexity of the centralised sdn controller, as well as the reliability of the network evolution. 6 conclusions in this article, we proposed to decouple 5g network control into two layers, namely, centralised coordination at the sdn controller and distributed adaptation of base stations. delay-tolerant topology control and delay-sensitive interference suppression are respectively handled by the two layers. the decoupling was based on a new evolutionary view of 5g network control. the decoupled network control is able to alleviate the requirement of delay and complexity, and therefore enhance network scalability. case studies show that our decoupled network control is effective for timely interference mitigation and reliable topology management. the stability and scalability of our approach are also demonstrated. 204 s.j. lin, j.g. yu, w. ni, r.p. liu acknowledgment this work was partly supported by national natural science foundation of china under the grant no.61701034 and no.61531007 and partly supported by china postdoctoral science foundation under the grant no 2017m620696. bibliography [1] alliance, n. g. m. n. (2012); small cell backhaul requirements, white paper, june, 2012. [2] bemporad, a.; heemels, m.; johansson, m. (2010); networked control systems, 406, berlin: springer, 2010. [3] docomo (2014); docomo 5g white paper: 5g radio access: requirements, concept and technologies, 2014. [4] gudipati, a.; perry, d.; li, l. e.; katti, s. (2013); softran: software defined radio access network, proceedings of the second acm sigcomm workshop on hot topics in software defined networking, 25-30, 2013. [5] kim, h.; feamster, n. (2013); improving network management with software defined networking. ieee communications magazine, 51(2), 114-119, 2013. [6] lin, s.; tian, h. (2013); clustering based interference management for qos guarantees in ofdma femtocell, wireless communications and networking conference (wcnc), 2013 ieee, 649-654, 2013. [7] ni, w.; collings, i. b. (2012); indoor wireless networks of the future: adaptive network architecture, ieee communications magazine, 50(3), 130-137, 2012. [8] ni, w.; collings, i. b. (2013). a new adaptive small-cell architecture, ieee journal on selected areas in communications, 31(5), 829-839, 2013. [9] ni, w.; collings, i. b.; lipman, j.; wang, x.; tao, m.; abolhasan, m. (2015); graph theory and its applications to future network planning: software-defined online small cell management, ieee wireless communications, 22(1), 52-60, 2015. [10] ni, w.; liu, r.p. ; collings, i.b.; wang, x. (2013), indoor cooperative small cells over ethernet, communication magazine, ieee, 51(9), 100-107, 2013. [11] pentikousis, k.; wang, y.; hu, w. (2013); mobileflow: toward software-defined mobile networks, ieee communications magazine, 51(7), 44-53, 2013. [12] shapley, l. s. (1971); cores of convex games, international journal of game theory, 1(1), 11-26, 1971. [13] wen, t.; zhu, p. (2013); 5g: a technology vision, huawei, 2013. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 385-386 foreword l.a. zadeh lotfi a. zadeh professor in the graduate school director, berkeley initiative in soft computing (bisc) 729 soda hall 1776 computer science division department of electrical engineering and computer sciences university of california berkeley, ca 94720-1776 e-mail: zadeh@eecs.berkeley.edu http://www.cs.berkeley.edu/ zadeh/ i feel honored by the dedication of the special issue of ijccc to me. i should like to express my deep appreciation to the distinguished co-editors and my good friends, professors balas, dzitac and teodorescu, and to distinguished contributors, for honoring me. the subjects which are addressed in the special issue are on the frontiers of fuzzy logic. the foreword gives me an opportunity to share with the readers of the journal my recent thoughts regarding a subject which i have been pondering about for many years fuzzy logic and natural languages. the first step toward linking fuzzy logic and natural languages was my 1973 paper," outline of a new approach to the analysis of complex systems and decision processes." two key concepts were introduced in that paper. first, the concept of a linguistic variable a variable which takes words as values; and second, the concept of a fuzzy ifthen rule a rule in which the antecedent and consequent involve linguistic variables. today, close to forty years later, these concepts are widely used in most applications of fuzzy logic. the second step was my 1978 paper, "pruf a meaning representation language for natural languages." this paper laid the foundation for a series of papers in the eighties in which a fairly complete theory of fuzzy logic-based semantics of natural languages was developed. my theory did not attract many followers either within the fuzzy logic community or within the linguistics and philosophy of languages communities. there is a reason. the fuzzy logic community is largely a community of engineers, computer scientists and mathematicians a community which has always shied away from semantics of natural languages. symmetrically, the linguistics and philosophy of languages communities have shied away from fuzzy logic. in the early nineties, a thought that began to crystallize in my mind was that in most of the applications of fuzzy logic linguistic concepts play an important, if not very visible role. it is this thought that motivated the concept of computing with words (cw or cww), introduced in my 1996 paper "fuzzy logic = computing with words." in essence, computing with words is a system of computation in which the objects of computation are words, phrases and propositions drawn from a natural language. the same can be said about natural language processing (nlp.) in fact, cw and nlp have little in common and have altogether different agendas. in large measure, cw is concerned with solution of computational problems which are stated in a natural language. simple example. given: probably john is tall. what is the probability that john is short? what is the probability that john is very short? what is the probability that john is not very tall? a less simple example. given: usually robert leaves office at about 5 pm. typically it takes robert about an hour to get home from work. what is the probability that robert is home at 6:l5 pm.? what should be noted is that cw is the only system of computation which has the capability to deal with problems of this kind. the problem-solving capability of cw rests on two key ideas. first, employment of so-called restriction-based semantics (rs) for translation of a natural language into a mathematical language in which the concept of a copyright c⃝ 2006-2011 by ccc publications 386 l.a. zadeh restriction plays a pivotal role; and second, employment of a calculus of restrictions a calculus which is centered on the extension principle of fuzzy logic. what is thought-provoking is that neither traditional mathematics nor standard probability theory has the capability to deal with computational problems which are stated in a natural language. not having this capability, it is traditional to dismiss such problems as ill-posed. in this perspective, perhaps the most remarkable contribution of cw is that it opens the door to empowering of mathematics with a fascinating capability the capability to construct mathematical solutions of computational problems which are stated in a natural language. the basic importance of this capability derives from the fact that much of human knowledge, and especially world knowledge, is described in natural language. in conclusion, only recently did i begin to realize that the formalism of cw suggests a new and challenging direction in mathematics mathematical solution of computational problems which are stated in a natural language. for mathematics, this is an unexplored territory. lotfi a. zadeh berkeley, ca august 13, 2011 international journal of computers communications & control issn 1841-9836, 13(3), 408-428, june 2018. latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system v.n. phu, v.t.n. tran vo ngoc phu* institute of research and development, duy tan university-dtu da nang, vietnam *corresponding author: vongocphu03hca@gmail.com; vongocphu@dtu.edu.vn vo thi ngoc tran school of industrial management (sim), ho chi minh city university of technology hcmut, vietnam national university ho chi minh city, vietnam vtntran@hcmut.edu.vn abstract: we have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. we have proposed a novel model using a latent semantic analysis (lsa) and a dennis coefficient (dnc) for big data sentiment classification in english. many lsa vectors (lsav) have successfully been reformed by using the dnc. we use the dnc and the lsavs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in english. this novel model uses many sentiment lexicons of our basis english sentiment dictionary (besd). we have tested the proposed model in both a sequential environment and a distributed network system. the results of the sequential system are not as good as that of the parallel environment. we have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification. keywords: english sentiment classification; parallel system; cloudera; hadoop map and hadoop reduce; dennis measure; latent semantic analysis 1 introduction in this survey, our novel model is performed as follows: firstly, we use the dennis coefficient (dnc) to identify the valences and polarities of the sentiment lexicons of the basis english sentiment dictionary (besd). then, the latent semantic analysis (lsa) is improved by using the sentiment lexicons. all the positive documents of the training data set are transferred into one lsav, called the positive lsav group. all the negative documents of the training data set are transferred into one lsav, called the negative lsav group. each document in the documents of the testing data set is transferred into one lsav. we use the dnc to cluster this lsav into either the positive lsav group or the negative lsav group. one similarity measure between the lsav and the positive lsav group is calculated certainly, called measure_1 and one similarity measure between the lsav and the negative lsav group is calculated certainly, called measure_2. if measure_1 is greater than measure_2, it means that the lsav being close to the positive lsav group is greater than the lsav being close to the negative lsav group, so copyright ©2018 cc by-nc latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system 409 the lsav (corresponding to the document of the testing data set) is clustered into the positive. if measure_1 is less than measure_2, it means that the lsav being close to the positive lsav group is less than the lsav being close to the negative lsav group, so the lsav (corresponding to the document of the testing data set) is clustered into the negative. if measure_1 is as equal as measure_2, it means that the lsav being close to the positive lsav group is as equal as the lsav being close to the negative lsav group, so the lsav (corresponding to the document of the testing data set) is not clustered into both the positive and the negative. the lsav is clustered into the neutral polarity. therefore, the sentiment classification of this document is identified successfully. finally, the sentiment classification of all the document of the testing data set is identified fully. we firstly implement all the above things in the sequential system, and then, we perform all the above things in the parallel network environment to shorten the execution times of the proposed model. our proposed model has the crucial contributions to many areas of research as well as commercial applications as follows: (1) many surveys and commercial applications can use the results of this work in a significant way; (2) the algorithms are built in the proposed model; (3) this survey can certainly be applied to other languages easily; (4) the algorithm of data mining is applicable to semantic analysis of natural language processing; (5) millions of english documents are successfully processed for emotional analysis; (6) our proposed model can be applied to many different parallel network environments such as a cloudera system; (7) this study can be applied to many different distributed functions such as hadoop map (m) and hadoop reduce (r); (8) the lsa − related algorithms are proposed in this survey; (9) the dnc − related algorithms are built in this work. 2 related work we summarize many researches which are related to our research. the surveys related the similarity coefficients to calculate the valences of words are in [1,13–18]. in the research [1], the authors generate several norwegian sentiment lexicons by extracting sentiment information from two different types of norwegian text corpus, namely, news corpus and discussion forums. the methodology is based on the point wise mutual information (pmi), etc. the english dictionaries are [19–21] and there are more than 55,000 english words (including english nouns, english adjectives, english verbs, etc.) from them. there are the works related to the dennis coefficient (dnc) in [2, 3, 5]. the authors in [3] collected 76 binary similarity and distance measures used over the last century and reveal their correlations through the hierarchical clustering technique, etc. there are the researches related the latent semantic analysis (lsa) in [4,6,7]. the study in [4] presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as latent semantic analysis, etc. the latest researches of the sentiment classification are [8–12]. in the research [9], the authors have explored different methods of improving the accuracy of sentiment classification. the authors’ proposed method based on the combination of termcounting method and enhanced contextual valence shifters method has improved the accuracy of sentiment classification, etc. 3 methodology our methodology comprises 3 sub-sections as follows: (1) first sub-section: creating the sentiment lexicons of the besd; (2) second sub-section: improving the lsa according to the 410 v.n. phu, v.t.n. tran sentiment lexicons of the besd a sequential environment and a distributed network system; (3) third sub-section: using the lsa and a dnc to cluster the documents of the testing data set into either the positive or the negative in both a sequential environment and a parallel distributed system. we built our the testing data set including the 11,000,000 documents in the movie field, which contains the 5,500,000 positive and 5,500,000 negative in english. we also built our the training data set including the 5,000,000 documents in the movie field, which contains the 2,500,000 positive and 2,500,000 negative in english. all the english documents in our testing data set and training data set are automatically extracted from millions of the documents of english facebook, english websites and social networks; then we labeled positive and negative for them. 3.1 creating the sentiment lexicons of the besd calculating a valence of one word (or one phrase) in english figure 1: overview of identifying the valence and the polarity of one term in english using a dennis coefficient (dnc) in this part, we calculated the valence and the polarity of one english word (or phrase) by using the dnc through a google search engine with and operator and or operator, as the following diagram in figure 1 shows. according to [1,13–18], pointwise mutual information (pmi) between two words wi and wj has the equation pmi(wi,wj) = log2 [ p(wi,wj) p(wi) ×p(wj) ] (1) and so (sentiment orientation) of word wi has the equation so(wi) = pmi(wi,positive) −pmi(wi,negative) (2) in the research [1], the authors generate several norwegian sentiment lexicons by extracting sentiment information from two different types of norwegian text corpus, namely, news corpus and discussion forums. the methodology is based on the point wise mutual information (pmi), latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system 411 etc. the authors in [13] used the ochiai measure through the google search engine with and operator and or operator to calculate the sentiment values of the words in vietnamese. the authors in [14] used the consine measure through the google search engine with and operator and or operator to identify the sentiment scores of the words in english. the authors in [15] used the sorensen coefficient through the google search engine with and operator and or operator to calculate the sentiment values of the words in english. the authors in [16] used the jaccard measure through the google search engine with and operator and or operator to calculate the sentiment values of the words in vietnamese. the authors in [17] used the tanimoto coefficient through the google search engine with and operator and or operator to identify the sentiment scores of the words in english. with the above proofs, we had the information about the measures as follows: pmi was used with altavista in english, chinese, and japanese with the google in english; jaccard was used with the google in english, chinese, and vietnamese. the ochiai was used with the google in vietnamese. the consine and sorensen were used with the google in english. according to [1,13–18], pmi, jaccard, consine , ochiai, sorensen, tanimoto, and dnc were the similarity measures between two words, and they can perform the same functions and with the same chracteristics; so dnc was used in calculating the valence of the words. in addition, we proved that dnc can be used in identifying the valence of the english word through the google search with the and operator and or operator. with the dennis coefficient (dnc) in [2,3,5], we had the equation of the dnc: dnc(a,b) = [(a∩ b) × [(¬a) ∩ (¬b)] − [(¬a) ∩ b] × [a∩ (¬b)]]√ n× [(a∩ b) + [(¬a) ∩ b]] × [(a∩ b) + [a∩ (¬b)]] (3) with a and b are the vectors. in this study, we chose n=1. therefore, we had eq. (4) as follows: dnc(a,b) = [(a∩ b) × [(¬a) ∩ (¬b)] − [(¬a) ∩ b] × [a∩ (¬b)]]√ [(a∩ b) + [(¬a) ∩ b]] × [(a∩ b) + [a∩ (¬b)]] (4) from the eq. (1), (2), (3), we proposed many new equations of the dnc to calculate the valence and the polarity of the english words (or the english phrases) through the google search engine as the following equations below. in eq. (3), when a had only one element, a is a word. when b had only one element, b is a word. in eq. (3), a was replaced by w1 and b was replaced by w2. dennismeasure(w1,w2) = denniscoefficient(w1,w2) = dnc(w1,w2) = b √ a (5) with (1) b=p(w1,w2)× p(¬w1,¬w2)-p(¬w1,w2) × p(w1,¬w2); (2) a=[p(w1,w2)+p(¬w1,w2)]× [p(w1,w2)+p(w1,¬w2)]. eq. (5) was similar to eq. (1). in eq. (2), eq. (1) was replaced by eq. (4). we had eq. (6) as follows: v alence(w) = so_dnc(w) = dnc(w,positive_query) −dnc(w,negative_query) (6) in eq. (5), w1 was replaced by w and w2 was replaced by position_query. we had eq. (7). eq. (7) was as follows: dnc(w,positive_query) = b7 √ a7 (7) 412 v.n. phu, v.t.n. tran with (1) b7=p(w,positive_query) × p(¬w, ¬positive_query)-p(¬w,positive_query) × p(w,¬positive_query); (2) a7=[p(w,positive_query)+p(¬w,positive_query)] × [p(w,positive_query)+p(w,¬positive_query)]. in eq. (5), w1 was replaced by w and w2 was replaced by negative_query. we had eq. (8). eq. (8) was as follows: dnc(w,negative_query) = b8 √ a8 (8) with: (1) b8=p(w,negative_query) × p(¬w,¬negative_query)-p(¬w,negative_query) × p(w,¬negative_query); (2) a8=[p(w,negative_query)+p(¬w,negative_query)] × [p(w,negative_query)+p(w,¬negative_query)]. we had the information about w, w1, w2, and etc. as follows: (1) w, w1, w2 : were the english words (or the english phrases); (2) p(w1, w2): number of returned results in google search by keyword (w1 and w2). we use the google search api to get the number of returned results in search online google by keyword (w1 and w2); (3) p(w1): number of returned results in google search by keyword w1. we use the google search api to get the number of returned results in search online google by keyword w1; (4) p(w2): number of returned results in google search by keyword w2. we use the google search api to get the number of returned results in search online google by keyword w2; (5) valence(w) = so_dnc(w): valence of english word (or english phrase) w; is so of word (or phrase) by using the dennis coefficient (dnc); (6) positive_query: active or good or positive or beautiful or strong or nice or excellent or fortunate or correct or superior with the positive_query is the a group of the positive english words; (7) negative_query: passive or bad or negative or ugly or week or nasty or poor or unfortunate or wrong or inferior with the negative_query is the a group of the negative english words; (8) p(w, positive_query): number of returned results in google search by keyword (positive_query and w). we used the google search api to get the number of returned results in search online google by keyword (positive_query and w); (9) p(w, negative_query): number of returned results in google search by keyword (negative_query and w). we used the google search api to get the number of returned results in search online google by keyword (negative_query and w); (10) p(w): number of returned results in google search by keyword w. we used the google search api to get the number of returned results in search online google by keyword w; (11) p(¬w,positive_query): number of returned results in google search by keyword (¬w and positive_query). we used the google search api to get the number of returned results in search online google by keyword (¬w and positive_query); (12) p(w, ¬positive_query): number of returned results in the google search by keyword (w and ( ¬positive_query)). we used the google search api to get the number of returned results in search online google by keyword (w and (¬positive_query)); latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system 413 (13) p(¬w, ¬positive_query): number of returned results in the google search by keyword (¬w and (¬positive_query)). we used the google search api to get the number of returned results in search online google by keyword ((¬w) and (¬positive_query)); (14) p(¬w,negative_query): number of returned results in google search by keyword (¬w and negative_query). we used the google search api to get the number of returned results in search online google by keyword (¬w and negative_query); (15) p(w, ¬negative_query): number of returned results in the google search by keyword (w and (¬negative_query)). we used the google search api to get the number of returned results in search online google by keyword (w and (¬negative_query)); (16) p(¬w,¬negative_query): number of returned results in the google search by keyword (¬w and (¬negative_query)). we used the google search api to get the number of returned results in search online google by keyword (¬w and (¬negative_query)). we have the information about the dnc as follows: (1) dnc(w, positive_query) ≥ 0 and dnc(w, positive_query) ≤ 1. (2) dnc(w, negative_query) ≥ 0 and dnc (w, negative_query) ≤ 1. (3) if dnc (w, positive_query) = 0 and dnc (w, negative_query) = 0 then so_dnc (w) = 0. (4) if dnc (w, positive_query) =1 and dnc (w, negative_query) = 0 then so_dnc (w) = 0. (5) if dnc (w, positive_query) = 0 and dnc (w, negative_query) = 1 then so_dnc (w) = -1. (6) if dnc (w, positive_query) =1 and dnc (w, negative_query) = 1 then so_dnc(w) = 0. so, so_dnc (w) ≥ -1 and so_dnc (w) ≤ 1. the polarity of the english word w is positive polarity if so_dnc (w) < 0. the polarity of the english word w is negative polarity if so_dnc (w) < 0. the polarity of the english word w is neutral polarity if so_dnc (w) = 0. in addition, the semantic value of the english word w is so_dnc (w). the result of calculating the valence w (english word) is similar to the result of calculating valence w by using altavista. however, altavista is no longer. in summary, by using eq. (6), eq. (7), and eq. (8), we identified the valence and the polarity of one word (or one phrase) in english by using the dnc through the google search engine with and operator and or operator. creating a basis english sentiment dictionary (besd) in a sequential environment in this part, we calculated the valence and the polarity of the english words or phrases for our besd by using the dnc in a sequential system in the algorithm 1. according to [19–21], we had at least 55,000 english terms, including nouns, verbs, adjectives, etc. algorithm 1 performing a besd in a sequential environment 1: input: the 55,000 english terms; the google search engine 2: output: a besd. 3: for all each term in the 55,000 terms do 4: by using eq. (5), eq. (6), eq. (7) and eq. (8) of the calculating a valence of one word (or one phrase) in english in the sub-section [overview of identifying the valence and the polarity of one term in english using a dnc], the sentiment score and the polarity of this term were identified. the valence and the polarity were calculated by using the dnc through the google search engine with and operator and or operator. 5: add this term into the basis english sentiment dictionary (besd) 6: end for 7: return besd our basis english sentiment dictionary (besd) had more 55,000 english words (or english phrases) and besd was stored in microsoft sql server 2008 r2. 414 v.n. phu, v.t.n. tran creating a basis english sentiment dictionary (besd) in a distributed system in this part, we calculated the valence and the polarity of the english words or phrases for our besd by using the dnc in a parallel network environment in the algorithm 2 and the algorithm 3. according to [19–21], we had at least 55,000 english terms, including nouns, verbs, adjectives, etc. this section included two phases: the hadoop map (m) phase and the hadoop reduce (r) phase. the input of the hadoop map phase was the 55,000 terms in english in [19–21]. the output of the hadoop map phase was one term which the sentiment score and the polarity are identified. the output of the hadoop map phase was the input of the hadoop reduce phase. thus, the input of the hadoop reduce phase was one term which the sentiment score and the polarity are identified. the output of the hadoop reduce phase was the basis english sentiment dictionary (besd). algorithm 2 performing the hadoop map phase 1: input: the 55,000 english terms; the google search engine. 2: output: one term which the sentiment score and the polarity are identified. 3: for all each term in the 55,000 terms do 4: by using eq. (5), eq. (6), eq. (7) and eq. (8) of the calculating a valence of one word (or one phrase) in english in the sub-section [overview of identifying the valence and the polarity of one term in english using a dnc], the sentiment score and the polarity of this term were identified. the valence and the polarity were calculated by using the dnc through the google search engine with and operator and or operator. 5: return this term 6: end for 7: return this term algorithm 3 implementing the hadoop reduce phase 1: input: one term which the sentiment score and the polarity are identified − the output of m. 2: output: a besd. 3: add this term into the basis english sentiment dictionary (besd); 4: return besd our besd had more 55,000 english words (or english phrases) and besd was stored in microsoft sql server 2008 r2. 3.2 improving the lsa according to the sentiment lexicons of the besd a sequential environment and a distributed network system reforming the lsa based on the sentiment lexicons of the besd according to the latent semantic analysis (lsa) [4,6,7], it is a technique in natural language processing, that provides a theory and method for extracting and representing the contextualusage and meaning of words by statistical computations applied to a large corpus of text. it closely approximates many aspects of human language learning and understanding. lsa produces a set of concepts (themes) exposed by the document analysis, which are based on the terms contained in the documents. it assumes that words that are similar in meaning occur in similar pieces of texts. we have the basic step involved in lsa based on the lsa [4, 6, 7] as follows: computing term-passage matrix -this is the document-term matrix where each row represents latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system 415 table 1: one latent semantic analysis vector lsav documents latent semantic analysis is very useful slow d1 1 1 1 1 2 1 0 d2 1 1 1 1 0 0 0 a document and the columns represent the terms (occurrence) in the document. typically, given two documents d1and d2 with d1= "latent semantic analysis is very very useful" and d2= "latent semantic analysis is slow". then, the document-term matrix is as shown below (called one latent semantic analysis vector lsav) in table 1. ⇒ the latent semantic analysis vector: lsav = ( 1 1 1 1 2 1 0 1 1 1 1 0 0 0 ) as known, in one sentence, there are sometimes many terms (meaningful words or meaningful phrases) bearing the neutral sentiment, the positive sentiment, or the negative sentiment. for example, we assume that in the besd, "useful" is the positive sentiment and its valence is +2.3. "very" is the positive sentiment and its sentiment score is +0.3. "slow" is the negative sentiment and its valence is -1.1. we see that the neutral terms are not the important role in a sentence. thus, if we still use them in calculating the sentiment of the sentence, there are many noises for this calculating. we also see that the negative terms make many noises for calculating the sentiment of the positive polarity and the positive terms make many noises for calculating the sentiment of the negative polarity. we use the valences of the terms combined with their frequencies to remove many noises of identifying the sentiment classification of one sentence. we apply the valences of the sentiment lexicons of the besd into the vd1 and vd2 as follows: according to the besd, it is assumed that "latent" is 0 of its valence; "semantic" is 0 of its sentiment value; "analysis" is 0 of its sentiment score; "useful" of +2.3 of its valence; "is" is 0 of its sentiment score; "document" is 0 of its valence; "classification" is 0 of its sentiment value; "an" is 0 of its valence; "very" is +0.3 of its sentiment score; the sentiment value of "slow" is −1.1. therefore, we have d1 and d2 in table 2. ⇒ d1 = (0, 0, 0, 0, 0.6, 2.3, 0) and d2 = (0, 0, 0, 0, 0, 0, 0) ⇒ emphasizing on the positive terms and the negative terms in one sentence. ⇒ the latent semantic analysis vector: lsav = ( 1 1 1 1 2 1 0 1 1 1 1 0 0 0 ) in one lsa vector (lsav), the value of each element is (its valences) × (its frequency). table 2: the value of d1 and the value of d2 documents latent semantic analysis is very useful slow d1 1 × (0) 1 × (0) 1 × (0) 1 × (0) 2 × (+0.3) 1 × (+2.3) 0 × (-1.1) d2 1 × (0) 1 × (0) 1 × (0) 1 × (0) 0 × (+0.3) 0 × (+2.3) 0 × (-1.1) transferring all the documents of the testing data set and the training data set into the lsavs in a sequential environmnent in this section, we proposed the algorithm 4, the algorithm 5, the algorithm 6, and the algorithm 7 in the sequential system as follows: all the positive documents of the training data 416 v.n. phu, v.t.n. tran set were transferred into one lsav, called the positive lsav group. all the negative documents of the training data set were transferred into one lsav, called the negative lsav group. each document in the documents of the testing data set was transferred into one lsav. we implemented the algorithm 4 to create an order list of the lsav which comprises all the meaningful terms of both the testing data set and the training data set in the sequential system. we proposed the algorithm 5 to transfer one document of the testing data set into one lsav in the sequential system. we built the algorithm 6 to transfer the positive documents of the training data set into one positive lsav in the sequential system, called the positive lsav group. we proposed the algorithm 7 to transfer the negative documents of the training data set into one negative lsav in the sequential system, called the negative lsav group. algorithm 4 creating an order list of the lsav 1: input: the documents of the training data set and the testing data set 2: output: an order list of the lsav − anorderlistofthelsav 3: set anorderlistofthelsav ← ∅ 4: for all each document into the documents of the training data set and the testing data set do 5: split this document into the sentences 6: for all each sentence in the sentences do 7: split this sentence into the meaningful terms based on the sentiment lexicons of the besd; 8: for all each term in the meaningful terms do 9: if checking this term in anorderlistofthelsav is false then 10: add this term into anorderlistofthelsav 11: end if 12: end for 13: end for 14: end for 15: return anorderlistofthelsav transferring all the documents of the testing data set and the training data set into the the lsavs in a distributed network system in this section, we implemented many algorithms in the distributed network system as follows: all the positive documents of the training data set were transferred into one lsav, called the positive lsav group. all the negative documents of the training data set were transferred into one lsav, called the negative lsav group. each document in the documents of the testing data set was transferred into one lsav. this section comprises the algorithm 8, the algorithm 9, the algorithm 10, the algorithm 11, the algorithm 12, the algorithm 13, the algorithm 14, and the algorithm 15. we created an order list of the lsav which comprises all the meaningful terms of both the testing data set and the training data set in the distributed network system in the algorithm 8 and the algorithm 9. this stage included two phases: the hadoop map phase (m) and the hadoop reduce phase (r). the input of m was the documents of the testing data set and the training data set. the output of r is one term. the input of r was the output of m, thus, the input of r was one term. the output of r was an order list of the lsav − anorderlistofthelsav. we transferred one document of the testing data set into one lsav in the parallel system in the algorithm 10 and the algorithm 11. this stage included two phases: the hadoop map phase latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system 417 algorithm 5 transferring one document of the testing data set into one lsav in the sequential system 1: input: one document in english and an order list of the lsa − anorderlistofthelsav; 2: output: one lsav; 3: set columns ← the length of anorderlistofthelsav 4: set rows ← the positive documents of the training data set 5: set lsav ← with its column is columns and its rows is rows 6: set i ← 0 7: for all each term in anorderlistofthelsav do 8: number := count this term in this document 9: valence := get the valence of this term based on the sentiment lexicons of the besd 10: set lsav[0][i] ← number × valence 11: set i ← i + 1 12: end for 13: for j:=1; j < rows; j++ do 14: for i:=0; i < columns; i++ do 15: set lsav[j][i] ← 0 16: end for 17: end for 18: return lsav (m) and the hadoop reduce phase (r). the input of m was one document in english and an order list of the lsa − anorderlistofthelsav. the output of m was one row of lsav. the input of r was the output of m, thus, the input of r was one row of lsav. the output of r was one lsav of this document. we transferred the positive documents of the training data set into one positive lsav in the distributed system, called the positive lsav group in the algorithm 12 and the algorithm 13. this stage included two phases: the hadoop map phase (m) and the hadoop reduce phase (r). the input of m was the positive documents in english and an order list of the lsa − anorderlistofthelsav. the output of m was one row of positivelsav. the input of r was the output of m, thus, the input of r was one row of positivelsav. the output of r was positivelsav. we transferred the negative documents of the training data set into one negative lsav in the parallel system, called the negative lsav group in the algorithm 14 and the algorithm 15. this stage included two phases: the hadoop map phase (m) and the hadoop reduce phase (r). the input of the hadoop map phase was the negative documents in english and an order list of the lsa − anorderlistofthelsav. the output of the hadoop map phase was one row of negativelsav. the input of the hadoop reduce phase was the output of the hadoop map, thus, the input of the hadoop reduce phase was one row of negativelsav. the output of the hadoop reduce phase was negativelsav. 418 v.n. phu, v.t.n. tran algorithm 6 transferring the positive documents of the training data set into one positive lsav in the sequential system 1: input: the positive documents of the training data set and an order list of the lsa − anorderlistofthelsav 2: output: one positive lsav the positive lsav group 3: set columns ← the length of anorderlistofthelsav 4: set rows ← the positive documents of the training data set 5: set positivelsav ← with its column is columns and its rows is rows 6: for j := 0; j < rows; j++ do 7: set i ← 0 8: for all each term in anorderlistofthelsav do 9: number := count this term in the document (j) of the positive documents of the training data set 10: valence := get the valence of this term based on the sentiment lexicons of the besd 11: set positivelsav[j][i] ← number × valence 12: set i ← i + 1 13: end for 14: end for 15: return positivelsav 3.3 using the lsa and a dnc to cluster the documents of the testing data set into either the positive or the negative in both a sequential environment and a parallel distributed system using the lsa and a dnc to cluster the documents of the testing data set into either the positive or the negative in a sequential environment this sub-section has the algorithm 16, and the algorithm 17. in this section, we used the lsa and a dnc to cluster the documents of the testing data set into either the positive or the negative in a sequential environment. we built the algorithm 16 to cluster one lsav (corresponding one document of the testing data set) into either positive polarity or the negative polarity in the sequential environment. we proposed the algorithm 17 to cluster all the documents of the testing data set into either the positive or the negative in the sequential system by using the lsa and the dnc. using the lsa and a dnc to cluster the documents of the testing data set into either the positive or the negative in a distributed system this part includes the algorithm 18, the algorithm 19, the algorithm 20, and the algorithm 21. in this part, we used the lsa and a dnc to cluster the documents of the testing data set into either the positive or the negative in a distributed system. we clustered one lsav (corresponding one document of the testing data set) into either positive polarity or the negative polarity in the distributed environment in the algorithm 18 and the algorithm 19. this stage comprised two phases: the hadoop map phase (m) and the hadoop reduce phase (r). the input of m was one lsav (corresponding one document of the testing data set); the positive lsav group and the negative lsav group. the output of m was the result of the sentiment classification of one document. the input of r was the output of m, thus, the input of r was the result of the sentiment classification of one document. the output of r latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system 419 algorithm 7 transferring the negative documents of the training data set into one negative lsav in the sequential system 1: input: the negative documents of the training data set and an order list of the lsa − anorderlistofthelsav 2: output: one negative lsav − the positive lsav group 3: set columns ← the length of anorderlistofthelsav 4: set rows ← the negative documents of the training data set 5: set negativelsav ← with its column is columns and its rows is rows 6: for j := 0; j < rows; j++ do 7: set i ← 0 8: for all each term in anorderlistofthelsav do 9: number := count this term in the document (j) of the positive documents of the training data set 10: valence := get the valence of this term based on the sentiment lexicons of the besd 11: set negativelsav[j][i] ← number × valence 12: set i ← i + 1 13: end for 14: end for 15: return negativelsav was the result of the sentiment classification of this document of the testing data set. we used the lsa and the dnc to cluster the documents of the testing data set into either the positive or the negative in the distributed system in the algorithm 20 and the algorithm 21. this stage comprised two phases: the hadoop map phase (m) and the hadoop reduce phase (r). the input of m was the documents of the testing data set. the output of m was the result of the sentiment classification of one document. the input of r was the output of m, thus, the input of r is the result of the sentiment classification of one document. the output of r was the results of the sentiment classification of the documents of the testing data set. 4 experiment to implement the proposed model, we have already used java programming language to save the 11,000,000 documents of the testing data set and the 5,000,000 documents of the training data set, and to save the results of emotion classification. the proposed model was implemented in both the sequential system and the distributed network environment. the configuration of one server is: intelâž server board s1200v3rps, intelâž pentiumâž processor g3220 (3m cache, 3.00 ghz), 2gb cc3-10600 ecc 1333 mhz lp unbuffered dimms; and the operating system of the server is: cloudera. our novel model related to the latent semantic analysis and a dennis coefficient is implemented in the sequential environment with the configuration as follows: the sequential environment in this research includes 1 node (1 server). the proposed model related to the latent semantic analysis and a dennis coefficient is performed in the cloudera parallel network environment with the configuration as follows: this cloudera system includes 9 nodes (9 servers). all 9 nodes have the same configuration information in table 3, we show the accuracy and the results of our novel model in the testing data set. the average time of the classification of our new model for the english documents in testing data set are displayed in table 4. 420 v.n. phu, v.t.n. tran algorithm 8 performing the hadoop map phase 1: input: the documents of the testing data set and the document of the training data set 2: output: one term;//the output of m. 3: input the documents of the testing data set and the document of the training data set into m in the cloudra system 4: for all each document into the documents of the training data set do 5: split this document into the sentences 6: for all each sentence in the sentences do 7: split this sentence into the meaningful terms based on the sentiment lexicons of the besd 8: for all each term in the meaningful terms do 9: return this term;//the output of the hadoop map. 10: for all each document in the documents of the testing data set do 11: split this document into the sentences 12: for all each sentence into the sentences do 13: split this sentence into the meaningful terms according to the sentiment lexicons of the besd 14: for all each term in the meaningful terms do 15: return this term;//the output of m 16: end for 17: end for 18: end for 19: end for 20: end for 21: end for 22: table 3: the accuracy and the results of our novel model in the testing data set. testing dataset correct classification incorrect classification accuracy negative 5,500,000 4,884,751 615,249 88.76%positive 5,500,000 4,878,849 621,151 summary 11,000,000 9,763,600 1,236,400 algorithm 9 implementing the hadoop reduce phase 1: input: one term from m 2: output: an order list of the lsav − anorderlistofthelsav 3: receive on term from m; 4: if checking this term in anorderlistofthelsav is false then 5: add this term into anorderlistofthelsav 6: end if 7: return anorderlistofthelsav latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system 421 algorithm 10 performing the hadoop map phase 1: input: one document in english and an order list of the lsa − anorderlistofthelsav 2: output: one row of lsav; //the output of m. 3: input one document in english and an order list of the lsa − anorderlistofthelsav into the hadoop map in the cloudera 4: set columns ← the length of anorderlistofthelsav 5: set rows ← the positive documents of the training data set 6: set onerowoflsav ← with its columns is columns 7: set i ← 0 8: for all each term in anorderlistofthelsav do 9: number := count this term in this document 10: valence := get the valence of this term based on the sentiment lexicons of the besd 11: set onerowoflsav[i] ← number × valence 12: set i ← i + 1 13: end for 14: return onerowoflsav algorithm 11 implementing the hadoop reduce phase 1: input: one row of lsav; //the output of m. 2: output: one lsav 3: receive one row of lsav 4: add this row into lsav 5: for j:= 1; j < rows; j++ do 6: for i := 0; i < columns; i++ do 7: set lsav[j][i] ← 0 8: end for 9: end for 10: return lsav algorithm 12 performing the hadoop map phase 1: input: the positive documents in english and an order list of the lsa − anorderlistofthelsav 2: output: one row of lsav; //the output of m. 3: set columns ← the length of anorderlistofthelsav 4: set rows ← the positive documents of the training data set 5: for j := 0; j < rows; j++ do 6: set onerowofpositivelsav ← with its column is columns 7: set i ← 0 8: for all each term in anorderlistofthelsav do 9: number := count this term in the document (j) of the positive documents of the training data set 10: valence := get the valence of this term based on the sentiment lexicons of the besd 11: set onerowofpositivelsav[i] ← number × valence 12: end for 13: set i ← i + 1 14: end for 15: return onerowofpositivelsav; //the output of m 422 v.n. phu, v.t.n. tran algorithm 13 implementing the hadoop reduce phase 1: input: one row of lsav; //the output of m. 2: output: positivelsav 3: receive one row of positivelsav 4: add this row into positivelsav 5: return positivelsav algorithm 14 performing the hadoop map phase 1: input: the negative documents in english and an order list of the lsa − anorderlistofthelsav 2: output: one row of lsav; //the output of m. 3: set columns ← the length of anorderlistofthelsav 4: set rows ← the negative documents of the training data set 5: for j := 0; j < rows; j++ do 6: set onerowofnegativelsav ← with its column is columns 7: set i ← 0 8: for all each term in anorderlistofthelsav do 9: number := count this term in the document (j) of the negative documents of the training data set 10: valence := get the valence of this term based on the sentiment lexicons of the besd 11: set onerowofnegativelsav[i] ← number × valence 12: set i ← i + 1 13: end for 14: end for 15: return onerowofpositivelsav; //the output of m algorithm 15 implementing the hadoop reduce phase 1: input: one row of lsav; //the output of m. 2: output: negativelsav 3: receive one row of negativelsav 4: add this row into negativelsav 5: return negativelsav table 4: average time of the classification of our new model for the english documents in testing data set average time of the classification 11,000,000 english documents. the latent semantic analysis and a dennis coefficient in the sequential environment 43,460,194 seconds the latent semantic analysis and a dennis coefficient in the cloudera distributed system with 3 nodes 13,120,064 seconds the latent semantic analysis and a dennis coefficient in the cloudera distributed system with 6 nodes 7,360,032 seconds latent semantic analysis and a dennis coefficient in the cloudera distributed system with 9 nodes 4,851,132 seconds latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system 423 algorithm 16 clustering one lsav (corresponding one document of the testing data set) into either positive polarity or the negative polarity in the sequential environment 1: input: one lsav (corresponding one document of the testing data set); the positive lsav group and the negative lsav group. 2: output: positive, negative, neutral; 3: measure_1 := similarity measure between this lsav and the positive lsav group by using the eq. (3) of the calculating a valence of one word (or one phrase) in english in the subsection [overview of identifying the valence and the polarity of one term in english using a dnc]. 4: measure_2 := similarity measure between this lsav and the negative lsav group by using the eq. (3) of the calculating a valence of one word (or one phrase) in english in the subsection [overview of identifying the valence and the polarity of one term in english using a dnc]. 5: if measure_1 is greater than measure_2 then 6: return positive 7: elsemeasure_1 is less than measure_2 8: return negative 9: end if 10: return neutral algorithm 17 clustering all the documents of the testing data set into either the positive or the negative in the sequential system by using the lsa and the dnc 1: input: the documents of the testing data set and the training data set. 2: output: positive, negative, neutral; 3: the creating a besd in a sequential environment in the sub-section [creating a basis english sentiment dictionary (besd) in a sequential environment]. 4: the algorithm 4 to create an order list of the lsav which comprises all the meaningful terms of both the testing data set and the training data set in the sequential system. 5: the algorithm 6 to transfer the positive documents of the training data set into one positive lsav in the sequential system, called the positive lsav group. 6: the algorithm 7 to transfer the negative documents of the training data set into one negative lsav in the sequential system, called the negative lsav group. 7: results := null; 8: for all each document in the documents of the testing data set do 9: one lsav := the algorithm 5 to transfer one document of the testing data set into one lsav in the sequential system. 10: oneresult := the algorithm 16 to cluster one lsav (corresponding one document of the testing data set) into either positive polarity or the negative polarity in the sequential environment. 11: add oneresult into results; 12: end for 13: return results; 424 v.n. phu, v.t.n. tran algorithm 18 implementing the hadoop map phase 1: input: one lsav (corresponding one document of the testing data set); the positive lsav group and the negative lsav group. 2: output: the result of the clustering − oneresult; //the output of m. 3: measure_1 := similarity measure between this lsav and the positive lsav group by using the eq. (3) of the calculating a valence of one word (or one phrase) in english in the subsection [overview of identifying the valence and the polarity of one term in english using a dnc]. 4: measure_2 := similarity measure between this lsav and the negative lsav group by using the eq. (3) of the calculating a valence of one word (or one phrase) in english in the subsection [overview of identifying the valence and the polarity of one term in english using a dnc] 5: if measure_1 is greater than measure_2 then 6: oneresult := positive; 7: else 8: if measure_1 is less than measure_2 then 9: oneresult := negative; 10: else 11: oneresult := neutral; 12: end if 13: end if 14: return oneresult; //the output of m algorithm 19 performing the hadoop reduce phase 1: input: oneresult; //the output of m. 2: output: positive, negative, neutral; 3: receive oneresult; 4: return oneresult; latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system 425 algorithm 20 implementing the hadoop map phase 1: input: the documents of the testing data set and the training data set. 2: output: positive, negative, neutral; 3: the creating a besd in a distributed system in the sub-section [creating a basis english sentiment dictionary (besd) in a distributed system]. 4: the algorithm 4 to create an order list of the lsav which comprises all the meaningful terms of both the testing data set and the training data set in the sequential system. 5: creating an order list of the lsav which comprises all the meaningful terms of both the testing data set and the training data set in the distributed network system in the algorithm 8 and the algorithm 9. 6: transferring the positive documents of the training data set into one positive lsav in the sequential system, called the positive lsav group in the algorithm 12 and the algorithm 13. 7: transferring the negative documents of the training data set into one negative lsav in the sequential system, called the negative lsav group in the algorithm 14 and the algorithm 15. 8: input the documents of the testing data set, the positive lsav group and the negative lsav group into the hadoop map in the cloudera system; 9: for all each document in the documents of the testing data set do 10: one lsav := transferring one document of the testing data set into one lsav in the parallel system in the algorithm 10 and the algorithm 11. 11: oneresult :=clustering one lsav (corresponding one document of the testing data set) into either positive polarity or the negative polarity in the distributed environment in the algorithm 18 and the algorithm 19. 12: return oneresult;//the output of m 13: end for 14: return oneresult;//the output of m algorithm 21 performing the hadoop reduce phase 1: input: oneresult − the result of the sentiment classification of one document (the input of r is the output of m). 2: output: the results of the sentiment classification of the documents of the testing data set; 3: receive oneresult − the result of the sentiment classification of one document. 4: add this oneresult into the results of the sentiment classification of the documents of the testing data set; 5: return the results of the sentiment classification of the documents of the testing data set; 426 v.n. phu, v.t.n. tran 5 conclusion in this survey, a new model has been proposed to classify sentiment of many documents in english using the latent semantic analysis and a dennis coefficient with hadoop map (m) /reduce (r) in the cloudera parallel network environment. based on our proposed new model, we have achieved 88.76% accuracy of the testing data set in table 3, and this is better than the accuracies of many previous models of the semantic analysis. besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. until now, not many studies have shown that the clustering methods can be used to classify data. according to table 4, the average time of the sentiment classification of using the latent semantic analysis and a dennis coefficient in the sequential environment is 43,460,194 seconds / 11,000,000 english documents and it is greater than the average time of the sentiment classification of using the latent semantic analysis and a dennis coefficient in the cloudera parallel network environment with 3 nodes which is 13,120,064 seconds / 11,000,000 english documents. the average time of the sentiment classification of using the latent semantic analysis and a dennis coefficient in the cloudera parallel network environment with 9 nodes is 4,851,132 seconds / 11,000,000 english documents, and it is the shortest time in the table. besides, the average time of the sentiment classification of using the latent semantic analysis and a dennis coefficient in the cloudera parallel network environment with 6 nodes is 7,360,032 seconds / 11,000,000 english documents the accuracy of the proposed model is dependent on many factors as follows: (1) the lsa − related algorithms. (2) the testing data set. (3) the documents of the testing data set must be standardized carefully. (4) transferring one document into one lsav. table 5: comparisons of our model′s positives and negatives the surveys related to the latent semantic analysis (lsa) studies approach positives negatives [4] unsupervised learning by probabilistic latent semantic analysis the survey presents perplexity results for different types of text and linguistic data collections and discusses an application in automated document indexing. the experiments indicate substantial and consistent improvements of the probabilistic method over standard latent semantic analysis. no mention [6] the latent semantic analysis theory of acquisition, induction, and representation of knowledge. a new general theory of acquired similarity and knowledge representation, latent semantic analysis (lsa), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. no mention our work lsa using a dnc the positives and negatives of the proposed model are given in the conclusion section. the execution time of the proposed model is dependent on many factors as follows: (1) the parallel network environment such as the cloudera system. (2) the distributed functions such as hadoop map (m) and hadoop reduce (r). (3) the lsa − related algorithms. (4) the performance of the distributed network system. (5) the number of nodes of the parallel network environment. (6) the performance of each node (each server) of the distributed environment. (7) the sizes of the training data set and the testing data set. (8) transferring one document into one lsav. latent semantic analysis using a dennis coefficient for english sentiment classification in a parallel system 427 the proposed model has many advantages and disadvantages. its positives are as follows: it uses the latent semantic analysis and a dennis coefficient to classify semantics of english documents based on sentences. the proposed model can process millions of documents in the shortest time. this study can be performed in distributed systems to shorten the execution time of the proposed model. it can be applied to other languages. its negatives are as follows: it has a low rate of accuracy. it costs too much and takes too much time to implement this proposed model. to understand the scientific values of this research, we have compared our model’s results with many studies in the tables below. our novel model has more benefits than the studies in the tables, and the results of this model are better than that of the works in the tables. in table 5, we present the comparisons of our model′s positives and negatives the surveys related to the latent semantic analysis (lsa). the comparisons of our model′s benefits and drawbacks with the studies related to the dennis coefficient (dnc) are shown in table 6. table 6: comparisons of our model′s benefits and drawbacks with the studies related to the dennis coefficient (dnc) studies approach benefits drawbacks [5] analysis of macromolecular polydispersity in intensity correlation spectroscopy: the method of cumulants a method is described by which the distribution function of the decay rates (and thus the extent of polydispersity) can be characterized, in a light scattering experiment, by calculation of the moments or cumulants. no mention [3] a survey of binary similarity and distance measures the authors collected 76 binary similarity and distance measures used over the last century and reveal their correlations through the hierarchical clustering technique no mention our work lsa using a dnc the advantages and disadvantages of this survey are shown in the conclusion section. conflict of interests the authors declare that there is no conflict of interests. bibliography [1] bai, a.; hammer, h.; yazidi, a.; engelstad, p. 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(2017); a valence-totaling model for vietnamese sentiment classification, international journal of evolving systems, 1-47, 2017. [17] phu, v.n., tran, v.t.n., chau, v.t.n. et al. (2017); semantic lexicons of english nouns for classification, international journal of evolving systems, 1-69, 2017. [18] turney, d. p.; littman, m.l. (2002); unsupervised learning of semantic orientation from a hundred-billion-word corpus, arxiv:cs/0212012, learning, 2002. [19] cambridge english dictionary (2017); http://dictionary.cambridge.org/ [20] longman english dictionary (2017); http://www.ldoceonline.com/ [21] oxford english dictionary (2017); http://www.oxforddictionaries.com/ jinijcccv12n6.pdf international journal of computers communications & control issn 1841-9836, 12(6), 839-853, december 2017. interval certitude rule base inference method using the evidential reasoning l.q. jin, x. fang liqian jin school of economics and management, chongqing university of posts and telecommunications, p. r. china jinlq@cqupt.edu.cn xin fang* 1. chongqing engineering research center for processing storage and transportation of characterized agro-products, p. r. china 2. school of business planning, chongqing technology and business university, p. r. china *corresponding author: xin_f@ctbu.edu.cn abstract: development of rule-based systems is an important research area for artificial intelligence and decision making, as rule base is one of the most general purpose forms for expressing human knowledge. in this paper, a new rule-based representation and its inference method based on evidential reasoning are presented based on operational research and fuzzy set theory. in this rule base, the uncertainties of human knowledge and human judgment are designed with interval certitude degrees which are embedded in the antecedent terms and consequent terms. the knowledge representation and inference framework offer an improvement of the recently developed rule base inference method, and the evidential reasoning approach is still applied to the rule fusion. it is noteworthy that the uncertainties will be defined and modeled using interval certitude degrees. in the end, an illustrative example is provided to illustrate the proposed knowledge representation and inference method as well as demonstrate its effectiveness by comparing with some existing approaches. keywords: interval certitude rule, knowledge representation, uncertainty inference, evidential reasoning. 1 introduction there are several types of uncertainties of knowledge such as incompleteness, randomness, fuzziness, inexactness and ignorance. the inference method which is based on uncertain knowledge is the uncertainty inference method. because of the uncertainty is ubiquitous, the uncertainty knowledge representation and uncertainty inference method have become the most visible and fastest growing branch of artificial intelligence [38]. in the existed methods of knowledge representation, rule base seems to be the most common form for expressing various types of knowledge [27]. the rule bases can be used to support decision making with both quantitative and qualitative knowledge under various types of uncertainties. specifically, different types of data such as exact number, fuzzy number, interval value, even subjective judgment can be represented by rule. in the last four decades, the development of uncertainty methods have received extensive attention. many methods have been proposed for showing uncertain knowledge and information. such as the certainty factor based inference method [36], inference method using the dempster-shafer theory [12], belief rule base inference methodology using the evidential reasoning (rimer) [31, 38–40], subjective bays method [36], fuzzy inference method [29], et al. copyright © cc by-nc 840 l.q. jin, x. fang in these methods, inference method using the dempster-shafer theory has a shortcoming in the aspect of dealing with conflict evidence, the subjective bayes method is mainly used in the randomness but does not apply to other uncertainties, fuzzy inference method is used in the vagueness. with the dempster-shafer theory, fuzzy set theory and if-then rule, yang et al [38] have researched and given rimer. the uncertain knowledge is described by belief rule base (brb), and it is able to handle many types of information. evidential reasoning (er) approach is an uncertainty inference method which make up for the deficiencies of the inference method using the dempster-shafer theory. however, there are still some defects [17]. liu et al. [5, 21] proposed the extended belief rule base (ebrb) based on brb and the extended belief rule base inference methodology using the evidential reasoning (rimer+). and the uncertainty of antecedent is described with belief structure and different evaluation grade set, but there is only one consequent attribute and knowledge should be converted to belief structure. the fuzzy set has been widely researched and applied in various fields [20,30,34]. interval number is an important fuzzy number, and it has been widely used in a lot of fields such as decision making science [2,32,37,41], operational research [6,9,19], [8,33]. the main reasons are as follows: • in the theoretical research, interval numbers are closely linked with other forms of uncertainty. • in the practical application, in order to evaluate the things, people are mainly inclined to give the upper and the lower bounds of information, and the interval number’s expression is nearly accord with this uncertainty characteristics of human thought. according to the above analysis, the certitude rule base is not accord with actual situation. because of the limitations of human cognition and the complexity of the objective things, decision maker can not always be confident enough to provide subjective judgment with exact certitude degrees. but at times, a range of the certitude degree can be assessed, such range is referred to as interval certitude degree. in order to apply the interval uncertainty, the knowledge representation which is based on the interval certitude rule and its inference method should be proposed. [16] this paper is organized as follows. in section 2, the interval certitude rule base inference method using the evidential reasoning (icrimer) is given. first, the structure and representation of interval certitude rule base (icrb) is presented. then a new generic knowledge base inference method icrimer is proposed. in section 3, the performances of icrimer is demonstrated by comparing with some existing approaches using a case study of classifications with eight data sets of uci machine learning repository. the paper is concluded in section 4. 2 interval certitude rule base inference method in this section, the basic knowledge of interval value fuzzy number is introduced, include concept, ranking method and similarity measure. then the interval certitude rule is proposed as knowledge representation method. in the end of this section, the interval certitude rule base inference method using the evidential reasoning with nonlinear programming is presented. 2.1 interval value fuzzy number note a = [ al, au ] be a bounded closed interval, and satisfies al ≤ au, al is the lower bound of a, au is the upper bound of a. if al, au ∈ r, then a = [ al, au ] is an interval number; if interval certitude rule base inference method using the evidential reasoning 841 al, au ∈ [0, 1], then a = [ al, au ] is an interval value fuzzy number (for short, interval number). specially, if al = au, then a = [ al, au ] is an exact number, note a = al. a ranking method based on the technique for order preference by similarity to ideal solution (topsis) and distance is given in order to rank the interval number. according to topsis [22], for interval number, the positive ideal solution is [1, 1], the negative ideal solution is [0, 0]. the relative similarity degree η of interval number a = [ al, au ] is as follows: η (a) = d− (a) d+ (a) + d− (a) where d− (a) = ∣ ∣ ∣ al+au 2 − 0 ∣ ∣ ∣ = a l+au 2 is the distance [23] between a = [ al, au ] and negative ideal solution [0, 0], d+ (a) = ∣ ∣ ∣ al+au 2 − 1 ∣ ∣ ∣ = 1 − a l+au 2 is the distance [23] between a = [ al, au ] and positive ideal solution [1, 1]. the relative similarity degree η satisfies that the larger the relative similarity degree is the larger the interval number will be. so the ranking of interval number can be given based on the relative similarity degree: • if η (a) > η(b) then a ≻ b , ’≻’ denotes the fuzzified version of ’>’ and has the linguistic interpretation ‘greater than’. • if η (a) = η(b) then a = b, means that the ranking orders of a = [ al, au ] and b = [ bl, bu ] are identical, a = [ al, au ] is equal to b = [ bl, bu ] , but al = bl and au = bu may be not always realized. the similarity measure of interval numbers is also an important content of interval type data processing. according to the lukasiewicz implication algebra on [0, 1], suppose a = [ al, au ] and b = [ bl, bu ] are interval numbers, the similarity measure s[] is as follows: s[] (a, b) = [ sl[] (a, b) , s u [] (a, b) ] s[] l (a, b) = min { min { 1 − al + bl, 1 − bl + al } , min { 1 − au + bu, 1 − bu + au }} su[] (a, b) = max { min { 1 − al + bl, 1 − bl + al } , min { 1 − au + bu, 1 − bu + au }} 2.2 interval certitude rule an interval certitude rule base (icrb) with k rules can be represented as follows: r = 〈 (x, a), (y, c), icd[], θ, w, f 〉 where x = {xi |i = 1, 2, · · · , i} is the set of antecedent attributes, the relationship among the antecedent attributes is taken as ‘∧’, ‘∧’ is the logical connective; ai = { ai,ii ∣ ∣ii = 1, 2, · · · , l a i } is the set of attribute values for antecedent attribute xi (i = 1, 2, · · · , i) , note a = {ai |i = 1, 2, · · · , i} be the set of antecedent attribute value sets; y = {yj |j = 1, 2, · · · , j } is the set of consequent attributes, the relationship among the consequent attributes is taken as ‘∧’; cj= { cj,j j ∣ ∣ ∣ jj = 1, 2, · · · , l c j } is the set of attribute values for consequent attribute yj (j = 1, 2, · · · , j), note c = {cj |j = 1, 2, · · · , j} be the set of consequent attribute value sets; θ= { θk |0 ≤ θk ≤ 1, k = 1, 2, · · · , k } is the set of the importance degree of each rule, θk is the rule weight of 842 l.q. jin, x. fang the kth rule; w = { wi ∣ ∣ ∣ ∣ 0 ≤ wi ≤ 1, i = 1, · · · , i, and i ∑ i=1 wi = 1 } is the set of the antecedent attribute weights, wi is the weight of xi ; icd[] = {icd (∆) |icd (∆) ⊆ [0, 1]} is interval certitude degree set, icd (∆) is the interval certitude degree of event ∆, icd (∆) is called interval certitude degree. icd (∆) satisfies the more higher the degree of certainty is, the more large icd (∆) will be. icd (∆) = [0, 0] means completely uncertainty, icd (∆) = [1, 1] means completely certainty, icd (∆) = [0, 1] means that we know nothing about the uncertainty of event ∆. f is a logical function. more specifically, the kth rule in the icrb r can be written as rk (k = 1, 2, · · · , k): if ( x1 = a k 1, icd k ( x1 = a k 1 )) ∧·· ·∧ ( xi = a k i , icd k ( xi = a k i )) then ( y1 = c k 1 , icd k ( y1 = c k 1 )) ∧·· ·∧ ( yj = c k j, icd k ( yj = c k j )) with rule interval certitude degree icdk ( rk ) , rule weight θk and antecedent attribute weights (w1, w2, · · · , wi) where ‘=’ means ‘is’; θk is the weight of the kth rule rk; (w1, w2, · · · , wi) are the weights of the antecedent attributes. for i = 1, 2, · · · , i , aki is the referential value of the ith antecedent attribute xi that is used in the kth rule rk , aki ∈ ai or a k i = φ, ( φ is default, means that the ith attribute ai of the kth rule. rk has no effect on the consequent); icdk ( xi = a k i ) ⊆ [0, 1] is the interval certitude degree of that aki is the referential value of xi in r k , means the degree of aki influence on the consequent; if aki = φ then icd k ( xi = a k i ) = 0 and this antecedent attribute can be left out. for j = 1, 2, · · · , j, ckj is the referential value of the jth consequent attribute yj that is used in rk , ckj ∈ cj or c k j = φ (means that the kth rule r k and its antecedent have no effect on the jth consequent cj ), icd k ( yj = c k j ) ⊆ [0, 1] is the interval certitude degree of that ckj is the referential value of yj in r k, means the degree of that yj is c k j , if c k j = φ then icdk ( yj = c k j ) = 0 and this consequent attribute can be left out; icdk ( rk ) ⊆ [0, 1] is the interval certitude degree of rk, means the degree of that rk is true. for shortly, the kth rule rk can be written as: if ( ak1, icd k ( ak1 )) ∧·· ·∧ ( aki , icd k ( aki )) then ( ck1 , icd k ( ck1 )) ∧·· ·∧ ( ckj, icd k ( ckj )) with rule certitude degree icdk ( rk ) , rule weight θk and antecedent attribute weights (w1, w2, · · · , wi) note: αki = icd k ( aki ) = [ ( αki )l , ( αki )u ] , βkj = icd k ( ckj ) = [ ( βkj )l , ( βkj )u ] γk = icdk ( rk ) = [ ( γk )l , ( γk )u ] where 0 ≤ ( αki )l ≤ ( αki )u ≤ 1, 0 ≤ ( βkj )l ≤ ( βkj )u ≤ 1, 0 ≤ ( γk )l ≤ ( γk )u ≤ 1; and the kth rule rk can be given as: if ( ak1, α k 1 ) ∧·· ·∧ ( aki , α k i ) , then ( ck1 , β k 1 ) ∧·· ·∧ ( ckj, β k j ) . with rule certitude degree [ ( γk )l , ( γk )u ] , rule weight θk and antecedent attribute weights (w1, w2, · · · , wi) interval certitude rule base inference method using the evidential reasoning 843 note: ak = { aki ∣ ∣ ∣ aki ∈ aiora k i = φ, i = 1, 2, · · · , i } ck = { ckj ∣ ∣ ∣ ckj ∈ cjorc k j = φ, j = 1, 2, · · · , j } ∧ak = ak1 ∧ a k 2 ∧·· ·∧ a k i ∧ck = ck1 ∧ c k 2 ∧·· ·∧ c k j where ak is the set of antecedent attribute values of the kth rule rk; ∧ak is the antecedent of the kth rule rk; ck is the set of consequent attribute values of the kth rule rk; ∧ck is the consequent of the kth rule rk. the kth rule rk can be given as: if ( ∧ak, αk ) then ( ∧ck, βk ) with rule certitude degree γk, rule weight θk and antecedent attribute weights (w1, w2, · · · , wi) where αk = [ ( αk )l , ( αk )u ] ⊆ [0, 1] is the interval certitude degree of ∧ak, βk = [ ( βk )l , ( βk )u ] ⊆ [0, 1] is the interval certitude degree of ∧ck, γk = [ ( γk )l , ( γk )u ] ⊆ [0, 1] is the interval certitude degree of the kth rule rk, θk is the weight of the kth rule rk, (w1, w2, · · · , wi) are the weights of the antecedent attributes. 2.3 inference method the interval certitude rule base inference method using the evidential reasoning is given as follows. the input actual vector can be noted as input() = {(a1, α1) , (a2, α2) , · · · , (ai, αi)} where ai ∈ ai or ai = φ (i = 1, 2, · · · , i) is one-to-one correspondence with the ith antecedent attribute xi; αi = [ (αi) l , (αi) u ] ( 0 ≤ (αi) l ≤ (αi) u ≤ 1, i = 1, 2, · · · , i ) is the interval certitude degree of ai. if ai = φ then αi = 0. suppose that the input fact input() and the kth rule rk match successfully, for all i = 1, 2, · · · , i, ai is equal to a k i without a k i = φ. but the interval certitude degrees of input fact value and antecedent attribute value may be different; so according to the similarity measure, there is a similarity degree should be given as activation certitude factors. the activation certitude degree of aki (i = 1, 2, · · · , i) under the input fact is given with similarity measure s[] as follows: α̃ki = { s ( aki , ai ) aki ∈ ai 1 aki = φ where s ( aki , ai ) = s[] ( αki , αi ) , αki is the interval certitude degree of a k i , αi is the interval certitude degree of ai, s ( aki , ai ) is the similarity degree of aki and ai. a k i =φ is a specific case, if aki =φ then the value of will not influence the matched conclusions, whether ai is default or some other values, the ith antecedent attribute in the kth rule always can be satisfied. note ãk = { α̃ki |i = 1, 2, · · · , i } ãk is the collection of activation certitude degree. 844 l.q. jin, x. fang the activation weight set of the kth rule rk is given as follows: w k = { wki |i = 1, 2, · · · , i } where wki = { wi a k i ∈ ai 0 aki = φ after normalization, the activation weight set is given as w̃ k = { w̃ki |i = 1, 2, · · · , i } where w̃ki = wki i ∑ t=1 wkt . because that the ‘∧’ connective is used in rules, so the aggregation function of antecedent is referred to as the t-norm operator. the interval certitude degree of antecedent ∧ak can be obtained as follows: α̃k = [ ( α̃k )l , ( α̃k )u ] = [ i ∏ i=1 i √ [ ( α̃ki )l ]w̄k i , i ∏ i=1 i √ [ ( α̃ki )u ]w̄k i ] where w̄ki = w̃ki max l=1,··· ,i { w̃k l } w̄ki means that the smaller the weight is, the less the impact on certitude degree ( ( α̃ki )w̄k i is approaches the limit of 1), if w̃ki = 0 then i √ [ ( α̃ki )l ]w̄k i = i √ [ ( α̃ki )u ]w̄k i = 1 [23]. according to er approach, { ∧ck } is the set of the consequent of the kth rule rk as the frame of discernment, let ωk = { ∅, { ∧ck } } , rk and ∧ak are evidences, the weights of them should be confirmed, the new weight is called credibility degree. by the definition of icrb, γk = [ ( γk )l , ( γk )u ] is the degree of that the rule rk is true, it can be seem as the degree of rk influence on ∧ck when rk is true; α̃k = [ ( α̃k )l , ( α̃k )u ] is the degree of ∧ak influence on ∧ck when ∧ak is true. based on the above analysis, the credibility degree wkr of r k and the credibility degree wka of ∧a k can be obtained as follows: wkr = [ ( wkr )l , ( wkr )u ] = [ ( γk )l (α̃k) u + (γk) l , ( γk )u (α̃k) l + (γk) u ] wka = [ ( wka )l , ( wka )u ] = [ ( α̃k )l (α̃k) l + (γk) u , ( α̃k )u (α̃k) u + (γk) l ] the basic probability masses are: mr ( ∧ck ) = wkrγ k = [ mlr ( ∧ck ) , mur ( ∧ck )] = [ ( wkr )l( γk )l , ( wkr )u ( γk )u ] interval certitude rule base inference method using the evidential reasoning 845 mr ( ωk ) = 1 − mr ( ∧ck ) = [ mlr ( ωk ) , mur ( ωk )] = [ 1 − ( wkr )u ( γk )u , 1 − ( wkr )l( γk )l ] m̄r ( ωk ) = 1 − wkr = [ m̄lr ( ωk ) , m̄ur ( ωk )] = [ 1 − ( wkr )u , 1 − ( wkr )l ] where mr ( ∧ck ) is the basic probability mass of rk; mr ( ωk ) is the remaining probability mass that is unassigned to ck which is caused by rk; m̄r ( ωk ) is amount of remaining support left uncommitted by the weight of rk. ma ( ∧ck ) = wkaα̃ k = [ mla ( ∧ck ) , mua ( ∧ck )] = [ ( wka )l( α̃k )l , ( wka )u ( α̃k )u ] ma ( ωk ) = 1 − ma ( ∧ck ) = [ mla ( ωk ) , mua ( ωk )] = [ 1 − ( wka )u ( α̃k )u , 1 − ( wka )l( α̃k )l ] m̄a ( ωk ) = 1 − wka = [ m̄la ( ωk ) , m̄ua ( ωk )] = [ 1 − ( wka )u , 1 − ( wka )l ] where ma ( ∧ck ) is the basic probability mass of ∧ak, ma ( ωk ) is the remaining probability mass that is unassigned to ∧ck which is caused by ∧ak; m̄a ( ωk ) is amount of remaining support left uncommitted by the weight of ∧ak. with the dempster combination method [12], the upper bound ( β̃k )u and the lower bound ( β̃k )l of the activation degree β̃k of consequent ∧ck are given as follows: max/minβ̃k s.t.β̃k = m∗r ( ∧ck ) m∗a ( ∧ck ) + m∗r ( ∧ck ) m∗a ( ωk ) + m∗r ( ωk ) m∗a ( ∧ck ) +m∗r ( ωk ) m∗a ( ∧ck ) 1 − m̄∗ r (ωk) m̄∗ a (ωk) m∗r ( ∧ck ) + m∗r ( ωk ) = 1 m∗a ( ∧ck ) + m∗a ( ωk ) = 1 m̄∗r ( ωk ) + m̄∗a ( ωk ) = 1 the solutions of the optimization problem are the upper bound ( β̃k )u and lower bound ( β̃k )l of the activation degree β̃k. β̃k = [ ( β̃k )l , ( β̃k )u ] ( β̃k )l = min β̃k ( β̃k )u = max β̃k then the interval certitude degree of consequent attribute value ckj (j = 1, 2, · · · , j) is obtained based on the similarity measure s[]: β̃kj = [ ( β̃kj )l , ( β̃kj )u ] 846 l.q. jin, x. fang ( β̃kj )l =        [ min { 1 − ( βkj )l + ( β̃k )l , 1 − ( βkj )u + ( β̃k )u }] ( βkj )l α̃k = 1 min { ( β̃k )l( βkj )l , 1 } α̃k < 1 ( β̃kj )u =        [ max { 1 − ( βkj )l + ( β̃k )l , 1 − ( βkj )u + ( β̃k )u }] ( βkj )u α̃k = 1 min { ( β̃k )u ( βkj )u , 1 } α̃k < 1 if there are t rules which are matching successfully with the input fact and having the same consequent attribute value c′, then the combination method should be given. first of all, the tth rules can be given as rt: if ( ∧at, α̃t ) then ( c′, β̃tn ) ∧·· ·∧ ( ctn−1, β̃ t n−1 ) ∧ ( ctn+1, β̃ t n+1 ) ∧·· ·∧ ( ctj, β̃ t j ) with rule certitude degree γt, rule weight θt and antecedent attribute weights w1, w2, · · · , wi where = 1, 2, · · · , t , n ∈ {1, 2, · · · , j} and n is determinate; ∧at is the antecedent of rule rt, α̃t = [ ( α̃t )l , ( α̃t )u ] ⊆ [0, 1] is the interval certitude degree of ∧at; c′ and ctj (j = 1, · · · , n − 1, n + 1, · · · , j) are the consequent attribute values of rule rt, β̃ t n = [ ( β̃tn )l , ( β̃tn )u ] ⊆ [0, 1] is the interval certitude degree of c′ under the input fact, β̃tj = [ ( β̃tj )l , ( β̃tj )u ] ⊆ [0, 1] (j = 1, · · · , n − 1, n + 1, · · · , j) is the interval certitude degree of ctj under the input fact; γ t = [ ( γt )l , ( γt )u ] ⊆ [0, 1] is the rule certitude degree of rule rt, θ t ∈ [0, 1] is the rule weight of rule rt, the activation weights of rules are under the influence of θ = {θt |t = 1, 2, · · · , t }, w1, w2, · · · , wi are antecedent attribute weights. the rule weights are the relative weights (preference weights), according to the preference weight method [25], the credibility degrees of rules are given as ̟ = {̟t |t = 1, 2, · · · , t }, and ̟t is the credibility degree of the tth rule rt. according to the evidential reasoning approach, {c′} is frame of discernment, let ω′ = { ∅, {c′} } , the basic probability masses are given as follows: mt (c′) = ̟tβ̃ t n = [ mlt (c′) , m u t (c′) ] = [ ̟t ( β̃tn )l ̟t ( β̃tn )u ] mt (ω′) = 1 − mt (c′) = 1 − ̟tβ̃ t n = [ mlt (ω′) , m u t (ω′) ] = [ 1 − ̟t ( β̃tn )u , 1 − ̟t ( β̃tn )l ] m̄t (ω′) = 1 − ̟t where mt (c′) is the basic probability mass of rt, mt (ω′) is the remaining probability mass that is unassigned to c′ which is caused by the incompleteness of rule rt; m̄t (ω′) is amount of remaining support left uncommitted by the weight of rt. suppose mλ(t) (c′) is the combined basic probability mass of c′ by aggregating the first t (t = 1, 2, · · · , t) rules (r1, · · · , rt), and mλ(t) (ω′) is unassigned to c′ which is caused by the incomplete-ness of the first t rules (r1, · · · , rt); m̄λ(t) (ω′) is amount of remaining support left uncommitted by the weight of the first t rules (r1, · · · , rt). obviously, for the first t = 1, mλ(1) (c′) = m1 (c′) = ̟1β̃ 1 j = [ mlλ(1) (c′) , m u λ(1) (c′) ] = [ ̟1 ( β̃1n )l , ̟1 ( β̃1n )u ] interval certitude rule base inference method using the evidential reasoning 847 mλ(1) (ω′) = m1 (ω′) = [ mlλ(1) (ω′) , m u λ(1) (ω′) ] = [ 1 − ̟1 ( β̃1n )u , 1 − ̟1 ( β̃1n )l ] m̄λ(1) (ω′) = 1 − ̟1 the combined masses of the first t rules are given as follows: • mλ(t+1) (c′) max/min mλ(t+1) (c′) = m ∗ λ(t) (c′) m∗t+1 (c′) +m∗ λ(t) (c′) m∗t+1 (ω′) + m ∗ λ(t) (ω′) m∗t+1 (c′) s.t. m∗ λ(t) (c′) m∗t+1 (c′) + m ∗ λ(t) (ω′) m∗t+1 (ω′) +m∗ λ(t) (c′) m∗t+1 (ω′) + m ∗ λ(t) (ω′) m∗t+1 (c′) = 1 m∗t+1 (c′) + m ∗ t+1 (ω′) = 1 the result of solving the optimization model is mλ(t+1) (c′) = [ mlλ(t+1) (c′) , m u λ(t+1) (c′) ] mlλ(t+1) (c′) = min mλ(t+1) (c′) muλ(t+1) (c′) = max mλ(t+1) (c′) • mλ(t+1) (ω′) max/min mλ(t+1) (ω′) = m ∗ λ(t) (ω′) m ∗ t+1 (ω′) s.t. m∗ λ(t) (c′) m∗t+1 (c′) + m ∗ λ(t) (ω′) m∗t+1 (ω′) + m∗ λ(t) (c′) m∗t+1 (ω′) + m ∗ λ(t) (ω′) m∗t+1 (c′) = 1 m∗t+1 (c′) + m ∗ t+1 (ω′) = 1 the result of solving the optimization model is mi(t+1) (ω′) = [ mli(t+1) (ω′) , m u i(t+1) (ω′) ] mli(t+1) (ω′) = min mi(t+1) (ω′) mui(t+1) (ω′) = max mi(t+1) (ω′) • m̄λ(t+1) (ω′) m̄λ(t+1) (ω′) = m̄λ(t) (ω′) m̄t+1 (ω′) the composition certitude degree of the consequent attribute value c′ is given as follows: β′ = [ (β′) l , (β′) u ] , (β′) l = ml λ(t) (c′) 1 − m̄λ(t) (ω′) , (β′) u = mu λ(t) (c′) 1 − m̄λ(t) (ω′) . obviously, interval certitude degree satisfies that the interval certitude degree is more greater the consequent attribute value is more certain. if there are more than one matches and values of each consequent attribute, then the consequent attribute value with the greater interval certitude degree is the final consequent attribute value under the actual input vector. 848 l.q. jin, x. fang 3 illustrative examples in this section, a case study of classification with uci data sets is provided. this example is used to illustrate the effectiveness of icrimer by comparing with some existing approaches. in order to compare icrimer inference method with other methods which are based on different data types as well as missing values, eight data sets from the uci machine learning repository are used. table 1 shows for each data set its name, the numbers of instances, attributes, linear attributes, nominal attributes, linguistic attributes, classes, and the percentage of the missing values. table 1: properties of eight data sets from uci dataset instances attributes linear attributes nominal attributes linguistic attributes classes missing values (%) cancer 699 10 0 0 10 2 0.25 glass 214 9 9 0 0 6 0 horse 368 27 7 20 0 2 24 ionosphere 351 34 34 0 0 2 0 iris 150 4 4 0 0 3 0 liver 345 6 6 0 0 2 0 pima 768 8 8 0 0 2 0 wine 178 13 13 0 0 2 0 these data sets are used to compare the performances of interval certitude rule base inference method using evidential reasoning approach (icrimer), rule based inference method using dempster-shafer theory (rimds) [15], feature interval learning algorithm (fil) [11], knearest neighbor algorithm (k-nn) [4] logistic regression (lor) [3,26], naive bayesian classifier (nb) [7], pruning decision tree (pdt) [13]. icrimer and rimds are interval feature projection based logical inference algorithms. fil is an interval feature projection based algorithm. lor and nb are probability based algorithm. pdt is a structure based algorithm and decision tree can be used to describe if-then rules. k-nn is a famous machine learning method and give excellent results on many real-world examples [24]. to illustrate the significance of the accuracy of the rule base, a comparison rule base is given, and the interval certitude degrees and weights of rules are given based on some artificial restrictions. for example, if two rules which have the same antecedent and the different consequent, then these two rules will be given the smaller interval certitude degrees and weights; the weights of attribute are given using the feature selection method based on relief [18]. the learning method based on icrimer and the comparison rule base is recorded as icrimer-c. in the eight data sets, there are different data types: linear values, nominal values, linguistic values (evaluation grade) and missing values. so the instances should be transformed into interval certitude rules firstly. table 2 and figure 1 report the correctly classified accuracy rate of the fil, k-nn, lor, nb, pdt, rimds and icrimer which are obtained by averaging the correctly classified accuracy rates over five repetitions of 5-folder cross validations. in this paper, k=5 for k-nn which exactly consistent with the case study in ref. 29 as 5-nn gives the best correctly classified accuracy rate. from table 2 and figure 1, we can find some phenomena as follows. (i) icrimer may not always be the best classification method except data sets cancer and iris, but icrimer outperforms the interval classification methods rimds and fil on five data sets, cancer, glass, horse, iris and liver. it means that icrimer has a higher classification capability on interval data sets. interval certitude rule base inference method using the evidential reasoning 849 table 2: correctly classified accuracy rates of the fil, k-nn, lor, nb, pdt, rimds, icrimer and icrimer-c learning method cancer glass horse ionosphere iris liver pima wine fil 0.9688 0.5502 0.7718 0.9253 0.9426 0.6185 0.7054 0.9663 k-nn 0.9691 0.6455 0.8157 0.8501 0.9640 0.6110 0.7353 0.9540 lor 0.9342 0.6355 0.8300 0.8775 0.8533 0.6870 0.6355 0.9607 nb 0.9740 0.5392 0.8027 0.8842 0.9293 0.6151 0.7262 0.9348 pdt 0.9299 0.6822 0.8033 0.8974 0.9600 0.6870 0.6822 0.9157 rimds 0.9629 0.5844 0.7700 0.8643 0.9739 0.6232 0.5195 0.9444 icrimer 0.9759 0.6047 0.7733 0.8942 0.9800 0.6318 0.6987 0.9055 icrimer-c 0.9900 0.6512 0.8132 0.8971 0.9899 0.6957 0.7532 0.9722 cancer glass horse ionosphere iris liver pima wine 0 0.2 0.4 0.6 0.8 1 1.2 data sets a c c u r a c y r a t e fil k−nn lor nb pdt rimds icrmier icrmier−c figure 1: correctly classified accuracy rates of the fil, k-nn, lor, nb, rimds, icrimer and icrimer-c (ii) with regard to if-then rules, icrimer outperforms or a bit underperforms pdt on five data sets, cancer, ionosphere, iris, pima and wine. it means that icrimer has advantages in this respect. (iii) icrimer-c is the best classification method on five data sets cancer, iris, liver, pima and wine, on the other three data sets the accuracy rate of icrimer-c is higher than the accuracy rate of icrimer and icrimer-c is the second best method in the eight methods. it means that, with a relatively accurately rule base, icrimer has general applicability and high classification capability. (iv) compare the classified accuracy rates of icrimer and icrimer-c, the classified accuracy rate of icrimer-c is higher than the classified accuracy rates of icrimer. it means that a relatively accurately rule base is an important influencing factors of icrimer and the rule base can be more accurately by adjusting the interval certitude degrees and weights of rules, the knowledge representation method icrb is effective. in general, icrimer is the better algorithm on this eight uci data sets, with the relatively accurate rule bases, the results of classification would be better. and the interval certitude structure can be used to describe the uncertainties of knowledge; the icrb can better reflect the uncertainty, correctness and importance of instance or knowledge. moreover, if the weights and interval certitude degrees are given by domain expert or gained through training, then the results will be more rational and better. 850 l.q. jin, x. fang 4 conclusions in this paper, a new knowledge representation, interval certitude rule base (icrb), was proposed to capture uncertainty and nonlinear causal relationships based on the if-then rule and fuzzy set theory. the inference process of interval certitude rule base was characterized by all interval certitude rules expression and was reasoning using the evidential reasoning. and an illustrative example with uci data sets was used to illustrate the application of the proposed method. there are several features of the interval certitude rule base inference method (icrimer). first, due to the use of interval certitude rule, the icrimer method gives a description framework of relationships between inputs and outputs which may be complete or incomplete, linear or nonlinear, discrete or continuous, or their mixture, which are based on both numerical data and human judgments. second, the uncertainty of human judgment is characterized with interval certitude structure, and icrimer is a multi-output uncertainty inference method with one or more than one consequent attributes. these features are more close to the experience and perception of human. acknowledgements this work is supported by doctor project of chongqing social sciences (grant no. 2016bs082, 2016bs032), project of chongqing municipal education commission (grant no. kj2016008391 44), chongqing commission of science and technology research projects (grant no. kj170616 5), chongqing engineering research center for processing, storage and transportation of characterized agro-products (grant no. kfjj2016026), fundamental science and frontier technology research project in chongqing (cstc2017jcyjax01301), national natural science foundation of china (no.71702015). bibliography [1] aczel j., saaty t.l. 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(2013); group decision making with multi-attribute interval data, information fusion, 14, 551–561, 2013. international journal of computers communications & control issn 1841-9836, 13(4), 521-536, august 2018. an approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural p systems h. rong, m. ge, g. zhang, m. zhu haina rong, mianjun ge, gexiang zhang* school of electrical engineering southwest jiaotong university chengdu, 610031, china ronghaina@126.com, 641294684@qq.com, zhgxdylan@126.com *corresponding author: zhgxdylan@126.com ming zhu chengdu university of information technology chengdu 610225, china zhuming@126.com abstract: this paper presents a novel approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural p systems. in this approach, six features of current/voltage signals in a small current grounding system are analyzed by considering transient and steady components, respectively; a fault measure is used to quantify the possibility that a line is faulty; information gain degree is discussed to weight the importance of each of the six features; rough set theory is applied to reduce the features; and finally a fuzzy reasoning spiking neural p system is used to construct fault line detection models. six cases in a small current grounding system prove the effectiveness of the introduced approach. keywords: membrane computing; p system; spiking neural p systems; fault line detection; feature analysis; information gain degree; rough set theory 1 introduction as a rapidly developing branch of natural computing, membrane computing, initiated by păun [3,12], focuses on the investigations of a computational model, called a membrane system or a p system, abstracting from the structure and functioning of living cells, as well as from the way the cells are organized in tissues or high order structures. numerous variants of p systems have turing computing power [5,15] or can solve computationally hard problems in a polynomial time [16, 26]. inspired by the information processing principles in a living cell, a p system has certain characteristics, such as distribution, parallelism and expansibility, which make it suitable to solve a variety of practical problems [14,46], such as engineering optimization [7,28], languages generation [12,40] and modeling biological and ecological systems [6]. in recent years, much attention is paid to spiking neural p systems (sn p systems), which were introduced in 2006 [5, 9] by considering the neurophysiological behavior of neurons sending electrical impulses (spikes) along axons from presynaptic neurons to postsynaptic neurons. except for theoretical results [10,13], sn p systems have been widely used to solve various application problems, such as combinatorial and engineering optimization problems [30,38], signal recognition [2], arithmetic operations [17,21,28] and fuzzy knowledge representation [23]. of a particular interest is the combination of sn p systems with fuzzy set theory, called fuzzy membrane computing [36], to solve fault diagnosis problems with respect to transformers [16, 42], transmission lines [8,24,25], traction power supply systems of high-speed railways [39] and metro copyright ©2018 cc by-nc 522 h. rong, m. ge, g. zhang, m. zhu traction systems [10], in electric power systems. in general, there are two kinds of fuzzy reasoning spiking neural p systems (frsn p systems) [36]: fuzzy reasoning spiking neural p system with real numbers (rfrsn p systems) [16] and fuzzy reasoning spiking neural p systems with trapezoidal fuzzy numbers (tfrsn p systems) [24]. the present work is motivated by two reasons. on the one hand, the application extension of frsn p systems requires further discussions. on the other hand, the fault line detection in a small current grounding system is a very important problem and its detection accuracy needs to be enhanced [4,9,32,35]. in china, most of medium-voltage distribution networks are small current grounding systems. furthermore, about 70%–80% of the faults in distribution networks result from single-phase grounding. therefore, the fault line detection problem with respect to single-phase grounding in a small current grounding system is a very common type of fault diagnosis in an electrical power system [1,15,30,31]. thus, a novel approach for detecting fault lines in a small current grounding system using frsn p systems is proposed to enhance the accuracy. this approach first analyzes six features of current/voltage signals in a small current grounding system by considering transient and steady components, respectively. then, the possibility that a line is faulty is quantified by using a fault measure. next, information gain degree is used to weight the importance of each of the six features and rough set theory is discussed to reduce the features. finally, frsn p systems are used to construct fault line detection models. the effectiveness of the introduced approach is verified by several typical cases of fault line detection in a small current grounding system. the organization of this article is as follows. section 2 describes the fault line detection problem in a small current grounding system. section 3 presents the proposed approach in detail. in section 4, six typical cases are used to conduct the experiments. conclusions are finally drawn in section 5. 2 problem description in this paper, a 110kv/35kv distribution network with 6 feeders is considered. the simplified model of a distribution network is shown in fig. 1. when a single-phase-to-ground fault occurs in the distribution network, any one of the 6 lines, 1–6, or the bus may be the faulty line. if the fault lasts for a long time, there will be much effect on the safe and stable operation of the distribution network, and even significant security incidents may happen, therefore, it is required to take a proper action to deal with the fault so as to restore power supply as soon as possible. when a line is faulty, the distribution network will generate zero sequence voltage and current. as usual, there means much difference in phase and amplitude of zero sequence current between the fault line and normal fault line. currently the widely used techniques are that a certain number of features are used to detect fault lines. there are three main techniques: steady state method, transient method and information fusion method. the steady state method is to select fault line by extracting the steady state fault characteristics of the distribution network, such as zero sequence current amplitude and phase, admittance, harmonic, power [1,15,30,31]. but these fault characteristics are easily affected by the way of neutral point grounding and different fault grounding methods. with the development of signal processing technology, the transient component that contains a lot of fault information is considered to detect the fault line. in [41], the wavelet transform was used to extract the features from zero sequence current of each feeder and the fault line detection is realized by comparing the polarity. wavelet analysis has a good characteristic of time-frequency localization and can decompose the signal into different frequency bands, so it is especially suitable for the analysis of non stationary signals and transient signals. in [27], a neural network was used to detect the fault line detection. in [47], a comprehensive fault line an approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural p systems 523 f line 1 line 2 line 3 line 4 line 5 line 6 figure 1: simplified model of a distribution network zero sequence current/voltage signals rough set theory fault line detection model with rfrsn p systems output fault lines transient component signal features steady component signal features information gain degree fault measures figure 2: framework of fault line detection approach selection method based on fuzzy theory was discussed. in [14], an information fusion method based on d-s evidence theory was presented to detect fault lines in a small current grounding system. 3 the proposed approach the fault line detection approach for a small current grounding system we propose here consists of four processes: feature analysis, fault measure calculation, feature information fusion and fault line detection model construction. fig. 2 shows the framework of this approach. this paper considers both transient and steady components features of current or voltage signals. in the process of feature information fusion, the information gain degree is used to weight the importance of features and rough set theory is applied to reduce the features. a fault line detection model is constructed by using a fuzzy reasoning spiking neural p systems with real numbers (rfrsn p systems). in what follows, the processes will be described step by step. 3.1 feature analysis when a single-phase-to-ground fault occurs in the distribution network, the steady-state characteristics are mainly analyzed in the time domain. the fault current in the fault line is the ground capacitance current with the direction from the bus to the line, or it is the sum of the capacitance current in other lines with the direction from the line to the bus. the transient characteristics are analyzed in the frequency domain, including the amplitude, phase and energy of transient zero sequence current. to intuitively show the features, this study considers an example of a system, which has a single phase to ground fault at 0.02s, the transition resistance (r0) is 0.2ω, 20ω, 2000ω, representing the metal grounding, low impedance grounding and high impedance grounding, respectively. in the sequel, the first four features of steady components and the last two features of transient components are analyzed by using different methods, due to space limitations, this paper only gives a simulation diagram that the transition resistance 524 h. rong, m. ge, g. zhang, m. zhu (r0) is 0.2ω. (1) zero sequence current analysis: when the neutral non-grounding system has a single phase grounding fault, the amplitude of the zero sequence current in the fault line equals the sum of the zero sequence currents in all the normal lines. the direction of the zero sequence current in the fault line is from the line to the bus and the direction of the zero sequence current in the normal line is from the bus to the line. thus, if the amplitude of the zero sequence current in a line is equal to the sum of the amplitude of the zero sequence current in other lines, the direction is opposite to the other lines, which indicates that the line is a fault one. fig. 3 shows the comparisons of zero sequence current between normal and fault lines, which refer to the lines in a neutral non-grounding system and a neutral grounding system, respectively. 0 0.05 0.1 0.15 0.2 −30 −20 −10 0 10 20 30 40 time (s) z er o se q u en ce c u rr en t (a ) normal line fault line (a) non-grounding system with r0 = 0.2ω 0 0.05 0.1 0.15 0.2 −30 −20 −10 0 10 20 30 40 time (s) z er o se q u en ce c u rr en t (a ) normal line fault line (b) grounding system with r0 = 0.2ω figure 3: zero sequence current comparisons between normal and fault lines (2) zero-sequence reactive power analysis: the zero sequence reactive power is the product of the corresponding zero sequence current and voltage. fig. 4 shows the comparisons of zero sequence reactive power between normal and fault lines, which refer to the lines in a neutral non-grounding system and a neutral grounding system by using arc extinction coil, respectively. in the neutral non-grounding system, the zero sequence reactive component of the fault line is opposite to the zero sequence reactive component of the normal line, and its amplitude is bigger than that of the zero sequence reactive component of the normal line. 0 0.05 0.1 0.15 0.2 −4 −2 0 2 4 6 8 x 10 5 time (s) z er o− se q u en ce r ea ct iv e p ow er ( va r) normal line fault line (a) non-grounding system with r0 = 0.2ω 0 0.05 0.1 0.15 0.2 −5 −4 −3 −2 −1 0 1 x 10 5 time (s) z er o− se q u en ce r ea ct iv e p ow er ( va r) normal line fault line (b) grounding system with r0 = 0.2ω figure 4: zero sequence reactive power comparisons between normal and fault lines (3) zero sequence admittance analysis: in the neutral non-grounding system, the admittance angle of the normal line is in the first quadrant of the admittance plane angle, while an approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural p systems 525 the admittance angle of the fault line is in the third quadrant. in the neutral grounding system by arc extinction coil, the admittance angles of the normal line and the fault line are in the first and second quadrant, respectively. fig. 5 shows the comparisons of zero sequence admittance angles between normal and fault lines, which refer to the lines in a neutral non-grounding system and a neutral grounding system by using arc extinction coil, respectively. 0 0.05 0.1 0.15 0.2 −400 −300 −200 −100 0 100 200 300 time (s) z er o se q u en ce a d m it ta n ce ( d eg re e) normal line fault line (a) non-grounding system with r0 = 0.2ω 0 0.05 0.1 0.15 0.2 −400 −300 −200 −100 0 100 200 300 time (s) z er o se q u en ce a d m it ta n ce ( d eg re e) normal line fault line (b) grounding system with r0 = 0.2ω figure 5: zero sequence admittance angle comparisons between normal and fault lines (4) the fifth harmonic analysis: the zero sequence current in a fault line contains a large number of odd harmonics, while the arc suppression coil will not greatly affect the magnitude and direction of the harmonic, so the harmonic can be used to detect the line. with the increase of harmonic frequency, the harmonic content becomes lower, so the fifth harmonic is usually used to realize fault line detection. fig. 6 shows the comparisons of the fifth harmonic between normal and fault lines, which refer to the lines in a neutral non-grounding system and a neutral grounding system by using arc extinction coil, respectively. 0 0.05 0.1 0.15 0.2 0 5 10 15 time (s) f if th h ar m on ic a m p li tu d e (a ) normal line fault line (a) non-grounding system with r0 = 0.2ω 0 0.05 0.1 0.15 0.2 0 5 10 15 time (s) f if th h ar m on ic a m p li tu d e (a ) normal line fault line (b) grounding system with r0 = 0.2ω figure 6: the fifth harmonic amplitude comparisons between normal and fault lines (5) wavelet waveform analysis of zero sequence current: the zero sequence current is decomposed into four layers by using wavelet transform, and the corresponding wavelet decomposition waveforms at scale 4 is shown in fig. 7, where the waveforms in (a)–(f) are obtained from the neutral unearthed system and the figures (g)–(l) are gained from the neutral grounding system by using arc extinction coil. the wavelet energy polarity of the zero sequence current in the fault line is the opposite of the normal line, and the amplitude is the maximum. thus, the fault line can be identified by comparing the energy 526 h. rong, m. ge, g. zhang, m. zhu amplitude and polarity of the zero sequence current. 0 500 1000 1500 2000 2500 3000 3500 −10 −5 0 5 10 sampling points h ig h f re q u en cy c oe ff ic ie n ts i n n or m al l in e (a) non-grounding system with r0 = 0.2ω 0 500 1000 1500 2000 2500 3000 3500 −10 −5 0 5 10 sampling points h ig h f re q u en cy c oe ff ic ie n ts i n f au lt l in e (b) grounding system with r0 = 0.2ω 0 500 1000 1500 2000 2500 3000 3500 −10 −5 0 5 10 sampling points h ig h f re q u en cy c oe ff ic ie n ts i n n or m al l in e (a) non-grounding system with r0 = 0.2ω 0 500 1000 1500 2000 2500 3000 3500 −10 −5 0 5 10 sampling points h ig h f re q u en cy c oe ff ic ie n ts i n f au lt l in e (b) grounding system with r0 = 0.2ω figure 7: wavelet waveform comparisons of zero sequence current between normal and fault lines (6) transient zero sequence current analysis: the direction and distribution of transient zero sequence currents shows similar phenomena to those of steady-state zero sequence currents. fig. 8 show the comparisons of the products of zero sequence current between normal and fault lines, which refer to the lines in a neutral non-grounding system and a neutral grounding system by using arc extinction coil, respectively. 0 0.05 0.1 0.15 0.2 −2.5 −2 −1.5 −1 −0.5 0 0.5 1 time (s) p ro d u ct o f ze ro s eq u en ce c u rr en t normal line fault line (a) non-grounding system with r0 = 0.2ω 0 0.05 0.1 0.15 0.2 −2.5 −2 −1.5 −1 −0.5 0 0.5 1 time (s) p ro d u ct o f ze ro s eq u en ce c u rr en t normal line fault line (b) grounding system with r0 = 0.2ω figure 8: comparisons of the products of zero sequence current between normal and fault lines an approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural p systems 527 the analysis of the features discussed in this section indicates that there exists a difference between normal and fault lines so that the features can be used to detect the fault line in a small grounding system. 3.2 fault measure following the feature analysis, a fault measure is discussed to quantify the possibility of a fault line [5, 33]. this article defines a fault measure as a real variable between 0 and 1. the smallest and biggest values 0 and 1 indicate that the line is normal and is faulty, respectively. suppose that the number of lines in a distribution network is n and the number of fault line detection methods is h. the fault measure function is constructed by adopting line detection method h (h = 1, 2, . . . ,n) for line k (k = 1, 2, . . . ,n) is xh(k) = xrh(k)xah(k), where xrh(k) is the relative fault measure function reflecting the difference of the fault measures between one line and the other lines in the distribution network; and xah(k) is the reliability fault measure function reflecting the reliable degree of the fault measure. as usual, different specific calculation methods are used for different fault features. to elaborate on the fault measure, we take the zero sequence current as an example. in a small current grounding system, the absolute values of the magnitudes of the zero sequence current in the fault line are equal to the sum of the absolute values of the amplitudes of all the normal lines. for line k, the magnitude of the zero sequence current is |ik|. if x = |ik|/ ∑n n=1 |in| is big, the line tends to a fault line, conversely, the line is inclined to a normal one. the relative fault measure function of the line is described as follows: xr1(k) =   1, x ≥ 0.5; 2n n−1x− 1 n−1, 1 2n < x < 0.5; 0, x ≤ 1 2n . (1) about the reliability fault measure function, when a single-phase-to-ground fault occurs in the system, the amplitudes of the zero sequence current in all lines reach to the maximum. if x = |imax|/ ∑n n=1 |in| is big, the fault characteristics of this method are obvious, on the contrary, they are not obvious. the reliability fault measure function of the line is described as follows: xa1(k) =   1, x ≥ 0.5; 10n 5n−6x− 6 5n−6, 3 5n < x < 0.5; 0, x ≤ 3 5n . (2) 3.3 information gain degree information gain degree is introduced to weight the importance of the six types of fault features in a small current grounding system. suppose that x is the data for a particular sample group like the lines in this paper and a is the attribute set for the fault line detection methods, a = {h1,h2, . . . ,hm}. the lines are divided into two types: fault and normal ones. so x = {x1,x2}, where |xi| is the number of sample instances of class i and |x| is the total number of sample instances of x. if h(xi) is the probability that a sample instance belongs to class i, then h(xi) = |xi|/|x|. assume that m(x) is the degree of uncertain information about x and it is ∑m i=1[−h(xi) log2 h(xi)], where method h has the characters h1,h2, . . . ,hj. in the case of h = hj, cij is the number of sample instances of class i and yj is the total number of sample instances, thus, the probability h(xi|h = hj) is cij/|yi|, the conditional entropy of h and the classification information entropy are described as m(x,h = hj) = ∑ j[−h(xi|h = hj) log2 h(xi|h = hj))], 528 h. rong, m. ge, g. zhang, m. zhu and b(x,h) = ∑ j[h(xi|h = hj)m(x,h = hj)]. the information gain of h is ∆qh = m(x)−b(x,h) = m(x)− ∑ j[h(xi|h = hj)m(x,h = hj)]. the information gain degree is normalized and the weight of the line detection method is xmh(k) = ∆qh/σ∆q. 3.4 rough set theory in this study, rough set theory is used to reduce the fault features in a small current grounding system. rough set theory is a tool to analyze the incomplete and imprecise data [24, 43]. it is used to simplify the rule reduction to information system. the main ideas of rough set theory include knowledge reduction and domain reduction, which are described in a very brief way as follows. knowledge reduction: suppose that r is an equivalence relation, ind(r) represents the intersection of all the equivalence relations in r, r ∈ r. if ind(r) = ind(r−{r}), then r can be omitted, otherwise, r cannot be omitted. domain reduction: suppose that f = {x1,x2, . . . ,xn} is a set family, xj ∈ u (u represents the domain). if ⋂ (f −{xi}) = ⋂ f , then xi can be omitted, domain reduction can delete the same decision rules and redundant attribute values in an information system so as to obtain the minimal solution. in this paper, the condition attributes are the fault line detection methods and the decision attribute decides whether a fault detection method corresponding to a kind of features is correct or not. conditional attributes require to be discretized. thus, this method is used to simplify the condition attributes and eliminate some unnecessary condition attributes and redundant decision rules. finally, the decisive solution is obtained. 3.5 fault line detection models with rfrsnps in this section, a fuzzy reasoning spiking neural p system with real numbers (rfrsnps) [16] is used to build the fault line detection model. the definition of rfrsn p system is first briefly described. subsequently, the reasoning algorithm and fuzzy production rules of fault line detection are discussed. finally, the fault line detection model is presented. fuzzy reasoning spiking neural p systems the definition and reasoning algorithm of rfrsnps is described in [16], due to space limitations, this paper no longer gives a specific process. fuzzy production rules of fault line detection the rfrsn p systems contain two types of neurons: proposition neurons and rule neurons, the rule neurons also express the fuzzy production rules. in this paper, we will describe three different kinds of fault line selection rules for rfrsn p system. (1) (general rules) ri (cf = ci): if pj(θj) then pk(θk), where pj and pk represent propositions, ci represents the certainty factor of rule ri, as a real number belonging to [0,1], θj and θk are real numbers in [0,1] representing the truth values of pj and pk, the truth value of pk is calculated as θk = θj ∗ ci (2) (and rules) ri (cf = ci): if p1(θ1) and . . . and pk−1(θk−1) then pk(θk), where p1, . . . pk are proposition, ci represents the certainty factor of rule ri, as a real number belonging to [0,1], θ1, . . . θk are real numbers in [0,1] representing the truth values of p1, . . . pk, the truth value of pk is calculated as θk = min(θ1, . . . ,θk−1) ∗ ci. an approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural p systems 529 (3) (or rules) ri (cf = ci): if p1(θ1) or . . . or pk−1(θk−1) then pk(θk), where p1, . . . pk are proposition, ci represents the certainty factor of rule ri, as a real number belonging to [0,1], θ1, . . . θk are real numbers in [0,1] representing the truth values of p1, . . . pk, the truth value of pk is calculated as θk = max(θ1, . . . ,θk−1) ∗ ci. fault line detection model based on the fuzzy production rules of fault line detection discussed above, we can establish the fault line detection model with rfrsnps for a line. the model is shown in fig. 9, where b, c, d, f and p1 represent the fault line measure values of the zero sequence current phase, the zero sequence reactive power, the zero sequence admittance amplitude, the transient zero sequence current and fused fault measure, respectively. the rfrsnps for fault line detection is described as follows: π1 = (o,σ1,σ2, . . . ,σ18,syn,in,out) where (1) o={a} is the singleton alphabet (a is called spike); (2) σ1, . . . ,σ11 are proposition neurons corresponding to the propositions with fuzzy truth values θ1, . . . ,θ11; (3) σ12, . . . ,σ18 are rule neurons, where σ12,σ15 are general rule neurons; σ16, σ17 are and rule neurons; σ18 are or rule neurons; (4) syn = {(1, 12) , (2, 13), (3, 14), (4, 15), (5, 16), (5, 17), (6, 16), (7, 17), (8, 16), (8, 17), (9, 18), (10, 18), (12, 5), (13, 6), (14, 7), (15, 8), (16, 9), (17, 10), (18, 11),}; (5) in={σ1, . . . ,σ4}; (6) out={σ11}. 4 case studies the distribution network system shown in fig. 1 is simulated on matlab/simulink to obtain training and testing samples for verifying the fault line detection. the transformer ratio is 110kv/35kv. the simulation time is 0.2s. the lines length are 10km, 15km, 20km, 28km, 35km, 50km, respectively, the positive sequence parameters of line are 0.17ohms/km, 1.21mh/km and 36.6+j172f/km, the zero sequence parameters of line are 0.23ohms, 5.48mh/km and 6pf/km. in the neutral ungrounded system, the testing samples are considered for several values of fault initial phase(0, 45 and 90 degrees), fault location(10%, 50% and 90% of lines 1 to 6) and transition resistance(0.2, 20 and 2000 ohms). in the process of the simulation experiment, we collect 162 features from each of lines 1 to 6 and totally 972 zero sequence signal features as data sets. next, the fault line measure values of the zero sequence current amplitude, the zero sequence current phase, the zero sequence reactive power, the zero sequence admittance amplitude, the wavelet energy of zero sequence current and the transient zero sequence current can be calculated by using the zero sequence signal. in what follows, six cases in the small current grounding system are used to test the introduced approach. 530 h. rong, m. ge, g. zhang, m. zhu b c d f figure 9: a certain line of fault line selection model with rfrsnps case 1: single-phase-to-ground fault occurring in line 1: initial phase is 90 degrees; the fault is located at 90% of the line; transition resistance is 20 ohms. table 1 gives fault measure values of zero sequence current signals in lines 1–6. then, the values between 0∼0.3, 0.3∼0.7 and 0.7∼1 are discretized as 0, 1, and 2, respectively. the results are shown in table 2. next, eqs. subsection 3.3 are used to compute the weights of the six feature analysis methods and the results are 0.4833, 0.6500, 0.6500, 0.4833, 0.6500 and 0.6500, respectively. subsequently, rough set theory is applied to reduce the condition attributes consisting of the features shown in table 1. the reduced result is (b∧f∧(c∨d)), where b and f are the core attributes. finally, the reasoning algorithm described above and fault line detection model with rfrsnps are used to obtain the detection result. the detailed steps are described as follows: step 1: g = 0, θ0 = (0.9583, 0.9667, 0.6467, 0.9439, 0, 0, 0, 0, 0, 0, 0, )t , c = diag(0.6500, 0.6500, 0.4833, 0.6500, 1, 1, 1),δ0 = 0. d1 = [ a1 0 0 1 ] 11×7 d2 =  0 00 a2 0 0   11×7 d3 = [ 0 0 0 a3 ] 11×7 et = [ 0 b1 ] 11×7 a1 =   1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1   4×4 a2 =   1 1 0 1 0 0 0 1 0 1 1 0   4×3 a3 =  0 10 1 0 0   3×2 b1 =   0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0   7×7 step 2: the firing condition of proposition neuron is satisfied and there is a postsynaptic rule neuron, so the proposition neuron fires and transmits a spike to the next rule neuron by the directed arc. step 3: δg+1 = (dt1 ⊗θg) +(d t 2 ⊕θg) +(d t 3 �θg), δ1 = (0.9583, 0.9667, 0.6467, 0.9439, 0, 0, 0)t . step 4: δ1 6= 0. the reasoning algorithm continues. step 5: g = 1. step 6: θg = et � (d⊗δg), θ1 = (0, 0, 0, 0, 0.6229, 0.6284, 0.3126, 0.6135, 0, 0, 0)t . step 7: δg+1 = (dt1 ⊗θg) +(d t 2 ⊕θg) +(d t 3 �θg), δ2 = (0, 0, 0, 0, 0.6135, 0.3126, 0)t . step 8: δ2 6= 0. the reasoning algorithm continues. step 9: g = 2. step 10: θg = et � (d⊗δg), θ2 = (0, 0, 0, 0, 0, 0, 0, 0, 0.6135, 0.3126, 0)t . step 11: δg+1 = (dt1 ⊗θg) +(d t 2 ⊕θg) +(d t 3 �θg), δ3 = (0, 0, 0, 0, 0, 0, 0.6135)t . an approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural p systems 531 table 1: fault measure values of zero sequence current signals in lines 1–6, where ca, cp, rp, aa, we and tc represent current amplitude, current phase, reactive power, admittance amplitude, wavelet energy and transient current, respectively. line ca cp rp aa we tc 1 0.4371 0.9583 0.9667 0.6467 0.9182 0.9439 2 0.0675 0.0167 0.4234 0.3175 0.0483 0.3203 3 0.0945 0.1083 0.1092 0.0945 0.2531 0.0000 4 0.0506 0.3917 0.0823 0.0506 0.0658 0.0000 5 0.1182 0.1518 0.3167 0.3182 0.2146 0.0186 6 0.3691 0.2417 0.2631 0.1694 0.0731 0.3034 table 2: discretized fault measure values, where ca, cp, rp, aa, we and tc represent current amplitude, current phase, reactive power, admittance amplitude, wavelet energy and transient current, respectively. line ca cp rp aa we tc 1 1 2 2 1 2 2 2 0 0 1 1 0 1 3 0 0 0 0 0 0 4 0 1 0 0 0 0 5 0 0 1 1 0 0 6 1 0 0 0 0 1 step 12: δ3 6= 0. the reasoning algorithm continues. step 13: g = 3. step 14: θg = et � (d⊗δg), θ3 = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6135)t . step 15: δg+1 = (dt1 ⊗θg) +(d t 2 ⊕θg) +(d t 3 �θg), δ4 = (0, 0, 0, 0, 0, 0, 0)t . step 16: δ4 = 0. the reasoning algorithm ends. the reasoning result can be obtained from output neuron p1. the pulse value of the spike in p1 is 0.6135(> 0.5). the rest may be inferred in the same way. the reasoning results of the other five lines can also be obtained and the pulse values of the spike in p2 to p6 are 0.0109, 0, 0, 0.0121, 0.1571(< 0.2), respectively. so line 1 is the fault line. case 2: single-phase-to-ground fault occurring in line 2: initial phase is 90 degrees; the fault is located at 90% of the line; transition resistance is 20 ohms. similar to case 1, the weights of the six feature analysis methods are 0.4833, 0.6500, 0.6500, 0.4971, 0.6500 and 0.6500, respectively. the reduced result is (c∧f∧(b∨d)), where c and f are the core attributes. the initial parameter matrices of rfrsnps for fault line detection are as follows: θ0 = (0.9252, 0.9321, 0.7172, 0.9986, 0, 0, 0, 0, 0, 0, 0) t , c = diag(0.6500, 0.6500, 0.4971, 0.6500, 1, 1, 1), δ0 = 0. according to the reasoning algorithm, we can get: θ1 = (0, 0, 0, 0, 0.6014, 0.6059, 0.3565, 0.6491, 0, 0, 0) t , θ2 = (0, 0, 0, 0, 0, 0, 0, 0, 0.6014, 0.3565, 0) t , 532 h. rong, m. ge, g. zhang, m. zhu θ3 = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6014) t , and δ4 = 0. so, the pulse value of the spike in p2 is 0.6014 (> 0.5). the pulse values of the spike in p1, p3, p4, p5 and p6 are 0.1109, 0.0251, 0.0459, 0 and 0.0241 (< 0.2), respectively. thus, line 2 is the fault line. case 3: single-phase-to-ground fault occurring in line 3: initial phase is 90 degrees; the fault is located at 90% of the line; transition resistance is 2000 ohms. similarly, the weights of the six feature analysis methods are 0.1909, 0.6500, 0.6500, 0.4833, 0.6500 and 0.6500, respectively. the reduced result is (b∧c∧(d∨e∨f)), where b and c are the core attributes; e is the fault measure value of the wavelet energy of zero sequence current. the initial parameter matrices of the rfrsnps for fault line selection are as follows: θ0 = (0.9167, 0.8723, 0.6842, 0.5974, 0.8783, 0, 0, 0, 0, 0, 0, 0, 0, 0) t , c = diag(0.6500, 0.6500, 0.4833, 0.6500, 0.6500, 1, 1, 1, 1), δ0 = 0. according to the reasoning algorithm, we can get: θ1 = (0, 0, 0, 0, 0, 0.5959, 0.5670, 0.3307, 0.3883, 0.5709, 0, 0, 0, 0) t , θ2 = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3307, 0.3883, 0.5670, 0) t , θ3 = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5670) t , and δ4 = 0. thus, the pulse value of the spike in p3 is 0.5670 (> 0.5). the pulse values of the spike in p1, p2, p4, p5 and p6 are 0.0705, 0.1308, 0, 0.0090 and 0.1588 (< 0.2), respectively. so line 3 is the fault line. case 4: single-phase-to-ground fault occurring in line 4: initial phase is 0 degrees; the fault is located at 90% of the line; transition resistance is 2000 ohms. the weights of the six feature analysis methods are 0.1909, 0.6500, 0.6500, 0.4971, 0.6500, 0.6500. the reduced result is (b∧f∧(c∨d)), where b and f are the core attributes. the initial parameter matrices of the rfrsnps for fault line selection are as follows: θ0 = (0.8918, 0.8816, 0.5999, 0.8871, 0, 0, 0, 0, 0, 0, 0) t , c = diag(0.6500, 0.6500, 0.4971, 0.6500, 1, 1, 1), δ0 = 0. according to the reasoning algorithm, we can get: θ1 = (0, 0, 0, 0, 0.5797, 0.5730, 0.2982, 0.5766, 0, 0, 0) t , θ2 = (0, 0, 0, 0, 0, 0, 0, 0, 0.5730, 0.2982, 0) t , θ3 = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5730) t , and δ4 = 0. so the pulse value of the spike in p4 is 0.5730(> 0.5). the pulse values of the spike in p1, p2, p3, p5 and p6 are 0.0644, 0.0203, 0.1860, 0.0164 and 0.0005 (< 0.2), respectively. thus, line 4 is the fault line. case 5: single-phase-to-ground fault occurring in line 5: initial phase is 0 degrees; the fault is located at 50% of the line; transition resistance is 20 ohms. the weights of the six feature analysis methods are 0.4833, 0.6500, 0.6500, 0.4833, 0.6500, 0.6500. the reduced result is (f∧e∧(b∨c)∧(c∨d)), where f and e are the core attributes. the initial parameter matrices of the rfrsnps for fault line selection are as follows: θ0 = (0.8866, 0.9167, 0.6879, 0.9336, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0) t , c = diag(0.6500, 0.6500, 0.4833, 0.6500, 0.6500, 1, 1, 1, 1)), δ0 = 0. according to the reasoning algorithm, we can get: θ1 = (0, 0, 0, 0, 0, 0.5763, 0.5959, 0.3325, 0.6069, 0.6500, 0, 0, 0, 0) t , θ2 = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5764, 0.3325, 0.3325, 0) t , θ3 = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5764) t , and δ4 = 0. so the pulse value of the spike in p5 is 0.5730 (> 0.5). the pulse values of the spike in p1, p2, p3, p4 and p6 are 0.0283, 0, 0.1528, 0 and 0.0664 (< 0.2), respectively. thus, line 5 is the fault line. case 6: single-phase-to-ground fault occurring in line 6: initial phase is 0 degrees; the fault is located at 10% of the line; transition resistance is 20 ohms. an approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural p systems 533 the weights of the six feature analysis methods are 0.4833, 0.6500, 0.6500, 0.4833, 0.6500, 0.6500. the reduced result is (b∧c∧f∧(e∨d)), where b, c and f are the core attributes. the initial parameter matrices of the rfrsnps for fault line selection are as follows: θ0 = (0.9284, 0.8987, 0.5847, 0.9312, 0.9021, 0, 0, 0, 0, 0, 0, 0, 0) t , c = diag(0.6500, 0.6500, 0.4833, 0.6500, 0.6500, 1, 1, 1, 1)), δ0 = 0. according to the reasoning algorithm, we can get: θ1 = (0, 0, 0, 0, 0, 0.6035, 0.5842, 0.2826, 0.6053, 0.5864, 0, 0, 0) t , θ2 = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2826, 0.5842, 0) t , θ3 = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5842) t , and δ4 = 0. so the pulse value of the spike in p6 is 0.5842 (> 0.5). the pulse values of the spike in p1, p2, p3, p4 and p5 are 0.1083, 0.0613, 0, 0.0328 and 0.0871 (< 0.2), respectively. thus, line 6 is the fault line. the experimental results of the six cases in a small grounding system indicate that the proposed fault line detection approach is not affected by fault locations, fault resistance and fault closing angles. 5 conclusions in this paper, a novel approach is introduced by fuzzy reasoning spiking neural p systems to detect fault lines in a small current grounding system. the feature analysis is performed on steady and transient components of zero sequence current of a small current grounding system. steady state features consist of zero sequence current signal amplitudes, zero-sequence reactive power, zero sequence admittance and the fifth harmonic. transient features are composed of wavelet energy of zero sequence current and transient zero sequence current signal amplitudes. experiments conducted on several cases of a distribution network system verify the feasibility and correctness of the presented approach. future work will focus on the improvement of the fault line detection accuracy and the reliability of results. following this work, micro grids and smart grids will be also considered in the future study. acknowledgment this work was supported by national natural science foundation of china (61672437, 61702428) and by sichuan science and technology program (2018gz0185, 2018gz0085, 2017gz0159). bibliography [1] chen, z.l.; 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(2006); research on the novel comprehensive fault line selection method for the nugs based on the fuzzy theory, proc. icpst, 1–6, 2006. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(4), 530-539, august 2019. performance evaluation and comparison of scheduling algorithms on 5g networks using network simulator d. perdana, a. n. sanyoto, y. g. bisono doan perdana* school of electrical engineering telkom university, bandung, indonesia *corresponding author: doanperdana@telkomuniversity.ac.id aji nur sanyoto school of electrical engineering telkom university, bandung, indonesia ajinsanyoto@gmail.com yoseph gustommy bisono school of electrical engineering telkom university, bandung, indonesia bisono@telkomuniversity.ac.id abstract: in this research, we compared the round robin (rr) and the proportional fair (pf) algorithms for different user equipment density scenarios using voice and video traffic, to evaluate the key impact on performance of 5g mmwave network. this research simulated on ns3.27 with an integrated mmwave module. based on the result, we found that the rr is a good choice for voice traffic. it has a throughput of 3.65% better than pf with similar fairness index. on the other hand, we found that the pf is the right choice for video traffic due to has better result for throughput. it has a throughput of 1.24% better than rr. for fairness index round robin has better result for voice and video traffic. keywords: network simulation, 5g networks, scheduling algorithm, round robin, proportional fair. 1 introduction increase of internet users give challenge for information and technology (it) industry especially service providers in order to provide high quality and low latency service quality. according to the situation above, the telecommunications industry began to move to fifth generation technology (5g). millimeter waves that have a frequency spectrum of 28 ghz 30 ghz appear as a central technology in fifth generation technology (5g), because of their potential with wide bandwidth to achieve the large throughput required by future networks [20]. it has been proposed to be an important part of the 5g network to provide multi-gigabit communication services [17]. research about mmwave generally uses 28-30 ghz, free-license bands at 60 ghz, and e-bands at 71-76 ghz, 81-86 ghz, and 92-95 ghz. [4]. mid-infrared elt imaginer and spectrograph (metis) 2020 provides requirements for 5g technology to have end-to-end (e2e) latency below 10 ms, this cannot be achieved by previous technology [9]. a lot of developer have already innovate not only on the physical layer, but also on several layers. data allocation for small packages on tdma scheduling lte systems is inefficient because the transmission process is sent at fixed 1 ms transmission time interval (tti). flexible tti which has a flexible tdma structure has been proposed in the study [8]. the tti variable system also has flexibility in scheduling resources, which can handle the characteristics of various networks efficiently. the concept of flexible tdma is a solution, considering 5g has various types of services with very copyright ©2019 cc by-nc performance evaluation and comparison of scheduling algorithms on 5g networks using network simulator 531 diverse traffic, ranging from applications, devices, and usage. according to all advantages, this flexible scheme must be handled by the right scheduling algorithm. in this research, we analyzed choice of the scheduler has a significant impact on performance of 5g mmwave network. in this research, we analyzed choice of the scheduler has a significant impact on performance of 5g mmwave network. the module to simulate scheduler algorithm that has been adapted to the flexibe tti concept has been done by [16] in network simulator 3. all wireless network protocols have similarities in terms of message scheduling [11]. this paper compare two scheduler algorithms and analyze performance parameters such as delay, throughput, and fairness index. we simulate round robin (rr) and proportional fair (pf) to find the best performing schedulers that are applied to flexible tti schemes. we analyze the effect of scheduler on network performance such as delay, throughput, and fairness index. round robin (rr) is a scheduler that provides resources for users without considering channel conditions. this is a simple procedure that provides fairness [14]. this algorithm works by rotating the queue process. each process has the same time allotment that is equal to time quantum (q). if this quantum time runs out, the server will handle the next process. there is a matrics to set user priorities for resource blocks. expressed with matrix m with user i in resource block k [5]. the value of the user metrics above compared with other user metrics during the system. users with the largest matrices will be served first. mi,k = wi(t−ti) (1) where notation on the (1) known as : wi = priority value for every service for user i t = current time ti = last time when user i was served proportional fair algorithm has main purpose to balance between throughput and fairness among all the users [1]. different from the previous algorithm, this algorithm considers the channel conditions in the calculation of the matrics.then the proportional fair algorithm calculates based on the value of the average data rate and throughput in the previous metrics calculation. there is a matrix to set user priorities for resource blocks. expressed with matrix m with user i in resource block k : mi,k = di,k(f) ri(f) (2) with ri(f) is average throughput of user i computed in subframe f, and di,k(f) is achieveable throughput user k in m resource block and f subframe which is a shannon expression for the channel capacity as di,k(f) = logb1 + snri,k(f)c (3) 2 related work in [17] research has been carried out stating that, with the increase in cellular data demand, 5g exploited the internet a large number of variations in the millimeter wave (mmwave) band to increase communication capacity. mmwave itself is suitable for 5g network devices, depending on the communication characteristics of mmwave capable of overcoming system complexity and design, interference management and spatial reuse, anti-blockage, and dynamics due to mobility. the fundamental difference between mmwave communication and other communication systems, 532 d. perdana, a. n. sanyoto, y. g. bisono namely in terms of opposing high propagation, directivity, and sensitivity to blockages. the characteristics of mmwave communication can be utilized as potential for mmwave communication, including integrated design and systems, interference management, spatial reuse, anti-clogging, and dynamic control. in [14] a tti-based design analysis was conducted and focused on flexible tti-based designs, in terms of how well they utilized the allocated radio resources, and found that flexible frame structures exceeded fixed structures in all traffic scenarios discussed, especially for small burst traffic. so it can be concluded that the flexible tti scheme will be very suitable to be applied on mmwave communication. in [16] a research has been conducted on the implementation and validation of the mmwave module in ns-3. they redesigned several layers because mmwave will require innovation not only in the physical layer, but also across all layers of the communication protocol stack to fully utilize high throughput, low latency capabilities and maximum performance. in [14] research has been carried out stating that, round robin performance and proportional fair scheduling provide good performance for downlink transmission mode. but for different transmission modes, proportional fair is able to provide good data rates. although round robbin provides individual data speeds that are better compiled and far from enodeb, the absolute value of this data speed is not as high as the proportional fair. therefore proportional fair may still be a good choice. in the [2] study conducted by b. barakat et al. it was stated that the growth of wireless traffic and the very high demand for data levels from users encouraged researchers to improve the performance of long term evolution-advanced (lte-a). to optimize it, package scheduling is done which is able to distribute radio resources among users to improve network performance and spectrum efficiency. in this case it has been proven that generalized proportional fair (gpf) schedulers are better than conventional other schedulers. several studies about scheduling algorithm on cellular technology has been done in lte system. s. ismail et al [12] have compared of several scheduling algorithms and evaluated in terms of throughput, delay, packet loss, and fairness index on vehicular environment for uplink transmission in lte networks. in reference [18] shows that, rr algorithm produces a good fairness index and has a poor throughput and has a delay. in contrast, the mt algorithm has good throughput and bad fairness index. the pf algorithm has increased fairness and throughput but has poor delays due to traffic requirements and channel condition independence. 3lha has good throughput for p1 and p2 connections but makes p3 connections starving. in contrast, 3lha requires fairness improvement. in [13], mohnish jha et al. compared round robin, priority set scheduler and proportional fair scheduler by transmitting real-time voice packages and best effort services with changes in the number of users using ns3.24. the simulation results show that the round robin scheduler is better qos performance compared to the other two at uplink and downlink. nevertheless, rarely research about scheduling algorithm on 5g network. research about 5g mmwave can be done to evaluate cross-layer and end-to-end performance. several studies about scheduling on 5g networks also have been researched before. k. gomez on research [10], provided a comparative study of a different scheduling disciplines that can be used in future 5g especially on emergency communications for public safety. in addition to proposing a new disciplinary scheduler, simulation results. 3 research method the simulations on this research were performed on the network simulator 3.27 with an additional mmwave module. the mmave module is designed for end-to-end simulations of 3gpp style cellular networks. figure 1 shows the flowchart system. after designing the module in the ns3 environment, performance evaluation and comparison of scheduling algorithms on 5g networks using network simulator 533 finish conclussion analysis throughput delay fairness index collecting data successfull scenario change node density change node density rr scheduler algorithm pf scheduler algorithm simulation scenario design design of 5g mmwave modul on ns3 start figure 1: flowchart system the simulation design is adjusted to the scenario. the scheduling algorithm is implemented and simulated alternately. changes on the number of nodes are set gradually from 20, 40, 60, 80, and 100 nodes. if the simulation is failed, the simulation scenario design will be reconfigured. if it is successful, we analyzed network performance such as throughput, delay, and fairness index. throughput defined as the effective ability of a network in sending data. throughput is the number of packets received in bits divided by the amount of delivery time [21]. throughput = ∑ rx packet size delivery time (4) delay, defined as the time from packet send from sender to received in destination [19]. average end to end delay, which is the average time of delivering the data packet from the sender to the receiver [15]. the delay value starts calculated when the source starts sending packets and ends when the destination actually receives the packet. the delay can be constant, time-varying, or even random depending on the scheduler [3] delay = trx −ttx∑ rx (5) 534 d. perdana, a. n. sanyoto, y. g. bisono table 1: simulation parameters parameter quantity mmwave carrier frequency 28 ghz mmwave bandwidth 1 ghz number of enodeb 1 number of user 20, 40, 60, 80, 100 user mobilility constant position datarate voice : 8 kbps, video : 386 kbps packet size voice : 20 bytes, video : 240 bytes transport layer udp scheduler algorithm round robin, proportional fair where notation on the (5) known as : trx = time of received packet on destination ttx = time of packet send on source∑ rx = received packet fairness index defined as the level of fairness of scheduling algorithms in schedule packages and allocation of resources to be sent. the theory and formula regarding the fairness index was revealed by [6]. metrics of the formula are known as jain’s fairness index. maximum value of this metrics is 1, where it indicates [7] f(x) = ( ∑n i=1 xi) 2 n ∑n i=1 x 2 i (6) where notation on the equation (6) known as : f(x) = fairness index n = number of user x = throughput user i figure 2: simulation topology the system design is shown in figure 2. the remote host node has a function as the sender and connected to the packet gateway (pgw) node in point to point mode. datarate between pgw and remote host is 100gbps. mme has role to control signaling session, pgw was connected to s-gw before enodeb to send radio transmission using the lte epc core network which indicates that the network to be simulated is a non-standalone 5g network. the parameters and its description are shown in table 1. the scenario in this research is performance evaluation and comparison of scheduling algorithms on 5g networks using network simulator 535 to change node density simulated by different scheduling algorithms. changes in the number of nodes in the scenario vary from twenty to one hundred with intervals of 20 ues. the scheduler algorithm that will be used is round robin and proportional fair. ues positions are arranged randomly with constant position mobility model. the simulations generate traffics for voice and video by remote hosts. the number of packet size and data rate used in the simulation is adjusted to the characteristics of the packet size and data rate on one of the voip codecs g.729 and h.264 video codec. for g.729 voice codec, which has 8 kbps data rate and 20 bytes packet size. h.264 video codec which has 386 kbps data rate and 240 bytes packet size. 4 result and analysis after simulating voice and video traffic from the 5g mmwave network in ns3, we obtained performance results such as throughput, delay and fairness index, then be analyzed. the analysis divided into two parts for voice and video traffic to find out which better scheduler for the two services. 4.1 simulation result for voice traffic figure 3: delay on change number of users for voice traffic figure 3 shows the effect when increasing the number of users to the delay obtained from the voice traffic simulation. the lowest delay in round robin occurred on 20 ue with 1.023 ms, for proportional fair lowest delay occurred on 20 ue with 1.285 ms. on 100 ue, round robin and proportional fair generating the highest delay with 1.321 ms and 1.755 ms. average delay obtained from round robin is 1.215 ms. this is 18.29 % lower than proportional fair with average delay of 1.487 ms. based on figure 5, it can be conclude that delay for both scheduler increase, due to increase of number of ue make waiting time for each users to be served is getting longer. round robin has a better delay because for small packages, users queuing don’t to take long time, different with proportional fair which must take consider the channel quality. figure 4 shows effect the when increasing the number of users to the throughput obtained from the voice simulation. it show that, round robin has higher throughput than proportional fair. round robin gets average throughput of 0.137 mbps. this is 3.65 % better than proportional fair with average throughput of 0.132 mbps. the lowest throughput in proportional fair occurred on 100 ue with 0.131 mbps, and for round robin occurred on 100 ue with 0.132 mbps. it show that the increase number of users, throughput decreased due to the bandwidth capacity will be shared with all users. round robin has a higher throughput because this algorithm not consider the channel condition and has main purpose to balance between throughput and fairness among all the users. 536 d. perdana, a. n. sanyoto, y. g. bisono figure 4: throughput on change number of users for voice traffic figure 5: fairness index on change number of users for voice traffic figure 5 shows the effect when increasing the number of users to fairness index in each scheduling algorithm for voice simulation. the average of fairness index obtained from the simulation of adding the number of users to the proportional fair algorithm is 0.994. it is lower than the round robin’s fairness index that has 0.995. the value obtained by the round robin algorithm is greater because this algorithm does not consider channel conditions so that it offers a higher fairness value. round robin and proportional fair have a decreasing fairness index value against the increase in the number of users. its happened because of the increasing number of users, more users were served and reduce the value of fairness. both schedulers show fairness due to the fairness index close to 1. 4.2 simulation result for video traffic figure 6 shows the effect when increasing the number of users to the delay obtained from the video traffic simulation. average delay obtained from round robin simulation with rising the ue is 3.105 ms. this is 2.19 % higher than proportional fair average delay with 3.037 ms which make proportional fair has better delay on video traffic. round robin has higher delay because round robin not consider the channel condition, that make the delay for video traffic. users served in sequence obtain longer delay. figure 7 shows the effect of increasing the users to the throughput obtained from the video traffic simulation. for proportional fair, the highest throughput occurred by 20 users with 2.869 mbps. the highest throughput in round robin occurred on 20 users with 2.811 mbps. it shown that round robin and proprotional fair has a decreasing throughput value towards increasing number of user. the average throughput of proportional fair obtained from the simulation is 2.820 mbps. this is 1.24 % higher than round robin with 2.785 mbps. performance evaluation and comparison of scheduling algorithms on 5g networks using network simulator 537 figure 6: delay on change number of users for video traffic figure 7: throughput on change number of users for video traffic figure 8: fairness index on change number of users for video traffic figure 8 shows the effect when changing the number of users to fairness index in each scheduling algorithm for video traffic simulation. the average of fairness index obtained from the simulation of adding the number of users to the round robin algorithm is 0.94. it is 3.19 % better than proportional fair’s fairness index that has 0.91. round robin and proportional fair have a decreasing fairness index value against the increase in the number of users. its happened because of the increasing number of users, more users were served and reduce the fairness. round robin has higher fairness index than proportional fair because round robin not consider channel condition and prioritize fairness among users. 538 d. perdana, a. n. sanyoto, y. g. bisono 5 conclusion in this paper, our work focuses on scheduling in a 5g network with a new mac layer structure that has been proposed in previous studies. based from simulation result, the choice of scheduling algorithm has affect on network performance. proportional fair is better than round robin for throughput on video traffic with similar value of fairness index. round robin has 3.19% better fairness index than proportional fairness and 2.19% higher average delay than proportional fair. it can be conclude that proportional fair is the right algorithm for video traffic. round robin is right choice for voice traffic due to has better result for fairness and throughput. for further research, it is expected that more scheduler algorithms can be implemented in the new design of mac layer on the 5g network. bibliography [1] angri, i.; mahfoudi, m.; najid, a.; bekkali, m. 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(2018). node density performance analysis on ieee 802.11ah standard for voip service, international journal of communication networks and information security, 10(1), 2018. international journal of computers communications & control issn 1841-9836, 10(5):678-685, october, 2015. pca encrypted short acoustic data inculcated in digital color images s.h. karamchandani, k.j. gandhi, s.r. gosalia, v.k. madan, s.n. merchant, u.b. desai sunil h. karamchandani* 1. indian institute of technology, bombay mumbai, india. *corresponding author: skaramchandani@rediffmail.com krutarth j. gandhi, siddharth r. gosalia d. j. sanghvi college of engineering mumbai, india. gandhi.krutarth@yahoo.co.in siddharth_destiny@hotmail.com vinod k. madan kalasalingam university krishnankoil (tn), india klvkmadan@gmail.com shabbir n. merchant indian institute of technology, bombay mumbai, india merchant@iitb.ac.in uday b. desai indian institute of technology, hyderabad hyderabad, india ubdesai@iith.ac.in abstract: we propose develop a generalized algorithm for hiding audio signal using image steganography. the authors suggest transmitting short audio messages camouflaged in digital images using principal component analysis (pca) as an encryption technique. the quantum of principal components required to represent the audio signal by removing the redundancies is a measure of the magnitude of the eigen values. the aforementioned technique follows a dual task of encryption and in turn also compresses the audio data, sufficient enough to be buried in the image. a 57kb audio signal is decipher from the stego image with a high psnr of 47.49 and a correspondingly low mse of 3.3266 × 10−6 with an equalized high quality audio output. the consistent and comparable experimental results on application of the proposed method across a series of images demonstrate that pca based encryption can be adapted as an universal rule for a specific payload and the desired compression ratio. keywords: colour image steganography, principal component analysis, eigen thresholding, pareto analysis. 1 introduction steganography or stego in it parlance, in greek means "covered writing" and is defined by markus kahn [1,2] as the art and science of communicating in a way which hides the existence of the communication. in contrast to cryptography, where the enemy is allowed to detect, intercept and modify messages without being able to violate certain security premises guaranteed by a copyright © 2006-2015 by ccc publications pca encrypted short acoustic data inculcated in digital color images 679 cryptosystem, the goal of steganography is to hide messages inside other harmless messages in a way that does not allow any enemy to even detect that there is a second message present. a modern steganographic system should defeat detection even by a machine. it replaces bits of useless or unused data in computer files such as graphics, sound, text, html with bits of different invisible information. this hidden information can be plain text, cipher text, sound, and even images. ideally steganography can be used for any communication channel. however in real life the cover media generally used are multimedia objects such as image, video, and audio files. the reasons include that cover media should be large compared to the size of the secret message, and the methods so far developed have less than one percent of the cover size, and the indeterminacy in the cover is necessary to achieve the necessary security. large objects without inderminacy like the value π at a very high precision are not suitable. the transmitting data should be plausible as in the modern digital society dependence on audio and image files are so prevalent. it is desirable that a steganography system should have a good embedding capacity, be secure and robust. modern steganography uses a number of techniques such as masking and filtering, algorithms and transformations, and least significant bit insertion [3]. in masking and filtering, the information is hidden inside of an image using digital watermarks that include information such as copyright, ownership, or licenses. it adds an attribute to the cover the image thus extends the amount of information presented. in algorithms and transformations technique data is hidden in mathematical functions that are often used in compression algorithms and facilitates to hide the secret message in the data bits in the least significant coefficients. the least significant bit insertion is the most common and popular method of the modern day steganography, and it uses lsb of a picture’s pixel information keeping the overall image distortion to a minimum while the message is spaced out over the pixels in the images. this technique works best when the image file is larger than the message file. we propose to inculcate audio information in color images following the encryption of the short audio messages using the principal component analysis (pca). the age old adage of pca incepts as an encrypting tool diversifying from its myth as a compression standard. the framework of the paper is as follows. literature review of steganography related to audio embedding is detailed in section 2. the subsequent section 3 discusses the proposed algorithm elaborating the pca encryption of audio data followed by the implementation of steganography and stegananalysis modules in section 4. simulation results are tabled in section 5 with the conclusions drawn in section 6. 2 related steganography techniques for audio concealment majority of the steganography techniques disguise either image or text within an audio or image file. the major contributions concerned with encryption of acoustic files, particularly short audio message in images are described herewith. the authors of [4] discuss a traditional steganography technique which involves substitution of the least significant bit of each pixel of the cover image with the encrypted bits of the audio file. an arbitrary color bit stream of the rgb image is considered as the envelope where the kilo byte audio information is saved. the ciphering of the audio signals is loosely based on the very basic fundamentals of logic design. the encryption is performed [4] using the boolean operations on the bit 5 and bit 6 of color information of the individual pixels given by (1) h = (b5 ⊕ b6)⊕m (1) where "h" represents the encoded bit generated from the x−or operation of the audio bit "m" with the bits of color information obtained from the image. the encoded bit then replaces the 680 s.h. karamchandani, k.j. gandhi, s.r. gosalia, v.k. madan, s.n. merchant, u.b. desai lsb of the respective color pixels in the cover image. since the exclusive disjunction of the three logical variables is associative and reversible the audio bit is recovered from (2) m = (b5 ⊕ b6)⊕h (2) the process of stegananalysis involves decoding of the secret "key" deposited in the header files. the knowledge of the header file exposes the entire stego process making it prune to brute force attacks. the algorithm also puts a cap on the length of the audio file to be embedded in the image. moreover, the technique proposed in this paper does not give any information regarding the evaluation of the performance parameters. exploiting the color information the authors [5–8] propose a steganography technique in which discrete cosine transform (dct) is performed on the rgb planes of the cover image by converting them to grayscale and creating blocks of size 8×8. the least significant bits of the frequency coefficients of b plane are swapped with the bits of the audio file [9]. manipulation of the color component in the individual blocks leads to a computationally extensive algorithm. in the process the low frequency coefficients are also being manipulated which when converted back to spatial domain, the changes can be easily perceived by the human eye. the pca technique incorporated in our proposed algorithm is a method of compression which is camouflaged as an encryption technique for the audio signal. the proposed technique performed in the spatial domain and suggests a robust encryption algorithm, thus overcoming the drawbacks of the existing methods. 3 proposed steganography and stegananalysis model for short audio data we propose to camouflage short audio signals encrypted by their immanent principal components within the digital color images.the wave or "wav", the most commonly used audio file format accommodates the audio information exclusively in the later 43 bytes. the header files, followed by four bytes representing the length of the file precede the audio data. the proposed model is sub divided in two modules: steganography and steganalysis for encryption and deciphering the data as illustrated in figure 1 figure 1: proposed model for smuggling audio information in image pca encrypted short acoustic data inculcated in digital color images 681 3.1 steganography: manipulation of the embedded audio the encryption is based on the gaussian assumption that an ndimension data evolves n directions of variance. the one dimension audio signal is trapped in a 2-d array and is represented as (3) z = wx (3) where "w" indicates the weight vector required to minimize correlation between the two dimensions. this sets up the basis for the pca technique which not only provides a good representation of the signal but also reduces the redundancy in the data. the feature similarity is obtained by decorrelating the covariance matrix as (4) zt1z2 = 0 (4) diagonalization of the covariance matrix suppresses cross-dimensional co-activity. the decorrelation (whitening) of the weights is obtained as a diagonalization problem given in (5) wcov (x)wt = i (5) where cov (x) denotes the covariance of the input data and i represents the identity matrix. the solution of the resulting covariance matrix is a function of the eigenvectors and eigenvalues of covariance of x. the decorrelating matrix w contains vectors that normalize the input’s variance also called as the principal components while the audio signal gets scaled to a well developed gaussian curve with unit variance in all dimensions. the decorrelating matrix is used to find the directions of maximal variance in the audio data. the variance of each principal component is reflected in the eigen values obtained from the as a solution of diagonalization algorithm.the audiosigna lxe,is obtained in (6) as a scrambled waveform with its redundancies eliminated. xe = xw (6) the scrambled lxe is then conventionally embedded in the image using the missionary lsb algorithm. 3.2 stegananalysis: validation of the recovered audio stegananalysis is art of reconnoitering the hidden information in a medium. the retrieval of speech signal from its redundant components is an essential criterion to discuss the robustness of the system. the generated principal components are used for deciphering the audio data using (7) x = xew (7) the audio samples are then retrieved by reducing the dimensionality of the resultant 2−d array x using the pareto analysis. 4 implementation of proposed scheme the dominant frequency band where human speech cognition is most sensitive is around 3 khz. the epiglottis transmits the vowel sounds at about 100 hz. the audio message of size 57 kb is disguised as a "wav" file in a 24-bit ".bmp" format color image of size 256 × 256 × 3. each sample is recorded in a 16 bit stereo.the audio signal sampled at 44100 samples/sec is down sampled to 22050 hz, causing the removal of samples with frequencies above 10.025 khz. the preprocessing eliminates whatever random noise existing in the wav file. 682 s.h. karamchandani, k.j. gandhi, s.r. gosalia, v.k. madan, s.n. merchant, u.b. desai 4.1 proposed algorithm the algorithm for hiding n audio samples by is structured by selecting the prime principal components. the eigen vector corresponding to the highest eigen value is the first principal component. by thresholding the eigen value, the least important eigen vectors can be eliminated and a matrix of retained eigen vectors in the order of significance is formed whose transpose is multiplied with the transpose of audio sample matrix to obtain final matrix. the least significant bit of each pixel of the cover image is replaced by the bits of binary value of each element of the final matrix. this technique reduces the number of bits to be hidden as compared to the actual number of bits to be hidden. 5 simulation results short hidden message is embodied as an audio signal. the pca performed on the final matrix contains the original data expressed in terms of retained eigen vectors that are orthogonal to each other, thus compressing the original data. 5.1 eigen thresholding the pareto analysis is used to determine the total number of eigen values corresponding to the principal eigen vectors to be considered for audio retrieval. this process we terms as eigen thresholding. a pareto chart in figure 2 (a) plots the individual variances against the percentage contribution of each eigen value. the first ten principal components contribute to about 95% information of the audio data. figure 2 (b) shows the range of the eigen values obtained from the covariance matrix. from the plot, it is indicated that the final 20% of the eigen values represent the actual content of information in the signal. a hard threshold and the number of principal components are determined by analyzing the plot of the eigen values. the audio data is therefore encrypted by compressing it into the fewer principal components. by figure 2: proposed model for smuggling audio information in image thresholding the eigen value, the principal components with least contribution have been ignored due to redundancy in theinformation. table 1 illustrates the quantum of principal components and compression ratio obtained for different eigen thresholds. the compression ratio between the data to be hidden and original audio signal are calculated as (8) compressionratio = originaldata−compresseddata originaldata (8) pca encrypted short acoustic data inculcated in digital color images 683 table 1: number of principal components and compression ratios achieved for various thresholds of eigen value for cover image (barbara) e igen threshold principal components array c ompression ratio 10−2 171×5 0.9707 10−3 171×11 0.9355 0.8×10−3 171×12 0.9297 10−4 171×31 0.8183 0.75×10−4 171×35 0.7948 the number of principal components exhibits an inverse relation with the eigen threshold as observed in table 1, causing the compression ratio to decrease. for threshold value 10_−4, sufficient number of principal components is retained to obtain the retrieved audio message with no distortion. decrease in the number of principal components cause significant eigen vectors to be ignored, resulting in misinterpretation of the derived audio. for threshold value greater than 10_−4, the additional principal components do not provide any information of the message and are of lesser significance. also, the compression ratio will be decreased due to the additional data of lesser significance to be hidden in cover image. hence the optimum value for thresholding the eigen values is 10_−4 with ideal compression ratio of 0.8183. 5.2 performance parameters for stegananalysis performance parameters such as mean square error (mse), peak signal to noise ratio (psnr), normalized absolute error (nae) [10], maximum difference (md) and structural content (sc) are used to determine the quality of stego image and are calculated as (9)− (13). table 2 charts the performance parameters of stego image for various thresholds of eigen value. mse = 1 mn n∑ j=1 n∑ k=1 (xj,k −x′j,k) 2 (9) psnr = 10 log 2552 mse (10) sc = ∑m j=1 ∑n k=1(xj,k) 2∑m j=1 ∑n k=1(x ′ j,k) 2 (11) md = max( ∣∣(xj,k −x′j,k∣∣) (12) nae = ∑m j=1 ∑n k=1 ∣∣∣(xj,k −x′j,k)∣∣∣∑m j=1 ∑n k=1 ∣∣∣(x′j,k)∣∣∣ (13) the cover image x of size m × n and the stego image of size x′ of size m × n, and x_jk and are pixel located at j_th row and k_th column of images x and x′ respectively. the maximum difference (md) is the maximum value of absolute difference between a pixel located at j_th row and k_th column of cover image and stego image and it is same, equal to 0.0039 for all thresholds of eigen value. the value of structural content also remains same, equal to unity which implies the cover image and stego image are similar. as the threshold for eigen values is decreased to retain the significant principal components, the size of final matrix increases thus increasing the number of bits to be replaced in cover image. hence, the value of mse and nae increase causing a decrease in the values of psnr. the above analysis and performance parameters 684 s.h. karamchandani, k.j. gandhi, s.r. gosalia, v.k. madan, s.n. merchant, u.b. desai table 2: performance evaluation parameters for different thresholds of eigen value for multiple images e igen i mage m ean square peak signal to n ormalized absolute t hreshold e rror (mse) n oise ratio (psnr) e rror (nae) 10−2 lena 5.4097×10−4 48.13 2.7513×10−4 barbara 5.4645×10−4 48.13 3.3014×10−4 cameraman 5.4535×10−4 48.13 2.9832×10−4 10−3 lena 1.1895×10−6 47.86 6.0493×10−4 barbara 1.1864×10−6 47.86 7.1679×10−4 cameraman 1.1882×10−6 47.86 6.5001×10−4 0.8×10−3 lena 1.2975×10−6 47.83 6.5986×10−4 barbara 1.2955×10−6 47.83 7.8267×10−4 cameraman 1.27555×10−6 47.83 6.9776×10−4 10−4 lena 3.3462×10−6 47.49 1.7×10−3 barbara 3.3404×10−6 47.49 2×10−3 cameraman 3.3188×10−6 47.49 1.8×10−3 0.75×10−4 lena 3.7630×10−6 47.45 1.9×10−3 barbara 3.7475×10−6 47.45 2.3×10−3 cameraman 3.7528×10−6 47.45 2.1×10−3 indicate successful implementation of hiding audio message in an image. the threshold for the eigen values can be easily visualized by the pareto chart which provides percentage contribution of the individual eigen values. figure 3 shows the stego image adjacent with the recovered audio signal. figure 3: stego image (barbara) for eigen threshold 0.0001 alongside retrieved audio sample 6 conclusions and future works we have developed a generalized procedure for concealing diminutive information using image steganography. the quantum of the principal components selected is a measure of the magnitude of the eigen values. a high psnr of 47.49 with reverse mse value of 3.3266×10−6 is obtained pca encrypted short acoustic data inculcated in digital color images 685 for the stego image along with a well audible recovered audio signal using the encrypted pca technique. the results demonstrated in table 2 illustrates comparable values of the parameters psnr, nae and mse across a set of images concluding that the proposed method can be custom-made universally across all sets of images. for steganography, principal components emerge as a method of encryption while simultaneously performing compression of the audio signal. the future scope would involve adaptive thresholding of the eigen value for principal component selection with the payload as a constraint. bibliography [1] http://www.jjtc.com/stegdoc/index2.html [2] bohme, r. (2010); advanced statistical steganalysis, springer-verlag, berlin. [3] samarth, k.n.; poornapragna, m.s. (2013); a novel technique of hiding an audio message in an image, international conference on electronics and communication engineering, isbn: 978-93-83060-04-7, 170-172 [4] khalil, m. i. (2011); image steganography: hiding short audio messages within digital images, j. computer science and technology, issn: 1860-4749, 11(2): 68-73. [5] abd, e. h.; abdulwahed, h. j.; mohammed, h. a. (2012); the use of discrete cosine transformation (dct), information hiding process, j kerbala university, 10(1):45-51. [6] shahana, t. (2013); a secure dct image steganography based on public-key cryptography, international journal of computer trends and technology (ijctt), 4(7): 2039-2043. [7] bucerzan, d.; ratiu, c.; manolescu, m. j. (2013); smartsteg: a new android based steganography application, international journal of computers communications & control, 8(5):681-688. [8] singh, k.m.; chanu, y.j.; tuithung, t; (2014), steganalysis of ±k steganography based on noncausal linear predictor, international journal of comuters communications & control, 9(5):623-632. [9] othman, s. e. (2012); hide and seek: embedding audio into rgb 24-bit color image sporadically using linked list concepts, iosr journal of computer engineering (iosrjce), issn 2278-0661, 4(1): 37-44. [10] wave and avi codec registries rfc 2361, microsoft corporation (june 1998). int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 739-748 computing by folding d. sburlan dragoş sburlan faculty of mathematics and informatics ovidius university of constanta, romania e-mail: dsburlan@univ-ovidius.ro abstract: the present paper introduces a new computing paradigm based on the idea of string folding. comparisons between the computational power of the proposed model with the classical families of languages from the chomsky hierarchy are studied. some preliminary results are reported and some conjectures are discussed. in this respect, the proposed model is promising not only because of the expected theoretical results, but also because of the possible indirect applications in various fields (as for instance, mathematical linguistics, dna computing, computing using light, and so on). 1 introduction the computing paradigm proposed in this paper has as motivation several completely different domains that naturally converge. whether we talk about the traditional japanese art of paper folding (origami), or about some in-vivo biochemical processes happening at the cellular level (for instance, the protein folding that produces particular 3-dimensional structures which are essential for their ”correct" functioning), or about the in-vitro folding of dna (by which one can arbitrary build two and three dimensional shapes at the nanoscale), a common feature is the folding of the physical support. aiming to design novel computational techniques, the present paper introduces a new model of computation which can be easily described by the controlled folding of the computational support. in order to be more eloquent, one may consider the origami perspective. assume for the sake of argument that the paper used in origami is actually a bi-dimensional array of symbols. hence the paper is not only the physical support for building two or three-dimensional shapes and forms but also it might be considered as the support for computation. in this simple framework a fold of the paper corresponds to a step of computation. even if in this informal presentation we are not interested to precisely define the outcome of performing such steps of computation, we only want to point out that, for instance, a simple fold along the vertical middle of a page corresponds actually to the simultaneous movement of symbols from one side to another. in this case, if to each symbol one associates a position in the two/three dimensional space, then this folding represents, in a certain sense, a parallel rewriting of symbols. more specifically, this simple procedure moves (rewrites) a lot of space in just one computational step. this is just one argument which indicates that, under certain conditions, the expected computational power for such systems exceeds the semi-linearity (see [1] for an operation that recalls in a certain sense the folding). in this general framework several issues have to be settled, namely the computational support, the method by which the computational support will be folded, and the way the output is read. here we only consider a restricted model where both the support of computation and the folding procedure are strings of symbols from some given languages. the model deals with the onedimensional case and in our construction “computing” represents the generation of a language by means of a folding operation, starting from two “simpler” languages. copyright c⃝ 2006-2011 by ccc publications 740 d. sburlan finally, it is worth noting that the folding operation appears in various forms and contexts in formal language and natural computing theories; we quote here only [7] and [9]. in addition, the type of string operation considered in this paper recalls in a certain sense the shuffle operation as defined in [3] and [6]. 2 preliminaries for the present work, we assume the reader be familiar with the basic notions and results of formal languages and theory of computation (for more details we indicate [2] and [8]). here, we only present some results related to the current work. if fl is a family of languages, then nfl denotes the family of length sets of languages in fl. we denote by reg, cf , cs, rec, and re the family of regular, context-free, contextsensitive, recursive, and recursively enumerable languages, respectively. it is known that reg cf cs rec re. for regular and context-free languages there are known necessary conditions also called pumping lemmas. in case of regular languages the pumping lemma can be stated as: lemma 1. let l be an infinite regular language over a finite alphabet σ. then there is a positive constant nl such that if w ∈ l, |w| ≥ nl, then w can be decomposed as w = xyz, x, y, z ∈ σ∗, with |y| ≥ 1, |xy| ≤ nl, and wi = xyiz ∈ l, for any i ≥ 0. in case of context-free languages the pumping lemma can be stated as: lemma 2. let l be an infinite context-free language over a finite alphabet σ. then there is a positive constant nl such that if z ∈ l, |z| ≥ nl, then z can be decomposed as z = uvwxy, u, v, w, x, y ∈ σ∗, with |vx| ≥ 1, |vwx| ≤ nl, and zi = uviwxiy ∈ l, for any i ≥ 0. let in be the set of nonnegative integers and k be a positive integer. a set s ⊆ ink is a linear set if there are the vectors v0, v1, . . . , vn ∈ ink such that s = {v | v = v0 +a1v1 + · · ·+anvn, ai ∈ in, 1 ≤ i ≤ n}. the vectors v0 (the constant vector) and v1, v2, . . . , vn (the periods) are called the generators of the linear set s. a set s ⊆ ink is semilinear if it is a finite union of linear sets. let σ = {a1, a2, . . . , an} be a finite alphabet. a mapping ψ : σ∗ → in |σ| such that ψ(w) = (|w|a1, . . . , |w|an) is called the parikh mapping. the mapping ψ can be extended to languages such that if l ⊆ σ∗ then ψ(l) = ∪ w∈l ψ(w). two languages l1 and l2 are called letter equivalent if ψ(l1) = ψ(l2). by length(l) we will denote the length set of l. a language l is called semilinear if ψ(l) is a semilinear set of vectors of numbers. semilinear sets are closed under complement, finite intersection and finite union. a permutation of symbols in a word preserves the parikh image. all context-free languages are semilinear. 3 folding systems let σ be a finite alphabet and γ = {u, d}. let f : σ∗ × σ × γ → σ∗ such that f(w, a, u) = aw, f(w, a, d) = wa. we define the folding function h : σ∗ × γ∗ → σ∗ such that computing by folding 741 h(w1, w2) =   f(. . . f(f(λ, a1, b1), a2, b2) . . . , ak, bk), if |w1| = |w2|, w1 = a1a2 . . . ak, w2 = b1b2 . . . bk; undefined, if |w1| ̸= |w2|. remark 3.1. if w1 ∈ σ∗, w2 ∈ γ∗, and |w1| = |w2|, then by applying h we mean that w1 is folded symbol by symbol according with the folding procedure described by the symbols of w2 (d means to fold the ”remaining" of w1 downwards and u upwards); the result can be seen as a stack of symbols that by definition we read from the top to the bottom (see figure 1). example 3. let w1 = aabb and w2 = dduu. then we formally have h(w1, w2) = f(f(f(f(λ, a, d), a, d), b, u), b, u) = f(f(f(a, a, d), b, u)b, u) = f(f(aa, b, u), b, u) = f(baa, b, u) = bbaa or graphically, the recurrent application of f is given in figure 1. figure 1: the computation of h(aabb, dduu). a folding system (in short an f-system) is a construct φ = (l1, l2) where l1 is an arbitrary language over an arbitrary alphabet σ (called the core language) and l2 is an arbitrary language over γ = {u, d} (called the folding procedures set). the language generated by φ(l1, l2) is l(φ) = ∪ w1∈l1,w2∈l2 |w1|=|w2| h(w1, w2), 742 d. sburlan where h : σ∗ × γ∗ → σ∗ is the folding function. for a class c of core languages and a class h of folding procedures sets, we denote by f(c, h) the family of all languages generated by f-systems, using components from the respective classes, that is f(c, h) = {l(φ) | φ = (l1, l2), l1 ∈ c, l2 ∈ h}. the following result shows the relation between the family of regular languages and the family of all languages generated by f-systems using regular languages as components. proposition 4. f(reg, reg) ! reg. proof: let l1 be an arbitrary regular language and the regular language l2 = {d}∗ ⊆ γ∗. then we have that for any word w1 ∈ l1 there exists w2 ∈ l2 such that |w1| = |w2|. since w1 is ”folded" always down (w2 consists only of d symbols), then it follows that h(w1) = w1, hence h(l1) = l1 and further more f(reg, reg) ⊇ reg. in order to prove the strict inclusion one can construct the following f -system: φ(l1, l2) where l1 = (ab)∗ and l2 = (ud)∗. it follows that for any even number i = 2k, k > 0, there exist exactly one word w1 = ab . . . ab ∈ l1 and exactly one word w2 = ud . . . ud ∈ l2 such that |w1| = |w2| = i. hence h(w1, w2) = a kbk and consequently l(φ) = {anbn | n > 0}, the well-known non-regular but context-free language. 2 by using context-free languages as core languages while preserving the regularity of folding procedure sets, one can surpass the class of context-free languages. proposition 5. f(cf, reg) ! cf. proof: one can use the same argument as given in the proof of proposition 4 to show that f(cf, reg) ⊇ cf. in addition, consider the context-free language l1 = {bn(ac)n | n ≥ 0} (as being generated for instance by the context-free grammar g = (n, t, p, s), where n = {s}, t = {a, b, c}, and p = {s → bsac, s → λ}) and the regular language l2 = {(ud)n | n ≥ 0} indicated by the regular expression (ud)∗. the length of any word in l1 is a multiple of 3, while any word from l2 has a length which is a multiple of 2. consequently, any word from φ(l1, l2) must have a length multiple of the least common multiple of 2 and 3. it follows that φ(l1, l2) = {a2nb2nc2n | n ≥ 0}, which by using the pumping lemma for context-free languages can be shown not to be a context-free language. consequently, we have that f(cf, reg) ! cf. 2 proposition 6. f(cf, cf) ! cf. proof: since obviously f(cf, cf) ⊇ f(cf, reg) then to prove the strict inclusion one can construct the following f-system, whose generated language does not belong to context-free languages family. let φ = (l1, l2), where l1 = {wwr | w ∈ {a, b}∗} is the context-free language of even palindromes and l2 = {undn | n ≥ 0} is a context-free language as well. it follows that l(φ) = {ww | w ∈ {a, b}∗} – a language that is not context-free. another interesting example which proves the strict inclusion regards the generation of the language l = {anbncn | n ≥ 0}. this can be achieved by an f-system φ(l1, l2) where l1 = {bn(ac)n | n ≥ 0} and l2 = {un(ud)n | n ≥ 0} are also context-free languages. 2 using a similar technique as the one presented in the proofs of proposition 4 and proposition 5 one can actually prove a more general result (that in addition suggests some interesting open problems regarding the usage of subfamilies of reg as folding procedures sets) computing by folding 743 lemma 7. let fl be a family of languages. then fl ⊆ f(fl, reg). moreover, one can state the following result: lemma 8. let fli, 1 ≤ i ≤ 4, some families of languages such that fl1 ⊆ fl2 and fl3 ⊆ fl4. then f(fl1, fl3) ⊆ f(fl2, fl4). the following result states that all f-systems using context-free languages as components are equivalent to f-systems with a particular relation between the core languages and the folding procedures sets. lemma 9. (normal form) let l ∈ f(cf, cf). then there exists an f -system φ(l1, l2), l1, l2 ∈ cf , with length(l1) = length(l2) and such that l(φ) = l. proof: let l = l(φ(l1, l2)) such that l1, l2 ∈ cf and length(l1) ̸= length(l2). we construct an equivalent f-system φ(l1, l2), l1, l2 ∈ cf , such that length(l1) = length(l2) as follows. it is known from [5] that nreg = ncf . this means that there exist the languages lb, lc ∈ reg such that length(lb) = length(l1) and length(lc) = lenght(l2). let σl1 = {a1, a2, . . . , ak} be the alphabet of l1, σb = {b1, b2, . . . , br} be the alphabet of lb, and σc = {c1, c2, . . . cs} be the alphabet of lc. in addition, let us consider the finite substitution given by ∆1 : σc → p(σ∗b) such that ∆1(ci) = σb, 1 ≤ i ≤ s. because regular languages are closed under finite substitutions and intersection then it follows that lb = lb ∩ ∆(lc) ∈ reg (lb contains all the words of length t ≥ 0 from lb for which there exists at least one word in lc with the same length). moreover, we have that length(lb) = length(lb) ∩ length(lc) = length(l1) ∩ length(l2). let us consider now the substitutions: ∆2 : σb → p(σ∗a) such that ∆2(bi) = σa, 1 ≤ i ≤ r, and ∆3 : σb → p({u, d}∗) such that ∆3(bi) = {u, d}, 1 ≤ i ≤ r. then, if we take l1 = l1 ∩ ∆2(lb) and l2 = l2 ∩ ∆3(lb) it follows that l1, l2 ∈ cf (as being the results of intersection of context-free languages with regular languages) and moreover length(l1) = length(l2) (both substitutions ∆2 and ∆3 do not change the length sets of the languages on which they are applied; in particular length(lb) = length(∆2(lb)) = length(∆3(lb))). similarly as before, in order to construct l1 we get from l1 only those words which match as length at least one word in lb. as a consequence, we have that l(φ(l1, l2)) = l(φ(l1, l2)) = l. 2 remark 3.2. the language generated by an f -system φ(l1, l2), where l1 and l2 are semilinear sets, is also semilinear. let us prove now a necessary condition for a language generated by a folding system using as components regular languages. let l1 ⊆ σ∗ and l2 ⊆ {u, d}∗ be two arbitrary infinite regular languages and assume that l(φ(l1, l2)) (the corresponding generated language) is also infinite. let nl1 and nl2 be the constants from the pumping lemma for regular languages applied to l1 744 d. sburlan and l2, respectively. let n = max(nl1, nl2). because l(φ(l1, l2)) is infinite, then there are the words r ∈ l1 and s ∈ l2 such that |r| = |s| ≥ n. according with the pumping lemma for regular languages, it follows that r can be written as r = xyz, where x, y, z ∈ σ∗ and such that |xy| ≤ n, y ≥ 1. similarly, s = uvt where u, v, t ∈ {u, d}∗, and such that |uv| ≤ n, v ≥ 1. in both cases, the pumping lemma states that ri = xyiz ∈ l1 and si = uvit ∈ l2, for any i ≥ 0. although in general |ri| ̸= |si|, for all i ≥ 0, one can remark that, based on the lowest common multiple between y and v, one can define the sub-sequences of words ri = xy lcm(|y|,|v|) |y| ·iz, for any i ≥ 0, and si = uv lcm(|y|,|v|) |v| ·it, for any i ≥ 0, such that |ri| = |si|, for any i ≥ 0. for simplicity of argument, let us consider now the double stranded structures [ ri si ] , for any i ≥ 0. first of all, remark that x, z, u, t have constant lengths. moreover, the above sub-sequences are infinite (because |y| ≥ 1 and |v| ≥ 1, one can remark that |ri+1| > |ri|, |si+1| > |si|, for any i ≥ 0). it follows that, starting from a certain positive constant ñ (that depends on |x|, |y|, |u|, |v|, hence on l1 and l2), in the double stranded structure [ rj sj ] , with j ≥ ñ, the substring from ri composed by multiple concatenations of y overlaps (partially) the one from si composed by multiple concatenations of v. moreover, assume in addition that ñ is the first rank in the sequence of double stranded structures such that the overlapping part is equal or longer than min( lcm(|y|,|v|) |y| , lcm(|y|,|v|) |v| ); this is necessary for having the same “context” repeated at least once. then, it follows that any term in the sequence [ ri si ] , for any i ≥ ñ, can be written as [ k1k j 2k3 q1q j 2q3 ] , where |kl| = |ql|, 1 ≤ l ≤ 3, and j ≥ 1 (see figure 2 for a particular case). figure 2: here is presented a particular case of a double stranded structure (when |u| > |x|). for instance, here we have that q1 = u and q2 = v3. finally, by folding the string k1k j 2k3 with respect to q1q j 2q3, one gets m1m j 2m3m j 4m5. the string k1 folded according with q1 produces m3. the string k2 folded according with q2 may produce a string m2 ahead of m3 and a string m4 behind of m3, but such that |m2m4| ≥ 1 (because |k2| ≥ 1). by repeating the above procedure for all strings k2 and q2, one gets m j 2m3m j 4. eventually, by folding k3 with respect to q3, one obtains m1m j 2m3m j 4m5. based on the above arguments, the following result holds true. lemma 10. let l ∈ f(reg, reg) be an infinite language. then there exists a positive constant n such that if w ∈ l, |w| ≥ n then w can be decomposed as w = uvwxy such that |vx| ≥ 1, |vwx| ≤ n, and wi = uviwxiy ∈ l, for any i ≥ 0. computing by folding 745 using lemma 10, one can easily prove that l = {a2nb2nc2n|n ≥ 0} ̸∈ f(reg, reg). consequently, one can prove that proposition 11. f(reg, reg) ⊂ f(cf, reg). a similar result stands also in case of a infinite language l ∈ f(cf, reg). more precisely, let l1 ⊆ σ∗ be a infinite context-free language and l2 ⊆ {u, d}∗ be a infinite regular languages and assume that l = l(φ(l1, l2)). using the same type of arguments as in the proof of lemma 10 it follows that there exists r ∈ l1 and s ∈ l2 such that |r| = |s| ≥ n = max(nl1, nl2), where nl1 is the constant for l1 given by the pumping lemma for context-free languages and nl2 is the constant for l1 given by the pumping lemma for regular languages. according to these lemmas we have • r can be decomposed into r = x1x2x3x4x5, where xi ∈ σ∗, 1 ≤ i ≤ 5, such that |x2x4| ≥ 1, |x2x3x4| ≤ nl1 ≤ n, and ri = x1x i 2x3x i 4x5 ∈ l1, for i ≥ 0; • s can be decomposed into s = uvt, where u, v, t ∈ {u, d}∗, such that |uv| ≤ nl2 ≤ n, v ≥ 1, and si = uvit ∈ l2, for i ≥ 0. based on the above relations one can define the sequences (an)n ⊆ in and (bn)n ⊆ in such that |rai| = |sbi|, for i ∈ in. more precisely if we denote by c1 = |x1|+|x3|+|x5|, c2 = |x2|+|x4|, and c3 = |u| + |t|, c4 = v, then the following relations have to be true. c1 + aic2 = c3 + bic4 , hence bi = c1 − c3 + aic2 c4 taking now ai = { (c4i − 1)(c1 − c3) , if c1 ≥ c3 (c4i − 1)(c3 − c1) , if c3 > c1 i ∈ in, i > 0 it follows that the subsequences ri = x1x ai 2 x3x ai 4 x5, for i > 0, and si = uv bit, for i > 0, have equal lengths, that is |ri| = |si|, for i > 0. considering the double stranded structures [ ri si ] , for any i ≥ 0, then it follows that, starting from a certain positive constant ñ (that depends on l1 and l2), in the double stranded structure[ rj sj ] , with j ≥ ñ, the substring from sj composed by multiple concatenations of v underlays (partially) the one from rj composed (in order) by multiple concatenations of x2, x3, and multiple concatenations of x5 (because the substrings x1 and x3 have constant lengths). moreover, assume that ñ is the first rank in the double stranded sequence such that the following conditions are satisfied: • the substring from rj composed by multiple concatenation of x2 overlaps the substring from sj composed by multiple concatenations of v; assume that the overlapping part is equal or longer than min(lcm(|x2|,|v|)|x2| , lcm(|x2|,|v|) |v| ); • the substring from rj composed by multiple concatenation of x4 overlaps the substring from sj composed by multiple concatenations of v; assume that the overlapping part is equal or longer than min(lcm(|x4|,|v|)|x4| , lcm(|x4|,|v|) |v| ). using similar arguments as in the proof of lemma 10, it follows that any term in the sequence[ ri si ] , for any i ≥ ñ, can be written as [ k1k h 2 k3k j 4k5 q1q h 2 q3q j 4q5 ] , where kl, ql, 1 ≤ l ≤ 5, are strings (of finite lengths), |kl| = |ql|, 1 ≤ l ≤ 5, and h, j ≥ 1. moreover, we have that |k2k4| ≥ 1 (because k2k4 746 d. sburlan contains at least one symbol from x2x4 and |x2x4| ≥ 1). although in general h ̸= j, one can decompose ri and si as[ k1k h 2 k3k j−h 4 k h 4 k5 q1q h 2 q3q j−h 4 q h 4 q5 ] , if j ≥ h, or [ k1k j 2k h−j 2 k3k j 4k5 q1q j 2q h−j 2 q3q j 4q5 ] , if h ≥ j. furthermore, based on the above construction, one can actually build an infinite sequence of type [ α1α i 2α3α i 4α5 β1β i 2β3β i 4β5 ] , for i ≥ 0, where |αl| = |βl|, 1 ≤ l ≤ 5, |α2α4| ≥ 1 and |α2α3α4| ≤ ñ. consequently, by folding α1αi2α3α i 4α5 according with β1β i 2β3β i 4β5, for i ≥ 0, one gets a string wi = w1w i 2w3w i 4w5w i 6w7w i 8w9, hence the following result is true. lemma 12. let l ∈ f(cf, reg) be an infinite language. then there exists a positive constant n such that if w ∈ l, |w| ≥ n then w can be decomposed as w = w1w2w3w4w5w6w7w8w9 such that |w2w4w6w8| ≥ 1 |w2w3w4w5w6w7w8| ≤ n and wi = w1wi2w3w i 4w5w i 6w7w i 8w9 ∈ l, for any i ≥ 0. using lemma 12 one can prove that the language l = {ww | w ∈ {a, b}∗} ̸∈ f(cf, reg). consequently, the following result holds true. proposition 13. f(cf, reg) ⊂ f(cf, cf). furthermore, one can show the following result. proposition 14. f(cs, cs) = cs. proof: assume that l1 and l2 are arbitrary context-sensitive languages. then one can construct a linear bounded automaton m such that l(m) = l(φ(l1, l2)) and which works as follows. m uses one read only input tape and three auxiliary tapes. a word w is placed on the input tape. the first auxiliary tape is used by m to nondeterministically generate a word w1 of the same length as the one present on the input tape, and then to check if w1 ∈ l1. the second auxiliary tape is used by m to nondeterministically generate a word w2 of the same length as the one present on the input tape, and then to check if w2 ∈ l1. finally, if w1 ∈ l1 and w2 ∈ l2, then m uses the third tape to simulate the folding of w1 according with w2 and to check whether or not the result is equal to w. because all the above mentioned tasks can be performed in linear space with respect to the length of the input, then it follows that l(φ(l1, l2)) ∈ cs. moreover, one can notice that all context-sensitive languages can be generated (to prove this, one can use a similar construction as in the proof of proposition 4). consequently, we have that f(cs, cs) = cs. 2 because the class o context-sensitive languages cs contains non semilinear languages, then using remark 3.2 it follows that proposition 15. f(cf, cf) ⊂ f(cs, cs) = cs. using a similar construction as in the proof of proposition 14 one can prove that computing by folding 747 proposition 16. f(rec, rec) = rec. proposition 17. f(re, re) = re. finally we can sum up all the results in the following hierarchy: theorem 18. reg ⊂ f(reg, reg) ⊆ cf ⊂ f(cf, reg) ⊂ f(cf, cf) ⊂ f(cs, cs) = cs ⊂ f(rec, rec) = rec ⊂ f(re, re) = re. 4 conclusion in this paper we introduced a new computing paradigm called folding systems. we investigated their computational power under certain constraints and we studied some of their properties. during our endeavor several open problems arose. one of them regards the existence of a pumping lemma for folding systems where both the core language and the folding procedure set are context-free languages. another open problem regards the existence of a polynomial time parsing algorithm for folding systems which use regular/context-free languages as core/folding procedure set. future investigations will focus on variants of folding systems where other families of languages will be used (for example those generated by lindenmayer systems), but also on defining more complex and realistic methods for performing the string folding. we conclude with the belief that many interesting problems can be pursued in this line of research. acknowledgment the work of the author was supported by the cncsis idei-pce grant, no. 551/2009, romanian ministry of education, research and innovation. bibliography [1] t. head, parallel computing by xeroxing on transparencies, algorithmic bioprocesses, 9, pp. 631–637, springer-verlag, berlin, 2009. [2] j. e. hopcroft, j. d. ullman, introduction to automata theory, languages and computation, addison-wesley publishing, reading massachusetts, 1979. [3] a. mateescu, g. rozenberg, a. salomaa, shuffle on trajectories: syntactic constraints, theoretical computer science, 197, 1-2, pp. 1–56, 1998. [4] m. minsky, computation – finite and infinite machines. prentice hall, englewood cliffs, nj, 1967. [5] r. j. parikh. on context–free languages. journal of the association for computing machinery, 13 (4), pp. 570–581, 1966. [6] g. păun, g. rozenberg, a. salomaa, grammars based on the shuffle operation, journal of universal computer science, 1 (1), pp. 67–82, 1995. 748 d. sburlan [7] p.w.k. rothemund, folding dna to create nanoscale shapes and patterns, nature (440), pp. 297–302, 2006. [8] g. rozenberg, a. salomaa, eds., handbook of formal languages, 3 volumes. springer-verlag, berlin, 1997. [9] g. rozenberg, gene assembly in ciliates: computing by folding. a half-century of automata theory, world scientific publishing company, pp. 93–130, 2000. 2dzitac.pdf international journal of computers communications & control special issue on fuzzy sets and applications (celebration of the 50th anniversary of fuzzy sets) issn 1841-9836, 10(6):772-788, december, 2015. the fuzzification of classical structures: a general view i. dzitac ioan dzitac 1. aurel vlaicu university of arad, elena dragoi 2, ro-310330 arad, romania ioan.dzitac@uav.ro 2. agora university of oradea, piata tineretului 8, ro-485526 oradea, romania rector@univagora.ro abstract: the aim of this survey article, dedicated to the 50th anniversary of zadeh’s pioneering paper "fuzzy sets" (1965), is to offer a unitary view to some important spaces in fuzzy mathematics: fuzzy real line, fuzzy topological spaces, fuzzy metric spaces, fuzzy topological vector spaces, fuzzy normed linear spaces. we believe that this paper will be a support for future research in this field. keywords: fuzzy real line, fuzzy topological spaces, fuzzy metric spaces, fuzzy topological vector spaces, fuzzy normed linear spaces, fuzzy f-space. 1 introduction an introduction in the classical set theory begins, in general, in the following way: by a set we understand a collection of objects, well individualized, such that we can decide without any ambiguity whether a given element belongs to that set or not. what should we do when we cannot answer this question? can we talk about sets described in natural language such as "the set of beautiful women" or "the set of tall men"? although these questions are natural they were formulated only in 1965 by lotfi a. zadeh. in order to give answers to these questions, l.a. zadeh [61] introduced the concept of fuzzy set. we present bellow some thoughts of lotfi a. zadeh, remembering the beginnings and the current impact of fuzzy sets theory. in [63] lotfi a. zadeh said: "in july of 1964, i was attending a conference in new york and was staying at the home of my parents. they were away. i had a dinner engagement but it had to be canceled. i was alone in the apartment. my thoughts turned to the unsharpness of class boundaries. it was at that point that the simple concept of a fuzzy set occurred to me. it did not take me long to put my thoughts together and write a paper on the subject. this was the genesis of fuzzy set theory. i knew that the word "fuzzy" would make the theory controversial. knowing how the real world functions, i submitted my paper to information and control because i was a member of the editorial board. there was just one review-which was very lukewarm. i believe that my paper would have been rejected if i were not on the editorial board. today (20 dec. 2010), with over 26,000 google scholar citations, "fuzzy sets"is by far the highest cited paper in information and control. my paper was a turning point in my research. since 1965, almost all of my papers relate to fuzzy set theory and fuzzy logic. as i expected, my 1965 paper drew a mixed reaction, partly because the word "fuzzy" is generally used in a pejorative sense, but, more substantively, because unsharpness of class boundaries was not considered in science and engineering. in large measure, comments of my paper were skeptical or hostile. an exception was japan. in 1968, i began to receive letters from japan expressing interest in application of fuzzy set theory to pattern recognition. in the years which followed, in japan fuzzy set theory and fuzzy logic became objects of extensive research and wide-ranging application, especially in the realm of consumer copyright © 2006-2015 by ccc publications the fuzzification of classical structures: a general view 773 products. a very visible application was the subway system in the city of sendai a fuzzy logicbased system designed by hitachi and kawasaki heavy industry. the system began to operate in 1987 and is considered to be a great success." on october 2, 2015 the paper "fuzzy sets" has already over 58,540 citations in google scholar and all zadeh’s papers have over 151,300 citations. "computation with information described in natural language (nl) is closely related to computing with words. nl-computation is of intrinsic importance because much of human knowledge is described in natural language. this is particularly true in such fields as economics, data mining, systems engineering, risk assessment and emergency management. it is safe to predict that as we move further into the age of machine intelligence and mechanized decisionmaking, nl-computation will grow in visibility and importance." (l.a. zadeh, [65]). "what is thought-provoking is that neither traditional mathematics nor standard probability theory has the capability to deal with computational problems which are stated in a natural language. not having this capability, it is traditional to dismiss such problems as ill-posed. in this perspective, perhaps the most remarkable contribution of computing with words (cw) is that it opens the door to empowering of mathematics with a fascinating capability the capability to construct mathematical solutions of computational problems which are stated in a natural language. the basic importance of this capability derives from the fact that much of human knowledge, and especially world knowledge, is described in natural language. in conclusion, only recently did i begin to realize that the formalism of cw suggests a new and challenging direction in mathematics mathematical solution of computational problems which are stated in a natural language. for mathematics, this is an unexplored territory." (l.a. zadeh, [64]). since then many authors have developed the theory of fuzzy set and its applications. especially, many mathematicians tried to extend in fuzzy context classical mathematics results. the success of the research undertaken has been demonstrated in a variety of areas such as: artificial intelligence, computer science, quantum particle physics, control engineering, robotics and many more. perhaps the main reason for this rapid development is that the world that surrounds us is full of uncertainty, the data we collect from the environment are, in general, vague and incorrect. so the notion of fuzzy set allows us to study the degree of uncertainty mentioned above in a purely mathematical way. 2 fuzzy sets the concept of fuzzy set was introduced by l.a. zadeh [61] in 1965. definition 1. [61] a fuzzy set in x is a function µ : x → [0, 1]. we denote by f(x) the family of all fuzzy sets in x. remark 2. in fact µ is the membership function of a fuzzy set a of x and the value µ(x) represents "the grade of membership" of x to fuzzy set a. but, in this paper, we adopt the convention to identify fuzzy sets with their membership functions. this identification was first used by j.a. goguen [19]. remark 3. as any subset of x can be identified with its characteristic function we remark that fuzzy sets generalize subsets. definition 4. [61] let µ, ν be fuzzy sets in x. the union of fuzzy sets µ şi ν, denoted µ∨ν, the intersection of fuzzy sets µ şi ν, denoted µ ∧ ν, the complement of fuzzy set µ, denoted 1 − µ, 774 i. dzitac are fuzzy sets in x, defined by (µ ∨ ν)(x) = max{µ(x), ν(x)} (1) (µ ∧ ν)(x) = min{µ(x), ν(x)} (2) c(µ)(x) = 1 − µ(x) (3) definition 5. the union of the fuzzy sets {µi}i∈i is defined by ( ∨ i∈i µi ) (x) = sup{µi(x) : i ∈ i} . the intersection of the fuzzy sets {µi}i∈i is defined by ( ∧ i∈i µi ) (x) = inf{µi(x) : i ∈ i} . definition 6. let α ∈ (0, 1], and let µ be a fuzzy set in x. the α-level set [µ]α is defined by [µ]α := {x ∈ x : µ(x) ≥ α} . the support of µ is supp µ := {x ∈ x : µ(x) > 0} . definition 7. [61] let x be a vector space over a field k (where k is r or c). a fuzzy set µ is called convex if µ(λx1 + (1 − λ)x2) ≥ min{µ(x1), µ(x2)} , (∀)x1, x2 ∈ x, (∀)λ ∈ [0, 1] . the extension principle is undoubtedly one of the most important of zadeh’s contribution in fuzzy set theory, allowing to extend in a fuzzy context almost any mathematical concept. the extension principle was introduced by zadeh [61] in 1965, and then it suffered many changes: zadeh [62]; jain [24]; dubois & prade [14]. for more details of this principle and its extensions we refer the reader to [66], [30]. let x = x1 ×x2 ×···×xr and µ1, µ2, · · · , µr be fuzzy sets in x1, x2, · · · , xr, respectively. let f : x → y . the extension principle allows us to define a fuzzy set in y by µ(y) = { sup (x1,··· ,xr)∈f−1(y) min{µ1(x1), · · · , µr(xr)} if f −1(y) 6= ∅ 0 if f−1(y) = ∅ . 3 fuzzy relations it is well known that the fuzzy relations play an important role in fuzzy modeling and fuzzy control and they also have important applications in relational databases, approximate reasoning, preference modeling, medical diagnosis. the concept of fuzzy relation was introduced by l.a. zadeh in his classical paper [61]. according to l.a. zadeh a fuzzy relation t between two nonempty sets x and y is a fuzzy set in x × y , i.e. it is a mapping t : x × y → [0, 1]. we denote by fr(x, y ) the family of all fuzzy relations between x and y . for x ∈ x we denote by tx the fuzzy set in y defined by tx(y) = t(x, y). thus, a fuzzy relation can be seen as a mapping x ∋ x 7→ tx ∈ f(y ), where f(y ) represents the family of all fuzzy sets in y . the fuzzification of classical structures: a general view 775 such mappings were investigated by various mathematicians under different aspects. thus n. papageorgiou [46] called these mappings fuzzy multifunctions and studied the continuity of these mappings. e. tsiporkova, b. de baets, e. kerre [56, 57] called these maps fuzzy multivalued mappings and they defined lower and upper semi-continuous fuzzy multivalued mapping. the relationships between these two types were studied completely. the continuity of fuzzy multifunctions was also studied by i. beg [6]. in papers [7, 8], i.beg studied the linear fuzzy multivalued operators and vector-valued fuzzy multifunctions. an application t : rm → f(rn) is called a fuzzy process (see y. chalco-cano, m.a. rojas-medar, r. osuna-gómez [9]). a special attention was given to convex fuzzy processes. they were introduced by m. matloka [36] in 2000. another concept of convex fuzzy process was proposed by y. syau, c. low and t. wu [55] in 2002. a comparative study of these fuzzy convex processes was made in 2010 by d. qiu, f. yang, l. shu [47]. to avoid any confusion d. qiu, f. yang and l. shu called the former m-convex fuzzy process and the latter slw-convex process. in paper [41] special types of fuzzy relations on vector spaces were considered : affine fuzzy relations, linear fuzzy relations, convex fuzzy relations, m-convex fuzzy relations. some fundamental properties of fuzzy linear relations between vector spaces are considered in [43]. the domain d(t) of t is a fuzzy set in x defined by d(t)(x) := sup y∈y t(x, y) (see [56]). we note that supp d(t) = {x ∈ x : tx 6= ∅} = {x ∈ x : (∃)y ∈ y such that t(x, y) > 0} . if for all x ∈ supp d(t) there exists unique y ∈ y such that t(x, y) > 0, then t is called fuzzy function (or single-valued fuzzy function). in this case, we denote this unique y by t(x). if µ ∈ f(x), then t(µ) ∈ f(y ) is defined by t(µ)(y) := sup x∈x [t(x, y) ∧ µ(x)] (see [6]). in particular, the range r(t) of t is a fuzzy set in y defined by r(t)(y) := sup x∈x t(x, y) [56]. let t ∈ fr(x, y ), s ∈ fr(y, z). the composition s ◦ t ∈ fr(x, z) (or simply st ) is defined by (s ◦ t)(x, z) := sup y∈y [t(x, y) ∧ s(y, z)] [61]. proposition 8. let t ∈ fr(x, y ), s ∈ fr(y, z). then (s ◦ t)x = s(tx), (∀)x ∈ x. proposition 9. the operation ” ◦ ” is associative. the inverse (or reverse relation) t−1 of a fuzzy relation t ∈ fr(x, y ) is a fuzzy set in y ×x defined by t−1(y, x) = t(x, y). it is obvious that r(t) = d(t−1) and r(t−1) = d(t). we remark that, for µ ∈ f(y ), we have t−1(µ)(x) = sup y∈y [t−1(y, x) ∧ µ(x)] = sup y∈y [t(x, y) ∧ µ(x)] . this type of inverse is usually called lower inverse [6]. 4 fuzzy real numbers for the concept of fuzzy real number, arithmetic operation and ordering on the set of all fuzzy real numbers we refer the reader to the papers [13, 14, 17, 25, 26, 38, 59]. definition 10. a fuzzy set in r, namely a mapping x : r → [0, 1], with the following properties: 1. x is convex, i.e. x(t) ≥ min{x(s), x(r)}, for s ≤ t ≤ r; 2. x is normal, i.e. (∃)t0 ∈ r : x(t0) = 1; 3. x is upper semicontinuous, i.e. (∀)t ∈ r, (∀)α ∈ (0, 1] : x(t) < α, (∃)δ > 0 such that |s − t| < δ ⇒ x(s) < α 776 i. dzitac is called a fuzzy real number. we will denote by r(i) the set of all fuzzy real numbers. remark 11. let x ∈ r(i). for all α ∈ (0, 1], the α-level sets [x]α = {t : x(t) ≥ α} are closed intervals [aα, bα], where the values aα = −∞ and bα = ∞ are admissible. when aα = −∞, the interval [aα, bα] will be denoted by (−∞, bα]. definition 12. a fuzzy real number x is called non-negative if x(t) = 0, (∀)t < 0. the set of all non-negative real numbers will be denoted by r∗(i). remark 13. for each r ∈ r we can consider the fuzzy real number r defined by r(t) = { 1 if t = r 0 if t 6= r . these fuzzy numbers are called crisp. thus r can be embedded in r(i). definition 14. [38] the arithmetic operations +,−, ·, / on r(i), are defined by: (x + y)(t) = ∨ s∈r min{x(s), y(t − s)}, (∀)t ∈ r (4) (x − y)(t) = ∨ s∈r min{x(s), y(s − t)}, (∀)t ∈ r (5) (xy)(t) = ∨ s∈r∗ min{x(s), y(t/s)}, (∀)t ∈ r (6) (x/y)(t) = ∨ s∈r min{x(ts), y(s)}, (∀)t ∈ r (7) remark 15. previous definitions are special cases of zadeh’s extension principle. remark 16. the additive and multiplicative operations are associative and commutative with the identities 0 and 1, where 0(t) = { 1 if t = 0 0 if t 6= 0 , 1(t) = { 1 if t = 1 0 if t 6= 1 . remark 17. it is obvious that 1. −x = 0 − x; 2. (−x)(t) = x(−t); 3. x − y = x + (−y); 4. −(x + y) = (−x) + (−y). definition 18. the absolute value |x| of x ∈ r(i) is defined by |x|(t) = { max{x(t), x(−t)} if t ≥ 0 0 if t < 0 . proposition 19. [26] the equations a + x = 0 and ax = 1 have unique solutions if and only if a is crisp. definition 20. [16] a partial ordering on r(i) is defined by x ≤ y if a1α ≤ a 2 α and b 1 α ≤ b 2 α , (∀)α ∈ (0, 1] , where [x]α = [a 1 α, b 1 α] and [y]α = [a 2 α, b 2 α]. the fuzzification of classical structures: a general view 777 proposition 21. [26] if [aα, bα], 0 < α ≤ 1, are the α-level sets of a fuzzy real number x, then: 1. [aα1, bα1] ⊇ [aα2, bα2], (∀)0 < α1 ≤ α2; 2. [ lim k→∞ aαk, lim k→∞ bαk] = [aα, bα], where {αk} is an increasing sequence in (0, 1] converging to α. conversely, if [aα, bα], 0 < α ≤ 1, is a family of non-empty intervals which satisfy the conditions (1) and (2), then the family [aα, bα] represents the α-level sets of a fuzzy real number. remark 22. as α-level sets of a fuzzy real number is an interval, there is a debate in the nomenclature of fuzzy real numbers. in [15], d. dubois and h. prade suggested to call this fuzzy interval. they developed a different notion of fuzzy real number by considering it as a fuzzy element of the real line. 5 fuzzy topological spaces from the notion of fuzzy set, to the notion of fuzzy topological space, there was one more step to be taken. thus, in 1968, c.l. chang [10] introduced the notion of fuzzy topological space. the definition is a natural translation to fuzzy sets of the ordinary definition of topological space. indeed, a fuzzy topology is a family t , of fuzzy sets in x, such that t is closed with respect to arbitrary union and finite intersection and x,∅ ∈ t . definition 23. [10] let x be an arbitrary set. a fuzzy topology on x is a family t ⊂ f(x) satisfying the following axioms: 1. ∅, x ∈ t , where ∅ is characterized by the membership function µ(x) = 0, (∀)x ∈ x and x is characterized by the membership function µ(x) = 1, (∀)x ∈ x; 2. if µ1, µ2 ∈ t , then µ1 ∧ µ2 ∈ t ; 3. if {µi}i∈i ⊂ t , then ∨ i∈i µi ∈ t . the pair (x,t ) will be called fuzzy topological space. the elements of t will be called open fuzzy sets. definition 24. [10] let (x,t ) be a fuzzy topological space. a fuzzy set µ1 is a neighborhood of a fuzzy set µ2 if there exists an open fuzzy set µ such that µ2 ⊆ µ ⊆ µ1. theorem 5.1. let (x,t ) be a fuzzy topological space. a fuzzy set µ is an open fuzzy set if and only for each fuzzy set µ2 ⊆ µ, we have that µ is a neighborhood of µ2. definition 25. [10] let x, y be arbitrary sets and f : x → y . if µ is a fuzzy set in y , then the inverse of µ, denoted as f−1(µ), is a fuzzy set in x defined by f−1(µ)(x) := µ(f(x)), (∀)x ∈ x . conversely, if µ is a fuzzy set in x, the image of µ, denoted as f(µ), is a fuzzy sets in y defined by f(µ)(y) = { sup x∈f−1(y) µ(x) if f−1(y) 6= ∅ 0 if f−1(y) = ∅ . definition 26. a function f from a fuzzy topological space (x,t ) to a fuzzy topological space (y,g) is said to be fuzzy continuous if the inverse of each open fuzzy set is an open fuzzy set. 778 i. dzitac in 1976, r. lowen [33] remarked that with chang’s definition constant functions between fuzzy topological spaces are not necessarily continuous. thus r. lowen suggested an alternative and more natural definition replacing the condition x,∅ ∈ t with every constant function belong to t . let (x,t ) be a topological space. we recall that a function f : x → r is said to be lower semi-continuous if for all a ∈ r, {x ∈ x : f(x) > a} is an open set in x. example 27. [33] let (x,t ) be a topological space. the lower semi-continuous fuzzy topology on x associated with t is ω(t ) := {µ : x → [0, 1] : µ is lower semi-continuous} . the usual fuzzy topology on k is the lower semi-continuous fuzzy topology generated by the usual topology of k. remark 28. [34] if (x,ti)i∈i is a family of topological spaces and t is the product topology on x = ∏ i∈i xi, then ω(t ) is the product of fuzzy topologies ω(ti), i ∈ i. definition 29. [33] the closure and the interior of a fuzzy set µ in a fuzzy topological space (x,t ) are defined by µ = inf{µ1 : µ ⊆ µ1 and c(µ1) ∈ t } ◦ µ= sup{µ1 : µ1 ⊆ µ and µ1 ∈ t } . we must note that, in paper [37], j. michálek defined and studied another concept of fuzzy topological space which is quite different from the classic chang’s definition. in paper [35] it is shown the divergences between these two types of fuzzy topological spaces. in paper [58], it is shown that the fuzzy continuous functions can be characterized by the closure of fuzzy sets, a subbasis of a fuzzy topology, and a fuzzy neighborhood. in [53] a more consistent approach to the use of ideas of fuzzy mathematics in general topology has been developed. definition 30. [53] a fuzzy topological space is a pair (x,t ), where x is an arbitrary set and t : f(x) → [0, 1] is a map satisfying the following axioms: 1. t (0) = t (1) = 1; 2. t (µ1 ∧ µ2) ≥ t (µ1) ∧t (µ2), (∀)µ1, µ2 ∈ f(x); 3. t ( ∨ i∈i µi ) ≥ ∧ i∈i t (µi), (∀){µi}i∈i ⊆ f(x). a nice survey concerning fuzzy topological spaces was written by a.p. shostak [54]. this survey contains: various approaches to the definition of fuzzy topology, fundamental interrelations between the categories of fuzzy topology and the category of topological spaces, the notion of a fuzzy point, the convergence structure in fuzzy spaces, important topological properties for fuzzy spaces etc. 6 fuzzy metric spaces one of the important problems concerning the fuzzy topological spaces is to obtain an adequate notion of fuzzy metric space. many authors have investigated this question, and several notions of fuzzy metric space have been defined and studied. we mention that the concept of the fuzzification of classical structures: a general view 779 fuzzy metric was introduced by i. kramosil and j. michálek [9] in 1975. their notion is equivalent, in certain sense, with that of statistical metric. we note that the statistical metrics were studied many years before, and a brief survey on them was made by b. schweizer and a. sklar in paper [52]. we also note that, in 1994, a. george and p. veeramani [18] modified the definition of fuzzy metric in order to obtain a hausdorff topology on a fuzzy metric space. definition 31. [52] a binary operation ∗ : [0, 1] × [0, 1] → [0, 1] is called triangular norm (t-norm) if it satisfies the following condition: 1. a ∗ b = b ∗ a, (∀)a, b ∈ [0, 1]; 2. a ∗ 1 = a, (∀)a ∈ [0, 1]; 3. (a ∗ b) ∗ c = a ∗ (b ∗ c), (∀)a, b, c ∈ [0, 1]; 4. if a ≤ c and b ≤ d, with a, b, c, d ∈ [0, 1], then a ∗ b ≤ c ∗ d. example 32. three basic examples of continuous t-norms are ∧, ·,∗l, which are defined by a ∧ b = min{a, b}, a · b = ab (usual multiplication in [0, 1]) and a ∗l b = max{a + b − 1, 0} (the lukasiewicz t-norm). definition 33. [9] the triple (x, m,∗) is said to be a fuzzy metric space if x is an arbitrary set, ∗ is a continuous t-norm and m is a fuzzy metric, i.e. a fuzzy set in x × x × [0,∞) which satisfies the following conditions: (m1) m(x, y, 0) = 0, (∀)x, y ∈ x; (m2) [m(x, y, t) = 1, (∀)t > 0] if and only if x = y; (m3) m(x, y, t) = m(y, x, t), (∀)x, y ∈ x, (∀)t ≥ 0; (m4) m(x, z, t + s) ≥ m(x, y, t) ∗ m(y, z, s), (∀)x, y, z ∈ x, (∀)t, s ≥ 0; (m5) (∀)x, y ∈ x, m(x, y, ·) : [0,∞) → [0, 1] is left continuous and lim t→∞ m(x, y, t) = 1 remark 34. in the definition of the fuzzy metric space, i. kramosil and j. michálek have imposed another condition: "m(x, y, ·) is nondecreasing, for all x, y ∈ x". m. grabiec [12] showed that this statement derives from the other axioms. indeed, for 0 < t < s, we have m(x, y, s) ≥ m(x, x, s − t) ∗ m(x, y, t) = 1 ∗ m(x, y, t) = m(x, y, t). example 35. [18] let (x, d) be a metric space. let md : x × x × [0,∞), md(x, y, t) = { t t+d(x,y) if t > 0 0 if t = 0 . then (x, md,∧) is a fuzzy metric space. md is called standard fuzzy metric. theorem 6.1. [18] let (x, m,∗) be a fuzzy metric space. for x ∈ x, r ∈ (0, 1), t > 0 we define the open ball b(x, r, t) := {y ∈ x : m(x, y, t) > 1 − r} . let tm := {t ⊂ x : x ∈ t iff (∃)t > 0, r ∈ (0, 1) : b(x, r, t) ⊆ t} . then tm is a topology on x. 780 i. dzitac proposition 36. [18] let (x, d) be a metric space and md be the corresponding standard fuzzy metric on x. then the topology td induced by the metric d, and the topology tmd induced by the standard fuzzy metric md are the same. definition 37. [18] let (x, m,∗) be a fuzzy metric space and (xn) be a sequence in x. the sequence (xn) is said to be convergent if there exists x ∈ x such that m(xn, x, t) = 1, (∀)t > 0. in this case, x is called the limit of the sequence (xn) and we write lim n→∞ xn = x, or xn → x. remark 38. [18] let (x, m,∗) be a fuzzy metric space. a sequence (xn) is convergent to x if and only if (xn) is convergent to x in topology tm . indeed, xn → x in topology tm ⇔ ⇔ (∀)r ∈ (0, 1), (∀)t > 0, (∃)n0 ∈ n : xn ∈ b(x, r, t), (∀)n ≥ n0 ⇔ ⇔ (∀)r ∈ (0, 1), (∀)t > 0, (∃)n0 ∈ n : m(xn, x, t) > 1 − r, (∀)n ≥ n0 ⇔ ⇔ lim n→∞ m(xn, x, t) = 1, (∀)t > 0 . definition 39. [18] let (x, m,∗) be a fuzzy metric space and (xn) be a sequence in x. the sequence (xn) is said to be a cauchy sequence if (∀)r ∈ (0, 1), (∀)t > 0, (∃)n0 ∈ n : m(xn, xm, t) > 1 − r, (∀)n, m ≥ n0 . a fuzzy metric in which every cauchy sequence is convergent is called complete fuzzy metric space. definition 40. [18] let (x, m,∗) be a fuzzy metric space. a subset a of x is said to be fuzzy bounded if there exist r ∈ (0, 1) and t > 0 such that m(x, y, t) > 1 − r, for all x, y ∈ a. remark 41. if (x, m,∗) is a fuzzy metric space induced by a metric d on x, then a ⊆ x is fuzzy bounded if and only if a is bounded. we say that a topological space (x,t ) is fuzzy metrizable if the topology is generated by a fuzzy metric. v. gregori and s. romaguera [22] proved that a topological space is fuzzy metrizable if and only if it is metrizable. in paper [21], the fuzzy metric m∗(x, y, t) := min{x,y}+t max{x,y}+t and other fuzzy metrics related to it were studied. this fuzzy metric is useful for measuring perceptual colour differences between colour samples. 7 fuzzy topological vector spaces the starting point of the theory of fuzzy topological vector spaces was a series of papers of a.k. katsaras (see [27], [28], [29]). let x be a vector space over a field k (where k is r or c). definition 42. [27] let µ1, µ2, · · · , µn be fuzzy sets in x. then µ = µ1 ×µ2 ×···×µn is a fuzzy set in xn defined by µ(x1, x2, · · · , xn) = µ1(x1) ∧ µ2(x2) ∧·· ·∧ µn(xn) . let f : xn → x , f(x1, x2, · · · , xn) = n ∑ k=1 xk. the fuzzy set f(µ) is called the sum of fuzzy sets µ1, µ2, · · · , µn and it is denoted by µ1 + µ2 + · · · + µn. in fact (µ1 + µ2 + · · · + µn)(x) = ∨{µ1(x1) ∧ µ2(x2) ∧·· ·∧ µn(xn) : x = n ∑ k=1 xk} . the fuzzification of classical structures: a general view 781 let µ be a fuzzy set in x and λ ∈ k. the fuzzy set λµ is the image of µ under the map g : x → x, g(x) = λx. thus, (λµ)(x) =      µ ( x λ ) if λ 6= 0 0 if λ = 0, x 6= 0 ∨{µ(y) : y ∈ x} if λ = 0, x = 0 . definition 43. [28] a fuzzy topological vector space is a vector space x over k equipped with a fuzzy topology such that the mappings + : x × x → x , (x, y) 7→ x + y · : k × x → x , (λ, x) 7→ λ · x are fuzzy continuous when k has the fuzzy usual topology and x × x and k × x have the corresponding product fuzzy topologies. in paper [28], the fuzzy vector topologies were characterized in terms of the corresponding families of neighborhoods of zero. theorem 7.1. [29] let x be a vector space over k, and t be a topology on x. then (x,t ) is a topological vector space if and only if (x, ω(t )) is a fuzzy topological vector space. 8 fuzzy normed linear spaces studying fuzzy topological vector spaces, a.k. katsaras [29], introduced in 1984 for the first time, the notion of fuzzy norm on a linear space. in 1992, c. felbin [17] introduced another concept of fuzzy norm by assigning a fuzzy real number to each element of the linear space. in 1994, s.c. cheng and j.n. mordeson [5] introduced another idea of fuzzy norm on a linear space such that their corresponding fuzzy metric was of kramosil and michálek type. following s.c. cheng and j.n. mordeson, in 2003, t. bag and s.k. samanta [2] introduced a new concept of fuzzy norm, and studied the properties of finite dimensional fuzzy normed linear spaces. a comparative study on fuzzy norms introduced katsaras, felbin and bag and samanta was made in paper [4]. other approaches for fuzzy normed linear spaces can be found in [1,7,10,44,48,51,60]. recently, s. nădăban introduced the concepts of fuzzy pseudo-norm and fuzzy f-space [11]. different types of fuzzy bounded linear operators and the relation between fuzzy continuity and fuzzy boundedness were studied in [3], in the context of bag-samanta’s type fuzzy normed linear spaces. the study of fuzzy continuous mappings and fuzzy bounded linear operators in fuzzy normed linear spaces initiated by t. bag and s.k. samanta in [3] was continued by i. sadeqi and f.s. kia [51] as well, as s. nădăban [45] in a more general setting. fuzzy bounded linear operators in felbin’s type fuzzy normed linear space were introduced by m. itoh and m. cho in [23]. j.z. xiao and x.h. zhu [59,60] gave a new definition for fuzzy norm of bounded operators. in [5], different definitions of strongly fuzzy bounded linear operators and weakly fuzzy bounded linear operators were given and a new idea of their fuzzy norm were introduced. in [25], some properties of the space of all weakly fuzzy bounded linear operators were studied. in 2006, r. saadati and j.h. park introduced the notion of intuitionistic fuzzy euclidean normed space (see [49], [50]). in paper [42] some special fuzzy norms on kn were introduced, and in this way, fuzzy euclidean normed spaces were obtained, . in order to introduce this concept it is proved that the cartesian product of a finite family of fuzzy normed linear spaces is a fuzzy normed linear space. 782 i. dzitac definition 44. [27] a fuzzy set ρ in x is said to be: 1. convex if tρ + (1 − t)ρ ⊆ ρ, (∀)t ∈ [0, 1]; 2. balanced if λρ ⊆ ρ, (∀)λ ∈ k, |λ| ≤ 1; 3. absorbing if ∨ t>0 tρ = 1; 4. absolutely convex if it is both convex and balanced. proposition 45. [27] let ρ be a fuzzy set in x. then: 1. ρ is convex if and only if ρ(tx + (1 − t)y) ≥ ρ(x) ∧ ρ(y), (∀)x, y ∈ x, (∀)t ∈ [0, 1]; 2. ρ is balanced if and only if ρ(λx) ≥ ρ(x), (∀)x ∈ x, (∀)λ ∈ k, |λ| ≤ 1. definition 46. [29] a katsaras fuzzy semi-norm on x is a fuzzy set ρ in x which is absolutely convex and absorbing. proposition 47. [31] let ρ be a katsaras fuzzy semi-norm on x. let pα(x) := inf{t > 0 : ρ (x t ) > α}, α ∈ (0, 1) . then p = {pα}α∈(0,1) is an ascending family of semi-norms on x. definition 48. [10] a fuzzy semi-norm ρ on x will be called katsaras fuzzy norm if ρ (x t ) = 1, (∀)t > 0 ⇒ x = 0 . remark 49. a) it is easy to see that [ ρ (x t ) = 1, (∀)t > 0 ⇒ x = 0 ] ⇔ [ inf t>0 ρ (x t ) < 1, for x 6= 0 ] . b) the condition [ ρ ( x t ) = 1, (∀)t > 0 ⇒ x = 0 ] is much weaker than that one imposed by a.k. katsaras [29], [ inf t>0 ρ (x t ) = 0, for x 6= 0 ] . proposition 50. [10] let ρ be a katsaras fuzzy semi-norm and pα(x) := inf{t > 0 : ρ (x t ) > α}, α ∈ (0, 1). then the family of semi-norms p = {pα}α∈(0,1) is sufficient if and only if ρ is a katsaras fuzzy norm. definition 51. [17] let x be a vector space over r, let || · || : x → r∗(i) and let the mappings l, r : [0, 1]×[0, 1] → [0, 1] be symmetric, nondecreasing in both arguments and satisfy l(0, 0) = 0 and r(1, 1) = 1. we write [||x||]α = [||x|| α 1 , ||x|| α 2 ], for x ∈ x, α ∈ (0, 1]. we suppose that (∀)x ∈ x, x 6= 0 there exists α0 ∈ (0, 1] independent of x such that for all α ≤ α0 we have (a) ||x||α2 < ∞ , (b) inf ||x||α1 > 0 . the quadruple (x, || · ||, l, r) is called fuzzy normed linear space and || · || a fuzzy norm, if the fuzzification of classical structures: a general view 783 1. ||x|| = 0 if and only if x = 0; 2. ||rx|| = |r| · ||x||, (∀)x ∈ x, r ∈ r; 3. for all x, y ∈ x, (a) whenever s ≤ ||x||11, t ≤ ||y|| 1 1 and s + t ≤ ||x + y|| 1 1, ||x + y||(s + t) ≥ l(||x||(s), ||y||(t)) , (b) whenever s ≥ ||x||11, t ≥ ||y|| 1 1 and s + t ≥ ||x + y|| 1 1, ||x + y||(s + t) ≤ r(||x||(s), ||y||(t)) . remark 52. c. felbin [17] proved that, if l(x, y) = min{x, y} and r(x, y) = max{x, y}, then the triangle inequality (3) in previous definition is equivalent to ||x + y|| ≤ ||x|| + ||y||. further || · ||iα are crisp norms on x, for each α ∈ (0, 1] and i = 1, 2. remark 53. in paper [5], felbin’s definition of fuzzy normed linear space is slightly modified in the sense that: 1. the value of the fuzzy norm is taken to be a fuzzy real number in the sense of j.z. xiao and x.h. zhu [59]; 2. the condition (a) and (b) of felbin’s definition are relaxed by the condition (a′) x 6= 0 ⇒ ||x||(t) = 0, (∀)t ≤ 0 . definition 54. [10] let x be a vector space over a field k and ∗ be a continuous t-norm. a fuzzy set n in x × [0,∞) is called a fuzzy norm on x if it satisfies: (n1) n(x, 0) = 0, (∀)x ∈ x; (n2) [n(x, t) = 1, (∀)t > 0] if and only if x = 0; (n3) n(λx, t) = n ( x, t |λ| ) , (∀)x ∈ x, (∀)t ≥ 0, (∀)λ ∈ k∗; (n4) n(x + y, t + s) ≥ n(x, t) ∗ n(y, s), (∀)x, y ∈ x, (∀)t, s ≥ 0; (n5) (∀)x ∈ x, n(x, ·) is left continuous and lim t→∞ n(x, t) = 1. the triple (x, n,∗) will be called fuzzy normed linear space (briefly fnl-space). remark 55. a) t. bag and s.k. samanta [2], [3] gave a similar definition for ∗ = ∧, but in order to obtain some important results they assumed that the fuzzy norm satisfies also the following conditions: (n6) n(x, t) > 0, (∀)t > 0 ⇒ x = 0 ; (n7) (∀)x 6= 0, n(x, ·) is a continuous function and strictly increasing on the subset {t : 0 < n(x, t) < 1} of r. the results obtained by t. bag and s.k. samanta can be found in these more general settings [10]. b) i. goleţ [7], c. alegre and s. romaguera [1] gave also the same definition in the context of real vector spaces. remark 56. n(x, ·) is nondecreasing, (∀)x ∈ x. 784 i. dzitac theorem 8.1. [10] if (x, n,∗) is a fnl-space, then m : x × x × [0,∞) → [0, 1], m(x, y, t) = n(x − y, t) is a fuzzy metric on x, which is called the fuzzy metric induced by the fuzzy norm n. moreover, we have: 1. m is a translation-invariant fuzzy metric; 2. m(λx, λy, t) = m ( x, y, t |λ| ) , (∀)x ∈ x, (∀)t ≥ 0, (∀)λ ∈ k∗. corollary 57. [10] let (x, n,∗) be a fnl-space. for x ∈ x, r ∈ (0, 1), t > 0 we define the open ball b(x, r, t) := {y ∈ x : n(x − y, t) > 1 − r} . then tn := {t ⊂ x : x ∈ t iff (∃)t > 0, r ∈ (0, 1) : b(x, r, t) ⊆ t} is a topology on x. moreover, if the t-norm ∗ satisfies sup x∈(0,1) x ∗ x = 1, then (x,tn) is hausdorff. theorem 8.2. [10] let (x, n,∗) be a fnl-space. then (x,tn) is a metrizable topological vector space. 9 conclusions lotfi a. zadeh, born on february 4, 1921, is a famous mathematician, electrical engineer, computer scientist, and professor emeritus at the university of california, berkeley, united state of america. he is father of fuzzy sets, fuzzy logic and computing with words. his pioneering paper, entitled "fuzzy sets" (1965, [61]), is cited over 58,540 time in many prestigious journals, and all his papers are cited over 151,300 time. some scientists, especially philosophers and mathematicians, had attempted to formalize the process of logical deduction. their work culminated in the invention of the programmable digital computer, a machine based on the abstract essence of mathematical reasoning. this machine and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an artificial brain. in this survey paper we mentioned some fuzzy mathematical structures as fuzzy real line, fuzzy topological spaces, fuzzy metric spaces, fuzzy topological vector spaces, fuzzy normed linear spaces and fuzzy f-space. acknowledgments as an editor of this special issue on fuzzy sets and applications, dedicated to celebration of the 50th anniversary of fuzzy sets, i should like to express my deep appreciation to the distinguished authors from belgium, canada, china, greece, lithuania, pakistan, romania, spain, taiwan, and usa, for their valuable contributions (in order of appearance in this issue): r.r. yager, s. ashraf, a. rehman, e.e. kerre, o. bologa, r.e. breaz, s.g. racz, z.-b. du, t.-c. lin, t.-b. zhao, v. kreinovich, c. stylios, s. nădăban, r.-e. precup, m.l. tomescu, e.m. petriu, x. tang, l. shu, h.-n.l. teodorescu, z. turskis, e.k. zavadskas, j. antucheviciene, n. kosareva, h. xu, r. vilanova, h.d. wang, s.c. guo, s. m. hosseini bamakan, y. shi, t. wang, g. zhang, m.j. pérez-jiménez, and f.g. filip. the fuzzification of classical structures: a general view 785 also many thanks to sorin nădăban (romania) and bogdana stanojevic (serbia) for their valuable comments that help improve the manuscript. it is my great pleasure to thanks prof. lotfi a. zadeh for his friendly collaborations with me in 2008 (fig.1, [65], [65]), in 2011 [64], and for his acceptation and helpful collaboration in editing of this special issue in 2015. celebrating the 50th anniversary of your pioneering paper "fuzzy sets", and your 94th birthday, i wish you, dear professor zadeh, a good health, long life, and new interesting achievements. 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(2001); fuzzy set theory and its applications, 4th edition, kluwer academic publisher. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 1, month: february, year: 2020 article number: 1010, https://doi.org/10.15837/ijccc.2020.1.3780 ccc publications bearing fault diagnosis method based on eemd and lstm p. zou, b. hou, l. jiang, z. zhang ping zou 1. school of economics and management, beijing jiaotong university, china no.3 shangyuancun haidian district beijing 100044 p. r. china 18113038@bjtu.edu.cn 2. beijing aerospace smart manufacturing technology development co., ltd, china 49 badachu road, shijingshan dist. beijing 100144 p. r. china ziapple@126.com baocun hou beijing aerospace smart manufacturing technology development co., ltd, china 49 badachu road, shijingshan dist. beijing 100144 p. r. china houbaocun@casicloud.cn lei jiang railway information center, china china national railway corporation, beijing 100844, china ginajiang1984@163.com zhenji zhang* school of economics and management, beijing jiaotong university, china no.3 shangyuancun haidian district beijing 100044 p. r. china *corresponding author: zhangzhenji@bjtu.edu.cn abstract the condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.in the past few years, deep neural network (dnn) has been recognized as an effective tool to detect rolling bearing faults. however, it is too complex to directly feed the original vibration signal to the dnn neural network, and the accuracy of fault identification is not high. by using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. in this paper, a new eemd-lstm bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the eemd method that can get clear fault feature signals, and lstm technology to extract fault features automatically that improves the efficiency of fault feature extraction. in the case of small sample size, this method can significantly improve the accuracy of fault diagnosis. keywords: fault diagnosis, eemd, lstm, motor bearing. https://doi.org/10.15837/ijccc.2020.1.3780 2 1 introduction rotating machinery is widely used in modern manufacturing, aerospace, vehicles, wind turbines and other fields. fault diagnosis of key components of rotating machinery, especially bearings, plays an important role in the reliability and safety of modern industrial system [19]. research on fault diagnosis methods of bearings is of great significance to maintain technical status of equipment and equipment and extend the service time.in the past few decades, major fault diagnosis technology based on vibration signal analysis can be divided into signal processing method [21], intelligent diagnosis method [22]and remaining service life prediction method [24].the signal preprocessing method is very important for bearing fault feature extraction and fault recognition accuracy.on the one hand, the bearing vibration signal is often disturbed by the external environment noise, which makes the signalto-noise ratio low.on the other hand, there are many kinds of bearing parts, the vibration signal measured shows obvious nonlinearity, and the vibration caused by large-scale transient fluctuation of load has strong non-stationary.therefore, it is necessary to preprocess the acquired original signal to enhance the feature information, so as to obtain better diagnosis results. emd (empirical mode decomposition) is a time-frequency signal decomposition tool proposed by n.e. huang for analyzing non-stationary and non-linear signals [15]. it is a typical signal processing method and widely used in the field of mechanical fault diagnosis [13]. emd can decompose complex signals into sum of a series of intrinsic mode function (imf), and each imf component represents an inherent vibration mode of signals, including different characteristic time scales, so that the signal characteristics are displayed at different resolutions.because the frequency component of each imf is not only related to the sampling frequency, but also changes with the change of the signal itself, emd method is an adaptive signal decomposition method with high signal-to-noise ratio, which is very suitable for processing time-varying and non-stationary signals.however, mode mixing is the most significant disadvantage of emd [25]. eemd proposed by z. wu and n.e. huang is an improved method [28], which solves the problem of mode mixing phenomenon by adding white noise into the original signal. the spectrum obtained by eemd method reflects the bearing fault characteristics more accurately.based on the eemd method, scholars have made some achievements in the field of fault diagnosis.yaguo lei proposed a fault feature extraction method based on eemd [17], which is applied to the early rub impact fault diagnosis of heavy oil catalytic cracking unit.in order to solve the problem of emd mode mixing, tong wang proposed a noise aided data analysis method called set experience [26], and studied the influence of emd and eemd on time-frequency.the pattern recognition methods used in these studies are mainly based on the "shallow learning" algorithm, which has high requirements for signal acquisition and processing, limited expression ability for complex functions, and poor generalization ability of the model. deep neural network (dnn) originated from artificial neural network (ann) [5], widely used in vision, speech recognition and natural language processing.various deep learning algorithms, such as auto encoder, stack auto encoder [6], dbm and dbn [14], have also been successfully applied to fault diagnosis.deep learning is a powerful feature learning ability, which can meet the requirements of adaptive feature extraction of mechanical fault diagnosis. its application in the field of fault diagnosis can reduce the dependence on expert fault diagnosis experience and signal processing technology, and reduce the uncertainty introduced by traditional fault diagnosis methods due to artificial design and feature extraction.in addition, deep learning can learn the complex mapping relationship between monitoring data and fault state by building a deep model with multiple hidden layers and multi-level abstraction in the way of nonlinear mapping, which is very suitable for fault diagnosis in the context of complex data. convolution neural network (cnn) and recurrent neural network (rnn) are the typical methods of dnn.most fault vibrations, such as flaking vibration, are periodic due to repeated impact. the amplitude and period depend on the bearing specifications and operating conditions. it is difficult for cnn to identify the continuous changes of these vibration characteristics. therefore, cnn is not enough to diagnose continuous and periodic faults.recursive neural network (rnn) architecture, especially longterm and short-term memory (lstm) neural network and its variants, has been successfully applied in the fields of image subtitle, speech recognition, genome analysis and natural language processing [3]. lstm is a kind of special rnn, which can extract the characteristic information contained in https://doi.org/10.15837/ijccc.2020.1.3780 3 the data of sea going volume efficiently, and can learn the dynamic change of time series adaptively, and can solve the problem of gradient disappearance and gradient explosion in the process of long series training. the experiment shows that the accuracy is better than the conventional rnn model, and can effectively improve the diagnosis rate [16]. zhao, r. obtains the long-term dependence of vibration signal through the lstm model and models the sequence data, monitors the health status of the machine [32]. xiang xu uses the method based on lstm to improve the accuracy of fault identification in power system [31]. this paper focuses on the intelligent fault diagnosis of the bearing of the rotating electrical machine by referring to the research progress of deep learning.according to the non-stationary characteristics of bearing vibration signal, eemd method is combined with lstm. eemd is used as the preprocessor of vibration signal. imf component with obvious fault feature information is selected according to the kurtosis of each component. then, the information of imf component is adaptively fused by lstm and the feature is extracted from it to complete the intelligent classification and recognition of bearing state.at last, the method is compared and analyzed with other fault diagnosis methods by test data. the results show that the performance of the proposed method is far better than svm and bpnn. compared with emd-cnn, the method has higher accuracy and better stability. 2 eemd ensemble empirical mode decomposition method in recent years, empirical mode decomposition (emd) is an effective method to deal with nonlinear and non-stationary signals. its main advantage is to decompose complex signals into several intrinsic mode functions (imf) arranged from high to low frequency, which overcomes the difficulty of selecting basis functions in wavelet transform, but it can not overcome the problem of modal aliasing caused by noise [29].in order to solve the mode aliasing problem, n.e huang added the gauss white noise assisted decomposition method on the basis of emd, which is the ensemble empirical mode decomposition(eemd). its basic principle is to use the uniform distribution characteristics of gauss white noise spectrum, add the white noise into the whole time-frequency distribution space, and take the "average" of the results of multiple decomposition. finally, they are cancelled each other to promote anti aliasing decomposition and avoid modal aliasing. the flow of eemd algorithm is shown in figure 1. the steps of eemd decomposition are as follows: figure 1: eemd decomposition steps (1) add n groups of different gaussian white noise si(t) into the original signal x(t), so that the signal and noise become n populations yi(t) = x(t) + si(t) (2) emd decomposition of n populations with white noise is carried out to obtain the corresponding intrinsic modal function components (imf) cij (t) and remainder terms ri(t). cij (t) represents the https://doi.org/10.15837/ijccc.2020.1.3780 4 j-th imf obtained by decomposition after the i-th white noise is added. (3) the final decomposition result of eemd is obtained by averaging the corresponding imf in step 2 ci(t) = 1/n n∑ i=1 cij (t) where cj (t) represents the j-th component obtained after eemd decomposition of the original signal. compared with one emd decomposition, eemd can eliminate mode aliasing, making the physical meaning of imf clearer. (4) imf component selection: each modal function component decomposed imfi is a narrow band signal from high frequency to low frequency. however, when eemd noise reduction, imf component should be properly selected to retain the most obvious imf component of fault feature information. kurtosis has nothing to do with bearing speed, size and load, etc. it is very sensitive to impact signal. it is particularly suitable for surface damage faults, especially early faults diagnosis. through kurtosis analysis of imfi components , imf components with obvious fault feature information are selected, and the signal kurtosis formula is as follows: cq = 1 n ∑n i=1(|xi|− x̄)4 x4rms where xrms represents the root mean square value of the signal and is an important index used to determine whether the operation state is normal in the mechanical fault. by kurtosis selection [18], the reserved component imfi fault feature signal is more significant. 3 lstm related work 3.1 lstm structure the fault signal component imf extracted by eemd usually has the characteristics of time cycle, and the fault features often have periodicity and repeatability in the time series. the long-term short-term memory (lstm) recurrent neural network can remember the observation results of longterm sequence interval. lstm adopts a network model which uses the form of similar list and has certain memory ability. compared with the composition of general neural network, rnn will add an additional propagation matrix from t-1 time hidden layer to t time hidden layer, as shown in figure 2 below. for sample data with time series, it is necessary to remember the past information to predict the current state, so that the fault features in the sample can be effectively extracted. long short-term memory (lstm) is a kind of time cycle neural network, which is specially designed to solve the long-term dependence problem of recurrent neural network(rnn) [1]. as shown in figure 2, lstm is a chain form of repeated neural network modules. figure 2: lstm structure https://doi.org/10.15837/ijccc.2020.1.3780 5 forgetting gate, input gate and output gate are the core components of lstm, which are composed of multiple feature maps.the forgetting gate determines how much front information passes, and the output results are as follows: ft = σ(wf zzt + whfht−1 + bf ) where σ represents the activation function, w represents the weight, zt represents the current input, and ht−1 is the input result of the previous neuron.bf represents offset. the input gate determines which information can be retained. the calculation formula is as follows: it = σ(wzizt + whiht−1 + bi) c̃t = tanh(wxczt + whcht−1 + bi) the output gate determines which information can be output as a unit, and the calculation formula is as follows: ct = ct−1ft + itc̃t ot = σ(wzozt + whoht−1 + bo) ht = ot × tanh(ct) after the lstm layer is the layer for classification, and the calculation formula is as follows: soft max(yi) = eui∑ eui 3.2 training methods in order to minimize the training error, gradient descent method is used [3]. the main problem of gradient descent method in recurrent neural network (rnn) was first found in 1991, that is, the error gradient disappears exponentially with the time length between events [4].when the lstm block is set, the error is also calculated along with the back propagation, from output to every gate in the input stage until the value is filtered out.therefore, the normal back transfer neural network is an effective method to train lstm block to remember long-term values. the cross entropy is chosen as the cost function to express the error between the real value and the estimated value: l = − ∑ xp(x) lnq(x) where p(x) represents the real classification result, and q(x) is the classification result output by soft max. using a method for stochastic optimization(adam) to train lstm and determine network parameters, that is, weight and deviation.adam can dynamically adjust the learning rate of each parameter by using the first-order moment estimation (mean) and second-order moment estimation (variance) of the gradient, and has achieved success in optimizing the learning rate of lstm. 3.3 lstm design lstm has been widely used in natural language processing(nlp) field [2], which is suitable for dealing with and predicting important events with relatively long interval and delay in time series. at present, it has been proved that lstm is an effective technology to solve the problem of long sequence dependence, and the general adaptability of this technology is very high. bearing fault information is also time series data. the fault information is sliced according to time-domain dimension, and fixed slice length is used as time-series sample, which is also suitable for lstm neural network. the key parameter of lstm is to select the time step, as shown in the figure below. the imf component obtained from the eemd decomposition of the signal sample xi(t) is taken as the time step, so that the lstm network can remember the fault feature signal, which is conducive to improving the accuracy of fault diagnosis. the imf component with obvious fault feature information is adaptively fused by lstm, and the lstm parameter obtained through training is the weight of each component fusion, among which the parameters of lstm neuron of time step are shared. https://doi.org/10.15837/ijccc.2020.1.3780 6 figure 3: time step design of lstm the performance of lstm is related to the number of network layers and the size of output layer.first, the learning ability of lstm is positively related to the number of network layers.to a certain extent, the deeper the network structure, the stronger the expression ability. multi layer lstm is to stack lstm. its advantage is that it can express features more abstractly at high level, reduce the number of neurons, increase recognition accuracy and reduce training time.if the structure of lstm is too simple and the learning ability is poor, the information contained in the input cannot be extracted effectively.the deep structure of the network means that more training data is needed to train a large number of parameters, otherwise, over fitting and local optimization problems may occur.at the same time, due to the increasing complexity of the network, it is very time-consuming to train the model, which increases the difficulty of building a suitable and efficient model.secondly, the output size of lstm needs to be tested and determined according to some basic design principles.generally speaking, the longer the time step is, the higher the output dimension of the lstm is, and the more information it remembers [7], more data can be obtained and more information can be provided for the deeper level. more importantly, for mechanical fault diagnosis, it can better suppress high-frequency noise and improve the accuracy of fault identification.in addition, the output dimension of lstm in the first layer is set to a large size to deepen the network and improve the expression ability of the network, so as to learn to obtain a good feature representation of the input signal.although some basic principles of setting super parameters are mentioned above, a lot of debugging is still needed in the actual process to get the appropriate parameter value [30]. 4 fault diagnosis method flow the bearing fault diagnosis based on eemd and lstm is divided into two main steps. the first step is signal preprocessing, which decomposes the sample signal into eemd and filters the fault characteristic signal according to the kurtosis. the second step is to combine the sample signal preprocessing as input and provide it to the rnn neural network of lstm. the flow is shown in figure 4. step 1: signal preprocessing (1) signal acquisition and division: obtain bearing vibration signal through acceleration sensor, divide it with equal length window, acquire sample signal, and more samples can be extracted from fixed length signal. using this method, time-domain signal with length of g can be divided into n samples, in which the length of sample is moving distance of slice window s, forming n samples with dimension length of s. (2) eemd decomposition of sample signal: eemd decomposition of sample signal obtained from vibration signal slice is carried out to decompose non-stationary signal x(t) into m stationary signals with different characteristic scales (imf1, imf2,..., imfm). then by analyzing the kurtosis of imf components,k imf components with obvious fault characteristic information are selected. (3) data set merging and creation: stack k imf components with obvious fault characteristics into a multi-channel sample to form a matrix of [k,s], perform the above operations on all sample signals, https://doi.org/10.15837/ijccc.2020.1.3780 7 figure 4: fault diagnosis method flow based on eemd and lstm merge and create a data set, form a sample data set of [n,k,s] dimension, and divide the data set into training set and test set. step 2: lstm neural network training (1) lstm design and training: design feasible lstm in accordance with the design principles described in section 3.3 and use the training set for training, debug the super parameters, reduce the network calculation through the pooling layer, increase the model generalization ability, use the full connection layer and classify the training results, and obtain a better performance lstm model through network iterative training. (2) qualitative diagnosis of bearing fault: verify the validity of bearing fault diagnosis model based on eemd and lstm through test set. 5 test 5.1 test description in this paper, phm 2009 challenge data of xichu university [33] is used as experimental data to analyze and verify the eemd + lstm method proposed in this paper. as shown in figure 5 and figure 6 below, the gearbox test bench includes a 2 horsepower motor (left), a torque sensor / encoder (middle), a dynamometer (right) and control electronic equipment. the test bearing is used to support the motor shaft. each pair of meshing gears contains a spur gear and a helical gear. acceleration data are measured near and far away from the motor bearing. single point fault is introduced into the test bearing by edm. the experimental environment is shown in table 1 below. the faults between 0.007 inch and 0.040 inch in diameter occur on the inner raceway, rolling element (i.e. ball) and outer raceway respectively, https://doi.org/10.15837/ijccc.2020.1.3780 8 figure 5: structural diagram of gearbox (a) figure 6: structural diagram of gearbox (b) and the vibration data of 0-3 horsepower (motor speed 1797-1720 rpm) motor load are recorded. 5.2 data description in this paper, 1797/min, load 0, 12k sampling rate normal data and 10 sets of data for inner ring, sphere and outer ring under 0.007, 0.014 and 0.021 fault diameter conditions are used. each group of sample signal contains 120000 sampling points (continuous acquisition for 10s), with an average of 12000 × 60 \ 1797 = 400 data points per cycle, forming 120000 × 10 \ 400 = 3000 samples.the imf sample component of the vibration acceleration sample signal of a single motor rotating periodic bearing after passing through eemd is shown in figure 7. different imf components contain different time scales, which can display the characteristics of signals at different resolutions, while the fault feature information generated by bearings is mainly contained in the first several imf components. when the rolling bearing is damaged locally, the impact component will be produced. the impact component usually contains the fault feature information, so kurtosis is selected as the judgment standard of effective imf.the kurtosis of each imf component f is shown in figure 8. generally, the higher the kurtosis is, the more impact components are contained in the signal. according to the kurtosis threshold, the six components with the largest kurtosis are selected as table 1: description of experimental environment parameters experimental environment parameter value approx. motor speed (rpm) 1797/min,1772/min,1750/min,1730/min sample rate 12k,48k(drive end bearing fault data) motor load (hp) 0,1,2,3 race fault diameter 0.007,0.014,0.021 ball failure diameter (ball) 0.007,0.014,0.021 diameter of outer ring failure 0.007,0.014,0.021 https://doi.org/10.15837/ijccc.2020.1.3780 9 figure 7: imf component diagram after eemd decomposition figure 8: imf component kurtosis the effective imf, which contains more vibration characteristic information generated by bearing fault.then, the six imf components with obvious fault characteristics are stacked into a multi-channel sample in the order of kurtosis from large to small, and the sample data dimension changes from 3000 × 400to3000 × 6 × 400. the sample data dimension changes from 3000 × 400to3000 × 6 × 400. carry out the above operations on all sample signals, create the data set, and divide the data set into training set and test set. the final data set is shown in table 2. 5.3 lstm model parameter design the lstm according to the design principles described in section 3.3, and adjust the relevant parameters through repeated tests. the final model structure and parameters are shown in table 3.units means the output length of the lstm network. dropout means that the neuron in the network is set to zero with a certain probability p, which can suppress the over fitting problem to a table 2: fault diagnosis training set and test set of rotating motor bearing state fault classification faulty diameter training set test set label normal normal 210 90 0 spur1 ball fault 007 210 90 1 spur2 ball fault 014 210 90 2 spur3 ball fault 021 210 90 3 spur4 inner ring fault 007 210 90 4 spur5 inner ring fault 014 210 90 5 spur6 inner ring fault 021 210 90 6 spur7 outer ring fault 007 210 90 7 spur8 outer ring fault 014 210 90 8 spur9 outer ring fault 021 210 90 9 https://doi.org/10.15837/ijccc.2020.1.3780 10 table 3: main structural parameters of lstm model serial number type parameter other parameters 1 input layer shape=[3000,6,400] 2 lstm layer nu=256, ts=6 3 lstm layer nu=256 epoch=50 4 lstm layer nu=128 activation="relu" 5 full connected layer nu=64 classifer="softmax" 6 dropout layer rate:0.25 optimizer="adamoptimizer" 7 output layer oc=10 table 4: test data accuracy under different super parameters batch size 20 40 60 80 100 accuracy date[%] learning rate 0.0005 99.8 99.6 99.2 99.8 99.6 0.001 99.4 99.8 99.8 99.4 99 0.002 99.8 99.8 99.4 99 99.6 0.004 99.6 99.6 98.6 100 99.6 0.006 98.8 98.4 97.6 98.8 99.6 certain extent, and improve the generalization performance of lstm. the designed lstm network is extracted by three lstm feature extraction units, and a full connection layer for feature integration and classification through the layer. the output of lstm in the first level feature extraction unit is 3 × 256, and then there are two feature extraction units, which realize the deep structure of the network and improve the expression ability of the network. nu=number of units;oc=output channel;ts=time steps. one of the important tasks of deep learning is to adjust the super parameters, as shown in table 4. in this paper, the super parameters of batch size and learning rate are as follows. the mini batch gradient method is used to calculate the loss function. the batch size represents the number of samples processed in each batch, and the learning rate represents the update speed of weight and paranoid amount in back propagation. the accuracy rate reflects the validity of the model. it can be seen from table 4 that the accuracy rate of 80 batches and 0.004 learning rate is the highest, which can reach 100%, and the average accuracy rate is 90%. 5.4 results and analysis in order to illustrate the effectiveness of the method proposed in this paper, figure 9 compares the results of bearing condition monitoring realized by eemd-lstm method and bpnn, 1-dcnn [27], emd-cnn [12] method. table 5 summarizes the results of bearing condition monitoring based on the same data set using the methods proposed in this paper, bpnn, 1-dcnn and emd-cnn.among them, bpnn structure is 2048-200-100-50-5, its activation function is relu, the learning rate of 1-dcnn method is 0.004, and the number of training is 500. emd-cnn method will extract nine imf components from the original data through emd, and identify the fault through cnn. in addition, in order to eliminate the influence of accidental error, each method has carried out 10 tests, taking the average value of various evaluation indexes as the test result, and the average value of 10 test results as the classification diagnosis performance evaluation index of this method.it can be seen that the diagnosis method based on eemd and lstm proposed in this paper is obviously superior to the traditional diagnosis method of bpnn.compared with the advanced diagnosis methods 1-dcnn and emd-cnn, the method proposed in this paper not only achieves 99.98% of the recognition indexes, which is better than 1-dcnn and emd-cnn, but also has stable diagnosis results. the reason is that after eemd processing, the signal-to-noise ratio of the signal is improved, and the effective imf component with high kurtosis is constructed as the multi-channel input of lstm..through information https://doi.org/10.15837/ijccc.2020.1.3780 11 figure 9: change of fault diagnosis accuracy tested by different deep learning methods table 5: statistics and general parameters of model test results method accuracy% recall% common parameter eemd-lstm 99.98 99.975 learning rate:0.004 tranning steps:50 batch size:80 bpnn 68.73 69.66 layers and cells: 2048-200-100-50-5 1-dcnn 99.34 99.33 learning rate:0.004 tranning steps:500 emd-cnn 99.79 99.79 4 convolutions layers fusion of lstm, the weight of imf component to the output is adaptively obtained. 6 conclusion in the first chapter, the paper reviews the machine learning methods in the field of fault diagnosis, points out the shortcomings of dnn method in the field of fault diagnosis, a fault diagnosis method based on ensemble empirical mode decomposition (eemd) and long and short term memory neural network (lstm) is proposed to solve the problem that the fault vibration signal of rotating machinery motor is usually nonstationary and the noise pollution is serious. it can realize the adaptive processing of data and simplify the operation of fault diagnosis. the paper introduces eemd and lstm methods in detail in the second and third chapter respectively, and puts forward the fault diagnosis process based on eemd-lstm in the fourth chapter, and in the fifth chapter experimental verification was carried out. in this paper, the proposed method is compared with 1-dcnn and emd-cnn.the experimental results show that: (1) combining the advantages of eemd in analyzing and processing non-linear and non-stationary random signals and the powerful automatic feature extraction ability of lstm, the intelligent bearing fault diagnosis is realized. (2) through kurtosis to determine the effective imf component and build a more centralized data set with vibration information, the classification performance of lstm network is improved. (3) through the test of test data, the proposed fault diagnosis method can achieve 99.98% accuracy, superior to the current advanced diagnosis methods 1-dcnn and emd-cnn, with higher accuracy and better stability of diagnosis results. deep learning has a strong learning ability. it can update the weight by iterative learning method in the signal and automatically raising the fault feature. it has more advantages than those traditional artificial intelligence methods. what is learned from vibration signal and what is its physical meaning in each layer of deep learning? although many literatures have done research and explanation in the field of image recognition, the relevant research in the field of fault diagnosis is not mature. the defects of long training time and poor generalization ability of lstm have not been objectively described. the characteristics of lstm in diagnosis need further study. using the characteristics of vibration signal to design a more "suitable" deep learning model will be the direction of future research [20] [23].in addition to the application in the field of industrial robots, fault diagnosis can also be derived https://doi.org/10.15837/ijccc.2020.1.3780 12 and applied to the fields of transportation [11], finance [11] and supply chain [9] [8], which has a broad application prospect. funding the research was funded by the national key r&d program of china, (no.2018yfb1702700). author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] alangari, h.; kimura, y. 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[33] [online]. available: www.phmsociety.org/competition/phm/09, accesed on 14 february 2019. https://doi.org/10.15837/ijccc.2020.1.3780 14 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: zou, p.; hou, b.; jiang, l.; zhang, z. (2020). bearing fault diagnosis method based on eemd and lstm, international journal of computers communications & control, 15(1), 1010, 2020. https://doi.org/10.15837/ijccc.2020.1.3780 international journal of computers communications & control issn 1841-9836, 12(4), 533-549, august 2017. automatic generation control by hybrid invasive weed optimization and pattern search tuned 2-dof pid controller n. manoharan, s.s. dash, k.s. rajesh, s. panda neelamegam manoharan department of electrical engineering, sathyabama university, chennai, india haran_mano_2000@yahoo.com subhransu sekhar dash*, kurup sathy rajesh department of electrical engineering, srm university, chennai, india munu_dash_2k@yahoo.com, rajeshks.srm@gmail.com *corresponding author: munu_dash_2k@yahoo.com sidhartha panda department of electrical engineering, vssut, burla-768018, odisha, india panda_sidhartha@rediffmail.com abstract: a hybrid invasive weed optimization and pattern search (hiwo-ps) technique is proposed in this paper to design 2 degree of freedom proportionalintegral-derivative (2-dof-pid) controllers for automatic generation control (agc) of interconnected power systems. firstly, the proposed approach is tested in an interconnected two-area thermal power system and the advantage of the proposed approach has been established by comparing the results with recently published methods like conventional ziegler nichols (zn), differential evolution (de), bacteria foraging optimization algorithm (bfoa), genetic algorithm (ga), particle swarm optimization (pso), hybrid bfoa-pso, hybrid pso-ps and non-dominated shorting ga-ii (nsga-ii) based controllers for the identical interconnected power system. further, sensitivity investigation is executed to demonstrate the robustness of the proposed approach by changing the parameters of the system, operating loading conditions, locations as well as size of the disturbance. additionally, the methodology is applied to a three area hydro thermal interconnected system with appropriate generation rate constraints (grc). the superiority of the presented methodology is demonstrated by presenting comparative results of adaptive neuro fuzzy inference system (anfis), hybrid hbfoa-pso as well as hybrid hpso-ps based controllers for the identical system. keywords: automatic generation control, interconnected power system, governor, dead band non linearity, 2 degree of freedom pid controller, invasive weed optimization, pattern search. 1 introduction automatic generation control (agc) loop in a power system calculates the required change in the generation based on the system frequency and tie-line flow deviations, and adjusts the set position of the generators in each area to maintain the time average of frequency and tie-line power changes at a low value [11], [5]. the researchers in the world over are developing a number of control strategies for agc to keep the tie-line flow system and frequency at their desired values both in normal and disturbed conditions [29]in recent times, soft computing based methods have been applied to tune the parameters of the controller [25] [1] ; [28]; [19]. [14] applied de to copyright © 2006-2017 by ccc publications 534 n. manoharan, s.s. dash, k.s. rajesh, s. panda tune the controller parameters for a multi-source power system and relative performances of different classical controllers were compared. [2] employed teaching learning based optimization (tlbo) technique for the design of i/pid controllers for a multi-units multi-sources power system and superiority of tlbo algorithm was demonstrated over de and optimal output feedback controller. [6] proposed an intelligent controller based on emotional learning for lfc of an interconnected power system with generation rate constraint (grc) and demonstrated the advantage their approach over pi, fuzzy logic, hybrid neuro fuzzy (hnf) controller. a firefly algorithm (fa) with on line wavelet filter was employed by [15] for agc of inter connected unequal three area power system. [8] employed artificial bee colony (abc) algorithm for agc and superiority of the approach was demonstrated over pso algorithm. [22] used gravitational search algorithm (gsa) to optimize pi/pidf controller parameters with conventional integral based objective functions for agc system and compared the results with de, bfoa and ga to show the superiority. a teaching learning based optimization algorithm has been applied by [24] for automatic generation control of multi-area power systems with diverse energy sources. it is seen in literatures that the performance of power system depends on the tuning technique, controller structure and choice of cost function. in this regard, it is observed that, a two degree of freedom controllers provide better performance than a single degree of freedom controller [21]. having known all this, in the present work, an ideal 2 degree freedom of pid (2-dof-pid) controller for agc of multi-area power systems. generally, all population centered heuristic optimization techniques offer acceptable results but there is no guarantee that a particular technique will give a better performance than other techniques in all optimizing problems [30] . hence, suggesting and realizing novel heuristic techniques are always desired. each heuristic technique has its own advantages and disadvantages. hybrid algorithms taking the advantage of two or more algorithms have been recently proposed in literature. [16] proposed a hybrid bfoa-pso algorithm to tune the controller parameters for agc systems. a hybrid pso-ps algorithm is proposed by [23] to tune the fuzzy pi parameters. a modified de optimized fuzzy pid controller for load frequency control with thyristor controlled series compensator has been proposed by [20]. in recent times, invasive weed optimization (iwo), a novel biologically motivated optimization technique was proposed by [12]. iwo is a robust, stochastic and derivative free optimization algorithm for the solution of complex real world problems. it is based on the invasive habits of growth of weeds in nature and having excellent exploration and exploitation ability in the search area. iwo has been successfully employed to a number of engineering problems such as recommender system design [18], antenna system design [10], state estimation of nonlinear systems [13] , unit commitment problem solution [27] and economic load dispatch of power systems [3]. to get excellent performance using any optimization technique, a balance of exploitation as well as exploration throughout the search procedure is to be maintained. iwo being a global search technique, searches the wide search area and may not give best solution if employed alone. alternatively, local search methods such as pattern search (ps) exploits the local but cannot perform extensive search [4] . owing to their individual strengths, there is a scope for hybridization of these algorithms [31]. in view of the above, a hybrid iwo-ps technique is suggested in this work for tuning the parameters of 2-dof-pid controller for agc of interconnected systems. in the present study, a two area thermal as shown in figure 1 is considered as the system under study. the same system is extensively used in literature for proposing new agc approaches [1]; [19]; [17]; [23]. in figure 1,b1 & b2 represent the frequency bias parameters;ace1 & ace2 stands for area control errors;u1 & u2 are the control outputs;r1 & r2 represent the regulation parameters in pu hz;tg1 & tg2 are the time constants of governor in sec; ∆pg1&∆pg2 are the incremental valve positions (pu); tt1&tt2 are the time constant of turbine in sec;∆pt1&∆pt2 are the incremental automatic generation control by hybrid invasive weed optimization and pattern search tuned 2-dof pid controller 535 figure 1: transfer function model of two-area non reheat thermal system turbine powers; ∆pd1&∆pd2 are the step load perturbations;∆ptie is the incremental tie-line power (p.u);kps1 & kps2 are the power system gains; tps1 & tps2 are the time constants of power system in sec; t12 represent the synchronizing coefficient in p.u. and ∆f1 and ∆f2 are the incremental frequency changes in hz. the parameters of the system are specified as shown. nominal parameters of the two area thermal power system are: pr = 2000mw(rating); pl = 1000mw(nominal loading); f = 60 hz; b1 = b2 = 0.425p.u.mw/hz; r1 = r2 = 2.4hz/p.u.; tg1 = tg2 = 0.08s; tt1 = tt2 = 0.3s; kps1 = kps2 = 120hz/p.u.mw ; tps1 = tps2 = 20s; t12 = 0.545pu the area control errors (ace)of each area are given [5]: ace1 = b1∆f1 + deltaptie (1) ace2 = b2∆f2 −deltaptie (2) in the present study, each component of the power system are represented by appropriate transfer functions. the transfer function of turbine is given by: gt (s) = ∆pt (s) ∆pv (s) = 1 1 + stt (3) governor transfer function is given by: gg(s) = ∆pv(s) ∆pg(s) = 1 1 + stg (4) the output of speed governing system ∆pg(s) is given by: ∆pg(s) = ∆pref (s) − 1 r ∆f(s) (5) 536 n. manoharan, s.s. dash, k.s. rajesh, s. panda the transfer function generator and load is given by: gp(s) = kp 1 + stp (6) where kp = 1d and tp = 2h fd the output ∆f(s) of generator load system has two inputs ∆pt (s) is given by: ∆f(s) = gp(s)[∆pt (s) − ∆pg(s)] (7) 2 ideal two degree of freedom pid controller depending on the number of closed-loop transfer functions which can be controlled individually, the degree of freedom of a control system is classified. in a control system design problem, numerous performance criteria are to be satisfied thus a 2-degree-of-freedom (2-dof) controller offers some advantages over the single degree of freedom control system [26]. the 2-dof controller calculates a weighted difference signal for each of the control actions as per the set point weights and gives an output signal which is the sum of the control actions on the respective difference signals [21]. a derivative filter is used for improved system performance in presence of noise or random error in the measured process variable. it also limits the huge controller output changes which derivative action causes due to presence of measurement noise and helps to lessen the controller output variations which may result in wear in the control parts. the structure of proposed ideal 2-dof-pid controller is given in figure 2 where r(s) represents the reference signal, y(s) is the feedback signal and u(s) represents the output signal,kp,ki&kd are the controller gains, pw & dw are the set point weights, and n is the filter coefficient of derivative term. a 2-dof-pid control system is given in figure 3 where c(s) a one degree figure 2: two degree of freedom (tdof) pid control structure of freedom controller,d(s) is the load disturbance and f(s) is the filter acting on the reference signal. in an ideal 2-dof-pid controller, and are specified by: f(s) = (pw + dwkd)s 2 + (pwn + ki)s + (kin) (1 + kdn)s2 + (n + ki)s + (kin) (8) automatic generation control by hybrid invasive weed optimization and pattern search tuned 2-dof pid controller 537 figure 3: tdof control system c(s) = kp (1 + kdn)s 2 + (n + ki)s + (kin) s(s + n) (9) an integral of time multiplied absolute error (itae) based objective function given in eq. (10) is chosen in this paper to design the proposed controllers. itae is chosen over other integral based objective functions because it gives less over shoots and settling times compared to other criterion such as integral of squared error (ise), and integral of absolute error (iae). other integral squared criteria such as integral of time multiplied squared error (itse) and integral of squared time multiplied error (iste) based design produces huge controller output when there is a sudden variation in reference which is not desirable. j = itae = ∫ tsim 0 ω1(|∆f1| + |∆f2| + |∆ptie|)t.dt (10) where, and are the system frequency changes; is the change in tie-line power and is the simulation time. the optimization problem can be expressed as: minimize j (11) subject to kpmin ≤ kp ≤ kpmax,kimin ≤ ki ≤ kimax,kdmin ≤ kd ≤ kdmax pwmin ≤ pw ≤ pwmax,dwmin ≤ dw ≤ dwmax,nmin ≤ n ≤ nmax (12) where kpmin,kpmax ; kimin,kimax and kdmin,kdmax are the lower and upper bounds of the control parameters. pwmin,dwmin and pwmax,dwmax are the lower and upper bounds of set point weights,nmin,nmax and are lower and upper bounds of derivative filter coefficient. the bounds of controller parameters, set point weights and filter coefficient are taken as -2 & 2, 0 & 5 and 10 & 300 respectively. 3 overview of invasive weed optimization technique invasive weed optimization (iwo) is a novel population based stochastic, derivative free optimization technique inspired from the biological growth of weed plants. it was first developed and designed by [12]. the iwo algorithm is based on the colonizing actions of weed plants [18]. some of the interesting characteristics of weed plants that are invasive, fast reproduction and distribution, robustness and self adaptation to the changes in climate conditions. the significant characteristic of the iwo algorithm is that it lets all the plants to contribute in the reproduction procedure. fitter plants yield more seeds than less fit plants and this results 538 n. manoharan, s.s. dash, k.s. rajesh, s. panda in the algorithm to converge. additionally, it is still probable that some of the less fit plants carry beneficial information in iteration process as compared to the some fitter plants. thus iwo algorithm provides an opportunity to the lesser fit plants to participate in reproduction process. if the seeds formed by lesser fit plants have better fitness in the colony, they can survive [12]. another significant characteristics of iwo algorithm is that reproduction is done without mating and every weed can yield new seeds, individually. this reproduction without mating characteristics augments a new quality to the technique as each agent may have not the same number of variables during the optimization process and the number of variables can be selected as one of the optimization parameters in iwo. also, iwo algorithm has more chance to avoid local minima points compared to ga and pso due to its continuous and normally distributed dispersal structure over search space which has a decreasing variance parameter centered on each parent plant [18]. the steps of the proposed approach are mentioned below: step 1: based on the number of chosen variables (d ) of the assumed problem, the seeds are initialized. the initial seed are distributed uniformly over the entire solution space. step 2: create each seed set, after generating all the selected variables of the given problem randomly within their effective lower and upper limits. thus, in the search space, each seed contains random values for all variable. each seed set represents a potential solution of the given problem. generate several seed set to create a seed matrix (s) of size (popmaxxd). the total number of plants in the population is selected as (popmax) after satisfying their limits. step 3: the fitness value of all individuals of the current seed set (s) (each row (plant) of s) is calculated according to the cost function considered in the given problem. these individuals evolve into weed plants which are capable of creating new units. step 4: as per the fitness value of each plant with respect to others, each plant is ranked. then, every weed yields new seeds depending on its rank in the set of seed. all plants are participating in reproduction process which adds a new attribute to the optimization providing chances to contribute useful information (good result) by less fit plants during iterative process. step 5: the number of seeds to be produced by all weed changes linearly from nmin to nmax which can be calculated by: seed number = fi −fworst fbest −fworst (nmax −nmin) + nmin (13) where,fi is the fitness associated with ith weed,fworst and fbest denotes the worst and best fitness in weed population. the created seeds are normally distributed over the field with zero average and variable standard deviation of σiter defined by σiter = [ itermax − iter itermax ]n(σ0 −σf ) + σf (14) where, itermax and itermin are the maximum number of iteration and current iteration, respectively. σ0 and σf , are the predefined initial and final standard deviations and n represent the modulation index. step 6: the new seeds breed to the flowering plants when all seeds found their positions over the search area. next, they are ranked together with their parents in the seed set matrix. plants with lower ranking in the colony are removed and the maximum number of plants in the colony (popmax) is maintained. step 7: survived plants can yield new seeds as per their ranking. the fittest individual (plant) is selected from the seed-parent combination of current seed set. if the stopping criterion is satisfied, the iterative process is terminated and the results (gain schedule) are displayed, otherwise go to step 3 for continuation. automatic generation control by hybrid invasive weed optimization and pattern search tuned 2-dof pid controller 539 4 overview of pattern search algorithm pattern search (ps) algorithm is an effective but simple technique applicable to the complex problems which cannot be solved by conventional optimization techniques. it has a flexible operator to fine tune the local explore capability [23]. the ps method consists of a series of polls xk k ∈ n . a number of trial steps with i = 1, 2, ...p are added to the polls xk to get trial points xik = xk + s i k at each poll. at these trial points the objective function value is calculated through a sequence of exploratory steps and compared with its previous value j(xk) . the trial step s∗k corresponding to least value of j(xk + s i k) − j(xk) < 0 is then selected to produce the subsequent estimation of the patterns polls xk+1 = xk + s∗k. the trial steps s i k are produced by a step length parameter ∆k ∈ r+in . the ∆k value is updated in subsequent polls as per xk+1 value. the improvement of ∆k , help the algorithm to converge. these elements are explained in more details in reference [4]. 5 results 5.1 application of hiwo-ps algorithm as the two areas are assumed identical, similar controllers are assumed in each area. the objective function is calculated by applying a 10percent step load disturbance in area-1. a series of runs are executed to properly select the algorithm parameters. number of search agents and iterations are taken as 20 and 50 respectively. the optimization process was repeated 10 times and the best solution obtained in 10 runs is selected as final controller parameters. in the next step, the proposed hiwo-ps algorithm is applied to optimize the controller parameters. in hiwo-ps algorithm, initially optimal iwo is executed for 40 iterations and then ps is employed for 10 iterations. the final solution corresponding to the minimum objective function value provided by optimal iwo is used as the beginning points of ps algorithm. for the implementation of ps algorithm, the following parameters are used: mesh size=1, mesh expansion factor=2, mesh contraction factor= 0.5, max. no. of function estimations=10, max. no. of iterations = 10. the optimized 2-dof-pid parameters are provided in table 1. for comparison, the optimized pi controller parameters are also specified in table 1. table 1: tuned controller parameters controller technique controller parameters iwo: pi kp=-0.3005, ki=0.4551 hiwo-ps kp =-0.3106, ki =0.4524 iwo: 2-dof-pid kp =1.764, ki =1.764, kd =0.4785, pw =7.201, dw =4.3767, n =298.2108 hiwo-ps: 2-dof-pid kp =1.889, ki =1.9398, kd =0.4941, pw =7.2088, dw =2.7742, n =318.1317 5.2 result analysis a 10 percent step load disturbance in area-1 is considered at t=0.0 sec. the itae values with iwo and hiwo-ps optimized pi/2-dof-pid controllers are shown in table 2. to demonstrate the efficiency of the proposed hiwo-ps technique, results are compared with genetic algorithm: ga, bacteria foraging optimization algorithm: bfoa [1], differential evolution: de [19], particle swarm optimization: pso, hybrid bfoa-pso cite16, non dominated shorting ga-ii: nsga-ii optimized pi controllers, nsga-ii optimized pid controller with derivative filter cite17, pattern search: ps, pso, and hybrid pso-ps cite23 optimized fuzzy pi controllers for the same interconnected power system. it is obvious from table 2 that 540 n. manoharan, s.s. dash, k.s. rajesh, s. panda table 2: itae values with different controllers and optimization techniques controller tuning method/ optimization technique itae value pi hybrid invasive weed optimization (iwo)pattern search 1.1761 pi invasive weed optimization (iwo) 1.1763 pi ziegler nicholas (ali, & abd-elazim, 2011) 3.7568 pi ga (ali, & abd-elazim, 2011) 2.7475 pi bfoa (ali, & abd-elazim, 2011) 1.7975 pi de (rout, sahu, & panda, 2013) 1.2551 pi pso (panda, mohanty, & hota, 2013) 1.2142 pi hybrid bfoa-pso (panda, mohanty, & hota, 2013) 1.1865 pi nsga-ii (panda, & yegireddy, 2013) 1.1785 2-dof-pid hybrid iwo-ps 0.1037 2-dof-pid iwo 0.1311 pidf nsga-ii (panda, & yegireddy, 2013) 0.387 fuzzy pi ps (sahu, panda, & sekher, 2015) 0.6334 fuzzy pi pso (sahu, panda, & sekher, 2015) 0.4470 fuzzy pi hybrid pso-ps (sahu, panda, & sekher, 2015) 0.1438 with the same pi controller, tuned using the same itae objective function, lowest itae value is obtained with proposed hiwo-ps technique (itae=1.1761) compared to iwo (itae=1.1763), z-n tuning (itae=3.7568), ga (itae=2.7475), bfoa (itae=1.7975), de (itae=1.2551), pso (itae=1.2142), hybrid pso-ps technique (itae=1.1865) and nsga-ii (itae=1.1785). in the above evaluation, identical interconnected power system with two similar pi controllers is assumed and the controller parameters are tuned using an itae objective function. therefore, it can be concluded that proposed hiwo-ps technique provides better performance than iwo, ga, bfoa, de, pso, hybrid pso-ps nsga-ii techniques as lowest itae value is achieved using hiwo-ps technique. from table 2, it is furthermore apparent that, value of itae is considerably reduced (itae=0.1311) with iwo tuned 2-dof-pid controller. the itae value is reduced (itae=0.1037) with hiwo-ps tuned 2-dof-pid controller. it is also evident from table 2 that hiwo-ps tuned 2-dof-pid controller gives minimum itae value compared to iwo optimized 2-dof-pid controller (itae=0.1349), pattern search (ps) tuned fuzzy pi controller (itae=0.6334), pso tuned fuzzy pi controller (itae=0.447), hybrid pso-ps tuned fuzzy pi controller (itae=0.1438) and nsga-ii tuned pidf controller (itae=0.387). in the next step, a step load perturbation (slp) of 10 percent is applied at t = 0 sec in area-1 and time domain simulation results are plotted. the system dynamic responses are shown in figures 4-6. the results of some recently published approaches like de cite19, bfoa cite1, hbfoa-pso cite16 tuned pi controller and pso fuzzy pi, ps fuzzy pi & hpso-ps fuzzy pi cite23 controllers for the identical system are also provided in figures 4. it can be seen from figure 4 that, considerable improvement is achieved with hiwo-ps tuned 2-dof-pid controller compared other methods. for a better illustration of advantage of proposed approach over various approaches proposed in recent times, itae values as well as settling times in tie-line power and frequency deviations for the above disturbance is summarized in table 3. it is evident from table 3 that best system performance in terms of minimum itae values and settling times are obtained with proposed hiwo-ps tuned 2-dof-pid controller as related to other recent methods. the dynamic response of the system for a concurrent 10percent slp in area 1 as well as 20 percent slp in area 2 at t = 0 s is assumed and the system dynamic responses are shown in automatic generation control by hybrid invasive weed optimization and pattern search tuned 2-dof pid controller 541 figure 4: change in frequency of area-1 for 10% step load increase in area-1 figure 5: change in frequency of area-2 for 10% step load increase in area-1 figures 7-9. it is obvious from figures 7-9 that the proposed controllers perform satisfactorily with change in the location and step size of the disturbance. better dynamic responses are obtained with proposed hiwo-ps tuned 2-dof-pid controller as compared to other recently reported methods in all the cases. robustness analysis is done to investigate the usefulness of the system when there are wide deviations in the loading conditions and parameters of the system. these parameters (loading condition and time constants) of speed governor, turbine, tie-line power are varied one after another from their initial values by +50 percent to -50 percent in steps of 25percent. the performance index under changed conditions are provided in table 4. the above sensitivity analysis is performed by assuming a slp of 10 percent in area-1 at t=0 sec. to demonstrate the advantage of the proposed approach, results are compared with hpso-ps tuned fuzzy pi controller [23] under the same varied conditions. in this comparison, hpso-ps tuned fuzzy pi controller values are selected for comparison as least itae value is attained with, hpso-ps 542 n. manoharan, s.s. dash, k.s. rajesh, s. panda figure 6: change in tie line power for 10% step load increase in area-1 table 3: performance comparison with recent agc approaches performance itae settling time value ∆f1 ∆f2 ∆ptie conventional zn: pi(ali, & abd-elazim, 2011) 3.7568 45 45 28 ga: pi (ali, & abd-elazim, 2011) 2.7475 10.59 11.39 9.37 bfoa: pi (ali, & abd-elazim, 2011) 1.7975 5.52 7.09 6.35 de: pi (rout, sahu,& panda, 2013) 0.9911 8.96 8.16 5.75 pso: pi (panda, mohanty, & hota, 2013) 1.2142 7.37 7.82 5.0 hbfoa-pso: pi (panda, mohanty, & hota, 2013) 1.1865 7.39 7.65 5.73 nsga-ii: pi (panda, & yegireddy, 2013) 1.1785 6.49 7.54 5.79 nsga-ii: pidf (panda, & yegireddy, 2013) 0.387 3.03 4.86 4.34 ps: fuzzy pi (sahu, panda, & sekher, 2015) 0.6334 6.05 7.10 5.56 pso: fuzzy pi (sahu, panda, & sekher, 2015) 0.4470 5.13 6.22 4.83 tuned fuzzy pi controller related to other methods. it is obvious from the simulation results that the system performances remain more or less the same with varied loading condition and system parameters. thus it can be concluded that, the hiwo-ps tuned 2-dof-pid controller offers a robust and efficient control strategy. also, the controller parameters which are tuned at the nominal system conditions, need not be retuned when there are wide variations in the system parameters. 5.3 extension to three unequal area non-liner hydro thermal power system to establish the capability of the proposed method to deal nonlinearity and several tielines, the method is applied to a unequal three area non-linear thermal hydro power system ( [16]; [10]; [23] as shown in figure 10. in this case different controllers are assumed in each area as the areas are unequal. a grc (generation rate constraints) of 3% min is assumed for thermal units. a grc of 270%min for rising and 360% min for lowering generation are considered for hydro unit. the related system parameters are specified. three-area hydro thermal power system with generation rate constraints: b1 = b2 = b3 = 0.425pu mw/hz; r1 = r2 = r3 = 2.4hz/pu mw ; tg1 = tg2 = automatic generation control by hybrid invasive weed optimization and pattern search tuned 2-dof pid controller 543 figure 7: change in frequency of area-1 for 10% step load increase in area-1 and 20% step load increase in area-2 figure 8: change in frequency of area-2 for 10% step load increase in area-1 and 20% step load increase in area-2 0.08s; tr1 = tr2 = 10.0s,tt1 = tt2 = 0.3s; tw = 1.0s; tr = 5s; kps1 = kps2 = kps3 = 120hz/p.u.mw ; tps1 = tps2 = tps3 = 20s; t12 = t23 = t31 = 0.086pu; a12 = a23 = a31 = −1 the objective function in this case is defined by: j = itae = ∫ tsim 0 (|∆f1| + |∆f2| + |∆f2 + |∆pt12| + |∆pt13| + |∆pt23|)t.dt (15) where ∆f1, ∆f2 and ∆f3 are the frequency devotions and ∆pt12, ∆pt13 and ∆pt23 are the tieline power deviations between individual areas. the final parameters obtained using proposed hiwo-ps algorithm are: 544 n. manoharan, s.s. dash, k.s. rajesh, s. panda figure 9: change in tie line power for 10% step load increase in area-1 and 20% step load increase in area-2 table 4: robustness analysis for two area two unit system parameter variation percent change performance index with hpso-ps optimized fuzzy pi (sahu, panda, & sekher, 2015) performance index with proposed hiwo-ps optimized 2-dof-pid settling time ts(sec) itae settling time ts(sec) itae ∆f1 ∆f2 ∆ptie value ∆f1 ∆f2 ∆ptie value nominal 0 2.26 3.74 2.94 0.1438 1.39 1.97 2.01 0.1037 loading condition +50 2.26 3.75 2.94 0.1438 1.39 1.97 2.01 0.1037 +25 2.26 3.75 2.94 0.1438 1.39 1.97 2.01 0.1037 -25 2.26 3.74 2.94 0.1437 1.39 1.97 2.01 0.1037 -50 2.26 3.74 2.94 0.1437 1.39 1.97 2.01 0.1037 tg +50 2.21 3.64 2.81 0.1321 1.29 1.9 1.99 0.1029 +25 2.22 3.70 2.88 0.1386 1.44 1.96 2.0 0.1034 -25 2.28 3.76 2.96 0.1460 1.39 1.97 2.01 0.1046 -50 2.31 3.77 2.97 0.1469 1.41 1.99 2.03 0.1053 tt +50 1.98 3.61 2.80 0.1348 1.02 1.81 1.89 0.991 +25 2.16 3.69 2.88 0.1409 1.21 1.86 1.92 0.1015 -25 2.33 3.76 2.95 0.1422 1.48 2.03 2.06 0.1064 -50 2.39 3.74 2.91 0.1354 1.58 2.12 2.13 0.1089 t12 +50 2.73 3.51 2.70 0.1361 1.42 1.89 2.08 0.0919 +25 2.56 3.60 2.80 0.1399 1.39 1.92 2.05 0.0967 -25 1.92 3.98 3.14 0.1513 1.4 2.0 1.87 0.1163 -50 3.02 4.48 3.53 0.1917 1.51 1.97 1.87 0.1403 kp1 = 1.8539,ki1 = 1.7880,kd1 = 0.1682,pw1 = 14.5646,dw1 = 11.2175,n1 = 495.2406 kp2 = 1.6238,ki2 = 1.8195,kd2 = 0.2642,pw2 = 10.1788,dw2 = 14.7692,n2 = 125.3624 kp3 = 0.0711,ki3 = 1.9101,kd3 = 0.0240,pw3 = 0.0122,dw3 = 18.9549,n3 = 139.4454 a 1% slp is applied at the same time in all the three areas at t=0 sec. the system dynamic responses are given in figures 11-13. the responses with anfis based controller [10], hbfoa-pso tuned pi controller [16] and hpso-ps based fuzzy pi controller [23] are also shown in figures 11-13 for comparison. figures 11-13 clearly establishes that system performance is appreciably enhanced with hiwo-ps tuned automatic generation control by hybrid invasive weed optimization and pattern search tuned 2-dof pid controller 545 figure 10: transfer function model of model of three-area hydro-thermal system with generation rate constraint figure 11: change in frequency of area-1 for 1% step load increase in all areas 2-dof-pid controller compared to anfis based controller, hbfoa-pso tuned pi controller and hpso-ps based fuzzy pi controller. finally, to demonstrate the advantage of the hiwo-ps tuned 2-dof-pid controller, itae values are compared with some recently reported optimization approaches [23]. in all the cases 1% slp is applied in all the areas at the same time. the results are briefed in table 5. it is obvious from table 5 that least itae value is achieved with proposed hiwo-ps tuned 2-dof-pid controller (itae=0.8378) compared to hpso-ps technique (itae=1.3999), rcga (itae=2.4873), gsa (itae=1.7805), de (itae=1.6857) and fa (itae=1.5344) algorithms. it is evident from table 9 that, hiwo-ps algorithm out 546 n. manoharan, s.s. dash, k.s. rajesh, s. panda figure 12: change in frequency of area-2 for 1% step load increase in all areas figure 13: change in frequency of area-3 for 1% step load increase in all areas table 5: comparison of itae values with different approaches for three area system techniques controller itae value ga (sahu, panda, & sekher, 2015) fuzzy pi 2.4873 gsa (sahu, panda, & sekher, 2015) fuzzy pi 1.7805 de (sahu, panda, & sekher, 2015) fuzzy pi 1.6857 fa (sahu, panda, & sekher, 2015) fuzzy pi 1.5344 hpso-ps (sahu, panda, & sekher, 2015) fuzzy pi 1.3999 hiwo-ps 2-dof-pid 0.8378 performs rcga, gsa, de, fa and hpso-ps techniques. automatic generation control by hybrid invasive weed optimization and pattern search tuned 2-dof pid controller 547 6 conclusion a 2 degree freedom of pid (2 dof pid) controller for automatic generation control (agc) of multi-area power systems is presented in this paper. the controller parameters are tuned tuned by hybrid invasive weed optimization and pattern search (hiwo ps) technique. an extensively used standard two area thermal system test system which is considered at the first instance for the agc design. at the outset, the superiority of hiwo-ps over iwo, ziegler nichols (zn), genetic algorithm (ga), bacteria foraging optimization algorithm (bfoa), differential evolution (de), particle swarm optimization (pso), hybrid bfoa pso, hybrid pso ps and nondominated shorting ga ii (nsga ii) is established. it is observed that proposed hiwo ps tuned 2-dof-pid controller achieves better system dynamic performances compared to several agc approaches reported in recent times. furthermore, robustness analysis is carried out and it is shown that the hiwo ps tuned 2-dof-pid controller perform satisfactorily when there are extensive variations in system parameters and operating load conditions. lastly, the proposed method is applied to three unequal area non-liner hydro thermal system. it is observed that proposed hiwo ps tuned 2dof pid controller gives better dynamic response 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(2014), a novel hybrid optimization algorithm of pattern search and iwo, applied mechanics and materials, 687-691, 1557-1559, 2014. international journal of computers communications & control issn 1841-9836, 12(2):265-275, april 2017. balancing between exploration and exploitation in aco a.e. negulescu, s.c. negulescu, i. dzitac alina eugenia negulescu university politehnica of bucharest romania, 060042 bucharest, sector 6, splaiul independentei, 313 alina.lascu@gmail.com sorin constantin negulescu lucian blaga university of sibiu romania, 550024 sibiu, victoriei bvd., 10 sorin.negulescu@ulbsibiu.ro ioan dzitac 1. aurel vlaicu university of arad romania, 310330 arad, elena dragoi, 2 ioan.dzitac@uav.ro 2. agora university of oradea romania, 410526 oradea, piata tineretului, 8 rector@univagora.ro abstract: in order to balance the preference of the artificial entities towards exploration or exploitation (in their transition rule), a novel technique is proposed for replacing the random function used by the classical ant colony optimization (aco) algorithms for solving the traveling salesman problem (tsp). the proposed beta distribution function (b), or random.betavariate(a,b) has the proven capability (depicted through test-runs) of influencing the algorithm’s solution quality and convergence speed. consequently, this paper will introduce in the related work section the classical aco algorithm, with a focus on the transition rule used for choosing the next node in the problem’s associated graph, followed by the related work on this topic, and it will continue with the introduction of the b function which will be presented both from a theoretical and practical perspective in relation with the scope: balancing between exploration and exploitation in order to improve the performance of the aco algorithm for the tsp. the paper concludes that the b-eas has the ability to find better solution than eas for a set of benchmarks from the tsplib library. keywords: aco, tsp, b, elitist ant system (eas), beta-elitist ant system (beas), transition rule. 1 introduction swarm inspired algorithms, and specifically the aco area, represent a steady interest over the past five years, as reflected in searches and recorded by google trends [7]. the constant focus on this family of algorithms is due to their simplicity in terms of modeling and, implicitly, the ease of understanding their functionality, which makes them, in turn, very appealing for computer scientists aiming to solve optimization problems. since they are inspired from nature, the principles of the aco algorithms are easy to grasp. their well-known applicability is for solving the tsp, which can also be very easily explained but, unfortunately, not that simple to solve: a traveling salesman has a list of cities which he must visit and return back to the starting point; there are several conditions that must be met: he must pass through each city only once, and he must find the shortest route to fulfill this task, as copyright © 2006-2017 by ccc publications 266 a.e. negulescu, s.c. negulescu, i. dzitac otherwise, longer routes will be associated with bigger costs. this problem can be extrapolated to the world of ants which, foraging for food, need to find the shortest path between the nest and the food source. because of these resemblances in terms of tasks, the ant colony inspired algorithms are directly applicable for solving the tsp. pertaining the parameters, by mirroring the natural ant colony model, the classical aco algorithm operates with artificial entities (i.e. the ants), positive feedback using pheromone deposit in the environment, heuristic information regarding distances in the search-space and negative feedback resulted from pheromone evaporation. for all intents and purposes, these parameters, which are inspired from nature, are the most influential ones in any aco algorithm. other common traits between aco algorithm variants are the initialization steps that include: • loading the problem’s associated graph; • setting up the initial parameter’s values; • initialization of the pheromone trails on the graph’s arches (using a minimum quantity of pheromone) as to "kick-start" the algorithm as otherwise, it would take a couple of iterations until the artificial ants would do this instead and this would result in additional computational costs. even though the last described step is obviously not characteristic to real ants, the model has been slightly altered so as to increase the convergence speed of the algorithm towards a solution. after performing the initialization, as long as the algorithm’s stop condition is not met (represented by, for instance, a maximum number of iterations or time limitation), every artificial ant needs to perform a set of steps, part of the construct solutions function: • choose a starting node on the graph – predictably (ant 1 in node 1, ant 2 in node 2 and so on) or randomly; • choose next node until all nodes are visited only once (function on which this paper is centered), during which the "artificial ant has to decide" which nodes are more attractive and choose one of them based on a probability formula that will be presented shortly; after all artificial ants have constructed solutions: • update pheromone trail function is employed for depositing a pheromone quantity on all the arches of the graph visited by the artificial ants during a tour, which, following the natural model, represents the positive feedback, or reinforcement; in this way the arches that have been used by multiple ants will have a greater pheromone intensity; • evaporate pheromone trail function will act as negative feedback in order for the algorithm to "forget" old and inefficient trails; this step is paramount as without it, the algorithm would converge too fast and get trapped in local optima; • store best solution function will compare all tour lengths of the artificial ants and record the smallest length value along with the order in which the nodes have been visited, for future comparisons. having presented the context, the paper will focus on the transition rule of aco algorithms which is based on a probability formula (as mentioned earlier). the state of the art is presented in "origins and related work", while the "rationale and approach" section pinpoints the drawbacks of current approaches and presents the beta distribution function as a possible solution. the balancing between exploration and exploitation in aco 267 section entitled "the model", describes the changes that have been applied to the eas algorithm in order to support the identified approach for improvement: beta-elitist ant system (b-eas) algorithm. the "conclusions and intentions" section rounds up the paper by presenting the strengths and weaknesses of the b-eas algorithm, together with the identified future research intentions in this direction. 2 origins and related work initial experiments, conducted with real ants (linepithaema humile), proved that when ants have to choose between two possible paths of different lengths (when going between a food source and the nest) they will initially travel on any of them with an equal probability but, as time passes, the shorter one will have a higher amount of deposited pheromone (reinforced by multiple, shorter trips) whereas the longer one will have a lower amount of pheromone (due to fewer, longer trips). moreover, considering that the evaporation speed of the pheromone is equal, the intensity of the pheromone on the longer path will decrease faster. even though the pheromone intensity on the two paths was different, a very small number of ants were still choosing the one with smaller pheromone intensity from time to time, which lead to the conclusion that the ants were deciding in a probabilistic manner. the findings in this "double-bridge experiment" [8] were adopted when the initial ant system (as) algorithm was modeled [5]. as such, almost all aco algorithms are using a probability formula for determining the next node. the formula coined by as, which is reproduced below (unaltered) defines, as called by its authors, a random-proportional rule: pki,j (t) =   [τij(t)] α·[ηij(t)]β∑ l∈nk i [τil(t)] α·[ηil(t)] β ifj ∈ nki 0 ifj /∈ nki (1) formula 1 corresponds to the probability p at the moment t for an ant k visiting node i to choose node j as the next node in which it will go. α and β are two general parameters used for weighting the pheromone intensity τij and the visibility ηij (defined as 1/distance) between nodes i and j when computing (at the moment t) the cost of choosing node j. the set nki contains the nodes the ant k is allowed to choose (i.e. all the nodes which were not yet visited, as constrained by the tsp). so, the formula can be expressed as being the ratio between the cost of choosing node j and the sum of costs for all the other nodes not yet visited. of course, this probability is 0 if node j is already visited. before the above presented formula is applied, a random real number between 0 and 1 is generated, representing the probability value. afterwards, pkij (t) is calculated for as many nodes j ∈ nki as necessary, until the sum of these computed costs is equal or greater than the selected probability and the last j node is chosen to move to. in this way, in many cases the formula is not applied for all the nodes not yet visited, sparing some time. the steps above are repeated until no more nodes are left to visit, resulting a list of nodes visited by the ant k. as a further improvement of the initial as algorithm, dorigo and gambardella proposed in the ant colony system (acs) algorithm a new transition rule that included exploration and exploitation traits [6]. this improvement, as described by its authors, is due to the pseudorandom-proportional rule for choosing the next node in the itinerary, where the ant k in the node i selects the next node j with a probability of 0% ≤ q0 ≤ 100%. as such, a q0 value is a priori selected when the algorithm starts, whereas the next node is selected by applying one of the following formulas depending on a randomly generated value q: • if q < q0 then the node for which τij (t) · [ηij (t)]β is maximum, is used; 268 a.e. negulescu, s.c. negulescu, i. dzitac • else, the node is selected by using the same formula introduced in the as algorithm. as described in [6], "acs algorithm’s state transition rule provides a direct way to balance between exploration of new edges and exploitation of a priori and accumulated knowledge about the problem". along with other several improvements, acs’ proven efficiency was demonstrated by its authors through a series of experiments whose results had shown that this algorithm outperformed other algorithms inspired from nature, as for instance, simulated annealing and evolutionary computation. another improvement of the as was the elitist ant system (eas) [5] that was reinforcing the pheromone intensity on the arches belonging to the tour of the ants that found the best solutions at the moment t. in this way one can state that the elitist ants can deposit a greater amount of pheromone. the experiments of the before mentioned authors indicated that a relatively small number of elitist ants can help the performance of the algorithm. nevertheless, as depicted in [1], the author claims that "the transition rule suggested in the original ant system and used extensively in modified versions [...] uses a power law scaling procedure to define the relative importance of the pheromone intensity and local information. [...] this form of transition rule, however, often leads to premature convergence of the ant algorithms to suboptimal solution when used with elitist strategy of pheromone updating". the author proposed, as a result, an alternative transition rule to be used with elitist strategy of pheromone updating in ant algorithms. moreover, the author of [1] considers that the classical transition rule presented in formula 1 would be the source of the local optima issue which called for the design of the max-min ant system (mmas) algorithm. further explained in [1], the reason for which the transition rule is not efficient is that the influence of the pheromone intensity (α) will play a main role in the decision making process of an ant choosing the next node to visit, while the heuristic information will have no impact. this is determining, as a result, the stagnation of the search. consequently, the author proposes an alternative method of transition that would prevent the pheromone intensity from "dominating the ants decision table at all stages of computation". this formula is represented as: pki,j (t) =   ατij(t)·βηij∑ l∈nk i [ατil(t)·βηil] ifj ∈ nki 0 ifj /∈ nki (2) the author in [1] claims that, this new transition rule based on addition, will ensure that for “the properly adjusted values of the pheromone, and local heuristics sensitivity parameters, the local heuristic term ηij will always have a saying in forming the decision table, irrespective of the distribution of pheromone intensities”. moreover, this method has the advantage of "reducing the number of controlling parameters, as one can always assume the value of unity for one of these parameters and adjust the value of the other one for the best performance of the method", as described by the aforementioned author. other solutions that were targeting the improvement of the original as algorithm were the rank-based ant system (rbas) [3], best-worst ant system (bwas) [4], hypercubeant colony optimization (hc-aco) [2] and grahp-based ant system (gbas) [9], to name a few of the most common. also, a respectable number of approaches went beyond the initial paradigm, implementing pheromone correction strategies [14] or creating hybrid solutions involving evolutionary computation [15] and neural networks [13]. whereas in the classical aco algorithm, α and β parameters are global, in the model presented in [11], they become local and "personal" to each individual ant. in this way, ants can behave differently in their transition rule. considering that the resource costs associated with this approach is minimal (in terms of memory) the benefits are various: this variant of aco balancing between exploration and exploitation in aco 269 algorithm has the ability to self-tune depending on the problem type and size (if local α and β parameters are adjusted based on the tour lengths found by each ant), the convergence speed can be improved and the parameters values can be easily adjusted automatically. all these benefits can assist in addressing the stagnation issue (i.e. local optima) that is typical to all meta-heuristic algorithms. 3 rationale and approach as it can be depicted from the selected examples provided in the previous section, there is an on-going interest regarding the improvement of the aco algorithms, due to their proven potential for solving combinatorial optimization problems. some improvement approaches are focusing on the update pheromone intensities rule, others are looking towards the parameters’ values setting, while this paper investigates the transition rule which the ants use to move through the nodes in the graph. all algorithms from the meta-heuristic class are relaying on a probability that implies the use of randomly generated numbers in different manners. without this intrinsic randomness factor the behavior would tend to be the same as the best-first algorithm and the solutions quality would be poor. the approach presented in this paper, that would allow to change the algorithm’s behavior towards exploration or exploitation, is based on another type of probability: b, that would allow a beta-proportional rule. beta distribution can be defined, according to [10] as "a mean of distribution probabilities", that essentially, represents "all the possible values of an unknown probability". this method is reasonably employed by statistics and probability theory and is defined in [0, 1] domain, having two positive shape parameters: a and b. its application is widely used in diverse areas as a technique that allows influencing the behavior of random variables limited to ranges of limited lengths. given these features, it can be used as a proper model for random behavior (pertaining the uncertainty of an outcome). for all practical purposes, a test-run may only have two possible results: either success (with a probability of x) or failure (with a probability of 1−x). as explained by the same author, this can be probabilistically illustrated by assigning to x a uniform distribution in the [0, 1] domain, as x being a probability, it can only take values between 0 and 1. also, the uniform distribution assigns respective probability density to every entity in the domain, which effectively means, that no possible value of x is a priori, more likely than the other. as such, for n independent iterations performed during an experiment, there can result both k successful outcomes, as well as n−k failed ones. consequently, the conditional distribution of x, based on the observation of k successful outcomes resulted from n iterations, represents, actually, a beta distribution with k + 1 and n−k + 1 parameters. therefore, as depicted in [10], for x, absolutely continuous random variable: rx = [0, 1] and a,b ∈ r++, x has a beta distribution with a,b as shape parameters of x’s probability density function, presented through the following formula, where b (a,b) represents the b function: f (x) = { 1 b(a,b) xa−1 ( 1 −xb−1 ) ifx ∈ rx 0 ifx /∈ rx (3) to visually grasp the behavior of this function for different parameter values, below are presented several histograms. the data set is represented by 10000 samples with values within the [0, 500] domain on the abscisa and the number of randomly generated samples is displayed on the ordinate. 270 a.e. negulescu, s.c. negulescu, i. dzitac 0 200 400 600 800 0 100 200 300 400 500 n u m b er o f g en er a te d sa m p le s value a = 0.7, b = 1.0 a = 1.0, b = 0.7 figure 1: histogram for beta distribution function with one sub-unitary and one unitary argument 0 100 200 300 400 500 600 0 100 200 300 400 500 n u m b er o f g en er a te d sa m p le s value a = 1.0, b = 1.0 a = 0.7, b = 0.7 a = 5.0, b = 5.0 figure 2: histogram for beta distribution function with equal arguments values balancing between exploration and exploitation in aco 271 according to the visual representation of b in figures 1 and 2, one can easily depict the fact that this function is highly flexible since it allows, depending on the values of its parameters, to randomly generate a higher number of values at one end or the other of the interval, a uniform distribution and a normal (inverse) distribution. also, it is important to mention that, if the a and b parameters equal 1, this function is behaving exactly like the classic random function where all the values have an equal probability of being generated and the algorithm would perform exactly like the as algorithm. 4 the model based on the previously introduced approach, consisting in replacing the random-proportional transition rule with the beta-proportional one, this section describes in detail, the model and the method used for testing it. the foundation of the model is the eas algorithm where the herein proposed transition rule has been modified to include the following steps: • determine the costs of moving from the current node to all the nodes not yet visited by applying the formula for computing pkji (t) using the same formula used by eas (as presented in the origins and related work section). • store these costs together with the corresponding node number in a vector by inserting them in such a way that the vector is ordered increasingly (so the nodes that have smaller costs are at the beginning of the vector). obviously, generating this vector in an ordered fashion has the advantage that it is much faster than generating and then sorting it. • generate a random number according to b that will indicate the position (in the vector) of the node selected as the next node to move to. the values of the parameters for the function must be chosen in such a way that it will generate with a higher probability small values so the elements at the beginning of the vector have a higher chance to be selected. 1 2 3 4 5 �1,5 �1,5 � 1 ,2 � 1 ,2 �1,3 �1,3 � 1,4 � 1,4 figure 3: example of associated graph with heuristic and pheromone information for a better understanding of the steps involved in the new model called beta-elitist ant system (b-eas), the graph in figure 3 is provided as an example. presuming that an ant starts in node 1, the cost of going to nodes 2, 3, 4 and 5 is taking into consideration the pheromone intensity and the visibility on each arch. the ordered vector, resulted after computing the costs, is presented in figure 4, together with the probability of selection according to b function. as it can be depicted in the example, node 3 which involves the smallest cost in terms of visibility and pheromone intensity, has a higher probability of being selected than 2, 4 and 5 if the arguments of b are properly chosen. test-runs were conducted for a full factorial experiment in order to determine appropriate values for the a and b parameters of b. subsequently, in order to generate smaller values with 272 a.e. negulescu, s.c. negulescu, i. dzitac ordered vector of nodes with computed values p ro b a b il it y o f se le c ti o n 100% 75% 50% 25% 0% 3 2 4 5 ∑ ��13+��13 ��1l+��1l)5 l=2 ∑ ��12+��12 (��1l+��1l)5 l=2 ∑ ��14+��14 (��1l+��1l)5 l=2 ∑ ��15+��15 (��1l+��1l)5 l=2 ∑ figure 4: example of selection probability for the ordered vector’s elements 1s t b et a d ist rib ut io n fu nc tio n pa ra m et er iterations t o u r l e n g th d e v ia tio n fro m o p tim u m (% ) < 100% < 50% < 40% < 30% < 20% < 10% < 60% < 70% < 80% < 90% 10 0 90 80 70 60 50 40 30 20 10 0 figure 5: solution length versus 1st parameter of beta distribution function) balancing between exploration and exploitation in aco 273 425 450 475 500 525 550 0 200 400 600 800 1000 s o lu ti o n l en g th iterations b-eas eas figure 6: comparison between eas and b-eas algorithms running on eil51 benchmark 1.5 · 104 1.6 · 104 1.7 · 104 1.8 · 104 1.9 · 104 2 · 104 2.1 · 104 0 1000 2000 3000 4000 5000 s o lu ti o n l en g th iterations b-eas eas figure 7: comparison between eas and b-eas algorithms running on d198 benchmark 274 a.e. negulescu, s.c. negulescu, i. dzitac a higher probability, the values for parameter a have been tested within [0.1, 1] domain, while b was maintained constant at 1. the results are presented further on in figure 5, where, for a set of problems with known solutions, the algorithm was constrained to run for 100 iterations. the quality of the obtained solutions were averaged and compared in a percentual manner to the known solutions so, the optimal solution obtained is 0% deviating from the best known ones (represented in darker color in the graph). the conclusion of this experiment is that the optimal parameters’ values of b function, used for generating random numbers, are a = 0.4 and b = 1. as displayed in figure 5 (where the dependent variable tour length is expressed versus parameter a and the number of iterations dependent variables), these values are driving the algorithm towards finding better solutions (with shorter lengths), and with a higher convergence speed. a comparison between the eas and b-eas algorithms using standard benchmarks from the tsplib [12] in regards to solutions quality and convergence speed is presented in figures 6 and 7. we have observed that without modifying the a and b parameters during the test-runs, the b-eas was converging slower than eas, but it continued to find better solutions, whereas eas would have stopped in a local optima. as stated before, when parameters have the values a < 1 and b = 1, ants tend to choose with a higher probability the nodes that give smaller costs of travel (exploitation), but there is also the probability to choose nodes that imply higher costs (exploration). this is why the graphs are displaying large improvements that are not further more developed over a high number of iterations, especially when a is much smaller than 1. conclusions and intentions the paper presents a new algorithm (b-eas) that can be stirred towards exploitation or exploration by modifying the a and b parameters used in the beta-proportional transition rule. specifically, the hereby authors propose replacing the classical random function for choosing the next node with the beta distribution function which allows a more flexible way of generating random numbers. to validate the proposed model, the optimal parameter values of b were determined for a set of benchmark problems using a full factorial experiment. the next logical step was to compare through test-runs the performance of b-eas with the original algorithm from which it was derived (eas). the results have shown that b-eas finds better solutions than eas, given enough time or iterations. considering the approach introduced by the same authors in [11], where the global parameters of the algorithm (α, β and � ) were transformed into local parameters for each artificial ant, creating thus a "population" of agents with individual characteristics (personalities), the same approach could be applied for the a, and b parameters of the b function, as well. we consider that this research 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(2014); a modified ant colony algorithm for traveling salesman problem, international journal of computers communications & control, 9(5): 633-643. international journal of computers communications & control issn 1841-9836, 12(4), 519-532, august 2017. pid and fuzzy-pid control model for quadcopter attitude with disturbance parameter e. kuantama, t. vesselenyi, s. dzitac, r. tarca endrowednes kuantama*, tiberiu vesselenyi, simona dzitac, radu tarca university of oradea 1 universitatii st., oradea, romania endrowednes@gmail.com, vtiberiu@uoradea.ro, simona@dzitac.ro, rtarca@uoradea.ro *corresponding author: endrowednes@gmail.com abstract: this paper aims to present data analysis of quadcopter dynamic attitude on a circular trajectory, specifically by comparing the modeling results of conventional proportional integral derivative (pid) and fuzzy-pid controllers. simulations of attitude stability with both control systems were done using simulink toolbox from matlab so the identification of each control system is clearly seen. each control system algorithm related to roll and pitch angles which affects the horizontal movement on a circular trajectory is explained in detail. the outcome of each tuning variable of both control systems on the output movement is observable while the error magnitude can be compared with the reference angles. to obtain a deeper analysis, wind disturbance on each axis was added to the model, thus differences between each control system are more recognizable. according to simulation results, the fuzzy-pid controller has relatively smaller errors than the pid controller and has a better capability to reject disturbances. the scaling factors of gain values of the two controllers also play a vital role in their design. keywords: quadcopter, pid, fuzzy-pid, simulink, attitude. 1 introduction a quadcopter, as its name suggests, has four symmetrical rotor-propellers which are mounted at the same height on each edge, forming a cross with identical radius from its center [9]. each rotor propeller creates an upward thrust which is highly maneuverable by controlling the speed variation, thus allowing the quadcopter for vertical take-off, landing and even hovering. however, to achieve good stability as well as to minimize interference in every movement, the quadcopter attitude must be converted into a suitable control algorithm. this is considered to be a challenging problem because quadcopters have a complex dynamic system with high nonlinearities, strong couplings, and under actuation [13]. according to the control strategy of quadcopters, the flight control systems can be divided into 3 types, i.e., linear flight control system, nonlinear flight control system, and learning based flight control system [5]. the linear flight control system, or feedback linearization, is a very useful method to decouple and linearize quadcopter attitude models. this paper will review in detail the algorithm of linear control system for quadcopters using conventional pid (proportional integral derivative) and fuzzy-pid control. considering the advantages of the ziegler-nichols rules for tuning the pid controller [22] and the feedback linearization method, we adopted this algorithm for the quadcopter attitude control and we analyzed the obtained results. the way of tuning pid controller parameters with mathematical modeling into feedback linearization for stability attitude is crucial. the way of tuning pid controller parameters with mathematical modeling using feedback linearization is crucial for attitude stability. on the other hand, fuzzy-pid algorithms have limitless ranges to flawlessly perform when rapid changes are encountered in high dynamic environments, moreover copyright © 2006-2017 by ccc publications 520 e. kuantama, t. vesselenyi, s. dzitac, r. tarca it does not require complex mathematical models [7]. a comparison of the pid and fuzzy-pid tuning is presented in which both controllers have been studied based on a dynamic model of a quadcopter. modeling of the quadcopter control and movement has been done using simulink matlab. through this modeling and control algorithm, differences between angular velocity with and without pid and fuzzy-pid control can be studied, as well as the impact of disturbances on the control system. to understand the control system of quadcopters, firstly one must comprehend the concept of quadcopter attitude based on the newton-euler equation. details of the quadcopter dynamic movement using this equation have been already covered in previous researches [12], [15]. this time, newton-euler equation is adapted to develop a quadcopter attitude model in which the quadcopter moves in a full circle while using pid and fuzzy-pid control systems for dynamic movement control. another aim of this study is to investigate the behavior of the quadcopter control system when faced with wind disturbances from multiple directions. 2 quadcopter attitude modelling vertical thrust is the result of combined angular velocity of four rotors which are directly connected to the propellers, thus driving the quadcopter attitude as a whole. naturally, rotor propellers placed in opposed position will rotate in the same direction and side-by-side ones will rotate oppositely. controlling the angular velocity of the four rotors will consequently result in a dynamic movement with force and moment transmission which can lead to lift, pitch, roll or yaw rotations moment inertia. fig. 1. illustrates these rotations, and are explained as follows: rotation around x-axis as roll movement, rotation around y-axis as pitch movement, and rotation around z-axis as yaw movement. figure 1: quadcopter schematic: (a) the structure of a quadcopter (b) description of pitch, roll, and yaw angles quadcopter attitude has a total of six degrees of freedom (dof), half are allotted to translational position in an earth inertial frame and the rest to angular positions of the quadcopter’s rigid body. all dofs are covered by the linear and angular orientation vectors of the quadcopter, thus it can be seen that the quadcopter has four inputs from the rotor propellers and six state outputs of the dynamic and complex system. in order to control these, the process of quadcopter modeling can be approached by interpreting any attitude movement with two reference frames in the form of the earth inertial frame (coordinate system) axes (xe, ye, ze) and the body frame axes (xb, yb, zb). from this point, we can formulate the equations and parameters needed to establish the transformation matrix based on the newton-euler equation model [3]. each parameter needed for attitude modeling can be seen in table. 1. pid and fuzzy-pid control model for quadcopter attitude with disturbance parameter 521 table 1: parameter for attitude modelling algorithm 2.1 newton-euler kinematics model the newton-euler modeling was adapted in designing linear(lt ) and angular motion (rt ) expressions of the quadcopter which can deliver lift up forces where change of attitude occur on the orientation of the body frame axes with respect to the earth inertial frame axes. orientation change of body frame axes results in angle alteration on roll rx(φ) , pitch ry(θ) , and yaw rz(ψ), respectively. based on the kinematic moving frame theory, the equations using transformation matrices for angular motion on each axis or for every movement can be written. matrix transformation between two rectangular coordinate systems is orthogonal and can be converted to 3x3 matrices for every axis. eq.(1). shows the rotation matrix around x-, y-, and z-axis and the transformation of the overall rotation movement (rt ) of the body frame can be calculated using eq.(2). rx(φ) =  1 0 00 cosφ sinφ 0 −sinφ cosφ   ; ry(θ) =  cosθ 0 −sinθ0 1 0 sinθ 0 cosθ   ; rz(ψ) =   cosψ sinψ 0−sinψ cosψ 0 0 0 1   (1) rt = rx(φ)ry(θ)rz(ψ) (2) the rate of change in linear motion from one position to another is related to the angular velocity and the difference in angle which will result in alteration of system output x, y, z position. this alteration can be computed using the angular rates measured by sensor of the quadcopter (p, q, r). the rotation angle in eq.(1). is used to compute the angular velocity of the quadcopter. the angular velocity with respect to the body frame can be computed by transforming the matrix from angular position [p′ q′ r′]t to euler angle rates [φ′ θ′ ψ′]t given in eq.(4). in fact, the values of p, q and r are obtained through orientation sensor readings on the quadcopter, thus the alteration of the angles of the body frame can be computed using the inverse of eq.(4)., as it is seen in eq.(5). v =  pq r   = rx(φ)ry(θ)rz(ψ)   00 ψ′   + rx(φ)ry(θ)  0θ′ 0   + rx(φ)  φ′0 0   (3)  pq r   =  1 0 −sinθ0 cosφ sinφcosθ 0 −sinφ cosθcosφ    φ′θ′ ψ′   (4)  φ′θ′ ψ′   =  1 sinφtanθ cosφtanθ0 cosφ −sinφ 0 sinφ/cosθ cosφ/cosθ    pq r   (5) the dynamic movement of the quadcopter, both on angular and translational basis are expressed with the newton-euler equation of motion as shown in eq.(6). the angular acceleration 522 e. kuantama, t. vesselenyi, s. dzitac, r. tarca of the inertia (iω′), the centripetal forces (ω × (iω)) and the gyroscopic forces (γ) are equal to the external torque applied to the bodyframe (τ). from this equation, angular velocity vector (ω′) can be determined as shown in eq.(8). τ = iω′ + (ω × (iω)) + γ (6) ω′ = i−1[−ω × (iω) + τ −γ] (7)  p′q′ r′   = i−1(  (iyy − izz)qr(izz − ixx)pr (ixx − iyy)pq   +  τφτθ τψ  − imωi   q−p 0  ) (8) where im is the inertia matrix, ω is the angular velocity, ω′ is the angular position, and ωi is the scalar angular speed of rotor. the inertia matrix for the quadcopter is diagonaldue to the symmetry of the quadcopter. quadcopter’s mass and its geometric distribution (especially inertia) are affecting the entire dynamics of the system. the symbols ixx, iyy, izz are the moments of inertia matrices which are the torque of the rotors. 2.2 circular trajectory model analysis of attitude control was done by modeling the dynamic of the quadcopter moving on a circular trajectory. this implies that the x-y position on the circular trajectory needs to be computed using stability control on roll and pitch motions while yaw angle is zero. the path of motion along xand y-axes can be seen in fig. 2. [18]. figure 2: quadcopter attitude trajectory model as it is known, two rotor propellers will cause movement on each axis around the center of gravity. in this modeling, rotors placed on the same axis will generate opposite thrust values, or in other words, the increasing thrust on one side is linear with the decreasing thrust on the other side in the same axis. the rotation torques on the body frame which derives from the rotor on each axis are dependent upon two parameters, i.e., the distance between rotor and quadcopter’s centroid (l) and the magnitude of thrust generated by the rotors on the same axis (x-axis or y-axis). the torque equation on the body frame on each axis is given in eq.(9). τx = l(trotor4 −trotor2); τy = l(trotor3 −trotor1) (9) from the quadcopter theory and attitude parameter, basic modeling was done with only a proportional control system. close loop feedback was designed to ensure a good functionality of the algorithm for quadcopter attitude, as well as to evaluate the output of the angular velocity on every axis. there are three subsystems of the designed model, i.e., input parameters in form pid and fuzzy-pid control model for quadcopter attitude with disturbance parameter 523 of inertia matrices ixx, iyy, izz and distance between rotor and centroid (l), the algorithm for quadcopter attitude and circular trajectory, and lastly the output in form of quadcopter movement on xand y-axis. the simulation block diagram for this model can be seen in fig. 3. the quadcopter parameters used in this model are as follows: l = 0.25 m and ixx = iyy = 0.005 kg.m2. because the modeling focused only on xand y-axis, izz was temporarily unneeded. figure 3: simulation block diagram 3 controller architecture analysis of the matrices function for every attitude reveals the fact that the quadcopter model requires a close feedback linearization control system for balancing and localization. this control architecture is necessary to enhance the dynamic control response of the quadcopter with respect to the quadcopter body frame. in this study, the movement on a circular trajectory was investigated and to achieve stability, pid and fuzzy-pid control systems have been used [2]. generally, the control system for the quadcopter moving on a circular trajectory, in fly/hover mode, or in other attitude movement can be done with the same control algorithm to attain stability. fig. 4 shows the quadcopter control system with close loop feedback and linearized pid or fuzzy-pid control. in this paper, the controller for x and y position will compute the desired roll and pitch angles depending on the desired values for quadcopter movement on each axis. these angles are fed into the ”attitude” block and with a closed-loop feedback the controller can continuously do a linearization process against any possible error factor. 524 e. kuantama, t. vesselenyi, s. dzitac, r. tarca figure 4: quadcopter control system diagram 3.1 pid control parameter implementation of pid controller tuned by ziegler-nichols rules to the linear model of quadcopter can be done by adopting the parameter feedback linearization items which are acting on the quadcopter position. the advantages of the pid controller is its simple control structure and the ease of implementation [4]. a pid controller regards an ”error” value as the difference between a measured variable and a desired set-point. the equation of a pid controller is: u(t) = kpe(t) + ki ∫ t 0 e(τ)dτ + kd d dt e(t) (10) e(t) = xd(t) −x(t) (11) in which u(t) is the control output, e(t) is the difference between the desired stated xd(t) and the actual state x(t), and kp,ki,kd are the proportional, integral, and derivative gains, respectively. alteration in pitch and roll angles may occur based on the torque generated torque by the rotors on x-axis or y-axis on the body frame. in this modeling, it was sufficient just to manage the kp and kd control parameters (p and d control), as ki was used to reduce the final error of the system and thus it is not needed [10]. the nominal coefficients of pd used to obtain the robust stability equation are based on the angular velocity equation of the quadcopter [14]. hence, the pd controller for the quadcopter in horizontal position can be calculated using eq.(12). based on two relations, i.e., the torque generated by each horizontal axis (shown in eq.(9).) and also the equation of angle alteration (shown in eq.(4).). the total mass of the quadcopter will have impact on the generated thrust, whereas the torque will be influenced by the moments of inertia (i). τx,y = [ l(trotor4 −trotor2) l(trotor3 −trotor1] ] = [ (kφ,p(φp′−φ′) + kφ,d(φd′−φ′))ixx (kθ,p(θp′−θ′) + kθ,d(θd′−θ′))iyy ] (12) the performance of pd controller was tested using stability simulation of quadcopter during the movement on the circular trajectory. the pd controller parameters can be seen in fig. 5. where the values of parameters kp and kd were obtained based on manual tuning which was done to get magnitude stability of the attitude value. parameter setting principles are based on the ziegler-nichols pd. the amount of error that occurs on the roll angle is not significant, so pid and fuzzy-pid control model for quadcopter attitude with disturbance parameter 525 it only takes the proportional gain value to adjust the generated output variable. the purpose of the stabilization is to obtain the same output of angular velocity in any desired position. figure 5: quadcopter pid control system simulink block diagram 3.2 fuzzy-pid control parameter as for the combination between pid control theory and fuzzy control theory, the fuzzypid control aims to establish a two variable continuous function between the pid parameters, absolute error value and absolute error value changes [11]. it has a self-tuning characteristic to automatically tune the control parameter to obtain optimal output [21]. optimal output can be obtained by adjusting the gain values of kp−fuzzy,ki−fuzzy,kd−fuzzy based on the comparison of the input in form of error and error rate values. the input will be analyzed using a fuzzy system in which if-then rules are applied as tuning parameter set of the received input [1], [19]. in accordance with the typical pid controller, the gain values of the fuzzy-pid controller can be adjusted with the parameters as seen on eq.(13). [8]. ufuzzy−pid = kp−fuzzye(t) + ki−fuzzy ∫ t 0 e(τ)dτ + kd−fuzzy d dt e(t) (13) a fuzzy-pid controller is proposed in this work to control the quadcopter stability in terms of attitude position on circular trajectory, considering that the quadcopter movements are heavily influenced by non-linear factors and dynamic coupling. the fuzzy-pid control attitude on pitch and roll positions are designed separately in order to accurately control the pid gain values. as it is known through pid control parameter, in this design, the pd gain values are enough to control the quadcopter movement on a horizontal path. the position of the quadcopter on a circular trajectory depends on the acceleration of the quadcopter and is based on the change in pitch and roll angles. each angle changes will be monitored and controlled using a fuzzy-pid controller. each controller has 2 membership functions where the first one, represented by in1, receives the current angle which is obtained from the orientation sensor reading of the quadcopter attitude; the other one (in2) reseives the error rate which is obtained from comparing current angle value with the reference angle value. through these 2 membership functions, the fuzzy pid system with if-then logic or fuzzy rule parameters can be applied to reduce the position error value on output in the form of quadcopter movement. mamdani-type inference is used for the inference engine with centroid defuzzification method [16] and it lays out the foundation rules for the fuzzy system [20]. each input and output are divided into 7 groups of linguistic variables, i.e., negative large (nl), negative medium (nm), negative small (ns), zero (z), positive small (ps), positive medium (pm), and positive large (pl). these linguistic variables express the degree of error and also the error rate in roll and pitch angle while moving on the circular trajectory. the details of these variables can be seen in fig. 6. two inputs and one output are normalized in the same interval [-1, 1] with a 526 e. kuantama, t. vesselenyi, s. dzitac, r. tarca figure 6: the membership function of the fuzzy logic controller: (a) input (b) output scaling factor variable of 0.2 for every linguistic group. the interval value must be equivalent with the circular trajectory movement values in the designed roll and pitch angle so that each scale will cover detailed changes. table 2 shows 49 fuzzy inference rules of this control action with the possible combinations of the two input values. table 2: fuzzy inference rules for quadcopter after establishing the fuzzy parameter values and its rules, the fuzzy-pid system as seen in fig. 7 can be designed. two identical fuzzy logic controllers are designed to control the movement in roll and pitch angles on the circular trajectory. inputs received by the fuzzy system are measured through the error normalization factor (ge) and the change in measurement normalization factor (gce). this factor can be calculated based on the values of kp−fuzzy(kpf), ki−fuzzy(kif), kd−fuzzy(kdf), and the allowed maximum error as seen on eq.(14). and eq.(15). ge = 1 max.error (14) gce = ge ∗ (kpf − √ k2pf − 4kifkdf kif 2 ) (15) output responses of the fuzzy are measured through response de-normalization factor (gu) and change in the response de-normalization factor (gcu) as seen in eq.(16) and eq.(17). this scaling factor is depending on gain information of conventional pid. gu = kdf gce (16) pid and fuzzy-pid control model for quadcopter attitude with disturbance parameter 527 gcu = kif ge (17) in this design, output normalization was done by directly triggering the changes of proportional and derivative action in which only the gu variable is used. the amount of designed error normalization factor is 0.5 with a change in measurement normalization factor of 0.3. the output of the pid controller will be used in euler integration for the quadcopter attitude on a circular trajectory. figure 7: quadcopter fuzzy-pid control system simulink block diagram 4 simulation results the circular movement of the quadcopter was simulated using simulink modeling which demonstrated the applicability of designed control in terms of control system stability on roll and pitch axes with steady high performance. the results are represented as angular velocity and position control responses of the control algorithm. circular trajectory modeling was performed by adjusting pitch and roll angle in horizontal position as shown in fig. 8(a). which was also used as reference angle. in quadcopter movement, the angular velocity algorithm can be developed using eq.(9). and the modeling was done with a closed loop system. practically, output angles for roll and pitch were obtained from attitude reading or orientation sensor data and the outcome was compared with the reference angles to observe how much error occurred. fig. 8(b). shows the output roll and pitch angles, and the differences between the desired angles and the outputs. due to the symmetry of the quadcopter, the same attitude error occurred on pitch and roll angles where noticeable error was seen in the settling time of 3-4 seconds and also in the settling time 7-8 seconds with an overshoot greater than 20 %. the differences between the generated attitude and the reference angles proved that the control algorithm is crucial to lessen the error. pid and fuzzy-pid control systems are essential to cope with this problem and the differences between their usages are seen clearly from the output. the tuning of the pid control parameters for both roll angle and pitch angle loops can be accomplished by adjusting the proportional and derivative coefficient. the proportional gain coefficient is used to manage acceleration and stability, while the integral coefficient is used to reduce permanent fault, and lastly the derivative 528 e. kuantama, t. vesselenyi, s. dzitac, r. tarca figure 8: quadcopter roll and pitch angle coefficient is used to reduce oscillation range and increase stability [17]. no steady state error was observed on roll and pitch angles, thus the pid controller could be simplified into a pd controller with integral gain ki = 0 . the complete position and attitude controller was developed to follow a circular trajectory with values kp of 0.8 and kd of 0.7, for which the simulink block diagram is presented in fig. 5. these gains of the pd controller were used as a set of operation points for the roll and pitch control. fig. 9. shows the comparison between the performance of the system with pd controller and the reference angle. the variable position output is explained on each axis in which x-axis position is called roll position and y-axis position is called pitch position. figure 9: quadcopter attitude with pd control system. (a) pitch and roll angle (b)trajectory position the influence of using pd control is very clear where overall output is almost identical to the reference, establishing an proper movement on circular trajectory. this data attest that the final values of proportional and derivative gain control can maintain proper operation on pitch and roll angles with maximum overshoot value no more than 7 % which occurs on the third quadrant, between 4 to 6 seconds of settling time. this circumstance is producing a shift in position on the trajectory as large as 0.12 m. the system using the pd control on roll and pitch axis has achieved a satisfying response in circular movement, and then pd control system was replaced with the fuzzy-pid control system to study the difference. a fuzzy-pid controller structure with feed forward loop was designed to obtain stabilization at steady state. the fuzzy rules for pitch and roll angles are alike and the values of fuzzy-pid sets used for the controllers are also identical. these values can be determined by an adaptive trial and error method. the values of fuzzy sets for kpf and kdf are 0.288 and 0.33 respectively.fig. 10. shows the modeling result of horizontal balancing control system on the pitch and roll axis. in the settling time between 0 to 8 seconds, the horizontal balancing control system on the roll axis can maintain the position/angle as well as on the yaw axis. this result shows that the fuzzy-pid control system is better than the pid control system pid and fuzzy-pid control model for quadcopter attitude with disturbance parameter 529 figure 10: quadcopter attitude with fuzzy-pid control system. (a) pitch and roll angle (b) trajectory position with a position shift less than 0.05 m and good flight stability on circular movement. this means that the fuzzy logic system can be used to change the output position to the different moments of control coefficients which can be done in the presence of fuzzy control along with the pid control [6]. it can be concluded that both systems are usable in this system and the difference between pid and fuzzy-pid controller only causing a small error in a no-disturbance condition, allowing the quadcopter to move on the desired circular trajectory. figure 11: attitude control system with disturbance parameter. (a) pid (b) fuzzy-pid to find out more about the difference between the two control systems on the quadcopter, a disturbance parameter of wind with 2 m/s velocity was added with various combination of wind direction. discrete gust wind models were used with gain values of 3.5, 3.5, 3.5 m/s and gust wind lengths of 120, 120, 80 m on each axis. gust wind length is the rise time needed to build up gust wind which expressed with length in each axis. the result of changes in pitch and roll angles with wind directions on each axis can be seen in fig. 11. disturbances affect the angle alteration in pitch and roll movement, thus resulting in greater tilt angle of the quadcopter. for a deeper analysis, fig. 12. shows the detail of changes in quadcopter flight position with various wind directions. during the circular movement with wind effect in xand ydirections, each control system has different behavior. in case of the pid control system, the shift in pitch and roll angles of movement happens almost at every settling time, but significant error in position occurs after 5 seconds of settling time. overall the fourth quadrant witnesses the maximum angle alteration of 0.14 radians from the reference angle and a maximum position shift of 0.1 m. in the case of the fuzzy-pid control, it can be clearly seen that the angle alteration happens in each position. this results in a wider flight position than the determined reference. the larger the settling time, the 530 e. kuantama, t. vesselenyi, s. dzitac, r. tarca figure 12: results obtained with attitude control system with disturbance on each axis. (a) pid (b) fuzzy-pid greater the error of position shift will be. this principle applies for every combination of wind directions. under wind effects in xand ydirection, the position shift occurs accordingly and this result shows that the quadcopter can still maintain the position of the movement on circular trajectory. details of position shift are as follows: maximum angle alteration of 0.17 radians and a position shift of 0.08 m, occurred in the third and fourth quadrant. due to the symmetrical shape of quadcopter between xand y-axis, the angle alteration between roll and pitch must be linear so as to keep the position on a circular trajectory. when the control sistem exposed with disturbance parameter, which is the common occurence in real environments, the fuzzypid is more reliable because it has the response system design through if-then logic parameters combined with adaptive pd control for error adjustment. 5 conclusions quadcopter attitude movement modeling on a circular trajectory can be performed based on the newton euler kinematic model. a control system for continuous movement of the quadcopter is necessary to guarantee the quadcopter stability when maneuvering, where there are continuous change in pitch and roll angles. without a control system, large amounts of overshoot were distinctly observable in the attitude movement in horizontal plane. to reduce the error, pid or fuzzy-pid control can be added into the quadcopter movement modeling algorithm. in the case of the pid control system, only components proportional and derivative were needed to maintain quadcopter flying position stability. pd controller has maximum position shift of 0.12 m on the circular trajectory, whereas in the case of fuzzy-pid control system the shift is only 0.05 m. this means that the quadcopter still moves on circular trajectory without significant error. wind disturbances affect both control systems and significant error occurs in the third and pid and fuzzy-pid control model for quadcopter attitude with disturbance parameter 531 fourth quadrant. under wind disturbance of 2 m/s, the fuzzy-pid control system can maintain the position on the circular trajectory better than the pid control system, with just 0.03 m of maximum position shift when compared to no disturbance. the fuzzy-pid control system works as an adaptive pid controller, the tracking response is automatically adjusted based on the fuzzy inference rules which allow comparing two inputs received by the fuzzy control system so it can adapt to possible errors and avoid overshoot. conclusively, the fuzzy-pid controller is superior to the pid controller in which manual gain adjustment has a crucial role in quadcopter flight stability. acknowledgment this work has been funded under the leaders erasmus mundus grant (agreement number 2014-0855/001-001) by european commission, through the education, audiovisual and culture executive agency, in the action plan 2 for the years 2014-2018, and supported by pncdi iii programme p2 transfer of knowledge to the economic operator (bridge grant pn-iii-p2 2.1 bg-2016-0296) funded by uefiscdi, romania. bibliography [1] ahmed s.f. et al (2015); attitude stabilization of quad-rotor (uav) system using fuzzy pid controller, second international conference on computing technology and information management (icctim), 99-104, 2015. 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(1942); optimum settings for automatic controllers, transactions of the american society of mechanical engineers, 64:759-768, 1942. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(3), 293-310, june 2019. network and traffic design aspects in network-coding-enabled wireless networks k. alic, m. mohorcic, a. svigelj kemal alic jozef stefan international postgraduate school jamova cesta 39, 1000 ljubljana, slovenia kemalic@gmail.com mihael mohorcic 1. department of communication systems, jozef stefan institute jamova cesta 39, 1000 ljubljana, slovenia 2. jozef stefan international postgraduate school jamova cesta 39, 1000 ljubljana, slovenia miha.mohorcic@ijs.si ales svigelj* 1. department of communication systems, jozef stefan institute jamova cesta 39, 1000 ljubljana, slovenia 2. jozef stefan international postgraduate school jamova cesta 39, 1000 ljubljana, slovenia *corresponding author: ales.svigelj@ijs.si abstract: practical experience of using opportunistic network coding has already been gained in several real network deployments, indicating the influence of some of the fundamental characteristics of the network and the traffic load. however, these aspects have not been systematically investigated in the scope of the construction of efficient and robust large-scale network-coding-enabled wireless mesh networks. in this paper we focus on these aspects using an example of two opportunistic networkcoding procedures: the well-known cope and the bearing opportunistic network coding (bon). in addition, the design aspects for network-coding-enabled wireless mesh networks and applications are discussed. we have shown that opportunistic network coding can improve the performance of different networks and supported applications in terms of throughput, delay and jitter, although the benefits are not significant in all the cases. thus, the use of opportunistic network coding should be considered upfront during the wireless network design phase in order to obtain the greatest benefits. keywords: opportunistic network coding, network coding algorithm, wireless multihop networks, simulations; traffic design. 1 introduction network coding has been successfully used to improve the performance of large, wireless, multi-hop networks. with network coding the conventional store-and-forward paradigm in relaying scenarios is replaced by a process-and-forward approach. instead of forwarding packets in the same form as they are received, the intermediate nodes combine the received outgoing packets using an algebraic function. the broadcast nature of wireless media is embraced as all the packets are distributed, for free, to all the neighbours, i.e., the nodes that can overhear the transmission. all the nodes store all the received packets for possible packet-decoding purposes. hence, a typical network-coding mechanism is composed of two main operations: (1) the coding of the outgoing packets on (intermediate) nodes and (2) the decoding of incoming packets upon their reception on nodes [19]. copyright ©2019 cc by-nc 294 k. alic, m. mohorcic, a. svigelj network coding was originally proposed to increase the throughput of a single-source communication, for instance, a multicast stream [1], in order to achieve the maximum data-transfer rate in multicast networks [12]. in this paper, we are interested in opportunistic network coding, where the packets for transmission are selected in a classic manner and the coding opportunities are searched for only amongst the packets currently present in the output queue [10, 13, 19]. we further focus our interest in fixed, wireless mesh networks, such as metropolitan wi-fi networks [6] or wireless sensor networks (wsns) [22] with unicast traffic, where the nodes can be seen as fixed wireless access points for the end users. the performance gains of opportunistic network coding in wireless mesh networks have already been proven in testbed deployments [8, 10]. the gains have also been analysed from the theoretical perspective [24] and compared to practical gains [25]. opportunistic network coding has also been considered for streaming applications. th code selection that takes into account video-stream characteristics, such as the distortion values and the play-out deadlines of the video packets, was studied in [15]. the well-known and widely adopted cope [10] has been under the spotlight, while so-called support mechanisms such as scheduling [21], queue management [7,16,17] and routing support [11,14,20] were used to bring about additional gains. most of these support mechanisms can also be used with other opportunistic network-coding approaches, such as [3,8,19]. in this paper we are not optimising the parameters of any particular network-coding algorithm or one of the support mechanisms, rather we are investigating the impact of different network characteristics on the performance of network-coding procedures. thus, our interest is in traffic distributions and patterns, different network topologies and node properties. as these parameters depend on the design and the purpose of the network, it is of paramount importance for the developers of wireless mesh networks and applications to know what benefits they can expect from the use of opportunistic network coding. as a particular example, this paper deals with the design and construction of an efficient and robust, large-scale (99 nodes) network-coding-enabled, wireless mesh network that ensures the delivery of high-data-rate and low-delay services for different applications used by a very large number of distributed users. we analyse and explain the major impacts on network performance in terms of a throughput and delay analysis of the opportunistic network-coding using two different opportunistic network coding procedures, namely, the well-known cope [10] as a representative of a procedure based on distributed information, and the recently proposed bon (bearing opportunistic network coding) [3–5], representing procedures that make coding decisions based only on local knowledge, thus reducing the overhead. both selected network-coding procedures are positioned between the mac and the network layer and are performing single-hop packet coding. support mechanisms that further improve the benefits of network coding are not considered. this article is organised as follows. in section 2 we briefly summarize the main characteristics of the cope and the bon. in section 3 the methodology for a subsequent performance comparison is presented. section 4 analyses the influence of wireless-network design parameters on the performance of opportunistic network coding from the quantity-of-service perspective (e.g., goodput). the end user perception of network coding in terms of delay and jitter is presented in section 5. next, coding fairness for different traffic conditions is evaluated in section 5 before section 6 concludes the article. 2 considered network-coding procedures in this study we consider cope [10] and bon [4] for use in wireless mesh networks. both procedures are inter-session network-coding schemes that use the xor operation for the coding of packets. they are fully distributed, able to discover coding opportunities and can be seen as network and traffic design aspects in network-coding-enabled wireless networks 295 an independent layer in the communication stack. after receiving an encoded packet, the bon and cope processes try to decode it. decoding is successful if there are enough packets in the packet pool. an ack message is sent for the native packet, but only if the recipient node is determined as one of the next hops for the packet. if there is not enough information to decode the packet, the encoded packet is dropped. both procedures use cumulative ack reports, which were first presented in [10]. cumulative ack-report messages are broadcasted periodically, every tu seconds. if the opportunity arises the ack reports are attached to the regular outgoing packets. in this case cumulative acks can be seen as an additional header. the cumulative ack messages reduce the overhead compared to the individual ack messages, thus optimizing the network coding from the throughput pointof-view. if an ack message is not received on the sending node within a predetermined time, the re-transmission event is triggered. a native packet that has been sent out as part of the encoded packet and has not been acked is to be re-transmitted. in the cope the packet is placed at the head of the output queue. in the bon the packet is placed in the re-transmission output sub-queue. this queue has a lower priority than the signalization queue, but a higher priority than the queue with regular outgoing packets. the packets in the retransmissions queue can be coded again. however, coding opportunities are only searched for within the packets in the regular output queue. this mechanism ensures that it is not possible for the same set of native packets that already failed in the decoding to be coded again. the cope also uses so-called reports. with these each node informs all its neighbours about which new packets it has received. the reports are broadcasted periodically, every tu seconds. if possible, the reports are attached to regular outgoing packets or ack messages as additional header. at the mac layer the bon and cope use a pseudo-broadcast mechanism, presented in [10]. given that the bon and cope rely on the same mechanisms of the underlying osi layers, in this paper we assume the use of the same mac layer that mimics the provision of resources as in csma-cd. the cope and bon coding algorithms are described in the following subsections. 2.1 cope in the cope the coding process depends of the nodes’ knowledge on what information (which packets) its neighbouring nodes have, through listening to the neighbours’ broadcasts (i.e., regular packets or reports). based on this knowledge the coding process is straightforward and the decoding process will have a high success rate. information arriving through particular messages and through listening to all the broadcasted messages provides only a few coding opportunities. in the case that the information about a packet’s presence at a particular neighbour’s node is not available, the coding needs to make a guess regarding the situation. the guessing is made through a delivery probability that is calculated in all of the etx-based (expected transmissions count) routing protocols. the node estimates the probability that the node n has the packet p by looking at the delivery probability for the link between packet’s previous hop and node n. with all the required information, the node can code together as many packets (p1, ..., pn) as possible provided that none of the packets were created on the coding node, all the packets have different next hops and that there is a strong possibility that all the next hops will be able to decode the packet. the next hop can decode the packet if it has already received all but one of the packets coded together. let the probability that a next hop has heard packet pm be pm. then, the probability, pd, that it can decode its native packet is equal to the probability that it has heard all of the n−1 native packets encoded with its own, i.e., 296 k. alic, m. mohorcic, a. svigelj pd = p1p2 . . .pn−1 (1) the coding algorithm ensures that the decoding probability pd for all the next hops for a given combination of encoded packets is above the threshold g. 2.2 bon the bon-coding process [4] relies only on information gathered locally in the networkcoding layer and does not record the state of the traffic flows. the bon-coding process is based on the local bearing of the packet p(r)ij which is defined on the relay node vr and depends on the positions of the p(r)ij previous hop vi(xi,yi) and the next hop vj(xj,yj). the bearing for the packet p(r)ij on the relay node is defined as a unit vector, calculated as: −→ b ij = (xj −xi,yj −yi) ‖(xj −xi,yj −yi)‖ (2) with the bon the packets p(r)ij and p (r) kl are codeable on the relay node if the previous hop of p(r)ij (vi) is in the vicinity of the next hop of pkl (r) (vl) and vice versa. this is where the vicinity of the node vj for the packet p (r) ij on the relay node vr is described as an area within the shape of an infinite cone, with the apex in the packet source vi, and the cone axis with the direction −→ bij and the aperture 2� (r) ij . � is referred to as the tolerance angle parameter and � ≥ 0. there is one tolerance angle on each node, i.e., �(r)ij = � (r) kl = � (r). by increasing the parameter �, the possibility of encoding multiple packets and the probability that one of the receivers will not be able to decode the packet is increased. by reducing the parameter � towards zero, the coding opportunities are reduced, but the probability of a successful packet decoding on the receiving nodes is increased. in the case of � = 0 only two packets can be coded. on each node the tolerance angle � is adjusted based on the retransmissions-ratio parameter (rr), reflecting the success of the packet coding, which is calculated on the node vi as: rr(m)vi = krmvi kcmvikpmvi (3) where m = {1, 2, ...}, kcmvi > 0 is the number of native packets that were sent out as part of the encoded packets, i.e., coded packets, on the node, and kpmvi is the number of packets that were passed from the network layer to the network coding layer. krmvi is the number of network-coding-layer retransmitted packets on the node. all the values are measured in the time interval [(m−1)t�u, m t�u ). the obtained value is compared to the maximum threshold rrmax and the minimum threshold rrmin. based on the comparison outcomes, the process provides a decision about increasing, decreasing or leaving the parameter � unchanged. 3 methodology for evaluating the performance in this section we define the performance metrics and the simulation parameters for a subsequent investigation of the impact that the network and traffic characteristics have on the efficiency of the network coding. network and traffic design aspects in network-coding-enabled wireless networks 297 3.1 performance metrics as the elementary metric reflecting the quantity of service, we observe the network goodput (g), which is the number of useful information bits delivered by the network to a certain destination per unit of time. the goodput in our graphs is defined as the sum of all the goodput on all the network nodes at a particular network load in the i-th simulation run, i.e., g(i). we further define the gain g(i) in the i-th simulation run as the relative increase of goodput obtained with the network coding with respect to the goodput without the network coding: g(i) = gnc(i) −gnocoding(i) gnocoding(i) 100% (4) where the gain can be observed for the all the traffic in the network or just on the individual flow level. in the latter case, we refer to it as the flow gain. as a typical quality of service (qos) metric we measure the end-to-end (ete) delay and jitter at the application layer. both the ete delay and the jitter are measured separately for each particular flow, and for all the flows. regardless of the case, we can write the following: ete(i) = ∑ka(i) n=1 dn ka(i) (5) where dn is the ete delay of the n-th packet and ka(i) is the number of packets received in the application layer. jitter is the standard deviation from the true periodicity of a presumed periodic signal. we only measure the jitter for flows with constant inter-arrival packet rates: jitter(i) = 1 f(i) f(i)∑ f=1 1 kf (i) kf (i)∑ n=1 (dnf −etef (i))2 (6) where f is the number of flows, kf is the number of packets received in the application layer belonging to the f-th flow, and etef is the ete for the f-th flow. when we are interested in the jitter of only one particular flow, this same equation is used for the calculation and f(i) = 1. the efficiency of the coding algorithms is evaluated through the number of individual overhead packets (i.e., the overhead that is attached to regular packets as headers is not taken into account in this measure). we also consider jain’s index [9], which is primarily used to identify underutilized channels or fairness in the load distribution. we use it to identify underutilised streams. j(i) = ( ∑f f=1 gf (i)) 2 f ∑f f=1 gf (i) 2 (7) where f is the number of flows in the network and gf is the goodput over the f-th stream. in the results where we show the dependency of the gain on other parameters (e.g. queue length, packet length, topology, etc.), we depict the maxgain, which is calculated as the average of the three highest gain values obtained among results for a given set of parameter values. we first sort the vector g(i), where i = {1, 2, ...,i} and i is the number of simulation runs from the highest to the lowest value: gs = sort{g(1),g(2), ...,g(i)} (8) and then the maxgain is: 298 k. alic, m. mohorcic, a. svigelj maxgain = 1 3 (gs(1) + gs(2) + gs(3)) (9) 3.2 network-coding settings each investigated characteristic was evaluated in a separate scenario. to this end, two opportunistic network-coding procedures, cope [10] and bon [4], as well as the reference scenario where network coding was not used (no coding), were considered in the evaluation. a purposely developed simulation model, presented in [2], built in the riverbed modeler simulation tool, was used. each simulation run took 150 s. the results were collected between the 90th and the 150th second to observe only the steady-state conditions. each scenario was evaluated using 30 different network loads. for each network load we made four simulation runs using different seeds. the cope and bon parameters remained fixed in all the scenarios, as in this study we are focusing on the impact of the network and the traffic characteristics on the efficiency of the network-coding procedures, rather than scaling (i.e., optimising) the network-coding parameters. both the bon and cope use up to two retransmissions in the network-coding layer. both procedures store packets in the packet pool for 15s after the packet has been received for the first time. the bon and cope send out cumulative acks at least every 0.5 s. the network-codinglayer packet re-transmissions are scheduled for 0.6 s after the initial packet transmission. if the opportunity arises, cumulative acks are attached to the regular outgoing packets. the cope reports are sent out at least every 0.5 s. when possible, the report packets are also attached to the regular outgoing packets or to the cumulative acks. the cope parameters were set to the values used in the test-bed presented in [10]. the bon parameters that are common to the cope procedures are set to the same values. the other bon parameters were set to the values used in [4]. 3.3 traffic, topology and variable parameters settings the udp-like traffic was generated in the origin nodes. the traffic intensity was regulated through the packet inter-arrival time and the packet lengths. we used several different constant packet lengths (2, 3, 4, 5, 6, 7, 8, 9, and 10 kbits) and variable packet length between 360 and 12,000 bits. the packet size distribution had two peaks and followed the measured internet traffic, as presented in [18]. the inter-arrival time between the packets was constant or calculated using an exponential distribution. different numbers of traffic flows were generated between selected nodes in the network. all the flows were selected in pairs (i.e., from node vx to vy and from nodevy to vx, where each node was selected only once. we selected n node pairs from the lowest to the highest hop-count distance, thus obtaining long flows, where the packets had several opportunities to be encoded on the path. in the case of the "all2all" traffic-flows distribution, all the nodes generated traffic flows and sent them to all the nodes in the network, resulting in f = nn (nn − 1) traffic flows, where nn is the number of nodes in the observed topology. the symmetry between the two flows in the load pair was set by the traffic-symmetry ratio. all the nodes used the same channel with a capacity of 2 mbit/s. the packet-delivery probability between the nodes changed dynamically during the simulations and throughout each simulation run, and depended on the amount of traffic between the neighbouring nodes and the distance between the nodes. in simulation runs with high loads the packet-delivery probability was between 0.7 and 0.97. routing tables were calculated at the beginning of each simulation run and these were updated every 30 s. dijkstra’s algorithm was used for the route calculation, taking into account network and traffic design aspects in network-coding-enabled wireless networks 299 the etx metric. the bon performance and the influence of the parameters on the network-coding performance were studied in different topologies to also show that the performance improvement does not depend on optimal parameter settings. we randomly deployed 99 fixed nodes over an area of 2 km×2 km. several node-transmission (tx) ranges were used in order to study different network topologies with the same node distributions. the node-transmission range indicates how far the signal from the node can be detected, which is reflected in the connectivity and the wireless links between the nodes. as an example, in fig. 1 two topologies with the shortest (280 m) and the longest (420 m) considered node-transmission ranges are depicted. we were interested in several parameters that influence the network-coding performance. all the variable parameters, with an included reference to the section where they were used, are presented in table 1. in general, for each case only one parameter was varied, and all the others remained fixed. table 1: simulation parameters for investigated characteristic transmiss. range number of traffic flows traffic-symm. ratio (%) packet length (kb) int. time dist. queue length (packets) topology design (4.1) 280 420 m ’all2all’ 100 10; exp 100 traffic distribution impact (4.2) 300 m 10 90, ’all2all’ 0-100 10; exp 100 packet length evaluation (4.3) 300 m ’all2all’ 100 2 10, var; exp 100 analysis of output queue length (4.4) 300 m ’all2all’ 100 10; exp 10 200 qos analysis (5.1) 300 m 2, 20 100 10; const. 100 coding fairness (5.2) 300 m 20, 90 100 10; exp 100 figure 1: a graphical presentation of selected network topologies used in the evaluation 4 quantitative impact of the network and the traffic characteristics on the network coding the core of this section is the analysis and evaluation of the influence of the different network and traffic characteristics on the performance efficiency of the network coding in wireless mesh networks. the investigated characteristics include (i) network topology (ii) traffic distribution, (iii) packet length, and (iv) packet-queue length. 300 k. alic, m. mohorcic, a. svigelj 4.1 topology design fig. 2 depicts the gain with respect to the increasing network load for network topologies with the node-transmission range set to 280, 340 and 400 m. the highest network-coding benefits were obtained for the topology where the nodes have the shortest transmission range. this might be counter-intuitive to a certain extent, as by a larger coverage area, it is possible to think that more nodes overhear transmissions, thus resulting in more coding opportunities and a higher decoding success. we wanted to present the gain, with its dependence on the node-transmission range, so the same results are presented in fig. 3 as maxgain, as obtained for all the topologies. the coding benefits decrease with a higher node-transmission range. there are several reasons for this. the diameter and the average hop-count distance of each network decreases with an increased nodes-transmission range. with this there are fewer possibilities for the packets to be encoded, as they need fewer hops from their sources to their destinations. in networks where the nodes have a shorter node-transmission range there are typically a few nodes that handle large portions of the traffic. nodes that handle a larger portion of the load are where normally the majority of the coding takes place. with a larger node-transmission range the number of possible paths between the source and the destination pairs increases, hence the load is more evenly distributed among the nodes. it often happens that the flows between two neighbouring nodes on one side of the network and two neighbouring nodes on the other side of the network use entirely disjointed paths, while often still competing for the same wireless resources, but not being able to be encoded in the intermediate nodes. this can be improved, for example, by using network-coding-aware routing which could in such a case increase the coding opportunities. figure 2: dependence of the gain (g) on the network load for topologies with a 280, 340, and 400 m node-transmission range figure 3: dependence of the maxgain on the node-transmission (tx) range furthermore, with a higher node-transmission range the competition to access the wireless channel is stiffer. hence, the time needed to receive the network-coding-layer acknowledgement message increases, especially when the network has to deal with higher traffic loads. thus, the acknowledgement messages can arrive at their destinations after the retransmission procedure was initiated. to some extent this can be monitored by adjusting (i.e. increasing) the waiting network and traffic design aspects in network-coding-enabled wireless networks 301 time for the network-coding layer acknowledgement messages, but this might affect the streaming applications in terms of unacceptable jitter. based on the above analysis we can conclude that the goodput of networks where the node transmission range is high can only be increased by a few percent using basic opportunistic network-coding approaches. if the network coding is considered for use in a mesh network, where the nodes have many neighbours, the support of a network-coding-aware routing protocol should be considered, or a network-coding procedure with a different approach should be selected such as e.g. bend [23]. 4.2 impact of the traffic distribution the potential benefits of using network coding are strongly dependent on the traffic distribution. we first examined the influence of traffic symmetry and later on the traffic-flows distribution on the performance of opportunistic network coding. for investigating the influence of traffic symmetry, we deployed 10 flows between 5 node pairs and varied the traffic-symmetry ratio, as defined in table 1, where also all the other parameter settings are stated. in fig. 4 we present the results for the dependence of maxgain on the traffic-symmetry ratio. as expected, the gains increase as the traffic within the traffic flows becomes more symmetric, essentially increasing the network-coding opportunities. with the lowest ratio (i.e., a traffic symmetry ratio = 0) small gains were noted. since the coding happens between the flows that cross their paths in opposite directions, the benefits of network coding increase with an increasing traffic-symmetry ratio. we can see that the network-coding procedures bring the most benefits when the traffic symmetry is above 60 %. figure 4: dependence of the maxgain on the traffic-symmetry ratio figure 5: dependence of the ete delay on the goodput (g) for 20 and 90 traffic flows and ’all2all’ traffic distribution in the next set of results, we evaluated the dependency of the network-coding benefits with respect to the number of traffic flows, i.e., the traffic-flows distribution. in fig. 5, the dependence of the ete delay on the goodput for 20 and 90 traffic flows and the ’all2all’ traffic distribution is presented. by increasing the number of traffic flows the network goodput increased with an unchanged ete delay. this is expected, as with the larger number of traffic flows, the same overall network load is more evenly distributed across the network. furthermore, the network 302 k. alic, m. mohorcic, a. svigelj coding decreases the delay in comparison to the no-coding scenario, while increasing the network goodput, but the delay is increasing due to the increased network load. in fig. 6 the dependence of maxgain on the number of traffic flows is shown. with very few flows in the network (i.e., 10) the maxgain was higher than 100 %. by increasing the number of flows also shorter flows, were injected into the network, and the shorter the traffic flow, the fewer coding opportunities its packets experience along the path. the lowest gains were obtained in the ’all2all’ traffic-flows distribution (shown as separate points in the graph), where an in-depth analysis also revealed that the fewest coding opportunities are found. nevertheless, in the ’all2all’ distribution of traffic flows, where the network-coding benefits were the lowest, the network-coding gain with both procedures was still higher than 20 %, and in addition both procedures decreased the delay significantly. figure 6: dependence of the maxgain on the number of traffic flows figure 7: network coding overhead in terms of the number of individual packets for 2 flows and ’all2all’ traffic distributions and topologies with node transmission (tx) range 300 and 400 m in topologies where the nodes have a higher node-transmission range, the signalization overhead also becomes more significant. in fig. 7 the overhead caused by the bon and cope in terms of the number of individual packets is presented for two extreme traffic distributions i.e. 2 flows and ’all2all’, and topologies with node-transmission ranges of 300 and 400 m. the cope uses the so-called report messages, with which the nodes inform their neighbours about which packets they have received. with the increased node-transmission range there are more nodes that can overhear each other’s transmissions, which means that report messages increase in number and in the size (each node composes reports for each of the neighbours from which it has received new, unreported) packets). a higher number of traffic flows means a higher number of cope reports, as there are more nodes that can overhear the packet transmission. in low trafficload conditions there are few opportunities to attach reports to regular outgoing packets, hence a high number of individual report messages. by increasing the traffic load the number of report messages also depends on the traffic-flow distributions. in an ’all2all’ traffic-flow distribution network and traffic design aspects in network-coding-enabled wireless networks 303 the number of stand-alone individual messages quickly decreases, as there are plenty of regular outgoing packets on all the nodes to which the report messages can be attached, and hence fewer individual packets. the opposite happens in the case of only two traffic flows. there is a constant number of nodes that relay packets to which report messages can be attached. due to the compact format of report messages the number of individual messages remains almost constant, regardless of the traffic load. using the bon, the number of overhead packets increases with the load. with higher loads there are more encoded packets and thus more acknowledgement messages which generate the only potential individual overhead packets in the bon. similarly, as with the cope, the acknowledgement messages are attached to regular outgoing packets, if possible. in a traffic distribution with two flows the inter-arrival time of the packets within the same flow is short enough so that the acknowledgement messages are attached to regular outgoing packets, hence there are almost no individual acknowledgement packets. it can be seen that the overhead also increases with the increased transmission range. this indicates that there can be more coding opportunities in topologies with a larger transmission range. nevertheless, the overhead produced by the bon is significantly lower than by the cope. figure 8: dependence of the gain (g) on the network load with packets of 2000 and 5000 bits, and of variable length figure 9: dependence of the maxgain on the packet length the influence of the packet length on the performance of opportunistic network coding reveals the overhead caused by the network-coding and how well the network coding procedures encode packets together. in this evaluation the parameters were set according to the specifications in table 1. in fig. 8 the dependence of gain on the network load for packets of 2000 and 5000 bits is shown (the results for a variable packet length are also shown, but they are discussed later). we can see that the highest gains were obtained with the bon for a packet length of 2000 bits. the bon self-adaptation process is based solely on information gathered in the network-coding layer. the more packets the network-coding layer processes, the better the decision process, and hence there are fewer retransmissions, indicating better coding decisions. however, with the bon the lowest gains are obtained with the largest packets, where, although the influence of the overhead is the lowest, incorrect coding decisions result in a high 304 k. alic, m. mohorcic, a. svigelj cost of retransmission. in addition, the impact on the signalization is higher than when smaller packets are in use. the channel is occupied for longer periods and the acknowledgement-message time out can be affected, resulting in additional packet retransmissions. the dependence of maxgain on the packet length is presented in fig. 9 for the bon and cope. with the bon the maxgainwas decreasing with the increased packet length since the overhead was less important and because the network-coding layer handled fewer packets. the latter resulted in poorer coding decisions as the bon self-adaptation process also depends on the number of processed packets. the cope generated a higher overhead than the bon, and hence the maxgain was lower with short packets. by increasing the packet length, the gains also grew to the point where the signalization did not affect the results and the maxgain shows no dependence on the size of the packet length . we were also interested in the network performance in terms of the variable packet lengths (also presented in fig. 8, and noted as "variable"). thus, additional experiments were carried out with the empirical packet-length distribution presented in [18], where the packet lengths varied between 360 and 12000 bits. when encoding packets with different lengths, the shorter packets are padded with zeroes, thus adding irrelevant information to the payload. as expected, the results for a variable packet-length distribution show the lowest gains. both coding algorithms favour the coding of a packet of approximately the same length, but the degrading impact of the packet extension cannot be compensated. 4.3 analysis of the length of the output queue in this subsection we examine the influence of the output-queue length on the networkcoding performance. the output-queue length is important because the cope and bon do not deliberately delay packets in order to find more coding opportunities. instead, both analysed algorithms only search for coding opportunities among the packets present in the output queue. for this analysis the parameters were set according to the specifications in table 1. in fig. 10 the dependence of maxgain on the queue length, ranging from 10 to 200 packets, is shown. as expected, both network-coding procedures provided the lowest gain with the shortest queue. although we would expect that more packets in the output queue will increase the coding opportunities, the results show that initially the maxgain grew very quickly with the queue length, but then the increase in the queue length had no effect on the performance of the network coding. 5 end-user perception of the network coding 5.1 quality-of-service parameters one particularly important consideration when introducing network coding in wireless communication systems is its potential effect on the quality of service. the bon and cope use asynchronous acknowledgement mechanisms to confirm the successful transmission and decoding of encoded packets. furthermore, cumulative acknowledgements are used to avoid a high overhead and to increase the goodput. this is reflected in the relatively long time periods used for packet-retransmission scheduling (in our case 0.6 s). such a long waiting period is expected to affect the streaming applications in terms of the delay and jitter (i.e., qos). we analysed this by observing a traffic flow between the two nodes with the highest hop-count distance under two traffic-flow distributions. in the considered network topology with the node-transmission range set to 300 m, these two nodes were 14 hops apart. we first investigated the case of two symmetric traffic flows between the two nodes. in such a traffic distribution the coding happened only benetwork and traffic design aspects in network-coding-enabled wireless networks 305 tween flows travelling in the opposite directions and retransmissions in the network coding layer were only due to packet loss and never due to an inability to decode the encoded packet. next, we created twenty symmetric traffic flows between ten node pairs with the highest hop-count distance, but we still observed the conditions on the same node pair as before. other parameters were set according to table 1. fig. 11 shows the dependence of ete delay on the increasing network load with two and twenty symmetric flows. the delay was not affected at lower loads in comparison to the nocoding scenario. however, with the higher loads, the delay obtained in the case of network coding was significantly lower than in the no-coding scenario. as shown in fig. 12, the jitter also remained lower or approximately the same as in the no-coding scenario for both networkcoding procedures, especially in the network with only two flows. with high loads there were occasions when the jitter increased, though this was in the case when the network was congested and packets were being dropped out of the packet queues. an in-depth analysis also revealed that with high loads there were rare occasions when the packet travelled the complete path between two nodes without getting encoded on at least one of the hops. for most of the cases on a 14-hop path the packets were encoded on several hops. figure 10: dependence of the maxgain on the queue length figure 11: dependence of the ete delay for the longest flow on the network load with 2 and 20 symmetric traffic flows. the jitter for a network load with 20 symmetric flows shows similar results. under light network-load conditions the jitter obtained for both the bon and cope was slightly higher than for the no-coding scenario. with a low number of packets in the network the impact of every encoded packet that was unsuccessfully delivered or decoded is very significant. with an increased load the jitter generally remained lower than in the no-coding scenario, although there were a few occurrences when the jitter for the case of using network coding, especially cope, was high. this happened when a higher number of opportunistically coded packets got lost during the transmissions and the arrival order of the packets was significantly affected. generally, we can conclude that network coding has a positive impact on the jitter. the benefits of network coding with respect to the no-coding scenario appear at high network loads, 306 k. alic, m. mohorcic, a. svigelj when the jitter is significantly affected in the no-coding scenario due to the congestion of the most-loaded nodes. it is worth pointing out that the impact of network coding was observed on a large-scale network using traffic flows that travel long distances. such networks are not primarily built for streaming applications with high qos demands, but were selected for the presented analysis so that packets were encoded several times on their path to the destination, and hence the effects of network coding would be observed with higher reliability. 5.2 analysis of coding fairness an important consideration for the network-coding procedure is also its fairness, i.e., how different traffic flows benefit from the network coding. in general traffic conditions only a small part of the native packets are encoded, thus observing the performance of the network coding for the entire network may blur the effects at the level of individual traffic flows. the aim of this section is to analyse the fairness of the network coding with respect to the traffic flows in the network, i.e., if it improves the network performance by significantly improving some traffic flows at the expense of some others. it is desirable that the network-coding algorithm improves the performance of all the traffic flows or at least that it does not degrade their performance in comparison to the no-coding scenario. figure 12: dependence of the jitter for the longest flow on the network load with 2 and 20 symmetric traffic flows figure 13: jain’s index (j) for the distribution with 20 traffic flows we carried out a detailed analysis of the individual traffic flows with respect to how successful the network was in transferring individual flows from the source to the destination. other parameters were set according to table 1. first, let us consider jain’s index, which is primarily used for identifying underutilized channels. in our case we are establishing whether users or applications were receiving a fair share of the system resources. in fig. 13 jain’s index for 20 flows of traffic distribution is presented. jain’s index indicates that the network-coding procedures treat the traffic at least as well as or better than when there is no coding; however, it does not reveal how the coding gains were distributed among the flows. network and traffic design aspects in network-coding-enabled wireless networks 307 in addition to jain’s index, in the following we use the gain of the particular traffic flow as the performance metric, so we refer to it as the flow gain. the flow gain results for a traffic distribution consisting of 20 traffic flows are shown in fig. 14. the flow gain was collected at an overall network load equal to 0.768 mbit/s, which is the maximum load at which the delay for the no-coding scenario was lower than 2 s. at such a network-load intensity, the goodput for the no-coding scenario was already affected by full queues on the nodes, i.e. the network was congested and several nodes were dropping packets due to the full queues. for the same traffic load, the cope and bon network-coding procedures did not have a negative effect on any of the flows in terms of goodput, as in this case the highest observed flow gain was above 30%. figure 14: gain (g) per individual flows for the traffic distribution with 20 traffic flows figure 15: histogram for gain (g) per individual flow for the traffic distribution with 90 traffic flows in fig. 15 we present the results for the case with an even higher number of traffic flows (i.e., 90). the results are presented as histograms for the overall network load equal to 1.24 mbit/s, which was the maximum load at which the delay for the cope was lower than 3 s. again, the network was in a state where packets on several nodes were dropped due to the full output queues. on the x-axis we have the flow gain while on the y-axis is the number of flows within a given flow-gain interval. for example, there were 20 flows for which the bon achieved a flow gain between 10 % and 40 %. although the goal of the network-coding procedures is to successfully transfer many flows with a high flow gain to their destinations, it is desirable that none of the flows are degraded. both coding procedures were able to improve the goodput of almost all the flows and there were only a few flows where the gains were low. a detailed analysis revealed that there were 2 out of 90 flows with the bon having a negative coding gain, where the lowest value was -1.15 %. with the cope there were four flows with a negative gain, with the lowest value being -16.7 %. although we present only a few representative results, the same conclusions can also be drawn for different traffic distributions and higher traffic loads. 6 conclusions two opportunistic network-coding algorithms the cope and bon were used for evaluation purposes. both procedures are similar in several aspects, but they differ in their core, i.e., the coding decision algorithm, which in case of the cope using exact distributed information about coding opportunities, whereas in the case of the bon it is based only on the local information 308 k. alic, m. mohorcic, a. svigelj available in a node. both procedures show similar tendencies, thus observations and conclusions gained can be generalised to other similar types of algorithms. the performance of both coding algorithms could be further improved for specific scenarios by optimising the particular parameters or using additional support mechanisms. however, in this investigation we focused on the impact of the network and traffic characteristics on the efficiency of the network-coding procedures, and thus the network-coding parameters remained fixed throughout the evaluation. in the following we summarise the observed and discussed design principles for wireless networks using network coding and their applications. table 2 summarises the winning parameters and protocol with an included reference to the section where they are analysed. table 2: summary of winning parameters for analysed scenarios parameter values win. value win. protocol topology design (4.1) transmission range 280 m 420 m 280 m bon traffic distribution impact (4.2) traffic-symm. ratio 0% 100% > 60% bon traffic distribution impact (4.2) number of traffic flows 10 90, ’all2all’ 10 bon packet length evaluation (4.3) packet length 2 kb 10 kb, ’var’ 2 kb bon analysis of queue length (4.4) queue length 10 p 200 p 50 p bon qos analysis ete delay (5.1) number of traffic flows 2, 20 20 bon ≈ cope qos analysis jitter (5.1) number of traffic flows 2, 20 2 bon ≈ cope coding fairness jain’s index (5.2) number of traffic flows 20 20 bon ≈ cope traffic distribution, and in particular, traffic symmetry have a significant impact on the performance of opportunistic network coding. in the case where only one-direction flows (completely asymmetric traffic) are considered, opportunistic network coding will not improve the overall network performance. by increasing the symmetry factor, the coding gains will increase, reaching the maximum benefits for a completely symmetric traffic load. furthermore, the highest gains are obtained when there is a relatively small number of flows in the network. by increasing the number of flows the network-coding gains decrease. the average length of the packets in the network is also an important parameter for the selection of the network-coding procedure. the overhead induced by the network-coding procedure has a larger influence on the network with smaller packets. when packets of the same length are encoded the gains obtained are higher than when the packets vary in length. in addition, the enlarging of the output queues improves the gains, but only to a certain level, which also depends on the number of flows in the network. hence, the length of the output queue should primarily be adjusted to handle the expected peak network loads. when planning the network topology, additional transmission power and thus an increased nodes-transmission range in network-coding-enabled networks will not necessarily improve the networks’ performance. the results show that with the increased nodes’ transmission range the benefits of opportunistic network coding fade, requiring cautious planning of the transmission power. the detailed delay and jitter analysis of individual flows revealed no negative effects on the qos due to the use of network-coding procedures. although the jitter might have slightly increased (almost negligibly in absolute terms) on rare occasions with low loads, the network performance in terms of delay and jitter with network coding in a heavily loaded network significantly outperformed the no-coding scenario where no network coding was used. it is worth noting that the jitter and delay were analysed on a flow that travelled 14 hops to its final destination and that there were only a few occasions where packets were not encoded at least one time along the path, and still we did not detect any degradation in the qos. finally, the coding-fairness analysis showed that both network-coding procedures considered in this study improved or at least maintained the throughput of all the 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(2012). comparison of analytical and measured performance results on network coding in ieee 802.11 ad-hoc networks, int. symp. on network coding (netcod), 2012, usa, 2012. international journal of computers communications & control issn 1841-9836, 13(6), 927-937, december 2018. model predictive control of stochastic linear systems with probability constraints c.f. caruntu, c.c. velandia-cardenas, x. liu, a.n. vargas constantin f. caruntu department of automatic control and applied informatics, gheorghe asachi technical university of iasi, str. prof. d. mangeron no. 27, iasi, romania. cristian c. velandia-cardenas research group mem, faculty of electronic engineering, universidad santo tomás, cra. 9 no. 51-11, bogotá, colombia. xinghua liu department of electrical engineering, school of automation and information engineering, xi’an university of technology, xian 710048, china. alessandro n. vargas* universidade tecnológica federal do paraná, utfpr, av. alberto carazzai 1640, 86300-000 cornelio procópio-pr, brazil. *corresponding author: avargas@utfpr.edu.br abstract: this paper presents a strategy for computing model predictive control of linear gaussian noise systems with probability constraints. as usual, constraints are taken on the system state and control input. the novelty relies on setting bounds on the underlying cumulative probability distribution, and showing that the model predictive control can be computed in an efficient manner through these novel bounds— an application confirms this assertion. indeed real-time experiments were carried out to control a direct current (dc) motor. the corresponding data show the effectiveness and usefulness of the approach. keywords: probability constraints, stochastic systems, linear systems, control. 1 introduction engineering applications show that constraints are not always met with probability one [4]. it is then reasonable to account constraints, together with performance indicators, under certain probability levels, e.g. [9]—this setup is referred to as model predictive control (mpc) with probability constraints, as detailed next. probability constraints on linear systems constitute a research topic that has attracted attention in recent years, in particular when it is combined with the mpc method, see for instance [2, 5, 10, 15, 20, 25], just to name a few. in this scenario, the model accounts additive disturbances [5, 15, 20, 25], multiplicative disturbances [2], and systems with mixed features; namely, systems with known: 1. probability distribution of the system output [15,20]; 2. gaussian distribution and general probability distribution of the disturbance [5]; 3. observable probability distribution of the disturbance at each time step [2]; 4. covariance and mean of the inputs and outputs [10]. copyright ©2018 cc by-nc 928 c.f. caruntu, c.c. velandia-cardenas, x. liu, a.n. vargas in particular, probabilistic constraints on inputs [5, 10, 11], states [2, 5, 10, 11, 25], and outputs [15,20] were transformed into deterministic constraints on the process input. to handle probability constraints is a task that the literature suggests the evaluation of element-wise probability constraints [2, 10, 11, 15, 16, 20, 25]; here, we follow the literature with a minor adaptation, that is, the element-wise evaluation yields nonlinear equations, difficult to compute. to overcome this difficulty, we propose an upper bound for the underlying nonlinear equations. as a byproduct, we obtain linear matrix inequalities—computing linear matrix inequalities increases efficiency on solving optimization problems [4, ch. 1]. the main contribution of this paper is to convert nonlinear equations, drawn from probability constraints, into linear matrix inequalities useful for real-time implementation. this assertion is illustrated in practice through the mpc applied in a direct current (dc) motor—experimental data are shown in section 4. these findings illustrate the practical benefits of our approach. the paper is organized as follows. section 2 states the problem to be solved and the derived solution. section 4 provides a performance evaluation of the proposed mpc with probability constraints based on the experimental results obtained on a direct current motor. finally, concluding remarks are given in section 5. notation and basic definitions. r, r+, z and z+ are the real, non-negative real, integer and non-negative integer numbers. z≥c1 and z(c1,c2] denote the sets {k ∈ z+ | k ≥ c1} and {k ∈ z+ | c1 < k ≤ c2}, respectively, for some c1,c2 ∈ z+. || · ||∞ denotes the infinity norm; given x ∈ rn and c ∈ r, by definition of the infinity norm, for ||x||∞ ≤ c to be satisfied, it is necessary and sufficient that ±[x]j ≤ c for all j ∈ z[1,n]. in is the identity matrix of order n and m ′ denotes the transpose of m. p [a] denotes the probability of event a and p(x) denotes the probability distribution function of random variable x. x̄ denotes the mean of the random variable x and sx denotes its covariance. 2 problem statement for some fixed and filtered probability space (ω,f,p), consider the following discrete-time linear time invariant system xk+1 = axk + buk + hwk, k ∈ z+, (1) where xk denotes an n-dimensional system state, uk represents an m-dimensional control input, and wk represents a q-dimensional noise or disturbance driven by an independent and identically distributed (i.i.d.) gaussian process. it is assumed that the initial state x0 is a gaussian random variable uncorrelated with wk. although it has been intensively studied over the last decades, (1) presents so far some interesting challenges. for instance, controlling (1) under probabilistic constraints remains an open problem [9]. the main contribution of this paper lies in showing a condition for controlling the system (1) under probabilistic constraints. we revisit some results from the literature, and show how to convert them into linear matrix inequalities, a useful scheme for numerical evaluations. to turn our contribution clear, we present some notation borrowed from [3]. stacking the model predictive control of stochastic linear systems with probability constraints 929 elements of (1) for n steps in the form x =   x1 x2 ... xn   , u =   u0 u1 ... un−1   , w =   w1 w2 ... wn   , (2) we obtain the nn-dimensional vector representation x = gxxx0 + gxuu + gxww. (3) where the matrices gxx, gxu and gxw are fixed, and their entries depend only on a, b, and h. here, n is called prediction horizon. note that x in (3) is a multivariate normal random vector, a direct consequence of the i.i.d. gaussian assumption on w. set x̄ as the mean value of x and let sx be its covariance, both given by [3] x̄ = gxxx̄0 + gxuu + gxww̄ sx = gxxsx0g ′ xx + gxwswg ′ xw. (4) the probabilistic constraints to be imposed on (1) are defined as follows. let fx be the set made up by the intersection of m linear inequality constraints, i.e., fx := m⋂ i=1 {x : a′ix ≤ bi}, (5) where ai and bi, i = 1, . . . ,m, represent vectors on rnn and r, respectively. moreover, consider fu as a set defined in similar fashion. assume for the moment that a given functional j : fx ×fu 7→ r+, convex with respect to its arguments, is associated with (1). hereafter, this functional is referred to as cost function. problem 1. given some δ ∈ r(0,1), find a solution for the next optimization problem. min u∈fu j(x̄,u) s.t. pr[x /∈ fx] ≤ δ, x = gxxx0 + gxuu + gxww (6) as a matter of fact, problem 1 means that the control input minimizes the cost function while ensuring that the system state may leave the feasible region with a probability less or equal than δ. to the best of the authors’ knowledge, problem 1 remains unresolved. the difficulty to solve such a problem stems from the fact that the probability in (6) represents a value taken over by multivariate integrals, requiring their evaluation, a difficult task to accomplish [12,17]. to avoid the evaluation of multivariate integrals, the authors of [3] suggest the element-wise evaluation of the set fx in probability; namely, find constants εi’s such that ∑m i=1 εi ≤ δ, and pr [ a′ix > bi ] ≤ εi, ∀i ∈ z[1,m]. (7) note that εi’s are slack variables, now in association with a univariate probabilistic inequality, which can be computed via the standard cumulative distribution function. even so, despite the progress researchers have made so far, the problem still remains difficult to solve because checking (7) may be cumbersome from the numerical point of view, as detailed next. 930 c.f. caruntu, c.c. velandia-cardenas, x. liu, a.n. vargas 0 0.2 0.4 0.6 0.8 1 −3 −2 −1 0 1 2 3 x y figure 1: the inverse cumulative distribution function (thick line) and upper linear approximations (straight lines tangent to the icdf function). let cdf(·) be the standard gaussian cumulative distribution function cdf(γ) = 1 √ 2π ∫ γ −∞ e− z2 2 dz, (8) and let icdf(·) be the corresponding inverse. one can show that (7) holds if and only if both inequalities ∑m i=1 εi ≤ δ and bi −a′ix̄ si ≥ icdf (1 −εi) , ∀i ∈ z[1,m], (9) hold true, where s1, . . . ,sm, are fixed, positive constants. even though the inequalities in (9) are convex with respect to εi’s, as shown in [3], finding a solution for (9) may be cumbersome—there is no closed, algebraic expression for computing a feasible solution for (9). consequently, to evaluate (9), it is necessary to use iterative algorithms, usually slow for computing the point of convergence [1, ch. 7]. but slow algorithms cannot be applied in some online control schemes, such as model predictive control [6,7,14,19,24,26]. in order to speed up the computation of a feasible solution for (9), we convert (9) into an affine function so as to obtain linear matrix inequalities (lmi)—it is known that lmi solvers outperform classical convex optimization algorithms [4, ch. 1]. the idea behind such conversion is now detailed. as described in fig. 1, the icdf function can be bounded from above by an affine function on some interval. this fact allows us to define an affine function h(·) such that h(1 −εi) ≥ icdf (1 −εi) , ∀i ∈ z[1,m]. (10) the novel condition hinges on checking whether the inequalities bi −a′ix̄ si ≥ h (1 −εi) , ∀i ∈ z[1,m], (11) hold true. in the positive case (9) is assured; as a byproduct, we obtain u that suffices to (6) in problem 1, the main condition pursued in this paper. the main contribution of this paper is capable of showing (11). yet derived from a simple idea of imposing upper bounds on the icdf function, the condition in (11) represents linear matrix inequalities, useful for real-time experiments—the paper shows a real-time application for a direct current motor (see section 4). this application illustrates the practical benefits of our approach. model predictive control of stochastic linear systems with probability constraints 931 3 model predictive control with probability constraints the problem we solve in this paper is presented in what follows. define yi = a′ix, and note that yi is a univariate gaussian random variable with mean and variance given by ȳi = a ′ igxxx̄0 + a ′ igxuu + a ′ igxww̄ syi = a ′ igxxsx0g ′ xxai + a ′ igxwswg ′ xwai. (12) using the novel condition in (11), we recast the problem 1 as the next problem. problem 2. given some δ ∈ r(0,1), find the solution to the optimization problem min u∈fu j(x̄,u) s.t. m∑ i=1 εi ≤ δ, bi − ȳi√ syi ≥ h (1 −εi) , ȳi = a ′ igxxx̄0 + a ′ igxuu + a ′ igxww̄, syi = a ′ igxxsx0g ′ xxai + a ′ igxwswg ′ xwai, ∀i ∈ z[1,m]. (13) the next result is a direct consequence of the aforementioned facts. theorem 3. a feasible solution to problem 2 is a feasible solution to problem 1. proof: recall that pr[x 6∈ fx] = pr [ x 6∈ m⋂ i=1 {x : a′ix ≤ bi} ] = pr [ x ∈ m⋃ i=1 {x : a′ix > bi} ] . (14) the rightmost term in (14) is bounded from above by ∑m i=1 pr[a ′ ix > bi] due to the boole’s inequality. when problem 2 has a solution, we have from (10) and (13) that bi − ȳi√ syi ≥ h (1 −εi) ≥ icdf (1 −εi) , ∀i ∈ z[1,m], which shows (9). both (9) and ∑m i=1 εi ≤ δ assure pr[a ′ ix > bi] ≤ εi for each i = 1, . . . ,m from (7), which in turn assures that pr[x 6∈ fx] ≤ δ. this argument completes the proof. 2 remark 4. theorem 3 assures that any optimal solution to problem 2 is a sub-optimal solution to problem 1—it means that the optimality of problem 1 is lost; however, the sub-optimal strategy turns the problem tractable from the numerical viewpoint. theorem 3 then contributes towards finding a solution to the so-far unsolved problem 1. recall that the model predictive control (mpc) works under the receding horizon control principle [14, ch. 1]; namely, the cost function j(·) in problem 2 should be minimized at each time instant k = k0, . . . ,k1. the current input uk in (1) is obtained by determining the input sequence {ûk, . . . , ûk+n−1} that solves problem 2, and next by setting uk = ûk. the remaining elements of the sequence are discarded, and this iterative procedure is repeated [14, ch. 1]. the aforementioned iterative procedure, key for the mpc scheme, was implemented in a laboratory testbed as detailed in the next section. 932 c.f. caruntu, c.c. velandia-cardenas, x. liu, a.n. vargas figure 2: equipment overview. 4 application of model predictive control to a direct current motor subject to probability constraints the goal of this section is to illustrate the proposed model predictive control scheme with probability constraints. to do so, we present data collected from real-time experiments—the experiments were carried out in a laboratory tested designed to control a direct current (dc) motor. 4.1 identification the laboratory testbed was assembled with “modulo 2208 servo-mecanismo”, a dc motor device assembled by datapool-brazil [18,21], which worked in association with a data-acquisition board (model usb-6008 daq) from national instruments (see fig. 2). the board was configured to work with fixed sampling time of 20 ms. the dc motor was equipped with a tachometer—it generated a voltage in the range [0, 5] v for an angular velocity in the range of [0, 26.17] rad/s, in a linear relationship. likewise, a current sensor generated the current consumed by the motor in the range [0, 5] a. pseudo random binary signals (prbs) with amplitudes between 0 and 5 v were generated and applied in a sixth-order butterworth low-pass filter with a cutoff frequency randomly chosen between 0.01hz and 70hz. the resulting signal was then applied in the input of the dc motor, and the corresponding data was collected and stored. each experiment for sake of identification was performed in chunks of 60s, that is, each chunk contained three thousand points. the data were then split into smaller chunks, each with 1300 points. these chunks were used to obtain a model in the observability canonical form through the subspace identification method [8,13]. the canonical model was then modified to cope with the prediction-error minimization algorithm. finally, the obtained model was validated through unused, uncorrelated data (see fig. 3 for illustration of a sample). this procedure produced the model (≈ 85% fitting): [ ωk+1 ik+1 ] = [ 0.9814 0.4582 −0.0087 0.7667 ][ ωk ik ] + [ 0.0064 0.0207 ] (uk + dk), (15) model predictive control of stochastic linear systems with probability constraints 933 0 1 2 3 4 5 6 7 8 9 0 5 q 0 1 2 3 4 5 6 7 8 9 0 5 10 15 20 25 z real simulation 0 1 2 3 4 5 6 7 8 9 0 1 2 y x figure 3: real and simulated data: experiment suggests that the model attained for the dc motor seem appropriate for representing the real-time equipment. −2.5 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 2.5 0 1 2 3 4 5 x 10 4 x y figure 4: histogram of disturbances. dashed curve shows the gaussian distribution. where ω, i, u, and d represent the angular velocity of the shaft, the electrical current consumed by the motor, the armature voltage applied in the terminals of the dc motor, and disturbance, respectively. the disturbance d stands for an i.i.d. stochastic process under gaussian probability distribution— the corresponding experimental data is summarized in fig. 4. 4.2 probability constraints armature voltage (control signal) that drove the motor was restricted by lower and upper bounds as umin ≤ uk ≤ umax, (16) where umin and umax are the minimum and maximum voltages. the rotor angular velocity and the armature current were bounded as well, i.e., ωmin ≤ ωk ≤ ωmax, imin ≤ ik, and pr[ik ≥ imax] ≤ δ, (17) where ωmin, ωmax, imin, and imax denote the corresponding limits. the control objective was to reach a desired value of the rotor angular velocity, say ωss, while fulfilling the constraints given in (16) and (17). 934 c.f. caruntu, c.c. velandia-cardenas, x. liu, a.n. vargas 0 0.2 0.4 0.6 0.8 1 −3 −2 −1 0 1 2 3 x y figure 5: inverse cumulative distribution function and its upper linear approximation (dashed line). 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 5 10 15 x y a reference 0.95 0.955 0.96 0.965 0.97 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 −0.1 0 0.1 0.2 0.3 x z b 0 2 4 6 8 10 12 14 −0.1 0 0.1 0.2 0.3 y z c 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 1 2 3 4 5 x q d figure 6: data from real-time experiments. problem 2 was evaluated for each δ = 0.95, 0.955, 0.96, 0.965, 0.97, and every curve shows the mean of one-hundred sample paths. the curves show (a) angular velocity of the dc motor; (b) armature current of the dc motor; (c) phase-portrait of the system; and (d) armature voltage of the dc motor. 4.3 experimental results recall that problem 2 requires a linear approximation for the inverse cumulative distribution function. to fulfill this requirement, the linear approximation h(1 − εi) = 2.38 − 4εi was considered, valid for εi ∈ [0.01, 1) (see fig. 5). associated with problem 2, we consider the cost j(x̄,u) = 1000||x̄||∞ + 2||u||∞. (18) problem 2 was evaluated with the following additional parameters: n = 2, sx0 = i2 and sw = i2. the limits for the constraints in (16) and (17) were set as umin = 0 v, umax = 5 v, ωmin = 0 rad/s, ωmax = 26.17 rad/s, imin = 0 a and imax = 0.38 a. the reference for the angular velocity was fixed at ωss = 13 rad/s. problem 2 was evaluated with δ taking values 0.95, 0.955, 0.96, 0.965, and 0.97, and every corresponding solution was checked in practice. indeed, one-hundred experiments were conducted for each value of δ—the experimental outcome is subsumed under the curves shown fig. 6. model predictive control of stochastic linear systems with probability constraints 935 the curves in fig. 6 show the statistical mean of the stored one-hundred samples. fig. 6(a) shows the angular velocity of the dc motor; it can be observed that as δ decreases, settling time increases. armature current is represented in fig. 6(b), which shows that the upper bound on the current is satisfied and the maximum value of the current decreases when the value of δ decreases. phase portrait of the process is illustrated in fig. 6(c)—data suggest that the dc model be second moment stable [22]. the curves of the armature voltage (control input) in fig. 6(d) suggest that as long as δ decreases, the upper bound on the armature voltage also decreases; as a result, δ successfully actuated as a constraint on the armature voltage. in summary, the experimental data indicate that our approach seems to be useful to handle real-time applications subject to physical constraints. 5 conclusion the paper presents an easy-to-check method to handle linear stochastic systems subject to probability constraints. here, constraints hinge upon gaussian distributions. however, nonlinear equations arise naturally from that gaussian-based evaluations, turning the problem difficult to solve. to overcome such a drawback, we have converted those nonlinear equations into simple-tocheck linear matrix inequalities (conversion guarantees only sub-optimal solutions, see remark 4). the derived conversion shows appropriate for real-time applications—specially when fast algorithms are necessary—as illustrated in the paper. acknowledgments this work was partially supported by the thousand talents plan of shaanxi province for young professionals, and the brazilian agencies fapesp grants 03/06736-7; cnpq grant 304856/2007-0; and capes grant program pve 88881.030423/2013-01. bibliography [1] bazaraa, m.s.; sherali, h.d.; shetty, c.m. 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(2014). flocking of multi-agents with a virtual leader, automatica, 50, 683–694, 2014. ijcccv11n1.dvi international journal of computers communications & control issn 1841-9836, 11(1):11-25, february 2016. a fuzzy networked control system following frequency transmission strategy h. benítez-pérez, j. ortega-arjona, o. esquivel-flores, j.a. rojas-vargas, a. álvarez-cid héctor benítez-pérez* iimas unam cto. escolar 3000, c. u., méxico d. f. *correspondig author: hector.benitez@iimas.unam.mx jorge ortega-arjona facultad de ciencias unam av. universidad 3000, c. u., méxico d. f. jloa@ciencias.unam.mx oscar esquivel-flores, jared a. rojas-vargas iimas unam cto. escolar 3000, c. u., méxico d. f. oaefmcc@hotmail.com, jared_36_23@hotmail.com andrés álvarez-cid facultad de ingeniería unam av. universidad 3000, c.u., méxico d. f. waveform@live.com.mx abstract: at present, network control systems employ a common approximation to solve the connectivity issue due to time delays coupled with external factors . however, this approach tends to be complex in terms of time delays, and the inherent local phase is missing. therefore, it is necessary to study the behavior of the delays as well as the integration of the differential equations of these bounded delays. the related time delays need to be known a priori, but from a dynamic real-time perspective in order to understand the dynamic phase behavior. the objective of this paper is to demonstrate the inclusion of the data frequency transmission and time delays that are bounded as parameters of the dynamic response from a real-time scheduling approximation, considering the local phase situation. the related control law is designed considering a fuzzy logic approximation for nonlinear time delays coupling. the main advantage is the integration of this behavior through extended state space representation. this keeps certain linear and bounded behavior leading to a stable situation during an events presentation, based on an accurate data transmission rate. an expected result is that the basics of the local phase missing as a result of the local bounded time delays from the lack of tide synchronization conforms to the modeling approximation. keywords: fuzzy networks control (fnc), frequency control, local phase challenge. 1 introduction real-time restrictions are the primary cause of time delays when general conditions tend to be periodic and repeatable . the control design and stability analysis of network-based control systems (ncss) have been studied in recent years, and approaches such as the codesign strategy have been developed [2]. the main advantages of these types of systems are their low cost, small wiring volume, distributed processing, simple installation, ease of maintenance, and reliability. copyright © 2006-2016 by ccc publications agora university 12 h. benítez-pérez, j. ortega-arjona, o. esquivel-flores, j.a. rojas-vargas, a. álvarez-cid a key issue in an ncs is the effect of network-induced delay on the system performance. the delay can be constant, time-varying, or even random depending on the scheduler, network type, architecture, or operating systems. [16] analyzed several important facets of ncss and introduced models for their delays, including first a fixed delay, then an independently random delay, and finally a markov process. this work introduced optimal stochastic control theorems for ncss based upon independently random and markovian delay models. [20], introduced static and dynamic scheduling policies for the transmission of sensor data over a continuous time linear timeinvariant (lti) system. they introduced the notion of the maximum allowable transfer interval (mati), which is the longest time after which a sensor should transmit a data. the mati determines that the lyapunov function of the system under consideration is strictly decreasing at all times. [22], extended the work of walsh et al. by developing a theorem that ensures the decrease of a lyapunov function for a discrete-time lti system at each sampling instant, using two different bounds. this used a number of different linear matrix inequality (lmi) tools for analyzing and designing optimally switched ncss. the mati is an important concept for bounded time delays and the maximum local dephasing problem. alternatively, [23] considered both the network induced delay and the time delay in a plant, and proposed a controller design method using the delay-dependent approach. an appropriate lyapunov functional candidate was used to obtain a memoryless feedback controller that was derived by solving a set of lmis. [21] modeled the network-induced delays of the ncss as interval variables governed by a markov chain. using the upper and lower bounds of the delays, a discrete-time markovian jump system with norm-bounded uncertainties was presented to model the ncss. based on this model, the h∞ state feedback controller was constructed via a set of lmis. [7] introduced a model transformation for the delay-dependent stability of systems with time-varying delays in terms of lmis. their model also refined results from delay-dependent h∞ control and extended them to the case of time-varying delays. based upon this review, the present paper defines a model that integrates time delays into a fuzzy control for ncss [18, 22] taking into consideration the local phase margin induced by the computer network as a result of online reconfiguration. since ncs is modified according to time delays, reconfiguration is a transition that modifies the structure of a system so it changes its representation of states. the objective of this paper is to show how the control of frequency transmission has a bounded impact over a network control system. in general there are two types of tasks in a ncs. the first is a periodic task that is timetriggered. in this type, tasks have a transmission time ci, a constant period of execution pi, and a deadline di. thus, the sum of the transmission times of n nodes’ tasks, divided into their periods pi for a fixed priority scheduler [15]. the network scheduler is a high priority in the design of a distributed system and it is also critical in an ncs, since if there is no scheduling between nodes, data transmissions may occur simultaneously which lead to collisions or bandwidth violations. this behavior results in a transmission with time delays, leading to failure in complying with deadlines, data loss, and an obvious decrease in system performance due to local phase misleading. a good scheduling control algorithm minimizes the decrease in system performance [4]; nevertheless, there are no global schedulers that guarantee optimal system performance [15]. some strategies include methods in which nodes generate proper control actions in order to optimally utilize bandwidth [11, 12]. in the digital control case, the performance only depends on the sampling frequency without uncertainties. for networked control, the minimum transmission frequency fm is necessary to guarantee suitable system performance. as the transmission frequency increases the system performance improves; however, the load on the network also increases until a maximum transmission frequency fm is reached, then the system performance decreases because the network is a fuzzy networked control system following frequency transmission strategy 13 overloaded. it is very important to modify the transmission frequency to obtain better system performance within a bounded region that is particularly defined for the current system needs. in control systems, several modeling strategies for managing time delay within control laws have been studied by different research groups. [16] proposed the use of a time delay scheme integrated to a reconfigurable control strategy, based on a stochastic methodology. [9] described how time delays are used as uncertainties, which modify pole placement of a robust control law. [8] presented an interesting case of fault tolerant control approach related to time delay coupling. [3] studied reconfigurable control from the point of view of structural modification, establishing a logical relation between dynamic variables and the respective faults. finally, [19] and [2] considered that reconfigurable control strategies perform a combined modification of system structure and dynamic response; thus, this approach has the advantage of bounded modifications over system response. the present paper proposes a mixed strategy using bounded variable time delays and frequency transmission as the data communication rate. the novelty of this approximation is that it guarantees schedulability as well as stability in the presence of bounded time delays, considering an accurate data transmission rate through the network. this is feasible since the time delays are bounded according to scheduler response. 2 frequency control as presented in [17], a fuzzy approach to network control systems allows a real representation of bounded time delays and control design. specifically, this can be used when time delays are the result of the controlling frequency transmission, as presented previously. several potential scenarios are presented that follow this time delay behavior. in fact, the number of scenarios is finite since the combinatorial formation is bounded. therefore, any strategy for designing a control law needs to take into account the gain scheduling approximation. therefore, an alternative strategy is the use of control for periodic messages, namely the control of frequency transmissions as presented by [6]. the change in frequency transmission of each node through time is represented using the relations of frequencies for a particular node and among all nodes. this means that the frequency transmission rate of each node is influenced by changes in the transmission rate of the other nodes. upper and lower bounding are necessary since the data transmission frequency of the network can be high. due to uncertainties during sensing, the system may become unstable; similarly, the low data frequency transmission may result in undersampling which goes to control performance weakness. having defined this dynamic bounding strategy, it becomes interesting to examine the effects of known time delays over a well-defined network control approximation. 3 modified frequency transmission for ncs’s the following section presents an illustrative experiment considering a twin rotor case study as well as a magnetic levitation system (fig. 1, 2) these cases include the local dynamics necessary for a complete networked control system where a global computer network is implemented. 3.1 twin rotor and magnetic levitation system equations the free-body diagram of the twin rotor is illustrated in figure 1 and from this the next nonlinear equations of motion are obtained using the euler-lagrange formula [24]: 14 h. benítez-pérez, j. ortega-arjona, o. esquivel-flores, j.a. rojas-vargas, a. álvarez-cid figure 1: twin rotor. θ̈ = 1 (jeqp + mhelil 2 cm) (kppvmp + kpyvmy− mhelil 2 cm(sin(θ)cos(θ))ψ̇ 2 − cos(θ)mheliglcm −bqpθ̇) ψ̈ = 1 (jeqy + mhelil 2 cmcos(θ) 2) (kypvmp + kyyvmy+ 2mhelil 2 cmcos(θ)sin(ψ)θ̇ψ̇ −bqyψ̇) (1) where: jeqp total moment of inertia about pitch axis jeqy total moment of inertia abut yaw axis kpp thrust force constant acting on pitch axis from pitch motor/propeller kyy thrust force constant acting on yaw axis from yaw motor/propeller kpy thrust force constant acting on pitch axis from yaw motor/propeller kyp thrust force constant acting on yaw axis from pitch motor/propeller bqp equivalent viscous damping about pitch axis bqy equivalent viscous damping about yaw axis lcm center of mass length along helicopter body from pitch axis mheli total moving mass of helicopter g gravitational constant table 1 shows the values of this parameters. in the case of magnetic levitation system model [25] specifications are derived from the mechanical and electric system representation as illustrated in figure 2. the nonlinear equations for the magnetic levitation system are: ẋ1 = x2 ẋ2 = −kmx 2 3 2mb(x1) 2 + g ẋ3 = 1 lc (−rx3 + u) where r = rc + rs and u = vc input voltage and a fuzzy networked control system following frequency transmission strategy 15 table 1: system specifications symbol value unit jeqp 0.0348 kg ·m 2 jeqy 0.0432 kg ·m 2 kpp 0.204 n ·m/v kyy 0.072 n ·m/v kpy 0.0068 n ·m/v kyp 0.0219 n ·m/v bqp 0.800 n/v bqy 0.318 n/v mheli 1.3872 kg lheli 0.186 m figure 2: magnetic levitation system table 2: system parameters symbol value unit lc 0.4125 h rc 10 ω rs 1 ω km 6.5308e −5 nm2/amp2 mb 0.068 kg g 9.81 m/s2 rc electromagnet resistance rs resistor in series wiht the coil km constant of electromagnet force mb mass of the ball g gravitacional constant lc coil inductance the values of the parameters are provided in table 2 these systems are distributed and communicated through an ethernet network to implement 16 h. benítez-pérez, j. ortega-arjona, o. esquivel-flores, j.a. rojas-vargas, a. álvarez-cid a complete networked control system, as depicted in figure 3. figure 3: ncss in this work, we define three rules of fuzzy model to approximate the twin rotor nonlinear system, as follows: rule 1 : if x1 is about 0◦, then ẋ = a1x + b1 rule 2 : if x1 is about 40◦, then ẋ = a2x + b2 rule 3 : if x1 is about −40◦, then ẋ = a3x + b3 where x1 is the pitch angle and a fuzzy networked control system following frequency transmission strategy 17 a1 =      0 0 1 0 0 0 0 1 0 0 −9.2593 0 0 0 0 −3.4868      b1 =      0 0 0 0 2.3611 0.0787 0.2401 0.7895      a2 =      0 0 1 0 0 0 0 1 18.8362 0 −9.2768 0 2.5148 0 0 −4.4618      b2 =      0 0 0 0 2.3667 0.0789 0.3073 1.0102      a3 =      0 0 1 0 0 0 0 1 −18.8362 0 −9.2768 0 −2.5148 0 0 −4.4618      b3 =      0 0 0 0 2.3667 0.0789 0.3073 1.0102      figure 4 shows the membership function for the rules of the helicopter fuzzy model. figure 4: membership functions of helicopter model here, two fuzzy rules are defined to approximate the magnetic levitation system by means of two linear models, as follows: rule 1 : if x1 is about 0.006m, 18 h. benítez-pérez, j. ortega-arjona, o. esquivel-flores, j.a. rojas-vargas, a. álvarez-cid then ẋ = a1x + b1 rule 2 : if x1 is about 0.014m, then ẋ = a2x + b2 where x1 is the ball position in meters and a1 =    0 1 0 4097.8 0 −25.6 0 0 −26.7    a2 =    0 1 0 1509.2 0 −14.2 0 0 −26.7    b1 = b2 =    0 0 2.4242    the membership functions for this case are shown in figure 5 figure 5: membership functions of levitator model the fuzzy controller for each system is design by pdc as follows: control rule i: if zi(t) is mi1 and zn(t) is min then u = −kix the feedback gain matrix ki was obtained by means of the linear-quadratic regulator (lqr) of each linear system. the stability analysis of the fuzzy control system should be performed employing a numerical mathematical program, for example, using matlab’s linear matrix inequalities toolbox to check feasibility. a fuzzy networked control system following frequency transmission strategy 19 k1 = [ 18.9369 1.9770 7.4917 1.5258 7.0292 0.7685 −2.2192 19.4463 −0.4496 11.8949 −0.7685 7.0292 ] k2 = [ 28.5899 2.0444 7.8331 1.6059 7.0264 0.7933 −2.3969 19.3161 −0.5528 11.5476 −0.7933 7.0264 ] k3 = [ 51.0473 6.1419 28.4307 4.7239 22.2386 2.3333 −6.3519 59.4161 −2.4313 41.5536 −2.3333 22.2386 ] following the same procedure for the fuzzy helicopter controller to find the matrix gains, we obtain the following matrix: k1 = [ −11971 −187 53 7 ] k2 = [ −5739 −147.7 32.1 10 ] now, in terms of frequency transitions, the dynamics representation is given as follows: ẋ =         0.1250 0.1300 0.1350 0.1400 0 0.1060 0.1111 0.1160 0.1210 0 0.0900 0.0950 0.1000 0.1050 0 0.0135 0.0185 0.0235 0.0286 0 0.001 0.001 0.001 0.001 1         x +         40 0 0 0 0 0 45 0 0 0 0 0 50 0 0 0 0 0 175 0 0 0 0 0 1         u y =         1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1         x where x1 = is the frequency transmission in node 1 x2 = is the frequency transmission in node 2 ... xn = is the frequency transmission in node n xc = is the network utilization and 20 h. benítez-pérez, j. ortega-arjona, o. esquivel-flores, j.a. rojas-vargas, a. álvarez-cid u1 = is the frequency input 1 of system u2 = is the frequency input 2 of system ... un = is the frequency input n of system uc = is the frequency input c of system we design a control for the trajectory tracking, which is the desired frequency transmission by means of the lqr algorithm, so that the gain matrix obtained is: k =        3.5521 0.0027 0.0023 0.0007 0.0003 0.0030 3.5101 0.0021 0.0006 0.0003 0.0029 0.0024 3.4763 0.0005 0.0003 0.0029 0.0022 0.0017 3.2792 0.0002 0.0000 0.0000 0.0000 0.0000 5.7417        and the integral gain matrix is ki =        −48.3570 0.0085 0.0150 0.0518 −0.0085 −48.3184 0.0064 0.0389 −0.0150 −0.0064 −48.2873 0.0297 −0.0398 −0.0299 −0.0228 −62.5185 0.0048 0.0048 0.0048 0.0050        the nominal values and schedulability regions were found using truetime [5] and are shown in table 3. these take into account that the frequency system models 5 network nodes; but in the experiment, only 4 nodes are employed because the last node corresponds to the use of the network. for the stability proof it is necessary to take into account there are two transmission bound, superior and lower bound and consider the schedulability restriction. the schedulability restriction is given taking the maximum frequency value if keeps the restriction. in the other hand for the lower transmission value it is necessary to prove that this values are the maximum allowable delay bound which guaranty stability of the ncs. in [10] it is presented a lmi formulation to find the maximum allowable delay bound. if there exist p > 0, qi > 0, xi, yi and zi, i = 1, ...,n, such that [ p11 f t l la −γ ] < 0, [ xi yi y ti zi ] > 0 where p11 = [ f11 pf1 −y ft1 p −y −d ] , f = [ f f1 ... fn ] , f1 = [ f1 ... fn ] , y = [ y1 ... yn ] , l = τ [ z1 ... zn ] , a fuzzy networked control system following frequency transmission strategy 21 table 3: nominal frequency, maximum, minimum and transmission time node ci (s) finom (hz) 0.9 f i min (hz) f i max (hz) 1 0.001 280 40 83 2 0.001 300 45 98 3 0.001 310 50 70 4 0.001 450 175 212 5 0.001 480 165 196 6 0.001 500 160 188 d = diag{q1, ...,qn} , γ = τ̄diag{z1, ...,zn} , f11 = f t p + pf + n ∑ i=1 { yi + y t i + τ̄xi + qi } the system is asymptotically stable for any time-delay τi satisfying 0 6 τi 6 τ̄i, i = 1, ...,n solving lmis with matlab toolbox for each subsystem of the fuzzy representation the next τi are found τ1 = 0.0058, τ2 = 0.0071, and τ3 = 0.0067 which in frequency terms are 172.42, 140.85 and 149.25 hz respectively being consistent with the values previously found. now in the twin rotor case the values are: τ1 = 0.050, τ2 = 0.067, and τ3 = 0.050 in frequency terms are 20, 15 and 20 hz respectively which approach to the values previously found in table 3. 4 frequency control design to implement this experiment, 7 nodes employed to obtain a complete distributed ncs configuration as follows: 3 nodes to the twin rotor system (sensor, actuator, and controller), 3 to the magnetic levitation system (sensor, actuator, and controller), and 1 more used for perform frequency control. the latter node assigns the changes in the transmission frequency to sensors and controller nodes of the systems. the platform was built with 10 mbps ethernet as communication medium and quarc adquisition boards to accomplish data exchange between nodes. quarc matlab’s toolbox provides an useful and straightforward way to stablish communication among several nodes which contain embedded simulink models (in this case twin rotor and levitation distributed models) by means its communication blocks. the processes run at nominal frequency transmission values according to table 3. the controller frequency node, which contains the embedded simulink frequency model, modifies nominal to schedulability values by means lqr controller action, as seen in figure 6. in this 22 h. benítez-pérez, j. ortega-arjona, o. esquivel-flores, j.a. rojas-vargas, a. álvarez-cid figure, the red and blue signals represent the helicopter’s sensor and controller transmission frequency, respectively, and the green and brown signals depict the levitator’s sensor and controller transmission frequency. figure 6: frequency transmission transition the results of these experiments are presented in fig. 7 , in which the twin rotor presents a suitable response even with frequency transitions. the red color signal is the desired pitch and yaw angle, and the blue color signal is the obtained response. figure 7: pitch and yaw angle when frequencies are whitin the bounds in the second experiment, the transmission frequencies are outside the lower bound of the schedulability region for short time instants. it can be seen at t = 10 and t = 30 in figure 8 where the frequencies leave the schedulability region for 5s and then return within the region. the magnetic levitation system response, in terms of the current value and ball position during the frequency transmission change, is presented in fig. 9. in this figure, the red color signal is the desired current and ball position, and the blue color signal is the system response. the next result is obtained (figure 10) when the frequencies change at t = 25 and t = 40 leaving slightly the schedulability region for 3s and 5s respectively and then the frequencies return within the region. a fuzzy networked control system following frequency transmission strategy 23 figure 8: pitch and yaw angle when frequencies by instants are not within the bounds figure 9: current and ball position when frequencies are within the bounds figure 10: current and ball position when frequencies by instants are not within the bounds 24 h. benítez-pérez, j. ortega-arjona, o. esquivel-flores, j.a. rojas-vargas, a. álvarez-cid 5 conclusion currently, time delays can be modeled using a bounded frequency transmission control approach; however, the resulting delays are time varying and stationary. therefore, a related local control law must be designed according to this characteristic, where time integration is the key global issue to be considered. global stability is reached by the use of the takagi sugeno fuzzy control design, where nonlinear combination is followed by the current situation of the states. these are partially delayed due to the communication behavior in terms of the phasing problem. in this case, the bounded local stochastic variables are presented in terms of local phases. the main contribution of this paper is the capability of determining the local time delays that can be aggregated per event, since the frequency transmission control contributes to the bound time response, resulting in a global problem of local synchronized elements. therefore, fuzzy control may be attractive to guarantee global stability, since any condition is bound to be less than the sampling period of the worst-case scenario with no loss of generality. however, local phase missing is still an open problem, since these are stochastically calculated and linked to a tight synchronization situation. the use of the dynamic scheduling approximation, as reviewed here, allows the system to be predictable and bounded; therefore, time delays can be modeled in these terms. moreover, the resulting dynamic representation tackles the inherent switching situation per scenario. this approximation has the main drawback that the context switch may be invoked every time that a periodic task takes place, and it is possible to be executed. in this case, inherent time delays to this action are taken into account to be processed as bounded known values and to modify the related local stochastic phase. thus, there is desynchronizing global behavior of the network. acknowledgments the authors acknowledge the support of papiit in100813 and conacyt 176556 as well as the collaboration from mr. adrian duran chavesti. bibliography [1] benítez-pérez, h.; benítez-pérez a.; ortega-arjona j. 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(2006); magnetic levitation experiment, quanser consulting. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(2), 199-211, april 2019. optimal data file allocation for all-to-all comparison in distributed system: a case study on genetic sequence comparison l.x. li, j. gao, r. mu leixiao li 1. college of computer and information engineering, inner mongolia agricultural university, hohhot 010018, china 2. college of data science and application, inner mongolia university of technology, hohhot 010080, china 3. inner mongolia autonomous region engineering & technology research center of big data based software service, hohhot 010080, china llxhappy@126.com jing gao* college of computer and information engineering, inner mongolia agricultural university, hohhot 010018, china *corresponding author: gaojing@imau.edu.cn ren mu state key laboratory, beijing jiaotong university, beijing 100044, china 568387304@qq.com abstract: in order to solve the problem of unbalanced load of data files in large-scale data all-to-all comparison under distributed system environment, the differences of files themselves are fully considered. this paper aims to fully utilize the advantages of distributed system to enhance the file allocation of all-to-all comparison between the data files in a large dataset. for this purpose, the author formally described the allto-all comparison problem, and constructed a data allocation model via mixed integer linear programming (milp). meanwhile, a data allocation algorithm was developed on the matlab using the intlinprog function of branch-and-bound method. finally, our model and algorithm were verified through several experiments. the results show that the proposed file allocation strategy can achieve the basic load balance of each node in the distributed system without exceeding the storage capacity of any node, and completely localize the data file. the research findings can be applied to such fields as bioinformatics, biometrics and data mining. keywords: distributed system, all-to-all comparison, mix integer linear programming (milp), file allocation, load balancing. 1 introduction the comparison between two random data files in a dataset is commonplace in bioinformatics, biometrics and data mining [1]. however, the all-to-all comparison between data files in a large dataset require special large scale computations. previous solutions to such a problem mainly fall into two categories: those grounded on centralized computing or those using distributed computing based on centralized storage [2]. the former requires access to supercomputer resources [3], while the latter is bottlenecked by limited storage capacity and task delay caused by waiting for data transmission [4]. the distributed computing based on distributed storage provides an efficient, reliable and scalable solution to large-scale computing problems like all-to-all comparison. by distributed copyright ©2019 cc by-nc 200 l.x. li, j. gao, r. mu computing, a large-scale problem is decomposed into several small problems, which are then handled separately on each node in a distributed system [5]. nevertheless, the performance of distributed computing depends heavily on data allocation, task decomposition and task scheduling, and might be dampened by the irrational data allocation, poor data locality and computing load imbalance in the distributed system [6]. there are two data allocation strategies for all-to-all comparison of data files in a large dataset via distributed computing based on distributed storage, namely, allocating all input files to each computing node, and allocating a number of copies of each input files randomly to the system node (i.e. the hadoop framework data allocation policy) [7–9]. hadoop framework can not guarantee the load balancing of the comparison task calculation of each node. storing all input files on each node is a common practice in centralized computing solutions, but it wastes storage and network resources [10] for the latter strategy, the computing performance is poor due to the frequency data movement between the nodes [11]. after all, hadoop, as a general distributed computing framework, is not designed specifically for all-to-all comparison [1,12]. to solve the above problems, this paper establishes a model to optimize the data file allocation optimization in all-to-all comparison of data files in a large dataset, puts forward a data file allocation algorithm and a task scheduling strategy, and verifies the proposed methods via experiments. the experimental results show that our model and algorithm can achieve data localization and load balancing of comparative files under the storage constraints of distributed system nodes, thus giving full play to the advantages of distributed system. the remainder of this paper is organized as follows: section 2 describes the all-to-all comparison problem and constructs a data file allocation model; section 3 designs the data allocation algorithm; section 4 performs the experimental verification and discusses the results; section 5 wraps up this paper with several conclusions. 2 problem description and data file allocation model 2.1 problem description all-to-all comparison refers to the multiple contrasts of all data files in a dataset. this problem can be illustrated by the graph in figure 1, where each vertex is a data file to be compared and each edge is a comparison task between two data files. if m is the number of data files to be compared, then the total number of comparison tasks is m(m − 1)/2. hence, the all-to-all comparison problem can be expressed as a graph with m vertices and m(m− 1)/2 edges. figure 1: all-to-all comparison problem in a distributed system with distributed storage, the all-to-all comparison of data files in a large dataset is implemented in two steps: first, all data files in the dataset are allocated to each computing node of the distributed system; then, the data files are compared in a pairwise manner. optimal data file allocation for all-to-all comparison in distributed system: a case study on genetic sequence comparison 201 table 1: symbol description number symbol symbolic description 1 m number of data files 2 n number of computing nodes in distributed system 3 s m row 1 column matrix, representing the size of each file 4 si, i = 1, 2, . . .,m size of i data file 5 u n row 1 row matrix, representing the maximum storage limit of each node in a distributed system 6 ui, i = 1, 2, . . . ,n the storage capacity of computing nodesi 7 wij, i = 1, 2, . . . ,m,j = 1, 2, . . .,m the size of the task compares with file i and file j. 8 cij, i = 1, 2, . . . ,m,j = 1, 2, . . .,m amount of calculation of the task compares with file i and file j. 9 xkt,k = 1, 2, . . . ,m(m− 1)/2, t = 1, 2, . . .,n whether or not allow the assignment of item k allocated to the t node 10 wkt = wijt assign the size of item k to the t node (the file number corresponding to item k is i,j). 11 ckt = cijt the amount of computation assigned to item k of the t node (the file number corresponding to item k corresponds to i,j). 12 taskno task number 13 av_work the average amount of tasks per computing node should be allocated in theory, 14 deci correspondence matrix between task and file 15 result task allocation result matrix 16 f objective function variable coefficient matrix 17 aeq coefficient matrix of equality constraint variables 18 beq equality constrained variable resource matrix 19 a inequality constraint coefficient matrix 20 b inequality constrained resource matrix 21 intcon integer variable subscript sequence number vector 22 lb lower limit of variable 23 ub upper limit of variable 24 [x,y ] x is the best solution for obtaining the variable value. y is the best solution. 202 l.x. li, j. gao, r. mu before allocating the data files, it is necessary to fully consider how the system performance is affected by node storage capacity, data transmission, network bandwidth, load balance and other factors. a desirable allocation strategy must satisfy the following conditions:(1) the data allocated to a node should not surpass the storage capacity of that node;(2) the two files to be compared on a node must be saved on that node to localize the data files for each comparison task;(3) the comparison tasks should be balanced among the computing nodes. for simplicity, all the symbols used in this paper are listed in table 1 below. for better system speed and performance, our goal is to balance the comparison tasks among the computing nodes under the storage capacity of each node. 2.2 data file allocation model the above description shows that all-to-all comparison is a typical constrained optimization problem: maximizing or minimizing objective functions under multiple constraints. the most effective solution to constrained problem is linear programming (lp), which works well when the objective functions and constraints are all linear [13,14]. with a strong modelling ability, the lp is also a desirable way to tackle control and programming problems. the main idea of the lp is to find a control sequence that satisfies all constraints and minimizes the objective function [15]. in this paper, node storage is added to the constraints of the all-to-all comparison problem, and load balancing is treated as the objective function. all constraints were expressed as an equality or inequality. as mentioned before, our problem involves m data files and n nodes in a distributed system with distributed storage. for multiple comparisons of all data files, the m data files should be distributed rationally to those nodes to fulfill load balancing, data localization and node storage constraint. as shown in figure 1, the total number of comparison tasks of these data files can be expressed as: c2m = m(m− 1) 2 (1) let si(i = 1, 2, · · · ,m) be the size of each of the m data files. then, the total size of the two files in each comparison task can be calculated as: wij = si + sj(i,j = 1, 2, · · · ,m,i < j) (2) then, the computing load of the comparison task between file i and file j can be obtained as: cij(i,j = 1, 2, · · · ,m,i < j) (3) thus, the total number of multiple comparison tasks between the m data files can be described as: m∑ i=1 m∑ j=i+1 cij (4) if the tasks are allocated equally to n nodes, then the theoretical mean number of tasks allocated to each computing node can be expressed as: m∑ i=1 m∑ j=i+1 cij n (5) then, xkt(k = 1, 2, · · · , m(m−1) 2 , t = 1, 2, · · · ,n) was introduced to specify whether data file k is allocated to node n: xkt = 0 or 1,k = 1, 2, · · · , m(m− 1) 2 , t = 1, 2, · · · ,n (6) optimal data file allocation for all-to-all comparison in distributed system: a case study on genetic sequence comparison 203 since a comparison task can only be allocated to one node, we have: n∑ t=1 xkt = 1,k = 1, 2, · · · , m(m− 1) 2 (7) let uj(j = 1, 2, · · · ,n) be the storage capacity of node n and wkt = wijt be the total size of the two files (file i and file j) in task k that are distributed to node t. then, the total size of the files distributed to each node in the distributed system should not exceed the storage capacity of that node: m(m−1) 2∑ k=1 wktxkt ≤ ut, t = 1, 2, · · · ,n (8) let ckt = cijt be the computing load of the comparison task k between file i and file j that are distributed to node t. then, the computing load distributed to each node in the distributed system can be expressed as: m(m−1) 2∑ k=1 cktxkt (9) then, the sum of the absolute difference between the actual and theoretical mean number of tasks actually allocated to each computing node can be expressed as: n∑ t=1 ∣∣∣∣∣∣∣∣∣   m(m−1) 2∑ k=1 cktxkt  − m∑ i=1 m∑ j=i+1 cij n ∣∣∣∣∣∣∣∣∣ (10) under the constraints of equations (6), (7) and (8), an optimal task allocation model [16] was established to minimize the value of equation (10): min n∑ t=1 ∣∣∣∣∣∣  m(m−1)2∑ k=1 cktxkt  − m∑ i=1 m∑ j=i+1 cij n ∣∣∣∣∣∣ s.t.   n∑ t=1 xkt = 1,k = 1, 2, · · · , m(m−1) 2 m(m−1) 2∑ k=1 wktxkt ≤ ut, t = 1, 2, · · · ,n xkt = 0 or 1,k = 1, 2, · · · , m(m−1) 2 , t = 1, 2, · · · ,n (11) if the objective function in equation (11) contains nonlinear terms, then established model is a nonlinear programming model. in this case, new decision variables d−t and d + t were introduced to equation (11) to transform the model into the linear form. the variable d−t means the actual computing load allocated to a node is greater than the theoretical mean computing load to that node, while the variable d+t has exactly the opposite meaning. the two variables are both numbers greater than or equal to zero. in other words, if the actual computing load allocated to a node is greater than the theoretical mean computing load to that node, the excess load d−t should be removed from the node; in the opposite scenario, the insufficient load d+t should be added to the node. in this way, the objective function is changed into finding the minimum sum of (d−t + d + t ) for each node. 204 l.x. li, j. gao, r. mu after introducing the new decision variables, equation (11) can be transformed into a linear programming model below. min n∑ t=1 (d−t + d + t ) s.t.   n∑ t=1 xkt = 1,k = 1, 2, · · · , m(m−1) 2 m(m−1) 2∑ k=1 wktxkt ≤ ut, t = 1, 2, · · · ,n   m(m−1)2∑ k=1 cktxkt  − m∑ i=1 m∑ j=i+1 cij n  −d−t + d+t = 0, t = 1, 2, · · · ,n xkt = 0 or 1,k = 1, 2, · · · , m(m−1) 2 , t = 1, 2, · · · ,n d−t ,d + t ≥ 0, t = 1, 2, · · · ,n (12) since the values of d−t and d + t cannot be integers, the model of equation (12) is a mixed integer linear programming (milp) model. 3 design of file allocation algorithm the milp model can be solved by commercial solvers like cplex [17] and gurobi [18] and non-commercial approaches like branch and bound method, cutting plane method, branchcutting plane method and heuristic method [19–21]. here, the intlinprog function of branch-andbound steps [16] in the matlab is selected to solve the milp in equation (12). table 2 lists the comparison tasks and data files in the problem. among them, branch and bound method is an effective method to solve combinatorial optimization problems. it can get the optimal solution, and the average speed is very fast. therefore, the idea of branch and bound method is adopted in this paper. table 2: comparison tasks and data files task number file1 number file2 number 1 1 2 2 1 3 . . . . . . . . . m− 1 1 m m 2 3 m + 1 2 4 . . . . . . . . . 2 ∗m− 3 2 m . . . . . . . . . m(m− 1)/2 m− 1 m then, a file allocation algorithm was designed to cover the following steps [16,22,23]: 4 experimental verification four file allocation experiments were carried out in the environment of matlab 2018a [24,25], with the aim to verify our model and algorithm. optimal data file allocation for all-to-all comparison in distributed system: a case study on genetic sequence comparison 205 algorithm 1 file allocation algorithm 1 step 1: define and initialize variables 2 define and initialize variables m,n,s,u : m ← the total number of files, n ← the total number of nodes, s ← [s1,s2, . . . ,sm],u ← [u1,u2,←,un]; if s.length==0 or u.length==0 then s ← unit matrix whose values are all 1 with m rows and 1 column; u ← unit matrix whose values are all infinite with n rows and 1 column. end if 3 step 2: calculate the corresponding matrix deci between the tasks and the files 4 define task ordinal variables: taskno ← 1; 5 for all i = 1 to m do for all j = i + 1 to m do deci(taskno, 1 : 4) ← [taskno,i,j,s(i) + s(j)]; taskno + +; end for end for 6 calculate the theoretical mean number of tasks per node: av_work sum (deci(, 4))/n; 7 step 3: set the values for general form of milp parameters i) set the value for the objective function variable coefficient matrix f. define temporary variables i,j; i ←length(deci(:, 1)) ∗n; j ← 2 ∗n; f ← the matrix with i + j rows and 1 column, the former i row elements are 0, and the latter j row elements are 1. ii) set the value of the variable coefficient matrixaeqfor the corresponding equality constraint. for all i = 1 to length(deci(:, 1)) do for all j = 1 to n do aeq(i, (i− 1) ∗n + j) ← 1; end for end for for all j = 1 to n do for all i = 1 to length(deci(:, 1) do aeq(length(deci(:, 1)) + j, (i− 1) ∗n + j) ← deci(i, 4); aeq(length(deci(:, 1)) + j,n*length(deci(:, 1)) + (j − 1) ∗ 2 + 1) ←−1; aeq(length(deci(:, 1)) + j,n*length(deci(:, 1)) + j ∗ 2) ← 1; end for end for iii) set the value of the resource matrix beq for the corresponding equality constraint define temporary variable i; i ← length(deci(:, 1)); beq ← (1 + n row 1 row matrix, the former i row element is 1, and the latter j row element is av_work; iv) set the value of the coefficient matrix a for the corresponding inequality constraint define temporary variable i; i ←length(aeq(1, :)); a ← unit matrix whose values are all 0 with n rows and 1 column. for all i = 1 to n do for all j = 1 to length(deci(:, 1)) do a(i, i + (j − 1) ∗n) ← deci(j, 4); end for end for v) set the value of the resource matrix b for the corresponding inequality constraint: b ← u; vi) set the value of the vector intcon for integer variable subscript sequence: intcon ←1: length(deci(:, 1)) ∗n; vii) set the value of lb for the lower bound and the value of ub for the upper bound. define the temporary variable i,j; i ←length(deci(:, 1)) ∗n; j ← 2 ∗n; lb ← unit matrix whose values are all 0 with i+j rows and 1 column; ub ← the matrix with i + j rows and 1 column, where the former i row elements are 1, and the latter j row elements are + infinity. 8 use the branch and bound intlinprog function to solve the milp problem, we can get the optimal solution: [x,y ] ← intlinprog(f, intcon, a, b, aeq, beq, lb, ub); 9 step 4: matrix of comparison task allocation result define temporary matrix variables sum, sum ← unit matrix whose values are all 0 with n rows and 1 column. for all i=1 to length(deci(:, 1)) do for all j = i + 1 to n do if x((i− 1) ∗n + j) > 0.99999 then sum(j) ← sum(j) + 1; result(j,sum(j)) ← i; end if end for end for 206 l.x. li, j. gao, r. mu (1) experiment 1 (same file size and equal distribution of comparison tasks) ten genetic sequence files of the same size (100m) were allocated to five nodes for sequence alignment. as shown in figure 2, the comparison task distributed to each node has the same computing load and achieves full load balancing. since m = 10 and n = 5, we have m(m − 1)%(2 ∗n) = 0, that is, each node was distributed with the same number of tasks. the detailed results of experiment 1 are shown in table 3 below. figure 2: load distribution to each node in experiment 1 table 3: detailed results of experiment 1 node number task number file number task amount calculation amount node1 6, 7, 22, 27, 30, 34, 35, 41, 43 1, 3, 4, 5, 7, 8, 9, 10 9 1800 node2 3, 9, 13, 16, 17, 25, 28, 31, 36 1, 2, 4, 5, 6, 7, 8, 9, 10 9 1800 node3 2, 10, 11, 19, 20, 23, 26, 33, 37 1, 2, 3, 4, 5, 6, 8, 9 9 1800 node4 1, 4, 12, 15, 18, 21, 29, 39, 42 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 9 1800 node5 5, 8, 14, 24, 32, 38, 40, 44, 45 1, 2, 3, 5, 6, 7, 8, 9, 10 9 1800 (2) experiment 2 (same file size and unequal distribution of comparison tasks) ten genetic sequence files of the same size (100m) were allocated to four nodes for sequence alignment. since m = 10 and n = 4, we have (m − 1)%(2 ∗ n)! = 0, that is, the nodes cannot achieve complete load balance. in this case, the load distribution to each node is shown in figure 3. the experimental results (table 4) show that three of the four nodes were distributed with 2200 tasks while the remaining one was distributed with 2400 tasks. the load between the nodes is basically balanced. (3) experiment 3 (different file sizes and equal distribution of comparison tasks) ten genetic sequence files of different sizes (150m; 220m; 180m; 300m; 190m; 95m; 200m; 160m; 320m; 260m) were allocated to five nodes for sequence alignment. since m = 10 and n = 5, we have m(m−1)%(2∗n) = 0. however, the nodes may not be able to achieve complete load balance due to the difference in file size. in this case, the load distribution to each node is shown in figure 4. as shown in table 5, four of the five nodes were distributed with 3735 tasks while the other two with 3775 tasks. the load between the nodes is basically balanced. optimal data file allocation for all-to-all comparison in distributed system: a case study on genetic sequence comparison 207 figure 3: detailed results of experiment 2 table 4: detailed results of experiment 2 node number task number file number task amount calculation amount node1 3, 6, 9, 14, 17, 20, 23, 24, 29, 39, 45 1, 2, 3, 4, 6, 7, 9, 10 11 2200 node2 7, 11, 13, 16, 18, 19, 21, 25, 27, 35, 37, 42 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 12 2400 node3 4, 5, 8, 22, 31, 33, 34, 38, 41, 43, 44 1, 3, 4, 5, 6, 7, 8, 9, 10 11 2200 node4 1, 2, 10, 12, 15, 26, 28, 30, 32, 36, 40 1, 2, 3, 4, 5, 6, 7, 8, 10 11 2200 figure 4: load distribution to each node in experiment 3 (4) experiment 4 (different file sizes and unequal distribution of comparison tasks) ten genetic sequence files of different sizes (150m; 220m; 180m; 300m; 190m; 95m; 200m; 160m; 320m; 260m) were allocated to four nodes for sequence alignment. in this case, the load distribution to each node is shown in figure 5. as shown in table 6, three of the four nodes were distributed with 4670 tasks while the remaining one with 4665 tasks. the load between the nodes is basically balanced. the results of the four experiments reveal that our algorithm can always reach load balance between the nodes, whether the files are of the same size, and ensure that the total size of the files distributed to a node never exceeds the storage capacity of that node. even if load balancing is 208 l.x. li, j. gao, r. mu table 5: detailed results of experiment 3 node number task number file number task amount calculation amount node1 2, 22, 26, 28, 32, 40, 41, 42, 43 1, 3, 4, 5, 6, 7, 8, 9, 10 9 3735 node2 5, 10, 12, 13, 18, 20, 31, 35, 36, 45 1, 2, 3, 4, 5, 6, 7, 9, 10 10 3735 node3 3, 4, 7, 16, 19, 21, 23, 25, 39 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 9 3775 node4 1, 17, 24, 27, 29, 30, 34, 37 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 8 3735 node5 6, 8, 9, 11, 14, 15, 33, 38, 44 1, 2, 4, 5, 6, 7, 8, 9, 10 9 3735 figure 5: load distribution to each node in experiment 4 table 6: detailed results of experiment 4 node number task number file number task amount calculation amount node1 1, 8, 11, 17, 19, 29, 32, 33, 41, 45 1, 2, 3, 4, 5, 7, 8, 9, 10 10 4670 node2 4, 13, 16, 20, 24, 26, 30, 31, 35, 37, 39, 42 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 12 4670 node3 3, 5, 10, 15, 18, 22, 27, 28, 34, 43, 44 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 11 4665 node4 2, 6, 7, 9, 12, 14, 21, 23, 25, 36, 38, 40 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 12 4670 theoretically impossible, our algorithm can minimize the load difference between the nodes and approximate the load balance. compared with hadoop-based data allocation strategy, this algorithm can ensure that the comparison tasks have 100% data locality, achieve load balancing between nodes, and improve storage saving and overall computing performance. optimal data file allocation for all-to-all comparison in distributed system: a case study on genetic sequence comparison 209 5 conclusions this paper probes deep into the all-to-all comparison between data files in a large dataset under the environment of a distributed system. after reviewing the problems existing in the existing methods, the author gives a formal mathematical description of the whole comparison problem. in order to achieve load balancing of each node in distributed system, a data file allocation model based on milp is constructed by using the technology and method of mathematical modeling. meanwhile, a file allocation algorithm was set up on the matlab using the intlinprog function of branch-and-bound method. finally, our model and algorithm were verified through several experiments. the results show that the proposed file allocation strategy can achieve the basic load balance of each node in the distributed system without exceeding the storage capacity of any node, and completely localize the data file. the research findings help to fully utilize the efficiency, stability and scalability of the distributed system to enhance the computing performance of all-to-all comparison. acknowledgements. funding the work is funded in part by the national natural science foundation of china (nsfc) under grant no. 61462070, the doctoral research fund project of inner mongolia agricultural university under grant no. bj09-44 and the inner mongolia autonomous region key laboratory of big data research and application for agriculture and animal husbandry. author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] borodin, v.; 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(2012). matlab from the introduction to proficiency (2nd edition), beijing: people’s post and telecommunications publishing house, 35–92, 2012. international journal of computers communications & control issn 1841-9836, 13(1), 50-61, february 2018. text classification research with attention-based recurrent neural networks c. du, l. huang changshun du*, lei huang school of economics and management beijing jiaotong university beijing 100044, china *corresponding author: summer2015@bjtu.edu.cn abstract: text classification is one of the principal tasks of machine learning. it aims to design proper algorithms to enable computers to extract features and classify texts automatically. in the past, this has been mainly based on the classification of keywords and neural network semantic synthesis classification. the former emphasizes the role of keywords, while the latter focuses on the combination of words between roles. the method proposed in this paper considers the advantages of both methods. it uses an attention mechanism to learn weighting for each word. under the setting, key words will have a higher weight, and common words will have lower weight. therefore, the representation of texts not only considers all words, but also pays more attention to key words. then we feed the feature vector to a softmax classifier. at last, we conduct experiments on two news classification datasets published by nlpcc2014 and reuters, respectively. the proposed model achieves f-values by 88.5% and 51.8% on the two datasets. the experimental results show that our method outperforms all the traditional baseline systems. keywords: machine learning, text classification, attention mechanism, bidirectional rnn, word vector. 1 introduction text classification refers to the process of determining text categories based on the text content under a given classification system. on the internet, major news sites, forums, blogs and so on are used as text for the main information subject and studying their automatic classification has a wide range of uses. in the field of journalism, press and publication need to be classified according to the columns in order to organize different news in different columns; intelligent recommendation system needs to be calibrated according to the user’s different personality characteristics and preferences for the corresponding category of news; in mail processing tasks, the mail system needs to be governed by the contents of the messages to determine whether the message is spam and decide whether to show to the user. therefore, the main goal of this paper is to study the text automatic classification algorithm and meet the current mass of automatic classification of text requirements. as early as the 1960s, people began to study text classification. at that time, it was artificial to write classification rules according to language phenomena and rules. by the 1990s, people began to study computer-based automatic classification technology. this method is first trained by pre-tagging data, learning discrimination rules or classifiers, and then starting to automatically classify new samples of unknown categories. the results show that in the context of large data volumes, its classification accuracy is much better than the expert definition of the rules. therefore, the current research focuses on automatic text categorization of computer algorithms. mingzhu yao [16] et al. used the latent dirichlet allocation (lda) model to automatically classify text. the lda model is expressed as a fixed probability distribution, the gibss sampling copyright ©2018 cc by-nc text classification research with attention-based recurrent neural networks 51 in markov chain monte carlo (mcmc) is used to reason and the parameters of the model are calculated indirectly. the probability distribution of the text on the fixed subject is obtained, the large probability is for the text of the category. aili zhang et al. [18] used the support vector machine (svm) algorithm for multi-class text classification. the method mainly uses the vector space model as a feature, which transforms the document into a high dimension sparse vector per the features of the text and then enters it into the svm classifier. liu hua [4] uses the key phrases of the text to classify, and he thinks that the key words or phrases of the response text category information are more important, so the vector features of the key phrases are first extracted by statistical methods, and then the cosine similarity is calculated to determine the category. with the rise of in-depth learning methods in recent years, the limited boltzmann machine has also been widely applied to text classification methods. hilton et al. [10] used the depth boltzmann machine simulation document to automatically learn the classification characteristics of the document, and in current document classifications has achieved good results. note that except of document classification, deep learning has been also drawn attention in other pattern classification tasks, such as images [15] [6]. the above methods have achieved some success in text classification tasks, but they each have their own shortcomings. in the literature [4], it is pointed out that the categories of texts are usually associated with some key phrases and words, so they are modeled using the method of extracting keywords. these keywords are important, but other words that link these keywords together also contain a lot of information about the document, and the direct abandonment of these words can seriously damage the information that the document represents. in [10], the neural network is used to study the document, taking into account the interrelations and sequences of words, has the ability to extract text features automatically, and has the strongest performance on current classical data sets. however, the whole model does not take the role of key words into account, but rather treats all the words as a network of input, not giving the key words any special treatment. therefore, we believe that if we can combine the advantages of the two methods, redesign the neural network model, and increase the weight keywords in the network, that the final text classification results should see a significant improvement. in order to verify this hypothesis, we designed the recursive neural network model to learn the representation of the text, and added the attention mechanism to the neural network [1]. the function of the attention mechanism is to learn a weight for each word of the input document, expecting key words to have a heavier weight, and the non-critical words to have a lighter weight, and the weight of the word reflects its contribution to the subject of the document. in the attention mechanism, the values of these weights are obtained through network learning, which is different from the previous subject model. here, we will assign a vector to each category. the weight of the word is calculated according to the similarity between the word vector and the category vector. all the vectors in the model, including the word vector and the class vector, are learned through the optimization algorithm. in the lab section, we collected data sets for news categories in chinese and english, and experimented with different settings on both datasets. the chinese data came from the 3rd natural language processing and chinese computing conference (nlpcc2014) public evaluation of news classification data, provided by the xinhua news agency and marked category tags into a total of 347 categories; english news data was selected from rcv1-v2 released by reuters [10], which contains a total of 103 categories. we first use the text preprocessing technique to remove low frequency words and stop words, then use the recursive neural network with the attention mechanism to extract the feature vector of the article, and finally pass the feature vector to the softmax classifier. the experimental results show that the attention mechanism is very effective in assigning a higher weight to the key words, and can effectively improve the accuracy of the classification. 52 c. du, l. huang the method proposed in this paper makes full use of the advantages of the depth learning model which can employ self learning characteristics, and embeds the traditional method of using keywords to classify the text in the neural network by way of the attention mechanism. the advantages of the two are organically combined in this paper. in part 2, the structure of the model is described in detail. part 3 gives the optimization objective function of the model and the parameter settings of the experiment. the fourth part shows the experimental results of the model on the above two datasets. part 5 is the model and experimental summary. the development of workable assessment systems is difficult largely due to the fact that the value of assessment is often controversial: 2 recurrent neural network model based on attention mechanism the model consists of two parts, the first part is the feature extraction operation which mainly utilizes the recursive neural network to gradually synthesize the vector characteristics of the text. we first introduce the recurrent neural network model, and then describe it in detail on the basis of this model to increase the structure of the attention mechanism; the second part is the classifier, the classifier has a dropout [14] layer and softmax layer composition. the biggest advantage of this model is that only simple preprocessing of text is required, you can use the attention mechanism to select keywords and learn the text of the feature representation. the various parts of the model are described in detail below. 2.1 the representation of the word in the text, we represent the word as a distributed word vector, and there are already many work studies in the vector field to learn word representations [11] [5] [8] [9] [7]. we use [8] the proposed language model to learn the representation of words. first, we collect an unsupervised text corpus and the new york times corpus (nyt), pre-training the word vector with the baidu encyclopedia. [13] [12] [17] and other work points out that in a large-scale unsupervised corpus, the learning the word vectors can improve the effectiveness of the model, and the model can also provide a better initial value. in this paper, we use e to represent the matrix of word vectors, each of which represents a vector of words, and the dimension of the column vector is d . the k th word is expressed as a one-hot vector vk (the k th position is 1 and the remaining position is 0), then the vector of the k th word can be denoted as evk . 2.2 the input layer of the network in the past, the input of the network was the word vector itself. here, we use this input method as a stepping stone to improve. we believe that the word definition cannot be determined by only the word itself, but also should look at the specific environment where the word is placed, and furthermore see how it works in different environments, where the meaning of the same word differs. this feature is especially important for chinese context. therefore, we get the word’s pre-training initial value, and the word in each text section, we use it before and after each word as background content, to calculate the given word as the center of the window for the average of the word vector 3, as a vector representation of the current word. here is an example. </s> shanghai forest coverage year by year increase </s> in this sentence, the word "coverage" of the vector is calculated as: v ′coverage = vforest + vcoverage + vy ear 3 , text classification research with attention-based recurrent neural networks 53 for the words in the beginning and ending of a text, we use the symbol </s> to fill it, which is also given a vector representation in the model, so the beginning and ending words can also take a similar calculation. it is worth noting that this method of calculation constitutes the input layer of the model, which is included in the objective function expression of the optimization model, not just the initial calculation. 2.3 recurrent neural network (rnn) the rnn (recurrent neural network) model has demonstrated a strong learning ability in many natural language processing tasks. it is characterized by good modeling of sequence data and full utilization of sequence information. since the rnn is to semantically synthesize each word in the text in turn, the rnn can adapt to the variable sentence, that is, the uniformity of the text length is not required, and both long and short texts can be learned.fig. 1 shows a traditional recurrent neural network structure. v o u x w w v ot-1 u xt-1 w ht ot xt w v u ht+1 ot+1 xt+1 w v u unfold h ht-1 figure 1: traditional structure of rnn in fig.1, xt is the input unit of step t , which represents the word vector of the t th word in the text; ht is the hidden state of step t ; ot represents the output of step t , the output of this step is a softmax classifier, the output is selected according to the needs of the model; u ,v , and w are the weight parameters of the network that all need to be learned in the model. as shown in fig. 1, the dotted line box is the calculation of the t th unit, as follows:{ ht = f(wht−1 + uxt + bh) ot = softmax(vht + b0) (1) where variables bh and b0 represent biased terms. as can be seen from eq.1, each hidden state of the recursive neural network is determined by the current input word and the hidden state of the previous step. if you do not need to add a classifier to each synthetic step in a particular task, ot can not be output. the disadvantage of the traditional recurrent neural network is that with the increase of the length of the text, the number of layers of the network is gradually deepened. the loss of the network in the process of information synthesis is relatively large, which tends to focus on the learning of the final stage of memory. therefore, the efficiency of long text learning is not good. in this paper, long short-term memory (lstm) [3] and gated recurrent unit (gru) [2] are used because of the shortcomings of traditional rnn in dealing with long text. the advantage of the lstm and gru nodes is that they can be set up in the process of synthesizing to control how much information should be received in the current synthesis step, how much is forgotten, and how much information is passed back. through these gate controls, rnn has a proficient learning ability for long text. the difference between lstm and gru is that lstm has more parameters. the gru has fewer parameters and thus has a faster calculation speed. 54 c. du, l. huang lstm and gru are two kinds of calculate nodes of rnn. in the method of calculating hidden state, it is different from traditional method, and it is consistent with rnn structure of main body. the lstm and gru nodes are calculated as shown in fig.2. c c ~ f i o in outo h h ~ in outo z r (a) lstm structure (b) gru structure figure 2: structures of lstm and gru unit lstm node:   i = σ(uixt + wiht−1) f = σ(ufxt + wfht−1) o = σ(uoxt + woht−1) g = tanh(ugxt + wght−1) ct = ct−1of + goi ht = tanh(ct)oo (2) gru node:   z = σ(uzxt + wzht−1) r = σ(urxt + wrht−1) s = tanh(usxt + wh(ht−1or)) ht = (1-z)os + zoht−1 (3) σ represents the sigmoid function, and the symbol o represents the operation of the corresponding vector element multiplication. in the lstm node, i ,f , o represent the input gate, the memory gate, and the output gate respectively, which control the proportion of the information throughput; g is the hidden state of the candidate, similar to the way the traditional rnn calculates the hidden state; ct is the internal memory, by the t− 1 step of the memory ct−1 and g through the memory gate and input gate weight to form; ht is the true output state, which is the amount of information that the internal memory ct outputs at the output gate. in the gru node, z is the update gate, r is the reset gate, s is the hidden state of the current candidate. it can be seen by s calculation that the reset node controls the amount of the previous node information ht−1 , and the final output state ht is weighted by the current candidate’s hidden state s and the previous node output state ht−1 by updating the gate z. 2.4 bidirectional rnn model of attention mechanism in this paper, we use the bidirectional rnn to learn the characteristics of the text, because the meaning of a word is not only related to the text content in front of it, but also related to the text content behind it. we use the bidirectional rnn method to implement the text representated from the learning, and then the two directions to learn the feature vector spliced together, this as a text vector, so that relative to the unidirectional rnn, the eigenvector of the semantics is more comprehensive and rich. at the same time, we add a mechanism of attention to the network model, for each word to learn a weight, making the key words have a heavier text classification research with attention-based recurrent neural networks 55 weight, and non-key words have a lighter weight, which can make important features become more prominent. fig.3 shows the overall architecture of the model. h1 h1 h2 h2 h3 h3 ht-1 ht-1 ht ht · · · · hα ht h1 α =(α ,α ,α ,···,α ,α ) v χ1 χ2 χ3 χt-1 χt head to tail tail to head concatenate softmax classifier dropout layer l 1 2 3 t-1 t · · figure 3: attention-based bidirectional rnn structure as shown in fig.3, the input sentence is x1 , x2 , x3 , l , xt−1 , xt. the recursive neural network rnn has both forward and backward directions, and in the forward rnn, the hidden state is −→ h1, −→ h2, −→ h3,l, −−→ ht−1, −→ ht (” ← ” indicates that the direction of the rnn is forward); in this setting, the hidden state of the word xi corresponds to hi = [−→ hi; ←− hi ] (i = 1, 2,l,t), that is, the hidden state of the two directions together, such as the original hidden state of the k dimensional vector, then the 2k vector after the stitching. in the traditional bidirectional recurrent neural network, the vector of −→ ht and ←− h1 is usually concatenated as a text representation. since the words that reflect the subject in a text are primarily a few keywords, the importance of each word is different. here we introduce the attention mechanism. we calculate the corresponding weights for each word by the attention mechanism, and then weighted the sum of the hidden states of all the words according to the weight, and the result of the summation ha is also taken as part of the textual feature. as shown in fig.3, vl represents the category vector, αi(i = 1, 2,l,t) represents the similarity between the hidden state of the i word and the category vector, that is, the weight of the i word, the similarity formula is [1] αi = eh t i mvl∑t j=1 e hti mvl (4) and i = 1, 2,l,t. s is a parameter matrix that calculate generalized similarity, when it is a unit array, the similarity of hi and vl is degenerated into the inner product. α = (α1,α2,l,αt) represents the weight vector. according to eq.4 we can see that it has been normalized. the resulting weighted feature vector ha is 56 c. du, l. huang ha = t∑ i=1 αihi (5) and then ha and rnn forward and backward results together, that is the character representation of the text. s = [ ha, −→ ht, ←− h1 ] 2.5 softmax classifier as shown in fig.3, after extracting the features of the text, the feature vectors are entered into the softmax classifier for classification. here we use dropout [14] to connect the feature vector with the softmax classifier. to illustrate the dropout method, we consider the eigenvector as an input to the classifier. the traditional neural network connection method is comprised of the whole connection mode, the dropout algorithm is connected to the random input data(the feature vector after splicing in this paper) according to a certain proportion of 0, and the only other elements that are not set to 0 are ones participating in the operation and connection. for the sake of convenience, suppose that a learning sample is updated once, and the specific process is as follows: first, the input vector is placed in proportion to a portion of the elements, and the element not set to 0 is involved in the operation and optimization of the classifier; then the second sample enters the vector, this time in accordance with the random set way to select the elements of training, until all the samples have been studied once. since for each input of a sample, the way to set 0 is randomized, each network update will have differing weight parameters. in the final prediction process, the entire network parameters are multiplied by 1 − ρ to get the final classifier network parameters. because the parameters of each update are not the same, the dropout algorithm can be seen as the neural network being cast into a combination of multiple models, and can effectively prevent over-fitting and improve the model’s prediction rate [14]. according to the literature [14], the dropout algorithm is similar to evolution, and the genes of the offspring are made up of half of the genes of the parents. this combination has a tendency to produce more vigorous genes. similarly, in the final network of the dropout algorithm the parameter is a combination of the parameters of multiple models, which is a process of choosing and keeping the advantages over the shortcomings, and thus yielding a better generalization ability. suppose that the vector obtained by a bi-directional recurrent neural network is c . the way the dropout algorithm sets its element to 0 can be represented by the bernoulli distribution. the bernoulli distribution is used to produce a binary vector r ( it only contains 0 or 1) equal to the latitude of c : r ∼ bernoulli (ρ) he vector entered into the softmax classifier is recorded as: cd = c ·r where softmax classifier’s network parameter is wc and the offset term is bc, the output of the network is : o = f (wccd + bc) text classification research with attention-based recurrent neural networks 57 f is a sigmoid function or a tanh function. the probability that the current text belongs to category i is: p (i|s) = eoi/ n∑ j=1 eoj oi represents the i th element of the vector o, and n represents the number of classes. 2.6 objective function in this paper, we mainly study the classification problem. the parameters that need to be optimized include the following parts: word vectors, parameters of bi-directional recurrent neural networks, generalized similarity matrices of attention mechanisms, class vectors and classifier parameters. a word vector is denoted by e, and the parameters of the bi-directional rnn are denoted by ŵ and û; the parameters of the attention mechanism layers are denoted as m , the vector of all the categories is represented by the matrix v , the i line vector vi represents the i th category; the parameters of the classifier are denoted by wc. the sample set of the training set is ω = { (t1,y1) , (t2,y2) ,l, ( t|ω|,y|ω| )} , where ti is the i th text, yi is its category label, and |ω| is the number of training set samples. θ = { e,ŵ,û,m,v,wc } ,p (yi|ti,θ) represents the probability that the category of the text ti is divided into yi when the parameter θ is known, so the optimized objective function is: l = |ω|∑ i=1 logp (yi|ti,θ) + λ‖θ‖22 λ is the parameter of the regular term. in the actual experiment, we use the random gradient descent method to optimize θ update method is: θ = θ −α ∂l ∂θ , α is the learning rate. 3 experiments and results 3.1 experimental data two data sets are used in the experiment. the first is the chinese data set, it is the 2014 chinese computer society organized by the natural language processing conference published by the news classification evaluation data set. it is responsible for organizing and annotating xinhua, which is a large-scale news classification corpus. it can be downloaded directly from the official website of nlpcc2014. the corpus training set has a size of 30,000 news articles, the test set contains 11,577 articles, and it’s test set and training set have good consistency in the distribution of each category. the data set has two categories, the first layer comprised of 24 categories, and the second layer with a total of 367 categories. in this paper, the category of text is unified as a single-level category, and for a multi-level hierarchy of trees, we consider the final small category as a single category and report the classification of the final category. the second data set is an english data set and is an rev1-v2 dataset published by reuters. this data set contains 804,414 news articles, comprising a total of 103 topics (103 categories, here is the hierarchical classification, processing with the previous data set). following the literature [10], we randomly divided the data set into a training set and test set, of which the training set contains 58 c. du, l. huang 794,414 news articles, and the test set contains 1,000 news articles. all of the experiments in this paper are performed on both datasets. 3.2 data preprocessing for the chinese data set, we first use the chinese word segmentation package nlpir developed by the chinese academy of sciences for chinese word segmentation. the functions of nlpir include chinese word segmentation, partnered annotation, named entity recognition, user dictionary function, support of a variety of chinese coding formats, and the ability to discover new words as well as facilitate keyword extraction. as the experiment in this article has a chinese data set, we need to call the software packet word. english data itself is a separate word, so the operation of word segmentation is unnecessary. after the word segmentation operation is completed, the word frequencies of the two data sets are calculated, and the low frequency words and stop words are deleted. because these words are not helpful in judging the subject, it may be helpful to use them when classifying them, which is not conducive to the prediction of the classification model. since we used the minibatch training model during the training process, we needed to perform a fixed-length operation on the length of the text. since the sentence lengths of the natural language text are inconsistent, we first calculate the longest sentence length lmax . for any sentence length less than lmax , the uniform use of the text </s> symbol to lmax ( </s> vector is always set to 0 ). the purpose of unifying the text length is to improve the efficiency of computing. when the length of the data is unified, you can use a matrix calculation, which when compared with circular computing is time-saving. 3.3 pre training of word vector before the model training, we need to pre-train the training vector on an unregulated largescale corpus. a word vector is a distributed representation of a word that expresses an input suitable for a neural network. many of the current studies have shown that word vectors without oversight learning in a large corpus are more conducive to the convergence of the neural network model leading to a good local optimal solution. in this paper, we use the skip-gram model to pre-train the training vector. the vector of this model has a strong performance in many natural language processing tasks. the skip-gram algorithm has been integrated in the word2vec package which we use directly to train chinese and english word vectors. we use the text content read on the baidu encyclopedia to carry out the pre-training of the chinese word vector, we pre-train the english word vector with the new york times corpus. 3.4 the setting of the experimental parameters in this paper, the model has the following super parameters: the dimension of the vector d , the dimension of the class vector, the dimension n of the hidden state in the recurrent neural network, the ratio ρ of the dropout algorithm, and the learning rate α of the sgd optimization algorithm. we use the grid search method to determine these parameters. the dimension of the word vector d is taken in {50, 100, 200, 300} ; the dimension of the class vector l is in {50, 100} , the dimension n of the hidden state is taken in {500, 1000, 2000} ; according to experience, the dropout algorithm ratio p is 0.6; the sgd algorithm learning rate α is in {1, 0.1, 0.01, 0.001} . for the xinhua newsroom data set, the best parameter value is: d = 100, l = 100,n = 1000,α = 0.05. for reuters rcv1-v2 datasets, the best parameter values are: d = 300, l = 100,n = 1000,α = 0.01 . the range of these parameters is based on experience, generally within the scope of the value can be achieve better experimental results. in text classification research with attention-based recurrent neural networks 59 this experiment, we use these parameters for multiple experiments, and then obtain the average of the results. 3.5 data experiment and comparative analysis in this paper, we design a bi-directional recurrent neural network based on an attention mechanism to deal with the problem of text classification. the attention mechanism can be used to learn a weight for each word in the text based on the information of the category, where words closely related to the category receive a relatively heavy weighting, and words that are relatively weak in relation to the category receive lighter weighting. in the experiment, we vectorize 20,000 words of high frequency occurrence in chinese, and vectorize 100,000 words of high frequency occurrence in an english data set. tab.1 lists some of the baseline models and the results presented in this paper. table 1: test results of document classification chinese and english data sets model accuracy recall f-value xinhua news agency reuters corpus rcv1v2 xinhua news agency reuters corpus rcv1v2 xinhua news agency reuters corpus rcv1v2 tf-idf+svm 72.1 31.8 88.7 45.8 79.5 37.5 avevec+svm 70.8 29.3 92.3 33.2 80.1 31.1 trnn 74.4 40.5 90.7 51.7 81.7 45.4 lda 77.3 35.1 93.3 44.3 84.5 39.2 docnade 76.5 41.7 84.5 42.5 80.3 42.1 replicated softmax 82.3 42.1 94.0 47.2 87.8 44.5 over-rep. softmax 82.7 45.3 89.3 51.4 85.9 48.2 bi-trnn 82.4 44.8 91.1 52.5 86.5 48.3 lstm bi-rnn 81.9 46.2 92.8 55.8 87.0 50.6 gru bi-rnn 83.3 45.8 93.9 51.9 88.3 48.7 attention lstm birnn 81.7 46.4 92.8 56.1 87.0 51.8 attentiongru bi-rnn 83.9 46.0 93.5 52.8 88.5 49.2 in order to verify the validity of the model, we compared it with the methods of some baseline systems. the first comparison method is to calculate the tf-idf feature of the text, form a set of vectors, and then use the support vector machine (svm) to classify the eigenvectors; the second method is to use the average of the text word vector (the text is preprocessed to calculate the mean of the vector of all words) and then use the svm classifier for classification; the third method trnn (traditional rnn) is to achieve the traditional rnn model, the fourth method is to call the matlab lda algorithm package for text classification; the fifth method is the neural autoregressive density estimation method; the sixth and seventh methods are the use of the depth of the boltzmann built rmb model [10], and the softmax classifier is transformed accordingly; bi-trnn is based on the traditional rnn, using the results of a two-way network; finally, the lstm bi-rnn and the gru bi-rnn represent the bi-directional recurrent neural networks based on the lstm and gru compute nodes, respectively. the bi-rnn is a bidirectional recurrent neural network. finally, the attention lstm bi-rnn and attention gru 60 c. du, l. huang bidirectional recurrent neural network of attention mechanism. from the results in table 1, it can be seen that the neural network model with an attention mechanism achieves the strongest performance on most indicators, reaching 83.9% accuracy and 88.5% f value when tested on the news corpus of xinhua news agency. when tested on the reuters corpus it reached a precision of 46.4% and f value of 51.8% effect. it can be seen that in the task of text learning, the advantages of the two methods of neural network representation learning and traditional keyword classification are taken into account, which has positive significance for the task of text classification. in addition, in regard to the lstm and gru contrast, regardless of whether there is no attention mechanism used in the network structure, because lstm has more fitting parameters than gru, it is more suitable for large data learning and prediction. so, for the reuters news corpus, the predictive effect of lstm is significantly better than gru, while for the xinhua news data, the gru results are better than the lstm node. 4 conclusion based on the task of text classification, this paper proposes a bi-directional recurrent neural network algorithm based on the neural network attention mechanism. after extracting the vector features of the text, the feature is input into the softmax classifier by dropout. previous methods either based on keywords, or the 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(in chinese) international journal of computers communications & control issn 1841-9836, 13(4), 550-565, august 2018. improving dtns performance by reduction of bundles redundancy using clustering algorithm r.o. schoeneich, p. prus radosław olgierd schoeneich*, piotr prus institute of telecommunications warsaw university of technology, poland *corresponding author: rschoeneich@tele.pw.edu.pl p.prus@stud.elka.pw.edu.pl abstract: this article presents complex clustering algorithm for delay tolerant networks (dtns) including neighborhood discovery, cluster creation, and data distribution within cluster. the general idea is to reduce the amount of messages being sent and of buffer utilization, by taking advantage of the nodes tendency to create groups and to share similar mobility patterns among each other. the main purpose of the algorithm is to improve the network performance without major changes in communication schemes between nodes. almost no extra message type is added. most extra features are available thanks to adding small extra fields into transmitted packages. neighborhood discovery is being realized passively by listening to other nodes messages. the proposed algorithms allow to reduce both the bandwidth occupation, as well as the problems related to the media access. furthermore, it can increase message delivery probability thanks to intelligent package distribution inside created cluster. simulations were carried out to evaluate the effectiveness of the proposed solution in terms of package delivery probability, mean buffer occupancy and mean hop number to delivery the message. results of simulation show that this solution is not necessary or recommended for small-scale networks with few nodes using clustering algorithms. however, with increasing number of nodes and messages, the performance of non-clustered dtns drops significantly while clustered network works efficiently. keywords: delay tolerant networks (dtns), mobile ad-hoc networks, clustering algorithms, cluster based routing, neighborhood discovery in ad-hoc networks. 1 introduction delay tolerant networks (dtn) [2] [9] are a kind of networks that can be used in extreme terrestrial environments with no telecommunication infrastructure provided, e.g. deserts, or rain forests. this kind of network can also be used in emergency situations, as an alternative way of communication, when conventional networks break down, for example as additional communication channel for emergency services in the case of terrorist attack. it can be also used for military purposes, as communication channel between groups of soldiers. the main advantage of this kind of network is the fact that no infrastructure is needed for its operation. therefore, it can be easily created in any situation. family of dtn networks that is taken into account is mobile ad-hoc networks that may lack continuous connectivity. it relates to situations when, in a particular moment, direct path between two nodes may be not available. however, communication is done through message ferring by mobile nodes. dtn are rather low transfer rate networks. the main assumption of dtn is to deliver the particular message as fast as possible, with maximum probability of delivery. however, in dtn networks it is possible that the path between two nodes will never appear, so the message might not be delivered. the main mechanism of message forwarding is presented in figure 1. a node that has a message to send broadcasts inv the invite message. this message contains a list of content identifiers, which node stores in its buffer. when a node that has received the inv message copyright ©2018 cc by-nc improving dtns performance by reduction of bundles redundancy using clustering algorithm 551 figure 1: basic message exchange scheme for dtn networks wants to get particular packages, it sends ack the acknowledge message. the ack message contains the list of desired content ids. after receiving ack message, the first node forwards desired packages via data packages. this mechanism is being repeated until message reaches its final destination. inv messages are being sent periodically. routing protocol that uses this mechanism is called epidemic routing [32]. it is a flooding protocol. it means that the message is being sent to each node met. epidemic routing allows to reach highest possible delivery probability, along with the shortest delivery time in particular mobility pattern. the main problem of this protocol is high redundancy. with increasing number of packages, the probability of message delivery drops significantly, because of limited buffers capacity. the second important problem is connected to the media access control. when nodes travel in a group, each node buffer will contain exactly the same content after some time. this means that each node will send identical inv messages. with increasing number of nodes, the access to the radio channel becomes more difficult. there is no need for each node of the group to send the new additional inv message. more advanced dtn routing protocols, such as spray and wait [29] or prophet [20], limit the redundancy by reducing the number of message copies forwarded. on the one hand, this solution reduces the utilization of buffers. however, on the other hand, it removes some possible paths, which may lead the message to the destination. when mobility pattern is not completely random, two nodes will meet each other sooner or later, and the delivery probability drops. in some mobility patterns, in which nodes are constantly mobile, there are many possible paths from the source to the destination. in such patterns, the number of copies forwarded can be reduced without a significant performance loss. on the other hand, for the mobility patterns, in which only some nodes are mobile, or nodes are moving in specified directions, the number of possible paths is much lower. reducing number of forwarded copies in such networks is highly ineffective. moreover, advanced routing protocols do not solve the problem with radio channel access. this considerations show two serious problems considering scalability of dtn networks: buffer and radio channel capacity. wise use of clustering algorithms can solve both problems. data distribution within improves significantly the message capacity and reduces the number of transmissions of the inv message, which increases the medium usage efficiency. the idea of clustering has been extensively investigated in ad-hoc networks. first solutions like clusterhead gateway switch routing [5] and cluster based routing protocol with extensions [28] [1] [31] was reference for next development. all of these solutions introduce the idea of two 552 r.o. schoeneich, p. prus figure 2: two level cluster topology level hierarchy composed of higher level managing nodes cluster heads and lower-level nodes cluster members with one-hop single cluster dimension. the multi-level hierarchy was proposed in [14]. multi hop cluster range was proposed in [34]. for wsn networks there were proposed similar solutions: based on two-level single hop clusters like low-energy adaptive clustering hierarchy [13], multi hop clusters e.g. hybrid energy-efficient distributed clustering [36], or multi-level clusters like in distributed weightbased energy-efficient hierarchical clustering protocol [8] and two-level hierarchy leach [24]. the other very popular routing protocols which are based on clusters in sensor networks are: panel [3], ucs [30], eecs [35], eeuc [19], ace [4], bcdcp [27], pegasis [21], teen [25], apteen [26], ttdd [23], ccs [16], hgmr [18], solutions like [10] [11] [12]. the detailed survey of clustering algorithms can be found in [33] and [22]. the clustering in dtn routing application was discussed in dtmn protocol [7] where authors proposed the solution for grouping mobile nodes with similar mobility pattern into a clusters. similar solution for nodes clustering in vehicle-based networks was proposed in [6]. other solution called hierarchical clustering-based forwarding scheme (hcs) was proposed in work [17]. the solution is based on implementing hierarchical clustering on social information. all these works are focused on routing schemes in wireless networks, the message storage efficiency is less important. our work focuses on achieving a high performance of the message storage and on the elimination of redundancy in a complex dtn network environment. 2 proposed clustering algorithm 2.1 cluster topology proposed algorithm involves grouping nodes with similar mobility patterns into two-level clusters. the general scheme of such cluster is presented in figure 2. a node in the area called level=0 is called cluster head (ch). this node is responsible for cluster management. address of this node is also considered as cluster id. level=1 nodes are directly connected to ch. these nodes are responsible for data storage. level 2 nodes are regular cluster members. the decision to organize the nodes into two-level clusters is due to the fact that such structures are still quite easy to manage and maintain in terms of their consistency. taking into account the range of radio communication, such approach is enough to cover area of node group with a similar mobility pattern. the structure of inv message sent by dtn network with cluster algorithms is presented in figure 3. improving dtns performance by reduction of bundles redundancy using clustering algorithm 553 figure 3: inv message structure the package contains information about address of message sender, cluster id, and its position in cluster. data part of message stores a list of packages available to be sent. when a node does not belong to any cluster, the value 0 as ch address is set. 2.2 related nodes metric the main problem in dtn networks is the fact that nodes are constantly mobile, which makes the cluster creation and maintenance tasks difficult. network topology changes constantly and, furthermore, the connection between two nodes can disappear permanently. we assume that nodes do not know their position via gps etc. the only information about the neighborhood we acquire is by listening to other nodes messages. to determine whether two nodes belong to the same cluster, some additional mechanisms have to be implemented. the solution to this problem has been presented on the basis of the clustering and cluster-based routing protocol for delay-tolerant mobile networks [7]. because of the fact that each node sends inv messages, there is no need to send extra hello messages, which are common for typical cluster-based ad-hoc networks. all the necessary/required information can be acquired from periodically sent inv messages. the period between inv messages is known. for each time slot, the related nodes metric is being evaluated. in general form, the metric formula is as follows: rny[n] = xy[n] + α 1xy[n− 1] + ... + αnxy[n−n] where: α the extinction coefficient, y the identifier of neighbouring node, xy[n] the binary factor which indicates if node received message from node y in time slot number n, n number of back slots, rny[n] value of the metric. by changing the metric parameters, the different behavior of nodes is being set. parameter α determines how long the information about relation is important. the higher the value of α the smaller impact on the metric value the previous meeting have. parameter n determines the number of time slots for which the metric is being evaluated. on the one hand the higher n, the more reliable the metric value is. on the other hand, it increases the memory requirements. additionally, the information about meetings from distant past is useless, because of a dynamic network topology change. value of parameters have to be set individually for each situation. depending on evaluated metric, two thresholds are defined, i.e. ytresh and ymin. these thresholds will be used to make decision. if the condition: rny[n] > ytresh is fulfilled, then we assume the node is strongly related with node y. on the other hand, if the condition: rny[n] < ymin is fulfilled, the relation between two nodes broke, and they no longer can be considered as strongly related nodes. these simple calculations allow to distinguish nodes with similar mobility pattern, which is very important in cluster formation and management. metric also stores information about cluster assignment of observed node. 2.3 new cluster formation if the number of the strongly related neighbors, which also do not belong to any cluster, exceeds threshold, the procedure of cluster initialization is carried out. node broadcasts or554 r.o. schoeneich, p. prus ganize message, which contains address of a sender and number of strongly related neighbors. each node, which receives such message has to answer and sends its own organize message. node with the highest number of neighbors becomes a new cluster head, and its neighbors become level 1 cluster members. such approach can lead to creation of two clusters, if two nodes are not in radio range, however, other mechanisms, which will be described later, will adjust this situation. 2.4 joining new node to cluster in order to join cluster, several conditions have to be fulfilled. each node listens to broadcasted messages and evaluates related nodes metric. nodes have to be strongly related to a sufficient number of cluster members to be concerned as strongly related to cluster. cluster topology provides two levels, so metric is only valid for level 0 and level 1 nodes. if node determines that it is strongly related to the cluster, it can send a message requesting to join the cluster. node becomes a part of the cluster of an adequate level. all packages from its buffer are being sent to the cluster and then distributed among level 1 cluster members. 2.5 leaving the cluster because of node mobility, cluster consistency has to be managed constantly. node located at level 1 has to be strongly related to cluster head, and a certain number of level 1 nodes. if the first condition is not fulfilled, node changes its level to 2. if second condition is not satisfied, node must leave the cluster. for level 2 nodes only the second condition has to be fulfilled. leaving node sends appropriate message to the cluster head and detaches from the cluster. it takes copies of as many packages from the cluster as its buffer can handle. 2.6 two clusters meeting situation when two clusters are within radio communication range is hard to manage. ch reelection and cluster reorganization common for cluster-based ad-hoc networks will be highly infective due to packages stored inside cluster. this could lead to the package loss caused by limited buffers capacity, and high network load associated with the package transmission to new level 1 nodes. proposed architecture prefers absorption of smaller clusters by the bigger ones. if node determines that it is better related to an adjacent cluster, it will become a member of a new cluster. however, it is required to counteract the situation, when node oscillates between two clusters. this can be achieved by defining another threshold. node can change cluster only if relation with new cluster is greater than current increased by threshold. this approach leads to the absorption of less numerous clusters by bigger ones. 2.7 cluster destruction or cluster head reelection last node leaving the cluster is the cluster head node. when nodes are leaving cluster, number of strongly related nodes seen by the cluster head is decreasing. when the number drops below a particular value the cluster head makes decision to destroy the cluster and broadcasts destroy message. reaction to such event can be different depending on the situation. decreasing number of strongly related nodes seen by ch may be result of cluster head mobility pattern change leads to leaving the group. node which receives destroy message checks a number of its strongly related neighbors. if it exceeds threshold of a new cluster creation, new cluster head is being reelected as in new cluster formation algorithm. if improving dtns performance by reduction of bundles redundancy using clustering algorithm 555 figure 4: the network topology after cluster formation the number of strongly related nodes is too small, and no organize message was heard by different node, the cluster is destroyed. 2.8 cluster head loss architecture must be resistant to the cluster head node failure. without additional mechanisms, such situation would lead to continuous migrating nodes to level 2 area and eventually to destruction of the cluster. to prevent the situation when level 1 node loses a strong relation to the cluster head, the first node ensures that the neighboring nodes see the cluster head node. a broadcast message is sent and response is expected. if message is not received, the cluster head re-election occurs. new cluster head is reelected among level 1 nodes. this makes the reorganization of cluster much easier because level 1 nodes are better related and closer to the disappeared cluster head. 3 cluster based routing organization of clusters changes topology of dtn networks. nodes with similar mobility pattern are grouped into clusters creating bigger virtual nodes of network. general idea is to treat clusters like regular dtn nodes. these nodes share functionality of storing and forwarding messages, however, thanks to organization, their buffer capacity is much bigger. in clustered dtn, the number of inv messages sent by the cluster is smaller than in flat networks. this makes clustered networks more scalable than regular dtn networks. network topology after cluster formation is presented in figure 4. 3.1 inter-cluster routing creation of virtual nodes allows coexistence of clusters and regular dtn nodes. no additional mechanisms have to be implemented. message exchange between two nodes is based on epidemic routing protocol described in introduction. the choice of this protocol is associated with its high performance under condition of package delivery probability and delivery time. furthermore, this protocol is easy to implement and do not need any additional information storage or evaluation. problem with radio channel access control is solved by preventing some nodes to send inv message. the solution is to introduce the probability of inv message sent. nodes with more neighbors send inv messages less often than these with lower number of neighbors. when randomly generated number is higher than threshold, the inv message is sent. value of threshold is evaluated based on neighbors’ quantity. after leaving too many time slots, the empty inv message is sent to indicate that node is still inside cluster. this approach limits the number of inv message sent without significant performance loss of message delivery. it is important to 556 r.o. schoeneich, p. prus mention that the cluster head has to broadcast inv messages in each time slot due to specific function of this node in cluster management. lack of connection with cluster head has to be counteracted as fast as possible. 3.2 intra-cluster routing nodes grouped into cluster creates consistent ad-hoc network. it is organized into two level hierarchy with one central node. moreover nodes have information about their neighbors based on related nodes metric evaluation. this makes creation of routing tables inside cluster easy. most appropriate routing protocol in such structure is optimized link state routing protocol (olsr) [15] typical for ad-hoc consistent mobile networks. path between two nodes within a cluster is almost always known. each node know which neighbor is connected directly to the cluster head. transmissions inside clusters are made on demand. data transfer inside cluster is much faster than between two dtn nodes. there is no offset made by time slots typical for inv messages. 4 data handling an objective of the proposed method was to reduce the redundancy of packages in dtn networks. it is also required that network load reduction does not cause performance degradation. single node failure, or unexpected cluster leaving, cannot lead to data loss. the purpose of this section is to describe the specifics of data handling for dtn cluster-based network. it presents the general assumptions and methods of data distribution within a cluster. due to their specific nature, no message delivery confirmation methods are implemented in dtn networks. using this methods would result in message flooding in the opposite direction. that means an additional load on the network without noticeable benefit. common concept is using time to live (ttl) parameter. after the time expiration, message is removed from the buffer. this solution has its explanation. if any node demands message for a long time, it may be assumed that it has already been duplicated and forwarded. optimal value of ttl is specific for each network, and should be set individually. 4.1 new package distribution in cluster new message in the cluster can only occur as a result of receiving the inv message from encountered node. the process of adding new nodes to a cluster takes place over several inv messages time slots and there is no need to consider the situation in which a new attached node carries packages that are not already in the cluster. each new package received by the cluster is sent to the cluster head node. then the message is forwarded to level 1 nodes. to avoid unexpected losses associated with unexpected node failure, the message is replicated in a number of nodes simultaneously. the cluster head remembers package sequence number and information about nodes that possess it. the cluster head manages the packages and in case of node failure sends the requests to replicate the package in another level 1 node. the ch manages the packages to be evenly distributed in nodes. the nodes through which a package has passed stores it through the entire duration of the ttl. this limits the number of transmissions and creates cache like buffers. after placing message in buffers the cluster head broadcasts the message about new available content. improving dtns performance by reduction of bundles redundancy using clustering algorithm 557 4.2 data search inv messages sent by cluster members contain a list of all packages available. if ack message is received the data search mechanism starts. first own buffer is being searched. if content is found, it is passed to requested node. if no data was found in buffer, request to the cluster head is sent. level 2 nodes send request through level 1 node. at first this node is also searching for its own buffer. if any of them finds desired content, the cluster head is asked. the ch responds with address of the node, which holds the content. then package is downloaded directly from the node. afterwards the package can be forwarded to desired location. 4.3 buffer overflow there may occur a situation, in which buffer of particular node overflows. threshold of 0.95 buffer capacity is set. if the buffer occupation exceeds this number, some content has to be deleted. there are two types of package in buffers: created by the cluster head, and created when package was forwarded by the node. the first to delete are cached messages. the node removes as much packages as necessary to reduce buffer occupation below the threshold. messages are deleted in order of time spent in buffer. if removal of cached packages is not enough to fulfill the buffer occupation condition, request to the cluster head is sent. at least two copies of one content have to be stored in the cluster to avoid data loss in case of node failure. if there are more copies, the cluster head permits to delete the message. otherwise it checks if there is any other level 1 node with free space in its buffer. if such node exists, the package is forwarded to this node. in other cases the oldest package is removed. 4.4 ttl expires in contrast to the standard dtn network cluster member nodes do not remove the messages with expired ttl automatically. when the cluster head notices that package expired its ttl broadcasts the message to delete desired content. each node, which received such message, deletes this package from its buffer. the list of available content sent in inv messages is updated. 5 simulations 5.1 mobility model the research was carried out using specialized mobility model for emergency and rescue situations designed by authors. it is a mobility model proposed by us to describe the mobility pattern of nodes in an emergency situation. scenario describes the situation when bomb explodes in the office. it takes into account different groups of people. the model describes mobility of injured civilians or people fleeing in panic. behavior of different rescue groups was implemented: police, firefighters and emergency medical services. this model is a complex attempt to maintain the mapping of different groups behavior in life-threatening situations. we find reasonable application for clustering algorithms in such situations. creating groups is common behavior for all living beings. in general the scenario consists of 3 phases: stable phase, chaos and rescue operation. during the stable phase civilians are moving around the office. they do their job, go smoke a cigarette, or sit at their desk. this phase is not interesting from the research point of view, however, it is used to generate a random distribution of nodes during the explosion. example of distribution of nodes in the stable phase is shown in figure 5. 558 r.o. schoeneich, p. prus figure 5: node distribution during stable phase figure 6: node distribution during chaos phase stable phase is interrupted by explosion inside the office. some civilians get injured, some are dead. rest flees in panic. the phase of chaos starts. it lasts until arrival of rescue teams. distribution of nodes in chaos phase is presented in figure 6. figure 7: node distribution during rescue phase evacuated people are grouping near office entrance. here we can observe clusters. those nodes are not highly mobile, but create a big buffer able to store message. next, and last phase is the rescue operation. firefighters enter the building, evacuate injured, police secure the area, improving dtns performance by reduction of bundles redundancy using clustering algorithm 559 and separates onlookers. medical services treat wounded people. distribution of nodes in this phase is presented in figure 7. simulation ends when last wounded person is sent to the hospital. figure 8: mobility algorithm the chaos phase has been implemented as follows: nodes inside a specific radius of explosion are seriously injured and are retained in place. these nodes are marked in red. other nodes will escape using corridors out of the office. due to smoke and reduced visibility nodes are moving along the walls. all mobile nodes in the phase are marked in black. to implement the mobility of nodes we proposed to use a set of edges of the door and edges of walls as points of the graph. all of this points are denoted as red dots. for each mobile node we executed algorithm that determines: 1) the closest distance between the black node and the nearest red point, then the algorithm 2) builds a graph between all red points and the building exit. 3) the algorithm performs the shortest path search in the graph based on the dijsktra algorithm. after finding the shortest path, nodes are moved with speed of 2-4m/s to the exit. the rescue phase looks as follows: in simulations we propose that after a predefined period of time rescuers (green nodes) are moved. the movement is based on previous graph, but nodes are moved in opposite direction to search all rooms in the office. rescue nodes which hit a injured (red) nodes, take them to the exit. the mobility algorithm was presented in figure 8. 5.2 results the research was performed in two stages. first for small scale network with small number of nodes and unlimited buffer capacity. this simulations show that clustered dtn works as efficient as non clustered network based on epidemic routing. the second stage is performed for a large scale network, where clustering algorithms show their advantage over regular dtn. during simulations following metrics were evaluated: 1. package delivery probability: we counted the metric as number of messages received divided by number of messages send. the metric is important because in emergency and rescue scenarios many messages sent should reach destination nodes. 560 r.o. schoeneich, p. prus table 1: parameters of simulations parameter min values max values parameter value α 0 1 radio radius 5m n 1 5 bandwith 11 mb/s ytresh 1.45 1.45 packet size 1500 b ymin 0.95 0.95 simulations time 750 sec. ycluster 3 30 messageperiod 180 sec. 2. mean buffer occupancy: the metric describes the mean number of packets stored per number of nodes. it describes how efficient the solution is in terms of minimizing buffer occupancy and total redundancy. 3. mean hop number: the metric specifies how many nodes have to pass the message before it was delivered to the recipient. the purpose of this metric is to determine the efficiency of packets transmission and to estimate energy requirements. it also partly determines the efficiency of use of the capacity of the radio channel. proposed mobility model is random and after each run different result is generated. to obtain reliable result simulation have to be run several times. after 50 simulations the mean value, and confidence interval is evaluated. for small scale networks the conditions are as follows: 1) unlimited buffer capacity, 2) number of messages created: 1000, 3) number of nodes: changes from 10 to 60 for all simulations we established nodes with different rules. number of firefighters 6 and 1 officer, num of policemans 6, num of rescuers 4 persons. total simulations time 750sec., messages sending period 180sec., the explossion moment was in 100 sec. after starting the simulation. optimal values of parameters: ytresh, ymin, ycluster and α have been set based on preliminary simulations. the parameters of the algorithm are presented in the table 1. results of simulations are presented in figure 9 a, b, c. results of simulation shows, that even in small scale networks cluster based dtn have advantage. using clustering mechanisms do not reduce network performance within meaning of package delivery probability. moreover it reduces the buffer occupancy and hop number. this leads to network load reduction. this advantages will be more noticeable in large scale network simulations. for those simulations conditions are: 1)buffer capacity: 100 packages, 2) number of messages created: 1000, 3) number of nodes changes from 150 to 200. buffer capacity is smaller than number of messages. this should lead to overload of dtn network without cluster algorithms. furthermore media access problems start being noticeable. results of simulations are presented in figures 10 a, b, c. as expected regular dtn network performance drops significantly. when number of nodes exceeds 190 problems with media access are noticeable. packages cannot be forwarded because network density is so high, that no inv message can be send without any collision. message delivery probability is at unacceptable level. this is the result of buffers overflow. hop numbers metric is evaluated only for successfully delivered messages, so this results do not carry any important information for regular dtn networks. more important information we get from results performed for clustered network. hop number do not differ noticeably in comparison to small scale research. a slight increase in the number of hops is correlated with the increasing number of nodes in the network. this means that cluster based network forwards messages efficiently without additional, unnecessary transmissions. improving dtns performance by reduction of bundles redundancy using clustering algorithm 561 (a) package delivery probability (b) mean buffer occupancy (c) mean hop number figure 9: results of simulations for small scale research 562 r.o. schoeneich, p. prus (a) package delivery probability (b) mean buffer occupancy (c) mean hop number figure 10: results of simulations for large scale research improving dtns performance by reduction of bundles redundancy using clustering algorithm 563 6 conclusions we have proposed and implemented complex clustering algorithms for dtn networks. it significantly improves performance of such networks and makes them more scalable. without many calculations, or sending extra control messages we achieved noticeable network load and buffer occupation reduction. this solution is designed to work in extreme terrestrial conditions where no telecommunication infrastructure is provided, and nodes moves in groups. this simple algorithm 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(2004); heed: a hybrid, energy-efficient, distributed clustering approach for ad-hoc sensor networks, ieee trans. mobile comput., doi:10.1109/tmc.2004.41, 3(4), 366–379, 2004. int j comput commun, issn 1841-9836 8(2):184-195, april, 2013. it outsourcing a management-marketing decision r.e. brandabur raluca ecaterina brandabur academia de studii economice bucuresti facultatea de marketing romania, bucuresti, 1, m. eminescu, 13-15 e-mail: raluca.brandabur@mk.ase.ro abstract: survival of an organization in an environment increasingly aggressive requires a more constant study of it, followed by careful planning of its activities on the market, accordingly with the organization’s mission. in this context, targets and specific modalities for achieving them are expressed by developing and implementing management-marking strategies. critical economic environment (recession) demands now thoughtful strategic movements, providing a satisfactory market share, constant cash-flow and customer loyalty. the evolution of the information technology (it) allowed for several years the development of it solutions based on cloud computing. the present paper describes how the decision of outsourcing it using cloud solutions is bounded from benefits in terms of reduced costs for the infrastructure, removes the burden of the infrastructure and networking management for the companies, offers the chance to use multi-tenant applications that are easy to be updated by the application developers and so on. keywords: crisis management, cloud computing, outsourcing it, marketingmanagement decisions 1 introduction the economical cycle seems to be one of the economy’s postulates and until we learn to manage the economic resources from a manner to linearize the economic processes, the single available option is to minimize the effects, often dramatically, of the negative components: the crisis point and the crisis itself. due to the economic crisis, the two main objectives firms have in this space are being able to appropriately value their portfolio by having adequate and reliable data, and cutting costs. the accent into a strong competitive environment comes on the competitive advantage [25] and nowadays a powerful it department can make the difference between failure or success. on the other hand, the complex technical nature of it requires expensive equipment and therefore capital is tied up into it department. the benefits generated by the dynamic of it have led in a relatively short time to the adoption of the new technological advances at all the levels of the marketing environment, exercising therefore, direct or indirect pressure on the company’s it to be able to adapt its own activity to the dynamics of the social and economical context. all categories of general public, companies, marketing intermediaries, economical, political or social entities currently operate using computer systems of various sizes. on the other hand, all these types of organizations are interconnected at different functional or operational levels, forming a big informatics system. it becomes therefore essential for a company to benefit from the it support in order to allow it to function as an active part of this macro-computer network. in other words, the it capability of an organization exceeds many concepts like strengths or competitive advantage and had become an absolute necessity. on the other hand, the acute need to cut the it costs at many organizations spurred the interest in the potential cost-savings promised by cloud computing. the traditional solution of outsourcing it turns in the current economic context less stable, more expensive and less effective in relation to the company’s real needs. copyright c⃝ 2006-2013 by ccc publications it outsourcing a management-marketing decision 185 the survival of an organization in an environment increasingly aggressive requires a more constant study of it, followed by careful planning of its activities on the market in accordance with the organization’s mission. in this context, targets and specific modalities for achieving them are expressed by developing and implementing management-marking strategies. figure 1: high speed it-ization of whole marketing environment critical economic environment (recession) demands now thoughtful strategic movement providing a satisfactory market share, constant cash-flow and customer loyalty. 2 managing outsourcing strategy faced with the alternatives of bankruptcy or dramatically decrease of activities, most of the companies focus on severe costs reduction, trying to pursue in the same time the lines of corporate social responsibility (csr) in terms of jobs preservation. one of the most effective solutions for costs reduction was proven to be the services outsourcing. this way, the company can have resources at costs that allow its survival. the csr requirements in terms of responsibility towards the employees are therefore respected by the fact that globally it creates new jobs inside outsourcing companies. these kinds of practical examples such payroll, transportation, trainings or marketing services proved to be again practical and functional. there are various levels of outsourcing (see figure 2): high/medium and low priority. outsourcing companies should be qualified and selected according to both their demonstrated effectiveness and their ability to work collaboratively. companies can create real sustained value routinely and use them for far more strategic ends-to gain capabilities that they don’t have in-house, or to strengthen capabilities they do have [12]. contracting third parties, specialized in areas where the firm has neither a critical strategic need, nor special capabilities [27], enables a company to focus its efforts on its core competencies. when the outsourcing is implemented for correct reasons, it could bring some benefits in terms of cost reduction and flexibility for the organizations that help them to overcome the crisis: * helps companies to focus on their main activities. during the financial boom period of time, some companies grow by adding supplementary activities that weren’t in the initial portfolio. these supplementary activities need specialized personnel that couldn’t be all the time trained inside the company and this leads to raise the company’s operating expenses. 186 r.e. brandabur figure 2: strategic outsourcing options, adapted after [30] externalizing these activities, even temporary, leads to refocusing on the core company’s activities and increasing the business revenues. * helps companies to optimize their costs by externalizing activities that are mandatory but having them inside the company is not effective (e.g. externalize the accounting activity to a specialized firm). * saves costs related to office space by outsourcing some less important activities to third parties or allowing teleworking for the employees. * helps performing a risk management policy in the periods of high turnover when the inconsistency in the employee number could increase. outsourcing possible activities to external companies could bring a certain level of stability and saves money that otherwise will be spent with new personnel training. the key risk is the growing dependence a on an outsourcer, thus limiting company future flexibility. the breakdown of classical outsourcing solutions has pushed the companies to identify new practical solutions. long term solutions are more complex and require more than gaining cost reduction and flexibility, outsourcing strategy must provide radical change and enterprise transformation [21]. 3 managing outsourcing it early in the ’90s, various organizations are looking for improving competitive advantages and obtaining better performance, it being critical in enabling business development in various domains, it enabled managerial innovation allowed organizations to compete more effectively and helped to quickly ramp up output to meet demand [8]. it outsourcing a management-marketing decision 187 during the last years, the high management and the achieved high productivity involved the intensive use of the it equipment. in fact, the efficient usage of the it infrastructure was from a certain point a competitive advantage. a more advanced and sophisticated equipment has triggered, under the astonishing rate of technological evolution, the increasing of the operational costs together with the enhancing of efficiency. the attempt to keep pace with innovations would mean permanent investments, changes and trainings, involving huge costs for the companies. meanwhile, the risk of not being compatible, if not with the latest technology, at least with the previous one, could generate losses. outsourcing choices represent alternate ways for organizations to leverage available resources to increase the value of it in meeting corporate objectives. [20]. however we can not see it outsourcing only as a decisional option, but rather as a piece of managerial plans of whole business and particularly we can develop an it strategy, where operational dimensions require such an approach. earl [4] suggests that outsourcing it is the first option when operational performance of it is low and is not a strength for the company, also in his "smart source" variant when business value of it is not a core of organization and operational performance of it trough outsourcing is improved. it outsourcing benefits include enhanced efficiency and cost savings, infusion of cash, reduced capital expenditure, quicker development of applications, improved services, access to new it knowledge and technologies, and greater flexibility in it resource management [32]. lacity and wilcocks [19] categorize the desired benefits of it outsourcing in terms of six strategic foci: financial restructuring (or cost efficiency), core competence, technology catalyst, business transition, business innovation and new market. the factors that lead to success are more business oriented than anchored in technical domain. it is important to first understand the problem, then find the right operation that fits the problem. this is the case when outsource it final results may place it to business needs, improving the to management of projects change and having the appropriate balance level between the management expertise and technical know-how. 4 cloud computing an attractive outsourcing opportunity the evolution of the computer science technologies allowed for several years the developing of it solutions based on cloud computing. starting as a centralized process [14] it seems to return at the beginnings, due to latest applications developed few years ago and tailored to the cloud technology which concentrates huge amount of it capacity. into a statutory document, the national institute of standards and technology [22], an agency of the u.s. department of commerce defines cloud computing as "a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction" [22]. gartner defines cloud computing as a style of computing in which massively scalable itrelated capabilities are provided ’as a service’ using internet technologies to multiple external customers [10]. in a more managerial approach "cloud computing is now an emerging technological phenomenon which aims to offer users complete infrastructure, platform and software solutions based on a pay-as-you-go financial model and also freeing them up of managing hardware, software and data by moving these tasks to the cloud providers side" [3]. the main benefits of moving toward the cloud are: increases productivity; reduces capital and ongoing maintenance costs which are now transferred to the cloud provider; provides access to the latest technologies as a basic condition for increased performance. another important 188 r.e. brandabur benefits are the ubiquitous access, global distribution and availability. cloud applications can be distributed anywhere around the globe and have the same highly available service levels at any location; measurable/metered services making possible a pay per use schema. this leads to better costs management and reduces the risks of paying unused resources. all of these have as result a better consumer satisfaction. also one of the most important advantage and characteristic of the cloud is the elastic provisioning, variable capacity depending on the customer needs. the cloud offers competitive advantage by its ease of use. [16–18] the most important risks associated with cloud computing are about security and privacy of data, in terms of data storage and data transfer protection, vulnerability management and remedial, personnel and physical security, applications security, data privacy and identity management, compliance requirements (e.g. disaster recovery, security standards, logs and audit trails), reliability, legal and regulatory concerns when providing cloud services. there are also issues related to the lack of standards for ensuring interoperability or migration between cloud providers and not at last, inquiries around the provider’s capacity in an very young industry, in terms of quality, company size, technology, communication [4, 17, 23, 29] related to the deployment of the cloud, nist identifies four models: private cloud, community cloud, public cloud and hybrid cloud [22]. practically, public cloud vs. private cloud discussion is, at this moment, about cost of it department vs. cost of privacy and safety of organizational data. a convenient way to share the computation resources among many organizations is the community cloud, which, according to [6] can be vertical, shared by a business entity together with its partners, by a consortium or can be shared by an it organization to provide services to other business units. examples of such community clouds are: the hr-xml consortium is the independent, nonprofit, volunteer-led organization dedicated to the development and promotion of a standard suite of xml specifications to enable e-business and the automation of human resources-related data exchanges and mount sinai hospital in toronto is building a community cloud in conjunction with the canadian government that will give 14 areas hospitals shared access to a fetal ultrasound application and data storage for patient informations [7]. the main obstacle in this case is the huge investment of consolidating cloud applications in terms of "fit" strategy between various it resources within cloud members. top 10 public cloud hosting include :ibm 1, csc 2, gogrid 3, joyent 4, 8x8 5, amazon 6 [31] when comes on business sector, public clouds seems to be a delicate subject, because associated risks, despite the fact that companies can use the public cloud with dynamic on-demand computing capacity much faster and without the up-front cost. for example, the software offered following the service model is growing at a 17 percent annual rate [1]. under before listed conditions, choosing right a cloud computing formula became a real challenge for executives. externalizing it is controversial in terms of risks, and cloud computing is even a highly controversial solution for outsourcing it. many professionals, 47% according to a 2010 study of isaca [14] , think that benefits are fewer than the associate risks, and only 17% consider that benefits outweigh the risks. same conclusion reveals a mckinsey global survey conducted in 2010 [24]. additional, the research revealed many barriers perceived in order to adopt the cloud technology from the interviewed it personnel (462 respondents) and non it (264 respondents) executives: evaluating and managing security or business continuity risks; managing regulatory 1http://www.ibm.com 2http://www.csc.com 3http://www.gogrid.com 4http://www.joyent.com 5http://www.8x8.com 6http://aws.amazon.com it outsourcing a management-marketing decision 189 risks or exposure; adapting existing business processes to cloud systems; addressing issues with migration or interoperability with their company’s current systems or data architecture; lack of awareness or interest in cloud systems in their company; adjusting technology governance processes for cloud systems (e.g. policies for control, monitoring) and developing the right set of skills to build, manage and support cloud systems. opinions are also different between it executives and non-it executives, first suggest that associated risks are too high, while according to the second category: the cloud increases business flexibility, increases the ability for it to scale up (or shrink) to meet business needs, has lower unit cost of it and offer disaster recovery and business continuity [1]. according to recent studies [24] more organizations intended to enlarge cloud computing usage within their activities (see figure 3) . figure 3: the cloud computing adopting model [15] results revealed that, from it executives (464 respondents), more than 80% say that their companies are using or experimenting cloud technologies. also 63% say that their companies are using cloud-based applications in some aspect of day-to-day operations, and over the next 12 to 18 months, deployment and piloting is expected to increase across all application types explored in the survey. this study confirm one of our hypotheses: as a product, the cloud enters in its second life stage: the growth. although, for now, it seems a viable option only for the organizations that are not dealing with sensitive data or for small or medium companies but soon cloud computing will surpass infancy stage. however a compromise solution for the reticent companies to the outsourcing solution could be the gradual adoption of virtualization, cloud experimentation, cloud foundation, cloud exploitation and ending with the hyper cloud paradigm [15]. the latest technologies in terms of resources virtualization with direct consequences in the apparition of the cloud computing paradigm allows the relocation of the it costs and various usage patterns while creating new ways for individuals to consume goods and services and for entrepreneurs and enterprises to dream up viable business models. in the same time the cloud computing makes possible the association and the collaboration between different organization by creating virtual enterprises that can compete through their offers with the largest players from their markets. 5 outsourcing it strategies based on cloud computing adoption of cloud technologies seems to be at the moment, according to the opinions of specialists [1, 15, 24] the best solution for companies of various sizes in order to cope the envi190 r.e. brandabur ronmental changes induced by it evolution. the strategic options for different sizes companies, in relation to the it outsourcing decision on cloud computing solutions, can be graphically represented by taking as analysis factors the following two attributes: reactivity of the company related to the benefits of the new technologies and the ratio between the cost of implementing and operating its own cloud the it competences to support new technologies (see figure 4). figure 4: outsourcing it strategies based on cloud computing a proactive attitude of the companies means implementing and using cloud computing technologies in the current activity and may lead, depending on the cost/competence ratio, for the large companies to assess four strategic options: * perform internally and develop private cloud a convenient implementation operating costs, associated with a higher than average it competences lead to the selection of a solution to develop their own cloud, which finally leads in obtaining various benefits. the private cloud can be used to solve their own infrastructure (virtual machines, storage, communication between applications) which eventually will host applications (whether on-premise, that is installed on local computers, whether they are exposed as services in the private or hybrid cloud). * perform internally and develop private-community hybrid cloud is the solution which one can choose in case the operational expenditures (opex) and deployment costs are too high and/or it skills do not allow the development of a private cloud system. the optimal solution becomes to join a hybrid system that allows lower costs and a certain level of load takeover by the cloud community. most often used when the private cloud can not handle all the load spikes (periods with high demands) or when the cloud is shared with other companies if it provides special capabilities that do not deserve a local/own investment. it outsourcing a management-marketing decision 191 * outsource the implementation of the it projects to private or hybrid private-community cloud provider is a convenient solution when the costs are too high and/or there is a lack of it competences or there are overloads for the technical personnel and it is impossible to complete the projects inside the company. in this way the company can have its own projects in private or hybrid cloud. it could be a trade between the company and third party service providers in terms of computation power (if surplus inside the private cloud) and it staff (if too few human resources, projects can be externalized). * outsource the implementation of all it projects to a private cloud or private–community hybrid cloud based on the concrete situation, the company may opt for outsourcing the implementation of all it projects, focusing the efforts on other specific activities. for the medium sized businesses, a proactive activity is translated in using a cloud computing system in the following variants: * perform internally and develop private-community or private-public hybrid cloud is the situation in which the company can opt for a private cloud formula, especially in case of high it competencies, or a hybrid containing private investments and shared in public, or community cloud, in a hybrid solution, depending on the security level of data handled. the second option allows, for the medium sized companies, using private and shared solutions simultaneously to avoid overloads and benefit of economy of scale, opposed to the exclusive use; * outsource it infrastructure (iaas) to private-community or private-public hybrid clouds or community cloud is a solution that allows the access to proper amount of high performance equipment that companies need at optimal costs considered for this service; * outsource it applications software-as-a-service (saas) to private-community or privatepublic hybrid clouds or community cloud is a solution that allows the access to the appropriate top software solutions that companies need at an optimal cost considered for this service; * outsource it infrastructure or services (iaas/saas) to private-community or private-public hybrid clouds or community cloud allows simultaneous access to applications and top equipment costs considered optimal for this service. the medium sized businesses access to this kind of cloud solutions is lower because the cost/competencies is often unfavourable. in the case of small businesses, proactive attitude consists primarily in using cloud solutions. often the small companies having limited access to all resource types (human, financial, material), technologies (including information) and although have problems concerning about transparency and regulatory burdens relative to the large companies [34]. typically, small businesses can not afford the access to the high competencies it specialists, excepting the cases where that company is a specialized one, making impossible an independent or private cloud computing approach. the options are: * develop it projects and share as components in public-community or public-private hybrid clouds or community cloud it development is the essential step that could lead to share beneficial option as the ratio between costs and competencies involved; * outsourcing infrastructure or services (iaas or saas) are similar situations to those available for medium-sized companies. 192 r.e. brandabur a passive attitude is associated to reluctance in adopting cloud solutions, on one hand because of the it competencies shortage, on the other hand by the lack of understanding of the benefits of technology. in the general given conditions of accelerated it-ization (see figure 1) this approach will eventually generate a competitive handicap to the company. for the large companies, the available options are: * invest to perform internally private cloud is a solution which basically will delay the optimal capacity use of the private cloud, the company being focused on it competencies development and cost optimization. the cloud applications development is in the background; * invest to use private-public hybrid clouds is a solution that will allow access to a hybrid environment, essential for data security but also having the capabilities to deal with the load peaks in terms of infrastructure. the access in the public side will be performed only temporary; * outsource some it tasks to private or private-public hybrid clouds or to community cloud is a solution that the company will perform only to outsource the projects or components of projects being in progress to the private or community cloud. fractionation of it projects will increase the it management difficulties which also lead to the growth of the operating costs. for the medium sized companies, the available options are: * invest to develop and use private-public hybrid clouds is an action aimed to achieve further benefits from the exploitation of cloud solutions * use occasionally saas and/or iaas from private-public or private-community hybrid clouds or community clouds this is a solution used when a temporary need of specialized services or infrastructure occurs. this is the most simple and affordable solution for it outsourcing to the cloud for the small sized companies, the available options are: * use occasionally saas and/or iaas from public, public-community hybrid clouds or community cloud providers the same solution used by the medium sized companies * use own it solutions may involve occasional and partly involuntary use of common solutions of the public cloud, like e-mail or social networks it outsourcing solutions in the cloud allow several options available for each company type. major differences lie in the magnitude and number of it projects developed by a company. while the large companies require large it projects whose implementation is complex and often expensive, small and medium-sized companies require projects whose management is less difficult, but the dysfunctions caused by impaired implementation it projects / operation can cause serious problems for any type of company. choosing a cloud computing solution and the outsourcing decision are important strategic choices in the overall strategic management of the company. the complexity of the current it projects are facing the companies to make important decisions related to the solution that have to be adopted. there is a thin line that makes the separation between a good or bad approach of the problem, by using enterprise class packages or very specialized libraries. however, wrong decisions are transformed in project delays, cost overruns or solutions that does not fit the business. it outsourcing a management-marketing decision 193 6 future work the author proposes to study the degree of acceptance of cloud computing solution in romania into b2b (business/organizational) market and furthermore the degree of developing of it outsourcing strategies. 7 conclusions the organizations’ infrastructure could be highly virtualized, stringing together mass quantities of it equipments into one or more easily managed logical resource pools, practically building the high quality cloud computing infrastructure. the resources needed to make this happen require massive investment in terms of expertise, equipment and support. such premises conduct on the strategic decision of outsourcing it to a cloud provider as a vendor targeted for an individual market though can distinguish that provider and allow it to offer complementary services to the industry as well, at reasonable costs and totally customized. the decision of outsourcing it is bounded from benefits in terms of reduced costs for the infrastructure, remove the burden of the infrastructure and networking management for the companies, offers the chance to use multi-tenant applications that are easy to be updated by the application developers and so on. a systematic analysis is necessary in order to support strategic marketing embraces activities and decisions that draw on some view of the future. the company size and type of business make, for instant, the difference between outsourcing it or keeping in house, but that will be not for so long. a managerial approach is needed also by it service providers. they will need to own or manage the full stack of it capabilities on a massive scale in order to provide deep expertise in delivery of services-on-demand. the contribution of the present paper is related to an analysis performed on the current it market (section 2), finding new opportunities for the companies that need to reduce their costs. cloud computing is a promising sector that could be a solution for many companies in terms of cost reduction and state of the art technologies. a pertinent review 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[34] oecd small business, job creation and growth: facts, obstacles and best practices,http://www.oecd.org/industry/smesandentrepreneurship/2090740.pdf international journal of computers communications & control issn 1841-9836, 13(4), 537-549, august 2018. enhanced interconnection model in geographically interdependent networks d.f. rueda, e. calle, x. wang, r.e. kooij diego f. rueda*, eusebi calle institute of informatics and applications universitat de girona p-iv building, campus montilivi, girona, spain u1930599@campus.udg.edu, eusebi.calle@udg.edu *corresponding author: u1930599@campus.udg.edu xiangrong wang, robert e. kooij faculty of electrical engineering, mathematics and computer science delft university of technology mekelweg 4, delft, the netherlands x.wang-2@tudelft.nl, robert.kooij@tno.nl abstract: interconnection between telecommunication networks and other critical infrastructures is usually established through nodes that are spatially close, generating a geographical interdependency. previous work has shown that in general, geographically interdependent networks are more robust with respect to cascading failures when the interconnection radius (r) is large. however, to obtain a more realistic model, the allocation of interlinks in geographically interdependent networks should consider other factors. in this paper, an enhanced interconnection model for geographically interdependent networks is presented. the model proposed introduces a new strategy for interconnecting nodes between two geographical networks by limiting the number of interlinks. results have shown that the model yields promising results to maintain an acceptable level in network robustness under cascading failures with a decrease in the number of interlinks. keywords: cascading failures, interdependent critical infrastructures, robustness, region-based interconnection. 1 introduction interdependent networks depend on a set of critical infrastructures (cis) that function collaboratively to produce and distribute the essential goods and services required for the defence and economic security of nations and the proper functioning of governments and society [11]. natural disasters (hurricanes, earthquakes, tsunami, tornadoes, floods or forest fires), man-made disasters (electromagnetic pulse (emp) or weapons of mass destruction (wmd) or terrorist attacks), technology-related disasters (power grid blackouts, hardware failures, dam failures or nuclear accidents), or cyber-attacks (viruses, worms or denial of services attacks) are responsible for large-scale disasters in networks [14] [10]. consequently, failures in critical infrastructures imply service disruptions that affect thousands of people, multiple communities, entire countries, or just one company [9]. telecommunication networks play a vital role in supporting the control, monitoring, connectivity and data transportation services of a number of critical infrastructures, including banking and finance, emergency and government services, water supply, transportation networks, power grids and oil and gas distribution networks. the interconnection between the nodes of these cis and telecommunication networks is usually carried out by their spatial proximity. this regionbased interconnection model generates a geographically interdependent network in which two copyright ©2018 cc by-nc 538 d.f. rueda, e. calle, x. wang, r.e. kooij nodes i and j, located in two separate networks are interconnected if the distance (dij) between them is less than or equal to a given radius (r). because of such interconnections, failures that occur in one infrastructure can directly or indirectly affect the other and impact large regions with catastrophic consequences [9]. an example of a large-scale failure in interdependent networks is the italian blackout of 2003, where a single failure in the power grid resulted in failures that propagated over a telecommunications network, ultimately affecting more than 55 million people [2]. therefore, network topologies, the geographic locations of nodes and their interdependency relationships have a huge impact on how robust interdependent networks are designed and maintained [9]. in contrast to the one-to-one interconnection studied in previous work [2], geographically interdependent networks exhibit a one-to-multiple interdependency model i.e., one node in one network can depend on an arbitrary number of nodes in the other network [16]. in terms of the functional giant component, a geographically interdependent network is more robust with respect to cascading failures when r is large [16]. this is due to the fact that with the increase of r, a node tends to have more interconnection nodes which, in turn, will decrease the probability of that node failing as result of the failures of its interconnection nodes. however, the regioninterconnection models proposed in our previous work [16] only consider the geographical distance between nodes to establish the interlink, whereas in most real scenarios, interlink allocation in geographically interdependent networks should be controlled with additional factors in order to mitigate other issues introduced by the large number of interlinks in each r e.g., high deployment cost or exceeding node capabilities. in the literature, most of the studies have been focused on modifying the interconnection patterns, according to a certain strategy, in order to improve the robustness of interdependent networks against cascading failures. yagan et al. [17] showed that the regular allocation of bidirectional interlinks always yields stronger robustness than random strategy and unidirectional interlinks do. li et al. [14] allocated weighted interdependency links under limited budget to obtain a more robust interdependent cyber-physical network. ji et al. [6] showed that the low inter degree-degree difference addition strategy (idd) and random inter degree-degree difference addition strategy (rid) are superior to the existing four link addition strategies (random addition, low degree, low betweenness and algebraic connectivity based) in improving the robustness of interdependent networks with high average inter degree-degree difference. however, these studies are focused on interdependent networks where the geographical location of the nodes is not considered to establish an interlink. j. martín-hernández et al. [8] showed the critical number of interlinks beyond which any further inclusion does not enhance the algebraic connectivity (λ2) of an interdependent network. therefore, controlling the number of interlinks in geographically interdependent networks is likely a valuable design feature in order to reduce the deployment cost of interdependent networks and not to exceed the capabilities of the nodes to be interconnected. unlike prior efforts, the major contributions of this paper are: 1) proposing a new strategy for interconnecting nodes between two geographical networks by limiting the number of interlinks and 2) analyzing the impact of limiting the number of interlinks has on the robustness of geographically interdependent networks against cascading failures. as a study case, we focused on interdependent telecommunication networks because they can represent the interconnection of two internet service providers or can refer to multilayer networks. moreover, in this paper we consider the vulnerability analysis of each network to a certain type of targeted attack to determine the influence the new region-based interconnection model has on the robustness of the resulting interdependent network. the remainder of this paper is organized as follows: section 2 describes the proposed interconnection model for region-based interdependent networks and cascading failure process in interdependent networks. section 3 presents the topologies of the networks to be interconnected enhanced interconnection model in geographically interdependent networks 539 figure 1: enhanced interconnection model in geographically interdependent networks and discusses the impact limiting the number of interlinks has on the robustness of region-based interdependent networks to cascading failures. finally, section 4 provides the conclusions and future work. 2 concepts and models in addition to the distance between the nodes, interlink allocation in geographic interdependent networks should be controlled by considering factors additional to the geographical constraint. this paper proposes a new region-based interconnection model in which a node i in network g1 and a node j in network g2 can be interconnected if 1) the distance dij between them is less than or equal to a given radius r and 2) the number of interlinks for nodes i and j do not exceed a given percentage for limiting the number of interlinks (φ1 and φ2, respectively). our new strategy for interlink allocation is based on dividing the nodes in both networks into subsets in accordance with a certain nodal property. thus, the model prevents φ1 and φ2 being exceeded for any node in g1 and g2, respectively. the model proposed is illustrated in fig. 1. the nodes in g1 are represented by filled circles and the nodes in g2 are represented by unfilled circles. for each node i in g1, there is a set of nodes in g2 that can be interconnected if the conditions 1) and 2) are satisfied. consequently, in contrast to our previous work [16], an enhanced interconnection model for limiting the number of interlinks in geographically interdependent networks is generated. the remainder of this section presents the proposed region-based interconnection model in detail and describes the failure model involving cascading failures. 2.1 interconnection model for limiting number of interlinks in geographically interdependent networks consider two undirected networks g1 (s,u) and g2 (t,v ), each with a set of nodes (s,t) and a set of links (u,v ) respectively. denote n1 and n2 as the number of nodes in g1 and g2, respectively, and l1 and l1 as the number of links in g1 and g2, respectively. when g1 and g2 interact, a set of bidirectional interlinks i joining the two networks is introduced. consequently, 540 d.f. rueda, e. calle, x. wang, r.e. kooij an interdependent network is defined as g(n,l) = (s ∪ t,u ∪ v ∪ i) [8]. let us define the adjacency matrix (a) of g as the n ×n matrix: an×n = ( a1 αb12 αbt12 a2 ) , (1) where α represents the coupling strength of the interaction, a1 is the n1×n1 adjacency matrix of the network g1, a2 is the n2 ×n2 adjacency matrix of the network g2, and b12 is the n1 ×n2 interconnection matrix representing the interlinks si ↔ tj, between g1 and g2. because we consider bidirectional interlinks, it follows that b21 = bt12 [8]. let bij denote as the (i,j) entry in the b12 matrix, where bij = 1 if the node i and node j are interconnected, and bij = 0 if they are not. the interdependency matrix (b) of the whole system is given by: bn×n = ( 0 b12 bt12 0 ) (2) in the region-based interconnection model previously proposed by us [16], the entry bij is determined by the geographical location of nodes. let (xi,yi) and (xj,yj) denote the spatial coordinates for nodes i and j, then, bij= 1 if the euclidean distance dij between node i in g1 and node j in g2 is smaller than a given threshold r. this link pattern generates a random geometric graph with a one-to-multiple interdependency model [16]. the euclidean distances dij is given by: dij = √ (xi −xj)2 + (yi −yj)2 (3) in the random geometric graph, a node i in g1 can depend on an arbitrary number of nodes in g2 that is no greater than n2, and vice versa. when the distance between two nodes is considered as the unique interconnection constraint, some issues are evidenced. specifically, the nodes in one network may have many interlinks from the other network, thus incurring high deployment cost. note that the cost can be related to the economic investment required to construct an interlink. for instance, in the case of interdependent networks constructed by power grids and telecommunication networks, a new interlink has an associated deployment cost as a function of the cable length. additionally, nodes in each network have limited capabilities to interconnect to a fixed number of nodes, and so the network’s extension requires additional investments. therefore, limiting the number of interlinks between the nodes in two networks contributes to keeping the deployment cost under control and adjusting to the operator’s budget. let us define the new factor to be considered in the interconnection of geographically interdependent networks for limiting the number of interlinks in each network. for g1, this factor is denoted as φ1 and is given by: φ1 = η1 n2 × 100%, (4) where η1 ≤ n2 is the maximum number of nodes from g2 that each node in g1 can interconnect to and n2 is the number of nodes in g2. similary, the limit of interlinks (φ2) for nodes in g2 can be calculated analogous to (4). therefore, the maximum number of interlinks that each node in g1 and g2 can interconnect to is controlled by φ1 and φ2. as part of our proposal, the nodes in g1 (g2) are divided into µ1 (µ2) subsets of nodes, each with a maximum of η1 (η2) nodes. subsets of nodes are a key aspect to controlling the allocation of a specific number of interlinks to each node. the number of nodes in a subset is directly related to the capacity of the nodes and the functionality performed by nodes in each network. for instance, in a fixed broadband access architecture, a subset of nodes in the access network can be interconnected to a subset of nodes in the core network. moreover, a core network can enhanced interconnection model in geographically interdependent networks 541 support the interconnection of a limited number of access nodes. without loss of generality, a subset of nodes in a network can group nodes with similar properties or randomly. then, the nodes of a subset in g1 will be interconnected to the nodes of a subset in g2 if the distance is less than or equal to a radius (r). as the number of nodes in each subset is limited, the number of interlinks in each node can be kept under control. let us consider that the nodes in g1 are divided into µ1 subsets of nodes, where µ1 is given by: µ1 = { round(n2 η1 ), if φ1 < 50% 2, if φ1 ≥ 50% (5) similarly, the nodes in g2 are divided into µ2 subset of nodes, where µ2 is given by: µ2 = { round(n1 η2 ), if φ2 < 50% 2, if φ2 ≥ 50% (6) let ai denote the property value of node i ∈ g1. then, nodes in g1 are ordered according to ai, i.e.„ a1 ≥ a2 ≥ ... ≥ ai−1 ≥ ai ≥ ai+1 ≥ ... ≥ an1−1 ≥ an1. moreover, let γsg denote the ordered set of nodes previously defined in g1. if γs1, γs2, ..., γsµ1 represent the subsets of γs, then, γs = ⋃µ1 g=1 γsg, and γsg is given by: γsg = { {i : (g − 1) ×η2 < i ≤ g ×η2}, if g < µ1 {i : (g − 1) ×η2 < i ≤ n1}, if g = µ1 , (7) where i represents the i − th element in γsg and g ∈ {1, 2, ...,µ1}. similarly, let cj denote the property value of node j ∈ g2. then, nodes j ∈ g2 are ordered according to cj, i.e.„ c1 ≥ c2 ≥ ... ≥ cj−1 ≥ cj ≥ cj+1 ≥ ... ≥ cn2−1 ≥ cn2. additionally, let γth denote the ordered set of nodes previously defined in g2. if γt1, γt2, ..., γtµ2 are subsets of γt , then, γt = ⋃µ2 h=1 γth, and γth is given by: γth = { {j : (h− 1) ×η1 < j ≤ h×η1}, if h < µ2 {j : (h− 1) ×η1 < j ≤ n2}, if h = µ2 , (8) where j represents the j − th element in γth and h ∈{1, 2, ...,µ2} let us define bφ as an n1×n2 interconnection matrix, whose entries or elements are bφij = 1 if nodes in the subset γsg are connected to nodes in the subset γth for g = h, otherwise bφij = 0. accordingly, the bφ matrix defines which nodes in the networks can be interconnected and establishes the limit for the number of interlinks that each node in the networks can handle. thus, each node in g1 or g2 will have a maximum of η1 or η2 interconnected nodes, respectively. finally, let us redefine the dependency matrix b12, whose entries are bji = 1 if dij ≤ r and bφij = 1, otherwise bij = 0. note that the new b12 matrix captures the interconnection conditions 1) and 2) proposed in this paper and thus the new interdependency matrix b, which is given by the equation (2), can be generated. therefore, the nodes in each geographical network will interconnect with a limited number of interlinks, consequently improving the model defined in [16]. for simplicity, in this paper we consider that g1 and g2 have the same number of nodes (n1 = n2) and that all the nodes in the interdependent network have the same limit of interlinks (φ1 = φ2). therefore, each network has µ1 = µ2 subsets of nodes with a maximum number of nodes η1 = η2. figure 2 presents two geographical networks being interconnected by employing the interconnection proposal described in this section. as can be seen in fig. 2, both networks 542 d.f. rueda, e. calle, x. wang, r.e. kooij figure 2: subsets for limiting the number of interlinks in geographically interdependent networks have n1 = n2 = 9 nodes and each node in g1 and g2 can support until φ1 = φ2 = 30% of nodes from the other. according to what has been described above, nodes in both networks are divided into µ1 = µ2 = 3 subsets, each one with a maximum of η1 = η2 = 3 nodes. then, the bφ matrix is generated with the subsets γsg and γth. finally, the interlinks between the nodes from g1 and g2 (dashed lines) are established if dij ≤ r and bφij = 1. 2.2 algorithm description algorithm1 summarizes the interconnection model proposed to limit the number of interlinks in geographically interdependent networks. algorithm1 requires two networks (g1 and g2) to be interconnected, the percentage for limiting the number of interlinks (φ1 and φ2) and the radius (r). the output of algorithm1 is a dependency matrix b12 with the conditions 1) and 2) previously described. as can be seen, algorithm1 calculates the maximum number of nodes that a node can interconnect to (lines 1 and 2) and the number of subsets (lines 3 and 4). then, the nodes are grouped in subsets according to one property (lines 5 and 6). the interconnection matrix (bφ12), in which each node in g1 (g2) has a maximum of η1 (η2) interconnected nodes (line 7) is generated. finally, the interdependency matrix b12 is generated by considering the distance constraint for a given r and the bφ matrix (lines 8 to 19). thus, an enhanced region-based interconnection model is defined for interconnecting the g1 and g2 networks and the interdependency matrix b, which is given by the equation (2), can be generated from the resulting b12 matrix. 2.3 cascading failure process in interdependent networks consider a geographically interdependent network g generated from the model proposed in this paper. when a random fraction of the nodes in g1 fails, a cascading failure process is induced. we assume the node i in network g1 is functional if a) at least one of its interconnected nodes in network g2 is operative, and b) the node i belongs to the giant component of the enhanced interconnection model in geographically interdependent networks 543 algorithm 1 interconnection model for limiting the number of interlinks in geographically interdependent networks. d ¯ ata: two geographical networks (g1 and g2), limit for number of interlinks (φ1 and φ2) and radius (r). r ¯ esult: dependency matrix b12. η1 = round(φ1n2/100) η2 = round(φ2n1/100) µ1 = round(n1/η2) µ2 = round(n2/η1) γsg ← getsubsetnodes(s,µ1,η2,nodal_property) γth ← getsubsetnodes(t,µ2,η1,nodal_property) bφ ← getbφmatrix (γsg, γth,η1,η2,µ1,µ2) for all i ∈ s do for all j ∈ t do dij = √ (xi −xj)2 + (yi −yj)2 if dij ≤ r and bφij == 1 then bij = 1 else bij = 0 end if end for end for return b12 functional nodes in network g1 [4]. due to interdependency, the failed nodes in g1 spread failures in g2. as the assumptions a) and b) are also applied to the nodes j in network g2, the failed nodes in g2 spread failures back into g1, and so on. the cascading failures continue until no more nodes fail. the remaining set of functional nodes is referred to as the largest mutually connected component (lmcc): lmcc = n1 + n2 n1 + n2 , (9) where n1 and n2 are the number of nodes that belong to the giant component of the functional nodes in g1 and g2, respectively, when the assumptions a) and b) are satisfied. the cascading failures described in this section can occur in real scenarios such as power grid blackouts [1] and disruptions in economic networks [15]. note that [16] also considered the case in which a node in g1 is functional if all of its interconnected nodes in g2 are operational. under that condition, in some cases, having more interconnected links makes the geographically interdependent network less robust. however, this case is outside the scope of this paper. 3 simulation results and discussion in this section, the topologies for geographically interdependent networks are described. moreover, the impact limiting the number of interlinks has on the robustness of geographically interdependent network is analyzed. 544 d.f. rueda, e. calle, x. wang, r.e. kooij table 1: nodes distribution in g1 and g2 according to interlink limits φ1 = φ2 µ1 = µ2 η1 = η2 10% 10 5 25% 4 12 50% 2 25 75% 2 37 100% 1 50 3.1 topologies for geographically independent networks the geographically interdependent networks considered as the study case represent two backbone telecommunication networks being interconnected with bidirectional interlinks. the random connection property of a backbone telecommunications network is modeled using an erdösrényi (er) random graph with a poisson nodal degree distribution [12]. this indicates that most nodes have approximately the same number of links close to the average nodal degree [3]. although, scale-free or other graph models can be also used to model telecommunication networks, these are more associated to large networks (such as multi-autonomous systems networks). moreover, some current backbone topologies are also scaling to other models which are out of the scope of this paper. in order to analyze the impact the model proposed has on the robustness of interdependent networks against cascading failures, the largest mutually connected component (lmcc) is measured in 100 interdependent telecommunication networks. each backbone telecommunication network to be interconnected is modeled as an er random graph with n1 = n2 = 50 nodes and the average nodal degree (〈k〉) equal to 6. the nodes in each network are placed uniformly in a two-dimensional square of the size z = 1 i.e., each node in the g1 and g2 networks has as spatial coordinates (x,y), where 0 ≤ x ≤ 1 and 0 ≤ y ≤ 1. the interconnection link pattern between the two er graphs is conditioned by a given radius r. the number of interlinks in each node is limited by a given percentage φ. the number of subsets (µ1,µ2) and the maximum number of nodes that a node in g1 and g2 can interconnect with (η1,η2) are presented in table 1. for instance, when φ = 25% , this is considered as the design constraint and, as such, the nodes in each network are divided into µ1 = µ2 = 4 subsets. thus, for a given radius r, it is expected that each node in g1 and g2 will have a maximum of 12 interlinks. as was described in subsection 2.1, a nodal property is also required to define how nodes in each network can be grouped. in the study case considered in this paper, node vulnerability to failures is selected as the property with which to group the nodes into subsets. in most real scenarios, the vulnerability of nodes to failures can be estimated from the historical failure database of their operation support systems (oss). however, given the difficulty of obtaining access to real data, centrality metrics could be used to measure the importance of nodes for the network connectivity under some failure scenarios [5]. previous studies have revealed that backbone telecommunication networks modeled as er are highly vulnerable to a sequential targeted attack based on nodal betweenness centrality (bc) [13]. figure 3 depicts a robustness analysis of the backbone telecommunication networks under targeted attacks when networks are not connected to other. the networks’ robustness is quantified as a function of the average two terminal reliability (attr) metric [9]. as can be seen in fig. 3, the telecommunication networks considered in this work exhibit high vulnerability to a sequential targeted attack by bc. whereas, the networks are more robust to a simultaneous enhanced interconnection model in geographically interdependent networks 545 figure 3: robustness analysis of backbone telecommunication networks (n1 = 50 and 〈k〉 = 6) in a single scenario targeted attack by bc and sequential or simultaneous targeted attacks based on degree centrality (dc). consequently, node vulnerability in each er network could be quantified by their bc values i.e., the higher the betweenness centrality of node is, the higher the node’s vulnerability is. 3.2 analyzing the impact limiting the number of interlinks has on the robustness of geographically interdependent networks to investigate the impact the region-based interconnection model has on the robustness of interdependent networks against cascading failures, the largest mutually connected component (lmcc) metric is measured when a fraction of nodes is removed. in the failure scenario considered in this paper, nodes in the network g1 are removed (according to their vulnerability to a sequential targeted attack by bc) until the percentage of removed nodes (p) is reached. removing the nodes in g1 leads to a cascading failure process as described in section 2.3. although several geographically interdependent networks can be generated by varying the radius and the limit of the number of interlinks, the two scenarios considered as case studies are: • scenario 1: the radius (r) is fixed to 0.2 and the limit for the number of interlinks (φ) ranges from 25% to 100%. this scenario can represent a real situation in which a telecommunication network operator has a geographical area limited by a radius r and is interested in controlling the number of interlinks to other infrastructures. • scenario 2: the number of interlinks is limited to 25% and r is varied from 0.1 to √ 2. this scenario can be used by a telecommunication network operator who has a certain capacity in their network, but wants to restrict its coverage area to a certain radius r to interconnect to fewer number of nodes from other infrastructures. both scenarios are replicated in 100 interdependent networks. the robustness analysis presented in this section is the average of the lmcc results measured in these interdependent networks. scenario 1: robustness analysis in geographically interdependent networks against variations in the limit of interlinks (φ) in this scenario, the radius (r) to interconnect the g1 and g2 networks is fixed to 0.2. then, for a given limit in the number of interlinks (φ), the lmcc of an interdependent network is 546 d.f. rueda, e. calle, x. wang, r.e. kooij (a) (b) figure 4: robustness analysis in geographically interdependent networks (r = 0.2) versus variations in the limit of interlinks (φ) a) largest mutually connected component (lmcc) as a function of the removed nodes (p) b) number of interlinks as a function of φ measured when a fraction of nodes (p) is removed in the g1 network. figure 4a depicts that for a given φ the lmcc first decreases almost linearly with the increase in the fraction of removed nodes (p ≤ 35%). later, the lmcc dramatically decreases until the networks are completely disconnected. networks with the highest slope in their lmcc curves are those that have less φ. this is because with the decrease of φ, nodes in the g1 and g2 networks are divided into more subsets (µ1 and µ2, respectively) which decreases the probability for interconnecting a large number of nodes. consequently, a node has fewer interconnected nodes and its failure probability is increased thanks to the failures of its interconnected nodes. also note that in fig. 4a there is a zone (p ≤ 20%) in which the robustness of interdependent networks for a given φ is similar to the robustness reached by a network modeled according to [16] with r = 0.2 and without limiting the number of interlinks (grg). moreover, in this zone all networks exhibit a high level of robustness against cascading failures (lmcc > 0.8). for example, when 20% of the nodes are removed from g1 and after the cascading failure process, lmcc = 0.89 for φ = 100% and 0.81 for φ = 10%. however, for p > 20%, there are more differences between the lmcc values reached by the networks with φ ≤ 25% and the network grg. however, in the case of networks with φ ≥ 50%, their robustness remains near to that achieved by grg until p ≤ 40%. therefore, for some p values, our model is able to maintain the lmcc in values near those achieved by our previous work [16] when the number of interlinks is limited to a certain value of φ. on other hand, as can be seen in fig. 4b, the number of interlinks is under the maximum number of interlinks reached by the grg network for φ < 100% (compare the dashed line grg and the blue bars g). this result is due to the strategy proposed in this paper whereby the nodes in the g1 and g2 networks are divided into subsets, with a maximum number of nodes η1 and η2, respectively. thus, our new region-based interconnection model guarantees that the number of interlinks in geographically interdependent networks is maintained below the limit φ. for instance, when φ = 75%, the maximum number of interlinks in the interdependent networks is 159. although this value is not exactly 75% of the maximum number of interlinks, it is below the limit of interlinks considered to be a design constraint. enhanced interconnection model in geographically interdependent networks 547 (a) (b) figure 5: robustness analysis in geographically interdependent networks (φ = 25%) versus variations in radius (r) a) largest mutually connected component (lmcc) as a function of removed nodes (p) b) number of interlinks as a function of r scenario 2: robustness analysis in geographically interdependent networks against variations in radius (r) in this scenario, the interdependent telecommunication networks are the result of interconnecting the g1 and g2 networks by limiting the interlinks (φ) to 25% and varying the radius (r). the largest mutually connected component (lmcc) as a function of the fraction of removed nodes from the g1 network is shown in fig. 5a. although the number of interlinks is limited to 25%, fig.5a shows that geographically interdependent networks better resist cascading failures because of a major number of interlinks when the r is large. this result is to be expected as the nodes in the g1 and g2 networks tend to be more probable to interconnect to a greater number of nodes as a wide geographical area is defined by a larger radius r. for example, when 20% of the nodes are removed from g1 and after the cascading failure process, lmcc = 0.90 for r = 1.2 and 0.83 for r = 0.2. additionally, fig. 5a depicts a zone (p ≤ 20%) in which the robustness of geographically interdependent networks for a given radius r remains near to the robustness of a network modeled according to [16] where r = √ 2 and the number of interlinks is not limited (grg). in this zone, all geographically interdependent networks have the lmcc > 0.8. as the percentage of removed nodes in g1 increases, the networks modeled with our new proposal maintain similar robustness levels until p ≤ 40%. consequently, limiting the number of interlinks to a certain percentage φ trends to control interlink allocation against increases in radius r. thus, our proposal based on subsets is effective in limiting the number of interlinks in geographically interdependent networks. regarding the number of interlinks, fig. 5b shows that for a given radius r our model generates interdependent networks where the interlinks are around 25% of the maximum reached by each network grg (compare light blue and dark blue bars).the reason is because, independent of the selected radius (r), the model proposed in this paper restricts the number of nodes that a node in the g1 and g2 networks can interconnect with to η1 and η2, respectively. for example, when r = 0.6, the maximum number of interlinks in the interdependent networks is 390, and the number of interlinks per node is 4 on average. consequently, for some p values our model yields promising results for maintaining network robustness under cascading failures by reducing the number of interlinks. 548 d.f. rueda, e. calle, x. wang, r.e. kooij 4 conclusions in this paper, an enhanced interconnection model in geographically interdependent networks has been proposed. in contrast to previous work, a new strategy based on subsets of nodes has been proposed to limit the number of interlinks in interdependent networks. the proposed region-based interconnection model has considered the percentage with which to limit the number of interlinks (φ) as a new factor in the design of geographically interdependent networks. moreover, the impact limiting the number of interlinks has on the robustness of geographically interdependent networks against cascading failures has been analyzed. the interconnection strategy proposed in this paper has proven to be effective in guaranteeing the number of interlinks in geographically interdependent networks is maintained under a certain limit φ. this is because for a given φ the nodes to be interconnected have been divided into subsets (µ1, µ2), each with a maximum number of nodes (η1, η2). results indicate that in some scenarios (p ≤ 20%) the robustness for a given φ has been maintained at levels close to those reached by [16] (lmcc ≥ 0.80). this is a relevant outcome because compared to the critical threshold at which lmcc equals zero, quantifying the impact of a small percentage of node failures (p) is essential for network providers to prevent networks from collapsing. furthermore, the two scenarios that have been analyzed in this paper represent some situations in which the model proposed can be applied by network providers. results have shown the robustness behaviour for geographically interdependent networks under cascading failures. in the first case, by limiting the coverage area to a certain radius r and varying the number of interlinks (φ), an interdependent networks is more robust against cascading failures when φ is increased. meanwhile, in the second case, by limiting the number of interlinks to a certain φ and varying the radius r, the robustness increases for large values of r. in both cases, the results are because with the increase in the number of interlinks, a node tends to be less likely to fail from the failures of its interconnection nodes. in the future work, the proposed region-based interconnection model can be studied in other interdependent networks and validated with real-world data. moreover, an in-depth cost-benefit analysis of limiting the number of interlinks in geographically interdependent networks can be carried out. acknowledgements this research was supported in part by the spanish ministry of economy and competitiveness and the dursi consolidated research group (csi reference sgr-1469) through the giros 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(2012); optimal allocation of interconnecting links in cyber-physical systems: interdependence, cascading failures, and robustness, eee trans. parallel distrib. syst., 23 (9), 1708-1720, 2012. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 656-667 an optimal rate control and routing scheme for multipath networks s. li, w. sun, y. zhang, h. zhang shiyong li, wei sun, yaming zhang school of economics and management, yanshan university, qinhuangdao, 066004, p.r. china e-mail(s): shiyongli@ysu.edu.cn, wsun@ysu.edu.cn, yaming99@ysu.edu.cn hongke zhang school of electronic and information engineering, beijing jiaotong university, beijing 100044, p.r. china e-mail: hkzhang@bjtu.edu.cn abstract: this paper considers optimal rate control and routing schemes for multipath networks which can be formulated as multipath network utility maximization problems. in these schemes, maximizing the aggregated user utility over the network with multipath routes under the link capacity constraints is the objective of utility maximization problems. by adopting the lagrangian method, sub-problems for users and paths are deduced and interpreted from an economic point of view. in order to obtain the optimal rate allocation, a novel distributed primal-dual algorithm is proposed, and the performance is evaluated through simulations under two different fairness concepts. moreover, window-based flow control scheme is also presented since it is more convenient to realize in practical end-to-end implementation than the rate control scheme. keywords: multipath networks, rate control, routing, network utility maximization, optimization. 1 introduction since the publication of the seminal paper [1], the single path rate control and routing schemes which can be formulated as network utility maximization problems have been extensively studied in the past years, mainly in the context of internet congestion control and flow control (e.g., [2][5], and the reference therein). recently, there has been much interest in multipath rate control and routing schemes [6][13]. in a multipath routing scheme, each source-destination pair can have several different paths along which data packet can be transmitted. in these schemes, maximizing aggregated user utility over the network with multipath routes under the link capacity constraints is the objective of the multipath network utility maximization problems. these problems are indeed a combination of multipath routing and rate control and can be viewed as an example of crosslayer optimization [5] for network architectures, where additional benefits are obtained by jointly optimizing at the routing (network layer) and rate control (transport layer). in order to solve the multipath network utility maximization problems, roughly speaking, multipath rate control and routing schemes can be classed into three categories: primal algorithms [6], [8], dual algorithms [7], [11], and primal-dual algorithms [9], [10], [12]. the primal algorithms have a dynamical law for adjusting user rate and a static law for generating link price, and conversely, the dual algorithms have a dynamical law for adjusting link price and a copyright c⃝ 2006-2011 by ccc publications an optimal rate control and routing scheme for multipath networks 657 static law for generating user rate. then, the primal-dual algorithms have dynamical laws for adjusting both user rate and link price. the primal algorithms are based on a penalty function approach, i.e., they replace the capacity constraints by a penalty function in the optimization objective [1], [6], [8]. they always tend to produce biased approximates of the optimal operating points, due to the fact that penalties are only incurred when the capacity constraints are violated. (in contrast, the optimal operating point is defined to be one that satisfies the capacity constraints.) as for the dual algorithms, the advantage is that they are designed to compute the exact optimal operating points when the step sizes are driven to zero in an appropriate fashion [7], [11]. then the primal-dual algorithms combine the advantages of both primal and dual algorithms, and possess a rapid convergence property to the optimums within reasonable convergence times [9], [10], [12]. in [1], after studying the single-path network utility maximization problem, the authors discuss the extension to the multipath case, where the flow between each source-destination pair can be globally split among multiple paths, and present a distributed rate-based congestion control algorithm. then in [6] the utility maximization framework for multipath case is further studied, and a primal algorithm with round-trip delay is proposed and investigated. meanwhile, a decentralized sufficient condition for local stability of the delayed algorithm is obtained. in [8], the sufficient condition for local stability of the primal algorithm with round-trip delay is improved, i.e., the gain parameter for each route is restricted by the round-trip delay of that route, but not by the round-trip delays of other routes, even those other routes serving the same users. dual algorithms that can produce exact optimal operating points are developed in [2] for singlepath networks. when extended to the multipath cases, the primal variables (i.e., the resource allocation) may not be unique although the dual variables (i.e., the price) in the dual algorithms converge. in order to obtain the unique optimum, some researchers relax the multipath network optimization problems and make them strictly concave. the authors in [9] address the problem by adding a quadratic term onto the objective problem and provide rigorous proof of convergence for the dual algorithm. then in [11] the author proposes a novel relaxed network utility maximization problem and derives a new class of controlled splitting multipath dual algorithms. in [12], by combining the first order lagrangian method and filtering mechanism, a new primal-dual algorithm is proposed to eliminate typical oscillation behavior in multipath networks. in this paper we investigate the optimal rate control and routing scheme formulated as multipath network utility maximization problem, decompose the optimization problem into two sub-problems for users and paths through lagrangian method, and give the interpretations for the sub-problems and dual problem from an economic point of view. furthermore, for each user and its available paths, we analyze the relationship between the prices charged by paths and that paid by the user, and on basis of the difference between them we propose a novel distributed primaldual algorithm for the optimal rate control and routing, which can converge to the optimum of the utility maximization problem, and present the practical end-to-end implementation in the internet. the rest of this paper is organized as follows: in section 2, we analyze the model for jointly optimal rate control and routing scheme known as multipath network utility maximization; in section 3, we present a novel distributed primal-dual algorithm for optimal rate control and routing; in section 4, we give some simulation results to illustrate the convergence of the proposed algorithm; finally, we conclude the paper in section 5. 658 s. li, w. sun, y. zhang, h. zhang 2 multipath network utility maximization 2.1 model description consider a network consisting of a set of links l, a set of paths p and a set of users s. each link l ∈ l has capacity cl. each user identifies a unique source-destination pair. there are multiple paths between each source-destination pair. associated with each user s ∈ s is a set of paths p(s) where each path p ∈ p(s) is a collection of links l(p). obviously, associated with a path p ∈ p(s) is a set of paths p(s) which are all associated with the same user s, and with identical source and destination. in following analysis, if a user s transmits along a path p, then we write p ∈ p(s); if a path p uses a link l, then we write p ∈ p(l), where p(l) is the set of paths that use link l. in this paper we make no assumptions about whether the paths p ∈ p(s) are link-disjointed. obviously the ability to generate link-disjointed paths can assist in the construction of highly robust end-to-end communication for the source-destination pair which is labeled by user s, but the model also covers the case where some or all of the paths p ∈ p(s) of user s share some common path segments. for user s, assume the transmission rate on path p is xsp(t), then the total flow rate of user s is ys(t) = ∑ p:p∈p(s) xsp(t). meanwhile, the aggregated rate on link l is zl(t) = ∑ p:p∈p(l) xsp(t). user s attains a utility us(ys(t)) when it transmits at rate ys(t). the multipath network utility maximization is modeled as the following nonlinear optimization problem max ∑ s:s∈s us(ys(t)) subject to ∑ p:p∈p(s) xsp(t) = ys(t)∑ p:p∈p(l) xsp(t) ≤ cl over xsp(t) ≥ 0. (1) here, maximizing the aggregated utility of the user rate ys(t) over all users with the constraints of link capacities is the objective of network. we are interested in the following class of utility functions proposed in [14] us(ys(t)) =   ws log ys(t), if αs = 1, ws ys(t) 1−αs 1 − αs , if αs ̸= 1, (2) where ws is considered as the willingness to pay of user s, αs is the parameter of fairness concept. this family of utility functions are known to characterize a large class of fairness concepts and have been investigated extensively [13], [15], [12]. in particular, if we set αs = 0, the optimization problem reduces to throughput maximization. if αs = 1, proportional fairness among competing users is obtained; if αs = 2, then harmonic mean fairness; and if αs = ∞, then max-min fairness [14]. for example, these utility functions are considered in wireless networks and optimal network performance and energy efficiency is achieved by jointly optimizing congestion control and power control [13]. 2.2 model analysis the utility maximization problem (1) with utility functions (2) is regarded as the primal problem with primal variables x = (xsp(t), p ∈ p(s)) and y = (ys(t), s ∈ s). then we can obtain the following theorem an optimal rate control and routing scheme for multipath networks 659 theorem 1. the rate allocation model (1) with utility functions (2) is a convex programming problem. the optimal total flow rate of each user, i.e. y∗s , exists and is unique. however, the optimal flow rate of each user on every path, i.e. xsp(t), may be not unique. proof: from the nonlinear programming theory [13], the objective of (1) is concave with respect to primal variables. the constraints of (1) are linear, and thereby all of them are convex. so the feasible set is compact. we deduce that (1) is a convex programming problem. furthermore, the optimization problem is strictly convex with respect to primal variable y = (ys(t), s ∈ s), then the optimal flow rate y = (y∗s, s ∈ s) exists and is unique as a consequence of strict convexity. however, the objective of (1) is not strictly concave with respect to primal variable x = (xsp(t), s ∈ s, p ∈ p), thus the optimal flow rate of each user on every path may be not unique. 2 in order to investigate the optimum of model (1), we firstly give the lagrangian of this nonlinear optimization problem l(x, y; λ, µ) = ∑ s:s∈s  us(ys(t)) + λs   ∑ p:p∈p(s) xsp(t) − ys(t)    +∑ l:l∈l µl  cl − ∑ p:p∈p(l) xsp(t) − ε2l  , (3) where λ = (λs, s ∈ s) is the price vector with element λs ≥ 0, which is the lagrange multiplier associated with the equality on user s, and can be considered as the price per unit bandwidth paid by user s; µ = (µl, l ∈ l) is the price vector with element µl ≥ 0, which is the lagrange multiplier associated with the inequality constraint on link l, and can be considered as the price per unit bandwidth charged by link l; ε2l is the slack variable, which can be considered as the spare capacity of link l. then the lagrangian (3) can be rewritten as l(x, y; λ, µ) = ∑ s:s∈s (us(ys(t)) − λsys(t))+ ∑ s:s∈s ∑ p:p∈p(s) xsp(t)  λs − ∑ l:l∈l(p) µl  +∑ l:l∈l µl ( cl − ε2l ) . (4) notice that the first term in (4) is separable in ys(t), and the second term is separable in xsp(t). thus the objective function of the dual problem is d(λ, µ) = max x,y l(x, y; λ, µ) = ∑ s:s∈s as(λs) + ∑ s:s∈s ∑ p:p∈p(s) bsp(λs, γsp) + ∑ l:l∈l µl ( cl − ε2l ) , (5) where as(λs) = max ys(t) us(ys(t)) − λsys(t), (6) bsp(λs, γsp) = max xsp(t) xsp(t)(λs − γsp), γsp = ∑ l:l∈l(p) µl. (7) then, the optimal flow rate can be denoted as y∗s(λs) = argmax us(ys(t)) − λsys(t), x∗sp(γsp) = argmax xsp(λs)(λs − γsp), where ∑ p:p∈p(s) xsp(λs) = y ∗ s(λs), γsp = ∑ l:l∈l(p) µl. 660 s. li, w. sun, y. zhang, h. zhang hence, the dual problem is min d(λ, µ) over λs ≥ 0, µl ≥ 0, s ∈ s, l ∈ l. (8) we can interpret the sub-problems (6)(7) from an economic point of view as follows. the sub-problem (6) is regarded as the user problem. in this problem, every user s tries to maximize its own utility which depends on the total flow rate ys(t). meanwhile, the user has to pay a price for its using bandwidth. since λs is the price per unit bandwidth paid by user s, then λsys(t) is the total cost that user is willing to pay. thus, the user problem (6) is a nonlinear optimization problem that every user is to maximize its own profit. the sub-problem (7) is regarded as the path problem. in this problem, µl is the price per unit bandwidth charged by link l, then γsp is the total price associated with the path p of user s. the product xsp(t)λs is the cost paid by user s for path p, and xsp(t)γsp is the total cost charged by path p. hence, the path problem (7) is a linear optimization problem that every path is to maximize its own revenue. meanwhile, the dual problem (8) can be considered as the network problem. the objective is to minimize the total price charged by all links under the constraints that users are guaranteed with certain levels of satisfaction. indeed, the price paid by a user and those charged by its paths is an equilibrium from the game theory point of view. for model (1) and its lagrangian (4) we can obtain the following theorem. theorem 2. at the positive optimum of model (1), the optimal total prices associated with paths for a user are all equal to the optimal price paid by the user. proof: indeed, at the optimum of model (1), from the karush-kuhn-tucker condition for optimality of an optimization problem, we can obtain u′s(y∗s) − λ ∗ s ⇒ { = 0, if y∗s > 0, ≤ 0, if y∗s = 0, ∀s ∈ s, λ∗s − γ ∗ sp = λ ∗ s − ∑ l:l∈l(p) µ∗l ⇒ { = 0, if x∗sp > 0, ≤ 0, if x∗sp = 0, ∀s ∈ s, ∀p ∈ p(s). thus, for the multiple paths that a user uses, e.g., p1, p2 ∈ p(s), the optimal total price associated with each path is∑ l:l∈l(p1) µ∗l = ∑ l:l∈l(p2) µ∗l = λ ∗ s = ws(∑ p:p∈p(s) x ∗ sp )αs . (9) 2 3 distributed algorithm 3.1 rate control algorithm to obtain the optimal flow rate in a decentralized architecture, we present the following distributed primal-dual algorithm, which depends on locally available information. each user s updates its flow rate xsp(t) on path p with the following primal algorithm dxsp(t) dt = (κxsp(t)(λs(t) − γsp(t)))+xsp(t) , (10) an optimal rate control and routing scheme for multipath networks 661 λs(t) = ws(∑ p:p∈p(s) xsp(t) )αs , (11) γsp(t) = ∑ l:l∈l(p) µl(t), (12) where κ > 0 is the step size; a = (b)+c means a = b if c > 0 and a = max{0, b} if c = 0. each link l updates its price µl(t) with the following dual algorithm dµl(t) dt = ( υ zl(t) − cl cl )+ µl(t) , (13) zl(t) = ∑ p:p∈p(l) xsp(t), (14) where υ > 0 is the step size. in the primal-dual algorithm, user s computes the price λs(t) paid for path p according to (11), obtains the total price γsp(t) charged by the path from (12), and updates its rate xsp(t) according to (10), which is a fluid model depending on the difference between the price paid by user s and the price charged by path p. meanwhile, link l observes the aggregated rate zl(t) on it, and updates its price µl(t) according to (13), which is identical to the packet loss rate on the link. obviously, the primal-dual algorithm is distributed, which only depends on locally available information. obviously, the rule for rate update (10)(11)(12) is a scaled gradient algorithm for the path sub-problem, and can be reduced to the single-path rate control algorithm when only a single path is available for each user. meanwhile, the rule for price update (13)(14) is also a gradient algorithm, and is similar to the single-path price algorithms designed for solving the dual problems. 3.2 end-to-end implementation in practical end-to-end implementation, window-based flow control where the window size is increased or decreased upon receipt of acks (positive acknowledgments) or nacks (negative acknowledgments) is more convenient to implement than rate-based flow control mechanism since the former is inherently self-clocking. in order to obtain a window flow control scheme, we discretize the system (10)(11)(12) and obtain xsp(t + δ) − xsp(t) δ = κxsp(t)(∑ p:p∈p(s) xsp(t) )αs  wr −   ∑ p:p∈p(s) xsp(t)  αs ∑ l:l∈l(p) µl(t)   . (15) here, we assume xsp(t) > 0. let wsp(t) be the window size of user s on its path p at time t. we follow the approximation relating data transmission rate and window size [14] xsp(t) ≈ wsp(t) rttp , where rttp is the round trip time of path p. 662 s. li, w. sun, y. zhang, h. zhang let ap(t, t + δ) and np(t, t + δ) denote the numbers of acks and nacks received by user s on path p in the time interval [t, t + δ), respectively. then ap(t, t + δ) δ ≈ xsp(t) ≈ wsp(t) rttp , and np(t, t + δ) δ ≈ xsp(t) ∑ l:l∈l(p) µl(t). thus, xsp(t + δ) − xsp(t) δ = wsp(t + δ) − wsp(t) ap(t, t + δ) ap(t, t + δ) rttpδ = wsp(t + δ) − wsp(t) ap(t, t + δ) wsp(t) rtt 2p . using the approximations above, the window-based flow control mechanism becomes wsp(t + δ) − wsp(t) = κwsrttp ( ∑ p:p∈p(s) wsp(t)/rttp) αs ap(t, t + δ) − κrttpnp(t, t + δ). (16) in order to damp the oscillation when window size is too small, we modify (16) into wsp(t + δ) − wsp(t) = κwsrttpap(t, t + δ) − κrttp   ∑ p:p∈p(s) wsp(t) rttp  αs np(t, t + δ). (17) the window-based flow control mechanism (17) can be interpreted as follows: when each ack is received, the window size is increased by a fixed amount which is in proportion to rttp; when each nack is received, the window size is decreased by a fixed amount which is in proportion to rttp( ∑ p:p∈p(s) wsp(t)/rttp) αs . hence, the window flow control scheme realizes the adaptive increase/multiplicative decrease (aimd) principle which is used in the original tcp version and other variants. for the total price associated with each path in practical end-to-end implementation, we present a way to communicate path prices to users implicitly, i.e., a user deduces the aggregated price on each path from observed round trip time. notice that queuing length bl(t) evolves according to dbl(t) dt = (zl(t) − cl)+. (18) comparing (18) with (13), we can observe that the link price µl(t) at time t is proportional to the current queuing delay ql(t), i.e., µl(t) = υ bl(t) cl = υql(t). then the price on path p is proportional to the end-to-end queuing delay at time t γsp(t) = ∑ l:l∈l(p) µl(t) = υ ∑ l:l∈l(p) ql(t). given the end-to-end propagation delay dp, the price associated with path p can be deduced from the round trip time rttp observed at the user s γsp(t) = υ ∑ l:l∈l(p) ql(t) = υ(rttp − dp). in practical implementation, dp can be estimated by the minimum rttp observed so far. an optimal rate control and routing scheme for multipath networks 663 4 simulation in this section, we investigate the performance of the proposed primal-dual algorithm and give simulation results to illustrate the convergence of our algorithm. we make no assumption that the paths for a user are link-disjointed and consider the general multipath communication where some of the paths for a user can share some path segments in common, just as shown in figure 1. there are two users in this simple network. two paths are available for each user, i.e., paths a→b→c and d→e→f→g for user 1 (pair of s1 and d1), and paths d→e→f→g and h→i→f→g for user 2 (pair of s2 and d2). obviously, the two paths for user 2 share a common link which is labeled by l5. there are seven unidirectional links with capacities c = (c1, c2, c3, c4, c5, c6, c7) = (3, 2, 3, 4, 4, 2, 3)mbps. in the proposed algorithm, we choose w = (w1, w2) = (2, 3), and κ = υ = 0.05. a d e i gs2 h bs1 d1 d2f l1 c l2 l3 l4 l5 l6 l7 figure 1: multipath network topology 4.1 proportional fairness we firstly consider the proportional fairness among competing users, thus the fairness parameter is α1 = α2 = 1. without loss of generality, assume the initial rate of each user on every available path is 1mbps. the optimum obtained by the proposed algorithm is listed in table 1. the optimal solution solved by nonlinear programming software lingo is also presented in the table. variable x∗11 x ∗ 12 x ∗ 21 x ∗ 22 y ∗ 1 y ∗ 2 algorithm 2.0000 0.4000 1.6440 1.9560 2.4000 3.6000 lingo 2.0000 0.4000 1.7360 1.8640 2.4000 3.6000 table 1. the optimum under multipath network topology: proportional fairness obviously, the algorithm is convergent and efficient to solve the optimum of multipath network utility maximization problem under the general multipath network topology. also as shown in table 1, the optimal rate of user 2 on its paths is not unique, which has been proved in theorem 1. however, the total optimal rate for each user is unique since the optimization problem is strictly convex with respect to primal variable y = (ys(t), s ∈ s). the simulation results for proportional fairness in this general multipath network topology are shown in figure 2, where (a) and (b) are the optimal prices for users 1 and 2, respectively, and (c) and (d) are the optimal rates of users 1 and 2, respectively. obviously, both the prices for users and rates of users converge to the optimum. it can also be observed that at the optimum the optimal total price associated with each path that a user uses is equal to the optimal price paid by the user. 664 s. li, w. sun, y. zhang, h. zhang 0 100 200 300 400 500 0 0.5 1 1.5 iterations p ri ce s γ 11 γ 12 λ 1 (a)prices for user 1 0 100 200 300 400 500 0 0.5 1 1.5 iterations p ri ce s γ 21 γ 22 λ 2 (b)prices for user 2 0 100 200 300 400 500 0 1 2 3 4 5 iterations r at es x 11 x 12 y 1 (c)rates of user 1 0 100 200 300 400 500 1 2 3 4 5 iterations r at es x 21 x 22 y 2 (d)rates of user 2 figure 2: performance of the algorithm: proportional fairness 4.2 harmonic mean fairness now we consider the harmonic mean fairness among competing users, thus the fairness parameter is α1 = α2 = 2. the optimums obtained by the proposed algorithm and lingo are both listed in table 2. obviously, the algorithm is convergent and efficient to solve the optimum of utility maximization problem. also, the optimal rate of user 2 on its paths is not unique, however, the total optimal rate for each user is unique since the optimization problem is strictly convex with respect to y = (ys(t), s ∈ s). variable x∗11 x ∗ 12 x ∗ 21 x ∗ 22 y ∗ 1 y ∗ 2 algorithm 2.0000 0.6969 1.5748 1.7283 2.6969 3.3031 lingo 2.0000 0.6969 1.6070 1.6961 2.6969 3.3031 table 2. the optimum under multipath network topology: harmonic mean fairness the simulation results for harmonic mean fairness in this general multipath network topology are shown in figure 3, where (a) and (b) are the optimal prices for users 1 and 2, respectively, and (c) and (d) are the optimal rates of users 1 and 2, respectively. obviously, the algorithm is convergent to the optimum of multipath network utility maximization problem. meanwhile, at the optimum the optimal total price associated with each path of a user is equal to the optimal price paid by the user. an optimal rate control and routing scheme for multipath networks 665 0 200 400 600 800 0 0.1 0.2 0.3 0.4 0.5 iterations p ri ce s γ 11 γ 12 λ 1 (a)prices for user 1 0 200 400 600 800 0 0.1 0.2 0.3 0.4 0.5 iterations p ri ce s γ 21 γ 22 λ 2 (b)prices for user 2 0 200 400 600 800 0 0.5 1 1.5 2 2.5 3 3.5 iterations r at es x 11 x 12 y 1 (c)rates of user 1 0 200 400 600 800 1 1.5 2 2.5 3 3.5 iterations r at es x 21 x 22 y 2 (d)rates of user 2 figure 3: performance of the algorithm: harmonic mean fairness 5 conclusions in this paper, we have studied the optimal rate control and routing schemes in communication networks with multipath routing, which are formulated as multipath network utility maximization problems. we decompose the utility optimization problem into two sub-problems, deduce the dual problem through the lagrangian method, and give the interpretation for them from an economic point of view. in order to obtain the optimum, we propose a novel primal-dual algorithm for jointly optimizing rate control and routing, and give the window-based flow control scheme in practical end-to-end implementation. we don’t assume that whether the paths of a user are node-disjoint, thus the algorithm also covers the case where the paths for a user are node-disjoint in parallel, which is regarded as concurrent multipath communication. then, we evaluate the performance of the proposed algorithm through simulations under two different fairness concepts. simulation results confirm that the algorithm can achieve the optimum within reasonable convergence times. acknowledgements the authors would like to thank the support from the national natural science foundation of china under grants 71101124, 71171174 and 60974018, and the national basic research program of china (973 program) under grant 2007cb307100. 666 s. li, w. sun, y. zhang, h. zhang bibliography [1] f. p. kelly, a. maulloo, d. tan, rate control in communication networks: shadow prices, proportional fairness and stability, journal of the operational research society, 49(3): 237252, 1998. 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[18] t. v. lakshman, u. madhow, the performance of tcp/ip for networks with high bandwidth-delay products and random loss, ieee/acm transactions on networking, 5(3): 336-350, 1997. international journal of computers communications & control issn 1841-9836, 13(6), 915-926, december 2018. defense scheme to protect iot from cyber attacks using ai principles t. ahamed tariq ahamed ahanger* college of computer engineering & sciences, prince sattam bin abdulaziz university, ksa *corresponding author: t.ahanger@psau.edu.sa abstract: even in its infancy, the internet of things (iot) has enticed most of the modern industrial areas like smart cities, automobiles, medical technology. since iot connects everything together, it is vulnerable to a variety of devastating intrusion attacks. being the internet of different devices makes it easy for attackers to launch their attacks. thus, to combat all these attacks, an attack analysis is presented in this article using the basic principles of artificial neural networks. internet packet traces are used to train to the supervised ann (multilevel perceptron) and evaluated after the training to decline the ddos attacks. this research article mainly focuses on the categorization of traffic patterns into legitimate traffic and attack traffic patterns in iot network. the ann processes are evaluated and tested in a simulated iot network. the experimental results show a greater accuracy in detection of various ddos attacks. keywords: ann, iot, ddos, security, ids, ai. 1 introduction in the current world more objects are associated with the internet than the people. and this difference will pursue to grow further, as the objects increases in their capability to directly interact with the internet or evolves into the physical objects of data which can be accessible through the internet systems. this scenario approaching towards greater independence in object interaction with the internet is generally interpreted as the (iot) internet of things [4,26]. the greater number of devices which avails the internet services is growing faster and to have them connected every time by wire or otherwise will establish a mighty source of data information at anyone’s finger tips [17]. to enable communication between smart machines is an advanced technology, but the technologies which compose the iot are not fresh for us. iot, as understood by its name, is the method of converting data to any virtual-platform on current internet infra, which is retrieved from diverse class of things [3]. the main concept of iot is to grant independent exchange of important information between unseen embedded distinctly identifiable devices in the real world around us, overwhelmed by the dominant technologies like rfid (radio-frequency identification) and wsns (wireless sensor networks) sensed by the sensor nodes and processed further for decision building [1], in order to perform an automated action. 2 security threats the complexity in the nature of iot security rotates around the reality that, since, it is a great challenge to combine several technologies into one; the system tries to connect devices securely which have limited computation capability, storage, and power [2]. few of the devices utilized by iot can hold only a little basic mechanism of security measures, some of which are not capable to maintain the confidentiality and integrity of the users’ information data [19]. furthermore, the devices-for example, rfids, and sensors has inadequate user interfaces, such as an on-off copyright ©2018 cc by-nc 916 t. ahamed button or status signal, hence represents a limited psychological visual for the owners when it approaches towards trusting these type of devices [13]. presently, privacy is considered as a major concern; which slows down the advancement of many upcoming technologies. moreover, it has been proved that the technology which doesn’t provide enough trust and exposes the individual’s identity diminishes [7]; in the recent past, many new technologies failed to maintain adequate privacy and security mechanisms, which causes pain and tremendous sufferings to the distressed. for the sake of gaining trust of a common man in the iot, the technologists need to make sure the same kind of failures with regard to security and privacy do not appear in the system, by safeguarding the required mechanisms to ensure such things existed from the beginning. 3 sources of threats subsequently, discussing the iot features and how they can be used in several scenarios, it is now time to discuss and to identify the potential threats confronting the communication mechanism in iot. there are three primary entities which poses threats to the privacy and security in iot: (i) malicious user: it is the possessor of the iot-device with power to carry out attacks to acquire the secrets of the device manufacturer, and also to acquire access to secret functionality. the malicious user reveals the shortcomings in the system to retrieve information, selling secrets to 3 rd parties, even launches an attack on the systems [12]. (ii) bad manufacturer: it is the builder of the device with the capability to explore the underlying technology to retrieve the information of the iot devices, or users. aforementioned, a manufacturer can intendedly introduce holes in the security design which can be exploited subsequently to access the user’s secret data and also revealing it to 3rd parties. similarly, the manufacturing of badly secured things results in the compromise of their users’ privacy. adding to that, in the context of iot where different devices connect one another, a device manufacturer just to harm the reputation of their competitors; can attack their devices [6]. (iii) external adversary: it is an external entity that is not considered as a member of the system and has no authorization to access it. an adversary aims to gather the information about the users’ of the system with the malicious intentions for example, creating financial losses and subverting the user’s reliability. it also, causes flaw in the system by maneuvering the data sensed and transmitted [11]. 4 classes of attacks to understand the risks attached, classification of attacks is essential to every system. we opted to focus on many categories to determine threats. • device tampering: as we know iot devices are smaller devices which are integrated in many other systems, for example; light, switches, tvs, cars, ovens, and many more. few of the iot devices remains unattended most of the time, therefore, they could be stolen easily irrespective being noticed by anyone. if a device got into the wrong hands, several types of attacks can be executed like software manipulation, hardware tampering, and secret stealing [20]. it’s essential to indicate that an attacker could tamper with the vulnerable device and utilize it to inject fake data into the system, utilize the device for attacking purpose or deviate it to its expected functionality. defense scheme to protect iot from cyber attacks using ai principles 917 • information disclosure: it’s an act of disclosing the information to an object which lacks the permission to access it. this comprises targeted attack, accidental exposure, and correlation or inference [18]. an adversary can retrieve information from the network links by eavesdropping, physically accessing the device. • privacy breach: contradictory to information disclosure, an attacker doesn’t essentially require access to the secret information of the user to learn about him. the attacker could ascertain confidential information from several other sources like traffic analysis and meta data [5]. • denial-of-service: dos is associated to the characteristic of being not accessible when an authorized user requests. the underlying system should be capable enough to continue operating even though some unintended action is being carried out by some adversary. dos attacks can be carried out by device stealing, disrupting the communication links, manipulating the software [14]. • spoofing: it is used to steal the credentials which belong to others so as to gain access to not accessible services. these credentials could be retrieved form a device directly, eavesdropping a channel of communication, or phishing [15]. 5 intrusion detection system any kind of unapproved or unauthorized activities in a network or a system are called intrusions. an ids (intrusion detection system) is a group of the tools, mechanism, resource to identify, assessment which describes intrusions [8]. intrusion detection is usually a part of a comprehensive protection system which is installed in a device or around a system and it can’t be taken as a stand-alone measure of protection. intrusion can be defined as: "any type of activities which tries to manipulate the truth, secrecy, or the resource availability" [9]. in the vice-versa intrusion prevention techniques are referred to as the first line of defense against these intrusions. nevertheless, as in any type of security system, total prevention of intrusion is impossible. the node compromise and intrusion heads to secret information like security keys being disclosed to the intruders in the system, which results collapse of the security mechanism [10]. consequently, idss are developed and designed to make intrusions public, before disclosing the system resources which are secured. idss are consistently acknowledged as a second line of defense from the view point of security. idss are considered as web equivalent of the robbers’ alarms that are being utilized in physical security systems currently [16]. the normal functional requirement of idss are; "high true positive rate, measured as the percentage of anomalies detected, and low false positive rate, measured as the percentage of normalcy deviations detected as anomalies". according to architecture, ids can be divided into two groups: network based ids: theids which is network based is responsible for the protection of the whole network environment from any type of intrusions. this type of ids architecture requires comprehensive knowledge of the system status and also monitors the different components of the network as well as the transactions which carry out between them [11]. a network ids watches the actions on an entire network and carries the traffic analysis for possible security breaches or threats. agent technology performs a primary role in this type of ids architecture. host based ids: the ids which is host based watches all the ongoing activities on an individual information system host. it makes sure that not a single security policy of information 918 t. ahamed system is being disrupted [12]. the host based ids are installed only on a single terminal/host and are given the responsibility of observing the status of that specific host(or server) only. 6 ids patterns the primary purpose of developing an intrusion detection system is to detect and identify the potential threats efficiently. there ids system can be classified into two categories: misuse detection: or signature-based detection. the behavior of the system is compared with previously known types of attack patterns (or signatures). those action patterns which may present a security threat should be described and stored in the system. afterwards, the misuse detection technique attempts to recognize any type of bad behavior with respect to these stored patterns in the system [18]. anomaly detection: targets on normal behaviors, instead of attack behaviors. firstly, these types of systems define what comprises a normal behavior which is normally carried out by an automated training and then intrusion activities are flagged that differ from this normal behavior by a specified threshold [22]. 7 intrusion detection techniques there is a diverse class of ids methods which are established on different formats, compositions and schemes. following are the description of those frequently used techniques: • evolutionary algorithm: the algo produces an application path that contributes standard operational models. this algorithm is designed to detect standard operational behavior, attempted intrusion, and error state by categorizing archetype which depends upon nonidentical conditions [13]. • rule based: this technique compares data against signatures with state transition analysis. every data packet is practiced to fsm (finite state machine) and follows transitions till the ultimate state reaches, resulted in the detection an attack [8,10]. • statistical analysis: this technique involves the comparison of present set of data pattern with the predefined set of basic criteria. the normal data behavior is compared with the deviations over a temporal period. in anomaly detection system this technique is used [21]. • protocol verification: this technique is based upon extensively checking the protocol behavior and their fields in comparison with the predefined set of standards. the data which violates the defined standards is considered to be malicious. this approach has good success rate in lucrative systems but the flaws are producing erroneous positives for undefined protocols [10]. defense scheme to protect iot from cyber attacks using ai principles 919 8 artificial neural network (ann) ann neurons are utilized to model complicated hypotheses. complexity of the hypotheses depends upon the number of neurons involved. the hypotheses evaluation is carried out by the setup of input nodes in the feedback process and the propagation of event data across the network towards output nodes where it is categorized as legitimate or suspicious [3]. gradient descents are used at this stage to thrust the error back from the output node across to the network using bpa(back propagation algorithm) to scan the hidden nodes for error measurement. thus the cost of function in terms of gradient descent can be calculated. to assimilate the structure and sequence developed in the system, neural network systems go through the training phase. a typical structure of an ann neuron is shown in fig. 1. figure 1: ann neuron 9 ann learning process two types of learning procedures for ann’s are as follows: • unsupervised learning procedure: the ann in this learning has an input g = {ai}ni=1 i.e., unlabeled data set; need to search certain patterns in that data. som is a category of artificial neural network which is trained by unsupervised learning method to generate less dimensional, discrete representation input space of samples of training called map. • supervised learning procedure: in this type of learning, the ann is presented with labelled set of training data which learns the mapping from data inputs a to outputs b, provided a labelled input-output sets of pairs, g = {(ai,bi)}ni=1 where g is called the training set and n is the total number of training samples. it is presumed that bi is a group variable from an infinite set bi ∈{1, . . . ,x} 920 t. ahamed the mlp (multi-layer perceptron) is a category of artificial neural network trained by supervised learning procedure. the mlp is utilized for the detection of intrusions which were established on an offline analysis approach. also, in a diverse outlook, it was used to detect intrusion in a data on a network by comparing it with the som self-organizing maps. 10 mlp architecture a three layer mlp architecture with feed-forward ann is shown in fig. 2. the network holds a function of unipolar transfer sigmoid in every output and hidden layer neurons. also, an algorithm with stochastic learning and a function of mean square error is utilized. the input nodes which represent neural network with labels x1, . . . ,x3 have been implemented, and +1 as bias units. figure 2: feed-forward neural network the ann architecture has total of three input units (l1), three hidden units (l2) and finally one output (l3) composed of the three layers of neural network. feed forward learning algorithm: the feed forward algorithm is the simplest form of devised artificial neural network. the information moves in only forward direction, which starts from the input layer, the hidden layer and finally to the output layer respectively. the algorithm states as follows; let vl is the number of total elements which excludes the bias elements. hence, the network parameters are {w,b} = {w1,b1,w2,b2} where w lrij represents the combined parameter connected with the element j in layer lr and element i in lr + 1. likewise, blri is the bias connected with i unit in the layer lr + 1. therefore above mentioned elements and functions reflect w1 ∈ o3∗3 and also, w2 ∈ o3∗1. suppose, a1i represent the output element in layer lr. for lr = 1, we just supposed that a1i = xi represent the ith input. thus, the ann model will derive hypotheses h(w,b)(x) which give a real number as output. thus we can represent defense scheme to protect iot from cyber attacks using ai principles 921 the above model in a mathematical form as: a (2) 1 = f{w 1 11x1 + w 1 12x2 + w 1 13x3 + b 1 1} (1) a (2) 2 = f{w 1 21x1 + w 1 22x2 + w 1 23x3 + b 1 2} (2) a (2) 3 = f{w 1 31x1 + w 1 32x2 + w 1 33x3 + b 1 3} (3) h(w,b)(x) = f{w 2 11a (2) 1 + w 2 12a (2) 2 + w 2 13a (2) 3 + b 2 1} (4) eq. 4, weighted total sum of the inputs from i in lr is the feedforward algorithm. backward learning algorithm: the process of learning in involves 4 stages, which are as follows: • for all the layers in the neural network, the feed forward algorithm calculates the activation. • the output from layer l3 computes the measure of error in the output: ∆ (l3) i = ∆ ∆ (l3) y 1 2 ‖z ·h(w,b)(x)‖ 2 = −{zi −a (l3) i } ·f ( y (l3) i ) (5) here, a(3)i = f ( y (l3) i ) is the sigmoid function. • to calculate the errors in lr = 2, lr = 3 for every node i in layer lr: ∆ (lr) i ( vt+1∑ k=1 w (lr) ki ∆ (l+1) k ) f ′ ( y (l3) i ) (6) • lastly, the intended partial derivatives are computed as: ∆ (lr) i ∆w (l) kj m(w,b; z,x) = a (l) j ∆ (l+1) k ∆ ∆b (l) k m(w,b; z,x) = ∆ (l+1) k 11 proposed system and evaluation we have used eight node sensors to compose an iot network. seven client nodes and one server relay node for data analytical purpose. network tap is used to capture traffic to avoid any kind of hindrance or change in the live traffic. the sensor node sends data towards server node and server node acknowledges by sending receive data reply that is based on data itself. this process enables the sensor nodes as shown in fig. 3, to acclimatize their behavior and respond to the ongoing phenomenon. in our research setup, we have an external intruder who attacks the iot network as shown in fig. 4. server node is the only target of target of attackers because the server node analyzes, keeps the record and responds to the sensor nodes. the attacker launches the dos attack by 922 t. ahamed figure 3: normal condition sending 107 packets towards the target node from a single host and the same amount of attack traffic was launched from four hosts to launch ddos towards the target node. we used a custom c script to craft udp packets to use them as attack packets. therefore, the server node comes to a halt and do not respond. the sensor nodes are not able to accommodate their changed behavior and finally cause the halt in the monitored system. therefore the detection of these attacks at the right time is very important to allow the uninterrupted working of sensor network and assure the reliability of the network. table 1: parameters for training. no. of sample patters used for training 2575 no. of sample patters used for validation 499 no. of sample patters used for test 499 table 2: amount of pattern traffic used for categorization. traffic size % ddos 2294 63.98 dos 2294 63.98 normal 1279 36.02 in this section we will evaluate the performance of designed artificial neural network intrusion detection method as discussed in the previous section. we trained the network with the defense scheme to protect iot from cyber attacks using ai principles 923 figure 4: attack situation following parameters as shown in table 1 and the amount of sample patterns used for categorization is shown in table 2. neural network confusion matrix is shown in fig. 5, is used to plot • training set • testing set • validation set • the final all confusion matrix the output of the system is of two types: 1. true positive 2. false positive the attack traffic that is correctly categorized is measured as true positive as shown in green block and the correctly categorized normal traffic is measured as false positive as shown in red block. after all the evaluation and tests as shown in 4th final confusion matrix in fig. 5, our proposed system showed the detection accuracy greater than 99% in the categorization of network traffic. our method proved that the artificial neural network algorithm used is capable of detecting ddos and dos attack traffic during the flow of genuine iot network traffic. it also improves the stability and reliability of the iot network by signaling the response system at the right time to avoid the network disruptions, thus improving and enhancing the performance of the iot network. 924 t. ahamed figure 5: confusion matrix 12 conclusion a ddos detection method is presented in this research article using ann for iot network. the detection method is mainly based on categorization of legitimate traffic patterns and attack traffic patterns. the proposed system is simulated and tested in an organized simulated iot network and the obtained results proved more than 99% detection accuracy. the system successfully identified the attack traffic and performed well in true and false negative accuracy. in the future, the system will be trained with latest threat patterns and will be tested to check its reliability with the modern world technology. acknowledgement this research was funded and conducted at prince sattam bin abdulaziz university, alkharj, saudi arabia during the academic year 2017–2018 under research number 2017/01/8029. bibliography [1] ahamad, t. 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(2016). internet of things service provisioning platform for cross-application cooperation, international journal of web services research (ijwsr), 13(1), 1–22, 2016. international journal of computers communications & control issn 1841-9836, 12(1):61-75, february 2017. big data on decision making in energetic management of copper mining c. lagos, r. carrasco, g. fuertes, s. gutiérrez, i. soto, m. vargas carolina lagos* facultad de administración y economía, universidad de santiago de chile av. libertador bernardo o’higgins 3363, santiago, chile *corresponding author: carolina.lagos@usach.cl raúl carrasco 1. departamento de ingeniería eléctrica, universidad de santiago de chile av. ecuador 3519, santiago, chile 2. departamento de matemáticas y ciencias de la computación, universidad de santiago de chile las sophoras 173, santiago, chile raul.carrasco.a@usach.cl guillermo fuertes departamento de ingeniería industrial, universidad de santiago de chile av. ecuador 3769, santiago, chile guillermo.fuertes@usach.cl sabastián gutiérrez 1. facultad de ciencias económicas y administrativas, universidad central de chile lord cochrane 417, santiago, chile 2. facultad de ingeniería, universidad andres bello antonio varas 840, providencia, santiago, chile sebastian.gutierrez@ucentral.cl ismael soto departamento de ingeniería eléctrica, universidad de santiago de chile av. ecuador 3519, santiago, chile ismael.soto@usach.cl manuel vargas facultad de ingenieria, universidad andres bello, santiago, chile manuel.vargas@unab.cl abstract: it is proposed an analysis of the related variables with the energetic consumption in the process of concentrate of copper; specifically ball mills and sag. the methodology considers the analysis of great volumes of data, which allows to identify the variables of interest (tonnage, temperature and power) to reach to an improvement plan in the energetic efficiency. the correct processing of the great volumen of data, previous imputation to the null data, not informed and out of range, coming from the milling process of copper, a decision support systems integrated, it allows to obtain clear and on line information for the decision making. as results it is establish that exist correlation between the energetic consumption of the ball and sag mills, regarding the east, west temperature and winding. nevertheless, it is not observed correlation between the energetic consumption of the ball mills and the sag mills, regarding to the tonnages of feed of sag mill. in consequence, from the experimental design, a similarity of behavior between two groups of different mills was determined in lines process . in addition, it was determined that there is a difference in energy consumption between the mills of the same group. this approach modifies the method presented in [1].a keywords: copper mining, energetic efficiency, big data, process management. areprinted (partial) and extended, with permission based on license number 3962080057504 © [2016] ieee, from "computers communications and control (icccc), 2016 6th international conference on". 1 introduction market conditions, global competence and care of environment have created the urgent need to improve the energetic efficiency. so, efficient management of energetic resources for big industrial customers shaping up a critical aspect. it is important for industries to economize in the copyright © 2006-2017 by ccc publications 62 c. lagos, r. carrasco, g. fuertes, s. gutiérrez, i. soto, m. vargas use of energy and make it sustainable in long terms. it has emerged a significant market and a technological opportunity for the tools and technologies that allow an efficient management of energetic resources. on the other hand, the quality and velocity of information interchange, requires a cooperation commitment among all the participants in the collecting process. among the reasons for a nonefficient flux of information are: (1) unfulfilling relations among the different levels that receive and process the information, (2) lack of preventive maintenance programs in the measurement equipment and (3) intrinsic factors of the process. data science and its predictive analysis its getting more and more important for the production of goods and services [2], with applications from data monitoring for the energetic consumption [3], system for the prediction of maintenance [4,5]. decision support systems (dss) can represent a solution, when human intervention is necessary [6–8], by using of techniques in the improvement of safety [9], and also, in the prediction for the analysis of natural resources consumption [10] and in models of optimization of processes [11]. this document discusses techniques and tools by consumption of energy data in mining, and the possible contribution that can do in the data analysis, for the decision making in the energetic efficiency. filip (2008) concludes that “the development and application of intelligent dss can help enterprise cope with problems and uncertainty and complexity, to increase efficiency and competitivity in production networks” [6]. the optimization and the control of the minerals processing plant, such as copper, has generate new research lines. with the increasing availability of data basis coming from the copper productive process, the amount of data gathered at an exponential rhythm. this creates and opportunity for the mining companies to analyse and optimized their operations observing those data. then as productive operations become bigger, with more variables, it is crucial for the direction and the area managers, to exploit the information in an efficient and timely way. to make this happen, it is important the synchronization among all the areas, through joint planning of all the areas participating in the data collection, and shared information [12]. other related works related with copper mining can be found in reference [13–15]. a previous preliminary work can be found in reference [1], that data analysis methods related to energetic consumption in copper mining. the main contribution of this paper is the generation of a systematic model for the analyses of the data and the use of statistical tools, allowing improvement of the energetic efficiency. this organization of the paper is: section 2, describes the problem of the sales of the mineral, knowing the energetic costs of the process and the evolution on time of the sales; section 3, details the methodology and the different focuses of big data; section 4, shows the design and analysis of results of the processing of big data, coming from the concentration process of copper, for the energetic efficiency and final conclusions of the research. 2 description of the problem with the purposes of improving the decisión making, data mining can be considered a tool of quantitative analysis, that still needs more exploration for the energetic management. in this sense, data mining and knowledge discovery in data basis have been attracting a significant amount of research [16], particularly to be used in the mining processes for its high data level [17]. the energetic costs hav been a relevant factor in the structure of costs of the mining processes.it is so, that the energy expenditure (electricity and fuel) it is the order of 15% of the operational cost (2008-2011), which increase affected negatively the operational margins, since a maximun of 65% in 2006, up to a 22% in 2014 [18]. so for a better understanding of the costs of big data on decision making in energetic management of copper mining 63 figure 1: costs and unit sales in the copper mining in chile between 2005 and 2014 the process, it is required to study more deeply the behavior of the management of the energetic efficiency, with the intention of founding the optimun energetic consume [19]. on the other hand, analyzing the unit costs of exploitation (dollar cents per pund of payable copper), it is observed that costs have increased, but not in the same direction than sales, which is appreciated comparing costs and unit sales in figure 1. besides the same graphic shows the decrease to a little more than the half of the average operational process, from 210 cus$/lb for the year 2010 up to 102 cus$/lb in 2014 [18]. the previous graphic, also accounts for the decrease in the operational margins that have experienced the mining companies in the last years, due to the increase in costs and less copper price. 3 problem formulation mining production companies as codelco, have been collecting and storing increasingly more data of their productive processes, which offer an enormous potential as source of new knowledge [20], given the complexity of the data and the mining process, this document is used to data analysis [21, 22]. the data mining for the analysis of processes efforts to establish correlations between the key indicators a data mining for analysis of process strives to establish correlations between indicators of performance and the consume in the process. if the correlations can establish with the enough precision, it is possible to predict the behavior of energy, given the current consumption and the attributes of the process. in this study, were used several techniques: 1. markov chain monte carlo this paper uses the markov chain monte carlo (mcmc) algorithms for imputing data, are a widely used and well-studied methodology that can be used to draw samples from posterior distributions [23]. the sampling is efficient for estimating parameters [24]. 2. canonical correlation analysis 64 c. lagos, r. carrasco, g. fuertes, s. gutiérrez, i. soto, m. vargas canonical correlation analysis (cca) is a known multivariate analysis method, the objective is to find and quantify the correlations between two sets of multidimensional variables [25]. in this study the cca, it is used as a right tool to establish the correlation between the energetic consumption as a dependant variable, and the independant variables of the ball mills and sag mill. cca can be used recursively depending on the amount of dependant and independant variables that can be related, forming possible variables and canonical correlations. [26,27], uses cca in industrial processes to perform the troubleshooting. on the other hand [28], proposes a model of a recursive and adaptative process, to improve the precision of variables of the process in time. 3. design and analysis of experiments the design and analysis of experiments are classical statistic models whose objective is to find out if some determined factors influence in a variable of interest, if exists influence of any factor and quantify the effect it has on it. the use of the design and analysis of experiments allow us to measure the effect of the mil as a factor on the energetic consume that is the variable of interest of this work, achieving quantify the effect it has the mil factor on the energetic consumption [29]. in this sense it is used in different problems of energy [23,30,31]. 4 design and analysis of results the analysis of the unit of the article is the production process of copper in the biggest state company of chile, in the division codelco chuquicamata. the data analysis, is done through a three phases procedure: imputation of data for the missing information; study to correlate between the variables that influence in the energetic consumption through a multivariable model of canonical correlation; and a comparison between means of consumption of the mills to verify potential significant differences between them. 4.1 variables of study the study is performed on six mills that operate in the process of the concentrator: mill of ball 16a (mobo 16a); mill of ball 16b (mobo 16b); mill of ball 17a (mobo 17a); mill of ball 17b (mobo 17b), mill sag 16 (sag 16) and mill sag 17 (sag 17), as shown in figure 2. the defined variables for the analysis of the energetic consumption are divided in two groups. in the first one, defined as mills mobo 16a, mobo 16b and sag 16 and the second group is defined as: mobo 17a y mobo 17b and sag 17, for both cases, the variable of study are: consumed energy, west temperature, east temperature, winding temperature and the tonnage, according with the defined variables with the maintenance management of codelco chuquicamata. 4.2 previous analysis in fig. 3 it is presented the potential of, mobo 16b. it is observed periods without information and the presence of atypical data in the last three years, which should affect the analysis to look for solutions to the proposed problem. big data on decision making in energetic management of copper mining 65 figure 2: measure variables in the sag mill and the mills of ball mobo 16a, mobo 16b, in the concentrate process figure 3: power of mobo 16b as example in fig. 4 it is shown the values of the power variable of the mobo 16b. in this graphic are appreciated the outlier values that will be correct thanks to the mcmc procedure. fig. 5 shows the dispersal among variables (power, tonnage,east temperature,west temperatura and winding temperature), at all times, it is observed that data do not distribute normally since the kernel distribution does not adjust to the normal distribution. 66 c. lagos, r. carrasco, g. fuertes, s. gutiérrez, i. soto, m. vargas figure 4: distribution of the power variable in mobo 16b figure 5: plot of the correlations of the variables of the mobo 16b according to table 1, a 10.77% of observations posses at least one missing data, so, the multiple imputation is used to recovered the lost information and correct the atypical data. big data on decision making in energetic management of copper mining 67 table 1: group of mobo 16b data, existing and non existing group east t. west t. wind t. power frequency percentage 1 x x x x 1905 89.23% 2 x x x . 60 2.81% 3 x x . x 1 0.05% 4 x . x x 1 0.05% 5 x . . x 3 0.14% 6 . x x x 1 0.05% 7 . x . x 1 0.05% 8 . . . x 1 0.05% 9 . . . . 162 7.59% 4.3 analysis of imputation in the case of the previous analysis and of imputation, it is presented the case of the mill of the mobo 16b, while in the canonical correlation, it is worked with the group of mills of sector 16 (of ball and sag); and for the experimental design. for the analysis it is used the software sas v9.3. though it does not exist all the information, and being a great number of atypical data in the period of three years, it is proceed to the imputation, not only for the problem of the missing data, but also “to correct" these atypical data (a 14% of the observations that have at least one missing data).for the imputation it must be deliver, before being used, initial values for the mean vector and the variance and covariance matrix to be able to estimate the coefficients. the matrixes are obtained from the bootstrap procedure, as it is shown shown on table 2: table 2: descriptive statistical of the variables in study variable eastt. westt. devt. power average 47.076154 35.790675 73.20456 3395.297468 likewise, it is presented the existing correlation between the independant variables or explanatory disposed in a double entry matrix, as it is shown on table 3: table 3: matrix of correlation variable eastt. westt. devt. power eastt. 102.449985 19.919678 41.713204 3271.145603 westt. 19.919678 81.730596 51.081713 2880.435635 devt. 41.713204 51.081713 236.074203 9127.414414 power 3271.145603 2880.435635 9127.414414 570276 while the procedure is repeated, new mean, variance and covariance matrixes are generated, to estimate the coefficients that constitute the process, this is repeated until the difference between parameters of an iteration with the next one being almost null. 4.4 test hypothesis below are shown on table 4, the coefficients obtained by the mcmc method, with its corresponding confidence intervals. 68 c. lagos, r. carrasco, g. fuertes, s. gutiérrez, i. soto, m. vargas table 4: test statistical variable coefficient est. error confidence interval (5%) value t p−value eastt. 47.104421 0.22795 [46.657; 47.552] 206.64 < 0.0001 westt. 35.764552 0.204824 [35.362; 36.167] 174.61 < 0.0001 devt. 73.163543 0.346796 [72.482;73.482] 210.97 < 0.0001 power 3394.130311 16.935236 [3360.873; 3427.873] 200.42 < 0.0001 it can be observed that each of these values is significant, what can be verified with the hypothesis test h0 : βi = 0 vs h1 : βi = 0, and the test statistical t−student, which decision criteria is to reject the hypothesis if p−value < 0.05, or if the coefficient value is within the confidence interval. in this case both are true, thus it demonstrate that this procedure is quite efficient and reliable to generate these new data basis. 4.5 application of the canonical correlation the use of the canonical correlation, allows not only to search a relation from variable to variable, but also the, between one of them and a set of them, as well as between two complete groups. below it is shown the equation of correlation for the mills 16 (ball a and b, and sag), structured in variables and abbreviated as it is seen in parenthesis: power of mobo 16a (p16a), mobo 16b (p16b), sag 16 (p16s), east temperatures, mobo 16a (te16a), west (to16a), winding (td16a) and of mobo 16b (te16b, to16b y td16a), and of the tonnage (ton16s), then: p16a+p16b+p16s = te16a+to16a+td16a+te16b+to16b+td16b+ton16s so, the process implies to create as much canonical correlations as it is needed to achieve the minimum of present variables between the dependant and the independant (min(# var. dependents,# var. independants)), that in this case corresponds to 3. table 5, shows the data obtained through this procedure. table 5: canonical correlation canonical correlation own value proportion accumulated 1 0.861034 2.8667 0.9239 0.9239 2 0.435197 0.2336 0.0753 0.9992 3 0.051116 0.0026 0.0008 1.0000 in this case, the canonical correlations are given for each group created inside the program, explains the relation between both groups of variables, that is to say, between the dependants and the independant, where it can be remarked that the first results in having a quite high value in comparison with the others, but to rectify how many groups must be considered, it can be performed the test of barttlet-lawley, which results can be appreciated below on table 6: table 6: statistical of the barttlet-lawley test aprox. f value. gl p−value 1 210.57 21 < 0.0001 2 39.74 12 < 0.0001 3 1.11 5 0.3505 this test, for the first line, works on the null hypothesis of h0 : ρ0 > ρ1 = ρ2 = ρ3 = 0 (with big data on decision making in energetic management of copper mining 69 ρ0 = 1), while the second line works on h0 : ρ1 > ρ2 = ρ3, and so on. thus, it can be observed that the two first hypothesis are rejected, analyzing the test by means of p−value < 0.05, but it is not the same for the last one, since it does not exists evidence to reject it. this implies that the two first correlations result significant and reliable to be taken in consideration in the study. the canonical correlation allows to modelate the information of the mills, with coefficient. the canonical correlation model allows for each of the variables within each group; to these made-up groups, are nominated as dependant and independant variables. on one hand, the sas program, gives two coefficients for each variable in each group, ones called raw coefficients, and the others standardized. the first ones correspond to the real coefficients and, at the same time, common of each variable, that is to say, are interpreted as it were a linear regression, this is, for each unit that increases some value, the response will increase / decrease in βi units. by the other hand the standardized coefficients are given only with the purpose of facilitating its interpretation, and to establish also a comparison between variables. avoiding differences between magnitudes or intervals these have. table 7: raw coefficients and dependant variables raw coefficients dependant 1 dependant 2 dependant 3 power mobo 16a 0.000420051 0.002660012 –0.000485457 power mobo 16b 0.0011697651 0.0011781961 –0.000018758 power sag 16 –0.049858651 –0.059131666 0.88859719798 table 8: raw coefficients and independant variables raw coefficients dependant 1 dependant 2 dependant 3 ton. sag 16 –0.000117482 –0.000269535 –0.001644198 east temp mobo 16a 0.0160939058 0.0454415296 0.0501536296 west temp mobo 16a 0.0129238742 0.0075762637 0.0630824701 dev temp mobo 16a 0.0106182145 –0.077979648 –0.005456832 east temp mobo 16b 0.0146624833 0.0514678025 –0.004535215 west temp mobo 16b 0.0065554753 0.0514678025 –0.034625719 dev temp mobo 16b 0.0467922347 0.0392654956 0.0003705063 table 9: standardized coefficients and dependant variables standardized coefficients dependant 1 dependant 2 dependant 3 power mobo 16a 0.1931 –1.2226 –0.2231 power mobo 16b 0.8838 0.8902 –0.0142 power sag 16 –0.0577 –0.0685 1.0259 70 c. lagos, r. carrasco, g. fuertes, s. gutiérrez, i. soto, m. vargas table 10: standardized coefficients and independant variables standardized coefficients dependant 1 dependant 2 dependant 3 ton. sag 16 –0.0368 –0.0845 –0.5155 east temp mobo 16a 0.1005 0.2837 0.3131 west temp mobo 16a 0.1562 0.0916 0.7626 dev temp mobo 16a 0.1963 –1.4415 –0.1009 east temp mobo 16b 0.1481 0.4138 –0.0458 west temp mobo 16b 0.0595 0.4668 –0.3140 dev temp mobo 16b 0.7170 0.6017 0.0057 in tables 7 to 10, it can be found important differences between the variables and the mills, with which it can be aroused the equations obtained in the analysis, considering that one of the groups (equation), it is discarded by the results obtained from the test of barttlet-lawley. below are presented two equations, of which it is chosen the second one that represents the greater correlation among the powers of the mills, temperature and tonnage. equation 1: 0.000420051 ∗ p16a + 0.0011697651 ∗ p16b – 0.049858651 ∗ p16s = 0.0160939058 ∗ te16a + 0.0129238742 ∗ to16a + 0.01061821145 ∗ td16a + 0.146624833 ∗ te16b + 0.0065554753 ∗ to16b + 0.0467922347 ∗ td16b – 0.000117482 ∗ ton16s equation 2: – 0.002660012 ∗ p16a + 0.0011781961 ∗ p16b – 0.059131666 ∗ p16s = 0.0454415296 ∗ te16a + 0.0075762637 ∗ to16a – 0.077979648 ∗ td16a + 0.0409671048 ∗ te16b + 0.0514678025 ∗ to16b + 0.0392654956 ∗ td16b – 0.000269535 ∗ ton16s 4.6 analysis of the design of experiments for this case, it is worked with all the mills, both sag and the ball 16 and 17, but only with the powers, since mills will be measured as factors. the design of the experiment allows to measure the effect of the mill as factor on the total energetic consumption, this to find some difference among the processes, that are expected to be totally independant among the mills. in the above anova table 11, it is considered the effect of the process, that is to say, it is worked with experimental designs nested, where the mills of ball a, b and sag 16, belong to the first group of the process, and the remaining to the second group. thus, it is concluded that it does not exist difference between mills 16 and 17 in the behaviour, but it exists in the consumption between the mills belonging to a same process (all this concludes starting from the test of the fisher, by means of its p−value, and considering a significance of α = 0.05). due the process in study, it starts in the sag mills, the energy consumption of the ball mills, it should requiere less consumption. however, there is no difference in the behaviour of the groups of ball mills 16 and sag 16, respect of the ball mills 17 and sag 17, which is consistent with the electric energy consumption for both groups of mills. big data on decision making in energetic management of copper mining 71 table 11: anova the model fv gl sc mc valor f p−value model 5 33306519304 6661303861 23023.6 < 0.0001 error 12804 3704525732 289326 total 12809 37011045036 in conclusion, if there is a difference in the process variable or group, as observed in table 12. table 12: paired sample mobo 16 variable means mobo 16a v/s mobo 16b stat t p−value power 90.2936 a>b 6.89 < 0.0001 eastt. -7.1599 a<b -34.63 < 0.0001 westt. 2.3912 a>b 7.60 < 0.0001 devt. 18.6154 a>b 59.00 < 0.0001 4.7 complementary analysis the procedure performed with the canonical correlations, does not result to be very different than the factorial analysis. these correlations are given by sas, and are shown below on table 13: table 13: correlations and dependant variables correlation dependant 1 dependant 2 dependant 3 power mobo 16a 0.7096 –0.7046 –0.0071 power mobo 16b 0.9874 0.1440 –0.0652 power sag 16 0.1680 –0.1513 0.9741 it is observed a strong correlation of the powers of the mills ball a and ball b respect the first group of mills, so it exists a relation between the energies that are consumed in both mills, from this it can be rescue that the work performed in the process inside mill a, it has certain relation with the one done by mill b, what can be due to the type of mineral and/or the tonnage that is given from the sag mill, which is divided among the ball mills. below are given on table 14, the correlations of the independant variables in relation with the set of variables of temperatures and tonnage. table 14: correlations and independant variables correlations independant 1 independant 2 independant 3 ton. sag 16 0.0775 –0.1130 –0.5006 east temp mobo 16a 0.6693 –0.1055 0.2723 west temp mobo 16a 0.1045 0.0018 0.7730 dev temp mobo 16a 0.8109 –0.5198 –0.0089 east temp mobo 16b 0.4764 0.3272 0.1090 west temp mobo 16b 0.4805 0.2050 –0.2313 dev temp mobo 16b 0.9219 0.0663 –0.2332 72 c. lagos, r. carrasco, g. fuertes, s. gutiérrez, i. soto, m. vargas it is observed that a great part of the variables show a strong correlation with the group 1, excepting the tonnage of the mill sag 16 and the temperature of the west engine of mill of ball 16 a. the fact is that a great part of the factors that act in the milling process (since this group of independant variables are strongly related with group 1 of the dependant variables), have a strong impact over the energetic consumption of the three mills of the process 16. all the variables considered in this set are from mill of ball a and b, which was expected due to the fact that the first set of dependant variables is strongly related with the power of this two mills. other important set to remark (despite of being discarted by the barttlet-lawley test) is the third set of variables, since this only considers the energy consumed by the mill sag 16 for the set of dependant variables. for the tonnage, it only works with the correlations of variables belonging to the mill sag 16. the above mentioned (for the first and third set of variables analyzed) can be corroborated with the following values given, where are shown the dependant variables related to the sets of independant variables and viceversa, and are shown below on table 15, in which are found similar results to the previous presented, with the exception of the third set of variables where it is demonstrated why it was descarted by the barttlet-lawley test. table 15: correlations and independant variables correlations independant 1 independant 2 independant 3 power mobo 16a 0.6110 –0.3066 –0.0004 power mobo 16b 0.8502 0.0627 0.0033 power sag 16 0.1446 –0.0658 0.0498 as it was expected, the powers of the mills ball a and ball b, have a strong relation with the set of independant variables of the first group, and thus the same independant variables present a strong correlation with the first set of dependant variables, giving in both cases, very similar values to the previous, and are shown below on table 16. table 16: correlations and dependant variables correlations dependant 1 dependant 2 dependant 3 ton. sag 16 0.0667 –0.0492 –0.0256 east temp mobo 16a 0.5763 –0.0459 0.0139 west temp mobo 16a 0.0900 0.0008 0.0395 dev temp mobo 16a 0.6983 –0.2262 –0.0005 east temp mobo 16b 0.4102 0.1424 0.0056 west temp mobo 16b 0.4137 0.0892 –0.0118 dev temp mobo 16b 0.7938 0.0289 –0.0119 it is concluded from the above table, that there is a strong relation between the temperatures (except the west engine of the mill of ball 16a) with the energetic consumption of the mills, mainly in the mill of ball 16a and mill of ball 16b. thus, it can be worked without problems for future analysis, descarting those that do not possess any relation with them, since these explain the behaviour of consumption in this machines together. big data on decision making in energetic management of copper mining 73 conclusions despite awareness of the wide potential of the energetic efficiency, there are few mining companies, that make decisions based on the analyzis of data coming from their productive processes, due to obstacles such as the complexity of the process and the lack of clear information. a database that integrates models for the energetic efficiency, it is a great input due to the non existence of them in the industrial explotation of copper. the great volume of data coming from the copper concentrate process, the ball mills and the sag mills, must be extracted, filtrated and transformed, for the variables related with the energetic consumption. big data processing, allows to identify the variables of interest (tonnage, temperature and power), and the relation that these variables have with the other variables of the process. for the analyzis of the data it is used the statistical tools, arriving to a model of improvement in the energetic efficiency. the main contribution of this paper, is obtain a model to improve energy efficiency, through the integrate dss and the data analysis using different statistical tools such as markov chain monte carlo, canonical correlation analysis and design and analysis of experiments. it is expected that the results are applicable in different areas of the production process for large-scale mining. there is an urgency in the creation and implementation of models to improve the energy efficiency in the mining sector, due to the restriction, environmental challenges and energy cost. acknowledgment the authors acknowledge the financial support of the "center for multidisciplinary research on signal processing" (con-icyt/act1120 project), the usach/dicyt 061413sg project, "continuous visible light communication access point for man to machine interaction and detection in high risk mining environments", corfo code 14idl2-29919 and "cooperation agreement between the universidad de santiago de chile and corporación nacional del cobre de chile, división chuquicamata", and the support given by professor francisco torres aviles (r.i.p). bibliography [1] c. lagos, f. cordova, s. gutierrez, g. fuertes, and r. carrasco, data analysis methods related to energetic consumption in copper mining . a test case in codelco, computers communications and control (icccc), 2016 6th international conference on, ieee xplore, e-issn 978-1-5090-1735-5, doi: 10.1109/icccc.2016.7496768, 1241–247, 2016. 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[31] g. s. dos reis et al., (2016); the use of design of experiments for the evaluation of the production of surface rich activated carbon from sewage sludge via microwave and conventional pyrolysis, applied thermal engineering, 93: 590–597. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(4), 489-502, august 2019. application of improved collaborative filtering in the recommendation of e-commerce commodities d. chang, h.y. gui, r. fan, z.z. fan, j. tian dan chang school of economics and management beijing jiaotong university, china no.3 shang yuan cun, haidian district, beijing, china 6787@bjtu.edu.cn haoyu gui school of economics and management beijing jiaotong university, china no.3 shang yuan cun, haidian district, beijing, china 17120610@bjtu.edu.cn rui fan* school of economics and management beijing jiaotong university, china no.3 shang yuan cun, haidian district, beijing, china *corresponding author: 17120607@bjtu.edu.cn zezhou fan school of economics and management beijing jiaotong university, china no.3 shang yuan cun, haidian district, beijing, china 17125463@bjtu.edu.cn ji tian beijing research institute of automation machinery industry co, ltd no.1 jiaochangkou street, xicheng district, beijing, china 15010118013@163.com abstract: problems such as low recommendation precision and efficiency often exist in traditional collaborative filtering because of the huge basic data volume. in order to solve these problems, we proposed a new algorithm which combines collaborative filtering and support vector machine (svm). different with traditional collaborative filtering, we used svm to classify commodities into positive and negative feedbacks. then we selected the commodities that have positive feedback to calculate the comprehensive grades of marks and comments. after that, we build svm-based collaborative filtering algorithm. experiments on taobao data (a chinese online shopping website owned by alibaba) showed that the algorithm has good recommendation precision and recommendation efficiency, thus having certain practical value in the e-commerce industry. keywords: recommendation precision, recommendation efficiency, support vector machine (svm), collaborative filtering. 1 introduction with the rapid development of the internet and mobile internet, the e-commerce industry is booming with broad attention from all walks of life. according to research report on market prospect and invest opportunity of e-commerce industry in china from 2018 to 2023, the overall copyright ©2019 cc by-nc 490 d. chang, h.y. gui, r. fan, z.z. fan, j. tian transaction scale of china’s e-commerce reached 24.1 trillion yuan in 2017, an increase of 17.4%. with the gradual improvement of the e-commerce industry, it is estimated that its transaction scale will reach 28.4 trillion yuan in 2018, a year-on-year increase of 17.8% [32]. the flourish of the e-commerce industry has led to the explosion of different kinds of data. and the data, which contain great value, are invisible assets for the e-commerce industry. however, not all data are valuable, so users have to spend much time in extracting useful and specific information from a vast amount of data. with the increasingly booming information in the e-commerce industry and the extraction of valuable information, recommender systems emerge as the times demand – e-commerce websites begin to solve the problem of "information overload" [3] through recommender systems. commodity recommender systems can record user’s characteristic information and their behavioral information such as purchasing and browsing. by analyzing the information obtained from modeling of user’s preference, commodity resources that fit user’s potential demand or may interest them will be extracted from the commodity information on e-commerce websites, and then recommended to users. collaborative filtering, with many advantages such as no need to consider the content of recommendation item, offering novel recommendation, little disturbance while browsing websites, as well as easy to achieve technically, becomes the basic algorithm of recommender systems. traditional collaborative filtering has the limitations of recommending only on the basis of single indicator — either user’s marks or comments. however, marks and comments should not be studied separately, because inconsistency between marks and comments often exists in real life. for example, a user may give a high mark for a commodity, but a dissatisfied comment at the same time and people’s cognition on mark differs. therefore, traditional collaborative filtering is defective in terms of precision. nowadays, it is a research hotspot to combine two or several different methods to solve practical problems to make up for the defects of traditional algorithm. for example, in [30] zhao et al. combined the lexicographic method and pareto method to optimize the flight ground support capability of airport. also, to extend the classical vehicle routing problem, they proposed a time-dependent and bi-objective vehicle routing problem with time windows [29]. therefore, we pretend to combine svm and collaborative filtering recommendation to improve the recommendation efficiency and precision. before giving comprehensive grades, commodities are divided into positive-feedback commodities and negative-feedback commodities in the paper with svm, namely commodities users like and dislike. collaborative filtering recommendation is only implemented on positive-feedback commodities. the original data for recommendation reduce greatly due to the classification in advance, so the improved collaborative filtering increases efficiency compared with the traditional one. finally, online data on taobao are used to verify that the improved collaborative filtering promotes recommendation precision and efficiency significantly. 2 literature review collaborative filtering is most commonly used in recommender system. it was first proposed by scholars such as goldberg in 1992, and implemented in tapestry — the experimental mail system, enabling the system to extract user-interested and effective email list [6]. mainstream collaborative filtering today can be divided into user-based collaborative filtering and item-based collaborative filtering. at present, collaborative filtering is the research focus in the academic circle. as traditional collaborative filtering has drawbacks such as cold start, data sparsity and poor extensibility, in order to come up with better solutions to these problems, scholars now focus on the improvement of traditional collaborative filtering. application of improved collaborative filtering in the recommendation of e-commerce commodities 491 for example, in [7] guo et al. proposed a novel method called "merge" to incorporate social trust information, and supplement user preference by merging users’ trusted neighbor ratings. in [8] hu et al. integrated time information into collaborative filtering similarity measure in collaborative filtering algorithm, and designed a hybrid personalized random walk algorithm; yong-ping du et al. [5] proposed item-based rbm, and used deep and multilayer rbm network structure to solve the problem of data sparsity; sedhain et al. [20] generalized matrix algebra framework, and they doesn’t need the target user’s data when the side information is available ; jian wei et al. [25] put forward two models on the basis of a framework based on tight-coupling collaborative filtering and the in-depth study into neural network; a. murat yagci et al. [26] focused on frequent co-occurrence items and proposed sascf to eliminate the cold start of the system; su hongyi et al. [22] proposed a new algorithm involving time decay factor in the cf algorithm, and deployed time weights on the mapreduce parallel computing framework ; xiuju liu et al. [13] presented a new algorithm of cf-isegb, and took the influence sets of current e-learning groups into consideration to effectively solve problems caused by sparse data sets. besides, recommendation precision and recommendation efficiency are also two important indicators to assess collaborative filtering. therefore, many scholars have improved the algorithm by promoting its recommendation precision and efficiency. for example, mehrbakhsh nilashi et al. [17] provided the probability of precise recommendation by considering users’ preference in many aspects of the items, and introduced vague method to eliminate the uncertainty of users’ preference. in [18] mahdi nasiri et al. promoted the predictive accuracy of collaborative filtering by initialized factor matrix. feng zhang et al. [27] designed linear time algorithm to calculate similarity, thus reducing the time of assessment. in [18] zhongya wang et al. calculated pb-level data by parallel computing and proposed effective collaborative filtering based on multi-gpu. kasra madadipouya in [14] added location factors to the traditional film collaborative filtering, and improved the accuracy and the quality of recommendation in practical application. nicola barbieri in [2] proved that basic probability framework is useful in the generation of the recommendation list, and enhanced the accuracy of recommendation. in [10] gai li et al. proposed a new model named ppmf by using rankrls to solve the problem of low recommendation accuracy and the high cost. with improved algorithms above, scholars have promoted recommendation precision and efficiency. therefore, it is of significance to improve traditional recommendation algorithm from the perspectives of recommendation precision and recommendation efficiency. the collaborative filtering in the paper adopts the classification model of svm. as an implementation method of statistical theory in practice, svm maps input variable on high-dimensional space by nonlinear mapping, then constrains questions through quadratic optimization and finds out the optimal classification hyperplane, thus maximizing the distance between data and the optimal classification hyperplane. the core concept is to reduce the error of classification to the greatest extent. many scholars at home and abroad use svm classifier to classify some data and establish prediction model. huayu li et al. separated data into positive and negative feedbacks by a svm-like task [11]. uricar et al. in [23] made classification with svm after figuring out deep characteristic data represented by people’s facial expressions, thus predicting their age, gender and smiles. in [15] asha s. manek et al. combined feature extraction with svm and made sentimental classification among online film reviews to predict the popularity of a film. anish jindal et al. in [9] combined dt with svm to precisely predict electricity theft, reducing false alarms greatly. a.s. ahmad et al. in [1] used the least square support vector machine (lssvm) to forecast electrical energy consumption of buildings. in [21] selakov et al. used the combination of pso and svm to forecast short-term electrical load according to the significant temperature variations. dongwen zhang et al. in [25] used svm in sentimental classification to extract the valuable information. 492 d. chang, h.y. gui, r. fan, z.z. fan, j. tian as svm can do well in predicting data, scholars at home and abroad combine it with collaborative filtering to predict items or products users may like and then recommend them to users. in order to solve the problem that recommender system is easy to be attacked by shilling, wei zhou et al. in [31] combined svm with tia and used borderline smote to relieve class-imbalance, thus detecting shilling in the system. lifang ren et al. in [19] combined svm with collaborative filtering to improve prediction precision as far as possible, which meant that svm was used to filter out services users might dislike, and services listed on top n would be recommended according to preference . problems related to recommendation precision and efficiency are generally caused by data missing, and scholars have proposed different solutions to these problems. in [16] mehrbakhsh nilashi et al. made the svm and multi-criteria collaborative filtering (mc-cf) as a combination to improve the recommendation precision. yeounoh chung et al. in [4] used the svm to find personalized experts, then these experts will be recommended to different users. in [12] zhan li et al. used multiple-kernel svm to recommend new videos, which can relieve the problems of data sparsity and item cold start . therefore, svm-based collaborative filtering proposed in the paper is of significance in promoting recommendation precision and efficiency. 3 svm-based collaborative filtering 3.1 commodity information acquisition in order to verify the actual effect of improved collaborative filtering on e-commerce industry, python-based scrapy is adopted in the paper to acquire online commodity information on taobao, mainly including commodity name, commodity information and user’s comments on it. commodities on taobao are graded by a 5-star marking system, and in the paper, it is shifted into a 5-point marking system. in addition, according to a huge amount of comments, taobao translates them into characteristic value of commodities by semantic analysis, such as good stuff, fast logistics, etc. in the experiment, 3,4000 pieces of data are acquired and part of them are demonstrated in table 1. 3.2 svm-based classification the data set in the experiment mainly includes marks and characteristic values of commodities. a 2500-dimension vector set is built based on the data, and the characteristic value which is nonexistent will be filled with 0. suppose that data set of commodities on taobao is {(xi,yi) |i = 1, 2, ...n}; the data set of commodities available for recommending is{xi|i = 1, 2, ...,n} ; xj = (xj1,xj2, ...,xjk) and xi = (xi1,xi2, ...,xik) are characteristic attributes of set i and set j; yi�{−1, 1} is the output type. yj = −1 means the commodity has negative feedback; yj = 1 means the commodity has positive feedback. svm builds classification model with commodity data set {(xj,yj) |j = 1, 2, ...n} to find optimal hyperplane g (x) = 〈w ·x〉 + b = 0 . the corresponding optimization of svm in the experiment is: max α l (w,b,α) = n∑ j=1 αj − 1 2 n∑ j=1 αiαqyjyqk (xjxq) s.t. n∑ j=1 yjαj = 0; 0 ≤ αj ≤ c (1) k (·) is the radial basis function; optimal solution α∗j can be obtained by optimization in formula (3-1) and thus figuring out the solution to the original question. w∗ = ∑n j=1 α ∗ jyjxj . therefore, the classification decision function of optimal hyperplane definition can be expressed application of improved collaborative filtering in the recommendation of e-commerce commodities 493 table 1: data of commodity information no. name price mark characteristic values 1 vero moda 2018 autumn new chic knitwear 599 4.9 comfortable (179) pretty (134) i like it (85) i haven’t wear it (59) good color (42) standard size (39) 2 lin shi mu ye cloth sofa bed 5960 4.9 skilled installation (1394) not bad quality (888) fast logistics (484) cost-effective (363) high price/performance ratio (104) no color difference (42) bad quality (6) 3 loose jeans for man in fall and winter 468 4.8 great quality (6681) comfortable (4132) suitable size (2816) good service (2585) good to wear (2366) cheap and fine (1996) thin cloth (613) 4 ntmpbins suitcase with universal wheel 1658 4.8 good quality (721) good service (273) good style (270) fast logistics (222) no color difference (195) smooth wheels (122) fair quality (51) 5 excerpts from historical records with annotations 298 4.9 not bad (79) clear printing (61) good quality (54) costeffective (50) thick paper (49) fast logistics (40) 6 2018 autumn new black dress with paillettes and tassels 513 4.7 good quality (193) beautiful (192) brighten your skin (192) soft cloth (153) non-deformation (135) new style (127) fair quality (39) as below: f (x) = sgn (g (x)) = sgn   n∑ j=1 α∗jyjk (xj,x) + b ∗   (2) 494 d. chang, h.y. gui, r. fan, z.z. fan, j. tian according to formula (2), all commodities can be divided into two categories. when f (x) = −1 , the commodity has negative feedback; when f (x) = +1 , the commodity has positive feedback. commodities fall into two categories through the classification of svm. then the data representing users’ dislike are eliminated and only data representing users’ affection are reserved. 3.3 comprehensive grade calculation after the classification with svm, the paper in this part will only make sentimental analysis of commodities with positive feedback and obtain quantified sentimental intensity, and then solve sentimental intensity after sentimental analysis and commodity marks with the method of weighted average, finally obtaining comprehensive grades. sentimental matching algorithm is adopted to match commodity comments with the ontology base and work out corresponding sentimental intensity. steps for computing the sentimental intensity of commodity comments are as follows: firstly, normalize the word frequency of characteristic values in commodity comments as showed in table 2. table 2: normalization of word frequency of commodity characteristic values id 1 2 3 4 5 6 7 8 9 description good stuff good service fast logistics good content official edition good packaging clear printing highquality paper fair quality word frequency 480 207 129 117 86 84 72 70 34 normalization 0.3752 0.1618 0.1008 0.0914 0.0672 0.0656 0.0562 0.0547 0.0265 next, use sentimental words matching algorithm to match commodity characteristic values with ontology base to obtain sentimental intensity and polarity of the corresponding characteristic value, and the formula to calculate the comprehensive sentimental intensity of a commodity’s comments is: intensity (fi) = k1p (wi1) t (wi1) + ... + knp (win) t (win) (3) wij represents sentimental words in commodity comments; p (wi1) and t (wi1) represent sentimental intensity and tendency of sentimental words respectively. when the polarity of wij = 1 , t (wi1) = 1. when the polarity of wij = 0 ,t (wi1) = 0. when the polarity of wij = 2, t (wi1) = −1 . kn is the corresponding normalized value of the characteristic value n . finally, calculate the comprehensive grade of the commodity with formula (4) α = 5 ; gi is the mark of the commodity: ce = αfi + (1 −α) gi (4) 3.4 similarity calculation and recommendation the data processed in table 3 are saved in matrix r (u,i) . u represents the number of users in the recommender system; i represents the number of items in the recommender system; rij is the mark of item ij given by user ui , representing users’ affection for the item.wij is the similarity between item ii and item ij . cosine similarity is adopted in the paper to measure the similarity between users or items. it measures similarity by the included angle between vector quantities. as it does not take users’ different rating scales into consideration, the cosine similarity in the modeling is improved by deducting users’ average mark ru. similarity wij between item ii application of improved collaborative filtering in the recommendation of e-commerce commodities 495 table 3: example of comprehensive grade calculation-programming python-from introduction to practice comment good stuff good service fast logistics good content official edition good packaging clear printing highquality paper fair quality sentimental classification ph ph pa ph ph ph ph ph nn intensity 3 7 7 5 4 6 5 3 9 polarity 1 1 1 1 1 1 1 1 2 assistant sentimental classification 0 0 ph 0 0 0 0 0 0 intensity 0 0 3 0 0 0 0 0 0 polarity 0 0 1 0 0 0 0 0 0 frequency 0.3752 0.1618 0.1008 0.0914 0.0672 0.0656 0.0562 0.0574 0.0265 sentimental mark 1.1256 0.1326 0.46368 0.457 0.2688 0.3936 0.281 0.1641 -0.477 comprehensive sentimental intensity 3.80938 and item ij can be expressed as wij = ∑ u�u(i,j)(rui −ru)(ruj −ru)√∑ u�u(i)(rui −ru)2 √∑ u�u(j)(ruj −ru)2 . (5) comprehensive grades of some commodities and users are firstly calculated with the formula in (3), as showed in table 4. then the similarity between commodities is calculated according to formula (5), and part of the results are demonstrated in table 5. table 4: comprehensive grades of some commodities commodity|user user1 user2 user3 user4 user5 python** 1 4.57 2.18 3.18 0.60 3.56 suit*** 2 3.35 1.88 0.00 4.58 4.92 children’s shoes**** 3 1.88 0.00 2.10 4.24 0.67 anchor**** 4 0.00 1.50 3.94 0.00 1.38 nuts*** 5 1.16 2.96 1.58 4.14 4.85 svm-based collaborative filtering obtains k nearest neighbors of the item to be recommended by calculating the similarity between items, and then use item similarity and users’ records to figure out the popularity grade of the item to be recommended by weighted calculation, finally forming a recommendation list according to the rank of grades. user u’s grade on item ij can be expressed as: puj = ∑ ii�iu ⋂ s(ij,k) wjirui (6) 496 d. chang, h.y. gui, r. fan, z.z. fan, j. tian table 5: similarity between commodities similarity python**,suit*** -0.15 python**,children’s shoes**** 0.29 python**,anchor**** -0.50 python**,nuts*** 0.15 suit***,children’s shoes**** 0.21 suit***,anchor**** -0.80 suit***,nuts*** 0.70 children’s shoes****,anchor**** -0.28 children’s shoes****,nuts*** 0.02 anchor****,nuts*** -0.26 lu represents the set of items user u like in the records; s (ij,k) represents the "k" item sets that are most similar to ij . svm is used to build positive-feedback set and negative-feedback set li = {li|rij = 1} is the positive-feedback set; dli = {li|rij = −1} is the negative-feedback set; bi = {li|rij = 0} is the unselected set. in svm-based collaborative filtering, the training set x = {(xi,yi) |xi�li ⋃ dli,yi�{−1, 1}} , test set tx = {txi�bi} .yiis the classification tag of xi; when xi�li ;yi = 1 when xi�dli, yi = −1 . the main steps of svm-based collaborative filtering are as follows: initialization: training set x = φ, test set tx = φ, • use pytho-based scrapy to acquire commodity information on taobao. • use svm to divide commodities into positive-feedback commodities and negative-feedback commodities, and build training set x and test set tx . • use training set x to train svm classifier. • use classifier to classify test set, filtering out negative items and reserving positive items. • make weighted calculation of marks and comments of positive-feedback commodities to get comprehensive grades. • use f (x) to predict the popularity grades of the items, and rank them according to the predicted grades, forming the final recommendation list. 4 experiment results and analysis 4.1 data preparation the experiment adopts python-based scrapy to acquire data of online commodity information on taobao, which include 7 categories of commodities (clothing, books, appliances, digital products, mobile phones, shoes and bags) and about 34,000 pieces of detailed comments. there are 4000 pieces of data for each category, among which 2500 pieces are taken as the training set and the rest are used for testing. hardware : thinkpad e445, 3.3ghz, 4gb ram. application of improved collaborative filtering in the recommendation of e-commerce commodities 497 4.2 assessment indicator predictive accuracy p represents the probability that the user may like an item in the recommendation list, which can show the accuracy of the recommender system. the formula to calculate predictive accuracy of recommender system is as follow: p = 1 m m∑ u=1 pu = 1 m m∑ u=1 |rlu ⋂ tlu| n (7) recall rate r represents the proportion of items users like in the recommendation list, which can show users’ satisfaction degree with the recommendation results. the higher the recall rate is, the higher satisfaction degree users have. the formula to calculate the recall rate of recommender system is as follow: r = 1 m m∑ u=1 ru = 1 m m∑ u=1 |rlu ⋂ tlu| tlu (8) f–measure is used to assess the overall recommending performance of the algorithm. a larger f-measure means the stronger recommending ability of the algorithm. the formula to calculate f-measure of the recommender system is as follow: f = 1 m m∑ u=1 fu = 1 m m∑ u=1 2 ∗pu ∗ru pu + ru (9) rlu is user u’s recommendation item set; tlu is the set of items user u like in the test set; "u" is the recommended item number; rlu = n. p , r and f are three main indicators to assess the algorithm. a larger f factor means the stronger recommending ability of the algorithm. 4.3 results and analysis (1)comparative analysis with traditional recommendation algorithm for the fairness and objectivity of the experiment, data recommendation in the paper is carried out according to traditional item-based recommendation algorithm and svm-based collaborative filtering respectively, so as to compare the effect of recommendation. traditional item-based recommendation algorithm is on the basis of neighborhood k . in order to be fairer and more objective, the recommended performance of algorithm with different neighborhood k will be analyzed in this part to choose the optimal k value. figure 1 is the recommended performance of item-based recommendation algorithm with different k values. three lines in the figure represents predictive accuracy, recall rate and f-measure with different k values. it can be seen from the figure that the optimal recommended performance exists when k = 10 , so k = 10 is selected in the subsequent experiment. figure 2, 3 and 4 represent the variation tendency of predictive accuracy p , recall rate r and f-measure of svm-based collaborative filtering and traditional recommendation algorithm respectively when the recommended item number n is different. it can be seen from the figures that when n < 20, predictive accuracy p , recall rate r and f-measure of svm-based collaborative filtering are obviously better than those of traditional recommendation algorithm. therefore, svm-based collaborative filtering has a big advantage over traditional recommendation algorithm when n < 20, especially when n = 5 . in real situation of recommending, due to users’ limited time and energy, too much recommendation is meaningless to users, and will affect their shopping experience on the contrary. therefore, what should be considered is the situation when n is relatively small. when n < 15 498 d. chang, h.y. gui, r. fan, z.z. fan, j. tian figure 1: recommended performance of item-based recommendation algorithm with different k values figure 2: predictive accuracy p of two models with different n values , the recommended performance of svm-based collaborative filtering is better than that of traditional recommendation algorithm, thus illustrating that when n is relatively small, svm-based collaborative filtering can achieve better recommended performance. to compare the efficiency of the two algorithms, during the performance comparison of traditional recommendation algorithm and svm-based collaborative filtering in the paper, the experiment is divided into training stage and predication stage, and then time consumption of the algorithm in data set ml-100k is calculated respectively. training time of traditional recommendation algorithm and svm-based collaborative filtering is 33.89 seconds and 11.23 seconds respectively; test time of them is 250.01 seconds and 90.08 seconds respectively, thus demonstrating svm-based collaborative filtering is more efficient. (2) analysis of the recommended performance of improved collaborative filtering table 6 and figure 5 are the recommended performance of svm-based collaborative filtering figure 3: recall rate r of two models with different n values application of improved collaborative filtering in the recommendation of e-commerce commodities 499 figure 4: f-measure of two models with different n values with different n (number of different recommended items). table 6: recommended performance of svm-based collaborative filtering with different n n (number of different recommended items) 5 10 15 20 25 30 f 0.2 0.24 0.25 0.255 0.25 0.24 r 0.14 0.22 0.255 0.3 0.34 0.36 p 0.34 0.28 0.245 0.225 0.2 0.16 figure 5: recommended performance of svm-based collaborative filtering with different n it can be seen from the table and figure that with the increase of n, predictive accuracy p starts to decline and eventually levels off; recall rate r starts to increase dramatically; f-measure shows the trend of increasing first and then decreasing, and finally levels off. these phenomena indicate that recommended performance does not always have a positive correlation with n . therefore, an appropriate n should be selected to improve the recommended performance of svm-based collaborative filtering. when n = 15 , f and p have relatively high values, which demonstrates that the recommender system has the best effect at this point, and too many items for recommendation will be a burden for users on the contrary. therefore, the algorithm in the paper has the optimal overall recommended performance when n = 15 . 5 conclusion the efficiency of recommending commodities to users in the e-commerce industry is decided by the scale of recommended items and the performance of the recommendation algorithm. in the paper, svm is adopted at first to classify items, and then items users may dislike are filtered out by negative-feedback information to reduce the scale of recommended items, which can not only significantly increase recommendation efficiency, but also decrease the disturbance of these 500 d. chang, h.y. gui, r. fan, z.z. fan, j. tian items on recommendation and promote recommendation precision. after figuring out positivefeedback commodities, marks and comments are taken into consideration to obtain comprehensive grades by weighted average, thus making it more objective and indirectly improving precision. finally, verification is conducted with the online data in the e-commerce industry. therefore, the algorithm is of certain practical value for the e-commerce industry. further studies can be carried out in the future with regard to how to further improve recommendation precision and efficiency of the recommendation algorithm in the situation of more recommended items. funding this work was funded by research supported by beijing logistics informatics research base and intelligent emergency logistics linkage system of public emergencies in beijing (project no.18jdglb019) author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. bibliography [1] ahmad, a.s.; hassan, m.y.; abdullah, m.p. et al. 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[32] [online].http://www.askci.com/reports/20180201/0946472814827719.shtml. international journal of computers communications & control issn 1841-9836, 13(6), 1047-1061, december 2018. a neutrosophic approach based on topsis method to image segmentation g. xu, s. wang, t. yang, w. jiang guojing xu 1. science and technology on avionics integration laboratory, shanghai 200233, china 2. china national aeronautical radio electronics research institute, shanghai 200233, china shiyu wang, tian yang, wen jiang* school of electronics and information, northwestern polytechnical university xi’an, shaanxi province, 710072, china *corresponding author: jiangwen@nwpu.edu.cn, jiangwenpaper@hotmail.com abstract: neutrosophic set (ns) is a formal framework proposed recently. ns can not only describe the incomplete information in the decision-making system but also depict the uncertainty and inconsistency, so it has applied successfully in several fields such as risk assessment, fuzzy decision and image segmentation. in this paper, a new neutrosophic approach based on topsis method, which can make full use of ns information, is proposed to separate the graphics. firstly, the image is transformed into the ns domain. then, two operations, a modified α-mean and the β-enhancement operations are used to enhance image edges and to reduce uncertainty. at last, the segmentation is achieved by the topsis method and the modified fuzzy c-means (fcm). simulated images and real images are illustrated that the proposed method is more effective and accurate in image segmentation. keywords: uncertainty; neutrosophic set; topsis method; fcm; image segmentation. 1 introduction recently, interests have been growing in the synergistic use of multimodal imaging to get better insights into phenomena or objects. therefore, the image analysis and processing has become more and more important [10,22,27,57,66]. in the existing image processing technology, image segmentation is one of the key tasks in many methods [2, 9, 46] and computer vision applications [13,14,21,35]. the purpose of image segmentation is to divide images into different regions for future processing based on the given criteria. detecting generic object categories is a fundamental issue in computer vision [23, 36, 50, 67]. its core content is to extract and separate the image information from lots of hurdles like noise, nonuniform illumination, and uneven contrast in otherwise homogenous region [8, 31]. for example, in the field of medicine, the complexity of breast cell histopathology (bch) images makes the reliable segmentation and classification of images quite difficult [20,53]. some uncertain information in the images, which is not useful for image segmentation, should be dealt properly or even discarded. in practical applications, getting the completely precise information is sometimes impossible [11,26,34,56,58]. therefore, how to deal with the uncertain information effectively is still an open issue. there are already some research about it, for example, information fusion techniques is able to decrease the information’s imprecision and uncertainty through the redundancy and complementarity, thereby improve the decision accuracy. to handle the uncertain information, many mathematical tools are presented such as d-s theory [6,25,28,48], neutrosophy theory [45,64], z-numbers [5,29,62], single valued neutrosophic cross-entropy [7,24,54,61] and d-numbers [12]. d-s theory was introduced by dempster and then developed by shafer, which can well describe and process uncertainty information by assigning copyright ©2018 cc by-nc 1048 g. xu, s. wang, t. yang, w. jiang probability to a set not an element, so it has been widely used in many fields of information processing and fusion. neutrosophic set (ns) method reduces the uncertainty information in the image and obtains the better image segmentation results. in neutrosophy set theory, an event a is described by three attributes 〈a〉, 〈anti−a〉 and 〈neu−a〉, represents the support, opposition and neutrality of a, respectively. the neutrality 〈neu−a〉 is neither 〈a〉 nor 〈anti−a〉, which denotes the uncertainty of evidence, in other words, the unknown part of the information. in many cases, the boundaries between these above concepts are vague and may even be crossed together. recently, ali proposed the complex neutrosophic set [1]. wang .et al extended ns to interval neutrosophic sets (inss) [51] and single-valued neutrosophic sets (svnss) [52] which enriched the ns theory and promoted its development. for example, neutrosophy theory has been applied to multi-criteria decision-making [15,59,60]. in addition, there are many researches combine ns to image segmentation. guo et al. [17,19] have applied the ns and clustering method to segment image. zhang et al. [65] use ns based on watershed method. some other researchers combine ns with some other method [39,49,55,63,68] to handle image segmentation. however, some methods do not consider make full use of ns information to segment image. in this paper, a new method with topsis method [32, 37, 44] is proposed to obtain a better image segmentation result. firstly, the image is transformed into the ns domain. secondly, two operations, a modified α-mean and β-enhancement operations are used to reduce the set indeterminacy enhance the image edges. finally, the segmentation is achieved by the topsis method and the modified fuzzy c-means (fcm). the remainder of this paper is organized as follows. the basic concepts are briefly introduced in section 2. in section 3, the new method is proposed. the simulated images with noise segmentation and real images segmentation are illustrated in section 4, which show the efficiency of the proposed method. finally, this paper is concluded in section 5. 2 fuzzy clustering by fcm the fcm algorithm makes use of fuzzy membership function which is used to assign a degree of membership for each class [33,38,43]. the analysis of fcm algorithm can be defined as the process of organizing objects into groups whose members are similar in some ways [18]. the fcm algorithm belongs to an unsupervised fuzzy clustering algorithm. under the condition of a general formulation, the data to be classified can be expressed with a vector x = {x1,x2, ...,xm}. the vector represents a finite data set of dimension m. assuming that c > 2 is an integer designating the number of clusters, which sorts the vector x. let rc×m be the set of all real c ×m matrices. a fuzzy c-partition of x is represented by a matrix u = [µik] ∈ rc×m, (1) where µik = µi(xk) is the degree of membership of the element xk in the cluster i. the entries of the partition matrix come in the form u. the constraints following can be verified [4,40]:  µik ∈ [0, 1], 1 ≤ i ≤ c, 1 6 k 6 m, c∑ i=1 µik = 1, 1 6 k 6 m, m∑ k=1 µik > 0, 1 6 i 6 c, (2) where u is used to depict the clusters of x, and a partition u of x can be obtained by the a neutrosophic approach based on topsis method to image segmentation 1049 minimization of the fcm objective functional [3]: jq(u,v : x) = m∑ k=1 c∑ i=1 (µik) q ‖ xk −vi ‖2a (3) where q ∈ [1, +∞) decides the the fuzzy degree of classification results [30], and v = (v1,v2, ...,vc) is the vector of the cluster centers. ‖ xk −vi ‖ is the distance between the gray level of the k-th pixel and the center of the i-th cluster. it is obvious that ‖ x ‖a = √ xtax is any inner product norm where a is any positive definite matrix. the approximate optimization of jq by the fcm algorithm can be obtained by iteration with the following necessary conditions for its local extreme value. in fcm algorithm [4, 40]: assume ‖ xk −vi ‖2a > 0, 1 ≤ i ≤ c, 1 ≤ k ≤ m. jq can be minimized by (u,v ), only if [4,40] as follows: µik =   c∑ j=1 ( ‖ xk −vi‖a ‖ xk −vj‖a ) 2 q−1  −1 (4) vi = m∑ k=1 (µik q ×xk) m∑ k=1 µik q (5) the fcm algorithm is made up of iterations alternating between eq. (4) and eq. (5). the algorithm is restrained to either a saddle point of jq or a local minimum. an algorithm of fast version of the fcm can be applied to an image, which is segmented is depending on a 1 − d attribute. for example, the gray level. the histogram of image of l levels is represented as hs, where l is the number of gray levels. each pixel of the image has a characteristic which lies in the discrete set. x = {0, 1, ...,l− 1} (6) in the circumstances, a gray level value is represented by each element of the data set. then µil = µi(l) is used to express the degree of membership of the element l to cluster i. the following functional is minimized by the fcm and it is similar to that of bezdek [3]: jq(u,v : l) = l−1∑ l=0 c∑ i=1 (µil) q·hs(l)· ‖ l−vi ‖2a (7) where µil shows the membership degree of the gray level l to cluster i. the fcm is faster than the conventional version processing the whole data set, because that the fcm operates only on the histogram [4]. where hs be the histogram of image of l levels, l is the number of gray levels, hs(l) is the number of occurrences of the level l. the computation of membership degrees of hs(l) pixels is decreased to that of only one pixel with l as gray-level value. general steps of the algorithm are shown as follows in fig. 1. in fig. 1, number of clusters c and the threshold value ε should be fixed firstly, the c is restricted to a range, where 2 < c < l, l = 0, 1, 2, ...,l− 1. gain the l gray levels to initialize the membership degrees µil as follows: c∑ i=1 µil = 1, l = 0, 1, ...,l− 1. (8) 1050 g. xu, s. wang, t. yang, w. jiang e il m i v il m e figure 1: fcm algorithm flow diagram centroid vi is calculated like this: vi = l−1∑ l=0 [µ q il ·hs(l) · l] l−1∑ l=0 [µ q il ·hs(l)] , i = 1, ...,c (9) then refresh the membership degrees as follows: a neutrosophic approach based on topsis method to image segmentation 1051 µ̃il =   c∑ j=1 ( |l−vi|a |l−vj|a ) 2 q−1  −1 (10) the defect measure is presented with e, where e = c∑ i=1 l−1∑ l=0 |µ̃il −µil|, and then estimate it as follows: if (e > ε){µil ←− µ̃il}. finally defuzzification. in this case, the value of clusters is fixed at some exact number, and the value of q is set to 2. the clustering operation is terminated when ε ≤ 10−3, which presents the quality of the clustering results [47]. 3 the proposed method the general steps of the proposed method are shown in fig. 2. the proposed method is achieved by using the modified α-mean operation, topsis method [41, 42] and the modified fcm. a modified α-mean is used to enhance image edges and reduce uncertainty. the βenhancement operation is used to enhance the set t , which is suitable for segmentation. the topsis method is used to take full advantage of the ns information. 3.1 transform the image into ns definition 1 (neutrosophic image). let u be a universe of the discourse, and w ⊆ u which is composed by the bright pixels. a neutrosophic image pns is characterized by three membership sets t , i and f [17]. a pixel p in the given image is described as the neutrosophic set p(t, i,f), which indicates that the pixel p belongs to w in the following way: the probability of t is true, probability of i is indeterminate, and f is false. the pixel p(i,j) in the image domain is transformed into the neutrosophic domain as follows, pns(i,j) →{t(i,j),i(i,j),f(i,j)} (11) where t(i,j), i(i,j) and f(i,j) are the membership values. they are calculated by the following equations, t(i,j) = g(i,j) −gmin gmax −gmin , (12) f(i,j) = gmax −g(i,j) gmax −gmin = 1 −t(i,j), (13) where g(i,j) = 1 w ×w i+w/2∑ m=i−w/2 j+w/2∑ n=j−w/2 g(m,n), (14) and gmax and gmin are the maximum and minimum of all g(i,j), respectively. the indefinite degree is obtained by i(i,j) = δ(i,j) −δmin δmax −δmin , (15) where δ(i,j) = |g(i,j) −g(i,j)|. (16) 1052 g. xu, s. wang, t. yang, w. jiang figure 2: the proposed method a neutrosophic approach based on topsis method to image segmentation 1053 3.2 take modified α-mean operation on the true subset t definition 2 (α-mean operation). the modified α-mean operation for pns, pns(α) is pns(α) = p(t(α),i(α),f(α)). (17) the α-mean operation of the neutrosophic set is defined as follows, t(α) = { t,i < α tmα ,i > α (18) tmα (i,j) = median(t(m,n)), i− w 2 6 m 6 i + w 2 , j − w 2 6 n 6 j + w 2 (19) where tmα (i,j) is the median of t(m,n), f(α) = 1 −t(α) (20) iα(i,j) = δt (i,j) −δt min δt max −δt min (21) δt (i,j) = |t(i,j) −t(i,j)| (22) t(i,j) = median(t(m,n)), i− w 2 6 m 6 i + w 2 , j − w 2 6 n 6 j + w 2 (23) after the modified α-mean operation, the distribution of the elements in i becomes more uniform. furthermore, the image edges are enhanced, and the membership set t becomes more distinct, which is suitable for segmentation. in this paper, α is 0.85 according to the experimental results. 3.3 take β-enhancement operation on the true subset t the intensification operation for the membership µ in fuzzy set is defined as follows µ′(i,j) = f(µ(i,j)) = { 2µ2(i,j),µ(i,j) 6 0.5 1 − 2(1 −µ(i,j))2,µ(i,j) > 0.5 (24) definition 3 (β-enhancement operation). a β-enhancement operation for pns, p ′ns(β), is defined as follows [17] p ′ns(β) = p(t ′(β),i′(β),f ′(β)) (25) t ′(β) = { t,i < β t ′β,i > β (26) f ′(β) = { f,i < β f ′β,i > β (27) where β = i(i,j) is a dynamic parameter that adjusts the threshold based on the image. t ′β(i,j) = { 2t2(i,j),t(i,j) 6 0.5 1 − 2(1 −t(i,j))2,t(i,j) > 0.5 (28) 1054 g. xu, s. wang, t. yang, w. jiang f ′β(i,j) = { 2f2(i,j),f(i,j) 6 0.5 1 − 2(1 −f(i,j))2,f(i,j) > 0.5 (29) i′β(i,j) = δ′t (i,j) −δ ′ t min δ′t max −δ ′ t min (30) δ′t (i,j) = |t ′(i,j) −t ′(i,j)| (31) t ′ (i,j) = 1 w ×w i+w/2∑ m=i−w/2 j+w/2∑ n=j−w/2 t ′(m,n). (32) after the β-enhancement operation, the membership set t becomes more distinct and has less information redundancy, which is appropriate for segmentation. 3.4 topsis method based on ns definition 4. assume that an completely accurate pixel pnsture(i,j)=[1,0,0], in the image is described that it belongs to w without any doubt. moreover, pnsfalse(i,j)=[0,1,1] is described that it does not belongs to w. d(i) in the image indicates the distance between the pixel pns(i,j) and pix pnsture(i,j) d(i) = d(pns(i,j),pnsture(i,j)) d(pnsture(i,j),pnsfalse(i,j)) = √ (t(i) − 1)2 + (i(i) − 0)2 + (f(i) − 0)2√ (1 − 0)2 + (0 − 1)2 + (0 − 1)2 (33) in the method, the information of the pixels are made full use. in addition to the probability t and f that the pixels is in a category or not, its degree of neutrality i is also considered. in other words, uncertain information in the obtained image is involved in the clustering process. accordingly, our method is more accurate and more reasonable in image segmentation. 3.5 modified fcm based on ns considering the effect of indeterminacy, two sets, t and i are composed into a new value for fcm clustering: x(i,j) = { t(i,j),i(i,j) 6 γ tγ(i,j),i(i,j) > γ (34) here, γ=0.85 is determined by experiments. then the modified fcm is applied to the subset t as introduced in section 2. first, the pixel information in the matrix x(i,j) is transformed into a vector x, then the image modified information vector x is subjected to the following recursive algorithm for cluster segmentation. jq(u,v : x) = m∑ k=1 c∑ i=1 (µik) q ‖ xk −vi ‖2a (35) µik =   c∑ j=1 ( ‖ xk −vi‖a ‖ xk −vj‖a ) 2 q−1  −1 (36) a neutrosophic approach based on topsis method to image segmentation 1055 vi = m∑ k=1 (µik q ×xk) m∑ k=1 µik q (37) where jq(u,v : x) can be minimized by (u,v ). the proposed method can be summarized as the following steps: step 1. transform the image into ns domain using eq. (12) to eq. (16). step 2. perform the modified α-mean and β-enhancement operations on the subset t through eq. (17) to eq. (32). step 3. compute the distance d(i) using eq. (33). step 4. if (d(i+1)-d(i))/d(i) < δ, go to step 5; else go back to step 2. step 5. apply the modified fcm to the subset t by eq. (34) to eq. (37). step 6. segment image. 4 experiments and discussions (a) (b) (c) figure 3: simulated image 1 used for segmentation: (a) original simulated image, (b) segmentation result of guo’s method [17], (c) segmentation result of proposed method figure 4: simulated image 2 used for segmentation:(a) original simulated image, (b) segmentation result of guo’s method [17], (c) segmentation result of proposed method in order to test the effectiveness of the proposed method, the gaussian noise whose expectation is 0 and the variance is 0.015, that is, n(0, 0.015) is added to two simulated images. as 1056 g. xu, s. wang, t. yang, w. jiang shown in fig. 3(a), the upper gray belt is close to the background gray, which is easy to be confused. in fig. 4(a), gray belts of two layers below are difficult to be distinguished. fig. 3(b) and fig. 4(b) are the segmentation results of the guo’s method [17]. in fig. 3(c) and fig. 4(c), segmentation results of the proposed method in this paper, the shape and size of image are segmented better. the three regions are distinct and easy to be recognized by the proposed approach. the edges of the image are also more clear. the error segmentation rates of fig. 3(b) and fig. 4(b) are 0.424 and 0.611, which show that many pixels are wrongly segmented by the guo’s method [17]. the error segmentation rates of fig. 3(c) and fig. 4(c) are 0.243 and 0.384, which perform that a less of pixels are wrongly segmented by the proposed method. then let’s consider the consequent of dividing some actual images. fig. 5(a) and fig. 6(a) are real lena images with different noise levers. the error segmentation rates are 0.815 and 0.718 in fig. 5(b) and fig. 6(b) segmented by guo, respectively. however, the false judgement rate of our proposed method is just 0.190 and 0.367 in fig. 5(c) and fig. 6(c). in the split photos, hat, hair and woman’s face, three main areas in the image are segmented more correctly and clearly. more pixels are segmented correctly and the regions are more consistent and homogenous in fig. 5(c) and fig. 6(c), which are better to be distinguished. (a) (b) (c) figure 5: real image used for segmentation: (a) original lena image with gaussian noise; (b) segmentation result of guo’s method [17]; (c) segmentation result of proposed method (a) (b) (c) figure 6: real image used for segmentation: (a) original lena image; (b) segmentation result of guo’s method [17]; (c) segmentation result of proposed method 5 conclusions in this paper, a new method with the topsis method, which effectively enhances the image edges and reduces the indeterminacy with the modified α-mean operation and β-enhancement operation, is proposed. with the topsis method, the ns information in the image is fully utilized. the information of t , i and f in the ns domain is made the best. the experimental results with simulated images and real images show that the proposed method is more effective in handling vague edges and in better making full use of ns information. with different noise levels and different kinds of noise, especially complex noise contained in the real medical images, a neutrosophic approach based on topsis method to image segmentation 1057 the proposed method performs better and more accurately than the existing method. image segmentation results are obtained more precisely and reasonably. at the same time, the proposed method shows that the notable validity in increasing boundary accuracy and region harmony. the comparison between the experimental results and the error segmentation rates can illustrate that our proposed method can handle the uncertain and indeterminate information better. in the further research directions, it may find more applications in diverse fields of control theory, computer vision, and artificial intelligence, especially in the image processing field. acknowledgments the work is partially supported by science and technology on avionics integration laboratory and aviation science foundation (program no. 20165553036). conflict of interests the authors declare that there is no conflict of interests regarding the publication of this paper. bibliography [1] ali, m.; smarandache, f. 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(2011). neutrosophic image segmentation approach based on thermal balance, computer engineering, 37(19), 210–212, 220, 2011. international journal of computers communications & control issn 1841-9836, 10(2):248-262, april, 2015. a comprehensive trust model based on multi-factors for wsns n. wang, y. chen na wang 1. moe research engineering center for software/ hardware co-design technology and application east china normal university no.3663 north zhongshan rd, shanghai 2. faculty of engineering shanghai second polytechnic university no.2360 jinhai rd, shanghai wnoffice@126.com yixiang chen* moe research engineering center for software/ hardware co-design technology and application east china normal university no.3663 north zhongshan rd, shanghai 200062 china *corresponding author: yxchen@sei.ecnu.edu.cn abstract: the goal of this paper is to introduce a novel trust model for wireless sensor networks. this trust model calculates trust value of nodes through two kinds of trusts: private trust and interactive trust of a node. private trust focuses on the past record of a node’s sensing and its remaining energy. interactive trust cares for the interaction of a node with its neighbors. this trust model can recognize faulty nodes inside a network, reduce their impaction to data acquisition, and select a trust routing for precise data transmission. a simulation is given and shows that this trust model has a higher performance than tms and eccr in some aspects. but it consumes more energy than eccr for its comprehensive structure of data. keywords: wireless sensor networks, trust model, private trust, interactive trust, trust routing. 1 introduction and related work a wireless sensor network (wsn, shortly) consists normally of thousands of tiny embedded computers which are equipped with a specific type of sensor to sense information from the surrounding environment. the collected information is relayed from sensor to sensor, using a secure multi-hop routing protocol, until the data reaches the desired destination node, which is called as a sink. the wsn technology has been applied in many areas, such as industry, environment, seismology, construction, transportation, military warfare, traffic control and agriculture [1]. sensor nodes in wsns suffer often from resource constraints such as low computational capability, limited storage capacity, limited communication bandwidth, and the use of insecure communication channel. wsns are also prone to varied types of attacks [4–8] such as black hole attack and sniffing attack. cryptographic solutions can successfully defend against outsider attack but may fail under insider malicious attacks. this vulnerability along with the cooperative nature of sensor networks requires one for assessing the trust relationships among the nodes in the network [10]. recently, some researches focusing on trust in wsn have been practiced based on different background such as gtms [12], rfsn [13], hatwa [14] and [15]. but all the above schemas are based on either routing or data sensing respectively. in [13], a method considering key factors in running of wsn is proposed. but the researches are far from adequate. in [11], nbbte (node copyright © 2006-2015 by ccc publications a comprehensive trust model based on multi-factors for wsns 249 behavioral strategies banding belief theory of the trust evaluation algorithm) is proposed, which integrates the approach of nodes behavioral strategies and modified evidence theory. the same authors as in [11] proposed in [3] a tms schema based on the method in [11]. direct trust value on each neighbor node is calculated by considering trust factors which are defined according to node behaviors in order to detect malicious attacks. at the same time, recommended trust value from common neighbor nodes is obtained through conditional transitivity and the weight of each recommendation is obtained by revised d-s evidence theory. both [11] and [3] consider factors of received packets rate, successfully sending packets rate, packets forwarding rate, data consistency, time frequency, node availability and security grade. data consistency is defined that the packets sent among neighbor nodes are similar in the same area according to the application. time factor is defined that the size of time grade is dependent on the specific situation. if it is established too large, then integrated trust value is affected by history heavily. on the contrary, if it is established too small, then trust value relies on a single period overly. except for the above three factors, others factors mainly aims on communication. the multi-factors in [11] make a progress in trust management for wsn. with regardless of the core algorithm in [11], the factors considered are reasonable and adequate to compute trust value of a common node. but it does not deal with the relationship of the factors. the reference [2] introduces a notion of trust evaluation to build trust mechanism for each node inside network. about routing, in order to improve reliability, some multipath routing technologies have been mentioned. the k heavier path is used between the source node and purpose and the packet is divided into different packet to transmit in [19]. in literature [20], they take to adjacent cluster head number as a topological construction weights, looking forward to a constant approximate rate based minimum network connected dominating sets, but this method does not consider cluster head of network node energy influence on the performance of the whole. according to the size of the nodes energy to network between the influence of choice, the literature [21] considers energy as weights, ensure constant approximation rate at the same time as a priority high-energy node cluster of communication between nodes, and, to some extent, improve the network energy efficiency, but it ignores the routing communication costs between clusters, existence of high communication costs of premature failure of the head node limitations. in [22], defining link reliability strategy constructed by remain energy, and communication cost of nodes as topology weight to synthetically reflect the energy efficiency of dominator, an energy-radio and communication cost route (eccr) is proposed to solve the problem that the average energy consumption in cluster and minimum communication cost. the author takes both node residual energy and distance into account to compete cluster head, at the same time, in order to reduce the cluster head energy cost, link reliability and hop are used to establish topological structure. the experimental results show that the algorithm not only has the energy saved characters, but also ensures the reliability of topology links and extends the network life-cycle efficiently. but eccr focus on energy efficiency but not data precision while trust may decide which route is trust for transmission to get more precise data, so the performance can be improved in view of trust value. in order to meet both data and energy requirement, it is necessary to build a trust model based on multi-factors which consider data, communication, clock and energy to help more application such as data aggregation, fault detection and route selection. the contribution of this paper is: 1. create a node trust model based on interactive factors and private factors. 2. give a routing algorithm to compute routing trust based on nodes’ trust value. 3. apply our model in fault detection. 250 n. wang, y. chen 4. evaluate and compare our model with other models. this paper introduces a novel trust model for wireless sensor networks. this trust model calculates trust value of nodes through two kinds of trusts: private trust and interactive trust. private trust focuses on the past record of a node’s sensing and its remaining energy. interaction trust cares for the interaction of a node with its neighbors. this trust model can recognize faulty nodes inside a network, reduce their impaction to data acquisition, and select a trust routing for precise data transmission. a simulation is given and shows that this trust model has a higher performance than tms and eccr in some aspects. but it consumes more energy than eccr for its comprehensive structure of data. the rest of the paper is organized as follows. the definitions and models are proposed in section 2 and 3. the routing algorithm based on our trust model is depicted in section 4 and we apply this model in fault detection on section 5. the comparison and evaluation of our trust model with other models are given in sections 6. the conclusions and future work are presented in sections 7. 2 definition of key attributes for trust in wireless sensor networks in order to defeat various attacks, we have to take all kinds of factors that depend on the interactions between neighbor nodes into account. however, there is an obvious trade-off between the number of factors and the energy consumption. in this paper, we pay attention on four key attributes which are called as to be connectivity, consistency, synchronization and adequacy to the node’s trust. since the structure of wsn is divided into inner indicating the level within one cluster and outer intra indicating the level between clusters heads. the structure is shown in figure 1. figure 1: the structure of wsn we focus on the evaluation of nodes in one cluster and then use the same method to evaluate the trust between cluster heads. before doing it, we depict the running process of stages within a wsn as follows. event-driving stage: when there is a request of detecting in a certain field from sink node, sink node will send a sensing order to its neighbor nodes. a comprehensive trust model based on multi-factors for wsns 251 self-organizing stage: the nodes received the request will be as the first level cluster heads. and the cluster heads will select their members according to cluster protocol such as leach. detecting stage: member node senses data after receiving the request message from its cluster head. the sensing action is frequent according to the sampling period. communication stage: member node sends its data to the cluster head and its neighbors. in this stage, interaction and data aggregation are crucial to trust value. when a member node sends data to other nodes, there will be two cases that are successful connection and failed connection with regard to communication. and the data sent from a certain member node may have two results comparing with other data that is consistent and inconsistent with regard to data. furthermore, the sensing moment should take into account when measure data consistency since data has time attribute. data aggregation: after data exchange, each node has the information of its neighbors and cluster head has the information of all members in cluster. in order to reduce information and energy consumption, data from member nodes will be aggregated in head with certain format and will be sent to high-level head. convergence stage: data from each level head will be aggregated and sent hierarchically till to the sink. during the process, attacks such as bad mouth and fault such as hardware fault may influence the result of aggregation. both attack and fault are called abnormal cases in this paper. in order to exclude the abnormal nodes, one of the resolve methods is giving a trust value to each node. when the trust value is keeping lower for certain duration, it should be deleted from the network. since the communication and sensing are the main action in wsn, the interaction and data should be regarded as the main parameters to construct trust value. 3 node trust model based on multi-factors in this section, we divide node trust into two parts: interactive trust and private trust. interactive trust describes the trust of a node’s interaction with its neighbor nodes based on interactive factors. private trust focuses on describing a node’s nature reputation based on private factors. 3.1 introduction of factors in wsn, there are interactions between nodes, so we abstract interactive factors. during the interactions, data including its time attribute will be exchanged in cluster head. we call them as interaction, data and time. considering the factors, we should give a combined trust model. but as the relations between factors are not simple, traditional model cannot be applied here. generally, relations between two factors are divided into three classes that are promoted, opposite and uncorrelated. promoted relationship means the promoting of a will make b promote. opposite relationship means the promoting of a will make b decline. and uncorrelated relationship means the change of a will make no influence on b. the factors within this paper have relationship as depicted in table 1. table 1 describes the relationship of interactive factors which involve success interaction between nodes, data similarity between nodes and approximate sensing time of two nodes. here, symbol plus means promoted relation between two factors, minus means opposite relation and blank means uncorrelated relation. it is shown in table 1 that when valid interaction increased, similar data will increase (here, we consume general case that normal data is more than fault data). time which is an attribute of data has the same relation with other factors as data. 252 n. wang, y. chen table 1: relationships between interactive factors interaction data time interaction + + data + + time + + table 2: private factors factors: data energy we also consider the private factors of a node in this paper. table 2 describes the private factors for a single node in which the variation of sensing data in a period and remaining energy are taken into account. the data factor relates the data correctness and energy factor relates to the node’s working ability. we give a reward coefficient to correct data and a penalty coefficient to error data. energy is a natural factor that can deduce from initial energy and remaining energy. it decides whether the node is running. 3.2 interactive factors we define the interactive factors as: interaction: when considering the number of continuous communication during t, we compute it as dcti,j(∆t) = ⌊( 100 · si,j(∆t) si,j(∆t) + fi,j(∆t) )( 1√ fi,j(∆t) )⌋ . (1) in order to compress the size of data for energy consumption reduction, the trust value is multiplied by 100 and get integer. where, si,k(∆t) is the success number of communication between node i and j in time ∆t, and fi,j(∆t) is the failure number of communication between node i and j in time ∆t. when failure number is larger than success number, we may think that these two nodes i and j are distrust. we make the value decline sharply by dividing √ fi,j(∆t). specially, if fi,j(∆t) = 0, we set dcti,j(∆t) = 100 [16]. data: dsti,j(∆t) = ⌊( 100 · ci,j(∆t) ci,j(∆t) + di,j(∆t) )( 1√ di,j(∆t) )⌋ . (2) ci,j = xixj x2i + x 2 j − xixj . (3) ci,j(∆t) is the total number of similar data comparison of node i with j in ∆t time, and di,j(∆t) is the total number of dissimilar data comparison. xi is sensing data of node i. specially, if di,j(∆t) = 0, we set dsti,j(∆t) = 100. time: ti,j = ⌊ 100 · titj t 2i + t 2 j − titj ⌋ . (4) the factors above can fulfill the complexity of trust evaluation. furthermore, the factors will change with the elapse of time not only by themselves but by other factors. a comprehensive trust model based on multi-factors for wsns 253 3.3 interactive trust related to interactive factors now, we present the interactive trust based on interactive factors. interactive factors mentioned above are abstracted from wsn which are crucial when computing the trust value between nodes. in order to depict the relations and factors’ importance in the model, we use a weighted trust model to compute trust value between two nodes as formula (5). ti = ⌊ n∏ i=1 y αi i ⌋ ( n∑ i=1 αi = 1 ) . (5) ti presents the direct interactive trust between two nodes, and yi is value of the ith factors with its weight αi. here, yi indicates four factors which come from formula (1)-(4). since α1 is the weight of factors, its value should display the importance of a factor in ti. with considering the relations described in table 1, we use reciprocal matrix to decide each factor’s weight. for example, from the perspective of optimization, the approach of measuring sort vector according to reciprocal matrix is based on the fact that when a = ( ai,j ) n×n is a reciprocal matrix, aω = nω and ω = ( ω1, ω2, . . . , ωn )t where ( ai,j ) = ωi/ωj. in our paper, we set the reciprocal matrix based on table 1 as figure2. and use method naming right characteristic root to calculate weight vector as: (interaction, data, time)=(0.5869, 0.3238, 0.0893).  1 2 61/2 1 4 1/6 1/4 1   figure 2: reciprocal matrix 3.4 private trust based on private factors in wsn, a node’s private trust will depend on its previous action. this private trust must be penalized when its sensing data is deviated far from the average and be awarded when its sensing data is correctly consecutively. for example, a fire can start near a sensor, so that sensor will read values higher than its neighbors at round one. if this is the case, and a large penalty is given to the sensor then it will considered as a fault node where in fact it is not. in our work, the node will be penalized with a small factor and will be rewarded in the next round, since the average will tend to be that of a disaster state. private trust has a combination method that is shown in formula (6). tp = { θ1 · tp−1 + θ2 · f + θ3 · (e−r − 1) + θ4 · e, if d > threshold; tp−1, if d ≤ threshold. (6) in this formula, θ1 + θ2 + θ3 + θ4 = 1. f presents the number of consecutive same sensing out of a predefined number whose value varies between 0 and 1, tp−1 is the last trust value in the previous round, d is the deviation from normal value of sensing, r is the number of misreading and e is whether the node can be found by its parent. θ2 and θ3 are reward and penalty coefficient respectively whose value can vary between 0 and 1. e is deduced from er/ei where er is remaining energy and ei is initial energy. when er/ei is lower than that can support transmit, e is set as −1, otherwise, e is set as 0. 254 n. wang, y. chen once tp is equal to 0, the node is regarded as faulty to be deleted from the network. once er/ei is equal to 0, the node is out of work to be deleted. in order to keep consistent with ti, tp should be multiplied by 100 to get an integer too. 4 routing algorithm based on multi-factors 4.1 double-weight trust diagram when consider nodes in one cluster, we get a g = (v, e, wv, we) consisting of vertexes v , edges e and weight w . each vertex is a node and each edge is the connection of two neighbors. we set private trust of a node as the vertex weight and interactive trust as the edge trust. the figure 3 is a double-weight trust diagram. figure 3: double-weight trust diagram in this diagram, node n1 has its private trust 0.96 and three interactive trusts 90, 95, 95 with n1, n4 and n5 respectively. it is assumed that n3 is the cluster head, when n1 transmits its sensing data to cluster head, it can route as {1, 2, 3}, {1, 5, 3}, {1, 4, 5, 3}, {1, 2, 5, 3}, {1, 5, 2, 3} and {1, 4, 5, 2, 3}. here, we do not consider circle since the circle can’t increase the trust of a routing. in order to select the most trustful routing, we must compute the trust of each routing. 4.2 routing trust in [18], chen et al propose a matrix-based computing method for max-mean measurement. the model defines a series of matrices sk = (skuv)n for max-mean degree inductively as s 1 = a and sk = a ⊙ sk−1, for any k2, skuv = { 0, u = v; 1/k max{aur ⊕ sk−1uv }, otherwise. (7) where a ⊕ b = { 0, min{a, b} = 0; a + b, otherwise. (8) our trust diagram in figure 3 describes the private trust and interactive trust respectively, but, when selecting a route, a combined trust should be considered. we indicate private trust as pt and interactive trust as it, then the combined trust can be computed through formula (9). ct = √ pt · it. (9) the trust matrix for fig.1 is as figure 4. a comprehensive trust model based on multi-factors for wsns 255   0 93 0 95 95 0 0 92 0 92 0 0 0 0 0 0 0 0 0 91 0 80 30 0 0   figure 4: trust matrix for figure.3 according to formula (7) and (8), the six routing trust is shown in table 3. table 3: routing trust for figure.3 routing {1, 2, 3} {1, 5, 3} {1, 4, 5, 3} {1, 2, 5, 3} {1, 5, 2, 3} {1, 4, 5, 2, 3} trust 92 93 72 71 89 89 it is clearly that, if precise is prior to length, routing {1, 5, 3} should be selected as routing between n1 and the cluster head n3. 5 application of our model in fault detection one of the critical tasks in designing a wireless sensor network is to monitor, detect, and report various useful occurrences of events in the network domain which is determined by the result of data aggregation. but sensor nodes are neither reliable nor stabile due to outer factors as environment and inner factors as energy. then fault detection is critical to the efficiency of data aggregation scheme. in our former work [18], we present an improved k-means data aggregation algorithm considering the proposal of outliers. each cluster includes three types of sets: aggregation data set, fault data set and abnormal data set. abnormal nodes can be detected according to the aggregation result. but the detection of fault nodes is completed in a hierarchical structure till the level of sink. when trust value reserved as an attribute of a node, the detection of fault node and outlier during data aggregation can deduce from trust values. if a node’s private value is higher than a threshold, it is regarded as a normal node. when nodes are not regarded as normal, they may be outlier or abnormal nodes which must be recognized with using interactive value. if its interactive trust is higher than a threshold while its private trust is low, it may be the case that the node is located on the edge of the event area and detects an event such as fire or insect pest. otherwise, it must be a fault node. the process is described in figure 5. 6 validations and evaluations 6.1 properties of ti as interactive trust value is depended on multi-factors, the value of each factor should impact the trust value. that is to say the increasing of factors’ value can lead to the increasing of interactive trust value. in another aspect, impacting of one factor must be limited. generally, trust value is set as a real that lower than 1. we can prove the property above. assertion 1. ti ≤ 1. proof: in formula (5): ti = ∏n i=1 y αi i ( ∑n i=1 αi = 1 ) , yi means interaction, data or time. 256 n. wang, y. chen figure 5: process of fault detection based on trust according to formula (1), (2) and (4): interaction ≤ 1; data ≤ 1; time ≤ 1;∑n i=1 αi = 1, αi ≤ 1. then ti ≤ 1. 2 assertion 2. ti is monotone. proof: t̄i = αi · yαi−1i · y α1 1 · · · · · y αi−1 i−1 · y αi+1 i+1 · · · · · y αn n ≥ 0. 2 this attribute ensure that the increasing of factors’ value can lead to the increasing of interactive trust value. assertion 3. ti is agglomerate. proof: ¯̄ti = αi(αi) · yαi−2i · y α1 1 · · · · · y αi−1 i−1 · y αi+1 i+1 · · · · · y αn n ≤ 0. 2 this attribute ensure that impacting of one factor is limited. 6.2 properties and evaluation of tp in formula (6), the initial private trust value is 1, with the running of wsn, the value either keeps unchanged or iterates with the penalty and reward coefficients which make the value being lower than 1. if we set θ2 = 0.5 and θ3 = 0.5, where the same 0.5 has different impact on correct reading and misreading since the misreading is depicted in exponential manner. when a round a comprehensive trust model based on multi-factors for wsns 257 of simulating is set as 10 reading and initial value of trust is set as 1, the private trust value of normal node, event node and fault node is shown in figure 6. if we only consider data, normal nodes’ private trust will not change during the process, while event nodes’ will decline at the beginning of event, then raise at the next round since the penalty will offset by the reward. fault nodes’ trust will decline immediately until reaching zero and the nodes will be deleted from the network. figure 6: private trust value for different nodes 6.3 evaluation of routing selection based on our model in wsn, it is important to choose a routing protocol, because the efficient routing paths between the sensor node and the sink change with time, especially in case of considering different factors. the above greedy forwarding is a candidate because it is simple and efficient about data transmission. in greedy forwarding, each node just needs to know three pieces of information: the trust value of its own and interaction with neighbors, its location, the location of neighbors. the relative location is displayed in figure 3 by directed arc, the direction is from nodes farther away from destination to nodes nearer to the destination. the impacts of interaction trust based on channel failure rate and private trust base on sensing failure rate on routing reliability can be described in figure 7. our simulation experiment is based on ns3. fifty sensor nodes are distributed in a space of 500×700, and the communication radius is set as 60. each node has two to five neighbors in the experiment and the node’s location is already known. the detailed value is shown in table 4. the comparison of routing reliability for eccr and our model with different fault rate is shown in figure 8. here, reliability displays valid interaction and precise data transmission. we can get that our model has a higher reliability than eccr in the whole. especially, when there are fault nodes in routing, our model is effected little in reliability while eccr’s reliability declines a lot just because it does not consider sensing failure caused by faulty nodes. but our model selects normal nodes with high private trust to transmit data to a neighbor node having highest interaction trust with it. 258 n. wang, y. chen figure 7: relationship between reliability and failure rate table 4: values in evaluation symbol description values n number of nodes 50 n number of cms in a cluster 6-8 m number of cm’s neighbors 4-6 θ1 history trust coefficient 0.9 θ2 reward coefficient 0.04 θ3 penalty coefficient 0.05 θ4 energy coefficient 0.01 t threshold for e 0.1 except for reliability, energy consumption is another important merit to measure the routing protocol based on the trust model. the structure of data is shown in table 5. simulation result shown in figure 9 indicates that the energy consumption of ours models is higher than eccr because our model keeps a larger size of data than eccr since eccr does not consider sensing data. it also indicates that the fault rate impacts a lot in our model because when a node is faulty, its private trust value is computed by the iterated part in formula (6). table 5: structure of data in our model node id the size of interaction the size of similar the size of trust si,j fi,j ci,j dx,y interactive private 2 bytes 1 bytes 1 bytes 1 bytes 1 bytes 1 bytes 1 bytesthe size of time | the size of sensing data | the size of energy a comprehensive trust model based on multi-factors for wsns 259 figure 8: reliability of different fault rate figure 9: energy cost of different fault rate 6.4 evaluation of fault detection based on our model in this subsection, we compare our model with tms since tms is an outstanding trust model considering different factors in wsn. the result is shown in figure 10 which indicates that the detection of our model is higher than tms during the running time because we introduce private trust to rapidly judge a fault node. but the fluctuation is larger than tms due to the temporary malicious judge of event nodes. 260 n. wang, y. chen figure 10: detection rate within running time 7 conclusion and future work in this paper, a trust model based on multi-factors is proposed. it defines multi-factors in wsn to help build trust model including private trust and interactive trust. private trust focuses on the past record, current record and remaining energy of a node and interactive trust focuses on the interaction of a node with its neighbors. interactive factors include communication, data and time to keep nodes connective, consistent and synchronized. private factors include data and energy to ensure a node consistent with itself and keeping active when working. the validation shows the increasing of factors’ value can lead to the increasing of interactive trust value and the impacting of one factor is limited. using the two types of trusts, a routing trust algorithm is proposed that is expressed as a two-weight diagram. with the routing model, a node can transmit its sensing data more accurately to the cluster head or sink than eccr, but consume more energy. furthermore, the trust model can be used in fault detection and the simulation results comparing with tms show that the proposed model can rapidly detect fault and effectively raise fault detection rate with a fluctuation due to event nodes. in the future, we will pay more attention on the application of the model and algorithm this paper proposed to real wireless sensor network, for example, the environment detecting. acknowledgements this work is supported by the national basic research program of china (grant no. 2011cb302802) and the innovation group project of the national natural science foundation(grant no. 61321064). the national natural science foundation of china (grant no. 61370100). shanghai knowledge service platform project (no. zf1213). wang acknowledges the supported of the key discipline of shanghai second polytechnic university named software engineering (no.xxkzd1301). the authors would like to thank the referees for their invaluable comments and suggestions. a comprehensive trust model based on multi-factors for wsns 261 bibliography [1] osman khalid, samee u.khan(2013); comparative study of 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[22] machado kassio, rosario denis(2013); a routing protocol based on energy and link quality for internet of things applications, sensors, 13(2):1942-1964. international journal of computers communications & control issn 1841-9836, 9(4):439-452, august, 2014. performance evaluation of enum name servers s.h. da mata, j.m.h. magalhaes, a. cardoso, h.a. carvalho, p.r. guardieiro saulo henrique da mata*, johann m. h. magalhaes, alexandre cardoso, paulo r. guardieiro federal university of uberlandia av. joao naves de avila, 2121, bloc 3n uberlandia, mg, brazil 38400-912 johann@iftm.edu.br, alexandre@ufu.br, prguardieiro@ufu.br *corresponding author: saulodamata@doutorado.ufu.br helio a. carvalho cpqd, rua dr. ricardo benetton martins, s/n parque ii do polo de alta tecnologia, campinas, sp, brazil 13086-902 helioac@cpqd.com.br abstract: enum is a protocol designed for mapping e.164 telephone numbers into internet uris. this protocol imposes new requirements and challenges on traditional dns servers. in this sense, this paper presents a performance evaluation of enum name servers considering these new scenarios. we selected four name servers implementations (bind, mydns-ng, nsd and powerdns) to compare their performance, using a benchmarking testbed. to perform the evaluation, we have defined three performance metrics: query throughput, response time, and cpu usage. these metrics required the development of a new procedure for the evaluation, since dnsperf presented limitations in the management of cpu resources. from results, we classified mydns-ng as not suitable for enum purposes, due to its quite low query throughput and poor scalability. powerdns server performed better than mydns-ng, but presented high sensibility to database size. on the other hand, bind and nsd servers achieved a high query throughput with great scalability features. since we have used extended scenarios and size of database that were not evaluated before, these results bring to light an interesting insight about enum servers. keywords: enum, benchmarking, bind, nsd, mydns-ng, powerdns 1 introduction nowadays, we are witnessing a continuous evolution of communication networks. in this evolution process, services previously exclusive to one communication network began to be shared with other networks. in this context, voice over ip (voip) is one of the best examples of this migration, where the voice service from public switched telephone network (pstn) is added to data service from internet and both services are available with a lower cost than the currently one offered by traditional networks. despite the expanding number of voip users and the availability of several providers, geographically, users of this technology are located on a set of "isolated islands", where the interconnection among these islands may occurs by means of pstn. this connection is performed by media gateways (mgws) and e1 links, which are commonly used to interconnect the voip carriers with the pstn. this interconnection infrastructure requires investments by operators in equipment and the payment of fees for the use of communication links. these expenses increase the total operating costs, burdening the end user. furthermore, one should consider that in spite of the advantages of voip application over the pstn, it is not possible the immediate replacement of this technology, because of the complexity involved. this implies a transition period when both technologies will coexist, since the migration copyright © 2006-2014 by ccc publications 440 s.h. da mata, j.m.h. magalhaes, a. cardoso, h.a. carvalho, p.r. guardieiro of pstn subscribers to voip can occur slowly and gradually. in this scenario, we have four basic types of calls: pstn-pstn, pstn-ip, ip-pstn and ip-ip. so, the problem is not restricted only to avoid the use of pstn for ip-ip calls, but also to allow calls between terminals of two different networks (pstn-ip, ip-pstn). traditionally, e.164 telephone numbers [1] are used in pstn and uniform resource identifiers (uris) [2] are used on the internet. signaling protocols such as sip and h.323 can work with these two types of identifiers. for internet users, who usually have the availability of a alphanumeric keyboard, the textual names (uris) are mostly used because of easy memorization and deduction. however, a problem arises when voip and pstn networks are interconnected, since pstn terminals typically provide a numeric keyboard to users. thus, it is necessary a mapping system between e.164 telephone numbers and uris which permits the interconnection of pstn and voip users. moreover, this mapping system must allow ip-ip calls without the connection with pstn. in this context, the use of electronic number mapping (enum) protocol stands out as a plausible solution. enum [3] is a technology that maps e.164 telephone numbers used in pstn into uris used on the internet. although enum is based on existing dns infrastructure, it has certain features, for example, a database with a huge number of records and the requirement of short resolution time to reach a pstn similar performance. in addition, it causes an increase in network traffic, since the size of the response message to enum queries is greater than in classic dns. therefore, it is of fundamental importance a study of how enum servers perform on this new environment. in this context, this paper presents a new performance evaluation of enum name servers. using a high performance hardware, we selected four different name servers implementations (bind, mydns-ng, nsd and powerdns) for the evaluation. our goal was to make a detailed study of the performance of each one of these servers and verify whether this performance can meet the enum protocol requirements. the rest of the paper is organized as it follows: section 2 describes related work. in section 3 we present our testbed and methodology. section 4 shows the benchmarking results. finally, concluding remarks are offered in section 5. 2 related work the performance study of enum servers has been reported in some works. in [4] and [5] we find a benchmarking study of three enum servers (bind, navitas and powerdns). according to authors, navitas and powerdns met the requirements of enum protocol, considering performance metrics as query throughput, response time and scalability. on the other hand, bind was not qualified as a serious enum server. in [6], we find another benchmarking study of two enum servers (bind and nsd). in this work, bind and nsd presented a similar performance for query throughput that is quite different from the results presented in [5]. in [7], there is an evaluation study of number portability and record updating process in enum systems. using dns zone transfer method for database synchronization, this work evaluated two enum servers (bind and powerdns). the considered metric was the information transference speed between different zones. the results showed that bind was 3 to 4 times faster than powerdns to deliver the number portability data. in [8] there is a performance evaluation of enum protocol considering different approaches for enum architecture. in this work, the focus was not the enum server performance, but the hierarchy arrangement of these servers. in [9], there is not a comparative study between enum servers, but bind was evaluated in the context of french enum architecture. the goal of this work was to build a simulation tool performance evaluation of enum name servers 441 based on the data of a real enum system. a similar work can be found in [10]. in all these works, we found different types of hardware and servers. besides, there are differences in the methodology and, as a consequence, it is not easy to compare the results presented by each work. however, among these works, [5] and [6] present a more complete study, a concise methodology, and good hardware and software profiles. although these works present similar objectives, they report divergent results. moreover, they were developed about 4 to 5 years ago. since then, we have been observing a great evolution for hardware, software and database technologies, creating the demand for updated and reliable studies about the performance of enum servers. in [11], one can find a preliminary evaluation of enum name servers. in the present paper, we extend this work by adding new scenarios of evaluation not contemplated in any previous works, for example, performance evaluation considering multiples naming authority pointer (naptr) records per fully qualified domain name (fqdn). in this sense, our study fulfills and updates all these former works. we built a powerful testbed that outperforms the hardware and software used in [5] and [6]. we improved the methodology presented in [5] by extending the range of the parameters of evaluation. besides, we report limitations of the dnsperf (version 1.0.0.1) and present results for new scenarios or evaluation. 3 testbed and methodology description 3.1 performance metrics three performance metrics were defined for the tests: • query throughput: the number of queries which the enum server can respond successfully per unit of time, i.e., queries per second (qps). • response time: time between sending of query to server and the moment the client receives the answer to that query. • cpu and ram usage: this metric evaluates how well the enum server uses these hardware resources. 3.2 enum servers nowadays, there is a great number of existing enum servers. we selected a set of servers in a way that we could evaluate different approaches for implementation of a name server. we also considered other factors such as the relevance of the server, availability, documentation and updating process. in this sense, we selected bind (version 9.8.1) and nsd (version 3.2.10) to represent the name servers based on zone files. mydns-ng (version 1.2.8.31) and powerdns (version 2.29) were selected to represent enum servers that allow the use of other types of database. for these servers we used mysql (version 5.1) as the database. 3.3 enumbenchtool enumbenchtool is a test management tool for benchmarking of enum servers developed by the network computer lab (ncl) of the federal university of uberlandia. this tool has been developed to automate, standardize and validate the tests and to facilitate the achievement of results. 442 s.h. da mata, j.m.h. magalhaes, a. cardoso, h.a. carvalho, p.r. guardieiro at the current stage of development, enumbenchtool admits the servers bind, mydns-ng, nsd and powerdns. this tool is not responsible for the benchmarking test itself, but it packs several other existing tools, simplifies the benchmarking management and makes configuration, synchronization and validation processes transparent to user. among these packed tools, we highlight dnsperf [12], which is a software developed by nominum, widely used to analyze the performance of authoritative dns servers. enumbenchtool is responsible for triggering each dnsperf test step, setting the number of emulated clients, the runtime, the query file and other parameters. in addition, enumbenchtool is responsible for processing the results from dnsperf. 3.4 number of dnsperf processes unfortunately, dnsperf (version 1.0.0.1) is not a multi-core software, i.e., it uses only one core of the processor for each operating system (os) process. depending on the performance of the enum server, dnsperf may not be able to send a sufficient amount of queries to saturate the name server using one single os process. thus, the saturation occurs on the client, invalidating the obtained results. so, it is necessary to increase the number of dnsperf os processes emulating clients to avoid this saturation. however, this increase cannot be indiscriminate, since the operating system has limitations to manage a very large number of processes. initial tests showed that the use of five processes is a reasonable choice for this type of evaluation, as showed in table 1. table 1: impact of the number of dnsperf processes in server throughput. processes clients/process max. throughput (qps) 1 500 202743 5 100 309927 10 50 305523 20 25 95189 500 1 60559 3.5 number of clients in [5], there is an evaluation interval from 2 to 60 clients. however, initial tests proved that this interval is too short to evaluate the server satisfactory, since 60 clients were not enough to saturate the enum server and, then, the results would not be plausible. therefore, we established that the number of emulated clients, who make simultaneously queries to server, would be in a range of 2 to 1000 clients. this range proved to be sufficient for stability of the system. 3.6 type of records we evaluated the enum servers considering two types of records: • existing records: the name server has a dns naptr record for the fqdn queried; • non-existing records: the name server has not a dns naptr record for the fqdn queried. 3.7 database record sets to check whether the server meets the requirement for scalability, three database record sets with 500 thousand (500k), 5 million (5m) and 50 million (50m) of records were defined. performance evaluation of enum name servers 443 3.8 query files using the enumbenchtool, we created query files for each database record set and for each record type as well. for each query file, these fqdns were generated in a random manner to avoid cache memory interferences. in this sense, we also generated independent random query files for each dnsperf processes. 3.9 dns zones each record set was divided into ten zones, with each zone consisting of one tenth of the total number of records. this division method, reported in [5], is a practical and efficient generation process for different types of records. furthermore, using bind, we evaluated the impact of the number of dns zones in the server query throughput, as showed in table 2. table 2: impact of the number of dns zones in server throughput. number of dns zones max. throughput (qps) 5 280994 10 324550 50 271256 100 270402 1000 265486 each zone file starts with a $ttl directive. we set the ttl as 86400 seconds. as we have a huge database and the records are queried using random and independent query files, the cache mechanism is not a great factor in our experiment. an example of the beginning of a zone file is as follow: \$ttl 86400 \$origin 0.3.3.4.3.5.5.e164.arpa. @ in soa ns.0.3.3.4.3.5.5.e164.arpa. root.e164.arpa. ( 2011080901 21600 3600 604800 3600 ) in ns ns.0.3.3.4.3.5.5.e164.arpa. ns in a 10.0.0.1 3.10 testbed layout the testbed consists of two linux machines, running ubuntu 11.10 and ubuntu server 11.10. each machine has two processors intel xeon 6-core e5645 ht 2.40 ghz, 16 gb of ram and two hard drives of 500 gb. both machines were connected via 1000 mbps ethernet connections. the testbed layout is based on [5] and is illustrated in figure 1. the master entity is responsible for synchronizing the activities of the machines under test. it sends commands to other two machines for the starting of each step of the test as well as the resource monitoring. control messages were sent through a link of 100 mbps. queries and responses were sent through an exclusive link of 1000 mbps. 444 s.h. da mata, j.m.h. magalhaes, a. cardoso, h.a. carvalho, p.r. guardieiro figure 1: testbed layout. 4 performance comparison of the enum servers the tests carried out at ncl were constituted of multiples steps. for each of these steps, the number of emulated clients was gradually incremented, according to commands sent by the master entity. each step had a duration of 60 seconds, in which dnsperf sent queries to enum server. at the end of the step, there was a timeout of 10 seconds before the starting of the next step to assure that the queries from the earlier step would not interfere in the results of the current step. 4.1 overall performance figure 2 shows enum servers performance for query throughput considering the record sets and record types previously defined. in figure 2(a), it is possible to observe that bind and nsd presented a great performance for throughput, reaching a maximum throughput of about 300 kqps. in this same figure, we can observe that powerdns and mydns-ng presented a maximum throughput much lower than the servers based on zone files. this occurred because servers that use zone files load all the records to physical memory (ram), which is known by its access speed. in our tests, mysql was not able to follow this speed. figures 2(b) and 2(c) show the performance of enum servers for throughput with an increased database. bind kept its performance even with a huge increase of the database. nsd also presented a great performance with 5m of records in the database, but the compiler of nsd zone files (zonec) failed to build all zones for the 50m record set. this failure of zonec was also reported in [6]. powerdns and mydns-ng were sensitive to database increasing and the throughput decreased as we increased the database size. another important parameter to be analysed is the server response time. to offer a service with quality similar as pstn, voip call establishment time should not be longer than the pstn call signalling interval. enum lookup response time is part of the total signalling interval for voip call setup. according to [13], the pstn signalling interval has mean of 205-218 ms (337-350 ms in the 95th percentile). thus, the lookup response time on the enum server should be lower than this mean value, since successive queries may occur in a single call setup. figures 3(a), 3(b) and 3(c) show the average response time for the evaluated enum servers. we note that all evaluated servers were able to satisfy the requirement of lookup response time in a voip call. this figure is also in consonance with the throughput results earlier commented. servers that use zone files are faster than those that use mysql database and, as consequence, the former servers presented a higher value for query throughput. figures 2(d) and 3(d) show a similar scenario, but with queries to non-existing records. in this scenario, bind and nsd kept their performance. on the other hand, powerdns and performance evaluation of enum name servers 445 1000 10000 100000 1e+06 1 10 100 1000 t h r o u g h p u t ( q p s ) number of clients bind mydns−ng nsd pdns (a) 500k/existing records 100 1000 10000 100000 1e+06 1 10 100 1000 t h r o u g h p u t ( q p s ) number of clients bind mydns−ng nsd pdns (b) 5m/existing records 100 1000 10000 100000 1e+06 1 10 100 1000 t h r o u g h p u t ( q p s ) number of clients bind mydns−ng pdns (c) 50m/existing records 100 1000 10000 100000 1e+06 1 10 100 1000 t h r o u g h p u t ( q p s ) number of clients bind mydns−ng nsd pdns (d) 5m/non-existing records figure 2: query throughput. mydns-ng presented a reasonable decrease in their performance when we considered queries to non-existing records. figures 2 and 3 indicate that bind and nsd reached saturation when 300 clients were sending queries to enum server. under the saturation condition, the throughput of the server stops to increase and enters an unstable zone. similarly, the response time stops increasing under this condition. in this situation, these servers start to experience increasing losses, as showed in figure 4. however, even under saturation condition these servers answers the queries with a response time near those found for maximum throughput and, therefore, the response time under saturation condition shows only a small variation. on the other hand, mydns-ng reached saturation before 10 active clients in the system. this throughput low performance is reflected in response time that continues to increase until the system starts to experience losses. powerdns also reached saturation before 10 active clients in the system, but it presented a different behavior for response time and loss rate. the response time continued growing up even when losses began to be perceived. figure 5 indicates servers cpu usage. we can note that none of the evaluated servers have overpassed the maximum cpu capacity of the server, confirming that server saturation was caused by the software and not by hardware restrictions. in figures 5(a) to 5(d), we can also note that to reach a high query throughput, bind uses up to 90% of server cpu resources. surprisingly, nsd reached a similar throughput with approximately 15% of cpu resources. the results for mydns-ng and powerdns indicate that these servers did not take advantage of the capacity of multi-core processors. we believe that this is the main factor for the differences in performance between the enum servers. while bind and nsd used all the cores of the processors, powerdns and mydns-ng used only one of the cores. the implementation details 446 s.h. da mata, j.m.h. magalhaes, a. cardoso, h.a. carvalho, p.r. guardieiro 0.0001 0.001 0.01 0.1 1 1 10 100 1000 r e s p o n s e t i m e ( s ) number of clients bind mydns−ng nsd pdns (a) 500k/existing records 0.0001 0.001 0.01 0.1 1 1 10 100 1000 r e s p o n s e t i m e ( s ) number of clients bind mydns−ng nsd pdns (b) 5m/existing records 0.0001 0.001 0.01 0.1 1 1 10 100 1000 r e s p o n s e t i m e ( s ) number of clients bind mydns−ng pdns (c) 50m/existing records 0.0001 0.001 0.01 0.1 1 1 10 100 1000 r e s p o n s e t i m e ( s ) number of clients bind mydns−ng nsd pdns (d) 5m/non-existing records figure 3: response time. 0.001 0.01 0.1 1 10 0 100 200 300 400 500 600 700 800 900 1000 l o s s r a t e ( % ) number of clients bind mydns−ng nsd pdns figure 4: loss rate considering 5m/existing records. that cause these different levels of cpu and memory resource utilization are out of the scope of this work. we also observe that there is only a slight variation in cpu usage when we evaluate different size of database and different types of records as well. table 3 shows memory usage for each enum server. bind and nsd are based on zone files and, as a consequence, the greater the size of database, the greater the consume of ram. on the other hand, mydns-ng and powerdns rely on mysql as their backend. thus, the ram usage is smaller than bind and nsd, with small variation when the database is increased. in order to verify if other variables interfered on the results, two auxiliary metrics were considered: dnsperf cpu usage and network usage. figure 6(a) shows that only part of the total cpu resources were used by dnsperf on the client machine, excluding the possibility of performance evaluation of enum name servers 447 1 10 100 1 10 100 1000 c p u u s a g e ( % ) number of clients bind mydns−ng nsd pdns (a) 500k/existing records 1 10 100 1 10 100 1000 c p u u s a g e ( % ) number of clients bind mydns−ng nsd pdns (b) 5m/existing records 1 10 100 1 10 100 1000 c p u u s a g e ( % ) number of clients bind mydns−ng pdns (c) 50m/existing records 1 10 100 1 10 100 1000 c p u u s a g e ( % ) number of clients bind mydns−ng nsd pdns (d) 5m/non-existing records figure 5: cpu usage. table 3: memory usage. records bind mydns-ng nsd powerdns 500k 1.1% 6.9% 1.2% 8.6% 5m 8.6% 6.9% 11.5% 8.9% 50m 82.8% 6.9% failed 9.8% saturation of hardware resources on the client. similarly, figure 6(b) illustrates the network usage, and we can observe that the average value of usage has not overpassed the maximum capacity of the link (1000 mbps). 4.2 performance for different types of database until now, bind and nsd has overcome mydns-ng and powerdns in all scenarios. these servers represent two different approaches of database for name servers, namely, zone files and mysql. in this context, we built a new scenario to evaluate the effect of these different approaches in the performance of the server. to accomplish this task, we chose powerdns that is the most flexible server among the evaluated servers. powerdns can work with a great number of different backends. therefore, we evaluated this server with two different databases: zone files and mysql. figure 7 shows the evaluation results. powerdns had a better performance when we used zone files. however, even with this improvement, powerdns performance is quite lower than bind performance. this occurs mainly because powerdns has a poor cpu usage. 448 s.h. da mata, j.m.h. magalhaes, a. cardoso, h.a. carvalho, p.r. guardieiro 0.1 1 10 100 1 10 100 1000 d n s p e r f c p u u s a g e ( % ) number of clients bind mydns−ng nsd pdns (a) dnsperf cpu usage 0.1 1 10 100 1 10 100 1000 n e t w o r k u s a g e ( % ) number of clients bind mydns−ng nsd pdns (b) network usage figure 6: dnsperf cpu and network usage considering 5m/existing records. 1000 10000 100000 1e+06 1 10 100 1000 t h r o u g h p u t ( q p s ) number of clients bind pdns (mysql) pdns (zone files) figure 7: query throughput performance for powerdns (mysql and zone file backends) and bind (zone files backend). 4.3 performance considering the use of swap memory despite the great performance of servers based on zone files, they have some drawbacks. each time the name server is restarted, it must load all records to ram. depending on the size of the record set, this can take a long time. moreover, in enum context, the update process of a record stored in zone files is not straightforward, even when we use dns dynamic updates [14]. other relevant factor is the available ram of the server. servers based on zone files rely on ram to reach high performance. if ram is not enough to load all the records stored in zone files, the os will use swap memory to load the rest of data. however, swap memory is quite slow when compared to ram speed. therefore, if swap memory is used, the performance of the enum server will decrease. in order to study this phenomenon, we evaluated bind in a scenario where the ram is not enough to load all records in the zone files. for this scenario we used 100m of existing records. figure 8 shows the result. as we have foreseen, the query throughput is severely affected. the throughput using swap memory is about 300 times lower than in the scenario using only ram. 4.4 performance considering multiples naptr records per fqdn. enum protocol is responsible for mapping e.164 telephone numbers into uris. in this context, the e.164 telephone number is converted into a fqdn for querying process. however, one fqdn can be mapped into multiples naptr records, according to available services for a given user. thus, it is interesting to know the behavior of enum servers in a scenario where performance evaluation of enum name servers 449 10 100 1000 10000 100000 1e+06 1 10 100 1000 t h r o u g h p u t ( q p s ) number of clients bind (ram) bind (ram + swap) figure 8: bind using only ram and ram plus swap memory. there are multiples naptr records for each fqdn. in our evaluation, the number of naptr records per fqdn can be different for each server, since each of these servers presented distinct sizes of response messages. the number of naptr records in each response message was incremented until the size of response message was near to 512 bytes, which is the typical maximum value of a udp segment. if a message is bigger than 512 bytes, this message is truncated. in this scenario we used only existing records and the 5m record set. figure 9 shows the evaluation results. in figure 9(a), bind presents the same performance for both evaluated cases until 100 clients. from this point, for 7 naptr/fqdn curve, the throughput remains constant, indicating a possible saturation state. however, analyzing figure 10(a), it is possible to see that the saturation occurred on the communication link and not on the server. in this case, it would be necessary a link with more capacity to get reliable results. on the other hand, this experiment shows that bind has a great performance even with more naptr records to manage. figures 9(b) to 9(d) show that the other servers were affected by the increase of response message size. differently from bind case, for these servers the communication link was not saturated (see figures 10(b) to 10(d)). 4.5 comparison with related work the comparison between different research works is not an easy task, because of different methodologies or lack of details about the experiment. nevertheless, [5] and [6] are similar works that allowed us to perform some comparison to verify the relevance of our results. table 4 shows the comparison of the approximated maximum throughput for the three researches. in this comparison, our results and results from [5] are related to a 5m record set. for [6], the results correspond to a 1m record set. for all cases we used existing records. table 4: comparison of the maximum throughput. maximum throughput (kqps) work bind nsd powerdns this 325 275 10 [5] 0.650 not evaluated 5.5 [6] 50 40 not evaluated from table 4 we observe the huge increase of throughput that we achieved in our experiments. results are specially surprisingly for bind due to its great evolution. authors in [5] evaluated bind version 9.2.4 and classified it as not suitable for enum due to its poor scaling properties. 450 s.h. da mata, j.m.h. magalhaes, a. cardoso, h.a. carvalho, p.r. guardieiro 0 50000 100000 150000 200000 250000 300000 350000 1 10 100 1000 t h r o u g h p u t ( q p s ) number of clients 1 naptr/fqdn 7 naptr/fqdn (a) bind 700 800 900 1000 1100 1200 1300 1400 1 10 100 1000 t h r o u g h p u t ( q p s ) number of clients 1 naptr/fqdn 6 naptr/fqdn (b) mydns-ng 0 50000 100000 150000 200000 250000 300000 1 10 100 1000 t h r o u g h p u t ( q p s ) number of clients 1 naptr/fqdn 6 naptr/fqdn (c) nsd 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 1 10 100 1000 t h r o u g h p u t ( q p s ) number of clients 1 naptr/fqdn 7 naptr/fqdn (d) 800 items figure 9: query throughput performance considering multiples naptr records per fqdn. 0 20 40 60 80 100 120 1 10 100 1000 n e t w o r k u s a g e ( % ) number of clients 1 naptr/fqdn 7 naptr/fqdn (a) bind 0.1 0.2 0.3 0.4 0.5 0.6 0.7 1 10 100 1000 n e t w o r k u s a g e ( % ) number of clients 1 naptr/fqdn 6 naptr/fqdn (b) mydns-ng 0 10 20 30 40 50 60 70 80 1 10 100 1000 n e t w o r k u s a g e ( % ) number of clients 1 naptr/fqdn 6 naptr/fqdn (c) nsd 0.5 1 1.5 2 2.5 3 3.5 4 1 10 100 1000 n e t w o r k u s a g e ( % ) number of clients 1 naptr/fqdn 7 naptr/fqdn (d) powerdns figure 10: network usage considering multiples naptr records per fqdn. in [6], the test was performed with bind version 9.4.0 and the throughput was more than 75 times greater than in [5]. in our evaluation, bind version 9.8.1 presented the best scaling performance evaluation of enum name servers 451 properties among the evaluated servers and the highest throughput that was 500 times greater than the throughput reported in [5]. nsd also presented a significant evolution from version 3.0.4 to version 3.2.10. it is important to mention that this increase in throughput is also caused by the evolution of the hardware, since we used a more powerful testbed than the former works. these results brings good expectations for the future of enum protocol, since the performance of the enum servers are getting improved, allowing the deployment of new services. 5 conclusions enum protocol has been considered as the most promising solution for the integration between the different existing communication networks. in this sense, in this paper, we have selected four different enum servers, namely, bind, mydns-ng, nsd and powerdns to study their behavior in the new scenarios of deployment. we chose bind and nsd to represent zone files name servers. mydns-ng and powerdns were selected to represent name servers with sql database. the evaluation was performed in a testbed with high performance machines, and the last stable versions of the enum servers selected for the benchmarking. we also developed a new tool to manage our tests. the enumbenchtool proved to be of fundamental importance to facilitate and validate our work. in our evaluation scenarios, we extended the range of clients sending queries to the server in a far greater range than the ones used in previous works. from results, we identified that mydns-ng presented the worst performance in all scenarios. we believe that this server is not mature enough to be used in an enum context. powerdns performed better than mydns-ng. however, due to its inefficient use of cpu and ram memory resources, it performed quite lower than bind and nsd. bind presented the highest query throughput and a great scalability features. this is a totally new and different result when compared with results from [5]. our results proved the evolution of this server in the last years. nsd had the most efficient cpu usage performance. it presented a query throughput similar to bind, but with a lower memory consume. on the other hand, nsd presented problems to store a huge amount of records, compromising its scalability. we also evaluated the impact of the response message size in the servers performance. this is a new scenario that was not contemplated in any of previous works. bind proved its superiority with a better performance when compared with the other enum servers. another important scenario is the evaluation of the performance of enum servers, that use zone files, when the database is greater than the ram capacity. it is worth to say that zone files allow the server to reach a higher throughput than mysql only if the name server has enough ram to load the records stored in the zone files. moreover, the update process is not straightforward when we use zone files. beyond the new results presented in this paper, we also highlight the improvements in the methodology for benchmarking of enum servers. we have identified that dnsperf saturates when only one os process is used to query the enum server. this limitation of dnsperf brings to light the necessity of improvement in this tool to follow the hardware advances. to the best of our acknowledge, we are the first ones to report this limitation. in short, we believe that we got important results for the benchmarking of enum servers research field. we presented new information about the performance of important enum servers and clarified some divergences between former works, as in [5] and [6]. moreover, we improved the methodology for the benchmarking of enum servers and presented results for scenarios that were not contemplated in any other works. 452 s.h. da mata, j.m.h. magalhaes, a. cardoso, h.a. carvalho, p.r. guardieiro acknowledgement the authors gratefully acknowledge financial support from the studies and projects finance organization (finep), under project no. 01.10.0560.00. bibliography [1] itu-t (2005). the international public telecommunication numbering plan. itu recommendation e.164. [2] berners-lee, t.; fielding, r.; masinter, l. (1998); uniform resource identifiers (uri): generic syntax. rfc 2396. [3] bradner, s.; conroy, l.; fugiwara, k. (2011); the e.164 to uniform resource identifiers (uri) dynamic delegation discovery system (ddds) application (enum). rfc 6116. [4] shen, c.; schulzrinne, h. (2007); measurement and evaluation of enum server performance. ieee international conf. on communications, icc 07, 1967-1972. [5] shen, c.; schulzrinne, h. (2006); evaluation and comparison of bind, pdns and navitas as enum server. technical report cucs-029-06, columbia university. [6] tetenyi, i.; szabo, g.; kiss, a.; toth, a. (2008); enum in everyday practice. is it a dream or an opportunity?. infocommunication journal, lxiii:48-54. [7] rudinsky, j. (2009); private enum based number portability administrative system evaluation. international conference on ultra modern telecommunications workshops, icumt 09, 1-7. [8] lee, h.; mun, y. (2004); performance evaluation of enum directory service design. lectures notes in computer science; 3038:1124-1130. [9] balakrichenan, s.; bugnazet, t.; becker, m. (2007); studying enum performance with modeling and simulation. first asia int. conf. on modelling simulation, ams 07 ; 82-87. [10] da mata, s.h.; guardieiro, p.r.; cardoso, a.; lamounier, e.; theodoro, l. (2011); performance study of enum service based in modeling and simulation. 8th int. conf. on information, communications and signal processing, icics 11 ; 1-5, doi:10.1109/icics.2011.6173557. [11] da mata, s. h.; magalhaes, j. m. h.; cardoso, a.; carvalho, h. a.; guardieiro, p. r. (2013); performance comparison of enum name servers. 22nd international conference on computer communication and networks (icccn2013), 1-5. [12] nominum (2007). dnsperf: dns performance tool manual. [13] lin, h. et al (2009); voip signalling performance requirements and expectations. internet draft. [14] vixie, p. et al (1997); dynamic updates in the domain name system (dns update). rfc 2136. international journal of computers communications & control issn 1841-9836, 13(3), 365-382, june 2018. identifying essential proteins in dynamic ppi network with improved foa x. lei, s. wang, l. pan xiujuan lei, siguo wang school of computer science shaanxi normal university xian 710119,shaanxi, china xjlei@snnu.edu.cn, wangsiguo@snnu.edu.cn linqiang pan* 1. key laboratory of image information processing and intelligent control of education ministry of china school of automation huazhong university of science and technology wuhan 430074, hubei, china 2. school of electric and information engineering zhengzhou university of light industry zhengzhou 450002, henan, china *corresponding author: lqpan@mail.hust.edu.cn abstract: identification of essential proteins plays an important role for understanding the cellular life activity and development in postgenomic era. identification of essential proteins from the protein-protein interaction (ppi) networks has become a hot topic in recent years. in this work, fruit fly optimization algorithm (foa) is extended for identifying essential proteins, the extended algorithm is called epfoa, which merges foa with topological properties and biological information for essential proteins identification. the algorithm epfoa has the advantage of identifying multiple essential proteins simultaneously rather than completely relying on ranking score identification individually. the performance of epfoa is analyzed on dynamic ppi networks, which are constructed by combining the gene expression data. the experimental results demonstrate that epfoa is more efficient in detecting essential proteins than the state-of-the-art essential proteins detection methods. keywords: essential proteins, protein-protein interaction (ppi), dynamic ppi networks, subcellular localization data, fruit fly optimization algorithm (foa). 1 introduction protein plays an important role in the cellular life activity, and essential proteins are critical for the growth and development of organisms under a variety of conditions [27]. the absence of a single essential protein is sufficient to cause lethality or infertility [50]. some recent results suggest that a comprehensive analysis of essential proteins can provide a deeper understanding of the relationship between mutations and human diseases, revealing the general principles of human diseases [12,15,59]. therefore, the identification of essential proteins is closely related to disease prediction and drug design [53]. with the development of high-throughput technologies, various biological data are available, e.g., yeast-two-hybrid, tandem affinity purification, and mass spectrometry. in [2], a greedy algorithm is proposed to optimize the detection of protein communities. existing methods for identifying essential proteins can be roughly divided into two types. the first type includes the biological experiment-based methods, e.g., gene knockouts [11], rna interference [7], and conditional knockouts [35], which are expensive and time-consuming. the copyright ©2018 cc by-nc 366 x. lei, s. wang, l. pan other type includes the topology-based centrality method, e.g., degree centrality (dc) [14], betweenness centrality (bc) [24], closeness centrality (cc) [52], subgraph centrality (sc) [9], eigenvector centrality (ec) [3], information centrality (ic) [40], neighborhood centrality (nc) [20], and local average connectivity-based method (lac) [19]. by defining and computing the topologically potential value of each protein, these methods can obtain a precise ranking score reflecting the importance of proteins in the protein-protein interaction (ppi) network [18]. some centrality analysis tools and rna detection tools [54] have been developed. for example, cytonca [43], a cytoscape plugin, integrated eight centrality measures, i.e., dc, bc, cc, ec, ic, sc, nc and lac. obviously, the topology-based centrality methods can improve the efficiency with less cost. however, these centrality methods also have their own shortcomings. it is well known that the performance of topology-based methods is closely related to the quality of the ppi networks, but there are many false positive and false negative in the ppi networks. in order to deal with the drawbacks of these methods, some new methods are proposed to predict essential proteins by integrating their topological properties with their biological properties. considering the interaction data and gene ontology (go) annotations, hsing et al. introduced a method to predict highly-interacting proteins [13]. later, a new prediction method called pec was proposed by li et al. [21], and another method called wdc was proposed by tang et al. [41], which integrate network topology with gene expression profiles. afterwards, tang introduced a new method to identify essential proteins in which topological features of ppi network is combined with subcellular localization information [42]. next, a new centrality measure is proposed by ren et al. to discover essential proteins, named harmonic centricity, which merges subgraph centrality with protein complexes to discover essential proteins [34]. recently, a new prediction method, named udonc, that combine the domain features of proteins with their topological properties in ppi networks, was proposed by peng et al. [30]. some machine learning methods, e.g., support vector machine, naive bayes, bayes network, and nbtree, were also adopted to predict essential proteins by using different features. for example, the random forest was adopted to predict essential proteins by qin et al. [32]. these methods that combine the network topological features with biological data is capable of improving the accuracy and efficiency of prediction significantly. these existing methods regard the ppi networks as static networks that ignore the time-course of the networks. the real ppi networks in cell keeps changing over different stages of the cell cycle [31], and they can be classified into stable or transient ppi networks [46], which are usually described as dynamic ppi networks (dpin). thus it is important to construct dynamic ppi networks to investigate the temporal properties of individual proteins and protein interactions. based on dynamic network topology and complex information, luo and kuang proposed a new method to predict essential proteins [22]. the results show that the identification of essential proteins in dynamic networks is more conducive than in static networks. fruit fly optimization algorithm (foa) is a novel swarm intelligent algorithm that mimics the foraging behavior of fruit flies for global optimization [25]. foa is easy to be understood and implemented, which has few parameters to be adjusted. due to its simplicity and efficiency, foa showed great success in solving some real-world complex problems like multidimensional knapsack problem [48]. here, foa will be used to find the essential proteins. in this work, we present a new algorithm, called epfoa, in which foa is merged with topological properties and biological information for essential proteins identification. to the best of my knowledge, most of the methods of essential proteins identification focus on static ppi networks and ignore the intrinsic features of organisms. in our method, we first integrate gene expression data with static ppi network to construct the dynamic network model. then a new topological centrality method that combines go annotation and edge aggregation coefficient (ecc) is proposed to measure the topological charidentifying essential proteins in dynamic ppi network with improved foa 367 acteristic of ppi networks with modular local average connectivity (lac) in dynamic networks. furthermore, the distribution of proteins in each compartment according to subcellular localization data is obtained, and the role of components in identifying essential proteins is analyzed. finally, epfoa is designed to identify essential proteins. to assess the performance of our method, epfoa is compared with some existing methods including dc, ec, ic, sc, nc, lac, pec and udonc, and the experimental results indicate that our method significantly outperforms with the existing methods. 2 method 2.1 fruit fly optimization algorithm fruit fly optimization algorithm (foa) is a novel method for global optimization, which is inspired by the foraging behavior of fruit flies. in sensory perception, the fruit fly is superior to the other species, especially in olfactory and vision. the olfactory organs of fruit flies can collect all kinds of scents floating in the air, even smell the food source from 40 kilometers away. after the fruit fly gets close to the food, it can also use the sensitive vision to find food and the companyążs flocking location, and fly to the direction [25]. the procedure of foa is presented in pseudo code as follows. step 1. randomly initialize the location of the fruit flies (xaxis,yaxis). step 2. give the random direction and distance for the search of food using osphresis by an individual fruit fly. { xi = xaxis + randomv alue yi = yaxis + randomv alue (1) step 3. the distance (disti) to the origin is estimated, then the smell concentration judgment value (si) is calculated, which is the reciprocal of the distance.{ disti = √ x2 + y2 si = 1 disti (2) step 4. substitute smell concentration judgment value (si) into smell concentration judgment function (or called fitness function) to find the smell concentration (smelli) of the individual location of the fruit fly. smelli = function(si) (3) step 5. find the individual with the maximal smell concentration among the fruit fly swarm according to the smell concentration value. [bestsmell bestinedx] = max(smell) (4) step 6. maintain the best smell concentration value x and y, where the fruit fly swarm will use vision to fly towards that location.  smell = bestsmell xaxis = x(bestindex) yaxis = y (bestindex) (5) step 7. repeat steps 2-5 until the smell concentration is superior to the previous smell concentration; otherwise, go to step 6. 368 x. lei, s. wang, l. pan 2.2 dynamic ppi network model construction gene expression data is valuable for revealing the dynamic properties of proteins and ppi. we integrate gene expression data with high-throughput ppi data to construct a dynamic ppi network. note that protein does not always become active at a cell cycle, a protein is active at the highest gene expression level. in order to mark the active time of each gene, the active threshold of each gene should be calculated, and the gene is active if its expression value is greater than the active threshold. the calculation of active threshold is proceeded on the 3-sigma model [45]. at(p) = µ(p) + 3 ×σ(p) × (1 − 1 1 + σ(p)2 ), (6) where µ(i) is the mean gene expression value of protein i and σ(i) is the algorithm standard deviation of the expression values over time 1 to t for protein i. since the gene expression data has three cycles and each cycle has 12 times tamps, the final gene expression at each time point is the average of the three cycles, which is defined as follows [16]: ft(i) = t(i) + t(i + 12) + t(i + 24) 3 , (i ∈ [1, 12]), (7) where t(i) denotes the gene expression value at time point i. at a certain times tamp, if both proteins are active with an interaction, the interaction of the two proteins is also active. eventually the entire ppi network was divided into 12 sub-networks, the dynamic ppi network was constructed. 2.3 topological characteristics of dynamic networks a ppi network is not only an important biological network but also a typical complex network, which meets the topological characteristics of complex network, such as small-world [49], scalefree [51], and modularity [10]. in this part, the role of the topological characteristics in the process of essential proteins identification is investigated, and a new topological centrality method based on the ecc and go annotation is proposed. furthermore, the modularity of the network that applied lac is also considered. dynamic network topology centrality strategy a ppi network can usually be expressed as an undirected graph g = (v,e), where the set of vertices v represents protein, and e represents all of interactions between pairs of proteins. in order to assess the centrality of dynamic network topology, we introduce the go annotation (since the ecc cannot fully reflect the characteristics). go annotation provides valuable information and a convenient method to study the gene function similarity, some researches have shown that the adoption of go semantic similarity term can improve the prediction accuracy of protein complexes gene and disease [36,56,57]. weighting the networks via ecc in order to measure the tightness of the two nodes, we use the ecc [41], which is defined as follows: ecc(u,v) = |nu ∩nv| + 1 min{du,dv} , (8) where nu (or nv) refers to the set of neighbours of node u (or v) in ppi networks, |nu ∩ nv| is the number of common neighbor nodes of u and v, which is consistent with the number of triangles which edge (u,v) belongs to du (or dv) indicates the degrees of node u (or v). weighting the networks using the gene ontology identifying essential proteins in dynamic ppi network with improved foa 369 the go information consists of three sub-ontologies: biological process (bp), cellular component (cc) and molecular function (mf) [6]. in order to measure the semantic similarity between the go terms to protein annotations in an interaction network, we applied the method developed by wang et al. [47]: go_sim(u,v) = ∑ t∈tu ⋂ tv (su(t) + sv(t))∑ t∈tu su(t) + ∑ t∈tv sv(t) , (9) where where tu and tv are the annotations of protein u and v; su(t) is the s-value of go term t related to term u and sv(t) is the s-value of go term t related to term v. generating new weighted networks based on the definition of the ecc and gene functional similarity, a new centrality measure, named eg, is proposed. for a protein u, the essentiality eg(u) is defined as the probability between the ecc and go information: eg(u) = ∑ v∈nu ecc(u,v) ×go_sim(u,v), (10) where n(u) denotes the set of all neighbors of node u. when computing dynamic eg(u), we should consider the number of times that each node appears in a dynamic ppi network, since some nodes are not included in all the time networks. dynamic eg(u) can be defined as the follows: deg(u) = ∑n i=1 eg i(u) tim(u) , (11) where n is the number of temporal networks in the dynamic network, egi(u) the eg of node u in the ith time point, tim(u) the number of time networks that contain node u. if node u does not appear at time point i, egi(u) is equal to zero. dynamic local average connectivity the lac of a node indicates its closeness [49], and the lac of a node v is defined as: lac(u) = ∑ v∈nu deg cu(v) |nu| , (12) where nu is the neighbors of node v, |nu| the number of nodes in nu, and cu the subgraph induced by nu. for a node u in cv, its local connectivity in cu is represented as deg(cu)(v). similar to deg(u), we define dynamic lac as follows [30]: dlac(u) = ∑n i=1 lac i(u) tim(u) , (13) where n is the number of temporal networks in the dynamic network, laci(u) the lac of node v in the ith time point, and tim(u) the number of time networks that contain node u. 2.4 subcellular localization score subcellular location is divided into different compartments, different compartments play different roles in cell activities. in order to understand the relationship between subcellular localization and essential proteins, we analyze the number of essential proteins in each subcellular location and propose a method to evaluate subcellular localization in previous research. assume 370 x. lei, s. wang, l. pan that in the nucleus, the wider the distribution of the proteins is, the greater the possibility of essential protein becomes [17]. let cmax denote the protein with the largest number of times appearing in subcellular localization of the nucleus, |u| represents the number of times of the protein u appearing in the nucleus. the importance of protein u, denoted as nsl(u), is calculated by the ratio of its size to the largest size of the nucleus. the value of nsl(u) is in the range of (0, 1]. nsl(u) = |u| |cmax| (14) 2.5 epfoa algorithm in order to make up for the shortcomings of traditional identification of essential proteins one by one, we propose the algorithm epfoa. the algorithm can identify p candidate essential proteins simultaneously, which greatly improves the recognition efficiency. in what follows, we introduce the algorithm epfoa. first, initialize the position of fruit fly and set the rules of location updating. then find p candidate essential proteins according to the characteristic of foa. finally, the identified p essential proteins are compared with known essential proteins to verify the number of essential proteins identified correctly. the initialization and update of the location of fruit flies the initialization and location update rules of fruit fly play an important role in the performance of epfoa. the position of the fruit fly is encoded as an integer set of p-dimensional set xi = (xi1,xi2, . . . ,xip), (i = 1, 2, . . . ,n) which denotes a candidate essential protein set. each element xij (xij ≤ |n|,j = 1, 2, . . . ,p) in xi is the sequence number of a protein. first we randomly selected p proteins to initialize a fruit fly position xi. then we compare the selected p proteins with the known essential proteins and keep the proteins that are successfully matched. after that the remaining positions that represent proteins are updated. in order to speed up the convergence of the proposed epfoa algorithm, we sort all the proteins based on degree except selected p proteins. a random value is assigned to the individual that is not essential protein in the xi and update the position in a sequence that is ranked by degree. encoding and decoding of epfoa the framework of epfoa is shown in fig.2. we set every fruit fly as essential protein candidate set, and the location of fruit fly is the serial number of the candidate proteins. for the purpose of evaluating the topological characteristics of the network comprehensively, we combine lac that represents the network modularity with the new network centrality. thus, when a fruit fly is in a certain position, we suppose its smell concentration judgment value s(i) can be calculated as following equation: s(i) = p∑ j=1 (dlac(µj) + deg(µj)), (15) where dlac(uj) denotes the dynamic local average connectivity of the jth protein among the p candidate essential proteins and deg(uj) denotes dynamic network topology centrality of the jth protein among the p candidate essential proteins. the topological characteristics and biological data are both indispensable in the process of identifying essential proteins and subcellular localization data plays an important role in essential proteins identification. we set the following smell concentration judgement function to measure the possibility of essential proteins represented by a fruit fly individual: fit(i) = α×s(i) + (1 −α) × p∑ j=1 nsl(µj), (16) identifying essential proteins in dynamic ppi network with improved foa 371 !"#$$ %&'%#'()*(+&' ,smelli !""#$%&'( )*+%#&$", -'$.&.'%#,"#% /+-'%&+$ 0 1 2 3 450065016 6507 451065116 6517 452065216 6527 453065316 6537 8%'%&-,))9, $#%:+*; 8<=-#((<('*, (+-'(&>'%&+$, .'%' ?#$#, #57*#""&+$, .'%' ?#$#,+$%+(+@a, .'%' ba$'3&-,))9, .'%' -#$%*'(&%a, 3#%c+. 4b!? 83#((,-+$-#$%*'%&+$, d<.@3#$%,e'(<#,s-i !"#$%%! &'(&$()*+),'(! -smelli ! "#$%&'($)*(+,$&-%.(/-*..(0+'0*'$1%$&+'(%'2( .+0%$&+' 3&456&786&98 86&, : ;*$*1-&'*(<)*$)*1($)*( -%6&-=-('=-#*1( +>(&$*1%$&+'(&/(1*%0)*2 ;?'%-&0(@ab ;?'%-&0(cbb >&'2($)*(>1=&$(>.?(<&$)($)*(-%6&-%.(/-*..( 0+'0*'$1%$&+'(%'2(1*0+12(&$d/(.+0%$&+'(8$)*'( =,2%$*($)*(.+0%$&+'(%00+12&'e($+($)*(1=.*/ figure 1: the framework of the algorithm epfoa. 372 x. lei, s. wang, l. pan where nsl(uj) denotes subcellular localization score of the jth protein among the p candidate essential proteins and α ∈ [0, 1], α is a parameter that regulates the proportion of the network topology and biological information in the process of identifying essential proteins. if α = 0, only subcellular location information works; else if α = 1, only network topology works. pseudo code of epfoa the process of epfoa can be divided into two steps. the first step calculates the topological and biological characteristics of protein nodes. the second step applies the process of foa algorithm to seek the optimal to find the essential proteins. the pseudo code of epfoa is shown in algorithm 1. algorithm 1 the pseudo code of epfoa ensure: g = (v,e) (the ppi network), gene expression data, gene ontology go, subcellular location data. require: essential protein set. 1: construct the dynamic ppi network 2: for each interacting protein pair (a,b) in ppi do 3: calculate ecc /*the closeness of the two nodes*/ 4: calculate go /*the functional similarity of the two nodes based on go annotation*/ 5: end for 6: for each node in g do 7: update the centrality deg(u) 8: calculate dlac(u) 9: calculate subcellular location score nsl(u) 10: end for 11: for fruit fly i do 12: initialize location x(i) and its best location b_x(i) 13: calculate the smell concentration smell(i)= fit(s(i)) 14: end for 15: for m in [1,maxiter] do 16: for fruit fly i do 17: update location x(i) = x(i) + random 18: if smell(i)<fit(s(i)) then 19: b_x(i) = x(i) 20: end if 21: end for 22: end for 3 results and discussion in this section, we first introduce the experimental data. then we analyze the parameter a towards the performance of epfoa. next, in order to evaluate the performance of epfoa more synthetically, we not only compare epfoa with some topology-based centrality methods (dc, ec, ic, sc, nc, lac) but also with some methods that integrate their topological properties with their biological properties (pec and udonc). in order to assess the essentiality of proteins in ppi networks, these methods are ranked in descending order based on their ranking scores including eight existing centrality methods (dc, ec, ic, sc, nc, lac, pec and udonc). after identifying essential proteins in dynamic ppi network with improved foa 373 that, top 1%,5%, 10%, 15%, 20% and 25% of the ranked proteins are selected as candidates for essential proteins. in this paper, the size of the set of essential proteins candidate is 1274. taking into account of the random optimization process of foa, we conduct ten experiments and then use the average of ten experiments as the final result to to analyze the parameter towards the performance of epfoa. the ten experiments are listed in the attachment 1. to further evaluate the epfoa performance, we randomly choose a candidate essential proteins from ten experiments to compare with other methods. the performance is presented in the form of histograms of the number of essential proteins predicted by each algorithm and also use six statistical measures to evaluate them. and precision-recall curves and jackknife curves are also used to evaluate the performance of the proposed epfoa method and the other eight methods. finally,we analysis the modularity of identified essential proteins. 3.1 experimental data to evaluate the performance of our proposed algorithm epfoa, we adopt ppi networks of s.cerevisiae which has been well characterized by knockout experiments and widely used in the evaluation of methods for essential proteins discovery. the ppi data of s.cerevisiae was downloaded from dip database [58], which contains 5093 proteins and 24743 interactions after removing the repeated interactions and the self-interactions. the known essential proteins data of s.cerevisiae contains 1285 essential proteins among which 1167 essential proteins present in the dip network, which are collected from four databases: mips [23], sgd [4], deg [55], and sgdp (http://www-sequence.stanford.edu/group). the gene expression data of s.cerevisiae are downloaded from geo database [44] that contains 7074 gene expression products. the gene ontology annotation data of s.cerevisiae is obtained from go consortium [5]. subcellular localization dataset of s. cerevisiae is downloaded from knowledge channel of compartments database [1], which includes 5095 yeast proteins and 206,831 subcellular localization records. 3.2 the effect of parameter α on performance in our proposed algorithm epfoa, evaluation function of proteins is changed with different values of α. to study the effect of parameter α on performance of epfoa, we evaluate the prediction accuracy by setting different values of α, ranging from 0 to 1. the detailed results are listed in table 1. as shown in table 1, the results are similar with α, ranging from 0.4 to 1. synthetically, we consider the optimal values to be α = 0.1. table 1: effect of parameter α on the performance of epfoa α 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 top 1% 37 45 42 40 40 40 39 40 40 39 39 5% 181 183 183 184 184 185 177 178 177 176 178 10% 350 341 334 317 304 295 287 289 288 284 284 15% 444 451 445 429 423 417 415 409 409 400 415 20% 563 542 537 532 531 528 529 530 530 529 544 25% 610 628 624 621 618 612 616 617 617 616 616 374 x. lei, s. wang, l. pan 3.3 comparison with other prediction measures in order to demonstrate the advantage of our proposed epfoa, we compare epfoa with eight existing methods including dc, ec, ic, sc, nc, lac, pec and udonc. the essential proteins candidate population size p is set to 1274 (5093*25%=1274). the top 1, 5, 10, 15, 20 and 25% proteins are selected as candidate essential proteins, respectively. then the prediction results are compared with the known essential proteins, and the experimental results are shown in fig. 3. it can be observed that the percentage of essential proteins predicted by epfoa is consistently higher than that achieved by the eight compared methods. taking top 1% (top 51) predicted essential proteins as an example, 46 essential proteins are correctly identified by epfoa while sc and ec have correctly predicted 24 essential proteins. dc ic sc ec nc lac pec udoncepfoa (a)top1% (top51) 20 25 30 35 40 45 50 n u m o f e s s e n ti a l p ro te in s 22 24 24 24 32 29 39 37 46 dc ic sc ec nc lac pec udoncepfoa (b)top5% (top255) 100 120 140 160 180 200 n u m o f e s s e n ti a l p ro te in s 101 102 96 96 159 156 170 170 184 dc ic sc ec nc lac pec udoncepfoa (c)top10% (top510) 200 220 240 260 280 300 320 340 360 n u m o f e s s e n ti a l p ro te in s 206 210 195 195 281 267 291 295 343 dc ic sc ec nc lac pec udoncepfoa (d)top15% (top764) 300 350 400 450 n u m o f e s s e n ti a l p ro te in s 320 316 279 279 373 376 372 400 456 dc ic sc ec nc lac pec udoncepfoa (e)top20% (top1019) 380 400 420 440 460 480 500 520 540 560 n u m o f e s s e n ti a l p ro te in s 413 406 377 377 464 473 439 491 540 dc ic sc ec nc lac pec udoncepfoa (f)top25% (top1274) 450 500 550 600 650 n u m o f e s s e n ti a l p ro te in s 502 504 467 467 545 552 493 573 627 figure 2: epfoa compared with several existing methods.(a) top 1% (top 51), (b) top 5% (top 255), (c) top 10% (top 510), (d) top 15% (top 764), (e) top 20% (top 1019), (f) top 25% (top 1274). 3.4 validation using six statistical measures in order to evaluate the performance of epfoa, we compare epfoa with the other methods using six statistical measures: sensitivity (sn), specificity (sp), positive predictive value (ppv), negative predictive value (npv), f-measure, and accuracy (acc). each statistical measure is defined as follows: sn = tp tp + fn , (17) sp = tn tn + fp , (18) ppv = tp tp + fp , (19) npv = tn tn + fn , (20) f −measure = 2 ×sn ×ppv sn + ppv , (21) acc = tp + tn tp + tn + fp + fn , (22) identifying essential proteins in dynamic ppi network with improved foa 375 where tp is the number of essential proteins correctly identified as essential proteins, fp is the number of nonessential proteins mistakenly identified as essential proteins, tn is the number of nonessential proteins correctly identified as nonessential proteins, and fn is the number of essential proteins mistakenly identified as nonessential proteins. the comparison results between epfoa and the other predicted essential proteins methods by six statistical measures performed on dip are shown in table 2. obviously, we can see that epfoa significantly outperforms all the compared methods. table 2: comparison of epfoa and the other methods in terms of sn, sp, ppv, npv, fmeasure, and acc on the ppi networks. method sn sp ppv npv f-measure acc dc 0.4302 0.8033 0.394 0.8258 0.4113 0.7178 ec 0.4002 0.7944 0.3666 0.8167 0.3826 0.704 ic 0.4319 0.8038 0.3956 0.8263 0.4129 0.7186 sc 0.4002 0.7944 0.3666 0.8167 0.3826 0.704 nc 0.467 0.8143 0.4278 0.8371 0.4465 0.7347 lac 0.473 0.8161 0.4333 0.8389 0.4523 0.7374 pec 0.4225 0.801 0.387 0.8235 0.4039 0.7143 udonc 0.491 0.8214 0.4498 0.8444 0.4695 0.7457 epfoa 0.5373 0.8352 0.4922 0.8586 0.5137 0.7669 3.5 comparison of the experimental results based on precision-recall curves to further validate the performance of epfoa, we study the precision-recall (pr) of epfoa on the ppi networks and compare with the other methods. the precision and recall of the top n ranked proteins are defined as follow: precision(n) = tp(n) tp(n) + fp(n) , (23) recall(n) = tp(n) p , (24) where tp(n) is the number of true predicted essential proteins among the top n ranked proteins, fp(n) is the number of false predicted essential proteins among the top n ranked proteins, p is the total number of essential proteins under consideration. fig. 4 shows the pr curves of epfoa and the other eight methods on the ppi networks. obviously, epfoa obtains the best performance, which demonstrates that the algorithm epfoa works well in identifying essential proteins. 3.6 validation using jackknife curves a more general comparison between the proposed algorithm epfoa and the eight previously proposed methods is tested by using a jackknife curves. the experimental results validated by jackknife curves are shown in fig. 5 the x-axis represents the proteins ranked in descending order from left to right according to the values computed using the corresponding methods, and the y-axis represents the number of true essential proteins among the top n proteins, where n is the number along the x-axis. the area under the curve is always used to measure the generality of a method. as shown in fig. 5, epfoa clearly performs better than the other methods. 376 x. lei, s. wang, l. pan 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 recall 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 p re c is io n dc ic sc ec lac nc pec udonc epfoa figure 3: the pr curves of gsp and that of other methods. 3.7 the modularity of essential proteins predicted by epfoa proteins usually perform tasks in biological system with protein complexes or functional modules and rarely act alone. therefore, protein modularity may be an appropriate measurement to evaluate the significance of essential proteins identified by epfoa. in order to examine the modularity of essential proteins identified by epfoa, we compare epfoa with dc that fully depend on network topology and pec that combine network topology with biological information. we show the top 1% identified essential proteins of each method. as illustrated in fig. 5, the number of essential proteins identified by epfoa is higher than dc and pec obviously. it also can be seen in fig. 5, it is worthy to note that the essential proteins identified by epfoa show more significant modularity than dc and pec. it indicates that epfoa is effective in identifying essential proteins. identifying essential proteins in dynamic ppi network with improved foa 377 0 200 400 600 800 1000 1200 number of top ranked proteins 0 100 200 300 400 500 600 n u m b e r o f e ss e n tia l p ro te in s dc ec ic nc sc lac pec udonc epfoa figure 4: the jackknife curves of gsp and the other nine methods. figure 5: the modules formed by the top 1% identified essential proteins predicted by dc, pec and epfoa. yellow circles are the essential proteins predicted by epfoa, and blue circles are the non-essential proteins that incorrectly predicted. 378 x. lei, s. wang, l. pan 4 conclusion it is believed that identification of essential proteins is very useful for understanding the minimal requirements for cellular life, and even the disease study and drug design. although there are many methods have been proposed, it is still a challenge to improve the predicted precision. it is a strong potential way to use computational methods to identify essential proteins. in this study, we propose a novel algorithm epfoa to boost the performance of essential proteins. we not only analyze the network topological characteristics in the dynamic ppi networks with go annotation, but also analyze the biological characteristics with the subcellular location information. by comparing with other existing methods, foca can more effectively identify the essential proteins with the higher precision. as future work, it would be interesting to apply the epfoa to other studies, such as gene and disease prediction. acknowledgements this paper is supported by the national natural science foundation of china (61672334, 91530320, 61502290, 61401263, and 61320106005) and the innovation scientists and technicians troop construction projects of henan province (154200510012). bibliography [1] binder, j. x., pletscher-frankild, s., tsafou, k., stolte, c., o’donoghue, s. i., schneider, r., jensen, 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(2014); computational approaches for human disease gene prediction and ranking, advances in experimental medicine biology, 799, 69, 2014. international journal of computers communications & control issn 1841-9836, 12(3), 393-401, june 2017. genetic algorithm with modified crossover for grillage optimization m. ramanauskas, d. sesok, r. belevicius, e. kurilovas, s. valentinavicius mikalojus ramanauskas, dmitrij sesok rimantas belevicius, saulius valentinavicius vilnius gediminas technical university, dept. of information technology, vilnius, lithuania vilnius, lt-10223 sauletekio al. 11, lithuania mikalojus.ramanauskas@vgtu.lt eugenijus kurilovas 1. vilnius gediminas technical university, dept. of information technology, vilnius, lithuania vilnius, lt-10223 sauletekio al. 11, lithuania jevgenij.kurilov@vgtu.lt 2. vilnius university institute of mathematics and informatics vilnius, lt-08663 akademijos str. 4, lithuania *corresponding author: jevgenij.kurilov@vgtu.lt abstract: modified genetic algorithm with special phenotypes’ selection and crossover operators with default specified rules is proposed in this paper thus refusing the random crossover. the suggested crossover operator enables wide distribution of genes of the best phenotypes over the whole population. during selection and crossover, the best phenotypes of the newest population and additionally the genes of the best individuals of two previous populations are involved. the effectiveness of the modified algorithm is shown numerically on the real-life global optimization problem from civil engineering the optimal pile placement problem under grillage-type foundations. this problem is a fair indicator for global optimization algorithms since the ideal solutions are known in advance but with unknown magnitudes of design parameters. comparison of the proposed algorithm with 6 other stochastic optimization algorithms clearly reveals its advantages: at similar accuracy level the algorithm requires less time for tuning of genetic parameters and provides narrower confidence intervals on the results than other algorithms. keywords: genetic algorithm; crossover operator; grillage optimization. 1 introduction in this paper, we propose a new genetic algorithm with modified crossover operator and compare it with other well-known stochastic optimization algorithms. as a benchmark the problem of pile placement optimization under grillage-type foundations is chosen. the grillage foundations consist of piles driven to the ground, and connected on the top with girders. the in-plane configuration of girders may be complex, and the number of piles may reach several tens (few examples of grillages are provided in the appendix). here we consider that the optimal grillage is the grillage with minimal number of piles of given stiffness characteristics, and the reactive forces in piles are equal. this can be achieved changing the positions of piles under girders. in ideal case the reactive forces in all piles are distributed evenly and are equal to the bearing capacity of pile. practically this is hardly possible, since the theoretical number of piles which is obtained dividing the total loading on the foundation (i.e., all active forces plus the dead weight of the erection) by bearing capacity of pile, is common case not the integer number. also, some technological requirements can hinder achieving the ideal scheme, e.g., the given minimal copyright © 2006-2017 by ccc publications 394 m. ramanauskas, d. sesok, r. belevicius, e. kurilovas, s. valentinavicius allowable distance between adjacent piles due to pile-driver characteristic, or the presence of the given immovable piles that are introduced into placement scheme by a designer (usually at the corners of girders) and do not change their position in the optimization process. the pile placement problem is ideal for comparison of global optimization algorithms. firstly, the global solution the reactive force that should be evened out in all piles is known in advance. secondly, the practical solution of the problem shows that the landscape of objective function is complex and has many local extremes. usually, the objective function is very sensitive to the pile positions: even small changes position of one pile sometimes leads to a large alteration of objective function. all this makes the problem a complex global optimization problem. the mathematical models of optimization of grillage-type foundations were formulated and the solution algorithms were suggested in [4], [3]. three problems were solved: pile placement seeking for even distribution of reactive forces in piles, pile placement seeking for least bending moments in the connecting girders, and integrated problem for minimization of reactive forces and bending moments. in case of two last problems the global solution cannot be obtained in advance, therefore it is not a right choice for comparison of optimization algorithms. the first problem was solved in [10] employing all popular at that time stochastic optimization algorithms. in all these algorithms a phenotype, or an individual, is the approximate mathematical model of the whole grillage. the fitness of a phenotype is measured by a maximum reactive force magnitude among all piles, i.e., the fittest individual has the least reactive force. in [15], combination of the sizing and topology optimization is observed, however the piles are aggregated to special groups of pile. exhaustive technical details on the design of grillages can be found, e.g. in [13]. also, this problem was solved by the new several dimension optimization method bacoor [8], [9]. the last method outperformed all other algorithms for the grillages where the piles have to be placed at very uneven distances. at more even distribution of piles the classical stochastic algorithms provided better results. in all cases genetic algorithm (ga) with carefully tuned genetic parameters provided best results or results close to the best solutions. among these parameters, the crossover operator plays significant role [4], [7] since it combines the information contained in the previous individuals in order to obtain fitter phenotypes. therefore, in this paper, the main attention is given to the crossover operator. the crossover was investigated, e.g., in [5] where a gender was assigned to each individual and the crossover was performed only between individuals of opposite genders. in many cases the crossover operator is dedicated for a particular type of problems, e.g., operator eax for traveling salesman problems [15], [11]. there are five chapters and appendices in this paper. in the second chapter, the ideas of new crossover operator and its implementation are described. the third chapter depicts the problem under consideration the analysis of grillage-type foundations via finite element method. in the fourth chapter, the optimization problem along with all requirements for fair comparison of optimization algorithms are given, and the obtained results are discussed. in the final section, some general conclusions are drawn. 2 modified crossover operator in the classical genetic algorithm, the initial population of a given length n is created. then, using selection and crossover operators the new individual generations are generated. during the selection, the fitter individuals have better chances to be included into the next generation. the probability of an individual to be selected for a next generation is usually directly proportional to the ratio of its objective function value with the best function value in the present population. instead of individuals that do not enter into the new generation, the new random individuals are created. after the selection, the random individuals interchange genes between them during genetic algorithm with modified crossover for grillage optimization 395 the crossover operation. this does not guarantee that after the selection all best genes will survive and will get into the new generation. if the non-elitist strategy is employed, always a certain probability exists that even the individual representing the global solution will not pass the selection. it also should be noted, that in classical algorithm the crossover operator is applied only to the individuals of newest generation. thus, the individuals of previous generations are completely lost. we suggest the following modified selection and crossover operations: • the new population is created not only of crossbred individuals, but also of the best individuals of two previous generations. thus in the current population coexist parents and grandparents. • one-third of the best individuals participate in the crossover, and each individual is interbred twice with in advance definite individuals. • thus, less random genetic parameters must be chosen for the algorithm; only the breeding point and mutation probability have to be selected. the crossover rules are summarized as follows: 1. initial population of n individuals a0 = {a0,a1, ...,an−1} . 2. the individual a′i of a succeeding population aj (j 6= 0) is obtained breeding n best individuals of the generation aj−1 according to the equations: • if i < n 2 ,a′i = ai ×ai+ n 2 • if i > n 2 ,a′i = ai ×an−i−1 where the × denotes the crossover operations. 3. population aj (j > 1) consists of 3·n individuals: n crossbred individuals of generation aj−1 and two groups of size n of the best individuals from generations aj−1 and aj−2 . thus, the recursive function for creation of populations is: a0 = {a0,a1,a2, ...,an−1} , a1 = a0 ⋃ a×0 , a2 = a0 ⋃ topn (a1) ⋃ topn ( a×1 ) , a3 = topn (a1) ⋃ topn (a2) ⋃ topn ( a×2 ) , · · · ai = topn (ai−2) ⋃ topn (ai−1) ⋃ topn ( a×i−1 ) , where topn (ai) is the set of n individuals of the ith generation with the best objective function values a×i , is the crossover operator breeding the individuals of the i th generation according to the rules shown. thus, the proposed rules do not require tuning of selection parameters. the third rule also guaranties a special elitist strategy, i.e., the individual with the best objective function value is retained from generation to generation. on the other hand, passing into the next generations only the fittest individuals of preceding populations may narrow the search space. sufficient diversity of a population therefore is achieved by a classical mutation with a rather high probability that should be tuned for the problem under consideration. 3 grillage optimization model one individual of a routine population is the approximate mathematical model of the grillagetype foundation. grillage is discretized by the finite element method into 2d beam element mesh with out-of-plane boundary conditions instead of piles. out-of-plane active forces consist of the self-weight of the erection, plus all active forces according to eurocodes. the girders of 396 m. ramanauskas, d. sesok, r. belevicius, e. kurilovas, s. valentinavicius a grillage are approximated as two-node beam elements with fixed cross-section and material characteristics. the piles are represented as the supports with specified displacements (zero displacements are the most common case). alternatively, piles are regarded as supports with specified stiffness characteristics. generally, the maximum reactive force among all piles is treated as the objective function. supports of the first type are rather non-realistic representations and sometimes yield misleading analysis results. for example, when multiple supports are needed to carry large concentrated load, this kind of supports will lead to a logjam. if odd number of supports is placed under load, the central support will be located just beneath the load and will take all the force. in case of even number of supports the "saw-teeth" like distribution of reactions is observed, and the more supports will be installed, the larger in absolute value reactions will arise. the optimization problem is defined as in min x∈d f (x) . (1) here f (x) is the objective function, d is the feasible shape of structure, which is defined by the type of certain supports, the given number and layout of different crosssections as well as different materials in the structure. f (x) is defined by the maximum difference between vertical reactive force at a support and allowable reaction for this support, thus allowing us to achieve different reaction at supports on different beams, or even at particular supports on the same beam: f (x) = max x∈d max 16i6ni |ri − cirallow| . (2) here ns denote the number of supports, rallow is allowable reaction, ci are factor to this reaction and ri are reactive forces in each support. finite element matrices and sensitivity analysis the problem has to be solved in statics and in linear stage [k]{u} = {f} . (3) here [k] is the stiffness matrix of grillage, {u} are the displacement of grillage nodes, and {f} the loadings. the reactive forces at a rigid supports are obtained using equation ri = ∑ j kijuj, i = 1, 2, ...,ns, (4) where a part of nodal displacements (displacements of free nodes) are already obtained via, and the displacements of nodes representing the rigid supports are specified (usually zero). if the supports have finite stiffness ki , ri ≈ kiui, i = 1, 2, ...,ns. (5) if the local search around the certain solution obtained by stochastic optimization algorithm is implemented, the sensitivity information is the must. the sensitivity analysis is performed using the pseudo-load approach; thus, the numerical calculation of derivatives can be avoided. denoting the support positions by xi, i = 1, 2, ...,ns ri,xi = [k],xi {u} + [k]{u},xi . (6) genetic algorithm with modified crossover for grillage optimization 397 here the derivative of stiffness matrix is obtained analytically, while the derivative of displacements supposes solution of the general sensitivity equation: [k]{u},xi = {f},xi − [k],xi {u} . (7) the derivatives of load vector are obtained also in a closed form, analytically. a simple twonode beam element with 6 d.o.f’s at a node (three displacements and three rotations about local element axes) is employed in the analysis. details on the stiffness matrices of an element may be found in many textbooks, e.g., [16]. program. original fortran program is used for obtaining the objective function value. first of all, the finite element mesh along with all needed data is prepared by a special pre-processor. some initial data for the pre-processor are constant and do change in the optimization process the configuration of the grillage, self-weight of the structure and active loadings, material characteristics. the locations of piles are obtained from the guess of optimization algorithm. since supports have to be placed under the girders of grillage, first of all the grillage is "unfolded" to one-dimensional construct, and locations of all supports are freely chosen along the whole length of this construct. after that, the initial configuration is restored. from all this input, the preprocessor automatically prepares the finite element mesh introducing nodes at support points, discontinuities of material and cross-sections properties, etc. the third independent program analyses the finite element results and provides the objective function value. model transformation patterns are obtained by using the formal concept analysis [10], where relations and element meta-classes of target and source models are linked together based on model classification group links that have similarities between them. 4 numerical results and discussion 10 different grillages possessing from 17 to 55 piles, i.e., the optimization variables were optimized. data for these problems (see appendix 1) are obtained from several dutch design bureaus which use the professional software package matrixframe (http://www.matrix-software.com/uk /structuralengineering/matrixframe/index.html) for structural engineering. seven different optimization algorithms are compared. 28 independent numerical experiments were performed with each algorithm. in order to have fair comparison, the objective function was evaluated 5000 times in each experiment. the following algorithms were employed [10]: modified random search (mrs), simulated annealing (sa), simplex (sm), the variable metric method newuoa [10], [12], [14], bacoor, and the proposed genetic algorithm with modified crossover (mcga). comparison of all algorithms is provided in the fig.1. since total number of objective function evaluations is 5000, small populations of 15 individuals were created in 333 generations. the mutation probability after few numerical experiments was set to 15%. all numerical results of 28 independent experiments are rendered in appendix 2. thus, in three cases the proposed algorithm outperforms all other algorithms. compared to the classical genetic algorithm, the results of mcga is better for almost all problems considered. the ideal solution was not found for any problem, however, the differences compared to the ideal solutions do not exceed 5% for problems no. 2, 5 and 7. results for problems no. 3, 4, 6, 7 and 9 differ from global solutions till 8%. summarizing, the sa showed the performance for the grillage optimization problems, however, the newuoa, mcga and ga (excluding the simplest structures no. 1 and 3) are not far behind. ga outperforms the mcga only for most complex grillages no. 8 and 10 with 34 and 55 design parameters. better results may be expected with larger populations. however, in this case we cannot fairly compare results with other algorithms. more important is, the 398 m. ramanauskas, d. sesok, r. belevicius, e. kurilovas, s. valentinavicius figure 1: the best objective function values in 28 runs obtained by mrs, sa, ga, sm, newuoa, bacoor and mcga, normalized to rideal . confidence intervals of objective function value are much narrower for results of mcga than for results of bacoor (we do not have the confidence intervals for other algorithms) (see in fig. 2). also, for all problems the upper values of mcga confidence intervals are better than ones of bacoor. in some problems the upper values are even better than the lower values of confidence intervals of bacoor. all numerical results objective function values and confidence intervals are provided in the tables 1 and 2. figure 2: confidence intervals. genetic algorithm with modified crossover for grillage optimization 399 table 1: summary of numerical results problem no best value found in mcga best value found in [10] exact solution 1 337.15 339.30 307.47 2 105.34 106.36 104.12 3 115.02 107.25 101.85 4 113.29 106.80 101.24 5 100.00 101.05 97.51 6 120.28 115.45 97.53 7 301.48 298.11 287.35 8 404.29 286.22 236.28 9 263.12 253.00 244.71 10 556.11 463.34 349.05 table 2: summary of confidence intervals problem no mcga lower mcga upper bacoor lower bacoor upper 1 351.98 361.85 360.29 461.26 2 107.46 110.48 115.29 131.41 3 119.01 125.32 111.67 137.62 4 116.65 121.18 95.61 153.22 5 101.72 106.96 107.92 127.86 6 121.87 141.36 108.08 154.73 7 300.02 332.12 319.70 390.43 8 375.90 541.41 174.99 606.98 9 260.63 302.23 246.69 384.22 10 539.95 642.83 387.85 772.54 5 conclusions in rather simple configurations of grillages where the optimal distribution of piles due to the geometry and given loadings is more or less even, the proposed method mcga outperforms other popular stochastic optimization algorithms. additionally, this algorithm requires less effort in tuning of algorithm parameters. the crossover and selection parameters are forecasted in advance by recursive crossover rules. also the confidence intervals for results of mcga are always narrower than those of bacoor. only for grillage configurations requiring larger number of supports and their uneven distributions, it loses to newuoa and bacoor. thus, the proposed method can be treated as a new global optimization algorithm 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(2009); benchmarking the newuoa on the bbob-2009 noisy testbed, f. rothlauf, editor, gecco (companion), acm, 2429-2434, 2009. [15] watson j.-p., ross c., eisele v., denton j., bins j., guerra c., whitley l.d., howe a.e. (1998); the traveling salesrep problem, edge assembly crossover, and 2-opt, ppsn v: proceedings of the 5th international conference on parallel problem solving from nature, london, uk, springer-verlag, 823-834, 1998. [16] zienkiewicz o.c., taylor r.l., nithiarasu p. (2005); the finite element method for fluid dynamics, butterworth-heinemann, oxford, 6th edition, 2005. genetic algorithm with modified crossover for grillage optimization 401 appendices table 3: appendix 1: characteristics of problems problem no number of supports foundation length rallw rideal 1 25 172,90 325 307,47 2 18 52,90 110 104,12 3 31 84,10 105 101,85 4 31 84,90 105 101,24 5 30 63,90 100 97,51 6 37 80,10 100 97,53 7 23 129,10 300 287,35 8 34 137,90 250 236,28 9 17 97,60 250 244,71 10 55 315,61 350 349,05 table 4: appendix 2: optimization results for all 10 problems in 28 independent numerical experiments probl/exper 1 2 3 4 5 6 7 8 9 10 1 375,87 108,33 122,88 119,18 105,21 136,06 308,04 540,96 283,54 614,04 2 359,09 111,01 124,15 118,72 107,04 129,00 312,08 520,77 277,54 591,94 3 391,24 107,81 127,80 121,81 110,12 129,23 313,24 434,80 275,06 580,37 4 356,93 111,85 117,99 119,09 102,96 122,32 321,58 414,26 282,97 606,22 5 341,47 107,69 123,78 118,68 105,85 126,39 313,00 404,29 287,37 579,35 6 368,95 106,81 120,46 113,29 105,79 120,28 319,00 425,89 265,36 563,57 7 352,35 107,28 124,94 119,06 101,28 133,34 315,42 419,62 309,76 658,62 8 358,81 105,86 121,88 123,05 105,82 127,83 310,84 538,27 268,19 593,13 9 340,49 108,43 124,31 119,79 107,36 122,32 305,63 407,56 290,50 576,15 10 348,00 112,49 119,78 117,50 103,19 138,99 319,01 523,01 299,97 556,11 11 352,52 107,82 123,30 120,11 105,54 141,05 327,97 532,61 273,09 590,31 12 349,46 107,75 118,99 119,69 101,86 125,69 341,05 432,63 284,85 567,98 13 376,92 110,01 126,53 118,31 105,97 126,22 301,49 525,76 279,00 607,32 14 351,34 105,45 118,58 121,63 102,85 136,55 315,65 420,27 286,73 563,72 15 341,32 106,93 115,70 118,35 103,23 131,61 312,04 429,79 276,59 606,96 16 352,12 117,91 123,82 119,26 106,82 143,18 318,38 425,28 270,80 602,00 17 337,15 108,61 123,21 121,01 107,00 127,17 305,69 442,52 263,12 638,64 18 345,64 107,62 118,43 114,78 105,90 138,09 311,00 439,41 276,29 613,01 19 364,22 109,22 117,90 121,67 101,88 143,99 313,38 408,72 298,48 578,94 20 344,76 111,06 128,80 115,69 103,36 134,57 354,81 445,75 268,88 612,41 21 369,08 110,98 115,02 113,43 100,00 124,37 316,54 457,68 290,61 566,76 22 370,84 106,44 117,24 119,15 103,18 147,41 319,63 449,23 266,89 593,30 23 354,59 105,34 131,69 117,51 105,93 132,50 307,67 537,75 279,97 557,41 24 361,88 110,66 118,33 120,48 103,45 136,64 311,88 446,24 278,44 590,55 25 355,11 108,59 127,67 118,72 103,24 122,41 315,93 441,27 290,06 563,00 26 376,52 106,98 120,09 122,02 102,57 126,20 309,44 534,65 280,92 578,71 27 341,46 112,65 120,74 120,05 103,12 136,93 315,33 423,97 290,76 621,52 28 355,41 109,58 126,61 117,59 101,11 124,86 314,31 419,46 284,29 586,92 402 m. ramanauskas, d. sesok, r. belevicius, e. kurilovas, s. valentinavicius figure 3: appendix 3: best pile placement schemes found with mcga int j comput commun, issn 1841-9836 8(2):235-246, april, 2013. quality system for production software as tool for monitoring and improving organization kpis v.c. kifor, n. tudor vasile claudiu kifor lucian blaga university of sibiu, 10 victoriei blv., 550024, sibiu, romania e-mail: claudiu.kifor@ulbsibiu.ro nicolae tudor conti temic microelectronic gmbh sieboldstrasse 19, d-90411 nürnberg, germany e-mail: nicolae.tudor@continental-corporation.com abstract: in this paper we propose a solution as support for quality systems for production software. the motivation behind this study was to reduce that cost in the production area caused by gaps in the quality of the production software. our proposal: qsps (quality system for production software) is offering support in the "vulnerable points" of these quality systems which usually generate nonconformities and have proved to be difficult or impossible to control. qsps is a method in seven steps or modules that integrates also software tools, templates, checklists, evaluating tools elaborated complying to products, process and system quality standards. if other analyzed methods like: scrum, xp, fuzzy, prompt, pta, prince2 or norms like: iso 9001, iso 9000-3, tickit, cmm and cmmi, aqap-110/aqap-150, ieee 730/983 are working in a reactive way, after the developing phase was finished, qsps is an active system, helping the software developer from the beginning of the implementation phase to improve the developing methodology and to fulfill the quality requirements. qsps model was applied in one of the largest european automotive company, the result being finalized in a practical approach of the qsps, named qsma − quality system for manufacturing application. using the qsma (quality system for the manufacturing application) for industrial projects and not only therefore, has led to accurate running of the production line from beginning of the sop (start of production). once this system was implemented and the production software applications were realized under the principles and rules of the qsma, we defined strategically measurable kpis (key performance indicators) out of the seven modules of the qsma. this kpis have the role to signal every time a production application has not the desired quality level and presents a high level of risk that could cause additional costs in the production. based on the kpis evolution, the weaknesses in the software applications can be identified in real time, so that the developer can react immediately, before occurrence of substantial damage. keywords: quality improvement, control, monitoring, efficiency, capability, performance, organization kpi (key performance indicator). 1 introduction this paper describes the deployment of software applications that is used in the production by using different product quality models, it identifies the flaws of these models and proposes a new quality model (qsps − quality system for production software) and its important kpis (key performance indicators), that is designed for the development of software applications used copyright c⃝ 2006-2013 by ccc publications 236 v.c. kifor, n. tudor in the automotive industry, a model that includes process and product norms and recommendations of use. the study is based on case studies of application of the most well-known quality standards and models for the development of a software application for production; it identifies the deficiencies of already used methods and proposes a model that is based on the quality requirements in the automotive industry, such as iso/ts 16949, with an optimal frame for production. we analyzed management methods for software applications, such as scrum, xp (extreme programming), fuzzy, prompt, pta, prince2 and quality norms such as iso 9001, iso 9000-3, tickit, cmm and cmmi, aqap-110/aqap-150, ieee 730/983. in order to identify the requirements of the automotive industry, we carried on the study and we have analyzed and applied the iso/ts 16949 to the earlier mentioned software project, nevertheless, we have analyzed also quality methods, such as automotive spice, iso/iec 25000/9126/14598. the choice of quality models for the case study was based on the following: • the most representatives and/or • the most frequently used in practice. due to special characteristics of software applications that accompany production (difficulty of testing, their impact upon the quality of the finite product), as well as their importance in assuring the continuity of the business environment in an organization (production control), these types of software application have to be treated in a particular way, different than other types of software, whose norms can be found in specialty literature. these differences of production software applications are shown in figure 1: figure 1: major differences among production software application the quality of production software should address two related but distinct notions [16]: functional quality (or fitness for purpose) − reflects how the software complies with specific requirements of the customer or conforms to specifications; structural quality − reflects how the production software meets non-functional requirement that support the delivery of functional requirement, such as robustness or maintainability, the degree to which the software was produced correctly. quality system for production software as tool for monitoring and improving organization kpis 237 production software quality measurement is about quantifying to what extent a software or system rates along each of five dimensions: reliability, efficiency, security, maintainability and (adequate) size [14]. a quality system for production software should be part of organization quality management system and can be expressed as objectives and means (processes, procedures, tools and responsibilities) designed to fulfill these objectives [8]. the objectives from the above definition are usually addressing customer (external and internal) focus, compliance with standards (effectiveness) waste reduction and a better use of resources (efficiency) and continual improvement. quality systems for production software are designed according to the requirements of the quality management standards (iso 9001), software engineering standards (iso 9000-3, iso/iec 9126 etc.), information security standards (iso 27000), and specific sectors standards (iso ts 16949 for automotive). in our view [17], a method for development, implementation and maintaining a production software should be based on traditional principles and methods of software engineering and project management, but has to incorporate specific features in order to cover the particularity of such products: • support and cover specific gaps of the quality systems for production software; • degree of difficulty and time required to implement; • system efficiency in saving resources, costs (or avoid potential additional costs), to control and ensure the project’s completion; • high transparency of project status and difficulties faced by using the method; • software quality assurance is not to endanger the "normal" flow of production; • adaptability of the model for extreme situations and different types of such softwares; • system feasibility; • easy for managers to understand the software, even with a low level of technical knowledge; • reduce the communication gap with the involved departments. 2 objectives the purpose of the qsps is to improve the effectiveness and efficiency of the quality systems for production software; applying qsps correctly would result in a proper running of the production software, a high quality product according to customer and standard requirements and lower production costs. effectiveness and efficiency represents today some of the most important key performance indicators and a permanent concern for every organization. while effectiveness is focusing on complying with specific quality outputs and standards requirements, efficiency aims higher results with lower resources [8]. as effectiveness and efficiency are considered to be very general, and difficult to define and measure, specific indicators and objectives are derived from the general objective and proposed for qsps project, in the table 1. the objectives are addressing customer (external and internal) focus, compliance with standards (effectiveness) waste reduction and a better use of resources (efficiency) and continuous improvement. qsps will support the quality system for production software with a method in seven steps or modules that integrates also software tools, templates, checklists, evaluating tools and an experienced database with case based reasoning. qsps will bring higher effectiveness for the quality system for productions software and give support for reaching more efficient operations. 238 v.c. kifor, n. tudor objectives/needs indicators targets improve the quality level of the profpy (first pass yield) 98% duction software from product perspective dpmo (defects per million opportunities) <10 reduce the risk of the impact of production software on product quality cpk cpk 1,5 (means 6 dpmo) reduce the risk of delay for rump-up rate and cycle time of the line. downtime of the production line due to software deficiency 0 reduce additional costs caused by software returns 0 production software software downtime 0 improve the capability of the production infrastructure, to eliminate the entire problems caused by the production software, so that the production achieves the maximum of yield at least 2,5% from entire lot of day production and 50% of analyse work per day by specialized engineer and technician, meaning a 10% better yield of the production line. transparency in classification of production problems (process vs. software) identification time less than 5 min. increase customer satisfaction supplier evaluation by customer 90% satisfaction quality system audit results iso 9001, iso ts 16949 system compliance levels 95% table 1: qsps objectives 3 qsps integration based on the objectives proposed, we applied the qsps in one of the biggest automotive company in the world. because of the special characteristics of the company, and the existing software lifecycle, the customized qsps became the name qsma (quality system for manufacturing application). qsma (method and tools for improving the efficiency and effectiveness of the quality systems for manufacturing software) aims to support the quality systems for production software, such systems been developed according to the specific standards specifications. this architecture will be integrated in the production information system lifecycle that controls the product development and industrialization processes. qsma will give support in the "vulnerable points" of these quality systems which usually generate nonconformities and have proved to be difficult or impossible to control (figure 2). qsma is a method in seven steps or modules that integrates also software tools, templates, checklists, evaluating tools elaborated complying to products, process and system quality standards, described also in the next chapter. 4 qsma practical results the technical and the economical departments from the automotive company have analyzed, in the period 2010-2011, the qsps model proposed and have confirmed its feasibility and quality system for production software as tool for monitoring and improving organization kpis 239 figure 2: integration of qsma the importance of its implementation in practice. therefore, the pilot process of implementation has been approved. this pilot process had considered five representative units of production from this company: core system, reporting solutions, integration (client) solutions, communication middleware and integration frameworks. the quality system for manufacturing applications (qsma), developed on the qsps structure, will be integrated in mes − manufacturing execution system, in the 92 plants of the company. qsma comprises 7 stages that will be individually applied for every department from the ones specified above. these stages contain templates, instructions and checklist, as follows: step 1: • checklist service development − criteria to start (functional spec. template, training, service level agreement, development closure characteristics etc.); • time and cost evaluation (on component level: reporting, equipment software etc.); • project organization/software development; • rasi chart (who is responsible for what); • handover/q-gates definition (what to do when). step 2: • process for developer − processing in application (presentation); • it requirements − non functional: – coding guideline (adaptability/ test adaptability/ stability/ portability/ coexistence/ changeability/ re-use study); – gui guideline; – performance guide line; – test guideline. • it requirements compare cross check to business requirements (cover ability check) − functionality compliance check frequency and timing; • best practices (check list) → plugin tcal frames.net; 240 v.c. kifor, n. tudor • document & source code control & process (version control − "subversion"); • it list of risks (template), technical risk (fmea); • it security requirements (checklist), see it at conti.; • ma dev. environment checklist-for example conti configuration model for camline. step 3: • requirements and recommendations for verification of the software (validation checklist): – design and development review/ verification/validation (checklist); – code review/security & vulnerability testing; – classification and verification of the software functionality in accordance to the defined it requirements (performance test) (checklist); – classification and verification of the software functionality in accordance to the defined business requirements. (requirements test). • process for verification and validation: template for verification criteria and result. step 4: • internal criteria of acceptance and approval of the implemented software kit by the developers before delivering it to the client. (check list) (software ppap); • acceptance protocol (with approval signatures) (template) (rasi from step 2); • user manual (form and template, content instructions); • deviations from specifications (document template/record medium: sharepoint... etc. to document requests/solutions and testing results after implementation); • internal functional & process audit for software (score list); • accuracy/maturity evaluation and classification (notification criteria for alfa, beta, release... version, definition of software maturity classification rules) (instructions); • usability: − fault tolerance understand ability/ learn ability analysis (instructions); • efficiency compliance (statistical reports: igate?, capability studies, software cpk, lifetime study) (checklist). step 5: • verification and approval of the implemented software kit by the customer. (functionality test-run @ rate); • process of bugs handling (environment definition test-integrationproduction) and • recording of bugs situations and solution (lesson learned for developer & bug tracking); • test scenario based on requirements-specification & implementation (action → expected result → run result); • statutory and regulatory conformity check (licenses, local law regulation); • system integration (rollout procedure: integration system → production system); • acceptance and release criteria & release process and protocol. step 6: • recommendations and requirements for functional monitoring and measurement of the software product during the production (instructions); • monitor results and long term improvements (template); • process of software validation and verification extension (template proposal); • calculation of ipm (number of software incidents per 1 million executing); • process of corrective and preventive action (chart); quality system for production software as tool for monitoring and improving organization kpis 241 • process of predictive actions (chart); • software quality assessment and capability (check/score list); • control and record of quality data collection and results (proposal for data structure: sharepoint, igate reports); • software control plan and handle of bugs in alignment with it incident mng.; • lesson learned, collection medium (sharepoint etc.); • qsma dynamic adjustment and improvement. potential improvement measurement. (mathematical simulation of known mes software: use software like statistica). step 7: • requirements and recommendations for handling customer complaints (instructions); • technical support agreement (contract template); • problem reporting/monitoring/recording and closing procedure (by customer acceptance of the solution) (system proposal: remedy, hp service management); • process of change management and problem/incidents resolution (templates for change request, templates for incidents: 8d... etc.); • training (process, methods and rules) → template with content recommendation; • customer communication process (methods, communication medium: e-mail, sharepoint, portal); • periodical customer feedback evaluation/ customer satisfaction reports(template); • statistics: over problems and solutions, reaction times, and solution efficiency: statistic proposal like pert diagram). 5 key performance indicators the preliminary data analyzed after implementation confirm a reduction of at least 60% of potential losses caused by software. it was preliminary confirmed the improvement of certain kpis such as figure 3. • functionality compliance: – business requirements cover ability: 95% – deviations from requirements: 1% – missing functionality during verification: 0% • dpmo (defects per million opportunities): < 10 • downtime of the production line due to software deficiency: 0 • software returns: 0 • supplier evaluation by customer: 90% satisfaction • non functional compliance: 95% • software availability: 99,99% this kpis have the role to signal every time a production application has not the desired quality level and presents a high level of risk that could cause additional costs in the production. based on the kpis evolution, the weaknesses in the software applications can be identified in real time, so that the developer can react immediately, before occurrence of substantial damage. 242 v.c. kifor, n. tudor figure 3: qsma kpi overview 6 qsma continuous improvements the scoring of the qsma system means a solid base of data entry in mathematical modeling; thereby the system can be monitored and subjected to continuous improvements (figure 4). the qsma system can be taken as a sum of combination quality system elements in the automotive industry iso/ts 16949 and that of software requirements of this industry automotive spice together with all other elements of the individually studied methods, with the help of formula 2: qsma = {e1, e2, e3, e4, e5, e6, e7} (1) where the e1÷e7 are the best combination of the elements of the known quality methods. e1 = 8∑ i=1 eo1isots16949e o 1automotivespicee1mi = e o 1isots16949e o 1automotivespice 8∑ i=1 e1mi (2) e2 = e o 2isots16949e o 2automotivespice 8∑ i=1 e2mi (3) e3 = e o 3isots16949e o 3automotivespice 8∑ i=1 e3mi (4) quality system for production software as tool for monitoring and improving organization kpis 243 e4 = e o 4isots16949e o 4automotivespice 8∑ i=1 e4mi (5) e5 = e o 5isots16949e o 5automotivespice 8∑ i=1 e5mi (6) e6 = e o 6isots16949e o 6automotivespice 8∑ i=1 e6mi (7) e7 = e o 7isots16949e o 7automotivespice 8∑ i=1 e7mi (8) so the mathematical representation of the qsps can be as followed: qsma = {e1, e2, e3, e4, e5, e6, e7} = 7∑ i=1 eoiisots16949e o iautomotivespice 8∑ j=1 ejmi (9) the obtained result of the software evaluation with qsps is a directly proportional function with the arithmetical average of these elements as followed: evaluation = f(qsma) = ∑7 i=1 ei 7 (10) in order to prove how close to these values are to reality, we created the simulation in figure 4, in which we calculated the average value that can be obtained from each element, regardless of the applied method, followed by a simulation of elements, to show their impact on the end result and the satisfaction of customers. as a result of these simulations, we can sum up the fact, that the already existing methods for software products deeply focus on element 2 − software specific requirements (management, risk, quality and security) and 3 − validation and verification of the implemented software application, so that we have paid an increased attention to the other elements in the implementation of the qsma system. 244 v.c. kifor, n. tudor figure 4: mathematical modeling of the method qsma 7 conclusions the qsma system leads to assurance of quality of production software applications and in the same time to avoidance of extra costs, that can occur in production due to software applications. other direct advantages, proved to be efficient in practice are: • assurance of quality of mes software applications in the production of mechanical components for motor vehicles; • helps in the development of sturdy mes software applications; • diminishes potential costs that can occur due to software errors and therewith the breakdown of production; quality system for production software as tool for monitoring and improving organization kpis 245 • offers high transparency in the classification and identification of production problems (process problems versus mes software problems); • acceptance of mes solutions by the customer and verification of fulfillment or requirements; • growth of customer satisfaction. the experimental system can be further improved and adapted to the actual needs that occur during practical situations. the projection of this system was created after a long study, that was the result of the growing need of a method that supports the deployment of software applications in the automotive industry, so that these fulfil the requirements of quality standards in this area assure a normal flow of production in order to avoid economic losses on organizational level. the software quality management gains more and more important in all areas of development and the economy, due to the following criteria [10]: the software quality is an exponential factor in competition, led by the growing awareness of the qualitative section of the beneficiaries; 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[24] iso 9001:2008, quality management systems requirements. international journal of computers communications & control issn 1841-9836, 12(5), 689-703, october 2017. gravitation theory based model for multi-label classification l. peng, y. liu liwen peng, yongguo liu* knowledge and data engineering laboratory of chinese medicine, school of information and software engineering, university of electronic science and technology of china, chengdu, 610054, pr, china *corresponding author: liuyg@uestc.edu.cn abstract: the past decade has witnessed the growing popularity in multi-label classification algorithms in the fields like text categorization, music information retrieval, and the classification of videos and medical proteins. in the meantime, the methods based on the principle of universal gravitation have been extensively used in the classification of machine learning owing to simplicity and high performance. in light of the above, this paper proposes a novel multi-label classification algorithm called the interaction and data gravitation-based model for multi-label classification (itdgm). the algorithm replaces the interaction between two objects with the attraction between two particles. the author carries out a series of experiments on five multi-label datasets. the experimental results show that the itdgm performs better than some well-known multi-label classification algorithms. the effect of the proposed model is assessed by the example-based f1-measure and label-based micro f1-measure. keywords: data gravitation theory, interaction, multi-label classification. 1 introduction as a major area of research into machine learning systems, the traditional supervised classification learning technology attributes each instance to one class (label). with x as the set of all instances and l as the label space, the traditional supervised classification learning system aims to get the function f : x → l from the training dataset {(xi, lj) : 1 ≤ i ≤ m, 1 ≤ j ≤ k}, where m and k denote the number of instances and the number of labels, respectively. in other words, the purpose of the system lies in predicting the one correct label for each test instance. there are many algorithms for single-label classification, such as the decision tree c4.5 [4], the support vector machine (svm) [1], the rough set [11], the neural network (nn) [8] and so on. the traditional single-label classification may apply to some fields, but many instances do not belong to a specific class in reality, namely text categorization, music information retrieval and video classification. the limitation of the traditional single-label classification has contributed to the growing popularity in multi-label classification algorithms. in general, multi-label classification methods are roughly divided into two categories [2]: the problem transformation method and the algorithm adaptation method. the former transforms multi-label dataset into other well-established learning scenarios, and processes the transformed dataset with the single-label classification algorithm. typical examples are the binary relevance algorithm (br), the label combination method (lc), high-order classifier chains [13], and random k label sets (rakel) [17]. by contrast, the algorithm adaptation method directly applies adaptive learning methods to solve the problem of multi-label learning. the representative algorithm adaptation methods include the k-nearest neighbor algorithm for multi-label learning (ml-knn) [24], the back-propagation multi-label learning (bpmll) [23], the bichromatic reverse k-nearest neighbor algorithm (brknn) [15] and the dependent multi-label k-nearest neighbor copyright © 2006-2017 by ccc publications 690 l. peng, y. liu algorithm (dml-knn) [21]. as an extended version of the knn method, the ml-knn approach employs the maximum posteriori probability to determine the label set of test instances. based on the label sets of the neighbor instances, the brknn method calculates the confidence of each label, and decides the k nearest neighbors of each test instance. the bpmll is another classic back-propagation multi-label classification algorithm which proposes a new error function for multiple labels. as far as we know, the methods based on the principle of universal gravitation has been extensively used in the classification or clustering of machine learning owing to simplicity and high performance. the existing physical gravitation-based classification or clustering methods mostly focus on two important factors: the data distance and density. for example, reyes et al. [14] proposed a multi-label lazy algorithm based on the data gravitation model, named the mldgc. it is the first algorithm developed for data gravitation model on multi-label classification. however, the data gravitation-based methods often ignore the interaction of data mass, and directly define data gravitation with density, resulting in inconsistency with human perception. in view of the necessity to include the attraction between two particles in the data gravitationbased classification, this paper proposes a novel algorithm based on gravitation theory called the interaction and data gravitation-based model for multi-label classification (itdgm). the rest of this paper is organized as follows: section 2 briefly introduces gravitation and reviews the related research on data gravitation, section 3 presents the proposed algorithm, section 4 describes the experiment, section 5 shows and discusses the experimental results, and section 6 wraps up the research with some conclusions. 2 literature review whenever there is an interaction between two objects, there is a force upon each of the objects. one of the forces can be characterized by newton’s law of universal gravitation. the universal gravitation-based methods are widely used in classification or clustering. wright [18] was the first to apply such a method in cluster analysis. gomez et al. [6] proposed a clustering algorithm based on feature space that considers each of the instances. on account of gravitational collapse, wang et al. [19] put forward an improved algorithm based on k-nn classifier. in light of the theoretical model of data gravitation and data gravitation field, yang et al. [22] presented a new classification model called data gravitation-based classifier (dgc), and used it in an intrusion detection system (ids). li et al. [9] developed a nonlinear classification algorithm to handle data gravity as data vector. shafigh et al. [16] proposed a method based on the gravitational potential energy of particles named gravitation-based classification (gbc) algorithm, sought for the equilibrium condition of the classifier, and proved that the algorithm had strong robustness but high computational complexity. wena et al. [20] came up with the cognitive gravitation model (cgm) algorithm, which uses the self-information of each instance instead of the mass based on the law of gravitation and the cognitive laws. starting from the dgc method, peng et al. [12] created the improved dgc (idgc) in an attempt to handle imbalance datasets with amplified gravitation coefficient (agc), which could strengthen and weaken the gravitational field of the minority and majority instances. reyes et al. proposed an algorithm based on lazy learning and data gravitation model for classification which simultaneously measured classification accuracy and label ranking. among the aforementioned data gravitation-based algorithms, most of them are applied in single-label learning systems. the only exception is the mldgc, which is adopted for multi-label classification. overall, few scholars have integrated the data gravitation model and multi-label classification. gravitation theory based model for multi-label classification 691 3 the proposed algorithm based on gravitation theory so far, the laws of physics have been extensively applied to the information domain. it is stressed that the interaction of data instances must conform to the physical laws. the laws are implemented to simulate actual data classification in many novel data gravitation-based classification algorithms. however, the simulation results may not fully correspond with the reality as the data gravitation is directly defined with the density instead of the interaction between data instances. to solve the problem, this paper proposes a multi-label learning algorithm based on data gravitation model (itdgm). in a multi-label dataset, an instance i can be transformed into a data particle expressed as (xi,yi,mi). the feature vector of the data particle i is defined as the label set of the particle and denoted by xi and yi. the symbol mi means the data mass of particle i. for any two particles i and j, the distance between them can be calculated as df (i,j) = √∑ ∀f∈f δ(xif,xjf ) 2. since the heterogeneous euclidean-overlap metric (heom) can handle both nominal and continuous features, the above heterogeneous distance function is adopted in the experiment. therefore, the term δ(xif,xjf )2 should be solved under three different conditions: • δ(xif,xjf )2 = 1 when xif 6= xjf and the data attribute is discrete; • δ(xif,xjf )2 = 0 when xif = xjf and the data attribute is discrete; • δ(xif,xjf )2 = |xif−xjf | max(f)−min(f) when the data attribute is continuous; where xif and xjf are the f-th eigenvalues of i and j, respectively; f is the feature space; δ(xif,xjf ) is the type of f-th feature; max (f) and min(f) are the maximum and minimum values, respectively. hence, it is possible to identify the k-nearest neighbors of particle i by the df (i,j) function. the k-nearest neighbors of particle i can calculate by ni = (i1, i2, . . . , ik) |df (i, i1) ,df (i, i2) , ...,df (i, ik), which is sorted by ascending order. according to the newton’s law of universal gravitation, the greater the mass of two objects, the greater the resulting interaction between them. in other words, the gravitation increases with the interaction between the two objects. in this research, a new concept is defined: the interaction-based gravitation coefficient (igc). the coefficient is created to evaluate the attraction between two particles like the interaction between two objects. data has not mass. for the reason that data has no mass, the data mass is usually set as one. however, the setting is inconsistent with human perception without considering the interactions between data particles. thus, the author defines the igc and a data gravitation-based model to measure the strong or weak gravitational force of particles. the dissimilarity between the label set of the particle i and that of particle j is expressed by the operator θ(yi,yj) and the interaction between the two particles m (xi,j) is defined as: p (i,j) = θ(yi, yj) q × 1 fj (1) m (xi,j) = log ( 1 p (i,j) ) = log ( qfj θ(yi,yj) ) (2) where p (i,j) is the dissimilarity degree between the label set of the particle i and that of particle j, and fj is the number of the instance labels, and q is the number of the total labels. 692 l. peng, y. liu for the sake of data stability, m (xi,j) is introduced to measure the dissimilarity in the classification between particles. from the above formulas, it is concluded that p (i,j) is inversely proportional to m (xi,j), that is, the smaller the p (i,j), the more intense the interaction between the particle i and j. based on neighborhood density concept of reyes et al. [14], the igc value of particle i is calculated by the formula mi = d wi i , where di is the neighborhood density or the distribution of the neighbor particles of i, and wi is the neighborhood weight of particle i. the greater the value of di, the more similarity between the label set of another particle and that of particle i. the neighborhood density is computed by the following formula: di = 1 + ∑ j∈ni (1 + m(xi,j))/df (i,j) (3) based on the above formula, the neighborhood weight calculation method is improved as below. first, pidy = ∑ j∈ni i(xi,j ) k and pidf = ∑ j∈ni df (i,j) k are identified as the nearest particles of i based on the principle of prior probability and the prior probability of the nearest particles of i in the feature space, respectively. the symbol k is the initial number of nearest neighbors, and ni is the nearest neighbors set of the particle i. then, the prior probability of the nearest neighborhood of i with similar label sets can be calculated by pi dy |df = ∑ j∈ni i(xi,j )·df (i,j) k . finally, the neighborhood weight (wi) is obtained by the formula wi = pi dy |df p i df pi dy − (1−pi dy |df )p i df 1−pi dy . through the above steps, it is possible to get the gravitational force between two instances. taking an instance from dataset, the k-nearest neighborhood of particle i can be computed as below. f (i, j) = g igc df (i,j) 2 = g mi df (i,j) 2 (4) in this experiment, the gravitational constant g is set to 1 for the calculation of the gravitation of instances. thus, the gravitational force f (i,j) = g mimj r2 is rewritten as f (i,j) = igc df (i,j) 2 , where the igc replaces the product of the mass of the objects. after obtaining the gravitational force between instances, it is necessary to determine which labels belong to the test instances. to this end, a simple statistical approach is implemented. the number of the y-th label that belongs to the k-nearest particles is expressed as n (i) = ∑ j∈fi f (i,j) ,y ∈ yj, and the number of the y-th label that does not belong to the k-nearest particles is expressed as n′(i) = ∑ j∈fi f (i,j) ,y /∈ yj, fi is the set of the nearest neighbors. if n (i) > n′(i), the y-th label belongs to the instance; otherwise, the y-th label does not belong to the instance. the proposed method is divided into three main steps. first, calculate the interaction between instances with the igc coefficient; second, compute the gravitation of each particle i; third, obtain the k-nearest neighbors of the instance based on gravitation. figure 1 illustrates the basic procedure of the itdgm method. the proposed algorithm seeks for k-nearest neighbors of each multi-label instance, and aims to transform the instances to particles in the learning phase. if linear search strategy is adopted for the k-nearest neighbors of instance i, the optimal time complexity of the learning phase is o ( n2 ·d ) for all training instances, i.e. it takes o (n ·k ·d) steps at most to classify k instances, where n is the number of instances and d is the cardinality of the feature space. gravitation theory based model for multi-label classification 693 algorithm 1 the itdgm algorithm input: tr → multi-label training dataset; ts → multi-label testing dataset; k → number of nearest neighbours output: label set of instances begin 1: learning phase 2: for each i ∈ tr do 3: ni ← k nearest neighbors (i,tr,k) 4: mi ← igc(i,ni) 5: end 6: test phase 7: for each i ∈ ts do 8: fi ← k nearest neighbors (i,ts,k) 9: yi ←∅ 10: for each y ∈ y do 11: n (i) = ∑ j∈fi f (i,j) ,y ∈ yj 12: n′(i) = ∑ j∈fi f (i,j) ,y /∈ yj 13: if n (i) > n′(i) then 14: yi ← yi ∪y; 15: end figure 1: basic procedure of the itdgm method 4 experiment due to the one-to-many relationship between instances and labels, the evaluation of multilabel classification performance is much more complicated than the traditional supervised singlelabel learning. the existing evaluation measures for multi-label classification mainly fall into two groups: the example-based measure [7] and the label-based measure [25]. in this research, the experimental results are verified by a string of different public datasets and discussed in details below. 694 l. peng, y. liu 4.1 datasets in this paper, five public multi-label datasets of different scales, labels and properties are selected from various domains to validate the proposed algorithm. the “genbase” datasets [3] store information on the gene functions; the “birds” contain bird species examples for acoustic classification; corel16k001, corel16k002 and corel16k006 encompass examples of corel images. the statistics on benchmark datasets are shown in table 1, where n is the number of instances in the datasets; d is the number of features; q is the number of labels; ds and lc are the label cardinality and the label density, respectively. table 1: statistics of the public multi-label benchmark datasets dataset domain n d q ds lc genbase biology 662 1186 27 32 1.252 birds audio 645 260 19 133 1.014 corel16k001 image 13766 500 653 4937 2.867 corel16k002 image 13761 500 654 4937 2.867 corel16k006 image 13859 500 652 4937 2.867 4.2 evaluation metrics the performance of multi-label classification is measured differently from the single-label classification learning system. let x ∈ rd be instances of data sets and express each instance as a d-dimensional vector x = {x1,x2, . . . ,xd},x ∈ x. let y = {l1, l2, . . . , lm} be a finite label set. every instance belongs to a subset of y and the subset is represented by an m-dimensional vector y = {y1,y2, . . . ,ym}; if yk = 1, label lk belongs to instance x; if yk = 0, the label does not belong to the instance. for a given training dataset t = {(xi,yi)|1 ≤ i ≤ n,xi ∈ x,yi ∈ y} composed of n instances, where x is the instance and y is the label subset of the instance, there is a corresponding test dataset d = {(xi,yi)|1 ≤ i ≤ p,xi ∈ x,yi ∈ y} composed of p instances based on the evaluation of the classification performance on test instances and the test set that returns the mean value. in this paper, the instance-based f1-measure (f1ex) is implemented to present the experimental evaluation results: f1ex = 1 m m∑ i=1 2|yi ∩zi| |yi| + |zi| (5) where m is the number of test instances, and zi is the predicted label set of a given test instance i. the greater the value of f1ex, the better the performance of the multi-label classification algorithm. different from the above example-based measure, the label based micro f1-measure relies on the harmonic mean between recall and precision. the precision refers to the relevance of the predicted labels and the recall reflects the percentage of the predicted labels that are relevant. such a measure is used in this research to present the algorithm performance. the f1-measure and the micro averaging are expressed by the following formulas: f1 − measure = 1 n n∑ i=1 2|yi ∩zi| |zi| + |yi| (6) gravitation theory based model for multi-label classification 695 mmicro = m ( m∑ l=1 tpl, m∑ l=1 fpl, m∑ l=1 tnl, m∑ l=1 fnl ) (7) where tpl is the true positives; fpl is the false positives; tnl and fnl are the true and false negatives for l labels after a binary evaluation, separately. the greater the micro f1-measure, the better the classification performance of the multi-label algorithm. 4.3 experiment setting the performance of the proposed algorithm itdgm is compared with that of five classic multi-label classification algorithms: the ml-knn, the br, the rakel, the brknn and the bpmll. in the rakel and br algorithms, the c4.5 decision tree is taken as the classifier. in this research, the number of the nearest neighbors is 2∼7 for the ml-knn, the itdgm and the brknn; the smoothing factor of the ml-knn is 1.0; the learning rate parameter of the bpmll is 0.05; the epoch parameter is 100; 20% of the input units are hidden units. the experiments are conducted on an interl core-i5 2.3 ghz processor with 8g memory, with the aid of matlab 2012 and the java-based open resource software mulan, which contains lots of classic and popular multi-label algorithms, evaluation targets and measures. the results are subjected to 5-fold cross validation. 5 results and discussion in this research, the example-based f1-measure (f1ex) and micro f1-measure are taken as the multi-label classifier evaluation criteria. in any case, the best results are highlighted and bolded. as mentioned above, the greater the f1ex and micro f1-measure, the better the performance of the corresponding evaluation method. tables 2∼3 show the results of multi-label classification algorithms on test datasets for f1ex and micro f1-measure. it is seen that the proposed algorithm itdgm has better performance than the other methods. based on the f1ex values in table 2, the itdgm outperforms the other five classic methods on the five public datasets. the f1ex index of the proposed algorithm surpasses that of the other algorithms more than 3% on the“bird” dataset, and 2% on the remaining datasets. table 3 displays the micro f1-measure values of the proposed algorithm and the other algorithms. the results show that the proposed algorithm boasts the best performance on the corel16k001, corel16k002 and corel16k006 datasets, and keeps a 2% lead over the other algorithms in "genbase" and "birds" datasets. table 2: example-based f1-measure (f1ex) values of all algorithms on the five public datasets algorithm dataset genbase birds corel16k001 corel16k002 corel16k006 mlknn 0.9649±0.0099 0.5330±0.0436 0.0071±0.0027 0.0063±0.0042 0.0109±0.0039 rakel 0.9705±0.0169 0.5608±0.0334 0.0459±0.0093 0.0739±0.0059 0.0798±0.0070 brknn 0.9565±0.0090 0.5649±0.0385 0.0410±0.0105 0.0522±0.0096 0.0567±0.0071 bpmll 0.0772±0.0457 0.4436±0.0594 0.0289±0.0011 0.0287±0.0026 0.0282±0.0017 br 0.9705±0.0169 0.5608±0.0334 0.0535±0.0054 0.0849±0.0088 0.0798±0.0070 itdgm 0.9766±0.0121 0.5661±0.0544 0.0851±0.0076 0.0946±0.0125 0.0995±0.0066 figures 2∼11 show the relationship between the values of f1ex and micro f1-measure and the number of nearest neighbors in all algorithms. it is demonstrated that the proposed algorithm 696 l. peng, y. liu table 3: micro f1-measure values of all algorithms on the five public datasets algorithm dataset genbase birds corel16k001 corel16k002 corel16k006 mlknn 0.9447±0.0142 0.2728±0.0300 0.0129±0.0050 0.0094±0.0056 0.0134±0.0039 rakel 0.9625±0.0094 0.3456±0.0445 0.0606±0.0062 0.1000±0.0054 0.1077±0.0109 brknn 0.9446±0.0123 0.3972±0.0303 0.0629±0.0144 0.0717±0.0087 0.0787±0.0044 bpmll 0.0870±0.0472 0.1528±0.0518 0.0414±0.0003 0.0408±0.0008 0.0396±0.0008 br 0.9625±0.0094 0.3456±0.0445 0.0718±0.0066 0.1108±0.0081 0.1077±0.0109 itdgm 0.9686±0.0118 0.4314±0.0571 0.1066±0.0082 0.1138±0.0101 0.1195±0.0065 has the best classification effect with the fewest k-nearest neighbors in the testing datasets. in figures 2 and 3, all algorithms perform well except the bpmll (f1ex: 0.0772, micro f1: 0.0870). the itdgm achieves the best overall result (micro f1: 0.9686) in figure 3, followed closely by the simply transformation methods the rakel and the br (micro f1: 0.9625). figure 2: relationship between f1ex and the number of nearest neighbors in “genbase” of all algorithms the itdgm is still the best performing algorithm in figures 4 and 5. although the mlknn and the brknn achieve fairly good performance at k=2 (f1ex: 0.5330) and k=3 (f1ex: 0.5649), respectively, the itdgm has a slight edge over the two algorithms at k=2. as shown in figure 5, the proposed algorithm has the best performance at almost any number of nearest neighbors on micro f1 metrics. according to figure 4 and figure 5, the rakel algorithm also has an outstanding performance. this is attributable to the random selection of label sets and unique strategies for training models in the algorithm. figures 6∼11 describe the metrics of all six algorithms on three corel16k datasets. all selective multi-label classification methods perform poorly in these datasets, owing to the low label density and huge amount of distinctive label sets in the three datasets. in this case, the proposed algorithm also outperforms the other algorithms. on the contrary, the ml-knn shows the worst performance in the three datasets due to the capacity of its classifier. despite the good performance in some cases, such as corel16k001 (f1ex: gravitation theory based model for multi-label classification 697 figure 3: relationship between micro f1 and the number of nearest neighbors in“genbase” of all algorithms figure 4: relationship between f1ex and the number of nearest neighbors in “birds” of all algorithms 0.0535, micro f1: 0.0718), corel16k002 (f1ex: 0.0849, micro f1: 0.1108), corel16k006 (f1ex: 0.0798, micro f1: 0.1077), the simple transformation method br costs much more training time than other algorithms. in comparison, the itdgm, the brknn and the ml-knn consumes less training time thanks to their roots in the lazy learning system. 698 l. peng, y. liu figure 5: relationship between micro f1 and the number of nearest neighbors in “birds” of all algorithms figure 6: relationship between f1ex and the number of nearest neighbors in corel16k001 of all algorithms gravitation theory based model for multi-label classification 699 figure 7: relationship between micro f1 and the number of nearest neighbors in corel16k001 of all algorithms figure 8: relationship between f1ex and the number of nearest neighbors in corel16k002 of all algorithms 700 l. peng, y. liu figure 9: relationship between micro f1 and the number of nearest neighbors in corel16k002 of all algorithms figure 10: relationship between f1ex and the number of nearest neighbors in corel16k006 of all algorithms gravitation theory based model for multi-label classification 701 figure 11: relationship between micro f1 and the number of nearest neighbors in corel16k006 of all algorithms 702 l. peng, y. liu 6 conclusion this paper presents a new multi-label classification algorithm based on data gravitation theory, introduces the interaction-based gravitation coefficient (igc) to the proposed algorithm, and uses the algorithm to calculate the gravitational force instead of the mass of the particles. the average classification accuracy of the proposed method is evaluated by experiments on five public open datasets, in comparison with five classic multi-label algorithms: the br, the rakel, the ml-knn, the bpmll and the brknn. the six algorithms are applied, under the example-based f1-measure (f1ex) and micro f1-measure, to the five public multi-label datasets at different number of nearest neighbors. in the experiment, the proposed algorithm outperforms all the other five algorithms, showcasing ideal capability and rationality. this research creates several new topics for future studies: the proposal of new multi-label classification method based on data gravitation model, integration with other fields [5] [10], and the improvement of the classification effect. acknowledgement this research was 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(2014); a review on multi-label learning algorithms. ieee transactions on knowledge and data engineering, 26(8), 1819–1837, 2014. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 4, month: august, year: 2020 article number: 3743, https://doi.org/10.15837/ijccc.2020.4.3743 ccc publications probability transform based on the ordered weighted averaging and entropy difference l. pan, y. deng lipeng pan institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 611731 yong deng* 1. institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 611731 2. medical center, vanderbilt university, nashville, tn. usa, 37205 *corresponding author: dengentropy@uestc.edu.cn, prof.deng@hotmail.com abstract dempster-shafer evidence theory can handle imprecise and unknown information, which has attracted many people. in most cases, the mass function can be translated into the probability, which is useful to expand the applications of the d-s evidence theory. however, how to reasonably transfer the mass function to the probability distribution is still an open issue. hence, the paper proposed a new probability transform method based on the ordered weighted averaging and entropy difference. the new method calculates weights by ordered weighted averaging, and adds entropy difference as one of the measurement indicators. then achieved the transformation of the minimum entropy difference by adjusting the parameter r of the weight function. finally, some numerical examples are given to prove that new method is more reasonable and effective. keywords: dempster-shafer evidence theory, probability transform, mass function, ordered weighted averaging, entropy difference. 1 introduction in engineering practice and scientific research, imprecise information often appears, which increases the difficulty of decision-making [21, 28, 50, 60]. the imprecise information generally has two characteristics: fuzziness and probabilistic. some researchers have proposed a lot of theories on the fuzziness of information. such as fuzzy sets[14, 76], intuitionistic fuzzy sets[4], z number[30, 77], pythagorean fuzzy sets[72], and so on[24, 27]. these theories are applied to decision-making[15, 32, 52, 64], expert systems[41, 48, 49, 66], pattern recognition[19, 55, 59, 81]. on the other hand, some theories have been proposed based on the probabilistic of information, such as probability theory[57, 75], dempster-shafer (d-s) evidence theory[12, 43], d number[9, 10], evidence reasoning[34, 80], complex evidence theory[58, 62, 63], and so on[33, 71, 74]. among them, d-s evidence theory has been https://doi.org/10.15837/ijccc.2020.4.3743 2 used in many fields due it has stronger processing ability in uncertain information. such as pattern recognition[7, 35, 61, 78], decision making[17, 26, 46, 79], expert system[16, 18, 51, 65], fault diagnosis[25, 31, 53, 54] and so on[20, 23, 36, 37]. in order to enhance the application of dempster-shafer evidence theory, there are studies about the mass function translated into probability distribution. smets proposed transferable belief model(tbm), it can transform mass functions to probability distribution and provide the expected utility theory for decision-making[44]. cobb and sheony proposed the plausibility transformation method, which translates each independent mass function to a corresponding probability distribution by plausibility function[6]. jiang proposed a probability transformation method of mass functions based on interval information, which used intermediate information of belief function and plausibility function to transform[22]. dezert proposed a probabilistic transformation method of adjustable parameters, it relies on the subjective judgment of decision makers[13]. however, existing methods can not consider the loss information after transformation. hence, it is still a open issue. in d-s evidence theory, entropy plays an important role. deng entropy is the extension of shannon entropy[8], which has attracted many people due it can measure the total nonspecificity and discord [2, 29]. hence, using deng entropy can handle the information measurement in this work. the paper proposed a new method to transform mass functions to probability distributions, which is based on ordered weighted averaging(owa) operator. the method can flexibly transform different probability distributions by adjusting the parameters r in owa operator. besides, the probability distribution with the minimum information loss can be obtained by using entropy difference of deng entropy. the rest of the paper is organized as follows. main preliminaries about dempster-shafer evidence theory, deng entropy and owa operator are introduced in section 2. in section 3, new method is presented, and some numerical examples are used to illustrate it. application in target recognition system is introduced in section 4. conclusion is given in section 5. 2 preliminaries in this section, the preliminaries about d-s evidence theory, deng entropy and ordered weighted averaging (owa) will be briefly introduced. 2.1 d-s evidence theory d-s evidence theory assigns probabilities to the power set of events [12, 43], so as to better grasp the unknown and uncertainty of the things, it offers a useful fusion tool for uncertain information [1, 3, 42, 69, 73]. some preliminaries in d-s theory are introduced as follows. for additional details about d-s evidence theory, refer to [12, 43]. definition 1. ( frame of discernment) for a mutually exclusive set θ composed of ai, it is defined as θ = {a1,a2, · · · ,an} (1) the power set of θ is indicated by 2θ : 2θ = {φ,{a1} ,{a2} , · · · ,{a1,a2} , · · · , θ} (2) definition 2. ( mass function) for a frame of discernment θ = {a1,a2, · · · ,an}, a mapping of m from 0 to 1 is defined as : m : 2θ → [0, 1] (3) which satifies m (φ) = 0 (4)∑ a⊆θ m (a) = 1 (5) where m(a) represents the degree of evidence supports a. https://doi.org/10.15837/ijccc.2020.4.3743 3 definition 3. ( belief function and plausibility function ) for any a ⊆ θ, the belief function bel :2θ → [0, 1] is defined as bel (a) = ∑ b⊆a m (b) (6) the plausibility function pl : 2θ → [0, 1] is defined as pl (a) = 1 −bel ( ā ) = ∑ b∩a 6=φ m (b) (7) obviously, pl (a) ≥ bel (a), the bel (a) and pl (a) are lower limit function and upper limit function of a. when a is a single set in power set 2θ, the equal sign is established. definition 4. ( pignistic probability transformation ) for a mass function m in the frame of discernment θ, its associated pignistic probability transformation(ppt) function betpm(w) : θ → [0, 1] is defined as[44]: betpm(w) = ∑ a⊆p(θ),w∈a 1 |a| m (a) 1 −m (φ) (8) where m (φ) 6= 1, |a| is the cardinality of subset a. in this way, mass functions can be transferred to probability distribution. definition 5. ( distance between betting commitments of the two mass functions ) let m1 and m2 be two mass functions on frame θ and let betpm1 and betpm2 be the results of two pignistic transformations from them respectively. then distance between betting commitments of the two mass functions is described as follows[5]: difbetpm2m1 = maxa⊆θ (∣∣∣betpm1(w) −betpm2(w)∣∣∣) (9) thus, similarity between betting commitments of the two mass functions is described as follows: simm1,m2 = 1 −difbetp m2 m1 (10) definition 6. ( deng entropy ) in d-s evidence theory, deng entropy can be used to measure uncertainty, it is defined as[8]: ed = ∑ m (a) log m (a) 2|a| − 1 (11) where m (a) is a mass function in the frame of discernment θ, |a| is the cardinality of subset a. 2.2 ordered weighted averaging yager proposed a kind of information settlement operator bounded between ”and” and ”or”, that is, the ordered weighted averaging (owa) operator[67, 70]. the operator reorders the data in the order from large to small, and then carries out weight processing combine with the location of the data, it can eliminate unreasonable situation well. thus, owa operator has been widely applied to complex network[56], fuzzy theory[45], information fusion[39], expert system[38], decision-making[68]. one of the core of owa opeartor theory is solution method of owa weight, which usually uses the function f : [0, 1] → [0, 1]. the function satisfies the following properties[68]. • 1© f (0) = 0; f (1) = 1. • 2© f is monotonic, f (a) ≥ f (b) if a > b. in particular using this function, the weight of owa wk (k = 1, 2, · · · ,q) can be obtain as[68]: wk = f ( k q ) −f ( k − 1 q ) (12) since the function f is monotonic, then wk = f ( k q ) −f ( k−1 q ) ≥ 0. https://doi.org/10.15837/ijccc.2020.4.3743 4 3 the proposed method in the section, a new method is presented, which translates the multielement subsets to single subset, and a example to illustrate the method. definition 7. (mean function) for a frame of discernment θ, m is a mass function. the mean function of bel (ai) and pl (ai) is defined as: m[bel,pl] (ai) = bel (ai) + pl (ai) 2 (13) where ai is a single subset. definition 8. (normalized function) % (ai) = m[bel,pl] (ai)∑ fj⊆p(θ),aj∈fj m[bel,pl] (ai) (14) where % (ai) is the result of m[bel,pl] (ai) normalization, satisfying ∑ % (ai) = 1. definition 9. (weight function) one classical of function that can be used to generate the weight, it is defined as[68]: f (x) = xr, (r > 0) (15) then, denoting ti = ∑i k=1 % (ak), and t0 = 0. finally, pm (ai) = (ti)r − (ti−1)r (16) obviously, pm (ai) is the result of translating the multielement subsets to single subset. hence, a example is used to illustrate the transformation process. example 1 : for a frame of discernment θ = {θ1,θ2,θ3}, m is a mass function in θ, there is m (θ1) = 0.2,m (θ2) = 0.1,m (θ1,θ2) = 0.3,m (θ2,θ3) = 0.25,m (θ1,θ2,θ3) = 0.15, firstly, eq.13 is used to calculate m[bel,pl] (θi): m[bel,pl] (θ1) = 0.425,m[bel,pl] (θ2) = 0.45,m[bel,pl] (θ3) = 0.2. then, standard normalization is realized by eq.14: % (θ1) = 0.395,% (θ2) = 0.419,% (θ3) = 0.186. thus, t0 = 0, t1 = 0.395, t2 = 0.814, t3 = 1. assuming r = 0.5, the final result is as follows: pm (θ1) = 0.628, pm (θ2) = 0.274, pm (θ3) = 0.098. or r = 1.5, the result is as follows: pm (θ1) = 0.248, pm (θ2) = 0.486, pm (θ3) = 0.266. as can be from the results of the case, a change in the value of r will cause a change in the result of transformation. obviously, when r = 1, then pm (θ1) = % (θ1) ,pm (θ2) = % (θ2) ,pm (θ3) = % (θ3). therefore, the transformation result will be more diverse under the adjustment of r value. but can r be any number? in example 1, when r → 0, then pm (θ1) = 1. if r → +∞, the pm (θ3) = 1. obviously, they are unreasonable. since in the frame of discernment θ, bel (θi) ≤ m (θi) ≤ pl (θi) must be satisfied, that is, bel (θi) ≤ pm (θi) ≤ pl (θi) is also satisfied, but pm (θ1) = 1 or pm (θ3) = 1 violated the rule. therefore, this conclusion cannot be admitted. in order to make the transformation result conform to the rule, belief function and plausibility function are considered in the transformation process, result in the following equations:{ f (tk) −f (tk−1) bel (θi) ≤ pm (θi) ≤ pl (θi) (17) https://doi.org/10.15837/ijccc.2020.4.3743 5 !" !# !$ %! %!& %!" %!# %!$ !% !& !' !" !( !# !) p ro b a b il it y d ri st ri b u ti o n vule of r ! " θ # ! ! " θ $ ! ! " θ % ! figure 1: the result of the probability transformation when r changes the range of r is [0.464, 1.732] by using eq.13 in example 1. under the condition, the transformation probability distribution is shown in figure.1. as can be seen from figure.1, the probability distributions of three element (θ1,θ2,θ3) are between belief function and plausibility function. r starts to change from 0.464, when r increases, θ1 can decrease, and θ2, θ3 can increase. and r = 1.025, then θ1 = θ2, after that, θ2 continued to increase. when r = 1.732, θ2 reaches a maximum value of 0.5001. during the process of translating mass functions to probability distributions, the change of information is also a noteworthy indicator. in this section, a loss function is used to measure the change of information, it is defined as follows: definition 10. (loss function) de = |ed −h| (18) where de is the difference between the deng entropy of mass functions before transformation and shannon entropy of the probability distribution after transformation. the smaller the value of de, the less information is lost. using the definition 10, eq. 17 is reconstructed as follows:  f (tk) −f (tk−1) bel (θi) ≤ pm (θi) ≤ pl (θi) min{de} (19) by using eq. 19, we can get that when r = 1.244, de is the minimum 1.9956, and pm (θ1) = 0.3149,pm (θ2) = 0.4592,pm (θ3) = 0.2259. in addition, ppt, pft, ipt and dsmp1 are used in example 1, and transformation results are shown in table 1. as can seen from table 1, the transformation result of the new method is relatively clear. figure. 2 shows the entropy difference before and after the transformation in this case. it can be seen from figure.2 that the entropy difference of the new method before and after the transformation is smallest, indicting that the information loss of the proposed method after the transformation is relatively small. this shows that the new method is relatively reasonable. https://doi.org/10.15837/ijccc.2020.4.3743 6 table 1: result of various methods ppt[44] pft[6] ipt[22] dsmp1[13] new method pm (θ1) 0. 4 0. 342 0. 4 0. 41 0. 3149 pm (θ2) 0. 425 0. 421 0. 42 0. 42 0. 4592 pm (θ3) 0. 175 0. 237 0. 18 0. 17 0. 2259 ! ! " ! #$% & '()* +,-( ./. ./0 &/. &/0 1/. 1/0 t h e d if fe re n c e b e tw e e n th e e n tr o p y (d e ) different models (1.9956) figure 2: the entropy difference in various methods 4 application in target recognition system in the section, a example is used to illustrate the application about new method. a numerical example about multisensor-based automatic target recognition system was proposed[11]. in multisensorbased automatic target recognition system, there are three known targets, θ1,θ2,θ3. the output results of sensors are shown in table 2. the main process of target recognition is described as follows: step 1: obtained evidence from sensors. independent evidences from different sensors, denoted as m1,m2, ...,mn, respectively. step 2: probability transform by new method. the probability distribution corresponding to mass function can be obtained by eq.19. step 3: measured the weight of evidence wmi. firstly, the similarity between probability distributions was measured by eq.9 and eq.10, denoted as simmi,mj . secondly, sum over each row of the similar matrix by crd mi = ∑k j=1 simmi,mj . finally, using the following equation to measure the weight of evidence: wmi = crdmi∑n i=1 crdmi (20) step 4: modified the mass functions of evidence. for event a, its mass function is modified as follows: m (a) = n∑ i=1 wmi ×mi (a) (21) step 5: used dempster rule by n-1. https://doi.org/10.15837/ijccc.2020.4.3743 7 the mass function can be combined n-1 times by dempster combination rule to get the result of recognition. step 6: probability transform by new method and decision making. table 2: output of the sensors[11] mi {θ1} {θ2} {θ3} {θ1,θ3} m1 0. 5 0. 2 0. 3 0 m2 0 0. 9 0. 1 0 m3 0. 55 0. 1 0 0. 35 m4 0. 55 0. 1 0 0. 35 m5 0. 6 0. 1 0 0. 3 the recognition results of various method are listed in table 3. as can be seen from table 3 that the final recognition target is θ1, but the recognition process is different. after the first combination, target θ2 was recognized in all methods. in the subsequent recognition process, the support of θ1 in the new method is higher, it is more effective than other methods. the maximum support for θ1 is 0.9852. therefore, the result of proposed method is more reasonable relatively. table 3: results of different combination rules of evidence m1,m2 m1,m2,m3 m1,m2,m3,m4 m1,m2,m3,m4,m5 m (θ1) = 0 m (θ1) = 0 m (θ1) = 0 m (θ1) = 0 dempster[12] m (θ2) = 0.8571 m (θ2) = 0.6316 m (θ2) = 0.3287 m (θ2) = 0.1404 m (θ3) = 0.1429 m (θ3) = 0.3684 m (θ3) = 0.6712 m (θ3) = 0.8596 m (θ1) = 0.1543 m (θ1) = 0.7057 m (θ1) = 0.9313 m (θ1) = 0.9839 deng. et. al[11] m (θ2) = 0.7469 m (θ2) = 0.2275 m (θ2) = 0.0257 m (θ2) = 0.0026 m (θ3) = 0.0988 m (θ3) = 0.0574 m (θ3) = 0.0354 m (θ3) = 0.0101 m (θ1) = 0.1543 m (θ1) = 0.3500 m (θ1) = 0.6027 m (θ1) = 0.7958 murphy[40] m (θ2) = 0.7469 m (θ2) = 0.5224 m (θ2) = 0.2627 m (θ2) = 0.0932 m (θ3) = 0.0988 m (θ3) = 0.1276 m (θ3) = 0.1346 m (θ3) = 0.1110 m (θ1,θ3) = 0 m (θ1,θ3) = 0.0093 m (θ1,θ3) = 0.0076 m (θ1,θ3) = 0.0029 m (θ1) = 0.299 m (θ1) = 0.766 m (θ1) = 0.9250 m (θ1) = 0.9624 song. et. al[47] m (θ2) = 0.527 m (θ2) = 0.076 m (θ2) = 0.029 m (θ2) = 0.006 m (θ3) = 0.174 m (θ3) = 0.158 m (θ3) = 0.046 m (θ3) = 0.032 m (θ1) = 0.2624 m (θ1) = 0.7924 m (θ1) = 0.9482 m (θ1) = 0.9868 new m (θ2) = 0.6455 m (θ2) = 0.1173 m (θ2) = 0.0162 m (θ2) = 0.0020 method m (θ3) = 0.0921 m (θ3) = 0.0903 m (θ3) = 0.0356 m (θ3) = 0.0112 5 conclusion in the paper, a transformation method is proposed, it based on owa, which can be transformation flexibly by adjusting the parameter r of weight function. the proposed method is constrained by belief function and plausibility function. in addition, the entropy difference is taken as the measurement standard, and the optimal solution of transformation can be finally obtained under the condition. examples are used to prove that the new method is relatively reasonable. however, there are some issues that need to be solved in the future. for example, m (θ1) = 13,m (θ2,θ3) = 2 3 , when entropy https://doi.org/10.15837/ijccc.2020.4.3743 8 difference is the smallest, the result of the new method is a uniform probability distribution, and other methods also can produce the same results. it increases the difficulty of decision-making. for this case, the current approaches are that reasonably improve the method of generating mass functions or collecting data again. thus, the mass function transformation to the probability distribution is still a lot of potential work to be done. acknowledgment the work is partially supported by national natural science foundation of china (grant no. 61973332). references [1] abellán, j. 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(2020). a new divergence measure of pythagorean fuzzy sets based on belief function and its application in medical diagnosis, mathematics, 8(1), 142, 2020. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: pan, l.; deng, y. (2020). probability transform based on the ordered weighted averaging and entropy difference, international journal of computers communications & control, 15(4), 3743, 2020. https://doi.org/10.15837/ijccc.2020.4.3743 international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(5), 629-646, october 2019. viners method for the multiple criteria analysis and neuromarketing of best places to live a. kaklauskas, d. dzitac, j. sliogeriene, n. lepkova, i. vetloviene arturas kaklauskas* vilnius gediminas technical university, vilnius, lithuania *corresponding author: arturas.kaklauskas@vgtu.lt domnica dzitac new york university abu dhabi, abu dhabi, uae domnica.dzitac@nyu.edu jurate sliogeriene vilnius gediminas technical university, vilnius, lithuania jurate.sliogeriene@vgtu.lt natalija lepkova vilnius gediminas technical university, vilnius, lithuania natalija.lepkova@vgtu.lt ingrida vetloviene vilnius gediminas technical university, vilnius, lithuania ingrida.vetloviene@vgtu.lt abstract: the best and worst places to live have been analysed in the world for many years and multiple criteria analysis has been used for that purpose. the quality of housing and its environment, pollution, green places, public spaces, physical movement and health, crime rates and individual safety, the wellbeing of youngsters, unemployment, job value, economic scarcity, governance, circadian rhythm, weekly rhythm and other factors are the focus of such analyses that aim to determine levels of positive emotions and happiness in built environment. questionnaires are the most common tool for such analyses, where inhabitants are asked to rank their happiness experience as a whole in built environment. many studies demonstrate that happy people are effective in multiple areas of their life including job efficiency, salary, health, human relations, etc. the innovative aspect of this research stems from the fact that biometric technologies (affective attitudes, emotional and physiological states) and the viners method developed by the authors are used to determine the best places to live and to serve neuro ads of homes for sale. to do this, rational segments of homebuyers are determined according to their demographic profiles (age, gender, education, marital status, families with children, main source of income), consumer psychographics and behaviour (happy, sad and angry along with valence and heart rate) and then select a rational video ad for such rational segment. the aim of our research is to develop the viners method for the multiple criteria analysis and neuromarketing of best places to live (viners method) by combining the somatic marker hypothesis, biometrics, neuromarketing and copras method. this article presents a case study to demonstrate the viners method put to practice. keywords: copras and viners methods, neuromarketing, multiple criteria analysis, best places to live. copyright ©2019 cc by-nc 630 a. kaklauskas, d. dzitac, j. sliogeriene, n. lepkova, i. vetloviene 1 introduction many authors [5,7,14,15,24,33,34,42,45,49,55] have analysed the effect of built environment on the happiness of the people in it. that effect (from the perspective of developing a sustainable community) has also been analysed in the field of sustainability [7,8,35,39]. common presumption is that happiness is an individual characteristic and each person is the one responsible for his or her own happiness. happiness is, however, a community characteristic as well, and factors external to the individual can have influence on its levels [8]. leyden [30] have analysed ten major cities and discovered that self-reported happiness of people is associated with aspects they see as important in their built environments, including issues such as maintenance, convenient public transportation and access to cultural amenities. the empirical data have been collected in the ten cities and they strongly suggest that built environment has an effect on social connections to places that play a vital role in happiness in a city [30]. florida et al. [16] have determined that the central role in the happiness of cities belongs to human capital, which outperforms income and all other variables. other researchers [27,45] have also determined that in places where educational attainment is higher people are happier. does a modern hospital offer true healing? healing places analyses different environments and their effect on our physical, spiritual, mental, emotional and social healing. four dimensions found in healing environments have been identified: built, natural, social and symbolic [18]. people tend to link city centres with feelings of excitement and/or anxiety, and see them as stimulating and lively. in the urban renaissance, an important role is played by evening activities because they keep a town or city alive way beyond normal business hours. a successful urban renaissance requires a more diverse pool of people attracted into city and town centres in the evening and at night [24]. urry [49] took interest in the ways visitors (and local people too) experience a place. what pleasures the place offers? what are the emotions people feel when they enter a place that is relatively unfamiliar? urry [49] focuses on popular tourist destinations and shows how specific sites are set up to saturate them with emotions, sometimes frightening and wild, sometimes relaxing and aesthetically pleasing. these emotions then become inseparable from how both tourists and residents imagine and portray those places. milligan et al. [33] explore positive emotional experiences of older people in the shared community spaces of gardening and social clubs. milligan et al. [33] demonstrate that the role of social spaces, and shared outdoor gardening activities in particular, can be important and constructive in contributing, in older people’s lives, to feelings of self-worth and belonging and emotional expression. emotions can be joyful, numbing or heartbreaking, but they all have the power to transform our lives, open new horizons or confine us, creating new fixtures or fissures we never expected to find [5]. this paper is structured as follows. this introduction is followed by section 2 with a demonstration of the viners method. section 3 deliver case studies. the paper ends with conclusions and follow-ups on forthcoming research in section 4. 2 viners method the introduction presents studies showing that a specific demographic segment of buyers should seek housing in the neighborhoods, which they often experience positively. to put it another way, it’s best to live feeling as best as possible. the aim of our research is to develop the viners method for the multiple criteria analysis and neuromarketing of best places to live (viners method) by combining the somatic marker hypothesis (1994), biometrics, neuromarketing and copras methods [52] developed by the applicants. the new viners method would make it possible to determine a rational segment of homebuyers by their demographic profiles, consumer psychographics and behaviour and then viners method for the multiple criteria analysis and neuromarketing of best places to live 631 figure 1: development of the viners method for the multiple criteria analysis and neuromarketing of best places to live to select a rational video ad for such rational segment. a rational segment of homebuyers by their demographic profiles, consumer psychographics and behaviour can be determined by creating a multiple criteria neuro matrix. in this research, we hypothesized that in housing neuromarketing an integrated approach is required to consider the most rational customer segments, the desired features of an apartment, individual differences, environmental influences on property, emotional and physiological states, valence and arousal and affective attitudes. our research launched on 6 november 2017 is tracking the 17 states of people at nine locations in vilnius. the goal is to collect data for anonymised affective attitudes, emotional and physiological tests (see figure 1). the data from our annual tracking activities done in vilnius with facereader 7.1 and the respiration sensor x4m200 were used to establish the aeps of passers-by taking into account their age and gender. we have to determine the priority and utility degree of suggested video ads and for that purpose the following system of criteria has been produced to define the specific rational homebuyer segments by their demographic profiles, consumer psychographics and behavior (see figure 1). statistics lithuania has provided the statistical information about the individual differences of potential buyers (x1-x6, x8, x9) under deliberation. the data on apartment attributes (x7, x10-x13) and environmental influences (x14-x19) came from eight experts on real estate marketing, brokerage and evaluating. currently over 250 million anonymised affective attitudes, emotional and physiological states (see figure 1) data entries have been accumulated. it would take a great deal of space to describe all the performed investigations. therefore, to simplify the write-up of the performed investigation, the use was only of x20-x24, rather than all the 632 a. kaklauskas, d. dzitac, j. sliogeriene, n. lepkova, i. vetloviene available aeps data (see table 1). the research consists of data gathered on these depersonalized bystanders relevant to 17 parameters numbering over 250 million items. it would require considerable room to demonstrate table 1: the neuro decision matrix of alternative video ads and the results of its multiple criteria analysis criteria describing alternatives sub-criteria describing alternatives measuring units weight * apartments video alternatives under comparison a1 . . . . . . aj . . . an buyer’s individual differences (demographic segmentation criteria) age (x1) x11 (20–30 years) points q11 + x111 . . . . . . x11j . . . x11n x12 (31–40 years) points q12 + x121 . . . . . . x12j . . . x12n x13 (41–60 years) points q13 + x131 . . . . . . x13j . . . x13n x14 (over 60 years) points q14 + x141 . . . . . . x14j . . . x14n gender (x2) x21 (male) points q21 + x211 . . . . . . x21j . . . x21n x22 (female) points q22 + x221 . . . . . . x22j . . . x22n education (x3) x31 (higher) points q31 + x311 . . . . . . x31j . . . x31n x32 (high/ special secondary) points q32 + x321 . . . . . . x32j . . . x32n x33 (secondary) points q33 + x331 . . . . . . x33j . . . x33n x34 (basic) points q34 + x341 . . . . . . x34j . . . x34n x35 (elementary) points q35 + x351 . . . . . . x35j . . . x35n x36 (incomplete elementary school) points q36 + x361 . . . . . . x36j . . . x36n marital status (x4) x41 (married) points q41 + x411 . . . . . . x41j . . . x41n x42 (divorced) points q42 + x421 . . . . . . x42j . . . x42n x43 (widowed) points q43 + x431 . . . . . . x43j . . . x43n x44 (never married) points q44 + x441 . . . . . . x44j . . . x44n main source of earnings (x5) x51 (salary/work compensation) points q51 + x511 . . . . . . x51j . . . x51n x52 (income from own or family business) points q52 + x521 . . . . . . x52j . . . x52n x53 (income from agricultural activities) points q53 + x531 . . . . . . x53j . . . x53n x54 (ownership or investment income) points q54 + x541 . . . . . . x54j . . . x54n x55 (pension) points q55 + x551 . . . . . . x55j . . . x55n x56 (governmental support) points q56 + x561 . . . . . . x56j . . . x56n x57 (support by family and/or other persons) points q57 + x571 . . . . . . x57j . . . x57n families with children (x6) x61 (families with children aged 0-17 yrs.) points q61 + x611 . . . . . . x61j . . . x61n x62 (families with no children) points q62 + x621 . . . . . . x62j . . . x62n price (x7) x71 points q71 x711 . . . . . . x71j . . . x71n viners method for the multiple criteria analysis and neuromarketing of best places to live 633 table 1: (continued.) criteria describing alternatives sub-criteria describing alternatives measuring units weight * apartments video alternatives under comparison a1 . . . . . . aj . . . an type of residential housing unit (x8) x81 (one unit house) points q81 + x811 . . . . . . x81j . . . x81n x82 (two-unit house) points q82 + x821 . . . . . . x82j . . . x82n x83 (multi-unit building dwelling) points q83 + x831 . . . . . . x83j . . . x83n ownership form of residential dwelling (x9) x91 (home owner resident) points q91 + x911 . . . . . . x91j . . . x91n x92 (resident in a rental unit) points q92 + x921 . . . . . . x92j . . . x92n dwelling characteristics residential space (x10) points q100 + x1001 . . . . . . x100j . . . x100n number of rooms (x11) points q110 + x1101 . . . . . . x110j . . . x110n aesthetic and comfort features (x12) points q120 + x1201 . . . . . . x120j . . . x120n construction year (x13) points q130 + x1301 . . . . . . x130j . . . x130n environmental influences aplinkos estetika (x14) points q140 + x1401 . . . . . . x140j . . . x140n street environment (x15) points q150 + x1501 . . . . . . x150j . . . x150n susisiekimas (x16) points q160 + x1601 . . . . . . x160j . . . x160n green areas (x17) points q170 + x1701 . . . . . . x170j . . . x170n commercial services (x18) points q180 + x1801 . . . . . . x180j . . . x180n cultural and sporting services (x19) points q190 + x1901 . . . . . . x190j . . . x190n consumer psychographic and behaviour segmentation criteria happiness (x20) points q200 + x2001 . . . . . . x200j . . . x200n sadness (x21) points q210 + x2101 . . . . . . x210j . . . x210n valence (x22) points q220 + x2201 . . . . . . x220j . . . x220n angriness (x23) points q230 + x2301 . . . . . . x230j . . . x230n heart rate (x24) points q240 + x2401 . . . . . . x240j . . . x240n priority of the project alternative p1 . . . pj . . . pn degree of utility of the project n1 . . . nj . . . nn tables 1–3 containing the data on these 17 parameters of depersonalized bystanders. therefore fragments of variables relevant to the theoretical study on these data appear in table 1. tables 2 and 3 illustrate fragments calculated practically. the system of criteria is then used as the basis for a multiple criteria neuro decision matrix where its columns represent n alternative video ads and its rows are filled with detailed quantitative information of each alternative. our quantitative information includes the systems of criteria, measuring units, values and weights, and the minimising or maximising criterion. in marketing segmentation, we take a large cohort of current and potential customers, and, based on the above features, divide it into customer sub-groups (segments). in general, the goal of segmentation is to identify the sub-groups of customers that are most likely to buy a dwelling or bring the best profit, and as a result these potential consumers can become target demographic, behavioral or other significant segments. in marketing segmentation, it is accepted 634 a. kaklauskas, d. dzitac, j. sliogeriene, n. lepkova, i. vetloviene that different marketing segments need different advertising approaches with different green housing options (based on characteristics, prices, environmental influences, etc.). by applying marketing segmentation, we seek to create profiles of the main potential home buyers. the authors analysed scientific articles and statistical data and their analysis shows that dwelling buyer needs and buying habits depend on age (x1), gender (x2), education (x3), marital status (x4) and other factors. buyers in the age group between 18 and 34, for instance, are usually looking for cheaper properties and tend to commit smaller sums for home buying than older buyers; smaller incomes is a factor. gender differences in the choice of home show that women are more interested in the size of the kitchen, bathroom and wardrobes. men, on the other hand, tend to focus on recreational spaces, pools and tubs. the size of and requirements for real estate investments can be also inferred from marital status. [11] compared married and single buyers and determine that those that are married have larger incomes and, naturally, can afford better apartment. the affordability of apartment and the environment in which their children will grow are the two main points stressed by these buyers. single buyers are quite different and their gender also plays a role: single men choose properties closer to entertainment, recreation and sports centres, whereas single women prefer properties closer to their friends or family. to do a good job, real estate brokers need to understand how buyers react to marketing stimuli such as needs, personality, motives, expectations and experiences. exposed to the same kinds of stimuli individuals often identify them in different ways, often focusing on elements they find important [2]. expectations among female participants are generally higher than among men, and also higher among younger participants than among older. the least-educated participants have the highest expectations regarding the time when they will buy their own property and with every step up the education ladder expectations fall lower. students had the highest expectations related to transport links, proximity to shops, schools and kindergartens, socio-economic and financial factors, greater autonomy, and a sense of peace [21]. eight experts of property marketing, brokers and valuators applied expert methods to determine criteria values and weights (see tables 2 and 3). the eight real estate experts evaluated how specific characteristics of housing units correspond with the needs of specific buyers based on a 10-point scale (where 1 is not very important, and 10 is very important). their basis for the evaluation included the aforementioned and worldwide practices as well as their long-time. a method of complex determination of the weights of the criteria taking into account their quantitative and qualitative characteristics [26] was applied for criteria weights calculations. the copras method [52] is applied to determine the priority and utility degree of a video ad. the utility degree (nj) can be between 0% and 100% and it depends, directly and proportionally, on the relative impact the criterial values xij and weights qi make on the end result. the copras method [52] ensures that an adequate system of criteria defines any analyzed alternatives. additionally, the weights and degrees of utility of these alternatives are presumed to link, both by direct and proportional dependence, to the values and weights of those criteria. 3 case study as of november 7, 2017, biometric tests of depersonalized passersby were conducted in nine locations in vilnius city by remote means, at a distance of approximately 20 meters. this investigation yielded 17 affective attitudes, physiological and emotional states parameters (see figure 1) of the depersonalized passersby over its course. over 250 million items of data were gathered on these 17 parameters of depersonalized passersby during the course of this investigation. since these 17 parameters of depersonalized passersby would take a tremendous amount of space to demonstrate in tables 1, 2 and 3 that are under deliberation, fragments of these data for a theoretical analysis are illustrated as variables in table 1. meanwhile fragments of practical viners method for the multiple criteria analysis and neuromarketing of best places to live 635 calculations appear as numbers in tables 2 and 3. 3.1 detailed system of criteria defining the alternative video ads this study has looked at the following groups of criteria: individual differences of potential buyers, apartment attributes, environmental influences, and consumer psychographic and behaviour segmentation criteria. buyer’s individual differences (demographic segmentation criteria) when users are looking for suitable options of apartment, the process can be lengthy and complicated because they may have many factors to consider. they need to take into account their budget and what facilities they need such as supermarkets, public transport or public schools available for their children. most home buyers also find it important to look for a property that will grow in value over time so that in a few years they can sell it and move up the property ladder easier [28]. socioeconomic and demographic characteristics may influence how a resident evaluates the neighbourhood, as may a normative element of the apartment environment preferred based on the person’s lifestyle, experience, and inclinations. the objective characteristics evaluated by the resident in residential environment are the attributes of a specific house and neighbourhood. when the subjective individual perception of the residential environment, including the quality of the neighbourhood, matches the person’s needs and expectations, it results in residential satisfaction [43]. when a person is satisfied with his or her residential environment, it means the perceived situation and the desired situation are a close match; deviation from preferred conditions, on the other hand, leads to dissatisfaction [19]. a range of studies spanning several decades show that neighbourhood satisfaction depends on age, sex, marital status, income, education, and race [6,25,40,41,48] focused on older residents in spain and found higher satisfaction among madrid residents living alone or only with a spouse. the satisfaction was also higher among women [19]. marriage, change in jobs, fertility, educational issues or living with old parents may make public rental apartment tenants reconsider the objective public rental apartment factors in light of changing personal requirements. looking at group and personal influences would mean focusing on mutual relationships between potential home buyers and their co-workers, family members, friends and other individuals. to limit the scope of this research, these factors were, therefore, excluded from the development of viners method, even though ethnicity, culture, group and personal influences affect the needs and behaviour of potential home buyers, as has been shown. these factors will be incorporated into the viners method at later stages of the research. dwelling characteristics and environmental influences intrinsic and extrinsic characteristics influence the choice of a home. intrinsic are the factors that define the apartment unit itself (flat, house or building), i.e. the number of rooms, balconies, windows, the floor, aesthetic properties, decor, features and amenities, etc. extrinsic are the features of the location, such as green spaces (public parks and gardens), urban quality (roads, buildings, squares), public transport, social context, proximity to commercial areas or other centralities, picturesque views, historical significance of the area, pollution (acoustic, atmospheric), and so on [10]. a rapid increase of railway lines, both operational and under construction, leads to an increase in the number of buildings that suffer adverse effects of ground-borne vibration (e.g. 636 a. kaklauskas, d. dzitac, j. sliogeriene, n. lepkova, i. vetloviene indoor noise and shaking) [17]. ventilation regulates indoor air parameters, such as air temperature, air speed, relative humidity and concentrations of chemical substances in the air, and this way creates thermally comfortable environments with acceptable indoor air quality [44]. building height determines various parameters. negative correlation, for instance, links air temperature, humidity, pm2.5, pm10, and co2 to building height—the higher the building, the lower the values. in contrast, ozone generally increased with building height, and the values of no2 were less consistent. the correlation between pm, co2, and o3 concentrations and floor height was stronger than between local wind speed and direction and floor height [1]. indoor temperature and humidity fluctuations can be moderated by means of building materials. phase change materials can be used to moderate the indoor temperature variations and reduce the energy consumption of hvac systems; they absorb or release large amount of thermal heat at a constant temperature in phase change process. well known is also the fact that indoor air humidity may have a significant influence on the living environment and, thus, on the comfort and health of its occupants. humidity is an important factor. it influences both indoor air quality and thermal comfort [50]. when households make the decision which home to buy, they consider such important factors as the costs of apartment, time and transport, and the benefits that the site offers [10]. available amenities and local incomes are usually the key factors in choosing a location. incomes, in part, depend on the interaction between the demand and supply of labour. in terms of the choice of a city, residents, workers and businesses also consider the overall quality of life offered by the city [10]. papamanolis [37] believes that environmental factors constantly change. those that affect buildings are all variable [38]; among them are air temperature, moisture, solar radiation, precipitation, wind, noise and air pollution. all of them display fluctuations in many aspects. the fluctuations in some cases span a wide range of values (e.g. in case of air temperature, solar radiation, humidity and wind speed), occasionally varying between negative and positive effects. other factors, such as pollution and ambient noise, have fluctuations ranging from negligible to (usually) negative. beyond the intensity, important is also the frequency with which environmental factors affecting buildings vary. the same can be said about air pollution, especially in urban areas. a storm or a pollution event is a good example. at the building shell surface, the wind can serve as a medium for ventilation, pollution dispersion, natural cooling or thermal exchange. it can also act as a factor involved in structural integrity. fluctuating values of environmental factors directly affect functioning of the systems installed in buildings in order to control the impact. the response of systems to variable conditions is a critical factor in their efficiency. only when they are adapted to address the effects of any prevailing factors, passive systems can be efficient. active systems are different. they show more flexibility when they respond to the fluidity of the environmental impact. a good example of a system that “behaves” according to the prevailing conditions are dynamic louvers—they react to the actual conditions to make sure the control of daylight and solar heat gains is optimal. every step towards higher levels of adaptability to a variety of conditions makes these systems more effective [37]. inclusion of the environmental factors mentioned above in the neuro decision matrix would involve a wide scope of research. potential home buyers make decisions slowly due to the complexities of decision making, often associated with a need for a large amount of funds and acceptance of long term obligations. they analyze various alternatives and circumstances as well as compare and consider different offers. researchers [13,46,47,51] noticed the huge influence emotions play on decision making. exceptional as well as digital marketing means must be employed to interest buyers in proposed apartment units. these promise buyers more possibilities for choosing a desired dwelling. viners method for the multiple criteria analysis and neuromarketing of best places to live 637 applications of integrated neuro-marketing means make it possible to evaluate the needs of potential real estate buyers relevant to their emotions, present the most suitable offers and encourage decision making. traditional marketing means are insufficient when wanting to interest buyers in new, innovative products and to orient them towards firm and perspective albeit untraditional decision making. thus this study undertook an analysis of the video neuro-advertising used in the real estate market of vilnius city. this video neuro-advertising was submitted to the inhabitants of four eldership districts of vilnius (specifically to naujamiestis, verkiai, old town and žirmūnai). the selection of these specific districts were due to their reflecting a social distribution that makes it possible to forecast the desired level of apartment for acquisition, specific needs for such and the abilities to accept adequate obligations. for example, homeowners in the old town manage the most expensive apartment in vilnius (where the price per one square meter is, on average, 2900 euros). these buyers are able to consider the highest-class projects being offered when their family structures and needs change. conversely, in the žirmūnai district, residential apartment constructed long ago dominate; these units are notable for their lesser liquidity and significantly lower prices (where the price per one square meter is, on average, around 1300 euros). real estate companies and brokers notice that residents change their apartment units 3-4 times on average during their entire lives. therefore the interest in real estate offerings is always relevant to various groups of residents during different stages of their lives. that is the reason different residents of these vilnius districts, representing various social groups, were selected for this study. data on the selected district residents came for the lithuanian department of statistics in 2018 [29] following the submission of a targeted query. the data received permitted dividing the residents by age, gender, education, marital status and family composition, main sources for earnings and the type of apartment in which the persons resided and the form of its ownership. conducted interviews established their needs to acquire or change their apartment units. consumer psychographic and behavioral segmentation a number of investigations in the area of consumer psychographic and behavioral segmentation have been performed [4,12,23]. for example, the research performed by [12] investigated emotions to determine if they can be considered as a suitable variable for segmenting museum visitors. the reports from the segment with more positive emotions indicated perceptions of the museum as being more attractive and unique at the museum, than the other segment did, along with a higher level of satisfaction [12]. 3.2 first-step segmentation in marketing segmentation, we take a large cohort of current and potential customers, and, based on the above features, divide it into customer sub-groups (segments). in general, the goal of segmentation is to identify the sub-groups of customers that are most likely to buy a dwelling or bring the best profit and as a result these potential consumers can become target demographics, behavioural or other significant segments. in marketing segmentation, it is accepted that different marketing segments need different advertising alternatives with different housing options (based on characteristics, prices, environmental influences, etc.). by applying marketing segmentation we seek to create profiles of the main potential home buyers. our research uses the two-step segmentation technique of potential home buyers. in geographic segmentation, the properties in question were grouped taking into account the urban districts (neighbourhoods). in the second stage dedicated to demographic, psychographic and behavioural segmentation, the aggregate neuro decision matrices created for each neighbourhood 638 a. kaklauskas, d. dzitac, j. sliogeriene, n. lepkova, i. vetloviene in question were narrowed down to cover more specific buyer segments. by using the two segmentation stages real estate brokers can optimise their marketing resources and reach the most rational potential clients. a team of eight property experts from vilnius evaluated ads and matched them with a certain group of residents following the definition provided by statistics lithuania. four relative age groups were established: group i with people aged 20 to 30, group ii with people aged 31 to 40, group iii with people aged 41 to 60 and group iv with people aged over 60. our approach includes the first step segmentation for which the object of our analysis, i.e. the urban district, was divided into four neighbourhoods (naujamiestis, verkiai, old town, žirmūnai). then, in the second stage, we reclassify the sub-segments established in the first stage and determine a rational number of segments (see table 2). table 2: the fragment of aggregate general neuro decision-making matrix during the time of first-step segmentation in the old town district criteria describing alternatives criteria no sub-criteria describing alternatives * weight number of inhabitants housing unit video ad alternatives under comparison 1 2 . . . 15 16 17 18 19 20 buyer’s individual differences (demographic segmentation criteria) age (x1) 1 20–30 years + 0.0079 4635 3 6 . . . 3 5 1 6 1 8 2 31–40 years + 0.0047 2895 6 6 . . . 6 8 2 8 3 9 3 41–60 years + 0.0381 4981 4 5 . . . 8 9 7 6 8 4 4 over 60 years + 0.027 3286 7 7 . . . 3 3 2 3 2 4 gender (x2) 5 male + 0.0097 7060 7 9 . . . 8 8 6 8 6 9 6 female + 0.0128 8737 8 10 . . . 9 8 5 7 8 8 education (x3) 7 higher + 0.060 7400 7 6 . . . 9 7 9 7 9 6 8 high and special secondary + 0.016 1938 7 8 . . . 5 7 4 7 4 8 9 secondary + 0.042 5227 6 7 . . . 2 7 1 7 2 8 10 basic + 0.012 1442 5 6 . . . 4 4 1 4 1 4 11 elementary + 0.012 1521 4 5 . . . 1 2 1 2 1 3 12 incomplete elementary school + 0.001 140 3 4 . . . 1 2 1 2 1 4 marital status (x4) 13 married + 0.057 6995 7 6 . . . 7 9 8 9 8 9 14 divorced + 0.016 1988 9 9 . . . 4 6 7 6 7 6 15 widowed + 0.011 1352 7 8 . . . 5 7 5 8 4 8 16 never married + 0.053 6556 9 8 . . . 4 6 7 6 7 8 main source of earnings (x5) 17 salary/work compensation + 0.069 8445 7 8 . . . 5 7 4 7 5 7 18 (income from own or family business + 0.004 534 8 8 . . . 9 8 9 7 9 7 19 income from agricultural activities + 0.0001 18 8 8 . . . 9 8 6 7 6 7 20 ownership or investment income + 0.0006 74 7 7 . . . 9 6 9 6 9 6 21 pension + 0.024 3021 4 5 . . . 2 2 1 3 1 4 22 governmental support + 0.004 551 1 5 . . . 1 1 1 1 1 1 23 support by family and/or other persons + 0.045 5493 1 4 . . . 1 1 1 1 1 1 families with children (total 4152) (x6) 24 families with children aged 0-17 yrs. + 0.016 2023 6 6 . . . 7 9 7 9 7 8 25 families with no children aged 0-17 yrs. + 0.017 2129 8 8 . . . 6 7 8 7 8 7 viners method for the multiple criteria analysis and neuromarketing of best places to live 639 table 2: (continued.) criteria describing alternatives criteria no sub-criteria describing alternatives * weight nnumber of inhabitants housing unit video ad alternatives under comparison 1 2 . . . 15 16 17 18 19 20 dwelling characteristics price (x7) 26 average price (euro /sq. m.) – 1 1830 1450 . . . 1770 1280 4050 1290 3710 1320 type of residential housing unit (x8) 27 one unit house + 0.002 285 5 4 . . . 9 7 9 7 9 7 28 two-unit house + 0.003 335 6 4 . . . 9 7 9 7 9 7 29 multi-unit building dwelling + 0.138 16969 8 9 . . . 7 9 7 9 7 9 ownership form of residential dwelling (18917) (x9) 30 home owner resident + 0.111 13681 8 7 . . . 7 9 6 9 6 8 31 resident in a rental unit + 0.027 3328 7 8 . . . 5 8 5 8 5 7 building materials (x10) 32 + 0.18 points 7 5 . . . 8 6 9 6 9 7 noise and air pollution (x11) 33 + 0.10 points 8 8 . . . 9 9 8 8 8 7 energy consumption (floor heating, renewable energy sources and etc.) (x12) 34 + 0.27 points 6 7 . . . 8 7 8 9 9 7 aesthetic properties (x13) 35 + 0.05 points 7 7 . . . 9 7 9 8 9 7 environmental influences urban quality (infrastructure) (x14) 36 + 0.22 points 7 7 . . . 8 6 9 6 9 7 green spaces (x15) 37 + 0.18 points 7 7 . . . 9 7 6 7 7 6 consumer psychographic and behaviour segmentation happiness 38 + 0.1 points 0.14 0.14 . . . 0.14 0.14 0.14 0.14 0.14 0.14 arousal 39 + 0.1 points 0.33 0.33 . . . 0.33 0.33 0.33 0.33 0.33 0.33 boredom 40 0.1 points 0.06 0.06 . . . 0.06 0.06 0.06 0.06 0.06 0.06 interest 41 + 0.1 points 0.01 0.01 . . . 0.01 0.01 0.01 0.01 0.01 0.01 heart rate 42 + 0.1 points 66 66 . . . 66 66 66 66 66 66 breathing rate 43 + 0.1 points 16.7 16.7 . . . 16.7 16.7 16.7 16.7 16.7 16.7 priority of housing unit video alternatives under comparison 18 12 . . . 20 6 1 7 2 9 utility degree of housing unit video alternatives under comparison (%) 74,17 75,88 . . . 74,17 78,48 100 78,28 94,49 77,7256 * the symbol ” + / − ” specifies that a bigger (smaller) criterion value corresponds to a bigger (smaller) importance for a user (stakeholder). comment: criteria evaluations ranging from 1 to 10 points, where 1–not very important, and 10–very important. per department of statistics data, distribution of residents by educational achievement does not apply to persons aged less than 10 years (not included in the calculations of residents in eldership districts). distribution of residents by marital status does not apply to persons aged less than 15 years. the study does not include persons aged under 19 years, because they are not yet active participants in the real estate market. a method of complex determination of the weights of the criteria taking into account their quantitative and qualitative characteristics [26] was applied for criteria weights calculations. the group of experts were shown 20 video ads taken from property offerings assigned to the relevant group of potential homebuyers. a sum segmentation neuro decision matrix for the old town was compiled based on this data (see table 2). on a 10-point scale, each expert assigned a certain number of points to each apartment in the matrix based on its acquisition/interest potential and taking into account the needs, financial capacity and family composition of typical social groups, as well as market trends, acquisitions and stereotypes. similar sum segmentation neuro 640 a. kaklauskas, d. dzitac, j. sliogeriene, n. lepkova, i. vetloviene decision matrixes were compiled for the other three districts of vilnius (verkiai, naujamiestis and žirmūnai). table 3 has been analogically filled out. the copras technique [52] and the data from table 2 has been used to determine the potential effect of the video ads being analyzed on passers-by. the seventeenth video ad (n17 = 100%) made the biggest potential impact on passers-by. the lowest potential impact on passers-by impressions and feelings was made by the fifteenth ad (n15 = 74,17%). with the buyer segments in the old town determined (see table 2), more rational marketing is then possible for the highest success. the individual buyer differences, environmental influences and apartment attributes related to each segment of buyers assists with more effective ad targeting. to determine the buyer segments in the old town, we start with the broadest possible sample (see table 2) and then narrow it down to establish more specific groups (see table 3) because the majority of property agents have multiple buyer personas. 3.3 second-step segmentation the old town’s sum neuro decision matrix created as part of the first-step segmentation (see table 2) has been used as the basis for a more specific neuro decision matrix in the second stage. table 3 shows one of the matrixes created in the second stage. fragments of available 250 millions aeps data (see figure 1) are displayed as variables in tables 1–3, because displaying the 17 parameters of these depersonalized passersby data in tables 1–3 which are under deliberation would take up a great deal of space. meanwhile fragments of the practical calculations appear as numbers in tables 2 and 3. an analysis of male and female respondents of various ages was for establishing an integrated, rational segment of potential buyers and an effective advertisement for housing in the old town of vilnius. the purpose of this analysis was to establish the project that would be most rational for advertising at this time to the group of a specific age. three video alternatives were shown to the eight aforementioned experts. the first alternative was video 1 on the live square project (https://livesquare.lt/en/). live square apartments are modern and high-quality home spaces. apartments with balconies and terraces are designed for three live square residential buildings. the second alternative was video 2 on magnus rezidencija [magnus residence] (http: //gedimino47.lt/). magnus rezidencija is an exceptional oasis of residential housing and commercial facilities on gedimino prospect, a central street of vilnius city. cozy terraces, high windows, bright staircases and a separate playground for children in a private yard have been equipped for this project. a safe neighborhood, privacy and convenience distinguish this project. the third alternative is centro rezidencija [central residence] (https://centrorezidencija. lt/galerija/). this project consists of eight residential buildings consisting of 4 to 9 stories. stage i of this project has concluded building 4-5 and 7-storey buildings that contain 115 housing units measuring from 35 to 68 square meters. the construction for stage ii of this project consists of three residential buildings: two have 7 storeys and one has 6 storeys. during this stage, the offer is for 133 housing units measuring from 29 to 85 square meters. stage iii of this project consists of three residential buildings, one is 8-stories and two are 9-stories high. the units offered for sale measure from 25 to 98 square meters. central to every stage of centro rezidencija will be the formation of a closed, internal yard complete with safe playgrounds for children, rest zones and an environment arranged by landscaping specialists. there is an underground parking lot for automobiles beneath the buildings. it is easily accessible by elevator operating directly from the staircases. viners method for the multiple criteria analysis and neuromarketing of best places to live 641 table 3: neuro decision matrix created as part of second-step segmentation in the old town criteria describing alternatives measuring units weight * video alternatives for the age groups video 1 video 2 video 3 age demographics for advertising (years, male & female) age demographics for advertising (years, male & female) age demographics for advertising (years, male & female) x11 x12 x13 x14 x11 x12 x13 x14 x11 x12 x13 x14 (20–30) (31–40) (41–50) (51–60) (20–30) (31–40) (41–50) (51–60) (20–30) (31–40) (41–50) (51–60) residential space points 0,1 + 7 8 8 6 7 8 9 6 8 9 9 7 number of rooms points 0,1 + 6 7 7 5 7 8 8 8 7 9 9 8 construction year points 0,02 + 10 10 9 6 10 10 10 7 10 10 10 8 aplinkos estetika points 0,09 + 10 10 10 9 10 10 10 10 10 10 10 10 street environment points 0,06 + 10 10 10 8 10 10 10 9 9 10 10 8 public transport points 0,12 + 9 10 10 10 9 10 10 10 9 10 10 10 price points 0,2 – 4 7 8 6 5 8 9 7 6 9 9 8 green areas points 0,14 + 8 10 10 10 9 10 10 9 8 10 10 9 commercial services points 0,09 + 8 9 9 8 9 10 10 9 8 10 10 9 cultural and sporting services points 0,08 + 10 10 9 6 10 10 10 7 10 10 10 8 heart rate points 0,05 + 71,65 74,49 84,31 82,80 79,46 76,39 82,94 91,12 77,85 78,16 76,01 70,18 happy points 0,2 + 0,139 0,108 0,085 0,070 0,130 0,115 0,083 0,075 0,134 0,116 0,101 0,104 sad points 0,05 + 0,191 0,141 0,121 0,109 0,192 0,160 0,157 0,112 0,190 0,157 0,146 0,149 angry points 0,05 + 0,115 0,099 0,100 0,087 0,094 0,102 0,102 0,103 0,104 0,105 0,108 0,110 valence points 0,15 + -0,224 -0,097 -0,107 -0,099 -0,107 -0,098 -0,139 -0,118 -0,104 -0,096 -0,114 -0,101 utility degree of housing unit video alternatives under comparison (%) 100,0 64,45 59,30 71,84 82,79 59,30 55,64 64,45 71,84 55,64 55,64 59,30 priority of housing unit video alternatives under comparison 1 6 7 4 2 9 12 5 3 10 11 8 * the symbol “+/-” specifies that a bigger (smaller) criterion value corresponds to a bigger (smaller) importance for a user (stakeholder). the copras method [52] and the data from table 3 has been used to define the possible result of the video ads being examined on passers-by. a multicriteria analysis of alternatives, performed with the application of the viners method, indicated that the group ranging in the ages of 20-30 years was the most receptive to the advertisement and showed the greatest interest in the projects under development (see table 3). 4 conclusions the researches described in the introduction indicate that a specific demographic segment of buyers would be best off looking for a dwelling in the same area where they usually feel well. in other words, people are best off living where they feel best. the best [20,32,36] and worst [9,20] places to live have been analysed in the world for many years and multiple criteria analysis has been used for that purpose [22]. other studies [3,31,42,53] also have analysed the environment that contributes to higher or lower positive emotions and happiness. lu [31] made a review of 178 published articles and was the first to look beyond the physiological and environmental effects of air pollution and take a systematic look at its psychological (affective, behavioural, cognitive), social and economic effects. air pollution contributes to 642 a. kaklauskas, d. dzitac, j. sliogeriene, n. lepkova, i. vetloviene decreased happiness and life satisfaction and increased anxiety, annoyance, self-harm, mental disorders, and suicide [31]. benita et al. [3] determined a positive correlation between immediate environmental factors, public spaces and happiness. benita et al. [3] unique contribution is the estimation, by using nearly real-time data, of the impact places, environment, and personal characteristics make on momentary happiness. to make people more happy, which is important to their well-being, planners are recommended to focus on designs that support opportunities for social interaction, make people feel safer in the environment, and increase access to open green spaces [42]. zhang et al. [54] estimate the monetary value of cutting pm2.5, one of the main sources of air pollution in china. zhang et al. [53] matched the hedonic happiness values from a representative country-level survey with daily air quality parameters by the dates and counties of interviews in china and determined how local concentration of particulate matter is related to individual happiness. over the course of the research, over 250 million items of data have been gathered on 17 aeps parameters (see figure 1) of these depersonalized passersby. our investigation has looked at the dynamic links between positive emotions and happiness of people and their built environment. our research shows that the innovative viners method is an essential new addition to multiple criteria neuromarketing analysis of integrated sustainable development, positive emotions, happiness and built environment. the research determines rational segments of homebuyers by their demographic profiles (age, gender, education, marital status, families with children, main source of income), consumer psychographics and behaviour (happy, sad and angry along with valence and heart rate). this article also presents a case study to demonstrate the viners method put to practice. the research confirmed our hypothesis that in housing neuromarketing an integrated approach is required to consider the most rational customer segments, the desired features of an apartment, individual differences, environmental influences on property, emotional and physiological states, valence and arousal and affective attitudes. by applying the neuro decision tables, the above data can be used in neuromarketing to discover the most rational customer segments and to perform multiple criteria analysis of video ads. certain limitations of the research were also noticed during the time of the case study. for example, this study only encompassed real estate examples, which the viners method then verified. the future expectation is to apply the neuro decision matrix and the viners method more broadly, performing neuro-marketing for business, services, industry, agricultural organizations, relationships and social units. furthermore, it is foreseen to supplement the neuro decision matrix on real estate with new criteria for more accurate descriptions of the objects under deliberation. when someone advertises a home for sale, they usually consider the individual differences of potential homebuyers, their desires for the flat or house (indoor environmental quality and human health; energy, water, and materials efficiency; recycled, reusable and low-impact building materials; waste reduction; low-carbon technologies; renewable energy sources), and the environmental influences (air, noise and water pollution, green architecture, green built environment, etc.) on the property. the affective reactions, physiological and emotional status, arousal and valence of potential buyers are only considered in real estate advertising in exceptional cases. but so far all these factors have never been considered as an integrated whole. this study aimed to take an integrated look at the physiological and emotional status of potential homebuyers, their affective reactions, individual differences, arousal and valence, as well as environmental influences on properties and the preferred features of homes. the above data in this study was used in multiple criteria analysis of video ads and neuromarketing; different customer segments were analyzed by means of neuro decision matrices. this research adds new pieces to "the big picture" of neuromarketing in green housing sales. this study, however, does not fully answer viners method for the multiple criteria analysis and neuromarketing of best places to live 643 some questions and many new questions have surfaced. in future studies, for instance, we intend to include a more extensive application of biometric and affective computing technologies in the viners method. acknowledgment this project has received funding from european regional development fund (project no 01.2.2-lmt-k-718-01-0073) under grant agreement with the research council of lithuania (lmtlt). bibliography [1] azimi, p.; zhao, h.; fazli, t.; zhao, d.; faramarzi, a.; leung, l.; stephens, b. 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[55] zidansek, a. (2007). sustainable development and happiness in nations, energy, 32, 91– 897, 2007. int j comput commun, issn 1841-9836 9(3):325-339, june, 2014. an adaptive ga in partitioned search space f. nadi, a.t. khader farhad nadi* school of computer schiences universiti sains malaysia, 11800 penang, malaysia *corresponding author: nadi@cs.usm.my ahamad tajudin khader school of computer schiences universiti sains malaysia, 11800 penang, malaysia e-mail: tajudin@cs.usm.my abstract: evolutionary algorithms are population based meta-heuristics inspired from natural survival of fittest phenomena. despite their reasonable performance, these algorithms suffer from some weaknesses including the need for finding the values of their parameters that affect their performance. a new algorithm is proposed that divide the search space into equal sized partitions. each partition is assigned with two parameters that determine the intensification and diversification rates. the partitions will be intensified or diversified adaptively with regards to the corresponding parameters. traditional crossover and mutation operators are replaced with two new parameter-free operators. the experiments conducted on a wide range of multi-modal and epistatic problems showed the superiority of the proposed method in comparison to other algorithms in literature. keywords: genetic algorithms,adaptive parameter control, crossover rate, mutation rate 1 introduction evolutionary algorithm (ea) is a search meta-heuristics that improvise the quality of a set of candidate solutions iteratively using variation and selection operators [8, 13, 27]. two major issues in the design of global search methods are intensification and diversification [3]. the ability to visit different regions of the search space is generally referred to as diversification. the ability to obtain high quality solutions within those locations is generally referred to as intensification [3]. the balance between these two and the way it is conducted is the main factor that differentiate these algorithms from each other [19]. eas consist of different components (operators), each with different role for intensification or diversification or both. usually, each operator in eas come with some parameters that could be used to change the role of that operator, and consequently the so called balance in the search. this way, the parameters are found to have important effects on performance of the algorithms [9, 24, 27]. the literature on parameter adjusting could be divided into two main approaches [23] as follows. the first approach is known as parameter assignment, where values for the parameters will be provided using different methodologies. whereas, in the second approach, which will be referred to as parameter-reduced, removal of the parameters is of interest. adaptive ga in a partitioned search space (agapss ) is a new ea that assign the parameters to portions of the search space. it divides the search space into a predefined number of regions and tries to search the regions with carefully designed intensification and diversification operators. two parameters are assigned to each partition of the search space holding the intensification and diversification rates. the probabilities for intensification or diversification of each copyright © 2006-2014 by ccc publications 326 f. nadi, a.t. khader region will be determined from the behaviour of the regions. the region’s behaviour is a function of overall fitness of the region and the number of visits from that region. the population will be distributed among the regions depending on their behaviour. the designed operators for intensification and diversification are working based on the probability vectors that holds the probability of the alleles on each loci for each individual. the probability vectors will be updated based on the changes in fitness of an individual. the agapss iteratively evolves until it reaches a stopping criteria. algorithm 1 shows a pseudo code of the proposed method. algorithm 1 pseudo code for the main loop of the proposed method. 1: while stopping criteria is not met do 2: for i = 0 to m //m is the number of regions in the search space see § 3.2 do 3: if u(0, 1) < gi.µd then 4: //u(0, 1) returns a random generated number between [0, 1] based on the uniform distribution 5: diversify(i1, i2) i1, i2 ∈ gi 6: end if 7: if u(0, 1) < gi.µi then 8: intensify(i1) i1 ∈ gi 9: end if 10: update related regions informations //see sub-section 3.2 11: update: gi.f, gi.v, gi.ib, gi.iw see § 3.2 12: update: gi.µd. see § 3.2 13: update: gi.µi. see § 3.2 14: end for 15: adjust region population sizes. see § 3.2 16: end while the remainder of this paper is organised as follows. section 2 briefly looks into the background and related works. explanation of the proposed method will be introduced in section 3. how the experiment has been conducted is mentioned in section 4. experimental results will be reported in section 5. finally, the last section, that is section 6, will be the conclusion and future works. 2 background this section will briefly review the literature on the research focusing on parameters. 2.1 parameter calibration approaches categorizing approaches that are dealing with parameters could be done in different ways [8]. to this end, the literature could be divided into two main branches [23], which are parameter calibration methods and parameter-reduced methods. in parameter calibration approaches, the parameters are actually within the algorithm and different methodologies are used for determining their values. whereas in parameter-reduced approaches, the effort is towards removal of the parameters of the algorithms. the ideal form would have no parameters within the algorithm. parameter control this category covers all of the approaches on which the optimal values for the parameters will be provided by different methods. this covers those that try to find the optimal parameter an adaptive ga in partitioned search space 327 values prior to the run, or the methods that are trying to find the values of the parameters during the run. revac [6], tries to find the parameters of a given ea by refining the possible parameter vectors iteratively. here, an estimation of distribution algorithm (eda) [25], is used for finding the best parameter set for a given ea. in an adaptive ga [20], a combination of static rules, inference engine of fuzzy logic controller and feedback from the algorithm are used for determining the values of the parameters. frequency of the best individuals within the population, number of duplicate individuals, and number of expected optimal values are used [4] for determining the parameter’s values. in a self-adaptive method [28], each individual is extended with a part that holds the mutation rate in itself. the global mutation rate will be calculated based on the individual mutation rate and a global value. similarly, in self-adaptive ga (saga) [1], every individual is extended with an extra bit (µ) holding the mutation rate. on each update, the new mutation rate (µ′) will be derived based on the previous rate (µ). hybrid self-adaptive ga (hsga) [7], add an extra gene to the end of the chromosomes, representing the size of tournament. in this method, the less fit individuals get less selection pressure while fitter individuals get higher selection pressure. lobo in his ph.d. thesis [18] addressed all of the parameters of the ga, either automatically (population size) or rationally (selection rate and crossover rate) based on theoretical foundations. an extension of this work [17] integrates local search with the previous work. it has been shown that the use of local search for exploitation of the search space is beneficial. parameter-reduced approaches this approach covers all of those methods where even one parameter is removed from the algorithm. generally, in parameter-reduced gas, the population and variation operators will be replaced with probabilistic model representation and generation models [2, 21, 26]. compact ga (cga) was proposed as a variation of ga where population is represented as a probability distribution over the set of solutions [12]. it processes each gene independently while it tries to create the same distribution like the previous population. in a canonical ga referred to as ssrga [32] crossover operator is removed from the algorithm, and mutation is replaced with another customised operator. in this method, firstly, the population will be divided into a set of sub-populations, secondly, a site-specific rate vector will be calculated from each sub-populations, and finally each sub-population will be mutated using its corresponding site-specific vector. the extended version of the ssrga, referred to as ssrga-ii [31], explores and exploits the search space simultaneously using a different strategy used in ssrga. the algorithms will select top m individuals from the population and a position frequency matrix will be constructed based on them. each component of this matrix refers to the probability of occurrence of allele i in site j. the probability for each site will be calculated proportionate to the fitness of the individuals. 3 proposed method in the proposed method, the search space will be divided into regions. each region is given two probability rates, one for diversification and the other one for intensification. in the beginning of the search, these probabilities will be initialised randomly. in other words, some of the regions will have more chance for either diversification or intensification. however, these probabilities will be updated as the search progresses. the probabilities of the regions change over time depending on the behaviour of the regions, which in turn will be determined by a rank given 328 f. nadi, a.t. khader to each region. the rank of each region is with regards to its overall fitness and the number of visits that have been done from that region. population of each region will also change over time, though in the beginning of the search, the same number of individuals will be assigned to each region. the rank of the region will also be used to determine the eligibility of a given region for attracting more individuals. regions will be prioritised based on their eligibility and individuals will be assigned to regions proportional to their eligibility. each individual is assigned with a counterpart probability vector, which holds the probability of having allele 1 for each loci of a given individual. the occurrence probabilities of the alleles will get updated depending on the changes in the fitness and the number of changes in each individual. the agapss is mainly inspired from pga [23] however, these two methods differ in some aspects including the variation operators. besides that, the agapss is an adaptive method due to utilization of parameter for intensification and diversification. the remaining of this section is organized as follows. first, a formal definition of the concepts that have been used in this research will be introduced in section 3.1. details on dividing the search space and management of the divisions will be explained in section 3.2. 3.1 terminology the formal definitions of the concepts used in the algorithm will be introduce in this section. for an individual (i), i = i1, ..., il | ik ∈ a, 1 ≤ k ≤ l, where l is the length of individual, and a = {0, 1} is the alphabet of each locus. there would be a probability vector (z) for every individual, ∀ ii ∃ zi , 1 ≤ i ≤ n, where n is the population size, and z = z1, ..., zl | zj ∈ [0, 1], 1 ≤ j ≤ l, where zj holds the probability of 1 in jth locus of individual (i). depending on the number of changes (τ) that have occurred on a given individual, its respective probability vector (z) will get updated [23] using, ź = z + r. where r ⊆ z is defined as in eq. 1 and ź is the updated probability vector. initially all of the z vectors will be initialized, in all of their loci, with 0.5. each locus in z vector will be bounded between 0 and 1 this way, equal chance will be given to each loci for being either 0 or 1. as the search progresses, the z vectors will get updated based on eq. 1, which is with regards to table 1. according to eq. 1 only those loci in z vector will be updated that their corresponding loci in individuals have been changed. the locations of the changed loci will be determined using another vector, u, (see eq. 2) which will be constructed by applying logical exclusive or operator on a given individual before and after changes. in eq. 1, ϵ = 1 is the learning factor, f : i → r is assumed to be the objective function of a maximization problem, τ = l∑ i=1 ui, u = u1, ..., ul|uj = ij ⊕ íj, 1 ≤ j ≤ l (2) where ⊕ is bitwise exclusive or operator. an adaptive ga in partitioned search space 329 r = r1, ..., rl|1 ≤ j ≤ l, rj =   ϵ τ · uj (f(i) > f(í) ∧ ij = 0) ∨ (f(i) < f(í) ∧ ij = 1) − ϵ τ · uj (f(i) < f(í) ∧ ij = 0) ∨ (f(i) > f(í) ∧ ij = 1) 0 otherwise (1) table 1: probability vector updates is based on the change in fitness prior and after the changes on individual. depending on the fitness, the probability vector will get positive or negative credit. change in locus fitness credit from to 0 1 improved + 0 1 worsen 1 0 improved 1 0 worsen + as an example, assuming the onemax function as optimization problem on which the number of ones within the individual will be the fitness. for an individual i = {1, 1, 0, 0, 1, 0, 0, 1}, the fitness will be 4. let assume this individual is changed to i′ = {1, 1, 1, 1, 0, 0, 0, 1}, the new fitness will be 5. here the fitness improved after the changes. accordingly, u = {0, 0, 1, 1, 1, 0, 0, 0} and τ = 3. consequently, r = {0, 0, 1/3, 1/3, −1/3, 0, 0, 0}. therefore, if we assume before the update z = {0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5}, after the changes it will be updated to z′ = {0.5, 0.5, 0.83, 0.83, 0.17, 0.5, 0.5, 0.5}. 3.2 dividing the search space initially, the search space will be divided into a predefined number (m) of regions (g) with the same size [16, 18, 22]. representation a set of loci in each individual will be reserved as indicators of regions [22]. it has to be noted that no extra genes will be added to the individuals. each individual in the population will be member of a region, formally, ii ∈ gj|1 ≤ i ≤ n, 0 ≤ j ≤ m − 1. (3) where n is population size, and m is the number of regions in the population, m = |a|x, x ≥ 1 (4) where x is the number of loci in the individual that is needed to be reserved for the region indicator, and |a| is the cardinality of loci. binary representation of the region index (i), in the first x loci of an individual, will be used for indication of the members of the ith region (gi). this way the search space could be divided into equal sized partitions. as an example, assume the case of dividing the search space into m = 4 regions. referring to equation 4, the number of loci needed to be reserved is log|a| m which in this case will be x = 2. consequently, values of the first two loci of each individual, will be set to either of 00, 01, 10, or11, 330 f. nadi, a.t. khader that refers to the regions 0, 1, 2, and 3 respectively. a sample population of this example is shown in figure 1. here, three individuals will be assigned to each region assuming n = 12. region i f f v δ γ pi pd ri rd χ n g0 0 0 1 1 0 2 1.67 3 h h 3 1 4 3 1 20 0 0 1 1 2 0 0 0 0 1 1 g1 0 1 0 0 1 2 3 3 h h 3 1 2 1 3 30 1 1 0 1 3 0 1 1 1 1 4 g2 1 0 0 1 1 3 2.33 3 h h 3 1 3 2 2 31 0 0 1 0 2 1 0 1 0 0 2 g3 1 1 0 0 0 2 2.67 3 h h 1 2 1 4 4 41 1 1 1 0 4 1 1 0 0 0 2 figure 1: the very first locus on each individual (highlighted in bold) will be used to indicate the regions. the onemax function is assumed as optimization problem in this example. region parameters as mentioned earlier, each region is assigned with two rates, one for intensification (µd) and the other for diversification (µi). in the beginning of the search, these probabilities will be set randomly for all of the regions. however, these rates change adaptively over time (iteration), depending on the ranks of the regions. the diversification rate will be adjusted using, µ́d(i) = µd(i) + 0.1(rd(i) − ŕd(i)), (5) where, ŕd(i) and rd(i) are diversification ranks of region gi at previous and current iterations respectively. on the other side, changes in intensification rates will be based on, µ́i(i) = µi(i) + 0.1(ri(i) − ŕi(i)), (6) where ŕi(i) and ri(i) are intensification ranks of region gi at previous and current iterations respectively. in both of the cases, the rates are bounded between 0.1 and 0.9 to prevent either neglect or over looking into a region. for example, assuming that in the beginning of the search, the diversification rank (rd(1)) for the first region is 3, and diversification rate (µ′d(1)) is 0.10. also assume that in the subsequent generation, the diversification rank (rd(1)) will be changed to 2. based on eq. (5) the diversification rate for the first region will change to 0.20. rank of the regions as mentioned, the regions’ parameters will be updated based on the ranks of the regions. the rank of the regions will be determined with regards to the priority and the fitness of the regions. the priorities of the regions will be determined based on the fitness of the regions and the number of visits that have been done from them. to this end, two behavioural statuses will be given to any given region, one for fitness and the other for the number of visits from a region. if the fitness value of a given region (see eq. (7)) was larger than mid-range value on that region, its status will be considered as high (h), otherwise it will be determined as not high (h). γ(i) = { h gi.f > f(gi.ib)+f(gi.iw ) 2 h otherwise. (7) an adaptive ga in partitioned search space 331 the number of visits from a given region (see eq. 8), will be determined as high (h) if the mid-range value of the number of visits among all of the regions is lower than the number of the visit from the given region. δ(i) =   h gi.v > argmax gi.v 0≤i≤m−1 +argmin gi.v 0≤i≤m−1 2 h otherwise . (8) as the first step, priorities of the regions will be retrieved using eq. (11) or eq. (12) for diversification or intensification respectively. a region with a low fitness and a relatively high number of visits would get the lowest priority (lowest value in eq. (11)) for either diversification or intensification. it is because that the expectation of finding a good solution within that region is low. this way, lower attention will be on this kind of regions. in the next step, ranks of the regions (rd(i) or ri(i)) will be determined. a list will be constructed where the regions are sorted first based on their priorities and then their fitnesses. the row index of each region in the sorted list will be returned as the ranks for the regions. in other words, rd(i) > rd(j) ⇔ (pd(i) > pd(j)) ∨(pd(i) = pd(j) ∧ gi.f > gj.f) | 0 ≤ i, j ≤ m − 1. (9) ri(i) > ri(j) ⇔ (pi(i) > pi(j)) ∨(pi(i) = pi(j) ∧ gi.f > gj.f) | 0 ≤ i, j ≤ m − 1. (10) where in both eq. 9 and eq. 10, ∨ is logical or operator, and ∧ is logical and operator. pd(i) =   2 (γ(i) = h) ∧ (δ(i) = h) 3 (γ(i) = h) ∧ (δ(i) = h) 1 (γ(i) = h) ∧ (δ(i) = h) 4 (γ(i) = h) ∧ (δ(i) = h) (11) pi(i) =   3 (γ(i) = h) ∧ (δ(i) = h) 4 (γ(i) = h) ∧ (δ(i) = h) 1 (γ(i) = h) ∧ (δ(i) = h) 2 (γ(i) = h) ∧ (δ(i) = h) (12) as another example, the overall fitness (f ) of the regions of the example population, and the number of visits from each population are also reported in figure 1 on page 330. consequently, δ, and γ are calculated for all of the regions. using the required information, the diversification and intensification priorities and also the ranks for the regions (according to eq. (8) and eq. (7)) are also calculated and shown in figure 1. region population as mentioned earlier, each region will be instantiated with the same number of individuals. however, during the progress of the search, the number of individuals within each region varies. it has to be noted that the population size is fixed and it is the membership of individuals that changes. 332 f. nadi, a.t. khader the population of regions changes with proportion to the diversification rank of the regions. the lower is the diversification rank of a given region, the higher is the eligibility (χ) of that region for attracting new individuals. formally, χi = m − rd(i) + 1. (13) in each iteration of the run, the new population size of each region will be calculated using, gi.n = gi.nmin + ⌊ χi∑ 1≤i≤m χi gi.nmax ⌋ , where nmax = n − nmin(m − 1). the extra individuals of each region will be distributed among the other regions with proportionate to their eligibility. the eligibility rank and population size of the regions for the sample population in the previous example are shown in figure 1. 4 experimental setup three different test functions are chosen from two different family of problems. selection of the compared algorithms was based on their relevance to the proposed method. the test functions are the same as those that have been used by the chosen algorithms for comparison [1,23,30–32]. this facilitate, the comparison of the results with other methods in literature. to come up with a fair experiment, the algorithms are tuned as it was reported in literature, which is reported in table 2. table 2: attributes of the comparing algorithms feature epistatic multimodal mpg population model steady state parent selection tournament selection survival selection delete oldest selection pressure 8 8 100 population size 50 50 100 max. no. of generations 500 100 10000 chromosome length based on the epistasis level 30 100 number of regions (m) 8 region population size 8 the performance of each algorithm is measured over 50 independent runs for each of the problems. the average (avg.) of the best results are derived for all of the experiments. however, standard deviation (stdev.) of the results are reported when the compared methods have reported them. best average is highlighted in bold, while the lowest standard deviation is underlined in each case. the following will be the explanation on the utilized test problems for the course of experiments. 4.1 max-sat problems max-sat is a generalized version of satisfiability (sat) decision problem that belongs to the family of np-hard optimization problems. the problem in sat is to find if there exists any assignment for the variables that satisfies the following formula, which is in conjunctive normal from (cnf),c1 ∧c2 ∧. . .∧cn.. however, an adaptive ga in partitioned search space 333 in max-sat the problem is to find an assignment which maximises the number of satisfied clauses [5, 10, 11]. multi-modal boolean satisfiability and epistatic problems are the two classes of benchmarks used in the comparisons. these problems will be constructed based on the max-sat problems. degree of multi-modality in a problems could be defined as a measure of difficulty of the problem. in these problems, the number of false peaks grow with the number of modality. while, the degree of epistasis of a problem expresses the relationship between the genes in a chromosome. dependency of a large number of alleles at other loci is a sign of high epistasis in a system [29]. the degree of epistasis has direct relation with difficulty level of the problem. the compared algorithms are canonical ga (cga) [32] with a randomly chosen constant mutation rate, ssrga [32], self-adaptive (saga) [1], adaptive (aga) [30] parameter control methods, compact ga (cga) [12] and a proposed method, [23] which will be referred to as pgacmco. epistatic problems spears [29] has introduced a method for the creation of epistatic problems using the boolean expressions. his proposed method could be tuned for different levels of epistasis. the method is based on conversion of the hamiltonian circuit (hc) problems into equivalent sat expressions. in a directed hc problem, the aim is to find if a given graph has a hamiltonian cycle. by definitions, a hamiltonian cycle is a cycle in a graph that meet all of the vertex exactly once. the definition of hc constrains the nodes of feasible solutions to have only one input edge and one output edge. as such, any tour that does not satisfies this constraint cannot be a solution [14]. max-sat multi-modal problems the multi-modal problems used for the course of experiment are created using a mechanism proposed by spears [29]. a uni-modal problem of length 30 could be created as follows, 1peak ≡ (x1 ∧ x2 ∧ . . . ∧ x30). using the above uni-modal problem, a bimodal problem will be, 2peak ≡ 1peak ∨ (x1 ∧ x̄1 ∧ x̄2 ∧ . . . ∧ x̄30). for the course of this experiments, multi-modal problems up to 5 peaks have been used. 4.2 multi-modal problem generator multi-modal problem generator (mpg) is another benchmark, proposed by spears [15, 29]. the benchmark has the ability to create problems with multiple peaks where the number of peaks defines level of problem difficulty. for all of the peaks we have used, the heights are linearly distributed and lowest height is 0.5. fitness value of a given individual will be calculated according to the hamming distance (hd) between the individual and the nearest peak [33]. the compared algorithms are compact ga (cga), [12] the pga , [23] gasat [9], gahsat [9], hand-tuned [9], meta-ga [6] and revac [6]. it has to be noted that the hand-tuned algorithm here is a canonical ga on which the values of the parameters are carefully tuned. 334 f. nadi, a.t. khader 5 results and discussion the mean of best fitness (mbf) [8] for all of the algorithms are reported. the mbfs of the mpg function reported in table 3 show that the agapss has obtained better performance in all of the instances of this function in comparison to the other compared methods. table 3: comparison of the agapss with compared methods over mpg benchmark in [6, 7] peaks agapss gasat gahsat ga meta-ga revac cga pga ssrga-ii 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.959 2 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.958 5 1 1.0 1.0 1.0 0.988 1.0 1.0 1.0 0.962 10 1 0.9956 0.9939 0.9961 0.993 0.996 0.9989 0.9994 0.960 25 0.9992 0.9893 0.9879 0.9885 0.994 0.991 0.9863 0.9933 0.963 50 0.9988 0.9897 0.9891 0.9876 0.994 0.995 0.9794 0.9896 0.962 100 0.9976 0.9853 0.9847 0.9853 0.983 0.989 0.9847 0.9909 0.965 250 0.9969 0.9867 0.9850 0.9847 0.992 0.966 0.9739 0.9879 0.969 500 0.9949 0.9834 0.9876 0.9865 0.989 0.970 0.9673 0.9885 0.967 1000 0.9935 0.9838 0.9862 0.9891 0.987 0.985 0.9649 0.9884 0.959 the results of the max-sat epistatic problem are reported in table 4. the rustles are very comparable with the results of the cga even though the agapss has obtained higher mbfs in five instances of the epistasis problems. table 4: comparison of the agapss with benchmark methods [23, 32] over epistatic problem with different levels of epistasis deg. of epistasis n=6 n=11 n=16 n=21 n=26 n=31 n=36 n=41 agapss avg. 0.986 0.986 0.982 0.965 0.946 0.926 0.91 0.896 stdev. 0.014 0.003 0.005 0.008 0.011 0.0081 0.011 0.012 pga avg. 0.991 0.990 0.994 0.967 0.909 0.869 0.842 0.827 stdev. 0.019 0.004 0.001 0.005 0.007 0.007 0.006 0.006 ssrga-ii avg. 0.953 0.926 0.885 0.858 0.839 0.823 0.811 0.805 stdev. 0.013 0.007 0.013 0.014 0.01 0.007 0.007 0.007 aga avg. 1 0.96 0.922 0.888 0.865 0.847 0.836 0.826 stdev. 0 0.007 0.008 0.007 0.006 0.005 0.004 0.004 saga avg. 0.980 0.943 0.904 0.873 0.853 0.837 0.827 0.817 stdev. 0.019 0.007 0.01 0.006 0.007 0.005 0.004 0.004 cga avg. 0.989 0.948 0.906 0.876 0.856 0.840 0.827 0.819 stdev. 0.017 0.011 0.009 0.007 0.008 0.005 0.005 0.004 compact avg. 0.985 0.983 0.983 0.979 0.945 0.926 0.909 0.894 ga stdev. 0.014 0.002 0.002 0.003 0.005 0.006 0.006 0.005 the results of the max-sat multi-modal problem are reported in table 5. in this benchmark the agapss has performed better in four out of the six algorithms. however, the results of the agapss and the pga are comparable for all the instances. it has to be noted that this test function as like the mpg function is a multi-modal problem. however, the obtained results of the agapss was far different from what that have been obtained over mpg function. the statistical test reported in table 6 allow systematic comparison of the agapss with the other compared methods. the proposed method is compared pairwise with other compared methods and the results of the statistical are reported for each pair. the statistical tests confirms that the results of the agapss have been significantly different from all of the other compared methods over mpg test function. according to the obtained ranks of the agapss it could be inferred that the proposed methods has obtained significantly better results in comparison to the other compared methods over this test function. considering the obtained rankings and the p-values, the statistical tests confirms the superiority of the proposed method over epistasis test function in four out of the six compared an adaptive ga in partitioned search space 335 algorithms. although the agapss has performed better than the pga and cga, regarding to the obtained ranks (r−), however the statistical difference between them was not significant. the statistical results have confirmed significant difference in favour of the proposed method for five out of six compared algorithm over max-sat multi modal problem. however, the proposed algorithm has performed significantly worse than the pga method. it would be more interesting to note that two different results have been obtained for the same family of problems. both of the max-sat multi-modal and mpg functions are multi-modal functions. while the proposed method has performed significantly better than the pga over mpg method, it has performed worst over mat-sat multi-modal problem. the main difference between the mpg and max-sat multi-modal function is that the mpg function is ran for 10000 fitness calls, while the max-sat problem stopped after 100 fitness calls. it suggest that the agapss might be able to outperform the pga over higher number of iterations. in order to check the validity of the aforementioned hypothesis, a new experiment has been conducted. both of the pga and agapss methods have been ran over max-sat multi-modal problem while the maximum number of fitness calls was set to 10000. figure 2 depicts the progress of improvement in fitness as the search progress over different instances of max-sat multi-modal function. figure 2: comparison of the progress in the performance of the agapss and pga over the max-sat multi-modal function in. 336 f. nadi, a.t. khader as it could be seen in all of the instances of the compared algorithms, the agapss have been able to obtain better results in comparison with the pga method. however, the agapss has obtained the better performance after some iterations. in other words, the performance of the pga method was better in the early stages of the run. the statistical analyses, on the results of the last four instances of the max-sat multi-modal function, confirmed that the agapss has performed significantly better than the pga . the performance of the agapss has also been significantly better than pga in longer iterations over the epistasis problems. as a result it has been shown that the assignment of the parameters to the parts of the search space could be beneficial. however, it take some generations, in the early stages of the run, for the agapss to conduct the proper balance between the partitions of the search space. in other words, it takes time for the proposed method to find the promising partition and focus on it. this time would slow down the algorithm from finding the promising area in the search space, however, the accuracy of the proposed method in comparison to the other methods has shown that the proposed method is able in finding significantly better solutions. table 5: the agapss in comparison to some of the relevent methods from literature over multi-modal max-sat problem with different levels of multi-modality. modality p = 1 p = 2 p = 3 p = 4 p = 5 agapss avg. 0.936 0.9155 0.9015 0.8996 0.9117 stdev. 0.023 0.033 0.031 0.031 0.037 pga avg. 0.999 0.976 0.970 0.964 0.961 stdev. 0.007 0.023 0.025 0.029 0.026 ssrga-ii avg. 0.871 0.835 0.842 0.835 0.846 stdev. 0.030 0.040 0.034 0.034 0.029 aga avg. 0.874 0.870 0.866 0.868 0.876 stdev. 0.022 0.029 0.026 0.022 0.025 saga avg. 0.827 0.842 0.840 0.853 0.846 stdev. 0.029 0.029 0.022 0.027 0.024 cga avg. 0.834 0.843 0.848 0.850 0.842 stdev. 0.029 0.029 0.022 0.027 0.024 cga avg. 0.8360 0.7642 0.7909 0.7729 0.7794 stdev. 0.0386 0.0274 0.0347 0.0292 0.0280 6 conclusions and future works a new adaptive algorithm is proposed that divides the search space into predefined number of regions. different search strategies will be applied on different regions based on ranks that are given to each region based on their behaviour. the behaviour of a regions will be determined in relation to the overall fitness of the regions and the number of visits that have been done from them. the proposed method has been examined using different instances of three benchmark problems: max-sat multi-modal problems, max-sat epistatic problems, and mpg problems. the performance of the proposed method shown to be superior over the mpg in comparison to all of the compared methods. however, the results are negotiable with the max-sat multi-modal test function. the proposed method shown to be able to perform better than the pga when the maximum number of fitness calls increased. ignoring the first instance of this benchmark, the statistical analysis shown significant difference the results over the epistasis problem has also shown that the agapss was significantly better in compare to the most of the compared methods. overall, based on the results of the benchmarks, the performance of the proposed method is superior compared to the compared algorithms. an adaptive ga in partitioned search space 337 table 6: the wilcoxon signed-rank test between the agapss and other algorithms. p-values below 0.05 are shown in bold. agapss vs. multi-modal epistatic mpg pga r− 0 26 53.5 r+ 15 10 1.5 p-value 0.043∗ 0.263∗∗ 0.012 ssrga-ii r− 15 36 55 r+ 0 0 0 p-value 0.043 0.012 0.005 aga r− 15 35 r+ 0 1 p-value 0.043 0.017 saga r− 15 36 r+ 0 0 p-value 0.043 0.012 hand-tuned r− 15 35 52 r+ 0 1 3 p-value 0.043 0.017 0.018 compact ga (cga) r− 15 25 52 r+ 0 11 3 p-value 0.043 0.327∗∗ 0.018 gasat r− 52 r+ 3 p-value 0.018 gahsat r− 52 r+ 3 p-value 0.018 meta-ga r− 53.5 r+ 1.5 p-value 0.012 revac r− 52 r+ 3 p-value 0.018 ** : no significant difference between the agapss and the compared algorithm. * : significant difference between the agapss and the compared algorithm in favour of the compared algorithm. bibliography [1] bäck, t., schutz, m. 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(2008). parameter control methods for selection operators in genetic algorithms. proceedings of the 10th international conference on parallel problem solving from nature620-630. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 387-402 reconfigurable takagi-sugeno fuzzy logic control for a class of nonlinear system considering communication time delays on peripheral elements h. benítez-pérez, f. cárdenas-flores, f. garcía-nocetti hector benítez-pérez, f. cárdenas-flores, fabian garcía-nocetti universidad nacional autónoma de méxico departamento de ingeniería de sistemas computacionales y automatización apdo. postal 20-726., admón. no. 20 del. a. obregón, méxico d. f., cp. 01000, méxico. e-mail: hector@uxdea4.iimas.unam.mx abstract: nowadays the study of faults and their consequences becomes an issue into highly safety critical computer network systems. how to bound the effects of a fault and how to tackle them into a dynamic system is still an open field. in here an approach to tackle this problem is presented. the use of takagi-sugeno fuzzy logic structure is given in order to accomplish two challenges, the presence of local faults and the respective time delays within a realtime distributed system. this approach is pursued as reconfigurable strategy according to communication time delays within a real-time distributed system. this approach is pursued as reconfigurable strategy according to communication delays. keywords: fuzzy logic, network control. 1 introduction the emergence of smart sensor and actuator technology removes the need for centralized control with feedback loops to dumb peripheral actuators replacing it with a databus connection [1]. this gives an autonomous actuator installation [2] as well as local control, self-calibration, health monitoring and reconfiguration availabilities. several strategies for managing time delay within control laws have been studied for different research groups. for instance [3] proposes the use of a time delay scheme integrated to a reconfigurable control strategy based upon a stochastic methodology. on the other hand, [4] proposes a reconfiguration strategy based upon a performance measure from a parameter estimation fault diagnosis procedure. another strategy has been proposed by [5] where time delays are used as uncertainties, which modify pole placement of a robust control law. in [6] presents an interesting view of fault tolerant control approach related to time delay coupling. reconfigurable control has been studied from the point of view of structural modification since fault appearance as presented by [7] it performs a combined modification of system structure and dynamical systems as studied by [8–10]. present approach takes time delays due to communication as deterministic measured variables. as well as actuator fault presence by modification of b matrix in order to propose a takagi-sugeno fuzzy control with two conditions loose of local peripheral elements and the related time delays. in here fuzzy logic control (flc) law [11] views time delays as a result of deterministic reconfigurable communications based upon scheduling algorithm. these time delays are a structural consequence determined by the insertion of new elements within communication channels due to fault appearance. in fact, fault presence is taken into account as the lost of the related peripheral element, specifically, sensor or actuator elements. the result of fault effects over the peripheral element is the lost of the related measurement, in this case, the structure of the plant is modified in terms of the lost of specific peripheral elements. as an obvious consequence control structure is modified. copyright c⃝ 2006-2011 by ccc publications 388 h. benítez-pérez, f. cárdenas-flores, f. garcía-nocetti fuzzy logic control engulf both undesirable situations by defining the time delays during fault appearance as well as the related lost of the faulty peripheral element. the goal of this paper is to define a strategy for control reconfiguration based upon communication system reconfiguration. some considerations need to be stated in order to define this approach. first, faults are strictly local in peripheral elements, faults are tackled by just eliminating the faulty element. in fact, faults are catastrophic and local. time delays are bounded and restrictive to scheduling algorithms. global stability can be reached using takagi-sugeno fuzzy logic approach. fuzzy logic has been a very powerfull tool to systematic modelling an design complex nolinear systems as presented by zadeh in [23–26]. this paper is placed as a strategy for reconfigurable systems as shown in figure 1. in fact, this paper is focused into reconfigurable control law due to the presence of local faults and time delays as consequences. time delays are measurable and bounded according a real-time scheduling algorithm. in this case scheduling algorithm is the well known earliest deadline first algorithm (edf). according to figure 1, structural reconfiguration takes place as result of edf performance and related user request. this action provokes control law modification. how this is modified is the scope of this paper by using takagi-sugeno approach. second reconfiguration state reconfiguration request plan valid plan database bus controller node computer network (sensor network) cotrol law database selection of the related control law control law node control law is chosen ) (if the plan is valid, the validation plan noyes first reconfiguration stage figure 1: general structure of reconfigurable system over a computer network the map of this paper is next, first section is current introduction, second section is scheduling approach, third section is plant design, fourth section is control approach fifth section presents a case study and finally some concluding remarks are given. 2 scheduling aproach the communication network plays a key role in order to define the behaviour of the dynamic system in terms of time variance from communications and processing although it presents a nonlinear behaviour. in order to understand such a nonlinear behaviour, time delays are incorporated by the use of real-time system theory that allows time delays to be bounded even in the case of causal modifications due to external effects. several algorithms can be pursued such as reconfigurable takagi-sugeno fuzzy logic control for a class of nonlinear system considering communication time delays on peripheral elements 389 rate monotonic (rm), deadline monotonic or earliest deadline first (edf) [15–17]. the use of last algorithm is pursued in here due to flexibility of task reorganisation during online performance. requires several characteristics from each task such as deadlines, consumption times and priorities. for instance, consider three tasks with next characteristics (table 1 and figure 2) first section under edf algorithm if a task changes its deadline at ∆t it would have a higher priority than those tasks already defined (table 2). table 1: task used to exemplified edf algorithm. task consumption periodic deadline priority number time (c) time (p) (d) 1 c1 p1 d1 pr2 2 c2 p2 d2 pr3 3 c3 p3 d3 pr1 from figure 2 there are two scenarios, in the first task 3 has the smallest slack time (ts3) therefore it has the highest priority pr1 , thereafter, task 1 has next priority (pr2 ) and task 2 has the lowest priority pr3 (table 1). 3 2 1 ts1 ts2 ts3c3 t′s1c ′ 1 t′s2 t′s3c ′ 3 p1 p3 p2 c1 c2 c ′ 2 tasks ∆t time p ′2 p ′3 p ′1 figure 2: time graph related to table 1. second scenario presents a different priority conformation according to slack times modifications. thats it for the case of deadline modification as display in figure 2 priorities are modified as shown in table 2 where task 2 has the smallest slack time (ts2) therefore it has the highest priority pr1 , task 3 has next priority and the task 1 has the lowest priority pr3 . therefore the task that is going to be executed first is the one who has the shortest deadline in comparison to the rest of alive tasks in a particular time. since this algorithm is performed, two conditions are stated upon this approach. control law must be executed after all sensors have performed their task and before any actuator takes an action over the plant, from these restriction system performance has two main scenarios fault present and fault free situations. these scenarios are 390 h. benítez-pérez, f. cárdenas-flores, f. garcía-nocetti table 2: new priority order after at reorganisation. task consumption periodic deadline priority number time (c) time (p) (d) 1 c1 p ′1 d ′ 1 pr3 2 c2 p ′2 d ′ 2 pr1 3 c3 p ′3 d ′ 3 pr2 exposed in figure 3, it shows a common configuration considering local time delays. sensors time τa τs τc control actuators τcomc−a τcoms−c figure 3: related time delays from control configuration. related time delays are defined in terms of scheduling algorithm behaviour. therefore time delays are stated as: delay sensor to controller τsc = τs + τcons−c delay of the controller τc delay controller actuator τca = τa + τcomc−a where τcoms−c and τcomc−a are the source of variations due to communication variation between elements. 3 plant approach the proposed dynamic plant is based upon the following structure: x(k + 1) = apx(k) + bpu(k) y = cpx(k), (1) reconfigurable takagi-sugeno fuzzy logic control for a class of nonlinear system considering communication time delays on peripheral elements 391 where ap ∈ ℜn×n, cp ∈ ℜn×1 and bp ∈ ℜn×1 are matrices related to the plant. x(k), u(k) and y(k) are the states, inputs and outputs respectively. specially bp is stated as: bp = n∑ i=1 ρibi m∑ j=1 ∫ τij−1 τij e−a p(t−τ)dτ, (2) where ρi ∈ {0,1} and ∑n i=1 ρi = 1 taking into account that n are the total number of possible faults and m are the involved time delays from each fault. current communication time delays are expressed as tij−1 and t i j. considering that ∑m j=1 τ i j ≤ t , where t is the sampling period and depends of the faults scenarios, then bi is integrated as: bi =   b1 b2 0i ...   →, i fault element, where bi → bn are the elements conformed at the input of the plant (such as actuators) and 0i is the lost element due to local fault where bp represents only one scenario following equation 2. therefore bpi defined as: b p i = bi m∑ j=1 ∫ tij−1 tij ea p(t−τ)dτ, (3) considers local faults and related time delays. for simplicity purposes bpi is used in order to depict local linear plants. from this representation fuzzy plant is defined as follows, taking into each time delay and fault cases: r1 if x1 is a1i and x2 is a2i and . . . and xℓ is aℓi then a p i x(k) + b p i u(k), (4) where x1 . . .xℓ are current state measures, ℓ is the number of states, i = 1, . . . ,n is one of the fuzzy rules, n is the number of the rules which is equal to the number of possible faults and aij are the related membership functions which are gaussians defined as: aij(xi) = exp ( − (xi − cij)2 σ2ij ) (5) where cij and σij are constants to be tuned. then it can be write hi as follows: hi = ℓ π j=1 aij(xj) (6) the final representation of plant as integrated system is based upon center of area defuzzification is expressed as: xp(k + 1) = ∑n i=1 hi(a p i x(k) + b p i u(k))∑n i=1 hi (7) it is important to remember equation 2 in order to pursue system response. from this representation of a global nonlinear system, the integration of several linear systems, it is necessary to define global stability as a result of this fuzzy system. this review is given in following section considering fuzzy logic control approach. the result of this system representation allows the integration of nonlinear stages and transitions to basically a group of linear plants. 392 h. benítez-pérez, f. cárdenas-flores, f. garcía-nocetti fuzzy logic actuators foultsu yref + control plants figure 4: plant configuration using fuzzy logic control. 4 control approach from the representation of the plant as fuzzy system [18] it is considered to develop the control law as a group of bounded local linear control laws related to each local linear system. the structure of each fuzzy rule is: r1 if x1 is ac1i and x2 is a c 2i and . . . and xℓ is a c ℓi then u(k) = −gix(k), (8) where i = 1, . . . ,n, n is the number of fuzzy rules which is the number of faults to be represented, x1, . . . ,xℓ are current states of the plant, acij are the gaussians members functions like: acij = exp ( − (xi − ccij) 2 (σcij) 2 ) (9) where ccij and σ c ij are constants to be tuned. furthermore gj represents the related control gain. similar to fuzzy system plant, fuzzy control representation is integrated as: wi = ℓ π j=1 acij(xj) (10) and u(k) = ∑n j=1 wi(gix(k))∑n i=1 wi (11) the configuration of flc is integrated to the already explored plant where final representation is given as closed loop system of linear feedback plant as shown in figure 4. x(k + 1) = ∑n i=1,j=1 hiwj((ai − cigjb p i )x(k) + b p i gjref)∑n i=1,j=1 hiwj y = ciui = cigjbj (12) where ref is the reference to be followed by controller and the variables i and j are used due to fuzzy rules interconnections as the representation of different linear plants and theirs controllers. from this representation stability needs to be stated as the following lyapunov function: v (x(k)) = xt (k)px(k) (13) and ∆v (x(k)) = v(x(k + 1)) − v(x(k)), (14) reconfigurable takagi-sugeno fuzzy logic control for a class of nonlinear system considering communication time delays on peripheral elements 393 where v (k + 1) = x(k + 1)t px(k + 1) = (∑n i=1,j=1 hiwj((ai − cigjb p j )x(k + 1) + b p j gjref)∑n i=1,j=1 hiwj )t p (∑n i=1,j=1 hiwj((aicigjb p j )x(k + 1) + b p j gjref)∑n i=1,j=1 hiwj ) . (15) therefore ∆v (x(k)) = (∑n i=1,j=1 hiwj((ai − cigjb p j )x(k + 1) + b p j gjref)∑n i=1,j=1 hiwj )t p (∑n i=1,j=1 hiwj((aicigjb p j )x(k + 1) + b p j gjref)∑n i=1,j=1 hiwj ) − xt px, (16) and ∆v (x(k)) = ∑n i=1,j=1   (hiwj) 2xt   − (ati pcigjb p j ) + a t i p(b p j giref)− (cigjb p j ) t pai− (cigjb p j )p(b p j gjref)+ (gib p j ref) t pai− (gjb p j ref) t p(cigjb p j )+ ati pai + (cigjb p j )p(cigjb p j )+ bpj gjrefp(gjb p j ref)   x − xpx   ∑n i=1,j=1(hiwj) 2 , (17) then by considering ref=0 ∆v (x(k)) = ∑n i=1,j=1 ( (hiwj) 2xt [ − (ati pcigjb p j ) − (cigjb p j ) t ai + ati pai + (cigjb p j )p(cigjb p j ) ] x − xpx ) ∑n i=1,j=1(hiwj) 2 . (18) it is important to remember that: ∆v (x(k)) ≤ 0, (19) and [ − (ati pcigjb p j ) − (cigjb p j ) t ai + ati pai + (cigjb p j )p(cigjb p j ) ] < 0, (20) where p > 0, || • || is the euclidean norm and it is possible to define: gj > ||bpj ||. (21) this condition has to be given for every single time delay and local fault appearance. furthermore the stability and the convergence of states should be assured by the adequate selection of matrices gi (equation 8) and the related parameters from both fuzzy systems. in this case recommendable procedure to follow is multi-objective optimisation in order to define those suitable values. 394 h. benítez-pérez, f. cárdenas-flores, f. garcía-nocetti i1 i2 i3 q13 q1 q20 q2 section a section a2 q32 figure 5: three tanks representation. i1 i2 i3 q13 q32 q1 q20 section a q2 sensor sensor sensor actuator actuator controller figure 6: three tanks representation based upon computer network. 5 case of study case study is based upon three tanks approach following figure 5. this is composed by three tanks with identical cross section a. the tanks are coupled by two pipes with cross section of a2. two pumps are driven by dc motors who supply two inflows q1 and q2. liquid level in each tank is measured and reported as i1, i2 and i3. the computer network integration is shown in figure 6. this system has a sampling period of 80 ms and nominal communication time delay of 20 ms. common representation of this system is stated in eq. 22 as state space representation [19]. this representation takes into account the inherent non-linearities of the model represented as: x(k + 1) =   − ql3 a q32x − q20x a ql3x − q32x a   +   1 a 0 0 1 a 0 0  u, (22) where x = [l1 l2 l3]t , y = [l1 l2]t and u = [q1 q2]t and qij = µija ∗ signum(li − lj) √ 2g|li − lj| q20 = µ20a √ 2gl2. (23) reconfigurable takagi-sugeno fuzzy logic control for a class of nonlinear system considering communication time delays on peripheral elements 395 here µij are the outflow coefficients. in terms of fuzzy representation, three rules are given due the possible number of faults which are two due to just two actuators are available. ri if x1 is ac11 and x2 is x c 21 and x3 is a c 31 then a p 1x(k) + b p 1u(k), r2 if x1 is ac12 and x2 is a c 22 and x3 is a c 32 then a p 2x(k) + b p 2u(k), r3 if x1 is ac13 and x2 is a c 23 and x3 is a c 33 then a p 3x(k) + b p 3u(k), in this case bp1, b p 2 and b p 3 are defined as: b p 1 = b1 (∫ 0.03 0.0 e−a p 1(t−τ)dτ + ∫ 0.08 0.03 e−a p 1(t−τ)dτ ) , b p 2 = b2 (∫ 0.04 0.0 e−a p 2(t−τ)dτ + ∫ 0.08 0.04 e−a p 2(t−τ)dτ ) , b p 3 = b3 (∫ 0.05 0.0 e−a p 3(t−τ)dτ + ∫ 0.08 0.05 e−a p 3(t−τ)dτ ) . (24) these three plant representations are modified just as b matrix where the reconfiguration is performed. in here two integrals are presented due to important time delays of related faults. for instance, bp2 has a time delay of 0.04 and b p 3 has a time delay of 0.05 seconds. in the same way control laws are represented, where three control laws are presented. ri if x1 is ac11 and x2 is x c 21 and x3 is a c 31 then u(k) = −g1x(k), r2 if x1 is ac12 and x2 is a c 22 and x3 is a c 32 then u(k) = −g2x(k), r3 if x1 is ac13 and x2 is a c 23 and x3 is a c 33 then u(k) = −g3x(k). based upon this equation, final closed loop equation is: x(k + 1) = ∑3 i=1,j=1 hiwj((ai − cigjb k j )x(k) + b k j gjref)∑3 i=1,j=1 hiwj . (25) since this representation is given and based upon stability proposal in last section, optimisation toolbox from matlab [20] is used to define current values of g matrices. 6 implementing approach following the review of case study, this is implemented over a computer network simulation [8,21,22] based upon true-time strategy. this simulation consists of a csma/ca can network integrated over ten nodes, where the typical time diagram is presented in figure 7. inconsistencies appear during communication and consumption times as well as the jitter play an important role. table 3 shows the consumptions, periods and variations of the involved nodes where sensors and actuators are organised according to edf algorithm. moreover, table 4 shows the related time delays according to figure 6. based upon these conditions, scheduling modifications are performed. this scheduling modification affects performance response of peripheral elements where bpi (equation 3) and cij (equation 5) matrices are altered as where time delays are defined as follows (table 5): 396 h. benítez-pérez, f. cárdenas-flores, f. garcía-nocetti tps1 s1 s1 s2 s3 s3 tps3 c tcs1 tcs2 c a1 tps2 s2 cs2 cs1 cs3 cc a2 ca1 tcca1 ca2 time tcs3 tcpp tcca2 figure 7: time diagram representation based upon computer network. table 3: consumption times and periods from tasks. (in seconds) component consumption variation time variation time % deadlines % s1 cs1 = 0.03 6-8 0.08 2-3 s2 cs2 = 0.03 6-8 0.08 2-3 s3 cs3 = 0.03 6-8 0.08 2-3 c cc = 0.05 7-9 0.08 2-3 a1 ca1 = 0.045 5-7 0.08 2-3 a2 ca2 = 0.045 5-7 0.08 2-3 reconfigurable takagi-sugeno fuzzy logic control for a class of nonlinear system considering communication time delays on peripheral elements 397 table 4: communication times needed (in seconds) communication spent time time tcs1 0.02 tcs2 0.02 tcs3 0.02 tcca1 0.02 tcca2 0.02 jitter 0.015 τ11 = 0,τ 1 2 = tcca1 + jitter + variation, and τ 1 3 = 0.08 τ21 = 0,τ 2 2 = tcca2 + jitter + variation, and τ 2 3 = 0.08 therefore control reconfiguration becomes necessary in order to keep certain response level. for this case study three different control laws are proposed as shown in table 6. the type of membership functions are gaussians normally distributed over the rank of each state as shown in figure 8. in the case that any state is gone further its effected are reduced through the related control law. minimum allowed value maximum allowed value state variable a11 a21 a31 figure 8: state variable. table 5: fuzzy logic rules considering the related plant. rule linearised plant if x1 is a11 and x2 is a12 and x3 and a13 x(k + 1) = a p 1x(k) + b p 1u if x1 is a21 and x2 is a22 and x3 and a23 x(k + 1) = a p 2x(k) + b p 2u if x1 is a31 and x2 is a32 and x3 and a33 x(k + 1) = a p 3x(k) + b p 3u 398 h. benítez-pérez, f. cárdenas-flores, f. garcía-nocetti table 6: fuzzy logic rules considering the related control law. rule control law if x1 is a11 and x2 is a12 and x3 and a13 u = g1x if x1 is a21 and x2 is a22 and x3 and a23 u = g2x if x1 is a31 and x2 is a32 and x3 and a33 u = g3x 7 results following the response of the case study, some interesting results are given related to three different scenarios named reconfiguration (stages) 1, 2 and 3 (figure 9a), where each stage is 20 ms long. and for each of the those cases three different responses like: dynamic response, consumption and communication times are given. the response shown, corresponds to three tanks performance during three different dynamic conditions with respect to communication time behaviour. the local response presented in the third scenario is performed due to modification of the response and dynamic conditions with respect to communication time delays. figure 10) presents communication system response of each element involved on the system. in this case, periodic activation per task and node is presented. where free time needs to be reorder to be more efficient. figure 10a) presents periodic time modification when reconfiguration was necessary and second graphic presents time used to communicate elements through the network. for instance, at first 20 seconds every period of activated task are relatively similar. from 20-40 seconds those modified periodic tasks change the relatively. similar situation is presented from 40-60 seconds. the reader may realize that some tasks reduce their periodic consumption times while others tends to spend more time. 8 concluding remarks as has been shown in this work, fuzzy logic control based upon takagi-sugeno structure allows the possibility of control reconfiguration as long as linear models based representation of the plant are available. despite of local faults and bounded time delays appearance, several conditions should be fulfil in order to be able to follow this proposal, for instance, plant should be observable and controllable during the whole nonlinear behaviour as well as the states should be present during undesirable situations. several approaches can be pursued such as robustness study during variations from one to another scenarios as well as further work is needed when not every state is available during fault conditions which is a realistic condition. in this case observer design under fault scenario need to be defined looking for smooth transitions between fault and fault free status. stability conditions are reached as long as system design is pursued by equations 15-19, where fuzzy logic variables and local control laws are established. following future work, a non-desirable drawback is related to a peripheral element fault without any propagation to the rest of the system. this can be possible by just considering some specific scenarios during fault propagation from local to general catastrophic faults. acknowledgements the authors would like to thank the financial support of disca-iimas-unam, and unampapiit (in106100, in103310 and in101307), icytdf picco10-53 méxico in connection with this work. reconfigurable takagi-sugeno fuzzy logic control for a class of nonlinear system considering communication time delays on peripheral elements 399 0 10 20 30 40 50 60 70 −0.5 0 0.5 1 1.5 2 a) error time 0 10 20 30 40 50 60 −1 0 1 time b) current states response state 1 state 2 state 3 0 10 20 30 40 50 60 −0.5 0 0.5 1 1.5 time c) current control response 0 10 20 30 40 50 60 −1 0 1 2 time d) current values to plant value 1 value 2 0 10 20 30 40 50 60 0 0.2 0.4 0.6 0.8 time e) periodical time modifications 0 10 20 30 40 50 60 0 5 10 time f) communication network response figure 9: dynamic response of case study following three scenarios. 400 h. benítez-pérez, f. cárdenas-flores, f. garcía-nocetti 0 10 20 30 40 50 60 70 80 90 −0.5 0 0.5 1 1.5 a) error time 0 10 20 30 40 50 60 70 80 90 −1 0 1 2 time b) current states response state 1 state 2 state 3 0 10 20 30 40 50 60 70 80 90 −0.5 0 0.5 1 1.5 time c) current control response 0 10 20 30 40 50 60 70 80 90 −1 0 1 2 time d) current values to plant value 1 value 2 0 10 20 30 40 50 60 70 80 90 −0.2 0 0.2 0.4 0.6 0.8 time e) periodical time modifications 0 10 20 30 40 50 60 70 80 90 0 5 10 time f) communication network response figure 10: dynamic response of case study following three scenarios considering periodic communication. reconfigurable takagi-sugeno fuzzy logic control for a class of 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[26] zadeh l. a., fuzzy logic and the calculus of fuzzy if-then rules, proceedings of synapse ‘91, tokyo, japan, 1991. international journal of computers communications & control issn 1841-9836, 12(3), 381-392, june 2017. a conceptual framework for artificial creativity in visual arts d. sirbu, i. dumitrache daniela sirbu department of new media university of lethbridge lethbridge, canada daniela.sirbu@uleth.ca ioan dumitrache department of automatic control and systems engineering university politehnica bucharest bucharest, romania ioan.dumitrache@acse.upb.ro abstract: the present paper introduces the conceptual framework for an artificial system for visual creativity addressing the idea of niche creativity that is domain specific and non-anthropocentric in its conceptual approach. we think that the visual creative output of the system reflects the artificial medium and the specific artificial processes engaged in its production and, therefore, it is an expression of the idea of embodied creativity with the proposed system offering in this sense an example of digital embodiment of creativity. although our approach to artificial creativity is non-anthropocentric, the system design is inspired by processes in the natural world that lead to the production of new and useful structures in both living and non-living systems with human creative cognition being included among these processes. the main problem raised by this abstract approach to artificial creativity in visual arts is the compatibility of its artistic production with human aesthetics, the ultimate goal of the proposed system being to produce visual output that would aesthetically engage human visual perception. keywords: artificial creativity, computational creativity, stochastic processes, evolutionary computing, multiagent systems. 1 introduction most approaches in computational creativity are concerned with emulating human creative cognition or processes of natural structure formation and growth in living organisms and nonliving systems. this goes hand in hand with an equally great effort to understand in more depth these processes and the material base from which they operate and which conditions them. these approaches raise the question if human creative cognition can be emulated in an artificial medium and if such a transfer is the most effective approach to computational creativity. the same question is raised when transferring processes of structure formation and growth from the natural world into the computational medium with the purpose to produce visual output of artistic value. our conceptual approach to the design of an artificial system for visual creativity (asvc) is different from this general trend in the sense that it is more holistic and is abstract in the interpretation of creativity, but remains more specific to the computational medium that embodies it. the purpose of this research is to demonstrate that no matter the form in which creativity is embodied, it is based on stochastic processes at its core, although the nature of its embodiment may affect the creative product due to the nature of influences exercised throughout its development. copyright © 2006-2017 by ccc publications 382 d. sirbu, i. dumitrache we aim to demonstrate that the stochastic nature of processes involved in creativity is what makes this process unpredictable, mysterious, and intimidating and we hope to bring a small contribution in debunking the myth of creativity and accept creativity as a natural part of everyday life just as much as intelligence and the very fact of being into the world are natural processes that unfold in a general and continuous process of change. we recognize the importance of approaching creativity research in interdisciplinary manner considering perspectives from cognitive psychology, neurobiology, learning, and complex systems as it has been previously suggested [13]. this approach allowed identifying some of the main aspects involved in creativity like: memory, divergent thinking, convergent thinking, and flow [13], which appear in previous theories and models of creativity [9], [1], [3], [10], [11], [14], [15], [23], [27], [37], although terminology and descriptions may vary. we also take into consideration limb’s viewpoint [13] that a general theory of creativity is hard to define because creativity is viewed as a "complex" with a multitude of facets involved. however, we think that there are some general principles and processes across many forms, if not all forms, of creativity in living and non-living systems. our purpose is to identify these general principles and processes and then test their validity empirically through an artificial system for visual creativity. we can then go back to particular forms of embodied creativity, among which human creativity is most important, and analyze their specifics while backed by principles identified through our holistic approach. we also hope this approach provides the basis for a practical approach to developing systems of specialized creativity which are less complex, but effective in their application domains. we present in this paper the fundamental concepts at the basis of asvc design and the hypothesis that underlies our approach. we briefly review the means to implement these concepts into an artificial system for visual creativity, and synthetically discuss our empirical approach in testing and interpretation of results and how these support our initial hypothesis. 2 background based on the general problematics identified in creativity research, we synthesize several aspects that are more relevant to our conceptual approach. there is a model of creativity based on four main stages [47] identified as preparation, incubation, illumination, and verification. this model is largely based on narratives from creative people [31] and therefore it is rejected by some authors [48], [49]. however, the model has continued to gain recognition in creativity research [23], [24], [25], [16], [14], [30] up to the present day. in our perspective, the wallas [47] model is important in emphasizing stochastic processes at the core of incubation and illumination stages when the creative product is conceived. this view received support from psychoanaltic theories of creativity [17], [18] and through experimental data from more recent research [23], [24]. in this sense, we point out that the concept of adaptive regression [17], [18] describing the generation of new ideas in a process of shifting cognition on a continuum between consciousness and subconsciousness. this shift during creative cognition leads to a state where rules over the knowledge domain are weakened allowing free associations and combinations of mental structures. therefore the generation of new ideas takes place through stochastic processes under some relaxed influences from the knowledge domain. the domain influence is manifested through the nature of the pre-existing mental structures and the limitations on the associations and combination between them. furthermore, behaviorist [4], [37], historiometric [38], [39], [44], and systemic [7], [8] approaches in creativity research link creativity to external factors and lead to evolutionary views of creativity. natural evolution is intrinsically based on stochastic processes under environmental influences and therefore creativity is viewed as such an adaptive process. a conceptual framework for artificial creativity in visual arts 383 in summary, we emphasize that interpretations of creativity as both intrapsychic and extrapsychic process link creativity to stochastic processes under influences that ultimately express in weak form the domain knowledge. this aspect is taken into consideration in computational models of creativity. it has been recognized [2], [30][35] that computational models of creativity based on evolutionary computing paradigms typically build on the wallas model [47]. some of these models emulate natural evolution processes without particular reference to human cognition [41], [42]. other models in this category focus on certain known or assumed processes in human creative cognition e.g. analogy making [28], [29], [14], [29], [22], or curiosity [34], while other models focus on the evolutionary paradigm and define a systematic theory for the development of self-improving algorithms as models of innovation [12]. 3 fundamental concepts in asvc design in our approach to artificial creativity, a central idea is that creativity reflects in its processes and output the organization of matter and processes that lead to structure formation in living and non-living natural systems. we believe that most processes in the natural world are stochastic in nature and pressures exercised due to physical phenomena lead to the formation of structures at all dimensional scales and complexities. these structures have intrinsic usefulness within the systems where they emerge. however, the notion of usefulness is a human construct and responds to human value systems. from a very holistic standpoint, there is nothing more or less useful in nature in the process of change that unfolds on a continuous basis. forms that seem to be destroyed or disappear under the influence of natural forces are, in fact, simply changing to become part of new structures. sometimes, or most of the times, we cannot even comprehend these new structures and the processes that lead to their formation due to their very small or very large scales in relation to our plan of observation. this is best described by the fractal theory advanced by mandelbrot [20] and further developed by other authors [5], and which describes features of self-similarity in natural forms at different scales in the visible world. therefore, we believe that creativity, in its strict definition as a process that produces new and useful artifacts, is intrinsic throughout the entire visible living and non-living systems as a process of change and structure formation. as similar ideas are expressed in most areas of human exploration, it is natural to turn to practical means made available in control engineering and computer science in order to empirically explore artificial forms of creativity in visual arts. the fundamental concept in our approach to asvc design is to create artificial stochastic processes which are then exposed to the influence of forces within the system leading to structure formation in the artificial world. our hypothesis is that if influences manifested on stochastic processes are integrated into an aesthetic system that governs loosely the artificial world, then there is a high probability that emerging structures are organized in visual compositions with aesthetic value. this way we unify in asvc design the stochastic nature of artificial processes with a system of influences that respond to human aesthetics and, therefore, to the idea of usefulness of the asvc output deeming the system to be creative. the research approach in exploring these questions is empirical. it is based on applying the principles that we hypothesize to underlie creativity to the development of artificial systems that are expected to have creative capabilities expressed in the production of new and aesthetically valuable visual compositions. these systems are then tested experimentally and results are analyzed based on an aesthetic system adapted to the specifics of artificial creativity. while the proposed system for artificial creativity is abstract in the sense that natural processes are not closely emulated, the system design is, however, inspired by principles and processes, which are effective in producing new forms in the visible living and non-living systems in 384 d. sirbu, i. dumitrache the real world. in doing so, we approach the design of the artificial system for visual creativity using stochastic models based on random walk algorithms and evolutionary computation and create artificial ecosystems in which aesthetic principles are manifested through relaxed pressures on the artificial stochastics. the main research contribution is based on the idea of embedding aesthetic knowledge in the system in a relaxed way that allows great stochastic freedom. this is key for the asvc capability to generate a large number of new visual compositions with aesthetic value. in this approach creativity is viewed as an adaptive process embodied in an artificial ecosystem with stochastic substrate and functioning for aesthetic performance. 4 general requirements for an artificial system for visual creativity based on the problematics identified in creativity research and computational creativity systems, we synthesize the following set of requirements for the asvc design: • the system must incorporate in some form specialized domain knowledge, which, in this case, is visual aesthetics. • asvc processes that lead to the production of new visual compositions must reflect the knowledge domain. • the same processes that lead to the formation of new visual structures must incorporate stochastic aspects. • the system must implement a digital process that effectively executes the drawing/painting as a counterpart of the drawing process in real life. • asvc must develop its own visual concepts. • the system must integrate the computational concept development process and the artificial drawing process. • the system must develop new visual output through computational processes that are adaptive in response to influences from a given artificial environment. • the asvc creative process must be best adapted to the computational medium that embodies it. these requirements define asvc creativity as an abstract process that liberally uses a blend of examples of structure formation in the living and non-living systems in the real world, but it is focused on the final product in the sense of synthetic creativity [19], [6] that is typical in the artistic domain. 5 asvc general architecture considering the requirements formulated above for an artificial system for visual creativity, we suggest a generic architecture (figure 1) that can be particularized for many versions of asvc depending on computational paradigms employed for the implementation of various components in its structure. this architecture is based on two main components as follows: • the kinetic drawing systems (kds). a conceptual framework for artificial creativity in visual arts 385 visual concept development system (vcds) kinetic drawing system (kds) visual aggregation and fading system (vafs) environment dynamics system environmentnumber of structuring zones size aspect ratio vafs initialization conditions initial positions for structuring zones visual output drawing agent population agent population behavior generator initial drawing agent parameters initial conditions algorithmic scheme(s) user input high resolution events and decision making low resolution events and decision making figure 1: generic asvc architecture showing main system components and their functional correlation. • the visual concept development system (vcds). these two main components suggest a hierarchical structure based on generating visual concepts at higher hierarchical level and their implementation at lower hierarchical level. we emphasize that the most important aspect in this architectural organization is, in fact, the level of resolution involved defining asvc as multiresolution system in the sense described in [26]. in this sense, the low resolution corresponding to kds is identified through local actions with corresponding small scale or micro influence in the system. the high resolution is related to actions that influence the visual development at large or macro level and this is associated with vcds (see figure 1 and figure 2). the important aspect is that both the virtual drawing through the kds and the artificial concept development through vcds are exploratory in nature being based to a large degree on stochastic processes that are under environmental influences. 6 kinetic drawing through a virtual ecosystem to further particularize the description of the generic architecture, asvc can be described as a virtual ecosystem populated by drawing agents. the motion of the drawing agents in the environment takes place at micro level in the system, which can be associated with a primary perceptual level where drawing agents seem to be independent in their actions, but responsive to environmental influences. the drawing process takes place through recording the motion trajectories of the drawing agents in the system. the motion of drawing agents is self-generated. therefore, the kds output at low perceptual level is influenced by the computational paradigm 386 d. sirbu, i. dumitrache that underlies agent motion. there are a number of pre-determined parameters that decide the agent appearance, which has a role as a unit form in visual structure aggregation and therefore has impact on the visual output from the system. this aspect is described in more detail with reference to a particular asvc implementation based on random walk algorithms in [43], but other computational paradigms can be employed. 7 design principles and visual concept development in asvc aesthetic principles are manifested in asvc through environmental dynamics. traces generated by the drawing agents in motion are steered towards aesthetic organization of form aggregations through interactions between the drawing agents and the environment. therefore, visual concept development in asvc depends on the structural elements in the environment configuration and the overall dynamics of the environment. if we employ an evolutionary computing paradigm, which is not mandatory, but is very intuitive, then defining a certain configuration and dynamics for the artificial environment means defining an environmental niche. in this case, the development of a visual concept in asvc can be described through an environmental niche in the virtual world. we re-emphasize that once the environmental niche is configured and the underlying computational paradigm is defined, this does not mean that the environmental niche is static. by design, the dynamics of the environmental components is largely based on stochastic processes that continuously unfold. this is meant to facilitate within aesthetic constraints a large variety of possible compositional developments. 8 artificial creativity as a feedback loop system starting from the idea that both intelligence and creativity are processes of adaptation to the environment and both are functions of the brain, we can discuss an interpretation of creativity as overlapping in many respects with intelligence as an adaptive system functioning in interaction with a given environment. the main difference between intelligence and creativity is that intelligence operates within the knowledge domain, while creativity operates to expand the knowledge domain. based on this interpretation, we can adapt the representation of intelligent systems as adaptive feedback loop systems [26] to incorporate aesthetic knowledge and relaxation of aesthetic principles through randomized algorithms at various levels in the asvc system as presented in figure 2. 9 implementations the conceptual framework discussed in the paper provides the basis for the development of several systems for assisted and autonomous artificial creativity based on random walk algorithms, genetic algorithms, and hybrid systems, which combine several computing paradigms through various components of the asvc generic architecture presented in figure 2. 9.1 random walk asvc a random walk asvc (rwasvc) has been developed based on kds with the motion of the drawing agents based on random walk algorithms. this is integrated with an environment with randomized areas of interaction that fracture the random walk continuity and reinitialize the algorithm with the agent placed in high recurrence areas. rwasvc systems have been extended a conceptual framework for artificial creativity in visual arts 387 into hybrid systems that incorporate drawing agents engaged in physical simulation systems in addition to the random walk drawing agent population (figure 3). perception processing information passed by drawing agents from the pictorial field stochastic aesthetic model of the pictorial field actual configuration pictorial field sensing world model analysis of aesthetic performance stochastic process with constraints value judgement high resolution behavior generator stochastic process low resolution behavior generator drawing actuation world visual concept development system (vcds) kinetic drawing system (kds) figure 2: asvc generic architecture adapted to emphasize computational creativity components and functionality in a feedback loop. this expands computational intelligent system representation proposed in [26] to accommodate rules relaxation over aesthetic knowledge domain in the world dynamics and drawing agents behavior in asvc. with this system we obtained the appearance of a natural garden fence with vegetation in continuous growth and having a distribution in the pictorial field that responds to aesthetic principles of visual composition organization. experiments with rwasvc systems show that for a defined size of the frame of reference and a certain range of proportional relationships with the agent size, these systems are very reliable in producing a large number of visual compositions in a reasonable amount of time. some recommendations for effective setups have been synthesized from these experiments. hybrid rwasvcs incorporating physical systems extend the range of visual styles, but more refined correlations of the agent size and numbers must be performed. 9.2 evolutionary asvc a number of evolutionary asvc (easvc) systems have been developed with drawing agents in movement under an evolutionary computing paradigm and with the environment being characterized by non-deterministic dynamics. experiments with these systems provide very interesting and consistent results based on experimental setups that allow a large degree of stochastic freedom in the environment dynamics and therefore in the visual concept development system of the easvc. these experiments emphasize that the systems creativity and the output quality 388 d. sirbu, i. dumitrache figure 3: selected cluster of sampled compositions generated by the hybrid asvc version 8 combining a random walk with 14 drawing agents and a physical simulation component with 10 drawing agents. random walk drawing agent: rectangle, size 15 pixels x 25 pixels. figure 4: dominant compositions with dense forms created during experiment number 8 with the easvc system. a conceptual framework for artificial creativity in visual arts 389 figure 5: correlations between total the number of dense forms per run and the total number of dominant compositions per run across experiments 2-9, case study 1. each run is of 50 generations of drawing agents. increases when aesthetic rules are loose allowing more freedom in creative combinations and associations of forms. sampled images from this experiment are presented in figure 4. experimental results are synthesized in figure 5 showing correlations between the total number of dense forms, which express better quality through better form definition, and total number of dominant compositions, which show quantitative increase of system productivity over generations. 10 conclusion we conclude emphasizing that experimental results support our hypothesis that stochastic freedom catalyzes asvc creative behavior when manifested both at conceptual level (visual concept development through the environment) and execution level (drawing through agents), provided that stochastic processes are under a system of aesthetic influences within asvc. therefore, the synthesis of a number of 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[49] weisberg r.w.(1999); creativity and knowledge: a challenge to theories, handbook of creativity, r.j. sternberg (ed.), cambridge, uk: cambridge university press, 1999. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 33-44 the flag-based algorithm a novel greedy method that optimizes protein communities detection r. bocu, s. tabirca razvan bocu national university of ireland, cork department of computer science e-mail: razvan.bocu@cs.ucc.ie sabin tabirca national university of ireland, cork department of computer science e-mail: tabirca@cs.ucc.ie abstract: proteins and the networks they determine, called interactome networks, have received attention at an important degree during the last years, because they have been discovered to have an influence on some complex biological phenomena, such as problematic disorders like cancer. this paper presents a contribution that aims to optimize the detection of protein communities through a greedy algorithm that is implemented in the c programming language. the optimization involves a double improvement in relation to protein communities detection, which is accomplished both at the algorithmic and programming level. the resulting implementation’s performance was carefully tested on real biological data and the results acknowledge the relevant speedup that the optimization determines. moreover, the results are in line with the previous findings that our current research produced, as it reveals and confirms the existence of some important properties of those proteins that participate in the carcinogenesis process. apart from being particularly useful for research purposes, the novel community detection algorithm also dramatically speeds up the proteomic databases analysis process, as compared to some other sequential community detection approaches, and also to the sequential algorithm of newman and girvan. keywords: interactome networks, protein-protein interactions, protein communities, cancer, greedy algorithm. 1 introduction 1.1 basic considerations on protein networks and their importance interactome networks, or, more specifically, networks of proteins, determine a fundamental biological theoretical entity. theoretical and practical endeavours often use interactome networks-related formalisms in order to analyze the protein interactions that determine a biological network, which is essential for the proper organization and function of a biological organism. these networks exhibit a complex structure, which implies that any research activity in the field is handled with inherent theoretical and technical difficulties. nevertheless, the dynamics and the structure of these biological networks have to be accurately understood, as they play an important role on the function of a biological organism seen as a whole, regardless their degree of structural complexity. as a consequence, it is highly required to design and implement efficient proteomic data analysis techniques that can be integrated in any research framework that study the structure and properties of the interactome networks. copyright c⃝ 2006-2011 by ccc publications 34 r. bocu, s. tabirca the aim of this paper is to present a novel and faster algorithm that performs the detection of communities in the interactome networks, based on a computationally-effective greedy technique. the significant influence that proteins exercise on fundamental physiological processes has been demonstrated in a series of recent contributions. in this respect, this paper re-states our research’s previous developments, apart from the algorithmic optimization itself, that cancer affects the most important proteins in the interactome network and, as a consequence, the normal function of the organism is greatly disturbed. an accurate understanding of the structure and importance of proteins requires the usage of efficient analysis techniques. in this context, the proper detection of protein communities is of utmost importance, because it is one of the fewer methods that allows an in-depth and informative analysis of protein networks, through the isolation of functionally-related protein communities. the paper will briefly enumerate the most relevant existing works regarding the community detection. furthermore, the novel algorithm will be described and analyzed. also, its practical usability is assessed on real proteomic data. 1.2 essential previous work the newman-girvan algorithm is one of the methods used to detect communities in complex systems. a community is built up by a subset of nodes within which the node-node connections are dense, and the edges to nodes in other communities are less dense. it is important to note that there are a number of alternative algorithmic techniques for the detection of communities in networks. they include hierarchical clustering, partitioning graphs to maximize quality functions such as network modularity, k-clique percolation, and some other interesting algorithmic methods [1,2]. nevertheless, the newman and girvan conceptual system is often chosen as a pretext for scientific contributions due to its structural articulation and its ability to be used in a wide range of practical situations [3]. the newman-girvan algorithm is particularly used to compute betweenness for edges (biological links) that connect the nodes (proteins) in a network. the algorithm of blondel et al. [26] is able to find high modularity partitions of large networks in a timely manner and, thus, to unfold a complete hierarchical community structure for the analyzed network. contrary to some other community detection algorithms, the network size limits that this algorithm faces are mainly generated by the limited storage capacity on the processing machines, rather than by the computational complexity. the algorithm of blondel et al. represents a different greedy approach to community detection that can be successfully applied to protein networks. the algorithm assigns proteins to their respective communities following a particular-to-general approach. thus, proteins are progressively added to their correct clusters considering only the existence of inter-protein links. this way, at some point the modularity cannot increase any more and thus, the algorithm stops. this approach has the significant advantage of avoiding the usage of computationally expensive data structures, as it is the case with the algorithm of clauset et al. therefore, it is rather limited by storage (memory) requirements than by computational time. the algorithm of clauset et al. [25] has the merit to point out that the updates that are performed by the algorithm of newman and girvan involve a significant number of pointless operations, as a consequence of the sparse nature of the adjacency matrix. as a consequence, the algorithm is optimized in order to properly handle the sparsity of the input data sets. thus, the three data structures that the algorithm uses contribute to optimizing the analysis process of protein networks. in this respect, it can be stated that the modularity can be updated in a sensibly quicker way using these three data structures, as compared to the algorithm of newman and girvan. consequently, it can be inferred it is one of the few algorithms that can be used to determine community structure in the case of protein networks. the flag-based algorithm a novel greedy method that optimizes protein communities detection 35 figure 1: the flag values are updated one by one, starting with the leftmost representation. as a consequence of the complete nature of this network, all the nodes are flagged identically, thus constituting the only and network-wide community. the following sections will describe the improved algorithm in more detail, along with some theoretical considerations that are mandatory for a good understanding of the new approach’s structure and strengths. 2 general presentation of the new approach 2.1 description of the algorithm the algorithm is designed according to a local-to-general approach. the iterative process starts with all the nodes considered as members of their own one-node community. then, each node assigns itself to one of the neighbouring communities considering local information about its proximity. in order to accomplish this, we attached to each node in the network an additional data structure called flag, which holds information about the community to which a node currently belongs. the mechanism that coordinates the flag-based community algorithm is the following. let us suppose that a certain node node has the set of neighbours {node1, node2, node3, ..., nodek}, with k ≤ n, where n represents the number of nodes in the network. each of these neighbours has a flag attached to it, which offers information about the community to which it belongs at a given time during the iterative process. in these conditions, node assigns itself to the optimal community, considering the community information offered by the flags of its neighbours. the algorithm is designed in such a way that it chooses the neighbouring community to which most of node’s neighbours belong. at the beginning of the first iteration, each node is initialized with unique community information contained in its own flag. during the course of the algorithm’s iterations, accurate information describing the community structure spreads network-wide. thus, functionally or otherwise related nodes feature, in their flag field, similar community information, after a fairly reduced number of iterations. as many such consensus-based groups of nodes build, they continue to attract nodes from smaller neighbouring communities, while it is possible to do so. when the algorithm completes its last iteration, nodes that are characterized by the same flag information are grouped in the same community. the flag information updating process is illustrated in figure 1 on a 4-node complete network configuration. we say that a network configuration is complete if, for any pair of vertices (u, v), there is an edge that connects them. the algorithm conducts the flags updating process in an iterative manner. at every iteration, each node updates its flag considering the community information that is stored in the flags of its neighbours. formally, this idea can be expressed by the following function: flagcurrent_node(t) = update_flag(flagnode1(t − 1), flagnode2(t − 1), flagnode3(t − 1), ..., flagnodek(t − 1)) . here, flagcurrent_node(t) represents the flag of the currently analyzed node at time t, which is computed through the function update_flag, which takes as arguments the flag values of its neighbours, (node1, node2, node3, ..., nodek). following the node processing suggestion that is introduced by 36 r. bocu, s. tabirca the algorithm of blondel et al., which is presented in a previous section, the order according to which nodes are selected to have their flag updated, is established in a random manner. it is immediate to note that, while there are n flags at the beginning of the first iteration, the number reduces over the course of the algorithm, resulting in the end in as many unique flag values as there are communities. the algorithm should continue to run until no node changes the information in its flag anymore. however, there could be nodes in the network that are featured by an equal maximum number of neighbors in two or even more neighbouring communities. we addressed this potential shortcoming by instructing the algorithm to stop when each node in the network is described by a flag value that the maximum of its neighbours have. let us consider {flag1, flag2, ..., flags} to be the set of the flags that exist in the network at a given moment. moreover, let us note by number_neighbours(current_node, flagcertain_flag), the number of vicinities current_node has with nodes described by a certain_flag. in these conditions, it can be stated that the algorithm is stopped when, considering each current_node in the network, the following condition is true: number_neighbours(current_node, flagcertain_flag) is less or equal than number_neighbours(current_node, flagsome_flag), for all possible selections of certain_flag, and considering that current_node is characterized by the information stored in flagsome_flag. the algorithm configures the community structure in the network by grouping together nodes with similar flags. let us note that, obeying this stop criterion, the algorithm establishes a community structure that ensures each node is connected to, at least, as many other nodes from its own community than it is with each other extra-community node. we are able now to state the algorithm’s execution pertains to the following general steps: 1. the flag that is attached to each node in the network is initialized with the appropriate value, which respects the following rule: flagcurrent_node(0) ← current_node_identifier. in other words, each node’s flag initially stores the identifier of the vertex, for example the number that denotes the node’s rank. 2. following, the first effective processing iteration of the algorithm is initiated. it implies the arrangement of the nodes in a random manner, let us note the resulting vertex set with r. 3. then, for each current_node ∈ r, and respecting the order of the elements, we have that flagcurrent_node(t) = update_flag(flagnode1(t − 1), flagnode2(t − 1), flagnode3(t − 1), ..., flagnodek(t−1)). let us recall that the function update_flag updates the information stored by the flag of current_node, considering the flag that appears with the highest frequency among its neighbours. 4. the algorithm continues to iterate until each node stores in its flag the same information as the majority of its neighbours do. otherwise, t ← t + 1, and the next iteration is initiated from step 2. the main steps of the algorithm are presented, in a very brief manner, in algorithm 1. the actual c implementation of the algorithm is sensibly more complex than it apparently seems to be, considering the pseudo code in algorithm 1, as it includes some auxiliary decision the flag-based algorithm a novel greedy method that optimizes protein communities detection 37 algorithm 1 the flag based community detection algorithm general structure //perform some initial checks if checkdataset() = false then displayerrormessage("it is a protein network, it should be un-weighted!") return errorcode end if do create adjacency list and store edges do create storage space to store and count flags do create the node ordering vector nodeorder[] do arrange nodes in random order relative to nodeorder[] do considering the random order, process all nodes and assign flags accordingly for i = 0; i < numberofnodes; i + + do unprocessed[i] ← true noofflags[i] ← 0 end for for i = 0; i < numberofnodes; i + + do currentflag ← getflagofnode(i) numberneighbourscurrentnode ← getnumberneighboursofnode(current_flag) unprocessed[i] ← false for j = 0; j < numberneighbourscurrentnode; j + + do currentflagneighbour ← getflagofnode(j) if unprocessed[j] = true then do continue else noofflags[currentflagneighbour] ← noofflags[currentflagneighbour] + 1 if numbernodeswithflag(currentflag) < numbernodeswithflag(currentflagneighbour) then do setflagofnode(i, getflagofnode(j)) end if end if end for end for //at this point, nodes are properly flagged do revert nodes ranks in nodeorder[] back to their un-randomized state, preserving flag information 38 r. bocu, s. tabirca statements and processing blocks of instructions. nevertheless, the pseudo code summarizes the core of the process that performs the proper assignment of values to each node’s flag and, as a consequence, the detection of the community structure. 2.2 remarks regarding the complexity and correctness in spite of the double for loop structure that is also displayed in algorithm 1, in practice, the flag based algorithm exhibits a near linear time complexity. there are several reasons that explain this efficient behaviour. the initial allocation of flag values to each node, which is performed in the first part of the algorithm’s execution, takes o(n) time to complete. considering each current_node, its neighbours are initially grouped according to the information stored in their flags, which generates a worst-case time complexity of o(number_neighbours(current_node)). then, in order to conduct all subsequent iterations, the algorithm selects the flag-related group that has the maximum size, and consequently assigns the relevant flag to current_node. this selective flag filtering process is repeated for all the nodes in the network. therefore, an overall run time of o(m) is required to complete the main processing loop. the global time complexity results following a simple join operation, which takes into account the complexity required to perform the initial initializations, together with the time complexity that is generated by the main processing loop. as a consequence, it can be stated that the global time complexity of the algorithm is o(n + m), where n denotes the number of nodes (proteins) in the processed network, while m represents the number of edges (biological links). another factor that explains the efficient behaviour of the algorithm resides in the fact the algorithm is able to correctly fill in the flags, and therefore assigns the nodes to the appropriate community, after only a few iterations. according to the experimental results of the tests we performed, the algorithm manages to fill approximately 80% of the flags with appropriate data after only three or four iterations. furthermore, it can be stated that the community structure is fully determined after only six or seven iterations, in most cases. the next section, which offers a detailed account on all the algorithms’ performance, will fully present the performance assessment process. we have performed an assessment of the algorithm’s correctness following the same empirical method with three stages, which has also been used to prove the correctness of the algorithm designed by clauset et al. first, we determined the community structure of a 9000-node subset that was extracted from the aggregated protein data set. the output that the flag-based algorithm produced was compared to that generated by the parallel version of the newman-girvan algorithm. we found the two community partitions to be similar. furthermore, we performed a comparative assessment considering the flag-based algorithm, together with blondel’s algorithm. the analysis took into account the amazon.com purchasing network [22], and the aggregated protein data set. let us recall that the blondel’s algorithm correctness has also been evaluated against the parallel version of the newman-girvan algorithm. we found that the flag-based algorithm produces a community structure that is identical to that generated by both reference algorithms, the newman-girvan parallel version and the approach of blondel et al. therefore, the algorithm’s correctness is sufficiently demonstrated through this empirical method. nevertheless, it is worth noting that we have also conducted a more formal verification regarding the algorithm’s correctness, by going through the main steps suggested in algorithm 1, and considering five randomized 20-node sample networks. the random networks were generated with network workbench tool [23], and following the suggestions contained in [24]. the step by step execution of the algorithm on these networks revealed that it is able to correctly assign the flag values to the networks’ nodes in all situations. the flag-based algorithm a novel greedy method that optimizes protein communities detection 39 table 1: execution times comparative analysis test number algorithm execution time 1 flag-based 502 2 blondel et al. 749 3 comparative analysis regarding the algorithm’s performance on interactome networks the algorithm was implemented in c and run, as a sequential process, on a beowulf class cluster of computers. each node in the cluster provides 12 mb of cache memory for the sequential or parallel processes that run on it. additionally, each node in the parallel world is powered up by an intel xeon 5420 processor, clocked at 2.5 ghz. the c language was selected as the backbone of all our implementations, because it is the closest-to-machine medium level programming language and, as a consequence, the gain in performance is noticeable. the comparative performance testing procedure made use of an aggregated biological data set that features 22, 573 proteins and 1, 886, 753 biological links. the size of this input data set determines a problem space that can be hardly handled properly in terms of execution times through a sub-optimal sequential approach. the testing procedure conducted a comparative performance assessment that made use of the biological data provided by the aggregated protein data set. the flag-based community detection algorithm is assessed against the optimal community detection algorithm that has already been introduced, the construct of blondel et al. let us recall that the algorithms were implemented in c and run on the same beowulf class cluster of computers, which has been standard during the course of our research, as sequential processes. in table 1, durations are expressed in seconds. additionally, the speedup generated by the flag-based algorithm is suggestively displayed in figure 2. it can be noticed that, compared to the fastest community algorithm, which has been introduced in the previous chapter, the flagbased algorithm performs noticeably faster. furthermore, seven smaller protein datasets (1000, 2000, 2500, 3000, 7000, 8000, 9000 proteins) have been considered. in this respect, figure 3 contains a visualization of a comparative analysis concerning the algorithms’ performance. 4 remarks concerning the accuracy of the algorithms’ output community detection algorithms determine the community structure with various degrees of accuracy. in this respect, the main problem that may impede the output of such an algorithm is represented by the possibly inaccurate allocation of nodes (proteins) to their respective communities. we have consistently faced this problem during the current phase of our research. when speaking about protein data sets that require a proper community structure detection, the accuracy of the algorithm’s output is mandatory, as even the slightest community structure misconfiguration may lead to incorrect deductions and conclusions. we made use of a measure called modularity, which assesses the quality of the community structure determined by the algorithm. suppose there are k clusters in the current iteration of the algorithm. a symmetric matrix e of size k×k is constructed according to the following procedure. an element eij in e represents the fraction of all edges that link the vertices in cluster i to the vertices in cluster j and eii represents the fraction of edges that connect vertices within cluster i. thus, summation of row (or column) elements ci = ∑k i=1 eij represents the fraction of all edges that connect vertices to and 40 r. bocu, s. tabirca figure 2: community detection optimization in protein networks the descending trend of the execution time induced by the flag-based community detection algorithm, as compared to the algorithm of blondel et al. the algorithm numbers denote the following: 1 flag-based algorithm, 2 blondel et al. figure 3: the flag-based community detection algorithm comparative performance analysis on seven smaller protein datasets. within cluster i. in these conditions, modularity is defined as q = σki=1(eii−c 2 i ), which measures the fraction of the edges that connect vertices within the same cluster minus the expected value of the same quantity in the network [25]. for a random network with random decomposition, q approaches 0. values approaching q = 1, which is the maximum, indicate strong clustering structure. the higher is the value, the stronger is the clustering structure in the network. the inclusion of modularity as a community assessment measure significantly improved the accuracy of the community detection process output. in this context, it can be stated that, taking into consideration some further comparative tests we conducted, the accuracy of the community structure generated by the algorithm is comparable to that produced by the algorithm of newman and girvan, both in its sequential and parallel version. therefore, the tradeoff created by the utilization of this greedy-based optimization the flag-based algorithm a novel greedy method that optimizes protein communities detection 41 strategy is, in practice, nonexistent. thus, while the sequential algorithm of newman and girvan generates, considering the aggregated protein data set, a community structure that is characterized by a modularity of 0.837, the flag-based algorithm builds a community structure whose modularity is 0.835. additionally, the algorithms of clauset et al. and blondel et al. determine, each of them, a protein community structure whose accuracy is characterized by a modularity of 0.821 and 0.811, respectively. it is immediate to note that the flag-based algorithm is not only the fastest community detection algorithm that has been used, but also the most accurate greedy algorithm, according to the modularity of the community structure that has been determined. 5 the algorithm and its suitability for protein networks analysis the flag-based community detection algorithm features a simple and intuitive structure that works around all the overheads that are inherent, in connection with sub-optimal algorithms, when a complex operation like community detection is performed. the degree of complexity and the associated overhead dramatically increase when a large networked structure, like the interactome, is processed. therefore, suitable algorithms have to avoid the usage of less efficient data structures. additionally, a proper balance between output’s accuracy and computational time has to be sought and implemented accordingly. the flag-based community detection algorithm has been designed with all the above principles in mind. thus, the algorithm uses only simple array structures. moreover, the community discovery method proposed by this algorithm determines a straightforward detection of protein communities. thus, more than 50% of all the proteins in the aggregated data set are assigned to their respective communities after only a few iterations. this behaviour suggests that the algorithm is able to process any existing biological data set, regardless the degree of enrichment with new data in the foreseeable future. as a final remark in this section, it is important to note that this gain in performance is not accomplished in the detriment of the output’s accuracy. in this respect, it can be stated that the value of modularity demonstrates that the algorithm assigns proteins to correct communities with almost the same accuracy as the algorithm of newman and girvan, which is an exact construct that needs significantly more computational time. considering all the greedy community detection algorithms that have been analyzed, the flag-based algorithm generates the most accurate proteomic community structure. 6 analysis of cancer-related protein communities in order to extract the data that is relevant to cancer, we used the valuable data on protein families that is made available in the pfam database [16]. let us recall that the testing procedure made use of a compiled biological data set that features 22, 573 proteins and 1, 886, 753 biological links. this size of the input data set determines a problem space that can be hardly handled properly in terms of execution times through a sequential approach. we examined the protein communities our method determined and some interesting differences in the community sizes were noticed. cancer proteins belong to more highly populated communities compared to non-cancer proteins. the explanation may reside in the fact that cancer proteins take part in more complex cellular (carcinogenic) processes than those proteins that are of lower importance in the interactome network and, consequently, have less influence on the 42 r. bocu, s. tabirca carcinogenesis. it can also be asserted that larger protein communities feature a larger or more complicated cellular mechanism, in which cancer proteins play an important role. proteins identified as members of more than one protein community are of particular interest. in general, each protein community represents and determines a distinct cellular process. therefore, proteins that are part of multiple communities may generate multiple cellular processes, and can be considered to be at the intersection of distinct but adjacent cellular processes that are determined by particular protein communities, which are isolated by our community detection technique. the comparison between the cancer proteins population and the non-cancer proteins population reveals that cancer proteins reside at community junctions at a sensibly greater extent than their non-carcinogenic siblings. this particular feature of cancer proteins enforces their special importance in the interactome network seen as a whole and, as a consequence, their influence on all the physiological processes and related disorders. existing contributions distinguish between highly connected domains in peripheral cores (locally central) and highly connected domains in central cores (globally central). we noticed that globally central proteins represent an essential backbone of the proteome, exhibit at a high degree evolutionary conservation, and are essential for the organism. it is important to note that cancerous disease provokes mutations exactly to these globally central proteins. this observation supports and extends the findings of wachi et al. (2005), who showed that differentially expressed proteins in squamous cell carcinoma of the lung tend to be global hubs [18]. moreover, the findings reported in this paper support and extend the results generated by our research’s previous stage. practically, the above findings reveal the topological features of cancer proteins that are primarily displayed for cancer mutated proteins in exhibiting the highest betweenness centrality compared to the proteins that didn’t loose their normal function. in other words, the carcinogenic process is generated by clusters of proteins that feature a central position in the protein network. as a consequence, the high adverse impact of any cancer form is, in our opinion, determined by the way the disease affects the fundamental proteins that coordinate the most essential processes in the metabolic and physiological chains. the already gathered experimental information can be summed up into the following conclusions: • the novel protein communities detection algorithm was designed and implemented and was found to accurately determine the functionally-related communities of proteins. • we practically assessed the suitability and performance of the new approach on real proteomic data related to cancer and the interesting properties of the determined protein communities allowed us to infer an explanation regarding cancer evolution. • the algorithm performs faster than the algorithms of blondel et al. and clauset et al., which represent two of the most efficient community detection solutions that have been designed by now. 6.1 conclusions and future developments the most important property of cancer proteins is their importance at the scale of the whole interactome. the flag-based algorithm was used to show that the globally central proteins are the ones that are the most affected in a carcinogenic process and are also located at the junction of the most important protein communities. the resulting clustering algorithm allows us to explore protein-protein connectivity in a more informative way than is possible by just counting the interaction partners for each protein. it allows us to distinguish between central and peripheral hubs of highly connecting proteins, revealing proteins that form the backbone of the proteome. the fact that we observe an enrichment the flag-based algorithm a novel greedy method that optimizes protein communities detection 43 of cancer proteins in this group and also their highest betweenness centrality values indicate the central role of these proteins. the domain composition of cancer proteins indicates the explanation for this topological feature: we have shown, based on our experiments’ results, that cancer proteins contain a high ratio of highly malign domains. therefore, all cancer drugs should be designed in such a way to prevent possible mutations to these highly-important proteins or, if the disease is already on the way, to contribute to reverting back to the original proteomic structure. moreover, it is important to note that the scientific presentation in this paper demonstrates that cautiously-designed greedy algorithms can produce an output whose accuracy is on par with that of similar conventional (exact) approaches. the next stages of our research will involve further optimizations of the algorithms that are used for an efficient community structure detection in protein networks. also, we intend to analyze even more biological data sets related to cancer and, possibly, other high-impact contemporary diseases. acknowledgment this work is supported by the irish research council for science, engineering and technology, under the embark initiative program. bibliography [1] j. yoon, a. blumer and k. lee, an algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality: bioinformatics, 2006. [2] m. girvan and m.e.j. newman, community structure in social and biological networks: state university of new jersey, 2002. [3] d. ucar et al., improving functional modularity in protein-protein interactions graphs using hub-induced subgraphs: ohio state university, 2007. [4] k. lehmann and m. kaufmann, decentralized algorithms for evaluating centrality in complex networks: ieee, 2002. [5] j. griebsch et al., a fast algorithm for the iterative calculation of betweenness centrality: technical university of munchen, 2004. [6] g.h. traver et al., how complete are current yeast and human protein-interaction networks? : genome biology, 2006. [7] r. bunescu et al., consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome: genome biology, 2005. [8] u. brandes, a faster algorithm for betweenness centrality: university of konstanz, 2001. 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[17] r. bocu and s. tabirca, sparse networks-based speedup technique for proteins betweenness centrality computation: international journal of biological and life sciences, 2009. [18] s. wachi et al., interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues: bioinformatics, 21, 4205-4208, 2005. [19] g. palla et al., uncovering the overlapping community structure of complex networks in nature and society: nature, 435, 814-818, 2005. [20] p.f. jonsson et al., cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis: bmc bioinformatics, 7, 2, 2006. [21] r. bocu and s. tabirca, proteomic data analysis optimization using a parallel mpi c approach: ieee computer society, the first international conference on advances in bioinformatics and applications, 2010. [22] a. clauset, m.e.j. newman and ch. moore, finding community structure in very large networks: phys. rev. e 70, 066111, 2004. [23] s. schnell, s. fortunato and r. sourav, is the intrinsic disorder of proteins the cause of the scale-free architecture of protein-protein interaction networks? : proteomics 7 no. 6, 961-964, 2007. [24] v. batagelj and u. brandes, efficient generation of large random networks: physical review e. 71:036113-036118, 2005. [25] r. bocu, detecting community structure in networks: eur. phys. j. b 38, 321-330, 2004. [26] v.d. blondel, j.-l. guillaume, r. lambiotte and e. lefebvre, fast unfolding of communities in large networks: journal of statistical mechanics, arxiv:0803.0476v2 [physics.soc-ph], 2008. international journal of computers communications & control issn 1841-9836, 13(5), 865-880, october 2018. running cells with decision-making mechanism: intelligence decision p system for evacuation simulation y. niu, y. zhang, j. zhang, j. xiao yunyun niu, yongpeng zhang, jieqiong zhang school of information engineering china university of geosciences in beijing, beijing 100083, china yniu@cugb.edu.cn, zyp940701@163.com, jieqiong1755@163.com jianhua xiao* the research center of logistics nankai university, tianjin 300071, china jhxiao2008@163.com *corresponding author: jhxiao2008@163.com abstract: cell migration is a central process which happens along with multicellular organisms' development and maintenance. the process that cells move to speci�c locations in particular directions has some similarities with pedestrian walking behaviour. in this work, we propose a simulation model called an intelligence decision p system (idps), which is inspired by the process of cell migration. each cell has its own decision-making mechanism and moving mechanism. they move towards its goals on a two-dimensional space under the guidance of external signals and its own regulations. cells also communicate with each other according to speci�c interaction mechanism. the environment is de�ned as a place for cell movement. it includes signal objects, some of which help start or end the migration and others have great in�uence on the speed and directions of cells. it also keeps a record of current position for each cell. comparing with traditional p systems, cells can be considered as intelligent particles with decision-making mechanism and they can move to their destination. a case study is about modeling and simulating a building evacuation problem in a �re emergency by using the idps model. to our best knowledge, the topic of evacuation simulation was not under study in the �eld of membrane computing before. the simulation result shows that the idps allows much easier and more precise modelling of pedestrian evacuation problems. so it is supposed to be a good simulation model for pedestrian walking behaviour. keywords: membrane computing, p system, modeling and simulation, pedestrian evacuation. 1 introduction membrane computing is a branch of natural computing, which is inspired by the structure and the function of living cells or the organization of cells in tissues and organs [21]. the computational models in membrane computing are called p systems. most p systems were proved to be computationally universal [16]. and many np-hard problems or pspace problems were solved by p systems in a polynomial time or even a linear time [1,36]. moreover, p systems provided distributed parallel and non-deterministic frameworks for computing and modeling [33], and then applied to various aspects of engineering [7,20]. on one hand, optimization algorithms with membrane structure, such as membrane-inspired evolutionary algorithms (miea) [21] and multi-objective membrane algorithm guided by the skin membrane (smg-moma) [34], were proposed and outperformed general optimization algorithms. on the other hand, some kind of copyright ©2018 cc by-nc 866 y. niu, y. zhang, j. zhang, j. xiao p systems are used as modelling notation for ecological systems [2] and crowd behaviour [26]. readers can �nd detailed assessments of various p systems in [22]. in traditional p systems, cells were designed for computing rather than moving. membrane structure divided the space to several parts at the beginning. objects or membranes evolved following speci�c rules. the computational results usually de�ned according to objects present in the output membrane in the halting condition. recently, some p systems have involved the idea cell moving. gemmating p systems introduces a new means of communication between membranes while keeping the de�nition of p system closer to the true structure of the cell. through the movement of the membrane, the system sends a signal to the area designated by the target indication. at the same time, cells can selectively receive these signals [5]. petre et al. got inspiration from mobile ambients and came up with mobile p-systems. by wrapping the object with the membrane and moving it to a predetermined destination, any two �static� cells can establish direct and secure communication [23]. the population p system allows cells to receive an object from other cells connected to it, which has no restriction on the transmission of the object [4]. in addition, cell movement can also be applied to the construction of the environment. zhang et al. let the cell play the role of an obstacle and achieves dynamic environmental changes through cell movement [37]. however, most of p systems do not involve the concept of position except the spatial p systems [3] and grid-exploring p system [13]. in spatial p systems, objects in membranes are associated with positions. membranes delimit space to di�erent regions. evolution rules are associated with the positions of objects. spatial p systems were proved to be universal and could be used to model the evolution of populations in presence of geographical separations [3]. the grid-exploring p system uses generalized membranes to form the grid elements. the changes of the positions of membranes leaded to di�erent spatial structures of the whole system. the arrangement of the inner membrane of the grid is optimized by arti�cial evolution to shorten the total time required for all particles to pass through the full channel. it takes the informationcarrying particles as the main body of the movement [13]. in this study, we propose a simulation model called intelligent decision p system (idps), which is inspired by the cell migration process. cells in this model are intelligent and movable. they choose their way to move according to their own mechanism and the interaction with their surroundings. cell migration is a central process along with the multicellular organisms' development. tissue formation during wound healing and immune responses all require the accurate movement of cells in particular directions to speci�c locations. cells' movement is targeted. for example, when the white blood cells chase and attack the invading bacteria, they ignore the red blood cells and other harmless substances. if they meet the obstructions on their way, they can adjust their actions to bypass them. during the migration process, there are also some kinds of competition behaviours as well as cooperation behaviours between cells. information can be exchanged between di�erent cells. cells often migrate in response to speci�c external signals. when they receive the signals, they open their internal switches and start the migration process. some of the signal molecules may even a�ect the speed and direction of cell migration. the cells receive signaling molecule by using cell-surface receptor, which triggers a series of biochemical reactions and protein-protein interactions in the cell. after that, the signal molecules inside cells pass the information to the cytoskeleton and the molecular motor to complete the migration process. the process of cell migration is illustrated by figure 1. first, a tip extends out of the polar feet. second, the cell precursor and extracellular matrix form new cell adhesion. third, cell shrinks. finally, the tail is separated from the surrounding matrix, and the cell moves forward. the process includes cytoplasmic displacement at leading edge (front) and laminar removal of dorsally-accumulated debris toward trailing edge (back). the actions are similar to a pedestrian's walking motions. when walking, the forefoot steps on the ground �rst and the rear foot rises. running cells with decision-making mechanism: intelligence decision p system for evacuation simulation 867 adhesion position polar feet new adhesion position old adhesion position cell migration direction figure 1: cell migration by comparison, we �nd that cells' migration has inherent similarities with pedestrians' walking behaviour. for example, both cells and pedestrians can move to their destination and choose routes according to their own decision-making mechanisms. they can communicate with each other to update their knowledge during these processes. they can adjust their behaviours according to the environment around them. there are blocking, pushing or competition behaviour in both cell migration process and crowd evacuation process. so a simulation model which is inspired by the cell migration process is expected to be used to model the crowd evacuation behaviour. this is the main motivation of this study. evacuation planning has become increasingly important in recent years which has attracted the attention of more and more researchers [10]. the e�cient evacuation of pedestrians is very important especially in the presence of a disaster such as a �re. e�ective evacuation strategy is the key to improve evacuation e�ciency and reduce the number of casualties. the public safety manager can use it to simulate pedestrian evacuation in the corresponding place, and then notice the potential danger in some evacuation process or draw up a reasonable evacuation plan [17]. in order to ensure the safety of evacuees, it is urgent to develop procedures for the evacuation of evacuees in emergency situations. however, the study of evacuation dynamics is very complex [35]. on one hand, a large number of people who belong to di�erent categories involved in the situation. the interaction between them is nonlinear and complex. the psychological factors also have great in�uence on their behaviours especially in a emergent situation. on the other hand, di�erent evacuation scenarios usually need di�erent strategies, and disasters often change the spatial accessibility of a scene. it is very di�cult for current modeling technologies to reproduce realistic situations completely. the basic task of indoor evacuation research is to simulate the building environment [25] and evacuees' behaviours [29]. some researchers have conducted empirical studies of evacuation behavior, but there are wide variations in the results of various studies [27]. the reason for this may be due to cultural and population di�erences [11] or di�erent motives for movement [24]. there are several modelling techniques for evacuation problems in the literature. both continuum models and network-based models are macroscopic models. both time and space are continuous. however, it is not very good at simulating the details of individual behavior [15]. the network-based models solve the problem of simulation of discrete events [8]. there are three famous microscopic models, that is, cellular automata models, agent-based models and socialforce models. cellular automata models represent the surroundings by a grid of cells [6]. each cell can be occupied by one pedestrian or several pedestrians. time is discrete, and at each step the movement of the individual depends on the state of the adjacent individuals and the prede�ned rules [31]. agent-based models use di�erent agents to model individual pedestrians and form the macroscopic behaviour based on the interactions between agents [18]. each agent has unique rules, so these models can model heterogenetic crowd [28]. but these models require a high cost of computing. in the social-force models, the movement of the individual is in�uenced by the forces from di�erent directions, such as the attraction of the direction of the target and the repulsion from other individuals [12]. game-theoretic models allow evacuees to predict the behavior of other evacuees, based on the microeconomic concept of maximizing subjective util868 y. niu, y. zhang, j. zhang, j. xiao ity [14,19]. it is very di�cult for current modeling technologies to describe both the environment and the pedestrians as realistically as possible. because of the similarity between cell migration and pedestrian walking behaviours, in this work we propose a simulation model called an intelligence decision p system (idps), which is inspired by the cell migration process to model the crowd evacuation behaviour. the main contributions of this paper can be summarized as follows. (1) intelligence decision p system is de�ned including decision-making mechanism, moving mechanism, interaction mechanism. it also involves an accurate description of environment around cells. each cell moves towards their goals on a two-dimensional space under the guidance of external signals and its own regulations. in each step, cells decide what is the next action to be performed through a deliberation decision-making process until the termination signal occurs. several characteristics, such as non-deterministic maximally parallel manner, priority rules and communication rules are also inherited by our model from traditional p systems. unlike traditional p systems, cells in the idps are intelligent and movable. they communicate with each other during the moving process. so the idps combines individual intelligence and swarm intelligence, where cells can be considered as intelligent particles. (2) to our best knowledge, the topic of evacuation simulation was not under study in the �eld of membrane computing before. in this study, the idps is used to model the building evacuation process in the presence of a �re disaster as a new modelling technique. evacuees with di�erent knowledge bases are described by cells with decision-making mechanism. they percept environment and interact with each other to update their knowledge bases during the evacuation process. each pedestrian is modelled individually. our model can describe the movement or the interactions of pedestrians as realistically as possible. the result shows that the idps model allows much easier and more precise modelling of building evacuation problems. 2 intelligence decision p system formally, an intelligence decision p system of degree n ≥ 1 is de�ned as follows π = (γ,e(0),c1, . . . ,cn,r,g,ss,st), where: � n ≥ 1 (the system contains n cells, labeled with 1, 2, · · · ,n; all these n cells are placed in the environment and the environment is labeled with 0); � γ is the alphabet of objects; � the environment e is de�ned as a set of objects to describe the scene of cell movement. it includes signal objects, some of which help start or end the migration and others have great in�uence on the speed and directions of cells. it also includes a system clock to keep a record of current time of the system and a counter to calculate the number of cells going out of the exits. the width of environment is denoted by w, and the height of environment is denoted by h. e(0) is the set of objects in the environment at the beginning of the simulation; � c (j) i = {p (j) i ,v (j) i ,k (j) i ,m (j) i } represents the state of cell i at step j, where p (j) i = (x (j) i ,y (j) i ) records the real-time location of cell i with 0 < x (j) i < w, 0 < y (j) i < h, v (j) i is the speed of the cell i at step j, k (j) i = {k1, . . . ,kn} describes the knowledge base of cell i at step j, and m (j) i means the type of cell. c (0) i denotes the initial state of cell i. running cells with decision-making mechanism: intelligence decision p system for evacuation simulation 869 � r is the set of evolution rules which includes the following types of rules: 1. knowledge base update rules: cells make decision based on their own knowledge bases. the initial knowledge bases are set according to cell types mi. and then they are updated in two di�erent ways. one is obtaining information from the environment. the other is exchanging information with other cells. (1) interaction with environment: cells obtain information from the environment, such as information about real-time tra�c, route guidance signals, obstacles, starting signal and termination signal. each information has a perception region. if a cell is in this region, it can get the information as shown in the following rules. k (j) i → k ′(j+1) i ,∀i ∈{1, . . . ,n}, k ′(j+1) i = { k (j) i ∪{a}, pi ∈ ra k (j) i , pi /∈ ra where ra indicates the perception region of information a. the knowledge base of cell i is updated by adding information a, if cell i reaches to ra. (2) communication with other cells: cells can share information with their neighbors. we suppose the distance of two neighbors is not no more than a threshold d. if lbest ∈ k ′(j+1) i ∪k ′(j+1) k , |pi−pk| ≤ d is the information which leads to the best running plan of cell i or cell j, and lbest is not in k ′(j+1) i , k ′(j+1) i is updated by adding information lbest. k ′(j+1) i → k (j+1) i ,∀i ∈{1, . . . ,n}, k (j+1) i = { k ′(j+1) i ∪{lbest}, lbest /∈ k ′(j+1) i k ′(j+1) i , lbest ∈ k ′(j+1) i 2. type transition rules: cell type is de�ned according to the knowledge base in the cell. the knowledge base change may lead to the cell type transition. k (j+1) i m (j) i → k (j+1) i m (j+1) i . 3. decision-making rules: according to the knowledge base and the current position, cell i can obtain several running schemes at step j. k (j) i p (j) i →{scheme (j) i,1 , . . . ,scheme (j) i,ik }. choose the best scheme scheme (j) i,best according to speci�c requirement, for example, minimizing distance from current position to the exit. {scheme(j)i,1 , . . . ,scheme (j) i,ik }→ scheme(j)i,best. it is worth mentioning that priorities can be easily added to these rules for decisionmaking. 870 y. niu, y. zhang, j. zhang, j. xiao 4. position-updating rules: the expected velocity and moving direction can be calculated from scheme (j) i,best, scheme (j) i,best → (v ′(j) i ,d ′(j) i ). in the process of moving, the actual situation may not allow cells to follow the direction of planning, then the cells need to adjust their behaviour. for example, when a cell encounters an obstacle (a wall or other cells in front of the cell), it needs to bypass the obstacle to move on. the actual velocity and direction of cell i at step j are denoted by v (j) i and d (j) i , respectively. (v ′(j) i ,d ′(j) i ){o1, . . . ,oq}→ (v (j) i ,d (j) i ), where o1, . . . ,oq represent obstacles. cell i updates its position according to the current position and speed. (x (j) i ,y (j) i )(v (j) i ,d (j) i ) → (x (j+1) i ,y (j+1) i ), where (x (j) i ,y (j) i ) means the position of cell i at step j, (v (j) i ,d (j) i ) means the speed and the moving direction of cell i at step j. � g is the set of destinations or exits of the moving cells. � ss is the starting signal for cell movement, while st is the termination signal. the rules of a system as above are used in the non-deterministic maximally parallel manner. when the starting signal ss appears in the environment, cells move towards their goals under the guidance of rules in r. in each step, cells decide what is the next action to be performed. they usually stop moving when they reach their destinations or they get the termination signal st from the environment. a con�guration of idps π is described by the multisets of objects in the cells and the environment. c (j) 1 , . . . ,c (j) n represent the states of all the cells present in the system at step j. they involve four basic characters of cells. that is, position, speed, knowledge base and type. the next system's con�guration is determined by rules in r from the previous one. all computations start from the initial con�guration and proceed. cells stop moving when they reach their destinations or when they get the termination signal st from the environment. the system stops evolving when all cells stop moving. the simulation results can be obtained by counting the number of cells going out of the exits. 3 intelligence decision p system for evacuation simulation this section describes how intelligence decision p system model the evacuation process of evacuees with di�erent knowledge bases in a �re emergency. a teaching building of china university of geosciences (beijing) was used as a sampling evacuation environment in this study, see figure 2. the classrooms, the walls, the doors, the corridor and the exits are represented by yellow, gray, blue, light blue and green, respectively. there are three exits, from left to right, exit 1, exit 2 and exit 3. the black area represents the safe area outside the building. for the sake of simpli�cation, cells in our model have the same size and the same moving speed. the de�ection angle of cell's moving direction is denoted by θ, θ ∈ [0, 360].the viewing angle is set by default to 120◦ [9]. evacuees can only see objects within their visual �elds, and evacuees cannot see the situation in other side of the wall (see figure 3). running cells with decision-making mechanism: intelligence decision p system for evacuation simulation 871 figure 2: the experimental scene wall exit figure 3: the visual �eld of a cell 3.1 knowledge base update there are eight categories of cells in our model. they are divided according to their familiarity of the exits. the �rst kind of cells have the knowledge about how to reach all the exits; the second, the third and the fourth kinds of cells have knowledge about two di�erent exits,respectively; the �fth, the sixth and the seventh kinds of cells know one exit only and the eighth kind of cells have no idea about any passable exits. evacuees knowing all the exits have the whole knowledge of the building, we call them "experts". others have partial information about the building, we call them "followers". in the initial state, every cell knows at least one exit. cells get information to update their knowledge base in three ways. (1) the followers tend to be attracted by the experts. followers identify experts based on the object mi in cell i. if the followers see the experts, they will move to the experts and then they get useful information, and update the knowledge base. (2) the followers have a certain probability to interact with each other. they share their own current optimal route information, and update their knowledge base. (3) the information about the nearest exit can be obtained from the guide signs. if cells in it's perception region and �nd these signs, they update their knowledge base immediately. the knowledge base can be updated by using the following rules. k′i → ki,∀i ∈{1, . . . ,n}, ki = { k′i ∪{lbest}, lbest /∈ k ′ i k′i, lbest ∈ k ′ i where lbest is the best information held by other evacuees who interact with the evacuee i. if the evacuee i interacts with other neighbors( the distance of two evacuees is less than 2) and acquires new information lbest, he updates the knowledge base k ′ i to k ′ i ∪{lbest}, otherwise the original knowledge base k′i is maintained. k′i = { ki ∪{g}, pi ∈ rg ki, pi /∈ rg 872 y. niu, y. zhang, j. zhang, j. xiao where rg indicates the perception region of the information g sent by guide signs. the knowledge base of evacuee i is updated by adding information g, if evacuee i reaches to rg. when cells get new information and update the knowledge bases, their categories may be changed at the same time. during the evacuation process, when a �re occurs, the exit may be blocked by �re. in that case, some evacuees may have no useful information to �nd any exit. they belong to the eighth category, and have to walk randomly until their knowledge bases are updated by interaction with other evacuees or the environment. when they get information about another exit, they will change their category once again. similarly, cells can become "experts" when they obtain information about all exits during the evacuation process. p0 represents evacuees who have no idea about any passable exits. p1, p2, and p3 represent evacuees who know one exit, two exits and three exits, respectively. mi =   p0, ki = ∅ p1, ki = {a} or {b} or {c} p2, ki = {a,b} or {a,c} or {b,c} p3, ki = {a,b,c} where a, b, and c respectively represent the information of the three exits. 3.2 behaviour adjustment according to the knowledge base, evacuees tend to choose the path with the shortest distance. scheme (j) i,best → (v ′(j) i ,d ′(j) i ) calculates the expected speed and moving direction. however, they cannot move as expected in many cases. to avoid collisions, cells attempt to keep a distance from the surrounding obstacles while moving. these obstacles include walls and other evacuees. so cells have to adjust their behaviour to avoid collisions. if the best way to the exit is blocked, the evacuee will choose another way to move. the feasible speed and moving direction can be calculated by (v ′(j) i ,d ′(j) i ){o1, . . . ,oq}→ (v (j) i ,d (j) i ), where o1, . . . ,oq represent obstacles. in the narrow corridor, the �ows of cells of two opposite directions are often encountered. to avoid the confusion caused by cross �ow of cells, cells attempt to communicate with other cells who move in the opposite direction. they exchange information with each other and update their knowledge base. according to the new knowledge base, they re-plan evacuation routes. some of them change their direction, and then communicate with other evacuees who are moving in the opposite direction. this process is repeated until all evacuees move in the same direction. this kind of behaviour adjustment is shown in figure 4. moving direction moving direction moving direction figure 4: interactive behaviour in cross �ow running cells with decision-making mechanism: intelligence decision p system for evacuation simulation 873 3.3 idps-based simulation of evacuation all cells have initial knowledge base k (0) i at the beginning. when the ss (initial signal) appears, cells start moving. in detail, the evacuation process can be simulated according to the following steps. step 1: the system checks whether there is a termination signal st. if there is a termination signal st, the simulation is �nished. otherwise, go to step 2. step 2: if the cell �nds the exit in its view �eld, go to step 7. otherwise, the simulation continues. step 3: if the cell interacts with the expert before, go to step 6. otherwise, the simulation continues. step 4: if the cell �nds no expert in its view �eld, go to step 6. otherwise, the simulation continues. step 5: the cell moves to the expert and gets information, go to step 8. step 6: if the cell �nds the guide sign in its view �eld, it gets the information provided by the guide sign, go to step 8. otherwise, the simulation continues. step 7: the cell has a certain probability to interact with its neighbours and share information with them. step 8: the cell updates the knowledge base and translates category. step 9: the cell makes path planning according to the decision-making rules. step 10: the cell adjusts its behaviour and moves with the actual velocity. step 11: if the cells reach the exit, the evacuation is successful. otherwise, the simulation continues and go to step 2. in general, the probability of interaction between cells is set to 20%. but when a cell cannot plan the route because there is no information available, its interaction with its neighbours will rise to 100% until it can plan an available evacuation route based on the information obtained. on the other hand, if a cell has interacted with one expert, he will not go looking for other experts because he has got enough information. 4 experiments and results analysis the simulation system is implemented using netlogo. two scenarios are considered in this experiment. in scenario 1, �re occurred in the classroom and did not block any exit. after the evacuation of the sta� in the classroom where the �re broke out, the classroom can be closed to ensure that the �re will not spread for a certain period of time. in scenario 2, the �re happened at the exit 1 and made exit 1 impassable. in a short period of time, the �re will block the exit 1, and evacuees need to consider what strategy should be taken if the planned evacuation route is blocked in the evacuation process. the total number of evacuees in experiment is set to 500. the proportion of experts can be adjusted. we will discuss it in section 4.1. there are six types of followers at the beginning. their proportion is given in table 1. and the initial position of evacuees is distributed randomly. table 1: initial proportion of the number of di�erent types of followers type of evacuees 1 2 3 4 5 6 exit 1 √ √ √ exit 2 √ √ √ exit 3 √ √ √ proportion 10% 10% 10% 20% 30% 20% 874 y. niu, y. zhang, j. zhang, j. xiao the results of experiment are evaluated by average evacuation time and the number of casualties. the average evacuation time is used to measure evacuation e�ciency. it is calculated by the following equation [30] avg.e(t) = (σnei=1tei) + tsnf ntotal (1) where ntotal is the total number of evacuees in the building, tei is the time to reach the exit for evacuees i, ts is the total simulation time, ne is the number of successful evacuations, and nf is the total number of evacuees who did not escape in the simulation. after some investigation, we found that the concentration of smoke generally reached the maximum value that evacuees can bear within 180s in the teaching building. so we set the simulation time to 180s. when the smoke concentration reaches this value, the evacuees in the building will get injured and not move any more. at the same time, the system sends out a termination signal to end the simulation process. some of the properties of the experiment are simulated by random control, so individual simulation results with the same environment and parameter speci�cations may be di�erent. the properties of experiment that are controlled in this way include the initial position of evacuees and evacuees's interactive behavior. because of this potential di�erence between simulations, we perform multiple simulations for each scenario and note the average result. for each experiment, the experimental result takes the average of the results of the ten experiments. 4.1 the e�ect of experts experiments were conducted to test experts' impact on evacuation e�ciency in scenario 1 and scenario 2 (figure 5 and figure 6, respectively). figure 5 illustrates the evacuation e�ciency in scenario 1 with 30 experts and without experts. when there was no experts to guide the crowd, it was not until 174s that the evacuation was completed. this value is very close to the terminal time (180s). under the guidance of 30 experts, all evacuees completed the evacuation within 140s. although no one was injured in the two experiments, the e�ciency of the evacuation with 30 experts was much higher than the evacuation without experts. figure 6 illustrates the evacuation e�ciency in scenario 2 with 30 experts and without experts. since exit 1 was blocked, more than 40 evacuees failed to evacuate without experts. when there are 30 experts involved, all evacuees can evacuate within 180s. although evacuees try to move to the nearest exit they know, sometimes that is not the best choice for them. without accurate information about the building, evacuees spend a lot of time on redundant paths. when experts appears in the crowd, this situation can be improved greatly. because they can provide information about the best exit to the followers. figure 5: the e�ect of experts in scenario 1 figure 6: the e�ect of experts in scenario 2 running cells with decision-making mechanism: intelligence decision p system for evacuation simulation 875 the impact of experts' quantity on evacuation e�ciency was also tested. the number of experts were set to 0, 20, 50 and 100 respectively, see table 2. in scenario 1, the average evacuation time decreased with the increasing number of experts. but if the number of experts exceeded a threshold, its e�ect on the evacuation process was not obvious. in scenario 2, the average evacuation time was increased comparing with that in scenario 1 and 42 evacuees got injured without experts. when experts appear in the crowd, the average evacuation time is shortened obviously and the number of injured evacuees reduced greatly. as shown in table 2, the experts' quantity plays an important role in the evacuation process. the more experts, the fewer injured evacuees. therefore, we should pay attention to popularize the knowledge of building structure to evacuees at ordinary times. table 2: the e�ect of experts experiments scenario 1 scenario 2 exp#1 exp#2 exp#3 exp#4 exp#1 exp#2 exp#3 exp#4 number of evacuees 500 500 500 500 500 500 500 500 number of experts 0 20 50 100 0 20 50 100 avg.e(t) 85.3 75.2 50.7 45.2 102.5 86.4 72.7 67.1 avg.hurt 0 0 0 0 42 5 0 0 4.2 the e�ect of guide signs in this subsection, we analyze the e�ect of guide signs. five experiments were conducted with di�erent numbers of guide signs. figure 7 shows the location distribution of guide signs in the building, where the orange areas represents the guide signs. guide sign 1 points to exit 1. both guide sign 2 and guide sign 3 point to exit 2. both guide sign 4 and guide sign 5 point to exit 3. there was no guide sign in exp# 1; the number of guide signs was increased sequentially from exp# 2 to exp# 6. figure 7: distribution of guide signs experiments were conducted to test the guide signs' impact on evacuation e�ciency in scenario 1 and scenario 2 (figure 8 and figure 9, respectively). figure 8 shows that the evacuation e�ciency increases with the increase of the number of guide signs in scenario 1. however, the number of evacuees near exit 1 will increase dramatically, and the probability of overcrowding and injury rise. in scenario 2, exit 1 is congested, the evacuation e�ciency is decreased. attention should be paid to evacuation guidance in this area. moreover, the invalid road signs will give evacuees the wrong guide. in exp#2, because of the wrong guidance of guide sign 1, many evacuees took the ine�ective path. therefore, in the early stage of evacuation, the evacuation e�ciency in exp#2 is 876 y. niu, y. zhang, j. zhang, j. xiao lower than that in exp#1, see figure 9. but after 80s, the evacuation e�ciency in exp#2 began to increase gradually, and �nally exceeded the evacuation e�ciency in exp#1. the reason for this phenomenon may be that guide sign 1 makes a large number of evacuees gathered near the exit 1, and the probability of successful interaction between them is increasing, so the evacuees can quickly �nd the location of the nearest exit. another phenomenon that we can observe from figure 9 is that the emergence of guide sign 2 and guide sign 3 makes the evacuation e�ciency improved signi�cantly. so guide sign 2 and guide sign 3 are more important than other signs. the average evacuation time and the average number of casualties are reported in table 3. it is shown that guide signs play an important role in shortening evacuation time and reducing the number of injured evacuees in most cases. but in scenario 2, when exit 1 is blocked, there are still a lot of evacuees moving towards exit 1 because of the guide sign 1. in that case, both the average evacuation time and the average number of casualties in exp#2 are more than those in exp#1. therefore, if an exit is blocked by �re, the corresponding guide signs may have a bad e�ect on the evacuation process. figure 8: the e�ect of guide signs in scenario 1 figure 9: the e�ect of guide signs in scenario 2 table 3: the e�ect of guide signs experiments scenario 1 exp#1 exp#2 exp#3 exp#4 exp#5 exp#6 avg.e(t) 85.7 82.3 71.5 66.7 60.2 57.2 avg.hurt 0 0 0 0 0 0 experiments scenario 2 exp#1 exp#2 exp#3 exp#4 exp#5 exp#6 avg.e(t) 104.8 112.5 97.3 89.5 81.2 78.2 avg.hurt 56 77 11 0 0 0 4.3 the e�ect of composition proportion of the crowd in this section, three experiments with di�erent proportions of evacuees were conducted. the parameters of three di�erent crowds are listed in table 4. table 5 shows the comparison of the experimental results under the in�uence of three di�erent proportions of evacuees. when the number of evacuees who knew exit 1 and exit 2 was more, the average evacuation time is relatively shorter, but there are injured evacuees. when the number of evacuees who knew exit 3 was higher, although the average evacuation time was longer, there were no injured evacuees. the reason for this phenomenon may be due to a large number of evacuees gathered in the vicinity of exit 1, resulting in a long time congestion phenomenon. this congestion hampered running cells with decision-making mechanism: intelligence decision p system for evacuation simulation 877 the movement of many evacuees who wanted to go to other exits, and some evacuees failed to evacuate in time. therefore, in an emergency, more attention should be paid to handle the crowd e�ciently and avoid congestion. table 4: the proportion of di�erent types of evacuees type of evacuees 1 2 3 4 5 6 exp# 1 20% 10% 20% 30% 10% 10% exp# 2 20% 20% 10% 10% 30% 10% exp# 3 10% 20% 20% 10% 10% 30% table 5: results of experiments with di�erent proportions of evacuees experiments scenario 1 scenario 2 avg.e(t) avg.hurt avg.e(t) avg.hurt exp#1 83.7 5 123.5 63 exp#2 76.3 2 82.6 32 exp#3 90.4 0 97.2 41 4.4 the e�ect of interaction probability in this section, experiments were designed to analyze the e�ect of interactive probability. the probability of interaction between followers are set to 0%, 20%, 40%, 60%, 80% and 100%, respectively. experts and guide signs were not involved in these experiments at the beginning. however, when an evacuee got information about all exits, he can convert his role from a follower to an expert. figure 10 and figure 11 show the experimental results in scenario 1 and scenario 2, respectively. as the increase of the probability of interaction, the evacuation time decreases rapidly in the early stage. but as the probability of interaction becomes higher and higher, this reduction trend slows down, and then there is even a rising trend. this phenomenon occurs because the interaction between the evacuees takes a certain amount of time, and frequent interactions may get a lot of repetitive information. this factor has an adverse e�ect on evacuation e�ciency. by the experiments, we can see that 60% is the best interaction probability in both scenario 1 and scenario 2. figure 10: the e�ect of interaction probability in scenario 1 figure 11: the e�ect of interaction probability in scenario 2 878 y. niu, y. zhang, j. zhang, j. xiao 5 conclusions in view of the similarity between cell migration process and the pedestrian walking behaviour, an intelligence decision p system (idps), which was inspired by the process of cell migration, was proposed in this paper to simulate the building evacuation. it involves decision-making mechanism, moving mechanism, interaction mechanism of cells, as well as accurate descriptions of external information around them. it can use both continuous and discrete time and space representations. in two-dimensional space, each cell moves towards their goals under the guidance of external signals and its own regulations. each cell has its own decision-making mechanism and moving mechanism. they can also communicate with each other or interact with external signals in their surroundings during the moving process. a case study was modeling and simulating the evacuation of pedestrians in buildings by using the idps. when a �re occurs, evacuees received signals and started to move toward the exits. they made decisions which were in their best interests. we analyzed some factors that a�ect evacuation e�ciency, including experts, guide signs, the proportions of di�erent types of evacuees, and the interaction probabilities. the results of experiment are evaluated by average evacuation time and the number of casualties. the results showed that the idps model allowed much easier and more precise modelling of pedestrian evacuation problems. this study combined the p system approach with new application scenarios. the crowd evacuation in buildings under emergency situations was modeled and simulated based on a novel p system. so far as we know, the topic was not under study in the �eld of membrane computing before. some characters of p systems, such as non-deterministic maximally parallel manner, priority rules and communication rules, were used to help simulate the building environment and evacuees' behaviours. the idps model can easily be combined with probabilistic approaches or other technologies to simulate more complex behaviours. our model can be applied to other types of crowd management problems, such as high-rise building evacuation simulation, with appropriate modi�cations. funding this work was supported by the beijing natural science foundation [grant numbers 4164096]; the national natural science foundation of china [grant numbers 61502012, 61373066, 61772290]; the humanity and social science youth foundation of ministry of education of china [grant number 13yjc630010]; the science and technology development strategy research program of tianjin [grant number 16zlzxzf00030]; the asia research center 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(2017); real-life applications with membrane computing, springer international publishing, 2017. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(4), 503-517, august 2019. energy optimization for wsn in ubiquitous power internet of things w. hu, h.h. li, w.h. yao, y.w. hu wei hu, huanhao li*, wenhui yao, yawei hu school of economics and management, shanghai university of electric power, shanghai 200090, china *corresponding author: 2625904776@qq.com abstract: this paper attempts to solve the problems of uneven energy consumption and premature death of nodes in the traditional routing algorithm of rechargeable wireless sensor network in the ubiquitous power internet of things. under the application environment of the upiot, a multipath routing algorithm and an opportunistic routing algorithm were put forward to optimize the network energy and ensure the success of information transmission. inspired by the electromagnetic propagation theory, the author constructed a charging model for a single node in the wireless sensor network (wsn). on this basis, the network energy optimization problem was transformed into the network lifecycle problem, considering the energy consumption of wireless sensor nodes. meanwhile, the traffic of each link was computed through linear programming to guide the distribution of data traffic in the network. finally, an energy optimization algorithm was proposed based on opportunistic routing, in a more realistic low power mode. the experimental results show that the two proposed algorithms achieved better energy efficiency, network lifecycle and network reliability than the shortest path routing (spr) and the expected duty-cycled wakeups minimal routing (edc). the research findings provide a reference for the data transmission of upiot nodes. keywords: ubiquitous power internet of things (upiot), energy-balanced routing, rechargeable wireless rechargeable network (wsn), routing algorithm, low power mode. 1 introduction during the "two sessions" (the annual sessions of china’s top legislature and top political advisory body) in 2019, the state grid corporation of china (sgcc) promised to step up the construction of strong smart grid and ubiquitous power internet of things (upiot) and act as a hub, platform and sharer of energy information, turning itself into a world-class energy internet enterprise with global competitiveness. before long, the sgcc confirmed that its most urgent and critical task is to speed up the upiot construction, which requires overall planning and deployment. as its name suggests, the upiot refers to the application of the iot technique in power systems. it is essentially a wireless sensor network (wsn) [15]. the wsn is a wireless network self-organized by numerous smart microsensor nodes, which collaboratively send the monitored data to the sink node for further processing. in the wsn, the safety, efficiency and speed of data transmission to the control center directly hinges on route optimization [9,20]. many sensors are deployed in the upiot. under harsh natural conditions, the nodes in the rechargeable wireless sensor network (wsn) will enter the low power mode after running out of energy, if they can only capture a limited amount of energy. in this case, the operation time of network nodes depends on the energy-balancing effect of the routing strategy. therefore, it is particularly important to manage the energy efficiency of sensors. the common routing protocols fall into two categories: planar routing and clustering routing. the planar routing protocols include flooding protocol, spin (sensor protocols for information via negotiation) protocol, sar (synthetized adaptive routing) protocol, and directed copyright ©2019 cc by-nc 504 w. hu, h.h. li, w.h. yao, y.w. hu diffusion mechanism [4]. in general, planar routing protocols are not applicable to the upiot, due to their fast energy consumption, slow response and high complexity. the typical clustering routing protocols are the leach (low energy adaptative clustering hierarchy) algorithm, the teen (threshold sensitive energy efficient sensor network protocol) algorithm, and the pegasis (power efficient gathering in sensor information systems) algorithm [3,16]. the latter two algorithms were extended from the leach. compared with planar routing protocols, the clustering routing protocols feature low energy consumption, fast response and simple implementation. unsurprisingly, this type of routing protocols has long been the research focus at home and abroad. below is a brief review of the studies on clustering routing protocols. in [12] lee at al. proposes an energy-optimization clustering algorithm based on multiple factors, which introduces the fuzzy rule algorithm to cluster head selection, thus extending the network lifecycle. reference [13] designs an energy-balanced non-uniform clustering routing algorithm, in which the cluster heads are selected by timer-based election. reference [5] puts forward an energy-balanced routing algorithm based on message importance; the algorithm increases the success rate and reduces the delay of message delivery, as the route is determined by the forwarding income of messages. reference [1] improves the low-energy adaptive clustering and stratification algorithm into an energy-balanced clustering routing protocol capable of adaptively adjust the cluster scale. xia et al. in [19] proposed an energy equalization method based on hybrid transmission to avoid energy holes in wireless sensor networks. hybrid transmission refers to the wireless transceivers of nodes in the network having different transmission powers. in a data collection sensor network, a wireless sensor near a sink node will deplete energy more quickly by receiving more data from a remote sensing node, and proposes to increase the transmission power of the distant node while probabilistically select the receiving node of the transmission to reduce the communication load of the node closer to the sink, delay the appearance of energy holes, and prolong the network life. the protocol ensure the balance of network energy by creating clusters of different scales based on the distance to sink node, residual energy and distribution density of network nodes, such that no low-energy node will be selected as cluster head. to sum up, the existing energy-balanced clustering routing protocols face the following problems: the distributed algorithm in the protocols cannot converge to the global optimal solution; the energy consumption of information reception is ignored when analyzing the energy consumed in data transmission between network nodes; the routing algorithm searches for the optimal path, overlooking the possibility of multipath routing. in light of the above, this paper proposes a multi-path routing algorithm based on link traffic distribution under the upiot. specifically, the energy-balanced routing was achieved based on the predictable energy supplement, and the opportunistic routing was adopted to tackle the timevariation and loss of wsn channels. these measures, coupled with the natural load-balancing feature of the wsn, ensure the balance of the energy consumption among wsn nodes. the experimental results show that the proposed method can optimize the energy of rechargeable wsn, and prolong the lifecycle of the entire wsn. 2 upiot overview the upiot is a smart service system that connects all things and all links in the power system, and supports effective human-machine interaction. this system can be implemented conveniently and flexibly to sense all kinds of states and process information in an efficient manner. the functions of the system are realized through modern information technologies like the mobile internet and artificial intelligence, and advanced communication technologies. in fact, the upiot is an application of 5g (the 5th generation mobile networks) and the iot in the energy optimization for wsn in ubiquitous power internet of things 505 power industry, linking up the human, machine and devices inside and outside the grid. as an extension of the energy internet, the system can serve users, grids, power generation companies, governments and the entire society. 2.1 construction principle unify standards and encourage innovation adhere to the unified data management, system construction must strictly follow the company’s unified sg-cim data model and data collection, definition, coding, application and other standards, to ensure data sharing. adhere to the unified application interface, unified portal entrance, unified technology line, to ensure the application of horizontal interconnection, longitudinal through; we will combine top-level design with grassroots innovation, and encourage grassroots units to adapt measures to local conditions and take the lead. inheritance and development, precise investment what is missing on the existing basis, integration and improvement, get through the data, avoid to scratch, find what you need and develop it, look for weak places and then strengthen it. technical and economic feasibility, vigorously promote; pilot reserves that are technically feasible but economically to be assessed. if the investment is large and the effect cannot be seen in a short term, the demonstration will be limited in scope. who to use, how to use, use frequency as the principle of whether to set up the project, to ensure accurate investment. saving money is making money. intensive construction, sharing and co-construction coordinate the company’s internal construction results, avoid repeated investment and development and pilot demonstration, promote the sharing and reuse of results, and give full play to the intensive effect. all business terminals should fully consider the needs of all other majors, and be equipped with electrical side acquisition devices, communication resources, edge computing and data resources for cross-professional reuse, so as to promote the co–construction and sharing of all majors. we will strengthen external cooperation in mature technologies, coordinate internal and external resources to advance them efficiently and ensure high-quality development. economic and practical, focusing on value the key to the construction of ubiquitous power internet of things is application. full consideration should be given to practicality, economy and convenience of grassroots application. only by working hard on practicality and practical effect can practical effect be achieved, so as to make front-line personnel better and more willing to use. 2.2 specific content of construction build a comprehensive service platform for smart energy with high-quality power grid services as the cornerstone and the entrance, we will make full use of the massive user resource advantages of state grid corporation to build a comprehensive intelligent energy service platform covering the government, terminal customers and the upstream 506 w. hu, h.h. li, w.h. yao, y.w. hu and downstream of the industrial chain, and provide information docking, supply and demand matching, transaction matching and other services to attract users for emerging businesses. we will strengthen the building of common capacity centers for equipment monitoring, power grid interaction, account management and customer service, empower power grid enterprises and emerging business entities, and support the three-level smart energy service system for companies, regions and parks. (1) drainage: integrate the external service application entrance and supply and demand information of various emerging businesses of state grid company, connect the energy efficiency service sharing platform of headquarter level enterprises, provincial customer side energy service platform, new energy big data platform, internet of vehicles, photovoltaic cloud network, intelligent energy control system and other systems, and give full play to the large-scale agglomeration effect. (2) enabling: integrating the common ability of state grid corporation for external services, providing all kinds of emerging business subjects with unified sharing services of network connection, monitoring, metering, billing, trading, operation and maintenance and other platform-based services. build an energy ecosystem we should establish a standard system to support the interconnection of equipment, data and services, establish a normal cooperation mechanism with well-known enterprises, universities and scientific research institutions at home and abroad, integrate the upstream and downstream industrial chains, reconstruct the external ecology, promote the aggregate growth of industries, and create an energy internet industrial ecosystem. we will build a national demonstration base for entrepreneurship and innovation, establish a mechanism for incubating emerging industries, serve small, medium and micro businesses, and actively foster new businesses, new formats and new models. (1) establishment mechanism: make use of the innovation and entrepreneurship platform, give full play to the supporting service role of "collaborative sharing of needs, resource sharing, crowdfunding and crowdsourcing", pool innovation achievements, improve the mechanism of achievement transformation, and build the platform of achievement transformation. (2) promotion and transformation: establish a special fund for achievement incubation, select outstanding achievements with broad market prospects and potential for state grid corporation operation, strengthen technical cooperation and capital cooperation, promote the industrialization of innovation achievements, and cultivate unicorn enterprises. cultivate and develop emerging businesses give full play to the sgc grid infrastructure, such as customers, data, brand unique advantages of resources, foster and develop a comprehensive energy service, internet finance, big data operations, data inquiry, photovoltaic cloud network, three stand one, online financial supply chain and virtual power plants, based on a new type of energy services, intelligent manufacturing, chain block its core and the combination of 5g resources such as communication, tower emerging business, such as commercial operation implement emerging business "flowers", as a new major profit growth point of sgc: (1) with the goal of serving the development of new energy industry, give full play to the unique resource advantages of state grid corporation, build new energy big data service platform, and carry out new business of new energy big data operation service. (2) by collecting all kinds of data related to equipment operation, environmental resources, weather and climate, load energy consumption and so on at the power generation side, power energy optimization for wsn in ubiquitous power internet of things 507 grid side and user side, we provide diversified services such as centralized equipment monitoring, equipment health management and energy efficiency diagnosis for power generation enterprises and comprehensive energy service providers. data sharing based on the unified data center and data model of the whole business, the data access transformation and integration are comprehensively carried out, the data standards are unified, the professional barriers are broken, and the data management system of the national network company is established. create a data center, unified data call and service interface standards, and achieve data application service. construction of an enterprise-level master data management system to support key tasks such as the transformation of multidimensional lean management systems. develop big data applications such as customer portraits, develop digital products, provide analytical services, and drive data operations: (1) for the internal network of the company: to achieve equipment status warning, power sales and load forecasting, new energy generation power forecasting applications, etc. , to improve lean management. (2) for the government industry: to achieve macroeconomic forecasting, energy conservation and emission reduction policy formulation, industry climate index analysis, big data credit and other services to support the government’s efficient and accurate decision-making. (3) for external enterprises: to achieve enterprise energy optimization recommendations, industry trend research, commercial location planning and other services to help enterprises save money and increase efficiency. (4) for customers who use electricity: to achieve home energy optimization advice, quality service improvement and other services to enhance the sense of power users. the upiot mainly consists of a sensing layer, a network layer, a platform layer, and an application layer. based on widely distributed sensors, the sensing layer collects data in all stages of the grid, from generation, transmission, distribution to consumption, and conducts preliminary data fusion and calculation. the network layer fully utilizes modern communication technologies like 5g to set up a wsn that links up all data. the platform layer schedules the facilities and processes the big data in the network, making preparations for application. the application layer maintains the safe and stable operation of the grid and pursues an energyefficient, smart and integrated power network. 3 upiot energy optimization 3.1 rechargeable wsn in the upiot, the data streams are transmitted via the rechargeable wsn [8]. the pattern of wireless charging determines the distance between the devices receiving and emitting the energy. the transmission distances, charging efficiency, power, frequency varies with the wireless charging patterns. this paper explores the charging mode based on radio waves. capable of charging devices several tens of meters away, this wireless charging mode is suitable for wsns with a large coverage. during wireless charging, the electromagnetic waves transmitted continuously from the power transmitter gradually attenuates with the increase of propagation distance and the change in the spatial environment. thus, the author firstly examined the charging model of a single node in the wsn. according to the electromagnetic propagation theory, the relationship between the electromagnetic wave power p1 at any point in space and the transmitting power p0 of the electromag508 w. hu, h.h. li, w.h. yao, y.w. hu netic wave transmitter can be established as: p1 = gp0( λ 4πd )2 (1) where g is the combined gain of the receiving and transmitting antennas; d is the distance of the receiving point from the transmitting source; λ is the wavelength of the electromagnetic wave. a rechargeable wsn that periodically collects data mainly consists of wireless sensor nodes, node links and power collection and transmission devices. such a rechargeable wsn can be described as g = (m,z,p), where m is the set of all wireless sensor nodes, z is the set of all node links and p is the set of power collection and transmission devices. m contains one sink node that collects data and uploads them to the processor and m-1 regular nodes that receive and transmit information. a node link exists between two wireless sensor nodes, if and only if the two nodes are within the transmission range of each other. in this case, the two nodes are called neighbors. it is assumed that each wireless sensor node in the rechargeable wsn collects data at a constant rate, and the acquisition rate of node j is rj. let t be the lifecycle of the rechargeable wsn. for each cycle t ∈ t , the transmitting power of p is a constant denoted as pfsj (t), with j ∈ p . for node j, the energy consumed to send a unit of data to node k can be denoted as efsjk, and that consumed to receive a unit of data as ejsk . the total amount of data transmitted by all wireless sensor nodes to the sink node via multiple paths can be denoted as qjk(t). 3.2 wsn opportunistic routing targeting unreliable networks, opportunistic routing increases network throughput through fewer transmissions and lower energy consumption, using multiple nodes in the next hop that may provide similar transmission quality [6,14]. the opportunistic routing is achieved in different ways, depending on the specific medium access control (mac) protocol. the key to this routing method lies in the selection of candidate set. a candidate set is a set of candidate nodes, i. e. the next hop nodes that are qualified by the algorithm to forward packets. in a wireless network, the nodes in the candidate set for opportunistic routing are all selected from single-hop neighbors. the main reason is that, in a highly dynamic wireless network, it is only possible to maintain the accurate information of single-hop neighbors. the topology of the candidate set for node s is shown in figure 1, where the black box stands for the size of the area centered on s. it can be seen that this area contains the neighbors within two hops (h=2) from s: the first-hop neighbors include nodes a∼e and the second-hop neighbors include nodes f∼k. among the second-hop neighbors, nodes g, h, i and k can interact with the nodes outside the area, and are thus called the exits of the area. thus, the candidate set of node s can be described as{g,h,i,k}. meanwhile, the first-hop neighbors and the second-hop neighbor that is not the exit of the area are service nodes for the candidate ones, and collectively form the set of service nodes {a,b,c,d,e,f,j}. if node s needs to forward data, the shortest path tree will be constructed directly from the link state database for deterministic forwarding, if the destination falls in the said area; the forwarding nodes will be selected by the priority and dynamic forwarding probability of candidate nodes, if the destination falls outside the area. determine the priority of candidate nodes. for a candidate node, the lower the end-to-end cost of reaching the destination node when it is selected, the higher the corresponding priority, and the higher the corresponding forwarding probability. for example, for two candidate nodes, if one is in the direction of the destination node and the other is in the opposite direction of the destination node. obviously, candidate nodes in the same direction have higher priority and energy optimization for wsn in ubiquitous power internet of things 509 figure 1: topology of an example on candidate set forwarding probability, and even worse, candidate nodes in the opposite direction can set the forwarding probability to zero. the cost corresponding to a candidate node includes two parts: the near cost and the far cost. the distant cost refers to the end-to-end cost expected by the candidate node to reach the destination node. according to link state database (lsdb), it is easy to calculate the approximate cost corresponding to the optimal path to a candidate node. the remote cost should be considered in two cases: if the destination node is located in the region with the candidate node as the center, the corresponding remote cost of the optimal path from the candidate node to the destination node can be obtained according to the lsdb of the candidate node; if the destination node is not in the region centered on the candidate node, considering the characteristics of opportunistic routing, this paper needs to consider the average end-to-end cost of all potential paths from the candidate node to the destination node, and take this as the remote cost of the candidate node. nodes have different forwarding probabilities under different conditions. for a particular destination node, not all candidate nodes (referring to candidate nodes after loop avoidance) have the opportunity to become the final forwarding node, because some candidate nodes will forward the packet to the more expensive path. in this paper, the factor α (0 ≤ α ≤ 1) is used to represent the proportion of candidate nodes with non-zero forwarding probabilities in the total candidate nodes: α = 1 indicates that each candidate node has a non-zero forwarding probability and has the opportunity to become the final forwarding node. in this case, the performance of load balancing is the best; when α ≤ 1/ns (ns is the number of sending node s candidate nodes), the opportunity route is degraded into a deterministic route, and the candidate node with the least end-to-end cost is determined as the final forwarding node. 510 w. hu, h.h. li, w.h. yao, y.w. hu 4 energy optimization model and routing algorithm for the upiot 4.1 energy balance analysis and routing algorithm for a wsn, it is assumed that each node i is charged by the same device at a time. to ensure the energy sufficiency of node i in each cycle t, the node should select the device with the highest charging power in each cycle. let the distance between node i and the power transmitter be di,r. then, the charging power of node i in cycle t can be expressed as: p maxi (t) = max r∈r   gpr( λ 4πdir )2   (2) according to the rechargeable wsn model, the data flow of a wireless sensor node i consists of three parts: the data stream flowing into node i qini (t), the data stream flowing out of node i qouti (t), and the data stream generated by node i itself eit. in a traffic-stable upiot network, the final data will all enter the sink node. in this case, each node i in the network should satisfy: qini (t) + eit = q out i (t) (3) in other words, the traffic of network nodes is dynamically balanced. the data flow rate can be expressed as: ∑ i,r∈lk qi,r(t) + ri = ∑ k∈lk qi,k(t) (4) where lk is the set of neighbor nodes that forward data for a randomly selected node in the network. here, it is assumed that the wireless sensor nodes collect monitored data at a low frequency and low power consumption. without considering the power consumed by the nodes in data acquisition, the energy consumption rate of node i in cycle t can be calculated by: vi = e fs jk · ∑ i,r∈lk qi,r(t) + ∑ k∈lk e js k qi,k(t) (5) it is also assumed that, the sum of the energy received by node i from the power transmitter in cycle t (ei(t)) and the remaining power of the battery in cycle t − 1 equals the energy consumption of node i in cycle t, i. e. the total energy for data transmission and reception of node i. then, the operation time of node i in cycle t can be expressed as: ti(t) = ei(t) + ei(t− 1) vi = ei(t) + p max i (t− 1) · tr e fs jk · ∑ i,r∈lk qi,r(t) + ∑ k∈lk e js k qi,k(t) (6) if ti(t) ≥ t, then the battery has surplus energy in cycle t and can support node i to operate beyond the cycle; otherwise, the energy received by node in cycle t, plus the remaining power of the battery, cannot support the normal operation of node i through cycle t. the relationship between ti(t) and t determines the amount of remaining power of the battery for node i in cycle t. an energy-balanced routing strategy must ensure that the remaining power of the battery for node i is neither zero or above the maximum battery capacity in any cycle. in actual application, it is possible in any cycle that the energy stored in a node is insufficient to support data transmission and reception at the node, calling for the design of an energy-balanced routing protocol. for any node with insufficient energy, the remaining energy in the previous cycle can be regarded as zero. energy optimization for wsn in ubiquitous power internet of things 511 definition 1 (network lifecycle). the network lifecycle in cycle t refers to the operation time of the rechargeable wireless sensor node which is shorter than that of any other node involving in data monitoring and transmission. the maximization of network lifecycle, i. e. the balancing network energy through traffic distribution of each sensor node in each cycle t, can be described as: max qi,r(t) {min i∈m ti(t)} (7) s.t. ti(t) = ei(t) + p max i (t− 1) · tr e fs jk · ∑ i.r∈lk qi,r(t) + ∑ k∈lk e js k qi,k(t)∑ i,r∈lk qi,r(t) + ri = ∑ k∈lk qi,k(t) qi,r(t) ≥ 0 i,r,k ∈ n where ei(t) represents the energy received by node i from the power transmitting device at stage t; p maxi represents the charging power of node i during time t; e fs jk is the transmission energy consumed by node j to transmit one unit of data packet to node k; ejsk is the received energy consumed by j to receive one unit of data packets; qi,r(t) represents the data flow rate between nodes. in essence, the network lifecycle maximization is a max-min linear programming problem for variable qi,r(t) under equal constraints. this problem can be solved by the linear programming tool fminimax in the matlab. in a rechargeable wsn, if a node consumes energy in data transmission and reception faster than it collects energy, then the node will die within a limited time; otherwise, the node energy will always surpass the battery capacity, causing a great waste of the energy provided by the charging device. the above calculation strikes a balance between the energy consumed in data transmission and reception and the energy collected from radio waves, thus maximizing the network lifecycle. if applied to an actual network, the routing strategy with the global optimal energy balance, which is obtained through the optimization of global information of the network, has a certain impact on the energy consumption and lifecycle of the network [10, 11]. to further extend the network lifecycle, it is of practical significance to optimize the energy of the upiot based on the low power mode. 4.2 upiot energy optimization based on the low power mode to further reduce the energy consumption and extend the lifecycle of the upiot sensor network, the low power mac protocol is often adopted to turn off the wireless transceiver of inactive nodes, preventing unnecessary power consumption, that is, initiate the low power mode [15,17]. under this mode, the wireless sensor nodes can turn off the wireless communication module in the idle period, and enter the sleep mode. then, the nodes can only send and receive data packet in the active state. the periodic sleep scheduling is an important aspect of the low power mode. by this strategy, the nodes fall asleep and wake up periodically at the preset time. the ratio of the sleep time to the scheduling cycle is called the duty ratio of the nodes in the low power mode. in the case of a low duty ratio, it is extremely rare for all nodes to wake up at the 512 w. hu, h.h. li, w.h. yao, y.w. hu same time. to successfully transmit a data packet, the sender needs to send the introduction packet continuously until a receiver wakes up and confirms for its active state. if the sender only selects one next hop node, then it has to wait for the next scheduling cycle to send the remaining data packet after the end of the current active cycle of the receiving node. in a sensor network with unreliable links, the sender must wait for several scheduling cycles, pushing up the data transmission time [2,18]. the opportunistic routing allows a sender to select a candidate set of neighbors to forward data, rather than only a next hop node. in this way, it is possible to effectively shorten the waiting time, and reduce the transmissions to successfully send a data packet. in this section, the author attempted to optimize the network energy by setting up the candidate set of forwarding nodes for each network node, when the routing strategy cannot achieve the optimal energy balance [7,10]. suppose the sender needs to send a packet and consider multiple packets in the traffic distribution policy. the forwarding nodes in the candidate nodes of the sender s can be ranked as fs = {a1,a2, · · · ,an} by their wake-up sequence. let p1,p2,p3 · · · ,pnbe the quality of the sender s to each of the forwarding nodes. then, the theoretical expected forwarding probability of node i in the candidate set can be determined by: p(i) = i−1 π l=1 (1 −pl)pi (8) equation (5) shows that the sender s fails to transmit data to any node in the candidate set until to node i. next, equation (7) can be transformed to the selection of the optimal candidate set fi(t)of forwarding nodes for node i within a rechargeable wsn in each cycle: max fi(t) {min ti(t) i∈m } (9) s.t. ti(t) = ei(t) + p max i (t− 1) · tr e fs jk · ∑ i.r∈lk qi,r(t) + ∑ k∈lk e js k qi,k(t)∑ i,r∈lk qi,r(t) + ri = ∑ k∈lk qi,k(t) qik(t) = pik fi∑ l=1 pil · ( ∑ i,j∈fj qij(t) + ri ) i,r,k ∈ n in equation (9), the traffic distribution depends on the following factors: the candidate set selected by opportunistic routing, the wake-up time, and the expected traffic considering node priority. the problem described by this equation can also be solved by the linear programming tool fminimax in the matlab. 5 case analysis 5.1 experimental setup the energy optimization routing protocol was simulated on the matlab, aiming to verify its effects on common rechargeable wsn and low power rechargeable wsn. a total of n − 1 rechargeable wsn nodes were deployed randomly in an 800m∗ 800m field, with the sink node at the coordinates (0, 0). the unreliable links were simulated by the free space propagation loss energy optimization for wsn in ubiquitous power internet of things 513 model. for simplicity, the charging scenario was simulated as a one-to-one mode. the charging device was placed at the height of 20m to supply energy to each node at a constant power. the energies consumed to transmit and receive a unit of data were respectively assumed as ejsk = e r and efs = et + αdβ. in addition, the scheduling cycle and duty ratio of the nodes in the low power mode were set as 2s and 60%, respectively. the specific simulation parameters are listed in table 1 below. table 1: simulation parameters sign definition value n total number of network nodes 10, 30, 50 and 80 tsim total simulation time 6h α amplification factor 80pj · b−1 ·m−3 β power index 4 ∆t cycle 1h tr charging time 0. 5h r sampling rate of wsn node 80b · min−1 er energy consumption for receiving a unit of data of wsn node 0. 0567 mj et energy consumption for transmitting a unit of data of wsn node 0. 0233mj the proposed energy optimization (eor) routing protocol is denoted as the eor-l in the low power mode. in the simulation, the eor was compared with the shortest path routing (spr), and the eor-l was contrasted with the expected duty-cycled wakeups minimal routing (edc). in the spr, the energy balanced routing is realized by taking the shortest path to the sink node based on the reciprocal of the ratio of the residual node energy to the rated battery capacity. in the edc, the candidate set with the shortest expected delay is selected under the low power mode and opportunistic routing. 5.2 evaluation standard this paper uses the normalized network life cycle as the evaluation criterion to evaluate the strategy of routing energy balance with network as the target. the normalized network life cycle is defined as the ratio of the average working duration to the total period length of all nodes in the network (excluding the charging cycle). taking figure 2 as an example for specific explanation: node i in the first observation cycle [0, t] before a third charge of electricity from 0 to 1e, subsequent data transmission, forwarding operation, after t1 time consumption of energy, finished the work ahead of time; in the second observation period, i obtained 3e energy and consumed 3e− 0.7e energy. at the end of this period, there was still electricity left. then the normalized network life cycle of i in the first two cycles is (t1 − t/3 + 2t/3)/(4t/3). 5.3 simulation results and analysis the energy optimization quality of each routing protocol was evaluated by normalized network lifecycle (nnl), i. e. the ratio of the mean operation time of all network nodes to the cycle. figure 3 shows the variation of the nnl with the number of nodes in the eor and the spr. it can be seen that the mean network lifecycle was shortened, as each node operated for 514 w. hu, h.h. li, w.h. yao, y.w. hu figure 2: battery energy cycle of node i figure 3: the variation of the nnl with the number of nodes less time in each cycle, with the increase in the number of nodes. both the eor and the spr saw a gradual decline in the nnl. comparatively, the eor achieved better energy optimization than the spr. this is attributable to the fact that the eor considers how the load distribution of multiple forwarding nodes, rather than a single next hop node, on the network optimization. figure 4 presents the variation of the nnl with the number of nodes in the low power mode. similar to figure 3, both the eor-l and the edc witnessed a declining trend in the nnl with the growth in the number of nodes. the eor-l, which focuses on energy optimization, outperformed the edc, which highlights time delay. comparing figures 3 and 4, it can be seen that the eor-l had a better nnl than the eor. generally speaking, the eor is a multi-path routing protocol, not an opportunistic protocol. by contrast, the eor-l can enter the sleep mode when there is no task, thus saving the energy consumed in idle state. under the same scenario, the eor led to a longer network lifecycle than the eor-l. there are two possible reasons: our simulation only considers the energy consumed in data transmitting and reception; the eor owns more information than the eor-l, because it optimizes network energy using the global information of the network, while the latter adopts a distributed routing energy optimization for wsn in ubiquitous power internet of things 515 figure 4: the variation of the nnl with the number of nodes in the low power mode algorithm. considering the complexity of the two algorithms, eor uses a linear programming algorithm with a polynomial level of time complexity n; eor-l adds constraints on the forwarding candidate set based on the former, the search space is larger than eor, but still n polynomial level time complexity. 6 conclusions the upiot is the development trend of the energy industry and the priority of china’s energy research. under the upiot, the information transmission relies on the wsn containing multiple sensors. the wsn involves widely distributed power devices, which may need to work under harsh natural environment. for the energy-balanced routing of rechargeable wsn, this paper puts forward a multi-path routing protocol for the normal mode and an opportunistic routing protocol for the low power mode. under the normal mode, the link traffic is planned through global linear programming for each forwarding node, according to charging rate, remaining battery power, and the energy consumed in data transmission and reception. under the low power mode, the wsn lifecycle is greatly extended under the framework of opportunistic routing. finally, matlab-based simulation shows that the proposed routing protocols can effectively optimize network energy, reduce energy consumption of network nodes and enhance the network reliability. next, we will investigate the routing problem of rechargeable wireless sensor networks in complex mobile scenarios. funding this work is supported by the humanities and social sciences project of chinese ministry of education (grant no. : 17yjczh062). bibliography [1] asha, g.; santhosh, r. 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(2019). energy-aware scheduling for information fusion in wireless sensor network surveillance, information fusion, 48, 95-106, 2019. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 473-502 human-inspired identification of high-level concepts using owa and linguistic quantifiers m.z. reformat, r.r. yager, z. li, n. alajlan marek z. reformat thinks2: thinking software and systems laboratory electrical and computer engineering university of alberta, canada e-mail: marek.reformat@ualberta.ca ronald r. yager 1. machine intelligence institute iona collage, new rochelle, ny, usa e-mail: yager@panix.com 2. visiting distinguished scientist king saud university, riyadh, saudi arabia zhan li thinks2: thinking software and systems laboratory electrical and computer engineering university of alberta, canada e-mail: zhanli@ualberta.ca naif alajlan advanced lab for intelligent systems research college of computer and information sciences king saud university, riyadh, saudi arabia e-mail: najlan@ksu.edu.sa abstract: intelligent agent based system can be used to identify high-level concepts matching sets of keywords provided by users. a new human-inspired approach to concept identification in documents is introduced here. the proposed method takes keywords and builds concept structures based on them. these concept structures are represented as hierarchies of concepts (hofc). the ontology is used to enrich hofcs with terms and other concepts (sub-concepts) based on concept definitions, as well as with related concepts. additionally, the approach uses levels of importance of terms defining the concepts. the levels of importance of terms are continuously updated based on a flow of documents using an adaptive assignment of term importance (aati) schema. the levels of activation of concepts identified in a document that match these in the hofc are estimated using ordered weighted averaging (owa) operators with linguistic quantifiers. a simple case study presented in the paper is designed to illustrate the approach. keywords: concept identification, text documents, ontology, hierarchy of concepts, ordered weighted averaging operator, importance of concepts. 1 introduction keyword-matching is the most popular approach used in current search engines for finding useful texts [1]. however, the meaning of words used as keywords is context-dependent. in copyright c⃝ 2006-2011 by ccc publications 474 m.z. reformat, r.r. yager, z. li, n. alajlan addition, users tend to use very few keywords (three or less) what created difficulties in identifying their proper meaning [2, 3]. in this paper we focus on human-like analysis of text, i.e., processes of identification of concepts based on terms/words from a document. the proposed approach reflects a real life scenario where a person links the information in a document to a specific topic by recognizing multiple terms that describe concepts associated with this topic. the terms that appear in a document “activate” terms known to the person that are related to the topic’s concepts. in such a way, a net of relevant words is activated. the more words related to a specific concept are activated, the more confidence the person has that the document contains this specific concept. in other words, in concept-based models, we try to “understand” the meaning of a set of keywords and attempt to determine presence of concepts defined by these keywords in documents. the proposed approach starts with a simple set of keywords provided by the user and representing an entity/item the user is interested in. this set is transformed into a hierarchy of concepts (hofc). the hofc is further enhanced with pieces of relevant knowledge obtained from ontology-based knowledge base. this ontology-based knowledge base is created based on ontology domains. its elements are equipped with values representing levels of contribution of individual keywords towards definitions of concepts. these values are automatically determined using aati – a schema proposed in ... for determining importance values of keywords based on continuous flow of documents. the proposed approach relies on the following idea: a process of identification of a concept in a document is equivalent to determining a level of activation of hofc representing this concept. once a hofc is built and enhanced using an ontology-based knowledge base, it is “checked against” a document. the words found in document are matched with terms and concepts from hofc. every time a term/concept is found it is activated. the activation levels of terms and concepts are aggregated using ordered weighted averaging (owa) operator. the weights of owa are determined based on activation levels and linguistic quantifiers, such as most, at least half, all and about one third. the activation and aggregation propagate in a bottom-up fashion. a final activation level – level of activation of the hofc – is treated as an indicator of how well the concept is present in the document. the paper is organized in the following way. section 2 contains a brief description of work done so far in the area of concept-based analysis of text with emphasis on applications of ontology to support definition and identification of concepts. the background is presented in section 3. section 4 contains an overview of the proposed approach. the next three section are dedicated to specific elements of that approach: the construction of ontology-based knowledge base is discussed in section 5, section 6 contains description of the process of building and enhancing hofcs, while the process of determining activation level of hofc for a document is described in section 7. a case study with two different linguistic quantifiers is included in section 8. the papers finishes with conclusions. 2 related work there are two most popular approached for concept-based analysis of documents: classifierbased approach, and concept structure-based approach. because our work is focused on a concept-identification method that involved ontology we do not describe the classifier-based techniques for identification of concepts. some example of the work related to this topic can be found in [4], [5], [6], and [7]. human-inspired identification of high-level concepts using owa and linguistic quantifiers 475 2.1 concept knowledge bases it can be noted that classifier-based techniques requires human-labor to set categories, build training sets, update models, etc. therefore, constructing a background concept structure as a knowledge base to define concepts is a more suitable approach. some example of such structures are: • synonym thesauri are treated as repositories of terms“related” to keywords provided by users. for example, anick [8] has proposed a system that automatically generates an extended condition: “a boolean expression is composed by oring each query term with any stored synonyms and then anding these clusters together.” that is, the whole query is ored together. each or term is composed of synonyms from an online thesaurus. • conceptual taxonomy is seen as a hierarchical organization of concepts. each concept in a conceptual taxonomy connects to both its superconcepts and its subconcepts. therefore, it provides a topological structure for efficient conceptual search and retrieval. in the project by sun microsystems, a conceptual indexing technique was proposed to automatically generate conceptual taxonomies [9]. • ontology is a model of a domain or a problem. ontologies can be used to provide formal semantics (meanings, concept-based information) to any sort of information, such as databases, web documents, etc. a commonly accepted definition states that “an ontology is an explicit and formal specification of a conceptualization of a domain of interest” [10]. in general, an ontology is a representation of a set of concepts and relations between those concepts in a domain. ontologies are gaining popularity because of their applications to text analysis. some of the popular ontologies are: wordnet, sensus, and gene ontology. wordnet is an english lexical ontology built at princeton university, which includes explanations of the terms and relations between terms (synonyms, antonyms, etc.) [11]. as the most popular linguistic ontology, wordnet is used by many researchers for expanded queries [12] [13]. however, the systems applying wordnet suffered from word sense disambiguation (wsd) because of polysemy1. this problem has been addressed in [14], [15], and [16]. additionally, researchers report difficulties in applying linguistic-based ontologies to non-linguistic applications [18]. sensus is a natural language-based ontology developed by the natural language group at information sciences institute (isi) [17]. it is an extension and reorganization of wordnet. it has been used for concept-based retrieval from online yellow pages and product catalogs [18]. gene ontology is composed of three structured controlled vocabularies describing gene products with their associated biological processes, cellular components and molecular functions [19]. this ontology is a part of an information retrieval system, kipar, to facilitate access to the literature relevant to kinetic modelling of a given metabolic pathway in yeast [20]. 2.2 ontology-based concept identification there are different ways to identify concepts in a text using ontology as a knowledge structure. we have divided those approaches into four major categories. 1polysemy means more than one words with the same meanings 476 m.z. reformat, r.r. yager, z. li, n. alajlan regular expressions, rules, and ontology in this type of approaches, regular expressions are used to identify concepts from texts. several predefined rules are then used to aggregate the identifications of single concepts. embley has proposed the use of information extraction ontologies, which are formalized over regular expressions [21]. muller et al. have constructed an ontology-based information retrieval and extraction system for biological literature, which is called textpresso [22] where biological concepts (e.g., gene, allele, cell or cell group, phenotype, etc.) are presented as regular expressions. natural language processing (nlp) with ontology informally, nlp aims to solve problems by making computers understand and process human language. there are two types of approach in nlp: deep approach and shallow approach. as styltsvig stated, “deep approaches presume access to a comprehensive body of world knowledge. these approaches are not very successful in practice, mainly because access to such a body of knowledge does not exist, except in very limited domains” [23]. shallow nlp techniques normally rely on simple rules to perform analysis. cimiano et al. have presented the lexonto model consisting of a domain ontology and a corresponding ontology for associating lexical information to entities of the given domain ontology [24]. morneau et al. have proposed seseionto using nlp to identify concepts from texts [25]. compared with the lexonto model, this approach involves more shallow nlp. first of all, seseionto removes articles and prepositions from nlp-based user queries (sentences) by tagging. the remaining words in the queries are matched to the concepts in the ontology. utilizing nlp and an ontology may be the most intuitive application to build conceptbased systems. however, the discussions on how much nlp should be involved never stops. in lexionto [24], nlp exists in the entire process; while in seseionto [25], nlp handles conversion from texts to concept-based structures. though the nlp techniques allow computer agents to recognize concepts from texts, their complexity cannot be ignored. vector space and ontology this type of solution is the most popular. in general, the ontology is utilized to expand the query, and vector space approach is used to evaluate similarity. vallet et al. have proposed an ontology based information retrieval system applying a vectorspace model to retrieve relevant documents [26]. after terms and concepts are connected through ontology-driven weighted annotations on the documents, a classic vector-space model is utilized to evaluate the relevance between documents and queries. the weights are based on the frequency of occurrence of the instances in each document. another ontology-based framework for semantic information retrieval has been proposed in [27]. with annotations based on gate [28] as an information extraction module, metadata for documents are generated. the metadata are extracted concepts from the document text. based on the term weighting technique cf ·idf (concept frequency inverted document frequency), which is similar to tf ·idf, concepts in the documents are recognized and indexed. castells et al. [29] have presented a complete concept-based information retrieval model. in their model, first a set of root ontology classes is constructed from three main base classes: domainconcept, topic, and document. documents are annotated with concept instances from the ontology. the annotations are weighted based on an adaption of the tf · idf scheme, and the ranking is then achieved through computing similarity values between queries and documents through the classic vector-space approach. in [30], tomassen et al. has presented an ontology-driven system webodir, where each concept is extended by associating it human-inspired identification of high-level concepts using owa and linguistic quantifiers 477 with a vector of key phrases describing the concept. the system starts from ranking concepts in the ontology according to ontology relevance. it then generates a query for each concept based on relations with other concepts. after submitting the queries to the underlying search engine, a set of documents for each concept is retrieved and then clustered. latent semantic indexing and ontology latent semantic indexing (lsi) is similar to the vector space approach that presents documents/queries in the manner of vectors. like vector space approaches, lsi relies on a termdocument matrix in which each column represents a document and each row lists frequencies of a term in different documents. lsi uses the singular value decomposition (svd) to reduce the dimensions of the term-document matrix and approximate the most important part of the original matrix. snasel et al. mapped lsi concepts to wordnet [31]. wordnet provides synonyms to expand queries: this improves recall through sacrificing precision, because it increased the number of keywords. lsi helps to retrieve the most relevant k terms/concepts from term matrix tmk. the authors argued that the expansion of these k terms instead of all keywords balanced the conflicts between precision and recall. 3 background 3.1 semantic web and ontology the semantic web [32], as an extension of the world wide web (www), makes the web content more machine-processable. tim berners-lee, who invented the www in the late 1980s, introduced this special vision of the web, in which the meaning of the content in web documents would play a much more crucial role than it does today. to accomplish that the semantic web needs a more firmly structured language for machine agents to process. the crucial first step has been the adaptation of the language rdf (resource description framework) [33] as a standard for the semantic web. the rdf is more a model than a language; it is designed to present information about web resources. rdf presents statement in the form of triples, i.e., subject-predicate-object. rdf is applied to build information-sharing models. in the triples, the subject denotes the resources, the object denotes the properties of the subject, and the predicate denotes the relations between the subject and the object. the rdf provides a foundation for defining the main data structure of the semantic web – ontology. the most popular definition of an ontology, in the context of the semantic web, is “an explicit and formal specification of a conceptualization of a domain of interest” [10]. ontologies are more than just a vocabulary, they are sources of knowledge of a specific domain. currently, most of ontologies are implemented in owl (web ontology language) which is based on rdf and designed by w3c. the most important aspect of ontologies used for semantic web applications is related to identifying two ontology layers: the ontology definition layer, and the ontology instance layer. the ontology definition layer represents a framework used for establishing an ontology structure and for defining classes (concepts) existing in a given domain. the structure of an ontology is primarily based on a relation is-a between classes. this relation represents a subclassof connection between a superclass and a subclass. in such a way, a hierarchy of classes is built. the ontology definition contains descriptions of all classes of the ontology. the classes are defined using datatype properties and object properties. both property types provide an accurate 478 m.z. reformat, r.r. yager, z. li, n. alajlan soccer player hasplayer inclub hasplayer inclub name:fc barcelona location: barcelona stadium:camp nou soccer club soccer club fc barcelonaplayer drogba player terry club chelsea first name: didier last name: drogba position:forward first name: john last name: terry position:midfielder name: chelsea location: london stadium:stamford figure 1: ontology snippet and complete description of a class, as described below: • the datatype property focuses on describing features of a class; datatype properties can be expressed as values of data types such as boolean, float, integer, string, and many more (for example, byte, date, decimal, time); • the object property defines other than is-a relations among classes (nodes); these relations follow the notion of the rdf that is based on the triple subject-predicate-object, where: subject identifies what object the triple is describing; predicate defines the piece of data in the object a value is given to; and object is the actual value of the property; for example, in the triple john likes books, john is the subject, likes is the predicate and books is the object. both types of property are important for defining ontologies. the possibility of defining class properties and relations between classes creates a versatile framework capable of expressing complex situations with sophisticated classes and the multiple different kinds of relationships existing among them. once an ontology definition is constructed, its instances, called individuals, can be built. the properties of classes are filled out: real data values are assigned to datatype properties, and links to instances of other classes (individuals) are assigned to object properties. a fragment of an ontology is presented in fig. 1. it contains three classes: soccer, soccer_club and soccer_club. both soccer_club and soccer_player are subclassof of the class soccer. class soccer_club has two individuals: club_chelsea and club_fc_barcelona; while class soccer_player has two players as individuals: player_drogba and player_terry. class soccer_player is defined by three data properties and one object property, as presented in fig. 1. the three data properties are first_name, last_name and position. the one object property is inclub. class soccer_club is also defined by three data properties and one object property. the three data properties are name, stadium and location. the one object property is hasplayer. two individuals of the class soccer player are player drogba and player terry. individual player drogba is defined by the term “didier” as the value of a datatype property first name, by the term “drogba” as the value of datatype property last name, by the term “forward” as the value of datatype property position, and by individual club chelsea as the value of the object property inclub. similarly, individual player terry is described by “john” as first name, “terry” as last name, “midfielder” as position, and individual club chelsea as the value of inclub. human-inspired identification of high-level concepts using owa and linguistic quantifiers 479 individuals of the class soccer club (club chelsea and club fc barcelona) are also described by three datatype properties and an object property. the three datatype properties listed are name, location, and stadium. the object property is hasplayer, which is the inverse property2 of the object property inclub. the values of the datatype properties and the object property for individuals club chelsea and club fc barcelona are shown in fig. 1. 3.2 ordered weighted averaging (owa) operators basic principles of owa aggregation of different pieces of information is a common aspect of any system that has to infer a single outcome from multiple facts. an interesting class of aggregation, ordered weighted averaging (owa) [34] operators, is a weighted sum over ordered pieces of information. in a formal representation, the owa operator, defined on the unit interval i and having dimension n (n arguments), is a mapping fw : in → i such that fw(a1, ...,an) = n∑ j=1 (wj · bj) (1) where bj is the jth largest of all arguments a1, a2, ..., an, and wj is a weight such that wj is in [0,1] and ∑n j=1 wj = 1. if id(j) is the index of the jth largest of ai then aid(j) = bj and fw(a1, ...,an) = ∑n j=1(wj · aid(j)). if w is an n-dimensional vector whose jth component is wj, and b is an n-dimensional vector whose jth component is bj, then fw(a1,a2, ...,an) = wt b. in this formulation, w is referred to as the owa weighing vector and b is called the ordered argument vector. at the beginning of the 1980s, zadeh [35] introduced the concept of linguistic quantifiers. those quantifiers describe a proportion of objects. according to zadeh, a person knows a vast array of terms that are used to express information about proportions. some examples are most, at least half, all and about one third. the important issue is to formally represent those quantifiers. in the mid-1990s, yager [36] showed how we can use a linguistic quantifier to obtain a weighing vector associated with an owa aggregation. yager introduced parameterized families of regular increasing monotone(rim) quantifiers. these quantifiers are able to guide aggregation procedures by verbally expressed concepts in a description independent dimension. a rim quantifier is a fuzzy subset q over i = [0,1] in which for any proportion r ∈ i, q(r) indicates the degree to which r satisfies the concept indicated by the quantifier q [36]. assuming a rim quantifier, we can associate with q an owa weighing vector w such that for j = 1 to n, wj = q( j n ) − q( j − 1 n ) (2) where n is a number of pieces of information to be aggregated. this expression indicates that the weighing vector w is a manifestation of the quantifier underlying the aggregation process. using this expression the values of the weighing vector can be obtained directly from the expression representing the quantifier. for example, let us take a look at the parameterized family q(r) = rp, where p ∈ [0,∞). here if p = 0, w1 = 1 and wj = 0 for j ̸= 1, and we obtain the existential (max) quantifier, which makes the owa operator f closer to an or operator; when p− > ∞, wn = 1 and wj = 0 for j ̸= n, and we have the quantifier for all (min), which makes the owa operator f closer 2if an object property o1 points object a from object b, and another object o2 points b from a, o1 and o2 are regarded as inverse properties. 480 m.z. reformat, r.r. yager, z. li, n. alajlan to an and operator; and when p = 1 we have q(r) = r, wj = 1n and we deal with the quantifier some. owa with argument importance in the previous section we showed how a quantifier q indicating interaction between pieces of information can be used to calculate an owa weighing vector w . however, not all pieces of information are of the same importance. a user may desire to assign different weights (importance) to different arguments (pieces of information). let mi ∈ [0,1] be a value associated with an argument a1 indicating its importance. in such a case, let m be a n-dimensional importance vector [m1,m2, ...mn], and the weighing vector w has to be calculated based on both q and m. the first step is to calculate the ordered argument vector b (eq. 1), such that bj is the jth largest of all arguments a1, a2, ..., an. furthermore, we assume µj denotes the importance weight associated with the attribute that has the jth largest value. thus if a3 is the largest value, then b1 = a3 and µ1 = m3. the next step is to calculate the owa weighing vector w using a modified version of eq.( 2), wj = q( xj t ) − q( xj−1 t ) (3) where xj = ∑j k=1 µk and t = xn = ∑n k=1 µk . so, xj is a sum of the importance of the jth most satisfied arguments, and t is the sum of all importance. when all arguments have the same importance, eq. (3) simplifies to eq. (2). 3.3 hierarchy of concepts (hofc) the idea of representing concepts as a hierarchy was introduced by yager [37], who represented concepts with atomic attributes, words or other concepts. using this method, a tree-like structure is established where each vertex is a concept, and terminal vertices (leaves) are attributes. the edges of the hierarchy of concepts (hofc) represent relationships that help to define concepts with other concepts and/or attributes. these edges (connections) are of significant importance to the hofc. if we assume that concept c1 is defined by two other concepts c2 and c3, then the hierarchy will have two edges connecting c2 with c1, and c3 with c1. the concept c1 is called a superconcept, and c2 and c3 are subconcepts. this also means that activation of concepts c2 and c3 leads to activation of c1. the hofc introduces a very important element, the activation of a superconcept by active sub-concepts is fully controlled by a user. there are two controlling components: importance vector m and linguistic quantifier q. the vector m indicates the significance of each subconcept in defining a superconcept. in other words, m determines the weight of each participating subconcepts in identifying an activation level of a superconcept. the linguistic quantifier q guides the aggregation of subconcept activations. both m and q determine how activation levels of subconcepts should be combined using the owa operator. a simple example of hofc is shown in the fig. 2. according to the hierarchy, concepta is defined as: concepta = (conceptb,conceptc,ma,qa) this means that concepta is defined by conceptb and conceptc. ma determines the importance of both of conceptb and conceptc in defining concepta. in this case, it is a two-value vector ma = [ma−b,ma−c] that implies the importance of activations of both subconcepts during calculation of the activation level of the concepta. the quantifier qa can be of any human-inspired identification of high-level concepts using owa and linguistic quantifiers 481 m_e concepte q_em_d conceptd m_b conceptb q_b q_d m_c conceptc q_c m_a concepta q_a a6 a1 a2 a3 a4 a1 a5 figure 2: simple hofc type, for example, most or some, and identifies a mechanism of combining activation levels of subconcepts. the rest of the concepts are defined in the following way, conceptb = (conceptd,concepte,mb,qb) conceptd = (a1,a2,a6,md,qd) concepte = (a3,a4,me,qe) conceptc = (a1,a5,mc,qc) as we can see, conceptd, concepte, and conceptc are defined by attributes only. activation levels of these concepts are calculated by aggregating activations of attributes. activation of an attribute means that the attribute is present, for example, in a web page. the aggregation process for each concept is controlled via the m and q associated with that concept. for conceptd, aggregation of activations of attributes a1, a2, and a6 is performed by the owa operators with a weighing vector determined by md and qd. 4 concept identification process: overview a concept-based information retrieval finds relevant documents based not on simple keyword matching but on concepts that are associated with terms from users’ sets of keywords. in order to accomplish that the process of concept identification is composed of the following stages: • firstly, a user’s set of keywords – called hereafter seed keywords for concept identification: seekcon – is translated into a structure of concepts; this is done by “mapping” keywords from seekcon to definitions of concepts, and constructing an equivalent hierarchy of concepts – hofc; • secondly, the constructed hofc is expanded using knowledge extracted from an ontologybased knowledge base – obkb; seekcons provided by users, who usually are not experts, contains simple terms; those terms are “linked” to definitions of concepts included in ontology; the terms, other concepts, and relations associated with those definitions are used to enhance the terms that are included in the equivalent hofc; this process leads to the expended hofc; 482 m.z. reformat, r.r. yager, z. li, n. alajlan knup hofcpp hofcsp obkb aati documents for estimation of levels of significance domain ontologies hofc document for evaluation seekcon figure 3: overview of the proposed approach for concept identification • finally, documents are checked how well they satisfy the extended hofc; for an hofc concept defined only by terms (no subconcepts), satisfaction of the concept is decided by the presence of those terms in a document; for an hofc concept defined by a list of terms and subconcepts, satisfaction of this concept is decided by the presence of those terms and the satisfactions of its subconcepts; in the end, the satisfaction of the extended hofc – and indirectly seekcon – is determined by the satisfactions of concepts defined in this hofc. an essential part of the proposed approach is a knowledge base containing definitions of concepts. these definitions are composed of (1) terms that describe these concepts together with their importance; (2) related concepts and their importance; (3) relations between terms and concepts. this knowledge base is built around ontology representing domains of interest. overall, the concept identification process contains a number of activities: a knowledge updating process knup, a hofc preparation process hofcpp, and a hofc satisfaction process hofcsp. knup works with a knowledge base to provide extra definitions of concepts and updates of single keywords importance in defining concepts. hofcpp translates users’ sets of keywords into hofcs, and expands them with knowledge from a domain knowledge base. hofcsp estimated levels of activation of concepts in documents what leads to identification of concepts. fig. 3 presents an overview of the proposed concept identification process. component knup builds and manages the obkb, the knowledge base. the importance values for terms or concepts in the obkb are obtained by the aati scheme, as shown in fig. 3. the document repository contains “unknown” to the aati documents. that is, there is no need for human experts to work on these documents to generate such knowledge as contained concepts, categories they belong to and so on. therefore, the process of updating the obkb does not stop. this is an outstanding advantage of the approach because no extra human labor is needed to build sample documents human-inspired identification of high-level concepts using owa and linguistic quantifiers 483 and the knowledge in the obkb can be kept up to date. hofcpp translates users’ seekcons into equivalent hofcs. it enriches these hofcs with knowledge stored in the obkb. hofcs provide lists of concepts to be identified and their definitions. the definitions of concepts, also known as the knowledge of concepts, include subconcepts, terms, their importance and their relations. as the output of this process, expanded hofcs are utilized for evaluation. hocsp determines how hofcs are activated by documents. documents are semantically annotated first. the annotated terms are pieces of information that satisfy concepts from the hofc. owa is integrated with the hofc to evaluate the satisfactions of queries by texts in documents. this process is mapped as the aggregation of the activations of the concepts from the hofc. the proposed process successfully implements concept identification. a term, as a piece of atom information, is not a binary string, but is related to a concept that is a meaningful entity defined in the obkb or in an hofc. that is, it has semantics. this is accomplished based on semantics instead of a simple presence. the main techniques, such as an ontologybased knowledge base (the obkb), an aati scheme, a hofc format, and owa operators for aggregation, are applied for this purpose. • an ontology provides a specification of a conceptualization. that is, it provides lists of concepts, terms and their relations in a domain. • an aati scheme assigns importance to terms and in turn to concepts in the obkb by “blindly” reading of documents. • users’ seekcons are organized into concepts in hierarchical structures (hofc). • owa operators aggregate the satisfactions of concepts in a hofc to evaluate the satisfaction of the whole hofc. the hofc accepts different linguistic quantifiers that make the approach flexible in defining concepts and representing different user interests. 5 ontologies and ontology-based knowledge base in order to identify high-level concepts we need to find out how many terms and subconcepts from concept definitions are present in a document. concepts which have their definitions "highly activated" are concepts identified in the document. the process of concept identification requires two essential parts: 1) definitions of concepts that contain lists of related concepts, subconcepts and terms, as well as relations between them: here, we use ontology as source of such information; 2) levels of importance of concepts and terms: they represents levels of contribution of terms/concepts toward concept activation levels; to address that issue we have developed adaptive assignment of term importance (aati) schema. there are two very important parts of knup: one that builds ontology-based knowledge base – obkb – from existing domain ontologies, and the other one that updates obkb. the process of updating the obkb is accomplished by the new self-adaptive scheme, aati (adaptive assignment of term importance), which assigns importance values to terms/concepts in the obkb, and continuously updates them. 5.1 ontology-based knowledge base (obkb) in an ontology, a class is described with properties. there are two types of properties: datatype property, and object property (section 3.1). in such a case, a concrete piece of infor484 m.z. reformat, r.r. yager, z. li, n. alajlan mation – called an individual – is an instance of a class and is created by assigning values to the properties. the ontology as defined above does not contain information required for concept identification in a suitable format. there are few main reasons for that: 1. a hofc contains concepts, while an ontology contains classes and individuals; it is difficult to expand hofcs directly from ontology without a transformation process; 2. hofc connections and ontology connections have different meanings; 3. an ontology does not contain importance (represented by vector m), and linguistic quantifiers (represented by q) which are necessary for owa operators to determine levels of activation of concepts contained in hofcs (section 3.2). ontology vs. hofc: component aspect in the semantic web definition of ontology, classes are abstract and individuals represent concrete information. that is, class defines properties that have no concrete values; while individuals are defined by values assigned to the properties. unlike an ontology that is designed to store a large amount of organized knowledge, a hofc represents a list of specific information relevant to a high level concept. if a class in an ontology has no individuals, i.e., no concrete information associated with it, it can be provided later by others. but it is not reasonable that a hofc should contain a vertex without a concrete value associated with it. such a concept is useless because there is no way to identify it in a document. therefore, both classes as well as individuals from domain ontologies are translated into concepts in the obkb. the following formats and operations pertain here: • concepts are used in the hofc and the obkb; classes and individuals are used in the ontology; • attributes are used in the hofc and the obkb; properties are used in ontology; • the attributes in the hofc and the obkb are called terms, which can be found in documents. • owa operators are applied to aggregate activation levels of terms and concepts included in hofcs; owa operators require m (importance) and q (linguistic quantifiers)3. ontology vs. hofc: conncection aspect another important difference between ontologies and hofcs is related to connections between components existing in both structures. the obkb has the same connections as ontology. these connections have different meanings. for example, let us assume there is a obkb built based on the ontology shown in fig. 1. the connection between the concept player drogba and the concept soccer player is different from the connection between the concept player drogba and the concept club chelsea. the former connection means “is-a” or “has-a.” that is, player drogba is-a soccer player or soccer player has-a player drogba. the latter connection means “inclub” or “hasplayer”. that is, player drogba inclub club chelsea or club chelsea hasplayer player drogba. the connections in hofcs represent relative levels in the hierarchy structure, that is, one concept is a subconcept (superconcept) of another concept. hofcs are built to represent seekcons where subconcepts contribute to defining their related superconcepts. the connection may be meaningless, because the user can connect any concepts to express his/her interests. for example, let us assume the knowledge in the ontology shown in fig. 1 is correct. a user can create 3details about m and q are in section 3.2 and section 3.3 human-inspired identification of high-level concepts using owa and linguistic quantifiers 485 his/her own hofc which has player player drogba as the superconcept, and club fc barcelona as the subcocnept if he/she assumes that player drogba will transfer to club fc barcelona and he/she wants to check if there is any rumour about it. in this case, the satisfaction of club fc barcelona contributes to the satisfaction of player drogba, and in turn to the satisfaction of the whole hofc, though it seems the connection should not exist. in general, subconcepts (superconcepts) are used to describe two connected concepts in a hofc; while the term related-concepts is used to describe two connected concepts in the obkb. overall, we define three types of connections. 1. typei connection connects concepts that are parts of users’ seekcons; in other words the concepts provided by users are related to each other through typei connections; they are the strongest type of connections; 2. typeii connection connects concepts defined in the domain ontology; these connections are “is-a” and “has-a” relations; the first stage of expanding an equivalent hofc is based on this type of connection (section 6.2); 3. typeiii connection also connects concepts defined in an ontology; however these connections represent all types of relations except “is-a” or “has-a” relation; this is the weakest connection; the second stage of expanding an equivalent hofc is based on this type of connection (section 6.2). mapping from ontology to obkb in a nutshell, the process of building the obkb from domain ontologies consists of a few steps which transforms ontology specific elements into components suitable for construction of hofc. those steps are: 1. a class from ontology is translated into a concept, however the definitions of properties are not translated; 2. an individual from ontology is translated into a concept, and • the values of object properties of this individual are translated into concepts attached to the concept created based on this individual; • the values of datatype properties of this individual are translated into attributes attached to the concept created based on this individual; 3. a default value of q is added to the created concept; the default q is most ; 4. the default value of m is set as zero for each concept and term. the obkb is used to expand equivalent hofcs created based on seekcons provided by users. 5.2 estimating importance levels the obkb is a model of a given domain. it contains concepts, terms, and relations that are specific for this domain. the aati scheme integrated with the obkb, determines importance values that represent levels of contributions of these concepts and terms towards concept activation. in [39], the aati was studied and validated through experiments. the aati scheme is implemented based on the power iteration [38], which is used to find eigenvectors of a matrix in linear algebra. the main steps of aati implementation are as follows: 1. translate an ontology into the obkb; 486 m.z. reformat, r.r. yager, z. li, n. alajlan 2. take a new document; 3. parse the document and annotate it with concept names an terms from the obkb; 4. for each term do one of the following: (a) if term weight is not zero (it means the term has already appeared in documents), take this as its new term weight; (b) if term weight is zero (the term has not been found in any documents), randomly generate a number between 0 and 1, and assign it as its term weight; 5. calculate the page value of this document; 6. update the term weights of those terms found in the document; 7. normalize term weights across all terms (make the sum of term weights equal to 1); 8. if there are no more documents, stop ; otherwise go back to step 2. the aati continuously updates term weights based on available documents. intuitively, we can say that the page value is high when terms occurring in a document have high term weights. at the same time, we can state that the term weight of a term is high when the page values of documents that contain the term are high. 5.3 obkb-based annotation of documents we have developed a java annotation module based on the uima (unstructured information management architecture) library created by ibm [40], which adds extra information to the texts of documents. annotation is the basis for concept identification processes. terms, which are literal values of the datatype properties – attributes in the obkb – are used to identify concepts in texts. all occurrences of obkb terms in a document are identified, and annotation information is added to them. this information includes its position (begin, end) in a document, the concept it belongs to, and the attribute it belongs to. annotation connects the obkb with documents: the frequency of a term presented in a document is stored and used later for determination how well the document satisfies the concept, to which the term is attached in the obkb. fig.4 shows how a document is annotated. for example, the term “chelsea” from obkb is the attribute name attached to the concept club chelsea. the annotation module finds that this term is present twice in the document. so, for the obkb and the annotation module, a string “chelsea,” which to most computer agents is meaningless (nothing but a binary string), has its own semantics/meaning: it is the name of a soccer club. 6 hofc preparation process the hofcpp preprocesses seekcons, builds hofcs from them, and expands these hofcs with knowledge from the obkb. the preprocessing of seekcons removes stop words and stems words. stop words include articles such as “the” and “a” and prepositions such as “in” and “from.” the stemming process removes suffixes and prefixes reducing words to their stems. for example, the term “extended” can be stemmed to “extend.” the process of building equivalent hofcs translates seekcons into concept structures as explained in section 6.1. next, knowledge is retrieved from the obkb to enhance the definitions of concepts. the details are presented in section 6.2. human-inspired identification of high-level concepts using owa and linguistic quantifiers 487 soccer player hasplayer inclub hasplayer inclub name:fc barcelona location: barcelona stadium:camp nou soccer club soccer club fc barcelonaplayer drogba player terry club chelsea first name: didier last name: drogba position:forward first name: john last name: terry position:midfielder name: chelsea location: london stadium:stamford bridge figure 4: document annotation: example 6.1 representation of users’ sets of keywords as hofcs a seekcon is a statement representing the interest of the user. the user provides keywords that describe objects that the user desires to identify in a document. the keywords are represented as a flat structure, that is, there is no indication that the keywords are related to each other, or that some keywords depend on others. this is a simplification suitable for automatic processing but it is not a realistic representation of a human-like way of finding concepts. for a human, all keywords are interconnected. they constitute a network of words representing concepts. the activation of a single word initiates activation of a concept and related concepts. a seekcon is represented by a hofc – a tree-like structure built of concepts (vertices) and terms (terminal vertices/leaves) (section 3.3). this structured form of a seekcon, that resembles a network of words and concepts, provides a more intuitive way of expressing things the user is looking for. a document that satisfies an equivalent hofc contains a high-level concept related to this hofc. the satisfaction of the hofc is a result of an aggregation of satisfactions performed at each vertex of hofc using owa, and associated with it the linguistic quantifier q, and the vector m of importance values. as presented in section 3.2, a linguistic quantifier q associated with a vertex is used at the time of the aggregation of satisfactions of terms and other concepts (subconcepts) attached to this vertex. the possible quantifiers are some, more, etc. the notion of linguistic quantifiers plays an important role in representing different ways of combining satisfactions. 488 m.z. reformat, r.r. yager, z. li, n. alajlan a vector m (section 3.2) represents the importance of each term and concept that is taken into consideration during an aggregation process. not all terms and concepts contribute uniformly to the activation of a concept associated with a vertex of hofc. rc concept1 (a) keyword rc concept1 concept2 concept3 (b) multiple keywords rc concept1 concept2 concept3 (c) structured keywords figure 5: sample root concepts (rcs) a seekcon represents a high-level concept to be identified in documents. the goal is to find out if a given document contained terms and subconcepts that are part of the equivalent hofc. this translates into a process of estimating a level of activation (or satisfaction) of the hofc. in order to simplify this process, a special concept, called root concept (rc), is automatically added to each equivalent hofc. in this case, the activation of the hofc is the same as the activation of the top level concept of the equivalent hofc. some possible structures of hofcs are presented in fig 5. in fig. 5a, a seekcon contains one single concept; in fig. 5b, a seekont contains multiple concepts in a flat structure; and in fig. 5c, a seekcon contains a few structured concepts. activation of these hofcs is simplified to the activation of a single root concept (rc), regardless the complexity of hofcs. 6.2 building and expanding hofc the process of identifying high-level concepts in a document depends on the ability to determine if hofc concepts can be inferred based on the texts of the document. this depends on the contents of the hofc itself – the more terms and concepts are used to build the hofc, the higher the chance of proper evaluation of presence of a high-level concept in a document. we do not expect users to provide comprehensive seekcons containing a large number of terms and concepts. the model accepts conventional keyword-based seekcons that are automatically translated into hofcs (section 6.1). that is, the keywords are translated into concepts with a specific hierarchy representing how those concepts are related to each other. the keywords from seekcon are regarded as connected with a typei connection. the names of concepts from an equivalent hofc (the same as keywords provided by the user) are used to retrieve knowledge of equivalent concepts from the obkb. the knowledge includes subconcepts, attributes, linguistic quantifiers (q), and importance vectors (m). the expansion process is as follows. for each concept from the hofc do search obkb for a concept equivalent to the concept if an equivalent concept is found then 1. copy all concepts from the obkb connected via connection typeii to the found concept as subconcepts of the concept human-inspired identification of high-level concepts using owa and linguistic quantifiers 489 2. copy all attributes of all just copied concepts from obkb as attributes of the subconcepts 3. copy all concepts, and their attributes, connected to the concept found in obkb via typeiii connection else add a string-type attribute “name” to the concept; the value of this attribute is the name of the concept end if end for a hofc includes concepts and attributes arranged in a hierarchy. identification of a concept in the obkb through a keyword means finding the matching keyword among the concept names or concept attributes in the knowledge base. once an equivalent concept is found, all knowledge regarding it present in the obkb is used to enhance the hofc. it is also possible that the user provides other linguistic quantifiers q different from the default quantifier contained in the obkb. different linguistic quantifiers mean different ways of aggregation of activation levels (section 3.2). 7 identification of concepts: hofc satisfaction process hofc satisfaction process (hofcsp) accepts expanded hofc (output from hofcpp), and identifies concepts in documents. an extended hofc contains not only concepts (user’s keywords) themselves but also concepts and attributes from the definitions of keyword-based concepts. those “new” concepts and attributes can be identified in documents. the evaluation of relevance is mapped as the “activation” of the hofc based on owa (section 3.2). the more of the hofc is activated, the higher the chance that the document contains a high-level concept represented by hofc. 7.1 satisfaction, importance and aggregation weights before explaining how hofcsp works, three terms used here: satisfaction, importance, and aggregation weights, need to be defined • satisfaction represents how well a criterion in the hofc is satisfied by the text from a document. a criterion can be an attribute, a concept or even a complete hofc. the satisfaction of the root concept (rc) (section 6.1) is equivalent to the satisfaction of the whole hofc. • importance represents how important a criterion is. with the aati scheme, each entity (term or concept) in the obkb includes a measure of importance. • aggregation weight is the weight of a criterion calculated by the owa operator (section 3.2). aggregation weight depends not only on the importance of the criterion but also on the linguistic quantifier. in section 7.2, the use of linguistic quantifiers and their importance in calculating aggregation weights is explained. 7.2 linguistic quantifier the concept of linguistic quantifiers was introduced by zadeh [35] in the early 1980s. some examples are all, most, at least half and about one third. to formally rep490 m.z. reformat, r.r. yager, z. li, n. alajlan resent those quantifiers, zadeh suggested using a fuzzy subset q(r) as a linguistic expression corresponding to a quantifier that indicates the degree to satisfy the concept for any proportion r ∈ [0,1]. (a) some (b) most figure 6: linguistic quantifiers yager [36] used the linguistic expression q(r) to obtain a weighting vector w associated with an owa aggregation. these quantifiers were then able to guide aggregation procedures by verbally expressed concepts in a description independent dimension. in the presented work, two linguistic quantifiers are considered: • q(r) = r represents the linguistic quantifier some, fig. 6a. the equation means that the aggregation weight of a criterion depends only on its importance value. that is, the relations between concepts do not change their aggregation weights. • q(r) = r2 represents linguistic quantifier most, fig. 6b. using this equation, the aggregation weight of a criterion tends to be large when it is less satisfied (more details are in section 3.2). 7.3 satisfaction of concept with attributes only a criterion for a single concept can be an attribute or a subconcept. the simplest situation is when a concept has only attributes attached to it. the literal values of attributes are called terms. the presence (more precisely, frequencies) of a term or terms in a document decides how well the criterion (attribute) is satisfied. mathematically, the satisfaction of a term si is expressed with the following formula: si = e − 1 freqi (4) where freqi is the frequency of the term in the document. the reasons for using eq. 4 are: 1. it is a monotonically increasing function, so when its frequency increases, the satisfaction increases. 2. the function “saturates” with increasing values of the argument, so if a given term occurs many times in a document, its satisfaction does not overshadow the satisfactions values of other terms. human-inspired identification of high-level concepts using owa and linguistic quantifiers 491 each criterion has an assigned value of importance. assuming there are n different datatype attributes attached to a single concept c, the importance vector m contains n elements, where each element represents the importance of a criterion. the satisfaction vector s also contains n elements, where each element represent the satisfaction level calculated for a criterion based on a given document. in such case, the ordered satisfaction vector sprime (eq. 1) is formed based on s, in which sprimej is the jth largest of all satisfactions s1, s2, ..., sn. furthermore, we assume µj denotes the importance weight associated with the attribute that has the jth largest value. thus if s3 is the largest value, then s prime 1 = s3 and µ1 = m3. based on this, the owa weighing vector w is obtained by q(r) in eq. 5, wj = q( xj t ) − q( xj−1 t ) (5) where xj = ∑j k=1 µk and t = xn = ∑n k=1 µk. that is, xj is the sum of the importance weights of the jth most satisfied arguments, and t is the sum of all importance weights. so, the satisfaction of concept sc is: sc = n∑ k=1 wk · s prime k (6) 7.4 satisfactions of concept with attributes and subconcepts as mentioned earlier, a criterion can be an attribute or a subconcept. the satisfaction of a subconcept is the satisfaction of a concept. the calculations of the satisfaction of a criterion based on terms (attributes) and the calculation of the satisfaction of a criterion based on concepts (subconcepts) are different. the calculation of the satisfaction of a criterion based on terms is quite simple, as shown in eq. 4, while the calculation of the satisfaction of a criterion based on concepts includes more complicated aggregation. when both attributes and subconcepts are attached to the concept, we aggregate separately satisfactions of attributes and satisfactions of subconcepts, and then aggregate the results of both aggregations. for example, for the conceptd in fig. 7 its satisfaction is calculated using eq. 6 using frequencies of terms. in the case of the concepta in fig. 7, its satisfaction is calculated in two steps: firstly, the satisfaction of the terma1 is determined as well as satisfactions of the conceptb and the conceptc; secondly, all satisfactions are aggregated using owa with m_a and q_a. 7.5 satisfaction of hofc activation of subconcepts the hofc is propagated upward as the process to calculate satisfaction of the hofc continues. that is, calculation of the satisfaction of the hofc starts from the attributes/concepts at the lowest level and ends with the concepts at the highest level. the presence of terms/attributes (for example, termd1,termd2,termc1, and termc2, see fig. 7) in a document contributes to the satisfaction of the corresponding concepts (conceptd and conceptc, respectively, fig. 7). the aggregation of the satisfactions of those concepts and other terms contributes to the satisfaction of the higher-level concepts (for example, termb1 and conceptd lead to activation of conceptb, fig. 7). the process is repeated until the satisfaction of the concept on the top level is calculated. the structure of hofc determines which terms/attributes and concepts contribute to the satisfactions of which concepts – satisfaction of a single concept is calculated based on the satisfactions of all terms and concepts that are attached (from the bottom) to it. the aggregation of satisfactions is performed at each concept of hofc using owa (eq. 6). 492 m.z. reformat, r.r. yager, z. li, n. alajlan m_a q_a concepta conceptd m_d q_d m_c q_c conceptc m_b q_b conceptb termc1 termc2 termd1 termd2 termb1 terma1 figure 7: enhanced hofc 8 case study in order to illustrate the proposed process for concept identification we present a simple case study where two seekcons: a:chelsea and b:drogba;terry;chelsea, are “checked” against two documents doc.1 and doc.2. the process is performed with two linguistic quantifiers most (q(r) = r2) and some (q(r) = r). the frequencies of terms in doc.1 and doc.2 are listed in table 1. roughly, doc.1 contains more information about concept chelsea and doc.2 contains more information about player drogba and player terry. table 1: term frequencies in documents doc.1 doc.2 concept chelsea name:chelsea 6 0 location:london 2 1 stadium:stamford 2 1 concept player drogba first name:didier 0 2 last name:drogba 1 2 position:forward 0 1 concept player terry first name:john 0 2 last name:terry 1 3 position:midfielder 0 0 8.1 building and expanding hofc both seekcons a and b are used to create hofcs. hofc_a is built from the keyword “chelsea”, fig. 8. the keyword “chelsea” itself is a piece of meaningless text for the machine. however, knup tries to figure out the concept embedded in this keyword. it finds the concept club chelsea in the obkb because the term “chelsea” is the value of its attribute name. then definitions of the concept in the knowledge base are added into the seekcon including the human-inspired identification of high-level concepts using owa and linguistic quantifiers 493 figure 8: construction of hofc_a values of its datatype attributes such as “london” (attribute location) and “stamford”(attribute stadium), and the values of the object attribute hasplayer associated with the two concepts: concept player drogba and player terry. the connection between concept club chelsea and concept player drogba and the connection between concept club chelsea and concept player terry are all typeiii connections (section 6.1 for more details). for simplicity, in this chapter we treat typei, typeii and typeiii connections equally in calculation. similarly, hofc_b is built from keywords “drogba,” “terry,” and “ chelsea”, fig. 9. since there is no indication about their relations, the process hofcpp places the three concepts in a flat (equal level) structure. the connections in this hofc are typei. figure 9: construction of hofc_b 8.2 importance of terms the importance (tw) of terms is assigned by the aati. the values are displayed in the table below: 494 m.z. reformat, r.r. yager, z. li, n. alajlan concept player drogba term didier drogba forward importance 0.06 0.11 0.02 concept player terry term john terry midfielder importance 0.03 0.07 0.06 concept club chelsea term chelsea london stamford importance 0.15 0.02 0.08 8.3 evaluation in total, there are four cases using the linguistic quantifiers most and some : hofc_a(most), hofc_a(some), and hofc_b(most), hofc_b(some). cases hofc_(most) and hofc_a(some) utilize hofc_a; cases hofc_b(most) and hofc_b(some) utilize hofc_b. table 2 shows the design. table 2: case study design case linguistic quantifier hofc_a(most) most hofc_a(some) some hofc_b(most) most hofc_b(some) some due to space limitations we show all computational details for two of those cases: hofc_a(some) and hofc_b(most). the results for the rest of the case are given in the table in section 8.4. case hofc_a(most) for doc. 1 hofc_a, shown in fig. 8, is generated from the keyword “chelsea.” it is composed of three concepts, which are club chelsea, player drogba, and player terry. the linguistic quantifier in this case is most. fig. 10 displays the aggregation orders in both cases: hofc_a(most) and hofc_a(some). the ellipses in the figure denote the aggregation processes; the numbers in the ellipses are the orders of the processes. as shown in fig. 10, satisfactions of the concepts player drogba and player terry are calculated firstly. as subconcepts of the concept club chelsea, their satisfactions are aggregated. then satisfactions of the terms in concept club chelsea are calculated and aggregate with the satisfactions of the aggregated subconcepts, which is the satisfaction of the whole hofc. player drogba has no subconcept, so its satisfaction is decided by attached terms. the term satisfaction is calculated based on eq. (4). we calculate the ordered satisfactions of the terms in player drogba: concept player drogba satisfaction importance term: drogba 0.3679 0.11 term: didier 0 0.06 term: forward 0 0.02 because the linguistic quantifier is most, which is defined by q(r) = r2. for the concept player drogba, the sum of the importance of all attached terms is 0.11 + 0.06 + 0.02 = 0.19. based on owa, the weights of its criteria/terms are: human-inspired identification of high-level concepts using owa and linguistic quantifiers 495 player terry m_4 q_4 player drogba m_3 q_3 m_5 q_5 club chelsea rc m_1 q_1 st am fo rd l on do n c he ls ea didier forward john terry midfielder 1 5 3 drogba 2 4 figure 10: hofc_a: satisfaction calculation process term drogba: wtdrogba = q( 0.11 0.19 ) − q( 0 0.19 ) = 0.3352 term didier: wtdidier = q( 0.17 0.19 ) − q( 0.11 0.19 ) = 0.4654 term forward: wtforward = q( 0.19 0.19 ) − q( 0.17 0.19 ) = 0.1994 for doc.1, concept player drogba has its satisfaction as: scdrogba = 0.3352 × 0.3679 + 0.4654 × 0 + 0.1994 × 0 = 0.1233 (7) then we calculate the ordered satisfactions of the terms in concept player terry: concept player terry satisfaction importance term: terry 0.3679 0.07 term: john 0 0.03 term: midfielder 0 0.06 the sum of the importance of all attached terms in concept player terry is 0.07+0.03+0.06 = 0.16. based on owa, the weights of its criteria/terms are calculated: term terry: wtterry = q( 0.07 0.16 ) − q( 0 0.16 ) = 0.1914 496 m.z. reformat, r.r. yager, z. li, n. alajlan term john: wtjohn = q( 0.10 0.16 ) − q( 0.07 0.16 ) = 0.1992 term forward: wtforward = q( 0.16 0.16 ) − q( 0.10 0.16 ) = 0.6094 for doc.1, the concept player terry has its satisfaction as: scterry = 0.1914 × 0.3679 + 0.1992 × 0 + 0.6094 × 0 = 0.0704 (8) defined by the structure of the hofc_a, satisfaction of the concept club chelsea is obtained by aggregating the satisfactions of its subconcepts (concept player drogba and concept player terry) and terms(“chelsea,” “london,” and “stamford”). because the methods for calculateing the satisfaction of subconcepts and terms are different, we calculate the satisfactions separately and then combine them. the details are as follows. the ordered satisfactions of sub-concepts in concept club chelsea are: concept player chelsea satisfaction importance weights concept: drogba 0.1233 0.19 0.2947 concept: terry 0.0704 0.16 0.7053 satisfaction of the combination of sub-concepts in concept club chelsea is: s prime cchelsea = 0.1233 × 0.2947 + 0.0704 × 0.7053 = 0.0860 (9) the ordered satisfactions of the terms in club chelsea are: concept player chelsea satisfaction importance weights term: chelsea 0.8465 0.15 0.3600 term: london 0.6065 0.02 0.1024 term: stamford 0.6065 0.08 0.5376 satisfaction of the combination of the terms in club chelsea is: s”cchelsea = 0.8465 × 0.3600 + 0.6065 × 0.1024 + 0.6065 × 0.5376 = 0.6929 (10) satisfaction of club chelsea is calculated by the combination of subconcepts sprimecchelsea and terms s”cchelsea as: concept club chelsea satisfaction importance weights comb. of terms 0.6929 0.25 0.1736 comb. of sub-concepts 0.0860 0.35 0.8264 finally, when applied to doc.1, satisfaction of the hofc_a is the same as satisfaction of concept club chelsea: scchelsea = 0.6929 × 0.1736 + 0.0860 × 0.8264 = 0.1914 (11) human-inspired identification of high-level concepts using owa and linguistic quantifiers 497 player drogba m_3 q_3 player terry m_4 q_4 m_5 q_5 club chelsea didier drogba forward chelsea stamford london john terry midfielder rc m_1 q_1 1 2 3 4 figure 11: hofc_b: satisfaction calculation process case hofc_b(some) for doc. 1 if a seekcon “chelsea drogba terry” is provided, a new hofc is created as shown in fig. 9. fig. 11 displays the aggregation order for hofc_b in cases hofc_b(most) and hofc_b(some). the ellipses in the figure denote the aggregation processes; the numbers in the ellipses are the orders of the processes. as shown in fig 11, the three single concepts (concepts player drogba, player terry, and club chelsea) are processed first. the satisfaction of the three separated concepts are aggregated as the satisfaction of the whole hofc. first, we calculate the ordered satisfaction of the terms in player drogba as: concept player drogba satisfaction importance term: drogba 0.3679 0.11 term: didier 0 0.06 term: forward 0 0.02 because the linguistic quantifier is some, which is defined by q(r) = r. for player drogba, the sum of the importance of all attached terms is 0.11 + 0.06 + 0.02 = 0.19. based on owa, the weights of its criteria/terms are: term drogba: wtdrogba = q( 0.11 0.19 ) − q( 0 0.19 ) = 0.5789 term didier: wtdidier = q( 0.17 0.19 ) − q( 0.11 0.19 ) = 0.3158 term forward: wtforward = q( 0.19 0.19 ) − q( 0.17 0.19 ) = 0.1053 for doc.1, concept drogba has its satisfaction as: 498 m.z. reformat, r.r. yager, z. li, n. alajlan scdrogba = 0.5789 × 0.3679 + 0.3158 × 0 + 0.1994 × 0 = 0.2130 (12) we calculate the ordered satisfactions of the terms in player terry: concept player terry satisfaction importance term: terry 0.3679 0.07 term: john 0 0.03 term: midfielder 0 0.06 the sum of the importance of all attached terms in player terry is 0.07+0.03+0.06 = 0.16. based on owa, the weights of its criteria/terms are: term terry: wtterry = q( 0.07 0.16 ) − q( 0 0.16 ) = 0.4375 term john: wtjohn = q( 0.10 0.16 ) − q( 0.07 0.16 ) = 0.1875 term forward: wtforward = q( 0.16 0.16 ) − q( 0.10 0.16 ) = 0.3750 for doc.1, concept terry has its satisfaction as: scterry = 0.4375 × 0.3679 + 0.1875 × 0 + 0.3750 × 0 = 0.1610 (13) the ordered satisfactions of the terms in club chelsea are: concept player chelsea satisfaction importance weights term: chelsea 0.8465 0.15 0.6000 term: london 0.6065 0.02 0.0800 term: stamford 0.6065 0.08 0.3200 the satisfaction of the combination of the terms in club chelsea is: s”cchelsea = 0.8465 × 0.6000 + 0.6065 × 0.0800 + 0.6065 × 0.3200 = 0.7505 (14) the ordered satisfactions of the three concepts are: rc satisfaction importance weights concept:chelsea 0.7505 0.35 0.4167 concept:drogba 0.2130 0.19 0.3167 concept:terry 0.1610 0.16 0.2667 therefore, the satisfaction of the rc, which is the satisfaction of hofc_b(some), is: scrc = 0.4167 × 0.7505 + 0.3167 × 0.2130 + 0.2667 × 0.1610 = 0.4231 (15) human-inspired identification of high-level concepts using owa and linguistic quantifiers 499 8.4 discussion the results for all cases are summarized in table 3. there are 12 pieces of information (sum of the frequencies of terms) to satisfy three aimed concepts (table 1) in both documents. for doc.1, 10 out of 12 are directly related to club chelsea, while for doc.2, 10 out of 12 are directly related to player drogba and player terry. in all of the results, the scores for doc.2 are greater than the scores for doc.1. this is mainly because different pieces of information have different importance (section 8.2). moreover, the scores differ when calculated with different hierarchical structures or with different linguistic quantifiers. table 3: the results of cases case linguistic quantifier doc.1 doc.2 hofc_a(most) most 0.1914 0.3234 hofc_a(some) some 0.4231 0.4391 hofc_b(most) most 0.1977 0.3115 hofc_b(some) some 0.4231 0.4391 for hoc_a(some) and hofc_b(some) the satisfaction of hofc_a is equal to the satisfaction of hofc_b when the same document is evaluated. that is, with the linguistic quantifier some, the hierarchical structures do not affect the calculation of satisfactions. this is because some is defined by q(r) = r (fig. 6a), which means the aggregation weight of a criterion is decided by its importance value directly. that is, with the linguistic quantifier some, the order of aggregation does not change the calculation. for information aggregated by the linguistic quantifier most (defined by q(r) = r2,fig. 6b) order is crucial. for example, if there are two pieces of information (i1 and i2) with importance imp1 = 0.2 and imp2 = 0.3, respectively, and the order of the aggregation is i1 then i2, with most their weights are: wgt1 = (0.2/0.5)2 = 0.16 and wgt2 = 1−(0.2/0.5)2 = 0.84, respectively. that is, i2 obtains larger aggregation weight than i1. if the order of the aggregation is i2 then i1, their weights are: wgtprime2 = (0.3/0.5) 2 = 0.36 and wgtprime1 = 1 − (0.3/0.5) 2 = 0.64, respectively. that is, i1 obtains larger aggregation weight than i2. thus with most, the order of aggregation is important for calculations. moreover, because the aggregation of pieces of information is in an order from most satisfied to less satisfied, the less satisfied piece of information tends to have larger weights. the difference in satisfactions between doc.1 and doc.2 with the linguistic quantifier some is much smaller than that with the linguistic quantifier most. for hofc_a, when the linguistic quantifier changes from some to most, the difference in satisfactions between doc.1 and doc.2 changes from 0.4391 − 0.4231 = 0.0160 to 0.3234 − 0.1914 = 0.1315. for hofc_b, when the linguistic quantifier changes from some to most, the difference in satisfaction between doc.1 and doc.2 changes from 0.4391 − 0.4231 = 0.0160 to 0.3115 − 0.1977 = 0.1138. this is because satisfaction calculated with most is affected by hierarchies while satisfaction with some is not. the case studies are good examples of how hotir, especially component eval, ranks documents. moreover, the detailed calculations illustrate the effects of the linguistic quantifiers some and most in hotir. in the next chapter, the designed experiments examine the performance of hotir with some and most. 9 conclusions the constant need for finding relevant documents leads to increased interests in concept-based search techniques. a necessary part of it is concept identification. in the paper we propose 500 m.z. reformat, r.r. yager, z. li, n. alajlan a human-inspired approach to detect concepts in text documents. the proposed approach is a fusion of multiple techniques: ontology, term importance evaluation schema, hierarchies of concepts, linguistic quantifiers and the aggregation operator owa. the following elements are proposed for the implementation of the approach: • obkb – the ontology-based knowledge base provides supplementary knowledge to define concepts in sets of keywords representing users’ interests; • aati – the scheme for assigning importance values to terms and concepts; • hierarchies of concepts (hofc) – structures built from concepts, terms/attributes; they are used as representation of users’ keywords; they are enhanced with knowledge from obkb; • linguistic quantifiers and owa – used to aggregate activation levels of concepts and terms from hofc and estimating overall activation of hofc which is equivalent to identification of concepts in documents. the case study has been designed to show how the proposed approach performs with different settings. the obtained results and inspection of the calculation processes confirm the ability of the approach 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[40] d. ferrucci and a. lally, uima: an architectural approach to unstructured information processing in the corporate research environment, natural language engineering, 10(3-4): 327-348, 2004. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 749-760 on fuzzy sequences, fixed points and periodicity in iterated fuzzy maps h.n. teodorescu horia nicolai teodorescu gheorghe asachi technical university, iasi, romania institute for computer science, romanian academy, iasi branch e-mail: hteodor@etc.tuiasi.ro abstract: i exemplify various elementary cases of fuzzy sequences and results related to the iteration of fuzzy mappings and to fuzzy logic systems (fls). several types of fuzzy logic system iterations are exemplified in relationship with oscillations in fls and with the problem of stability in fuzzy logic control. i establish several conditions for fixed points and periodicity of the iterations based on fuzzy systems. keywords: modeling, control, fuzzy map iteration, fuzzy sequence, behavior. 1 introduction in this paper, i provide simple examples of iteration of fuzzy sets and i discuss the asymptotic behavior of their successive iterations. i discuss several cases of iterated fuzzy logic and arithmetic systems with and without defuzzification and determine conditions for fuzzy sets that are fixed points. the topic was dealt with in the literature in the early approaches by kloeden [1] and in [2][9], but attracted little interest during the last two decades. however, sequences of fuzzy numbers and various generalizations constituted recently the subject of a large number of papers [10][21], while many applications indirectly referred to fuzzy sequences [22][24]. however, few recent papers, like [24], addressed fuzzy sequences and the problem of fixed points in fuzzy mappings with relation to fuzzy systems and control. in this paper, a fuzzy map is any application from a set of fuzzy sets to itself. a fuzzy system is a function with fuzzy set-valued variables, also named inputs, and fuzzy set values, named output. an iterated fuzzy system can be seen as a fuzzy system, in the sense used in control, but without defuzzification, that has a connection from the fuzzy output to the fuzzy input through a unitary delay loop. analyzing fuzzy iterations has therefore applications in control, in decision making, and in modeling, among others. the connection between iterations and sequences is straightforward: while iterations are defined by f[n+1] = f(f[n]), many sequences are obtained as an+1 = f(an), an+1 = f(an−1, an), an+1 = f(g(an−1), an), or by similar formulas. a fixed point of a specified fuzzy function (system) iteration is a fuzzy set, ã, (membership function, µ ã ) that is preserved under the iteration; in other words, when presented to the input of the fuzzy system, it is found unchanged at its output.a periodical fuzzy system will recover after a fixed number of iterations the period the initial input membership function as its output. assume that a metric space has been defined on a space of fuzzy sets. subsequently, when convergence of sequences is considered in the respective metric space, i mean that ãn → ã∞ iff d(ãn, ã∞) −→n→∞ 0 . whenever absolute convergence will be meant, the following definition applies: ãn → ã∞ iff |µãn(x) − µã∞| −→ n→∞ 0 ∀x. in this paper, the primary interest is in absolute convergence. copyright c⃝ 2006-2011 by ccc publications 750 h.n. teodorescu a basin of attraction of a specified input fuzzy set, which is a fixed point for a specified fuzzy system, is the set of membership functions that determine the output of the fuzzy system to tend during the iteration to the specified fuzzy set. the applications of the above concepts in the real space are well known to control engineers, biologists, and virtually in all disciplines. i denote by [0, 1]r the set of functions from r to [0,1]. membership functions used at the input and at the output of the fuzzy logic systems belong to [0, 1]r. being given an application φ : [0, 1]r → [0, 1]r, the successive iteration of φ with initial condition ã0 is ã0 → ã1 = φ(ã0), ã2 = φ(ã1) = φ [2](ã0), ã3 = φ(ã2) = φ [3](ã0), ... . a fuzzy logic system (fls) is basically an application φ : [0, 1]r → [0, 1]r, which is defined in a specific manner, using rules. when a discrete-time feedback loop is added to a fuzzy logic system, the equivalent operation is an iteration of the function φ starting with an initial input membership function ã0. the operation of the loop is expressed by the successive iterations φ[n](ã0), n=1,2,3,... . from the application point of view, an iteration of a fls can be seen as a fls with a discrete time, constant loop, like in fig.1. figure 1: iteration of a fls represented as a fls with a delay loop. the loop "gain" is unitary. the operation is in discrete time. the set [0, 1]r, when endowed with one of the usual definitions of distance between functions, becomes a metric space and has the corresponding induced topologic structure. therefore, we are able to talk about limit points in that topologic space and about the asymptotic behavior of the successive iteration of an application represented by a fls. when a metric is needed, i use d(ã, b̃) = ∫ ∞ −∞(µã(x) − µb̃(x)) 2dx, where ã, b̃ stand for the output of the flss, assuming integrability. in the next section, which is preparatory, i exemplify various ways for building sequences of fuzzy sets. in section 3, i discuss sequences generated by flss. conclusions are derived in section 4. throughout this paper, ♢ denotes the end of a proof, example, or a line of reasoning. 2 elementary examples of fuzzy sequences 2.1 games based on fuzzy sequences produced by logic operations over fuzzy sets i start illustrating the topic of fuzzy sequence by a simple example based on logic operations with fuzzy sets. consider the following game: three school colleagues have a high spirit of contradiction. when they start a discussion, they typically start with two disparate opinions; then, each of them is ready to contradict what the last speaker told and partly agree and partly disagree with the ante-precedent speaker; to do so, they answer with what at the same time contradicts the predecessor, yet agrees with the ante-predecessor. they take turns in expressing their opinions in that way. can they ever come to a conclusion? if yes, after how many steps of mutual contradictions? (i would say that the game is not far from what seldom happens in real life, so this example is not devoid of interest. the problem is if a group of three skillful contradictors still can agree under fuzzy logic.) on fuzzy sequences, fixed points and periodicity in iterated fuzzy maps 751 this "game" is not possible in binary logic, but makes sense in the frame of fuzzy logic. consider two fuzzy sets, ã0 and ã1 representing the first two opinions. the third colleague’s and the subsequent responses define a sequence of fuzzy sets by ãn+1 = ¬ãn ∩ ãn−1, n ≥ 1, where ¬ãn denotes the complement of the fuzzy set ãn. the above recurrence, together with the initial conditions ã0 and ã1, defines the fuzzy set sequence. it is easy to see that the sequence is ã0, ã1, ã2 = ¬ã1 ∩ ã0, ã3 = ¬ã2 ∩ ã1,... . the membership functions of ã3 is µ3(x) = min(µ1(x), 1 − µ2(x)), or µ3(x) = min(µ1(x), 1 − min(µ0(x), 1 − µ1(x))). (i) assume µ0(x) < 1 − µ1(x). then µ3(x) = min(µ1(x), 1 − µ0(x)). because µ0(x) < 1 − µ1(x), µ1(x) < 1 − µ0(x), thus µ3(x) = min(µ1(x), 1 − µ0(x)) = µ1(x). (ii) assume µ0(x) ≥ 1 − µ1(x). then µ3(x) = min(µ1(x), 1 − (1 − µ1(x))) = µ1(x). therefore, ã3 = ã1. next, ã4 = ¬ã3 ∩ ã2 = ¬ã1 ∩ ã2 and µ4(x) = min(µ2(x), 1 − µ1(x)), or µ4(x) = min(min(1 − µ1(x); µ0(x)), 1 − µ1(x)). (i) assume µ0(x) < 1 − µ1(x). then µ4(x) = min(µ0(x), 1 − µ1(x)) = µ0(x). (ii) assume µ0(x) ≥ 1 − µ1(x). then µ4(x) = min(1 − µ1(x), 1 − µ1(x)) = 1 − µ1(x). thus, ã4 = ¬ã1. next, ã5 = ¬ã4 ∩ ã3 = ã1 ∩ ã1 = ã1. if the response strategy is changed, that is ãn+1 = ãn∩¬ãn−1, n ≥ 1, the game is less simple. indeed, the membership functions of ã2 and ã3 are µ2 = min(µ1, 1−µ0), µ3 = min(min(µ1, 1− µ0), 1 − µ1). whenever µ1(x) < 1 − µ0(x), µ3(x) = min(µ1(x), 1 − µ1(x)), ã3 = ã1 ∩ ¬ã1, else ã3 = ¬ã0 ∩ ¬ã1. the next step is ã4, µ4 = min(µ3, 1 − µ2). 2.2 fuzzy sequences produced by arithmetic operations over values of the membership functions arithmetic operations with fuzzy numbers have been used to build chaotic processes by kloeden [1]. here i exemplify how, based on sequences of fuzzy sets and operating them through logic operations and using arithmetic manipulations of the elements of the sequences, one can obtain new sequences with the desired properties. consider the fuzzy set ã0 with µ0(x) = { x2 x ∈ [0, 1] 0 elsewhere. and the sequence of singletons with the membership functions µn(x) = { 1 x = 1 n 0 elsewhere. define c̃n by c̃0 = ã0 and c̃n = c̃n−1 ∪ b̃n for n > 0. the graph of the fuzzy set c̃n looks like in figure 2, where, for convenience, i have drawn a line to show where a singleton occurs. recall that a small full circle on the graph conventionally denotes the actual value at a point of discontinuity of a function, while a small empty circle denotes the lack of value on the graph of a continuous function. the number of singularities (discontinuities) of the membership function of the sets c̃n is equal to n.♢ define a sequence of fuzzy sets, {ũn, n = 1, 2, ...}, based on the fuzzy numbers ν0, ν1, µν̃0(x) = { 0 if x < 0 or x > 3 1 − x/3 else , µν̃1(x) = { 0 if x ̸= 1 1 else , 752 h.n. teodorescu figure 2: . as ũn = ν̃0 ∩ ¬(3 · ν̃1)n. i propose to graphically and analytically find the first two sets in the series, then to write their expressions, determining if there is a limit of the sequence. according to the operations with fuzzy numbers in the framework of the fuzzy arithmetic, µ(3−ν1)n(x) = { 1 if x = 3n 0 elsewhere. this expression is obtained by applying the definition of the product of fuzzy numbers: m̃ · ñ = q̃, µq(w) = max all y,z,s,t;y·z=w min(µm(y), µn(z)). for singletons ν̃, the expression min(µm(y), µn(z)) ̸= 0 only if z corresponds to the value of the singleton. from the defining relations, ũ1 = ν̃0 ∩ ¬(3 · ν̃1)1 and µ3ν1(x) = { 1 if x = 3 0 elsewhere, µ−3ν1(x) = { 1 if x ̸= 3 0 if x = 3, µu1(x) =   0 if x < 3 or x > 3 1 − x/3 if x ∈ [0, 3]|{3} 0 if x = 3. therefore, µu1 = µν0. similarly, ũ2 = ν̃0 ∩ ¬(3 · ν̃1)2, µu1(x) = { 0 if x < 0 or x > 3 1 − x/3 else thus, µu1 = µν0 . moreover, for all n > 2, µun = µν0 (see figure 3). therefore, ν̃0 is the limit of the constant sequence {ũn}. further sequences are obtained by varying the manipulation of the membership functions in the precedent sequence. for example, changing the definition condition ũn = ν̃0 ∩¬(3 · ν̃1)n with the condition ũn = ν̃0 ∩ ¬ ( 2 3 · ν̃1 )n , a new sequence is obtained. on fuzzy sequences, fixed points and periodicity in iterated fuzzy maps 753 figure 3: explanatory graphs . 2.3 sequences defined by conditions on the membership functions sequences of fuzzy sets can be conveniently defined based on sequences of real numbers standing for α-cuts. another fuzzy sequence with limit is exemplified subsequently. consider a fuzzy set ã0 satisfying the condition µ0(x) > 0 if x ∈ (0,1) and µ(x) = 0 elsewhere. i denoted µ ã0 by µ0. apply the following iterative procedure to generate a sequence of fuzzy sets: µn(x) = { µn−1(x) if µn−1(x) < ( 2 3 )n , n ≥ 1, 0 elsewhere, where µn represents the membership function of the fuzzy set obtained at the n-th iteration. it is easy to check that µn −→n→∞ ∅ (see figure 4.) figure 4: a sequence of fuzzy sets tending to the null fuzzy set, µ(x) = 0 ∀ x ∈ r. periodical fuzzy sequences are defined as a direct extension of periodicity for real valued sequences. i define a sequence of fuzzy sets as follows: ã0 : µã0(x) = { 0 if x < 0 or x > 3 1 − x/3 else, ã1 : µã1(x) = { 0 if x < 0 or x > 3 x/3 if x ≥ 0 and x ≤ 3, ãn+1 = ãn−1 ∩ ¬ãn. the first two sets in the sequence are shown in fig.5. i start by drawing the graphs of the membership functions of the first two sets in the sequence (see figure 5.) from ãn+1 = ãn−1 ∩ ¬ãn, derive ã2 = ã0 ∩ ¬ã1. notice that on the interval [0,3], ¬ã1 = ã0, ¬ã0 = ã1. moreover, for x < 0 and for x > 3, µ ã0∩¬ã0(x) = 0, µã1∩¬ã1(x) = 0 and µã0∩¬ã1(x) = 0. therefore, ã2 = ã0 ∩ ã0 = ã0.♢ next, write the expression of the membership functions of ã2 and ã3 like ã2 : µã2(x) = { 0 if x < 0 or x > 3 1 − x/3 if x ≥ 0 and x ≤ 3; 754 h.n. teodorescu figure 5: explanatory graphs. also, ã3 = ã1 ∩ ¬ã2, ã3 = ã1, where ã3 : { 0 if x < 0 or x > 3 x/3 if x ≥ 0 and x ≤ 3; there is no limit for this sequence. indeed, ãn = ãn−2 ∀ n. concluding, this sequence of fuzzy sets has the form ã0, ã1, ã0, ã1,..., that is, it oscillates with period 2. 2.4 fuzzification-defuzzification-based sequences consider the set of fuzzy sets φ = {ã|µa : r → [0, 1]}. define an operation ◦ : r×φ×φ → φ, where r is the set of real numbers. an example of such operation is the fuzzification followed by an inference (applying rules and truncation). the operation ◦ is defined by (x ∈ r, (ã, b̃) ∈ φ × φ) → φ, the result being the fuzzy set with the membership function µ(u; (ã, b̃, x)) = min(µ ã (x), µ b̃ (u)). then, define a "seed" x0 ∈ r and the sequence ã0, ã1 = x0 ◦ ¬ã0, ã2 = x0 ◦ ¬ã1,... . consider, as above, the sets r and φ, and the operation ◦ : r × φ → φ. moreover, consider a map ◦̂ : φ → r. an example of such a map is the defuzzification, but a more interesting case would be the defuzzification followed by a map from r to r. define a "seed" x0 ∈ r, then the sequence ã0, x1 = ◦̂(◦(x0, ã0, ã0)), ã1 = (◦(x1, ã0, ã0)), x2 = ◦̂(◦(x1, ã0, ã0)),... the generation of this sequence relates to the main subject of study in this topic. indeed, the procedure above corresponds to an "identity (mamdani-type) fuzzy logic system", that is, a system based on a single rule "if input is ã, then output is ã, followed by a defuzzifier, the defuzzified output value being fed back at the input for fuzzification. both the defuzzification and the feedback loop are included in the same ◦ operation. this amounts to a simplified fuzzy system in a discrete-time feedback loop configuration. as a matter of example, consider a mamdani-type fuzzy system with a single rule, ã → ã, where the graph of the membership function of ã is shown in figure 6 a) or b). figure 6: . for case (a), the sequence ã0, x1 = ◦̂(◦(x0, ã0, ã0)), ã1 = (◦(x1, ã0, ã0)), x2 = ◦̂(◦(x1, ã0, ã0)),... becomes constant (ã) after two steps, for whatever x0 such that µã(x0) > 0. on fuzzy sequences, fixed points and periodicity in iterated fuzzy maps 755 for case (b), there is a limit when x = ◦̂(◦(x, ã, ã)) has a solution, that is, the application ◦̂(◦(·, ã, ã)) has a fixed point. 3 iterated fuzzy logic systems essentially, a fuzzy logic system without defuzzification operation at the output is a function from a set of fuzzy sets (the "input" fuzzy sets) to another set of fuzzy sets (the "output" fuzzy sets). a fls is described by: i) a finite set of "input" fuzzy sets; i denote these sets by ãi, i = 1, ..., p. ii) a finite set of "output" fuzzy sets; i denote these sets by b̃j, j = 1, ..., q. iii) an index function ı : {1, ..., p} → {1, ..., q}. iv) an actual "input" fuzzy set (equivalently, membership function), denoted by x̃; x̃ is the value of the variable of the function represented by the fuzzy logic system. v) the equations defining the "output" (the fuzzy set value of the function) corresponding to the input are: wj=ı(i) = max x min⌊µ x̃ (x), µ ãi (x)⌋, µ ỹ (x) = max j ⌊min(w1, µb̃1(x)), ..., (w2, µb̃2(x)), ..., (wq, µb̃q(x))⌋. in applications, fuzzy logic systems are described by rules, in the form if "input" x̃ is ãi then "output" ỹ is b̃j. where ãi corresponds to an input fuzzy set and b̃j to an output fuzzy set, according to the rule. in principle, nothing prevents that two rules, with the same antecedent have different consequents. also, rules in the form: if "input" x̃ is ã then "output" y is b̃ and ỹ is b̃′; are allowed. however, in the discussion in this section, i do not include such cases. with the above definition, introduce iterated fuzzy systems as follows: i) assume the sets of the input and output fuzzy sets are defined on the same universe of discourse. this condition allows us to perform the iteration. while it is not required, we may require that the input fuzzy sets are identical to the output ones; that is, the function represented by the fuzzy logic system maps a set of fuzzy sets into itself. ii) at time moment "0", present the fuzzy set x̃[0] at the input of the fuzzy system. iii) assign to the actual input fuzzy set x̃[n], which is presented at the input of the fuzzy system at the time moment n, the output at time moment n-1, ỹ [n − 1], for n > 0. this procedure produces the repeated iteration φn of the mapping represented by the fuzzy system φ : mu → mu , where mu is the set of fuzzy sets on the universe of discourse u. in what follows, i present some examples related to typical applications of flss. i first address the general case of sugeno iterated system; next, i address an example involving sugeno systems; finally, i address an example involving an iterated mamdani system. consider a directed graph g=(v,e), where v is the set of vertices and e is the set of directed edges. the graph can be interpreted as the pictorial description (graph) of the index function used in the definition of the input-output mapping represented by the fls. when the input membership functions differ from the output membership functions, the graph is bipartite. intuition may induce us to believe that the iteration of a fls system can have a fixed point only if the graph has a node with a self-loop. this is not true. even flss with bipartite graphs may have a fixed point. iterated fuzzy logic systems with singleton-type output. general case. 756 h.n. teodorescu figure 7: (a) bipartite graph and loop for a fuzzy logic system with the index functions 1 → b, 2 → c, 3 → a. (b) respectively 1 → 2, 2 → 3, 3 → 1. (c) example of membership functions for the case (b). arrows indicate the input to output correspondence. mamdani-type system. (d) sugeno-type system. consider the set of membership functions mr = {µ : r → [0, 1]} and the set of weighted singletons on r, denoted by sr = {(β, w)|β ∈ r, w ∈ [0, 1]}. in (β, w), β are the values where the singletons occur, that is, x ̸= β ⇒ µβ(x) = 0, and w ∈ [0,1] are the corresponding weights (truth-values), that is x = β ⇒ µβ(x) = w. subsequently, formalize the definition of fuzzy logic systems with singleton-type output, which is similar to the classic 0-order sugeno system, but "deprived" of the defuzzification of the output. such a system always produces at the output a set of singletons. thus, the system is a fuzzyinput, fuzzy-output system. when iterated, such a system may have as fixed point a set of singletons that, when presented at the input of the system, produces the same set of singletons at the output. first, define: i) a finite set of "input" fuzzy sets; denote these sets by ãi, i = 1, ..., p. ii) a finite set of "output" singleton-type fuzzy sets; denote these sets by (bj, wj = 1), j = 1, ..., q. iii) an index function ı : {1, ..., p} → {1, ..., q}. vi) an actual "input" fuzzy set (equivalently, membership function), denoted by x̃; x̃ is the value of the variable of the function represented by the fuzzy logic system. iv) the equation defining the truncation of the "output" singletons corresponding to the input x̃ is wj=ı(i)(x̃) = maxx min⌊µx̃(x), µãi(x)⌋. here, wj=ı(i)(x̃) is the truncation value of the singleton with the current number j, produced by the rule which for an antecedent ãi has consequent βj . notice that the above relation determines the weights of the output singletons, based on the intersection of the fuzzy sets x̃ and ãi. flss as above will be named sugeno-type. proposition. the iteration of a fls as above defined may have a fixed point. the proof is based on an example. consider the fls defined as in fig. 8. assume the membership functions and the rules are: ã1 → β2 = 2.5, ã2 → β1 = 1.5, µã1(x) =   x − 1 x ∈ [1, 2] 3 − x x ∈ [2, 3], 0 else µ ã2 (x) =   x − 2 x ∈ [2, 3] 4 − x x ∈ [3, 4]. 0 else then, x̃, where µ x̃ (x) =   0.5 x = 1.5 0.5 x = 2.5 is a fixed point of the fls.♢ 0 else it is not difficult to see that: on fuzzy sequences, fixed points and periodicity in iterated fuzzy maps 757 figure 8: . consider a sugeno-type fls as above, with continuous input membership functions and with two rules ãi → βj ãi+1 → βk where µãi(βj) ̸= 0, µãi+1(βk) ̸= 0 and ∀ h such that h ̸= i&h ̸= i+ 1 µ ãh (βj) = 0 and µãh(βk) = 0. then, the fls has the fixed point (βj, µãi(βj))∪(βk, µãi+1(βk)). the condition of continuity is needed only to prevent undefined values. subsequently, i consider mamdani-type fls, that is, systems having "complete" (not singletons) fuzzy sets at the input and the output. assume that the input and the output membership functions are the same and are isosceles triangular, overlapping two by two (typical case in applications, see figures 7,8). also assume that every rule has a single consequent, that is rules have the form ãi → ãj, while rules like ãi → ãj ∨ ãk are not allowed. also assume the output of the fls is defuzzified by the center of gravity (c.o.g.) method. then, proposition. if there is a cycle in the graph of the above described fls, then there is an initial state of the system that produces an oscillatory iteration. indeed, there is an input real value where only one input membership function is non-null and thus selects a single output membership function. defuzzification of the output by c.o.g. produces a real value; by the assumed conditions, for that value only one input membership function is non-null. continuing, the cycle in the graph is thus completed.♢ as a last case, i deal with sugeno-type fls. the equation defining the output fuzzy (overall) set (the fuzzy set value of the function represented by the fuzzy system so defined) is: µy (y, x̃) = max j [ min(w1(x̃), µβ1(y), ..., (wj(x̃), µβj(y), ..., (wq(x̃), µβq(y) ] notice that the last relation represents the union of the weighted output singletons. the iteration is obtained by imposing µx[n](x) = µy [n − 1](y). for allowing the iteration, the fuzzy system must satisfy the condition that the input and output membership functions must be the defined and take values on the same space. it is also apparent that, whatever the initial input membership function is, after the first iteration, the input membership function can be but a union of singletons. consequently, with no loss of generality, i assume that the initial input in the iteration is a set of singletons with the same positions as the output singletons, (βj, wj), j=1,...,q. these singletons determine a set of p×q values of the input membership functions, µj(βj), some of them possibly null. therefore, at iteration n > 1, µi(βj[n − 1]). the input truncation values become max(µi(βj[n − 1]), wj[n − 1]). thus, the new values of (truth-degree of) the singletons are: wh[n] = max j s.t.h=ı(j) max i (µi(βj[n − 1]), wj[n − 1]); the fixed point condition becomes: wh = max j s.t.h=ı(j) max i (µi(βj), wj); 758 h.n. teodorescu because the equality must hold for whatever time moment is considered at the two sides. the p-period condition is obtained by requesting that wh[n] = max j s.t.h=ı(j) max i (µi(βj[n − p − 1], wj[n − p − 1]). example. in this example i deal with a fuzzy logic system as above, with two fuzzy sets defining the input and two singletons defining the output. consider the two input functions µa1(x) = { 4x(1 − x) if x ∈ [0, 1] 0 elsewhere , µa2(x) =   3x if x ∈ [0, 1/3] 1 − (3/2)(x − 1/3) if x ∈ [1/3, 1]. 0 elsewhere the index function is described by 1→2, 2→1. the output singletons are at β1 = 0.27 and β2 = 0.6 (see figure 9). figure 9: explanatory graphs for the iteration. the initial input is µx(0) =   1 if x = 0.2 1 if x = 0.8. 0 elsewhere the first value of the output is (0.27, w1[0])(0.6, w2[0]) where: w1[0] = max [min(µa2(0.2), 1), min(µa2(0.8), 1)] = max(0.6, 1 − .7) = 0.6, w2[0] = max [min(µa1(0.2), 1), min(µa1(0.8), 1)] = max(0.64, 0.64) = 0.64. the output becomes the new value of the variable of the function represented by the fuzzy system. the second value of the output is (β1 = 0.27, w1[1] = 0.6) ∪ (β2 = 0.6, w2[1] = 0.64) where: w1[1] = max [min(µa2(0.27), w1[0]), min(µa2(0.6), w2[0])] = max[min(0.81, 0.6), min(0.6, 0.64)] = max(0.6, 0.6) = 0.6, w2[1] = max [min(µa1(0.27), w1[0]), min(µa1(0.6), w2[0])] = max(0.64, 0.64) = 0.64. therefore, the output for the second iteration is identical to the output of the first, (β1 = 0.27, w1[1] = 0.6) ∪ (β2 = 0.6, w2[1] = 0.64); thus, the iteration result is constant. the result is a fixed point of the system. on fuzzy sequences, fixed points and periodicity in iterated fuzzy maps 759 4 conclusions in this paper, i focused on preparatory topics, namely fuzzy sequences, the iteration of fls, which are functions defined on sets of (membership) functions, and on their fixed points and their periodicity. whatever narrow is this topic, it is central in the theory of dynamic fuzzy systems, because both fuzzy logic systems without defuzzifier, and fuzzy algebraic systems apply sets of functions to sets of functions, while fuzzy logic systems with defuzzifier apply sets of functions to real numbers. the iteration may be convergent (either in a specified metric, or uniformly in the space of the output, that is in the space of the membership functions values) to a limit fuzzy set, or may be periodical, as it has been illustrated using simple examples. more complex behaviors can appear when the output of the fls is defuzzified. i primarily dealt with fuzzy systems without defuzzifier at the output. the systems with defuzzified output will be dealt with in subsequent papers. acknowledgment. i thank several colleagues and the referees for their comments and suggestions. the paper was not supported by, but is related to research performed in the frame of the romanian academy. bibliography [1] p.e. 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saman k. halgamuge, approximate symbolic pattern matching for protein sequence data. international journal of approximate reasoning 32 (2003) 171-186 [24] j.-y. dieulot, p. borne, inverse fuzzy sum-product composition and its application to fuzzy linguistic modelling. studies in informatics and control, vol. 14, no. 2, 2005, pp. 73-78 international journal of computers communications & control issn 1841-9836, 12(2):276-290, april 2017. a solution for problems in the organization, storage and processing of large data banks of physiological variables f. palominos, h. díaz, f. córdova, l. cañete, c. durán fredi palominos*, hernan díaz, lucio cañete, claudia durán departaments: mathematics, biology, industrial technology and industrial engineering. universidad de santiago de chile avda. bernardo o’higgins 3363, santiago, chili. (fredi.palominos, hernan.diaz, lucio.canete, claudia.duran)@usach.cl *corresponding author: fredi.palominos@usach.cl felisa córdova director at engineering school finis terrae university felisa.cordova@gmail.com abstract: the proliferation and popularization of new instruments for measuring different types of electrophysiological variables have generated the need to store huge volumes of information, corresponding to the records obtained by applying this instruments on experimental subjects. together with this must be added the data derived from the analysis and purification processes. moreover, several stages involved in the processing of data is associated with one or more specific methods related to the area of research and to the treatment at which the base information (raw) is subjected. as a result of this and with the passage of time, various problems occur, which are the most obvious consequence of that data and metadata derived from the treatment processes and analysis and can end up accumulating and requiring more storage space than the base data. in addition, the enormous amount of information, as it increases over time, can lead to the loss of the link between the processed data, the methods of treatment used, and the analysis performed so that eventually all becomes simply a huge repository of biometric data, devoid of meaning and sense. this paper presents an approach founded on a data model that can adequately handle different types of chronologies of physiological and emotional information, ensuring confidentiality of information according to the experimental protocols and relevant ethical requirements, linking the information with the methods of treatment used and the technical and scientific documents derived from the analysis. consequently, the need to generate specific data model is justified by the fact that the tools currently associated with the storage of large volumes of information are not able to take care of the semantic elements that make up the metadata and information relating to the analysis of base records of physiological information. this work is an extension of our paper [25].a keywords: data models; big data; eeg data organization; physiological information, metadata. areprinted (partial) and extended, with permission based on license number 3947080516854 [2016] ©ieee, from "computers communications and control (icccc), 2016 6th international conference on". 1 introduction this paper is an extension of [25] which delves into the specification of the different types of relevant information on the eeg records, the mathematical formalization of data and interactions expands, and indicators to quantify and predict storage requirements are proposed. understanding the mechanisms of human reasoning and brain functioning is a central topic in neuroscience [16] [17] [18] [30] . technological development has multiplied alternatives of copyright © 2006-2017 by ccc publications a solution for problems in the organization, storage and processing of large data banks of physiological variables 277 technological tools to record brain activity. in turn, the development of computers has created new opportunities for processing and analyzing the data through different tools [19] [20] [21]. moreover, the greater availability and lower prices of technology allow researchers to access many instrumental that was previously reserved for medical specialists, allowing researchers also raised new objectives and scientific concerns. as a result, in addition to the generation of new knowledge, there has been a huge growth in the quantity of eeg records, generating huge volumes of research and clinical data, in centers worldwide. the enormous size of these records, evidence that it is a collection of large volumes of data in the field of big data, which to be registered in computer systems, requires enormous storage media, which escape the most feature traditional computer systems. the origin of the electroencephalogram, an instrument that allows the capture of signals known as eeg, date back to 1875 when richard caton first detected electrical activity on the brain surface of animals [22]. however, the expanding use of this technology beyond the traditional medical field occurs only in the last decades together with new quantitative approaches (qeeg) allowing a deeper data comprehension beyond qualitative characterizations. [23]. the potential to use this technology spans multiple areas, such as neuromarketing [24], education [26], psychology [27], labor relations [28] and work [29] . 1.1 the eeg data and theirs specificities electroencephalographic data (eeg) are the record obtained by measuring brain activity, using the instrument called electroencephalographs. the measurements are obtained through a set of electrodes that are located at certain points of the human scalp, generally non-invasively, such that each electrode receives an analog signal which is then digitized by a computer. because of the nature of the eeg data recording and digitization procedure, the temporal resolution of the signal will only be limited by the sample rate used in the experiment. while a good amount of knowledge has been gathered from an ample range of eeg frequency span (mainly between 0,5 and 30 hz), which mean between the eeg wave ranges of delta and beta, the gamma band (>30 hz) has been scarcely explored. to have a look at gamma frequencies it is necessary to increase the sample rate up to 256 hz to have a confident resolution measuring brain phenomena that occur at 128 hz maximum. if we want to go further, we only need to double the sample rate to 512 hz to have a new maximum resolution of 256 hz for the brain phenomena. starting with a standard eeg configuration of sample rate 128 hz, we can explore the initial part of the gamma oscillation between 30 hz (the end of beta) until 64 hz (the maximum resolution allowed by nyquist theorem). by increasing our sample rate to 256 hz we increased data file from 64 data points in a second to 128 data points. by setting the sample rate to 512 hz, we ended with 256 data points per second. the fractal nature of the eeg signal makes it only finite when our technology to study it fails to go beyond. to reach real time performances in brain-machine communication or to study very precise stimulus-response experiments, sample data must be recorded at thousands of hz with the purpose of seeing deeper into the different time-dimensions of the processes happening in the brain. the application of ecg in humans, although it is conditioned to the experimental protocols of each investigation, which has management, storage and processing of data concerned, have common characteristics. sequences series of values recorded during application of an electroencephalogram (eeg), can be described as a set of analog sequences, called channels, such that each channel corresponds to record one of the electrodes of the electroencephalograph. the number of channels varies according to the instrument. the duration of each record depends on the experimental design and conditions. however, each channel will have the same duration as the 278 f. palominos, h. díaz, f. córdova, l. cañete, c. durán remaining channels of the raw register. the storage format may vary depending on the instrument type. in the case of the data corresponding to this job, they are stored in edf format [1]. the length of each record should be as the experiment or clinical study established. due to the nature of brain activity, and the scope of the eeg technology, raw records are not completely clean and can be affected by different perturbations. in the clinical field are called artifacts (residual noise from the instruments, interactions between channels, and twitching or muscle movements). 1.2 problems on the egg data management regarding the different factors related to storage, organization, processing and analysis of eeg records, it is clear that this is a complex problem of data management. the constant and often excessive amount of growth data and great accumulation of metadata generated in the processing and analysis of raw records. loss of the meaning of information as a result of growth in the amount of data that over time hidden links between data, data processing, and results. many physiological data should be organized according to the time in which they originate, whether as instrumental records or as a result of processing. therefore, proper management of the timestamps associated with the data is required. in order not to incur losses of information and meaning, it is required to manage data in conjunction with information on related research, researchers, experimental people, derived data and results. moreover, it is necessary to ensure the confidentiality of information. finally, it is necessary to record information about the nature of the data, their growth potential, the way they have been processed and the results of the analyses performed. 1.3 the eeg records and their relation to the area of big data current alternatives to storing and distributing data from the scope of big data are still weak in their ability to include metadata and meaning. ’big data refers to enormous amounts of unstructured data produced by high-performance applications falling in a wide and heterogeneous family of application scenarios’ [7] . on the other hand, the problem of big data can be discerned as two distinct problems: big data collections and big data objects [8] . ’there are three fundamental issue areas that need to be addressed in dealing with big data: storage issues, management issues, and processing issues’ [12] and big data features can be summarized as follows: data volume; data velocity; data variety; data value and complexity [12] [10] . the problems in the area of big data arise mainly from the needs of scalability, the massive scale of the data, the heterogeneity of information, unstructured data and their distribution across multiple platforms. resulting in problems of management, storage, portability and processing of information. because data can be distributed across multiple sites, there are incompatibilities in the interfaces, in the definition and representation of data, which are often connected to the platform containing them. furthermore, there are many metadata respect of the information and problems arise in the transmission of data over networks [8] . moreover, "big data also brings about new opportunities for discovering new values, helps us to gain an in-depth understanding of the hidden values and incurs also new challenges, for example how to effectively organize and manage such datasets" [9]. advances in information technology (it), the rapid growth of so-called cloud computing and the internet of things (iot) [9] have increased opportunities to generate and accumulate data, exceeding the capacity of researchers and technology companies to respond to this problem with a systematic and integrated solution. until 2003, "five exabytes (1018 bytes) of data a solution for problems in the organization, storage and processing of large data banks of physiological variables 279 were created by human. today this amount of information is created in two days. big data requires a revolutionary step forward from traditional data analysis, characterized by its three main components: variety, velocity, and volume " [10] . "for solutions of permanent storage and management of large-scale datasets disordered, distributed file systems [24] and nosql [26] databases are good choices" [9]. mckinsey global institute [11] , the potential of big data is mainly in five sectors: healthcare, public sector, retail, manufacturing and personal location data. moreover, the research trends in the field of big data analytics, point to the heterogeneity of the data and subsequent incongruity that occurs in highly unstructured data; the scalability; the combination of rdbms and nosql database query optimization systems issues in hiveq [7]. experience with eeg records and derived information concerning this work shows that we are in the presence of a problem of massive data rather than a problem of big data, however, they share many of the characteristics and problems associated with big data. in fact, the volume of data not only increases but that each action generates new records or associated metadata that systematically increase storage needs. moreover, when there is greater availability of instrumental eeg, the growth rate of the volume of data increases linearly in proportion to the increased availability of devices. treatment, storage organization and eeg records and their analysis, share the problems inherent of big data, in several aspects: • there are differences between different instruments to capture eeg records and have a high degree of inconsistency. • the eeg data and its derivatives within the scope of those who is called big data collections [8]. egg data management is fully compatible with the integration of rdbms, particularly in the management of metadata to facilitate analytical eeg records. • eeg records management and its derivatives is a data management problem high scalability. in many types of research, data usually are not discarded but remain stored for future reference, links or reviews. although there storage formats for eeg records, such as the european data format (edf), these records are rather unstructured nature. "in order to design meaningful analytics, it is mandatory that big data input sources are transformed into a suitable, structured format" [7]. 2 stages in the treatment of egg records the processing and analysis of eeg data go through different stages or states. initially, after his capture, the eeg records untreated (called raw) can contain different types of disturbances (artifacts), which should be identified and subtracted from the register, before applying methods of analysis. then the treatment of previously released data, which will be subjected to different methods, create new records of information (in digital format), which are the input stage of analysis and preparation of scientific reports and clinical report. the different stages being experienced treatment eeg records can be seen in figure 1. the following sections provide a brief description of each of the stages or states of the processing and analysis of eeg registrations. 2.1 data cleaning the aim the cleaning process is the removal of artifacts [2]. the results of the data cleaning process have important effects on eeg records both in its duration as its size. during the 280 f. palominos, h. díaz, f. córdova, l. cañete, c. durán figure 1: steges in the treatment of eeg records process, the analog sequences captured in each of the channels (raw) are converted to digital streams. thus, the accuracy and relevance of the digital signal generated will depend on the method parameters that are applied in the purification process. there are different types of alternative processes for the cleaning of raw records each eeg record may be subjected to a sequence of various cleaning methods. some of these results will be discarded during treatment and others will be stored for later analysis. in order to properly interpret the results of the clearance records, you must record the sequence of the methods applied, and the specific values of the parameters that each of the methods was applied. in the case of eeg records considered in this work, methods and purification processes used are the following: • importing data: due to the emotiv epoc instrument records the sensor signals in 14 useful channels (called: af3, f7, f3, fc5, t7, p7, o1, o2, p8, t8, fc6, f4, f8 , af4), a file format of european data format (edf), the import is done through the built-in tool biosig eeglab [4] software. generally, you must import all channels, but there is the possibility of importing a few. • epoch’s selection: it refers to select periods of records that contain events of interest to the investigation, in which people objects of study, they are subject to some kind of stimulus. at this stage, you can also perform other actions considered pre-processing such as changing the sampling rate, filter the data or re-reference them. 2.2 processing and data exploration it is understood by data processing, to all actions that aim to inquire into the information contained in the records or derive new information from existing data. there are different methods and techniques that can be used at this stage [4]. the specific order in which they are used, the number of times that it was applied as well as its parameters, depend on the objectives of the research and the findings that researchers performed during the same. processing methods according to their purpose and their effect on the data can be classified into: • exploratory methods: specifically, rather than processing methods they are techniques to explore the characteristics and peculiarities of each eeg record. generally make use of the help of graphical tools that allow the display of one or more channels, thus forming a a solution for problems in the organization, storage and processing of large data banks of physiological variables 281 better impression and understanding of the records. including loading and channel display (load and view channel locations), erp plotting images, and plotting spectra and maps channel [4] are frequently mentioned. • methods of processing and generation of macro data: it refers to those methods that act on the data to modify or to generate new data. within this group are, for example, those for determining the baseline (to avoid skewed by the presence of low-frequency artifacts or analysis), ica using data decomposition [2], work with ica components, the decomposition time / frequency [4], etc. 3 context of the capture, storage, processing, and analysis of eeg data scientific and clinical studies that require registration, treatment, storage and analysis of eeg data, demand the articulation of investigative processes as well as different types of elements, such as clinical patients, volunteers, premises, equipment, technical staff, clinicians, and researchers (see figure 2). the relevant part of these activities corresponds to drawing conclusions: technical, scientific and clinical diagnostics, which must be properly documented. the intervention of so many different factors, not only generates abundant records of information of different nature but also requires an organization and storage that includes the different interactions between different components, in order to avoid levels of confusion and distancing from reality, a product the abundant data. this paper addresses the definition of a data model (see figure 4) that supports the development of an information system based on a storage strategy and organization that considers existing relevant interactions in the context of studies related to the eeg data. figure 2: the context in which they exist and the eeg data is generated the following sections describe the conceptual data model developed to meet the requirements described previously. 3.1 scientific/clinical studies, research and dependences in any context of treatment and analysis of eeg data, there will be planned investigative processes (projects) conducted by researchers or clinical staff specialized, which will be assigned to a set of physical units (clinics or laboratories), where all the experimental activities will be 282 f. palominos, h. díaz, f. córdova, l. cañete, c. durán conducted. the enclosures also house the instruments and computers with the necessary software for the treatment of captured eeg records. 3.2 research experiences and records the projects require the development of various clinical or experimental activities, which are subject to strict protocols, which therefore must be developed, conducted and supervised by qualified researchers and clinicians. the result of these activities is the generation of several eeg records (raw), which will be generated in each of the sessions which will be subjected patients or experimental subjects. therefore, it comes to many eeg records, which require adequately linked to experiences in which they originate, the instruments used, laboratory personnel and dependencies in which they perform. 3.3 dimensioning and predicting storage needs the storage, management, processing, and documentation of the results of processing and analysis of eeg records obtained from research and clinical studies, requires a hardware platform that, in addition to processing power, ensure responses in appropriate time lapses and adequate storage management. investigations that require storage of eeg records and derivates of them require computers able to process and manage files that may have average sizes of hundreds of megabytes. the type of study that originates the data, affect considerably on the number of records, duration, and size. in the medical field, the amount, duration, and size of eeg records is directly related to the pathology under study and its severity. experts know beforehand the size and approximate duration of eeg records that are necessary to diagnose with a significant degree of reliability of the presence or absence of certain pathologies. however, in the field of new applications of eeg technology, which have opened to very different areas outside of classical medicine, the problems have storage requirements and processing needs very variability. any useful prediction requires the determination of significant variables that serve as the basis for accurate estimates. suppose that s is the set of study areas related to eeg data and is is the set of research (projects) linked around these areas, then let: • p(i): the set of all persons involved in a research i, i ∈ is. • r(x,i): the set of eeg records captured a person x in an investigation i, i ∈ is and x ∈ p(i). • rd(x,t,r): the set of records derived from treatment of a record eeg r obtained from a person x in research i, i ∈ is, x ∈ p(i), and r ∈ r(x,i). if we denote by i(r) to all of the records included in a full investigation i, then the storage space of research related to the different areas of study contained in s, is given by the following expression: z(s) = ∑ i∈is ∑ x∈p(i) ∑ r∈r(x,i) (z(r) + ∑ rd∈rd(x,i,r) z(rd)) (1) where z(x) represents the size of an object x measured in mb. a solution for problems in the organization, storage and processing of large data banks of physiological variables 283 to study this problem, it has been chosen a set of indicators whose behavior is expected to permit characterize the different types of studies and estimating storage needs requiring treatment and analysis of neurophysiological information. the indicators initially considered are as follows: indicator description mb amount of storage required by raw records and derivatives in each investigation. ns amount of subjects involve in each investigation. nr amount of records raw captured in each investigation. nd amount of (digital) derived records raw records in each investigation. ms the total sum of the minute records captured in a research. ni amount of reports generated in each investigation. table 1: indicators relating to a representative set of eeg investigations. 3.4 organization of processing methods and reports one of the ultimate goals of the research is drawing conclusions and scientific / technical reports. this requires the systematic application of various methods of processing and analyzing the results, which will provide researchers with lights on the nature of the phenomena under study. the effectiveness and relevance of each method are directly related to its parameters and the order or sequence in which they are applied. in many cases, each method may require multiple applications to find a suitable parameterization for the problem, something that must be recorded in the study as a log for further review. the characterization of a processing method m any can be represented by m(n, ~v ), where n represents the number of parameters involved in the application of the method and ~v is a vector containing the formal description of parameters. processing methods are applied to the original eeg records (raw) or records arising from the application of previous methods (eg cleaning artifacts). the fact of applying a processing method on a specific eeg record r, can be represented by the cinchona (m,r,t,n,~v), such that the vector ~v contains the specific values of the parameters applied to register r at time t, which can give rise to a new data record r′. thus, the full process a record eeg r can be represented as the set (m(ni, ~v )i,ri, ti,ni,~vi)/i =, 1 . . .k (2) we denote as m(r) a sequence of methods applied in a temporal sequence t1, . . . , tk over an r record. given the research process, i involves the generation of one or more records for each individual, we represent by mx(r) the set containing all sequences m(r) associated with all eeg records applied to the subject s. finally, the results obtained from the analysis of all processing sequences contained in m(r) in a research i, give rise to many technical documents or scientific reports that should be properly cataloged and stored for any use present or future. scientific and clinical reports contains the conclusions of the analysis made in the investigation and therefore should be linked to each of the records and data used in its construction. 284 f. palominos, h. díaz, f. córdova, l. cañete, c. durán figure 3: interaction between system components 4 data model for the organization, storage and processing of large data sets eeg it needs to consider the context in which eeg records are recorded, processed and analyzed from multiple investigations; consider that the data from the processing of each of the records are in the presence of a problem of management of complex data, which also requires the coordination and integration of multiple sources of information and technologies. to keep the whole meaning of the information is necessary to include descriptive information concerning research, technical reports, and the conclusions drawn from the records and the various associations that occur between these entities. the eeglab [3] [4] tool has addressed this problem from the standpoint of ease of processing and exploration of the data but does not provide for integration into the information system of the context in which the records are generated. neither includes information on technical documents with the conclusions drawn from his treatment. this paper presents a conceptual model for the organization, storage and processing of eeg records (under the model entity / relationship [5]), which takes over the metadata associated with the problem. considers context information where records are generated. it takes over the need to store the associations between records, treatment, scientific papers and technical reports, which contain the findings of the analysis developed. this proposal consists in adding information on the context in which data are generated to information concerning the organization, storage and processing of eeg records. this is achieved by a technology-based relational database component, which incorporates contextual information and semantic elements that give meaning to data volumes (see figure 4). subsequently, on this platform may implement other applications, such as web-based interfaces or other tools that use the metadata system for maintaining and managing the links between different elements of information required to manage information eeg. 4.1 scientific/clinical studies, research and dependences the data eeg focus of this study is captured in scientific or clinical studies during the development of properly planned processes (projects), conducted by researchers or clinical professional, in installations or laboratories that house the instruments required to capture data and experimentation. the projects are framed within these studies, and in turn, researchers or clinicians may be linked to different projects. the diagram is shown in figure 4 depicts this dynamic. in particular, it must be considered clearly states that all projects should be assigned to a field concerning clinical trials or scientific experience, which comprise a wide range of investigations. a solution for problems in the organization, storage and processing of large data banks of physiological variables 285 4.2 research, experimental situations, experimental subjects and eeg records normally, an investigation involves making different experimental situations or activities, which can obtain new eeg records of experimental subjects, using various instruments. then each eeg record, although will be associated with only one subject and a specific instrument can be used in other investigations. a similar situation applies to clinical records obtained from patients. this situation creates a set of eeg records, previously termed i (r), which can be represented as in figure 4 by record entity. in research contexts addressed in this work, records obtained from the application of an individual experience, are always linked to a single instrument. however, you can simultaneously record multiple records across different instruments. this situation also has been reflected in figure 4 as an aggregation (high-level entity) between experience and instruments entities. on the other hand, as shown in figure 4, the association between a research and high-level entity experience/instruments, is the type many to many, because an investigation can involve more than one pair experience/instruments, and in turn, each pair experience/instrument may be associated with multiple investigations. this relationship between experience/instrument and research frequently applied to clinical studies where the same experimental situation, for example, diagnostic procedures are applied to multiple patients. on the other hand, people involved in an investigation or are subjected to a clinical procedure may eventually be required to participate in other research or studies. 4.3 methods, sequences processing, and parameter settings. the systematic and successive application of various treatment methods on eeg records in raw format or those derived from previous treatments is an essential feature of eeg data analysis. though several treatments do not give useful results, many treatments if they are significant for research, so that should be recorded, including parametrization, identifying the set of input data, and the order and the moment in time they were applied. consequently, in the exploration and processing of each eeg recording, you must register the application sequence of processing methods. the explorations on the results, all associated with a set of timestamps. processing said the sequence of previously denoted by the expression (2). these timestamps introduce a natural chronological order that besides being a historical record of the treatment records, allows us to analyze the strategy analysis and correct it, if necessary. this situation it’s represented in figure 4. the application of different parametrized methods results in the generation of new data records, which add to existing records. this situation is collected as a reflexive relationship called "originates", between the high-level entity called "applies" and the entity called record. 4.4 management of technical reports it is considered that, in general, an investigation involves different experimental situations, which are applied to different people. this creates a set of eeg records, previously has been called i(r). the processing of eeg records, either through exploratory methods or data processing allows obtaining derived information that is the basis of the conclusions contained in the technical and scientific reports. the preparation of the documents can take information from many records corresponding to a specific research process or multiple investigations, a reason that is it necessary to link the eeg records all the scientific/technical reports involved. 286 f. palominos, h. díaz, f. córdova, l. cañete, c. durán figure 4: conceptual model for the context of eeg data the biggest challenge in the treatment of biometric information is not only the need for storage space for raw records or derivates but also store reports and other results containing the summary and details of the conclusions. 4.5 overview of the model for the management, storage and processing of eeg records integrating all requirements described in the preceding paragraphs, is represented in the conceptual data model is presented in figure 4. to simplify the figure, the attributes have not been incorporated into the model. this data model aims to implement a database (with documentary features), to incorporate contextual information and references to the physical location where the data is stored (url). specifically, the purpose of the database is as follows: (1) integrate records and metadata to facilitate the analytic eeg records and derived data; (2) establish a catalogue of eeg records, scientific and technical documents as well as from the investigation; (3) allow the generation of a system capable of recording the activities of research centres and evolve if necessary; and (4) allow the development of tools for analysis and processing of data, which are made in the context of the system, automatically integrating partial and final results of interest to research centres. to facilitate the implementation of the de model in any relational platform and therefore the portability of applications that make use of it, in figure 5 shows the standard logical design (dls) of the conceptual model. a solution for problems in the organization, storage and processing of large data banks of physiological variables 287 figure 5: dls for the conceptual model about context of eeg data conclusions the growing needs to store large amounts of information relating to various areas of human and scientific work, has prompted different approaches and approaches related to the storage of information, is now called big data. although these proposals address many of the problems associated with the storage, access, and distribution of information on computer media, they do not have sufficient tools to manage the semantic elements of the stored information, which is specific to each problem type. in the case of eeg records and technical and scientific reports, it is required of mechanisms for the organization, storage and processing of data, allowing the integration of the various elements of information contained in the records and mainly those generated from their treatment. current developments in the area of storage [13] [14] provide an alternative for managing large volumes distributed in different storage media, which however are limited by the heterogeneity of the different types of data. proposals made in this document provide a solution for these semantic constraints in the context of specific data, by integrating other technologies, such as database services (daas) [15] [7], which also allow manipulation of metadata and integrate different types of information, enabling the development of applications and interfaces that automate the recording and metadata generation. in addition, the technology database allows inheriting mechanisms to ensure the confidentiality of information regarding experimental subjects, the analysis, and conclusions of the records, research, and researchers. in this scenario the data model proposed is able to leverage solutions in the field of big data, incorporating semantic elements that enable researchers and technical staff not to lose control over data and new knowledge derived from them. the data model becomes a significant interface between the information processing methods, facilitating data analysis and generation of conclusions. moreover, the increased availability of metadata and recording timestamps on different moments related to the processing, analyzing and drawing conclusions, promote more efficient use of computing resources by decreasing the need to reprocess data unnecessarily when those results are registered for the system level. finally, the use of technologies of conventional databases available in the field of free software or proprietary contributes to the greater integration of information value in the new scenario underlying the current developments in the field of human-computer interfaces in science and medicine. 288 f. palominos, h. díaz, f. córdova, l. cañete, c. durán acknowledgment this work was developed under the project "definición, desarrollo e implementación de nuevos indicadores y procedimientos para la evaluación y seguimiento del rendimiento académico estudiantil y el desempeño docente, en base información curricular, variables psico-neuro-cognitivas y herramientas basadas en matemática y lógica difusa", which receives funding from the department of scientific and technological research (dicyt) of the university of santiago of chile (usach), chile. bibliography [1] european data format (edf). 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[29] matthew d. lieberman (2007); social cognitive neuroscience: a review of core processes, annu. rev. psychol. 58:259-289. 290 f. palominos, h. díaz, f. córdova, l. cañete, c. durán [30] raja parasuraman, george mason, glenn f. wilson (2008); putting the brain to work: neuroergonomics past, present, and future, human factors, 50(3): 468-474. doi 10.1518/ 001872008x288349. international journal of computers communications & control issn 1841-9836, 13(1), 8-23, february 2018. a comparative study of the pso and ga for the m-mdpdptw e. ben alaïa, i. harbaoui, p. borne, h. bouchriha essia ben alaïa*, imen harbaoui dridi, hanen bouchriha laccs : laboratoire d’analyse, conception et commande des systèmes école nationale des ingénieurs de tunis, lr11es20, tunisie université de tunis el manar *corresponding author: essia.benalaia@enit.rnu.tn imen.harbaoui@issatkr.rnu.tn hanen.bouchriha@enit.rnu.tn pierre borne cristal : centre de recherche en informatique signal et automatique de lille ecole centrale de lille villeneuve d’ascq, france pierre.borne@centralelille.fr abstract: the m-mdpdptw is the multi-vehicles, multi-depots pick-up and delivery problem with time windows. it is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers for the purpose of satisfying precedence, capacity and time constraints. this problem is a very important class of operational research, which is part of the category of np-hard problems. its resolution therefore requires the use of evolutionary algorithms such as genetic algorithms (ga) or particle swarm optimization (pso). we present, in this sense, a comparative study between two approaches based respectively on the ga and the pso for the optimization of m-mdpdptw. we propose, in this paper, a literature review of the vehicle routing problem (vrp) and the pick-up and delivery problem with time windows (pdptw), present our approaches, whose objective is to give a satisfying solution to the m-mdpdptw minimizing the total distance travelled. the performance of both approaches is evaluated using various sets instances from [10] pdptw benchmark data problems. from our study, in the case of m-mdpdptw problem, the proposed ga reached to better results compared with the pso algorithm and can be considered the most appropriate model to solve our m-mdpdptw problem. keywords: : particle swarm optimization (pso), genetic algorithm (ga), vehicle routing problem (vrp), pick-up and delivery problem with time windows (pdptw), m-mdpdptw, optimization. 1 introduction the multi-vehicles, multi-depots pick-up and delivery problem with time windows (m-mdpdptw) combine several variant of the well-known vehicle routing problem (vrp), which is part of the category of np-hard problems. even for a small problem size, the resolution of such complex problem requires to use heuristic and meta-heuristic methods, since these allow finding feasible solutions, in a reasonable calculation time. the m-mdpdptw principle is to construct a set of routes in order to pick up and to deliver goods between a set of suppliers (pickup nodes) and a set of customers (delivery nodes). we consider several depots which does not contain any goods and where is based a homogeneous fleet of vehicles. every single vehicle has a limited capacity and must leave and return to its starting depot. and each load must be transported by one vehicle without any transshipment at copyright ©2018 cc by-nc a comparative study of the pso and ga for the m-mdpdptw 9 other locations. a time window is associated with each pick-up and delivery node, thus defining for each vehicle the earliest time to visit and the latest permitted time to leave each node. we aims to minimize the sum of travel distance without violating the different problem constraints, which are: (1) the capacity constraint which ensures that at any time in the route, the load on the vehicle must not exceed its maximum capacity. and all the vehicles leave and return to depot unloaded. (2) the precedence constraint which ensures that for each request, the origin (suppliers) precedes the destination (customers). (3) the soft time constraint: we consider that if the vehicle arrives before the earliest permitted time to visit the node, then it must waits the beginning of the time window to serve it. but, if the end of service time, in the node, is after the latest permitted time to leave it, then a tardiness time is calculated and the solution is accepted but penalized. in this context, we develop and compare two improving optimization approach based on population search methods, which are genetic algorithm (ga) and particle swarm optimization (pso) for solving our m-mdpdptw. this paper is organized as follows. the literature review of the vehicle routing problem and the pick-up and delivery problem is presented in section 2. the problem formulation and the mathematical model for the m-mdpdptw is described in section 3. section 4, proposes our developed approach, based on ga and pso, for solving a m-mdpdptw to minimize the total travel distance. section 5 validates and compares the proposed approaches by numerical example. finally, the concluding remarks and further research are included in section 6. 2 related work 2.1 genetic algorithm for the vehicle routing problem and the pick-up and delivery problem genetic algorithms are the most popular and most used evolutionary algorithms. they evolve a set of coded solutions called individuals populations. for each individual, the degree of adaptation to their local environment is measured by a predefined objective function to optimize, called fitness. from one generation to another, the best adapted individuals are selected for the reproduction by applying of genetic operations of crossover and mutation in order to produce better populations with better performing individuals. this process is repeated until a stop criterion is reached. a literature review was proposed by [19], in which the authors detail 15 variants of the vrp and give a synthesis of the 48 heuristics for these problems. in [20], the authors propose an approach based on metaheuristic method, which combines a hybrid genetic algorithm for research and adaptive control for diversification to solve the multi-depot and periodic vehicle routing problems. in the work of [18], the authors present two metaheuristics for the resolution of the multi depot vehicle routing problem (mdvrp) which are based on a local research and a hybrid genetic algorithm. a genetic algorithm was developed by [6] for the resolution of the multi-criteria pdptw with multiple vehicles, based on the aggregation method and minimizing the compromise between the total travel cost, the total tardiness time and the vehicles number. this algorithm has been treated in the dynamic case in [5]. the authors in [1] proposed a multi-criteria approach based on ga for the optimization of the m-mdpdptw. the aim is to discover a set of satisfying solutions (routes) minimizing total travel distance, total tardiness time and the total number of vehicles. a tabu method, a genetic algorithm based on chromosome permutation, a "split" procedure and a local search are proposed by [9] to solve a particular problem of pdptw. this problem 10 e. ben alaïa, i. harbaoui, p. borne, h. bouchriha considers several requests: the delivery of medicines and medical care pharmacy at home patients and the pick-up of biological samples and unused drugs at the patients. the authors in [13] present a memetic algorithm for the resolution of pdptw with an efficient crossover operator. memetic algorithm is obtained by combining ga with local search. a new genetic algorithm has been introduced in [14] for optimization of the mdvrp with capacity constraints and restrictions on the traveled distance. these authors use indirect coding and adaptive mutation operator inter-depots for the affectation of customers. for the resolution of the mdvrp, [7] use a stochastic approach based on the ga and the fuzzy logic to adapt the crossover and mutation rates. they consider the total traveled cost and the time spent in the objective function. [2] propose an approach which is based on the combination of genetic algorithm (ga) with the clustering algorithm for the optimization of the m-mdpdp. the main contribution in this work is to find new depot locations in order to obtain feasible solution (routes) for the m-mdpdp. the disadvantage of the ga is that it requires a high number of iterations. particle swarm optimization (pso) is a relatively recent heuristic algorithm which is based on the behavior of swarming characteristics of living organisms. these approaches are similar, nevertheless they each have its own particularities, its own research strategy and two different ways to develop the set of solutions. 2.2 pso for the vehicle routing problem and the pick-up and delivery problem the principle of the pso is to start from an initial swarm (population) and apply a research strategy based on the cooperation of its ne particles (individuals). in the pso algorithm, the speed is the basic mechanism which drives the search in the promising areas of the solution space. this speed allows to put update the particles positions. in [4], the authors present a solution approach based on particle swarm optimization (pso) in which a local search is performed by variable neighborhood descent algorithm (vnd). this approach was developed to solve the vehicle routing problem with simultaneous pickup and delivery (vrpspd). a hybrid particle swarm optimization (hpso) is proposed by [16] to solve the multi-objective pdptw. this algorithm adds particles neighbor information to diversify the particle swarm and use the variable neighborhood search (vns) to enhance the convergence speed. the pso is also used to solve the vehicle routing problem with multi-depot. [8] propose an improved pso for the multi-depot vehicle routing problem with time windows. another pso algorithm is proposed for solving the practical case of multi-depot vehicle routing problem with simultaneous pickup and delivery and time window [17]. a ga which evolves the vrp solutions using a pso is proposed in [12]. this algorithm improves the performance of each individual of the population. we find other metaheuristic methods that have been developed for the resolution of the vrp who is one of the most famous combinatorial optimization problems [15], [21]. we choice to study two evolutionary algorithms ga and pso. these are two optimization techniques based on the populations and have been widely compared in the literature. these two approaches provide a coding that perfectly represents the data of our problem. the difference is in their research strategy: the genetic algorithm is categorizing as global research heuristics that uses crossover and mutation operators and a competition between individuals to find desired solutions, while the pso has no evolutionary operator and in its research strategy, it gives more importance to cooperation between individuals. many researches in the literature showed that the pso gives better results for the vehicle a comparative study of the pso and ga for the m-mdpdptw 11 routing problems. for our problem studied case, gas gave better results compared to the pso [11]. 3 mathematical model like the pdptw problem, our m-mdpdptw takes into account the following variables and parameters: l: set of depots; h: set of nodes (pick-up and delivery) {1, 2, ...,n}; h+: sets of pick-up nodes; h−: sets of delivery nodes; hc: set of couples: delivery and pickup; ci: the couple (ci,fi) : the pick-up node (fi) with its corresponding delivery node (ci), ∀i ∈ {1, ..., (n/2)}; vm: set of available vehicles from depot m,{v1, ...,vdep}; dij: euclidean distance between node i and j; k: the total number of vehicles available for all the depots; qi: goods quantity request of the node i, (if qi < 0 it is a delivery node else if qi > 00 it is a pick-up node); tkij: time taken by the vehicle k to travel from node i to node j; q: the maximum capacity of a vehicle; yki : the load of vehicle k after leaving the node i; eti: the earliest time that node i can be serviced by a vehicle; lti: the latest permitted time to leave node i; si: service time at node i; ai: arrival time of the assigned vehicle at the node i; di: departure time of the vehicle from the node i; wi: waiting time of the vehicle at node i; ti: tardiness time of the vehicle at node i. we add the above sets l: set of depots 1, ...,dep; hc: set of couples: delivery and pickup; ci: the couple ci,fi: the pick-up node fi with its corresponding delivery node ci, ∀i ∈ {1, ..., (n/2)} vm: set of available vehicles from depot m v1, ...,vdep. our decision variable is defined as follows: xmkij = { 1 if vehiclek originates from depotmtravel along arc (i,j) 0 otherwise the m-mdpdptw considered in this study aims to minimize the total distance travelled (f1). the objective function is formulated as: f1 = ∑ m∈l ∑ k∈vm ∑ i∈(h∪m) ∑ j∈(h∪m) dijx mk ij (1) subject to: ∑ m∈l ∑ i∈h∪l ∑ k∈vm xmkij = 1 (∀j ∈ h ∪l) (2) ∑ m∈l ∑ j∈h∪l ∑ k∈vm xmkij = 1 (∀i ∈ h ∪l) (3) 12 e. ben alaïa, i. harbaoui, p. borne, h. bouchriha ∑ j∈h xmkij = ∑ j∈h xmkji (∀i = m ∈ landk ∈ vm) (4) xmkij = 1 ⇒ y k i = 0 (∀i ∈ l, j ∈ h andk ∈ vm) (5) xmkji = 1 ⇒ y k i = 0 (∀i ∈ l, j ∈ h andk ∈ vm) (6) xmkij = 1 ⇒ y k j = y k i + qj(∀i,j ∈ h andk ∈ vm ) (7) 0 < yki ≤ q (∀i ∈ handk ∈ vm) (8) xmkij = 1 ⇒ aj = di + t k ij (∀k ∈ vm) (9) di = ai + si (∀i ∈ h) (10) di = si = 0 (∀i ∈ l) (11) eti > ai ⇒ wi = eti −ai (∀i ∈ h) (12) ti = max(0,di −lti) (∀i ∈ h) (13) dfi < dci (∀i ∈ hc ,fi ∈ h +and ci ∈ h−) (14) in this formulation, constraints (2) and (3) ensure that each request is served once by the same vehicle, while constraint (4) guarantee that each vehicle starts and ends its route at the same depot. constraints (5) and (6) impose that all the vehicles which leave and return to depot are unloaded. for each vehicle of each depot, the load of vehicle k leaving node i to j is defined in (7), while capacity constraint (8) guarantee that at any time the load, on the vehicle k, must not exceed the vehicle capacity. each node i have time interval [eti, lti] in which service at location i must take place. this time windows define in constraints (9) to (11) the arrival time, the departure time, service times at every depot, respectively. if the vehicle arrives at customer before the beginning of the applicable service time, a waiting time is calculated according the equation (12). and if the departure time from a node is later than its latest time of service, we calculate a tardiness time by equation (13). finally, the precedence constraints (14) ensure that the pick-up node (fi) of every couple i must be visited before the corresponding delivery node (ci). 4 optimization approach for solving the m-mdpdptw 4.1 solution representation we have adopted a coding that is easy to use and to program, which fits well with the needs of our problem. our choice was based on direct-type permutation list encoding to represent the solutions (individuals for the ga or particles for pso) of the m-mdpdptw. our solution is a a comparative study of the pso and ga for the m-mdpdptw 13 sequence of genes encoded in integers. each gene identifies a node and the order of the genes gives for each vehicle the order in which these nodes will be visited. each coded individual contains both customers and suppliers. we chose to indicate the start and the end of each path by the depot indices. the index 808 is not used throughout the work. figure 1 shows a solution encoded as a direct type permutation list. the example consists of twenty nodes numbered 1 to 20, which is ten pairs (customers/suppliers), three depots (index 21, 22 and 23) and two vehicles located in each depot. in the first depot the two vehicles are used, so we will have two routes that start at the depot (index 21) and ends at the same depot. the first vehicle visit two nodes in this order (13 before 4) and the second one visit three couple that is to say six nodes. on the other hand in the third depot just one vehicle is used to visits four nodes. d epot1 21 13 4 21 8 17 10 5 12 20 21 d epot2 22 7 18 3 6 22 19 2 14 16 22 d epot 3 23 1 15 11 9 23 figure 1: solution representation 4.2 genetic algorithm optimization for the m-mdpdptw our developed approach based on ga, manipulates several types of populations and the solutions of the m-mdpdptw are constructed using different heuristics which breaks down the problem into two major parts: regrouping then routing. structure of the initial population the choice of the initial population is very important because it has an influence on the convergence speed of the genetic algorithm used. the initial population is constructed into two steps: step 1: the depot-grouping phase: creation of the population couple/depot the m-mdpdptw considers several depots in which a fixed number of vehicles are located. it is therefore necessary to determine from which depot the nodes will be served. the process rules of depot grouping phase is explained in details in the following reference [1]. at the end of this strategy, each couple (pick-up node and its associated delivery node) are assigned to the nearest depot. step 2: generating the initial solution a group of initials solutions is randomly generated, constructing the first population named pcouple/depot containing n individuals. the chromosomes of the solution are encoded using path representation in which, for each depot, the couples are listed in the order in which they are visited [21]. the routing phase for each depot, the number of vehicles used and the order of delivery and pick-up within each route are specified by the population named pnode/vehicle/depot. to construct this population type we should follow three steps: 14 e. ben alaïa, i. harbaoui, p. borne, h. bouchriha step1: creation of population pvehicle/depot knowing the number of vehicles available in each depot, we start by creating 2n individuals of a new population named pvehicle/depot. this population is generated at each iteration to indicate the new number of vehicles used and the new number of couples to be visited by each vehicle. step 2: use of ga operators the genetic operators are used to create new population pcouple/depot containing 2n individuals. its first part represents an exact copy of the n individuals of pcouple/depot created in phase 2, while the remaining 50 percent are created by applying ga operators on the first part. in our case, we select two parent chromosomes from population of step 2 by using tournament selection. for recombination, it is difficult to determine the most effective crossover method in advance. therefore, we have designed our recombination operator based on the one point crossover and uniform crossover. our crossover algorithm is adapted for permutation coding with individuals containing multiple depots. the principle is to start by applying a binary mask of the same length as the number of depots. this mask will determine: (1) the depot that will be copied: the genes of the first parent contained in this depot will be copied in the second child and those of the second parent will be copied into the first child). (2) the depot that will be crossed: the same crossing point is chosen in each depot, for the two parent chromosomes. the first part of each child is copied, gene by gene, from its parent. the first child will be completed with genes that were not inherited from the first parent but rearranged according to their order of appearance in the second parent. and we apply the same procedure to complete the second child using the order of appearance in the first parent.this procedure is repeated until the crossing rate (tc = 0.8) is reached (80% of children population size is reached). for diversification, what remains of the population (2 individuals) will be mutated with applying swap mutation according to a fixed probability (tm = 0.2). step 3: population pnode/vehicle/depot for each depot, the number of vehicles used and the order of delivery and pick-up within each route are specified by the population named pnode/vehicle/depot. to construct this population type we should follow three steps: knowing the number of vehicles available in each depot, we start by creating 2n individuals of a new population that indicates the number of nodes visited by each vehicle, named pvehicle/depot. after, we affect visited couples to vehicles by coding each individual of the population pcouple/depot created in phase 3 by an individual of the population pvehicle/depot. this new population, named pcouple/vehicle/depot, verifies that each couple belongs exactly to same route. to find, for each depot the order in which all pick-up and delivery nodes are visited, we develop a heuristic algorithm. its principle is to choose randomly, in each route, a starting node from the assigned couples. then, we follow it by the closest node. our heuristic minimizes the total distance traveled for each individual of pnode/vehicle/depot. figure 2 and figure 3 show an example of the individual of pvehicle/depot and pnode/vehicle/depot. k1 k2 k3 depot1 6 2 0 depot2 4 4 depot3 4 0 figure 2: example of individual of pvehicle/depot for this example we have a vehicles total number equal to 7 and 10 couples customer/supplier which are defined as follows: a comparative study of the pso and ga for the m-mdpdptw 15 depot 1 21 8 13 4 17 10 5 21 12 20 21 depot 2 22 19 7 2 18 22 3 6 14 16 22 depot 3 23 1 15 11 9 23 figure 3: example of individual of pnode/vehicle/depot c1(13, 8),c2(10, 4),c3(20, 12),c4(5, 17),c5(7, 19),c6(18, 2),c7(14, 3),c8(16, 6),c9(9, 1),c10(11, 15). heuristics for creation of feasible solutions each individual of population pnode/vehicle/depot must respect different constraints. the precedence constraint ensures that each delivery point, on the same route, is not visited before its supplier. the capacity correction constraint ensures that the total load of the vehicle must be smaller than or equal to the maximum capacity of the vehicle. the heuristics algorithms for precedence and capacity corrections procedures, to transform each individual into feasible solution, are explained in details in [1]. the structure of the genetic algorithm proposed for the m-mdpdptw optimization is illustrated in table 1. in our genetic algorithm we use the elitism strategy for each generation, the best n solutions in the current population are copied in the new initial population. the best solution found throughout the search is returned when a fixed number of iterations is reached. this number is determined after several experiments. table 1: structure of the genetic algorithm proposed for the m-mdpdptw optimization begin step 1: apply depot-grouping phase step 2: generate randomly an initial population pcouple/depot containing n individuals. repeat until maximum of generation reached. step 3: create a new pcouple/depot containing 2 × n individuals. the first part of this population represents one copy of the n individual pbest−couple, while the remaining 50 percentage of this population are created by applying ga operators on population pbest−couple. we select two parent chromosomes from population of step 2 by using tournament selection. for recombination, we apply our designed crossover and for diversification, we apply swap mutation according to a fixed probability. step 4: generate pvehicle/depot containing 2 × n individuals and respecting constraint vehicle numbers. step 5: apply routing phase to create pnode/vehicle/depot. this population with size [2 × n ×m] specifies, for each depot, the number of routes (that are vehicles) and the order of delivery and pick-up within each route. step 6: apply the precedence then the capacity correction procedure, to transform each individual into feasible solution. step 7: calculate for every individual of pnode/vehicle/depot fitness values (f1, f2). step 8 : elitism: copy the n best pcouple/depot/vehicle solution into the new initial population. increment the generation number end 16 e. ben alaïa, i. harbaoui, p. borne, h. bouchriha 4.3 particle swarm optimization for the m-mdpdptw general principle of the algorithm pso proposed pso shares many similarities with gas. all two techniques begin with a group of a randomly generated population; both utilize a fitness value to evaluate the population. the principle of the pso is to start from an initial swarm and to apply a research strategy based on the cooperation of its ne particles. the search for optimums is done by generating several generations. in every generation, a potential solution to the problem is generated, and then evaluated to record the best solutions found. the solution to our m-mdpdptw represents the best solution found on all generations. the main difference between the pso approaches compared to ga, is that pso does not have genetic operators such as crossover and mutation. particles update themselves with the internal velocity. in pso, only the best particle gives out the information to others. it is an on way information sharing mechanism, the evolution only looks for the best solution. in most applications, the particles positions represent the solutions of the problem studied, but in our case the solution to m-mdpdptw is decoded from the new particle position. the travel strategy of a particle i is illustrated in figure 4. new position xi(t+1) ne towards the best neighbors position to neig ne towards the best personal performance s the best current position xi(t) figure 4: analysis of the particle travel for the creation of the initial swarm pnode/depot and the decoding of the initial solutions: routing phase for the creation of the swarm pnode/vehicle/depot we follow the same steps as in the genetic algorithm. initializing and updating speed and position we consider that the particles movement is controlled by the limitation of their traveled maximum distance during iteration. thus, in order to escape the deviation problem of the particles from the search space, we use a technique of interval confinement. the speed of each particle is initialized by values between 0 and n (n is the nodes number to be visited). the update of the speed and the best position is obtained by the particle (~pbesti : personal best) and by the swarm (~gbesti: global best), using equations (15), (16) and (17). the new position of the particle is calculated from equation (18). ~pbesti(t + 1) = { ~xi(t + 1), if f(~xi(t + 1)) isbetterthanf( ~pbesti(t)) ~pbesti(t), else (15) a comparative study of the pso and ga for the m-mdpdptw 17 ~gbest(t + 1) = arg min ~pbesti f ( ~pbesti(t + 1) ) , 1 ≤ i ≤ n (16) vi,j(t + 1) =   w vi,j(t)+ c1r1i,j (t)(pbesti,j(t) −xij(t))+ c2r2i,j (t)(gbestj(t) −xi,j(t)) (17) xi,j(t + 1) = xi,j(t) + vi,j(t + 1) (18) with : xi,j(t): the position of the particle i in dimension j at time t; vi,j(t): the speed of the particle i in dimension j at time t; pbesti,j(t): the best position obtained by the particle i in dimension j at times t; gbesti,j(t): the best position known by the particle i at time t; c1, c2: acceleration coefficients; r1, r2: random numbers drawn uniformly in [0, 1], at each iteration t and for each dimension j; w~vi: the inertia component of the movement; c1r1(pbesti −~xi): the cognitive component of the particle movement (moving to its best known position); c2r2(gbesti −~xi): the social component of the particle movement (closer to the best position of its neighbors). decoding the new particle position the elements of the new particle position after its updating do not reveal directly the nodes index in the route. a decoding phase of the new visit order is therefore necessary in order to find the solutions adapted to m-mdpdptw. we consider that the speed is defined as a probability vector, where the value of each element corresponds to the probability of its permutation in the route. the decoding phase of the new particles gives us the new permutation of the nodes order in the route. in the same route, nodes will be visited from the nearest to the furthest. if there are several nodes that are in the same position, then they will be visited according to the speed of their movement. the node with the highest speed will be visited first. we therefore reorder in ascending order of the values of ~xi(t + 1). these values are subsequently replaced by the corresponding node index to build the new visit order of the nodes. the structure of the adaptation algorithm of the pso for the m-mdpdptw optimization is illustrated in the following. n: nodes number to visit; f : function to minimize ; t : maximum number of iterations; dep: number of depot; t = 0; xmax = n; xmin = 0; 1. initialization : ne : swarm size ; c1; c2; r1; r2; 2. generating ne initial particles of pnode/depot; 3. application of the routing phase for the creation of initial solutions pnode/vehicle/depot; 18 e. ben alaïa, i. harbaoui, p. borne, h. bouchriha 4. application of the corrections heuristics of precedence, capacity and belonging of each couple to the same route; 5. evaluate the ne initial particles of pnode/vehicle/depot (1); 6. initialize the speed of each particle by random values between [0; n]; 7. initialize the position of each particle ~x(0) = pnode/depot 8. initialize ~pbesti(0) = pnode/vehicle/depot[i] 9. initialize the best solution found by the swarm ~gbest(0) = arg min → p besti f( ~pbesti(0)) 10. while (t < t) do; 11. for i = 1 to ne do; 12. speed update (18); 13. update of the new position (18); 14. verify the new particle does not leave the search space: if(~xi(t + 1) > xmax)then~xi(t + 1) = xmax; elseif(~xi(t + 1) < xmin)then~xi(t + 1) = xmin; 15. decoding the new particles position; 16. generating new solutions: routing phase; 17. apply corrections heuristics; 18. evaluate the ne particles of pnode/vehicle/depot (1); 19. save the best result found by the particle; for i = 1 to ne do; iff(pnode/vehicle/depot(i)) ≤ f(pbesti(t))then pbesti(t + 1) = f(x(t + 1)) elsepbesti(t + 1) = pbesti(t) end for; 20. update the best solution found by the swarm ~gbest(t + 1) = arg min → p besti f( ~pbesti(t + 1)) a comparative study of the pso and ga for the m-mdpdptw 19 21. end for; 22. end while; save the best solution found on all generation; the same, to find the optimal solution, we browse the swarm pnode/vehicle/depot particle by particle, to determine that which minimizes our objective function (1). 5 computational results this section describes computational results to assess and compare the performance of the two proposed algorithms. we programmed the algorithms in c language using microsoft visual studio2010 and ran them on mobile intel core i7, cpu 2.50 ghz and 6.00 go memory (ram) under the operating system windows 8 professional. in literature, the existing benchmark instances do not combine all the characteristics of the mmdpdptw. so our simulation results are obtained using the problem instances generated by li and lim [10] which are created for the single depot pickup and delivery problem with time windows. we only added the additional information that can accommodate the m-mdpdptw by generating multi depot locations using the algorithm described in our work [2]. the locations of the different depots considered in our simulations for each type of li and lim’s problems are summarized in table 2. table 2: location of the different depots instances coordinates of the 1st depot coordinates of the 2nd depot lc (40 , 50) (34 , 32) lr (35 , 35) (60 , 40) lrc (40 , 50) (65 , 30) the parameters of the two approaches are selected after many experiments. the parameters values for pso are: number of swarms=1, swarm size=500 particles, the maximum number of generation=1000 iterations. the inertia weight linearly decreasing from 0.8 to 0.5, the acceleration constants, random numbers for personal best and global best are 0.2, 0.2, 0.3 and 0.5. the parameters values for ga are: population size equal to number of particles=500, maximum number of generations=1000. the crossover rate and mutation rate are 0.8 and 0.2, respectively. the best results obtained from our two proposed ga and pso approaches solving our mmdpdptw were compared as shows in table 3. we consider the depot locations already given in the table 1 and we evaluate the total distance travelled, considered in equation 1, for some selected categories of problem instances with different sizes: clustered locations with short schedule horizon (lc101 and lc102), clustered locations with long schedule horizon (lc201 and lc202), randomly distributed locations with short schedule horizon (lr101 and lr102), randomly distributed locations with long schedule horizon (lr201 and lr202), and finally, problems category that have partially random and 20 e. ben alaïa, i. harbaoui, p. borne, h. bouchriha partially clustered locations with a tight time window width (lrc101 and lrc102) and with a large time window width (lrc201 and lrc202). table 3: comparison of the results of our ga and pso approaches to solve our m-mdpdptw problem instances best distance of ga approach best distance of pso approach lc101 916.078 1839.3962 lc102 910.598 1606.801 lc201 1081.395 947.914 lc202 1204.900 1472.292 lr101 1938.980 2137.17 lr102 1898.109 2128.639 lr201 1979.990 2060.405 lr202 1241.264 2497.547 lrc101 1079.137 2513.376 lrc102 1129.326 2567.438 lrc201 1453.306 2937.202 lrc202 1421.923 2829.999 from table 3, we can observe that the values of the total distance traveled given by our approach based on the ga are better than those given by the pso. this last only gave a low improvement of the fitness for clustered locations instance lc201. however, these results show the strength of gas that is in the parallel nature of their research and their ability to manage multiple large data sets. compared with ga, the advantages of pso are that it is easy to implement and there are few parameters to adjust. a source of the ag’s power is their used genetic operators: crossover and mutation. the crossover attempts to preserve the beneficial aspects of candidate solutions and to eliminate undesirable components, while the random nature of mutation is probably more likely to degrade a strong candidate solution than to improve it. through its genetic operators, even weak solutions may continue to be part of the makeup of future candidate solutions and thus allows the creation of new solutions that have, a higher probability of exhibiting a good performance. this tends to make the algorithm likely to converge towards high quality solutions within a few generations. to validate and evaluate our proposed ag approach, we solve the single depot pdptw a comparative study of the pso and ga for the m-mdpdptw 21 table 4: comparison of the results of our ag approaches using the li and lim instances of single depot problem case best known solutions of li and lim best known solutions of our ga approach lc101 828.94 861.24 lc102 828.94 926.941 lc103 1035.35 959.303 lc104 860.01 940.05 lc105 828.94 867.609 lc106 828.94 955.339 lc107 828.94 922.66 lc108 826.44 914.211 lc109 1000.60 869.537 problem. the comparison with the best results published by li and lim with respect to the total traveled distance minimization is shown in table 4. the proposed ga approach shows the promising result in the clustered problem with short schedule horizon. compared with the lim and lim’s best solutions approach for the pdptw problem with single depot, this algorithm produced good quality results that are sometimes even better than the results obtained, making it even more suitable for population-based solution algorithms. furthermore, the total traveled distances for the benchmark data sets are near the best result and are a little better than the best known distance in lc103 and lc109 category. 6 conclusion in this paper we have developed two meta-heuristic approaches based on the genetic algorithm (ga) and particle swarm optimization (pso), in order to compare and identify the best approach that can be used for solving a multi depots multi vehicles pickup and delivery problems with time windows (m-mpdptw). we proposed a brief literature review on the vrp and the pdptw. the mathematical formulation of our problem was subsequently presented. then, we have detailed the use of ga and pso algorithm to determine the solution which minimizes our 22 e. ben alaïa, i. harbaoui, p. borne, h. bouchriha objective function. simulation was presented in a last part by using benchmark’s data. the experimental results on a large number of benchmark instances indicate that the use of ga seems to be the most favorable method to reach final best solutions for our m-mdpdptw. for our future work, we propose to study the multi-objective optimization by comparing the genetic algorithms, the particle swarm optimization and other metaheuristic methods. bibliography [1] ben alaia, e.; harbaoui, d.i.; bouchriha, h.; borne, p. 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(2014); an efficient solution for the vrp by using a hybrid elite ant system, international journal of computers communications & control, 9(3), 340-347, 2014. int j comput commun, issn 1841-9836 8(2):196-205, april, 2013. a fuzzy rules-based segmentation method for medical images analysis h. costin hariton costin 1. faculty of medical bioengineering, grigore t. popa university of medicine and pharmacy, iasi, romania; m. kogalniceanu str., no. 9-13, 700454, iasi, romania; 2. institute of computer science of romanian academy, iasi branch, carol i blvd., no. 11, 700506, iasi e-mail: hcostin@gmail.com abstract: medical imaging mainly manages and processes missing, ambiguous, complementary, redundant and distorted data and information has a strong structural character. this paper reports a new (semi)automated and supervised method for the segmentation of brain structures using a rule-based fuzzy system. in the field of biomedical image analysis fuzzy logic acts as a unified framework for representing and processing both numerical and symbolic information, as well as structural information constituted mainly by spatial relationships. the developed application is for the segmentation of brain structures in ct (computer tomography) images. promising results show the superiority of this knowledge-based approach over best traditional techniques in terms of segmentation errors. the quantitative assessment of this method is made by comparing manually and automatic segmented brain structures by using some indexes evaluating the accuracy of contour detection and spatial location. though the proposed methodology has been implemented and successfully used for modeldriven in medical imaging, it is general enough and may be applied to any imagistic object that can be expressed by expert knowledge and morphological images. keywords: biomedical image processing, fuzzy systems, image segmentation, fuzzy rules. 1 introduction within the current clinical setting, medical image segmentation is a vital component of a large number of applications, such as to: (i) study anatomical structure; (ii) identify regions of interest, i.e. locate tumor, lesion and other abnormalities; (iii) measure tissue volume to assess growth or decrease in size of tumor; (iv) help in treatment planning prior to radiation therapy and in radiation dose calculation. contour detection for segmentation still remains a challenging problem in medical imaging, for instance in radiotherapy treatment, where both ct and mr images can be employed. the former is needed to accurately compute the radiation dose, while the latter is usually better suited for accurate delineation of tumor tissue. any contour delineation error above 10% is considered in clinical practice that may lead to an unacceptable risk to irradiate healthy tissues instead of diseased ones. this is the main raison of our approach to deploy a (semi)automatic and more effective segmentation procedure for ct images, based on a strong paradigm in computational intelligence: fuzzy logic. imprecision in medical image information may be due to several factors, ranging from the observed phenomenon to the algorithms’ precision. for instance, a soft transition between healthy and pathological tissues may make difficult to distinguish tumor margins from edematous normal brain. in this context, the theory of fuzzy sets is an interesting and useful tool, as it provides a good theoretical basis to represent imprecision of the information and it constitutes a unified framework for representing and processing both numerical and symbolic information, as well as structural information. proceeding from the grounds of artificial intelligence, soft computing, copyright c⃝ 2006-2013 by ccc publications a fuzzy rules-based segmentation method for medical images analysis 197 and image processing, for instance from [4], [9], [11], [18], [19] a number of authors have used these techniques to aid in the analysis of medical images, e.g. in [1], [2], [3], [5], [6][8], [10], [12][13], [20][22]. our new segmentation procedure is one of supervised-type, as it requires a model image of the analyzed structure. moreover, the adding of domain knowledge about the characteristics of objects increases the segmentation accuracy. the created fuzzy system for this application uses a rule-based linguistic description of the objects belonging to the region of interest, that serves to pilot the system for the edge detection-based segmentation. at the entry of the system, for each input image a symbolic representation is determined. then, fuzzy rules are applied. among all points of interest (candidate points for contour detection) only those satisfying the rule set are kept for further analysis, by applying some criteria of decimation. these criteria include the spatial conformity with rules and the compactness between boundary points. at the exit, the system yields the position of contour points. 2 the used fuzzy system figure 1 shows a block diagram of the used fuzzy system. it represents a supervised approach that converges to the contour points of the object of interest. first, a knowledge base is established going from a reference (model) image for which the contour points are manually delineated (figure 1(a)). the written fuzzy rules include the features of the known boundary, e.g. the transitions of the brightness for neighboring pixels of the contour points. figure 1: the block diagram of the used fuzzy system 2.1 knowledge base settlement linguistic description of the image. membership functions and fuzzification first, for gray level images the main parameter used for their description is the brightness. in fuzzy logic we have to consider brightness as a fuzzy variable, composed by different fuzzy classes: black, dark, gray, bright and white. thus, for a 256 gray level image one may write the fuzzy variable (brightness ; [0, 255] ; { black, dark, gray, bright, white }). we have chosen for our study trapezoidal membership functions (µ) corresponding to brightness, and the fuzzification process is made according to these membership functions. determination of a point position the position of points in a digital image may be represented using a system of polar coordinates (θ, r) as in figure 2. the origin is chosen as the object center, c. the number of 198 h. costin contour points forming the knowledge base is detected by counting the intersections between different sampling rays (αj) and the border of the (reference) object. thus, a new fuzzy variable position is derived from this graphical representation and it codifies the value of angle θ associated to a contour point, depending on the number of sampling rays used: (position; [0, 360];{α1, α2, ..., αn}). (1) for n=36 the membership functions for fuzzy variable position is shown in figure 3. 2.2 fuzzy rules based knowledge base fuzzy rules settlement the manually traced border of the reference object is obtained using computing the average gray level respectively within two moving (outer and inner) windows situated on the two sides of the contour point (shaded area in figure 2). this technique yields a good noise reduction and is made for every direction αj. thus, one establishes the existing relations between the average gray levels within the two windows and the value of angle θ. through the fuzzification of the fuzzy variables brightness and position the relations between these two variables are directly obtained. then, if...then fuzzy rules are derived, that show the n (n=35) relations between the two fuzzy variables. for each case when the membership value to a class αj is maximum (1) for position, i.e. {1/αj}, a new fuzzy rule may be defined. for instance, some of these fuzzy rules might be: (1) if the position is {1/α1} then the transition of the brightness each side of the contour varies from {1/dark} to {1/gray} ; ... (35) if the position is {1/α35} then the transition of the brightness each side of the contour varies from {0.9/dark ; 0.1/gray} to {1/gray} . fuzzy rules encoding the knowledge base uses the fuzzy rules encoding, i.e. their conversion into numerical values. indeed, for a certain direction αj two numerical values are defined: the inner brightness, gint(αj), and the outer one, gext(αj): gint(αj) = 5∑ i=1 µi,int(αj)ci 5∑ i=1 µi,int(αj) ; gext(αj) = 5∑ i=1 µi,ext(αj)ci 5∑ i=1 µi,ext(αj) (2) where ci are the centers of the classes brightness, i.e. the median values of the gray levels of each class. the values µi,int(αj) represent the membership degrees for the i-class of brightness, for the direction j and the inner contour. a similar definition stands for µi,ext(αj). 3 the used fuzzy system at work the involved steps for supervised contour points detection are the image fuzzification, application of fuzzy rules, points aggregation, then applying decimation criteria, and the final stage represents the defuzzification. a fuzzy rules-based segmentation method for medical images analysis 199 figure 2: the block diagram of the used fuzzy system figure 3: the block diagram of the used fuzzy system 3.1 obtaining fuzzy image according to our approach, when utilizing polar co-ordinates and representation from figure 2, the angle values are fuzzified in classes αj by the fuzzy variable position. as an example, for θ = 25 degrees, the membership function becomes µposition (25) = {0.5/α3 ; 0.5/α4} (figure 3). the set of points taken on n* directions constitutes the universe of discourse of a new fuzzy variable named pixel, whose elements are: pix(θj, ri) ∈ pixel, j = 1, ..., n∗, i = 1, ..., p ∗; pix(θj, ri) = [gint(θj, ri); gext(θj, ri)] (3) 3.2 application of fuzzy rules and points aggregation the application of fuzzy rules on the point set of the variable pixel yields a measure of resemblance between input image data (to be analyzed) and the non-zero values encoded in the corresponding knowledge base. the measure of resemblance with the knowledge base uses rules encoded for every class αj, by the pair of brightness variation, e.g.: if the position is {1/α1} then the brightness is (50 ; 100) ; the variable resemblance, based on the classes inner and outer, measures the "degree of similitude" between the brightness values in input image and the fuzzy rules, for a certain direction αj. in this case the fuzzy rules are written in the following manner, in which the value of resemblance appears: if the position is {1/α1} and the brightness is ( 50 ; 100 ) then one has a total resemblance of { 1/inner ; 1/outer } ; 200 h. costin the membership degrees to the classes inner and outer of the variable resemblance are read by means of the correspondent membership functions in figure 4, in which we obtain, e.g., µresemblance(θ1, ri) = { 1/inner ; 0.4/outer }. if the number n’ of the considered directions for establishing variable pixel differs from the n classes of the variable position, then an interpolation of fuzzy rules is needed. a contour image pixel has high values of the membership degrees to the classes inner and outer, that means the use of min operator in fuzzy logic. let’s consider another class, global, of the resemblance series, resulting from the intersection between two membership degrees: µresemblance(θj, ri) = {µinner/inner; µout er/outer; min[µinner; µout er]/global} (4) so, we may determine the candidate series, which is a subset of pixel series and represents a support of the global class. in this manner, the candidate series is defined by candidate = s(pixel) = {pix(θ, ri)/µglobal(θ, ri) > 0} (5) 3.3 decimation criteria we have two decimation criteria in order to choose the right contour points: a spatial resemblance criterion and a compactness criterion. they both allow to reduce the number of candidate pixels and these criteria measure the areas having a high pixel density and characterized by a high resemblance degree. the spatial resemblance criterion assesses for each candidate point, within an appropriate window, the average value of the matching to the image model. denoting by µspatial this value, the region of interest is limited by the n directions situated on the two sides of the considered direction. on these directions we use p points surrounding the position ri . thus, one obtains: µspatial(θj, ri) = ∑n u=−n ∑p v=−p µglobal(θj+u, ri+v) (2n + 1)(2p + 1) (6) figure 4: membership degree of the pixel (θ1, 1) to the pixel variable the pre-segmentation points are those pixels yielding a preliminary position of the contour, on n’ directions. these points must have high membership degrees µglobal to the global class and µspatial to the spatial class. thus, the pre-segmentation series contains those points having the maximal value of the combined two memberships: µpreseg(θj) = maxi[µspatial(θj, ri) ·µglobal(θj, ri)] (7) a fuzzy rules-based segmentation method for medical images analysis 201 figure 5: membership function of the fuzzy variable contour the compactness criterion also evaluates areas of pixels with the highest belief to be contour points. from the candidate points pix(θj, ri) matching very well the image model, this criterion chooses those pixels being very "close" to the pre-segmentation points θj. the distance, belonging to the [0, 1] interval, between the current point pix(θj, ri) and the pre-segmentation point pix(θj+u) of the θj+u direction, is computed according to the formula [1-d(θj+u; θj, ri)], considering n directions on both sides of the j direction. thus, for each candidate point one computes the value µcompactness = ( n∑ u=−n [µglobal(θj, ri) ·µpreseg(θj+u) · [1−d(θj+u; θj, ri)]] ) /(2n + 1) (8) 3.4 deffuzification process the last stage of segmentation is obtaining contour points through a deffuzification process, i.e. by translating fuzzy series into real numbers. the output data offer the position of the n’ contour points situated within a circular area of radius dθj from the considered centroide. the position of the edge pixels on a certain direction θj represents the median position of the points on that direction, weighted by a membership degree, µcontour, associated to the contour output class (figure 5). µcontour normalizes the compactness degree canceling those points with small values of the compactness. the contour series keeps those pixels having the value µcompactness between an average compactness value, m(θj), obtained for p’ points of the candidate series on the direction θj, and the maximum value max(θj): m(θj) = ( p′∑ i=1 µcompactness(θj, ri) ) /p′ (9) the deffuzification stage yields the contour point dθj for each direction θ. the value dθj is obtained using a weighting of the position of the points p’ on that direction through their membership degree to the obtained contour. dθj = ( p′∑ i=1 µcontour(θj, ri) ) /p′ (10) 202 h. costin 4 application of this method of segmentation to the cerebral images analysis we have used this method to segment some cerebral tomographic (ct) images with a graylevel resolution of 10 bits (figure 6). the chosen reference image is shown in figure 7. we used a geometric system composed by 32 directions for knowledge base construction. for each direction we used 50 points on which fuzzy rules act, so a total universe of discourse of 1600 points was considered (figure 8). also, we choose a (5 x 5) window for computing the average value of local gray levels. the number of pixels having a non-zero resemblance, representing candidate series, was 985 (figure 9). for each point of this set one averages the resemblance value to the reference image, in order to keep on every 32 directions the points having the maximum spatial resemblance (figure 10). in this way the pre-segmentation points have been obtained. the areas involved in computing this average value are formed by two opposite directions on each analyzed point and by 20 points per directions all around that point. the computation of compactness yields areas with high pixels density having contour characteristics. the deffuzification stage offers the median point of high compactness on each direction or a set of 32 points. the segmentation of cerebral ventricles is made by the interpolation of the 32 contour points, using a polynomial interpolating method [16], that yields contour tracing and is adapting properly to the anatomic pattern of the analyzed structure (figures 11, 12). figure 6: input image figure 7: reference image figure 8: universe of discourse figure 9: candidate series figure 10: pre-segmentation points figure 11: interpolated points on input image the accuracy of this segmentation method is assessed by superposing the contours detected automatically and manually, by an expert, and compute the difference between them (figure a fuzzy rules-based segmentation method for medical images analysis 203 figure 12: interpolated points only figure 13: the difference between manually and automated traced points figure 14: contours of the two ventricles over input image 13). as objective parameters of the segmentation precision we have to compute two indexes: (1) the relative surface difference, ∆s, between the two contours ; (2) the average relative difference of ray length, ∆d, also between the contours. the first index represents the sum of the n elementary surfaces ∆si existing between the contours, normalized by the surface of the manually traced contour, smanual : ∆s = ∑n i=1 |∆si| smanual (11) the second parameter is computed by using the length differences ∆di between the two contours, for a certain direction of analysis, normalized by the average ray length, dimanual, of the manually traced contour, and averaged by the n considered directions: ∆d = 1 n n∑ i=1 |∆di| dimanual (12) we have obtained the following average values of the two indexes: ∆saverage = 5.6%, and ∆daverage = 4.5%, on 12 tomographic (ct) images used in our experiment (figure 14 is an example). these values act as a measure of the segmentation errors, and in general are low enough for further steps of image analysis (classification, 3d reconstruction, etc.). in order to compare our results with previous ones, obtained for the same image modality (ct), we used the fuzzy c-means (fcm) clustering algorithm [1], [15], that yet does not address the intensity inhomogeneity artifact. also, we have applied other traditional approach otsu’s multithreshold segmentation method, as shown in [14] and [17]. as main software tool, fuzzy logic toolboxtm of matlabr and simulinkr proove to be appropriate object-oriented programming environments for our purposes. the computing time is not a critical parameter for this application of still images analysis. as shown, the proposed method doesn’t rely on iterative algorithms and it is susceptible to partial parallelization. in fact, after the training stage on a reference image, 204 h. costin that implies computing of fuzzy rules, their encoding into a data base and an interpolation procedure, the contour detection is achieved on each search direction. in this manner, the whole computation may be done almost in parallel in four main stages: image fuzzification, fuzzy rules application, decimation criteria calculus (after the end of the second stage), and defuzzification. table 1. segmentation errors for the image in figure 6 segmentation method error (%) otsu’s multithreshold method 12.2 fuzzy c-means 9.4 proposed method 5.6 5 conclusions the application of fuzzy logic for biomedical image segmentation proved superior results in terms of segmentation errors, in comparison with other methods developed in [14], [15], [17], taking as reference the manually contour-based segmented version. a main advantage of our method is that knowledge is directly represented in the image space by means of fuzzy sets. the automatic detection of contour points is facilitated by using a data fusion approach by combining information brought by fuzzy rules (selected after a training process) with that coming from each used searched direction. thus, the obtained average accuracy of 5,6% was found very good by the medical neuro-surgeon who helped us during experiments, even more as the recognized variability between different medical experts when manually contouring structures in brain images is at least 10%. moreover, this adaptive technique proves to be robust, as it may approach with good results noisy images, for which the contour area may be not well delimited even for experts. in this case, the parameters characterizing contours must be expressed by average values taken on larger research areas. this is also a working manner for increasing the robustness of the main method. another advantage of the method is that the computing time when implementing this noniterative algorithm is kept at a low value and it is possible to be lowered by further parallelization. acknowledgment the author would like to thank his colleagues, prof. ion poeată, phd, md, and lecturer cristian rotariu, phd, for their help in providing him the brain ct images, manually segmentation, some software implementation and for results evaluation during experiments. bibliography [1] ahmed, m.n., et al (2002); 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[22] zhou, j.; rajapakse, j.c.; segmentation of subcortical brain structures using fuzzy templates, neuroimage, 28: 915-924, 2005. ijcccv11n1.dvi international journal of computers communications & control issn 1841-9836, 11(1):26-38, february 2016. fuzzy linear physical programming for multiple criteria decision-making under uncertainty a. elsayed, e. kongar, s.m. gupta ahmed elsayed department of computer science and engineering university of bridgeport 221 university avenue, bridgeport, ct 06604, usa aelsayed@my.bridgeport.edu elif kongar* departments of mechanical engineering and technology management university of bridgeport 221 university avenue, bridgeport, ct 06604, usa *corresponding author: kongar@bridgeport.edu surendra m. gupta department of mechanical and industrial engineering northeastern university 360 huntington avenue, boston, ma 02115, usa gupta@neu.edu abstract: this paper presents a newly developed fuzzy linear physical programming (flpp) model that allows the decision maker to introduce his/her preferences for multiple criteria decision making in a fuzzy environment. the major contribution of this research is to generalize the current models by accommodating an environment that is conducive to fuzzy problem solving. an example is used to evaluate, compare and discuss the results of the proposed model. keywords: decision support systems, fuzzy goal programming, fuzzy sets and systems, heuristics, linear physical programming, logistics, multi-criteria decision making, supply chain. 1 introduction many real life decisions are often complex and typically involve several parties with different goals and objectives. today, as also stated by filip et al. [1], technological and organizational improvements have led to more complex and complicated decision making environments. as a result, current decision models are stretched to the limit to handle these changes as the data availability, number of participants in the decision making process, size of markets and several other factors grow. that is, decision making has become a cumbersome task requiring models that are able to consider various multiple goals, constraints that are somewhat uncertain and flexible in nature. for instance, the success of supply chains heavily depends on the joint effort focusing on the overall value creation as opposed to optimizing segregated objectives. furthermore, some of the supply chain outcomes, viz., responsiveness, sustainability, innovation, are less likely to be expressed with crisp objective values [2]. in many cases desired business outcomes are fuzzy in nature and contain a considerable degree of flexibility. the reason for this is twofold. first, the copyright © 2006-2016 by ccc publications agora university fuzzy linear physical programming for multiple criteria decision-making under uncertainty 27 outcome measures are difficult to calculate and quantify. in addition, the conflicting objectives among different parties in the supply chain are likely to mandate varying levels of preference levels even for an identical performance measure. therefore, it is reasonable to model supply chain performance measurement problems via fuzzy and multiple objective approaches which are, in fact, more suitable for such problem environments. with this motivation, this paper presents a decision support model which would accommodate the uncertainty involved in the problem environment caused by the flexibility in the goals, information, perspective and/or different visions. the model, based on linear physical programming (lpp), is suitable for individualized decisions as well as decisions where multiple participants are involved and has the ability to integrate and reflect varying preferences of the decision maker. linear physical programming (lpp) is a preference based optimization method, which operates in the environment of multiple criteria and uses a utility function to represent the decision maker’s (dm) preference levels. the key distinguishing feature of lpp is that the decision maker is excluded from the process of choosing appropriate weights [3] which is one of the major challenges when defining a utility function. furthermore, similar to other decision support systems [4], the fuzzy lpp formulation can assist the decision maker in overcoming individual limitations and constraint by (i) handling the computational complexity which would otherwise be practically prohibitive and by (ii) introducing vagueness that is naturally embedded in real life decisions or introduced due to multiparticipant nature of the decision environment. due to these advantages, physical programming has been the topic of various theoretical and practical studies. the technique has been extensively used in various industrial and mechanical engineering applications as well as in other disciplines. this paper presents a newly developed fuzzy linear physical programming (flpp) model to accommodate the vagueness of the problem environment while eliminating the need for precision in decision makers’ preference levels. the model presented significantly improves the basic methodology and generalizes it to assist in overcoming the decision maker’s vagueness. 2 literature review related body of knowledge offers a large variety of models where the decision maker’s preference levels are integrated into an objective function. most techniques require that the decision maker construct an aggregate objective function using the weights determined as a result of an exasperating trial and error process. in lpp, however, the decision maker specifies the ranges of different degrees of desirability instead of defining the weights. lpp then uses these ranges of desirability to formulate the aggregate objective function, thus eliminating the tedious weight assignment process by providing decision makers with a flexible and more natural problem formulation. maria et al. [5] proposed a production-planning model based on linear physical programming (lpp) that would utilize previous design knowledge when available. messac et al. [6] applied lpp to reconfigure a distribution network including various quantitative and qualitative factors such as costs of relocation/consolidation, inbound and outbound transportation, relocation, customer delivery time, labor quality, labor-management relations and tax incentives. the proposed model is then utilized to determine the demand allocations of plants to warehouses and warehouses to customers. ondemir and gupta [7] a applied linear physical programming to a multi-criteria advanced repair-to-order and disassembly-to-order (artodto) system for sensor embedded products with rfid tags. the proposed approach aimed at determining how to process eol products 28 a. elsayed, e. kongar, s.m. gupta to satisfy life expectancy based demand. various types of demand are met by a combination of disassembly, repair, and recycling operations. the model allowed third party procurement to prohibit backorders. a study that introduces heuristics into physical programming (pp) was proposed by sanchis et al. [8]. in this work, the authors replaced pp optimizer by a genetic algorithm to avoid potential local minima issue in addition to simplifying the algorithm that constructs the pp preference functions. relevant research combining disassembly sequencing and scheduling via linear physical programming have previously been conducted by kongar and gupta [9] and lambert and gupta [10]. another multi-objective method that has been extensively used in problem environments with high uncertainty is fuzzy programming. the method has been widely used due to its ability to respond to the imprecision associated with the input data in multi-objective problems and hence its ability to handle vagueness. in fuzzy programming each objective function value is interpreted as a fuzzy number bounded by the best and worst values of the particular objective. the corresponding fuzzy membership function value of the objective is the deviation from the goal divided by that objective’s range, thus a value between 0 and 1. fuzzy programming aims at finding the solution that minimizes the largest fuzzy membership function. in addition to the goals, other input parameters can be represented by fuzzy numbers and fuzzy arithmetic can be used to combine fuzzy numbers based on fuzzy set theory first introduced by zadeh [11]. one of the earlier studies in fuzzy was published by narasimhan [12] who solved a goal programming problem in a fuzzy environment. in order to illustrate the capability of the proposed model, the author provided solutions for the typical goal programming model that aims at a single optimum solution. a similar study has also been proposed by hannan [13] demonstrating the incorporation of fuzzy goals into typical goal programming formulation via numerical examples. the study also provides a discussion on the advantages and shortcomings of fuzzy and standard goal programming modeling. tian et al. [14,15] developed a fuzzy physical programming model for multidisciplinary design optimization. in their study, the authors utilized fuzzy pp for the optimization modeling of through passenger train plan and solved it via genetic algorithm. zhang et al. [16] utilized a fuzzy physical programming approach in a linear environment. however, a major drawback in this approach was that it limited the fuzziness only to the boundaries of the problem whereas fuzziness can occur in the variables of both the goals and the classes resulting in more complicated fuzzy relationship between the two. another relevant work in the area of fuzzy physical programming (fpp) was published by huang et al. [17]. in that study, the authors proposed a fuzzy aggregate objective function with fuzzy constraints for each soft and hard class. these studies utilized a single membership function for each goal, and calculated the class function as a function of the membership values as opposed to as a function of the goal values as proposed by the original physical programming methodology. class functions are considered to be aggregation of goal values and its membership function. the problem is then converted back to its crisp form and solved by genetic algorithm. ilgin and gupta [18] provide a state of the art review of physical programming (pp) by classifying the pp related studies into four areas, viz., (1) methodological papers, (2) industrial engineering applications, (3) mechanical engineering applications, and (4) other applications. the methodology described in this paper combines linear physical programming (lpp) and fuzzy sets and proposes a multi-objective model for disassembly sequencing under uncertainty. fuzzy linear physical programming for multiple criteria decision-making under uncertainty 29 figure 1: lpp soft class functions for a generic uth objective 3 theoretical background and methodology 3.1 linear physical programming the physical programming algorithm requires that the decision maker expresses his/her preferences with respect to each criterion using one of the eight different classes. the first four classes are “soft class functions”, and represent minimization (class-1s), maximization (class-2s), value (class 3s), and range (class 4s) optimization. the remaining four are “hard class functions” and are used to introduce inequality and range restrictions into the problem environment. in this regard, class 1h and class 2h define upper and lower bounds, respectively, while class 3h imposes equality and class 4h imposes range related restrictions to the problem environment. the qualitative and quantitative depiction of each class is provided in figures 1 and 2. the soft class functions allow the dm to express varying levels of preferences for each criterion. this is done by introducing corresponding constraints for each preference level in each of the classes. to provide better understanding, consider class 1-s and class 2-s, depicted in figure 1, which are used for “smaller is better” and “larger is better” cases respectively. table 1 demonstrates the ranges and corresponding constraints for the problem. note that all the soft class functions will be embedded in the aggregate objective function to be minimized. in figure 1, the uth generic criterion is indicated as gu (x) where x is the decision variable vector. the goal value, gu is represented on the horizontal axis while, zu, the class function that is subject to minimization is represented on the vertical axis. since lpp algorithm considers the lower values of the class functions as “better” values but prohibits negative values, the class function that corresponds to the ideal value is set to zero. 30 a. elsayed, e. kongar, s.m. gupta table 1: preference levels and constraints for class-1s and class 2-s class 1-s class 2-s range index preference level constraint range index preference level constraint 1 ideal gu ≤ t + u1 1 ideal gu ≥ t − u1 2 desirable t+u1 ≤ gu ≤ t + u2 2 desirable t−u1 ≤ gu ≤ t − u2 3 tolerable t+u2 ≤ gu ≤ t + u3 3 tolerable t − u2 ≤ gu ≤ t − u3 4 undesirable t+u3 ≤ gu ≤ t + u4 4 undesirable t − u3 ≤ gu ≤ t − u4 5 highly undesirable t+u4 ≤ gu ≤ t + u5 5 highly undesirable t−u4 ≤ gu ≤ t − u5 6 unacceptable t+u5 ≤ gu 6 unacceptable t − u5 ≥ gu figure 2: lpp hard class functions for a generic uth objective as for the hard classes, only two ranges are defined namely, acceptable and unacceptable. hard class functions appear as constraints in the lpp model. they can be considered as the hard constraints in goal programming and are handled in the same manner. therefore, they are used for bounding and hence creating the feasible search space (figure 2). it is important to note that both soft and hard class functions are piecewise linear and convex. this can be achieved by ensuring that the calculated weights are positive. lpp’s built-in-weight calculation algorithm, linear physical programming weight (lppw), ensures this condition and guarantees that is each criterion is associated with a piecewise linear class function. briefly, lppw, first proposed by messac [19], uses the decision maker’s target values for each criterion to calculate a weight vector. following this, lpp utilizes these weights to form the objective function to be minimized. 3.2 fuzzy linear physical programming model the methodology proposed in this paper utilizes separate membership functions for each linguistic variable for each goal, and converts the relationship between the class variable and the goals to a fuzzy relationship. furthermore, the weight for each goal is also calculated based on the fuzzy relationship prior to problem solving via fuzzy goal programming. therefore, the algorithm detailed in this paper is fuzzy in nature; modeled, initialized and solved in a fuzzy environment throughout. the concept of creating a fuzzy relationship between the goals and the classes was introduced fuzzy linear physical programming for multiple criteria decision-making under uncertainty 31 figure 3: trapezoidal membership function by kosko [20]. the steps of the proposed algorithm are provided below. 1. select a class type for every goal s1, s2, s3, s4 or h1, h2, h3, h4 2. decision maker should define the membership function of each linguistic variable for each goal. assuming that the membership function is trapezoidal, then the function can be expressed mathematically as follows: µideal (gi) =              0, (gi < a) or (gi > d) gi−a b−a , a ≤ gi ≤ b 1, b ≤ gi ≤ c d−gi d−c , c ≤ gi ≤ d (1) where gi is the goal number i to be used in the decision process, and, a,b,c, and d are the parameters of the membership function of the ideal linguistic variable as shown in figure 3. 3. the class function zi will also turn into a set of linguistic variables, each member of the set corresponding to the respective linguistic variable for the goal, i.e., ideal, desirable, etc., with a trapezoidal assumption the membership function will be as demonstrated follows: µzi (z) =              0, (z < a) or (z > d) z−a b−a , a ≤ z ≤ b 1, b ≤ z ≤ c d−z d−c , c ≤ z ≤ d (2) for instance µzideal (z) value for z̃ ideal will be as follows (figure 4): µzideal (z) =        0, (z > c) 1, a ≤ z ≤ b c−z c−b , b ≤ z ≤ c (3) the values of a,b,c, and d of each membership function will be determined according to the lppw algorithm to guarantee that the fuzzy relationship between the class function and each goal is a convex fuzzy relationship. 32 a. elsayed, e. kongar, s.m. gupta figure 4: sample trapezoidal membership function for the ideal linguistic variable for class 1-s 4. after determining the values of membership function limits of zi sets, the fuzzy arithmetic is utilized to obtain the value of the fuzzy weight of each linguistic variable for each goal. through a fuzzy division, the ideal set will be calculated as follows: w̃ideal = z̃ideal g̃ideal (4) which will generate a membership function for the fuzzy weight. please note that equation 4 can easily be solved using a computer program based on the fuzzy division operation described in [21, 22]. 5. the weight of each linguistic variable can be obtained by defuzzification of the fuzzy weights. in this paper the center of gravity (centroid) defuzzification technique is utilized to defuzzify the weights in order to compute the corresponding crisp values. following defuzzification, each crisp weight is multiplied by the membership function of the corresponding goal’s linguistic variable. this way each linguistic goal is shaped according to its priority. the resulting modified membership function becomes: µideal (gi) =              0, (gi < a)or (gi > d) wideal(gi−a) b−a , a ≤ gi ≤ b wideal, b ≤ gi ≤ c wideal(d−gi) d−c , c ≤ gi ≤ d (5) 6. calculate the union of all the membership functions of the linguistic variables of each goal to obtain the final membership function for the corresponding goal values: µgoali (gi) = µideal (gi)∨µdesiarble (gi)∨µt olerable (gi)∨µundesirable (gi)∨µhundesirable (gi)∨µunacceptable (gi) (6) 7. define the membership function of the constrains µci (g1,g2,g3, . . .) where ci is the constraint number i defined by the decision maker (can be considered as one of the hard classes). 8. apply fuzzy goal programming by calculating the minimum point that provides the highest value for the membership function of the intersection of every defined membership function : µd (g1,g2,g3, . . .) = min i (µci (g1,g2,g3, . . .))∧min j (µj (gj)) (7) fuzzy linear physical programming for multiple criteria decision-making under uncertainty 33 where µd (g1,g2,g3, . . .) will be the membership function of the required objectives. then the optimum goal values will be: g = min g (max (µd (g1,g2,g3, . . .))) (8) where g is the optimum values of all the goals which satisfy the required constrains. 4 model application and discussion to demonstrate the applicability of the proposed algorithm, consider the numerical example provided in messac et al. [3]: a company manufactures two types of products, product a and product b. due to factory’s physical and human-resource limitations, the company can comfortably manufacture 25 units per month of product a, and 10 of product b. these levels are considered ideal and independent of other considerations. the profits generated per unit from product a and b are respectively $12k and $10k. at these levels of manufacturing, the resulting monthly profit becomes $400k. investors have decided that if the company is to remain in business, a minimum monthly profit of $580k must be achieved at the optimal level. after appropriate market research and examination of the situation, the dm decides that, the preferences regarding production rate are as follows: preference level product a product b ideal <25 <10 desirable 25-31 10-18 tolerable 31-36 18-26 undesirable 36-44 26-33 highly-undesirable 44-50 33-40 unacceptable >50 >40 the profit (constraint) equation for the factory takes the form: 12g1 + 10g2 ≥ 580, where g1 and g2 represent two criteria that denote the respective monthly production levels of products a and b. the following demonstrates the application of the proposed algorithm to the problem described above. both goals (g1 and g2) belong to class-1s since they are “smaller is better” in nature. then the membership function of the goals’ linguistic variable is assumed as shown in figure 5 (i.e., ’*’ for ideal, ’+’ for desirable, ’.’ for tolerable, ’o’ for undesirable and ’–’ for highly-undesirable). following this assumption, the lppw algorithm is then employed to calculate the parameters of the class function membership limits to guarantee the convexity of the fuzzy relation between the goals and the class function. figure 6 shows the resulting membership function for the class function. figure 7 shows the fuzzy relation between the class function z and the goal for product a. the membership function of the constrains is also assumed as shown in figure 8,where the constraints are fuzzified so that, for instance, “less than 25 is ideal” statement is converted into “less than 25 is somewhat ideal”. by calculating the weights and applying equation 6, the final membership function for the intersection of all goals become as shown in figure 9. the final membership function after applying equation 7 is depicted in figure 10. the algorithm results in (g1 , g2) = (25.5, 20.2) for product a and product b respectively in the case of trapezoid membership functions (shown figures). product a result (25.5) is in the 34 a. elsayed, e. kongar, s.m. gupta 0 5 10 15 20 25 30 35 40 45 50 product a values m e m b e rs h ip f u n ct io n s v a lu e s 0 0.2 0.4 0.6 0.8 1 0 5 10 15 20 25 30 35 40 product b values m e m b e rs h ip f u n ct io n s v a lu e s 0 0.2 0.4 0.6 0.8 1 figure 5: membership functions of the product a and product b linguistic variables −0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 class function values m e m b e rs h ip f u n ct io n s v a lu e s 0 0.2 0.4 0.6 0.8 1 figure 6: membership functions of the class function linguistic variables figure 7: two views of the fuzzy relation between product a values and the corresponding class function fuzzy linear physical programming for multiple criteria decision-making under uncertainty 35 figure 8: membership functions of the constraints figure 9: the intersection region between the membership functions of product a and product b figure 10: the intersection region between the membership functions of product a and product b and constrains 36 a. elsayed, e. kongar, s.m. gupta table 2: number of samples and corresponding execution times number of samples execution time (seconds) 150 1.02 300 2.79 400 4.70 500 7.35 600 10.78 desirable range with close proximity to the ideal. as for product b, the result (20.2) is in the tolerable range closer to the desirable boundary. when the membership function is defined as triangular, the best goals become (g1 , g2) = (28, 22.4) for product a and product b, respectively. here, product a result (28) is in the desirable range, whereas product b result (22.4) is in the tolerable range. messac et al. [19] first modeled and solved the problem via goal programming (gp) and calculated the (g1 , g2) coordinates as (25, 28) and (40, 10). the authors described the solution alternatives as quite restrictive and highly dependent on the choice of weights and on the goals/targets. furthermore, the authors note that dm’s knowledge cannot be implemented in the problem model. following this, the study proposed the utilization of lpp for the same problem. the resulting value is located at the desirable/tolerable boundary for g1, and within the tolerable range for g2, with (g1 , g2) = (31, 20.8). compared to both gp and lpp results, the proposed methodology provides better solutions for all goals. in addition, the proposed algorithm eliminates the need for deciding on the weights of goals. the approach can be easily adapted to general purpose problems as well as specialized problem environments with narrower focus. that is, if a problem can be formulated with an objective function and one or more technological constraints and sign restrictions the presented model will be able to solve the problem in a complete fuzzy environment. the model can also be easily converted into a multiple objective optimization and regardless of the number of objectives in the problem environment, it would still be able to preserve its fuzzy nature. it should also be noted that the only alpha cut used in the computation is in the final step of the optimum value for calculation. here, the maximum membership function is: α = max (µd (g1,g2,g3, . . .)) , whereas the corresponding optimal value becomes: g = min (aα) . . all the computations in the example are numerical, since they were computed via vonneumann architecture computer system and not via a fuzzy processor. for the example shown, the number of samples used for all variables constitutes a domain of 300 samples (i.e., n =300). increasing the number of samples would adversely affect the overall execution time since calculations and complexity of the fuzzy relations are similar to regular matrix multiplication. table 2 depicts the relationship between the number of samples and the execution time for a selection of numerical values. fuzzy linear physical programming for multiple criteria decision-making under uncertainty 37 5 conclusions unlike conventional goal programming techniques, fuzzy goal programming, including the proposed fuzzy linear physical programming model, implies a fuzzy system. therefore, the model’s computational complexity on regular digital machines not only depends on the number of input and output variables, but also on the number of discretization levels, viz., samples, from both variables [15]. using this principle, the computational complexity of the problem under test can be restricted to a number that improves result accuracy while improving the computational time (approximately 300 discretization level for each input and output variable universe of discourse). this simplified fuzzy logic control is proven be suitable for a loop controller with regard to the computation time and the required memory. bibliography [1] filip, f.g., a.-m. suduc, m. bazoi (2014); dss in numbers. technological and economic development of economy, 20(1): 154-164. 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[19] messac, a. (1996), physical programming: effective optimization for computational design, aiaa journal, 34(1): 149-158. [20] kosko, b. (1994), fuzzy systems as universal approximators, ieee transactions on computers, 1994. 43(11): 1329-1333. [21] peeva, k.; y. kyosev (2005), fuzzy relational calculus theory, applications and software: world scientific, 2005 (advances in fuzzy systems applications and theory). [22] sumathi, s.; s. paneerselvam (2010), computational intelligence paradigms: theory & applications using matlab, boca raton, fl, usa. : crc press. international journal of computers communications & control issn 1841-9836, 14(1), 63-77, february 2019. using an adaptive network-based fuzzy inference system to estimate the vertical force in single point incremental forming s.g. racz, r.e. breaz, o. bologa, m. tera, v.s. oleksik sever-gabriel racz, radu-eugen breaz*, octavian bologa, melania tera, valentin-stefan oleksik department of industrial machines and equipment lucian blaga university of sibiu, romania, 550024 sibiu, victoriei, 10, romania gabriel.racz@ulbsibiu.ro *corresponding author: radu.breaz@ulbsibiu.ro octavian.bologa@ulbsibiu.ro, melania.tera@ulbsibiu.ro, valentin.oleksik@ulbsibiu.ro abstract: manufacturing processes are usually complex ones, involving a significant number of parameters. unconventional manufacturing processes, such as incremental forming is even more complex, and the establishment of some analytical relationships between parameters is difficult, largely due to the nonlinearities in the process. to overcome this drawback, artificial intelligence techniques were used to build empirical models from experimental data sets acquired from the manufacturing processes. the approach proposed in this work used an adaptive network-based fuzzy inference system to extract the value of technological force on z-axis, which appears during incremental forming, considering a set of technological parameters (diameter of the tool, feed and incremental step) as inputs. sets of experimental data were generated and processed by means of the proposed system, to make use of the learning ability of it to extract the empirical values of the technological force from rough data. keywords: adaptive network-based fuzzy inference system, cnc milling machines, incremental forming, technological force. 1 introduction manufacturing processes are usually involving a significant number of parameters. complex analytical models were developed to express the dependencies between these parameters, for every manufacturing processes, but the complexity of the factors involved in these processes often affect the utility and accuracy of such models. to compensate this drawback, methods involving artificial intelligence techniques, such as fuzzy logic, artificial neural networks, adaptive network-based fuzzy inference systems and adaptive neuro fuzzy inference systems were used to extract significant information, such as empirical relationships between parameters, form large amount of manufacturing processes rough data, where analytical dependencies between inputs and outputs were difficult to be derived. these methods make use of the learning ability of the artificial intelligence systems to overcome the complexity of the phenomenon involved in machining processes. a synthesis regarding the main ideas and activities or the renowned scientist lofti zadeh, the father of fuzzy logic was presented in [8]. among others, fuzzy data processing was pointed as a very useful application of fuzzy logic. a comprehensive presentation regarding the fuzzification of classical structures was introduced in the survey work [9], which can be used as starting theoretical support for every research using fuzzy logic methods and tools. fuzzy linguistic modeling was the subject of the review presented in [12], which stated the fact that fuzzy linguistic modeling has proven as a useful tool for information accessing systems. copyright ©2019 cc by-nc 64 s.g. racz, r.e. breaz, o. bologa, m. tera, v.s. oleksik as presented above, fuzzy logic has numerous useful applications, industrial control being one of the most important ones. a survey paper synthesizing the industrial applications of fuzzy control was introduced in [21]. another work regarding modeling and simulation of a combined pid-fuzzy control structure for a telerobot was introduced in [11]. the work presented in [25] used fuzzy logic to predict the surface roughness in milling processes. a fuzzy inference system using cutting regime parameters (such as cutting speed, and feed per tooth, cutting forces values), parameters linked with the tool geometry (tool nose radius) and parameters related to the cutting conditions (the use of cutting fluids) as inputs was designed. the system was able to calculate the surface roughness as an output, in an empirical way. experimental work unfolded during this research had shown that the data predicted by the fuzzy system were close to the experimental ones. the development of a system based upon fuzzy adaptive networks to overcome the complexity introduced by high-speed machining in turning operations was introduced in [15]. a model based upon fuzzy adaptive networks was built and used to predict surface roughness in turning. experimental data was used to validate the proposed model. the researches presented in [16] proposed the use of adaptive neuro-fuzzy inference systems to predict the surface quality obtained by means of end milling. four parameters (feed, speed, vibration and depth of cut) were used as inputs, while the considered output parameter was roughness. the experimental data set was used for both training and validation for the proposed technique. the approach was found to be superior to the use of artificial neural networks from the point of view of prediction accuracy. the authors considered adaptive neuro-fuzzy inference systems as hybrid intelligent techniques, while the use of artificial neural networks was considered a simple intelligent one. in [4] the research team developed an adaptive neuro-fuzzy inference systems model to predict the white layer thickness and surface roughness for an electro discharge machining process. some process parameters such as duration of the pulse, feed rate of the wire, open circuit voltage and dielectric pressure were used as inputs for the adaptive neuro fuzzy inference systems system. experimental data was used for validating the approach, which was found to provide feasible results. the work presented in [25] compared the use of adaptive neuro fuzzy inference systems and backpropagation artificial neural networks to predict the values of some specific process parameters (roughness, tool wear and metal removal rate) for an electro discharge machining process. the above-mentioned parameters were used as outputs, while capacitance, feed rate, gap voltage and threshold were considered as inputs. the results had also shown that adaptive neuro-fuzzy inference systems outperformed artificial neural networks for the prediction task. the fusion of a support vector machine with adaptive neuro-fuzzy inference systems for fault detection of an industrial system was introduced in [22]. a simulation study had proven that the fusion-based approach performed better than individual support vector machine and adaptive neuro fuzzy inference systems. artificial intelligence techniques, mainly their ability to cope with experimental sets of data and extract and/or predict crisp empirical values for significant output parameters were also used in [19], were an air pollution forecasting system based upon artificial neural networks was designed and tested. robot control is another filed of application of such computational intelligence methods. the work from [6] used reduced training examples for artificial neural networks to control a robot with six degrees of freedom. using an adaptive network-based fuzzy inference system to estimate the vertical force in single point incremental forming 65 2 single point incremental forming technological forces single point incremental forming (spif) is an unconventional forming process which allows the user to manufacture prototypes or small batches of sheet metal parts by combining three or more technological movements in a flexible manner [14] [18] [10] [2]. a brief description of the spif process principle is depicted in (fig. 1). the blank (2) is fixed by means of the blank holder (3). in order to realize the shape of the sheet metal part, one of the active elements, usually the punch (1) has an axial feed vertical movement direction, continuous or incremental (with the step s), while the other element, the active plate (4) carries out a plane horizontal movement. figure 1: single point incremental forming principle the spif process still has many drawbacks, which hinder its industrial implementation. one of them is related with the lack of specific technological equipment. prototype machines are used in research facilities for unfolding the process. a literature survey has reported also cnc milling machines and serial industrial robots [5], [23] as technological equipment used for the process, with a special emphasis, by the usage point of view, on cnc milling machines. consequently, it may be stated that the process is unfolded most frequently on cnc milling machines, which are sensitive and expensive equipment. another drawback of the process is related to the difficulty of estimating the technological forces, which became very important when cnc milling machines are involved. forces exceeding the allowable values on each kinematic chain could appear and damage the cnc machine. some modern cnc working centers are equipped with electronic safety systems, which can stop the movements on all axes when the resistant forces and torques are surpassing the allowable values, but this is not a general situation. there are cnc machines which are not fitted with such systems, or these systems could be too slow to stop the process before damages occur. the user must take into consideration both scenarios (best and worst case) and, of course try to avoid the risk of damaging the machine. it is here noticeable that even if the safety systems intervene and stops the process, productivity will be affected (fig. 2). a lot of researches were conducted recently to develop some methods of calculating and assessing the technological forces within the spif process, not only for avoiding machine damage, but also because accurate information about the values of the technological forces are also important to predict the plastic behavior of the sheet metal part. in [7] the influence of the tool diameter, vertical depth increment and steepness of the part’s wall or wall angle is studied, by manufacturing a simple geometry part, a cone. the result shows 66 s.g. racz, r.e. breaz, o. bologa, m. tera, v.s. oleksik figure 2: flowchart of the influence of unknown technological forces upon the cnc milling machines that if the vertical step size, tool diameter or wall angle are increased the forces will increase as well. the research presented in [1] was focused on establishing practical formulae enabling the calculation of forces occurring during the single point incremental forming process, based on experimental results as well as analytical relations derived from finite element analysis (fea) results. the experiments were unfolded for manufacturing a conical part. the formula for the vertical force takes into consideration the characteristics of the material (tensile strength), the material thickness, the diameter of the tool, the vertical step (expressed by the influence of it on the scallop height) and the wall angle of the conical part. another approach, presented in [17], was focused on experimental measurement on forming forces in the conical frustum-forming process to analyze the trend of forming force. finally, analytical models for tangential force prediction were proposed and validated through experimental data. an empirical combined model has been constructed which could provide the prediction of tangential force with an average error less than 11 % for the experimental conditions employed in the experimental research. the work presented in [20] compared the formulae developed in [1] with experimental results. it was verified that the proposed analytical formula has been adequate for constant wall angle slopes but limited to predict the forming force of variable wall angle geometries. in [26], a discussion regarding the process parameters upon the technological forces is presented, based upon a synthesis of the results presented in the literature. according to that, the main parameters which influence the behavior of the forces are: • tool diameter (proportional influence, an increase in the tool diameter leads to an increase in the force value); • vertical step size (proportional influence); • sheet thickness (proportional influence); • wall angle slope (proportional influence). it is noticeable the fact that the influence of these parameters (mainly the wall angle slope was synthesized and reported for different shapes of the parts (cone, conical frustum and pyramid) and for different materials (steel and aluminum alloys). using an adaptive network-based fuzzy inference system to estimate the vertical force in single point incremental forming 67 most of the researches reported that the experimental measurements of the technological forces employed specialized devices, such as 3d dynamometers or custom designed data acquisition systems, which are very expensive and nod widely available. the latest mentioned systems also take a long time for setting-up and calibration. some previous work of the authors of this paper was presented in [24]. a reduced experimental data set was used in that approach and only two parameters were considered as input for the adaptive network-based fuzzy inference system (feed in the horizontal plane and depth). it was considered that only two parameters are not enough to describe the evolution of the vertical technological force. the approach presented here significantly developed the preliminary adaptive network-based fuzzy inference system presented in [24], by using more parameters within the spif process as inputs and significantly extending the amount of data processed by the system (268 data sets instead of 90 data sets). in this paper, the authors set forth a method for measuring the technological forces which can be used even at workshops level and involves no supplementary costs, out of the initial costs of the cnc milling machine. also, using a neuro-fuzzy approach, the experimentally gathered database was organized as a technological knowledge base, which allow the user to assess the value of the vertical technological force outside the process (table 4). table 1: process parameters process parameters process parameters considered in the considered in the observations literature proposed approach tool diameter tool diameter vertical step size vertical step size the approach was to develop knowledge sheet thickness databases for every sheet thickness of a given material, so sheet thickness was not considered as an input variable the influence of the wall angle slope could wall angle slope not be predicted by analytical approaches [20]. moreover, industrial implementation of the process involves manufacturing parts with highly variable wall angle slope feed spindle speed considered only during initial approach the proposed approach indicates as influence factors for the value of the vertical technological force, in the initial phase, the tool diameter, the vertical step size, the feed and the spindle speed. in order to preserve the degree of generality, no particular geometry of the processed part was taken into consideration (cone, conical frustum or pyramids), so no wall angles were considered as influence factors in this stage of the researches. 3 method for calculating the vertical technological force the technological movements required by the spif process are performed on cnc milling machine tools by means of the feed drives. the typical structure of a linear feed drive, either horizontal or vertical consists of a rotary servomotor axially coupled with a ball screw system. the schematic diagrams of a horizontal and a vertical feed drives are presented in (fig. 3 a and 68 s.g. racz, r.e. breaz, o. bologa, m. tera, v.s. oleksik b). figure 3: schematic loads diagram for a horizontal (a) and a vertical (b) feed drive the notations from (fig. 3 a) have the following significations: 1 drive motor, 2 axial coupling, 3 horizontal slide and part, 4 lead ball-screw. the notations from (fig. 3 b) have the following significations: 1 drive motor, 2 axial coupling, 3 vertical slide and working unit (with the main spindle), 4 tool, 5 lead ball-screw, 6 counterweight for balancing the weight of the working unit. the drive motor of a cnc feed drive must overcome both the static and the dynamic torques which appear during the machining process: tmot = tst + tdin (1) where: tmot motor torque; tst resistant static torque; td resistant dynamic torque. the total static torque for a horizontal feed drive may be calculated with the following relation: tst = (ftech + ff )p 2πηtot (2) where: ftech resistant technological force [n]; ff friction force; p ball-screw step [mm]; i transfer ratio between motor and ball screw; ηtot total mechanical efficiency. for the considered cnc milling machine-tool the value of step is p = 4.233 mm when vertical feed drives are considered, the friction force opposes the weight force of the machine slide (g), consequently, relation (2) must be rewritten as: using an adaptive network-based fuzzy inference system to estimate the vertical force in single point incremental forming 69 tst = (ftech −g + ff )p 2πηtot (3) where only the down movement has been taken into consideration (the technological force appears only when the tool is driven down, towards the workpiece). a 3-axis (x, y, z) haas cnc mini mill was used as technological equipment. on each axis, the actuating motor is directly coupled with the ball screw, so the transfer ratio i=1. according to this, the resistant static torque on z-axis (vertical direction) may be expressed as: tst = (ftechz −g + ffz)p 2πηtot (4) the cnc equipment displays at the level of operator panel the current value of the motor torque, for each axis, as a percent value. the percent value is linked with a certain crisp value of the motor torque, which is different from both nominal and maximal values. if the readings are done during the constant velocity phase, the dynamic torque (4) may be considered zero and consequently neglected. to calculate the amount of the motor torque which is considered as 100%, the values of the static torque in the no-load conditions were subtracted from the values in load conditions, according to the following relations: ∆tst = (ftechz −g + ffz)p 2πηtot − (−g + ffz)p 2πηtot = ftechz ·p 2πηtot (5) where: ∆tstz difference between static torque in load and no-load conditions on z-axis. expressing the left-hand side of equation (5) based on the maximum feed force allowed on z-axis (8896 n, taken from the machine documentation) leads to: ∆tmax_stz = ftechz ·p 2πηtot = 8896 · 4.233 · 10−3 2 · 3.14 · 0.9 = 6.65[nm] (6) where: ∆tmax_stz the maximum difference between static torque in load and no-load conditions on z-axis. the efficiency factor for the screw nut assembly was considered η = 0.9. it can be observed that the values of ∆tmax_stz is in-between the values of the nominal torque (tn = 5.39 nm) and maximum torque (tmax = 13.8 nm) of motors. considering ∆tz the difference between the load and no-load static torques read from the operator panel of the machine (in percent), the following relation may be written: ∆tstz = ∆tmax_stz · ∆tz 100 (7) equation (7) allows now the user to calculate of the vertical technological force, by using the difference between the values of the torques, displayed by the cnc equipment. the percent displayed values of torques can be replaced by crisp numerical values, knowing the 100% reported motor torque calculated by means of equations (6) and (7). the technological forces can now be calculated using the relation: ftechz = 2πηtot p ∆tstz = 2πηtot p · ∆tmax_stz · ∆tz 100 (8) 70 s.g. racz, r.e. breaz, o. bologa, m. tera, v.s. oleksik for a given movement cycle, it is only necessary to program the same cycle in no-load conditions, prior to unfolding it in load conditions, to be able to read and subtract the no-load and load-conditions torques. to validate the proposed calculation method, the technological force on z-axis was measured using a data acquisition system. the differences between measured and calculated values were found to be in the range of 1% resolution of the cnc operator panel display, leading to a maximum error of 88.6 n. considering that for incremental forming of steel sheets the vertical technological force could reach values greater than 2000 n, this error was considered acceptable. 4 generating the experimental database an experimental program was unfolded to gather the data which will be processed by the adaptive network-based fuzzy inference system. the dc04 steel, with a thickness of 0.7 millimeters was used as workpieces material. simple toolpaths were programmed (fig. 4), and the punch was moved along them. on each component of the toolpath (segments from 1 to 10, each one with an overall length of 10 millimeters) the manufacturing inputs were modified. the inputs taken into consideration were tool diameter, spindle speed, feed on horizontal plane, incremental vertical step. figure 4: toolpaths used for generating the experimental database some examples of the processed parts are presented in (fig. 5). figure 5: examples of processed parts as presented in equation (1), the dynamic resistant dynamic torque also appears during the acceleration and deceleration phases of the movement. to avoid its influence, the data form the using an adaptive network-based fuzzy inference system to estimate the vertical force in single point incremental forming 71 operator panel was read in the constant velocity phase of the movements, when due to zero value of the linear and angular acceleration, the dynamic torque is also zero (fig. 6). every toolpath was generated in no-load and load conditions and the no-load values were subtracted form the first ones. figure 6: the readings from the operator panel were made after reaching the constant velocity on the axis the data form the operator panel of the cnc equipment were captured by a digital camera and afterwards the needed information was extracted and processed. the image processing method used in this approach was described in detail in [3]. after this process, the ∆tz values were obtained and using equation (8) the vertical technological forces could be calculated. for generating the rough data to be processed by the proposed adaptive network-based fuzzy inference system, a total of 268 experimental data sets were obtained by experimental tests. a reduced selection of these data is presented in (table 1). table 2: example of experimental data set (selection) tool spindle feed on vertical technological diameter speed horizontal step force [mm] [rev/min] plane [mm] [n] [mm/min] 8 400 200 -1 1790.161 8 360 130 -2 1716.338 8 450 175 -2 1492.168 10 300 50 -1 977.211 10 480 200 -2 2043.60 10 450 90 -2 1066.049 12 110 290 -3 1425.840 12 120 350 -2.5 2186.288 12 400 350 -2.5 1901.120 for each group of parameters from table two variation intervals were stated. the values for the tool diameter were set to only three values: 8, 10 and 12 mm. for the spindle speed, the limits were set between 0 and 500 mm/min while for the feed on horizontal plane the limits were 72 s.g. racz, r.e. breaz, o. bologa, m. tera, v.s. oleksik set between 50 and 350 mm/min. for the vertical step increments of 1, 2, 2.5 and 3 mm were used. the vertical step was increased using the above-mentioned increments up to a total depth of 7.5 mm. 5 adaptive network-based fuzzy inference system the first stage of building the adaptive network-based fuzzy inference system model involved a selection process in order to find the most influential combination of input parameters upon the output parameter. as stated before, the data sets have taken into consideration four parameters as inputs (tool diameter, spindle speed, feed on horizontal plane, vertical step) and one parameter as output (technological force). it was considered that a system with four inputs could be to complex and consequently a method of selecting only three inputs (the optimal ones) was considered. to fulfill this goal, the matlab exhsrch command (from fuzzy logic toolbox) was used. the function exhsrch performs an exhaustive search within the available inputs to select the set of inputs that most influence the output. essentially, exhsrch builds an adaptive network-based fuzzy inference model for each combination and trains it for one epoch and reports the performance achieved. the results from exhsrch (fig. 7) indicate that "diameter", "feed" and "step" form the best combination of three input attributes. the training (continuous line with circle markers) and checking errors (dotted line with asterisks markers) are getting distinguished, indicating the outset of overfitting. the left-most input set of variables in (fig. 7) has the least error or in other words the most relevance with respect to the output. figure 7: tool diameter, vertical feed and incremental step the optimal combination of three inputs these results are in accordance with the literature survey, which in most of the cases considered speed as the least influential factor within the spif process. most of the authors consider that a nonzero speed of the punch is only required as a measure to reduce friction and it only influences the overall roughness of the surface. with the selection of input parameters done, the adaptive network-based fuzzy inference system was designed to calculate the vertical technological force, considered as an output. as for any adaptive network-based fuzzy inference system, a training and checking process had to be unfolded. the experimental data sets were divided into training and checking sets. the division was made by selecting the odd values from the 268 experimental data set as training set while the even values were selected as checking set. at the beginning of the training process, a preliminary fuzzy inference system was generated. at this initial stage the fuzzy system does not reflect entirely the behavior of the considered system. during the training process, artificial neural networks were used to improve the fuzzy system, by means of a learning process. the learning process was completed after 3 stages (epochs), needed for the modelling errors to reach using an adaptive network-based fuzzy inference system to estimate the vertical force in single point incremental forming 73 acceptable values. at the end of the learning process, the fuzzy inference system can accurately reflect the real relationships between inputs and output, for the entire range of variations. the adaptive network-based fuzzy inference system was built and trained using the interactive graphical user interfaces provided by matlab software packages and its modules, fuzzy logic and neural network toolboxes. the structure of the fuzzy inference system is presented in (fig. 8). triangular membership functions, presented in (fig. 9) for the "feed" input, were selected. figure 9 also presents the intervals of variation for the selected input. figure 8: structure of the fuzzy system figure 9: membership function for the input variable "feed" the final structure of the proposed system, presented in (fig. 10) has three inputs, an output, 5 levels of ann, 193 neurons and 81 fuzzy rules. figure 10: the structure of the neural networks for the proposed system the adaptive network-based fuzzy inference system has an interactive graphical user interface, 74 s.g. racz, r.e. breaz, o. bologa, m. tera, v.s. oleksik presented in (fig. 1), which allows the user to calculate the output (vertical force) for any intermediary value from the inputs variation ranges. thus, the system can predict the result for data which were not experimentally available. the user can either drag the vertical bars through any values of the two input variables, or manually introduce input data for them and the model will automatically calculate the value of the output (technological vertical force). figure 11: graphical user interface of the adaptive network-based fuzzy inference system (only first 31 rules are visible in the figure, out of 81) no related fuzzy models were found in the literature for calculating the vertical technological force during spif. to evaluate the efficiency of the proposed model, a comparison between it and a linear regression model was unfolded. the comparison was made by comparing the rmse (root mean square) values against checking data for both models. for the proposed model the rmse was 153.353, while for the linear regression model the rmse error was 159.290. this result proves to a certain extent the usefulness of the proposed model. however, it should be stressed the fact that further experimental tests must be performed to validate the proposed model for different sheet thicknesses and different materials. the experimental data (both training and checking data sets) and the adaptive networkbased fuzzy inference system (.fis file to be run using the graphical user interface anfis from matlab) can be downloaded from the following link: https://www.dropbox.com/s/s0gdpjap2istku5/anfis.zip?dl=0 6 conclusion the approach presented in this paper did not aim to develop an analytical expression for calculating the vertical force values, but to develop an adaptive network-based fuzzy inference system which allows the user to estimate in an empirical way the force for a given set of process parameters and their variation intervals. numerical simulation, using finite elements analysis is today able to calculate the forces within the spif process and this allows to evaluate if the process is safe or not for the technological using an adaptive network-based fuzzy inference system to estimate the vertical force in single point incremental forming 75 equipment. however, numerical simulation requires both very expensive software packages and highly qualified personnel, able to develop and work with complex and accurate fea models of the incremental forming process and run the above-mentioned simulations. the approach presented in this paper offers a cost-effective alternative to that, the method requiring some simple experiments and a cost-effective software package. moreover, the building of the network-based fuzzy system is a straightforward process, based only upon matlab integrated graphical user interfaces and does not require a highly trained user. the proposed method is dedicated to speed the adoption of the spif process by the industry, by offering a simple, yet (considered by authors) effective method for estimating the forces, which can be applied at workshops level, rather than in research laboratories. future researches will be oriented of taking into consideration different sheet thicknesses and different materials, besides the already considered parameters, to build a more comprehensive knowledge base. funding this research was partially funded by the romanian ministry of research and innovation cccdi-uefiscdi, project number pn-iii-p1-1.2-pccdi-2017-0446/nr. 82pccdi/2018, within pncdi iii, project title: "smart manufacturing technologies for advanced production of parts from automotive and aeronautics industries". author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. bibliography [1] aerens, r.; eyckens, p.; van bael, a.; duflou, j. r. 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(2015); advanced numerical framework to simulate incremental forming processes, ph.d. thesis, university of aveiro, portugal, 2015. international journal of computers communications & control issn 1841-9836, 13(6), 938-955, december 2018. ann based short-term load curve forecasting v. chis, c.barbulescu, s. kilyeni, s. dzitac violeta chis mathematics and computer science department aurel vlaicu university of arad arad, romania violeta.chis@uav.ro constantin barbulescu*, stefan kilyeni power systems department politehnica university timisoara romania timisoara, romania *corresponding author: constantin.barbulescu@upt.ro stefan.kilyeni@upt.ro simona dzitac power engineering department university of oradea oradea, romania simona@dzitac.ro abstract: a software tool developed in matlab for short-term load forecasting (stlf) is presented. different forecasting methods such as artificial neural networks, multiple linear regression, curve fitting have been integrated into a stand-alone application with a graphical user interface. real power consumption data have been used. they have been provided by the branches of the distribution system operator from the southern-western part of the romanian power system. this paper is an extended variant of [4]a. keywords: artificial neural networks; short-term load forecasting; artificial intelligence; load curve. apartially reprinted and extended, with permission based on licence number 4453460839057 © ieee, from "2018 7th international conference on computers communications and control (icccc)." 1 introduction the current paper is an extended version of [4]. it deals with real power systems data applied for short-term daily load forecasting. in [4] one single distribution branch has been discussed. currently, the work has been extended for all of the distribution branches belonging to the distribution system operator (dso) involved. a new set of data, corresponding to the entire network of the dso has been added. finally, a comparative analysis has been provided for all the distribution branches involved and the entire network assembly. load forecasting has a great impact on future decisions, predicting the energy demand for the operation and planning of power systems plays an important role in the control, power security, market operation, and scheduling of reasonable dispatching plans for smart grids. load forecasting is an important component of power system to establish economical and reliable operations for power stations and their generating units. an accurate load forecasting approach used to predict load demand is essential part of any energy management system. the load forecasting methods are generally classified in statistical-based methods like exponential smoothing, regression, kalman filter, state space model and artificial intelligence based copyright ©2018 cc by-nc ann based short-term load curve forecasting 939 methods such as expert system techniques [7], neural networks (nn), fuzzy expert system [20], fuzzy time series, fuzzy neural networks, genetic algorithms [8], support vector machine, pattern recognition and hybrid methods. load forecasting is carried out over a wide range of intervals ranging from a few seconds to decades and is mainly based on retrospective load variation data. according to the time span, load forecasting can be divided into very short (a few minutes), short term load forecasting (stlf), medium term load forecasting (mtlf) and long term load forecasting (ltlf). ltlf covers a period of several years and is applicable for system and long term network planning. the mtlf covers a period of a few weeks and is applicable for scheduling of the fuel supply, the planning of maintenance, etc. the stlf refer to hourly prediction of the load for a lead time ranging from one hour to several days out and it’s used to predict load demands so that the day-to-day operation of a power system can be efficiently planned [9]. the accuracy of load forecasting depends significantly on the availability of historical consumption data as well as on the knowledge about the main influence parameters on the energy consumption [16]. the quality of the forecast methods is influenced by several factors including: • weather datatemperature, humidity, wind speed, fog, precipitation, solar radiation; • seasonal (time) factors seasons’ sequence, day of the week and hour of the day (with low periods during the night hours and with peaks at different day hours); • causative factors (holidays, strikes and some specific public events, etc.); • economic factors (industrial activity, demographic change, economic growth). the influence of these factors is different from one type of forecast to another. short-term load forecasting (stlf) performs load forecasting of the system from few hours to weeks, using generally historical load data and weather data as inputs. the forecasted data are used to estimate load flows, for transmission line loading, for transient stability studies. many decisions, like generating capacity, planning for energy transactions or system security assessment are based on stlf. researchers have considered different approaches to stlf, like time series method, regression analysis [1], intelligent techniques, such as nn [2], [17], fuzzy logic [10], [14], neuro-fuzzy [6] or data mining [18], [21]. time series have been used for decades in areas such as the economy, digital signal processing and the load forecasting. arma (autoregressive moving average) [3], arima (auto regressive integrated moving average) [11] and arimax (autoregressive moving average with exogeneous variables) [5] are the most commonly used time series methods. since 1990, the focus has been on using different artificial intelligence (ia) techniques. thus, the authors of the paper [15] were among the first research groups that chose to use nn for the stlf. in [12], a model based on decision trees is used for load forecasting. in [19] the neural networks are also used for load forecasting problem, due to the non-linear character of the load. but, the authors are going further, combining the anns with optimization techniques. they are proving that the forecast error is reduced by applying this method, in order to estimate the parameters of the network. another hybrid model is applied in [22]. the authors are focusing on empirical mode decomposition (iemd), autoregressive integrated moving average (arima) and wavelet neural network (wnn). all these algorithms are optimized by fruit fly optimization algorithm (foa). this kind of approach is recommended in case of load forecasting affected by natural and social factors. 940 v. chis, c.barbulescu, s. kilyeni, s. dzitac paper [13] is one of the papers that are dealing with dynamic neural networks, in order to predict the daily power consumption. these networks are able to adaptively learn de patterns from historical data. they are capable to tackle the high non-linear degree between input and output data. the introduction is presented within the 1st section of the paper. the 2nd one focuses on describing the applied models. the software-tool is briefly presented within the 3rd one. the case study and the results are largely discussed within the 4th section. finally, the 5th one synthesizes the conclusions. 2 models used several models have been implemented within the developed software tool. 2.1 multiple linear regression model regression methods are used to model the relationship between load consumption and other factors such as weather, day type, stochastic influences such as average loads and customer class [23]. the following data have been used within the current model: previous day, type of the day (working day or weekend), previous day same hour load and previous 24 hour average load. the following stages have been tackled: • generate predictors (previousdayhour, pre24houraverload, day, dayweek); • forming validation input data (vpreviousdayhour, vpre24houraverload, vday, dayweek); • create regression coefficients (b, bint, r); • validate the regression coefficients (evaluating the input data) calculate the mean absolute percent error (mape) and plot actual load vs. predicted load; • use the regression coefficients to forecast one day ahead calculate mape and plot actual load vs. forecasted load. • use the regression coefficients to forecast one day ahead calculate mape and plot actual load vs. forecasted load the time factors include the time of the day of the week, and the day hour. there are important differences in load between weekdays and weekends. the load on different weekdays also can behave differently. 2.2 curve fitting model from the fourier library models, fourier8 has been used. multiple regression has been used to obtain an average estimate. figure 1 presents synthetically the algorithm of this method for stlf. ann based short-term load curve forecasting 941 figure 1: curve fitting algorithm 2.3 the neural network model several papers propose the use of nn for complex problems like stlf, which are highly non-linear. the main advantages of nn are that it can learn to gain on any nonlinear function from a large number of data. to identify the assumed relation between the future load and the earlier load data a multilayer perceptron (mlp) network with a single hidden layer is used. mlp is one of the most well-known nn architectures for prediction algorithms, and is popular because of its flexibility in assuming shapes of complex patterns. the introduction of hidden layer(s) makes it possible for the network to exhibit non-linear behavior. for calculation of hidden layer neurons does not exist any specific formula. researchers have observed that too few hidden layer neurons can cause the network to not learn the convergence and too many hidden layers can cause the network to memorize the scheme rather than forecasting [2], [17]. the nn is trained using historical data and use back propagation (bp) method. bp is widely applied for stlf because of its ability to study and remember the relation between inputs and outputs as well as to approach any types of function. for training, the network uses the default bp training algorithm, levenberg-marquardt. this is the fastest method in the toolbox for training moderate-sized feed-forward neural networks. the overall design of nn is presented in figure 2. figure 2: the overall design of nns for the current paper the nn architecture has the following configuration: one output (load), six inputs: load on previous day at same hour, load on previous week at same hour, month, day, day of the week (there are important differences in load between weekdays and weekends). 3 software tool a software tool has been developed in matlab environment for stlf. of course, there are commercial software packages. their main disadvantage is that they are a black box, offering no modeling transparency and they are more difficult to modify. data are retrieved from microsoft excel files. for different loads, the influence of different factors is not the same: temperature could have a major influence for residential loads, but may 942 v. chis, c.barbulescu, s. kilyeni, s. dzitac have little impact on some industrial loads. in this case the temperature has not been taken into account. future power demand is estimated based on the historical load data. the developed software allows a quick graphical comparative analysis: actual load versus load forecasting. the mape is used as a performance criterion. mape = 1 n ( n∑ i=1 |yi −pi| yi ) ∗ 100 (1) where yi,pi− actual and forecasted load of i hour; n− the forecasting horizon this application was developed using the matlab gui tool-box and implements different models. the models developed have been integrated into a stand-alone application with a graphical user interface. starting from the idea that the interface design activity should be centered on the user, it was intended to meet the recommended requirements for such interfaces: friendly, intuitive, easy-touse, extensible but also consistent. radio buttons have been used to select the model (figure 3). depending on data, one may be better than another. the developed software allows comparing different models automatically. figure 3: the software tool main window 4 results and discussions real data have been used, that are belonging to the distribution system operator managing the distribution network from the western side of romania. the load curves forecasting is performed for the most significant summer day (21st june). 10 years known period is considered (2004-2013). results validation refers to the range of 2014-2016 years. the forecasts have been performed using artificial neural networks (ann), multiple linear regression (mlr) and curve fitting (cf). ann based short-term load curve forecasting 943 table 1 and figure 4 contain the known load curves for the 2004-2013 period, for the most significant summer day. table 1: load curves for known period 2004-2013 [mw] year/hour 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 1 67.70 72.40 68.70 69.90 65.00 55.30 61.20 63.30 64.80 61.30 2 64.30 68.90 65.50 67.40 61.20 52.30 58.00 59.30 59.90 56.40 3 62.10 66.10 62.80 66.20 58.00 50.10 55.80 56.90 57.60 54.10 4 61.80 64.90 61.90 65.10 57.30 49.00 54.10 55.70 55.80 54.30 5 63.90 68.40 64.50 66.30 59.00 50.90 56.00 58.00 58.10 58.50 6 68.70 74.90 68.90 69.30 63.60 54.60 59.80 61.70 60.30 58.60 7 75.60 81.40 73.60 72.30 65.80 56.40 64.20 63.40 63.60 61.80 8 89.90 97.10 86.40 84.90 80.70 70.60 82.90 79.90 81.30 78.30 9 92.60 101.6 92.60 89.90 86.00 77.60 86.50 85.00 84.80 83.00 10 90.30 99.30 89.30 88.10 88.10 78.60 84.50 85.00 85.40 81.60 11 85.40 95.20 81.60 84.10 84.00 75.30 81.30 80.80 81.10 78.50 12 85.50 95.10 81.40 83.60 85.00 75.90 80.50 80.60 82.30 80.30 13 85.10 94.00 82.80 84.60 84.60 77.80 81.60 83.20 84.90 81.50 14 85.00 93.10 83.40 84.40 84.60 78.20 81.90 84.80 88.00 81.70 15 82.70 91.50 82.40 83.10 79.70 77.20 82.20 82.20 87.30 80.00 16 75.60 84.20 75.70 78.20 73.80 70.80 76.00 77.60 81.90 74.50 17 72.70 81.50 73.30 75.30 72.00 68.60 73.40 74.20 78.10 71.50 18 72.40 80.60 73.20 73.90 70.50 66.50 71.50 70.60 76.60 69.70 19 70.30 80.40 72.20 72.30 67.80 64.80 69.60 69.50 72.10 66.80 20 71.80 81.90 72.20 71.60 67.20 65.00 67.70 68.30 68.50 66.00 21 88.40 95.20 81.90 80.20 73.60 69.10 70.00 69.80 72.80 68.10 22 95.50 101.9 94.20 90.50 89.00 83.60 80.10 79.50 74.60 76.40 23 86.20 91.20 86.70 81.50 84.80 81.40 81.10 81.30 75.40 82.40 24 75.50 79.20 76.40 71.70 73.60 69.50 70.80 70.80 69.00 72.30 figure 4: load curves’ variation for the 2004-2013 period [mw] table 2 and figure 5 are presenting the load curves for the 2014-2016 period. they are going to be used in order to validate the forecast based on previously presented load curves (table 1). 944 v. chis, c.barbulescu, s. kilyeni, s. dzitac table 2: load curves for 2014-2016 period [mw] validation period year/hour 2014 2015 2016 1 62.60 65.00 71.00 2 57.60 59.90 65.20 3 56.30 58.50 63.00 4 55.40 57.40 61.20 5 55.20 58.40 62.20 6 58.30 58.20 64.10 7 60.60 60.70 67.50 8 78.80 78.70 82.20 9 84.60 83.00 87.60 10 85.50 84.30 87.50 11 81.90 80.80 85.00 12 83.70 80.90 86.30 13 84.50 82.30 87.20 14 86.80 83.80 87.00 15 84.00 84.10 86.60 16 78.60 79.90 80.00 17 75.90 79.00 79.00 18 75.00 74.50 77.40 19 71.80 71.70 76.30 20 71.40 71.20 74.30 21 74.00 72.40 74.40 22 87.00 88.10 89.30 23 81.00 83.30 84.20 24 69.50 75.40 76.10 figure 5: load curves’ variation for the 2014-2016 period [mw] the following comments are suitable for the case of tables 1 and 2 (respectively figure 4 and figure 5): • a rigorous consumed power ascending or descending trend is not highlighted, in generally; ann based short-term load curve forecasting 945 • the load curves are crossing each other (thus, their shape is different), meaning that they are slightly correlated (on horizontally); • an ascending consumed power trend is highlighted for the 2011-2013 period (with few deviations); • load curves’ correlation degree is reduced (time evolution and daily shape). the results applying different methods are subjected to: • forecasted values; • differences from the known values (relative deviation in %); • relative square deviation; • performance index for each forecasted year (2014-2016). it is computed as the sum of relative square deviations for the 24 hourly values. these results are presented in tables 3 5 (fv forecasted value, rd relative deviation, rsd relative square deviation) for the 2014-2016 years and graphically in figures 6-8. figure 6: 2014 forecasted value comparative analysis [mw] figure 7: 2015 forecasted value comparative analysis [mw] the performance indices have been gathered in table 4. the forecast errors are presented in table 7. comparative analysis of the results leads to the following conclusions: 946 v. chis, c.barbulescu, s. kilyeni, s. dzitac table 3: 2014 year load curves forecasted [mw] hour know value ann cf mlr fv rd [%] rsq fv rd [%] rsq fv rd [%] rsq 1 62.6 61.51 -1.75 3.05 54.57 -12.83 164.5 64.49 3.03 9.16 2 57.6 58.49 1.54 2.37 50.79 -11.82 139.7 60.16 4.44 19.72 3 56.3 57.72 2.51 6.32 51.18 -9.10 82.83 58.16 3.31 10.96 4 55.4 57.73 4.21 17.73 51.49 -7.06 49.82 58.43 5.46 29.87 5 55.2 59.36 7.54 56.78 52.78 -4.39 19.25 62.31 12.88 165.8 6 58.3 59.99 2.90 8.39 53.19 -8.76 76.75 62.49 7.18 51.59 7 60.6 63.40 4.62 21.32 57.05 -5.87 34.41 65.47 8.04 64.59 8 78.8 82.76 5.03 25.28 73.04 -7.31 53.46 80.48 2.14 4.56 9 84.6 84.05 -0.66 0.43 77.52 -8.37 70.09 84.85 0.30 0.09 10 85.5 83.76 -2.03 4.13 76.62 -10.38 107.8 83.71 -2.09 4.37 11 81.9 82.53 0.77 0.60 74.48 -9.06 82.07 81.03 -1.06 1.12 12 83.7 83.13 -0.68 0.46 76.32 -8.82 77.78 82.78 -1.10 1.20 13 84.5 83.52 -1.16 1.36 77.82 -7.91 62.57 83.99 -0.60 0.37 14 86.8 83.60 -3.69 13.59 78.44 -9.64 92.83 84.29 -2.89 8.33 15 84.0 83.18 -0.98 0.96 77.37 -7.90 62.35 82.88 -1.33 1.77 16 78.6 79.76 1.48 2.19 73.81 -6.09 37.1 78.03 -0.72 0.52 17 75.9 76.47 0.75 0.56 71.22 -6.17 38.03 75.43 -0.62 0.38 18 75.0 74.39 -0.81 0.66 69.41 -7.45 55.54 73.91 -1.45 2.10 19 71.8 70.36 -2.01 4.03 66.54 -7.32 53.61 71.4 -0.56 0.31 20 71.4 69.39 -2.81 7.92 66.17 -7.32 53.64 70.78 -0.87 0.76 21 74.0 75.17 1.59 2.52 68.62 -7.27 52.82 72.78 -1.65 2.73 22 87.0 83.09 -4.50 20.21 77.46 -10.96 120.2 80.38 -7.60 57.82 23 81.0 83.95 3.65 13.29 82.64 2.03 4.12 85.93 6.08 36.99 24 69.5 75.60 8.78 77.11 74.84 7.69 59.13 76.92 10.68 113.9 pi2014 291.3 pi2014 1650 pi2014 589.1 figure 8: 2016 forecasted value comparative analysis [mw] • classical forecasting methods (cf, mlr) are inadequate. the global performance index (table 6) sustains this fact; • ann method: the best case is recorded for the 2015 year (table 6: performance index ann based short-term load curve forecasting 947 table 4: 2015 year load curves forecasted [mw] hour know value ann cf mlr fv rd [%] rsq fv rd [%] rsq fv rd [%] rsq 1 65.0 62.36 -4.05 16.44 55.14 -15.17 230.1 63.23 -2.72 7.42 2 59.9 58.70 -2.01 4.03 54.67 -8.73 76.23 58.83 -1.79 3.20 3 58.5 57.94 -0.95 0.90 53.22 -9.03 81.46 57.75 -1.28 1.63 4 57.4 57.47 0.13 0.02 52.40 -8.71 75.88 57.03 -0.64 0.40 5 58.4 57.42 -1.67 2.79 52.80 -9.59 91.95 56.95 -2.49 6.20 6 58.2 59.50 2.23 4.97 52.70 -9.45 89.31 59.83 2.80 7.84 7 60.7 61.31 1.00 1.00 54.70 -9.88 97.71 62.00 2.14 4.57 8 78.7 78.94 0.30 0.09 69.18 -12.10 146.5 78.49 -0.26 0.07 9 83.0 83.78 0.94 0.88 74.59 -10.13 102.6 83.85 1.02 1.05 10 84.3 84.49 0.23 0.05 76.14 -9.68 93.74 84.80 0.59 0.35 11 80.8 81.92 1.39 1.93 73.56 -8.96 80.22 81.70 1.11 1.23 12 80.9 83.40 3.09 9.56 75.76 -6.36 40.40 83.45 3.16 9.97 13 82.3 84.06 2.14 4.57 77.16 -6.25 39.02 84.31 2.45 5.98 14 83.8 85.64 2.20 4.84 80.13 -4.38 19.19 86.52 3.25 10.57 15 84.1 83.88 -0.26 0.07 78.05 -7.19 51.75 84.15 0.05 0.00 16 79.9 79.64 -0.33 0.11 74.34 -6.96 48.51 79.42 -0.60 0.36 17 79.0 77.24 -2.22 4.94 72.87 -7.76 60.21 77.12 -2.38 5.68 18 74.5 76.49 2.67 7.13 72.72 -2.40 5.74 76.43 2.59 6.72 19 71.7 73.39 2.36 5.58 70.15 -2.16 4.65 73.67 2.75 7.56 20 71.2 73.11 2.68 7.18 70.29 -1.28 1.63 73.43 3.13 9.81 21 72.4 75.83 4.74 22.5 73.56 1.60 2.56 75.89 4.82 23.24 22 88.1 86.31 -2.03 4.12 85.27 -3.21 10.32 87.72 -0.43 0.18 23 83.3 82.35 -1.14 1.30 80.44 -3.43 11.77 82.46 -1.01 1.01 24 75.4 71.64 -4.98 24.83 70.66 -6.28 39.46 72.24 -4.20 17.61 pi2015 129.8 pi2015 1500 pi2015 132.7 129.8); the worst case is recorded for the 2014 year (table 6: performance index 291.3); • the forecast errors are ranging between 4.7 % and 8.8 % in case of ann based method. values of 18 %, respectively 13 % have been obtained in case of cf, respectively mlr. the load curves for a period of 10 years (2004-2013) are presented in table 8, for the most significant summer day. these ones are corresponding to the entire network assembly of the considered distribution network operator. these data are going to be used in order to perform the forecast for 2014-2016 period. the same information is graphically presented in figure 9. the load curves for 3 years period are presented in table 9 and figure 10. these ones are used in order to validate the performed forecast. the trend is unclear for the 2004-2013 period. there are several increasing periods, alternating with decreasing ones, during a relatively short set of values. the load curves are not crossing each other. this means that they are characterized by a high correlation degree. the 2014-2016 period is also characterized by an unclear evolution. the load curves correlation degree during a day is relatively good, taking into consideration their shape. thus, it is envisaged that the ann based forecasting methods could lead to better results. 948 v. chis, c.barbulescu, s. kilyeni, s. dzitac table 5: 2016 year load curves forecasted [mw] hour know value ann cf mlr fv rd [%] rsq fv rd [%] rsq fv rd [%] rsq 1 71.0 68.29 -3.82 14.57 58.06 -18.23 332.2 64.50 -9.15 83.75 2 65.2 63.51 -2.60 6.74 54.80 -15.95 254.4 60.05 -7.91 62.49 3 63.0 62.00 -1.58 2.51 53.22 -15.52 240.9 58.90 -6.51 42.37 4 61.2 61.12 -0.12 0.02 52.20 -14.71 216.3 58.02 -5.20 27.01 5 62.2 62.17 -0.05 0.00 53.40 -14.15 200.2 59.02 -5.11 26.11 6 64.1 62.85 -1.95 3.79 55.82 -12.92 166.9 58.95 -8.04 64.58 7 67.5 65.21 -3.39 11.52 58.25 -13.70 187.8 61.30 -9.19 84.40 8 82.2 82.93 0.88 0.78 67.25 -18.19 330.9 77.56 -5.64 31.86 9 87.6 87.15 -0.52 0.27 71.34 -18.56 344.4 81.56 -6.89 47.51 10 87.5 88.45 1.08 1.18 73.25 -16.29 265.3 82.88 -5.28 27.87 11 85.0 85.3 0.36 0.13 71.12 -16.33 266.8 79.89 -6.01 36.08 12 86.3 85.62 -0.79 0.63 71.94 -16.64 276.9 80.13 -7.15 51.08 13 87.2 86.65 -0.63 0.40 73.79 -15.38 236.5 81.54 -6.50 42.19 14 87.0 87.79 0.90 0.81 75.82 -12.86 165.3 83.03 -4.56 20.79 15 86.6 87.29 0.80 0.64 76.85 -11.25 126.7 83.46 -3.63 13.19 16 80.0 83.18 3.98 15.80 74.21 -7.24 52.36 79.84 -0.20 0.04 17 79.0 82.21 4.06 16.52 74.26 -6.00 36.01 79.18 0.23 0.05 18 77.4 78.34 1.22 1.48 71.28 -7.90 62.45 75.29 -2.73 7.45 19 76.3 75.57 -0.96 0.91 69.18 -9.33 87.04 72.91 -4.44 19.73 20 74.3 75.12 1.11 1.22 69.31 -6.71 45.06 72.59 -2.30 5.28 21 74.4 77.49 4.16 17.30 71.25 -4.24 17.97 73.80 -0.81 0.65 22 89.3 91.78 2.78 7.71 85.83 -3.89 15.13 88.02 -1.43 2.06 23 84.2 88.10 4.64 21.5 81.88 -2.75 7.57 83.87 -0.39 0.15 24 76.1 80.20 5.39 29.02 76.35 0.33 0.11 76.94 1.10 1.21 pi2016 155.5 pi2016 3935 pi2016 697.9 table 6: performance indices comparative analysis ann cf mlr pi2014 291.3 1650 589.1 pi2015 129.8 1500 132.7 pi2016 155.5 3935 697.9 pitotal 576.6 7085 1420 table 7: maximum forecast errors ann cf mlr 2014 8.78 12.83 12.88 2015 4.74 15.17 4.82 2016 5.39 18.23 8.19 maximum 8.78 18.23 12.88 the same forecasting methods have been applied (as the ones presented in tables 2-5). the ann based short-term load curve forecasting 949 table 8: load curves for 2004-2013 period [mw] year/hour 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 1 455.5 460.2 472.1 436.3 418.7 398.4 411.6 401.7 401.7 388.6 2 427.8 452.8 457.7 427.3 415.0 382.6 390.8 381.4 380.6 372.2 3 417.9 441.0 436.1 421.0 410.6 378.5 394.7 386.6 389.0 387.8 4 413.9 454.8 447.6 418.4 393.8 365.7 382.7 374.7 385.8 384.1 5 434.4 456.8 459.3 424.5 404.1 380.7 403.8 391.4 389.8 376.5 6 459.4 465.2 469.0 441.7 411.6 388.1 403.5 404.8 382.1 358.6 7 477.9 503.0 489.1 455.6 422.1 396.2 395.5 404.5 393.6 377.7 8 534.9 570.2 517.9 525.6 473.9 449.2 464.4 446.3 437.0 432.3 9 554.1 607.2 529.7 574.0 525.4 479.5 481.7 489.0 483.4 486.0 10 536.9 604.8 530.5 564.6 530.8 492.4 490.5 506.5 494.3 479.4 11 519.1 583.5 499.0 549.3 529.6 483.5 472.7 492.0 490.5 487.8 12 512.3 557.3 527.7 531.5 514.1 477.6 465.7 469.2 472.6 474.8 13 496.2 551.3 539.4 525.2 500.5 468.6 445.1 467.3 464.9 460.9 14 502.0 567.3 547.0 518.9 495.3 473.4 452.3 489.4 472.0 456.7 15 491.1 546.9 529.1 518.6 488.2 462.2 438.2 463.9 467.6 458.5 16 452.2 505.2 485.6 487.7 451.3 450.6 447.0 451.2 451.7 428.9 17 462.9 490.6 492.0 479.3 439.1 442.8 440.0 449.3 443.6 426.8 18 448.5 502.7 490.6 476.5 424.6 443.0 445.4 445.5 440.9 414.9 19 440.4 495.6 488.9 464.3 405.2 432.4 419.4 453.3 443.5 413.5 20 436.8 476.2 445.0 442.2 393.6 418.9 392.2 418.8 427.2 409.2 21 510.1 534.7 496.2 479.5 426.3 421.5 386.5 405.2 432.0 424.6 22 545.3 561.3 534.7 528.4 494.5 478.9 432.7 429.4 445.7 453.3 23 562.8 580.5 573.5 540.3 524.9 518.9 498.9 475.3 463.7 480.3 24 536.7 538.0 524.1 501.8 488.1 482.1 443.3 444.7 437.5 438.7 figure 9: known load curves for 2004-2013 period extended results are not presented anymore. only a synthesis of the forecasted values is presented in table 10. the same results are graphically presented in figures 11-13. the performance indices are synthesized in table 11. the conventional forecasting methods (cf and mlr) are totally inadequate (table 11, global performance indices values). among them, the mlr provides more accurate results. comparing the yearly performance indices for the ann based results it is highlighting that the 2016 year the best situation is recorded (around 59). the worst one is recorded for the 2014 950 v. chis, c.barbulescu, s. kilyeni, s. dzitac table 9: load curves for validation period 2014-2016 [mw] year/hour 2014 2015 2016 1 389.4 384.8 384.2 2 374.7 370.3 364.6 3 377.4 380.9 373.9 4 370.8 364.9 363.3 5 370.6 367.4 372.5 6 368.2 358.1 364.3 7 382.0 381.3 380.1 8 430.4 421.4 422.7 9 464.1 450.9 449.9 10 482.8 472.9 471.2 11 490.3 486.2 481.9 12 460.6 453.9 452.7 13 449.0 442.3 450.7 14 459.8 454.9 450.5 15 453.0 450.5 441.4 16 433.3 431.1 430.0 17 437.5 440.2 433.8 18 427.2 418.0 423.0 19 419.9 409.6 415.9 20 408.3 402.2 405.6 21 410.9 411.6 406.0 22 450.0 439.8 436.4 23 472.6 474.6 467.7 24 438.6 439.4 434.6 figure 10: load curves corresponding to the forecasted period year (around 152). the maximum forecast errors are presented in table 12. based on the results presented in table 12, acceptable values have been obtained for ann based forecasting method (bellow 5 % errors). in case of cf and mlr forecasting based methods, they are ranging towards 17 % respectively 8 %. the considered distribution system operator is divided into a number of 4 distribution branches. a comparison between the obtained values is performed in the following. only the results corresponding to the ann and mlr based forecasting methods have been considered. ann based short-term load curve forecasting 951 table 10: performance indices comparative analysis 2014 2015 2016 ann cf mlr ann cf mlr ann cf mlr 387.7 358.8 365.2 365.8 349.6 357.7 379.9 343.5 353.8 382.5 355.6 353.1 361.0 342.8 347.2 366.1 341.0 343.3 386.1 354.2 368.3 367.3 343.7 351.4 377.0 343.2 354.1 385.7 353.5 367.1 367.5 342.2 347.7 362.0 339.7 342.2 384.1 351.7 362.5 369.8 344.2 349.4 364.7 341.2 346.1 380.6 348.4 349.1 368.5 341.2 349.2 356.2 338.6 339.9 386.2 348.7 367.1 378.1 343.2 362.8 379.0 339.8 361.5 421.7 375.9 415.6 411.1 353.6 405.9 418.0 349.6 397.6 477.3 438.8 463.5 446.8 390.8 436.6 442.5 383.3 424.9 472.0 437.3 460.3 474.7 423.5 454.9 457.4 399.4 445.8 476.4 441.2 469.8 486.6 426.5 463.7 464.9 418.6 459.5 468.5 438.5 461.2 457.1 398.5 441.0 445.8 398.4 434.2 456.9 437.4 451.7 446.4 394.8 433.5 437.5 392.4 426.4 454.4 432.2 450.4 458.5 407.2 444.9 447.3 407.3 439.3 454.4 413.3 454.2 451.7 407.2 441.5 444.6 408.5 437.7 419.7 390.9 431.2 429.8 393.8 427.1 430.0 392.5 423.3 419.2 393.0 431.5 431.8 402.3 432.9 437.9 406.5 433.1 409.9 384.0 423.5 422.0 396.1 426.3 419.6 388.3 416.2 409.2 386.9 424.3 415.3 392.5 422.3 412.4 384.8 411.0 402.5 387.6 422.7 406.8 386.1 414.6 405.8 381.6 406.7 420.3 407.8 437.9 410.0 392.3 418.9 414.8 394.2 416.7 454.9 440.6 464.5 443.8 435.0 454.1 438.6 425.4 442.7 485.9 461.3 489.7 464.1 452.8 475.6 461.6 453.6 474.6 439.7 436.9 456.6 432.2 433.9 449.1 439.1 433.5 446.8 figure 11: 2014 year forecasted load curve the specific performance indices have been synthesized in table 13. these indices have been obtained by dividing the performance index with 24 (3 years x 24 hours = 72). the distribution system operator entire network assembly is ranking on the 1st place. it is a logic result: the errors are attenuating through summation, due to the opposite signs. it closely 952 v. chis, c.barbulescu, s. kilyeni, s. dzitac figure 12: 2015 year forecasted load curve figure 13: 2016 year forecasted load curve table 11: performance indices comparative analysis ann cf mlr pi2014 151.5 1200 281.0 pi2015 78.89 1855 350.9 pi2016 58.72 2176 473.1 pitotal 289.1 5231 1105 followed up by distribution branch tm and, at a considered distance, by distribution branch ar and distribution branch hd. distribution branch cs is ranking on the last place. the distribution branch tm is ranking on the 1st place, if the mlr based results are discussed. the entire network assembly is following up. the distribution branches ar and hd are the next ones. the distribution network cs is also ranking on the last place. the 1st two places have been changed comparing with the ann based hierarchy. the ann specific indices ann based short-term load curve forecasting 953 table 12: maximum forecast errors ann cf mlr 2014 4.17 12.67 6.56 2015 4.94 16.09 7.75 2016 3.52 17.30 7.90 maximum 4.94 17.30 7.90 table 13: specific performance indices no distribution branch ann mlr specific indices ratio total specific total specific 1 entire network assembly 298.1 4.02 1105 15.35 3.82 2 distribution branch tm 354.1 4.92 958.5 13.31 2.71 3 distribution branch ar 576.6 8.01 1420 19.72 2.46 4 distribution branch hd 821.5 11.41 2690 37.36 3.27 5 distribution branch cs 2278 31.64 10530 146.2 4.63 are covering a large domain, from 4.02 (entire network assembly), to 31.64 (distribution branch cs). their values are influenced by the input data (2004-2013), the load curves’ correlation degree and the real consumption evolution (2014-2016). high values for the specific performance index highlight the presence of possible wrong load consumption data (discrepancy is recorded). an acceptable situation could be obtained if they are eliminated (corrected). the maximum forecast errors are presented in table 14. table 14: maximum forecast errors nr. crt distribution branch ann cf mlr 1 entire network assembly 4.94 17.30 7.90 2 distribution branch tm 5.11 21.50 8.01 3 distribution branch ar 8.78 18.23 12.88 4 distribution branch hd 8.11 15.63 12.28 5 distribution branch cs 14.91 45.26 33.33 the provided conclusions, regarding the quality of the results are sustained. the ones provided by the anns are the most acceptable ones. among the conventional methods, the mlr based ones are the most suitable. 5 conclusion accurate load forecasts are critical for distribution planning, for utilities. the quality of the forecast methods depends on the available historical data as well as on the knowledge about the main influence parameters on the energy consumption. 954 v. chis, c.barbulescu, s. kilyeni, s. dzitac different forecasting methodologies have been integrated into a stand-alone application with 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[23] [online]. available: www.mathworks.com matlab users guide, accesed on 12 february 2018. international journal of computers communications & control issn 1841-9836, 13(4), 477-491, august 2018. an improved abc algorithm for energy management of microgrid r. gao, j. wu, w. hu, y. zhang ren gao* hubei university of economics no. 8, yangqiaohu avenue, jiangxia district, wuhan, china *corresponding author: gr@hbue.edu.cn juebo wu 1. department of geography, national university of singapore arts link, singapore 117570 2. zte corporation no.55, science and technology south road, shenzhen, china wen hu hubei university of economics no. 8, yangqiaohu avenue, jiangxia district, wuhan, china yun zhang wuhan university no. 299, bayi road, wuchang district, wuhan, china abstract: microgrids are an ideal way of electricity generation, distribution, and regulation for customers by means of distributed energy resources on the community level. however, due to the randomness of photovoltaic and wind power generation, it is a crucial and difficult problem to achieve optimal economic dispatch in microgrids. in this paper, we present an optimal economic dispatch solution for a microgrid by the improved artificial bee colony (abc) optimization, with the aim of satisfying load and balance demand while minimizing the cost of power generation and gas emission. firstly, we construct a mathematical model according to different characteristics of distributed generation units and loads, and improve the performance of global convergence for abc in order to fit such model. secondly, we explore how to solve the optimal economic dispatch problem by the improved abc and give the essential steps. thirdly, we carry out several simulations and the results illustrate the benefits and effectiveness of the proposed approach for optimal economic dispatch in microgrid. keywords: artificial bee colony (abc), optimization, economic dispatch, microgrid, swarm intelligence. 1 introduction the energy crisis and environmental degradation is a global burning issue confronting humanity today, and the microgrid provides an effective way to solve this problem [12]. the microgrid is a micro system consisting of a set of distributed power supply, loads and energy storage systems and control devices, which can supply various forms of power (electricity, heat, etc.) to loads with high reliability. it is able to meet the electricity and heat demand through the output of distributed generation units and energy storage units, as well as improve the energy efficiency and reduce power generation cost and discharge system. at the same time, users in microgrid are free to choose the power supply mode by the optimization demand, and all these factors are the impetus for the development of smart grid [6]. however, there are still many problems on operation control, energy management and scheduling in microgrid. the diversity of distributed copyright ©2018 cc by-nc 478 r. gao, j. wu, w. hu, y. zhang generation units and flexibility of their composition makes its energy management become more complex. thus, it is a challenging issue to achieve optimal economic dispatch in microgrid [11,20]. significant research has been conducted and reported in the fields of economic dispatch for microgrids, related to the operation costs as well as minimizing emissions. dynamic programming method was introduced to economic dispatch in microgrid, which obtained the global optimal solution by partitioning the complex problems into smaller stages [3, 18]. but the computation time and memory requirement will increase greatly when the dimension of stage, state and decision-making variable becomes bigger, that is, the dimension disorder. compared with the traditional optimization algorithms, evolution algorithm is a kind of effective global search technology, which has been successfully applied in the areas of unit combination of electric power system and optimization scheduling, such as genetic algorithm (ga) [2,14], differential evolution (de) [5,16], and bacterial foraging algorithm (bfa) [4,7]. although evolution algorithm is a good solution to economic dispatch, it may lead to a long iterative time and poor convergence due to the presence of prematurity and random walk. by evolution algorithm, the searching process starts at a larger random state and ends in a small random state, in which this mechanism of combination search is easy to cause the algorithm converges into local optimal solution. in addition, swarm intelligence was also introduced to microgrid for economic dispatch, such as particle swarm optimization (pso) [1,19]. swarm intelligence has the advantages of simple and easy to realize quick convergence and relying on few parameters, which has received more and more attention in the field of microgrid. in addition, some autonomous systems are developed to achieve management automatically, such as cyber-physical system [8]. artificial bee colony optimization, as a specific application of swarm intelligence, has the ability to solve optimization issues by means of local optimization searching behavior of individual artificial bee with a fast convergence speed [15,17]. the contribution of this paper is as below. 1) a mathematical model of optimal economic dispatch is established in a microgrid based on abc, by considering time-of-use pricing strategy for both grid-connected and islanded microgrid. 2) the conventional abc is improved in order to speed up the convergence and avoid local minimum. 3) simulations are carried out to demonstrate the feasibility and benefits of the proposed approach. the remainder of the paper is organized as follows. section 2 improves the conventional abc algorithm. the mathematical model and economic dispatch strategy in microgrid is presented in section 3. section 4 describes the optimal economic dispatch solution for a microgrid by the improved abc. simulation and results analysis are performed in section 6. a brief conclusion is given as well as the future work. 2 improvement of abc the goal of economic dispatch is to solve the optimal composition problem of distributed generation units in microgrid, in order to reduce the cost and emissions. but the traditional abc is easy to fall into local optimum and with slow convergence. in this section, we firstly modify abc by introducing the choice mechanism of the bee’s neighborhood individual. then, we improve the global convergence performance and dynamic regulation of sharing information among individual bees by integrating cross operation, mutation ability and greedy strategy. 2.1 neighborhood factor in abc algorithm, the location update of food source for employed bees and onlookers is defined as: lij = pij + r × (pij −pkj) (1) an improved abc algorithm for energy management of microgrid 479 where i is the location of food source, k ∈ {1, 2, . . . ,m}, j ∈ {1, 2, . . . ,d} and k 6= j. r is a random number within [−1, 1]. pij is the current position for the bee and pkj is the food source position by random selection in the neighborhood individuals. the selection probability for food source is defined as: pi = fiti/ m∑ i=1 fiti (2) where fiti means the higher earnings ratio is, the larger probability will be. when the continuous search for food source by an individual is more than a certain number, a new food source is produced, defined as: x j i = r × (x j max −x j min) + x j min (3) where j ∈{1, 2, . . . ,d}, r is a random number within [−1, 1]. xmax and xmin are the maximum and the minimum value respectively. in abc, the optimal solution is calculated by the food source update according to generating new food source from bees’ neighborhood. the neighborhood individuals are chosen randomly without considering the relationship between the quality of food source in neighborhood and in current position. meanwhile, the bee’s food source doesn’t combine with the search process in food source update. therefore, the process control is insufficient in abc algorithm, which causes the relatively slow speed of convergence. neighborhood factor can adjust search process dynamically according to the quality of individual food source in the neighborhood. the onlooker compares the quality of the current food source with the food sources in the neighborhood when it choosing the neighborhood individual. if the quality of the food source in the neighborhood is better, the food sharing information should be increased. on the contrary, the food sharing information will be increased. the location update of food source for onlookers is re-defined as: vij = τ ×xij + η ×r × (xij −xkj) (4) where τ is forgetting factor, and it means the memory strength of the current food source to search the next food source. η is neighborhood factor that determines the strength of sharing information according to the quality of food sources in neighborhood. the forgetting factor is going down dynamically in the next search in order to make the bees make full use of the search information in neighborhood individuals and get the better global optimization results, defined as: τ = λ×wτ (5) in order to get a better global optimization results in later search period, the neighborhood factor is changing from small to big, defined as: η = λ×wη (6) λ is a constant in eq. 5 and eq. 6. λ > 1 when the quality of food source in the neighborhood is better than the current food source. otherwise, λ < 1. wτ and wη is changing dynamically along with the search process. wτ = w2 − ((itermax − iter)/itermax)α × (w2 −w1) (7) wη = w3 − ((itermax − iter)/itermax)β × (w4 −w3) (8) 480 r. gao, j. wu, w. hu, y. zhang where w1, w2, w3, w4 are constants limited within [0.1, 1.5] and w2 > w1, w4 > w3. in forgetting factor τ, wτ decreases from w2 to w1 along with the search process, which makes the bees move to the optimal food source area rapidly. to achieve quick convergence, the value of α is less than 1. since the value is too small to global convergence, the value of α is usually set to [0.8, 1]. in η, wη increases from w3 to w4 along with the search process, which will reach the information sharing degree between the bee and its neighborhood individual. the value of β is bigger than 1 in order to avoid bees missing some optimal food source and limited within [1, 1.2]. 2.2 abc combined with genetic algorithm (1) cross operation cross operation refers to the mutual exchange of genes between two pairs of chromosomes in certain way, so as to form two new individuals. cross operator can enhance the global search ability in wide range with global parallelism. in abc algorithm, role conversion mechanism is realized after bees starting searching the paths and the number of iterations is the same as the one of conversions. in general applications, less iterations can make the algorithm have strong ability in local search but with weak global search ability. by adding cross operator, it can make the abc algorithm have better ability for global optimization. the detail of the process is as follows. step 1: perform cross operation for all paths by combining any two paths together as pi and pj. step 2: select a node position p randomly, and add the area (from pj to the end) to the left. delete the node that pi has done cross operation with pj. step 3: do the same operation for pj. (2) mutation ability for abc algorithm, there may be better solution or even the global optimal solution in the neighborhood of current solution. we add mutation factor to further realize global optimization operation for optimal economic dispatch in microgrid. mutation operation is conducted for all paths and the procedure is as below. j 6= 1: select the jth food source and change the position j and j − 1. j = 1, change the first food source with the last food source. j is the food source position. (3) greedy strategy during computing process, the worst path will be replaced by the optimal path in each population. there are two optimal paths being preserved for the employed bee in each iteration. for onlookers and scouts, greedy algorithm is adopted for cross operation. if the candidate solution is better than the current solution, then choose the candidate solution as optimal one. the bee doesn’t record the path information of the current solution any longer. otherwise, the path information doesn’t need to be changed by the candidate solution. by greedy algorithm and cross operation, the abc algorithm can be enhanced by sub-operator. 3 mathematical model and economic dispatch strategy in microgrid fig. 1 shows the microgrid architecture studied in this paper, which encompasses wind turbine (wt), micro turbine (mt), photovoltaic (pv), fuel cell (fc), battery (bt), other storage components, general loads and essential loads with diverse features. in this architecture, the connection point, called point of common coupling (pcc), is the bride between the utility system and the microgrid system. thus, there are two kinds of modes an improved abc algorithm for energy management of microgrid 481 !"#$ %$ &'(")*+ *,)-"./).0,/ 12 3"),1#,"4 5'5./3 $ %! figure 1: typical architecture of microgrid system in microgrid according to the state of pcc, that is, connected state (grid-connected) and disconnected state (islanded mode). 3.1 mathematical model of economic dispatch the aim of optimal economic dispatch in microgrid is to get the best composition of distributed generation units and reduce the consumption and emission at the same time. we present the mathematical model of economic dispatch by considering both grid-connected mode and islanded mode. proposed objective function a) grid-connected mode in grid-connected mode, the microgrid can exchange electricity with the utility system through the pcc, so the selling and the purchasing behaviour are considered in order to get better economic dispatch. the objective function of operating cost is defined as: f = min { t∑ k=1 ∆tk [ n∑ i=1 (cfi + comi + poi) + cbi,k −csi,k ]} (9) where t is the total operating time of microgrid, n is the total number of generating units and ∆tk is the duration of time interval k. cfi means the fuel cost of the micro generation unit i while comi denotes the operation and maintenance cost of the micro generation unit i. poi is environmental cost generated by exhaust emissions. cbi,k stands for the purchased electricity of the micro generation unit i in ∆tk if the load demand goes beyond the generated power, and csi,k implies the income from sold electricity of the micro generation unit i in ∆tk if the generated power is too much for the load demand. the fuel cost of cfi is computed by: cfi = kfi,k ×pi,k (10) 482 r. gao, j. wu, w. hu, y. zhang where kfi,k is the fuel coefficient ($/kwh) of the micro generation unit i in ∆tk and pi,k is the generated power (kwh) for the micro generation unit i in ∆tk. the operation and maintenance cost of comi is calculated by: comi = komi,k ×pi,k (11) where komi,k is the operation and maintenance coefficient ($/kwh) of the micro generation unit i in ∆tk and pi,k is the same meaning as in eq. 10. the environmental cost of poi is computed by: poi = kri,k ×epi,k ×pi,k (12) where kri,k is emission coefficient, epi,k is emission price and pi,k is the same meaning as in eq. 10. the purchased electricity of cbi,k is computed by: cbi,k = kbi,k ×pbi,k (13) where kbi,k is the buying coefficient ($/kwh) of the micro generation unit i in ∆tk and pbi,k is the bought power (kwh) for the micro generation unit i in ∆tk. the income from sold electricity of csi,k is computed by: csi,k = ksi,k ×psi,k (14) where ksi,k is the buying coefficient ($/kwh) of the micro generation unit i in ∆tk and psi,k is the income (kwh) for sold by the micro generation unit i in ∆tk. b) islanded mode under this circumstance, the microgrid is disconnected to the utility system and controlled by microgrid itself as an islanded entity. no power is exchanged between microgrid and utility system through the pcc. thus, the objective function of operating cost is defined as: f = min { t∑ k=1 ∆tk [ n∑ i=1 (cfi + comi + poi) ]} (15) where all the parameters are the same meanings as in eq. 9 except removing the cost for purchasing power and the income for selling surplus power. equality constraints a) grid-connected mode according to the charge-discharge of battery, there are two kinds of situations for power balance constraints, namely charging balance constraints and discharging balance constraints. for charging battery, the power balance constraints are defined as: pload = n∑ i=1 pi,k −αchpch,k + pbi,k −psi,k (16) where pload is the load power in microgrid, pi,k is the generated power (kwh) for the micro generation unit i in k, pch,k is the power for charging the battery, αch is charging efficiency coefficient, pbi,k and psi,k are the same meanings as in eq. 13 and eq. 14. an improved abc algorithm for energy management of microgrid 483 for discharging battery, the power balance constraints are defined as: pload = n∑ i=1 pi,k −αdispdis,k + pbi,k −psi,k (17) where all parameters are the same as in eq. 16 except pdis,k is the power for discharging the battery and αdis is discharging efficiency coefficient. b) islanded mode in islanded mode, microgrid cannot exchange any power with utility system, so it doesn’t need to consider purchasing power and selling power. for charging battery, the power balance constraints are redefined as: pload = n∑ i=1 pi,k −αchpch,k (18) where the parameters are the same meanings as in eq. 16. for discharging battery, the power balance constraints are redefined as: pload = n∑ i=1 pi,k −αdispdis,k (19) where the parameters are the same meanings as in eq. 17. inequality constraints the constraints of micro generation units, buying and selling electricity, and charging and discharging battery constitute the inequality constraints in microgrid. a) inequality constraints for micro generation units pmini,k ≤ pi,k ≤ p max i,k (20) where pmini,k and p max i,k are the minimum and the maximum operating power of the micro generation unit i. b) inequality constraints for buying and selling electricity pbmini,k ≤ pbi,k ≤ pb max i,k (21) psmini,k ≤ psi,k ≤ ps max i,k (22) where pbmini,k and ps min i,k are the minimum buying and selling electricity from/to utility system while pbmaxi,k and ps max i,k are the maximum buying and selling electricity from/to utility system. c) inequality constraints for charging and discharging battery pminbt,k ≤ pbt,k ≤ p max bt,k (23) eminbt,k ≤ ∣∣∣∣∣ebt,0 − j∑ k=1 pbt,kt ∣∣∣∣∣ ≤ emaxbt,k (24) where pminbt,k and p max bt,k are the minimum and the maximum charging/discharging efficiency. e min bt,k and emaxbt,k are the minimum and the maximum battery capacity. 484 r. gao, j. wu, w. hu, y. zhang 3.2 economic dispatch strategy (1) grid-connected strategy in grid-connected microgrid, the economic dispatch problem should consider not only the power scheduling strategy of distributed generation units, but also the influence of the system performance of power trading between microgrid and external network. a) utilize wt and pv units as output at first, track and control the maximum power. b) determine whether to use mt for generating power according to actual load. c) when the generated power by wt, pv and mt units is beyond the load demand, make battery storage discharge to supply the loads and monitor the state of battery charging and discharging. d) if the energy from storage battery is enough to meet the load demands, increase the output by storage battery to sell the electricity to the utility system. e) if the energy from storage battery is insufficient to satisfy the demands in microgrid, purchase the power from the utility system. f) if the generating cost is higher than the price to purchase electricity from utility system, the microgrid buys the power from outside to meet internal load demand. and if the internal cost is smaller than external price, the microgrid generates the power and sells to utility system. (2) islanded mode strategy in islanded microgrid, it doesn’t need to take power trading into account and load demands are completely supplied by the internal micro sources and energy storage devices. the energy control system is responsible for real-time management and maintenance of system reliable operation. the economic dispatch is realized by the optimum combination of power supply. a) utilize wt and pv units as output at first. b) when the generated power by wt, pv and mt units is beyond the load demand, turn off the units with high generating cost to achieve electricity balance. c) when the generated power by wt, pv and mt units is not enough for the load demands, make battery storage discharge to supply the loads. d) if the energy from storage battery is enough to meet the load demands, utilize all the distributed generation units and storage batteries together to meet residual load demands according to comprehensive generating cost. e) perform load shedding if the generated power is insufficient when all distributed generation units are running and the storage batteries are discharging. 4 optimal economic dispatch solution for a microgrid by the improved abc optimal economic dispatch in microgrids can be regarded as optimal decisions for multistage decision problems to get different combinations of distributed generation units in different periods [9, 21]. the abc algorithm is a useful way to deal with multistage decision problems that can compare the performance of combination in each stage. by means of the idea in dynamic programming, we firstly explore the optimal economic dispatch solution based on the improved abc and then present the key steps of algorithm. 4.1 implementation of optimal economic dispatch the abc algorithm is mainly to solve the non-constraint problem, while the economic dispatch in microgrids is a kind of constraint optimization problem. the key point is to convert the parts of constraint optimization problem to non-constraint problem. the abc algorithm an improved abc algorithm for energy management of microgrid 485 can solve the equality constraints of distributed generation units directly, and the necessary conversion has to do for the inequality constraints of distributed generation units. in the proposed approach, the definition of path for optimal composition of distributed generation units is each state of unit combination in a time period constitutes for a decision set from time 1 to time t . so the problem of optimal economic dispatch is a multistage search problem, with the goal of finding a decision path with minimum total cost. the dynamic model is defined as: ft ( ult ) = min { ϕlt ( ukt−1,u l t )} t ∈ t (25) where ult is the state l in the t period, ϕ l t ( ukt−1,u l t ) is the accumulation of operating cost from the state k in the t− 1 period to the state l in the t period. ft ( ult ) is the minimum cost in the t period. the constraint conditions are followed the presented mathematical model of economic dispatch. (1) conversion of objective function the objective function of operating cost is redefined as: min ( n−1∑ i=1 tc ( sπ(i),sπ(i+1) ) + tc ( sπ(n),sπ(1) )) (26) where tc(si,sj) is the transfer cost from the state i to the state j, and π(i) is the optional state set. (2) treatment of constraint conditions we build tabu list to restrict the state that cannot meet some constraints. tabu is used to record all the states that allow to be transferred in the period t. there is no direct connection between any two periods. if the state meets the demand, its element in tabu is set to 1. otherwise, its element in tabu is set to 0. on one hand, the number of transfer can be restricted by tabu. on the other hand, the search behaviour can be run in a feasible solution, which plays certain guidance for bees search behaviours. in the process of economic dispatch, when the output of a micro source is beyond its upper limit, other micro sources have to adjust their output to keep the system balance. 4.2 key steps of algorithm (1) divide the loads into diverse classes, general loads and essential loads, ordered by the importance. (2) create composition states by different distributed generation. compute the maximum and the minimum output for each state to generate the upper limit and lower limit. (3) compare states with loads. if lower limit of one state is bigger than the maximum loads or upper limit is smaller than the minimum loads, remove this state. otherwise, record this state and its upper limit and lower limit. (4) determine the initial state. (5) initialize bee number, swarm evolution parameters, random food sources etc. (6) search objective food source by scouts and generate employed bees. if the current food source is better than the previous one, store it. otherwise, ignore this food source. (7) if the process reaching final time in the iteration, go to step 9. otherwise, go to step 8. (8) compute list of the current period and the earnings rate by the proposed constraint processing methods. calculate the output and generating cost at this state and update the path information. go to step 6. 486 r. gao, j. wu, w. hu, y. zhang table 1: time-of-use pricing (rmb/kwh) time 07:00-10:00 10:00-15:00 15:00-19:00 19:00-21:00 21:00-23:00 23:00-07:00 usage mid peak on-peak mid peak on-peak mid peak off-peak b-price 0.47 0.8 0.47 0.8 0.47 0.16 s-price 0.35 0.64 0.35 0.64 0.35 0.12 table 2: emission factor and external cost of nox, so2, co2 type ec (rmb/kg) ef-fc (kg/mwh) ef-mt (kg/mwh) co2 0.023 489 724 nox 8 0.014 0.2 so2 6 0.0027 0.0036 (9) record the shortest path from step 8 and update the path information for global optimization according to the optimization algorithm. (10) if not reaching the end of iterations and not appearing stagnation phenomenon, go to step 6 for the next iteration. otherwise, stop the process. 5 simulations and results analysis to demonstrate the feasibility and effectiveness of the proposed approach in optimal economic dispatch for microgrids, we carry out several case studies based on the real data, for both gridconnected and islanded microgrid. the basic data are chosen for the typical summer and winter days from the central region of china. 5.1 testing environment the collection of the basic data in this experiment includes typical day load curve in winter/summer, local energy data, and technical performance parameters for micro sources. in grid-connected mode, since the battery charging/discharging is only restricted by the performance of itself, we assume the battery charging and discharging have the same situations in winter and summer. the gas price is 2.05 rmb/m3. we adopt the time-of-use pricing here, which is classified into three groups: on-peak, midpeak and off-peak. in different time period, the prices for selling and buying power to/from the utility system are also different, as shown in table 1. because mt and fc power are relying on burning fuel, it is inevitable to produce atmospheric pollutants such as co2, nox, so2, and other solid dust particles. the handling cost of emissions is computed by the estimated external discount cost multiplied by the total generated power and the emission factor (ef). table 2 gives the emission of fc and mt as well as external cost (ec) [10,13]. 5.2 case study 1: typical winter days according to the proposed algorithm, the output power generated by micro sources is calculated for typical days in winter, also including the best economic dispatch for operation and optimal operation cost. in winter, the micro sources are controlled dynamically followed by the heating requirement. (1) grid-connected strategy an improved abc algorithm for energy management of microgrid 487 -82 -72 42 t2 742 7 : ; 5 t 77 7: 7; 75 7t <7 <: ] a b % 0 y^ b >'i%y_a=0 ]9] va#( z> ]` ?@ &> /> figure 2: power balance of grid-connected microgrid in typical winter days !"" #"" $"" %"" &"" '"" ("" )"" " ! $ & ( *" *! *$ *& *( !" + , . /01 23 43 figure 3: comparison of total cost of daily power generation in the grid-connected microgrid the power balance of the grid-connected microgrid in typical winter days is shown in fig. 2, where psp means the buying power (negative value) and the selling power (positive value) from/to the utility system. it can be seen from the results that the microgrid bought the power from outside during the off-peak hours and sold the power to utility system during the peak hours, in order to get the best economic dispatch. we carried out a comparison of total cost of daily power generation for no optimization and after optimization in winter days, in which the number of observed days is 20. the average cost is rmb 712.7 before optimization (bo) while it is rmb 540.95 after optimization (ao), as shown in fig. 3. (2) islanded mode the power balance of the islanded microgrid in typical winter days is shown in fig. 4 and the related system cost is given in fig. 5, including comprehensive cost (c_cost), fuel cost (f_cost), operation and maintenance cost (om_cost), and environmental cost (e_cost). 5.3 case study 2: typical summer days in summer, the micro sources are controlled dynamically followed by the cooling requirement. (1) grid-connected strategy the power balance of the grid-connected microgrid in typical summer days is shown in fig. 6. it can be seen from the results that the mt and fc run together in peak hours because the power price is higher than it generated by micro sources. the microgrid system purchased the power from outside for supplying loads and battery charging in off-peak time. the average cost of daily power generation of the grid-connected microgrid is rmb 1007.05 before optimization (bo) while it is rmb 810.95 after optimization (ao) in summer, as shown in fig. 7. (2) islanded mode the power balance of the islanded microgrid in typical summer days is shown in fig. 8 and the related system cost is given in fig. 9. 488 r. gao, j. wu, w. hu, y. zhang figure 4: power balance of islanded microgrid in typical winter days figure 5: system cost of islanded microgrid in typical winter days 5.4 results analysis the simulation results showed that the proposed approach can achieve the optimal economic dispatch for grid-connected and islanded microgrid. in grid-connected mode, the microgrid system used the generated power firstly in peak time in order to reduce purchasing power from the utility system, and it also sold the surplus electricity to the utility system. in mid-peak or off-peak hours, it bought the electricity from the utility system as much as possible so as to reduce the cost of generating power. in winter, the characteristics of charging and discharging storage battery had great impact on electric power trade. in summer, since the full running time of mt is longer than it in winter and the efficiency of pv is better than in winter, the purchasing power from outside system is obviously decreased. the decision factors of buying or selling power in grid-connected microgrid is the generating cost of micro sources, outputs and power price in the utility system. in islanded mode, it can be seen from the results the load shedding basically happened from 16:00 to 21:00 when the battery needed charging and the power generated by wt was decreasing. we can also learn that the fuel cost occupied most of the total cost. the second expense was operation and maintenance cost, and the environmental cost was minimum. by the influence of battery charging and discharging process, the high cost of the system has occurred in battery charging process. therefore, to reduce the comprehensive cost in islanded microgrid, the key is to find a better way for flexible and effective scheduling of the storage battery. an improved abc algorithm for energy management of microgrid 489 d&" d*" $" )" *$" * # % ' ) ** *# *% *' *) !* !# q , a b ; tu a j69btv,n; qrq w,0@ xj qy 5+ ej 2j figure 6: power balance of grid-connected microgrid in typical summer days k?? a?? l?? @?? f?? g?? >??? >>?? ? m k l f >? >m >k >l >f m? n 0 & ) o*. =d hd figure 7: comparison of total cost of daily power generation in the grid-connected microgrid figure 8: power balance of islanded microgrid in typical summer days figure 9: system cost of islanded microgrid in typical summer days 490 r. gao, j. wu, w. hu, y. zhang 6 conclusions and future work microgrids are the component of the smart grid revolution offering a number of important advantages in reducing energy consumption and environmental pollution, and improving power system reliability and flexibility, which changes the way to deal with the increasingly growth of loads. in this paper, we presented a novel approach of optimal economic dispatch in a microgrid with the improved abc, including grid-connected and islanded mode. in order to overcome the problems of slow convergence and easy to fall into local optimum in traditional abc, we improved it by introducing neighborhood factor, cross operation, mutation ability and greedy strategy. we constructed the mathematical model of optimal economic dispatch and also gave the economic dispatch rules for a microgrid both in grid-connected and islanded mode. the abc-based optimal economic dispatch solution was discussed and the detail steps were given. simulations of typical cases were carried out and the results showed that the improved abcbased approach is feasible and effective in optimal economic dispatch for microgrids. future work will focus on exploring hybrid swarm intelligence algorithms to economic dispatch in microgrid, such as altruism algorithm and cuckoo search algorithm. moreover, flexible switching strategies for real-time scheduling by considering more constraint conditions in microgrid would be a promising direction of future research. acknowledgments this paper is supported by the natural science foundation of hubei province (2016cfb208, 2017cfb773). bibliography [1] abid, s.; zafar, a. 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(2017); reducing the time of task execution with existing resources ¨c comparison of approaches, international journal of simulation modelling, 16(3), 484-496, 2017 international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(3), 311-328, june 2019. research on key technology of web hierarchical topic detection and evolution based on behaviour tracking analysis m. chen mo chen* business college of beijing union university a3, yanjingdongli, chaoyang district, beijing, 100025, p.r. china *corresponding author: mo.chen@buu.edu.cn abstract: in the development background of today’s big data era, the research direction of web hierarchical topic detection and evolution characterized by the semistructured or unstructured data has caught wide attention for academicians. this paper proposes an idea of web hierarchical topic detection and evolution based on behaviour tracking analysis taking the network big data as the research object, and expounds main implementation methods, which include the instance analysis of the usage mode, the instance analysis of the seed, the set analysis of similar instance supporting the topics, the set analysis of similar instance supporting the events, the evolution analysis of the event, and expounds the algorithm of web hierarchical topic detection and evolution based on behaviour tracking analysis. the process of experimental analysis is organized as follows, first of all, the experiment analyses the quality of topic detection, the accuracy rate with the number of instance concerned and the seed threshold variation trend, the accuracy rate with the number of instance concerned and the probability threshold variation trend, secondly, the experiment analyses the quality of topic evolution, the accuracy rate with the variation trend of parameter adjustment, the accuracy rate with the number of instance concerned and the similar threshold variation trend, finally, the experiment analyses the time consuming to solve main research problem under different method, the qualitative result of topic detection and evolution under different data set. the results of experimental analysis show the idea is feasible, verifiable and superior, which plays a major role in reconfiguring web hierarchical topic corpus and providing an intelligent big data warehouse for the network information evolution application. keywords: web hierarchical topic, topic detection, event evolution, behaviour tracking analysis. 1 introduction in the development background of web text mining technology and big data era, so far, the field of intelligent technology has also developed into a more challenging stage [7, 13, 28], the network has become one of services, which can transmit most popular information for users. according to deep survey, the number of network data has gone through eb level in different domain [14, 18, 19, 27]. academicians should cogitate how to analyse intricate big data, nevertheless, it is an important and key application direction for researching web hierarchical topic detection and evolution based on behaviour tracking analysis. in the network big data, the number of internet news is showing explosive growth as a kind of flow resource with on-going events, which has shown 5v features of volume, variety, value, velocity and veracity [3,23,26]. based on above characteristics, the internet news should reflect high currency and reliability [5], on this basis, the topic of internet news should be quickly detected, and its evolution path should be tracked in real time. however, how to research web hierarchical topic detection and evolution based on behaviour tracking analysis, it has become an urgent problem to build a web hierarchical topic corpus and provide a real-time big data source for the network information evolution application. copyright ©2019 cc by-nc 312 m. chen through researching the literature related to topic detection and evolution technology, this paper proposes an idea of analysing the process for web hierarchical topic detection and evolution, and expounds main implementation methods and algorithms following with interest web hierarchical topic detection and evolution from behaviour tracking analysis. this process does important contribution for researching a method of analysing the detection and evolution for web hierarchical topic, the results of experimental analysis show that the implement of this idea is feasible, verifiable and superior. 2 related works in recent years, some scholars have done certain research about the technology of web topic detection and evolution. a statistical model is proposed [2], in this model, it can combine context with related topics by jointly modelling the topic word with the hash tag and the time stamp, in order to detect and track interpretable topics over time along with their distribution of the hash tag, in this technical context, the experiment demonstrates that this model effectively reveals the process of topic detection and evolution by using the real dataset, this model is different from the traditional topic mining model, it shows serious improvement due to this fact that the distribution of the metadata containing in user content generated can be analysed, so the whole research result does main contribution in the area of topic detection and evolution in the context of the statistical analysis. a topic detection and evolution method is proposed by analysing the semantic word shift, the topic trend, and the evolving dynamic using the data set [4], in this method, it can merge and split local topics in different time periods, in order to track the process of knowledge transfer among topics, in this technical context, the experimental results show that the process of topic detection and evolution usually follows pattern from adjusting status to mature status, and sometimes with readjusting status, this method is different from the statistical analysis, it shows serious improvement due to this fact that the word migration via topic channels has been defined, and three migration types of non-migration, dual-migration, and multi-migration are better to understand topic detection and evolution, so the whole research result does main contribution in the area of topic detection and evolution in the application direction of information retrieval. a topic detection and evolution method is proposed [30], which is called the citation-content-latent dirichlet allocation method, in this method, it can account for the document citation relation and the content of document itself via a probabilistic generative model, this model can deal with the citation and text information, and its parameters are estimated by a collapsed gibbs sampling algorithm, in addition, a topic detection and evolution algorithm is designed, which can run in two steps of the topic segmentation and the topic dependency relation calculation, this model and algorithm have been tested by using the online dataset, in this technical context, the experimental results demonstrate that the implementation of the model and algorithm can more effectively detect important topics and reflect topic evolution process comparing with the topic tracking in the knowledge transfer context, so the whole research result does main contribution in the area of topic detection and evolution for designing the model and algorithm. a topic detection and evolution framework is proposed based on the probabilistic topic model [31], in this framework, firstly, the notations, the terminology, and the basic topic mining model is introduced, secondly, three technologies of the topic detection and evolution are applied, which are the discrete time topic detection and evolution, the continuous time topic detection and evolution, and the online topic detection and evolution, thirdly, the application of this framework is discussed, in this technical context, the comparative experiments are completed for different technologies of the topic mining, this framework shows serious improvement than single probabilistic model and does main contribution in the area of the topic detection and evolution performance evaluation. a topic detection and evolution method is proposed based on the analysis of the content similarity research on key technology of web hierarchical topic detection and evolution based on behaviour tracking analysis 313 or dissimilarity using the textual material [12], in this method, the graph-theoretical technology is applied, in order to deal with the network relationship among the content-similar topics, in this technical context, the explanatory experiment more effectively illustrates usefulness of the approach using the online news articles in different situations, so the whole research result does main contribution in the area of topic detection and evolution in the context of the network analysis. based on above analysis of the literature about the technology of web topic detection and evolution, through comparing the difference among technologies and analysing the main contributions in the research area, if want to research the process of web topic detection and evolution, in addition to using web mining technology based on the structure and content, but also using web mining technology based on the behaviour tracking. therefore, in recent years, some scholars have also done certain research about the technology of web behaviour tracking analysis. a collaborative tagging model is proposed [24], in this model, the technology of the usage behavior tracking analysis is applied to the information extraction direction for web user query in the ontology environment due to different structure data, which is based on the idea of the block acquiring page segmentation, in order to retrieve the tag-based information, in this technical context, the comparative experiments are completed regarding the average precision rate, the time cost, and the storage space rate with existing information retrieval model, it shows that the application of this model can assure research more effectiveness, so the whole research result does main contribution in the area of the behavior tracking analysis. a tracking method of the usage behavior is proposed [17], in this method, a relational graph is established by mining the temporal and causal information among aggregated http request, and an algorithm is designed and implemented for primary request identification, which is a critical task of web usage mining, in order to demonstrate higher value and effectiveness, in this technical context, the experimental result shows that it is a more useful method of analysing a large-scale dataset for the realworld web access log, so the whole research result does main contribution in the direction of mining value for information available. a web usage mining method is proposed [1], this method can be applied to solve the problem of developing more accurate and efficient recommendation systems, the traditional data protection mechanism focuses on the access control and the secure transmission, which provide only security against malicious the third parties, but not the service provider, so this method can mine efficiently and intelligently data, in order to guide and track the users’ usage behavior, and does main contribution in the application direction of the modern ebusiness. a web usage mining method is proposed [11], in this method, the state-of-the-art session identification technology is used in terms of limitation, feature, and methodology, which provide a structured overview of the research development, in this technical context, the comparative experiments critically review existing session identification technology, the whole research result does main contribution in highlighting the limitation and related challenge, identifying the area where further improvement is required, in order to complement the performance of existing technology. a web usage mining method is proposed [21], in this method, an algorithm is also designed for the data cleaning and filtering using the web log dataset, this algorithm mainly complete the process of preprocessing and clustering the usage behavior, which consists of the use cases for the data cleaning and filtering, the user and session identification, in this technical context, the experiments are carried out to obtain the aggregate clustering results, through this process, two datasets of web usage log are collected and processed, so the whole research result does main contribution in the area of web usage behavior analysis. based on above analysis of the literature about the technology of web behavior tracking analysis, through comparing the difference among technologies and analysing the main contributions in the research area, the scholars have studied two research directions including the 314 m. chen technology of web topic detection and evolution, but do not fully take into account the hierarchical series induced by the usage behavior, if do not fully consider this point, then ignore the process to track the usage behavior from the angle of topic detection and evolution. so this paper mainly takes web usage behaviour record of news big data as the research source, utilizes the method of analysing web usage behaviour tracking to perfect news big data corpus for topic detection and evolution research, and proposes an idea of analysing the process for web hierarchical topic detection and evolution to solve difficult problems existing in current research status. 3 problem definition and notation under the background of web big data development, users can retrieve and browse web news from different dimension, granularity and frequency, which has been analysed and evaluated [15,20,22]. in this process, the time sequence trajectory of users’ behavior can be recorded. these data not only record the characteristic of web news that users use, but also contain the topics reflected in web news, and the events that are generated based on these topics. therefore, the knowledge hidden in web news big data can be mined, the topics that users are concerned about can be detected, a series of events under the topics can be tracked, and the evolution process of events can be combed out based on the process of analysing web news usage characteristic. based on the analysis of web news structure, contents and semantic feature, every web news that users concern can be regarded as an instance node in every authoritative web news network from the perspective of global usage. the social events supported by set of some related nodes can be considered as a topic, and a series of events will be created under each topic. in this way, when users are concerned about a series of topics reflected in a social event, they can not only browse many web news instances supporting topics, but also browse a series of events that are generated by this topic. when users are concerned about an event, it can also browse more than one web news instance content that supports this event. from the perspective of local usage, when users retrieve web news instances, besides inputting the keywords related to social events reported by web news, they can also input the keywords with five tuple semantic description. therefore, during the analysis process of web news topic detection and evolution, the social events can be mined utilizing structure, contents and semantic feature, a series of events caused by the topics can be mined focusing on mining web news instances supporting the events, the logical hierarchical relationship can be mined between mining objects, which will construct a multi-level structural corpus, it can represent visual topics and events of web news with a high degree of quality. based on the analysis of web news utility feature, users can retrieve web news in the search process with a high degree of currency, which is called the time accuracy reporting web news core events. users can retrieve web news with a high degree of truthfulness, which is called the releasing source reliability of web news. users can retrieve web news of high currency with a high degree of truthfulness, therefore, during the analysis process of topic detection and evolution, it should weigh the factors of web news currency and authenticity, and provide web news utility instances supporting the topics and events from the perspective of users’ utility characteristic for web news. based on the behaviour tracking analysis of web usage characteristic for user, s−u can link the search keywords and the url instances synchronously, in the s −u relation, s represents the set of the keywords, u represents the set of the url instances. as shown in the formula 1, fq(s,u) can indicate the clicking frequency of instances, fqi(u) can indicate the clicking frequency of homologous url, fq(u) can indicate the clicking frequency of instances in a certain period. as shown in the formula 2, rti(u) can indicate the clicking rate of homologous url. research on key technology of web hierarchical topic detection and evolution based on behaviour tracking analysis 315 fq(u) = n∑ i=1 fqi(u) (1) rti(u) = fqi(u) fq(u) (2) newsset can be defined using the {ns1, ...,nsi−1,nsi,nsi+1, ...,nsk}, the range of array is from one to k. nsi.url defines the url instance address, nsi.title defines the instance title, nsi.pubtime defines the instance releasing time, nsi.pubsource defines the instance releasing source, nsi.content defines the instance contents, nsi.keyword defines the instance keywords. userbehavior can be defined using the {ub1, ...,ubi−1,ubi,ubi+1, ...,ubn}, the range of array is from one to n. ubi.username defines the user name, ubi.searchword defines the keywords, ubi.url defines the url clicked, ubi.systemtime defines the system time. based on above definition and notation, the issue should be solved to mine topics contained in the instances, mine instances set supporting related topics. from research angle of mining web news instance set supporting topics concerned by users, the issue should be solved to mine events that are happening under these topics, analyse the instance set supporting these events, so as to reflect the evolution process of web news topics continuously. this result can be denoted using the topicurl, it can be represented using the {tu1, ..., tui−1, tui, tui+1, ..., tum}, the range of array is from one to m, tui can be represented using the < topic,topicurl,event,eventurl >, tui.topic can express the topic description detected, tui.topicurl can indicate the seed instances url set supporting related topics, tui.event can express the description of events under topics mined, tui.eventurl can indicate web news instance of events supporting topics detected. 4 the analysis of web hierarchical topic detection and evolution in view of the problem definition and notation, this paper proposes an idea of analysing the process for web hierarchical topic detection and evolution shown in figure 1. this framework is used for completing the analytical process for web hierarchical topic detection and evolution, which include the instance analysis of the usage mode, the instance analysis of the seed, the set analysis of similar instance supporting the topics, the set analysis of similar instance supporting the events, and the evolution analysis of the event. the algorithms are designed for completing the functions and methods of this framework, which include the algorithm of analysing the process for web hierarchical topic detection, the algorithm of analysing the process for web hierarchical topic evolution. 4.1 the algorithm of analysing the process for web hierarchical topic detection the algorithm of analysing the process for web hierarchical topic detection is implemented by designing two methods of the usage mode analysis and the topic series construction, the inputting content of this algorithm is the result of semantic five tuple description analysis and utility evaluation for web news instances, and the user usage behaviour record set, the outputting content of this algorithm is the similar and sequential set of web news instances that can support corresponding topics. according to web usage behaviour record, the explosive and attention mode of web news instances are analysed, in order to infer the click mode of web news instances, the degree distribution and similarity mode of web news instances are also analysed, in order to infer the retrieval mode of web news instances. in accordance with these results of analysing web usage 316 m. chen figure 1: the framework of analysing the process for web hierarchical topic detection and evolution mode, the seed set of web news instances can be mined, the similar set of web news seed instances can also be mined. referring to the utility feature that have been analysed [6], the semantic five tuple can describe the topics under the time series. for the process of analysing the explosion mode, the execution process can be viewed a sensor for the social event. the process of analysing the mode quotes the entropy characteristic, and the analytical result is a speculation about the sharpness of the click rate change. for the process of analysing the attention mode, the execution process makes up for the problem existing in the burst mode, that is, when the research instances are followed by web users, the measurement standard will be an absolute phenomenon. based on the analysis of the usage mode, the click mode of web news instances can be inferred shown in the formula 3. clickmode(u) = (1 − (− n∑ i=1 rti(u) × lognrti(u))) × × log(fq(u)) −minui∈u (log(fq(ui))) maxui∈u (log(fq(ui))) −minui∈u (log(fq(ui))) (3) in the formula 3, n indicates the number of the granular unit that the instances are followed. for the sudden event, it can be set in day. for the normal occurrence event, it can be set in week or month. if the fluctuation is not large for the click rate of web news instances, then the attention mode is smaller. if web news instances have obvious fluctuation, then the attention mode will be large. according to the click process of web news instances, it has the power law distribution characteristic. therefore, the click frequency of web news instances has carried on logarithm transformation. for the process of analysing the degree distribution mode, the degree of web news instances presents the power law distribution, so its logarithmic transformation can be executed. for the process of analysing the similarity mode, it not only makes up for the problem existing in the degree distribution mode, which ignores the degree origin of web news instances through retrieval keyword, but also solves the problem of the sparse record in the click behaviour of web user. based on the analysis of the usage mode, the retrieval mode of web news instances can be speculated shown in the formula 4. research on key technology of web hierarchical topic detection and evolution based on behaviour tracking analysis 317 searchmode(u) = 2 n(n + 1) × log(d(u)) −minui∈u (log(d(ui))) maxui∈u (log(d(ui))) −minui∈u (log(d(ui))) × × n∑ i≤j ∑∞ k∈dataitem(sik(u)sjk(u))√∑∞ k∈dataitem(sik(u)) 2 √∑∞ k∈dataitem(sjk(u)) 2 (4) based on the formula 3 and 4, the set of web news instances can be mined by using the formula 5, seedurl(u) should be larger than or equal to the seed threshold. as shown in the equation 6, < ti,pi,oi,cei,rei > (nsi.uc) represents the semantic five tuple description for the seed instance. for the utility evaluation result, the sort sequence of the utility can be executed. in the subsequent experiments, the optimal value of the seed threshold will be analysed. seedurl(u) = clickmode(u) ×searchmode(u) (5) timeseries(ns) = (< t1,p1,o1,ce1,re1 > (ns1.uc), ..., < ti,pi,oi,cei,rei > (nsi.uc), ...,< tn,pn,on,cen,ren > (nsn.uc)) (6) algorithm 1 method 1 analysing the usage mode 1: input: userbehavior, threshold; 2: output: topicurl; 3: let userrecord ← userbehavior; 4: let groupuserrecord ← groupbyurl(u),topicurl ← φ; 5: for each gur[i](0 ≤ i ≤ gur.size() − 1) do 6: seedurl ← calculate clickmode and searchmode; 7: if seedurl ≥ threshold then 8: tu.add(seedurl); 9: end if 10: end for for the construction of the sequence topic, the execution process uses the probability for its first transfer, in order to judge whether it is similar to the seed instance supporting topics by using web news instances and taking web news seed instance as the research center. if su indicates the seed instance, then the variable tu expresses that whether the instance can support the topic of su, the variable ts expresses that whether the search keyword can support the topic of su. if the seed instance is able to support the topic of su, then tu = 1, conversely, tu = 0, if the search keyword is able to support the topic of su, then ts = 1, conversely, ts = 0. in initial status, tsu is one, p(tsu = 1) is one, the probability is zero. as shown in the formula 7 and 8, p(ts = 1) is able to calculate su probability, p(tu = 1) is also able to be recalculated, when p(tu = 1) is larger than or equal to the probability threshold, the web news instances are able to be found, so as to mine the similar instance set. in the subsequent experiments, the optimal value of the probability threshold will be analysed. p(ts = 1) = ∞∑ u:(s,u)∈e fq(s,u)∑∞ (s,ui)∈e fq(s,ui) ×p(tu = 1) (7) 318 m. chen p(tu = 1) = ∞∑ s:(s,u)∈e fq(s,u)∑∞ (si,u)∈e fq(si,u) ×p(ts = 1) (8) algorithm 2 method 2 constructing the topic series 1: topicurl, userbehavior, threshold; 2: topicurl; 3: for each tu[i](0 ≤ i ≤ tu.size() − 1) do 4: swset1 ← existset(tu[i].getset(”topicurl”),ub); 5: while swset1 is not null do 6: while each swset1 is exist do 7: p(ts) ← calculateresult(swset1.getelement(j).position,tu[i].getset(”topicurl”),ub); 8: if p(ts) ≥ threshold then 9: swset2.addset(swset1.getelement(j)); 10: end if 11: ub ← (swset1.getelement(j).position,p(ts)); 12: end while 13: while each swset2 is exist do 14: wnuset1 ← existset(swset2.getelement(j).position,ub); 15: if(wnuset1 ← equalset(wnuset1,wnuset2)) is not null then 16: while each wnuset1 is exist do 17: p(tu) ← calculateresult(wnuset1.getelement(k).position, 18: swset2.getelement(j).position,ub); 19: if p(tu) ≥ threshold then 20: wnuset2.addset(wnu1.getelement(k)); 21: end if 22: ub ← (wnuset1.getelement(k).position,p(tu)); 23: end while 24: end if 25: end while 26: swset1 ← existset(wnuset2,swset2,ub); 27: end while 28: tu[i] ← wnuset2; 29: describe topic tu[i]; 30: end for 4.2 the algorithm of analysing the process for web hierarchical topic evolution the algorithm of analysing the process for web hierarchical topic evolution is implemented by designing two methods of the event series construction and the event evolution analysis, the inputting content of this algorithm is the result of analysing semantic five tuple for web news instances, the user usage behavior record set and web news topic set, the outputting content of this algorithm is the topic set that has been excavated under the events and the evolution result of analysing the events belonging to the topics. according to the set of web news instances that can support the topics mined, the user usage behavior record is used to analyse the time sequence of web news instances that are followed, the similarity degree of the core events reported among web news instances is calculated by research on key technology of web hierarchical topic detection and evolution based on behaviour tracking analysis 319 using the result of analysing semantic five tuple description, and the evolution state of the events supported by the similar web news instances and the topics belonging to the events can also be analysed. the calculation result of the time series similarity can show the topics mined among web news instances, the user can pay much attention to web news instances that describe the same events, which have occurred within a certain time period, and the time sequence concerned is similar. on a certain granularity, the vector of the time sequence can be expressed as shown in the formula 9 representing the attention rate of web news instances. in this formula, rti(uj) represents the attention rate of the instance uj in i component for a granularity, n indicates the number of the granular units that the instances are continuously followed. for the sudden events, the granularity can be set in days, for the normality events, the granularity can be set in weeks or months. timeseries(tui) = ({rt11(tu11), ...,rt1j(tu1j), ...,rt1m(tu1m)}, ...,{rti1(tui1), ...,rtij(tuij), ...,rtim(tuim)}, ...,{rtn1(tun1), ...,rtnj(tunj), ...,rtnm(tunm)}) (9) according to the semantic five tuple description of web news instances supporting the topics, the core events can be extracted, the similarity degree can be calculated among the core events reported by web news instance. as shown in the formula 10, fs(fsi,fsj) can be calculated with a threshold, which is greater than or equal to the similarity threshold. the semantic five tuple is used to describe the core events reported by the aggregated web news instance set, the web news instances that represent the node of each event are arranged in ascending order according to the occurrence time of the core events. if the core events occur in the same time, then web news instances are arranged in descending order according to its utility characteristic. the web news instances that are clustered together can be arranged in ascending order according to the occurrence time of the core events, if the core events occur in the same time, then web news instances are arranged in descending order according to its utility characteristic. < ti,pi,oi,cei,rei > (< ti,ei > .uc) expresses the seed topic and the event description, < tij,pij,oij,ceij,reij > (< ti.uc >,< eij.uc >) expresses the seed topic of the evolution event description. in the subsequent experiments, the optimal range of analysing the parameters and the similarity threshold value will be analysed. fs(fsi,fsj) = α× ∑n m=1(tsim, tsjm)√∑n m=1(tsim) 2 √∑n m=1(tsjm) 2 + +β × ∑n m=1(ceim,cejm)√∑n m=1(ceim) 2 √∑n m=1(cejm) 2 (10) topicurl(t,e) = ({< t11,t1,e11 >,...,< t1j,t1,e1j >,...,< t1m,t1,e1m >}, ..., {< ti1,ti,ei1 >,...,< tij,ti,eij >,...,< tim,ti,eim >}, ...,{< tn1,tn,en1 >,..., < tnj,tn,enj >,...,< tnm,tn,enm >}) (11) 5 the experimental analysis and result in the process of completing the experiments based on designing the algorithms, the experimental environment of the software and hardware is used as follows. java language is used for the 320 m. chen algorithm 3 method 3 constructing the event series 1: input: topicurl, userbehavior, newsset, threshold, parameters; 2: output: topicurl; 3: for each t[i](0 ≤ i ≤ t.size() − 1) do 4: generate timeseriesvector; 5: fs ← calculate similarity of timeseries and coreevent; 6: if fs ≥ threshold then 7: adjust event under topic t[i]; 8: end if 9: describe event under topic t[i]; 10: end for programming design to implement the algorithms, myeclipse platform of the software research and development is used for the framework implementation, sql server of the database management system is used for web big data storage and process. the processor is intel 2.40ghz, the memory is 32gb [?,8,9,16,29]. the experiments mainly use the standard data set for the social event of german a320 airliner crash, the data source is from massive web news analysis corpus for the real data, the experimental analysis and result can verify feasibility and effectiveness of the research idea. 5.1 the qualitative analysis of the topic detection as shown in the figure 2, the accuracy rate represents the quality of the topic detection by using three web usage behaviour mining processes. firstly, the red column represents the accuracy rate of analysing the instance clicking mode, this quality is not high, although it has improvement, but the maximum is able to only arrive on about 0.64. secondly, the blue column represents the accuracy rate of analysing the instance searching mode, this quality is not also high, although it has also improvement in several monitoring points, but the maximum is able to also only arrive on 0.63. thirdly, the green column represents the accuracy rate of the algorithm designed in this paper, this quality is significantly improved, and the maximum is able to arrive on about 76 figure 2: the qualitative analysis of the topic detection research on key technology of web hierarchical topic detection and evolution based on behaviour tracking analysis 321 5.2 the analysis of the accuracy rate with the number of the instances concerned and the seed threshold variation trend as shown in the figure 3, the accuracy rate of the topic detection is indicated through the x and y axis adjustment. the accuracy rate represents the quality of the topic detection, if the seed threshold is defined, then it is able to increase in a stable trend, because the number of the instances followed is less, the relationship among big data is simpler. if the number of the instances is increasing, then the relationship of the link exists, so the accuracy rate of the topic detection is increasing in a stable trend. if the number of the instances is defined, firstly, then the quality of the topic detection expresses an increasing trend, secondly, then it will decrease with the increasing threshold, because the threshold is less, the topics of inaccurate accuracy are able to be found. if the threshold can arrive at a stable range, then the topics of approximate accurate are able to be found. if the threshold can arrive at a value, then the accurate topics cannot be found. this experiment expresses when the number of the instances is one hundred and sixty, and the seed threshold is zero point seven five, the quality of the topic detection can get the highest about 0.78. figure 3: the analysis of the accuracy rate with the number of the instances concerned and the seed threshold variation trend 5.3 the analysis of the accuracy rate with the number of the instances concerned and the probability threshold variation trend as shown in the figure 4, the accuracy rate represents the quality of the instances mined that support the topics by adjusting the probability threshold of the x axis and the number of the instances of the y axis. the accuracy rate indicates the quality of the instances mined, if the threshold is defined, then the quality of the instances mined is able to increase in a stable trend, because the number of the instances followed is less, the relationship among big data is simpler. if the number of the instances concerned is increasing, then the relationship of the link exists, so the accuracy rate of the instances mined is increasing in a stable trend. if the number 322 m. chen of the instances is defined, firstly, then the quality of the instances mined expresses an increasing trend, secondly, then it will decrease with the increasing threshold, because the threshold is less, the instances of inaccurate accuracy can be mined. if the threshold can arrive at a stable range, then the instances of approximate accurate can be mined. if the threshold can arrive at a value, then the accurate instances cannot be mined. this experiment expresses when the number of the instances followed is one hundred and forty, and the probability threshold is zero point seven, the quality of the instances mined can get the highest about 0.76. figure 4: the analysis of the accuracy rate with the number of the instances concerned and the probability threshold variation trend 5.4 the qualitative analysis of the topic evolution as shown in the figure 5, the accuracy rate represents the quality of the topic evolution by using three web usage behaviour mining processes. firstly, the red solid line represents the accuracy rate of analysing the time series similarity, which shows that the accuracy rate is not high with the increase in the number of web news instances concerned, although it has risen, but the highest can only arrive on about 0.64. secondly, the blue solid line represents the accuracy rate of analysing the core event similarity, which shows that the accuracy rate is not also high with the increase in the number of web news instances concerned comparing with analysing the time series similarity, and the accuracy rate has also a little decreasing slightly trend, the highest can only arrive on about 0.64. thirdly, the green solid line represents the accuracy rate of the algorithm designed in this paper, which shows that the accuracy rate has greatly improved because of integrating the time series similarity based on the semantic evaluation and the similarity analysis for the core events. although the accuracy rate is similar comparing with other two methods under the circumstance of less web news instances concerned, but the accuracy rate has gradually widening the gap comparing with other two methods with the increase in the number of web news instances concerned, the highest can arrive on about 0.75. so this experiment expresses that the quality of analysing the topic evolution is higher than other two methods by using the algorithm designed in this paper. research on key technology of web hierarchical topic detection and evolution based on behaviour tracking analysis 323 figure 5: the qualitative analysis of the topic evolution 5.5 the accuracy rate of analysing the topic evolution with the variation trend for the parameter adjustment as shown in the figure 6, the accuracy rate indicates the quality of analysing the topic evolution for web news topics according to the parameter adjustment of the sequence event construction process. the red dashed line represents the accuracy rate, in which the alpha parameter values are different aiming at the formula 10. from its trend, when the alpha value is adjusted from 0.6 to 0.65, and the beta value is adjusted from 0.35 to 0.4, the quality of analysing the topic evolution is more high and stable, and the accuracy rate is close to 0.70. in general, the parameter adjustment can make the quality of analysing the topic evolution more stable to the maximum for web news topics, which accords with the experimental effect expected, and can determine the optimal range of the parameter. figure 6: the accuracy rate of analysing the topic evolution with the variation trend for the parameter adjustment 5.6 the accuracy rate analysis with the number of the instances concerned and the similar threshold variation trend as shown in the figure 7, the accuracy rate represents the quality of analysing the topic evolution by adjusting the similarity threshold of the x axis and the number of the instances of the y axis. if the threshold is defined, then the quality of analysing the topic evolution is able to increase in a stable trend, because the number of the instances followed is less, the relationship among big data is simpler in the analytical process of the time series and core event similarity. 324 m. chen if the number of the instances followed is gradually increasing, then the relationship among big data adds also the semantic feature for analysing the process of the topic evolution, so its accuracy rate can increase. if the number of the instances is defined, firstly, then the quality of analysing the topic evolution can increase, secondly, then it will decrease with the increasing threshold, because the threshold is less, the inaccurate or approximate accurate analysis of the topic evolution mays be completed. if the threshold can increase to a stable range, then the approximate accurate result of analysing the topic evolution can be excavated. if the threshold can increase to a value, then the accurate result of analysing the topic evolution cannot be excavated. this experiment expresses when the number of the instances followed is one hundred and eighty, and the similarity threshold is zero point seven, the quality of analysing the topic evolution can get the highest about 0.76. figure 7: the accuracy rate analysis with the number of the instances concerned and the similar threshold variation trend 5.7 the time consuming analysis for solving main research problem under different methods as shown in the figure 8, in view of german a320 airliner crash social event, the x axis represents massive web news time released through the authoritative web news network platform, the red solid line represents the time consuming for analysing web hierarchical topic evolution under the method based on the content and description, the blue solid line represents the time consuming for analysing web hierarchical topic evolution under the method based on the behaviour tracking. according to the change trend of two solid lines, the number of web news instances has increased sharply in several time intervals with the progress of the event development, therefore, the time consuming has also increased sharply, in other time intervals, the time consuming has relatively stable trend. while the time consuming is lower for the blue solid line, because the analytical process of web hierarchical topic detection and evolution uses the behaviour tracking method based on the semantic five tuple description and the utility evaluation. this experiment expresses that the non-deterministic problem can be solved efficiently using the research on key technology of web hierarchical topic detection and evolution based on behaviour tracking analysis 325 method, which is proposed by this paper. figure 8: the time consuming analysis for solving main research problem under different methods 5.8 the qualitative analysis of web hierarchical topic detection and evolution result under different data sets as shown in the figure 9, the qualitative analysis of web hierarchical topic detection and evolution result uses also other four standard data sets in addition to the data set of german a320 airliner crash social event, which include shanghai bund trample, taiwan revival airliner falling river, nepal 8.1 earthquake and orient star cruise overturn social event. according to the change trend of five columns, there is little difference in the qualitative analysis of web hierarchical topic detection and evolution result at the start, development and end stage of five social events. this experiment shows that the analytical process of web hierarchical topic detection and evolution is stable under different social events. moreover, the qualitative analysis of web hierarchical topic detection and evolution result has only little effect in different stages of the social events with the increase of the number of web news instances. figure 9: the qualitative analysis of web hierarchical topic detection and evolution result under different data sets 6 conclusion this paper completes the research on an idea of analysing the process for web hierarchical topic detection and evolution in view of the behaviour tracking technology taking the network 326 m. chen big data of web news as the processing object, this result of designing and implement is more valuable for the scholars in the research field. in the research process, this paper proposes the analytical algorithm of web hierarchical topic detection and evolution, in which this paper has also proposed the methods of analysing the usage mode, the topic series construction, the event series construction and the evolution analysis for the events, so as to solve the problem existing in current research status. the results of experimental analysis show that the idea is feasible, verifiable and superior, which plays a major role in reconfiguring web hierarchical topic corpus, improves understanding efficiency of the network big data, enhances the website availability, constructs and improves the website service function, improves the efficiency of the business operational and website clicking rate, provides an intelligent big data warehouse for the network information evolution application. funding this paper is supported by national natural science foundation of china under grant nos.71572015, support project of high-level teachers in beijing municipal universities in the period of 13th five-year plan under grant nos.cit&tcd201704072, premium funding project for academic human resources development in beijing union university under grant nos.bphr2018as01. bibliography [1] ahila, s.s.; shunmuganathan, k.l. 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(2017). topic evolution based on the probabilistic topic model: a review, frontiers of computer science, 11(5), 786-802, 2017. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 1, month: february, year: 2020 article number: 1003, https://doi.org/10.15837/ijccc.2020.1.3783 ccc publications a fuzzy bayesian network model for quality control in o2o e-commerce h. zhang, h. feng, y. cui, y. wang hao zhang* logistics management department beijing technology and business university, china *corresponding author: zhhaozhhao@126.com huixia feng logistics management department beijing technology and business university, china fenghuixia2019@163.com yan cui logistics management department beijing technology and business university, china cui82986637@163.com yuting wang logistics management department beijing technology and business university,china firstyuting@gmail.com abstract with the popularization of the online to offline (o2o) e-commerce on fresh food products, how to control the quality is becoming increasingly important. to adequately address this problem, this paper presents a fuzzy bayesian network model for effectively controlling the quality in o2o ecommerce. reasoning about uncertain events and incomplete data through an intelligent simulation with bayesian networks provides a convenient and fast method of evaluation and analysis for ecommerce platforms to quickly select fresh food suppliers. such a model is capable of appropriately modelling the uncertainty inherent in the fresh food product distribution process. it focuses on the identification of the critical factors that affect the food product quality along the supply chain. this leads to the development of a complete selection and evaluation system for the quality in o2o e-commerce. a simulation study is conducted that shows the proposed model is applicable for effectively controlling the quality in o2o e-commerce. ultimately, the unloading level, warehouse inspection and warehouse monitoring are determined as the entry points for quality control, with corresponding degrees of influence of 44%, 37%, and 34%. the main points to protect the quality of food are introduced, which provides a theoretical basis for solving fresh food safety problems for business platforms. keywords: fresh food quality control; o2o mode; fuzzy bayesian network; triangular fuzzy numbers. https://doi.org/10.15837/ijccc.2020.1.3783 2 1 introduction online-to-offline (o2o) commerce is a new type of e-commerce that has emerged in recent years. it involves in an organic combination of online channels and offline channels for selling various products in the marketplace. o2o commerce makes the connection between online and offline more closely. this provides a new opportunity for the development of various industries and pushes the development of e-commerce to a higher level. the o2o commerce relies on advanced mobile internet technologies with the integration of online platforms with offline entities. it can use online platforms to introduce customers who often shop online to offline stores and let them feel the product in person. more importantly it can use publicity and good services of offline physical stores to achieve good brand awareness, and then use online promotions to attract customers to return to online consumption, and ultimately reach the maximum benefit. it is a kind of business model in which carry out bidirectional drainage and cross development between online and offline. the benefit of the o2o model is that it provides a localization services with the combination of online and offline to more effectively promote corporate values and services. the integration of online payment methods and traffic, with the help of data that can be monitored, continues to carry out customer relationship management in a continuous manner so as to conduct accurate marketing. in addition, the o2o model reduces intermediate links and greatly reduces logistics costs. with the development of o2o commerce in recent years, there is an increasing use of the o2o commerce for fresh food products including fresh vegetables, fruits, meat and seafood. there are several major o2o commerce platforms including yihaodian, amazon china, and jingdong mall that have launched online fresh food business in china. in 2015, the transaction volume in china’s fresh o2o commerce market reached 49.71 billion yuan. in 2017, the transaction volume of china’s fresh o2o commerce market exceeded 100 billion yuan. this shows that there is a huge potential for the fresh food product market in o2o commerce in china. as people are increasingly pursuing high quality in their lives, fresh produce occupies an increasingly important place in the human food supply because of health-promoting nutritional properties. considering the special properties of fresh food, factors that affect the quality of fresh food are also more complicated due to many links related to the supply of fresh food under the o2o mode. therefore, it is of great importance and a challenge to accurately judge the factors that have impact on the quality of fresh food. fresh food has been found to contain listeria spp..there has been an increase in foodborne outbreaks and cases associated with fresh produce , but data in the literature on the prevalence of pathogen contamination are inconsistent [5] . dong et al. pointed out the technical difficulties and solutions to carrying out the quantitative risk assessment of food microorganisms and discussed the application prospects [9]. lei yang developed a fresh product supply chain under o2o and freshness-keeping decisions are investigated [30]. in addition, food authenticity issues such as the illegal substitution of frozen meats for fresh meats constitute one of the public concerns among food authorities, industry, and consumer groups. an enzymatic method based on the activity of beta-hydroxyacyl-coa-dehydrogenase was used to differentiate between fresh/chilled and frozen pork meats [6]. to strengthen the quality and safety management of fresh food, a farm food safety-risk assessment tool has become popular, as it serves as a self-assessment and educational tool for fresh produce farms to evaluate their food safety practices [23]. an inter-organizational information system has been applied to the iois architecture for the traceability of fresh vegetables [1, 26]. a previous study examined the key drivers and constraints for implementing traceability [20]. zhang et al. elaborate the role and existing problems in the food safety from the perspective of enterprises, government and regulatory power of third parties [32]. akshit singh et al. propose a big-data analytics-based approach and inform supply-chain decision makers about quality of food products [22]. there are various attempts in the development of specific approaches for tackling the factors influencing the quality of fresh product in different circumstances. due to the uncertainty and the risk is not easy to quantify, davidson et al. defined the initial hazard level of the food supply chain system and the potential changes in circulation operations. then, the fuzzy comprehensive evaluation method was used to evaluate the early risk of the system and to quantify or classify the fuzzy values of the evaluation output [8] xu et al. solved the single-plant location decision problem and the multiple location decision problem [28]. martínezsimarro et al. studied the relationship between food quality https://doi.org/10.15837/ijccc.2020.1.3783 3 and safety and the reality of enterprises by using fuzzy neural theory and established a evaluation model [21]. however, the above studies have not recognized the risk from the aspect of factors, belc et al. used the delphi method to identify the quality and safety risk factors [4]. however, the existing methods for controlling the quality of fresh food products can not find precisely the relationship between factors and make reasonable reasoning. their application are scattered without organization and systems that can fully integrated complete information in the fresh food supply process in the o2o mode and fundamentally solve the problem of quality and safety. in addition, the methodology used lacks objectivity and does not adequately quantify the problem. this paper sort out the key factors influencing the quality control of fresh food and integrate a system of fresh food quality indicators to provide an entry point for quality control using fuzzy bayesian network that combines triangular fuzzy numbers [10] and bayesian network based on the analysis of the supply of fresh food. this method overcomes shortcomings of the fault tree analysis, risk matrix method and other evaluation methods in visually describing the abstract problem and achieves reasonable reasoning of data that can not be quantified . this paper is organized as follows: section 2 presents the fresh food supply in the o2o model. section 3 provides details of the fuzzy bayesian network model. section 4 presents the simulation study. based on the simulation results in section 5, this paper discusses the direction and standards for fresh food quality control. finally, the conclusions are presented in section 6. 2 fresh food supply in o2o commerce at present, e-commerce is becoming more and more popular. fadwa chalfoun et al. analyzed electronic media importance and prepared classification of e-media [11]. at the same time , aurelija et al. discussed general trends of online shopping and the consumer protection [2]. in the context of e-commerce, we need to make various decisions. xu et al. provided more choices for decision-making and obtained a universal decision model [29]. the storage condition and cold chain structure are important factors influencing fresh food quality the smart freezing technology enables real-time monitoring of quality during the freezing process and helps improve the product quality and freezing efficiency [17, 25]. supercooling is also a foodprocessing technique with the potential to significantly increase the shelf life of foods [24]. however, the loss of fresh food in the transportation process is serious because it is perishable [16]. we must perfect the logistics of the transportation of fresh food and decrease the loss rate of fresh food in the transportation process [34]. based on fresh food e-commerce enterprises, a forward and reverse logistics network and route planning in the environment of low carbon emissions are established [14]. zou pointed the safety reliability of fresh food cold chain logistics [35] xin et al. use an euler algorithm to optimize the flow patterns in a perishable food supply chain network [27]. because of the increase in consumers’ awareness of fresh foods, food packaging material is important for food safety. the demand for packaging materials is no longer limited to ordinary materials but has gradually changed into safe, healthy and efficient materials [12]. modified atmosphere packaging can become one of the most appropriate technologies for packaging fresh and fresh-cut produce to extend the shelf life of fresh produce and provide a high-quality product [3, 19, 33]. however, fresh food cannot be sterilized by heat, so a sterilization method using cold plasma for packaged fresh food was proposed [15]. in addition, the behaviours of retail and food service employees regarding food safety for fresh and fresh-cut produce are often associated with food illness outbreaks, so it is important to identify the factors influencing employees’ practices [7]. so, it is important to link data sharing with the sustainability performance of the entire fresh food supply chain, and it is not considering only one aspect [18]. fresh food supply analysis in the o2o mode aims to clarify the factors affecting fresh food quality in the supply process. by dividing the supply chain in detail and clearly analysing the specific operation processes, the quality and safety factors involved in the supply chain such as stocking, distribution and processing, tallying and distribution are clarified, and the unloading level, warehouse inspection and other key influencing the quality of food factors are extracted. this article sorts out fresh food supply operation processes in the o2o mode based on expert consultation and field research, as shown https://doi.org/10.15837/ijccc.2020.1.3783 4 in fig. 1. production of fresh food fresh vegetables fresh seafood fresh livestock cold chain storage cold chain transportation direct delivery wholesale processing cold chain storage cold chain transportation chain supermarket convenience store intelligent cold store terminal consumption e x p e rien c e o fflin e information service platform stocking information carding information integrating information merging orders sending orders figure 1: supply process for fresh food in the o2o mode this study focuses on the process from chain supermarkets and convenience stores to consumer terminals. large supermarkets and fresh merchants under o2o act in accordance with customer demand forecasts and purchase plans in conducting procurement; then, they receive the goods for acceptance, storage, food information registration and classification. considering the special nature of some fresh food, businesses must also carry out food circulation processing to extend the shelf life or increase its added value. after the customer’s new order is received, to satisfy the customer’s order requirements, the fresh food is sorted, picked, packaged, etc., and is moved to the waiting area. to ensure the efficient delivery and quality safety of fresh food, by making delivery plans and specifying the delivery route, the merchants send the delivery notice to the customer, load the goods into the refrigerated distribution vehicles, and control the temperature during the transport process until the fresh food is safely served to customers. the analysis of the factors influencing fresh food quality is the basis and standard for quality inspection in the o2o mode. this comprehensive system reflects different attributes of the complex system of fresh food enterprises and the environment in the o2o mode and provides a direct basis and entry point for quality control. so, a scientific and completely comprehensive analysis and evaluation system must be established. considering the needs and requirements of consumers in combination with the attributes and characteristics of fresh food supply links in the o2o model, this paper analyses and establishes a comprehensive evaluation index system of fresh food quality from the preparation, circulation processing, tally, and delivery links to clarify all factors in the supply links, as shown in table 1. 2.1 preparation for goods preparation for goods include purchase and storage. purchase is the fresh food from the upstream to accept the goods from the truck unloaded, check the number and status of goods. and in accordance with the sorting and picking process requirements, we should do a small amount of storage preparation https://doi.org/10.15837/ijccc.2020.1.3783 5 table 1: the factors influencing the quality of fresh food in each step of the supply chain stages factors influencing quality preparation for goods purchase plan, acceptance inspection, unloading, receiving, classifying fresh food, inspection in warehouse, monitoring the warehouse distribution processing picking outbound, cleaning and cutting, packing, stock-in tally links sorting, allocation inspection, outer packaging delivery links delivery plan, delivery notice, outgoing quality control, loading, transport logistics, whole-process temperature control, unloading in the tally venue. reasonable purchase plan to help the supplier make their arrival time be reasonable , or else it will make the supplier too much time to arrive, resulting in the receipt of the scene is very congested, extending the time of fresh food exposed to room temperature. after the arrival of fresh food, if the receiving staff did not follow the standard to the arrival of fresh food for basic visual inspection and vehicle temperature record check, then it may danger the safety of fresh food . according to the beijing cold chain market research shows that most of the fresh food packaging damage occurred in the loading and unloading operations process. in the unloading operations, different operating equipment on the quality of fresh food safety and safety of the extent of a great difference. when the traditional manual handling method is used, not only the work efficiency is low, but also the time of exposure of the fresh food to the room temperature is greatly prolonged, and the quality of the freshman is affected by the quality of the personnel. which will make fresh food packaging damage probability and fresh food itself greatly increased the probability of damage. in the process of receiving food in the warehouse, this process is carried out in the outdoor temperature, so it may lead to the risk of damage to product quality. meanwhile, unreasonable fresh food classification operations can infect other food and make fresh food by cross-bacterial infection. after storaging, if the "quantitative test" errors occur, the quality of fresh food will not be affected. however, if the "quality inspection" errors occur, for example, they can not correctly check out the pesticide residues, hormones and other chemical agents excessive food, which will make these unsafe fresh food into the distribution channels, serious harm to fresh food quality and safety. if treasury monitoring operations is unreasonable, it may damage the quality of fresh food from four aspects. first of all, the treasury temperature monitoring will make fresh food is not conducive to the preservation of the temperature, to speed up the deterioration of fresh food. second, the treasury health is not timely clean up, deterioration of fresh food will breed bacteria. once again, most of the operations are carried out in the warehouse, if the cold storage equipment failure, will make the entire distribution center of fresh food are not conducive to the preservation of the temperature, so that its food safety has been seriously affected. finally, the treasury is likely to fire, theft and other accidents, but also may be affected by natural disasters such as the impact of the earthquake, which will be on the distribution center of fresh food quality and safety impact. according to the analysis of the preparation for goods, the factors affecting the quality of fresh food mainly are divided into purchase plan, acceptance inspection, unloading, receiving, classifying fresh food, inspection in warehouse and monitoring the warehouse. 2.2 distribution processing fresh food circulation processing is based on the characteristics of fresh food distribution. before stocking, we can extend its shelf life, increase the added value and process fresh food. the process of picking outbound is carried out in the outdoor temperature, there is also a risk of exposure to fresh food and damage to quality and safety. cleaning and cutting operations are very strict with environmental requirements. the workplace must be disinfected and cleaned in strict https://doi.org/10.15837/ijccc.2020.1.3783 6 accordance with national food hygiene requirements and relevant laws and regulations. if the cleaning operation and cutting operation are not clean, the cleaning solvent used does not meet the national health requirements, and the disinfection and cleaning work of cutting equipment is not in place, it will cause damage to the safety and quality of fresh food. if the packaging of fresh food is unreasonable, and it is not sealed or packed in dark box according to the characteristics and requirements of fresh food, it will not only fail to protect the safety of fresh food, but also cause great harm to the safety of fresh food. in addition, due to the negligence of the operators, the packing is not tight or there is leakage, gas, etc., which will also endanger the safety of fresh food. in the process of storage, the quality and safety of fresh food will be affected by outdoor temperature. therefore, fresh food quality distribution process will be affected by various interference factors, mainly in the following aspects: picking outbound, cleaning and cutting, packing and stock-in. 2.3 tally links tally link is an important part of fresh food distribution. the impact of different equipment on the quality and safety of fresh food is different in sorting operations. the traditional manual sorting operations are inefficient and prone to appear the wrong sorting situation, and if the sorting operations staff quality is low, it will greatly increase the chance of fresh food damage. allocation inspection is to ensure that fresh food are good quality, if there are inspection errors, resulting in substandard fresh food into the delivery link, which will damage the quality of fresh food. therefore, the tally process of each process has different degrees of impact on the quality of fresh food, mainly reflected in the following aspects: sorting, allocation inspection and outer packaging. 2.4 delivery links delivery plans mainly include the delivery quantity, object, delivery route and distribution centers location[31], which will impact on the delivery time. if the communication with the customer is not smooth, delivery notice is not timely or delivery of information are wrong, they will enable customers to prepare the goods and the equipment in advance, so that the probability of fresh food damage is reduced after the fresh food arrives at the destination and is unloaded. shipping inspection errors may lead to fresh food delivery errors, resulting in unnecessary returns to extend the fresh food in the distribution channel within the residence time, thereby affecting the quality of fresh food safety. the loading process needs to consider the factors that affect the safety of fresh food from two aspects. first of all, different loading equipment has different impact on the quality and safety of fresh food. secondly, the rationality of fresh food loading will affect the temperature control of fresh food. if the load density is too high, the temperature of the box will be uneven, and if the load density is too low, the refrigerator resources will be wasted. the reasonable loading sequence will also affect the smooth transportation and loading and unloading of fresh food, and the unreasonable loading will extend the transportation time, thus affecting the quality and safety of fresh food. transportation logistics includes refrigerated vehicle failure factors and traffic environment factors. in the process of transportation, if the refrigerator car can not fail, it will greatly extend the delivery time of fresh food and increase the probability of deterioration of fresh food. the transportation process of transportation in the transportation environment will also have an impact on fresh food, such as traffic congestion, accidental traffic accidents will cause damage to fresh food. if the temperature control equipment in the transport process failure, it will make fresh food is not conducive to the preservation of the temperature environment, thereby increasing the chance of fresh food deterioration. distribution and loading and unloading operations are facing the same problems, but also affected by the quality of fresh food. the impact of the delivery on the quality of fresh food is mainly reflected in the following operations: delivery plan, delivery notice, outgoing quality control, loading, transport logistics, wholeprocess temperature control and unloading. the links involved in the supply of fresh food are complex, resulting in interference and difficulty in the evaluation of quality. it is necessary to establish an evaluation system based on the analysis of multiple influencing factors according to the different links and processing flow in the supply process https://doi.org/10.15837/ijccc.2020.1.3783 7 to conduct a comprehensive and reasonable fresh food quality evaluation from a variety of aspects so as to effectively manage the quality and safety of fresh food. however, in order to carry out effective quality control of fresh foods, it is necessary to analyse the influence of each factor reasonably, find out the key factors and provide the entry point of quality control so as to reduce the risk of deterioration of fresh food. 3 the fuzzy bayesian network model to study the key factors influencing the quality control of fresh food in the o2o mode, a series of factor indicators must be analysed and compared. because the degree of influence of each factor is difficult to quantify and because of the uncertainty, the fuzzy bayesian network model combining the bayesian network and the triangular fuzzy numbers which organically integrates the bayesian method and graphical theory based on probability uncertainty and statistical theory can be used to achieve forward and reverse complex reasoning. the reasoning results are directly reflected in the network diagram to provide an intuitionistic macroscopic description of the abstract problem domain. the key influencing factors are analysed by examining the probability. in the forward reasoning, the bayes network algorithm is based on the joint probability formula to obtain the joint probability distribution of each node. the concrete calculation process is as follows: suppose that n nodes in the bayesian network are represented by x1, x2, ..., xn. the values of the nodes are represented by small letters. for example, the value of xi is xi. for the bayesian network containing n nodes, according to the chain rule, the joint probability distribution can be described as follows: p (x1, x2, ..., xn) = n∏ i=1 p (xi|x1, x2, ..., xi−1) (1) if the parent node set of xi is parent(xi), the probability of node xi is p (xi|x1, x2, ..., xi−1) = p (xi|parent(xi)) (2) the above equation can be simplified as follows: p (x1, x2, ..., xn) = n∏ i=1 p (xi|parent(xi)) (3) in the inverse reasoning, the bayesian formula is used as the algorithm basis of the bayesian network. the posterior probability is deduced by the prior probabilities of the influencing factors to analyse the influence degree of the factors, and the main factors for the quality control of fresh food in the o2o mode are obtained. the bayesian formula is expressed as follows: suppose that the sample space for experiment e is ω and that the events a1, a2, ..., an are mutually incompatible. a1, a2, ..., an form a complete event group, i.e., ⋃n i=1 ai = ω, aiaj = ∅, p (ai) > 0 , according to the multiplication theorem and conditional probability: p (ai|b) = p (b|ai)p (ai)∑n i=1 p (b|ai)p (ai) (4) where p (ai) is the prior probability and p (ai|b) represents the posterior probability [13] the determination of conditional probabilities is the premise and basis of bayesian network reasoning, the set of parent nodes of node xi is u (xi) = u1, u2, ..., um, xi is the value of xi, ui is the vector of the parent node variables, and ui is the value of vector ui. the conditional probability of node xi is p (xi|u (xi)) = p (xi, u (xi)) p (u (xi)) = p (xi = xi, ui = ui) p (ui = ui) (5) for node xi with three parent nodes, the conditional probability is calculated as follows. when the parent node is in state0, the conditional probability value that node xi is in state0 is https://doi.org/10.15837/ijccc.2020.1.3783 8 p (xi = state0|u1 = state0, u2 = state0, u3 = state0) = p (xi = state0, u1 = state0, u2 = state0, u3 = state0) p (u1 = state0, u2 = state0, u3 = state0) (6) where the parameter state0 indicates the state of the node, and its specific meaning can be described as the poor or good condition of the event. the above formulas show that the calculation of conditional probability requires a large amount of sample data to satisfy different valued requirements of each node. the amount of calculation increases with the increase in number of parent nodes. in the absence of an accurate probability, the concept of group decision making is required based on expert experience; i.e., experts are consulted on the conditional probability of the nodes with the use of questionnaires and triangular fuzzy number for related data processing. the ipcc (intergovernmental panel on climate change) uses the language variables of the occurrence probability with seven-stage classification to describe the probability values. the language variables and their corresponding probability values and triangular fuzzy numbers are shown in table 2. table 2: the semantic value of event probability and the corresponding triangular fuzzy number probability range triangular fuzzy number expression statements <1% ((0.0,0.0,0.1) very low 1%-10% (0.0,0.1,0.3) low 10%-33% (0.1,0.3,0.5) a little low 33%-66% (0.3,0.5,0.7) moderate 66%-90% (0.5,0.7,0.9) a little high 90%-90% (0.7,0.9,1.0) high >99% (0.9,1.0,1.0) very high with the questionnaire survey method, the probability table of each node is obtained. if the number of experts is q, the probability of node xi in state j given by the expert k is converted into a triangular fuzzy number according to the above table p̃ kij = (a k ij , m k ij , b k ij )(k=1,2,...,q) (7) to obtain a relatively reasonable fuzzy probability based on the opinions of several experts, the average fuzzy probability of node xi in state j is p̃ ′ij = p̃ 1ij ⊕ p̃ 2ij ⊕ ... ⊕ p̃ q ij q = (a′ij , m ′ ij , b ′ ij ) (8) the fuzzy probability is transformed into the exact probability by using the mean area method. the exact probability of g in node xi in state j is p ′ij = a′ij + 2m ′ ij + b ′ ij 4 (9) to satisfy the requirements that the total probability of each node in different states is 1, it is necessary to "normalize" the exact probability of each node in each state. after normalization, the exact probability of node xi in state j is pij = p ′ij∑ p ′ij (10) compared with the methods of delphi method, fuzzy netural theory, risk-return model and fuzzy comprehensive evaluation, the fuzzy bayesian network is a combination of the bayesian method, the triangular fuzzy number and graph theory, combining prior knowledge with the sample information. this approach can address incomplete data and is an ideal model for data mining and uncertainty https://doi.org/10.15837/ijccc.2020.1.3783 9 representation. first, because a large amount of sample data is obtained based on the experience of experts using the concept of group decision-making, in the case where accurate data cannot be obtained and the calculation process is complicated, the proposed model can convert uncertain data into triangular fuzzy numbers for specific processing, and the processing results are introduced into bayesian networks. then the bayesian network quantitatively analyses the problem and qualitatively describes the causal relationship between the variables and their probability values. the fuzzy bayesian network can conduct a reverse reasoning function to predict the key factors of certain links and the extent of the specific effects and use the posterior probability to find the key influencing factors. 4 simulation study genie 2.0 used in this simulation (graphical network interface) is a simulation software developed to build a graphical decision theory model. it has a visual interface and facilitates the construction and analysis of bayesian network models. it can simultaneously conduct learning and reasoning about bayesian networks, and support structure and parameter learning. the simulation environment can build complex bayesian network models for large-scale nodes. the variables that need to be input are the prior probability value of each node and the conditional probability value of the non-root node. the output variables are the priori and posterior probability value of each node. the simulation process from the data to the model until the final result features less influence of the subjective method, simple operation process and high degree of visualization. by analysing the supply parts in the o2o mode, 46 factors influencing the quality control of fresh food in processes were determined and divided into 46 network nodes. the fresh food quality is measured as the final bayesian network node. the number and range of the nodes in the bayesian network are shown in table 3. in this paper, the bayesian network structure is constructed manually based on expert knowledge. then, the bayesian network model is obtained through correction by analysing the database, as shown in fig. 2. figure 2: bayesian network of fresh food quality a complete fuzzy bayesian network model contains the network topology and parameters of the model, and the model parameters refer to the probability distribution table of each node. after establishing the network topology, it is necessary to quantitatively describe the probability relations between the nodes, which is the basis of bayesian network reasoning. through the questionnaire survey and data processing of triangular fuzzy numbers, the appropriate conditional probability table is introduced for each node. an example of the conditional probability table of a node is shown in table 4. from the conditional probability table, we can clearly see the probability of node b10 in two states https://doi.org/10.15837/ijccc.2020.1.3783 10 table 3: range of bayesian networks number node range a quality of fresh food (0, 1) a1 preparation for goods (0, 1) a2 distribution processing (0, 1) a3 tallying (0, 1) a4 delivery (0, 1) b1 purchase plan (0, 1) b2 acceptance inspection (0, 1) b3 unloading (0, 1) b4 receiving (0, 1) b5 classifying fresh food (0, 1) b6 inspection in warehouse (0, 1) b7 monitoring the warehouse (0, 1) b8 picking outbound (0, 1) b9 cleaning and cutting (0, 1) b10 packing (0, 1) b11 stock-in (0, 1) b12 sorting (0, 1) b13 allocation inspection (0, 1) b14 outer packaging (0, 1) b15 delivery plan (0, 1) b16 delivery notice (0, 1) b17 outgoing quality control (0, 1) b18 loading (0, 1) b19 transport logistics (0, 1) b20 whole-process temperature control (0, 1) b21 unloading (0, 1) c1 unloading equipment (0, 1) c2 quality of unloading personnel (0, 1) c3 quality inspection (0, 1) c4 temperature monitoring in warehouse (0, 1) c5 hygienic cleaning in warehouse (0, 1) c6 refrigerator in warehouse (0, 1) c7 contingency in warehouse (0, 1) c8 packing requirement (0, 1) c9 packing leakproofness (0, 1) c10 sorting equipment (0, 1) c11 quality of sorting personnel (0, 1) c12 logistics container (0, 1) c13 quality of operating personnel (0, 1) c14 loading equipment (0, 1) c15 loading rationality (0, 1) c16 loading sequence (0, 1) c17 refrigerator car fault (0, 1) c18 traffic environment (0, 1) c19 temperature control equipment (0, 1) c20 quality of delivery personnel (0, 1) https://doi.org/10.15837/ijccc.2020.1.3783 11 table 4: conditional probability table of the packing level condition triangular fuzzy number probability state0 state1 state0 state1 state0 state1 c8, c9 (0.57,0.77,0.93) (0.03,0.17,0.37) 0.8 0.2 c8 c9 (0.37,0.57,0.77) (0.23,0.43,0.63) 0.57 0.43 c9 c8 (0.43,0.63,0.83) (0.17,0.37,0.57) 0.63 0.37 c8, c9 (0.33,0.5,0.7) (0.23,0.43,0.63) 0.54 0.46 when parent nodes c8 and c9 are in different conditions. for example, when both c8 and c9 are in state0, the probability of b10 being in state0, which is defined as the better state in this paper, is 80%. when c8 and c9 are in state1, the probability of b10 being in state1, which is defined as the poor state, is 54%. 5 result and discussion to reflect the factors influencing the quality control of fresh food and make an objective evaluation, this paper investigated businesses that have joined the o2o fresh platform, such as jd, excellent fresh on a daily basis, and sf-best, and we collected relevant data influencing the quality of fresh food according to expert opinions. the simulation was conducted when the data were input into the software genie after the triangular fuzzy number processing. the simulation data come from reality, and the factors in the supply of fresh food under o2o mode are truly reflected in the simulation environment. according to forward reasoning, we can draw the probability of each node. the probability of the "quality of fresh food" in state0 is 65%, as shown in fig. 3, where "state0" is the better state and "state1" is the poor state. figure 3: prior probability model based on the inverse reasoning of the bayesian network, when the quality of fresh food is poor, i.e., p (a = state1) = 1, the link of goods preparation has the greatest impact on the quality safety, and the corresponding probability value is 56% according to the bayes network simulation using genie, followed by the delivery link, tallying link and circulation processing link. consequently, the degree of impact on the quality and safety of fresh food is gradually weakened. in the link of goods preparation, unloading, inspection in warehouse, and monitoring the warehouse are the key factors for controlling the quality and safety of fresh food in the o2o mode. the probability values showing the degree of influence are 44%, 37% and 34%, as shown in fig. 4. therefore, the assurance of the quality and safety of fresh food should focus on the three aspects to strengthen implementation. https://doi.org/10.15837/ijccc.2020.1.3783 12 figure 4: posterior probability model the research for the cold chain market shows that in the process of unloading operations, the probability that the packaged fresh food itself is damaged significantly increases. the use of automatic forklift and other operating equipment instead of traditional handling operations effectively reduces the rough work, shortens the exposure time at room temperature and improves the efficiency of unloading operations so that fresh food is well protected. after the food enters the warehouse, strengthening the quality inspection provides a timely way to select the fresh food that has excessive pesticide residues, hormones and other chemical agents in order to prevent these unsafe fresh foods from entering the distribution channels, thus preventing serious harm with respect to fresh food quality and safety. effective warehouse monitoring operations can reduce the damage to the quality and safety of fresh food. first, powerful warehouse temperature monitoring can offer a suitable storage temperature to prevent fresh food from putrefaction. second, timely cleaning of the warehouse prevents decayed food from breeding bacteria and from polluting the storage environment and stops the spreading of pests and harmful biological bacteria. in addition, the effective monitoring of cold storage equipment failure helps to improve the effect of fresh food refrigeration. finally, strengthening monitoring for fire, theft and other accidents strongly protects the quality safety of fresh food in distribution centres. 6 conclusion refining the factors influencing fresh food quality is an important measure for controlling quality safety. this paper fully integrates information on the supply process and considers the rule of e-commerce to introduce and perfect a series of evaluation indices considering the aspects of preparation, circulation, tally and delivery. through the establishment of a bayesian network and backward reasoning, the unloading level, warehouse inspection and warehouse monitoring are determined as the entry points for quality control, with corresponding degrees of influence of 44%, 37%, and 34%. the main points to protect the quality of food are introduced, which provides a theoretical basis for solving fresh food safety problems for business platforms. in short, the methods and ideas adopted in this article have proposed a qualification standard for food circulation regulation in the o2o model. in addition, the intelligent analysis of the indicator data solves the problem of information occlusion for downstream enterprises and customers and makes information in platforms more open. consumers can reasonably select methods of procurement. this process integrates e-commerce platforms to provide products and control supervision. there are still some improvements to be made in this paper. first, we only considered a single individual in the process of discussing the influencing factors of distribution. further research can be https://doi.org/10.15837/ijccc.2020.1.3783 13 extended to multiple distribution companies for joint distribution. secondly, in the process of identifying factors that affect quality, the analysis of logistics supply chain is more about transportation, and improvement can also be considered from the supply chain itself. acknowledgements this research was sponsored 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(2020). a fuzzy bayesian network model for quality control in o2o e-commerce, international journal of computers communications & control, 15(1), 1003, 2020. https://doi.org/10.15837/ijccc.2020.1.3783 international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(2), 233-252, april 2019. routing in wsns powered by a hybrid energy storage system through a cear protocol based on cost welfare and route score metric r. senthilkumar, g.m. tamilselvan, s. kanithan, n. arun vignesh r. senthilkumar* department of electrical and electronics engineering, roever enginering college, perambalur, tamil nadu621212 *corresponding author: rajsen1985@gmail.com g.m. tamilselvan electronics and communication engineering, bannari amman institute of technology, sathyamangalam, tamil nadu638401 tamiltamil@rediffmail.com s. kanithan department of electronics and communication engineering, avs college of technology-salem, tamilnadu kani.rex@gmail.com n. arun vignesh department of electronics and communication engineering, griet, hyderabad arunvignesh44@gmail.com abstract: implementing a low cost, power efficient and high performance routing protocol in wireless sensor networks (wsns) is an important requirement for transmitting a packet through network. in this paper we propose, a new cost and energy aware routing protocol (cear) that works based on the two metrics such as cost welfare metric and route score metric.a hybrid electrical energy storage (hees) framework which holds numerous banks of heterogeneous electrical energy storage (ees) components to be specific battery and a ultra-capacitor is used for providing energy to the network exhibit in the wsn for routing. the simulation results shows that our proposed routing protocol routes the packet efficiently by choosing the best path that also reduces the cost and routes the packet with reduced power consumption. the quantitative metrics in terms of packet delivery ratio of 0.93, average end to end delay of 110 secs, packet loss ratio of 0.75, average throughput attained of 250 bits/sec and efficiency of 98-99.9% overpowers the performance of our proposed work. keywords: hybrid energy storage system; cear protocol, routing; cost welfare metric, route score metric. 1 introduction in a wide spectrum of applications such as wireless sensor networks (wsns), the energy harvesting technology from the solar panels is a primary issue, which is to be tackled for grant effectiveness. the harvested energy should be stored and controlled using appropriate devices to power the sensor nodes present in the wireless networks [4]. batteries are currently used for powering in most wireless sensors, where the power requirements are modest. the primary copyright ©2019 cc by-nc 234 r. senthilkumar, g.m. tamilselvan, s. kanithan, n. arun vignesh batteries are usually chosen for their high energy densities, low leakage rates and low cost. for sensor node applications, lifetimes of battery is at least a year is desirable [8].a hybrid energy framework, shaped by consolidating various energy storage systems (e.g., battery, ultra-capacitor, flywheel, power module, and so on.) and generators (e.g., smaller scale turbine, wind turbine, pv board, and so on.), has turned out to be a solution to meet the energy/power prerequisites with enhanced adaptability, unwavering quality and cost proficiency [5].subsequent to the framework arrangement and conduct of the hybrid framework are turning out to be more unpredictable, the streamlined administration and control of a hybrid energy framework is still a testing errand [20]. to accomplish the ideal solution for this energy administration issue, research activity in recent years has focused on the design and development of novel hybrid energy storage systems that use renewable energy technologies [3]. hybrid energy storage systems (hess) are essential, when single renewable energy technologies need to be utilized in a more effective way and if they need to be more appealing from a financial point of view [10]. the significant point of interest of this hybrid energy storage systems is that they can, at the same time guarantee distinctive types of energy, for instance electricity, hot water, heating/cooling capacity, and so on, which is by and large an interest in diverse sorts of building offices [25]. another ecological aspect of this hess is they are critical and absolutely contemplated with respect to present atmosphere issues and the ones that anticipate us. regardless of the way that the structure of hesss is reasonably fundamental, those systems have been generally connected [6]. the fundamental idea in hess is to utilize ultra-capacitors (ucs) as a right hand energy storage system along with the batteries to enhance the execution of the whole energy storage systems, in terms of effectiveness, unwavering quality, and element reaction [29]. ucs give quick and effective energy conveyance and long cycle-existence without any concoction response included. likewise, the state of charge (soc) of a uc can be precisely acquired in light of the fact that its soc is relative to the square of the cell voltage [7]. the scope of uc working temperature (40 to +70oc) is too more extensive than that of batteries. the essential disservice of ucs is their moderately low energy thickness contrasted with batteries [9], [17]. hence hybridization of batteries (and/or power modules) and ucs is considered in nature to be the best utilization of ucs without a doubt applications [2]. such stored energies are efficiently managed and supplied to the wireless sensor nodes when there is a demand of power [13][14]. in order to operate the self-sustainable wsns in isolated places, the energy aware routing protocol forwards the data packets through network, while extending the lifetime of network [11]. before its capacity falls below than 80% of its initial rate capacity, the nodes are still limited by the number of charge/discharge cycles of a rechargeable battery (rb) [16], [25]. wireless communications require more energy consumption than sensing and computing tasks [28]. the hess is composed by rb and a super-capacitor (sc). a power management device is utilized to control all the operations in the energy system [23]. to reduce the power consumption in the sensor nodes and manage the power between the sensor nodes, this paper we propose a cost and energy aware routing (cear) protocol, which reduces the cost and power consumption by the evaluation of two metrics namely cost welfare metric and route source metric. the performance evaluation are conducted based on packet delivery ratio, average end to end delay, packet loss, packet loss ratio, average throughput. the remaining of this paper is organized as follows: section 2 presents some the recent works related to our proposed work. section 3 presents the detailed explanation about our proposed methodology. section 4 presents the results obtained by our proposed routing protocol followed by the conclusion in section 5. routing in wsns powered by a hybrid energy storage system through a cear protocol based on cost welfare and route score metric 235 2 related work some of the most recent works related to our proposed routing method is explained as follows:zhang, bo, et al. [27] described a broadly useful multi-hop wsn architecture capable of supporting time-critical cps systems utilizing energy harvesting. at that point they exhibited a set of harvesting aware speed selection (hass) algorithms. that boosts the base energy save for every one of the nodes in the network, accordingly guaranteeing highly strong performance under emergency or fault driven circumstances. at that point they introduced an ideal centralized solution, alongside a distributed solution and actualized a cps-specific trial procedure. arabinda nandha et.al [19] explained a work distance, energy and the angle formed by the node with the base station are taken as input parameters. the mamdani fuzzy inference system is used to select the chance of a sensor node to become a cluster head (ch). a-star search algorithm is used to locate an optimum route from source to sink node. the data packets are routed on the selected path. the a-star search algorithm finds the shortest route path from source node to sink node. this approach is admissible, complete, and optimal one as it uses a-star algorithm. selvi et.al [24] described a technique called the rule based clustering for routing model provides better performance in terms of network lifetime than the other existing techniques since they consume more energy during the formation of clusters and finding the shortest path. moreover, additional overhead on the cluster head selection is tackled also using rules in this proposed model in an efficient manner by building balanced clusters. the main advantage of the proposed approach is that it extends the lifetime of the network and increases the throughput, energy efficiency, link quality, and scalability. roshani h.padyal et.al [21] implemented an opportunistic based neighbor coverage routing. in this hop-by-hop opportunistic routing is done by selecting a forwarding node on the base of the current load on the node, energy, and neighbor coverage information. in this choosing of the reliable node in the path, an establishment is based on load information table. proposed protocols minimize energy consumption and enhance network performance as compared to existing routing protocol. suneet kumar gupta et.al [22] discussed an energy efficient and balanced route generation algorithm is proposed with considering both energy efficacy and energy balancing issues. here, we consider the distance and residual energy of the nodes as energy efficiency parameters and energy is balanced by diverting the incoming traffic to other nodes having comparably lower incoming traffic. to develop the routing schedule, we have applied genetic algorithm which can quickly compute the routing schedule as per the current state of the network. it is observed that the performance of proposed algorithm is better than existing algorithm in terms of first node die and energy consumption in the network. nilanjan mukherjee and dani strickland [18] actualized a particular boost multilevel buck converter based topology that coordinate the half breed second life batteries with the grid tie inverter. a reasonable module based distributed control architecture was introduced to totalize each converter modules according to its characteristics autonomously. the converter and control architecture were observed to be adaptable to coordinate different batteries with an inverter dc link. modeling, investigation and test approval were performed on a solitary stage secluded half breed battery energy storage system model to comprehend the operations of the control technique with different cross breed battery configurations. abeywardana, db wickramasinghe,et al. [1] built up a battery-supercapacitor based cross breed energy storage system (hess) comprising of two dc/ac support converters, battery, supercapacitors, matrix connection, state of charge (soc) estimation and an associated control 236 r. senthilkumar, g.m. tamilselvan, s. kanithan, n. arun vignesh systems were tentatively verified and further moved forward. the switching frequency current swell component in both battery and supercapacitor currents were reduced by stage shifted interleaved operation. in the meantime, the control system keeps up the supercapacitor voltage in pre-characterized esteem and the battery’s soc. the estimation can be finished by a broadened kalman channel kept up within the specified soc limits. qing xie et al. [26] formally described the worldwide charge allocation issues in hees systems, in particular, conveying a specified level of incoming energy to a subset of destination ees (electrical energy storage) banks, with the goal that efficiency of most extreme charge allocation was achieved. to set the worldwide charge allocation efficiency, the issue was figured as a mixed integer nonlinear program with the objective function, where the constraints capturing key requirements and features of the system such as the energy conservation law, power conversion losses in the chargers, rate capacity and self-discharge effects in the ees components. huang, et al. [12] actualized an energy sharing controller in view of distributed battery energy storage system architecture. the re-designed dc-dc control stage and the energy sharing controller are used to achieve soc balancing among the battery cells while in the meantime giving dc bus voltage direction to whatever is left of the system or load. as a result, there is no requirement for two autonomous converter systems for cell soc balancing and dc bus voltage control. this prompts reduced design complexity of the battery energy storage system. the proposed energy sharing controller tends to the battery cells’ soc imbalance issue from the root by changing the discharge/charge rate of each battery cell. zhang, bo, et al. [27] described a broadly useful multi-hop wsn architecture capable of supporting time-critical cps systems utilizing energy harvesting. at that point they exhibited aset of harvesting aware speed selection (hass) algorithms. that boosts the base energy save for every one of the nodes in the network, accordingly guaranteeing highly strong performance under emergency or fault driven circumstances. at that point they introduced an ideal centralized solution, alongside a distributed solution and actualized a cps-specific trial procedure. 3 routing in wsn using cear protocol mobilizing the natural resources such as solar energy in the maximum range and storing the energy which is used to power the sensor nodes present in the wireless sensor network (wsn) is done by the design of hybrid accumulator that consist of a battery and a ultra-capacitor is implemented in the previous work. also the stored energy between battery and the ultracapacitor is controlled and managed using an adaptive power organizing algorithm in the previous work. the power supplied to the sensor nodes by means of the battery and ultra-capacitor in the hybrid accumulator depending on the demand of power. the success of wireless sensor networks and their pervasive use is somehow constrained by energy supply which generally provided by batteries. the network lifetime depends on the balance between energy consumption and reliable delivery of data packets. the nodes in the wireless networks powered by a hybrid energy storage system (hess) require a specific management unit to control the different energy flows. however, limited battery life has been a barrier for widespread deployment of wireless networks. to overcome such issues, in this paper we proposed a cost and energy aware routing (cear) protocol for efficient and inexpensive routing. the architecture of our proposed method is shown in figure 1. from figure 1 we can see that the sensor nodes present in the wireless sensor network is powered by means of renewable energy source such as solar energy extracted from the pv panels. the extracted energy is stored and supplied to the wsn through the hybrid energy storage systems which is a combination of a battery and a capacitor. the supplied energy is consumed by the nodes to perform routing and using cear protocol the best path which reduces the power consumption and the cost is selected and the data packets get transmitted through routing in wsns powered by a hybrid energy storage system through a cear protocol based on cost welfare and route score metric 237 such paths efficiently. figure 1: architecture of our proposed method 3.1 powering up of wsn nodes by hess model the hess energy model considers the battery cycle life as well as a hybrid energy harvesting system. also we propose a cost welfare and route source metric that takes into account the nodes’ battery residual energy, harvesting rate and energy requirement to route the packet. a battery and an ultra-capacitor (uc) which is combined in the hybrid energy storage system do not have a regular connection always and also there is no direct connection between the uc and the harvesting unit. when a battery exceeds its life cycle then the node will be considered as dead. the battery is recharged when its residual energy falls below a pre-set threshold (1-d)ub where d is "depth of discharge (dod)" and ub is the maximum capacity of the battery. let ζkdenote the arrival time of the k-th packet request at a node. if the routing protocol selects the node, the node relays the packet. the period of activity consists of route selection and relaying of packet, in which the node does not harvest energy is assumed to be ζp seconds long. we defineζk+=ζk+ ζp. the node harvests energy during the remainder of the time slot, which ζh seconds long. it follows that ζk+1 = ζk+ + ζh. the residual energy of battery and uc for our models is described as follows. ucable = e ∧ uc(n,ζk) − l(j)e(n,r(j)) > 0 (1) bable = e ∧ b(n,ζk) − (1 −d)ub − l(j)e(n,r(j)) > 0 (2) brecharge = e ∧ b(n,ζk) ≤ (1 −d)ub (3) where ucable is the time in which the uc has enough energy to route the packet. bableis the time that the battery has enough energy to route the packet.brechargeis the event that the battery has exceeded its depth of discharge and cannot accept further route requests until it has recharged. e∧uc(n,ζk) denotes the residual energy on the n th node uc at time ξk, l(j) is the route for the jth packet, e(.) is the energy per bit required to fulfill the route r(j), e∧b(n,ζk) denotes the residual energy on the nth node battery at time ξk, and ub denotes the maximum capacity in joules of the battery. to define the switch states s1 and s2 for the nth node at time 238 r. senthilkumar, g.m. tamilselvan, s. kanithan, n. arun vignesh ξk, we shall use the indicator function i(a)is 1, if the condition a is true and i(a)is 0, when a is false. we observe that s1=1 only, if the node is selected to relay the packet. which implies s1(n,ζk) = i{relay ∩ (ucable cupbable} (4) s2=0 under different conditions depending on if the node is harvesting or transmitting. s2(n.,ζk) = 1 − i(ucable ∩bable ∩relay ) − i(bable ∩ relay ) (5) the first indicator function can be 1, if the node is chosen to relay in which case the second indicator function must be zero. the updated equations for the uc at timeξk−1 to time ξkbecause of leaking, harvesting and loading is given below. e∧uc(n,ζk) = min[(1 −α(ζk−1,ζk))euc(n,ζ + k−1) + s2(n,ζ + k−1)γn(ζ + k−1,ζk), uuc (6) e∧load,uc(n,ζk,r(j)) = l(j)e(n,r(j)s1(n,ζk)s2(n,ζk) (7) euc(n,ζ + k ) = β(n,d,ζk)[e ∧ uc(n,ζk) −e ∧ load,uc(n,ζk,r(j))] (8) where α(ζk−1,ζk) denotes the time invariant fraction of energy leaked in the uc over a time slot, e∧load,uc(n,ζk,r(j)) is the energy consumed by a packet if the uc is used, uuc denotes the maximum capacity in joules of the uc and γn(ζ + k−1,ζk) denotes the energy harvested at the n-th node during time slot k-1. euc(n,ζ + k ) denotes the residual energy on the n th node uc at ζ+k and β(n,d,ζk) is an indicator function for the event that the battery on node n has not exceeded its finite cycle life at the beginning of time slot k. β(n,d,ζk) is a non-increasing function of sξkand for a fixed ξk, is a strictly decreasing function of d, the depth of discharge on the battery. similarly the update equations of battery from time ξk−1 to time ξk because of leaking, harvesting and loading is given below. e∧b(n,ζk) = min{[(1 −ϕ)eb(n,ζ + k−1)] + [1 −s2(n,ζ + k−1)]γn(ζ + k−1,ζk)ub} (9) e∧load,b(n,ζk,r(j) = l(j)e(n,r(j))s1(n,ζk).[1 −s2(n,ζk)] (10) eb(n,ζ + k ) = β(n,d,ζk)[e ∧ b(n,ζk) −e ∧ load,b(n,ζk,r(j))] (11) where ϕdenotes the time varying fraction of energy leaked in the battery in one time slot e∧load,b(n,ζk,r(j))is the energy consumed by a packet if the battery is used and eb(n,ζ + k ) denotes the residual energy on the nthnode battery at ζ+k . 3.2 routing in wsn routing of packets with low power consumption and low cost is a major contribution in the wireless sensor networks. in order to perform such routing, a routing protocol is needed to be designed which is cost and energy aware. in our proposed method, the major challenges in the wsns can be implemented by the cear protocol. it presents an efficient and reliable routing protocol those routes the packets to the sink node. in our proposed method, the sensor nodes present in the wsn utilizes the energy supplied by the hybrid system that carries the power from the renewable source. setup phase the setup phase of the cear protocol handles the routing table replenishment of every node. it is loaded with no less than a route to the sink node keeping in mind the end goal to route the sensing packets. this is continued with the broadcast of control packets that conveys route information concerning the sink node. routing in wsns powered by a hybrid energy storage system through a cear protocol based on cost welfare and route score metric 239 cear protocol in routing, the data packets are forwarded to the hubs in the network which is a special node with an additional communication technology used to route the packets to the base station (bs). in all sensor network applications, reliable and fast delivery of messages with reduced cost should be a major requirements. the routing performed in the wsn using our proposed cear routing protocol is shown in figure 2. figure 2: routing in wsn using cear protocol during data transmission, the source node transmits a route request (rreq) packet to any destination hub. after the reception of packet, the destination hub transmits a route reply (rrep) packet to source hub. this process finds the nodes (selected nodes) that have linked with the hub which reduces the unwanted routing delay in the network which also delays the setup phase and the energy consumption. by means of this selected nodes the best path for routing is selected based on a cost function request (cfr) and cost function packet (cfp) which is shown in figure 3. figure 3: path selection for routing based on cfp to find an energy and cost efficient route from the selected nodes the source node again sends a cost function request (cfr) to all the selected nodes. when the nodes receive the cfr, each node send a cost function packet (cfp) which consist of the details of the cost welfare metric and the route source metric calculated for each node. 240 r. senthilkumar, g.m. tamilselvan, s. kanithan, n. arun vignesh 3.2.2.1 cost welfare metric (cwm) cost welfare metric helps the source node to select the node with lowest cost. to ensure, an extended lifetime for the network, the cost function must reflects the node ability to forward the packet through nodes with high uc residual energy and high harvesting energy rates. cost welfare metric based on some parameters used for routing with low cost such as hop count, transmission queue, residual energy, harvested energy, life cycle of the battery which is given by the following equation cwm(n,ζ) = (hc(n,ζ) ∗whc + qoc(n,ζ) ∗wqoc) −euc(n,ζ) ∗wuc +eb(n,ζ) ∗wb + γ(n,ζ) ∗wγ + lc(n,ζ) ∗wlc (12) where hc is the hopcount which represents the minimum path length between the source node and the hub/sink node. euc and eb represent the uc and battery residual energies in joules. γis the harvested energy the node scavenged in the last time slot. lcrepresents the battery cycle lifetime. qoc is the length for the node’s transmission queue occupation. the hop count and transmission queue parameters are called undesirable parameters and the remaining parameters residual energy, harvested energy and battery cycle life are called as benefit parameters which supports the extension of network. the value of each parameters vary from 0 to 100. the weight function for each parameter is denoted as whc,wqoc, wuc,wb, wγ, wlcthis allows the priority scheme in the cost metric calculation. the weight limits for each parameter is set as follows, whc + wqoc = 1 (13) wuc + wb + wγ + wlc = 1 (14) where whc,is the weight distribution of the hopcount,wqoc is the weight distribution of the transmission queue, wuc is the weight distribution of the ultra-capacitor, wbis the weight distribution of the battery, wγis the weight distribution of the harvested energy ,wlcis the weight distribution of the lifecycle of battery. the weight distribution for such parameters based upon the above equations (13), (14) is given in the below table 1. table 1: weight distribution for the parameters used in cost function metric characteristics parameters weight distribution positive uc 0.45 battery 0.2 harvested energy 0.15 life cycle 0.2 negative hop count 0.5 transmission queue 0.5 the battery characteristics (residual energy and cycle life) are assigned with an importance of 20%, while the harvesting energy has weight of 15%. the two "negative" (cost) parameters share responsibility of 50% of in the cost function result. wsn applications have lower harvesting energy rates that can assign higher weight to wγ, in order to customize the data flow. this formulation increases the flexibility of the cost function metric. 3.2.2.2 route score metric route score metric based on energy level and weight assigned to the next hope node, link quality and weight assigned to the next hope node which is given by routescore = pe.we + pl.wl (15) routing in wsns powered by a hybrid energy storage system through a cear protocol based on cost welfare and route score metric 241 where pe is the energy level of the next hope node, we is the assigned weight for pe, pl is the link quality to the next hope node, and wl is the assigned weight for pl. the route score takes values from 0 to 100. higher values indicate a better route and the packets are usually routed through nodes with higher route score metric value. the nodes with higher route score is selected as best nodes for routing and the packets are transmitted through such nodes. the route score value of each node will be stored in the routing table and that must be refreshed at each time slot in order to choose the best node. the refreshment of route score will be done by sending a feedback packet from the neighbours with the packet length of one byte for each time slot. low energy and low cost routing by the above two metric calculations cear routing protocol chooses the best route that consumes less power and cost. the hess metric is zero for a node that has sufficient energy on its uc to route a packet. if the uc has insufficient energy, then a non-zero value will be calculated for hess based on the energy state of the battery. the hess metric depends on the cost capacity metric of, which treats battery with interminable cycle life and 100% profundity of release. be that as it may, we have to likewise have a segment of cost connected with spending the battery cycle life; for this, we can see the cycle life of a battery correspondingly to the life of a non-rechargeable battery. consequently, we characterize two energy depletion functions for the battery one to demoralize choice of a node that is close to its predefined profundity of release, and another to discourage utilization of a node that is close to the end of its battery cycle life. the general cost capacity ought to increment, if both of these cost segments are increased. let us first consider the cost component for discharge within one cycle, calculate energy depletion exponent be defined as follows λb(n,ζk) = ub −e∧b(n,ζk) dub (16) in words, the above calculated value will be zero, when the battery is fully charged and is one, when it is discharged down to its specified level of discharge. next, the "cost component" for the within-cycle discharge is defined as follows chess(n,ζk,r(j)) = dub (γn + ε) log µ .(µλb(n,ζ + k ) − 1)l(j).e(n,r(j)) (17) 4 results and discussions this section provides the simulation results and the comparison of our proposed method with the existing methods by evaluating various performance measures. the proposed method is implemented in matlab/simulink platform. the proposed system configuration is given below: operating system: windows 8; processor: intel core i3; ram: 4 gb; platform: matlab/simulink. 4.1 simulation results the simulation results obtained by our proposed method are presented in this section. our proposed work consists of two phases which is carried out in two platforms. the simulation parameters utilized by the pv panels are given in table2 and the specification of the other parameters used in storage system and wsn are given in table 3. 242 r. senthilkumar, g.m. tamilselvan, s. kanithan, n. arun vignesh table 2: parameters of the pv panel parameters specification maximum power(w) 100 maximum current(a) 5.86 short circuit current(a) 6.44 open circuit current(a) 21.4 number of cells 36 weight (kg) 12 length(mm) 1490 depth(mm) 35 table 3: specifications of the simulation parameters storage system parameters specification wsn parameters specification converter efficiency 80% protocol cear uc leakage current 1 ma sampling time 5 us capacity of the battery 20% traffic type cbr charge current 300 ma simulation time 600sec discharge current 150 ma number of nodes 30,50,75,100 reference dc link voltage 300 v simulation area 500*500m nominal battery voltage 144v packet size 512 mb nominal uc voltage 125 v transmission range 250 m switching frequency 15 khz node speed 20 m/s the initial process of our research work is the extraction of energy from the solar panels and harvesting the energy to provide supply to the wsn. our objective of proposed method is to extract the maximum amount of energy from the available solar panel and manage it by means of a storage device and then supply power to the sensor nodes. the energy obtained from the solar is based on the intensity of the light. as the intensity is high means greater voltage is obtained from the solar panel. the hees stores the energy obtained from the solar panel and which is utilized for powering up the sensor nodes in the wsn. the harvested solar energy is interfaced with our hybrid accumulator by the use of a converter placed between the solar panels and the accumulator. the current and voltage signals obtained by the converter are shown in figure 4(b) and (c).the energy from the harvesting unit reaches the storage unit which is the combination of both the battery and an ultra capacitor. the charging mode output of the storage unit is shown in figure 4(c).the terminal voltage obtained by the storage unit indicates both the combination of the capacitor and battery voltage. the terminal voltage obtained by the storage unit is shown in figure 4(d).soc is normally used when discussing the current state of a battery in use. an alternate form of the same measure is the depth of discharge (dod), the inverse of soc. the state of charge of the battery used in our proposed architecture is shown in figure 4(e).then the energy output from the solar panel is stored efficiently by hees which can be further utilized for wireless sensor powering process. the resulting power from hees is utilized by the wireless sensor network to perform a cost and energy aware routing. the simulation is carried out in 4 different scenarios by varying the number of nodes connected in the network. the results obtained by the proposed routing protocol with different number of nodes is given the below in table 4. the simulation is carried out in 4 different scenarios by varying the number of nodes connected in the network such as 30, 50, 75 and 100 .the energy harvested from the solar panel by utilizing hybrid accumulator is utilized to power up the sensor nodes in the wsn.inturn wsn utilizes the energy for routing routing in wsns powered by a hybrid energy storage system through a cear protocol based on cost welfare and route score metric 243 figure 4: energy obtained from solar panel,(a) voltage obtained by the solar panel, (b) current obtained by boost converter, (c) voltage obtained by boost converter (d) charging of storage unit, (e) terminal voltage of the storage unit, (f)state of charge of the battery process by selecting best node from other sensor nodes for routing which is done by utilizing a cost and energy aware routing protocol. the simulation results of selecting the best node by the source node among all other sensor nodes by our proposed methodology is given below, thus the wsn source nodes which get supplied with the energy harvested from the solar panel is utilized for routing process which is done by utilizing the cost and energy aware routing protocol. initially, the source node analysis with the 30 nodes based on the cear protocol and alternatively it searches to best nodes through the protocol along 50.75 and 100 nodes and finally chooses the best one among them and continuous the routing process. the energy is efficiently utilized by means of cear protocol for selecting best node for routing. table 4: simulation results of cear protocol number of nodes 30 50 75 100 packet delivery ratio 2.651 2.891 2.912 2.9867 average e2e delay 110.442 117.981 121.157 127.982 packet loss 200 210 213 219 packet loss ratio 0.953 1.237 1.567 1.864 average throughput 210.225 212.872 218.902 221.086 energy consumption 97 95 90 85 the various parameters obtained by the simulations performed in different scenario that is, with different number of nodes such as 30, 50, 75, and 100 is shown in figure 9. the simulation results shows that, our proposed routing protocol selects the best node from the total available nodes and forward the data with high efficiency and low cost. above 244 r. senthilkumar, g.m. tamilselvan, s. kanithan, n. arun vignesh figure 5: simulation output for selecting best node by alternate path analysis (a) with 30 nodes, (b) with 50 nodes, (c) with 75 nodes, (d) with 100 nodes and (e) selection of best node. results are the evident to show the efficiency of our proposed cear protocol. in order to justify our proposed protocol is better than the existing protocols. the performance of the different protocols are calculated in terms of different parameters and compared in the upcoming sections. 4.2 performance evaluation of cear: the performance of the protocol depends on various parameters and chosen for simulation. the main parameters are packet size, no of nodes, transmission range and the structure of the network. based on the above parameters the following evaluation is calculated. packet delivery ratio (pdr): the packet delivery ration is defined as the performance measure of routing protocol in any network in terms of packet delivery level. if pdr is high, then the performance is better. the relation for packet delivery ratio is as follows packet delivery ratio = ∑ (totalpacketsreceivedby alldestinationnode)∑ (totalpacketssendby allsourcenode) (18) average end-to-end delay: the average end-to-end delay can be obtained by computing the mean of end-to-end delay of all successfully delivered messages. if the distance between source and destination increases, routing in wsns powered by a hybrid energy storage system through a cear protocol based on cost welfare and route score metric 245 figure 6: simulation results of cear protocol in terms of pdr, e2e delay, packet loss, plr and throughput and the energy consumption. then the probability of packet drop is also increases. the average end-to-end delay is expressed as follows d = 1 n n∑ i=1 (tri −tsi ) ∗ 1000[ms] (19) where d is the average end to end delay, i is the packet identifier, n is the number of packets successfully delivered, triis the reception time and tsiis the send time. packet loss: packet loss is the ratio of the number of packets that never reached the destination to the number of packets originated by the source which are given by pl = (nsentpackets−nreceivedpackets) nsentpackets (20) packet loss ratio: packet loss ratio is the percentage of ratio of the number of packets that never reached the destination to the number of packets originated by the source (pl*100) which is given by plr = (nsentpackets−nreceivedpackets) nsentpackets ∗ 100 (21) 246 r. senthilkumar, g.m. tamilselvan, s. kanithan, n. arun vignesh average throughput: it is the average of the total throughput. it is also measured in packets per unit time interval length (til). averagethroughput = receivedsize (stoptime−starttime) ∗ ( 8 1000 ) (22) 4.3 comparison results the use of hess in our proposed routing method is suggested due to the better storage capacity, efficiency, energy density, power density, response time, cycle life time and cost when compared to other storage devices used in the existing methods. the importance of our proposed hess in our proposed routing protocol is shown by the below table 3. table 5: comparison of several energy storage systems battery smes flywheel sc nas hybrid ess(proposed) efficiency (%) 60-80 95-98 95 95 70 98-99.9 energy density (wh/kg) 20-200 30-100 5-50 50 120 30-200 power density (w/kg) 25-1000 10-1000 2000 4000 120 4500 response time (ms) 30 5 5 5 100 3 cycle life (time) 200-2000 1000 20000 50000 2000 50000 cost (s/kw h) 150-1300 2000 380-2500 250-350 450 100-150 the comparison graph between hess and other existing energy storage devices regarding the above table is shown in figure 7. figure 7: comparison results for efficiency, energy density, power density, response time, cycle life time, cost the figure 7 shows the comparison graph, the efficiency of the hess is likely to be the same when comparing to the smes, flywheel, and super capacitor. but when comparing in all other factors such as energy density, power density, cycle life, response time, cost hess is proved to be better solution. we can conclude that the use of hess in our proposed routing provides better solution and improves the overall performance of the network. by the utilization of the hess in our proposed routing protocol selects the path with best nodes that consumes less power and low routing in wsns powered by a hybrid energy storage system through a cear protocol based on cost welfare and route score metric 247 cost. ad hoc on-demand distance vector (aodv), proactive source routing (psr), greedy perimeter stateless routing (gpsr) are compared with our proposed routing protocols is shown below in table 6. table 6: comparison of our proposed routing protocol with existing protocols number of nodes type of routing protocols packet loss average e2e delay packet delivery ratio average throughput packet loss ratio energy consumption 30 aodv 248 120.442 9.3671 207.864 2.890 99 psr 246 127.754 70.809 210.56 1.590 99.2 gpsr 235 110.750 70.809 218.56 1.050 99.56 proposed 205 109.543 93.608 240.9 1.152 97 50 aodv 644 131.145 2.891 212.872 2.890 96 psr 173 74.7002 40.4567 214.55 1.678 97.25 gpsr 160 70.7008 45.1786 248.55 1.150 98.32 proposed 210 117.981 93.6028 240.9 1.237 95 75 aodv 799 130.306 1.4598 136.936 5.999 93 psr 383 193.11 10.99 150.9 1.909 94.25 gpsr 280 110.90 11.4177 190.18 1.190 95.12 proposed 265 102.375 98.1747 240.58 1.143 90 100 aodv 1285 129.825 1.2843 146.431 6.789 88 psr 313 142.524 10.99 150.9 1.909 89.21 gpsr 280 110.90 1.2919 198.33 1.190 90.25 proposed 274 108.532 92.3664 266.87 1.678 89 the figures 8 to 13 shows the comparison graphs in terms of the important parameters resulted by the routing protocol for different number of nodes regarding to the above comparison tables. figure 8: packet loss comparison of proposed cear protocol with exisiting protocols figure 8 shows the packet loss comparison of different type of routing protocols with our proposed cear routing protocol with varying number of nodes. the packet loss denotes the inability of the path selected for transmission in the network. for a successive node the packet loss should be as minimum as possible. from the figure 11 it is visible that the packet loss of our proposed protocol is very less compared to other protocols. this makes us to say that our proposed protocol is best for routing in wsn. figure 9 shows the e2e delay comparison of different type of routing protocols with our proposed cear routing protocol with varying number of nodes. if the e2e delay exists in a 248 r. senthilkumar, g.m. tamilselvan, s. kanithan, n. arun vignesh figure 9: e2e delay comparison of proposed cear protocol with exisiting protocols network at a maximum range the performance of the overall network is degraded even though the power consumption and the cost of routing is low. from figure 12 we can demonstate that our proposed cear routing protocol produces minimal delay compared to other existing protocols. figure 10: packet delivery ratio comparison of proposed cear protocol with exisiting protocols figure 10 shows the packet delivery ratio comparison of different type of routing protocols with our proposed cear routing protocol with varying number of nodes. packet delivery ratio is the maximum ability of the network to deliver a packet from source to destination. from figure 13 it is evident that the packet delivery ratio of our proposed routing protocol is higher than the exisiting protocols which makes us to come a decision that we can use our proposed routing protocol to deliver maximum packets without loss in the network. figure 11: throughput comparison of proposed cear protocol with existing protocols figure 11 shows the throughput comparison of different type of routing protocols with our routing in wsns powered by a hybrid energy storage system through a cear protocol based on cost welfare and route score metric 249 figure 12: packet loss ratio comparison of proposed cear protocol with exisiting protocols figure 13: energy consumption comparison of proposed cear protocol with existing protocols proposed cear routing protocol with varying number of nodes. for an efficient network routing protocol the throughput should be maximum as possible. from figure 11, it is shown that the proposed routing protocol results in high throughput than the existing protocols. from this we can come to know that our proposed routing protocol is efficient than the other existing protocols. figure 12 shows the packet loss ratio comparison of different type of routing protocols with our proposed cear routing protocol with varying number of nodes. packet loss ratio indexes the variation between the number of packets send from the source to the number of packets received at the destination. the reduced packet loss ratio indicates the value of the routing protocol. it is clear that our proposed routing protocol produces less packet loss ratio which indicates that the number of delivered packets is maximum.from the figure 13 it is visible that the energy consumed by our proposed methodology is very less compared to that of other routing protocols. from the above results and comparison, it clearly shows that our proposed routing protocol selects the best node with less power consumption and low cost and also it shows that the utilization of hess for routing by the sensor nodes in our proposed method is the most optimal solution to reduce the power consumption. 5 conclusion in this paper we have proposed energy and cost aware routing protocol used in wireless sensor network. the sensor nodes present in the wsn utilizes the power, which is generated by the renewable energy source such as solar energy by means of a storage system. the storage and management of the extracted energy is done by means of a hybrid storage device such as the combination of a battery and an ultra-capacitor (uc). the uc suits the primary buffer of the storage system by preventing the quick exhaustion of the battery cycle lifetime. by using a cost and energy aware routing protocol at the network level, we acquired a noticeable increment in the network residual energy without trading off the data packet delivery. moreover in this 250 r. senthilkumar, g.m. tamilselvan, s. kanithan, n. arun vignesh paper we also analysis the performance 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(2013). a review of energy storage technologies for marine current energy systems, renewable and sustainable energy reviews, 18, 390-400, 2013. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 1 (march), pp. 45-52 domain ontology of the virdent system c.m. bogdan crenguţa mădălina bogdan ovidius university of constanta, romania numerical methods and computer science department 124 mamaia blvd., 900527 e-mail: cbogdan@univ-ovidius.ro abstract: the goal of using virtual and augmented reality technologies in therapeutic interventions simulation, in the fixed prosthodontics (virdent) project, is to increase the quality of the educational process in dental faculties, by assisting students in learning how to prepare teeth for all-ceramic restorations. its main component is an e-learning virtual reality-based software system that will be used for the developing skills in grinding teeth, needed in all-ceramic restorations. this paper presents a domain ontology that formally describes the knowledge of the domain problem that the virdent e-learning system dealt with. the ontology was developed based on the uml models of the virdent information system, making sure in this way the ontology captures knowledge identified and described in the analysis of the information system. at first, we constructed the taxonomy of these concepts, using the dolce ontology and its modules. then, we defined the conceptual relations between the concepts. we also added swrl rules that formally describe the business rules and knowledge previously identified. finally, with the assistance of the pellet reasoner system, we checked the ontology consistency. keywords: knowledge, ontology, taxonomy, information system, uml diagram. 1 introduction the goal of using virtual and augmented reality technologies in therapeutic interventions simulation in the fixed prosthodontics (virdent) project is to increase the quality of the educational process in the dental faculties, by helping students to learn how to prepare teeth for ceramic crowns. the virdent system is an e-learning system that will be used as a software tool by the students of the dental medicine faculty, to developing their skills in grinding teeth during the dental laboratories of fixed prosthetic department. these skills are required in making one of the crown types: all-ceramic. all-ceramic restorations are one of the most successfully restorations available from the point of view of aesthetics and biocompatibility. the first criterion is fulfilled by the crown translucent close to the dental hard tissues allowing light to pass through, at gingival level. biocompatibility refers to the adhesion degree of the ceramic with the surrounding tissue of the teeth. it also refers to how much gingivae tolerate the crown. at present, ceramic appears to be more biocompatible than other dental material [15]. however, this kind of restorations is not widely used because ceramic crowns preparation requires strict and delicate precision. in addition, the bonding techniques employed are somewhat complex and adverse effects could appear, such as pulp inflammation. that is why, the virdent system will be a useful tool to the students of dental medicine faculties. copyright c⃝ 2006-2011 by ccc publications 46 c.m. bogdan the first phase of the developing process of the virdent software system consisted of the analysis of the information system supported by virdent. during the analysis a series of models have been created such context diagram, domain model, business process model, and so on [2]. all these models have been created using the unified modelling language (uml) [9] which is a standard language and allows creating graphic models of the information or software system. the ontology construction method is based on these models and therefore we give further two of the models: domain model and business process model. domain model describes structurally the virdent information system, i.e. considers only persistent information used by the system. information is classified in classes together with their relationships which form the uml class diagram of the model domain. the classes emerged from the concepts of the problem domain such as tooth, root, gingiva, enamel, diamondtool, and so on (figure 1). the classes are linked together by generalization/specialization relations or different kinds of associations such as aggregations or compositions. figure 1: virdent domain model to describe the system from the behavioral point of view we created the business use case diagram which form the business model business processes. the diagram contains eight business use cases that describe the preparation of teeth for all-ceramic crowns. one of the business domain ontology of the virdent system 47 use cases is "central incisor preparation" whose uml business activity diagram is presented in figure 2. the diagram contains activities that are part of the preparation techniques of the central incisors, their execution order and the business objects used as inputs or created or modified as outputs by the activities. business objects are central incisors, grooves, incisal edges, and so on, and the tools used during execution of the activities, such as diamond tools and step-down burs. figure 2: business activity diagram of the virdent system an ontology is a formal specification of the concepts intension and the intensional relationships that can exist between concepts. according to guarino’s definition, ”an ontology is a logical theory accounting for the intended meaning of a formal vocabulary, i.e. its ontological commitment to a particular conceptualization of the world” [6]. in this paper, we present a domain ontology of the virdent system. 48 c.m. bogdan 2 method of construction of ontology the methodology of ontology construction is based on the few existent methodologies, like ontology development 101 [8], concern and business rule-oriented [1] and other ones. our method performs a translation of the knowledge semantics described semi-formally in the uml models of the virdent information system, making sure in this way the ontology captures knowledge identified and described in the analysis of the information system. the method consists of the following steps: a) define the classes and the class hierarchy; b) define the relations of classes; c) ontologically describe the business rules; d) write additional constraints; e) verify the ontology. in addition, we mention that we used to build ontology language owl dl (web ontology language description logic) [16] and editor protégé [5]. 3 virdent ontology construction nowadays, there are some top-level ontologies (such as dolce, sumo and bfo) which describe very general concepts like space, time, matter, object, event, etc., i.e. independent concepts by a particular domain or problem. among these, we used the dolce ontology [7] and its modules such as d&s [4], temporal relations, and so on. dolce is an ontology of particulars, in the sense that its domain of discourse is restricted to particulars. other top-level ontologies might be used. 3.1 identify classes and creating taxonomy first, classes are identified based on the uml class diagram of the model domain virdent system. so, each class in the class diagram has a corresponding owl class, since an owl class represents a set of individuals that form the extension of the concept mapped by class. second, the most of the attributes of the classes have been transformed in physical, temporal and abstract qualities which are subclasses of the quality class of the dolce ontology. for example, thickness and form are two physical qualities, i.e. subclasses of the dolce:physical-quality class. third, the activities of the business use cases are described ontologically as dolce processes, i.e. perdurants that fulfill the properties of cumulativity and homeomericity. for example, reduction is a process that executes during the ”create orientation grooves for incisive decrease” business activity (figure 2). we classified reduction as a process, because the sum of two reductions is still a reduction occurrence and there are temporal parts of a reduction that are not reductions. these classes form the basis of the virdent ontology as during construction of taxonomy and ontology appear other new owl classes. the classes are organized in a taxonomy created on the basis of the subsumption relation. two classes a and b are linked by subsumption relation if and only if every individual (instance) of the b class is also an individual of the a class [7]. in this case, we can say that a subsumes b, or a is the superclass of the b class or b is a subclass of a. for example, the taxonomy of the amount of matters of the virdent ontology is presented in figure 3. 3.2 defining the conceptual relationships most of the conceptual relations of our ontology map the relations found in the dolce ontology and its sub modules, such as inherent-in, generic-constituent-of, participate-in, genericallydependent-on and so on. so, the qualities are linked by the concepts which they inhere in through the has-quality relation. for example, between the enamel and color concepts there is the has-quality relation (see figure 3). domain ontology of the virdent system 49 figure 3: a subtaxonomy of our ontology the aggregation relations from the uml class diagram (figure 1) have been transformed into the temporary-component d&s relation between the aggregate class and the class aggregated. for example, between the pair of concepts diamondtool-rod and dentalbur-rod there is the temporary-component relation. the composition relations from the class diagram (figure 1) have been transformed into the specific-constant-constituent dolce relation which states that an entity consists of another entity for a period of time. for example, every individual of the alveolarprocess concept is specific and constant constituent by individuals of the alveolus concept. most of the associations of the uml class diagram (figure 1) have correspondent imported ontological relations. for example, between the enamel and dentin concepts we have the approximate-location-of relation of the imported spatial relations ontology (figure 3). observe in figure 1 that each attribute has an uml data type, i.e. a classifier whose instances are values that attribute can take. ontological modeling of data types of attributes is by datatype properties whose domains are the owl classes associated with attributes and ranges are xml 50 c.m. bogdan predefined data types. for example, the thicknessvalue is a datatype property that describes the data type of the thickness attribute of the tooth class (figure 1). the property domain is the thickness class and range is the nonnegativeinteger predefined data type. furthermore, according to dolce ontology in any perdurant (process, state, etc.) at least one endurant participates in. for example, in the makegrooves process there are three participants: a groove, the incisal edge and a cylindrical-conical diamond tool with a flat top. so, any individual of the groove concept participates in the makegrooves process for a period of time. this piece of knowledge is ontologically described introducing the dolce participant-in relation between the groove and makegrooves concepts. 3.3 ontological description of the business rules business rules were identified during analysis of the virdent information system [2]. they describe knowledge that can be expressed ontologically using owl dl or swrl language. swrl (semantic web rule language) allows us writing rules expressed in terms of owl concepts: classes, properties and individuals [14]. for instance, the swrl rule that expresses the piece of knowledge: ”the incisal edge is decreased by 2 mm” is shown in the figure 4. finally, we checked the ontology consistency with the help of the protégé tool [5] version 3.4 and the pellet reasoner system [11]. 4 related work there are few medical ontologies within dentistry. park et al [10] created an owl tooth ontology that embeds spatial relations. these relations describe the position of the tooth compared to other teeth. the relations derive from the formal relations in biomedical ontologies [12]. pathogenic pathway database for periodontitis was created based on an ontology that describes the molecular pathology of periodontal disease [13]. the database is used by programs which provide the following functionalities: displaying of the taxonomy in a tree-like view, key word search, showing causal relationships associated to a concept and a pathway browser. somweb is a semantic web-based system that provides it support for clinicians and researchers in oral medicine to meet to review patient cases, establish a diagnosis, and decide on the most appropriate treatment plan for the patient. owl ontologies are used in somweb to represent oral medicine templates and knowledge, as well as to represent community models and data [3]. 5 conclusions and future works we presented in this paper an ontology of the protocols for preparation of teeth for all-ceramic crowns. the ontology was developed based on the virdent information system. furthermore, we intend to use ontology to design the virdent software architecture and for the construction of a knowledge base that will form the persistence layer of the e-learning system. acknowledgements we would like to thank dr. c. amariei, dr. c. ştefănescu, dr. a. zaharia and dr. l. petcu, professors at faculty of dental medicine, ovidius university of constanta, romania, for valuable comments to the study that forms the base of this paper. this research was supported by the romanian ministry of education, research and innovation, national centre of programs management, inside pncdi-2 partnerships program. domain ontology of the virdent system 51 figure 4: the swrl rule of a piece of 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[16] world wide web consortium. owl web ontology language reference. w3c recommendation, 2004. int j comput commun, issn 1841-9836 8(2):247-254, april, 2013. energy optimization in mobile wireless sensor networks with mobile targets achieving efficient coverage for critical applications g.a. montoya, c. velásquez-villada, y. donoso germán a. montoya, carlos velásquez-villada, yezid donoso universidad de los andes colombia, cra 1 este no 19a 40 bogotá e-mail: ga.montoya44@uniandes.edu.co ce.velasquez917@uniandes.edu.co ydonoso@uniandes.edu.co abstract: the mobile wireless sensor networks (mwsn), classified within manets, have multiple applications for critical situations management such as target monitoring and tracking in conflict zones, supporting urban security, critical infrastructure monitoring, remote locations exploration (i.e. aerospace exploration), and patients monitoring and care in health facilities, among others. all of these applications have requirements of certain intelligence in the network that can be used for network’s self-configuration in order to find targets, guarantee connectivity and information availability until its reception. this paper proposes a mwsn architecture with an initial random distribution in a specific work area, and a centralized management to perform autonomous decision making about the movement and connectivity of the sensors. the work area presents mobile targets with interesting events which must be covered by the mobile sensors, and thus, send the collected information through the network to any base station available. our work shows a dynamic mathematical model used to maximize targets’ coverage and send its sensed information to the base stations available, while minimizing system’s power consumption and maximizing operation time. the heuristic algorithm we used to construct and find a feasible solution is also shown. keywords: mwsn, multiobjective optimization, shortest path, coverage, location, energy efficiency. 1 introduction mobile wireless sensor networks (mwsn) are systems with a large amount of limitations and restrictions inherited from wsn systems but with the additional degree of liberty to move the sensor nodes which make them complex systems. nodes’ mobility within the network enhance the applications of these kind of systems but makes decision making more difficult, since it introduces a new dynamic feature in the network that changes all other parameters in every step of the movement (connectivity, bandwidth, noise and energy). however, these kind of problems can be solved to be used in a series of critical applications with mobile scenarios like warfare situations, where targets will be moving in time and sensors can be deployed randomly to track these targets and send the information back to the base; disaster recovery situations, where robots with a central information management can move around the disaster area looking for targets like people trapped inside structures and send this information through a direct link or using other robots to convey the information to the base; and space exploration, where robotic rovers must move around to look for targets and communicate with satellites to send the information back to earth. copyright c⃝ 2006-2013 by ccc publications 248 g.a. montoya, c. velásquez-villada, y. donoso the model presented in this work assumes that every node is mobile and can act as a router, sending their own information and forwarding information from other nodes to base stations (bs). it is also assumed that the bss don’t have sensors embedded and can’t move. given mobile targets, it is necessary to adjust mobile sensors positions in order to maintain connectivity and minimize energy consumption. in other words, it is possible that certain sensors must move to cover a specific target, maintaining connectivity with their neighbours to send properly collected data to a base station. since all network elements can move, it is necessary to use a dynamic mathematical model that takes into account each network state in order to guarantee connectivity and minimum energy consumption. the remainder of the paper is organized as follows: section ii states the problem and some related work. section iii presents the solutions’ mathematical model and computational algorithm, showing the methodology to implement it, and additionally, explaining several criteria to design the heuristic according to the mwsn constraints. section iv shows some results for several instances with different parameters. finally, in section v, conclusions and future work are presented. 2 problem statement and related work this paper presents a difficult problem of mobile wireless sensor networks with mobile targets (mwsn-mt), which consists of an area of interest with mobile targets, mobile sensors and one or more base stations available inside, which can’t move and its position is known. the targets position is unknown and the sensors positions can be changed with an order from a centralized management system. the problem consists in finding all the targets inside the area and create communication paths from them to the base stations while at the same time minimizing the energetic cost associated to the mobile sensors sensing, movement, transmission and reception of data. the problem can be seen as the iterative solution of multiple shortest path (sp) problems from the targets found to any base station available, multiple coverage problems where the mobile sensors will try to maximize the area covered looking for all the targets, in topologies were the paths may not exist or could change in future iterations. some related work used as basis for our research can be found in the book by ahuja et al. [1] where the authors explain optimization for network problems and several algorithms used to solved them. church and revelle [2] state the maximal covering location problem and set the basis for further research on the subject. chvátal [3] states the set covering problem and solves it through a greedy heuristic. baldacci et al [4] combine routing and covering problems and solve them with exact and heuristic algorithms. powell [5] in his book states the basis of approximate dynamic programming (adp) and gives models, examples and applications for its use. chabini [6] presents solutions for all to one shortest path problems in discrete dynamic networks. also, works on mobile robotics by chakraborty and sycara [7] present a dynamic approach for a mobile robotic network where the decisions can be taken centralized or distributed while maintaining connectivity in the network. zavlanos et al. [8] use a distributed hybrid approach to control mobility of the robotic network while maintaining connectivity at desired rates. works on mobile wireless sensor networks by miao et al. [9] explore the problem of deployment and distribution of mobile sensor networks through swarm intelligence. wang et al. [10] review mobile sensor networks challenges and applications. finally, cortes et al. [11] present control and coordination algorithms for mobile sensors. while the previous works serve as the basis for our research, this paper introduces the system energy consumption consideration in a distributed target searching application, a research that can lead to mobile, autonomous and efficient networks for disaster inspection and remote site exploration among other applications. energy optimization in mobile wireless sensor networks with mobile targets achieving efficient coverage for critical applications 249 3 problem solution 3.1 mathematical formulation the network begins with an initial graph g = (s, b, t, e, a), where s is the node set composed of mobile sensors, b is the set of base stations (nodes without mobility nor sensing capabilities but with connectivity outside the network) and t is the targets set; e is the edges set, which are the feasible connections between mobile sensors and between sensors and base stations, and a is the arcs set, which are the feasible links from targets to sensors. the graph g is converted in a new graph g′ = (s′, b′, t ′, e′, a′) after each run of the algorithm. this process is performed iteratively to reach the targets and guarantee connectivity between mobile sensors and base stations as the network elements move. the sensors have transmitting and sensing features which follow the well known disk coverage model where each sensor is assumed to cover a disk centered at itself with fixed sensing and transmitting ranges as disk radii. if a sensor cannot reach any target, it can move seeking it or serving as a relay node for communications. however, the use of these capabilities affects the energy consumption of the network and reduces its lifetime; therefore, in the initial state, sensors have an energy level which is reduced by movement, data transmission and reception. in each following state it is necessary to minimize the energy consumption in order to extend the lifetime of the network and increase the probability to find targets. the system also has to maximize coverage trying to find all targets. in figure 1, the basic structure of the optimization model is shown. figure 1: optimization model structure the mathematical model notation is shown in table 1. a is the arcs set, representing sensors to targets connections. e is the edges set, representing sensors to sensors or to base stations connections. rc is the maximum distance for wireless links between sensors and sensors to base stations and rs is the maximum distance for sensing (sensor to target). parameters rc and rs are assumed to be constants for all nodes. t targets set t batik battery level available at edge (i, k) ∈ e s mobile sensors set i, j capik capacity of edge (i, k) ∈ e b base stations set b distik length of edge (i, k) ∈ e e edges set (i, k), i ∈ s, k ∈ s ∪b ft data flow demand (in bps) from target t ∈ t a arcs set (i, t), i ∈ s, t ∈ t cik energetic cost of edge (i, k) ∈ e dit cost of arc (i, t) ∈ a rc communication radius between nodes in s ∪b rs sensing radius between (s) and (t ) table 1: mathematical model notation decision variables: xtik is "1" if edge (i, k) is in the path for target t to any base station and 250 g.a. montoya, c. velásquez-villada, y. donoso "0" otherwise; and zit is "1" if target t is covered by sensor i, "0" otherwise. min w1 ∑ t∈t ∑ e∈e cikx t ik + w2 ∑ (i,t)∈a ditzit (1) subject to: w1 + w2 = 1 (2) construction constraints ∑ t∈t distikx t ik ≤ rc ∀(i, k) ∈ e (3) distitzit ≤ rs ∀i ∈ s, t ∈ t (4) energy consumption constraints ∑ t∈t cikx t ik ≤ batik ∀(i, k) ∈ e (5) ∑ i∈s zit = 1 ∀t ∈ t (6) ∑ t∈t zit = 1 ∀i ∈ s (7) data flow and connectivity constraints∑ t∈t ftx t ik ≤ capik ∀(i, k) ∈ e (8) ∑ (i,t)∈a ftzit = ∑ (j,b)∈e ftx t jb (9) ∑ j∈s ∑ t∈t xtij − ∑ j∈s ∑ t∈t xtji = 0 ∀i ∈ s (10) integrality constraints xtik ∈{0, 1} ∀(i, k) ∈ e, t ∈ t (11) zit ∈{0, 1} ∀(i, t) ∈ a (12) constraints (3) and (4) state that the links between nodes can only exist if the distance between them is less than the communications coverage radius parameter (rc) and a sensor can only cover a target if the distance from the node to the target is less than the sensing coverage radius parameter (rs). constraint (5) states that energy consumption on a link between two nodes can’t be greater than the energy available at that link. constraint (6) assures that a target must be covered by at least one sensor while (7) assures that at most one sensor covers a target actively. constraint (8) states that data flow through a link must be less or equal than the links capacity. constraint (9) states that the data flow generated from the targets covered must arrive at the base stations. constraint (10) is the balance constraint, stating that all the flow entering a sensor node must be equal to the flow leaving that sensor node. energy optimization in mobile wireless sensor networks with mobile targets achieving efficient coverage for critical applications 251 3.2 computational implementation figure 2 presents the solution algorithm pseudo-code. in this table the algorithms functional characteristics are shown. the solution seeks to cover all targets one at a time by looking for a communications path from a sensor covering the target to any base station in the grid. if a target isn’t covered by a sensor, the algorithm will move the n most energetic sensors spreading them around unexplored areas, maximizing the area coverage while looking for the target(s) missing for a predefined number m of steps or until the target(s) is covered. our solution uses a greedy heuristic procedure based in minimum cost (cost is defined as a combination of distance and energy consumption). some relaxations from the mathematical model discussed before are implemented in the algorithm, those relaxations are as follows: • constraints (6) and (7). each target must be covered by at most one mobile sensor. whether there is more than one mobile sensor covering the target, it is necessary to select the sensor with minimum cost and greater energy. if there is no sensor covering the target, the algorithm will try to find it through sensors movement. • constraints (9) and (10). for a given target, it is required to find a path form the target to any base station. therefore, a base station is able to receive information from different targets, and it is possible that a base station will not be selected to receive information. also, if the target can’t send its information to a base station, the algorithm will try to create a path to a base station through sensor movement. figure 2: solution algorithm pseudo-code 4 results this section presents computational results for the heuristic algorithm described in the previous section. the algorithm was coded in matlabž from mathworks. the experiments were performed on a core 2 duo personal computer equipped with 4 gb of ram and running under microsoft windows 7. we considered 5 random instances with parameters stated in table 2. the instances present variations in the numbers of mobile sensors, targets and base stations and nodes and targets initial positions. parameters like communications coverage radius (rc), sensing coverage radius (rs), initial energy level (einit), energy consumption by communications (ecomm), sensing (esens) and movement actions (emove), number of maximum steps to move looking for a target (m) and number of mobile sensors to move in each step (n) are kept constant 252 g.a. montoya, c. velásquez-villada, y. donoso 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 1 2 3 4 1 2 3 0 20 40 60 80 100 120 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 1 2 3 4 1 2 3 0 20 40 60 80 100 120 0 20 40 60 80 100 120 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 1 2 3 4 1 2 3 a) b) c) 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1516 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 1 2 1 2 d) 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 1 2 1 2 e) 0 50 100 150 200 250 0 20 40 60 80 100 120 140 160 180 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 2728 29 30 31 32 33 34 35 3637 38 39 40 1 2 1 2 f) figure 3: graphical results for two different instances instance name mobile sensors targets base stations mwsn50.2.2 50 2 2 mwsn50.4.3 50 4 3 mwsn50.3.5 50 3 5 mwsn40.2.2 40 2 2 mwsn40.4.3 40 4 3 table 2: instances used for all instances; these parameters values are shown in table 3. the graphical results shown in figure 3 are graphs representations for two different instances were a) and d) show the initial graph and c) and f) show a feasible solution found. restriction compliant links are painted in the figures. triangular nodes (n) represent the target to be covered. circular nodes (⊙) represent the sensor nodes that can cover the targets and provide connectivity to base stations. square nodes (�) represent base stations nodes that can be reached to convey target information. the path(s) shown were found searching for the most efficient path in terms of jumps and energy saving from coverage node to base station node. the numerical solution results for figure 3 and the other instances in table 2 are shown in table 4. we performed 10 runs of the algorithm for each instance and the values for the number of iterations, energy consumption and cpu time were averaged over those 10 runs, however in the cases were the algorithm didn’t find a path, the results weren’t taken into account. the algorithm couldn’t find a first path 5% of the runs and the last path (covering all targets) 20% of the runs. 5 conclusions and future work we have presented a novel dynamic multi objective optimization approach using a greedy heuristic and a two step optimization procedure where several objectives are searched at a time energy optimization in mobile wireless sensor networks with mobile targets achieving efficient coverage for critical applications 253 parameter value rc 15[m] rs 2[m] einit 10000[ma/hr] ecomm 10[m] emove 40[m] esens 5[m] m 1[step] n 20%[nodes] table 3: parameters values used for all instances first path first path first path last path last path last path instance iterations energy cpu time[s] iterations energy cpu time[s] mwsn50.2.2 120,4 18,5267 0,49452 313,2 46,4009 1,04676 mwsn50.4.3 31,5 9,672 0,3354 191,8 53,8483 1,15752 mwsn50.3.5 4,7 0,6747 0,21372 92,6 18,8868 0,546 mwsn40.2.2 57 10,78841 0,3354 209,5 37,16683 0,74256 mwsn40.4.3 52,8 20,52101 0,4368 162,8 59,17033 0,92508 table 4: numerical results for all instances and where the network configuration can change at any time. the instances presented were constructed randomly and the algorithm was tested several times with each one of them achieving promising results. the system was able to find a path to at least a target 95% of the times before the energy consumption forbids it. also a path from all targets was achieved 80% of the times in the instances tested, showing some baseline performance over further research can be contrasted and compared. if the network has few mobile sensors, the movement action will waste most of the energy to find the targets, this action should be restricted and used wisely in order to enhance systems lifetime. the number of targets in the system related with the mobile sensors number will determine the emphasis of the algorithm, coverage or path finding. the results in this paper show that it is possible to automate the decision making in critical response time scenarios where information can flow in the system and a centralized management system can take actions to ensure coverage and information flow to support decision at a higher level. further research can be done using real life scenarios, taking into account propagation models for the communications links, realistic areas with obstacles for mobility and connectivity, target mobility models based on different applications, exact algorithms in conjunction with heuristics and distributed computing like swarm intelligence for problem solving. bibliography [1] ahuja r. k., magnanti t. l. and orlinj. b., network flows, prentice-hall, isbn 9780136175490, 1993. [2] church r. and revelle c., the maximal covering location problem, papers in regional science, 32(1):101-118, 1974. [3] v. chvátal. a greedy heuristic for the set-covering problem, mathematics of operations research, informs. 4(3):233-235, 1979. 254 g.a. montoya, c. velásquez-villada, y. donoso [4] baldacci r., dell’amico m., salazar gonzález j., the capacitated m-ring-star problem, operations research, 55(6):1147-1162, 2007. [5] powell w. b., approximate dynamic programming, john wiley & sons, 2nd edition, isbn 978-0-470-60445-8, 2012. [6] chabini i., discrete dynamic shortest path problems in transportation applications: complexity and algorithms with optimal run time, transportation research record, 1645(-1):170-175, 1998. [7] chakraborty n., sycara k., reconfiguration algorithms for mobile robotic networks, robotics and automation (icra), 2010 ieee international conference on, pp.5484-5489, 3-7 may 2010. [8] zavlanos m. m., ribeiro a. and pappas g. j., distributed control of mobility & routing in networks of robots, signal processing advances in wireless communications (spawc), 2011 ieee 12th international workshop on, pp.236-240, 26-29 june 2011. [9] miao l., qi h. and wang f., biologically-inspired self-deployable heterogeneous mobile sensor networks, intelligent robots and systems 2005, (iros 2005). 2005 ieee/rsj international conference on, pp. 23632368, 2-6 aug 2005. [10] wang y.-c., wu f.-j. and tseng y.-c., mobility management algorithms and applications for mobile sensor networks, wireless communications and mobile computing, 12(1):7-21, 2012. [11] cortes j., martinez s., karatas t., and bullo f., coverage control for mobile sensing networks, robotics and automation, ieee transactions on, 20(2):243-255, april 2004. int j comput commun, issn 1841-9836 8(2):294-303, april, 2013. developing integrated performance measurement system using component based approach s. sekkat, k. kouiss, j. saadi, l. deshayes souhail sekkat lm2iensam. universita moulay ismail marjane ii beni mahammed, bp 4024, meknes alismailia, meknes, maroc s souhail@hotmail.com khalid kouiss limos ifma. campus de clermont-ferrand/les czeaux bp 265 63175 aubire cedex, france. kouiss@ifma.fr janah saadi liser. ensem. universit hassan ii. an chok, bp 8118, oasis, casablanca, maroc. janah1@menara.ma laurent deshayes manbat technology. 3 rue abou baker mohammed ibnou zohr, residence la tulipe, quartier des hpitaux, casablanca, maroc. laurent.deshayes@gmail.com abstract: in an industrial context defined by more acute competition, performance measurement becomes a control tool. the business intelligence module of manufacturing execution system (mes) software achieves performances analysis function. however, the implementation of performance indicators in information system is a difficult problem. indeed, the enterprises need methods to specify and to install their performance measurement system (pms). in this paper, we propose a methodology of performance indicators implementation. we use the uml language to develop pms model and component based programming for its implementation. this method facilitates performances measurement system design and implementation. keywords: manufacturing execution systems, business intelligence, performances measurement systems, component based approach. 1 introduction nowadays, the level of integration determines the effectiveness of modern technical systems. application of the system engineering results in that systems become distributed, and consist of a number of physically or logically distributed components that constitute the system to reach the common goal. communication between such components is a crucial issue. the mes software allows efficient integration of computer control systems and achieves the coordination of all production resources. performance measurement function is provided by business intelligence (bi) module of manufacturing execution systems mes [1]. it is based on the computer controller systems and allows: data collection, performance indicators computation and scorecards display. the development and the implementation of a performance measurement system (pms) is a difficult problem. indeed, to design and implement a pms, we must properly model identities, attributes and operations of individual objects as well as the sequence of operations and data copyright c⃝ 2006-2013 by ccc publications developing integrated performance measurement system using component based approach 295 flow in a shop floor control system. what will allow, thereafter, the time follow-up of the key performance indicator (kpi). therefore, in this paper, we present a pms model and its implementation using component oriented approach. the main body of this article is organized as follows. in the next section, the mes software, that allows production control, is presented. then, the design methods of integrated production system is presented and discussed. after then, proposed approach for specification and implementation of pms is outlined. the article closes with a practical example and drawing a conclusion. 2 the manufacturing execution systems softwares the rapid progress of computer technologies brings shop floor control functions into a new area. many advanced functions have been introduced as the results of improving computing power. these functions include real time scheduling, networking, cell coordination and performance analysis [2]. in this section, we will present the mes software and the functions they achieve. first, we are going to describe the computer integrated manufacturing (cim) pyramid. then we will position the pms in this pyramid. finally we are going to present business intelligence module and its functions. 2.1 cim pyramid and shop floor control since the functions of shop floor control have become more complex than before, several control architecture and models were proposed. one of the most famous models is nist amrf (national institute of standard and technology / advanced manufacturing research facility) proposed by national bureau of standards. the amrf model is applying hierarchical control architecture; it is composed of five-level hierarchy: facility, shop, cell, workstation and actuator/sensor. each level has its specific functions. the business functions such as; cost accounting, aggregate planning, are executed in the facility level. it is done generally by erp software. shop level is responsible for coordinating production tasks including resource allocation and task assignment. the tool used at this level is the manufacturing execution systems mes. cell level is subject to sequence similar parts in batch jobs and supervises supporting tasks such as material handling and transportation. the supervision is achieved, generally by scada software. a workstation level is responsible for monitoring the execution of production tasks. it consists of a robot, a fabrication station or a material storage buffer. we seek interest in shop floor control functions provided by mes software’s. we will then present the functions achieved by theses software’s. indeed, before the advent of the mes software, the work orders printed by the erp software, were transmitted by hand to the shop floor manager. based on shop floor state (breakdowns, delays), he directs the products lots rooting and allocates to each operator the tasks to achieve. the technical information on product is handed to all workstations by technical manager. for data collection, production sheets are elaborated and the workers are asked to fill them. these sheets allow to summaries the production, time, and defaults data. the raised data is thereafter recorded in a computer system. they allow maintaining a production history and achieve product tracking and performance indicators computation. this method has, until now, given satisfaction. however, the companies must be nowadays more reactive and effective. firstly the mes is supplied with work orders, then it sequences and times activities based on finished resources capacities, allocate resources and define what people, machines and tools should do. it also manages and distributes information on products, processes and designs. then 296 s. sekkat, k. kouiss, j. saadi, l. deshayes it gives the order to send materials to certain parts of the plant to begin a process. finally it directs the flow of work in the plant based on planned and actual production activities, and monitors the progress of products units, batches or lots. figure 1: information flow between mes and other systems when the product is achieved, the mes collect data about products; people and machines work and track what they have done, to create a full history of the product. it gathers, also, certification statements of work and conditions. then it records, tracks, analyzes product and process characteristics with engineering ideals and compares measured results in the plant with goals and metrics set by the corporation. an mes software is generally a production server that collects permanently the state of the available variables on device controllers and scada systems distributed in the shop and record this information in database system. to achieve database management it must include a communication tool that allows automatic operation integration with external systems (erp, gpao). to meet the needs of a variety of manufacturing environments, mesa identified 11 principal mes functions. thus, if the mes functions set is theoretically invariable, the priority between them and the configuration can vary and be adapted according to the aims of the company. among the solutions provided by software editors we mention : wonderware mes 4.0 [3] and mapex production manager v7 [4]. the integrated actual market solutions answer partly company needs and require a complete review of existing applications. the eai (enterprise applications integration) solution is an alternative to the integrated mes solutions for the company [5]. 2.2 business intelligence (bi) module of mes software the manufacturing execution systems mes achieve performance analysis function provided by business intelligence (bi) module. the pms is based on the computer controller systems for: 1. the data collection (starting time, break times, completion time, products quality), 2. the computation of performance indicators and developing integrated performance measurement system using component based approach 297 3. the display of scorecards. bar-codes are used to identify work orders. sensors linked to workstation controller are used for produced parts counting. the workers collect events occurred on machine keyboard. the information are collected by scada software and recorded to relational data base. data aggregation and indicators computation is assured by mes software. the mes software includes integrated business intelligence bi module used by all others mes modules to improve business decision making. indeed, business intelligence bi must be able to provide production reporting, end-user query, dashboard/screen tools, data mining tools, and planning and modeling tools. it must provide the right information to the right person throughout the organization. a bi system is a data-driven dss that primarily supports the querying of a historical database and the production of periodic summary reports [6]. the product data used by mes software to generate kpi have high frequency traffic pattern. also, the shop floor and company departments have their own applications that use and produce data. this generate data heterogeneity problem. it is necessary therefore to provide interfaces between the different databases, important memories size and achieve data codification. we propose, then, to adopt structured approach for performance measurement system development. in the next paragraph, we will present the different methods of an integrated production control systems design. 3 the design of integrated production system the integration of the information required by the co-operation of manufacturing applications has become a crucial issue for enterprises. however, ther is wide spectrum of information sources and the existing software components are heterogeneous. an integrated infrastructure that provides a set of services for information access and management, production resource control and monitoring interprocess communication must be developed. based on well accepted standards this infrastructure will convert from closed proprietary systems towards interoperable and networked applications. enterprise integration (ei), which emerged after the computer integrated manufacturing (cim) era, has been heavily discussed and investigated since the late 80s with the seminal work on the cimosa architecture [7]. also, the component approach can be used to achieve the interoperability of computer control systems and to allow components reuse [8]. in this section, we will present integrated production systems and different methods used for its design. first, we are going to describe the method used for computer integrated manufacturing system design. then we will present the cimosa framework and component approach for enterprise integration. finally we are going to return to our problematic to redefine it. 3.1 the methods used for computer control system design a design of an integrated production system is a big project that includes several jobs; programmers, automation engineer, mechanics. a design of shop floor controller is achieved through several steps: 1 requirement analysis, 2 existing system analysis as is, 3 system specification, 4 solutions proposition and feasibility validation to be, 5 equipments installation, 6 controller implementation, 7 operation and maintenance. in the requirement analysis step the requirements of the proposed system are collected by analyzing the user needs. this phase is concerned with establishing what the ideal system has to perform. however it does not determine how the software will be designed or built. usually, the users are interviewed and the user requirements document is generated. during the specification, it is necessary to describe functionalities of equipments (production system) and the controller (information system). thus, modeling methods are used. business 298 s. sekkat, k. kouiss, j. saadi, l. deshayes process modeling is the activity of representing a company processus, so that the current process may be analyzed and improved. bpm is typically performed by managers who are seeking to improve process efficiency and quality. it allows the construction of models of a determined part of an enterprise. the evolution of the complexity of information systems, the worry of optimization of software applications design drove the scientists to develop some modeling methods. the classic modeling methods describe the information system and proceed to separate treatment of data and processes. the object-oriented methods are new approaches of development. it considers a system like a set of objects in interactions (every object achieves or undergoes some operations), examples of these methods (uml, omt). the methods dedicated to distribute systems are those allowing the development of multi agents systems (with distributed execution objects). the implementation is a setting step, which consists of machines and control softwares choice, supervisory control interfaces programming and tests. shop floor control must be integrated system. interoperability requirement is difficult to achieve. indeed, many companies selling industrial software tools and hardware, such as distributed control systems, scada (supervisory control and data acquisition) packages, programmable controllers, etc., provided external interfaces for interoperability but employed proprietary protocols requiring custom software development. integration of applications meant substantial development effort to provide adapter layers so that applications could operate collaboratively and share data. 3.2 cimosa framework for enterprise integration the concept of abstract manufacturing services, which came up with the cimosa framework for enterprise integration, is to provide the manufacturing processes with a set of abstract services supporting the execution of industrial operations by the resources. these services are system-wide, callable through an interface consisting of basic functions for starting or stopping an operations and independent of any resource implementation details. the actual dialogue with a specific resource occurs outside the application, within a manufacturing service provider accessible through this interface. the manufacturing application limits its role to the control of operations, and remains unaware of their physical implementation. applications based on abstract manufacturing services can be easily configured and extended for a new manufacturing layout, as any technological detail is encapsulated within the manufacturing service provider. the integrating infrastructure (iis) as specified in the cimosa formal reference base is made up of a set of distributed services, which allow generic system-wide functionalities such as the control of manufacturing processes, the administration and exchange of information, the scheduling and monitoring of resources, and the interfacing with human beings, machines and application programs. business services (bs) provide services for controlling the manufacturing processes: they control the execution of functional operations by the different resources, following the procedural rules described in the functional model of the manufacturing processes. they allocate the resources and retrieve the necessary information in the information system. information services (is) manage system-wide the enterprise information (location, replication, access right, etc.) and provide a homogeneous interface for accessing the different data storage units such as databases and file systems. presentation services (ps) provide a homogeneous interface to the various manufacturing resources (machines, humans and applications). they provide abstract manufacturing services to the business services, and control the execution of the corresponding functional operations by the resources. developing integrated performance measurement system using component based approach 299 3.3 component bsed approach in the same topic, component based approach is used to enterprise integration. indeed, component programming have introduced revolutionized changes in software development, like engineering methods, component-based development, and assembling components based on their interface and semantics. this changes ease system interoperability and entreprise integration. software components are associated with their component infrastructure. a component infrastructure is the basic, underlying framework and facilities for component construction and component management. the different concerns involved in the realization of a software system can be divided into problem-specific and crosscutting ones. problem-specific concerns relate to the main functionality that should be provided by the system. distribution transparencies are typical examples of crosscutting concerns. their realization imposes the need for additional functionality, which should be spread across the systems problem-specific abstractions. any distribution transparency involves using middleware services that require a broker, which provides access transparency. achieving transaction transparency amounts in using services that implement a distributed atomic commitment protocol. these services further require others that enable synchronization and persistence. the middleware provide some high-level services related to applications communication needs called inter process communication, ipc. it is located upon the transport layer in the osi model (layers 5, 6 and 7). the double mission of middleware interfacing is: the client server process, that achieves remote process calls implementation and manage call and results return functions between applications. the data translation, which defines data format and syntax, so that the transport layer can send this data across network. 3.4 problematic definition several researchers have applied object-oriented and component-oriented concepts in implementing manufacturing execution systems mes. for instance, yang [9].applied object-oriented programming (oop) to develop a cim system. in addition, a distributed automation and control system has been implemented in [10].for system integration a middleware corba were used. another work was addressed on applying cop methodology to develop a controller, in robotic palletization area [11]. although various works related to the modeling and implementation of shop floor controllers were presented, the implementation of pms was not addressed by those researchers. specially integration of pms with other applications. component approach can be used to support this issue. basically, it focuses on: identifying objects, their related properties and building the relationships among objects, integration of the pms within the control system (data collection and interoperability). 4 proposed approach as a first step, we propose to model the system by using uml language. we adopt the use cases diagram to elaborate the functional model of the system. the use cases identified are the different activities proposed by the isa s95 standard [12]: the product definitions and production resources management the detailed production scheduling, production dispatching and execution. the production data collection, production tracking and production performance analysis. as far as we are considered, we deal with big interest with production data collection and performances analysis activities. 300 s. sekkat, k. kouiss, j. saadi, l. deshayes figure 2: tha use case diagram of manufacturing execution system to describe the static view of the system, we must identify all its classes and objects, including physical and conceptual system entities. the products, processes, workers and other industrial entities are modeled in uml static view by class diagram. it shows the class attributes, and the relationships among the classes. a product routine is a set of operations that describe the methods used to manufacture a product. an operation is allocated to a resource. a work order wo is associated to a product. the data about wo operations are collected and recorded in a report. figure 3: tha class diagram of performance measurement system the script achieved by the bi module of mes software is as follow: the erp software sends to mes software the information about launched work order wo. the mes schedule the wo and launch its execution via the scada system to workstation. during production process the scada system collects the data about each wo. when the wo is finished, production information is feedback from scada toward mes developing integrated performance measurement system using component based approach 301 software. the mes software transfers to the erp wo reports, that computes and displays performance indicators. as a second step, we propose to adopt a component-oriented approach for pms implementation. indeed, it is necessary to elaborate an integrating infrastructure that allows an interoperability of the distributed system. we propose to adopt enterprise application integration architecture (eai), for control system integration and pms implementation. we propose, therefore, an integrating infrastructure based on computer standards, like xml format, that allows the different heterogeneous applications to communicate by messages sends and receipt. the integration platform regroups the components that support the interoperability. it provides both data centralization and communication services that allow tools to share data and communicate between themselves. in addition to communication services, the integration platform contains a user interface module and a runtime coordination that contribute to integrated operation. 5 practical example the proposed method is applied to the flexible machining cell in ensam. we will develop a pms for its control. the pms developed is the oee overall equipment effectiveness measures. there are three underlying metrics that provide understanding as to why and where the oee gaps exist. these measurements are described below: availability: the portion of the oee metric represents the percentage of scheduled time that the operation is available to operate. often referred to as uptime. performance: the portion of the oee metric represents the speed at which the work center runs as a percentage of its designed speed. quality: the portion of the oee metric represents the good units produced as a percentage of the total units started. commonly referred to as first pass yield. oee is computed as follows: oee = availability x performance x quality. to implement the pms, we must integrate it within the control system. indeed, it should collect the data, from the shop floor control and send the performance indicators to the business information system. we will describe the oee measures implementation. firstly, we are going to describe the technical architecture of the system (data collection material). then, we will present, the software architecture that allow system implementation (products data configuration, bi module deployment). the manufacturing system is organized around a transport system which allows a routing of the work-pieces, placed upon work-piece carriers, toward a cnc milling machine. a jointed-arm robot is used to load and unload parts for cnc machine. in the control architecture of the system, a scada software (ciros control) is installed in the top-level computer. an ethernet network is connecting several machine controllers. the cnc machine is equipped with a dnc link based on rs-232 , the robot and transport system with ethernet extension. profibus dp connect stopper boxes to the transport system controller. the strategy of integration was to provide a common software infrastructure (integration platform) that would allow a wide variety of application tools to communicate and share data, while minimizing the specific development for each tool. the heart of the integration platform is scada software ciros control. it is a real-time, object-oriented development environment. the pms must communicate with the scada software ciros, it must also communicate with a mes software icim production manager. let us consider a work order sent by the icim manager to the cell controller ciros. the cell controller will select the production routine of the considered part from the routine file 302 s. sekkat, k. kouiss, j. saadi, l. deshayes figure 4: the ensam machining cell database. it will check about the resources status. if all necessary resources are available, it will determine adequate material and start the tasks. to develop reports it is necessary, at first, to extend the icim manager data base and add report tables. then we must determine the necessary data site, on the icim manager data base tables. after then, we must establish computation formulas of the indicators to display. we must also, develop sql queries to select the data from data base tables. the reporting tools crystal reports allow reports development. it is necessary to connect at first to the icim manager data base through odbc server, in order to reach all data that will be used in the reports. icim manager provided xml files that allow the communication of the data to reporting tool. 6 conclusions and future works the use of object and component approach, proven industrial technology coupled with message oriented middleware considerably eased the process of application tool integration. it allowed a better flexibility of the pms. although the proposed model is focused on a cim system in the educational environment, several concepts developed from this model still can be applied in the shop floor controller utilized in other environment. as perspective, we will consider, in addition to interoperability problem, the possibility of kpi component reuse. to do this, we will explore different possibilities, for example, the use of uml profile. which means, applying stereotypes, tag definitions and constraints, to the classes, attributes and operations, that we previously defined in our research and define specific profiles in production control area. bibliography [1] mesa international. 2010. data architecture for mom: the manufacturing master data approach. white paper 37. https://services.mesa.org/resourcelibrary/. [2] halang, w. a., sanz, r., babuska, r., roth, h., 2006. information and communication technology embraces control. status report prepared by the ifac: coordinating committee on computers, cognition and communication. annual reviews in control, 30: 31-400. developing integrated performance measurement system using component based approach 303 [3] wonderware. wonderware manufacturing execution systems software solutions. http://www.wonderware-benelux.com/ [4] mapex. production and timing control. http://www.emapex.com/en. [5] cross database technology. dataexchanger l’eai 100 % oprationnel. www.dataexchanger.com. [6] m. ghazanfari, m. jafari, s. rouhani. a tool to evaluate the business intelligence of enterprise systems, scientia iranica, 18 (6): 1579-1590, 2011. [7] amice. (1993). cimosa: cim open system architecture (2nd revised and extended ed.), berlin: springer-verlag. [8] adrian alexandrescu. a development process for enterprise information systems based on automatic generation of the components, int j comput commun, 3(s):168-172. [9] yang. c. o., guan, t. y., lin, j. s., 2000. developing a computer shop floor control model for cim systems using object modelling technique, computers in industry 41. pp: 213-238. [10] krassi. b. a. 2002. distributed computing and automation: industrial applications. http://www.automationit.hut.fi/julkaisut. [11] chiron, f., 2009. contribution ă la flexibilităš et ă la rapidităš de conception des systă¨mes automatises avec lutilisation duml. these de doctorat en informatique ; universitc blaise pascal clermont. [12] ansi/isa-95.00.03-2005 (2005). enterprise-control system integration. part 3: activity models of manufacturing operations management, isbn: 1-55617-955-3. international journal of computers communications & control issn 1841-9836, 14(1), 56-62, february 2019. iot devices signals processing based on multi-dimensional shepard local approximation operators in riesz mv-algebras d. noje, r. tarca, i. dzitac, n. pop dan noje 1. university of oradea 410087 oradea, universitatii, 1, romania dan.noje@gmail.com 2. primus technologies srl 410248 oradea, onestilor, 3, romania dan.noje@primus-tech.ro radu tarca* university of oradea 410087 oradea, universitatii, 1, romania *corresponding author: rtarca@uoradea.ro ioan dzitac 1. aurel vlaicu university of arad 310330 arad, elena dragoi, 2, romania ioan.dzitac@uav.ro 2. agora university of oradea 410526 oradea, p-ta tineretului 8, romania, idzitac@univagora.ro nicolae pop institute of solid mechanics of the romanian academy 10141 bucharest, sector 1, constantin mille, 15, romania nicpop@gmail.com abstract: in this article we continue the study started in [8] to use riesz mvalgebras for iot devices signals processing. the shepard local approximation operators introduced in [8] were defines such that to approximate single variable functions. in real industrial usage, the signals coming from iot devices may be influenced by mode than a parameter, and thus we introduce generalized shepard local approximation operators that can approximate multi-dimensional functions and some numerical experiments are considered. keywords: iot devices; signal processing; shepard local approximation operators; local approximation operators; approximation algorithms; riesz mv-algebras, vectorial mv-algebras. 1 introduction with the aim to provide new mathematical tools that are useful to develop algorithms that are suitable for iot devices signals processing, in [1] were introduced shepard local approximation operators that can approximate one-dimension functions. since in real industrial applications, the iot devices signals are not depending on one single parameter, there is a real need to introduce generalized shepard local approximation operators that can approximate multi-dimensional copyright ©2019 cc by-nc iot devices signals processing based on multi-dimensional shepard local approximation operators in riesz mv-algebras 57 functions. this new approximation operators can be later used to develop software algorithms that act as input validators for industrial automated control systems [10,11] based on the riesz mv-algebra structure of the iot devices signals [8]. in 1958, multivalued algebras, shortly named mv-algebras, were introduced by chang [2,3] as the algebraic structures corresponding to the ∞ valued lukasiewicz logic. definition 1. an mv-algebra is a structure a = (a,⊕,¬, 0a) if and only if the following axioms are fulfilled: (a,⊕,¬, 0a) is an abelian monoid, ¬¬x = x, x⊕¬0a = ¬0a, ¬(¬x⊕y) ⊕y = ¬(¬y ⊕x) ⊕x. in an mv-algebra a, the constant 1a and the binary operations � and can be defined as follows: 1a = ¬0a, x�y = ¬(¬x⊕¬y), x y = x�¬y. also we can define a distance function d : a×a → a as follows: d(x,y) = (x y) ⊕ (y x). this distance as it is defined is a metric and plays a very important role in image and signal processing. by introducing an additional external operation, in 2003, was defined the concept of vectorial mv-algebras [6], shortly named vmv-algebras. let consider an mv-algebra a and an external operation defined as follows: • : r+ ×a → a. definition 2. the mv algebra a is an vmv-algebra if and only if the following axioms are fulfilled: 1 •x = x,∀x ∈ a, (a + b) •x = a•x⊕ b•x,∀x ∈ a and ∀a,b ∈ r+, a• (b•x) ≤ (a · b) •x,∀x ∈ a and ∀a,b ∈ r+, d(a•x,a•y) ≤ a•d(x,y),∀x,y ∈ a and ∀a ∈ r+. vmv-algebras inspired new algebraic structures, mv-modules [4] and riesz mv-algebras [1]. definition 3. an mv-algebra a is a truncated mv-module over the unital latticeal ring (r,v) if there is defined an external operation • : r+ ×a → a, such that the following properties are fulfilled for ∀α,β ∈ r+ and ∀x,y ∈ a. (α + β) • x = α• x⊕β • x, α• (x⊕y) = α•x⊕α•y, if x ≤¬ y, α• (β •x) = (α ·β) • x, if α,β ∈ [0,v]. 58 d. noje, r. tarca, i. dzitac, n. pop if property v •x = x, is also fulfilled, then a is an unital mv-module over unital ring (r,v). definition 4. if an mv-algebra is a truncated unital module over (r, 1), it is a riesz mvalgebra. theorem 5. in any riesz mv-algebra, the following properties are fulfilled [4]: α• (β •x) ≤ (α ·β) •x, d(α•x,α•y) ≤ α•d(x,y), 0 •x = 0, α• 0 = 0, x ≤ y ⇒ α•x ≤ α•y, α ≤ β ⇒ α•x ≤ β •x, α• (x⊕y) ≤ α•x⊕α•y, for any x,y ∈ a and α,β ∈ r+. it was also proved that any riesz mv-algebra is an vmv-algebra, but the reciprocal statement isn’t true, but the set of values of iot devices signals can be organized as riesz mvalgebras [8]. in [1] was proved that riesz mv-algebras are algebraic and topological structures for data processing, because: theorem 6. any method developed in the classical numerical analysis is applicable in riesz mv-algebras if the riesz mv-algebras operations are used. based on this statement, in [8] were introduced shepard local approximation operators on riesz mv-algebras, to approximate one-dimension functions. were considered a riesz mvalgebra a and a function f : [0,n] → a and a shepard kernel [9], which is a strictly decreasing function k : [0, 1] → r+. also, was considered the set b(x,r) = {y ∈ [0,n] | |x−y| 6 r} (1) definition 7. a shepard local approximation operator, is a function s : [0,n] → a defined as follows: s (f,x) = ⊕xi∈b(x,r) k ( |x−xi| r ) ∑ xi∈b(x,r) k ( |x−xi| r ) •f (xi) where ⊕ and • are the riesz mv-algebra operations. iot devices signals processing based on multi-dimensional shepard local approximation operators in riesz mv-algebras 59 2 generalized shepard local approximation operators for iot devices signal processing we us consider a riesz mv-algbra a and a function f : [0,n 1 ] × [0,n 2 ] × ...× [0,nm] → a. in this generalized situation, in the definition 1 of the set b the distance has to be replaced by a norm. the norms we consider in the numerical experiments are: ‖x‖ = √ c21 + c 2 2 + ... + c 2 m (euclidean norm) ‖x‖ = max (|c1| , |c2| , ..., |cm|) (supremum norm) ‖x‖ = |c1| + |c2| + ... + |cm| (l1 norm) where x = (c1,c2, ...,cm) ∈ [0,n1 ] × [0,n2 ] × ...× [0,nm]. considering this, we can define the generalized shepard local approximation operators as follows: definition 8. a generalized shepard local approximation operator, is a function sg : [0,n 1 ] × [0,n 2 ] × ...× [0,nm] → a defined as follows: sg (f,x) = ⊕xi∈bg(x,r) k ( ‖x−xi‖ r ) ∑ xi∈bg(x,r) k ( ‖x−xi‖ r ) •f (xi) where ⊕ and • are the riesz mv-algebra operations and bg (x,r) = {y ∈ [0,n 1 ] × [0,n 2 ] × ...× [0,nm] | ‖x−y‖ 6 r}. (2) in [1] was considered that the set of possible values for iot devices signals is the interval [0, 2t − 1], where t is the number of bits used to store these values. in [10] was proved that the structure ([0, 2t − 1],⊕,¬, 0) is a mv-algebra, if the following definitions are used: x⊕y =def min(2t − 1,x + y), ¬ x =def 2t − 1 −x, ∀x,y ∈ [0, 2t − 1]. in [11] was proved that if we consider the external operation • : r+ × [0, 2t−1] → [0, 2t−1], defined as follows: a•x =def min(2t − 1,a•x), ∀a ∈ r+ and ∀x ∈ [0, 2t − 1], the structure ([0, 2t − 1],⊕,¬, 0,•) is a vectorial mv-algabra and is easy to see that this is also a riesz mv-algebra, as mentioned in [8]. if we use the above definition of ⊕ and • operations and the formula of the general shepard local approximation operator from definition 8, we can define an algorithm that can be used to fill in the missing data of signals received from iot devices. in [1] were considered the following types of kernels: k(u) = 1 u2λ , (shepard kernel) k(u) = e−λu 2 , (exponential kernel) k(u) = ( sin (qπu) sin (πu) )2λ , (shepard-jackson kernel) 60 d. noje, r. tarca, i. dzitac, n. pop where λ is a parameter that can influence the performance of obtained results, and q is the degree of the shepard-jackson kernel. also in [1] was determined by numerical experiments that the best results are given by shepard and exponential kernels, thus only these will be considered in the following numerical experiments. let now consider an industrial rectangle shape grid of iot sensors that collect temperature of an environment. the temperatures collected at a certain moment can be represented by a two-dimension function f : [0,n1] × [0,n2] → [0, 2t − 1]. in the formula of definition 8, xi = (cir,cic ) are the sensors located on row cir and column cic have transmitted data and x = (cr,cc) is the sensor located in row cr and column cc that was not transmitted data. the fill in algorithm has the following steps: 1. a kernel has to be selected; 2. the parameter λ is set; 3. the radius r, that influence how many received values are considered in the approximation of missing values, is set; 4. a grid traversing method is selected and each missing value is approximated. 3 numerical results in the approximation process, there is also the possibility to use the previously new approximated values or to ignore them. also to reduce the computational complexity, we replace the ball defined in 2 with a square having the side length 2r + 1 and we used the supremum norm. a grid of 31 × 31 sensors was considered, and we assumed that the collected values should be f(x) = (cr+cc) 2 · sin(π(cr+cc) 40 ). it was considered a continuous function, because one of the purposes of this algorithm is to be used to approximate missing values collected by the new fiber optic bragg grating sensors system designed to monitor the ethanol fermentation during the bioethanol and wine production. this new fiber optic bragg grating sensor system was developed using financing through grant pn-iii-p2-2.1-ped-2016-1955. the approximation error was determined using the formula p = ∑ x∈[0,n1]×[0,n2] |f(x) −sg(f,x)| , because we are interested in the overall error cumulated error. 3.1 experiment 1 in this experiment we assume that we received only data transmitted by sensors that are located on rows and columns that are both even numbers. several parameterizations are considered. after running the tests, we get the approximation errors listed as follows: iot devices signals processing based on multi-dimensional shepard local approximation operators in riesz mv-algebras 61 parametrization shepard kernel exponential kernel r = 2,λ = 2 48.1403 74.1296 r = 2,λ = 10 36.3588 36.3465 r = 2,λ = 20 36.3587 36.3587 r = 3,λ = 2 58.0658 183.302 r = 3,λ = 10 36.3588 42.9482 r = 3,λ = 20 36.3587 36.5376 r = 5,λ = 2 68.9431 476.092 r = 5,λ = 10 36.3588 110.7 r = 5,λ = 20 36.3587 54.642 r = 10,λ = 2 80.0381 1431.53 r = 10,λ = 10 36.3588 463.169 r = 10,λ = 20 36.3587 237.138 3.2 experiment 2 in this experiment we assume that we couldn’t receive data transmitted by sensors that are located on rows and columns that are both odd numbers. same parameterizations like in the previous experiment are considered. after running the tests, we get the approximation errors listed as follows: parametrization shepard kernel exponential kernel r = 2,λ = 2 15.1187 20.5379 r = 2,λ = 10 13.1902 13.2046 r = 2,λ = 20 13.1902 13.1902 r = 3,λ = 2 18.6535 58.1864 r = 3,λ = 10 13.1902 14.2934 r = 3,λ = 20 13.1902 13.2233 r = 5,λ = 2 21.7479 152.12 r = 5,λ = 10 13.1902 33.5822 r = 5,λ = 20 13.1902 16.5552 r = 10,λ = 2 24.8594 455.862 r = 10,λ = 10 13.1902 146.969 r = 10,λ = 20 13.1902 74.6371 4 conclusion nowadays, industrial information systems are depending on signals received form iot devices. there can be several problems in acquiring data from these iot devices, problems that can led to missing values. without a complete set of data, the automation of processes isn’t possible or is not satisfying enough. the algorithm proposed in this paper has the role to fill in the missing values of signals sent by iot devices. as mentioned in [8], for industrial usage of the algorithm, this methods should be further developed to determine the proper set of parameters for each of the kernels, based on the particularities of the industrial processes handled and on the amount of missing values. depending on the constrains of the real processes that have to be modeled, other error measures can be considered as well. 62 d. noje, r. tarca, i. dzitac, n. pop funding this research was funded by unitatea executiva pentru finantarea invatamantului superior, a cercetarii, dezvoltarii si inovarii (uefiscdi) romania, grant number 198ped/2017, grant code: pn-iii-p2-2.1-ped-2016-1955. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. bibliography [1] bede, b.; 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[11] [online]. sisteme informatice industriale, universitatea politehnică din bucuresti. available online: http://shiva.pub.ro/?page_id=345, accessed on 2 october 2018. international journal of computers communications & control issn 1841-9836, 12(5), 677-688, october 2017. a new deep learning approach for anomaly base ids using memetic classifier s. mohammadi, a. namadchian shahriar mohammadi*, amin namadchian department of industrial engineering ak.n. toosi university of technology, tehran, iran *corresponding author: mohammadi@kntu.ac.ir anamadchian@mail.kntu.ac.ir abstract: a model of an intrusion-detection system capable of detecting attack in computer networks is described. the model is based on deep learning approach to learn best features of network connections and memetic algorithm as final classifier for detection of abnormal traffic. one of the problems in intrusion detection systems is large scale of features. which makes typical methods data mining method were ineffective in this area. deep learning algorithms succeed in image and video mining which has high dimensionality of features. it seems to use them to solve the large scale of features problem of intrusion detection systems is possible. the model is offered in this paper which tries to use deep learning for detecting best features.an evaluation algorithm is used for produce final classifier that work well in multi density environments. we use nsl-kdd and kdd99 dataset to evaluate our model, our findings showed 98.11 detection rate. nsl-kdd estimation shows the proposed model has succeeded to classify 92.72% r2l attack group. keywords: deep learning, kdd99, memetic algorithm, nsl-kdd, classification function, anomaly base intrusion detection, intrusion-detection system (ids). 1 introduction in recent years, security industry has been actively playing roles in dealing with security threats against computer organizations and networks through employing various technologies such as encryption, authentication, and access control. however, all these technologies are also involved with their specific limitations, allowing an attacker to enter the system.idss 1 are security mechanisms which intelligently monitor computer and network systems in real time to detect intrusions and take quick appropriate measures in response [3]. anomalyand signature-based are two main methods used in idss. the former is based on the statistical description of users or application programs which is ultimately intended to detection any activity deviating from the profile of normal behavior, which in fact is an indication of an abnormal behavior conducted by users or application programs [7]. signature-based idss work based on collecting and storing the signature of known attacks, and the ids attempts to search the logged events for patterns matching the signature of stored attacks. both methods have their specific advantages and drawbacks. signature-based systems have an appropriate accuracy in detection of known attacks and generate few number of false positives. on the other hand, they are only capable of detecting previously modeled attacks. on the contrary, anomaly-based idss are capable of detecting new attacks, but also produce a large number of false positives and may identify a normal behavior as suspicious due to deviation from the defined threshold. another challenge in using these systems is their difficulty in adapting to dynamic environments [10]. 1intrusion detection systems copyright © 2006-2017 by ccc publications 678 s. mohammadi, a. namadchian many challenges are faced in order to improve the idss. the large number of application programs has led to extensive features in describing normal behavior. moreover, there are more complex attacks developed everyday taking advantage of several vulnerabilities in different application programs. as a result, many parameters of high dimensions are involved in the idss. as a solution, attempts were made in many studies to further simplify the problem through selecting effective features and decreasing their numbers [8]. different studies attempted to select appropriate features for intrusion detection and to develop a learning model through an algorithm. [1]. classified these studies into four categories, namely classification, clustering, statistical, and information theory. in [12, 14], cross-entropy analysis was employed to derive the appropriate features for each attack type. all the mentioned methods are highly dependent on the dataset as they attempts to extract the appropriate features according to those of the dataset. similar to intrusion detection, we are also faced with the problem of selecting appropriate features from among a large number of features in the real environment in applications such as image, video, and sound processing. recent studies have managed to achieve acceptable results in feature selection through a deep-learning approach [15]. the main idea behind deep-learning is the assumption that data are composition of factors or features created in a hierarchical manner. many other general assumptions can further improve deep learning. these seemingly simple assumptions allow exponentially finding relationships between some of the regions and samples. this can be a solution to some of the high-dimensional challenges of the problem [6]. in the proposed algorithm, we used a deep learning algorithm for regression function is obtained through deep auto-encoder.then use memetic algorithm to generate a liner classification function to detect attack. this algorithm helps system to bypass local minima and become convergent, faster. therefore, unlike papers which used genetics [14], [13] we used all kdd training datasets and enhanced our precision as well. the architecture and components of proposed system are introduced in section 2. section 4 deals with deep learning algorithm and its characteristics for obtain linear classification function for each class. in section 5 you know how memetic algorithm works to combine results of classification functions is implied. and finally discussion and conclusion are presented in section 7. 2 the proposed algorithm our proposed algorithm is composed of three phases. in first phase we prepare and normalized dataset.in second phase we use an deep learning auto-encoder model to produce a regression function and in last phase we use an memetic for produce a classifier function by those model.as shown in fig 1 our dataset compose from three parts: training and validation part for learning auto-encoder model and test part for evaluation the proposed algorithm.each record in dataset represent by data and it’s label, normalized data feed into input layer of deep auto-encoder model, and labels are assigned as input the stochastic guardian descent to find optimal model that fit with validation data. after that, we apply memetic algorithm on reached features as final classification function that can detect normal and abnormal traffic. finally at test phase, this model is evaluated with test data.the details of proposed architecture will be in the next sections. a new deep learning approach for anomaly base ids using memetic classifier 679 figure 1: architecture of the proposed algorithm in first phase we must prepare our data for learning. for this purpose all extracted features from data must be normalized to real number with method that is described in section 3. in next phase we train a deep learning for producing a model as regression function that can assign attack score which is explained with detail in section 4. our objective in the third phase is to combine the mentioned functions to learn classification that is able to report an attack is occurred or not occurring. for generating this function, the memetic algorithm is used. this algorithm works based on an evaluation function. after obtaining a proper classification, system may isolate attacks from ordinary traffic through this function. kdd99 dataset was used to test this algorithm. 3 normalize dataset the quality of analyzed data plays a significant role in enhancing the precision of data mining algorithms. we use a gradient descent optimization algorithms in our deep neural network model then need data with zero mean and equal variance. z-score is one method that have a distribution with a mean of zero and a standard deviation of one and perfect for normalization.x is feature data, µ is mean and σ is standard deviation in equation 1. 680 s. mohammadi, a. namadchian xnormalized = x −µ σ (1) kdd use text and number. for text feature we assign a number for every value of text can give features. for example for service feature we use 0 for ftp and 1 for http and so on. after that all feature has a number value. we use this formula to assign a number between -1 and 1 to each features. 4 deep learning approaches deep learning is a branch of machine learning based on several layer of non-linear operations that attempt to provide high-level abstractions for complex data. several algorithms can be building blocks that put on each other as stacks to shape a deep architecture like: convolutional neural networks, deep belief network, boltzmann machine, restricted boltzmann machine, neural networks, auto-encoder, gated auto-encoder; and many models of this group are based on unsupervised learning.the output of each layer becomes the input of the next layer; therefore features that learned in upper layers are more abstracted [6]. the aim of an auto-encoder is to learn a compressed representation for a set of data, typically for the purpose of dimensionality reduction. auto-encoder is based on the concept of sparse coding [11]. ae can be considered as a discriminative dnn in which the target output would be similar to the input, and the number of hidden layer nodes is lower than input. therefore, it can be an unsupervised or supervised method. its training measure is usually composed of two terms: minimization of the construction error and regularization policy. considering different relations for regularization, various types of auto-encoders such as de-noising ae, contractive ae, sparse ae can be created [4,5,9]. after training of the network is finished the hidden layer result considered as compressed representation of the input data. we have five group data in kdd include attack and normal traffic. we use deep autoencoder that encode 41 features to 5 features group that show the score of is member of five group dos , normal , probe , u2r , r2l. as shown in fig 2, we design an auto-encoder deep model composed of three layers of encoder-decoder and a regression at last layer. this supervised model received vectors with 41 dimension as input and attempt to learn a representation that can discriminant them according to their label. after training of this neural network is done, we have a feature vector with five dimension at last hidden layer of encoders as representation of input data attack score.this encoders can work as a regression function. a new deep learning approach for anomaly base ids using memetic classifier 681 figure 2: architecture of the auto-encoder deep learning for generate regression function for five groups 682 s. mohammadi, a. namadchian deep auto-encoder can learn features as well as regression function.so we can use this algorithm without any feature selection.our goal in this step is find best representation of data for five group in kdd after this step we need to combine this functions and generate one classification function. 5 memetic as final classifier whenever connection information is bestowed to auto-encoder of each class, it may have different features due to the mentioned connection belonging to attacks or normal traffic. thus, we need an algorithm which concludes such results and makes decision about normal or abnormal condition of a connection. to do this, a linear memetic classifier were employed. in the training phase, we evaluated data of each connection using regression function of each class and map to five numbers that show score of attack probabilities then this new features is given to the memetic classifier as input.we try in memetic algorithm learn from high level features that show attack probabilities final decision about the normal or attack traffic. as it can be seen in fig 1 in our proposed algorithm has been used to obtain linear classification functions values of each gene are coefficients of linear classifier function and are in the range of (-1023,1023). evaluation function for each chromosome is as 2 which reflects classification precision: cr = tp + tn sizeofdataset (2) algorithm 1 architecture of the memetic algorithm for combine five regression function for detect normal or attack data: training data set result: memetic classifier for intrusion detection initializes p randomly (p = population); perform local search and find best fitness in its neighbourhood explored chromosome of each individual in p; repeat select parents from p; generate offspring applying recombination to the parent selected; if an individual is selected to undergo mutation then then apply local search; end if evaluate fitness of current individual and its neighbours; adopt best chromosome; until best chromosome fittness > σ for local search in algorithm 1 we use a simple local search algorithm like hill claiming, we try to find best fitness by explore neighbourhood of chromosome by changing the gene value. where σ (a double value that can be in 0,1 range) which shows precision rate. the more this value is close to 1 result more take runtime and the more precise classification functions. the fitness function for algorithm 1 can compute equation 1 shows in 2. a new deep learning approach for anomaly base ids using memetic classifier 683 algorithm 2 fitness function for memetic algorithm data: individual chromosome gene[0-n] and learning dataset result: fitness of individual sum=0; for all connection record k in learning data set do selectedfeaturek= deepencode(x); if connecction k is attack then if ∑n−1 i=0 genei ∗normalized(k) < genen then sum = sum + 1 end if else if ∑n−1 i=0 genei ∗normalized(k) >= genen then sum = sum + 1 end if end if end for fittness = sum sizeofdataset ; as you can see in algorithm 2 if correction rate for a classification function compute as fitness each classification function is "n" gens that multiple to normalize(according section 3 method) feature of connection if the compute value less than last gen value classified as attack otherwise classified as normal. 6 evaluation result for evaluating intrusion detection, a proper dataset should be selected in the first phase which either meet the necessary standards or be comparable with other works. kd99 [17] is a proper dataset. [16] produce a dataset called nsl-kdd 2 was introduced that have some benefit to evaluate intrusion.we use both kdd and nsl-kdd datasets to test our algorithm in order to both preserve our work’s comparability with its previous counterparts and to negate some drawbacks of kdd99 in our evaluation. we use correction rate percent that show in equation 2 on testing part of kdd99 for evaluate our model. results of the proposed algorithm are presented and compared with the similar algorithm in table 1. our deep auto-encoder have four layer in each encoder and decoder phases. memetic algorithm parameters is number of population, mutation probability and crossover probability were selected as 100, 0.03 and 0.9 respectively and dscg local searching function was used in our simulation. we compare our algorithm with four algorithms on kdd99 dataset in table 1 on the first column we use only memetic algorithm without deep auto-encoder on our model as you can see result improve with select five best regression function for all group. more abstraction and manifold learning feature of the deep auto-encoder and diminution reduction cause better result. tao xia, et al. [18] try to use genetic with information theory on kdd99 we compare with their result on column "ga" you can see 42.49 percent better result in r2l attack. we have a little records on train for r2l and as result show feature generation and manifold learning of deep auto-encoder can solve this problem better than information theory.nahla ben amor, et al. [2] 2http://nsl.cs.unb.ca/nsl-kdd/ 684 s. mohammadi, a. namadchian use the native bayesian network and show its better result than decision tree. their result shows on "native bayes" and "decision tree" columns to show deep learning and memetic have very competitive results than this ordinary neural networks. table 1: comparison of our cr results with relevant studies class our(deep learning) memetic ga native bayes decision tree normal 98.11 97.22 98.34 97.68 99.50 dos 98.75 98.4 99.33 96.65 97.24 probe 83.34 81.23 93.95 88.33 77.92 r2l 48.35 42.23 5.86 8.66 0.52 u2r 74.28 66.22 63.64 11.84 13.60 the number of selected nsl-kdd records is based on the classification hard scattering in kdd99, thus training algorithms precision is analyzed in a larger and more precise span. in nsl-kdd, numbers of records in both test and training sets are reasonable, thus intricate methods would be implemented without a random selection of dataset records [16].kddtrain+, was used for training phase and kddtest+, was used to evaluate the algorithm.seven algorithms run three times on each record on nsl-kdd.successfulprediction is a number in the range of 0 to 21 which indicates how many time that algorithm be to succeed on correct detection that record.successfulprediction field is a criterion to detect difficulty of classification of present records of nsl-kdd. in fig 3 8, system precision in terms of successfulprediction field is shown. for instance, in fig 6 for r2l class, our system has succeeded to classify 90.72% of records correctly. it is worthy to say that 0 in the mentioned field indicates that all seven algorithms have failed to classify these records in all three times. figure 3: comparison correction rate on nslkdd normal attack our model with memetic algorithm a new deep learning approach for anomaly base ids using memetic classifier 685 figure 4: comparison correction rate on nslkdd dos attack our model with memetic algorithm figure 5: comparison correction rate on nslkdd u2r attack our model with memetic algorithm figure 6: comparison correction rate on nslkdd r2l attack our model with memetic algorithm 686 s. mohammadi, a. namadchian figure 7: comparison correction rate on nslkdd probe attack our model with memetic algorithm figure 8: comparison correction rate on all nslkdd groups our model with memetic algorithm 7 conclusion anomaly-based intrusion detection system was presented which can learn feature of attacks using dnn-auto-encoder and use the memetic algorithm for final classifier. the performance of present model show it can detect most of attacks correctly. deep learning, which is proper for large scale features and memetic algorithm which can solved local minimum optimization. in order to evaluate the performance of the proposed algorithm, results were obtained on kdd and nsl-kdd datasets. this study aimed at offering a deep learning model for anomaly detection engine. however future works include using deep recurrent network for on-line model. for example deep lstm model for add signature-based intrusion detection systems as a supervisor on operational environment. signature-based intrusion detection systems have a trivial false positive rate but their detection rate is very low because they are able to detect only those attacks which follow their pattern. therefore, when a signature-based intrusion detection system recognizes a connection a new deep learning approach for anomaly base ids using memetic classifier 687 as a single attack, it is reliable mostly and it may even be used as an attack label to train an lstm detection system. bibliography [1] ahmed m., naser mahmood a.,hu j. 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[13] owais s., snasel v., kromer p., abraham a. (2008); survey: using genetic algorithm approach in intrusion detection systems techniques, 2008 7th computer information systems and industrial management applications, 300–307, 2008. [14] qu g., hariri s., yousif m. (2005), a new dependency and correlation analysis for features, ieee transactions on knowledge and data engineering, 17(9), 1199–1206, 2005. [15] schmidhuber j. (2015), deep learning in neural networks: an overview, neural networks, 61, 85–117, 2015. 688 s. mohammadi, a. namadchian [16] tavallaee m., bagheri e., lu w., ghorbani a. a. (2009), a detailed analysis of the kdd cup 99 data set, ieee symposium on computational intelligence for security and defense applications, cisda 2009, 2009. [17] university of california, i. kdd cup 1999, 1999. [18] xia t., qu g., hariri s., yousif m. (2005), an efficient network intrusion detection method based on information theory and genetic algorithm, performance, computing, and communications conference, 2005. ipccc 2005, 24th ieee intl., 11–17, 2005. international journal of computers communications & control issn 1841-9836, 12(1):76-89, february 2017. speed computation for industrial robot motion by accurate positioning l.m. matica, h. oros liliana marilena matica* faculty of electrical engineering and information technology university of oradea oradea, romania *corresponding author:lmatica@uoradea.ro horea oros faculty of sciences, department of mathematics and computer science university of oradea oradea, romania horos@uoradea.ro abstract: in this paper we define a new method for speed (velocity) computation, named mixt profile. the mixt profile of speed variation assures an accurate positioning at the end of motion (movement), in a well determinate time lapse. the method is linked with computation of location (position) matrix, about an industrial robot. mixt profile of speed may be applied about motion on linear or circular trajectories. the paper continues the explanation from [6]a regarding this method. keywords: kinematics of industrial robots; linear or circular trajectory; acceleration and deceleration stage of movement areprinted and extended, with permission based on license number 3979260943291 [2016] ieee, from "computers communications and control (icccc), 2016 6th international conference on" 1 introduction this paper contains others explanation regarding mixt profile of speed, published in [6], those are: more detailed explanations about the computation method named mixt profile of speed; the adaptation of mixt profile method of computation for a circular trajectory and a concrete example of computation; a graphic about deceleration stage explaining software implementation of this computation method; formulas of reverse kinematics about rrrrrr robotic arm, which was done for illustrate the method of computation, figure 1; formulas of direct kinematics about this robotic arm; explanations about the solid kinematics principles that establish the direct kinematics; enunciation of or algorithm about determining the direct kinematics of an robotic arm (a simple algorithm defined in this paper; it analyses only those two relative positions: parallel or perpendicular). concerning movement command of an industrial robot, [1][6] it is necessary to define the next location of it. the industrial robot location is defined by the location matrix. starting with the values of location matrix, the values of kinematics joints parameters of the industrial robot may be computed. those computation formulas are named, reverse kinematics. the reverse kinematics is the solution of an equation system formed by forward kinematics. forward kinematics of an industrial robot is the result of a kinematics analysis. an example of kinematics analysis about an industrial robot, (more precise, about a robotic arm type rrrrrr) is illustrated in figure 1 [3]. in figure 1, the notations defines several cartesian coordinate systems with its axles: xi, yi, zi and its origins: oi (index i goes from 1 to 6; i = 1..6); then six rotation driving kinematics copyright © 2006-2017 by ccc publications speed computation for industrial robot motion by accurate positioning 77 figure 1: kinematics analysis of robotic arm, type rrrrrr couples (d.c.c.) of robotic arm: ci (i = 1..6); variable parameters of d.c.c.: θi (i = 1..6); constant parameters of the robotic arm: d1; a2; d4; d6; the versors: −→n ; −→o ; −→a (about sense and direction of axles ox6; oy6 and oz6), the cartesian coordinate system of index 6 has the origin in the tool centre point (tcp) of the robotic arm. all cartesian coordinate systems have the position of axles according with denavit-hartenberg convention. [3], [6] about industrial robot movement (motion), if the motion time is defined, the positioning precision at the end of the motion is not very good. a very good positioning precision, at the motion end, can’t be obtained, in a defined motion time. both conditions are very hard to accomplish. the mixt profile speed variation, defined in this paper, during industrial robots motion, may accomplish those two conditions. the method is linked with the location matrix computation of an industrial robot. [6] 2 the location matrix of an industrial robot the industrial robot location, (position and orientation) is defined by the location matrix that contains axles components of position vector: −→p and orientation versors: −→n ; −→o ; −→a ; figure 2, [3]; a versors have the module equal to 1. figure 2: the position vector and orientations versors that define the location matrix of an industrial robot a versor describes only the orientation; about an industrial robot, the orientation versors: −→n ; −→o ; −→a ; describe the orientation of tool centre point, tcp, regarding the cartesian coordinate 78 l.m. matica, h. oros system considered. the location matrix of the industrial robot contains three components of those versors and the position vector (three components along well known three axles of a cartesian coordinate system: ox; oy ; oz). gi =   nx ox ax px ny oy ay py nz oz az pz 0 0 0 1   (1) index i of the location matrix makes reference to index i cartesian coordinate system. according to denavit-hartenberg convention, a coordinate system, index i, is obtained by homogeneous transformations, from previous one, index i−1. those homogeneous transformations are (always in this order): 1. rotation of θi(t) + β angle, around ozi−1 axle (the parameter t show that the angle varies in time, it is programmable, because that d.c.c. is an rotation one); 2. translation of di distance, along ozi−1 axle; 3. translation of li distance, along oxi axle; 4. rotation of αi angle, around ozi−1 axle. [3], [6] the previous homogeneous transformations define the transformation matrix, i−1ai: [3] i−1ai = rot(ozi−1,θi(t) + βi) ·trans(ozi−1,di) ·trans(oxi, li) ·rot(oxi,αi) (2) for example, the forward kinematics formulas of the robotic arm from figure 1 are (a robotic arm is a particular kind of industrial robot, it is similar with the human arm): 0a1 = rot(oz0,θ1(t) + π/2) ·trans(oz0,d1) ·rot(ox1, +π/2) 1a2 = rot(oz1,θ2(t)) ·trans(ox2,a2) 2a3 = rot(oz2,θ3(t) + π/2) ·rot(ox3, +π/2) 3a4 = rot(oz3,θ4(t)) ·trans(oz3,d4) ·rot(ox4,−π/2) 4a5 = rot(oz4,θ5(t)) ·rot(ox4, +π/2 5a6 = rot(oz5,θ6(t) ·trans(oz5,d6) (3) let us discus about transformation matrix 0a1. the kinematics joint, named c1, is a rotational one; so, relation 2 is useful and must be adapted (index i is equal with 1): 0a1 = rot(oz0,θ1t + βi) ·trans(oz0,d1) ·trans(ox1, l1) ·rot(ox1,α1) (4) about relation 4, the variable parameter (programmable) is the angle: θ1(t); the constant values are named: β1 ; k1 ; l1 and α1. in purpose to determine the constant value named β1, the relative position of axle ox1 to axle ox0 must be analyzed, figure 3; it is perpendicular (it is not parallel, it define an angle); this parameter has a different from zero value [3]. in purpose to determine the α1 constant value, it must be analyzed the axle oz1 relative position to axle oz0; figure 4; it is perpendicular, this parameter has a non-zero value. the translations are defined by relative position of origins o0 and o1; so, it is necessary a translation along axle oz1 with constant value named d1. [3] it result the 0a1 transformation matrix, relation 3. speed computation for industrial robot motion by accurate positioning 79 figure 3: analysis about β1 value figure 4: analysis about α1 value let us discuss other two examples [3]. from figure 5 graphical analysis, it results the 1a2 matrix. a constant value (equal with a2) characterizes a translation along axle ox2; the constant value named β2 has a zero value, because axle ox1 is parallel with axle ox2; the constant value named α2 has a zero value, because axle oz1 is parallel with axle oz2. from figure 6 graphical analysis, it results 5a6 matrix, (relation 3); the significant constant value, d6, is about a translation along axle oz6; β6 and α6 have zero value. similar analyses are performed for determination of every transformation matrix (of forward kinematics). it results all the transformation matrices (relation 4) and the location matrix of the industrial robot. more precisely, the location matrix defines the position and the orientation of the robotic arm tool center point, tcp. lets makes a clear distinction between parameters of a translational or a rotational kinematics joint of an industrial robot. about a rotational kinematics joint, relation 2, the variable parameter is named: θi(t). about a translational kinematics joint, the variable parameter is named: di(t); the i−1ai transformation matrix is defined as follow: i−1ai = rot(ozi−1,βi) ·trans(ozi−1,di(t)) ·trans(oxi, li) ·rot(oxi,αi) (5) 80 l.m. matica, h. oros figure 5: analysis about 1a2 matrix figure 6: analysis about 5a6 matrix in relation 2 and 5, the constant values of forward kinematics computation are named: βi; ki; li; αi, whatever the kinematics joint is a rotational or a translational one. usually, about forward kinematics of industrial robots, the explanations work with a single formula (relation 6), without a clear distinction (regarding the parameter name), concerning variable and constant values: i−1ai = rot(ozi−1,θi) ·trans(ozi−1,di) ·trans(oxi, li) ·rot(oxi,αi) (6) the relations 2 and 5, described in this paper, make a clear distinction between constant and variable values, involved in forward kinematics computation. according with denavit-hartenberg convention, the algorithm explained in this paper, determines the constant values involved in forward kinematics computations, based on observation: two axles are parallel or are not parallel. the first step of the algorithm consists in cartesian coordinate systems settlement, identical with denavit-hartenberg convention. every kinematics joint, named ci, is characterized by a cartesian coordinate system, named oxy zi properly settled; index i goes from 1 value to n, n is the number of kinematics joints. the axle ozi is settled along the axis of index i + 1 kinematics joint, named ci+1 ; the axle oxi is settled perpendicular of the plane formed by axles ozi−1 and ozi. the oxy zn cartesian coordinate system is settled linked with tcp position and orientation; in figure 1 n = 6. every cartesian coordinate system is obtained from the previous one, by several homogenous transformations; those define the transformation matrices, i−1ai. the next step of the algorithm consists on the characteristics identification of each ci kinematics joint and determination of each i−1ai matrix. regarding a rotational kinematics joint, relationship 2 is useful for transformation matrix determination; the variable (programmable) parameter is θi(t); the others values are constant. regarding a translational kinematics joint, relationship 5 is useful for transformation matrix determination; the variable (programmable) parameter is di(t); the others values are constant. speed computation for industrial robot motion by accurate positioning 81 concerning industrial robot motion, the speed variation (as it is defined by mixt profile of speed), had influence (it changes the parameters values, in time) upon programmable (variable) parameters of kinematics joints. the third step of the algorithm consists on transformation matrixes determination, i−1ai, (several analysis about determination of each transformation matrix, were explained on graphical analysis in figures 4, 5 and 6) it may be described as follow: the variable parameter is defined by the kinematics joint type, on step two of the algorithm; about constant values, there is a rotation, named βi, around axle ozi−1, if axle oxi−1 and axle oxi are not parallel (those axle may be perpendicular, according with the construction of the industrial robot and the angle may be ±π/2; there are translations along axle ozi−1 or along axle oxi if the cartesian coordinate systems origins, oi−1 and oi, are not identical (very simple to be identified); there is a rotation around axle oxi, named αi, if axle ozi−1 and axle ozi are not parallel, (those axle may be perpendicular and the angle may be ±π/2). the previous explanations develop an algorithm about forward kinematics determination, (it asks about each kinematics joint of the robotic arm: is it a translational or a rotational one; it asks about two axles: are those axles parallel or not; this algorithm may be named algorithm or). it analysis only those two relative positions: parallel or perpendicular; the analysis described by denavit-hartenberg convention analysis any relative position about similar cartesian coordinate axles involved. the formulas of forward kinematics, to compute position vector components of an industrial robot, more precise, about the robotic arm type rrrrrr from figure 1, are [3] (notations si; i = 1..6, means sine of θi angle and ci means cosine of same angle, the others notation are identical with those explained): px = (s1 ·c2 ·s3 + s1 ·s2 ·c3) ·c4 ·s5 ·d6 + c1 ·s4 ·s5 ·d6 −s1 ·c2 ·a2+ +(s1 ·s2 ·s3 −s1 ·c2 ·c3) · (c5 ·d6 + d4) py = (−c1 ·c2 ·s3 −c1 ·s2 ·c3) ·c4 ·s5 ·d6 + s1 ·s4 ·s5 ·d6+ +c1 ·c2 ·a2 + (c1 ·c2 ·c3 −c1 ·c2 ·s3) · (c5 ·d6 + d4) pz = (−s2 ·s3 + c2 ·c3) ·c4 ·s5 ·d6 + (s2 ·c3 + c2 ·s3) · (c5 ·d6 + d4)+ +s2 ·a2 + d1 (7) the formulas to compute the orientation versors components of the same robotic arm, figure 1, are: nx = (s1 ·c2 ·s3 + s1 ·s2 ·c3) · (c4 ·c5 ·c6 −s4 ·s6)+ +c1 · (s4 ·c5 ·s6 + c4 ·s6) −s5 ·c6 · (s1 ·s2 ·s3 −s1 ·c2 ·c3) ny = (−c1 ·c2 ·s3 −c1 ·s2 ·c3) · (c4 ·c5 ·c6 −s4 ·s6)+ +s1 · (s4 ·c5 ·c6 + c4 ·s6) −s5 ·c6 · (c1 ·c2 ·c3 −c1 ·s2 ·s3) nz = (−s2 ·s3 + c2 ·c3) · (c4 ·c5 ·s6 −s4 ·s6) −s5 ·s6 · (s2 ·c3 + c2 ·s3) (8) ox = (s1 ·c2 ·s3 + s1 ·s2 ·c3) · (−c4 ·c5 ·s6 −s4 ·c6)+ +c1 · (−s4 ·c5 ·s6 + c4 ·c6) + s5 ·s6 · (c1 ·c2 ·c3 −c1 ·s2 ·s3) oy = (−c1 ·c2 ·s3 −c1 ·s2 ·c3) · (−c4 ·c5 ·c6 −s4 ·c6)+ +s1 · (−s4 ·c5 ·s6 + c4 ·c6) + s5 ·c6 · (c1 ·c2 ·c3 −c1 ·s2 ·s3) oz = (−s2 ·s3 + c2 ·c3) · (−c4 ·c5 ·s6 −s4 ·c6) + s5 ·s6 · (s2 ·c3 + c2 ·s3) (9) 82 l.m. matica, h. oros ax = (s1 ·c2 ·s3 + s1 ·s2 ·c3) ·c4 ·s5 + s1 ·s4 ·s5+ c5 · (s1 ·s2 ·s3 −s1 ·c2 ·c3) ay = (−c1 ·c2 ·s3 −c1 ·s2 ·c3) ·c4 ·s5 + s1 ·s4 ·s5+ +c5 · (c1 ·c2 ·c3 −c1 ·s2 ·s3) az = (−s2 ·s3 + c2 ·c3) ·c4 ·s5 + c5 · (s2 ·c3 + c2 ·s3) (10) the reverse kinematics (as a result of forward kinematics) computes the d.c.c. parameters starting with position matrix elements values; it is the solution of the equations system (12 equations and 12 unknown values) defined by direct kinematic. it results this conclusion: in order to command an industrial robot motion, it is necessary to compute the position matrix components, for every sampling period of time; those components describe the position and orientation of the robotic arm. the speed (velocity) of motion is defined by vector −→p (position vector) variation. [6] 3 acceleration, motion on trajectory and deceleration about motion on a trajectory, a condition could be a certain speed profile. this speed profile may be trapezoidal or parabolic, figure 7 and figure 8 (graphics consider continuous time). figure 7: trapezoidal profile (of speed) figure 8: parabolic profile the motion of an industrial robot may contain three stages: 1. the acceleration from zero motion speed to the programmed motion speed; 2. the motion with programmed motion speed (constant); 3. the deceleration from programmed speed to zero. [6] speed computation for industrial robot motion by accurate positioning 83 commonly, acceleration and deceleration depend on the speed profile that was selected. this paper describes another method about deceleration; the method describes another speed profile, named mixt profile of speed, figure 9. [6] figure 9: mixt profile of motion speed if the trajectory is imposed (linear or circular), it must be computed the position of the intermediary points, named waypoints, (on the trajectory), during acceleration stage, motion on trajectory stage and deceleration stage. intermediary positions of the robotic arm are defined by different location matrix. if the trajectory is imposed, we must compute location matrices for every waypoint. considering reverse kinematics, it results the motion commands for kinematics joints of the robotic arm; starting with location matrix of every waypoint that composes the trajectory, the parameter of every kinematics joint may be computed. 4 acceleration and deceleration stages for mixt profile of speed usually, about acceleration stage, the acceleration variation depends of the maximum acceleration possible, on a sample period of time, considering a numerical computation system with numerical processor. about an robotic arm motion, the numerical process of command computation, is a discrete one. [1] variation of robotic arm position, variation of motions speed, acceleration and deceleration values (and others values) depend of a discrete variable defined by relation: k ·t , where t is the sampling period of time, and k is the number of the sample periods of time considered from the commands beginning (for example, the variable had the value 11 · t after eleven sampling periods of time from the start of motion). [6] about computation described in this paper, the value of maximum possible acceleration in a sample period of time is named amax. about this computation method described in this paper, in the acceleration stage for mixt speed profile, the variation of speed is defined by the relation: [6] v(kt) = v0 + k ·amax (11) often, the motion speed initial value is zero: v0 = 0; it results: v(kt) = k ·amax, figure 9, but this speed increasing computation method may be applied for any initial value. considering the defined speed increase, relation 11, (in the acceleration stage of mixt profile), the position varies with the values: t ·v(kt) = t ·k ·amax; after each sampling period of time. the acceleration value may be considered about axle component of position vector, it results the maximum possible acceleration along every axle, named am (instead of amax); the position vector axle components varies, during the acceleration stage: [6] 84 l.m. matica, h. oros pk,x = pk−1,x + k ·t ·am ; k = 1..ka pk,y = pk−1,y + k ·t ·am pk,z = pk−1,z + k ·t ·am (12) index k goes from 1 value to ka value, till the end of the motion stage. the computation starts from axle components values of initial position vector, named: p0,x; p0,y; p0,z. let consider a motion with a programmed (imposed) speed value, named vp ; the acceleration stage ends when the speed reach this programed speed value. the programed value of motion speed defines the number of sampling periods of time necessary for the acceleration stage; named ka, it results: ka = vp/t ·am (13) the variation is a discrete one, so, the value of ka must be an integer value; the ka value must be adapted of this condition: it will be the next bigger integer value of the computed value. because of this aspect, the last step of speed increase value must be adapted, in purpose to reach the programmed speed value (it is obvious that the last step of speed increase value will not be bigger then: am). if speed axle components, named: vp,x; vp,y; vp,z; have different values, the ka value is determined by the maximum value of speed component: ka = max(vp,x; vp,y; vp,z)/t ·am (14) the acceleration may be different for each axle, the maximum value of speed component, max(vp,x; vp,y; vp,z), defines the axle with maximum acceleration. about other axles, the acceleration is computed, in order to have o constant value for every sampling period of time. the acceleration values are: vp,x/ka; vp,y/ka; vp,z/ka. about next stage, the motion with a programmed speed, the position vector is described by the relations: [6] pk+1,x = pk,x + t ·vp,x pk+1,y = pk,y + t ·vp,y pk+1,z = pk,z + t ·vp,z (15) in relation 15 index k starts from ka and goes till is necessary the deceleration stage. this relation, rel. 15 defines those significant values: δx = t ·vp,x; δy = t ·vp,y; δz = t ·vp,z; its mean the linear space steps (because the trajectory is linear), performed at each sampling period of time, during motion with programed speed, on every axle. those values are named axle steps. motion on a circular trajectory defines angle steps (about spherical coordinates). about deceleration stage (the third stage of motion), the speed variation is not a linear one: v(kt) = vp −t ·ad(kt) = vp − b ·k2; k = 1..kd (16) in previous relation, the deceleration value, named ad, is a variable value and b is a constant value. the b value defines the characteristics about robotic arm motion. the speed decreases till the motion end; considering the condition: 0 = vp −b ·k2d; it results the number of sampling period of time necessary for the deceleration stage, named kd (the axle components of speed are: vp,x; vp,y; vp,z): speed computation for industrial robot motion by accurate positioning 85 kd = √ max(vp,x; vp,y; vp,z) b (17) about motion on trajectory (the second stage), the necessary distance for deceleration stage, named dd, determine its end (the motion on trajectory ends in the point situated at distance dd before the end point of motion). [6] the resulting speed profile, named mixt profile, figure 9 (the graphic considers continuous time) ensures a better precision about stop point proximity. typically, for precise positioning at the motion end, it can’t be specified the time needed; the mixt profile of speed specifies exactly the time needed for precise positioning at the motion end. [6] the described method, named mixt profile (of speed), was implemented at a flexible welding cellule (for manufacture of mining machinery), and the agreed motion characteristics (with the beneficiary) were ok. the maximum weight of processed pieces (with this welding cellule) was 2.5 tons. [6] about deceleration stage, the software implementation considered 25 values about speed decrease, from speed maximum value possible, those values were written in a table, named: deceleration table. inertial reason imposes the number of table values (25). in purpose the determine the deceleration start, the programed speed, vp, was compared with those values, from deceleration table; the comparison result defines the value of kd and every speed value, for every sampling period of time, during deceleration stage; a graphical explanation of this process is described in figure 10. figure 10: about software implementation of deceleration process in figure 10, the example works with 4 steps till the end of the motion, (when the speed has zero value). the software implementation of deceleration stage considered a different deceleration table for oz axle component of speed, because a vertical motion has different inertial characteristics, comparing with a horizontal motion (about axle ox and oy the deceleration table is identical). 5 example of computation about a linear trajectory for example, considering a linear trajectory and constant orientation of the robotic arm (along the motion), the value of initial speed zero; the values of programed speed: vp = 5 √ 2mm/s; vp,x = 3mm/s; vp,y = 4mm/s; vp,z = 5mm/s; am = 25mm/s2 and t = 10−2s; this method of computation determines: ka = 20, the number of sampling periods of time necessary for acceleration stage: [6] ka = max(3; 4; 5)mm/s/(10 −2s · 25mm/s2) = 20 (18) 86 l.m. matica, h. oros during acceleration stage, the speed increases with those values: δvx = 3/20mm/s; δvy = 4/20mm/s; δvz = 5/20mm/s; (because of inertial reasons, the acceleration have different values, for each axle components). considering the initial values of position vector components: p0,x = 1, 1mm; p0,y = 2, 2mm; p0,z = 3, 3mm, after first sampling period of time, the position vector has the axle components: p1,x = p0,x + 1 ·t · (v0,x + δvx) = 1, 1 + 10−2 · 3/20 = 1, 1 + 0, 0015 = 1, 1015mm p1,y = p0,y + 1 ·t · (v0,y + δvy) = 2, 2 + 10−2 · 4/20 = 2, 2 + 0, 002 = 2, 202mm p1,z = p0,z + 1 ·t · (v0,z + δvz) = 3, 3 + 10−2 · 5/20 = 3, 3 + 0, 0025 = 3, 3025mm (19) during acceleration stage, after 10 period of time the axle components of position vector differs (from the previous one, in the previous period of time) with: δpx = 10 · t · δvx = 10 · 3/2000 = 0, 015mm; δpy = 10 · t · δvy = 10 · 4/2000 = 0, 02mm; δpz = 10 · t · δvz = 10 · 5/2000 = 0, 025mm. after 20 periods of time, begin the stage of motion on trajectory. in this moment (considering the initial values of position vector components), the position vector has the axles components: p20,x = 1, 1 + 10 −2 · 3/20 · (1 + 2 + ... + 20) = 1, 1 + 0, 315 = 1, 415mm; p20,y = 2, 2 + 10−2 · 4/20 · (1 + 2 + ... + 20) = 2, 2 + 0, 42 = 2, 62mm; p20,z = 3, 3 + 10−2 · 5/20 · (1 + 2 + ... + 20) = 3, 3 + 0, 525 = 3, 825mm. the stage of motion on trajectory is described by relations relation 15, (it is similar with acceleration stage, but speed has a constant value): δpx = t ·vp,x; δpy = t ·vp,y; δpz = t ·vp,z; (20) for each axle, the axle steps have constant values. because axle steps have constant values, the algorithm is named: numeric difference analysis, more exactly: interpolate algorithm of numeric difference analysis. [6] for example, after 80 periods of time, on the stage of motion with constant speed (and after 100 periods of time from the beginning of the motion) the components of position vector have the values: px = 1, 415 + 80 · 10−2 · 3 = 3, 815mm; py = 2, 62 + 80 · 10−2 · 4 = 5, 82mm; pz = 3, 825 + 80 · 10−2 · 5 = 4, 225mm. let apply this computation (mixt profile of speed), to the robotic arm from figure 1. let consider the orientation of robotic arm defined by those versors: −→n = 1· −→ i ; −→o = 1· −→ j ; −→a = 1· −→ k (where −→ i , −→ j , −→ k are the versor defining the cartesian coordinate axles, ox; oy and oz). after 100 sampling period of time, the location matrix is: g0(100 ·t) =   1 0 0 3, 815 0 1 0 5, 82 0 0 1 4, 225 0 0 0 1   (21) during the motion, the location matrix has different values, at every sampling period of time. knowing the location matrix, it results parameters of robotic arm kinematics joints, considering the formulas of robotic arm reverse kinematics. the value of θ1(100 · t) parameter may be computed with this relationship (formula from reverse kinematics of the robotic arm [3]): θ1(100 ·t) = arctan −(p100,x −d6 · sin r2 · cos r1) p100,y −d6 · sin r2 · sin r1 (22) speed computation for industrial robot motion by accurate positioning 87 in previous relationships, the angle r1 is the polar angle and the r2 angle is the azimutal angle, about −→a versor, figure 11. figure 11: polar and azimutal angle those angles may be computed; the value of n1 and n2 depends of the sign of versor components; let remember that arctan can have values in [−π; π]; but those angles may have values in [0; 2π]: r1 = n1 ·π + arctan ∣∣∣∣ayax ∣∣∣∣ ; r2 = n2 ·π + arctan √ a2x + a 2 y |az| (23) from the considered orientation of the robotic arm: −→a = 1 · −→ k ; it results the value of those two angles are: r1 = 0; r2 = 0; the parameter value is: θ1(100 ·t) = arctan −(p100,x) p100,y = arctan(−3, 815/5, 82) = −33, 245 (24) previous relationship defines a negative angle, it means: the rotation sense of motion, about c1 kinematics joint (the angle is the parameter of this kinematics joint) is opposite considering the positive sense, as it is designed in figure 1. in purpose to determine the end of the second stage (motion with constant speed), it is necessary to compute the number of sampling period of time for deceleration; let considers b = 5mm/900s; it results: [6] kd = √ max(3; 4; 5)mm/s 5mm/900s = 30 (25) during the deceleration, the computation of waypoints coordinates involves those speed values, from relation 16 it results: vx(kt) = vp,x − vp,x k2 d ·k2 = 3 − 3 900 ·k2,k = 1..kd vy(kt) = vp,y − vp,y k2 d ·k2 = 4 − 4 900 ·k2 vz(kt) = vp,z − vp,z k2 d ·k2 = 5 − 5 900 ·k2 (26) for each sampling period of time, the position differs with values: δpx = t ·vx(kt),k = 1..kd δpy = t ·vy(kt) (27) δpz = t ·vz(kt) 88 l.m. matica, h. oros about deceleration stage, it must be computed the maximum value of distance axle components (necessary for deceleration stage), named ddmax: ddmax = kd∑ k=1 t · [max(vp,x; vp,y; vp,z) − b ·k2] (28) the ddmax value considers the maximum distance of the three axles, necessary for deceleration stage. the deceleration begins when it remains the distance ddmax, till the motion end, on respective axle. according with the considered example, after 24 period of time on deceleration stage, the axle components of speed have the values: [6] vx(24 ·t) = vp,x − vp,x k2 d · 242 = 3 − 3 900 · 242 = 1.08[mm/s] vy(24 ·t) = vp,y − vp,y k2 d · 242 = 4 − 4 900 · 242 = 1.44[mm/s] vz(24 ·t) = vp,z − vp,z k2 d · 242 = 4.0752[mm/s] (29) 6 about computation for a motion on a circular trajectory, with mixt profile of speed the previous example considered a linear trajectory. a circular trajectory imposes the computation of waypoints on spherical coordinates, named radium: r, polar angle: ϕ and azimuthal angle: φ; figure 11, and conversion on cartesian coordinates of those values. [6] the acceleration and deceleration is similar with the method described about a linear trajectory, regarding tangential speed. the variation of tangential speed defines the variation of angular speed, named ω, figure 10. [6] software implementation considered the maximum acceleration possible of motion speed, about rotation around each cartesian coordinate system axle. a table about deceleration stage was defined about vertical rotations another table was defined about horizontal rotations. an example of computation may consider ka = 3; this values is defined by imposed values of motion speed. let consider the motion a rotation of 2π/4 = 90◦ around axle oy . in the first period of time, the motion speed is: v1 = v0 + am, it result the angular speed of rotation ω1 = ω0 + ∆ω; in the second and third period of time the angular speed increase with the same value ∆ω; it results: ω2 = ω1 + ∆ω = ω0 + 2 ·∆ω and ω3 = ω2 + ∆ω = ω0 + 3 ·∆ω. in the fourth period of time, (after motion beginning), the angular speed reaches the programed speed, ωp. comparing the angular programmed speed value with values from deceleration table, it may results the value kd = 5 and all the values of angular speed, about deceleration stage. the distance necessary for deceleration stage on a circular trajectory, named dec may be computed, where ωdec(k) are the smallest five values from deceleration stage (the deceleration table simplifies the software implementation of deceleration stage): dec = kd=5∑ k=1 t ·ωdec(k) (30) it results the number of sampling period of time for motion on trajectory stage (let consider ω0 = 0): kt = 2π/4 − (∆ω + 2 · ∆ω + 3 · ∆ω) ·t −dec t ·ωp (31) a variable orientation of robot arm, during the motion, involves a similar computation as described for a circular trajectory, but applied about computation of azimuthal and polar angle of each orientation versor; (the orientation versors are: −→n ; −→o ; −→a ). speed computation for industrial robot motion by accurate positioning 89 conclusions about a robotic arms motion, those two conditions are very difficult to accomplish: best precision at the motion end and exact defined motion time. those two conditions are accomplished by mixt profile (of motion speed variation), described in this paper. the advantages of mixt profile are: the best precision to reach the end point of robotic arms motion, exact determination of motion time and minimum time of acceleration up to programed motion speed. the method may have others diverse applications, about motion on a linear or circular trajectory; for example about turning or milling process. motion execution with exact speed gives processing quality; the described method of mixt profile, about speed variation, was implemented on numerical control equipment and precision was accurate (for example: numerical control equipment for workpieces of sintered metal carbides). bibliography [1] horsch, t.; juttler, b.; cartesian spline interpolation for industrial robots. university of technology, departament of mathematics, darmstadt, germany (http://www.ag.jku.at/pubs/csi98.pdf) [2] matica, l.m.; kovendi, z. (2011); structure analysis for an industrial robot, journal of computer science and control systems, issn 1844-6043, 4(1): 89-92. [3] matica, l.m. (2008); conducerea robotilor industriali, edit. univ. din oradea, isbn 978973-759-481-5. [4] choset, h. and all, principles of robot motion; https://mitpress.mit.edu/books/principlesrobot-motion [5] laumond, j.p.; robot motion planning and control, isbn: 978-3-540-76219-5 (print) 978-3540-40917-5 (online), http://link.springer.com/book/10.1007%2fbfb0036069; [6] matica, l.m.; oros, h (2016); speed computation in movement followed by accurate positioning of industrial robots, computers communications and control (icccc), 2016 6th international conference on, ieee xplore, e-issn 978-1-5090-1735-5, doi: 10.1109/icccc.2016.7496741, 75 79. international journal of computers communications & control issn 1841-9836, 13(3), 429-439, june 2018. autopilot design for unmanned surface vehicle based on cnn and aco d. zhao, t. yang, w. ou, h. zhou dongming zhao, tiantian yang, wen ou, hao zhou* school of automation wuhan university of technology wuhan 430070, hubei, china dmzhao@whut.edu.cn, ttyang@whut.edu.cn, wenou@mail.hust.edu.cn *corresponding author; 18601157377@163.com abstract: there is a growing concern to design intelligent controllers for autopiloting unmanned surface vehicles as solution for many naval and civilian requirements. traditional autopilot’s performance declines due to the uncertainties in hydrodynamics as a result of harsh sailing conditions and sea states. this paper reports the design of a novel nonlinear model predictive controller (nmpc) based on convolutional neural network (cnn) and ant colony optimizer (aco) which is superior to a linear proportional integral-derivative counterpart. this combination helps the control system to deal with model uncertainties with robustness. the results of simulation and experiment demonstrate the proposed method is more efficient and more capable to guide the vehicle through los waypoints particularly in the presence of large disturbances. keywords: usv, autopilot, predictive control, convolution neural network (cnn), ant colony optimization (aco), rolling optimization. 1 introduction in recent years, the application of usvs is ever increasing in the fields of oceanography [13] [3], meteorology [7], military and commercial applications. autopilot is a device for controlling the heading of usv in a truly autonomous mode, which plays a key role in course-keeping for usv [24]. when usv is working under harsh sea condition and high-risk environment, the performance of traditional autopilot declines in the presence of time-varying ocean disturbances, and measurement noises. a lot of autopilot designs for usv have been proposed to solve this problem. gao et al. proposed a fuzzy neural network controller based on chaos neural network forecast model for the usv in complex sea condition [9, 15, 17]. an optimizing sliding mode cascade control structure is proposed to determine the optimal sliding surface parameters for sliding mode control of underactuated usv systems [18]. further, nonlinear controller of usv is designed by backstepping method [1, 10]. the fuzzy control approach is also applied to the control of usv [8,11,19]. li et al. proposed an adaptive radial basis function based on neural network controller for the nonlinear control of usv which contains modeling errors and unknown bounded environment disturbances [4]. however, it’s still an open problem that how to develop effective method for robustness and high-precision steering of usv in extreme sea condition. this paper reports a kind of autopilot based on convolutional neural network(cnn) and ant colony optimizer. a nmpc based on cnn is used to compensate this predicted disturbance and the ant colony algorithm is used to predict the disturbances. this approach helps the autopilot to deal with model uncertainties and so on. the simulation and experiment results verified the efficacy of the proposed method. the rest paper is organized as follows: section 2 states the specifications of the usv and the problem formulation. section 3 elaborates the autopilot design based on cnn and aco. section 4 provides the simulation results and experiment results. section 5 gives the conclusion of this paper. copyright ©2018 cc by-nc 430 d. zhao, t. yang, w. ou, h. zhou figure 1: wh-01 usv 2 problem formulation consider a robotic marine surface vehicle, called wh01 (as shown in figure 1), whose specifications are listed in table 1.the vehicle, designed in the control laboratory at whut(wuhan university of technology), has a hydraulic jet propeller and one rudder driven by servo motors for controlling its surge speed and heading. the maximum angular detection for rudder is ±35◦. it is equipped with navigation sensors including beidou, 3-axis magnetometer, 3-axis accelerometer, and 3-axis rate gyro. the magnetometer and gyro are used to measure its yaw and yaw rate during trails, respectively. in addition, a 4g communication module is installed to control and monitor the vehicle from remote computer. table 1: specifications of wh-01 usv parameter value length /m 8.075 vertical length /m 8 draft depth /m 0.6 moulded depth/m 1.15 full loaded displacement/t 3.2 maximum speed /kn 12 cruising speed /kn 6 propulsion mode water jet propulsion because of the interference of wind, waves and currents, the steering model of usv is expressed as follows: t1t2ψ̈ + (t1 + t2)ψ̇ + ψ + aψ 3 = kδ + kt3δ̇ + fa + fω + fl (1) t1t2 = (m + λ22)(iz + λ66)/c (2) t = t1 + t2 −t3 (3) where ψ is the heading angle of the usv, δ is the rudder angle of the usv , t is the turning lag index, t1t2t3 is the second-order turning lag index, k is the turning ability index, m is the mass of the usv, fa is equivalent disturbance rudder angle of the wind, fω is equivalent disturbance rudder angle of the disturbances due to waves, fl is equivalent disturbance rudder angle of the disturbances due to currents. autopilot design for unmanned surface vehicle based on cnn and aco 431 aco plant angle deviation + rudder prediction angle angle feedback predictive controller target heading wind, waves, flow interference + cnn model figure 2: a block diagram of the autopilot based on cnn and aco for usv according to the calculation method in the literature [6] and the zigzag experimental data of ψ/δ = 15◦/15◦ for the draft state in the taihu lake, it can be obtained that: k = 2.9097,t = 55.8855 (4) zigzag experimental result is shown in table 2. table 2: zigzag experimental data of usv time /s rudder angle /◦ heading angle /◦ remark 1′82 15 15 5′82 15 28.2 maximum of forward heading angle 9′44 -15 0 12′68 -15 -15 maximum of backward heading angle 15′62 15 -24.7 23′06 15 15 maximum of forward heading angle 26′56 -15 28.2 3 robust adaptive autopilot the autopilot design of usv is based on convolutional neural network(cnn) and ant colony optimizer. closed-loop state prediction and rolling horizon optimization are included in the predictive controller. particularly, the future dynamic trend is predicted by the nmpc based on historical data of input and output. an ant colony algorithm acting as nonlinear optimizer is used to improve the rolling horizon. the predictive value is corrected by negative feedback and then compared with reference input. the difference between the predicted value and the expected value is minimized in the next period of time. the proposed autopilot design is shown in figure 2. 3.1 cnn model the architecture of cnn based model is shown in figure 3. the delay lines with taps are represented by tdl(tapped delay line). the cnn is in parallel with the process of system. 432 d. zhao, t. yang, w. ou, h. zhou figure 3: architecture of cnn based model the predictive error is used as training signal of the network. the single-step cnn’s prediction model is defined as: ym(k + 1) = f̂[y(k),y(k − 1), · · · ,y(k −n + 1); u(k),u(k − 1), · · · ,u(k −m + 1)] (5) where y(k) is the output of the heading angle at sampling time k, u(k) is the input of the heading angle at sampling time k, m is the system input order, n is the system output order, f̂(·) is the input and output mapping function of the cnn model, ym(k + 1) is the predictive value of the heading angle. because the single-step neural network model has less predictive information, the multistep prediction method is used to improve the anti-interference and robustness of the autopilot. the prediction model of single-step neural network is used to infer the multi-step one, which is expressed as: ym(k + p) = f̂[y(k + p− 1), · · · ,y(k + p−n); u(k + p− 1), · · · ,u(k + p−m)] (6) at sampling time k, y(k + p − 1), · · · ,y(k + 1) is the actual output data of future, which can’t be measured. so, the predictive value is used to replace the output of future. in addition, the predicted data before the kth moment can be replaced by historical data. so, the p step predictive model is formulated as: ym(k + p) = f̂[ym(k + p− 1), · · · ,ym(k + p−n); u(k + p− 1), · · · ,u(k + p−m)] (7) the cnn model structure of multi-step prediction is shown in figure 4. figure 4: structure of multi-step neural network prediction 3.2 feedback correction the reference trajectory function of the autopilot is based on the reference trajectory provided by the model control algorithm, which is expressed as: yγ(k + 1) = ay(k) + (1 −a)ω (8) autopilot design for unmanned surface vehicle based on cnn and aco 433 because the predictive controller has the model mismatch problem, a large deviation is produced between prediction model and actual output of the object. the predictive output should be corrected to reduce the predictive error. at sampling time k, we must calculate the error between the actual output value y(k) and the output value predicted by the model(ym(k)) at first. then, the controller adds the error to the model predictive output(ym(k + 1)) to get the closed-loop predictive output. the future closed-loop predictive output of p step is as follows: ypj(k + j) = ym(k + j) + rjej(k) + βj[ej(k) −ej(k − 1)] (9) where rj is the error correction coefficient, βj is the change rate of correction coefficient, ej(k) = y(k) −ym(k) is the difference between actual output and the expected output, j = 1, 2, · · · ,p is the number of predictive steps, ypj(k + j) is the predictive value of the output component after the (k + j) moment, ypj(k + j) is the model predictor of the output component at sampling time (k + j). 3.3 rolling optimization usually, the difference between the predictive value of heading and the expected value of heading is supposed to be zero. the range of the control increment should not be too large. for this reason, the optimal law of predictive controller is determined by the performance index. the secondary performance indicator is used as the objective function. the optimal control objective function is introduced by: j = 1 2 { s∑ j=1 p∑ i=1 qij[ypj(k + i) −yγj(k + j)]2 + γ∑ j=1 m∑ i=1 λij[uj(k + i− 1) −uj(k + i− 2)]2} (10) where p is the length of predictive time domain, m is the length of time domain, m ≤ p , qj is the error weighting factor, λj is the control incremental weighting factor, yj(k + j) is the reference trajectory of the output at k + j moment. the ant colony optimizer(aco) is used to maximize the objective function of the controller within the range of rudder angle, which has good robustness and can be implemented in parallel. the implementation steps of the aco are enumerated below: 1. initialize the ant pheromone distribution, set the number of iterations; 2. 10 ants are initialized in the neighborhood, moving according to the following transfer probability. for each ant i, the objective function ji is defined, the transfer probability for ant i at time k is as follows: pij(k) = [τj(k)] 1.2[4jij(k)]2∑ r [τr(k)]1.2[4jir(k)]2 (11) where τj(k) is the j-neighborhood attracting intensity of the ants at k time. 4jij = ji−jj is the difference value of the target function. 3. calculate the objective function value of each ant, and record the optimal control sequence in the current ant colony; 434 d. zhao, t. yang, w. ou, h. zhou 4. revise the intensity of pheromone according to the pheromone update equation, following the next equation: { τj(k + 1) = 0.7τj(k) + ∑ 4τj 4τj = 1/jj (12) 5. number of iterations n = n + 1 6. if the number of iterations does not reach the ending iteration number 100, the second step is returned, otherwise, the loop is terminated and the optimal control sequence is outputted. 4 results and discussion 4.1 simulation results the disturbance of wind and wave on usv is simulated using white noise and a second-order wave transfer function [26]. the disturbance of ocean current is treated as a constant value. y(s) = kcω(s) kωs s2 + 2ζω0s + ω 2 0 (13) where ω(s) is a gaussian white noise with a mean of zero, the value of power spectrum density is 0.1, and kc = 5 is a constant disturbance coefficient, ζ = 0.3 is a damp coefficient, kω = 0.42 is the gain constant, ω0 = 0.606 is the frequency of the dominant wave. in the case of wind, wave and current interference, when the setting heading angle is 15◦, the effect of traditional controller and predictive controller simulated using matlab is shown in the following figure. 0 50 100 150 200 0 5 10 15 20 time/s h e a d in g a n g le / ° pid control predictive control figure 5: heading using pid(proportion integration differentiation) and the mpc it is shown from fig.5, fig.6 that the predictive controller based on cnn has good control accuracy, robustness and fault tolerance. compared with the traditional model predictive control, this controller’s stability has become stronger with external disturbances, which can realize the stable path tracking with the characteristics of strong anti-interference. finally, the problem of model mismatch in conventional generalized predictive control is also solved by this proposed method. autopilot design for unmanned surface vehicle based on cnn and aco 435 0 50 100 150 200 −5 0 5 10 15 20 time/s ru d d e r a n g le / ° pid control predictive control figure 6: rudder angle using pid and the mpc table 3: latitude and longitude of the target point target number coordinate longitude latitude 1 120◦08′03.7′′e 31◦26′31.4′′n 2 120◦08′07.7′′e 31◦26′27.9′′n 3 120◦08′06.5′′e 31◦26′23.1′′n 4 120◦08′02.3′′e 31◦26′21.0′′n 5 120◦07′57.4′′e 31◦26′24.3′′n 6 120◦07′59.0′′e 31◦26′29.4q′′n 4.2 experimental results the predictive controller is applied to the wh-01, in which the course control experiment was carried out in taihu lake, wuxi, jiangsu province. six target points are selected for the typical trajectory experiment in the lake, of which the latitude and longitude are shown in table 2. the usv traveled at the average speed of 12 knot during the experiment.this simulation data is derived from the average of three experimental data. fig.7 and fig.8 show the waypoints follow by the usv by the predictive controller and the pid scheme. fig.9 and fig.10 illustrate the distance of the vehicle from next waypoint and total time taken to operate under the predictive controller and the pid scheme. fig.11 and fig.12 demonstrate mean average deviation of the waypoints by the predictive controller and the pid scheme. it can be shown from fig.7, fig.8 and table 4 that the usv can also pass through the target points when disturbed by wind and waves. it is observed that the predictive controller is actual trajectories desired trajectories 2 3 4 5 1 6 figure 7: waypoints followed by the predictive controller systems 436 d. zhao, t. yang, w. ou, h. zhou actual trajectories desired trajectories 1 2 3 4 6 5 figure 8: waypoints followed by the pid gc systems 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 time(sec) d is ta n c e o f th e v e h ic le f ro m n e x t w a y p o in t( m ) distance way point arrival time (31) (91) (130) (164)(64) figure 9: distance of the vehicle from next waypoint and total time taken to operate by the predictive controller systems 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 time(sec) d is ta n c e o f th e v e h ic le f ro m n e x t w a y p o in t( m ) distance way point arrival time (35) (75) (107) (143) (169) figure 10: distance of the vehicle from next waypoint and total time taken to operate by the pid gc systems(guidance and control systems) table 4: average performance measures of predictive control case number of target points arriving total distance(m) rd (m/s) pid(kp = 8,ti = 8,td = 0) 6 393.57 8.01 predictive controller 6 347.98 4.23 autopilot design for unmanned surface vehicle based on cnn and aco 437 0 20 40 60 80 100 120 140 160 180 −20 −10 0 10 20 30 time(sec) d e v ia ti o n f ro m i d e a l tr a je c to ri e s (m ) figure 11: mean average deviation of the waypoints by the predictive controller systems 0 20 40 60 80 100 120 140 160 180 −20 −10 0 10 20 30 time(sec) d e v ia ti o n f ro m i d e a l tr a je c to ri e s (m ) figure 12: mean average deviation of the waypoints by the pid gc systems more efficient and capable to control the usv tracking the target points especially in the case of disturbances. 5 conclusion a predictive controller based on convolutional neural network(cnn) is designed for autopiloting an usv. it is shown from the experimental results that the predictive controller has good robustness and anti-jamming. in extreme sea condition, the problem of control overshooting and poor anti-interference is solved by the algorithm. in addition, the difficulty in hydrodynamic modeling can be solved by the proposed approach. finally, the feasibility of the method is verified by numerical simulations and experiments on lake. however, the steering mathematical model based on the cnn needs to be improved in the future. particularly, the training samples accumulated from the model experiment, the actual ship test and expert knowledge need to be analyzed. in these conditions, it is difficult to build the nonlinear steering law, which is worthy of further research. the next work will be related to research the optimal strategy and design a new predictive controller with good accuracy, adaptability and 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(2013); analysis and design of simplified dual neural network based model predictive controller, msc thesis, shanghai jiao tong university, 2013. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 668-680 effectiveness of program visualization in learning java: a case study with jeliot 3 s. maravić čisar, d. radosav, r. pinter, p. čisar sanja maravić čisar, robert pinter subotica tech-college of applied sciences, department of informatics serbia, 24000 subotica, marka oreškovića 16 e-mail: sanjam@vts.su.ac.rs, probi@vts.su.ac.rs dragica radosav university of novi sad, technical faculty "mihajlo pupin" zrenjanin, department of informatics serbia, 23000 zrenjanin, djure djakovica bb e-mail: radosav@tfzr.uns.ac.rs petar čisar telekom srbija, serbia, 24000 subotica, prvomajska 2-4 e-mail: petarc@telekom.rs abstract: this study was carried out to observe, measure and analyze the effects of using software visualization in teaching programming with participants from two institutions of higher educations in serbia. basic programming learning is notorious for complex for many novice students at university level. the visualizations of examples of program code or programming tasks could help students to grasp programming structures more easily. this paper describes an investigation about the possibilities of enhancement of learning java using the visualization software jeliot. an analysis of 400 students’ test results indicates that a significant percentage of students had achieved better results when they were using a software visualization tool. in the experience of the authors jeliot may yield the best results if implemented in with students who are new to the art of programming. keywords: software visualization, computer assisted learning, programming learning, jeliot 3. 1 introduction programming is a difficult cognitive skill to learn. mastering the basis of a programming language is a huge problem for many students. in order to write a simple program they need to have a basic knowledge of variables, input/output of data, control structures and other areas. an even greater problem is mastering the more complex concepts such as pointers, abstraction or exception handling. and even when they have the necessary theoretical knowledge, the problem arises when they have to apply their knowledge as a whole and actually write a programming code. jenkins [1] identified reasons for these difficulties, such as: • the need for good competence in problem solving; • students are used to courses that depend mostly on theoretical knowledge and memorization, but learning the basics of programming needs a more practical approach, based mostly on problem solving activities; copyright c⃝ 2006-2011 by ccc publications effectiveness of program visualization in learning java: a case study with jeliot 3 669 • traditional teaching, generally based on lectures and specific programming language syntaxes, often fails to motivate students to get involved in meaningful programming activities; • programs have a dynamic nature, but most learning materials have a static format which makes it difficult to analyze the program’s dynamic behavior; • students’ learning needs are frequently very different within the same group. different learning styles and previous experience make a common approach to the whole group rather difficult. group sizes often make an individualized support to students difficult. educational software visualization tools are used to give students graphical representations of aspects of software systems that are inherently intangible and not obvious from them, such as the exact control flow, side effects in expression evaluations, and data dependencies. when students are given the ability to visually explore programs and algorithms, teachers expect them to be able to make better sense of program executions and programming concepts. 2 related work boyle, bradley, chalk, jones and pickard [2] defined the new curriculum for london metropolitan university’s course of introductory programming. they specifically focused on visual approach. over 600 students took part in the course. the increase in pass rates was from 12% to 23% compared to the previous year. boyle et al. reported some significant problem concerning handling the course transition, but on average they described the graphical approach as ’very successful with the students’. kannusmäki, moreno, myller and sutinen [3] evaluated the use of the jeliot 3 program visualization system during the second course of programming in the virtual studies of computer science distance learning program at the university of joensuu, finland. gathered data showed that those who were most successful in the course used jeliot more than the other groups involved in the research. however, most of the students (in general) by and large still used other tools to code and test their programs. the usage problems reported were mostly of a technical nature or related to the usability of the editor. the animation was criticized for being too slow and some students even found the whole system unnecessary and unsuitable for advanced courses. the positive aspects identified in the feedback included the ability to make conditional statements, loops, and objects more understandable. hundhausen, douglas and stasko [4] conducted a meta-study, analyzing 24 experimental studies on the effectiveness of algorithm visualization. they state that one of the main reasons for why visualizations are not widely used is because the teachers responsible for the courses refuse to use new methods in teaching. they also found out that the main focus in articles about visualizations is normally on their graphical means of expression instead of their learning benefits. of the 24 studies examined, 11 showed statistically significant results of visualizations positive effects on learning, meaning that the group using a visualization system gained better learning results than the control group. hundhausen et al. [4] also discovered that the sole use of visualization systems does not necessarily improve the learning results; it is more important to engage the learners in the subject using visualization system as an aid. myller and bednarik [5] presented their experiences with three empirical methodologies to study human behavior, interaction and learning with program visualization. each of the approaches provides the research agenda with important source of data. classroom studies inform about the practices taking place in this context and can generate testable hypotheses. controlled experiments, when designed well, can provide answers to the previously established hypotheses and can give accurate insights into interaction and cognitive processes involved in programming. data from surveys and questionnaire studies can be used both to collect data related to attitudes and current practices, 670 s. maravić čisar, d. radosav, r. pinter, p. čisar and generate testable hypotheses. furthermore, all these methods can indicate issues for further development in the form of usability problems or unexpected behavior of users. programming is one of the essential areas taught in university studies of computer science and other engineering degrees and some experiences in teaching programming with e-learning application could be found in [6] and [7]. 3 software visualization software visualization is "the visualization of artefacts related to software and its development process" [8] and is used in the presentation, navigation and analysis of software systems. price, baecker and small [9] presents the following general definition of software visualization: "software visualization is the use of the crafts of typography, graphic design, animation and cinematography with modern human-computer interaction and computer graphics technology to facilitate both the human understanding and effective use of computer software." given that the underlying purpose of algorithm visualization is to be educationally effective, it is worth nothing that eight extant taxonomic reviews of algorithm visualization and software visualization technology have focused largely on system expressiveness, as shown in table 1. sv taxonomy descriptive dimensions myers aspect (code, data, algorithms) × form (static, animated) shu what is visualized (data or information about data, program and/or execution, software design) brown content (direct, synthetic) × persistence (current, history) × transformation (incremental, discrete) stasko and patterson aspect × abstractness × animation × automation kraemer and stasko visualization task (data collection, data analysis, storage, display) × visualization purpose (debugging, performance evaluation or optimization, program visualization) roman and cox scope × abstraction × specification method × interface × presentation price et al. scopex content × form × method × interaction × effectiveness table 1: the descriptive dimensions of the eight extant taxonomies of sv [4] in particular, these taxonomies have focused on three main questions [9]: 1. what kinds of programs can be visualized with a given visualization system? 2. what kinds of visualizations can a given visualization system produce? 3. what methods can one use to produce and interact with visualizations? the primary goal of visualization is to convey information. it should convey this information in an understandable, effective, easy-to-remember way [4]. despite its intuitive appeal as a pedagogical aid, algorithm visualization technology has failed to catch on in mainstream computer science education [9]. while those few educators who are also algorithm visualization technology developers tend to employ their own algorithm visualization technology, the majority of computer science educators tend to stick to more traditional pedagogical technologies, such as blackboards, whiteboards and overhead projectors. why do computer science educators tend not to use algorithm visualization technology? instructors commonly cite several reasons, including the following: • they feel they do not have the time to learn about it. effectiveness of program visualization in learning java: a case study with jeliot 3 671 • they feel that using it would take away time needed for other class activities. • they feel that creating visualizations for classroom use requires too much time and effort. note that, in the algorithm visualization technology literature, this reason is frequently used to motivate new technology that is easier to use, and that supports the more rapid creation of visualizations. • they feel that it is simply not educationally effective. the reason that "it is simply not educationally effective" stands out as very important because there is no reason to adopt new technology if it does not bring some improvements in the educational process. so, the aim of this paper was to research the possibilities of enhancement in learning programming language java using the visualization software jeliot 3. the authors concentrate on effectiveness of (algorithm) visualizations i.e. how well one performs with the visualization tool compared to others who do not use the tool. the results of successful solution of certain programming problems are compared, solutions given by students who have used this tool, and those who have not. this will lead to answers regarding the efficient application of visualization software in education. a number of classroom studies have been conducted in order to evaluate and analyze the usage of tools in the jeliot family by using both qualitative and quantitative methods [8]. 3.1 jeliot 3 jeliot 3 is a program visualization application. it visualizes how a java program is interpreted. method calls, variables, operation are displayed on a screen as the animation goes on, allowing the student to follow the execution of a program step by step as shown in figure 1. programs can be created from scratch or they can be modified from previously stored code examples; all the visualization is automatically generated [10]. jeliot 3 understands most of the java constructs and is able to animate them. figure 1: a screenshot of jeliot 3 jeliot 3 can be used in several ways for teaching and learning to program. here are some examples from [3]: 672 s. maravić čisar, d. radosav, r. pinter, p. čisar • lecturers can use jeliot 3 as a part of the lecture material. they can explain the different concepts of programming through jeliot animations. this will facilitate the construction by the students of the correct relationship between the animation and the concept, and enable them to apply it later with a reduced possibility of error [11]. • the students may use jeliot 3 by themselves after lectures to do assignments. • jeliot 3 can be used in an interactive laboratory session, where students may utilize their recently acquired knowledge by writing programs and debugging them through jeliot 3. • finally, jeliot 3 provides a tool that can aid in courses where external help is not available (e.g. in distance education). its visualization paradigm creates a reference model that can be used to explain problems by creating a common vocabulary between students and the teacher [11]. one of the possibilities that the software jeliot 3 provides is the ability to select the ask questions during animation option from the main menu. whenever an expression is to be evaluated, a popup window will ask for the result. however, currently questions are generated only for assignment statements (figure 2). the continuation of the animation is not possible until a student gives an answer to the question. in this way students have the opportunity to self evaluate their knowledge. figure 2: a screenshot with multiple choice questions the jeliot family’s key feature has been the fully or semi-automatic visualization of the data and control flows. the development of the jeliot family has taken more than ten years with different kinds of stages. several versions of the concept have been developed, namely eliot (developed at university of helsinki, finland), jeliot i (developed at university of helsinki, finland), jeliot 2000 (developed at weizmann institute, israel). this has led to the stage when, as a product the software has become both usable and stable. the new version jeliot 3 is a free piece of software published under general public license (gpl). this means that the future platforms can be developed by networked teams presenting the idea of learning communities. in these communities the distinction between a teacher, a learner and a developer disappears, thus the learner can develop the tools he or she needs with the other members of the community. effectiveness of program visualization in learning java: a case study with jeliot 3 673 jeliot-together with its documentation, research publications, and learning materials-can be downloaded for free from http://www.cs.joensuu.fi/jeliot/. 4 research wolfgram [12] states the following, "people only remember 15 percent of what they hear and 25 percent of what they see, but they remember 60 percent of what they interact with". the aim of this study was to explore the impact of the software for visualization jeloit 3 in learning the programming language java. emphasis is placed on examining whether there are differences in the achievements of students who were taught java with the help of jeliot and those who were not. java was selected as it has evolved to be most predominant and popular general purpose programming language of the current age. the research was the result of cooperation between two institutions of post-secondary education in serbia: the technical faculty "mihajlo pupin" in zrenjanin and subotica tech-college of applied science in subotica. preparations for the survey began in 2006, a year before the start of the experimental part of the research. because the students from both institutions have similar education profile and many students from subotica tech continue their education in zrenjanin’s technical faculty, a team-based approach was taken for adjustment of the teaching materials. in this way one of the principals of the bologna process of students’ mobility was satisfied. the semester lasts 15 weeks and the students have a weekly lecture. the java course consists of 30 hours of lectures and 30 hours of lab works. the lesson duration is 2x45 minutes. during the lectures the assignments for lab works were given to students. they had the task to analyze the problem at home and to draw an algorithm flow chart based on which they could write the program code at school, at lab works. at these practices students worked in pairs, two students per one pc. considering the fact that the participants in the survey were students from two different higher education institutions with different experience in object-oriented programming, and the fact that they were mainly beginners in programming, teaching process involved procedural programming and an introduction to object-oriented paradigm as in the material [13]. during the programming course the following topics were taught: variables, operators, decision-making statements (if-then, if-then-else, switch), the looping statements (for, while, do-while), the branching statements (break, continue, return), arrays (one-dimensional and two-dimensional), methods, constructors and inheritance. the research lasted for two school years (2007-2009) and 400 students were included. in the first year of study the sample consisted of 200 students. from the total number of respondents 47 were female (23.5%) and 153 were male students (76.5%). in the second year of study the sample consisted again of 200 students, among them 34 were female (17%) and 166 were male (83%). a certain part of the results of this research relating to only 45 students from subotica tech collected in the spring of 2009 could be found in [14]. the paper includes the results of a questionnaire that was used to collect reflections on programming and the application of the jeliot program of students. the aim of this questionnaire was to find out how students had accommodated jeliot into their learning processes. in the research the model of pedagogical experiment with parallel groups was applied. students were divided into three groups. the control group learned and worked in the traditional way, in the classrooms and under teachers monitoring. this group has lectures in the traditional way. the lecturers in this group used blackboard or whiteboard to present teaching material. all necessary explanations were drawn on the board, such as explaining the program code that was used as example during the class, or what took place during checking conditions in if-then-else statement, or in an algorithm flow chart. students of the control group used jcreator (lightweight development environment for java technologies) for writing and debugging code examples at the lab exercises, as well as for solving homework tasks. students of this group used a textbook for 674 s. maravić čisar, d. radosav, r. pinter, p. čisar studying. for the first experimental group, in addition to the traditional ways of learning, an experimental factor was introduced learning using visualization software jeliot and powerpoint presentations (blended learning) while in the second experimental group only e-teaching materials were applied. the lectures notes were in form of powerpoint presentations. for explaining the program code that was used as example during the classes the lecturers used jeliot. in this way the lectures become more interactive. students of both experimental groups had a two-hour workshop at the beginning of the course to become familiar with it. the main purpose of jeliot 3 is not to be used as a debugging tool, but it is convenient for analyzing smaller program codes as was used in this research. during the first month of the semester (a total of 8 lab exercises) they solved their tasks with the help of the lecturer. based on the algorithm flow chart which they had to do as preparation for the class, they had to write program code in jeliot and to use jeliot as compiler. the students debugged the errors which were indicated by jeliot together with the lecturer. after this first period of learning with jeliot they had to complete all task alone (in groups of two students). of course, during the entire class the lecturer was present and his role was to give an extra explanation if it was necessary. most of the time, students are trained to develop very simple programs starting from scratch. in fact, many students will be involved in evolution-related software after completion of their studies, so it is very important for them to know how to read and change existing large program codes [14]. however, evolving and understanding large existing software includes quite different activities such as recovering/understanding the actual architecture of the system, understanding some part of its source code and documentation (may be in some programming language that the student had not studied before). including a new feature in such software then requires performing impact analysis, regression testing, etc [15]. considering this, the next type of problems that students had was to figure out what an unknown program code did and to create appropriate test cases. depending on which group they belonged to, students used jeliot or jcreator to complete this task. the comparison of the results of the control and experimental groups was performed by testing the hypothesis. significant differences of the three groups of students were determined based on the critical values for probability levels of 0.05 and 0.01. the following hypothesis was stated: application of software for visualization jeliot 3 does not affect the process of learning java programming language. the test was used to examine the students’ knowledge. the exam was a traditional paper-and-pencil test with 20 multiple choice questions, where a standard grading procedure has been applied to grade these test items (one point for a correct answer, zero points for an incorrect one). the statistical analysis of the performed test included calculation of the arithmetic mean, standard deviation, standard error of the mean and interval of variation. the one-way anova was used to test for differences among three groups of students. in order to determine which groups differ from each other the post-test for pair-wise comparisons tukey’s hsd test (honestly significant difference) was implemented. statistical analysis of the results was done using the site vassarstats [16] that offers the possibility of online statistical calculations. the total number of students who participated in the first year of the research was 200. the control group consisted of 60 students who had lectures in the traditional way. in the first experimental group there were 65 students who had the traditional way of lecturing accompanied with the powerpoint presentations and software for the visualizations. in the second experimental group there were 75 students who only had e-learning material. the results of the research are given in table 2. the analysis of the descriptive statistical parameters has shown that the average value of the scored points for the first experimental group was the largest (11.05) with a standard deviation of 4.78 points. the lowest average of points had a control group, 7.33 with standard deviation of 4.85 points. based on these results, it can be said that the first experimental group had the effectiveness of program visualization in learning java: a case study with jeliot 3 675 group n mean std.dev. std.err. min max control 60 7.3333 4.8526 0.6265 0 18 experimental i 65 11.0462 4.7777 0.5926 0 20 experimental ii 75 10.9067 5.5585 0.6418 0 20 table 2: descriptive statistical parameters of the first year of the research highest average score and thus showed the best results on the test. graphical representation of the average number of points is given in figure 3. figure 3: the average number of points in the first year of the experiment the results of the anova analysis are shown in table 3 (p ≤ 0.05). source of variation ss df ms f p-value f crit between groups 556.5784615 2 278.2892308 10.67313056 3.97e-05 3.04175303 within groups 5136.541538 197 26.07381492 total 5693.12 199 table 3: the results of the anova analysis in the first year of the experiment (p ≤ 0.05) the results of the anova analysis are shown in table 4 (p ≤ 0.01). the calculated value for f of 10.67 indicates significant differences between the studied groups. significant differences between individual groups were determined using the tukey test. the absolute (unsigned) difference between any two sample means required for significance at the designated level (m1=mean of the control group, m2=mean of the first experimental group and m3=mean of the second experimental group) is: m1-m2=3.7162 m1-m3=3.5767 m2-m3=0.1395 next the hsd is computed (hsd). if the difference is larger than the hsd, then the difference is said to be significant. 676 s. maravić čisar, d. radosav, r. pinter, p. čisar source of variation ss df ms f p-value f crit between groups 556.5784615 2 278.2892308 10.67313056 3.97e-05 4.71452043 within groups 5136.541538 197 26.07381492 total 5693.12 199 table 4: the results of the anova analysis in the first year of the experiment (p ≤ 0.01) hsd[.05]=2.1; hsd[.01]=2.62 m2 vs. m3 insignificant m1 vs. m3 p < .01 m1 vs. m2 p < .01 based on the results of hsd, it can be concluded that there is a significant difference between the control and experimental groups i and ii (p < 0.01). the hypothesis of equality of the control and experimental groups cannot be accepted, which means that the implementation of software for visualization jeliot 3 does have an influence on the process of learning java. no significant differences between the experimental groups have been recognized. the total number of students who participated in the second year of the research was 200. the control group consisted from 68 students who had lectures in the traditional way. in the first experimental group there were 66 students who had the traditional way of lecturing accompanied with the powerpoint presentations and software for the visualizations. in the second experimental group there were 76 students who had only e-learning material. the results of the research are given bellow in table 5. group n mean std.dev. std.err. min max control 68 7.2206 4.6548 0.5645 0 17 experimental i 66 10.8333 4.6923 0.5776 1 19 experimental ii 66 11.5 5.3787 0.6621 1 20 table 5: descriptive statistical parameters of the second year of the research the analysis of the descriptive statistical parameters has shown that the average value of the scored points for the second experimental group was the largest (11.5) with a standard deviation of 5.378 points. the lowest average of points had a control group, 7.22 with a standard deviation of 4.65 points. based on these results, it can be said that the second experimental group had the highest average score and thus showed the best results on the test. the graphical representation of the average number of points is given in figure 4. the results of the anova analysis are shown in table 6 (p < 0.05). source of variation ss df ms f p-value f crit between groups 713.5172 2 356.7586 14.7546 1.07e-06 3.04175303 within groups 4763.358 197 24.17948 total 5476.875 199 table 6: the results of the anova analysis in the second year of the experiment (p ≤ 0.05) the calculated value for f of 14.75 indicates the existence of significant differences between effectiveness of program visualization in learning java: a case study with jeliot 3 677 figure 4: the average number of points in the second year of the experiment the studied groups. significant differences between individual groups were determined using the tukey test. the absolute (unsigned) difference between any two sample means required for significance at the designated level is: m1-m2=-3.6127 m1-m3=-4.2794 m2-m3=0.6667. the following step is computing hsd: hsd[.05]=2.01; hsd[.01]=2.51 m1 vs. m2 p < .01 m1 vs. m3 p < .01 m2 vs. m3 insignificant. the results of the anova analysis are shown in table 7 (p ≤ 0.01). source of variation ss df ms f p-value f crit between groups 713.5172 2 356.7586 14.7546 1.07e-06 4.71452043 within groups 4763.358 197 24.17948 total 5476.875 199 table 7: the results of the anova analysis in the second year of the experiment (p ≤ 0.01) based on the results of hsd, it can be concluded that there is a significant difference between the control and experimental groups i and ii (p < 0.01). the hypothesis of equality of the control and experimental groups cannot be accepted, which means that the implementation of the software for visualization jeliot 3 does have an influence on the process of learning java. there are no recognizable differences between the experimental groups. based on the results of the two-year research in which a total of 400 students have been included it can be stated that the experimental groups i and ii have been highly successful compared with the control group in learning java. thus, the set hypothesis that application of the software for visualization jeliot 3 has no influence on the process of learning java programming language was refuted. between the experimental groups no significant statistical difference was found. students who were in the experimental groups after completing the course filled out the survey in order to obtain information about their opinion on learning using visualization software. first, 678 s. maravić čisar, d. radosav, r. pinter, p. čisar at the middle of the semester, students’ satisfaction with their progress in learning and their motivation for further work was examined. to express views on the motivation of the students the likert scale with 5 responses was applied (very high, high, medium, low and very low). the results are shown in fig. 5 (all values are expressed in percentages). figure 5: the motivation of the students figure 6 shows the results (given in percentages) of students’ satisfaction in their learning progress. they could express their attitude with the following responses: very satisfied, satisfied, neutral, dissatisfied, very dissatisfied (likert scale). figure 6: student’s satisfaction in learning progress the students of the experimental group were asked what they think about the educational possibilities of jeliot 3. their answers depended on the level of knowledge of java and objectoriented programming concepts in general. responses were ranged from the answer that jeliot3 is a great help for beginners, that it is a powerful educational tool thanks to the visualization and because of that, it made learning java easy, to the opinions of some students who have already had experience in java programming that it was a pure waste of time for them. students effectiveness of program visualization in learning java: a case study with jeliot 3 679 who expressed a negative opinion about jeliot said that it was difficult for them to adapt their knowledge of object-oriented programming and their style of programming to the new tool, also they had objections about the elements of visualization code which were disturbing their attention. what all the negative comments had in common was that they were given by students who had already used the java language, but almost all of them pointed out that they believed that jeliot could be very useful at the beginning of the process of learning java. the results of the research showed that the passing rate of the exam increased for a few percent when using the jeliot3 (for 3% in the first year and 2.67% in the second year of research). but, in order to be able to claim that result of increased passing is the consequence of using jeliot3, it would be necessary to perform some additional research focused solely on that piece of information. 5 conclusions the paper discussed the problem of the applications of software for visualization jeliot3 in learning the programming language java. the study involved 400 students of two higher education institutions in serbia. based on the research, which lasted for two years, it can be stated that there are significant differences in the achievements of students who were taught in the traditional way, and those who have used jeliot3. through well-defined messages from the compiler jeliot indicates to the user where the syntax errors in code are. the goal is to help novices understand basic concepts of algorithms and programming like assignment, i/o and control flow, whose dynamic aspects are not easily grasped just by looking at the static representation of an algorithm in a programming language [11]. software visualization tools are intended to be used in the early stages of the learning path of a programmer, teaching the students the basics of programming, algorithms, and the software development cycle [17]. while a future experiment involving jeliot and more advanced students (in terms of programming) may provide interesting results, it is the belief of the authors that this particular program works best with beginner students. it is not possible to "eject" teachers from the teaching process and replace them with a computer, but it is necessary to ensure their active participation during the animation code so that students receive the necessary explanation. using visualization tools should be intense, in the sense that teachers need to use it in the teaching process, and students in laboratory exercises as well as when working at home. of course, further research in this field is required. although this study did not directly deal with the influence of jeliot to accelerate learning processes, based on previous research of the authors [18] about the visualization of some selected parts of the course analogue and digital electronics that has shown that interactive animations can significantly contribute to increasing the speed of learning, it can be assumed that jeliot could be especially helpful to beginners in learning java in the same way. it must be noted that it is already clear there is a need for different tools for learning depending on the level of students’ knowledge of the studied materials. advanced students and even students with only superficial experience in programming are very sensitive to the change of tools that are used as a code editor, if it does not provide significant improvement over the tools they are used on. in other words, the individual characteristics of students, including the level of knowledge, must be taken into thorough consideration because the demands of students are changing rapidly. bibliography [1] t. jenkins, "on the difficulty of learning to program", in proc. of 3rd annual ltsn-ics conference,loughborough university, uk, 53-58, 2002. 680 s. maravić čisar, d. radosav, r. pinter, p. čisar [2] t. boyle, c. bradley, p. chalk, r. jones, p. pickard, using blended learning to improve student success rates in learning to program. journal of educational media, special edition on blended learning, 28(2-3): 165-178, 2003. 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[13] [online]. available: http://stwww.weizmann.ac.il/g-cs/benari/lov/lov.html [14] s. maravić čisar, r. pinter, d. radosav, p. čisar, software visualization: the educational tool to enhance student learning, proc. of 33rd international convention mipro 2010, computers in education, may 24-28, 2010, opatija, croatia, issn 1847-3938, isbn 978-953233-054-0, 4: 234-238, 2010. [15] a. van deursen, j. m. favre, experiences in teaching software evolution and program comprehension. available: http://www.tzi.de/st/papers/teaching-iwpc03.pdf [16] available: http://faculty.vassar.edu/lowry/vassarstats.html [17] a. moreno, m. s. joy, jeliot 3 in a demanding educational setting, fourth international program visualization workshop, 29-30 june 2006, florence, italy [18] r. pinter, d. radosav, s. maravić čisar, interactive animation in developing e-learning contents, proceedings of 33rd international convetion mipro 2010, computers in education, may 24-28, 2010, opatija, croatia, issn 1847-3938, isbn 978-953-233-054-0, 4: 251-254 international journal of computers communications & control issn 1841-9836, 13(4), 492-502, august 2018. an optimized dbn-based coronary heart disease risk prediction k. lim, b.m. lee, u. kang, y. lee kahyun lim1*, byung mun lee2, ungu kang2, youngho lee2 1. department of it convergence engineering; 2. department of computer engineering gachon university, republic of korea 1342 seongnamdaero, sujeong-gu, seongnam-si, gyeonggi-do, korea bmlee@gachon.ac.kr, ugkang@gachon.ac.kr, lyh@gachon.ac.kr *corresponding author: lyh@gachon.ac.kr abstract: coronary heart disease (chd) is the world’s leading cause of death according to a world health organization (who) report. despite the evolution of modern medical technology, the mortality rate of chd has increased. nevertheless, patients often do not realize they have chd until their condition is serious due to the complexity, high cost, and the side effects of the diagnosis process. thus, research on predicting chd risk has been conducted. the framingham study is a widely-accepted study in this field. however, one of its limitations is its overestimation of risk, which threatens its accuracy. therefore, this study suggests a more advanced chd risk prediction algorithm based on optimized-dbn (deep belief network). optimizeddbn is an algorithm to improve performance by overcoming the limitations of the existing dbn. dbn does not have the global optimum values for number of layers and nodes, which affects research results. we overcame this limitation by combining with a genetic algorithm. the result of genetic algorithm for deriving the number of layers and nodes of optimized-dbn for chd prediction was 2 layers, 5 and 7 nodes to each layers. the accuracy of the chd prediction algorithm based on optimizeddbn which is developed by applying results of genetic algorithm was 0.8924, which is better than framingham’s 0.5015 and dbn’s 0.7507. in the case of specificity, optimized-dbn based chd prediction was 0.7440, which was slightly lower than 0.8208 of existing dbn, but better than framingham’s 0.65. in the case of sensitivity, optimized-dbn is 0.8549, which is better than framingham 0.4429 and dbn 0.7468. auc of suggesting algorithm was 0.762, which was much better than framingham 0.547 and dbn 0.570. keywords: artificial neural networks (ann), deep belief network (dbn), coronary heart disease (chd), computational intelligence, genetic algorithm, chd prediction. 1 introduction coronary heart disease (chd) is a disease in which a waxy substance called plaque builds up inside the coronary arteries [11]. it has been a leading cause of death globally for 15 years according to a world health organization report [1]. once chd is developed, it is nearly impossible to cure completely and patients are at risk of sudden death due to sudden myocardial infarction or etc. therefore, the accurate risk prediction of chd is very important in that it gives patients a chance to live better life by caring themselves more cautiously [6]. with this reason, there have been many researches to develop the method for predicting the risk of chd. among these, the framingham heart study which was developed through prospective cohort study and the us adult treatment panel (atp) are widely used as standards when predicting the risk of chd [15]. and as artificial neural networks (ann) are becoming increasingly popular because of its outstanding classifying performance, it is begin to be adopted into establishing the risk prediction model for chd [20]. research on developing a chd risk copyright ©2018 cc by-nc an optimized dbn-based coronary heart disease risk prediction 493 prediction model based on various ann algorithms such as random forest (rf), support vector machine (svm), and deep belief network (dbn) have been actively conducted. although the risk prediction model based on ann algorithms boasts a much better performance than the previous model, nevertheless, there is still room for improvement [9]. therefore, these days, to develop a more improved version of prediction model, an ensemble technique which means combination of two different algorithms has been attempted [21]. the performance depends on how to combine two algorithms and if those are able to complement each other’s disadvantages, it can significantly improve the prediction performance. however, developing chd risk prediction model by combining two ann algorithms have not tried much yet. among many ann algorithms, deep belief network is one of the most widely-used ann algorithm because it has advantage in overfitting issue compared to other ann algorithms [5]. overfitting is one of the bothersome issue to most of the ann algorithm researchers because it degrades prediction performance when applied to actual field [14]. through regulating the parameter values of hidden or visible layers, dbn can be able to solve the overfitting problem to a certain degree. though, dbn also has a limitation that there is no optimal value adequate to all datasets. the optimal values of each parameters are differed depending on datasets. thus, researchers set values randomly and try several times, and select the values which show the best performance. however, there is no conviction that it is the optimal value set. therefore, in this study, our goal was to develop an chd risk prediction model based on optimized-dbn by combining dbn with genetic algorithm (ga). ga is an optimization algorithm which is useful for finding the optimal value. we utilized the ga to find the optimal value of the dbn parameter values of hidden and visible layers which is adequate to the dataset to predict the risk of chd. in section 2, we explained background knowledge about dbn and ga which are needed to understand the optimized-dbn algorithm we suggest through this paper. in section 3, we described process and methodology of research to develop and verifying optimized-dbn. the results of research such as performance comparison with the traditional dbn and other ann algorithms which are widely being used now are recorded in section 4. lastly, we concluded the paper with suggestion of future works in section 5. 2 backgrounds 2.1 deep belief network (dbn) deep learning refers to a collection of algorithms that pursues the improvement of performance by setting multiple hidden layers and passing information to those hidden layers several times. in 2006 jeffrey hinton, a professor at the university of toronto who is called the master of artificial intelligence, first proposed the dbn in the paper "a fast learning algorithm for deep belief nets" [7]. with this paper, he proved that a dbn can greatly improve existing artificial neural networks, which take a lot of time to optimize and perform worse through the unsupervised pre-learning method. a dbn consists of a restricted boltzmann machine (rbm) and artificial neural network and the rbm is composed of visible neurons and hidden neurons. once the rbm obtains the training result of the input data, it uses the result as the input data for the next layer. it accumulates hidden neurons and learns [16]. the energy function of rbm is as in the following equation (1). e (v,h) = − ∑ i,j vihj − ∑ i∈v aivi − ∑ j∈h bjhj (1) in equation (1), vi and vj are the binary values of the visible layer of i and the hidden layer 494 k. lim, b.m. lee, u. kang, y. lee of j. in addition, hi and hj mean the bias of the visible layer of i and the hidden layer of j. w means a weighting matrix. therefore, the energy function of the rbm and the boltzmann machine without bias is the same as the following equation (2). e (v,h) = − 1 2 ∑ i,j wijvihj = − 1 2 v twh (2) in this case, the network v and h vectors follow the probability of the following equation (3). p (v,h) = 1 ze −e (v,h) (3) z is the partition function, and p satisfies a probability value between 0 and 1. the updating of the weights uses k-step contrastive divergence (cd-k). 〈vihj〉 data is the mean value of the input patterns of the visible layer of i and the hidden layer of j, and 〈vihj〉 model represents the average value of the network [3]. the dbn sets the initial weights by performing unsupervised pre-training before performing the error-propagation algorithm [9]. after the initial weight value is determined, it performs an error back-propagation algorithm with the supervised learning method and performs fine-tuning to optimized performance [22]. however, research on the optimum value of the number of layers and nodes is still lacking, especially when performing pre-training through rbm. the number of layers and nodes is a variable that influences research results, but there is a limit to the global optimal value because the optimal value depends on the type and characteristics of the dataset to be learned. most studies using dbn have been conducted by researchers to set several values randomly and apply values which showed the best performance. however, this method has limitations because it is difficult to confirm whether its result is the optimum result. our study overcomes this problem of dbn by applying a genetic algorithm. a genetic algorithm (ga) is a method to find the optimum value. ga simulates a mathematical evolutionary method using crossover, mutation and selection. in other words, ga can be used to generate an optimal value of the number of layers and nodes of the dbn to maximize its performance. through a genetic algorithm, we improve the performance of the chd risk prediction by finding the optimal number of layers and nodes for the data through a genetic algorithm and applying it to the dbn. 2.2 genetic algorithm (ga) genetic algorithms were created by john holland [8], a professor of computer science and psychology at the university of michigan. this is an algorithm in which the initial population of artificial chromosomes is reproduced through crossover and mutation, and in the process, chromosomes with a low fitness value are extinguished and chromosomes with a high fitness value survive. survived chromosomes thus dominate the population. a genetic algorithm is an optimization algorithm performed in this form [13]. the genetic algorithm assigns the rank of each object through the fitness function and sets as many genes as the researcher set. it transmits the characteristics of genes having high fitness by conveying those to the next generation. after that, the next generation of chromosomes is generated through a crossover process. the crossover process has the effect of accelerating the convergence speed to its optimal value. finally, through the mutation process which changes chromosomes at a certain probability, the global search effect is maximized in this stage, converging toward its optimum value. the genetic algorithm is based on the schema theorem. the schema is a set of strings consisting of 0, 1, and asterisks (*), and the genetic algorithm is executed by adjusting these schema. when the reproduction rate in the an optimized dbn-based coronary heart disease risk prediction 495 genetic algorithm is proportional to the fitness, the probability that a particular schema (h) will survive the next generation can be predicted by the following equation (4) [19]. p (c) h = 1 −pc ( ld l− 1 ) (4) the probability that schema h will survive after the mutation is as following equation (5). the genetic algorithm maximizes the global search effect by the mutation process. p (m) h = (1 −pm) n (5) it is possible to get the probability of the schema growing according to the above two expressions, the formula is as following equation (6) mh (i + 1) = f̂h (i) f̂ (i) mh (i) [ 1 −pc ( ld l− 1 )] (1 −pm)n (6) in this study, a genetic algorithm was used to derive the optimal dbn layer and node number values. the initial population size, n, was set to 256, pc was set to 0.7, pm was set to 0.001, and the termination condition was set to 200 generations following regular settings. the sigmoid function was also used as a fitness function. 3 optimized dbn based coronary heart disease risk prediction 3.1 experimental design the purpose of this study was to develop and validate a predictive algorithm for coronary heart disease risk based on an optimized-dbn. the optimized-dbn improves performance by applying optimal values for the layer and node number derived by genetic algorithm to dbn. we developed a predictive algorithm for chd risk through chd risk factor data and verified it using a confusion matrix and roc curve. the research method for this paper is shown in figure 1. the training data was applied to the rbm, and when the rbm configuration was completed, the dbn was constructed through the error propagation algorithm. then, the optimum value of the number of nodes and layers of the dbn was derived through the genetic algorithm, and the optimized result was applied to generate the optimized-dbn. the performance of the optimizeddbn based chd risk prediction was verified by the confusion matrix and the roc curve. and we utilized matlab as a toolbox for developing ann model and r 3.0 to visualize results. 3.2 data the optimized-dbn based chd risk prediction proposed by this study predicts the risk chd of patients through relevant variables. data from the 6th national health and nutrition examination survey (knhanes) was used for training and validation [12]. the knhanes is a national health and nutrition survey conducted by the korea center for disease control and prevention (kcdc) to identify the status of and trends in people’s health and nutritional status [10]. this data includes the chd risk factor data and chd incidence data for each subject, which is useful for training and testing for the chd risk prediction [17]. this study also utilized the latest data from the national health and nutrition examination survey of korea. variables for predicting the risk of chd were as in the framingham study. eight factors including gender, age, systolic blood pressure (sbp), diastolic blood pressure (dbp), total cholesterol (tcl), high density cholesterol (hdl), obesity, and smoking were set as predictors of chd. we defined 496 k. lim, b.m. lee, u. kang, y. lee figure 1: experimental design for developing optimized-dbn based coronary heart disease risk prediction age, total cholesterol (tcl), high density cholesterol (hdl), systolic blood pressure (sbp), and diastolic blood pressure (dbp) as continuous variables and gender, smoking, and obesity as nominal variables. the above 8 variables were extracted from knhanes-6 data. a total of 8,108 patients were enrolled in the knhanes-6 study. 7,329 patients were excluded from the study because of the lack of data on risk factors for chd suggested by the framingham study or the presence of chd. furthermore, as in the framingham study, data for subjects under the age of 30 was excluded. the remaining 748 data were used for analysis and prediction. in addition, 70% of the data was randomly extracted and used as training data for the creation of the optimized-dbn. the remaining 30% of the data was used to evaluate the performance of the optimized-dbn. of the knhanes-6 data, 748 data points met the conditions of the study. therefore, this data was used for research. a randomly chosen 70% of this training data was applied to the rbm first. the rbm uses the training result as training data for the next layer. in this way, the rbm is constructed by stacking hidden layers. once the rbm configuration is complete, the dbn is configured via the error propagation algorithm. in this process, the sigmoid function is used as the activation function. a genetic algorithm is then performed to derive the optimum value of the number of nodes and layers of the dbn. the initial population size (n) of the an optimized dbn-based coronary heart disease risk prediction 497 figure 2: summary of data from korea national health and nutrition examination survey according to framingham risk score guidelines genetic algorithm was set to 256, the possibility of crossover (pc) was set to 0.7, the possibility of mutation (pm) was set to 0.001, and the termination condition was set to 200 generations. after the genetic algorithm is implemented, the optimal value of the layer and node values with the lowest error rate is selected, and this value is applied to generate optimized-dbn. when the error rate gradually decreases over the generations and converges toward its lowest value, the rbm performs learning for chd risk prediction. as a result of the genetic algorithm, the number of layers was 2 and the number of nodes was 5 and 7 nodes to each layers, and this value was applied to the optimized-dbn configuration. after the optimized-dbn is constructed, an error backpropagation algorithm is performed, performance is improved through fine-tuning, and the optimized-dbn configuration is completed. 3.3 performance measure in this study, confusion matrix and receiver operating characteristic (roc) curves were used as performance evaluation indexes. the confusion matrix can be used to assess the performance of the proposed algorithm by comparing the predicted risks with actual values. tp (true positive) means that an actual chd risk patient is correctly predicted as a chd risk patient. tn (true negative) means that patients with low chd risk are correctly predicted as those with low chd risk. these two indicators are values that are precisely classified [18]. fp (false positive) means that a person with a low chd risk is misdiagnosed as a chd risk patient. false negative (fn) means that patients with high chd risk are incorrectly classified as those with low chd risk. these two indicators are not well classified [2]. the roc curve is a graph showing how sensitivity and specificity are related to each other. in this study, the roc curve was used to show the accuracy of the optimized-dbn based chd risk prediction. sensitivity means the probability that the prediction is correct when the prediction algorithm assumes that the chd risk is high. it is a measure of how accurately the proposed algorithm predicts patients with high chd risk. specificity means the probability that the prediction is correct when the prediction algorithm determines that the chd risk for a patient is low. it is a measure of how accurately the proposed algorithm predicts patients with low chd risk. the roc curve can be interpreted 498 k. lim, b.m. lee, u. kang, y. lee figure 3: process for developing optimized dbn based coronary heart disease risk predictions as the proposed algorithm’s accuracy increasing as the graph converges to 1. in other words, as the area under the curve (auc), which is the area under the line of the roc curve graph, approaches 1, it can be estimated that the accuracy of the prediction algorithm is high. 4 results 4.1 genetic algorithm results our results show that the proposed chd risk prediction algorithm performs better than the existing chd risk prediction algorithm. the proposed chd risk prediction algorithm is based on dbn, which is an artificial neural network algorithm. we overcome the limitations of dbn by using a genetic algorithm. after constructing the rbm through the training data and finishing the dbn configuration, the genetic algorithm was applied to derive the optimal value of the number of nodes and layers. the initial population (n) of the genetic algorithm was set to 256, the possibility of crossover (pc) was set to 0.7, the possibility of mutation (pm) was set to 0.001, and the termination condition was set to 200 generations. as shown in figure 4, the error rate gradually decreased over the generations. the error rate, which was close to the initial 3.8, an optimized dbn-based coronary heart disease risk prediction 499 decreased as the number of generations increased. it can be seen that the error rate converges to 0.3033 near the termination condition of 200 generations. after the genetic algorithm was terminated, the layer and node values with the lowest error rate were selected as the optimal solution. the value was 2 layers and 5 and 7 nodes to each layers. after applying these values to the dbn, the optimized-dbn generation was completed by supervised learning. figure 4: error rate of genetic algorithm to develop optimized-dbn figure 5: confusion matrix of optimized-dbn based chd risk prediction the confusion matrix of the optimized-dbn based chd risk prediction is shown in figure 5. tp, which means that the actual chd risk patient is correctly predicted as a chd risk patient, was 837; tn, which means that patients with low chd risk are correctly predicted as those with low chd risk, was 218; fp, which means that a person with a low chd risk is misdiagnosed as a chd risk patient, was 75; and fn, which means that patients with high chd risk are incorrectly classified as those with low chd risk, was 218. the sensitivity of the optimized-dbn was 0.855, specificity was 0.744 and accuracy was 0.829. this was confirmed to be superior to the results of the framingham study, which showed sensitivity as 0.4430, specificity as 0.65, and accuracy as 0.5015. in addition, it performed well when compared with other classifiers. in the case of sensitivity, all of the classifiers are generally high. in particular, the bolzmann perceptron network is slightly higher than suggesting algorithm. but the performance of the proposed algorithm is relatively superior to bpn in other factors such as specificity, accuracy and auc. even in case of specificity, the performance of the proposed algorithm is superior to other classifiers, but slightly lower than dbn’s 0.8209. however, suggesting algorithm is also superior to dbn in other factors. in the case of accuracy and auc, it is confirmed that the proposed algorithm shows much better performance than all other classifiers. the performance comparison between the proposed optimized-dbn and other 500 k. lim, b.m. lee, u. kang, y. lee table 1: performance comparison by classifiers algorithm sensitivity accuracy auc nb 0.8482 0.6385 0.7917 0.736 lr 0.8283 0.6962 0.8003 0.716 bpn 0.8654 0.6211 0.7909 0.701 rf 0.8219 0.6144 0.7720 0.696 dbn 0.7469 0.8209 0.7508 0.570 framingham 0.4430 0.65 0.5016 0.548 optimized dbn 0.8550 0.7440 0.8294 0.762 figure 6: comparison graph to evaluate the optimized-dbn based chd risk prediction figure 7: comparison graph to evaluate the optimized-dbn based chd risk prediction prediction algorithms is shown in the figure 6 and figure 7. the auc of the optimized-dbn based chd prediction was 0.762. the framingham auc an optimized dbn-based coronary heart disease risk prediction 501 was 0.546, which confirms that the optimized-dbn based chd prediction was more accurate. in addition, it performed well when compared with other classifiers such as the support vector machine (svm) and random forest(rf) which shows 0.501 and 0.696. more precise results are listed the figure 6. 5 conclusions and future work the purpose of this study is to develop the optimized-dbn, a more advanced ann based prediction model, by combining two ann algorithms, dbn and ga. and we validate its performance through predicting chd risk. the results show the improved performance of optimizeddbn when comparing to not only the conventional dbn but also other major ann algorithms and famingham risk score based prediction model which is widely used in healthcare system. this study demonstrates the use of a ga as a tool to draw the optimal parameter values for setting the dbn classifier to improve the performance. it is very useful since it can be applied regardless of datasets because the optimized-dbn is able to bring the optimal value sets out which are tailored to the data set. this study will contribute to helping not only medical staffs to make decisions but also patients to prevent chd by realizing their risk in advance. and it is also able to be applied to any other diseases, if there is enough patient medical data to train and validate the prediction model. however, since this study used only the national health data of republic of korea, to more stable and reliable verification, more precise, objective evaluation through further samples is needed. furthermore, despite its superiority, still, the prediction accuracy is not perfect so we should keep working to develop a more improved way of classification and prediction. acknowledgements this research was supported by the msit(ministry of science and ict), korea, under the itrc(information technology research center) support program(iitp-2018-2017-0-01630) supervised by the iitp(institute for information & communications technology promotion). bibliography [1] blackwell, d. l.; lucas, j. w. 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(2016); the frequency based study of the applicability of dbn algorithm on language acquisition modeling the korean journal of cognitive and biological psychology, 28 (4), 617–651, 2016. international journal of computers communications & control issn 1841-9836, 13(6), 956-971, december 2018. multi-objective tabu search to balance multihoming loads in heterogeneous wireless networks j. a. huertas, y. donoso jorge a. huertas center for optimization and applied probability (copa) industrial engineering department universidad de los andes, bogotá, colombia huertas.ja@uniandes.edu.co yezid donoso* computing and systems engineering department universidad de los andes, bogotá, colombia *corresponding author: ydonoso@uniandes.edu.co abstract: the advantages of the increasing usage of mobile devices that operate under the multihoming scheme are changing the communications world drastically. therefore, next generation networks operators have the challenging task to distribute connections of mobile devices efficiently over their access networks, creating a big heterogeneous wireless network for telecommunications. we present a mixed integerlinear programming (milp) model to balance the load of multiple services over wireless networks taking into account three key indicators: connection loads of access networks, connection costs, and battery consumption of connections. to solve the multi-objective problem, we propose a multi-objective tabu search procedure that is capable to find non-supported solutions in the online efficient set. to test the performance of our multi-objective tabu search, we tested it over four instances of the literature. in the first instance, a small instance, our procedure finds the true efficient set of solutions. for the other three instances, large instances with over a thousand mobile devices, our procedure finds good online efficient sets of solutions in less than 30 seconds. finally, using appropriate multi-objective metrics, we compare the results of our multi-objective tabu search against the results of a state of the art multi-objective genetic algorithm in the literature for the same problem, outperforming the genetic algorithm in every instance tested. keywords: heterogeneous networks, multihoming, vertical handover, optimization, multi-objective, tabu search. 1 introduction multihoming refers to the ability mobile devices have to connect to different access networks at a time through multiple network interfaces [12]. on the other hand, vertical handover (vho) is the ability to change the access network that provides any service, while maintaining its quality [10]. multihoming facilitates vho, making it more seamless to the user [11]; and enhances the flow of data of multiple services across various network interfaces [2], allowing prioritized data to flow through next generation networks (e.g., 4g), and less important data to flow through legacy networks. this behaviour converges towards heterogeneous wireless networks (hwns). thanks to the exponential growth of mobile devices that operate under a multihomming scheme, authorities operating access networks must rely on tools capable to perform vho processes, while ensuring users’ quality of service (qos). standards such as ieee 802.21 mh provide only the framework for performing vho, yet the decision-making algorithms to do so are an open copyright ©2018 cc by-nc multi-objective tabu search to balance multihoming loads in heterogeneous wireless networks 957 discussion topic among the research world [15]. the methods in charge to perform vho in a whn must recover useful information from mobile devices to decide how to efficiently distribute the connections across different available access points. to illustrate this issue, assume that an operator has three access networks (i.e., a, b, and c), and there are five mobile devices using at least one of three services (i.e., web, video, and voice). a possible bad “distribution" of the connections is presented in figure 1(a), where all the services are provided by the same access network, which may not have enough bandwidth to do so. assuming that all services in our example demand the same bandwidth, have the same connection cost, and generate the same power consumption, figure 1(b) presents a balanced distribution of the connections, which is a better scenario for qos. (a) (b) figure 1: connections example: (a) possible distribution of connections; (b) balanced solution in this paper we present an optimization model to achieve a balanced distribution of connections to multiple access networks, taking into account three key indicators: load of access networks, cost of connections, and battery consumption due to connections. to solve the model we propose a multi-objective tabu search heuristic. we test our procedure under different small and large-scale instances and compare our results against a genetic algorithm in the literature. the remainder of this paper is organized as follows: section 2 discusses the relevant related work in the literature; section 3 formally introduces the problem description; section 4 presents the proposed multi-objective tabu search; section 5 presents the computational experiments, as well as its results; finally, section 6 concludes the paper and outlines future work. 2 literature review multihoming and load balancing strategies have been topics of several research projects. for example, [3] presents a model that minimizes the load of networks by re-allocating services from the more loaded network. [1] presents a queuing model that combines multihoming and network coding to improve the allocation of network resources and the communication performance in heterogeneous technologies. [4] proposes an optimization model to perform fairness balance in whs. additionally, [9] extended the previous work to a multi-objective model to balance whns considering service loads, connection cost, and battery consumption. the two former models only balance the services in the intersection of the access networks’ coverage zone. we attempt to extend these models by balancing services in the union of all the coverage zones of the access networks. 958 j. a. huertas, y. donoso multiple meta-heuristics have proven to be an excellent way to solve combinatorial problems. simulated annealing algorithms mimic the cooling processes of metals to find good solutions. particle swarm algorithms reproduces the movements of swarms across the globe to solve problems. genetic algorithms combine the way we evolve to find good solutions across generations. all of these heuristics and meta-heuristics reproduce the nature’s processes to find solutions to specific problems. [3] presents multiple heuristics to solve problems related to computer networks. for the load balancing across heterogeneous networks problem [9] uses a multi-objective genetic algorithm to obtain an efficient set of solutions across generations. unlike them, we specifically focus on tabu search to solve the problem. introduced by [7], tabu search (ts) allows to search the solution space and enables early stopping of algorithms due to local optima. it has been applied to solve a large variety of problems, including set covering problems. regarding wireless sensor netowrks (wsns) problems, numerous work has been accomplished using tabu search meta-heuristic. [14] provides a ts-based routing protocol (tsrp) for data routing in wsns. [6] proposes a centralized clustering method for a data collection mechanism in wsns that seeks the optimization of the consumption of energy while collecting data. they achieve their mechanism through a tabu search meta-heuristic that returns better solutions in terms of cost and execution than other meta-heuristics such as simulated annealing or distributed methods. regarding the multi-objective nature of problem we address, [8] provides a framework for the usage of tabu search in wsns problems with multiple objectives. it also addresses multiple wsns related problems, such as the wireless lan planning problem (wlp problem), and the evaluation of wireless sensor networks. using multi-objective tabu search meta-heuristics, the authors achieve high-quality solutions regarding the paretto optimal frontier, proving that this type of heuristic is suitable to handle problems with numerous combinatorial and continuous variables, such as the ones related to wsns. as showed in the previous paragraphs, although many wsn-related problems have been solved using a tabu search approach, having extremely good results, many hwn-related problems have been tackled using other types of heuristics. we attempt to fill this gap by balancing multiple service loads in a whn using a multi-objective tabu search procedure. 3 problem description we proposed a formulation based on the model presented in [4] and extended to consider multiple objectives in [9]. while both models balance connections from services in the intersection of the access networks’ coverage zones, we do so in the union of the available access networks’ coverage space. let n , k, and i be the set of the available access networks, mobile devices, and all services in use, respectively. subset sk references the services being used in mobile device k ∈ k. let cj and wj be the cost of connection to the access network j ∈n and the theoretical bandwidth of network j ∈n , respectively. let fk be the maximum cost the user of mobile device k ∈k is willing to pay to connect to any access network. let di be the demanded bandwidth by service i ∈i. multi-objective tabu search to balance multihoming loads in heterogeneous wireless networks 959 let ms be the minimum signal strength required by any mobile device to connect to any network. let skj be the signal strength perceived by mobile device k ∈k from network j ∈n . to model the power consumption generated in a mobile device when connected to a network we use a power consumption indicator that relates the signal strength perceived by the device. let okj be the indicator of power consumption generated in mobile device k ∈k if connected to network j ∈n . the power consumption indicator is inversely proportional to the signal strength perceived; it is low (i.e., 1) if the perceived signal is above a signal upper limit us; high (i.e., 3) if the perceived signal is below a signal lower limit ls; and medium (i.e., 2) if it lays between ls and us. eq. 1 describes how the power consumption is defined. okj =   1, if us < skj; 2, if ls ≤ skj ≤ us; 3, if ls > skj; ∀ k ∈k,j ∈n . (1) let pk be the percentage of battery remaining in mobile device k ∈k. let bk be an indicator of the battery percentage remaining in mobile device k ∈k; it takes a high value (i.e., 3) if the battery percentage pk is above a battery-percentage upper limit ub; it takes a low value (i.e., 1) if pk is bellow a battery-percentage lower limit lb; finally, it takes a medium value (i.e., 2), if pk is between lb and ub. eq. 2 describes how the battery percentage indicator is defined. bk =   1, if lb > pk; 2, if lb ≤ pk ≤ ub; 3, if ub < pk; ∀ k ∈k. (2) this model considers xikj, a binary decision variable that takes the value of 1, if the mobile device k ∈k connects to network j ∈n to satisfy service i ∈sk; 0, otherwise. also, it considers ykj, another binary decision variable that takes the value of 1, if mobile device k ∈ k connects to network j ∈ n to satisfy any of its services i ∈ sk; 0, otherwise. finally, α, β, and γ are non-negative decision variables representing the maximum load of the networks, the maximum connection cost to the networks, and the maximum consumption indicator from connections to the networks, respectively. thus the formulation of our milp is as follows: minimize α (3) minimize β (4) minimize γ (5) 960 j. a. huertas, y. donoso subject to, ykj ≤ ∑ i∈sk xikj, ∀ k ∈k,j ∈n ; (6) xikj ≤ ykj, ∀ k ∈k,j ∈n , i ∈sk; (7)∑ j∈n xikj = 1, ∀ k ∈k, i ∈sk; (8) di ·xikj ≤ wj, ∀ k ∈k,j ∈n , i ∈sk; (9) cj ·ykj ≤ fk, ∀ k ∈k,j ∈n ; (10) okj ·ykj ≤ bk, ∀ k ∈k,j ∈n ; (11) ms ·ykj ≤ skj, ∀ k ∈k,j ∈n ; (12)∑ k∈k ∑ i∈sk di wj ·xikj ≤ α, ∀ j ∈n ; (13)∑ k∈k cj ·ykj ≤ β, ∀ j ∈n ; (14)∑ k∈k okj ·ykj ≤ γ, ∀ j ∈n ; (15) xikj ∈{0, 1}, ∀ k ∈k,j ∈n , i ∈sk; (16) ykj ∈{0, 1}, ∀ k ∈k,j ∈n ; (17) α,β,γ ≥ 0. (18) objective function (3) minimizes the maximum load of the networks, objective function (4) minimizes the maximum connection cost to a network, and objective function (5) minimizes the maximum battery consumption from connections to the networks. constraints (6) and (7) relate decision variables ykj and xikj by determining if a mobile device connects to a network to satisfy the demand of one of its services, or not. nevertheless, to ensure that every service in the mobile devices is served, constraints (8) guarantee the accessibility of the services in every mobile device to a network. constraints (9) guarantee that the services being used can only be provided by networks with enough bandwidth to meet its demand, not overloading any network. constraints (10) guarantee that the connection cost to a network does not exceed the maximum connection cost established by the user of the mobile device. constraints (11) ensure that the consumption indicator generated by the connection to a network does not exceed the battery level of a mobile device. one of the big contributions of this paper is the ability to guarantee accessibility to the services being used in all the mobile devices located in the overall coverage area of the access networks. constraints (12) guarantee the connection of a mobile device to a reachable network. constraints (13) capture the maximum load of the available networks. constraints (14) capture the maximum connection cost to the networks. constraints (15) capture the maximum power consumption indicator due to connection to networks. finally, constraints (16)-(18) define the nature of the decision variables. 4 multi-objective tabu search approach tabu search (ts) heuristic explores the solution space of a problem in an iterative way, prohibiting previous solutions to be visited during a number of iterations; and storing some of these previous solutions (which is a good aspect to extend to the multi-objective procedure). we propose a multi-objective ts (mots) procedure for solving the model described in section 3. multi-objective tabu search to balance multihoming loads in heterogeneous wireless networks 961 the remainder of this section is organized as follows: sub-section 4.1 introduces the encoding of our mots. sub-section 4.2 shows the move operator. sub-section 4.3 presents the memory management used. sub-section 4.4 defines the dominance criteria for our mots. sub-section 4.5 discusses some of the diversification and intensification strategies used. sub-section 4.6 presents the pseudo-codes of the mots. finally, sub-section 4.7 presents appropriate measures to devise the quality of the mots. 4.1 encoding in the problem defined in section 3, the decision variable that defines the structure of the problem is xikj; it tells how the connections and the multihoming are managed. the other decision variables depend on this binary decision variable. the encoding of the solution is guided by this variable; therefore, we encapsulate the values of the solution in a three-dimensional array x as the one presented in fig. 2. figure 2: encoding of the solution the structure of x is faithful to the variable notation: the first index (i.e., the rows) references the mobile devices; second index (i.e., columns), the networks; third index (i.e., depth), the services. each cell of the array can take the values defined by the variable xikj, and the value of the remaining decision variables can be obtained by iterating x. for example, in fig. 2, the fourth mobile device is connecting to the fourth access network to provide the first service (i.e. x14,4 = 1); no other access network can provide this service for this mobile device (i.e., x14,j = 0,∀ j 6= 4). 4.2 move operator constraints (8) guarantee that every service in use in every mobile device gets connected to a network. to guarantee such constraint, we defined a swap move. more formally, any possible swap move has the form: swap(xikj,x i kj′), ∀ k ∈k, i ∈sk,j,j′ ∈n | x i kj = 1 ∧x i kj′ = 0. (19) move operator (19) defines the neighbourhood of the current solution x. with this definition, the neighbourhood of x includes both feasible and infeasible reachable solutions. in our mots we only move to a feasible solution, due to the convexity of the model defined in section 3. 4.3 memory management managing short-term and long-term memory in ts is crucial for a good performance of the procedure [7]. for short-term memory we defined a temporary list of tabued moves t given the 962 j. a. huertas, y. donoso chronological realizations of the swap move. at any iteration of the algorithm, a new cell in x takes the value of 1. we prohibit the removal of the new cell (i.e., the cell takes the value of 0) during certain number of iterations. more formally, the tabu move (k,j,i, l) tells a cell xikj cannot be zero (i.e., xikj 6= 0) during l iterations. constant insertion and deletion of tabu moves to the temporary list allows the exploration of the solution space over solutions that have not been reachd yet; this is a key process in any meta-heuristic [3]. on the other hand, some services won’t be used in some devices, because of subset sk,∀k ∈k. additionally, a device k ∈k cannot connect to a given network j ∈n if the perceived signal skj is below ms, or the connection cost cj is above fk. these cases are checked through a constant tabu list that allows predefined infeasibility to be taken into account. some of the impossible connections are preprocessed for the procedure, and taken into account with a dynamic 3d array to preserve the dimensionality of the wsns related problems. 4.4 dominance let α(x), β(x) and γ(x) be the associated values of the maximum load of a network, the maximum connection cost to a network, and the maximum consumption of battery level for connecting to a network in solution x, respectively. let x and x′ be two different feasible solutions to the problem. solution x dominates x′ if (α(x) < α(x′) and β(x) ≤ β(x′) and γ(x) ≤ γ(x′)) or (α(x) ≤ α(x′) and β(x) < β(x′) and γ(x) ≤ γ(x′)) or (α(x) ≤ α(x′) and β(x) ≤ β(x′) and γ(x) < γ(x′)). (20) additionally, solution x alternates with x′ if α(x) = α(x′) and β(x) = β(x′) and γ(x) = γ(x′) (21) a solution x is efficient when there is no other solution x′ that dominates it. on the other hand, an efficient solution can be either supported or non-supported. supported solutions are found through the linear convex combination of the objective functions (i.e., weighted sum of objectives). non-supported efficient solutions cannot be found through the weighted sum of objective functions. for further information on multiobjective concepts, the interested reader is referred to [3,5]. we defined a set of current solutions s̄ = {x1, x2, . . . , xv} that changes iteratively depending on the swap operator. at the end of any iteration, we obtain the efficient solutions s in s̄ (i.e., s ⊆ s̄). after n iterations, the algorithm stops, finding the online efficient set s, which is the set containing all the efficient solutions found after n iterations of the procedure. 4.5 search strategies diversification strategies in ts are conceived to explore the solution space that has not been visited until a given iteration. intensification strategies are made to guide the search through solutions found better historically [7]. to generate both diversification and intensification strategies in our mots, at any iteration we move to a random non-dominated solution of the multi-objective tabu search to balance multihoming loads in heterogeneous wireless networks 963 current neighbourhood. with this decision we explore the remaining solution space, because the new solution is non-dominated, while searching good solutions, for the same reason. 4.6 pseudo-codes algorithms 1, 2 and 3 show the pseudo-codes of our mots. in algorthm 1, we define a set of current solutions (i.e., line 3.) that is iteratively mutated (i.e., line 6.) to explore the whole search space. additionally, iteratively we compare the set of current solutions with online efficient set (i.e., line 7.) to replace it. in algorithm 2, the random component of the procedure is taken into account in line 3. the mutation of the current solution is performed in lines 4. to 8.; also, the addition of the new tabu move is executed in these lines as well (i.e., line 7.). finally, during the dominance check in algorithm 3 we replace the online efficient set in line 23. algorithm 1 multi-objective tabu search input: n,v output: s 1: procedure mots(n,v) 2: s, t ←∅ 3: s̄ ← generateinitialsolutions(v) 4: s ← checkdominance(s, s̄) 5: for it < n do 6: s̄ ← move(s̄, t) 7: s ← checkdominance(s, s̄) 8: t ← iteratetabulist(t) 9: it ← it + 1 10: end for 11: return s 12: end procedure algorithm 2 move input: s̄, t output: s̄ 1: procedure move(s̄, t) 2: for index < |s̄| do 3: if random(0, 1) ≥ 0.5 then 4: x ← s̄(index) 5: x′← non–dominated solution of neighborhood(x) 6: (k,j,i) ← swap done to achieve x′ 7: t ← t + {(k,j,i, l)} 8: s̄(index) ← x′ 9: end if 10: index ← index + 1 11: end for 12: return s̄ 13: end procedure 964 j. a. huertas, y. donoso algorithm 3 check dominance input: s, s̄ output: s 1: procedure checkdominance(s, s̄) 2: for index < |s̄| do 3: x ← s̄(index) 4: if |s| = 0 then 5: s ←{x} 6: else 7: dominates, alternates, isdominated, enters ← false 8: d ←∅ 9: for index2 < |s| do 10: x′← s(index2) 11: dominates ← x dominates x′ . see eq. 20. 12: isdominated ← x′ dominates x . see eq. 20. 13: alternates ← x alternates x′ . see eq. 21. 14: enters ← true 15: if dominates = true then 16: d ← d + {x′} 17: end if 18: end for 19: if dominates = true then 20: s ← s − d 21: end if 22: if enters = true ∧ isdominated = false ∧ alternates = false then 23: s ← s + {x} 24: end if 25: end if 26: index ← index + 1 27: end for 28: return s 29: end procedure 4.7 quality of mots to measure the quality of our mots we compute the spacing and the spread of the online efficient set, as [13] did to measure the performance of a multi-objective particle swarm optimization procedure. these are suitable metrics to measure multi-objective procedures performance. the spacing of an efficient set measures how distant are the efficient solutions in an efficient set; as this metric takes into account the standard deviation of the distances between the efficient solutions, a low value of spacing tells that the solutions are uniformly spaced, thus, a low variable is desirable. the spread measures how close the efficient solutions are to the optimal values of the single-objective problems. let q be the number of solutions in the efficient set. let ei be the distance to the nearest neighbour within the set of solution i. let ē be the average distance of ei. finally, let e∗δ be the distance of the optimal solution of objective δ ∈ {α,β,γ} to its nearest neighbour within the efficient set. eq. (22) and (23) present the spacing and spread equations, respectively. multi-objective tabu search to balance multihoming loads in heterogeneous wireless networks 965 spacing(s) = √√√√1 q q∑ e=1 (ē−ei)2 (22) spread(s) = ∑ δ∈{α,β,γ}e ∗ δ + ∑q e=1 |ē−ei|∑ δ∈{α,β,γ}e ∗ δ + q · ē (23) 5 computational experiments to test the performance of our mots we used four instances instances based on the literature: a small instance and three large instances. 5.1 small instance we used the same instance presented in the first case study in [9]. tables 1 4 present the parameters used in the instance. table 1: mobile devices information. mobile voice video web fk pk devices ($ units) (% charge) k1 1 1 0 62 85 k2 1 0 1 44 34 k3 1 1 1 98 74 k4 1 0 1 73 28 k5 1 1 1 82 50 table 2: networks information. network wj cj technology (mbps) ($ units) lte 70 80 wifi g 54 0 hspa+ 15 40 table 3: perceived signal of networks by mobile devices. mobile lte wifi g hspa+ devices k1 87 78 64 k2 55 68 28 k3 92 47 88 k4 0 85 25 k5 91 95 93 966 j. a. huertas, y. donoso table 4: services’ demanded bandwidth. service voice video web di 0.1 3 0.5 (mbps) we ran our experiments using a macbook pro with 8gb ram with intel i7 processor running at 3.2ghz with turbo boost. we used gurobi optimizer on java to obtain the results of the single objective problem described in section 3. table 5 summarizes the results obtained. column one show the objective optimized, column 2 the id of the solution obtained, columns 3,4, and 5 present the value of the decision variables. table 5: mip results. objective solution α β γ minimize α 1 0.086 160 6 minimize β 2 0.213 0 9 minimize γ 3 0.207 160 4 minimize γ 4 0.447 80 4 when minimizing the load (i.e., solution 1), the value of α is minimum regarding the ones obtained with the other solutions. as well, when minimizing the cost of connections, the value of β reduces to 0, this is because all services are provided by the wifi g network, which has no cost. finally, minimizing γ we found two alternated solutions; solutions 3 and 4 have the same value for γ, yet the value of the other variables are different. to test our mots, we generated objective function pairs in order to graph the solutions of the meta-heuristic. after fine-tuning of the parameters, we set v = 10, n = 2, 000, and l = 1, 000. fig. 3 presents the efficient set for the three pairs of objective functions (i.e., α vs. β, α vs. γ, and β vs. γ), and the contrasted solution of the single objective problem. black dots represent the efficient set; white squares represent the points associated to the optimal solutions of the single-objective problem. note that solutions 1 and 2 (minimizing load and cost, respectively) are the extreme points of the efficient set in fig. 3 (a); solutions 1 and 3 are the efficient set of fig. 3 (b); finally, solutions 3 and 4 are the extreme points of the efficient set in fig. 3 (c). thus, our mots finds optimal efficient set for small instances of the problem. our meta-heuristic is able to calculate not only efficient supported solutions, but also the nonsupported ones. fig. 4 illustrates how the efficient set in fig. 3(a) distributes over supported and non-supported solutions. black-dotted solutions can be found through the convex linear combination of objective functions α and β; white-dotted solutions are non-supported ones. traditional optimization of the milp presented in section 3 would have never find non-supported efficient solutions. this proves our mots is a good method to solve this type of problems. finally, we ran our mots taking into account all three objectives. as a result, we obtained the efficient set presented in table 6 in a computational time of 0.96 seconds. note that all optimal solutions of table 5 are present in the efficient set found. for the optimal efficient set shown in table 6, we obtained a spacing equal to 13.54 and a spread equal to 1.157. [9] reported a spacing of 21.505 for this instance. multi-objective tabu search to balance multihoming loads in heterogeneous wireless networks 967 (a) (b) (c) figure 3: efficient set when balancing pair of objectives: (a) α vs. β ;(b) α vs. β ; (c) β vs. γ figure 4: supported and non-supported solutions for α – β. table 6: multi-objective efficient set. solution α β γ 5 0.207 40 7 6 0.200 40 9 7 0.207 80 5 8 0.447 80 4 9 0.213 0 9 10 0.207 160 4 11 0.086 160 6 12 0.133 80 7 5.2 large instances to test the scalability of our mots we reproduced the experiments in [9]. we generated three random instances changing the amount of mobile devices: instance 2 with 20 mobile devices, in968 j. a. huertas, y. donoso stance 3 with 500 devices, and instance 4 with 1,000 mobile devices. we maintained, at most, five services per device that can connect to seven access networks, having the information presented in [9]. table 7 and table 8 present the information of the networks and services used, respectively. table 7: large-scale instances’ networks information. network wj cj technology (mbps) ($ units) lte 70 80 wifi n 300 0 wifi g 54 0 wimax 15 60 hspa+ 15 40 hsdpa 2 20 umts 0.3 10 table 8: large-scale instances’ networks information. service di (mbps) voice 0.1 video 3.0 web 0.5 game 2.0 chat 0.2 we ran our mots over the three large instances 30 times. since the number of possible solutions is higher over the instances, after fine-tuning of the parameters we set v = 10, n = 5, 000, and l = 2, 500. the results obtained are different from the ones in [9] because we assumed some of the parameters that [9] did not specified in the case studies. table 9 presents our results. column 1 references the instance. columns 2, 3 and 4 reference the average minimum values of α, β, and γ, respectively. column 5 references the average number of solutions in the efficient set. column 6 shows the average time running the algorithm. finally, columns 7 and 8 present the average spacing and spread of the efficient set, respectively. table 10 presents the standard deviation of the values shown in table 9. table 9: large-scale instances’ average results. instance α β γ solutions time (s) spacing spread instance 1 0.086 000.000 4.000 8.000 0.960 13.539 1.156 instance 2 0.427 156.000 10.067 26.867 7.308 8.357 0.958 instance 3 2.186 548.667 27.333 18.300 14.557 16.590 1.013 instance 4 4.447 1,134.667 50.400 21.433 20.618 19.499 1.001 as the mobile devices grow, the minimum values of the objective functions are greater too. nevertheless, the average time to solve the problem is quite reasonable, taking into account the multi-objective tabu search to balance multihoming loads in heterogeneous wireless networks 969 table 10: large-scale instances’ standard deviations results. instance α β γ solutions time (s) spacing spread instance 1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 instance 2 0.0500 24.7295 0.2494 156.0224 0.3881 4.9527 0.2154 instance 3 0.4250 46.7470 0.4618 126.3764 0.3095 6.8807 0.2512 instance 4 0.5419 76.4933 0.5384 140.1392 0.8100 7.1564 0.1929 average number of solutions in the efficient set. due to the units of the objective functions, the average spacing of the solutions obtained are obviously large, yet reasonable. finally, the spread of the solutions obtained oscillates around one, which is good. once we take into account the values presented in table 10, it is possible to see that the procedure is quite stable regarding cpu time. also, the standard deviation of the spreads are really low, ensuring that our metaheuristic is always near to the real optimal values of the single-objective solutions. taking into account the units of the instances, the variances of the spacing are reasonable, telling that the uniformity of the efficient sets found is quite stable. we are only presenting the minimum values found for each objective, and the decision of using one solution against another one depends on the decision-taker. nevertheless, considering the metrics measured, the solutions are well distributed along the search space. the values of the standard deviation of β are larger than the other ones, because of the monetary units of this statistic, making reasonable to oscillate around the 50 monetary units. we compared the results of our average spacing metric per instance against the spacing obtained with the genetic algorithm in [9]. table 11 shows the spacing obtained in each of the instances by [9] with the genetic algorithm and the average spacing obtained with our proposed mots. as they do not provide any spacing metric for the fourth instance (i.e., 1,000 mobile devices), we only compare spacings of the second and third instances. table 11: spacing results. [9] mots average spacing spacing instance 1 21.505 13.54 instance 2 35.542 8.357 instance 3 30.168 16.590 instance 4 – 19.499 as the spacing metric takes into account the standard deviation between solutions in the efficient set, a low value is always better. it is possible to notice that, although optimality was only proved in the first instance, our meta-heuristic produces better online efficient sets than the ones obtained with the genetic algorithm. in all the comparable instances our average spacing outperforms the ones obtained by [9]. 6 conclusions we developed a milp model that takes into account the multihoming scheme; additionally, it only takes into account the destination network during the vho process. this is a versatile 970 j. a. huertas, y. donoso consideration, because in real life, operators know a priori the initial networks that the mobile devices are connected to. our model balance service loads in the union of the coverage areas of the access networks, representing a major contribution to the literature. we tested our milp using commercial optimizer gurobi in a small instance to obtain optimal single-objective solutions. its result were compared to the efficient set obtained with our mots, and matched with the extreme points of the non-dominated frontier. we found more than one non-supported solution, proving that the meta-heuristic was quite successful compared to the convex linear combination of the objective functions; but furthermore, our mots reaches the real efficient set for small instances of the problem. we additionally tested the mots over large instances, and achieved good solutions within a reasonable computational time. in addition, we obtained some metrics on the online efficient set proving the efficiency of our meta-heuristic, yet optimality could not be proved. we compared the metrics of the online efficient sets obtained with our mots against a multi-objective genetic algorithm in the literature under the same instances. our results are always better than those we compared to. with the intuition behind our mots we were able to calculate good solutions, yet optimality was not proved. future work includes merging efficient exact algorithms over networks with our meta-heuristic as a procedure to find good initial solutions; therefore, it could be possible to achieve optimality while solving multi-objective large scale problems. author contributions jorge huertas formulated the new milp model. he also designed and programmed the multi-objective tabu search, as well as the computational experiments. yezid donoso was the investigation project director. he contributed guiding and reviewing the investigation. all authors contributed equal to the paper. conflict of interest the authors express no conflict of interests. bibliography [1] capela, n.; sargento, s. 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(2010). a survey of vertical handover decision algorithms in fourth generation heterogeneous wireless networks, computer networks 54, 1848–1863, 2010. international journal of computers communications & control issn 1841-9836, 13(5), 881-894, october 2018. a financial embedded vector model and its applications to time series forecasting y.f. sun, m.l. zhang, s. chen, x.h. shi yanfeng sun1,2, minglei zhang1,2, si chen1,2, xiaohu shi1,2,3* 1. key laboratory of symbol computation and knowledge engineering of the ministry of education jilin university, china changchun, 130012, china 2.college of computer science and technology jilin university, china changchun, 130012, china 3.zhuhai laboratory of key laboratory of symbolic computation and knowledge engineering of ministry of education zhuhai college of jilin university, china zhuhai, 519041, china sunyf@jlu.edu.cn, 632987245@qq.com, 1075083128@qq.com *corresponding author: shixh@jlu.edu.cn abstract: inspired by the embedding representation in natural language processing (nlp), we develop a �nancial embedded vector representation model to abstract the temporal characteristics of �nancial time series. original �nancial features are discretized �rstly, and then each set of discretized features is considered as a "word" of nlp, while the whole �nancial time series corresponds to the "sentence" or "paragraph". therefore the embedded vector models in nlp could be applied to the �nancial time series. to test the proposed model, we use radial basis functions (rbf) neural networks as regression model to predict �nancial series by comparing the �nancial embedding vectors as input with the original features. numerical results show that the prediction accuracy of the test data is improved for about 4-6 orders of magnitude, meaning that the �nancial embedded vector has a strong generalization ability. keywords: embedded vector, �nancial daily vector, �nancial weekly vector, rbf neural networks. 1 introduction financial time series is nonlinear, non-stationary, and often with high noise [7]. moreover, the �nancial market is a dynamic complex system which is vulnerable to various external factors [23]. therefore, it is a challenging job to analyze the �nancial time series, especially for its forecasting problem. unlike the traditional predictive methods such as the autoregressive integrated moving average (arima) model, the computational intelligence-based approaches are data-driven models that do not require any speci�c assumptions about the distribution of the data. they are widely used in the �nancial market, the exchange rate market and corporate �nancial forecasting and other �elds [6].the intelligent computing methods, such as multilayer perceptron(mlp) [12, 29], radial basis function (rbf) neural network [1, 24, 32], adaptive neural-fuzzy inference system [8], support vector regression (svr) [5,31], deep belief network [14,25], simulation model [9], lifecycle model [22], and particle swarm optimization (pso) [21] are often used for forecasting jobs. an important work in the �eld of natural language processing (nlp) is how to represent words or documents. with the applications of the deep learning algorithms [2, 16] and the machine learning algorithms [13,27] in the �eld of nlp, a number of word representation methods have copyright ©2018 cc by-nc 882 y.f. sun, m.l. zhang, s. chen, x.h. shi been proposed. one-hot representation [30] is simple and one of the most widely used methods, but the weaknesses are involved in high dimension of the word vector, easily leading to dimension disaster and easily resulting in the phenomenon of lexical division. the weaknesses have been solved to a great extent of the advent of the distributed representation methods, or word embedding, which are vectors whose relative similarities correlate with semantic similarity. one of the most in�uential early models is latent semantic analysis (lsa), developed in the context of information retrieval [10]. latent dirichlet allocation (lda) is the re�nement of lsa, which is the most well-known topic model [4]. both lsa and lda use documents as contexts, which become computationally very expensive on large data sets. another type of distributed representation method is neural networks based. bengio et. al. proposed a neural network statistical language model by learning a distributed representation of words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences [3]. while until mikolov et. al. proposed the word2vec model, and provided open source toolkit [17,18], word embedded vector had been widely used in di�erent kinds of nlp applications. word2vec models are two-layer neural networks that are trained to reconstruct linguistic contexts of words. unlike the earlier language model, word2vec represents all the words in the corpus with a real dense vector, namely the word embedding. during the learning process, the embedding vectors are trained together with language model parameters. the most advantage of word2vec is that it could be better represented by latent semantics. two topology frameworks, namely continuous bag of words (cbow) and skip-gram, could be adopted in word2vec model, as well as two training techniques, hierarchical softmax and negative sampling, could be selected, respectively. compared with other neural network models, the hidden layer is removed from word2vec, and the mapping layer no longer emphasizes the order of words. paragraph vector is an extension of word2vec, which adds the id of a paragraph (might be a phrase, a sentence or a document) into the inputs and outputs paragraph representation vectors together with word embedded vectors [15]. paragraph vector includes two models: pv-dm (distributed memory model of paragraph vectors) and pv-dbow (distributed bag of words version of paragraph vector). word embedded vector is also widely applied to other �elds. in machine translation [26], word embedded vector can be used to mine the relationship between words in the two languages. after training word vectors of the two languages, it can be translated by a matrix transformation. from used sentences in image understanding [11]. images and sentence knowledge are integrated by the neural network. task extraction is built on semantic or knowledge relations. in the social network [19], network embedding method is widely used. deepwalk [20] model is proposed based on word2vec. it is a new method of learning the potential representation of vertices in complex network. these potential representations encode social relations in continuous vector space and can be easily utilized by other machine learning models. tang [28] proposed a novel embedding method-line, which embedded the large complex network into the low dimensional vector space. the classical stochastic gradient descent restriction is solved by using a sampling algorithm for edges. line is suitable for many type of information network. in the paper, the idea of word embedded vector is introduced to abstract the temporal characteristics of �nancial time series. after discretizing original �nancial features, we consider each set of discretized features as a "word" of nlp, and the �nancial time series as the "paragraph". then we apply the embedded vector models to �nancial series data and obtain �nancial embedded vectors. next we adopt the �nancial embedded vectors as the input of the rbf neural network for single-step �nancial time series prediction. to test the proposed �nancial embedded vector model, we use the daily data of s&p500 index for experiments. compared with the rbf neural network without �nancial embedded vector, the numerical results have been improved greatly in the prediction accuracy. a financial embedded vector model and its applications to time series forecasting 883 2 financial embedded vector model considering each set of discretized features of �nancial time series as a 'word' of nlp, we apply the embedded vector models in nlp to the �nancial time series. appling word2vec and paragraph vector, we can construct �nancial daily vector and �nancial weekly vector, respectively. in section 2.1, word2vec and paragraph vector will be introduced brie�y. and then our proposed �nancial embedded vector model will be stated in section 2.2. 2.1 embedded vector model in nlp 1) word2vec word2vec has two topology frameworks, namely continuous bag of words (cbow) and skipgram. the task of the former is to predict the word given its context, while that of latter is to predict the context given a word. on the other hand, there are two training methods to train the parameters for both of the topology frameworks. one is hierarchical softmax, the other is negative sampling. in this paper, cbow framework and hierarchical softmax training method are used in our proposed method. therefore word2vec with cbow and hierarchical softmax will be described in the following. cbow framework is a neural network model with three layers: input layer, projection layer and output layer (fig.1). unlike neural network language model(nnlm) [3], there are no nonlinear hidden layers in cbow. the representation vectors of the context of a word are the inputs of the model. they are mapped to the projection layer by simply summation. the output layer represents the central word, which is expressed by a leaf node of the hu�man tree. v(context(wt)1) v(context(wt)2) v(context(wt)2n-1) v(context(wt)2n) xw wt input layer project layer output layer sum p(wt | context(wt)) figure 1: diagram of cbow model structure suppose the tth sample is the pair of (context(wt), wt),where context(wt) is consisted of n words before and after the word wt, v (w) is the representation vector or embedded vector of word w. cbow predict the probability of occurrence of the target word wt by context words context(wt). the target function is the following logarithmic likelihood function l = ∑ w∈c log p(w|context(w)) (1) where p(wt|context(wt)) is the probability of wt on condition of context(wt). the goal is to maximize the logarithmic likelihood function, which could be solved by stochastic gradient ascent (sga) [17]. 2) paragraph vector 884 y.f. sun, m.l. zhang, s. chen, x.h. shi based on word2vec model, le and mikolov added paragraph id into the inputs and proposed paragraph vector model, which could learn the representation vectors for both word set and paragraph set [15]. similar with word2vec, paragraph vector has two frameworks, namely distributed memory model of paragraph vectors (pv-dm) and distributed bag of words (pv-dbow ). fig.2 gives the network structure of pv-dm, which could be considered as the cbow framework of word2vec adding "paragraph id" in the input layer. the learning algorithm is the same with that of cbow model. after being trained, the paragraph vectors can be used as features of the paragraph. there is one thing should be noticed that, it should be performed a short inference stage to get a representation vector for a new paragraph, by making gradient descended on the paragraph vector while holding the learned word embedded vectors �xed. paragraph id d v(context(wt)1) v(context(wt)2n-1) v(context(wt)2n) xw wt input layer project layer output layer sum p(wt | context(wt), d) figure 2: diagram of pv-dm model structure 2.2 financial embedded vector model the key point of word2vec (or paragraph vector) is to abstract the context relationships among di�erent words by a neural network model and then obtain the embedding representation vectors of all the words in the dictionary. similar with natural language, �nancial time series has obviously temporal characters, therefore based the embedding model in nlp, we propose a �nancial embedded vector model to represent the "context" relationships between di�erent discretized feature sets of �nancial time series. according to word2vec and paragraph vector models, "daily embedding vector" and "weekly embedding vector" are proposed, respectively. in the following of this section, the former will be described in detail, the latter is similar. assume the �nancial time series has m features, all of which are discretized. for example, it has 5 features and each of which is discretized into 3 sets. then we could get the discretized feature sets from '11111' to '33333', 35 = 243 sets in total. that is the "dictionary" size of �nancial embedding models. firstly, we train the word2vec model to get the �nancial daily embedded vector by using cbow model and hierarchical softmax framework. assume the ith discretized feature set be '23213', that is to say wi='23213', and denote the "context" set of wi as context(i), then we will show the training procedure for the sample (wi, context(i)), which is shown in fig.3. the structure of fig.3 is the same as fig.1, each leaf node of hu�man tree represents a discretized feature set, the position of which is predetermined by its frequency in the training corpus. for example, '23213' is a leaf node of hu�man tree in fig.3. there is a single path pi from root node to the leaf node '23213', which includes 5 nodes and 4 branches. each branch could be considered as a binary classi�cation problem. let the left branch be a positive class a financial embedded vector model and its applications to time series forecasting 885 v(context(wt)1) v(context(wt)2) v(context(wt)2n-1) v(context(wt)2n) xi 23213 input layer project layer output layer � i 1 � i 2 � i 3 � i 4 � i 5 d i 2 = 0 d i 3 = 1 d i 4 = 0 d i 5 = 1 figure 3: diagram of cbow model structure for the training of '23213'. and the hu�man code be 1, while the right branch be a negative class and the hu�man code be 0. the hu�man code of wi is the binary code of the nodes in pi (except root node), namely that d2 id3 id4 id5 i='0101'. it is easy to know the probability of the positive class is σ(xti θ) = 1 1 + e(−x t i θ) (2) where θ is the undetermined non-leaf node vector, and xi is the summation of input context vectors. the probability of the negative class is 1 −σ ( xi tθ ) . therefore, the probability of the binary classi�cation on the jth node of path pi is p(dij|xi,θ i j−1) = { σ(xti θ i j−1),d i j = 1 1 −σ(xti θ i j−1),d i j = 0 (3) where j=2,3,4,5. it can be equivalently written as p(dij|xi,θ i j−1) = [σ(x t i θ i j−1) 1−dij ] · [1 −σ(xti θ i j−1) dij ] (4) then we could use the probability of the path pi to represent how likely to generate wi on condition of its context discretized feature set is context(i), which could be computed by p(wi|context (i)) = lwi∏ j=2 p(dij|xi,θ i j−1) (5) where lwi is the node number of pi , being 5 in this example. of course, the probability of each sample pair is expected as large as possible. then the objective functions of the whole model could be set as l = ∑ w∈c log (p(w|context(w))) = ∑ w∈c log (∏lw j=2 p ( dwj |xi,θ i j−1 )) = ∑ w∈c log (∏lw j=2 σ ( xti θ i j−1 )1−dij[1 −σ1−dij (xti θij−1)]dij) = ∑ w∈c ∑lw j=2 { (1 −dij) log σ ( xti θ i j−1 ) + dij log [ 1 −σ1−d i j ( xti θ i j−1 )]} (6) 886 y.f. sun, m.l. zhang, s. chen, x.h. shi the goal is to maximize the objective function by training parameters v(x) and θ. it could be solved by stochastic gradient ascent (sga), and �nally the well trained v is the "daily �nancial embedded vector" set. for the detailed algorithm of sga, one can refer to [17]. the pseudo codes of the method are shown in fig.4. figure 4: pseudo codes for sga furthermore, we divide the �nancial series into weekly segments, and each weekly segment could be considered as a "paragraph" in nlp which consists of 5 discretized feature sets as the daily �nancial vector. then by introducing the paragraph vector model, we could get "weekly �nancial vector" for each week. the method is similar with that of "daily �nancial vector". 3 the algorithm framework in section 2, a �nancial embedded vector model is proposed, from which we could get "daily �nancial vector" and "weekly �nancial vector", respectively. and then the trained �nancial vectors could be set as the features for �nancial series analysis. compared with the original features of �nancial series, the �nancial vectors abstract more temporal characters and are expected to get better results. in this section, we develop a �nancial series prediction framework based on �nancial embedded vectors and rbf neural network. there are three main processes, namely data discretization, �nancial embedded vector construction, and single step prediction based on rbf neural network, which will be described in the following of this section. 3.1 data discretization a �nancial time series is usually a set of time-dependent consecutive real numbers. in our �nancial embedded vector model, the �nancial vector at each time point is a "�nancial word", therefore, the real features should be discretized �rstly and the feature set could be divided to a "�nancial vocabulary". assume there are m real features of a �nancial time series, the ith feature is discretized into ni segments, then the size of "�nancial vocabulary" is: s = ∏m i=1 ni (7) in our method, k-means algorithm is adopted for the discretization. for all the values of the ith feature, they are clustered into ni categories, which are labelled with the set {1, 2, ...,ni}. then a financial embedded vector model and its applications to time series forecasting 887 the ith feature of series is discretized into ni segments according to clustering results. k-means is an unsupervised clustering algorithm and often be used as discretization tool. compared with other discretization algorithms, k-means algorithm has lower computational complexity and is advantageous for dealing with large-scale data. 3.2 financial embedded vector construction after data discretization, each time point of the �nancial series could be mapped to a "�nancial word" in the "�nancial vocabulary". and the whole �nancial series is a long "�nancial paragraph" consists of these "�nancial words". take the context of a "�nancial word" as inputs, and the target "�nancial word" as output, the �nancial embedded vector model could be trained on cbow framework and by sga algorithm. then we could obtain so called "daily �nancial vectors". part of them are shown in table1. table 1: part of daily �nancial vectors discretization results daily financial vectors 21232 [-0.0078 -0.0112 0.0211 0.0196 -0.0059 0.0211 0.0241 0.0091 0.0249 -0.0156 -0.0077 -0.0105 -0.0145 0.0141 0.0079 0.0021 -0.0110 0.0076 0.0153 0.0010] 33112 [-0.0182 -0.0200 0.0228 0.0003 -0.0129 -0.0152 -0.0154 0.0085 0.0010 0.0067 -0.0211 -0.0190 -0.0007 0.0057 0.0127 -0.0159 0.0204 0.0046 0.0056 -0.0030] 33113 [0.0077 -0.0190 -0.0205 0.0248 0.0190 0.0155 -0.0030 0.0201 -0.0025 0.0037 -0.0007 -0.0149 0.0066 -0.0067 -0.0191 0.0238 0.0219 -0.0055 -0.0011 0.0025] we consider �ve days' "�nancial words" of a week as a weekly "�nancial sentence". adding the sentence id into the �nancial embedded vector model, it could be trained by stochastic gradient descent algorithm on pv-dm framework, and get "weekly �nancial vectors". part of them are shown in table2. table 2: financial weekly vectors discretization results (weekly) weekly financial vectors [33112 31232 21232 23113 31112] [0.0429 -0.0725 -0.0783 0.2201 0.0254 -0.1170 0.0982 -0.0089 0.0813 -0.0245 -0.1699 -0.1943 -0.0463 -0.1262 0.0201 0.1168 -0.1370 0.0347 -0.1303 -0.1324] [23113 33113 31212 33113 33113] [0.0318 -0.0232 -0.1067 0.1555 0.0652 -0.1573 -0.0302 -0.1005 0.0855 0.0142 -0.0757 -0.1824 -0.0594 -0.0842 -0.0969 0.1414 -0.1923 0.0295 -0.0568 -0.1683] [12313 31233 21233 23323 12132] [0.0407 -0.0812 -0.1055 0.2603 -0.0162 -0.1914 0.1718 -0.0282 0.0654 0.0374 -0.2742 -0.1971 -0.0573 -0.1199 0.0504 0.1890 -0.1782 0.0816 -0.1934 -0.1976] 3.3 single step prediction based on rbf neural network to predict the �nancial series data, we select radial basis function (rbf) neural network as the forecasting model. rbf neural network is a particular type of multilayer perceptron (mlp) neural network, with input, hidden and output three layers (fig.5) [26]. the hidden layer consists of rbf neurons, which use radial basis function as their active functions. after getting the �nancial embedded vectors, we use rbf neural network to build the predicting models for one-step forecasting of the daily and weekly closing price, respectively. the original percentage data, the daily �nancial vector and the weekly �nancial vector are used as input data for comparison. therefore we build six models for two goals with three types of inputs, which will be described in detail in section 4.2. in the hidden layer, the mostly used gaussian function is selected as the active function. there is only one output node in all six models for our goal is one-step daily or weekly closing price predicting. 888 y.f. sun, m.l. zhang, s. chen, x.h. shi figure 5: rbf network structure 4 experiments the s&p500 index has a longer trading history and enough sample which can re�ect the laws of a mature securities market. the data are more stable and truthful. we collect the s&p500 index data from yahoo finance [33]. date is from january 4, 1950 to november 14, 2016, a total of 16826 trading days. every daily trading data includes �ve-dimensional data, namely the opening price, the highest price, the lowest price, the closing price and the volume. to eliminate the dimension in�uence of the �ve-dimensional data and improve the stability of the data, we will �rstly perform the data pretreatment. next, using rbf neural network and selecting di�erent feature combinations, daily closing price and weekly closing price are predicted, respectively. 4.1 data pretreatment firstly, eq.8 is performed for percentage processing of the 5-dimensional data, respectively. x′t = (xt −xt−1)/xt−1 (8) where xt is the data for the day t,xt-1 is the data for the day t-1, x′t is the percentage of the data for the day t. secondly, the k-means algorithm will be adopted for the discretization of the original data. we take three clusters in the k-means algorithm. finally, all the �ve features are clustered into three classes independently. that is to say, the discretized feature space size is 35 = 243. set the cluster labels as 1, 2, 3, a �ve-dimensional data of a time point will be discretized into {1, 2, 3}5. take for example, the clustering results of the �ve dimensions for the date 1950-01-04 are 2, 1, 2, 3, and 2, respectively. therefore, the discretization result for the date is '21232', which is "�nancial word" of that day. after discretizing 5-dimensional time series data, we apply the �nancial embedded vector models to obtain "daily �nancial vectors" and "weekly �nancial vectors", which is described in section 3.2. for example, the daily �nancial vector of '21232' is shown in the �rst line of table 1. 4.2 daily closing price forecasting based on �nancial embedded vector to test the performance of the obtained �nancial embedded vectors, they are set as the input features to predict daily closing price in this section. rbf network is applied as the forecasting model, and three types of feature combinations are arranged for comparison. denote them as model1, model2, and model3, the detail of which are described as follows: model1-comparison daily model. the model output is the closing price for day t, that is, the closing price for day t will be forecasted, which is 1-dimensional data. the inputs are the a financial embedded vector model and its applications to time series forecasting 889 data three days before, the percentage data for day t-1, t-2, and t-3. the data for each day includes 5 percentage data of original features, namely that, the opening price, the highest price, the lowest price, the closing price and the volume. therefore the dimension of the inputs is 15. the previous ten thousand data are used for training, the rest for testing. model2-financial daily vector daily model. the model output is the same as model1. the inputs are the �nancial daily vectors of three days before, namely day t-1, t-2, and t-3. in this model, we will take the dimension of each �nancial daily vector as 20. then the dimension of input is 60. the division of training data and testing data is the same with model1. model3-financial daily and weekly vector daily model. the model output is the same as above two models. the inputs are the �nancial daily vectors for day t-1 and t-2, and the �nancial weekly vector for the last week of the date. in this model, we will take the dimension of each �nancial daily (and weekly) vector as 20, and then the dimension of input is also 60. the division of training data and testing data is the same with above two models. rbf neural network training function is the built-in function in matlab. an important parameter is distribution density ('spread' in the built-in function). a trade-o� between over �tting and under �tting will be considered by di�erent value of spread. the value of spread is set as 10 values in our experiments, which are 0.01, 0.05, 0.1, 0.3, 0.4, 0.7, 1, 5, 10, 20 and 50, respectively. mean square error (mse) is used as the evaluation index of forecasting e�ect, which is de�ned by mse = 1 n ∑n i=1 (zi −yi)2 (9) where zi is the real value of the ith data, yi is the forecasting value of the ith data, n is the sample number. the smaller the mse value is, the more accurate the prediction is. the results of mse for both training set and testing set are shown in table 3. from the results in table 3, the mse values of the three models for the training set are very similar. model1 even gets the least average mse among the three models, though all of them reach 10-4 magnitude. while for the testing set, the mse values di�er greatly in magnitude for di�erent spreads and di�erent models. model2 and model3 are far better than model1, and model3 is superior to model2. for example, the average mse value of model2 is 99.8% improved from model1, and that of model3 is 92.0% decreased to model2. according to the big di�erences among the three models results of the testing data, it could be concluded that the �nancial embedded vector can improve the generalization ability of the models, especially for the combination of daily �nancial vector and weekly �nancial vector. fig.6 shows the comparing results of the predicted daily closing price curves of the three models. the curves in the top �gure are the local enlarged of the ones in bottom �gure. from the bottom �gure, it is easy to �nd that both curves of model2 and model3 are obvious closer to actual data curve than that of model1. when we focus on the enlarged curves, we could �nd that the performance of model3 is better than that of model2. 4.3 weekly closing price forecasting based on �nancial embedded vector to further examine the e�ect of �nancial embedded vectors, longer periods of data are adopted. in this section, experiments are performed to predict the weekly closing price. similar with the above section, rbf is chosen as the forecasting model, and three types of feature combinations are arranged for comparison. they are denoted as model4, model5 and model6, which are described below. model4-comparison weekly model. assume the closing day (friday) of the target week is day t, the closing price of day t is the output of the model, which is a 1-dimensional value. the 890 y.f. sun, m.l. zhang, s. chen, x.h. shi table 3: prediction results of daily close price mse for training mse for testing spread model1 model2 model3 spread model1 model2 model3 0.01 0 0.000029 0.000053 0.01 0.00410 0.000169 0.000198 0.05 0 0.000029 0.000054 0.05 0.10830 0.000168 0.000194 0.1 0.000007 0.000029 0.000054 0.1 287.808 0.000470 0.000191 0.3 0.000014 0.000029 0.000054 0.3 1225.83 0.000334 0.000266 0.4 0.000013 0.000029 0.000054 0.4 48.7838 0.005158 0.000216 0.7 0.000031 0.000029 0.000054 0.7 35.3886 1.243979 0.029582 1 0.000039 0.000029 0.000054 1 88.7457 2.229143 0.214848 5 0.000059 0.000030 0.000054 5 12.8574 0.003093 0.033577 10 0.000063 0.000042 0.000057 10 2.10287 0.000398 0.000277 20 0.000065 0.000053 0.000061 20 2.71368 0.000214 0.000193 50 0.000066 0.000065 0.000066 50 1.30023 0.000166 0.000161 average 0.000032 0.000036 0.000056 average 155.058 0.316663 0.025428 c lo si ng p ric e day model1 model2 model3 actual data figure 6: forecasting and actual daily closing price in the training set inputs are the percentage data of original features of 5 days before, namely from day t-1 to t-5. therefore the dimension of the input is 25. also, the previous ten thousand data are used for training, the rest for testing. model5-financial daily vector weekly model. the output is the same as model4. the inputs are the �nancial daily vectors of 5 days before, namely from day t-1 to t-5. for the �nancial daily vector is 20-dimensonal, the input dimension is 100. the division of training set and testing set is the same with model4. model6-financial weekly vector weekly model. the output is the same as model4. the input is the weekly vector of the week before, which is 20 dimensions. the division of training set and testing set is the same with model4. the results of mse for both training set and testing set are shown in table 4. the results are very similar with those of above section. the mse values of the three models for the training set are similar and very small. for the testing set, the mse values di�er greatly from di�erent models. model5 and model6 are far better than model4, and model6 is superior to model5. the average mse value of model5 is only 1.16 x 10-5% of that of model4, and that of model6 is 13.52% of that of model5. a financial embedded vector model and its applications to time series forecasting 891 table 4: prediction results of weekly close price mse for training mse for testing spread model4 model5 model6 spread model4 model5 model6 0.01 0 0.000003 0.000004 0.01 0.00018 0.000149 0.000148 0.05 0 0.000003 0.000004 0.05 0.00140 0.000134 0.000211 0.1 0 0.000003 0.000004 0.1 0.00984 0.000126 0.000185 0.3 0 0.000003 0.000004 0.3 54.2725 0.002544 0.000455 0.4 0 0.000003 0.000004 0.4 30.7013 0.000280 0.000526 0.7 0 0.000003 0.000004 0.7 2779.44 0.001075 0.000735 1 0 0.000003 0.000004 1 211338 0.016169 0.000740 5 0.000021 0.000003 0.000045 5 6941.79 0.002522 0.000148 10 0.000034 0.000003 0.000051 10 2492.17 0.001514 0.000133 20 0.000043 0.000003 0.000052 20 756.955 0.001221 0.000133 50 0.000049 0.000010 0.000058 50 0.68581 0.000465 0.000124 average 0.000013 0.000004 0.000021 average 20399.5 0.002382 0.000322 fig.7 shows comparing results of the predicted weekly closing price curves of the three models. the graph in the top of fig.7 is the enlarged one of the local area of the bottom �gure. from the �gure, it is easy to �nd that both curves of model5 and model6 are obviously closer to the actual data curve than that of model4, and model6 is the best one. also, the �nancial embedded vector can greatly improve the generalization ability of the models, especially for the weekly �nancial vector. c lo si ng p ric e week model4 model5 model6 actual data figure 7: forecasting and actual weekly closing price in the testing set 5 conclusion in the paper, inspired by the idea of embedded vector idea in nlp, we propose the �nancial embedded vector model to represent the �nancial time series, which could abstract the temporal features very well. focused on the �nancial time series prediction, a framework is developed 892 y.f. sun, m.l. zhang, s. chen, x.h. shi based on the �nancial embedded vector model and rbf neural network. to test the e�ectiveness of our method, it is applied to s&p500 index historical data for the daily and weekly closing price prediction. comparing di�erent kinds of input features, it could be concluded that the �nancial embedded vectors could improve the precision greatly to the original features. funding the authors are grateful to the support of the national natural science 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[33] [online]. http://�nance.yahoo.com, accesed on 1 jan 2017. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 2, month: april, year: 2020 article number: 3859, doi.org/10.15837/ijccc.2020.2.3859 ccc publications optimal control applications in the study of production management l. popescu, n. d. militaru, o. m. mituca liviu popescu* department of statistics and economic informatics faculty of economics and business administration, university of craiova, al. i. cuza st. no. 13, craiova 200585, romania *corresponding author: liviunew@yahoo.com nicolae daniel militaru university of craiova al. i. cuza st. no. 13, craiova 200585, romania postdoctoral researcher militarunicolaedaniel@yahoo.com ovidiu mircea mituca academy of economic studies piata romana, no. 6, bucharest 010374, romania phd student mircea.mituca@gmail.com abstract a mathematical model for an economic problem of production management is proposed. the continuous optimal control problem is solved, by using the pontryagin maximum principle at the level of a new space, called lie algebroid. the controllability of the economic system is studied by using lie geometric methods and involves restrictions on the final stock quantities. finally, a numerical application is given. keywords: optimal control, automatic control, controllability, production management, pontryagin maximum principle, lie algebra. 1 introduction the mathematical methods are applied successfully in optimal control theory with many applications in economics, business, finance or engineering. one of the motivations for this paper is the study of some type of lagrangian systems with external constraints. these systems have important applications in many different areas as optimal control theory, econometrics, cybernetics, operational research (caputo [3], feichtinger & hartl [8], liu [14], sethi & thompson [22], weber, [23] ) or automatic control (caruntu et al. [4], chitsaz et al. [6], dubins [7]). also, in control theory, the lie doi.org/10.15837/ijccc.2020.2.3859 2 geometric methods have been applied by many authors, see for example agrachev & sachkov [1], brocket [2], isidori [10], jurdjevic [11], lavalle [13], popescu [20]. in this paper we propose a mathematical model for a problem of production management, using the optimal control techniques. we find the optimal solution with minimum costs of production and storage applying the pontrygin maximum principle (pmp) at the level of a new working space, called lie algebroid, which is a holonomic distribution of the tangent space. moreover, it is provided that the framework of lie algebroids is more suitable than the tangent space in the study of control affine systems with holonomic distributions. the controllability of our economic system is studied by using lie geometric methods and applying the frobenius theorem. such type of problems including inventory and production management are intensely studied. thus, chazal, jouini & tahraoui [5] studied the production planning and inventories optimization, using a backward approach for the case of convex storage cost. gayon, vercraene & flapper [9] dealt with optimal control of a production inventory system with two disposal options and product returns. maccini, moore & schaller [15] studied the inventory behavior with permanent sales stocks. olsson [18] presented some modelling techniques for base-stock inventory systems in the case of state dependent demand rates. ortega & lin [19] gave a review on control theory applications to the production and inventory problems. popescu [21] used the framework of a driftless control affine system in the study of inventory and production problems. the present paper is organized as follows. in the second section we present the known results about lie geometric methods in optimal control theory for control affine systems, including the controllability problems in the case of holonomic distributions. in section three, that contains the novelty of the paper, we give an application of optimal control to a problem of production management and show that the hamilton-jacobi-bellman equations, provided by pmp on cotangent space, lead to a system of differential equations, very difficult to solve. in order to find the optimal solution, we will use a different way, applying the pmp in the framework of lie algebroids. next, we present only the necessary notions about lie algebroids (see mackenzie [17] for more details) and the geometric viewpoint of the optimal control. using the relation between the hamiltonian functions on dual lie algebroid and the cotangent space, we apply the pmp at the level of a lie algebroid, which in this case is a distribution on the tangent bundle. next, we prove that the distribution generated by the vector fields is integrable and it determines a foliation in the state space. it results that the proposed economic system is not controllable, in the sense that we can only manufacture a certain final quantity of products. in the last part of the section, we find the complete solution of the control system using the pmp on lie algebroids. finally, a numerical example is given. 2 theoretical basic of control affine systems let us consider m be a smooth and n-dimensional manifold. a continuous control system is given by the differential equations, depending on some parameters dxi(t) dt = fi(x(t),u(t)), where x ∈ m represents the state of the system and u ∈ u ⊂ rm represents the controls. considering x0 and x1 two points of m (two states of the system), then the optimal control problem consists of finding the optimal trajectories of control system that connect x0 and x1 and minimizing the cost min ∫ t 0 l(x(t),u(t))dt, x(0) = x0, x(t) = x1, where the function l is the lagrangian (energy, cost, time, distance, etc.). the most important and powerful tool for studying the optimal solutions in control theory is pmp. it generates the differential equations of first order, necessary for the optimal solutions. for each optimal trajectory, c(t) = (x(t),u(t)), it offers a lift on the cotangent space (x(t),p(t)) satisfying hamilton-jacobi-bellman equations. the hamiltonian function is given by h(x,p,u) = 〈p,f(x,u)〉−l(x,u), p ∈ t∗m, and it results that the maximization condition with respect to u, given by h(x(t),p(t),u(t)) = max v h(x(t),p(t),v), doi.org/10.15837/ijccc.2020.2.3859 3 yields ∂h ∂u = 0 (h is assumed to be smooth with respect to u). the extreme trajectories must satisfy the following equations ẋ = ∂h ∂p , ṗ = − ∂h ∂x . (1) we recall that a control affine system has the form [2] ẋ = x0(x) + m∑ i=1 uixi(x), (2) where x = (x1, ...,xn) represent the local coordinates on the manifold m, u(t) = (u1(t), ...,um(t)) ∈ u ⊂ rm, m ≤ n and x0,x1...xm are smooth vector fields on m. usually, x0 is called the drift vector field which describes the dynamics of the affine system in the absence of control. the vector fields xi, i = 1,m are called the input vector fields and the function u(t) represents the control. we can say that the system is controllable if for any two states x0 and x1 of the system, there exists a finite time t and a control u : [0,t] → u such that for x(t) with x(0) = x0, we have x(t) = x1. in other words, the system is controllable if for any two states x0, x1, there exists a solution curve of the system (2) which connects x0 to x1. controllability is the ability to move a system from a given initial state to any final state, in finite time, using some available controls. a distribution ∆ on m is an application which assigns for every point in m a subspace of the tangent space at this point. the distribution ∆ is called locally finitely generated if there is a set of vector fields {xi}i=1,m (called local generators of ∆) which spans ∆ , i.e. ∆(x) = span{x1(x), ...,xm(x)}⊂ txm. the distribution ∆ has dimension k if dim ∆(x) = k, for all points x in m. the lie bracket of two vector fields is given by [xi,xj](x) = ∂xj ∂x (x)xi(x) − ∂xi∂x (x)xj(x), ( ∂xj ∂x is the jacobian matrix of xj). a distribution ∆ on m is said to be involutive if, for any x ∈ m and xi(x),xj(x) ∈ ∆(x), we have [xi,xj] (x) ∈ ∆(x). if the involutive distribution is generated by vector fields {xi}i=1,m then it results [xi,xj] (x) = m∑ k=1 lkij(x)xk(x). therefore, we can say that each lie bracket is expressed as a linear combination of the system vector fields. we recall that a foliation {sα}α∈a of m is a partition of the space m = ∪sα of m in disjoint and connected submanifolds sα, called leaves. a distribution ∆ of constant dimension on m is called holonomic (integrable) if there exists a foliation {sα}α∈a on m whose tangent bundle is ∆, that is txs = ∆(x), where s is the leaf passing through x. frobenius’s theorem says that if the distribution ∆ has constant dimension, then ∆ is integrable if and only if ∆ is involutive. the presence of the drift x0 in the study of control affine systems complicates the question of controllability. in the following we deal with the driftless control affine system in the form ẋ = m∑ i=1 uixi(x). (3) it is assumed that the vector fields xi, i = 1,m, generate a distribution ∆ on the connected manifold m so that the rank of ∆ is constant. if the distribution ∆ = span{x1,x2, ...,xm} is holonomic with constant rank, then [xi,xj] ∈ ∆ for every i 6= j and the system is not controllable. using the frobenius theorem it results that the distribution ∆ is holonomic and determines a foliation on m. thus, two points can be joined by a trajectory of the system if and only if they are situated on the same leaf of the foliation. 3 production management problem we consider that a company can manufacture n types of products, denoted p1, p2,..., pn. in a certain period of time t (fixed) it must produce a certain amount (s1,s2, ...,sn) of each type of product. the quantity of pn product depends on the quantities of products p1, p2 ,..., pn−1 by a given law. we know that the unit storage costs of holding inventory are given by constants (β1,β2, ...,βn) for each doi.org/10.15837/ijccc.2020.2.3859 4 product. also, we assume that the unit production costs increase linearly with the production level and the cost of production operations for pn are considered negligible (pn is a final product packed and unassembled). we are looking for a plan of production for filling the order at the specified delivery data at minimum cost. the case of a single product is studied by kamien and schwartz [12]. we consider xi = xi(t) the inventory accumulated by time t. the inventory level is the cumulated past production pi = pi(t) and considering xi(0) = 0, we have xi(t) = ∫ t 0 pi(s)ds. hence the rate of change of inventory level ẋi is the production and we have ẋi = pi. the unit production costs ci increase linearly with the production level, i.e ci = αipi, where α1, ...,αn−1 ∈ (0, 1) are positive constants and we have that the total cost of production is c1p 1 + ... + cn−1pn−1 = n−1∑ i=1 αi ( pi )2 = n−1∑ i=1 αi ( ẋi )2 . we obtain that the total cost (including storage), at time t is n−1∑ i=1 αi ( ẋi )2 + n∑ i=1 βix i. considering, ẋi = ui, i = 1,n−1, the control variables and assuming that the rate of change of inventory for final product pn is given by the law ẋn(t) = u1 x1(t) k1 + ... + un−1 xn−1(t) kn−1 , where ki > 0, i = 1,n− 1, we obtain the following optimal control problem  ẋ1 = u1 ẋ2 = u2 ..... ẋn−1 = un−1 ẋn = u1 x 1 k1 + ... + un−1 x n−1 kn−1 xi(0) = 0, xi(t) = si, i = 1,n u1, ...,un−1 ≥ 0 (4) min ∫ t 0 ( α1(u1)2 + ... + αn−1(un−1)2 + β1x1 + ... + βnxn ) dt. we are looking for the optimal solutions starting from the point (0, 0, ..., 0) and (s1,s2, ...sn) as endpoint. the system can be written in the form (driftless control affine system): ẋ = n−1∑ i=1 uixi, x =   x1 ... xn   , x1 =   1 0 ... x1 k1   , ...,xn−1 =   0 ... 1 xn−1 kn−1   ∈ rn+ (5) min ∫ t 0 f(u(t),x(t))dt, f(u(t),x(t)) = α1(u1)2 + ... + αn−1(un−1)2 + β1x1 + ... + βnxn. the distribution ∆ = span{x1, ...,xn−1} which is generated by the vector fields x1, ...,xn−1 has constant dimension, dim ∆(x) = n− 1, for all x ∈ rn. the vector fields are given by x1 = ∂ ∂x1 + x1 k1 ∂ ∂xn , x2 = ∂ ∂x2 + x2 k2 ∂ ∂xn , ...,xn−1 = ∂ ∂xn−1 + xn−1 kn−1 ∂ ∂xn . doi.org/10.15837/ijccc.2020.2.3859 5 the lie bracket are [xi,xj] = [ ∂ ∂xi + xi ki ∂ ∂xn , ∂ ∂xj + xj kj ∂ ∂xn ] = 0, i,j ∈ 1,n− 1 and it results that the associated distribution ∆ = span{x1, ...,xn−1} is holonomic and has the constant rank n − 1. using the frobenius theorem, it results that the system is not controllable, in the sense that we cannot reach any final stock quantity. moreover, from the system (4) we obtain ẋn = ẋ1x1 k1 + ... + ẋn−1xn−1 kn−1 , which yields, by integration xn = ( x1 )2 2k1 + ... + ( xn−1 )2 2kn−1 + c. (6) (c is a constant) and it results that ∆ determines a foliation on rn+ given by the hipersurfaces (6). from the initial condition xi(0) = 0 it results c = 0 and using that xi(t) = si we have that the system is controllable (the problem has the solution) if and only if the final amounts satisfy the condition sn = s21 2k1 + ... + s2n−1 2kn−1 . to solve the optimal control problem we can use the pontryagin maximum principle on the cotangent space. we get the hamiltonian h(u,x,p) = n∑ i=1 piẋ i −f = p1u1 + p2u2 + ... + pn ( u1 x1 k1 + ... + un−1 xn−1 kn−1 ) −α1(u1)2 − ...−αn−1(un−1)2 −β1x1 − ...−βnxn, the conditions ∂h ∂ui = 0 lead to the following formulas of control variables ui = piki + pnxi 2αiki , (7) which replaced into the expression of the hamiltonian function leads to h = n−1∑ i=1 ( piki + pnxi )2 4αik2i −β1x1 − ...−βnxn. (8) using the hamilton-jacobi-bellman equations (1) we obtain a very complicated system of differential equations. therefore, we will use a different way, involving the framework of lie algebroids. 3.1 lie algebroids we consider m a real, c∞-differentiable, n-dimensional manifold and txm its tangent space in the point x ∈ m. the tangent bundle of m is usually noted (tm,πm,m), where πm is the canonical projection map πm : tm → m applying a tangent vector x(x) ∈ txm ⊂ tm to the base point x ∈ m. a vector bundle is a triple denoted (e,π,m) with e the total space, m the base space and the map π : e → m is a surjective submersion. using mackenzie [17] we have: definition 1. a lie algebroid is given by (e, [·, ·]e,σ), where (e,π,m) is a vector bundle of rank m over the manifold m, satisfying the following conditions: i) c∞(m)-module of sections γ(e) is endowed with a lie algebra structure [·, ·]e. ii) the map σ : e → tm (called the anchor) induces a lie algebra homomorphism from the lie algebra of sections (γ(e), [·, ·]e) to the lie algebra of vector fields (�(m), [·, ·]) and verifies the leibniz rule [s1,fs2]e = f[s1,s2]e + (σ(s1)f)s2, ∀s1,s2 ∈ γ(e), f ∈ c∞(m). doi.org/10.15837/ijccc.2020.2.3859 6 considering the local coordinates (xi) on an open subset u ⊂ m and a local basis {sα} of the sections of the bundle π−1(u) → u, these generate the local coordinates (xi,yα) on e. the local functions σiα(x), l γ αβ(x) on the base m given by following relations σ(sα) = σiα ∂ ∂xi , [sα,sβ]e = l γ αβsγ, i = 1,n, α,β,γ = 1,m, are called the structure functions of lie algebroids. we recall that a control system on the lie algebroid (e, [·, ·]e,σ) (see martinez [16]) with the control space τ : a → m is given by a section ρ of e along τ. a trajectory of the control system ρ is an integral curve of the vector field σ(ρ). considering the cost function l∈ c∞(a), we must minimize the integral of l over the set of those system trajectories which satisfy certain optimal conditions. the hamiltonian function h is given by h(µ,u) = 〈µ,ρ(u)〉−l(u) and the critical trajectories are given by hamilton-jacobi equations on lie algebroids (martinez [16]) dxi dt = σiα ∂h ∂µα , dµα dt = −σiα ∂h ∂xi −µγl γ αβ ∂h ∂µβ . (9) with ∂h ∂ua = 0. using [20] we have: remark 3.1. the connection between the hamiltonian function h on the dual space t∗m and the hamiltonian function h on the dual lie algebroid e∗ has the form h(p) = h(σ?(p)), µ = σ?(p), p ∈ t∗xm, µ ∈ e ∗ x. (10) which locally yields µα = σ∗iα pi, (11) and the hamiltonian h(p) is degenerate on the subset kerσ? ⊂ t∗m. 3.2 solution of production management problem we will solve the problem of production management using the framework of lie algebroids. thus, we consider the space e = ∆ (holonomic distribution with constant rank), the anchor map σ : e → tm is given by inclusion and [, ]e is the induced lie bracket. the anchor σ has the components σiα =   1 0 ... 0 0 1 ... 0 ... ... 0 0 0 ... 1 x1 k1 x2 k2 ... x n−1 kn−1   , and we get the lagrangian function l on e given by l = α1(u1)2 + ... + αn−1(un−1)2 + β1x1 + ... + βnxn, which is regular, because det ( ∂2l ∂ui∂uj ) 6= 0. next, we use the legendre transformation in order to find the hamiltonian function on dual lie algebroid. theorem 2. the hamiltonian function on dual lie algebroid e∗ has the following expression h(x,µ) = n−1∑ i=1 µ2i 4αi −β1x1 − ...−βnxn. (12) proof. the legendre transformation from e to e∗ induced by the regular lagrangian l has the form (x,u) → (x,µ), µi = φi(x,u) = ∂l ∂ui = 2αiui, doi.org/10.15837/ijccc.2020.2.3859 7 and the hamiltonian function is given by h(x,µ) = µφ−1(x,µ)−l(x, φ−1(x,µ)). in (x,u) coordinates we get h = ui ∂l ∂ui −l = α1(u1)2 + ... + αn−1(un−1)2 −β1x1 − ...−βnxn, and using the relations ui = µi2αi , we find (12). ut using the relation (11) we can find the hamiltonian function h on t∗m in the form h(x,p) = h(µ), µ = σ?(p), with   µ1 ... µn−1   =   1 0 ... 0 x 1 k1 0 1 ... 0 x 2 k2 ... ... ... ... 0 0 ... 1 x n−1 kn−1     p1 ... pn−1 pn   . we get that µi = piki + pnxi ki and it results the hamiltonian on the dual space, given in relation (8). theorem 3. the optimal solution of production management problem is given by xi(t) = ai1e √ βn 2αiki t + ai2e − √ βn 2αiki t − βiki βn , i = 1,n− 1, (13) xn(t) = ( x1(t) )2 2k1 + ... + ( xn−1(t) )2 2kn−1 , (14) where ai1 = 1 di + 1 βiki βn + disi d2i − 1 , ai2 = di di + 1 βiki βn − disi d2i − 1 , di = e √ βn 2αiki t , (15) and control variables ui(t) = √ βn 2αiki  ai1e √ βn 2αiki t −ai2e − √ βn 2αiki t   . (16) proof. from the relation [xα,xβ] = l γ αβxγ we obtain the components l γ αβ = 0, while from (9) we deduce that dxi dt = σiα ∂h ∂µα , dµα dt = −σiα ∂h ∂xi , which leads to   ẋi = µi2αi , i = 1,n− 1 ẋn = ∑n−1 i=1 µix i 2αiki , µ̇i = βi + x i ki βn, i = 1,n− 1 (17) we deduce, using (17) ẍi = µ̇i 2αi ⇒ ẍi = βn 2αiki xi + βi 2αi , i = 1,n−1, which yields the linear nonhomogeneous second order differential equations. next, considering the linear homogeneous differential equations ẍi − βn 2αiki xi = 0, doi.org/10.15837/ijccc.2020.2.3859 8 and the characteristic equation λ2 − βn2αiki = 0, we get the solutions λ1,2 = ± √ βn 2αiki . it results the general solutions of the homogeneous differential equations xi(t) = ai1e √ βn 2αiki t + ai2e − √ βn 2αiki t . also, we find the general solution of the nonhomogeneous second order differential equations given by (13). the solution is optimal because the hamiltonian is a convex function. the constants ai1,ai2 can be found from the initial conditions xi(0) = 0, xi(t) = si, which lead to the system{ ai1 + ai2 = βiki βn ai1di + 1 di ai2 = βiki βn + si where we denote di = e √ βn 2αiki t > 1. from the above system, by direct computation, the coefficients (15) are obtained. finally, using (4) we have ẋi = ui, i = 1,n− 1 and it results the control variables (16), which ends the proof. ut numerical application: we consider a numerical example for the case n = 3: fixed period of time is t = 1; final stock quantities from products p1, p2, p3 are s1 = 6, s2 = 4, s3 = 8; storage costs are given by β1 = 4, β2 = 2, β3 = 4; the coefficients are α1 = 2/3, α2 = 1/2, k1 = 3, k2 = 4. the economic system is controllable, because s3 = s21 2k1 + s22 2k2 = 8. if s3 6= 8, then the system is not controllable. applying the formulas (13)-(16) the following optimal solution is obtained x1(t) = 3, 3595786488et − 0, 3595786488e−t − 3, x2(t) = 2, 2397190992et − 0, 2397190992e−t − 2, x3(t) = ( x1(t) )2 6 + ( x2(t) )2 8 , with control variables (production rates) given by u1(t) = 3, 3595786488et + 0, 3595786488e−t, u2(t) = 2, 2397190992et + 0, 2397190992e−t. 4 conclusions and future work in this paper a mathematical model for a problem of production management is proposed. we have to find the optimal solution so that different products are manufactured in the required quantities and at a fixed date with minimum production and storage costs. the continuous optimal control problem is solved by using the pontryagin maximum principle at the level of a new working space, called lie algebroid, which is a holonomic distribution on tangent spaces. the controllability of our economic system is solved by using the lie geometric methods and the properties of the lie brackets for the vectors fields which generate the distribution. the system is controllable if and only if the final stock quantities satisfy a given law. finally, a numerical application is presented. we have to remark that the optimal solution of this numerical example can be easily represented in three-dimensional space using mathematical software. as future work, we will consider the case of nonholomic and strong bracket generating distribution [20], where the system is controllable. this type of distributional system has many applications in automatic control, see for instance the paper [6], where the minimum wheel-rotation paths for differential-drive mobile robots are studied and the mathematical model is a driftless control affine system. it is interesting to use the pmp on the lie algebroid, which in this case is the whole tangent space with the base given by vectors of distribution, together with the first iterated lie brackets. doi.org/10.15837/ijccc.2020.2.3859 9 references [1] agrachev, a.; sachkov, y. 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(2011). optimal control theory with applications in economics. mit press, 2011. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: popescu, l.; militaru, n.d.; mituca, o.m.(2020). optimal control applications in the study of production management, international journal of computers communications & control, 15(2), 3859, 2020. https://doi.org/10.15837/ijccc.2020.2.3859 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 3, month: june, year: 2020 article number: 3885, https://doi.org/10.15837/ijccc.2020.3.3885 ccc publications modelling and analysis of iot technology using neural networks in agriculture environment emre özbilge, yönal kırsal, ersin çağlar emre özbilge* department of computer engineering european university of lefke, north cyprus tr-10 mersin, turkey *corresponding author: eozbilge@eul.edu.tr yönal kırsal department of electronics and communication european university of lefke, north cyprus tr-10 mersin, turkey ykirsal@eul.edu.tr ersin çağlar department of management information systems european university of lefke, north cyprus tr-10 mersin, turkey ecaglar@eul.edu.tr abstract the rapid development of internet, cloud computing and sensor networks lead to develop and deploy the internet of things (iot) which is a hot topic for the researchers. it has started to be used in various areas. thus, agriculture is one of the most popular iot research area. in agriculture environment, farming platform area is being a huge open structure and farmers must protect the crops from extreme weather conditions namely; wind speed/direction, precipitation, air temperature, solar radiations, and relative humidity etc. these extreme weather conditions effect crops and farms very significantly. but with the benefits of internet of things technologies, an agriculture business become more easy and efficient despite extreme weather conditions. this paper provides a model of smart agriculture environment using neural networks that helps the farmers to make more accurate predictions for the future according to weather conditions. this paper proposed a time-delay radial basis function (tdrbf) network approach to model temporal and sequential relationship between the various weather condition sensor readings from the agricultural environment. the performance of the acquired network model was analysed statistically and presented in this paper. as a result, the results of the neural network model show that it could be used to predict the desired weather condition sensor readings beforehand in order to increase the productivity in agricultural environment and also it is possible that by using such an intelligent learning system could provide a life-long learning for the changing weather conditions in the farming area over the years. keywords: neural networks, internet of things, agriculture, machine learning, intelligent systems. https://doi.org/10.15837/ijccc.2020.3.3885 2 1 introduction due to the improvement of the internet technology, many technologies came to our daily life such as cloud computing, green computing and internet of things (iot) etc. iot is the most popular technology due to its wide range of applications. the terminology of iot was proposed in 1999 by kevin ashton as an interconnected devices [1]. as a definition, iot is the network of physical devices that enables these devices to collect and exchange data. iot has two important parts as a hardware and software. hardware can be any object such as vehicles, furniture, building, home appliance with an embedded sensors. software is the framework that creates connection with devices to identify and communicate each other [21]. with this unlimited connectivity as well as capacity, iot has a lot of application areas. thus, agriculture is one of the application areas too. agriculture is the backbone of business sector for every country. in addition, agriculture is the basis for the human being as it is the main source of food and it plays important role in the growth of countries economy. the changing of natural factors such as climate, temperature, rain etc. affect all aspects of agriculture and creating many new problems all around the world [15]. the usage of iot technology can be improved by the efficiency of overall agriculture business. hence, this helps farmers to control unpredictable environmental factors to minimize the effects on the production cycle [27]. in order to maximize the production efficiency and the quality of the services (qos) for agriculture environment, the environmental variables need to be constantly checked. hence, there should be methods that able to change and/or adapt them according to the changing variables. the process of iot applications can be realised with the combination of sensors, localization technology, processing platforms, wireless connectivity and low power devices integrated in small packages, which can be used to measure the environment variables to create an iot agriculture system [24]. according to gartner, approximately 25 billion identifiable devices are expected to be a part of this iot technology by year 2020. thus, agriculture can be a vast area to iot with distributed autonomous sensors to monitor environmental condition of grain stores and to analyse data and exchange the information [28]. in agriculture, it is important to know the behaviour of the weather in advance, therefore, farmers are able to harvest the crops before they suffer from the bad weather conditions. in order to estimate these conditions, artificial neural network is such a powerful tool to model the non-linear weather data. in this paper, a time-delay radial basis function (tdrbf) network [5] is proposed for predicting the future expected weather sensor readings. this network uses time-delay inputs, so that, it is capable of learning the relationship between the past and future weather sensory readings. the aim of this paper is to implement iot technology in agriculture to analyse the unpredictable environment factors and help farmers to increase productivity. the structure of the paper is as follows: section 2 explains the researches done in the past, section 3.1 describes the experimental setup of the agricultural environment, section 3 presents the overview of the proposed system for agriculture. in section 4, a neural network approach and its learning algorithm used in this study is demonstrated and afterwards the experimental results of the proposed system using real world data is given in the section 5. finally, section 6 points out some conclusion and suggests related future work. 2 literature review agriculture is an important sector in any country and it plays a crucial role for economic growth [2]. hence, because of this reason, agriculture environments should be supported with latest technology to get much more benefit. in literature, different technologies have been used by the researchers depending on these benefits. the authors in [25] proposed a polyhouse monitoring system with wireless sensor network (wsn). the proposed system measures the temperature, humidity, carbon dioxide level and detection light level of the polyhouse. on the other hand, the authors designed small greenhouse model in lab environment and used smart system in this greenhouse model as a smart solution to help farmers in [16]. the research study implemented in [22] used iot and cloud computing to monitor the crop production based on temperature and rainfall. another research model proposed for real time monitoring the soil depends on temperature, moisture, ph. and implemented decision support system for crop disease identification by using an image analysis based on sms alert https://doi.org/10.15837/ijccc.2020.3.3885 3 [20]. in another study, authors presented an empirical model with the combination of iot and cloud computing [9]. this approach promotes the fast improvement of agriculture and helps to realise smart solution for agriculture. with these improvement and solutions, researchers try to solve problems efficiently related to farmers [26]. on the other hand, the authors designed and implemented iot based monitoring system with graphical user interface visualisation software for crop environment in [18]. the aim was to improve the agricultural forecast. another management system was proposed in [23] as a smart cloud-controlled irrigation management system. the authors used sensors and actuators for monitoring water of the plants and cloud-based framework hosts a whether forecast [3]. in another research study, authors used iot technology and image processing together to determine the environmental factors. this will help farms to protect farm field and get a better production [14]. the authors in [17] proposed a smartfarm platform. the platform contains many technologies such as weather prediction, satellite imagery, soil and water monitoring. the main aim of this platform was to collect data effectively and efficiently. however, none of the models above have not considered neural network models to predict desired output considering the different weather conditions. hence, in this paper, such an intelligent system is proposed and presented. the proposed model results are promising and explained in the following sections. 3 the proposed model and methodology in this section, the proposed model and methodology are presented using neural networks in agricultural environment. a tdrbf network approach is used to model temporal and sequential relationship between the various weather condition sensors readings from the agricultural environment. 3.1 experimental setup the data used in this paper are taken from the north wyke farm data portal where they are made publicly available [8, 10, 11]. the farm platform provides three farming systems in farmlets and each consisting of five component catchments. each farmlet is managed using alternative approaches to livestock production from grassland and measurements on water, air and soil are also recorded. most of the data obtained have been received in every 15 minute in this platform. please note that we have used only met station data for whole of farm platform for 2017 and 2018. the farm platform provides meteorological stations for whole system, soil and precipitation for each catchment as well as water flow and properties for each catchment. in other words, many parameters (data) are collected for usage. however, precipitation p(t), air temperature at(t), relative humidity rh(t), wind speed ws(t), wind direction wd(t) and solar radiation sr(t) have been used in this paper for analysis. hence, the sample data used for analysis in this paper is given in figure 1. various sensors were allocated at the farming area in order to obtain data. however, please note that for the meteorology station data, it is not necessary to select a catchment from the map since the collected data is for global evaluation. the detailed information on the farm platform data collections and guides to their use can be found in [8, 10, 11]. 3.2 the proposed model in this section, the proposed model and methodology used are explained using neural networks. figure 2 shows an overview of the proposed system for the farm model. before the data was presented to the acquired model of farm, each sensory data was pre-analysed individually and the result showed that the sampling rate from the sensors was found very high. this is very important because modelling the farm parameters requires to use of relevant information from the data streaming in each time, otherwise, it is possible that the model receives same information in each consecutive time as a result of the model might fail to predict expected future values of the required farm parameters. to analyse sensory data, autocorrelation test [13] was applied separately for each sensor. briefly, the sampling rate could be selected when autocorrelation between x(t) and x(t + τ) has fallen below e−1. https://doi.org/10.15837/ijccc.2020.3.3885 4 0 500 1000 1500 2000 2500 3000 -50 0 50 100 150 200 250 300 350 400 450 time-step (every 15min) figure 1: sample data from real-time streaming at the north wyke farm platform [11] raw sensory data downsampling predicting expected future relative humidity tdrbf network figure 2: the proposed framework for the model of farm. figure 3: autocorrelation results for the precipitation sensor where the lag equal to 5 at which has fallen below e−1. https://doi.org/10.15837/ijccc.2020.3.3885 5 due to use of all sensors data at the same time as each an input to the farm model, the minimum lagged sensor data was selected as the down-sampling rate of all the sensors. the result of this analysis was given in figure 3 where the precipitation data was found that the relevant data was received in every five time-steps. consequently, the sensors data were down-sampled by taking the average of the every five consecutive streamed data so that the sampling rate became 75 minute after down-sampling process. afterwards, the down-sampled data was presented to a tdrbf network. figure 4 shows the inputs and output configuration of the tdrbf network for this experiment. various sensors were allocated at the farming area, these are precipitation p(t), air temperature at(t), relative humidity rh(t), wind speed ws(t), wind direction wd(t) and solar radiation sr(t) sensors as clearly seen in the figure 4. the data from these sensors were sampled every 15 minute. the network receives six inputs which are the data from six sensors and then predicts the expected future value of the relative humidity. here, it is important to note that the proposed farm model network predicts 1-week ahead desired relative humidity sensor values. tdrbf network figure 4: inputs and outputs of the tdrbf network model for predicting the expected future value of the desired sensor. 4 tdrbf network model the radial basis function (rbf) network has been commonly used for function approximation and classification problems [6, 12]. the rbf network consists of two layers; hidden and output layers. a hidden layer represents a number of nodes often each node is called a kernel which perform a nonlinear transformation. the kernel is usually a gaussian function which is defined with two specific parameters; center vector and a width of the function. the inputs of the network are first presented to the hidden layer, then the output of the network is calculated by simply integrating the weighted outputs of the normalised hidden nodes linearly. one limitation of using standard rbf network in the farm experiment is that the network cannot learn the temporal or sequential dependencies in order to predict the future output of the desired sensor. to overcome this limitation, time delayed inputs are also added to the inputs of the network so that the network is capable of learn the relationship between the past sensory data and the future desired sensor. an overview of the tdrbf network is shown in figure 5. the network receives the normalised sensory inputs x(t) : [p(t),at(t),rh(t),ws(t),wd(t),sr(t)] in the range of [0, 1] at time t. then, time delayed input vector is constituted by storing the actual input vector x(t) with nx time-delays: z(t) = [x(t), x(t− 1), . . . , x(t−nx)] . (1) to compute the output of each hidden node, the euclidean distance d(t) between the time delayed input vector and the center vector ci of each hidden unit are computed as: di(t) = ‖z(t) − ci‖ = √√√√√ k∑ j=1 (zj(t) − ci,j)2, (2) https://doi.org/10.15837/ijccc.2020.3.3885 6 rbf layer inputs output output layer figure 5: the structure of tdrbf network. where i is an index of the current hidden node (i = 1, . . . ,h, h is the number of hidden nodes), k = n ·(nx + 1) represents the dimensionality of the center vector, here n = 6 indicates the number of sensory inputs and ci,j is the center from the time delayed input j to the hidden node i. the output of the hidden nodes a(t) is then calculated using a normalised gaussian function. ai(di(t),si) = exp ( − d2i (t) 2 ·σ2i ) ∑h j=1 exp ( − d2j (t) 2 ·σ2j ), (3) where σi is the width of hidden node i. finally, the network-predicted outputs x̂(t + 1) for the expected future sensory input value is calculated by linearly integrating the output of the hidden nodes: x̂(t + 1) = h∑ i=0 wi ·ai, (4) where wi is the connection weight from hidden node i to the output, w0 indicates the bias term and a0 = 1. 4.1 training tdrbf network training of the tdrbf network requires two stages of learning. firstly, center vectors ci (i = 1, 2, . . . ,h) of the hidden nodes are trained by using k-means algorithm. after having obtained center vectors, the width of each hidden node σi is computed using the p-nearest neighbours heuristic approach [4]: σi = 1 p · √√√√√   p∑ j=1 ‖ci − cj‖2  , ∀i 6= j, (5) where cj represents the p-nearest neighbours center vector to the current center vector ci and the parameter p is set to be 2. https://doi.org/10.15837/ijccc.2020.3.3885 7 afterwards in the second learning stage, the normal equation solution with regularisation is applied to obtain the weights of the output layer w. summary of the training algorithm of the tdrbf network is given in algorithm 1. algorithm 1: summary of the tdrbf network training. input : providing training dataset < xtrain, ytrain >, validation dataset < xvalid, yvalid >, setting the number of hidden nodes h and input time-delays nx. output: obtaining the center c, width σ of the hidden nodes and the weights w of the output layer. 1 constituting time lagged inputs z of all the training data using (1) train the center c = k-means(xtrain,h). 2 obtain the width σ of the hidden node using (5). 3 compute the distance d of the hidden nodes for the training data using (2). 4 compute the activation values a of the training data using (3). 5 optimising weights w of the output layer using different regularisation parameter λ: 6 for i = 1; each λi; i = i + 1 do 7 use normal equation solution and obtain wλi. 8 compute network predictions x̂ for validation inputs xvalid using (4) 9 compute the average error ei = xvalid − x̂ 10 end 11 select the best network parameters: w=min(ewλi ) 4.2 parameter selection there are two important parameters which have to be defined, these are time delay parameter nx and the number of the hidden nodes h. to predict 1-week ahead value of the relative humidity sensor, the network requires sufficient number of past input sensory data according to model the future value of the desired sensor. therefore, the training parameter nx was set to be 2-weeks of data points receiving from the sensors in the farming area. to create 2-weeks memory for tdrbf network, simply nx = 19×14 = 266 past data points were used where 1-day data information consists of 19 data points and 14 represents the number of the day here. more time delayed input data points could be added to the network but it could cause the network unable to generalise unseen sensory data and also less time delayed input data outcomes unable to model the temporal or sequential sensory informations [7]. in this case, the network predicts 19×7 = 133 data points ahead value of the desired sensor which implies 1-week ahead network-prediction. it is also essential to choose the right number of hidden node for the neural network training. because selecting a small or a large number of hidden node can lead underfitting or overfitting problem respectively. hence to define the parameter h, different number of hidden node were tested for the network eventually 1500 hidden nodes yielded the best performance in this case. in the neural network literature, to find the optimal number of hidden nodes, there are various techniques, however, this is not the purpose of this paper. 5 experimental results after the network was trained using 1-year of data from the farming environment, the obtained farm model was tested using unseen data which were logged from the other year. the result of the network predictions is shown in figure 6. it is clearly seen that there is some small fluctuating between the actual and predicted relative humidity values. this is expected result because the network predicts future values of the desired sensor instead of nearer expected values. to assess the quality prediction performance of the network, pearson’s correlation coefficient was computed and the correlation coefficient between the actual and predicted values was found to be 0.8 (note that coefficient value 1 indicates perfect matching). https://doi.org/10.15837/ijccc.2020.3.3885 8 0 20 40 60 80 100 120 140 160 180 200 30 40 50 60 70 80 90 100 110 time-step re la ti v e h u m id it y [ % ] actual predicted (a) 20 25 30 35 40 45 50 55 60 65 30 40 50 60 70 80 90 100 time-step re la ti v e h u m id it y [ % ] actual predicted (b) figure 6: (a) actual relative humidity sensor data and the network-predicted values of the sensor. total of 5455 data points were tested. for the clarity, only the first 200 test data points are displayed on the graph. (b) graph is zoomed to display more detail predicted versus actual sensory readings about time-steps between 20 and 65. https://doi.org/10.15837/ijccc.2020.3.3885 9 1 2 3 4 5 6 7 8 9 10 11 12 -10 -8 -6 -4 -2 0 2 4 6 8 10 month # e rr o r (a) 1 2 3 4 5 6 7 8 9 10 11 12 -50 -40 -30 -20 -10 0 10 20 30 month # e rr o r (b) figure 7: (a) error bar, and (b) box plot of the prediction error. https://doi.org/10.15837/ijccc.2020.3.3885 10 furthermore, t-test was also applied to the network residual error and it was found that the mean value of the residual error is statistically similar to zero at the 1% significance level. in addition, to analyse how much deviation between the model-predicted outputs and the actual relative humidity sensor readings occurred, confidence interval of the mean of the residual error is also computed at the same significance level and was between [−0.24, 0.17]. further analyses were also carried out in order to understand deeply the behaviour of the neural network model on the farm data in the course of the predicted year. figure 7 shows the error bar and box plot of the prediction errors associated with each month of the predicted year. it is clearly seen that the acquired network model showed varied performances in comparison between the months of the corresponding year. these uncertainties were shown with the standard deviations of the prediction errors of each month as given in figure 7a. especially, the month 7 and 9 (i.e. july and september) are the highest deviation occurred from their corresponding mean error with approximately 8.8 standard deviation. similar results can be also seen in the figure 7b where the end of the whiskers, in the case of july and september, indicate the maximum and minimum distances to the median error (median error is the red line in the box) and these values are the largest in comparison with other months. surely in month 4, 5 and 6 (april, may and june) in the figure 7b have similar maximum and minimum distances to the their corresponding median error but the number of the outliers that are indicated with plus symbols, are lesser than the month 7 and 9. as a result, the volume of the outliers increased the standard deviation of the month 7 and 9. in addition, although the month 8 (august) has lower minimum and maximum distances to its median error, the volume of the outliers are high, so that it has similar standard deviation with month 4, 5 and 6. the input data of july and september from the training and testing dataset were examined statistically whether or not both datasets are from same distribution. thus, kolmogorov-smirnov (ks) test was applied to compare the training and testing data of each sensor on the network. the results showed that; the null hypothesis was rejected for all the sensory inputs data between the training and testing sets at the 5% significance level in july. similarly for the september, only the wind speed ws sensor data from the training and testing sets come from same distribution and the null hypothesis was rejected for the other sensory data. this was expected results due to the high standard deviations for the both months. however, we believe that uncertain sensory data is normal between years due to the fact that global warming. nevertheless, the acquired tdrbf network generalised well on the noisy sensory data from the farming area. predicting expected future relative humidity non-linear filter final predicted sensory value figure 8: possible extension for the proposed framework of the model of farm. one way to overcome the problem of the noisy data is that to extend the proposed framework given in figure 2 with a non-linear filter [19]. therefore, after the network predicts the future value of the corresponding sensors, the raw predicted values pass through the some smoothing processes by using previously predicted values before the final decision on the predicted sensory values. this extension is illustrated in figure 8. 6 conclusion and future work in this paper, a tdrbf network-based agricultural weather data forecasting model was demonstrated. the steps of the proposed agricultural tdrbf model-based framework was described in section 3. due to the high sampling rate from the sensors in the agricultural area, down-sampling process was suggested which was found out in autocorrelation analysis given in figure 3. afterwards, the structure of tdrbf network, training algorithm and the parameter selection approach for the agrihttps://doi.org/10.15837/ijccc.2020.3.3885 11 cultural problem were described in section 4. the tdrbf network performed very promising results for the predictions of the expected future weather sensor values. to verify the network performance, statistical analyses were also carried out in section 5 and the prediction errors between the obtained network model and the actual sensor readings were found very low at the 1% significance level. in addition, figure 6 proved that how robust the network predictions on the unseen noisy weather sensor data. consequently, the generalisation capability of the network was the desired outcomes for the globally changing weather data over the years in the world. for the future work, the study can be further explored in many direction as follows; • comparison of the different type of neural networks for the same scenario; • developing a new region-based agricultural weather forecasting framework, so that, for the different regions, a specific sensors network model will be selected for generating more reliable future predictions. this is because in the large agricultural environment, it is possible that weather sensors readings could be different from each other which are located at the different regions in the same environment. acknowledgement the authors are deeply grateful to north wyke farm platform and rothamsted research in providing of the data publicly available. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] ashton, k. 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[28] [online]. available: https://www.gartner.com/en/newsroom/press-releases/2017-02-07-gartnersays-8-billion-connected-things-will-be-in-use-in-2017-up-31-percent-from-2016, accesed on 22 december 2019. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: özbilge, e.; kırsal, y.; çağlar, e. (2020). modelling and analysis of iot technology using neural networks in agriculture environment, international journal of computers communications & control, 15(3), 3885, 2020. https://doi.org/10.15837/ijccc.2020.3.3885 international journal of computers communications & control issn 1841-9836, 12(3), 365-380, june 2017. computational intelligence-based pm2.5 air pollution forecasting m. oprea, s.f. mihalache, m. popescu mihaela oprea*, sanda florentina mihalache, marian popescu automatic control, computers and electronics department petroleum-gas university of ploieşti romania, 100680 ploieşti, bd. bucureşti, 39 {mihaela, sfrancu, mpopescu}@upg-ploiesti.ro *corresponding author: mihaela@upg-ploiesti.ro abstract: computational intelligence based forecasting approaches proved to be more efficient in real time air pollution forecasting systems than the deterministic ones that are currently applied. our research main goal is to identify the computational intelligence model that is more proper to real time pm2.5 air pollutant forecasting in urban areas. starting from the study presented in [27]a, in this paper we first perform a comparative study between the most accurate computational intelligence models that were used for particulate matter (fraction pm2.5) air pollution forecasting: artificial neural networks (anns) and adaptive neuro-fuzzy inference system (anfis). based on the obtained experimental results, we make a comprehensive analysis of best ann architecture identification. the experiments were realized on datasets from the airbase databases with pm2.5 concentration hourly measurements. the statistical parameters that were computed are mean absolute error, root mean square error, index of agreement and correlation coefficient. keywords: computational intelligence, pm2.5 air pollution forecasting, anfis, ann, ann architecture identification. areprinted (partial) and extended, with permission based on license number 3957050363449 [2016] ©ieee, from "computers communications and control (icccc), 2016 6th international conference on". 1 introduction this paper is an extension of [27] (doi: 10.1109/icccc.2016.7496746). a comprehensive comparative study is here presented. in addition, an extended analysis for the identification of best neural network architecture is included. we report our new experimental results. air pollution forecasting is an important research topic especially for the improvement of life quality in cities. among currently used forecasting methods, computational intelligence methods proved to be more efficient in real time forecasting systems. the deterministic methods which take into account many variables related to the forecasted parameter and use a precise mathematical model with embedded physical and chemical factors (e.g. those based on climate models) give better solutions, but in a longer period of time. in contrast, computational intelligence based methods are approximate methods, that give solutions with a good forecasting accuracy, in short periods of time. thus, the real time forecasting systems used for urban population early warning of air pollution episodes occurrence can be based on computational intelligence techniques. artificial intelligence (ai) provides several techniques for building forecasting systems, mainly from its computational intelligence part and less from its symbolic part. such applications in different domains were reported in the literature, most of them in the economic, energy and environmental fields [1], [18], [25]. symbolic ai is used as a knowledge based approach in selecting some important parameters that influence the forecasting systems performance, usually for the prediction model features selection. on the other hand, computational intelligence techniques copyright © 2006-2017 by ccc publications 366 m. oprea, s.f. mihalache, m. popescu can be used as effective predictors’ builders (see e.g. [5], [12], [16], [29], [30]). an important environmental problem that needs better solutions nowadays is urban air quality improvement and reducing human health effects due to air pollution in cities. for this, good real time air pollution short-term forecasters have to be developed and included in the environmental management systems or in the early warning system of intelligent environmental decision support systems. particulate matter with diameter less than 2.5 µm (pm2.5) is an air pollutant that has potential negative effects on human health, when its concentration exceeds the admissible standard upper level. our research work focuses on the development of a good real time pm2.5 air pollution forecasting model that will be integrated in the rokidair decision support system (rokidair dss) to be used in two pilot romanian cities, ploieşti and târgovişte, by the rokidair early warning system. starting from a literature review and a comparative study between most used computational intelligence based forecasting methods, we have selected the best model which is a neural network model and we have performed an analysis of best neural network architecture identification via trial and error method. computational intelligence is a paradigm introduced in [2] which combines mainly three computing technologies: fuzzy computing, neural computing and evolutionary computing. fuzzy computing and neural computing are used as forecasting models, while evolutionary computing can be applied mainly for the optimization of a forecasting model. in the last decade, other nature-inspired computing methods were added to computational intelligence, such as swarm intelligence with various techniques: ant colony optimization (aco), particle swarm intelligence (pso), artificial bee colony algorithm (abc) etc. these last techniques are commonly used for optimizing the forecasting model and not as a forecasting model. the remainder of the paper is organized as follows. in section 2 it is described computational intelligence based forecasting focusing on most used techniques: artificial neural networks and adaptive neuro-fuzzy inference system (anfis). a comparative study of the two techniques (ann and anfis) applied to pm2.5 air pollution forecasting is presented in section 3, concluding that the best experimental results were obtained by the ann forecasting model. the identification of the best pm2.5 ann forecasting architecture is discussed in section 4. the final section concludes the paper and highlights some future work. 2 computational intelligence based forecasting computational intelligence provides data-driven methods. the neural methods applicable to solve forecasting problems are: artificial neural network (ann) and adaptive neuro-fuzzy inference system (anfis). they can perform air pollution forecasting more efficiently than the deterministic methods by capturing the knowledge accumulated in the historical data sets (time series) which is learned via a training algorithm and is used to accurately predict specific air pollution parameters (e.g. air pollutants concentrations). 2.1 artificial neural networks (ann) artificial neural networks are universal approximators of non-linear functions [14]. they are composed by a number of non-linear processing units named artificial neurons which are structured in layers. forecasting problems are solved mainly by feed-forward anns (multi-layer perceptron mlp and radial basis function rbf) and recurrent anns. figure 1 shows the general architecture of a feed-forward ann, which has an input layer, some hidden layers and an output layer. computational intelligence-based pm2.5 air pollution forecasting 367 inp1 inp2 inpm out1 outn . . . . . . . . . . . . . . . . . . w11 w12 w1m input layer hidden layers output layer figure 1: the general architecture of a feed-forward ann a recurrent neural network is a type of artificial neural network that has a directed cycle made by the connections between the artificial neurons, allowing it to have an internal state. thus, it exhibits a dynamic behaviour which provides the ability to process and predict chaotic time series for long-terms [19]. a recurrent ann is an ann that has feedback, allowing arbitrary connections between neurons, both forward and backward (i.e. recurrent). thus, it propagates data bi-directional, from input to output and from output to input. recurrent anns are universal approximators [34]. they provide a very good performance in temporal structures modeling, as well as in real world problem solving ( [4], [6]). recurrent anns exhibit a dynamic temporal behavior and can process arbitrary sequences of inputs (e.g. chaotic time series). the best structure of an ann is experimentally determined. usually, a single hidden layer is enough to capture the nonlinearity of any function. deep networks are anns with several hidden layers. the number of hidden nodes is chosen by experiments, while the number of input and output nodes is set according to the forecasting problem that has to be solved. the number of input nodes represents the input window (in the case of time series, the number of past hours measurements) and the number of output nodes represents the forecast horizon (number of future time steps, hours, days etc., for which the prediction is determined). the ann is trained with a training set (which is extracted from a data set) by using a specific training algorithm (the most used being backpropagation and the levenberg-marquardt algorithm), after that following the validation and testing steps which are executed on the validation set and testing set, respectively. details on the ann computational algorithms are given in the literature (see e.g. [13]). the levenberg marquardt algorithm [20] is an iterative algorithm that estimates the weights vector of the ann model by minimizing the sum of the squares of the deviation between predicted and target values. if ann training is too long then overfitting can occur. to avoid this, the ann training is stopped earlier, as soon as the performance on testing data is not improved any more. 2.2 anfis the anfis method applied to prediction uses a hybrid architecture composed by a fuzzy inference system fis enhanced with ann features proposed by jang [15]. the advantages of fis are mainly its design that emulates human thinking and the simple interpretation of the results. integrating the ann part into a fuzzy inference system enhanced the fis part with learning/adapting capabilities. the prediction model does not use a mathematical model as well as the case of ann. the anfis architecture has the structure given in figure 2. 368 m. oprea, s.f. mihalache, m. popescu inputs forecasted outputdefuzzificationfuzzy inference enginefuzzification fis training and validating data fis structure testing data input membership functions rule weight output membership functions knowledge base ann figure 2: the general architecture of anfis the fis part is formed by five functional units: a fuzzification unit (from crisp value to fuzzy set), a defuzzification unit (from fuzzy set to a crisp value), the database unit (containing the description of membership functions for input/output variables), a rule base unit (all the rules defined for fis), and the decision unit (performing the inference operations on the fuzzy rules) [26]. the neuro-fuzzy architecture is capable to learn new rules or membership functions, to optimize the existing ones (figure 2). the training data determine restrictions on the design methods for the rule base and membership functions. usually the particular type of datasets for pm2.5 eliminates the subclustering method in generating the fis structure, a good choice being the grid partition method. the anfis architecture (figure 2) has five layers, with takagi-sugeno rules. the first layer (adaptive) forms the premise parameters (the if part with inputs and their membership functions). the second layer computes a product of the involved membership functions. the third layer normalizes the sum of inputs. in layer 4, the adaptive i-node computes the contribution of i-th rule to anfis output, forming the consequence parameters (the then part with output and its membership function). the fifth layer makes the summation of all inputs. the ann part can improve the membership functions associated with fis structure. usually these membership functions are the tuning parameters of the fis. their initial values are chosen from experience or trial and error methods. in the training mode the ann finds the most suited membership functions for the input-output relation described by fis, according to training and checking dataset. anfis applies a hybrid learning algorithm (h) or backpropagation (bp) algorithm. the hybrid learning algorithm identifies premise parameters with gradient method and consequence parameters with least square method. at feedforward propagation step from h, the system output reaches layer 4, and the consequence parameters are formed with least square method. with backpropagation (bp) optimization method, the error signal is fed back and the new premise parameters are computed through gradient method. the prediction method is tested with datasets that respect the main features of training dataset. the prediction precision decreases with enlarging the prediction window from one hour in advance to six hours in advance. 2.3 an overview on pm2.5 computational intelligence based forecasting the main computational intelligence techniques that were used for pm2.5 forecasting are: feed forward ann, radial basis function ann, recurrent ann, and anfis. other ai techniques such as genetic algorithms and swarm intelligence were applied to optimize the forecasting model. we have selected some pm2.5 forecasting systems based on computational intelligence that were computational intelligence-based pm2.5 air pollution forecasting 369 reported in the literature. in the first years of the current century, the pm2.5 forecasting ann models (usually, of mlp type) were compared mostly with statistical models such as linear regression, arma, arima, revealing a very good performance of the neural models. one of the earlier pm2.5 forecasting neural models were proposed in [21] and [31]. the first ann model was applied in canada, while the latter was applied in santiago de chile. the experimental results described in both papers showed a very good performance of the ann model in comparison with the traditional statistical models (e.g. linear regression). in the next years, various comparisons between different types of anns models used to pm2.5 forecasting were performed. for example, an analysis of three ann models (mlp, rbf-ann and square mlp) applied to pm2.5 short term prediction in an area on the us-mexico border is presented in [28]. the experimental results revealed that for the analyzed area the rbf-ann model outperformed the other two models. another work that analyzes the performance of different neural network models and regression model applied to forecasting expressway fine pm (i.e. pm2.5) in indiana, usa is described in [35]. a feed forward ann with backpropagation training algorithm is described in [?], for 3 days in advance forecasting of pm10, so2 and co air pollutants (ap) levels in the besiktas district in istanbul, turkey. the ann is integrated in the airpol system (http://airpol.fatih.edu.tr). the ann inputs are daily meteorological forecasts and the ap indicator values. the authors applied some geographical models, the most complex one being based on the distance between two sites in the case of using three selected neighborhood districts. in [10] it is demonstrated the efficacy of using envinnet, a prototype stochastic ann model for air quality forecasting in cities from italy (rome, milan and napoli) to predict pm10 in phoenix, arizona in comparison with the use of cmaq system. the ann is a mlp that uses the conjugate-gradient method for training. in the last years, the research work was focused on combining pm2.5 forecasting ann models with other techniques, such as statistical techniques, data mining, genetic algorithms, wavelet transformation, deep learning, that can improve the forecasting accuracy. some examples are briefly described as follows. a research work that reports the successful use of anns and principal component analysis for pm10 and pm2.5 forecasting in two cities, thessaloniki (greece) and helsinki (finland) is described in [36]. the authors used a mlp and the meteorological and aq pollutants to predict the next day mean concentration of pm10 and pm2.5. a genetically optimized ann and k-means clustering was applied in [8] to predict pm10 and pm2.5 in a coastal location of new zealand. a hybrid pm2.5 forecasting model that uses feed forward ann combined with rolling mechanism and accumulated generating operation of gray model, that was experimented in three cities from china is introduced in [11]. another recent work on pm2.5 neural forecasting is described in [9]. the authors propose a novel hybrid model that combines air mass trajectory analysis with wavelet transformation in order to improve the accuracy of the average pm2.5 concentration two days in advance neural forecasting. the ann is a mlp trained with a backpropagation algorithm and the levenberg-marquardt (lm) algorithm. also, early stopping was used to avoid overfitting. some meteorological parameters were used. finally, one of the newest achievements for pm2.5 prediction in 52 cities from japan is reported in [24]. each city has several thousands of pm2.5 sensors. a deep recurrent neural network (drnn) is proposed for pm2.5 prediction based on real data sensors, which applies a novel pre-training method (dynpt) using time series prediction especially designed auto-encoder. the experimental results showed that the drnn model outperformed the current climate models used in japan, which are based on eulerian and lagrangian grids or trajectory models. the use of pure fis for specific air pollutants is reported in literature. in [3] it is presented a study for mexico city air pollution. the paper present a solution of classifying different air 370 m. oprea, s.f. mihalache, m. popescu parameters via fuzzy reasoning and include this solution in the air quality index calculation. in [7] it is formulated a fuzzy based forecasting model used in polish environmental agency, a model that forecast specific air pollutants based on decades of measurements. examples of anfis based systems for the prediction of air pollutants concentrations are presented in [22], [23], [32] and [33]]. in [23] it is proposed a fuzzy inference system to forecast pm2.5 concentrations at specific hours using as additional input the medium temperature. in [33] it is developed a fuzzy inference system to forecast ozone concentrations levels based on other pollutants and meteorological parameters. the provided fis model accuracy has the best values for coefficient of determination. for the city of konya, turkey the literature present the solution of anfis forecasting model for pm10 trained with large datasets [32]. in [22] there are presented three case studies from three different cities from romania. each time the proposed anfis forecasting model for pm10 is tested and there are made recommendations on how to adjust the model parameters to improve forecasting accuracy. the main conclusion of the literature overview is that an efficient pm2.5 forecasting ann model can be derived only by experiment, depending on the pm2.5 measurements data sets and several characteristics (climatic, geographic, industrial, economic, social etc) specific to the analyzed area. thus, there is no pre-set ann model type for pm2.5 that is proper for any area. another important conclusion is that both ann and anfis can model non-linear time series. 3 a comparative study of pm2.5 forecasting with anfis and ann 3.1 data sets anfis / ann forecasting modelpm2.5(t-1) pm2.5(t) pm2.5(t-2) pm2.5(t-3) pm2.5(t+1) figure 3: structure of the proposed architecture for this study were chosen two urban traffic pm2.5 monitoring stations. taking into consideration the lack of data for the romanian stations, the data used for training and validation were from a münchen (germany) station where sufficient data are available (4 years) in the airbase database. in addition, the testing was done using also data (from 2015) from ploieşti (romania), a city which presents interest as part of a larger project (rokidair projectwww.rokidair.ro). the data set from münchen contains 34000 samples and the pm2.5 hourly concentration has a range of 0.52-168.5 µg/m3. the additional testing data from ploieşti contains 4000 samples and the domain of pm2.5 concentration is 3.24-36.45 µg/m3. the data were normalized and then divided (randomly) as follows: 70% for training, 15% for validation and 15% for testing the model. if the amount of data is large enough, it is supposed that the data contains the effect of other pollutants and meteorological data, so the proposed architecture (figure 3) for the forecasting computational intelligence-based pm2.5 air pollution forecasting 371 model (anfis or ann) has as inputs the values of pm2.5 concentrations from current hour to three hours ago and as output the prediction for the next hour. in order to determine the suitability of the models the following statistical parameters were calculated: mae mean absolute error; rmse root mean square error; ia index of agreement; r correlation coefficient. the two errors measure how close the predicted data are to the true values and have to be as small as possible, and the last two indices are numbers that indicate how well the data fit the prediction model and they should be close to 1. the experiments presented in the following sections were performed using matlab© environment. 3.2 experimental results for anfis model in this study, for the generation of the fuzzy inference system was used the grid partition method, imposed by the specific of data. the membership functions were triangular and gaussian and the optimization algorithms of the ann were backpropagation and hybrid. the anfis model was tested for all combinations between the modifiable parameters, namely the membership functions types and the optimization methods. table 1 presents the experimental results of the anfis model tested with data from münchen. table 1: statistical indices for anfis model (münchen) anfis structure mae [µg/m3] rmse [µg/m3] ia r trimf/hybrid 1.9614 3.2564 0.9796 0.9604 gauss/hybrid 1.9419 3.2089 0.9801 0.9616 trimf/backprop. 2.0494 3.3933 0.9778 0.9573 gauss/backprop. 2.2177 3.4471 0.9770 0.9556 analyzing table 1 it can be seen that the best configuration is when a gaussian function is used for the membership functions associated to inputs, and for the training of the neural network a hybrid algorithm is used. in this case, the rmse is the smallest, and the ia and r have the biggest values. the smallest training and validation errors are around 0.02. the best configuration of the anfis structure was also tested with data from the ploieşti station. the values of the statistical parameters are presented as follows: mae [µg/m3]: 1.0166; rmse [µg/m3]: 1.9160; ia: 0.9711; r: 0.9447. 3.3 experimental results for ann model the structure of the neural network contains four neurons in the input layer, one hidden layer and one neuron in the output layer. there were used two types of neural networks, namely feed forward backpropagation (ff) and layer recurrent (lr), with levenberg-marquardt as training algorithm, and the adaptive learning functions were gradient descent with momentum weight and bias (learngdm lgdm ) and gradient descent weight and bias (learngd lgd). the simulations were performed modifying also the number of neurons in the hidden layer (from three to twelve). the training and validation errors are with two orders of magnitude smaller than the ones obtained in the case of anfis model, with values around 0.0004. in table 2 is presented a selection with the values of the statistical parameters for the ann models. the best configurations of the ann models are highlighted, being associated to the 372 m. oprea, s.f. mihalache, m. popescu layer recurrent structure with 4 neurons in the hidden layer and the learngd adaptation learning function, and the feedforward structure with 5 neurons in the hidden layer and the learngdm adaptation learning function, respectively. in this case the mean absolute error and the root mean squared error have the smallest values, and ia and r indices have the biggest values. table 2: statistical indices for ann model with one hidden layer (münchen) ann structure mae [µg/m3] rmse [µg/m3] ia r 4x4x1/lgdm ff 1.9421 3.2086 0.9802 0.9616 lr 1.9683 3.2359 0.9798 0.9609 4x4x1/lgd ff 1.9421 3.2086 0.9802 0.9616 lr 1.9278 3.1931 0.9804 0.9619 4x5x1/lgdm ff 1.9340 3.1966 0.9804 0.9619 lr 1.9836 3.2360 0.9797 0.9609 4x5x1/lgd ff 1.9609 3.2152 0.9800 0.9614 lr 1.9486 3.2138 0.9801 0.9614 4x6x1/lgdm ff 1.9415 3.2149 0.9801 0.9614 lr 1.9676 3.2207 0.9800 0.9613 4x6x1/lgd ff 1.9471 3.2292 0.9799 0.9611 lr 1.9519 3.2182 0.9801 0.9613 4x7x1/lgdm ff 1.9402 3.2185 0.9801 0.9613 lr 1.9528 3.2252 0.9800 0.9612 4x7x1/lgd ff 1.9489 3.2128 0.9801 0.9615 lr 1.9567 3.2224 0.9800 0.9612 4x8x1/lgdm ff 1.9516 3.2218 0.9800 0.9612 lr 1.9498 3.2130 0.9801 0.9615 4x8x1/lgd ff 1.9433 3.2148 0.9801 0.9614 lr 1.9613 3.2290 0.9799 0.9611 4x10x1/lgdm ff 1.9657 3.2272 0.9799 0.9611 lr 1.9527 3.2183 0.9800 0.9613 4x10x1/lgd ff 1.9568 3.2167 0.9800 0.9614 lr 1.9791 3.2367 0.9797 0.9609 in addition, as in the anfis case, the best configurations of the rnn and ffnn structure were tested with data from the ploieşti station and the values of the statistical parameters from table 3 were obtained. table 3: statistical indices for ann model (ploieşti) mae [µg/m3] rmse [µg/m3] ia r rnn 0.9672 1.3713 0.9852 0.9714 ffnn 0.9852 1.3970 0.9846 0.9702 the experimental results confirms that the best ann configuration (rnn) previously obtained provides very good results for the testing data set from ploieşti. computational intelligence-based pm2.5 air pollution forecasting 373 3.4 discussion a comparison between the best values of the statistical parameters obtained for the anfis model and the ann models, respectively, using münchen datasets is synthesized in table 4. table 4: comparison between best results forecasting method mae [µg/m3] rmse [µg/m3] ia r best anfis 1.9419 3.2089 0.9801 0.9616 best rnn 1.9278 3.1931 0.9804 0.9619 best ffnn 1.9340 3.1966 0.9804 0.9619 it can be observed that the results obtained with ann are slightly better than the anfis results, and corroborated with the fact that for ploieşti data the statistical indices are much better in the case of ann (see sections 3.2 and 3.3) and the time for training is much smaller (with at least 10 times) for ann, it can be concluded that the ann forecasting model is most suitable for the prediction of pm2.5 concentrations. therefore, in the next section the focus will be on the ann forecasting model. 4 best ann architecture identification for pm2.5 forecasting usually, an ann with one hidden layer is enough for any complex nonlinear function (see e.g. [37]). however, in order to make a complete analysis, several ann architectures with one hidden layer and two hidden layers were experimented. thus, the selected architectures (rnn and ffnn) with one hidden layer from the previous section were tested with two hidden layers. also, different adaptive learning functions were used: gradient descent with momentum weight and bias and gradient descent weight and bias, and different number of neurons in the second hidden layer (from three to twelve). a trial and error method was applied in order to identify the best ann forecasting model architecture. the best ann architecture selection was determined using the same statistical indices as in the section 3. it is interesting to analyze how is influenced the forecasting accuracy (error) of the ann model by the number of hidden layers and the number of hidden neurons in each hidden layer. more hidden layers involve a deeper learning ability of the ann model. however, the number of hidden layers is dependent on the application specific data sets (e.g. in our case pm2.5 concentration hourly measurements) and must be determined by experiment until optimum prediction (i.e. minimum rmse). table 5 presents a selection of the statistical indices values for an ann architecture with two hidden layers, which has four neurons in the first hidden layer and 3 to 12 neurons in the second hidden layer. 374 m. oprea, s.f. mihalache, m. popescu table 5: statistical indices for ann model with two hidden layers (münchen)) ann structure mae [µg/m3] rmse [µg/m3] ia r 4x4x4x1/lgdm ff 1.9319 3.2041 0.9803 0.9617 lr 1.9491 3.2159 0.9801 0.9614 4x4x4x1/lgd ff 1.9303 3.1923 0.9804 0.9620 lr 1.9338 3.2120 0.9802 0.9615 4x4x5x1/lgdm ff 1.9397 3.2065 0.9802 0.9616 lr 1.9494 3.2228 0.9800 0.9612 4x4x5x1/lgd ff 1.9475 3.2192 0.9801 0.9613 lr 1.9491 3.2258 0.9800 0.9612 4x4x7x1/lgdm ff 1.9460 3.2292 0.9800 0.9611 lr 1.9776 3.2450 0.9796 0.9607 4x4x7x1/lgd ff 1.9291 3.2028 0.9803 0.9617 lr 1.9102 3.1890 0.9805 0.9621 4x4x8x1/lgdm ff 1.9562 3.2264 0.9799 0.9611 lr 1.9428 3.2356 0.9800 0.9610 4x4x8x1/lgd ff 1.9472 3.2131 0.9801 0.9615 lr 1.9320 3.2061 0.9803 0.9617 4x4x9x1/lgdm ff 1.9350 3.2149 0.9801 0.9314 lr 1.9310 3.2062 0.9802 0.9616 4x4x9x1/lgd ff 1.9353 3.2165 0.9801 0.9614 lr 1.9630 3.2346 0.9799 0.9609 4x4x10x1/lgdm ff 1.9359 3.2105 0.9802 0.9615 lr 1.9481 3.2137 0.9801 0.9615 4x4x10x1/lgd ff 1.9377 3.2149 0.9802 0.9614 lr 1.9516 3.2327 0.9799 0.9610 changing the number of neurons in the first hidden layer from 4 to 5 the results from table 6 are obtained. the number of neurons in the second hidden layer is the same as in the previous case (from 3 to 12 neurons). computational intelligence-based pm2.5 air pollution forecasting 375 table 6: statistical indices for ann model with two hidden layers (münchen) ann structure mae [µg/m3] rmse [µg/m3] ia r 4x5x4x1/lgdm ff 1.9455 3.2076 0.9802 0.9616 lr 1.9491 3.2159 0.9801 0.9614 4x5x4x1/lgd ff 1.9512 3.2137 0.9801 0.9614 lr 1.9338 3.2120 0.9802 0.9615 4x5x5x1/lgdm ff 1.9627 3.2302 0.9799 0.9610 lr 1.9489 3.2265 0.9799 0.9611 4x5x5x1/lgd ff 1.9389 3.2012 0.9802 0.9617 lr 1.9835 3.2450 0.9796 0.9607 4x5x7x1/lgdm ff 1.9313 3.2147 0.9802 0.9614 lr 1.9556 3.2257 0.9799 0.9612 4x5x7x1/lgd ff 1.9314 3.1957 0.9804 0.9619 lr 1.9791 3.2499 0.9796 0.9606 4x5x8x1/lgdm ff 1.9256 3.1926 0.9804 0.9620 lr 1.9350 3.1961 0.9803 0.9619 4x5x8x1/lgd ff 1.9380 3.2025 0.9803 0.9617 lr 1.9446 3.2249 0.9800 0.9612 4x5x9x1/lgdm ff 1.9505 3.2251 0.9800 0.9612 lr 1.9184 3.1926 0.9804 0.9620 4x5x9x1/lgd ff 1.9181 3.1678 0.9807 0.9626 lr 1.9658 3.2414 0.9797 0.9608 4x5x10x1/lgdm ff 1.9336 3.2041 0.9802 0.9617 lr 1.9429 3.2145 0.9801 0.9614 4x5x10x1/lgd ff 1.9164 3.2053 0.9803 0.9617 lr 1.9441 3.2136 0.9801 0.9614 analyzing the results from tables 5 and 6 it can be observed that the best ann architecture with two hidden layers is feedforward with five neurons in the first hidden layer, nine neurons in the second hidden layer and gradient descent weight and bias as the adaptive learning function (figure 4). in this case, mae and rmse have the smallest values and ia and r have the biggest values. figure 4: best ann architecture for the best architecture mentioned above are presented in figures 5 and 6 the testing error evolution and a partial view of the comparison between testing and forecasted data for münchen data set. 376 m. oprea, s.f. mihalache, m. popescu 0 1000 2000 3000 4000 5000 samples -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 t e st in g e rr o r figure 5: testing error for best ann architecture (ff 4x5x9x1) 4100 4150 4200 4250 4300 4350 4400 samples 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 d a ta testing data forecasting data figure 6: comparison of testing vs forecasted data for best ann architecture (ff 4x5x9x1) table 7: best ann architecture best ann architecture mae [µg/m3] rmse [µg/m3] ia r with one hidden layer 1.9278 3.1931 0.9804 0.9619 with two hidden layers 1.9181 3.1678 0.9807 0.9626 table 7 concludes that using an ann architecture with two hidden layers improves the results obtained with the ann architecture one hidden layer suggesting a possible use of this type of structure for pm2.5 forecasting. computational intelligence-based pm2.5 air pollution forecasting 377 5 conclusions and future works this paper is an extension of [27] which proposed pm2.5 air pollution forecasting models based on computational intelligence techniques. starting from the study presented in [27], in this paper we extend the comparative study between artificial neural networks (anns) and adaptive neurofuzzy inference system (anfis) which are the most accurate computational intelligence models used for particulate matter (fraction pm2.5) air pollution forecasting by increasing the number of neurons in the hidden layer of the ann architecture. we also present an extended overview of computational intelligence techniques based on neural networks approach with up to date solutions of air pollution forecasting. the data used for training and validation were from a münchen (germany) urban traffic station available from the airbase database with hourly pm2.5 concentrations (for 4 years). for the testing of the model data from ploieşti (romania), a city which presents interest as part of a larger project (rokidair project www.rokidair.ro) were also used. the proposed models have four pm2.5 hourly concentrations as inputs and the prediction of the next hour pm2.5 concentration. we compared the performances of the models using statistical indices such as mean absolute error, root mean square error, index of agreement and correlation coefficient. the simulation results obtained with ann are better than the anfis results, and corroborated with the fact that for ploieşti data the statistical indices are much better in the case of ann and the time for training is much smaller (with at least 10 times) for ann, it can be concluded that the ann forecasting model is most suitable for the prediction of pm2.5 concentrations. therefore, the next part of the paper concentrates on finding the best neural network architecture by increasing the number of hidden layer of the ann architecture. in this new approach there are performed numerous simulations with different architectures starting from the best architectures with one hidden layer obtained in section 3. therefore we kept the number of the neurons in the first hidden layer and varied the number of neurons in the second different layers. the paper proposes an ann model for pm2.5 forecasting, whose architecture was identified via trial and error method, during an extended analysis that was performed. the simulation results pointed out that introducing another hidden layer is beneficial in terms of statistical indices that assess the forecasting performance. this is a good premise to find other solutions using deep learning as future work. the model can be used to real time forecasting and is incorporated in the rokidair dss, being used by the rokidair early warning system in case some pm2.5 air pollution episodes arise in different areas from the two pilot cities: ploieşsti and târgovişte. the selection of the most proper computational intelligence based pm2.5 forecasting model was made during the comprehensive comparative study of applying the ann and anfis models, which proved to be efficient in real time forecasting systems. the ann pm2.5 forecasting model can detect concentrations above the standard limit values for the human health protection. as future work we will extend the study for the improvement of the forecasting model performance through deep learning or combining neural networks with 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(2007); model for forecasting expressway fine particulate matter and carbon monoxide concentration: application of regression and neural network model, journal of air waste management association, issn: 2162-2906, 57(4), 480-488, 2007. [36] voukantsis d.; karatzas k.; kukkonen j.; räsänen t.; karppinen a.; kolehmainen m. (2011); intercomparison of air quality data using principal component analysis and forecasting of pm10 and pm2.5 concentrations using artificial neural networks, in thessaloniki and helsinki, science of the total environment, issn: 0048-9697, 409, 1266-1276, 2011. [37] zhang g., patuwo b.e.; hu m.y. (1998); forecasting with artificial neural networks: the state of the art, international journal of forecasting, issn: 0169-2070, 14(1), 35-62, 1998. international journal of computers communications & control issn 1841-9836, 13(4), 566-573, august 2018. nonparametric regression-based step-length estimation for arm-swing walking using a smartphone p.h. truong, n.d. nguyen, n.h. ho, g.-m. jeong phuc huu truong, nhan duc nguyen, ngoc-huynh ho, gu-min jeong* school of electrical engineering, kookmin university, jeongneung-dong, seongbuk-gu, 02707 korea. phtruong@kookmin.ac.kr, nhannd@kookmin.ac.kr, ngochuynh.0303@kookmin.ac.kr *corresponding author: gm1004@kookmin.ac.kr abstract: in this paper, we propose an adaptive step-estimation method to estimate the distance traveled for arm-swinging activities at three level-walking speeds, i.e., low, normal, and high speed. the proposed method is constructed based on a polynomial function of the pedestrian speed and variance of walking acceleration. we firstly apply a low-pass filter with 10 hz cut-off frequency for acceleration data. then, we analyze the acceleration data to find the number of steps in each sample. finally, the traveled distance is calculated by summing all step lengths which are estimated by the proposed method during walking. applying the proposed method, we can estimate the walking distance with an accuracy rate of 95.35% in a normal walking speed. the accuracy rates of low and high walking speeds are 94.63% and 94.97%, respectively. furthermore, the proposed method outperforms conventional methods in terms of accuracy and standard deviation at low, normal, and high speeds. keywords: walking distance estimation, pedestrian dead reckoning (pdr), nonparametric regression, arm-swinging. 1 introduction recently, pedestrian dead reckoning (pdr), which uses sensor information to estimate the location and orientation of a pedestrian in a global positioning system-denied scenarios [2, 6, 11,15], has received considerable attention from researchers. moreover, pdr technology brings many advantages on energy consumption, weight, cost compared with the inertial navigation system for indoor navigation. this leads to numerous researches on the pdr field over the last few years. pdr algorithms commonly compute the walking distance by detecting users’ steps and estimating the step lengths. due to the location of mounted sensors, step detection and analysis can be done in many different ways. fixing sensors on the user’s foot, stance phases of the foot and the step detection can be determined [15, 17]. zero velocity update (zupt) method is commonly used in this foot mounting configuration to bound the error accumulation [7,13,15]. in the second configuration, researchers fixed the inertial sensor on users’ belts [1], or kept sensors in a constant pose [8]. the inertial force experienced by the sensor is directly linked to the motion of the human body’s center-of-mass (com) and, subsequently, to user’s movement [16]. therefore, in arm-swinging activities, where an inertial force of the sensor is not linked to the human’s motion, an extra acceleration affects significantly the accuracy of walking distance estimation. suh et al. [14] proposed a peak detection algorithm using five data points to find the number of walking steps. this algorithm utilizes the constraints between the times and consecutive peak values to detect the steps. tian et al. [16] presented a step frequency-based copyright ©2018 cc by-nc nonparametric regression-based step-length estimation for arm-swing walking using a smartphone 567 and individual-height-based distance estimation for pedestrian swinging arms. however, their method only focused on walking at the normal speed. in this paper, we propose a distance estimation method for arm-swinging cases where a pedestrian holds a smartphone in hand and naturally walks at three speed levels. in general, data from smartphone are collected in the sensor coordinate system; thus, we firstly need to transform experimental data into the world coordinate system for synchronizing recorded data with the pedestrian movements. a low-pass filter (lpf) with 10 hz cut-off frequency is applied to remove noise from the raw data. then, we analyze arm-swing-related gait cycles and define step phases in human walking. finally, we estimate the length of each step using an adaptive conversion as a polynomial function of the walking speed. the polynomial function is obtained by using a nonparametric regression called locally weighted polynomial regression [5,10]. in addition, this step-length estimation is an improvement of method [18]. the proposed method estimates the walking distance with an accuracy rate of 96.05%, 94.79%, and 94.20% in the normal, low, and high walking speeds, respectively. to demonstrate the performance of the proposed method, we compare experimental results of our method with two reference methods [16,18]. 2 problem formulation during walking, pedestrians hold smartphones in their hands and swing their arms forward or backward. this action varies the acceleration collected in smartphones, and thus, affects the estimation results of walking distance. we propose a distance estimation method for armswinging pedestrians in three speed levels. figure 1 illustrates the walking condition of an arm-swinging pedestrian with three levels of speed. the rightmost sub-figure presents not only the phone-holding posture but also the smartphone orientation. low walking normal walking fast walking xy z holding mode figure 1: walking speed levels and smartphone holding posture. sensor inertial data preprocessing coordinate transformation step detection step-based feature extraction nonparametric regressor figure 2: block diagram of walking distance estimation figure 2 illustrates our proposed method for estimating the traveling distance. after collecting inertial data from the smartphone, we apply a low-pass filter to acceleration signals to isolate the force of gravity and remove the noise created during movement. then, we transform the accelerations from smartphone’s coordinates to the world coordinates using euler’s rotation theorem. by analyzing the arm-swinging phase, we find the number of steps in each trial. a feature is extracted as the magnitude of the average velocities on three dimensional axes in each step. a nonparametric regressor uses the extracted feature and ground truth to estimate the walking distance. 568 p.h. truong, n.d. nguyen, n.h. ho, g.-m. jeong 3 data preprocessing using euler’s rotation theorem [12], we transform the raw data of the accelerometer from the smartphone’s frame into the world frame. generally, a step frequency is below 10 hz for human walking [3]. therefore, we design a kaiser window-based lpf to remove noise from raw signals. the kaiser window is defined as follows: w (n) =   i0 ( β √ 1 − ( n−n/2 n/2 )2) i0 (β) , 0 ≤ n ≤ n 0 otherwise, (1) where, i0 is the zeroth-order modified bessel function; n is the length of the sequence without the first element; and β is the kaiser window parameter that affects the sidelobe attenuation of fourier transform of the window. to obtain kaiser window with a sidelobe attenuation of α db, β is determined using the following formula: β =   0.1102 (α− 8.7) , α > 50 5.5842(α− 21)0.4 + 0.07886 (α− 21) , 50 ≥ α ≥ 21 0, α < 21. (2) figure 3 shows a demonstration of magnitude response of an x-axis acceleration of the fir filter-based kaiser window. 200 400 600 800 1000 1200 1400 1600 time (s) -6 -4 -2 0 2 4 6 a c c e le ra ti o n ( m /s 2 ) raw signal filtered signal figure 3: magnitude response of low-pass fir filter 4 arm-swing analysis in human walking, an arm-swinging cycle is divided into two phases: forward-swing and backward-swing, as shown in figure 4. the forward-swing phase starts when the hand holding the smartphone is at the highest position behind the waist with maximum acceleration (black "x" symbol) and the same-side foot touches the ground. then, the hand moves forward to the front of the body and the acceleration drops to a minimum (pink cycle) when the body is in a stance phase. eventually, the hand stops at the highest forward position and the acceleration reaches the highest position (other black "x" symbol) while the opposite-side foot hits the ground. on the other hand, the backward-swing phase starts when the observed hand starts moving backward from the front of the waist while the acceleration decreases from the maximum value and the nonparametric regression-based step-length estimation for arm-swing walking using a smartphone 569 opposite-side foot touches the ground. acceleration in the backward-swing phase decreases to the smallest value (other pink cycle) in the stance phase, and then increases to the highest value (other black "x" symbol) when the hand is at the back of the user’s body. meanwhile, a same-side foot hits the ground. therefore, there are two steps in a complete arm-swinging cycle. 660680700720 740760780 -4 -3 -2 -1 0 1 2 highest point backward highest point backward stance point forward stance point backward highest point forward forward swingbackward swing a c c e le ra ti o n ( m / s 2 ) -2 -3 -4 -1 0 1 2 time (s) 6.66.87.07.27.47.67.8 figure 4: arm-swing cycle analysis 5 adaptive step-length estimation a distance estimation method for pedestrian walking based on vertical acceleration was proposed by weinberg [18]. his well-known formula is expressed as follows: lw ≈ kw × 4 √ amax −amin, (3) where, lw is the step length; kw is a constant; and amax, amin are the highest and smallest vertical acceleration values in a single step, respectively. based on the weinberg’s formula, we propose a k-factor adapted along steps as a polynomial function of step velocity. the adaptive k-factor for each step is considered as a general regression model: k = f(v,β) + e, i = 1, 2, ...,n, (4) where v =   1 v1 · · · v1p 1 v2 · · · v2p ... ... ... ... 1 vn · · · vnp   , (5) k = (k1,k2, ...,kn) t , (6) β = (β0,β1, ...,βp) t , (7) 570 p.h. truong, n.d. nguyen, n.h. ho, g.-m. jeong e = (e1,e2, ...,en) t . (8) it should be noted that v is the magnitude of the average velocities on x,y, and z-axes in each step; e and n are the noise or measurement error and the number of observations, respectively. we assume the noise e is uncorrelated, mean zeros, and random variables. then, the locally weighted polynomial regression is obtained through the solution of the weighted least squares problem: β̂ = argmin β (k −v β)tw(k −v β), (9) where, w is a diagonal matrix with the gaussian weights which can achieve a more accurate local approximation model [4]. the solution respecting to the coefficient β is β̂ = (v twv )−1(v twk). (10) additionally, the k-factor is obtained as ki = β0 + viβ1 + ... + vi pβp. (11) the proposed adaptive step-length estimation equation is lstepi = (β0 + viβ1 + ... + vi pβp) × 4 √ amax −amin. (12) the walking distance within each trial is calculated by summing the distance traveled of all the steps. d = n∑ i=1 lstep(i), (13) where, n is the number of walking steps in each trial. 6 experimental results in our experiments, three participants were required to walk forward 15 m, and swing their hands in their casual manner. we collected 300 samples of the acceleration and gyroscope signals at the sampling rate of 50hz. to validate the performance of a proposed method and selecting the degree of a polynomial function, we applied a k-fold cross-validation (k = 6) on the dataset. we divided the dataset into 6 roughly equal-sized parts. we, then, took alternately 5 parts for the training set and used the remaining part as the test set. figure 5 shows the mean squared errors versus degrees of freedom [9] of the training set for different degrees of the polynomial function (k = 2, 3, ..., 6). we selected the degree of the polynomial equal to 6 to minimize both errors of the training set and test set. we compared the estimation results of our proposed method with tian et al. [16] and weinberg et al. [18] methods. figure 6 shows the accuracy rates of three methods versus three speed levels, i.e., low, normal, and high speed. note that the speed levels are obtained based on our previous approach [8]. we defined the speed levels in the experimental samples based on a normal distribution of average walking velocities v and velocity deviation σ. thus, the ranges of the low, normal, and high speeds are v ≤ v −σ, v −σ < v < v + σ, and v ≥ v + σ, respectively. table 1 describes the average results of error rates and standard deviations (std.) of three methods. the proposed method achieved a higher accuracy and a lower standard deviation in comparison with the two reference methods. in particular, the proposed method obtained the average nonparametric regression-based step-length estimation for arm-swing walking using a smartphone 571 0 10 20 30 40 50 60 70 80 1.65 1.7 1.75 1.8 1.85 1.9 1.95 2 2.05 ×10 -3 global, degree = 2 local, degree = 2 global, degree = 3 local, degree = 3 global, degree = 4 local, degree = 4 global, degree = 5 local, degree = 5 global, degree = 6 local, degree = 6 degrees of freedom m s e figure 5: mean squared error (mse) versus polynomial degree 0 10 20 30 40 50 60 70 80 90 100 weinberg et al. tian et al. proposed method low normal high speed levels a c c u ra c y figure 6: estimation accuracy of the three methods table 1: comparison of estimation results of the proposed method with reference methods speed level low normal high error (%) std (m) error (%) std (m) error (%) std (m) weinberg [18] 25.24 0.74 17.10 0.89 8.92 0.67 tian [16] 12.32 1.02 5.44 1.16 11.18 0.51 proposed method training 3.87 0.56 4.02 0.54 4.09 0.65 testing 5.37 0.54 4.65 0.68 5.03 0.52 error rate and average standard deviation of 5.01% and 0.56 m in the testing phase, respec572 p.h. truong, n.d. nguyen, n.h. ho, g.-m. jeong tively, which significantly surpassed the reference methods’ results. between the two reference methods, the method proposed by tian et al. shows lower error rates for the slow and normal walking speeds (12.32% and 5.44%, respectively), but suffers a higher error for the high speed level (11.18%). on the contrary, the average standard deviation of weinberg et al.’s method was better than tian et al.’s method. generally, the application of these two methods into walking distance estimation for the arm-swing activities is a case-by-case issue. contrarily, the proposed method was well-performed for all three cases of walking speeds. 7 conclusion in this paper, we proposed an adaptive distance estimation method for arm-swinging activities at the three levels of speed, i.e., low, normal, and high speed. we analyzed the phases of the arm-swinging activities to determine the number of steps in each sample. the degree of the polynomial function was selected by analyzing the mean squared error versus the degrees of freedom of each polynomial value using the cross-validation technique. the results revealed that the proposed method estimated the distance traveled better than the conventional methods in terms of the accuracy and standard deviation of the results. acknowledgment this research was supported by the basic science research program through the national research foundation of korea (nrf) funded by the ministry of education (nrf2015r1d1a1a01 060917), the korean government (msip)(nrf-2016r1a5a1012966), and global scholarship program for foreign graduate student at kookmin university in korea. bibliography [1] alvarez, d.; gonzalez, r. c.; lopez, a.; alvarez, j. c. (2006); comparison of step length estimators from wearable accelerometer devices, ieee engineering in medicine and biology society, 5964-5967, 2006. [2] capurso, n.; song, t.; cheng, w.; yu, j.; cheng, x. (2016); an android-based mechanism for energy efficient localization depending on indoor/outdoor context, ieee internet of things journal, 4(2), 299-307, 2016. [3] chen, k.y.; bassett, d.r. (2005); the technology of accelerometry-based activity monitors: current and future, medicine and science in sports and exercise, 37(11), s490-s500, 2005. [4] cleveland, w. s.; loader, c. (1996); smoothing by local regression: principles and methods, physica-verlag hd, 10-49, 1996. [5] cleveland, w. s.; devlin, s. j. (1988); locally weighted regression: an approach to regression analysis by local fitting, j. am. stat. assoc., 83(403), 596-610, 1988. [6] donoso, y.; montoya, g. a.; solano, f. 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(2016); a multi-mode dead reckoning system for pedestrian tracking using smartphones, ieee sensors journal, 16(7), 2079-2093, 2016. [17] truong, p. h.; lee, j.; kwon, a. r.; jeong, g.-m. (2016); stride counting in human walking and walking distance estimation using insole sensors, sensors, 16(6), 8-23, 2016. [18] weinberg, h. (2002); using the adxl202 in pedometer and personal navigation applications, analog devices, 2002. international journal of computers communications & control issn 1841-9836, 13(2), 268-279, april 2018. epak: a computational intelligence model for 2-level prediction of stock indices l. tang, h. pan, y. yao li tang 1. school of management and economics university of electronic science and technology of china chengdu 611731, china 2. tianfu college of southwestern university of finance and economics chengdu 610052, china tangli@std.uestc.edu.cn heping pan* intelligent finance research center chongqing institute of finance chongqing 400067, china *corresponding author: panhp@swingtum.com yiyong yao tianfu college of southwestern university of finance and economics chengdu 610052, china yiyongyao@yahoo.com abstract: this paper proposes a new computational intelligence model for predicting univariate time series, called epak, and a complex prediction model for stock market index synthesizing all the sector index predictions using epak as a kernel. the epak model uses a complex nonlinear feature extraction procedure integrating a forward rolling empirical mode decomposition (emd) for financial time series signal analysis and principal component analysis (pca) for dimension reduction to generate information-rich features as input to a new two-layer k-nearest neighbor (knn) with affinity propagation (ap) clustering for prediction via regression. the epak model is then used as a kernel for predicting each of all the sector indices of the stock market. the sector indices predictions are then synthesized via weighted average to generate the prediction of the stock market index, yielding a complex prediction model for the stock market index. the epak model and the complex prediction model for stock index are tested on real historical financial time series in chinese stock index including csi 300 and ten sector indices, with results confirming the effectiveness of the proposed models. keywords: empirical mode decomposition, principal component analysis, affinity propagation, k-nearest neighbor, time series, stock index prediction. 1 introduction time series prediction is a practical issue. especially the financial time series prediction with important economic significance has attracted serious attention from both finance and computer science researchers. a fair large of literatures research on financial prediction methods[23], typically including arima [8,27], arch [3,13],garch [1,16], chaos-theoretical [17,18], artificial neural network (ann) [10,20], support vector machine (svm) [9,19], and k-nearest neighbor (knn) [12,28]. from these equivalent researches, it is realized that the foremost key factor for effective prediction is the feature extraction which should generate essential information effectively. practically, the financial time series feature extraction is equivalent to the signal analysis. thus we copyright © 2018 by cc by-nc epak: a computational intelligence model for 2-level prediction of stock indices 269 can apply a signal processing technique such as the empirical mode decomposition [6] which is an effective method used widely in dealing with nonlinear and non-stationary signals [21,25]. however, the end effect of emd [15] has not been considered usually in most of the researches except the forward rolling emd with a sliding window proposed by zhang and pan in 2015[26]. through a forward rolling emd, the original time series is decomposed into multilevel imfs with a high dimension, thus the principal components analysis (pca) [11] can be applied for dimension reduction. generally, we propose a nonlinear feature extraction procedure integrating a forward rolling emd and pca in this paper. for prediction modeling, knn is a nonparametric algorithm which can predict via nonlinear regression. however, it should be note that knn has a large amount of calculations and a large prediction deviation may be occurred when the samples are in disequilibrium. to tackle these issues, this paper proposes a two-layer knn with affinity propagation (ap) clustering. ap is an effective clustering method with sensory signals process and data patterns detection [5]. integrate the nonlinear feature extraction procedure and a two-lay knn, this paper proposes a new computational intelligence model for univariate time series prediction called epak and construct a complex prediction model for stock market index synthesizing all the sector index predictions applying epak as a kernel. 2 an emd-pca-ap-knn (epak) financial prediction model 2.1 frame work of an epak financial prediction model the epak model which is a computational intelligence financial prediction model needs to learn, adapt to and evolve along with changing financial situations. in general, we should first define a time frame and assume a historical financial time series long enough exists. in this paper, we focus on a daily time frame thus we can express a financial price time series on day t as x(t) = (o(t),h(t),l(t),c(t),v (t)), (1) where o(t),h(t),l(t),c(t) and v (t) are the open price, high price, low price, close price and volume. in this paper we only consider the close price, so c(t) is set as x(t). we can define a relative return of the price for x(t) as r(t,λ) = x(t) −x(t−λ) x(t−λ) , (2) where λ is the prediction step length of time series with a basic step length of λ = 1 . without any other specification, we use r(t) as r(t,λ). therefore, we can express a historical relative return data set as dr(t,t) = (r(t− (t −w) + 1), · · · ,r(t + 1),r(t)), (3) where t is the number of total days and w << t is the width of a sliding window applied for intercepting the historical data set. in general, an epak financial prediction model can be expressed as epak : p(t) =⇒ aknn(k) =⇒ r(t + λ), (4) or mathematically epak : r(t + λ) = aknn(p(t),k), (5) 270 l. tang, h. pan, y. yao where aknn stands for a two-layer knn with ap, p(t) means the principal components generated by pca, k is the structural parameter, and r(t + λ) is the prediction output. for details, the epak model works through two processes, first the feature extraction integrating emd for signal decomposition of financial time series and pca for dimension reduction, second the prediction procedure applying a two-layer knn regression with ap. thus we can re-express the epak model as dr(t,t) => emd => pca => p(t) => ap => aknn(k) => r(t + λ), (6) or mathematically r(t,λ) = aknn{ap [fe(pca(emd(dr(t,t))))]}, (7) where fe() denotes the feature extraction process. 2.2 a nonlinear feature extraction process integrating a forward rolling emd and pca as a foremost key process for financial time series prediction, feature extraction should concentrate essential information from the historical data set and input to the prediction model. the epak model encompasses a complex nonlinear feature extraction procedure special for financial time series, integrating a forward rolling emd and pca. a forward rolling emd for financial time series to start a feature extraction process, we apply the forward rolling emd for signal decomposing on the historical data set dr(i − 1,t),i = t,· · · ,t − (t − w) + 1 and make it as the input,r(t),i = t,· · · ,t−(t −w) + 1 as the output of prediction. therefore, the input-output data set can be expressed as dp(t,t −w) =   emd(dr(t− 1),t) −→ r(t) emd(dr(t− 2),t) −→ r(t− 1) · · · · · · · · · emd(dr(t− (t −w)),t) −→ r(t− (t −w) + 1)   . (8) after emd, the original data set has been decomposed to multilevel imfs which can satisfy two conditions according to imf definition [6]: 1) for a whole series, the total number of extrema and zero-crossing points should be equal or differ no more than one, 2) for any time period, the mean of the upper envelope and the lower envelope that are formed by local maxima and minima equals zero. take the emd decomposition of dr(t) as an example, the emd decomposition consists of three parts: 1) sifting process: • calculate all the local maxima and minima of dr(t) . • generate upper and lower envelopes with maxima and minima by cubic spline, and calculate the mean value mi(t) = (ue(t) + le(t)) 2 , (9) where i = 1, 2, · · · indicates the ith-order,ue(t) and le(t) are the upper and lower envelopes. epak: a computational intelligence model for 2-level prediction of stock indices 271 • generate a detail component of decomposition hi(t) = dr(t) −mi(t), (10) when i = 1,dr(t) = dr(t). 2) imf checking: check whether hi(t) can satisfy the two conditions of imf definition: • if it can, hi(t) is an imf, and the residual is r(t) = dr(t) − c(t), (11) c(t) = hi(t). (12) let dr(t) = r(t) and continue to next sifting. • if it cannot, let dr(t) = hi(t) and continue to next sifting by 1). 3) sifting stop: • to ensure the instantaneous frequency defined by imf has sufficient physical significances, a metric sd is defined by huang [6] to determine whether to stop sifting. sd can be expressed as sd = n∑ i=1 [ |hi+1(t) −hi(t)|2 (hi(t))2 ], (13) when 0.2 < sd < 0.3, stop sifting. • if the number of extrema including maxima and minima of r(t) is smaller than two, stop sifting. • return all the results as dr(t) = ( n∑ i=1 ci) + r, (14) or in more detail dr(t) =   imf1(t,w)... imfn(t,w)   + r. (15) equation (15) means that emd can decompose the original data set into n (generally, let n <= 5) multilevel imfs and a residual r,thus eq.(8) can be rewritten as dp(t,t −w) = {d −→ r}, (16) d =   imf1(t− 1,w) · · · imfn(t− 1,w) imf1(t− 2,w) · · · imfn(t− 2,w) ... ... ... imf1(t− (t −w),w) · · · imfn(t− (t −w),w)   , (17) r =   r(t) r(t− 1) ... r(t− (t −w) + 1)   . (18) it should be note that each row of d is high dimensional since it consists of multilevel imfs time series. therefore, a pca algorithm can be applied for reducing the high dimension. 272 l. tang, h. pan, y. yao pca for dimension reduction compared to other methods of dimension reduction such as linear discriminant analysis (lda), locally linear embedding (lle), and laplacian eigenmaps (le), pca can maintain the information contained in the original data as much as possible after dimension reduction. however, for financial time series prediction, the feature extraction needs to keep the more information of original data the better robustness and prediction accuracy of the model. therefore, pca is used for dimension reduction and feature extraction in this paper. pca reduces data dimension by an orthogonal linear transformation which is actually a singular value decomposition process. thus we can express a pca algorithm as three processes: 1) form a matrix consists of principal components: normalize the high-dimensional matrix d to y y = (normalization(d)) t . (19) apply a singular value decomposition of y y = v σw t , (20) where v and w are orthogonal matrices formed by the eigenvectors of y y t and y t y respectively, σ is a nonnegative rectangular diagonal matrix whose left part consists of the eigenvalues λ(i),i = 1, 2, · · · ,p of y y t . thus we can generate a transformed matrix p that consists of principal components in turn, p = st v = w σt v t v = w σt . (21) 2) find a new lower dimension: pca is used for reducing dimension, thus the dimension of matrix p should be reduced. actually, the first r in l principal components concentrate the most essential information of matrix p . to find the value of r, we can use the cumulative contribution rate (ccr) which is generally required to be more than a threshold (such as 85%) [15] for help, ccrr = ∑r i=1 λ(i)∑l i=1 λ(i) > 85%, (22) where r is the new lower dimension and r � l. 3) generate an information-rich matrix: construct a new matrix σr as a r × l matrix by σr = ir×lς. (23) then we can form a lower dimensional matrix pr of p pr = w (σr) t , (24) where pr is the new low-dimensional matrix which should be input to a two-layer knn with ap for prediction. 2.3 a two-layer knn algorithm with ap knn is a nonparametric algorithm that can be used for nonlinear regression. to improve the knn algorithm, this paper proposes a two-layer knn with ap. we apply ap to transform the feature into clusters as input to a two-layer knn for prediction. epak: a computational intelligence model for 2-level prediction of stock indices 273 ap for clusters generation assume the feature extracted by a forward rolling emd and pca comprises n data points. ap initially regards every data point as a potential cluster center. then measure the similarity between any data pairs and accumulate evidences for an iterative procedure which can find the final suitable exemplars. for details, we can describe an ap algorithm as three procedures: 1) construct a similarity matrix: in this paper, we measure the similarity between each data pairs with euclidean which is one of the classical similarity metric for time series. thus the similarity can be defined as sim(i,j) = −‖i− j‖2, (25) where i and j are any data pairs of the n data points. when j = i, sim(i, i) = p(i),means the preference of point i can be an suitable exemplar. therefore, p is set as a preference parameter which can affect the clustering solutions. the similarity matrix sn×n can be composed of all sim values in order. 2) find suitable exemplars: an iterative procedure based on sn×n can be helpful to find suitable exemplars. in this procedure, the evidences of ”responsibility”r and ”availability”a should be accumulated. assume point c is the exemplar candidate and i is any point. r(i,c) indicates the suitability of c for being the exemplar of i, and a(i,c) indicates the appropriateness of i for choosing c as the exemplar, r(i,c) = sim(i,c) − max j 6=c {a(i,j) + sim(i,j)}, (26) a(i,c) = min{0,r(c,c) + ∑ j 6=i,c max[0,r(j,c)]}, (27) when r(i,c) + a(i,c) achieves the maximum, c is the most suitable exemplar of i . 3) choose an optimal clustering solution: after finding the final exemplars, there are several clustering solutions and the optimal one should be chosen. an effective evaluation of clustering solution is the silhouette coefficient which can reflect the separability and compactness between clusters [22]. assume there are clusters ci ,i = 1, 2, · · · ,n, sc for any point in cluster ci can be expressed as sc(xi) = min[a(xi,cj)] − b(xi) max{b(xi), min[a(xi,cj)]} , (28) where a(xi,cj) is the mean dissimilarity between xi and all the other points in cluster cj, j 6= i,b(xi) is the mean dissimilarity between xi and all the other points in cluster ci . calculate the mean value of overall sc as scm = mean{ n∑ i=1 sc(xi)}, (29) the higher the scm value is, the better the clustering solution [4]. therefore, we can choose the optimal clustering solution with a highest scm . a two-layer knn we propose a two-layer knn with ap consists of three functions aknn, aknn1, and aknn2. assume x(t + λ) = dr(t + λ,t) is the future prediction point and x(t) = dr(t,t) as input. aknn is equivalent to the input-output of a two-layer knn. for the first layer which can reduce the computation, aknn calls aknn1 to find x(t)’s nearest exemplar cnear and ascribes x(t) to the same cluster cnear . and for the second layer which can ensure equilibrium samples, 274 l. tang, h. pan, y. yao aknn calls aknn2 to find the k nearest neighbors in cnear for prediction. for details, a twolayer knn can be defined as function aknn inputs x(t) and ci,i = 1, 2, · · · ,n which are the clusters generated by ap, outputs the prediction x(t + λ) = dr(t + λ,t) , (x(t + λ)) = aknn(x(t),ci,k). (30) aknn generates predictions by calling aknn1 and aknn2 in turn. function aknn1 inputs x(t) and ci,i = 1, 2, · · · ,n, outputs cnear and its exemplar cnear (cnear,cnear) = aknn1(x(t),ci,k = 1), (31) where k = 1 means cnear is x(t)’s nearest exemplar. calculate the similarity between x(t) and each exemplar ci,i = 1, 2, · · · ,n as sim(x(t),ci) = −‖x(t) − ci‖2. (32) when sim(x(t),cnear) achieves the maximum, cnear is x(t)’s nearest exemplar and correspondingly outputs the cnear. function aknn2 inputs x(t) and cnear ,outputs x(1 + λ) , (x(t + λ)) = aknn2(x(t),cnear,k), (33) calculate the similarity between x(t) and each point xi in the same cluster cnear as sim(x(t),xi) = −‖x(t) −xi‖2. (34) when sim(x(t),xj),j = 1, 2, · · · ,k achieves the first k max(sim) values, xj,j = 1, 2, · · · ,k are the nearest neighbors of x(t). thus the prediction x(t + λ) can be generated as x(t + λ) = ∑k j=1 xj k . (35) according to eq. (35), the parameter k is a critical factor which can affect the results, thus find the optimal k value is a practical problem. in general, different specific historical data set has different optimal k value which is usually found by experiments. therefore, in this paper, we find each optimal k value for each specific model by experiments, setting the original k value as k = 1 , generating predictions based on nearest neighbors and increasing value with step length λ = 1. if k value continues to increase three times with no effect on improvement of prediction, we can stop and find the optimal k value makes the best performance of prediction. however, other methods should be studied in future work. 2.4 three structural parameters of an epak financial prediction model a specific epak financial prediction model can be constructed by three structural parameters: 1) λ is the prediction step length, 2) w is the sliding window width used for a forward rolling emd, and 3) k means the nearest neighbors selected by the second layer of aknn. thus eq. (7) which is a general definition of an epak model can be re-expressed as r(t + λ) = aknn{ap[pca(emd(dr(t,w)))],k}. (36) epak: a computational intelligence model for 2-level prediction of stock indices 275 3 a complex prediction model for stock market index synthesizing all sector indices predictions using epak as a kernel a stock market index can reflect the overall trend of the stock market such as the chinese benchmarked stock index csi 300, the standard and poor’s composite index and so on. it is generally calculated by paasche idex as ir = cavr b × 1000, (37) where cavr and b indicate the adjusted market values of the constituent stock (cav ) during the reporting period and on the base date, and b equals a constant that set as a divisor. moreover,cavr can be calculated as [2] cavr = n∑ i=1 pi ×asi, (38) where n is the number of total constituent stocks, pi is the price of each constituent stock, and asi is the number of adjusted share capital of each constituent stock. note according to paasche index, the circulation or volume of the constituent stock during the report period is set as a weight cwi, thus asi can be calculated as asi = ts × cwi, (39) where ts is the number of total share capital. therefore, eq. (37) can be rewritten as ir = 1000 d × n∑ i=1 ts ×pi × cwi, (40) in general, the constituent stocks of the stock market index are belongs to different industries, accordingly different sector indices can be calculated. therefore, we can construct a complex prediction model for stock market index synthesizing all sector indices predictions. applying an epak as a kernel, this complex model can be expressed as mip = n∑ i=1 sipi ×swi, (41) where sipi is the prediction of each sector index generated by epak, swi is the a sector weight which sums the weights of the constituent stocks, and n is the number of total sector indices. 4 empirical test and results to test the effectiveness of an epak model for predicting a real data set, we do an empirical test on chinese stock index and collect real historical data set from wind and china securities index co., ltd (china). 4.1 performance metrics of a specific epak prediction model for evaluating the performance of a prediction model, some metrics can be applied, generally including mean absolute percentage error (mape), mean absolute difference (mad), and 276 l. tang, h. pan, y. yao root mean square error (rmse). however, we apply hit rate [14] that can reflect accuracy of the predicted direction as a metric in this paper. it is defined as hitrate = ∑n i=1 hi n ,hi = { 1, ri ×rp > 0 0, ri ×rp < 0 , (42) where n indicates the number of samples, ri and rp are the real and predicted values. 4.2 a specific epak model for predicting csi 300 model epak_csi300d1 is proposed to predict the t+1 daily return of chinese benchmarked stock index csi 300. this model can be expressed as r(t + 1) = aknn{ap [pca(emd(csi300d1_dr(t,w)))],k}. (43) the historical csi 300 price time series from 4th january 2006 to 29th december 2017 comprises 2917 trading days is used, with the earlier 80% part for in-sample training and the later 20% part for out-of-sample testing. in terms of hit rate, the performance of epak_csi300d1 is shown in table 1 (note: the level of imfs is determined by the emd decomposition of csi 300), resulting a highest hit rate of 72.78% with w = 100, n = 3 and k = 1. therefore, we can say that epak_csi300d1 can predict the t + 1 daily return of csi 300 effectively. table 1: hit rates of epak_csi300d1 for t + 1 daily return of csi 300 prediction hit rate% w k=1 k=2 k=3 k=4 k=5 100 72.78 71.35 68.12 68.29 70.63 150 72.29 70.27 71.19 67.90 67.90 200 70.47 69.91 68.98 69.35 68.05 250 70.85 70.66 67.82 70.10 67.44 300 72.22 70.29 69.13 67.58 69.32 350 68.92 72.66 71.48 68.32 69.31 4.3 a specific complex model for predicting csi 300 synthesizing ten sector indices predictions using epak as a kernel the constituent stocks of csi 300 are belongs to ten sectors, thus we construct ten specific epak models for predicting the t + 1 daily return of these ten sector indices as shown in table 2 (note: the data is collected from http://www.csindex.com.cn/). the historical data set for each sector index during the same period as csi 300’s is used. table 3 shows the test results, where the highest hit rate of each model is selected and the best performance in terms of hit rate is 79.60% with the epak model predicting the t + 1 daily return of telecom svc index. according to eq. (41), we construct a prediction model for predicting csi 300 synthesizing ten sector indices predictions as csi300 = 10∑ i=1 ri(t + 1) ×swi, (44) where ri(t + 1) is the prediction of each sector index generated by the epak prediction model. this model test achieves a hit rate of 73.43% which is higher than the performance of epak: a computational intelligence model for 2-level prediction of stock indices 277 table 2: ten sector indices form csi 300 index weight (%) constituent number energy 2.61 12 materials 7.04 33 industrials 13.31 62 cons disc 11.92 37 cons staples 7.97 11 health care 5.15 22 financials 39.81 75 info technology 7.62 30 telecom svc 2 7 utilities 2.57 11 table 3: hit rates of epak models for daily return of ten sector indices prediction index hit rate (%) index hit rate (%) energy 73.54 health care 74.55 materials 77.58 financials 69.49 industrials 78.59 info technology 71.52 cons disc 78.58 telecom svc 79.60 cons staples 76.57 utilities 73.54 epak_csi300d1. this result implies the complex prediction model for prediction the stock market index can improve the effectiveness of the prediction model with more comprehensive information as input. 5 conclusion this paper proposes a financial prediction model called epak, integrating a nonlinear feature extraction procedure and a two-layer knn with ap for prediction. the nonlinear feature extraction procedure is adaptable and comprehensive for financial time series analysis and the new two-layer knn with ap can tackle the main deficiencies of knn and perform better. applying the epak model as a kernel, we construct a complex model for stock market index prediction which synthesizes the predictions of each sector index of the stock market via weighted average to generate the prediction of the stock market index. specific epak models for univariate time series are implemented for predicting the t + 1 daily return of csi300 and ten sector indices of csi300, achieving a highest hit rate of 79.60% on telecom svc index prediction. then the predictions of ten sector indices are synthesized via weighted average for generating the prediction of csi 300, which performs better than the direct prediction of csi 300. as a prediction model for financial time series, epak comprises two key processes, feature extraction and modeling of prediction which are also the main factors for the performance of prediction. thus in order to improve the prediction model, we can focus on these two procedures in our future work. for feature extraction, we can advance four aspects on: 1) taking more comprehensive information from different financial markets that interact with each other [7,24], 2) applying other effective nonlinear dimension reduction algorithm integrating other methods which should be more suitable for financial time series, 3) finding a similarity metric to take place of euclidean measurement which is special for financial time series, 4) improving the prediction modeling, such as apply the auto encoder, random forest and so on. 278 l. tang, h. pan, y. yao bibliography [1] beckers, b.; herwartz, h.; seidel, m. 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(2017); multidimensional k-nearest neighbor model based on eemd for financial time series forecasting, physica a: statistical mechanics and its applications, 477, 161-173, 2017. international journal of computers communications & control issn 1841-9836, 12(5), 599-611, october 2017. proportional-integral-derivative gain-scheduling control of a magnetic levitation system c.-a. bojan-dragos, r.-e. precup, m.l. tomescu, s. preitl, o.-m. tanasoiu, s. hergane claudia-adina bojan-dragos, radu-emil precup, stefan preitl, oana-maria tanasoiu, stefania hergane politehnica university of timisoara department of automation and applied informatics bd. v. parvan 2, 300223 timisoara, romania claudia.dragos@aut.upt.ro, radu.precup@upt.ro, stefan.preitl@upt.ro, oanatanasoiu96@gmail.com, stefania.hergane@yahoo.com marius l. tomescu* aurel vlaicu university of arad romania, 310330 arad, elena dragoi, 2 *corresponding author: tom_uav@yahoo.com abstract: the paper presents a gain-scheduling control design procedure for classical proportional-integral-derivative controllers (pid-gs-c) for positioning system. the method is applied to a magnetic levitation system with two electromagnets (mls2em) laboratory equipment, which allows several experimental verifications of the proposed solution. the nonlinear model of mls2em is linearized at seven operating points. a state feedback control structure is first designed to stabilize the process. pid control and pid-gs-c structures are next designed to ensure zero steady-state control error and bumpless switching between pid controllers for the linearized models. real-time experimental results are presented for validation. keywords: gain-scheduling, magnetic levitation system, proportional-integralderivative control; real-time experiments. 1 introduction during the last years several classical and adaptive control structures have been proposed for positioning control system and applications for magnetic levitation systems. some of these structures are presented in [49], [52], [1], [35], [47], [56], [11], [27], [23], [19], [55], [5]. for example, a high gain adaptive output feedback controller is designed in [5] by introducing two different virtual filters and using back-stepping. another adaptive control scheme that copes with the modifications of the structural parameters of magnetic levitation systems is suggested in [1]. gain-scheduling control solutions are popular nowadays, and they are briefly analyzed as follows: fuzzy-based gain scheduling of exact feed-forward linearization control and sliding mode controllers for magnetic ball levitation system are proposed in [28]. a high gain adaptive output feedback control to a magnetic levitation system is discussed in [29]. a proportional-integralderivative (pid) gain-scheduling controller for second order linear parameter varying, which exclude time varying delay using a smith predictor is given in [44]. other interesting adaptive gain scheduling control techniques for real practical applications are given in [6], [54], [7], [17], [53]. the paper is dealing with the position control of a ferromagnetic sphere in a magnetic levitation system with two electromagnets (mls2em) laboratory equipment. a state feedback control structure (sfcs) is first designed in order to stabilize the system by applying the control signal only to the top electromagnet [8]. the simulated external disturbance can be applied copyright © 2006-2017 by ccc publications 600 c.-a. bojan-dragos, r.-e. precup, m.l. tomescu, s. preitl, o.-m. tanasoiu, s. hergane to the bottom electromagnet. starting with [9] the sfcs is next controlled by a proportionalintegral-derivative gain-scheduling control (pid-gs-c) structure. in the presented scheme, the proportional, derivative and integral gains are adapted to the modifications of the operating points. the paper proposes relatively simple classical and adaptive control structures which belongs to the general case of linear and nonlinear control system structures [43], [38], [50], [20], [42], [51], [32], [18], [36]. the paper is organized as follows: the nonlinear model and the linearized mathematical model (mm) of mls2em are given in section 2. the proposed control structure is next developed in section 3. the real-time experimental results are presented in section 4 and the conclusions are highlighted in section 5. 2 process modeling the controlled mls2em laboratory equipment includes: two electromagnets (em1 the top electromagnet and em2 the bottom electromagnet), the ferromagnetic sphere, sensors to detect position of the sphere, computer interface, drivers, power supply unit, connection cables and an acquisition board. the nonlinear state-space mm of ml2sem is [24]: ṗ(t) = v(t), v̇(t) = −i 2 em1 (t)·femp 1·exp(−p(t)/femp 2) m·femp 2 + g + i2 em2 (t)·femp 1·exp(−(xd−p(t))/femp 2) m·femp 2 , i̇em1(t) = ki·uem1(t)+ci−iem1(t) fip 1 fip 2 ·exp(−p(t)/fip 2) , i̇em2(t) = ki·uem2(t)+ci−iem2(t) fip 1 fip 2 ·exp(−(xd−p(t))/fip 2) , y(t) = km ·p(t), (1) where: p ∈ [0, 0.0016] the sphere position (m), v ∈ rthe sphere speed (m/s), iem1, iem2 ∈ [0.03884, 2.38] the currents in the top electromagnet (em1) and bottom electromagnet (em2) (a), uem1,uem2 ∈ [0.005, 1] the signals applied to em1 and em2, respectively (v), and y the process output (m), i.e., the measured sphere position. the parameters of the process are determined analytically and experimentally [24], [16]. the model (1) is linearized around seven operating points (o.p.s) with the coordinates p (j)(p(j),v(j), iem1 (j), iem1 (j)) where j is the index of the current operating point, j = 1...7. the linearized state-space models and their matrices are: ∆ẋ(j) = a(j)∆x(j) + b(j)∆u(j), ∆y(j) = ct (j) ∆x(j), ∆x(j) = [∆p(j) ∆v(j) ∆i (j) em1 ∆i (j) em2] t , ∆u(j) = [∆u (j) em1 ∆u (j) em2] t , a(j) =   0 1 0 0 a (j) 21 0 a (j) 23 a (j) 24 a (j) 31 0 a (j) 33 0 a (j) 41 0 0 a (j) 44   ,b(j) = [ b(j)uem1 b(j)uem2 ] =   0 0 0 0 b (j) 31 0 0 b (j) 42   ,ct (j) = [1 0 0 0], (2) where t stands for matrix transposition, with the following elements of the matrices a(j) and b(j), which depend on p (j) : proportional-integral-derivative gain-scheduling control of a magnetic levitation system 601 a (j) 21 = i (j) em1 2 m femp 1 f 2 emp 2 exp(−p(j)/femp2) + i (j) em2 2 m femp 1 f 2 emp 2 exp(−(xd −p(j))/femp2), a (j) 23 = − 2i (j) em1 m femp 1 femp 2 exp(−p(j)/femp2), a (j) 24 = 2i (j) em2 m femp 1 femp 2 exp(−(xd −p(j))/femp2), a (j) 31 = −(kiu (j) em1 + ci − i (j) em1) p(j) fip 1 exp(p(j)/fip2), a (j) 33 = − fip 2 fip 1 ·e x10 fip 2 , a (j) 41 = −(kiu (j) em2 + ci − i (j) em2) p(j) fip 1 exp((xd −p(j))/fip2), a (j) 44 = − fip 2 fip 1 exp((xd −p(j))/fip2), b (j) 31 = ki fip 2 fip 1 exp(p(j)/fip2), b (j) 42 = ki fip 2 fip 1 exp((xd −p(j))/fip2). (3) the operating points were chosen as follows such that to belong to the steady-state zone of the sphere position sensor input-output map [33], to cover the usual operating regimes and to avoid the extremities of the input-output map due to instability that may occur: p (1)(0.0063, 0, 1.22, 0.39),p (2)(0.007, 0, 1.145, 0.39),p (3)(0.0077, 0, 1.07, 0.39), p (4)(0.0084, 0, 1, 0.39),p (5)(0.009, 0, 0.9345, 0.39),p (6)(0.0098, 0, 0.89, 0.39), p (7)(0.0105, 0, 0.83, 0.39). (4) the transfer function (t.f) of the state-space linearized mm (2) used in the control system design is: h (j) pc(s) = c t (j)(si−a(j))−1b(j)uem1 = k(j) 3∏ k=1 (s−p(j)k ) = k (j) p 3∏ k=1 (1 + t (j) k s) , (5) where k(j)p = k(j)/ 3∏ k=1 p (j) k , i is the third-order identity matrix and the time constants are t (j) k = −1/k(j)p , k = 1...3, j = 1...7. the expressions of the t.f.s h (j) pc(s) are given in [22], [23]. 3 control solutions design in order to support the development of next control solutions, the sfcs is designed for the reduced third-order linear mathematical model (u2 = uem2 = 0) of unstable magnetic levitation system [22]. the first three state variables are kept and they lead to the state vector x = [ p v iem1 ] t . the pole placement method is applied to compute the state feedback gain matrix, kt (j) c = [k (j) c1 k (j) c2 k (j) c3 ] t , j = 1...7 . therefore, for each linearized mm, with the t.f. h(j)pc(s) (5), the closed-loop system poles p∗(j)k , k = 1...3, j = 1...7, [22], [23], have been imposed in order to guarantee the stability of the linearized system. with the obtained state feedback gain matrix, ktcbest = k t c_5 = [ 66.63 1.62 −0.15 ], two types of closed-loop t.f.s of the new state feedback control structure (nsfcs), h(j)sfcs_5(s) result as: h (j) sfcs_5(s) = h (j) x_5(s) = c t (j)(si−a(j)x_5) −1b (j) uem1 = c t (j)[si− (a(j) −b(j)uem1k t c_5kas)] −1b (j) uem1 =   k (j) sf cs_5 (1+t (j) 1x_5s)(1+2ζ (j) 5 t (j) 2x_5s+t (j) 2x_5 2 s2) , j = 1...3, j ∈{6, 7}, k (j) sf cs_5 (1+t (j) 1x_5s)(1+t (j) 2x_5s)(1+t (j) 3x_5s) , j ∈{4, 5}. (6) 602 c.-a. bojan-dragos, r.-e. precup, m.l. tomescu, s. preitl, o.-m. tanasoiu, s. hergane table 1: nsfcs poles and parameters nsfcs poles nsfcs parameters o.p. p∗(j)1_5 p ∗(j) 2_5 p ∗(j) 3_5 k (j) sfcs_5 t (j) 1x_5 t (j) 2x_5 ζ (j) 5 t (j) etax_5 (1) -0.79+1.02i -0.79-1.02i -0.10 0.084 0.0988 0.6 0.0077 (2) -0.92+0.88i -0.92-0.88i -0.13 0.065 0.0778 0.7 0.0078 (3) -1.07+0.62i -1.07-0.62i -0.16 0.054 0.0618 0.9 0.0081 (4) -1.65 -0.82 -0.21 0.046 0.0485 0.0123 (5) -2.32 -0.41 -0.32 0.041 0.0314 0.0244 (6) -3.07 -0.28+0.16i -0.28-0.16i 0.038 0.0033 0.9 0.0308 (7) -3.78 -0.23+0.20i -0.23-0.20i 0.034 0.0026 0.7 0.0332 the new sfcs poles obtained with the state feedback gain matrix ktc_5 and the nsfcs parameters used next in the pid-gs-c structure are given in table 1. due to the fact that the natural sfcs does not contain an i component, so it cannot ensure the zero steady-state control error, the sfcs, as controlled plant, is included in a cascade control structure (ccs) with pid controller in the outer loop. depending on the operating points, seven control solutions with pid controllers have been designed using pole-zero cancellation. the t.f.s of the designed pid controllers extended with a first order lag filter can be expressed as: h (j) pid_5(s) =   k (j) c_5(1+2ζ (j) c_5t (j) c_5s+t (j) c_5 2 s2) s(1+t (j) fd_5s) ,j ∈{1, 2, 3, 6, 7}, k (j) c_5(1+t (j) c1_5s)(1+t (j) c2_5s) s(1+t (j) fd_5s) ,j ∈{4, 5}, (7) with the tuning parameters k(j)c_5, t (j) c_5, t (j) c1_5, t (j) c2_5 and t (j) fd_5: k (j) c_5 =   1/(2 ·k(j)sfcs_5 ·t (j) etax_5), j = 1...3 0.05/(2 ·k(j)sfcs_5 ·t (j) σx_5), j ∈{4, 5} 0.01/(2 ·k(j)sfcs_5 ·t (j) 1x_5), j ∈{6, 7}, ,   t (j) c_5 = t (j) etax_5, j ∈{1, 2, 3, 6, 7}, t (j) c1_5 = t (j) 1x_5, j ∈{4, 5}, t (j) c2_5 = t (j) 2x_5, j ∈{4, 5}, t (j) fd_5 = 0.1 ·t (j) 1x_5, j = 1...7, ζ (j) c_5 = ζ (j) 5 . (8) due to the oscillatory regime, the t.f.s coefficients k(j)c_5 must be adjusted. the numerical values of pid-c parameters and the system performance indices – overshoot and settling time – are synthesized in table 2. due to the process nonlinearities, which also depend on the operating point, the switching from one pid controller to another one is a useful solution to ensure improved performance according to [48]. therefore, a pid-gs-c structure illustrated in figure 1 is designed. the pid-gs-c structure is developed on the basis of the pid controller (with the notation pid-c in figure 1), with the t.f.: ∆u1x(t) = kp(t)e(t) + ki(t) ∫ e(t)dt + kd(t)ė(t), (9) where kp is the proportional gain, kd is the derivative gain and ki is the integral gain. proportional-integral-derivative gain-scheduling control of a magnetic levitation system 603 table 2: pid controller parameters o.p. pid controller tuning pid control system parameters performance indices k (j) c_5 t (j) c_5 t (j) r_5 σ (j) 1_5 (1) 60.65 0.009 4,25 0,24 (2) 98.23 0.008 4,25 0,24 (3) 150.83 0.006 4,5 0,24 (4) 89.88 0.005 5,0 0,23 (5) 28.67 0.003 4,0 0,23 (6) 40.98 0.003 3,5 0,24 (7) 111.24 0.003 4,0 0,24 figure 1: block diagram of pid-gs-c system structure as shown in figure 1, the gain-scheduling (gs) block uses the reference input r(t) and the control error e(t) as input variables, and the tuning parameters of the pid-c, namely kp, kd and ki, as output variables. the pid tuning parameters are obtained as follows: kp(t) = kp max − (kp max −kp min) exp[−(α(t)|e(t)|)], kd(t) = kd max − (kd max −kd min) exp[−(α(t)|e(t)|)], ki(t) = (1 −α(t))ki max. (10) the parameters kp max, kp min, kd max, kd min, ki max and ki min are determined from the pidgs-c tuning parameters: kp max = { max[k (j) c_5 · (t (j) c1_5 + t (j) c2_5)],j ∈{4, 5}, max[2 ·k(j)c_5 · ς (j) c_5 ·t (j) c_5],j ∈{1, 2, 3, 6, 7}, kp min = { min[k (j) c_5 · (t (j) c1_5 + t (j) c2_5)],j ∈{4, 5}, min[2 ·k(j)c_5 · ς (j) c_5 ·t (j) c_5],j ∈{1, 2, 3, 6, 7}, kd max = { max(k (j) c_5 ·t (j) c1_5 ·t (j) c2_5),j ∈{4, 5}, max(k (j) c_5 ·t (j)2 c_5),j ∈{1, 2, 3, 6, 7}, kd min = { min(k (j) c_5 ·t (j) c1_5 ·t (j) c2_5),j ∈{4, 5}, min(k (j) c_5 ·t (j)2 c_5),j ∈{1, 2, 3, 6, 7}, ki max = max(k (j) c_5),ki min = min(k (j) c_5),j = 1...7. (11) the parameter 0 ≤ α(t) ≤ 1 is included in order to have a smooth and continuous variation of the switching from one pid controller to another one. the following equation is used to get the value of this parameter: α(t) = tanh(ηβ(t)) = [exp(2ηβ(t)) − 1]/[exp(2ηβ(t)) + 1], (12) 604 c.-a. bojan-dragos, r.-e. precup, m.l. tomescu, s. preitl, o.-m. tanasoiu, s. hergane where the parameter η determines the rate at which α(t) changes between 0 and 1 and chosen in order to ensure a certain dynamics of the variation of α(t). the parameter β(t) is set in terms of [35]: β(t) = { 1, |e(t)| > ξ, 0, |e(t)| < ξ, , ξ = 0.9 ·r(t). (13) as shown in [35], to design the pid-gs-c structure, the following conditions can be taken into account: when the system is in steady-state error (i.e., |e(t)| is large), kp max and ki min are activated in order to produce a large control signal and to overcome the undesirable oscillation and overshoot; during the steady-state regime, ki max and kp min are activated to obtain a small value of |e(t)| and to overcome the undesirable problem of overshoot. 4 experimental results all control structures, namely, sfcs, pid-c and pid-gs-c, were tested on the nonlinear laboratory system and validated by real-time experiments. in all cases, the reference input was set to 0.007 m from the top electromagnet and the control structures responses were tested on the time frame of 20 s. the responses of the sphere position, the current and the control signal in the top electromagnet were plotted. the values of the parameters of the pid-gs-c block in figure 1 are kp max = 5516, kp min = 0.6, kd max = 0.1225, kd min = 0.0036, ki max = 151, ki min = 0 and η ∈ {0.001, 0.1}. they have been obtained using several experiments such that to get the best values in the context of a compromise to tradeoff to overshoot. but other empirical performance indices can considered in this tuning. the following experimental scenarios were considered and performed: (a) the state feedback control system structure with the best state feedback gain matrix was tested on the laboratory equipment and the results are presented in figure 2. (b) the pid controller designed at seven operating points was tested on laboratory equipment. the experimental results of the control system with pid controller designed only for three o.p.s defined in tables 1 and 2, i.e., (1), (5) and (7) are presented as responses of several variables measured in the laboratory setup in figures 3 to 5 as follows: the control signal versus time in figure 3, he current through em1 versus time in figure 4 and the controlled output versus time in figure 5. these results are better in comparison with the results presented in figure 2 because of the pid-c, which ensures the zero steady-state control error and the reference input is tracked. the pair of complex conjugated poles from the cases that correspond to the o.p.s (1) to (3) (similarly to the o.p.s (6) and (7)) and the nonlinearities of the process lead to oscillations at the beginning of transient responses and during the real-time experiments. (c) the experimental results of the control system with pid-gs controller are presented as responses of several variables measured in the laboratory setup in figures 6 to 8 as follows: the control signal versus time in figure 6, he current through em1 versus time in figure 7 and the controlled output versus time in figure 8. these results are better in comparison with the results presented in figures 3 to 5 because of the pid-gs controller that ensures improved dynamic behavior characterized by smaller overshoot and settling time. proportional-integral-derivative gain-scheduling control of a magnetic levitation system 605 figure 2: real-time experimental results of state feedback control system structure with the best state feedback gain matrix figure 3: control signal u1 = u (j) em1, j ∈ {3, 5, 6}, versus time for control systems with pid controller for o.p.s. (3), (5) and (6) figure 4: current iem1 versus time for control systems with pid controller designed for o.p.s. (3), (5) and (6) 606 c.-a. bojan-dragos, r.-e. precup, m.l. tomescu, s. preitl, o.-m. tanasoiu, s. hergane figure 5: sphere position p versus time for control systems with pid controller designed for o.p.s. (3), (5) and (6) figure 6: control signal u1 = u (j) em1 versus time for control systems with pid-gs controller figure 7: current iem1 versus time for control systems with pid-gs controller proportional-integral-derivative gain-scheduling control of a magnetic levitation system 607 figure 8: sphere position p versus time for control systems with pid-gs controller 5 conclusions the paper has presented three control structures developed to control the position of the sphere in an mls2em laboratory setup. in order to use relatively simple control structures, the presented nonlinear model of the mls2em was linearized around seven operating points. to stabilize the process, a state feedback control structures was designed and the best state feedback gain matrix was found. since the sfcs does not ensure the zero steady-state control error, seven pid controllers were designed. to ensure the switching between different pid controllers, a pid-gs controller was developed and implemented. all control system structures were tested on the nonlinear model, accepting the main values of the parameters given in [24]. this paper has considered a low-cost implementation of the controllers but this must be viewed in connection with the complexity of the control algorithms, and several approaches can be used [10], [12], [37], [13], [15], [31], [46]. the real-time experimental results prove that the pid-gs-c structure discussed in this paper guarantees the improvement of control system performance regarding to step modifications of reference input. they ensure zero steady-state control error, small overshoot and settling time. as shown in the previous section, the choice of the parameters of the pid-gs-c block in figure 1 has been carried out by conducting several experiments such that to aim the best values from the point of view of the compromise to the achievement of best overshoot and settling time. this is a limitation of the control system structure presented in this paper. the systematic choice of the parameters of pid-gs-c will represent a direction of future research by ensuring the optimal tuning using classical [4], [22], [39], [2], [25], [14] and modern optimization algorithms [40], [41], [45], [26], [33], [30], [34], [21], [3] or their combinations. future research will also be focused on the design of control systems with pi(d) fuzzy gain-scheduling controllers, takagi-sugeno fuzzy controllers and hybrid structures including sliding mode control and gain-scheduling control for improved performance indices. acknowledgment this work was supported by grants from the partnerships in priority areas – pn ii program of the romanian ministry of national education and scientific research (mencs) – the executive agency for higher education, research, development and innovation funding 608 c.-a. bojan-dragos, r.-e. precup, m.l. tomescu, s. preitl, o.-m. tanasoiu, s. hergane (uefiscdi), project numbers pn-ii-pt-pcca-2013-4-0544, pn-ii-pt-pcca-2013-4-0070 and pn-ii-ru-te-2014-4-0207. bibliography [1] an s., ma y., cao z. 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(2011); constrained predictive control of a levitation system, proceedings of 16th international conference on methods and models in automation and robotics, miedzyzdroje, poland, 278-283, 2011. international journal of computers communications & control issn 1841-9836, 12(4), 475-491, august 2017. asymptotically unbiased estimation of a nonsymmetric dependence measure applied to sensor data analytics and financial time series a. caţaron, r. andonie, y. chueh angel caţaron department of electronics and computers transilvania university of braşov, romania cataron@unitbv.ro razvan andonie 1. department of computer science central washington university, usa 2. department of electronics and computers transilvania university of braşov, romania *corresponding author: andonie@cwu.edu yvonne chueh department of mathematics central washington university, usa chueh@cwu.edu abstract: a fundamental concept frequently applied to statistical machine learning is the detection of dependencies between unknown random variables found from data samples. in previous work, we have introduced a nonparametric unilateral dependence measure based on onicescu’s information energy and a knn method for estimating this measure from an available sample set of discrete or continuous variables. this paper provides the formal proofs which show that the estimator is asymptotically unbiased and has asymptotic zero variance when the sample size increases. it implies that the estimator has good statistical qualities. we investigate the performance of the estimator for data analysis applications in sensor data analysis and financial time series. keywords: machine learning, sensor data analytics, financial time series, statistical inference, information energy, nonsymmetric dependence measure, big data analytics. 1 introduction statistical machine learning is based on the strong assumption that we use a representative training set of samples to infer a model. in this case, we select a random sample of the population, perform a statistical analysis on this sample, and use these results as an estimation to the desired statistical characteristics of the population as a whole. the accuracy of the estimation depends on the representativeness of the data sample. we gauge the representativeness of a sample by how well its statistical characteristics reflect the probabilistic characteristics of the entire population. many standard techniques may be used to select a representative sample set [16]. however, if we do not use expert knowledge, selecting the most representative training set from a given dataset was proved to be computationally difficult (np-hard) [10]. the problem is actually more complex, since in most applications the complete dataset is either unknown or too large to be analyzed. therefore, we have to rely on a more or less representative training set. for example, a common statistical machine learning problem is to estimate information theory measures (such as entropy) from available training sets. this can be copyright © 2006-2017 by ccc publications 476 a. caţaron, r. andonie, y. chueh reduced to the construction of an estimate of a density function from the observed data [21]. we refer here only to nonparametric estimation, where less rigid assumptions will be made about the distribution of the observed data. although it will be assumed that the distribution has the probability density f, the data will be allowed to speak for themselves in determining the estimate of f more than would be the case if f were constrained to fall in a given parametric family. the estimation of information theory measures has an important application area the detection of dependency relationships between unknown random variables represented by data samples. there are two information theory strategies one can adopt when studying the relationship between two random variables: the first is to measure their interdependence thought as a mutual attribute and the second is to measure how much one system depends on the other. in the first case we have symmetric (bilateral) measures of dependence, whereas in the second one we have nonsymmetric (unilateral) measures. the literature review summarizes these two strategies as follows. strategy i. several symmetric dependence measure were proposed (see [20]). among them, the shannon entropy based mutual information (mi), mi(x,y ) = h(x) + h(y )−h(x,y ) = mi(y,x), which measures the information interdependence between two random variables x and y . estimating mi is known to be a non-trivial task [3]. naïve estimations (which attempt to construct a histogram where every point is the center of a sampling interval) are plagued with both systematic (bias) and statistical errors. other more sophisticated estimation methods were proposed [19], [23], [14]: kernel density estimators, b-spline estimators, k-th nearest neighbor (knn) estimators, and wavelet density estimators. an ideal estimator does not exist, instead the choice of the estimator depends on the structure of data to be analyzed. it is not possible to design an estimator that minimizes both the bias and the variance to arbitrarily small values. the existing studies have shown that there is always a delicate trade off between the two types of errors [3]. strategy ii. several continuous entropy-like nonsymmetric dependence measures have been proposed [15]. but information measures can also refer to certainty (not only to uncertainty, like shannon’s entropy), and probability can be considered as a measure of certainty. for instance, onicescu’s information energy (ie) was interpreted by several authors as a measure of expected commonness, a measure of average certainty, or as a measure of concentration. the second strategy can be illustrated by a unilateral dependence measure defined for discrete random variables by andonie et al. [2]: o(x,y ) = ie(x|y ) − ie(x), where ie(x) = ∑n k=1 p 2 k is onicescu’s information energy of a discrete random variable x with probabilities pk, and ie(x|y ) is the conditional information energy between two variables, as defined in [18]. for a continuous random variable x with probability density function f(x), the ie is [11,18]: ie(x) = e(f(x)) = ∫ +∞ −∞ f(x)f(x)dx = ∫ +∞ −∞ f2(x)dx (1) where x is a random variable with probability density function f(x). in other words, ie(x) is the expectation of the values of a density function f. for two continuous random variables x and y , with their joint probability density function f(x,y), we introduced the continuous version of the o(x,y ) measure in [1,8]: o(x,y ) = ie(x|y ) − ie(x) = ∫ +∞ −∞ ∫ +∞ −∞ f(x,y)f(x|y)dy dx− ∫ +∞ −∞ f2(x)dx (2) the discrete and the continuous o(x,y ) measure the “additional” average certainty (or information) of x occurring under the condition that y has already or simultaneously occurred asymptotically unbiased estimation of a nonsymmetric dependence measure applied to sensor data analytics and financial time series 477 (or is certain) “over” the average certainty of x when the certainty (or information) of y is not available. thus, o(x,y ) can be regarded as an indicator of the unilateral dependence of x upon y . as a unilateral measure, o(x,y ) gives a different insight than the mi into the dependencies between pairs of random variables. the o(x,y ) measure can easily be computed if the data sample is extracted from known parametric probability distributions. when the underlying distribution of data sample is unknown, the o(x,y ) has to be estimated. more formally, we have to estimate o(x,y ) from a random sample x1, x2, ..., xn of n d-dimensional observations from a distribution with the unknown probability density f(x). this problem is even more difficult if the number of available points is small. contributions. in previous work [4,5], we introduced a knn method for estimating ie and o(x,y ) from data samples of discrete or continuous variables. in the present paper, we give a more detailed description of this method. for the first time, we provide the formal proofs showing that the estimator is asymptotically unbiased and has asymptotic zero variance when the sample size increases. this implies that the estimator has good statistical qualities. the potential application of our results in statistical machine learning for multivariate data drives the motivation for the present work and we refer to two real world applications. the first one is a study of the unilateral interactions between temperature sensor data in a refrigerator room. the second one is an analysis of the dynamics of interaction between assets and liabilities evidenced by the historical event that matches the time series formed by the unilateral dependence measures observed from the financial time series reported by kodak and apple. this evolution is captured by our unilateral dependence measure over time. the rest of the paper is structured as follows. in section 2 we summarize our previous work, we review the knn estimation method for the continuous o(x,y ) measure, and we derive our ie estimator in a d-dimensional space. in section 3 we analyze and prove the asymptotic unbiased behavior of this ie estimator. in section 4 we derive the o(x,y ) estimator for the purpose of measuring the unilateral dependence relation between a pair of random variables. section 5 discusses applications and section 6 contains our final remarks. 2 background fitting multi-dimensional data to joint density functions is challenging. we aim to summarize the knn estimation technique which can be used for this problem. we will refer to our previous results on ie and o(x,y ) knn estimation. 2.1 knn estimation of the unilateral dependence measure in an unidimensional space our goal is to approximate ie and o(x,y ) from the available dataset, for random variables x and y with unknown densities, using the knn method. the knn estimators represent an attempt to adapt the amount of smoothing to the “local” density of data. the degree of smoothing is controlled by an integer k, chosen to be considerably smaller than the sample size. we define the distance ri,k,n between two points on the line as |xi −xk| out of n available points, and for each xi we define the distances ri,1,n ≤ ri,2,n ≤ . . . ≤ ri,n,n, arranged in ascending order, from xi to the points of the sample. the knn density estimate f(xi) is defined by [21]: f̂(xi) = k 2nri,k,n 478 a. caţaron, r. andonie, y. chueh the knn was used for non-parametrical estimate of the entropy based on the k-th nearest neighbor distance between n points in a sample, where k is a fixed parameter and k ≤ n − 1. based on the first nearest neighbor distances, leonenko et al. [13] introduced an asymptotic unbiased and consistent estimator hn of the entropy h(f) in a multidimensional space. when the sample points are very close one to each other, small fluctuations in their distances produce high fluctuations of hn. in order to overcome this problem, singh et al. [22] defined an entropy estimator based on the k-th nearest neighbor distances. a knn estimator of the kullback-leibler divergence was introduced by wang et al. [24]. faivishevsky et al. used a mean of several knn estimators, corresponding to different values of k, to increase the smoothness of the estimation [9]. the knn method works well if the value of k is optimally chosen, and it outperforms histogram methods [23]. however, there is no model selection method for determining the number of nearest neighbors k, and this is a known limitation. 2.2 knn estimation of the ie and the o(x, y ) measure in [5], we introduced a knn ie estimator, and we stated in [4], without mathematical proofs, that this estimator is consistent. by definition [12], a statistic whose mathematical expectation is equal to a parameter is called unbiased estimator of that parameter. otherwise, the statistic is said to be biased. a statistical estimator that converges asymptotically to a parameter is called consistent estimator of that parameter. in the following, we will review this result, introducing also the basic mathematical notations used in section 3. the ie is the average of f(x), therefore we have to estimate f(x). the n observations from our samples have the same probability 1 n . a convenient estimator of the ie is: ˆie (n) k (x) = 1 n n∑ i=1 f̂(xi). (3) the mass probability at value xi determined by its k nearest surrounding points and standardized by the sphere volume vk(xi) these k nearest observations occupy measures the density of probability at value xi. the k-th nearest neighbor estimate is defined by [4], [21]: f̂(xi) = k n vk(xi) , i = 1...n (4) where vk(xi) is the volume of the d-dimensional sphere centered in xi, with the radius ri,k,n measuring the euclidean distance from xi to the k-th nearest data point. the estimate (4) can be re-written as: f̂(xi) = k nvdr d i,k,n , i = 1...n. (5) given vk(xi) = vdrdi,k,n where vd is the radius of the unit sphere in d-dimensions, that is v1 = 2, v2 = π, v3 = 4π3 , etc. by substituting f̂(xi) in (3), we finally obtain the following ie approximation: ˆie (n) k (f) = 1 n n∑ i=1 k nvdr d i,k,n . (6) in [6], we showed how to estimate the o(x,y ) dependence measure from an available sample set of discrete or continuous variables and we experimentally compared the results of the knn estimator with the naïve histogram estimator. asymptotically unbiased estimation of a nonsymmetric dependence measure applied to sensor data analytics and financial time series 479 3 asymptotic behavior of the ie approximator consistency of an estimator means that as the sample size gets large the estimate gets closer and closer to the true value of the parameter. unbiasedness is a statement about the expected value of the sampling distribution of the estimator. the ideal situation, of course, is to have an unbiased consistent estimator. this may be very difficult to achieve. yet unbiasedness is not essential, and just a little bias is permitted as long as the estimator is consistent. therefore, an asymptotically unbiased consistent estimator may be acceptable. in [4], we stated (without proofs) that the ie approximator is asymptotically unbiased and consistent. in the this section, we provide the formal proofs for this statement. lemma 1. let us consider the identically distributed random variables t (n)1 , t (n) 2 , ..., t (n) n defined by t (n) i = k nvdr d i,k,n . (7) for a given x, the random variable tx has the following probability density function: htx(y) = f(x) [ k y f(x) ]k y2(k − 1)! e −k y f(x) , 0 < y < ∞. proof: see appendix 6. 2 theorem 2 (asymptotically unbiasedness). the information energy estimator ˆie (n) k (f) is asymptotically unbiased. proof: we will find the asymptotic value of the average: lim n→∞ e [ ˆie (n) k (f) ] . from lemma 1, for a given x, the random variable tx has the pdf: htx(y) = f(x) [ k y f(x) ]k y2(k − 1)! e −k y f(x) , 0 < y < ∞. the conditional expectation of t (n)1 |x1 = x, given the fixed center x1 = x is: limn→∞e [ t (n) 1 |x1 = x ] = ∫ ∞ 0 yh(y)dy = ∫ ∞ o f(x) (kf(x)) k (k − 1)! ·y−1−ke− kf(x) y dy = f(x) (kf(x)) k (k − 1)! ∫ ∞ 0 y−1−ke −kf(x) y dy. we make the following change of variable: u = kf(x) y , with y ∈ (0, +∞). the corresponding u ∈ (+∞, 0) is decreasing. we have: du = − 1 y2 kf(x)dy 480 a. caţaron, r. andonie, y. chueh and y−1−k = ( u kf(x) )k+1 . we obtain:∫ ∞ 0 yh(y)dy = f(x) (kf(x)) k (k − 1)! ∫ 0 ∞ −uk+1e−udu · 1 (kf(x))k+1 ( kf(x) u )2 · 1 kf(x) . the negative sign before uk+1 allows us to change the limits of the integral: ∫ ∞ 0 yh(y)dy = f(x) (kf(x)) k (k − 1)! ∫ ∞ 0 uk−1e−udu · 1 (kf(x))k = f(x) (k − 1)! ∫ ∞ 0 uk−1e−udu = f(x) (k − 1)! γ(k) = f(x). we take the complete expectation of the above, then we have: limn→∞e [ e [ t (n) 1 |x1 = x ]] = limn→∞e [ t (n) 1 ] = e[f(x)]. let us define ĝ(n)(f) = 1 n ∑n i=1 t (n) i , the unbiased estimator of f(x) (like in [22]). we obtain: e [ ĝ(n)(f) ] = 1 n n∑ i=1 e [ t (n) i ] = 1 n [e [f(x)] + · · · + e [f(x)]] = e [f(x)] . limn→∞e [ ĝ(n)(f) ] = e [f(x)] = ∫ f(x) ·f(x)dx = ie(f). this proves that our ie estimator is asymptotically unbiased. 2 next, we aim to prove that it has asymptotic zero variance. lemma 3. given the identically distributed random variables t (n)1 , t (n) 2 , ..., t (n) n defined by (7), we have: lim n→∞ 1 n e [ t (n)2 1 ] − lim n→∞ 1 n e [ t (n) 1 ]2 = 0. proof: see appendix 6. 2 lemma 4. given the identically distributed random variables t (n)1 , t (n) 2 , ..., t (n) n defined by (7), the following relation is true: lim n→∞ n(n− 1) n2 ( e [ t (n) 1 t (n) 2 ] −e [ t (n) 1 ] e [ t (n) 2 ]) = 0. proof: see appendix 6. 2 theorem 5 (asymptotic zero variance). lim n→∞ v ar [ ĝ (n) k (f) ] = 0. asymptotically unbiased estimation of a nonsymmetric dependence measure applied to sensor data analytics and financial time series 481 proof: t (n)1 ,t (n) 2 , . . . ,t (n) n are identically distributed. v ar [ ĝ (n) k (f) ] = 1 n2   n∑ i=1 v ar [ t (n) i ] + ∑ i 6=j i<j 2cov ( t (n) i ,t (n) j ) = 1 n v ar [ t (n) 1 + n(n− 1) n2 ] cov ( t (n) 1 ,t (n) 2 ) . lim n→∞ v ar [ ĝ (n) k (f) ] = lim n→∞ 1 n e [ t (n)2 1 ] − lim n→∞ 1 n e [ t (n) 1 ]2 + lim n→∞ n(n− 1) n2 ( e [ t (n) 1 t (n) 2 ] −e [ t (n) 1 ] e [ t (n) 2 ]) . from lemma 3 and lemma 4, we obtain: lim n→∞ v ar [ ĝ (n) k (f) ] = 0. 2 our main result is synthesized in: theorem 6 (consistency). the information energy estimator ˆie (n) k (f) is consistent. proof: this results from theorems 2 and 5, and the following property: an asymptotically unbiased estimator with asymptotic zero variance is consistent [17]. 2 4 the knn o(x, y ) estimator our goal is to infer o(x,y ) from the random samples x1, x2, ..., xn. we will use the results from section 2.2 to deduct the knn estimator of o(x,y ). first, we substitute îe (n) k (x) from eq. (6) in eq. (2): ô(x,y ) = îe (n) k (x|y ) − îe (n) k (x) (8) where: îe (n) k (x|y ) = m∑ j=1 f̂(yj)îe (n) k (x|yj) (9) and îe (n) k (x) = 1 n n∑ i=1 k1 nvd1(x)r d1 i , (10) is an adaptation of eq. (6). 482 a. caţaron, r. andonie, y. chueh we can write: îe (n) k (x|yj) = 1 n n∑ i=1 f̂(xi|yj) = 1 n n∑ i=1 f̂(xi,yj) f̂(yj) = 1 n n∑ i=1 f̂(xi,yj) f̂(yj) , (11) where f̂(xi|yj) = f̂(xi,yj) f̂(yj) . (12) from (9) and (11) we can write: îe (n) k (x|y ) = m∑ j=1 f̂(yj) 1 n n∑ i=1 f̂(xi,yj) f̂(yj) = 1 n m∑ j=1 n∑ i=1 f̂(xi,yj). the estimate of the joint probability density can be written as: f̂(xi,yj) = k2 pvd2(x,y )r d2 i,j , where p is the number of pairs (xi,yj), then re-write the eq. (9) as: îe (n) k (x|y ) = k2 npvd2(x,y ) m∑ j=1 n∑ i=1 1 rd2i,j . ri is the euclidean distance between the reference point xi and its kth1 nearest neighbor, when the points are drawn from the one-dimensional probability distribution f(x): ri = ‖xi −xi,k1‖. similarly, rj is the euclidean distance between the reference point yj and its kth1 nearest neighbor, when the points are drawn from the one-dimensional probability distribution f(y ): rj = ‖yj − yj,k1‖. then, rij is the euclidean distance between the reference point (xi,yj) and its k th 2 nearest neighbor, when the points are drawn from the joint probability distribution f(x,y ): rij = √ (xij −xij,k2 )2 + (yij −yij,k2 )2. the estimate of o(x,y ) is: ô(x,y ) = k2 npvd2(x,y ) m∑ j=1 n∑ i=1 1 rd2i,j − k1 n2vd1(x) n∑ i=1 1 rd1i . (13) although we do not have a general method to set the nearest neighbor parameter, silverman [21] suggested that k should be proportional to n4/(d+4). (14) in our case, the optimal values of k1 and k2 may not be equal, because the these two parameters refer to different samples. the r code for computing the estimate of o(x,y ) is publicly available on github1. 5 applications there are tremendous opportunities for applications using o(x,y ) estimation to analyze potential dependencies between data represented by samples. we illustrate with an example with numerical implementations and two applications using real-world data, all extracted from our previous work and summarized here. 1https://github.com/cataron/information-energy asymptotically unbiased estimation of a nonsymmetric dependence measure applied to sensor data analytics and financial time series 483 figure 1: the knn estimated values, where k was determined with formula (14). the theoretical values ie(x) = 1.12, ie(x|y ) = 1.1351, o(x,y ) = 0.0151, ie(y ) = 1.128, ie(y |x) = 1.14787, o(y,x) = 0.01987 are marked with dashed lines. the estimates converge towards their theoretical values for an increasing number of samples, under various k values (the horizontal axis): 2, 4, 6, 8, 10. 5.1 a specified joint probability distribution let us illustrate with the following joint probability density function from [6]: fx,y (x,y) = 6 5 ( x + y2 ) ,x ∈ [0, 1],y ∈ [0, 1], (15) which has the marginal probability density functions fx(x) = ∫ 1 0 6 5 ( x + y2 ) dy = 6 5 ( x + 1 3 ) (16) and fy (y) = ∫ 1 0 6 5 ( x + y2 ) dx = 6 5 ( 1 2 + y2 ) . (17) the conditional probability density function is: fx|y (x|y) = fx,y (x,y) fy (y) = x + y2 1 2 + y2 , and the theoretical value of o(x,y ) is: o(x,y ) = ie(x|y ) − ie(x) ie(x) = ∫ 1 0 f2(x)dx = 1.12 ie(x|y ) = ∫ 1 0 ∫ 1 0 fx,y (x,y)fx|y (x|y)dxdy = 1.1351 and o(x,y ) = 1.1351 − 1.12 = 0.0151. 484 a. caţaron, r. andonie, y. chueh similarly, we obtain ie(y ) = 1.128, ie(y |x) = 1.14787, and o(y,x) = 0.01987. we observe that o(x,y ) < o(y,x). the theoretical values of ie(x), ie(y ), ie(x|y ), ie(y |x), o(x,y ), and (y,x) are represented in fig. 1 with dashed lines. their knn estimates are represented with continuous lines, for an increasing number of data samples. 5.2 sensor data when studying the interaction between two random variable, why is a unilateral dependence measure useful, and why do we not simply use the well-known mi? let us consider two sets of experiments, characterized respectively by random variables x and y . the experiments run simultaneously and interact probabilistically. our question is which variable influences probabilistically more the other one. thus, x can be viewed as x|y and y can be viewed as y |x. while the correlation quantifies linear dependence and mi describes the degree of interdependence between two random variables, o(x,y ) helps us understand which random variable, x or y , has a higher influence on the other one. if both x and x|y are available, we can estimate ie(x|y ) as well as o(x,y ), and similarly for y and y |x. when the data is acquired from real world experiments or from simulators, we need to store series of values for x and y featuring the two phenomena running independently, as well as measurements of values generated by the two phenomena running simultaneously, in order to capture x|y and y |x. moreover, the precision of the ie(x|y ) and o(x,y ) estimators increase when more values of x|y are available for each value of y . in our application, which can be found in [6], we study the evolution of the temperature measured at the surface of two packages, x and y , introduced in a refrigerated room. when one package at the outside temperature is placed in the room, it is getting cold under the influence of the air conditioning devices. the situation changes when a second package is placed in the room because it will change the ambient conditions and the temperature may decrease slower. emulating the real world circumstances, we are able to measure the temperatures with various experimental setups. when we only have one package in the room, we measure the time series of temperatures as samples of an unconditional random variables. when we have both packages in the room, we measure simultaneously the time series of temperatures as conditional random values. according to these experiments, we can assess if package x has a stronger influence on package y , or vice versa. this might lead to the decision to remove the sensor which is more influenced by the other. 5.3 financial time series studying the relationship between stocks is a very interesting business case for finance. this is just an example which can be extended to all kind of financial parameters of companies. in [7] we have presented an application using the public available data offered by kodak and apple for the period 1999–2014. we analyzed the unilateral dependence between the kodak and apple series in order to understand how they influence one to each other. we aim to describe in the following our general approach for detecting unilateral dependencies between time series variables, without specifically referring to the kodak-apple case. the information energy and the unilateral dependence measure both are the result of two random variables. the estimation becomes more precise when the number of observations is high. when we analyze time series, we consider the series as a set of n observations xi, i = 1...n of the random variable x. our goal is to find synchronous relations between two time series which have been considered as two samples of two random variables x and y . a point xi from the first series was paired with a point yi from the second one producing a joint observation, with asymptotically unbiased estimation of a nonsymmetric dependence measure applied to sensor data analytics and financial time series 485 i = 1, . . . ,n. therefore, from two time series with n points each we obtain n pairs (xi,yi). the unilateral dependence o(x,y ) between x and y can be estimated with formula (13) based on x and (x,y ), while the unilateral dependence o(y,x) can be estimated with the same formula based on y and (y,x). we note that the set of pairs (x,y ) is equal with the set of pairs (y,x) since for each observation xi exactly one observation yi is used. it is interesting to study the evolution of the unilateral dependence in time. the values o(x,y ) and o(y,x) can be estimated at the moment of time tm using the history of the m observations of xi and yi between the initial moments t0 and tm. a new set of two observations xm+1 and ym+1 allow us to re-estimate o(x,y ) and o(y,x). our model captures not only the unilateral dependencies between two simultaneous time series, but also how these dependencies evolve in time, which can be valuable and insightful for forecast or investment in financial research or applications. 6 conclusion we introduced a non-parametric asymptotically unbiased and consistent estimator of the ie, and the unilateral dependency measure o(x,y ) between random variables x and y . we estimated o(x,y ) from available data samples using the knn technique. in our applications, we showed how the nonsymmetric dependence measure can provide information that cannot be expressed by a symmetric measure. the examples illustrated in this paper are all one-dimensional random variables, for the purpose of simplicity. the knn estimation can be also applied on multidimensional variables. for instance, the o(x,y ) measure can be used to analyze the unilateral dependence between bi-variate time-series data acquired from various fields. because of the mathematical properties proved here, the o(x,y ) estimator works the best for large data sets, so it is suitable for big data analytics. our method can be applied to both continuous and discrete variable spaces, meaning that we can use it both in classification and regression problems. bibliography [1] andonie r., caţaron a. 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(2009), estimation of mutual information: a survey, proceedings of the 4th international conference on rough sets and knowledge technology, springerverlag, berlin, heidelberg, 389–396, 2009. asymptotically unbiased estimation of a nonsymmetric dependence measure applied to sensor data analytics and financial time series 487 [24] wang q., kulkarni s. r., verdu s. (2006), a nearest-neighbor approach to estimating divergence between continuous random vectors, proc. of the ieee international symposium on information theory, seattle, wa, 242-246, 2006. appendix proof of lemma 1 the random variables t (n)1 , t (n) 2 , ..., t (n) n are identically distributed, therefore: e [ ˆie (n) k (f) ] = e [ t (n) 1 ] . let us denote ρr,n = ( k nvdr )1 d , with ρr,n → 0 when n →∞. for a real number r we have: p [ t (n) 1 > r|x1 = x ] = p [ρr,n > r1,k,n|x1 = x] , (18) because: t (n) 1 > r ⇔ k nvdr d 1,k,n > r ⇒ k nvdr > rd1,k,n ⇒ ( k nvdr )1 d > r1,k,n we write the probability p [ t (n) 1 > r|x1 = x ] as a binomial distribution, and approximate it with the poisson probability function. the binomial distribution is given by f(k; n,p) = ( n k ) pk(1 −p)n−k, and the binomial distribution of the cumulative distribution function is p(x ≤ x) = ∑ xi≤x p(x = xi) = ∑ xi≤x f(x). the probability expressed by the poisson formula is p(x) ≈ e −np(np)x x! , where p is the successful probability out of n samples. using the poisson approximation for the binomial distribution by letting the sample size n →∞ we get: p[ρr,n > r1,k,n|x1 = x] = 1 −p [r1,k,n ≥ ρr,n] = 1 − k∑ i=1 ( n− 1 i )[ p(sρr,n)(1 −p(sρr,n)) ]n−i−1 = p [tx > r]. we note that: p(sρr,n) = ∫ sρr,n f(t)dt, ρr,n = ( k nvdr )1 d , vρ = vdρ d = vd k nvdr = k nr ⇒ n = k rvρ . 488 a. caţaron, r. andonie, y. chueh let n →∞, then: lim n→∞ np(sρr,n) = k r lim n→∞ p(sρ) vρ = k r f(x). thus, from the poisson approximation we obtain: p [tx > r] = 1 − k∑ i=0 ( k r f(x) )i i! e− k r f(x). to calculate the pdf for random variable tx, we differentiate its cumulative density function with respect to r: d dr p [tx ≤ r] = d dr [1 −p [tx > r]] = d dr [ 1 − ( 1 − k∑ i=0 [ k r f(x) ]i i! e− k r f(x) )] = d dr k∑ i=0 [ k r f(x) ]i i! e− k r f(x) = d dr [ e− k r f(x) + ∑ i=1 k [ k r f(x) ]i i! e− k r f(x) ] = kf(x) r2 e− k r f(x) + k∑ i=1 [ k r f(x) ]i−1 (i− 1)! ( − kf(x) r2 ) e− k r f(x) + k∑ i=1 [ k r f(x) ]i i! · kf(x) r2 e− k r f(x) = kf(x) r2 e− k r f(x) − k∑ i=1 kf(x) r2 · [ k r f(x) ]i−1 (i− 1)! e− k r f(x) + k∑ i=1 kf(x) r2 · [ k r f(x) ]i i! e− k r f(x) = kf(x) r2 e− k r f(x) − kf(x) r2 e− k r f(x) + kf(x) r2 · [ k r f(x) ]k k! e− k r f(x) = e− k r f(x)f(x) [ k r f(x) ]k r2(k − 1)! . then, for a given x, the random variable tx has the pdf: htx(y) = f(x) [ k y f(x) ]k y2(k − 1)! e −k y f(x) , 0 < y < ∞. proof of lemma 3 we have: lim n→∞ e [ t (n)2 1 ∣∣∣x1 = x] = ∫ ∞ 0 y2h(y)dy = ∫ ∞ 0 f(x) (kf(x)) k (k − 1)! y−ke −kf(x) y dy = f(x) (kf(x)) k (k − 1)! ∫ ∞ 0 y−ke −kf(x) y dy. we make the following substitution: u = kf(x) y , with y ∈ (0, +∞), u ∈ (+∞, 0) decreasing, and y = kf(x) u . then du = − 1 y2 kf(x)dy and y−k = ( u kf(x) )k . asymptotically unbiased estimation of a nonsymmetric dependence measure applied to sensor data analytics and financial time series 489 we obtain: lim n→∞ e [( t (n) 1 )2 |x1 = x ] = f(x) (kf(x)) k (k − 1)! ∫ 0 ∞ − ( u kf(x) )k e −kf(x) y y2 1 kf(x) du = f(x) (kf(x)) k (k − 1)! ∫ 0 ∞ −uk−2e−u ( 1 kf(x) )k−1 du = f(x) (kf(x)) k (k − 1)! ∫ ∞ 0 −uk−2e−udu = kf(x)2 (k − 1)! γ(k − 1) = k k − 1 f(x)2. lim n→∞ e [( t (n) 1 )2] = lim n→∞ e [ e [( t (n) 1 )2 |x1 = x ]] = lim n→∞ e [ k k − 1 f(x)2 ] = k k − 1 e [ f(x)2 ] = k k − 1 [ v ar [f(x)] + (e [f(x)]) 2 ] = k k − 1 [ v ar [f(x)] + ie(f)2 ] . lim n→∞ v ar [ t (n) 1 ] = lim n→∞ e [( t (n) 1 )2] − lim n→∞ ( e [ t (n) 1 ])2 = k k − 1 v ar [f(x)] + k k − 1 ie(f)2 − ie(f)2 = k k − 1 v ar [f(x)] + 1 k − 1 ie(f)2. lim n→∞ 1 n v ar [ t (n) 1 ] = lim n→∞ 1 n [ k k − 1 v ar [f(x)] + 1 k − 1 ie(f)2 ] = 0. proof of lemma 4 the relation above can be proved if the limiting covariance between t (n)1 and t (n) 2 is zero. this can be achieved by showing limiting probability distributions of t (n)1 and t (n) 2 are independent. 490 a. caţaron, r. andonie, y. chueh given the real number r, s > 0: p [ t (n) 1 > r,t (n) 1 > s|x1 = x,x2 = y ] = p [r1,k,n < ρr,n,r2,k,n < ρs,n|x1 = x,x2 = y] = p [ at least k of x3,x4, . . . ,xn ∈ sρr,n;x and at least k of x3,x4, . . . ,xn ∈ sρs,n;y ] . note: for x 6= y, ρr,n → 0 and ρs,n → 0 as n → +∞. for large enough n, we have sρr,n;x ∩sρs,n;y = φ. we obtain: p [ t (n) 1 > r,t (n) 1 > s|x1 = x,x2 = y ] = ∑ k≤i,j i+j≤n−2 (n− 2)! i!j!(n− 2 − i− j)! [ p(sρr,n;x) ]i [ p(sρs,n;x) ]j · [ 1 −p(sρr,n;x) −p(sρs,n;y ) ]n−2−i−j where p(sρr,n;x) = ∫ sρr,n;x f(t)dt. recall: lim n→∞ np(sρr,n;x) = k r f(x) = lim n→∞ k r · p(sρr,n;x) vρ . then, we have: p [ t (n) 1 > r,t (n) 1 > s|x1 = x,x2 = y ] = ∑ k≤i,j i+j≤n−2 (n− 2)! i!j!(n− 2 − i− j)! ( k nr )i ( k ns )j · ( p(sρr,n;x) vρr,n )i ( p(sρs,n;x) vρs,n )j ·[ 1 − 1 n ( k r · p(sρr,n;x) vρr,n + k s p(sρs,n;x) vρs,n )]n−2−i−j since: (n− 2)! (n− 2 − i− j)!ninj → 1 as n →∞[ 1 − 1 n ( k r · p(sρr ) vρr + k s p(sρs) vρs )]n−2−i−j → e−( k r f(x)+ k s f(y)) as n →∞ lim n→∞ ( 1 − 1 n )n = e−1. asymptotically unbiased estimation of a nonsymmetric dependence measure applied to sensor data analytics and financial time series 491 we obtain: lim n→∞ p [ t (n) 1 > r,t (n) 2 > s|x1 = x,x2 = y ] = n−2−k∑ i=k n−2−k∑ j=k ki i!ri [f(x)] i · kj j!sj [f(y)] j ·e−( k r f(x)+ k s f(y)) = [ n−2−k∑ i=k ki i!ri [f(x)] i e− k r f(x) ] ·  n−2−k∑ j=k kj j!sj [f(y)] j e− k s f(y)   = p [tx > r] ·p [ty > s], where for given z, the random variable tz has the pdf: htx = f(z) ( k y f(z) )k (k − 1)!y2 e −kf(z) y = kkf(z)k+1 (k − 1)!yk+2 e −kf(z) y . therefore, by the theorem of independent variables: lim n→∞ e[t (n) 1 t (n) 2 ] = limn→∞ [ e[t (n) 1 ] ·e[t (n) 2 ] ] ⇒ lim n→∞ cov[t (n) 1 ,t (n) 2 ] = 0. international journal of computers communications & control issn 1841-9836, 12(3), 330-346, june 2017. walking motion generation and neuro-fuzzy control with push recovery for humanoid robot p.e. mendez-monroy paul erick mendez-monroy iimas universidad nacional autonoma de mexico parque cientăfico y tecnologico de yucatan km. 5.5 carretera sierra papacal chuburna c.p. 97302 sierra papacal, yucatan erick.mendez@iimas.unam.mx abstract: push recovery is an essential requirement for a humanoid robot with the objective of safely performing tasks within a real dynamic environment. in this environment, the robot is susceptible to external disturbance that in some cases is inevitable, requiring push recovery strategies to avoid possible falls, damage in humans and the environment. in this paper, a novel push recovery approach to counteract disturbance from any direction and any walking phase is developed. it presents a pattern generator with the ability to be modified according to the push recovery strategy. the result is a humanoid robot that can maintain its balance in the presence of strong disturbance taking into account its magnitude and determining the best push recovery strategy. push recovery experiments with different disturbance directions have been performed using a 20 dof darwin-op robot. the adaptability and low computational cost of the whole scheme allows is incorporation into an embedded system. keywords: push recovery, neuro-fuzzy systems, reinforcement learning, biped walking. 1 introduction with the recent growth of humanoid robots performing tasks within the human environment, research has focused on improving their movement and design for stable bipedal walking. where dynamic walking is a general technique with predefined trajectories and feedback control to ensure that the robot is always rotating around a point in a base of support (bos). to achieve dynamic walking the centre of gravity (cog) can be outside of the bos of the robot, but the zero momentum point (zmp) cannot. the zmp is the point where the total angular momentum in the ankle is zero and the cog is the projection of the centre of mass (com) on the ground, inside the bos established by the feet. these concepts can be associated through an inverted pendulum model as the simplest model. but this technique is susceptible to external disturbance and irregularities in the surface, it requires strategies to maximise the balance and minimise the adverse effects of these disturbances. the aim of push recovery is to perform an action that counteracts an external disturbance to prevent the robot from falling. the problem of push recovery has been researched using several approaches. hyon et al. [5] use force-controlled actuators and a complete dynamic model to reject external disturbances. park et al. [10] also use force-controlled actuators with optimisation algorithms for active balancing. nevertheless, the requirements for multi-axis force sensors, an accurate model and intensive computing are their disadvantages. other research considers biomechanical movements using the knowledge of human behaviour in responses of unexpected disturbances with simple movements. [11], [17] [7]. generally, these strategies are classified into three groups: ankle, hip and stepping. copyright © 2006-2017 by ccc publications walking motion generation and neuro-fuzzy control with push recovery for humanoid robot 331 some researchers have presented their work in push recovery when a robot walking in place. pratt et al. [11] introduced the concept of "capture point" to determine where the foot should step after being pushed to return to its original pose. pratt et al. [12] extended the concept to multiple steps. the use of learning strategies to adapt the push recovery has increased in the last years. rebula et al. [14] used capture point learning to improve simple step push recovery. while missura et al. [8] proposed online learning to define the amplitude of the leg depending on the robot posture and the error in the desired step. some researchers have integrated several methods to complement the simple strategies for this level of complexity. stephens [17] combines hip and ankle strategies with the purpose of balance control by defining a larger area return of a push and he established theoretical analytic bounds for both strategies. hyon et al. [5] presented a multi-level posture balance method for humanoid robots and demonstrated push recovery with a biped sarcos pushed from behind. semwal et al. [15] used a hierarchical fuzzy control to decide by several push recovery strategies and used a simulated robot with good performance and low-cost computation but without online learning obtaining the same boundaries for each push recovery strategy as an analytic method. however, few researchers have presented push recovery strategies in the walking phase, in order to continue the walking before a push. komura et al. [6] proposed a feedback controller for the biped walking and applied a hip strategy as push recovery in a simulated robot. wieber et al. [19] used a predictive control model to minimise the jerk and zmp error, this improved the robustness to disturbances in walking. the objective of this work is to design a practical strategy according to external disturbances using a neuro-fuzzy system to define the best push recovery behaviour to counteract the disturbance. the novel push recovery approach can maintain the robot walking, counteracting pushes from any direction and in any phase with a certain magnitude of disturbance. this scheme has the advantages of using a simple model and a controller of low computational cost. furthermore, the design is applicable to generic robots with position controlled actuators, inertial measurement unit and joint position sensors. the remainder of the paper proceeds as follows. section 2.1 reviews three biomechanical push recovery controllers and their implementation on position-controlled actuators. section 2.1 explains the details of the neuro-fuzzy system, structure, design and estimation. section 2.1 describes the reinforcement learning process with a modified levengerg-marquardt algorithm. section 2.1 explains the walking pattern. section 6 shows the experimental results using the neuro-fuzzy system with reinforcement learning. the paper is finished with some concluding remarks and future work. 2 biomechanical push recovery biomechanical studies of human walking have shown that humans perform three different movement patterns as recovery strategies (ankle, hip, step) to reject a sudden external disturbance. the ankle strategy modulates torque at the ankle joint to keep humans upright, the hip strategy converts unpredicted linear momentum into angular motion at the torso, and the stepping strategy moves the swing foot in the direction of the disturbance. recent work has focused on using such biomechanical strategies [12] [17]. the next review of these biomechanical strategies assumes simplified physical models of the robot and explains how they can be implemented on real humanoid robots with position controlled actuators. the ankle strategy maintains the cog in the bos by applying torque to the ankle joints. for position controlled actuators, the torque at the ankle can be modified either by controlling the zmp or modulating the target angle of the ankle servo. in this approach, the latter is used 332 p.e. mendez-monroy as the strategy of push recovery in the ankle. δzmp = −kaxa,swing (1) where ka is the ankle feedback gain and the xa,swing is the current position of the swinging ankle. the hip strategy applies angular acceleration in the torso to generate a reaction force that returns the cog to the bos from a elapse time {t0,t1}, decelerate in time {t1,t2} and revert to the initial position slowly {t2,t3}, this control called "bang-bang" can be represented for position controlled actuators as δθt = { kh 0 ≤ t < t2 kh t3−t t3−t2 t2 ≤ t < t3 (2) where δθt is the torso position bias, t3 the time to complete the control, and kh is the hip feedback constant. for even larger disturbances, no amount of body torquing will result in push recovery. in this case, it is necessary to take a step. the stepping strategy moves the bos by taking a step. this strategy looks for the point on the ground where the robot can step to bring it to its original speed or even full stop [17]. the new step (walking phase) or modified step (stance phase) is calculated using the relative target foot position xstep [1]. xstep = θ̇x √ zc g = ks (3) the definition of a real analytic boundary to apply each strategy is difficult without the exact knowledge of the current system state as well as the kinematic and mechanic restrictions. in addition, its design becomes impractical if some of the parameters, such as the weight or cog change. instead, the problem is formulated through learning techniques and the parameters are obtained online using past experiences. some learning strategies have been performed in other robots [3], [18], where the aim is to learn a policy that maps robot states and establish appropriate actions. 3 neuro-fuzzy system for push recovery the push recovery is one of the most complex tasks for humanoid robots and it requires an adaptive intelligent controller, the push recovery method is achieved using simulation, the manipulation of the real system and with expert knowledge on intuitive biped push recovery. thus, a neuro-fuzzy system is proposed to select the appropriate control parameters for the three strategies mentioned above. the selection uses the current walking state and the torso position. the fuzzy controller is designed using system constraints (eg. maximum torque for the actuators, maximum angles for the joints, maximum step size, etc.), expert knowledge and is updated using new reinforcement training with a low computational cost. figure 1 shows the architecture of the neuro-fuzzy system with an "actor-critic" reinforcement learning. the strategy is divided in the sagittal and the coronal planes but, only the sagittal plane will be described as the design for the coronal plane is similar. the controller determines a proper action of push recovery in the presence of an external disturbance using the current states {θt, θ̇t} and the internal reinforcement signal ˆ̂r from the predictor. the scheme separates the push recovery system from the motion generator to simplify the design. thus, the motion generator performs its normal movements when there is no external walking motion generation and neuro-fuzzy control with push recovery for humanoid robot 333 figure 1: the neuro-fuzzy reinforcement structure disturbances or unbalance. as soon as an external disturbance or unbalance occurs the neurofuzzy controller will establish the best learning strategy to counteract its effect. the neuro-fuzzy controller uses the trunk position θt and its angular velocity θ̇t to define the feedback gains for the ankle, hip and stepping strategies. it is initialised offline using simulation, expert knowledge and the robot constraints (limit of the actuator torques, maximum step size, etc.) and it learns using reinforcement learning with low computational cost. the neuro-fuzzy predictor has the same structure as the controller but with the internal reinforcement signal as the output. its objective is to evaluate the performance of the whole neuro-fuzzy system using the external reinforcement signal (reward) to update the parameters of the controller and predictor. learning is performed by a modified levenberg-marquardt algorithm. in the state estimation, the trunk position and angular velocity in the roll and pitch are estimated based on the accelerometer and gyroscope measurements and mixing with the measured joint angles (q) to use in the forward kinematic equations. a walk pattern generator is presented to modify the walking trajectory according to the adopted push recovery strategy. 3.1 neuro-fuzzy controller the neuro-fuzzy controller ("actor") can determine a better action for the next time instant based on the current system state and the internal reinforcement signal. the internal reinforcement signal from the predictor enables both the controller and the predictor to learn without waiting for external reinforcement, which may only be available at a time long after a sequence of actions has occurred. this scheme can speed up the learning of the complete system. the fuzzy structure comprises five principal components: inputs, antecedent labels, rules, normalisation and consequent labels. moreover, at the computational level, a fuzzy system can be seen as a feedforward neural network with five layers. thus, the fuzzy structure is defined in five layers with node’s basic functions for an online training. each layer has a input vector {uk1...p}, a parameter vector {w k 1...p} and a output vector {o k 1...q} defining a activation function. uki = f ( uk1,u k 2, · · · ,u k p,w k 1,w k 2, · · · ,w k p ) (4) okj = aj(u k i ) where a(·) is the activation function (5) 334 p.e. mendez-monroy where k = {1, ..., 5} is the kth layer, with p inputs and parameters for the jth output of the layer. the next paragraph describes the functions of the nodes in each of the five layers of the proposed neuro-fuzzy controller with x = {x1, ...,xin} inputs, y = {y1, ...,yout} outputs with r defined fuzzy rules like: rj : if x1 is µ1,j and ... and xin is µin,j then y1 is ν1,j and ... and yout is νout,j (6) where µij and νlj are fuzzy sets, the indexes i = {1, ..., in} inputs, l = {1, ...,out} outputs and j = {1, ...,r} fuzzy rules. layer 1 (inputs): the nodes in this layer transmit the inputs directly to the next layer (the vector parameters has unity values w1i = 1 ∀i) eq.(7). layer 2 (antecedents): each single node to perform a membership function. the gaussian membership function is used in this work eq.(8). where m2ij and σ 2 ij correspond to the centre and the width of the gaussian function of the jth rule with the ith input variable. layer 3 (rules): implements the conjunction of all or some antecedent conditions in the jth rule. hence, the rule nodes should perform the fuzzy "and" operation, in this case, the product function is used, in fuzzy theory, it is called fire strength eq.(9). layer 4 (normalised): the number of nodes in this layer is equal to that of the rules layer. the effect of the layer is to perform a normalisation on each rule. it is called normalised fire strength for the jth rule eq.(10). layer 5 (consequent): this layer will have as many nodes as there are output variables. each output node combines the normalised fire strength with the fuzzy action and the defuzzification is computed with the centre of area method eq.(11) [4]. f1i = u 1 i and a 1 i = f 1 i i = 1, 2, ..., in (7) f2ij = µij ( a1i ; m 2 ij, c 2 ij ) = − ( a1i −m 2 ij )2 ( σ2ij )2 and a2ij = exp(f2ij) (8) f3j = 2∏ i=1 a2ij and a 3 j = f 3 j w 3 ij = 1 ∀i (9) f4j = a3j∑r j=1 a 3 j and a4j = f 4 j where r∑ l=1 a4j = 1 (10) f5l = ∑r j=1(m 5 ljσ 5 lj)a 4 j∑r j=1 σ 5 lja 4 j and a5l = f 5 l (11) the complete fuzzy system f with fuzzy rules (7) is: f = {fl, l = 1, ...,out}, fl = ∑r j=1 ν − lj ∏in i=1 µij(xi)∑r j=1 ∏in i=1 µij(xi) , ν−lj = ∑r j=1(m 5 ljσ 5 lj)a 4 j∑r j=1 σ 5 lja 4 j (12) where ν−lj is the centre of the lth output fuzzy set and the jth fuzzy rule. thus, f is the fuzzy control action with the parameter vector pc = {m2ij, σ 2 ij, m 5 lj, σ 5 lj}. the parameter vector pc = {m2ij, σ 2 ij, m 5 lj, σ 5 lj} of the all weights in the controller is updated by minimising the internal reinforcement signal r̂, such that, the system ends up with a good recovery strategy and avoids failure. the learning rule used to adjust the parameter vector is walking motion generation and neuro-fuzzy control with push recovery for humanoid robot 335 a gradient-based adaption δpc and levenberg marquardt with the objective function ec to be minimised in the controller. δpc = ηc ∂ec ∂pc = ηc ∂ec ∂v ∂v ∂f ∂f ∂pc , ec(t) = 1 2 v2(t) (13) where ηc is the learning rate factor and v is the prediction of the external reinforcement signal. 3.2 neuro-fuzzy predictor to update a neuro-fuzzy system supervised learning [4] is the most common strategy used. these schemes need accurate training data to indicate the correct desired output to compute the training error. but, in the case of biped robots is difficult to get the correct output for each push recovery strategy because is a dynamic system with simplifications and uncertainties in the model and controller. however, most of the real systems can provide a right-wrong binary decision (reinforcement signal) based on the current control, making a reinforcement training a better option to learn. thus, a neuro-fuzzy predictor ("critic") is used to determinate the parameter update using the external reinforcement signal r and to predict an internal reinforcement signal r̂. the predictor structure is the same as the controller but only with the internal reinforcement signal like output. the learning algorithm will be applied to the fuzzy controller and the fuzzy predictor simultaneously and only conducted by an external reinforcement feedback (r) using an orbital energy. r = θ̇2x + w 2θ2x 2 (14) this is the "critic" information available for learning and indicates whether the output is right or wrong. in this paper, r is mapped to a range r = {−1 → failure, 1 → success} to get a continuous reinforcement signal. thus, the predictor output v approximates the discounted total reward-to-go r̃(t) = r(t + 1) + αr(t + 2)... [16]. it is the future accumulative reward-to-go, α is a discount factor for the infinite-horizon problem and r(t + 1) is the external reinforcement. defining the prediction error ep and the objective function to be minimised as ep(t) = αv(t) −v(t− 1) −r(t) ep(t) = 1 2 e2p(t) (15) therefore, the parameter vector pp = {m 2p ij , σ 2p ij , m 5p j , σ 5p j } is updated using a a levenberg marquardt algorithm and gradient-based update rule given by δpp = ηp ∂ep ∂pp = ηp ∂ep ∂ec ∂ec ∂v ∂v ∂pp (16) 3.3 state estimation state estimation uses the joint angles measured by the encoders, the inertial acceleration and angular velocity of the trunk measured by the imu and a forward kinematic model to reconstruct the whole-body pose of the robot. with the reconstruction of the whore-body pose is obtained the current com and which leg is the standing in. by fusing the gyroscope and accelerometer data is estimated the trunk position. the trunk position θ̂t = (θtx,θty) is expressed using the inertial acceleration vector a = (ax,ay) ∈ [−1, 1] 336 p.e. mendez-monroy measured by the accelerometer, first, a raw angle estimate is obtained. then, it is mixed with the angular velocity in the n iteration of the control loop (eq. 19). rlθ̇tx = arcsin(ax) (17) θ̇ty = arcsin(ay) (18) θn = (1 −κ)(θn−1 + ρ( ˆ̇ θn − bn)) + κθ̂n (19) where ˆ̇θn is the raw angular velocity, κ is a blending factor, ρ is the control time, and bn is the gyro drift. there are other more advanced options for mixing the gyroscope and accelerometer measurements [2]. on the other hand, the measurements of the joints (q) obtained through the encoders of each motor are applied to a direct kinetic algorithm to get the current pose. this kinematic model is rotated around the centre of the supporting foot such that the trunk position equals the roll and pitch angles θq = (θtx,θty) obtained in equation (19). thus, the difference between the trunk angles achieved by the imu and the joint angles is used by the neuro-fuzzy controller. ∆θ = θt −θq θ̇ = ˆ̇ θ (20) 4 reinforcement levenberg-marquardt learning as mentioned earlier, a neuro-fuzzy system is used in both the controller and the predictor where their parameters are updated using reinforcement learning. according to eq.(13) and eq.(15), it is possible use a modification of the levenberg-marquardt algorithm [13] to speed up the update to the neuro-fuzzy system. the parameters p = {pc,pp} are updated using normalisation in both networks to confine the parameter values into some appropriate range (21). p(t + 1) = p(t) + δp(t) ‖p(t) + δp(t)‖1 with δp(t) = −(h(p))−1jt (p)e(p) (21) where approximation of the hessian matrix h and the jacobian matrix is calculated using the backpropagation algorithm. h(p) = jt (p)j(p) + µi with j = [ ∂e ∂p1 , ∂e ∂p2 , ..., ∂e ∂pn ] (22) where p0 are the initial parameter values for the controller and predictor, ε1, ε2 and ε3 are specific stop criteria in the lm algorithm, kmax is the maximum iteration number and ε4 is the controller or predictor error prediction. enew is the value of the objective function evaluated with the new parameters. the jacobian matrix in eq.(22) for the controller is formed by partial derivatives in eq.(13), they are calculated as ∂ec ∂v = v ∂v ∂f ≈ dv df ≈ v(t) −v(t− 1) f(t) −f(t− 1) (23) the term ∂v/∂f in eq.(13) is indirectly dependent on f and complex computation, so that an approximation can be computed by the instantaneous difference ratio. the other term ∂f/∂pc is more tractable. so that, with f known and differentiable, a few applications of the chain rule walking motion generation and neuro-fuzzy control with push recovery for humanoid robot 337 algorithm 1 the modified levenberg-marquardt algorithm data: pc or pp, ec or ep, ec or ep result: pnew k := 0; v := 2; p = po h := jtj; g := jte (k) stop:=(‖ g ‖∞≤ ε1); λ = τ ·max (diag (h)) while (not stop) and (k < kmax) and (e (k) > ε4) do h = −(h + λi)−1 g if (‖ h ‖≤ ε2 ‖ p ‖) then stop=true else pnew= p + h ρ := ‖e(k)‖2−‖enew‖2 ht (µh+g) if ρ > 0 then p = pnew h := jtj; g := jte (k) stop=(‖ g ‖∞≤ ε1) or ( ‖ e (k) ‖2≤ ε3 ) µ = µ ·max ( 1/3, 1 − (2ρ− 1)3 ) ; v = 2 else µ = µ ·v; v = 2v end if end if end while through the five layers of the controller give the following set of learning rules. ∂f ∂m5lj = ∂f ∂a5j ∂a5j ∂f5j ∂f5j ∂m5lj = ( f5l )( 1 )( σ5lja4j∑r j=1 σ 5 lja 4 j ) ∂f ∂σ5lj = ∂f ∂a5j ∂a5j ∂f5j ∂f5j ∂σ5lj = ( f5l )( 1 ) (∑r j=1 σ 5 lja 4 j ) m5lja 4 j − (∑r j=1 m 5 ljσ 5 lja 4 j ) a4j(∑r k=1 σ 5 lja 4 j )   (24) the equations in (24) are the partial derivatives to update the consequent parameter values p5lj = {m 5 lj, σ 5 lj}. the antecedent parameter values p2ij = {m 2 ij, σ 2 ij} are updated when the error is propagated to the preceding layers as ∂f ∂p2ij = ∂f ∂a5j ∂a5j ∂f5j ∂f5j ∂p2ij = (∑r j=1 σ 5 lja 4 j )( m5lja 4 j ) ∂a4j ∂p2ij − (∑r j=1 m 5 ljσ 5 lja 4 j )( σ5lj ) ∂a4j ∂p2ij(∑r k=1 σ 5 lja 4 j )2 ∂a4j ∂p2ij = ∂a4j ∂f4j ∂f4j ∂a3j = ∑r j=1 a 3 j ∂a3j ∂p2ij −a3j ∂a3j ∂p2ij(∑r k=1 a 3 j )2 ∂a3j ∂p2ij = ∂a3j ∂f3j ∂f3j ∂a2ij ∂a2ij ∂f2ij ∂f2ij ∂p2ij = (∏ k 6=i a2kj )( exp(f2ij) ∂f2ij ∂p2ij )(2 (a1i −mij)( σ2ij )2 )(2 ( a1i −mij )2( σ2ij )3 ) (25) 338 p.e. mendez-monroy in the case of the predictor, the partial derivatives are very similar to those of the controller. where the differential is: ∂ep ∂ep = ep ∂ep ∂v = αv (26) the partial derivative ∂v/∂pp is calculated as being equal to ∂f5l /∂pc. 5 walking pattern the walking pattern generates trajectories from the design parameters (mode, sway amplitude, step time, direction) in real time. the parameters are modified at the start of each step. 5.1 zmp and cog trajectories the objective of defining the trajectories of the zero moment point (zmp) and the centre of gravity (cog) is to guarantee the balance of the robot in open loop, keeping these trajectories within the base of support (bos). the trajectory is defined in the sagittal plane, the trajectory in the coronal plane is designed in a similar way. in the case of the zmp, a dynamic zmp can be represented with two equations in axes x-y, using the simple inverted pendulum model (figure 2) and assuming a constant movement zc of the pelvis in the transverse plane, the equation (27) is simplified. in the case of the cog, it is assumed to be identical to the centre of the pelvis. thus, the zmp equation in the sagittal plane is represented as xzmp = ∑n i=1 mi(z̈i + g)xi − ∑n i=1 miẍizi − ∑n i=1 iiyω̈iy∑n i=1 mi(z̈i + g) ≈ xcog − ẍcog l g (27) where mi is the mass, xi, zi is the mass position, ii is the inertia moment and ωi is the angular velocity. the first term (xcog) is the static component and the second term (ẍcog) is the dynamic component of the zmp. figure 2: (inverted pendulum model (left); step parameters in the x direction (right) in this paper a third-order polynomial interpolation is used to obtain the desired zmp trajectory. a polynomial can be used to generate a trajectory in a straightforward and direct form. moreover, the cog trajectory is designed with this trajectory. the polynomials in eq.(28) represent the trajectories of position and velocity of the zmp in the sagittal plane setting a normalised time to start tn = 0 and end tn = 1 of the step [10] without loss of generality. xzmp(tn) = ∑3 t=0 bit i n ẋzmp(tn) = ∑3 t=1 ibit i−1 n b = ω −1x boundary zmp (28) walking motion generation and neuro-fuzzy control with push recovery for humanoid robot 339 the boundary conditions {xzmp(0), ẋzmp(0)} and {xzmp(1), ẋzmp(1)} of the lifting and landing of the zmp in each step define the polynomial coefficients in eq.(28) to get a smooth zmp trajectory:   0 0 0 1 0 0 1 0 1 1 1 1 3 2 1 0     b0 b1 b2 b3   =   xzmp(0) ẋzmp(0) xzmp(1) ẋzmp(1)   ωb = xboundaryzmp (29) by assuming that the cog xcog is located to the centre of the pelvis, it is defined with the same form as (29) but with different coefficients. the boundary conditions for the cog {xcog(0), ẋcog(0), xcog(1), ẋcog(1)} are used to design the polynomial coefficients ai. xcog(tn) = ∑3 t=0 ait i n ẋcog(tn) = ∑3 t=1 iait i−1 n a = ω −1x boundary cog (30) by substituting the xcog and ẋcog in eq.(30) and xzmp in eq.(27), a direct match between zmp and cog is obtained:∑3 t=0 bit i n = ∑3 t=0 ait i n − ( l g )∑3 t=1 iait i−1 n (31) the relationship between the boundary conditions allows defining all the coefficients of the polynomials of the zmp and the cog using (31) in a matrix form b = γa, where γ is the coefficient matrix. x boundary zmp = φγω −1x boundary cog with γ =   1 0 −2 l g 0 0 1 0 −6 l g 0 0 1 0 0 0 0 1   (32) in the sagittal plane, each step can be defined by the amplitude of the feet during the double support phase before and after the single support phase (figure 2). the boundary conditions of cog are defined by with these parameters.[ xcog(0) ẋcog(0) xcog(1) ẋcog(1) ]t = [ −0.5ci αci 0.5ci+1 αci+1 ]t (33) where sx = ci + ci+1 is the length stride in x. to define this trajectory, it is assumed that the position of the pelvis during a step is in the centre of the feet. the boundary conditions are parametrized by a factor α to modify the pelvis velocity. this allows acceleration at the start and end of the step and deceleration when the leg is swinging with a high value of α. by establishing trajectories with a direct relationship, and defining boundary conditions based on the step parameters, it is simpler to define trajectories and modify them in the presence of a disturbance. 5.2 ankle trajectory once the cog and zmp trajectories are defined, it is necessary to identify the trajectories of the ankles (support and swing) according to several movement conditions, which complete the pattern. the swinging foot trajectory (x̂a,swing) is defined by a cycloid function. it has zero velocity at the start and the end, and a maximum velocity in the swinging phase. the time (td) for the 340 p.e. mendez-monroy double support phase (dsp) is considered. the trajectory x̂a,swing is defined from the dsp to the other dsp, between the start time (t1) and the final time (t2) (figure 3 (left)). x̂a,swing(ta) = (sx) ( ta − sin(2πta) 2π ) − ci with ta = t− (t1 + td/2) t2 − t1 − td (34) t 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x̂ a -200 -150 -100 -50 0 50 100 t 0 1 r e la ti v e x p o s it io n s pelvis support swing figure 3: position of the swinging ankle (left); relative trajectories of the feet and the centre of the pelvis (right) the supporting foot trajectory (x̂a,support(t)) must be the opposite action of the centre of the pelvis (xp). these trajectories are generated in the local coordinate frame located in the front of supporting foot, and for its implementation must be transferred to the global coordinates of the robot (figure 3). in fig.(3 (left)) an example of the trajectories of the ankles is shown, it is assumed that the right ankle is the one supporting and the left ankle is swinging. the relative position in the global coordinate frame is: xa,support = −xp(tn) xa.swing = x̂a,swing(ta) −xp(tn) (35) the walking pattern is completely defined for the sagittal plane. where the input parameters (sx, t1, t2) can generate the trajectories of the ankles, knees and hip, the zmp and the cog directly. the tuning parameters (α,td) are determined for the forward, turning and sideways modes. the trajectories for the coronal plane have the same structure but with different condition boundaries. 6 experimental results a darwin-op humanoid robot developed by the romela lab was used to implement the neuro-fuzzy system experimentally. it is 45 cm tall, weighs 2.8kg, and has 20 degrees of freedom. it has a web camera for visual feedback, and 3-axis accelerometer and 3-axis gyroscope for inertial sensing. position-controlled dynamixel servos are used for actuators, which are controlled by a cm730 microcontroller connected by an intel atom-based embedded pc at a control frequency of 100hz. the neuro-fuzzy design begins when the fuzzy rules eq.(6) are defined incorporating information from the darwin robot and expert knowledge. once the structure is defined, it is necessary to reinforce learning. first, learning is applied to a realistic physical simulation to avoid damages in the real system. finally, the neuro-fuzzy system is implemented in the darwin robot and tested with random pushes, its performance and strategies show the advantages of the proposed scheme. walking motion generation and neuro-fuzzy control with push recovery for humanoid robot 341 6.1 design the neuro-fuzzy system for the controller and the predictor are started with the same rules (r = 9) and with two inputs {∆θx, θ̇x}. but, the controller has three outputs {ka,kh,ks} and the predictor has one output {v}. so, with r defined fuzzy rules: rj : if θx is µ1j and θ̇x is µ2j then ka is ν1j and kh is ν2j and ks is ν3j (36) where µij and νlj are fuzzy sets, the indexes i = {1, 2} inputs, l = {1, 2, 3} outputs and j = {1, ...,r} fuzzy rules. {ka,kh,ks} are the recovery variables for the ankle eq. (4), hip eq.(5) and stepping eq.(3) strategies respectively. with the fuzzy system defined, expert knowledge and limitations of the humanoid robot will be employed to set the initial rule base before its learning. three membership functions are used for each input variable, and five membership functions for each output variable. the rule base is defined as follows: table 1: fuzzy rule base for the feedback gains ka ∆θx nt zt pt nv pa spa sna θ̇x zv spa za sna pv spa sna na kh ∆θx nt zt pt nv ph sph snh θ̇x zv sph zh snh pv sph snh nh ks ∆θx nt zt pt nv ns zs sps θ̇x zv sns zs sps pv sns zs ps the membership functions in table 1 are defined using expert knowledge and the minmax ranges for each push recovery strategy. the term sets of the inputs and output variables and their limits are selected as follows: t(θx) = {µ11,µ12,µ13} = {nt,zt,pt} {−45, 45} t(θ̇x) = {µ21,µ22,µ23} = {nv,z,pv} {−120, 120} t(kxa) = {ν11,ν12,ν13,ν14,ν15} = {na,sna,za,spa,pa} {−0.35, 0.35} t(kxh) = {ν21,ν22,ν23,ν24,ν25} = {nh,snh,zh,sph,ph} {−1, 1} t(kxs) = {ν31,ν32,ν33,ν34,ν35} = {ns,sns,zs,sps,ps} {−0.85, 0.85} (37) where the letters for each fuzzy set are: negative (n), small negative (sn), zero (z), small positive (sp), positive (p), trunk (t), velocity (v), ankle (a), hip (h) and step (s). for example, spa meaning small positive ankle. 6.2 learning the neuro-fuzzy system learns from random pushes and during walking and is able to successfully absorb multiple pushes. simulation trials were performed for 3 push types (frontal, behind and lateral) with 6 repetitions in different strength ranges (small, medium, large). reinforcement learning in the simulation was applied to 54 trials resulting in the following fuzzy surface fig.(4), for each output gain k. the ankle gain surface shows that the strategy is applied in all ranges, with a large or medium push. its gain is high, but with a small push it is regulated. the hip strategy is applied when 342 p.e. mendez-monroy 0.05 0.1 -0.15 -0.1 -0.05 0 0.15 1 -0.6 -0.4 0.2 -0.2 0 -1 0.4 0.6 0.8 -0.8 -0.04 -0.02 0 0.02 0.04 0.06 -0.08 -0.06 0.08 ka kh ks figure 4: ankle feedback surface after simulation learning (left); hip feedback surface after simulation learning (middle ); step feedback surface after simulation learning (left) the disturbance is greater, allowing a recovery without having to execute a step. finally, the step strategy surface shows boundaries where it is inevitable to apply the step, but inside the robot kinematic. 6.3 results after designing and training the neuro-fuzzy system in a physically realistic simulation, the system was implemented in the darwin robot. a trial was defined with multiple pushes to evaluate the ability of the neuro-fuzzy system to represent expert knowledge and respecting the limits, as well as assess the reinforcement learning algorithm to update the system. figure (5) shows an experimental test with 6 random pushes in walking forward mode, the first and second plots show θx and θ̇x, respectively. the unknown strength push is frontal (3s,29.9s and 40s) and behind (9s, 15.5s and 21.6s) are marked with vertical dash lines in the plots. the first push is the strongest requiring application of the three strategies as shown in the fig.(6), where the robot steps forward and corrects the trunk position angle to an upright position. the feedback gains are weighted using the effect of the inferior strategy learnt in the simulation trails respecting the limits defined in the design. note the sudden velocity change at the moment of pushes. then, the robot is able to recover its velocity to the normal level and maintain walking. this is because the external reinforcement signal is applied with minimum delay to the neuro-fuzzy system, and this has a low computational cost with 18 fuzzy rules and 64 parameters to update. in a comparison between the levenberg marquardt algorithm and the backpropagation algorithm. the proposed algorithm has an average time of 0.5 ms with 8 average iterations, while, the backpropagation has an average time of 4 ms with 80 average iterations. this allows efficient implementation in embedded systems. finally, a phase plot between θx and θ̇x is shown in fig. (7), it shows the theoretical boundaries for each push recovery strategy using a lineal inverted pendulum model. the boundaries are calculated using the following parameters: zc = 0.295, d = 0.05, th = 0.3, m = 2, g = 9.81, walking motion generation and neuro-fuzzy control with push recovery for humanoid robot 343 t 0 5 10 15 20 25 30 35 40 45 θ̇ x -200 -100 0 100 0 5 10 15 20 25 30 35 40 45 θ x -50 0 50 figure 5: experimental test with 6 random pushes without failure 0 5 10 15 20 25 30 35 40 45 k a -0.2 0 0.2 0 5 10 15 20 25 30 35 40 45 k h -1 0 1 t 0 5 10 15 20 25 30 35 40 45 k s -0.05 0 0.05 figure 6: gains of the 3 push recovery strategies with 6 random push after learning 344 p.e. mendez-monroy w = √ g/zc, τmaxhip = 1 and x max capture = 0.08 [17]. ackle : ∣∣∣ θ̇xw + θx∣∣∣ < dzc ankle + hip : ∣∣∣ θ̇xw + θx∣∣∣ < dzc + τmaxhip (e−wth−1)2mgzc ankle + hip + stepping : ∣∣∣ θ̇xw + θx∣∣∣ < dzc + τmaxhip (e−wth−1)2mgzc + xmaxcapturezc (38) the phase plot shows that the neuro-fuzzy system improved the robustness to strong pushes with figure 7: the phase plot shows how the neuro-fuzzy system responds to 6 unknown pushes. the coloured areas are the theoretical boundaries for the ankle(white), hip (light green), step (light blue) and unstable (light red). a larger stable region. so, the robot already learnt the right ankle, hip and stepping strategies to cope with pushes from any direction. 7 conclusions in this paper, a practical method to implement a full body push recovery system on a general humanoid robot without specialised sensor and actuators is proposed. the method consists of a neuro-fuzzy system designed by combining expert knowledge, system constraints and online reinforcement learning. this approach has a number of advantages over previous approaches. it is able to recover from unknown disturbances from any direction performing a combination of push recovery strategies based on the current system state without an accurate dynamical model of the robot or environment. a straightforward and robust online reinforcement learning algorithm updates the parameters for the neuro-fuzzy controller and predictor to improve the push recovery performance using both simulation environment as well as on a small-size humanoid. implementation using the modified lm algorithm guarantees low-cost computation. it is integrated with a non-periodic, step-omnidirectional walking generator where step parameters produces soft curves and can be flexibly changed as a response to a disturbance. moreover, it does not attempt to follow a future zmp reference, only the joint trajectories are prescribed using the step parameters and a simple inverted pendulum model. walking motion generation and neuro-fuzzy control with push recovery for humanoid robot 345 experimental results show that the trained system can successfully develop a full body push recovery under external perturbations while walking in an arbitrary direction. there is a significant improvement of the stable region in the sagittal plane from a low number of experiences, but enough for the neuro-fuzzy controller to produce convincing push recovery capabilities. possible future work includes incorporating strategies to unreliable terrains and robustness. acknowledgment thanks to the support of the laboratory of humanoid robots at pcc-unam for the facilities provided. to dr. fernando arambula cosio and dr. mario pena cabrera for the facilities and knowledge provided during the development of the project. bibliography [1] adiwahono a.h., chew c.m., liu b. 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(2015); less computationally intensive fuzzy logic (type-1)-based controller for humanoid push recovery. robotics and autonomous systems, 63, 122-135, 2015. [16] si j., wang y.t. (2001); on-line learning control by association and reinforcement, ieee trans. on neural networks, doi: 10.1109/72.914523, 12(2), 264-276, 2001. [17] stephens b. (2007); humanoid push recovery, ieee-ras int. conf. humanoid robots, ieee press, pittsburgh, pa, 589-595, 2007. [18] tedrake r. (2004); stochastic policy gradient reinforcement learning on a simple 3d biped, proc. of the 10th int. conf. on intelligent robots and systems, 2849-2854, 2004. [19] wieber p.b. (2006); trajectory free linear model predictive control for stable walking in the presence of strong perturbations, ieee-ras int. conf. humanoid robots, ieee press, nashville, doi: 10.1109/ichr.2006.321375, 137-142, 2006. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(3), 344-358, june 2019. selection of cluster heads for wireless sensor network in ubiquitous power internet of things w. hu, w.h. yao, y.w. hu, h.h. li wei hu, wenhui yao*, yawei hu, huanhao li school of economics and management, shanghai university of electric power, shanghai 200090, china *corresponding author: 272828408@qq.com abstract: this paper designs a selection algorithm of cluster heads (chs) in wireless sensor network (wsn) under the ubiquitous power internet of things (upiot), aiming to solve the network failure caused by premature death of wsn sensors and overcome the imbalance in energy consumption of sensors. the setting of the cluster head node helps to reduce the energy consumption of the nodes in the network, so the choice of cluster head is very important. the author firstly explains the low energy adaptive clustering hierarchy (leach) and the distance and energy based advanced leach (deal) protocol. compared with the leach, the deal considers the remaining nodal energy and the sensor-sink distance. on this basis, the selectivity function-based ch selection (sf-chs) algorithm was put forward to select chs and optimize the clustering. specifically, the choice of chs was optimized by a selectivity function, which was established based on the remaining energy, number of neighbors, motion velocity and transmission environment of sensors. meanwhile, a clustering function was constructed to optimize the clustering, eliminating extremely large or small clusters.finally, the simulation proves that the deal protocol is more conducive to prolonging the life cycle of the sensor network. the sf-chs algorithm can reduce the residual energy variance of nodes in the network, and the network failure time is later, which provides a way to improve the stability of the network and reduce energy loss. keywords: wireless sensor network (wsn), ubiquitous power internet of things (upiot), cluster head (ch) selection, clustering optimization. 1 introduction the ubiquitous power internet of things is built around all aspects of the power system, fully utilizing modern information technologies such as mobile internet and artificial intelligence, and advanced communication technologies to realize the interconnection of all things and humancomputer interaction in all aspects of the power system. it has comprehensive state perception, efficient information processing and application [18]. a smart service system with convenient and flexible features. it is a specific application of "5g" technology and the internet of things in the power industry system. it realizes a mode of interconnecting people-machine-devices inside and outside the power grid, and generates real-time shared data during the running process.ubiquitous power internet of things (upiot) is a wireless sensor network (wsn) formed after applying the iot technique in power systems. the wireless sensors in the network enable the information exchange between different links in power systems. the wsn is the key to the construction of the upiot, for it carries various strengths of distributed processing systems, including high monitoring accuracy, strong fault tolerance, large coverage, remote telemetry and control, self-organization and multi-hop routing [16]. in order to extend the network life cycle, the node energy in the sensor network can be balanced by randomly selecting the cluster head nodes to spread the energy consumption in the network. copyright ©2019 cc by-nc selection of cluster heads for wireless sensor network in ubiquitous power internet of things 345 however, there is a contradiction between the largescale and high complexity of the upiot and the limited energy of each sensor. when it transmits information to the sink, the sensor may lose efficacy due to excessive energy consumption, leading to the failure of the entire network. to extend the network lifecycle and balance sensor energy, the data transmitted by common sensors can be collected and integrated by a cluster head (ch), before being sent to the sink. this strategy eliminates the long-distance transmission between common sensors and the sink, and reduces the energy consumed in data transmission, thus extending the network lifecycle [5] [6]. much research has been done at home and abroad on ch selection. for example, reference [10] proposes a ch selection algorithm based on grid dynamic energy threshold, which selects chs according to the sensor-grid center distance and the remaining energy of each sensor; considering network dynamics, the algorithm enhances the energy efficiency of sensors [19]. reference [11] takes account of the energy consumed in data transmission of ch selection and information aggregation, and presents a reservation mechanism that lowers the energy consumption by controlling the number of messages generated in clustering and ch selection. reference [13] designs a ch selection algorithm based on the fuzzy balance cost, which determines the how each sensor qualifies for ch by the sensor-sink distance and neighbor density, thus ensuring the load balance. based on the dynamic characteristics of the nodes, residual energy and node location, the above papers provide an effective solution for the election of cluster heads, which plays an active role in improving the stability of wireless sensor networks and extending the life of the network. overall, the existing studies on ch selection have the following defects:(1) the sensor’s propagation rate and energy consumption will change in the harsh environment, and the life of the sensor will be affected. therefore, the network environment factor should be considered in the process of selecting the cluster head; (2) in the current research, the ubiquitous power internet of things is not combined with the wireless sensor network, and the description of the ubiquitous power internet of things is also less. how to ensure the stability of the ubiquitous power internet of things operation needs to be solved urgently. in the context of the upiot, this paper attempts to create a ch selection algorithm for the wsn to overcome the energy imbalance between sensors, which is common in wsns [1] [2] [8] [9] [14]. firstly, the author introduced the classic ch selection method called low energy adaptive clustering hierarchy (leach), and further optimized the selection results considering the remaining energy and sensor-sink distance, creating the distance and energy based advanced leach (deal) protocol [4] [17]. next, the selectivity function-based ch selection (sf-chs) algorithm was proposed based on sensor energy, the number of neighbors, sensor motion velocity, and transmission environment. to optimize the clustering results (clustering optimization), the clustering function was set up to prevent extremely large or small clusters. finally, the simulation analysis shows that the deal protocol outperformed the leach protocol in extending network lifecycle and minimizing energy loss, and that the ch selected by the sf-chs algorithm and the cluster allocated by the proposed function enhanced the stability and lifecycle of the wsn. 2 ch selection for the wsn 2.1 wsn in the upiot, wireless sensors and other intelligent acquisition devices are applied to various links, ranging from grid infrastructure, energy storage stations, thermal power plants, photovoltaic power plants, wind farms to power users. these intelligent acquisition devices form a wsn, in which the links are called sensors [7] [15]. the wireless sensors are responsible for collecting information like power, device parameters, and environmental status [3]. during power dispatching, each sensor transmits its own information wirelessly to the control center at the 346 w. hu, w.h. yao, y.w. hu, h.h. li virtual power plant. known as the sink, the virtual power plant is responsible for making a scheduling plan based on the load and power generation information, aiming to effectively improve the grid access of distributed energy and the proportion of the controllable capacity in the grid. it is assumed that each wsn sensor has the following features: (1) each sensor is deployed randomly through the network, knowing nothing about its location. (2) each sensor has a limit on energy, which attenuates in data transmission and monitoring, and can sense its remaining energy. (3) each sensor is unique, work independently of others, and can communicate directly with the sink. (4) each sensor is free to send and receive data, and judge its distance to the data sender by the perceived signal strength. (5) located at a fixed position, the sink has an unlimited amount of energy and can communicate with all sensors. the energy consumed to transmit information between sensors can be calculated by: etx(k,d) = { keelec + kεfsd 2, d ≤ d0 keelec + kεmpd 4, d > d0 (1) erx(k) = k ×eelec where etx and erx are the energies consumed by a sensor to send and receive information, respectively; k is the number of bits being transmitted; eelsc is the energy needed to transmit 1 bit of information; εfs and εmp are the power amplification factors under the said two models, respectively; d is the sender-receiver distance; d0 is the threshold (if d ≤ d0 , the free space model should be adopted, which assumes that the energy consumed to send information is positively correlated with d2 ; if d > d0 , the multipath attenuation model should be adopted, which assumed that the energy consumed to send information is positively correlated with d4 ), d0 = √ εfs εmp . in the upiot, the energy of a sensor far away from the virtual power plant may be attenuated to zero after multiple communication, owing to the limited amount of sensor energy. the attenuation could affect the lifecycle of the entire network. thus, a clustering mechanism was introduced to the network, dividing the power network into several subsets. each cluster consists of a ch and a few common sensors (figure 1). in a cluster, cluster members mainly perform information collection or information transmission, and communicate with cluster heads in a single-hop or multi-hop manner. the cluster head is the same as the ordinary node, but can function like a gateway. it has a certain data processing capability, and communicates with the base station through the network formed between the cluster head and the cluster head. in the data transmission process, the ordinary node sends its own information to the cluster head, and the cluster head fuses the data in the cluster and sends it to the aggregation node. for the monitoring sensor nodes of each device, the distance from the cluster head is shorter than that of the sink node, so it consumes less energy, thus prolonging the life of the network, and also ensuring that the data communication effectively covers the entire network, and is easier. optimize management of nodes within the network. selection of cluster heads for wireless sensor network in ubiquitous power internet of things 347 figure 1: the wsn 2.2 ch selection (1)deal the leach is a typical clustering routing algorithm. aiming to extend the lifecycle of the entire network, this protocol randomly selects chs from the sensors that have never been chs, such that the each sensor consumes a similar amount of energy. in the leach protocol, the chs selection is carried out in the following manner: each sensor randomly generates a number in the interval of [0, 1], and the sensors whose numbers are smaller than threshold ti will be the chs. the ch selection contains rtotal cycles, where rtotal is the ratio of the number of sensors divided by the number of chs. during the selection process, every sensor has the potential to be the ch. the only difference lies in the number of cycles. once selected, a sensor should not be ch again in the following cycles. the threshold ti can be expressed as: t(i) = { p 1−p×[r mod (1/p)] , i ∈ g 0 , otherwise (2) where p is the proportion of chs in all sensors; mode is the remainder; r is the number of the current cycle; g is the set of sensors that have never been selected as chs. the leach algorithm effectively extends the network life cycle, but there are still some problems and some limitations: 1) since leach assumes that all nodes can communicate directly with the aggregation node, and each node has the ability to support different mac protocols, the protocol is not suitable for use in large-scale wireless sensor networks; 2) the protocol does not specify how the number of cluster head nodes can be distributed to the entire network. therefore, it is very likely that the selected cluster head nodes are concentrated in a certain area of the network, so that there are no cluster head nodes around some nodes; 3) since leach assumes that in the initial cluster head selection round, all nodes carry the same energy, and each node that becomes the cluster head consumes roughly the same amount 348 w. hu, w.h. yao, y.w. hu, h.h. li of energy. therefore, the protocol is not suitable for networks with unbalanced node energy; 4) in the cluster head election, the impact of the death node on the network is not considered. the number of cluster heads does not change dynamically with the death of the network node. the number of cluster heads cannot always be optimal, and the death time will be earlier. affect the stability of the network; 5) uneven clustering can cause uneven distribution of network energy consumption. singlehop transmission makes energy consumption of cluster head nodes far from the base station large. as mentioned before, the deal is an advanced leach protocol through optimization based on distance and energy. it considers both the mean and variance of the remaining energy of each sensor. in the deal, the threshold for sensor i can be defined as: t(i) = (1 −p) ×gp + p ×csp (3) where p is the optimal proportion of chs in all sensors; gp and csp are the probabilities for a common sensor and the current sensor to be selected as ch, respectively. the gp and csp can be calculated by formulas (4) and (5), respectively: gp = k n −k × (rmodn k ) (4) where k is the number of chs generated per cycle; n is the number of all network sensors; r is the number of the current cycle. csp = ( std (ei) mean(ei) eoi ) − ( p × ln2b lfarthestn2b ) + n xm ×ym (5) where std(ei) and mean(ei) are the mean and variance of the remaining energy of sensor i, respectively; eoi is the initial energy of sensor i; li2b is the distance from sensor i to the sink; l farthest n2b is the distance from the sink to the farthest surviving sensor; xm ×ym is the size of the entire network. the deal retains the ch selection method of the leach: each sensor randomly generates a number in [0, 1], and the sensors whose numbers are smaller than the corresponding threshold will be the chs. the optimization of the threshold function makes it more likely for sensors with high energy and close sensor-sink distance to be chs. thus, the ch selection is less random and the network can survive for a longer time. (2) sf-chs in order to ensure the energy balance of sensor nodes, extend the life cycle of the network, and ensure the timely collection and effective management of power equipment information, this paper proposes a cluster head selection algorithm based on the selection function, and selects nodes with larger value of selection function in the network as the cluster head node, which effectively avoids the problem of node failure due to excessive energy consumption and prolongs the network life. definition 1. selectivity function the selectivity function can be established according to the remaining energy, the number of neighbors, the relative motion velocity, and transmission environment of each sensor: maxz = λ1 ×erest + λ2 ×ni + λ3 ×vi + λ4 ×ωr (6) where λ1 + λ2 + λ3 + λ4 = 1, and λ3 < 0. if the current sensor has the largest value of the selectivity function, it should be elected as the ch. selection of cluster heads for wireless sensor network in ubiquitous power internet of things 349 the remaining energy of a sensor erest can be obtained as: erest = ecurrent emax (7) where ecurrent and emax are the current remaining energy and the maximum initial energy of the sensor, respectively. the high energy sensor should be preferred in ch selection, because ch usually consumes more energy than common sensors. for example, the ch needs to collect the information about the power equipment from all common sensors in the cluster, sort out and integrate the information, and then send them to the sink. let ni be the number of neighbors of a wireless sensor. definition 2. neighbor a neighbor refers to any sensor within the circle (radius: r = √ s π×q) centering on the current sensor. the expression of the radius can be proved as follows: it is assumed that all sensors are located in a square with a side length of one, and all clusters have the same coverage. then, the optimal number of chs in the network can be defined as: q = √ n ×d0 2π m d2bs (8) where n is the number of network sensors; m is the side length of the network; dbs is the mean sensor-sink distance. if the network remains stable, the number of chs remains basically the same after the replacement in each cycle, and the communication radii of the chs can cover the entire network. suppose adjacent clusters do not overlap in their communication ranges between adjacent clusters, the network coverage s must satisfy: q×πr2 ≥ s (9) thus, we have: r = √ s π ×q (10) the sensors with many neighbors should be preferred in ch selection, because the distance to ch is positively correlated with the energy loss in information transmission. let vi be the motion velocity of the sensor i. in the wsn, some sensors are constantly moving. if these sensors be chs, the stability of the network may be dampened. what is worse, such a ch may enter the adjacent clusters, increasing its distance to the sensors in its cluster and to the sink. in this case, more energy will be consumed to transmit the same amount of data, and the data may even get lost. considering the negative effects of the moving sensors, the author set up a weighting factor λ3 < 0 for the motion velocity of the sensors. the sensors with a low motion velocity will be preferred in the selection process. letωr be the environmental impact factor of the transmission process. this factor was introduced to the selectivity function to describe how the natural conditions affect the wireless communication bandwidth and thus the data transmission rate, as well as the impacts of other unknown, uncontrollable changes. 350 w. hu, w.h. yao, y.w. hu, h.h. li 3 clustering optimization 3.1 extremely large and small clusters let x = {x1,x2,x3 · · ·xn} be the chs being selected, with n be the number of clusters. the remaining common nodes determine the joined cluster according to the clustering function, and communicate with the cluster head of the cluster. after receiving the "request to join" message of all member nodes, the cluster head allocates the order in which the member nodes in the cluster send data, calculates the sending time of each cluster member, and sends the generated tdma message to all member nodes in the cluster. the member node receives the message and saves its own time slot, and it is the turn to communicate with the cluster head when it sends itself. definition 3. clustering function each sensor selects its cluster according to its distance to and the energy of each ch. in general, a sensor prefers a nearby ch with the most remaining energy. the clustering function can be expressed as: minri = α×dij −β ×e xj rest (11) where α and β are the influence factors of distance and energy, respectively; dij is the distance between sensor i and ch j; exjrest is the remaining energy of ch j. sensor i will be allocated to the cluster of the ch that minimizes the value of the clustering function. it is difficult to predict the size and number of sensors of each cluster, as the chs generated in each cycle differ in remaining energy and have random positions. this may lead to extremely large and small clusters (figure 2). figure 2: extremely large and small clusters as shown in figure 2, the randomly distributed sensors can be divided into 7 clusters, in which clusters 3, 4, 5 and 7 are extremely small clusters while clusters 1, 2 and 6 are extremely large clusters. for each extremely large cluster, more energy is consumed as each sensor transmits its data to the ch, and as the ch integrates the received information; for each extremely small cluster, the ch is far away from the sink, pushing up the energy consumption in data transmission to the sink; also, because the number of nodes in the cluster is small, the time for collecting and processing data is much shorter than that of extremely large cluster, so the data is sent to the sink node more frequently, resulting in excessive consumption of extremely small clusters and unbalanced energy consumption; in addition, extremely small clusters frequently send data to the sink node, and the sink node needs to participate in the collaborative acceptance of data, thereby selection of cluster heads for wireless sensor network in ubiquitous power internet of things 351 occupying the sink node more frequently, so that other cluster head nodes cannot cooperate with the sink node when data needs to be transmitted, thereby a conflict of collaboration has arisen. therefore, both extremely large and small clusters may cause energy depletion and premature death of the chs, resulting in a shorter network lifecycle. to solve the problem, the maximum and minimum capacity constraints can be defined for each cluster: caj ≥ nav + η(e xj rest −e av rest) × nav eavrest (12) caj ≤ nav + η(e xj rest −e av rest) ×nav (13) where caj is the number of sensors in the cluster of chj;nav is the mean number of sensors of all clusters in the network; exjrest is the remaining energy of chj; e av rest is the mean remaining energy of all chs; 0 < η < 1 is the relationship factor between remaining energy and the number of sensors. 3.2 data transmission procedure the data transmission process in the network was divided into cycles. each cycle was further splits into the cluster formation stage and the stable transmission stage. the former involves ch selection and clustering. the specific process is shown in figure 3. (1) cluster formation stage. before the first cycle, the sink confirms all surviving network sensors, while each sensor transmits its information about the initial energy to the sink, and the chs are determined according to the best number of chs and the selectivity function. the sensors elected as chs will broadcast its information to the entire network, so that each common sensor selects its desired cluster according to the clustering function. the common sensor will send the clustering request to the corresponding ch, applying to join the cluster. the ch will always keep its receiver open. if the cluster being applied is saturated with sensors, the common sensor will apply to enter the cluster with the next optimal conditions. if failing to join any cluster after the end of the current cycle, the general cluster will form a cluster itself. after confirming the sensors accepted to its cluster, the ch will return an acceptance signal to these sensors and assign a time interval for communication to each of these sensors. in addition, the ch will also create a time division multiple access (tdma) schedule, and broadcast it across the network. after that, the common sensors will send data according to the schedule, preventing timing disorder in the transmission process. in order to reduce energy consumption, each cluster node uses the energy control mechanism to set the amount of energy used for transmission according to the strength of the cluster head transmission signal. when the time slot is not in turn, the nodes in the cluster can turn off the wireless communication module to save energy, and the cluster head node must remain active to receive data. (2) stable transmission stage. in the stable transmission stage, each common sensor transmits its monitored information to the corresponding ch. after receiving all the information, the ch will integrate the information and send the result to the sink. the data fusing process can remove unnecessary information, reduce traffic and energy consumption, and extend the network cycle. during data transmission, the time is divided into a number of intervals according to the tdma schedule, so that each intra-cluster sensor can send data to the ch at its time interval. if a part of data is not sent in the current time interval, it should be transmitted gain in the next time interval. after receiving certain data, the cluster head node integrates the data, strengthens meaningful signals, reduces uncorrelated noise, and sends the data to the sink node. this mode can 352 w. hu, w.h. yao, y.w. hu, h.h. li figure 3: data transmission procedure reasonably reduce the traffic, but when the cluster head is far away from the sink node and the data packet is large, the energy of the cluster head node is quickly consumed. in order to reduce the inter-cluster interference, the dsss mechanism is used for inter-cluster communication. the intra-cluster nodes use a unique spreading code to transmit data to the cluster head. the cluster head uses the spreading code to filter all received energy. in order to reduce inter-cluster interference and its own energy consumption, the node can adjust the transmit power by itself. the cluster head sends data to the sink node using a fixed spreading code. the cluster head transmits the data using the csma sounding channel mechanism. if another cluster is transmitting data using the spreading code, the cluster head waits for other selection of cluster heads for wireless sensor network in ubiquitous power internet of things 353 cluster heads to complete the data transmission; if not, the cluster head node directly uses the spread spectrum based the code is used for data transmission. after the data is stably transmitted for a period of time, the network restarts a round of cluster head elections, and the above process is repeated. 4 simulation experiment 4.1 parameter setting the three ch selection algorithms, namely, the leach, the deal and the sf-chs, were simulated on the matlab, aiming to test the algorithm’s network lifetime and network performance. the network coverage was set to 100m × 100m, with the sink at the center. all the sensors were evenly distributed in the network, all of which know the coordinates of the sink. the initial energy of each sensor was set to 1j, the data packet size was set to 4,000 bits, the energy consumed to transmit 1 bit of data eelec was set to 50nj/bit, the εfs and εmp were set to 10pj/bit/m2 and 0.0013pj/bit/m4, respectively, and the number of cycles were set to 300. after each cycle, the energy of each sensor will be attenuated to a certain extent. if the energy of a sensor falls to zero, the sensor can be deemed as failed. multiple sensor failures will make the monitoring ineffective. hence, the end of the network lifecycle was defined as the failure of 95% of all network sensors [10]. 4.2 results analysis the three algorithms were simulated under a 200-sensor network and a 300-sensor network. the ch selection and clustering results are shown in figure 4 below. figure 4: clustering results it can be seen from that the sink was located at the network center, and the sensors were clustered relatively evenly, the number of nodes in each cluster is within the range of suitable cluster capacity, with no extremely large or small clusters. the clusters had no significant difference in energy loss. taking the 200-sensor network as an example, the relationships between the number of failed sensors and the number of cycles of the three algorithms are presented in figure 5. under the operation conditions of the leach algorithm, the first sensor failure appeared in cycle 25, and the network lifecycle ended at cycle 225. under the operation conditions of the deal, the first sensor failure occurred in cycle 35, and the network stopped operation in cycle 231. under the 354 w. hu, w.h. yao, y.w. hu, h.h. li ch selection and clustering by the sf-chs, the first sensor failure was observed in cycle 42 and the network lifecycle terminated at cycle 275. figure 5: number of failed sensors of the three algorithms the above results show that the network lifecycle ended at basically the same moment under the deal and the leach. however, the first sensor failure appeared later and the network survived longer under the deal. under the proposed sf-chs, the network lifecycle was longer than that under the leach and the deal, the death of the first sensor was further delayed, and the network lifecycle was extended by 16.5%. to sum up, the proposed sf-chs can ensure the balance of energy consumption and extend network lifecycle, thanks to the consideration of neighbors, motion velocity and transmission medium and the optimization of the clustering process. in order to study the stability of the network, this paper compares the average residual energy of the nodes in the network and the variance of the residual energy of the nodes under the three algorithms. the results are shown in figure 6 and figure 7. figure 6 compares the residual energy of the network under three different algorithms, reflecting the change in energy consumption rate. when using the leach algorithm, due to poor cluster head selection, the energy consumption of the previous network is large, and the slope of the curve is large. in the later stage, the number of nodes in the network is reduced, the network energy consumption is also reduced, and the slope is reduced. the deal algorithm is used for the leach algorithm. optimized, so the energy consumption is lower, the slope of the curve is smaller and smaller as the surviving node is reduced. the sf-chs algorithm is not only better in cluster head selection, but also better clustering result. the consumption is basically the same, the curve tends to be a straight line, and the same, the overall energy consumption is reduced due to node death in the later stage. as shown in figure 7, the leach brought about the maximum variance of remaining energy, while the sf-chs minimized the variance, and the variance is reduced by 32%. this means the deal protocol reduces the variance of remaining energy from the level of the leach, while the proposed sf-chs can effectively enhance network stability and extend network lifecycle. figure 8 compares the energy values consumed per round under the operating conditions of the three algorithms. the energy consumption per round reflects the cluster head selection and clustering results. when the cluster head selection is correct, the energy consumed per selection of cluster heads for wireless sensor network in ubiquitous power internet of things 355 figure 6: network residual energy comparison curve figure 7: the variance of remaining energy of sensors of the three algorithms round is naturally less. large networks typically consume less energy than smaller networks. as shown in figure 7, based on the optimized leach algorithm, the deal algorithm consumes less energy than the leach algorithm. therefore, it can be seen that the deal algorithm has a better selection of cluster heads. in addition, before 240 rounds, the sf-chs algorithm the energy consumption per round is much less, indicating that the clustering result of the sf-chs algorithm is better; at the end of the running cycle, the energy consumption per round of sf-chs and deal is higher than that of the leach algorithm. the reason may be in the leach algorithm, the node is too dead, so it consumes less energy, while in the other two algorithms, there are more surviving nodes, which naturally consume more energy. in addition, this paper analyzes how the location of the aggregation node affects the number of dead nodes, as shown in figure 9. the two cases are simulated separately. one case is that the aggregation node is located at the center of the network, and the other case is that the aggregation node is located at the edge 356 w. hu, w.h. yao, y.w. hu, h.h. li figure 8: comparison of energy consumption per round under the three algorithms figure 9: number of dead nodes when the sink node is in different locations of the network. it can be seen from the figure that the aggregation node has a dead node in the network center and the network failure time is later, and the network life is longer. based on the above analysis, the proposed sf-chs algorithm is beneficial to improve network stability and extend network lifetime, and can play a positive role in the development of ubiquitous power internet of things. 5 conclusion the upiot is an important research topic of the future power industry. the performance of the upiot depends heavily on wireless sensors. improving the stability and survival period of wireless sensor networks can better promote the development of ubiquitous power internet of things. to solve the energy depletion of wireless sensors in data transmission, a common selection of cluster heads for wireless sensor network in ubiquitous power internet of things 357 method is to set up chs and divide wsn sensors into several clusters, thus enhancing the energy efficiency. in this paper, a wsn is set up in the context of the upiot, taking the application links of the power system as sensors, and the information exchange between the sensors is realized through sensors. on this basis, the basic principle of the leach and deal protocols was explained: each sensor generates a random number, and the sensors whose numbers are smaller than the corresponding threshold will be the chs. next, the sf-chs algorithm was designed in two steps: the cluster selectivity function was created based on the remaining energy, number of neighbors, motion velocity and data transmission environment of each sensor, and the clustering function was established to optimize the clustering method, eliminating extremely large or small clusters. finally, the simulation shows that compared with the leach protocol and the deal protocol, the sf-chs algorithm has a late failure node, the network lifetime is extended by 16.5%, and the variance of the remaining energy of the nodes in the network is reduced by 32%, which effectively improves the stability of the network. in addition, when the aggregation node is located in the center of the network, the time of the failed node is later, and the network life is longer. therefore, the sf-chs algorithm can provide a solution for extending the network life and improving the stability of the network. the research work of the thesis is based on the ubiquitous power internet of things, but all the research is only based on the theoretical level. the simulation results are only verified in the virtual environment. the future research work will apply the research results to the actual environment for verification. development and application verification for specific wireless sensor network monitoring environment. funding this work is supported by the humanities and social sciences project of chinese ministry of education (grant no.: 17yjczh062). author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] aghera, k.; pambhar, h.; tada, n. 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(2018). energy efficient reservationbased cluster head selection in wsns, wireless personal communications wireless personal communications, 100(3), 667-679, 2018. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 5, month: october, year: 2020 article number: 3825, https://doi.org/10.15837/ijccc.2020.5.3825 ccc publications reduce effect of dependent malicious sensor nodes in wsns using pairs counting and fake packets a. ahmed, m. hababeh, a. abu-hantash, y. abuhour, h. musleh ashraf ahmed* computer graphics and animation department princess sumaya university for technology, jordan p.o.box 1438 al-jubaiha, amman 11941, jordan *corresponding author: a.ahmad@psut.edu.jo mohammed hababeh computer engineering, german jordanian university amman madaba street, p.o. box 35247, jordan m.hababeh@gju.edu.jo alaa abu-hantash, yousef abuhour, husam musleh university of science and technology, irbid, jordan p.o.box 3030, irbid 22110, jordan amabuhantash12@cit.just.edu.jo ysabuhour14@sci.just.edu.jo hskhairimusleh13@cit.just.edu.jo abstract in this paper, we propose a new technique for the enhancement of target detection in wireless sensor networks (wsns) in which sensor nodes are responsible for taking binary decisions about the presence or absence of a given target and reporting the output to the fusion center. we introduce the algorithm; fail silent pair (fsp) to calculate global decision in the fusion center. the fsp algorithm randomly distributes all sensor nodes into pairs then considers pairs of the same local decision. also, we present new detection and prevention methods to reduce the effect of dependent malicious sensor nodes. the detection method is based on the deception of suspicious sensor nodes with fake packets to detect a subset of the malicious sensor nodes, as these nodes eavesdrop on other sensor nodes packets to use their local decisions as a reference to build an intelligent decision. while the prevention method allows the fusion center to correct local decisions of some malicious sensor nodes with identified strategies, assisting in the increase of the accuracy of global decisions. we introduce a mathematical analysis to verify our methods, in addition to simulation experiments to validate our technique. keywords: wireless sensor networks (wsn), dependent malicious sensor nodes, detection and prevention methods. https://doi.org/10.15837/ijccc.2020.5.3825 2 1 introduction recently, autonomous sensors have been used in a wide range of various applications. an autonomous sensor consists of the sensor itself and a low-power computational unit. there are many types of autonomous sensors that can measure different physical phenomena such as temperature, pressure, sound, vibration, or proximity [19]. the sensor type is selected based on its use. for example, a temperature sensor can be used to measure the temperature of a car engine. furthermore, a location sensor can be used on a specific area to detect the existence of an object. since most implemented applications require many autonomous sensors, manufacturers tend to produce cost effective sensors using low-cost components [7]. a sensor network is formed by many autonomous sensors. in a wired sensor network, usually, the star topology is used. a wired sensor network consists of one dominant central node and a group of sensor nodes physically connected to the central node. all sensor nodes send measurements directly to the central node to calculate the final decision. wired sensor networks were widely used for network reliability due to low interference, low latency, and privacy [10]. however, the main problem with wired sensor networks is the scalability, specifically when covering large areas. adding a new sensor node to the network requires high-cost wire cabling. along with the complexity of network management. therefore, wireless sensor networks (wsns) [15] were introduced to overcome these problems. a wsn can be defined as a network of devices, denoted as sensor nodes, which sense the environment and send information gathered from the monitored field (e.g. an area or a volume) through wireless links [4] to a base station called the fusion center. the fusion center is responsible for calculations and decision making. wsns are flexible, cover large areas, and easy to deploy [12]. wsns work in various hostile environments difficult for humans to reach. moreover, wsns are inexpensive and consume low power. due to these features, wsns attracted the attention of researchers, and as a result, several applications were implemented using wsns [16], [11], and [2]. wsns are more vulnerable to communication failures due to its nature, being that wsns have dynamic network topology which means the number of nodes can vary with time zhang2016. these disadvantages make the communication in wsns less reliable and increase interference. in addition, network sensor nodes have low computational power [6]. in brief, wsns are more vulnerable to attack than wired sensor networks. therefore, using wsns for critical applications such as military applications is risky [17]. wsns are exposed to different types of attacks in [20] and [3]. for instance, attacks aimed to affect the calculations of the fusion center, in which malicious sensor nodes send misleading packets to sabotage the wsn global decision. network sensor nodes cannot be distinguished due to of the lack of unique identifiers for each node because of the large number of nodes. additionally, network sensor nodes can easily connect and disconnect from the network to save power. thus, malicious sensor nodes can easily attack wsns. network sensor nodes in wsns cannot run complex topologies, and unable to use malicious software detection algorithms. due to these limitations, wsns suffer from several problems such as lack of privacy [18] (which is the main security concern) because of the interference between wsns. thus, creating an algorithm which runs in the fusion center has been a research challenge. these algorithms must include intelligent methods to assist the fusion center with calculating correct decisions as well as dealing with malicious sensor nodes. various types of attacks have been proposed. in [1], attacks have been classified into dependent and independent attacks. in independent attacks, malicious sensor nodes behave statically during the entire period of the attack. for example, detecting the existence of an object requires binary location sensors. binary location sensors respond either with 1 (present) or 0 (absent). accordingly, the fusion center calculates the final result after collecting the measurements from all sensor nodes. based on that, independent malicious sensor nodes send false measurements to the fusion center. an independent malicious sensor node follows one of the following patterns: • always-zero; in which the malicious sensor node always sends zero regardless of the correct measured value. https://doi.org/10.15837/ijccc.2020.5.3825 3 • always-one; in which the malicious sensor node always sends one regardless of the correct measured value. • always-false; in which the malicious sensor node always sends the opposite of the measured value. therefore, detection of independent attack is easy as it follows a simple pattern. in dependent attacks, malicious sensor nodes have the ability to modify the values sent to the fusion center. these sensor nodes can make advanced and intelligent decisions based on wsn’s behavior. if the malicious sensor nodes are unable to affect the fusion center decision, they will act normally in order to avoid exposure. in this paper, we present a new technique for the management of the decision-making process in the fusion center. we have divided the technique into three phases; the fail silent pair (fsp) algorithm, the detection phase, and the prevention phase. the fusion center uses the fsp algorithm to calculate the global decision. while the detection and prevention phases are used to detect a subset of the malicious sensor nodes and prevent them from influencing the global decision. the rest of this paper is organized as follows. section 2 gives some related works. section 3 presents the system model. section 4 details the proposed methods. section 5 illustrates the simulation results. finally, discussion and conclusions are drawn in section 6 and section 7, respectively. 2 related work in literature, many detection and prevention methods of malicious sensor nodes in wsns have been proposed. authors of [1] proposed an algorithm for detecting all types of malicious sensor nodes in wsns, which has enhanced the reliability of network reports. since the performance of an intelligent node heavily depends on its reporting order, the algorithm is based on changing the reporting order of all sensor nodes in the network periodically. this approach is effective and provides accurate results. however, it only works for the long-term and increases the overhead of the fusion center as well as its processing time, considering the results depend on many rounds to increase the detection accuracy. alternative methods proposed in [5], [14]. the autoregressive (ar) model was used to detect and block the malicious sensor nodes. each sensor node sends its output to the base station, and an autoregressive predictor computes an estimated value for each node. the base station detects a sensor node as a malicious node and blocks it if the difference between the sensor’s output and the estimated value is greater than a chosen threshold. in addition to the autoregressive predictor, a neural predictor is used in [14] to calculate the estimated value of each node. another detection approach proposed in [8]. the authors introduced an abnormality-detection approach based on the abnormality detection in data mining. in the independent attacks, it is shown that the attacker can always be detected as the number of spectrum sensing rounds tends towards infinity. in contrast, a dependent attack is applicable, in which the attackers can avoid detection if they possess exact information on the miss-detection and false-alarm probabilities. in [9], attackers have been divided into two types; a static attacker who sends false data to the sink, and a dynamic attacker who sends both correct and incorrect information to the sink. malicious node detection has been studied under the byzantine attack, in which the attacker has full control over a subset of verified nodes. the attacker may confuse the system by transmitting packets through non-optimal paths or dropping packets. this can be done using distributed discovery and probability state checking in the byzantine environment. protocols for identifying unusual transmissions were presented in [13] to discover malicious sensor nodes in a wsn by detecting malicious message transmission in a network. a message transmission is supposedly suspicious if its signal strength is incompatible with its originator’s geographical location. 3 system model we have considered a wsn under a non-interference environment with a fixed number of sensor nodes arranged in a star topology. the sensor nodes send their measured values to the fusion center https://doi.org/10.15837/ijccc.2020.5.3825 4 in a collision-free time division multiple access (tdma) protocol. sensor nodes were unable to detect the media access control address (mac address) of other sensor nodes in the network. each sensor node operated independently. all normal sensor nodes had the same behavior giving accurate measurement values of 80%. the reporting order is defined as the order in which sensor nodes send their local decisions to the fusion center. a sensor node with reporting order t will send its local decision after the sensor node with reporting order t− 1 sends its local decision. in this paper, we have focused on a specific type of sensor nodes; binary sensor nodes. the binary sensor node responds by either present (h1) or absent (h0). 3.1 mathematical technique analysis let the space of n sensor nodes be defined by s = {si : 1 ≤ i ≤ n}. each sensor node gives a local binary decision, denoted by ln , in which ln = { 1, for target-present, 0, for target-absent. the topological pairs space is defined as all possible pairs of n sensor nodes, and one fusion center, such that t = (( {pk :k∈{1,...,n 2}},fucion center )) , in which pk is a pair of sensor nodes, and n is an even number of sensor nodes. if n is an odd integer then we choose a random sensor node, and uses its decision twice (e.g. if sensor node si was chosen then the first local decision is denoted by 1li, and the second local decision is denoted by 2li). for a chosen round ν, we define τν as a collection of distinct random pairs (i.e τν = {pk = {si, sj} : i 6= j and i, j ∈{1, . . . ,n}} in which τν ⊆ s×s and ‖τν‖= n/2, in which ‖.‖ is the cardinality (number of elements)). also, we define the local decision of the kth pair pk that consists of the ith and jth sensor nodes at a certain round ν as lkν : τν 7 −→{0, 1, φ}, in which lkν(pk) = lkν(si, sj) = { li, if li ⊕ lj = 0 φ(ignored), if li ⊕ lj = 1 in which ⊕ is the bitwise xor operation, pk = {si, sj}, i, j ∈ {1, . . . ,n}, and lkν(si, sj) = lkν(sj, si) since bitwise the xor operation is commutative. the set of local decisions made by τν (all pairs of sensor nodes at a certain round ν) is denoted by lν = {lkν : lkν 6= φ, k = 1, . . . ,n/2, andi 6= j ∈{1, . . . ,n}} and m = ‖lν‖≤ n/2. the ignored pairs are defined as silent pairs with cardinality ‖silentpairs‖= n/2 −m. the collection of all possible sets of sensor node pairs that are randomly chosen is denoted by l = {lν,ν = 1, . . . , π n/2 t=1(2t − 1)}, in which the cardinality of l represents the number of distinct random rounds the fusion center can runs. the cardinality of l can be calculated by multiplying the number of choices (n− 1) for the first pair by the number of choices (n− 3) for the second pair, and so on. more formally, ‖l‖ is determined by the product of all positive odd numbers less than n. the local performance of each pair pk is estimated by two factors, which are the local detection (pdk ) probability and the false-alarm (pfk ) probability. pdk is the probability that the target is correctly identified as present by a given pair k when it is actually present (i.e., pdk = pr{l k ν = 1|h1}), while pfk is the probability that the target is falsely identified as present by the pair when it is actually absent (i.e., pfk = pr{l k ν = 1|h0}). the definition of the global detection (pd) probability and the false-alarm (pf ) probability for m non-silent pairs, is defined as: pd = m∑ r=κ (mr )∑ t=1 ∏ k∈b(m, r)t pdk ∏ k/∈b(m, r)t (1 −pdk ) (1) https://doi.org/10.15837/ijccc.2020.5.3825 5 and pf = m∑ r=κ (mr )∑ t=1 ∏ k∈b(m, r)t pfk ∏ k/∈b(m, r)t (1 −pfk ) (2) in which b(m,r)1 ,b (m,r) 2 , . . . ,b (m,r) (mr ) represent all the possible (m r ) combinations of r integers drawn from the interval [1, m]. in order to estimate the reliability of the network, both (pd) and (pf ) probabilities are combined together to calculate the correct global decision (pcd) probability, given as: pcd = ph1pd + ph0 (1 −pf ), (3) in which ph1 is the probability that the target is present, and ph0 is the probability that the target is absent. 3.2 fusion center strategy the fusion center considers a sensor node as a present sensor node if its local decisions is present, and as an absent sensor node if its local decision is absent. the fusion center uses the k-out-of-n rule. this rule determines that the global decision is present if the number of present sensor nodes is not less than a certain threshold, denoted by k. based on the k-out-of-n rule, the fusion center determines that the target is present if ∑n k=1 lk ≥ k, and the target is absent if ∑n k=1 lk < k. 3.3 malicious sensor node behavior in this paper, we have focused on the dependent malicious sensor nodes. dependent malicious sensor nodes behave based on local decisions of other sensor nodes in the network. these sensor nodes can take advanced and intelligent decisions based on network behavior. if the malicious sensor nodes are unable to affect the fusion center decision, they will act normally in order to avoid exposure. as assumed, the malicious sensor nodes recognize the value of k, but are unable to figure the reporting order of other sensor nodes in the network. moreover, they are unable to distinguish between malicious and normal sensor nodes. the behavior of a malicious sensor node depends on its reporting order. a malicious sensor node with reporting order t has the ability to attend to only local decisions of the previous t−1 sensor nodes and save these decisions. suppose γt is the sum of the first t − 1 local decisions (i.e. γt = σt−1i=1li). the local decision of a dependent malicious sensor node can be expressed as in eq. (4), in which do, dz and df refer to the subsets of dependent malicious sensor nodes with always-one, always-zero, and always-false strategies, respectively. also, h means that the dependent malicious sensor node will choose to act maliciously and send the opposite of its real local decision. lk =     1, if k ∈do, 0, if k ∈dz, h, if k ∈df , ifk − (n− t + 1) ≤ γt < k h1, if γt ≥ k h0, if γt < k − (n− t + 1) (4) the dependent malicious sensor node chooses to act normally and report a correct local decision as the following two cases: • if γt ≥ k : in this case, the number of reported ones in the first t− 1 local decisions is not less than k. so, regardless of the local decisions of the remaining sensor nodes the global decision will be present. therefore, the malicious sensor node will send h1 as a local decision. https://doi.org/10.15837/ijccc.2020.5.3825 6 • if γt < k−(n−t + 1): in this case, the number of reported ones in the first t−1 local decisions is so low that, even if all the remaining sensor nodes report ones the threshold k is unreachable and the target will be detected as absent. therefore, the malicious sensor node will send h0 as a local decision. otherwise, the malicious sensor node will choose to act maliciously because its still has a chance to influence and amend the global decision (i.e. when k − (n− t + 1) ≤ γt < k). 4 proposed methods in this section, we introduce the fail silent pair (fsp) algorithm, detection and prevention phases. the fusion center uses fsp algorithm to calculate the global decision. while the detection and prevention phases are used to detect a subset of the malicious sensor nodes and prevent them from influencing the global decision. both phases require only one iteration with o(n) execution time. the difference is that the fusion center runs the prevention phase at each round after collecting local decisions from sensor nodes, while the detection phase runs after every r rounds. both phases require additional o(n) memory. initially, the fusion center distributes the reporting order of each sensor node in the setup phase. upon each round, the fusion center collects local decisions from all sensor nodes in the network. subsequently, the fusion center uses the prevention method to reverse the local decision of each sensor node identified as malicious with a known strategy if it acts maliciously during this round. afterwards, the fusion center calculates the global decision using the fail silent pair (fsp) algorithm. according to each r rounds, the fusion center runs the detection phase to detect a subset of malicious sensor nodes. the detection method depends on deceiving the malicious sensor nodes in an unrealistic environment using sequences of fake packets. depending on the reporting order, some malicious sensor nodes respond with different local decisions for each sequence of fake packets, thus leading to detection. 4.1 fail silent pair (fsp) algorithm the fusion center strategy is based on the fail silent pair (fsp) algorithm. the fail silence method is discussed in [22]. a component using the fail silence method either provides a correct result or none at all. the fsp algorithm uses the same approach to handle the local decisions of sensor nodes. all sensor nodes send their local decisions to the fusion center. when the fusion center collects all local decisions, it randomly distributes all sensor nodes into pairs. then, initializes two counters; one for present pairs and another for absent pairs. finally, the fusion center runs the fsp algorithm to calculate the global decision. this process is done in each round. the fsp algorithm takes into consideration the pair pk if both sensor nodes of the pair have the same local decision. in case the local decisions of both sensor nodes at pk are present, the fusion center increases the number of present pairs. in case the local decisions of both sensor nodes at pk are absent, the fusion center increases the number of absent pairs. otherwise, the fusion center ignores the pair pk. in our technique we consider κ to be a dynamic threshold computed as κ = dk n × me, in which m = ‖lν‖. κ value is variable since the value of m can be variable at each round ν. the fsp algorithm uses κ−out−of−n to calculate the global decision. this rule determines that the global detection is present if the number of present pairs is larger than κ. the mathematical formula of the κ−out−of−n is represented by (5), in which lkν ∈lν. g = { 1 ≡ target-present, if ∑m k=1 l k ν ≥ κ, 0 ≡ target-absent, if ∑m k=1 l k ν < κ, (5) comparing the local decisions of two sensor nodes within the same pair reduces the effect of incorrect local decisions. incorrect local decisions may be reported either by a malicious sensor node or by a normal sensor node giving accurate measurement values of 80%. since we cannot determine which sensor node has an incorrect local decision, we exclude the pair. this is the principle of the https://doi.org/10.15837/ijccc.2020.5.3825 7 algorithm 1 pseudocode of fail silent pair algorithm 1: function fsp (n, k, localdecision) 2: randomper ← permutation(1, n) 3: absentpairs ← 0 4: presentpairs ← 0 5: for i ← 1 to n / 2 do 6: p1 ← localdecision[randomper[i]] 7: p2 ← localdecision[randomper[i + 1]] 8: if p1 + p2 = 2 then 9: presentpairs ← presentpairs + 1 10: else if p1 + p2 = 0 then 11: absentpairs ← absentpairs + 1 12: end if 13: end for 14: κ = d((k / n) × (presentpairs + absentpairs))e 15: if presentpairs ≥ κ then 16: globaldecision ← 1 17: else 18: globaldecision ← 0 19: end if 20: return globaldecision 21: end function fail silent pair algorithm; either to consider the local decision of a pair if both sensor nodes in the pair agree on the same local decision, or to ignore the pair if there is a difference between the local decisions of the sensor nodes in the pair. 4.1.1 fsp algorithm example fusion center si1, sj1 l1ν1 si2, sj2 l2ν1 si3, sj3 l3ν1 si4, sj4 l4ν1 si5, sj5l5ν1 figure 1: fsp algorithm https://doi.org/10.15837/ijccc.2020.5.3825 8 table 1: fusion center decision. sensor id i1 j1 i2 j2 i3 j3 i4 j4 i5 j5 local decision of the sensor node 1 0 0 0 1 1 0 0 1 0 local decision of the pair (lkν1) drop 0 1 0 drop to better understand the fsp algorithm, let us consider a fusion center with 8 sensor nodes as shown in figure 1, in which the fusion center has the local decisions of each sensor node. initially, the fusion center randomly distributes the sensor nodes into pairs then runs the fsp algorithm. the global decision of the fusion center is h0 (absent), as the number of absent pairs is larger than the number of present pairs (in which si1, . . . ,sin and sj1, . . . ,sjn in figure 1 are randomly chosen from the set of sensor nodes s. the pairwise counting technique is shown in table 1). 4.2 the detection phase as mentioned in section 3.3, a malicious sensor node with a reporting order t attends to the previous t− 1 local decisions and saves them to determine if they act normally or maliciously. this means the reporting order plays an important role in the malicious sensor node decision. for that purpose, the fusion center uses the reporting order feature to detect the malicious sensor nodes. as assumptions, the target will be under a certain situations (either present or absent) before starting the detection phase, and its situations will not change until the end of the phase. the fusion center assumes that each sensor node in the network is a suspicious sensor node. a suspicious sensor node is a sensor node that may be a malicious sensor node, in which the fusion center must test its behavior to determine its type (either normal or malicious). in order to detect the malicious sensor nodes, the fusion center sends two sequences of fake packets to all suspicious sensor nodes and collects the local decisions of all sensor nodes after each sequence. the detection technique is based on the dependent malicious sensor nodes strategy as described in 3.3. the aim of the algorithm is to trick a suspicious sensor node with two sequences of fake packets denoted by f1 and f2, respectively, and requests the local decision of the sensor node after each sequence. building the sequences of fake packets depends on the reporting order of the suspicious sensor node. assuming the existence of a suspicious sensor node with t as a reporting order, then f1 will contains t− 1 packets such that all of them are ones (i.e. f1 = (1, · · · , 1 )), and f2 will contains t− 1 packets such that all of them are zeros (i.e. f2 = (0, · · · , 0 )), in which in both sequences the ith packets denotes the local decision of the ith sensor node (1 ≤ i < t). because of the nature of the sensor nodes (i.e. sensor nodes are low power consumption units), the malicious sensor nodes are unable to distinguish between normal and fake packets. so, a malicious sensor node will attend to fake packets and save them as normal packets sent from another sensor node in the network. this will create an unrealistic environment for the malicious sensor node, thus send its local decisions based on this environment. in contrast, a normal sensor node will not interact with fake packets, and send a correct local decision (of 80% accuracy). if the suspicious sensor node is normal, its local decision will be identical for both sequences f1 and f2. on the other hand, if the suspicious sensor node is malicious, its strategy may force it to respond, in some cases, with two different local decisions, thus, be detected as a malicious sensor node. based on that, the fusion center can detect a subset of malicious sensor nodes, as follows: • if t > k, the malicious sensor node will respond with h1 for f1 and with h0 for f2. hence, be detected as malicious. • if t ≤ k and k − (n− t + 1) > 0, then we have two cases, as follows: – if the strategy of the malicious sensor node is always-one, it will respond with h1 for f1 and with h0 for f2. thus, be detected as malicious. – if the strategy of the malicious sensor node is always-false and the target is currently absent (i.e. h = h0), it will respond with h1 for f1 and with h0 for f2. thus, be detected as malicious. https://doi.org/10.15837/ijccc.2020.5.3825 9 algorithm 2 pseudocode of detection algorithm function fakepackets (n) 2: for i ← 1 to n do nodestate[i] ← normal 4: end for for i ← 1 to n do 6: f1 ← getones(i) send f1 8: receive localdecision1 f2 ← getzeros(i) 10: send f2 receive localdecision2 12: if localdecision1 6= localdecision2 then nodestate[i] ← malicious_unknown 14: end if end for 16: return nodestate end function when the fusion center detects a suspicious sensor node as malicious, it will not be able to identify its strategy. therefore, the fusion center considers the strategy of new malicious sensor nodes as unknown at the end of the detection phase. the fusion center does not run the detection phase at each round as there is a small percentage of change in the network topology between successive rounds. instead, the fusion center runs the detection phase after every r rounds. the fusion center cannot guarantee the integrity of the network by running the detection phase once as some normal sensor nodes may be compromised with time. so, the detection phase must run periodically. 4.3 the prevention phase as shown in section 4.2, the fusion center can detect a subset of the malicious sensor nodes, assisting in the prevention of influencing the global decision. when a malicious sensor node has been detected, the fusion center cannot prevent it from sending its local decisions. as a substitute, the fusion center takes advantage of its knowledge of the behavior of malicious sensor nodes to correct their local decisions if possible and use the amended decisions alongside local decisions of normal sensor nodes to calculate the global decision using fsp algorithm. the fusion center must identify the strategy of malicious sensor nodes to correct its local decision. the fusion center can only correct local decisions of malicious sensor nodes with always-false. therefore, the fusion center ignores local decisions of other malicious sensor nodes with strategies but always-false. the fusion center tracks the strategy of each malicious sensor node to handle its local decision (i.e. either to ignore or to correct it). recall that when the fusion center detects a suspicious sensor node as a malicious, it will not be able to identify its strategy. therefore, fusion center considers the strategy of new malicious sensor nodes as unknown at the end of the detection phase. the fusion center runs the prevention phase at each round after collecting local decisions of all sensor nodes and before calculating the global decision. the prevention phase depends on the behavior of malicious sensor nodes, and the saved strategy and current local decision of each malicious sensor node. the prevention method is divided into 7 rules as shown in table 2: https://doi.org/10.15837/ijccc.2020.5.3825 10 table 2: rules of prevention method. saved strategy local decision new strategy new local decision unknown present always one present unknown absent always zero absent always one present always one present always zero absent always zero absent always one absent always false reverse the local decisionalways zero present always false always false present or absent always false based on expression 4, the fusion center can determine if a malicious sensor node is acting normally or maliciously. so, the prevention method is only used with malicious sensor nodes whose has been identified as acting maliciously during this round. when the prevention phase is over, the fusion center runs the fsp algorithm to calculate the global decision. the fsp algorithm will take into consideration normal sensor nodes, malicious sensor nodes acting normally, and malicious sensor nodes acting maliciously with always-false saved strategies during this round. algorithm 3 pseudocode of prevention algorithm function preventionalgorithm (n, k, localdecision, nodestate) newlocaldecision ← localdecision 3: for i ← 1 to n do sum ← sumoflocaldecision(localdecision, 1, i) if nodestate[i] 6= normal and sum ≥ k − (n − i + 1) and sum < k then 6: if nodestate[i] = malicious_unknown then if localdecision[i] = 1 then nodestate[i] ← always_one 9: else nodestate[i] ← always_zero end if 12: else if nodestate[i] = always_one and localdecision[i] = 0 then nodestate[i] ← always_false else if nodestate[i] = always_zero and localdecision[i] = 1 then 15: nodestate[i] ← always_false end if if nodestate[i] = always_false then 18: newlocaldecision[i] ← 1 − localdecision[i] end if end if 21: end for return newlocaldecision end function 5 results in this section, we present the simulation results to evaluate our methods. the simulation was done using matlab r2017a platform and ubuntu 16.04 lts machine. we define the success rate as the ratio between the number of rounds in which the fusion center finds a correct global decision to the total number of rounds. in this section, percentage of h1 is defined as the probability that the target is actually present in each round. this value is used to generate an assumed global decisions for each round before starting the simulation. https://doi.org/10.15837/ijccc.2020.5.3825 11 0 5 10 15 20 number of malicious sensor nodes 0.4 0.5 0.6 0.7 0.8 0.9 1 s u cc e ss r a te counting nodes fsp algorithm (a) 0 5 10 15 20 number of malicious sensor nodes 0.4 0.5 0.6 0.7 0.8 0.9 1 s u cc e ss r a te counting nodes fsp algorithm (b) 0 5 10 15 20 number of malicious sensor nodes 0 0.2 0.4 0.6 0.8 1 s u cc e ss r a te counting nodes fsp algorithm (c) 0 5 10 15 20 number of malicious sensor nodes 0 0.2 0.4 0.6 0.8 1 s u cc e ss r a te counting nodes fsp algorithm (d) figure 2: fsp algorithm versus counting sensor nodes technique. 5.1 compare fsp algorithm with counting sensor nodes technique we compare the performance of fsp algorithm against a previous technique such as the counting sensor nodes technique over a different number of malicious sensor nodes. the fsp algorithm was introduced in section 4.1. while counting sensor nodes was explained in section 3.2. figure 2 shows the results of the comparison in different parameters. table 3 shows the used parameters for each experiment, and the improvement rate. the improvement rate is defined as the ratio between the number of cases in which fsp algorithm is better than the counting sensor node technique to the total number of cases. table 3: used parameters for each experiment in figure 2 figure rounds n k malicious sensor nodes percentage of h1 improvement rate do dz df 2a 100 20 0.6 ×n 100% 0 0 50% 90.48% 2b 100 20 0.6 ×n 0 100% 0 50% 100% 2c 100 20 0.6 ×n 0 0 100% 95% 95.24% 2d 100 20 0.6 ×n 30% 30% 40% 95% 90.48% the results show that the fsp algorithm performs better than the counting sensor nodes technique in different scenarios and with at least 90.00% improvement rate. 5.2 detection and prevention we compare the fsp algorithm with detection and prevention methods to the fsp algorithm without detection and prevention against the counting sensor node technique. figure 3 results of the comparison in different parameters. table 4 shows the used parameters for each experiment, and the improvement rate. the improvement rate is defined as the ratio between the number of cases in https://doi.org/10.15837/ijccc.2020.5.3825 12 which the fsp algorithm with detection and prevention is better than the fsp algorithm without detection and prevention to the total number of cases. table 4: used parameters for each experiment in figure 3 figure rounds n k malicious sensor nodes percentage of h1 improvement rate do dz df 3a 100 20 0.6 ×n 100% 0 0 50% 100% 3b 100 20 0.6 ×n 0 100% 0 50% 100% 3c 100 20 0.6 ×n 0 0 100% 95.00% 100% 3d 100 20 0.6 ×n 30.00% 30.00% 40.00% 95.00% 90.48% 0 5 10 15 20 number of malicious sensor nodes 0.4 0.5 0.6 0.7 0.8 0.9 1 s u cc e ss r a te fsp with fake packets fsp without fake packets counting nodes (a) 0 5 10 15 20 number of malicious sensor nodes 0.4 0.5 0.6 0.7 0.8 0.9 1 s u cc e ss r a te fsp with fake packets fsp without fake packets counting nodes (b) 0 5 10 15 20 number of malicious sensor nodes 0 0.2 0.4 0.6 0.8 1 s u cc e ss r a te fsp with fake packets fsp without fake packets counting nodes (c) 0 5 10 15 20 number of malicious sensor nodes 0 0.2 0.4 0.6 0.8 1 s u cc e ss r a te fsp with fake packets fsp without fake packets counting nodes (d) figure 3: compare the performance of fsp algorithm with and without detection and prevention. the results show that using the detection and prevention methods increase the performance of the fsp algorithm with at least 90% improvement rate. 5.3 k− thresholds values the fusion center uses k value to calculate κ that is used to determine if the target is either present or absent. choosing k value will significantly affect the performance of the fusion center. figure 4 shows the performance of the fusion center under different values of k and different numbers of malicious sensor nodes. table 5 shows the used parameters for experiments in figure 4. table 5: used parameters for experiments in figure 4. figure rounds n malicious sensor nodes percentage of h1 do dz df 4 100 16 0 0 100% 50% https://doi.org/10.15837/ijccc.2020.5.3825 13 5 10 15 20 number of malicious sensor nodes 0.4 0.45 0.5 0.55 d e te ct io n r a te s1 s2 s3 s4 figure 5: detection rate over different number of malicious sensor nodes. 10 20 30 40 50 60 70 80 90 100 k 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 s u cc e ss r a te malicious nodes = 6 malicious nodes = 8 malicious nodes = 10 malicious nodes = 12 figure 4: performance of the fusion center with different values of k. 5.4 fake packets detection technique the detection technique using fake packets was introduced in section 4.2. we have evaluated the performance of this technique in figure 5. table 6 shows the used parameters for each experiment in figure 5. in figure 5, the detection rate is defined as the ratio between the number of malicious sensor nodes identified as malicious to the total number of malicious sensor nodes. table 6: used parameters for experiments in figure 5. scenario rounds n k malicious sensor nodes percentage of h1 do dz df s1 100 20 0.6 ×n 100% 0 0 50% s2 100 20 0.6 ×n 0 100% 0 50% s3 100 20 0.6 ×n 0 0 100% 50% s4 100 20 0.6 ×n 30% 30% 40% 50% the results show that using the fake packets technique assists the fusion center in reducing the effect of malicious sensor nodes through its ability to detect some. after detecting a sensor node as malicious, the fusion center must identify its strategy in order to correct its local decision. figure 6 studies the fusion center’s ability identify strategies of malicious https://doi.org/10.15837/ijccc.2020.5.3825 14 sensor nodes. table 7 shows the used parameters for experiment in figure 6. in figure 6, the detection rate is defined as the ratio between the number of malicious sensor nodes with correct identified strategies to the total number of malicious sensor nodes of that strategy. table 7: used parameters for experiment in figure 6. rounds n k malicious sensor nodes percentage of h1 do dz df 100 20 0.60 ×n 30.00% 30.00% 40.00% 50.00% 5 10 15 20 number of malicious sensor nodes 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 d e te ct io n r a te always-one always-zero always-false figure 6: identifying strategies of malicious sensor nodes. the results show that the fusion center is able to identify strategies of always-one and always-false malicious sensor nodes with various detection rates. moreover, the results show that malicious sensor nodes with always-zero strategy are able to withstand the fake packets technique. 6 discussion in this paper, we have introduced a new technique to improve the performance of target detection in wireless sensor networks (wsns). our technique is divided into three phases: the fail silent pair (fsp) algorithm, the detection phase based on the fake packets technique, and the prevention phase. the fsp algorithm was introduced in section 4.1. the fsp algorithm randomly distributes the sensor nodes into pairs. then it takes into consideration the pair pk if both sensor nodes in the pair have the same local decision. otherwise, the fsp algorithm ignores the pair pk. section 5.1 shows that the fsp improves the accuracy of the global decision. however, we think the fsp algorithm will have a larger effect when utilized with other types of sensors such as sensors with the ability to measure distance or determine location. the detection technique was introduced in section 4.2. the detection technique is based on misleading malicious sensor nodes with an unrealistic environment using sequences of fake packets. section 5.4 showed that this technique assists the fusion center to reduce the effect of malicious sensor nodes through its ability to detect some. the prevention technique was introduced in section 4.3. the fusion center uses the prevention technique to correct the local decisions of malicious sensor nodes of always-false strategy. section 5.4 fusion center’s ability to identify the strategies of malicious sensor nodes. the fusion center is able to identify strategies of always-one and always-false malicious sensor nodes with various detection rates. in contrast, malicious sensor nodes with always-zero strategies can be detected as malicious as shown in figure 5, but the fusion center is incapable to identify their strategy as shown in figure 6. https://doi.org/10.15837/ijccc.2020.5.3825 15 the introduced methods can enhance the fusion center’s performance and the accuracy of the global decision. however, more improvements can be made. future work will focus on using other types of sensor nodes, enhancing the detection and prevention methods to increase the detection rate, optimizing the fake packets technique to reduce the overhead of the fusion center, and using artificial intelligence to optimize the process of choosing the system’s parameters (e.g. k, r, etc.). 7 conclusions in this paper, we have presented a new and simple technique to improve the performance of target detection in wireless sensor networks (wsns). we introduced the fail silent pair (fsp) algorithm to calculate the global decision in the fusion center. the fsp algorithm randomly distributes all sensor nodes into pairs then only consider pairs in which both sensor nodes have the same local decision. this technique can reduce the effect of incorrect local decisions. also, we introduced a new technique to improve the detection of dependent malicious sensor nodes in the network leading to the improvement of the accuracy of the global decision. the detection technique depends on misleading the malicious sensor nodes with an unrealistic environment using sequences of fake packets. a malicious sensor node will respond with different local decisions for each sequence of fake packets, leading to its detection. finally, we introduced a new prevention technique to handle the local decisions of the malicious sensor nodes. this technique is based on prior knowledge of the behavior of malicious sensor nodes. the fusion center runs the prevention technique before calculating the global decision to reverse local decisions of malicious sensor nodes of identified strategies. this assists in improving the accuracy of the global decision. funding this work is supported by king abdullah ii design and development bureau (kaddb). author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] althunibat, s.; antonopoulos, a.; kartsakli, e.; granelli, f.; verikoukis, c. 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(2005). embedded systems handbook. crc press, 2005. https://doi.org/10.15837/ijccc.2020.5.3825 17 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: ahmed, a.; hababeh, m.; abu-hantash, a.; abuhour, y.; musleh, h. (2020). reduce effect of dependent malicious sensor nodes in wsns using pairs counting and fake packets, international journal of computers communications & control, 15(6), 3825, 2020. https://doi.org/10.15837/ijccc.2020.5.3825 yonal136-155-pb.pdf international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(5), 647-659, october 2019. exploring analytical models for performability evaluation of virtualized servers using dynamic resource y kirsal yonal kirsal* department of electronics and communication engineering european university of lefke, lefke, north cyprus tr-10 mersin, turkey *corresponding author:ykirsal@eul.edu.tr abstract: virtualization of resources is a widely accepted technique to optimize resources in recent technologies. virtualization allows users to execute their services on the same physical machine, keeping these services isolated from each other. this paper proposes the analytical models for performability evaluation of virtualized servers with dynamic resource utilization. the performance and avalability models are considered separately due to the behaviour of the proposed system. the well-known markov reward model (mrm) is used for the solution of the analytical model considered together with an exact spectral expansion and product form solution. the dynamic resource utilization is employed to enhance the qos of the proposed model which is another major issue in the performance characterization of virtulazilation. in this paper, the performability output parameters, such as mean queue length, mean response time and blocking probability are computed and presented for the proposed model. in addition, the performability results obtained from the analytical models are validated by the simulation (des) results to show the accuracy and effectiveness of the proposed work. the results indicate the proposed modelling results show good agreement with des and understand the factors are very important to improve the qos. keywords: analytical models, markov reward model, performability evaluation, virtulazilation, dynamic resource utilization. 1 introduction with the rapid growth of network applications, the management of resources and bandwidth in future system is becoming more complicated. virtualization is one of the techniques that serve large number of tasks and multimedia applications with high demands by using a single physical machine [12]. virtualization allows users to better utilize the resources that networks have available in a physical machine. the physical machine considered for virtualization purpose has a lot more resources than a personal computer such as a lot more cpu, ram and the hard disk space. thus, all of those resources can be emulated inside a software and with this software many virtual machines can be put on a single physical machine [18]. virtualization allows service providers to lower their capital expenditures instead of buying many physical machines. in addition, centralized management with virtualization can be easily reachable instead of having to reach many individual physical machines providers only to manage one physical machine. in addition, all the updates or patches that the network to do for each of those individual machines can be done through from one central location. so, with the copyright ©2019 cc by-nc 648 y kirsal centralized management service providers have lower operational expenses because it takes less effort to manage these multiple machines [1]. however, putting multiple machines on one machine is not anything new. currently, service providers have servers running these virtual machines for their subscribers in cloud computing environment and need additional computing resources for different server. thus, they begin to add other virtual machines and these could be larger virtual machines depending on the business need [15]. obtaining the best qos requirements and improve the system performance of virtualized servers in cloud computing is a challenging task. even though it is not a new topic, qos modelling and evaluation of virtualized servers in such environment is still one of the key issues in order to obtain qos measurements. modelling and performance evaluation of such system in an analytical point of view help to service providers to predict the complex system behaviour. this might lead to improve the system qos characterization in different aspects. therefore, the main focus in this paper is to model and analyse the qos of the proposed work considering the availability issues of a physical and virtual machines together with dynamic resource utilization. in other words, the physical and virtual machine failures and repairs are considered in analytical point of view. in addition, dynamic resource utilization is also employed to enhance the qos of the proposed model which is another major issue in the performance characterization. using the proposed model, important performability measures, such as mean queue length, mean response time and blocking probability can be computed. the well-known markov reward model (mrm) is used for the solution of the analytical model considered together with an exact spectral expansion and product form solution. the rest of the paper is organized as follows: section ii gives motivation of the proposed model. section iii describes the proposed model and the solution approach. numerical results and discussions are given in section iv. conclusions and future-work are provided in section vi. 2 motivation with the widespread deployment and development of cloud computing facilities, the importance of virtualized cloud systems has significantly grown. the resource allocation is one of the important factors which affect the qos of the virtualized servers [7]. the modeling of resource allocation and evaluation in cloud computing is considered in [2,6,13,19,21]. an analytic probabilistic model is presented in [2] to calculate profit in a virtualized cloud data center. the proposed model accurately calculate the arrival rates based on the external and internal requests for virtualized cloud data centers. in [13] the authors proposed a web application model via simulation to get the behavioral patterns of different users and an performance analysis is done for resource utilization in cloud computing. the energy-efficient scheduling of virtual machine resource reservations in the cloud data center is presented in [19] focusing on cpu applications. the main aim is to schedule all reservations non-preemptively considering the limitations of a physical machine capacities to minimize the total energy consumption. the authors in [21] proposed an effective evolutionary approach for virtual machine allocation that can maximize the energy efficiency of a cloud data center. this is done by designing a simplified simulation using cloudsim that speed up the process of the proposed work. however, those proposed models do not consider availability issues of both virtual and physical machines for dynamic resource utilization. in addition, many existing studies mainly focus on performance analysis of virtual servers in cloud computing systems [5, 6, 10, 11, 17, 20]. performance modelling and evaluation of the virtual servers in the cloud computing is considered in [5]. however, two virtual machines and a physical machine have been considered for performance evaluation which is not practical case. in [6] and [17] a performance management system is proposed based on an analytical exploring analytical models for performability evaluation of virtualized servers using dynamic resource 649 queuing model on cloud. in those studies, the web applications are modeled as queues and virtual machines are modeled as service centers. thus, the queuing theory models are applied to dynamically create and remove virtual machines in the cloud to enhance the system performance. the performance models for service migration have been addressed in [20] to predict the virtual machine migration time and the resources availability. however, the analysis done above studies considers only the pure performance model which ignore failure and recovery behaviour of the system. but in reality such systems are prone to failures due to hardware and software failures. an optimization method for the scheduling of scientific work flows on cloud systems is presented in [16]. a performability model is presented which provides the fitnesses of explored solutions by use of a meta-heuristic algorithm. in [9] a hierarchical model is employed for analysis of virtual machines failure and recovery with respect to the system behavior. in addition, the proposed model used in [9] for virtual machine mode was continuous time markov chain (ctmc) and a stochastic reward nets (snr) is employed in [8] to model and analysis of the availability of a virtualized system. in summary, however, the performability analysis of virtual machines with physical machine failure and recovery behaviour together with dynamic resource utilization have not been considered in none of the presented studies for an analytic probabilistic model. in this paper, the proposed model considers analytical models for performance and availability issues together with dynamic resource utilization in an attempt to understand, and improve system qos. in addition, the analytical models results are validated by the discrete event simulation (des) results to show the accuracy and effectiveness of the proposed work. the results indicate the proposed modelling results show good agreement with des. 3 proposed model and solution approach in this section, the proposed analytical models and the solution approaches are presented to evaluate the qos of the virtualized machines considering availability of physical as well as virtual machines together with dynamic resource utilization. in order to obtain realistic qos output measurements dynamic resource utilization is also considered which can maximize the memory usage depending on the number of requests and failures within the proposed model. the main idea is to enhance the overall performance of the entire system by sharing the available resources. all the virtual machines used by the same physical machine are perfectly coordinated and synchronized. hence, the tasks can be serviced more efficiently since the service ability will change dynamically when the virtual machine failures occur. the performance and availability models are considered separately due to the behavior of the proposed model. the exact spectral expansion solution approach is used to obtain steady state probabilities for the availability model, on the other hand, the steady state probabilities of the performance model are obtained by using the product form solution approach. therefore, the mrm is employed to obtain performability output parameters for the proposed model. in mrm, the steady state probabilities of performance model are obtained (πpi ) and are passed as reward rates to the availability model. 3.1 model description the handling of an incoming tasks for the proposed system with single physical machine (pm), a number of parallel homogeneous virtual machines (vms) and a finite queue, l are illustrated in fig. 1. hence the total system capacity is k where k=l+vm. as shown in the figure, the arriving tasks reach first in a finite buffer and then distributed by the pm with a mean service time 1/µ according to virtual machine availability. the pm will distribute the tasks for processing if and only if any of the vms are available to handle a new task. hence, each vm 650 y kirsal can also service and process the tasks in the system with a mean service time 1/µ. a vm can only serve one task at a time. figure 1: the proposed system the resource allocations of such systems can be implemented in various ways depending on the system architecture. the dynamic resource allocation is presented which assures fair sharing of the cpu or the memory. the proposed allocation considers the avalability of the virtual machines and number of vms in the system. it determines if the vm is busy or idle, if it is idle, it starts to transmit. if the vm is busy, the physical machine sends the tasks to vms when they available. however, if the vm fails the shared cpu will not be wasted and can be dynamically used by the others available vms until the failed vm will be repaired. thus, the marginal distribution of the number of operative vms is easily seen to be binomial [14]: qi,. = ∞ ∑ j=0 qi,j = ( v m i ) ( ηv ηv + ξv )i ( ξv ηv + ξv )v m−i where i = 0, 1, . . . , v m. (1) where qi is the probability that i, vm are operative. hence, the processing capacity of the system, which is defined as the average number of operative virtual machines, is equal to e(i)=vm(ηn/ηn+ξn). the ηn and ξn are recovery and failure rates of virtual machines in the proposed system. the failure and recovery behaviour of the proposed system is explained in section 3.2 in more detail. however, for a good qos measurements in such system the availability of virtual machines and impact of on system performance should be considered. therefore, in order to get more realistic results, dynamic resource utilization (effective service rate) for the proposed system is calculated considering all possible combinations of the number of operative vms. then the expected effective service rate is calculated as follows: µeff = v m ∑ i=0 qi · µi (2) equation 2 is used to calculate effective service rate of the proposed work when a vm failure occurs and used to optimize the resource allocation in the proposed work. in other words, let’s define t as a service time of a virtual machine and teff as dynamic service time of virtual machine with a failure with means µ and µeff, respectively. hence, for dynamic resource utilization the service time of a task is equal to the smaller one between t and teff. since the random variables exploring analytical models for performability evaluation of virtualized servers using dynamic resource 651 t and teff have exponential distribution, the service times of a task is exponentially distributed with mean; e[t ] = 1 µ = e[min(t, teff)] = 1 µ + µeff (3) as shown in fig. 1, the tasks join the system with the poisson distribution, hence the average arrival rate is λ [4,10,11]. on the other hand, the physical and virtual machines in the system are prone to failures, each vms and pm get corrupted with an average rate of ξv and ξp, respectively. the failed vms and a pm then repaired with an average repair rate of ηv and ηp, respectively. the repair priority is given to pm since the vms can not operate when a pm failure occurs. if there are tasks in the queue, the operative vm cannot be in a pending state. however, when any vm fails due to the corruption, new tasks can be served by the first available vm. it is also assumed that if an operative vm fails, it becomes available again at the breakpoint. if all vms are busy or failed, the queue is growing with average rate of λ. in order to obtain more realistic qos output parameters both performance and availability issues are considered together with dynamic resource allocation. the performance and availability models considered are presented with the resulting mrm solution approach in detail following sections. 3.2 the availability model of the proposed system the availability model shows possible vms and a pm failures and repairs in the proposed system. an exact spectral expansion solution approach is used for availability model to obtain the steady state probabilities. the both failures and repairs behaviour are shown in fig. 2. as mentioned before, the distribution of time intervals between vms and a pm failures are exponential and given by mean 1/ξv and 1/ξp, respectively. at the end of the vms and a pm failures, the vms and a pm require an exponentially distributed repair time with mean 1/ηv and 1/ηp. as clearly seen from the fig. 2 that multi-repairman facility is assumed for all of the vms in order to get realistic qos measurements. figure 2: the availability model considered assume that πai,j represents the steady state probabilities of the availability model. hence, the state of the availability model at time t can be described by a pair of integer valued random variables, i(t) and j(t), specifying the vms and the pm failures and repairs, respectively. as clearly seen from the fig. 2, j(t) describes the pm failures and has two modes. let j(t)=0,1 denote a binary value indicating whether or not the system is down due to a pm failure. in other words, 0 indicates the whole system is down due to a pm failure and 1 indicates all vms are operate. on the other hand, i(t) represents the operative states of the vms and there are v m + 1 configurations. thus, i(t) = 0, 1, · · · , v m. hence, z = [i(t), j(t)]; t ≥ 0 is an irreducible markov process on a lattice strip that models the system. its state space is (0, 1, · · · , v m +1) x (0, 1). in other words, the failure and repair behaviour of the vms can be represented in the horizontal as well as the failure and recovery behaviour of the pm in the vertial direction of a lattice strip. the three matrixes of the spectral expansion can be defined when the state 652 y kirsal diagram is obtained. hence, a, b, and c are the matrixes that indicate of the transition rates of the proposed model. the definition and the formation of the matrixes can be found in [3,11]. therefore, clearly, the elements of a,b and c depend on the parameters v m, ξv, ηv, ηp,ξp. the transition matrices of a system with v m machines are of size (v m +1)×(v m +1). it is possible to specify the numbering of the matrices as (0,1,2,· · · ,v m + 1) for states (0,1,2,· · · ,v m + 1) respectively. the state transition matrices a, aj, b, bj, c, and cj can be given as follows: a = aj =           0 v mηv 0 0 0 0 0 ξv 0 (v m − 1)ηv 0 0 0 0 0 2ξv 0 (v m − 2)ηv 0 0 0 0 0 3ξv 0 . . . 0 0 0 0 0 . . . 0 2ηv 0 0 0 0 0 (v m − 2)ξv 0 ηv 0 0 0 0 0 (v m − 1)ξv 0 0 0 0 0 0 0 v mξv           b = bj =     ηp 0 0 0 0 0 ηp 0 0 0 0 0 . . . 0 0 0 0 0 ηp 0 0 0 0 0 ηp     c = cj =        0 0 0 0 0 0 0 0 ξp 0 0 0 0 0 0 0 ξp 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 ξp 0 0 0 0 0 0 0 ξp 0 0 0 0 0 0 0 0        therefore, the proposed system can be solved using the well-known exact spectral expansion solution method. thus, the steady state probabilities for the availability model, πai,j can be obtained using the steady state solution. the solution is given for systems with bounded queuing capacities. the steady-state probabilities of the system considered can be expressed as: pi,j = lim p(i(t)=i,j(t)=j) t→∞ ; 0 ≤ i ≤ v m + 1, 0 ≤ j ≤ 1 (4) let’s define certain diagonal matrices of size (v m + 1) × (v m + 1) as follows: daj (i, i) = v m+1 ∑ k=0 aj(i, k); d a(i, i) = v m+1 ∑ k=0 a(i, k); (5) dbj (i, i) = v m+1 ∑ k=0 bj(i, k); d b(i, i) = v m+1 ∑ k=0 b(i, k); (6) dcj (i, i) = v m+1 ∑ k=0 cj(i, k); d c(i, i) = v m+1 ∑ k=0 c(i, k); (7) and q0 = b, q1 = a−d a −db −dc, q2 = c. hence, all state probabilities in a row can be defined as: vj = (p0,j, p1,j, · · · , pv m+1,j); j = 0, 1 (8) exploring analytical models for performability evaluation of virtualized servers using dynamic resource 653 the steady-state balance equations for bounded queuing systems (0 ≤ j ≤ 1) can now be written as follows: v0[d a 0 + d b 0 ] = v0a0 + v1c1 (9) vj[d a j + d b j + d c j ] = vj−1bj−1 + vjaj + vj+1cj+1; 0 ≤ j ≤ 1 (10) vl[d a + dc] = vl−1b + vla (11) the normalizing equation is given as follows: l ∑ j=0 vje = 1 ∑ j=0 v m+1 ∑ i=0 p(i, j) = 1.0 (12) from the equations above, the following equation can be written: vjq0 + vj+1q1 + vj+2q2 = 0; 0 ≤ j ≤ 1 (13) furthermore, the characteristic matrix polynomial q(λ) can be defined as: qλ = q0 + q1λ + q2λ 2; q̄β = q2 + q1β + q0β 2; (14) where ψqλ = 0; |qλ| = 0; φq̄β = 0; |q̄β| = 0; (15) λ and ψ are eigenvalues and left-eigenvectors of qλ and β and φ are eigenvalues and lefteigenvectors of q̄β, respectively. note that, φ and β are vectors defined as: φ = φ0, φ1, . . . , φs+1 (16) β = β0, β1, . . . , βs+1 (17) furthermore, vj = v m+1 ∑ k=0 (akψkλ j+1 k + bkφk(i)β 2−j k ),0≤j≤ 1 and in the state probability form, pi,j = v m+1 ∑ k=0 (akψkλ j−m+1 k + bkφk(i)β 2−j k ) 0 ≤ j ≤ 1 (18) where λk(k = 0, 1, . . . , v m + 1) and βk(k = 0, 1, . . . , v m + 1) are v m + 1 eigenvalues [3,4,11]. the more details of the exact spectral expansion method can be found in [3]. therefore, all steady state probabilities of the availability model, πai,j can be obtained using the exact spectral expansion method. 3.3 the performance model of the system the performance model considers the arrival and service rates transitions of the single pm and multiple vms in this section. the state transition diagram of performance model of a proposed system is given in fig. 3. 654 y kirsal figure 3: the performance model considered let’s define the states i (i=0,1,2,· · · ,vm+l) as the number of tasks in the system at time t. in order to obtain perofrmability measures of the proposed model, the performance model is solved using general product form solution technique to get steady state probabilities. once all πpi obtained, they pass as reward rates to the availability model. ρ is the traffic intensity in the system where ρ=λ/µ. hence, the state probabilities, πpi can be obtained and are given in equation 19 [9,10]. πpi =      ρi i! · πp0 0 ≤ i ≤ v m ρi v mi−v m .v m! · πp0 v m < i ≤ v m + l (19) in equation 19, πpi is the probability that there are i tasks in the proposed system and π p 0 can be defined as follows: πp0 = [ v m−1 ∑ i=0 ρi i! + v m+l ∑ i=v m ρi v mi−v m.v m! ]−1 (20) hence, the average number of tasks in the proposed system, nv m can then be calculated as nv m = v m+l ∑ i=0 i · πpi which gives: nv m = [ v m ∑ i=0 i · ρi i! + v m+l ∑ i=v m i · ρi v mi−v m.v m! ] · πp0 (21) similarly, the blocking probability pb of the system can be calculated as: pb = p(v m + l) = λv m+k v mlv m!µv m+l · πp0 (22) using little’s formula, the average value of time of a task in a the system is can be calculated as follows: mrt = mql (1 − pb)λ (23) therefore, a mrm approach can be used to obtain the overall performability output parameters such as mean queue length, blocking probability and mean response time. equation 21 gives the mql assuming that all vms are operative. however, since only i vms are operative at any time, the mql can now be represented by ni where i is the number of operative vm. thus overall mql can be calculated as follows; mql = k ∑ i=0 ni 1 ∑ j=0 πai,j (24) exploring analytical models for performability evaluation of virtualized servers using dynamic resource 655 similarly, the blocking probability, pb and mrt of the proposed system can be evaluated as follow: pb = k ∑ i=0 pb,i 1 ∑ j=0 πai,j (25) mrt = k ∑ i=0 mrti 1 ∑ j=0 πai,j (26) 4 results and discussion in this section, numerical results are presented to understand the beahviour of the system and show the affect of the availability issues for virtualized servers with dynamic resource utilization. the performance and availability models are considered separately and a mrm is employed for steady state solution. as stated before, the results obtained from the analytical model and the simulation for each analysis are presented in order to validate as well as to show accuracy of the proposed analytical model. numerical results are presented here for performability measures of virtualized servers with dynamic resource utilization. the parameters used are mainly taken from the literature in order to be consistent [2,4–6,8–13,17,19,20]. the mean service rate is mainly application dependent. in fig. 4 qos results are presented as a function of the arrival rate for the proposed system with vm=50 and l=100 where, k=vm+l=150. the other parameters are µ = 0.016(tasks/sec), ηv=ηp = 0.5/h,ξv=ξp = 0.001/h and mean arrival rate per tasks varies from 0.05 tasks per second. qos output parameters, mean queue length and blocking probability, have been computed for both fixed resource allocation (fra) and dynamic resource allocation (dra) in fig. 4(a) and 4(b), respectively in order to show efficiency of the dynamic resource allocation in the proposed system. it can be clearly seen that dynamic resource allocation gives more promising performance results compared to fixed resource allocation scheme in terms of mean queue length and blocking probability. fig. 4 shows that as the rate of the incoming tasks increases, mean queue length and blocking probability results are also increases for both reservation schemes. however, when the virtual as well as the physical machine failures are considered, the system with dynamic resource allocation policy performs better than fixed resource allocation especially for the loaded system. this is due to the service utilization continues to increase by dynamic resource allocation. thus, the tasks can be serviced since the service ability will change dynamically when the virtual machine failures occur. on the other hand, all virtual machines have an equal share of resources in fra and some of the resources will be wasted due to failures. as the rate of the incoming requests increases this difference becomes less evident since the queuing capacity, k=150 becomes the main limiting factor. hence, please note that dra is used for the rest of the analysis since it gives better qos output results compare to fra when the both failures are considered. in figs. 5 and 6, qos output measurements are presented as a function of mean arrival rate for the proposed model with different virtual machines and a physical machine failure rates, respectively. the mean queue length, blocking probability and mean response time results are shown in fig. 5(a), 5(b) and 5(c), respectively with different virtual machines failures. the parameters are vm=50, k=150, µ = 0.016(tasks/sec), ηv=ηp = 0.5/h, ξp = 0.001/h and mean arrival rate per tasks varies from 0.05 tasks per second. as clearly seen that even though the dynamic resource utilization is used, the failures of the virtual machines significantly effect the system performance. in fig. 5 the pure perforamnce results gives best qos output measurements 656 y kirsal (a) mean arrival rate, λ (tasks/sec) 0.2 0.3 0.4 0.5 0.6 0.7 0.8 m e a n q u e u e l e n g th ( ta s k s ) 0 50 100 150 155 vm=50, k=150, µ=0.016 (tasks/sec), η v =η p =0.5/h, ξ v =ξ p =0.001/h fixed reseource allocation-analytical fixed reseource allocation-simulation dynamic resource allocation-analytical dynamic resource allocation-simulation (b) mean arrival rate, λ (tasks/sec) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 b lo c k in g p ro b a b il it y 0 0.1 0.2 0.3 0.4 0.5 0.6 vm=50, k=150, µ=0.016 (tasks/sec), η v =η p =0.5/h, ξ v =ξ p =0.001/h fixed reseource allocation-analytical fixed reseource allocation-simulation dynamic reseource allocation-analytical dynamic reseource allocation-simulation figure 4: qos results of the proposed model with fixed resource allocation and dynamic resource allocation: (a) mean queue length; (b) blocking probability (a) mean arrival rate, λ (tasks/sec) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 m e a n q u e u e l e n g th ( ta s k s ) 0 50 100 150 155 vm=50, k=150, µ=0.016 (tasks/sec), η v =η p =0.5/h, ξ p =0.001/h pure performance-analytical pure performance-simulation ξ v =0.01-analytical ξ v =0.01-simulation ξ v =0.5-analytical ξ v =0.5-simulation ξ v =0.1-analytical ξ v =0.1-simulation (b) mean arrival rate, λ (tasks/sec) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 b lo c k in g p ro b a b il it y 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.97 vm=50, k=150, µ=0.016 (tasks/sec), η v =η p =0.5/h, ξ p =0.001/h pure performance-analytical pure performance-simulation ξ v =0.01-analytical ξ v =0.01-simulation ξ v =0.5-analytical ξ v =0.5-simulation ξ v =0.1-analytical ξ v =0.1-simulation (c) mean arrival rate, λ (tasks/sec) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 m e a n r e s p o n s e t im e ( s e c o n d s s ) 50 100 150 185 vm=50, k=150, µ=0.016 (tasks/sec), η v =η p =0.5/h, ξ p =0.001/h pure performance-analytical pure performance-simulation ξ v =0.01-analytical ξ v =0.01-simulation ξ v =0.5-analytical ξ v =0.5-simulation ξ v =0.1-analytical ξ v =0.1-simulation figure 5: qos results of the proposed model with different failure rates: (a) mean queue length; (b) blocking probability; (c) mean response time for each analysis since the system never fails. however, the results show in fig. 5 that as virtual machine failure rate increases the qos degradation becomes more evident. for instance, for ξv = 0.1/h and ξv = 0.5/h the all qos values dramatically increases. in other words, the tasks will be piled up in the system quickly as shown in fig. 5(a) when the virtual machine failure increases. the system will not serve the tasks and the system reaches the maximum capacity quickly (i.e, k=150). in fig. 5(b), the system started to block incoming tasks due to frequent virtual machine failures hence the blocking probability increases as the virtual machine failure increases. similarly, the system will not able to serve the tasks due to virtual machine failures hence the mean response time of the proposed system also increases as shown in fig. 5(c). on the other hand, in fig. 6 the affect of the physical machine failures are shown. similarly exploring analytical models for performability evaluation of virtualized servers using dynamic resource 657 (a) mean arrival rate, λ (tasks/sec) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 20 40 60 80 100 120 140 150 vm=50, k=150, µ=0.016 (tasks/sec), η v =η p =0.5/h, ξ v =0.001/h ξ p =0.001-analytical ξ p =0.001-simulation ξ p =0.01-analytical ξ p =0.01-simulation ξ p =0.1-analytical ξ p =0.1-simulation (b) mean arrival rate, λ (tasks/sec) 0.2 0.3 0.4 0.5 0.6 0.7 0.8 b lo c k in g p ro b a b il it y 0 0.1 0.2 0.3 0.4 0.5 0.6 vm=50, k=150, µ=0.016 (tasks/sec), η v =η p =0.5/h, ξ v =0.001/h ξ p =0.001-analytical ξ p =0.001-simulation ξ p =0.01-analytical ξ p =0.01-simulation ξ p =0.1-analytical ξ p =0.1-simulation (c) mean arrival rate, λ (tasks/sec) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 m e a n r e s p o n s e t im e ( s e c o n d s ) 60 80 100 120 140 160 180 vm=50, k=150, µ=0.016 (tasks/sec), η v =η p =0.5/h, ξ v =0.001/h ξ p =0.001-analytical ξ p =0.001-simulation ξ p =0.01-analytical ξ p =0.01-simulation ξ p =0.1-analytical ξ p =0.1-simulation figure 6: qos results of the proposed model with physical machine failure rates: (a) mean queue length; (b) blocking probability; (c) mean response time to the fig. 5, the mean queue length, the blocking probability and the mean response time results are presented in fig. 6 with a physical machine failure and repair. the parameters used are the same as used in fig. 5. however, in this analysis the virtual machine failures are kept constant, ξv = 0.001/h and a physical machine failure varies from 0.001 to 0.1. as clearly seen from the fig. 6, the system qos degrades with a physical machine failure in terms of mean queue length, blocking probability and mean response time in figs. 6(a), 6(b) and 6(c), respectively. when a physical machine fails all of the virtual machines do not operate, hence the failure of the physical machine limits the access to the virtual machines. as the failure rate of a physical machine increases all of the performability measures increases and the qos is getting worst. in other words, less physical machine failure, the better the system performance gets. in the case of a physical machine fails, it will cause of blocking the incoming tasks as shown in fig. 6(b), increase of the tasks in the queue as well as late responses from the system as shown in fig. 6(a) and 6(c), respectively. therefore, it is very important to understand the failure and recovery of both vm and pm as well as dynamic resource utilization so that such practical systems could be incorporated improved in terms of qos for such systems. 5 conclusions in this paper, the analytical models have been modelled for performance and availability issues together with dynamic resource utilization in order to understand and improve system qos parameters. the behaviour of failure and recovery of the virtual machines and a physical machine are considered as an availability model and the exact spectral expansion solution approach is used to obtain steady state probabilities. the steady state probabilities of the performance model 658 y kirsal are obtained by using the product form solution approach. therefore, the mrm is employed to obtain performability output parameters for the proposed model. in addition, dynamic resource utilization is also employed to enhance the qos of the proposed model. the main focus in the analysis is given to performability output parameters such as mean queue length, blocking probability and mean response time. numerical results obtained clearly show that, the virtual machine and especially a physical machine failures affect the system qos significantly. the failures of a physical machine cause more significant performance degradation. it can be clearly seen from the numerical results that dynamic resource allocation gives more promising performance results compared to fixed resource allocation. the qos results obtained from the analytical model are compared to the des results in order to show the accuracy and effectiveness of the proposed models. findings show that the analytical models and a mrm technique presented show good agreement with des. the models presented in this paper are flexible and useful for modelling systems with similar behaviour and queuing considerations. bibliography [1] borangiu, t.; 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(2019). energy-aware virtual machine allocation for cloud with resource reservation. journal of systems and software, 147, 147–161, 2019. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 4 (december), pp. 761-778 a multi-agent system architecture for coordination of the real-time control functions in complex industrial systems j. wu, r. tzoneva jiang wu, raynitchka tzoneva cape peninsula university of technology south africa, 7350 bellville, symphony way email: wolfgang.jw@gmail.com, tzonevar@cput.ac.za abstract: multi-agent system architecture for coordination of the real-time control functions in complex industrial systems is presented. the problem which must be solved out is how efficiently to organize the interactions between tasks in order to satisfy the functionality and the time restriction of the system. in order to solve this problem, the treatment of the task interactions is separated from the tasks and is implemented by the proposed multi-agent system. a general three level multi-agent system is introduced to manage the interactions and schedule of tasks. a framework of building of the schedule of the tasks is also presented. finally, the benefits of the proposed architecture are discussed. keywords: middleware, multi-agent system architecture, real-time task, coordination. 1 introduction real-time systems are usually more complex than other systems because they are based on concurrency, have to deal with multiple independent streams of input events and to produce multiple outputs [1]. real-time systems are not means to get the results as soon as possible, the requirements towards their operation is to get the results at a prescribed point of time within defined time tolerances [2]. real-time systems are divided into two classes on the basis of their time tolerances (see [3] and [4]): hard real-time systems and soft real-time systems. hard realtime systems are those whose deadlines for implementation of the tasks must absolutely be met or the system will be considered to have failed (and this failure might be catastrophic), while soft real-time systems allow for some deadlines, at least occasionally, to be missed with only a degradation in performance but not a complete failure. on the basis of the said above it can be concluded that not only the functions of the systems must be completed, but also the time restriction has to be achieved in order to obtain the goals of the real-time system. the functions of the real-time system are represented by different software tasks having a lot of interactions between themselves. in order to achieve the time requirements of the real-time system a schedule of system’s tasks and coordination of their interactions must be organized efficiently. it is supposed in the paper that every real-time function is realized by a task and each task is coded in one individual software program. this means the interactions between the functions or tasks only may be implemented via ports which are virtual/logical data connections that can be used by the programs to exchange data directly, instead of going through a file or other temporary storage location [5]. agent theory is applied to build a multi-agent system which coordinates the execution of real-time tasks efficiently using the following capabilities of the agents: autonomy [6], social relations [7], reactivity [8] and pro-activeness [9]. the presented in the paper multi-agent system architecture consists of three agents which represent three levels of processes for interactions and schedule manipulation. the interaction agent, the lowest agent, is used to receive requisitions copyright c⃝ 2006-2011 by ccc publications 762 j. wu, r. tzoneva for interactions from the tasks; the coordinating agent, the middle level agent, is applied to do the coordination of the tasks; the mapping agent, the highest level agent, takes charge of storing the information of how to deal with the interactions of tasks. the development and running of tasks which realize the functionality of the real-time system are simplified by adopting this multi-agent architecture. the tasks may only focus on how to achieve the aim of the functionality and how to send and receive the interaction information from the multi-agent system. all interactions are treated by the multi-agent system. 2 the problem of real-time control functions the real-time system is a system in which not only functionality but also the time restriction for implementation of these functions (tasks) must be satisfied. interaction packets between tasks and the order of execution of these tasks are two main factors which impact the period of time for completion of a given function of the system. traditionally, the interaction packets between two tasks are implemented directly and it is not necessary to be managed. thus, task ta must know how to connect to task tb and what is the format of the message which tb has to receive if ta wants to send a message to tb. this obviously increases the burden of the engineering work during the development, running and maintenance of the system. usually the order of execution of tasks is scheduled by a high level task which calls some type of scheduler. the scheduler evaluates the time restriction of each task and assigns a priority to the task by a specific arithmetic, and then the task possesses the computation resource (cpu and memory) ordered according to this priority. different strategies of scheduling have different arithmetic, but all of them consider that the task is the smallest unit to be processed. the process of determination of priority of the tasks is also necessary to be dynamic one because the environment of the real-time system is open and dynamic one. some methods such as giotto [10], let [11], tdl (see [12] and [13]) and htl [14] try to solve the problem for scheduling by division of the task into several subtasks which have no internal interaction. these methods dodge the problem that the priority of tasks should be dynamic but this is still no an ideal solution for the problem for interaction packets management. 3 method for coordination of the real-time control functions to implement real-time system efficiently, the interaction packets between tasks must be managed and the scheduling of tasks must be dynamic. the method provides a middleware to supply the function of the interaction packet management and the dynamic scheduling control. the method is not supposed to stop a task which is executed, it schedules tasks by controlling the time which invokes and continues execution of the tasks. 3.1 time points of the task there is no concurrency within a task, but the tasks in the real-time structure could be executed in parallel [1]. the tasks often execute asynchronously and are relatively independent of each other for significant periods of time. the tasks need to communicate and synchronize their operations with each other from time to time. therefore, the tasks need to be paused some times to wait for the feedback of communications and synchronization. a task is also possible to be paused by another task which has higher priority and to be re-executed again after the higher priority task releases the computation resource. a multi-agent system architecture for coordination of the real-time control functions in complex industrial systems 763 start time point and end time point are essential time points for every task. a task is invoked with parameters and then executed, see figure 1. that means the necessary parameters are prepared before the start time point. a task releases the computation resource which is used for execution after it completes the execution and output results of the execution at the end time point. figure 1: task with start and end time point of execution almost all of the interaction packets between the tasks (exclude loosely coupled message communication) make task to pause and continue its execution when the condition is satisfied, see figure 2. the pause can happen zero or several times during the execution of the task. figure 2: task to be paused loosely coupled (asynchronous) message communication is a special interaction of the task, it only sends a message out and does not need to wait for any feedback from the other task, see figure 3. as well as other interactions, it can happen zero or several times during the execution of the task. this point could be called iwf point (interaction without feedback point). figure 3: task’s interaction without feedback as mention above, the time points of a task could be start point, end point, paused point, continue point and iwf point. start point and end point happen one and only one time during a completed execution of task. other three time points may happen zero or several times during a completed execution of a given task, and must be noted that paused point and continue point always happened in couples. 764 j. wu, r. tzoneva thus, a task may have five type points which determine the run-able situation of the execution, they are start point and end point, paused point and continue point, and iwf point. the iwf point does not affect the execution of the task. before start point and after end point, as well as for the time from paused to re-executed points, the task is not able to execute, and the computation resources (at least cpu) are able to be assigned to other tasks. 3.2 separation of the interactions between real-time tasks the traditional way of interaction is changed in the new method, the interaction does not directly happen between two tasks in which one is sender and other is receiver. a requisition which contains the information of an interaction packet is sent to the middleware and then this information is sent to the target task from the middleware after the requisition is treated by the middleware. thus, operation of an interaction packet from task ta to task tb is divided into three phases which are: interaction packet sending (ta to middleware), interaction packet treatment (middleware), and interaction packet receiving (middleware to tb). to make the execution of the real-time system more efficient, all possible types of interaction must be registered in the middleware. and then, the task which sends an interaction packet does not need to know which particular task takes charge to receive the interaction packet information, the middleware will appoint a task which has capability and also is available to receive and treat the interaction packet. this aspect makes the real-time system more flexible during the development, runtime and even maintenance. engineers don’t need to know which task must receive the interaction packet during development, any interaction packet is simply just sent to the middleware. there are multi choices of tasks which may treat the interaction packet; the middleware will appoint one or more tasks to treat the interaction packet by evaluating the status of execution of the real-time system at runtime. this aspect also reduces the workload and limits the effect range when a function is added or removed because the tasks which realize other functions don’t need to do any modification to fit the change. the only one requirement is to register the interaction which the new task may send and receive. a task may receive an interaction packet only when it is not started and then be invoked or it is paused and waiting to receive an interaction packet to continue execution. a task does not possess the computation resource (cpu) before the task starts and continues. the middleware may schedule the tasks by controlling the time at which it sends the interaction packet to the target task. all changes of the status of the tasks must be sent to the middleware to monitor the status of the real-time system and schedule the tasks. figures 4 and 5 compare the treatment of interaction packets of the traditional way and the new method. one interaction packet is separated into two parts, interaction request sending and interaction packet receiving. interaction 1 in figure 4 is separated according to the proposed method into interaction 1a and interaction 1b as can be seen in figure 5; interaction 2 in figure 4 is separated into interaction 2a and interaction 2b in figure 5; interaction 3 in figure 4 is separated into interaction 3a and interaction 3b in figure 5. all the interactions in the section a and the section b are connected by the middleware. the target task of section b is selected by the middleware and the time at which the middleware sends interaction packet to the target task is decided by the middleware by evaluating the situation of the real-time system. 3.3 the mechanism of the coordination the middleware takes charge to receive interaction packet and send interaction packet to the target task. an interaction packet must be processed by several phases in the middleware and then could be sent to the target task. there are six phases to treat an interaction packet in the middleware. in figure 6, each ellipse which is located in the middleware represents a phase of a multi-agent system architecture for coordination of the real-time control functions in complex industrial systems 765 figure 4: tasks interact directly figure 5: tasks interact via middleware 766 j. wu, r. tzoneva treatment of interaction packet. by the order of treatment, they are: receive, change status of sending task, find the treatment plan, schedule the tasks, send and change the status of receiving task. receiving a requisition of an interaction packet is the first phase. the middleware must supply a common port to receive the interaction packet. the interaction packet should have several parts. first at all, the sender of the interaction packet must be indicated. the second is the type of the interaction packet. these two parts of the interaction packet is used to find the treatment plan. the third part is the main body of the interaction packet, the message. the last part indicates the time restriction of the interaction packet. this part may not be attached with the interaction packet because some interactions may have no time restriction. after the interaction packet is sent, the task may continue or pause its execution depending on if the interaction needs reply or not. the second phase is changing the status of the task which sends the interaction packet. any change of the status of tasks must be recorded to monitor the status of the real-time system. the status of the real-time system is foundation for scheduling of the tasks in the middleware of the real-time system. figure 6: treatment of interaction packet in the middleware finding the treatment plan is the third phase to treat the interaction packet. the middleware searches the treatment plan of the interaction packet in a database which stores the information of how to treat the interaction packets according to the sender indication and the type of the interaction packet. this search may find several plans, all of these plans must be sent to the next phase. the fourth phase is scheduling the tasks of the real-time system. the middleware decides which treatment plan will be performed and what time for performing of the plan to be selected based on evaluating the status of the real-time system and the time restriction from the information of the treatment plan and the interaction packet. if the interaction packet is not sent out immediately, it should be stored in a waiting queue, and be sent in time to the target task. the time when the interaction packet will be sent is changeable, every time the middleware makes the decision affects the waiting queue and the sending time of all the interaction packets which are waiting for sending could be reassigned. there are two statuses of tasks at which they could receive the interaction packet, one is before the task starts, and other one is the tasks is paused. when a task is executing, it could not be possible to receive any information from outside of the task because the task has no internal concurrency. in addition the interaction packet could contain only the parameters to invoke a a multi-agent system architecture for coordination of the real-time control functions in complex industrial systems 767 task which is not started or could be a message to make the task to continue the execution. the status of the task which receives the interaction packet is changed when the interaction packet arrives. thus, the receiving task’s status is necessary to be changed at the middleware for next evaluation of the real-time system. the graphic description of the treatment of an interaction packet is presented in figure 6. the arrows represent the work flow of treatment of the interaction packet. the middleware takes charge to find the target task to receive the interaction packet and schedule the tasks by controlling the interaction packet sending time. there are two times where the status of the real-time system changes during treating an interaction packet and reassigning the sending time of the interaction packets waiting in the queue. 4 general multi-agent system architecture for coordination of the real-time control functions the main function of the middleware is for a specific time to receive the interaction packet from a task, to analyze it, and to send the packet to other tasks which should do further treatment of the packet. to achieve this target, the middleware should have some information for the system: * knowledge of the relationship of the tasks except the tasks of middleware; * the time restriction of each interaction; * a map of the status of tasks which are running except the tasks of middleware. the knowledge of the relationship of the tasks is a static global view of the system. it records how to treat an interaction packet and supply the information for further treatment. the time restriction is specified in the relationship of the tasks, it is a guide to indicate when to do the communication. 4.1 analysis of the middleware the objectives of the middleware are achieved by the cooperation of the three agents. objectives of the middleware the middleware does not perform any real function which is required for real-time system from the outside of the system, but it plays an important role in the system to coordinate the tasks which perform the real functions by controlling the communications between them. the middleware is also employed to reduce the complexity of the real-time system and makes the system to be developed, maintained and run easily. to achieve this aim, the new method is applied into the middleware. therefore, the task which performs some function in the real-time system is required to focus on completing this function only. the task communicates only with the middleware as follows: * sends interactions to the middleware * receives interactions from the middleware. the interactions between the tasks are treated by the middleware to reveal the relationship of the tasks in the system. 768 j. wu, r. tzoneva thus, the main function of the middleware is treating the interactions between the tasks of the real-time system to make the system running correctly and in the logic way. the treatment of the interactions also has to satisfy the time restriction because it is working for a real-time control system. the correctness of the real-time system is based on the correctness of both of these two factors. so, the objectives of the middleware are: * treat the interactions between the tasks to reveal the relationship of the tasks in the system * treat the interactions on time to satisfy the time restriction of the real-time system. middleware’s agents it is proposed the middleware functions to be implemented by three agents: an interacting agent, a coordinating agent, and a mapping agent. each agent plays a distinct role to complete a specified function. these agents are self-existent, they work together by exchanging information to achieve the objectives of the middleware. each agent performs a specified function by itself and cooperates with others by exchange of information. the mapping agent is an agent which supplies a static, global description of the real-time system. the description is expressed by the view of the relationships of the tasks of the system. this view is detailed through the records of the interactions between the tasks of the system. in fact, the rules for the running of the real-time system (for the interactions and their corresponding treatment) are predefined in the mapping agent only, and no undefined rules could be executed in the system. the coordinating agent is an agent which supplies a dynamic, real-time status description of the real-time system. the description of the running system is expressed by the view of the status of the tasks. the status of tasks is changed in real-time; it indicates each task’s working condition and some details. usually, once a task starts, the most details are not changed except the working condition. the coordinating agent also schedules the tasks by scheduling the sending time of the orders based on the information which is supplied by the mapping agent, the view of the status of the tasks of the system and the schedule arithmetic which is predefined. the coordinating agent is the core agent in the middleware. it executes the schedule arithmetic and the rules of the system by obeying the real-time status of the system. the interacting agent is an agent which temporary stores the main body of the interaction information, which is the real content that should be treated by other tasks. the content of the interaction is only sent to the target tasks and no more treatment is done in the middleware. the content should be kept until it is not useful. the interacting agent separates the interaction into the content and the metadata which is information for the interaction. the interacting agent also combines the content and the orders from the coordinating agent to form a complete order. the receiving and sending of the interactions and the orders are other two important functions of the interacting agent. the interacting agent is the only agent which communicates directly with the other tasks of the real-time system. thus, the middleware consist of three agents and each agent represents a control level of the middleware, a rough view of the structure of the middleware is given in figure 7. environment of the middleware in the real-time system the middleware coordinates the tasks by controlling the interactions between the tasks in the real-time system. in a real-time system which does not employ the middleware to coordinate the tasks, a task has to spend a lot of time to treat the interactions with other tasks. in this process the developer a multi-agent system architecture for coordination of the real-time control functions in complex industrial systems 769 figure 7: a rough view of the structure of the middleware has to know all the details of the tasks which are supposed to be interacted. additionally various tasks may be forced to do modification when one task is modified. it is also difficult to check the running status of the tasks and difficult to add or remove calculating resources from the system. the development, maintenance and running of the tasks in the real-time system which employs the middleware are much simpler than others because they may interact with other tasks by a few steps which are simple and standard. for instance, a sensor task collects value from an electrical sensor. the value then should be sent to a database to be stored and sent to a calculation module to do the further treatment. in a real-time system which does not employ the middleware, the sensor task should do two interactions with other two tasks to treat one value, and also should have the information of other two tasks. in the real-time system which employs the middleware, the sensor task should only send the value to the middleware and only need to have the information of the middleware. the difference between these two ways of treatment is present in figure 8. an interaction usually transfers the information from an initiator (task) to a receiver (task). there are two basic parts of the interaction, one is the content of the information, and other is the order of the information which indicates the receiver how to treat the content of the information. the content of the information could be null when the initiator only wants to inform the receiver that the status of the initiator is changed. the receiver mode is the same in the system with middleware and in the system without middleware. in the system without middleware, the initiator sends the interactions to the receiver directly and without middle treatment between the initiator and the receiver, so the interaction which is sent by the initiator must exactly be the same as the interaction which is received by the receiver. in the system with middleware, the interactions are treated by the middleware. therefore, the interaction which is sent from the initiator does not indicate how the interaction will be treated by the receiver, but it indicates which type the interaction is. the type of the interaction 770 j. wu, r. tzoneva figure 8: difference of the interactions between the system with and without middleware and the type of the task constitute a unique symbol which could be analyzed in the middleware. the middleware may parse the symbol and obtain several orders to the receivers and then send to them these orders with the content of the information. figure 9 presents different types of the interactions. the grey area of the two charts are the same, they represent that the orders which the receiver has received are the same. the different is the initiator which only sends one interaction to the middleware in the system which has middleware, and the initiator sends two interactions to the receivers in the system without middleware. actually, no matter how many tasks receive orders to treat the content of the information, the initiator only needs to send one interaction to the middleware in the system which is with middleware. the middleware parses the task type and the interaction type to obtain the receivers and the orders and then sends the orders to receivers. in the system without middleware, the initiator has to send separate orders to every receiver by itself. in a complex system, the number of orders is very big, it could be hundreds even thousands orders that have to be sent by the initiator itself. the workflow of treatment of the interaction there are three types of interactions, and the different type of interactions should be treated by different steps. the interactions are: * task register interaction; * task status change interaction; * the task should communicate with other tasks. the procedure of treatment of an interaction starts once the middleware receives the interaction. the whole procedure usually consists of different steps for different type of interactions. for the first type interaction, the procedure is: * assign a task no. to the task; * return the task no. to the task; * register the task at the middleware. for the second type interaction, the procedure is: a multi-agent system architecture for coordination of the real-time control functions in complex industrial systems 771 figure 9: different type of interaction procedures * return a received signal to the task; * change the status of the task. for the third type interaction, the procedure is: * return a received signal to the task; * separate the metadata from the interaction; * change the status of the task; * find the order plans for the interaction; * select a plan to be executed; * combine the order and the content; * execute all orders. the whole workflow of treatment of the interaction is presented in figure 10. the interactions between the agents according the figure 10, there are two bounds between the agents, they are: the bound between the mapping agent and the coordinating agent, and the bound between the coordinating agent and the interacting agent. each bound has two data transfer directions. in additional of these, the interacting agent also has other two data transfer directions to communicate with the outside of the middleware (receive an interaction and execute the order). therefore, there are six different data transfers in the middleware, and they are listed by the order of treatment: * from outside of the middleware to the interacting agent; 772 j. wu, r. tzoneva * from the interacting agent to the coordinating agent; * from the coordinating agent to the mapping agent; * from the mapping agent to the coordinating agent; * from the coordinating agent to the interacting agent; * from the interacting agent to outside of the middleware the description below gives the types of data transfer for the third type of the interactions of a task with other tasks. * in the first data transfer, the intact interaction is transferred from outside of the middleware to the interacting agent. and then the whole interaction is separated into metadata and the content. the content is stored in the interacting agent and the metadata is sent to the coordinating agent. * the second data transfer is: the coordinating agent receives the metadata from the interacting agent. the metadata should indicate the initiator of the interaction, and then the coordinating agent is able to change the status of the task which is the initiator. * the third data transfer is: the mapping agent receives the metadata from the coordinating agent. the metadata should indicate the unique interaction type, and the interaction type will be used to find the order plans from the database. * the fourth data transfer is: the mapping agent returns the search results which are a set of order plans to the coordinating agent. each plan consists of several orders. the scheduler is able to select a plan to be executed on the bases of the status of the tasks. * the fifth data transfer is: the coordinating agent sends the orders to the interacting agent to combine the content which was separated before. all of these orders should be able to obtain the correct content which should be carried to be sent to other tasks. * the last data transfer is: the interacting agent sends the intact orders to the tasks which should receive the orders. for the other two types of the interactions only the first and the second data transfer are involved. the data containers in the agents to supply a static, global description of the real-time system, the mapping agent involves a database to store the information of the system. the database should have the information of the task type, its interaction type and how to deal with the interaction. it should be able to return the order plans by supplied task type and the interaction type. to supply a dynamic, real-time status description of the system, all running tasks’ status must be stored in a date container and must be changed dynamically. it is the basis for scheduling the tasks. no matter which schedule arithmetic is applied, the real-time status of the system is always useful for the scheduling. the interacting agent keeps the content of the interaction in a data container. the content should be connected to the order must be removed when it will not be used again. the data in the mapping agent is static and should only be read. other two data containers have both a multi-agent system architecture for coordination of the real-time control functions in complex industrial systems 773 figure 10: the workflow of treatment of the interaction 774 j. wu, r. tzoneva read and write (add, remove, change) operations. for each of these, it is possible that two write operation to happen at the same time or a read and a write operation to happen at the same time. this may lead to potential dangerous (unpredictable results) of the data. to avoid this dangerous situation, no other operation may be allowed to be executed when a write operation is happening. 4.2 architecture of the middleware the middleware is working in a real-time system, and has to communicate with other tasks of the system. each task is a self-existent program, and they may be distributed in the network and working concurrently. a real-time control system usually is a concurrent system and requests the coordination component to have high response efficiency. figure 11 presents the detailed architecture of the middleware. data is the core three data containers exist to store the information of the system. each data container is the core of the agent in which it stays. the agents’ actions are all around these data containers. the data containers and specific agents are listed below in a table 1: data agent relationship of the tasks the mapping agent running status the coordinating agent content of interactions the interacting agent table 1: data containers and specific agents queues and communicators the queues and the communicators are the channels of the transfer of the data between the agents. a queue is a data structure which may store elements and order them in a fifo (first-infirst-out) manner. the communicators are pair operations on the queue, one is to get the element and other is to put the element. seven queues exist in the middleware to transfer data between the agents. they are listed below in a table 2: queue from (get) to (put) qsisiin sisi the interacting agent qincoor the interacting agent the coordinating agent qcoormap the coordinating agent the mapping agent qmapcoor the mapping agent the coordinating agent qcoorina the coordinating agent the interacting agent qcoorinb the coordinating agent the interacting agent qinsisi the interacting agent sisi table 2: queues in the middleware a multi-agent system architecture for coordination of the real-time control functions in complex industrial systems 775 figure 11: detailed architecture of the middleware 776 j. wu, r. tzoneva simple interaction standard interface (sisi) simple interaction standard interface (sisi) is a special module of the interacting agent of the middleware. it is the only module that communicates with the tasks directly. it receives the interaction packets from the tasks, put them into the qsisiin and sends the order packets from the qinsisi to the tasks. threads two types of threads exist. one is running during the lifetime of the middleware, and the other is started by other thread. the first type of the threads is named "listen thread" because they always listen the queues and create a second type threads which are executing the real treatment. so the second type is named "function thread". listen threads are established when the middleware starts and they run during the lifetime of the middleware. each of them is monitoring a specific queue to check if there is any element in the queue and get the element from the queue by the rule "first-in-first-out". to shorten the time of the response, the listen thread creates a function thread to do the treatment of the element which is gotten from the queue and go back to monitoring the queue immediately. a process head which is a special thread listens the port to receive the interaction packet in sisi. it creates a new thread to do the first treatment of the interaction packet and put the result as an element into the queue qsisiin. function threads execute the real treatment of the elements. they usually read or write something from the data container, and also obtain a new element for the further treatment except no further treatment is needed. if the thread obtains a new element which needs further treatment, the function thread then invokes a put function to put the element into the queue which receives the element for next step treatment. a process end is created by a listen thread qgetinsisi. it sends the order packet to the tasks which should receive the order packet. it is the last function in the treatment cycle. timer timer is a module in the coordinating agent of the middleware. it executes the specific function in a specific time. the specific function here is put the element into the queue qcoorina. the element arrives to the queue qcoorina at the specific time. the middleware receives the interactions from the tasks and sends the orders to the tasks. each agent has a data container to store the information which should be treated. the queues and the listen threads and the put functions provide the capability that the agents may communicate with each other. the real treatments are executed in the threads which are created by listen threads. 5 scheduling in the coordinating agent coordinating agent does real coordination in the middleware for the whole real-time control system. it schedules tasks in the real-time system and manages the interactions between them. it is placed between the interacting agent and the mapping agent and consists of a scheduler and running status. scheduler is used to organize executions of tasks of the system in the coordinating agent. it schedules tasks based on the clock, the status of the system, the meta-data of interaction packets from the interacting agent and the sets of treatment plans from the mapping agent. scheduler decides which plan of treatment is used for response to the interaction packet based a multi-agent system architecture for coordination of the real-time control functions in complex industrial systems 777 on the status of the system. scheduler also decides at what time to send the order to the interacting agent. that is the scheduler decides which order or orders are used to response to the requisition of interaction packet, and at what time the order is sent to which task. scheduler controls the execution of the system dynamically through control of the execution of the tasks and the interaction packets between the tasks. running status is a real-time table to record execution details of the tasks which are running at the time. any change of the status of the task is recorded in the running status after scheduler receives the meta-data of the interaction packet from the interacting agent. running status supplies the information to the scheduler for scheduling the orders. the order which is sent to the interacting agent is also recorded in the running status. several tasks may run the same code of a program at the same time or a same code of a program may be run several times in the system. a code of the task exists in the running status to indicate which code of the program is run by the task. 6 conclusions the middleware coordinates the tasks in a real-time control system by scheduling them and managing the interaction packets between them. the middleware could be thought as a post office. the letters (interaction packets) are collected by postman (interacting agent) first. then the post office (coordinating agent) sorts the letters based on the yellow page (mapping agent). at the end, the postman sends the letter to the addressee. the whole process of treatment of an interaction packet in the middleware could be divided into three phases: * requisition collection; * finding solution; * processing of the solution. comparing the traditional solutions with the proposed method, the benefit of the method is obvious. first at all, the whole real-time control system is separated into three parts: function realization, schedule execution and system architecture. this makes each part of the system to be developed in more effective and professional way. each of these three parts is easy to be changed. the table of interaction packets may be changed to realize another purpose. the scheduler may be realized by other type arithmetic for changing the strategy of the scheduling. the tasks may be added, removed or upgraded easily because only some corresponding changes are necessary to be added to the database which is placed in the mapping agent. system status is easy to be monitored during system run, because this status is a snapshot of the system. acknowledgements the investigations are funded by the national research foundation of south africa under the grant 62364 “substation automation and energy management systems”. bibliography [1] h. gomaa, designing concurrent, distributed, and real-time applications with uml, addison-wesley, 2000. 778 j. wu, r. tzoneva [2] a. gambier, real-time control systems: a tutorial, the fifth asian control conference, melbourne, australia, 2004. 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[14] a. ghosal, t. henzinger, d. iercan, c. kirsch, a. sangiovanni-vincentelli, hierarchical coordination language for interacting real-time tasks, in: proceedings of the 6th acm international conference on embedded software, seoul, korea, acm (oct 2006), pp.132–141, 2006. international journal of computers communications & control issn 1841-9836, 13(6), 972-987, december 2018. scheme for statistical analysis of some parametric normalization classes a. krylovas, n. kosareva, e.k. zavadskas aleksandras krylovas department of mathematical modelling, vilnius gediminas technical university, sauletekio av. 11, vilnius, lithuania aleksandras.krylovas@vgtu.lt natalja kosareva department of mathematical modelling, vilnius gediminas technical university, sauletekio av. 11, vilnius, lithuania natalja.kosareva@vgtu.lt edmundas kazimieras zavadskas* laboratory of operational research, institute of sustainable construction, vilnius gediminas technical university, sauletekio av. 11, vilnius, lithuania *corresponding author: edmundas.zavadskas@vgtu.lt abstract: in this research 7 parametric classes of normalization functions depending on 1 or 2 parameters proposed for mcdm problem solution. monte carlo experiments carried out to perform comparative statistical analysis and find optimal parameter values for the case of gaussian distribution of decision making matrix elements. optimal parameter values were ascertained for each normalization method. normalization formulas were compared with each other in the sense of their efficiency. logarithmic and max normalization formulas demonstrated highest values of the best alternative identification. the proposed methodology of determining optimal parameter values of normalization formulas could be applied by approximation of real data with appropriate probability distributions. keywords: normalization methods, multi-criteria optimization, monte carlo method, comparative statistical analysis, saw. 1 introduction multiple criteria decision making (mcdm) methods deal with ranking of alternatives (projects) by measurements or evaluations of the projects according to finite number of attributes (criteria). ranking results depend on many components of this process, the main of them that influence finite results are • data normalization formula, • weight determination method, • data aggregation method. data normalization is an important part of a decision-making problem, but it is still not given enough attention in scientific literature. nevertheless, it was shown in the number of scientific papers that data normalization method selection often significantly affects the accuracy of mcdm problem solution. the main topic of existing articles is investigation of various normalization formulas together with topsis as one of the most popular mcdm method for ranking alternatives. jahan and edwards [4] proposed the comprehensive systematized review of existing copyright ©2018 cc by-nc scheme for statistical analysis of some parametric normalization classes 973 normalization methods. some of them are traditionally used with the certain mcdm methods, for example, the well known tandem of vector normalization (van delft and nijkamp [17]) and the technique for order of preference by similarity to ideal solution (topsis) method (hwang and yoon [3]). the study of celen [1] revealed that topsis with vector normalization generated the most consistent results among the most popular four normalization procedures. among the linear normalization procedures, max-min and max methods appeared as the possible alternatives to the vector normalization procedure. a simulation comparison of normalization procedures for topsis in terms of their ranking consistency and weight sensitivity was carried out by chakraborty and yeh [2]. the study results also justify the use of the vector normalization procedure for topsis. influence of normalization tools on copras-g method applied for material selection task proposed by yazdani et al. [19] in the study of podviezko and podvezko [13] it is shown that different types of transformation and normalization of data applied to popular mcda methods, such as saw or topsis may produce considerable differences in evaluation. authors stated that attention has to be paid to making a choice of the type of normalization, which reflects preferences of decision-maker. kosareva et al. [7] accomplished comparative statistical analysis of 5 widely used normalization methods with saw method for ranking the alternatives and ternary estimates matrix. it is notable that results strongly differ for benefit and cost type attributes. minmax method in most cases is significantly better than other. in the study of peldschus [12] the impact of linear, concave and convex function profiles for mapping on a dimensionless interval (normalization) was investigated. review of the normalization methods used in construction engineering and management, and their applications there are presented by kaplinski and tamošaitienė [6]. milani et al. [11] examined how different families of norms affect the result of solving engineering decision problem by entropy and topsis methods. it was verified that the linear optimization norms cannot affect the rank of alternatives significantly. in contrast, nonlinear norms may yield some deviations, mainly for alternatives that are inherently close. recently, some new normalization formulas were proposed in the literature. research of zavadskas and turskis [20] is focused on introducing a new logarithmic method for decision making matrix normalization. based on weitendorf [18] and juttler [5] formulas, stanujkic et al. [14] proposed a new normalization procedure. the idea is to use the distance from the preferred ratings, which respects the decision-maker’s preferences. this procedure, adapted for negotiations, was integrated with step-wise weight assessment ratio analysis (swara) and additive ratio assessment (aras) methods in the research of stanujkic et al. [15] data normalization, as well as the measurement scales, inconsistency issues, missing judgement estimation methods, etc. have been extensively studied in the pairwise comparison matrix (pcm). a comprehensive literature review on pcm provided in kou et al. [8] a group decision-making (gdm) method for integrating heterogeneous information proposed by li et al. [9] the purpose of this study is to propose a new methodology of constructing parametric normalization methods and to carry out their statistical comparative analysis. 7 classes of parametric data normalization procedures are presented. some of the proposed normalization methods with particular parameter values are well known and widespread, other methods aren’t so popular. nevertheless, all these methods were not being applied using the wide range of parameter values so far. the article is organized as follows. in the section 2 seven classes of parametric normalization functions are introduced, their properties and dependency on parameter values are discussed. experiment design and detailed description of initial data matrices generation procedure is given in the section 3. monte carlo experiment results and comparative statistical analysis of normalization methods depending on the parameter values presented in the section 4. conclusions and future research are discussed in the section 5. 974 a. krylovas, n. kosareva, e.k. zavadskas 2 parametric data normalization methods in this article simple additive weighting (saw) method (see ref. 19) with equal weights is applied for mcdm problem solution. let us suppose that the initial data are the results of some measurements, expert evaluations, etc., and are written in m×n-dimension matrix x = (xij)m×n. the element xij of decision making matrix is evaluation of alternative i(i = 1, 2, . . . ,m) by the criterion j(j = 1, 2, . . . ,n). decision making matrix after normalization procedure is noted as follows: x̃ = (x̃ij)m×n, 0 ≤ x̃ij ≤ 1. let wj,j = 1, 2, . . . ,n be criteria weights satisfying conditions ∑n j=1 wj, 0 ≤ wj ≤ 1. then, saw criteria aggregated value is calculated for each alternative: qi = n∑ j=1 wjx̃ij, i = 1, 2, . . . ,m. if q1 ≥ q2 ≥ . . . ≥ qm, then the alternatives are ranked as follows: altern1 � altern2 � . . . � alternm. the essence of any normalization procedure is mapping of the real values xij ∈ [mj,mj] ⊂ (−∞, +∞) having certain meaning in the interval [0, 1], which in general case represents these numbers unevenly. for example, functions xα : [0, 1] → [0, 1] depending on the parameter α represent half of the maximum values to the 13% of the maximum normalized values, when α = 0.2, to the 30%, when α = 0.5, 75%, when α = 2 and even 97%, when α = 5 (see figure 1). figure 1: normalization functions xα : [0, 1] → [0, 1]. it means that the aggregated values qi can not only depend strongly and not so much on the absolute values of xij as on the relationships xi′j < xi′′j. in this research we’ll limit ourselves with direct optimization, when higher values of criteria are better, omitting inverse optimization, when lower values are treated as better. so, the only essential requirement for normalization functions is – they must be monotonously increasing. the other restriction of our research is dealing with random generation of initial decision making matrix elements that have the gaussian (normal) probability distribution. practically applied normalization methods can be more complex compared to functions depicted in figure 1 and can therefore have a great deal of influence in alternative comparisons and decision-making results. therefore, the literature deals with a large number of normalization methods, the new methods are developed, their researches and comparisons are carried out. denote mj = min 1≤i≤m xij,mj = max 1≤i≤m xij,j = 1, 2, . . . ,n. in the table 1 we propose 7 classes of normalization functions [mj,mj] → [0, 1] depending on 2 parameters α ≥ 0 and k ≥ 0. scheme for statistical analysis of some parametric normalization classes 975 these classes describe 7 different normalization approaches. note, that (1), (6) and (7) methods depend exclusively on the parameter α, meanwhile the rest (2)–(5) methods are depending on two parameters α ≥ 0 and k ≥ 0. when α = 1, methods (1), (6) and (7) are known from the literature and are entitled as: (1) – minmax normalization, (6) – logaritmic normalization, (7) – max normalization. plots of functions (1)–(7), when α = 1,k = 1, are given in figure 2, plots of functions (1), (6) and (7), when α = 0.5, 2 – in figure 3). graphs of functions (2)–(5) with corresponding parameter values α = 0.5, 2; k = 2, 3, 5 are depicted in figure 4. in the table 2 the example of data matrix normalization by methods (1)–(7) is presented when α = 1, k = 1 and initial data matrix is (xij)(4×4) =   120 1.2 1 4 250 2.5 2 5 3600 36 5 8 6400 64 10 13   . the second column elements of matrix (xij) are obtained dividing the first column elements by 100, while the fourth column elements are calculated by adding constant 3 to the third column elements. let us see that data normalization formulas (1)–(5) are invariant with respect to linear transformation αx + β, formula (7) is invariant with respect to multiplication/division, but does not preserve addition, while formula (6) does not preserve nor multiplication, neither addition. all the first 5 methods map the lowest value to 0, and the highest value to 1, method (7) maps the highest value to 1. several questions naturally arise considering this issue. are the mutual differences between the results of these methods application for mcdm problem solution significant? what are the “best” values of parameters α and k? how do the results vary when varying parameter α and k values? how do the results vary when varying m and n values? table 1: formulas for 7 parametric normalization methods in the case of direct normalization. formula number normalization method direct normalization formula (1) minmax normalization (weitendorf, 1976) x̃ij = ( xij −mj mj −mj )α (2) exponential normalization x̃ij = e −k  mj −xij xij −mj  α (3) logaritmic maxmin normalization x̃ij = 1( 1 + k ln ( mj −mj xij −mj ))α (4) arctangent normalization x̃ij = ( 2 π arctan ( k xij −mj mj −xij ))α (5) double exponential normalization x̃ij =  1 −e −k ( xij −mj mj −xij ) 1 + e −k ( xij −mj mj −xij )   α (6) logarithmic normalization (zavadskas and turskis, 2008) x̃ij = ( ln(xij) ln( ∏m i=1 xij) )α ,xij ≥ 1 (7) max normalization (stopp [16], 1975) x̃ij = ( xij mj )α 976 a. krylovas, n. kosareva, e.k. zavadskas figure 2: functions (1)–(7), when α = 1,k = 1. figure 3: functions (1), (6) and (7), when α = 0.5 and α = 2. 3 experiment design the case under investigation is when the decision making matrix elements are some measurements done with sufficient precision and having gaussian probability distribution xij ∼ n(µi,σ). we’ll suppose, that the first row elements of the matrix have at the average higher values than elements of other rows µ1 > µ2 = . . . = µm. only such matrices were analysed, for which the first alternative (first row) has no domination property in comparison with any other alternative, i.e. when the following conditions are not fulfilled: (x11,x12, . . . ,x1n) � (xi1,xi2, . . . ,xin), if x11 ≥ xi1,x12 ≥ xi2, . . .x1n ≥ xin. (8) if condition (8) is valid, weighted averages qi = ∑n j=1 wjx̃ij will satisfy inequalities q1 ≥ qi with any values of weights wj. therefore, the result of all normalization methods will be the same – the first alternative is better than i-th alternative. so, when random matrices are being generated, such matrices whose first and any (at least one) i-th row satisfy domination property (8) are being rejected. as a result, the number of cases of a fair decision has decreased. for example, the first and the second rows elements of decision making matrix x(1) =   52.34 66.31 63.38 67.01 48.90 62.05 56.54 53.93 72.05 56.24 61.58 48.05 52.10 65.00 71.95 56.82 77.69 70.34 65.00 55.65   scheme for statistical analysis of some parametric normalization classes 977 table 2: normalization methods application example when α = 1 and k = 1. normalization method normalized matrix x̃ij formula (1)   0 0 0 0 0.021 0.021 0.11 0.11 0.55 0.55 0.44 0.44 1 1 1 1   x̃ij = xij −mjmj −mj (2)   0 0 0 0 2.8 · 10−21 2.8 · 10−21 0.0003 0.0003 0.45 0.45 0.29 0.29 1 1 1 1   x̃ij = e−  mj −xij xij −mj   (3)   0 0 0 0 0.21 0.21 0.31 0.31 0.63 0.63 0.55 0.55 1 1 1 1   x̃ij = 1 1 + ln ( mj −mj xij −mj ) (4)   0 0 0 0 0.01 0.01 0.08 0.08 0.57 0.57 0.43 0.43 1 1 1 1   x̃ij = 2π arctan ( xij −mj mj −xij ) (5)   0 0 0 0 0.01 0.01 0.06 0.06 0.55 0.55 0.38 0.38 1 1 1 1   x̃ij = 1 −e − ( xij −mj mj −xij ) 1 + e − ( xij −mj mj −xij ) (6)   0.18 0.02 0 0.18 0.20 0.10 0.15 0.21 0.30 0.41 0.35 0.27 0.32 0.47 0.5 0.34   x̃ij = ln(xij)ln(∏mi=1 xij) (7)   0.02 0.02 0.1 0.31 0.04 0.04 0.2 0.38 0.56 0.56 0.5 0.62 1 1 1 1   x̃ij = xijmj satisfy inequalities 52.34 ≥ 48.90, 66.31 ≥ 62.05, 63.38 ≥ 56.54, 67.01 ≥ 53.93, therefore (52.34, 66.31, 63.38, 67.01) � (48.90, 62.05, 56.54, 53.93) and matrix x(1) is being rejected. matrix x(2) =   57.56 66.42 58.31 55.51 58.51 63.08 67.93 46.31 55.36 57.58 59.16 72.82 38.95 58.40 40.93 57.93 53.68 45.57 70.41 59.98   is appropriate. the results of 7 normalization procedures when α = 1,k = 1 are as follows: x̃(2)(1) =   0.95 1.0 0.59 0.35 1.0 0.84 0.92 0.0 0.84 0.58 0.62 1.0 0.0 0.62 0.0 0.44 0.75 0.0 1.0 0.52   , x̃(2)(2) =   0.95 1.0 0.50 0.15 1.0 0.83 0.91 0.0 0.83 0.48 0.54 1.0 0.0 0.54 0.0 0.28 0.72 0.0 1.0 0.39   , 978 a. krylovas, n. kosareva, e.k. zavadskas a) function (2) b) function (3) c) function (4) d) function (5) figure 4: graphs of functions (2)–(5) with different parameter α and k values. x̃(2)(3) =   0.95 1.0 0.65 0.49 1.0 0.85 0.91 0.0 0.85 0.65 0.68 1.0 0.0 0.67 0.0 0.55 0.78 0.0 1.0 0.60   , x̃(2)(4) =   0.97 1.0 0.61 0.31 1.0 0.88 0.94 0.0 0.88 0.60 0.65 1.0 0.0 0.64 0.0 0.42 0.80 0.0 1.0 0.52   , x̃(2)(5) =   1.0 1.0 0.62 0.26 1.0 0.99 1.0 0.0 0.99 0.59 0.67 1.0 0.0 0.66 0.0 0.37 0.91 0.0 1.0 0.49   , x̃(2)(6) =   0.21 0.21 0.20 0.20 0.21 0.20 0.21 0.19 0.20 0.20 0.20 0.21 0.19 0.20 0.18 0.20 0.20 0.19 0.21 0.20   , x̃(2)(7) =   0.98 1.0 0.83 0.76 1.0 0.95 0.96 0.64 0.95 0.87 0.84 1.0 0.67 0.88 0.58 0.80 0.92 0.69 1.0 0.82   . we can see that matrices x̃(2) (1)−x̃(2) (5) mutually differ slightly, meanwhile there are essential differences between the first 5 matrices and x̃ (2) (6) − x̃(2) (7). when proper matrix x is generated and some kind of normalization (1)–(7) is done, saw criteria aggregated values with scheme for statistical analysis of some parametric normalization classes 979 equal weights are calculated for each row: qi = n∑ j=1 1 n · x̃ij, i = 1, 2, . . . ,m. (9) the first alternative is considered to be the best one when q1 > q2, q1 > q3, . . . , q1 > qm. 10 series by 100 experiments were conducted. after 100 realizations of experiments denote id the number of cases, when the first alternative identified as the best one (the number of correct mcdm problem solutions from 100). denote also wid = id100, i.e. the percent of the correct mcdm problem solutions. table 3: numbers of cases with the best first alternative (id) for normalization methods (1)–(7), when α = 1 and k = 1. (1) 62 54 62 56 58 52 49 58 52 45 (2) 69 60 66 57 56 56 50 57 54 46 (3) 55 50 59 55 51 45 45 57 55 44 (4) 64 56 60 58 56 51 50 57 52 46 (5) 62 56 59 59 53 49 47 53 51 47 (6) 69 66 66 73 63 64 58 64 63 56 (7) 69 65 66 72 63 62 61 65 63 54 table 3 shows experiment results – how many times after application of methods (1)–(7) with α = 1 and k = 1 it was found that the best option is the first alternative. each row contains results of 10 experiment series for each normalization method. next, monte carlo experiments were conducted by changing parameters α and k values as follows. first row elements of matrix x were generated according to the gaussian distribution with the average value µ1 = 67 and standard deviation σ = 15, elements of other rows – with the average values µi = 57, i = 2, 3, . . . ,m and standard deviation σ = 15. calculations were performed using the c++ program. 4 experiment results 4.1 dependence of the best alternative detection accuracy on parameter α at the first stage of the experiments we fixed k value at k = 1 and varied α choosing the individual range for each normalization method. 100 series of 100 experiments were repeated for each parameter α value and empirical averages wid = 1100 ∑100 i=1 widi calculated. the purpose is to detect α value which maximizes wid . in the table 4 the dynamics of wid values depending on α is depicted. 95% confidence intervals [w1_0.95; w2_0.95] for mean values ewid are given in the last column of the table. maximum values of correct mcdm problem solution empirical averages wid were achieved for such α values: α = 2 for methods (1), (3), (4) and (7), α = 0.75 for method (2), α = 4 for method (5), α = 20 for method (6). the dependence of empirical mean values on α as well as upper and lower bounds of confidence intervals of the mean values are presented graphically in figs. 5–6. for improvement of graphic images, smoothing spline fitting of table 4 data was produced with matlab. the question arises: are the detected differences between normalization methods with different α values statistically significant? student’s t-tests were applied for testing the hypotheses h0 of equal average values ewid in the cases of α = 1 and corresponding optimal parameter α values. table 5 shows p-values of the respective t-tests. significant differences in the best alternative 980 a. krylovas, n. kosareva, e.k. zavadskas table 4: wid values and confidence intervals for mean values ewid for 7 normalization methods at k = 1. normalization method α wid [w1_0.95; w2_0.95] (1) 1 0.4784 [0.4678; 0.4890] 1.25 0.4899 [0.4790; 0.5008] 1.5 0.4998 [0.4885; 0.5111] 2. 0.5015 [0.4911; 0.5119] 3 0.4958 [0.4860; 05055] 4 0,4905 [0.4804; 0.5006] 5 0.4859 [0.4758; 0.4960] (2) 0.5 0.4952 [0.4854; 0.5050] 0.75 0.4987 [0.4876; 0.5098] 1 0.4936 [0.4836; 0.5036] 1.25 0.4897 [0.4796; 0.4998] 1.5 0.4874 [0.4774; 0.4974] 2 0.4807 [0.4707; 0.4907] (3) 0.5 0.3438 [0.3342; 0.3534] 1 0.4485 [0.4373; 0.3534] 1.5 0.4859 [0.4758; 0.4597] 2 0.4979 [0.4875; 0.4960] 3 0.4959 [0.4862; 0.5083] 4 0.4919 [0.4821; 0.5017] (4) 1 0.4751 [0.4643; 0.4859] 1.5 0.4947 [0.4842; 0.5052] 2 0.5009 [0.4904; 0.5114] 3 0.498 [0.4880; 0.5080] 4 0.4929 [0.4827; 0.5031] 5 0.4906 [0.4803; 0.5009] (5) 1 0.4684 [0.4582; 0.4786] 2 0.4914 [0.4812; 0.5016] 3 0.4951 [0.4845; 0.5057] 4 0.4952 [0.4844; 0.5060] 5 0.494 [0.4834; 0.5046] 6 0.4927 [0.4823; 0.5031] 7 0.4923 [0.4822; 0.5024] (6) 1 0.5370 [0.5258; 0.5482] 2 0.5396 [0.5287; 0.5505] 3 0.5444 [0.5336; 0.5552] 4 0.5486 [0.5377; 0.5595] 5 0.5508 [0.5401; 0.5616] 10 0.5596 [0.5496; 0.5696] 20 0.5609 [0.5512; 0.5706] 30 0.5523 [0.5420; 0.5626] (7) 0.25 0.5394 [0.5284; 0.5504] 0.5 0.5400 [0.5290; 0.5510] 0.75 0.5396 [0.5286; 0.5506] 1 0.5403 [0.5293; 0.5513] 2 0.5419 [0.5310; 0.5528] 3 0.5384 [0.5276; 0.5492] 4 0.5340 [0.5232; 0.5448] scheme for statistical analysis of some parametric normalization classes 981 detection accuracy on significance level 0.05 were observed for normalization methods (1), (3)– (6). methods (2) and (7) didn’t show significant differences. consequently, it makes sense using formulas (1), (3)–(6) with the appropriate optimal parameter α values that differ from α = 1 (see optimal α values in the table 5), while it makes sense using α = 1 for methods (2) and (7). 1 1.5 2 2.5 3 3.5 4 4.5 5 0.465 0.47 0.475 0.48 0.485 0.49 0.495 0.5 0.505 0.51 0.515 α method 1: dependence of w id on alpha w id w 1_0.95 w 2_0.95 0.5 1 1.5 2 0.47 0.475 0.48 0.485 0.49 0.495 0.5 0.505 0.51 0.515 α method 2: dependence of w id on alpha w id w 1_0.95 w 2_0.95 0.5 1 1.5 2 2.5 3 3.5 4 0.32 0.34 0.36 0.38 0.4 0.42 0.44 0.46 0.48 0.5 0.52 α method 3: dependence of w id on alpha w id w 1_0.95 w 2_0.95 1 1.5 2 2.5 3 3.5 4 4.5 5 0.46 0.47 0.48 0.49 0.5 0.51 0.52 α method 4: dependence of w id on alpha w id w 1_0.95 w 2_0.95 1 2 3 4 5 6 7 8 9 10 0.45 0.46 0.47 0.48 0.49 0.5 0.51 α method 5: dependence of w id on alpha w id w 1_0.95 w 2_0.95 0 5 10 15 20 25 30 0.53 0.54 0.55 0.56 0.57 0.58 0.59 0.6 0.61 α method 6: dependence of w id on alpha w id w 1_0.95 w 2_0.95 figure 5: dependence of empirical average accuracy wid on the parameter α values for (1)–(6) normalization methods and 95% confidence intervals bounds. next, anova procedure was applied to check the hypothesis of equal averages ewid for 7 normalization methods with optimal α values. procedure results show significant differences existing between methods. bonferroni test was chosen for post hoc multiple comparisons. it revealed that there are nor significant differences between averages ewid for (1)–(5) methods, neither between (6) and (7) methods. however, when comparing any method from the first group 982 a. krylovas, n. kosareva, e.k. zavadskas 0 0.5 1 1.5 2 2.5 3 3.5 4 0.52 0.525 0.53 0.535 0.54 0.545 0.55 0.555 0.56 α method 7: dependence of w id on alpha w id w 1_0.95 w 2_0.95 figure 6: dependence of empirical average accuracy wid on the parameter α values for (7) method and 95% confidence intervals bounds. figure 7: mean plot from anova procedure comparing ewid for optimal α values corresponding to 7 normalization methods. ((1)–(5) methods) with any of the methods of the second group ((6)–(7) methods), statistically significant differences were found. the conclusion can be made that (6) and (7) methods at the average are more precise than (1)–(5) methods, since the average accuracy ewid obtained by (6)–(7) methods is significantly higher than the accuracy of (1)–(5) normalization formulas. in figure 7 the mean plot from anova procedure output is represented. ewid for optimal α values corresponding to 7 normalization methods are depicted. table 5: hypotheses of equal average values ewid testing results for α = 1 and optimal α value for (1)–(7) normalization methods for the first experiment. normalization method optimal α value h0 p-value (1) 2 ewid α=1 = ewid α=2 0.002 (2) 0.75 ewid α=1 = ewid α=0.75 0.499 (3) 2 ewid α=1 = ewid α=2 0.000 (4) 2 ewid α=1 = ewid α=2 0.001 (5) 4 ewid α=1 = ewid α=4 0.000 (6) 20 ewid α=1 = ewid α=20 0.002 (7) 2 ewid α=1 = ewid α=2 0.838 table 6: wid values, corresponding optimal α values and t-test results for (1)–(7) normalization methods for the second experiment. normalization method optimal α value wid h0 p-value (1) 2 0.6967 ewid α=1 = ewid α=2 0.000 (2) 0.75 0.6966 ewid α=1 = ewid α=0.75 0.594 (3) 2 0.6959 ewid α=1 = ewid α=2 0.000 (4) 2 0.701 ewid α=1 = ewid α=2 0.000 (5) 3 0.6879 ewid α=1 = ewid α=3 0.002 (6) 10 0.7775 ewid α=1 = ewid α=20 0.003 (7) 1 0.7631 scheme for statistical analysis of some parametric normalization classes 983 table 7: recommended normalization formulas (1)–(7) with optimal α values and k = 1. x̃ij = ( xij−mj mj−mj )2 (1) x̃ij = e − ( mj−xij xij−mj ) (2) x̃ij = 1( 1+ ln ( mj−mj xij−mj ) )2 (3) x̃ij = ( 2 π arctan ( xij−mj mj−xij ) )2 (4) x̃ij =  1−e− ( xij−mj mj−xij ) 1+e − ( xij−mj mj−xij )  4 (5) x̃ij = ( ln(xij) ln( ∏ m i=1 xij) )20 , xij ≥ 1 (6) x̃ij = xij mj (7) due to randomness of the experiments we observe some variability in the obtained results: optimal α value for (5)-th normalization method changed from 4 to 3, for (6)-th method – from 20 to 10, for (7)-th method – from 2 to 1. however, t-test didn’t detect significant differences between ewid at the mentioned α levels. anova results and conclusions also are the same. so, the conclusions remained unchained – it is recommended to use optimal α values, which differ from 1 for normalization methods (1), (3)–(6) and apply α = 1 for methods (2) and (7). moreover, (6) and (7) normalization formulas lead to significantly higher average accuracy of best alternative detection. the results of the experiments revealed that it is reasonable to use normalization formulas with α values specified in the table 7. to evaluate the stability of obtained results, monte carlo experiments were repeated with other values of decision making matrices. the elements of matrix x were generated as follows: x1j ∼ n(67, 15), xij ∼ n(52, 15), i = 2, 3, . . . ,m. as the differences µ1 − µi increased, it is natural to expect that the best alternative detection accuracy will be higher than in the previous experiment. the results of the second experiment are shown in the table 6. they essentially confirmed results of the first experiment. 4.2 dependence of the best alternative detection accuracy on parameter k data normalization formulas (2)–(5) are depending on both parameters α and k. so, it is interesting to reveal whether ewid change significantly while changing k. calculations were carried out with few values of parameters α, and for each fixed α for few values of k. the elements of matrix x were generated as follows: x1j ∼ n(67, 15), xij ∼ n(57, 15), i = 2, 3, . . . ,m. 100 monte carlo experiments were repeated for each parameter values combination and empirical mean values wid calculated. then maximum value of wid was detected for some optimal parameter k value. calculation results for normalization formulas (4) and (5) are presented in the table 8. maximum wid values and corresponding parameter α and k values are as follows: normalization method (2) – wid = 0.511,α = 0.75,k = 1; (3) – wid = 0.505,α = 2,k = 1; (4) – wid = 0.516,α = 2,k = 1; (5) – wid = 0.509,α = 1,k = 0.5. in figure 8 the dependence of average accuracy wid on k = 1 is depicted. for normalization formulas (2)–(4) optimal parameter k value is 1, only for method (5) optimal value differs from 1 (k = 0.5). next, t-test was applied for testing the hypotheses h0 of equal average values ewid for k = 1 and corresponding optimal parameter k values. the difference between ewid with parameters α = 1,k = 0.5 and ewid with parameters α = 1,k = 1 is significant at significance level 0.05. 984 a. krylovas, n. kosareva, e.k. zavadskas 0 2 4 6 8 10 0.35 0.4 0.45 0.5 0.55 0.1 ≤ k ≤ 10 method 2: dependence of w id on k α=0.5 α=0.75 α=1.0 α=1.5 α=2.0 0 2 4 6 8 10 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.1 ≤ k ≤ 10 method 3: dependence of w id on k α=0.5 α=0.75 α=1.0 α=1.5 α=2.0 0 2 4 6 8 10 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.1 ≤ k ≤ 10 method 4: dependence of w id on k α=0.5 α=0.75 α=1.0 α=1.5 α=2.0 0 2 4 6 8 10 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.1 ≤ k ≤ 10 method 5: dependence of w id on k α=0.5 α=0.75 α=1.0 α=1.5 α=2.0 figure 8: dependence of empirical average accuracy wid on parameter k values for (2)–(5) normalization methods. table 8: wid calculated by varying α and k values for normalization methods (4) and (5). method (4) α k 0.5 0.75 1 1.5 2 3 0.1 0.497 0.486 0.500 0.482 0.486 0.479 0.2 0.510 0.494 0.500 0.496 0.484 0.479 0.5 0.475 0.500 0.501 0.509 0.495 0.497 1 0.422 0.460 0.486 0.502 0.516 0.506 2 0.353 0.399 0.435 0.478 0.488 0.504 5 0.303 0.317 0.358 0.381 0.401 0.449 10 0.285 0.297 0.307 0.324 0.345 0.378 method (5) α k 0.5 0.75 1 1.5 2 3 0.1 0.487 0.482 0.479 0.485 0.484 0.485 0.2 0.497 0.483 0.479 0.486 0.484 0.483 0.5 0.483 0.502 0.509 0.504 0.497 0.498 1 0.423 0.466 0.482 0.489 0.494 0.494 2 0.339 0.385 0.411 0.442 0.454 0.473 5 0.296 0.302 0.318 0.339 0.354 0.381 10 0.287 0.278 0.290 0.299 0.296 0.323 scheme for statistical analysis of some parametric normalization classes 985 so, it is recommended to use α = 1,k = 0.5 for normalization method (5): x̃ij = 1 −e −1 2 ( xij−mj mj−xij ) 1 + e −1 2 ( xij−mj mj−xij ) , whereas for methods (2)–(4) it is reasonable to use k = 1 with respective optimal α value (see relevant formulas in table 7). 5 conclusions and future research the purpose of this article is to analyze some parametric normalization formulas and establish how various data normalization methods and parameter values affect the accuracy of mcdm problem solution. 7 data normalization methods were investigated (see formulas (1)–(7)). some of them with certain parameter values are generalizations of the well known normalization methods. data matrices were randomly generated according to gaussian probability distribution. in all conducted monte carlo experiments decision making matrices were generated with the first alternative as the best one. then the alternatives were ranked by the saw method overall aggregated value with equal weights. the measure of identification accuracy is the percentage of correct identifications of the best alternative. the results of experiments are as follows. 1. identification accuracy obtained by methods (6)–(7) is significantly higher than the accuracy for normalization methods (1)–(5). 2. variation of parameter k revealed that for normalization method (5) it is reasonable to use combination of parameters α = 1,k = 0.5, whereas for methods (2)–(4) it is reasonable to use k = 1 with respective optimal α value (see relevant formulas in table 7). 3. optimal α value have some “degrees of freedom”, for example, it is possible to choose another optimal value α = 10 instead of α = 20 for normalization formula (6), since there isn’t significant difference between identification accuracy at these values. 4. this research is accomplished in the special case when elements of decision matrix have gaussian distribution. if we possess more information, we can apply the same methodology by approximating real data with appropriate probability distributions. 5. parameters of initial decision making matrix were chosen so that identification of the best alternative would be higher than 50%. experiments revealed that identification accuracy is higher with the bigger number of criteria n, and is lower with fewer alternatives m. bibliography [1] celen, a. 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(2008). a new logarithmic normalization method in games theory, informatica, 19(2), 303–314, 2008. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(4), 574-589, august 2019. frequent patterns algorithm of biological sequences based on pattern prefix-tree l.y. xue, x.k. zhang, f. xie, s. liu, p. lin linyan xue#, xiaoke zhang#, fei xie, shuang liu college of quality and technical supervision, hebei university, baoding 071002, china lineysnow@163.com; lianlianfushi@126.com; xiefei-1214@163.com; liushuang99@hotmail.com #joint first authors. these authors contributed equally to this work. peng lin* college of management, hebei university, baoding 071002, china *corresponding author: linpeng1982@139.com abstract: in the application of bioinformatics, the existing algorithms cannot be directly and efficiently implement sequence pattern mining. two fast and efficient biological sequence pattern mining algorithms for biological single sequence and multiple sequences are proposed in this paper. the concept of the basic pattern is proposed, and on the basis of mining frequent basic patterns, the frequent pattern is excavated by constructing prefix trees for frequent basic patterns. the proposed algorithms implement rapid mining of frequent patterns of biological sequences based on pattern prefix trees. in experiment the family sequence data in the pfam protein database is used to verify the performance of the proposed algorithm. the prediction results confirm that the proposed algorithms can’t only obtain the mining results with effective biological significance, but also improve the running time efficiency of the biological sequence pattern mining. keywords: bioinformatics, frequent patterns, biological sequence, pattern prefixtree, data mining. 1 introduction bioinformatics is a new comprehensive cross discipline involving biology, mathematics, physics, informatics and computer science. it plays a vital role in the development of life science, and becomes the frontier of life science research. the core issue in bioinformatics is genome informatics which includes the obtaining, processing, storing, assigning and explaining of the genome information. by using computers and network as tools, based on the mathematical theory, methods and technology, genome informatics studies the biopolymers include the sequences, structures and functions of dna and protein. the key issue in genome informatics is to understand the meaning of the order in nucleotide sequences, namely, to understand the exact locations of the genes in the chromosome and the functions of the dna segments. these are very vital in the research of disease gene of people, the function of gene and the designing of pharmacy. to achieve those goals, pattern mining in biological data is the critical techniques [3,7,9]. biological individuals have their particularity in the process of evolution, because a part of the sequence or region in the organism will affect the survival of the entire organism. therefore, these sequence patterns will remain well-conserved in the evolutionary process. one of the vital contents of current biological sequence data analyzing is to mine proper biological sequence patterns. so far, researchers have proposed a large number of biological sequence pattern mining algorithms. these algorithms mainly find the following patterns in biological sequences: copyright ©2019 cc by-nc frequent patterns algorithm of biological sequences based on pattern prefix-tree 575 (1) repeat patterns in a biological sequence. for example, some continuous repeating patterns appeared in the dna sequence called tandem repeats (trs). it has been proved that these trs play a key role in the evolution of genes. in the evolution of living individuals, using repeated sequences can help the formation of new genes [13]. conversely, the generation of some human diseases may be caused by mutations in repeated sequences, such as digeorge syndrome, williams syndrome, musculoskeletal muscular dystrophy [15], etc. however, we still do not fully understand these trs and their biological functions. therefore, finding and studying the repeat sequences in dna sequences are very important for the establishment of repeat sequence databases and the unknown functional identification of biological sequences. in general, the above problems are included in the frequent pattern mining of biological single sequence. (2) conservative patterns in multiple biological sequence sets. some sequence regions in the process of genetic variation in organisms will affect the survival of organisms, so these sequences will be highly conserved during the entire evolution of organisms. for example, most or even all of the sequences from the same family sequence set (such as the protein family) often contain conserved sequence pattern regions that play a vital role in the structure and function of protein [2]. important functional sequences (transcription factor binding sites), which would be generally located in upstream region of co-expressed gene sequence, tend to be more conserved and can regulate the expression of genes [17]. these examples can all be seen as mining their conservative patterns in multiple biological sequences. (3) sequence patterns with different frequencies appear in sequences in multiple biological sequence sets. a repeating sequence is called a copy. in the human population, there are often differences in the number of repeats (the number of copies) of the repeat sequence, known as repeat copy number polymorphisms. this feature is widely used in genetic diversity analysis, individual identification, genetic diagnosis, genetic mapping and other applied research. for example, saghai et al. conducted an experiment to verify the polymorphism through the rflp linkage map [16]. in the experiment, they compared and analyzed the conserved regions covering 24% of rice gene sequences and 17 conserved regions covering 31% of barley gene sequences. they found that 72% of single copy genes in barley were similarly expressed in rice in a single copy. similarly, about 60% of rice single-copy sequences are found in barley. this result shows that the difference between the grass crops is caused by the difference in repeat sequences, not due to structural differences in the genes. excavating the repetitive patterns in the multi-sequences of organisms and their frequency of occurrence in each sequence has guiding significance in genetic recognition and so on [18]. this type of problem can be incorporated into the study of frequent patterns mining of biological multi-sequences. 2 related works at present, there are two main kinds of computational methods for studying biological sequence pattern discovery. each kind of algorithm has a different search strategy. one kind of algorithm uses a heuristic search strategy and it is an approximate algorithm. in the artificial intelligence field, most machine learning methods use heuristic algorithms. in sequence pattern mining, such methods mainly include gibbs sampling algorithm, em method [11], meme algorithm [4] and so on. this kind of algorithm is usually an iterative process, and it gets better solutions through iterations. in the algorithm, some approximate description of the sequence pattern information is needed to determine the quality of a certain measurement standard. in the iteration, the solution is optimized according to the criterion, and it is judged whether the iterative termination condition is satisfied. the advantage of this kind of algorithm is that the computational complexity is low and it is suitable for searching for a longer sequence pattern. however, the disadvantage is that the solution it obtains may be a local optimal solution, and 576 l.y. xue, x.k. zhang, f. xie, s. liu, p. lin it may not be able to obtain a global optimal solution. however, a large number of application practices have proved that such approximate solutions obtained by machine learning algorithms can be used to solve practical application problems. since the frequent pattern mining was defined by agrawal and srikant in 1995 [5], related research has become an important field of data mining and has received extensive attention from researchers. they proposed many algorithms, some of which are suitable for efficient mining of large-scale sequential patterns, such as spade, freespan, prefixspan, gsp, apriori and so on. however, biological sequences are different from sequence data such as transaction sequences. when applying the above algorithm directly to biological sequence data, there are many difficulties and problems to be solved. for example, the meaning of fuzzy matching between patterns is difficult to understand, the mining results cannot meet the needs of biological research, some pruning strategies or data structures cannot be effectively applied to biological sequence data, and algorithm for data volume scalability. it is necessary for biological sequences to design frequent pattern detection algorithms. the existing algorithms mainly have the following categories: (1) tandem repeats mining algorithm. tandem repeats are a special type of sequence pattern, which is a subsequence that is arranged end-to-end in a dna sequence, has repeating units in a string, and has a frequency exceeding a certain threshold. tandem repeats include perfect tandem repeats (ptr), longest pattern repeats (lpr), approximate tandem repeats (atr) and so on. jiang et al. in [8] proposed the definition of the lpr for the exact search for new repeats, and designed an lpr search algorithm based on the subsequent array structure. kurtz et al. [10] gave reputer algorithm that depended on the data structure of the suffix tree. the repeated sequences are mined by pairwise alignment of subsequences. however, these algorithms are still difficult to find for frequently occurring repeats in dna sequences [1,6]. in addition, won et al. proposed a mosaic silhouette algorithm to identify repeated sequences [19]. (2) mining sequence models with conditional restrictions. in practical applications, we often want to mine sequence models with conditional constraints based on the characteristics of biological sequences and the needs of application problems. such constraints usually include the need to mine patterns of biological sequences that may contain intervals of any length, to allow fuzzy matches between sequence patterns, and so on. the existing sequential pattern mining algorithm with some constraints does not consider the biological sequence’s various constraint features. liao et al. proposed a two-stage algorithm that can mine sequence patterns containing arbitrary length intervals [12]. the algorithm is divided into two phases: the first phase searches for all short frequent patterns, and these is no gap in these patterns; the second phase generates long patterns containing these short frequent patterns. the algorithm can use the short frequent mode information to reduce the search time of the spaced global mode. although the algorithm can mine more sequence patterns that are more biologically meaningful, it takes more time to generate long patterns. (3) frequent subtree mining. with the continuous expansion of data mining applications, frequent patterns of mining objects are constantly diversified. there are sequences, transaction itemsets, and complex data structures such as graphs and trees to adapt to web mining, biological structure data mining semi-structured document mining. therefore frequent subgraph mining and frequent subtree mining have become important research fields in frequent pattern mining. after several years of research, there have been some methods for mining frequent patterns such as trees and graphs. in the research of frequent subtree mining algorithms, the identification of tree isomorphism and the matching of tree patterns are the two key issues to be solved. in the frequent subtree mining algorithm, it is determined whether the tree in the database has a subtree that is isomorphic to a certain known tree. let the tree in the database be t and the known tree be p , |t|≥|p|. if there is a one-to-one correspondence between the vertices of p to t , and the edges of the corresponding vertices have the same relationship, t is a subtree of p . frequent patterns algorithm of biological sequences based on pattern prefix-tree 577 in the tree pattern matching problem of biological data, the trees t and p are ordered trees, and each vertex and edge have markers. at this point, the isomorphism of the subtree requires that the corresponding vertices have the same mark [14]. 3 single sequence frequent pattern mining algorithm 3.1 basic mode first of all, it is stipulated that dna sequences always consist of four characters a, c, g, t and $ as the terminator. on this basis, the relevant definitions of patterns are given. definition 1. for alphabet σ = {a,c,g,t}, pattern p =< p1,p2, . . . ,pn > and pattern p ′ =< p′1,p ′ 2, . . . ,p ′ n > are given, approximation degree is defined as follows: approximation degree ( p,p ′ ) = |e| length(p) where e = {j|p′j = pj,j = 1, 2, . . . ,n}, it represents the set of same characters between two patterns. the approximation degree takes into account not only the hamming distance between patterns, but also the influence of pattern length. assuming that the hamming distances between patterns are the same, the pattern lengths are 2 and 10, respectively, it is obvious that if the pattern length is 10, it means that the two patterns are more similar. when the degree approximation is 1, it means that the two patterns match perfectly. definition 2. for alphabet σ = {a,c,g,t}, pattern p =< p1,p2, . . . ,pn > and pattern p ′ =< p′1,p ′ 2, . . . ,p ′ n > are given, approximation match(p,p ′) represents whether pattern p and pattern p ′ satisfy the minimum approximation specified by the user. definition 3. (frequent approximation patterns). given sequence s, pattern p =< p1,p2, . . . ,pn > is a frequent approximation pattern in sequence s if and only if ∑ e=1 approximation match(p,p ′) ≥ m, where (1) pi is a substring of s, (2) |pi| = |p |, (3) m represents the minimum frequent threshold specified by the user, (4) for pi = s[a.. .b], pj = s[c. . .d], where i 6= j, if approximation match(p,pi)=1 and approximation match(p,pi) = 1, then it must be b < c or d < a. definition 4. (support of frequent approximation patterns). given sequence s, pattern p =< p1,p2, . . . ,pn > is a frequent approximation pattern in sequence s, then we call support(p) = ∑n i=1 approximation match (p,pi) [length(s)/ length(p)] as support of frequent approximation patterns p on sequence s. the higher the support degree, the higher the frequency of pattern occurrence in the sequence. when the support degree is 1, the pattern is a frequent and accurate pattern in the sequence. lemma 5. 0≤approximation degree(p,p ′)≤1 it is obvious that approximation degree(p,p ′)≥0, according to definition 1, for pattern p =< p1,p2, . . . ,pn > and pattern p ′ =< p′1,p ′ 2, . . . ,p ′ n >, i = {i|p′i = pi, i = 1, 2, . . . ,n}, it is obvious that |i| ≤ length(p), so approximation degree(p,p ′)≤1. 578 l.y. xue, x.k. zhang, f. xie, s. liu, p. lin lemma 6. 0≤sup(p)≤1 it is obvious that sup(p)≥0, according to definition of frequent approximation patterns, for pi = s[a.. .b], pj = s[c. . .d], if approximation match(p,pi)=1, and approximation match (p,pj)=1, then it must be b < c or d < a, so sup(p)≤1. p is a frequent approximation pattern in sequence s, if there are m patterns p1, p2, . . . , pm in sequence s, where p1 = s [a1,a′1], p2 = s [a2,a ′ 2], · · · , pm = s [am,a ′ m] (a1 < a2 < · · · < am), they are all satisfied approximation match(p,pi)=1, in order to maximize the number of nonoverlapping patterns in s sequence, the first non-overlapping pattern must be p1. according to definition of frequent approximation patterns, |pi| = |p |. let |p | = l, then p1 = s[a1,a1 +l−1], p2 = s[a2,a2 +l−1], · · · , pm = s[am,am+l−1]. if the first non-overlapping pattern is not s[a1,a1 + l − 1], it is the i-th s[ai,ai + l − 1], where ai > a1. if ai+1 − ai ≥ l, then the second non-overlapping pattern is s[ai + 1,ai+1 + l−1]. if ai+1 −ai < l, then ai+2 and ai are compared, if ai+1 − ai < l, then ai+3 and ai are compared, until ak+1 − ai ≥ l. so the second non-overlapping pattern is s[ak,ak + l−1]. and so on, the n-th non-overlapping pattern is obtained. obviously, the second non-overlapping pattern will not overlap with the first non-overlapping pattern. because ai > a1, it doesn’t overlap with pattern s[a1,a1 + l − 1]. the method can be used to clip candidate patterns and quickly find frequent approximate patterns satisfying conditions. 3.2 basic mode table after getting all the basic patterns of a sequence s, a basic pattern table of s can be constructed. for ease of searching, it needs to sort all the basic patterns of s by the lexicographic order of the characters in |σ|. for example, for basic pattern of s="yxzxxyzyxy", after sorting, it can get the basic schema list of s, it is shown in table 1. in table 1, each item is in the form of (num, sm, loc), where: num represents the number of item; sm represents the basic pattern; loc is starting position of the basic pattern sm in s. table 1: basic mode table for s num sm loc 1 x 3 2 xy 7 3 xyzy 6 4 xz 3 5 y 9 6 yx 7 7 yxzxx 2 8 yz 4 9 zxxy 1 10 zyxy 6 of course, there may be duplicates in the basic pattern, such as: s′="xxyxxyxyxy", the basic pattern "x","yx" all appear twice, "x" appears 4 times. in this case, the above basic model table can be improved and designed as shown in table 2. in table 2, each item is still in the form of (num, sm, input, loc), but where loc is the set of starting positions of the basic pattern sm in s. frequent patterns algorithm of biological sequences based on pattern prefix-tree 579 table 2: basic mode table for s′ num sm loc 1 x 1 3 2 xy 1 3 6 8 3 y 9 4 yx 5 7 5 yxx 3 3.3 construct basic frequent pattern prefix tree definition 7. for a basic pattern table, the basic pattern prefix tree which the basic pattern table corresponds to is a rooted tree, whose path from root to each leaf node represents a basic pattern. every side of path represents a substring, which represented by edges of the same node do not have the same prefix. a basic pattern is achieved through sequentially arranging substrings represented by edges on the path in the basic pattern prefix tree. for the basic model table 1, the basic pattern prefix tree shown as figure 1 could be constructed by using the above recursive algorithm. figure 1: the basic pattern prefix tree corresponding to s in the tree, each node has a set of starting points {l1, l2, · · · , lk}. its meaning is: x is the substring represented by the edge of the node connected to its father node, then {l1, l2, · · · , lk} represents the substring x in the initial position collection of s. for root node, {l1, l2, · · · , lk} represents the set of initial position of the empty string in s, that is, the collection {1, 2, · · · , |s|} for all positions in s. by the above analysis and its definition, a novel recursive algorithm to construct a basic pattern prefix tree is designed. starting from root node, the prefix subtree is constructed layer by layer for each vertex. the core is to apply the following node-extend(s,d) extension algorithm to a node. since we need to calculate displacements for the set of starting positions in this algorithm, we define the addition of sets and numbers for this purpose. 580 l.y. xue, x.k. zhang, f. xie, s. liu, p. lin definition 8. given the set t = (t1, t2, · · · , tk) and the number l, their addition result is a set: t + l = {t1 + l, t2 + l, · · · , tk + l}. the node’s extension algorithm node-extend(s,d) establishes a prefix tree with d as the root for collection s of basic pattern string sets. the basic pattern table of the string h is s(h), then constructing the basic pattern prefix tree of h is the following recursive called nodeextend(s(h),root). 3.4 pruning of the basic pattern prefix tree using the basic pattern prefix tree, basic frequent patterns can be mined. firstly, we trim the basic pattern prefix tree as follows. if loc={l1, l2, · · · , lk} and loc is less than minloc sup in a pattern (num, sm, loc), the node and the subtree rooted from the node can be subtracted. because in a pattern, if its subpattern is not frequent, then according to the nature of apriori algorithm, the pattern itself must not be frequent. if a basic pattern is not frequent, all patterns including it would be certainly not frequent. deleting infrequent nodes and their subtrees in the basic pattern prefix tree will delete the infrequent subpatterns. given loc sup is 2, after pruning the basic pattern prefix tree in figure 1, the prefix tree which is shown as figure 2 can be obtained. figure 2: prefix tree after pruning from the prefix tree we can know that the original string. the frequent patterns in s="aabaababab" are: the frequency of "a" is four, the frequency of "b" is four, the frequency of "c" is two, the frequency of "ab" is two, and the frequency of "ba" is two. 3.5 general frequent pattern mining the basic frequent mode is limited to that there is no letter in the mode that is the same as the start letter. the above basic pattern prefix tree can only mine the basic frequent mode, and cannot mine the general frequent mode. in order to enable it to mine general frequent patterns, we designed a general frequent pattern detection algorithm based on basic sub-pattern prefix tree. the algorithm is a recursive algorithm. from root node, every node is model-expanded layer-bylayer. the kernel of the algorithm will be to apply the expansion algorithm span(s,es,a) to a node. there is a pattern s, whose set of end positions in the string s is es. in the basic pattern prefix tree, the last node of the path of the pattern s is a. the algorithm could detect all the frequent patterns prefixed by s. in order to detect all frequent patterns in the string s, span(φ,e,root) can be called where e = {1, 2, · · · , |s|}.in this way, the algorithm starts with an empty string, expands from tree to frequent patterns algorithm of biological sequences based on pattern prefix-tree 581 tree, and records the extended set of end positions at each layer. when extended to a leaf node, it will return to the root node and continue to expand until the extended substring is infrequent. by synthesizing the above several steps, the single sequence pattern mining for biological data algorithm for single biological sequence mining can be obtained. the algorithm quickly detects all basic frequent patterns firstly, and then implements pattern growth on this basis to obtain all frequent patterns of the sequence. the flow of the algorithm is defined as: algorithm 1 step 1. a candidate pattern p is extracted from set c in turn, if pattern p is already in the approximate frequent pattern set s, the next candidate pattern is selected. otherwise, the candidate pattern p is compared with other element c [index] of set c. step 2. if approximation degree(p,c[index]) is greater than minimum approximation, then the number of repetitions of candidate pattern p is increased by 1, index = index+i. candidate pattern p continues to compare with element c [index] of set c. otherwise index = index + 1, candidate pattern p continues to compare with element c [index] of set c. step 3. repeat step 2 until index is greater than or equal to the maximum subscript in set c, repetition times and support degree of candidate pattern p are counted, if the repetition times and support degree of candidate pattern p satisfy the specified minimum frequent threshold m and the minimum support degree minsupport, then candidate pattern p is a frequent approximation pattern and is added to the approximate frequent pattern set s. step 4. repeat step 1 until every element in set c is taken out. 4 multiple sequence frequent pattern mining algorithm above algorithm for mining single sequence frequent patterns can be extended for mining multiple sequence frequent patterns. definition 9. distribution support was set a collection of biological sequences d and subsequences t , the number of sequences containing subsequences t in d is called the distribution support of t . the distribution support degree of sub-sequence t is the quantity of sequences containing subsequence t in d, denoted as dis supd(t). definition 10. given biological sequence set d and subsequence t , if the sub-sequence support distribution dis supd(t) is greater than or equal to mindis sup, t is said to be a frequent pattern. for understanding the process of constructing a multiple sequence basic model table and its prefix tree easier, we would give the following definition: definition 11. (set vector) there are vectors t = (t1,t2, · · · ,tn) where each ti is a set, and t is a set vector. definition 12. (support function of the set vector) for the set vector t , its support function f(t) is defined as the number of non-empty sets in t , f(t) = |{ti|ti 6= ∅; 1 ≤ i ≤ n}| . (1) definition 13. (operation of set vector) if there are set vectors s = (s1,s2, · · · ,sn) and t = (t1,t2, · · · ,tn), the intersection of the two vectors is defined as: t ∩ s = (t1 ∩ s1, t2 ∩ s2, . . . , tn ∩ sn) . (2) 582 l.y. xue, x.k. zhang, f. xie, s. liu, p. lin definition 14. (addition of set vectors and numbers) with the set vector t = (t1,t2, · · · ,tn) and the number l, there is t + l = t1 + l,t2 + l, · · · ,tn + l. (3) "+" represents the addition of sets and numbers as defined in definition 3. 4.1 multiple sequence basic pattern table for the mining of multiple sequence frequent patterns, firstly we would intercept all the basic patterns in each sequence, and then sort the basic pattern tables of each sequence to obtain the basic pattern table of multiple sequences in the sequence set. suppose there are four sequences in the sequence set d. s1="xyzyxz", s2="xzyzx", s3="yzyxyz", s4="xzyxyz". algorithms 1 is invoked for each sequence. the basic mode table for each sequence of d is shown as table 3. table 3: basic patterns of multiple sequences basic pattern table of s1 num sm loc 1 xyzy 1 2 xz 4 3 yxz 3 4 yz 3 5 z 5 6 zyx 6 basic pattern table of s2 num sm loc 1 xy 4 2 xzyz 1 3 y 5 4 yzx 2 5 zxy 3 6 zy 2 basic pattern table of s3 num sm loc 1 xyz 3 2 yx 4 3 yz 1 4 4 z 5 5 zyxy 2 basic pattern table of s4 num sm loc 1 xyz 3 2 xzy 1 3 yx 2 4 yz 4 5 z 5 6 zyxy 3 frequent patterns algorithm of biological sequences based on pattern prefix-tree 583 in order to mine multiple sequence frequent patterns, after we get the basic pattern tables of multiple sequences, it must integrate the tables to get a merged multiple sequence pattern table. for example, the basic pattern table of 4 sequences in table 3 is integrated to obtain basic frequent pattern table of multiple sequence sets similar to table 1, it is shown in table 4. table 4: basic pattern table after merging num sm loc set 1 x {1,5},{1,5},{4},{4} 2 xy {1},{5},{4},{4} 3 xyz {1},∅,{3},{3} 4 xz {4},{1},∅,{2} 5 xzy ∅,{1},∅,{1} 6 y {1,3},{2,5},{1,2,4},{2,4} 7 yx {3},∅,{2},{3} 8 yz {2},{2},{4},{4} 9 z {2,5},{1,3},{1,4},{2,4} 10 zx {2},{1},{2},{3} 11 zyx {2},∅,{1},{1} 12 zyxy ∅,∅,{2},{1} from table 4 basic frequent patterns could be easily discovered. let (num,sm,loc set) be the term of a basic mode sm, then the frequency of sm is the support function f(loc set) of the set vector loc-set. for example, if dis sup is set to 2, then according to table 4 we can easily tap out the basic frequent patterns: the frequency of "x" is four, the frequency of "xy" is four, the frequency of "xyz" is three, the frequency of "xz" is three, the frequency of "xzy" is two; the frequency of "y" is four, the frequency of "yx" is three, the frequency of "yz" is four; the frequency of "z" is four, the frequency of "zy" is four, the frequency of "zyx" is three, the frequency of "zyxy" is one. 4.2 multiple sequence basic frequent pattern prefix tree assuming that the basic pattern table of the multiple sequence set d is d(h), then constructing the basic pattern prefix tree of d can invoke the node’s extension algorithm. the algorithm is a recursive algorithm that can be invoked with node-extend (d(h),root). in contrast to the mining of single sequence frequent patterns, the "+" in the "(p ’,loc+|xi|)" of the algorithm represents the set vector and number defined in the definition of 12. since the basic patterns in table 4 are all frequent patterns, the following recursive algorithm is used to construct the following multiple sequence basic frequent pattern prefix tree. assume that there are k sequences 1, 2, · · · ,k in d. in the tree, each side of x represents a character or substring; x is connected to the node in the direction of the leaf is b, there is a set vector t (b) = (t (b)1 ,t (b) 2 , . . . ,t (b) k ) on b node represents the mode prefixed with x in each sequence in d. tbi = {l1, l2, . . . , lki} is the collection of initial positions of x in sequence si. a basic frequent pattern in table 4 can be obtained by sequentially arranging the characters represented by the nodes on the path from root to every leaf node. by synthesizing above several steps, multiple sequence pattern detection for biological data algorithm for multiple biological sequence frequent pattern mining can be obtained. the algorithm quickly detects all basic frequent patterns firstly, then implements pattern growth on this basis to obtain all frequent patterns of sequence set. algorithm’s framework is as follows: 584 l.y. xue, x.k. zhang, f. xie, s. liu, p. lin figure 3: the basic frequent pattern prefix tree corresponding to d algorithm 2 multiple sequence pattern mining for biological data algorithm input: biological sequence s, minimum local support mindis sup; output: all frequent pattern sets freq set; begin 1. for each sequence si in s do 2. intercept(s); /* the intercept algorithm is used to get all the basic pattern string sets h of sequence s.*/ 3. sort(smi,s′′mi); /* using the algorithm, the basic schema table si(h) in order of lexicographic order can be gotten.*/ 4. end for 5. merge the basic schema tables of multiple sequences and sort them. 6. the basic pattern frequency table of multiple sequence sets is constructed and all basic frequent pattern sets h are extracted; 7. node-extend (s(h),root) is recursively called to construct the basic frequent pattern prefix tree t ; 8. mfreq-mining(s,root); end. 5 experimental results and analysis in order to verify effectiveness of the algorithm, two sets of experiments were compared. first set of experiments compares the traditional algorithm with the proposed two fast and efficient biological sequence pattern detection algorithms. it is mainly used for verifying that these algorithms change with the supportability threshold, which has little impact. the second set of experiments compares the traditional algorithm with the proposed algorithms to verify that the proposed algorithms in this paper have better elapsed time efficiency under the same supportability threshold. under the same biological sequence set, with the increasing of support threshold, running time changes of prior, biopm, and the single sequence frequent pattern mining algorithm are shown in figure 4. frequent patterns algorithm of biological sequences based on pattern prefix-tree 585 from figure 4, it can be seen that the elapsed time of each of the three algorithms will gradually decrease with increasing of the support threshold. however, overall elapsed time of the single sequence frequent pattern mining algorithm changes steadily, and it is obviously smaller than the other two algorithms. especially when the threshold is small, the apriori algorithm will generate a large number of candidate modes and interference modes during the mining process, which inevitably leads to a high complexity of the space-time of the algorithm. similarly, the biopm algorithm needs to construct a projection database frequently during the mining process, and there are also a large number of short-term generations, which greatly affects the efficiency of algorithm. the single sequence frequent pattern detection algorithm starts from length of basic pattern. it prunes basic pattern prefix tree in the process, avoiding the generation of a great quantity of short biological patterns and candidate patterns to speed up the operation speed and improve the efficiency. figure 4: the relation between support threshold and average elapsed time the following experiment is used to verify that the single sequence frequent pattern mining algorithm has better excavation efficiency under the same support degree threshold. the experimental data were from 10 families in the pfam protein database. the same or similar part of the length is selected as the test set, and set a 15% support threshold for them. from the 100-sequences, the sequence was gradually increased. the patterns were mined using the biopm and the single sequence frequent pattern mining algorithm respectively, and the overall time of pattern mining of all sequences was obtained. as the number of sequences increases, the overall elapsed time trends of the two algorithms are shown in figure 5. from figure 5, it can be seen that under certain fixed support threshold conditions, the running time of both the biopm algorithm and single sequence frequent pattern mining algorithm will increase as the quantity of sequences increases. however, running time of the single sequence frequent pattern mining algorithm is much smaller than that of the biopm algorithm, and the trend of change is always stable. the reason is that the biopm algorithm needs to construct a projection database frequently during the mining process and also a large number of shortterm generations causes the complexity of the algorithm’s space-time and increase the efficiency. however, the single sequence frequent pattern mining algorithm is to start frequent pattern mining from the basic pattern length, which avoids the elapsed time overhead of generating a great quantity of short patterns. because classic apriori algorithm, biopm algorithm, and multiple sequence frequent pattern 586 l.y. xue, x.k. zhang, f. xie, s. liu, p. lin figure 5: the relationship between the number of sequences and the time detection algorithm are deterministic algorithm of multiple sequence frequent patterns, the set of frequent patterns mined for the same biological sequence collection and support threshold is exactly the same. in order to verify the performance of the multiple sequence frequent pattern mining algorithm, two sets of experiments were conducted and then compared based on the experimental results. in the first set of experiments, these algorithms are compared with the multiple sequence frequent pattern mining algorithm to verify that the multiple sequence frequent pattern mining algorithm is less affected by changes in the support threshold. the second set of experiments compares the biopm algorithm, the mbiopm algorithm, and the multiple sequence frequent pattern mining algorithm. it is verified that under the same premise (with the same support threshold), the elapsed time efficiency of the multiple sequence frequent pattern mining algorithm is better, and it has superior excavation performance. through the group of experiments, it can be confirmed that the multiple sequence frequent pattern mining algorithm is affected less by the change of the support threshold set by the user. experimental data were sampled from 3 families in the pfam protein sequence database (galpha, calici coat, glyco hydro 19). a subset of the same or similar lengths (out of a total of 50) are selected as test sets to ensure that this algorithm is suitable for different types of sequence sets. under each of the specific support conditions, 50 sequences of data were tested using four algorithms respectively. under the same set of biological sequences, as the support threshold is increased, the elapsed time changes of the prior, biopm, mbiopm, and the multiple sequence frequent pattern mining algorithm are shown in figure 6. from figure 6, it can be seen that the elapsed time of the four algorithms will gradually decrease as support threshold increases. however, the trend of overall elapsed time of the multiple sequence frequent pattern mining algorithm is relative stability, especially when the threshold becomes small, the elapsed time will be obviously less than the other three algorithms. the main reason for this result is that the apriori algorithm will generate a great quantity of candidate modes and interference patterns during mining process, which will inevitably lead to a higher complexity of the space-time algorithm; the biopm algorithm requires frequent construction of the projection database during the mining process, and there are also a large number of shortmode generations, which greatly affect the efficiency of the algorithm; the multiple sequence frequent pattern detction algorithm is started from length of basic model to avoid generating a great quantity of short biological models, through the merge operation can quickly basic frequent pattern detection and use basic frequent pattern prefix tree for pattern growth to improve frequent patterns algorithm of biological sequences based on pattern prefix-tree 587 figure 6: the relation between support threshold and average elapsed time efficiency. the set of experiments is used to verify that the multiple sequence frequent pattern mining algorithm has better excavation efficiency under the same support degree threshold. the experimental data were from 10 families in the pfam protein database. the same or similar part of the length is selected as the test set, and set a 15% support threshold for them. from the 100-sequences, the sequence was gradually increased. the patterns were mined using the biopm and the multiple sequence frequent pattern mining algorithm respectively, and the overall time of pattern mining of all sequences was obtained. as the number of sequences increases, the overall elapsed time trends of the two algorithms are shown in figure 7. figure 7: the relationship between the number of sequences and the time from figure 7, under the conditions of a certain fixed support threshold, the elapsed time of the three algorithms for comparison will increase without exception with the increase of the number of sequences. however, it can be noted that the elapsed time of the multiple sequence frequent pattern mining algorithm is more stable, especially when the model is longer, the overall time is significantly less than the other two algorithms. it is due to the fact that the biopm 588 l.y. xue, x.k. zhang, f. xie, s. liu, p. lin algorithm needs frequent construction of the projection database during the mining process, and there are also a large number of short-term generations that cause the complexity of the algorithm’s space-time and increase the efficiency. for the mbiopm algorithm, since each time a frequent pattern of k-class length is mined, the existing pattern needs to be compared with the buffer pattern one by one. at the same time, the buffer area needs to be cleared and opened repeatedly during the pattern growth. these will definitely reduce the speed of the algorithm. the multiple sequence frequent pattern mining algorithm starts frequent pattern mining from the basic pattern, avoids the runtime overhead of generating a great quantity of short patterns, and at the same time uses basic frequent pattern prefix trees for pattern growth after fast mining to obtain basic frequent pattern string sets to avoid "interference patterns". the generation of the mining efficiency of the algorithm has been greatly improved. 6 conclusion according to the characteristics of biological sequence patterns and mining requirements, fast and effective biological single sequence and multiple sequence pattern mining algorithms are proposed in this paper, respectively. first of all, the concept of the basic pattern of the sequence is defined. the algorithm can connect the basic patterns of the sequence to obtain the basic pattern table of the sequence. for multiple sequences, the basic pattern table of multiple sequences can be obtained through the merge operation. based on the basic model, overall basic frequent patterns could be easily detected. in order to mine general frequent patterns, a basic frequent pattern prefix tree is constructed based on a single or multiple sequence basic frequent pattern table, and the general frequent pattern mining is quickly implemented using the set vector operation, which improves the mining efficiency. at the same time, the use of pruning technology avoids producing a great quantity of unnecessary short patterns. the analysis results of the experiment show that the two proposed algorithms significantly improve the mining efficiency of the biological sequence model, especially can get better mining results in the case of a small support threshold. funding this research was supported by the natural science foundation of hebei province (h2019201378). bibliography [1] apostolico, a.; preparata, f. p. 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(2006). an efficient approach for sequence matching in large dna databases, journal of information science, 32(1), 88-104, 2006. zhangijcccv12n6.pdf international journal of computers communications & control issn 1841-9836, 12(6), 886-902, december 2017. using blockchain to protect personal privacy in the scenario of online taxi-hailing n. zhang, s. zhong, l. tian ning zhang, shan zhong, li tian* school of information, central university of finance and economics, beijing, china zhangning@cufe.edu.cn, 2015211624@email.cufe.edu.cn *corresponding author: tianli@email.cufe.edu.cn abstract: personal privacy protection issues has gradually caused widespread concern in society which will lead to economic and reputation losses, hinder network and e-commerce innovation or some other consequences if not handled properly. in this paper, we make use of the de-centralization, permanent and audibility of the blockchain to propose a blockchain-based personal privacy protection mechanism, which uses online taxi-hailing as the application scenario. we not only provide the details of the blockchain custom transaction domain used by the scene, but also expound the information exchanging and blockchain auditing between passengers, online taxi-hailing platform and drivers in online taxi-hailing scene, providing a case model for the blockchain solution to personal privacy protection and a technical mechanism solution for further study of personal privacy protection issues. keywords: online taxi-hailing, blockchain, personal privacy protection, 1 introduction with the popularization of computer and network, the issue of personal privacy protection has gradually become a concern and concern. if we ignore the personal privacy protection issue or handle it improperly, it may lead to reputation and economic losses or even hinder the network and e-commerce innovation. the problem of personal privacy protection can be solved by both legal and technical means, and it can be also effectively solved by blockchain technology currently. the blockchain is a distributed book that records the history of each transaction sent and verified, as well as the additional information contained in the transaction [3]. all the blocks in the blockchain are arranged in chronological order [11], created by the miners [5], and each node has a copy of the whole blockchain [12]. because of the features of de-centralization [14], permanent recording and convenient auditing [10], blockchain technology can be used to meet the data security requirements such as integrity and audibility of privacy data, so it can be used as an effective solution of personal privacy protection issues. there are few literatures on blockchain technology and the literature on the blockchain technology’s application in personal privacy protection is more scarce. in the application aspect of blockchain’s protecting personal privacy, melanie swan [13], referring to the seriousness of health privacy issues, argued that blockchain could provide a mechanism for protecting the privacy of personal health data against infringement, but she didn’t give the scenario implementation details of blockchain’s protecting personal privacy. in the aspect of using blockchain technology to solve practical problems and providing specific implementation details, guy zyskind [15] proposed a decentralized personal information management system in which bit-coins can be used to pass storage, query, and data analysis instructions through some agreement, ensuring that users own and control their own information, but the paper doesn’t propose a specific case of application, neither does it provide any personal privacy theory on the issue. ahmed kosba [7] copyright © cc by-nc using blockchain to protect personal privacy in the scenario of online taxi-hailing 887 argue that the current blockchain trading environment lacks the privacy protection of transactions, and that money flows between virtual addresses are completely exposed to the blockchain environment, so they propose a decentralized intelligent contract system called hawk to protect the privacy of transactions by avoiding the clear text storage form of financial transactions on the blockchain. but the method can only protect the privacy of transaction information, which lacks a broader field of real life application. amir lazarovich [8] gave a detailed discussion on how to use blockchain technology protect personal privacy, and proposed a distributed-storage based third-party database named escrow, and a blockchain-audit based invisible ink system, taking medical information privacy protection as an example to illustrate the blockchain technology’s application in personal privacy issues. but it lacks a discussion on individual privacy issues and the presentation of blockchain application details. in summary, domestic and foreign scholars’ research on blockchain application for personal privacy protection is very limited. therefore, by putting the solution method into the specific application scenarios, we will explain the details of blockchain’s privacy protection application one by one, which can provide a mechanism for scholars to further study on the issue. some people concern about the information security risks of blockchain technology application like the haker attacks, as the attack sources of information security are characterized as spectral and concealed[9]. the practice shows that compared with other techniques, the possibility of blockchain being attacked by a hacker successfully is very low. furthermore, it is the time we have come to start thinking about a new paradigm of law, which could balance the power of blockchain technology and emerging autonomous systems in ways that promote economic growth, free speech, democratic institutions, and the protection of individual liberties [14]. as businesses and government strive to accommodate this new way of doing business, countries have been started for blockchain application legal provisions. such as arizona governor doug ducey signed hb 2417 into law in march 2017, delaware’s historic blockchain law became effective in august 2017 and chinese first standard under the guidance of the government released in may 2017. in addition, it is pointed out that the technical solution of personal privacy protection needs to consider the problems and needs of anonymity, data access control, auditing, online social network privacy protection, mobile location privacy protection and database privacy protection. online taxi-hailing service just fit those above problems and has more universality and representation than others, so we use it as the application scenario of blockchain’s protecting personal privacy. the didi, one of the hottest online taxi-hailing platform, happened to information leakage because several staff use their permissions to check user travel records and make profits illegally. leaked information, include the driver’s identity, vehicle information and passenger’s identity and common address and so on, are likely to threaten users’ property and life security. 2 the online taxi-hailing scene and its exposure to personal privacy 2.1 online taxi-hailing and its process online taxi-hailing is a "car rental & designated driving" model for passengers, providing a new way for urban travel. consider a scenario where passenger u1 wants to go to work by using online taxi-hailing software (such as the current popular dididache in china). first of all, the passenger input departure and destination on the online taxi-hailing software. then the online taxi-hailing software automatically obtain departure of passengers, and recommend drivers to the passenger and send passenger’s information and travel routes to those drivers. finally, drivers receive 888 n. zhang, s. zhong, l. tian passenger’s routes and decide whether to grab the order. the first one who completes it has the right to complete the transaction. u (t h e p a s s e n g e r ) a (i n t e r n e t c a r r e n t a l p la t fo r m ) c (d r iv e r ) ① u p lo a d p a s s e n g e r ’ s i t i n e r a r y ② se le c t p r o p e r d r iv e r s a n d p r o v id e t h e u s e r ’ s r o u t e d a t a t o t h e m ③ d r iv e r g r a b t h e o r d e r a n d c o m p le t e t h e t r a n s a c t io n figure 1: the original online taxi-hailing scene 2.2 exposed problems of processes the passenger uses the software every day and his route is hardly changed. in the process, the passenger encounters some personal privacy issues: • problem 1: the passenger can’t guarantee that the online taxi-hailing platform will be confidential to his personal information, and his itinerary and other information may be stolen by the taxi platform to promote advertising, as well as acquisition of other commercial value (such as selling it to third-party data company). • problem 2: platform and drivers can freely view passenger’s driving route information, leading to passenger’s anxiety that he can’t control his personal privacy information. • problem 3: when the passenger decides to stop using the online taxi-hailing software, his information stored on it can’t be emptied. • problem 4: imagine that the passenger has asked the online taxi-hailing software to delete his personal information, and soon after the online taxi-hailing platform was attacked by hackers. unfortunately, the passenger found out that his name was on the list while he didn’t have any evidence to file a lawsuit against the online taxi-hailing software. 2.3 solution in order to solve the above four personal privacy issues, we use the third-party database and the data interactive audit platform to protect the personal privacy. the third-party certification, reputation, and return policy are interacted to produce a different overall effect on the level of trust [2]. third-party database means that the user can choose his own trusted database to achieve personal privacy data hosting. third-party database is an open source database that users can use it through its corresponding link. different users may choose different third-party database, and through such a distributed data storage we can increase the security of data [7] and reduce the amount of data stored in an online taxi-hailing software service. moreover, the third-party database stores user data in the form of encryption, as a result of which the third-party database can’t see the specific content of user’s privacy and can only serve a loyal guardian on user’s data. data interactive audit platform is a system built on the blockchain that audits all data operation behaviors. the platform uses blockchain to record all operations on the user data, including data-reading, data-writing, data-updating, and license management for the online taxi-hailing platform, drivers, and other groups. forced by pressures of personal privacy protection needs, using blockchain to protect personal privacy in the scenario of online taxi-hailing 889 online taxi-hailing software platform and other profit institutions can rent the data interactive audit platform to announce to the public their improvement on personal privacy protection. the platform can ensure that all relevant data operations are recorded in the blockchain which will further strengthen the operation compliance of online taxi-hailing platform and other parties, allowing users to truly control and master their own data. in addition, due to the online taxi-hailing software platform renting data interactive audit platform for some privacy purposes, so one data interactive platform may correspond to a number of online taxi-hailing software platforms. e (third party databese) u (passenger) s (data interactive audit platform) a1 blockchain ① initialkey exchange ③upload the privacyinformation a2 an c1 c2 cn… … internet car rentalplatform drivers ④ ⑤ internet car rentalplatform and drivers obtain passenger's data through the data interactive audit platform ⑥ passenger readsvisitors' records ④ the taxisoftware platform reads the passenger'sdeparture position ⑤ theselected driver reads the passenger's itinerary ⑦revoke authority ②grant permissions audit audit read the audit messages figure 2: solve online taxi-hailing privacy problems we can use third party database and data interactive audit platform to solve the problem of privacy protection in the online taxi-hailing scene and its process is as follows. firstly, the passenger, the third party database, and data interactive audit platform have their initial key exchange between each other, and the passenger grants permissions to the data interactive audit platform. in order to rent a car, the passenger will upload his itinerary and other privacy information to his third party database. through third party database and blockchain audit method, he can avoid the online taxi-hailing software platform getting his entire itinerary (only get the departure, but not the destination). as a result of it, the problem 1 has been solved. then, the online taxi-hailing software platform reads the passenger’s departure location and recommends the corresponding matching driver to him. the selected driver reads the entire itinerary of the passenger and decides whether to receive the order. the third party database can’t see the passenger’s data cause the uploaded data is encrypted, so we can protect passenger’s data security by the third party database’s decentralized storage of user data. online taxi-hailing software platform and the driver can access to passenger data through data interactive audit platform, and all operations are recorded in the blockchain. as a result of it, the passenger gets the ability to control his own personal privacy information. the problem 2 has been solved. passengers can improve their control of the data by reading the visitor records on the blockchain. at the same time, if the platform leak passenger’s private information, the passenger can also have evidence against platform’s illegal operation. therefore, the problem 4 will be solved. finally, when the user doesn’t want to continue to use the online taxi-hailing software platform, he can withdraw the operating authority of the data interactive audit platform on the online taxi-hailing platform to protect his privacy from infringement. so the problem 3 will be solved. 890 n. zhang, s. zhong, l. tian 3 model design and definition 3.1 actors profiles • passenger mobile terminal (u1). the passenger mobile terminal can be a user handset that downloads the online taxi-hailing platform app, representing the user identity. u1’s action is to upload passenger’s itinerary, read other groups’ information access records, and grant / revoke the data interactive audit platform s1’s access to its data and so on. • data interactive audit platform (s1). s1 has been described above in detail, so we will not repeat them here again. s1’s action is to help online taxi-hailing software platform and drivers to read user data, and audit each operation on the blockchain. • the server of the third party database (e1). e1 has been described above in detail, so we will not repeat them here again. e1’s action is to find the blockchain audit records and to determine whether to accept the user’s write-data request, and whether to accept software and drivers’ read-data request. • online taxi-hailing platform (a1). a1 is the online taxi-hailing company’s platform. the action that a1 needs to complete is to obtain the passenger’s departure location in order to provide optimal compatible drivers (for example, drivers less than three km away from u1). • online taxi-hailing software driver (c1). online taxi-hailing software drivers can be those drivers’ phones that download the online taxi-hailing platform. the position of the driver is not required to be protected as personal information, so it is visible to the passenger (u1). when a1 finds out a plurality of optimum drivers (c1) that can be matched, then it will send u1’s departure point to c1. c1 automatically applies to view the itinerary of u1, and then decides whether to compete for the transaction with u1. 3.2 blockchain databases for the design of blockchain database, it is necessary to refer to the specification of blockchain’s transaction. herbert and litchfield [6] mentioned two approaches to solve the problem of authorization, which are the master bitcoin model and the bespoke model. the problem they want to solve is how to use the blockchain to complete the authorization of software using, and how to protect software integrity, prevent software piracy, and complete software updating. in the master bitcoin model, the software vendors transfer 1 unit of master bitcoin [6] to the user’s wallet address via blockchain to granting user the right of using the software. user’s software can automatically read transactions. by verification, if the software find that the master bitcoin in the user address does come from the specific vendor address, it will automatically starts the installation, otherwise it will not be allowed to install. the bespoke model uses blockchain with a special specification [6], which contains a number of additional domain components in the specification. these additional domains are tailored for flexible requirements of software licensing. for example, we can add token, license, hash software, signature and other domains in the specification, to further implement the software ownership transfer, integrity checks and other advanced features. in addition, the blockchain specifications for different encryption currency is different, and sometimes we can build such a specification based on our own needs to meet the specific needs of the application. the method also has some inspiration for our online taxi-hailing case. through different individuals’ sending transactions on the blockchain, we can easily audit the content of their using blockchain to protect personal privacy in the scenario of online taxi-hailing 891 transactions. of course, we can also construct a more unique blockchain specification in accordance with our specific purpose. our specific specification is shown below. transaction specification tx-id hash(meta-data) token signature figure 3: a special definition of specification fields hash(meta-data) is the hash value of the message meta-data that will be conveyed by the transaction, the purpose of which is to store the longer message meta-data in a shorter way. token is used to record who has seen or operated the passenger u1’s information. signature is used to record the sender’s signature, mainly to verify the identity of the sender, so as to determine the authenticity of the transaction. tx-id is a specific transaction number, which plays a role in the blockchain that can uniquely identify the transaction. in fact, tx-id is automatically generated after the completion of the transaction. we put it in the transaction specification to effectively distinguish those different transactions. in the custom transaction, the sender encrypts the message to a hash value using the public key of the receiver, and the receiver uses its own private key to decrypt it. the sender uses his own private key to generate the signature, and the receiver uses the sender’s public key for signature verification. 3.3 third party database in addition, the third party database needs to have the ability to establish and maintain the chain of title for the user to record all blockchain transactions’ number tx-id it has validated and its corresponding transaction hash(meta-data1) /token /signature /encrypt(meta-data2) of the tx-id. the third party database needs to maintain a table for each user and data interactive audit platform’s combination. the table is constructed as follows. the definitions of tx-id, token and signature are the same as the transaction specification mentioned above, while the content of hash(meta-data1) is other actors’ manipulation on user’s private data, and encrypt(mata-data2) is the encryption of user’s private data. thechainoftitle tx-id hash(meta-data1) token signature encrypt(meta-data2) figure 4: the chain of title in addition, we should also note that hash(meta-data1) is the hash value of meta-data1, which is invisible to e1. e1 can read token’s content and e1 can take appropriate measures based on the token’s specific content. signature is the part that e1 need to verify by using sender’s public key which can guarantee the authenticity of the transaction, as long as the verification is successful. encrypt(meta-data2) is the content that e1 can write in the table in accordance with other actor’s submitted information. 892 n. zhang, s. zhong, l. tian 3.4 data interactive audit platform database as mentioned above, the data interactive audit platform is a system which builds on the blockchain and audits all data operation behaviors. the data interactive audit platform’s database stores various actors (such as u/e/a/s)’s necessary information when they make some data exchange, including blockchain address (such as uaddress), id number (such as uid), etc. in this section, the symmetric key (such as keyus) and the private key of asymmetric key (such as uprivatekey) are also put into the parameter information in order to express the logical structure of the system more clearly. but in the real world, for the sake of information security, the key can’t be stored in the data interactive audit platform database, but should be stored and maintained by a special mechanism (such as the pki system). this database’s uml, attributes and methods are shown below. +generate() +send() +read() +decrypt() +validate() +write () -eid -eaddress -eprivate key -uid -uaddress -sid -saddress -keyue e +generate() +send() +broadcast() -uid -uaddress -uprivatekey -eid -eaddress -keyue -keyus u +send() -sid -saddress -sprivatekey -uid -eid -eaddress -aid -keyus s +send() -aid -keya -sid -uid a * * * * 1* 1 * 1 * figure 5: uml of data interactive audit platform database actors’ attribute definitions are shown as follow. • xid: x’s non-blockchain address. it is the only identification of x. for example, x1’s email address. • xaddress: x’s blockchain address, but also x’s public key. it is not unique because x1 can have multiple blockchain addresses. • xprivatekey: x’s private key. for x1, a blockchain address (public key) corresponds to a private key. • keyxy: symmetric key between x and y. as for u1, he can select multiple e at the same time, but he has a specific symmetric key with each e. actors’ method definitions are shown as follow. using blockchain to protect personal privacy in the scenario of online taxi-hailing 893 • generate(x): there are different kinds of keys’ generating methods. 1. symmetric key generation: the system will automatically generate the symmetric key by the user’s mouse or touch screen sliding route. we can use route data as input, and use des tripledes algorithm to generate the key which can served as the shared key. 2. asymmetric key generation: the system will automatically generate the asymmetric key by the user’s mouse or touch screen sliding route. we can use route data as input, and use des tripledes algorithm to generate the public/private keys. • send(x;y): send content y to x. the receiving address includes the blockchain address and the nonblockchain address. 1. sending to blockchain address means that we can send transaction to actor’s blockchain wallet address. because the custom domain in the blockchain contains customized content such as meta-data, we can audit its loading information by this transaction. the general form of transmission is u. send(xaddress; hash(meta-data, token, signature)). 2. sending to non-blockchain address means that we can send information via email or some other means. like e1 and s1, each actor has its own address and its receiving new message will trigger the corresponding action. the general form of transmission is u.send(x;y). the receiver has the ability to identify the contents of y and thus triggers the corresponding action. there are several types as follows. (1) send general information, such as u.send (eid; uaddress, keyue). it triggers the receiver to record the contents such as "uaddress, keyue" into the database. (2) send authorization instructions, such as u.send (eid; permission). it triggers the receiver to check whether the sender’s authorization action has been audited in the blockchain. (3) send request instruction, such as u.send (aid; request). it triggers the receiver to query the sender what information he needs. (4) send authorization instruction, such as u. send(sid; authorization). it triggers the recipient to continue to perform the desired action (as has been authorized). • broadcast(x): broadcast content x on the blockchain. the so-called broadcast means that the user send the transaction on the blockchain which will be verified by each node, so as to be able to learn the specific content of x. • read(x): read the transaction x on the blockchain. e1’s reading transaction means that finding corresponding transaction from blockchain via tx-id. for example, we can find out a corresponding transaction according to tx-id "002", and then extract it for preparing for the next operation step. • decrypt(x): decryption means that, after e1 extract corresponding transaction, he will use his own private key to get the specific content of the transaction, which includes tx-id, hash (metadata), token, as well as signature. 894 n. zhang, s. zhong, l. tian • validate(x): verification means that e1 uses sender’s public key to verify token and signature that he decrypts. after verification, e1 will save his extracted information to his own maintenance table the chain of title. e.validate (token) as an example, the pseudo code of its general process is as follows: e.read(token); / e reads the current token e.check(u.the_chain_of_title); / in the table the chain of title, e finds the table belongs to u. test=ture / set test variables to true for(tx_id=1;tx_id<=tx_id.max; tx_id++) / loop, in turn to traverse tx-id in u’s table the chain of title {if(u.token =“ u revoke s’s authority on operating u’s data”) {test=false;break;}} / if the tx-id’s corresponding token’s content is revocation, the test variable change to false; if there is no revocation, the test variable is still true if(test=true) / after loop ending, check the value of the test variable {insert token into u1.the_chain_of_title; execute(token)} / if test is true, insert the token at the end of the table the chain of title ( of course, in details, it should be inserted together with tx-id, hash(meta-data1), token,signature, encrypt(meta-data2). then execute the contents of the token. else refuse. / if test is false, the execution is rejected. • write(x): writing content x in the database. when e1 receives tx-id and its corresponding content x from other actors to e1’s non-blockchain address, he will write this information to the tx-id’s corresponding place. for example, when e1 receives the information sent by u1 whose tx-id is "002" and corresponding content information is encryptkeyus(meta-data2), he will write this message to the "002" entry. 3.5 online taxi-hailing software platform database online taxi-hailing software platform database is maintained by the online taxi-hailing software platform itself. in the real world, online taxi-hailing software platform database are generally more complex, and it is designed according to the specific function and structure of the online taxi-hailing software. however, this paper simplifies the database, leaving only the key parts of the blockchain interaction to display the database. this database’s uml is shown as follows while its role’s attributes and methods are defined in the front tables so we will not repeat them here. 4 model framework and implementation overall, the model includes processes such as the initial key exchange, granting permissions, uploading privacy information, reading the passenger departure location, reading the passenger travel, reading the visitor log and revoking permissions. using blockchain to protect personal privacy in the scenario of online taxi-hailing 895 +send() -uid u +send() -uid -cid a +send() -cid -public keyc -private keyc c 1* 1 * * * figure 6: uml of online taxi-hailing software platform database 4.1 initial key exchange u, s, e will exchange their keys firstly. its process is as shown below. e u s blockchain ② ua dd re ss， ke yu e ④ ua dd re ss( ea dd re ss) ⑦ ea dd re ss( sa dd re ss) ⑤ keyus,uid,eid,eaddress ⑥ saddress ① uaddress, uprivatekey ③ eaddress,epriv atekey figure 7: initial key exchange 1○ u.generate(uaddress,uprivatekey) 2○ u.send(eid; uaddress,keyue) 3○ e.generate(eaddress,eprivatekey) 4○ e.send(uid; uaddress(eaddress)) 5○ u.send(sid; keyus,uid,eid,eaddress) 6○ s.send(uid; saddress) 7○ u.send(eid; eaddress(saddress)) 4.2 grant permissions passenger u1 requests to use the data interactive audit platform s1 on the online taxi-hailing software platform and grants the privilege to s1. after authorization, s1 will have the right to read or manipulate u1’s data. 1○ u.send(eaddress; hash(),token,signature) note that the content of hash () is empty, and the content of token is “u grant data operation rights to s”. 2○ b.return(tx-id) 3○ u.send(eid; permission,tx-id) 4○ e.read(eaddress.transaction) e.decrypt(eaddress.transaction) 896 n. zhang, s. zhong, l. tian e u s blockchain ①hash(),token,signature ② tx-id ⑤ broadcast(permission, uaddress,saddress) ③ pe rm iss ion ,tx -id ④ check-permission figure 8: grant permissions e.validate(signature) e.validate(token) note that e view the table the chain of title which belongs to some user u and find the according transaction through tx-id, finding that the transaction token’s all following tokens do not write “u revoke s’s authority to operate its data” at all. so we can conclude that it is still valid for s to operate u’s data. 5○ u.broadcast(permission,uaddress,saddress) 4.3 upload the privacy information to carry out an online taxi-hailing order, u1 should first provide personal privacy information such as his own departure and destination, and upload the above itinerary route information to e1 and audit them in the blockchain. as the block size is limited, the audited information uploaded to the blockchain is a shorter hash value. after the passenger upload the departure and destination to the third party database, it will automatically trigger a series of actions (such as the online taxi-hailing platform recommends several drivers to the passenger). the automatic triggering mechanism is achieved through passenger’s sending request to the online taxi-hailing platform. thus, we solved the problem1 that the travel route of the passenger is leaked to a1, so that a1 can’t obtain the entire trip route privacy of the passenger or sell passenger’s itinerary to other groups. 1○ u.send(eaddress; hash(meta-data),token,signature) meta-data’s content is “u1’s itinerary and other private information”, while token’s content is empty. 2○ b.return(tx-id) 3○ u.send(aid; request) 4○ u.send(eid; encryptkeyus(meta-data),tx-id) 5○ e.read(eaddress.transaction) e.decrypt(eaddress.transaction) e.validate(signature) e.validate(token) note that, although the token’s content is empty, e still has to perform the validation process in a flow. 6○ e1.write(encryptkeyus(meta-data),tx-id) using blockchain to protect personal privacy in the scenario of online taxi-hailing 897 e u s blockchain ①hash(metadata),signature ② tx-id ④ en cr yp t ke yu s (m et ada t a ) , t x i d ⑤ c heck-perm ission a ③request ⑥ write figure 9: upload privacy information 4.4 the online taxi-hailing software platform reads the passenger’s departure position the taxi software platform a1 reads the departure position of the passenger u1 and provides recommended drivers to the passenger on the basis of it. s1 is the privacy protection system selected by the taxi software platform a1. therefore, if a1 wants to acquire the departure of u1, it still needs to apply to the third party database e1 through s1. 1○ a.send(sid; keya, meta-data1) the content of meta-data1 is “a want to get the initial position of u ". 2○ s.send(uid; meta-data1) 3○ u.send(sid; authorization) 4○ s.send(eaddress; hash(meta-data1), token,signature) the content of token is “u1 authorized a1 to read its initial position". 5○ b.return(tx-id) 6○ s.send(eid; meta-data1,tx-id) 7○ e.read(eaddress.transaction) e.decrypt(eaddress.transaction) e.validate(signature) e.validate(token) 8○ e.send(sid,encryptkeyus(meta-data2)) the content of meta-data2 is “the initial position of u1”. 9○ s.send(aid,encryptkeya(meta-data2)) 4.5 the selected driver reads the passenger’s itinerary and compete for the order the taxi software a1 has read the passenger departure position, matched the optimum position driver c1 through the system, and sent the departure of u1 to c1. c1 will automatically apply to view the itinerary (if user think it’s not convenient, he can set the system to automatically authorize, because the entire reading process will be recorded in the blockchain), and 898 n. zhang, s. zhong, l. tian e s blockchain a ④ token,hash(meta-data1),signature⑤ tx-id ⑧ encrypt keyu s(m eta-data2) ⑥ m eta-data1,tx-id ① ke ya , m et a-d ata 1 ⑨ en cry pt k ey a (m et a-d ata 2) u ③ authorization ② meta-data1 ⑦ check-permission figure 10: the online taxi-hailing platform reads passenger departure location decide whether to compete for it. the entire process avoids a1’s obtaining u1’s privacy of the itinerary information. after successful competition, some lucky drivers will have the opportunity to provide taxi service to the user. thus, we solve problem 2 and then all operations of a1 and c1 are recorded on the blockchain, which can provide u1 the ability to control his own personal privacy information. e s blockchain a u ④ token,hash(meta-data1),signature ⑤ tx-id ⑧ encrypt keyu s(m eta-data2) ⑥ m eta-data1,tx-id ① p ublic keyc , me ta-d ata1 ⑨ e ncry pt publi ckey c(m etadata 2) ② meta-data1 ③ authorization ⑦ check-permission c ⑩ encryptpublickeyc(meta-data2) figure 11: the selected driver reads the passenger’s itinerary 1○ a.send(sid; publickeyc, meta-data1) the content of meta-data1 is "c1 wants to get u1’s itinerary". 2○ s.send(uid; meta-data1) 3○ u.send(sid; authorization) 4○ s.send(eaddress; hash(meta-data1), token,signature) the content of token is: "u1 authorizes c1 to get the itinerary of u1". 5○ b.return(tx-id) using blockchain to protect personal privacy in the scenario of online taxi-hailing 899 6○ s.send(eid; meta-data1,tx-id) 7○ e.read(eaddress.transaction) e.decrypt(eaddress.transaction) e.validate(signature) e.validate(token) 8○ e.send(sid; encryptkeyus(meta-data2)) the content of meta-data2 is "u’s itinerary". 9○ s.send(aid; encryptpublickeyc(meta-data2)) 10○ a.send(cid; encryptpublickeyc(meta-data2)) 4.6 the passenger reads visitors’ records passenger u1 can figure out which actor has attempted to read his data. here we will use the information stored in the token before, because the token contains u1’s authorization records about which visitor visited which information of u1. thus, we solved problem 2 and problem 4. u1 can not only see all the visitor records to strengthen his control over the data, but also have evidence to inform the privacy breach platform’s illegal operation (failed to clear u1’s personal information in time). e u s blockchain ① h ash( met a-da ta),s igna ture ② tx -id ③ m et ada ta ,t xid ⑤ re a d :a ll-to k e n ④ ch e ck -p e rm issio n ⑥ en cr yp t ke yu e (a llto ke n) figure 12: the passenger reads the visitor record 1○ u.send(eaddress; hash(meta-data),token,signature) the content of metadata is "u1 wants to read which group has tried to access his privacy information", or “u1 want to read all his related tokens that have been audited in the blockchain”. the content of token is null. 2○ b.return(tx-id) 3○ u.send(eid; meta-data,tx-id) 4○ e.read(eaddress.transaction) e.decrypt(eaddress.transaction) e.validate(signature) e.validate(token) 5○ e.read(all-eaddress.all-token) e.decrypt(all-eaddress.all-token) e1 reads all tokens related to u1 from his addresses and then extracts them ( e1 can use his own private key to decrypt them) . 900 n. zhang, s. zhong, l. tian 6○ e.send(uid; encryptkeyue(all-token)) 4.7 revoke permissions passenger u1 may, for some reason, wish to terminate the use of the current online taxihailing software platform a1 and wish to terminate a1’s manipulation on his own data by revoking the permissions of its corresponding data interactive audit platform s1. on the other hand, u1 can give up the current third-party database e1 and select another one, but he doesn’t need to change the current e1 because e1 can’t see his privacy information at all. thus, we solved problem 3, so that once u1 decide to stop using the online taxi-hailing software, he can immediately revoke s1’s access to his data to protect his privacy from infringement. e u sblockchain ① hash(),token,signature ③ wr ite (m et a-d ata ,tx -id ) ④ ch e ck -p e rm issio n ⑥ broadcast ② tx-id ⑤ write figure 13: revoke permissions 1○ u.send(eaddress; hash(),token,signature) the content of hash() is null. the content of token is “u want to revoke s’s data operation authority on u ”. 2○ b.return(tx-id) 3○ u.send(eid; write(meta-data,tx-id)) the content of meta-data is: "u request to revoke s’s data operation authority on u ". write means that e is expected to be able to write meta-data in its corresponding table the chain of title. 4○ e.read(eaddress.transaction) e.decrypt(eaddress.transaction) e.validate(signature) e.validate(token) 5○ e.write(meta-data,tx-id) e writes meta-data and tx-id. from now on, as long as e reads “u request to revoke the operating authority of s on u’s data” in meta-data, he will refuse to continue to provide s services related to u’s data. e can also read tokens related to u in the chain of title. once e finds that the last token record of u on s is “u want to revoke s’s data operating authority on u ”, e will refuse to provide u’s data related services for s. 6○ u.broadcast(token) using blockchain to protect personal privacy in the scenario of online taxi-hailing 901 5 conclusions and future works personal privacy problem of online taxi-hailing has not been effectively solved. in addition to lack of improvement motivation due to users’ privacy protection consciousness, the online taxi-hailing platform also lacks the improvement ability due to the current deficiency of privacy protection technology. many developing countries, like china, have yet to enact special laws on personal information protection. however, blockchain technology provides an effective mechanism solution to personal privacy protection currently as a strong complement to the legal system. through the application of our blockchain model, firstly, the taxi software platform can’t obtain the entire itinerary privacy of the user, so that the privacy information won’t be sold to other for-profit organization anymore. secondly, the user can see all the visitor records, thereby enhancing his control over his privacy data. moreover, once the user decides to stop using the online taxi-hailing software, he can revoke its data interactive audit platform’s access to his data. finally, if the online taxi-hailing software platform is attacked by hacker which leads to user’s information leakage, the user can sue the platform for that it doesn’t clear his information promptly, with the evidence of visitor records. in fact, in our opinion that blockchain technology can solve the problem of identity hacking, because if your identity is controlled by a private key, and your own holds that the private key, then there is no way to hack your identity, or at least compared with the traditional database system, the possibility of attack is very low. in addition, maybe separate blockchain may not be the solution to data hacking and identity theft from a technical point of view, but combining with other blockchains rather than fighting alone can solve it. we boldly apply the blockchain technology to the issue of personal privacy protection, and take the lead in the current popular online taxi-hailing scenario, where we comprehensively describe the details of blockchain’s application in individual privacy protection through processes such as rights-granting, data-writing, data-reading and permissions-revoking, providing a technical mechanism solutions for scholars to further study personal privacy protection issues. however, this paper still has some limitations. firstly, from the theoretical point of view, our analysis to personal privacy protection is relatively a little less due to space limitations. secondly, from the application point of view, our domestic online taxi-hailing passengers’ awareness is relatively weak about personal privacy protection issue. finally, from the implementation point of view, we provides the data exchange between entities, but doesn’t provide the specific implementation and simulation of the system, meaning that further study will be carried out to complete the specific implementation and simulation experiments of the system refer to papers of barlas et al. [1] and cotet et al. [4]. acknowledgment this research work was supported by national social science foundation of china under grant no. 13axw010. bibliography [1] barlas p., heavey c., dagkakis g. (2015); an open source tool for automated input data in simulation, international journal of simulation modelling, 14(4), 596-608, 2015. [2] chang m.k., cheung w., tang m. (2013); building trust online: interactions among trust building mechanisms, information & management, 50, 439-445, 2013. 902 n. zhang, s. zhong, l. tian [3] claassen r. a. (2016); an introduction to bitcoin and blockchain technology, kaye scholer llp, 2016. [4] cotet c.e., popa c.l., enciu g., popescu a., dobrescu t. (2016); using cad and flow simulation for educational platform design and optimation, international journal of simulation modelling, 15(1), 5-15, 2016. [5] forte p., romano d., schmid g. (2015); beyond bitcoin part 1: acritical look at blockchainbased systems, iacr cryptology eprint archive:1164, 2015. 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(2015); certification of witness: mitigating blockchain fork attacks, undergraduate thesis in mathematics, columbia university in the city of new york, 2015. [12] swan m. (2015); blockchain: blueprint for a new economy, o’reilly media, inc. 2015. [13] swan m. (2015); blockchain thinking : the brain as a decentralized autonomous corporation, ieee technology & society magazine, 34(4), 41-52, 2015. [14] wright a., de filippi p.(2015); decentralized blockchain technology and the rise of lex cryptographia, social science electronic publishing, 2015. [15] zyskind g., nathan o., pentland a.(2015); decentralizing privacy: using blockchain to protect personal data, security and privacy workshops ieee, 180-184, 2015. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 5, month: october, year: 2020 article number: 3906, https://doi.org/10.15837/ijccc.2020.5.3906 ccc publications a convolutional long short-term memory neural network based prediction model y. h. tian, q. wu, y. zhang yonghong tian* college of data science and application inner mongolia university of technology hohhot 010080, china *corresponding author: tyh@imut.edu.cn qi wu college of data science and application inner mongolia university of technology hohhot 010080, china wq0103wq@163.com yue zhang college of data science and application inner mongolia university of technology hohhot 010080, china yueer_zhang@163.com abstract in recent years, the market demand for online car-hailing service has expanded dramatically. to satisfy the daily travel needs, it is important to predict the supply and demand of online car-hailing in an accurate manner, and make active scheduling based on the predicted gap between supply and demand. this paper puts forward a novel supply and demand prediction model for online carhailing, which combines the merits of convolutional neural network (cnn) and long short-term memory (lstm). the proposed model was named convolutional lstm (c-lstm). next, the original data on online car-hailing were processed, and the key features that affect the supply and demand prediction were extracted. after that, the c-lstm was optimized by the adabound algorithm during the training process. finally, the superiority of the c-lstm in predicting online car-hailing supply and demand was proved through contrastive experiments. keywords: online car-hailing, supply and demand prediction, long short-term memory (lstm), convolutional neural network (cnn), adabound. https://doi.org/10.15837/ijccc.2020.5.3906 2 1 introduction with the development of internet technology, online car-hailing platforms are growing in number and scale. based on supply-demand information, the platforms connect drivers and passengers with a sharing mechanism called internet taxi booking. online car-hailing has become a main travel mode in many countries. take china for example; there are 392 million online car-hailing users by the end of 2019. many online car-hailing platforms, namely, didi, ucar and uber [16], are very popular in urban areas, facilitating daily trips and relieving the traffic pressure. the boom of online car-hailing platforms also faces a major problem: the supply-demand balance is gradually disrupted in some areas, because users may change the travel mode. thus, it is urgent to dispatch the drivers in advance to meet the travel demand of passengers. this calls for dynamic forecast of supply and demand. traditionally, the supply and demand of online car-hailing is predicted by the logit model [1], autoregressive–moving-average (arma) model [12] and backpropagation neural network (bpnn) [6]. yang et al. [19] established a taxi demand model by analyzing the demands of passengers on taxis at different times. jiang et al. [7] established a model based on the online carhailing order data on the mobile app and completed the prediction of online car-hailing. however, the lack of spatiotemporal characteristics may affect the prediction effect of online car-hailing. considering data sparsity, zhang et al. [20] proposed a double ensemble gradient boosting decision tree to predict the supply and demand gap of online car-hailing. based on nonlinear support vector machine (svm) and bpnn [3], lu et al. [10] created a two-stage supply and demand prediction model for online car-hailing, and extracted the features that affect the prediction; however, the bpnn, as a traditional neural network (nn), perform poorly in solving time series problems and converges slowly in the face of largescale data. with the aid of deep learning (dl) technique [13], xu et al. [18] predicted online car-hailing supply and demand based on long short term memory (lstm) with sliding window, but wasted too much time in lstm training. this paper attempts to solve the defects in the above studies on supply and demand forecast of online car-hailing, and accurately predict the range of supply-demand gap. for these purposes, the convolutional neural network (cnn) [4, 8, 15, 21] and the lstm were combined into the convolutional lstm (c-lstm) model, which was then trained with adabound. firstly, the cnn was adopted to extract the features of supply and demand forecast for online car-hailing, while reducing data size and enhancing model generalization. since the forecast is a time series problem, the lstm was employed to arrange the extracted features in chronological order, to complete the forecast of the range of online car-hailing supply-demand. the prediction effect of the proposed c-lstm model was proved through contrastive experiments. 2 the cnn the cnn is a feedforward nn widely adopted for computer vision [14] and natural language processing [9]. the network structure is translation invariant, and generally composed of one or more convolutional layers, pooling layers and fully-connected layers. in each layer, the neurons are connected locally to extract and transform the hierarchical features of the input; meanwhile, these neurons are also connected to those on the superior layer, with the same connection weight. in each convolutional layer, several feature maps are generated through convolution by different kernels. each feature map consists of several neurons arranged in matrix. every neuron is only connected to some of its neighboring neurons. the neurons in the same feature map share a kernel, which is equivalent to weight. therefore, the weight sharing strategy can reduce the connections between different layers and lower the risk of overfitting. the eigenvalues of the supply and demand prediction of online car hailing can be expressed as: gn = [ g1,g2, · · · ,gtn ] ,n = 1, 2, · · · ,n (1) where, gt = [ gt1,g t 2, · · · ,g t m ]t ∈ rm×1 is the eigenvalue at time t; n is the number of eigenvalues. l is the length of the time series of the convolution kernel. then, the eigenvalue in the period [t, https://doi.org/10.15837/ijccc.2020.5.3906 3 t + t − 1] can be described as: gt:t+t −1n = [ gt,gt+1, · · · ,gt+t −1 ]t (2) the output βt+t −1 of convolution at time t + t − 1 can be defined as: βt+t −1 = σ(wti •g t:t+t −1 n + b) (3) where, σ is the activation function; wi is the 1d kernel; • is matrix multiplication; b is the bias term. in addition to convolution, pooling is another important operation in the cnn. this operation reduces the dimensionality of each feature map, making the computation less complex. the pooling layer, a.k.a. the subsampling layer, either implements mean pooling or max pooling. if max pooling is adopted, the maximum eigenvalue can be obtained as ĝ = max{βt+t −1}. 3 the lstm the lstm is a time-recurrent neural network (rnn) proposed by hochreater et al. [5] to solve the problem of long-term dependency [17]. this nn has been widely applied in machine translation [2], subtitle generation and stock forecast, thanks to its excellence in natural language processing and ability to avoid the vanishing gradient problem. figure 1: structure of the lstm the lstm memory unit is mainly composed of four modules: cell status, input gate, output gate and forget gate. the input gate controls the update of memory state; the output gate controls the output in the current state; the forget gate clears useless information. the functions of the three gates are equivalent to write, read and reset operations. together, the three gates ensure that the state of each moment is stored in the cell. the lstm structure diagram is shown in figure 1. the input at time t includes xt, ct−1 and st−1, and the output includes ct and st, wi, wf, wc and wo be the weights of input gate, forget gate, cell, and output gate, respectively, bf, bi, bc, and bo be the biases of input gate, forget gate, cell, and output gate, respectively. then, the cell state of the lstm can be updated as follows: the main function of the input gate is to determine how much of the input xt can be retained in the ct. the realization formula is shown in formula (4):{ it = σ (ui ·xt + wi •st−1 + vi · ct−1) c̃t = tanh (uc ·xt + wc ·st−1) (4) https://doi.org/10.15837/ijccc.2020.5.3906 4 here i stands for "input"; it represents the input of the input gate at time t, while it ⊗ c̃t represents the components stored in ct after the input gate, and " ⊗ " represents the multiplication of the corresponding elements in the corresponding vector. the main function of the forget gate is to determine how many components of ct−1 can be retained in ct at time t, and the realization formula is shown in formula (5): ft = σ (uf ·xt + wf ·st−1 + vf · ct−1) (5) the f here stands for "forget"; as with the input gate c̃t, that is, after forgetting the gate, the component retained in the ct is ft ⊗ c̃t−1. the output gate determines the components retained in the hidden layer st into the output ot through the control unit ct. state c after the input gate and the forgotten gate, as shown in formula (6): ct = it ⊗ c̃t + ft ⊗ c̃t−1 (6) the former term refers to the components retained in the ct by the input gate, and the latter term refers to the components retained in the ct by the forgotten gate. secondly, in order to determine how many components in ct are retained in st, the specific implementation is shown in formula (7): ot = σ (uo •xt + wo •st−1 + vo • ct) (7) where, ot represents the state of the output layer at time t. after passing through the output gate, the components retained on the hidden layer are implemented as shown in formula (8): ht = ot � tanh (ct) (8) lstm network has been widely used in many fields because it can avoid the problem of gradient vanishing effectively and can process the input of a longer time sequence at the same time. 4 design and optimization of the c-lstm based on the above analysis, this section combines the cnn and the lstm into the c-lstm model, and optimizes the model training with adabound. 4.1 model design figure 2 provides the structure of the c-lstm model for supply and demand prediction of online car-hailing. the c-lstm firstly processes the input data into a matrix of time series. next, the cnn extracts features and reduces the dimensionality of the input data, creating a low-dimensional feature map which contains the data features. the feature map is then imported to the lstm, where the extracted features are arranged by temporal relationship. finally, the lstm output is normalized by the softmax function, and outputted as a k-dimensional vector. each dimension of the vector is the probability that the samples fall in one of the k classes preset for predicted values. here, the supply-demand gap was divided into six classes: ga (0), gb (1-10), gc (10-20), gd (20-30), ge (30-50) and gf (>50), which correspond to the six dimensions of the output layer. the probabilities of the k classes add up to 1. the final prediction result can be expressed as: t (bt) = max (p (y = m|bt)) (9) where, bt is a sample; p (y = m|bt) is the probability for bt to fall into class m. formula (9) shows that the maximum of the probabilities for the six classes is chosen as the prediction range of our model. https://doi.org/10.15837/ijccc.2020.5.3906 5 figure 2: structure of the c-lstm 4.2 training optimization the prediction effect of the c-lstm model hinges on the training quality. to improve the prediction effect, the training of the c-lstm was optimized by adabound, a desirable tool for computer vision, natural language processing and the dl. proposed by luo et al. [11], adabound is a dl optimization algorithm with adaptive learning rate. the algorithm is improved from the adam optimization algorithm, whose instability and extreme learning rate have led to poor generalization ability. the adabound draws the boundaries of dynamic changes in learning rate, and ensures the gradual and smooth transition from the adam optimization algorithm to stochastic gradient descent (sdg). moreover, adabound is not very sensitive to hyper-parameters, which saves lots of time for parameter adjustment and further improves the learning effect. the specific steps of the adabound are illustrated below algorithm 1. algorithm 1 adabound 1: input: x1 ∈ f , initial step size α, {β1t}tt=1, β2, lower bound function η1, and upper bound function ηu 2: set m0=0, v0=0 3: for t=1 to t do 4: gt = ∇ft (xt) 5: mt = β1tmt−1 + (1 −β1t) gt 6: vt = β2vt−1 + (1 −β2) and vt = diag (vt) 7: η̂t = clip ( α/ √ v t,ηt(t),ηu(t) ) and ηt = η̂t/ √ t 8: xt+1 = ∏ f,diag(η−1t ) (xt −ηt �mt) 9: end for the adabound involves six parameters, namely, the learning rate or step size (α), the exponential https://doi.org/10.15837/ijccc.2020.5.3906 6 decay rate of the first-order moment estimation (β1), the exponential decay rate of the second moment estimation (β2), the hyper-parameter (ε) that prevents the denominator from being zero, the final learning rate (final_lr), and the convergence rate (γ). because the c-lstm model is mainly trained on keras, the six parameters above were set to α=0.001, (β1)=0.9, β2=0.999, ε=1e-08, the final-lr=0.1, and γ=1e-3, respectively. the adabound-based optimization of c-lstm training is described as follows algorithm 2. algorithm 2 1: input: m training samples; the number of neurons in each hidden layer and the output layer, the total number of layers h; the activation function; the loss function; iterative step s; maximum number of iterations max; the termination threshold ε. 2: output: weights w and bias b of each hidden layer and the output layer. 3: initialize weights w and biases b of each hidden layer and the output layer in a random manner. 4: for iter to 1 to max: 5: for i=1 to m: perform forward propagation to calculate the activation value. 6: for l=2 to h, perform forward propagation to calculate the activation value. 7: calculate the gradient of the output layer by mean squared loss function. 8: for l=h to 2, perform back-propagation to calculate the gradient of each layer. 9: for l=2 to h, update the weights w and biases b of the lst layer using adam optimization algorithm. 10: if the variations in weights w and biases b are below the termination threshold , go to the next step. 11: output weights w and biases b of each hidden layer and the output layer. 5 experimental verification 5.1 experimental environment to verify its performance, the c-lstm was subjected to experiments on a computer (gpu: geforce gtx 1060) running on windows. the software development environment, programming language, and dl framework are pycharm community edition (64bit), python 3.6 (64bit), and keras, respectively. besides, various python libraries such as sklearn, panda and numpy were installed in the development environment. 5.2 feature selection selecting reasonable feature data can effectively improve the training effect of the prediction model. in this section, we will analyze the original data of online car-hailing and select the factors that affect the forecasting research of online car-hailing supply-demand prediction, including weather, temperature, traffic congestion, air quality, time, area identification and the difference between supply and demand. next, we will introduce it in detail based on the selected features. when people choose online car-hailing to travel, they will encounter difficulties in taking a taxi. for example, it is difficult to get a car-hailing car when the weather conditions are bad and the temperature is not suitable. it is also very busy during holiday peak hours and commuting hours it is difficult to get a car; and in traffic congestion and good weather, people may not choose to ride a car online; in addition, passengers who take online ride-hailing in the previous period will also affect the status of passengers who take a taxi at the current time. therefore, there are many factors that influence people to travel by online car-hailing. 5.3 experimental data the experimental data on online car-hailing were provided by didi. the data from february 23 to march 17 were selected as the experimental dataset. each day was divided into 144 10min-long time slices. in this way, a total of 200,448 pieces of data was obtained for our experiments. the experimental dataset was split into a training set and a test set at the ratio of 4:1. https://doi.org/10.15837/ijccc.2020.5.3906 7 for convenience, the raw data were converted to the csv format and stored in the mongodb, a document database. then, the database was used to view, manage and query the data on online car-hailing. the processed data were stored in csv format. the experimental dataset covers multiple features, including regional feature, climate feature, traffic congestion feature, period feature, and supply-demand gap feature. (1) regional feature the regional feature reflects the influence of different regions on supply and demand prediction. the data on this feature were provided by didi. the target city was divided into 58 areas of equal size. hence, the hash tags of the 58 areas were converted in turn into numerical tags and stored in csv format. (2) climate feature there are three kinds of climate feature: weather feature, temperature feature and pm2.5 feature. these features were extracted with the aid of the mongdb. the temperature, weather, and pm2.5 of each area were queried, recorded every 10min on each day, and stored in csv format. (3) traffic congestion feature this feature reflects the number of road segments with each degree of congestion. the traffic data in mongodb were used to write a program to convert the scattered data (table 1) into integrated congestion information. the processed data were recorded every 10min on each day, and stored in csv format. the scattered data can be converted by: total−level = 4∑ n=1 c−leveln∗ n (10) (4) period feature the online car-hailing situation varies from period to period. our experimental data were collected on 18 workdays and 6 weekends. in general, more people travel in morning peak hours on weekdays, and the entertainment and dining hours on weekends. therefore, each workday was split into three periods: morning and evening peak hours (7: 00 9: 00 and 17: 00 19: 00), working hours (9: 00 17: 00), and the remaining hours; each weekend day was split into two periods: peak hours (10: 00 14: 00 and 17: 00 21: 00) and the remaining hours. the features of the simultaneous periods were differentiated by numerical values. the data were recorded every 10min on each day, and stored in csv format. (5) supply-demand gap feature the supply-demand gap of online car-hailing is affected by the gap value at the previous moment. thus, the gap value at the previous moment was taken as the feature of the supply-demand gap. the gap value at time t−1 is denoted as gt-1. considering waiting time and other factors, the gap values at time t-3 to time t− 1 were selected. the data were recorded every 10min on each day, and stored in csv format. after the features were constructed, the various feature data were fused and stored in csv format. the final feature dataset is described in table 1, where id is the regional feature, weather is the weather feature, temp is the temperature feature, pm is the pm2.5 feature, traffic is the traffic congestion feature, period is the period feature, and gt-3, gt-2 and gt-1 are the supply-demand gap feature; the time is the duration of feature collection (10min). 5.4 evaluation indices the supply and demand prediction of online car-hailing is a multi-classification problem. therefore, the predictive ability of our c-lstm model was evaluated by precision, recall and f1: precison = tp tp + fp (11) recall = tp tp + fn (12) f1 = 2 ×precison×recall precison + recall (13) https://doi.org/10.15837/ijccc.2020.5.3906 8 table 1: feature dataset time id weather temp pm traffic period gt−3 gt−2 gt−1 13:00:00-13:10:00 21 1 13 80 6634 0.3 9 10 7 13:10:00-13:20:00 21 1 13 80 6252 0.3 10 7 16 13:20:00-13:30:00 21 1 13 80 6370 0.3 7 16 19 13:30:00-13:40:00 21 1 14 80 6005 0.3 16 19 25 13:40:00-13:50:00 21 1 14 69 6372 0.3 19 25 50 13:50:00-14:00:00 21 1 14 69 6375 0.3 25 50 32 14:00:00-14:10:00 21 1 14 69 6099 0.3 50 32 31 14:10:00-14:20:00 21 1 14 69 6516 0.3 32 31 24 14:20:00-14:30:00 21 1 14 69 6131 0.3 31 24 19 14:30:00-14:40:00 21 1 14 69 6475 0.3 24 19 12 14:40:00-14:50:00 21 1 14 68 6446 0.3 19 12 25 14:50:00-15:00:00 21 1 14 68 6148 0.3 12 25 20 15:00:00-15:10:00 21 1 14 68 6389 0.3 25 20 31 15:10:00-15:20:00 21 1 14 68 6262 0.3 20 19 31 15:20:00-15:30:00 21 1 14 68 6126 0.3 19 31 101 15:30:00-15:40:00 21 1 14 68 6334 0.3 31 101 76 15:40:00-15:50:00 21 1 14 68 6985 0.3 101 76 10 15:50:00-16:00:00 21 1 14 68 6817 0.3 76 10 16 where, tp is true positive (a result indicating that a given condition is present when it is); fp is false positive (a result indicating that a given condition is present when it is not); fn is false negative (a result indicating that a given condition is not present when it is). besides, accuracy was selected to measure the prediction accuracy of our model: accuracy = numberofcorrectpredictions numberoftotalpredictions (14) 5.5 parameter settings the most important part of the structure design of network model is the design of hidden layer. the number of layers and the parameters of each layer should be determined. the c-lstm neural network in this paper includes four parts: input layer, convolutional network part, lstm, and output layer. for the convolutional network part of the model, select max pooling method and activate function select relu. the size and number of layers of cnn convolution kernel need to be optimized and adjusted. in the following, the size and number of layers of convolution kernel will be introduced through experiments, and accuracy will be used as the evaluation standard. for the selection of the size of the convolution kernel, we simulate the method of extracting features that is common in time series processing, that is, extracting features of different time series by setting the convolution kernel of different sizes. the experimental results of setting the sizes and layers of different convolution kernels are shown in table 2. as can be seen from table 2, when the number of layers is 2 and the size of the convolution kernel is 64,32, the prediction accuracy of the model is the highest. the experimental results of the number of lstm nodes are shown in table 3. it can be seen from the table that when the number of nodes is 32, the prediction accuracy of the model is the highest. the c-lstm parameters were adjusted through repeated tests. the final parameter settings are listed in table 4. it can be seen that the cnn contains two convolutional layers. the first convolution layer has 64 neurons, and the second layer has 32 neurons. the activation functions of the two layers are both rectified linear unit (relu). each convolutional layer is followed by a pooling layer. there are two lstm layers in our model, each with 32 neurons. the last layer is a dense layer with softmax as the activation function. this layer outputs the predicted result. https://doi.org/10.15837/ijccc.2020.5.3906 9 table 2: experimental results of different convolution kernel size and convolution layer number cnn layer convolution kernel size accuracy 2 (32,1), (16,1) 0.7219 (64,1), (16,1) 0.7217 (64,1), (32,1) 0.7246 (128,1), (64,1) 0.7220 3 (128,1), (64,1), (32,1) 0.7155 table 3: experimental results of different number of lstm nodes lstm nodes accuracy 16 0.7237 32 0.7246 64 0.7212 128 0.7221 table 4: parameter settings layers type output activation functions 1 conv1d (none, 9, 64) relu 2 maxpooling1d (none, 4, 64) 3 conv1d (none, 2, 32) relu 4 maxpooling1d (none, 1, 32) 5 lstm (none, 1, 64) tanh 6 lstm (none, 1, 64) tanh 7 dense (none, 6) softmax 5.6 results analysis to prove its superiority in predicting supply and demand of online car-hailing, our c-lstm model was compared with other prediction algorithms on actual data. besides, the improving effect of adabound on c-lstm prediction was contrasted with that of other optimization algorithms. (1) prediction effect of the c-lstm the prediction effect of the c-lstm was compared with that of the classic lstm, random forest (rf) and the svm. the effect of each model was measured by precision, recall, f1 and accuracy. to eliminate stochasticity, 30 comparative experiments were carried out, and the mean values of these experiments were taken as the final results (table 5). table 5: prediction results of different models algorithms indices accuracy precision recall f1 c-lstm 0.7253 0.719 0.725 0.714 lstm 0.7139 0.706 0.714 0.703 rf 0.6893 0.687 0.691 0.683 svm 0.6784 0.627 0.679 0.637 as shown in table 5, the proposed c-lstm achieved greater precision, recall, f1 and accuracy than the three contrastive prediction models, an evidence of its excellence in the forecast of supply and demand of online car-hailing. besides, the dl models (lstm and c-lstm) both outperformed the rf and svm, indicating that dl is more suitable for our problem than machine learning. to sum up, our model has a good prediction effect of online car-hailing supply and demand. https://doi.org/10.15837/ijccc.2020.5.3906 10 (2) analysis of loss value convergence under different optimizers in this section, the experiment mainly verifies that the adabound algorithm improves the convergence rate of the predictive model training. the optimization effect of adabound algorithm will be analyzed through the convergence of functions in the process of model training, and the adabound algorithm will be compared with the traditional sgd algorithm and rmsprop algorithm. the experimental results show that the model cost function of adabound algorithm converges most quickly in the process of model training, while the traditional sgd algorithm and rmsprop algorithm converge more slowly. at the same time, the cost function value of adabound algorithm is lower than sgd algorithm and rmsprop algorithm. in the training process of the model, the smaller the loss value is, the better the learning effect of the model will be (see figure 3). figure 3: loss convergence comparison of different optimizer (3) optimization effect of adabound this subsection aims to verify the advantages of adabound in optimizing the training of the c-lstm model. for comparison, the adam optimization algorithm and root mean square prop (rmsprop) were separately introduced to train the c-lstm. the following parameters were set to the same values as those of adabound: step size (α), the exponential decay rate of the first-order moment estimation (β1), the exponential decay rate of the second moment estimation (β2), the hyperparameter (ε) that prevents the denominator from being zero. besides, 30 comparative experiments were carried out, and the mean values of these experiments were taken as the final results (table 6). table 6: prediction results of the c-lstm trained by different algorithms algorithms indices accuracy precision recall f1 adabound 0.7253 0.719 0.725 0.714 adam 0.7134 0.705 0.713 0.708 rmsprop 0.7006 0.698 0.701 0.701 table 6 shows that the c-lstm trained by adabound had better indices than that trained by other optimization algorithms. this means the adabound can greatly improve the prediction effect of the c-lstm model on online car-hailing supply and demand. 6 conclusions this paper puts forward a novel model called the c-lstm to predict the supply and demand of online car-hailing. firstly, the cnn and lstm were subjected to structural analysis, and combined into the c-lstm model. next, the online car-hailing data provided by didi were processed, and divided into a training set and a test set. after that, the c-lstm model was optimized by the adabound algorithm to improve its prediction effect. the superiority of the c-lstm in supply and https://doi.org/10.15837/ijccc.2020.5.3906 11 demand prediction of online car-hailing was confirmed through contrastive experiments. the research results provide a reference for online scheduling of cars, and help to reduce the waiting time of users, making online car-hailing platforms more efficient. the future research will further optimize the structure of the c-lstm model based on the extended versions of the lstm. funding the work was supported by the natural science foundation of inner mongolia (grant no.: 2013ms0920), and science and technology planning project of inner mongolia (grant no.: 201502015). conflict of interest the authors declare no conflict of interest. references [1] castro, m.; martínez, f.; munizaga, m.a. 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(2019). a novel job-shop scheduling strategy based on particle swarm optimization and neural network, international journal of simulation modelling, 18(4), 699–707, 2019. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: tian, y. h.; wu, q.; zhang, y. (2020). a convolutional long short-term memory neural network based prediction model, international journal of computers communications & control, 15(5), 3906, 2020. https://doi.org/10.15837/ijccc.2020.5.3906 international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(6), 633-652, december 2019. studies in informatics and control: a bibliometric analysis from 2008 to 2019 y. li, z.s. xu, x.x. wang, f.g. filip yang li business school sichuan university, china no. 24, south section 1 of the first ring road, chengdu, 610064, china liyang_ly18@163.com zeshui xu* business school sichuan university, china no. 24, south section 1 of the first ring road, chengdu, 610064, china *corresponding author: xuzeshui@263.net xinxin wang business school sichuan university, china no. 24, south section 1 of the first ring road, chengdu, 610064, china wangxinxin_cd@163.com florin gheorghe filip romanian academy 125 calea victoriei, bucharest, 010071, romania ffilip@acad.ro abstract: as an international scientific journal, studies in informatics and control (sic ) covers the field of information technology (it) and topics related to research areas, as well as important applications in it, with particular emphasis on advanced automatic control, modeling and optimization. sic has greatly contributed to the areas where it involves since its first online publication way back in 1992. this paper sets out to analyze the structure and the underlying trend of the journal by making use of bibliometric methods. firstly, the classical indicators are provided to illustrate the performance of the journal. this current study performs an in-depth analysis of the most productive and the most influential authors, institutions, and countries/regions, as well as the most cited research works published in sic. secondly, the visualization tools vos viewer and citespace are used to create scientific maps that may explain the structure of the journal in an intuitionistic way. in the science mapping, the co-citation maps, and the co-authorship networks of various items (such as authors, institutions, and countries/regions) are conducted. also, the bursts detection of these items are derived. the co-occurrence of keywords and their bursts detection and timeline review are shown, respectively. finally, some conclusions are given. this paper provides a comprehensive and visual understanding of this well-regarded scientific journal. keywords: studies in informatics and control, bibliometric analysis, science mapping analysis, web of science, information technology. copyright ©2019 cc by-nc 634 y. li, z.s. xu, x.x. wang, f.g. filip 1 introduction as an international scientific journal, studies in informatics and control (sic) is one of the leading journals in the field of information and communication technologies (i&ct) in romania. it provides important perspectives on topics relevant to information technology (it), with an emphasis on useful applications in the most important areas of it such as automatic control, system modeling and optimization. the journal was first published in 1989 under the name "studies in computers and informatics" as a publication of the institute for computer technique and informatics (itci). it was renamed as the current name sic in 1990. in 1992, the journal became an international one with an editorial board of scientists from 17 countries and prof. theodore williams of purdue university played a major role in shaping journal profile. since 1992, sic has been published both in print and online by the romanian institute for r&d in informatics, ici burcharest under the auspices of the romanian academy [12]. according to the current homepage of the journal (https://sic.ici.ro/about-sic/), this quarterly journal has been included within the coverage of inspec database (since 1993), doaj (since 1996), google scholar (since 2006) and philadelphia-based institute of scientific information (isi thomson-reuters) / clarivate analytics science citation index expanded (scie) (since 2008), scopus (since 2010) and other relevant academic databases and search engines listings. the journal impact factor in 2018 was 1.347 according to the journal citation reports (jcr) published in web of science (wos). the ranking of the journal in the research field of automation & control system was 45 out of 62 while in the field of operations research & management science it was 53 out of 84. prof. f.g. filip, a member of the romanian academy, is the founder and editor-in-chief of the journal. bibliometrics analyze patterns that appear in the publication and communication of documents by using mathematical and statistical methods [11]. it provides a systematic, transparent, and reproducible review to a large body of information by providing a structural analysis [1]. bibliometric methods are used in various fields for the purpose of research evaluation [19]. because bibliometrics is effective in revealing the evaluation of a research direction, it has been applied in many fields, such as economy [10], management [21], innovation [5,13], decision support [14] and entrepreneurship [16,17]. in addition, it also has been widely used to evaluate a certain journal, including european journal of operational research [15], information sciences [29], ieee transaction on fuzzy systems [28], international journal of intelligent systems [18], knowledge-based system [8], technological and economic development of economy [27], international journal of strategic property management [31], international journal of computers communications & control [25], etc. the bibliometric evaluation comprises two procedures [20]: performance analysis and science mapping. the performance analysis evaluates the performance of different scientific actors based on the number of publications and citations [8] and it is illustrated by a series of bibliometric indicators. the science mapping mostly aims at displaying the structure, evaluation and dynamic aspects of scientific research [2,3]. in fact, many visualization tools or softwares are created to make science mapping analysis, and cobo et.al [7] made a comparation of them. two of the most common science mapping softwares, vos viewer and citespace are applied in this paper. vos viewer represents the bibliometric map in an easy-to-interpret way [24] and can construct the maps of co-citation, co-authorship, co-occurrence, citation and bibliographic coupling. besides, burst-detection provided by citespace helps scholars to identify emergent terms regardless of how many times their host documents are cited [6]. the hot topics and research trends can be detected from the time-line view of citespace. the main contribution of this paper consists in performing a bibliometric analysis of the research works published in sic. the bibliomteric indicators are applied to various levels such studies in informatics and control: a bibliometric analysis from 2008 to 2019 635 as authors, institutions, and countries/regions. the analysis of co-citation, co-authorship and co-occurrence concerning various items, as well as the burst detection and time-line view are presented in science mapping. the rest of this paper is structured as follows: section 2 describes the source of data used in this paper, and the performance analysis of sic is illustrated in section 3. in section 4, scientific maps and relative evaluation are presented. furthermore, some conclusions are given in section 5. 2 data description according to the website of sic (https://sic.ici.ro/), the journal has been included within the coverage of clarivate analytics scie (since 2008). clarivate analytics is a company with an entrepreneurial mission to help customers reduce the time from new ideas to life-changing innovations. scie created as sci in 1964 indexes more than 9,200 of the world’s most impactful journals across 178 scientific disciplines. and it contains more than 53 million records and 1.18 billion cited references way back from 1900 to 2019. since the scie is included in the wos core collection, relative data have been downloaded from wos core collection scie (https://apps.webofknowledge.com/) to analyze the performance of research works published in the journal from 2008 to november 1, 2019. with the database set at wos core collection, the citation indexes set at scie and the timespan set at 2008-2019, full records and cited references of documents in sic are searched and exported in plain text form in november 1, 2019. the performance analysis and science mapping analysis of the journal are based on 527 papers that have been searched and their relative information derived from wos. 3 performance analysis of sic in this section, the annual publications and citations of the journal are described. depending on the bibliometric indicators, the most productive and influential authors, countries/regions, and institutions are exhibited, as well as the most cited documents. 3.1 annual publications and citations since 2008, 527 research works have been published in sic, among which 517 documents are articles, which accounts for 98.102% of the total number of research works. the other four types of the documents are editorial materials (6; 1.139%), proceeding papers (6; 1.139%), reviews (3; 0.569%) and biographical items (1; 0.19%). it can be seen from figure 1 that, the annual publications in sic are constant. the minimum number of publications was 38 in 2008 and 2014, and the maximum number of publications was 51 in 2016 and 2017. since sic was included within the coverage of clarivate analytics sice in 2008, the number of annual citations of this scientific journal keeps to increase rapidly and exceeded 50 in 2010 and 100 in 2012 for the first time. a great augment of citations appeared in 2018. the number of citations is 351 in 2018, which is 82 higher than that in 2017 (269 citations). during the past 10 months of 2019, the research works in sic has been cited 312 times, which is higher than the number of citations in 2017. in order to illustrate the citation structure of the publications in sic, the relative information is listed in table 1. the number of publications (p), the number of citations (c), the number of cumulative publications and citations (cp and cc), the number of document whose number of citations is more than 50 (≥ 50), 30 (≥ 30), 20 (≥ 20), 10 (≥ 10), 5 (≥ 5) and 1 (≥ 1), h-index (the abbreviation of hirsch-index) [9] and the total citations of annual publications (tc) are listed by year to evaluate performance. 636 y. li, z.s. xu, x.x. wang, f.g. filip * as of november 1, 2019. figure 1: annual publications and citations from 2008 to 2019*. the number of c became greater than that of p in 2010 for the first time and the number of cc is higher than the number of cp since 2012. it means that all the publications in sic have been cited at least once on average. during the 12-year period, only 2 documents published in 2012 and 2015 are cited not less than 50 times. 6 of 527 documents are cited not less than 30 times, and 2 of the 6 documents published in 2013. 14 of 527 documents are cited not less than 20 times, and 4 of the 14 documents published in sic in 2010. the 44 documents published in 2010 gained much attention and made much influence with the highest number of citations (254). 4 of the 44 documents are cited not less than 20 times, 8 of the 44 documents are cited not less than 10 times, and the h-index of the 44 documents is 9. these 3 indicators are the highest compared with them of other 11 years. the highest numbers of documents with number of citations not less than 30 and not less than 5 are in 2013. in the year of 2016, 44 of the 51 documents are cited at least once, which is the highest compared with other 11 years. based on the above analysis, it can be found that the documents in 2010, 2012, 2013, 2015 and 2016 have greatly contributed to sic. 3.2 the most productive and influential authors and documents in this subsection, some bibliometric indicators concerning the publications and the citations are utilized to evaluate authors and documents. the most productive and the most influential authors table 2 lists the top 8 productive authors who published not less than 10 articles in sic since 2008. some indicators such as p, the year of first publication (yfp), the year of latest publication (ylp), average publications (ap) and type of their research works, are shown in table 2. benrejeb, m. is the most productive author who has published 13 documents in the journal since 2008. thorsteinsson, g. from iceland published 12 articles in this journal from 2008 to 2016 and the ap of him is 1.33. niculescu, a. from romania published 11 articles and 1 editorial studies in informatics and control: a bibliometric analysis from 2008 to 2019 637 table 1: characteristics of annual publications from 2008 to 2019*. year p c cp cc ≥50 ≥30 ≥20 ≥10 ≥5 ≥1 h-index tc 2008 38 2 38 2 0 0 0 4 7 30 6 113 2009 41 7 79 9 0 0 1 3 10 35 6 147 2010 44 54 123 63 0 0 4 8 16 37 9 254 2011 40 66 163 129 0 1 1 2 11 37 6 162 2012 48 102 211 231 1 1 1 6 14 41 8 239 2013 46 148 257 379 0 2 2 7 17 39 8 247 2014 38 186 295 565 0 0 2 2 8 32 6 131 2015 47 208 342 773 1 1 1 4 10 39 5 192 2016 51 225 393 998 0 0 0 2 10 44 5 141 2017 51 269 444 1267 0 1 2 4 16 38 8 217 2018 48 351 492 1618 0 0 0 3 4 22 4 82 2019* 35 312 527 1897 0 0 0 0 0 5 1 5 * as of november 1, 2019. table 2: the top 8 productive authors in sic from 2008 to 2019*. rank authors countries p yfp ylp ap type 1 benrejeb, m tunisia 13 2008 2018 1.18 article (13) 2 thorsteinsson, g iceland 12 2008 2016 1.33 article (12) 3 niculescu, a romania 12 2008 2019 1.00 article (11), editorial material (1) 4 page, t england 11 2008 2013 1.83 article (11) 5 wang, h. p china 11 2010 2019 1.10 article (11) 6 tian, y china 11 2014 2019 1.83 article (11) 7 borne, p france 10 2008 2019 0.83 article (10) 8 radulescu, c. z romania 10 2009 2019 0.91 article (10) * as of november 1, 2019. material in sic since 2008 and he published 1 document in the journal per year on average. the 2 authors with highest number of ap (1.83) is page, t. from england and tian, y. from china. page, t published his 11 articles in sic during the period from 2008 to 2013, while tian, y. published 11 articles in the journal in the last 6 years. in terms of yfp, 5 of the top 8 productive authors started to publish in the journal since 2008. 5 of the top 8 productive authors continue to publish in the journal until 2019. 2 of the top 8 productive authors come from romania, and another 2 of the 8 authors are from china. the other 4 authors come from tunisia, iceland, england and france, respectively. the research areas of all these 8 authors are operations research & management science and automation & control systems, according to the classification of wos. according to some common indicators about citations, table 3 lists the top 3 influential authors with the number of citations higher than 100. table 3: the top 3 influential authors in sic from 2008 to 2019*. rank authors country p c ac ≥50 ≥30 ≥20 ≥10 ≥5 ≥1 h-index 1 zavadskas, e. k lithuania 8 170 21.25 1 2 3 5 7 7 7 2 turskis, z lithuania 5 128 25.6 1 2 3 4 4 4 4 3 tuba, m serbia 7 115 16.43 1 2 3 4 4 6 4 * as of november 1, 2019. in table 3, p and c are the publications and citations of the authors respectively, and 638 y. li, z.s. xu, x.x. wang, f.g. filip ac represents the average citations of an author. zavadskas, e. k. from lithuania published 8 articles in sic and obtained 170 citations in total. the h-index of him is 7, which means that there are 7 of his articles published in the journal cited at least 7 times. the ac of turskis, z. from lithuania (25.6) is the highest, for he published only 5 articles in the journal and obtained 128 citations. all of the 3 authors have 1 article cited not less than 50 times and have 1 article has not been cited until november 1, 2019. there is some interesting information observed on the website of wos. only 2 articles in sic are cited not less than 50 times, and 1 of them is a cooperative article published in june 2015 by zavadskas, e.k, turskis, z. and antucheviciene, j., while another one is the work of bacanin, n and tuba, m. the 2 articles written by the 3 authors with on citations are published in september 2019. 1 of the 2 articles is "a novel extended edas in minkowski space (edas-m) method for evaluating autonomous vehicles" [30] written by zavadskas, e.k, stevic, z, turskis, z and tomasevic, m. the another one is written by tuba, m together with tuba, e, namely article "generative adversarial optimization (goa) for acute lymphocytic leukemia detection" [23]. the most cited documents to analyze the influence of the documents published in the journal during the period from 2008 to november 1, 2019, the top 6 cited publications which have been cited not less than 30 times, are listed in table 4. the relative information, namely c, ac, the number of authors (na), the type of document, the publication year, and research area (ra) of these publications are also listed. table 4: the top 6 influential documents in sic from 2008 to 2019*. rank title authors c ac na type year ra 1 selecting a contractor by using a novel method for multiple attribute analysis: weighted aggregated sum product assessment with grey values (waspas-g). zavadskas, e. k; turskis, z; antucheviciene, j. 54 10.8 3 article 2015 automation & control systems; operations research & management science. 2 artificial bee colony (abc) algorithm for constrained optimization improved with genetic operators. bacanin, n; tuba, m. 50 6.25 2 article 2012 automation & control systems; operations research & management science. 3 multi-criteria assessment model of technologies. zavadskas, e. k; turskis, z; volvaciovas, r; kildiene, s. 42 6.00 4 article 2013 automation & control systems; operations research & management science. 4 a state of the art on supply planning and inventory control under lead time uncertainty. dolgui, a; ben a. o; hnaien, f; louly, m. a. o. 35 5.00 4 article 2013 automation & control systems; operations research & management science. 5 simulation-based optimization using genetic algorithms for multi-objective flexible jssp. nicoara, e. s; filip, f. g; paraschiv, n. 33 3.67 3 article 2011 automation & control systems; operations research & management science. 6 adjusted fireworks algorithm applied to retinal image registration. tuba, e; tuba, m; dolicanin, e. 31 10.33 3 article 2017 automation & control systems; operations research & management science. * as of november 1, 2019. all of the 6 most influential documents are articles and their research areas cover automation & control systems and operations research & management science. the 6 articles are results of cooperation among authors, and only 1 of them was written by 2 authors while the other 5 of studies in informatics and control: a bibliometric analysis from 2008 to 2019 639 the 6 articles were written by 3 or 4 authors. 3.3 the productive and influential countries/regions and institutions in this subsection, the countries/regions and institutions that mostly contribute to the journal from the perspective of productivity and influence respectively are analyzed. the indicators such as p, c, ac and h-index are exhibited. the most productive and the most influential countries/regions to analyze the productivity of countries/regions, the top 6 most productive countries/regions with publications not less than 20 and their annual publications are shown in figure 2. * as of november 1, 2019. figure 2: the annual publications of top 10 productive countries/regions from 2008 to 2019*. the top 6 productive countries/regions are romania (209), france (46), tunisia (39), china (37), chile (30) and india (25) in a strict order. the number of the total publications of the 6 countries/regions reached the highest value (39) in 2010, 2015 and 2016. romania published more than 20 documents in the journal in 2010, 2015, 2016, and 2017 and it is the most productive country among the 6 countries/regions per year except for in 2008. in this year, france published 11 documents in the journal while the publication of romania is 10. as the second productive country, the publication of france is more than 10 documents only in 2008, and it published just 1 to 6 documents in the journal per year from 2009 to 2019. chile had its first 3 publications in the journal way back in 2009, and has continued to publish at least one document in the journal until 2019. some countries/regions published no documents in the journal only occasionally. the third productive country tunisia published no documents in sic in 2013 and 2014, and published at least 2 documents in the journal in other years. china published only 1 article in the journal in the first 3 years, while it published frequently in sic in the last 10 years. india published nothing in the journal during the period from 2010 to 2012. to analyze the influence of countries/regions, the indicators are used to describe the citation structures of top 7 most cited publications which are cited not less than 100 times, as listed in table 5. 640 y. li, z.s. xu, x.x. wang, f.g. filip table 5: the top 7 influential countries/regions in sic from 2008 to 2019*. rank country/regions p c ac ≥50 ≥30 ≥20 ≥10 ≥5 ≥1 h-index 1 romania 209 682 3.26 0 1 3 15 45 163 10 2 france 46 209 4.54 0 1 2 6 14 38 8 3 lithuania 14 198 14.14 1 2 3 6 10 11 8 4 serbia 17 183 10.76 1 2 4 7 7 14 7 5 chile 30 133 4.43 0 0 2 3 8 26 5 6 tunisia 39 112 2.87 0 0 0 3 9 27 7 7 china 37 107 2.89 0 0 0 3 8 25 5 * as of november 1, 2019. the most productive country romania is also the most influential one with 682 citations and the highest value of h-index 10. lithuania and serbia are the only two countries with the value of ac over 10, which means their publications in the journal are cited more than 10 on average. they are also the only 2 countries with 1 of their publications in the journal cited not less than 50 times, and the 2 countries published less than 20 documents in the journal while obtained more than 180 citations. although lithuania published just 14 documents in sic, it is the third influential country with the highest value of ac 14.14. as 1 of the top 6 productive country, india was not included for its publications in the journal are cited just 39 times in total. it is reasonable to infer that the number of citations have no any direct connection with the number of publications. in other words, a high number of publications does not always involve a high citation score. the most productive and the most influential institutions table 6 lists the top 10 productive institutions whose number of publications exceeds 15. the relative indicators are also listed in order to illustrate their productivity and influence. table 6: the top 10 productive institutions from 2008 to 2019*. rank institutions countries/regions p c c/p h-index 1 national institute for r&d in informatics ici romania 56 222 3.96 7 2 university politehnica of bucharest romania 33 102 3.09 6 3 université lille nord de france (comue) france 29 117 4.03 6 4 romanian academy of sciences romania 27 148 5.48 7 5 fondation i-site université lille nord-europe (ulne) france 27 115 4.26 6 6 université de lille france 27 115 4.26 6 7 école nationale d’ingénieur de tunis tunisia 22 74 3.36 6 8 université de tunis el manar tunisia 21 71 3.52 6 9 école centrale lille france 19 62 3.26 5 10 pontificia universidad católica de valparaiso chile 17 69 4.06 5 * as of november 1, 2019. from table 6, it can be figured out that the national institute for r&d in informatics ici bucharest is the leading productive institution, with a number of publications almost twice higher than that of the second productive institution university politehnica of bucharest. 4 of 10 institutions are in france, 3 of 10 institutions are in romania, and other 2 are in tunisia. pontificia universidad católica de valparaiso is the only institution from chile. the publications of the top 6 institutions are more than 25, while the publications of other 4 are around 20. in fact, there is no significant difference in the list of top 10 productive and cited institutions. vilnius gediminas technical university (lithuania) takes the place of école centrale lille studies in informatics and control: a bibliometric analysis from 2008 to 2019 641 (france) and is the top 2 cited institutions with 13 p, 183 c, 14.54 ac and the value of h-index 8. the relative indicators: p, c, ac and h-index, are used to illustrate the influence of the top 10 influential institutions in figure 3. * as of november 1, 2019. figure 3: the radar map of top 10 productive institutions in terms of p, c, ac and h-index from 2008 to 2019*. the number of publications of the national institute for r&d in informatics ici bucharest (romania) is distinctly higher than these of other institutions, while there are no obvious differences among other institutions. the number of citations is higher than the number of their publications, and the citations of the national institute for r&d in informatics ici bucharest (romania) and vilnius gediminas technical university (lithuania) are one or half more than those of other institutions. there are no evident differences among the h-indices of these 10 institutions. the ac of vilnius gediminas technical university (lithuania) is 14.54, while the ac of other institutions are less than 6. 4 science mapping analysis of sic in order to intuitively display the structure of publications in sic, science network maps have been developed by using the vos viewer and citespace regarding three different aspects: cocitation analysis, co-authorship analysis, and co-occurrence analysis. parts of burst information are detected by citespace and listed in tables. the co-citation analysis of authors, references and journals, the co-authorship of authors, institutions, and countries/regions, co-occurrence of keywords and the corresponding burst detection of the 527 documents published in sic are presented, respectively. 642 y. li, z.s. xu, x.x. wang, f.g. filip 4.1 co-citation analysis co-citation was defined as the frequency with which two documents are cited together in the very beginning [22]. it was expanded into the co-citation analysis of journals and authors [26]. in the network of co-citation analysis, different nodes represent different references/journals/authors, and they are clustered by similarity and presented by a color. the size of the node represents the total number of citations with which the reference/journal/author is cited. the link between two nodes means that they are co-cited, and the size of the link represents the strength of their co-citation relationship. in the following, three types of co-citation analysis: references co-citation analysis, journals co-citation analysis, and authors co-citation analysis, are made, and all the network maps are established using vos viewer. depending on the visualization tool vos viewer, 8,335 cited authors are derived. in order to show them clearly, the minimum number of an author’s citations is set at 10, and 52 authors meet the threshold. the cluster resolution is set at 0.5. the largest set of connected items consists of 49 authors, as shown in figure 4. figure 4: authors co-citation network. as figure 4 illustrates, all of the 49 authors are classified into six clusters, which are represented by six different colors. the bigger the node is, the more the author is cited by documents published in sic from 2008 to november 1, 2019. the most-cited author is filip, f.g with 51 citations of his publications. the link shows the co-citation relationship between two authors. the width of the link is positively related to the strength of the relationship. the link between andrei, n and dai, y. h is the widest, and the link strength is 93. this means that andrei, n and dai, y. h are co-cited 93 times. furthermore, the top 10 cited authors with the strongest citation bursts are listed in table 7. the cited author with the strongest citation bursts is banciu, d from romania. the strength of bursts is 3.9566 and the period of citation bursts is 2010-2014. the citation of liouane, n from tunisia burst in 2008 and 2009, and the citation bursts of other 9 authors occurred later than 2009. from 2010 to 2014, the citations of authors successively burst, and the citations of 3 authors burst in 2010. only the citation of zadeh, l. a from usa and wang, h. p from china burst in last 3 and 4 years respectively, which means that the works of zadeh, l. a and wang, h. p have been taken into consideration and cited by many scholars in the last 3 or 5 years. figure 5: the references co-citation networks. studies in informatics and control: a bibliometric analysis from 2008 to 2019 643 table 7: the top 10 cited authors with the strongest citation bursts from 2008 to 2019*. rank cited authors strength begin end 2008-2019 1 banciu, d 3.9566 2010 2014 2 suduc, a. m 3.4812 2011 2013 3 zadeh, l. a 3.4076 2017 2019 4 gabrera, g. g 3.1207 2014 2015 5 susnea, i 3.0751 2011 2014 6 thorsteinsson, g 3.0739 2010 2013 7 liouane, n 2.8432 2008 2009 8 peng, y 2.7815 2012 2013 9 page, t 2.6353 2010 2012 10 wang, h. p 2.5943 2016 2019 * as of november 1, 2019. to visualize the references co-citation networks, the minimum number of citations of a reference is set at 3, and 209 of 10,810 references have met the threshold. figure 5 illustrates the largest set of connected items which consists of 204 references. with the cluster resolutions set as 0.5, there are six clusters differentiated by six colors. two linked items are cited together at least once. table 8 lists the top 1 reference with the strongest citation bursts from 2008 to 2019. the reference was published by cabrera, g.g in 2012, and the citation of the reference bursts in 2014 and 2015. this means that the citations of this article in these 2 years are more numerous than those of the other years. cabrera g.g et al. [4] proposed a hybrid algorithm which combines the particle swarm optimization (pso) algorithm and the simulated annealing (sa) algorithms to solve the probabilistic traveling salesman problem (ptsp). table 8: the top 1 reference with the strongest citation bursts from 2008 to 2019*. rank reference year strength begin end 2008-2019 1 cabrera, g.g, 2012, stud inform control, v21, p49 2012 2.7439 2014 2015 * as of november 1, 2019. by setting the minimum number of citations of a journal at 20 and the resolution at 1.0, the journals co-citation network can be visualized in figure 6. 46 of 6,286 journals are selected and sic is the most cited journal (651 citations) followed by european journal of operational research with 115 citations. figure 6(a) shows the co-citation relationship between the 46 journals. the thicker the link is, the more the linked 2 journals are cited together. to make it clearer, the minimum strength of the link is set at 150 and the result can be seen in figure 6(b). the existing links from figure 6(b) show that the linked journals are co-cited not less than 150 times. only 6 links meet the threshold, and the linked journals by the 6 links are illustrated in table 9. for further studying of the cited journals, the citespace has been applied in order to detect the citation bursts of them. the top 10 cited journals with the strongest citation bursts and the related information are listed in table 10. international journal of computers, communications & control (ijccc) is the journal with the strongest citation bursts 11.578, which is at least twice than those of others. compared 644 y. li, z.s. xu, x.x. wang, f.g. filip (a) (b) figure 6: the journals co-citation network. to other 2 journals whose bursts lasted for four years from 2009 to 2012, ijccc is cited more than them. the citation bursts of most journals began in 2009, 2011 or 2013 and lasted for 2-4 years. the journals isa transactions is more cited than before, because its citation bursts occurred in the last 4 years. 4.2 co-authorship network co-authorship reflects the collaborative relationship between authors/institutions/countries/regions that have published documents in the journal. in the following, the cooperation relationship is analyzed from the perspective of authors (microcosmic scale), institutions (middle scale) and countries/regions (macroscopic scale). the vos viewer is applied in order to create the network maps and the citespace in order to detect the citation bursts information. it can be found that 1,147 authors published at least 1 document in sic. in figure 7, the largest set of connected items consists of 76 authors and their co-authorship networks are shown. with the clustering resolution set at 0.5, 11 clusters are derived and represented by 11 colors. the size of the node reflects the number of publications of the author, and the strength of the studies in informatics and control: a bibliometric analysis from 2008 to 2019 645 table 9: the 6 couples of journals with strongest co-citation strength from2008 to 2019*. rank link strength journal 1 journal 2 1 172 international journal of production research international journal of production economics 2 165 computers & operations research european journal of operational research 3 160 studies in informatics and control ieee transactions on automatic control 4 155 studies in informatics and control expert system with applications 5 153 studies in informatics and control automatica 6 150 studies in informatics and control european journal of operational research * as of november 1, 2019. table 10: the top 10 cited journals with the strongest citation bursts from 2008 to 2019*. rank cited journals strength begin end 2008-2019 1 international journal of computers communications & control 11.578 2009 2012 2 european journal of operational research 4.6737 2011 2015 3 operations research 4.4687 2013 2016 4 computers in industry 4.2578 2011 2014 5 mathematical programming 3.9211 2008 2009 6 communications of the acm 3.9011 2009 2012 7 ieee transactions on neural networks 3.8133 2009 2012 8 journal of the operational research society 3.7977 2013 2015 9 journal of intelligent manufacturing 3.7971 2011 2013 10 isa transactions 3.7841 2016 2019 * as of november 1, 2019. link represents the frequency of cooperation. the linked authors are cooperators and they finish at least 1 document with each other. in each cluster, almost every author has cooperated with the most productive author. by setting the minimum strength of link at 4, the figure 7(b) is derived. the strongest cooperation relationship is between wang, h. p and tian, y, they have written 10 documents in cooperation. wang, h. p and christov, n have published 4 documents together, and they represent the second strongest cooperative relationship. to analyze the co-authorship of the institutions, the vos viewer has been employed to map the co-authorship networks. the minimum number of documents of an institution is set at 5, and 32 of 457 institutions meet the threshold. among all the 32 institutions, 17 are part of the largest connected network as shown in figure 8 (with the clustering resolution set at 0.5). 17 institutions are grouped into 4 clusters which are marked in red, yellow, green and blue, and figure 8 shows the cooperation relationship between them. the national institute for r&d in informatics, ici bucharest and the university politehnica of bucharest are the two most productive institutions of them. the top 6 groups with the strongest cooperation are listed in table 11. the national institute for r&d in informatics maintains a strong cooperation with many other institutions. the co-authorship network of countries/regions is shown in figure 9. it is the largest linked network consisting of 54 countries/regions (there are 60 countries/regions in total) obtained by applying the vos viewer. international cooperation relationships among 54 countries/regions are exhibited in figure 9. romania, france, china and tunisia are the four most productive countries. table 12 lists 646 y. li, z.s. xu, x.x. wang, f.g. filip (a) (b) figure 7: co-authorship networks of authors. figure 8: co-authorship network of institutions. the main international cooperation information of the top 6 most productive countries/regions. the links represent the number of cooperators, and total link strength represents the number of international cooperative publications. cooperation strength is the percentage of total link strength from the number of documents. the main cooperators of the 6 countries/regions and their link strength are listed, and the link strength represents the number of cooperative documents. romania is the most productive (209 documents) country in the past 12 years and owns the most collaborative countries/regions. however, the total link strength of romania (31) is not the highest, and the cooperation strength (14.83%) is the lowest. france has published 46 documents in sic since 2008, and 76.09% of these research works were written with authors from other 10 countries/regions. as the country with the highest cooperation strength (76.09%), france establishes a strong international collaborative relationship. the collaborative strength between france and tunisia is the strongest, and they have cooperated for 17 times. in the last 12 years, the authors from tunisia have written documents with authors from france, lithuania and saudi arabia. indian authors prefer to cooperate with the authors who come from romania, england, france and oman. studies in informatics and control: a bibliometric analysis from 2008 to 2019 647 table 11: the top 6 groups with the strongest cooperation from 2008 to 2019*. rank link strength institutions 1 institutions 2 1 7 spiru haret university university of iceland 2 6 école centrale lille ecole nationale d’ingénieur de tunis 3 5 bucharest university of economic studies national institute for r&d in informatics ici 4 5 casa acad romane national institute for r&d in informatics ici 5 4 university politehnica of bucharest national institute for r&d in informatics ici 6 4 loughborough university university of iceland * as of november 1, 2019. figure 9: co-authorship networks of countries/regions. next, citespace is used to detect the citation bursts of authors, institutions, and countries/regions. table 13 lists the top 1 author, the top 4 institutions and the top 5 countries/regions with the strongest citation burst from 2008 to 2019. tian, y is the only one author with citation bursts over the past 12 years, and the citation bursts began in 2014 and lasted until 2019. the documents published by tian y in sic have been cited more often in the last 6 years than in the first 6 years starting 2008. the documents of école centrale de lille has been cited more often in the preceding 4 years, while the documents of vilnius gediminas technical university have gained a lot of attentions recently. university politehnica of bucharest has the highest strength of citation bursts (6.2612) from 2014 to 2017. the citation bursts of tunisia and england occurred in 2008, and lasted for 2 or 3 years respectively. from 2011 to 2014, spain had the highest strength of citation bursts (4.8054). the citation bursts of documents from turkey have continued to 2019 from 2017. 4.3 co-occurrence analysis as a quantitative analysis method, co-occurrence analysis is useful to support the knowledge services and knowledge mining. the keywords show the research fields and the underlying trends of the publications. thus, the keyword co-occurrence analysis is a good way to reveal the hot topics and research trends of the journal. the following research is based on 2,988 keywords detected by employing the citespace. figure 10 shows the largest connected co-occurrence network and classification map of the keywords. the two linked keywords appear together in not less than 1 document. the top 7 most frequently used keywords are labeled. the bigger the node is, the higher the occurrence is. the network is divided into 6 clusters and the smaller the cluster id is, the more components 648 y. li, z.s. xu, x.x. wang, f.g. filip table 12: the international cooperation of the top 6 most productive countries/regions from 2008 to 2019*. rank countries/regions documents links total link strength cooperation strength main cooperators countries/regions link strength 1 romania 209 14 31 14.83% iceland 7 france 6 india 3 england 3 2 france 46 10 35 76.09% tunisia 17 romania 6 china 5 3 tunisia 39 3 19 48.72% france 17 lithuania 1 saudi arabia 1 4 china 37 10 21 56.76% france 5 pakistan 4 south korea 3 5 chile 30 6 9 30.00% usa 2 peru 2 mexico 2 6 india 25 4 7 28.00% romania 3 england 2 france 1 oman 1 * as of november 1, 2019. figure 10: the keyword co-occurrence network and classification map. the cluster has. the cluster is labeled with indexing terms and three algorithms: latent semantic index (lsi), log-likelihood ratio (llr) and mutual information (mi) in sequence. to analyze the hotspots and the underlying trends of the journal, the citespace is applied in order to detect the top 5 keywords with the strongest citation bursts and to map the timeline view of keywords. the main attention is paid to the keywords ’system’, ’model’, and ’design’ which have frequently appeared in the journal during 2010-2011, 2012-2015, and 2012-2013 respectively, and ’model’ is the keyword with the strongest citation bursts. in the last 4 years, the keyword ’algorithm’ has gained much attention, and the research concerning this keyword has been prevalent. the keyword ’mobile robot’ was paid much attention during 2012-2014. from figure 11, it can be observed that most keywords of the publications are a combination of these three words. sic not only focuses on the research of algorithms, models and system designs, but also pays attention to the application of these technologies and combination with other theories. especially, they are combined with decision-making techniques, internet, supply chain and energy. studies in informatics and control: a bibliometric analysis from 2008 to 2019 649 table 13: the top 1 author, 4 institutions and 5 countries/regions with the strongest citation bursts from 2008 to 2019*. rank authors strength begin end 2008-2019 1 tian, y 4.0459 2014 2019 rank institutions strength begin end 2008-2019 1 university politehnica of bucharest 6.2612 2014 2017 2 école centrale de lille 4.4988 2008 2011 3 petroleum-gas university 3.0844 2014 2016 4 vilnius gediminas technical university 3.0109 2015 2019 rank countries strength begin end 2008-2019 1 spain 4.8054 2011 2014 2 england 4.0777 2008 2010 3 turkey 3.5189 2017 2019 4 tunisia 3.4792 2008 2009 5 usa 2.9427 2009 2012 * as of november 1, 2019. table 14: the top 5 keywords with the strongest citation bursts from 2008 to 2019*. rank keywords strength begin end 2008-2019 1 model 4.5285 2012 2015 2 design 3.3160 2012 2013 3 system 3.0302 2010 2011 4 algorithm 2.9440 2016 2019 5 mobile robot 2.6314 2012 2014 * as of november 1, 2019. 5 conclusions the bibliometric analysis of the journal sic is illustrated in this paper. the productivity and influence of authors, countries/regions, and institutions that have published documents in sic, and the citations of the most cited documents is analyzed. the productivity and influence are reflected in terms of publications and citations, but also the h-index and other statistic information are used. a comprehensive performance analysis of the documents published in sic between 2008-2019 is presented and illustrated. for an intuitionistic exhibition of the publications in sic, science maps of the documents published in sic from 2008 to 2019 have been developed, by using the visualization tools vos viewer and citespace. co-citation analysis, co-authorship network and co-occurrence analysis are represented in visualization. based on the above analysis, some conclusions are summarized as follows. (1) sic has had a more important influence in the area of it in the last 10 years, and has gained more consideration and attention. authors and institutions from romania contribute most publications to the journal and gain most citations, while the average influence of their publications is not the highest. there is no doubt that romania is one of the most productive and influential country in sic, followed by france, tunisia, china, chile and india, respectively. (2) the source of the references is extensive, as there are 8,335 cited authors and 6,286 650 y. li, z.s. xu, x.x. wang, f.g. filip *as of november 1, 2019 figure 11: the timeline view of keywords in sic from 2009 to 2019*. cited journals. sic has the strongest co-citation relationship with other journals as the only one reference with the strongest citation bursts was published in sic in 2012 by cabrera, g.g. (3) international cooperation relationship is not strong enough in this journal. only 14.83% of the publications from romania are collaborative documents, while romania is the most productive country of sic. on the contrary, 76.09% of the publications from france were written in international cooperation, 17 out of 46 in collaboration with authors from tunisia. this is the strongest international collaboration relationship in sic. (4) the journal focuses mainly on it and innovations in the relative technologies and applications. the research areas are getting wider, especially for the applications linked with other research directions. overall, this paper provides an objective and intuitionistic view of sic by employing bibliometric analysis. it provides scholars a scientific way to understand the trends and the hot topics published in this journal. it might be of particular interest to researchers and professionals in the field of it with its diverse applications. last but not least, the relevant and reliable assessment data provided by this study may be beneficial to the authors, readers and editors of this well-established scientific quarterly. bibliography [1] aria, m.; cuccurullo, c. (2017). bibliometrix: an r-tool for comprehensive science mapping analysis, journal of informetrics, 11(4), 959–975, 2017. [2] braam, r.r. 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(1999). combining mapping and citation analysis for evaluative bibliometric purposes: a bibliometric study, journal of the american society for information science, 50(2), 115–131, 1999. [21] podsakoff, p.m.; mackenzie, s.b.; podsakoff, n.p.; bachrach, d.g. (2008). scholarly influence in the field of management: a bibliometric analysis of the determinants of university and author impact in the management literature in the past quarter century, journal of management, 34(4), 641–720, 2008. [22] small, h. (1973). co-citation in the scientific literature: a new measure of the relationship between two documents, journal of the american society for information science, 24(4), 265–269, 1973. [23] tuba, m.; tuba, e. (2019). generative adversarial optimization (goa) for acute lymphocytic leukemia detection, studies in informatics and control, 28(3), 245–254, 2019. [24] van eck, n.; waltman, l. 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(2019). a bibliometric overview of international journal of strategic property management between 2008 and 2019, international journal of strategic property management, 23(6), 366–377, 2019. international journal of computers communications & control issn 1841-9836, 13(6), 988-1006, december 2018. indoor localisation through probabilistic ontologies i. mocanu, g. scarlat, l. rusu, i. pandelica, b. cramariuc irina mocanu* computer science department, university politehnica of bucharest romania, ro-060042 bucharest, splaiul independentei, 313 *corresponding author: irina.mocanu@cs.pub.ro georgiana scarlat computer science department, university politehnica of bucharest romania, ro-060042 bucharest, splaiul independentei, 313 georgiana.scarlat@cti.pub.ro lucia rusu faculty of economics and business, babes bolyai university of cluj-napoca romania, 400591, cluj-napoca, teodor mihali, 58-60 lucia.rusu@econ.ubbcluj.ro ionut pandelica agora university of oradea romania, 410526, oradea, piata tineretului, 8 ionut.pandelica@univagora.ro bogdan cramariuc it center for science and technology romania, 11702, bucharest, av. radu beller, 25 bogdan.cramariuc@citst.ro abstract: for elderly people that are living alone in their homes there is a need to permanently monitor them. one of this aspect consist in knowing their indoor position and motion behavioural status, in real time. one possibility for indoor positioning of an user consists in understanding the images provided by supervising cameras. in this case the main aspect is represented by recognition of objects from these images. thus, object recognition plays an essential part in understanding the environment and adding meaning to it. this paper presents a method for indoor localisation based on identifying the user’s context. the user’s context is computed based on object recognition and using a probabilistic ontology. the key element is represented by the probabilistic ontology that describes objects, scenes and relations between them. this ontology contains probabilistic relations that are learned using a large database. results show that given a set of object detectors with high detection rate and low false positive rate, the system can recognize the user’s context with high accuracy. keywords: object recognition, detection rate, probabilistic ontology, context identification. 1 introduction the high number of elderly who are living alone, or who are spending too much of the day without supervision of specialized people is increasing exponentially. with the development of active and assisted living (aal) technologies the localisation of persons is easier. indoor positioning/localisation systems (ips) is similar with gps and can be used successfully to locate people or objects inside buildings via mobile devices (smartphones or tablets). ips relies on cameras mounted on walls or ceiling that work together to recognize user’s context or copyright ©2018 cc by-nc indoor localisation through probabilistic ontologies 989 objects. results are in highly accurate position. like gps, ips systems can detect the direction of movement, and it can predict the path based on this information so that accurate position remains as the space is displaced [20]. hospitals and medical centers can benefit from this indoor localisation systems for staff, patients and managerial purposes. ips staff includes: rapid location of colleagues in the building, finding records and mobile devices, notifications when and where patients are checked. ips for patients that are seek can achieve the below benefits: automatic checking of the building’s entrance, turn-by-turn dynamic way finding meetings/appointments with relevant information based on location, way finding back to the parked car. for people with alzheimer, ehealth and mhealth solutions includes complex ips technologies, which are essential for the pursuit of both indoor and outdoor patients [1]. therapy acceptance and commitment (act) encourages seniors (patients in general) in two directions: (1) acceptance of thoughts and emotions, difficult and undesirable, and (2) adoption and simultaneous promotion, of actions and behaviors in daily practice, which is a consistent of individual values. act includes mindfulness exercises that promotes contact with the present [1]. the goal of the system proposed in this paper is to offer a reliable context detection of a user in his home. the user’s context will be represented by surrounding objects. thus the main problem that must be solved consist in object recognition. object recognition has evolved very much in the past decade but the current state of the art solutions are still far from what the human brain can do. moreover, training object detectors involves a big amount of resources such as computational power and large image training set. hence, object recognition is the main part of the proposed system especially extracting the meaning from the object configurations found and detect the type of the scene in which they are in. for this scope, we propose a probabilistic ontology that describes objects, scenes and relations between them. the system is described by a generic probabilistic ontology which can be instantiated according to the set of objects and scenes that need to be recognized. the ontology contains probabilistic relations that are learned using a large database containing thousands of images (labelme image database [15]) of annotated images with object and scene information. the results of analyzing the co-occurrence and spatial relations between objects are then used to improve the object recognition process. for implementing a semantic information based solution for scene recognition, a large number of object detectors are needed because each scene can have so many different object configurations. training so many object detectors is one problem, but it can be solved given the necessary resources. another important problem is that using so many object detectors to scan through an image can be time consuming. the solution proposed in this paper tries to reduce the number of image scans to a minimum. this can be achieved if the algorithm would somehow "knows" what objects to search for. this would be possible in scenarios in which some objects are already detected and the next searched objects would be the ones that are relevant to the object context found so far. by doing this, the algorithm could recognize the scene after only a small number of object scans. the rest of the paper is organized as follows: section 2 presents some existing methods for object recognition. section 3 contains the proposed method for improving scene recognition by using semantic information organized as a probabilistic ontology model. section 4 presents the evaluation of the proposed system. conclusions and future work are given in section 5. 2 related work the ips systems have made significant progress in the last years. the utility of those types of systems on gps tracking devices is appreciated both for seniors, and also for health care 990 i. mocanu, g. scarlat, l. rusu, i. pandelica, b. cramariuc professionals (hcps) and caregivers. bellow are described some of the most popular systems and applications for hcps and caregivers [11]. balance is an application geared specifically for alzheimer’s caregivers, which works on iphone and ipad. the balance features are: alzheimers disease references and information, alzheimer’s caregiving and advice, advanced medication management features (refill date, start date and dosage), native scheduling features, adds categories, relevant to caregivers, "doctor diary" for logging symptoms and taking notes, news about alzheimer’s. mobicare is a simple, straightforward and free iphone and web application. their features are: profile of loved ones who are receiving personal information, including birth date, gender, basic insurance information, the contact information for one physician, basic symptoms, tracking based on 15 preset choices (i.e. insomnia, wandering, etc.), basic medication, tracking but with some limitations. dementia caregiver solutions is an informational application for dementia caregivers. its features are: perform advices for addressing the difficult behaviors associated with alzheimer’s and other types of dementia. they also have bookmark or "star" articles you wish to read in the future. object recognition is a vastly studied topic for which many systems were developed, however none of them are even close to the performance with which human brain can recognize objects, even though there are many variations in light, shape and color. the process through which the human brain accomplishes object recognition with very high speed and accuracy has been intensely studied by neurologists. based on [2]: "the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of rflexive, largely feed forward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. however, the algorithm that produces this solution remains poorly understood". in [3] suggests that "object recognition does not only involve physical properties (such as shape, color and texture) of the objects but also semantic information which includes the understanding of its use, previous experience with the object and how it relates to others". some approaches in object recognition were purely based on visual properties of the objects. for example, paper [5] tries to detect general classes of objects, not just specific ones by using part-based modelling and recognition of objects. the pictorial structure models were first introduced in [4]. they describe how a set of object parts arranged in a flexible configuration are used to model an object. the object parts represent their localized visual properties. the flexible object parts configuration is represented by pair-wise object connections. this approach is suitable for generic recognition problems because of the complex description of object visual features. another machine learning approach is presented in [18]. their solution is capable of processing images extremely fast and achieves high detection rates. integral images are used to speed up computations. also an important aspect is the fact that they train extremely efficient classifiers using an algorithm based on adaboost, but that are used only a small number of visual features which are selected as being critical features. many regions of the image than on background areas are discarded in the early stages of the algorithm. this is accomplished by combining increasingly more complex classifiers in a cascade. a major advantage of this solution is that it can run in real-time applications at 15 frames per second. both of the previously described systems are very promising but they lack semantic information about objects, which is crucial in recognizing objects with large variations in shape, such as furniture objects and others. a synergy between google and toronto university research team obtain a remarkable result called multimodel, which solve multiple translation tasks, image captioning with coco dataset, a speech recognition corpus, and an english parsing task. this model can caption images, catindoor localisation through probabilistic ontologies 991 egorize them, then translate to french and german and construct parse trees, by spanning multimple domains. multimodel used encoder-decoder architectures, and applied to neural machine translation extended neural. gpu is another model which used a recurrent stack of gated convolutional layers and bytenet used left-padded convolutions in the decoder. compared to extended neural gpu and bytenet, multimodel idea improves efficiency.and obtained good results in image classification [7]. the inception deep convolutional architecture was called googlenet or inception-v1, than the was refined by the introduction of batch normalization in inception-v2, or by additional factorization ideas in the third iteration inception-v3, later inception-v4 with similarly expensive hybrid inception-resnet versions for both residual and non-residual inception networks [17]. another results was offered by capsnet, which used a 3 layer architecture for convolutional neural networks (cnns) for translated replicas of learned feature detectors. the primary capsules are the lowest level of multi-dimensional entities and corresponds to inverting the rendering process. the second layer (primarycapsules) is a convolutional capsule layer with 32 channels of convolutional 8d capsules. the third layer (digitcaps) has one 16d capsule per digit class and each of these capsules receives input from all the capsules in the layer below.the implementation is in tensorflow using the adam optimizer [16]. there are, however, approaches that use semantic information concerning objects. one such approach is presented in paper [12,13]. object recognition and scene understanding is strongly influenced by semantic and context-based information from a psychological point of view. therefore, they use the context information to improve object recognition based on visual properties. their approach presents how to extract context probability maps from images. also, based on these maps, they learn specific configurations for a set of object classes. the final goal is to filter out false positives. there are new methods based on deep learning for object detection and recognition. in the context of object detection, several network architectures have been proposed [8], [9] and found to outperform methods based on traditional hand-crafted features. most of these models were trained on rgb datasets such as pascal voc to predict the bounding boxes of objects from images. the main difference between the solutions presented in paper [12] and the solutions presented in this paper is that, here the context is used not only to remove objects that don’t fit the context, but also for inferring what other objects can be found in the same context. for example, if a keyboard was detected at a previous step, then there is a high probability that a mouse may also be present in the same scene. this reasoning is used to make the system converge faster in order to recognize the scene by running the most relevant object detectors for the current context. for example, if a tv was detected, the system shouldn’t run an object detector for finding a car, but should run an object detector for finding a coach. also, this system is designed in a generic way, thus allowing the use of any objects and scenes. this means that entities modeled by the ontology are abstract and can be mapped to any set of objects and scenes as long as these have the needed correlations between them (objects are relevant for the chosen set of scenes). 3 system description the system uses as stored data both a pre-trained probabilistic ontology model containing object and scene entities and relations among them and a set of object recognizers mapped on the object entities contained by the probabilistic ontology model, as given in figure 1. the object recognition module can run, at request, a specific object recognizer and provide the result as a pair of object confidence and object bounding box. the inference module represents the main reasoning algorithm and, at each iteration, sends a request to the object 992 i. mocanu, g. scarlat, l. rusu, i. pandelica, b. cramariuc recognition module for finding a certain object. based on the found/not found object results, the inference module uses the probabilistic ontology to filter out false positives, to determine what object to inquire about next and to determine if a scene can be recognized. figure 1: system architecture in order to implement a reliable system for scene recognition, we consider the following steps: • selecting the ontology’s structure: finding the set of relevant scenes and objects and mapping the meaningful relations among them into ontology relation types with associated attributes that reveal their semantic understanding. • choosing a large training data set that contains diverse context configurations for the chosen set of objects. • implementing a component that scans through the training dataset to compute object probabilities, scene probabilities and relation probabilities and aggregate all this information into a probabilistic ontology model. • implementing a object recognition method that allows testing the system and analyzing the influence on performance that object detection rate has on it. • doing ontology-based reasoning using already found objects to determine if a new found object belongs or not to the current context, thus filtering object recognition false positives. • doing ontology-based reasoning using previous object search results to determine what other objects can appear in the current scene in order to make the number of object recognition scans needed to recognize a scene much lower. • doing ontology-based reasoning to recognize a scene based on the previously found objects. the system can run in two modes. the first one is the mode in which the system is used for running tests batches and computing system statistics. the tests are run on a big test database indoor localisation through probabilistic ontologies 993 (approximately 3000 images) and the computed statistics consist of scene recognition accuracy, mean number of iterations needed to converge to recognise a scene and total number of removed false positives from the entire test set. this data is used to evaluate the system and analyze how different changes and improvements can affect its performance. the second mode is used for viewing the analyzed image and obtaining the label associated to the image. an object is searched into the image if the object is found then the most probable scene’s name will appear. after that, correlated objects with already found are searched in the image. the rectangles for founded objects are displayed on the input image and the recognized scene’s name is provided. 3.1 object recognition the scene recognition problem can also be approached using only image processing algorithms that analyze low level information about color, shapes and texture. this approach has the advantage that the classification model is easier to learn and design, but the disadvantage is that this method cannot distinguish very well between different indoor scenes such as living room, bedroom and so on. this is due to the fact that indoor scenes usually have similar colors, shapes and textures. such scenes with similar visual features can only be distinguished using more top level information about the image such as objects and correlations between them. moreover, neurological studies [20] show that even the human brain cannot distinguish well objects and scenes unless there is some semantic meaning attached to them. as a result, scene recognition is best approached using semantic information regarding the scene. using object recognition for solving a scene recognition problem has the advantage that once the objects are found, recognizing the scene becomes a much simpler problem. however, there is a huge disadvantage to this approach. object detectors can sometimes generate false positives. an object that is "alien" to the current object context can affect the final recognized scene very much, especially if there are only a few objects in the scene. the approach presented in this paper shows how contextual information can also be used to filter out false positives, thus increasing overall scene recognition accuracy. both ideas presented above about reducing the number of object scans and filtering out false positives are based on the relations that exist between objects. some objects are strongly related among them (for example: keyboard and mouse, table and chair, etc.) and others are hardly related and can’t be seen together in the same scene very often (for example: tree and tv, refrigerator and car, etc.). however, these relations are not always applicable (for example: keyboard can appear in a scene without a mouse). therefore, they should be modeled stochastically. in this paper, the chosen model for representing the relations between objects is a probabilistic ontology. the ontology used in this paper contains as entities objects and scenes and as relations the stochastic relations among objects and among scenes and objects. every ontology entity has an apriori probability and every relation is also described by the probability of it to be true in a scene. the object to object relations from the ontology are described by an attribute called interpretation that helps distinguish between positive and negative relations. positive relations are those for which the objects are semantically connected to each other, meaning that this makes it more probable for them to appear in the same scene. the negative relations are exactly the opposite and they mean that the two objects are less likely to be found together. for filtering out false positives obtained as a result of object detection, the current approach uses the previously found objects in the scene and the relations that connect them to the newly found object. if these relations are positive ones, this will increase the detection confidence of the checked object, but if the relations have a negative interpretation then this will decrease the 994 i. mocanu, g. scarlat, l. rusu, i. pandelica, b. cramariuc detection confidence. if the newly found object’s detection confidence has decreased considerably after this update, then it is considered to be a false positive and it will be eliminated from the found objects list. if the first detected object is a false positives there are not afterward corrections applied. however, a prevention method is used in order to decrease the chances of this to happen. this measures is to use the false positive rate of each object detector as an influencing factor in choosing what object to search for in the case when no objects were found so far. this means that objects whose object detectors have lower false positive rates are preferred in the initial steps of the algorithm. choosing what objects to search for so that a scene can be recognised faster uses the positive and negative relations between previously searched objects and current objects that are candidates for search. this means that candidate objects that are in positive relations with previously found objects with high detection confidence are more suitable to be searched for next than candidate objects that are in negative relations with previously found objects. also, this reasoning is applied not only for the searched and found objects, but also for the searched and not found objects. if an object was not found after scanning the current scene, then candidate objects that are in a negative relation with it are more suitable for the next search than the ones that are in a positive relation with it. this reasoning is applied so that the next object choosing criteria can be more complex even if no objects were found yet. the weight of the influence the not found objects have on choosing a new candidate is smaller than the weight for the found objects. this is justified by the fact that even though an object is not found in a scene, it does not necessarily mean that the current context is not suitable for it. for example, in a kitchen scene there might not be a stove object found, but this does not mean that other objects related to it cannot exist in that scene. however, if there are two equally suitable objects candidates according to their relations with already found objects in the scene, a tie breaker between them can be the same criteria based on not found objects after scanning the image. this reasoning is useful if the algorithm is in a state when all the previous searched objects are not found and it should try to search objects from different contexts than the ones searched before so that the chances of finding a new object increase. all the criteria for choosing the next object to scan the image described earlier are combined into a fitness formula and the candidate object with the highest fitness value is chosen. the fitness value can also take into account the object’s associated object detector false positive rate in case no objects were found yet in order to avoid the situation when the first found object is a false positive. in order to recognize a scene after some objects were found, the proposed solution uses the semantic information stored as relations between scenes and objects inside the probabilistic ontology. the relation inscene is described by the probability of an object to be in a certain scene. at each step of the algorithm, the set of already found objects in the scene is used together with the relations between them and the entire set of scenes to compute the a fitness value for each scene. the number of objects needed to recognize a scene can vary very much depending on the objects and how correlated they are with a certain scene. in some cases, only a few objects can be enough to know the scene as long as their detection confidence is big enough. for example, if a stove object was found and it has a very high detection confidence, then the most probable scene by far is kitchen. every time a new object is found, the fitness value is computed for each scene and if the scene with the highest fitness value is much beyond average then it is returned as the recognized scene and the object recognition process ends. the probabilities contained in the ontology are computed based on a large annotated image dataset, such that it can be applicable in general cases. the entities and the topology of the ontology can vary according to the use case it indoor localisation through probabilistic ontologies 995 is designed for, but it is important that they reflect meaningful semantic information about objects and how they relate to one another and scenes and what objects are most probable to be contained by them. probabilistic ontology the probabilistic ontology can be modeled using any topology structure and any entity set as long as these are relevant to the current use case. however, there are some restrictions on how to build the probabilistic ontology. more precisely, the entity set must contain both scenes and objects. the object set has to be identical to the object set used by the object recognition module, and the objects must be relevant for the scene set. the general structure of the probabilistic ontology is given in figure 2. the possible relations are: inscene, is-a, object-object relation, hasvisualfeature. figure 2: ontology structure there is no restriction regarding the stochastic relations between objects, except the fact that the relations have to be either positive or negative and weights have to be provided for each relation. the relation between objects and scenes is restricted to be of the type inscene, which reflects how probable it is for an object to be in a scene. also, this relation should fully connect all the objects and scenes. another restriction regarding the ontology building is that there should be no 0 value probabilities (in the case there are, they should be replaced with a very small value, close to 0). an example of a part of the probabilistic ontology is given in figure 3. each object to object relation contained by the probabilistic ontology has a fixed format. we consider the following relations properties: • name: a string that uniquely identifies each relation • interpretation: represents how the relation should be interpreted during reasoning (positive, negative). 996 i. mocanu, g. scarlat, l. rusu, i. pandelica, b. cramariuc figure 3: ontology example • check weight: represents how important is this relation for checking newly detected objects and filtering out false positives. • next weight: represents how important this relation is for determining the next object to query the object recognition module. • check rule: some relations are based on spatial requirements. this rule is used to check if the spatial requirements are met. for the relations that have no spatial meaning attached to them, this rule should be none. this ontology contains as entities objects from a house and the following scenes: kitchen, living room, street, office. we pointed relationship description between objects: • usedtogether: two objects are often used together. the object pairs that are in this relation are given by the user according to common sense information about the objects in the object set. • insamescene: two objects often appear together in the same scene. the object pairs are determined by the object recognition algorithm according to the probability of the co-occurrence of each pair of objects. • cannotcoexist: two objects can’t both be present in the same scene. the object pairs are determined by the object recognition algorithm according to the probability of the co-occurrence of each pair of objects. • areoddtogether two objects may appear together in the same scene, but their combination seems unnatural. the object pairs that are in this relation are given by the user according to common sense information about the objects in the object set. • ontop: most of the cases when the two objects appear together, one of them is placed on top of the other. the object pairs that are in this relation are given by the user according to common sense information about the objects in the object set. indoor localisation through probabilistic ontologies 997 • canoverlap: most of the cases when the two objects appear together, their areas overlap. the object pairs that are in this relation are given by the user according to common sense information about the objects in the object set. • isneighbour: most of the cases when two objects appear together, they are very close to one another. the object pairs that are in this relation are given by the user according to common sense information about the objects in the object set. properties of these relationship between objects are syntetize in table 1. the rules are the following: ontoprule : center(object1).y > center(object2).y isneighbourrule : distance(object1,object2) ≤ diagonal(object1) + diagonal(object2) 2 canoverlaprule : left(object1).x < left(object2).x < right(object1).x and left(object1).y < left(object2).y < right(object1).y table 1: properties of relationship between objects relation name interpretation checkweight nextweight checkrule usedtogether positive 0.8 0.95 none insamescene positive 0.6 0.9 none cannotcoexist negative 0.98 0.98 none areoddtogether negative 0.75 0.85 none ontop positive 0.8 0.7 ontoprule canoverlap positive 0.8 0.65 canoverlaprule isneighbour positive 0.8 0.7 isneighbourrule inscene belonging 0 0 none the values for the relation checkweight and nextweight attributes are chosen manually so that they reflect how reliable a relation is for checking for false positives (checkweight) and how reliable a relation is for predicting the presence of one of its referred objects when the other one is already found in the scene (nextweight). for example, the relation usedtogether describes a much stronger bond between objects than the relation insamescene, therefore its checkweight is much bigger (0.8 vs. 0.6). however, both usedtogether and insamescene are relations between objects that are frequently found together, therefore their nextweight is very big (0.95 and 0.9). these weights are used so that not all relations influence in the same way false positive filtering and next object choosing, because each of them has a different semantic interpretation and should affect the reasoning process in its own custom way. the checkweight and nextweight attributes have the role of quantifying semantic relation attributes as numbers in the [0, 1] interval, that can be used as components in the inference module reasoning formulas. the current probabilistic ontology contains the relation between objects and scenes: inscene describes how probable is for an object to appear in a scene. the probabilities of each object, each scene and each relation were computed based on the labelme database [15] that currently contains 78840 annotated images. 998 i. mocanu, g. scarlat, l. rusu, i. pandelica, b. cramariuc 3.2 object recognition the object recognition module has access to a set of predefined object detectors which are run at the request of the inference module on the input image or on a region of the input image. the result that is supplied by this module is represented by a tuple of the form (foundobject, boundingbox, confidence). the bounding box represents the area in the image where the found object is placed. this information is used by the inference module to check ontology rules that have spatial requirements. the confidence of object detection is also used by the inference engine as a measure of how much the detected object influences reasoning regarding other related objects. an important aspect worth mentioning is that not all the object detectors are necessarily run in order to recognize a scene, the inference module decides to inquire about a certain object which it believes it is more probable to appear in the image based on previous detections, and when it has enough information to infer the scene with a high probability, the system returns and no more detectors are run. this means that, given a powerful inference module that obtains reliable information from all the other components, the final result can be obtained very fast, avoiding the costly image scans that the object detectors apply. the object recognition module is responsible for running object detectors on request. also, this component has access to information regarding each object recognizer that is relevant to the inference module. more precisely, the object recognition module can provide information about each object detector’s detection rate and false positive rate. this information is meaningful for the inference module because it influences what object to inquire about. 3.3 inference module the inference module represents the system’s component that implements the main algorithm for scene recognition. a scene can be inferred based on the objects recognized using the object recognition module. the information regarding found or not found objects is used to interrogate the probabilistic ontology. this ontology contains information about the stochastic relations between scenes and objects and between objects. the algorithm starts by choosing a first object to interrogate the object recognition module about. the first object is chosen based on false positive rate. therefore, the first interrogated object is the one with the lowest false positive rate. this is done because at the initialization of the system, there is no information available about the input image, therefore it is important not to start with invalid information that can compromise future reasoning. after the initialization step when the first object is chosen, the algorithm enters a repetitive loop. at each iteration, the object recognition module is queried about the existence of a certain object inside the input image. if the object is found, then its confidence and bounding box are available. next, a set of inference rules are used. the first inference rule has the role of filtering out false positives. the information used to update an object’s detection confidence is represented by the positive and negative relations between the current object and the previously found objects. if the newly found object is in a positive relation with a previously found object then its detection confidence will increase. on the other hand, if the current object is in a negative relation with a previously found object, then its detection confidence will decrease. a visual description of the first inference rule is given in figure 4. the first inference rule are the following rules (ri), i=1,7: • (r1) compute the list of previously found objects rejected by the current found object. • (r2) compute rejection factor of the found object rejfound according to each rejected object relation. indoor localisation through probabilistic ontologies 999 figure 4: description of the first inference rule • (r3) compute the list of previously found objects attracted by the current candidate object. • (r4) compute the found object attraction factor. attrfound according to each attracted object relation • (r5) update the current found object’s detection c(o) rate according to the rejection and attraction factors already computed: attrfound, rejfound c(o) = c(o) ∗ (1 − rejfound |objectsrejected| + attrfound |objectsattracted| ) • (r6) if the found object detection confidence c(o) has decreased more than 20%, then it is filtered out. the second inference rule has the role of determining what the next searched object should be. this rule is important because it helps the algorithm converge faster to the recognized scene and it avoids running all the object recognizers on the input image. in order to determine what object to search for next, information about the positive and negative relations from the probabilistic ontology is used against the set of previously found and not found object sets. therefore, the next object to inquire the object recognition module about is the one that best fits the current context. more precisely, not searched objects that are in a positive relation with previously found objects will have a bigger chance at being next and not searched objects that are in a negative relation with previously found objects will have a smaller chance at being next. a visual description of the second inference rule is given in figure 5. similar reasoning is used also in the case of previously not found objects (objects that were searched for in the input image but were not found), but in this case the positive relations have a negative impact on the new object’s chance at being next and the negative relations have a positive impact on the new object’s chance at being next. however, the reasoning based on previously found objects has a bigger weight in influencing the detection rate than the one on previously not found objects. treating these two cases differently is justified by the fact that, even though an object is not found in the input image, this does not necessarily mean that the object cannot belong to the current context. 1000 i. mocanu, g. scarlat, l. rusu, i. pandelica, b. cramariuc figure 5: description of the second inference rule the scene recognition algorithm reasoning can be compromised if the first found object is a false positive. this can lead to filtering out other following correct detection as false positives and the final result becomes meaningless. therefore, when choosing the next object to search for, the false positive rate of the object detector is also taken into consideration if no objects have been found so far. this reduces the risk of starting the algorithm with a false positive detection. • (r1) obtain the list of previously found objects rejected by the current candidate object objectsrejected • ( r2) compute the found object rejection factor rejfound according to each rejected object relation • (r3) obtain the list of previously found objects attracted by the current candidate object objectsattracted • (r4) compute the found object attraction factor attrfound according to each attracted object relation • (r5) repeat the steps above for previously not found objects and compute rejnot−found and attrnot−found factors. • (r6) compute the fitness of the current candidate object: f(ocand) = (attrfound−rejfound)+α∗(rejnot−found)−attrnot−found) where α represent a sub-unit weight for decreasing the influence of the not found objects. if there was no object found, the fitness value is also influenced by the false positive rate (fp): fp(detectorocand ) of the candidate objects: f(ocand) = (1 −fp(detectorocand ) ∗f(ocand) • (r7) choose the next object to search for from the list of candidate objects by finding the one with the biggest fitness value: onext = argmax(f(ocand)) indoor localisation through probabilistic ontologies 1001 the third inference rule has the role of determining the fitness value of each scene for the current image. if the scene with the highest fitness value is bigger than a threshold proportional to the average scene fitness, then the algorithm returns the scene and the object searching process is ended. for computing the fitness value of each scene the inference module uses the set of objects found so far to interrogate the probabilistic ontology about the probability of having each found object in the current candidate scene. the ontology relation that contains information about the probability of an object to appear in a scene is called inscene. this is a mandatory relation that should exist in any instance of the probabilistic ontology, no matter what object set and scene set is used or what other relations are used between objects. a visual description of the third inference rule is given in figure 6. figure 6: description of the second inference rule the fitness value for a scene s is computed as: fitness(s) = ∑ o∈objectsf ound log p(inscene(o,s) p(o) where objectsfound represents the set of the previously found objects, p(inscene(o,s)) represents the probability of the inscene relation between object o and scene s and p(o) represents the apriori probability of object o (obtained from the probabilistic ontology). the best candidate scene is chosen by: scenebest = argmax(fitness(s)) the best candidate scene is returned as the final recognized scene if it’s fitness is bigger than a threshold proportional to the average scene fitness: fitness(scenebest ) > δ ∗ 1 |scenes| ∗ ∑ s∈scenes fitness(s) (1) if no scene is recognized at the current iteration, then the object searching process continues and the previous three inferences are applied again at each iteration until a scene can be inferred or all the objects have been searched for. in the latter situation, the returned scene is the one with the biggest fitness value, without taking into consideration the equation 1. 1002 i. mocanu, g. scarlat, l. rusu, i. pandelica, b. cramariuc 4 system evaluation the system is implemented to be flexible regarding replacing any of the components implementation. every module communicates with the others through an interface, making the components loosely coupled. therefore, in order to replace the current implementation of a module all that is needed is to implement the module interface. the scene recognition application is written in java language using eclipse as ide. for object recognition algorithm we use the yolo network [19]. the scene recognition application was tested using images from labelme database [15]. this database includes images for many indoor and outdoor scene types. for testing the system, only images with relevant scene types were used: kitchen, office, living room and street. the images are annotated with scene information under the attribute scenedescription. after the probabilistic ontology probabilities are computed based on object and scenes co-occurrences inside the training image set, the model is stored inside an xml file. the xml ontology is then parsed by the application and mapped into java objects that are used by the inference module. the pseudocode given in algorithm 1 that describes the reasoning algorithm implemented in the inference module: algorithm 1 reasoning algorithm 1: procedure reasoningalgorithm(imageimage) 2: searchobject ← getobjectwithlowestfp() 3: foundobjectssofar ⇐ [] 4: notfoundobjectssofar ⇐ [] 5: while true do 6: (confidence,boundbox) ⇐ findobject(searchobject,image) 7: updatedconfidence ⇐ checkfoundobject(searchobject,confidence,foundobjectssofar) 8: if updatedconfidence− confidence > threshold then 9: foundobjectssofar ⇐ foundobjectssofar ∪searchobject 10: else 11: notfoundobjectssofar ⇐ notfoundobjectssofar ∪searchobject 12: if [] 6= foundobjectssofar then 13: scenefitnesslist ⇐ computeallsceneprobabilities() 14: bestscene ⇐ maxfitnessscene(scenefitnesslist) 15: meanfitness ⇐ meanfitnessv alue(scenefitnesslist) 16: if bestscene.fitness > �∗meanfitness then 17: return bestscene 18: end if 19: end if 20: end if 21: searchobject = getnextmostprobableobj(foundobjsofar,notfoundobjsofar) 22: end while 23: end procedure the object recognition module has access to a set of object detectors and to information regarding their performance: detection rate and false positive rate. we use yolo (you only look once) network [19]. it is a very robust method, which is almost invariant to position and lightning. it simultaneously predicts multiple bounding boxes and class probabilities for those boxes. yolo trains on full images and directly optimizes detection performance. yolo reasons globally about the image when making predictions. unlike sliding window and region proposalindoor localisation through probabilistic ontologies 1003 based techniques, yolo sees the entire image during training and test time so it implicitly encodes contextual information about classes as well as their appearance. and also yolo learns generalizable representations of objects. for testing the system, only images with relevant scene types were used: kitchen, office, living room and street. the images are annotated with scene information under the attribute scenedescription. the database contains many object types that appear in many combinations. the images in the database are annotated with object information. for the current system, the relevant annotated object information is represented by the object outline polygon and the "verified": flag. the object polygon is used to obtain the object’s bounding box for the object recognition stubs and the "verified" flag is used to select the object’s confidence. if an object is verified, then it was annotated correctly and the object recognition will give it a high confidence, otherwise a lower confidence value is assigned. some examples of labelme annotated images can be seen in figure 7. figure 7: labelme annotated images example figure 8 shows some examples of recognising kitchen, office, living room. the current system was evaluated on a test set containing a total of 3381 annotated images from labelme database [15]. the aggregated evaluation results obtained after running the application on the test database can be seen in table 2. table 2: system evaluation results using stubs scene name accuracy mean iterations removed fp test images kitchen 89% 7 16 651 office 90% 9 35 914 living 91% 8 30 775 1004 i. mocanu, g. scarlat, l. rusu, i. pandelica, b. cramariuc figure 8: example of scene recognition 5 conclusions and future work this paper presents a method for context detection of a user in an indoor space. the proposed method is based on the results of an object recognition process. in order to be able to recognize a wide range of scenes, the number of objects that need to be recognized can become very big. running a large number of object detectors can be time consuming; therefore the current approach uses semantic information about objects and scenes to speed up the scene recognition process and to eliminate false positives that can have a negative impact on the final result. this semantic information is organized as an object and scene probabilistic ontology model. reasoning is done using the stochastic relations between objects and between objects and scenes and its outcome can influence in which order objects are searched for or if a newly detected object is eliminated for being a false positive. scene recognition is influenced by the semantic relations between scenes and objects, and the object recognition process ends as soon as there are enough objects found to determine the scene. results show that given a set of object detectors with high detection rate and low false positive rate, the system can recognize a scene with high accuracy and in a small number of iterations. as future work, the ontology model can be extended to meet domain-specific requirements because it is easily adaptable to different domains. acknowledgment this research and paper was co-founded by both the executive unit for financing higher education, research and development and innovation through partnership program, project "mobility pattern assistant for elderly people", project number pn-ii-pt-pcca-2013-4-2241, by university politehnica of bucharest, through the "excellence research grants" program, upb gex. identifier: upb-excelenta-2016 "optimizarea activitatilor zilnice folosind deep learning implementata pe sisteme reconfigurabile" / daily activities using deep learning implemented on fpga, contract number 8 / 26.09.2016 (code 341) and by the grant of the romanian national authority for scientific research and innovation, cccdi uefiscdi and of the aal programme with co-funding from the european union’s horizon 2020 research and innovation programme project "ionis improving the quality of life of people with dementia and disabled persons", project number aal2017-aal-2016-074-ionis (contracts 52/20017 and 53/2017). indoor localisation through probabilistic ontologies 1005 bibliography [1] burm, c. 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[20] https://senion.com/indoor-positioning-system/, last accessed october 2018. international journal of computers communications & control issn 1841-9836, 10(4):520-538, august, 2015. game theoretic distributed power control algorithms for uplink wireless data in flat fading channels m. hayajneh, c. abdallah mohammad hayajneh* united arab emirates university college of it , alain, p.o.box 17551 *corresponding author: mhayajneh@uaeu.ac.ae chaouki abdallah university of new mexico department of electrical & computer engineering msc01 1100, 1, albuquerque, nm 87131-0001, usa chaouki@ece.unm.edu abstract: in this paper we present a game-theoretic power control algorithms for wireless data in cdma cellular systems under two realistic channels: (a1) fast flat fading channel and (a2) slow flat fading channel. the fading coefficients under both (a1) and (a2) are studied for three appropriate small scale channel models that are used in the cdma cellular systems: rayleigh channel, rician channel and nakagami channel. this work is inspired by the results presented by [1] under nonfading channels. in other words, we study the impact of the realistic channel models on the findings in [1] through the followings: we evaluate the average utility function, the average number of bits received correctly at the receiver per one joule expended, for each channel model. then, using the average utility function we study the existence, uniqueness of nash equilibrium (ne) if it exists, and the social desirability of ne in the pareto sense. results show that in a non-cooperative game (npg) the best policy for all users in the cell is to target a fixed signal-to-interference and noise ratio (sinr) similar to what was shown in [1] for non-fading channel. the difference however is that the target sinr in fading channels is much higher than that in a non-fading channel. also, for spreading gain less than or equal to 100, both npg and non-cooperative power control game with pricing (npgp) perform poorly, where all the terminals except the nearest one were not able to attain their corresponding minimum sinr even if sending at the maximum powers in their strategy spaces. keywords: code-division-multiple-access (cdma), utility function, power control, game theory, non-cooperative game (npg), wireless data. 1 introduction the mathematical theory of games was introduced by von neumann and morgenstern in 1944 [18], and by the late 1970’s became an important tool whenever a player’s decision depends on what the other players did or will do. a core idea of game theory is how strategic interactions between rational agents (players) generate outcomes according to the players’ utilities [10], [19]. game theory thus forms a suitable framework to obtain more insight into the interactions of selfinterested rational agents with potentially conflicting interests. a player in a non-cooperative game responds to the actions of other players by choosing a strategy (from his strategy space) in an attempt to maximize a utility function that quantifies its level of satisfaction. in a cellular system each user desires to have a high sinr at the base station (bs) coupled with the lowest possible transmit power. it is important in such systems to have high sinr, as this will reflect a low error rate, a more reliable system, and high channel capacity, so that more users can be served per cell [11]. it is also important to decrease the transmit power to lengthen copyright © 2006-2015 by ccc publications game theoretic distributed power control algorithms for uplink wireless data in flat fading channels 521 battery life and to alleviate the near-far problem [17]. in power control algorithms exploiting game theory however, the tendency of each user to maximize his/her utility function in response to other users’ actions, leads to a sequence of power vectors that converges to a point where no user has incentive to individually increase his power. this operating point is called a nash equilibrium. due to the lack of cooperation between the users this point may not be efficient, in the sense that it may not be the most desirable social point [3]. in pareto sense, the most desirable social point is actually the power vector that pareto dominates all other power vectors. it should be noted that extensive work has been done on non-game theoretic power control algorithms for wireless data and multimedia cdma cellular networks, e.g. [9, 10, 15, 16]. the power control problem for wireless data cdma systems was first addressed in the game theoretic framework in [1][8]. in this paper the work in [1], which only dealt with deterministic (nonfading) channels, is extended to a realistic wireless cdma channels by considering the following cases of fading models: a rayleigh fast/slow flat fading channel model, a rician1 fast/slow flat fading channel model and a nakagamifast/slow flat fading channel model. where we use the same utility function and evaluate its average in the fading channels mentioned above, then we use these averaged utility functions to study the existence, uniqueness and social desirability of ne operating point under each channel model. the remaining of this paper is organized as follows: in section 2 we present the utility function and the system model studied in this paper. in section 3 we evaluate the performance of the system for the channel models mentioned above. non-cooperative power control game (npg) and non-cooperative power control game with pricing (npgp) are discussed briefly in sections 4 and 5, respectively. we then point out the constraints on the new modified strategy spaces to guarantee the existence and uniqueness of nash equilibrium points for npg and npgp under the assumed channel models in section 6. simulation results are outlined in section 7, and our conclusions are given in section 8. 2 utility function and system model in general utility functions are used to quantify the satisfaction level a player achieves by choosing an action from its strategy profile, given the other players’ actions. a utility function thus maps the player’s preferences onto the real line. a formal definition of a utility function may be found in [10]. in a cdma cellular system, a number of users sharing the spectrum and air interface. henceforth, each user’s transmission adds to the interference of all users at the bs. each user desires to achieve a high quality of reception at the bs, i.e., a high sinr, while using the minimum possible amount of power in order to extend the battery’s life. the conflicting goal of each user to have a high sinr at the bs makes the game theoretic framework suitable for studying and solving the problem. in this paper we consider the same system model and the same utility function of [1]: uplink single-cell direct sequence code division multiple access (ds-cdma) system with n users, where each user transmits frames (packets) of m bits with l information bits. the rate of transmission is r bits/sec for all users. let pc represent the average probability of correct reception of a frame at the bs, and let p represent the average transmit power level. the utility function for a cdma system is given by: u = lr m p f(γ) (1) 1for space limitation, we omit the findings related to rician channel model. the reader can find these findings in the supplementary document [22]. 522 m. hayajneh, c. abdallah where f(γ) is an efficiency function that approximates pc, u thus represents the number of information bits successfully received at the bs per joule of consumed energy. with the assumption of no error correction, and random packet correct reception rate p̃c, i.e., pc = e{p̃c}, is then given by ∏m l=1(1−p̃e(l)), where p̃e(l) is the random bit error rate (ber) of the lth bit at a given sinr γi. pe is the average ber, that is pe = e{p̃e} (c. f. (9) and (23)). we are assuming that all users in a cell are using non-coherent binary frequency shift keying (bfsk) modulation, and are transmitting at the same rate r. it should be noted that the efficiency function f(γ) has the same expression of pc in terms of p̃e, except that p̃e is replaced by 2p̃e [1]. 3 evaluation of the performance in this section we find closed-form formulas of the average utility functions under the six assumed channel models. we then use these formulas to study the existence and uniqueness of nash equilibrium point in section 6. the sinr γi at the receiver for the ith user is assumed to be large (γi ≫ 1) to combat the fading effect, it is given by [13]: γi = w r pi hi α 2 i∑n k ̸=i pk hk α 2 k + σ 2 (2) where αi is the path fading coefficient between ith user and the bs and is constant for each bit in a fast flat fading channels (a1), while it is constant for each packet in a slow flat fading channels (a2). w is the spread spectrum bandwidth, pk is the transmitted power of the kth user, hk is the path gain between the bs and the kth user, and σ2 is the variance of the awgn (additive-white-gaussian-noise) representing the background thermal noise in the receiver. for simplicity we express the interference from all other users as xi, i.e. xi = n∑ k ̸=i pk hk α 2 k (3) therefore (2) can be written as: γi = γi(αi,xi) = w r pi hi xi + σ2 α2i = γ ′ iα 2 i (4) for a given αi and xi, the ber, p̃(e|αi,xi), of the ith user using bfsk is given by [13]: p̃(e|αi,xi) = 1 2 e− γi(αi,xi) 2 (5) the average ber and average utility functions for this modulation scheme is evaluated next under the previously mentioned channel models. 3.1 rayleigh flat fading channel in this case αi is modelled as a rayleigh random variable with a probability distribution given by: fαi(ω) = ω σ2r e−(1/2σ 2 r)ω 2 , i = 1,2, · · · ,n (6) where σ2r = e{α2i}/2 is the measure of the spread of the distribution. in all following calculations, and as a consequence of the multiplicative effect of small and large scale models, it is assumed that σ2r = 1/2. using (4) and (6) the distribution of γi for a given xi becomes: fγi|xi(ω) = 1 γ′i e −( 1 γ′ i )ω game theoretic distributed power control algorithms for uplink wireless data in flat fading channels 523 rayleigh fast flat fading channel for the lth bit in the frame, we can rewrite the sinr (4) and the interference (3) for the ith user as follows: γi(l) = wr pi hi α 2 i (l) xi(l)+σ2 , and xi(l) = ∑n k ̸=i pk hk α 2 k(l). in this paper, we assume that the fading channel is fast enough to have the fading coefficients {αi(l)}ml=1 statistically independent, that is iid (identical independent distributed) random variables. henceforth, the averaged correct reception pc is given as (1 −pe)m , while the efficiency function f(γi) is given as (1−2pe)m , where pe is averaged ber for each bit in the frame, that is pe = e{p̃e}. we will calculate the averaged pe next. we can find the conditioned error probability p̃(e|xi) by taking the average of (5) with respect to fγi|xi(ω): p̃(e|xi) = e { p̃(e|γi,xi) } = ∫ ∞ 0 p̃(e|ω,xi)fγi|xi(ω)dω = 1 2γ′i ∫ ∞ 0 e −( 2+γ′i 2γ′ i )ω dω = 1 2 + γ′i (7) notice that we dropped the bit index l because the average ber does not depend on l. for large sinr, (7) behaves like: p̃(e|xi) ≈ 1 γ′i = xi + σ 2 w r pi hi (8) now, we can find the averaged ber pe by taking the expectation of (8): pe = e { p̃(e|xi) } = e{xi}+ σ2 w r pi hi = 1 γi (9) where γi is the ratio of the mean of the received power from user i to the mean of the interference at the receiver and given by: γi = w r pi hi∑n k ̸=i pk hk + σ 2 (10) therefore, the average utility function of the ith user is given by: ui = l r m pi (1− 2 γi )m (11) rayleigh slow flat fading channel in a slow flat fading channel model, αi is assumed to be constant for each packet/frame. the averaged efficiency function f(γi) is therefore given as the expectation of (1 − 2p̃(e|αi,xi))m with respect to the random variables αi and xi. one can evaluate ui(p|xi) as follows: ui(p|xi) = ∫ ∞ 0 ui(p|ω,xi)fγi|xi(ω)dω = ∫ ∞ 0 l r m pi (1−e−ω/2)m 1 γ′i e −( 1 γ′ i )ω dω = l r m pi γ ′ i m∑ k=0 (−1)k ( m k ) × ∫ ∞ 0 e −( k 2 + 1 γ′ i ) ω dω = l r m pi m∑ k=0 ( m k ) 2 (−1)k k γ′i + 2 (12) for large sinr (γ′i ≫ 1), (12) can be approximated by: u(p|xi)≈ l r m pi ( 1 + 1 γ′i m∑ k=1 ( m k ) 2 (−1)k k ) (13) 524 m. hayajneh, c. abdallah averaging (13) with resect to xi we obtain the average utility function for high sinr: ui = e{ui(p|xi)}≈ l r m pi ( 1 + e{xi}+ σ2 w r pi hi m∑ k=1 ( m k ) 2 (−1)k k ) = l r m pi ( 1 + 1 γi m∑ k=1 ( m k ) 2 (−1)k k ) (14) ui ≈ l r m pi ( 1− β γi ) where β = − ∑m k=1 ( m k ) 2 (−1)k k > 0. 3.2 nakagami flat fading channel here, the fading coefficient αi is modelled as a nakagami random variable with a probability distribution given by [13]: fαi(ω) = 2m m γ(m)ωm ω2m−1 e(− m ω )ω2; i = 1,2, · · · ,n where ω = e{α2i} controls the spread of the distribution. the fading figure m = ω 2 e{(α2i −ω) 2} is a measure of the severity of the fading channel, where m = ∞ corresponds to a nonfading channel. in the following it is assumed that ω = 1. then the distribution of γi for fixed xi is given as: fγi|xi(ω) = 1 γ(m) ( m γ′i )m ωm−1 e −( m γ′ i ) ω nakagami fast flat fading channel we find the conditioned error probability p̃(e|xi) as: p̃(e|xi) = ∫ ∞ 0 p̃(e|ω,xi)fγi|xi(ω)dω = 1 2γ(m) ( m γ′i )m ∫ ∞ 0 ωm−1 e −( γ′i+2m 2γ′ i ) ω dω = 1 2 ( 2m 2m + γ′i )m (15) for fixed m and γ′i ≫ 1, (15) can be rewritten as: p̃(e|xi) ≈ 1 2 ( 2m γ′i )m (16) to find the average pe, we need to find the mean of (xi + σ2)m. here, xi is a summation of independent random variables each distributed according to a gamma density function. this makes the evaluation of (xi + σ2)m tedious and it may be easier to find an approximate density function of xi. to do this, let us recall esseen’s inequality which estimates the deviation of the exact distribution of a sum of independent variables from the normal distribution [21]. theorem 1. let y1, · · · ,yn be independent random variables with eyj = 0, e|yj|3 < ∞ (j = 1, · · · ,n). let σ2j = ey 2 j , bn = ∑n j=1 σ 2 j , ln = b −3/2 n ∑n j=1 e|yj| 3. let ψn(z) be the c.f. (cumulative distribution ) of the random variable b−1/2n ∑n j=1 yj. then |ψn(z)−e−z 2/2| ≤ 16ln |z|3 e−z 2/3 (17) game theoretic distributed power control algorithms for uplink wireless data in flat fading channels 525 define ỹk = pkhkα2k and yk = ỹk −pkhk. by simple calculations we can find that ỹk,(k = 1, · · · ,n) are gamma distributed random variables, such that fỹk(ω) = (m/pkhk) m γ(m) ωm−1 e−(m/pkhk)ω and eỹk = pkhk, which means that yk,(k = 1, · · · ,n) are zero mean random variables. note that σ2k = ey 2 k = (pkhk) 2/m,∀k = 1, · · · ,n, and therefore, bn = 1m ∑n k=1(pkhk) 2. it is fairly simple to find out that the third moment e|yk|3 = ey 3k = 2(pkhk) 3 m2 (yk ≥ 0), and ln = 2 ∑n k=1(pkhk) 3 √ m ( ∑n k=1(pkhk) 2)3/2 . for large n, ln has a very small value, i.e., ln << 1. examining (17) for small values of z, ln takes care of the bound and making it very small, while for large values of z, the exponential will decrease the bound and make it approach zero. in conclusion, we can approximate xi as a gaussian random variable with mean ζxi and variance σ2xi given by: ζxi = e{xi} = e   n∑ k ̸=i α2kpk hk   = n∑ k ̸=i pk hk e{α2k} = n∑ k ̸=i pk hk (18) and σ2xi = e{x 2 i}− ζ 2 xi = e   n∑ l ̸=i n∑ k ̸=i plhlpk hk α 2 l α 2 k  − ζ2xi = 1m n∑ k ̸=i (pk hk) 2 (19) where (19) was obtained using the fact that αk and αl are statistically independent for all k ̸= l. so, we can write fxi, the pdf of xi, as follows: fxi(w) = δi√ 2πσxi e − (w−ζxi ) 2 2σ2xi , where w ≥ 0 and δi = 2/(1 + erf[ζxi/ √ 2σxi]) is a scaling factor such that f xi(w) is a valid pdf. erf[.] is the error function. by examining equations (18) and (19), one can see that ζxi ≫ σxi , therefore δi ≈ 1. averaging (16) over fxi(ω) we obtain the average error probability pe for high sinr below: pe ≈ 1 2 ( 2m w r pi hi )m ∫ ∞ 0 ( xi + σ 2 )m × 1√ 2πσxi e − (xi−ζxi ) 2 2σ2xi dxi = 1 2 ( 2m w r pi hi )m ∫ ∞ σ2 ym × 1 √ 2πσxi e − (y−(ζxi +σ 2))2 2σ2xi dy ≈ 1 2 ( 2m w r pi hi )m ∫ ∞ 0 ym × 1 √ 2πσxi e − (y−(ζxi +σ 2))2 2σ2xi dy (20) where we used the change of variable y = xi + σ2 and the last approximation in (20) used the fact that σ2 ≪ 1. by examining (20) one can see that it is the mth moment of a random variable y normally distributed with mean ζy = ζxi + σ 2 and variance σ2y = σ 2 xi . therefore, the average pe is given by: pe = 1 2 ( 2m w r pi hi )m e{ym} = 1 2 ( 2m w r pi hi )m e{((y − ζy) + ζy)m} = 1 2 ( 2m w r pi hi )m m∑ k=0 ( m k ) ζm−ky ck = 1 2 ( 2mζy w r pi hi )m m∑ k=0 ( m k ) ck µky = 2m−1 ( m γi )m m∑ k=0 ( m k ) ck ζky (21) 526 m. hayajneh, c. abdallah where γi is given in (10), and ck is the kth central moment and it is given by [13]: ck = { 1.3 · · ·(k −1)σkxi k even 0 k odd by splitting up the summation in (21), we obtain: m∑ l=0 ( m l ) cl ζly =1 + ( m 2 ) σ2xi (σ2 + ∑n k ̸=i pk hk) 2 + · · ·+ ( m m′ ) 1.3 · · ·(m′ −1)σm ′−1 xi (σ2 + ∑n k ̸=i pk hk) m′ (22) where m′ = m if m is even and m′ = m − 1 if m is odd. since σ2x (see (19)) is very small compared to ζxi (see (18)), we can approximate the summation by its leading term which is 1. therefore the average pe at high sinr behaves like: pe ≈ 2m−1 ( m γi )m (23) and the average utility function of the ith user is given by: ui = l r m pi ( 1−2m ( m γi )m)m (24) notice that if we set m = 1, we obtain the same performance as in the rayleigh fast flat fading case. nakagami slow flat fading channel as done earlier, ui(p|xi) can be determined as follows: ui(p|xi) = ∫ ∞ 0 ui(p|ω,xi)fγi|xi(ω)dω = ∫ ∞ 0 l r m pi (1−e−ω/2)m 1 γ(m) ( m γ′i )m ωm−1 e −( m γ′ i ) ω dω (25) by factorizing (1−e−γi/2)m and using the identity ∫ ∞ 0 yne−a y dy = γ(n+1) an+1 we obtain: ui(p|xi) = lr m pi m∑ k=0 (−1)k ( m k )( 2m k γ′i + 2m )m (26) for fixed m and high sinr, γ′i ≫ 1 (26) can be approximated as: ui(p|xi) ≈ lr m pi [ 1 + ( 1 γ′i )m m∑ k=1 (−1)k ( m k )( 2m k )m] (27) averaging (27) with respect to the distribution of xi and using the same argument as in (20), (21) and (22) we end up with the final approximate averaged utility function given by: ui ≈ lr m pi [ 1 + ( 1 γi )m m∑ k=1 (−1)k ( m k )( 2m k )m] ui ≈ lr m pi [ 1− ξ ( 1 γi )m ] (28) where ξ = − ∑m k=1(−1) k ( m k )( 2 m k )m > 0. in the following two sections, we introduce briefly both npg and npgp games. game theoretic distributed power control algorithms for uplink wireless data in flat fading channels 527 4 non-cooperative power control game (npg) let n = {1,2, · · · ,n} be the index set of the users currently served in the cell and {pj}j∈n represent the set of strategy spaces of all users in the cell. let g = [n ,{pj},{uj(.)}] denote a noncooperative game, where each user, based on local information, chooses a power level from a convex set pj = [pj−min,pj−max] and where pj−min and pj−max are the minimum and the maximum power levels in the jth user strategy space, respectively. assuming that the power vector p = [p1,p2, · · · ,pn] is the result of npg, the utility of user j is given by [1]: uj(p) = uj(pj, p−j) (29) where pj is the power of user j, and p−j is the vector of powers transmitted by all other users. the right side of (29) emphasizes the fact that user j can only control his own power. we rewrite (1) for user j as: uj(pj, p−j) = lr m pj f(γj) (30) the formal expression for the npg is given in [1] as: g : max pj∈pj uj(pj, p−j), for all j ∈n (31) this game will produce a sequence of power vectors until it converges to a point where all users are satisfied with their utility level. this operating point is called a nash equilibrium operating point of npg. in the next subsection, we define the nash equilibrium point and describe its physical interpretation. 4.1 nash equilibrium in npg definition 2. [1] a power vector p = [p1,p2, · · · ,pn] is a nash equilibrium of the npg defined above if for every j ∈n ,uj(pj, p−j) ≥ uj(p′j, p−j) for all p ′ j ∈ pj. one interpretation of nash equilibrium is that no user can increase its utility by changing its power level unilaterally. if we multiply the power vector p by a constant 0 < λ < 1 we may get higher utilities for all users, as was the case in nonfading channels. this means that the nash equilibrium is not efficient, that is, the resulting p is not the most desired social operating point. this results from the lack of cooperation between the users currently in the system. to impose a level of cooperation between users in order to reach a pareto dominant nash point, a pricing technique was introduced in [1]. we discus this modified npg game next. 5 non-cooperative power control game with pricing (npgp) in npgp each user maximizes the difference between his/her utility function and a pricing function. this aims to allow more efficient use of the system resources within the cell, as each user is made aware of the cost of aggressive resources use, and of the harm done to other users in the cell. we use here a linear pricing function, i.e., a pricing factor multiplied by the transmit power. the base station broadcasts the pricing factor to help the users currently in the cell reach a nash equilibrium that improves the aggregate utilities of all users at power levels lower than those of the pure npg. in other words, the resulting power vector of npgp is pareto dominant compared to the resulting power vector of npg, but is still not pareto optimal in the sense that we may multiply the resulting power vector of npgp by a constant 0 < λ < 1 to get higher 528 m. hayajneh, c. abdallah utilities for all users. let gc = [n ,{pj},{ucj(.)}] represent an n-player noncooperative power control game with pricing (npgp), where the utilities are [1]: ucj(p) = uj(p)− cpj for all j ∈n (32) where c is a positive number chosen to get the best possible improvement in the performance. therefore, npgp with a linear pricing function can be expressed as: gc : max pj∈pj {uj(p)− cpj} for all j ∈n (33) 6 existence and uniqueness of nash equilibrium point in this section we show that npg and npgp introduced by [1] admit a unique nash equilibrium points under the assumed channel models. however, to guarantee the existence and uniqueness of ne point in both games, the terminals’ strategy spaces defined in [1] should be constrained more. that is, some transmit power values which were allowed in a non-fading channel, may not be allowed under a fading channel. in the following, we refer to the unconstrained maximizing transmit power level of user i by pmaxi . pi refers to the convex strategy space of user i. lemma 3. in npg under rayleigh fast flat fading channel with the average utility function ui given in (11), the existence of a nash equilibrium point is guaranteed if and only if the strategy space is modified to pi = {pi : γi ∈ ( γi−min,γi−max ) }, where γi−min = 2(m + 1) −√ 2m(m + 1) and γi−max = 2(m + 1) + √ 2m(m + 1). the best response vector of all users r1(p) =(r11(p),r 1 2(p), · · · ,r 1 n(p)), where r 1 i (p) = min(p max i ,pi−max), and pmaxi = 2(m + 1)ii, ii = r ( ∑n k ̸=i hkpk + σ 2) w hi (34) is a standard interference function, therefore by [12], the nash equilibrium point is unique. ii is the effective interference for user i. proof: in all following proofs we make use of the classical results of game theory, where the existence of a nash equilibrium point is guaranteed if the utility function is quasiconcave and optimized on a convex strategy space. thus, to prove the existence of nash equilibrium point, it is enough to prove that the utility function ui is concave (a concave function on some set is also a quasiconcave function on the same set) in pi given p−i on the convex set pi = {pi : γi ∈( γi−min,γi−max ) }. let us find the first and second order derivatives with respect to pi as follows: ∂ui ∂pi = lr m p2i ( 2(m + 1) γi −1) (1− 2 γi )m−1, (35) then ∂ 2ui ∂p2i = 2l r (1− 2 γi )m [2(m+1)(2+m)−4(1+m) γi+γi2] m p3i (−2+γi) 2 . therefore, ∂2ui ∂p2i < 0,∀ γi ∈ ( γi−min,γi−max ) , where γi−min = 2(m +1)− √ 2m(m + 1) and γi−max = 2(m +1)+ √ 2m(m + 1). this implies that the strategy space should be modified to the convex set pi = {pi : γi ∈ ( γi−min,γi−max ) } to guarantee the concavity of the utility function, and then to guarantee the existence of nash equilibrium point. to prove the uniqueness of nash equilibrium point we need to prove that r1(p) is a standard function. by setting (35) to zero we find the maximizing transmit power level that lies in the convex strategy space pi is given as in (34). before proving that r1(p) is a standard interference function we introduce the definition of an arbitrary standard interference function ϕ(p) as follows: game theoretic distributed power control algorithms for uplink wireless data in flat fading channels 529 definition 4. [12] an interference function ϕ(p) is called a standard interference function if it satisfies the following: 1) positivity: ϕ(p) > 0, i.e., each element is positive, 2) monotonicity: if p > p̂ then ϕ(p) ≥ ϕ(p̂) (component wise), and 3) scalability: ∀δ > 1, δϕ(p) > ϕ(δ p) (component wise). to prove that r1(p) is a standard interference function we proceed as follows: the proof of positivity is trivial, since pi ⊂ r+ and r1i (p−i) ∈ pi, ∀i ∈ n , where r 1 i (p−i) = r 1 i (p). also, it is obvious that pmaxi (p) > p max i (p̂) for all i if p > p̂ by looking at (34), henceforth the monotonicity of r1(p) is satisfied. to prove the scalability, it is enough to prove that pmaxi (p−i) is a scalable function and then the scalability of r1(p) comes through. let us rewrite equation (34) as follows: pmaxi (p−i) = 2r (m+1) ( ∑n k ̸=i hkpk+σ 2) w hi then pmaxi (δp−i) = 2r (m + 1) (δ ∑n k ̸=i hkpk + σ 2) w hi , (36) while δpmaxi (p−i) = 2δ r (m + 1) ( ∑n k ̸=i hkpk + σ 2) w hi (37) it is clear that δpmaxi (p−i) > p max i (δ p−i), therefore r 1(p) is a standard interference function, and the nash equilibrium point is unique. 2 in the following lemmas we omit the proof of existence and/or uniqueness as they are similar to those of lemma 3. lemma 5. in npg under rayleigh slow flat fading channel with the average utility function (14), the existence of a nash equilibrium point is guaranteed if and only if the strategy space is modified to the convex set pi = {pi : γi ∈ (1,3β)}. the best response vector of all users r2(p) =(r21(p),r 2 2(p), · · · ,r 2 n(p)), where r 2 i (p) = min(p max i ,pi−max), and p max i = 2β ii, is a standard interference function, therefore by [12] nash equilibrium point is unique. proof: similarly, we need to find the first and second order derivatives of ui with respect to pi: ∂ui ∂pi = l r m p2i ( 2 β γi −1 ) , then ∂ 2ui ∂p2i = 2 l r m p3i ( 1− 3 β γi ) , which means that ∂ 2ui ∂p2i < 0,∀ γi ∈ (1,3β) so the convex strategy space should have the following form: pi = {pi : γi ∈ (1,3β)} to guarantee the concavity of ui and then to guarantee the existence of nash point. 2 lemma 6. in npg under nakagami fast flat fading channel with the average utility function ui given in (24) with m = 2, the existence of a nash equilibrium point is guaranteed if the strategy space is modified to the following convex set pi = {pi : γi ∈ ( γi−min,γi−max ) }, where γi−min = √ 8 √ 2 + 5m − √ m (8 + 17m) and γi−max = √ 8 √ 2 + 5m + √ m (8 + 17m). the best response vector of all users r5(p) =(r51(p), r 5 2(p), · · · , r5n(p)), where r 5 i (p) = min(p max i ,pi−max), and p max i = 4 √ 1 + 2m ii, is a standard interference function, therefore by [12] nash equilibrium point is unique. proof: we find the first and second order derivatives of ui in (24) after setting m = 2 with respect to pi as follows: ∂ui ∂pi = lr m p2i ( 16(2m + 1) γ2i −1) (1− 16 γ2i )m−1, (38) then ∂2ui ∂p2i = 1 m p3i (−16 + γ 2 i ) 2 ( 2lr (1− 16 γ2i )m × [ 256(1 + m)(2m + 1)−16(2 + 5m)γ2i + γ 4 i ]) (39) 530 m. hayajneh, c. abdallah and this implies that ∂ 2ui ∂p2i < 0,∀ γi ∈ ( γi−min,γi−max ) , where γi−min = √ 8(2 + 5m)−8 √ m (8 + 17m) and γi−max = √ 8(2 + 5m) + 8 √ m (8 + 17m). henceforth, the strategy space should have the following convex set: pi = {pi : γi ∈ ( γi−min,γi−max ) } to guarantee that ui is strict concave on pi, then a nash equilibrium exists. 2 lemma 7. in npg under nakagami slow flat fading channel with the average utility function ui given in (28), a nash equilibrium point is guaranteed if and only if the strategy space is the following convex set pi = {pi : γi ∈ ( 1, √ 6ξ ) }. the best response vector of all users r6(p) = (r61(p), r 6 2(p), · · · , r 6 n(p)), where r 6 i (p) = min(p max i ,pi−max), and p max i = √ 3ξ ii, is a standard interference function, therefore by [12] nash equilibrium point is unique. proof: the first derivative and second order derivatives of ui after setting m = 2 with respect to pi are given by: ∂ui ∂pi = l r m p2i ( 3 ξ γ2i −1 ) , and ∂ 2ui ∂p2i = 2 l r m p3i ( 1− 6 ξ γ2i ) , therefore ∂ 2ui ∂p2i < 0,∀ γi ∈( 1, √ 6ξ ) . as a result, the convex strategy space should be pi = {pi : γi ∈ ( 1, √ 6ξ ) } to guarantee the strict concavity of ui and then the existence of a nash equilibrium point is guaranteed. 2 now, we turn to the existence and uniqueness of nash equilibrium point of npgp under the assumed channel models discussed above. lemma 8. in npgp under rayleigh fast flat fading channel model with utility function uci = ui −c pi, where ui is given in (11), a nash equilibrium point existence is guaranteed if and only if the strategy space is the following convex set: pi = {pi : γi ∈ ( γi−min,γi−max ) }, where γi−min = 2 (m + 1) − √ 2m(m + 1) and γi−max = 2 (m + 1). the best response vector of all users r7(p) =(r71(p),r 7 2(p),· · · ,r 7 n(p)), where r 7 i (p) = min(p max i ,pi−max), and pmaxi ≈ −6 21/3 a + 22/3(27bi + √ 108a3 + 729b2i ) 2/3 6 (27bi + √ 108a3 + 729b2i ) 1/3 ; a = lr m c , bi = 2 (m + 1)lrii m c (40) is a standard interference function, therefore by [12] nash equilibrium point is unique. proof: let us find the maximizing power pmaxi in terms of the sinr γi as follows: ∂uci ∂pi = lr m p2i ( 2 (m + 1) γi −1) (1− 2 γi )m−1 − c = 0, (41) then pmaxi = √ lr m c ( 2 (m + 1) γi −1) (1− 2 γi )m−1 (42) for pmaxi to be feasible, i.e., real and positive we need to satisfy the following condition on the strategy space: pi = {pi : γi ∈ (1,m + 1)}. but, to guarantee the concavity of the utility function uci , we have to have pi = {pi : γi ∈ ( γi−min,γi−max ) }, where γi−min = 2 (m + 1) −√ 2m(m + 1) and γi−max = 2 (m + 1) + √ 2m(m + 1). therefore, to fulfill the two conditions the convex strategy space should be the intersection of the two sets, that is pi = {pi : γi ∈( γi−min,γi−max ) }, where γi−min = 2 (m + 1) − √ 2m(m + 1) and γi−max = 2 (m + 1). since γi >> 1 on the convex strategy space pi given above, one can approximate pmaxi , the solution of (41), as the feasible solution of the following equation: p3i + lr m c pi − 2 (m + 1)lrii m c = 0 (43) game theoretic distributed power control algorithms for uplink wireless data in flat fading channels 531 this equation has only one real and positive solution which is given in (40). it is fairly easy to prove that r7(p) with pmaxi as given in (40) is a standard interference function. therefore, nash equilibrium point is unique. 2 in the following lemmas we may omit the proof of existence and/or uniqueness if it can be argued the same way as in lemma 8. lemma 9. in npgp under rayleigh slow flat fading channel model with utility function uci = ui−c pi, where ui is given in (14), a nash equilibrium point existence is guaranteed if and only if the strategy space is the following convex set: pi = {pi : γi ∈ (1,2 β)}. the best response vector of all users r8(p) =(r81(p),r 8 2(p),· · · ,r 8 n(p)), where r 8 i (p) = min(p max i ,pi−max), and pmaxi = −6 21/3 a + 22/3(27bi + √ 108a3 + 729b2i ) 2/3 6 (27bi + √ 108a3 + 729b2i ) 1/3 ; a = lr m c , bi = 2lrβ ii m c (44) is a standard interference function, therefore by [12] nash equilibrium point is unique. we have equality in (44) because there was no approximation in getting pmaxi . lemma 10. in npgp under nakagami fast flat fading channel model with utility function uci = ui −c pi, where ui is given in (24), a nash equilibrium point existence is guaranteed if and only if the strategy space is the convex set: pi = {pi : γi ∈ ( γi−min,γi−max ) }, where γi−min = √ 8 √ 2 + 5m − √ m (8 + 17m) and γi−max = 4 √ 1 + 2m. the best response vector of all users r11(p) = (r111 (p), r 11 2 (p), · · · , r 11 n (p)), where r 11 i (p) = min(p max i ,pi−max), and pmaxi ≈ √ lr 2m c √ −1 + √ 1 + 64 (1 + 2m)i2i m c lr (45) is a standard interference function, therefore by [12] nash equilibrium point is unique. proof: the maximizer transmit power pmaxi is the feasible solution of ∂ ui ∂ pi − c = 0, where ∂ ui ∂ pi is given in (38), and results in a polynomial of degree 2m + 4. it is a tedious and may be impossible to find a closed-form for the feasible solution of this polynomial. recall that γi > 4 to guarantee ui(p) > 0, so the maximizer transmit power level pmaxi can be approximated by the feasible solution of the following equation. p4i + lr m c p2i − 16 (1 + 2m)lri2i m c = 0 (46) the only feasible solution of the equation above is given by (45). 2 lemma 11. in npgp under nakagami slow flat fading channel model with utility function uci = ui −c pi, where ui is given in (28), a nash equilibrium point existence is guaranteed if and only if the strategy space is the following convex set: pi = {pi : γi ∈ ( 1, √ 3ξ ) }. the best response vector of all users r12(p) = (r121 (p), r 12 2 (p), · · · , r 12 n (p)), where r 12 i (p) = min(p max i ,pi−max), and pmaxi = √ lr 2m c √ −1 + √ 1 + 12ξ i2i m c lr (47) is a standard interference function, therefore by [12] nash equilibrium point is unique. 532 m. hayajneh, c. abdallah proof: the maximizer transmit power level pmaxi is the feasible solution of the following equation. p4i + lr m c p2i − 3ξ lri2i m c = 0 (48) the only feasible solution of the equation above is as given by (47). it is simple to check that r12(p) with the maximizer power in (47) satisfies all the conditions of a standard interference function. henceforth, the nash equilibrium point is unique. 2 observing lemmas 3-7, we see that the maximizing sinr γmaxi for all users are the same: γmaxi = 2(m + 1), ∀i ∈ n under fast rayleigh and fast rician fading channels. on the other hand γmaxi = 2β, ∀i ∈ n under slow rayleigh and slow rician fading channels. γ max i = 4 √ 1 + 2m ∀i ∈ n under fast nakagami fading channels, and γmaxi = √ 3ξ∀i ∈ n under slow nakagami fading channels. for nonfading channels it was shown in [1] that γmaxi = 12.4, ∀i ∈n . this implies, as expected, that in order to overcome the fading effect, users in fading channels have to target higher sinr values. next, we introduce an algorithm that converges to nash equilibrium point of npg and npgp. we need to keep in mind that the strategy space denoted by pi in the algorithm differs according to the channel model. the algorithm is the same as in [1] except that the strategy spaces are modified to the forms given in lemmas 3-7 to guarantee the existence of nash equilibrium point under the studied channel models. assume user j updates its power level at time instances that belong to a set tj, where tj = {tj1, tj2, · · ·}, with tjk < tjk+1 and tj0 = 0 for all j ∈ n . let t = {t1, t2, · · ·} where t = t1 ∪ t2 ∪ · · · ∪ tn with tk < tk+1 and define p to be the smallest power vector in the modified strategy space p = p1 ∪ p2 ∪ · · · ∪ pn . algorithm 12. consider non-cooperative game g as given in (31) and generate a sequence of power vectors as follows: initialize k = 0; n = total numberof active users; p[0] = p; for j = 1 to n do set tj = set of times user j updates its power; end for set t = ∪ j∈n tj = {t1, t2, t3, ...}; k ← k + 1; step1 : for j = 1 to n do if tk ∈ tj; set pmaxj (tk) = arg max pj∈ pj uj(pj, p−j(tk−1)); else pmaxj (tk) = p max j (tk−1); end if; end for if p(tk) = p(tk−1) stop and declare p(tk) as the ne point; else k ← k + 1 goto step1 the next algorithm finds the best pricing factor cbest for npgp, keeping in mind that the strategy space should be according to lemmas 8-11. game theoretic distributed power control algorithms for uplink wireless data in flat fading channels 533 algorithm 13. initialize ∆c = real positive number; c = 0; use algorithm 12 to obtain uc=0j for all users j = 1 to j = n ; step2:for j = 1 to n do set cj ← c + ∆c; {broadcast the pricing factor to all users} ; end for use algorithm 12 to obtain uc+∆cj for all users j = 1 to j = n ; if uc+∆cj is pareto dominates u c j for all users; goto step2 else stop and declare cbest = c−∆c 7 simulation results we show the effects of time-varying, fast and slow fading wireless channels on the equilibrium utilities and powers, that are the outcomes of the extended npg and npgp algorithms (algorithms12 and 13) which were originally studied for non-fading wireless channels in [1]. the system studied is a single-cell ds-cdma cellular mobile system with 9 stationary users, all are using the same data rate r and the same modulation scheme, non-coherent bfsk. the system parameters used in this study are given in table 1. the distances between the 9 users and the bs are d = [310,460, 570,660, 740,810, 880,940,1000] in meters. the path attenuation between user j and the bs using the simple path loss model [17] is hj = 0.097/d4j , where 0.097 approximates the shadowing effect. results of simulations show that under rayleigh, rician, and nakagami fast flat fading channels with spreading gain w/r = 102, users do not reach a nash equilibrium point where all users except the nearest user to the bs are using the highest power level in the strategy space without achieving the maximizing sinrs (γmaxi = 2(m +1), ∀i ∈n). in fig.1 we demonstrate the equilibrium utilities and the equilibrium powers of npg under a fast fading channels (a1) with the three small scale fading models with spreading gain w/r = 103. all users were able to achieve their maximizing sinr under two small scale fading models, namely, nakagami and rician channels. under the rayleigh channel model however some users failed to achieve the maximizing sinr. one can see in fig.1 that the farthest 4 users in the rayleigh channel were forced to send at their maximum allowable power to achieve their minimum sinrs. the equilibrium utilities and equilibrium powers of the npgp under (a1) are shown in the left and right graphs of fig.2, respectively. results show that a pareto improvement over npg for rayleigh, rician, and nakagami channels was obtained such that all users succeeded to attain sinrs more than their corresponding minimum sinrs (γi > γi−min, ∀i ∈n). then we present the effect of a slow flat fading channels (a2) on the equilibrium utilities and powers which are the outcomes of npg algorithm 12 as shown in fig. 3. this figure shows that, unlike fast fading channels, all users succeeded to achieve the maximizing sinr (γmaxi = 2β = 19.8619 under rician and rayleigh channels and γ max i = √ 3ξ = 25.1182 under nakagami channels). left graph of fig. 3 shows that under both rayleigh and nakagami channel models users were equally satisfied, i.e., the equilibrium utilities are the same for both models. however, under rayleigh channels, the equilibrium powers are less than those under nakagami channels. this could be due to the fact that users under nakagami channels target a higher maximizing sinr as we just mentioned above. as for the effect of slow fading channels on the outcomes of npgp, equilibrium utilities and equilibrium powers, our simulations showed that pareto improvement (dominance) over npg 534 m. hayajneh, c. abdallah was not possible under the three small scale models. at c = cbest, simulations showed that the best policy is that all users to target a fixed sinr, that is γmaxi = 19.8619 under rician and rayleigh channels and γmaxi = 25.1182 under nakagami channels, which is exactly the same situation as in npg. to demonstrate this result for the three small scale models more clearly, we present fig. 4 for rician and rayleigh channel models and fig. 5 for nakagami channel model. fig. 4 shows that with c = cbest the maximizing transmit power pmaxi given in (44) behaves with respect to the effective interference ii (feasible values of ii ) the same as pmaxi = 2β ii given in lemmas 2 and 4. while fig. 5 shows that pmaxi given in (47) behaves with feasible values of ii the same as pmaxi = √ 3ξ ii given in lemma 6. surprisingly, both figures suggest that npgp with linear pricing does not admit a pareto dominance over the ne operating point of npg in a slow flat fading channels under the three small scale fading models. 10 2 10 3 10 −6 10 −5 10 −4 10 −3 10 −2 10 −1 10 0 d (meters) e q u ili b ri u m p o w e rs ( w a tt s) 10 2 10 3 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 10 d (meters) e q u ili b ri u m u til iti e s (b its /j o u l) deterministic rayleigh rician nakagami figure 1: equilibrium powers and equilibrium utilities of npg for rician fast flat fading channel gain (+), rayleigh fast flat fading channel gain (o), nakagami fast flat fading (△) and deterministic channel gain (*) versus the distance of a user from the bs in meters with w/r = 103. table 1: the values of parameters used in the simulations. l, number of information bits 64 m length of the codeword 80 w , spread spectrum bandwidth 106, 107 hz r, data rate 104 bits/sec σ2, awgn power at the bs 5×10−15 n, number of users in the cell 9 s2, specular component 1 w/r, spreading gain 102, 103 m, fading figure 2 pi−max, maximum power in ith user’s strategy space 1 watts game theoretic distributed power control algorithms for uplink wireless data in flat fading channels 535 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 10 d (meters) e q u ili b ri u m u til iti e s (b its /j o u l) deterministic c best =6e8 nakagami c best =6e3 rician c best =1e9 rayleigh c best =1e8 10 2 10 3 10 −6 10 −5 10 −4 10 −3 10 −2 10 −1 d (meters) e q u ili b ri u m p o w e rs ( w a tt s) figure 2: equilibrium utilities and equilibrium powers of npgp for rician fast flat fading channel gain (+), rayleigh fast flat fading channel gain (o), nakagami fast flat fading (△) and deterministic channel gain (*) versus the distance of a user from the bs in meters with w/r = 103. 10 2 10 3 10 6 10 7 10 8 10 9 10 10 d (meters) e q u ili b ri u m u til iti e s (b its /j o u l) 10 2 10 3 10 −6 10 −5 10 −4 10 −3 10 −2 d (meters) e q u ili b ri u m p o w e rs ( w a tt s) rician nakagami rayleigh figure 3: equilibrium utilities and equilibrium powers of npg for rician slow flat fading channel gain (+), and rayleigh slow flat fading channel gain (o), nakagami slow flat fading (△) versus the distance of a user from the bs in meters with w/r = 103. 536 m. hayajneh, c. abdallah 10 −6 10 −5 10 −4 10 −3 10 −2 10 −5 10 −4 10 −3 10 −2 10 −1 10 0 interference plus noise, i i (watts) u se r i t ra n sm it p o w e r le ve l, p im a x (w a tt s) p i max in eq.(54) p i max (lemma 2 or 4) figure 4: pmaxi as a function of ii as in eq. (44) (o), and the linear expression p max i = 2β ii given in lemmas 2 and 4 (solid line) with c = cbest. 10 −6 10 −5 10 −4 10 −3 10 −2 10 −5 10 −4 10 −3 10 −2 10 −1 10 0 interference plus noise, i i (watts) u se r i t ra n sm it p o w e r le ve l, p im a x (w a tt s) p i max in eq.(32) p i max (lemma 2) figure 5: pmaxi as a function of ii as in eq. (47) (o), and the expression of p max i = √ 3ξ ii given in lemma 6 (solid line) with c = cbest . game theoretic distributed power control algorithms for uplink wireless data in flat fading channels 537 8 conclusions we studied a noncooperative power control game (npg) and noncooperative power control game with pricing (npgp) introduced in [1] for realistic channel models, where we studied the impact of power statistical variation in rayleigh, rician and nakagami fast/slow flat fading channels on the powers and utilities vectors at equilibrium. the results showed that an equilibrium with an equal maximizing sinr is not attainable in both games with spreading gain (w/r = 102). in fast fading with spreading gain w/r = 103, fixed target sinr npg admitted ne point only under both rician and nakagami small scale models. and unlike fast fading, npg admitted ne point under all small scale fading models in slow fading channels. results demonstrated that in slow flat fading channels, npgp with linear pricing does not exhibit a pareto dominance over npg outcomes at equilibrium. in order to overcome the fading effects the sinrs targeted at equilibrium are higher for all users at equilibrium in the rician, rayleigh and nakagami flat fading cases than sinrs under deterministic (nonfading) channels. bibliography [1] c. u. saraydar, n. b. mandayam, d. j. goodman (2002); efficient power control via pricing in wireless data networks, ieee tras. comm., 50(2): 91-303. 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[22] https://www.dropbox.com/s/62uyd038cjnrwss/hayajneh_supplementary.pdf. international journal of computers communications & control issn 1841-9836, 13(2), 162-174, april 2018. moving object detection and tracking using genetic algorithm enabled extreme learning machine g. jemilda, s. baulkani g. jemilda* jayaraj annapackiam csi college of engineering, nazareth, india. *corresponding author: jemildajeba@yahoo.com s. baulkani government college of engineering, tirunelveli, india. ramabaulkani@yahoo.co.in abstract: in this proposed work, the moving object is localized using curvelet transform, soft thresholding and frame differencing. the feature extraction techniques are applied on to the localized object and the texture, color and shape information of objects are considered. to extract the shape information, speeded up robust features (surf) is used. to extract the texture features, the enhanced local vector pattern (elvp) and to extract color features, histogram of gradient (hog) are used and then reduced feature set obtained using genetic algorithm are fused to form a single feature vector and given into the extreme learning machine (elm) to classify the objects. the performance of the proposed work is compared with naive bayes, support vector machine, feed forward neural network and probabilistic neural network and inferred that the proposed method performs better. keywords: curvelet transform, speeded up robust features, enhanced local vector pattern, histogram of gradient, extreme learning machine, genetic algorithm. 1 introduction tracking an object (or multiple objects) on an image plays a vital role in the field of computer vision. numerous applications of object tracking in computer vision are video compression, video surveillance, visual interface, man-and-computer management, medicine, augmented reality and robotics. quick movements of objects change shapes, scenes, unstructured structures of objects and cameras have trouble tracking objects. tracking is normally performed in the context of a program that requires the location and/or shape of the object in each frame. one of the most advanced algorithms for feature selection is the genetic algorithm. this is a stochastic method for function optimization based on the mechanics of natural genetics and biological evolution. faheem and gungor [5] proposed a novel dynamic clustering based energy efficient and qos aware routing protocol (called eqrp) which is made by the real behavior of the bird mating optimization (bmo) for smart grid applications. fadel et al. [3] introduced cardio radio sensor to serve as a reliable, robust and efficient communication address. in this paper, honey bee mating and cooperative channel assignment algorithms have been proposed. this significantly decreases the probability of packet loss, energy consumption, improving the life time of crsns in smart grids. faheem et al. [4] discussed a novel nature-inspired evolutionary link qualityaware queue-based spectral clustering routing protocol for uasn-based underwater applications. to overcome the challenges faced by the commonly used uwsn performance indicator and to overcome the inefficiencies existing clustering based routing protocol, a novel qos aware evolutionary cluster based protocol mentioned by faheem et al. [6]. the main advantage is that it is robust and efficient. it is used for optimization, searching, feature extraction, segmentation and classification. this genetic algorithm reduces the processing time and increases the accuracy. the main contributions of this paper can be described as follows: copyright © 2018 by cc by-nc moving object detection and tracking using genetic algorithm enabled extreme learning machine 163 • the use of color information (instead of grey levels only), during the entire detection process, which improves sensitivity; • the use of background subtraction function is a good approximation of a mode function and assures high responsiveness to background changes and good accuracy; • the inclusion of knowledge-based feedback from the soft thresholding segmentation module abates false positives and prevents from deadlock situations in background update; • the system is conceived to be auto-adaptive, i.e., to work with the best possible performances on all possible scenarios. results show that accurate results are aligned with other state-of-the-art approaches, but they are more stable in all corner-case situations and higher frame-per-second rates. • it also shows very limited memory (of all types) requirements, as it is usually demanded for embedded applications. the rest of this paper is organized as follows: section 2 discusses the related work. section 3 explains the proposed method including its design idea and practical implementation approach. section 4 presents simulation parameters. section 5 provides experimental results where the effectiveness of the proposed work is compared to the existing methods. finally, section 6 concludes this paper by summarizing our results, significance and future possibilities of the work. 2 related work a new tool developed by arnab roy et al. [12] to speed up the dramatic change of detection of low-end laptop webcam stuff with specific hardware for processing high-speed images. the first algorithm is fast and ensures the second algorithm edge of the object found more clearly at the expense of slow image processing. this method is used to reduce the average delay in motion of the objects up to 45.5% and the memory usage by about 14%, while keeping the same accuracy. hsu-jung cheng et al. [2] proposed a framework for detecting vehicles in aeronautical surveillance using the dynamic bayesian network and found that this method has flexibility and good generalization capabilities. felix m. philip et al. [11] proposed visibility model based on the second derivative to predict the objects, spatial tracking model and tangential weighted function to track multiple objects. but tracking multiple objects in low resolution videos is not possible. 3 methodology this section briefly explains about the methods used in this paper for tracking the objects and it was shown in the figure 1. 3.1 input video the input video is chosen as it consists of the objects to track. the objects in the video must be moving, since this project is to capture the moving objects in the video. 164 g. jemilda, s. baulkani figure 1: architecture diagram 3.2 divide video into image frames video technology is widely used for electronically capturing, recording, processing, storing, transmitting and analyzing a sequence of still scenes showing images in motion. the number of still pictures of videos per unit of time ranges from six or eight frames per second (frame/s) to 120 frames per second or more. these frames are saved and processed consequently. in this context, the video is divided into number of frames for example, the video of up to 10 seconds is break down into 80 to 85 frames. 3.3 object localization the moving object is localized from each and every frame by using background subtraction algorithm. the traditional background subtraction algorithm is modified and a new concept is implemented. the curvelet transform is applied on each and every frame. then soft thresholding is applied to remove noise. next, frame difference process is applied to segment the moving object and the background. finally, mathematical morphology concept is applied to fill the gap and edges in the detected objects. moving object detection and tracking using genetic algorithm enabled extreme learning machine 165 curvelet transform curvlet is not a coordinate technique for presenting multiple objects. it differs from the other wave in the direction of transition to the extent of oriented translation. the use of this transform is optimally sparse representation of objects with edges and optimal image reconstruction in illposed problems [9]. soft thresholding soft thresholding is a popular tool in computer vision and machine learning. it is used to remove noise from an image [1]. frame difference approach frame differencing is a technique used to find the difference between two video frames. it is used to segment the moving object from the background. if the pixels have changed, apparently there was something changed in the image. the algorithm of frame difference is relatively simple. it is described in very detailed manner as follows: 1. convert the incoming frame to grayscale. 2. subtract the current frame from the background model, which is the previous frame. 3. for each pixel, if the difference between the current frame and background is greater than a threshold then the pixel is considered as part of the foreground. otherwise it is considered as the background. 4. the foreground pixel is denoted as white color and the background pixel is denoted as the black color. finally the output image is produced. mathematical morphology mathematical morphology is a powerful tool for extracting structural characteristics in an image and is useful for characterizing shape information.the obtained segmented object may include a number of disconnected edges due to non-ideal segmentation of moving object edges. therefore, some morphological operation is needed to generate connected edges. here, a binary closing morphological operation is used and it is determined by a structuring element. the effect of the operator is to preserve background regions that have a similar shape to this structuring element, or that can completely contain the structuring element, while eliminating all other regions of background pixels [8]. 3.4 object identification after locating the moving object, the next step is to identify the object. to do this first the feature extraction techniques are applied on to the localized object. in this paper the texture, color and shape information of objects are considered. shape feature extraction using surf speeded up robust features (surf) is a local feature detector and descriptor that can be used for tasks such as object recognition, registration, classification and 3d reconstruction. it is partly inspired by the scale-invariant feature transform (sift) descriptor. the standard version of surf is several times faster than sift and claimed by its authors to be more robust against different image transformations than sift. surf descriptors can be used to locate and 166 g. jemilda, s. baulkani recognize objects, people or faces, to make 3d scenes, to track objects and to extract points of interest [15]. surf uses the determinant of hessian for selecting the scale, as it is done by lindeberg. given a point p=(x, y) in an image i, the hessian matrix h (p, σ) at point p and scale σ, is defined as follows: h (p,σ) = ( lxx (p,σ) lxy(p,σ) lx,y (p,σ) lyy (p ,σ) ) (1) where lxx (p,σ) etc. are the second-order derivatives of the grayscale image. the box filter of size 9×9 is an approximation of a gaussian with σ=1.2 and represents the lowest level (highest spatial resolution) for blob-response maps. the pseudo code for surf is shown in algorithm 1. algorithm 1: pseudo code of surf algorithm input : input image output : surf key points 1. begin 2. for all pixel i=1 : p 3. do 4. compute the gradient. 5. compute gradient magnitude and orientation 6. find the scalespace response using doh filters with different σ. 7. find local maxima lm with different scales and octaves. 8. if lm==highest maxima value 9. select as surf key points 10. end if 11. end 12. end colour feature extraction using hog the histogram of oriented gradients (hog) is a feature descriptor used in computer vision and image processing for the purpose of object detection. it is used to count the occurrences of gradient orientation in localized portions of an image [10]. the pseudo code for hog is shown in algorithm 2. algorithm 2: pseudo code of hog algorithm input : input image output : hog feature 1. begin 2. for all block i=1 : b 3. do 4. compute gradient values in horizontal directions. 5. compute gradient values in vertical directions. 6. create histograms of gradient image 7. end 8. concatenate normalized histograms from all of the block regions. 9. end texture feature extraction using elvp enhanced local vector pattern (elvp) is a novel vector representation developed to represent the 1d direction and structure information of local texture and the adjacent pixels with moving object detection and tracking using genetic algorithm enabled extreme learning machine 167 diverse distances from different directions. based on the vector representation, the lvp descriptor is proposed to provide various 2d spatial structures of micro patterns with various pair wise directions of vector of the referenced pixel and its neighborhoods. the proposed local pattern descriptor encodes the lvps by using the four pairwise directions of vector of the referenced pixel and its neighborhoods. especially, each pairwise direction of vector of the referenced pixel generates the transform ratio which is used to design the weight vector of dynamic linear decision function for encoding the distinct 8-bit binary pattern of each lvp. since the binary code can be considered as a two-class case by using dynamic linear decision function to calculate the comparative space transform (cst) values of the neighborhoods for encoding a bit string via the sign function [7]. the pseudo code for elvp is shown in algorithm 3. algorithm 3: pseudo code of elvp algorithm input : input image output : elvp feature 1. begin 2. for all pixel i=1 : p 3. do 4. take the one pixel gc 5. take the pixel n in direction of angle β 6. calculate the difference d between gc and n vβ,d (gc) = (i (gβ, d)−i (gc)) 7. calculate the binary pattern lvpp,r (gc) = { lv pp,r,β (gc) |β= 0o, 450, 900, 1350 } 8. calculate the binary description and store it into the bin value. 9. store descript ion in the bin array 10. end 11. end 3.5 feature selection the genetic algorithm (ga) is used for finding the best features from the surf, hog and elvp. a better solution can be evolved using a population of strings (called chromosomes or the genotype of the genome) and by encoding candidate solutions (called phenotypes) to an optimization problem. solutions are represented in binary as strings of 0s and 1s. the evolution usually starts from a population of randomly generated individuals and happens in generations. in each generation, the fitness of every individual in the population is evaluated. multiple individuals are selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. the new population is then used in the next iteration of the algorithm. the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. if the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached. ga proceeds to initialize a population of solutions randomly, and then improve it through repetitive application of mutation, crossover, and inversion and selection operators [13]. apply the genetic algorithm for each feature extraction method such as surf, hog and elvp. after applying the genetic algorithm the reduced surf, reduced hog and reduced elvp is produced. 168 g. jemilda, s. baulkani 3.6 feature fusion the next step is to combine the reduced surf, reduced hog and reduced elvp into single feature vector. to do this, concatenate all features into a single array. finally the single feature vector is produced. 3.7 object classification a new learning algorithm called extreme learning machine (elm) is used to classify objects for single-hidden layer feed forward neural networks (slfns) which randomly chooses hidden nodes. this algorithm provides good performance at fast learning speed. the elm is an emerging learning technique provides efficient solutions to generalized feed-forward networks including (both single and multi-hidden-layer) neural networks, radial basis function (rbf) networks, and kernel learning. elms have classification capability. elms have significant features like fast learning speed, ease of implementation, and minimal human intervention. they thus have strong potential as a viable alternative technique for large-scale computing and machine learning [14]. 3.8 generating video from frames to generate a video from a set of sequences or set of frames, it starts with the number zero. this will work as long as the sequence is unbroken. the frame rate of the resulting video is 3 frames per second so that each still can be seen for a short period of time. the rescale of the picture to the desired resolution can be done to manage the size of the resulting video. 3.9 output video the output video is generated from the filtered and classified frames. these frames are combined to form the output video. 4 simulation the simulation results on five different sequences recorded with three different platforms is presented in table 1. in all cases, the sensor was a pair of forward-looking avt marlin f033c cameras, which deliver synchronised video streams of resolution 640 x 480 pixels at 13-14 frames per second. bahnhofstrasse (999 frames) and linthescher (1208 frames) have been recorded with a child stroller (baseline ≈ 0.4 m, sensor height ≈ 1 m, aperture angle ≈ 65◦ ) in busy pedestrian zones, with people and street furniture frequently obstructing large portions of the field of view. loewenplatz (800 frames), bellevue (1500 frames) and city (3000 frames) have been recorded from a car (baseline ≈ 1 m, sensor height ≈ 1.3 m, aperture angle ≈ 50◦) driving on inner-city streets among other cars, trucks and trams. pedestrians appear mostly on sidewalks and crossings, and are observed only for short time spans. lighting and contrast are realistic, with most sequences recorded on cloudy days. 5 experimental images the datasets used in this work are taken from https://motchallenge.net/vis/mot1713-sdp and https://motchallenge.net/vis/eth-crossing. only the frames of the datasets for various movements of the moving object 1 and 2 are shown in the figure 2 and figure 3. moving object detection and tracking using genetic algorithm enabled extreme learning machine 169 table 1: simulation results simulation parameter value population 12 no of generation 10 video resolution 640 x 480 background updation frame 10 figure 2: various movements of the moving object 1 5.1 experimental results to evaluate the performance of the proposed system, this paper used the dataset with various objects like moving objects, lemming, occlusion, multiple objects, and indoor objects. to examine the effectiveness of the proposed system, it is compared with various performance metrics. the various performance metrics used to compare the effectiveness are average center error, overlap rate, detection accuracy, sensitivity, specificity, f-measure, detection error, execution time, precision rate and recall rate. overlap rate it shows the overlapping of each frame by frame. overlaprate = area (rt ∩rg) area (rt ∪rg) (2) where rt tracking result of each frame rg corresponding ground truth average center error rate it shows the error rate for each frame averagecentererrorrate = area (rt ∪rg) −area (rt ∩rg) (3) where rt tracking result of each frame rg corresponding ground truth figure 3: various movements of the moving object 2 170 g. jemilda, s. baulkani precision rate the precision is the fraction of retrieved instances that are relevant to the find. precision = tp tp + fp (4) where tp true positive (equivalent with hit) fp false positive (equivalent with false alarm) detection accuracy it is one of the parameter which is used to analyse the performance of the proposed method. detection accuracy = no. of correctly classified objects total no. of objects (5) f-measure f-measure is the ratio of product of precision and recall to the sum of precision and recall. the f-measure can be calculated as fm= (1+a)∗ precision∗recall a∗precision + recall (6) where a real value sensitivity sensitivity also called the true positive rate or the recall rate measures the proportion of actual positives. it is calculated using sensitivity = tp (tp + fn) (7) where tp – true positive (equivalent with hit) fn false negative (equivalent with miss) specificity specificity measures the proportion of negatives which are correctly identified. it is calculated using specificity = tn (fp + tn) (8) where tn – true negative (equivalent with correct rejection) fp – false positive (equivalent with false alarm) detection error it is one of the parameter which is used to analyse the performance of the proposed method. it is calculated using the below said formula. detection error = no. of wrongly classified objects total no. of objects (9) moving object detection and tracking using genetic algorithm enabled extreme learning machine 171 figure 4: average center error for various methods with different datasets figure 5: overlap rate for various methods with different datasets execution time it is one of the parameter which is used to analyse the performance of the proposed method. it is calculated using the below said formula. execution time = ending time− starting time (10) to analyse the performance of the proposed system, it is compared with the above mentioned performance metrics. the performance comparison of the average center error value of the proposed method and other four existing approaches such as nb, fnn, pnn and svm are plotted in the graphs as below. in the figure 4, the average center error value of the five methods including the proposed method is compared. the average center error value of the proposed method is lower than the other four existing approaches. because of the low average center error value, the proposed method is better than the other four existing approaches. the performance comparison of the overlap rate of the proposed method and other four existing approaches such as nb, fnn, pnn and svm is compared and shown in the figure 5. the overlap rate value of the proposed method is higher than the other methods. because of the high overlap rate value, the proposed method is better than the other four existing approaches. the performance precision rate of the proposed method and other four existing approaches such as nb, fnn, pnn and svm is plotted in the figure 6. the precision rate value of the proposed method is higher than the other four existing approaches. because of the high precision rate value, the proposed method is better than the other four existing approaches. the detection accuracy value of the proposed method is higher than the other four existing approaches as shown in the above figure 7. because of the higher detection accuracy value, the proposed method is better than the other four existing approaches. the sensitivity value of the proposed method is higher than the other four existing approaches as shown in the above figure 8. because of the high sensitivity value, the proposed method is better than the other four existing approaches. 172 g. jemilda, s. baulkani figure 6: precision rate for various methods with different datasets figure 7: detection accuracy for various methods with different datasets figure 8: sensitivity value for various methods with different datasets figure 9: sensitivity value for various methods with different datasets moving object detection and tracking using genetic algorithm enabled extreme learning machine 173 the figure 9 shows the specificity value of the proposed method is higher than the other four existing approaches. because of the high specificity value, the proposed method is better than the other four existing approaches. 6 conclusion in this paper, the moving objects in the video are tracked and it is highlighted to show that the object is moving in the video. for the first the video is divided into frames. first the input given is a video, which is divided into frames. then the moving object is localized from each and every frame by using background subtraction algorithm. after locating the moving object the next step is to identify the object. to do this first the feature extraction techniques are applied on to the localized object. after extracting all the features these feature are reduced using the genetic algorithm and then these reduced feature set are fused to form a single feature vector. finally these feature vectors are given into the extreme learning machine (elm) to classify the objects. the classified frames are then combined to form a video. in these output video, the moving object is highlighted. to know the performance of the proposed method, we compared the results with the various methods. from the experiment results, our proposed methods shows better results for identifying the moving object in the video file and are very efficient and reliable. as a future work, an algorithm that detects the tracking failure and recovers the tracking process when the target tracking fails due to long duration of heavy occlusion is planned. bibliography [1] biswas, m.; om h. 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(2014); a sift based object recognition using contextual information, iranian conference on intelligent systems (icis), 1-4, 2014. int. j. of computers, communications & control, issn 1841-9836, e-issn 1841-9844 vol. vi (2011), no. 3 (september), pp. 503-529 adaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications n. sahab, h. hagras nazanin sahab, hani hagras university of essex, united kingdom the computational intelligence centre e-mail: nsahab@essex.ac.uk, hani@essex.ac.uk abstract: real world environments are characterized by high levels of linguistic and numerical uncertainties. a fuzzy logic system (fls) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. the first generation of flss were type-1 flss in which type-1 fuzzy sets were employed. later, it was found that using type-2 flss can enable the handling of higher levels of uncertainties. recent works have shown that interval type-2 flss can outperform type-1 flss in the applications which encompass high uncertainty levels. however, the majority of interval type-2 flss handle the linguistic and input numerical uncertainties using singleton interval type-2 flss that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. this ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the fls. even in the papers that employed non-singleton type-2 flss, the input signals were assumed to have a predefined shape (mostly gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. in this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 fls where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. the non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. we will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 fls will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties. keywords: type-2 fuzzy logic, non-singleton fuzzy logic systems, interval type-2 fuzzy logic systems, type-2 non-singleton type-2 fls. 1 introduction professor lotfi zadeh introduced fuzzy sets in 1965 [1]. he has written several papers since then, but his comprehensive paper on the concept of linguistic variables [2] became very famous as a reference on fuzzy logic theory. he introduced fuzzy logic for the application of approximate copyright c⃝ 2006-2011 by ccc publications 504 n. sahab, h. hagras [a] [b] figure 1: two examples of domestic products that used fuzzy logic. (a) aeg washing machine [8]. (b) hitachi air conditioner [9]. reasoning and mentioned the need of a humanistic system whose behavior is strongly influenced by human judgment, perception or emotion for the purpose of computing with words to solve the problems of human-oriented fields such as philosophy, psychology, politics, law, sociology and literature. professor zadeh also described the use of his fuzzy logic approach in [3] in describing the behaviors of too complex or too ill-defined systems. this has inspired control engineers from over the world to investigate the power of fuzzy logic to control applications which are difficult to mathematically model. in 1975, the first fuzzy logic controller (flc) was developed by mamdani and assilian for controlling a steam engine [4]. since then fuzzy logic systems (flss) have been applied with great success to many real world applications. it was reported in 1995 that over the last two decades, the number of applications of flss have dramatically increased. several industrial applications of fuzzy logic were reported from which we can mention cement kilns [5], steel furnaces [5], aircraft engine control, steam turbines, power supply controllers, etc [6]. flss have also been used domestically in elevators, washing machines [6], fridges [7], air conditioners, automobiles [5], automatic transmission, camera autofocus control, etc [6]. figure 1 shows two examples of fuzzy logic usage in domestic appliances which are the aeg washing machine [8] (shown in figure 1a) and the hitachi air conditioner [9] (shown in figure 1b). according to the john lewis website about a top brand washing machine from aeg and reporting about the fuzzy logic washing machine, the website mentions "fuzzy logic circuit detects when the laundry is out of balance and re-jig it accordingly, ensuring minimum wear and tear to the drum bearings. the fuzzy logic also detects half loads, if too much detergent has been added and adds extra rinses if required" [8]. hence, for the past thirty years fuzzy logic and its applications became embedded in our everyday environments. fuzzy logic laid the basis for a successful method to model uncertainty, vagueness, and imprecision [10]. the flss are general knowledge base systems [11] with linguistic rules that can be constructed using the knowledge of experts in a given field. during more than four decades from the invention of fuzzy logic, great progress in using fls has been achieved. while traditionally type-1 flss have been widely employed in real world applications, recent years have shown a rapidly growing interest in the research and application of type-2 flss. this is because, it has become apparent that type-1 flss cannot fully handle the large amounts of uncertainties encountered in real world environments and applications. interval type-2 flss have shown to provide better performance when compared to type-1 flss in applications with high levels of uncertainty. the difference in performance has been attributed to the nature of interval type-2 fuzzy sets which can better account for the uncertainties as they incorporate a footprint of uncertainty (fou) and they can be assumed to embed a large number of typeadaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications 505 1 fuzzy sets. however, the majority of the type-2 flss [12] handle the linguistic and input numerical uncertainties using singleton interval type-2 flss that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. input numerical uncertainties are associated with the noise, imprecision and uncertainty associated with sensors and input devices. however, the linguistic uncertainties are associated with human words and perceptions. hence, it seems paradoxical to use singleton type-2 flss to handle the input numerical uncertainties, as this ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the fls. thus we cannot treat the incoming fls inputs as perfect signals as in the case of singleton flss. hence, there is a need to employ non-singleton flss to handle the input numerical uncertainties by modeling the inputs as fuzzy inputs rather than crisp values. although, there are many papers on type-1 flss, quite limited number of papers considered the usage of type-1 non-singleton flss such as [13][25] and even fewer number considered type-2 non-singleton fls [25][31]. the work done so far in (type-1 and type-2) non-singleton flss use predefined shapes for the uncertainty distribution affecting the flss inputs (mainly gaussian and triangular) which might not reflect the real uncertainty distribution associated with the given sensor [32][35]. for example in the case of a sonar sensor, the numerical input uncertainties depend on many factors such as the shape and type of objects reflecting the sonar signal as well as wind speed, humidity and temperature. moreover, the uncertainty distribution also varies with the measured values where for the case of a sonar sensor, the amount of uncertainty affecting measurement readings at 20cm distance could be much less than the uncertainties affecting the measurement at large distances such as 3m. hence, it is not fair to consider that there is specific shape for the uncertainty distribution (triangular, gaussian, etc.) with fixed parameters (average, standard deviation, etc.) for all the measured values. [12] in this paper, we will present some of the theoretical basis for generating an adaptive type-2 fuzzy input which is better able to represent the encountered numerical uncertainty at a given measurement. the non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they are piece-wise linear type-2 fuzzy sets which are changing for different measurements to model the encountered uncertainty. piece-wise linear fuzzy sets were used in [36] for the representation of membership functions. however, we use piece-wise linear fuzzy sets to represent the type-2 fuzzy input variables. in this paper, we will present an overview on how the adaptive type-2 input based non-singleton interval type-2 fls can operate in real time. we will present real world experiments using a mobile robot which will show how under high input uncertainty levels, the non-singleton type-2 fls can give a good performance and outperform its singleton type-2 and type-1 flss counterparts. section 2 will review the singleton type-1 fls. section 3 will review the singleton interval type-2 fls while section 4 will review non-singleton flss. section 5 will provide an overview on modeling a data based type-2 fuzzy input. section 6 will provide an overview on the proposed approach for type-2 non-singleton type-2 fls with the type-2 fuzzy input. section 7 will provide an overview on the system operating in real-time in real world environments. section 8 provides the experiments and results and section 9 provides the conclusions and future work. 2 singleton type-1 fls the first generations of the flss were singleton type-1 flss. in the singleton type-1 fls, both the antecedents and the consequents of the fls are type-1 fuzzy sets and the input values are crisp and precise values. a type-1 fuzzy set, a, for a variable x ∈ x is characterized by a type-1 membership function µa(x) which is limited between [0,1] [32]: 506 n. sahab, h. hagras a = {(x,µa(x))|∀x ∈ x} (1) in flss, membership functions are associated with terms that appear in antecedents or consequents of the fls rules [32]. figure 2 shows an example of the linguistic labels, close, moderate and far modeled by type-1 fuzzy sets considered for the antecedent of a type-1 fls. figure 2: an example of type-1 fuzzy sets which is also used in our experiments as antecedent membership functions. figure 3 shows the structure of singleton type-1 fls (the usual mamdani type) where the crisp inputs are first fuzzified to input type-1 fuzzy sets which trigger the inference engine and rule base to generate output type-1 fuzzy sets which are then defuzzified to obtain a final crisp output of the fls. in singleton flss, the incoming inputs to the fls are considered as crisp numbers which assume that the incoming inputs to flss are not associated with noise or uncertainty. figure 3: structure of a singleton type-1 fls [32]. considering having p inputs x1 ∈ x1, ...,xp ∈ xp and one output y ∈ y, where f lk is the antecedent membership function for input k and rule l, and gl is consequent membership function for rule l, the lth rule is shown as: rl : if x1is f l 1 and ... xp is f l p, then y is g l. l = l, ...,m (2) singleton fuzzifier maps a crisp input −→x = (x1, ...,xp) ∈ x1 ×x2 × ...×xp ≡ −→ x into a fuzzy set ax in −→ x. singleton fuzzifier is nothing more than a fuzzy singleton where the output of the singleton fuzzifier related to input k and rule l is µf l k (xk′) when the incoming input is xk = xk′. the firing strength of rule l can be found as follows [34]: µl = µf l1 (x1′) ∗ µf l2(x2′) ∗ ... ∗ µf lp(xp′) (3) where * represented the t-norm operation. the defuzzification produces a crisp output for the fls from the fuzzy sets that appear at the output of inference engine. there are many different types of defuzzifiers such as centroid, height, and center of sets defuzzifiers [32]. for example for the center sets defuzzifier (which we have used in our experiments) when cl is the adaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications 507 centroid of the related consequent fuzzy set to rule l and t is t-norm operation, the defuzzified output ycos( −→x ) could be written as follows [32]: ycos( −→x ) = ∑m l=1 c lt p i = 1µf li (xi)∑m l=1 t p i = 1µf li (xi) (4) there are many sources of uncertainty facing the fls in dynamic unstructured real world environment and many real world applications; some of them are: [37] • uncertainties in inputs to fls due to noise, change of the conditions of observation, etc.; • linguistic uncertainties as the meaning of words that are used in antecedent and consequent linguistic labels can have different meaning to different experts [32]; • uncertainties associated with the use of noisy training data; • uncertainties associated with the change in the operation conditions of controllers; • uncertainties in control outputs which can be due to the wear and tear associated with the fls actuators. all of these uncertainties translate into uncertainties about membership functions. type-1 fls cannot handle linguistic and numerical uncertainties associated with dynamic situations, as they use precise membership functions that the user believes capture uncertainties. when the environment change, the chosen type-1 fuzzy sets might not be appropriate any more. this can cause degradation in the performance of the fls and the designer might end up wasting time to frequently tune type-1 fls to deal with various uncertainties [37], [32]. hence, it was claimed that type-1 fuzzy sets might have limited capabilities to directly handle uncertainties related to dynamic uncertain situations. that sounds paradoxical because the word fuzzy has the connotation of uncertainty. the paradox about type-1 fuzzy set has been known for a long time, that a representation of fuzziness is made using membership values which are themselves crisp numbers [38]. a type-2 fuzzy set is characterized by a fuzzy membership function, i.e. the membership value for each element of this set is a fuzzy set in [0,1], unlike a type-1 fuzzy set where the membership value is a crisp number in [0,1] [32]. the membership functions of type-2 fuzzy sets are three dimensional and include a footprint of uncertainty (fou), it is the new thirddimension of type-2 fuzzy sets and the fou that provide additional degrees of freedom that can make it possible to directly model and handle the uncertainties [32]. therefore flss that use type-2 fuzzy sets have the potential to handle higher uncertainty levels than their type-1 counterparts [37], [38]. 3 singleton interval type-2 fls type-2 fuzzy logic was also introduced by zadeh in 1975 in [2], but little works [39] were published about them until the middle of previous century. until then few people were studying on type-2 fls, as people were learning what to do with type-1 fuzzy sets [39]. type-2 flss are the extension of the type-1 fls. in type-2 flss, the antecedent and/or consequent membership functions of type-2 fls are type-2 fuzzy sets [40]. a type-2 fuzzy set ã is characterized by a type-2 membership function 0 ≤ µ ã (x,u) ≤ 1, where x ∈ x and u ∈ jx ⊆ [0,1], as [32]: ã = {((x,u),µ ã (x,u))|∀x ∈ x,∀u ∈ jx ⊆ [0,1]} (5) jx is called primary membership of x, where jx ⊆ [0,1] for ∀x ∈ x. the uncertainty in the primary membership grades of a type-2 fuzzy membership function consist of a bounded 508 n. sahab, h. hagras region, that we call the footprint of uncertainty (fou) of a type-2 membership function. it is the union of all primary membership grades [41]. figure 4a shows the type-1 membership function which is totally crisp and precise. figure 4b shows the primary membership function of a type-2 fuzzy set and related fou. the fou of a type-2 membership function handles the rich variety of choices that can be made for a type-1 membership function [37], [43]. a type-2 fls can be thought of as a collection of a large number of type-1 fls’s. when the antecedent and consequent membership grades in the type-2 fls have a continuous domain, the number of embedded type-1 fls’s is uncountable [40]. interval type-2 fls [33] is a special case of general type-2 fls when the secondary membership functions are interval sets. the fou can be described in terms of upper and lower membership functions. figure 4b illustrates an upper membership function and a lower membership function that are two type-1 membership functions that are bounds for the fou of a type-2 fuzzy set. the upper membership function is denoted by µ ã (x), and the lower membership function is denoted by µ ã (x) [32]. figure 4c also shows the 3-d view of an interval type-2 fuzzy set. in a type-2 fls the rules are defined as equation (2), however the antecedent and/or consequents are type-2 fuzzy sets. we can mark type-2 fuzzy sets with sign "∼" to be different from type-1 fuzzy sets as [32]. the structure of singleton type-2 fls is shown in figure 5 where crisp inputs are first fuzzified into input type-2 fuzzy sets (in singleton fuzzification) which then activate the inference engine and the rule base to produce output type-2 fuzzy sets. these output type-2 fuzzy sets are then processed by the type-reducer which combines the output sets and then performs a centroid calculation, which leads to type-1 fuzzy sets called the type-reduced sets [32]. the defuzzifier can then defuzzify the type-reduced type-1 fuzzy outputs to produce crisp outputs to be fed to the actuators. more information about the singleton interval type-2 fls can be found in [32], [37], [40]. figure 4: (a) a type-1 fuzzy set. (b) the primary membership function of a type-2 fuzzy set. (c) a 3-d view of interval type-2 fuzzy set. figure 5: structure of singleton interval type-2 fls [37], [32]. using type-2 fuzzy sets to represent the inputs and outputs of a fls has many advantages when compared to the type-1 fuzzy sets, we summarize some of these advantages as follows [37]: • the type-2 fuzzy sets of membership functions are themselves fuzzy and include a fou, so adaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications 509 they can model and handle the linguistic and numerical uncertainty associated with the inputs and outputs of the fls in changing and dynamic unstructured environments and handle the difficulty associated with determining the exact membership functions for the fuzzy sets [41]. hence, flss based on type-2 fuzzy sets have the potential to produce better performance that type-1 fls. • using type-2 fuzzy sets to represent the flc inputs and outputs will result in the reduction of the flc rule base when compared to using type-1 fuzzy sets as the uncertainty represented in the footprint of uncertainty in type-2 fuzzy sets lets us cover the same range as type-1 fuzzy sets with smaller number of labels and the rule reduction will be greater when the number of the flc inputs increases [32]. • each input and output will be represented by a large number of type-1 fuzzy sets which are embedded in the type-2 fuzzy sets [42], [41]. the use of such a large number of type-1 fuzzy sets to describe the input and output variables allows for a detailed description of the analytical control surface and gives much smoother control surface and response [37]. during the past decade, type-2 fuzzy sets and systems have become very popular [44]. there are many applications done using type-2 flss such as video streaming control [45], induction motor control [46], two-axis motion control [47], hot strip mill temperature control [19], marine diesel engines, dc-dc converter, mobile robots, ambient intelligent environment, etc. [37]. in these applications, the type-2 flss have outperformed their type-1 counterparts. although, there are many papers on interval type-2 flss, the vast majority of these papers used singleton flss which mix the numerical and linguistic uncertainties to be handled only by linguistic labels type-2 fuzzy sets. this looks paradoxical, as if numerical uncertainties were present, they should affect the incoming inputs to the fls, and therefore we cannot treat the incoming fls inputs as perfect signals in the case of singleton flss [48]. hence, there was a need to consider non-singleton type-2 flss to handle the incoming numerical uncertainties. 4 non-singleton fls the major difference between a non-singleton fls and a singleton fls is in the fuzzification part. zadeh introduced fuzzification in 1973 as "the operation of fuzzification (or, more specifically, support fuzzification) has the effect of transforming a non-fuzzy set into a fuzzy set or increasing the fuzziness of a fuzzy set" [3]. since 1975 when mamdani used fls in a steam engine [4], the majority of the flss were using singleton fls employing singleton fuzzification. the reason for widespread employment of singleton fuzzification is because of its simplicity and speed of computation which allows for real time operation. in singleton fuzzification, inputs are considered to be crisp data and the effect of noise, imprecision, and uncertainty is ignored, while the non-singleton fuzzification models the fls inputs as fuzzy sets. flss in real world environments [48] can face high levels of uncertainties, which can also be categorized into linguistic and numerical uncertainties. the linguistic uncertainties is related to human words and perceptions, and the input numerical uncertainties are related to noise, imprecision and uncertainty related to input devices and sensors. in non-singleton flss, the numerical uncertainties can be modeled and handled by non-singleton fuzzy inputs and the linguistic uncertainties will be handled by antecedent fuzzy sets to represent the linguistic labels. dealing with numerical uncertainty is especially important in engineering applications in which measurement instruments, sensors, or input devices are employed to produce inputs to flss. there has been some research from instrumentation and measurements researchers in modeling uncertainty measurements using type-1 fuzzy sets and even type-2 fuzzy sets. there are a number of non-ideal situations affecting measurement results such as measurement method, measurement condition, and operator [49]. this has caused the issue of measurement uncertainty 510 n. sahab, h. hagras in which the true value is unknown. ferrero [49] also mentioned that up to that date, the most widely known and assessed theory to deal with incomplete information was probability. but probability just covers random uncertainty which is a part of incomplete knowledge and it is needed to consider both random and unknown systematic effect [49]. quite limited number of papers (compared with large number of paper in flss) used nonsingleton type-1 fuzzification. since 1997 mouzouris and mendel presented some closed forms of non-singleton fuzzifier [32], [34][35]. liang and mendel [32][33] in 2000 presented some closed form of non-singleton type-2 fls for the case of gaussian type-2 fuzzy inputs with uncertain standard deviation, and some limited number of papers have used type-1 non-singleton type-2 fls in their applications such as [19][25] and more limited number of authors have used type2 non-singleton type-2 [25][31]. however, all the papers assume a predefined shape (such as triangular or gaussian) as the fuzzy input variable. however, these predefined shapes might not accurately represent the real shape of data distribution related to the input device or sensor. three categories of non-singleton flss have been seen so far in papers which are: non-singleton type-1 fls, type-1 non-singleton type-2 fls, and type-2 non-singleton type-2 fls. in the first two categories, the input is modeled as a type-1 fuzzy set and in the third category type-2 fuzzy sets are used to model incoming inputs to the fls. in this paper, we will present a new approach to obtaining an interval type-2 fuzzy input variable [50] directly from data with no assumption of any predefined shapes for inputs. we will then present our novel adaptive type-2 non-singleton type-2 fls. 5 overview of modeling a data-based type-2 fuzzy input we will start by presenting our data based method to model non-symmetric non-specified shapes of convex type-2 fuzzy inputs, which are directly obtained from data and we do not assume a specific shape about the uncertainty distribution with the given sensor [50]. a summary of the process is as follows: 1. record 10,000 continuous measurements (at a specific measurement condition) and obtain a histogram for that condition to show how many times a measurement value is recorded. 2. obtain a normal convex type-1 fuzzy set from the data distribution as follows [50]: obtain the first point from the left side in which the distribution value is in its maximum (shown as maxl in figure 6). consider the graph from the starting point to the first left maximum (maxl) and connect the points at each distribution value if the value is increasing, otherwise keep the line horizontally at previous value. continue the previous step until getting to the point maxl. then obtain the first point from right side in which the distribution value is in its maximum (maxr). consider the graph from the right ending point to the first right maximum (maxr) and connect the points at each distribution value if the value is increasing or fixed, otherwise keep the line horizontally at the previous value. continue the previous step until getting to the point maxr. to having a convex set, the value from maxl to maxr is kept constant equal to maximum distribution value (distribution_no(maxl) = distribution_no(maxr)). divide the whole set by the maximum value to change the scale of the function to the acceptable interval [0,1] as a normal fuzzy set. 3. repeat the first and second steps whilst changing different possible conditions which could affect the input measurements. as for the case of a sonar sensor [50], we considered changing temperature, sound noise, and wind while recording measurements at a fixed distance. adaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications 511 figure 6: obtaining a convex function from data distribution [50]. 4. create a convex type-2 fuzzy set from the various type-1 fuzzy input variables, as shown in figure 7. the method to generate type-2 fuzzy sets from type-1 fuzzy sets is similar to the approach in [51], but we considered non-specified shapes instead of triangular type-1 fuzzy sets. the details of the approach were described in [50]. another method (which can be done in a discrete form) is to obtain the upper membership function by taking the maximum t-conorm of all the type-1 fuzzy sets, whilst keeping the area from the left point with maximum membership value (maxl) to the right point with maximum membership value (maxr) constant at one. lower membership function could be also considered as the minimum of all the type-1 fuzzy sets. [50] figure 7: obtaining type-2 fuzzy input from type-1 fuzzy inputs, the colorful lines show each type-1 fuzzy set and upper membership function and lower membership functions of the resulted interval type-2 fuzzy input is shown by black thick lines [50]. figure 8 illustrates a type-2 fuzzy input variable modeled from the case of one of the sonar sensors at around 3000mm from a cylindrical object. the black lines define type-2 fuzzy input variable, and the colorful dash lines show the type-1 fuzzy sets from which the type-2 fuzzy input is created. 512 n. sahab, h. hagras figure 8: a sample of the modeled type-2 fuzzy inputs at the distance of around 3000mm [12]. in the following section, we present a summarized view on how to employ the type-2 fuzzy input in a type-2 non-singleton type-2 fls. 6 type-2 non-singleton type-2 fls the type-2 non-singleton type-2 fls employs the created type-2 fuzzy input variables to handle the encountered numerical uncertainty, and uses type-2 fuzzy sets to model the linguistic labels like "close" , "moderate", and "far" which represent membership functions of antecedents to handle the encountered linguistic uncertainty. figure 9 illustrates the structure of the type-2 non-singleton type-2 fls [48]. the following subsections presents the detailed operation of the type-2 non-singleton type-2 fls. figure 9: the structure of the type-2 non-singleton type-2 fls [50]. 6.1 fuzzification the antecedent type-2 fuzzy sets to represent the linguistic labels were considered as triangular, left shoulder, and right shoulder membership functions (as shown in figure 10). the input type-2 fuzzy input variables were considered to be a non-specified piece-wise linear shape as explained in section 5 (as shown in figure 11). the fuzzifier converts the inputs into linguistic labels with given membership value to activate the rule base and inference engine. the operation between the linguistic labels antecedent fuzzy sets (defined by the interval type-2 fuzzy set ) and the fuzzy input variable to provide the lower and upper membership values of input k within rule l is given by equation (6) and equation (7) respectively as follows: the antecedent type-2 fuzzy sets to represent the linguistic labels were considered as triangular, left shoulder, and right shoulder membership functions (as shown in figure 10). the input type-2 fuzzy input variables were considered to be a non-specified piece-wise linear shape as explained in section 5 (as shown in figure 11). adaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications 513 the fuzzifier converts the inputs into linguistic labels with given membership value to activate the rule base and inference engine. the operation between the linguistic labels antecedent fuzzy sets (defined by the interval type-2 fuzzy set f̃ lk) and the fuzzy input variable to provide the lower and upper membership values of input k within rule l is given by equation (6) and equation (7) respectively as follows: µlk(xk) = sup xk∈xk (µxk(xk) ∗ µf lk(xk)) (6) µl k (xk) = sup xk∈xk (µ xk (xk) ∗ µf l k (xk)) (7) the µxk(xk) and µxk (xk) are the upper and lower membership functions of the input fuzzy variable, and µf l k (xk) and µf l k (xk) are the upper and lower membership functions of the antecedent fuzzy sets representing the linguistic labels respectively. in this work, for the sup operation, we have used the maximum t-conorm, and for the ∗ operation, we have used minimum t-norm. as we have considered three forms of antecedent type-2 fuzzy sets, therefore six algorithms are required to obtain the upper membership value and lower membership values of the output of the fuzzier for the different antecedents. figure 10: the considered antecedent type-2 fuzzy sets [48]. figure 11: a considered shape of type-2 fuzzy input variable for the non-singleton fuzzification [12]. due to the limited space, we will present the algorithms to obtain the upper and lower membership values of the type-2 non-singleton fuzzification when only right shoulder antecedent is considered. the rest of the algorithms follow the same logic. 514 n. sahab, h. hagras the first example of fuzzification is on finding upper membership value of the fuzzifier, when antecedent membership function is a right shoulder (shown in figure 12 with green and black colors). following equation (6), we need to perform the max-min operation between the upper membership functions of the type-2 fuzzy input variable and the right shoulder antecedent type2 fuzzy set. the minimum operation between the type-1 membership functions and the type-1 fuzzy input variable will resulted in a type-1 fuzzy set which is shown in figure 12 by red line, where we need to find its sup (maximum) to find µlk(xk). when the ending point of the upper membership function of the fuzzy input variable is larger than ar1, it is required to consider the operations involving the right shoulder antecedent type-2 fuzzy set, as it is not zero. two cases can happen, where case 1 is shown in figure 12a and case 2 is shown in figure 12b and figure 12c. case1 happens when maxr (shown in figure 12) is smaller than br. in that case, the line of antecedent from ar1 to br would be considered. figure 12: different cases for obtaining the upper membership value of the fuzzifier output when the right shoulder antecedent membership function is considered in rule (a) case1. (b) and (c) case 2. the algorithm in figure 13 shows how to obtain the µlk(xk) where we find the intersection of the sets of lines j (which form the piece-wise linear type-2 fuzzy input variable) and the antecedent type-2 fuzzy sets. the given line j (of fuzzy input) ranges from point[j] to point[j+1] in the x-axis line where j+1 is the line connecting to the line j and located at its right in the piecewise linear function. the u[j] and u[j+1] are also membership value at point[j] and point[j+1] respectively. the membership value for any point xk located on line j is obtained from (8) as follows: µxk(xk) − u[j] xk − point[j] = u[j + 1] − u[j] point[j + 1] − point[j] µxk(xk) = u[j + 1] − u[j] point[j + 1] − point[j] xk + point[j + 1]u[j] − point[j]u[j + 1] point[j + 1] − point[j] (8) the membership value µf l k (xk) of any point xk on a line of the antecedent fuzzy set (starting from an extreme left point "left" (ar1 in our case) and ending at an extreme right point "right" (br in our case)) is obtained as follows: µf l k (xk) − µf l k (left) xk − left = µf l k (right) − µf l k (left) right − left µf l k (xk) = µf l k (right) − µf l k (left) right − left xk + rightµf l k (left) − leftµf l k (right) right − left (9) adaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications 515 at the intersection point xk,c between an antecedent line and a line j of the fuzzy input variable, µxk(xk) in equation (8) is equal to µf l k (xk) in equation (9). hence xk,c could be found as follows: xk,c = [ (rightµf l k (left) − leftµf l k (right))(point[j + 1] − point[j])+ (right − left)(point[j]u[j + 1] − point[j + 1]u[j]) ] [ (right − left)(u[j + 1] − u[j])+ (µf l k (left) − µf l k (right))(point[j + 1] − point[j]) ] (10) subsistuting with the value of xk = xk,c in equation (8) or (9), we can find the intersection points when it is needed as µlk(xk) = µ l k(xk). case 2 happens when maxr is larger or equal br, which is the case shown in figure 12b and figure 12c. hence the output of fuzzifier for the upper membership value is one. the algorithm given in figure 13 summarizes the method to obtain µlk(xk) in the case that the antecedent type-2 fuzzy set is a right shoulder. figure 13: the algorithm to obtain the upper membership value of the fuzzifier for input k and rule l (when right shoulder antecedent is considered in the rule). the second example of fuzzification is on finding lower membership value of the fuzzier, when antecedent membership function is a right shoulder (shown in figure 14 with green and black colors). following the equation (7), we need to obtain the t-norm (minimum) between the type-1 membership function µ xk (xk) and µf l k (xk). this will result in a type-1 fuzzy set (shown by red line in figure 14), where we need to find its sup (maximum) to find related µl k (xk). if the ending point of lower membership function of the type-2 fuzzy input is smaller or equal ar2(the start of the lower membership function of right shoulder antecedent), the lower membership value of the fuzzifier for the right shoulder is zero. if it is not zero, then two cases can happen, where case 1 is shown in figure 14a and case 2 is shown in figure 14b. case 1 happens when the lower membership value of fuzzy input at point maxr(shown in figure 14a) is larger than the membership value of the antecedent point maxr, which can be considered as: 516 n. sahab, h. hagras figure 14: different cases for obtaining the lower membership value of the fuzzifier output when the right shoulder antecedent membership function is considered in rule (a) case1 (b) case 2. maxr < right(µxk(maxr) − µf l k (left)) + left(µf l k (right) − µxk(maxr)) µf l k (right) − µf l k (left) (11) in this case, the max-min will be located on the antecedent line from ar2 to br. because of the positive slope of the antecedent line, the most right intersection between the lower membership function of the type-2 fuzzy input and the antecedent line has the highest membership value, and its membership value is µl k (xk), when the right shoulder is considered in rule. case 2 happens when the lower membership value of fuzzy input at point maxr is not larger that the membership value of the antecedent at point maxr (when case1 does not happen). in this case, µl k (xk) is equal to the maximum membership value of the lower membership function of the type-2 fuzzy input. figure 15 illustrates the algorithm to obtain the lower membership value of the fuzzifier for input k and rule l when right shoulder antecedent is considered in rule. figure 15: the algorithm to obtain the lower membership value of the fuzzifier for input k and rule l (when right shoulder antecedent is considered in the rule). adaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications 517 6.2 rule base and inference engine the outputs of fuzzifier trigger the inference engine and the rule base to generate type-2 fuzzy outputs which are then type-reduced and defuzzified to generate the final crisp output of the fls. the same approach as singleton fls is applicable to the type-2 non-singleton type-2 fls after obtaining the output of fuzzifier. the rule base, inference engine, type-reduction and defuzzification follow as: • rule base: the rule base of singleton and non-singleton fls are similar. considering a fls having inputs x1 ∈ x1, ...,xp ∈ xp and one output y ∈ y, the rule l is defined as [32]: if x1 is f̃ l 1 ... and xp isf̃ l p, then y is g̃ l (12) where l=1,..., m and m is the total number of rules in the rule base. the f̃ lk is the antecedent membership function for input k and rule l, and g̃l is consequent membership function for rule l. • inference engine: we have shown in the fuzzifier subsection, how to find the upper and lower membership values of a given type-2 fuzzy input variable to a given linguistic label. the process needs to be done for all inputs (k=1,..., p). at the end, the firing strength of each rule f l = [fl,f l ] could be found where fl and f l will be obtained as follows: fl = t p k=1{µ l k (xk)} (13) f l = t p k=1{µ l k(xk)} (14) 6.3 type-reduction after obtaining the lower and upper firing strengths for each rule from the inference engine and obtaining the centroid of consequent membership function, the center of sets type-reducer [32] is used to produce the type-reduced sets. 6.4 defuzzification the final defuzzified crisp output is obtained by taking the average of the type-reduced set. 7 adaptive type-2 input based non-singleton type-2 fls we considered the non-singleton type-2 fuzzy inputs to be dynamic and automatically generate from data and the shapes are changing for different measurement values. as we cannot generate the type-2 fuzzy sets for all the measurement values, we need to interpolate the type-2 fuzzy set for an incoming measurement value by interpolating the type-2 fuzzy sets representing the measurements to the left and right of incoming measurement value. figure 16 shows the process of the on-line singleton type-2 fls. [12]. we obtained some sample of original type-2 fuzzy input variables for different measurement values within considered range according to the instructions given in section 5, and then obtained the rest of the type-2 fuzzy input variables by a linear interpolation defined for type-2 fuzzy sets. the interpolated membership value for the desired crisp incoming input (d) can be obtained as follows: ud[i] = (d − d2) (d1 − d2) u1[i] + (d − d1) (d2 − d1) u2[i] (15) 518 n. sahab, h. hagras the u1[i] is the membership value of the left original fuzzy input variable, and u2[i] is the right original fuzzy input variable. the d1 is the given measurement input for the left fuzzy input variable and d2 is the given measurement input for the right neighboring fuzzy input variables, and d is the crisp value (new measurement data) in question which is to be modeled by an interpolated type-2 fuzzy input variable. figure 16: the process of on-line non-singleton type-2 fls [12]. the interpolation is considered to be done separately for the lower membership functions and the upper membership functions for the fuzzy input. the resulting interval type-2 fuzzy sets need to be convex and normal and if this is not the case, we need to convert the type-2 fuzzy set to a normal convex fuzzy set [48]. 8 experiments and results in this section, we will present real world experiments in a real world environment and we will describe the characteristics of the considered flss and test environment. we will then present control surface analysis and the real world experiments results. 8.1 experimental setup in this section, we will present real world experiments which were carried using a real world robot navigating in a real world environment. we will compare the performance of the adaptive type-2 non-singleton type-2 fls against its singleton type-1 and type-2 flss counterparts. in our experiments, we have employed a four-wheel mobile robot (shown in figure 17) to do a left edge following behavior using two sonar sensors. a hair dryer was located on robot to blow hot adaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications 519 air through the path of front sonar sensor to create larger measurement uncertainty while robot is moving. figure 17: the four wheel mobile robot used in the experiments. in order to test the robot under unseen input uncertainties [12], we have applied various sources of uncertainties to the input sonar sensors (as shown in figure 18a, figure 18b) where we have used a hair dryer stuck on robot to blow hot air while a fan fixed at a certain position on ground to create cold wind. these employed uncertainties are introduced during the online operation of the robot and they have not been seen by the robot during the fls design. we chose changing the wind speed (through the fan and dryer) and temperature (through the hair dryer) to introduce input uncertainties to the sonar sensors. this is because the wind, temperature variations and atmospheric turbulences are all meteorological conditions that have an effect on sound propagation (as we used sonar sensors) [52]. the speed of sound in air is proportional to the square root of absolute temperature as follows: c = 20.05 √ t + e/p (16) where t is absolute temperature in kelvin, and e is partial pressure of water vapor, and p is barometric pressure. wind is also another factor affecting the sound travel. the wind in the same direction of sound will cause the wave propagation to be bent downward, and in the upwind direction sound waves move upward which cause no direct sound penetration [52]. to obtain the position of the robot in a precise way, the vicon system is used in which six cameras are employed. to be able to recognize the robot, four markers are used (as shown in figure18). the top of the cylinders are also marked to be able to check if the edges of the cylinders are in a same line where the starting and ending cylinders are modeled in vicon to be able to distinguish the certain position of wall (one of them is shown in figure18c) [12]. in our experiments, we considered a type-1 fls which was able to give a good performance under the normal lab conditions. the performance of type-1 fls was considered to be acceptable in a usual room temperature without applying external uncertainty. two main factors were considered to evaluate the performance of the flss, the first one is the root mean square error (rmse) which is considered as a measurement factor for the average of error for the distance between the robot’s path from the desired path. the second factor is standard deviation (std) of error in following edge to check the variance of the error around the average error value. the shapes of the type-2 fuzzy sets used to represent the linguistic labels are shown in figure 19 where we blurred the type-1 membership functions (shown in thin lines in figure 19) to obtain the fou of the type-2 fuzzy sets (shown in figure 19 by thick line) for the interval (singleton and non-singleton) type-2 fls. the consequent membership functions (for all flss) are type-1 fuzzy sets (shown in figure 20) as there are no major uncertainties associated with robot outputs. the rule-base (employed for all the flss) is shown in table 1. 520 n. sahab, h. hagras (a) (b) (c) figure 18: mobile robot experiments when (a) left edge following set in a straight line. (b) left edge following set in straight line with a right angle in the way. (c) one of the cylinders modeled in vicon system using three markers to know the exact position of wall. figure 19: antecedent membership functions for both the front and back senors. the type-1 fuzzy sets are shown in thin line while the interval type-2 fuzzy sets are shown in thick lines [12]. figure 20: the consequent type-1 fuzzy sets for all the used flss [12]. front sonar back sonar close alright far close low low low alright low medium low far high high high table 1: the rule base for all the flss; the output in the rule base is the speed of right wheel [12]. adaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications 521 8.2 control surface analysis the control surface graphically represents the unknown function articulated by the fls. figure 21 shows the control surfaces for the singleton type-1 and type-2 flss and the type-2 non-singleton type-2 fls. the control surfaces show the inputs of front and back sensors plotted against the speed of right wheel as output of the flss. note that a smooth shape of a control surface translates to a smooth control response that can deal with uncertainty and imprecision. figure 21: the control surface of (a) the singleton type-1 fls. (b) the singleton interval type-2 fls. (c) the type-2 non-singleton intreval type-2 fls. as seen from figure 21a, the control surface of the type-1 fls is rather steep which means it can be affected by high levels of uncertainties in the inputs where small change in the inputs 522 n. sahab, h. hagras can result in large output changes which might cause high overshoots/undershoots and steady state errors in addition to moving towards an unstable situation. on the other hand the control surface of the singleton type-2 fls (shown in figure 21b) is smoother than the type-1 fls, which means it can handle higher level of uncertainties. however, the control surface of the type-2 nonsingleton type-2 fls (shown in figure 21c) is much smoother where there are smooth transitions at all parts of the control surface where any small change in inputs will result in a small change in the outputs. thus using the type-2 input based non-singleton type-2 fls result in a control surface which is smoother than the singleton type-1 and type-2 flss. this smooth response will consequently give a very good control performance that can handle the high levels of uncertainties and disturbances where small variations in inputs will not cause significant changes to outputs. thus, it is expected that the type-2 non-singleton type-2 fls will be able to handle higher levels of uncertainties than its singleton type-1 and type-2 flss counterparts. this will be evidenced in the real world experiments reported in the following subsection. 8.3 real world experiments figure 22a and figure 22b show the results of the robotic experiments illustrated in figure 18a, which compare the performance of singleton type-1, singleton type-2 and type-2 input based non-singleton type-2 fls to realize an edge following behavior at a desired distance of 2m. the robot starts from the distance of 1.8m from the nearest edge. figure 22a shows the robot’s paths of singleton type-1, type-2 and the non-singleton type-2 fls under no induced uncertainty. as shown in figure 22a and table 2 as expected under no uncertainty, the performance of all the flss will be quite similar where the type-1 fls will be slightly better. however, as high levels of uncertainties are introduced (by blowing hot wind through the way of one sonar) as shown in figure 22b and table 2, the type-2 non-singleton type-2 fls gives the best performance in terms of the rmse and the std (which could represent the size of the overshoots/undershoots from the average). in terms of control performance, the type-2 non-singleton type-2 fls provides the best performance followed (by a large margin) by the singleton type-2 fls and then the singleton type-1 fls which performs poorly. figure 22b evidences the control surface analysis in figure 21 where the type-2 nonsingleton type-2 fls recovers quickly from the uncertainties with relatively smaller overshoot/undershoot. in the other experiments we are presenting in the paper, we consider the same type-1 fuzzy antecedent while we increase the fou as illustrated in figure 23 to have larger difference between type-1 fls and type-2 fls. the rule base and consequent membership functions were considered the same as table 1 and figure 20 respectively. the other differences are introducing a right angle in the middle of the edges set in straight line and changing the position of fan (shown in figure 18b). different conditions different types of fls type-1 fls type-2 fls type-2 nonsingleton type-2 no uncertainty std = 45.5617 std =46.5435 std= 47.9173 rmse =45.5695 rmse=46.5823 rmse= 51.2167 (stopped) large uncertainty std = 624.8140 std =477.2452 std =244.2411 rmse=1268.2 rmse=1037.8 rmse=634.4 table 2: results of following a straight wall for the singleton type-1 fls, singleton type-2 fls, and adaptive type-2 input based non-singleton type-2 fls under different conditions (related to figure 22). adaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications 523 figure 22: the robot’s path when employing singleton type-1 fls, singleton type-2 fls, and the adaptive type-2 input based type-2 fls, while following a straight wall under different conditions (a) no external uncertainty. (b) large external uncertainty. figure 23: the type-2 fuzzy sets for the antecedent membership functions considered in the experiments whose results are shown in figure 24 and figure 25, which is actually considering uncertainty bound of 3 around type-1 fuzzy sets. figure 24 shows the robot’s paths when employing the type-2 non-singleton type-2 fls with its singleton type-1 fls and singleton type-2 fls counterparts to do left edge following at a desired distance of 2m. the robot was started from the distance of 1.8m from the nearest edge. figure 24a shows that the robot’s paths under no induced uncertainty for the three fls are quite similar. looking at table 3, it can be seen that the performance of the type-2 input based non-singleton type-2 fls is slightly better than the other flss because it has the least 524 n. sahab, h. hagras std and rmse compared with its singleton type-1 fls and singleton type-2 fls counterparts. when considering moderate uncertainty (the related robot’s paths are shown in figure 24b), the difference between the performance of the adaptive type-2 non-singleton type-2 fls and the singleton type-1 fls and singleton type-2 fls are becoming more distinguishable. the robot’s path for the non-singleton type-2 fls is closer to the desired path specified by black dashed lines, so it has the best performance while the type-2 fls has also outperformed the type-1 fls. table 3 also shows the least std and rmse for the non-singleton type-2 fls, then the singleton type-2 fls which outperform the type-1 fls by having less std and rmse. when considering large uncertainty (the related robot’s paths are shown in figure 24c), the non-singleton type-2 fls outperforms its singleton type-2 and singleton type-1 fls counterparts, while the type-1 fls is performing very poorly. table 3 also shows the least std and rmse for the non-singleton type-2 fls. figure 25 shows the robot’s paths when employing the type-2 non-singleton type-2 fls and its singleton type-1 fls and singleton type-2 fls counterparts to do left edge following at a desired distance of 2m. the robot was started from the distance of 1.9m from the nearest edge. figure 25a shows the robot’s path when employing the flss under no induced uncertainty. the performances of the flss are quite similar as it can be seen from the figure 25a while the adaptive type-2 non-singleton type-2 fls was slightly better as it has the least std and rmse (illustrated in table 4). figure 25b and figure 25c also shows that the type-2 input based non-singleton type-2 fls outperforms its singleton type-2 and type-1 fls counterparts when moderate and large uncertainty are applied respectively. table 4 also confirms that the type-2 input based non-singleton type-2 fls outperforms its counterparts flss in the experiments as the type-2 input based non-singleton type-2 fls has the least std and rmse, while the type-1 fls performs poorly. different conditions different types of fls type-1 fls type-2 fls type-2 nonsingleton type-2 no uncertainty std =202.5274 std =203.8221 std =191.3634 rmse =202.7028 rmse=204.0164 rmse=191.5175 moderate uncertainty std =907.7865 std =700.8891 std =533.0486 rmse =910.7622 rmse=703.5157 rmse=546.5797 large uncertainty (stopped) std =694.8356 std =669.3059 rmse=699.5279 rmse=676.0831 table 3: summary results of the edge following behviour for the singleton type-1 fls, singleton type-2 fls, and adaptive type-2 input based non-singleton type-2 fls under the different three conditions related to figure 24. different conditions different types of fls type-1 fls type-2 fls type-2 nonsingleton type-2 no uncertainty std = 201.7354 std = 201.7226 std = 193.5605 rmse = 201.7395 rmse=201.7245 rmse=193.5647 moderate uncertainty std =562.0358 std=548.4835 std= 345.9873 rmse=588.3824 rmse=584.5484 rmse=352.3825 large uncertainty (stopped) (stopped) std =747.6389 rmse=748.5229 table 4: summary results of the edge following behavior for the singleton type-1 fls, singleton type-2 fls, and adaptive type-2 input based non-singleton type-2 fls under the different three conditions related to figure 25. adaptive non-singleton type-2 fuzzy logic systems: a way forward for handling numerical uncertainties in real world applications 525 [a] [b] [c] figure 24: comparison between singleton type-1 fls, singleton type-2 fls, and the adaptive type-2 input based non-singleton type-2 fls when the robot started from 1.8 meter distance from the edge of wall while applying (a) no external uncertainty (b) moderate uncertainty (c) large uncertainty. 9 conclusions and future directions in this paper, we have presented an adaptive type-2 fuzzy input based non-singleton type-2 fls which is better able to handle the encountered input uncertainties. the non-singleton type2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the uncertainty distribution associated with the given sensor. we have presented an overview on how the adaptive type-2 input based non-singleton interval type-2 fls can operate in real time. we have shown through control surface analysis, how the nonsingleton type-2 fls can produce much smoother control surface than the singleton type-1 and type-2 flss. this should result in a better control response that handles high uncertainty levels. this was evidenced through the real world experiments using mobile robots where under high uncertainty levels, the non-singleton type-2 fls can outperform its singleton type-2 and type-1 flss counterparts. this work has shown the benefits achieved when using type-2 non-singleton type-2 flss in applications characterized by high input uncertainty levels. in our future work, we are going to tune the parameters of the type-2 fls by neural networks. we aim also to work towards modeling more generalized type-2 fuzzy input variable or even doing non-singleton fuzzifier in general type-2 flss. last but not least, we are also considering developing a self-tuning adaptive type-2 input based non-singleton interval type-2 fls, which is updating the shape of the fuzzy input variable whenever the performance of the fls was deteriorated. 526 n. sahab, h. hagras [a] [b] [c] figure 25: comparison between singleton type-1 fls, singleton type-2 fls, and the adaptive type-2 input based non-singleton type-2 fls when the robot started from 1.9 meter distance from the edge of wall while applying (a) no external uncertainty. 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[52] j. lamancusa (2000), outdoor sound propagation, noise control, me 458: engineering noise control, penn state university, pp.10-19, 2009. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(2), 253-271, april 2019. the biological as a double limit for artificial intelligence: review and futuristic debate a. tugui, d. danciulescu, m.-s. subtirelu alexandru tugui* "a. i. cuza" university iaşi 700506 iasi, carol i, 11, romania *corresponding author: altug@uaic.ro daniela danciulescu university of craiova 00585 craiova, a.i.cuza, 13, romania danadanciulescu@gmail.com mihaela-simona subtirelu university of medicine and pharmacy, craiova 200349 craiova, petru rares, 2, romania e-mail: mihaela.subtirelu@yahoo.com abstract: this paper aims to identify to what extent artificial intelligence (ai) is biologically limited and to launch a debate on the issue of overcoming these limitations. to achieve our goal, we utilized a qualitative research methodology framework, providing an in-depth analysis of ai limitations formulated by prominent scholars within this field of specialization. we found that the biological boundary imposes a double limitation on ai, both from a gnoseological perspective and from a technological perspective. this twofold limitation of ai underpins the idea that as long as the biological cannot be understood, formalized, and imitated, we will not be able to develop technologies that mimic it. by adopting an original approach, our research paper focused on mapping out the twofold limitation of the biological with reference to the success of ai. special attention was paid to the motivational analysis of this limitation in terms of human existence, the opportunity and utility to create artificial intelligences as superior to the human-like condition. we have opened the door for future debates on the need to decode cellular communication by understanding and developing a natural language of the living cell (n2lc). based on the present research, we proposed that within the current technological context, biological computers (biocomputing) could represent a so-called invisible hand outstretched by biological systems towards ai. keywords: biological computer, super-ai-slices, technological singularity; limits of artificial intelligence, natural language of the living cell (n2lc). 1 introduction nowadays society is dominated by technology [2, 27, 35], communications,and interaction (analog and/or digital) between different chaotic systems [25], which are sometimes too complicated [1,38]. change is essential [4,7,24,33,44] in our society. as we currently find ourselves at the end of the 801st tofler lifetime [50], further predictions are being made about how society will develop in terms of technology in the following toflerian lifetime. one of these predictions, advocated by ray kurzweil [30], is that in 2045, human society will reach the point where "the accelerated technological progress will overcome the human ability to understand, evaluate, and control all of its consequences, and when the non-biological intelligence created in that year will copyright ©2019 cc by-nc 254 a. tugui, d. danciulescu, m.-s. subtirelu be a billion times stronger than human intelligence today" [53]. this technological stage, mentioned by kurzweil [30] and other field-related specialists [57], is referred to as the technological singularity, which translates into technological simplicity with artificial intelligence (ai). the term technological singularity—put forward by a. turing [18, 53, 54] in the middle of the 20th century, just before the launching of the ai concept—has played a central role in maximizing profits within various industry and business sectors. the focus on technology simplification imposed by technological singularity has been taken over by artificial intelligence that intersects with genomics and synthetic biology [45], hence placing the biological system as a key element of the future great computing platform [37]. as highlighted by gartner [8,12], ai has become the core of new technologies, motivating the need to identify ai limitations in order to avoid and even overcome them. embarking to highlight the central role of the biological system in the success of artificial intelligence systems, the present paper aims to identify to what extent the biological system stands as a technological limitation of ai. 2 methodological issues to reach the goal set in the present research study, we set out to identify those ai limitations governed by the nature of the imitation of the biological system (brain-body-behavior), hence formulating the following research question: to what extent is the biological system limit for ai? to obtain an answer to this question, we mainly utilized a qualitative approach, since throughout time various reliable approaches have already been created with regard to the limits of ai, which made our mission easier. under the circumstances, we carried out an in-depth retrospective analysis via a meta-analysis [61] of the opinions expressed by prestigious authors within this field of specialization, i.e: hubert l. dreyfus, the author of what computers can’t do; jacob t. schwartz, mathematician, computer scientist, former professor of computer science at new york university, creator of mathematical and computer theories, and author of technical report # 212 of march 1986 on ai limits; donald norman, cognitive scientist; gordon bell, senior researcher at microsoft and computer industry consultant; james n. gray, specialist in database and transaction processing computer systems; franz l. alt, former president of the acm; paul w. abrahams, consulting computer scientist and former president of the acm; experts invited by denning and metcalfe [13] to put forward their views in the volume beyond calculation: the next fifty year in computing; max lungarella, fumiya iida, josh c. bongard, and rolf pfeifer, authors of numerous ai research studies and projects and coordinators of the proceeding volume, 50 years of artificial intelligence. essays dedicated to the 50th anniversary of artificial intelligence framing the limitations of ai in the 21st century with historical reflections [34]. figure 1 illustrates the scope of our meta-analysis for the first 50 years following the launch of the ai concept, as well as references to the predictions launched by the selected authors. figure 1: the meta-analysis of the timeline for ai limits the biological as a double limit for artificial intelligence: review and futuristic debate 255 to design and develop our approach, we departed from the outline of the main ai milestones, as well as the initial goals as imagined by the visionary parents of ai. to extract those limitations imposed by the formalization and production of biological-type phenomena, we have undertaken a retrospective analysis of ai limitations as formulated by the selected authors. having framed the above-mentioned limitations, we went a step further, highlighting their similarities to biological entities in order to establish to what extent such limitations impose actual limitations on ai. we underpinned our research enquiry with systematic implementation of a hybrid approach between biological entities and computers [41] to finally formulate some predictions with regard to what is within the reach of ai and what is not until the technological singularity is attained in our society. figure 2: the research modeling stages the overall approach of our research designed to achieve the goal set is outlined in figure 2. 3 artificial intelligence: a short history and its original goals humans have constantly resorted to technology to achieve goals with the interest of survival and controlling the others. defined as the scientific study (logos from λóγoς [gk]) of craftsmanship (techne from τέχνη [gk]), technology has dominated the last 10,000 years of human existence [30] and been the catalyst for the leap from one technological era to the next, including the current era of cybernetics, which was marked by the launch of the first electronic computer in 1946 (eniac-electronic numerical integrator and computer). in the first decade of the cyber revolution, the idea of technological intelligence emerged with the launch of the concept of artificial intelligence at the 1956 dartmouth conference, a concept suggested by john mccarthy to his colleagues marvin minsky, allen newell, claude shannon, herbert simon, oliver selfridge, and ray solomonoff [34]. the concept itself was the scientific response to science fiction ideas [53], which included elektro and sparko at the 1939 world’s fair; isaac asimov’s three laws of robotics published in the may 1941 issue of astounding science fiction; the 1950 novel i, robot, written by i. asimov; and a. turing’s test in 1951 to answer the question "can machines think?". newell, shaw, and simon’s validation of 38 of the 52 theorems of the principia mathematica by means of the logic theorist software; the 1958 launch of lisp (the first language of ai) by john mccarthy; the launch of the general problem solver in 1959 by newell, shaw, and simon to solve complex problems, such as the missionaries and cannibals scenario; the publishing of the article "pattern recognition by machine" by selfridge and neisser [47]; and the development, between 1959 and 1962, by j. mccarthy and his students at mit of the first credible chess game 256 a. tugui, d. danciulescu, m.-s. subtirelu software, known as a chess playing program for the ibm 7090 computer, are a few concrete landmarks on the phase i timeline of ai (1957-1962), as delineated by dreyfus [15]. shadowed by the highly interesting achievements carried out during phase i, the following five years (19631968), framed by dreyfus [15] as phase ii of ai, seem to be less spectacular in reaching further ai objectives such as the simulation of human behavior (particularly in relation to what the human brain can do), highlighting the fact that progress in ai has not been exciting or spectacular [51] and that the following years’ predictions (after 1968) are not bright [26]. this view of limited achievements in ai is also shared by schwartz [46], who adopts the perspective put forward by dreyfus [15] and highlights that even after 14 years, ai faces "very limited success in particular areas, followed immediately by failure to reach the broader goals at which these initial successes seem at first to hint", motivated in particular by the technological limitations at the time of analysis. even 50 years after the term artificial intelligence was launched, at the july 2006 conference in monte verita (ascona, switzerland), lungarella, iida, bongard, and pfeifer [34] mentioned the shared opinion of fellow researchers that ai is far from approaching the goals originally set by the first generation of ai visionaries, whereas natural intelligence is still far from being understood. over the last decade, and especially after 2013, an increasing number of organizational studies and reports have been registered, focusing mainly on digital economy-related industries. when accessing the gartner.com platform, in a simple search for artificial intelligence, ai, intelligent, automation, and robot for the year 2018, we got 1358 entries, of which the content analysis would indicate that the first 427 entries (i.e. 31.44%) have one or more of the requested keywords in their headline. setting out to analyze the newsletters received from mckinsey & company, we carried out an analytical study to validate the information explosion trend in terms of the usability of ai applications in the organizational field. thus, based on 1189 newsletters received from mckinsey between 2014 and 2018, we developed our investigation for each year and set as query parameters the same keywords as in the gartner.com analysis, i.e. in the title (subject) and summary. table 1 indicates the synthesis of our newsletter queries for 2014-2018 by keywords (title and/or summary), and figure 3 illustrates the values reported in table 1. according to our analysis, by april 26, 2017 there were no query titles (subj.) directly querying artificial intelligence or ai. the situation changed radically after april 27, 2017, when we found that there were 42 pieces of information in which artificial intelligence and ai appeared both in the title and the summary of the investigated information. it is important to note that a mckinsey newsletter can include from one to ten independent notices in which keywords appear either in the summary or in the title and summary. considering only the newsletter content, we found that after april 27, 2017, the use of the terms selected either in the title or in the title and summary increased from 32 entries in 2014-2017a to 237 entries between 2017a -2018, which means an increase of 877% after april 26, 2017. consequently, for both the gartner and mckinsey data processed in relation to the reports, research studies, and surveys about technological trends at the societal level, we marked out an industry-oriented expansion of applied ai theoretical concepts to solve practical issues in order to simplify problem solving and/or replace individuals from various processes. in addition, our organizational analysis registered the occurrence of an inflection point on april 27, 2017, at which time it can be seen that the mckinsey reports focused on the technological expansion of intelligent applications in different industries. this is in fact another argument, an obvious proof of the social view in favor of complexity simplification via the instruments of artificial intelligence on the way towards the technological singularity. the biological as a double limit for artificial intelligence: review and futuristic debate 257 table 1: synthesis of mckinsey queries total 2014 2015 2016 2017a 2017 2018 artificial intelligence (kw) 7 6 0 0 21 48 artificial intelligence (subj.) 0 0 0 0 5 11 ai (kw) 0 0 0 0 21 56 ai: (subj.) 0 0 0 0 2 16 robot (kw) 2 0 1 2 9 6 robot: (subj.) 1 0 1 0 1 2 automation (kw) 2 1 1 3 31 33 automation: (subj.) 2 1 1 3 10 11 intelligent (kw) 0 0 0 2 3 4 intelligent: (subj.) 0 0 0 1 1 1 legend: kw=key words (subject+summary); subj.=subject; 2017a: until april 26 2017. 4 the limits of ai in time — a meta-analysis according to economic theory, every technological revolution is emphasized by the emergence of a new production factor that triggers the concrete manifestation of the new technological age. under the circumstances, tapscott [49] has labeled the current economy the digital economy, stating the significant contribution of information to the creation of gdp (gross domestic product) as a specific factor in the cybernetics era [59]. from a societal perspective, tapscott records an accelerated transition of human society from the industrial society of the early twentieth century to the internet-dominated post-industrial society and digital technologies specific to the late twentieth century. in terms of artificial intelligence as a digital technology of the 21st century, our research study aimed to pinpoint the main landmarks as highlighted by the ai visionary parents from its very beginning and analyze to what extent these limitations have been preserved over time and how they influence the evolution of this field of specialization. in essence, ai was seen as an attempt to build systems with human-like or even super-human capacities in certain domains [60], traditionally considered closely related to the human mind. in this endeavor, in order to better understand their target, the pioneers of ai were keen to discover how the human brain functions. originally considered a meat machine by m. minsky in the late 1960s, two decades later the human brain was characterized by schwartz [46] as a biochemical computer. without actually defining the human brain, p.w. abrahams [1], a former student of m. minsky at mit, postulated in his essay "the world without work" that the human brain is a target that is hard to understand and mimic using artificial intelligence. the author compares the human brain to the moon towards which the earthlings have set out to build a tower, but no matter how hard they work to raise it, it is still not enough compared to the earth-moon distance. 4.1 ai limitations according to hubert l. dreyfus in his 1972 work entitled what computers can’t do, hubert l. dreyfus carried out a critical analysis of what ai managed to do or not with regard to the expectations postulated by the ai visionary parents during 1956-1972. as illustrated in figure 1, dreyfus’s [15] answer to the book title question, what computers can’t do, formulated from a critical perspective on ai, explicitly mentions in the conclusion the limits of artificial intelligence. dreyfus [15] explained what computers cannot yet do due to technological limits even with 258 a. tugui, d. danciulescu, m.-s. subtirelu figure 3: a graphical representation of the organizational explosion of the ai keywords the creation of equipment capable of huge performances with up to 1010 10 states (dreyfus’s number). he referred to the limitations of processes of informational formalization in the brain and the body, and, additionally, the limits of human behavior formalization (for which there are sometimes no rules), as well as the limitation of the formalization of the non-material aspect of the human soul (immaterial soul) inspired by descartes’ [14] vision and the motives of his discourses. in fact, irrespective of the research hypotheses (tested and untested) formulated by ai titans such as minsky, shannon, simon, shaw, turing, neumann, mccarthy, fodor, and feigenbaum focused on and connected to ideas and theories of interconnected research areas such as mathematics, physics, chemistry, psychology, and philosophy, ai limitations in dreyfus’s opinion were centered on two key words: technology and formalization. dreyfus regarded formalization as the main limitation, in the sense of the impossibility of heuristic modeling from the biological, psychological, ontological, and epistemological perspective of the brain-body-behavior-soul (sobrbobe) grouping in relation to human needs optimization functions. 4.2 ai limitations according to j.t. schwartz a state-of-the-art approach to ai limitations was developed by j.t. schwartz [46], former computer science professor at new york university. schwartz analyzed what had been achieved in the field of ai in the 30 years after the concept release (1956-1986), as illustrated in figure 1. schwartz featured two categories of limitations, namely the limitations imposed by the concrete physical and logical issues of artificial intelligences design and those required by the ethical dimension of their existence. in the first category, schwartz included a) the fundamental limits to the constructability of artificial intelligences (ais), which refers to the (limited) possibilities of systems designed similarly to the human brain (br) in performance; b) limits imposed by the quantitative theory of computational complexity, motivated by the remarkable, but complicated to simulate, ability of the human brain to manipulate complexity and to reveal it in a simple and the biological as a double limit for artificial intelligence: review and futuristic debate 259 efficient manner; and c) knowledge-based limitations on ai detailed on three levels: sensors, with reference to the analysis of images (computer vision) and the analysis of nature; motor control, modeling of spatial environments, and motion planning; and reasoning, planning, knowledge representation, and expert systems with reference to graphical searching, predicate systems, expert systems, knowledge representation, and learning. in the second category of ethical limits, schwartz included the fear induced when such systems are out of control, as well as the methods and rules of human interaction with these systems. 4.3 ai limitations upon the 50th anniversary of the foundation of the acm the debates launched in march 1997 on the occasion of the 50th anniversary of the association for computing machinery (acm) via 20 essays authored by it experts and specialists [13] triggered an increased awareness of the technological limitations of artificial intelligence, highlighted in a direct or indirect manner by g. bell, j.n. gray, d. norman, f.l. alt, and p.w. abrahams in their works. in fact, these limitations are found in the first section, "coming revolution," as well as the second section, "computers and human identity," of the volume edited by p.j. denning (chair of the computer science department in the school of information technology and engineering at george mason university) and r. metcalfe (the inventor of ethernet technology), i.e. the debates on the predictions launched with reference to the future of computing. bell g. and j.n. gray: "the revolution yet to happen" in their chapter "the revolution, yet to happen," g. bell and j.n. gray [5] discussed cyberspace, where information about all real-world physical objects will be found online by encapsulating it in a chip, leading to fully networked systems. the authors’ prediction for 2047 was that the operating and storage performance of the computer would equal the human brain (br) and that the so-called on-a-chip systems, body area networks, and robots would make their presence felt in cyberspace. essentially, bell and gray were of the opinion that there was a technological limitation to the development (understanding, formalizing, and building) of systems capable of human-brainlike performance that could be overcome by 2047. however, hybrid systems were considered as immediate solutions (after 2025), in which body area networks (as an intermediary step towards biological computers or biocomputing) would play a considerable role. norman d.: "why it’s good that computers don’t work like the brain" "why it’s good that computers don’t work like the brain" is the chapter [41] where psychologist donald norman (keen on both human and computer behavior) adopted a positive approach to the differences between the individual, as an intelligent, unpredictable, robust, relatively error-insensitive, and redundant being, and the computer (including robots) as an abstract, linear, consistent, rational, and precise machine. this mirror characterization reflects the irrefutable limits of artificial intelligence. norman is of the opinion that computers and robots will never come to mimic or surpass people, and that due to technology, the human species is condemned to an ever-growing complexity, which will lead to a continued loss of privacy and freedom of action. within this context, the technological limitation of computers (including artificial intelligence) compared to the human brain is decided from the concept, design, and realization stages, since we place into discussion two totally different entities, namely the human brain (br)— the result of an evolution marked by continuous adaptations and interactions over millions of years, where the natural selection criterion was that of survival of the species — and computer technology, which is limited in terms of its the evolution over time (little over 50 260 a. tugui, d. danciulescu, m.-s. subtirelu years at the date of the author’s analysis) as well as its design and development by reference to efficiency optimization functions and computational algorithms. norman was categorical and clearly stated that a computer would not be able to mimic or surpass people. the solution recommended by the author to overcome this technological limitation envisages the human-computer relationship seen as an interaction between cooperative and even hybrid systems towards the development of the so-called biological computer. alt f.l.: "end-running human intelligence" the marked difference between human intelligence and artificial intelligence is also shared by f.l. alt [3] in his chapter "end-running human intelligence." the author features several domains where ai las proven to be less successful, such as "chess playing, legal problems, medical diagnosis, weather prediction, public opinion surveys, and the understanding of natural language." some limitations identified by alt are already outdated at present, proving that ai has made remarkable progress over the past two decades. abrahams p.w.: "a world without work" disappointed with the failure to meet the goals he had predicted during ai’s debut in the late 1950s and early 1960s when he was m. minsky’s student at mit, 40 years later, p.w. abrahams [1], in his chapter "a world without work," outlined some of the most important ai failures, including not having met the goal set by japan in the fifth generation computer project, despite the contemporary appreciations of feigenbaum and mccorduck back in 1983 [22], and the cultural and emotional limitations of robots’ interactions with human subjects in the field of service provision (such as telephone services, taxi services, and cuisine). for example, with regard to the interaction with human subjects, abrahams added one of the most challenging limits, i.e. the inability to feel the same type of love for a robot as for a person, or even the same intensity of sexual attraction (even if 20 years later this limitation seems to be somewhat conceptually overcome via conferences on topics such love and sex with robots, lsr 2016 and the special issue "love and sex with robot" in the robotics journal [62]). towards the end of his chapter, abrahams launched a series of rhetorical questions to highlight ai behavioral limitations in comparison with the biological system (brbobe), as well as various aspects with regard to the utility of intelligent humanoid machine design. among such rhetorical questions, we identified those related to the capability of intelligent humanoid machines to engage in activities or situations specific to humans, such as eating, bleeding, dying, procreating, and the feeling of pleasure or pain. moving a step towards an affirmative answer related to procreation, abrahams wondered if robot children would be subject to the same ethical imperatives specific to our children? could the robots be treated like slaves without any compassion? and what would then be the reason for designing robots, apart from intellectual curiosity and desire for power, provided that humans can easily create people and do so with pleasure? we considered that all these questions, which are difficult to answer, impose the limitations of ai as a form of manifestation and level of development. in addition, abrahams also drew societal boundaries (including ethical ones) in the sense that robots cannot succeed in creating a better world than the one people created on their own, even if robots undertook all social/economic tasks and overcame different cultural, racial, and religious differences. consequently, the role assigned to intelligent computers is to complement the individual in his/her actions, such as managing, exploiting, recovering, and recycling the planet’s limited resources. abrahams’ approach encompasses both constructivist limitations, which involve the understanding-formalization-construction process, and behavioral limitations (including feelings, states, and manifestations specific to humans) as well ethical ones. abrahams’ recommendation the biological as a double limit for artificial intelligence: review and futuristic debate 261 was to create systems assigned to humans (living systems) similar to those put forward by d. norman in his chapter. 4.4 ai limitations on the 50th anniversary of the concept launch on july 9-14, 2006, ai specialists attending the 50th anniversary summit of ai, held at centro stefano franscini, monte verita, ascona, switzerland, celebrated 50 years since the term artificial intelligence was launched. although the summit agenda listed the launch of speculations about the future of ai, we found that no actual limits were discussed, but rather the challenges and development directions applicable in different areas. lungarella, iida, bongard, and pfeifer [34] wrote "ai in the 21st century with historical reflections", as forward-thinking paper showing the evident advantage to having obtained a clear picture of the stage reached by ai in the first 50 years since its launch, and to having synthesized in a prudent manner expectation forecasts for the next 25-50 years. we shared the same motivation to have selected this paper for our meta-analysis timeline. in the first decade of the 21st century, lungarella et al. [34] stated that there was still a technological limitation imposed by the fact that natural intelligence (the topic of behavioral emulation by artificial intelligences) was "far from being understood", saying that the "basic theories of natural intelligence are lacking" while "artificial forms of intelligence are still much more primitive than natural ones". thus, the authors insisted on the limitation of the understanding and the conceptual formalization of the biological system (the brain-body-environment triplet) within the constrained limitations still imposed by a rudimentary and unavailable technology. this overview of conceptual and technological limitations was articulated amid the paradigm shift in the stated purpose of artificial intelligence. thus, if intelligence was initially thought to be located in a box in the human brain (br), after 2000, a novel perspective focused on the distributed intelligence located throughout the whole organism (bo), which interacts with and explores the environment (en). in other words, the authors endorsed the paradigm shift from a computational approach to an embodied perspective. 5 the result is a biological limitation following our meta-analysis of the main limitation-related views put forward by dreyfus, schwartz, norman, bell, gray, abrahams, lungarella, iida, bongard, and pfeifer, we synthesized a series of methodological assertions to highlight the essence of ai limitations. the first methodological assertion, based on the limitations of ai as formulated by dreyfus, states the existence of a technological limitation to building systems with huge performances (1010 10 states, a value that even today does not work), which is a performance with which the biological system is indirectly credited via the brain-body binomial (brbo), complemented by the individual’s inability to achieve complete self-understanding and the formalization of his/herbehavior (be) and soul (so) as a manifestation of the whole sobrbobe. departing from the ai limitations as formulated by schwartz, we extracted the second methodological assertion, namely the existence of a concomitant limitation of scientific and technological knowledge in constructing artificial intelligences similar to the human brain (br) in terms of its remarkable ability to manipulate and reveal complexity in various ai applied domains, to which ethical limitations of the biological interaction (brbobe) are added. we noted that schwartz focused on the biological component (brbo) of ai limitations, which cannot yet be understood or formalized so as to design ais. the experts summoned by dening and metcalfe [13] as contributors to the anniversary volume of the 50th anniversary of the foundation of the acm clearly identified a series of in262 a. tugui, d. danciulescu, m.-s. subtirelu teresting ai limitations, from which we extracted the third methodological assertion to identify the technological limitation in system designs (computers) that mimic or surpass people [41] in their behavior regarding survival, feelings, moods, and manifestations within a lack of utility context [1]. in essence, the proper design involves the understanding and formalization of the biological system (brbobe), and the authors endorse this as a possible solution to the design of cooperative systems based on human-computer [1] interaction, hybrid body area networks [5], and the development of the biological computer [41]. the fourth methodological assertion, extracted from the ai limitations identified by lungarella, iida, bongard, and pfeifer [34], consists of the lack of basic theories with regard to what natural intelligence is as the subject of ai emulation and the continued existence of a limitation of the understanding and formalization of the biological system (brbo) in the interaction with and exploration of the environment (en). essentially, it is a reformulation of human behavior (be) with the interaction with and biological exploration (brbo) of the environment (en) that forms the brboen triplet, while the biological system (brbo) is still largely unknown. by examining the four methodological assertions, we found that the biological represents a common and important limitation of ai, hence it must be first understood, then formalized, and finally imitated by technology. as long as the biological cannot be understood, formalized, and imitated, we will not be able to develop technologies that can mimic it. surface imitation of natural intelligence can only lead to superficial results, such as the tower that the earthlings would propose to build in order to reach the moon, and no matter how much of it would be built each day, it would remain insignificant compared to the earth-moon distance [1]. subsequently, we can say that both the gnoseological and technological boundaries of human beings are automatically limitations of ai. all previously mentioned authors investigated the key role of the biological component in ai limitations by limiting the initial understanding of natural intelligence — in fact, selfunderstanding, which must be the premise of success in the theorization and formalization of natural intelligence and the technological stage in the process of ais design that mimics the biological system. in other words, in compliance with the four methodological assertions, all the limitations identified by dreyfus, schwartz, norman, bell, gray, abrahams, lungarella, iida, bongard, and pfeifer are located on the following logical trace: biological − > technology − > biological − > ai (bio-tech-bio-ai). we can then explain the logical route bio-tech-bio-ai in the simplest way; the individual is not capable of self-understanding (the first biological limit) in order to create fundamental theories of natural intelligence and is still limited in the design and use of appropriate technologies (tools and materials), technologies that in turn would be used to mimic the biological system (the second biological limit) in order to design what we should have understood, i.e. artificial intelligences. consequently, our meta-analysis, based on the four methodological assertions, highlights that the biological boundary remains the main limitation of ai, both from a gnoseological perspective (due to the impossibility of self-knowledge) and from a technological perspective (given by the impossibility of formalization, design, and use of technologies — instruments and materials — that imitate natural intelligence). when implying the limitation of the biological systems, we found that the natural intelligence of the individual is limited from a gnoseological perspective. the gnoseological and technological limitations fall into a spiral pattern similar to dna, in which the two boundaries communicate, but as of yet do not intersect. the above analysis justifies the conclusion that the human being is still at this moment a double limit for artificial intelligence. the biological as a double limit for artificial intelligence: review and futuristic debate 263 6 current ai records and limitations — futuristic debate the two limitations — gnoseological and technological — approached as a double limit of the biological in terms of the success of ai, were analyzed in a direct manner by norman [41]. in the attempt to answer "why it’s good that computers don’t work like the brain", the author asserted that computers and robots would never come to mimic or surpass people. accordingly, norman advocated the idea of parallelism between the two boundaries, while he identified a bridge between them through biological computation via a biological computer. even though the merriam-webster dictionary notes that the term biological computer (bio computer) was first used in 1952 in the sense of a "computer that uses components of biological origin (such molecules of dna) instead of electrical components", this sense was taken over in 1958 and heinz von foerster [55, 58] created the biological computer laboratory at the department of electrical engineering, the university of illinois. it was not until 1997 that norman endorsed a fusion-oriented approach (in the sense of merge) between the biological cell and the classical computer that would lead in the coming years to a hybrid biological computer capable of notable performance in the applicative field of artificial intelligence. within the framework of the double biological limitation highlighted above, corroborated with societal expectations of ai design that would predominantly replace human activities, we considered it appropriate to continue our discussion with some promising achievements in recent years as arguments for the success of ai and launch further questions about future challenges and limitations. 6.1 some actual evidence of ai the idea of the biological cell’s assimilation into the future biological computer gives us biocomputing, a concept used since 1965 [36] as an "application of computer science to biological research", though in tandem with the notion of biological computation (not yet defined in merriam-webster’s collegiate dictionary [36]), meaning that a system of neurons, grown biologically [41] is used to design ais. if at first the biological computer was defined as a system of neurons, artificially grown, capable of problem solving via biologically real, brain-like operations [13], later on we could speak of dna computers [29, 45] or computers using synthetic biological components in order to manipulate, store, and retrieve data. as in any technological field, the field of biological computers is mainly concerned with achieving a stable and reliable technology. in terms of the understanding and formalization of the biological system and ultimately the development of stable and reliable technologies leading to the development biological computers, it is worth highlighting some of the most important achievements of the past five years. parallel computation between computers and molecular motors in the attempt to concretize norman’s idea of efficiently involving the biological system in the creation of hybrid systems [41], the complex research team coordinated by professor nicolau of mcgill university (canada), with representatives from the lund university (sweden), molecular sense ltd. (uk), technische universitat dresden (germany), philips research and philips innovation services (the netherlands), and linnaeus university (sweden) [39], obtained in 2016 the "proof of concept" of a computer that operates in parallel using mobile molecular proteins that exploit a nanotechnology-based network and codes a problem still unsolvable by electronic computers. in particular, the authors [39] put forward a "parallel-computation approach, which is based on encoding combinatorial problems into the geometry of a physical network of lithographically defined channels, followed by exploration of the network in a parallel fashion using a large number of independent agents, with very high energy efficiency." this project, initiated 264 a. tugui, d. danciulescu, m.-s. subtirelu by d.v. nicolau [39], a professor at mcgill university, incorporates the development of various ideas on the design of molecular motors at the microand nano-biocomputation level, published in 2006 in microelectronic engineering [40]. stable storage of digital data in dna a practical achievement of great importance in the field of bio computers was the research study completed in 2016 by erlich and zielinski [19], researchers at columbia university and the new york genome center, who informed the scientific community about a major improvement in dna information storage and retrieval when they succeeded in implementing "a new coding strategy to encode text, images, a movie, and an operating system in 2 megabytes of dna, and retrieve it back perfectly in multiple trials" [21]. the two researchers, through their strategy, managed to store 215 petabytes of data on a single gram of dna [11]. biological computers inside living cells one of the most striking achievements in the field of bio computers was announced by a. green, an engineer at arizona state university, who informed the public that together with researchers from harvard university, they had developed a biological computer "that controls how cells behave," [28, para. 1] including the construction of biological circuits that behaved similarly to digital circuits and used the logical operators and, or, and not to make decisions. the research team thus managed to create a biological transistor (called a transcriptor), a specialized computer that could be programmed to monitor and affect the functions of living cells. to achieve this, the team used dna that could store up to 455 exabytes of data per gram [28]. biology: the next great computing platform within the last two decades, and particularly after 2012, biology has enjoyed increased attention from researchers in different areas of specialization. for example, a step forward was made in the formulation of theories such as the theory of electrodynamic instabilities in biological cells [32] and the design of specialized sensors for the reception of emotions-on-a-chip [31], towards the recording of the extracellular neural activity [20], not to mention the continuous-flow biochips [43], the design of revolutionary gene-editing technologies such as crisprs [37], and the design of nano-biologic computers as reliable alternatives for quantum computers [9, 39]. all these intersections between ai, genomics, and synthetic biology as highlighted by rosso [45] will help specialists turn biology into the next great computing platform [37] of society. 6.2 futuristic debate and next questions the topic of ai limitations has always been a sensitive issue for society in general and for the individual in particular. following our meta-analysis, we highlighted the importance of ethical [46] and spiritual or soul-related [15] limitations that are difficult to overcome [48]. to successfully outline ai limitations, since the individual stands as the double limit meant to secure the success of ai, motivates us to shed some light on our limits as a species in the universe. in what follows, we discuss some central topics of reflection on our human-like interaction with ai. do we really know our limitations as a species? it is quite obvious that the individual, as a human, manifests a rather limited understanding and self-understanding. for example, from an existential perspective, we do not yet know with utmost certainty where we come from and where we are going on the path of our existence. we cannot yet understand ourselves or explain and formalize our own intelligence in a credible way, and we do not have yet an explanation of why humanity displays (at least for now) superior intelligence to the other species on earth. we certainly do not know, and there is no clear evidence of our appearance on earth and which of the biological as a double limit for artificial intelligence: review and futuristic debate 265 the creationist or evolutionary theories is true [10,16,17,42]. in addition, even if we were to be given a proof to demonstrate one of the two theories, we would still wonder how this evidence should be exhibited to be irrefutably credible. are there temporal, spatial or other limits to our ability to understand that proof? on this level, the list of questions remains open. the only certainty is that we have many limitations as a species-that is, we know very little about what we want to know. is it necessary and appropriate to plan the design of such ai that go beyond the human being? the usefulness of the descriptive truism formulated in the previous question, rounded by the obvious conclusion that we have many limitations, helps us to clearly understand another aspect: someone (from the creationist perspective) or something (from the evolutionary perspective) has contributed to our development as a species. are we able to compete with the creator or the millennia-long evolutionary process in our attempt to design ais? should we attempt to do so? and if yes, are we firmly convinced that we will succeed? and if we succeed, is it prudent for the human species that these ais be superior to us in terms of intelligence? it is obvious that the human has been overcome in many areas by technology, as far as efficiency is concerned [56]. is it necessary and opportune that we be overcome by technology and from intelligence perspective? the positive answer to such a question, correlated with the ethical limitation regarding the exploitation of these ai as highlighted by abrahams [1], should encourage a serious reflection on the future sharing of the exploited and exploiter roles. how far should ai reach? the answer is simple. as long as the individual has his/her own gnoseologic and technological limits to which the existential ones are added, ai cannot overcome us for a very simple reason—we cannot build something we can not formalize, to which we add our fears and/or pride in relation to a technological entity created by ourselves. however, biocomputing, bio computers, and dna computers could lead to the design of ais that overcome natural intelligence on certain levels, which means we could talk about the slices of super ai (super-ai-slices), without the scope to completely overcome the natural intelligence. biocomputing—the invisible hand of ai? fascinated by the secrets of medicine, in an informal discussion in 2014, we asked the famous surgeon i. lascar, a professor at the university of medicine and pharmacy in bucharest, what the secret was to a successful operation. among the syntheses and content-related explanations, professor lascar pointed out that surgery is assisted, besides a number of strictly scientific factors, by a so-called invisible hand that contributes to the success of an operation and which all physicians rely on. in this context, the success of biocomputing research and development as part of the bio computer could be the catalyst for leaping to a level of ai that surprises us in terms of intelligent performance and behavior. current achievements, such as the design of the biological transducer; the monitoring, programming, and behavioral control of the live cell (via logical operations and, or, and not); and technological challenges such as the decoding of live cell communication and the future development of a natural language of living cells (n2lc) used in biocomputing could turn biocomputing into the invisible hand of biological systems stretched towards artificial systems, especially ai. all the questions raised in our present paper are aspects of ai boundaries in relation to natural intelligence, but also future research topics in relation to the following premise: nature is very simple and efficient in everything she makes [52]. it is very important for us, as humans, to understand the simplicity of nature in creating biological entities, decoding the biology, and applying it to communicate via an nllc to build calm technologies [6,23] at the societal level. 7 conclusion the research study carried out in this paper, motivated by the need to clarify to what extent technological limitations are based on biological limitations and departing from the methodolog266 a. tugui, d. danciulescu, m.-s. subtirelu ical assertions extracted via our meta-analysis, highlighted that the biological boundary represents the main limitation of ai from both a gnoseological and a technological perspective. thus, we have come to the conclusion that as long as the biological system cannot be understood, formalized, and imitated, we will not be able to develop technologies that can mimic it. we highlighted the double biological limitation as a conclusion of the bio-tech-bio-ai logical pathway, to which we assigned the results of our meta-analysis with respect to ai limitations as identified in dreyfus, schwartz, norman, bell, gray, abrahams, lungarella, iida, bongard, and pfeifer. we registered on the bio-tech-bio-ai path the inability of the individual regarding selfunderstanding the first biological limitation in order to develop theories about natural intelligence, which induces a limitation on the creation and use of technologies appropriate to the process of imitation/mimetization of the biological system (the second biological limitation) in order to design what we should have understood, i.e. artificial intelligences. within this framework, we conceptually embraced the gnoseological and technological limitations in a dna spiral model through which the two boundaries communicate, but which do not intersect as of yet. at this stage of human scientific knowledge, as far as the double limitation of ai is concerned, we highlight that the technological limit is not a mere consequence of the gnoseological limit, motivated by the fact that from a causal perspective we are talking about the same biological system which is analyzed at two different moments. at the first moment, the biological system searches for and does not find the answer to the question "what do we imitate?", while, at a second moment, the same biological system is looking for the answer to "with what (technology) do we imitate?". that we have to deal with two limitations resides also from the procedure of forcible and sequential elimination of one of the two limits, hence reaching the obvious result that the un-eliminated limit will always be valid. plainly, if we assume that we were now provided a wonder technology, we would grow aware that we still do not have an answer to the question of what to imitate with this technology. likewise, if we suddenly understood what natural intelligence is, we would find that we do not have at the same time (simultaneously) a technological solution to imitate natural intelligence. considering the current level of technological development, it is important to understand this dual limitation of ai in order to establish clear and separate research goals aimed at advancing from both directions to achieve the original goals assigned to artificial intelligence. furthermore, the motivational analysis of ai limitations was supported by our futuristic discussion structure on three levels. the first level approached via the existentialist dimension, i.e the perspective of knowing our limitations as a species on earth, a discussion that ends with the certainty that humans have numerous limitations and that we know very little about what we would like to know, alongside a long series of unanswered questions. the second level was the launching of unanswered questions about the opportunity to design ais that are superior to human beings. the third level focused on the usefulness of designing such ais. here we placed into discussion the idea of building super-ai-slices, i.e. those ais that overcome natural intelligence in certain directions and that would be more useful at the societal level than a global artificial intelligence. motivated by the outstanding practical achievements of biocomputing, we focused on the technological challenge of decoding cellular communication through the understanding and development of the natural language of the living cell (n2lc), leading to the understanding and acquisition of novel information needed to monitor, coordinate, and direct cellular behavior, and we highlighted the topic of the so-called invisible hand outstretched by biocomputing towards ai. the biological as a double limit for artificial intelligence: review and futuristic debate 267 funding this work was partial supported by a mobility grant of the romanian ministery of research and innovation, cncs uefiscdi, project number pn-iii-p1-1.1-mc-2018-2607, within pncdi iii, by the faculty of economy and business administration from "alexandru ioan cuza" university iasi, and by the university of craiova. author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] abrahams, p.w. 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[62] [online]. love and sex with robot, robotics. special issue lse 2016. available online: https://www.mdpi.com/journal/robotics/special_issues/lsr, accessed on 10 december 2018. international journal of computers communications & control issn 1841-9836, 13(1), 71-82, february 2018. a knowledge base completion model based on path feature learning x. lin, y. liang, l. wang, x. wang, m. yang, r. guan xixun lin, xu wang key laboratory for symbol computation and knowledge engineering of national education ministry, college of computer science and technology, jilin university, changchun 130012, china limin wang school of management science and information engineering, jilin province key laboratory of internet finance, jilin university of finance and economics, changchun 130117, china mary qu yang midsouth bioinformatics center and joint bioinformatics ph.d. program, university of arkansas at little rock and university of arkansas for medical sciences, 2801 s. university avenue, little rock, arkansas 72204, usa yanchun liang, renchu guan* key laboratory for symbol computation and knowledge engineering of national education ministry, college of computer science and technology, jilin university, changchun 130012, china zhuhai laboratory of key laboratory of symbolic computation and knowledge engineering of ministry of education, zhuhai college of jilin university, zhuhai 519041, china *corresponding author: guanrenchu@jlu.edu.cn abstract: large-scale knowledge bases, as the foundations for promoting the development of artificial intelligence, have attracted increasing attention in recent years. these knowledge bases contain billions of facts in triple format; yet, they suffer from sparse relations between entities. researchers proposed the path ranking algorithm (pra) to solve this fatal problem. to improve the scalability of knowledge inference, pra exploits random walks to find horn clauses with chain structures to predict new relations given existing facts. this method can be regarded as a statistical classification issue for statistical relational learning (srl). however, large-scale knowledge base completion demands superior accuracy and scalability. in this paper, we propose the path feature learning model (pflm) to achieve this urgent task. more precisely, we define a two-stage model: the first stage aims to learn path features from the existing knowledge base and extra parsed corpus; the second stage uses these path features to predict new relations. the experimental results demonstrate that the pflm can learn meaningful features and can achieve significant and consistent improvements compared with previous work. keywords: knowledge base completion, random walks, path features, extreme learning machine. 1 introduction large-scale knowledge bases (kbs), such as never-ending language learning (nell) [6], freebase [3], yago [35], and dbpedia [11], construct their own ontologies derived from facts that copyright ©2018 cc by-nc 72 x. lin, y. liang, l. wang, x. wang, m. yang, r. guan are manually or automatically extracted from databases, such as wikipedia or other unannotated web pages. these kbs usually contain billions of facts, and each fact can be organized as a triple rk(ai,bj), such as athleteplaysforteam (messi, barcelona) and professions (van gogh, artist). the variables a and b represent entities or attributes in the real world, and r represents the binary relationship between them. billions of these facts constitute a large complicated knowledge network. the construction of such large-scale kbs is significant for many types of natural language processing research, e.g., question answering [2], semantic analysis [1], and information retrieval [13]. however, existing facts stored in kbs are not comprehensive compared with real-world knowledge. many important entity-relationships are missing due to improper manual operations or the drawbacks of fact-extraction models [38]. fortunately, most of the information can be inferred from existing facts in kbs. thus, the task of knowledge base completion (kbc) has aroused intense attention in both academic and industry research [27,36]. graph-based approaches for kbc is an important subfield of statistical relational learning (srl) [10]. a classic learning method in srl is markov logic network (mln) [32], which combines the probabilistic graphical model with first-order logic for knowledge inference. although mln is very powerful, it needs to explore all derivation trees and combine them to calculate even a single ground fact; therefore, mln does not perform well when the amount of data is large. inductive logic programming (ilp), such as the first-order inductive learner (foil) and its variants, is another type of srl [18,23,31]. foil deploys the separate-and-conquer strategy to learn the first-order logic rule set; however, similar to mln, foil cannot handle large-scale kbs. proppr [37] is a new direction that attempts to improve the scalability of foil. the path ranking algorithm (pra) [19] aims to reason new facts directly from facts observed in the kbs and achieves state-of-the-art performance compared with previous work. pra attempts to encode the entire kb as a large edge-labeled directed graph and exploits random walks to extract informative path features. each path feature can be viewed as the most frequent sequence of relations in the graph. however, pra is deficient in terms of addressing long-tail data distributions because some rare entities lack common path features. another direction emphasizes the extraction of relations from a large text corpus and combination with current kbs to enhance the performance of pra [7–9,20,21]. the extracted relations are added to the original directed graph as new edges to compensate for the lack of sufficient facts. however, directly adding relations with similar semantics to the kb results in severe path explosion and feature sparsity. in addition to the aforementioned models, many approaches focus on latent feature models [27], which aim to learn latent representations of entities and relations by minimizing a reconstruction loss or a margin-based ranking loss [4, 12, 25, 28, 29]. latent feature models are effective to encode knowledge representations, but when the kb tensor constructed from data has a higher rank, it is more difficult to obtain meaningful embeddings. in this paper, motivated by pra, we propose a more general framework called the path feature learning model (pflm). the pflm is a two-stage model: in the first stage, two types of path features (directed relation paths (drps) and supplement relation paths (srps)) are generated from different target entity pairs by random walks; in the second stage, we incorporate the learned features into the kernel extreme learning machine (kelm) [14, 15] for kbc. in addition to the advantages of good generalization and fast learning speed [16], kelm is robust in terms of triple classification, which is illustrated in our experiments. the main highlights of this paper are summarized as follows: 1. with the stage of path feature learning, plenty of classification algorithms (in our case, we use elm and kelm) can be easily incorporated into our framework. using the new path a knowledge base completion model based on path feature learning 73 features and the single-hidden layer feedforward neural network, the pflm can perform kbc effectively and efficiently. 2. the pflm is an extensible scheme. in addition to the drps introduced by pra, the proposed srps and other expected path information (e.g., immediate nodes) can be collected in the path feature learning stage. 3. the results of experiments show that our model achieves significant and consistent improvements compared with baseline models, such as pra [19] and cpra [7]. the rest of our paper is organized as follows: section 2 introduces the background of basic model setting. in section 3, we introduce the detailed implementation strategies for our model. experimental details and discussions are provided in section 4. the last section draws our conclusions and identifies future work. 2 background in this section, we first give a brief overview of pra and kelm. pra is a classic algorithm of kbc. kelm is an efficient neural network architecture that we employ to implement triple classification. triple classification [33] is a standard way to evaluate kbc. 2.1 path ranking algorithm (pra) pra leverages multiple random walks to reach tail entity b from head entity a for each entity pair (a,b). the filtered paths that connect entity pair (a,b) serve as different path features. then, the random probability of head entity a reaching tail entity b through path-constrained random walks is calculated as the feature value. finally, pra adopts logistic regression based on the limited-memory broyden-fletcher-goldfarb-shanno(l-bfgs) to perform triple classification. the effectiveness of pra depends on the power of the horn clause rules to obtain the entity constraints. a relation path is generated by the conjunction of a sequence of triples, for example, given writercreatedrole (hemingway, santiago) → roledescribledinbook (santiago, the old man and the sea), the predicative information writerwrotebook (hemingway, the old man and the sea) can be inferred. by exploiting the implicit relation paths, novel facts that do not originally exist in the kbs are produced. the more detailed procedures are as follows: pra encodes the whole kb as a directed edgelabeled graph g(n, t , e). n is the set of entities in kb, e is the set of edges connecting entity pairs, and t is the collection of edge types representing first-order logic rules. a path p is the ordered sequence of edges p={r1,. . . ,rn}. pra applies multiple random walks, starting from the head entity a, to obtain the common path set s(p) = {p1, . . . ,pk} for the common tail entity b. s(p) is relevant to a specific relation r. the most frequent paths from s(p) are selected as path features. each path feature value va,p (b) is calculated using the recursive formulas defined as follows. if p is an empty path: va,p (b) = 1 if a = b (1) va,p (b) = 0 otherwise. (2) if p is not an empty path, p ′ = r1, . . . ,rn−1: va,p (b) = ∑ b′∈range(p ′) va,p ′ (b ′) ·p(b|b′; rn), (3) 74 x. lin, y. liang, l. wang, x. wang, m. yang, r. guan where p(b|b′; rn) is the probability of reaching target node b from b′ with one edge labeled rn, and range(p ′) is the set of target nodes where the path p ′ ends. 2.2 kernel extreme learning machine (kelm) extreme learning machine is a learning algorithm aims to train single-hidden layer feedforward neural networks. huang provided strict theoretical proof that the standard single-hidden layer feedforward neural networks training process can be considered as finding a least-squares solution β′ for the linear system hβ = t that allows the hidden node parameters wi and bi to be randomly generated. h is the hidden layer’s output matrix, β is the output weights and t represents the training sample outputs. in most cases, when the number of hidden nodes is much less than the number of distinct training samples, β′ = h†t can be calculated with zero error to approximate these training examples, where h† is the moore-penrose generalized inverse of h. elm shows good generalizability and learning speed; it performs better than the conventional learning algorithm in many areas, such as face recognition [40], text classification [39], image classification [5], and medical diagnosis [24]. kernel extreme learning machine (kelm), in contrast to traditional elm, conducts classification and regression with kernel functions and kernel parameters (c,γ) instead of the number of hidden-layer nodes and path feature mappings h(x). the reason we choose kelm as our classifier is its robustness, which will be discussed in our experiments. the kernel matrix is defined as ωelm = hh t (4) ωelm (i,j) = h(xi) ·h(xj) = k(xi,xj), (5) where k(xi,xj) is the kernel function. the output weights and output function are expressed as follows: β = ht ( i λ + hht )−1t (6) f(x) = h(x)β =   k(x,x1) . . . k(x,xn )   t ( i λ + ωelm ) −1t. (7) 3 proposed method motivated by pra, we propose a novel model called the path feature learning model (pflm), which combines path feature learning with kelm to achieve triple classification. fig. 1 illustrates the framework for our model. the inputs of our model are the knowledge base and the large parsed corpus. the path feature learning aims at providing a large real-valued matrix for kelm. the final output of pflm is predicting whether the entity pair contains the specific relation, which is a binary classification problem. the main reason for combining these operations is that the first stage of our model assumes that all background knowledge and samples are ground facts, which makes it more suitable for machine learning models. on the premise of representing the kb as a large directed heterogeneous graph with encodable horn clause rules, random walk inference outperforms traditional searching methods, such as the breadth-first search algorithm, in the process of the feature searching. the retrieved first-order horn clause rule sets, which are represented as paths, can be recognized as candidate path features. we first introduce the a knowledge base completion model based on path feature learning 75 knowledge base match subjects and objects large parsed corpus collect verbs to produce knowledge matrix exploit pca to generate latent embeddings use the stratified sampling and down sampling techniques pathfinders search drps pathfinders search srps pathfollowers compute path feature values x1 x2 xn xn-1 h1 hn-1 hn y h2 ...... β1 β2 βn βn-1 ...... path feature learning figure 1: flowchart of the path feature learning model. concept of path feature learning, which includes drps and srps. then, we introduce two practical techniques in subsection 3.2 for efficient implementation. 3.1 path feature learning in the path feature learning stage, we define two path types according to their components. drps are the existing chains of the horn clause rules that are directly searched by pra. srps are the sequences of relations with some latent embeddings that are learned from factorization of the kb matrix m. specifically, each row of the matrix is a tuple t = (subject,object), while subject and object are entities that exist in the kb and also occur in 500 million dependencyparsed web documents [7, 21]. each column of the matrix is the verb in t. the elements of the matrix are the frequencies of (t, verb) in the extra corpus. after row normalization and centering, we apply pca to m to obtain the latent embeddings of the verbs. as shown in the fig. 1, both drps and srps can be searched and computed by pra according to the equations 1, 2 and 3. we consider a specific example to explicitly describe the two path types. an important reasoning path for the triple athleteplaysinleague (lebron james, nba) can be expressed by drp as athleteplaysforteam (lebron james, cleveland cavaliers) → teambelongedtoleague (cleveland cavaliers, nba). unfortunately, the knowledge repository may miss the valuable relational edge athleteplaysforteam (lebron james, cleveland cavaliers), so it seems impossible to infer the fact athleteplaysinleague (lebron james, nba), which is known to us but disappears in the kb. this feature sparsity problem leads to severe over-fitting for many isolated entity pairs, which limits the performance of pra. in our method, the proposed srps combine the extracted subject-verb-object (svo) information that expresses similar semantics of athleteplaysforteam from the extra text corpus. the method exploits verb clustering techniques to map the lexicalized edge labels (e.g., ‘works for’ , ‘plays for’, ‘leads to’) to some latent embeddings, forming a new edge type: latentem1 (leborn james, cleveland cavaliers). the probability of extracting important path features is increased by using both drps and srps compared with that of pra, which only considers drps in 76 x. lin, y. liang, l. wang, x. wang, m. yang, r. guan kbs. the path features are quite logical for our understanding and effective for the large feature space. after the entity pair path information has been taken into account for all datasets, the most frequent m candidate path features are selected as the final path features. each path feature is composed of relations or latent embeddings or mixtures of both. it is reasonable to regard the random probability values as path feature values, even if the computation requires enormous running time. the larger the probability is, the more likely a specific path feature will be selected for our target relation. this attribute cannot be reflected by binary features. finally, the abstract graph information is mapped to relevant feature vectors, and the kelm completes the task of triple classification. it is noted that the pflm is a general scheme. we chose kelm as our second stage classification model, in contrast to the logistic regression model adopted by pra. different kernels and parameters can be selected for different relation decisions. in general, in the stage of path feature learning, in addition to drps, we propose srps to explictly identify path features from the kbs and extra corpus; in the second stage, the feature matrices are transferred to the kernel extreme learning machine classifier to complete the task of triple classification. furthermore, each feature matrix is computed by random walks following the concrete relations indicated by drps or srps. missing relations can be added to the kbs based on accurate classification results. 3.2 two practical sampling techniques due to the knowledge bias in kbs, sampling techniques must be employed to balance the datasets. when adopting random walks to obtain positive and negative samples, two points must be considered. first, the relation types contained in kbs are probably uneven, and this phenomenon may affect the overall model’s feature distribution. therefore, we consider [21], which employs stratified sampling [32] to obtain identical sample numbers for different types of relations when possible. secondly, pra holds the closed-world assumption. in addition to the small portion of positive samples already existing in knowledge graph g, most of the samples are negative ones produced by random walks, which may lead to a serious distribution imbalance. we use a downward sampling technique to solve this problem. to control the positive and negative samples within a reasonable proportion (in our case, the ratio is 1:10), the pflm selects the common relation paths in accordance with the sampling numbers. for example, in a large-scale kb, we present thousands of entity pairs for athleteplaysinleague. in addition to the path athleteplaysforteam → teambelongedtoleague, many other related paths, such as athleteplayssport → athleteplayssport−1 → leagueplayers−1, athleteplayssport→stadiumhometosport−1→ leaguestaduim−1, and athleteplaysforteam → athleteplaysforteam−1 →leagueplayers−1 are contained in the set. we denote r−1 as the inverse of relation r (i.e., writerwrotebook−1 is equivalent to bookwritenbywriter). after sampling and feature computing, the pflm provides the final feature matrices composed of multiple entity pairs with their feature vectors for the target relation athleteplaysinleague to the kelm classifier. 4 experiments and discussion we use the never-ending language learning (nell) dataset to evaluate our model. nell is a large-scale kb whose contents are learned by reading from the web over time. the dataset we employ is a benchmark and can be downloaded from http://rtw.ml.cmu.edu/emnlp2013_pra/. this dataset contains 15 relations, and each relation is split into two parts: 10% test data and 90% training data. to rigorously compare and decrease the extensive feature computing, we follow the same rules as cpra [7] and set the number of each sample’s path features as 750. a knowledge base completion model based on path feature learning 77 1 2 3 4 5 6 7 8 9 10 0.5 0.6 0.7 0.8 0.9 companyceo 1 2 3 4 5 6 7 8 9 10 0.65 0.70 0.75 0.80 0.85 athleteplaysinleague 1 2 3 4 5 6 7 8 9 10 0.08 0.18 0.28 0.38 0.48 publicationjournalist 1 2 3 4 5 6 7 8 9 10 0.20 0.35 0.50 0.65 0.80 roomcancontainfurniture 1 2 3 4 5 6 7 8 9 10 0.20 0.25 0.30 0.35 0.40 headquarteredin 1 2 3 4 5 6 7 8 9 10 0.43 0.46 0.49 0.52 0.55 animalistypeofanimal 1 2 3 4 5 6 7 8 9 10 0.025 0.050 0.075 0.100 0.125 cityliesonriver 1 2 3 4 5 6 7 8 9 10 0.22 0.28 0.34 0.40 0.46 citylocatedincountry 1 2 3 4 5 6 7 8 9 10 0.35 0.45 0.55 0.65 0.75 stadiumlocatedincity kelm elm 1 2 3 4 5 6 7 8 9 10 0.42 0.46 0.50 0.54 0.58 teamplaysagainstteam figure 2: comparison of 10 relations’ data with elm and kelm based on rbf kernel function. the horizontal axis represents the experiment times, and the vertical axis represents the f1measure. table 1: the comparisons of four different kernels for kelm macro-precision macro-recall macro-accuracy macro-f1 linear kernel 0.5249 0.3798 0.8939 0.4130 wavelet kernel 0.5974 0.3862 0.8960 0.4332 rbf kernel 0.8825 0.4172 0.9488 0.5279 polynomial kernel 0.8021 0.4034 0.9446 0.4986 we first compare the robustness of elm and kelm for triple classification on ten diverse relation datasets; the corresponding path features are the same and are both provided by path feature learning. the results are shown in fig. 2. from fig. 2, it can be seen that when the number of samples is small, the elm’s f-measure oscillates between several fixed values, and the vibration is drastic in some relation datasets, such as stadiumlocatedincity. when the number of samples is larger, we intend to implement more hidden layer nodes to improve the network generation, and elm fluctuates more severely, thus causing a decline in accuracy and stability, such as the relation roomcancontainfurniture. by contrast, when we choose kelm and fix the corresponding kernel parameters, kelm is more robust and performs better than elm. therefore, we chose kelm as our classifier instead of elm based on the experimental results. moreover, we compare the performance of different kernel functions on the same task. table 1 presents the experimental results. the best performances are indicated in bold for all tables in this section. we employ the grid-search strategy to select the best parameters c and γ for different kernel functions. the parameters are both tuned in {2−5, 2−3, . . . , 215}. we evaluate the linear, wavelet, rbf and polynomial kernels on four measurements among 15 relation datasets. from the results, we can observe that the effects of different kernels vary considerably, and the rbf kernel achieves the best performance. the most notable result is that the rbf kernel is 35.8%, 3.8%, 5.5%, and 11.5% higher than the linear kernel, which indicates that the dot 78 x. lin, y. liang, l. wang, x. wang, m. yang, r. guan table 2: running time comparison for pra, cpra and pflm pra cpra pflm time(min) 1313 1160 1191 table 3: detailed comparison of the f1-measure for pra, cpra, and pflm pra cpra pflm animalistypeofanimal 0.5214 0.5270 0.6069 athleteplaysforteam 0.2156 0.6387 0.6667 athleteplaysinleague 0.8099 0.7402 0.8370 cityliesonriver 0.0493 0.3076 0.5484 citylocatedincountry 0.1538 0.5454 0.5778 companyceo 0.2857 0.3529 0.7273 countryhascompanyoffice 0.0000 0.0000 0.1481 drughassideeffect 0.9629 0.9427 0.9474 headquarteredin 0.3076 0.6382 0.6047 locationlocatedwithinlocation 0.3950 0.4147 0.4286 publicationjournalist 0.0967 0.1594 0.5444 roomcancontainfurniture 0.7206 0.7320 0.7985 stadiumlocatedincity 0.5263 0.6666 0.7143 teamplaysagainstteam 0.4736 0.2086 0.5800 writerwrotebook 0.5911 0.8000 0.8218 product in an infinite-dimensional space is more suitable for our problem. therefore, we choose the rbf kernel as our kernel function in the following experiments. in table 2 we compare the running times of pra, cpra, and pflm. from table 2 we can conclude the following. 1) pra adopts logistic regression with l2 regularization optimized by l-bfgs to complete the triple classification. it calculates the path feature values with dozens of iterations, which means that its convergence is slow. 2) cpra reduces the number of relation paths that random walkers need to search, therefore it is the fastest method. 3) although the computation and selection for the path feature learning of the drps and srps consume substantial running time, the pflm is not as time-consuming as pra. in table 4 we compare pra, cpra and pflm on 15 relations. the experimental results show that the pflm achieves significant and consistent improvement. for example, the pflm is 12% and 23% higher than cpra and pra on the macro f1 criterion, respectively. we believe the pflm outperforms cpra because the pflm can better incorporate expressive path information to address the problem of feature sparsity, which limits the performance of cpra and pra. we reproduce the experiments in [30], and table 3 shows a more detailed comparison of the f1 measurement for the three models on 15 nell relations. from table 3, we can conclude that the pflm shows significant improvement in the triple classification of 13 table 4: macro measurement comparison for pra, cpra and pflm (%) macro-precision macro-recall macro-f1 macro-accuracy pra 0.7458 0.3443 0.4073 0.9373 cpra 0.8094 0.4241 0.5116 0.8708 pflm 0.9009 0.5152 0.6367 0.9439 a knowledge base completion model based on path feature learning 79 table 5: most impressive path features in the three relations path type element of path atheleteplaysforteam drps -athleteledsportsteam-athelteledssportsteam-teamhomestadium-teamhome stadium−1srps -le1−1-le2-le1−1-le2-teamhomestadium-teamhomestadium−1cityliesonriver drps -proximityfor-subpartof−1-riverflowsthroughcity−1-statecontainscity−1-atlocation-riverflowsthrough city−1srps -le1le5−1-le1le5−1-riverflowsthroughcity-riverflowsthrough city−1companyceo drps -organizationleadbyperson-worksforsrps -le1le5le1le5−1-agentcollaborateswithagent-le1−1-le2-le1le2-agentcollaboratesswithagentrelations, with the largest increases of 463.0 % and 241.5 % compared with pra and cpra on the relation publicationjournalist. table 5 displays the impressive path features (drps and srps) that are searched by path feature learning on three different relations. these paths are common to all entity pairs, and their importance in triple classification are reflected by their own path feature values. for each path type, we present two examples, and we can observe that the highest-weighted drps and srps have similar semantics with the target relations. 5 conclusions and future work in this paper, we propose the pflm to solve the problem of large-scale kbc. the pflm extracts drps and srps during the path feature learning stage and sends these features to the kernel extreme learning machine. the pflm shows superior classification ability compared with the original algorithm and its variants on the benchmark datasets. for our future work, we plan to 1) incorporate more path information into the path feature learning schemes, e.g., the path-type limitation and immediate nodes; 2) enhance the efficiency for computing path feature values by exploiting parallel or distributed computing. acknowledgements the authors are grateful for the support of the national natural science foundation of china (no. 61572228, no. 61472158, no. 61300147, no. 61602207 and no. 61572225), the united states national institutes of health (nih) academic research enhancement award (no.1r15gm114739), the national institute of general medical sciences (nih/nigms) (no.5p20gm103429), the science technology development project from jilin province (no. 20160101247jc), the premier-discipline enhancement scheme supported by the zhuhai government and the premier key-discipline enhancement scheme supported by guangdong government funds. 80 x. lin, y. liang, l. wang, x. wang, m. yang, r. guan bibliography [1] agirre, e.; 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(2011); face recognition based on extreme learning machine, neurocomputing, 74, 2541–2551, 2011. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 6, month: december, year: 2020 article number: 3977, https://doi.org/10.15837/ijccc.2020.6.3977 ccc publications water demand forecasting using deep learning in iot enabled water distribution network l.k. narayanan, s. sankaranarayanan, j.j.p.c. rodrigues, s. kozlov lakshmi kanthan narayanan department of computer science and engineering chennai institute of technology chennai, india lakshmikanth.vlsi@gmail.com suresh sankaranarayanan* srm institute of science and technology chennai, india *corresponding author: sureshs3@srmist.edu.in joel j. p. c. rodrigues 1 federal university of piauí (ufpi), teresina – pi, brazil 2 instituto de telecomunicações, portugal. 3 itmo university, st. petersburg, russia sergei kozlov itmo university, st. petersburg, russia abstract most of the water losses occur during water distribution in pipelines during transportation. in order to eradicate the losses, an “iot based water distribution system” integrated with “fog and cloud computing" proposed for water distribution and underground health monitoring of pipes. for developing an effective water distribution system based on internet of things (iot), the demand of the consumer should be analysed. so, towards predicting the water demand for consumers, deep learning methodology called long short-term memory (lstm) is compared with traditional time series methodology called auto regressive integrated moving average (arima) in terms of error and accuracy. now based on demand prediction with higher accuracy, an iot integrated “water distribution network (wdn)” is designed using hydraulic engineering. this wdn design will ensure minimal losses during transportation and quality of water to the consumers. this will lead to development of a smart system for water distribution. keywords: internet of things, recurrent neural network, long short-term memory, auto regressive integrated moving average, water distribution network. https://doi.org/10.15837/ijccc.2020.6.3977 2 1 introduction in the present era, people are facing many health hazards due to accessing unsafe water. a report of world health organization (who) states that the imbalance in the quantity and quality of water leads to the downtime of human activities. it is the need of the hour to construct a safe water distribution and management system in order to save our future generations. the major reason for the water quality issues are because of breakdown or crack in pipes, poor monitoring and demand forecast system, loss in quantity of distribution due to water theft and leakages. also, the current water distribution system that is in practice in india is the traditional system which has not changed over more than 100+ years. figure 1 illustrates the traditional water distribution system. figure 2 shows a typical “water sub-distribution” system which describes how water is distributed to all the consumer nodes. figure 1: water distribution system figure 2: water sub distribution system there are two types of water distribution network (wdn) based on the location of pipes laid which are above ground water distribution network (agwdn) and underground water distribution network (ugwdn)[3]. in water distribution systems, the supervisory control and automation is not in practice throughout the network. all the process are completely automated only at the water treatment unit using scada and not done yet especially in the distribution part of the network[5]. in the traditional water distribution system, there is no part for the demand forecast analysis. for the determination of rate of flow in the pipe network, fuzzy based method is used in the traditional system[4]. it is hard to deploy the sensors under the surface of ground which plays a vital role in making decision for operational control by the engineer. so with advent of iot [22], we can make this into reality. in order to take real time decision based on data communicated by the underground sensor, fog/edge computing introduced by cisco been integrated with iot. the computation delay, latency and bandwidth are reduced to a great extent by the evolution of fog computing which is an extension of cloud computing in iot[8, 29]. so accordingly, an “iot” integrated with “fog and cloud computing” for underground “water distribution management” and pipe health monitoring proposed where all the real time processing happen at the edge level called fog computing. all the data is captured from various sensors that are deployed internally and externally over the surface of pipe with laminated protection under the surface of ground. for storing the processed and unprocessed data for future operations like billing, consumption analysis etc., cloud computing employed which is a part of the system[17]. for developing such an iot based system for the water distribution management, the first and foremost step is identifying the consumption pattern and behavior of conhttps://doi.org/10.15837/ijccc.2020.6.3977 3 sumer through the detailed analytical study of historical data. there are various realistic models that are used for demand forecasting like “svm, regression, ann, arima” which has produced proven results as available from literature[10, 13, 18]. the current trend is more towards deep learning which uses various numbers of interconnected different layers present in the neural network. deep learning ensures neural network to learn from a large volume of data and complex algorithms are used to train the neural net. figure 3 shows the design layer of the neural network system. figure 3: a neural network label so, the proposed work aims at designing an efficient water distribution system based on demand forecast for smart city with minimal transportation losses. so, towards this, historical “water consumption” data is used for studying the consumer behaviour. so accordingly, the water demand predictions were made for a daily consumption over 3-month period as a case study using “lstm and arima” in terms of error and accuracy. the major contribution of the paper are as follows. •prediction of daily consumption water demand using “deep learning” method called “lstm”. •prediction of daily consumption water demand using “arima”. •comparative analysis of lstm versus arima based demand forecasting. •design and analysis of “water distribution network” based on “lstm” based forecast using epanet for 24 hr period. the rest of the sections in this paper organised as follows. section 2 discusses about the various technologies adopted in construction of water distribution system and also about various methods for demand forecasting. section 3 discusses on water distribution architecture for iot followed by the operation of water distribution system and water demand forecasting using “lstm” which is a variant of “recurrent neural network”. section 4 talks on the results and discussion which compares the water demand forecasting using “lstm” with traditional “time series analysis” method called “arima” in terms of error and accuracy. also based on demand forecasting using lstm, water distribution design carried out using epanet. section 5 is the concluding and future work section which gives the concluding remarks of the research with future direction of research. 2 literature review many researches are made on designing an “iot water distribution system” using sensors in order to monitor the supply and quality of water. this “wds” will display the real time water consumed by the customers[6]. in terms of scada for water distribution, data is collected from various “sensors” deployed in the network from remote locations and correspondingly processes the data[7, 28]. research been done towards identifying the quality of water by employing randomized pollution matrix and maximum column coverage methodologies[23]. researchers have developed a system employing microphone and acoustic signature for sensing the water flow and actively controlling the water flow. in addition, determination of fault or leak in the pipeline analyzed too[19]. now towards “water distribution automation”, solution is provided towards “fault-diagnosis, fault-detection, flow-pressure variation regulation and control, isolation and restoration” of pipes in the network, enhancing and improving the efficiency and performance of the “water distribution system”. for towards development of smart sensors, “micro electro mechanical system (mems)” has played a very important role[1]. for covering up large distance between placement location of water tanks, “ultrasonic sensors and sub-ghz based systems” are developed. for controlling the operation of “water distribution system”, “smart valves” are introduced[24]. research carried out towards the development of contamination warning system (cws) for “drinking water distribution systems”. for detection of contaminants rapidly, placement of sensors is the critical aspect in the design of “cws”[14]. research been done https://doi.org/10.15837/ijccc.2020.6.3977 4 towards measuring the hydrogen ion concentration present in the water flow through the pipe by deploying ph sensors. this is helpful in assuring the quality of water that is supplied to customer[11]. research also been done by measuring conductivity in water by employing conductivity sensor for water quality. this sensor allows you to measure water conductivity which is included in open garden hydrophonics[15]. “internet of things” refers to networking the devices towards sensing and collecting the data from the environment and sharing the data across different devices towards processing for various purposes[16]. researcher have employed iot towards the leakage detection in the pipe which is based on the “drip-out” happening outside the surface of the pipe. “moisture sensors” are deployed at the top and bottom surface of the pipe outside[27]. for effective water management, “electromagnetic wave” communication happen between “underground” and “aboveground” sensors. the communication range is “2.4ghz frequency” and “operating range is 73 meters to 100 meters[? ]. research been done towards proposing an iot based reference architecture called the integrated water resource management which specially focuses on the water management. it is a unified architecture for linking and embedding process control mechanism. research has been done in connecting the intermediate gateway to the control system to the internet. the web based application is used as a gateway to communicate to the internet. a system for servicing the city piped network intelligently using iot is developed using dynamic simulation model, which is used to monitor the drainage pipes underground. geographical information system is used as spatial management system[20]. for monitoring the water supply and controlling the water theft, novel and innovation system developed. “adafruit” server used for simulation of the system[25]. researchers have developed a system for collecting the data sources and the data analysis carried out. the consumption of data is done online through data visualization[2]. it has been seen so far that good amount of work done by employing iot for water distribution, water flow, water quality and water theft. now in terms of water demand prediction, research been done by employing “machine learning” which are discussed below. a system to predict the water demand for a region is done using “artificial neural network (ann)”. the forecast data of “laminga water treatment plant and distribution network” is taken into consideration for 60 days. the supervisory control is designed to control the login security through user name and password protection, interfacing the demand node for the activation / deactivation of pumps and also interfacing the activation/deactivation of valves[13]. research done towards a deterministic model for the prediction of water demand in the austin household area is taken into considerations. the consumption pattern is correlated with the number of persons in the household. “kohenen self-organization maps” are used for clustering. the graphical analysis of consumption is made in 7*7 formats. the application of time series analysis in data analytics has made many improvements in terms of accuracy of the prediction made. the water distribution of milan city is validated over the demand forecasted and actual consumption through a test bed setup. svm is the method used for performing this analysis[13]. on top of scada system, an artificial neural network -ann is deployed. this system is implemented on the basis of case study done in nigeria. solution is provided towards remote monitoring of hydrology parameters and drawing a feasible solution towards scarcity of water in the water distribution network[10]. adaptive seasonal regression model and auto regressive model with fixed seasonality is used for short term water demand prediction. this analysis was carried out by using a statistical forecasting method based on the data obtained from prototype test bed. this resulted in derivation of a framework called time series forecasting framework(tsff)[9]. a decision-making model is derived as a result of analysis carried out on case study of demand forecasting of water consumed by people of residential buildings in korea. demand forecasting is made by back propagation neural network with considerations of climate, seasonality components and geometrical values. two years consumption of residents from 2012 to 2014 is used as training input[26]. from the data analytics employed in water demand prediction, it has been concluded that good amount of research done by employing machine learning algorithms like ann, svm, bpnn and regression. none of the system has employed deep learning for water demand prediction. in addition, none of these water demand predictions been integrated into iot based water distribution system towards water distribution design. none of the system is implemented using short term distribution like daily water demand forecast integrated with water distribution design with minimal loss of water and meeting the demand of consumers. the forthcoming session will discuss about integration of fog and cloud computing with iot based water https://doi.org/10.15837/ijccc.2020.6.3977 5 distributed system architecture design in detail. 3 water distribution architecture using iot there are different types of systems that are in practice for demand forecasting, quantity measurement, automation of distribution etc. all these systems are standalone applications. "iot based water distribution architecture” integrated with “fog and cloud computing” been proposed [17, 21] based on the above-mentioned points. figure 4 illustrates an “iot” based architecture for “water distribution and underground pipe health monitoring system”. figure 4: an iot based water distribution architecture[17] 3.1 operation of water distribution system the design of the “water distribution system" is based on the prediction of consumer demand. the water distribution forecasting is done based on historical water consumption data. the water supply in the network will be initiated by the “scada” engineer on the basis of forecast made. this forecast based distribution enables the scada engineer to look for alternate source if the required quantity is not available. the hydraulic parameters like “flow, pressure, velocity and also quality” is monitored through the various sensors positioned at pre-defined intervals in and around the pipe. for keeping track of pipe health, quality of water flow and hydraulic parameters like the flow, pressure inside the pipe need to be monitored. different methodologies pertaining to “water demand prediction” need to be looked into based on historical water consumption data for predicting the water demand towards water distribution design. these are discussed below 3.2 water demand forecasting this section will discuss in detail about the prediction of water demand towards effective implementation of wds design. the water demand forecasting is done by using “long short-term memory (lstm)” which is a variant of “recurrent neural network” under “deep learning”. the demand forecast analysis using “lstm” is compared with the traditional “time series analysis” called “auto regression integrated moving average (arima)” in terms of error and accuracy. towards the demand forecasting and analysis, the day-wise water consumption data of the residents of austin city-texas, usa is considered. the dataset consists of water meter readings of various types of consumers like residents, industrial and public etc. the dataset also consists of maximum consumption data labeled as peak 1 and peak 2 along with total consumption of the day. the dataset details are furnished in public domain of us web [30]. it contains consumption data for 8 years and 3 months from jan-01-2010 till march-31-2018.the following figure illustrates the consumption of austin city over the period of jan 2010 to march 2018. 3.2.1 recurrent neural network before getting into the details of lstm which is a variant of rnn for time series forecasting, details about recurrent neural network been discussed. there are many difficulties that arise in handling sequential inputs, memorizing previous outputs etc. in the traditional neural network. so, with the upcoming of deep learning, rnn is developed. rnn is smart enough to handle sequential https://doi.org/10.15837/ijccc.2020.6.3977 6 figure 5: water consumption of austin from january 2010 to march 2018 data, keep track of current input and previous inputs and also keeps track of previous inputs due to the in built provision of memory. figure 6 illustrate the basic rnn model. figure 6: rnn processing states where ht is the output of the network after processing, xt is the input states. data at the previous states is denoted by successor -1 and future state is denoted with a successor of +1. figure 7 illustrates layered architectural representation of rnn. in rnn, there are three layers namely input, output and hidden layers respectively. here h is the new state formed after performing a geometrical operation called tan (h). in order to determine the outcome of hidden layer (h(t)) parameter, the following equation is used. h(t) = sh(whxt + uhht−1 + bh) y (t) = sy (wy ht + by ) here “xt” is input vector, ht is input layered vector, “yt“ is output vector. “w,u and b” are metrics and vector, sh is activation function within the hidden layer which can be tanh or relu. the output activation function sy is either sigmoid or softmax or linear. rnn vanishes the gradient problem that occurred in the neural network model by exploding gradients through back propagation and the gradients are vanished by using long short-term memory (lstm) networks. 3.2.2 lstm networks lstm is a special type of rnn which learns the long term dependencies at a rapid pace.it has a unique feature incorporated in it viz. keeping track of information over a longer duration of time. the rnn network is formed by repeated chains of neural network. the structural formation of these modules is very simple in structural construction. it is built with only one tan(h) layer. lstm follows a very special way to interact between the four different layers present in the structure. figure 7 shows the different layers in lstm. figure 7: lstm computation layer https://doi.org/10.15837/ijccc.2020.6.3977 7 3.2.3 working of lstm* step 1: decision over the size of remembrance of the past data the primary step in lstm is eliminating the irrelevant data from the input cell during the specific time step. this is performed by function shown in the above figure called sigmoid (s).this sigmoid layer will act as an input gate which helps in making the decision of which contents of the memory cell to be processed forward. ft = s(wf .[ht−1, xt] + bf ) where ft is forget gate, which decides on which information to be left out from the previous state of time, ht-1 is the previous state and xt is the current input. step 2. selective cell update values after the decision made on the size of memory of previous state, the comparison is made between the current input xt and previous output ht-1. it helps the lstm to determine how much of the past unit is needed to be included with the present input. a vector of new candidate value ct’ is created by tanh layer in order to write to the memory cell. the following equation is used for this selection. it = s(wf .[ht−1, xt] + bf ) ct = tanh(wc.[ht−1, xt] + bc) ct = ft ∗ ct−1 + it ∗ c′t here it is input gate which helps in deciding which of the information can be passed forward on the basis of present time step. step 3: decides which part of the present state can be proceeded to output state this state helps in setting up the output. sigmoid layer is executed formerly to determine which are the components of the cells through to the output. then tanh function is applied over the cell state in order to limit the values between -1 and 1. finally this outcome is multiplied with the sigmoid gate output.the sigmoid layer decides which part of the memory is to be written to the output gate. ot = s(wo.[ht−1, xt] + bo) ht = ot ∗ tanh(ct) here ot is the output gate, which creates an impact on the output gate by the passed information in the present time step. here for the water distribution forecast analysis, the model is trained with 43000 input sequence of daily consumption from jan 2010 till march 2017 which is the training data set. the model is designed with these inputs for performing long time prediction. the secondary model is built with automated test set training from the pre-trained model in order to perform short term prediction based on nearly six-month data as validation data set. the range of feedback delay is set within a range of 1 to 5 which is based on the number of neurons in each hidden layer, the maximum range can be between 1 to 40. the number of hidden layers is set with a range of 1 to 3 for training functions. this is set with reference to bayesian regularization and levenberg marquardt. the initial learning rate of the system is obtained as 0.0009 using solver called adam optimizer. this is shown in figure 8. here the data set consist of vectors like date, consumption in million gallons, peak-1 consumption and peak-2 consumption. the entire data is fed as input through the input layer. the forget gate is programmed to forget the null values and also the peak-1, peak-2 values in order to feed the consumption of water to predict the future demand. the output gate will have the vectors called date and predicted quantity of water in million gallons. https://doi.org/10.15837/ijccc.2020.6.3977 8 figure 8: summary of data trained with lstm model 4 results and discussion based on the lstm and arima applied for water consumption demand prediction over the data set of water consumption of austin city, this section will briefly discuss on the forecast analysis result in terms of accuracy of forecast and percentage of error in prediction. based on this forecast analysis, an iot based distribution network design construction is discussed in the following section. the algebraic expression which plays a vital role in determining effectiveness of the prediction model in terms of efficiency is described below. et = ot − ft here et is the calculated error, qt is actual amount of water consumed and ft is the predicted demand. m eanerror[m e] = 1 n ∑ i=1n et m eanabsoleteerror[m ae] = 1 n ∑ i=1n |et| the percentage of error is calculated as follows p e = ot − ft ot ∗ 100 here pe is error in percentage. the following figure shows the lstm model trained with the data set.comparative analysis of prediction vs. consumption of water is done and the results are discussed below. figure 9 and 10 shows the lstm and arima forecast versus consumption for 2018. table-1 and 2 shows the prediction for 3 months in 2018 for lstm and arima. table-3 represent the computation of errors and accuracy that are found during forecast analysis using recurrent neural based lstm and statistical based arima models. from the table it is evident that the accuracy of lstm model is nearly 12 percent higher when compared with arima. from the table it is also proven that in spite of the prediction, the consumption is higher over a greater period of time. we cannot claim that the entire volume is properly consumed by the consumers. the consumption data includes volume of water lost during transportation and water theft occurred etc. it is necessary to minimize the losses https://doi.org/10.15837/ijccc.2020.6.3977 9 figure 9: lstm forecast vs consumption for 2018 season 1 figure 10: arima forecast vs consumption for 2018 season 1 table 1: prediction for 3 months using lstm date (o) 2018 (f) 2018 act(o-f)/o mod(2018 percentage actual 2018 sq. consumed -ual predicted =error error) error error error 1 jan 13953.60 14853.76 0.06 0.06 6.45 -900.16 810279.82 2 jan 13861.20 14168.71 0.02 0.02 2.22 -307.51 94565.27 3 jan 15435.60 14396.96 -0.07 0.93 93.27 1038.64 1078780.87 29 mar 14768.40 14043.00 -0.05 0.95 95.09 725.40 526203.74 30 mar 13683.60 14043.00 0.03 0.03 2.63 -359.40 129170.47 31 mar 13683.60 14043.00 0.04 0.04 4.41 -593.40 352128.20 total 40.41% 3689248.26 table 2: prediction for 3 months using arima date (o) 2018 (f) 2018 act(o-f)/o mod(2018 percentage actual 2018 sq. consumed -ual predicted =error error) error error error 1 jan 13953.60 13172.44 -0.06 0.94 94.40 781.16 610205.31 2 jan 13861.20 13513.51 -0.03 0.97 97.49 347.69 120891.50 3 jan 15435.60 13604.39 -0.12 0.88 88.14 1831.21 3353314.18 29 mar 14768.40 13888.32 -0.06 0.94 94.04 880.08 774542.09 30 mar 13683.60 14186.21 0.04 0.04 3.67 -502.61 252616.78 31 mar 13683.60 13941.76 0.04 4.41 -492.16 242222.18 total 52.45% 97274284.40 since fresh water availability has started diminishing. in order to overcome this flaws need a wdn with smartness. the graphical representation of error and prediction accuracy is illustrated in the figure 11 and figure 12 respectively. https://doi.org/10.15837/ijccc.2020.6.3977 10 table 3: comparison of accuracy betweeen lstm and arima lstm arima mape 40.41 52.45 accuaracy 59.59 47.55 mase 3.6x10^5 9.7x10^6 m eanabsolutep ercentageerrorm ap e = 1 n ∑ i−1n m od[p e] accuracy= 100 mape the accuracy is derived by reducing the percentage of error from hundred. the accuracy obtained using lstm model is better than that of forecast analysis done by arima. the accuracy of the lstm is obtained nearly 60 percentage whereas arima is only 48 percentage on validating the entire data set. figure 11: mape comparison between arima and lstm figure 12: comparative analysis of percentage error and accuracy 4.1 water distribution design using epanet on the basis of the demand forecast done using lstm model for the first quarter of the year 2019, the distribution network is designed on the basis of following assumptions as shown in table 4. table 4: assumptions for water distribution design s.no name of the parameter description 1 no. of distribution points 50 2 material of pipe pvc 3 no. of junctions 26 4 diameter of pipe 100mm 5 length b/w each junction 500m 6 roughness co.efficient 140 7 calculation method hazen williams formula the predicted data of jan 17, 2019 is taken into simulation for the pipe network design. the total quantity of water predicted is taken as an average for 24 hrs. the epanet design is constructed with an assumption of 50 distribution mains supplied by one main reservoir. • the maximum and average demand of jan 2019 is computed as 14391 million gallons based on https://doi.org/10.15837/ijccc.2020.6.3977 11 prediction done for jan 17 2019 – 14535 mg is the max requirements as per the prediction analysis done over a period of 3 months (jan 2019 to march 2019). • the prediction is assumed for one postal zone and it is assumed that 50 sub-tanks supply the entire zone and 25 no.s of junctions are used to connect all of them. • the length of the pvc connects the upper zone is assumed as 1000m and the tanks in bottom zone is 500m in length. table 5: hourly water supply on 17th jan 2019 epanet supply 17th jan 2019(in hrs) supply (mg) multiplier factor 0 400 0.2 1 500 0.06 2 500 0.02 3 540 0.03 4 695 0.06 5 1000 0.13 6 1200 0.9 7 1500 1.68 8 1700 1.35 9 1400 1.35 10 400 0.67 11 400 0.56 12 300 1 13 250 2.56 14 250 3.56 15 250 2.81 16 250 1.24 17 400 0.75 18 500 0.9 19 500 1.75 20 400 1.25 21 400 1.25 22 400 0.34 23 400 0.34 total 14535 the forecast analysis of water demand for the year 2019 is illustrated in the figure 13. the construction of water distribution network based on the forecast analysis using lstm is illustrated in the figures 14 to 16. figure 14 to 16 show the simulation of water distribution system using epanet based on lstm forecast. this simulation helps to determine the hydraulic parameters such as flow, pressure, velocity etc. throughout the wdn. the epanet design in figure 14 shows wdn design. figures 15 and 16 describes the water flow in the pipe network in the due course of time. it also helps in determining the systematic flow of water including the transportation losses like head loss which is the pre requisite for providing lossless distribution. this analysis also helps to determine the exact flow including the losses that happen during the transmission in order to match the supply as per requirement. https://doi.org/10.15837/ijccc.2020.6.3977 12 figure 13: lstm prediction for 2019 season 1 figure 14: wdn based on forecast using lstm figure 15: water flow during distribution stage on 17 jan 2019 at node 25 during 3.00 hrs figure 16: water flow in wdn on 17th jan 2019 https://doi.org/10.15837/ijccc.2020.6.3977 13 5 conclusion and future work to conclude, an iot based water distribution architecture integrated with fog computing is proposed along with a hydraulic wdn design using epanet. in addition a comparative demand forecast analysis is done for the efficient water distribution network design. this analysis is carried out on the basis of day to day consumption of water over a 3 months period using lstm and arima. the demand forecast analysis using lstm provides a higher accuracy and minimal error compared to arima. on the basis of lstm based forecast result, the water distribution system design for an iot based system is done with an aim of effective supply of water with minimal losses and well-defined quality using hydraulic engineering which will result in establishing a smart water distribution network (swdn). in future, this work can be extended towards development of software agent-based model for underground pipe health monitoring and consumption monitoring using agents built with intelligence, which will intimate the scada engineer for immediate control action and supply restoration. during critical stages this intelligent agent would bring control automation as a preventive measure. acknowledgements this work is partially funded by fct/mctes through national funds and when applicable cofunded eu funds under the project uidb/50008/2020; and by brazilian national council for scientific and technological development cnpq, via grant no. 309335/2017-5. references [1] abdelkader outtagarts. 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[30] [online] https://catalog.data.gov/dataset?q=water+consumption+demand+forecasting copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: narayanan, l.k.; sankaranarayanan, s.; rodrigues, j.j.p.c.; kozlov, s. (2020). water demand forecasting using deep learning for water distribution system design in iot enabled water distribution network, international journal of computers communications & control, 15(6), 3977, 2020. https://doi.org/10.15837/ijccc.2020.6.3977. introduction literature review water distribution architecture using iot operation of water distribution system water demand forecasting recurrent neural network lstm networks working of lstm* results and discussion water distribution design using epanet conclusion and future work international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 2, month: april, year: 2020 article number: 3862, doi.org/10.15837/ijccc.2020.2.3862 ccc publications pose manipulation with identity preservation a. t. ardelean, l. m. sasu andrei-timotei ardelean* transilvania university of brasov 500036 brasov, b-dul eroilor, 29, romania *corresponding author: andrei.ardelean@student.unitbv.ro lucian mircea sasu 1. transilvania university of brasov 500036 brasov, b-dul eroilor, 29, romania lmsasu@unitbv.ro 2. xperi corporation 500152 brasov, turnului, 5, romania lucian.sasu@xperi.com abstract this paper describes a new model which generates images in novel poses e.g. by altering face expression and orientation, from just a few instances of a human subject. unlike previous approaches which require large datasets of a specific person for training, our approach may start from a scarce set of images, even from a single image. to this end, we introduce character adaptive identity normalization gan (caingan) which uses spatial characteristic features extracted by an embedder and combined across source images. the identity information is propagated throughout the network by applying conditional normalization. after extensive adversarial training, caingan receives figures of faces from a certain individual and produces new ones while preserving the person’s identity. experimental results show that the quality of generated images scales with the size of the input set used during inference. furthermore, quantitative measurements indicate that caingan performs better compared to other methods when training data is limited. keywords: pose manipulation, image generation, adaptive normalization, generative adversarial network. 1 introduction developing a way to easily manipulate face expression and head pose of an individual has been the focus of many research groups in the last decade. the baseline solution for this task involves creating a 3d model and animate it accordingly. however, building a photo-realistic head model using standard techniques can take substantial amount of time for a human artist. many industries could benefit from optimizing this process such as cinematic, advertising and video games; furthermore, it has potential for image enhancement and editing software. doi.org/10.15837/ijccc.2020.2.3862 2 a traditional automatic method for face manipulation is based on 3dmm fitting [2]. parameters can be estimated from a single image and then changed to obtain different expressions. this method is not sufficient by itself to work with hidden regions, e.g. teeth and closed eyes [27]. other approaches rely on warping from one or more source images to the desired pose to generate guided head images [26]. a drawback of this solution is the limited amount of variation between the source and target pose it can manage without great loss of quality [28]. new approaches for face image generation have been brought by recent advances in generative adversarial networks (gans) [10]. seminal papers in this area [14, 15] show that one can generate high resolution realistic figures of human faces using gans. our contribution is a new model, character adaptive identity normalization gan (caingan), that receives figures of faces from a certain individual and produces new ones while preserving the person’s identity. caingan generates images in novel poses starting from a small set of source pictures with the individual, i.e. a few-shot setting, without any fine-tuning as found in [9, 28]. we conducted experiments to compare images generated by caingan, with alternative systems using image-to-image translation [13, 25] and conditioning based on adaptive instance normalization [12] using computed embeddings [28]. by performing quantitative measurements on the self-reenactment task we show that our model is able to achieve state-of-the-art results using less data compared to other methods and without fine-tuning. the rest of the paper is structured as follows: in section 2 we make a literature review; the subsequent section describes caingan in detail; section 4 contains a comparison between different methods and an ablation study. eventually, we summarize our contributions in section 5. code for the implementation of caingan is available at https://github.com/tardelean/caingan 2 related work a number of works on face generation with preservation of identity focus on the talking face task, i.e. the area of interest is the mouth region with motion driven by either audio sources [4, 5, 22, 30] or video to be imitated [21, 30]. these methods cannot be easily extended to synthesize full head images that require handling more variation between poses or hidden elements in the source images. while it is possible to replace the face from an existing head footage, by using a face modeling approach as in [23], the result of pose manipulation would be limited to face expression. providing conditional information to several layers of the generator has been widely used to prevent input constraints from vanishing. good results were obtained especially by modulating activations using adains [6, 12, 15] and spade [19] that employs spatial denormalization to introduce semantic map constraints. our model is based on a conditional gan framework, i.e. instead of generating starting from noise as done traditionally [10], the input of the generative network can take different forms including images [13, 19] as done in this work. the use of multiple discriminators to stabilize gan training has been recently studied in several works [1, 8, 18]. specifically our model uses two discriminators with different objectives. 3 the caingan model in this section we present the architecture of the proposed model, followed by the training algorithm. eventually we give some implementation details. 3.1 the architecture the goal is to train a generative model that is able to synthesize new images starting from k existing source pictures with the same person. let xi denote the i-th image of an image sequence x; we uniformly sample k + 1 distinct images from x. the model receives as input xi1,xi2, ...,xik along with their corresponding landmark images [3] l(xi1 ),l(xi2 ), ...,l(xik ) and target landmark l(xik+1 ), then generates a new image x̂t that must follow target landmark and preserve identity of the person https://github.com/tardelean/caingan doi.org/10.15837/ijccc.2020.2.3862 3 figure 1: full architecture and model workflow. top side depicts the embedding which is used at each upsampling step to spatially modulate activations. the generator (shown in the bottom side of the figure) starts from the target landmarks l(xt) to synthesize a new image x̂t. di and dp represent the identity and pose discriminators, respectively. from the k input images. the generated image is expected to be similar to the ground truth image xt := xik+1. we propose caingan, a model that consists of 4 networks (figure 1) which we will describe in the following. the embedder computes a spatial embedding exi from a single input image: exi = e(xi,l(xi)). implementation details can be found in section 3.3. in order to use multiple source images, a method to combine the embeddings must be devised. zakharov et al. in [28] simply averages the one-dimensional tensors computed by the embedder. we observed that weighing the features by their relevance helps to better capture the identity, therefore a responsibility based combining method was developed (eq. (1), figure 2a). to achieve this, the embedder will also output a weighing tensor rxi representing the certainty for the computed features. the final identity embedding is calculated by a function ψ as: ex = ψ ((ex1,rx1 ), . . . , (exk ,rxk )) = ∑ i∈{i1,...,ik } exi ·rxi∑ i∈{i1,...,ik } rxi (1) we explored the use of target landmarks l(xt) as input to the embedder along with xi and l(xi) and found this helpful for the generation process, since the identity features can be aligned to the final pose earlier. hence, this is the version used in our experiments, denoted targeted embedder, as illustrated in figure 2b. the generator starts from the target landmark and generates a new image x̂t = g(l(xt),ex). the landmark image is provided through the input layer while the combined spatial embedding ex is used to modulate the activations at several resolutions with spade blocks. in order to assess the quality of the generated image we employ two discriminators: identity discriminator and pose discriminator. identity discriminator di (xa,xb) follows the multi-scale architecture from pix2pixhd [25] and is used to estimate identity resemblance. at this stage we only consider characteristic traits and the results are expected to be invariant to pose. thus, the input consists of two rgb images of the same person in arbitrary positions. pose discriminator dp (xa,l(xa)) has the same architecture as di with its own set of parameters. the network receives a frame and the appropriate target landmarks and checks the correspondence. while both discriminators will also assess general realism of a given image, dp is used specifically to avoid pose mismatch, whereas di encourages identity preservation. this disengagement allows us doi.org/10.15837/ijccc.2020.2.3862 4 (a) combining embeddings based on responsibility, as in equation (1). the weights of the targeted embedder (e) are the same for each of the k inputs. the targeted embedder is detalied in figure 2b. (b) the structure of the targeted embedder. the real frame xi, its landmark image l(xi) and target landmarks l(xt) are concatenated and processed to get rxi and exi . figure 2: embedder network to assign different importance factors to each discriminator while training. different combinations may give results that correlate better with human visual perception. 3.2 training videos are used as image sequences during training. for each epoch we sample a sequence of k + 1 distinct frames for each training video and compute landmark images: l(xi1 ),l(xi2 ), ...,l(xik+1 ) using a pretrained face alignment network [3]. the embedding tensor ex is then computed according to (1). using l(xt) and ex, caingan generates the new image x̂t which is further fed to the identity discriminator di (x̂t,xi1 ) along with a source frame xi1. the choice of the identity frame index i1 is arbitrary as the generation process is not influenced by the order of input images. x̂t and l(xt) are also fed to the pose discriminator dp (x̂t,l(xt)). importance factors λi and λp control the weight of each discriminator in the objective function given by the hinge loss [16, 17]: ladv(g,e,d) = −λi ·di (g(l(xt),ex),xi1 ) −λp ·dp (g(l(xt),ex),l(xt)) (2) lid(di ) = max(0, 1 −di (xt,xi1 )) + max(0, 1 + di (x̂t,xi1 )) (3) lp ose(dp ) = max(0, 1 −dp (xt,l(xt)) + max(0, 1 + dp (x̂t,l(xt)) (4) the identity and pose discriminators are updated using lid and lp ose, respectively. the weights of the generator and the embedder are updated together using the full objective: l(g,e,d) = ladv(g,e,d) + λf m (λi ·lf m (di )+ λp ·lf m (dp )) + λv gg ·lv gg (5) λf m and λv gg represent hyperparameters that control the importance of the loss in the full objective. lv gg is a perceptual loss that compares features extracted at several layers by a pretrained vggnet [20] from the original and the generated image. feature matching loss lf m is also a perceptual loss, comparing activations in the layers of the discriminator according to (6). importance factors λi and λp also affect the weight of feature matching losses. the fm loss is similar to the one used in [25] as we employ multiscale discriminators: lf m (d) = s∑ i=1 t∑ j=1 l1(d (j) i (xt,y),d (j) i (x̂t,y)) nj (6) where s represents the number of scales, t is the number of layers in d, nj is the number of elements in layer j and l1 denotes the standard manhattan distance. y is either the value of l(xt) when doi.org/10.15837/ijccc.2020.2.3862 5 using the perceptual loss induced by the pose discriminator, or xi1 for the identity discriminator, respectively. to ease the training process we start with a low importance factor for the identity discriminator and linearly increase it to its maximum value over the first 10 epochs. this allows the model to learn the easy task first, generating realistic face images in given pose, after which we gradually impose identity preservation. we alternate between (g, e) and d updates, with twice more steps for the discriminator and using two time-scale update rule [11] to stabilize training. 3.3 implementation details the generator resembles an encoder-decoder architecture for image translation. there are 4 downsamplings residual blocks with learned skip connections, 3 same resolution residual blocks and 4 upsampling layers. instance normalization [24] is used after every downsampling and upsampling layer. a spade residual block with spectral normalization [17, 29] is used after each upsampling. nearest interpolation is used to bring the spatial embeddings to the appropriate resolution for each spade block. the discriminators are based on the architecture proposed by wang et al. in [25], and use 2 scales as the images are relatively small. the embedder consists of 2 downsampling and 4 same resolution residual blocks which are shared while computing rxi and exi. two independent same resolution residual blocks are then used to get rxi and exi. 4 experiments and results to evaluate our approach, we conduct extensive experiments on the voxceleb2 [7] dataset. to emphasize the ability of our model to learn from less data, we only use a small subset of the actual dataset. originally, the train set contains almost 6000 different speakers featuring more than a million videos. for our experiments we randomly selected 150 speakers and their corresponding videos (around 30,000), less than 3% of the grand total. a video dataset was used since it is an accessible way to obtain multiple images with the same identity. quantitative comparison is performed against two baselines: pix2pixhd [25] and previously stateof-the-art method for talking head generation [28] denoted fshm (few-shot head models). we trained the pix2pixhd model from scratch as described in the original paper and official implementation. in order to use the model without fine-tuning, the network input consists of all source frames and their landmarks as well as the target landmark; these are also given to the discriminator. we also implemented a version of fshm (feed-forward only) in order to assess the results in a limited training data setting. three different metrics are used to compare the described methods: structural similarity metric (ssim) between the ground truth and the generated image is used to measure low-level structural similarity, cosine similarity (csim) between embedding vectors as computed by a pretrained face recognition network and fréchet inception distance (fid) [11] measuring perceptual realism which usually better captures the similarity of real and fake images. we follow the same training setup presented by zakharov et al. [28], using 50 video sequences with 32 test frames for each. the comparison given in table 1 shows that caingan is able to get better quantitative results using only a fraction of the dataset. additionally, the method is able to generate realistic images in the desired pose with a good preservation of identity. from qualitative comparison in figure 3 we can see that while fshm can synthesize the face with the right alignment there is a high identity mismatch. clearly, small amounts of training images severely affect the ability of the fshm model to generalize to unseen faces. we also obtain the uncanny artifacts present in images generated by pix2pixhd, as reported in [28]. 4.1 ablation study we performed an ablation study to analyze the influence of different components of our method. quantitative results of the experiments are visible in table 2. the variants are: caingan without doi.org/10.15837/ijccc.2020.2.3862 6 method (k) ssim ↑ csim ↑ fid ↓ pix2pixhd(1) 0.66 0.80 72.26 fshm (1) 0.64 0.72 93.17 fshm-ff-full (1) 0.61 n/a 46.61 fshm-ft-full (1) 0.64 n/a 48.5 caingan (1) 0.69 0.85 35 pix2pixhd(8) 0.66 0.81 71.89 fshm (8) 0.65 0.73 83.13 fshm-ff-full (8) 0.64 n/a 42.2 fshm-ft-full (8) 0.68 n/a 42.2 caingan (8) 0.77 0.91 24.92 table 1: k is the number of source frames used for testing. for ssim and csim higher is better, for fid lower is better. caingan (8) was stopped after 20 epochs to avoid overfitting, all other models were trained for 30 epochs. the “full” suffix refers to the models being trained on the entire dataset. these results are taken from [28]. csim is not reported here, as a different face recognition network was used for the original results. source pix2pixhd fshm caingan real image k=1 k=8 k=1 k=8 figure 3: visual assessment on the voxceleb2 dataset. first column represents the number of source frames, the next column illustrates one of the k source images and the last column contains the ground truth (xt) images. in between are the generated frames by different methods. the figure is best viewed in color. targeting (caingan w/o t) where only the source frame and its landmarks are given to the embedder, caingan without discriminator importance weighing (caingan w/o i) where λd = λi are fixed and caingan without responsibility based embedding mixing (caingan w/o r) where the weighted version in equation (1) is replaced by: ex = 1 k ∑ i∈{i1,...,ik } exi (7) doi.org/10.15837/ijccc.2020.2.3862 7 method (k) ssim ↑ csim ↑ fid ↓ caingan w/o t (1) 0.69 0.85 36.26 caingan w/o i (1) 0.68 0.84 46.44 caingan (1) 0.69 0.85 35 caingan w/o t (8) 0.72 0.87 38.08 caingan w/o i (8) 0.76 0.91 28.72 caingan w/o r (8) 0.75 0.90 30.05 caingan (8) 0.77 0.91 24.92 table 2: ablation study on selection of voxceleb2 dataset. all models were trained for 30 epochs, the best result between epochs 20 and 30 was reported. k is the number of source frames. this variant is not applicable for k = 1 as in this case the two expressions yield the same result. we can observe that all components are essential to obtain the best results. using targeted embedder has a greater influence in the k = 8 setting, which is expected since more images can benefit from early alignment. 5 conclusions and future work we introduced a new method for synthesizing images in novel poses while preserving the identity of a given subject. caingan uses spatially adaptive normalization with a proper combining function of spatial feature maps in the embedding space. experimental results show that caingan behaves better on scarce training sets compared to other methods. furthermore, realistic images can be generated without the need for fine-tuning. the ablation study demonstrates caingan’s non-redundant structure whereas the difference between scores in the k = 1 and k = 8 settings illustrate the ability to capitalize on more source images when available. further development directions include designing a method to extend the applicability of caingan beyond the task of self-reenactment and closing the gap between one-shot and multi-shot results by improving on single source image generation. references [1] albuquerque, i.; monteiro, j.; doan t.; considine b.; falk t.; mitliagkas i. 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(2019). talking face generation by adversarially disentangled audio-visual representation, aaai conference on artificial intelligence, 33, 9299– 9306, 2019 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: ardelean, a. t.; sasu, l. m. (2020). pose manipulation with identity preservation, international journal of computers communications & control, 15(2), 3862, 2020. https://doi.org/10.15837/ijccc.2020.2.3862 introduction related work the caingan model the architecture training implementation details experiments and results ablation study conclusions and future work international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 6, month: december, year: 2020 article number: 4037, https://doi.org/10.15837/ijccc.2020.6.4037 ccc publications a social network image classification algorithm based on multimodal deep learning j. w. bai, c. chi junwei bai school of art and media, wuhan polytechnic university, wuhan 430023, china daqianzi007@126.com cheng chi* college of engineering and technology, hubei university of technology, wuhan 430068, china *corresponding author: aling420@126.com abstract the complex data structure and massive image data of social networks pose a huge challenge to the mining of associations between social information. for accurate classification of social network images, this paper proposes a social network image classification algorithm based on multimodal deep learning. firstly, a social network association clustering model (snacm) was established, and used to calculate trust and similarity, which represent the degree of similarity between users. based on artificial ant colony algorithm, the snacm was subject to weighted stacking, and the social network image association network was constructed. after that, the social network images of three modes, i.e. rgb (red-green-blue) image, grayscale image, and depth image, were fused. finally, a three-dimensional neural network (3d nn) was constructed to extract the features of the multimodal social network image. the proposed algorithm was proved valid and accurate through experiments. the research results provide a reference for applying multimodal deep learning to classify the images in other fields. keywords: multimodal deep learning, social network, image classification, three-dimensional neural network (3d nn). 1 introduction the model of our social interactions has been reshaped by social networks, which are booming under the proliferation of information technology and smart mobile terminals [2, 4, 6, 10, 11, 21]. the frequency actions of social network users (e.g. interaction, image upload, and sharing) complicate the data structure and inflate the scale of image data on social networks, posing a severe challenge to data mining. https://doi.org/10.15837/ijccc.2020.6.4037 2 the social network images are associated with various social information of the users, including their locations, followees, comments, and reposts. the associations contain information that are valuable for user portrayal, precision advertising, and community discovery. as a result, the clustering, classification, and frequent pattern mining of social network images have become research hotspots [12, 15, 18, 22, 24]. radcliffe-brown was the first to study social network and its structure, kicking off the social network analysis. since then, great progress has been made in the theories, methods, and techniques of social structure [5, 9, 16, 23, 25]. roy et al. [19] reviewed the development and analyzed the strengths and weaknesses of various social networks at home and abroad, namely, facebook, twitter, qzone, and wechat moments. the current research on social network images mainly focuses on image indexing, image classification, and image annotation. li et al. [13] classified multi-class social network images through kernel canonical correlation analysis (kcca), and achieved a classification accurate of over 89%. mallick et al. [14] screened social network images by matrix factorization model, and mined the visual features of each image and its associated images, thereby improving the effect of image classification. based on depth-first search and breadth-first search, hang et al. [8] extended deep-walk with word2vec model, and divided the random paths of the network through greedy binarization. the existing processing methods for social network images cannot mine the relationship between social information. fortunately, the deep learning has been successfully applied to analyze the image associations in other fields [1, 3, 7, 20]. zhu et al. [26] designed an image clustering algorithm for image capturing robot, based on the convolutional neural network (cnn) for multi-classifier decisionmaking. paoletti et al. [17] combined hierarchical matching pursuit with multithreaded asynchronous reinforcement learning to classify multi-source remote sensing images. for accurate classification of social network images, this paper proposes a social network image classification algorithm based on multimodal deep learning, which fully mines the associations between social network images and other social information. the remainder of this paper is organized as follows: section 2 establishes a social network association clustering model (snacm), and calculates the trust and similarity, which represent the degree of similarity between users; section 3 adopts the artificial ant colony algorithm to analyze the importance of the users with the highest similarity, and conducts weighted stacking of the snacm based on the update of pheromone; on this basis, the social network image association network was constructed, and the social network images of three modes, i.e. rgb (red-green-blue) image, grayscale image, and depth image, were fused. at the end of section 3, the features of the multimodal social network image were extracted with a three-dimensional neural network (3d nn). finally, the proposed algorithm was proved valid and accurate through experiments. 2 snacm the social networks have various strong or weak associations. the effect and coverage of social network image clustering directly hinge on the user activity. to balance the effect and coverage of image clustering and overcome data sparsity of social networks, this paper created the snacm based on artificial ant colony algorithm, which searches for the users highly similar to the core user to improve the clustering accuracy. 2.1 similarity calculation suppose each user association in a social network based on trust propagation have two weights. let p be the pearson correlation coefficient representing user similarity, and t be the trust representing the trust propagation relationship. then, the dual weight coefficient can be expressed as: (ωp , ωt )=(p(xi,xj), t(xi,xj)). figure 1 provides an example of the snacm for 7 users. as shown in figure 1, the snacm combines the two types of associations in figures 1(a) and (b), which greatly improves the coverage. in addition, every association is directed. for instance, the weight of x2 → x4 is (p(x2,x4), t(x2,x4)), for user x2 trusts user x4; the weight of x2 → x4 is (p(x2, x4), 0), for user x4 does not trust user x2. the core user m of the social network can be defined as: https://doi.org/10.15837/ijccc.2020.6.4037 3 (a) the similarity-based associations between social network users (b) the trust-based associations between social network users (c) the snacm for 7 users figure 1: an example of the snacm m = min ∑ xi,xj∈c dis (xi,xj) (1) where, c is the set of user clusters; dis(xi,xj) is the distance between users xi and xj: dis (xi,xj) = √ d2p (xi,xj) + d 2 t (xi,xj) (2) where, dp and dt are similarity-based distance and trust-based distance, respectively; ωp and ωt are measurement functions of similarity and trust, respectively:{ dp (xi,xj) = 1 −ωp (xi,xj) dt (xi,xj) = 1 −ωt (xi,xj) (3) the next step is to search for the users highly similar to the core user c through initialization, and weighted stacking. firstly, the similarity between the core user and every other user in the trust-based social network was quantified, and the top-t users were selected. the similarity can be quantified by: sim (c,xk) =   2×p (c,xk)×t (c,xk) p (c,xk)+t (c,xk) p (c,xk) + t (c,xk) 6= 0 t (c,xk) p (c,xk) = 0,t (c,xk) 6= 0 p (c,xk) p (c,xk) 6= 0,t (c,xk) = 0 (4) https://doi.org/10.15837/ijccc.2020.6.4037 4 formula (4) shows that, the similarity between user xk and the core user c can be directly calculated, if the two users have direct trust-based associations like mutual or unilateral following. otherwise, the similarity can be mined from comments and ratings, using the pearson correlation coefficient: t (c,xk) = dismax −dismax (c,xk) + 1 dismax (5) the user similarity can be computed by: p (c,xk) = ∑h h=1 (sh(c) − s̄(c)) (sh (xk) − s̄ (xk))√∑h h=1 (sh(c) − s̄(c)) 2 √∑h h=1 (sh (xk) − s̄ (xk)) 2 (6) where, sh is the attention of a user to event h; s is the mean attention of a user to events; h is the set of events that attract the attention of the user. the user similarity was determined against a suitable threshold, such that the users in the same range are allocated to the same class. 2.2 weighted stacking based on the snacm, the artificial ant colony algorithm was introduced to analyze the importance of the t users with the highest similarity. figure 2 shows the trust-based association model of the core user. figure 2: the trust-based association model of the core user the pheromone released by the ant walking in the model is determined by the similarity between the users corresponding to the path it walks. the pheromone is updated iteratively by: τ ′ (c,xk) = (1 −ρ)τ (c,xk) + g∑ g=1 ∆τg−xi (c,xk) (7) where, τ ′ is the updated pheromone of user xk, representing its association with the core user c; ρ is the volatilization coefficient. let cg−xk be the cost of the g-th ant to solve the optimal path. then, the pheromone ∆τg−xk released by the g-th ant on path between of user xk and the core user c can be described as: ∆τg−xi (c,xk) = { k cg−xi(c,xk) , xi ∈ xg 0, xi /∈ xg (8) where, k is a constant; xg is the set of users visited by the g-th ant. as shown in formulas (7) and (8), if user xk does not belong to that set, the pheromone will volatize iteratively with ρ; otherwise, https://doi.org/10.15837/ijccc.2020.6.4037 5 the vitalization should be combined with the sum of the pheromone released by all ants on the paths between u user xk and other users. to avoid the local optimum trap, the probability for the g-th ant to walk from user xk to the core user c can be defined as follows, in order to select the users with high similarity to the core user and low redundancy: pg (c,xk) =   [τ(c,xk)α[1/p (c,xk)]β∑ l∈yg [τ(c,xl)]α[1/p (c,xl)]β ,c ∈ nxk 0,c /∈ nxk (9) where, 1/p(c,xk) is the heuristic value, representing the probability to walk from user xk to the core user c; α and β are controlling parameters of the effects of pheromone and heuristic value, respectively; nxk is the set of neighbors of user xk; yg is the set of users not yet visited by the g-th ant. 3 social network image classification algorithm the snacm can reveal the similarity-based association vectors between users. here, the cnn is adopted to extract the salient features of the associations between social network images. the obtained salient features, coupled with the similarity-based association vectors, were used to train a neural network model, aiming to accurately classify social network images. the structure of the social network image classification algorithm is presented in figure 3. figure 3: the structure of the social network image classification algorithm 3.1 building an image association network the social image association network was constructed through one-to-one mapping of the n users in the snacm to the set m of images uploaded by them. let f(f,mk) be the association between the image mk uploaded by user xk and the image f uploaded by the core user c, and matrix r be the degree of the association. then, the weights of the association between mk and f can be characterized by the corresponding element r(f,mk) in matrix r. hence, the probability for the g-th ant to walk from image mk to image mj can be defined as: pg (f,mk) =   [τ(f,mk)]α[1/f (f,mk)]β∑ l∈vg [τ(f,ml)]α[1/f (f,ml)]β ,f ∈ nmk 0, f /∈ nmk (10) where, nmk is the set of neighbors of image mk; vg is the set of images not yet visited by the g-th ant. the line algorithm based on the second-order similarity can be used to calculate the conditional probability of every directed association in the social network: p̂g (f,mk) = exp [τ (f,mk) τ ′ (f,mk)]∑n q=1 exp [τ (f,mq) τ ′ (f,mq)] (11) https://doi.org/10.15837/ijccc.2020.6.4037 6 the associations between snacm images were taken as training samples. then, the image association network can be finalized through learning the kullback–leibler (k-l) divergence between formulas (10) and (11). the objective function of the algorithm can be defined as: min ∑ f,mk∈m g∑ g=1 kl ( p̂g (f,mk) ,pg (f,mk) ) = min ∑ f,mk∈m g∑ g=1 [r (f,mk) lg (pg (f,mk))] (12) the conditional probabilities cannot be calculated unless the pheromones are iteratively optimized throughout the image association network. however, it is an arduous task to directly optimize formula (12). to solve the problem, lg (pg (f,mk)) can be processed by the negative sampling algorithm and sigmoid function: lg sig (pg (f,mk)) + s∑ s=1 pnoise [sig (ps−g (f,mk))] (13) where, the second term is the negative sampling item; pnoise is the noise distribution of negative sampling; ps−g is the total number of negative samplings with conditional probability s obtained in the s-th sampling. then, the gradient descent algorithm was introduced to optimize the above formula. in this way, the salient features of image associations can be obtained through learning. 3.2 multimodal fusion figure 4: the workflow of the classification of multimodal social network image many social network images come from video clips. for the accuracy of image classification, the images extracted from videos were subject to modal analysis and fusion, and trained with the cnn. for a social network image, the texture is reflected by the grayscale image of the object, and the 3d surface features are depicted by rgb and depth images. to fully utilize the feature information of social network images, this paper fuses the rgb image, grayscale image, and the depth image, and extracts the salient features from the fused high-dimensional image (figure 4). during the fusion, the low-level features were extracted from the rgb image, grayscale image, and the depth image by the 3d nn, using convolutional and pooling techniques, and used as the inputs of the objective functions of multiple path-searching ants, thereby obtaining more abstract and effective high-level features. after the fusion, the image mk corresponding to user xk can be expressed as: imk = [irgb (mk) ,igray (mk) ,idepth (mk)] (14) where, irgb, igray , and idepth are the images uploaded in the rgb, grayscale, and depth modes, respectively. the different features were extracted from the three modes, and used to create a training set t under mode fusion based on image association network: t = {t1 (if,im1 ) , · · · , tk (if,imk) , · · · , tn (if,imn)} (15) https://doi.org/10.15837/ijccc.2020.6.4037 7 where, tk(if,imk) is the k-th training sample of the image association network. based on the image association network, the training path involves a feature extraction model and a feature amplification module. the former consists of a 3d convolution layer, a regularization layer, and a max pooling layer; the latter consists of 3d sampling and feature cascading. since twodimensional (2d) cnn cannot effectively mine the information from the grayscale and depth images, this paper adopts the 3d nn to extract the features from the multimodal social network image. in the 3d nn, the input of the input layer, and the outputs of kernels and output layer are both 3d data (figure 5). figure 5: the sketch map of the 3d nn in the 3d nn for multimodal fusion, the pixel value of point (a,b,c) in the c-th image of the m-th feature block on the n-th layer can be calculated by: iabcnm = f  ∑ q xn−1∑ xn=0 yn−1∑ yn=0 zn−1∑ zn=0 ωnmq ×u (a+xn)(b+yn)(c+zn) (n−1)q + dnm   (16) where, xn, yn, and zn are the width, height, and length of the kernel on the n-th layer; xn ×yn ×zn is the step length of 3d convolution; ωnmg is the weight between point (a,b,c) in the c-th image of the m-th feature block on the n-th layer and the q-th feature map on the i-1-th layer; u is the input from the n-1-th layer to the m-th layer; dnm is the deviation. the 3d pooling after convolution has a similar principle as the convolution. this operation not only reduces the dimensionality of the features of the social network images in the three modes, but also enhances the saliency of these features. if the region of interest (roi) is of the size w1×w2×w3. then, the 3d pooling can be expressed as: irst = max 0≤ε1≤w1;0≤ε2≤w2;0≤ε3≤w3 (ur×η+ε1,s×δ+ε2,t×τ+ε3 ) (17) where, (r,s,t) is the pixel to be pooled; n×δ×τ is the step length of 3d pooling; ur×η+ε1,s×δ+ε2,t×τ+ε3 is the output eigenvalue of 3d pixel (r ×η + ε1,s× δ + ε2, t× τ + ε3). to prevent data jitter and speed up the convergence of the multimodal fusion 3d nn, instance normalization was employed to normalize each channel of every social network image. let upoo be the output of the pooling layer. then, the mean λ of each social network image can be calculated along the channel by: λ = 1 rs r∑ r=1 s∑ s=1 upoo (18) the variance σ can be obtained by: σ = √√√√ 1 rs r∑ r=1 s∑ s=1 (upoo −λ)2 (19) the normalized result can be obtained by: https://doi.org/10.15837/ijccc.2020.6.4037 8 inor = upoo −λ√ σ2 + ε (20) the layer-by-layer normalization effectively prevents vanishing or exploding gradient, reduces the reliance of the 3d nn on initial parameters, and accelerates network convergence. on this basis, the social network images can be classified accurately through subsequent visualization analysis. 4 experiments and results analysis to verify its effectiveness, the proposed social network image classification algorithm, which is grounded on multimodal deep learning, was tested on single modal and multimodal social network images. a total of 360 social network images, which has been classified, was taken as the training samples. then, 100 samples were selected to serve as test samples. each of them contains images in three modes, namely, rgb image, grayscale image, and depth image. figure 6 presents the convergence curves of our algorithm on the training set and test set under different image modes. (a) multimodal images (b) single modal images figure 6: the convergence curves of our algorithm on the training set and test set under different image modes as shown in figure 6, the losses of our algorithm were -0.8735 and -0.8232 on the training set and test set of single modal social network images, respectively, and -0.8924 and -0.8045 on the training set and test set of multimodal social network images, respectively. obviously, the training loss and test loss on multimodal images were 0.0189 and 0.0187 smaller than those on single modal images, indicating that our algorithm performs better on multimodal images. our algorithm has obvious advantage over other classification algorithms in classification performance. the classification metrics of the proposed 3d nn are compared with several training networks in table 1. table 1: the comparison of classification metrics between different training networks type of network weighted stacking? accuracy mean accuracy precision recall bp nn no 90.2% 83.2% 82% 80% bp nn yes 92.1% 86.3% 86% 87% rbf nn yes 93.7% 92.4% 92% 94% sofm nn yes 95.2% 92.9% 93% 94% 3d nn yes 96.8% 97.0% 95% 95% note: bp, rbd, and sofm are short for backpropagation, radial basis function, and self-organizing feature map, respectively. as shown in table 1, the bp nn had a great difference between accuracy (90.2%) and mean accuracy (83.2%) in direct training. the accuracy of the bp nn on social network images was improved from 90.2% to 92.1%, after the importance of the most similar users was analyzed through https://doi.org/10.15837/ijccc.2020.6.4037 9 the artificial ant colony algorithm, as well as weighted stacking. compared with bp nns, rbf nn, and sofm nn, the 3d nn achieved the highest accuracy, which averaged at 97.0%. hence, our model is highly effective in classifying social network images. figure 7 compares the loss curves of the above nns on the test set. it can be seen that the proposed model produced the highest classification accuracy and tolerable losses. (a) accuracy (b) loss curve figure 7: the comparison of accuracy and loss curve between different training networks 5 conclusions based on multimodal deep learning, this paper designs a classification algorithm for social network images. firstly, the authors developed the snacm, and relied on the model to derive the trust and similarity, which represent the degree of similarity between users. then, the importance of the most similar users was analyzed by the artificial ant colony algorithm, and the snacm was subject to weighted stacking. next, the social network images of rgb, grayscale, and depth modes were fused. the features of the multimodal social network image were extracted by a 3d nn, realizing the accurate classification of social network images. experimental results show that our algorithm has smaller loss on multimodal social network images than on single modal ones, and clearly outperforms other methods in classification performance; the proposed 3d nn boasts the highest accuracy among the tested training networks, and controls the loss within the acceptable range. funding this work is supported by philosophy and social science research project of education department (19g091), humanities and social sciences project of hubei provincial department of education (17g134), ministry of education collaborative education project (201902256010), humanities and social sciences project of hubei provincial department of education (18g623), ministry of education collaborative education project (201901088004). author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. https://doi.org/10.15837/ijccc.2020.6.4037 10 references [1] anantha, n.l.; battula, b.p. 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(2018). generative adversarial networks for hyperspectral image classification, ieee transactions on geoscience and remote sensing, 56(9), 5046-5063, 2018. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control https://doi.org/10.15837/ijccc.2020.6.4037 12 cite this paper as: bai, j. w.; chi, c. (2020). a social network image classification algorithm based on multimodal deep learning, international journal of computers communications & control, 15(6), 4037, 2020. https://doi.org/10.15837/ijccc.2020.6.4037 introduction snacm similarity calculation weighted stacking social network image classification algorithm building an image association network multimodal fusion experiments and results analysis conclusions international journal of computers communications & control issn 1841-9836, 13(4), 590-601, august 2018. an abc algorithm with recombination x. you, y. ma, z. liu, m. xie xuemei you*, yinghong ma, zhiyuan liu, mingzhao xie business school, shandong normal university, shandong, 250014, p. r. china *corresponding author: sdxmyou@126.com yinghongma71@163.com liuzhiyuan@sdnu.edu.cn abstract: artificial bee colony (abc) is an efficient swarm intelligence algorithm, which has shown good exploration ability. however, its exploitation capacity needs to be improved. in this paper, a novel abc variant with recombination (called rabc) is proposed to enhance the exploitation. rabc firstly employs a new search model inspired by the updating equation of particle swarm optimization (pso). then, both the new search model and the original abc model are recombined to build a hybrid search model. the effectiveness of the proposed rabc is validated on ten famous benchmark optimization problems. experimental results show rabc can significantly improve the quality of solutions and accelerate the convergence speed. keywords: artificial bee colony (abc), recombination, hybrid search model, global optimization. 1 introduction in recent years, swarm intelligence (si) has become a research focus in optimization field. the si refers to establish mathematical model to simulate the social behaviors from nature. in the past decades, different si algorithms have been proposed, including ant colony optimization (aco) [8,17,25], particle swarm optimization (pso) [22], artificial bee colony (abc) [9], firefly algorithm (fa) [20], hybrid algorithm (ha) [14], bat algorithm (ba) [2], and cuckoo search (cs) [5,30]. except pso and aco, abc can be regarded as the most popular si algorithm. the main reason contains: 1) abc has less control parameters than other si algorithms; and 2) abc has powerful exploration ability. due to the superiority of abc, it has received much attention. in the original abc, individuals are divided into three types: i.e., employed bees, onlooker bees and scouts. during the search, all individuals (bees) fly in the search space and try to improve the food sources (find better solutions). compared to other si algorithms, abc uses a different search equation to generate new solutions. for the current solution, an individual (employed bee or onlooker bee) randomly chooses a different solution in the population and uses their difference to obtain a new solution by modifying one dimension. based on this search mechanism, abc shows slow convergence speed and exploitation ability. to strengthen the exploitation capacity, this paper presents a new abc variant (called rabc), which employs a recombination method between the original abc search model and a modified search model. for the latter model, it is inspired by the updating equation of pso. by combining the search information of the global best solution and previous best, rabc can improve the exploitation ability. experiment is validated on ten well-known benchmark problems. simulation results of rabc are compared with the original abc and two improved abc variants. the rest of this paper is organized as follows. the original abc is briefly introduced in section 2. a short review of recent progress on abc is given in section 3. in section 4, our copyright ©2018 cc by-nc an abc algorithm with recombination 591 proposed rabc is described. section 5 presents the simulation results and discussions. finally, conclusion and future work are summarized in section 6. 2 artificial bee colony (abc) there are two famous si optimization algorithms: pso and aco, which were developed in the 1990s. recently, different si algorithms were proposed. among these algorithms, abc becomes popular because of its superiorities [10]. in the original abc, it consists of three types of individuals: employed bees, onlooker bees and scouts. for all food sources (solutions), the employed bees conduct the first round search around food sources and try to find new better solutions. the onlooker bees conduct the second round search around some selected better food sources. for a food source, if the above bees cannot find a better one to replace it after some rounds search, the scout will randomly find a food source to replace it. 2.1 population initialization in the following, we will describe the original abc in details. first, a initial population consists of n food sources (solutions) {xi|i = 1, 2, ...,n}, where xi is the ith food source and n is the population size. the initial food sources are randomly generated as follows. xij = lowj + randj · (upj − lowj) (1) where xij denotes the jth component for ith food source xi, i = 1, 2, ...,n, j = 1, 2, ...,d; d is the dimensional size; randj is a real random number between 0 and 1; upj and lowj are the boundaries for the jth dimension. 2.2 employed bee phase for each food source xi in the current population, each employed bee flies to its neighborhood and tries to find a new solution vi. this process can be described as below [9]. vij(t) = xij(t) + φij(xij(t) −xkj(t)) (2) where j�[1,d] is a random index of the population; xk is a randomly selected food source and it is mutually different with xi; t represents the iteration index; φij is a real random number in the range [-1, 1]. the abc employs a greedy selection method to determine whether the new candidate solution (food source) vi should be entered in the next generation. if vi is better than xi, then xi is updated by vi; otherwise xi is unchangeable. the greedy selection can accelerate the population convergence. 2.3 onlooker bee phase the onlooker bees only fly to some selected food sources and search their neighborhood to find new food sources. the selection of each food source xi is related to its fitness quality. a better food source has a higher selection probability. in the original abc, the selection probability pi for each food source xi is defined by [9]: pi = fiti∑n i=1 fiti (3) 592 x. you, y. ma, z. liu, m. xie where fiti is the fitness value of xi. when xi is selected, the onlooker bee searches the neighborhood of xi and obtain a new solution vi according to eq.(2). similar to the employed bees, the same greedy selection is utilized by the onlooker bees to determine whether the new solution vi should be entered in the next generation. 2.4 scout bee phase if the employed or onlooker bees cannot improve the quality of xi in limit iterations, xi may be stagnated. then, a scout bee re-initializes xi according to eq.(1). 3 a brief review of abc although the original abc has shown good performance, it still has some drawbacks. to tackle these issues, many improved abcs have been proposed. in this section, a brief review of recent advance on abc is presented. karaboga and akay [10] compared abc with some evolutionary algorithms on a number of benchmark problems. simulation results demonstrate abc is competitive to those compared algorithms. zhu and kwong [32] proposed a new abc called gabc , which introduced the global best individual into the original solution search equation. akay and karaboga [1] designed a new parameter mr to adjust the probability of dimension perturbation. results demonstrate the modified approach can accelerate the search. wang et al. [23] combined multi-strategy ensemble learning and abc. it aims that multiple strategies can effectively balance the global and local search. inspired by gaussian de [21], zhou et al. [31] introduced gaussian sampling into abc to obtain good performance. cui et al. [4] used a ranking method to choose the parent solutions when generating new solutions. simulation results show the ranking based abc is very effective. in [6], cui et al. developed another version of abc, which employs a dynamic population mechanism. during the search, the population size is not fixed and dynamically updated. in [3], chen et al. combined teaching learning based optimization into abc, and proposed an improved abc variant to optimize the parameters estimation of photovoltaic. to improve the exploitation, xiang et al. [26] used a grey relational model to choose the neighbor individuals. then, the de mutation operator is employed to generate new candidate solutions. in [27], xiang et al. proposed another abc variant based on the cosine similarity. to select some good neighbor individuals, a novel solution search model is designed. the frequency of parameters perturbation is also modified to share more search information between different solutions. simulation results on twenty-four benchmark functions show that the proposed cosabc is competitive. yaghoobi and esmaeili [29] designed an improved abc by using three new strategies: chaos theory, multiple searches, and modified perturbation. song et al. [18] presented an improved abc based on objective function value information, which employs two modified solution search models. the objective function value is incorporated to adjust the step size. experiments on thirty test functions show the proposed approach is better than other six abcs. li et al. [13] designed a new gene recombination operation to accelerate the convergence of abc. some good solutions in the population are chosen to generate offspring through the gene recombination. results demonstrate the gene recombination operation can effectively strengthen the exploitation capacity of abc. kong et al. [12] presented a new abc variant with two strategies: elite group guidance and combined breadth-depth search. the proposed algorithm was verified on twenty-two benchmark functions. sulaiman et al. [19] presented a robust abc to balance exploitation and exploration. experiments on 27 test functions and economic environmental dispatch (eed) problems show the effectiveness of the approach. an abc algorithm with recombination 593 4 abc with recombination (rabc) in section 2, it can be seen that abc mainly uses eq.(2) to find new food sources (solutions) during the iterations. from eq.(2), the new solution vi is very similar to its parent solution xi, because they are different on only one dimension. based this search mechanism, abc shows powerful exploration ability, but its convergence speed is slow. to improve this case, some researchers introduced some good search experiences into abc to accelerate the search. in [32], the global best solution is used to improve the search equation. results show the modification can significantly improve the performance. pso is a successful si optimization algorithm. the original pso references has more than 100,000 citations. the main advantages of pso focus on fast convergence speed and strong search ability. in pso, each individual (also called particle) flies to its previous best (pbest) and the global best (gbest) found so far. so, pso takes full advantage of the search experiences of those best individuals. the pso updating model is defined by [11]. vij(t + 1) = w ·vij(t) + c1r1(pbestij(t) −xij(t)) + c2r2(gbestj(t) −xij(t)) (4) where vi and xi are the velocity and position, respectively; w�[0, 1] is inertia weight; c1 and c2 are two learning factors; r1,r2�[0, 1] are two random numbers. in this paper, a new solution updating equation is proposed inspired by pso. however, this paper is not the first time to introduce pso model into abc. in [32], a gbest guided abc (gabc) was proposed. in [28], xiang et al. used the gbest and an elitist method to modify the solution search model. liu [16] used pbest to update the employed bees and gbest to the onlooker bees. in [15], li et al. defined a modified model as follows. vij(t) = w ·xij(t) + 2φij(xij(t) −xkj(t))φ1 + ϕij(gbestj(t) −xkj(t))φ2 (5) where φ1 and φ2 are two positive values; φij and ϕij are two random numbers in [0,1]. differs from these existing models, we design a new one by taking full advantage of pbest and gbest. the detailed model is defined as follows. vij(t) = w ·xij(t) + r1(pbestij(t) −xij(t)) + r2(gbestj(t) −xij(t)) (6) where j is a randomly selected dimension index, r1,r2�[0, 1] are two random numbers, and the weight factor w�[0, 1]. based on eq.(6), we propose an alternative model by combining the original abc model. the recombined model is described as below. vij(t) = { xij(t) + φij(xij(t) −xkj(t)), ifrand(0, 1) < pr w ·xij(t) + r1(pbestij(t) −xij(t)) + r2(gbestj(t) −xij(t)), otherwise. (7) where rand(0, 1)�[0, 1] is a random value, and pr�[0, 1] is the probability rate. in our approach rabc, both employed and onlooker bees use eq. 6 to generate new solutions during iterations. like the original abc, a greedy selection method is also utilized to accelerate the search. in comparison to the original abc, rabc adds a probability parameter pr to control the usage of pbest and gbest. when pr is large, bees mainly use the original abc model to generate new solutions. then, rabc is similar to abc. when pr is small, bees mainly employ the modified model to generate new solutions. then, more search experiences of pbest and gbest are utilized in the search process. therefore, pr plays an significant role in balancing exploration and 594 x. you, y. ma, z. liu, m. xie exploitation in rabc. moreover, rabc only modifies the search model, and does not employ other operations. so, abc and rabc have the same computational time complexity. 5 experimental study 5.1 test functions in this paper, ten famous classical benchmark functions are utilized to validate the performance of rabc. these functions were early used in many optimization papers [24]. the dimension d is set to 30 in the experiments. table 1 briefly describes the ten benchmark problems. all functions should be minimized and their mathematical definitions are listed as below. f1: sphere f1(x) = ∑d i=1 x 2 i f2: schwefel 2.22 f2(x) = ∑d i=1 |xi| + ∏d i=1 xi f3: schwefel 1.2 f3(x) = ∑d i=1( ∑i j=1 xj) 2 f4: schwefel 2.21 f4(x) = maxi(|xi|, 1 ≤ i ≤ d) f5: rosenbrock f5(x) = ∑d−1 i=1 [100(xi+1 −x 2 i ) 2 + (xi − 1)2] f6: step f6(x) = ∑d i=1(bxi + 0.5c) 2 f7: quartic with noise f7(x) = ∑d i=1 ix 4 i + rand(0, 1) f8: schwefel 2.26 f8(x) = ∑d i=1 −xi sin( √ |xi|) f9: rastrigin f9(x) = ∑d i=1[x 2 i − 10 cos(2πxi) + 10] f10: ackley f10(x) = −20 ·exp(−0.2 · √ 1 d ∑d i=1 x 2 i ) −exp( 1 d ∑d i=1 cos(2πxi)) + 20 + e an abc algorithm with recombination 595 table 1: search range and global optimum for the benchmark problems. functions search range global optimum f1 [-100,100] 0 f2 [-10,10] 0 f3 [-100,100] 0 f4 [-100,100] 0 f5 [-30,30] 0 f6 [-100,100] 0 f7 [-1.28,1.28] 0 f8 [-500,500] −418.98 ·d f9 [-5.12,5.12] 0 f10 [-32,32] 0 table 2: pcomputational results of rabc with different pr values. functions pr = 0.1 pr = 0.3 pr = 0.5 pr = 0.7 pr = 0.9 mean mean mean mean mean f1 5.94e-75 6.51e-57 6.75e-48 7.46e-40 4.35e-33 f2 8.78e-36 9.61e-33 1.54e-26 8.44e-20 5.65e-14 f3 5.87e+03 6.40e+03 6.32e+03 5.56e+03 5.83e+03 f4 1.47e+00 3.37e+00 9.89e+00 1.84e+01 3.32e+01 f5 2.74e+01 2.54e+01 2.31e+01 1.61e+01 1.43e-01 f6 0.00e+00 0.00e+00 0.00e+00 0.00e+00 0.00e+00 f7 2.79e-02 2.25e-02 3.59e-02 4.57e-02 1.06e-01 f8 -10925.2 -12545.9 -12569.5 -12569.5 -12569.5 f9 0.00e+00 0.00e+00 0.00e+00 0.00e+00 0.00e+00 f10 2.90e-14 3.26e-14 2.55e-14 2.89e-14 1.73e-12 596 x. you, y. ma, z. liu, m. xie sensitivity analysis of the parameter pr according to eq.(7), a new parameter pr is introduced in our approach rabc. as mentioned in section 4, pr is beneficial for balancing the exploration and exploitation. a large pr is good for exploration and a small pr is helpful for exploitation. so, how to choose a suitable pr is worthy to be investigated. to analyze the parameter pr, we try to test different pr on the benchmark set. the parameter pr is set to 0.1, 0.3, 0.5, 0.7, and 0.9, respectively. for other parameters n and limit, they are set to 50 and 100, respectively. the termination criterion for running an algorithm is maximum number of function evaluations (maxfes). when the number of function evaluations reaches to maxfes, the algorithm is stopped. according to the literature [23], maxfes is equal to 1.5e+05. for each parameter pr, rabc is run 25 trials. table 2 displays the results of rabc with different pr, where "mean" is the mean best function value. as seen, when pr = 0.1, rabc outperforms other pr values on two functions f1 and f2. for functions f3 −f5, all pr values cannot help rabc find good solutions. on function f3, pr = 0.7 is slightly better than other pr values, and pr = 0.1 is better on function f4. for function f5, pr = 0.9 can find reasonable solution, while other pr values fail. all pr values and find the global optimum on f6 and f9. when pr > 0.3, rabc can converge to the global optimum, but rabc with pr = 0.1 and 0.3 falls into local minima. for function f7, rabc with pr = 0.2 is better than other pr values. when pr = 0.5, rabc achieves better results on function f10. from the above analysis, rabc with a fixed pr value cannot obtain better results than other pr values. so, it is not easy to select which pr is suitable for the benchmark set. in order to choose the relatively best pr, friedman test is used to calculate the mean rank of each pr on the benchmark set. table 3 shows the mean rank values of rabc with different pr values. as shown, rabc with pr = 0.1 achieves the best mean rank. it demonstrates that pr = 0.1 is the relatively best choice among five different pr values. in the following experiment, pr = 0.1 is used for rabc. table 3: mean rank of rabc with different pr values. rabc mean rank pr = 0.1 2.70 pr = 0.3 3.00 pr = 0.5 2.80 pr = 0.7 2.90 pr = 0.9 3.60 comparison of rabc and other well-known abc algorithms in this section, we compare rabc with the standard abc and two other well-known abcs. the compared algorithms are listed as below. • abc [9] • gabc gabc [32] • mabc [7] • our approach rabc an abc algorithm with recombination 597 in the following experiments, all algorithms use the same population size and stopping condition. both n and limit are set to 100. maxfes is equal to 1.5e+05. the parameter c is equal to 1.5 in gabc [32]. in mabc, the probability p is set to 0.7 by the suggestions of [7]. the parameter pr in rabc is equal to 0.1 based on experimental study. each algorithm is run 25 trial on each function. table 4 presents the results among rabc, abc, gabc, and mabc on the ten benchmark functions, where "mean" indicates the mean best function value. from the table, rabc outperforms the standard abc on 7 functions, while abc is better than rabc on f5 and f8. for the last function f6, all abcs can find the global optimum zero. compared to gabc, rabc achieves better solutions on 5 functions. gabc performs better than rabc on 3 functions. besides the function f6, rabc, gabc, mabc can converge to the global minima on f9. mabc is better than rabc on 2 functions, but rabc outperforms mabc on six functions. table 4: comparison results among rabc, abc, gabc and mabc. functions abc gabc mabc rabc mean mean mean mean f1 9.67e-16 6.86e-16 2.98e-40 5.94e-75 f2 2.36e-10 1.39e-15 2.13e-21 8.78e-36 f3 9.21e+03 4.29e+03 1.01e+04 5.87e+03 f4 3.73e+01 1.91e+01 5.71e+00 1.47e+00 f5 1.21e+00 6.77e-01 2.27e-01 2.74e+01 f6 0.00e+00 0.00e+00 0.00e+00 0.00e+00 f7 1.68e-01 7.98e-02 4.02e-02 2.79e-02 f8 -12332.6 -12569.5 -12569.5 -10925.2 f9 5.33e-14 0.00e+00 0.00e+00 0.00e+00 f10 1.65e-09 3.97e-14 3.26e-14 2.90e-14 fig.1 lists the convergence graphs of rabc and three other abcs on six test functions. as shown, rabc converges faster than mabc, gabc, and abc on f1, f2, f4, f9 and f10 on the whole search process. for function f3, rabc is faster than other three algorithms at the beginning of the search. gabc is faster than rabc at the last stage. because rabc converges to a local minima when fes reaches to 3.0e+04. for function f10, rabc converges much faster than other three algorithms at the beginning and middle stages. the convergence curves of rabc, mabc, and gabc are similar at the last stage of the search. 598 x. you, y. ma, z. liu, m. xie 0 30000 60000 90000 120000 150000 -200 -150 -100 -50 0 50 b es t f un ct io n va lu e (l og ) fes (f 1 ) abc gabc mabc f1 0 30000 60000 90000 120000 150000 -90 -60 -30 0 30 b es t f un ct io n va lu e (l og ) fes (f 2 ) abc gabc mabc f2 0 30000 60000 90000 120000 150000 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 b es t f un ct io n va lu e (l og ) fes (f 3 ) abc gabc mabc f3 0 30000 60000 90000 120000 150000 0 2 4 6 b es t f un ct io n va lu e (l og ) fes (f 4 ) abc gabc mabc f4 0 30000 60000 90000 120000 150000 -40 -30 -20 -10 0 10 b es t f un ct io n va lu e (l og ) fes (f 9 ) abc gabc mabc f9 0 30000 60000 90000 120000 150000 -40 -30 -20 -10 0 10 b es t f un ct io n va lu e (l og ) fes (f 10 ) abc gabc mabc rabc f10 figure 1: the convergence graphs of four abcs on six selected functions. an abc algorithm with recombination 599 6 conclusion in order to strengthen the exploitation ability of abc, a new abc variant with recombination (called rabc) is proposed in this paper. rabc firstly employs a new search model inspired by the updating equation of pso. then, both the new search model and the original abc model are recombined to build a hybrid search model. a set of ten famous benchmark optimization problems are tested in the experiments. results show rabc performs better than abc, mabc, and gabc on most test functions. the parameter pr aims to control the frequency of using pbest and gbest. how to choose the best pr is studied in the experiments. results show that rabc with a fixed pr value cannot obtain better results than other pr values. the statistical test demonstrates that pr = 0.1 is the relatively best choice among five different pr values. however, a fixed pr is not a good choice. a dynamic pr may be more suitable. this will be studied in the future work. acknowledgments this work was supported by the national natural science foundation of china (nos. 71701115, 71471106, 61502284, and 71704098), the natural science foundation of shandong province (nos. zr2017mf058 and zr2016gq03), and the higher school science and technology foundation of shandong province (no. j17ka172). bibliography [1] akay, b.; karaboga, d. 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(2010); gbest-guided artificial bee colony algorithm for numerical function optimization, applied mathematics and computation, 217, 3166-3173, 2010. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(4), 459-474, august 2019. esbl: design and implement a cloud integrated framework for iot load balancing g. balakrishna, m.n. rao gubba balakrishna koneru lakshmaiah educationfoundation department of computer science and engineering me2balu@gmail.com nageswara rao moparthi koneru lakshmaiah education foundation department of computer science and engineering rao1974@gmail.com abstract: the continuous growth in wireless communication, the demand for sophisticated, simple and low-cost solutions are also increasing. the demand motivated the researchers to indulge into inventing suitable network solutions ranging from wireless sensor networks to wireless ad-hoc networks to internet of things (iot). with the inventions coming from the researchers, the demand for further improvements into the existing researchers have also growth upbound. initially the network protocols were the demand for research and further improvements. nevertheless, the iot devices are started getting used in various fields and started gathering a huge volume of data using complex application. this invites the demands for research on load balancing for iot networks. several research attempts were made to overcome the communication overheads caused by the heavy loads on the iot networks. theses research attempts proposed to manage the loads in the network by equally distributing the loads among the iot nodes. nonetheless, in the due course of time, the practitioners have decided to move the data collected by the iot nodes and the applications processing those data in to the cloud. hence, the challenge is to build an algorithm for cloud-based load balancer matching with the demands from the iot network protocols. hence, this work proposes a novel algorithm for managing the loads on cloud integrated iot network frameworks. the proposed algorithm utilizes the analytics of loads on cloud computing environments driven by the physical host machines and the virtual environments. the major challenge addressed by this work is to design a load balancer considering the low availability of the energy and computational capabilities of iot nodes but with the objective to improve the response time of the iot network. the proposed algorithm for load balancer is designed considering the low effort integrations with existing iot framework for making the wireless communication world a better place. keywords: iot, cloud integrated load balancer, inter quartile correlation, static threshold utilization. 1 introduction the most recent ground-breaking innovation in the field of networking is internet of things (iot). the iot networks have gained a huge popularity for making the communication protocols working with three simple components as gather the data from the open operational environment using sensors, the independent machine to machine communication and the cloud computing environment for the applications and the data collected by the sensor agents. an important aspect of iot is their protection against cyber attacks [1]. the increasing popularity of smart conceptual factors of modern appliances and life style made iot an integral part of the modern wireless copyright ©2019 cc by-nc 460 g. balakrishna, m.n. rao communications. iot frameworks are frequently considered as a layered secluded engineering of a computerized innovation. the gadget layer alludes to the physical parts: cps, sensors or machines. the system layer comprises of physical system transports, distributed computing and correspondence conventions that total and transport the information to the administration layer [3], which comprises of uses that control and consolidate information into data that can be shown on the driver dashboard. the best most stratum of the stack is the substance layer or the ui. the historical backdrop of the iot starts with the development of the programmable rationale controller by dick morley in 1968, which was utilized by gm in their programmed transmission producing division. these plcs took into consideration fine control of individual components in the assembling chain. in 1975, honeywell and yokogawa presented the world’s first dcss, the tdc 2000 and the centum, independently. these dcss were the following stage in permitting adaptable process control all through a plant, with the additional advantage of reinforcement redundancies by conveying control over the whole framework, disposing of a solitary purpose of disappointment in a focal control room. nonetheless, with the increasing demand for further improvements on iot load balancing for cloud integrated applications and frameworks this work proposes a novel load balancing algorithm. the rest of the work is furnished such as in the section 2, the routing demands for any iot network is analysed, in section 3, the benefits of cloud computing service models are analysed, in section 4, the outcomes from the parallel researches are analysed, in the section 5, the novel proposed load balancing algorithm is presented, in the section 6, the integration possibilities of the proposed algorithm into any existing iot framework is discussed, in the section 7, the results obtained from the proposed framework is discussed and analysed, in the section 8, the comparative analysis with the standard protocols are carried out to establish the significance of improvements and this work presents the final conclusion of the research in the section 9. 2 iot routing strategies demands in this section of the work, the fundamental demands for routing strategies on iot is elaborated and discussed to realise the break points for performance improvements using load balancing. some expansive scale remote information securing, and activation related applications utilize low-fuelled installed gadgets. these applications incorporate accuracy horticulture, building the board/mechanical robotization, vehicular specially appointed systems, and urban systems/vitality and water lattices to fabricate more intelligent urban communities. in these remote sensors organizes, the installed gadgets work under serious vitality limitations, which results in calculation, stockpiling, and radio-transmission related imperatives [fig. 1]. they likewise impart over a lossy channel. traffic examples and information stream inside lln are profoundly directional. the examples can be characterized as multipoint-to-point traffic, point-to-multipoint traffic, or point-topoint traffic. in mp2p traffic, for instance, detected data from various detecting hubs is steered to an internet application by means of lbr. p2mp traffic is seen when an inquiry asks for is produced using the internet and steered by means of the lbrs and lln switches to various field hubs. p2p traffic happens when control data should be sent to actuator or ready data is gotten from a sensor. the rfc reports depict the key usefulness and steering prerequisites for urban llns: esbl: design and implement a cloud integrated framework for iot load balancing 461 figure 1: iot routing building blocks 2.1 arrangement of nodes in a run of the mill urban system organization, hundreds or thousands of nodes with precustomized functionalities are taken off. the steering convention should represent these variables and bolster self-association and self-design at the most reduced conceivable vitality cost. 2.2 affiliation and disassociation of nodes after the instatement stage, nodes may join or leave the system at self-assertive occasions. the steering convention likewise ought to most likely handle circumstances where a breaking down node may influence or endanger the general directing effectiveness. 2.3 ordinary estimation detailing the information directing calculation and choice may rely upon the detected information, the recurrence of revealing, the measure of vitality staying in the nodes, the reviving example of the vitality searched nodes, or different variables. 2.4 scalability the directing convention must almost certainly bolster a field organization of a couple of hundred to a huge number of sensor nodes without decaying chosen execution parameters beneath configurable edges. hence, it is natural to understand that the scalability is one of the key factors for low power consuming devices, such as iot devices. thus, the scalability of the iot network can be improved using cloud-based load balancing strategies. thus, in the next section of the work, the cloud computing service types with related advantages are discussed. 3 cloud computing services this work proposes a novel framework to balance the iot network data processing loads using the cloud computing services. hence, it is a prime importance to realize the cloud computing service types and identify the benefits, which can be utilized for balancing iot network loads. 3.1 using cloud software capabilities the ability gave to the data consumers is to utilize applications running on a cloud foundation. the applications are available from different gadgets through either a thin interface, for 462 g. balakrishna, m.n. rao figure 2: cloud provisioning capabilities using vm example, an internet browser, or a programable interface. the consumer does not oversee or control the hidden cloud framework including system, servers, working frameworks, stockpiling, or even individual application capacities, with the conceivable exemption of constrained client explicit application design settings. 3.2 using cloud operational capabilities the ability gave to the application consumers to send application onto the cloud framework, data centre owners made or gained applications made utilizing programming dialects, libraries, administrations, and apparatuses bolstered by the supplier. the application owner does not oversee or control the basic cloud framework including system, servers, working frameworks, or capacity, however has power over the conveyed applications and potentially design settings for the application-facilitating condition. 3.3 using cloud provisioning capabilities the capacity gave to the application owners to arrangement handling, stockpiling, systems, and other key registering assets where the application consumers can send and run self-assertive programming, which can incorporate working frameworks and applications. the application owner does not oversee or control the basic cloud foundation but rather has command over working frameworks, stockpiling, and conveyed applications; and potentially constrained control of select systems administration parts henceforward, it is natural to realize that the provisioning capabilities of cloud computing can be utilized to handle the loads on applications running and generating data from any iot network. the provisioning of resources on cloud can be achieved using virtual machines [figure 2]. virtual machines are operating frameworks or application situations that is introduced on programming, which emulates devoted equipment. the end client has indistinguishable experience on a virtual machine from they would have on committed equipment. specific programming, called a hypervisor, copies the pc customer or server’s cpu, memory, hard circle, arrange, and other equipment assets totally, empowering virtual machines to share the assets. the hypervisor can imitate numerous virtual equipment stages that are disengaged from one another, enabling virtual machines to run linux and windows server operating frameworks on the equivalent fundamental physical host. virtualization limits cost by lessening the requirement for physical equipment frameworks. esbl: design and implement a cloud integrated framework for iot load balancing 463 4 outcomes from the parallel researches the origination of the modern research on iot started with the newer directions explored by the s. oteafy et al. in [15]. the work [15] defines the possibilities of utilizing the existing frameworks and strategies from wireless sensor networks. though, this work formulated the foundation of the research, the deficiencies identified in this work is the designed networks are highly generalized and cannot be extended to the modern day demands from iot networks. soon the demand for specified routing strategies are identified and q. le et al. in [9] have proposed the enhanced routing strategies for wsn matching with the demands of iot networks. the shortcoming from this proposed algorithm was the missing component of balancing load. this shortcoming was restructured by the c. petrioli et al. in [17]. with the initiations by c. petrioli et al. [17], the newer challenges are identified, and multiple parallel research outcomes are also demonstrated. the work by j. guo et al. [6] for managing the large-scale network and the work by h. y. kim et al. [8] for increasing the life time of the network have demonstrated significant improvements over the existing situations. yet another challenge of the present iot networks is the iot nodes are deployed for various purposes and must collected information from various sources, in various formats and in various time durations. hence, managing the load for higher to lower data ranges is obviously a challenge and must be addressed. the work of s. m. a. oteafy et al. [14] demonstrates the possibilities for self-adaptive or self-adjusting algorithm for balancing loads. the iot frameworks are based on the standard network protocols and the standard network stack, thus for balancing loads, the capabilities can be utilized. this ideology was proposed by di marco et al. in [4] for managing the loads using the network stack functionalities. during the routing of the data, load balancing being the prime concern, the energy efficiency is also an important factor. with the improvements of the routing algorithms, it is been observed that, the higher complexities of the routing algorithms are pooling a lot of energy for execution. the energy consumptions must be reduced in order to increase the life span of the iot network.s. a. alvi et al. in [2] proposes a guideline for framing the routing algorithms following the green computing policies. hence, with different recommendations from various researchers on various aspects of iot frameworks, there was a significant demand to summarize concrete guidelines for iot design, deployment and managements. the recommendations were well summarized by m. wu et al. in their work [19]. in spite of number of several prominent outcomes from the research community on iot, it was a great difficulty to influence the practitioners to use the iot frameworks as the initial implementations are costly for changing into a newer dimension. after the work of d. evans et [5] explaining the benefits of using iot networks for various demands, a high amount of adaptations was observed. within few months, the demand for iot implementation was increased to a greater extend. naturally, the research community was interested to find the details of implementation as to what extend the innovations were helping the practitioners. the survey on the findings was reported timely by l. mainetti et al. in [12]. the report by l. mainetti et al. [12] have uncovered a newer research problem for the research community. the iot network frameworks were designrf to sustain for shorter period and cannot be maintained for a longer duration. thus, the practitioners have raised a serious concern about the stability of the networks as further improvements or implementation can increase the cost to a greater extend. the solution to this problem was proposed by s. a. karthikey et al. [7] for making the iot networks fault tolerant and cost effective. further, the resource management schemes are also to be made economical. many of the parallel research attempts have demonstrated the best use of economic planner to drive the 464 g. balakrishna, m.n. rao resource management as demonstrated by t. menouer et al. [13] building a resource clusters on cloud for iot based networks also proven to be significant for load optimization as suggested by r. peinl et al. in [16]. nevertheless, these strategies are primarily applicable for private cloud setups, hence these strategies are criticised by a larger research community. the moderated strategy parallel to this is proposed by c. c. tarek menouer et al. [18] to overcome the challenges of isolated cloud. further the work of c.c. tarek menouer et al. [18] is enhanced by keqin li et al. [10] by using the multiple heterogeneous servers specialized for edge computing. the working models of these proposed strategies are numerous as one of the most popular deployment is described by yue xu et al. in [20] for traffic management and prediction based on the proposed framework by longjiang li et al. in [11]. nonetheless, the demands and purposes for the iot networks have came a long way from the initial research outcomes. the present iot networks are utilized for higher and higher capacitive situations. the data collected by the network nodes have also grown in volume and the applications handling the data must also be scaled. hence, the practitioners have started utilizing the benefits from cloud computing. the research outcomes from the cloud computing have proposed a good number of solutions to balance the load for cloud specific applications handling the data from the cloud services. nevertheless, the algorithms are not designed and not suitable for handling applications dealing with iot data. henceforth, this work proposes a novel framework for balancing loads for cloud integrated iot framework. the novel algorithm is elaborated in the next section of this work. 5 iot cloud integrated load balancing algorithm the proposed framework deploys an algorithm for migrating the highly loaded virtual machine to a lesser loaded destination physical host. the proposed algorithm utilizes 4 major properties of virtual machines handling iot node data as cpu utilization, power consumption or the energy, memory utilization and finally the sla violation due to the overload. cloud stack adjusting is the way toward disseminating remaining tasks at hand and registering assets in a cloud processing condition. load adjusting enables ventures to oversee application or remaining task at hand requests by dispensing assets among various pcs, systems or servers. cloud stack adjusting includes facilitating the circulation of outstanding burden traffic and requests that live over the internet. the algorithm is furnished here. from the understanding of the parallel research outcomes, it is natural to realise that most of the load balancing algorithms have failed in justifying the iot network load balancing demands as all the parameters involved in analysing the load of any virtual machine were given equal weightage. in the contradiction, this work proposes a novel weightage metric to justify the iot load balancing demands as studied in the previous sections of this work. considering the limited power and processing capabilities of any iot node in the network, this algorithm improves the energy efficiency and the processing capabilities to a greater extend. also, the loading balancing process time complexity is reduced significantly. the working capabilities of the proposed esbl algorithm with existing iot framework is elaborated in the next section of this work. 6 proposed framework for cloud integrated iot load balancing in this section of work, the proposed framework to balance load for cloud integrated iot network is elaborated. the proposed esbl algorithm can get embedded in the iot function esbl: design and implement a cloud integrated framework for iot load balancing 465 algorithm 1 energy sensitive for balancing load algorithm (esbl) 1: initialize the iot network routing table 2: initialize all nodes 3: accumulate the data from the iot nodes into cloud 4: instantiate the virtual machines 5: analyse the present load as vm[i] 6: for each virtual machine 7: a. calculate cpu utilization as cpuv[i] 8: b. calculate power consumption as powerv[i] 9: c. calculate memory utilization as memv[i] 10: d. calculate sla violation as slav[i] 11: for each vm[i] 12: a. calculate the loadv[i] = (cpuv[i] * 0.4) + (powerv[i] * 0.1) +(memv[i] * 0.3) + (slav[i] * 0.2) 13: for loadv 14: a. if loadv [i] > loadv [j] 15: b. highv m ← loadv [i] 16: c. lowv m ← loadv [j] 17: sourcev m ← highv m 18: destinationv m ← lowv m 19: calculatev mshutdown 20: calculateenergyv ← powerv [destinationv m] 21: migratev m[destinationv m] process. the proposed modified framework is elaborated in figure 3. in the initial phase of the network operations, the network components must be started and generate the beacon signal as part of the initial bootstrap process. the iot involves extending internet connectivity beyond standard devices, such as desktops, laptops, smart phones and tablets, to any range of traditionally dumb or non-internet-enabled physical devices and everyday objects. embedded with technology, these devices can communicate and interact over the internet, and they can be remotely monitored and controlled. all the devices associated with the network must be up and running in this initial phase of the framework and the framework is also responsible for assuring connectivity between the components. in the next phase of the framework, the network routing table must be updated to ensure the appropriate data migration to the cloud. a routing table uses a similar thought that one does when utilizing a guide in bundle conveyance. at whatever point a hub needs to send information to another hub on a system, it should initially realize where to send it. if the hub can’t straightforwardly associate with the goal hub, it needs to send it through different hubs along an appropriate course to the goal hub. most hubs don’t endeavor to make sense of which course may work; rather, a hub will send an ip bundle to an entryway in the lan, which at that point chooses how to course the "bundle" of information to the right goal. every door should monitor which approach to convey different bundles of information, and for this, it utilizes a routing table. a routing table is a database which monitors ways, like a guide, and uses these to figure out which approach to forward traffic. entry ways can likewise share the substance of their routing table with different hubs asking for that data. furthermore, the network controller using this framework must migrate all the data collected by the network to be migrated to the cloud hosts. the applications running on the cloud hosts shall use these data for further purposes. 466 g. balakrishna, m.n. rao figure 3: proposed cloud integrated iot application load balancing process flow table 1: initial experimental setup algorithm name no. of hosts number of vms total simulation time(sec) iqr mmt 50 100 86400 iqr mu 50 100 86400 lr mmt 50 100 86400 lr mu 50 100 86400 mad mmt 50 100 86400 mad mu 50 100 86400 thr mmt 50 100 86400 thr mu 50 100 86400 eslb 50 100 86400 once the applications are up and running, the esbl algorithm, will identify the heavily loaded source vm host instance and less loaded destination physical host. finally, after the identification of source and destination nodes, the migration happens. the framework continues to collect data from the iot network and performs the load balancing strategies as and when imbalanced nodes are identified. 7 results and discussions the results obtained from the proposed algorithm and the cloud integrated framework are highly satisfactory. the obtained results are furnished in this section of the work and discussed further. the results obtained from the proposed framework is analysed in few different parts as load analysis of the iot network before and after the migration, initial virtual machine setups, energy consumption, number of node shutdowns and finally execution time of the total load balancing time. during the analysis of the results, the results obtained from the proposed algorithm is also compared with other parallel research algorithms for cloud-based load balancing. 7.1 initial virtual machine setups firstly, the initial virtual machine setups are discussed in this section. all the algorithms, proposed and comparable, are tested under a similar situation. the initial setup is discussed in table 1. esbl: design and implement a cloud integrated framework for iot load balancing 467 figure 4: initial setup comparisons table 2: energy consumption analysis algorithm name energy consumption (kwh iqr mmt 47.85 iqr mu 49.32 lr mmt 35.37 lr mu 35.38 mad mmt 45.61 mad mu 47.36 thr mmt 41.81 thr mu 44.08 eslb 36.81 the detailed of the comparative algorithms are discussed in the further section of this work. the results are also visualized graphically in figure 4. 7.2 energy consumption secondly, the energy consumption by the virtual machines depend on the energy consumption for running the applications on the virtual machines and the load balancing. the calculation of the energy or power consumption is calculated for the complete virtual structure of the network, where all the virtual machines are included. this strategy helps in realizing the effective utilization of the power. the load balancing algorithms are intended to reduce the load on one single physical instance or a specific virtual machine to reduce the power consumption. the power consumption of the proposed algorithm is analysed and compared with the other parallel standard load balancing algorithms [table 2]. the results ate visualized graphically in figure 5. it is natural to understand that the overall energy consumption of the virtual network for iot application and data management is significantly less compared to the other standard applications. 468 g. balakrishna, m.n. rao figure 5: energy consumption analysis table 3: number of node (physical hosts) shutdowns algorithm name number of node shutdowns iqr mmt 1549 iqr mu 1622 lr mmt 806 lr mu 816 mad mmt 1528 mad mu 1632 thr mmt 1424 thr mu 1578 eslb 795 7.3 number of physical hosts shutdowns thirdly, the number of physical hosts during the idle phase tend to shutdown if for a longer duration no load is been assigned. hence, the poorest load balancing algorithm will have maximum number of host shutdowns. the physical hosts shutdown analysis is furnished in table 3. the results are been analysed graphically in figure 6. it is natural to realize that, the proposed algorithm is demonstrating much lesser hosts shutdowns, which clearly signifies that the utilization of the available resources is significantly high compared with the other algorithms. 7.4 load balancing execution time fourthly, the load balancing algorithm time complexity is analysed. the code responsible for balancing the loads are also deployed on the same physical host instances and buy out the time required to run, or time required for application to respond. the time complexity is analysed in table 4. the results are been analyzed graphically in figure 7. esbl: design and implement a cloud integrated framework for iot load balancing 469 figure 6: host n node shutdown analysis table 4: load balancing execution time algorithm name execution time (sec) iqr mmt 0.002510 iqr mu 0.003100 lr mmt 0.001680 lr mu 0.001530 mad mmt 0.003310 mad mu 0.014710 thr mmt 0.001330 thr mu 0.001600 eslb 0.001560 figure 7: execution time analysis 470 g. balakrishna, m.n. rao figure 8: initial insanitation of the network 7.5 iot network performance finally, the performance of any iot network can be measured by analysing the responsiveness of the network. during a network performance improvement process, if the response time or the number of response requests in the same time increases, then it is natural to accept that the performance of the network has improved. the network is observed during the routing phases and some of the scenarios are furnished in figures 8 – 10. the performance comparison of the configured iot network is analysed in table 5. table 5: load balancing effects on iot network source node number of connect requests average response with esbl average response without esbl 1 491 2.063 1.941 2 390 2.086 1.921 3 297 2.092 1.916 4 398 2.187 1.843 5 971 2.093 1.915 6 393 2.015 1.985 7 291 2.109 1.902 8 491 2.098 1.911 9 285 2.192 1.839 10 395 2.147 1.872 the improvement is visualized graphically in figure 11. it is natural to observe that the response time have significantly improved without comproesbl: design and implement a cloud integrated framework for iot load balancing 471 figure 9: routing at time instance t1 figure 10: routing at time instance t2 472 g. balakrishna, m.n. rao figure 11: response time improvement analysis mising the processing capabilities and energy efficiently. in the next section of the work, the comparative analysis is carried out. 8 comparative analysis to realize the benefits or improvements obtained from any proposed algorithm or framework can be understood by comparing the results with parallel or standard research outcomes. hence in this section of the work, the comparative analysis is carried out [tab. 6]. from the comprehension of the parallel research results, it is normal to understand that the vast majority of the heap adjusting calculations have flopped in advocating the iot organize load adjusting requests as every one of the parameters associated with examining the heap of any virtual machine were given equivalent weight age. in the logical inconsistency, this work proposes a novel weight age metric to legitimize the iot load adjusting requests as concentrated in the past areas of this work. considering the constrained power and handling capacities of any iot hub in the system, this calculation improves the vitality effectiveness and the preparing abilities to a more noteworthy expand. likewise, the stacking adjusting process time multifaceted nature is decreased altogether. table 6: comparative analysis name selection policy allocation policy comparison eslb energy consumption number of node shutdowns execution time iqr mmt minimum migration time inter quartile range improved improved improved iqr mu minimum utilization inter quartile range improved improved improved lr mmt minimum migration time local regression not improved improved improved lr mu minimum utilization local regression not improved improved not improved mad mmt minimum migration time median absolute deviation improved improved improved mad mu minimum utilization median absolute deviation improved improved improved thr mmt minimum migration time static threshold improved improved not improved thr mu minimum utilization static threshold improved improved improved hence, it is natural to realize that the proposed algorithm has outperformed most of the parallel research outcomes. esbl: design and implement a cloud integrated framework for iot load balancing 473 9 conclusions the increasing demand for higher complex data processing applications for managing iot network data make motivated the application and network designer to push the application on to the cloud computing environment. considering less dependencies of services and platform from the cloud service models, the major need is consisting of only infrastructure from the cloud service type offerings. with this decision of utilizing the benefits of infra as a service, the demand for balancing the loads on cloud computing is also unavoidable. observing the parallel standard load balancing strategies, this work identifies that the existing algorithms are not designed to match the demands of iot networks. hence this work proposes a novel algorithm for balancing loads for cloud integrated iot frameworks utilizing the characteristics of loads such as cpu utilization, memory utilization, energy consumption, sla non-violation and equal distribution of the virtual machines with equal distributions of application loads on virtual machines. the algorithm demonstrates significant improvements over standard algorithms and also improves the iot network response time by 60. bibliography [1] ahanger, t.a. 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(2019). wireless traffic prediction with scalable gaussian process: framework, algorithms, and verification, ieee journal on selected areas in communications, 37(6), 1291–1306, 2019. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 2, month: april, year: 2021 article number: 4189, https://doi.org/10.15837/ijccc.2021.2.4189 ccc publications a resource allocation algorithm for ultra-dense networks based on deep reinforcement learning h. s. zhang, t. m. wang, h. w. shen huashuai zhang college of applied science and technology beijing union university beijing 100101, china 20202045111106@buu.edu.cn tingmei wang* college of applied science and technology beijing union university beijing 100101, china *corresponding author: yykjttingmei@buu.edu.cn haiwei shen college of applied science and technology beijing union university beijing 100101, china yykjthaiwei@buu.edu.cn abstract the resource optimization of ultra-dense networks (udns) is critical to meet the huge demand of users for wireless data traffic. but the mainstream optimization algorithms have many problems, such as the poor optimization effect, and high computing load. this paper puts forward a wireless resource allocation algorithm based on deep reinforcement learning (drl), which aims to maximize the total throughput of the entire network and transform the resource allocation problem into a deep q-learning process. to effectively allocate resources in udns, the drl algorithm was introduced to improve the allocation efficiency of wireless resources; the authors adopted the resource allocation strategy of the deep q-network (dqn), and employed empirical repetition and target network to overcome the instability and divergence of the results caused by the previous network state, and to solve the overestimation of the q value. simulation results show that the proposed algorithm can maximize the total throughput of the network, while making the network more energy-efficient and stable. thus, it is very meaningful to introduce the drl to the research of udn resource allocation. keywords: ultra-dense networks (udns), deep reinforcement learning (drl), resource allocation, throughput, energy efficiency https://doi.org/10.15837/ijccc.2021.2.4189 2 1 introduction in recent years, wireless resource allocation has become an important issue, with the proliferation of long-term evolution (lte) systems and fifth generation (5g) communication systems. meanwhile, the recent surge in business demand of users poses severe challenges to resource optimization. the white papers of cisco show that the amount of wireless network data is increasing exponentially, and is expected to grow by 1000% in the next decade. to improve the service quality and business experience of the network, scholars have shifted their attention towards ultra-dense networks (udns). with the rapid development of network technology, lots of small cells could now be deployed on udns [8]. however, the growing density of base stations increases the total power consumption of base stations, intensify the interference between small base stations in the network, and make these base stations more unstable [27]. to solve the problems arising from the largescale deployment of small cell base stations, it is of great significance to allocate wireless resources by improving the energy efficiency and stability of the udns. the existing research on udn wireless resource allocation mainly focuses on increasing the total network throughput and improving network energy efficiency. liu [17] maximized the total network capacity with a distributed resource allocation algorithm, satisfied the requirement on quality of service (qos), and optimized the sub-channel allocation and power allocation through geometric programming. wang et al. [30] adopted a non-cooperative game model with penalty factors, proposed a way to virtualize the local information of small cells for power allocation, and optimized network energy efficiency through effective control of small cell base stations, in the light of their load conditions. drawing on nash bargaining and cooperative game theory, zhang et al. [36], zhang et al. [35], and zhang [34] studied the power allocation of sub-channels in small cell networks, and solved the game model by the lagrangian dual decomposition method. in this way, the model converged to the pareto optimal solution, maximizing the energy efficiency of the entire system. in the above research, the wireless resources are mostly allocated by traditional methods, which involve a huge computing load during implementation. in the scenario of udns, the growing number of small cell base stations will continuously push up the computing complexity. to simplify the algorithm, many researchers have resorted to wireless resource allocation through reinforcement learning (rl). so far, fruitful results have been achieved in the application of rl in wireless communication. amiri et al. [3] applied rl to power allocation of wireless networks, allocated the power of network base stations reasonably through online learning, and thus maximized the total throughput of the network. chen et al. [6] integrated wireless network with rl, and implemented q-learning to rationalize the allocation of spectrum resources and increase network throughput. however, these two rl approaches can only optimize network throughput, failing to consider the energy efficiency of the network. nevertheless, the rl could not effectively handle the ultra-large state space of udns. this gives prominence to deep reinforcement learning (drl). many scholars have probed extensively into the drl [21]. for example, chang et al. [5] developed a drl-based architecture for fog network, which offloads the transcoding tasks in network services to fog nodes under the dynamics of wireless networks, strikes a balance between high qos and low delay, and thereby offers the best user experience. xiong [32] investigated the slice-level resource reservation and physical resource allocation based on the drl algorithm, and constructed an automatic resource management system for wireless virtual networks; the constructed system can maximize the resource utilization of the whole system, without sacrificing the quality of user services. he et al. [11] discussed the actual channel state under the interference alignment mechanism, and obtained the optimal user selection strategy under the cache condition, using the drl algorithm; their strategy eliminates the interference to the signal, and significantly improves the total throughput and energy efficiency of the network. currently, the udns mainly face the following problems due to the dense deployment of small cell base stations and the huge network space: inter-signal interference, slow algorithm convergence, and poor signal stability. these problems could be alleviated by coupling the drl technology with concepts like empirical repetition and target network. this paper proposes a network resource allocation algorithm based on drl in the context of udns, aiming to increase network throughput and speed up convergence. firstly, a model was established to maximize the total system capacity response to the interference caused by the dense deployment of https://doi.org/10.15837/ijccc.2021.2.4189 3 small cells in udn. next, the drl algorithm was adopted to make the allocation of wireless resources more efficient, with the goal to solve the complex resource allocation problem in udn. finally, the deep q-network (dqn) was employed for resource allocation, and the concepts of empirical repetition and target network were introduced. our algorithm stores all operation processes in the memory pool, and randomly selects the data in the memory pool for training. this effectively solves the correlations between samples, and overcomes the instability and divergence of the results caused by the previous network state, thereby improving stability [5]. in the iterative process, the estimation network is updated iteratively at any time according to the current state and action. then, at every interval, the estimation network assigns the network parameters to the target network, such as to avoid the overestimation of the q-value [32]. the research contents are detailed in four parts below: first, a small cell network scenario was summarized, and the relevant hypotheses and model were established for the scenario. next, the drl was introduced to convert the research problem, and to analyze the corresponding algorithm. afterwards, an application scenario was created to simulate the algorithm, and to compare our algorithm with other algorithms; the simulation results were analyzed reasonably. finally, several findings were drawn from the whole process of the research. 2 hypotheses and modeling this paper summarizes the system model of an ultra-dense small cell network as figure 1. figure 1: system model as shown in figure 1, the base stations are distributed densely, so that the networks of the same frequency cannot be used by users in the same cell, but by those in other cells. on the architecture of communication network, the network system should consider the interference caused by all base stations to users, due to the dense distribution of multiple base stations. suppose a core controller can collect relevant information in the entire network, including transmission power and signal-to-noise ratio (snr); each user can transmit location information, interference, and transmission rate to the core controller through pilot signals, and make a unified plan for spectrum allocation. let n = {1, 2, 3, . . . ,n} be the nsmall cells in the system, each of which uses k subcarriers k = {1, 2, 3, . . . ,k}. the distribution of base stations follows the poisson point process model, multiplexing k orthogonal subcarriers. during the access to the base station, each cell user adopts the transmission power of pnt . at time t, the cell user can only connect to one base station; each subcarrier can only be allocated to one user. at time t, in the small cell n, the signal to interference plus noise ratio (sinr) on the k-th subcarrier can be expressed as: sinkkn = gknp (n,k) t∑ n6=ń g k ńp (ń,k) t + σ2 (1) where, p (n,k)t is the total transmission power of small cell n on subcarrier k; gkn is the channel gain; σ2 is white gaussian noise (wgn). hence, the channel capacity of small cell n on channel k can be https://doi.org/10.15837/ijccc.2021.2.4189 4 obtained as: rk=n b k log2 ( 1 + sinkkn ) (2) where, b is the total bandwidth of the system. thus, the maximum total throughput of the system can be expressed as: argmaxr = n∑ n=1 k∑ k=1 rknb k n s.t.c1: p (n,k)t ≥ pmin,∀n, k c2: ∑ n,k p (n,k) t ≤ pmax,∀n, k (3) where, bkn = 1 or 0 is an indicator of whether the small cell base station n allocates subcarrier k to the user (if bkn = 1, the subcarrier k of small cell nis occupied; if bkn = 0, the subcarrier k of small cell n is not occupied); pmin and pmax are the minimum and maximum transmission powers required by the subcarrier, respectively. the overall capacity of the entire system can be enhanced by adjusting the transmission power p (n,k)t of the base stations on the subcarriers. note that the water injection algorithm was adopted to allocate the initial power between the subcarriers, for this algorithm can maximize the transmission rate by reasonably allocating the transmission power as per the planned criteria and the real-time channel conditions [11]. during channel allocation, the power consumption of each small cell base station is divided into two parts: the minimum fixed power consumed to maintain the operation of the station, and the power consumed to serve users. in the period t, the power consumption of the small cell base station n can be expressed as: p ′(t,n) = p op n + p (n,k) t b k n (4) where, popn is the fixed operating loss of small cell base station n; p (n,k) t is the transmission power of small cell base station n in period t. energy efficiency, as the ratio of total throughput to the total power consumption, can effectively measure the network performance. therefore, the total energy efficiency of the system at time t can be expressed as: eettotal = max rt∑n n=1 p ′ (t,n) (5) to optimize the energy efficiency of the network, it is necessary to control the carrier allocation and transmission power of small cell base stations. however, this non-convex optimization problem cannot be efficiently solved by traditional algorithms in a short time. what is worse, the traditional algorithms cannot make real-time adjustment according to the network environment. to solve the problem, artificial intelligence (ai) techniques like drl provides a possible tool to intelligently and effectively enhance user satisfaction with qos and the utilization rate of network resources [25]. 3 problem transformation and algorithm analysis 3.1 basic model of rl the rl is an important tool widely used to solve markov dynamic programming and other problems [39]. during the rl, the learner can acquire the optimal strategy by interacting with the complex environment. according to the network state, the learner takes an action by a certain strategy. once taken, the action will change the network state, and receive a reward. then, the strategy will be further optimized. this process will be repeated until the strategy is optimal. hence, network state, action, and reward are three key factors of the rl framework. in this paper, the three factors are defined as follows: (1) state space the state space is defined as s(t) = { b (t) 1 ,c (t) 1 ,p (t) 1 , . . . ,b (t) n ,c (t) n ,p (t) n } , where b(t)1 is the traffic volume of small cell base station at time t; c(t)1 is the channel state at time t; p (t) 1 is the transmission https://doi.org/10.15837/ijccc.2021.2.4189 5 power of small cell base station at time t. to reduce the complexity of the algorithm and the state space of the network, the transmission power of the base station needs to be discretized by: p (t) n = τ( pfmax −aτ ) ≤ ∑k k=0 p (n,k) t < ( pfmax −aτ+1 ) (6) where, τ ∈{0, 1, 2, 3, 4, 5}; a0 = pfmax; a6 = 0; a1 ∼ a5 are arbitrary thresholds. (2) action space the action space is defined as a(t) = { u (t) in ,p (t) in ,t (t) nk | n ∈ n, k ∈ k } , where u(t)in is the indicator of whether small cell base station needs to be connected with user i at time t; p (t)in is the power allocation by small cell base station n to user i at time t; t (t)nk is the adjusted value of the energy efficiency on the k-th subcarrier of base station n at time t. (3) reward to optimize energy efficiency, the reward function can be set as the energy efficiency at time t: rt = eettotal = max rt∑n n=1 p ′ (t,n) (7) 3.2 drl strategy to maximize the throughput of the entire network, it is a must to maximize the cumulative reward by choosing a suitable method for allocating network resources. the q-learning presents an effective way for rl, and has been widely applied by researchers [29]. the significance of q-learning is to search for the optimal strategy π∗ that maximizes the cumulative discount return in the long term. in each period t, the learner belongs to the state of s(t), and executes the action a(t) = π ( s(t) ) corresponding to strategy π. he/she will be rewarded with rt ( s(t),a(t) ) , depending on the current state and action. then, the next state will be obtained. the strategy will be optimized through iterative learning by the small cell base station. based on strategy π, the expected long-term return v π ( s(t),a(t) ) of the state-action strategy can be expressed as: v π ( s(t),a(t) ) = eπ [ i∑ i=1 λiri ( s(i),a(i) ) | s(1) = s(t),a(1) = a(t) ] (8) where, ri ( s(i),a(i) ) is the instant reward generated by performing action a(i) in state s(i); λ is a discount factor (0 < λ < 1). to maximize the long-term return v π ( s(t),a(t) ) , the optimal state value function can be expressed as: π∗ ( s(t) ) = max q ( s(t),a(t) ) (9) the optimal strategy can be obtained through iterative update of the optimal q value: q ( s(t),a(t) ) ← ( 1 −α)q ( s(t),a(t) ) + α [ rt ( s(t),a(t) ) + λ max q ( s(t+1),a(t+1) )] (10) where, α is the learning rate that affects the update speed of q value (0 < α < 1). 4 dqn in the real-world udn, both state space and action space are huge. it is difficult for q-learning to achieve desirable results. therefore, the dqn algorithm that combines deep learning with q-learning can effectively overcome this defect. apart from the dqn, the authors also introduced two novel concepts: empirical repetition and target network. https://doi.org/10.15837/ijccc.2021.2.4189 6 during q-learning, the q-value might be overestimated, for the strategy selection and update are based on the same q function table [2]. the target network was introduced to prevent the overestimation. there are two neural networks with different parameters in dqn. one of them is an estimation network, i.e., an optimal value function q ( s(t),a(t) | θ ) ≈ q ( s(t),a(t) ) , with network weight θ, fitted from the currently inputted state and action; the other is the target network, whose target value can be calculated by: r = rt ( s(t),a(t) ) + λ max q ( s(t+1),a(t+1) | θ − ) (11) where, θ− is the parameter of the target network. any small deviation of the function value will affect the entire strategy. to stabilize q ( s(t),a(t) | θ ) , the estimation network needs to be trained in each step to minimize the loss function, so as to approximate the actual q ( s(t),a(t) ) . thus, the deviation between q values of the target network and estimation network can be minimized by gradient descent: loss(θ) = e [( r − q ( s(t),a(t) | θ ))2] (12) during the training, only the estimation network is trained, and updated to the target network through multiple online trainings. in this way, the correlation between the two q values is greatly weakened, avoiding the overestimation of q value. to prevent the target strategy from falling into the local optimum trap and search for a better q value, the ε-greedy strategy was introduced to the dqn. the random selection of the ε-greedy strategy was employed to find the action with the greatest value. however, a huge amount of data is needed for neural network fitting [10, 13, 16, 22, 24, 26, 38]. besides, it may take a long time for the action taken under a strategy to produce benefits. thus, empirical repetition was implemented to store each step of computation into a preset memory pool. then, the stored data were extracted from the pool for training: at time t, the algorithm belongs to the state s(t). the corresponding action a(t) is chosen as per the optimal function π∗ ( s(t) ) . then, the instant reward rt and the next state s(t+1) will be obtained. next, the data ( s(t),a(t),rt,s(t+1) ) will be stored in the memory pool with the capacity of n . after the sample size accumulates to a certain level, the dqn will randomly select data from the pool for iterative computation, and then update the parameters of the estimation network. after certain iterations, the estimation network parameters will be synced to the target network [7, 9, 12, 15, 19, 28]. this approach helps to alleviate the instability and divergence of the results caused by the past states, thereby improving the algorithm stability. 5 simulation and results analysis our simulation was carried out under a udn with 11 macro base stations and 80 small cell base stations. the area of udn is 1,000m × 1,000m, and each base station has a radius of 250 m. the users are uniformly distributed within the coverage areas. each user is allocated at least one channel [18, 23, 31, 37]. if one base station is interrupted, then the service of the user connected to this base station will be interrupted. during the simulation, the power of the small cell base stations was discretized and divided into 5 levels, corresponding to p = {10, 15, 20, 25, 30}. a neural network with two hidden layers was designed, with 200 hidden layer nodes. meanwhile, the memory pool capacity n, number of selected samples s, and learning rate α were set to 10,000, 300, and 0.0005, respectively. due to the instability of udn, the energy efficiencies obtained by simulation were all mean values. to verify the dqn performance, q-learning algorithm was selected as the contrastive algorithm. the simulation results are shown in figure 2. in the first 600 iterations, dqn performed poorer than q-learning, for the dqn requires a preliminary training and applies function fitting to training. at the initial phase of training, the dqn has a much poorer fitting effect than the table look-up method of q-learning. https://doi.org/10.15837/ijccc.2021.2.4189 7 figure 2: simulation results after the 600-th iteration, dqn far outperformed q-learning, owing to the following reason: qlearning relies on the table look-up method; in the udn, however, not all scenarios are stored in the table; thus, q-learning involves lots of random explorations, which drag down its performance [1, 4, 14, 20, 33, 40]. in addition, the number of base stations has an impact on the overall energy efficiency of the system. table 3 compares the energy efficiency of base stations at different numbers of base stations. note that the random power algorithm was introduced as another contrastive algorithm against dqn algorithm. figure 3: energy efficiency at different numbers of base stations as shown in figure 3, the overall energy efficiency of the system increased with the number of base stations under the dqn algorithm. this is because more small cell base stations mean the algorithm controls more targets, making the scheme more flexible; a flexible scheme pushes up the overall efficiency. by contrast, under q-learning, the growing number of base stations led to an exponential rise in the number of candidate strategies; then, it is much harder to find the optimal strategy. that is why the q-learning algorithm had a decline in learning performance. figure 4 presents the convergence curves of dqn algorithm and q-learning algorithm. as shown in figure 4, our algorithm achieved faster convergence and better energy efficiency than q-learning. https://doi.org/10.15837/ijccc.2021.2.4189 8 figure 4: network convergence 6 conclusions this paper proposes a network resource allocation algorithm based on drl to increase network throughput and energy efficiency of the udn. drawing on the theories on deep learning, a longterm reward function was constructed with energy efficiency as the reward. the weight parameters of neural network were updated through repeated training. simulation results show that, with the growing number of iterations, our algorithm could optimize system energy efficiency more effectively than other algorithms, and provide an effective solution to resource allocation under dynamic complex network environment. the future research will further refine the details and improve the performance of the optimization algorithm, carry out simulations on a larger scale, and apply the algorithm in real-world scenarios. 7 funding the apc was funded by r&d center "cercetare dezvoltare agora" of agora university. 8 author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. references [1] abtahi, f.; 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(2019). application of the asynchronous advantage actor–critic machine learning algorithm to real-time accelerator tuning, nuclear science and techniques, 30(10), 1-9, 2019. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: zhang, h. s.; wang, t. m.; shen, h. w. (2021). a resource allocation algorithm for ultra-dense networks based on deep reinforcement learning, international journal of computers communications & control, 16(2), 4189, 2021. https://doi.org/10.15837/ijccc.2021.2.4189 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 2, month: april, year: 2021 article number: 4108, https://doi.org/10.15837/ijccc.2021.2.4108 ccc publications prognosis prediction of stroke based on machine learning and explanation model q. qin, x. zhou, y. jiang qiuli qin*, xuehan zhou department of information management beijing jiaotong university, china beijing 100044, china *corresponding author: qlqin@bjtu.edu.cn xhzhou@bjtu.edu.cn yong jiang china national clinical research centre for neurological diseases beijing tiantan hospital,capital medical university , china jiangyong@ncrcnd.org.cn abstract the prognosis prediction of stroke is of great significance to its prevention and treatment. this paper used machine learning to predict stroke prognosis, and use shap method to make feature importance and single sample analysis. firstly, feature engineering, use borderline-smote algorithm to deal with data imbalance, use support vector machine(svm) to build a prognostic prediction model, and use random forest(rf), decision tree(dt), logistic regression(lr) for comparative analysis, and find the performance of svm after feature engineering better than other models, the accuracy, specificity, f1 score, auc value reach 0.8306, 0.8356, 0.8415 and 0.9140. then, the model was further analyzed for explainability, and it was found that the top three causes of the disease were glasgow coma score, nihss and atrial fibrillation. finally, try to analysis a single sample, which is performed to determine that the patient is a low-risk patient, and suffering from atrial fibrillation is the largest potential risk factor for the patient. keywords: machine learning; stroke; prognosis prediction; explanation model. 1 introduction stroke is a global public health problem, and the prevention and treatment of stroke is also the primary task of public health in all countries. according to the joint statistics of the american heart association and the national institutes of health [30], stroke is the second leading cause of death and the third leading cause of disability in the world population. according to data published by the who[31], among the 56.9 million deaths worldwide in 2016, ischemic heart disease and stroke caused 15.2 million deaths, which was the main cause of death worldwide in 2001-2016. https://doi.org/10.15837/ijccc.2021.2.4108 2 with the increase in the incidence and recurrence rate of stroke, the burden of stroke has a serious impact on family and public medical care. in the absence of specific treatments, prevention and intervention are the best treatments. therefore, it is of great significance for public health management to deeply understand the influencing factors of stroke disease and prognosis and to do a good job of secondary prevention of stroke. risk factors of stroke can be divided into modifiable and non-modifiable. who[32] shows, among the modifiable risk factors, hypertension, diabetes, obesity, and lack of physical activity are the important risk factors for stroke, among which hypertension is the most important risk factor for stroke. among the non-modifiable factors, aha/nih[30] shows that age, gender and previous stroke history are important risk factors, and family stroke history and race are also listed as risk factors[33]. there is also [30], [5] evidence that recent use of antihypertensive drugs, rapid weight changes, and atrial fibrillation are also risk factors for stroke. with the improvement of hospital information management system and the rapid development of machine learning, the study of stroke prognosis prediction based on machine learning has also developed. prognosis prediction can help doctors make better medical decisions and achieve a better prognosis. heo et al. [14] used deep neural networks, random forest and logistic regression algorithms to predict the long-term prognosis of ischemic stroke, and found that deep neural networks had the best performance. machine learning can also analyze brain images and make prediction. kuang et al.[18] used random forest to analyze brain ncct images and realized automatic aspects scoring. the sensitivity of the score reached 97.8%, and the auc value was 0.89, which reflected good performance. xie et al.[19] used gbm and xgb models for image segmentation of ct images and 90-day predicted mrs scores, and found that decision tree-based gbm performed better. shameer[20] built a risk prediction model based on machine learning. by processing electronic medical record(emr) data, predict the readmission of stroke patients with an accuracy rate of 83.19% and an auc value of 0.78. sung[29] used a supervised machine learning technique to mine emr text to distinguish different stroke subtypes and found that binary classification based on decision tree and random forest is better than multi-classification. the use of machine learning to analyze medical data is a hot topic of current research. most stroke prediction studies focus on brain imaging (ct) and electronic medical records(emr). afify h.m. et al.[1] automated diagnosis of different types of breast carcinoma histopathological images by machine learning algorithms, which was a multi-classification task. romana c.h. et al.[7] used firefly algorithm combined with k-means clustering(km-fa) in brain image segmentation and showed a great performance. additionally, machine learning has been applied in electronic commerce, power system, education and other fields. jing li et al.[21] used machine learning in custom behavior prediction, include clustering, decision tree and naive bayesian algorithm, to explore the characteristics of target groups in which purchase behavior would occur. in the study of stroke prediction based on machine learning, commonly used algorithms can be divided into interpretability models and difficult-to-interpret models. an interpretable model is a highly interpretable model structure, such as decision trees and logistic regression. the difficult-to-interpret model, which can also be called a complex model, usually has good predictive performance[11]. therefore, although complex machine learning models show superior performance in the field of medical prediction, they usually lack the interpretability analysis of the results. at present, there are different interpretation methods for complex models. the more popular one is to construct an explanation model, namely an explanation, to assist in the interpretation of complex models. ribeiro [26] et al. proposed a lime (local interpretable model-agnostic explanation) method in 2016. the principle is to perturb the input data and observe the changes in the prediction results to fit an interpretable model and use the pseudo the combined model explains the prediction results. the lime method can be independent of the model. it can explain a sample from the perspective of the model as a whole, which can explain the results prediction, image recognition, text classification and other issues. inspired by the lime method, lundberg et al. [22] proposed the shap (shapley additive explanations) method combined with the shapley value method. the shapley value method is the benefit distribution plan of coalition members proposed by shapley l.s. in 1953 [28], which distributes benefits according to the marginal contributions of the coalition members. the shap rule treats alliance members as features in the dataset to calculate the marginal contribution of each feature. both lime https://doi.org/10.15837/ijccc.2021.2.4108 3 and shap can interpret the model well and are independent of the model. in contrast, shap can provide local accuracy and consistency guarantees in the process of interpreting the model. in this paper, we firstly proves that the complex machine learning method after feature engineering and parameter tuning has better performance than the simple machine learning method. with the development of machine learning, human not only pursue high performance, but also try to enhance the interpretability of machine learning, which help human can understand the prediction results. in the current research, many studies[[16], [9], [3]] showed the prediction results of machine learning are lack of interpretability. in the context of precision medicine, the interpretability model can effectively deal with the limitation of the machine learning prediction result, such as lack of robustness, lack of interpretability, and difficulty in application. in this paper, based on game theory, the interpretability research is divided into global interpretability and local interpretability. one part applies global interpretability to analyze the importance of different prognostic factors, and the other part applies local interpretability to analyze the risk factors of individual patients. this method can provide more timely, efficient and interpretable prediction results for clinical treatment decision-making, and use the analysis results to solve clinical practical problems. based on the above, in this paper, we use the dataset from the third international stroke trial(ist3), which collected multi-dimensional dataset(266 variables). after feature selection, we use basic information, detection indicators, functional check, past medical history and other information. then, we use different machine learning algorithms to build prediction models. finally, we analyze the explanation of the models’ results to improve the application further, which include feature important and single sample analysis. the purpose of this paper is to enhance the understanding of the influencing of stroke prognosis and to explore an explanation model applied to stroke diseases, hoping to help medical staff assist in diagnosis and treatment. 2 materials 2.1 ist-3 data the third international stroke trial (ist-3)[27] is a large-scale randomized trial and the largest randomized controlled trial in the history of acute ischemic stroke, which recorded stroke patients and the three-year follow-up data of the thrombolytic control group provided 266 variables including basic information, treatment plan, and follow-up information of the patient. the dataset included a total of 3035 patients older than 18 years old, of which 1617 (53%) patients were over 80 years old. 2.2 input many risk factors affect the prognosis of stroke. in this paper, among the 266 variables in the original dataset, combined with the characteristics of the early stroke management guide[25] and the ist-3 dataset, we divide the independent variables into 5 categories, namely basic information, detection indicators, functional checks, and past the medical history, medication history, and stroke subtypes, and the variable assignment methods are shown in table 1. 1) basic information: the basic information of the patient includes age, gender, weight, and whether they live alone. age and gender are important uncontrollable factors for stroke[30]. obesity and lack of physical activity are also risk factors for stroke[32]. therefore, weight and whether living alone before the stroke occurs can be considered as potential risk factors. 2) testing indicators: the patient’s testing indicators include systolic blood pressure, diastolic blood pressure, and blood sugar. the systolic and diastolic blood pressure reflect the patient’s blood pressure level, and hypertension and diabetes are the most important risk factors[32]. 3) functional check: the functional examination of the patient includes nihss, adl scale, and glasgow coma score. to assess the condition of patients, there are currently many scales that can quickly quantify the degree of functional impairment of patients, for example, nihss (nih stroke scale) used to assess the degree of neurological impairment[25], adl ( activity of daily living scale), gcs (glasgow coma scale) to assess the degree of coma in patients, etc. https://doi.org/10.15837/ijccc.2021.2.4108 4 table 1: dataset features, feature description and value assignment methods feature description assignment method dead6mo died within 6 months yes=1,no=2 basic information age age continuous variable gender gender female=1, male=2 weight weight(kg) continuous variable livealone_rand live alone yes=1,no=2 detection indicator sbprand systolic blood pressure(mm hg) continuous variable dbprand diastolic blood pressure(mm hg) continuous variable glucose blood sugar (mmol/l) continuous variable function check indepinadl_rand independent in adl before stroke? yes=1,no=2 gcs_score_rand total glasgow coma scale score at randomisation continuous variable nihss total nih stroke score at randomisation continuous variable medical history / medication history antiplat_rand received antiplatelet drugs in last 48 hours? yes=1,no=2 atrialfib_rand patient in atrial fibrillation at randomisation? yes=1,no=2 stroke_pre history of previous stroke or tia? yes=1,no=2 diabetes_pre treatment for diabetes before admission? yes=1,no=2 hypertension_pre treatment for hypertension before admission? yes=1,no=2 stroke subtype stroketype stroke subtype 1=’taci’, 2=’paci’, 3=’laci’, 4=’poci’, 5=’other’ 4) past medical history & medication history: the patient’s past medical history and medication history include antiplatelet drugs, atrial fibrillation, past stroke history, and hypertension. because stroke has a high recurrence rate, the history of past stroke is also an important factor. antiplatelet drugs have been shown to have a preventive effect on ischemic stroke in previous studies[17], but at the same time, they may have a risk of hemorrhagic transformation[10]. there is a strong correlation between atrial fibrillation and stroke, and atrial fibrillation is also an important risk factor for stroke[24]. 5) stroke subtypes: in this dataset, stroke subtypes include five types, namely 1=’taci’, 2=’paci’, 3=’laci’, 4=’poci’, 5=’other’. 2.3 output in this paper, we selected the survival of patients within 6 months as the output. 3 methods 3.1 data analytics framework the input of the model is the information when the patient is admitted to the hospital, and the output is the binary prediction result of whether the patient will die after 6 months. the data analysis framework is shown in figure 1. 3.2 data preprocessing 1)process discrete features for discrete features that exist in the dataset, such as gender, stroke subtype, etc., the values of these features are not numerically differentiated, so one-hot encoding (one-hot) is used for processing. taking gender as an example, the gender feature is converted into two features gender_female and gender_male. if the sample value of the original gender feature is 1, the sample value of the converted https://doi.org/10.15837/ijccc.2021.2.4108 5 figure 1: data analysis framework for the prediction of stroke prognosis [gender_female, gender_male] is [1, 0], thereby reducing the effect of meaning value on model training results. 2)data standardization for continuous features in the dataset, such as systolic blood pressure, body weight and other features, because different features have different scales, features with a large scale have a greater impact on the results, so the dataset needs to be standardized. in this paper, z-score standardization is used to standardize the data by giving the mean and standard deviation of the original data. the processed data conforms to the standard normal distribution, that is, the mean is 0 and the standard deviation is 1. the conversion function is: x∗ = x− x̄ σ (1) where x̄ is the mean value of the feature, and σ is the standard deviation of the feature. 3)deal with unbalanced dataset by analyzing the original dataset, it is found that the outcome feature "dead6mo" has 2220 positive samples and 815 negative samples, which means the dataset is unbalanced. since machine learning tends to improve the accuracy of the majority class, the performance of the model will decrease, and the ability to judge minority samples will decrease. in the medical field, it is often necessary to keenly find high-risk patients. therefore, it is necessary to deal with unbalanced dataset to improve the machine’s ability to judge minority samples. this paper uses borderline-smote algorithm to deal with imbalanced dataset. borderline-smote is an improved oversampling algorithm based on smote, which uses only a few samples on the border to synthesize new samples, to improve the imbalanced distribution of the dataset[12]. the borderline-smote method divides minority samples into 3 categories, namely safe, danger, and noise. samples with more than half of the nearest neighbors are in the minority class are classified as safe, samples with more than half of the nearest neighbors are in the majority class are classified as danger, and all of the nearest neighbors are in the majority class are classified as noise. since the model usually has a weak distinguishing ability for danger class, the borderline-smote method uses danger class k nearest neighbors to randomly generate minority class samples. based on smote, the improved borderline-smote improves the ability to distinguish boundary samples. 4)training and test set before training the model, the dataset needs to be divided into training set and test set. the training set is used to train and fit the model, and the test set is used to test the trained model. in this experiment, the dataset was randomly divided according to the ratio of 7:3, and the training set and the test set were constructed. 3.3 hyper-parameter optimization before model training, model parameters need to be determined. such parameters are also called hyperparameters. optimizing the hyperparameters and selecting a set of optimal hyperparameters for the machine learning model can improve the performance and effect of learning to a certain extent. this paper uses grid search algorithm to optimize the parameters. https://doi.org/10.15837/ijccc.2021.2.4108 6 the grid search algorithm (gridsearch, gs) is one of the most basic exhaustive parameter optimization algorithms, and the parameters obtained are relatively reliable[4]. the principle is to divide the parameters to be searched into grids in a specific spatial range, and then traverse all points in the grid to find the parameter values that optimize the performance of the model. the grid search algorithm is simple, convenient, easy to understand, and fast in finding the best. 3.4 machine learning model in this paper, we chose 4 machine learning models to predict whether they would die after 6 months of stroke, include support vector machine, random forest, decision tree and logistic regression. these 4 models were selected because they have great performance in binary classification. among the 4 models, support vector machines and random forests could process high-dimensional data well while maintaining great performance. but as a representative of complex models, it means that their results are always difficult to understand by human. logistic regression and decision tree are baseline algorithms for comparison, because they have interpretable advantages, people can usually understand the prediction results of logistic regression and decision tree models well. 1)support vector machine the support vector machine[8] divides all samples in the training set into two categories by finding an optimal hyperplane, and at the same time maximizes the classification interval between the two types of samples. the samples located on the optimal hyperplane are called support vectors. the solid dots and hollow dots respectively represent the samples of the two categories, h is the classification hyperplane, and the optimal classification plane not only ensures that the two types of samples are accurately separated, but also requires their classification maximum interval. the former is to ensure that the empirical risk value is minimized, while the latter is to minimize the confidence range of the generalized community and ultimately lead to the smallest real risk. 2)random forest random forest[15] is a classifier based on the idea of ensemble learning. multiple weak classifiers composed of decision trees are integrated into a strong classifier by voting. for each decision tree, the training set of each tree is randomly selected training samples with replacement. this method is called the bootstrap sample method. 3)decision tree decision tree is a classic machine learning algorithm, which is composed of internal nodes, directed edges, and leaf nodes. through the decision tree, you can clearly see the choice of each root node when making a decision, so it has good interpretability. 4)logistic regression logistic regression methods are classic machine learning and statistical methods, which can not only solve regression problems, but also solve binary classification problems. through the parameters of logistic regression fitting, the prediction results can be well understood, so it has good interpretability. for example, after a linear logistic regression model is fitted with y = xβ + ε , the importance of each feature can be understood through β. 3.5 explanation model in the medical field, while pursuing a high rate of prediction accuracy, it is also necessary to explain the results of disease prediction to help doctors analyze the causes of patients and achieve the purpose of auxiliary diagnosis and treatment. this paper uses the shap method to analyze the predictive results. the shap method can either rank the overall feature importance or a single sample. the principle is as follows[22]: let s ⊆ f (f is the full set of features), s represents the set of elements in the sequence before the feature i , and f\s\i is the set of elements after the feature i, then the elements of the s set before i and f\s\i after the feature i can form |s|! (|f|−|s|−1)! sequence. in order to calculate the contribution value of the feature i , it is necessary to train the model fs ⋃ {i} and the model fs first, and after the feature value xn is substituted into the model, the contribution value fs ⋃ {i}(xs ⋃ {i})−fs (xs ) https://doi.org/10.15837/ijccc.2021.2.4108 7 is calculated. finally, calculate the weighted average of each sequence, which is the shap value of each feature: φi = ∑ s⊆f\{i} |s|! (|f|−|s|−1)! |f|! [fs ⋃ {i}(xs ⋃ {i}) −fs (xs )] (2) 4 experimental results to compare and intuitively see the model performance and experimental results, we first select the evaluation index and evaluate the model. then, analyze the explanation for the problem that the complex model is difficult to explain. 4.1 assessment measurement the commonly used evaluation indicators of the binary classification model are accuracy, precision, specificity, f1-score, auc (area under curve) value, etc. 1) confusion matrix in the classification model, the prediction results can be recorded as tp (true positive), fn (false negative), fp (false positive), tn (true negative). and extend the accuracy rate, specificity, f1-score and other indicators, see eq. (3)-(7). accuracy = tp + tn tp + fp + tn + fn (3) sensitivity = tp tp + fn (4) specificity = tn fp + tn (5) precision = tp tp + fp (6) f1 −score = 2 ×precision×recall precision + recall (7) 2) roc curve the roc curve refers to the receiver’s operating characteristic curve. each point on the curve reflects the susceptibility to the same signal stimulus, so it is also called the susceptibility curve. the horizontal axis of the roc curve is the false positive rate, and the vertical axis is the true rate. the auc value is the area under the roc curve, and the value range is [0,1]. the larger the value, the better the classification effect. 3) selection of evaluation indicators in the two-class model, accuracy is the most intuitive and commonly used evaluation index to judge the performance of the model. in the scenario of stroke prognosis prediction, the model needs to prevent missed diagnosis as much as possible, so specificity is selected as one of the evaluation indicators. also accuracy and specificity cannot fully evaluate the performance of the model, and f1score can comprehensively evaluate the performance of the model. therefore, this paper uses accuracy, specificity, f1-score and auc value as the evaluation indicators of the model to comprehensively evaluate the model. https://doi.org/10.15837/ijccc.2021.2.4108 8 table 2: comparison of classification prediction results model accuracy specificity f1-score auc svm 0.7747 0.6133 0.4279 0.7741 svm + borderline-smote 0.8306 0.8356 0.8415 0.9140 rf 0.7802 0.6428 0.4285 0.7965 rf + borderline-smote 0.8045 0.8106 0.8172 0.8997 lr 0.7827 0.6351 0.5137 0.8056 dt 0.7131 0.4504 0.4739 0.6394 4.2 assessment results and analysis to compare the performance of the complex model and the baseline model, this paper uses the evaluation indicators mentioned above and obtains the performance results of different models (see table 2). 1) the importance of balancing the dataset in this experiment, by observing the specificity and f1-score, it can be found that after processing the imbalanced dataset, the model performance has been greatly improved. in terms of specificity, 0.8106(rf +borderline−sm ot e)> 0.6428(rf ), 0.8356(sv m +borderline−sm ot e)> 0.6133(sv m ). for f1score, 0.8172(rf +borderline−sm ot e)> 0.4285(rf ), 0.8415(sv m +borderline−sm ot e)> 0.4279(sv m ). it can be seen that in the same machine learning algorithm, using the improved borderline-smote algorithm to process imbalanced dataset can greatly improve the prediction effect of the model in the minority class and improve the performance of the model. and it is suitable for different complex machine learning models, such as svm and rf, has good portability. therefore, the balance of sample categories is very important to the classification model. 2) better performance of the complex models compared with the baseline model, the model after a series of processing has better performance under most evaluation indicators, and the complex model has strong predictive ability and significant advantages. in terms of accuracy, the accuracy of decision trees (0.7131) is lower than other models. although the accuracy of the logistic regression model is slightly higher than that of the svm and rf models after feature engineering, the accuracy of the complex model is significantly improved after data changes, which is much higher than lr. at the same time, the specificity and f1-score of dt and lr are much lower than svm and rf models. but it is undeniable that after a series of data processing, the complex model is far inferior to the baseline model in terms of interpretability. after data preprocessing, the dataset has lost part of information, and the dataset at this time has lost part of its interpretability. then, after svm and rf model training, the prediction results are more difficult to understand. therefore, further analysis of the forecast results is needed. 4.3 explanation machine learning model 1) feature importance etiology research is an important part of modern medical research. researching disease factors and risk factors can help people recognize and prevent diseases. the importance ranking of the overall sample can help people understand the importance of different features, so this paper uses importance ranking to help analyze the importance of different prognostic factors. first, use the shap method to analyze the importance of features, calculate the shap value of the features of the overall sample, and sort the feature values. through the overall feature importance ranking (see figure 2), it can be found that the main features that affect the prognosis of stroke are the glasgow coma score, the nihss, and whether the patient is atrial fibrillation. the patient’s state at the time of admission has a great effect on the level of patient prognosis, which is basically consistent with the results of existing studies [[6],[2],[13]]. 2) single sample analysis at present, most machine learning disease prediction models can achieve high prediction accuracy, but lack the interpretation of the prediction results of a single sample, and it is difficult to make targeted recommendations. although the random forest and support vector machine constructed in https://doi.org/10.15837/ijccc.2021.2.4108 9 figure 2: feature importance this paper have more advantages in predictive performance, they are not as good as decision trees and logistic regression models in terms of interpretability. especially after data preprocessing, the data becomes more difficult to understand. when the shap method is used in the analysis of a single sample, it can enhance the interpretability of the model to a certain extent, help doctors understand the model, choose a better treatment plan, and improve the prognosis. this experiment randomly selects a sample for single-sample analysis (figure 3). it can be seen from figure 3 that the prediction result shows that the patient’s risk value is 0.15, which is lower than the base value of 0.493, so the risk of death is low. further analysis of this patient’s low risk of death is due to the better performance of nihss and glasgow coma scores, but since this patient is a patient with atrial fibrillation, which has a negative impact on the prognosis, standard anticoagulant therapy can be considered for this patient[23] to improve prognosis. figure 3: single sample feature contribution value 5 discussion this paper mainly solves two problems. one is to study which machine learning method can improve the prediction effect of stroke as much as possible and to better serve public health; the other is how to explain the prediction results of the model, so that people can understand and use the results given by the model prediction. https://doi.org/10.15837/ijccc.2021.2.4108 10 1) data preprocessing data preprocessing is one of the research focuses on this paper. the data preprocessing in this paper is mainly divided into three parts, namely discrete value processing, standardized processing and unbalanced dataset processing. the discrete value processing part, through the processing of onehot encoding, will perform feature processing on features that do not have numerical values, such as gender. the standardized processing part avoids the problem of model bias caused by dimensional differences by standardizing all features. in terms of unbalanced dataset, by using the improved smote method and the k-neighbor algorithm, new minority data are randomly generated at the edge of the classification, to avoid the problem of unbalanced data degrading model performance. through the experimental results, it can be found that the performance of the model after data preprocessing is significantly superior to that of the unprocessed model. especially after using the improved smote algorithm, the specificity of the model is significantly improved. in practical applications, the probability of missed diagnosis can be reduced. 2) machine learning algorithm selection this paper considers different types of machine learning models in algorithm selection. decision trees and logistic regression are baseline models with strong interpretability, while support vector machines and random forests are complex models with better prediction performance. it can be seen from the performance of the model that the complex model has obvious advantages when dealing with a large number of feature dataset, and the accuracy rate is higher than that of the baseline model. however, the results of complex models are difficult to interpret after data preprocessing and black-box model fitting, so they have not been widely used. this leads to the next research focus, interpretability analysis. 3) interpretability analysis this paper uses an advanced interpretability method, referring to the shapely value idea in game theory, and assists in explaining complex models by constructing an explanatory model. this method can not only rank the overall importance and analyze the important prognostic factors on the dataset, but also rank the feature importance of a single sample to analyze the main prognostic factors of a patient. research on the interpretability of the model can help us understand the black box model, thereby better helping medical staff set up treatment plans and intervention measures. 4) research limitations and future prospects first of all, stroke is a complex disease, and current research usually focus on a kind of data structure, such as image data, electronic medical record data, streaming data, etc. in fact, we can try the method of data fusion, and make predictions after the fusion of multi-modal data. the prediction results may be more accurate and more stable. but the premise is that medical institutions and public health organizations consciously collect multi-modal data of stroke patients, and provide a good database platform for researchers who build models. meantime, more data and more features will decrease classical machine learning performance, and we can try deep learning algorithm. deep learning can deal with big data and high-dimensional features well and have a great performance. secondly, stroke disease may change dynamically over time. this paper uses death after 6 months as the outcome variable to predict, but this prediction method predicts the time point. in the future, we can try to use other methods for dynamic prediction, such as dynamically predicting the prognosis risk of patients in the form of a timeline. thirdly, this paper explores the explanation method and analyzes the overall feature importance and the feature importance ranking of individual samples. the ranking of overall feature importance can be confirmed by previous research. however, we don’t have the dataset which is labeled, thus the interpretation of a single sample lacks expert verification, which is the limitation of the research process of this paper. finally, although this paper uses the dataset of the international stroke trial, due to differences in race, geographic environment, and dietary habits, the results of studies in different regions, such as the importance of prognostic factors, may be different from this paper. however, the method in this paper is portable. researchers in different regions can use the dataset and characteristics of local hospitals, regions, and countries to try the method proposed in this paper and verify the results. https://doi.org/10.15837/ijccc.2021.2.4108 11 6 conclusion in this paper, a representative ist-3 is used as the research dataset, and in response to the problems in the dataset, data preprocessing is used to process the original dataset. then, different machine learning models were constructed, and the comparison found that the prediction effect of the support vector machine after data preprocessing was the best. then the predictive results of the model were analyzed for interpretability, using feature importance to analysis the important prognostic risk factors of stroke. and we randomly selected a sample to assess prognostic risk, and try to give a treatment to improve the prognosis of patient. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] afify, h.m.; mohammed, k.k.; hassanien, a.e.(2020). multi-images recognition of breast cancer histopathological via probabilistic neural network approach, journal of system and management sciences, 1(2), 53-68, 2020. 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(2021). prognosis prediction of stroke based on machine learning and explanation model, international journal of computers communications & control, 16(2), 4108, 2021. https://doi.org/10.15837/ijccc.2021.2.4108 introduction materials ist-3 data input output methods data analytics framework data preprocessing hyper-parameter optimization machine learning model explanation model experimental results assessment measurement assessment results and analysis explanation machine learning model discussion conclusion international journal of computers communications & control issn 1841-9836, 13(5), 759-771, october 2018. a spectral clustering algorithm improved by p systems g. chen, j. hu, h. peng, j. wang, x. huang guangchun chen, juan hu, hong peng*, xiangnian huang school of computer and software engineering xihua university, chengdu, 610039, sichuan, china gchen@mail.xhu.edu.cn, jhu@mail.xhu.edu.cn *corresponding author: ph.xhu@hotmail.com xhuang@mail.xhu.edu.cn jun wang school of electrical and information engineering xihua university, chengdu, 610039, sichuan, china wj.xhu@hotmail.com abstract: using spectral clustering algorithm is di�cult to �nd the clusters in the cases that dataset has a large di�erence in density and its clustering e�ect depends on the selection of initial centers. to overcome the shortcomings, we propose a novel spectral clustering algorithm based on membrane computing framework, called msc algorithm, whose idea is to use membrane clustering algorithm to realize the clustering component in spectral clustering. a tissue-like p system is used as its computing framework, where each object in cells denotes a set of cluster centers and velocity-location model is used as the evolution rules. under the control of evolutioncommunication mechanism, the tissue-like p system can obtain a good clustering partition for each dataset. the proposed spectral clustering algorithm is evaluated on three arti�cial datasets and ten uci datasets, and it is further compared with classical spectral clustering algorithms. the comparison results demonstrate the advantage of the proposed spectral clustering algorithm. keywords: machine learning, spectral clustering, membrane computing, tissue-like p systems. 1 introduction membrane computing initiated by gheorghe p�aun [17], was inspired from the structure and functioning of living cell as well as from the cooperation of cells in tissues, organs, and biological neural networks. membrane computing is a class of distributed parallel computing models, known as p systems or membrane systems. in the past years, many variants of p systems have been proposed [7, 8, 11, 15, 16, 18, 19, 32, 41, 46], and they have been applied to di�erent real-world problems [47], for example, combinatorial optimization [42,44,48], robots [1], image processing [4,5,22,25,40,43], signal processing [23,36,45], knowledge representation [26,34,37], fault diagnosis [21, 28, 33, 35, 38, 39], ecology and system biology [3, 9, 10]. most of membrane systems have been proved to be powerful (equivalent with turing machine) and e�ective (able to solve the np hard problems in a feasible time). in the recent years, p systems were used to deal with data clustering problems. zhao et al [49] presented an improved clustering algorithm, in which the rules in cell-like p systems were used to realize classical k-medoids algorithm. in peng [24], evolution-communication p systems are used to deal with fuzzy clustering problems. in [27] and [29], two di�erent mechanisms of p systems were considered to investigate automatic clustering problems. liu et al [12] used a cell-like p systems with promoters and inhibitors to develop a k-medoids clustering algorithm. copyright ©2018 cc by-nc 760 g. chen, j. hu, h. peng, j. wang, x. huang in [20], fuzzy clustering problems were viewed as a multiobjective optimization problem and a tissue-like p system was designed to solve the optimization problem. spectral clustering is a popular method for solving clustering problems in a wide range of noneuclidian spaces, linearly non-separable clusters and detecting non-convex patterns [13]. the key idea in spectral clustering is to achieve graph partitioning by performing eigen-decomposition of a graph laplacian matrix. the obtained eigenvectors are used as the low dimensional representation of the data, and then the k-means algorithm is applied to generate the clusters. spectral clustering approaches di�er in how they de�ne and construct the laplacian matrix and thus which eigenvectors are selected to represent the partitioning. moreover, di�erent objective functions are used to derive the best cut. chan et al. [2] proposed the ratio cut to minimize the total cost of the edges crossing the cluster boundaries, normalized by the size of the k clusters, to encourage balanced cluster sizes. shi and malik [31] established the normalized cut (ncut), which can measure the dissimilarities among groups and within clusters. in [6], ding et al. proposed min-max cut criterion, which can avoid to segment the smaller subgraphs that contains only a few vertices. according to di�erent partitioning criteria and spectral mapping methods, many di�erent methods have been developed to realize spectral clustering algorithms. perona and freeman [30] proposed pf algorithm based on iterative spectrum, which is the simplest spectral clustering algorithm. ng et al. [14] proposed the njw algorithm, which is based on the k channel segmentation. however, there are a number of shortcomings in spectral clustering algorithms, for example, it is di�cult to �nd the clusters with a large di�erence in density and their clustering e�ect depends on the selection of initial centers. this paper focuses on application of membrane computing model in spectral clustering to overcome the shortcomings and presents a novel spectral clustering algorithm based on a membrane computing model, called msc algorithm. a tissue-like p system is considered as a computing framework, and a membrane clustering algorithm is developed based on the computing framework and is embedded in a classical spectral clustering algorithm. to the best of our knowledge, this is the �rst attempt to use membrane computing model for improving spectral clustering algorithm. the remainder of this paper is organized as follows. section 2 reviews classical spectral clustering algorithm. section 3 describes in detail the proposed membrane spectral clustering (msc) algorithm. experimental results and analysis are provided in section 4. conclusions is given in section 5. 2 spectral clustering and the njw method spectral clustering method is a widely used graph-based approach for data clustering. given a dataset x = {x1,x2, . . . ,xn} in rn×d with k clusters. we expect the dataset x will be transformed into a weighted undirected graph g = (v,e), in which v = {xi}ni=1 is the vertex set composed of n data points, and e = {wij}ni,j=1 is the set of weighted edges, where wij indicates the pairwise similarity between the xi and xj. v and e contain all vertices and edges, respectively, let w = (wi,j)1≤i,j≤n be the a�nity matrix. usually, wij in a�nity matrix can be measured by a gaussian function: wij = { e − d(xi,xj) 2 σ2 , i 6= j 0, i = j (1) the degree matrix d is a diagonal matrix, whose element dii is the degree of data point xi, i.e., dii = ∑n j=1 wij. based on the two matrices, we can obtain the laplacian matrix, l. there are three forms of laplacian matrices: (i) unnormalized laplacian matrix (l = d − w), and two a spectral clustering algorithm improved by p systems 761 normalized laplacian matrices, (ii) symmetric laplacian matrix (lsym = d−1/2wd−1/2) and (iii) random-walk laplacian matrix (lrw = i −d−1w). as a spectral approach for graph partitioning problem, njw method is one of the most widely used spectral clustering algorithms. its idea is to �nd a new representation of patterns on the �rst k eigenvectors of the laplacian matrix. algorithm 1 gives the details of njw method. algorithm 1 njw method input: x ∈ rn×d, k ∈ n output: v = {vi|i = 1, 2, . . . ,k} 1: construct the a�nity matrix w ∈ rn×n according to eq. (16); 2: compute the degree matrix d; 3: compute the normalized laplacian matrix lsym = d −1 2 wd− 1 2 ; 4: let 0 ≤ λ1 ≤ λ2 ≤ ··· ≤ λk be the k least eigenvalues of lsym and p1,p2, . . . ,pk be the corresponding eigenvectors; construct the matrix p = [p1,p2, . . . ,pk] ∈ rn×k, where pi is ith column vector; 5: construct the matrix y from p by renormalizing each rows of p , i.e., yij = pij/( ∑ j p 2 ij) 1/2; 6: treat each row of y as a point in rk, and cluster them into k clusters c1,c2, . . . ,ck via k-means algorithm; 7: output the clusters that corresponds to the original data set, v1,v2, . . . ,vk, where vi = {xj|yj ∈ ci}. 3 spectral clustering algorithm based on membrane computing framework in this paper, we will try to use membrane computing algorithm (mca) to replace the k-means component in classical spectral clustering algorithm to realize the optimal data partitioning. the spectral clustering algorithm optimized by membrane computing model is called msc algorithm in this paper. by contrast, classical spectral clustering algorithm is called csc algorithm. figure 5 shows the structural comparison of csc and msc algorithms. from figure 5, we can �nd that �rst component of msc algorithm is the same to that of csc algorithm, but msc algorithm uses mca algorithm rather than k-means algorithm in component 2. therefore, in the following, we only describe the mca algorithm. since the core of mca algorithm is a tissue-like p system, we �rst describe the tissue-like p system, and then illustrate the proposed mca algorithm. 3.1 a tissue-like p system we design a tissue-like p system (consisting of q cells) as the computing framework of mca algorithm: π = (o1,o2, . . . ,oq,r1,r2, . . . ,rq,r ′, io) where oi is the set of objects in ith cell, ri is the set of evolution rules in ith cell, r′ is the set of communication rules between the cells, and the io = 0 indicates that the environment is the output region of the system. figure 2 shows the tissue-like p system, which consists of q cells labeled by 1, 2, . . . ,q respectively. each cell has m objects, and the environment is labeled by 0. denote by zij the jth 762 g. chen, j. hu, h. peng, j. wang, x. huang dataset affinity matrix laplacian matrix eigenmatrix k-means dataset affinity matrix laplacian matrix eigenmatrix mca csc algorithm msc algorithm component 1 component 2 component 1 component 2 figure 1: structural comparison of csc and msc algorithms. object in ith cell, i = 1, 2, . . . ,q, j = 1, 2, . . . ,m. the arrows in the �gure indicate the communication of objects. the communication of objects is between these cells and the environment. the environment is also the output region of the system. when the system halts, the object in the environment is the optimal solution (a set of optimal cluster centers). 1 2 q 0 figure 2: the designed tissue-like p system. each object in the cells, z, is used to represent a set of candidate k cluster centers, z1,z2, . . . ,zk ∈ rd. thus, object z can be represented as a k ×d dimensional vector: z = (z11,z12, . . . ,z1d, . . . ,zi1,zi2, . . . ,zid, . . . ,zk1,zk2, . . . ,zkd) (2) where (zi1,zi2, . . . ,zid) corresponds to ith candidate cluster center, i = 1, 2, . . . ,k. these objects in cells will be evolved during the computation. initially, a set of objects is generated randomly. based on data points in a data set, we can determine a lower bound and an upper bound for each dimension, aj = min{x1j,x2j, . . . ,xnj}, bj = max{x1j,x2j, . . . ,xnj}, j = 1, 2, . . . ,d. thus, zij = rand([aj,bj]), where rand() denotes a random function that can generate the random number in [aj,bj]. the tissue-like p system uses the communication rule of the form < i,a; λ, 0 > to update the object in the environment, which means that object a in cell i is transported to environment 0, where λ denotes the empty object. the object in the environment is called the global optimal object, denoted by zbest. for each cell, communication rule is used to communicate its best object to the environment and update the optimal object. the updating formula can be given as follows: zbest = { zi,best, if j(zi,best) < j(zbest) zbest, otherwise (3) where zi,best is the best object in ith cell. the object judgement is based on the following �tness function: j(z1,z2, . . . ,zk) = k∑ i=1 n∑ j=1 (uij) m ‖ xj −zi ‖2 (4) a spectral clustering algorithm improved by p systems 763 where uij denotes membership degree of xj belonging to ith class, and m is a power exponent. during the computation, tissue-like p system uses evolution rules to evolve the objects in cells. in this work, the velocity-location model of pso is used as the evolution rules. the velocity-location model can be described as follows: { v ij = wz i j + c1r1(p i j −z i j) + c2r2(zbest −z i j) z′ij = z i j + v i j (5) where v ij corresponds to the speed of z i j, z ′i j is the new value of z i j after evolving, and p i j is the best position so far for jth object in ith cell; w is the inertia weight constant, c1 and c2 are learning rate constants, and r1 and r2 are two random real numbers in [0, 1]. in the implementation of mca algorithm, a linear decreasing strategy is used, i.e., w = (0.9 − t 2t ), where t is the current iteration number and t is the maximum number of iterations. in this paper, the maximum iteration number is used as halting condition. after the system halts, the best object zbest in the environment is regarded as the solution. finally, according to the optimal cluster centers, c1,c2, . . . ,ck, n data points are classi�es into k clusters. 3.2 membrane clustering algorithm as stated above, mca algorithm is used as second component of the msc algorithm. the mca algorithm uses the designed tissue-like p system to automatically search for the optimal cluster centers for a data set to be clustered. under the control of the evolution and communication rules, the p system continuously evolves the objects in cells and updates the global optimal object in the environment until the system halts. figure 3 shows the �ow chart of mca algorithm. randomly generate initial object for q cells evaluate each object of the q cells communication rule communication rule communication rule evolution rule evolution rule evolution rule cell 1 cell 2 cell q halting? n export final result in the environment y stop figure 3: the �ow chart of membrane clustering algorithm (mca) used in msc algorithm 764 g. chen, j. hu, h. peng, j. wang, x. huang 4 experimental results and analysis 4.1 dataset in order to evaluate the performance of msc algorithm, three benchmark synthetic dataset and ten uci dataset were used in experiments. the three synthetic dataset are threecircles, twommoons and spirl respectively, shown in figure 4(a)-(c). table 1 gives the basic information for all data sets. (a) (b) (c) figure 4: three synthetic datasets. (a) threecircle; (b) twommoons; (c) spirl. table 1: data sets used in experiments datasets no.of data dimension no.of clusters threecircle 3603 2 3 twommoons 1502 2 2 spirl 944 2 2 iris 150 4 3 wine 178 13 3 sonar 208 60 2 diabetes 1151 19 2 glass 214 9 6 ecoli 336 7 8 heart 297 13 2 liver 345 6 2 ionosphere 351 34 2 sym 350 2 3 4.2 experimental results three commonly used indexes of quality were used to measure the clustering performance. (1) adjusted rand index (ari): ρari ∈ [−1, 1]. this index measures the agreement between two compared partitions, namely, the ground truth (denoted as u) and the estimated by the tested clustering approach (denoted as v ), and it is expressed by ρari = a11 − (a11 + a01)(a11 + a10)/a00 (a11 + a01) + (a11 + a10)/2 − (a11 + a01)(a11 + a10)/a00 (6) where a11 ∈ n is the number of sample pairs belonging to the same subset in u and in v , a10 is the number of sample pairs belonging to the same subset in u and to di�erent subsets in v , a01 ∈ n is the number of sample pairs belonging to di�erent subset in u and a spectral clustering algorithm improved by p systems 765 to the same one in v , and a00 ∈ n is the number of sample pairs belonging to di�erent subsets in u and in v . (2) purity index (pur): ρpur ∈ [0, 1]. this index matches the clustering partition v with the ground truth u as a weighted sum of the maximal precision values for each subset. ρpur = 1 n k∑ i=1 max j |vi ∩uj| (7) (3) jaccard index (jac): ρjac ∈ [0, 1]. this index matches the similarity among two sets, u and v , as follows: ρjac = a11 a11 + a10 + a01 (8) in the experiment, two classical spectral clustering algorithms, k-sc and �-sc, were introduced to implement two msc algorithms, where membrane clustering algorithm is used to replace k-means component in the original spectral clustering algorithms. thus, two msc algorithms and the corresponding classical spectral clustering algorithms were compared in experiment. table 2 and table 3 show the comparison results of these algorithms on synthetic and uci datasets, respectively. for each dataset, these tables provide the experimental results of four algorithms in terms of three indexes. note that these experimental results are average value of 10 times independently running for each algorithm on a dataset. moreover, we also provide the averages of these algorithms for each clustering index, respectively. from table 2, we can see that the average value of msc algorithm is the largest and can reach 1. the results show that the spectral clustering algorithm based on membrane computing framework has an obvious advantage in improving the average performance of spectral clustering algorithm. comparison results of msc and tsc on the uci datasets show that k-sc and �sc algorithms based on membrane computing framework can signi�cantly improve the jaccard index, indicating that the proposed msc algorithm is more robust and has a certain ability to deal with noise data. for the ari and the purity indexes, msc algorithm achieves a comparable result in comparison to the classical algorithm. iris dataset is used as an example to analyze the in�uences of parameters in msc algorithm. figure 5 (a)-(c) shows the in�uences of three parameters, including bandwidth � of the gaussian kernel function, the number of cells m and the maximum number of iterations maxstep. as can be seen from the �gures, msc algorithm is more sensitive to m and �, and the curve of parameter maxstep rises slowly and �nally tends to straight line, which indicates that the performance of the algorithm is not improved when the maximum number of iterations is reached. 766 g. chen, j. hu, h. peng, j. wang, x. huang table 2: clustering quality assessment results (synthetic datasets). dataset quality k-sc �-sc measure k-means mca k-means mca threecircle ari 1.0 1.0 0.51 1.0 purity 1.0 1.0 0.84 1.0 jaccard 1.0 1.0 0.56 1.0 twommoons ari 0.37 1.0 0.59 1.0 purity 0.81 1.0 0.89 1.0 jaccard 0.56 1.0 0.70 1.0 spirl ari 1.0 1.0 0.89 1.0 purity 1.0 1.0 0.73 1.0 jaccard 1.0 1.0 0.95 1.0 average ari 0.79 1.0 0.66 1.0 purity 0.93 1.0 0.82 1.0 jaccard 0.85 1.0 0.73 1.0 table 3: clustering quality assessment results (uci repository datasets). dataset quality k-sc �-sc measure k-means mca k-means mca iris ari 0.44 0.66 0.63 0.75 purity 0.87 0.86 0.84 0.90 jaccard 0.50 0.64 0.60 0.71 wine ari 0.03 0.30 0.29 0.0 purity 0.57 0.67 0.53 0.40 jaccard 0.24 0.37 0.38 0.34 sonar ari 0.02 -0.01 0.00 -0.0058 purity 0.57 0.51 0.55 0.50 jaccard 0.34 0.48 0.34 0.48 diabetes ari 0.16 0.01 0.16 0.0 purity 0.70 0.53 0.70 0.53 jaccard 0.43 0.51 0.43 0.50 glass ari 0.17 0 0.16 0.01 purity 0.51 0.36 0.52 0.36 jaccard 0.25 0.26 0.33 0.26 ecoli ari 0.37 0.20 0.48 0.42 purity 0.56 0.45 0.66 0.70 jaccard 0.32 0.29 0.42 0.65 heart ari 0.31 0.04 0.37 0.40 purity 0.78 0.55 0.80 0.75 jaccard 0.49 0.51 0.52 0.63 liver ari 0 -0.02 -0.01 0.12 purity 0.65 0.58 0.98 0.67 jaccard 0.36 0.51 0.50 0.56 ionosphere ari 0.15 0.01 0.13 0.25 purity 0.70 0.64 0.68 0.73 jaccard 0.44 0.54 0.41 0.55 sym ari 0.56 0.75 0.49 0.38 purity 0.68 0.87 0.75 0.54 jaccard 0.79 0.72 0.43 0.43 average ari 0.28 0.20 0.28 0.23 purity 0.66 0.61 0.70 0.61 jaccard 0.41 0.49 0.43 0.52 a spectral clustering algorithm improved by p systems 767 (a) (b) (c) figure 5: free parameter analysis over uci datasets based on the purity: (a) bandwidth � of the gaussian kernel function; (b) the number of cells m; (c) the maximum number of iterations maxstep. 768 g. chen, j. hu, h. peng, j. wang, x. huang 5 conclusion in this paper, we used membrane computing framework to develop a novel spectral clustering algorithm, called msc algorithm. the core component of msc algorithm is a tissue-like p system which is composed of several cells and uses the improved pso algorithm as evolution mechanism. we evaluated the performance of the proposed algorithm on three arti�cial data sets and ten uci datasets. the results show that compared with the classical spectral clustering algorithm, the proposed algorithm can improve the clustering performance. this study also demonstrates the e�ectiveness of using the membrane computing framework to solve data clustering problems. msc algorithm used membrane clustering algorithm (mca) instead of k-means component in classical spectral clustering algorithm, which searches for the optimal solution by both the evolution of objects in multiple cells and the communication of objects between the cells. it is well known that membrane computing is a distributed computing model. however, msc algorithm is not implemented in parallel due to limitation of the computer's serial architecture. therefore, our further work is to discuss the parallel implementation of msc algorithm on gpgpu and/or fpga. funding this work was partially supported by the national natural science foundation of china (no. 61472328), chunhui project foundation of the education department of china (nos. z2016143 and z2016148), research foundation of the education department of sichuan province (no. 17td0034), china, and the innovation fund of postgraduate, xihua university(no. ycjj2017073). bibliography [1] buiu, c.; 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(2012); the k-medoids clustering algorithm by a class of p system, journal of information & computational science, 9(18), 5777-5790, 2012. widjcccv12n6.pdf international journal of computers communications & control issn 1841-9836, 12(6), 871-885, december 2017. implementation of leader-follower formation control of a team of nonholonomic mobile robots a. widyotriatmo, e. joelianto, a. prasdianto, h. bahtiar, y.y. nazaruddin augie widyotriatmo*, endra joelianto, yul yunazwin nazaruddin instrumentation and control research group institut teknologi bandung, indonesia ganesha 10, bandung 40132, indonesia *corresponding author: augie@tf.itb.ac.id ejoelianto@yahoo.com, yul@tf.itb.ac.id agung prasdianto, hafidz bahtiar engineering physics program institut teknologi bandung, indonesia ganesha 10, bandung 40132, indonesia prasfoster@gmail.com, hafidzbahtiar@students.itb.ac.id abstract: a control method for a team of multiple mobile robots performing leaderfollower formation by implementing computing, communication, and control technology is considered. the strategy expands the role of global coordinator system and controllers of multiple robots system. the global coordinator system creates nocollision trajectories of the virtual leader which is the virtual leader for all vehicles, sub-virtual leaders which are the virtual leader for pertinent followers, and virtual followers. the global coordinator system also implements role assignment algorithm to allocate the role of mobile robots in the formation. the controllers of the individual mobile robots have a task to track the assigned trajectories and also to avoid collision among the mobile robots using the artificial potential field algorithm. the proposed method is tested by experiments of three mobile robots performing leader-follower formation with the shape of a triangle. the experimental results show the robustness of formation of mobile robots even if the leader is manually moved to the arbitrary location, and so that the role of a leader is taken by the nearest mobile robot to the virtual leader. keywords: leader-follower, formation, nonholonomic, trajectory control, collision avoidance, multiple mobile robots. 1 introduction leader-follower formation of a team of mobile agents has received special attention from for researchers because of its usefulness to many applications, e.g. military applications, transportation, warehouse automation, etc. in performing a formation of team of mobile robots, multidisciplinary technologies of computing, communication, and control need to be conducted. in particular applications, such as transportation, leader-follower formation of multi agents has bounded regulation. for example, in road transportation and warehouse automation, some vehicles should follow particular track, designed by the global coordinator, and have to avoid collisions among others. in this case, some vehicles, which are assigned to follow the same track, can cooperate performing leader-follower formation. one advantage is that it can avoid congestion due to width limitation of the lane. this paper discusses a strategy for a team of mobile robots to perform leader-follower formation. the mobile robot used in this paper is differential-type mobile robot which is categorized as nonholonomic system. formation control of nonholonomic system copyright © cc by-nc 872 a. widyotriatmo, e. joelianto, a. prasdianto, h. bahtiar, y.y. nazaruddin is also of special interest because of its difficulty in controlling a system with nonholonomic constraints. several methods in control of nonholonomic system have been shown in [12][24]. the problem of multi-vehicle formation control has been studied in [16][14], and many others, where the focus is on consensus based formation control. ren and cao in [16] classify the formation control problems become formation shaping problems, in which the objective control is to establish formation shape, and formation tracking problem, which is to find a control algorithm so that the agents track the predefined trajectories. in [11] and [14], graph theoretic methods and consensus, cooperation in networked multiagent systems are the focus. in the case of road transportation or warehouse automation, the tracks or lanes have been determined and the tasks have been provided by a fleet system or a global coordinator. this paper focuses on the architecture and control strategy for mobile robots in performing leader-follower formation in which the tracks and tasks are predetermined by the global coordinator. oh, park, and ahn in [13] studies the categorization of formation control which is focused on sensory systems and topology between agents. it leads to three categories, which are positionbased, displacement-based, and distance-based formation controls. in the position-based control, agents control to achieve the predetermined formation based on the measurement of their own position in global coordinate system. in the displacement-based control, the measurement used is displacement of neighboring agents. in the distance-based control, the agents control their distance in performing formation. the position-based control needs more advanced sensing capability and lesser interactions inter-agents comparing to the two other methods. the advancement of position sensors have been shown including global positioning system (gps) [1], inertial navigation system(ins) [4], vision[19], [20], laser-based localization [21], [22], and many more. this paper focuses on the position-based formation control. several position-based of multi vehicle controls for have been presented in several literature. dong and farrel in [6] and widyotriatmo, pamosoaji, and hong in [23] study the position-based method for nonholonomic agents. ren and atkins in [15] proposes the position-based method for formation control double integrator agents. the trajectory tracking problem for agent with unicycle-type kinematic model is proposed in [3]. the position based-method requires the ability of mobile agents to measure their position and orientation in the global coordinate. a system architecture performing feedback coordination between mobile robots and global coordinate system is usually used in the position-based method formation control [7], [26]. the method consists of two steps, which are: first, the global coordinator generate formation virtual trajectories; second, the mobile robots execute control to track the desired virtual trajectory. in this paper, a leader-follower formation control for a group of mobile robots is proposed. the main contributions of this paper is the extension of functions of the global coordinator and of the controller of mobile robots in performing leader-follower formation. the global coordinator performs the virtual trajectories generation of mobile robots. the generated virtual followers are classified by stages. the scheme creates main virtual leader which is the virtual leader for all agents and sub virtual leaders which are the leaders for the consecutive stage followers. in addition, task of global coordinator also assign roles for mobile agents to fill positions of leader or followers. using the scheme, the minimum communication link between the robots is between the main leader or the sub-leaders and their pertinent followers. the mobile robots’ controller has task to track the virtual trajectory given by the global coordinator, and also executes collision avoidance algorithm using potential field methods [8][25], in such a way that the mobile robots track the virtual trajectories while avoids collision among other mobile robots and obstacles. the rest of the paper is organized as the following. section 2 depicts the proposed leaderfollower formation strategy which consists of system architecture of leader-follower formation, the formation of virtual leader and followers, the role assignment algorithm, and the trajectory tracking control of the mobile robots which also includes collision avoidance strategy. section implementation of leader-follower formation control of a team of nonholonomic mobile robots 873 3 shows the simulation and experimental results. section 4 draws conclusion and the future research directions. 2 leader-follower formation control strategy 2.1 system architecture of leader-follower formation the leader-follower formation control of mobile robots study purposes to design control that drives multiple mobile robots to achieve certain moving or static formation. the strategy of leader-follower formation control of mobile robots can be described as follows. first, virtual trajectories of virtual leader and followers, as well as the formation parameters are generated from a global coordinator system. several methods in the global coordinator system are artificial potential field, sample-based motion planning, cell decomposition, or others. in this paper, the virtual trajectories of the virtual leaders and followers form a triangle. the formation parameters are assumed to be predetermined from a motion planning algorithm. second, the role of leader and follower of mobile robots position of leader and followers is voted based on the shortest distance to the leader-follower trajectories. finally, the leader and followers employ the designed trajectory tracking control and obstacle avoidance algorithms to keep them following individual trajectories while also to prevent them to collide with each other. the architecture of the leader follower strategy is shown in fig. 1. figure 1: the architecture of the formation control of a team of n mobile robots 2.2 formation of virtual leader and followers in this subsection, we consider mobile robots with one leader and two followers performing triangular formation. then, we show that the number of followers can be expanded by assigning the virtual followers to become sub-leaders. let (x, y) be a global coordinate system. fig. 2(a) shows the formation of one virtual leader and two virtual followers which are located at the first stage follower. the coordinate of the virtual leader is denoted by (xv,1, yv,1) and that of the virtual followers are denoted by (xv,i, yv,i), i = 2, 3. the virtual leader and two first stage followers form a triangle formation. the velocity and the direction angle of the virtual leader are vv,1 and θv,1, and those of the virtual followers are vv,i and θv,i, i = 2, 3. the distance between leader and follower are denoted by bv,i:1, i = 2, 3. the angle between the line projecting from the coordinate of one follower of the first stage follower to the leader and the x-axis is represented by γv,i:1 , i = 2, 3. the angle between the line projecting from the coordinate of one follower to that of the other follower in the same stage and the line projecting from the coordinate of that follower to the leader is denoted by ϕv,i:1, i = 2, 3. 874 a. widyotriatmo, e. joelianto, a. prasdianto, h. bahtiar, y.y. nazaruddin figure 2: (a) the configuration of virtual leader and two virtual followers shaping a triangular formation until the first stage follower; (b) the formation configuration is expanded to the second stage follower. the individual agents of first stage virtual follower become the virtual leader of agents in the second stage followers. hence the determination of virtual trajectories configuration can be expanded for the succeeding stages (fig. 2(b)). for the second stage followers, the virtual trajectory 5 can be developed from virtual trajectory 2 as the second follower or trajectory 3 as its first follower. the virtual leader trajectory is developed by the following equations: ẋv,1 = vv,1cosθv,1 = vx,v,1, (1) ẏv,1 = vv,1sinθv,1 = vy,v,1. (2) the relation of γv,i:1, ϕv,i:1, and θv,1, for i = 2, 3, are γv,i:1 = ϕv,i:1 + θv,1(t) − π/2. (3) the position of the first stage followers at time t are calculated as follows: xv,i(t) = xv,i(t) − bv,i:1 cos(γv,i:1(t)), (4) yv,i(t) = yv,i(t) − bv,i:1 sin(γv,i:1(t)), (5) i = 2, 3. 2.3 role assignment algorithm once the trajectories of virtual leader and followers have been developed, a group of vehicles are assigned to those trajectories. let us consider one virtual leader and two first-stage-virtualfollowers as in the previous section. three mobile robots are to be assigned to individual virtual trajectories. the configuration is illustrated in fig. 3. let the position of the j-th robot be defined as (xr,j, yr,j), j = 1, 2, 3 and that of the k-th virtual trajectory be (xv,k, yv,k), k = 1, 2, 3. implementation of leader-follower formation control of a team of nonholonomic mobile robots 875 figure 3: the configuration of robot and virtual trajectory. the assignment of a robot to a particular trajectory is based on the distance from the robot to each trajectory. the distance between a robot and a virtual trajectory are denoted by rr:v;j:k, which is calculated as follows: rr:v;j:k = √ (xr,j − xv:k) 2 + (yr,j − xy:k)2. (6) each robot knows the information of each virtual trajectory position and that of each robot position. the role assignment algorithm is described as follows. algorithm: role assignment of robots-virtual trajectories 1. calculate the distance for every pair robots-virtual trajectories; 2. compare the distance for each pair robot to the virtual leader. the robot with minimum distance to the virtual leader is assigned to become the leader; 3. compare the distance of other robots, which have not been assigned as the virtual leader, to the virtual followers. the robot that has the shortest distance to the first follower is assigned as that virtual follower trajectory. if the distance between the robots are the same, the vehicle with lower assigned number is delegated to that virtual follower trajectory. the algorithm is checked every loop time so that the roles of individual robots can be changed according to the distance of one mobile robot to a configuration of virtual trajectories. 2.4 trajectory tracking control and collision avoidance of the differentialwheel-type mobile robots after the virtual trajectories are developed and the robots have been assigned to individual virtual trajectories, the control of a mobile robot to track a particular trajectory is designed. for the first step of control design, the model of differential-wheeled mobile robot is derived. fig. 4 shows the configuration of a mobile robot. 876 a. widyotriatmo, e. joelianto, a. prasdianto, h. bahtiar, y.y. nazaruddin figure 4: the schematic of differential-wheeled mobile robot. the coordinate of the mobile robot is (xmr, ymr) and the angle of the robot with respect to x-axis is θmr. let the distance between the left and right wheels be 2l and the radius of left and right wheels be r. linear and rotational velocities, vmr and ωmr, of the mobile robot are obtained by arranging the right wheel velocity ωr and left wheel velocities ωl, as follow: [ vmr ωmr ] = r [ 1 1 1 l − 1 l ] [ ωr ωl ] . (7) if the linear and the rotational velocities, vmr and ωmr, are designed, the solution of the right and left wheel velocities, ωr and ωl, are directly obtained as: [ ωr ωl ] = 1 2r [ 1 l 1 −l ] [ vmr ωmr ] . (8) the mobile robot kinematic equation is derived as:   ẋmr ẏmr θ̇mr   =   cos θmr 0 sin θmr 0 0 1   [ vmr ωmr ] . (9) a reference point (xr, yr) is chosen as in fig. 5, satisfies the following: xr = xmr + l cos θmr, (10) yr = ymr + l sin θmr. (11) the motion of reference point is obtained as follows: ẋr = ẋmr − θ̇mrl sin θmr, (12) ẏr = ẋmr + θ̇mrl sin θmr. (13) the linear and angular velocities (vr and ωr) of the reference point are the same as those of implementation of leader-follower formation control of a team of nonholonomic mobile robots 877 figure 5: the chosen reference point (xr, yr). mobile robot coordinate (vmr and ωmr). the angle θmr and θr are also equal. substituting (9) into (12) and (13), we obtain ẋr = vr cos θr − ωrl sin θr, (14) ẏr = vr sin θr + ωrl cos θr, (15) the relationship between linear and angular velocities and the motion of reference point is obtained as follows: [ vr ωr ] = [ cos θr sin θr − sin θr l cos θr l ] [ ẋr ẏr. ] (16) note that l can be chosen as a non-zero positive value, thus (16) always has a solution. from (16), if the velocity of the reference point (ẋr, ẏr) is designed, the linear and rotation velocities (vr and ωr) of the mobile robot can be directly calculated, and so do the rotational velocities of right and left wheels (ωr and ωl) by applying (8). the problem becomes how to obtain a control algorithm so that the reference point of a particular robot follows the virtual trajectory which has been assigned to that robot. the errors between the reference point of j-th robot and its assigned k-th virtual trajectory (ex,j:k, ey,j:k) are as follow: ex,j:k = xv,k − xr,j, (17) ey,j:k = yv,k − yr,j. (18) the differentiation of the errors in (17) and (18) with respect to time can be derived as follows: ėx,j:k = ẋv,k − ẋr,j, (19) ėy,j:k = ẏv,k − ẏr,j. (20) 878 a. widyotriatmo, e. joelianto, a. prasdianto, h. bahtiar, y.y. nazaruddin the velocity of reference point (ẋr,j, ẏr,j) is designed as ẋr,j = ẋv,k + kpx,j ( (xv,k − xr,j) + 1 tix,j ∫ t 0 (xv,k − xr,j) dt + tdx,j d(xv,k − xr,j) dt ) , (21) ẏr,j = ẏv,k + kpy,j ( (yv,k − yr,j) + 1 tiy,j ∫ t 0 (yv,k − yr,j) dt + tdy,j d(yv,k − yr,j) dt ) , (22) where kpx,j, tix,j, tdx,j, kpy,j, tiy,j, and tdy,j are positive constants. the control laws (21) and (22) utilize the measurement of robot coordinate (xr,j, yr,j) and its correspond virtual trajectory (xv,k, yv,k). substituting (21) and (22) into (19) and (20), it is obtained kpx,j ( ex,j:k + 1 tix,j ∫ t 0 ex,j:k dt + (1 + tdx,j)ėx,j:k ) = 0, (23) kpy,j ( ey,j:k + 1 tiy,j ∫ t 0 ey,j:k dt + (1 + tdy,j)ėy,j:k ) = 0. (24) from (23) and (24), it can be seen the uniform asymptotic stability of errors (ex,j:k, ey,j:k) = (0, 0) since the values of kpx,j, tix,j, tdx,j, kpy,j, tiy,j, and tdy,j are positive and thus the equations (23) and (24) satisfy the routh-hurwitz stability criterion. therefore, the errors (ex,j:k(t), ey,j:k(t)) converge to zero as t → ∞ for any initial conditions (ex,j:k(0), ey,j:k(0)). the spectrum response of the errors (ex,j:k(t), ey,j:k(t)) can also be designed by choosing the parameters tix,j, tdx,j, tiy,j, and tdy,j. when the trajectory control is executed, it is possible that the mobile robots collide with each other. to avoid the circumstances, the control algorithms (23) and (24) are modified by applying additional input to repel a mobile robot towards collision. this technique is known as artificial potential field method [17][25]. 3 simulation and experimental results 3.1 simulation of three mobile robots performing leader-follower formation the simulation is intended to assure that the chosen of reference point as well as of the control parameters are suitable for the mobile robots. the constant l which determines the reference point of the mobile robot can be analyzed as follows: the smaller value of l, the motion of robot becomes more oscillation; whereas the larger value of l, the lag between the robot and the virtual trajectory is larger. the motion also depends on the distance between right and left wheels. from the simulations, the value of l between 2 cm and 8 cm shows good performance. the controller parameters kpx,j, tix,j, tdx,j, kpy,j, tiy,j, and tdy,j, j = 1, 2, 3 are designed by using the pole placement method with the desired poles of equations (23) and (24) are: -0.2 and -0.01. thus, the control parameters are obtained as follow: kpx,j = kpy,j = 0.21, tix,j = implementation of leader-follower formation control of a team of nonholonomic mobile robots 879 tiy,j = 105.0, and tdx,j = tdy,j = 0.01 for j = 1, 2, 3. using these parameters, the simulation is shown in fig. 6. for the assigned three virtual trajectories v1, v2, and v3, the robots r1, r2, and r3 which are started from different locations successfully perform leader-follower formation. the control inputs given to right and left wheels of individual robots stay below 14% of their maximum power which is suitable for the design (fig. 7). figure 6: robots r1, r2, r3 performs leader-follower formation using the proposed method. figure 7: motor power given to individual motors (left and right wheels) of robots r1, r2, r3 in performing leader-follower formation of fig. 6. 3.2 experiments of leader-follower formation using three mobile robots fig. 8(a) shows the experimental setup which includes the arena, three mobile robots, camerabased localization system, and personal computer (pc) as the global coordinator system. the 880 a. widyotriatmo, e. joelianto, a. prasdianto, h. bahtiar, y.y. nazaruddin arena is colored white, and three robots are colored with three different colors: red, green, and blue. the localization sensor is a camera mounted at the height of 114 cm. the camera is utilized to differentiate the colors of individual mobile robots and of the arena. two colors patched in a mobile robot are utilized to measure its orientation. fig. 8(b) shows the picture from the camera point of view. the position and orientation of all robots are calculated in a microcontroller from the measured position of colored rectangular shape patched on the mobile robots. then, the control input for each mobile robots are is calculated and sent to mobile robots using wireless bluetooth communication. the control parameters used in the experiment are the same as those used in the simulation. figure 8: (a) the experimental setup consists of arena (white pad), three robots, camera, and pc as the global coordinator system; (b) top-view picture is captured from the camera. the position of front and back rectangular is obtained so that the position and orientation of the three mobile robots can be calculated. fig. 9 shows the first experiment of three mobile robots performing leader-follower formation without being disturbed. the mobile robots are initially located near the virtual trajectories. from fig. 9, the mobile robots perform leader-follower formation following the given individual virtual trajectories. using the proposed control method, the mobile robots r1, r2, and r3 follow the pertinent virtual trajectories v1, v2, and v3, respectively. in this case, the mobile robot r1 becomes the leader and mobile robots r2 and r3 become the followers, all together perform triangular formation. fig. 10 shows the control signals, which are power signals, given to the motors of mobile robots for this experiment. in the second experiments (fig. 11), while three mobile robots perform leader-follower formation, the leader, mobile robot r1, is intentionally disturbed by moving its location the back. in this case, the virtual trajectories are still moving forward. the role assignment algorithm is performed so that the r2 becomes the leader and the r1 replaces the position of r2 in the formation. while tracking the new trajectories, the mobile robot r1 also performs obstacle avoidance algorithm so there is no collision between r1 and r2. fig. 12 shows the control signals which are the power given to the individual motors of mobile robots for this experiment. implementation of leader-follower formation control of a team of nonholonomic mobile robots 881 figure 9: the motion of three mobile robots in the first experiment: three mobile robots are located in various location and then performs leader-follower formation. figure 10: the motion of three mobile robots in the first experiment: three mobile robots are located in various location and then performs leader-follower formation of fig. 9. 882 a. widyotriatmo, e. joelianto, a. prasdianto, h. bahtiar, y.y. nazaruddin figure 11: the motion of mobile robots in the second experiment: the leader r1 is intentionally disturbed by moving its location to the back. the role assignment algorithm is performed in which r2 replaces the leader formation and r1 replaces the r2 position in the formation. figure 12: the power signals given to the individual motors (left and right wheels) of the mobile robots in performing leader-follower formation of fig. 11. implementation of leader-follower formation control of a team of nonholonomic mobile robots 883 4 conclusions and future work in this paper, a strategy of leader-follower formation of mobile robots is presented. a role assignment algorithm is proposed to allocate mobile robots following individual virtual trajectories in the leader-follower formation. a trajectory tracking control law is developed. control parameters are designed by the pole-placement method for mobile robots to track their individual virtual trajectories. the combination of trajectory tracking control and artificial potential field assures that the mobile robots track the pertinent virtual trajectories and avoided collisions among others. the proposed method is tested by simulation and experiments of mobile robots performing leader-follower formation. the method is also tested by experiment in which the leader is intentionally disturbed by moving the leader to the back. by the implementation of the role assignment algorithm, the mobile robots successfully switch their position to maintain the leader-follower formation. the future works include the integration of the motion planning and control which can handle double collision avoidance layers. one collision avoidance layer lies on the motion planning which is designed at the trajectories generation, and the other is on the low level controller as it is implemented in this paper. by establishing the double collision avoidance layers, the safety is more guaranteed and thus the implementation of multiple wheeled vehicles performing leader-follower formation is closer to a real application. acknowledgment this work was supported by the ministry of research, technology and higher education of indonesia. bibliography [1] barrios c., motai y. 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(2014); backstepping-based robust control for wmr with a boundary in prior for the uncertain rolling resistance, international journal of computers communications & control, 9(3), 348-355, 2014. muthuijcccv12n6.pdf international journal of computers communications & control issn 1841-9836, 12(6), 854-870, december 2017. shrp secure hybrid routing protocol over hierarchical wireless sensor networks b. muthusenthil, h. kim balasubramanian muthusenthil 1. information security research institute, wookyoung information technology, daegu, south korea. 2. computer science and engineering, valliammai engineering college, india. bmssen@gmail.com hyunsung kim* 1. dept. of mathematical sciences, university of malawi, malawi 2. dept. of cyber security, kyungil university, korea *corresponding author: kim@kiu.ac.kr abstract: a data collection via secure routing in wireless sensor networks (wsns) has given attention to one of security issues. wsns pose unique security challenges due to their inherent limitations in communication and computing, which makes vulnerable to various attacks. thus, how to gather data securely and efficiently based on routing protocol is an important issue of wsns. in this paper, we propose a secure hybrid routing protocol, denoted by shrp, which combines the geographic based scheme and hierarchical scheme. first of all, shrp differentiates sensor nodes into two categories, nodes with gps (ng) and nodes with antennas (na), to put different roles. after proposing a new clustering scheme, which uses a new weight factor to select cluster head efficiently by using energy level, center weight and mobility after forming cluster, we propose routing scheme based on greedy forwarding. the packets in shrp are protected based on symmetric and asymmetric cryptosystem, which provides confidentiality, integrity and authenticity. the performance analyses are done by using ns2 and show that shrp could get better results of packet loss rate, delivery ratio, end to end delay and network lifetime compared to the well known previous schemes. keywords: wireless sensor network (wsn), routing protocol, information security, anonymity, greedy forwarding. 1 introduction wireless sensor networks (wsns) are complex distributed system which comprises of large number of tiny wireless sensor nodes. these sensor nodes are widely deployed over a geographical area for monitoring and observe data in various ambient conditions. this real time data could be used to design various applications with intelligence. wsn is a technology which becomes more mature and is gaining momentum as one of the enabling technologies for the future internet. the major applications of wsn focus predictive maintenance, intelligent buildings, enhanced safety & security, smart home, health care and disaster management etc. the characteristics of wsn such as rapid deployment, self-organization and fault tolerance make a very promising sensing technique for military applications [2,10]. wsn plays a dominant role and lots of researches and practical applications have been contributing to improve in terms of device size, date rate, energy etc. but the main bottleneck is based on energy factor. since wsn operates on resource copyright © cc by-nc shrp secure hybrid routing protocol over hierarchical wireless sensor networks 855 constrained environment, either changing or recharging batteries is an unmanageable task. even the failure of single node due to low energy can prostrate the entire network. this problem forced researchers for developing an energy efficient protocol at network level [33]. at the network level various energy efficient routing protocols were developed [8, 9, 28]. mainly the routing protocols in wsn are classified in three main categories: data centric protocols, geographic based protocols, heterogeneous protocols and hierarchical protocols. recently, heterogeneous wireless sensor network (hwsn) routing protocols have drawn more and more attention. various hwsn routingprotocols havebeenproposed to improve theperformance ofhwsns likeedfcm,mcr, eepca, leach and sep. in sep, the cluster head of the advanced node frequently becomes the cluster head than the normal node [9]. this paper mainly proposes a secure hybrid routing protocol (shrp) which combines the concepts of geographic based and hierarchical. shrp is consisted of two phases (i) clustering and cluster head selection phase (ii) secure routing phase. shrp uses a weight factor to select cluster head by considering energy level, center weight and mobility after clustering. secure routing phase is designed based on greedy forwarding and the packets are protected based on symmetric and asymmetric cryptosystem. thereby the routing scheme could provide confidentiality, integrity and authenticity. wsn is composed of a set of clusters. in each cluster, a node called cluster head (ch) and remaining sensor nodes are called as cluster member nodes (cm). the role of each ch is to carry out the following three roles. the first role is to gather sensed data from the cluster nodes periodically and aggregates the data to remove redundancy among correlated values [30]. the second role is to generate a time division multiple access (tdma) schedule in which sensor nodes receive a time slot for data transmission. the third role is to transmit the aggregated data to the destination by using secure routing. hence the lifetime of ch would be a very short span of time performs all three roles and it becomes essential to shift the cluster head periodically in a well-structured manner. in shrp, (1) a novel cluster head selection scheme is proposed between the sensor nodes based on the three factors center weight, residual energy and mobility factor, and (2) secure routing scheme is designed. the performance analysis by varying the percentage of nodes with gps (ng) and nodes with antenna (na) is done using ns2 and shows results of the parameters like packet delivery ratio, control overhead, percentage of attacks and energy consumption varies in shrp. thispaper is organizedas follows: section2discusses about relatedworks. section3proposes our network model and proposed system in detail and devises a new secure hybrid routing protocol. performance analysis and results are provided in section 4. section 5 concludes with future direction of this research. 2 related work this section discusses on the various clustering schemes, cluster head selection schemes and secure routing schemes over wsns [5,9]. baker et al. proposed linked cluster algorithm (lca) mainly focuses on forming an efficient network topology to handle mobility of nodes [5]. xu et al. proposed random competition based clustering (rcc) applicable to wsn which applies "first declaration win" rule [29]. nagpal et al. proposed clubs algorithm in which clusters are formed by local broadcast and converge in time proportional to the local density of nodes [19]. bandyopadhyay et al. proposed energy efficient hierarchical clustering (eehc) with the objective of minimizing the network lifetime [6]. heinzelman et al. proposed low energy adaptive clustering hierarchy (leach) which is one of the popular clustering algorithm in which clusters are formed based on received signal strength 856 b. muthusenthil, h. kim and uses the cluster head as routers [11,12]. leach obtains energy efficiency by partitioning the nodes into clusters which comprises of setup phase and steady state phase. during setup phase the cluster head selectionprocess is basedonpredeterminedprobability, and steady state phase is for data transmission. wang et al. proposed clustering scheme based on queries and attributes of data [27]. mostafaei et.al [18] presents an algorithm based on imperialist competitive algorithm for improving the network lifetime in wsn by diving the nodes into various cover-sets. alasem et al. [3] proposed a location based energy-aware reliable routing protocol (lear) for wsn based on enhanced greedy forwarding (egf) protocol which selects the nearest node to be active node based on its distance but it practically fails. leach also does the cluster head selection process but it is based on predetermined probability does not considered the energy efficiency for cluster head selection. leach-centralized (leach-c) uses centralized algorithm for the cluster head selection where the base station collects the position and energy level of the sensor nodes and the node having greater energy than average energy of all sensor nodes would be elected as cluster head. since this approach only considers the energy level of sensor nodes while selecting the cluster head, there may be a greater probability of elected cluster head is far away from base station which consumes more energy for the communication between base station and cluster head. mehmood et.al [16] proposes leach-vh for improving the performance of leach in which introducing the concept of vice cluster head (vh) to support ch but it leads to additional energy for electing vh. younis et al. presented hybrid energy-efficient distributed clustering (heed) protocol, which periodically selects cluster head according to their residual energy [31]. but the disadvantage is the authors do not make any assumptions about infrastructure or node capabilities, other than the availability of multiple power levels in sensor nodes. however, heed supports two-level hierarchy. ming et al. proposed a new energyefficient dynamic clustering scheme where each node estimates the number of active nodes in real-time and computes its optimal probability of becoming a cluster head by monitoring the received signal power from its neighbor nodes [10]. jung et al. proposed a cluster based energyefficient forwarding scheme which uses the binary exponential back off algorithm for cluster head selection [14]. han [9] proposes heterogeneous cluster-based protocols which has ability to manage the clusters and member nodes for better balance energy consumption of the nodes in the whole network whereas it does not satisfy for unequal distribution of clusters. song [24] proposes a heterogeneous sensor network to improve the efficiency of network coverage but optimization needs to be addressed. ndiaye et al. [20] proposed that software defined networking (sdn) provides a promising solution in flexible management wsns by allowing the separation of the control logic from the sensor nodes/actuators [17]. the advantage with this sdn-based management in wsns is that it enables centralized control of the entire wsn making it simpler to deploy network-wide management protocols and applications on demand. the cluster head selection algorithms described above is considering the two important parameters such as distance among the nodes and residual energy of the nodes. the proposed solution uses different approach from the previous where cluster head selection process is done based on the weight factor of center weight, residual energy and mobility of each node. bohge et al. proposed secure hierarchical routing protocol by using tesla certificates for authentication [7]. but it cannot prevent intruders from coming into the network and sending packets and cannot protect against eavesdropping. tubaishat et al. proposed a secure routing protocol uses the symmetric key cryptography and proposed a group key management scheme and drawback associated with this protocol is that, while changing the ch all group key i.e. inter-cluster and intra-cluster key should have to compute once again, which is a cumbersome task [26]. parno et al. proposed lha-sp on securing heterogeneous hierarchical wsns uses the symmetric key scheme authentication and confidentiality is maintained by shared pairwise key shrp secure hybrid routing protocol over hierarchical wireless sensor networks 857 but it deals with orphan node problem [23]. oliveria et al. proposed fleach, a protocol for securing node to node communication uses random key pre-distribution scheme with symmetric key cryptography but it is vulnerable to node capturing attack [22]. ibriq et al. proposed a secure hierarchical energy efficient routing protocol (sheer) which provides secure communication at the network layer which uses hikes a secure key transmission protocol and symmetric key cryptography [13]. leao et al. proposed an alternative-route definition (around) communication scheme for wsns. the underlying idea of around is to setup alternative communication paths between specific source and destination nodes, avoiding congested cluster-tree paths [15]. srinath et al. proposed cluster based secure routing protocol which uses both public key (in digital signature) and private key cryptography [25]. this protocol deals with interior adversary or compromised node but it requires high computational requirement (use of public key cryptography), which is not efficient for the wsns. oliveira et al. proposed sec-leach an efficient solution for securing communications uses random-key pre distribution and µtesla for secure hierarchical wsn with dynamic cluster formation [21]. quan et al. proposed secure routing protocol cluster-gene-based (srpbcg) for wsns [34]. biological ’gene’ as encryption key but it only deals with the adversary’s attack and compromised nodes but computation and communication burden are more in this protocol. adnan et al. [1] proposed a secure region-based geographic routing protocol (srbgr) to increase the probability of selecting the appropriate relay node. by extending the allocated sextant and applying different message contention priorities more legitimate nodes can be admitted in the routing process but it fails when increasing number of nodes with different scenarios of network terrain. 3 shrp: secure hybrid routing protocol this sectionproposesanovel securehybrid routingprotocol (shrp) inwsn.wedifferentiate sensor nodes into two categories: nodes with gps (ng) and nodes with antennas (na). in order to propose shrp, we need to undergo two phases: (1) clustering and cluster head selection and (2) secure routing. inphase1, clustering isdonebasedonanyoneof thebest approaches fromthe previous clustering schemes. however the clustering approach should satisfy that the percentage of ng must be at least three nodes in each cluster in order to support position requirement from each node. after that the cluster head selection process is done based on the weight factor of center weight, residual energy and mobility of each node. in phase 2, secure routing is designed where the packets are protected by using symmetric and asymmetric cryptosystem to support confidentiality, integrity and authenticity. the network architecture is composed of ch and cm as entities: • cluster head (chi): it is node which acts as a coordinator of each cluster. we assume that na only could be a candidate of cluster head. any na nodes in a cluster could be selected as the cluster head which has maximum weight factor and but with less mobility factor. • cluster member (cmi): it is a node in a wsn is capable of performing some processing, gathering sensory information and communicating with cluster head in the network. any na or ng nodes could be member nodes in each cluster which is attached with ch exclusively. based on these assumptions, a transmission model between a source node (cms) and a destination node (cmd) can be designed as follows: 858 b. muthusenthil, h. kim table 1: notations notation meaning ng nodes with gps na nodes with antennas chi cluster head i cms cluster member source cmd cluster member destination µi weight factor of node i ci center weight of node i eri residual energy of node i mi mobility of node i nmid center of each cluster ei(0) initial energy level of node i ei(t) initial energy level of node i at time t rreq, rrep route request and route reply message ae(k, m) asymmetric key encryption function with 2 inputs of key k and message m ad(k, m) asymmetric key decryption function with 2 inputs of key k and message m se(k, m) symmetric key encryption function with 2 inputs of key k and message m sd(k, m) symmetric key decryption function with 2 inputs of key k and message m h() secure hash function prd, pud private-public key pair for destination prs, pus private-public key pair for source ids, idd real identities for source and destination aids, , aidd amplified identities for source and destination sks session key generated by source ti time stamp for node i ens, end encrypted messages by source and destination macs, macd message authentication codes for source and destination aus, aud authenticated values by source and destination ‖ concatenation operator sf security field q query message mid message id table 2: recommended clustering algorithms algorithms required parameters cluster overlapping location awareness lca no required adaptive clustering no required rcc no required gs3 low required eehc no required dwehc no required attribute based clustering no required shrp secure hybrid routing protocol over hierarchical wireless sensor networks 859 • if cms is located within the distance rs from cmd, cms transmits the packet to cmd via the cluster head chs. • when cmd is outside of the transmission range from cms, the packet is forwarded to the intermediate cluster heads in the direction of cmd. figure 1: network configuration 3.1 clustering and cluster head selection phase clustering wsn involves large number of sensors for which clustering is an effective and efficient way for managing high volume of nodes. there are many clustering schemes, which were proposed by various researchers based on different categories. many of the clustering algorithms are given in related works [8,10]. this objective of clustering is out of scope to our research but the research objective we tabulated few clustering schemes which suits for our network model . therefore clustering is done based on any of the clustering approaches in in table 2. however, the clustering should satisfy that the percentage of ng nodes must be at least three nodes in each cluster in order to support requisition requirement from each node. cluster head selection as we mentioned at the network initialization and transmission model, each node could get their location information with the help of ng nodes. the cluster head selection process is done based on the parameters of weight factor (µi) along with center weight (ci), residual energy (eri) and mobility (mi) of each node i. the weight factor of the node is defined as the weight assigned to a node based on its residual energy and mobility, in order to give less or more importance to the other nodes in the cluster. weight factor of the node i (µi) is computed by (1). µi = (x1 ∗ ci) + (x2 ∗ eri) − (x3 ∗ mi), (1) where x1, x2 and x3 are threshold values such that x1 + x2 = 1. x3 is a deduction factor due to its mobility. 860 b. muthusenthil, h. kim table 3: cluster head selection message format node id weight factor (µi) node mobility (mi) let nmid be the center of each cluster which can be determined by help of ng nodes. the center weight (ci) of the node i is computed by using (2). ci = nmid ∗ α, (2) where α is the distance from the border node of its cluster to nmid, which ranges from 0 to 1 depending upon the location. let ei(0) be the initial energy level (eri) of the node i. at a time period t , the energy consumed by the node i (ei(t)) is computed by using (3). ei(t) = ntx ∗ β + nrx ∗ γ, (3) where ntx and nrx are the numbers of data packets transmitted and received by the node i at time t , respectively. β and γ are in the range (0, 1) to measure energy consumption level. the residual energy of the node i(eri) at time t is computed using (4). eri = ei(0) − ei(t). (4) the node mobility (mi) of node i is computed using (5). mi = √ (u2 − u1)2 + (v2 − v1)2 t2 − t1 , (5) where (u1, v1) and (u2, v2) are the coordinates of the node i at time t1 and t2, respectively. figure 2: node mobility the steps involved in cluster head selection are as follows. 1. when the nodes are deployed in a wsn, all the nodes compute µi and broadcast cluster head selection message to its neighbors, which follows the format of table 3. it includes the parameters such as node id, weight factor and node mobility. 2. when node receives the message, it forms a member list (ml), and checks whether it has the maximum weight factor µmax by using the obtained ml. 3. node with µmax is elected as cluster head (chi) as shown in figure 1 4. if there are more than one node with µmax value, less mobility factor node is selected as the cluster head and it transmits cluster head election message (chelec), which contains idchi to every nodes in the cluster. shrp secure hybrid routing protocol over hierarchical wireless sensor networks 861 if a node in ml needs to leave from the cluster, it sends leave request (lreq) message to chi. chi broadcasts the updated ml to every member nodes which removes the node from ml. similarly, when a node in ml needs to join into the cluster, it sends join request (jreq) message to chi. chi broadcasts the updated ml to every member nodes which adds the node into ml. 3.2 secure routing phase the task of secure routing is to form route from source cms to destination cmd by sending packets while complying with the condition of that cms is informed about the position of cmd. the secure routing is described as the following steps: 1. when cms wants to transmit a route construction request with a query to cmd, it invokes form_message(), unicasts rreq to its chs and stores mid in its route cache termed as session table (st), which helps to distinguish the respective rreq while receiving rrep . table 4: route request message format message id source id destination id location of chi destination location security field query (mid) (aids) (idd) (nl) (ld) (sf) (q) 2. when chs receives rreq, it checks ld and invokes request_route_ch(rreq). 1: function request_route_ch(rreq) 2: if ld is within ml or idd is member of chs then 3: send rreq directly to cmd 4: else 5: send rreq directly to cmi towards direction of ld 6: do 7: chi broadcast to nearest nl towards ld 8: while (not reach to chd) 9: end if 10: end function 1: function form_message() 2: cms generates a session key ks and forms a message ms = ids‖idd‖ks‖pus‖ts with ts 3: encrypts ms with pud by applying asymmetric encryption ens = ae(pud, ms) 4: computes macs = h(ens‖ks) 5: computes authenticated value aus = ae(prs, ts) 6: sets nl = null 7: returns (ens, macs, aus) 8: end function 3. if rreq reaches to mnd, chd invokes verify_message() and respond_route(rrep ) to return back rrep to chs 1: function verify_message() 2: cmd decrypts ens by using prd and retrieves m ′ s = id ′ s‖id ′ d‖k ′ s‖pu ′ s‖t ′ s by using ad(prd, ens) 3: computes mac′s = h(ens‖k ′ s) 4: checks the validity macs by comparing with mac ′ s 5: checks authenticity of source by ad(pus, aus) 862 b. muthusenthil, h. kim 6: cmd forms acknowledgement message ma = ids‖idd‖ks‖pud‖td 7: if verification is successful then 8: compute end = se(ks, ma) 9: computes macd = h(end‖td) 10: end if 11: aud = ae(prd, td) return end 12: end function 1: function respond_route_ch(rrep ) 2: if ls is within ml or ids is member of chd then 3: send rrep directly to cms 4: else 5: send rrep directly to cmi towards the direction of ls 6: do 7: chi broadcast to nearest nl towards ls 8: while (not reach to chs) 9: end if 10: end function 4. if cms receives rrep , the secure routing process is successful. 4 analysis this section provides performance analysis and security analysis after providing simulation results on shrp. we used ns2 to provide simulation results of shrp, which uses parameters of table 5. simulations were carried out based on leach, eehc and shrp [?,?] table 5: simulation parameter parameters values initial energy of nodes eunit 0.5j amplification coefficient of the free space model efs 10pj · m 2/b amplification coefficient of the multipath transmission model eamp 0.0013pj · m 2/b table data fusion rate eda 5nj/b circuit loss eelec 50nj/b clustering probability of nodes p 0.05 data packet length 4000b control packet length 80b 4.1 performance analyses figure 3 and figure 4 show the packet loss rate results depending on the different number of nodes to form clusters based on various clustering techniques. shrp minimizes the packet loss rate approximately 3.27% in the number of ng nodes and 3.34% in the number of na nodes than eehc. furthermore, it reduces the rate approximately 40.02% in ng nodes and 47.06% in na nodes than leach. shrp has less end to end delay compared to eehc and leach as shown in figure 5 and figure 6. as shown in figure 5 and figure 6, shrp has better performance than leach and eehc shrp secure hybrid routing protocol over hierarchical wireless sensor networks 863 figure 3: packet loss rate depending on changes of the number of ng nodes figure 4: packet loss rate depending on changes of the number of na nodes figure 5: end to end delay depending on changes of the number of ng nodes for the number of ng and na nodes changes. hence the delivery latency of shrp in the number of ng nodes changes is higher than the other case. figure 7 and show the variations on the three schemes and they characterize that incurs approximately 97.9% packet delivery ratio. leach and eehc achieve the packet delivery ratio 864 b. muthusenthil, h. kim figure 6: end to end delay depending on changes of the number of na nodes in average of 95.2% and 91%, respectively. from figure 8 the shrp in number of na nodes incurs 98.2% (approx) delivery of data packets and results in better rate compared to delivery rate in ng nodes. figure 7: delivery ratio depending on changes of the number of ng nodes from figure 9 and figure 10 it is understood that shrp has 32% of delay in delivering data packets. leach and eehc delivery delay rate at a rate of 48% (approx) and 43.05%. hence shrp result has better performance than leach and eehc for number of ng nodes and shrp has 28% (approx) of delay in delivering data packets for na nodes.hence delivery latency of shrp in number of ng node is high than delivery latency of shrp in number of na nodes. figure 11 and figure 12 shows the network lifetime in number of ng nodes. shrp has the network lifetime of 30.05% (approx). leach and eehc has the network lifetime of 32.6% (approx) and 35.09% (approx). figure 11 and figure 12 show the comparison of network lifetime, which shows that shrp has longest lifetime compared to leach and eehc. shrp secure hybrid routing protocol over hierarchical wireless sensor networks 865 figure 8: delivery ratio depending on changes of the number of na nodes figure 9: delivery latency depending on changes of the number of ng nodes figure 10: delivery latency depending on changes of the number of na nodes 4.2 security analysis the focus of this analysis is to ensure how secure the message transmissions in shrp between cms and cmd, which is only focused on the secure routing phase. 866 b. muthusenthil, h. kim figure 11: network lifetime depending on changes of the number of ng nodes figure 12: network lifetime depending on changes of the number of na nodes once the messages to establish route are secured, it is inferred that communications over the wsn are secure. 1. unlinkability/anonymity: shrp achieves anonymity by using encrypted message of identities, which could provide security of ids. this is achieved by sending the encrypted message ens during route construction request message formatting. there is no way an attacker can discover the source node’s real identity because no user identification information is transmitted in plain. therefore, shrp provides entity anonymity. further unlinkability is provided because the timestamp ts is protected from the preying eyes of an adversary and therefore cannot be related to a particular node by anyone other than cmd. so, entity privacy is guarded against eavesdropper. this means the session key derived after authentication ensures privacy of end entity information like sensed data or any encrypted messages. 2. impersonation attack: an attacker may attempt to use a bogus chi or cmi to impersonate the real one that the attacker has access to. as much as the attacker has no knowledge of any entity in the network due to anonymity and unlinkability properties, the attacker cannot manage to impersonate any entity in wsn with a malicious chi or cmi. even shrp secure hybrid routing protocol over hierarchical wireless sensor networks 867 from the transmitted message, (ens, macs, aus) and (end, macd, aud) relayed between cms and cmd, the attacker cannot modify them to pass authentication because he/she will need to have the secret values related to the message in order to impersonate either cmi or chi to pass the counterpart’s verification. this attack is difficult to materialize because the real identity of the entity is still concealed to all players in shrp. 3. replay attack: an attacker may wish to initialize a replay attack from eavesdropped data packets of an authenticated communication between cms and cmd and retransmit them at a later time as if it comes from the real entity. this attack is thwarted in shrp because the authenticated message ens = ae(pud, ms) for route construction message contains timestamp ts meant to be used once, so there is no way an attacker can devise a replay of any message encrypted with the related secret keys. in the same way the session key sk is unique per session and is updated after any successful secure routing procedure. so, its arguable shrp resists against the replay attack. 4. man-in-the middle attack: in man-in-the-middle attack, an adversary eavesdrops and intercepts the communication between or among communicating legal entities in wsn and relays authentic messages to the victims to make them that believe they are communicating confidentially. thus, the adversary controls the whole communication sessions without knowledge of the intended entities in wsn. however this attempt though cannot succeed in shrp because no attacker can manage to initiate the fabrication of the legal message that seems acceptable to cmd. since to achieve this attack, the adversary must find a means of sending verifiable components ens, macs and aus in order to pretend as cms to cmd. obviously, there is no other way of forging ens without knowledge of the parameters of ms. furthermore, the extraction of ms from ens means the ability to solve the discrete logarithm problem that can be solved by cmd only. therefore the attacker will not succeed and besides the message ms is not sent in plain, thus the attacker will not know the information targeted to cmd. conclusively, shrp is resilient against impersonation attack. 5. mutual authentication: in shrp, both end point the origin and the destination of a transmitted message authenticate and verify the counterpart, thereby providing mutual authentication. before cms and cmd can communicate securely they first share the counter part’s public key. so based on the public key, the parties transmit messages authentic and verifiable only between themselves. for instance, when cms sends login message ens, macs and aus to cmd, it is formed in a way that only cmd with the knowledge of the private key can extract the message. having extracted ens, cmd verifies the counterpart entity and establish a session key sk securely only after the proper authentication success. on the other hand cmd authenticates an cms by checking the received macs and aus. cmd trust that it is communicating with an unintended party is based on the assumption that computing ens, macs and aus without knowledge of cm ′ds private key involves solving the discrete logarithm, which is infeasible by an attacker. at the end, cms and cmd mutually authenticate each other. 5 conclusion this paper has been proposed a secure hybrid routing protocol (shrp), which combines the geographic based scheme and hierarchical scheme. shrp classified sensor nodes into two categories, ng nodes and na nodes, to put different roles in wsns. shrp is consisted with 868 b. muthusenthil, h. kim two phases: the clustering and cluster head selection phase and the secure routing phase. in the clustering and cluster head selection phase, shrp selects a clustering scheme from the previous schemes to satisfy that the percentage of ng must be at least of three nodes in each cluster in order to support location requirement of each node. after that the cluster head selection process is done based on a new weight factor of center weight, residual energy and mobility of each node. in the secure routing phase, a secure 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(2009); secure routing protocol cluster-gene-based for wireless sensor networks, 1st international conference on information science and engineering (icise), ieee, 2009, 4098–4102, 2009. international journal of computers communications & control issn 1841-9836, 12(1):103-115, february 2017. two flow problems in dynamic networks c. schiopu, e. ciurea camelia schiopu*, eleonor ciurea transilvania university of braşov romania, 500091 braşov, iului maniu, 50 camelia.s@unitbv.ro, e.ciurea@unitbv.ro *corresponding author: camelia.s@unitbv.ro abstract: in this paper we study two flow problems: the feasible flow problem in dynamic networks and the maximum flow problem in bipartite dynamic networks with lower bounds. we mention that the maximum flow problem in bipartite dynamic networks with lower bound was presented in paper [8]a. for these problems we give examples. keywords: dynamic networks, feasible flow, bipartite network, maximum flow. areprinted (partial) and extended, with permission based on license number 3917170356624 ©[2016] ieee, from "computers communications and control (icccc), 2016 6th international conference on" 1 introduction the theory of flow is one of the most important parts of combinatorial optimization. the static network flow models arise in a number of combinatorial applications that on the surface might not appear to be optimal flow problems at all. the problem also arises directly in problems as far reaching as machine scheduling, the assignment of computer modules to computer processor, tanker scheduling etc. [1]. however, in some applications, time is an essential ingredient [3], [4], [5]. in this case we need to use the dynamic network flow model. on the other hand, the bipartite static network also arises in practical context such baseball elimination problem, network reliability testing etc. and hence it is of interest to find fast flow algorithms for this class of networks [1], [6]. the maximum flow problem in bipartite dynamic networks with lower bounds was presented in paper [8]. in this paper, which is an extension of [8], we present in addition the feasible flow problem in dynamic networks. these problem have not been treated so far. further on, in section 2.1 we discuss some basic notions and results for the maximum flow problem in general static networks with lower bounds. section 2.2 deals with the maximum flows problem in general dynamic networks with lower bounds. in section 2.3 we present algorithms for flow problems in bipartite static network and in section 2.4 we discuss the feasible flows in static networks. in section 3 we discuss the feasible flows problem in dynamic network and give some examples. section 4 deals with maximum flows in bipartite dynamic networks with lower bounds and an example. 2 terminology and preliminaries in this section we discuss some basic notations and results used throughout the paper. 2.1 maximum flows in static networks with lower bounds let g = (n,a,l,u) be a static network with the set of nodes n = {1, . . . ,n}, the set of arcs a = {a1, . . . ,ak, . . . ,am}, ak = (i,j), i,j ∈ n, the lower bound function l : a → n, the upper copyright © 2006-2017 by ccc publications 104 c. schiopu, e. ciurea bound (capacity) function u : a → n, with n the natural number set, 1 the source node and n the sink node. for a given pair of subset x,y of the set of nodes n of a network g we use the notation: (x,y ) = {(i,j)|(i,j) ∈ a,i ∈ x,j ∈ y} and for a given function f on set of arcs a we use the notation: f(x,y ) = ∑ (x,y ) f(i,j) a flow is a function f : a → n satisfying the next conditions: f(i,n) −f(n,i) =   v, if i = 1 0, if i 6= 1,n −v, if i = n (1a) l(i,j) ≤ f(i,j) ≤ u(i,j), (i,j) ∈ a (1b) for some v ≥ 0. we refer to v as the value of the flow f. the maximum flow problem is to determine a flow f for which v is maximum. we further assume, without loss of generality, that if (i,j) ∈ a then (j,i) ∈ a (if (j,i) /∈ a we consider that (j,i) ∈ a with l(j,i) = u(j,i) = 0). a preflow f is a function f : a → n satisfying the next conditions: f(n,i) −f(i,n) ≥ 0, i ∈ n −{1,n} (2a) l(i,j) ≤ f(i,j) ≤ u(i,j), (i,j) ∈ a (2b) for a preflow f the excess of each node i ∈ n is e(i) = f(n,i) −f(i,n) (3) and if e(i) > 0, i ∈ n −{1,n} then we say that node i is an active node. given a flow (preflow) f, the residual capacity r(i,j) of any arc (i,j) ∈ a is r(i,j) = u(i,j) − f(i,j) + f(j,i) − l(j,i). the residual network with respect to the flow (preflow) f is g̃ = (n,ã,r) with ã = {(i,j)|(i,j) ∈ a,r(i,j) > 0}. in the residual network g̃ = (n,ã,r) we define the distance function d : n → n. we say that a distance function is valid if it satisfies the following two conditions d(n) = 0 (4a) d(i) ≤ d(j) + 1, (i,j) ∈ ã (4b) we refer to d(i) as the distance label of node i. we say that an arc (i,j) ∈ ã is admissible if satisfies the condition that d(i) = d(j) + 1; we refer to all other arcs as inadmissible. we also refer to a path from node 1 to node k consisting entirely of admissilbe arcs as an admissible path. whereas the maximum flow problem with zero lower bounds always has a feasible solution (since the zero flow is feasible), the problem with non-negative lower bounds could be infeasible. therefore the maximum flow problem with non-negative lower bounds can be solved in two phases: (p1) this phase determines a feasible flow if one is exists; (p2) this phase converts a feasible flow into a maximum flow. the problem in each phase essentially reduce to solving a maximum flow problem with zero lower bounds. consequently, it is possible to solve the maximum flow problem with non-negative lower bounds by solving two maximum flow problems, each with zero lower bounds. in the next presentation we assume familiarity with maximum flow algorithms, and we omit many details. the reader interested in further details is urged to consult the book [1]. two flow problems in dynamic networks 105 2.2 maximum flows in dynamic networks with lower bounds dynamic network models arise in many problem settings, including production distribution systems, economic planning, energy systems, traffic systems, and building evacuation systems [3], [4], [5]. let g = (n,a,l,u) be a static network with the set of nodes n = {1, . . . ,n}, the set of arcs a = {a1, . . . ,am}, the lower bound function l, the upper bound (capacity) function u, 1 the source node and n the sink node. let n be the natural number set and let h = {0, 1, . . . ,t} be the set of periods, where t is a finite time horizon, t ∈ n. let use state the transit time function h : a×h → n the time lower bound function e : a×h → n, the time upper bound function q : a×h → n, e (i,j; t) ≤ q(i,j; t), for all (i,j) ∈ a and for all t ∈ h. the parameter h(i,j; t) is the transit time needed to traverse an arc (i,j). the parameters e(i,j; t) and q(i,j; t) represents the minimum and respective maximum amount of flow that can travel over arc (i,j) when the flow departs from i at time t and arrives at j at time θ = t + h(i,j; t). the maximal dynamic flow problem for t time periods is to determine a flow function g : a×h → n, which should satisfy the following conditions in dynamic network d = (n,a,h,e,q) : t∑ t=0 (g(1,n; t) − ∑ τ g(n, 1; τ)) = w̄ (5a) g(i,n; t) − ∑ τ g(n,i; τ) = 0, i 6= 1,n, t ∈ h (5b) t∑ t=0 (g(n,n; t) − ∑ τ g(n,n; τ)) = −w̄ (5c) e(i,j; t) ≤ g(i,j; t) ≤ q(i,j; t), (i,j) ∈ a , t ∈ h (6) max w̄, (7) where τ = t − h(k,i; τ), w̄ = t∑ t=0 v(t), v(t) is the flow value at time t and g(i,j; t) = 0 for all t ∈{t −h(i,j; t) + 1, . . . ,t}. obviously, the problem of finding a maximum flow in the dynamic network d = (n,a,h,e,q) is more complex than the problem of finding a maximum flow in the static network g = (n,a,l,u). fortunately, this issue can be solved by rephrasing the problem in the dynamic network d into a problem in the static network r1 = (v1,e1, l1,u1) called the reduced expanded network. the static expanded network of dynamic network d = (n,a,h,e,q) is the network r = (v,e,l,u) with v = {it|i ∈ n,t ∈ h}, e = {(it,jθ)|(i,j) ∈ a,t ∈ {0, 1, . . . ,t − h(i,j; t)}, θ = t+h(i,j; t),θ ∈ h}, l(it,jθ) = e(i,j; t), u(it,jθ) = q(i,j; t), (it,jθ) ∈ e. the number of nodes in the static expanded network r is n(t +1) and number of arcs is limited by m(t +1)− ∑ a ◦h(i,j), where ◦h(i,j) = min{h(i,j; 0), . . . ,h(i,j; t)}. it is easy to see that any flow in the dynamic network d from the source node 1 to the sink node n is equivalent to a flow in the static expanded network r from the source nodes 10, 11, . . . , 1t to the sink nodes n0,n1, . . . ,nt and vice versa. we can further reduce the multiple source, multiple sink problem in the static expanded network r to a single source, single sink problem by introducing a supersource node 0 and a supersink node n + 1 constructing the static super expanded network r2 = (v2,e2, l2,u2), where v2 = v ∪{0,n + 1}, e2 = e ∪{(0, 1t)|t ∈ h}∪{(nt,n + 1)|t ∈ h}, l2(it,jθ) = l(it,jθ), u2(it,jθ) = u(it,jθ), (it,jθ) ∈ e, l2(0, 1t) = l2(nt,n + 1) = 0, u2(0, 1t) = u2(nt,n + 1) = ∞, t ∈ h. 106 c. schiopu, e. ciurea we construct the static reduced expanded network r1 = (v1,e1, l1,u1) as follows. we define the function h2 : e2 −→ n, with h2(0, 1t) = h2(nt,n + 1) = 0, t ∈ h, h2(it,jθ) = h(i,j; t), (it,jθ) ∈ e. let d2(0, it) be the length of the shortest path from the source node 0 to the node it, and d2(it,n + 1) the length of the shortest path from node it to the sink node n + 1, with respect to h2 in network r2. the computation of d2(0, it) and d2(it,n + 1) for all it ∈ v are performing by means of the usual shortest path algorithms. the network r1 = (v1,e1, l1,u1) have v1 = {0,n + 1}∪{it|it ∈ v,d2(0, it) + d2(it,n + 1) ≤ t}, e1 = {(0, 1t)|d2(1t,n + 1) ≤ t,t ∈ h}∪{(it,jθ)|(it,jθ) ∈ e,d2(0, it)+h2(it,jθ)+d2(jθ,n+1) ≤ t}∪{(nt,n+1)|d2(0,nt) ≤ t,t ∈ h} and l1, u1 are restrictions of l2, u2 at e1. now, we construct the static reduced expanded network r1 = (v1,e1, l1,u1) using the notion of dynamic shortest path. the dynamic shortest path problem is presented in [3]. let d(1, i; t) be the length of the dynamic shortest path at time t from the source node 1 to the node i and d(i,n; t) the length of the dynamic shortest path at time t from the node i to the sink node n, with respect to h in dynamic network d. let as consider hi = {t|t ∈ h,d(1, i; t) ≤ t ≤ t −d(i,n; t)}, i ∈ n, and hi,j = {t|t ∈ h,d(1, i; t) ≤ t ≤ t −h(i,j; t) −d(j,n; θ)}, (i,j) ∈ a. the multiple sources, multiple sinks static reduced expanded network r0 = (v0,e0, l0,u0) have v0 = {it|i ∈ n,t ∈ hi}, e0 = {(it,jθ)|(i,j) ∈ a,t ∈ hi,j}, l0(it,jθ) = e(i,j; t), u0(it,jθ) = u1(i,j; t), (it,jθ) ∈ e0. the static reduced expanded network r1 = (v1,e1, l1,u1) is constructed from network r0 as follows: v1 = v0∪{0,n+1},e1 = e0∪{(0, 1t)|1t ∈ v0}∪{(nt,n+1)|nt ∈ v0}, l1(0, 1t) = l1(nt,n+1) = 0, u1(0, 1t) = u1(nt,n + 1) = ∞, 1t,nt ∈ v0, l1(it; jθ) = l0(it,jθ) and u1(it,jθ) = u0(it,jθ), (it,jθ) ∈ e0. we notice that the static reduced expanded network r1(r0) is always a partial subnetwork of static super expanded network r2(r). in references [4], [5] it is shown that a dynamic flow for t periods in the dynamic network d with e = 0 is equivalent with a static flow in a static reduced expanded network r1. since an item released from a node at a specific time does not return to the location at the same or at an earlier time, the static networks r,r2,r0,r1 cannot contain any circuit, and therefore, are acyclic always. in the most general dynamic model, the parameter h (i) = 1 is waiting time at node i, and the parameter e(i; t), q(i; t) are lower bound and upper bound for flow g(i; t) that can wait at node i from time t to t + 1. this most general dynamic model is not discussed in this paper. the maximum flow problem for t time periods in the dynamic network d formulated in conditions (5), (6), (7) is equivalent with the maximum flow problem in the static reduced expanded network r0 as follows: f0(it,v0) −f0(v0, it) =   vt, it = 1t, t ∈ h1 0, it 6= 1t,nt, t ∈ h1, t ∈ hn −vt, it = nt, t ∈ hn (8) l0(it,jθ) ≤ f0(it,jθ) ≤ u0(it,jθ), (it,jθ) ∈ e0 (9) max ∑ h1 vt, (10) where by convention it = 0 for t = −1 and it = n + 1 for t = t + 1. in stationary case the dynamic distances d(1, i; t), d(i,n; t) become static distances d(1, i), d(i,n). many notions from this section are presented and in papers [4], [7]. two flow problems in dynamic networks 107 2.3 maximum flows in bipartite static networks in this section we consider that the static network g = (n,a,l,u) is bipartite static network. a bipartite network has the set of nodes n partitioned into two subsets n1 and n2, so that for each arc (i,j) ∈ a, either i ∈ n1 and j ∈ n2 or i ∈ n2 and j ∈ n1. let n1 = |n1| and n2 = |n2|. without any loss of generality, we assume that n1 ≤ n2. we also assume that source node 1 belongs to n2 (if the source node 1 belonged to n1, then we could create a new source node 1′ ∈ n2, and we could add an arc (1′, 1) with l(1′, 1) = 0, u(1′, 1) = ∞). a bipartite network is called unbalanced if n1 << n2 and balanced otherwise. the observation of gusfield, martel, and fernandez-baca [6] that time bounds for several maximum flow algorithms automatically improves when the algorithms are applied without modification to unbalanced networks. a careful analysis of the running times of these algorithms reveals that the worst case bounds depend on the number of arcs in the longest node simple path in the network. we denote this length by l. for general network, l ≤ n− 1 and for a bipartite network l ≤ 2n1 + 1. hence for unbalanced bipartite network l << n. column 3 of table 1 summarizes these improvements for several network flow algorithms. table 1: several maximum flows algorithms algorithm running time, running time, running time general network bipartite network modified version dinic n2m n21m does not apply karazanov n3 n21n n1m + n 3 1 fifo preflow n3 n21n n1m + n 3 1 highest label n2 √ m n1n √ m n1m excess scaling nm + n2 log ū n1m + n1n log ū n1m + n 2 1 log ū ahuja, orlin, stein, and tarjan [2] obtained further running time improvements by modifying the algorithms. this modification applies only to preflow push algorithms. they call it the two arcs push rule. according to this rule, always push flow from a node in n1 and push flow on two arcs at a time, in a step called a bipush, so that no excess accumulates at nodes in n2. column 4 of table 1 summarizes the improvements obtained using this approach. we recall that the fifo preflow algorithm might perform several saturating pushes followed either by a nonsaturating push or relabeled operation. we refer to this sequence of operations as a node examination. the algorithm examines active nodes in the fifo order. the algorithm maintains the list q of active nodes as a queue. consequently, the algorithm selects a node i from the front of q, performs pushes from this node, and adds newly active nodes to the rear of q. the algorithm examines node i until either it becomes inactive or it is relabeled. in the latter case, we add node i to the rear of the queue q. the algorithm terminates when the queue q of active nodes is empty. (see [1]) the modified version of fifo preflow algorithm for maximum flow in bipartite network is called bipartite fifo preflow algorithm. a bipush is a push over two consecutive admissible arcs. it moves excess from a node i ∈ n1 to another node k ∈ n1. this approach means that the algorithm moves the flow over the path d̃ = (i,j,k),j ∈ n2, and ensures that no node in n2 ever has any excess. a push of α units from node i to node j decreases both e(i) and r0(i,j) by α units and increases both e(j) and r0(j,i) by α units. (see [2]) in the paper [2] is presented the following bipartite fifo preflow (bfifop) algorithm: 108 c. schiopu, e. ciurea algorithm 1 the algorithm for a feasible flow in r0. 1: algorithm bfifop; 2: begin 3: preprocess; 4: while q 6= ∅ do 5: begin 6: select the node i from the front of q; 7: bipush/relabel(i); 8: end 9: end. 1: procedure preprocess; 2: begin 3: f =: 0; q := ∅; 4: push u(1,j) units of flow on each (1,j) ∈ a and add j to rear of q; 5: compute the exact distance labels d(i) from t to i; 6: end; 1: procedure bipush/relabel(i); 2: begin 3: if there is an admissible arc (i,j) then 4: begin 5: select an admissible arc (i,j); 6: if there is an admissible arc (j,k) then 7: begin 8: select an admissible arc (j,k); 9: push α := min{e(i),r(i,j),r(j,k)} units of flow along the path (i,j,k) and adds k 10: to q if k /∈ q; 11: end 12: else 13: d(j) := min{d(k) + 1|(j,k) ∈ a,r(j,k) > 0} 14: end 15: else 16: d(i) := min{d(j) + 1|(i,j) ∈ a,r(i,j) > 0}; 17: end; for more information see [2]. we notice the fact that have used the notations from this paper and specify that this algorithm runs on networks g with l = 0, a single source node 1, a single sink node n. 2.4 feasible flows in static networks we consider the flow problem satisfying the conditions (1a) and (1b). the condition (1a) is the flow conservation condition and the condition (1b) is the feasibility condition. let f be a flow of value v in the static network g = (n,a,l,u). let be ḡ the static network we get by adding the return arc (n, 1) to the network g. we extend the mappings l,u and f to ḡ as follows: two flow problems in dynamic networks 109 l̄(n, 1) = 0, ū(n, 1) = ∞, f̄(n, 1) = v (11) then f̄ is a circulation on ḡ: f̄(i,n) − f̄(n,i) = 0, i ∈ n (12a) l̄(i,j) ≤ f̄(i,j) ≤ ū(i,j) (12b) we notice that the static network ḡ = (n̄,ā, l̄, ū) has n̄ = n, ā = a ∪{(n, 1)}, l̄(i,j) = l(i,j), ū(i,j) = u(i,j), f̄(i,j) = f(i,j), (i,j) ∈ a and usual, ∞ means a sufficiently large number, for example ū(1,n). therefore the feasible flow problem we are looking for corresponding to an feasible circulation on network ḡ. however, note that the feasible flow problem might well be unsolvable, because there might not be any feasible circulations. a cut in the static network g = (n,a,l,u) is an arc set [x,y ] = (x,y )∪(y,x) with x ⊂ n, y = n −x, (x,y ) = {(i,j)|(i,j) ∈ a,i ∈ x,j ∈ y}, (y,x) = {(j,i)|(j,i) ∈ a,j ∈ y,i ∈ x}. the set (x,y ) denote the set of forward arcs in the cut and the set (y,x) denote the set of backward arcs in the cut. we refer to a cut [x,y ] as a 1 −n cut if 1 ∈ x and n ∈ y . for the maximum flow problem the capacity of 1 −n cut [x,y ] is c[x,y ] = u(x,y ) − l(y,x) (13) a 1 −n cut whose capacity is the minimum among all 1 −n cuts is a minimum cut. in [1] the next theorems are proved. theorem 1. (generalized max-flow min-cut theorem). the maximum value of the flow from a source node 1 to a sink node n in a static network g = (n,a,l,u) is equal to the capacity of a minimum 1 −n cut. theorem 2. (feasible circulation theorem). a necessary and sufficient condition for the existence of a feasible circulation on g = (n,a,l,u) is that l(y,x) ≤ u(x,y ) for any cut [x,y ] of g. theorem 3. (feasible flow theorem). a necessary and sufficient condition for the existence of a feasible flow on g = (n,a,l,u) is that l(y,x) ≤ u(x,y ) for all partitions n = x ∪y with 1 /∈ y or n /∈ x. theorem 3 result from theorem 2 if transform the flow f on g = (n,a,l,u) into circulation f̄ on ḡ = (n̄,ā, l̄, ū). 3 the feasible flow problem in dynamic networks 3.1 the feasible flow in dynamic networks we consider the flow problem satisfying the conditions (5) and (6). a flow g on the dynamic network d = (n,a,h,e,q) satisfying the conditions (5) and (6) is equivalent with the flow f0 on the multiple sources, multiple sinks static reduced expanded network r0 = (v0,e0, l0,u0) satisfying the conditions (8) and (9). recall the construction of sets hi and hi,j: hi = {t|t ∈ h,d(1, i; t) ≤ t ≤ t − d(i,n; t)}, i ∈ n, and hi,j = {t|t ∈ h,d(1, i; t) ≤ t ≤ t − h(i,j; t) − d(j,n; θ)}, (i,j ∈ a). this sets make the connection between the dynamic network d and the static network r0. therefore we reformulate theorem 1, theorem 2, theorem 3 for the static network r0. let v0 be v0 = v01 ∪ . . .∪v0i ∪ . . .v0n with v0i = {it|i ∈ n,t ∈ hi}, i = 1, . . . ,n. we refer to a cut [x0,y0] with x0 ⊂ v0, y0 = v0 −x0 as a v01 −v0n cut if v01 ⊂ x0 and v0n ⊂ y0. 110 c. schiopu, e. ciurea theorem 4. (generalized dynamic max-flow min-cut theorem). the maximum value of the flow from a source node v01 to a sink node v0n in a static network r0 = (v0,e0, l0,u0) is equal to the capacity of a minimum v01 −v0n cut. theorem 5. (feasible dynamic circulation theorem). a necessary and sufficient condition for the existance of a feasible circulation on r0 = (v0,e0, l0,u0) is that l0(y0,x0) ≤ u0(x0,y0) for any cut [x0,y0] of r0. theorem 6. (feasible dynamic flow theorem). a necessary and sufficient condition for the existence of a feasible flow on r0 = (v0,e0, l0,u0) is that l0(y0,x0) ≤ u0(x0,y0) for all partitions v0 = x0 ∪y0 with v01 6⊂ y0 or v0n 6⊂ x0. if h,e,q are constant over time, then a dynamic network d = (n,a,h,e,q) is said to be stationary. ford and fulkerson [5] have devised an algorithm that generates a maximum flow in the stationary dynamic network d = (n,a,h, 0,q), the case when e = 0. the flow obtained with the algorithm ford and fulkerson is called temporally repeated flow. let c : a → n be the fucntion cost. for many details is urged to consult the book [5]. there are two inconveniences for the flow problem in the stationary dynamic network d = (n,a,h,e,q). the first is that although in the planar network g = (n,a,c, l,u) with c = h,l = e,u = q exist a feasible flow, it is possible that in the static network r0 = (v0,e0, l0,u0) will not be any feasible flow. the second inconvenience consist in the fact that it is possible that the temporally repeated flow in the network r0 of a feasible and minimum time flow in the network g = (n,a,c, l,u), will not be any feasible in the network r0. 3.2 examples the stationary dynamic network d = (n,a,h,e,q) is presented in figure 1 and the time horizon set to t = 5, therefore h = {0, 1, 2, 3, 4, 5}. the transit time h(i,j), the lower bound e(i,j) and the upper bounds q(i,j) for all arcs (i,j) ∈ a are indicated in this order near arcs. figure 1: the stationary dynamic network d. we obtain: d(1, 1) = 0,d(1, 6) = 4, d(1, 2) = 1,d(2, 6) = 3, d(1, 3) = 1,d(3, 6) = 3, d(1, 4) = 3,d(4, 6) = 1, d(1, 5) = 2,d(5, 6) = 2, d(6, 6) = 0; h1 = {0, 1}, h2 = {1, 2}, h3 = {1, 2}, h4 = {3, 4}, h5 = {2, 3}, h6 = {4, 5}; h1,2 = {0, 1}, h1,3 = {0, 1}, h2,3 = {1}, h2,4 = {1, 2}, h2,5 = {1}, h3,5 = {1, 2}, h4,6 = {3, 4}, h5,4 = {2, 3}, h5,6 = {2}. the static network r0 = (v0,e0, l0,u0) is presented in figure 2. the lower bounds l0(it,jθ) and the upper bounds u0(it,jθ) for all arcs (it,jθ) ∈ e0 are indicated in this order near arcs. for all partitions n = x ∪ y with 1 /∈ y or n /∈ x of the static network g = (n,a,c = h,l = e,u = q) is verified the condition from theorem 3. therefore exist a feasible flow on g. also, is verified theorem 6 on the network r0 = (v0,e0, l0,u0). example 1. we replace in the network from figure 1 e(1, 2) = 4, q(2, 4) = 4, q(2, 5) = 4 with e(1, 2) = 7, q(2, 4) = 2, q(2, 5) = 2. now, we consider the partition n = x∪y with x = {2} and two flow problems in dynamic networks 111 figure 2: the static network r0. y = {1, 3, 4, 5, 6}. we have (x,y ) = {(2, 3), (2, 4), (2, 5)}, (y,x) = {(1, 2)} and in the network g = (n,a,c = h,l = e,u = q) we obtain l(y,x) = l(1, 2) = 7 > 6 = u(2, 3) + u(2, 4) + u(2, 5) = u(x,y ). therefore not exist a feasible flow on g. example 2. we replace in the network from figure 1 q(2, 4) = 4 with q(2, 4) = 3. for q(2, 4) = 3 the theorem 3 is verified. therefore exist a feasible flow on network g = (n,a,c = h,l = e,u = q). if q(2, 4) = 3 then u0(21, 43) = u0(22, 44) = 3. we consider the partition v0 = x0 ∪y0 with x0 = {22} and y0 = v0 −x0. we have (x0,y0) = {(22, 44)}, (y0,x0) = {11, 22} and l0(y0,x0) = l0(11, 22) = 4 > 3 = q(2, 4) = u0(22, 44) = u0(x0,y0). from theorem 6 we obtain that the flow problem in the static network r0 (dynamic network d) is infeasible. 4 maximum flows in bipartite dynamic networks with lower bounds 4.1 maximum flows in this section the dynamic network d = (n,a,h,e,q) is bipartite. we construct the static reduced expanded network r0 = (v0,e0, l0,u0) and we notice the fact that the network r0 is a bipartite network with v0 = w1 ∪ w2, w1 = {it|i ∈ n1, t ∈ h}, w2 = {it|i ∈ n2, t ∈ h}. let w1,w2,ε0 be w1 = |w1|, w2 = |w2|, ε0 = |e0|. if n1 << n2 then obvious that w1 << w2. in the static bipartite network r0 we determine a maximum flow f0 with a generalization of bipartite fifo preflow algorithm. we generalize the bfifop for a network r0 = (v0,e0, l0,u0) where l0 > 0, there are multiple source nodes 1t, t ∈ h1 and there are multiple sink nodes nt, t ∈ hn. also, we present a pseudocode in detail. we specify that maintain the arc list e+0 (it) = {(it,jθ) |(it,jθ) ∈ e0}. we can arrange the arcs in these lists arbitrarily, but the order, once decided, remains unchanged throughout the 112 c. schiopu, e. ciurea algorithm. each node i has a current arc, which is an arc in e+0 (it) and is the next candidate for admissibility testing. initially, the current arc of node it is the first arc in e + 0 (it). whenever the algorithm attempts to find an admissible arc emanating from node it, it tests whether the node’s current arc is admissible. if not, it designates the next arc in the arc list as the current arc. the algorithm repeats this process until it either finds admissible arc or it reaches the end of the arc list. the generalised bipartite fifo preflow (gbfifop) is presented in algorithm 2. we notice that any path in the residual network r̃0 = (v0, ẽ0,r0) can have at most 2w1 + 1 arcs. therefore we set d(1t) := 2w1 + 1 in procedure preproces. the correctness of the gbfifop algorithm results from correctness of the algorithm for maximum flow in bipartite network [2]. theorem 7. the gbfifop algorithm which determines a maximum flow into the bipartite dynamic network d = (n,a,h,q) has the complexity o(n1mt2 + n31t 3). proof: in section 3 we specify that the maximum flow problem for t time periods in the dynamic network d = (n,a,h,e,q) is equivalent with the maximum flow problem in the static reduced expanded network r0 = (v0,e0, l0,u0). the networks d and r0 are bipartite with n = n1∪n2, v0 = w1 ∪w2. we have n1 = |n1|, n2 = |n2|, m = |a|, w1 = |w1|, w2 = |w2|,ε0 = |e0|. the bipartite fifo preflow algorithm determines a maximum flow into the bipartite static network g = (n1 ∪ n2,a,l,u) in o(n1m + n31) how is specified in table from section 4. we apply the generalizate bipartite fifo preflow algorithm in the static reduced expanded bipartite network r0. hence the algorithm has the complexity o(w1ε0 + w31). from section 4 we have w1 = n1t and ε0 ≤ mt . as a result the algorithm has the complexity o(n1mt2 + n31t 3). we specify that in the first phase the feasible flow f0 is zero flow and in the second phase the feasible flow f0 is the feasible flow f0 determined in the first phase. 2 algorithm 2 the generalised bipartite fifo preflow (gbfifop) algorithm 1: algorithm gbfifop; 2: begin 3: preprocess; 4: while q 6= ∅ do 5: begin 6: select the node it from the front of q; 7: bipush/relabel(it); 8: end; 9: end. 1: procedure preprocess; 2: begin 3: f0 is a feasible flow in r0; q := ∅; 4: compute the exact distance labels d(it); 5: for t ∈ h1 do 6: begin 7: f0(1t,jθ) := u0(1t,jθ) and adds node jθ to the rear of q for all (1t,jθ) ∈ e0 8: d(1t) := 2w1 + 1; 9: end; 10: end. two flow problems in dynamic networks 113 1: procedure bipush/relabel(it); 2: begin 3: select the first arc (it,jθ) in e + 0 (it) with r0(it,jθ) > 0; 4: β := 1; 5: repeat 6: if (it,jθ) is admissible arc then 7: begin 8: select the first arc (jθ,kτ ) in e + 0 (jθ) with r0(jθ,kτ ) > 0; 9: if (jθ,kτ ) is admissible arc then 10: begin 11: push α := min{e(it),r0(it,jθ),r0(jθ,kτ )} units of flow over the arcs 12: (it,jθ), (jθ,kτ ); 13: if kτ /∈ q then 14: adds node kτ to the rear of q; 15: end 16: else if (jθ,kτ ) is not the last arc in e + 0 (jθ) with r0(jθ,kτ ) > 0 then 17: select the next arc in e+0 (jθ) 18: else 19: d(jθ) := min{d(kτ ) + 1|(jθ,kτ ) ∈ e+0 (jθ),r0(jθ,kτ ) > 0}; 20: if e(it) > 0 then 21: if (it,jθ) is not the last arc in e + 0 (it) with r0(it,jθ) > 0 then 22: select the next arc in e+0 (jθ) 23: else 24: begin 25: d(it) := min{d(jθ) + 1|(it,jθ) ∈ e+0 (jθ),r0(it,jθ) > 0}; 26: β := 0; 27: end; 28: end 29: until e(it) = 0 or β = 0 30: if e(it) > 0 then 31: adds node it to the rear of q; 32: end; 4.2 example we have n1 = {2, 3, 7} and n2 = {1, 4, 5, 6}. the support digraph of the bipartite dynamic network is presented in figure 3 and time horizon being set t = 5, therefore h = {0, 1, 2, 3, 4, 5}. the transit times h(i,j; t) = h(i,j), t ∈ h, the lower bounds e(i,j; t) = e(i,j) and the upper bounds (capacities) q(i,j; t) = q(i,j), t ∈ h for all arcs are indicate in table 2. applying the gbfifop algorithm in the first phase and the second phase we obtain the flows f0(it,jθ), f∗0 (it,jθ) (the feasible flow, the maximum flow) which are indicated in figure 4. we have w1 = {21, 22, 23, 31, 32, 33, 73, 74, 75} and w2 = {10, 11, 12, 44, 52, 53, 54, 62, 63, 64}. a minimum (10, 11, 12)−(73, 74, 75) cut in the static network r0 is [y0, ȳ0] = (y0, ȳ0) ∪ (ȳ0,y0) with y0 = {10, 11, 12, 22, 23, 31, 32, 33} and ȳ0 = {21, 44, 52, 53, 54, 62, 63, 64, 73, 74, 75}. hence [y0, ȳ0] = {(10, 21), (22, 53), (22, 64), (23, 54), (31, 62), (31, 44), (32, 63)} ∪ {(52, 33)}. we have w̄0 = f∗0 (y0, ȳ0) −f ∗ 0 (ȳ0,y0) = 40 − 0 = 40 = u0(y0, ȳ0). hence f ∗ 0 is a maximum flow. 114 c. schiopu, e. ciurea figure 3: the support digraph of network d = (n,a,h,e,q) figure 4: the network r0 = (v0,e0,f0,f∗0 ). table 2: the functions h,e,q (i,j) (1,2) (1,3) (2,4) (2,5) (2,6) (3,4) (3,6) (4,7) (5,3) (5,7) (6,7) h(i,j) 1 1 3 1 2 3 1 1 1 1 1 e(i,j) 3 5 1 1 1 0 4 1 0 1 5 q(i,j) 12 10 8 3 3 4 5 12 3 4 10 two flow problems in dynamic networks 115 conclusions in this paper we approached two flow problems: the feasible flow problem in dynamic networks and the maximum flow problem in bipartite dynamic networks with lower bounds. for the maximum flows problem in bipartite dynamic networks with lower bounds we developed an algorithm. these problem have not been treated so far. we demonstrate the fact that if the dynamic network d = (n,a,h,e,q) is bipartite, then the static reduced expanded network r0 = (v0,e0, l0,u0) is bipartite. for solving, we have rephrased the maximum flows problem in bipartite dynamic networks with lower bound into a problem in bipartite static network. then, we have extended the bipartite fifo preflow algorithm of ahuja et al. [2] for the static reduced expanded network with multiple source and multiple sinks r0 = (v0,e0, l0,u0). also we have presented the complexity for the generalization bipartite fifo preflow algorithm. for each of the two problems we have presented one example. flow problems in bipartite dynamic networks like: the parametric maximum flow problem, the minimum cost flow problem, the generalization of the highest label preflow push algorithm or the generalization of the excess scaling algorithm are still open for research. bibliography [1] r. ahuja, t. magnanti and j. orlin (1993), network flows. theory, algorithms and applications, prentice hall, inc., englewood cliffs, new jersey, 1993. [2] r. ahuja, j. orlin, c. stein and r. tarjan (1994), improved algorithms for bipartite network flows, siam journal of computing, 23:906-933. [3] x. cai, d. sha and c. wong (2007), time-varying nework optimization, springer, 2007. [4] e. ciurea (2002), second best temporally repeated flow, korean journal of computational and applied mathematics, 9(1):77-86. [5] l. ford, d. fulkerson, flow in networks., princeton university press, princenton, new jersey, 1962. [6] d. gusfield, c. martel, and d. fernandez-baca (1987), fast algorithms for bipartite network flow, siam journal of computing, 16:237-251. [7] c. schiopu, e. ciurea (2016), the maximum flows in planar dynamic dynamic networks, international journal of computers communications & control, 11(2):282-291. [8] c. schiopu, e. ciurea, the maximum flows in bipartite dynamic networks with lower bounds. the static approach, computers communications and control (icccc), 2016 6th international conference on, ieee xplore, e-issn 978-1-5090-1735-5, doi: 10.1109/icccc.2016.7496731, 10-15. international journal of computers communications & control issn 1841-9836, 13(3), 391-407, june 2018. attribute selection method based on objective data and subjective preferences in mcdm x. ma, y. feng, y. qu, y. yu xiaofei ma, yi feng technology planning dalian commodity exchange dalian city, china, 116023 maxf@dce.com.cn, fengyi@dce.com.cn yi qu*, yang yu agricultural bank of china data center 88 aoni road, pudong new area shanghai city, china, 200131 *corresponding author: 57638907@qq.com yuyinyang@126.com abstract: decision attributes are important parameters when choosing an alternative in a multiple criteria decision-making (mcdm) problem. in order to select the optimal set of decision attributes, an analysis framework is proposed to illustrate the attribute selection problem. then a two-step attribute selection procedure is presented based on the framework: in the first step, attributes are filtered by using correlation algorithm. in the second step, a multi-objective optimization model is constructed to screen attributes from the results of the first step. finally, a case study is given to illustrate and verify this method. the advantage of this method is that both external attribute data and subjective decision preferences are utilized in a sequential procedure. it enhances the reliability of decision attributes and matches the actual decision-making scenarios better. keywords: attribute selection, multi-criteria decision-making (mcdm), multiobjective optimization, attribute correlation. 1 introduction multi-criteria decision-making (mcdm) is successfully applied to help decision makers (dms) choose optimal alternatives. during the past few decades, various mcdm methods have been proposed based upon different philosophies such as multi-attribute utility, the analytic hierarchy process (ahp), outranking methods and so on. meanwhile, many decision support systems (dsss) have been designed in mcdm to assist dms in analyzing problems and making decisions more easily. mcdm deals with a general class of problems that contains multiple attributes, objectives and criteria [20]; and alternatives are determined based on many qualitative or quantitative criteria which are generally complicated and assessed by more relevant attributes. however, not all of them can be used in decision-making procedure, because they contain plenty of redundant or "noisy" attributes and it will lead to useless decision alternatives. therefore, the effectiveness of an alternative is highly dependent on the set of decision attributes. the concept of "optimal" can be illustrated by two aspects: (1) elements of the set are highly related to the mcdm problem for decision purpose; (2) the set of attributes is parsimonious, and the selected alternative will be suboptimum if one of these attributes is omitted. the rationale of attribute selection is similar to feature selection in data mining field. there are a lot of feature selection methods have been proposed, but only a few of them can be applied in decision-making process directly which are mentioned in the literature review part. most of these methods can’t copyright ©2018 cc by-nc 392 x. ma, y. feng, y. qu, y. yu account for both objective and subjective perspectives, so there is a low reliability of decision attributes. this study is focused on how to select the optimal set of decision attributes for a mcdm problem, external attribute data and subjective decision preferences are utilized in a sequential procedure to enhance the reliability of decision attributes. the attribute selection method for decision problem should account for both objective and subjective perspectives, more precisely, this paper merges attribute data (objective) with dms’ requirement (subjective). utilize the former for rational, constrained modeling and the latter for adapting specific problem issues to the decision-making process. in order to obtain the optimal set of attributes, an analysis framework for attribute selection problem and a two-step screening procedure is conducted in this paper. the specific procedure is illustrated in figure 1. figure 1: the illustration for the presented method this new attribute selection method in mcdm contributes to selecting the optimal set of decision attributes and helping dms choose optimal alternatives better. meanwhile, this method merges objective data and subjective preferences to improve performance for actual decision scenarios. 2 literature review many feature selection methods have been proposed, and they are usually classified into three classes: "filter" methods, "wrapper" methods and "embedded" methods [2,7,8,14]. wu [19] proposed using the fuzzy and grey delphi methods to identify a set of reliable attributes and, based on these attributes, transforming big data to a manageable scale to consider their impacts. meinshausen et al. [11] demonstrated linear model and gaussian model of variable selection consistency in higher dimensional case. although they have good performance, they cannot be applied in decision-making process directly (partial principle may be accepted). only a few papers mentioned attribute selection in decision-making problems; for instance, chun [4] considered the "optimizing" and "satisficing" (a portmanteau of satisfy and suffice) attributes and deal with the multi-attribute decision problem with sequentially presented decision alternatives; dai et al. [6] constructed three attribute selection approaches in context of incomplete decision systems based on information-theoretical measurement of attribute importance; in order to screen the critical factors influencing the stability of perilous rock, meng et al. [12] used fuzzy compromise topsis method to calculate the importance of attributes in the decision problem. attribute selection method based on objective data and subjective preferences in mcdm 393 wu [18] used fuzzy delphi method to screen out the unnecessary attributes to deal with the complex interrelationships among the aspects and attributes. attribute selection procedure can improve the decision performance, but no papers used this procedure in mcdm problems. 3 method 3.1 preliminaries trapezoidal fuzzy numbers definition 1. let x be a universe set. a fuzzy ã in a universe of discourse x is characterized by a membership function µã(x), which associates with each element x in x, a real number in the interval [0,1]. the function is termed the grade of membership of x in ã. definition 2. a tuple ã = (a,b,c,d),a ≤ b ≤ c ≤ d, is called a trapezoidal fuzzy number (tfn) if its membership function is µã(x) =   (x−a)/(b−a) a ≤ x ≤ b 1 b ≤ x ≤ c (d−x)/(d− c) c ≤ x ≤ d 0 otherwise where a, b, c, d are real numbers. definition 3. given two trapezoidal fuzzy numbers ã = (a1,b1,c1,d1), ã = (a2,b2,c2,d2), and a real number λ, the main operations can be expressed as follows: 1○ã1 ⊕ ã2 = (a1 + a2,b1 + b2,c1 + c2,d1 + d2) 2○ã1 ⊗ ã2 = (a1a2,b1b2,c1c2,d1d2) 3○λ ⊗ ã2 = (λa1,λb1,λc1,λd1) multi-objective optimization let χ be a vector containing n decision variables and in a universe of discourse x. mathematically, an optimization problem with p objective functions can be expressed as (mahdi and seyed, 2012): minimize fi(x) for i = 1, 2, ...,p subject to: gj(x) ≤ 0,j = 1, 2, ...,q, hj(x) = 0,j = q + 1, 2, ...,m where x = (x1,x2, ...,xn), xl is the lth decision variable; p and m are respectively the numbers of objective function and constraint. a variety of methods can be used to solve this problem. one popular method is to combine those objectives into a single composite objective so that traditional mathematical programming methods can be applied. and other approaches are based on the pareto optimum concept, and more specifically, let y = (y1,y2, ...,yn) be another vector containing n decision variables in x. definition 4. (domination) x ∈ x dominates y ∈ x, denoted x � y, if ∀j ∈ p, vj(x) ≤ vj(y) with at least one strict inequality. definition 5. (pareto optimal) x is a pareto optimal (po) solution, if there is no y ∈ x such that y � x. the set of all pareto optimal solutions in x is denoted po(x). 394 x. ma, y. feng, y. qu, y. yu these methods include multiple objective genetic algorithm (moga), non-dominated sorting genetic algorithm (nsga), nsga-ii, multi-objective multi-state genetic algorithm (moms-ga), niched-pareto genetic algorithm (npga) and multi-objective particle swarm optimization (mopso) [9,21]. an analysis framework of attribute selection under mcdm multi-criteria decision making (mcdm) has been widely applied as a well-known branch of decision making, and can be further divided into multi-objective decision making (madm) and multi-attribute decision making (modm) [5,13]. the whole hierarchical structure of a mcdm problem is shown in figure 2, and it can be unfolded from four hierarchies from top to the bottom, namely, objective, criteria, attribute and alternative. (1) objective hierarchy: generally contain multiple decision objectives given by dms which reflect different decision requirement. (2) criteria hierarchy: criteria hierarchy is to realize a fair comparison of alternatives from the various aspects of objective hierarchy, and it is also developed in a hierarchical fashion, starting from some general but imprecise criteria description, which is refined into more precise suband sub-sub criteria. (3) attribute hierarchy: attribute hierarchy is determined by criteria hierarchy and objective hierarchy. it is generally assumed that each criterion can be represented by some measurable attributes of the consequences arising from implementation of any particular decision alternative [16], as well as objective. no matter what kind of mcdm methods, decision attributes are the bottom and direct evaluative parameters to determine alternatives. (4) alternative hierarchy: consist from candidate alternatives evaluated by the set of decision attribute. figure 2: the hierarchical structure of a mcdm problem this paper focus on how to obtain the optimal set of decision attributes from attribute hierarchy for evaluating alternatives. attribute selection method based on objective data and subjective preferences in mcdm 395 rationale of the proposed method let o = {o1,o2, ...,oγ} be a finite set of objectives, and c = {c1,c2, ...,cn} be the set of criteria. in most actual cases, objectives and criteria are always predetermined by mds according to different knowledge background and decision experiences, thus we assume that all of objectives and criteria (or sub-criteria) are given. we define decision attribute selection procedure as a sequential selection procedure based on objective and subjective information. it means that a series of screening steps will be applied in sequence to obtain an optimal set of attributes. a sequential screening can be described as follows [3]: scrh,h−1,...,1(aoriginal) = scrh(scrh−1(...(scr1(aoriginal))...)) (1) where scrk,k = 1, 2, ...,h are screening steps, and scrh is the final screening step in this sequential screening; aoriginal = {aj|j ∈ n} is the original set of decision attributes. and two screening steps are conducted in proposed selection procedure, namely, scr2,1(a) = scr2(scr1(a)). scri is a screening step based on real-time attribute data, and the corresponding screening algorithm is established in the step. the result scri(a) is to obtain a relatively optimized set of decision attributes ascr1 by deleting redundant attributes. scr2 is a further screening step based on the results of scr2(a) to acquire the absolutely optimal set of decision attributes ascr2. decision goals that represent subjective preference are utilized in this step, and the selected set should satisfy all decision goals. the whole procedure of attribute selection is shown by figure 3 figure 3: the procedure of the proposed method where ascr1 = {{a1},{a1}, ...,{ap}, ...,{an}}, and ã b scr2 is candidate optimal set of scr2. 3.2 a new method for attribute selection preprocessing of the data we consider three data types of decision attributes in this paper, namely, real numbers expressed as α0, intervals expressed as [αl,αu ] and linguistic term set expressed as a linguistic term set s = {s0,s1, ...,sk}. and real-time attribute data can be listed as attribute value series in table 1. where c ≤ t1 < t2 < ... < tm ≤ d and [c,d] is a time interval. 396 x. ma, y. feng, y. qu, y. yu table 1: attribute value series α1 α2 ... αj ... t1 α1(t1) α2(t1) ... αj(t1) ... t2 α2(t2) α2(t2) ... αj(t2) ... ... ti αi(ti) α2(ti) ... αj(ti) ... ... tm αm(tm) α2(tm) ... αj(tm) ... both scr1 and scr2 are quantification procedures and therefore, in order to guarantee the scientificity and preciseness of the presented method, preprocessing of attribute data need to be carried out firstly (referring to our previous work [10]). (1) convert attribute values to tfns for a real number α0, it can be denoted as a tfn (α1,α2,α3,α4), where α1 = α2 = α3 = α4 = α0; for internal value bαl,αuc, the corresponding tfn can also be expressed as (α1,α2,α3,α4), where α1 = α2 = αl and α3 = α4 = αu. it is a little complex for a linguistic term set with odd cardinality s = {s0,s1, ...,sk}, and the element sk is converted to the corresponding tfn as follows: (α1k,α 2 k,α 3 k,α 4 k) = max { 2k − 1 2k + 1 , 0 } , 2k 2k + 1 , 2k + 1 2k + 1 , min { 2k + 2 2k + 1 , 1 } (2) where k = 0, 1, ...,k. (2) normalize values of attributes in real-world decision scenarios, some decision attributes are cost ones, which mean the lower the values of attributes, the better they will be; the others are benefit ones, which mean the higher the values of attributes, the better they will be. without loss of generality, we assume that the attribute values can be expressed as αj(ti) = (α1j (ti),α 2 j (ti),α 3 j (ti),α 4 j (ti)), and normalized attribute values can be denoted as γj(ti) = (r1j (ti),r 2 j (ti),r 3 j (ti),r 4 j (ti)). the normalized methods are given as follows: • for cost attributes: rvj (ti) = max i ( α4j (ti) ) −αvj (ti) max i ( α4j (ti) ) − min i ( α1j (ti) ) v = 1, 2, 3, 4. (3) • for benefit attributes: rvj (ti) = αvj (ti) − min i ( α1j (ti) ) max i ( α4j (ti) ) − min i ( α1j (ti) ) v = 1, 2, 3, 4. (4) and the normalized attribute value series can be denoted as γ1 = {r1(t1),r1(t2), ...,r1(tm)}; attribute selection method based on objective data and subjective preferences in mcdm 397 γ2 = {r2(t1),r2(t2), ...,r2(tm)}; ...; γj = {rj(t1),rj(t2), ...,rj(tm)}; .... the first step for screening scr1 in scr1, one "relatively best" representative attribute will be chosen by dms for each criterion. it means that, for ∀cp ∈{c1,c2, ...,cn}, 1 ≤ p ≤ n, ∃ap1 ∈ aoriginal,st.cp ⇔ ap1. for example, "economic losses" is a criterion in emergency decision, and "amount of loss" is the "relatively best" attribute to evaluate loss degree. thus the set {ap1|1 ≤ p ≤ n} is a significant by-product after criteria determination, and actually it is the input of scr1. scr1 is used to screen decision-relevant attributes from aoriginal by analyzing attribute interrelation. more precisely, we will investigate geometrical relationship between attributes based on attribute value series; select highly correlative attributes for ap1 from aoriginal to constitute a "relatively best" representative set for each criterion ci, and omit the rest of decision-irrelevant attributes. the step is entirely implemented on attribute value series without subjective impacts, and the outcome ascr1 is a smaller and relative optimal set which the elements are determined by evaluation criteria. the idea of scr1 arises from grey relational analysis (gra) theory which provides a valid way to quantify the interrelation of different factors using geometric methods [22]. and the detailed process of scr1 is given in the following. step 1.1: normalize attribute value series it is different from normalization of attribute values, and it guarantees comparability of different attribute value series. the specific process is yj = {aj(ti)/dj, i = 1, 2, ...,m} (5) dj = 1 m− 1 m∑ i=2 |aj(ti) −aj(ti−1)| (6) where yj is normalized attribute value series, and dj is increment average of yj. step 1.2: calculate increment series ∆yj = {∆yj(ti) = yj(ti) −yj(ti−1), i = 2, 3, ...,m} (7) step 1.3: calculate correlation coefficient ascr1 is obtained based on the input set {ap1|1 ≤ p ≤ n} and thus, step 1.3 will be stepwise conducted for each element ap1. the correlation coefficient of different ti between ap1 and aj can be defined as follows: ξ(ti) = { sgn(∆yp1(ti) · ∆yj(ti)) · min(|∆yp1(ti)|,|∆yj(ti)|) max(|∆yp1(ti)|,|∆yj(ti)|) ; j 6= p1 0 ∆yp1(ti) · ∆yj(ti) = 0 (8) where sgn(·) is a sign function; and sgn(∆yp1(ti) · ∆yj(ti)) = 1, if ∆yp1(ti) · ∆yj(ti) > 0; sgn(∆yp1(ti) · ∆yj(ti)) = −1, if ∆yp1(ti) · ∆yj(ti) < 0. step 1.4: calculate correlation degrees the correlation degree between ap1 and aj can be obtained by 398 x. ma, y. feng, y. qu, y. yu τ(ap1,aj) = ∣∣∣∣∣ 1d− c m∑ i=2 ∆ti · ξ(ti) ∣∣∣∣∣ (9) the correlation degrees reflect the closeness of two attributes; the greater the value of τ(ap1,aj) is, the closer the two attributes are, and the better the capability of aj is to evaluate criterion cp. step 1.5: set threshold and screen correlation attributes for ap1 for ∀cp, a threshold of correlation degree τ∗ should be set by dms, and the attribute aj will be reserved, if τ(ap1,aj) > τ∗. and the reserved attributes for evaluating cp can be denoted as {ap} = {ap1,ap2, ...,apθp}, where θp is the sum of attributes. in addition, the correlation degree τ(ap1,apqp) between ap1 and apqp will be abbreviated as zpqp, where 1 ≤ qp ≤ θp. step 1.6: acquire the screening result set ascr1 repeat step 1.1 to step 1.5 for each ap1, where 1 ≤ p ≤ n; and the result set ascr1 can be obtained, namely, ascr1 = {{a1},{an}, ...,{an}}. the second step based on multi-objective optimization scr2 implementing decision attribute selection is aimed to assist dms in evaluating alternatives for given decision problems and therefore, the selected attributes should be sensitive to the decision problem as well as familiar to dms. sensitivity reflects whether the selected attributes are accurate enough to represent the decision problem, and familiarity is used to estimate whether dms are certain enough to make decision based on the selected attributes. for example, "fire duration" and "indoor fire load" are more important (sensitive) attributes than "carbon monoxide concentration" for evaluating fire grades; while it is more reliable for medical staff to estimate the physical condition of the wounded with "carbon monoxide concentration" than "radiative heat flux". multi-objective optimization theory is a feasible way to quantify and synthesize these requirements, and it has solved similar problems in other areas successfully [1]. thus three main objectives will be considered in this paper: attribute amount, attribute utility and attribute familiarity. attribute amount should be as few as possible without reducing the validity of alternative, and it is the demand of both scr2 and attribute selection method. attribute utility is used to distinguish attribute sensitivity in estimating the same decision problem, and attribute familiarity explores dms’ cognition differences for decision attributes. the outcome ascr2 drawn from scr2 should satisfy mentioned three goals, and now the optimal set of decision attributes have been found after scr1 and scr2, namely, ascr2 = scr2(scr1(aoriginal)). a set of zero-one binary vectors {vb} with t = (θ1 +θ2 +...+θn) dimensionality will be defined to represent different candidate outcome set ãbscr1, where one represents the attribute belonging to the set, otherwise it is zero. the optimal set of decision attributes is ascr2 ∈{ã b scr1 |b ∈ n}, and {ãbscr1} contains all the subsets of ascr1. three objective functions are defined firstly, and we will show that all the objective functions can be expressed as the functions with independent variable vb. defining objective functions (1) attribute utility function attribute utility function measures decision sensitivity of ãbscr1, and it can be defined as follows: u(ãbscr1 ) = g(mu(ã b scr1 ),w(ãbscr1 )) (10) attribute selection method based on objective data and subjective preferences in mcdm 399 where 1○ mu(ãbscr1 ) = v b ·mu∗ (11) mu∗ represents attribute utility matrix, and it is diagonal matrix with the diagonal elements ∆a11 ∆a11 , ..., ∆a11 ∆a1θ1 , ..., ∆ap1 ∆apqp , ..., ∆an1 ∆an1 , ..., ∆an1 ∆anθn . more precisely, mu∗ can be explained by a marginal utility function u(·), namely, u(apqp) = ∆ap1 ∆apqp 1 ≤ p ≤ n, 1 ≤ qp ≤ θp; (12) where ∆ap1 is increment of ap1, and ∆apqp is the corresponding varying of apqp. and the function u(·) quantifies the assessment performance on criterion ci of the attribute apqp according to calculate the ratio between increments of the two attribute values. in fact, mu∗ can be predetermined by domain experts, because plenty of field studies have been conducted to investigate utility relation of attributes. 2○ w(ãbscr1 ) = w ∗ · (vb)′ (13) w∗ represents attribute weighting matrix, and it is also a diagonal matrix with diagonal elements w11, ...,w1θ1, ...,wpqp, ...,wn1, ...,wnθn. where wpqp = ωp ·zpqp, 1 ≤ p ≤ n, 1 ≤ qp ≤ θp; (14) and {ω1,ω2, ...,ωn} are the know weights of c = {c1,c2, ...,cn}. g(·) is a dot product function and therefore, u(ãbscr1 ) can be transformed into the following function with independent variable vb, namely, u(vb) = (vb ·mu∗) · (w∗ · (vb)′) (15) (2) attribute familiarity function attribute familiarity function is established to quantify dms’ cognition degrees on different candidate set ãbscr1, and it is defined as f(vb) = η ·σ · (vb)′ (16) in eq. 16, η = (η1,η2, ...,ηl)1×l is the weight vector of dms, and l is the number of dms; σ is attribute familiarity matrix with the following expression σ =   σ1(1) σ2(1) ... σt (1) σ1(2) σ2(2) ... σt (2) · · · · · · · · · σ1(l) σ2(l) ... σt (l)   l×t (17) and σh(l) represents the lth dm’s familiarity degree with attribute ah, where 1 ≤ l ≤ l, and 1 ≤ h ≤ t . attribute familiarity is usually evaluated qualitatively by dms, and many methods can be applied to quantify assessment results and obtain matrix σ, such as ahp and fuzzy set theory. (3) attribute count function 400 x. ma, y. feng, y. qu, y. yu attribute count function calculates the attribute number of a candidate set ãbscr1, and it can be defined by means of vector length formula, count(ãbscr1 ) = |v b|2 (18) where |vb| represents the length of vector vb. establish multi-objective optimization model for scr2 after define relevant objective functions, the multi-objective optimization model of scr2 is established to obtain the pareto optimal solutions ascr2, as well as the optimal set of decision attribute selection problem. maximize u(vb) = (vb ·mu∗) · (w∗ · (vb)′) f(vb) = η ·σ · (vb)′ (19) minimize count(ãbscr1 ) = |v b|2 subject to : |vb|2 ≥ n the model eq. 19 can be solved by aforementioned algorithm, such as moga, nsga, moms-ga and traditional mathematical programming methods, and it depends the specific model drawn from given decision problems and decision requirements. 4 empirical results 4.1 accident scenario and computational settings in order to validate the presented method, a fire accident scenario will be constructed and simulated. the fire happens in a factory dormitory, and two workers are asleep; some inflammable are stacked near the door which arise spontaneous combustion for some reasons. the total layout of this room is illustrated in figure 4, containing three beds, two workers marked by fellow cuboids, fire origin and plenty of fire smoke. now two experts need to make rescue alternative based on the collected data; one is fire expert who are engaged in fire research for many years and have in-depth study of the fire attributes, and the other is an experienced firefighter who know some medical knowledge. and they are weighted with 0.6 and 0.4 respectively. a fire simulation software fds [17] is used to establish fire scenario and generate fire data. the purpose of simulation is not only to provide a fire scenario, and more importantly, it can generate relevant fire attributes data for the following experiment. the data can be exported from fds containing attribute names, physical dimensions, attribute values and attribute types. in this paper, we generate twelve fire attributes and 125 data records. 4.2 select fire attributes by utilizing the presented method in this fire scenario, three main criteria will be considered, i.e., fire behavior, personal security and environmental conditions, and corresponding attributes are shown in table 5 where "enthalpy" is an interval attribute, "indoor fire load" and "circuit aging degree" are linguistic attributes, and the rest are real number attributes. the bold attributes are "relatively best" representative attributes for each criterion. (1) data preprocessing attribute selection method based on objective data and subjective preferences in mcdm 401 figure 4: a simulated fire scenario table 2: list of criteria and corresponding attributes criterion attributes fire behavior temperature / enthalpy / heat release rate / relative humidity / burning rate / pressure / radiative heat flux personal security carbon monoxide / carbon dioxide / oxygen environmental conditions indoor fire load / circuit aging degree as aforementioned, the real number attributes and interval attributes can be denoted by tfns directly. the linguistic attributes "flammable of storage items" and "circuit aging degree" can be transformed into tfns in table 3. table 3: linguistic attributes and corresponding tfns linguistic terms tfns not dangerous (0,0,0.111,0.222) medium dangerous (0.111,0.222,0.333,0.444) dangerous (0.333,0.444,0.555,0.666) very dangerous (0.555,0.666,0.777,0.888) highly dangerous (0.777,0.888,0.999,1) then we carry out normalization by eq. 3 and eq. 4 to obtain corresponding tfns. (2) the first step for screening scr1 without loss of generality, the criterion "fire behavior" will be utilized to illustrate the first screening procedure and the other two criteria "personal security" and "environmental conditions" can be dealt with similarly. 402 x. ma, y. feng, y. qu, y. yu the criterion "fire behavior" is represented by seven attributes, namely, "temperature", "enthalpy", "heat release rate", "relative humidity", "burning rate", "pressure" and "radiative heat flux"; and "temperature" is the "relatively best" representative attribute for the criterion "fire" behavior. step 1.1 to step 1.4 are implemented to calculate correlation degrees between ""temperature" and the remaining six attributes, and the results are given in table 4. table 4: correlation degrees between "temperature" and the other six attributes attribute enthalpy heat release rate relative humidity burning rate pressure radiative heat flux correlation degree 0.53745 0.96987 0.33271 0.74787 0.47665 0.88712 step 1.5: set threshold of correlation degree τ∗ = 0.6 thus the attributes will be reserved whose correlation degrees is greater than or equal to 0.6, i.e., "burning rate", "heat release rate" and "radiative heat flux". step 1.6: repeat step 1.1 to step 1.5 for each criterion for criteria "personal security" and "environmental conditions", the "relatively best" representative attributes are respectively "carbon monoxide" and "indoor fire load", and relevant correlation degrees are shown in table 5. table 5: relevant correlation degrees for the other two criteria criterion personal security environmental conditions attribute carbon dioxide oxygen circuit aging degree correlation degree 0.80961 0.93428 0.76865 we reset thresholds of correlation degree for each criteria, viz., τ∗personal = 0.9 and τ ∗ environmental = 0.8. finally, the result of the first screening ascr1 can be obtained, namely, ascr1 = {"temperature", "heat release rate", "radiative heat flux", "burning rate", "carbon monoxide", "oxygen", "indoor fire load"}. (3) the second step for screening scr2 before conducting scr2, mu∗, σ and w∗ should be given firstly. as aforementioned, mu∗ and σ can be determined by relevant research achievements and expert experiences, and therefore, we collected and integrated evaluation results from ten fire researchers to assign mu∗ and σ as follows: mu∗ =   19.5 15.6 7.4 11.2 15.4 18.7 18.2   and σ = ( 9 8 8 9 4 4 5 4 5 4 4 9 9 7 ) where the elements values of mu∗ are in the interval [0, 20], and the greater the value is, the more sensitive the attribute is for evaluation. the elements values of σ are in the interval [0, 10], and the greater the value is, the more familiar the attribute is to dms. attribute selection method based on objective data and subjective preferences in mcdm 403 moreover, w∗ can be acquired with the given criteria weight vector (ω1,ω2,ω3) = (0.4, 0.4, 0.2), namely, w∗ =   0.299 0.388 0.215 0.354 0.352 0.374 0.197   after determine relevant parameters mu∗, w∗, σ, and the seven-dimensional independent variable vb, scr2 based on the multi-objective optimization model eq 19 is performed in matlab r2013b. we choose the traditional mathematical programming method to solve this model, because the research [15] has proven that the programming method has better performance than aforementioned other algorithms to solve such a problem with the zero-one binary vector variable and the small size of data. the results are shown in figure 5 where the curve represents all possible solutions and the pareto optimal solution is the intersection of the curve and the x-axis, namely, vb = (1, 1, 0, 1, 1, 1, 1) . it means that the attribute "radiative heat flux" represented by zero can be omitted, and the optimal set of decision attributes is: ascr2 = {"temperature", "heat release rate", "burning rate", "carbon monoxide", "oxygen", "indoor fire load"}. figure 5: the results of the second step scr2 4.3 validation of the presented method in order to verify the result is the optimal set for a mcdm problem, multi-attribute utility theory will be used in the following. a conclusion underlies the presented method: removing decision-irrelevant attributes from the set of decision attributes will improve the effectiveness of decision alternatives. thus it is reasonable and feasible to verify our work starting from scr2, because the purpose of scr1 is to omit decision-irrelevant attributes. 404 x. ma, y. feng, y. qu, y. yu multi-attribute utility theory (maut) is used to choose decision alternatives by evaluating the utility values of alternatives which is calculated based on the utility of decision attributes, and it is an effective way to quantify decision attributes’ impact on decision results. more precisely, the utility evaluation model of an alternative is given as follows: u(x1,x2, ...,xn) = n∑ i=1 kiui(xi) if n∑ i=1 ki = 1 (20) where xi is the ith decision attribute, and ki is the corresponding weight; ui(xi) is the attribute utility function of xi, and u(x1,x2, ...,xn) is the utility function of an alternative evaluated by x1,x2, ...,xn. thus the utility values of all possible candidate sets ãbscr1 are calculated based on eq. 20, where attribute utility function ui(xi) can be defined as eq. 12, and the weights are acquired by eq. 14. it should be pointed that normalization of weights need be carried out to guarantee n∑ i=1 ki = 1. and the results are given in table 6, where si represents the candidate set of decision attributes, and 1 ≤ i ≤ 16. the correspondence between si and the set of attributes are shown in table 7. table 6: the utility values of attribute sets attribute set s1 s2 s3 s4 s5 s6 s7 s8 utility value 23.621 24.343 28.496 28.923 29.665 31.745 28.474 29.500 attribute set s9 s10 s11 s12 s13 s14 s15 s16 utility value 25.908 28.447 32.631 31.229 30.335 35.774 31.658 33.439 table 7: the correspondence between si and the set of attributes si the correspondence set si the correspondence set s1 (1, 0, 0, 0, 1, 0, 1) s9 (1, 0, 0, 0, 1, 1, 1) s2 (1, 1, 0, 0, 1, 0, 1) s10 (1, 1, 0, 0, 1, 1, 1) s3 (1, 0, 1, 0, 1, 0, 1) s11 (1, 0, 1, 0, 1, 1, 1) s4 (1, 0, 0, 1, 1, 0, 1) s12 (1, 0, 0, 1, 1, 1, 1) s5 (1, 1, 1, 0, 1, 0, 1) s13 (1, 1, 1, 0, 1, 1, 1) s6 (1, 1, 0, 1, 1, 0, 1) s14 (1, 1, 0, 1, 1, 1, 1) s7 (1, 0, 1, 1, 1, 0, 1) s15 (1, 0, 1, 1, 1, 1, 1) s8 (1, 1, 1, 1, 1, 0, 1) s16 (1, 1, 1, 1, 1, 1, 1) in table 6, for ∀i, 1 ≤ i ≤ 16, it has u(s14) > u(si), where i 6= 14. the set s14 has the maximum utility value u(s14) = 35.774, and thus the alternative chosen based on s14 (the optimal set ascr2) is the optimal alternative. in addition, s1 contains three attributes with minimum utility value u(s1) = 23.621, and s16 contains all attributes with utility value u(s16) = 33.439. it means that too few or too many attribute selection method based on objective data and subjective preferences in mcdm 405 attributes are not available in a mcdm problem; too few attributes are not enough to evaluate the decision problem, while too many attributes may lead to conflict and interfere decision results. the set of decision attributes obtained by utilizing the presented method has the maximum utility value in alternative evaluation, and it demonstrates the effectiveness of the presented method. 5 implications this paper utilizes attribute selection procedure in mcdm problems innovatively. mcdm problems refer to getting the optimal alternatives which are determined based on many qualitative or quantitative criteria, and these criteria are conflicting and assessed by more relevant attributes. therefore, the set of decision attributes determines the effectiveness of an alternative. in order to improve the reliability of mcdm, the method proposed in this study can select the optimal set of decision attributes which are highly related to the mcdm problem and remove redundant or "noisy" attributes. meanwhile, the selection of optimal set can reduce the attribute dimension and improve the efficiency of decision making while maintaining the great effect. the method combines external attribute data with subjective decision preferences in the sequential procedure to improve decision performance. external attribute data are objective factors, which show the features of the data. these attributes contain its own regularity and all kinds of information that decision-making needs, so the first step to screen is based on objective attribute data. subjective factors show the decision makers’ preferences. because of the different profession and background knowledge, decision makers have different recognition of attributes. considering the subjective decision preferences contributes to improve the reliability and accuracy of decision-making. using the former to build the method and using the constraint conditions of the latter to adjust the method can make it more in line with the actual situation. the method combines external attribute data with subjective decision preferences in the sequential procedure to improve decision performance. external attribute data are objective factors, which show the features of the data. these attributes contain its own regularity and all kinds of information that decision-making needs, so the first step to screen is based on objective attribute data. subjective factors show the decision makers" preferences. because of the different profession and background knowledge, decision makers have different recognition of attributes. considering the subjective decision preferences contributes to improve the reliability and accuracy of decision-making. using the former to build the method and using the constraint conditions of the latter to adjust the method can make it more in line with the actual situation. 6 conclusions in this paper, a new attribute selection method with the context of mcdm is proposed. according to the analysis of attribute selection problem, a two-step procedure is established to reduce original set to the optimal set of decision attributes. gra theory and multi-objective optimization method are respectively used to implement these two screening steps. and then a fire example is shown to illustrate application and validity of screening method. the attribute selection method presented in this paper is a dynamic and flexible procedure which depends on decision data resource and decision requirement. it eliminates the drawback that decision attributes are chosen completely subjectively, and it will show better performance for new decision scenarios. attribute selection is also a critical process for many domains, such as classification problem, clustering problem, risk assessment, credit assessment, etc. and even solution of these issues 406 x. ma, y. feng, y. qu, y. yu depended on results of attribute selection. attribute selection problem is also discussed in data mining called "feature engineering", and still has needs to continue thorough research and the technique problem to be solved. generally it exists plenty of missing data among decision information, and how to select the optimal decision attributes in this case will be our future research. bibliography [1] babaei, s., sepehri, m. m., babaei, e. 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(2016): a hierarchical clustering approach based on threedimensional gray relational analysis for clustering a large group of decision makers with double information, group decision and negotiation, 25(2), 325-354, 2016. international journal of computers communications & control issn 1841-9836, 12(5), 728-740, october 2017. a hybrid social network-based collaborative filtering method for personalized manufacturing service recommendation s. zhang, w.t. yang, s. xu, w.y. zhang shuai zhang*, wenting yang, wenyu zhang school of information zhejiang university of finance and economics no.18 xueyuan street, xiasha, hangzhou 310018, china *corresponding author: zhangshuai@zufe.edu.cn 1123797538@qq.com, wyzhang@e.ntu.edu.sg song xu business school nankai university no.94 weijin road, tianjin 300071, china xs_xsong@zufe.edu.cn abstract: nowadays, social network-based collaborative filtering (cf) methods are widely applied to recommend suitable products to consumers by combining trust relationships and similarities in the preference ratings among past users. however, these types of methods are rarely used for recommending manufacturing services. hence, this study has developed a hybrid social network-based cf method for recommending personalized manufacturing services. the trustworthy enterprises and three types of similar enterprises with different features were considered as the four influential components for calculating predicted ratings of candidate services. the stochastic approach for link structure analysis (salsa) was adopted to select top k trustworthy enterprises while also considering their reputation propagation on enterprise social network. the predicted ratings of candidate services were computed by using an extended user-based cf method where the particle swarm optimization (pso) algorithm was leveraged to optimize the weights of the four components, thus making service recommendation more objective. finally, an evaluation experiment illustrated that the proposed method is more accurate than the traditional user-based cf method. keywords: manufacturing service recommendation, social network, collaborative filtering, salsa, pso. 1 introduction given the parallel and almost simultaneous rapid development of both web 2.0 and global manufacturing resources, a large number of manufacturing enterprises have turned to social network-based resources to improve their core competitiveness. for example, service-oriented architectures (soas) [7] and their corresponding web services offer dynamic methods that allow manufacturing enterprises to communicate with their suppliers and customers [4]. however, selecting the most appropriate recommendation technique is still a common challenge among numerous manufacturing enterprises. recommender methods have been widely applied to address the information overload problem often encountered in recommending personalized products or services by using the collaborative filtering (cf) [1] method, mostly in the commercial domain, including amazon and netflix. the traditional cf-based recommender systems usually face serious drawbacks, such as data sparsity and cold-start, which seriously affect the effectiveness of the service recommendation. to solve data sparsity and cold-start problems, social network information can be explored to leverage copyright © 2006-2017 by ccc publications a hybrid social network-based collaborative filtering method for personalized manufacturing service recommendation 729 service preferences of users, trust relationships among users, and influence of users on others, thus making personalized service recommendations more accurate and objective [6]. through comparison with and analysis of previous works, this study proposed that the social network-based cf method can also be applied in the area of manufacturing services not only as a solution to the industry’s information overload problem but as an alternative to its need for a more accurate and objective personalized recommendation technique. it is assumed that the opinions of trustworthy enterprises have significant influence on the consumers who use social network-based information to access information on required services or products. apart from trust relationships of enterprises, similarity of consumer enterprises is another important factor for calculating predicted ratings to recommend services, since two enterprises with similar features may have higher similarity of preference ratings on required services. however, conventional social network-based cf methods still have some serious drawbacks to be explored. first, the traditional trust evaluation methods usually ignore the reputation propagation in social networks. second, traditional cf recommendation methods basically use single similarity between consumer enterprises (i.e., rating similarity), making the result of service recommendation ineffective. furthermore, the corresponding weights of influential attributes for calculating predicted ratings are subjectively or averagely determined, resulting in inaccurate manufacturing service recommendation. to overcome the above barriers, this study proposed a hybrid social network-based cf method to make manufacturing service recommendation more effective and accurate. first, stochastic approach for link structure analysis (salsa) [15] was leveraged to compute the global reputation values of enterprises in selecting top k1 trustworthy enterprises by considering reputation propagation in enterprise social network. in addition to the selection of trustworthy enterprises, three types of similar enterprises were selected as influential components for predicting ratings. the types of enterprise similarity include rating similarity, development stage similarity, and category similarity. after the four influential components were selected, the predicted ratings of candidate services were calculated by using an extended user-based cf method where particle swarm optimization (pso) [12] was leveraged to automatically and objectively obtain the weights of the four influential components. finally, the manufacturing services with higher predicted ratings can be recommended to the consumer enterprise, by using user-based cf method. the rest of this paper is organized as follows: section 2 summarizes several related works. section 3 further elaborates on the proposed hybrid method. section 4 presents the experimental evaluation to verify the effectiveness of the proposed method. lastly, section 5 gives the conclusion of this paper, as well as suggestions for further research. 2 related works 2.1 social network-based recommender method social networks derive their name from social associations among people, and model social connections among individuals or objects [20]. in recent years, large amounts of useful information from social networks have been mined and integrated with recommender systems to enhance the quality of recommendation, and consequently, social network-based recommender methods have been broadly applied and extended to various domains. for instance, eirinaki et al. [8] presented a trust-aware system for personalized recommendation, employing both implicit trust and explicit trust between users in the social network. meanwhile, the liu et al. [17] study proposed a novel recommendation system that considered social relations and item contents into the bayesian probabilistic matrix factorization (bpmf) [23] to improve the accuracy of recommendation. additionally, sun et al. [25] proposed a dynamic 730 s. zhang, w.t. yang, s. xu, w.y. zhang competitive approach to overcome the problem of environmental change for social network service recommendation. a previous work [29]-also done by the present writers-presented quality of service (qos)-aware service recommendation by combining social network and cf technology to predict the missing qos values for manufacturing service recommendation. however, the previous social network-based recommender methods [8, 17, 23, 25, 29], have not considered reputation propagation between consumer enterprises, which makes the result of manufacturing service recommendation ineffective. therefore, this study employed salsa, which is an effective link analysis method to compute global and accurate reputation value of enterprises in a social network. 2.2 stochastic approach for link structure analysis (salsa) salsa is an effective link analysis method that combines the idea of pagerank with the hub and authority idea of hits [21] and it is usually used in ranking of web pages. white et al. [28], for instance, proposed a general framework to estimate the relative importance of nodes in a graph by using salsa algorithm. najork et al. [18] proposed some definitions of neighborhood graph to enhance the effectiveness and the efficiency of query-dependent linkbased ranking algorithms including salsa algorithm. furthermore, borodin et al. [2] presented an extended salsa-called popularity salsa (psalsa)-to extract useful information about the relative ranking of the web pages. subsequently, langville et al. [14] proved that salsa is the best ranking algorithm for web information retrieval by comparing hits and pagerank. we find that the link structure of salsa is similar with transactions among consumer enterprises in a social network. therefore, this study adopted salsa to calculate the global reputation values of trustworthy enterprises in a social network. 2.3 particle swarm optimization (pso) pso [12] was initially proposed by kennedy and eberhart in 1995, and the concept was derived from the foraging behaviors of a swarm of birds to address optimization problems. in the past two decades, pso has been continually extended to facilitate its application in various domains, including education, economics, and engineering. for instance, sobecki [24] proposed five swarm intelligence algorithms in the field of student course recommendation, including ant colony optimization (aco) [5] and pso. specifically, pso was employed to find the set of the optimal neighborhood of students for further grade prediction. the study of park et al. [19] presented an improved pso that combined chaotic sequences with the linearly decreasing inertia weights to enhance the global searching capability to overcome nonconvex economic dispatch (ed) problems. esfahani et al. [9] provided an optimal control system to enhance power quality-where the fuzzy pso method was proposed for optimization of proportional-integral-derivative (pid) controller. the tyagi et al. [26] research utilized the multi-objective pso-based association rule mining model to objectively obtain minimum support and minimum confidence to extract the useful association rules, so that the collaborative filtering recommendations are obviously enhanced. according to these aforementioned studies, pso is a promising stochastic optimization technique for handling multi-objective problems; however, it is rarely employed as an optimal service recommendation in the domain of manufacturing services. in the present study, we takes advantage of the pso algorithm to automatically and objectively obtain corresponding weights of influential components to recommend the most effective and efficient manufacturing services. a hybrid social network-based collaborative filtering method for personalized manufacturing service recommendation 731 3 the proposed hybrid method to enhance the quality of manufacturing service recommendation, this paper proposes a hybrid social network-based cf method to modify the traditional user-based cf method. trustworthy enterprises and three types of similar enterprises with different features can be regarded as influential components for calculating predicted rating. fig. 1 demonstrates the overview of the proposed method, and it is divided into four parts: (a) selection of top k1 trustworthy enterprises-considering global reputation propagation in enterprise social network; (b) selection of three types of top k similar enterprises-with different features, including rating similarity, development stage similarity, and category similarity; (c) optimization of four corresponding weights-by means of pso algorithm; and (d) manufacturing service recommendation-by calculating rating value of required service through extended user-based cf method. figure 1: overview of our proposed method 3.1 selection of top k1 trustworthy enterprises by using salsa to select the trustworthy enterprises more accurately, first, the relative trust weight based on social network was identified. then, salsa was employed to compute the global reputation value by considering reputation propagation in social networks. trust weight identification in a social network each enterprise was linked with one or several friend enterprises in social networks and each link carried a corresponding trust weight that was first identified. inspired by a previous workalso conducted by the present writers-in the domain of attention (doa) [30], it was established that the more number of times that a consumer enterprise has connected with friend enterprises such as visiting home page and inquiring services, the more trust the consumer enterprise has on a friend enterprise. therefore, this study considered average connection times as trust weight 732 s. zhang, w.t. yang, s. xu, w.y. zhang and the formulation is represented as follows: twij = conntimes(ei,ej)∑k k=1 conntimes(ei,ek) , (1) where conntimes(ei,ej) denotes the number of times that enterprise i has connected to enterprise j, and denominator means the total times that enterprise i has connected with all enterprises in social network. the number of connection times can be extracted from the enterprise profile. calculation of global reputation value by using salsa a large amount of reputation evaluation methods have been widely used to compute reputation value. however, the conventional reputation evaluation methods usually ignore the reputation propagation in enterprise social networks. largely, salsa [15] is known as a promising link analysis algorithm, combining advantages of both pagerank [3] and hits [13]. this study found that the link structure of salsa is similar to reputation propagation in an enterprise social network, such that it is appropriately adopted to calculate the global reputation values of enterprises based on the trust weight. in an enterprise social network, the relationships among consumer enterprises can be regarded as a bipartite undirected graph g = (vh,va,e), where vh and va denote a set of hub enterprises (all the enterprises in a social network with out-degree) and a set of authority enterprises (all the enterprises in a social network with in-degree), respectively. e is the set of directed interactions edge between the enterprises. each edge is arranged with a corresponding trust weight calculated by equation (1). an adjective matrix m can be built based on the link structure of the social network. the initial mij = 1 if the enterprise i points to enterprise j, otherwise, mij = 0. each edge is assigned a trust weight, so that mij = 1×twij or 0. the equations of hub matrix h and authority matrix a are represented as follows [16]: h = mrm t c , a = m t c mr, (2) where mr and mc denote each nonzero row divided by row sum of matrix m and each nonzero column divided by its column sum, respectively. this research considered the authority value of salsa as the reputation value of an enterprise. when g is connected, salsa assigns each page an authority weight, which is proportional to the sum of weights of its incoming edges [16]. however, if g is not connected, the authority value of enterprise i can be represented as follows [16]: ai = |aj| |a| × |b(i)|∑ j=1 twij |ej|∑ j=1 twij , (3) where |aj| and |a| denote the number of enterprises in jth connected component and total number of enterprises in the set of va, respectively; |b(i)| and |ej|, respectively, denote the number of in-degree enterprise i and total number of in-degree enterprises in jth connected component; enterprise i belongs to jth connected component. an example of trust relationship among enterprises in social networks is shown in fig. 2. a hybrid social network-based collaborative filtering method for personalized manufacturing service recommendation 733 figure 2: an example of trust relationship in enterprise social network the connection times among enterprises can be extracted from enterprise profile in table 1. table 1: connection times among enterprises e1 e2 e3 e4 e5 e1 0 30 50 0 0 e2 0 0 40 0 10 e3 0 10 0 0 30 e4 10 0 0 0 0 e5 0 0 0 0 0 the trust weights between the enterprises can be computed by using formulation 1, and adjective matrix m can be built. the trust relationship in enterprise social network in fig. 2 can be transformed as bipartite graph in fig. 3. figure 3: g: bipartite graph of the example according to the aforementioned formulation 2, we can calculate the hub matrix h and authority matrix a in the enterprise social network. m = [ e1 e2 e3 e4 e5 e1 0.000 0.375 0.625 0.000 0.000 e2 0.000 0.000 0.800 0.000 0.200 e3 0.000 0.250 0.000 0.000 0.750 e4 1.000 0.000 0.000 0.000 0.000 e5 0.000 0.000 0.000 0.000 0.000 ] h = [ e1 e2 e3 e4 e5 e1 0.629 0.278 0.094 0.000 0.000 e2 0.445 0.405 0.150 0.000 0.000 e3 0.188 0.185 0.625 0.000 0.000 e4 0.000 0.000 0.000 1.000 0.000 e5 0.000 0.000 0.000 0.000 0.000 ] a = [ e1 e2 e3 e4 e5 e1 1.000 0.000 0.000 0.000 0.000 e2 0.000 0.344 0.469 0.000 0.188 e3 0.000 0.209 0.703 0.000 0.089 e4 0.000 0.000 0.000 0.000 0.000 e5 0.000 0.188 0.200 0.000 0.613 ] according to the structure of matrix h and matrix a, g is not connected and the authority value of enterprise i can be calculated by using formulation 3. the result of global reputation values of enterprises can be represented as [a1,a2,a3,a4,a5]t = [0.250, 0.156, 0.356, 0, 0.238]t . therefore, this study selected top k1 trustworthy enterprises in social networks as an influential component of manufacturing service recommendation. 734 s. zhang, w.t. yang, s. xu, w.y. zhang 3.2 selection of three types of similar enterprises in addition to trustworthy enterprises, this paper considered the similarity of past consumer enterprises to recommend suitable manufacturing service. conventional user-based cf method usually only considers rating similarity of past consumer enterprises to predict ratings of candidate manufacturing services. however, other similar features of enterprises also have significant influence on predicting ratings of candidate manufacturing services. this section discusses the three types of top k similar enterprises-chosen for this study-as the additional three influential components of manufacturing service recommendation, including rating similarity, stage similarity, and category similarity. selection of top k2 enterprises with similar ratings pearson correlation coefficient (pcc) [22] is a popular and effective technology to calculate similarity among users based on the historical ratings. in this section, pcc is also leveraged to calculate the rating similarity of consumer enterprises according to their historical ratings to services, and the equation is presented as follows [29]: rate_sim(ea,eb) = ∑i i=1(ra,i −ra) × (rb,i −rb)√∑i i=1 (ra,i −ra) 2 × √∑i i=1 (rb,i −rb) 2 , (4) where i means the total number of manufacturing services co-rated by consumer enterprise ea and eb; ra,i and rb,i denote the rating value of manufacturing service i provided by ea and eb, respectively; ra and rb mean the average ratings of ea and eb, respectively. the value of rate_sim(ea,eb) will be within the interval [-1, 1], and the larger it is, the higher is the similarity between ea and eb. we can select top k2 enterprises with similar ratings. selection of top k3 enterprises with similar development stages for a manufacturing enterprise, the stage of enterprise development also plays an important role in service selection since the different stages of development have a different focus on manufacturing services. for example, start-up stage enterprises usually pay more attention on price of manufacturing service to reduce costs, while mature enterprise may rather put more emphasis on the quality of manufacturing service to attract more consumers. however, the development stages similarity is usually ignored in the process of manufacturing service recommendation. therefore, this study considered the development stages of enterprise as an influential factor of manufacturing service recommendation. according to the lifecycle of enterprise development, each enterprise can be divided into four stages: start-up, growth, maturity, and decline. the stage of each enterprise can be assessed by the hwang method [11] and we will not repeat here. the equation of stage similarity is shown as follows: stage_sim(ea,eb) = 1 − |ea,s −eb,s| dec−start , (5) where ea,s and eb,s denote the stages of enterprises ea and enterprise eb, respectively; dec and start mean the start-up stage and declining stage, respectively. we define the four stages as real numbers: 1, 2, 3, and 4. the top k3 enterprises with similar development stage were selected. selection of top k4 enterprises with similar enterprise category the category of manufacturing enterprise can be divided into three levels, namely broad heading, medium-class, and subclass. the broad heading can be further segmented into 25 a hybrid social network-based collaborative filtering method for personalized manufacturing service recommendation 735 types, including food manufacturing, tobacco manufacturing, and medical manufacturing. it can be further decomposed into medium-class and subclass. the semantic similarity of enterprise category is an indispensable factor for recommending manufacturing service. food manufacturing enterprise, for instance, and medical manufacturing enterprise have completely different demands in terms of manufacturing services. a previous work [4]-by these writers-explored a rich body of owl-based manufacturing service ontologies, and it was referenced to develop the ontology of enterprise category to calculate the semantic similarity of this category. the particle of the ontology is illustrated in fig. 4. the formulation of the semantic similarity of enterprise category can be represented as follows [4]: category_sim = µ n(csuper(ea,o) ∩csuper(eb,o)) n(csuper(ea,o) ∪csuper(eb,o)) + ν n(cmidd(ea,o) ∩cmidd(eb,o)) n(cmidd(ea,o) ∪cmidd(eb,o)) + λ n(csub(ea,o) ∩csub(eb,o)) n(csub(ea,o) ∪csub(eb,o)) , (6) where n(csuper(ea,o) ∩nsuper(eb,o)) means the number of the broad heading to which both enterprise a and enterprise b belong within ontology. n(csuper(ea,o)∪nsuper(eb,o)) denotes the number of the broad heading to which either enterprise a or enterprise b belongs within ontology. by that analogy, the similarity of the medium-class and subclass of enterprise category can be computed, and µ + ν + λ = 1. according to the result of semantic similarity of manufacturing enterprise category, the top k4 enterprises with similar location were selected. figure 4: particle of the ontology of manufacturing enterprise category 736 s. zhang, w.t. yang, s. xu, w.y. zhang 3.3 four weights optimization by using pso the weight optimization of the four influential components is inspired by genetic algorithm (ga)-based learning method [10], which was employed to determine the degree of importance of corresponding criteria of electronic service of internet banking. however, pso algorithm is more efficient than ga, since pso has no complicated operators, including selection, crossover, and mutation. therefore, the study adopted the pso algorithm to determine the objective weights of the four influential components to calculate the predicted ratings of target manufacturing services. fitness function based on the principle of pso algorithm, the study first constructed the fitness function to judge the performance of positions of particles. the main ideology behind constructing a fitness function is to compare actual average rating of services, which the query enterprise has consumed and rated, with predicted rating of past consumer enterprises. the actual average rating of j services that was rated by consumer enterprise is denoted as ra. the equation for predicted average rating of j services rated by consumer enterprise is represented as follows: parj = 1 j × j∑ j=1 pr(ea,sj), (7) where j means total number of services rated by enterprise ea. pr(ea,sj) denotes the predicted rating of the jth service using user-based cf, and it can be calculated as following formulation: pr(ea,sj) = 4∑ i=1 ai × ∑ki ki=1 r(eki,sj) × compi(ea,eki)∑ki ki=1 compi(ea,eki) , (8) where ki means ith set of top k enterprises (i.e., k1, k2, k3 and k4); compi(ea,eki) is expressed as ith component calculated by the four equations (3-6); r(ea,sj) is the rating provided by kith enterprise eki in top ki enterprises; and αi denotes corresponding weight which will be optimized. through comparison of actual average rating and predicted average rating, the distance between them seems closer; hence, the performance of the position of the particle is better. the final fitness function was constructed as follows: fva = 1 1 + |ra −parj| , (9) where fva denotes the fitness value of the four weights, and ra means average rating of services that the consumer enterprise has rated. identification of optimal weights of each influential component the four influential components are regarded as four particles in corresponding four-dimensional space and the range of each axis was set on interval [0, 1]. each particle has a velocity to update its position in its corresponding dimensional space. the position of the particle with highest fitness value was set as the global best position (gd) in each iteration. the ith particle kept its best personal position (pid) which was visited in the corresponding dth dimension of search space. the velocity and position of each particle were updated as follows [27]: vt+1id = ωv t+1 id + c1r1(p t id −x t id) + c2r2(p t gd −x t id), (10) a hybrid social network-based collaborative filtering method for personalized manufacturing service recommendation 737 xt+1id = x t id + v t+1 id , (11) where ω means a random inertia weight between 0 and 1; vtid denotes the velocity of ith particle in generation t; c1 and c2 are two positive constants; r1 and r2 are two random numbers among 0 and 1; and xtid means the current position of ith particle in generation t. through multiple iterations, the best personal positions of the four particles in their corresponding dimensional spaces were obtained. the weights of four influential components were normalized as follows: αi = xid∑4 i=1 xid , (12) where xid denotes the best personal position of ith particle in corresponding dth space. 3.4 manufacturing service recommendation the missing rating of manufacturing service sr which consumer enterprise ea needs can be predicted using a user-based cf method. the final equation of manufacturing service recommendation can be referred to equation (8), and the corresponding weight αi was objectively obtained through the optimization process that is illustrated in sub-section 3.3 in detail. accordingly, the predicted missing ratings of various manufacturing services, the top k manufacturing services with higher predicted ratings, can be recommended to the consumer enterprise. 4 an evaluation experiment in this section, a comparison experiment to evaluate the quality of manufacturing service recommendation was conducted through the use of the study’s proposed hybrid method. evaluation metric of mean absolute error (mae) was employed to evaluate the accuracy of our proposed hybrid method. the formulation is expressed as follows [29]: mae = ∑t t=1 |pt −at| t , (13) where t means the total number of predictions, and pt and at denote the tth predicted rating and tth actual rating, respectively. 4.1 comparative methods two recommender methods were selected to compare with the proposed hybrid method of the study to demonstrate the accuracy of the method. these are: (1) traditional user-based cf method, which only considers rating similarity between past consumer enterprises to predict ratings of candidate services. (2) combined social network and cf method [29], which is an effective method for manufacturing service recommendation, while it has not considered reputation propagation in enterprise social network, and only subjectively assigned relative weights of the influential components. 4.2 comparison results in the historical rating registry, there are 8796 ratings of 372 manufacturing services provided by 156 consumer enterprises with their relative trust information and profiles. the ratings varied from 1 to 5, and 80% of the rating data were randomly selected to act as training set and the rest were represented as testing set. 738 s. zhang, w.t. yang, s. xu, w.y. zhang fig. 5 demonstrates the three sets of mae values calculated by using three recommender methods with different sizes of neighbors from 5 to 17. according to the results of experiments, the mae values calculated by our proposed method are lower than the other two methods. therefore, our proposed method in this study is more accurate than traditional user-based cf method and the combination of the social network and cf method. the results also reveal that the performance of our proposed method is the best at the point of 11 in neighbor size. figure 5: comparison of mae values of three methods 5 conclusion in this paper, a hybrid social network-based cf method is proposed to make manufacturing service recommendation more accurate and objective. the result of the evaluation experiment illustrates that the proposed hybrid method is more accurate than the other traditional recommender methods. to sum up, the major contributions of this paper are as follows: first, trustworthy enterprises are obtained by identifying trust weight and leveraging salsa that considers reputation propagation based on social network in the process of calculating reputation value; second, the trustworthy enterprises and three types of similar enterprises with different features are considered as four influential components for calculating predicted ratings of candidate services to enhance the quality of manufacturing service recommendation; third, personalized manufacturing service can be recommended by using an extended user-based cf method where pso algorithm is employed to automatically and objectively obtain the weights of four influential components. still, this paper has some limitations, which can be further explored in future research. for instance, more useful information on enterprise social networks can be mined to enhance the accuracy of manufacturing service recommendation. acknowledgement the work has been supported by national natural science foundation of china (no. 51475410, no. 51375429) and zhejiang natural science foundation of china (no. ly17e050010). a hybrid social network-based collaborative filtering method for personalized manufacturing service recommendation 739 bibliography [1] adomavicius g., tuzhilin a.(2005); toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, ieee transactions on knowledge and data engineering, 17(6), 734–749, 2005. 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(2013); a social network-based expertise-enhanced collaborative filtering method for e-government service recommendation, advances in information sciences and service sciences, 5(10), 724-735, 2013. international journal of computers communications & control issn 1841-9836, 14(1), 90-106, february 2019. a fuzzy group decision-making model for determining the most influential persons in the sustainable prevention of accidents in the construction smes z. turskis, s. dzitac, a. stankiuviene, r. šukys zenonas turskis faculty of civil engineering vilnius gediminas technical university sauletekio al. 11, vilnius, lithuania, 10223 zenonas.turskis@vgtu.lt simona dzitac university of oradea universitatii, 1, oradea, romania, 410087 simona@dzitac.ro aušra stankiuviene*, ritoldas šukys faculty of civil engineering vilnius gediminas technical university sauletekio al. 11, vilnius, lithuania, 10223 *corresponding author: ausra.stankiuviene@vgtu.lt ritoldas.sukys@vgtu.lt abstract: safety in the sustainable construction is a game with uncertain reality, with human’s life. managers in power, involved in the construction project life cycle, create rules for the game and are the main players. the project’s success can be perceived differently by stakeholders. a company’s competitiveness, successful implementation of the projects and effective safety management depends on the strategic allocation of its human resources in order to select a proper project team in alignment with employee capabilities. this requires detailed factors reflecting their role (in line with their interests and attitudes) and knowing which of them has a decisive influence on the successful implementation of the project. the real data describing key factors in this concept can be provided as uncertain conditions. this work presents a novel integrated modified fuzzy group decision-making approach to select and rank the most influential persons ensuring the sustainable prevention of accidents at work in the small and medium-sized construction enterprises. this model includes the delphi method and fuzzy extensions of eckenrode’s criteria rating method. the proposed model could be expanded in order to select the most suitable individuals for sustainable management of safety and, moreover, for the effective implementation of safety and health measures. keywords: construction site, accidents prevention, occupational safety, risk management, fuzzy, rating, group, mcdm. 1 introduction sustainability is defined as a condition of equilibrium, stability, and interoperability, enabling people to meet their needs and support ecosystems [42]. the application of sustainability principles in any activity, including the construction, is becoming increasingly important for the development in terms of economic, social effectiveness, safety, and sustainability [1]. the uncertain challenges affect planning and practices [17]. a growth in the construction industry creates a demand for suitable materials and techniques [70]. the construction integrates the copyright ©2019 cc by-nc a fuzzy group decision-making model for determining the most influential persons in the sustainable prevention of accidents in the construction smes 91 achievements of the various fields of science, and proper safety management is inseparable [27]. in the modern world buildings and infrastructures are becoming increasingly complex [47]. the construction sustainability, safety culture and zero accidents at work aspiration are challenges of crucial importance for modern society. knowledge and education, to enhance understanding and adaptation of these paradigms, are fundamental to solving these dilemmas [6,19] widespread in organizations even nowadays when many organizations work on several complex projects at the same time [47]. project leaders control many risks and disadvantages during of construction works and play different roles in safety management in the construction process. a successful implementation of sustainable projects and effective implementation of safety management are the characteristics of a company’s competitiveness in the market [74] and reduce environmental, social and economic losses [31]. consequently, effectiveness of safety management has become a core topic for researches. the construction industry is one of the most dangerous sectors of the economy. during the last decade, the number of accidents at work has increased. the small and medium-sized enterprises (smes) have a higher accidents risk compared to large construction enterprises [25, 53]. the increased number of accidents at work in the construction impacts on a sustainable working environment. a reduction of accidents at work, as a priority, must be included in the action plans of construction projects [41]. hasle et al. [23] has investigated smes accidents causation criterion and prevention actions, and concluded that the owners of smes may deal with safety and health issues quite differently compared to those in the large construction enterprises. many factors influencing accidents at work in the construction are still largely unexplained. in general, accidents at the construction sites could be qualified as defects of the safety management system which occur due to the number of aspects, including technical, technological, organisational and other types of factors which affect human safety performance and working environment [33, 34, 52]. indrees et al. [26] presented that psychological factors such as an attitude of workers, a workload, organizational relationships, a clarity of roles, a mental stress, job insecurity, and job satisfaction strongly affect workers’ safety. scientists and practitioners mainly concentrated on a risk assessment process [4,56,59]. dozens of researchers concentrate on reactive (accident frequency and the seriousness index) [53] and proactive indicators (organisational, technical and behavioural), and each of them has their strengths and weaknesses for the safety performance [24,36,39,43,45,60]. yau [66] and zhou et al. [76] presented that the development of a safety culture significantly reduced the number of accidents. after reviewing the literature, it can be concluded that most of the accidents at work in smes construction sites are essentially related to negligent management or insufficient employee safety awareness [67]. management problems are mainly due to inappropriate management skills, lack of opportunities to implement safety and health strategies, non-compliance with labour safety laws and regulations, and the inappropriate use of security measures [33,58]. according to pasman et al. [46], a prediction what could happen under specified conditions is the most elementary step to the better safety. meanwhile, it is evident that accidents at work in the construction is a result of behaviour of stakeholders (owners, developer, project leaders, contractors, subcontractors, occupational safety and health (osh) specialists, osh coordinators). therefore, the stakeholders must understand principles of the sustainable construction and the impact of safety. the selection, preparation, and implementation of the right strategies for effective prevention of accidents is the biggest challenge for the most enterprises [18]. therefore, it is very important to know which is the most influential person and which person can continuously improve the level of safety, ensure a sustainable and proper prevention of accident at work in construction. until now, there is a lack of research on who the main stakeholders on onsite construction are and how they influence the effective safety on sme’s construction site. this research examines 92 z. turskis, s. dzitac, a. stankiuviene, r. šukys the understanding of the role of stakeholders and their importance in ensuring the management of accident prevention in projects. therefore, all stakeholders need to be identified, categorized and their concerns, interests and influence need to be analysed and prioritized using group fuzzy model. the authors consider that this work presents a useful approach to the accidents prevention in the construction, and suggest a novel integrated group fuzzy modified approach to determine the most influential person in the prevention of accidents at work in the sme’s. this model integrates the delphi technique and a modified fuzzy extension of eckenrode’s rating method. the presented model will be expanded and applied for the sustainable construction project development and the ranking of the main persons for preparation and implementation of a proper safety prevention projects. 2 fuzzy mcdm safety management problems’ solution models in construction site a successful project is considered when all risks are properly managed in all project stages. the selection of proper techniques and strategies depends on all players involved in construction project implementation. the project’s owner, project manager, project osh coordinator, contractor, subcontractor, osh coordinator, osh specialist are examples of stakeholders that are influential persons in accidents prevention during the design and organization of the project. stakeholders must constantly seek and promote their mutual interests in reducing any risks and improving their management skills. it should be emphasized that all stakeholders involved in construction project need to be aware of the risks they are facing and how they apply risk management in order to create better working conditions. safety in the sustainable construction is a stakeholders’ game against nature, environment, and with human’s life. the stakeholders and project participants play significant role in this game dealing with problems. the owner or client of the project is the person concerned and has overall responsibility for project management. it is often related to the financial part; while other organizations or individuals interested in the project have contractual relations with the project owner. however, the project owner can transfer management responsibility to other stakeholders in the project. but it is not exempted from liability for the safety and health of workers in accordance with established legislation. the project owner (client) or project manager designates one or more osh coordinators at the site who coordinate the preparation of a safety and health plan and ensures that site workers apply all the preventive measures they need to take on their safety and health site. in addition, employers or their designated osh specialist must comply with the minimum safety and health requirements applicable at construction sites and must take into account the directions of the osh coordinator. it is obvious that stakeholders, participating in project management and accident prevention, must constantly seek and promote their mutual interests in reducing any risks and improving their management skills. it is important that the project clearly defined the role and influence of stakeholders in preventing accidents. in addition, stakeholders need to be equally guided by the need to manage the threats, opportunities and uncertainties associated with developmental achievements and commit to reaching the desired goal in the relationship. there are direct and indirect reciprocal relations between the participants and the organization or project; because everyone has the ability to influence and can affect other activities. due to the variety of different stakeholders involved in the project, the risk of conflicts increases, and this can increase the risk associated with the construction project and may affect the successful completion of the project. in the implementation of the project, the co-operation plan of stakeholders can be considered a fuzzy group decision-making model for determining the most influential persons in the sustainable prevention of accidents in the construction smes 93 figure 1: the main influencing factors of accident at work an important part of overall risk management. the stakeholder input may be beneficial to the project, or, conversely, may jeopardize the success of the project. an assessment of the role of project stakeholders at the initial design stage and the ability to manage unpredictable responses throughout the construction phase are crucial to the success of the project. the solutions are not completely known, more than half of them are uncertain and insecure. the uncertainty and insecurity arise from many rules which has permanently ordained, and it depends on how many approaches are caused by mental factors, while the solution’s implementation in practice without any limitations becomes possible. while the accidents at work cannot be exactly predicted, in general, the solution becomes possible only when risk management is carried out under the current real-life conditions. the problem of measuring according to some criteria in construction is known as multi-criteria decision-making (mcdm) [75]. whereas there is no method which is the most suitable for all decision-making situations, many mcdm models and their extensions have been developed [40]. a number of authors promote their own mcdm methods and models. however, there is a lack of methods and models of evaluating 94 z. turskis, s. dzitac, a. stankiuviene, r. šukys the efficiency of personnel. mcdm methods in the construction can be used on the national, organizational and project levels. even so, the most assessment methods are trying to find out how to make the most economic construction decisions, and mostly all these decisions are only intended for economic objectives [55]. therefore, mcdm approaches are very useful to solve different management problems. a selection of safe and effective management systems in the construction is a complex mcdm task. the analytic hierarchy process (ahp) [49] is the most widely used mcdm method. a lot of studies have been made on mcdm methods and applications. usually the ahp is applied to calculate significance of criteria. šaparauskas et al. [51] assessed and prioritised the relative importance of various criteria based on the saaty’s analytic hierarchy process (ahp). a multiplicative exponential weighting (mew) was applied in order to calculate the best performed project [67]. keshavarz et al. [29] introduced a new method of evaluation based on distance from average solution (edas) for multi-criteria inventory classification problems. zavadskas et al. [71] merged two different multi-criteria decision-making (mcdm) methods and developed an original weighted aggregated sum product assessment method (waspas), while the original combination of three different mcdm methods was introduced as a new multiple objective optimisation on the basis of ratio analysis plus full multiplicative form (multimoora) method. the similar problems could be solved using different methods. zavadskas et al. [72] applied three different hybridised well-known mcdm techniques to rank and assess different technological alternatives in construction: step-wise ratio assessment technique for order of preference by similarity to ideal solution (swara-topsis), elimination and choice expressing the reality (et choix traduisant la realité (swara-electre iii elimination)), multicriteria optimization and compromise solution (visekriterijumska optimizacija i kompromisno resenje in serbian language (swara-vikor)). recently turskis and juodagalvienė [61] presented a novel peculiar model, which is based on ten different multi-criteria decision-making methods: game theory, ahp, simple additive weighting (saw), multiplicative exponential weighting (mew), topsis, edas, additive ratio assessment (aras), full multiplicative form, laplace rule, and bayes rule. a philosophy and logic are the basis of mathematical models of real-life issues. in 1657 christopher wren in his chair of astronomy at gresham college said that mathematical models could be used when dealing with uncertainty [65]. most of the construction safety management problems includes vague and uncertain values of criteria. the fuzzy set theory provides a decision framework to incorporate imprecise judgments inherent in the personnel selection process. in 1965 zadeh [69] introduced the fuzzy set theory to deal with inaccurate and uncertain data, even in situations when information is based on subjective evaluations and defined in the lexical conditions. zadeh used fuzzy set theory to a gradual transition from one class to another in the development of decision support models. it was later applied in mcdm applications [8,32]. risk management decision determined by various fuzzy criteria and used by practitioners and supervisors in the construction sector [10]. the fuzzy set theory and fuzzy extensions of multicriteria decision-making models play principal roles in the risks’ weighting, responses and choices of means to manage them [15,20]. the group decision-making processes are necessary to design and evaluate a set of different alternatives. one of the most important task is to reject those alternatives that do not meet lower bounds of the significant criteria values. for a long time, a rigorous agreement was seen as a final group’s opinion. in the most cases, a group of experts who make real-life decisions have no strict and steady opinion about the same criteria and alternatives. an agreement of the group reached when the most dominant players agree with the criteria ratings and performances of the considered alternatives. real-life problems’ modelling and solution lead the group of a fuzzy group decision-making model for determining the most influential persons in the sustainable prevention of accidents in the construction smes 95 decision-makers to situations when models are based on vague logic. in addition, most often the models based on the criteria rating in words. such type of ratings cannot be replaced by the strict (crisp) numerical values. fuzzy set theory allows decision makers to use incomplete or partially obtained information into the problem solving model [62]. a fuzzy set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging [69]. various types of membership functions are available. in this research the most commonly used triangular membership function is used [14]. a fuzzy triangular number will be denoted as (α, β, γ) (α lower value of fuzzy number, β modal value of fuzzy number, γ upper value of fuzzy number). van laarhoven and pedrycz [63] introduced the basic operations of fuzzy triangular number. many researchers investigated such problems and developed different fuzzy models to solve problems of safety and risk management in the construction. debnath et al. [12] developed a fuzzy inference model for assessing occupational risks in the construction sites. grassi et al. [21] presented a fuzzy multi-criteria model for risk evaluation in the workplaces. liu and tsai [38] used the fuzzy anp analysis to identify and assess the underlying hazards and causes. 3 an integrated delphic-eckenrode’s likert-type scale-based fuzzy rating it is very important to identify the importance of the activities of the different process managers before starting to assess the key challenges of workplace safety’s management, efficiency level of safety solutions and quality improvement. to order to achieve this, experts can use weighting methods for criteria. there are a lot of different subjective approaches for assessing weights [44]: ahp [49], anp [50] expert method [73], swara [28, 30], fare (factor relationship) [21], etc. nominal group technique delphi [13,37] is a useful tool solving complicated problems which needs expert data. this is a group decision-making process and includes idea generation, problem description, data assessment, and generation of feasible alternatives. five types of measurement scales (nominal, ratio, ordinal, interval, or fuzzy) commonly could be used to measure criteria values and importance [64]. lot of scales evaluating importance of criteria are proposed in scientific papers. for example, rating of perceived exertion borg scale [7] is a frequently used quantitative measure of perceived exertion (rated exertion on a scale of 6-20). a data is expected to fit into one or another of the absolute categories. in any case, decision-makers must apply methods appropriate to their data and to the questions they wish to answer. likert scales were introduced by likert in 1930s [35] as a tool for the measurement and assessment of attitudes. since then likert-type scales have become more popular in many fields of decision-making, including management [57]. it is the most widely used approach to scale responses in survey research. the reason for this is that the likert scale is a very simple tool to use and can be analysed effectively as interval or fuzzy scales [1]. the likert-type scale is often used interchangeably with rating scale, even though these two ones are not synonymous. in 1965 eckenrode [16] presented seminal work on criteria weights elicitation. the six methods were compared for their reliability and efficiency in collecting the judgment data: ranking [2, 3]; rating [35]; the three paired comparisons methods: partial paired comparisons i buel [9]; partial paired comparisons ii; complete paired comparisons [5]; successive comparisons churchman et al. [11,50]. eckenrode [16] found no significant differences among the techniques. the values calculated by all of methods correlates. rating is effective for personal assessment, and it’s especially effective for group decision making. it works well because it forces expert to get clarity on his 96 z. turskis, s. dzitac, a. stankiuviene, r. šukys own criteria and create a shared set of criteria. eckenrode’s rating method is selected and modified by applying basics of fuzzy sets theory in this study. the problem solution flow chart is presented in figure 2. figure 2: proposed framework of the study at the beginning of the problem solution experts determined the main participants (stakeholders) influencing accidents prevention in the construction. each participant differently affects accidents prevention processes, and therefore has different importance in these problems solving. delphi method selects the most important stakeholders of the problem under consideration. a a fuzzy group decision-making model for determining the most influential persons in the sustainable prevention of accidents in the construction smes 97 table 1: likert-type scale to determine participant’s importance (tfn = triangular fuzzy number) importance value importance tfn tfn tfn abbreviation level α γ β l 1 little 0.5 1 1.5 m 2 medium 1 1.5 2 i 3 important 1.5 2 2.5 v 4 very 2 2.5 3 e 5 extreme 2.5 3 3.5 team of experts determined a set of the most important participants (table 1). the likert-type scale (important, irrelevant, ...) made below for the significance of the participant’s importance (fig. 3). rating: the raw rating assigned by the judge to each criterion against the scale of 0 to 5 (5 most valuable or important) treated as follows (table 2 and 3): wcj = pcj/ m∑ c=1 pcj (1) where, wcj weight computed for criterion c from the rating given by judge j pcj rating given by judge j to criterion c, and wc is calculated as follows: wc = n∑ j=1 wcj/ n∑ j=1 m∑ c=1 wcj (2) figure 3: likert-type scale to determine participant’s importance the equations (1) and (2) using rules of fuzzy arithmetic are modified as follows: w̃cj = p̃cj/ m∑ c=1 p̃cj = ( pcαj/ m∑ c=1 pcγj; pcβj/ m∑ c=1 pcβj; pcγj/ m∑ c=1 pcαj ) ; (3) and 98 z. turskis, s. dzitac, a. stankiuviene, r. šukys w̃c = (wcα; wcβ; wcγ) = n∑ j=1 w̃cj/ n∑ j=1 m∑ c=1 w̃cj =   n∑ j=1 wcαj/ n∑ j=1 m∑ c=1 wcγj; n∑ j=1 wcβj/ n∑ j=1 m∑ c=1 wcβj; n∑ j=1 wcγj/ n∑ j=1 m∑ c=1 wcαj   (4) where, wjl − mink yjk, j = 1,n, k = 1, p is minimum possible value, wjα = (∏p k=1 yjk )1 p , j = 1, n, k = 1,p is the most possible value and wjβ = maxk yjk,j = 1,n, k = 1,p is the maximal possible value of j-th criterion wjγ = mink yjk, j = 1,n, k = 1,p. a defuzzification should be applied before final decisions are made. the defuzzification is a process of producing a quantifiable result in crisp logic, given fuzzy logic, and corresponding membership degrees. a common and useful defuzzification technique is a centre of gravity. this method is selected in the case study. wc = 1 3 (wcα + wcβ + wcγ) . (5) 3.1 problem solution: fuzzy group multi-criteria method in assessing the most influential person in sustainable prevention of accidents at work in order to ensure sustainable prevention of accidents at work in the construction, stakeholders should use an expert advice which will allow to select the effective and rational alternatives. to achieve the above mentioned goals, an integrated determination method of criteria significance depending on their characteristics, is developed. the problem could be solved based on the survey of experts’ data. all of experts have university degree in a civil engineering and actively work with accidents prevention. at first step there was formed a team of eighteen osh specialists, osh coordinators, project contractors and subcontractors from different construction companies which employs less 50 people. all of them were male. respondents were 26 – 55 years old (26-30 – 6; 31-35 – 4; 36-40 – 4; 41-45 – 2; 46-50 – 1; 51-55 – 1) and their work experience in the construction was 2 – 25 years (2-5 – 2; 6-10 – 5; 11-15 – 6; 16-20 – 1; 21-25 – 4). the standard seven-stage delphi procedure is applied in the case study. firstly, a facilitator explains to the participants the purpose and procedure of the problem. secondly, members of the group silently explain their opinion about the solution (criteria), with a short explanation in written not consulting or discussing their ideas with other participants. this ensures that all participants get an opportunity to make an equal contribution. thirdly, a facilitator encourages a sharing and a discussion of reasons for the choices made (criteria) by each group member to identify a common ground. fourthly, participants verbally explain in details all presented ideas which are not clear for all participants of the groups or further details about any of the ideas that colleagues have produced and which may not be clear to them. fifthly, a facilitator eliminates duplicate solutions (criteria) from the list of all solutions, and the members proceed to rank the solutions starting from the most important to the least important. sixthly, a facilitator involves a prioritizing procedure of the recorded ideas in relation to the original problem. following the voting and ranking process, a facilitator asks some questions to participants who have a different opinion about ranks from average alternatives (criteria) ranking. seventhly, a final ranking and rating of alternatives (criteria) should be done. the participants of experts’ group, based on their experience of accidents at work prevention in sme’s enterprises, generated as many ideas as they can. the ideas were evaluated against all consequences and sequels factors. then experts ranked and rated the influential persons in a fuzzy group decision-making model for determining the most influential persons in the sustainable prevention of accidents in the construction smes 99 table 2: participants’ importance (pi) lexical evaluation based on likert-type scale pi experts e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12 e13 e14 e15 e16 e17 e18 p1 e m v i m m l m m m e i e i v m m l p2 m l l i m i m v l m m v i v l i l m p3 e i v e e e v e i v e e e e v v e i p4 v v v e e e i e e i e e e e i v i e p5 i e e e e e e e v e e e e e m i v v p6 i m l l e i i v v v v i e e v e e e table 3: participants’ importance level expressed by triangular fuzzy numbers corresponding to the linguistic scale e1 e2 e15 e18 α β γ α β γ .. . α β γ .. . α β γ p1 2.5 3 3.5 1 1.5 2 . . . 2 2.5 3 . . . 0.5 1 1.5 p2 1 1.5 2 0.5 1 1.5 . . . 0.5 1 1.5 . . . 1 1.5 2 p3 2.5 3 3.5 1.5 2 2.5 . . . 2 2.5 3 . . . 1.5 2 2.5 p4 2 2.5 3 2 2.5 3 . . . 1.5 2 2.5 . . . 2.5 3 3.5 p5 1.5 2 2.5 2.5 3 3.5 . . . 1 1.5 2 . . . 2 2.5 3 p6 1.5 2 2.5 1 1.5 2 . . . 2 2.5 3 . . . 2.5 3 3.5 prevention of accidents at work. later, the resulting data were analysed and the list of the main participants such as a developer (p1), a project leader (p2), a contractor (p3), osh specialist (p4), osh coordinator (p5) and a subcontractor (p6) was made. then the experts were requested to rate the main participants according to linguistic significance scale. finally, linguistic variables and converted to fuzzy numbers and ranks of influential persons determined. fuzzy participants’ importance values defuzzified as follows (figure 4): figure 4: final assessment of the influential person in sustainable prevention of accidents at work in construction the last stage is a calculation of a relative importance index (ri ) of each considered participant (figure 5): ric = wc maxc wc (6) 100 z. turskis, s. dzitac, a. stankiuviene, r. šukys figure 5: ri -relative importance of the influential person in sustainable prevention of accidents at work in the construction 4 results key stakeholders, including the developer, the project leader, the osh specialist, the osh coordinator, the contractor, and the subcontractor, are the most influential persons who can prevent accidents at work in the smes construction sites. the results of the investigation show that these stakeholders could have a positive impact on the sustainable prevention of accidents at work for smes. the importance of criteria has influence on their values in the multi-attribute utility function. besides, it is necessary to define values of quantitative and qualitative criteria (or people). the data identifying the most influential persons in the prevention of accidents at work are shown in figure 5. according to the shown values of the relative importance ri, the main participants are divided into three groups. the most influential persons are the osh coordinator (p5), the contractor (p3) and the osh specialist (p4) whose ri rates are the highest 1.00, 0.99 and 0.98 respectively. it means that the osh coordinator, the osh specialist and the contractor play a special role in the construction process, including n the areas of preventing an accident at work. another influential person is the subcontractor (p6) at level 0.86. he represents the second most influential group of persons. at the third importance level is the client (p1) and the project leader (p2) with the lowest ri levels – 0.69 and 0.64 respectively. the above results show that leadership positions can have a positive impact on sustainable safety practices in the sme’s construction enterprises. managers such as the osh coordinator, the osh specialist and the contractor can also help to realise the targeted strategy of collectivism and consumer communication between a contractor and subcontractors. 5 conclusions the successful implementation of a sustainable modern construction project in a real environment is influenced by many complex procedural, social, economic, political and technological factors and affects various groups of people differently. therefore, the best solutions can be achieved by applying scientific methods involving a large amount of information and calculations. an important problem for each country is prevention of accidents at work in the sme’s which dominate in the construction market. introducing necessary precautions on the construction sites prior to construction begins with an aim to ensure a safe and healthy environment is one of key tasks of building project leaders (stakeholders). each participant of the construction process plays his own and specific role in the execution process of construction projects and stands against various risks. a fuzzy group decision-making model for determining the most influential persons in the sustainable prevention of accidents in the construction smes 101 their different attitudes, rights, abilities, and responsibilities in the prevention of accidents at work depends on education, work experience in management of construction projects, skills and knowledge of specific approaches to prevent accidents. besides education, an ability to cope with risk is very important factors. the developer, the project leader, the osh specialist, the osh coordinator, the contractor, and the subcontractor are the most influential persons who have the ability to an effective sustainable prevention of accidents at work in the construction. the initial data in similar group decision-making approaches are presented by experts in words. each of experts has own opinion about criteria values. the significance of expert estimations was assessed with the help of the modified fuzzy group eckenrode’s rating method. the proposed method is superior to conventional techniques because the proposed method has the capability to make group decisions in fuzzy environment. therefore, fuzzy set theory is a powerful tool to solve such problems. it is very important to know which of the project life cycle ’s participants, depending on their position in the decision hierarchy, is the most influential on direct decisions, or on the choice of project management groups which can most effectively implement projects and supervise these processes. it is also necessary to determine the most effective strategy for a sustainable development. when designing a project team, the size, complexity, duration, type of the project and specific requirements for the stakeholders should be evaluated. the research shows that the most influential persons in a sustainable prevention of accidents at work in the sme’s construction enterprises rank as follows: the osh coordinator, the contractor, the osh specialist (rates from 1 to 0.98), the subcontractor (rates as 0.86), the client, and the project leader (rates 0.69 and 0.64 respectively). the model presented in this study is suitable for identifying and selecting key players and their influence or for determining the importance of criteria in the multi-criteria utility function. the results and findings will be expanded and used by future researchers. bibliography [1] allen, d. e.; mcaleer, m., singh, a. k. 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(2015); overview and analysis of safety management studies in the construction industry, safety science, 72, 337–350, 2015. international journal of computers communications & control issn 1841-9836, 13(4), 503-520, august 2018. a simulation based analysis of an multi objective diffusive load balancing algorithm i.d. mironescu, l. vinţan ion dan mironescu* lucian blaga university of sibiu, romania *corresponding author: ion.mironescu@ulbsibiu.ro lucian vinţan lucian blaga university of sibiu, romania lucian.vintan@ulbsibiu.ro abstract: in this paper, we presented a further development of our research on developing an optimal software-hardware mapping framework. we used the petri net model of the complete hardware and software high performance computing (hpc) system running a computational fluid dynamics (cfd) application, to simulate the behaviour of the proposed diffusive two level multi-objective load-balancing algorithm. we developed an meta-heuristic algorithm for generating an approximation of the pareto-optimal set to be used as reference. the simulations showed the advantages of this algorithm over other diffusive algorithms: reduced computational and communication overhead and robustness due to low dependence on uncertain data. the algorithm also had the capacity to handle unpredictable events as a load increase due to domain refinement or loss of a computation resource due to malfunction. keywords: petri net simulation, high performance computing, load balancing, diffusive algorithm, multi-objective optimisation 1 introduction 1.1 the problem the main problem approached through our research is how we can optimally distribute the computational effort on an application among the multiple processing units (pus) of a high performance computing (hpc) machine. computational fluid dynamics (cfd) applications [8], which are the focus of our research, perform alternatively intensive computation and communication (data exchange) steps for a high number of points of a discretized computational domain. the hardware on which these applications run is a collection of computing nodes connected through a hierarchical communication network. each node has a heterogeneous collection of processing units with different processing speed communicating through a hierarchy of global and local memories. shorter computation time, lower energy consumption and an equilibrated load are all important for the application user and for the hardware operator. therefore, this represents a multi-objective optimization problem. 1.2 state of the art taking into account that the computation platform and the application have many parameters, each one with many distinct discrete values, the load-balancing problem is a np-hard one [7]. this type of application needs a large-scale distributed computing system. as the underlying numerical method is an approximation method, the computational load can be known completely only at computation time. to handle this problem’s particularities, modern solutions use metaheuristic [3], distributed [16] and dynamic load balancer [17]. the scale of the system copyright ©2018 cc by-nc 504 i.d. mironescu, l. vinţan and the heuristic behaviour of the used algorithm make modelling and simulation indispensable in the development and assessment of a load balancer algorithm. the modern tools for modelling and simulation of computing systems are using the discrete event formalism [5] [9] [22]. from the current existing methods, petri nets presents the advantage of having graphical representation and sound formal definition [2]. the graphical representation facilitates rapid development of models and expressive simulation. the formal definition allows formal verification of the resulted models. 2 the proposed high performance computing system in this research, we continue and complement our work presented in [20]. the hardware we consider in our approach is a cluster of heterogeneous nodes, implementing a high performance computer system (hpc). for the simulations, we modelled the nodes of this hpc after those of an existing system, the coka cluster [25]. a node from coka has two cpu sockets and eight pcie slots. the cpu sockets are occupied by intel xeon e5 2630v3 cpus with 8 cores. nvidia k80 gpu boards, each having two gpu processors, occupy the pcie slots. the cpus and gpus are communicating through the pcie bus. four gpu cards are sharing the same bus to a cpu socket. each node has two network interfaces – one on each socket; they connect each node to an infiniband (ib) interconnection network with a fat tree topology. we selected this model because performance and energy consumption models and measurements were presented in [4] for the same class of application we use. the software runtime allowing the transparent and distributed use of the hardware resources has a two level architecture. the internode level is implemented in the charm++ framework [14]. the charm++ runtime supports the transparent, asynchronous, message based communication among chares distributed on the system nodes. chares are the basic entities at this level and follow the actor paradigm. the heterogeneous node level is managed with the support of the starpu framework [1]. in this framework, the application is represented as a directed acyclic graph of tasks. the tasks are dispatched to the processing units by a scheduler component that uses a scheduling/load balancing algorithm. required memory transfers and dependencies between tasks are managed transparently. the application we designed implements the lattice boltzmann method (lb) cfd numerical method [8]. the computation in lb is an iterative process. in each iteration, for each point of the discrete computational space, two consecutive computational steps are performed: collision (only local data to the point is used) and propagation (data from neighbours is needed). the distributed character of the computation is implemented through domain decomposition. at the upper level (charm++) the workload is distributed / redistributed among the nodes. the global domain is divided in subdomains. each subdomain is distributed to a chare, which will manage it until the end of computations. the chare divides the subdomain in blocks, creates for each the corresponding tasks and feeds them to the lower level (starpu); this dispatches the task on pu and executes them. 3 proposed load balancer the proposed load balancing algorithm described in [20] and schematically presented in figure 1 has two levels, corresponding to the software architecture levels. at the charm++ level, the load distribution is performed by the chares. in the first iteration, one of the chares receives the domain and starts, as initiator, a negotiation process with other nodes; the negotiations are following the contract net protocol [24]. in each negotiation round the initiator selects its a simulation based analysis of an multi objective diffusive load balancing algorithm 505 neighbours from the nodes bidding to its announcement; the initiator then sends to its neighbours bigger subdomains to be further redistributed. the process continues recursively with each responder becoming initiator until no node responds to the call. the resulting neighbourhood relation will be kept until the computation ends and each node will communicate only with its neighbours implementing thus the diffusive character of the algorithm [12]. after receiving its subdomain, each node’s chare decomposes it in blocks and creates tasks for the next level. at the starpu level, the local scheduler dispatches the tasks received from the node’s chare to the queues of the node’s pus. we developed a scheduler based on the dequeue model data aware sorted decision (dmdasd) scheduler [15] with some adaptations. the scheduler is using a cost function representing a weighted sum of the three optimization objectives. the function has the following form: fobj = α · tcomputation + β ·ecomputation + γ ·load (1) where fobj is the scheduling function, tcomputation is the estimated computation time , ecomputation is the estimated energy consumption, load is the estimated queue load. the weighting coefficients must satisfy the normalisation (equilibrium) relation α+β+γ = 1. the scheduler estimates the values of the objective function for each pu; it sends the task for computing the block to the pu with the lowest value of the objective function. our variant includes a supplementary border queue, priority based dispatching and work stealing. exterior blocks are placed in the border queue based on assigned priority, but they can be transferred to another node if this becomes idle and requests ghost border zones [18]. when all pus become idle, starpu signals to the node’s chare the end of computation for the current iteration. then, the chare demands the ghost border zones from its neighbours and its frequency is lowered. if the neighbour responds with a ghost border zone, the computation continues normally; if it responds with a bigger memory zone, the load is redistributed and the neighbour frequency is raised. the receiving chare integrates the new memory zone in its subdomain. if the neighbour is not responding in a pre-set time, it is considered malfunctioning; its subdomain is recovered from backup by its neighbours and recomputed. charm++ starpu starpu starpu cpu1 gpu1cpu2 cpu1 cpu2 cpu1 gpu2gpu1 scheduler q u e u e q u e u e q u e u e cpu1 cpu2 gpu1 q u e u e border q u e u e q u e u e cpu1 cpu2 q u e u e border q u e u e q u e u e cpu1 gpu1 q u e u e border q u e u e gpu2 scheduler scheduler chare 1 chare 2 chare 3 1 2 3 4 5 node 1 node 2 node 3 figure 1: scheme of the load balancing two level architecture: 1) distribution; 2) task creation; 3) scheduling; 4) work stealing; 5) redistribution 506 i.d. mironescu, l. vinţan 4 modelling of the system 4.1 underlying mathematical model the execution of the hpc application on the given hardware architecture was modelled as a combination of atomic computation and communication steps. these steps can be performed concurrently on the available units. we adopted from literature the models for the time and the energy needed to perform these steps on a given pu [4] [21]. the computation is divided in two phases: collision and propagation. on the cpu, the unit optimally processed by each core is the 1024x1024 sites block. the time of performing the computation for this number of sites in the collision phase (tcol_cpu, in s) depends on the clock’s frequency (f, in mhz) through the formula: tcol_cpu (f) = 1380 f , (2) the energy used in this phase (ecol_cpu, in j) is related to the clock’s frequency (f, in mhz) through the following relations: ecol_cpu (f) = pcol_cpu (f) · tcol_cpu (f) (3) pcol_cpu (f) = 30 + 0.0062 ·f1+0.2, [w ] (4) the time needed to perform the propagation phase for the same block (tprop_cpu) is independent of the frequency: tprop_cpu (f) = 0.15s (5) the energy used by the cpu in the propagation phase eprop_cpu is related to the clock’s frequency (f, in mhz) through the following relations: eprop_cpu (f) = pprop_cpu (f) · tprop_cpu (6) pprop_cpu (f) = 30 + 0.055 ·f1+0.2, [w ] (7) the gpu processes in one step a block of 8192x1024 sites. the time needed by the gpu in the collision phase (tcol_gpu, in s) depends on the clock’s frequency (f, ’in mhz) by the expression: tcol_gpu (f) = { 71.4 f f < 650mhz 0.033 f ≥ 650mhz (8) the energy used in this phase (ecol_gpu, in j) is given by the following formulas: ecol_gpu (f) = pcol_gpu (f) · tcol_gpu (f) (9) pcol_gpu (f) = { 42.5 + 0.109f f < 650mhz 42.5 + 0.109f + +0.005e0.0099f f ≥ 650mhz (10) where (pcol_gpu) is the power (in w) required for the collision step on the gpu. for the propagation phase on the gpu, the time needed (tprop_gpu, in s) is: tprop_gpu (f) = { 27.2 f f < 800mhz 0.085 f ≥ 800mhz (11) the energy used for the propagation phase (eprop_gpu, in j) is calculated on the basis of the power required (pprop_gpu, in w) with the relation: eprop_gpu (f) = pprop_gpu (f) · tprop_gpu (f) (12) a simulation based analysis of an multi objective diffusive load balancing algorithm 507 pprop_gpu (f) = 42.94 + 0.096f (13) the blocks to be processed are in the main memory. time and energy for accesses to the local memory of the pus are included in the models presented above. in order to be processed by the gpus, blocks must be transferred from the main memory (host memory) to the gpu’s local memory (device memory). the results should be transferred back from device to host memory. in order to model these transfers over pcie, we used the model described in [23]; this model adapts the latency (l), overhead (o), short message gap (g), long message gap (g) model to the host-device communication over the pcie bus. the expression of the transfer time from host to device (thd) as a function of the number of transferred bytes (b) is given by the following formula: thd(b) = lhd + ohd + ghd ·b + ghd (14) with the values from [23], the expression (14) is: thd(b) = 0.009420 + 8.318392e − 008b + 0.002503, [ms] (15) the energy used for the host-device transfer (ehd(b), in j) is: ehd(b) = ppci · thd(b) (16) ppci is the power consumed on the pcie bus; ppci = 25w [26]. the transfer time from device to host (tdh) is given by the formula: tdh(b) = ldh + odh + gdh ·b + gdh (17) with the values from [23] the expression is: tdh(b) = 0.009023 + 7.924734e − 008b + 0.002674, [ms] (18) the energy used for the host-device transfer (edh(b)) is: edh(b) = ppci · tdh(b) (19) for the network, the looggp model proposed in [19] was used. this model introduces a second overhead (o) supplementary to the latency (l), overhead (o), short message gap (g), long message gap (g) parameters; o is linearly dependent on data size. the transfer time between two nodes over ib (tib) for b bytes is then: tib(b) = lib + oib + oib ·b + gib ·b + gib (20) with the values from [19], tib(b) is (in ms): tib(b) = 181.5 + 34.7 + 1.88b + 37.9b + 1.88 (21) 4.2 reference model as reference we developed a model estimating the time and the energy needed to perform one iteration for a given system configuration. a system configuration specifies: the number nn of nodes and for each node i with 0 ≤ i < nn: the number of pus of each type (ncpu (i) and ngpu (i)); the clock frequencies of each pu(fcpu (j,i) ,0 ≤ j < ncpu (i) and fgpu (k,i) ,0 ≤ k < ngpu (i)); the number of blocks allocated to each node nb(i). the time of computing a block on the cpu j of the node i (tcpu (j,i)) is: tcpu (j,i) = tcol_cpu (fcpu (j,i)) + tprop_cpu (fcpu (j,i)) (22) 508 i.d. mironescu, l. vinţan where tcol_cpu and tprop_cpu are calculated with the formulas (2), respectively (5). the energy is given by the following formula: ecpu (j,i) = ecol_cpu (fcpu (j,i)) + eprop_cpu (fcpu (j,i)) (23) where ecol_cpu and eprop_cpu are calculated with the formulas (3) , respectively (6). if the gpu is alone on the link to the host memory, the total time of computing eight blocks (blgpu = 8 ·blcpu bytes) on the gpu k of the node i (tgpu (k,i)) is: tgpu (k,i) = thd(blgpu ) + tcol_gpu (fgpu (k,i)) + tprop_gpu (fgpu (k,i)) + tdh(blgpu ) (24) where thd and tdh are the transfer times from host to device and from device to host, respectively, given by equations (14) and (17), tcol_gpu and tprop_gpu are the times for computing in collision and in propagation phase respectively, calculated with the formulas (8) and (11). the energy used to compute eight blocks on the gpu k of the node i (egpu (k,i)) is given by the following equation: egpu (k,i) = ehd + ecol_gpu (fgpu (k,i)) + eprop_gpu (fgpu (k,i)) + edh (25) where the computation energies ecol_gpu and eprop_gpu are calculated with the formulas (9) and (12) and the transport energies ehd and ehd are calculated with the formulas (16) and (19). therefore, for the maximal time needed for the computation on the gpu (tmax_gpu), we derived the formula for the case that m gpus are sharing the same link to memory: tmax_gpu = (m mod 2) · thd(blgpu ) + floor( m 2 ) · thd(2 ·blgpu ) + tcol_gpu (fgpu (k,i)) + tprop_gpu (fgpu (k,i)) + ((m− 1) mod 2) · tdh(2 ·blgpu ) + (m mod 2) · tdh(blgpu ) (26) with these considerations, if we denote with nbo(i) the number of blocks which completely occupy the units of the node i, with q the quotient of nb/nbo(i) and with r the reminder of nb/nbo(i), the time to compute nb blocks on node i is given by the following formula: t(nb, i) = (q + 1) ∗max(tcpu (j,i), tmax_gpu ),j = 1,ncpu (i) (27) the energy is given by the equation: e(nb, i) = q ∗ ( ncpu(i)∑ j=1 ecpu (j,i) + ngpu(i)∑ k=1 egpu (k,i)) + r 8∑ k=1 egpu (k,i) + r mod 8∑ j=1 ecpu (j,i) + ncpu(i)∑ j=(r mod 8)+1 ecpuidle(j,i) + ngpu(i)∑ k=r 8 +1 egpuidle(k,i) (28) where ecpuidle(j,i) is the energy consumed by the cpu j of the node i in idle mode and egpu (k,i) is the energy consumed by the gpu k of the node i in idle mode. the cpu and gpu idle energies are calculated with the equations: ecpuidle(j,i) = pcpuidle · tcpuidle(j,i) (29) egpuidle(k,i) = pgpuidle · tgpuidle(k,i) (30) a simulation based analysis of an multi objective diffusive load balancing algorithm 509 the cpu and gpu idle times are given by the formulas: tcpuidle(j,i) = { t(nb, i) − (q + 1) · tcpu (j,i) ifj ≤ r mod 8 t(nb, i) −q · tcpu (j,i) ifj > r mod 8 (31) tgpuidle(k,i) = { t(nb, i) − (q + 1) · tgpu (k,i) ifk ≤ r8 t(nb, i) −q · tgpu (k,i) ifk > r8 (32) we considered pcpuidle = 30 w and pgpuidle = 42 w, the free coefficients from equations (4) and (10). after all pus have terminated their local computation, the exchange phase takes place. in this phase, each node transfers the border zones to its neighbours. the averaged dimension (dbord) of the border zone for a subdomain with nb blocks is: dbord(nb) = 4 · √ nb · 1024 ·wbord ·dsite, bytes (33) where wbord is the border width (stencil dimension for the computation of one site) and dsite is the dimension in bytes of the data for one site. the average is computed considering the square with the equivalent number of blocks. considering a duplex channel of communication, the time for the node i to be ready for the next iteration (titer(i)) is the time needed to compute the nb blocks and the time to transfer the border zones: titer(nb, i) = t(nb, i) + tib(dbord(nb)) (34) the energy consumed for this is: eiter(nb, i) = e(nb, i) + eib(dbord(nb)) (35) an iteration is finished when all nodes have completed their transfers. the performance objectives of the whole system for a given distribution of blocks nb(i), 1 < i ≤ nn are: a) the time of computation for one iteration (titer) (eq. 36); b) the energy of computation for one iteration(eiter) (eq. 37), with the idle energy eidle given in (eq. 38); c) the load imbalance li = 1 −lb where the load balancing lb is given by (eq. 39) as defined in [27] . we used li in order to perform the multi-criterial minimisation for all the objectives, as lb is an objective to be maximized. titer = max i∈[0...nn−1] titer(nb(i), i) (36) eiter = nn−1∑ i=0 eiter(nb(i), i) + eidle (37) eidle = nn−1∑ i=0 (titer − titer(nb, i)) · ( ncpu(i)∑ j=1 pcpuidle) + ngpu(i)∑ k=1 pgpuidle) (38) lb = av g i∈[0..nn−1] titer(nb(i), i) titer (39) 510 i.d. mironescu, l. vinţan 4.3 petri net model the system was modelled using the petri net formalism. specifically, extended petri nets were used [2]. the modelling and simulation were performed using the snoopy tool [9]. hardware and software components were modelled as described in [20]. each pu is represented by a token; this token is initially located in the idle place and travels to the other places representing points of the computation reached by the pu. the process of computation is represented by two transitions corresponding to the two phases: propagation and collision. each of the computation phases introduces a time delay and an energy consumption; to model them, we implemented in the net the relations described in section 4.1. the frequency of each pu is modelled by tokens residing in the frequency place; this place is connected to the transitions representing each of the two computing phases, collision and propagation, through a read arc. in this way, transition’s delay and number of generated tokens are expressed as function of the number of frequency tokens. the process of modifying the frequency of pu for dynamic voltage and frequency scaling (dvfs) is simulated by increasing or decreasing the number of tokens in the frequency place. in the cpu model, each transition corresponds to the computation of an elementary block. the delay of the transitions corresponds to the time given by the formulas (2) for collision and (5) for propagation. for the gpu model, each transition corresponds to the computation of eight blocks representing a gpu block. the delay corresponds to the time obtained with the formulas (8) for collision and (11) for propagation. the transition for each computing phase produces tokens that accumulate in an energy place. the number of produced energy tokens depends on the number of frequency token through the formulas (3) for collision on cpu, ((6) for propagation on cpu, (9) for collision on gpu and respectively (12) for propagation on gpu. by counting the tokens in the energy place, the energy consumed by each computational process can be evaluated. the pus, global memories and network interfaces are connected through a simplified model of the pcie bus. the tokens flowing through this part of the model represent data packets. each component connected to the bus can gain the exclusive access to the bus by putting a token in a place which inhibits the transition for other units connected to the bus. the values of the coefficients from the formulas (15) and (18) were used as timing values for the transitions of the cpn model. overhead (o) is used for the transition representing the preparing of the message. this transition needs a cpu core token in the idle place. latency (l) is used as a delay in the next transition modelling the transport on the pci. the bandwidth for a large message (g) is used to calculate the passing rate through the transition. the bandwidth for short messages (g) is used for the transition enabling sending of a new data stream. the transition delay simulates the bus contention. the specific mainboard architecture is also considered; it has a separate link to each processor. in this way, only half of the gpu boards from a node shares the same host-device link. for the network simulation, the values of the parameter from the formula (21) were used for the transitions delays. the two overheads (o, o) were used in the transmission preparing transition, which also consumes a cpu core token from the idle state. the two gaps (g, g) were used in the transmission transition simulating the network bandwidth and contention. the application is composed of the scheduler net, which connects to the starpu run time net, and the load balancer, which connects to the charm++ runtime. we re-used the charm++ structure presented in [20]. details of the scheduler for one pu are presented in figure 2. the scheduling is done by computing an objective function for each pu. the next arriving task is dispatched to the pu with the minimal value for the objective function. the computation of the objective function is implemented through a net simulating the addition of values through the accumulation of corresponding number of tokens. for each pu, one such net was build. this net has an accumulator place for each optimisation criteria. the place contains the current value a simulation based analysis of an multi objective diffusive load balancing algorithm 511 of the criteria for the pu. for each task in the waiting queue and for each pu, a number of tokens are generated for energy consumption, completion time and load. the number of tokens corresponds to the estimated values of the criteria for the task execution on the pu. these tokens are transferred to an estimation place. an amount equal to the tokens for the current values is also transferred in the same place. this gives the estimation of the cumulative values for energy, completion time and load after the task execution on the given pu. all estimated values are connected to a cumulative transition. this transition functions like a minimum operator. it transfers tokens until the place with the smallest number of tokens becomes empty. the empty place activates the scheduling transition. the transition transfers the task token from the waiting queue to the corresponding pu queue. the accumulator places of the selected pu are then updated with the number of tokens corresponding to the scheduled task. if a frequency change takes place, the computational speed and the energy used by the pu are changing, meaning that the estimation for the completion time and energy for the tasks still in queue are not accurate any more. the number of tokens in energy and completion time accumulators places are increased correspondingly. in addition, if a task is transferred from queue to execution, a token is removed from the load accumulator place. by explicitly modelling the scheduling process, it is possible to model the incurring overhead. each operation corresponding to a computational process is represented through a transition. the transition is enabled only if a token for a cpu core is present in the idle core place; it consumes this token and returns it to the idle place only after the processing time passed. ready task frequency pu1 terminated task idle pu1 assigned task load pu1 time task energy task energy pu1 time pu1 energy estimation time estimation load estimation objectiv function estimation sum sum sum ponderated sum comparison assigning update update update schedule on pu1 collision propagation from pu2 dvfs figure 2: schematics of the scheduling function for a pu 5 simulation 5.1 computing system modelled we modelled a system with 16 nodes. to give relevance to the computational case, each node was configured differently from the other in respect to the available cpus and gpus. this configuration becomes relevant in the context of the partition of computing resources among different applications [10] or in the context of temporary or permanent resource unavailability. in both cases, even if the nodes are identical at start, the resources available to an application differ from a node to the other. the configuration for each node is given in table 1, where 512 i.d. mironescu, l. vinţan table 1: nodes configuration node i 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ncpu (i) 8 8 8 8 8 8 8 8 16 16 16 16 16 16 16 16 ngpu (i) 2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16 nbo(i) 23 38 53 68 83 98 113 128 31 46 61 76 91 106 121 136 ncpu(i) and ngpu(i) are the number of pus of each type and nbo(i) the number of blocks completely occupying the units of the node i. considering the optimal blocks load of each pu given in [4] and that a cpu core is needed to manage the operation of a gpu board (=two processors), nbo(i) is given by the formula: nbo(i) = ncpu (i) − ngpu(i) 2 + 8 ·ngpu (i) (40) 5.2 design space the input variables for the three objective functions of the multi-criterial optimisation problem are the partition of the lb grid in 16 subdomains and the running frequency for each pu of the system. the dimension of the design space (i.e. the number of possible hardware-software combinations) is equal to the number of possible partitions x number of possible frequency configurations. the number of possible frequency configurations is equal to ((number of cpu frequency levels) x (number of gpu frequency levels))16. even by considering only the frequencies effectives for dvfs (as shown in [4]), we have four levels for cpu and 12 levels for gpu giving a set of 4816 possible configurations. the number of possible partitions depends on the dimension of the partition unit(the smallest part to be transferred between nodes). if a node is idle, the minimal optimal load to be transferred to it will be of 16 blocks(the load of a gpu card with two gpu processor). the node 15 can host maximal 256 blocks over its optimal load of 136; this gives 256/16 = 16 units of partition. for the 16 nodes, we determined experimentally 300540195 possibilities of distributing these 16 units. this gives a total of 4816 · 300540195 ≈ 1035 possible configurations. this design space obviously cannot be explored exhaustively in order to obtain the pareto set. 5.3 reference surface to have a measure of the algorithm efficiency, we built an approximation of the pareto surface in the space of the three objectives: energy consumption, completion time and load balancing. we designated this surface as reference. given the design space dimension, we used a metaheuristic algorithm to generate a reference surface approximating the pareto set. we preferred this approach to other metaheuristics (e.g. nsga2 multi-objective genetic algorithm) because it can find points in the pareto set like the configurations using minimal energy and the configurations with minimal computing time. the pseudocode is presented in algorithm 1 to reduce calculation, we used a dictionary type structure perf_data to store the values for the time needed and energy consumed by each individual node for the computing of each of the possible subdomain’s dimension (in blocks), with each of the possible frequency configurations. the values were calculated with formulas (27) and (28) from section 4.2. the dictionary allows the retrieving for each node i and for each subdomain dimension b of the minimal computing time (tmin) and corresponding energy (etmin), of the minimal energy (emin) and corresponding time (temin) and of the corresponding frequencies configurations, freqtmin and freqemin. we obtained a set of starting points by generating a manageable set of partitions. we considered a simulation based analysis of an multi objective diffusive load balancing algorithm 513 algorithm 1 metaheuristic search 1: generate perf_data dictionary 2: generate schedules 3: for all schedules, s do . global candidate selection 4: (freqtmin, tmin,etmin,litmin) ← generate tmin configuration(s,perf_data) 5: (freqemin, temin,emin,liemin) ← generate emin configuration(s,perf_data) 6: if insert(ref_set, (s,freqtmin, tmin,etmin,litmin)) then 7: new_freq ← freqtmin 8: repeat . local search for candidates that minimise li 9: new_freq ← decreasefrequency(new_freq) 10: (tnew,enew,linew) ← generatenewliconfig(s,new_freq,perf_data) 11: until insert(ref_set, (s,new_freq,tnew,enew,linew)) 12: end if 13: if insert(ref_set, (s,freqemin, temin,emin,liemin) then 14: new_freq ← freqemin 15: repeat . local search for candidates that minimise li 16: new_freq ← increasefrequency(new_freq) 17: (tnew,enew,linew) ← generatenewliconfig(s,new_freq,perf_data 18: until insert(ref_set, (s,newc_emin,tnew,enew,linew) 19: end if 20: end for packets of 16 blocks as the partition units and we generated all possible distributions of these 16 units on the 16 nodes (300540195 schedules). the reference set ref_set is the structure that will contain the approximation of the pareto set at the algorithm completion. for the ref_set we used a dictionary of dictionaries structure. each of the three optimization objectives is key at one level. the keys are sorted, allowing efficient comparison of a given element with the set members. this is used by the insert operation testing the argument element before insertion. if the element is not dominated, it is inserted in the set and the operation returns true. if the element is dominated, it is discarded and the operation returns false. if the element dominates a set member, this member is deleted from ref_set. using the same algorithm, we built reference sets with less blocks by decreasing the dimension of the partition unit. these configurations were used as starting point for the investigation of load increase case (see section 5.5). with a similar process, reference sets were calculated for partitions on 10 to 15 nodes to be used for the node failure case (see section 5.6). 5.4 initialization case the simulation aims to test if the developed algorithm can achieve a mapping (domain decomposition plus scheduling) near to the reference set and how fast it can reach this mapping. an initialization message is sent to one of the nodes with the location and dimension of the complete domain. the node starts the contract net protocol initiation. after receiving biddings, the starting node divides the domain, considering its processing power (type and number of pus) and the processing power of the bidding nodes. the distribution phase continues recursively until no nodes are bidding. each node starts computation in parallel with redistribution. the computations start with the blocks situated at the domain core, allowing the outer blocks to be redistributed. the normal redistribution takes place automatically at the end of each iteration. a simulation was run for each node as first node. 514 i.d. mironescu, l. vinţan 5.5 load increase case the simulation goal was to show how the algorithm reacts in case of a dynamic increase in load; this situation arises when the calculation imposes the mesh refining in order to increase computation’s accuracy. refining is done by halving the distance between the grid sites (one step of refining). the number of sites in each direction is doubled. the total number of block sites is multiplied by four. we started with a configuration that is on the reduced reference set and increased four times the load of some selected nodes. in the cpn model, the load increase was simulated by marking the places corresponding to the selected nodes with four times more tokens than in the reference mapping. the effect of a single affected node was investigated at first. each node was loaded with four times its complete load indicated in table 1. the cases of multiple nodes were then investigated. as reference we used the reference set computed for the maximal number of sites. as starting configuration we used a configuration in the reference set for a lower number of sites. 5.6 node failure case in this case, we investigated how the load balancing will react to a node failure. we used the reference set computed with the same number of sites for a system with fewer nodes. in the simulation case, a scheduled transition injects at a random time a token, blocking the functioning of one or more nodes. the first goal is to find if the system could recover after losing one or more nodes; the second one is to see in how much time the mapping reaches the reference surface for the corresponding number of nodes. 6 results and discussion 6.1 initialisation case in the first step, the chare on the init node distributed the data to eight other nodes with the shortest path to it. each of these nodes already have some fixed neighbours (the node from which it received the task and some of the nodes that accepted the task.) the recursive process ends when no other nodes respond or a node has a subdomain that it can certain compute. figure 3 presents one example of the distribution process for a 4x4 grid. in each step, the announcing nodes are in blue, the nodes that have finished distribution and started computing are in green and the nodes that cannot distribute further are in red. the arrows are presenting established contracts. figure 3: initial distribution of load on available nodes after all nodes start calculation, blocks are also exchanged in the communication step to balance the load. this happens until the computation time is approximately equal on all nodes. after the computations were started, the simulations were run until a stable mapping (meaning no full block exchange) took place in the communication step. the simulations were run for each node as starting node. a mean of 40 iterations are needed for reaching a grid distribution that is near to the reference set. a simulation based analysis of an multi objective diffusive load balancing algorithm 515 figure 4 presents one of the reached mapping as a red point, compared with the reference set in blue. we considered the time and the used energy as more important than the load; so, we selected the values α = 0.40; β = 0.40; γ = 0.2 for the weighting coefficients of the objective function given by formula (1). therefore we expected that the resulted mapping will be nearer to the points representing also the weighted combination of the three objectives on the reference set. the cause of not reaching the configuration on the reference set is that the redistribution stopped before reaching the optimal schedule. the faster node has finished earlier and requested the border block. the request arrived only after the slower node started to process the last blocks so they were not transferred anymore. figure 4: reference set (blue) with stable configuration (red) reached after redistribution 6.2 load increase case figure 5 presents time (a) and iteration number (b) needed to reach the proximity of the pareto front for a single affected node. 600 500 400 300 200 100 t im e [ s ] 0 2 4 6 8 10 12 14 node 69 114 159 204 249 294 339 384 93 138 183 228 273 318 363 408 (a) time needed to reach the near optimal solution. 80 50 40 n o . o f it e r a t io n s 0 2 4 6 8 10 12 14 node 69 114 159 204 249 294 339 384 93 138 183 228 273 318 363 408 60 90 70 (b) number of iterations needed to reach optimal solution. figure 5: handling of the load increase on one node. point label indicates number of blocks to be transferred the numbering of the nodes are in the order from the slowest node (node 0 only two gpus available) to the fastest node (node 15 all possible gpus=16 available). in each case, the affected node was considered to have the minimal optimal load (nbo(i) from table 1) before the refinement was started. in figure 5, each point is labelled with the number of blocks to be 516 i.d. mironescu, l. vinţan transferred which is approximated to 3*optimal load. point annotation is the number of supplementary blocks to be redistributed (3*optimal load, see table 1). the break in the ascending trend at the nodes 3 and 11 can be explained by some architectural details. starting with the two nodes, there is a better distribution of gpus on the cpu-gpu interconnect network. in this way, the transfer time between the host and devices is smaller for the same amount of data. the time needed to reach a stable near optimal mapping, in the proximity of the reference set, for the refinement started in two(a) to six(f) nodes is presented in figure 6. number of blocks on the x-axis is the total number of blocks to be redistributed from the affected node. all possible distinct combinations of two, three, four, five and respectively six nodes that can be formed from the 16 nodes, were simulated. as the configurations are limited, there are more combination 500 400 300 200 100 t im e [ s ] number of blocks 200 300 400 500 600 700 800 (a) refining on two nodes . 500 400 300 200 600 t im e [ s ] number of blocks 800400 600 1000 (b) refining on three nodes 500 400 300 800 600 t im e [ s ] number of blocks 400 600 800 1000 1200 1400 700 (c) refining on four nodes 500 400 900 800 600t im e [ s ] number of blocks 600 800 1000 1200 1400 1600 700 1000 1800 (d) refining on five nodes 500 900 800 600 t im e [ s ] number of blocks 700 1000 1100 1200 800 1000 1200 1400 1600 1800 2000 (e) refining on six nodes figure 6: handling of the load increase on 2 to 6 nodes of nodes with the same total number of blocks. depending of the node combination, we have different timings for the same total point number (grid sites). this is the result of their hardware configuration and of the number of neighbours from each of them. the time is increasing with the number of nodes as they share the same communication bandwidth. 6.3 node failure case figure 7 shows the time until the stable, near optimal mapping is reached depending on the number of points to be redistributed. the points label indicates the number of defective nodes. three classes of nodes failure behaviour are observed: • slow recovering nodes: 0, 1, 3, 13 and 15; • intermediate recovering nodes: 8, 9, 2, 10, 4, 12, 6, 14 and 7; • fast recovering nodes: 11 and 5. the three classes are given by the variation in the number and the hardware configuration of the node neighbours. failure in nodes with many neighbours with better architectural characteristics a simulation based analysis of an multi objective diffusive load balancing algorithm 517 (such as nodes 11 and 5) is recovered faster than failure in nodes that have less and/or suboptimal neighbours (such as nodes 0 and 5). 250 300 200 100 t im e [ s ] 0 2 4 6 8 10 12 14 number of blocks 11 15 5 20 9 1 13 80 7 3 60 40 150 100 140120 figure 7: time for the redistribution of the load of a failed node. the label of each point indicates the node that has failed. 7 conclusion we have investigated through simulation the behaviour of the proposed algorithm for the multi-objective optimization of the workload distribution on a heterogeneous computing system. the simulations have shown that the algorithm can perform as expected. the algorithm succeeds: • to distribute the workload by passing it from neighbour to neighbour. the maximal number of iterations needed is equal to the maximal distance between the starting node and the other nodes in the tree of neighbourhood that is forming; • to reach a stable state in the proximity of the reference set of a tri-objective optimisation problem. the factors influencing the iteration number and the time needed for reaching a stable configuration were also revealed by this investigation. they are the hardware configuration and the position in the node neighbourhood hierarchy/topology. from the hardware perspective, the number and distribution of gpus on the available slots is determined. the optimum is for a moderate number of gpus uniformly distributed on the slots, to fully exploitation of the communication hierarchy. from the topological perspective, nodes with fewer neighbours recover slower from the increase of load as they cannot efficiently evacuate the load; • to handle well sudden load increases caused by mesh refining or node failure. the behaviour observed through the simulation reveals that our algorithm has some advantages over other algorithms: • it does not need an explicit, distinct imbalance check. this is implicitly detected by the slower node that receives a border zone request from is faster neighbours. the load balancing is also resolved with the transfer that is already taking place only with an increased payload. therefore, our developed algorithm has a speed and resource advantage over all algorithms that make an explicit balance check and have an explicit load balancing phase; • it can detect and solve imbalances resulted from special situations, like mesh refining or node failure without explicit checks. the detection is implicitly codified in the exchanges protocol between neighbours. after the supplementary load is dispatched to some queues, the load balancing is taking place like in a normal situation. the consideration of special casesrefining and failure is an original aspect. this is important because these cases are unexpected, but not rare for the application investigated; 518 i.d. mironescu, l. vinţan • it uses less uncertain data. it starts the transfer only when the imbalance is certain and directs the data only to the nodes that can handle the supplementary load. so, our proposed algorithm is more robust than algorithms basing their decision exclusive on estimations. the second level scheduler uses estimates in its decision, but at this level predictability is higher. at first level, the decision of transferring the load is not based on estimations but triggered by a real imbalance; • it follows three objectives for optimisation, instead of classical solution that considers only time and energy [6] [13]. analysis of computing load is also important in the context of system cooling and of a datacenter reliability. the heuristic algorithm used to approximate the pareto surface is also original and has showed potential at least for producing reference sets and for off-line scheduling. it must be also further analysed. more investigation has to be done on the model of a system with a much higher number of nodes in the magnitude order of a real hpc configuration. this will be done using a simulator that can handle this number of nodes and a metaheuristic automatic design space tool (fadse) [11] to build the reference set. conflict of interest the authors declare no conflict of interest. bibliography [1] augonnet, c.; samuel, t.; namyst, r.; wacrenier, p.-a. 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[25] [online]. available: http://www.fe.infn.it/coka/doku.php?id=start, accesed on 26 february 2018 [26] [online]. available: https://pcisig.com/specifications/pciexpress/base2/, accesed on 26 february 2018 [27] [online]. available: https://pop-coe.eu/node/69, accesed on 26 february 2018 international journal of computers communications & control issn 1841-9836, 12(2):238-253, april 2017. webira comparative analysis of weight balancing method a. krylovas, n. kosareva, e.k. zavadskas aleksandras krylovas, natalja kosareva*, edmundas kazimieras zavadskas vilnius gediminas technical university sauletekio al. 11, lt-10223 vilnius, lithuania aleksandras.krylovas@vgtu.lt, natalja.kosareva@vgtu.lt, edmundas.zavadskas@vgtu.lt *corresponding author: natalja.kosareva@vgtu.lt abstract: the attributes weight establishing problem is one of the most important mcdm tasks. this study summarizes weight determining approach which is called webira (weight balancing indicator ranks accordance). this method requires to solve complicated optimization problem and its application is possible by carrying out non trivial calculations. the efficiency of webira and other mcdm methods – saw (simple additive weighting) and emdcw (entropy method for determining the criterion weight) compared for 4 different data normalization methods. the results of the study revealed that more sophisticated webira method is significantly efficient for all considered numbers of alternatives. efficiency of all methods decreases with increasing number of alternatives, but webira is still applicable, while application of other methods is impossible as the number of alternatives is greater than 11. webira is the least affected by the data normalization, while emdcw is the most affected method. keywords: webira, saw, emdcw, multi-attribute decision making (madm), entropy, kemira. 1 introduction from the large diversity of mcdm methods some are very simple to use methods, and the other – complex, requiring more effort and computing resources. this article analyses the attributes weighting task, which is solved by different methods. one of the main and well known multiple criteria decision making (mcdm) methods is calculation of weighted averages of the the performance values of alternatives evaluated in terms of attributes (criteria): s(j) (w,r) = n∑ i=1 wir (j) i , j = 1, 2, . . . ,m, (1) here w = (w1,w2, . . . ,wn), 0 6 wi 6 1, n∑ i=1 wi = 1 is vector of weights, n – number of attributes, r =   r (1) 1 r (1) 2 . . . r (1) n r (2) 1 r (2) 2 . . . r (2) n . . . . . . . . . . . . r (m) 1 r (m) 2 . . . r (m) n   – matrix of alternatives j ∈ {1, 2, . . . ,m} estimates, which elements 0 6 r(j)i 6 1 are obtained by certain measurements or expert assessments applying different calculation procedures, which are usually referred to as normalization methods (see [1, 2]). for example, in this article maximum method (max), sums method (sum), minmax method (minmax) and vector normalization (vctr) will be used. the mentioned mcdm method is known as wsm (weighted sum model). it was noticed by churchman in 1954 [3] that "course of action that maximizes the expected total weighted copyright © 2006-2017 by ccc publications webira comparative analysis of weight balancing method 239 efficiency (effectiveness) is optimum". this method proposed for solving mcda problems in [4]. zionts and wallenius in 1976 [5] emphasysed that the basis of this method is the intuitive understanding that the "overall utility function is assumed to be implicitly a linear function, and more generally a concave function of the objective functions". triantaphyllou in 2000 [6] noticed that in the maximization case, the best alternative is the one that yields the maximum total performance value (1). though the method is rather old it is still relevant and frequently used, the articles with its applications appearing in solid academic journals nowadays. the article [7] proposes a modified version of the weighted sum model that takes into account decision-maker preferences and provides a possibility of higher interactivity in the selection of the most suitable alternative. a mathematical model for a dynamic weighted sum method (dwsm) is presented in [8]. in hwang and yoon [9] this method was called simple additive weighting (saw). the name stuck, and today one can find a number of articles in which this method is actually called as saw. triantaphyllou in [10] drew attention to aggregating of benefit and cost criteria in four different mcda methods. in [11] simple additive weighting is proposed as a metamodel for other mcda methods. generalized saw under fuzzy environment and the relative preference relation proposed in [12] to easily and quickly solve fmcdm problems. a new comprehensive overview of multiple attribute decision-making techniques and their applications is presented in [13]. in the review [14] of mcdm literature it was noted that the analytic hierarchy process (ahp) in the individual tools and hybrid mcdm in the integrated methods were ranked as the first and second methods in use. maccrimmon in [15] noticed that 1) as the number of relevant attributes and alternatives increases, the ability of the decisionmaker to handle the problem decreases; 2) using a combination of mcdm methods frequently may be more feasible than using any one method separately. a study [16] presents a hybrid mcdm method combining saw, techniques for order preference by similarity to an ideal solution (topsis) and grey relational analysis (gra) techniques. the ranking results show that multiple mcdm methods are more trustworthy than those generated by a single mcdm method. a new combinative distance-based assessment (codas) method to handle mcdm problems is proposed in [17]. to improve the accuracy of weighted sum and weighted product models (wsm and wpm), in [18] the weighted aggregated sum product assessment (waspas) method was applied as an aggregation operator on wsm and wpm. in the paper [19], an extended version waspas-ivif method is proposed which can be applied in uncertain decision making environment. in [20] authors revealed that mcdm methods work fairly well in estimating the number of clusters in a data set. another aspect of the mcdm methods – weight coefficients w selection, which can be accomplished by using a priori information (expert’s estimates, subject specific knowledge) or a posteriori information of matrix r itself. the latter are sometimes called objective weight determining methods [21]. in the articles [22–25] a new weight determining approach which is applicable to the tasks when matrix r is composed of two or more components is proposed. all mcdm methods using this methodology can be assigned to the group of weights balancing methods, which hereinafter we call webira (weight balancing indicator ranks accordance). the main idea of this approach is maximizing compatibility of the two (or more) sets of attributes which are treated as independent. optimization task is being solved and criteria weights are sought throughout the weight balancing procedure. webira so far have not been compared with other mcdm methods, thus relevant is the question when it is appropriate to use. webira is suitable for a very important economic benefit carrying tasks, it has both scientific and practical meaning. for example, developers constructing sustainable products and technologies must pay attention to three main components such as economic development, social development and environmental protection. mcdm problems of sustainability could be solved by applying 240 a. krylovas, n. kosareva, e.k. zavadskas webira for 3 sets of attributes – economic, social and environmental components of sustainable development. a major criticism of madm is that different techniques may yield different results when applied to the same problem. it does not exist multiple criteria evaluation method which is "best under any circumstances". therefore the comparative analysis of various mcdm methods determining which method is best for a particular case is relevant and important task. the performance of eight methods: elimination and choice expressing reality (electre), topsis, multiplicative exponential weighting (mew), saw, and four versions of ahp investigated in simulation experiment in [26]. simulation parameters are the number of alternatives, criteria and their distribution. in general, all ahp versions behave similarly and closer to saw than the other methods. electre is the least similar to saw. the following performance order of methods was established: saw and mew (best), followed by topsis, ahps and electre. the comparative analysis of mcda methods saw and copras (complex proportional assessment) describing their common and diverse characteristics is proposed in [27]. the paper [28] presents an empirical application and comparison of six different mcdm approaches (between them wsm, ahp, topsis, copras) for the purpose of assessing sustainable housing affordability. topsis, saw, and mixed (rank average) for decision-making as well as ahp and entropy for obtaining the weights of attributes have been compared in [29]. mixed method as compared to topsis and saw is the preferred technique, moreover, ahp is more acceptable than entropy for weighting. the comprehensive study [30] carried out the comparative analysis among well-known and widely-used methods wpm, wsm, topsis, ahp, promethee, electre, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. the outcomes of this research highlight that more sophisticated methods, such as topsis and preference ranking organization method for enrichment evaluation (promethee), better predict the optimum design alternative. webira method requires to solve complicated optimization task and therefore it relates to executing non trivial computer calculations. naturally, the question arises as to when it would appear reasonable to apply webira, and when – the less sophisticated approaches. in this article monte carlo-type experiments are performed and webira is compared with two simple objective weight determining methods: avrg – the simple arithmetic average, i. e. the weighted sum with equal weights and the entropy method for determining the criterion weight (emdcw) described in [21]. the shannon entropy method [31] is one of the most famous approach for determining the objective attribute weights. entropy measures the uncertainty associated with a random variable, i.e. the expected value of the information transmitted to the decision maker. the authors of the paper [21] have combined the best features of the entropy method and the cilos (the criterion impact loss) approach to obtain a new method – integrated determination of objective criteria weights, or (idocriw). in [32] three methods have been used for estimating criteria weights: entropy, cilos and idocriw, while for the selection of priority well-known and widely used mcdm methods saw, topsis and copras have been used in mcdm analysis of operating of rotor systems with tilting pad bearings. another problem the article dealt with – the comparative analysis of efficiency of some data normalization methods. a state-of-the-art survey on the influence of normalization techniques in ranking is proposed in [33]. thirty-one methods were identified, classified and evaluated for use in materials selection problems. review of normalization methods used in construction engineering and management, and their applications presented in [34]. the article is organized as follows. in chapter 2 the algorithm of solving the optimization problem and the case study of its application is proposed. in chapter 3 random matrices generating scheme is described. in chapter 4 the transformation formulas for matrix of estimates proposed. chapter 5 describes the process of numerical experiments and methods of efficiency webira comparative analysis of weight balancing method 241 comparison. chapter 6 describes the statistical analysis of the results of numerical experiments, summarizes and proposes recommendations for application of various mcdm methods and normalizing procedures. 2 algorithm of webira method suppose, that matrix r is composed of two components r = (p |q), p = ( p (j) i ) m×np , q = ( q (j) i ) m×nq , np + nq = n. two weighted sums are calculated s (j) p = np∑ i=1 wpip (j) i , s (j) q = nq∑ i=1 wqiq (j) i , j = 1, 2, . . . ,m. (2) coefficients wp = ( wp1,wp2, . . . ,wpnp ) , wq = ( wq1,wq2, . . . ,wqnq ) satisfy monotonicity conditions: 1 > wp1 > wp2 > · · · > wpnp > 0, 1 > wq1 > wq2 > · · · > wqnq > 0. (3) inequalities (3) are set from expert estimates, when k experts line up attributes pie, qie according to their importance: pi1e � pi2e � ···� pinpe, qi1e � ···� qi2e � qinqe, e = 1, 2, . . . ,k. (4) when we have a priori information about experts evaluations (4), inequalities (3) could be obtained by different methods. in the article [22] kemeny median has been adapted for this purpose and the name kemira (kemeny median indicator ranks accordance) proposed for the method. in the articles [23–25] inequalities (3) were set by calculating entropy values or by application of voting theory methods. as the inequalities (3) indicating the weight preferences are established, all of the mentioned methods can be assigned to the objective weight determining, because there is not need of further information to set the weights of the attributes. we consider the task when inequalities (3) already established and the weights wp , wq determining task is being solved. the task is formulated as minimization of a certain distance or measure: s (wp ,wq) = min wp ,wq √√√√ 1 m m∑ j=1 ( s (j) p −s (j) q )2 , (5) where the weights wp , wq are satisfying the inequalities (3). so, all the mentioned mcdm methods [22–25] can be assigned to the group of weight balancing methods, which we call webira. in this article we analyze only the benefit type attributes, i.e. whose higher value is better. when optimization task (5),(3) is already solved, the ranks can be assigned to the alternatives j ∈ {1, 2, . . . ,m} depending on the size of the weighted sums s(j)p and s (j) q . suppose that j p 1 , jp2 , . . ., j p m, j q 1 , j q 2 , . . ., j q m are such numbers of alternatives that the weighted sums (2) are satisfying the inequalities: s jp1 p > s jp2 p > · · · > s jpm p , s j q 1 q > s j q 2 q > · · · > s j q m q . (6) denote a set of the best k alternatives according to the first np attributes apk = {jp1 ,jp2 , . . . ,jpk }, according to the last nq attributes – a q k = {j q 1 ,j q 2 , . . . ,j q k } and their intersection a = apk ∩a q k . 242 a. krylovas, n. kosareva, e.k. zavadskas the meaning of the sets apk and a q k is selecting the best k alternatives according to the attributes p and q respectively while the meaning of the set a – the best alternatives according to the both attributes. the purpose of webira method is to balance weights so that a number of elements |a| of the set a would be not less than the certain number. so, it is required to find a sufficient number of the best alternatives according to both attributes p and q. in the articles [22–25] the minimizing tasks (5),(3) have been solved together with max |a| under various parameter k values of the sets apk and a q k . in the current article we limit ourselves to the case k = 1 and require a 6= ∅, i. e. we will search the only one the best alternative according to the both attributes p and q. this additional condition can be formulated as follows: s (jp0 ) p = max j=1,2,...,m {s(j)p }, s (jq0 ) q = max j=1,2,...,m {s(j)q }, j p 0 = j q 0 . (7) algorithm of solving the problem (5),(3),(7) is as follows. 1. by random re-selection among weights wp , wq satisfying conditions (3) the weights satisfying an additional condition (7) are being searched. 2. if the weights w 0p , w 0 q were not found after iter0 iterations, algorithm is finishing work and it is concluded that the weights can not be determined. 3. if the weights w 0p , w 0 q are set, the loss value (5) is fixed as s 0, directions ∆wp , ∆wq are selected at random and the new weights w 1p = w 0 p + h∆wp , w 1 q = w 0 q + h∆wq calculated, here h is the predetermined value. 4. the correction of weights w 1p , w 1 q is performed as follows w → w̃ : w̃ = (w̃1, w̃2, . . . , w̃n), w̃i =   1, if wi > 1, 0, if wi 6 0, wi,else, then the weights are normalized wi = w̃i n∑ i=1 w̃i . 5. checking whether the new weights w 1p , w 1 q satisfy the condition (7). if they satisfy and the number of iterations does not exceed the established limit iter1 the algorithm moves to the step 7. 6. if the number of iterations exceeds the established limit iter1, algorithm finishes its work with the determined weights w 0p , w 0 q. 7. the loss value (5) is calculated s1 ( w 1p ,w 1 q ) . if s1 6 s0, we substitute w 0p = w 1 p , w 0q = w 1 q, s 0 = s1 and then the new weights w 1p , w 1 q are calculated with the same directions ∆wp , ∆wq. go to the weight correction procedure to the step 4. 8. if s1 > s0, the directions ∆wp , ∆wq are changed randomly, i. e. go to the step 3 of algorithm. webira comparative analysis of weight balancing method 243 note. algorithm parameters iter0, iter1 and h are set empirically and their values were set respectively to 225, 200 and 0.05. example. provide the case study of the described algorithm application. set parameter values: m = 6, nx = 5, ny = 4, iter0 = 225, iter1 = 100, h = 0.05, p =   0.8333 1.0000 0.6667 0.8000 0.3333 0.6667 0.2000 0.6667 1.0000 1.0000 0.8333 0.8000 1.0000 0.2000 0.1667 1.0000 0.8000 0.8333 0.6000 1.0000 0.1667 0.4000 0.3333 1.0000 0.3333 0.3333 0.2000 1.0000 0.6000 0.8333   , q =   1.0000 1.0000 0.3333 0.4000 0.3333 0.6000 0.6667 0.8000 0.3333 0.4000 0.3333 0.6000 0.3333 1.0000 0.3333 1.0000 0.5000 0.6000 0.5000 0.8000 0.1667 0.6000 1.0000 1.0000   . (8) the initial point: (n = 1) wp = (0.5556, 0.1111, 0.1111, 0.1111, 0.1111), wq = (0.3636, 0.3636, 0.1818, 0.0910), sp = (0.7741, 0.6889, 0.7037, 0.9148, 0.3222, 0.4778), sq = (0.8242, 0.5333, 0.3818, 0.6363, 0.5636, 0.5515). the ranks of the alternatives: rangsp = (4, 1, 3, 2, 6, 5), rangsq = (1, 4, 5, 6, 2, 3). we see, that condition (7) is not satisfied, i. e. two criteria p and q differently determine the best alternative. let’s skip some of the checked weights wp , wq and provide only some of the calculations results: (n = 119) wp = (0.3478, 0.3478, 0.1739, 0.0870, 0.0435), wq = (0.2500, 0.2500, 0.2500, 0.2500). sp = (0.8377, 0.5478, 0.7667, 0.8667, 0.3565, 0.4478), sq = (0.6833, 0.6000, 0.4166, 0.6666, 0.6000, 0.6917). the ranks of the alternatives are: rangsp = (4, 1, 3, 2, 6, 5), rangsq = (6, 1, 4, 5, 2, 3) and again the condition (7) is not satisfied. the initial point was found when (n = 169), wp = (0.5000, 0.5000, 0.0000, 0.0000, 0.0000), wq = (0.3636, 0.3636, 0.1818, 0.0910), sp = (0.9166, 0.4334, 0.8167, 0.9000, 0.2833, 0.2667), sq = (0.8242, 0.5333, 0.3818, 0.6363, 0.5636, 0.5515). the ranks of the alternatives: rangsp = (1, 4, 3, 2, 5, 6), rangsq = (1, 4, 5, 6, 2, 3) already responding the condition (7). the initial loss calculated by the formula (5) is s0 = 0.660953. randomly determined directions: ∆p = (0.30, −0.30, −0.22, −0.13, −0.05), ∆q = (−0.41, 0.31, −0.20, −0.08). the function (5) is increasing in this direction, therefore, the opposite direction was chosen and the weights were set as follows: w 1p = w 0 p − h · ∆p , w 1q = w 0q − h · ∆q. then the weights were adjusted (step 4 of algorithm). the minimum value s1 = 0.653260 of the function (5) in the direction ∆p , ∆q was obtained with the weights wp = (0.4900, 0.4900, 0.0111, 0.0067, 0.0022), wq = (0.3772, 0.3416, 0.1882, 0.0930). the second iteration of the algorithm (random change in direction) is as follows. the direction vectors are: ∆p = (0.35, 0.24, −0.22, −0.16, 0.04), ∆q = (−0.40, 0.39, −0.20, 0.00). in this case the function declines in the direction w 1p = w 0 p − h · ∆p , w 1q = w 0q − h · ∆q and the minimum value s1 = 0.648413 of the function (5) is obtained when the weights are wp = (0.4813, 0.4813, 0.0224, 0.0147, 0.0003), wq = (0.3932, 0.3188, 0.1962, 0.0918). we present some further iterations results: iter = 17, s1 = 0.575767, wp = (0.4384, 0.4321, 0.0779, 0.0482, 0.0033), wq = (0.3942, 0.3456, 0.1468, 0.1134). 244 a. krylovas, n. kosareva, e.k. zavadskas iter = 29, s1 = 0.508748, wq = (0.4119, 0.4057, 0.0832, 0.0804, 0.0188), wq = (0.4094, 0.4035, 0.0935, 0.0935). iter = 65, s1 = 0.450759, wp = (0.3447, 0.3447, 0.1488, 0.1488, 0.0130), wq = (0.4454, 0.4380, 0.0583, 0.0583). iter = 93, s1 = 0.388068, wp = (0.2453, 0.2453, 0.2453, 0.2453, 0.0187), wq = (0.3954, 0.3954, 0.1227, 0.0866). notice that iter1 = 100 iterations were accomplished, but we failed to reduce the value s1 = 0.388068 of the loss function, i. e. in all randomly selected directions the function (5) increased. given the weights obtained in the 93-th iteration the values of criteria are sp = (0.8158, 0.6402, 0.6982, 0.8119, 0.4723, 0.5389), sq = (0.8662, 0.5201, 0.3828, 0.6546, 0.5655, 0.5124). the ranks of the alternatives: rangsp = (1, 4, 3, 2, 6, 5), rangsq = (1, 4, 5, 2, 6, 3). please note that for the algorithm realization it was necessary to apply a relatively complicated computer program which was realized in c++. however, one can easily check the calculations and this may be done in each step of the algorithm independently of other steps. 3 random matrices estimates generation the elements x(j)i , y (j) i of the estimates matrices x = ( x (j) i ) m×nx and y = ( y (j) i ) m×ny are the integers simulating the scores of the expert estimates x(j)i ∈ {1, 2, . . . ,bxi }, y (j) i ∈ {1, 2, . . . ,byi } of the alternatives i ∈ {1, 2, . . . ,m}. each row of matrices x and y corresponds to one alternative x =   x (1) 1 x (1) 2 · · · x (1) nx x (2) 1 x (2) 2 · · · x (2) nx · · · · · · · · · · · · x (m) 1 x (m) 2 · · · x (m) nx   ,y =   y (1) 1 y (1) 2 · · · y (1) ny y (2) 1 y (2) 2 · · · y (2) ny · · · · · · · · · · · · y (m) 1 y (m) 2 · · · y (m) ny   . (9) the columns of matrices (9) arranged in descending order of attributes priorities. the first line of matrix is generated with preset probabilities pxik , p y ik : p{x(1)i = bxi −k} = pxik , k = 0, 1, 2, . . . ,bxi − 1, p{y(1)i = byi −k} = pyik, k = 0, 1, 2, . . . ,byi − 1. (10) antecedent probabilities pxik , p y ik chosen in such way, that the first alternative should have on average higher estimates. other alternatives estimates generated with the equal probabilities: p{x(j)i = bxi −k} = 1bxi , k = 0, 1, . . . ,b x i − 1, i = 1, 2, . . . ,nx, p{y(j)i = byi −k} = 1byi , k = 0, 1, . . . ,b y i − 1, i = 1, 2, . . . ,ny, j = 2, 3, . . . ,m. (11) therefore, the second and all other alternatives are treated as a kind of noise making heavy recognition of the first – the best alternative. the more alternatives we have, the more difficult is the task of identification. the experiments were carried out with the following parameter values: bxi = 6, i = 1, 2, 3, 4, 5, b y i = 6, i = 1, 2, 3, 4. probabilities of the first alternative estimates: p{x(1)1,2 = 6} = p{x (1) 1,2 = 5} = 0.5; p{x (1) 1,2 = l} = 0., l = 1, 2, 3, 4. webira comparative analysis of weight balancing method 245 p{x(1)3,4,5 = 6} = 0.; p{x (1) 3,4,5 = l} = 0.25, l = 2, 3, 4, 5; p{x (1) 3,4,5 = 1} = 0. probabilities of other alternatives estimates are equal: p{x(j)i = l} = 1 6 , j = 2, 3, . . . ,m, i = 1, 2, 3, 4, 5, l = 1, 2, 3, 4, 5, 6. similarly selected estimates probabilities of the first and other alternatives according to y : p{y(1)1,2 = 6} = p{y (1) 1,2 = 5} = 0.5; p{y (1) 1,2 = l} = 0., l = 1, 2, 3, 4. p{y(1)3,4 = 6} = 0.; p{y (1) 3,4 = l} = 0.25, l = 2, 3, 4, 5; p{y (1) 3,4 = 1} = 0. p{y(j)i = l} = 1 6 , j = 2, 3, . . . ,m, i = 1, 2, 3, 4, l = 1, 2, 3, 4, 5, 6. the example of generated matrix with the best first alternative: (x|y ) =   5 6 3 4 2 6 5 4 4 4 4 4 2 3 1 2 5 3 3 4 5 1 3 2 2 5 5 3 3 6 6 5 6 4 4 4 4 3 4 3 5 6 5 6 4 2 1 5 5 2 4 6 3 1 1 2 3 2 2 2 5 6 4 4 2 2 3 1 6 2 5 6   . (12) 4 transformations of estimates matrix recall that randomly generated matrices x, y elements x(j)i , y (j) i are the integers while input data of webira method – matrices p , q elements acquire real values in the interval [0, 1]. they are the normalized values of matrices x, y elements. in this article four transformation (normalization) methods (x,y ) → (p,q) are applied: max method: p(j)i = x (j) i max j∈{1,2,...,m} x (j) i ,q (j) i = y (j) i max j∈{1,2,...,m} y (j) i , sum method: p(j)i = x (j) i m∑ j=1 x (j) i ,q (j) i = y (j) i m∑ j=1 y (j) i , minmax method: p(j)i = x (j) i − min j∈{1,2,...,m} x (j) i max j∈{1,2,...,m} x (j) i − min j∈{1,2,...,m} x (j) i , q (j) i = y (j) i − min j∈{1,2,...,m} y (j) i max j∈{1,2,...,m} y (j) i − min j∈{1,2,...,m} y (j) i , vector normalization: p(j)i = x (j) i√ m∑ j=1 ( x (j) i )2 ,q(j)i = y (j) i√ m∑ j=1 ( y (j) i )2 . (13) notice that formulas (13) applicable when all attributes are the benefit type of optimization direction, (i. e., the higher value is better, see, for example, [1]). another case – cost type criteria, (i.e., the lower value is better) will be not discussed in this article. 246 a. krylovas, n. kosareva, e.k. zavadskas suppose that matrices p , q and their concatenation – matrix r = (p |q) = ( r (j) i ) m×(nx+ny) , i. e. r(j)i = { p (j) i , if i 6 nx, q (j) i−nx, if nx + 1 6 i 6 nx + ny are obtained from randomly generated matrices x, y by one of the four methods (13). webira method will be compared with two mcdm methods – simple arithmetic average: avrg: s(j) = 1 nx + ny ( nx∑ i=1 p (j) i + ny∑ i=1 q (j) i ) (14) and emdcw (entropy method for determining the criterion weight, see [21]: emdcw: s(j) = nx+ny∑ i=1 wir (j) i , wi = 1−ei nx+ny− nx+ny∑ i=1 ei , ei = − 1m m∑ j=1 r̃ (j) i · ln ( r̃ (j) i ) , r̃ (j) i = r (j) i m∑ j=1 r (j) i (15) next, we provide the case study of formulas (14) and (15) application. the result of matrix (12) transformation using max method:  1.0000 1.0000 0.5000 0.6667 0.4000 1.0000 0.8333 0.6667 0.6667 0.8000 0.6667 0.6667 0.3333 0.6000 0.1667 0.3333 0.8333 0.5000 0.6000 0.6667 0.8333 0.1667 0.6000 0.3333 0.3333 0.8333 0.8333 0.6000 0.5000 1.0000 1.0000 1.0000 1.0000 0.6667 0.6667 0.6667 0.8000 0.5000 0.6667 0.5000 1.0000 1.0000 0.8333 1.0000 0.6667 0.4000 0.1667 0.8333 0.8333 0.4000 0.6667 1.0000 0.5000 0.1667 0.2000 0.3333 0.5000 0.3333 0.4000 0.3333 0.8333 1.0000 0.6667 0.8000 0.3333 0.3333 0.5000 0.2000 1.0000 0.3333 0.8333 1.0000   weighting sums (15) of 8 alternatives obtained by emdcw method are as follows: 0.7747; 0.4847; 0.5110; 0.8141; 0.7650; 0.5522; 0.4435; 0.5803. weighting sums calculated by avrg method (14) are: 0.1470; 0.1069; 0.1135; 0.1550; 0.1521; 0.1084; 0.1004; 0.1164. thus, both methods assign the fourth as the best alternative and we treat it as a mistake, because the best is considered the first alternative. webira method was applied with initial weights values wx = (0.3314, 0.1953, 0.1581, 0.1581, 0.1571), wy = (1.0000, 0.0000, 0.0000, 0.0000). weighted averages (2) calculated with these weights are s j x = (0.7739, 0.6476, 0.5813, 0.7698, 0.7043, 0.4914, 0.3259, 0.4933), s j y = (1.0000, 0.1667, 0.3333, 1.0000, 1.0000, 0.6667, 0.3333, 1.0000). the initial loss (5) in this case is s0 = 0.8785. after 134 iterations webira method allowed to reduce this value to s1 = 0.7147 and the following weights were found: wx = (0.4623, 0.1373, 0.1373, 0.1373, 0.1258), wy = (0.5901, 0.4099, 0.0000, 0.0000). webira comparative analysis of weight balancing method 247 weighted averages are: s j x = (0.8100, 0.6741, 0.5816, 0.7464, 0.7245, 0.4869, 0.3029, 0.5551), s j y = (0.9316, 0.2349, 0.3333, 0.8633, 0.9316, 0.8032, 0.5382, 0.7267). ranks of alternatives according to the x: 1, 4, 5, 2, 3, 8, 6, 7 and according to the y : 1, 5, 4, 6, 8, 7, 3, 2. so, webira method set as the best the first alternative and we treat this as the right decision. notice that all three methods set the same three best alternatives: 1, 4 and 5. submit normalized matrices, calculated by other methods. minmax method:   1.0000 1.0000 0.2500 0.6000 0.2500 1.0000 0.7500 0.3333 0.6000 0.7500 0.6000 0.5000 0.2000 0.5000 0.0000 0.0000 0.6667 0.4000 0.5000 0.6000 0.7500 0.0000 0.5000 0.2000 0.0000 0.6667 0.8000 0.5000 0.4000 1.0000 1.0000 1.0000 1.0000 0.5000 0.3333 0.6000 0.7500 0.4000 0.5000 0.4000 1.0000 1.0000 0.7500 1.0000 0.6000 0.2500 0.0000 0.7500 0.8000 0.2500 0.6000 1.0000 0.0000 0.0000 0.0000 0.2000 0.2500 0.2000 0.2500 0.2000 0.7500 1.0000 0.6000 0.7500 0.2000 0.0000 0.4000 0.0000 1.0000 0.0000 0.6667 1.0000   . emdcw and avrg methods determined as the best the fifth alternative, webira – the first alternative. matrix transformated by sum method:   0.1923 0.2400 0.0937 0.1538 0.0869 0.1818 0.1612 0.1052 0.1290 0.1538 0.1600 0.1250 0.0769 0.1304 0.0303 0.0645 0.1315 0.0967 0.1154 0.1600 0.1562 0.0384 0.1304 0.0606 0.0645 0.1315 0.1612 0.1154 0.1200 0.1875 0.2307 0.2173 0.1818 0.1290 0.1052 0.1290 0.1538 0.1200 0.1250 0.1153 0.2173 0.1818 0.1612 0.1578 0.1290 0.0769 0.0400 0.1562 0.1923 0.0869 0.1212 0.1935 0.0789 0.0322 0.0384 0.0800 0.0937 0.0769 0.0869 0.0606 0.1612 0.1578 0.1290 0.1538 0.0800 0.0625 0.1153 0.0434 0.1818 0.0645 0.1315 0.1935   . in this case, as in another – vector normalization method the obtained results coincide with the max method, i. e. emdcw and avrg methods set as the best the fourth alternative, while webira – the first. matrix transformated by vector normalization:  0.5103 0.6155 0.2535 0.3922 0.2222 0.4615 0.4240 0.2917 0.3442 0.4082 0.4103 0.3380 0.1961 0.3333 0.0769 0.1696 0.3646 0.2581 0.3061 0.4103 0.4225 0.0980 0.3333 0.1538 0.1696 0.3646 0.4303 0.3061 0.3077 0.5070 0.5883 0.5556 0.4615 0.3392 0.2917 0.3442 0.4082 0.3077 0.3380 0.2941 0.5556 0.4615 0.4240 0.4375 0.3442 0.2041 0.1025 0.4225 0.4902 0.2222 0.3076 0.5089 0.2187 0.0860 0.1020 0.2051 0.2535 0.1961 0.2222 0.1538 0.4240 0.4375 0.3442 0.4082 0.2051 0.1690 0.2941 0.1111 0.4615 0.1696 0.3646 0.5163   . 5 numerical experiments numerical experiments were conducted as follows. random matrices x, y generated in such a way that on average more often the best alternative is the first. matrices p , q are calculated by 248 a. krylovas, n. kosareva, e.k. zavadskas four normalization methods and the best alternative is determined by three methods emdcw, avrg and webira. when the best is the first alternative the result of the experiment is marked with (+) and recorded to the table. if the best is any other (not the first) alternative (−) is recorded to the table. it is possible that webira can not set the best alternative. we then record (n). notice, that in the cases of emdcw and avrg methods such experimental result is impossible. in the table 1 the results of 5 experiments are presented. methods emdcw, avrg, webira are denoted respectively as (e), (a), (w). table 1: fragment of experimental results. max method minmax method sum method vector normalization nr. (e) (a) (w) (e) (a) (w) (e) (a) (w) (e) (a) (w) 1 + + + + + + + + + + + 2 + + + + + + + + n + + n 3 + + + + + 4 + + + + + + + + 5 n n n n after a series of experiments, we calculate the number of pluses (+) denoted as p in each of the 12 columns of the table 1, the number of minuses (-) denoted as m and undetected cases (n). webira method peculiarity compared to avrg and emdcw – possible non zero values of parameter n. it means that webira quite often eliminates cases when it can not detect the best alternative. consider the following indicator to compare methods performance: en = p−m p + m + n . (16) indicator en shows reliability of the correspondent method. our purpose is the detection of significantly different average values of en in the groups. 100 series of monte carlo experiments were carried out by 100 in each series. the common number of experiments was 10000. random matrices estimates generation procedure is described in chapter 3. the number of alternatives varied m = 3, 4, ..., 50. table 2 presents the average values of en dependence on the number of alternatives m, mcdm and data normalization methods. conclusions and future research webira method allows quite effectively separate the cases when it is not possible to select the best alternative. otherwise, when the method is applicable its efficiency is significantly higher compared to the two selected simple methods: avrg and emdcw. this is true for all four matrices normalization methods: max, minmax, sum and vector normalization. efficiency decreases with increasing number of alternatives, but it is still applicable for webira method, while application of avrg and emdcw is impossible as the number of alternatives is greater than 11, as their efficiency became negative value. in the figure 1 graphs average values of efficiency indicator en depending on the mcdm method are presented. for the max normalization and m = 1, 2, . . . , 30 the average efficiency of webira is significantly higher than efficiency of emdcw and avrg methods. for minmax, sum and vector normalizations all 3 mcdm methods: emdcw, avrg and webira mutually significantly differ comparing the average values of efficiency indicator en when m = 1, 2, . . . , 15, while webira is significantly efficient than emdcw and avrg for all considered numbers of alternatives. webira comparative analysis of weight balancing method 249 table 2: average values of en dependence on the number of alternatives m, mcdm and data normalization methods. max method minmax method sum method vctr method m (e) (a) (w) (e) (a) (w) (e) (a) (w) (e) (a) (w) 3 0.7224 0.7268 0.8837 0.4736 0.6086 0.8398 0.6944 0.7344 0.8820 0.7018 0.7286 0.8820 4 0.5762 0.5896 0.8122 0.3298 0.4868 0.7781 0.5358 0.5934 0.8006 0.5468 0.5890 0.8039 5 0.4760 0.4860 0.7721 0.2746 0.4082 0.7479 0.4288 0.4842 0.7591 0.4438 0.4770 0.7599 6 0.3870 0.4010 0.7354 0.2220 0.3358 0.7059 0.3426 0.3958 0.7075 0.3552 0.3918 0.7055 7 0.2968 0.3308 0.6694 0.1580 0.2758 0.6539 0.2556 0.3180 0.6449 0.2682 0.3200 0.6472 8 0.2060 0.2236 0.6318 0.0824 0.1744 0.6074 0.1606 0.2166 0.6003 0.1782 0.2110 0.6016 9 0.1520 0.1816 0.5999 0.0626 0.1470 0.5797 0.1134 0.1732 0.5637 0.1272 0.1724 0.5662 10 0.0998 0.1200 0.5644 0.0300 0.0884 0.5430 0.0574 0.1064 0.5314 0.0718 0.1082 0.5300 11 0.0404 0.0628 0.5275 -0.0412 0.0414 0.5149 0.0108 0.0494 0.4906 0.0168 0.0500 0.4905 13 -0.0646 -0.0374 0.4745 -0.0992 -0.0556 0.4653 -0.0776 -0.0490 0.4282 -0.0724 -0.0556 0.4274 15 -0.1356 -0.1032 0.4349 -0.1602 -0.1016 0.4293 -0.1572 -0.1208 0.3943 -0.1534 -0.1220 0.3903 20 -0.2824 -0.2512 0.3427 -0.2988 -0.2424 0.3378 -0.2930 -0.2786 0.2990 -0.2864 -0.2826 0.2989 30 -0.4726 -0.4368 0.2639 -0.4840 -0.4272 0.2606 -0.4818 -0.4660 0.2233 -0.4764 -0.4678 0.2183 50 -0.6592 -0.6250 0.1658 -0.6572 -0.6172 0.1725 -0.6604 -0.6530 0.1482 -0.6616 -0.6524 0.1488 figure 1: the dependence of en average values on the number of alternatives and mcdm method for max, minmax, sum and vector normalization respectively. in the figure 2 graphs of indicator en average values depending on the data normalization method are presented. webira is the least affected by the data normalization method, while emdcw is the most dependent on normalization. one way anova was performed to compare 250 a. krylovas, n. kosareva, e.k. zavadskas figure 2: the dependence of en average values on the number of alternatives and normalization method for webira (w), emdcw (e) and avrg (a) method respectively. average values of indicator en for the fixed m values at significance level 0.05. in the case of emdcw (e) method it was established that en average values for minmax data normalization are significantly lower than the average values obtained by max, sum and vector methods when m 6 11, and that max normalization is significantly more efficient than minmax and sum methods. hence, minmax normalization reduces the efficiency of the emdcw method. in the case of avrg (a) method average value of en obtained by minmax data normalization are significantly lower than en averages received by the three other data normalization methods. in the case of webira (w) method at a low number of alternatives max data normalization significantly increases the average efficiency of the method. random matrices generated in the article are simulating repeated expert evaluations of the same alternatives and depend on a priori probabilities (10)–(11). these probabilities can be such that the recognition of the best alternative will be very easy or almost impossible. it is obvious that in the first case application of webira method is irrational, and in the second – any method will not determine the best alternative. in these cases the alternatives separation requires further research. this article is limited to the case when the task of the best alternative recognition is of medium difficulty. references describe more matrices transformation methods such as max, minmax, sum and vector normalization. their efficiency could depend on the expert evaluation scales. in this article all attributes were assessed in 6-point scale, i. e. x(j)i ,y (j) i ∈{1, 2, 3, 4, 5, 6}. there are many other simple alternative mcdm methods similar to avrg and emdcw. their efficiency comparison using indicator en proposed in the article is a separate interesting webira comparative analysis of weight balancing method 251 task. it is appropriate to look for the most efficient methods and investigate the cases when it makes sense to apply the method webira. webira method is extended and applicable in the case of three or more 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(2015); analysis of normalization methods influencing results: a review to honour professor friedel peldschus on the occasion of his 75th birthday, procedia engineering, 122:2–10. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(4), 590-607, august 2019. a factored similarity model with trust and social influence for top-n recommendation x.f. zhang, x.l. chen, d.w. seng, x.j. fang xuefeng zhang school of computer science and technology, hangzhou dianzi university hangzhou 310018, china xiuli chen school of computer science and technology, hangzhou dianzi university hangzhou 310018, china dewen seng* school of computer science and technology, hangzhou dianzi university hangzhou 310018, china *corresponding author: sengdw@163.com xujian fang school of computer science and technology, hangzhou dianzi university hangzhou 310018, china abstract: many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional collaborative filtering (cf) recommendation algorithms. however, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. to make up for the defect, this paper designs a novel top-n recommendation model based on trust and social influence, in which the most influential users are determined by the improved structural holes (ish) method. specifically, the features in matrix factorization (mf) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. in addition, a trust measurement model was created to quantify the strength of implicit trust. the experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy. keywords: recommendation system, matrix factorization, trust, social influence, deep learning, top-n recommendation. 1 introduction the dawn of the big data era has brought abundant information resources. however, human beings are sometimes provided with too much information, that is, faced with information overload. to solve the problem, many portals and e-commerce systems have adopted recommendation systems to push the desired information to users. these systems learn users’ preferences from their historical behaviors, aiming to recommend the items meeting their interests and needs. cf is one of the most popular and successful personalized recommendation algorithms. the algorithm discovers the preferences of users by mining the data on their historical behaviors, divides the users into different groups based on their preferences, and recommends similar items to users in each group. the recommendation accuracy of the cf hinges on two issues: inferring user preferences from historical data, and identifying similar users or items. the traditional cf algorithm faces two major challenges, namely, data sparsity and cold start. many methods have been developed to cope with these challenges. some scholars have introduced various auxiliary copyright ©2019 cc by-nc a factored similarity model with trust and social influence for top-n recommendation 591 data to improve the cf, including membership [7,26], trust [2,8,14,23,27,28] and other social information [5,21,25]. with the development of social networking, it is increasingly convenient to acquire the data on social relationship. against this background, a series of trust-based approaches have been proposed and applied in various domains, making up an integral part of recommendation algorithms. trust relationship, closely related to social information, is a hotspot in the research of recommendation systems. in social networks, users always prefer the items recommended by those they trust. therefore, trust-aware recommendation can greatly improve the recommendation effect of sparse users (i.e. data sparsity), leading to better user experience and enhanced loyalty. based on trust propagation mechanism, jamali and ester [10] designed a mf-based model for recommendation in social rating networks (socialmf). the potential feature vectors of target users were described as the weighted average of their trustee’s feature vectors, which significantly reduced the recommendation error, especially for cold-start users. guo et al. [8] extended the svd++ algorithm [14] into the trustsvd algorithm, which achieves excellent recommendation effect through considering the explicit trust relationship of the target user. the above studies fully demonstrate the promotion effect of the trust relationship on recommendation accuracy. however, these trust relationships are single and incomplete. the observations on real-world datasets like filmtrust, epinions and ciao reveal two problems of trust relationship: (1) the trust relationship may be implicit due to privacy concerns, resulting in the sparsity of trust information; (2) the trust value is usually binary, i.e. each trusted friend has the same impact on the target user, which goes against the reality. therefore, the existing trust information can not fully mine the implicit user preference information in social networks. it is necessary to accurately measure the trust relationship, and examine its role in item recommendation. the user behavior in the social network is also greatly affected by social influence. the behavior or opinion of a person is easily affected by influential users, who seem to be authoritative. to study user behavior, it is both necessary and difficult to analyze the influence of different users out of the massive information in the social network. the social influence analysis [18] refers to the evaluation of the impact of each user against his/her information or social behavior by a certain standard, with the aim to identify those with significant impact in his/her group or the social network. in fact, the analysis of social influence has attracted much attention from scholars engaging in sociology, management, information science, and economics [1,12,13,15,22]. for example, kitsak et al. [13] empirically investigated social networks and email networks, and divided network nodes by k-kernel decomposition to different levels from the edge to the core, revealing that the most influential nodes are not necessarily the most connected nodes, i.e. those with high betweenness centrality, but those with high k-shells. nevertheless, in the field of recommendation system, trust plays an important role, and social influence is neglected by most researchers, although it is also a key factor affecting the behavior of social network users. our research is mainly motivated by two issues. on the one hand, the existing trust-based recommendation methods usually use the binary trust relationship of social networks directly to improve the recommendation quality, seldom considering the difference and potential impact of trust intensity among users, and also can not fully tap the implicit user preference information in social networks, which can not achieve good recommendation effect in the case of sparse trust data. on the other hand, the existing recommendation systems rarely utilize social influence, despite its significant impact on user behavior in the social network. to overcome these two defects, this paper fully excavates the implicit social relationships among users in the social network, and propose a top-n item recommendation algorithm which fusion of trust and social influence, called 592 x.f. zhang, x.l. chen, d.w. seng, x.j. fang factored user and item similarity model with trust and social influence based on deep learning (fstid). the architecture of our recommendation algorithm is shown in fig. 1. finally, the proposed algorithm was proved more accurate and effective than other recommendation methods on three datasets (epinions, ciao and filmtrust). figure 1: the architecture our recommendation algorithm there are four main contributions of this research: (1) a novel trust measurement model was conducted to quantify the implicit trust in social network. the trust value covers three aspects, namely, user interaction, item rating and user preference. the combination of the two kinds of relationship between trust and similarity are compact, which are effective to solve the extreme data sparsity of recommendation system. in addition, our model has played a significant positive role correspond to accurate positioning neighbor users. (2) the ish method was proposed to pinpoint the key nodes, i.e. the most influential users, in the social network. (3) considering the influence of user trust and social influence on recommendation, an innovative top-n recommendation model with the incorporation of user and item similarity, trust and social influence is proposed. besides, deep autoencoder (dae) technology was employed to optimize the initial values of the latent features for mf. (4) many top-n recommendation algorithms were compared through experiments on three realworld datasets. the comparison shows that our approach can alleviate the sparse problem of rating data to a certain extent and obtain more reliable recommendations. the remainder of this paper is organized as follows: section 2 details the construction of our algorithm; section 3 compares our algorithm with other top-n recommendation algorithms through experiments; section 4 puts forward the conclusions and foretells the future research. a factored similarity model with trust and social influence for top-n recommendation 593 2 model construction this section introduces the overall framework of the fstid and details the four key stages of the method in seven steps. before presenting the fstid, a new trust measurement model was first established, the ish method was employed to identify the most influential nodes in the social network, and the dae was adopted to extract user and item feature vectors. let u = {u1,u2, · · · ,um} be the set of m users, i = {i1, i2, · · · , in} be the set of n items, r = [ru,i]m×n be the binary matrix of the item ratings by the user (ru,i = 1 means user u has purchased or rated item i; otherwise, user u has not purchased or rated item), g = (v,e) be the graph of social network (v is the set all nodes (users) and e is the set of all edges), and < i,j > be the edge from node i to node j, that is, the trust relationship between users i and j. 2.1 overall framework figure 2: the overall framework of the fstid as shown in fig. 2, the procedure of the fstid algorithm roughly contains four key stages: the calculation of trust value, the identification of influential users, feature extraction, and topn recommendation. firstly, the trust value is computed based on the initial trust value and preference degree of each user, forming the trust network. then, the ish is adopted to identify the key nodes in the social network, and allocate them into the set of influential users. after that, the features of users and items are extracted by the dae. finally, the cf is applied to predict the item ratings, and provide personalized recommendation. the four stages are implemented in seven steps: (1) calculation of trust value step 1: the trust value of each user is initialized based on his/her interaction information with others. the term interaction is defined as two users rating on the same item i. step 2: considering the mutual influence between trust value and interactions, and the users’ tendency to trust those who have interacted successfully on their favorite items, the author measured the user preference over different items by preference degree. step 3: based on user preference over item i, different weights are assigned to items in each interaction, then generate the final trust value, and the trust network is formed after filtering 594 x.f. zhang, x.l. chen, d.w. seng, x.j. fang out the weak trust relationship. (2) identification of influential users step 4: the constraint value of each node is computed according to the topology of its neighbors in the directed trust network. taking the constraint value as the indicator, the importance of each node is evaluated and recorded as "influence". finally, the most influential users are determined and allocated to the same set. (3) feature extraction steps 5 and 6: by using dae to learn users’ behaviors without supervision so that the high-dimensional, sparse users’ behaviors can be compressed into low-dimensional, dense users and items feature vectors. compared to initial features, these features are no longer sparse, but also more representative. (4) top-n recommendation step 7: combining the user similarities and item similarities, we introduce trust users and influential users to predict rating and offer personalized recommendation for users 2.2 calculation of trust value (steps 1 3) the trust value can be expressed as t = [tu,v]m×m, where tu,v is a nonzero element indicating the existence of social relationship between users u and v. in the real world, the trust relationship of most online social networks, e.g. facebook, epinions and flixster, is usually represented in binary form (0 or 1), which is not exactly the same as the trust relationship of users. this subsection sets up a novel trust measurement model, regardless if the trust value obeys explicit distribution. first, two assumptions were put forward: (1) an interaction is defined as two users perform a rating on the same item; (2) the trust value comes from cumulative experience of subjective individuals. under these assumptions, the trust value can be initialized as: init(u,v) = min(|iu ⋂ iv|,du) du (1) where iu ⋂ iv is the number of interactions between users u and v, i.e. the number of items rated by both users; du = √ |iu| is an adjustable threshold specifying the minimum number of interactions between the two users that fully trust each other. the trust relationship is not a stable factor, which along with influence of interaction experience will be changed. a successful interaction would increase the trust value and an unsuccessful interaction act oppositely. if both users u and v have rated item i, then the interaction is successful if the two ratings are both above or below the average rating of user himself:{ successful, (ru,i − ū) ∗ (rv,i − v̄) > 0 unsuccessful, (ru,i − ū) ∗ (rv,i − v̄) < 0 (2) where ū and v̄ are the average rating of users u and v on all items, respectively. the trust value differs with the preference degree of each user. let pre(u,i) be the preference degree of user u over item i, ui be the set of users that have rated item i, and o be a user in the set ui. then, the similarity between users u and o can be calculated using the pearson correlation coefficient (pcc): sim(u,o) =  1 2 + ∑ i∈iu∩io(ru,i − ū)(ro,i − ō) 2 ∗ √∑ i∈iu∩io(ru,i − ū) 2 √∑ i∈iu∩io(ro,i − ō) 2  ∗ |iu ∩ io| |iu| (3) pre(u,i) = ∑ o∈ui sim(u,o) |ui| (4) a factored similarity model with trust and social influence for top-n recommendation 595 based on the user preferences in successful or unsuccessful interactions, different weights were assigned to different items. on this basis, the final trust value t(u,v) can be obtained as: t(u,v) = ∑ i∈successful pre(u,i) − ∑ i∈unsuccessful pre(u,i)∑ i∈successful pre(u,i) + ∑ i∈unsuccessful pre(u,i) ∗ init(u,v) (5) 2.3 identification of influential nodes (step 4) in the social network, each user collects and disseminates information as a node. some of them are more influential than others. these key nodes are similar to structural hole (sh) occupants in the competitive group theory proposed by burt [3]. the sh occupants play a key role in information exchange between the local groups and enjoys more opportunities and options than other group members. therefore, the author improved the sh method to integrate the impacts of inand outdegrees of neighbor nodes in the directed trust network. the ish is illustrated with an instance in fig. 3 and table 1. here, the in-degree is defined as the number of nodes pointing towards a node, and the out-degree has the opposite meaning. the sh is generally evaluated by two indices: the betweenness centrality and the index given by burt himself. the former refers to freeman’s betweenness centrality [6] for the overall network and its extended form. if a node is on the shortest path of many pairs of other nodes, then the node has a high betweenness centrality, and a high probability to be a sh occupant. the latter involves four aspects: effective size, efficiency, constraint and hierarchy. constraints refer to the node’s ability to use sh in its own networks, which is more important than the other three aspects. the calculation formula of constraint is as follows, describing the degree to which a node in a network is directly or indirectly connected to other nodes: c(i) = ∑ j∈γ(i)  p(j,i) + ∑ q∈γ(i) p(j,q) ∗p(q,i)  2 , i 6= q 6= j (6) where γ(i) refers to the nodes directly connected to node i in the undirected graph; p(j,i) is the proportion of the energy that node i devotes to maintain its relationship with node j to the total energy of node i. since the trust network is a directed graph, γ(i) was replaced with t−i to describe the set of nodes pointing towards node i. the value of p(j,i) can be calculated by: p(j,i) = zji∑ j∈t−i zji (7) where zji = 1 if < i,j >∈ e (otherwise, zji = 0); p(j,i) + ∑ q∈γ(i) p(j,q)∗p(q,i) represents the time and energy that node i invests directly or indirectly to maintain its connection with node j, when node j is the only node pointing to node i, the item reaches a maximum value of 1; this item reaches a minimum when there is no bridge node between node i and node j. based on the c(i) value obtained from eq. (6), the number and closeness of neighbors can be evaluated in a comprehensive manner. the c(i) value is negatively correlated with the number of nodes pointing towards the target node, and positively with the closeness between these nodes. if closely distributed, the nodes are less likely to acquire new information. on the contrary, nodes with small constraint coefficients can promote information propagation. table 1 lists the constraint values of all nodes in fig.3 (a), respectively calculated by the sh and the ish. during the calculation, any node with no node pointing towards it was neglected. as shown in the table, nodes u4 and u7 have the same constraint value, i.e. the same influence. the information flow theory [20] holds that the information on the internet often propagates to the opinion leader, before spreading to the others distributed in a wide range. in this sense, 596 x.f. zhang, x.l. chen, d.w. seng, x.j. fang figure 3: two toy examples of trust network topological graph table 1: constraint values of all nodes in fig. 3(a) obtained by the sh and the ish c(i) u1 u4 u7 u8 sh 0.6667 0.3333 0.3333 1 ish 0.5555 0.3333 0.0625 1 a node connected to multiple opinion leaders is likely to become an sh occupant. taking the in-degree of node as the evaluation standard for opinion leader, nodes u1,u4 and u7 with the highest in-degrees must be opinion leaders, and play a greater role than the other nodes (e.g. u8 and u9) in information dissemination. among them, node u7 are connected to two opinion leaders at the same time, indicating that it is more conducive to information dissemination than nodes u1 and u4. in other words, node u7 is more likely to occupy an sh than the other two nodes. the sh results in table 1 are obviously unreasonable, as many important nodes are not found. this is because the traditional sh only measures the relationship between a node and its nearest neighbor, without considering that between the node and its two-hop neighbors. for convenience, it is assumed that a key node j can boost the influence of the node i it points towards. the nodes pointing away from node j were also taken into account. as shown in fig.3(b), the influence of node u6 is reduced due to the fact that the only node pointing towards it u1 has a node pointing away from it u5. therefore, the ish integrating the impacts of inand out-degrees of neighbor nodes can be expressed as: c(i) = ∑ j∈t−i |t +j | |t +j | + |t − j | ∗  p(j,i) + ∑ q∈t−i p(j,q) ∗p(q,i)  2 , i 6= q 6= j (8) where t +j is the set of nodes pointing away from node j. the validity of the ish was tested with the instance in fig.3(a). the test results are recorded in table 1. it can be seen that node u7, with the lowest constraint value, is the most influential node. both u1 and u4 had an in-degree of 3, yet u8, a node pointing towards u1, has a connection with u5, which reduces the influence of u1 on u8. thus, the influence of u4 is higher than that of u1. a factored similarity model with trust and social influence for top-n recommendation 597 after the constraint values of all nodes were obtained by eq. (8), the top k% of users with the smallest constraint values were selected as global influential users iu. 2.4 feature extraction (step 5 and step 6) figure 4: the flowchart of feature extraction. considering the sheer number of users and items, the user vectors and item vectors were respectively mapped by the mf to d-dimensional joint potential factor spaces pd×m and xd×n. explain intuitively that matrix p represents the preferences of m users on d topics, and x represents the d topics which are concerned by users behind n items, then the rating matrix r can be approximately expressed as the product of two matrices: r = ptx. most mf-based recommendations initialize p and x randomly and iteratively output local optimal solutions, which depends heavily on the initial values [4]. this paper attempts to make more accurate recommendations by mining out accurate feature vectors from the implicit features of the mf. to this end, the dae, a deep neural network, was adopted to compress high-dimensional, sparse user behaviors into low-dimensional, dense feature vectors of users and items. this approach provides good initial values to the network through unsupervised layer-by-layer pre-training, and adjust the network weights by supervised fine-tuning training, making it possible to extract key information from the data and form features [24]. the primary goal is to design an item-based (user-based) dae to map the observed ui(iu) into a low-dimensional potential (implicit) space, and then reconstruct ui(iu) in the output space to learn advanced, abstract features. the entire workflow is illustrated in fig.4. let s = [i1, ...,ii, ...,im] be the input. the specific steps of the dae to extract user feature matrix p are given below: step 1: given an input s, let wi ∈ rd×m(i ∈ [1,l− 1]) be a weight matrix, bi ∈ rd be the bias vectors and σ(x) = 1 1+ex be the sigmoid formula. then, the d-dimensional implicit feature hi can be expressed as: h1 = σ(w1s + b1) (9) hi = σ(wihi−1 + bi) (10) step 2: given the weight matrix wl ∈ rm×d between hidden and output layers, and bias 598 x.f. zhang, x.l. chen, d.w. seng, x.j. fang vector bl ∈ rm, the dae can reconstruct the original data ŝ from the hidden layer hl−1 by: ŝ = σ(wlhl−1 + bl) (11) step 3: the pretraining program of the dae intends to minimize the objective function by adjusting weight matrices w and bias vectors b: l = 1 2m m∑ i=1 ∣∣∣ŝi −si∣∣∣2 + λ 2 |w1|2 + . . . + λ 2 |wl| 2 (12) where ŝi and si represent the i-dimensional vectors of ŝ and s, respectively; |·|2 is the squared l2 norm of vectors or matrices, i.e. the sum of squared dimensional values; the first factor in objective function denotes the error which is employed to minimize the error of reconstructed data ŝi and original data si; the other factors are regular terms used to prevent overfitting. step 4: update weight matrix w in each iteration by: w = w − l× ∂l ∂w (13) where l refers to learning rate. according to the above formula, bias vectors b update process act in the same way. the d-dimensional feature vector of the users and the items were obtained by repeating the above steps to continuously optimize the model until the end of training. 2.5 top-n recommendation agent (step 7) our approach fstid, is based on the model proposed by guo called factored user and item similarity model with social trust (fst) [9], which predict user u’s rating on item i through four parts: (1) item bias bi; (2) the similarity between user u and another user v who has rated item i: ptv qu, where pv,qu are the implicit feature vectors of users v and u, respectively; (3) the similarity between item i and another item j which has been rated by user u: xtj yi, where xj and yi are the implicit feature vectors of items j and i, respectively; (4) influence of any user u’s trusted user w on targeted item i: ptwyi. the rating prediction formula can be expressed as: r̂u,i = s|ui−u|−β ∑ v∈ui−u ptv qu + (1 −s)|iu−i| −α ∑ j∈iu−i xtj yi + |tu| −z ∑ w∈tu ptwyi + bi (14) where ui−u refers to the set of users having rated item i except user u; iu−i refers to the set of items having been rated by user u except item i; tu is the set of users trusted by user u; s ∈ [0, 1] is the relative importance of user similarity; α,β,z are parameters related to the number of rated items, similar users and trusted users, respectively. taking β for instance, β = 1 refers to the mean user similarity, item i will get a high rating only if all users in ui−u are similar to user u; when β = 0, this item calculates the sum of the similarities between user u and user in ui−u, which means that item i will get a high rating even if only a few users are similar to user u. in conclusion, the probability of obtaining a high predictive rating decreases with the increase of parameter β. the above analysis shows the critical importance of the influential users’ ratings on item i. in addition, guo et al. [9] confirmed that the trustee of user u also affects user u’s rating on item i. thus, the rating prediction formula of the fst can be modified as: r̂u,i =s|ui−u|−β ∑ v∈ui−u ptv qu + (1 −s)|iu−i| −α ∑ j∈iu−i xtj yi + δ|t + u | −z ∑ o∈t+u pto yi+ (1 − δ)|t−u | −z ∑ c∈t−u ptc yi + |iu| −µ ∑ f∈iu ptf yi + bi (15) a factored similarity model with trust and social influence for top-n recommendation 599 the trustees in the fst were divided into a set of trustees t +u and a set of trustors t − u , aiming to disclose their impacts on the rating of item i. the parameter δ ∈ [0, 1] was adopted to control the weights of the two types of users. when δ = 0, it represents the influence of trustees is totally ignored. to the contrary, δ = 1 denotes only the influence of trustees is considered. µ ≥ 0 refers to the parameter of the number of influential users. for each influential user f ∈ iu, its influence over item i is described by the inner product ptf yi. in addition, the model parameters b,p,q,x and y can be trained by minimizing the objective function below: j = 1 2 ∑ u∈u ∑ i∈i+u ,j∈i−u |(ru,i −ru,j) − (r̂u,i − r̂u,j)|2f + λ 2 (|p |2f + |q| 2 f + |x| 2 f + |y | 2 f + |b| 2 f ) (16) where u is the set of all users; i+u and i − u respectively refers to the rated items and unrated items of user u. for simplicity, parameter λ was taken as the regularization term in all cases. the pseudocode for model learning is shown in algorithm 0, where α, β, z, µ, s and δ are control algorithm 1 the learning algorithm of fstid data: u ← user set; r ← user item matrix;t ← user trust matrix; iu ← influential user set; p ← user feature matrix; x ← item feature matrix; input: α, β, z, µ, s, δ, ρ, λ, η and iteration limitation l; output: b, p, q, x, y 1: initialize bias vector b with random values in (0, 0.01), and set q = p,y = x; 2: while j not converged or the number of iterations < l do 3: for all u ∈ u do 4: for all i ∈ i+u do 5: z ← sample(ρ,i−u ) 6: compute loss j by eq. (16) 7: update bi,bj,qu,yi,yj,pv,xk,po,pc,pf according sgd: 8: bi ← bi −η × ∂j∂bi , qu ← qu −η × ∂j ∂qu , yi ← yi −η × ∂j∂yi 9: for all j ∈ z do 10: bj ← bj −η × ∂j∂bj , yj ← yj −η × ∂j ∂yj 11: ∀v ∈ uj−u,pv ← pv −η × ∂j∂pv 12: end for 13: ∀v ∈ ui−u,pv ← pv −η × ∂j∂pv , ∀k ∈ uu−i,xk ← xk −η × ∂j ∂xk 14: ∀o ∈ t +u ,po ← po −η × ∂j ∂po , ∀c ∈ t−u ,pc ← pc −η × ∂j ∂pc 15: ∀f ∈ iu,pf ← pf −η × ∂j∂pf 16: end for 17: end for 18: iteration number++ 19: end while 20: return b, p, q, x, y parameters, λ is the regularization parameter, η is the initial learning rate and ρ is the number of samples. firstly, ρ unrated items are selected randomly for each user to train the model (line 6). 600 x.f. zhang, x.l. chen, d.w. seng, x.j. fang then, the parameters are optimized continuously in the training phase by stochastic gradient descent (sgd) (lines 7-15) until the loss function converges or the preset maximum number of iterations is reached (line 3). finally, the learning vectors and matrices are outputted (line 20). as for comparative experiments, we will make detailed comparisons and explanations in subsequent experiments to express the advantages of our algorithm. 3 experiments and results analysis this section carries out a series of experiments on three real-world datasets, trying to answer the following questions: (1) does the ish enjoy higher accuracy in identifying influential users than other influence measurement methods? (2) how do model parameters like α, β, z and µ affect recommendation accuracy? (3) how does the number of influential users affect recommendation accuracy? (4) does the fstid outperform the other advanced trust-aware recommendation algorithms on typical sparse datasets? 3.1 datasets and evaluation metrics there real-world datasets were selected for our experiments, including filmtrust, ciao and epinions, which are currently well-known test data sets. most trust-based recommendation algorithms are used to test the performance of algorithms because they contain both user ratings and trust relationships. filmtrust is a dataset from a movie sharing website. the data entries in the dataset are movie ratings based on a four-point scale. ciao and epinions are datasets from two famous consumer review websites, on which users rate commodities against a five-point scale and refer to others’ ratings before deciding on whether to purchase a commodity. as shown in table 2, all the three datasets are extremely sparse in nature. table 2: specification of the used datasets data set users items ratings density epinions 40163 139738 664824 0.0118% ciao 7375 99746 139738 0.0379% filmtrust 1508 2071 40163 1.14% *density= #ratings #users×#items the 5-fold cross validation was adopted in our experiments. each dataset was split randomly into 5 equal parts. in each iteration, 1 part was selected as the test set and the other 4 parts as the training set. the mean results of the 5 parts were taken as the final results. different from the evaluation of rating prediction, the top-n recommendation performance was measured by two metrics: precision and f1-measure. f1-measure is the weighted average of precision and recall rate (the proportion of all recommended items in the items rated by the target user). let u ′ be the set of users in the test set and rn (u) be the top-n items recommended to user u. then, the precision, recall rate and f1-measure under n recommended items, respectively a factored similarity model with trust and social influence for top-n recommendation 601 denoted as p@n, r@n and f1@n, can be calculated by: p@n = 1 |u ′| ∑ u∈u′ |rn (u) ∩ iu| n (17) r@n = 1 |u ′| ∑ u∈u′ |rn (u) ∩ iu| iu (18) f1@n = 2 ∗p@n ∗r@n p@n + r@n (19) where n ∈{5, 10} is the number of items recommended to the target user. the greater the values of p@n and f1@n, the more accurate the top-n recommendation. 3.2 social influence analysis the susceptible infected (si) epidemic model [15] was selected to evaluate the effects of model parameters on recommendation results. mimicking the virus propagation process, this classic model is a mature method to simulate the transmission of information. in this model, each network node either exists in the susceptible s status or in the infected i status. once infected, a susceptible node will irreversibly become an infected node, and transmit virus to its neighbor nodes at the probability of γ ∈ (0, 1). in our experiments, the probability γ was set to 0.001 and the network nodes were initialized randomly. the influence of each network node on the three datasets were evaluated by the parameter si at transmission time t = 10. the mean value of si can be calculated by: s̄i = 1 m m∑ m=1 si (20) where m is the repeated times of node i. our method ish was compared with the sh and the degree centrality (dc) in terms of the social influence over the three datasets. the experimental results are presented in fig.5, where the x-axis is the calculated influence of each node and the y-axis is the actual influence of each node. the purple area in fig.5 are low-impact nodes. the sh and the dc had almost the same number of low-impact nodes on all three datasets. comparatively, the sh nodes ranked lower than the dc nodes, indicating that the sh enjoyed better effect than the dc. meanwhile, the ish achieved the lowest proportion of low-impact nodes among the three methods. most ish nodes concentrated in the second half of the ranking. the red area in fig.5 are high-impact nodes. by analysis of top-25 nodes, the number of high-impact nodes calculated by dc, sh, and ish are 18, 15 and 18 in filmtrust; the number of high-impact nodes is 8, 9, 11 respectively in ciao and also is 9, 8, 10 in epinions. the results are very close, revealing that the node with higher degree is more important. as shown in fig.5, the ish achieved the highest mean value on all datasets, that is, our algorithm outperformed the other methods in identification. to sum up, the ish can identify the top k% influential users correctly, without mistaking low-impact users as high-impact users. 3.3 effects of model parameters effect of parameters α, β, z, µ the parameters α, β, z, µ respectively control the influence of item similarity, user similarity, trustees, and influential users on the item ratings. to save time and space, the values of 602 x.f. zhang, x.l. chen, d.w. seng, x.j. fang mean:2198.6 500 1000 1500 2000 2500 3000 3500 c ish f ish figure 5: correlation analysis of different methods and their actual influence. (a)-(c) results on filmtrust; (d)-(f) results on ciao; (g)-(i) results on epinions. these parameters were limited to a small set: 0.5, 1, 2 while the values of s and δ were fixed at 0.5 in our experiments. for simplicity, only the five best p@10 results on each dataset are listed in table 3. obviously, the different parameter configurations led to different results, and the optimal parameters changed from dataset to dataset. the optimal values of α, β, z and µ on epinions, ciao and filmtrust were (0.5, 2, 2, 0.5), (0.5, 2, 0.5, 0.5) and (0.5, 1, 0.5, 0.5), respectively. it can be concluded that the optimal combination is α = 0.5, β, z > 1 and µ < 1, that is, the item similarity and influential users are more important than user similarity and trustees. table 3: the five best p@10 results on each dataset. epinions ciao filmtrust 1 0.010486 (0.5, 2, 2, 0.5) 0.02378 (0.5, 2, 2, 2) 0.352258 (2, 1, 1, 0.5) 2 0.010467 (1, 2, 0.5, 0.5) 0.023609 (0.5, 0.5, 0.5, 0.5) 0.351964 (2, 0.5, 2, 2) 3 0.010379 (2, 1, 2, 0.5) 0.023411 (1, 2, 1, 1) 0.351948 (0.5, 1, 0.5, 1) 4 0.010243 (1, 2, 1, 1) 0.023396 (0.5, 2, 1, 0.5) 0.351819 (1, 1, 0.5, 0.5) 5 0.010231 (0.5, 2, 0.5, 2) 0.02336 (2, 0.5, 0.5, 1) 0.351775 (0.5, 0.5, 0.5, 0.5) effect of s and δ the parameters s and δ from eq.(15) respectively describe the relative importance of user similarity and trustees on the prediction of item ratings. the value of s is positively correlated with the impact of user similarity, while the value of δ is negatively correlated with the impact of trustees. in our experiments, α, β, z, µ were set to the optimal values, while the values of a factored similarity model with trust and social influence for top-n recommendation 603 s and δ were increased at a step of 0.1 in the range [0, 1]. firstly, the value of s was set to 0.5 to obtain a series of results on δ and determine the value of δ. then, the experiments were conducted with s. according to the experimental results in fig. 6, the value of p@10 was worse than most other values at δ = 0 or δ = 1. this means the recommendation accuracy can be improved effectively through the proper combination of trustors and trustees. this conclusion also applies to parameter s. the value of s can be set to 0.3, despite the difference in its optimal value on different datasets. 0.0 0.2 0.4 0.6 0.8 1.0 0.00910 0.00936 0.00962 0.00988 0.01014 0.0 0.2 0.4 0.6 0.8 1.0 0.0210 0.0216 0.0222 0.0228 0.0234 0.0 0.2 0.4 0.6 0.8 1.0 0.3480 0.3492 0.3504 0.3516 0.3528 0.3540 p @ 1 0 p @ 1 0 s (a)epinions (b)ciao s p @ 1 0 (c)filmtrust s figure 6: the effects of s and δ on fstid at p@10 0.0 0.2 0.4 0.6 0.8 1.0 0.00910 0.00936 0.00962 0.00988 0.01014 0.0 0.2 0.4 0.6 0.8 1.0 0.0210 0.0216 0.0222 0.0228 0.0234 0.0 0.2 0.4 0.6 0.8 1.0 0.3480 0.3492 0.3504 0.3516 0.3528 0.3540 p @ 1 0 p @ 1 0 s (a)epinions (b)ciao s p @ 1 0 (c)filmtrust s figure 7: the effects of parameters k on fstid at p@10 effect of parameter k the fstid takes the top k% users with higher influence as influential users, as only a tiny fraction of users in the social network have a great influence. to verify the impact of parameter k on the recommendation performance, the value of the parameter was increased at the step of 1 in the interval [0, 10]. the experimental results are displayed in fig. 7. it can be seen that the optimal value of k was 2, 4 and 7 in epinions, ciao, and filmtrust, respectively. moreover, the poorest result was observed on the three datasets at k = 0, i.e. ignoring influential users. hence, the consideration of influential users can indeed enhance the recommendation effect. 3.4 comparison with other methods several top-n recommendation algorithms were selected to evaluate the effect of our method, such as mostpop, fst, group bayesian personalized ranking (gbpr), the factored item similarity model (fism), and the fstid-. the mostpop is the baseline method that ranks the ratings of an item by popularity, i.e. how frequently the item is rated or consumed by the user. proposed by guo et al., the fst [8] takes account of implicit user feedback, similarity and social trust. the gbpr was improved from the bayesian personalized ranking (bpr) by pan and chen [16], which introduces the influence of social groups on user preference to enhance the item recommendation quality. fism is a top-n recommendation method based on item similarity 604 x.f. zhang, x.l. chen, d.w. seng, x.j. fang proposed by kabbur et al. [11]. the fstidremoves the dae pretraining from our approach and randomly sets the values of p,q,x and y in the interval of (0, 0.01). table 4: results of different methods at n=5 and 10. the optimal results are in bold characters. datasets n metrics mostpop gbpr fism fst fstidfstid epinions 5 p@n 0.01169 0.009353 0.01147 0.01179 0.011888 0.01231 10 f1@n 0.01298 0.01103 0.01307 0.0133 0.013107 0.01402 5 p@n 0.009171 0.00756 0.00902 0.009187 0.009486 0.01024 10 f1@n 0.01305 0.01111 0.01315 0.01328 0.014431 0.01459 ciao 5 p@n 0.02677 0.02228 0.02704 0.02741 0.027382 0.0283 10 f1@n 0.02436 0.02063 0.02495 0.02523 0.025442 0.02644 5 p@n 0.02142 0.01827 0.02141 0.02174 0.022376 0.02329 10 f1@n 0.02662 0.02116 0.02687 0.0272 0.028402 0.02914 filmtrust 5 p@n 0.4170 0.4124 0.4171 0.4191 0.418567 0.4198 10 f1@n 0.4095 0.4051 0.4087 0.4099 0.411364 0.4116 5 p@n 0.3503 0.3470 0.3503 0.3514 0.352159 0.3532 10 f1@n 0.4518 0.4458 0.4516 0.4521 0.452825 0.4541 it can be seen from table 4 that the fstid achieved better results than the contrastive methods on all datasets, proving that our approach can alleviate data sparsity and make reliable recommendations. besides, the fstid, which relies on the dae to initialize the mf, outperformed the fstid-, revealing that the features extracted by the dae are more suitable than randomly generated features. on the other hand, fstidbecause there is no need to utilize dae for feature initialization, it has lower computational cost than fstid, and in most experiments it has achieved the best results compared with other approach except the fstid method, which indicates the effectiveness of the integration of social influences to improve the recommended performance. furthermore, the fstid’s advantage over the other methods were smaller on filmtrust than ciao and epinions, due to the relatively small scale of filmtrust. this means the dae can capture features more effectively in big data. 4 conclusion this paper proposes a novel factored similarity model for top-n recommendation based on trust and social influence. firstly, we quantify the trust value between users based on the user’s interaction information and similarity, and finally build the user’s trust association graph. in the process of calculating the trust degree, the penalty factor based on the common rating item is added to reduce the accidental effect caused by the low number of common rating items, which greatly increases the attack cost of malicious users, makes it difficult to become the neighbors of target users, and effectively enhances the stability of the algorithm. then, the ish was utilized to identify the influential users in a complex network. moreover, the dae was adopted to compress high-dimensional, sparse user behaviors into low-dimensional, dense vectors of user and item features. compared with other top-n recommendation approaches, our approach achieved excellent recommendation effects on three real-world datasets. the future research will consider the dynamic changes in user preference and trust with the time of user rating, and try to recommend items preferred by users in a timely manner. a factored similarity model with trust and social influence for top-n recommendation 605 acknowledgment this work is supported by the national natural science foundation of china (no. 61473108), the fundamental public welfare research program of zhejiang province (no. gf19f020052) and the national experimental teaching demonstration center of computer-science in china. bibliography [1] anagnostopoulos, a.; kumar, r.; mahdian, m. 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(2017). collaborative filtering with social local models, 2017 ieee international conference on data mining (icdm), 645–654, 2017. a factored similarity model with trust and social influence for top-n recommendation 607 appendix in order to facilitate the reader to read better, we have listed the abbreviations used in this article in lexicographic order, as shown in table 5. table 5: acronyms and initialisms dictionary. abbreviation full name cf collaborative filtering dae deep autoencoder dc degree centrality fism factored item similarity model fst factored user and item similarity model with social trust fstid factored user and item similarity model with trust and social influence based on deep learning gbpr group bayesian personalized ranking ish improved structural holes mf matrix factorization sgd stochastic gradient descent sh structural hole si susceptible infected international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 4, month: august, year: 2020 article number: 3888, https://doi.org/10.15837/ijccc.2020.4.3888 ccc publications hesitant fuzzy degroot opinion dynamics model and its application in multi-attribute decision making z. su, z. xu, h. zhao, s. liu zhan su 1. command & control engineering college army engineering university of pla, nanjing, jiangsu 210007, china 2. department of general education army engineering university of pla, nanjing, jiangsu 210007, china suz123456789@163.com zeshui xu business school sichuan university, chengdu, sichuan 610065, china xuzeshui@263.net hua zhao* department of general education army engineering university of pla, nanjing, jiangsu 210007, china *corresponding author: zhaohua_nj@sina.com shousheng liu department of general education army engineering university of pla, nanjing, jiangsu 210007, china ssliunuaa@sina.com abstract the research on the evolution law of the opinions can help the decision makers (dms) improve the decision-making efficiency, predict the trend of events and make the right decision. these opinions are always described by one number, which is inaccurate and incomplete. to solve such a problem, in this paper, the hesitant fuzzy degroot (hf-degroot) opinion dynamics model is proposed. in order to simulate the transformation of hesitant fuzzy opinions, we introduced the multiplications for real matrix and hesitant fuzzy matrix. then three kinds of transformation matrices with the consideration of the similarity degree, self-confidence degree and authority degree are constructed based on the hesitant fuzzy data and the consensus condition for the model is discussed as well. furthermore, the hf-degroot opinion dynamics decision-making method is proposed from a prediction perspective and is applied to the emergency decision for the public health events. finally, the effectiveness, feasibility and practicability of this method are shown by the comparison and simulation results. keywords: hesitant fuzzy set, opinion dynamics, consensus, decision making, opinion transition matrix, degroot opinion dynamics model. https://doi.org/10.15837/ijccc.2020.4.3888 2 1 introduction nowadays, it is difficult for individuals or groups to make decisions without being influenced by public opinions. however, public opinions are becoming more complicated due to the increasing ways of information transmission. people are difficult to distinguish the right and meaningful opinions and are easily affected by negative ones meanwhile. therefore, it is very necessary for the decisionmakers (dms) to study the evolution law of opinions which can not only help dms to understand a large number of opinions in a short time and improve the decision-making efficiency, but also provide targeted and predictive information to dms to help them make the right decisions. based on the theories of mathematics, physics and cybernetics, the opinion dynamics [2, 17] simulates the communication, influence and diffusion process of opinions in the transmission process by establishing a model and predicts the tendency of evolution. it has been studied and applied in many fields, such as politics [4], marketing [18], and venture capital [25], etc. according to the opinion space being a discrete numerical set or a continuous interval, the opinion dynamic models can be divided into two types: discrete and continuous. discrete models are performed well in simulating large-scale social activities such as mayor election [1] and the elections in germany [19], mainly including ising model [5, 12, 25], voter model [23] and sznajd model [4, 18, 19, 21], etc. continuous models such as degroot opinion dynamics model [3, 9, 10], deffuant model [13] and hegselmann-krause [11] model mainly study the evolution of the group’s opinions from the perspective of individual social actors, henceforth called agents [17]. among these models, the degroot model has always been very representative. it is expressed in a very concise form, using a stochastic matrix to represent the interactions between agents, and assuming that the interactions are time-invariant, it has been extensively studied these days. for example, stamatelatos et al. [24] used it to reveal diverse aspects of the nodes of interest’s political profile in the twitter online social network. askarzadeh et al. [2] extended it to describe the evolution of the probability distribution of interacting species. however, both the degroot opinion dynamics model and other opinion dynamics models use a single value to describe the opinion. in fact, due to the cognitive ability’s improvement and the increasing complexity, the traditional way of a single value can no longer meet the needs. in this case, professor zadeh [31] proposed the concepts of membership degree and fuzzy set (fs), which enable people to describe the attributes of things rationally by using the uncertainty method. on this basis, torra [26] proposed the hesitant fuzzy set (hfs), which specifies that the attributes can be represented by multiple membership degree values and shows the hesitant mentality of dms. due to its outstanding ability of expression, hfss have been widely used in many fields [30], such as communication network [7], construction industry [8], venture investment [22]. for the common multi-attribute decision making (madm) problems under the hesitant fuzzy environment, there are many mature solutions, such as hesitant fuzzy topsis method [29] and hesitant fuzzy todim method [32] which are often used to deal with the energy policy selection problem [29] and service quality evaluation [32]. but those methods based on static information at a certain time will not be applicable when the actual situation is constantly changing. for example, in the early stage of outbreak of the novel coronavirus (covid-19) in 20191, the virus was not particularly harmful and infectious, but with the virus spread rapidly, the corresponding problems related to politics, economy, culture and other aspects had also been changing rapidly. in this situation, the static decision-making methods cannot grasp the dynamic change of the events and the corresponding changes in the attribute values cannot be well reflected, by contrast, the dynamic mcdm methods is more appropriate. if we look at the decision-making process from the perspective of opinion dynamics, the dms are the agents whose hesitant fuzzy opinions form the group’s opinions. with the changes of time and environment, dms interact with each other through communication and learning. finally, these opinions are aggregated into one or several opinions which represent the final decision. in the forming process of the group’s opinions, there are some intersecting theories which can be taken advantage of each other although their research objectives are different. therefore, in this paper, we will integrate 1who, coronavirus disease (covid-19) pandemic, https://www.who.int/emergencies/diseases/novel-coronavirus2019, 2020. https://doi.org/10.15837/ijccc.2020.4.3888 3 the dms’ opinions and establish the corresponding decision-making methods based on the appropriate opinion dynamics model. due to the good mathematical properties, the degroot opinion dynamics model is suitable to be used in this study. in order to make the hesitant fuzzy data fit the matrix operations better, we also define and discuss the operation rules of the hesitant fuzzy matrices. the remainder of this paper is arranged as follows: in section 2, basic concepts of hfss and degroot model are presented in detail. section 3 presents the hesitant fuzzy degroot (hf-degroot) opinion dynamics models and the corresponding decision-making algorithms are shown in section 4. next, we apply the hf-degroot decision-making algorithms to the emergency decision for the public health events in section 5 and show the rationality of the proposed methods by a series of comparative analyses in section 6. section 7 ends the paper with some conclusions. figure 1: organizational structure of this paper 2 the hfss and opinion dynamics this section introduces the necessary prior knowledge regarding hfss and the degroot opinion dynamics model in opinion dynamics as the basis for the subsequent research. 2.1 hfss torra [26] introduced the concept of hfs firstly. compared with traditional fss, the hfss is superior in the expression of the uncertain information. in order to make hfs easily understood, xia and xu [27] proposed the mathematical expression of hfs and defined the hesitant fuzzy element (hfe) ha(x). so the hfs a on a fixed set x is described as: a = {〈x,ha (x)〉 |x ∈ x}, where ha(x) includes several different values between 0 and 1, and represents the membership degrees of the element x to the set a. the hfes can’t be compared directly so the score function s (h (x)) = 1 l (h) l∑ k=1 γk (1) was proposed that the higher the score values is, the greater the hfe is where l (h) is the number of membership degree in h(x) which can be abbreviated as l. but they are difficult to be distinguished https://doi.org/10.15837/ijccc.2020.4.3888 4 when the hfes have the same scores. so the deviation degree [6] of the hfe d (h (x)) = [ 1 l l∑ k=1 (γk −s (h (x))) 2 ]1 2 (2) provides an effective solution that the larger the values, the smaller the hfe. the basic operational laws and the aggregation operators proposed by liao et al. [14] are of great importance for decision making. definition 1. let h, h1, h2 be three hfes and c be a positive real number, then (1) hc = l⋃ i=1 {( γσ(i) )c} ; (2) ch = l⋃ i=1 { 1 − ( 1 −γσ(i) )c} ; (3) h1 ⊕h2 = lmax⋃ i=1 { γ σ(i) 1 + γ σ(i) 2 −γ σ(i) 1 γ σ(i) 2 } ; (4) h1 ⊗h2 = lmax⋃ i=1 { γ σ(i) 1 γ σ(i) 2 } . where γσ(i)j is the largest value in hj and lmax = max{l (h1) , l (h2)}. if the numbers of membership degree values contained in the hfes are different, the pessimistic or optimistic principle introduced by xu and xia [28] can be adopted to extend the shorter ones. if there is no special explanation, the pessimistic principle is used in this article for extension. definition 2 [14]. an adjusted hesitant fuzzy weighted averaging (ahfwa) operator is a mapping fn → f, ahfwa (h1,h2, ...,hn) = n ⊕ k=1 wkhk = lmax⋃ i=1 { 1 − n∏ k=1 ( 1 −γσ(i)k )wk} (3) mo et al. [16] found and proved that when the ahfwa operator and pessimistic principle are adopted, the operation between the real number and hfe satisfies the distributive law. besides the above conclusions there are some other properties which will be listed in theorem 1: theorem 1. suppose that λ and µ are two positive numbers and h,h1,h2,h3 are hfes, if the ahfwa operator is adopted, then the following conclusions can be got: (1) (λ + µ) h = λh⊕µh [16]; (2) h⊕h = 2h [16]; (3) h1 ⊕h2 = h2 ⊕h1; (4) λ (h1 ⊕h2) = λh1 ⊕λh2; (5) (h1 ⊕h2) ⊕h3 = h1 ⊕ (h2 ⊕h3); (6) λ (µh) = (λµ) h. proof. (1) and(2) have been proven by mo et al. in ref. [16]. (3) h1 ⊕h2 = lmax⋃ i=1 { γ σ(i) 1 + γ σ(i) 2 −γ σ(i) 1 γ σ(i) 2 } = lmax⋃ i=1 { γ σ(i) 2 + γ σ(i) 1 −γ σ(i) 2 γ σ(i) 1 } =h2 ⊕h1 (4) λ (h1 ⊕h2) = λ ( lmax⋃ i=1 { γ σ(i) 1 + γ σ(i) 2 −γ σ(i) 1 γ σ(i) 2 }) = lmax⋃ i=1 { 1 − ( 1 −γσ(i)1 + γ σ(i) 2 + γ σ(i) 1 γ σ(i) 2 )λ} = lmax⋃ i=1 { 1 − 2∏ k=1 ( 1 −γσ(i)k )λ} = λh1 ⊕λh2 (5) (h1 ⊕h2) ⊕h3 = lmax⋃ i=1 { 1 − 2∏ k=1 ( 1 −γσ(i)k )} ⊕h3 = lmax⋃ i=1 { 1 − 3∏ k=1 ( 1 −γσ(i)k )} h1 ⊕ (h2 ⊕h3) = h1 ⊕ lmax⋃ i=1 { 1 − 3∏ k=2 ( 1 −γσ(i)k )} = lmax⋃ i=1 { 1 − 3∏ k=1 ( 1 −γσ(i)k )} = (h1 ⊕h2) ⊕h3 (6) λ (ηh) = λ [ l⋃ i=1 { 1 − ( 1 −γσ(i) )η}] = l⋃ i=1 { 1 − [ 1 − 1 + ( 1 −γσ(i) )η]λ} = l⋃ i=1 { 1 − ( 1 −γσ(i) )ηλ} = (λµ) h the proof is completed. https://doi.org/10.15837/ijccc.2020.4.3888 5 note 1. in theorem 1, hfe must be extended by pessimistic principle or optimistic principle. because mo et al. [16] found that if 0.5 or the average value of the previous several membership degree values is used to extension, the conclusion in theorem 1 is not valid. in addition to the basic operational rules, similarity measure also occupies a very important place in the hesitant fuzzy theory. for two hfss a and b, xu and xia [28] defined several similarity formulas based on the traditional hamming distance and euclidean distance. (1) normalized hesitant fuzzy weighted hamming similarity: s1(a,b) = 1 − 1 n n∑ i=1   1 l (xi) l(xi)∑ j=1 ∣∣∣hσ(j)a (xi) −hσ(j)b (xi)∣∣∣   (4) (2) normalized hesitant fuzzy weighted euclidean similarity: s2(a,b) = 1 −   1 n n∑ i=1   1 l (xi) l(xi)∑ j=1 ∣∣∣hσ(j)a (xi) −hσ(j)b (xi)∣∣∣2    1/2 (5) these two formulas above can also be used in the calculation of the similarity measures between two hfes. 2.2 degroot opinion dynamics model the degroot opinion dynamics model [9] introduced in 1974 was used to study the simple procedure to reach consensus among the agents. assume that {1, 2, · · · ,n} is a set of agents and the opinion of the ith agent at the time t ∈ n is xi (t) ∈ r (i = 1, 2, · · · ,n) . then at the time t+1, the evaluation of the opinions can be expressed as the weighted average of its own and other neighbors’ opinions: xi (t + 1) = n∑ j=1 pijxj (t) , t = 0, 1, 2, · · · (6) where pij ≥ 0 (i = 1, 2, · · · ,k; j = 1, 2, · · · ,k) is the weight that the ith agent gives to the jth agent and n∑ j=1 pij = 1. if x (t) = {x1 (t) ,x2 (t) , · · · ,xn (t)}t and p = (pij)n×n. then the ith agent’s opinion xi (t) at the time t+1 in eq.(6) can be rewritten as x (t + 1) = px (t) , t = 0, 1, 2, · · · (7) where p is a stochastic matrix. the consensus of the model as the core content in the model research should be considered because the convergence of the degroot opinion dynamics model cannot guarantee the formation of a unified final opinion. definition 3 (consensus) [9]. if the model satisfies that for any initial opinion x (0) the limit exists lim t→∞ x (t) = lim t→∞ ptx (0) = µ∗1n (µ∗ is a real number), then the model (7) can reach a consensus, where 1n is the column vector whose elements are all 1. that is, when the model reaches a consensus, there is x1 (∞) = x2 (∞) = · · · = xn (∞) for all the initial opinions. the fully regularity of the stochastic matrix p is the most important factor to make the model reaches a consensus. definition 4 (fully regularity) [20]. the matrix p is fully regular if the limit p∞ = lim k→∞ pk = 1n (p1,p2, · · · ,pn), pi ∈ r (i = 1, 2, · · · ,n) exists and the rows of p∞ are identical. the degroot opinion dynamics model assumes that the opinion is time-invariant, so it can be analyzed by using the existing conclusions in markov chain (mc) theory [20]. the function of stochastic https://doi.org/10.15837/ijccc.2020.4.3888 6 matrix p in the degroot opinion dynamics model is equivalent to the transition probability matrix in mc theory. if p is a doubly stochastic matrix, the following theorem 2 can be concluded: theorem 2 [15]. let a markov chain with the finite state space i = {1, 2, · · · ,n} be irreducible and aperiodic, the transition probability matrix p = (pij)n×n be doubly stochastic matrix, then lim n→∞ pn = 1 n in where in is the n order matrix whose elements are all 1. in addition, degroot [9] gives the sufficient conditions for the model to reach a consensus by using the mc theory. theorem 3 [9]. if there exists a positive integer n and all the elements in at least one column of the matrix pn are positive, the degroot opinion dynamics model reaches a consensus. 3 the evaluation of the hesitant fuzzy opinion the main work of this section is to study the dynamic evolution of the hesitant fuzzy opinion by using the degroot opinion dynamics model. because the transformation of hesitant fuzzy opinions needs to be expressed by the matrix operations, but there’s a big difference between the hesitant fuzzy operators and the real arithmetic, so at the beginning of this section, we need to define the multiplication between the real number matrix and hesitant fuzzy matrix, and study the operation rules between real numbers and hfes. 3.1 multiplication between the real matrix and hesitant fuzzy matrix the following is the definition of the multiplication between the real matrix and the hesitant fuzzy matrix: definition 5. a = (aij) is a m×s real matrix, z = (zij) is a s×n hesitant fuzzy matrix, the hesitant fuzzy matrix z is pre-multiplied by the real matrix a to get an m×n hesitant fuzzy matrix c = (cij) where aij ∈ r, zij and cij are the hfes, and cij = ai1z1j ⊕ai2z2j ⊕···⊕aiszsj = s ⊕ q=1 aiqzqj, i = 1, 2, · · · ,m; j = 1, 2, · · · ,n (8) where γσ(k)lj denotes the kth largest value in zlj, denoted as c = az. especially the product of a 1 × s real row matrix and a s × 1 hesitant fuzzy column matrix is a first-order square matrix or a hfe. when the ahfwa operator is used in eq.(8), the multiplication between the real matrix and the hesitant fuzzy matrix satisfies the associative law. theorem 4. suppose that a is a m×s real matrix, b is a s×n real matrix, and z is a n×p hesitant fuzzy matrix, then (ab) z = a (bz). proof. let a = (aij)m×s and b=(bjk)s×n be real matrixes, and z = (zkr)n×p be a hesitant fuzzy matrix. then d = ab = (dik)m×n be a real matrix, and e = bz = (ejr)s×p be a hesitant fuzzy matrix, where dik = s∑ j=1 aijbjk,ejr = n ⊕ k=1 bjkzkr. according to theorem 1, the operations between the real numbers and the hfes satisfy the commutative law, the associative law and the distributive law [16], so the elements in dz are n ⊕ k=1 dikzkr = n ⊕ k=1   s∑ j=1 aijbjk  zkr = n⊕ k=1 ( s ⊕ j=1 (aijbjk) zkr ) = n ⊕ k=1 ( s ⊕ j=1 aij (bjkzkr) ) = s ⊕ j=1 aij ( n ⊕ k=1 bjkzkr ) . based on the associative property of the operation between the real numbers and hfes, the elements in ae are s ⊕ j=1 aijejr = s ⊕ j=1 aij ( n ⊕ k=1 bjkzkr ) , dz = ae. the proof is completed. https://doi.org/10.15837/ijccc.2020.4.3888 7 3.2 hf-degroot opinion dynamics model hesitant fuzzy data allow the opinions to be expressed more accurately and truthfully. in this section, its evolution in the transmission is simulated. assume that n agents give their opinions z1 (t) ,z2 (t) , · · · ,zn (t) at the time t which are in the form of hfes respectively. influenced by others, the agents adjust their opinions at the next moment. p is the n×n stochastic matrix comprising the elements pij ∈ r (i = 1, 2, · · · ,n; j = 1, 2, · · · ,n) which represents the transition weight corresponding zi (t) to zj (t) at the time t. and each element pij is nonnegative and the sum of the elements in any given row is 1. the hf-degroot opinion dynamics model is time-invariant. then, at the time t+1, the opinion of the ith agent can be expressed as: zi (t + 1) = n∑ j=1 pijzj (t), t = 0, 1, 2, · · · (9) let z (t) = {z1 (t) ,z2 (t) , · · · ,zn (t)}t , then, z (t + 1) = pz (t) , t = 0, 1, 2, · · · (10) from theorem 4 and the theory of the degroot opinion dynamics model, we can get theorem 5. let p = (pij)n×n be a stochastic matrix, and z (t) = (z1 (t) ,z2 (t) , · · · ,zn (t)) t be a hesitant fuzzy column matrix, then z (t) = pz (t− 1) = ptz (0) , t = 1, 2, 3, · · · (11) proof. according to theorem 4, z (t) = pz (t− 1) = p 2z (t− 2) = ptz (0). the proof is completed. eq.(11) in theorem 5 simplifies the operation of hesitant fuzzy matrix. the agents’ opinions at the time t can be got by the multiplication of t power of the stochastic matrix p and the hesitant fuzzy matrix only one time. definition 6 (consensus of the hesitant fuzzy opinions). if the model satisfies that for any initial hesitant fuzzy opinion x (0) the limit exists lim t→∞ z (t) = lim t→∞ ptx (0) = 1nh∗ (h∗ is a hfe), then the model of eq.(10) can reach a consensus, where 1n is the column vector whose elements are all 1. there is z1 (∞) = z2 (∞) = · · · = zn (∞) for all the initial hesitant fuzzy opinions when the model reaches a consensus. 4 decision making method based on the hf-degroot opinion dynamics model in this section we will discuss the following questions in combination with the characteristics of the hesitant fuzzy opinions: – how to explore the relationships between the hesitant fuzzy opinions? – when will a group of hesitant fuzzy opinions reach consensus? 4.1 the structure of the transition matrix in the research of degroot opinion dynamics model, the stochastic matrix p which represents the transition weights between opinions plays an important role. due to the difficulty in obtaining information the stochastic matrix p is mostly obtained by subjective weighting, but this method is not objective enough and often lacks of mining the hidden information in the opinions. therefore, in the following research, several methods of obtaining the stochastic matrix p will be introduced which take into account both the objective and subjective factors. since people’s opinions are not always the same, the original opinions will be adjusted while learning others’ opinions. here we assume that there are three factors that affect the transition of opinions: (1) similarity degree–similar opinions are more easily transferred to each other. (2) self-confident degree–confident people are more likely to get others’ trust and less likely to accept others’ opinions. https://doi.org/10.15837/ijccc.2020.4.3888 8 (3) authority degree–authority figures are more likely to influence others. now we will construct the opinion transition matrix from these three aspects. plan a: opinion transition matrix based on the similarity degree according to people’s thinking habits, similar opinions are more acceptable to each other. and in the hesitant fuzzy environment, the similarity degree between opinions can be described by similarity measure. based on this idea, the opinion transition matrix can be established. suppose that at the time t = 0, the initial opinions of the dms are z1 (0) ,z2 (0) , · · · ,zn (0) and sij is the similar measure between zi (0) and zj (0). then we get the opinion transition matrix p=(pij)n×n, where pij(1) = sij n∑ j=1 sij (12) is the opinion transition weight assigned by zi to zj and the hf-degroot opinion dynamics model is time-invariant. plan b: opinion transition matrix based on the self-confident degree in the hesitant fuzzy theory, the fluctuation of membership degrees in hfes is measured by the deviation degree, which is similar to the concept of mean square deviation in statistics. the dm’s selfconfidence can be reflected by the deviation degree of the hfes. the more confident the dm is, the smaller the deviation degree of the data will be. meanwhile in real life, dms with higher self-confidence are hard to transform their own opinions with others, so we should appropriately increase the weights of their own opinions. based on the above analysis, we will add the factor of self-confidence, namely the deviation degree, to expand the transition weights of the opinions. the values of deviation degrees α (zi) for zi (0) (i = 1, 2, · · · ,n) are almost close to 10−2 since the membership degrees are within [0,1]. to be better compared with the similarity measures, the deviation degrees should be standardized and then adjusted by the importing parameter ρ > 1. the specific steps to calculate the transition weights are shown below: algorithm i input: a set of hfes z1,z2, · · · ,zn and the parameters ρ. output: the opinion transition matrix p for zi (i = 1, 2, · · · ,n). step 1. use eq.(2) to get the deviation degrees α (zi) for zi (i = 1, 2, · · · ,n). step 2. standardize these deviation degrees by using eq.(13): ᾱ (zi) = max i (α (zi)) −α (zi) max i (α (zi)) − min i (α (zi)) , i = 1, 2, · · · ,n (13) step 3. combining with eq.(12), determine the parameters ρ, and get the weight assignment of zi in the transformation of opinion as follows: pij(2) =   sij 1+ρᾱ(zi)+ n∑ j=1,j 6=i sij , i 6= j 1+ρᾱ(zi) 1+ρᾱ(zi)+ n∑ j=1,j 6=i sij , i = j (14) step 4. end. note 2. the method mentioned in eq.(13) is not the only formula for standardizing deviation degree. there are other valid formulas such as eq.(15). this method is to standardize the deviation degree by its entire value range [0,0.5]. ᾱ′ (zi) = 0.5 −α (zi) 0.5 , i = 1, 2, · · · ,n (15) the two methods of standardization of deviation degrees have their own advantages. eq.(15) still can be used when eq.(13) is invalid for a group of deviation degrees are all equal. but eq.(13) https://doi.org/10.15837/ijccc.2020.4.3888 9 considers more interaction between the opinions. plan c: opinion transition matrix based on the authority degree in the common decision-making process, since the authority in some areas is powerful enough to change people’s opinions, it is reasonable to give more weights to their opinions. based on the above analysis, the parameter η > 1 is introduced to simulate the influence of authority. for convenience, the qth dm is assumed to be the authority. in this way, the transition weight assigned by zi to zj is determined as follows: pij(3) =   sij ηsiq + n∑ i=1,j 6=q sij ,j 6= q ηsiq ηsiq + n∑ i=1,j 6=q sij ,j = q (16) 4.2 evolution analysis of the hf-degroot opinion dynamics model previously, we defined the hf-degroot model, mined the implicit information in hesitant fuzzy data, and determined the weight transition matrices. next, the evolution of various opinions in the hesitant fuzzy environment will be specifically analyzed according to the hypothesis that the hfdegroot opinion dynamics model is time-invariant. with the help of the opinion transition matrix established in section 4.1, we consider the following two scenarios. scenario 1. there are only two types of opinions: {1} and {0} at this time, dms have very different opinions, but whether they can reach consensus requires further analysis. because the similarity measures of {1} {1} and {0} {0} is 1 but the similarity measure of {0} {1} is 0. for the sake of convenience, the same opinions will be put together in the following, which will not affect the final results. let there be n dms, among which m opinions are {1} and n-m opinions are {0}. we discuss the evolution results of these opinions under the three different transfer modes mentioned in plan a, plan b and plan c respectively: (1) evolution based on plan a according to eq.(12) in plan a, the opinion transition matrix of the opinion set {1} , · · · ,{1}︸ ︷︷ ︸ m ,{0} , · · · ,{0}︸ ︷︷ ︸ n−m   t is: q1 =   1 m · · · 1 m 0 · · · 0 ... ... 1 m · · · 1 m 0 · · · 0 0 · · · 0 1 n−m 1 n−m · · · ... 0 0 1 n−m 1 n−m   since q1t = q1, then lim t→∞ q1 t = q1, z (∞) = q1  {1} , · · · ,{1}︸ ︷︷ ︸ m ,{0} , · · · ,{0}︸ ︷︷ ︸ n−m   t =  {1} , · · · ,{1}︸ ︷︷ ︸ m ,{0} , · · · ,{0}︸ ︷︷ ︸ n−m   t . this means that these dms can’t reach consensus and no one changes their opinion. (2) evolution based on plan b since these dms only have two kinds of opinions {0} and {1}, whose deviation degrees are both 0. they correspond to the highest degree of confidence. the opinion transition matrix obtained by eqs.(14) and (15) is: https://doi.org/10.15837/ijccc.2020.4.3888 10 q2 =   1+ρ m+ρ · · · 1 m+ρ 0 · · · 0 ... ... 1 m+ρ · · · 1+ρ m+ρ 0 · · · 0 0 · · · 0 1+ρ n−m+ρ 1 n−m+ρ · · · ... 0 0 1 n−m+ρ 1+ρ n−m+ρ   (17) let q21 =   1+ρ m+ρ · · · 1 m+ρ ... 1 m+ρ · · · 1+ρ m+ρ   ,q22 =   1+ρ n−m+ρ · · · 1 n−m+ρ ... 1 n−m+ρ · · · 1+ρ n−m+ρ   , then in the matrix q2, q2 = ( q21 o o q22 ) , both q21 and q22 are doubly stochastic matrixes. according to theorem 2, lim t→∞ qt21 =   1 m · · · 1 m ... 1 m · · · 1 m   , lim t→∞ qt22 =   1 n−m · · · 1 n−m ... 1 n−m · · · 1 n−m   , qt2 = ( qt21 o o qt22 ) , we can get lim t→∞ q2 t = q1, z (∞) = q1  {1} , · · · ,{1}︸ ︷︷ ︸ m ,{0} , · · · ,{0}︸ ︷︷ ︸ n−m   t =  {1} , · · · ,{1}︸ ︷︷ ︸ m ,{0} , · · · ,{0}︸ ︷︷ ︸ n−m   t . this suggests that when all dms are confident, these opinions do not reach consensus. (3) evolution based on plan c assume that the authority parameter of the first dm is η > 1 for the convenience. according to eq.(16), the opinion transition matrix is obtained as: q3 =   η η+m−1 · · · 1 η+m−1 0 · · · 0 ... ... η η+m−1 · · · 1 η+m−1 0 · · · 0 0 · · · 0 1 n−m 1 n−m · · · ... 0 0 1 n−m 1 n−m   (18) let q3 = ( q31 q32 ) ,qt3 = ( qt31 o o qt32 ) , q31 =   η η+m−1 1 η+m−1 · · · 1 η+m−1 ... ... ... ... η η+m−1 1 η+m−1 · · · 1 η+m−1   m×m =   1 1 ... 1   m×1 ( η η + m− 1 , 1 η + m− 1 , · · · , 1 η + m− 1 ) 1×m , https://doi.org/10.15837/ijccc.2020.4.3888 11 qt31 =   1 1 ... 1     ( η η+m−1, 1 η+m−1, · · · , 1 η+m−1 )   1 1 ... 1     t−1 · ( η η + m− 1 , 1 η + m− 1 , · · · , 1 η + m− 1 ) = q31 q32 t = q32, then lim t→∞ q3 t = q3, z (∞) = q3  {1} , · · · ,{1}︸ ︷︷ ︸ m ,{0} , · · · ,{0}︸ ︷︷ ︸ n−m   t =  {1} , · · · ,{1}︸ ︷︷ ︸ m ,{0} , · · · ,{0}︸ ︷︷ ︸ n−m   t . the results show that no one has changed his opinion. from the perspective of mc theory [20], all the opinions compose the state space d = {1, 2, · · · ,n} which is decomposed into two irreducible closed sets d1 = {1, 2, · · · ,m}and d2 = {m + 1,m + 2, · · · ,n}. no matter what is the transfer probability between the internal states of d1 and d2, the states in d1 and d2 cannot communicate with each other. the principle can be simply expressed by the following figure 2: figure 2: transformation of opinions in scenario 1. (states 1 and 2 represent the opinions of {1}, states 3 and 4 represent the opinions of {0}) it can be seen from the above simulation that when there are only two diametrically opposed opinions, neither the influence of similarity, self-confidence, or authority will cause the dms to change their original opinions. this refusal to transfer from one opinion to another is the cause of the failure to reaches a consensus, which conforms the fact. in addition, when there are only {0} and {1} opinions, these hfes have been completely reduced to membership degrees, so scenario 1 describes only the extreme situation of the evolution of the hesitant fuzzy opinions. in the following, the hf-degroot opinion dynamics model is used to study the transformation trend of the opinions of incompletely opposite groups, which is also the general situation of the evolution of opinions. scenario 2. there are not just {1} and {0} among dms the following is a discussion on the general situations in which multiple opinions coexist. for convenience, we replace the last dm in scenario 1, whose opinion is neither {0} nor {1}. suppose that there are n dms, the first m-bit opinion is {1}, the n-m-1 bit opinion is {0}, and the last 1-bit dm opinion is expressed by a hfe h, the similarity measure between his opinion and the previous dms is as follows: sin = sni =   s1n, i = 1, 2, · · · ,m sm+1,n, i = m + 1,m + 2, · · · ,n− 1 1, i = n . https://doi.org/10.15837/ijccc.2020.4.3888 12 using eq.(12), the opinion transition matrix is obtained as follows: q4 =   p11 · · · p11 0 · · · 0 p1n ... ... ... p11 · · · p11 0 · · · 0 p1n 0 · · · 0 pm+1,m+1 · · · pm+1,m+1 pm+1,n ... ... ... ... ... ... ... 0 · · · 0 pm+1,m+1 · · · pm+1,m+1 pm+1,n pn1 · · · pn1 pn,m+1 · · · pn,m+1 pn,n   (19) here p11 = pij = 1 m + s1n (i = 1, 2, · · · ,m; j = 1, 2, · · · ,m) , p1n = pin = s1n m + s1n (i = 1, 2, · · · ,m) , pm+1,m+1 = pij = 1 n−m− 1 + sm+1,n (i = m + 1, · · · ,n−m− 1; j = m + 1, · · · ,n−m− 1) , pm+1,n = pin = sm+1,n n−m− 1 + sm+1,n (i = m + 1, · · · ,n− 1) , pn1 = pnj = s1n ms1n + (n−m− 1) sm+1,n + 1 (j = 1, 2, · · · ,m) , pn,m+1 = pnj = sm+1,n ms1n + (n−m− 1) sm+1,n + 1 (j = m + 1,m + 2, · · · ,n− 1) , pnn = 1 ms1n + (n−m− 1) sm+1,n + 1 . since sin > 0 (i = 1, 2, · · · ,n), then the elements of the n column of q4 are all positive, which satisfies the condition in theorem 3, the dms’ opinions will finally reach consensus. that is to say, based on the similarity degree of opinions, as long as there is a third opinion besides agreement and disagreement, these opinions will reach consensus in the end. similarly, by adding the influence of the self-confidence degree or authority degree, these opinions can also reach a consistent conclusion. in scenario 2, the opinions reach consensus because the transition matrix p is fully regular in which lim t→∞ pt = 1n (p1,p2, · · · ,pn). and pi (i = 1, 2. · · · ,n) can be seen as the weight of the initial hesitant fuzzy opinion zi (0) in the final opinion. by referring to the theory of mc, the transformation relationship between the opinions shown in scenario 2 can be expressed in figure 3 as follows: figure 3: transformation of opinions in scenario 2. (state 1 represents the opinions of {1}, state 2 represents the opinions of h, and state 3 represents the opinions of {0}) this is actually an irreducible aperiodic mc with the finite state space or ergodic mc. the final consensus opinion is the weighted average of the weight vector (p1,p2, · · · ,pn) and the initial opinion matrix (z1 (0) ,z2 (0) , · · · ,zn (0))t , which is also the prediction of the final opinion of the group and can provide help for the hesitant fuzzy decision-making problem. https://doi.org/10.15837/ijccc.2020.4.3888 13 4.3 the decision-making method based on the hf-degroot opinion dynamics model based on the above analysis in section 4.2, when the initial opinions are presented, we can get the prediction of the final opinions from a dynamic perspective. therefore, it can be applied to deal with the decision-making problems that change quickly. in the following, three corresponding algorithms will be given. algorithm ii. hf-degroot decision-making method based on the similarity degree step 1. identify the decision-making problem and the alternatives in s = { s(1),s(2), · · · ,s(m) } , and obtain the evaluation information of the alternatives in s(k) given by dms m1,m2, · · · ,mn in the form of hfss { h (k) 1 ,h (k) 2 , · · · ,h (k) n } (k = 1, 2, · · ·m) . step 2. obtain the similarity measure matrix. this step includes the use of eqs.(4) or (5) to calculate the similarity measures s(k)ij between the opinions from mi and mj on the alternative s (k). s(k) =   s (k) 11 · · · s (k) 1n ... · · · ... s (k) n1 · · · s (k) nn   (20) step 3. according to eq.(12), we obtain the dm’s opinion transition matrix p (k) on the alternative in s(k) as follows: p (k) =   p (k) 11 · · · p (k) 1n ... · · · ... p (k) n1 · · · p (k) nn   (21) step 4. calculate the limit of the matrix ( p (k) )t . if the p (k) is fully regular, lim t→∞ ( p (k) )t =   w (k) 1 w (k) 2 · · · w (k) n w (k) 1 w (k) 2 · · · w (k) n ... ... ... ... w (k) 1 w (k) 2 · · · w (k) n   (22) then we extract w(k) = ( w (k) 1 ,w (k) 2 , · · · ,w (k) n ) from the matrix in eq.(22) which is dms’ final weight vector for s(k), then go to the next step, otherwise the algorithm stops. step 5. use the operator (3) and w(k) to get the final opinions for s(k)(k = 1, 2, · · · ,m), and then sort them according to the score function in eq.(1) by the descending order. algorithm iii. hf-degroot decision-making method based on the self-confidence degree step 1-2. same as steps 1-2 in algorithm ii. step 3. determine the parameter ρ > 1 and use algorithm i to calculate the opinion transition matrix p (k). step 4-5. same as steps 4-5 in algorithm ii. algorithm iv. hf-degroot decision-making method based on the authority degree step 1-2. same as step 1-2 in algorithm ii. step 3. determine the parameter η > 1 and use eq.(16) to calculate the opinion transition matrix p (k). step 4-5. same as steps 4-5 in algorithm ii. 5 numerical examples and analysis this section presents a case of public health emergency with three different methods. https://doi.org/10.15837/ijccc.2020.4.3888 14 5.1 background although modern medicine has been quite developed, there are still many public health emergencies such as the h1n1 pandemic influenza in 2009 and the novel coronavirus (covid-19) in 20191. in response to the emergence and international spread of the emerging infectious diseases, who has enacted the international health regulations (ihr)2 that regulate the detection, assessment and response to the public health events. many countries have also established crisis response mechanisms to deal with the public health emergencies. the chinese government has established and revised a series of laws3 and regulations4 which specify that when public health emergency occurs, the health administration department should organize experts to comprehensively evaluate the events, then the central government and local governments shall establish corresponding emergency response plans. the national response framework5 established by the united states government is a guide for emergency response to all types of threats and hazards, including health emergencies. the response framework specify that the federal government, state and local governments shall form a three-level response system, and each of them shall play the required role in the process of incident assessment and graded emergency response. the assessment of the events is an important step in the active response to the public health emergencies, both for who and for countries around the world. but making a reasonable assessment is often very difficult. firstly, the assessment of such events is full of uncertainty because that many public health emergencies are unpredictable and lack of a basic understanding of hazard and transmission mode in the early stage. secondly, in the process of evaluation, the dms is hesitated to make a decision due to considering the event comprehensively from the economic, political, cultural and other aspects besides the medical perspective. thirdly, the events are constantly changing. so the hf-degroot opinion dynamics model in this paper can be used to support dms to make decision analysis from the perspective of dynamic development. suppose that a local government sets up a decision-making group m = {m1,m2, · · · ,m5} composed of government officials, medical experts, disease control experts, etc. they want to evaluate a public health event according to the pre-set four response levels s = { s(1),s(2),s(3),s(4) } . table 1: the hesitant fuzzy decision matrix m1 m2 m3 m4 m5 s(1) {0.2} {0.4, 0.3} {0.7, 0.2} {0.9, 0.8} {0.8, 0.7, 0.6} s(2) {0.9, 0.8} {0.4, 0.3, 0.2} {0.7, 0.4} {0.6, 0.4, 0.3} {0.3, 0.2} s(3) {0.7, 0.2} {0.9, 0.8} {0.2} {0.4, 0.2, 0.1} {0.7, 0.3} s(4) {0.7, 0.6} {0.8, 0.4} {0.5, 0.4} {0.2, 0.1} {0.6, 0.5, 0.4} the three decision-making algorithms introduced in section 4.3 will be applied in the following problems. 5.2 decision-making process based on algorithm ii the detailed decision-making process based on the similarity measure mentioned in section 4.3 will be shown as follows: step 1. according to the above analysis, the decision-making problem is to select the appropriate response level from the four alternatives. here, the dm’s opinions on the four response levels are listed in table 1. 1who, emergencies, https://www.who.int/emergencies/crises/en/, 2020. 2who, strengthening health security by implementing the international health regulations (2005), https://www.who.int/ihr/en/, 2019. 3the npc standing committee, law of the people’s republic of china on the prevention and treatment of infections diseases, http://www.npc.gov.cn/npc/c238/202001/099a493d03774811b058f0f0ece38078.shtml, 2020 4c. s. council, overall emergency plan for national public emergencies, http://www.gov.cn/yjgl/2006-01/08/ content_21048.htm, 2006. 5fema, national response framework, fourth edition, https://www.fema.gov/media-library/assets/documents /117791, 2020. https://doi.org/10.15837/ijccc.2020.4.3888 15 step 2. calculate the similarity degrees between these decision opinions for the response level s(k) (k = 1, 2, 3, 4) by using eq.(5). these results are listed in table 2-5. table 2: similarity degrees between dms’ opinions for s(1) s (1) ij m1 m2 m3 m4 m5 m1 1.0000 0.8419 0.6464 0.3481 0.4934 m2 0.8419 1.0000 0.7764 0.5000 0.6303 m3 0.6464 0.7764 1.0000 0.5528 0.6258 m4 0.3481 0.5000 0.5528 1.0000 0.8586 m5 0.4934 0.6303 0.6258 0.8586 1.0000 table 3: similarity degrees between dms’ opinions for s(2) s (2) ij m1 m2 m3 m4 m5 m1 1.0000 0.4646 0.6838 0.5918 0.4000 m2 0.4646 1.0000 0.7840 0.8586 0.9184 m3 0.6838 0.7840 1.0000 0.9184 0.6838 m4 0.5918 0.8586 0.9184 1.0000 0.7840 m5 0.4000 0.9184 0.6838 0.7840 1.0000 table 4: similarity degrees between dms’ opinions for s(3) s (3) ij m1 m2 m3 m4 m5 m1 1.0000 0.5528 0.6464 0.8174 0.9000 m2 0.5528 1.0000 0.3481 0.3945 0.4615 m3 0.6464 0.3481 1.0000 0.8709 0.7085 m4 0.8174 0.3945 0.8709 1.0000 0.8709 m5 0.9000 0.4615 0.7085 0.8709 1.0000 table 5: similarity degrees between dms’ opinions for s(4) s (4) ij m1 m2 m3 m4 m5 m1 1.0000 0.8419 0.8000 0.5000 0.8586 m2 0.8419 1.0000 0.7879 0.5257 0.8709 m3 0.8000 0.7879 1.0000 0.7000 0.9184 m4 0.5000 0.5257 0.7000 1.0000 0.6303 m5 0.8586 0.8709 0.9184 0.6303 1.0000 step 3. according to eq.(12), we calculate the dms’ transition weights for the response level s(k) (k = 1, 2, 3, 4) listed in table 6-9. table 6: the opinion transition matrix based on the similarity degrees for s(1) p (1) ij m1 m2 m3 m4 m5 m1 0.3003 0.2528 0.1941 0.1045 0.1482 m2 0.2246 0.2668 0.2071 0.1334 0.1681 m3 0.1795 0.2156 0.2777 0.1535 0.1738 m4 0.1068 0.1534 0.1696 0.3068 0.2634 m5 0.1367 0.1747 0.1735 0.2380 0.2772 https://doi.org/10.15837/ijccc.2020.4.3888 16 table 7: the opinion transition matrix based on the similarity degrees for s(2) p (2) ij m1 m2 m3 m4 m5 m1 0.3185 0.1480 0.2178 0.1884 0.1274 m2 0.1154 0.2484 0.1948 0.2133 0.2281 m3 0.1680 0.1926 0.2457 0.2256 0.1680 m4 0.1425 0.2068 0.2211 0.2408 0.1888 m5 0.1056 0.2426 0.1806 0.2071 0.2641 table 8: the opinion transition matrix based on the similarity degrees for s(3) p (3) ij m1 m2 m3 m4 m5 m1 0.2534 0.1401 0.1638 0.2072 0.2355 m2 0.1897 0.3431 0.1194 0.1353 0.2125 m3 0.1844 0.0993 0.2853 0.2485 0.1824 m4 0.2114 0.1020 0.2252 0.2586 0.2027 m5 0.2340 0.1559 0.1610 0.1974 0.2518 table 9: the opinion transition matrix based on the similarity degrees for s(4) p (4) ij m1 m2 m3 m4 m5 m1 0.2500 0.2104 0.2000 0.1250 0.2146 m2 0.2091 0.2484 0.1957 0.1306 0.2163 m3 0.1902 0.1873 0.2377 0.1664 0.2183 m4 0.1490 0.1566 0.2086 0.2980 0.1878 m5 0.2007 0.2036 0.2147 0.1473 0.2337 table 10: final weight assignment matrix based on algorithm ii w(i) m1 m2 m3 m4 m5 s(1) 0.1898 0.2136 0.2052 0.1858 0.2056 s(2) 0.1638 0.2099 0.2123 0.2166 0.1975 s(3) 0.2168 0.1601 0.1925 0.2124 0.2182 s(4) 0.2014 0.2027 0.2117 0.1689 0.2153 step 4. obtain the final opinion transition matrix for dms shown in table 10. step 5. use the ahfwa operator expressed in eq.(3) to obtain the final result and sort it as s(1) � s(2) � s(4) � s(3), so s(1) is the emergency response level of the event. 5.3 decision-making process based on algorithm iii the study on how a person’s self-confidence affects the final decision will be shown in this section. assume that parameter ρ = 5 in the algorithm. step 1-2. the similarity matrix for s(k) (k = 1, 2, 3, 4) is obtained in the same way shown in step 1 and step 2 in section 5.2. step 3. use algorithm i to calculate the transition weight of the dm’s opinion for s(k) (k = 1, 2, 3, 4) listed in table 11-14. step 4. obtain the final opinion weight matrix shown in table 15. step 5. use eq.(3) to aggregate evaluations and sort the four levels as: s(1) � s(2) � s(3) � s(4). so s(1) is the final emergency response level. https://doi.org/10.15837/ijccc.2020.4.3888 17 table 11: the opinion transition matrix based on self-confidence degree for s(1) p (1) ij m1 m2 m3 m4 m5 m1 0.7203 0.1011 0.0776 0.0418 0.0592 m2 0.1087 0.6453 0.1002 0.0645 0.0813 m3 0.1795 0.2156 0.2777 0.1535 0.1738 m4 0.0479 0.0689 0.0761 0.6888 0.1183 m5 0.0707 0.0904 0.0897 0.1231 0.6261 table 12: the opinion transition matrix based on self-confidence degree for s(2) p (2) ij m1 m2 m3 m4 m5 m1 0.7371 0.0571 0.0840 0.0727 0.0491 m2 0.0624 0.5935 0.1053 0.1154 0.1234 m3 0.1680 0.1926 0.2457 0.2256 0.1680 m4 0.1092 0.1585 0.1695 0.4180 0.1447 m5 0.0455 0.1045 0.0778 0.0892 0.6829 table 13: the opinion transition matrix based on self-confidence degree for s(3) p (3) ij m1 m2 m3 m4 m5 m1 0.2534 0.1401 0.1638 0.2072 0.2355 m2 0.0799 0.7231 0.0503 0.0570 0.0896 m3 0.0760 0.0409 0.7055 0.1024 0.0752 m4 0.1283 0.0619 0.1367 0.5501 0.1230 m5 0.1869 0.1245 0.1286 0.1577 0.4023 table 14: the opinion transition matrix based on self-confidence degree for s(4) p (4) ij m1 m2 m3 m4 m5 m1 0.6666 0.0935 0.0889 0.0556 0.0954 m2 0.2091 0.2484 0.1957 0.1306 0.2163 m3 0.0869 0.0856 0.6517 0.0760 0.0998 m4 0.0598 0.0629 0.0838 0.7180 0.0754 m5 0.1044 0.1059 0.1117 0.0767 0.6014 table 15: final weight assignment matrix based on algorithm iii w(i) m1 m2 m3 m4 m5 s(1) 0.2456 0.2285 0.1062 0.2140 0.2057 s(2) 0.2404 0.2198 0.1202 0.1600 0.2595 s(3) 0.1285 0.2252 0.2769 0.2075 0.1619 s(4) 0.2319 0.1037 0.2372 0.2153 0.2119 5.4 decision-making process based on algorithm iv this section discusses the role of the authority in decision making. as in section 5.3, our calculation starts from step 3 in algorithm iv and it is assumed that the authoritative parameter η = 4. step 1-2. the similarity matrix for s(k) (k = 1, 2, 3, 4) is obtained in the same way as shown in step 1 and step 2 in section 5.2. step 3. according to eq.(16), we calculate the transition weight of the dm’s opinion corresponding to the response level s(k) (k = 1, 2, 3, 4) listed in table 16-20. step 4. obtain the final opinion weight matrix shown in table 20. step 5. use eq.(3) to obtain the aggregation result and get s(2) � s(4) � s(3) � s(1), so the best https://doi.org/10.15837/ijccc.2020.4.3888 18 table 16: the opinion transition matrix based on the authority degree for s(1) p (1) ij m1 m2 m3 m4 m5 m1 0.6319 0.1330 0.1021 0.0550 0.0779 m2 0.5367 0.1594 0.1237 0.0797 0.1005 m3 0.4667 0.1401 0.1805 0.0998 0.1130 m4 0.3235 0.1162 0.1284 0.2324 0.1995 m5 0.3879 0.1239 0.1230 0.1687 0.1965 table 17: the opinion transition matrix based on the authority degree for s(2) p (2) ij m1 m2 m3 m4 m5 m1 0.6515 0.0757 0.1114 0.0964 0.0651 m2 0.3429 0.1845 0.1447 0.1584 0.1695 m3 0.4468 0.1281 0.1634 0.1500 0.1117 m4 0.3993 0.1448 0.1549 0.1687 0.1323 m5 0.3209 0.1842 0.1371 0.1572 0.2006 table 18: the opinion transition matrix based on the authority degree for s(3) p (3) ij m1 m2 m3 m4 m5 m1 0.5759 0.0796 0.0931 0.1177 0.1338 m2 0.4835 0.2187 0.0761 0.0863 0.1354 m3 0.4750 0.0639 0.1837 0.1600 0.1175 m4 0.5174 0.0624 0.1378 0.1583 0.1241 m5 0.5499 0.0916 0.0946 0.1160 0.1479 table 19: the opinion transition matrix based on the authority degree for s(4) p (4) ij m1 m2 m3 m4 m5 m1 0.5714 0.1203 0.1143 0.0714 0.1226 m2 0.5140 0.1526 0.1202 0.0802 0.1329 m3 0.4844 0.1193 0.1514 0.1060 0.1390 m4 0.4119 0.1082 0.1442 0.2059 0.1298 m5 0.5011 0.1271 0.1340 0.0920 0.1459 table 20: final weight assignment matrix based on algorithm iv w(i) m1 m2 m3 m4 m5 s(1) 0.5442 0.1349 0.1191 0.0925 0.1094 s(2) 0.5224 0.1153 0.1302 0.1261 0.1061 s(3) 0.5461 0.0899 0.1070 0.1242 0.1329 s(4) 0.5296 0.1239 0.1250 0.0918 0.1296 response level is s(2). 6 comparative analysis by the discussions in section 5, the decision-making method based on the proposed hf-degroot opinion dynamics model will be further studied, including the comparison of the influence of the three factors on the decision making, the comparison with other decision-making method, and the discussion on the sensitivity of the parameters. to further analyze these algorithms, algorithm v which gives the dms the weight w = { 1 2, 1 8, 1 8, 1 8, 1 8 } https://doi.org/10.15837/ijccc.2020.4.3888 19 is added and uses the ahfwa operator to perform the operation. the results of the four algorithms are listed in the following table 21: table 21: comparison of sorting results by different methods s(1) s(2) s(3) s(4) rank algorithm ii 0.5578 0.5016 0.4625 0.4840 s(1) � s(2) � s(4) � s(3) algorithm iii 0.5678 0.5315 0.4950 0.4681 s(1) � s(2) � s(3) � s(4) algorithm iv 0.4177 0.6901 0.4243 0.5538 s(2) � s(4) � s(3) � s(1) algorithm v 0.4551 0.6789 0.4482 0.5416 s(2) � s(4) � s(1) � s(3) 6.1 comparison between algorithms the following analysis is divided into two categories, including the comparison among these three decision-making methods and the comparison between the three methods and algorithm v. 6.1.1 comparison of algorithms ii, iii and iv to validate these proposed methods, we will provide some comparative analysis on the final decision results. the results of table 21 show that both algorithms ii and iii choose the response level s(1), and the ranking results of the two algorithms are similar. this result shows that the influence of the self-confidence on the final decision-making result is not particularly obvious. different from the previous two algorithms, after adding the authority degree in algorithm iii, a completely different order is obtained. it can be seen that the authority degree has a much greater impact on the final decision-making result than the similarity degree and self-confidence degree. in terms of the speed to reach a consensus for the three algorithms, we compare the power r ∈ r of the opinion transition matrix when the same criterion is reached for the first time. it should be noted that the criterion of reaching a consensus here is that the difference between all the elements in the power matrix and the corresponding elements in the limit matrix is less than 10−4, which means that if the element p̃ (k)ij (i,j = 1, 2, · · · , 5; k = 1, 2, 3, 4) in ( p (k) )r satisfies ∣∣∣p̃ (k)ij −w(k)j ∣∣∣ ≤ 10−4, the smallest r that satisfies the inequation is the object by comparison. the speed to reaching a consensus for the three algorithms are shown in the following figure 4. figure 4: the speed to reaching a consensus for algorithms ii, iii and iv by using matlab2017b, the results are calculated and shown in figure 4. it can be seen that in algorithms ii and iv, the group’s opinions can reach consensus after at most 10 times of transmission, while algorithm iii needs at least 20 times. this shows that the similarity degree and the authority degree have little difference on the influence of consensus, while confidence is the most important factor to delay the consensus reaching. https://doi.org/10.15837/ijccc.2020.4.3888 20 6.1.2 comparison between algorithm iv and algorithm v to demonstrate the effectiveness of the proposed algorithms, a static decision algorithm v is introduced to compare with algorithm iv. because in section 5.4, the parameter η=4, the corresponding weight of algorithm v is set as w = { 4 8, 1 8, 1 8, 1 8, 1 8 } . it can be seen from table 21 that although the final opinion ultimately chooses the response level s(2), the ranking results are different. the reason for this difference is that the original opinions are constantly learning and changing as they spread which exactly reflects the effectiveness and rationality of these dynamic algorithms. 6.2 sensitivity analysis based on parameter values in order to further understand the model, the following is mainly about the parameter sensitivity analysis for algorithm iii and algorithm iv. 6.2.1 parameter sensitivity analysis of algorithm iii in algorithm iii, the parameter ρ is used to adjust the self-confidence degree. the following figure 5 will show the change of the final score values of the response levers caused by ρ. 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 the response level 0.46 0.48 0.5 0.52 0.54 0.56 0.58 t h e s co re o f th e f in a l o p in io n s figure 5: scores of the response levels corresponding to different values of the parameter ρ here, four groups of data (ρ = 2, 5, 10, 15) are selected to compare the final scores of each response level. it can be seen that ρ has little impact on the final score and ranking results, especially when the values of ρ increase from 10 to 15, the scores of each response level do not increase rapidly, which indicates that algorithm iii has good robustness. 6.2.2 parameter sensitivity analysis of algorithm iv in algorithm iv, η is a parameter used to adjust the authority degree of dms. the following figure 6 will show the change of the final score values of the response levels caused by η. figure 6: the score of each response level corresponding to different parameter values of η https://doi.org/10.15837/ijccc.2020.4.3888 21 four groups of data ( η = 2, 4, 10, 15) are selected to compare the final scores of each response level. it can be seen that η has a great impact on the final scores and ranking results and enlarge the gap between the scores of each response level, especially in η = 10, 15. meanwhile, when η is small, such as η = 2, 4, the gap increases rapidly, and when η is large, such as η = 10, 15, the gap increases slowly. it can be concluded that the change of the authority degree has big impact on the final opinion obviously. 7 conclusions the study on the law of opinion dissemination can help dms to understand information comprehensively, improve decision-making efficiency and predict the tendency of opinions to make correct decisions. with the help of degroot opinion dynamics model, this paper has studied the dynamic evolution of hesitant fuzzy information, and proposed the corresponding decision-making methods. firstly, the multiplication of real matrix and hesitant fuzzy matrix have been defined for the description of the evolution law of the hesitant fuzzy opinions. moreover, the corresponding operation properties have been discussed, which have provided the foundation for the application and extension of the hesitant fuzzy theory. secondly, the hf-degroot opinion dynamics model has been proposed. this includes combining the characteristics of the hesitant fuzzy data, constructing the transformation matrix from the three aspects: the similarity degree, self-confidence degree and authority degree, and simulating the learning and transformation process of the hesitant fuzzy opinions. the research has shown that, under the premise of the hypothesis in this paper, as long as there are not only two opinions of agreement and disagreement, these hesitant fuzzy opinions will eventually reach consensus. therefore the hfdegroot opinion dynamics model can be used to predict the final consistent opinions based on dms’ initial opinions. finally, the hf-degroot decision-making method has been proposed and applied to the emergency decision making for the public health events. the comparison and simulation results have shown that this method 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(2014). the todim analysis approach based on novel measured functions under hesitant fuzzy environment, knowledge-based systems, 61, 48-58, 2014. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: su z.; xu z. s.; zhao h., liu s. s. (2020). hesitant fuzzy degroot opinion dynamics model and its application in multi-attribute decision making, international journal of computers communications & control, 15(4), 3888, 2020. https://doi.org/10.15837/ijccc.2020.4.3888 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 3, month: june, year: 2020 article number: 3836, https://doi.org/10.15837/ijccc.2020.3.3836 ccc publications a deep belief network and case reasoning based decision model for emergency rescue d. chang, r. fan, z.t. sun dan chang school of economics and management beijing jiaotong university, china no.3 shang yuan cun, haidian district, beijing, china dchang@bjtu.edu.cn rui fan* school of economics and management beijing jiaotong university, china no.3 shang yuan cun, haidian district, beijing, china *corresponding author: 17120607@bjtu.edu.cn zitong sun school of finance central university of finance and economics, china shahe education park, changping district, beijing, china 2019210333@email.cufe.edu.cn abstract the frequent occurrence of major public emergencies in china has caused significant human and economic losses. to carry out successful rescue operations in such emergencies, decisions need to be made as efficiently as possible. using earthquakes as an example of a public emergency, this paper combines the deep belief network (dbn) and case-based reasoning (cbr) models to improve the case representation and case retrieval steps in the decision-making process, then designs and constructs a decision-making model. the validity of the model is then verified by an example. the results of this study can be applied to maximize the efficiency of emergency rescue decisions. keywords: deep belief network, case-based reasoning, decision support, emergency rescue, earthquake. 1 introduction with the rapid development of the times today, the society is also suffering from all sides of the test. as small as daily life, such as carbon dioxide emissions from automobiles [7], to various disaster events such as chemical enterprises’ production pollution [13], earthquakes [17], etc., will cause great harm to the environment on which we live. this paper takes an earthquake as a context for studying https://doi.org/10.15837/ijccc.2020.3.3836 2 emergency rescues. an earthquake can endanger human life and property and can have significant social, economic, political, and environmental impacts. decision-making guiding the emergency response should therefore be as efficient as possible. research shows many studies of emergency responses to earthquakes, but most of them are text-based. moreover, there are still errors made in emergency rescuing, mainly due to inaccurate decision-making systems. to solve this problem, this paper mainly uses the deep belief network (dbn) and case-based reasoning (cbr) models to construct an earthquake emergency rescue decision-making [9, 21] model. first, a large number of text-based cases are normalized and then case retrieval is performed to match the cases with the highest similarity [19, 23]. and last, this paper applies the case study [11, 15] to verify the model. the earthquake emergency rescue decision-making model constructed in this paper improves the existing decision-making reasoning method and expands its applicability and feasibility. 2 literature review deep belief network (dbn) is a probability generation model developed by bioneural networks and shallow neural networks to infer the distribution of data samples from joint probability distributions [14]. the dbn model generates data based on maximum probability by training the weights of neurons in the network structure, forming high-level abstract features, and improving the classification performance of the model. it is considered a good method for knowledge representation and uncertainty reasoning, and its application is a hot topic in data mining. yushi chen et al. applied dbn to the classification of hyperspectral data and improved its accuracy [4]. takashi kuremoto et al. applied dbn to forecasting the time series and proved the forecasting accuracy. and they pointed out that the model proposed can be applied to the approximating and short-term prediction of chaotic time series [12]. oriol vinyals et al. applied dbn to the speech recognition task under mismatched noise conditions, and proved that dbn was better than mlp under the clean condition [20]. walter h. l. pinaya et al. applied dbn to characterize differences in brain morphometry in schizophrenia and proved that dbn was more accurate than svm [16]. a. deoras et al. applied dbn to the semantic tagging which was a sequence classification task. and they proved the model had improved generalization capability especially when some features were missing or noisy [5]. yan gao et al. applied the dbn algorithm to the problem of the performance prediction of cloud service, and proved its effectiveness by experiment [8]. jinsheng yang et al. constructed a wireless local area network (wlan) fingerprint location database with an improved dbn algorithm [22]. these studies show that the dbn algorithm is effective in solving the problem of data loss in data mining. case-based reasoning (cbr) simulates the process of learning from previous incidents to solve current problems. case experience obtained according to cbr semantics can be readily applied to the target case. cbr can prevent the repetition of past errors and accelerate the learning process by adding case correction and case reasoning to the case base. jaeseok huh et al. applied the cbr algorithm to the travel routes for large-scale as/rss and proved the method proposed made travel routes optimized quickly [10]. petr berka introduced that how the cbr approach can be used for sentiment analysis[2]. jiyong ding et al. applied the improved cbr algorithm to the predictions of project performance, then took nanjing hf project as an example, and proved the innovation of the method [6]. d. a. adeniyi et al. proposed the chi-square case-based reasoning model and applied it to the realization of an automated risk calculator and death prediction, and then proved the precision rate and predictive quality [1]. chanvarasuth et al. applied the cbr algorithm to investment decisions, solved the choice of optimal future investment, and proved the method was superior to the traditional method by experiment [3]. michael schnell et al. applied the cbr algorithm to make the medical coding practices and a short evaluation more easily [18]. in summary, dbn can retain the characteristics of sample attributes, adapt dynamic data to a large extent, and solve the problem of data deletion. the cbr algorithm is suitable for handling cases with high repetition, and through case reuse and correction, can improve the existing case library, widely used in decision support. therefore, this paper combines with the improved dbn algorithm and uses cbr technology to design an earthquake emergency rescue decision model, aiming to improve https://doi.org/10.15837/ijccc.2020.3.3836 3 the reliability and accuracy of emergency rescue decision-making and improve the applicability and feasibility of the decision-making model in an earthquake disaster. 3 decision model of earthquake emergency rescue based on dbncbr 3.1 analysis of earthquake emergency rescue decision 3.1.1 analysis of the constituent elements of earthquake emergency rescue decision by analyzing the history of earthquakes, the main causes of human fatalities due to earthquakes can be classified into three categories: building collapse, secondary disasters, and social environment effects. the level of destruction resulting from an earthquake is related not only to the seismic characteristics of the earthquake itself, but to such things as the seismic capacity of local buildings and local population density as well as secondary disasters like floods and fires. thus, the factors influencing earthquake destructiveness can be classified into three categories: earthquake characteristics, natural environment and social environment. 1. earthquake characteristics (1) magnitude earthquake magnitude is a direct measure of earthquake strength: the higher the magnitude, the greater the energy released by the earthquake and the stronger its destructive force. in general, the direct economic loss produced by the earthquake will increase with the level of its magnitude. table 1: comparison of direct economic loss and capital demand for reconstruction of china’s post-2008 earthquake disaster earthquake name earthquake magnitude direct economic losses /$100 million estimated funding requirements for reconstruction /$100 million completion time for rehabilitation /years requirement/loss ratio 2008 wenchuan 8.0 8523.09 10000.00 3 1.17 2010 yushu 7.1 228.47 320.00 3 1.40 2013 lushan 7.0 665.14 860.00 3 1.29 2014 ludian 6.5 198.49 270.00 3 1.36 2017 jiuzhaigou 7.0 118.00 3 table 1 demonstrates that the destructive force of the earthquake increases with its magnitude. this is especially clear when we note that the direct economic loss from the wenchuan earthquake (magnitude 8.0) is 37.3 times that of the yushu earthquake (magnitude 7.1). (2)intensity earthquake intensity is the degree of influence of the earthquake on the surface and the building engineering. in general, the higher the intensity of an earthquake, the greater the damage to local buildings, the higher and faster the building collapse rate, and the less likely people will be able to https://doi.org/10.15837/ijccc.2020.3.3836 4 escape from the disaster site, leading to an increase in the number of casualties. (3)focal depth focal depth refers to the vertical distance from the earthquake source to the surface. the three focal depth categories are shallow, middle and deep source earthquakes. shallow source earthquakes with a depth of less than 70 km account for more than 90% of all measured earthquakes globally. so far, the greatest depth measured of an earthquake source is 720 km. for earthquakes of similar magnitude, the focal depth determines the earthquake’s intensity and thus indirectly determines its damage intensity. because the energy released by the earthquake is constantly attenuated in the process of propagation, the deeper the earthquake source, the lower its effect on the surface and its damage intensity; conversely, the shallower the source, the higher the earthquake intensity and its destructiveness. (4) earthquake time the time when the earthquake occurs directly affects the number of people inside buildings and their ability to respond, such as if the earthquake strikes late at night when most people are sleeping indoors. often, nighttime earthquakes cause more casualties and damage than those occur during the day. 2. natural environment (1) physical characteristics the physical characteristics of the earthquake zone has a major effect on the destructiveness of the earthquake. search and rescue work in a mountainous region can be very difficult and can be further hampered by secondary hazards such as landslides. (2) weather weather conditions such as rain, heat, cold and wind after an earthquake affect the launch of rescue work and the survival rate of people who are buried or injured and can lead to further losses. (3) secondary disasters secondary disasters are disasters caused by earthquake damage, such as fires, floods, explosions, contamination of air and water, landslides, etc. for example, the 1906 san francisco earthquake ruptured gas pipelines, leading to further casualties and property damage. in 2008, after the wenchuan earthquake, landslides and other secondary disasters killed tens of thousands of people or about 14.4 percent of the total number of deaths attributed to the earthquake. 3. social environment (1) population density population density mainly includes two aspects: the number of people indoors and the population density in the earthquake area. when other conditions are the same, the higher the population density, the higher the number of casualties caused by the earthquake, and vice versa. (2) seismic performance of buildings the seismic performance of buildings and their fortification ability are related to the strictness of the local building construction standards. the better the seismic performance of buildings, the lower the building collapse rate and speed, and the less the economic losses and casualties caused by the earthquake. (3) people indoors or outdoors the more people there are indoors when an earthquake occurs, the more the casualties. conversely, if more people are outside in an open area at the time of the earthquake, the number of casualties will be lower. https://doi.org/10.15837/ijccc.2020.3.3836 5 (4) building collapse rate casualties caused by an earthquake are directly proportional to the building collapse rate. (5) capability and rapidity of regional emergency rescue the 72-hour golden law of life-saving shows that the earlier the start of rescue operations, the more people can be saved. rescue teams in the affected region and the organizations and government authorities behind them therefore need to maximize their planning and implementation speed. in addition, the better a rescue team’s ability, the greater the number of people saved. (6) material reserves if there are enough material reserves in the place where the earthquake has occurred, the transportation of materials can be appropriately reduced during emergency rescue, which can also avoid the waste of materials. but if there is very little material reserves in the area, a lot of materials will be needed after the earthquake, which must be considered in emergency rescue. from the above analysis, we can conclude the earthquake emergency rescue decision-making factor table, as shown in table 2. table 2: decision factors for earthquake emergency rescue classification decision-making factors earthquake characteristics magnitude intensity focal depth earthquake time natural environment physical characteristics weather secondary disasters social environment population density seismic performance of buildings people indoors or outdoors building collapse rate capability and rapidity of emergency rescue material reserves 3.1.2 analysis of decision process of earthquake emergency rescues to respond quickly after an earthquake, every step in the decision-making process of emergency rescuing should be analyzed in detail. from a macro point of view, this process is circular and the target requiring a decision changes according to the rescue situation. after completing all of the rescue mission, a new command center should be set up, and the aid construction activities will be carried out. then the new decision-making objectives will be determined according to the new scene situation. the crisis caused by an earthquake occurs suddenly, but its scope and extent lasts a long time. the emergency decision-making process for earthquake disasters is shown in figure 1. after receiving the disaster information of the earthquake scene, the command center, the government function department and the transfer advisory body will make the decision target according to the emergency and corresponding decision plan. optimal plans will be identified and implemented through analysis and evaluation of multiple groups of procedures. after the rescue, those organizations will judge whether the meeting meets the decision goal, or redefine the decision goal until the rescue work is over, and then start the rescue work. after the earthquake, the police will be informed by the alarm notification, and the local government departments started the emergency plan according to the earthquake level, and reported the disaster information to the higher authorities. establish a rescue command center to communicate https://doi.org/10.15837/ijccc.2020.3.3836 6 figure 1: earthquake emergency rescue decision process chart instructions to emergency departments at the same level and below. the purpose of the immediate start-up of the emergency plan is to first minimize the disaster level; at the same time, a rescue expert group is established to consult on specific emergency measures, and a special department is set up under the emergency rescue command center to participate in the rescue, to clarify its responsibilities and to initiate specific rescue measures. after receiving the notice of the emergency command center, the main rescue organizations will organize personnel to set up relevant special departments to carry out rescue activities. although the rescue activities of various organizations will be carried out at the same time, there will be a certain priority due to the constraints of objective conditions. https://doi.org/10.15837/ijccc.2020.3.3836 7 3.2 learning and construction of deep belief networks 3.2.1 learning method of dynamic incremental expectation maximization of bayesian network parameter after obtaining the new sample data information, the prior probability of the node will change from p(θ|i0) to p(θ|d,i0), in which d is dynamically added information after data update, d = d1,d2, · · · ,dn, and i0 is prior information. when the sample data changes, the prior information involved in the calculation needs to synthesize the current input information d based on the original prior information i0. it can be seen that the idea of this algorithm is to dynamically adjust the new information based on the original sample data. using dynamic incremental expectation maximization bayesian network parameter learning method can simplify the research process and carry reasonable sample data. before learning the parameters, two concepts should be assumed as the basis: 1. for conditional probability parameters θ, the dynamically added sample information d and prior information i0 are independent of each other; 2. obtain conditional probability parameters θ through calculation of the likelihood function; however, since the learning information is derived from dynamic updates, in order to improve the efficiency of obtaining conditional probability parameters, each data update relies entirely on new data, and re-learning parameters is inefficient. we can change our thinking, consider the original sample data set separately from the new data set, and treat the new learning process as a “superposition” of conditional probability parameters. however, the above-mentioned superposition is not a simple numerical superposition. there is correlation knowledge between the historical condition probability parameter and the new parameter, which needs to be taken into consideration during the calculation process. the considerations are as follows: 1. conditional probability parameters θ of the original data; 2. likelihood function calculation of new data. the above two aspects are the basis for the establishment of dynamic incremental expectation bayesian network parameter learning methods. here, the likelihood function of the dynamically added sample data set is represented by a function f(θ) , written as: f (θ) = ηmd (θ) −d ( θ, θ̄ ) (4 − 1) where, θ̄ is old parameter value; θ is new parameter value; ηmd(θ) represents the data information after introducing the likelihood function of the dynamically added data d is independent, and is not affected by the posterior conditional probability parameter θ̄; η is learning rate, the learning rate determines the degree of correlation between the old and new parameters. the value range of η is [0, 1], when it approaches 1, it means that the parameter update speed is faster, and the effect is more significant; when it approaches 0, it means that the parameter update speed is slow, and the effect becomes weak; d(θ, θ̄) is estimated distance between θ̄ and θ. calculating the distance estimate can reduce the parameter adjustment range and make the calculated new value closer to the historical value. this probability is a χ2 distance. its calculation is as follows: d ( θ, θ̄ ) = ∑ i ∑ j pθ̄ (pai = j) χ 2 ( θij, θ̄ij ) (4 − 2) the above algorithm is a learning step based on the expectation maximization algorithm. in order to adapt to the dynamic update of sample data, it is required that the parameter learning results should also be dynamically updated as the data changes. therefore, the above algorithm is adjusted as: θtijk =   θtijk + η ( p[xi=k,pai =j|d]−p[pai =j|yt+1,θt]θtijk p̂(pai =j) ) , p (pai = j) 6= 0 θtijk, p (pai = j) = 0 (4 − 3) https://doi.org/10.15837/ijccc.2020.3.3836 8 if there is no missing sample data, the function (4-3) can be simplified as: θtijk =   η + (1 −η) θtijk, p ( xi = k|yt+1,θt ) = 0andp ( pai = j|yt+1,θ t ) = 1 (1 −η) θtijk, p ( xi = k|yt+1,θt ) = 1andp ( pai = j|yt+1,θ t ) = 0 θtijk, others (4 − 4) in this paper, an adaptive adjustment method which uses no difference to drive learning is proposed, the basic idea is: when the parameter learning method tends to converge, slow down the learning rate η, and when the parameter learning method reaches the maximum value, the error will expand, and the learning rate η needs to be accelerated. assumption: if there is a node xi in the bayesian network topology, the prior information of the node is pai = j, the learning rate of the conditional probability parameter θij is ηij. at the beginning of the error-driven η adaptive adjustment algorithm, the initial setting of the relevant data of the algorithm value: 1. set the prior probability of the node xi: p [xi = k|pai = j] = θ t ijk,k = 1, 2, · · · ,m; 2. set the initial learning rate ηij. it is generally set to close to 1 in the initial situation so that the learning rate can be increased at the beginning. 3. t = 0,δt = 0. t is the number of occurrences, δt indicates the number of occurrences of pai = j since the latest update ηij. according to the conditions set above, the calculation process of the adaptive adjustment algorithm is as follows: after the sample set is updated, the dynamic incremental expectation maximization algorithm is adopted to estimate its conditional probability parameters as θtijk, η in function (4 − 3) turn into ηij. if the error between the estimated value and the mean is large, increase the learning rate of the learning conditional probability parameter θij, and θt = 0. in other cases, θt = θt + 1. 3.2.2 calculation of decision property weight at present, the methods of constructing and analyzing multi-indicator system mainly include hierarchical analysis and main component analysis. among them, the hierarchical analysis method (ahp) decomposes the relevant elements of decision analysis into targets, standards, indicators, etc. on this basis, quantitative and qualitative analysis can be used for the thinking process of mathematical decision-making and reducing quantitative information. it is mainly applicable to situations where the factor structure is complex and the indicator needs to be quantified according to experience. there are many factors affecting the casualties and economic losses of earthquake disasters, based on expert experience, the target layer, the standard layer and the index layer are used to construct the index system of the key influencing factors of earthquake casualty. the target layer is the key factor of earthquake casualty, and the standard layer includes earthquake, natural environment and economic and social factors. the indicator layer contains 13 indicators. details of the indicator system are shown in table 3. after determining the hierarchy of the indicator system, it is necessary to quantify the effect of various factors on the casualties at the indicator level. according to the indexing process of the above analysis method and the knowledge associated with the decision factors, the judgment matrix is constructed by comparing the indicators under the same standard layer, the weight values of each indicator are calculated, and the transformation from qualitative index to quantitative index is completed. 3.3 introduction of cbr 3.3.1 pre-processing of properties in case in the process of earthquake emergency rescue decision-making, a variety of decision-making factors are handled, and in the case of unprocessed, the decision factors are usually characterized by different https://doi.org/10.15837/ijccc.2020.3.3836 9 table 3: decision-making factors for earthquake emergency rescue the target layer decision-making factors for earthquake emergency rescue the standard layer earthquake characteristics natural environment social environment the index layer magnitude intensity focal depth earthquake time physical characteristics weather secondary disasters population density seismic performance of buildings people indoors or outdoors building collapse rate capability and rapidity of emergency rescue material reserves types of data, such as numerical type (continuous type, discrete type, etc.), descriptive data (boolean type, image) and so on. when establishing a case library, the presentation of the case should be considered firstly and the data type of the decision factor needs to be unified. the general method of processing data is to divide continuous numerical values and symbolize non-numeric information. at the same time, it is necessary to unify the unit of data and the value interval. after the data is processed, the system assigns equal weights to each property in the initial stage. the basic type of earthquake emergency rescue case data information is shown in table 4. table 4: types of earthquake case data information type specific unit symbol numerical data numerical data are generally divided into discrete and continuous data discrete data: the number of values is limited, intermittent, and discontinuous continuous data: data that takes values within a full numeric interval, or may be indeterminate values indeterminate values intensity: magnitude 8 descriptive data non-numerical data is generally descriptive and is usually described when non-numerical data records are recorded collapse: loose, completely collapsed the essence of discrete non-discrete data is to divide the attribute value intervals of non-discrete data into several regions, so that they are in the same region, or to convert the data within the description range into discrete amounts of the same property values, thus updating the original data to discrete values. 3.3.2 case library building based on k-d tree 1. define the set of case properties x , xi ∈ x, i = 1, 2, · · · ,m where is ith attribute. 2. according to the expert experience, the initial weights of the case characteristic attributes are given, and the attributes xi in the case characteristic attributes are arranged in descending order according to the size of the initial weight. the weight of the case characteristic attributes is recalculated as the case data is updated. https://doi.org/10.15837/ijccc.2020.3.3836 10 3. select the attribute xmax with the highest weight among the root nodes of the k-d tree. 4. cut the attribute value range of the root node as ymax = y1max ∪y2max · · · ∪ynmax according to the area range, and yimax ∩yjmax = ∅, i 6= j. segmentation criteria are determined by attribute type. 5. the region node obtained after the property value domain is split as the root node, adding the second most weighted property to split the new property value domain. 6. repeat the steps of 4 and 5 to split and add properties, all of which are split, and end the process. 3.4 case retrieval based on similarity function k-d tree 3.4.1 similarity evaluation functions establishing bayesian network parameters learning to improve similarity function: similarity (x,y) = 1 − √√√√ m∑ i=1 wif (xi,yi) (4 − 5) where xi and yi is ith attribute value of history case xi and target case yi; m is number of attributes in the case. f (xi,yi) =   0, xi = yi 1 −p, xi = xandp (yi = x|y1,y2, · · · ,yi,yi+1, · · · ,ym) = p 1 −q, yi = y andp (xi = y |x1,x2, · · · ,xi,xi+1, · · · ,xm) = q q, others (4 − 6) 3.4.2 the k-d case library retrieving the retrieval of the k-d case library starts with the root node of the k-d tree and traverses the k-d tree structure, and the similarity of the target case to the historical case is calculated by the similarity function. if the similarity of the matching case is within the set expert value range, the retrieved case is determined to be available and stored in the output sequence. after the search for the entire k-d case library is completed, the output of the most similar historical case is used as the result of the search. the search process is divided into four steps: 1. during the retrieval process, if there is no searchable node, exit the retrieval; 2. if the similarity of the matching case calculated from the current node is lower than the set expert value, the result is discarded and the d value of the k-d tree is recorded as val; 3. if the historical case is not fully fit in the retrieval process, the search will be continued in depth according to the nature of the k-d tree, and the case with the highest similarity obtained will be taken as the output result; 4. exit the retrieval process. 3.5 earthquake emergency rescue model structure based on dbn-cbr the state of crisis after an earthquake is a sudden outbreak, but the scope and depth of its impact cannot be dealt with and ended in a short time. after receiving the earthquake information, the command center, the government function department and the transfer advisory body will make the decision-making objectives according to the emergency plan, and make the corresponding decision plan, through the evaluation, analysis and comparison of multiple sets of programs, to determine the optimal plan, so as to implement the rescue work. after the rescue, those organizations will judge the extent of the decision-making objectives according to the effectiveness of the rescue, or re-set the https://doi.org/10.15837/ijccc.2020.3.3836 11 decision-making objectives until the end of the rescue work, then start the construction work. in summary, the dbn-cbr-based earthquake emergency rescue model is shown in figure 2. the implementation of the earthquake emergency rescue decision model can be summarized into the following three steps: 1. according to the historical case of earthquake emergency rescue obtained from data mining, establish the original earthquake emergency rescue case library, unify the representation of cases, and delete unreasonable and unrelated cases of earthquake emergency rescue. 2. on the basis of the earthquake emergency rescue case database, the deep confidence network of earthquake emergency rescue properties is established, the bayesian network parameters are studied, the relevant knowledge between the attributes is obtained, and the weight of each attribute is calculated. 3. the decision maker takes the matching case as a reference to further analyze the target case to determine that the matching result will lead to the solution that is closest to the decision objective. after the case is corrected, the modified case is stored in the case library. 4 experimental results and analysis 4.1 data preparation 4.2 system analysis this paper selects the 6.5-magnitude earthquake in ludian county, zhaotong city, yunnan province, as an example, and collect the original case information as shown in table 5. table 5: original data table of zhaotong earthquake in yunnan classification decision factors earthquake characteristics magnitude magnitude 6.5 intensity magnitude8,9 focal depth 12km earthquake time 16:30 natural environment physical characteristics unstable zone, very vulnerable to damage weather lightning strike secondary disasters severe landslides, floods social environment population density 277/km2 seismic performance of buildings bad people indoors or outdoors 75% building collapse rate 97% capability and rapidity of emergency rescue 85% material reserves bad pre-processing of case data, discrete data standards as shown in table 6. based on this, the raw data of zhaotong earthquake in yunnan are processed, and the sample data after the dispersion are obtained, as shown in table 7. according to the calculation of the dynamic incremental maximum expected parameter learning method in equation 4-1, the weights of the attributes of each seismic decision factor can be obtained, as shown in table 8. https://doi.org/10.15837/ijccc.2020.3.3836 12 figure 2: the dbn-cbr-based earthquake emergency rescue model https://doi.org/10.15837/ijccc.2020.3.3836 13 table 6: discrete properties corresponding to the decision factors of earthquake emergency rescue attribute code decision factors discretization interval a magnitude 1:(1-4.5) 2:(4.5-6) 3:(6-) b intensity 1:(1-4) 2:(5-8) 3:(9-12) c focal depth 1:(300-) 2:(60-300) 3:(0-60) d earthquake time 1:(9:00-17:00) 2:(17:00-23:00) 3:(23:00-9:00) e physical characteristics 1:(stable) 2:(general) 3:(fragile) f weather 1:(normal) 2:(general) 3:(serious) g secondary disasters 1:(normal) 2:(feneral) 3:(serious) h population density 1:(0-25) 2:(25-100) 3:(100-) i seismic performance of buildings 1:(good) 2:(general) 3:(bad) j people indoors or outdoors 1:(0-29%) 2:(30%-49%) 3:(50%-100%) k building collapse rate 1:(0-29%) 2:(30%-49%) 3:(50%-100%) l capability and rapidity of emergency rescue 1:(50-100%) 2:(30%-49%) 3:(0%-29%) m material reserves 1:(adequate) 2:(general) 3:(worse) 4.3 system modeling and experiment the earthquake emergency rescue decision model system is built on the platform environment of matlab r2018b, visual studio 2018 and access 2016. the design idea is to provide the calculation support for the sample data by matlab, where the fuzzy reasoning is used to evaluate the seismic hazard level, to determine whether a rescue plan is needed immediately, and the case reasoning results are displayed in the form established in vs. 1. earthquake danger level simulation set the input variable membership function: earthquake magnitude membership function, natural environment membership function and social environment membership function, set output variable membership function, that is, earthquake danger level membership function. the fuzzy reasoning decision algorithm is designed, and the fuzzy rule browser user displays the membership function of the input and output corresponding to each fuzzy control rule. by setting the input amount, the fuzzy rules used and the corresponding output can be obtained directly by fuzzy reasoning. in the fuzzy inference system editor interface, click view-surface, which pops up the surface viewer interface to display a 3d graphic of the fuzzy system simulation results. the result shows that the earthquake danger level in zhaotong, yunnan, is serious and the rescue plan needs to be given immediately. 2.dbn-cbr-based earthquake emergency rescue decision-making module interface enter the information for the target case and calculate the similarity to match the case. in the case library, the matching case with the highest similarity of 0.754 was retrieved, and this case is the sichuan lushan earthquake, its rescue plan was to send 7,491 rescue soldiers to the disaster area to carry out the rescue. also, there were more than 10,000 soldiers as a reserve force in chengdu military region and the armed police force. after arriving in the disaster area, a total of 24 townships in lushan county, baoshan county and other surrounding areas were checked, more than 2,000 people had their medical treatment and health checks. and health and epidemic prevention had been carried out. 4.4 results and analysis from the experiment results, the seismic factor of the zhaotong earthquake in yunnan was 0.955, the natural environment factor was 0.537, the social environmental factor was 1.04, the danger level of 2.04 was obtained by fuzzy reasoning, and the danger level of zhaotong earthquake in yunnan was https://doi.org/10.15837/ijccc.2020.3.3836 14 table 7: discrete properties corresponding to the decision-making factors of zhaotong earthquake emergency rescue in yunnan attribute code decision factors discrete attribute a magnitude 3 b intensity 2 c focal depth 3 d earthquake time 1 e physical characteristics 3 f weather 2 g secondary disasters 3 h population density 3 i seismic performance buildings 3 j people indoors or outdoors 3 k building collapse rate 3 l capability and rapidity of emergency rescue 1 m material reserves 3 table 8: weight of factors in earthquake emergency rescue decision attribute code decision factors weight earthquake characteristics magnitude 0.1286 intensity 0.1117 focal depth 0.0814 earthquake time 0.1014 natural environment physical characteristics 0.0640 weather 0.0463 secondary disasters 0.0841 social environment population density 0.1160 seismic performance buildings 0.1035 people indoors or outdoors 0.0650 building collapse rate 0.0273 capability and rapidity of emergency rescue 0.0557 material reserves 0.0150 https://doi.org/10.15837/ijccc.2020.3.3836 15 figure 3: 3d map of earthquake danger level simulation results figure 4: earthquake emergency rescue decision case match results based on dbn-cbr https://doi.org/10.15837/ijccc.2020.3.3836 16 severe according to the fuzzy rules, and the solution needed to be given immediately. then this paper sets the decision goal and starts the case search. during the case matching, the lushan earthquake magnitude 7.0, intensity of 9, the depth of the earthquake is 13km, while the zhaotong earthquake magnitude 6.5, intensity of 8 and 9, the depth of the earthquake is 12km; there are a wide range of secondary mountain disasters, manifested in mudslides and landslides. as for social environment, the level of economic development is low, there are several state-level poor counties. and seismic performance of houses is weak, collapse rate of buildings after the earthquake is very high, and there were heavy casualties in these two earthquakes. and when we determine the earthquake danger level, the earthquake danger level of these two places is the same as serious. it is immediately to have a rescue plan. the seismic characterization of the two places is very similar. the result of the final case match is that the lushan earthquake has the highest similarity with the zhaotong earthquake in yunnan, so the emergency rescue plan of the lushan earthquake can be used as the decision-making support of the zhaotong earthquake in yunnan. in fact, after the zhaotong ludian earthquake, nearly 10,000 officers and soldiers from chengdu, beijing, the air force’s second artillery and armed police military regions all arrived in 13 disasterstricken counties and 13 heavily affected townships. it is similar with the sichuan lushan earthquake. the actual rescue plan of the two earthquakes both focuses on sending a large number of troops to check the personnel and prevent secondary geological disasters. the actual situation and the experimental results of this paper are highly consistent. 5 conclusions taking earthquakes as an example, this paper combines dbn and cbr algorithms to apply to emergency rescue decision-making, improves case representation and case retrieval steps in the case reasoning process, and then, on the basis of the existing seismic emergency rescue theory system, designs a decision-making model for an earthquake emergency rescue by using the logical structure of case reasoning. finally, the proposed model is verified by a real case. therefore, the model proposed in this paper has practical significance in emergency rescue decision-making. it can improve the deficiency of the existing decision-making reasoning method, enrich existing emergency decision-making theory, and can help decision makers make more targeted decisions. on this basis, this paper makes the following suggestions: 1. when making emergency decisions, in addition to drawing on the advice of experts, scientific decision-making methods are necessary; 2. continuously improve the existing case library to improve the selectivity of case matching; 3. optimize decision-making methods from various angles to improve the accuracy of case matching. funding this work was funded by beijing 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(2020). emergency rescue decision model based on deep belief network and case-based reasoning, international journal of computers communications & control, 15(3), 3836, 2020. https://doi.org/10.15837/ijccc.2020.3.3836 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 1, month: february, year: 2020 article number: 1001, https://doi.org/10.15837/ijccc.2020.1.3690 ccc publications stochastic fuzzy algorithms for impairment of assets management m.i. bolos, v. bogdan, i.a. bradea, c.d. sabau-popa, d.n. popa marcel-ioan bolos, victoria bogdan*, claudia-diana sabau-popa, dorina-nicoleta popa department of finance and accounting, faculty of economic sciences, university of oradea, oradea, romania, marcel_bolos@yahoo.com, dianasabaupopa@yahoo.ro, dorinalezeu@yahoo.com *corresponding author: victoria.bogdan10@gmail.com ioana-alexandra bradea department of informatics and economic cybernetics, bucharest university of economic studies, bucharest, romania, alexbradea1304@yahoo.com abstract the present paper aims to analyze the impairment of tangible assets with the help of artificial intelligence. stochastic fuzzy numbers have been introduced with a dual purpose: on one hand to estimate the cash flows generated by tangible assets exploitation and, on the other hand, to ensure the value ranges stratifications that define these cash flows. estimation of cash flows using stochastic fuzzy numbers was based on cash flows generated by tangible assets in previous periods of operation. also, based on the lagrange multipliers, were introduced: the objective function of minimizing the standard deviations from the recorded value of the cash flows generated by the tangible assets, as well as the constraints caused by the impairment of tangible assets identification according to which the cash flows values must be equal to the annual value of the invested capital. within the determination of the impairment value and stratification of the value ranges determined by the cash flows using stochastic fuzzy numbers, the impairment of assets risk was identified. information provided by impairment of assets but also the impairment risks, is the basis of the decision-making measures taken to mitigate the impact of accumulated impairment losses on company’s financial performance. keywords: fuzzy logic-based methods, tangible assets, impairment, management tool, risk, fuzzy stochastic number, cash flows, performance. 1 introduction impairment of assets plays an important role in ensuring the financial performance of companies in the medium and long term, but also in identifying those tangible assets that no longer generate economic benefits for the company. impairment expenses may have high values at different times which may affect the financial stability of the company, with direct consequences on the financial results but also on the remuneration of the company’s shareholders. https://doi.org/10.15837/ijccc.2020.1.3690 2 this is the reason why, managers and shareholders are interested in developing management tools that provide solutions for early identification of possible impairment of tangible assets but also of the possible impairment risks. ias 36 impairment of assets, offer an algorithm for testing the impairment of tangible assets. according to this algorithm, a tangible asset is considered to be impaired when its recoverable amount is lower than its carrying amount. therefore, the fundamental equation of impairment of assets is based on the two values, the carrying amount obtained after deducting from the book value the accumulated depreciation and the recoverable amount of the tangible assets. on the other side, as ias 36 defines, an asset’s recoverable amount is the higher value of its fair value less disposal costs and its value in use. most often the fair value less disposal costs is the net selling price, for those tangible assets for which there is an active market. the net selling price, as a component part of the impairment test of tangible asset is obtained according to the market rules. in the situation where on the market there is a tangible asset traded according to the market rules, on the basis of a sale contract, then the sale price from that contract will be taken into account for the impairment test. in case such a sale contract does not exist, then the best estimate of the net sale price is the market price from which the costs of disposing of the asset are deducted. finally, if there is neither sale price in the market, nor sales contract, then the international accounting standard recommends the use of the latest sale prices resulting from transactions with similar tangible assets in the same economic branch [23]. therefore, the net sale price of the tangible assets taken into account for the impairment test must comply with the requirement for its best estimate, as well as the requirement regarding the source of information regarding the formation of the net sale price of the tangible asset. concerning the asset’s value in use, this can be determined in two different stages. in the first stage, cash flows are estimated for the entire asset’s life cycle, as well as for its residual value. in the second stage, cash flows can be updated with an adequate discount rate to allow estimations for future capitalization generated by tangible assets in present values, using the discounting techniques. when evaluating the updated cash flows that are the base for computing the value in use of the tangible asset, the following will be considered: the best estimates established on the basis of the best economic life conditions existing for the remaining life of the tangible asset, five years forecasts approved by the company, exploration of previous forecasts adjusted with certain growth / decrease rates, etc. the value in use of the tangible asset although it requires complex calculations, however, is most often used in performing the impairment tests. as this impairment test is a rational algorithm based on comparing the carrying amount of the tangible assets with the recoverable value of the tangible assets, however, it has a number of disadvantages resulting from the mechanism of performing the impairment test, namely: 1. it is based on estimated values of the cash flows generated by the asset’s exploitation, although the depreciation is based on the actual cash flows generated by the asset’s exploitation; 2. the impairment test is carried out at different periods of time which cannot surprise in real time whether the asset is impaired or not, or if there are real indications that the tangible asset has started to lose value; 3. does not provide to company decision makers the operational information about impairment of assets, in order to take measures to mitigate the impact of the financial loss. the purpose of this paper is to provide solutions to the above-mentioned problems with the help of stochastic fuzzy numbers and langrange multipliers to identify in real-time the loss of tangible assets value, in order to determine the value of the impairment, but also to provide an appropriate tool expense management with impairment. this is because any loss of tangible assets value means an expense to the company. the introduction of fuzzy stochastic numbers in the study of impairment of tangible assets has a double impact: on one hand they allow the estimation of the cash flows for future periods based on the actual cash flows resulting from the exploitation of the tangible assets, and on the other hand the fuzzy numbers allow stratification. of the value ranges resulting from estimating future cash flows in order to accurately identify those intervals where the impairment of tangible assets risk is high. https://doi.org/10.15837/ijccc.2020.1.3690 3 at the same time, the lagrange multipliers method allows the determination of the impairment value, respectively of the loss of value for the tangible assets in the conditions of establishing the objective function as the one minimizing the deviation of the cash flows values from their planned value and of introducing the condition that the value of the annual cash flows is equal to the annual value of the capital invested in tangible assets. the innovative nature of the research paper is thus the introduction of stochastic fuzzy numbers in the management of expenses generated by the impairment of tangible assets but also in identifying the value ranges for which the impairment of the tangible assets risk is the highest. 2 literature review the impairment of tangible assets represents an important area of research, due to the fact that affects in a direct way the financial performance of companies in the medium and long term and identify those tangible assets that no longer generate economic benefits. over time, researchers from all over the world have devoted their attention for studying this problem, in present being recorded 388 isi scientific papers in the wos database that include in their topic the key words: "impairment of assets". the first articles published in this area of research appeared in 1993, and over time, their number has grown steadily, as shown in the figure 1. most of these falls into the following areas of research: finance (30%), management (15%), economics (15%) and business (8%). regarding the country of origin, usa ranks first with 35% of all published scientific studies, followed by china (13%), australia (10%), england (8%), italy (5%), germany (4%), canada (4%), romania (3%), france (3%) and portugal (3%). in this section most important related articles conducted on this topic were reviewed. thus, andre et al.(2015)[3] has analysed the effects of mandatory ifrs adoption on conditional conservatism in europe. they demonstrated that firms which do not record asset impairment when evidence suggests the probable need to do so, will show an important reduction in conditional conservatism. the conservative accounting practices were targeted also by lawrence et al.[19]. they have modelled the impact of extant accounting rules on the accounting rules, on conservative accounting rules, especially those that require assets to be written down when their fair values drop sufficiently below their book values. feng et al. [10] has analysed the correlations between the internal control over the financial reporting and the operating activity of a company. their findings support the general hypothesis that internal control over financial reporting has an economically significant effect on firm operations. glaum et al.[13] has analysed compliance for a large sample of european companies mandatorily applying ifrs, targeting the disclosures required by ifrs 3 business combinations and ias 36 impairment of assets. the results have showed substantial non-compliance, both at the companies and country level, indicating that accounting traditions and other specific factors play an important role despite the use of common reporting standards under ifrs. riedl [22] realized an examination of long-lived asset impairments. his results reveal that economic factors have a weaker association with write-offs reported after sfas no. 121. among the most relevant papers that studied the impairment of assets, was noticed the study of [2] agarwal et al. (2009) which analysed the financial decision over the life cycle and the implications for regulation, focusing on the financial mistakes and regulatory strategies that might help avoid these mistakes. gox and wagenhofer [15] studied the optimal impairment rules and the optimal precision of the accounting information, in order to provide comparative static results that lead to predictions on the determinants of impairment rules. the impact of impairment of assets in the bank sector was studied by badertscher et al. [4]. analysing the fact that fair value provisions in u.s. accounting rules enabled the financial crisis by depleting banks’ regulatory capital, which triggered asset sales, their results have shown that fair value accounting losses had minimal effect on regulatory capital and these provisions did not affect the commercial banking industry in the ways commonly alleged. https://doi.org/10.15837/ijccc.2020.1.3690 4 figure 1: number of isi articles targeting the impairment of assets chalmers et al. [8] analysed whether the adoption of ifrs can be associated with a loss of potentially useful information about intangible assets. their results showed that there is a negative correlation between accuracy and dispersion of analysts’ earnings forecasts and aggregate reported intangibles after ifrs adoption, especially for firms with high levels of underlying intangible assets. carlin and finch [7] studied the connection between the discounted cash flow modelling and the asset’s recoverable amount. the reporting entities consider the rate selection as a factor of influence for the outcomes of the impairment testing process conducted under ifrs. their study showed the existence of variances between independently generated risk-adjusted discount rates and those disclosed as having been used by a sample of large listed companies. the effects of managerial discretion and stock market reactions after adopting the ifrs standards were studied by hamberg [17]. their results have shown that by replacing the historical cost with fair value measures, the amount of capitalized goodwill increased, due to the fact that goodwill impairments under ifrs are considerably lower than goodwill amortizations and impairments made under gaap. the conclusions revealed that the adoption of ifrs increased reported earnings. godfrey and koh [14] examined whether goodwill impairment write-offs reflect firms’ investment opportunities during the first years of the us goodwill impairment accounting regime. the results supported the international accounting standards board and financial accounting standards board statement that an impairment test regime can reflect firms’ underlying economic attributes and also can reflect that managers use discretion to reduce contracting costs. recently, goodwill impairment was the most studied issue in the accounting literature, as we found in abughazaleh et al. [1], hamberg and beisland [16], giner and pardo [11], korosec et al. [18], schatt et al. [24]. bolos et al. [5], analysing the investment decisions optimisation process within a company, created a fuzzy logic managerial decision tool for assets acquisition that combines, with very good results, the acquisition cost of assets with their economic performance. after explaining and testing their proposed model, they concluded that the fuzzy logic multi-criteria algorithm becomes a tool that provides for any company, regardless the number of assets and the number of selection criteria, the most convenient combination of assets cost and their economic and technical performance. this is relevant, as matemilola and ahmad [20] highlighted, because most times tangible fixed assets are more preferred by firms when they want to obtain long term debt financing. another study developed by bolos et al. [6], targeted the differences registered between the projected assets production capacity and the volume of finished goods and services that are sold to consumers. the company is exposed to cash-flow deficits risk, which makes it difficult for the company to pay the debts to its financial creditors. in these terms, mamdani fuzzy logic system was developed and the probabilistic values of the influence factors in the credit process are identified, in order to subsequently detect the risks due to probabilistic cash-flow deficits. to date, there is no indexed article in the wos database, which use stochastic fuzzy algorithms https://doi.org/10.15837/ijccc.2020.1.3690 5 to analyse the impairment of assets. fuzzy intelligence is used especially in the following areas of research: computer science, artificial intelligence, engineering electrical electronic, automation control systems, operation research and management science. in business intelligence, however, applications of fuzzy technology are still rather scarce, even though the original motivation and justification of fuzzy intelligence was to model and improve human communication (see meyer and zimmermann [21]). only 2% of the total isi articles targets the economic field, but none of them can be framed in the accounting domain. only 2% of the total isi articles targets the economic field, but none of them can be framed in the accounting domain. the purpose of the research paper is to provide a tool needed to manage the impairment expenses of the tangible assets by introducing the fuzzy stochastic numbers and lagrange’s multipliers method, in order to determine the value of the impairment but also to determine the value ranges in which the impairment of tangible assets risk is higher. the research methodology involved the following stages: stage i theoretical foundation of stochastic fuzzy numbers; stage ii modeling the impairment value of an asset, two assets and n-assets using fuzzy stochastic numbers; stage iii testing the methods for computing the value of the impairment and the impairment risk for one asset, respectively for two assets. 3 stochastic elements and the impairment of assets as noted, the impairment of an asset is recognised when the carrying amount (vca) exceeds the recoverable amount of the asset (vra). the recoverable amount is defined as the largest value of the net fair value of an asset (fair value less costs of disposal) and the asset’s value in use. the fundamental equation of asset impairment is always described by the existence of the inequality between its carrying amount (vca) and recoverable amount (vra), with two possible situations, namely: (a) if vca > vra then the asset is impaired and the company will record an impairment expense that will affect its financial performance. the causes that lead to such situations are diverse and depend most on the behaviour of the assets when they are used in the company’s operational activity. as we know, the causes of impairment can be both internal and external. in case of internal causes, we considered that the maintenance and revision costs of tangible assets contribute significantly to the impairment of assets, as they could have an exponential evolution as their useful life increases; (b) if vca < vra then the asset is not impaired and the company will continue to benefit from its operation. during this period, the evolution of the operating revenues generated by the asset’s operations must be monitored. these revenues are dependent on the production capacity of the asset (cp) and the possibility that the products/services are sold on the market (cap,s). also, the operating expenses of the asset that can completely deplete the operational revenues with the passage of time, must be monitored. in this case, the impairment test can not be avoided. the amount of value in use of an asset is dependent on the amount of cash flows (cfa) generated each year of its useful life. theorem 1. if the cash flow value is a constant over the asset’s useful life (cfa = c), with c ∈ r and the asset’s useful life is lfa ∈ [0; t], then the updated value (cfu) at the discounted rate (dr) of the cash flows over the life of the asset will be as follows: cfu = c ∫ t 0 e−drtdt = c dr ( 1 −e−drt) (1) proof. let a be the asset that generates a constant cash flow of the form cfa = c with c ∈ r and with lfa ∈ [0; t] , then the present value of the future cash flows generated by the use of the asset will be: https://doi.org/10.15837/ijccc.2020.1.3690 6 1. for the base year of asset’s life span cfa (0) = c 1 (1+dr)0 2. for the first year of asset’s life span: cfa (1) = c 1 (1+dr)1 ; 3. for the second year of asset’s life span: cfa (2) = c 1 (1+dr)2 ; 4. for year t of asset life: cfa (t) = c 1 (1+dr)t ; throughout the life of the asset lfa ∈ [0; t] the aggregate form of cash flows will be quantified as follows: cfa (lfa) = t∑ i=0 c 1 (1 + dr)i ; lfa ∈ [0; t] (2) if the economic life of the asset is indefinite, cfa (lfa) →∞, then we can write: cfa (lfa) = lim i→∞ c [( 1 + dr i ) i dr ]−rai = ce−drt (3) if cash flows occur continuously over the life of the asset, then they shall be determined as follows: cfa (lfa) = ∫ t 0 ce−drtdt = c dr ( 1 −e−drt ) (4) theorem 2. the cash flows generated over the life of the asset lfa ∈ [0; t] as a stochastic process with standard normal distribution (µ = 0 and σ = 1) have the dynamic equation of the form: ∆cfa = µcfa∆t + σcfa∆z (5) proof. the asset’s life span is shared in time intervals of the form t0 ≤ t1 ≤ t2 ≤ . . . ≤ tt−1 ≤ tt so that any increase/change in the cash flows value generated by the company’s asset can be written as follows: cfa(tt) cfa(t0) = cfa(t1) cfa(t0) × cfa(t2) cfa(t1) × . . .× cfa (tt) cfa (tt−1) (6) by logarithmizing of the above expression, it is obtained: ln cfa(tt) cfa(t0) = ln n∑ t=1 cfa (tt) cfa (tt−1) (7) according to the central limit theorem in the probability theory, it is known that for very high values of n, when n →∞, we will obtain that ∑n t=1 cfa(tt) cfa(tt−1) → 0, which means that the sum of the ratios of the cash flow formula, between two consecutive moments, has the mean µ = 0 and the distribution σ = 1. this will mean that the ratio cfa(tt) cfa(tt−1) has a log-normal distribution, ln cfa(tt) cfa(tt−1) is normally distributed, and ln ∑n t=1 cfa(tt) cfa(tt−1) the terms of the sum are random, independent and finite variation variables. to write the cash flows variations (∆cfa) as a stochastic process, is used the expression of the asset’s growth rate that generated cash flows over its entire lifetime (rcfa): rcfa = ∆cfa cfa = cfa (tt) −cfa(t0) cfa(t0) = cfa (tt) cfa(t0) − 1 (8) the above relation can be rewritten as: rcfa + 1 = cfa (tt) cfa(t0) (9) by logaritmizing, the above expression becomes: ln(rcfa + 1) = ln cfa (tt) cfa(t0) (10) https://doi.org/10.15837/ijccc.2020.1.3690 7 for lower values of rcfa, the expression ln(rcfa + 1) = rcfa will be: ln cfa(tt) cfa(t0) = rcfa. because lncfa(tt) cfa(t0) has a normal distribution with the arithmetic mean µ = 0 and the variance σ = 1, we can write that ∆cfa cfa = µ∆t + σ∆z. the above expression can be rewritten as such: ∆cfa = µcfa∆t + σcfa∆z (11) where: ∆z = ε √ ∆t with ε – random variable with standard normal distribution respectively e (ε) = 0 and σ2ε = e ( ε2 ) − [e (ε)]2 = e ( ε2 ) = 1. theorem 3. the probability that the cash flows cfa (lfa) take the values [cfamin (lfa) ; cfamax (lfa)] throughout the assets lifetime, as they evolve after a logarithmic function, is established with the relation: cfa (t0) e (µ−12 σ2)tt−σ √ tt ≤ cfa (tt) ≤ e( µ−12 σ 2)tt+σ √ ttcfa (t0) (12) proof. the cash flows generated by companies’ tangible assets are the basis for the assets’ value in use determination. their evolution is logarithmic as the operating revenues begin to decrease as their useful life increase. the asset’s value in use (v ua) and the cash flows generated over its lifetime (cfa (lfa)) establishes the relation: v ua = lncfa (lfa) . the equation of stochastic dynamics was described in theorem 2, according to which: ∆cfa = µcfa∆t + σcfa∆z (13) according to ito’s lemma between v ua and cfa (lfa) the following relation is established: dv ua = ( ∂v ua ∂t + ∂v ua ∂cfa µcfa + 1 2 ∂2v ua ∂cfa 2 σ 2cfa 2 ) dt + ∂v ua ∂cfa σcfadz (14) the calculation of derivatives leads to the following results, namely: ∂v ua ∂t = 0; ∂v ua ∂cfa = 1 cfa ; ∂2v ua ∂cfa 2 = − 1 cf2a . the results obtained are replaced in the equation above and we obtain: dv ua = ( 1 cfa µcfa − 1 2 σ2 1 cfa 2 cfa 2 ) dt + σ 1 cfa cfadz (15) or d (lncfa ) = ( µ− 1 2 σ2 ) dt + σdz (16) from theorem 2 can be noticed that ln cfa(t) cfa(t0) is normally distributed with the following distribution: ln cfa(tt) cfa(t0) ≈ θ (( µ− 12σ 2 ) tt,σ √ tt ) . for standard normal distribution we apply the formula of probabilities and it is obtained: p (µ−ασ ≤ z ≤ µ + ασ) = 2n (α) − 1 (17) it results that: p (( µ− 1 2 σ2 ) tt −ασ √ tt ≤ ln cfa (tt) cfa (t0) ≤ ( µ− 1 2 σ2 ) tt −ασ √ tt ) = 2n (α) − 1 (18) solving the above inequation it results that: e(µ− 1 2 σ 2)tt−ασ √ tt ≤ cfa (tt) cfa (t0) ≤ e(µ− 1 2 σ 2)tt+ασ √ tt (19) or cfa (t0) e (µ−12 σ2)tt−ασ √ tt ≤ cfa (tt) ≤ e( µ−12 σ 2)tt+ασ √ ttcfa (t0) (20) https://doi.org/10.15837/ijccc.2020.1.3690 8 in conclusion, it can be argued that in regard to impairment of tangible assets, the elements of stochasticity that play an important role in the determination of the asset’s value in use are the cash flows that are generated over the asset’s useful life. if the cash flows are constant over the useful life of the asset, then they have the value equal to: cfa (lfa) = ∫ t 0 ce −drttdt = c dr ( 1 −e−drtt ) . if the cash flows have a stochastic evolution, after a normally distributed allocation function, over the asset’s useful life, then the likelihood that the cash flows will take certain values within a certain range will be: cfa (t0) e (µ−12 σ2)tt−ασ √ tt ≤ cfa (tt) ≤ e( µ−12 σ 2)tt+ασ √ ttcfa (t0) (21) 4 the use of stochastic fuzzy numbers in modelling discounted future cash flows generated by the use of tangible assets companies’ managers are required to make assumptions and judgements regarding the estimated future cash flows and discount rate. because estimates of future cash flows and determination of discount rate often require significant estimates, judgments, and assumptions to be made, they are vulnerable to potential misstatement. as reflected in ias 36, estimated future cash flows should be based on appropriately detailed underlying assumptions, which include changes in working capital and capital expenditure. overhead costs relating to the day-to-day use of asset, as well as future overheads costs, are only included to the extent that they can be attributed directly, or allocated on a reasonable and consistent basis. as we found by ginevičius and gudačiauskas [12] value of the discounted net cash flows was calculated for brand valuation. we can observe that most evaluation models of estimated future cash flows used for other non-current assets, both tangible and intangible, are based on discounting rate in order to calculate an updated value. as dzitac et al. highlighted in [9], when we assess, judge or decide we usually use a natural language in which the words do not have a clear, definite meaning. consequently, we need fuzzy numbers to express linguistic variables, to describe the subjective judgement of a decision maker in a quantitative manner, and the most often used are triangular fuzzy numbers. the use of stochastic fuzzy numbers allows the modelling of estimated future cash flows on the range of values and time that result from probability calculations when the cash flow distribution function is normally distributed, with the mean µ = 0 and the square mean variance σ = 1. the purpose of modelling using stochastic fuzzy numbers is to identify the impairment of asset risk, respectively, if in the future, the company will record an expense that may cause deterioration in financial performance. the triangular fuzzy numbers will also be introduced for modelling the estimated future cash flows generated by the use of assets. definition 4. let a ∈ r and b, c two positive real numbers. we say about the variable (cfa) that is a triangular fuzzy number and we denote ã∆cfa = (a,b,c) if the fuzzy subset ã∆cfa ∈ r and its function of membership is given by the relation: 1. if b,c > 0, then the membership function has the form: ã∆cfa (cfax) =   1 − a−cfax a−b for any b ≤ cfax ≤ a 1 − cfax−a c−a for any a ≤ cfax ≤ c 0 in the rest (22) 2. if b = c then ã∆cfa (a) = 1 and ã ∆ cfa (cfax) = 0 for any cfax 6= a, where: cfax is the cash-flow value at a certain time. the level sets of the fuzzy triangle number described above are: [ ã∆cfa ]α = [a1 (α) ; a2 (α)] , where a1 (α) = b + (a− b) α and a2 (α) = c− (c−a) α with α ∈ [0, 1]. https://doi.org/10.15837/ijccc.2020.1.3690 9 definition 5. the triangular fuzzy number is called the stochastic fuzzy number if for any a ∈ r and for any two positive real numbers b, c, the sub-set ã∆cfa fulfils cumulatively the following conditions: the membership function for any b,c > 0 is: ã∆cfa (cfax) =   1 − a−cfax a−b for any b ≤ cfax ≤ a 1 − cfax−a c−a for any a ≤ cfax ≤ b 0 in the rest (23) for b = c it results according to definition 4 that ã∆cfa (a) = 1 and ã ∆ cfa (cfax) = 0 for any cfax 6= a. the values of the variable cfa (lfa) on the interval [b,c] are given by the relation: cfa (t0) e (µ−12 σ2)tt−ασ √ tt ≤ cfa (tt) ≤ e( µ−12 σ 2)tt+ασ √ ttcfa (t0) (24) thus: b = cfa (t0) e (µ−12 σ2)tt−ασ √ tt and c = cfa (t0) e (µ−12 σ2)tt+ασ √ tt; the set of membership degree determined by the membership function will be:[ µ ( cfa (t0) e (µ−12 σ2)tt−ασ √ tt ) ; µ ( cfa (t0) e (µ−12 σ2)tt+ασ √ tt )] , corresponding to the variation level considered [b,c]; the variable cfa (lfa) is a random variable with density distribution function f (cfa) = 1√ 2πσ2 e −(cfa−µ) 2 2σ2 and with the distribution function written as primitive of the density distribution function∫ +∞ −∞ 1√ 2πσ2 e −(cfa−µ) 2 2σ2 dt = 1. the fuzzy stochastic triangular number ã∆cfa (cfax) introduced above is used to model the estimated future cash flows generated by the normal company’s asset operation. the company purpose is to maximize the revenues obtained from asset’s operation within a reasonable period of time, in order to ensure the payments recovery related to the invested capital in the asset (kinv) , but also to cover the payments generated by its running costs. the major advantages of using stochastic triangular fuzzy numbers is that allow the setting of cash flows value ranges for future periods of time and an easy processing of these flows with fuzzy numbers. 5 modeling the impairment of assets risk with stochastic fuzzy numbers 5.1 stochastic fuzzy modelling for impairment of assets risk for one asset theorem 6. is considered an asset a1 whose contribution to company sales is (w1) , generating cash flows cfa1 expressed with the stochastic fuzzy numbers, ã∆cfaef : x → [0, 1]. the impairment of the asset value (vimp) will be established as follows: vimp = w1ã∆cfaef −r1kinv1; (25) with: w1 = r1kinv1 ã∆cfa ef https://doi.org/10.15837/ijccc.2020.1.3690 10 proof. when the company owns only one asset (a1) , the following assumptions apply: the asset generates inflows from the obtained revenues modelled with the stochastic fuzzy triangular numbers ã∆cfa1 (reva1) and outflows generated by the operating expenses of the asset, also modelled with stochastic triangular fuzzy numbers ã∆cfa1 (expa1) of the form: w1cfa1 = w1 1 dr ( 1 −e−drt ) [ã∆cfa (reva1) − ã ∆ cfa ( expa1 ) ] = w1ã∆cfaef (26) the generated cash flow must be at least equal to the part of the invested capital in the asset, over the reference period in which the impairment analysis is performed, denoted (rikinvi). also, to avoid impairment and expenses that affect the financial performance, the company sets as its objective the minimization of the deviation between the expected cash flows and the effective ones realized during the period of the asset’s operation after a relation of the form: min w21 { 1 dr ( 1 −e−drt )[ ã∆cfapl− ã ∆ cfa ef ]}2 (27) the lagrangian function is: l = min w21 { 1 dr ( 1 −e−drt )[ ã∆cfapl− ã ∆ cfa ef ]}2 −λ ( w1ã ∆ cfa ef −r1kinv1 ) (28) for which the optimum conditions are: { ∂l ∂w1 = 0 ∂l ∂λ = 0 (29) by carrying out the calculations, we obtain:  2w1 { 1 dr ( 1 −e−drt )[ ã∆cfa pl− ã∆cfaef ]}2 −λã∆cfaef = 0 w1ã ∆ cfa ef −r1kinv1 = 0 (30) we note α = 2 1 dr ( 1 −e−drt ) and we obtain that: w1 = λã∆cfa ef[ ã∆cfa pl− ã∆cfaef ]2 (31) by replacing the expression of w1 in the last equation, we obtain: λã∆cfa ef[ ã∆cfa pl− ã∆cfaef ]2 ã∆cfaef = r1kinv1 (32) hence, it results the calculation formulae for parameter λ of the form: λ= r1kinv1 [ ã∆cfa pl− ã∆cfaef ]2 ã∆cfa ef 2 (33) by replacing the parameter λ in the formula obtained for w1 the way to calculate w1 is obtained: w1 = r1kinv1 ã∆cfa ef (34) the value of impairment of the asset (vimp) will be given by the formula: vimp = w1ã∆cfaef −r1kinv1 (35) in conclusion, it can be said that the asset impairment value depends on the asset’s contribution to sales (w1) , but also on the volume of cash flows ( ã∆cfa ef ) as well as the invested capital (r1kinv1). https://doi.org/10.15837/ijccc.2020.1.3690 11 5.2 stochastic fuzzy modelling of assets’ impairment risk for a 2-asset group theorem 7. there are considered two assets (a1, a2) participating in the sales volume with the weights (w1,w2) , each generating cash flow (cfa1,cfa2) , modelled based on stochastic triangular fuzzy numbers, ã∆cfaef : x → [0, 1]. the impairment of the assets value (vimp) will be given by the relation: vimp = w1ã∆cfaef + w2ã ∆ cfa ef − (r1kinv1 + r2kinv2) (36) with values for (w1) and (w2) established below: w1 = (r1kinv1 + r2kinv2) [ ã∆cfa pl(2) − ã∆cfaef(2) ]2 ã∆cfa ef(1)2 [ ã∆cfa pl(2) − ã∆cfaef(2) ]2 + ã∆cfaef(2) 2[ ã∆cfa pl(1) − ã∆cfaef(1) ]2 (37) w2 = (r1kinv1 + r2kinv2) [ ã∆cfa pl(1) − ã∆cfaef(1) ]2 ã∆cfa ef(1)2 [ ã∆cfa pl(2) − ã∆cfaef(2) ]2 + ã∆cfaef(1) 2[ ã∆cfa pl(1) − ã∆cfaef(1) ]2 (38) proof. for modelling estimated future cash flows using stochastic triangular fuzzy numbers, is assumed that the company holds a minimum of two assets denoted symbolically by (a1, a2) that generate cash flows throughout their useful life (cfa1 = r1kinv1,cfa2 = r2kinv2). the cash flows generated during the time frame that is the object of fuzzy modelling, are composed of inflows modelled with the stochastic triangular fuzzy numbers, ã∆cfai (revai) and outflows, also modelled with stochastic triangular fuzzy numbers, ã∆cfa1 (expai). for each asset owned by the company, the fuzzy equation describing the cash flow is as follows: cfai = 1 dr ( 1 −e−drt )[ ã∆cfa (revai) − ã ∆ cfa ( expai )] = ã∆cfaef(i) (39) in addition, each of the assets (ak) held by the company makes a certain contribution to the company’s sales that are determined by the production capacity of each (cpak), but also by the possibility of selling the products obtained on the market. we note this asset contribution (ak) for making sales with (wk). so, we will have: for asset a1: an effective cash flow is generated w1 (cfa1)=w1 { 1 dr ( 1 −e−drt )[ ã∆cfa1 (reva1) − ã∆a1 (expa1) ]} = w1ã∆cfa1ef (1) and the objective is to minimize the deviation of the effective cash flows values ã∆cfaef (1) from the planned cash flows ã∆cfapl (1) , so that the use value of the asset does not change significantly and impair, respectively min w21 { 1 dr ( 1 −e−drt )[ ã∆cfa pl(1) − ã∆cfaef(1) ]}2 . the value of the cash flows generated by asset (a1) must be equal to the part allocated from the invested capital for the year of analysis denoted by (r1kinv1) , so the asset not to be affected by cash-flows fluctuations generated by it, so the usefulness value begin to diminish. for asset a2: a cash flow of the following form is generated: w2 (cfa2) = w2 { 1 dr ( 1 −edrt )[ ã∆cfa (reva2) − ã∆cfa (expa2) ]} = w2ã∆cfaef and the objective is also to minimize the deviations of the effective cash flows ã ∆ cfa ef from the planned cash flows ã∆cfapl (2) , so that the use value of the asset does not change significantly and impair, respectively, min w22 { 1 dr ( 1 −e−drt )[ ã∆cfa pl− ã∆cfaef ]}2 . the amount of the cash flow generated by asset a2 must be equal to (r2kinv2) , so that the asset’s value of use is not affected. this creates an optimization problem where the aim is to minimize the deviations of the cash flows from their expected value: https://doi.org/10.15837/ijccc.2020.1.3690 12 min w21 { 1 dr ( 1 −e−drt )[ ã∆cfapl(1) − ã ∆ cfa ef(1) ]}2 + min w22 { 1 dr ( 1 −e−drt ) [ ã∆cfapl(2) − ã ∆ cfa ef(2) ]}2 (40) the problem restrictions are that the amount of cash flows generated by each asset is equal to the share of the invested capital for each asset, for the reference year for which the analysis is performed: w1ã ∆ cfa ef(1) + w2ã∆cfaef(2) = r1kinv1 + r2kinv2 (41) the lagrangian of the optimization problem is: l = min w21 { 1 dr ( 1 −e−drt )[ ã∆cfapl(1) − ã ∆ cfa ef(1) ]}2 + min w22 { 1 dr ( 1 −e−drt ) [ ã∆cfapl(2) − ã ∆ cfa ef(2) ]}2 − λ ( w1ã ∆ cfa ef(1) + w2ã∆cfaef − (r1kinv1 + r2kinv2) ) we establish the optimization conditions for the situation above and we obtain:  ∂l ∂w1 = 0 ∂l ∂w2 = 0 ∂l ∂λ = 0 thus, after making the calculations we will obtain the following system of equations:   2 w1 { 1 dr ( 1 −e−drt )[ ã∆cfa pl(1) − ã∆cfaef(1) ]}2 −λã∆cfaef(1)= 0 2 w2 { 1 dr ( 1 −e−drt )[ ã∆cfa pl(2) − ã∆cfaef(2) ]}2 −λã∆cfaef(2)= 0 w1ã ∆ cfa ef(1) + w2ã∆cfaef(2) = r1kinv1 + r2kinv2 (42) from the equations above we take out w1 and w2 and we obtain the following: w1 = λã∆cfa ef(1) α [ ã∆cfa pl(1) − ã∆cfaef(1) ]2 (43) where α is the constant α= 2 1 dr ( 1 −e−drt ) . w2 = λã∆cfa ef(2) α [ ã∆cfa pl(2) − ã∆cfaef(2) ]2 (44) by replacing w1 and w2 in the last equation of the system above, we obtain: λã∆cfa ef (1) α [ ã∆cfa pl (1) − ã∆cfaef (1) ]2 ã∆cfaef(1)+ + λã∆cfa ef (2) α [ ã∆cfa pl (2) − ã∆cfaef (2) ]2 ã∆cfaef(2) = (r1kinv1 + r2kinv2) (45) λα [ ã∆cfaef(1) 2][ ã∆cfapl(2) − ã ∆ cfa ef(2) ]2 + ã∆cfaef(2) 2[ ã∆cfapl(1) − ã ∆ cfa ef(1) ]2 = (r1kinv1 + r2kinv2) α [ ã∆cfapl(1) − ã ∆ cfa ef(1) ]2[ ã∆cfapl(2) − ã ∆ cfa ef(2) ]2 https://doi.org/10.15837/ijccc.2020.1.3690 13 from the equation above the value of parameter λ is obtained: λ= (r1kinv1 + r2kinv2) [ ã∆cfa pl(1) − ã∆cfaef(1) ]2[ ã∆cfa pl(2) − ã∆cfaef(2) ]2 ã∆cfa ef(1)2 [ ã∆cfa pl(2) − ã∆cfaef(2) ]2 + ã∆cfaef(2) 2[ ã∆cfa pl(1) − ã∆cfaef(1) ]2 (46) by replacing the expression obtained above for parameter λ in the expressions of w1 and w2 is obtained: w1 = λã∆cfa ef(1) α [ ã∆cfa pl− ã∆cfaef(1) ]2 and respectively w2 = λã∆cfa ef(2) α [ ã∆cfa pl− ã∆cfaef(2) ]2 w1 = (r1kinv1 + r2kinv2) [ ã∆cfa pl(2) − ã∆cfaef(2) ]2 ã∆cfa ef(1)2 [ ã∆cfa pl(2) − ã∆cfaef(2) ]2 + ã∆cfaef(2) 2[ ã∆cfa pl(1) − ã∆cfaef(1) ]2 (47) w2 = (r1kinv1 + r2kinv2) [ ã∆cfa pl(1) − ã∆cfaef(1) ]2 ã∆cfa ef(1)2 [ ã∆cfa pl(2) − ã∆cfaef(2) ]2 + ã∆cfaef(1) 2[ ã∆cfa pl(1) − ã∆cfaef(1) ]2 (48) conclusion: the impairment of assets depends on the size of the invested capital (rikinvi), the contribution of the companies’ assets to the sales volume (w1, w2), but also on the value of cash flows modelled by the stochastic fuzzy numbers ã∆cfapl (1) − ã ∆ cfa ef (1) , ã∆cfapl(2) − ã ∆ cfa ef. 5.3 stochastic fuzzy modelling for impairment of assets risk for a group of n assets theorem 8. is considered that the company own n-assets (a1,a2 . . . . . .an ) holding the weights in total sales (w1,w2 . . .wn ). each of these assets generates cash flows of the form (cfa1,cfa2, . . . . . .cfan ) modelled by stochastic fuzzy numbers ã∆cfa1ef (1) , ã ∆ cfa2 ef (2) , . . . . . . ã∆cfanef (n) . let us say that the impairment value of all assets denoted by (vimp) is a matrix equation:  vimp1 vimp2 . . . vimpn   =   w1 w2 . . . wn     ã∆cfa ef(1) ã∆cfa ef(2) . . . ã∆cfa ef(n)  −   rkinv1 rkinv2 . . . rkinvn   (49) where: w = 1 ∝ rkinv[ ã∆cfa ef ] proof. the assets held by the company (a1,a2 . . . . . .an ) generate cash flows (cfa1,cfa2, . . . . . .cfan ) modelled by stochastic fuzzy numbers ã∆cfa1 ef (1) , ã∆cfa2ef (2) , . . . . . . ã ∆ anef (n). https://doi.org/10.15837/ijccc.2020.1.3690 14 the system of equations formed for testing the impairment of the assets will be:  min ∑n k=1 w 2 kα [ ã∆cfa pl(k) − ã∆cfaef(k) ]2 ∑n k=1 wkã ∆ cfa ef(k) = ∑n k=1 rkkinvk (50) the lagrangian of the problem becomes: l = min n∑ k=1 w2kα [ ã∆cfapl(k) − ã ∆ cfa ef(k) ]2 −λ ( n∑ k=1 wkã ∆ cfa ef(k) − n∑ k=1 rikinvi ) (51) with the conditions of optimum: { ∂l ∂wk = 0 ∂l ∂λ = 0 (52) after calculations, we will have:  ∑n k=1 2wkα [ ã∆cfa pl(k) − ã∆cfaef(k) ]2 −λ ∑n k=1 ã ∆ cfa ef(k) = 0∑n k=1 wkã ∆ cfa ef(k) − ∑n k=1 rkkinvk = 0 (53) we write in the matrix form the equations from above which will become: α   w1 w2 . . . wn     ã∆cfa pl(1) − ã∆cfaef(1) ã∆cfa pl(2) − ã∆cfaef(2) . . . ã∆cfa pl(n) − ã∆cfaef(n)   2 = λ   ã∆cfa ef(1) ã∆cfa ef(2) . . . ã∆cfa ef(n)   (54) and respectively:   w1 w2 . . . wn     ã∆cfa ef(1) ã∆cfa ef(2) . . . ã∆cfa ef(n)   =   r1kinv1 r2kinv2 . . . rnkinvn   (55) or the matrix form of the equation system  ∝ w [ ã∆−cfa ]2 = λã∆cfaef wã∆cfa ef = rkinv (56) from the equation system from above results that: w = 1 ∝ ˘ã∆cfaef[ ã∆−cfa ]2 (57) by replacing the expression of parameter w in the matrix equation we obtain: 1 ∝ λ [ ã∆cfa ef ]2 [ ã∆−cfa ]2 = rkinv (58) hence it results that: λ= rkinv [ ã∆−cfa ]2 [ ã∆cfa ef ]2 (59) https://doi.org/10.15837/ijccc.2020.1.3690 15 by replacing the expression of λ we will obtain: w = 1 ∝ λã∆cfa ef[ ã∆−cfa ]2 = 1∝ rkinv [ ã∆− cfa ]2 [ ã∆ cfa ef ]2 ã∆cfaef [ ã∆−cfa ]2 = rkinv[ ã∆cfa ef ] (60) expression w can be written in matrix form thus:   w1 w2 . . . wn   =   r1kinv1 r2kinv2 . . . rnkinvn     ã∆cfa ef(1) ã∆cfa ef(2) . . . ã∆cfa ef(n)   (61) the impairment of asset value will be written in matrix form, thus:  vimp1 vimp2 . . . vimpn   =   w1 w2 . . . wn     ã∆cfa ef(1) ã∆cfa ef(2) . . . ã∆cfa ef(n)  −   r1kinv1 r2kinv2 . . . rnkinvn   (62) conclusion: it can be argued that in the previous modelling situation, for a group of n assets, the impairment value depends on the value of the invested capital (rkkinvk), the size of cash flows generated by each asset ( ã∆cfa ef (k) ) and the contribution that each asset has to the company sales (wk). 6 testing the efficiency of the stochastic fuzzy numbers in asset impairment testing 6.1 single asset case in order to simulate the efficiency of the stochastic fuzzy numbers in asset impairment testing, were considered as indicators: the initial investment cost (kinv1), the lifetime of the asset (lf 1), an investment rate for the 10% impairment calculation and respectively r1kinv1. the monthly average expected future cash flows for each year of the tangible asset life (10 years) and also the cash flows generated by the tangible asset for 10 months period, are illustrated in the figure 2. it was determined whether in the next period of the tangible asset operation the asset will be influenced by the impairment process, for which the company will incur an impairment expense. in the first stage, based on the data of the cash flows generated over the lifetime of the asset, the mean µ and variance σ shall be determined using the statistical formulas µ = 110 ∑10 i=1 cfai and respectively σ = 1 n−1 √∑n i=1 ( cfi −cf )2 . by making the calculations we obtain µ = 1, 98% and the value of σ = 3, 04%. with the two determined statistical parameters, namely the mean µ = 1, 98% and the value σ = 3, 04%, the expected cash flows for the next operation period is estimated to be between (cfmin; cfmax) as follows:1, 2 mil. euro ≤ cf ≤ 1, 8 mil. euro. in this interval, three triangular fuzzy numbers are constructed that characterize the cash flows generated by the asset’s operation in the form of: https://doi.org/10.15837/ijccc.2020.1.3690 16 figure 2: cash-flow forecast over the asset life and for each month of asset operation   ã∆cfa1 ef (1) = [1, 2 1, 3 1, 4] ã∆cfa1 ef (2) = [1, 4 1, 5 1, 6] ã∆cfa1 ef (3) = [1, 6 1, 7 1, 8] (63) the triangular fuzzy number ã∆cfa1ef (1) = [1, 2 1, 3 1, 4] characterizes the cash flows of the tangible asset with high risk of impairment, the fuzzy triangular number ã∆cfa1ef (2) = [1, 4 1, 5 1, 6] characterizes the cash flows of the asset with medium risk of impairment, while the triangular fuzzy number ã∆cfa1ef (3) = [1, 6 1, 7 1, 8] characterizes the asset cash flows with low risk of impairment. according to theorem 6, the value of the impairment of assets will be determined with the formula vimpa1 = w1ã∆cfa1ef −r1kinv1 (64) where: w1 = r1kinv1 ã∆cfa1 ef (65) after calculations, we will have: vimpã ∆ cfa1 ef (1) = [1, 2 1, 4] − 1, 5 = [−0, 3 − 0, 1] vimpã ∆ cfa1 ef (2) = [1, 4 1, 6] − 1, 5 = [−0, 1 + 0, 1] vimpã ∆ cfa1 ef (3) = [1, 6 1, 8] − 1, 5 = [+0, 1 + 0, 3] conclusion: it is observed that within the range delimited by the first triangular fuzzy number vimpã ∆ cfa1 ef (1) = [1, 2 1, 4] the tangible asset is completely impaired and it is necessary to record the expense for impairment of the asset whose maximum value is – 0,3 mil ∈ and the minimum value is – 0,1 mil. euro. also, in the range delimited by the second fuzzy number vimpã ∆ cfa1 ef (2) = [1, 4 1, 6] the value of the impairment to be recognized is of minimum – 0,1 mil. euro , while for the interval delimited by the third fuzzy number vimpã∆cfa1ef (3) = [1, 6 1, 8] it is not necessary to recognize the impairment. full analysis of impairment was made after a period of time and the cash flows generated by the use of the asset during the operation period were monitored. https://doi.org/10.15837/ijccc.2020.1.3690 17 figure 3: assets impairment assessment using stochastic fuzzy numbers 6.2 two assets case for the two-assets case, were considered the assets [a1,a2] for which we know the following: 1. for the (a1) asset, information on expected future cash flows, actual cash flows and impairment tests are those resulting from calculations for a single asset. 2. for the (a2), information on the following indicators: the initial investment cost (kinv2), the asset’s useful life (lf2) 10 years and an investment rate for calculating the 10% impairment, respectively r2kinv2, as shown in the figure 4. figure 4: cash-flow forecast over the asset life and for each month of asset operation based on cash flows data, the two statistical indicators are identified; the mean ¯ and the variance œ, respectively µ= 1, 59% and the variance σ= 2, 75%, for which the expected cash flows for the next period of operation are obtained, respectively:1, 8 mil. euro ≤ cf ≤ 2, 8 mil. euro. https://doi.org/10.15837/ijccc.2020.1.3690 18 within this range, a number based on three triangular fuzzy numbers is also constructed, that characterize the asset a2 operation, over this range as follows:  ã∆cfa2 ef (1) = [1, 8 2, 13] ã∆cfa2 ef (2) = [2, 13 2, 43] ã∆cfa2 ef (3) = [2, 43 2, 80] (66) the fuzzy numbers constructed on the value range 1, 8 mil. euro ≤ cf ≤ 2, 8 mil. euro characterize the cash flows generated by the tangible asset a2, as follows: 1. ã∆cfa2ef (1) = [1, 8 2, 13] characterize the cash flows generated by the tangible asset with high risk of impairment; 2. ã∆cfa2ef (2) = [2, 13 2, 43] characterize the cash flows generated by the tangible asset with medium risk of impairment; 3. ã∆cfa2ef (3) = [2, 43 2, 80] characterize the cash flows generated by the tangible asset with low risk of impairment; according to theorem 6, the value of the asset impairment will be determined with the formula: vimpa2 = w2ã∆cfa2ef −r2kinv2 (67) where: w2 = r2kinv2 ã∆cfa2 ef (68) after calculations, we will have: vimpã ∆ cfa2 ef (1) = [1, 80 2, 13] − 2, 00 = [−0, 20 + 0, 13] vimpã ∆ cfa2 ef (2) = [2, 13 2, 43] − 2, 00 = [+0, 13 + 0, 43] vimpã ∆ cfa2 ef (3) = [2, 43 2, 80] − 2, 00 = [+0, 43 + 0, 60] conclusion:for the (a2) asset, it follows from the impairment calculations that for the first triangular fuzzy number ã∆cfa2ef (1) = [1, 80 2, 13] it is necessary to recognize an impairment expense of a maximum value of 0.200 mil. euro, for the second triangular fuzzy number ã∆cfa2ef (2) = [2, 13 2, 43] as well as for the third triangular fuzzy number ã∆cfa2ef (3) = [2, 43 2, 80] the asset is not impaired and consequently there is no need to recognize an expense for impairment because the value of impairment is positive respectively vimpã∆cfa2ef (2) = [+0, 13 + 0, 43] and vimpã ∆ cfa2 ef (3) = [+0, 43 + 0, 60]. for the situation where the two assets are taken into account, (a1, a2), the impairment value will be obtained either by applying theorem 8 or by summing the values obtained for impairment over the three intervals considered, namely: vimp1ã ∆ cfa1,a2 ef (1) = [−0, 3 − 0, 1] + [−0, 20 + 0, 13] = [−0, 50 + 0, 03] vimp2ã ∆ cfa1,a2 ef (2) = [−0, 1 + 0, 1] + [+0, 13 + 0, 43] = [+0, 03 + 0, 53] vimp3ã ∆ cfa1,a2 ef (3) = [+0, 1 + 0, 3] + [+0, 43 + 0, 60] = [+0, 53 + 0, 93] there may be ckn combinations of intervals that characterize the forecasted cash flows generated by the two assets (a1,a2) for the period of operation considered, but the objective is to determine the maximum impairment value that the two assets may register. in our case, the maximum impairment value of the two assets is 0.50 million euro for which the company will record an expense, according to ias 36. in this case also, full analysis of impairment was made after a certain period of time and generated assets’ cash flows were monitored during the operational period. https://doi.org/10.15837/ijccc.2020.1.3690 19 7 final conclusions and research limits the use of stochastic triangular fuzzy numbers in the valuation of impairment of tangible assets enables companies, during the period of using the assets in the current activity, to identify potential impairment risks that may occur when the carrying amount of assets (invested capital) is higher than the discounted cash flows in the value in use of the asset, under the condition that the fair value less costs of disposal of the assets is lower than these cash flows. these asset impairment risks are very important for the company’s financial performance for at least two reasons. firstly, any identified and quantified impairment of assets involves expense recognition, which diminishes the accounting result of the period. secondly, early identified risks in the operation of tangible assets allow the company to develop strategies and adopt measures to mitigate the impact that asset impairment has on the company’s financial performance. thus, we consider that there are at least three advantages of using triangular fuzzy numbers: (1) first advantage is that the minimum and maximum value of discounted future cash flows can be estimated on the basis of the cash flows generated by tangible assets in prior periods of operation; (2) second advantage is that the discounted future cash flows generated by the asset’s operation can be stratified by means of professional judgments into one or more triangular fuzzy numbers by which the risk of impairment of assets is analysed. the triangular fuzzy numbers thus constructed, in a stochastic environment, allow the identification of those categories of discounted future cash flows of tangible assets with the highest risk of impairment of the assets for which the company has to bear the highest impairment expense; (3) third advantage is that the use of stochastic triangular fuzzy numbers can determine the value of impairment of assets when it exists, depending on the estimated discounted future cash flows. thus, managers can make realistic estimates of asset’s impairment in order to avoid the risk of its financial deterioration in medium and long term. from the fuzzy modelling of asset’s impairment risk, but also from the testing of simulation of this modelling for the situation of a single tangible asset or a group consisting of several tangible assets, it is obvious that value of the impairment for a certain period of time is influenced by three important elements: (a) the value of the invested capital that most often is represented by the carrying amount of the tangible asset which makes the subject to the impairment test; (b) the amount of discounted future cash flows generated by the use of the asset over its useful life; (c) the contribution of each tangible asset for which the impairment test is performed to achieve the company’s turnover. the obtained results allow the asset impairment to be stratified in order to identify those discounted future cash flows ranges for which the company will register impairment costs in line with ifrs’s. these impairment values always appear to be negative and are a signal that the financial performance of the company will deteriorate. the positive value of impairment indicates that the company is sheltered from asset impairment risk and its financial performance will not be affected in short and medium term by asset impairment. also, the results obtained in this research are the basis of a new chapter in the complex asset impairment field, which allows the quantification of impairment for future periods of time. as far as the limits of this research are concerned, they can be found in scenarios designed to test the risk of impairment of assets and in the construction of the stochastic fuzzy approach. future research aims to use real data on the assets of listed companies and to build an asset’s performance optimization model taking into account all variables from the initial moment of evaluating the cost of assets up to their disposal or derecognition moment. author contributions m.i.b and v.b conceived the study and were responsible for the design and development of the analysis. m.i.b, i.a.b and c.d.s.p were responsible for introduction of stochastic fuzzy numbers. https://doi.org/10.15837/ijccc.2020.1.3690 20 v.b and d.n.p were responsible for data interpretation. m.i.b wrote the first draft of the article. i.a.b was responsible for the second draft of the study. disclosure statement authors do not have any competing financial, professional, or personal interests from other parties. references [1] abughazaleh, n. m.; al-hares, o. m.; roberts, c. 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(2016). do goodwill impairments by european firms provide useful information to investors?, accounting in europe, 13, 307-327, 2016. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: bolos, m.i.; bogdan, v.; bradea, i.a. et al.(2020). stochastic fuzzy algorithms for impairment of assets management, international journal of computers communications & control, 15(1), 1001, 2020. https://doi.org/10.15837/ijccc.2020.1.3690 int j comput commun, issn 1841-9836 8(2):255-262, april, 2013. raising energy saving awareness through educational software a.e. pitic, i. moisil, s. dzitac alina elena pitic lucian blaga university of sibiu, faculty of sciences, department of mathematics and informatics e-mail: alinap29@yahoo.com ioana moisil lucian blaga university of sibiu, hermann oberth engineering faculty e-mail: im25sibiu@gmail.com simona dzitac university of oradea, romania e-mail: simona.dzitac@gmail.com abstract: a study that i have conducted on a sample of 395 children aged 6 to 12, from both the urban and the rural environments, shows that an increasing number of them use computer related technologies. today there are an increasing number of web sites that inform the user of different ways to save energy and to reduce energy consumption because it is only natural that the modern information society would go online to research such topics. the development of an educational application that is focused on teaching the children about energy saving is represented on a timeline. we conducted a study in which a number of 29 children, aged between 8 and 10, use this educational application and a questionnaire. we describe the methodology used in the process of data gathering and then the results are interpreted. the state of happiness and fatigue of the child user has a great potential of influencing the way in which he or she uses the application, and implicitly it’s educational impact. because we want to be able to reform these concepts, we will base our work on a series of markov models and we will define some measures that are relevant to our goal. keywords: energy saving, educational software, elearning, case study. 1 introduction today there are an increasing number of web sites that inform the user of different ways to save energy and to reduce energy consumption because it is only natural that the modern information society would go online to research such topics. the major issue with these sites is that although scientifically correct and very complete from an informational standpoint they do not meet the user’s expectations, being much too serious and end up being considered boring. the fast development of some technologies like computer gaming, which are very attractive to children and teen, may present an opportunity. indeed those who have worked with children and teen on a day to day basis have seen how much they love computer games. this is backed up by a number of researchers that have conducted empirical studies [7]. thus the technology used in computer games could be used to create educational software, raising the motivation and engagement of children and making the learning process a fun activity. many researchers agree to use computer games for educational purposes. in [2] we see that computer games help teach children faster, the lessons being more dynamic and engaging. this is seen as a great alternative to the slow pace and the boredom of regular school lessons. boyle [3] points out that computer games can lead to greater engagement and pleasure in the learning copyright c⃝ 2006-2013 by ccc publications 256 a.e. pitic, i. moisil, s. dzitac process, strengthening the educational environment. moreover there are studies that show that carefully selected computer games can improve thought processes [4]. in response to these studies many researchers have developed games for educational purposes [5], [6]. 2 initial case study a study that i have conducted on a sample of 395 children aged 6 to 12, from both the urban and the rural environments, shows that an increasing number of them use computer related technologies [9]. given their exposure to these technologies it is imperative that educational applications be designed in a way that takes into account the abilities, interests and the development demands of children. figure 1 and 2 shows some interesting yet somehow expected results like the high number of children having access to a computer at home, the amount of time spent by them using it or the internet navigation preferences [9]. figure 1: the number of children who own a pc / computer usage by theme figure 2: child’s internet navigation preferences / number of hours spent in front of the pc in one day as expected, the recreational aspect of technology usage will overcome the educational one. possibly the most interesting question for us, "have you ever played a computer game that has taught you things useful for school?", was answered with yes by 72% of the children from our sample. however, only 20% of the children have read electronic books. a pretty large number of children (18%) declare that they use computers for communication. with regard of their age, they provided us with an interesting information on how technologies influences children at small ages. we can tell that the informational technologies is influencing children more and more by the increasing number of hours spent by them in front of a pc (figure 2). only one third of those raising energy saving awareness through educational software 257 questioned spend more than two hours using a pc and of these only a handful cross into the computer dependent category. a probable cause for this could be the children’s relatively small age. they are between 7 and 10 years old, primary school pupils, which means that the role of their teachers and parents is still leaving a powerful impression on their education. this theory is confirmed by figure 3 which show a connection between the child’s age and the number of hours spent using a pc. just as predicted, as the child’s age goes over 10 years the time spent in front of the computer rises to between three and four hours each day. figure 3: chart showing child’s age and the hours per day of pc usage seeing such an exposure to technology it is of the utmost importance to have educational applications that corroborate these interests and demands for the children’s future development. these are some of the observations that can be formulated from the initial case study data: • the time spent in from of a pc increases with the child’s age; • multimedia applications and computer games are the preferred content for children; • the number of children using the world wide web is quite large; • the computer has an important (second) place in a child’s free time activities program; • not many children have read e-books; • educational games steadily increase in popularity among children. here are some of our conclusions derived from our observations: • educational applications over the world wide web may have a big impact; • there is little interest in text format; • the practice of using computers in the educational environment must intensify; • the process of learning by playing and the idea of learning through discovery must be made a priority; • educational applications should have elements that attract a child’s attention. 3 modelling the emotional state we define the set of emotional states es={pleased (p), normal (n), displeased (d)} and the set of arousal states as={aroused (a), normal (n), sleepy (s)}. in order to define the model we will consider: for the emotional states (es) we will have pleased > normal > displeased; for the arousal states (as) we will have aroused > normal > sleepy. where ">" has the meaning of "better than". to model user emotions we use markov chains to model the transition between different states as shown in figure 4 [10]. the "good" transitions are the blue dotted lines (figure 4), the red lines represent the "bad" transitions and the black ones are "neutral" transitions. high values for p(p,p), p(n,p), 258 a.e. pitic, i. moisil, s. dzitac figure 4: modeling pleasure and arousal of the user (mes and mas) p(d,n), p(d,p), and low values for p(d,d), p(n,d), p(p,n) and p(p,d) will describe a good interaction between user and our application. the p(n,n) transition doesn’t offer information about emotional state. we define a measure of "wellbeing" (user feelings during the use of an application) as des= (α1 p(p,p)+ α2 p(n,p) + α3 p(d,n) + α4 p(d,p) (β1 p(d,d) + β2 p(n,d) + β3 p(p,n) + β4 p(p,d)), where α = (α1, α2, α3, α4) and β = (β1, β2, β3, β4) are nonnegative real numbers, empirical determined, with at least one non-zero value. we also define dnes=des /( ∑4 i=1 αi + ∑4 i=1 βi) , a normalized measure of the "wellbeing". greater values of dnes characterizes a good user-application intereaction. to model user arousal, we will define the markov model as elements of the as set (figure 4). similarly mes, the blue dotted arcs represent "good" transitions, the black ones "neutral" and the red represent "bad" transitions. a good user-application interaction will have greater values for p(s,s), p(n,s), p(a,n) and p(a,s), and lower values for p(a,a), p(n,a), p(s,n) and p(s,a). the p(n,n) transition doesn’t offer any useful information. we define a measure of the "fatigue stare" as das= α1p(a,a)+ α2p(n,a) + α3p(s,n) + α4p(s,a) (β1 p(s,s) + β2 p(n,s) + β3 p(a,n) + β4 p(a,s)), α = (α1, α2, α3, α4) and β = (β1, β2, β3, β4) are nonnegative real numbers, empirical determined, with at least one nonzero value. the normalized measure of the "fatigue state" is defined as dnas=das /(( ∑4 i=1 αi+∑4 i=1 βi)). to characterize a user session of an application we can use dnes and dnas [10]. 4 the "energy" educational application this application began it’s history in january of 2010. we wanted to create an educational web application with a topic focused on the "adaptive web application for citizens’ education teaching children the value of electrical energy" [1]. a succession of the most important changes suffered by the application and it’s interface is shown in figure 5. we conducted a study in the 2009-2011 timeframe, on a sample of 276 children, aged 6 through 12. the children have responded to a questionnaire and some partial results were used as a starting point. intermediate results were used because of the ongoing study and the work on the "energy" application. the questionnaire is made up of three open answer questions ("do you know how to save energy?", "how is electricity produced?", "how do you save electricity?"). methodology: • children where given the questions during the civic education classes; raising energy saving awareness through educational software 259 figure 5: the versions of the "energy" application • the evaluator shows the questionnaire to the children in the classrom in the presence of a teacher and then distributes it to all children (5 minutes); • the children fill in the questions (a maximum of 15 minutes); • in lower classes (age group from 6 to 8) the children where given aid in reading and had their question answered; • the questionnaires are then gathered by the evaluator/teacher. the answers where free form and they were gathered in a list of answers obtained after the results were interpreted. the final results are given in figure 6: figure 6: did you save energy/how the energy is produced/how can you save energy? some of the conclusions are: • 245 of the children (88%) are aware of the importance of saving energy; • 91 children (33%) don’t know how electricity is produced; • 130 children (47%) asociate the saving of energy with turning the lights off. based on these conclusions we chose the educational content for the first version of the "energy" application. a part of the results from figure 6 where used in [1]. the first design session took place in january of 2010. we chose four children (two where 8 years old and the other two 9 years old) who together with two adults formed the design team for the application. the programming team was seldom present at the meetings. up to the meeting in march 2010 the design team gained one 9 year old. in the beginning meetings took place at a rate of two per week, each meeting being two or three hours long. among the challenges encountered is the inability to capture the children attention for more than half the meeting time. in time, by limiting meetings to two hours and by combining work 260 a.e. pitic, i. moisil, s. dzitac with fun the children were drawn more and more to the project. discussions related to technology where always followed by examples that children could try out for themselves. the last meeting in which children where involved took place in june of 2010. the changes in the applications up to version 1.5 where of a technical nature (optimizing and bug fixing). in october of 2011 we conducted one last study in which the children had the role of the user. the study was comprised by the "energy" application (version 1.5) together with a questionnaire. 29 children aged 8 through 10 took part in the study, all pupils of the no. 4 general school in sibiu, together with three teachers (figure 7). figure 7: study progress in order to determine the emotional state of the subjects we have used a variant on the affect-grid for children method [8]. here follows a part of the results obtained from the study. the tendency of the user to pass from an emotional state to another can be seen in figure 8. here the initial state is given by the start of the application and the final one by exiting the application. the diagrams do not take into account possible variations during the use of the application. if a child goes through a succession of emotional states like (f, n, t, n, f), the result will be 0, even if there have been variations in between. figure 8: going from an emotional state to another no observable rule can be set, users passing randomly from one emotional state to another. at first sight this result could appear strange because we were expecting the application to be a success resulting in a positive emotional state in the subjects after its use. we have stumbled upon the reason by chance and from one of the children participating in the study. the reasons given by him for being sad at the end of the reserved time where that he "still wanted to play" and "didn’t want to go back to class". having found out this we have revised the diagram, and raising energy saving awareness through educational software 261 came up with figure 9. in it the initial state is given by the start of the application but the final one is given by the last page reached. figure 9: going from an emotional state to another (version 2) it is now clear that our application is interesting for the children that took part in the study: • only 9% of the have felt a drop in their happy state, a possible explination beeing that they did not succed in finding all the usefull pages, thus not finishing the story; • 24% have felt an increase and the rest where stagnant; • none of the children where more tired after using the application; • 42% of the children felt more stimulated. other results were obtained by interpreting the results of the questionnaire given to the children, a part of these being shown in figure 10. figure 10: what did this application teach you? / how did you save energy? 5 conclusions the cost of educational software for children is very high because they imply a greater effort both in time spent and in developing abilities for working with children. one solution would be to offer instruments that would permit software developers to test the quality of their applications at a lower cost, on small groups of users. the models that we have proposed permit the testing of the emotional aspect, but not the cognitive one. gaining children as design partners or as users is possible and useful but it presents a real challenge. developing educational software together with children comes with a series of inherent and specific difficulties that where surpassed by treating them as equal partners and because we had the help of teachers trained to work with children. we developed an application with the aid of children that where treated as design partners and afterwards as users. a study conducted on the latest version of the application proves that children like it and it has a positive influence on how they think on the subject of saving energy. 262 a.e. pitic, i. moisil, s. dzitac bibliography [1] moisil, i., dzitac, s., popper, l., pitic, a., adaptive web aplication for citizens‘ education teaching choldren the value of electricall energy, int j comput commun, 5(5): 819-825, 2010. [2] papert, s., the children’s machine: rethinking school in the age of the computers, new york: basic books, 1993. [3] boyle, t., design for multimedia learning, london: prentice hall, 1997. [4] aliya, s. k., the role of computer games in the development of theoretical analysis, flexibility and reflective thinking in children. a longitudinal study, international journal of psychophysiology, 149, 2002. [5] virvou, m. k., combining software games with education: evaluation of its educational effectiveness, educational technology & society, 8: 54-65, 2005. [6] conati, c., & zhou, x., modeling students’ emotions from cognitive appraisal in educational games., proceedings of the intelligent tutoring systems, 994-954, 2002. [7] mumtaz, s., children’s enjoyment and perception of computer use in the home and the school, computers and education, 36: 347-362, 2001. [8] widen, s. c., & russell, j. a., children’s scales of pleasure and arousal, aps conference, toronto: 1-8, 2001. [9] pitic, a., moisil, i., computers in school. a case study with children 6 to 12 years old, the 8th international scientific conference elearning and software for education, bucharest, 26-27, april 2012. [10] pitic, a., moisil, i., a novel method to characterize user sessions of educational software. 3rd word conference on psychology, counselling and guidance, efes kusadasi izmir,turkey, wcpcg 09-12 may 2012. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 5, month: october, year: 2021 article number: 4274, https://doi.org/10.15837/ijccc.2021.5.4274 ccc publications multi-objective pso with passive congregation for load balancing problem m. marufuzzaman, m. a. timu, j. sarkar, a. islam, l. f. rahman, l. m. sidek mohammad marufuzzaman*, lariyah mohd sidek institute of energy infrastructure universiti tenaga nasional, malaysia 443000, kajang, selangor, malaysia m.mohammad@uniten.edu.my, lariyah@uniten.edu.my *corresponding author: m.mohammad@uniten.edu.my muneed anjum timu, jubayer sarkar, aminul islam department of computer science american international university, bangladesh dhaka, bangladesh timu.muneef@gmail.com, jubayersarker31@gmail.com, aminul.irony@gmail.com labonnah farzana rahman institute for environment and development universiti kebangsaan malaysia 43600 bangi, selangor, malaysia labonnah.deep@gmail.com abstract high-level architecture (hla) and distributed interactive simulation (dis) are commonly used for the distributed system. however, hla suffers from a resource allocation problem and to solve this issue, optimization of load balancing is required. efficient load balancing can minimize the simulation time of hla and this optimization can be done using the multi-objective evolutionary algorithms (moea). multi-objective particle swarm optimization (mopso) based on crowding distance (cd) is a popular moea method used to balance hla load. in this research, the efficiency of mopso-cd is further improved by introducing the passive congregation (pc) method. several simulation tests are done on this improved mopso-cd-pc method and the results showed that in terms of coverage, spacing, non-dominated solutions and inverted generational distance metrics, the mopso-cd-pc performed better than the previous mopso-cd algorithm. hence, it can be a useful tool to optimize the load balancing problem in hla. keywords: high-level architecture, load balancing, particle swarm optimization, crowding distance, passive congregation, mopso-cd-pc. https://doi.org/10.15837/ijccc.2021.5.4274 2 1 introduction high-level architecture (hla) is a medium used to interact with another computer by communicating with data transfer or synchronizing regardless of the computing platforms [3]. hla contains run time infrastructure (rti) used to manage received data from different software applications and redirect those data to other simulation applications [2]. we can separate each of those simulations as an entity. in the context of hla, that entity refers to the federates and a group of federates is called the federation. the rti contains an application programming interface (api) with a programming library compliant with the interface specification. the interface specification is designed to be object-oriented and gives support for any object-oriented programming language. federation object model (fom) is a model for hla federates which specifies how a federate will broadcast data during simulation. the programmer can configure this fom to create the new object and interaction types to adjust to their own simulation needs. hla supports the transfer of proprietary rights, meaning one federate that controls some object can give this control of that object to another federate. the hla and distributed interactive simulation (dis) are commonly used for the distributed system [12]. the distribution system is becoming more and more popular, and so the workload of distributed systems is increasing too [8]. hla is capable of providing a large-scale simulation of the distributed systems. however, the major problem arises when the hla load balance is not optimized when the hla goes through a large operation and therefore, shows low efficiency. the load can be split up into two loads: computation load, which is a measurement of system load. this gives a load average of the system load of a runtime. another is the communication load which maximizes the use of resources throughout the system so that every load is efficiently used the allocated resources for specific tasks while reducing the volume of unused resources. hla and dis usually deal with a large volume of tasks and therefore, require huge computational time. nonoptimized allocation leads to load balancing problems because of computing resources and eventually degrade the simulation efficiency and confidence. moreover, the load balancing problem deals with 2 loads where computation load sometimes overlaps with communication load, so the load balance in hla is considered to be a multiobjective problem [11], [13]. in the modern research era, almost all the problems are tried to solve using artificial intelligence [6], [7]. optimization is another promising area where the algorithms continuously develop and enhancing day by day [1], [9]. in multi-objective problems, the load-balancing method is divided into static load balancing (slb) and dynamic load balancing (dlb). dlb algorithms monitor changes on the system and redistribute the work accordingly. however, continually working with new federate dynamic load balance processes is very complex, increasing the overhead of workload significantly [10]. on the other hand, the static load balance approach provides preliminary information about the system. the main task is to find the optimized allocation of simulation tasks by providing information from the system. if the fluctuation of information is minimum in the static load balance method, it offers the best possible solution in a distributed system. the key area to solve the load balancing problem lies in the efficient migration of federates. therefore, an effective optimization technique is necessary for the federate migration process. a multi-objective evolutionary algorithm (moea) is usually incorporated to optimized the federation migration process [15]. among those algorithms, multi-objective particle swarm optimization (mopso) is popular in the field of moea. for example, dahmani et al. solved aircraft cargo transportation problems using the bi-objective load balancing method by maximizing the cargo loaded into the aircraft and giving them a priority queue to balance each load using discrete mopso [1]. kumar mishra et al. used non-dominated sorting genetic algorithms with elitist strategy (nsga-ii) and mopso to solve the portfolio optimization problem using the markowitz mean-variance model where a low complexity single-layer neural network was used [9]. the research outperforms other algorithms using the elitist learning strategy in combination with mopso. moreover, researchers further improved the mopso algorithm by including crowding distance (mopso-cd); adaptive grids (mopso-grid); comprehensive ranking (mopso-cr) and perturbation methods [2],[5], [14]. among those methods, mopso-cd is popular for the global best selection method. therefore, optimization using moso-cd will enhance the performance of mopso. in a different application, shabbir et al. showed that another approach named passive congregation (pc), which is inspired by a biological https://doi.org/10.15837/ijccc.2021.5.4274 3 mechanism for animals to aggregate into groups usually enhanced the performance of any pso algorithm [4]. pc model is suitable for use with the pso model, and this hybrid pso outperforms the standard pso. in this research, the existing mopso-cd algorithm will be further modified using the psopc approach for optimizing the load balance in hla architecture. the generated mopso-cd-pc algorithm will be evaluated in terms of different performance metrics and compared with the existing mopso-cd algorithm in the load balancing application. 2 research methodology pso is used to solve complex optimization problems by simulating swarms of birds or other groups. each entity of a swarm is identified as a particle. particles can be differentiating using two parts, position xi and velocity vi of ith particle in standard pso as shown in equation (1),(2). vi (t + 1) = wvi (t) + c1r1 (pi − xi (t)) + c2r2 (pgi − xi (t)) (1) xi (t + 1) = vi (t + 1) + xi(t) (2) here, xi = [xi1,xi2, ...,xid] represents iteration index of the particle, and d is the max number, t represents the current time, pi is the personal best which is the previous best location of the ith particle, pgi represents global best which is called the overall best location, w is inertia weight, c1and c2 are called acceleration constants, r1 and r2 are independent random numbers within [0, 1]. the hybrid pso with the pc can increase a standard pso’s performance [4]. after introducing the pc, the representation of pso-pc is depicted in equation (3),(4). vi (t + 1) = wvi (t) + c1r1 (pi − xi (t)) + c2r2 (pgi − xi (t)) + c3r3(ri − xi (t)) (3) xi (t + 1) = vi (t + 1) + xi(t) (4) here, the additional ri represents a randomly selected particle from within the swarm of particles, c3 represents the passive congregation coefficient and r3 is same as r1 and r2. the overall pseudocode of the mopso-cd-pc algorithm is depicted in figure 1. figure 1: pseudocode of mopso-cd-pc algorithm https://doi.org/10.15837/ijccc.2021.5.4274 4 table 1: mopso parameter ranges and levels parameters range low medium high ps 50–100 50 75 100 c1 1.0–2.0 1.0 1.5 2.0 c2 1.0–2.0 1.0 1.5 2.0 nog 100–300 100 200 300 table 2: values are taken in dang et al. for mopso-cd performance[2] ps c1 c2 nog 100 1 1.5 300 even though we implemented pso to solve the multi-objective optimization problem, mopso takes all the good things from the pso. however, at the same time, some of the flaws of pso such as low convergence accuracy and low divergence also existed as well. therefore, the researchers modified the mopso for increasing the performance. the pbest in the first iteration becomes the gbest. on the second iteration, a new pbest is selected and this pbest is compared with the gbest. if the pbest is better than the gbest, we update the gbest; otherwise, we keep the gbest. this cd approach is taken in this research for the selection of the global best. mopso-cd uses its external archive to store non-dominated solutions, which are also used to find the relative density [5]. the selection of gbest is essential as all the particles eventually follow the gbest location. the external archive is controlled by giving it a pre-defined size. improvement of the performance of mopso-cd is made by introducing pc with pso, hence the new method is named mopso-cd-pc. 3 results and discussion to evaluate our implementation results, we are following the result set for mopso-cd used in ding et al.[2]. in this research, the most frequently used performance metrics were used to analyze mopso-cd and mopso-cd-pc methods’ performances. the parameters are shown in table 1 and the values used in the mopso-cd approach are shown in table 2. few performance metrics need to be considered for performance evaluation. the first performance metric is set coverage or c-metric, a measurement method introduced by zitzler and thiele [15]. considering there are two pareto fronts (pf) p and q. where, q is dominated through at least one of the solutions that are in p. c (p, q) represents the percentage of the solutions that are in q as described in equation (5). c (p, q) = |{q ∈ q | ∃p ∈ p : p dominates q}| |q| (5) where |q| represents the length of pareto front q. this research took account of the correct pareto front p* and an estimation of the pareto front p. if the value achieved from c (p*, p) is lower, then the solution in p is better. if c (p, q) = 1, then p is weakly dominated by all solutions in q, and if c (p, q) = 0, then p does not dominate any solution in q. spacing (sp) is another performance measurement method used to evaluate the allocation of the non-dominated solutions within the pareto front. the representation of the metric is depicted in equation (6), sp (spacing) = √√√√ 1 n n∑ i=1 (di − d) 2 (6) where n shows the approximation of non-dominated solutions (di), which can be represented in equation (7) di = minj m∑ k=1 ∣∣∣f ik − f jk∣∣∣, i, j = 1, 2, 4, . . . , n, m (7) https://doi.org/10.15837/ijccc.2021.5.4274 5 table 3: performance comparison between mopso-cd and mopso-cd-pc algorithm moea cmetric sp-metric ns igd-metric a1 a2 a1 a2 a1 a2 a1 a2 mopso-cd [2] 0.91 1.00 290.53 108.62 34 45 82.6 75.93 mopso-cd-pc [this research] 0.181 0.40 61.566 48.518 33 32 32.488 32.82 equation 7 shows how many number-of-objectives present i.e. d = ∑n i=1 di/n. when the value is close to zero, the solutions are distributed uniformly. non-dominated solutions (ns) are the measurement method that gives how many non-dominated solutions are allocated. depending on the value of ns, it is understandable that the answer is better or worse. if the value is large, the solution is considered as good. inverted generational distance (igd) is another performance measurement method used in this research. this is used to reflect both convergences of the solution and the diversities of the solution. let’s consider an even distribution of pareto front p* and estimation of pareto front p, the representation of igd is shown in equation (8), igd (p, p∗) = ∑ v∈p d(v, p∗) |p∗| (8) where d (v, p*) represents the minimum euclidean distance between v and all the points in p*. the value of igd can measure the solution quality. if the value is small, the result is considered better. we used these four performance metrics to evaluate our mopso-cd-pc algorithm with the previous work in load balancing applications. the parameter ranges and the simulation results have been compared with the recently published research works, which are shown in table 3. to compare with the method for mopso-cd, this research took the first two numerical problems (a1 & a2) from that simulation tasks. according to the table, it is being shown that in c-metric, the mopso-cd-pc values are lower compared to the previous mopso-cd, which indicated that this research performed better in terms of c-metric performance. according to the sp-metric method, the research method shows more uniformity than the previous method. although the value of ns is almost similar for both algorithms, the value of igd-metric value is lower in the mopso-cd-pc method, which indicates that the performance is better according to the igd-metric values as well. in this research and enhancement of the existing mopso-cd method is done by introducing the pc in pso to optimize the load balancing problem. the performance of mopso using the cd approach is better due to the pc approach, which improves the performance of standard pso by transferring information among the individuals in swam. previously, pso-pc has already proven to be a better solution than pso and by evaluating this research, it is now clear that the improvement can be achieved in multiple objective solutions too. the comparison of the experimental results indicates that the mopso-cd-pc gives better performance and an ideal optimization solution for load balancing. moreover, hla-based simulations are highly dependent on the resources of distributed environments, therefore, optimized load balance will increase the performance of the distributed system by lowering the execution time. 4 conclusion in this research, the load balancing problem is optimized, which is a key issue in the distributed system or hla. the goal of load balancing is to reduce the disparity of the computation load and communication load. mopso based on cd is a popular algorithm to solve load balance in hla architecture. mopso is a part of pso and by optimizing pso with passive congregation theory, this research has improved the mopso-cd method performance. performance metric simulation was done, and results were compared with recently published research works. the simulation results show that the mopso-cd-pc outperforms the previous mopso-cd approach in terms of different https://doi.org/10.15837/ijccc.2021.5.4274 6 metrics. this research proves that by introducing the pc into the existing mopso-cd approach, the new mopso-cd-pc method can be an optimized solution for load balancing problems in hla. funding the authors would like to thanks the american internaitonal university of bangladesh for supporting this research work. additionally, this research is funded by the boldrefresh2025 grant from universiti tenaga nasional, malaysia. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] dahmani, n.; krichen, s. 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(1998). multiobjective optimization using evolutionary algorithms a comparative case study, in international conference on parallel problem solving from nature, 292-301, springer, berlin, 1998. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: marufuzzaman, m.; timu,m. a.; sarkar,j.; islam,a.; rahman,l. f.; sidek,l. m. (2021). multiobjective pso with passive congregation for load balancing problem, international journal of computers communications & control, 16(5), 4274, 2021. https://doi.org/10.15837/ijccc.2021.5.4274 introduction research methodology results and discussion conclusion int j comput commun, issn 1841-9836 8(4):499-513, august, 2013. combining feature methods for content-based classification of mammogram images k. chikamai, s. viriri, j-r. tapamo keith chikamai serestina viriri* school of mathematics, statistics and computer science university of kwazulu-natal, westville campus durban, south africa viriris@ukzn.ac.za *corresponding author jules r. tapamo school of engineering university of kwazulu-natal, howard college durban, south africa tapamoj@ukzn.ac.za abstract: breast cancer is among the leading cause of death among females. studies show that early detection allows for a better prognosis. mammography is one of the successful ways for early detection of breast cancer. it mostly involves manual reading of mammograms, a process that is difficult and error-prone. this paper discusses a classification model for mammograms based on microcalcification characteristics, as a way of helping radiologists make quick and accurate diagnostic decisions by availing to them similar past cases. the images are pre-processed by gaussian smoothing and median filtering with 5×5 and 3×3 kernels respectively. gabor and haralick features are then extracted to form the image signatures over which similarity measurements are made. experimental results show an average precision value between 0.5 and 0.61 using haralick features, 0.49 and 0.57 using gabor features, and 0.51 and 0.78 using combination of gabor and haralick features. keywords: mammogram, classification, gabor filters, grey level co-occurrence matrix, haralick features. 1 introduction breast cancer is a high-mortality disease, and one of the leading causes of death among women [1] . if detected early, this disease is manageable, and can be cured in some instances. mammography is one of the methods used to detect early signs of cancer, and has proven to be very effective [23]. it involves generating images of the breast through x-ray photography, enabling the visualization of the internal breast structure for analysis that can expose any abnormality. traditionally, mammogram analysis has been manually done by radiologists through visual inspection. the amount of medical images being generated is increasing exponentially. according to geneva radiology, at geneva university and hospitals, images in excess of 30000 are being produced daily [1]. a large image database increases the referential space, providing a solid foundation for solving new cases easily. however, the large size increases the time needed for processing of the images. speed of decision making is important, especially in medical diagnosis. computer aided diagnosis (cadx) employs techniques in image processing and artificial intelligence to assist pathologists arrive at objective conclusions about a given image [3]. it is commonly used for identification of suspicious regions in a mammogram, as well as for determination of malignancy. a major part of malignancy determination involves extracting and computing features that are used to characterise the image. accurate characterization of these copyright c⃝ 2006-2013 by ccc publications 500 k. chikamai, s. viriri, j-r. tapamo features is important to the overall performance the cad system and can significantly reduce the rate of unnecessary biopsies. researchers have explored feature extraction methods to characterize breast pathology that include: morphological [3], wavelets [12], fractal and histogram-based measures. however, efficient and accurate retrieval of images based on their content as a field of computer vision is still an open problem [2, 4]. this research work implements the gabor filter and the grey level co-occurrence matrix (glcm) in an attempt to enable efficient and accurate retrieval of mammogram images containing microcalcifications, as described by a pathologist. the rest of the paper is organized as follows. section 2 looks at related relevant work in the literature. section 3 discusses the proposed system including the features used. experimental setup and results are discussed in section 4 and the paper is concluded in section 5. 2 related work mammogram images lack color information, and usually exhibit low intensity ranges as well as noise occlusions. overlying vessels and tissues also present a lot of challenges to the detection of malignant objects. this restricts the type of applicable features to those that exploit shape and textural characteristics of objects, with the requirement that these features be stable and robust against the mentioned limitations. researchers have attempted haralick features for the determination of malignancy in mammograms. hamid et al. [12] attempted a comparison between wavelet, haralick and shape features for classification of benign and malignant tumors in mammograms. pre-processing phase included segmentation using adaptive filtering banks described in [14]. martins et al. [13] combined haralick features with shape features as input to the k-means and svm classifier, achieving considerable success rate of 85%. muller et al. [4] look at developments in content-based image retrieval (cbir) in medical domain and present some future promising research directions. the authors note that speed as an evaluation parameter is rarely mentioned yet is important for an interactive system. they also propose that performance comparison for different feature sets needs to be done to identify well performing visual features and their optimal applications. in pointing out future research directions, the study revealed that availability of good quality features could increase accuracy in data mining and related applications. specialization is also proposed as a means of including the domain knowledge as a measure of improving accuracy. wei et al. [15] analytically look at the potential of cbir in medical image database retrieval, and discuss the benefits and feasibility of applying it, or extending the current techniques in order to apply them to daily medical practice. they review the limitations of the current noncbir approach, as well as obstacles of the application of cbir to medical image retrieval. they propose a textural analysis approach based on grey-level co-occurrence matrices for cbir in mammography as a case study. the method involves two stages: image analysis and image retrieval. image analysis determines the discriminating textural features that best act as descriptors for the image, later to be used for the subsequent image retrieval process. twelve glcms were constructed in four directions at three distances. eleven haralick features [11] were then calculated for the 12 glcms giving a total of 132 features for each region of interest (roi). the l2 norm was used for similarity measurement, with the smallest distance indicating most similarity. a total of 329 rois were used for evaluation from images sourced from the mammographic images analysis society (mias). precision and recall were used to test accuracy, with the system achieving 51% and 19% as the highest scores respectively. both these values were scored at a glcm distance of 5. the study identified a number of research issues, which include: semantic gap, systems integration, usability and performance evaluation. the major problems identified with current retrieval systems include subjectivity, financial and time costliness and combining feature methods for content-based classification of mammogram images 501 inefficiency of image data representation. obstacles to cbir application include image noise and many image formats available. 3 general structure of proposed model the proposed model follows the typical cbir model (figure 1). it is generally composed of two stages: off-line feature extraction and on-line image retrieval. off-line feature extraction involves extraction of features from each of the database images and their storage into the feature domain space. features are stored in feature vectors which are descriptors of their corresponding images. during on-line image retrieval, a user supplies a query example image whose features are also extracted and used by subsequent algorithms against the database features. figure 1: different steps of a content based classification of a candidate mammogram 3.1 pre-processing breast roi extraction and denoising the three techniques applied prior to features extraction are: median smoothing, gaussian smoothing, and region growing. given an image i with r rows and c columns, the preparation of this image for further processing is carried out in the three following phases: phase 1: the image is first processed using a median filter to remove sporadic sharp frequencies that are characteristics of digital noise. this filter has some edge-preserving characteristics since it does not adversely blur edges. assuming n is the kernel, made of neighborhood 502 k. chikamai, s. viriri, j-r. tapamo pixels around the target pixel ,(x0, y0), defined as n = {(x−n, y−m), (x−n+1, y−m), ..., (xn, ym)} (1) where n × m is the kernel size. the median filtering of the (x0, y0) is calculated as follows, – sort n in the sequence s = (si)i=0,1,...,(n−1)×(m−1), – assign the median value of the sorted sequence to the target coordinates i.e. i(x0, y0) = s(n×m) 2 in this case, n = m = 3 phase 2: gaussian smoothing is then applied on the output image using a kernel g(x, y) of dimension 5 × 5. the kernel used is of the form, g(x, y) = 1 2πσ2 e x2+y2 2σ2 (2) phase 3: after denoising, region growing is applied to detect the breast region of interest and isolate artificial labels. in order to increase the efficacy of the process by insensitizing the algorithm against small random signal perturbations characteristic of noise, the region growing considers blocks of 8 pixels at a time instead of individual pixels. local gradient and contrast enhancement the image is further enhanced using gradient and contrast enhancement techniques [20]. gradient enhancement increases the intensity of pixels in an adaptive manner. taking i(x, y) as the intensity function of a 2d image, the gradient at a pixel (x, y) in a mammogram image is given by, g(x, y) = 1 n × n m 2∑ i=− m 2 n 2∑ j=− n 2 |i(x + i, y + j) − i(x, y)|∀x, y ∈ s (3) the calculated gradient values are then added to the original image to give the gradient enhanced image i′(x, y), i′(x, y) = i(x, y) + g(x, y) (4) where s = {(x, y) | 0 ≤ x ≤ c − 1, 0 ≤ y ≤ r − 1} is the set of all image pixels m, n are the vertical and horizontal spatial dimensions of the kernel m and n determine the extent over which the gradient value is calculated and by implication, the size of objects that are enhanced. a square kernel is used in this work i.e. m = n = 3. this technique increases the intensity of pixels relative to the gradient of their local neighborhood. those areas presenting a higher gradient will thus have their intensities increased more, as determined by the kernel size. the kernel size is intuitively chosen to approximate the spatial extent of microcalcifications in order to enhance their gradient. contrast enhancement uses the mean of a region to alter its pixels intensities. the mean of a pixel neighborhood is iteratively calculated as follows, µk(x, y) = 1 m × n m 2∑ i=− m 2 n 2∑ j=− n 2 µk−1i(x + i, y + j)∀x, y ∈ ∑ ∗ (5) combining feature methods for content-based classification of mammogram images 503 the contrast enhanced image is represented as follows, i′′(x, y) = µk(x, y) l − 1 i′(x, y) (6) where k specifies the iterations over which the mean is calculated, µk is the mean at the kth iteration, l is the highest intensity in the image * the other variables are defined as for gradient enhancement. while gradient enhancement increases the intensities of high gradient areas without affecting the rest, contrast enhancement diminishes the effect of low contrast areas by lowering their intensity values. the neighborhood determines the objects (by size) that are enhanced. the number of iterations, k, determines how much the original signal is attenuated. a value of k = 2 is empirically chosen for the experimental runs in this work. similar to the gradient kernel, a square mask of dimension 9 (i.e. m = n = 3) is used for the contrast kernel. these filters are useful in reducing the effect of the monotonous pelvic muscle which mimics the gradient levels of microcalcifications. 3.2 feature extraction the gabor filter and haralick features are used for textural analysis of the pre-processed image. the gabor filtering is an intermediate stage, with the first and second moments being calculated from the gabor filtered image to give the final features. the techniques and the specific parameters used are discussed in the following sections. gabor filtering gabor filter is a transform function related to the fourier transform which can be used to convey spatial information in addition to frequency properties of a signal. it is commonly applied as a band-pass filter in signal processing where it is used to determine the sinusoidal frequency and phase content of local sections of a time varying input signal, and has been found useful in image compression. among other useful properties, the gabor filter has been found to better minimize the conjoint time-frequency information resolution of a signal [4]. a gabor filtering is obtained by multiplying a complex sinusoidal plane wave of a certain frequency with a gaussian envelope as follows [5]: g(t) = kejθw(at)s(t) (7) where w(t) = e−πt 2 is the gaussian envelope, s(t) = ej(2πft) is the sinusoidal function, where f is the frequency of the sinusoidal plane wave, k is the constant, and ejθ determines the orientation the strength of the gabor filter response depends on the filter’s congruence with the local signal; where the filter’s sensitivity is determined by tuning of the parameters that include: orientation, phase and frequency [5]. given that gabor filters are not inherently orthogonal [9], this section determines an optimal set of parameters for designing gabor filter jets that will detect the desired range of object characteristics with minimum redundancies. based on the work in [6], this project implements the following 2-d gabor filter: gλθφ(x, y) = 1√ 2πσxσy e − ( x′2 σ2x + γy′2 σ2y ) cos(2πx′w + φ) (8) 504 k. chikamai, s. viriri, j-r. tapamo x′ = xcos(θ) + ysin(θ) (9) y′ = ycos(θ) − xsin(θ) (10) where λ, θ and φ are configurable input parameters specifying wavelength, orientation and phase, respectively; γ specifies the aspect ratio of the gaussian envelope. its value is empirically set to 0.5; σx and σy define the convergence of the gaussian envelope along the x− and y− axes respectively. they are defined as follows, a = ( uh ul ) 1 s−1 σu = uh (a − 1) (a + 1) √ 2log2 σv = tan ( π 2k )[ uk − 2log ( 2σ2u uh )][ 2log2 − (2log2) 2 σ2u u2h ]− 1 2 σx = 1 2πσx , σy = 1 2πσy the spatial width of the filter (fw) is then linearly scaled from the derived standard deviation as follows, fw = 4σ + 0.5 (11) this filter width calculation is empirically established to give a good compromise since it does not greatly affect border pixels. the pre-processed input image i(x, y) is convolved with gabor filter g(s, t) to give the response image r(x, y) as shown in equation (12), r(x, y) = ∫ ∫ (x,y) i(s, t)g(x − s, y − t)δsδt (12) this implementation (equation 12) is computationally expensive in the spatial domain, making it impractical for large input images and gabor kernels. this study thus takes advantage of the convolution theorem to implement filtering in the frequency domain as defined in equation 14. since convolution in the spatial domain is equivalent to multiplication in the frequency domain, the computational complexity is reduced using the latter method [21]. it uses the comparatively optimal fftw library to carry out the fourier-spatial transformations. f (r(x, y)) = f {i(x, y)} f {g(x, y)} (13) g(x, y) = f−1 {f {i(x, y)} f {g(x, y)}} (14) this work considers a set of gabor filters configured with four orientations: 0, 45, 90 and 135. these values are calculated according to equation 15 considering recommendations in [ [7, 8]]. θk = kπ n , k = {0, . . . , n − 1} (15) where n is the number of orientations, k represents the kth orientation. combining feature methods for content-based classification of mammogram images 505 this method ensures equidistant spacing in the orientation field. furthermore, the orientation space is chosen from the range θk ∈ [0, π], which provides sufficient coverage since it has been established that response values in the range [π, 2π] only differ from those in [0, π] by phase shift [7, 8]. glcm and haralick features in this work, haralick features are extracted to describe the mammogram’s textural characteristics. textural characteristics are described by patterns of pixel intensities [11]. practically, these intensities are described by a distance-angular relationship model using a grey level co-occurrence matrix (glcm) as proposed by haralick et al. [11]. glcms are second-order statistics that define relationships between distinct tonal intensities by measuring the frequency with which they occur together at certain directions (θ) and distances d, and fall under statistical textural classification approaches. grey level co-occurrence matrix feature method is also used to model grey level dependencies of mammogram images. similar to the gabor wavelets, set of matrices are defined over four directions: 0, 45, 90 and 135, at a distance d and is represented as p(i, j, θ, d). a subset of nine haralick features [11] are calculated over the glcm to describe the mammograms’ textural characteristics. for notational convenience, lets denote p(i, j) as the probability of i occurring alongside j, the haralick features are then defined as follows: energy = √∑ i,j p(i, j)2 (16) entropy = − ∑ i,j p(i, j)log(p(i, j)) (17) contrast = ∑ i,j pi,j(i − j)2 (18) homogeneity = ∑ i,j pi,j 1 + (i − j)2 (19) max prob = max(p(i, j)) (20) correlation = ∑ i,j pi,j  (i − µi)(j − µj)√ σ2i σ 2 j   (21) dissimilarity = ∑ i,j pi,j|i − j| (22) idm = ∑ i,j 1 1 + (i − j)2 p(i, j) (23) 3.3 classification of a candidate mammogram the cbir system designed, returns results ranked in order of relevance to the query image. the number of returned images (represented by k in this context) impacts on accuracy or precision of the system if factored during system evaluation. the k nearest neighbor (k-nn) classifier is used for classification; we used the version described in [9, 22]. in our context there are two classes m0, and m1: 506 k. chikamai, s. viriri, j-r. tapamo • m0: class of mammograms without microcalcifications • m1: class of mammograms with microcalcifications insert(s, y, cm) inserts y in s, in increasing order of distance between y and cm. given a set a, ia(.) is and indicator function defined as follows: ia(x) = 1 if x ∈ a and ia(x) = 0 otherwise. the classifier in algorithm 1 finds the minimum distance between the given query vector cm and all mammograms in m, builds a sequence s of k vectors representing the mammogramms with the minimum distance. the query vector is finally assigned to the class ml(l = 0, 1) that has the majority of elements of s. experiments are conducted with values of k taken from an set of 5 elements, {1, 3, 5, 7, 9}. 4 results and discussion 4.1 image dataset experimental tests were conducted on a set of 60 images sourced from the mammographic image analysis society (mias) database [10]. this database contains 322 mammogram images of mixed pathologies, and is accompanied by ground truth that has been verified and marked by radiologist. the ground truth gives the severity of the pathology (malignant/benign) as well as the spatial locality and extent of the pathology. the pathology classes are: • calcification • well-defined/circumscribed masses • spiculated masses • other, ill-defined masses • architectural distortion • asymmetry combining feature methods for content-based classification of mammogram images 507 • normal the breast tissue of each mammogram has been classified depending on density. the density of a breast tissue alludes to the amount of fat present in that tissue. fatty tissues appear as relatively darker areas in mammograms. in order to reduce the amount of fatty tissue, the mammograms are classified under any of these three types: fatty, fatty-glandular and dense glandular. all dataset images have been quantized to 256 grey levels, and were digitized at a resolution of 200 microns. they are padded and clipped to occupy a standard size of 1024 × 1024 pixels. 4.2 experimental setup sixteen images were selected from the database to form the query image set. these images were taken from both classes of pathology, i.e., normal and malignant. normal images in this experiment were defined as those images not containing microcalcifications. this definition covers images diagnosed as positive for other malignancy indicators such as circumscribed masses and asymmetries. results were then collected for each round for every query image. for generalized results, precision values calculated are averaged over all query images instead of one. the query process was repeated ten times (ten iterations) using a randomized set of 8 images from the normal class, the average precision values obtained in each round were then averaged over the ten iterations to give the statistical base for reporting. this process was done for every value of k ∈ [1, 3, 5, 7, 9]. 4.3 performance metric the average precision curve [18] was used to evaluate the performance of the system. precision gives the general classification performance of the system. it measures the ability to correctly classify both sample sets. sixteen images from both classes are used as query images, and results collected after every round as explained in section 4.2. for every returned result set, precision is calculated as follows, precision = r k , where r is the number of accurate predictions and k the number of neighbors algorithm 2 is used to compute precisions. the average of precision values for both sets of query images is then taken as the precision value for the round. the precision values are then used to sketch the precision curve for diagrammatic representation. the precision value ranges from a minimum of 0 to a maximum of 1. a high precision value implies that the system has a commensurately high ability of correctly classifying a given sample. 4.4 discussion the first results of the experimental runs are given in figures 3, 4 and 5, show the haralick results for single, double and combined class query image sets, respectively. the figures 6, 7 and 8, show the gabor filter results for single, double and combined class query image sets, respectively. the figures 9, 10 and 11 show the results for single, double and combined class query image sets, respectively, using combined gabor and haralick features. the first results (figures 3, 4 and 5), show precision values obtained by querying the database using 8 images randomly selected from the ”normal” class. the querying process (section 4.2) is evaluated over five rounds. the lowest precision value of 0.71 is scored at distance k = 3, with the highest value of 0.88 scored at the distance k = 1. the system gives a low score for images identified positive for microcalcifications . the highest score of 0.375 is recorded at distance k = 1, and the lowest score of 0.13 scored at distance k = 9. for mixed class query images (figure 4), 1−nearest neighbor gives the best precision score at 0.69, with the 9−nearest 508 k. chikamai, s. viriri, j-r. tapamo figure 2: query result using normal class images figure 3: query result using normal class images figure 4: query results using mixed class images neighbor giving the lowest score at 0.47. overally, the performance degrades with an increasing value of k (figure 5). the highest value scored is 0.61 at k = 1, with the lowest value of 0.50 combining feature methods for content-based classification of mammogram images 509 figure 5: average precision for combined class query image set figure 6: query result using normal class images figure 7: query results using mixed class images being scored at k = 9. compared to haralick features, the gabor feature set gives slightly higher average values 510 k. chikamai, s. viriri, j-r. tapamo figure 8: average precision for combined class query image set figure 9: query result using normal class images figure 10: query results using mixed class images (figures 6, 7 and 8) for queries involving benign classified images with a high score of 0.85 (considering all values of k). however, the gabor vector gives relatively lower high scores for the combining feature methods for content-based classification of mammogram images 511 figure 11: average precision for combined class query image set calcification and combined class query image set, at 0.25 and 0.57 respectively. it however gives consistently better average results considering all values of k for all three query classes. comparison of retrieval performance was done considering a mixed gabor and haralick feature set (figures 9, 10 and 11), which gave mixed results for various values of k. the combined feature set gave the highest score for queries involving normal classified images, with a score of 0.88 at k = 9 (figure 9). it also registered the highest scores for k ∈ [1, 7], with the gabor vector giving the highest values for the remaining values of k. this set also gave high values for the calcification and mixed class query images at 0.625 and 0.79 respectively, both values attained at k = 1. summarily, the combined feature set of gabor and haralick features enhances retrieval performance for all classes of query images. the best consistent performance is achieved at k = 1 for all query classes. for comparison, wei et al. [19] implement a glcm-based mammogram retrieval system for comparison with their algorithm. regions of interest (rois) are cropped from mammogram images of multiple pathologies. the images are then gabor-filtered before calculation of haralick features. the glcm matrix is calculated over three distances and four orientations. their average precision values range between 0.33 and 0.64. our system proves to be more discriminating towards images not containing microcalcifications than for those containing microcalcifications. a possible explanation is that a lot of unnecessary breast information is being included for similarity calculations. this means that the algorithm needs to be enhanced more to reduce the impact of non-calcification regions. the high dimension of features might also have a negative effect on the accuracy of the algorithm by introducing redundancies. our precision value is however not far-off the one obtained in [19]. this adds to the fact that the proposed model automates roi selection. 5 conclusions and future work mammography allows the detection of breast cancer in its early stages, which makes possible early remedial measures that can reduce the high mortality rates associated with the disease. this paper discussed a content-based classification model for mammogram images, with the objective of availing a second opinion to a radiologist for reference during diagnosis. it implemented the gabor filter and haralick features for textural analysis and description for similarity assessment. this work evaluated haralick features at five distances, k ∈ [1, 3, 5, 7, 9] and four orientations east, north, south and west. the best value is attained using a combined gabor and haralick 512 k. chikamai, s. viriri, j-r. tapamo feature set, with a score of 0.79 at the distance of k = 1, with lowest value as 0.49 at the distance k = 9 using gabor features only. the moderate precision value could be attributed to the impact of non-calcification breast areas, as well as redundant and less discriminating features. work is underway to remove redundant features as well as those features that do not discriminate well with respect to microcalcifications. bibliography [1] rangayyan, r.m. 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(2007); effective extraction of gabor features for adaptive mammogram retrieval, proc. of icm : 1503-1506. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 4, month: august, year: 2021 article number: 4226, https://doi.org/10.15837/ijccc.2021.4.4226 ccc publications performance evaluation of river water quality monitoring using lora connectivity with fuzzy algorithm d. perdana, j. naufal, i. alinursafa doan perdana* telecommunication engineering department electrical engineering faculty, bandung, indonesia *corresponding author: doanperdana@telkomuniversity.ac.id julian naufal telecommunication engineering department electrical engineering faculty, bandung, indonesia julian@student.telkomuniversity.ac.id ibnu alinursafa telkom dds ibnu@telkom.co.id abstract this study was proposed a river water quality monitoring application, connected by sensors such as ph, turbidity and total dissolved solids (tds) sensor to measure acidity, turbidity levels and amount of dissolved solids, respectively, as well as reduce bad effect of polluted river water. this river water quality monitoring tool was able to process input data from sensors using fuzzy algorithms and determine whether the river water quality. lora functions as data transmission communication and antares as a cloud service to store data obtained from sensors. furthermore, data obtained was displayed in the smartphone android application. the rivers that were tested are located in citarum river sector 6 and 21. the results showed that the accuracy of the temperature, tds and ph sensor were 98.69%, 89.69% and 99.39%, respectively. furthermore, the average value of rssi citarum sector 6 and 21 were -111,576 db and -112,855 db, respectively. meanwhile the average snr of sector 6 was -6,46 db and citarum sector 21 was -12,85851 db. keywords:water quality, lora, fuzzy algorithm, citarum river. 1 introduction based on reports from the 2018 indonesian environmental statistics, 25.1% of villages that has rivers experienced water pollution [1]. this data proved that the quality of river water in indonesia is generally in a heavy polluted status and its impact is capable of damaging the river environment, https://doi.org/10.15837/ijccc.2021.4.4226 2 as well as cause diseases that are detrimental to humans. therefore, water quality monitoring is very important in order to maintain its good quality, keep it safe for use in daily needs and avoid the bad effect due to water pollution [2]. this study aims to create a water quality monitoring tool with sensors that are be able to provide quality data on river water such as ph, tds, turbidity and temperature [3]. the data obtained was further processed by fuzzy algorithm to determine whether the water quality is good or not [4, 5, 6]. in addition, this tool was monitored remotely by using lora connectivity and the data was subsequently accessed using the application on android. the reference for the determination of river water quality was the government regulation number 82 of 2001 concerning management of water quality and pollution control [7, 8]. the other was the government regulation number 32 of 2017 concerning environmental quality standards and water health requirement on hygiene needs for sanitation, swimming pools and solus per aqua [9]. furthermore, this study was carried out based on the concept of iot and lora. it is said to be a network of devices which communicates among itself using ip connectivity without human interference available anywhere, anytime, and for any purpose [10, 11]. meanwhile, fuzzy to compression data [12, 13]. end-devices also uses it across a single wireless hop to communicate to gateways, connected to the internet, therefore acting as transparent bridges and relaying messages between these end-devices and central network server [14, 15]. in indonesia, lora operates on 923 mhz-925 mhz frequency band [16]. 2 related works several studies or experiments have been carried out using the fuzzy logic method. for example, a flood detector using fuzzy logic and lora technology was designed by khuen [17]. in the study by khuen, a more reliable and robust system design. based on syamsul et al. [18] was proposed control system was based on fuzzy logic, with two main parameters namely temperature and vapour pressure. it proved that controlling the nutmeg oil distillation system using fuzzy logic will improve the performance of the system i.e. accelerate the distillation time, optimal use of energy from fuel gas. moreover, yi zhao et al. [19] study a fuzzy logical based algorithm was proposed to suppress the remaining ground clutter from base data of x-band doppler weather, with the aim of controlling or improving data quality before its application. meanwhile, asep [20] proposed the application of fuzzy logic methods in attempting the velocity of a hexapod mobile robot by using the takagi-sugeno-kang type of its inference system. the fuzzy logic controller is used to drive a hexapod mobile robot. zina et al. [21] study design for nonlinear uncertain system is succeeded presents a robust fault tolerant tracking control (fttc) by takagi sugeno (t-s) fuzzy model. system can count from unmeasurable premise variables subject to sensor faults . putra et al. [22] utilized the fuzzy, which is succeeded on the scoring process of question answering system and increasing the accuracy level of question answering system. the next study was designed by mandala and proposed an iot digital thermometer called hi-thermo, which implements fuzzy logic. fuzzy system, when applied is able to set the interval to send data on continuous monitoring the energy efficiency of the system [23]. in addition, the fuzzy implementation by alireza can use a fuzzy estimator to design a force or position tracking system in a robot manipulators [24]. 3 system design and simulation 3.1 design system figure 1 shows the system design of the river water quality monitoring tool. the data from this device were sent to the lora gateway, which was further connected to the lora server. the function of the lora server is to connect the lora gateway to the database and platform. after the data reached antares, it was stored temporarily. https://doi.org/10.15837/ijccc.2021.4.4226 3 figure 1: design system. subsequently, the application made in android that was connected to antares retrieved the stored data. furthermore, the data or results obtained from these sensors were shown in real time using an android application called monriv (monitoring river) on a smartphone. 3.2 block diagram figure 2: block diagram. figure 2 shows a block diagram of this river water quality monitoring tool. in this study, the system was designed using an esp32 microcontroller, which was connected to several sensors to monitor and measure river water quality parameters. furthermore, the data was sent to the lora gateway using a frequency of 920-923 mhz. after arriving at the gateway lora, it was sent to network server lora. the function of network server is to connect the lora gateway with the platform antares. the water quality data that was already stored in antares was sent to the android application that has been created and displayed on the application on the android smartphone. 3.3 hardware design figure 3: hardware design. https://doi.org/10.15837/ijccc.2021.4.4226 4 the hardware design used in this study in shown in figure 3.the hardware design was designed to connect all 4 sensors such as temperature, ph, total dissolved solid and turbidity sensor to the esp32 board lora microcontroller [25, 26, 27? ]. furthermore, it was connected to lora hope rfm95 as remote communication and antares cloud as a database or data storage place, while the data that was earlier obtained from antares was displayed in an application form on android. antares is one of the iot platforms developed by pt telekomunikasi indonesia. it is able to store and send data in real time from sensors using arduino or raspberry pi. furthermore, it uses the restful approach in the development of api (application programming interface) for easy operation and good networks by users [28, 29]. 3.3.1 fuzzy algorithm 1. fuzzy variable in designing this monriv tool, 4 variables were used as input, namely the variable ph, turbidity (in ntu), tds (in mg / l) and temperatures (in ◦c). 2. linguistic values four variables used in this study for linguistic values are as follows: (a) the ph variable, divided into 3 fuzzy sets, namely: acids, bases and neutral. (b) turbidity variable, divided into 3 fuzzy sets, namely: clear, turbid and very turbid. (c) variable tds, divided into 3 fuzzy sets, namely: large, moderate and a little. (d) temperature variable, divided into 3 fuzzy sets, namely: cold, hot and normal. 3. fuzzification is the process of mapping crisp (numeric) values into a fuzzy set and determining the degree of membership. mapping the crisp value into the fuzzy set is explained in the following figure: (a) the ph variable was divided into 3, namely acids, neutral and bases from the range 0-6, 6.5-7.5 and 8-14, respectively: (b) in the temperature membership function there were 3 linguistic variables, which include cold, normal and heat, from the range of 0-20, 21-28 and 31-100, respectively: https://doi.org/10.15837/ijccc.2021.4.4226 5 (c) in the turbidity membership function there were 3 linguistic variables, namely clear, turbid and very turbid, ranging from 0 to 5 ntu, 6 to 25 and above 25 ntu, respectively. (d) in the tds membership function there were 3 linguistic variables, namely little, moderate and large, ranging from 0 to 500 mg/l, 600 to 1000 mg/l and over 1000 mg/l, respectively. 4. defuzzification this stage variable linguistic values were processed into crisp values as output. data from the four water quality sensors were further divided into 4 categories namely "excellent", "good", "medium" and "poor". the data is displayed in the graph below: (a) bad category has a range of 0 to 25. (b) medium category has a range of 26 to 50. https://doi.org/10.15837/ijccc.2021.4.4226 6 (c) good category has a range of 51 to 75. (d) excellent category has a range of 76 to 100. 4 results and analysis this chapter discusses the water quality, fuzzy algorithm and lora network testing of the citarum river. this was carried out in two locations, namely citarum river sector 6 and 21 and testing lora network testing from citarum to nearest gateway. 4.1 water quality testing citarum river the water quality testing was carried out in the citarum river sector 6 or more precisely on jalan babakan leuwi and citarum river sector 21 located on sukabirus. the results obtained for figure 4: defuzzification. https://doi.org/10.15837/ijccc.2021.4.4226 7 the quality of citarum river water in sector 6 include: 4.1.1 water quality testing after carrying out water quality testing for about 30 minutes, the average results of the water quality of the citarum river was obtained. in citarum river water sector 6, the average water quality was at a ph of 9.67, tds 300.98 mg/l, temperature 26.46 and turbidity 1281.581 ntu and were included in the bases category, little category, normal and very turbid category, respectively. meanwhile, for the citarum river in sector 21, the average water quality obtained was at a ph of 8.05, tds of 327.75 mg/l, temperature 26.38 and turbidity 1405.128 ntu and were included in the bases, little, normal and very turbid category, respectively. the conclusion was that sector 21 has a bad quality compared to sector 6, due to the higher tds and turbidity value. table 1 shows the average water quality of citarum. river in sector 6 and 21: table 1: water quality of citarum river. water quality of citarum river no location ph tds temperature turbidity 1 ciatrum river sektor 6 9,67 300,98 26,46 1281,581 2 ciatrum river sektor 21 8,05 327,75 26,38 1405,128 4.2 fuzzy algorithm testing on river water 4.2.1 fuzzy sector 6 the first test was carried out at the citarum river sector 6 for 30 minutes. the quality results obtained were the citarum river in sector 6 including "medium" with an average fuzzy value of 50. the value of 50 was included in the "medium" deffuzzyfication, therefore it was concluded that citarum river sector 6 is in the "medium" category. the fuzzy results are shown below: figure 5: fuzzy sector 6. 4.2.2 fuzzy sector 21 the first test was carried out at the citarum river sector 21 for 30 minutes. the quality results obtained were the citarum river in sector 21 including "medium" with an average fuzzy value of 50. the value of 50 was included in the "medium" deffuzzification, therefore it was concluded that citarum river sector 21 is in the "medium" category. the fuzzy results are shown below: 4.3 lora network testing lora network testing was carried out by installing the rfm95w lora antenna to the monriv device. the monriv tool also acts as the lora network receiver from the lora gateway located at telkom sto around bandung. this testing process was carried out by measuring rssi, snr, delay https://doi.org/10.15837/ijccc.2021.4.4226 8 figure 6: fuzzy sector 21. and packet loss through the lora network within 30 minutes at the citarum river sector 6 and 21 locations. the test uses 923-925 mhz frequency, 125000 bandwidth and spreading factor of 10. the definition of signal noise ratio (snr) is the power or signal strength received by the user with noise. the greater the value of snr, the greater the power obtained by the user. however, if the snr value is low then the greater the noise received. this snr affects the quality of the network in data transmission. the definition of received signal strength indicator (rssi) is a parameter that shows the end user’s receiving power from all signals in the channel frequency band used. the further away from the beam distance, the weaker the signal strength received and the slower the data sent. the value of rssi is closer to 0, the better the signal. the rssi value is expressed in dbm and is a negative value. the definition of packet loss is the number of packets that fail to reach their destination when delivering the package. and the definition of delay is the amount of time it takes for a package to travel from source to destination. table 2: water quality of citarum river. average lora network testing no location snr (db) rssi (db) delay packet loss 1 ciatrum river sektor 6 -6,46 -111,6 18 s 44 % 2 ciatrum river sektor 21 -12,86 -112,855 30 s 65 % table 2 shows the conclusion of the average network quality. it is seen that citarum river sector 6 has better average network quality compared to citarum river sector 21. this was proven because sector 6 has a better snr, rssi, delay and packet loss compared to 21. 4.4 lora network testing from river to gateway sto cijawura this test aims to analysis the performance or network quality of the lora from monriv tool network data delivery, which was on the river sector 6 and 21 to lora gateway in sto cijawura and lembong. this was carried out by obtaining sample data for about 30 minutes. furthermore, the data in antares was collected and either the sto cijawura or lembong were selected for the data to pass through. the parameters to be measured in this test include rssi, snr, packet loss and delay. the first test was used to measure monriv tool to gateway sto cijawura. 4.4.1 rssi the graph in figure 7 shows that the average result for rssi sector 6 and 21 include -109.9 dbm and -111.8 dbm, respectively. furthermore, rssi sector 6 value was better compared to snr sector 21. this proves that apart from distance, obstacles such as buildings and trees greatly affect the rssi value. this was proven by the citarum river sector 2, which is located near the telkom campus, consequently it has many obstacles such as high-rise buildings and housing for residents. meanwhile, the citarum river sector 6 is located in a wide area. therefore, the rssi value obtained was better compared to 21. https://doi.org/10.15837/ijccc.2021.4.4226 9 figure 7: rssi sto cijawura. 4.4.2 snr figure 8: snr. the graph in figure 8 shows that the average result for snr in sector 6 and 21 are -11.9731 db and -12.731 db, respectively. it is seen that the value of snr for sector 6 is better compared to that of sector 21, which proves that besides distance, obstacles such as tall buildings and trees greatly affect the snr value. this was proven by the citarum river sector 21, which is located near the telkom campus, that has many objects such as high-rise buildings and residential areas. meanwhile, the citarum river sector 6 is located in a wide and spacious place. therefore, the chances of the snr value obtained are better compared to sector 21. 4.4.3 delay and packet loss the table 3 shows that the delay and packet loss on lora greatly affect distance and obstacles. the value of delay and packet loss in citarum river sector 21 was better compared to sector 6. this is because the distance from sector 21 is closer to the sto cijawura route. therefore, test distance and packet loss are in direct proportion. table 3: delay and packet loss. no location snr (db) rssi (db) 1 sektor 6 69 s 85 % 2 sektor 21 47 s 77 % 4.5 lora network testing from river to gateway lembong 4.5.1 rssi the graph in figure 9 shows that the average result for rssi sector 6 and 21 is -111.1 dbm and -114.27 dbm, respectively. this implies that the rssi sector 6 value is better compared to sector 21, which proves that apart from distance, obstacles such as buildings and trees greatly affect the rssi value. this was proven by the citarum river sector 21 which is located near the telkom campus, consequently it has many obstacles such as high-rise buildings and housing for residents. meanwhile, https://doi.org/10.15837/ijccc.2021.4.4226 10 figure 9: rssi. the citarum river sector 6 is located in a wide area. therefore, the rssi value obtained was better than sector 21. 4.5.2 snr figure 10: snr. figure 10 shows that the average result for snr sector 6 and 21 is -3.55 db and -12.91 db, respectively. this implies that the value of snr for sector 6 is better compared to that of sector 21, which proves that even though the gateway is different, distance and obstacle such as tall buildings and tree may affect the quality of lora data transmission. this is because, the citarum sector 21 river is located near the telkom campus and consequently has many obstacles, such as high-rise buildings and residential housing. meanwhile, for the citarum sector 6 river, which is located in a spacious place, there are no tall buildings around it, therefore the ssnr value space is better compared to sector 21. 4.5.3 delay and packet loss table 4: delay and packet loss. no citarum river delay packet loss 1 sector 6 69 s 78% 2 sector 21 47 s 88% the table 4 shows that the delay and packet loss on lora greatly affect distance and obstacles. the value of delay and packet loss in citarum river in sector 6 is compared to sector 21, due to its fewer obstacles. apart from distance, obstacles or noise also affect the quality of data transmission. 5 conclusion based on tests carried out, the water quality of citarum river sector 6 is better compared to that of 21, which is shown by citarum river sector 21 having a higher number of tds and turbidity compared sector 6. this was due to the closeness of sector 21 to residential areas, which resulted to the presence of waste in river sector 21 and in turn damaged its quality. for the results of the fuzzy calculation, these two rivers have the same number of definitions, namely 50. the value of 50 was included in the range of the river category "medium". therefore, it was concluded that these https://doi.org/10.15837/ijccc.2021.4.4226 11 two rivers have a quality that is "medium". this tool is only able to send fuzzy score to the antares. however, it can’t send the fuzzy result like “medium” or “good” to the antares and application in android. for testing the quality of the lora network, citarum river sector 6 was of better quality compared to 21, which was evident from 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(2013) "performance comparison of ieee 1609.4/802.11p and 802.11e with edca implementation in mac sublayer," 285-290. paper presented at 2013 5th international conference on information technology and electrical engineering, icitee. 2013, yogyakarta, indonesia. https://doi.org/10.1109/iciteed.2013.6676254. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: perdana, d.; naufal j.; alinursafa, i. (2021). performance evaluation of river water quality monitoring using lora connectivity with fuzzy algorithm, international journal of computers communications & control, 16(4), 4226, 2021. https://doi.org/10.15837/ijccc.2021.4.4226 introduction related works system design and simulation design system block diagram hardware design fuzzy algorithm results and analysis water quality testing citarum river water quality testing fuzzy algorithm testing on river water fuzzy sector 6 fuzzy sector 21 lora network testing lora network testing from river to gateway sto cijawura rssi snr delay and packet loss lora network testing from river to gateway lembong rssi snr delay and packet loss conclusion international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(5), 733-752, october 2019. design, modeling and control of bionic knee in artificial leg h.l. xie, y. xie, f. li hualong xie* department of mechanical engineering and automation, northeastern university, shenyang 110819, china hlxie@mail.neu.edu.cn yao xie department of mechanical engineering and automation, northeastern university, shenyang 110819, china 1870220@stu.neu.edu.cn fei li department of information science and engineering, shenyang university of technology, shenyang 110870, china lifeisut@163.com abstract: the biped robot with heterogeneous legs (brhl) greatly facilitates the development of intelligent lower-limb prosthesis (illp). in the brhl, the remaining leg of the amputee is simulated by an artificial leg, which provides the bionic leg with the precise gait following trajectory. therefore, the artificial leg must closely mimic the features of the human leg. after analyzing the motion mechanism of the human knee, this paper designs a four-link bionic knee in light of the coexistence of rolling and sliding between the femur, the meniscus and the tibia. drawing on the driving mechanism of leg muscles, two pneumatic artificial muscles (pams) were adopted to serve as the extensor and flexor muscles on the thigh. the two pams move in opposite direction, driving the knee motions in the artificial leg. to overcome the complexity of traditional pam modelling methods, the author set up a pam feature test platform to disclose the features of the pams, and built static and dynamic nonlinear mathematical models of the pams based on the test data. next, a proportional-integral-derivative (pid) closed loop controller and sliding mode controller was designed for the bionic knee, referring to the kinetics equation of the knee. through experimental simulation, it is confirmed that the proposed controller can accurately control the position of the four-link bionic knee, and that the designed bionic knee and pam driving mode are both correct. keywords: bionic knee, biped robot with heterogeneous legs (brhl), pneumatic artificial muscle (pam), high-speed on-off valve, sliding mode control 1 introduction millions around the world have lost their lower extremities due to varied reasons, ranging from wars, earthquakes, diseases, work-related injuries, traffic accidents to accidental injuries. in 2006, china launched the second national sample survey on disabled people [4]. the survey results show that 29.07% (24.12 million) of chinese with disabilities suffered from extremity disability; about 8% (2.26 million) of them had one or more of their limbs amputated, including copyright ©2019 cc by-nc 734 h.l. xie, y. xie, f. li 1.58 million of lower extremity amputees. in the us, around 1.60 million have lost some or all their limbs, and the majority (97%) of amputations caused by vascular complications target the lower extremities. a good chunk (25.8%) of these lower extremity amputations removes the leg above the knee [7]. currently, there is not yet any biomedical technology capable of regenerating human tissues. in this case, the intelligent lower-limb prosthesis (illp) [6,10,19] is the most desirable way to compensate for the walking function of lower extremity amputees. this paper designs a novel biped robot with heterogeneous legs (brhl) [15,17,21], laying a solid basis for the r&d of the illp. for example, the brhl can be applied in prosthesis tests to disclose the walking features of the disabled wearing the illp, eliminating the need for the disabled to walk repeatedly in prosthesis, the physical and mental pains of the disabled in the tests, and the human interference in the test results. as shown in figure 1, our brhl consists of an artificial leg, a bionic leg and a robotic upper body. the artificial leg and the bionic leg correspond to the remaining good leg and the illp of the amputee, respectively. the artificial leg is required to simulate the normal gait of the remaining leg in an accurate manner, and provide the bionic leg with the precise gait following trajectory. therefore, the control of the artificial leg is critical to the development of the entire brhl [16,18]. figure 1: the structure of the brhl to satisfy the requirements on the artificial leg, this paper adopts the pams based on highspeed on-off valve control to replace the “flexor and extensor muscle groups”of the biceps and quadriceps of the human leg, and drives the joint movement. compared with traditional robot driving modes (e.g. motor, hydraulic unit and cylinder), the two-pams driving mode bears great resemblance with the driving mode of human knee in motion mechanism and smoothness. during the normal motions of human knee, rolling and sliding coexist between the femur, the meniscus and the tibia; the trajectory of the instant center of rotation (icr) is in the form of a j-shape curve. in our design, the j-shape icr trajectory of the knee is achieved through a four-link bionic mechanism, which also enhances the system flexibility and stability [8]. it is a complex task to establish a model for the pams, due to their various nonlinear uncertainties, including but not limited to the friction of the woven mesh, gas compression and irregular deformation. the flow of the high-speed on-off valve is also hard to simulate, adding to the difficulty in system control. many attempts have been made to overcome the difficulty. for instance, jouppila et al. [5] designed a full-state observer based on the smooth variable structure filter (svsf) and the sliding mode control (smc), which regulates the pam position via the smc, and verified the robustness of the observer in controlling the pam stretching design, modeling and control of bionic knee in artificial leg 735 system for pulling reciprocating masses. using hysteresis compensation, vo-minh et al. [12] improved the control precision with two closed loops for feedforward compensation: the inner loop compensates for the nonlinear pressure within the pam, while the outer loop makes up for the nonlinear dynamics of the pam. chen and ushijima et al. from tokyo university of science [1] improved the precision of the pam model based on chou’s theoretical model [2], but the improved model is too complex to applied in actual control. in this paper, a pam feature testing platform is constructed, following the pam modelling theories proposed by shaofei wang from tokyo university of technology [14], shameek ganguly from indian institute of technology [3] and many other scholars [13, 22]. then, the author fitted the test data by polynomial function, and created a desirable static model for the pams. considering the actual control scenarios, two opposite pulse-width modulation (pwm) signals were applied simultaneously on the high-speed on-off valve bank, respectively acting on the onoff valves of the two pams. on this basis, a valve port flow model was established, in light of sanville’s valve flow formula [9]. after that, a proportional-integral-derivative (pid) closed loop controller was designed, referring to the lagrangian-eulerian kinetics equation of the knee. finally, the position control of the four-link bionic knee was achieved successfully through a simulation on matlab simulink. 2 design of bionic knee on the artificial leg the human skeleton is a masterpiece of evolution. it is the most suitable biological mechanism for walking on both legs. the knee, a critical joint in two-legged walking, mainly encompasses the medial and lateral condyles of the femur, the tibia, the meniscus, cruciate ligaments, muscles and nerves [?,20]. the cruciate ligaments refer to the anterior cruciate ligament (acl) and posterior cruciate ligament (pcl) of knee joint. as shown in figure 2, the interface between the lower end of the femur and the meniscus, and that between the meniscus and the upper end of the tibia, are both irregular in shape. during walking, both rolling and sliding motions occur on the two interfaces. figure 2: structure of human knee in flexion and extension, the horizontal axis of the knee is not fixed, and the icr trajectory is in the form of a j-shape curve. the icr motion adjusts the torque of the ground reaction force to the knee, and affects the knee’s following of hip motion, exerting a direct impact on the walking stability and energy consumption. therefore, whether the icr trajectory exists in j-shape is an important criterion to evaluate the design of bionic knee. the single-axis bionic knee is highly flexible, but lacks stability. meanwhile, the four-link bionic knee can output a j-shape icr trajectory, and ensure the motion flexibility and stability. the icr trajectory enables the artificial leg to have a higher height from the ground and better 736 h.l. xie, y. xie, f. li obstacle avoidance ability during swing phase. in addition, the icr trajectory allows the artificial leg to effectively use the ground reaction to maintain stability during support phase [6]. this explains the popularity of the four-link mechanism in the design of intelligent prothesis. in view of the human knee mechanism, three plans were prepared for the design of bionic knee on the artificial leg (figure 3). in the first plan, the artificial leg takes up too much space, which may interfere in the motion of the bionic leg. thus, this plan was abandoned. despite the rational space arrangement, the second plan has a common defect with the first plan: excessively long pams are required for the bionic knee to complete the normal gait (at least 0∼90◦)); the pams, coupled with the driving chain, will not match the length of the thigh. hence, the second plan was also discarded. the third plan was formulated based on these two plans. in the third plan, a multiplying wheel (gear ratio: 2.5) was introduced to increase the knee rotation angle per unit of pams’contraction length and effectively reduce the length of pam. in addition, the third plan increases the transverse distance between the two pams, effectively avoiding interference caused by the inflation of pams. (a) first plan (b) second plan (c) third plan figure 3: design plans for bionic knee on the artificial leg 3 motion analysis and dynamic modeling of knee joint 3.1 analysis of the four-link mechanism the icr trajectory is the key criterion to evaluate the bionic performance of the fourlink mechanism. therefore, the analysis of the four-link mechanism focuses on two issues: the correspondence between the rotation angle of the knee (the angle between the thigh and the shank) and that of the chain wheel, and the icr trajectory. for convenience, a cartesian coordinate system was set up as in figure 4(b), where the origin o lies at the center of the driving shaft. according to the closed chain vector equation of the four-link mechanism, the links must satisfy the following constraint: l1e iθ1 + l2e iθ2 − l3eiθ3 − l4 = 0 (1) where θi is the rotation angle of the linkage i; li(i=1,2, . . . , 4) is linkage length of the fourlink bionic knee; θ is the rotation angle of the chain wheel on the transmission chain; ϕ is the rotation angle of the bionic knee; α and β are initial values of θ1 and θ2; θ1 = α−λθ, θ2 = ϕ+β. the rotation angle of the bionic knee can be computed by the two constants by: design, modeling and control of bionic knee in artificial leg 737 ϕ = 2 arctan ( a± √ a2 + b2 − c2 b− c ) −β (2) where a = −2l1l2 sin(α−λθ); b = 2l2 (l1 cos(α−λθ) − l4); c = l21 +l 2 2 −l 2 3 +l 2 4 −2l1l4 sin(α− λθ). (a) knee joint model (b) knee joint diagram figure 4: structure of the bionic knee on the artificial leg according to kennedy’s theorem, the icr of the links, the shank and the load lies in the intersection between the extension lines of rockers 1 and 3. thus, the value of the icr can be derived from the slopes of the extension lines. the coordinates of each point were solved in light of the coordinate system in figure 4. next, the icr trajectory of the four-link mechanism was drawn using the plot commands in matlab. as shown in figure 5, the obtained icr trajectory was obviously a j-shape curve. figure 5: icr trajectory of the bionic knee 3.2 kinetics modelling of the bionic knee on the artificial leg the movement analysis diagram of artificial leg is shown in figure 6. the bionic knee on the artificial leg is a single-degree of freedom (1 dof) system. based on the coordinate system in figure 6, the kinetics equation of the system was established by the lagrangian-eulerian method as: 738 h.l. xie, y. xie, f. li l = k −e = 3∑ i=1 ( 1 2 mi ( ẋ2i + ẏ 2 i )) + 3∑ i=1 ( 1 2 jiθ̇ 2 i ) − 3∑ i=1 (migli · ξ (θi)) (3) where k is the kinetic energy term; e is the potential energy term; θi are the rotation angles of rocker 1, rocker 3, the shank (connected to link 2) and the load on the ankle; ξ (θi) is the vertical motion component; ji is the equivalent moment of inertia; mi is the equivalent mass; (xi,yi) are the centroid coordinates of each link. figure 6: movement analysis diagram of artificial leg the kinetic equation of knee joint of artificial leg can be written as: t = d dt ( ∂l ∂θ̇2 ) − ∂l ∂θ2 = [ 1 3 m2l 2 2 + 1 2 m2l2lϑ sin (θ2 + ϑ1) + 1 2 m2l1l2 cos θ2 + 1 2 m3 ( l2 + d 2 )2 1 2 m3 ( l2 + d 2 ) lϑ sin (θ2 + ϑ1) + 1 2 m3l1 ( l2 + d 2 ) cos θ2 + 1 4 m3d 2 ] θ̈1 + [ 1 3 m2l 2 2 + 1 4 m3 ( l2 + d 2 )2 + 1 4 m3d 2 ] θ̈2 + [ −1 2 m2l2lϑ cos (θ2 + ϑ1) + 1 2 m2l1l2 sin θ2 −1 2 m3 ( l2 + d 2 ) lϑ cos (θ2 + ϑ1) + 1 2 m3l1 ( l2 + d 2 ) sin θ2 ] θ̇21 + 1 2 m2gl2 sin (θ1 + θ2) + m3g ( l2 + d 2 ) sin (θ1 + θ2) = m21θ̈1 + m22θ̈2 + c21θ̇ 2 1 + g2 (4) where ϑ1 = π6 and ϑ2 = π 3 are transformation angles of coordinate system; lϑ is coordinate offset; d is equivalent cylinder diameter of load; m21 is coupling inertia of the knee joint; m22 is effective inertia of shank and load; c21 is centripetal acceleration coefficient; g2 is gravity item of shank and load. during the calculation, the masses of rockers 1 and 3 were neglected, for they are much smaller than the loads on the shank, the ankle and the artificial foot. then, the lagrangianeulerian equation can be simplified as: design, modeling and control of bionic knee in artificial leg 739 t = d dt ( ∂l ∂q̇ ) − ∂l ∂q = m(q)q̈ + g(q) = (fa −fb) r (5) where q is generalized coordinate vector; fa and fb are the output forces of extensor and flexor pams, respectively; r is the reference radius of the chain wheel. 4 experimental modeling of the pams due to a series of nonlinear factors, such as elastic deformation of rubber tube, friction between woven mesh and gas compression, the modeling of the pams is very difficult. firstly, the static characteristics modeling of pams is established by experiment. then, by referring to the sanville flow formula, the functional relationship between the gas mass flow of the pams and the pwm signal is analyzed, and then the dynamic equation of pam charging and discharging is solved. 4.1 static characteristics modeling of pams in our design, the bionic knee is driven by two pams, which works similar to a spring with variable stiffness. when the pam is inflated, the diameter increases and the length is shortened, outputting an axial tensile force. it is obvious that the output force f of the pams depends on the internal pressure p and the shrinkage ratio ε. currently, the pams are usually modelled based on their stress-induced deformation, using the law of conservation of energy. however, this modelling approach is relatively complex, and inconvenient in actual application. to solve the problem, this paper establishes a pam testing platform (figure 7) to disclose the isobaric and isometric features of the pams, and sets up the pam model based on the test results. the workflow of the isobaric and isometric test is presented in figure 8. the bionic knee on the artificial leg uses the mas-20-100n-aa-mc pams (festo ag, germany), whose initial length l0 is 100mm and the maximum shrinkage ratio is 20∼ 25% (no pressure, no load). figure 7: pam feature testing platform with the aid of the curve fitting toolbox of matlab, the data of the isobaric and isometric test were fitted by the least squares method, using the polynomial function. the fitting results can be expressed as: f = p (a0 + a1ε) + a2ε 3 + a3ε 2 + a4ε + a5 (6) where ai is the polynomial coefficient and its value is shown in table 1. 740 h.l. xie, y. xie, f. li table 1: parameters value of ai a0 a1 a2 a3 a4 a5 2314 -7455 -151900 56710 -6699 -70.64 figure 8: workflow of isobaric and isometric test according to formula (6), the relationship of pam output force f with internal pressure p and shrinkage rate ε is shown in figure 9. the contour shows the function relationship between internal pressure p and shrinkage ε under different loads. figure 9: fitting model of pam the comparison of pam fitting value with isobaric and isometric experimental value is shown in figure 10 and 11. it can be known that the experimental modeling method can accurately express the static characteristics of pam, which is simpler than analytical modeling method and can be used for simulation and controller design. design, modeling and control of bionic knee in artificial leg 741 figure 10: comparison of fitting value and isobaric experimental value of pam figure 11: comparison of fitting value and isometric experimental value of pam 4.2 dynamic characteristics modeling of pams the charging and discharging process of pams is realized by controlling the valve port flow of a group of high-speed on-off valves through pulse width modulation (pwm) signal. in the process of dynamic characteristics modeling of pams, it is assumed that the whole pneumatic system is in an ideal state. the pressure change rate ṗ of the pams can be illustrated as: ṗ = γrtṁ v − γpv̇ v (7) where ṁ is the mass flow at the valve port; r is the ideal gas constant; γ is the isentropic index (γ=1.4 for the air); t is the thermodynamic temperature of the gas; v is the internal volume, which is approximately linear with the shrinkage ratio. in this paper, charging and discharging experiments were carried out on the pams under no load. the working diagram of pam is shown in figure 12. the variation of its internal volume was obtained by actual test, as shown in figure 13. the internal volume was approximately proportional to the shrinkage rate, and the test data was approximately fitted by a linear curve, that is: v = b1ε + b2 (8) where b1 and b2 are the fitting parameters, b1 = 1.9e− 4, b2 = 3.21e− 5. figure 12: charging and discharging processes of the high-speed on-off valve based on sanville’s valve flow formula, the total valve port flow of a single pam under simultaneous charging and discharging can be established as: 742 h.l. xie, y. xie, f. li ṁ = ṁin (ain,p,µ) − ṁout (aout,p, 1 −µ) =   µpupγ1 − (1 −µ)pγ2 p < 0.189 µpupγ1 − (1 −µ)pγ1 0.189 ≤ p ≤ 0.317 µpupγ3 − (1 −µ)pγ1 p > 0.317 (9) where γ1 = amax√ rt √ γ ( 2 γ+1 )γ+1 γ−1 , γ2 = amax√ rt √ 2γ γ−1 [( patm p )2 γ − ( patm p )γ+1 γ ] , γ3 = amax√ rt √ 2γ γ−1 [( p pup )2 γ − ( p pup )γ+1 γ ] ; pup is the stabilized pressure outputted by the air pump, pup=0.6mpa; patm is the atmospheric pressure, patm=0.1mpa; amax is the maximum opening area of the valve. figure 13: relationship between internal volume and shrinkage rate of pams the simulation results of the gas mass flow model inside a single pam are shown in figure 14. through lateral comparison and analysis, with the increase of internal pressure, the gas flow of the charging valve port becomes smaller and smaller, while the gas flow of the discharging valve port increases. through longitudinal comparison and analysis, the gas flow of the charging valve port increases and the discharging valve port decreases when the duty ratio of pwm signal is increased. the value of gas mass flow is consistent with the rated value 100l/min of high-speed switching valve, indicating that the modeling of gas mass flow inside pam is correct. figure 14: valve port flow of pam to sum up, the function relationship between internal pressure of pams and pwm signal can be described as: design, modeling and control of bionic knee in artificial leg 743   pa = ∫ ( µa,pa,θ, θ̇ )∣∣∣ pa0=0.1mpa pb = ∫ ( µb,pb,θ, θ̇ )∣∣∣ pb0=0.6mpa (10) assuming that the internal volume of pam is fixed at the initial value, the change curve of the internal pressure of pam a and b with duty ratio of pwm signal can be obtained, as shown in figure 15. as can be seen from the figure, when the duty ratio is equal to 0.9, the pam reaches a stable pressure value at a faster rate, and the stable value is roughly the same as the upstream pressure source of 0.6 mpa. on the contrary, the stable pressure value of the pam is similar to the atmospheric pressure of 0.1 mpa. thus by adjusting the duty ratio of pwm signal, the control of internal pressure value and charging and discharging rate of pam is realized. (a) internal pressure change of pam a (b) internal pressure change of pam b figure 15: change curve of the internal pressure of pams 5 control simulation and analysis 5.1 simulation based on pid control the pid control is one of the most popular automatic control strategies. besides strong robustness and adaptability, the pid control operates on a simple philosophy: adjusting the system response through proportional, integral and derivative corrections of error. the design of a pid controller does not require the solving of an accurate mathematical model of the object. the functional relationship between the rotation angle of the chain wheel and the shrinkage ratio of the pams can be described as:{ εa = θ ·r/l0 εb = ε0 −θ ·r/l0 (11) where ε0 = 0.2 is the preload shrinkage ratio of the pam b. by combining equations (6), (10) and (11), the functional relationship between the output force of pam and the pwm signal can be obtained:  fa = fa ( µa,θ, θ̇ ) fb = fb ( µb,θ, θ̇ ) (12) 744 h.l. xie, y. xie, f. li based on the solution of the dynamic equation of the artificial leg and the motion analysis of the four-bar knee joint, the functional relationship between the knee torque and the rotation angle of the chain wheel can be obtained: t = m21θθ̈1 + m22θθ̈ + c21θθ̇ 2 1 + g2θ (θ1,θ) = fa ( µb,θ, θ̇ ) ·r −fb ( µb,θ, θ̇ ) ·r (13) in order to simplify the control simulation, during the control of the knee joint, fix the hip joint, i.e. θ1 = 0, equations (13) can be converted into the general form: θ̈ = 1 m22θ [ fa ( µa,θ, θ̇ ) ·r −fb ( µb,θ, θ̇ ) ·r −g2θ(θ) ] (14) the pid control law and transfer function can be respectively expressed as: u(t) = kp ( e(t) + 1 ti ∫ t 0 e(t)dt + td de(t) dt ) (15) g(s) = u(s) e(s) = kp ( 1 + 1 tis + tds ) (16) where kp, ki = kp/ti and kd = kp×td are the gain parameters of the proportional, integral and derivative operations, respectively; ti and td are the time constants of the integral and derivative operations, respectively. the pid controller for the bionic knee with valve signal is presented in figure 16. the block “knee sprocket”is a switch from knee angle to sprocket angle and the block “sprocket knee”is a switch from sprocket angle to knee angle. the block “model of knee”represents the knee dynamics model of the artificial leg. for pam b, pwm signal contrary to ideal angle information is given, and pwm signal duty ratio of pam a is adjusted by pid controller. the angle information of sprocket is collected by the encoder for feedback, which is used for solution of control error. figure 16: structure of the pid controller of the bionic knee to verify the four-link bionic knee and the model of the pams driving mode, a simulation model was set up on matlab simulink, and adopted to simulate how the bionic knee followed the ideal trajectories under irregular square wave signal and sinusoidal signal, respectively. the pid controller parameters were configured through trial and error: kp = 3.9; ki = 5.88; kd = 2.05; n (differential filter coefficient) = 100. considering the actual response time of highspeed on-off valve under continuous working mode ((< 0.2ms)), the step length was fixed at 0.01s; the duty cycle range of the pwm control signal was set to 0.12∼ 0.88; the integrator output range of the internal pressure of the pams was set to 0.1∼ 0.6mpa; the initial pressure of design, modeling and control of bionic knee in artificial leg 745 the two pams were set to pa = 0.1mpa and pb = 0.6mpa, respectively. the simulation results are shown in figures 17 ∼ 22. figure 17: following curve under irregular square wave signal figure 18: angle error under irregular square wave signal figure 19: internal pressure variation under irregular square wave signal as shown in figures 17 ∼ 19, under the pid control, the different positions of the bionic knee rotated quickly by the required angles, following the ideal trajectory under irregular square wave signal, and the internal pressure of the pams changed between 0.1 and 0.6mpa. thus, the pid controller can satisfy the requirements for actual application. as shown in figures 20∼ 22, for the sinusoidal signal, the bionic knee can achieve ideal follow on the whole under the pid control, but the tracking error was about 0.06 rad. it can be seen that the pid controller basically meets the control requirements for the linear system, but has little capacity for the nonlinear system. due to the strong nonlinear of the electric-pneumatic system of the knee joint, the design of nonlinear controller needs to be considered. 746 h.l. xie, y. xie, f. li figure 20: following curve under sinusoidal signal figure 21: angle error under sinusoidal signal figure 22: internal pressure variation under sinusoidal signal 5.2 simulation based on sliding mode control sliding mode control is essentially a nonlinear control. compared with other control methods, the structure of the sliding mode control system is not fixed and purposefully changing according to the current state of the system (such as deviation) during the dynamic process, forcing the system to move in accordance with the predetermined sliding mode state trajectory. this method has the advantages of rapid response and insensitivity to parameter change and disturbance, and is suitable for the control of electric-pneumatic system of bionic knee joint. considering that the bionic knee system of artificial leg is a second-order equation about angle θ of sprocket wheel, the designed sliding mode function is: s(t) = λ1e + ė (17) where λ1 > 0 and meets hurwitz conditions. the angle tracking error of the knee joint system is: e = θd −θ ė = θ̇d − θ̇ (18) design, modeling and control of bionic knee in artificial leg 747 where θd is the angle of sprocket wheel in ideal movement of knee joint. the derivative of the sliding mode function is: ṡ = λ1ė + ë = λ1 ( θ̇d − θ̇ ) + ( θ̈d − θ̈ ) (19) in order to ensure the stability of the control system of the knee joint, the motion points in the area around the sliding mode surface should eventually tend to the sliding mode surface s = 0. therefore, lyapunov function is defined as follows: v1 = 1 2 s2 (20) according to lyapunov stability criterion, there should be: lim s→0 v̇1 = lim s→0 sṡ ≤ 0 (21) weighing the approaching speed and stability of the control system, this paper adopts the exponential approaching law: ṡ = −δ1 · sign(s) −δ2s δ1 > 0 δ2 > 0 (22) exponential term −δ2s can guarantee that the system state can approach the sliding mode with a large speed when s is large. however, when s is small, its approach speed is slow and cannot guarantee to arrive in a finite time. therefore, an isokinetic approach term −δ1 · sign(s) is added. combined with the knee system model of artificial leg, the sliding mode controller based on the approach law is obtained: fa ( µa,θ, θ̇ ) = m22θ r ( λ1 ( θ̇d − θ̇ ) + (δ1 · sign(s) + δ2s) + θ̈d ) + g2θ(θ) r + fb ( µb,θ, θ̇ ) = pa ( µa, θ̇,θ ) ·κa1(θ) + κa2(θ) (23) where κa1 and κa2 are polynomials about shrinkage in the static model of pam. according to the charging and discharging dynamic model of pam, and combining the functional relationship between the angle of sprocket wheel and the shrinkage rate of pam, the functional relationship between the internal pressure pa of flexor and the duty ratio µa of pwm, the control signal of high-speed switching valve group, is established. µa =   v +paγ2a pupγ1a+paγ2a pa < 0.189 v +paγ1a pupγ1a+paγ1a 0.189 ≤ pa ≤ 0.317 v +paγ1a γ3a+paγ1a pa > 0.317 (24) where v = v ṗa+γpav̇ γamax √ rt , γ1a = √ γ · ( 2 γ+1 )γ+1 γ−1 , γ2a = √ 2γ γ−1 · [( patm pa )2 γ − ( patm pa )γ+1 γ ] and γ3a = √ 2γ γ−1 · [( pa pup )2 γ − ( pa pup )γ+1 γ ] . the simulation system of artificial knee joint based on sliding mode control is shown in figure 23. in the simulation process, the step size setting is consistent with the pid simulation. λ1 = 8, δ1 = 5.88, , δ2 = 2.05 and the setting range of duty cycle output of high-speed switching 748 h.l. xie, y. xie, f. li valve is 0.12∼0.88. the initial values of internal pressure inside the pam is also set as pa= 0.1 mpa and pb= 0.6 mpa. the simulation results are shown in figures 24∼31. it can be seen from figures 24∼26 that, for irregular square wave signals, the sliding mode control system can achieve better tracking, with smaller error and faster response time than the pid controller. the internal pressure of the pam varies within 0.1∼0.6 mpa and is reasonable. it can be seen from figures 27 and 29 that, for the trajectory curve of sinusoidal signal, the sliding mode controller can achieve ideal following, with the error range controlled within 0.03 rad and a fast response time, which fully meets the precision requirements of knee motion control. as shown in figures 30∼31, the sliding mode controller can realize the ideal following of human normal gait, which proves the correctness of the structure design and system modeling in this paper. figure 23: simulation system of artificial knee joint based on sliding mode control figure 24: following curve under irregular square wave signal figure 25: angle error under irregular square wave signal design, modeling and control of bionic knee in artificial leg 749 figure 26: internal pressure variation under irregular square wave signal figure 27: following curve under sinusoidal signal figure 28: angle error under sinusoidal signal figure 29: the internal pressure variation under sinusoidal signal 750 h.l. xie, y. xie, f. li figure 30: following curve under human normal gait figure 31: angle error under human normal gait 6 conclusions in this paper, the authors propose a new prosthesis platform and design a bionic artificial leg that simulates the healthy leg of human body. based on the analysis of human knee motion, this paper designs a four-link bionic knee, and simulates the flexor and extensor muscles as two pams. the two pams move in opposite direction, driving the knee motions in the artificial leg. in this way, the artificial leg can move similarly to a human leg. after testing the pam features, a static model was developed for the pams based on the test results, using the least squares method. based on sanville’s valve flow formula, a dynamic model was constructed for the total valve port flow of each pam under simultaneous charging and discharging. finally, the pid closed loop controller and sliding mode controller were designed for the bionic knee. the simulation results show that compared with the pid closed-loop controller, the sliding mode controller can significantly improve ideal track following effect and realize the ideal track following of the human normal gait, which indicates the correctness of the structural design and system modeling in this paper. acknowledgment financial supports from the fundamental research funds for the central universities (id n170313025) and scientific study project of liaoning province education department (id lqgd2017029) are highly 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(2017); position/stiffness control of antagonistic bionic joint driven by pneumatic muscles actuators, journal of mechanical engineering, 53(13), 64-74, 2017. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(2), 170-182, april 2019. heterogeneous data clustering considering multiple user-provided constraints y. huang yue huang* school of information science beijing language and culture university, beijing, china *corresponding author: huang.yuet@blcu.edu.cn abstract: clustering on heterogeneous networks which consist of multi-typed objects and links has proved to be a useful technique in many scenarios. although numerous clustering methods have achieved remarkable success, current clustering methods for heterogeneous networks tend to consider only internal information of the dataset. in order to utilize background domain knowledge, we propose a general framework for clustering heterogeneous data considering multiple user-provided constrains. specifically, we summarize that three types of manual constraints on the object can be used to guide the clustering process. then we propose the userheteclus algorithm to solve the key issues in the case of star-structure heterogeneous data, which incorporating the user constraint into similarity measurement between central objects. experiments on a real-world dataset show the effectiveness of the proposed algorithm. keywords: clustering, heterogeneous networks, relational data, multi-typed objects, user constraints. 1 introduction with the advent of "big data", data mining has become a widely accepted tool for data analysis and various data mining-related research appeared [4,5,25,26]. among all the techniques of data mining, clustering presents an effective way of exploring data, especially scenarios with no available labelled data. compared with homogeneous networks, heterogeneous information networks [20] which consist of different types of objects and links can be found in many actual scenarios. clustering on heterogeneous data has become a key emerging challenge during the recent twenty years [21,22,24]. in earlier study of clustering, we only have to deal with objects of the same type and numerous methods have been proposed [10]. later, clustering on data with two different types of objects emerged, such as two-way clustering [9], co-clustering [1,3,6,7], and bi-clustering [15]. recently, more attention has been paid to multi-way clustering [2], also called high-order coclustering [8]. generally, in the datasets with real heterogeneity [13], the object type that contains less number of distinct values among heterogeneous data are called central type or target type, while the other types are called attribute types [19]. whereas, most existing research focus on the star-structure of heterogeneous networks [11, 14, 18, 20], where links only exist between objects of the central type and objects of the attribute types, representing many scenarios in the real world. besides, several methods have also been put forward for other types of heterogeneous networks [12] or arbitrary heterogeneous networks [16,17]. in some scenarios, it is impossible to perform effective cluster analysis without taking advantage of the external information of the dataset, such as domain knowledge provided by users. taking document analysis as an example, for a heterogeneous dataset containing tens of thousands of documents, thousands of words, and several document clusters, if the information of cluster assignment of one hundred pairs of documents is provided, then the clustering efficiency copyright ©2019 cc by-nc heterogeneous data clustering considering multiple user-provided constraints 171 on the documents can be improved. to solve the issue of clustering heterogeneous data considering user-provided constraints, we firstly analyzed that there are three types of constraint information provided by the user to help the clustering process of the objects. then we propose a complete general analysis framework, based on which we propose corresponding solutions to solve the key issues in the case of star-structure heterogeneous data. the rest of this paper is organized as follows. section 2 defines the research scope of the problem and analyzes the hypotheses. section 3 first proposes a general framework for solving the heterogeneous data clustering analysis that takes into account user-provided constraints, then it analyzes and resolves the key issues with the proposed algorithm userheteclus for heterogeneous data clustering considering multiple user-provided constraints. section 4 gives the experimental results and analysis. conclusion is given in section 5. 2 problem definition and hypothesis similar to previous study on semi-supervised clustering [23], it is easy to find that in heterogeneous data, user-provided constraints can be of three types: (1) the user labels which central objects should belong to (or do not belong to) the same cluster; (2) the user indicates which central objects with which attribute values must belong to (or not belong to) the same cluster; (3) the user indicates which attribute values actually correspond to the same (or different) meanings. when the user can determine the cluster attribution of the central object by providing the values of the central object for one or some attribute objects, it indicates that the user provides a decisive attribute object. in this case, assigning the object cluster under such a constraint condition is similar to classifying central objects, requiring the user to provide very precise knowledge, so the condition is clearly too high to meet. according to previous studies and applications of semi-supervised clustering, what the user generally provides is information about the relationships between objects, but in practical applications, the type of constraint that the user may provide is not limited to this, so we have the following hypotheses: hypothesis 1: in the problem of heterogeneous data clustering considering user-provided constraints, the constraints may be on the central object or on the attribute object. hypothesis 2: in the problem of heterogeneous data clustering considering user-provided constraints, it is assumed that the heterogeneous dataset has a star structure. 3 key issues and steps of the userheteclus in this section, we propose the userheteclus algorithm for solving the key issues in the procedure of clustering star-structured heterogeneous data. 3.1 framework for clustering heterogeneous data considering user constraints the information available in the analysis of heterogeneous data with a star structure considering user-provided constraints includes internal information (object type information, object attribute information, and object relation information) and external information. in the clustering of heterogeneous data with a star structure considering user-provided constraints, on the one hand, the composition of the dataset itself is very complex; on the other hand, it is necessary to integrate external information. therefore, comprehensively, in the semisupervised clustering of heterogeneous data considering user-provided constraints, we still need to follow the principle of "paying more attention to relations and less to attributes" [12], and it is necessary to separate the attribute similarity computation from object clustering. 172 y. huang in addition, relative to unsupervised clustering, semi-supervised clustering still has a small amount of useful sample information. therefore, how to effectively utilize the domain knowledge contained in the labeled samples to guide the clustering process in the clustering algorithm is a key issue that distinguishes the semi-supervised clustering algorithm from the unsupervised clustering algorithm. in the traditional general framework for attribute-based clustering studies, the measurement of object similarity or dissimilarity (or distance) is the primary issue to be addressed, but in practice, the constraint information provided by the user is mostly at the object level rather than the attribute level, so it is very difficult to integrate user-provided constraints into the measurement process of similarity or dissimilarity. therefore, except for the case in which the values of the user-provided attributes are identical, other user-provided constraint information should be used in the clustering process of the central object. therefore, regarding this problem, the measurement of central object similarity and the process of central object clustering are two secondary key issues that need to be solved. based on the above discussions, we conclude that a complete procedure of heterogeneous data clustering considering user-provided constraints include four steps: (1)the presentation of user-provided constraints; (2) the measurement of the central object similarity; (3) the semisupervised clustering of central objects considering user-provided constraints; (4) the clustering of attribute objects. 3.2 presentation of user-provided constraints at present, it is agreed in semi-supervised clustering studies that it is necessary to impose certain constraints on clustering results, i.e., the descriptions of the relation constraints between data objects can be categorized into two categories of must-link constraints and cannot-link constraints, with some respective properties. however, according to the above analyses, userprovided constraints are not limited to the above mentioned user constraints on object relations and may also include decisive attribute constraints and attribute value equality constraints. definition 1. user constraint on object relation: for the given heterogeneous data of d=(c,a), assume that the user-provided constraint is on the central objects of c = {ci|i = 1, 2, ...,n} and that the central objects cluster as the set of clus that contains n central object clusters, described as c.clus={clus1,clus2,...,clusn}. suppose that there are two central objects, i.e., cs and c t (s 6= t). the must-link constraint and the cannot-link constraint of the user-provided constraint on object relation can be specifically described as follows: • if cs and ct should be in the same cluster, then must− link(cs,ct) = true, which can be specifically described as cs ∈ clusi, ct ∈ clusj, i = j; • if cs and ct should not be in the same cluster, then cannot− link(cs,ct) = true, which can be specifically described as cs ∈ clusi, ct ∈ clusj, i 6= j. therefore, the must-link constraint and the cannot-link constraint are both boolean functions and have the following properties: remark 1. the must-link constraint and the cannot-link constraint of the user’s two types of constraints have symmetry, and for cs,c t∈c : • must-link(cs,c t)⇔must-link(c t,cs); • cannot-link(cs,c t)⇔cannot-link(c t,cs). remark 2. the must-link constraint and the cannot-link constraint of the user’s two types of constraints have limited transitivity, and for cr,cs,c t∈c : heterogeneous data clustering considering multiple user-provided constraints 173 • must-link(cr,cs)&&must-link(cs,c t)⇒must-link(cr,c t); • must-link(cr,cs)&&cannot-link(cs,c t)⇒cannot-link(cr,c t). definition 2. user constraint on decisive attribute: for the given heterogeneous data of d=(c,a), assume that the user-provided constraint is on the central objects of c = {ci|i = 1, 2, ...,n} and that the central objects eventually cluster as the set of clus that contains n central object clusters, described as c.clus={clus1,clus2,...,clusn}. suppose that there are two central objects, i.e., cs and c t (s 6= t), and that the cluster assignments of cs and c t are represented by cs.clus and c t.clus, respectively. then, a user-provided constraint on decisive attributes can be described as cs. akp=c t.akp ⇔ cs.clus=c t.clus, in which ak is denoted as the decisive attribute. definition 3. user constraint on attribute value equality: for the given heterogeneous data of d=(c,a), assume that the user-provided constraint is on the central objects of c = {ci|i = 1, 2, ...,n} and that there are two central objects, i.e., cs and c t (s 6=t), the user-provided constraint on attribute value equality can be described as akp = akq (p 6= q). 3.3 measurement of central object similarity the measurement of central object similarity in heterogeneous data with a star structure adopts the following ideas: first, if the user provides a constraint on the attribute value equality, then it is used to get the heterogeneous data with unique identifiers; otherwise, this step is skipped. in a practical problem, the user may not be able to provide all the above-described three types of constraints. then, when the central object similarity is measured pairwise, the similarity between the two central objects is represented using linear combinations of the two objects on each of attribute objects, in which the coefficient of each attribute object in the linear combination constitutes the contribution coefficient vector, which can be adjusted according to the actual situation. therefore, the following basic concepts are defined. definition 4. star-structured data with unique identifiers (ids): star-structured heterogeneous data with unique ids containing n central objects and r (r> 1) attribute objects are described as d’=(c,a,id,r) (d’=(c,a) for short). the specific meanings are as follows: • c represents the collection of central objects, i.e., c = {ci}ni=1, where c i represents the ith central object. • a represents the collection of attribute objects, i.e., a = {ak}rk=1 (k∈{1,2,...,r}), where ak = {akp} nak p=1 represents the object collection of the kth attribute (p ∈{1, 2, ...,nk}), akp represents the pth attribute object of the kth attribute object (p ∈{1, 2, ...,nk}), and nak represents the number of attribute objects included in ak. • id represents the collection of all objects, i.e., id = c.id ⋃ a.id. c.id represents the collection of central objects with unique ids, and c.id = {ci.id}ni=1, where ci.id represents the unique id of the ith central object (i∈{1,2,...,n}). a.id represents the collection of attribute objects, and a.id = {ak.id}rk=1 (k∈{1,2,...,r}), in which ak.id = {akp.id} nk p=1 represents the collection of the objects with the kth attribute (p ∈{1, 2, ...,nk}), where akp represents the pth attribute object of the kth attribute object (p ∈{1, 2, ...,nk}) and nk represents the number of attribute objects included in ak. • r represents the undirected relationship collection present in the dataset of d, i.e., r = {rl} nr l=1 (l ∈ {1, 2, ...,nr}), and for any relationship that is rl =< rl.one,rl.theother >, 174 y. huang it satisfies the following condition: rl.one ∈ c and rl.theother ∈ a or rl.one ∈ a and rl.theother ∈ c. given that no relationship between central objects is available in the star-structured heterogeneous data, the measurement of central object similarity can only rely on attribute objects, so the similarity of central object in terms of the remaining attribute objects is defined as follows: definition 5. similarity between central objects in terms of the kth type of attribute object: for the given heterogeneous dataset of d’=(c,a), the calculation formula of the similarity, sk(ci,cj), between two central objects ci and cj in terms of the kth type of attribute object is as follows: sk(ci,cj) = 2 ×|ci.ak.id ∩cj.ak.id| |ci.ak.id| + |cj.ak.id| (1) where ci.ak.id represents the collection of ids of the kth type of attribute object correlated to ci and cj.ak.id represents the collection of ids of the kth type of attribute object correlated to cj. obviously, the value range of sk(ci,cj) is [0, 1], and it is easy to prove that it meets the properties of similarity measurement. the linear combinations of the similarities of the central object on each of various attributes are considered to be used to measure the similarity between the central objects, but the roles of different attribute objects also differ, so we provide the following definition: definition 6. similarity between central objects: for the given heterogeneous dataset of d’=(c,a), the similarity, s(ci,cj), between two central objects ci and cj is the linear combinations of similarities between the two in terms of the kth type of attribute object, with the following calculation formula: s(ci,cj) = r∑ k=1 wk ·sk(ci,cj) (2) where wk is called the contribution coefficient, representing the contribution of the kth type of attribute object to the judgment of whether ci and cj are similar and satisfying: (1) r∑ k=1 wk = 1 and (2) wk > 0,wk ∈ r, which jointly constitute the contributing coefficient vector w = (w1,w2, ...,wk, ...,wr). obviously, the value range of s(ci,cj) is [0, 1], and it is easy to prove that it meets the properties of similarity measurement. wk is evaluated according to the actual situation. central object similarity measurement of the userheteclus input: star-structured heterogeneous data with unique id (d’=(c,a)) and contribution coefficient vector (w = (w1,w2, ...,wk, ...,wr)), in addition to the constraint of attribute value equality. output: the similarity matrix simmatrix(c) of the central objects of c = {ci}ni=1. procedures: step 1: the central objects of c = {ci}ni=1 are represented using their attribute objects. step 2: according to the user-provided attribute value equality constraint, the value ids of attributes with equal value are represented by one of the ids; if the user does not provide the attribute value equality constraint, then skip this step. step 3: the similarity between any two central objects on each of various attribute objects sk(ci,cj) is calculated according to definition 5. step 4: the similarity between any two central objects s(ci,cj) is calculated according to definition 6 to obtain the central object similarity matrix simmatrix(c). heterogeneous data clustering considering multiple user-provided constraints 175 3.4 semi-supervised clustering of central objects considering user-provided constraints definition 7. similarity between central object clusters: for the given heterogeneous dataset of d’=(c,a) and several central object clusters clus, the similarity between central object clusters cluss and clust, s(cluss,clust), the average of the similarities between central objects contained in one cluster and those contained in another cluster, has the following formula: s(cluss,clust) = |cluss|∑ i=1 |clust|∑ j=1 s(ci,cj) |cluss|× |clust| (3) where s(ci,cj) represents the similarity between ci and cj. obviously, the value range of s(cluss,clust) is [0, 1], and it is easy to prove that it meets the three properties of similarity measurement. for the above mentioned three forms of user-provided constraint, the constraint of attribute value equality is related to the id of the object, so it needs to be addressed first; the user constraint of the object attribute can be converted into the user constraint of the object relation, which is related to the clustering process, and can be incorporated into the specific clustering process. therefore, the rationale for the semi-supervised clustering of central objects considering multiple user-provided constraints is that first, if the user provides a constraint on decisive attributes, then it is converted into a user constraint on the object relation, and then, the pairwise central objects similarities are sorted in descending order. after completing the above preparatory steps, the actual clustering process is executed. first, each object is treated as a separate cluster, and the objects in the must-link set are first linked. then, the two most similar central objects are judged in terms of whether they meet the condition for the linking and sequentially repeated; if the two are in the cannot-link collection, then proceed to the next pair of central objects. otherwise, the two are judged in terms of whether they have already in the same cluster, and if they are, then proceed to judging the next pair of central objects. otherwise, the similarity between the cluster in which two objects reside is calculated, and if the similarity threshold is met, then the two objects are linked; if it is not, then the two are not linked. the process is repeated until the objects are linked as one cluster or the number of clusters is reached. semi-supervised clustering of central objects considering user-provided constraints of the userheteclus input: the collection of central objects c = {ci|i = 1, 2, ...,n}, the central object similarity matrix simmatrix(c), the must-link set, the cannot-link set, the constraint of decisive object, the constraint of attribute value equality, and central object similarity threshold λ. output: central object clustering result c.clus. procedures: step 1: if the user provides a constraint on a decisive attribute, then convert it into a user constraint on object relation. step 2: the central object similarities are sorted in descending order. step 3: each central object is treated as a separate cluster. step 4: search the must-link collection to link the clusters in which the objects with the must-link constraint reside. step 5: sequentially, for the two central objects with the highest similarity: step 5.1: determine whether they are in the cannot-link set; if they are, then they are not linked, and continue to determine the next pair of central objects. 176 y. huang step 5.2: determine whether the two belong to the same cluster: if they do, then continue to determine the next pair of central objects. step 5.3: determine whether the central object pair formed by the central objects composed of the two clusters that the two are from is present in the cannot-link set; if it is, then they are not linked, and continue to determine the next pair of central objects. step 5.4: calculate the similarity between the two clusters that the two central objects are from. if the similarity is greater than or equal to λ, then link the two; otherwise, do not link, and continue to determine the next pair of central objects. step 5.5: repeat steps 5.1-5.4 until the objects are linked as one cluster or reach the required number of clusters. 3.5 clustering of attribute objects in the process of heterogeneous data clustering considering user-provided constraints, central objects are the focus of heterogeneous data clustering; because it is insufficient for the original attribute object information to support the clustering of the central objects, the user-provided partially labeled data are introduced and integrated into the process of clustering the central objects. therefore, in this study, we believe that the clustering of attribute objects should be based on the clustering result of the central objects and adopt the approach of "voting" using the nearest cluster of the attribute object to classify the attribute object to the central object cluster with the most votes and then separate it from the type of attribute object cluster, which is used in our previous study [12]. 4 experimental analysis of the userheteclus 4.1 experimental data preparation to test the clustering effectiveness of the userheteclus on actual data, in this section, we used the china national knowledge infrastructure (cnki) literature data source to extract nonredundant records (2467 entries, searched on march 19, 2014) from the cnki dataset of the donlinks school of economics and management (dsem) of university of science and technology, beijing, using the four textual segments of paper title, author, source, and keyword. for convenience of describing the clustering results of the algorithm, records (in a total of 168 entries) that have no reference and the chinese library classification (clc) containing t were used as the testing dataset of the userheteclus (part of which is presented in table 8) to analyze the research fields of the papers that are related to computer applications by authors from dsem. the constraints for this dataset include the following: (1) the constraints of attribute value equality a. the keywords "steelmaking concasting", "concasting", and "steelmaking and concasting", and they were involved in 9 records. b. the keywords of "clustering", "cluster analysis", and "clustering algorithm", and they were involved in 17 records. (2) the constraints on user relations a. must-link constraint. the papers titled "chinese keyword extraction algorithm based on high-dimensional clustering techniques", "pattern aggregation theory-based text feature dimensionality reduction method and its application in text classification", and "text classification based on granular network generation rules" belonged to the same cluster and were involved in three records. heterogeneous data clustering considering multiple user-provided constraints 177 table 1: cnki experimental dataset for the userheteclus id title author source keywords 59 overview and analysis of manufacturing execution system models li tieke metallurgical automation manufacturing execution system, information system, system model, enterprise model 62 research on cryptographic algorithm in data transmission of internet of things of rfid system wang xiaoni, wei guiying journal of beijing information science and technology university (natural science edition) internet of things, radio frequency identification, cryptographic algorithm 137 telecom customer segmentation methods and applications chen fengjie technology and industry data mining, clustering, clementine 223 global neighborhood algorithm for job shop scheduling problem cui jianshuang, li tieke computer integrated manufacturing system neighborhood structure, critical path, job shop scheduling, neighborhood switching, scheduling algorithm 259 research on cure algorithm of hierarchical clustering method wei guiying, zheng xuanxuan technology and industry cure algorithm, hierarchical clustering, clustering 268 hybrid vehicle routing problem based on improved fuzzy genetic algorithm zhang qun, yan rui chinese management science vehicle routing problem, fuzzy genetic algorithm, multidistribution center ... ... ... ... ... 2456 research on the problem of determining the number of rolling units chen xiong, pan yongquan control and decision rolling unit, simulated annealing algorithm, random variable variance, scheduling 2473 diagnostic theory of two fuzzy control charts chen zhiqiang, zhang gongxu, yan zhilin journal of beijing university of science and technology shewhart control chart, selection control chart, total quality, subquality, fuzzy judgment b. cannot-link constraint. the papers titled "basic framework and method for china’s overseas mining investment decision process" and "high temperature compressive strength of coke for blast furnace" did not belong to the same cluster and were involved in two records. in this experiment, two constraints were provided, and in practice, the three types of userprovided constraints may all be provided, or only one type may be provided. 4.2 analysis of experimental results (1) analysis of object clustering accuracy the userheteclus (wauthor = 0.3,wvenue = 0.2,wterm = 0.5,λ = 0.01) generated 22 clusters 178 y. huang on 168 paper objects of the experimental dataset, which included two major clusters and 13 clusters of isolated points (table 2). the meaning of each cluster was explained by looking at the title and keywords of the paper, and the cluster of isolated points was explained based on the title of the paper. table 2: clustering result on paper objects of the experimental dataset with userheteclus no. cluster size objects in the cluster interpretation 1 72 "1191", “1005", “1109", “945", “982", ... manufacturing execution system, production scheduling algorithm, and other papers 2 63 “383", “284", “947", “1865", “436", ... data mining papers 3 7 “687", “1067", “1284", “468",... software project management research 4 3 “2284", “2294", “2253" calculate accounting indicators with excel 5 2 “2437", “2436" management information system development 6 2 “2346", “1383" supply chain and erp 7 2 “1902", “1045" system design and implementation using asp.net, etc. 8 2 “1796", “1551" temperature of nanoporous vacuum insulation panel 9 2 “955", “325" lean improvement research 10 1 “881" the basic framework and method of china’s overseas mining investment decision-making process 11 1 “2330" eu textile environmental label and its comprehensive evaluation 12 1 “1178" control technology of pipeline steel inclusions 13 1 “2271" application of value engineering in the development of building material products 14 1 “1921" research on factors and methods of reservoir management post evaluation 15 1 “393" recovery of the thorium resources of the mine in baotou and its research status for nuclear fuel 16 1 “395" elliptic curve encryption algorithm and case analysis 17 1 “917" combination forecast of technical maturity of patented products of industrial pulverized coal boiler based on triz theory 18 1 “298" investigation on the construction of evaluation index system for energy efficiency reform of existing buildings 19 1 “618" internet and e-commerce and logistics 20 1 “1680" enterprise system semantic complexity based on isomorphic ontology structure 21 1 “1535" current situation and development strategy of ups 22 1 “274" high temperature compressive strength of coke for blast furnace the analysis of the central object clustering results indicates the following: a. the classification on the macroclusters via the userheteclus is clear, with high intercluster discriminability, and has identified two major directions of computer-related studies, i.e., "manufacturing execution system" and "data mining", in dsem, which are in line with the actual meaning of the cluster, with ideal clustering effectiveness. b. for each specific cluster, clear meanings of the cluster are present, indicating that the intracluster similarity between the objects generated by the userheteclus is high. c. the clusters of isolated points identified by the userheteclus are different from the major clusters, and the difference between the isolated points is also large. (2) analysis of the effect of main parameters of the algorithm the userheteclus has two sets of parameters: w and λ. specifically, the parameter set of w is used to control the weight of each type of attribute object when calculating the similarity heterogeneous data clustering considering multiple user-provided constraints 179 between the central objects, and λ is the central object similarity threshold, which affects and controls the clustering process of central objects. in this study, we also examined the effect of λ on the clustering result. since different values of λ lead to different numbers of central object clusters, the relationship between different values of λ and the number of central object clusters was tested on the userheteclus (wauthor = 0.3,wvenue = 0.2,wterm = 0.5) using the cnki experimental dataset (fig. 1). it can be observed that a monotonically increasing relationship was present between λ and the number of central object clusters (paper cluster); specifically, when λ = 0, the number of paper clusters was 1; as the value of λ gradually increased, the number of paper clusters also gradually increased; when λ = 1.0, the number of paper clusters reached 168, i.e., the number of paper objects. understanding the relationship between λ and the number of central object clusters helps to determine the optimal value of λ in practical applications. figure 1: relationship between the userheteclus parameter λ and the number of paper clusters 5 conclusion in this study, we investigated the issue of clustering heterogeneous data considering multiple user-provided constraints, in which the auxiliary external information about the object provided by the user is considered in addition to the information of the object itself. firstly, we analyzed and we presented three types of constraint information that are provided by the user to help the clustering process of the objects, including constraints on user relations, constraints on user decisive attributes, and constraints on attribute value equality, which provide bases for future classification processes involving user-provided constraints in clustering algorithms. secondly, we proposed a complete general analysis framework for clustering heterogeneous data considering user-provided constraints and then summarized the key issues for the case of star-structured heterogeneous data, including (1) the representation of user-provided constraints, (2) measurement of central object similarity, (3) semi-supervised clustering of central objects and (4) attribute 180 y. huang objects considering user-provided constraints. lastly, we proposed using linear combinations of similarities of two central objects with all attribute objects to measure the similarity between the two. in addition, we defined the contribution coefficient to quantify the weight of various attribute objects on the central object similarity, based on which the similarities were sorted, enabling fast clustering of central objects under different types of user-provided constraints. for future work, we intend to investigate the issue of clustering heterogeneous data considering user constraints for arbitrary structure. funding this work was partially supported by the humanity and social science youth foundation of ministry of education of china (17yjczh069), science foundation of beijing language and culture university (supported by "the fundamental research funds for the central universities") ( 19yj040001) and blcu youth talent development program. bibliography [1] banerjee, a.; dhillon, i.s.; ghosh, j. merugu s.; modha, d.s. 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(2014). automatic crack detection and classification method for subway tunnel safety monitoring, sensors, 14(10), 19307–19328, 2014. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 1, month: february, year: 2020 article number: 1002, https://doi.org/10.15837/ijccc.2020.1.3762 ccc publications a multi-criteria picture fuzzy decision-making model for green supplier selection based on fractional programming g. cao guo cao* 1. school of management northwestern polytechnical university 127 west youyi road, beilin district, xi’an shaanxi, 710072, p.r.china 2. school of economics and management, changzhou institute of technology 666 liahoe road, changzhou, jiangsu 213032, china *corresponding author: caog@czu.cn abstract due to the increasing complexity in green supplier selection, there would be some important issues for expressing inherent uncertainty or imprecision of decision makers’ cognitive information in decision making process. as an extension of intuitionistic fuzzy sets (ifss) and neutrosophic sets (nss), picture fuzzy sets (pfss) can better model and represent the hesitancy and uncertainty of decision makers’ preference information. in this study, an attempt has been made to present a multi-criteria picture fuzzy decision-making model for green supplier selection based on fractional programming. in this approach, the ratings of alternatives and weights of criteria are represented by pfss and ifss, respectively. based on the available information, some pairs of fractional programming models are derived from the technique for order preference by similarity to an ideal solution (topsis) and the proposed biparametric picture fuzzy distance measure to determine the relative closeness coefficient intervals of green suppliers, which are aggregated for the criteria to generate the ranking order of all green suppliers by computing their optimal degrees of membership based on the ranking method of interval numbers. finally, an example is conducted to validate the effectiveness of the proposed multi-criteria decision making (mcmd) method. keywords: multi-criteria decision making (mcmd), picture fuzzy sets (pfss), fractional programming, biparametric picture fuzzy distance measure, green supplier selection. 1 introduction in the past two decades, increased environmental pollution is forcing companies to obtain sensitivity to environmental awareness [6]. manufacturers have applied various approaches to improve their supply chain’s environment performance [25]. green supply chain integrates environmental thinking into supply chain management, including product design, material procurement, production processes, product delivery [2], and managing end-of-life products and waste. green supplier selection is a critical component of the green supply chain management strategy [10, 23]. as a result, many researchers https://doi.org/10.15837/ijccc.2020.1.3762 2 and practitioners have put their efforts on developing various strategies to improve the green supply chain management process. it has been asserted that selecting the right green suppliers has proven to be one of the key issues faced by operations and purchasing managers to remain competitive [15], and also helps manufacturers in increasing customer satisfaction and quality of the product. since it requires considering many new problems, it is obvious that the decision-making process would be more complex and comprehensive in the green supply chain management. an important phenomenon among these issues is uncertain and imprecise data. if the fuzziness and vagueness presented in the decision-making process is ignored, the results could be misleading. aiming at reflecting the bounded rationality of decision makers, fuzzy set theory is of importance as a result of its merits of mathematically expressing ambiguity and vagueness. numerous decision making methods has been extended to multiple fuzzy environments, such as neutrosophic sets, pythagorean fuzzy sets [9], hesitant fuzzy sets, intuitionistic fuzzy sets, and probability hesitant fuzzy sets [16]. based on the concept of fuzzy sets (fss), atanassov [3] first introduced the concept of ifs, which is an extension of zadeh’s fss [8]. and then, cuong [7] proposed the concept of the picture fuzzy set (pfs) to address such a problem that human opinions involve several types of answers such as yes, abstain, no, or refusal and so on. pfs is characterized by a positive-membership function (µ(x)), a negative-membership function (ν(x)) and a neutralmembership function (γ(x)) such that 0 ≤ µ(x) + ν(x) + γ(x) ≤ 1. different from ns, there is a restriction on the sum of the three types of membership degrees in pfs, which implies that they are dependent with each other. pfs is also a very effective tool to express inherent uncertainty or imprecision of decision makers’ cognitive information in decision making process. however, the application of multi-criteria picture fuzzy decision-making for green supplier selection is a limited area of study in literature. green supplier selection is a multi-criteria decision-making problem [14]. decisions are performed based on the assessment of quantitative and qualitative criteria in two fields including economic and environmental criteria. nowadays, various techniques were adopted to select green suppliers. liang et al. presented a multi-criteria decision making method based on multi-granularity interval 2-tuple fuzzy linguistic set for solving the green supplier selection problems. govindan et al. [14] proposed an approach integrated of multi-criteria decision making and multi-objective linear programming. lo et al. [20] developed a hybrid model that integrated the best–worst method, topsis and fuzzy multi-objective linear programming to solve problems in green supplier selection and order allocation. gitinavard et al. proposed an extended elimination and choice translating reality method in intervalvalued hesitant fuzzy environment [13]. mohammadi et al. presented a method of supplier selection based on interval type-2 fuzzy sets to deal with uncertain information. it can be concluded that various studies have focused on different fuzzy-based techniques to assess and select green suppliers, including probabilistic linguistic preference relations[11], interval type-2 fuzzy sets[18, 22, 29], intuitionistic uncertain linguistic set[17], triangular fuzzy number[4], intuitionistic fuzzy sets[21, 32], trapezoidal fuzzy numbers [28? ] and neutrosophic set [1]. most of these studies are based on the assumptions that the weights of a criterion are represented by the known crisp numbers. however, there exist little investigation on multi-attribute decision-making problems with both ratings of green suppliers on criteria and weights being expressed with fuzzy sets [12]. thus, an interesting and important issue is how to utilize the unknown weights information of a criterion to select the most desirable green supplier. consequently, it is beneficial to combine the advantages of picture fuzzy sets in representing information and the superiority of fractional programming. in this paper, we propose a new mcdm method with the concept of the relative-closeness coefficient, where the weight and the evaluating value of a criterion are represented by intuitionistic fuzzy sets and picture fuzzy sets, respectively. moreover, the fractional programming model is used to obtain the optimal weights of the criteria based on topsis and the proposed biparametric picture distance measure, which provides us with a very useful way for dealing with mcdm problems in picture fuzzy environments. the remainder of this paper is organized as follows. section 2 introduces the definition of a pfs, some existing picture fuzzy distance measures and the concept of likelihood for interval numbers. section 3 proposes a new biparametric picture fuzzy distance measure. based on the concept of https://doi.org/10.15837/ijccc.2020.1.3762 3 relative-closeness coefficients, section 4 establishes some auxiliary fractional programming models and develops a new approach to solve the decision making problems under the pfss environment. section 5 presents a numerical example to illustrate the effectiveness of the proposed approach. finally, a concrete conclusion is drawn in section 6. 2 preliminaries in this section, we present some basic definitions and results for pfs, picture fuzzy distance measure and interval numbers. definition 1. [7]. a picture fuzzy set (pfs) a in a finite set x is defined as follows: a = {< x,µa(x),νa(x),γa(x) >,x ∈ x}, where µa(x), νa(x) and γa(x) represent the positive-membership function, negative-membership function and neutral-membership function of x to set a, respectively. for each point x in x, we have µa(x),νa(x),γa(x) → [0, 1] and 0 ≤ µa(x) + νa(x) + γa(x) ≤ 1. similar to the ifs, πa(x) = 1 − (µa(x) + νa(x) + γa(x)) could be called the refusal-membership agree of x to set a. for convenience, we can use x = (µa,νa,γa) to represent an element in pfss. definition 2. [7]: given any two pfss a and b in a finite set x, their inclusion, union, intersection and complement are respectively defined as follows: (1) a ⊆ b iff ∀x ∈ x, µa(x) ≤ µb(x),νa(x) ≥ νb(x),γa(x) ≥ γb(x) (2) a = b iff ∀x ∈ x, a ⊆ b and a ⊇ b (3) a∪b = {x, (max(µa(x),µb(x)),min(νa(x),νb(x)),min(γa(x),γb(x))} (4) a∩b = {x, (min(µa(x),µb(x)),max(νa(x),νb(x)),min(γa(x),γb(x))} (5) coa = ā = {< x,νa(x),µa(x),γa(x) >,x ∈ x} definition 3. d(a,b) is a distance measure between a ∈ pfs(x) and b ∈ pfs(x) if it satisfies the following properties: (1) 0 ≤ d(a,b) ≤ 1 (2) if a = b, then d(a,b) = 0 (3) d(a,b) = d(b,a) (4) if a ⊆ b ⊆ c, then d(a,b) + d(b,c) ≥ d(a,c) definition 4. [26, 27]. given any two pfss a and b, the normalized picture hamming distance, the normalized picture euclidean distance and the generalized picture distance measure between a and b are respectively defined as follows: dh(a,b) = 1 n n∑ j=1 (|µa(xj) −µb(xj)| + |νa(xj) −νb(xj)| + |γa(xj) −γb(xj)|) (1) de(a,b) = √√√√ 1 n n∑ j=1 (µa(xj) −µb(xj))2 + (νa(xj) −νb(xj))2 + (γa(xj) −γ(xj))2 (2) dg(a,b) = 1 5 (|µa(xj) −µb(xj)|p + |νa(xj) −νb(xj)|p + |γa(xj) −γ(xj)|p + |max(µa(xj),νb(xj)) −max(µb(xj),νa(xj))|p + |max(µa(xj),γb(xj)) −max(γa(xj),µb(xj))|p )1/p (3) https://doi.org/10.15837/ijccc.2020.1.3762 4 definition 5. [31]. given any two interval numbers a = [ a−,a+ ] and b = [ b−,b+ ] , the likelihood of a ≥ b is defined as follows: p(a ≥ b = max { (1 −max( b+ −a− l(a) + l(b) ), 0), 0 } (4) where l(a) = a+ −a− and l(b) = b+ − b−. 3 a new distance measure between picture fuzzy sets distance measure plays an important role in a lot of areas. initially, many studies have focused on this issue on fuzzy sets, ifss, nss and so on, such as euclidean distance and hamming distance. later, other extensions of the above distance measures were developed for picture fuzzy sets, such as the normalized picture hamming distance, the normalized picture euclidean distance and the generalized picture distance measure. although the picture hamming distance measure and the normalized picture euclidean distance measure have their successful application in picture fuzzy clustering and multicriteria decision-making, some counter-intuitive phenomenon may occur in practical application. for example, let a = (0.1, 0.1, 0), b = (0.2, 0.2, 0), and c = (0.2, 0.1, 0) be three picture fuzzy sets. as we know, when the degree of neutral-membership is equal to zero, pfs reduces to ifs. thus, a, b, and c all belong to ifss. according to the ranking function for the ifss given in definition 2, we have a < b < c, and the hamming distance measure between a and c should be higher than that between a and b. according to definition 4, however, we have dh(a,b) = 0.1, dh(a,c) = 0.05, and dh(a,b) > dh(a,c), which is against our intuition. to overcome this drawback, we propose a new biparametric distance measure for pfss, which is a generalization of the biparametric intuitionistic distance measure proposed by boran and akay [5]. definition 6. given any two pfss a and b, the biparametric distance measure between pfss a and b is defined as follows: d(a,b) = ( 1 3n(t + 1)p n∑ j=1 (|t(µa(xj) −µb(xj)) − (νa(xj) −νb(xj)) − (γa(xj) −γb(xj))|p + n∑ j=1 |t(νa(xj) −νb(xj)) − (µa(xj) −µb(xj)) − (γa(xj) −γb(xj))|p + n∑ j=1 |t(γa(xj) −γb(xj)) − (µa(xj) −µb(xj)) − (νa(xj) −νb(xj))|p )1/p (5) where t=2, 3, 4, ... , parameter t identifies the level of uncertainty. the biparametric parametric picture distance measure has the capability of distinguishing positive difference from negative difference, and does not yield the counter-intuitive phenomena. for example, suppose a = (0.1, 0.1, 0), b = (0.2, 0.2, 0), and c = (0.2, 0.1, 0) be three pfss. let t=2 and p=1, according to definition 5, we have d(a,b) = 0.017 and d(a,c) = 0.033. the distance measure between a and b is lower than that between a and c. meanwhile the measure is consistent with the fact that a < b < c. however, with definition 4, we have dh(a,b) > dh(a,c). this implies our proposed parametric distance measure is more reasonable than the normalized picture hamming distance measure. theorem 7. d(a,b) is a biparametric parametric distance between two pfss a and b in x. proof. we prove theorem 1 according to definition 6. we first show that 0 ≤ d(a,b) ≤ 1. note that |t(µa(xj) −µb(xj)) − (νa(xj) −νb(xj)) − (γa(xj) −γb(xj))| = |(tµa(xj) −νa(xj) −γa(xj)) − (tµb(xj) −νb(xj) −γb(xj))| https://doi.org/10.15837/ijccc.2020.1.3762 5 |t(νa(xj) −νb(xj)) − (µa(xj) −µb(xj)) − (γa(xj) −γb(xj))| = |(tνa(xj) −µa(xj) −γa(xj)) − (tνb(xj) −µb(xj) −γb(xj))| |t(γa(xj) −γb(xj)) − (µa(xj) −µb(xj)) − (νa(xj) −νb(xj))| = |(tγa(xj) −µa(xj) −νa(xj)) − (tγb(xj) −µb(xj) −νb(xj))| since µa(x),νa(x),γa(x) ∈ [0, 1], and µa(x) + νa(x) + γa(x) ∈ [0, 1], we have the following inequations: −1 ≤ (tµa(xi) −νa(xi) −γa(xi)) ≤ t −t ≤−(tµb(xi) −νb(xi) −γb(xi)) ≤ 1 then we have −(t + 1) ≤ (tµa(xj) −νa(xj) −γa(xj)) − (tµb(xj) −νb(xj) −γb(xj)) ≤ t + 1 this implies 0 ≤ |(tµa(xj) −νa(xj) −γa(xj)) − (tµb(xj) −νb(xj) −γb(xj))|p ≤ (t + 1)p similarly, we have the following inequations: 0 ≤ |(tνa(xj) −µa(xj) −γa(xj)) − (tνb(xj) −µb(xj) −γb(xj))|p ≤ (t + 1)p 0 ≤ |(tγa(xj) −µa(xj) −νa(xj)) − (tγb(xj) −µb(xj) −νb(xj))|p ≤ (t + 1)p finally, we have the following inequations: 0 ≤( 1 3n(t + 1)p n∑ j=1 (|(tµa(xj) −µb(xj)) − (νa(xj) −νb(xj)) − (γa(xj) −γb(xj))|p + |t(νa(xj) −νb(xj)) − (µa(xj) −µb(xj)) − (γa(xj) −γb(xj))|p + |t(γa(xj) −γb(xj)) − (µa(xj) −µb(xj)) − (νa(xj) −νb(xj))|p ))1/p thus, 0 ≤ d(a,b) ≤ 1 now we prove that d(a,b) = 0 if a = b. if µa(xj) = µb(xj), νa(xj) = νb(xj) and γa(xj) = γb(xj), we have µa(xj) − µb(xj) = 0, νa(xj) − νb(xj) = 0 and γa(xj) − γb(xj) = 0. thus, the distance measure d(a,b) is equal to zero. we now prove that d(a,b) = d(b,a). note that |t(µa(xj) −µb(xj)) − (νa(xj) −νb(xj)) − (γa(xj) −γb(xj))|p = |(−1){t(µb(xj) −µa(xj)) − (νb(xj) −νa(xj)) − (γb(xj) −γa(xj))}|p ; |t(νa(xj) −νb(xj)) − (µa(xj) −µb(xj)) − (γa(xj) −γb(xj))|p = |(−1){t(νb(xj) −νa(xj)) − (µb(xj) −µa(xj)) − (γb(xj) −γa(xj))}|p |t(γa(xj) −γb(xj)) − (µa(xj) −µb(xj)) − (νa(xj) −νb(xj))|p = |(−1){t(γb(xj) −γa(xj)) − (µb(xj) −µa(xj)) − (νb(xj) −νa(xj))}|p based on definition of absolute value, we have |t(µa(xj) −µb(xj)) − (νa(xj) −νb(xj)) − (γa(xj) −γb(xj))|p = |t(µb(xj) −µa(xj)) − (νb(xj) −νa(xj)) − (γb(xj) −γa(xj))|p |t(γa(xj) −γb(xj)) − (µa(xj) −µb(xj)) − (νa(xj) −νb(xj))|p = |t(γb(xj) −γa(xj)) − (µb(xj) −µa(xj)) − (νb(xj) −νa(xj))|p |t(νa(xj) −νb(xj)) − (µa(xj) −µb(xj)) − (γa(xj) −γb(xj))|p = |t(νb(xj) −νa(xj)) − (µb(xj) −µa(xj)) − (γb(xj) −γa(xj))|p https://doi.org/10.15837/ijccc.2020.1.3762 6 thus, d(a,b) = d(b,a). we now prove the fourth condition of definition 6. by definition 6, the distance measures d(a,b) and d(a,c) are respectively given as follows: d(a,b) = ( 1 3n(t + 1)p n∑ j=1 (|t(µa(xj) −µb(xj)) − (νa(xj) −νb(xj)) − (γa(xj) −γb(xj))|p + |t(νa(xj) −νb(xj)) − (µa(xj) −µb(xj)) − (γa(xj) −γb(xj))|p + |t(γa(xj) −γb(xj)) − (µa(xj) −µb(xj)) − (νa(xj) −νb(xj))|p ))1/p d(a,c) = ( 1 3n(t + 1)p n∑ j=1 (|t(µa(xj) −µc(xj)) − (νa(xj) −νc(xj)) − (γa(xj) −γc(xj))|p + |t(νa(xj) −νc(xj)) − (µa(xj) −µc(xj)) − (γa(xj) −γc(xj))|p + |t(γa(xj) −γc(xj)) − (µa(xj) −µc(xj)) − (νa(xj) −νc(xj))|p ))1/p if a ⊆ b ⊆ c, according to definition 6 we have 0 ≤ µa(xj) ≤ µb(xj) ≤ µc(xj) ≤ 1, 0 ≤ νc(xj) ≤ νb(xj) ≤ νa(xj) ≤ 1 and 0 ≤ γc(xj) ≤ γb(xj) ≤ γa(xj) ≤ 1. then we obtain the following inequations: |t(µa(xj) −µc(xj)) − (νa(xj) −νc(xj)) − (γa(xj) −γc(xj))|p = (|t(µa(xj) −µc(xj)) − (νa(xj) −νc(xj)) − (γa(xj) −γc(xj)) +tµb(xj) − tµb(xj) + νb(xj) −νb(xj) + γb(xj) −γb(xj)|)p = (|[t(µa(xj) −µb(xj)) − (νa(xj) −νb(xj)) − (γa(xj) −γb(xj))] [t(µb(xj) −µc(xj)) − (νb(xj) −νc(xj)) − (γb(xj) −γc(xj)]|)p ≤ (|t(µa(xj) −µb(xj)) − (νa(xj) −νb(xj)) − (γa(xj) −γb(xj)|) +|t(µb(xj) −µc(xj)) − (νb(xj) −νc(xj)) − (γb(xj) −γc(xj))|)p similarly, we have the following inequations: |t(νa(xj) −νc(xj)) − (µa(xj) −µc(xj)) − (γa(xj) −γc(xj))|p ≤ (|t(µa(xj) −µb(xj)) − (νa(xj) −νb(xj)) − (γa(xj) −γb(xj)|) + |t(νb(xj) −νc(xj)) − (µb(xj) −µc(xj)) − (γb(xj) −γc(xj))|p ) |t(γa(xj) −γc(xj)) − (µa(xj) −µc(xj)) − (νa(xj) −νc(xj))| ≤ |t(γa(xj) −γb(xj)) − (µa(xj) −µb(xj)) − (νa(xj) −νb(xj))| + |t(γb(xj) −γc(xj)) − (µb(xj) −µc(xj)) − (νb(xj) −νc(xj))|p ) and then, we have the following inequations: dp (a,c) = ( 1 3n(t + 1)p n∑ j=1 (|t(µa(xj) −µc(xj)) − (νa(xj) −νc(xj)) − (γa(xj) −γc(xj))|p + |t(νa(xj) −νc(xj)) − (µa(xj) −µc(xj)) − (γa(xj) −γc(xj))|p + |t(γa(xj) −γc(xj)) − (µa(xj) −µc(xj)) − (νa(xj) −νc(xj))|p ) ≤ 1 3n(t + 1)p n∑ j=1 (|t(µa(xj) −µb(xj)) − (νa(xj) −νb(xj)) − (γa(xj) −γb(xj))|) +|t(µb(xj) −µc(xj)) − (νb(xj) −νc(xj)) − (γb(xj) −γc(xj))|)p + 1 3n(t + 1)p n∑ j=1 (|t(νa(xj) −νb(xj)) − (µa(xj) −µb(xj)) − (γa(xj) −γb(xj))|) + |t(νb(xj) −νc(xj)) − (µb(xj) −µc(xj)) − (γb(xj) −γc(xj))|p ) https://doi.org/10.15837/ijccc.2020.1.3762 7 + 1 3n(t + 1)p n∑ j=1 (|t(γa(xj) −γb(xj)) − (µa(xj) −µb(xj)) − (νa(xj) −νb(xj))|) + |t(γb(xj) −γc(xj)) − (µb(xj) −µc(xj)) − (νb(xj) −νc(xj))|p ) ≤ 1 3n(t + 1)p n∑ j=1 [(|t(µa(xj) −µb(xj)) − (νa(xj) −νb(xj)) − (γa(xj) −γb(xj))| +|t(µb(xj) −µc(xj)) − (νb(xj) −νc(xj)) − (γb(xj) −γc(xj))| +(|t(νa(xj) −νb(xj)) − (µa(xj) −µb(xj)) − (γa(xj) −γb(xj))|) + |t(νb(xj) −νc(xj)) − (µb(xj) −µc(xj)) − (γb(xj) −γc(xj))|) +(|t(γa(xj) −γb(xj)) − (µa(xj) −µb(xj)) − (νa(xj) −νb(xj))|) + |t(γb(xj) −γc(xj)) − (µb(xj) −µc(xj)) − (νb(xj) −νc(xj))|]p ≤ (d(a,b) + d(b,c))p we know that d(a,c) ≥ 0, d(a,b) ≥ 0, and d(b,c) ≥ 0, and therefore we have the following inequalities: d(a,c) ≤ d(a,b) + d(b,c) we thus prove that d(a,b) is a biparametric parametric distance measure between picture fuzzy sets a and b, since d(a,b) satisfies definition 3. definition 8. given any two pfvs α1 and α2, a biparametric distance measure between α1 and α2 is defined as follows: d(α1,α2) = ( 1 3(t + 1)p (|t(µα1 −µα2 ) − (να1 −να2 ) − (γα1 −γα2 )| p + |t(να1 −να2 ) − (µα1 −µα2 ) − (γα1 −γα2 )| p + |t(γα1 −γα2 ) − (να1 −να2 ) − (µα1 −µα2 )| p )1/p (6) where t = 2, 3, 4, · · · , parameter, and t identifies the level of uncertainty. 4 fractional programming models for solving mcdm problems with pfss environments for a mcmd problem under picture fuzzy environment, let c = (c1,c2, · · · ,cn) be a set of criteria, and x = (x1,x2, · · · ,xm) be a set of alternatives to be selected. let x = (αij)m×n be a picture fuzzy decision matrix, where αij = (µαij ,ναij ,γαij ) is a pfv for alternative xi with respect to criteria cj provided by decision-maker, such that 0 ≤ µαij ≤ 1,0 ≤ ναij ≤ 1, 0 ≤ γαij ≤ 1, 0 ≤ µαij + ναij + γαij ≤ 1, i = 1, 2, · · · ,m and j = 1, 2, · · · ,n. therefore, a positive ideal solution x + and a negative ideal solution x− may be defined in the following way. the degree µ̄αij of positive membership, the degree ν̄αij of negative membership, and the degree γ̄αij of neutral membership with respect to attribute cj ∈ c are 1 and 0, respectively, which can be written as α+ij = (µ + αij ,ν+αij ,γ + αij ) = (1, 0, 0) and α−ij = (µ − αij ,ν−αij ,γ − αij ) = (0, 1, 0). then, the distance matrix can be given as d+ = (d+ij), and d− = (d−ij), where d + ij is the distance between α + ij and αij, and d − ij is the distance between α − ij and αij, respectively. assumed that the decision maker constructs a pfs wj = (aj,ωj), where ωj is the weight of the criteria cj ∈ c on the fuzzy concept “importance”, where ωj ∈ [0, 1], and ∑n j=1 $j = 1. the importance indices allow us to calculate the best final result as well as the smallest one, which we can expect in a process leading to a final decision. during the making decision process, the decision maker may change his/her evaluating weights by adjusting the value of the weight index. so in fact his/her weight lies in the closed interval ωj = [ ωlj,ω u j ] , where 0 ≤ ωlj ≤ ω u j ≤ 1. based on topsis, the relative-closeness coefficient of each xj ∈ x, (i = 1, 2, · · · ,m) with respect to x+i is defined as follows: https://doi.org/10.15837/ijccc.2020.1.3762 8 ci = √∑n j=1(ωjd + ij)2√∑n j=1(ωjd + ij)2 + √∑n j=1(ωjd − ij)2 (7) where ωlj ≤ ωj ≤ ω u j for all i = 1, 2, · · · ,m and j = 1, 2, · · · ,n. obviously, ci is different for different ωj ∈ [ ωlj,ω u j ] . values of ci should be in some range when ci takes all values in the interval [ ωlj,ω u j ] . in other words, ci is an interval number, denoted by rj = [ clj,c u j ] . the lower and upper bounds clj and c u j of ci can be captured by solving the following pair of fractional programming models: cli = minci = √∑n j=1(ωjd + ij)2√∑n j=1(ωjd + ij)2 + √∑n j=1(ωjd − ij)2 (8) s.t.clj,≤ cj ≤ c u j ; j = 1, 2, · · · ,n , and cli = maxci = √∑n j=1(ωjd + ij)2√∑n j=1(ωjd + ij)2 + √∑n j=1(ωjd − ij)2 (9) s.t.clj,≤ cj ≤ c u j ; j = 1, 2, · · · ,n due to the fact that ∂ci ∂ωj = (ωj)2d+ij √∑n j=1(ωjd − ij)2/ ∑n j=1(ωjd + ij)2 + (ωj) 2d−ij √∑n j=1(ωjd + ij)2/ ∑n j=1(ωjd − ij)2 ( √∑n j=1(ωjd + ij)2 + √∑n j=1(ωjd − ij)2)2 ≥ 0 therefore, for j = 1, 2, · · · ,n, ci is a monotonically increasing function of ωij, which means that cj reaches its maximum at ωuj and arrives at its minimum at ω l j. then, the relative closeness coefficient interval rj = [ clj,c u j ] of the alternative xi ∈ x for the decision-maker can be obtained by solving eqs. (8) and (9) using the existing optimization software such as lingo and excel. later, using eq. (4), the likelihood of xs � xt for alternatives xs and xt in x can be determined as follows: p(xs � xt) = p(cs � ct) = max { (1 −max( cut −cls l(cs) + l(ct) ), 0), 0 } (10) where l(cs) = cuj − c l j and l(ct) = c u j − c l j. then the likelihood matrix can be obtained as follows: p = p(pst)m×n =   p11 p12 · · · p1m p21 p22 · · · p2m ... ... · · · ... pm1 pm2 · · · pmm   where pst = (ps ≥ pt) for alternatives xs and xt in x. thus, an optimal degree of membership for alternative xi ∈ x, i = 1, 2, · · · ,m is defined as follows [30]: rank(xi) = 1 m(m− 1) ( m∑ s=1 pis + m 2 − 1) (11) obviously, rank(xi) ∈ [0, 1] for alternative xi ∈ x, i = 1, 2, · · · ,m , and xs ≥ xt if and only if rank(xs) ≥ rank(xt). https://doi.org/10.15837/ijccc.2020.1.3762 9 the complete procedure for multi-criteria decision making based on the picture fuzzy sets and fractional programming models can be summarized as follows: step 1: obtain the performance data in the form of picture fuzzy sets αij = (µαij ,ναij ,γαij ) (i = 1, 2, · · · ,m; j = 1, 2, · · · ,n) for m alternatives over n criteria and establish a picture fuzzy decision matrix x = (αij)m×n. step 2: transform the criteria values of cost type into those of benefit type. i.e., transform x̃ = (α̃ij)m×n into the normalized picture fuzzy decision matrix r̃ = (r̃ij)m×n, where r̃ij = { αij, for benefit criteria (αij)c, for cost criteria (12) where (αij)c is the complement of αij such that (αij)c = (νij,µij,γij). step 3: identify the picture fuzzy positive ideal solution (pfpis) and picture fuzzy negative ideal solution (pfnis) based on the pfss, and calculate the positive / negative picture fuzzy distance matrix d+ = (d+j ) and d − = (d−j ). step 4: determine the relative closeness coefficient of each alternative by using eqs. (8) and (9). step 5: determine the likelihood of xs � xt for alternatives xs and xt in x. step 6: calculate the optimal degree of membership for alternative, and rank the alternatives according to optimal degree rank(xi). 5 a numerical example and comparison with the results of other method 5.1 a numerical example in this section, a numerical example adapted from [24] is worked out to illustrate the proposed method. a company located in ankara wants to select a best green supplier, where there are four alternatives to be selected: x1, x2, x3, and x4. x1 is located in the marmara region and exports to 4 countries. x2 is located in the marmara region and exports to 2 countries. x3 is located in the central anatolia region. moreover, x4 is located in the marmara region and exports to 6 countries. the four alternatives currently have approximately 200–500 employees. the team of decision makers is composed of experts from the finance department, engineering department and purchasing department. they believe that the information and data about performance of the green supplier can be reported using fuzzy approach. after experts’ preliminary screening, 5 criteria are determined, such as quality (c1), cost (c2), service and delivery(c3), sustainability (c4), and environmental management and control (c5). to obtain an optimal green supplier, the following steps are given as follows. step 1. to select an ideal green supplier, the team of decision makers has agreed to assess each criteria cj (j = 1, 2, · · · , 5) of alternative xi (i = 1, 2, 3, 4) under picture fuzzy environment and constructs a decision matrix x = (aij)4×5,which are shown in tab.1. the importance of each attribute cj, (j = 1, 2, · · · , 5) are given in the form of intervals weight vectors as ω1 = [0.15, 0.83], ω2 = [0.26, 0.68], ω3 = [0.47, 0.48], ω4 = [0.16, 0.82] and ω5 = [0.35, 0.63]. table 1: picture fuzzy decision-making matrix c1 c2 c3 c4 c5 x1 [0.78, 0.11, 0.11] [0.85, 0.05, 0.10] [0.80, 0.09, 0.11] [0.64, 0.27, 0.08] [0.63, 0.29, 0.08] x2 [1.00, 0.00, 0.00] [1.00, 0.00, 0.00] [0.85, 0.05, 0.10] [1.00, 0.00, 0.00] [1.00, 0.00, 0.00] x3 [0.76, 0.12, 0.11] [0.70, 0.20, 0.10] [0.58, 0.34, 0.08] [0.43, 0.53, 0.04] [0.37, 0.52, 0.11] x4 [1.00, 0.00, 0.00] [1.00, 0.00, 0.00] [0.82, 0.07, 0.11] [1.00, 0.00, 0.00] [1.00, 0.00, 0.00] step 2. considering that cost is a criterion of cost type, it must transform cost type criterion into that of benefit type. with eq. (12), the normalized picture fuzzy decision matrix is shown in tab.2. https://doi.org/10.15837/ijccc.2020.1.3762 10 table 2: normalized picture fuzzy decision-making matrix c1 c2 c3 c4 c5 x1 [0.78, 0.11, 0.11] [0.05, 0.85, 0.10] [0.80, 0.09, 0.11] [0.64, 0.27, 0.08] [0.63, 0.29, 0.08] x2 [1.00, 0.00, 0.00] [0.00, 1.00, 0.00] [0.85, 0.05, 0.10] [1.00, 0.00, 0.00] [1.00, 0.00, 0.00] x3 [0.76, 0.12, 0.11] [0.20, 0.70, 0.10] [0.58, 0.34, 0.08] [0.43, 0.53, 0.04] [0.37, 0.52, 0.11] x4 [1.00, 0.00, 0.00] [0.00, 1.00, 0.00] [0.82, 0.07, 0.11] [1.00, 0.00, 0.00] [1.00, 0.00, 0.00] step 3. in this paper, pfpis is defined as [(1, 0, 0), (1, 0, 0), (1, 0, 0), (1, 0, 0) and (1, 0, 0)], and pfnis is defined as [(0, 1, 0), (0, 1, 0), (0, 1, 0), (0, 1, 0) and (0, 1, 0)], respectively. and then, with eq. (5), let t=2 and p=2, the biparametric distance of each green supplier from pfpis and pfnis is given in tab. 3. table 3: distance matric between alternatives and pfpis and pfnis d+ d− c1 0.156 0.816 0.167 0.816 0.686 0.000 0.674 0.000 c2 0.108 0.816 0.216 0.816 0.738 0.000 0.616 0.000 c3 0.142 0.738 0.315 0.128 0.702 0.108 0.509 0.719 c4 0.264 0.816 0.450 0.816 0.562 0.000 0.369 0.000 c5 0.275 0.816 0.476 0.816 0.550 0.000 0.356 0.000 step 4. according to eqs. (8) and (9), four pairs of fractional programming models for alternatives xi (i = 1, 2, 3, 4) can be obtained. let us illustrate this step by using (cl1,cu1 ) as an example. cli = min( √ (0.156ω1)2 + (0.108ω2)2 + (0.142ω3)2 + (0.264ω4)2 + (0.275ω5)2( √ (0.156ω1)2 + (0.108ω2)2 + (0.142ω3)2 + (0.264ω4)2 + (0.275ω5)2+√ (0.686ω1)2 + (0.738ω2)2 + (0.702ω3)2 + (0.562ω4)2 + (0.550ω5)2 )) s.t. { 0.15 ≤ ω1 ≤ 0.83; 0.26 ≤ ω2 ≤ 0.68; 0.47 ≤ ω3 ≤ 0.48; 0.16 ≤ ω4 ≤ 0.82; 0.35 ≤ ω5 ≤ 0.63 , and cui = max( √ (0.156ω1)2 + (0.108ω2)2 + (0.142ω3)2 + (0.264ω4)2 + (0.275ω5)2( √ (0.156ω1)2 + (0.108ω2)2 + (0.142ω3)2 + (0.264ω4)2 + (0.275ω5)2+√ (0.686ω1)2 + (0.738ω2)2 + (0.702ω3)2 + (0.562ω4)2 + (0.550ω5)2 )) s.t. { 0.15 ≤ ω1 ≤ 0.83; 0.26 ≤ ω2 ≤ 0.68; 0.47 ≤ ω3 ≤ 0.48; 0.16 ≤ ω4 ≤ 0.82; 0.35 ≤ ω5 ≤ 0.63 using winqsb, the optimal values of the objective function given in these models are cl1 = 0.0213 and cu1 = 0.0829, i.e., the relative-closeness coefficient interval of green supplier x1 is r1= [0.0213, 0.0829]. similarly, the relative-closeness intervals of the green suppliers x(k) (k = 2, 3, 4) are respectively obtained as r2 = [0.9908, 0.992], r3= [0.079, 0.3165] and r4= [0.5771, 0.9747]. step 5. based on those relative closeness intervals of the green suppliers, with eq. (10), the likelihood probability values of the pairwise components of x (k) (k = 1, 2, 3, 4) can be calculated as follows: p = p(pst) =   0.50 0.00 0.01 0.00 1.00 0.50 1.00 1.00 0.99 0.00 0.50 0.54 0.576 0.00 0.00 0.50   step 6: the optimal degrees of the membership for the green suppliers xk (k = 1, 2, 3, 4) can be obtained as rank (x1) = 0.126, rank (x2) = 0.375, rank (x3) = 0.207 and rank (x4) = 0.292, respectively. this shows that rank (x1) < rank (x3) < rank (x4) < rank (x2), and hence x2 is the best green supplier. this order is the same as the result in [24] and explains the validity of our proposed method. https://doi.org/10.15837/ijccc.2020.1.3762 11 5.2 comparison with the result of topsis method now, we make a comparative analysis of the proposed method with the existing topsis approach. first, by extending intuitionistic fuzzy weighs to picture fuzzy ones, we have the picture fuzz weight vectors of criteria as w1 = [0.15, 0.83, 0.02], w2 = [0.26, 0.68, 0.05], w3 = [0.47, 0.48, 0.05], w4 = [0.16, 0.82, 0.03] and w5 = [0.35, 0.63, 0.03]. considering that picture fuzzy set is a special form of neutrosophic sets, we have the weighted picture fuzzy matrix by using the operating laws of neutrosophic sets defined in [19], which is shown in tab. 4. table 4: weighted picture fuzzy decision-making matrix c1 c2 c3 c4 c5 x1 [0.117, 0.849, 0.002] [0.221, 0.696, 0.005] [0.376, 0.527, 0.006] [0.102, 0.869, 0.002] [0.221, 0.737, 0.002] x2 [0.150, 0.830, 0.000] [0.260, 0.680, 0.000] [0.400, 0.506, 0.005] [0.160, 0.820, 0.000] [0.350, 0.630, 0.000] x3 [0.114, 0.850, 0.002] [0.182, 0.744, 0.005] [0.235, 0.657, 0.004] [0.069, 0.915, 0.001] [0.130, 0.822, 0.003] x4 [0.150, 0.830, 0.000] [0.260, 0.680, 0.000] [0.385, 0.516, 0.006] [0.160, 0.820, 0.000] [0.350, 0.630, 0.000] next, pfpis is defined as [(1, 0, 0), (1, 0, 0), (1, 0, 0), (1, 0, 0) and (1, 0, 0)], and pfnis is defined as [(0, 1, 0), (0, 1, 0), (0, 1, 0), (0, 1, 0) and (0, 1, 0)], respectively. according to eq. (1), the normalized picture hamming distance of each green supplier from pfpis and pfnis is given in tab. 5. table 5: distance matric between alternatives and pfpis and pfnis d+ d− c1 c2 c3 c4 c5 c1 c2 c3 c4 c5 x1 1.734 1.480 1.156 1.769 1.519 0.271 0.530 0.855 0.236 0.486 x2 1.680 1.420 1.112 1.660 1.280 0.320 0.580 0.899 0.340 0.720 x3 1.739 1.567 1.426 1.848 1.696 0.266 0.443 0.582 0.155 0.310 x4 1.680 1.420 1.137 1.660 1.280 0.320 0.580 0.875 0.340 0.720 at last, we use the relative-closeness coefficient of an alternative to determine the final ranking, and we have rank (x1) = 0.237, rank (x2) = 0.286, rank (x3) = 0.175 and rank (x4) = 0.283, respectively. this shows that rank (x3) < rank (x1) < rank (x4) < rank (x2), and hence x2 is the best green supplier. this order is the same as our method proposed in this study. 6 conclusion pfs is a useful tool to express inherent uncertainty or imprecision of decision makers’ cognitive information in human decision making process. in this paper, we have proposed a method for solving the multi-criteria decision making problems under the picture fuzzy environment, in which the ratings of alternatives on criteria and weights of criteria are represented by pfss and ifss, respectively. moreover, some fractional programming models are established to obtain a relative closeness coefficient interval where preference information is independently determined for each alternative. this allows us to use flexible ways to simulate real decision scenarios. finally, a numerical analysis is conducted to illustrate the practicality and effectiveness of the proposed mcmd approach. in future research, the proposed method could be extended to interval-valued picture fuzzy sets and trapezoidal picture fuzzy sets. another interesting direction could be to develop other types of picture fuzzy aggregation operators. funding this research was funded by the social science foundation of jiangsu province (no. 16glb014). conflict of interest the author declare no conflict of interest. https://doi.org/10.15837/ijccc.2020.1.3762 12 references [1] abdel-basset, m.; manogaran, g.; gamal, a.; smarandache, f. 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(2019). multi-attribute decision making based on novel score function of intuitionistic fuzzy values and modified vikor method, information sciences, 488, 76-–92, 2019. https://doi.org/10.15837/ijccc.2020.1.3762 14 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: cao, g.(2020). a multi-criteria picture fuzzy decision-making model for green supplier selection based on fractional programming, international journal of computers communications & control, 15(1), 1002, 2020. https://doi.org/10.15837/ijccc.2020.1.3762 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 3, month: june, year: 2020 article number: 3864, https://doi.org/10.15837/ijccc.2020.3.3864 ccc publications navigation decision support: discover of vessel traffic anomaly according to the historic marine data a. daranda, g. dzemyda andrius daranda* vu institute of data science and digital technologies vilnius university, lithuania *corresponding author: andrius.daranda@gmail.com gintautas dzemyda vu institute of data science and digital technologies vilnius university, lithuania gintautas.dzemyda@mif.vu.lt abstract during the last years, marine traffic dramatically increases. marine traffic safety highly depends on the mariner’s decisions and particular situations. the watch officer must continuously observe the marine traffic for anomalies because the anomaly detection is crucial to predict dangerous situations and to make a decision in time for safe marine navigation. in this paper, we present marine traffic anomaly detection by the combination of the dbscan clustering algorithm (densitybased spatial clustering of applications with noise) with k-nearest neighbors analysis among the clusters and particular vessels. the clustering algorithm is applied to the historic marine traffic data – a set of vessel turn points. in our experiments, the total number of turn points was about 3 million, and about 160 megabytes of computer store was used. a formal numerical criterion to com-pare anomaly with normal traffic flow case has been proposed. it gives us a possibility to detect the vessels outside the typical traffic pattern. the proposed meth-od ensures the right decisions in different oceanic scale or hydro meteorology conditions in the detection of anomaly situation of the vessel. keywords: marine anomaly detection, marine traffic, spatial data, dbscan, clustering, knearest neighbors, regression. 1 introduction marine traffic generates multidimensional and dynamic navigation data. the enormous amount of marine traffic data creates significant challenges to detect anomalies for situation awareness. due to data complexity and specificity, unique methods are necessary for proper processing and resolving marine navigation data in different contexts. usually, the human is an essential part of the marine industry despite excessive mechanization and technical advancements. the human in the marine field plays a vital role in controlling vessels and making decisions to ensure vessel safety. https://doi.org/10.15837/ijccc.2020.3.3864 2 however, many studies show that the main reason for marine accidents is human error [8], [2], [3], [15]. many accidents happen because the vessel watch officer decides to take an unconventional route. the best-known example is the costa concordia disaster. the vessel ran aground and overturned after hitting an underwater rock. also, human error could be caused by overworking, wrong managerial decisions, insufficient knowledge, lack of maintenance of standards, etc. [10]. therefore, it is crucial to detect marine traffic anomaly and alert the watch officer about it as soon as possible. so, the early detection of such abnormalities is the essential key to ensure the safety of marine traffic, security, and protection of the environment. 2 the marine traffic it is essential to get full navigational information about marine traffic. therefore, the maritime safety committee (msc) of the international maritime organization (imo) adopted and required the use of the automatic identification system (ais) in the vessels [20]. this system ensures the exchange of data among vessels, ais base stations, and satellites. the ais highly enhances the safety and efficiency of marine traffic and complements situational awareness and assessment [17], [1], [5]. however, due to marine traffic complexity, it is not easy to decide about the navigational situation without a full analysis of the situation. the detection of anomaly navigation situation is highly required for all marine traffic participants [13]. however, the use of ais alone is not enough ][13]. the anomaly navigation situation could appear not only due to human error but also it may be a reason of piracy or robbery at sea, mi-grant smuggling, and organized crime within the fishing, oil spilling, and oil bunkering [13], [16]. usually, such activities are trying to hide or mask by fake vessel ais data [4], [14]. the ais data could be divided into kinematic (vessel position – latitude & longitude, speed (sog), course (cog, hdg), rate of turn (rot)) and static (vessel name, maritime mobile service identity (mmsi), vessel type, dimension, the port of destination) information. ais broadcasts the navigational data every 2-10 seconds (depending on vessel speed) [12] from every vessel in the vicinity throughout self-organized time division multiple access (stdma) [9] vessel-to-vessel datalink. this broad-cast creates an enormous volume of spatial data. for example, the vessels near danish coasts (including greenland) generate about 60 gb data every month. in this case, if we want to process one-year data, we have to deal with 720 gb. consequently, the maritime authorities need methods to detect and prevent illegal activities. likewise, the watch officer in the vessel should be warned about unusual activity near passing vessels. the abnormal vessel behavior should take notice and take appropriate action in time to avoid collision or disaster. many techniques have been proposed for marine anomaly detection. all methods can be divided into three main categories [11]: supervised, unsupervised, and semi-supervised anomaly detection. another way to separate anomaly detection is short-term and long-term. the short-term anomaly may be detected in coastal waters, e.g., using self-organizing map application for processing of ais data[18]. long-term marine anomaly detection is necessary for open seas or oceans [19]. 3 dbscan algorithm for clustering the turn points this paper proposes the method of detecting the potential anomaly situations based on the densitybased spatial clustering of applications with noise (dbscan) clustering algorithm [6] applied to the historic ais marine data. partitioning methods (k-means, pam clustering) are suitable only for compact and well-separated clusters, but they are not designed to find concave clusters that are nonlinearly separable. the results of these methods are negatively affected by the noise in the data and outliers. dbscan algorithm separates noise data. moreover, unlike the k-means algorithm, dbscan itself computes the number of the clusters. dbscan algorithm was created for the clustering of the large volume of spatial data. dbscan algorithm has two control parameters: a radius of the neighborhood with respect to some point (�), and the minimum number of neighbors (data points) within radius � (minpts). dbscan https://doi.org/10.15837/ijccc.2020.3.3864 3 classifies data points into three groups: core points, reachable points, and noise or outliers. a point q is a core point if at least minpts points are within distance (�) from it, including this point itself. a part of reachable points is reachable directly. a point q is directly reachable from q if point q is within distance � from core point q. points of the cluster may be reachable directly from core points, only. a point q is reachable from q if there is a path of points p1, ...,pn with p1 = p and pn = q, where each pi + 1 is directly reachable from pi. note that all points on the path are the core points, with the possible exception of q. all points that are not reachable from any other point are outliers or noise points. each cluster contains at least one core point. in order to use the dbscan algorithm for anomaly detection, the vessel positions (latitude & longitude of the vessel) were extracted from historic marine data. the historic marine traffic data allows getting the experience of vessel navigation to the particular port of destination. the vessel turn points only were analyzed and clustered by dbscan. they are filtered from the whole data. it is assumed that the turning point is the position where the vessel changes course for more than 4 degrees. moreover, the use of a turning point is useful for filtering the vessel yaw and pitch motion. the turn points to the same port of destination are different for every vessel. it depends on the planned route, different navigational, and hydro meteorology conditions. likewise, all vessels usually go slightly differently for the same planned trajectory every time. however, there are regions of turning, and the clustering may be applied to disclose these regions. to imagine the volume of data, e. g. in our research, the turn point data set contain information about 1350 vessels whose destination port was rotterdam. the total number of turn points was about 3 million, and about 160 megabytes of computer store was used. as a result, we have obtained 450 clusters. figure 1 shows an example of the clustered turn points by dbscan and the general trajectory of vessels. figure 1: dbscan algorithm applied to for clustering the turn points the calculated clusters must be filtered by removing turning points that are outliers, defined by dbscan. the clustered turn points make the recognized path to the port of destination, as shown in figure 2. however, sometimes dbscan forms the out-laying clusters which do not belong to the typical path to the port of destination. the reason for such outlying clusters may be e.g. the mistake in setting the port of destination in the ais. such clusters and their groups are put in boxes in figure 2. they are removed from further analysis because we need the data about proper marine traffic flow to the ports of destination. further, the spatial data should be filtered by the port of the destination. all vessels find a different path to the same port of destination, but the moderate trail can be easily recognized. the extracted remaining clusters of turning points reflect the navigation experience and regular marine traffic flow. this experience depends on the port of destination but could be used from any initial port of the voyage if this route is on the way. https://doi.org/10.15837/ijccc.2020.3.3864 4 figure 2: the result of dbscan algorithm 4 anomaly detection as a result of the analysis and filtering above, we get the marine traffic to some particular port of destination. the example of the clustered traffic flow is shown in figure 3. points denote the centers of turning point clusters. then such centers are numbered and marked by symbol ×. these points are chosen in the set at random for further analysis. let us consider a particular vessel. the vessel that is not in the normal clustered flow (e.g. somewhere outside the traffic flow that is given in figure 3 could be classified as suspicious. there are various reasons for being not in the normal flow. for example, the navigator forgot to change the port of destination in the ais settings. however, being not in the marine traffic flow could mean illegal fishing, smuggling, environmental pollution, or other criminal activity. as an example, let us analyze the same marine traffic flow with some anomaly, see figure 3. there are four cases of marine traffic anomaly. these cases are depending on the distance to the normal traffic flow. in this case, we assume that the outlier vessel proceeds to the same port of destination as the marine traffic flow. the reason for such an assumption may be grounded by information from the ais. figure 3: the clustered marine traffic flow – the centers of turning point clusters https://doi.org/10.15837/ijccc.2020.3.3864 5 let us analyze the data in figure 3 more in detail using k-nearest neighbors comparison. for each point, we find k-nearest points and compute the average distance among this point and the remaining k points. afterward, the results are averaged for all points of the traffic flow. as a result, we get one average value for a fixed k. in figure 5, we present an average distance among points for different k = 1, . . . , 8. the average distance increases depending on the number of neighbors. some regression may approximate this dependence. linear, second-order polynomial and logarithmic functions were examined. in fact, we see the linear dependence with an r-squared statistical measure near 1. the k-nearest neighbors from k = 1 to 8 for each of 14 marked points from figure 3 and figure 4, were calculated and dependencies on k of the average distance were determined. we noticed differences among the dependencies on the average distance for the particular point from the normal traffic flow and the anomaly cases. the examples of dependencies of the average distance among the neighboring points are presented in figure 6 and figure 7. table 1 summarizes the results: variances of estimates, when k runs from 1 to 8, i.e., each variance was evaluated using eight numbers. in the anomaly cases, the variances are much smaller than in the normal traffic flow. moreover, table 1 leads to the criteria to detect the vessels outside the typical marine traffic pattern: 1. the distance between the abnormally acting vessel and the nearest neighboring point from the normal traffic flow obtained by dbscan is much bigger than the average distance among the points of the normal traffic flow; 2. the variance of the average distance between neighboring points obtained for k = 1, . . . , 8 is much higher for the vessels from the normal traffic flow as compared with the abnormally acting vessel; 3. the variance decreases with growing the distance between the vessel and the normal traffic flow. figure 4: the marine traffic flow with anomaly cases https://doi.org/10.15837/ijccc.2020.3.3864 6 figure 5: the average distance among the neighboring points in normal traffic in de-pendence on k figure 6: the average distance among the neighboring pints in normal traffic in de-pendence on k for case no. 7 figure 7: the average distance among the anomaly case no. 4 and the neighboring points in normal traffic in dependence on k https://doi.org/10.15837/ijccc.2020.3.3864 7 table 1: variances of estimates points variance points in traffic 1 72.71 2 52.83 3 25.49 4 52.51 5 38.72 6 28.09 7 139.36 8 22.72 9 70.82 10 84.57 points out of traffic 1 15.65 2 10.16 3 8.00 4 5.70 average for normal traffic 53.92 5 navigation decision support the traditional navigational methods like radar and ais ensure getting navigational data, but humans always must make a decision. the proposed anomaly detection could be used in the navigation decision support. there are different groups of users for such a decision support system (dss): particular vessel, marine authorities that control marine traffics in narrow channels and ports, rescue services, environmental monitoring, and law enforcement authorities. our method is fast because we need to apply the dbscan to a large amount of spatial data once. further calculations are based on sufficiently small data containing coordinates of turn point clusters that are results of clustering by dbscan. this data is sufficient to determine whether the particular vessel is the outlier of the normal marine traffic. depending on the dss, the warning should be sent to the officer-in-charge. besides, we may need the real-time coordinates and ports of destinations of particular vessels that are in the area of interest if the dss is devoted to control the particular vessel and to suggest the proper decision for its navigation officer. these coordinates come from the automatic identification system (ais). in this case, anomaly detection could be a part of the technical solution in dss. 6 conclusion the anomaly detection is crucial to predict dangerous situations and to make a decision in time for safe marine navigation. in this paper, we present marine traffic anomaly detection by the combination of dbscan clustering algorithm (density-based spatial clustering of applications with noise) with k-nearest neighbors analysis applied to the vessel turning point data. the data extracted from the historic marine traffic data serves as the reference point when evaluating the abnormal behavior of the vessel. a formal numerical criterion to compare anomaly with normal marine traffic flow case has been proposed. it gives us a possibility to detect the vessels out-side the typical traffic pattern. this method could be beneficial for modeling marine traffic flow. besides, this method could be used to detect marine traffic anomaly on many vessels at once and in real-time. also, the proposed method is useful in different hydro meteorology conditions. therefore, the presented approach provides a solution for marine surveillance and marine traffic situational awareness. our experiments show the possibility of processing a large amount of spatial data (up to several terabytes). as a result, a sufficiently small set of cluster centers was aggregated using dbscan for further analysis and decisions. further research should be addressed to the evaluation of further behavior of the vessel and its attempts to return to the normal marine traffic. https://doi.org/10.15837/ijccc.2020.3.3864 8 author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] brusch, s.; lehner, s.; fritz, t.; soccorsi, m.; soloviev, a.; van schie, b. 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[19] wang, y.; han, l.; liu, w.; yang, s.; gao, y. (2019). study on wavelet neural network based anomaly detection in ocean observing data series, ocean engineering, 186, 2019. [20] [online] imo resolution msc 74 (69), annex 3, recommendation on performance standards for an universal shipborne automatic identification system (ais), http://www.imo.org/en/knowledgecentre/indexofimoresolutions/maritime-safetycommittee-(msc)/documents/msc.74(69).pdf, accesed on 10 december 2019. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: daranda, a.; dzemyda, g. (2020). navigation decision support: discover of vessel traffic anomaly according to the historic marine data, international journal of computers communications & control, 15(3), 3864, 2020. https://doi.org/10.15837/ijccc.2020.3.3864 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 4, month: august, year: 2020 article number: 3910, https://doi.org/10.15837/ijccc.2020.4.3910 ccc publications analytical modelling of share price value using computational intelligence methods m. petrescu, m. cuc, i. oncioiu, a.-g. petrescu, f.-r. bîlcan, l. ivan marius petrescu* valahia university of targoviste 130024 targoviste, 2 carol i bvd, romania *corresponding author: marius.petrescu@valahia.ro mădălina cuc academia nationala de informatii „mihai viteazul” 075100 bucures, ti, odăii 20-22, romania madalina.cuc@animv.ro ionica oncioiu titu maiorescu university 040051bucharest, 189 calea văcăreşti street, romania ionica.oncioiu@prof.utm.ro anca-gabriela petrescu, florentina-raluca bîlcan, lucian ivan valahia university of targoviste 130024 targoviste, 2 carol i bvd, romania anca.petrescu@valahia.ro raluca.bilcan@valahia.ro lucian.ivan@valahia.ro abstract the liberalization, volatility and high competition of financial markets are all factors that expose companies to new risks and challenges, requiring a continuous innovation of models, techniques and tools for managing share price value and other related risks. this paper provides a model to implement a subsystem that should support the management decision on the trading segment of its own shares, based on intelligent agents with regards to identifying, collecting, structuring and updating data instruments, with analysis mechanisms of the main price and volatility indicators. our conclusion is that the use of computational intelligence methods in modeling of share price value is both a requirement and an option in managing financial-foreign exchange risks, given that companies are subject to a wide range of risks and volatility is the defining feature in which they operate. keywords: neural networks, intelligent systems, share price, financial daily vector, exchange risks. https://doi.org/10.15837/ijccc.2020.4.3910 2 1 introduction in the actual context, of exponential growth of data amount and computing power, of cloud computing as a result of the need for infrastructure spending decrease, at the same time with defining platform services (paas), software services (saas) and infrastructure services (iaas), the possibility of using intelligent agents in identifying, collecting, structuring and updating data in databases is generated, the core of the transactional decision support system being represented by the specific transactional risk assessment instruments that will run on these databases [1, 7, 11]. from a practical point of view, the analysis of the efficiency of intelligent agents can be performed using neural models. being nonlinear models, their capacity for representation is significantly more pronounced than that of linear equivalents (where such a thing exists), but a nonlinear model is not always needed in prediction. in romania the impact of the social factor, the absence of management efficiency measuring metrics, including for the economic, technological and last but not least ecological factors, did not enable the development of certain models which would foster the creation and evolution of some substantiation tools and decision support specific to developed economies. currently, the roles of managerial decision substantiation and support systems consisted, at most, in their great majority, in statistical data collection, the measurement of classic indicators, alert and a set of damage control measures in case of exceeding critical performance indicators (not trading indicators). there is an almost nonexistent methodology to create indicators which would be the result of applying metrics on spatial elements given by the multitude of simulations of convergent, stable and controllable models, and this happens due to the lack of argument elements, meaning model simulations, caused by the lack of specifically built models [3, 9]. starting from the premise of the necessity of building a decision support system specific to each national company and not a general one, customized for each company, as a result of the evolutions which would occur following the listing of these closed owned companies, the aim of this paper is to implement a subsystem to support the management decision on the trading segment of its own shares, based on intelligent agents in identifying, collecting, structuring and updating data instruments, with analysis mechanisms of the main price and volatility indicators [3, 9]. our paper has two major goals. first, we focus on assessment the model of price prediction for share based on neural networks and the model of volatility prediction, encapsulated in an agent-based system make up a subsystem of a decision support system. second, this research helps creating a subsystem of managerial decision assistance on the trading segment of own shares, based on modelling of share price value using computational intelligence methods. the novelty elements of this research covered some existing gaps in the literature by highlighting aspects related to the training of a neural network to make more accurate predictions for the future share price value according to romanian capital market conditions. the structure of the paper is as follows: section 2 describes a brief presentation of the literature. section 3 provides the proposed model and methodology. section 4 analyses the empirical results and discussions implications and section 5 drives conclusions. 2 literature review in literature, the first techniques specific to computational intelligence dates back 22 years ago and emerged from the need to resolve problems that cannot have solutions by traditional methods or when the available data is not sufficient to define a logical model leading to an algorithm that would enable one unique solution or more [2, 4, 7, 12]. thus, there are numerous question marks regarding the metric for determining the degree of difficulty of the problems submitted to solution that would impose an exact approach specific to artificial intelligence or an "almost exact" one, specific to computational intelligence [13, 14]. related to this, the notion of intelligence can be defined as the ability of machines to solve or assist man in solving "difficult problems". such a situation proposes to offer alternatives to achieving an objective by using the available resources. in the case of natural intelligence, the natural selection based on competihttps://doi.org/10.15837/ijccc.2020.4.3910 3 tion offers the solution to the existence and evolution of the living organism, the adaptation to the changes that have occurred in the evolutionary environment being its most important characteristic. furthermore, beyond the living organisms’ struggle for survival, intelligence is not limited to biological organisms and their struggle for survival but passes to a new level, that of the possibility of choice, seen as a decision taken by a decision-maker or a factor of decision and which can be influenced by various factors [15, 16]. generally speaking, computational intelligence was defined as the capacity of a system to interact with its environment, being able to take decisions based on previous and current information/data, using evolutionary fuzzy computation, neural networks and combinations of the two [8, 18, 20]. compared to the concept of (traditional) artificial intelligence, computational intelligence does not use an exact model, and the input information for the model is incomplete, with high uncertainty and low accuracy [19, 22]. the present methodologies for the use and application of computational intelligence are incorporated into an elastic framework, under which new methods are added to the existing ones, with its permanent extension. it is worth noting the common aspect of this feature elasticity with cloud computing, the natural stage of development of these methodologies moving toward the technologies distributed in the cloud. on the other hand, other authors document the computation intelligence concept by describing the most applied components of this field: neural networks, a component which will be used in this study to create intelligent agents that will be implemented in the agent system, built in an analysis model of the trading risk management of shares [1, 5, 6, 17]. the study implemented in [21] used artificial neural networks (anns) to monitor the information collection/processing systems based on simple connected elements, similar to the biological neurons, capable of collecting and processing information. these simple connected elements are distributed throughout all the areas of the neural network, processing data / information in parallel. as in the case of biological neural systems, the network’s function is defined by the parameters of the elements’ connection that constitute it. the parameters of the connections between neurons, expressed as weights, are the ones that store the information collected in the network. the intelligent component of the neural networks is given by network’s learning/teaching feature and is achieved by adjusting the weights according to specific algorithms. this learning is done as in the case of neurological systems through lessons/examples. following the learning process, ann can be configured for pattern recognition or classification. in the case of biological systems, learning/ training modifies the tensions of the synaptic connections between neurons. in artificial neural networks, learning is achieved by adjusting the weights. the representation of a network’s neurons can be regarded as a parallel processing assembly and distributed by interconnected processing modules (nodes). the input data in each node are values in the closed range [0, 1]. 3 the proposed model and methodology the proposed model and methodology are based on the works of mcculloch and pitts [15], who first defined the computing machines as neural networks. according to these authors the artificial neurons (an) can be defined as a nonlinear function from f : rn → [0 , 1 ] or f : rn → [−1 , 1 ] in relation to the "activation function" used, where n is the number of the input values which are also called input signals [13]. at the same time, this model assumes the existence of a value threshold called excitation threshold, a situation in which the net input signal is added to the threshold value, s = wx + b. the value of input x is multiplied by the corresponding weight w and the result is the value of https://doi.org/10.15837/ijccc.2020.4.3910 4 input signal s = wx which is the same real value as x. for positive weights w > 0 we will call them excitatory synaptic weights, and for negative w < 0 we will call them inhibitory synaptic weights. the value of the net input signal is transformed by the activation function and results in the answer given by the artificial neuron. usual activation functions are: step function (hardlim), signum function (hardlims), sigmoid transfer function (logsig), linear transfer function (purelin), hyperbolic tangent sigmoid transfer function (tansig) and the enumeration is not exhaustive. the n-dimensional artificial neuron with correction or activation threshold can be described by a function hβ : rn × rn → [−1, 1] or hβ : rn × rn → [0, 1] where n is the size of the input system rn 3 x = (x1, x2, ... xn)t as well as the size of the system weights rn 3 w = (w1, w2, ... wn)t and scalar b will be called a correction or activation threshold and represents a factor that influences the neuron activation, with the role of increasing or decreasing the value of the input signal to the activation function. in the model in this study, another equivalent description of the above neuron can be made by adding a new +1 input synapse and weight equal to the activation threshold, meaning: xn+1 = 1 and wn+1 = b briefly, the net input signal and the output of the neuron representation being exemplified in figure 1. the proposed model and methodology are based on the works of mcculloch and pitts[15], who first defined the computing machines as neural networks. according to these authors the artificial neurons (an) can be defined as a nonlinear function f : rn → [0 , 1 ] or f : rn → [−1 , 1 ] in relation to the "activation function" used, where n is the number of the input values which are also called input signals[13]. at the same time, this model assumes the existence of a value threshold called excitation threshold, a situation in which the net input signal is added to the threshold value, s = wx + b the value of input x is multiplied by the corresponding weight w and the result is the value of input signal s = wx which is the same real value as x. for positive weights w > 0 we will call them excitatory synaptic weights, and for negative w < 0 we will call them inhibitory synaptic weights. the value of the net input signal is transformed by the activation function and results in the answer given by the artificial neuron. usual activation functions are: step function (hardlim), signum function (hardlims), sigmoid transfer function (logsig), linear https://doi.org/10.15837/ijccc.2020.4.3910 5 transfer function (purelin), hyperbolic tangent sigmoid transfer function (tansig) and the enumeration is not exhaustive. the n-dimensional artificial neuron with correction or activation threshold can be described by a function hβ : rn × rn → [−1, 1] or hβ : rn × rn → [0, 1] where n is the size of the input system rn 3 x = (x1, x2, ... xn)t as well as the size of the system weights rn 3 w = (w1, w2, ... wn)t and scalar b will be called a correction or activation threshold and represents a factor that influences the neuron activation, with the role of increasing or decreasing the value of the input signal to the activation function. in the model in this study, another equivalent description of the above neuron can be made by adding a new +1 input synapse and weight equal to the activation threshold, meaning: xn+1 = 1 and wn+1 = b. briefly, the net input signal and the output of the neuron representation being exemplified in (figure 1). figure 1: form of the non-linear n-dimensional neuron model haykin[13]) the methodology for building a neural network in this study started with the following steps: • analysis and quantification of the situation in terms of input and output values by specifying the requirements. • it starts with the most parsimonious model which is also a solution to the problem. • the values of the weights and the threshold values are determined so as to pro-duce the output results corresponding to the desired answers for all the elements in the training data set. • the values of the weights and the threshold values are determined so as to pro-duce the output results corresponding to the desired answers for all the elements in the test data set to verify the generalization capacity. • the stop condition relative to the test data should be checked. if this is not met, resume at step 3 for other values of weights and thresholds. • if more than k resumes of step 3 are made and the stop condition is not verified, step 2 is resumed with the modification of the network topology (number of neurons and layers). https://doi.org/10.15837/ijccc.2020.4.3910 6 • if more than p resumes of step 2 are made and the stop condition is not verified, step 1 is resumed and the variables representing the problem are re-quantified. furthermore, the neural networks involve the transformed transmission of input-to-output (forward) signal values and the methodology for carrying out the learning process defines the learning algorithm and operationalizes this process by the weights’ modification function (synapses) of the neural network until it reaches the desired objective. however, the primary element of neural intelligence, the learning, consists in transforming the weights (w1, w2, ... wn) of the processing elements from the input to the output. the transformation of a connection’s weight or more exactly of a processing element depends on the importance of its role in minimizing the difference between the output and the desired result. the importance degree or the power of a connection will be expressed depending on the weight wij during the learning process. within this learning process there can be learning sequences based on accurate data subsets in the inaccurate data sets and certain conditions in uncertain conditions sets, and these sequences use a mathematic model to adjust weights. such a learning sequence using the data and conditions previously described is called a learning rule. in the prediction of share price, the problem of training a neural network is described through the minimization of a criterion function. when its value is less than a threshold called error, we say that the training is done with a given error threshold. the rule for updating weights is under the form: wk+1 = wk + c 2 (dk − yk) where: wk+1 is the weights’ vector at step k + 1; wk is the weights’ vector at step k; dk is the desired output vector at step k ; yk is the obtained output vector at step k ; xk is the input vector at step k ; c is the correction constant (0 < c ≤ 1) . simultaneously, the network’s convergence and performance will be calculated based on the testing data, this being the stopping condition for the whole training and parametrization process. the data used in this paper for romanian companies are taken from the bucharest stock exchange data portal where they are made publicly available [23]. the neural network performing the price prediction for companies listed will be built, trained, validated and tested according to the following steps. using the matlab program, we will perform the numerical and the graphical simulations that will represent all the stages. the analysis interval comprises a period between 2013 and 2016, with an aggregate of 688 recordings for the data series of the variable – daily closing price. also, the series of values for the daily traded volume was also stored in the database. of the 688 values 488 were retained for the network training data and 100 values for validation and testing. the endogenous variable considered is the share price (daily closing) and the exogenous variable considered is the trading volume. the data used are for the daily closing quotas and are represented in figure 2. as a rule, overfitting appears when a model is more flexible than it should be or when it is excessively complex, having too many parameters in relation to the number of observations. 4 experimental results following the proposed model and methodology presented above, taking into consideration the neural network multi-perceptron with the endogenous variable given by daily closings and the exogenous variable given by the volume of open-loop architecture transactions, the predictive performance measures will be rn and mse. the result of this analysis was given in figure 3. the training data (488) will generate the calculus of the gradient and weights of the network. the validation data (100) will be used for stopping the training as a protection to the “overspecialization”. https://doi.org/10.15837/ijccc.2020.4.3910 7 figure 2: representation of the variables (endogenous and exogenous)) for the neural network presented, the training data set is used to the calculus of the network gradient and weights without stopping the process. the process stopping will be performed through the validation data after error evaluation. this error decreases during the initial training stage, just as the error obtained at the error evaluation for the validation data. however, when the network starts overfitting the data (the model overspecialization effect), the error evaluated for the validation data usually starts increasing. when the error for the validation subset increases for a specified number of iterations, the training process is stopped and the returned weights are those corresponding to the minimum validation error. a supplementary check of the performances of model can be made using the error histogram figure 4. the histogram can show the aberrant values, that are values for which the fitting is much “worse” than for most of the other data. the figure 5 display the autocorrelation function (acf) of the errors for the training data for the figure 3: the neural network for model https://doi.org/10.15837/ijccc.2020.4.3910 8 figure 4: error histogram for the training, validation and testing data for model model. figure 5: the autocorrelation function (acf) of training data the figure 6 and figure 7 display the autocorrelation function (acf) of the errors for the validation and testing data for model, visualizing the way the prediction errors are autocorrelated in time. for a perfect predictive model there should be only one value differing from zero of the autocorrelation function and it should appear for a zero-time lag. this would mean that the predictive errors were totally uncorrelated with each other (the white noise). in our case there is a slight autocorrelation for the short time lags, the rest of the correlations being approximately within the limits of the trust interval of 95 around zero, therefore the model is adequate. the result of the network predictions is shown in figure 8. also, from figure 8 one may notice that between the observed values and the predicted ones there are very small differences, difficult to seize at the resolution of graphs presentation. https://doi.org/10.15837/ijccc.2020.4.3910 9 figure 6: the autocorrelation function (acf) of validation data figure 7: the autocorrelation function (acf) of testing data figure 8: graph of the observed, respectively predicted time series for the three data sets (training, validation, testing) of model https://doi.org/10.15837/ijccc.2020.4.3910 10 5 conclusion and future work in this paper, the findings reveal that the neural networks are able to extract semantic contexts from unstructured big data, they create and classify models and recognize complex patterns. while validating the “learning”, the neural network becomes an “expert” in the date subset it was trained on, being then used to predict other new situations of the type “what if”. therefore, the remarkable calculus power of the parallel distributed structure of a neural network is cumulated with the generalization capacity, respectively, the capacity to produce output rational values (in a reasonable convergence range) for unknown input values within the training process. these two qualities of the neural networks make possible finding reasonable solutions for complex problems operating in uncertain environments with inaccurate data, sometimes impossible to be solved by classical methods, based on exact mathematical models. in addition, the results proved that for every training data, the network modifies its synaptic weights to minimize the difference between the desired response and the properly response of the network. the network training is done until it reaches a status when the synaptic weights do not modify significantly. in this way, the neural network builds a mapping of input data at output data from the capital market in romania. in conclusion, one can say that the strength of neural networks is their ability to accurately predict outcomes for share price value. in tests of accuracy performed in the present study, compared with other approaches, neural networks were able to get very good scores. hence, using neural networks for the share price prediction of some companies is a good alternative to the traditional technical analysis used by the trading platforms. for the future work, the study can be further explored in many direction as follows: when designing the neural network evaluation and error correction tools are necessary in order to be sure that the network is error tolerant, and in order to profit from the benefits of adaptability, the network’s time constants must be long enough for the system to ignore the fake troubles. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] alberola, j. m.; such, j. m.; botti, v.; espinosa, a.; garcia-fornes, a. 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[23] [online]. bucharest stock exchange. available: http://www.bvb.ro/, accessed on 22 january 2020. https://doi.org/10.15837/ijccc.2020.4.3910 12 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: petrescu, m.; cuc, m.; oncioiu, i.; petrescu, a.-g.; bîlcan, f.-r.; ivan, l. (2020). analytical modelling of share price value using computational intelligence methods, international journal of computers communications & control, 15(4), 3910, 2020. https://doi.org/10.15837/ijccc.2020.4.3910, 2020. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 2, month: april, year: 2021 article number: 4112, https://doi.org/10.15837/ijccc.2021.2.4112 ccc publications a new semantic-based tool detection method for robots w.b. chen, c. he, w.z. chen, q.l. chen, p.l. wu wenbai chen*, chao he, weizhao chen, qili chen school of automation beijing information science & technology university beijing 100192, china *corresponding author: chenwb03@126.com 1026853315@qq.com 670924002@qq.com qilichen@hotmail.com peiliang wu school of information science & technology yanshan university qinhuangdao 066004, china peiliangwu@ysu.edu.cn abstract home helper robots have become more acceptable due to their excellent image recognition ability. however, some common household tools remain challenging to recognize, classify, and use by robots. we designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (mask-r-cnn). this method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. it provides accurate recognition of the functional components of tools. the method is compared with existing algorithms on the dataset umd part affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. the proposed method helps the robot understand and use household tools better than traditional object detection algorithms. keywords: functional components, mask-r-cnn network, tool classification, functional semantics. 1 introduction tool use marks the beginning of human civilization, and cognitive function is the prerequisite for using tools. cognitive science research has shown that the functional cognition of tools in humans is a process in which structural information on the tools is obtained by various sensory organs using primarily stereo vision. the information on the functional components of tools is processed by the brain and organically combined to represent the tool [1]. in the process of human cognition, a model of a tool is typically constructed by learning (the idea of clustering and supervised learning) the semantic https://doi.org/10.15837/ijccc.2021.2.4112 2 features and functions of similar tools. in recent years, li’s research team [2, 3] and saxena’s research team [4] almost unanimously agreed that functional cognition is crucial for detecting semantic features in objects and scenes and a vital component of human cognition. functional semantic information can provide a more meaningful semantic description than a name or spatial location; it plays an important role in human activities, is used in daily language, and deeply profoundly people’s understanding and interactions. in robot research, the development of machine intelligence has been based on imitating human abilities. at present, home service robots can imitate human perception, although there are limits, such as the ability of the robot to interact with people naturally and provide a pleasant service. the important reason is that the robot’s cognition of tools is not comprehensive and in-depth. the research on service robot tool cognition has gone through two stages, namely, the perception stage (recognition and positioning) and the cognitive stage (natural interaction). in the perception stage, the tool representation is primarily based on the underlying features, such as color, texture, and depth. the features extracted in this stage are suitable for tool detection and location. however, due to the lack of consideration of the semantics of objects and scenes, the robot cannot recognize the service environment in the same or similar manner as human beings, making it difficult to have natural interactions with people and to achieve satisfactory service. in the cognitive stage, the middle-level and high-level semantic information of objects are considered. by adding this semantic information to the traditional perceptual information, we establish a process for learning the functional components of tools similar to the human cognition process. therefore, robots can understand and use the tools using semantic information and imitating the human cognitive mode. 2 releated work at present, the relationship between the robot and the tool and its components can be obtained and analyzed using various sensors, but information at the semantic level has to be obtained using semantic tags. however, it is difficult for the robot to obtain the semantic tags in a natural environment. in recent years, research has emerged on active functional cognition methods based on reasoning and learning [2, 4]. scholars have used functional methods to analyze functional representation and perform tool classification, such as three-dimensional (3d) cad models of chairs and other objects [5]. subsequently, numerous studies focused on the detection of object grabbing points in two-dimensional (2d) images based on apparent features [6, 7, 8]. with the emergence of red, green, blue depth (rgb-d) sensors, such as optrima and kinect, fast and low-cost 3d data acquisition has become possible, leading to novel research results in tool function detection. grabner et al. detected a surface suitable for sitting in 3d data [9], kjellstrom et al. classified the function of tools from a video [10], and zhu et al. proposed a task-oriented object model for learning construction patterns [11]. hassan et al. integrated human, object, and environmental aspects for attribute modeling and achieved accurate functional detection [12]. kemp et al. detected a tool tip that can be grasped by a robot [13]. mar et al. determined the correct grasping method for grasping a tool [14], and lenz et al. used a sparse autoencoder (sae) to detect the grab position of an object [15]. redmon detected the object’s grasping position based on a deep learning method [16]. myers et al. used a structured random forest (srf) and the superpixel-based hierarchical matching pursuit (s-hmp) to detect seven functional components of common household tools, namely, grab, cut, scoop, contain, pound, support, and wrap-grasp [17]. abelha et al. used a model-based approach to find the best alternative tool [18]. based on ontology and semantics, worgotter constructed six component primitives to describe the functional components of tools, including contain, cut, hit, hook, poke, and sift and applied them to the functional analysis of tools [19]. in the above-mentioned research, refs. [15, 16] only focused on the grasp component of the tool but did not consider other functions. reference [17] analyzed seven different functional components and their detection. reference [18] regarded the tool as a single component and developed tool models and alternative tool searches based on 3d visual data. the same kind of tools may have different shapes, materials, and colors; however, they can be regarded as organic components of several main functional components. in this study, we focus on the bottomup cognitive mode of human beings and construct an edge feature representation of the functional components of the tool. the tool representation and model of the functional semantic combination https://doi.org/10.15837/ijccc.2021.2.4112 3 of the components is established in the high-level semantic space. we perform tool classification and similarity assessment of different tools based on the tool function. we use a deep learning framework, i.e., mask regional convolutional neural network (mask r-cnn), to implement the model for detecting the functional components of the tool. mask r-cnn accurately detects the target in the image and generates high-quality segmentation results for each instance. the method is more accurate for target detection than a single detection network. in addition, due to the pixel-level annotation provided in model training, sufficient information can be obtained, and the target and background can be classified precisely. therefore, the mask r-cnn algorithm provides high accuracy. 3 methods the detection model to obtain the functional components of the tool is divided into an offline training stage and an online detection stage. the framework of the proposed method is illustrated in (figure 1). figure 1: the framework of the proposed method • offline training stage. first, we perform a deep convolution operation on the training image to extract the feature map of the target image. we divide the original image into multiple anchor boxes using the region proposal network (rpn) and perform classification and regression analysis to obtain the region of interest (roi) with a high score. then, we perform two operations on the feature map. the first is to input the feature map of the corresponding size obtained from the feature extraction stage into a fully-connected neural network for classification and regression. the second is to input the feature map into the mask branch module and classify the feature maps by pixel to obtain the segmentation results. finally, training is used to obtain the final detection model for the tool’s functional components. • online detection stage. we input the target image into the trained detection model. the corresponding feature data are obtained from the deep convolutional network and the rpn and input into different network modules, such as the fully-connected neural network and the mask network, to obtain the test results. 3.1 the detector of the tool’s functional components the mask-r-cnn framework is used for detecting and positioning the functional components of common household tools to facilitate the recognition of the tools by the home service robot. the https://doi.org/10.15837/ijccc.2021.2.4112 4 mask-r-cnn detection framework has a mask module for object detection and functional instance segmentation to classify the tool’s functional components. the system model is based on the concept of the faster-r-cnn, which is also a two-stage detector. the first stage is the rpn, which creates rois. the second stage consists of feature extraction in the rois proposed in the first stage and subsequent three-step processing, i.e., classification, border regression, and segmentation. the features of the two stages are extracted by the underlying backbone network and are shared to increase the prediction speed. in the offline training stage, several processing operations are performed on the input image using the following modules: • backbone network: the resnet network is used to extract the features of the input image to obtain the feature map of the entire image. • feature pyramid networks (fpn): the fpn network is used to extract different levels of target features; large targets are detected with deep features, and small targets are detected with shallow features. • region proposal network (rpn): a sliding window strategy is used, and the original image is divided into multiple anchors. classification and bounding box regression are performed on all anchors, and the roi with the highest score is extracted. non-maximum suppression is performed, and the results are input to the roi align layer for feature extraction. • region of interest (roi) align: the features in the roi selected by the rpn are extracted from the feature map proposed by the backbone network (the value of the floating-point position is obtained by nonlinear interpolation), and the fixed-size feature map of each roi is obtained. • classification and regression: the roi selected by the rpn is classified, and bounding box coordinate regression is performed. the classification module uses the softmax activation function. • segmentation: the mask network branch uses the fcn network to segment each roi into k output units with the size of m2, k is the number of categories, m is the length and width of the module. each category corresponds to a segmentation module (to minimize the competition between the classes in the segmentation task). the sigmoid activation function is used for each pixel. 3.2 backbone network the resnet50-fpn network is used as the network architecture of the component detection model. resnet has a residual network structure. resnet provides high accuracy due to the depth of the network layers. the structure of resnet is shown in (figure 2). resnet50 contains 50 convolution operations and 4 residual blocks. figure 2: the structure of resnet https://doi.org/10.15837/ijccc.2021.2.4112 5 the resnet is combined with the fpn for multi-scale detection. the fpn is a well-designed multi-scale detection method. the fpn consists of three types of connections: bottom-up, top-down, and horizontal connections. the schematic diagram is shown in (figure 3). the structure integrates the characteristics of each level to obtain sufficient semantic and spatial information. figure 3: the schematic diagram of the fpn the feature extraction network resnet50+fpn is shown in (figure 4). the image information is input into the residual network for feature extraction, and the fpn and the resnet are combined to perform multi-scale feature extraction. figure 4: the structure of resnet50+fpn 3.3 mask branch this system model uses resnet101+fpn as a feature extraction network to achieve state-of-the-art performance. in addition, we use roialign instead of roipooling to improve the pooling operation. an interpolation process is used (bilinear interpolation to 14*14 and pooling to 7*7) to minimize the misalignment problem caused by direct sampling using only pooling. finally, we add the mask branch to achieve semantic segmentation. the mask-r-cnn framework uses multi-task loss, total loss (l), classification loss (lcls), prediction box loss (lbox), and mask loss (lmask). the classification loss is defined as the cross-entropy loss function, the regression loss function is defined as the smoothl1 loss https://doi.org/10.15837/ijccc.2021.2.4112 6 function, and the segmentation loss function is defined as the average binary cross-entropy loss. the schematic diagram of the loss functions is shown in (figure 5). figure 5: schematic diagram of the loss definition of the mask r-cnn the mask r-cnn is based on the faster-r-cnn, but the feature extraction uses the resnet-fpn architecture, and the mask prediction module is added. there are three main differences between the mask r-cnn and the faster r-cnn framework: (1) the improved resnet-fpn structure is adopted in the basic feature extraction network, and the multi-layer feature map is conducive to the detection of multi-scale objects and small objects. (2) the roi align method is proposed to replace roi pooling. since roi pooling has two rounding operations, the extracted features deviate from the initial regression position; this case is referred to as a misalignment, which affects the accuracy of the detection and segmentation tasks. therefore, mask r-cnn does not use the rounding operation but retains all floating-point numbers. it obtains the values of multiple sampling points through bilinear interpolation and performs the maximum pooling operation on the values of multiple sampling points to obtain the final value. (3) after obtaining the features of the roi, the mask module is added to perform classification and regression and predict the pixel category. 4 experiment and results 4.1 dataset this experiment uses the university of maryland (umd) part affordance dataset [17], which is a relatively complete dataset for the functional components of tools. the rgb-d information of 17 household tools, including kitchen and gardening tool, are included, namely turners, trowel, tenderizer, spoon, shovel, shear, scoop, scissors, saw, pot, mug, mallet, ladle, hammer, cup, and bowl. each tool is depicted in nearly 300 different perspectives, for a total of more than 30,000 sets of rgb-d data. about one-third of the tools have been marked for component functionality. the ratio of training data to test data is about 4:1. (table 1) lists seven functional components of tools and their descriptions. table 1: seven functional components of tools in the umd part affordance dataset function description grasp it can be grasped by hand. (handle) cut it can be used to separate objects. (blade) scoop it has a curved surface for collecting soft materials. (spoon) contain it has a deep cavity to hold the liquid. (bowl) pound it can be used to strike other objects. (hammerhead) support it can accommodate loose flat parts. (scraper) wrap-grasp it can be held by hand and palm. (cup, outer surface) https://doi.org/10.15837/ijccc.2021.2.4112 7 4.2 model training and loss function the pytorch framework was used in our experiments, and the basic code was the mask r-cnn benchmark from facebook research [20, 21]. we used the pre-trained resnet-50 model for initialization, and the pre-training weights were the training weights of mask r-cnn on the coco dataset. the gpu was an nvidia titan xp with 10 gb of memory. the training and test images had a size of 768×768. during training, the batch size was 2, the initial learning rate was 0.0025, and the weight attenuation was 0.0001. the maximum number of iterations was 72000. the training process required 10 hours. the loss curves obtained during the training process are shown in (figure 6). (a), (b), (c), and (d) show the total loss l, the classification loss lclass, the prediction box loss lbox, and the mask branch loss lmask. the value of the loss function l and lclass + lbox + lmask, in the mask r-cnn was minimized, and the optimal model was used to predict the functional components of the tool. the trained model was applied to predict and analyze with test data. the loss function of the mask r-cnn is defined as: l = lclass + lbox + lmask (1) where lclass + lbox are identified the same as in the faster r-cnn: lclass + lbox = 1 ncls ∑ i lcls (pi, p∗i ) + 1 nbox ∑ i p∗i l smooth 1 (ti − t ∗ i ) (2) lcls ({pi, p∗i}) = −p ∗ i log (1 − p ∗ i ) − (1 − p ∗ i ) log (1 − p ∗ i ) (3) the lmask is the average binary cross-entropy loss: lmask = − 1 m2 ∑ 1≤i,j≤m [ yij log ykij + (1 − yij ) log ( 1 − ykij )] (4) figure 6: the loss curves https://doi.org/10.15837/ijccc.2021.2.4112 8 4.3 results in this study, 80% of the 20186 images in the tool dataset were used as the training set and 20% as the test set. the model performance evaluation index was the mean average precision (map). the ap (iou=0.50) of each functional component of the model on the test set is shown in (table 2). table 2: detection accuracy and segmentation accuracy of the mask r-cnn model type bbox segm grasp 0.7931 0.8155 cut 0.9375 0.9586 scoop 0.9973 0.9973 contain 0.9886 0.9886 pound 0.9925 0.9735 support 1.0000 1.0000 warg-grasp 1.0000 1.0000 map 0.9584 0.9622 the test results indicate that the proposed model detects the functions of the tool’s components accurately, with an map of more than 90%. therefore, the model is suitable for use in home service robots to detect common household tools. we used traditional machine learning algorithms and deep learning-based detection algorithms to detect the functional components of common household tools. the accuracy of the component detection algorithm based on the deep learning framework mask-rcnn is higher than that of traditional machine learning algorithms, such as the functional component detection algorithm based on srf; however, the former requires more computing resources than the latter. the detection accuracies of different methods are listed in (table 3). table 3: detection accuracies of different methods type proposed method srf s-hmp srf + coarse-to-fine rgb-d part-based grasp 0.815 0.314 0.367 0.554 0.50 cut 0.958 0.285 0.373 0.224 0.57 scoop 0.997 0.412 0.415 0.573 0.37 contain 0.988 0.635 0.810 0.605 0.68 pound 0.973 0.429 0.643 0.511 0.23 support 1.000 0.481 0.524 0.489 0.49 warg-grasp 1.000 0.666 0.767 0.787 0.36 (figure 7) (a) shows the detection result of ref. [22], (figure 7) (b) shows the detection result of ref. [23], and (figure 7) (c) shows the experimental result of the proposed method. the objective of the detection methods in refs. [22] and literature [23] is to detect the functional component of the tools. in (a), the functional components of the tools from left to right are contain, cut, grasp, pound, scoop, support, and wrap-grasp. if the tool has this feature, the position is highlighted in the detection result. in (b), the functions are the same as (a). the background color is blue, the target component is red, and the other functional components are yellow. the detection of functional components of the tools in this study focuses on the home service robot’s cognition of the tools. the objective is to provide the service robot with an understanding of the tool, including its usage method and the functions of various components. thus, the goal of this experiment is the detection of the functional components in a tool. as shown in (c), all the functional components of the tools, such as spatula, hammer, soup ladle, fruit knife, mug, and shovel (from left to right), are detected correctly. the detection results depict the segmentation results of the functional component instances and their classification, the borders, and the confidence score. thus, the proposed algorithm exhibits higher accuracy than the other two algorithms and has advantages in terms of practicability. https://doi.org/10.15837/ijccc.2021.2.4112 9 figure 7: the results of the functional component detection and instance segmentation results of different algorithms 5 conclusion we proposed a method for detecting the functional parts of common tools using the mask-r-cnn network for use in intelligent robots. the method extracted features from the resnet+fpn network layer, performed classification and regression of the target frame using the rpn layer and the roi align layer, and included a module to obtain the semantic meaning of the segmented region after full convolution for up-sampling to detect the functional components of various tools. the multi-task loss function used in the network training of the algorithm included the classification loss, target frame regression loss, and mask segmentation loss. the value of the loss function was reduced in the learning process until the global optimal solution was obtained. the research results showed that the proposed method could identify the categories of commonly used household tools and their functional components efficiently and accurately, indicating that a household service robot would be able to detect and understand the usage of commonly used tools. the algorithm had higher accuracy and robustness than the two traditional machine learning algorithms. in a future study, we will optimize the detection algorithm to recognize common household tools in multiple complex scenes with multiple objects at different viewing angles and different light intensities. acknowledgment this work is supported by beijing natural science foundation (grant no. 4202026) and the national key r & d program of china (grant no. 2018yfb1308300). references [1] gibson, j. j. 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(2019). towards affordance detection for robot manipulation using affordance for parts and parts for affordance. autonomous robots, 43(5), 11551172. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: chen w.b., he c., chen w.z., chen q.l., wu p.l. (2021). a new semantic-based tool detection method for robots, international journal of computers communications & control, 16(2), 4112, 2021. https://doi.org/10.15837/ijccc.2021.2.4112 introduction releated work methods the detector of the tool’s functional components backbone network mask branch experiment and results dataset model training and loss function results conclusion international journal of computers communications & control issn 1841-9836, 10(1):49-61, february, 2015. traffic control based on contention resolution in optical burst e. dhavarudha, c. charoenlarpnopparut, s. runggeratigul ekularn dhavarudha*, chalie charoenlarpnopparut school of information, computer, and communication technology sirindhorn international institute of technology, thammasat university rangsit campus p.o.box 22, pathum thani 12121, thailand chalie@siit.tu.ac.th *corresponding author: ekularn.d@egat.co.th suwan runggeratigul air products industry co., ltd. 84/8 mu14 petchakasem rd., nongkaem, bangkok, 10160 thailand suwan@apithailand.com abstract: traffic control based on contention resolution process (tccr) is proposed in this study as a quality of service (qos) mechanism to offer service level agreement (sla) for optical burst switch (obs). qos of the high and the low priority classes are issued upon their sla. the first one is defined for real-time application as internet protocol television (ip tv) and voice over internet protocol (voip) while the other is for soft real-time service as internet protocol remote terminal unit (ip rtu) based on iec 60870-5-101/104 protocol. a combination of burst aggregation (ba), extra offset time, and fiber delay line (fdl) is utilized in tccr to offer absolute service differentiation qos to the high priority class. the experiments show tccr can offer the high priority class of its satisfied sla for both blocking probability and delay. it also relatively improves the performances of the low priority class bounded in its sla because tccr does not force to drop this class. the performances of tccr are compared with the other techniques such as no class isolation and bandwidth allocation processes. the comparisons show tccr gives the best solution of the defined sla which enhances obs performances and properly differentiates class of services. keywords: optical burst switching, contention resolution, voice and video applications, telecontrol application, service level agreement. 1 introduction optical burst switch (obs) is all-optical switching which makes connection setup with out of band signaling. obs is a promising and future proofed technology for backbone network since it can support tremendous bandwidth and eliminate drawbacks of optical-electrical-optical (oeo) switching. for illustration, obs requires lower power consumption than oeo switching. also, it is a protocol transparent and data rate independent which brings about network scalability. nowadays, network bandwidth consumption has been dramatically increased due to the new internet protocol (ip) applications such as high definition television, interactive games, triple plays, and so on. although huge bandwidth is provided, consumer needs are not restrained. in order to maintain customers’ requirement and network utilization, quality of service (qos) is a crucial factor to support multiple classes of services. implementing qos to ip network can be categorized into two models which are the integrated service (intserv) and the differentiated service (diffserv) models. intserv model is per-flow guaranteed qos. on the internet network there are plenty of information flows; therefore, intserv requires enormous amount of state information for all flows causing unscalability to the network. on the other hand, diffserv model copyright © 2006-2015 by ccc publications 50 e. dhavarudha, c. charoenlarpnopparut, s. runggeratigul provides traffic differentiation based on per-hop qos and specifies basic mechanisms on the way to treat packets. difserv is defined into two services which are absolute service differentiation and relative service differentiation. the first one provides the worst case service to guarantee the application in each class whereas the second one relatively defines qos based on other classes. there are several research works in the area of obs qos based on contention resolution. yoo et al. [1] propose offset time based qos offering relative qos for multi-class obs. in this scheme, each class requires offset time differentiation for their class isolation. it also implements limited fiber delay line (fdl) for all classes to enhance each class’s performance but not a purpose for class isolation. although this scheme is effective to enhance network performances but it is not good enough to isolate multi-classes depending on their practical requirement. therefore, more contention resolution mechanisms as burst aggregation (ba) or other techniques are required to provide the qualified qos for multiple classes. shin and yang [2] propose ba timer based scheme to isolate services between real-time and non real-time applications. in addition, long et al. [3] also present ba adjustable timer and burst size based schemes to differentiate ip services into three classes. both studies illustrate their schemes can offer relative qos for the real-time application but they result in performance degradation of non real-time application. moreover, both studies do not present fdl in obs core nodes to reduce burst loss in the core network. cherif and fatima [4] present a study of relative qos oriented based on contention resolution by using fdl and deflection routing techniques. although, they use various combinations of both schemes to reduce blocking probabilities of obs; the major contribution come from fdl. the combinations of both schemes are also used as service differentiation of the two classes. however, the performances of the high classes are enhanced by those techniques but the low classes experience more contention due to the deflected bursts from the high class traffic. moreover, lacking of ba and offset time schemes in this study yields limitation in obs network enhancement. the study of absolute guaranteed qos mechanism using ba timer and threshold based techniques is presented by choi et al. [5]; however, this study is not proposed to isolate service differentiation classes. a proactive wavelength pre-emption technique supporting absolute qos is proposed by phuritatkul et al. [6] in order to guarantee qos in absolute term for the high class. in contrast, the low class experiences more burst drops due to their wavelength preemption scheme. this scheme can guarantee the high class qos but the mechanism is complex and it is not scalable for large networks. form the previous works, there are some openings based on providing qos to obs with contention resolution schemes. in this study, we focus on the study based on the two classes of traffic: the high and the low priority classes. we propose a new technique to offer absolute qos with traffic control based on contention resolution (tccr) applied to the high priority class. because there is no electronic buffer device in obs, the combination of the three contention resolution schemes in time domain as extra offset time, ba, and fdl are utilized in tccr as qos mechanisms to enhance obs performances by reducing network blocking probabilities in both edge and core nodes. tccr also gives distinctive service differentiation between the high and the low priority classes by allowing the high priority class to be controlled with the three mentioned mechanisms. next, we compare our proposed schemes with an absolute qos based on the ip qos bandwidth allocation scheme [7] and also obs implementing various contention resolution schemes with no class isolation. we organize this research into four sections. the process of our proposed tccr is demonstrated in the next section. section 2 presents the proposed models and section 3 shows the experimental results. lastly, the contribution is concluded in section 4. traffic control based on contention resolution in optical burst 51 2 traffic control based on contention resolution schemes the technologies of optical memory and optical logic device are immature in this moment; therefore, contention resolution plays very important role for obs to reduce burst losses in the network. several contention resolution schemes are applied as qos mechanisms. however, this paper highlights on the schemes in time domain which are extra offset time, ba, and fdl. extra offset time scheme is a technique that allows extra timing accumulated to the original offset time for reducing burst losses in the network. ba electronically buffers several bursts at ingress node and smoothes burst traffics which lead to reduce network contention. fdl acts as a light buffer. it allows a contending burst traveling along optical fiber line in order to temporarily delay that burst before sending to an available channel. the detail of tccr technique is illustrated in fig.1. the high priority class (class 1) incoming traffics are queued in ingress buffers for ba figure 1: the process of tccr thresholds and their offset times are also set to be longer than their base values. in addition, at the intermediate nodes, if their control packets cannot reserve available channels for traffic class 1, then available fdl channels are selected. however, all mentioned contention resolution schemes are not provided for the low priority class (class 0) as to offer distinguished service differentiation between the two classes. 2.1 quality of service and service level agreement service level agreement (sla) is a key component of service level that service providers specify their performance agreement or qos agreement to end users such as guaranteed delay 52 e. dhavarudha, c. charoenlarpnopparut, s. runggeratigul and guaranteed bandwidth. in this paper we consider the two agreements for the two classes according to their qos as follows. firstly, traffic class 0 is ip remote terminal unit (ip rtu) traffic based on iec 60870-5101/104 protocol [8]. this application is ip based information of supervisory control and data acquisition (scada) system for remote controlling and monitoring utilized in utility business. this application is very important for power grids, water treatment, distribution utilities and other utilities such as oil and gas pipeline utilities. this application is one of smart grid applications provides data acquisitioning, remote monitoring and remote controlling of the equipment in power grids including power plants and substations. in addition, this kind of traffic becomes more important for remote sensor and remote control applications based on ip network. for qos aspect, this application requires soft real-time communication. the latency requirement is less than 1 s [9]. as this service is a request and response communication and its nature is designed for client and server concept, one session consumes very low bandwidth. it is approximately 100 kbps for one session stream [9]. because its nature is master/slave soft real-time communication, when the server detects communication failure the server is able to restart pooling and recollect the information from the client. therefore, no requirement based on blocking probability is defined for this application. however, this application is critical for operational works; blocking probability of this case shall be as minimal as possible which shall not affect ip rtu operation and overall network performance. secondly, traffic class 1 is assumed to carry both ip television (ip tv) and voice over ip (voip) applications which are real-time services in ip networks. they require high qos to offer satisfied quality to users. normally high grade of service network for voice application requires blocking probability less than 0.03 [10]. for video traffic, it needs blocking probability below 0.02 [2] [10]. therefore, setting sla of traffic class 1 in term of blocking probability to 0.02 will give satisfied blocking agreement to both ip tv and voip applications. the standardized recommendations recommend one way end-to-end packet delay for video service of 150 ms to 400 ms [2] [10] and for voice service of 150 ms [10]. however, these values are too high for backbone network; there are some extra delays causing from other elements such as queuing delay and processing delay produced by edge nodes. in addition, propagation delays shall be taken in to consideration for network designing. therefore, the maximum sla for delay aspect shall be set in order to allow the network can tolerate more compensation on other extra delay. as the sprint ip backbone network and some obs and public frame relay networks offer one way delay of 31 ms [11], we adopt this idea to our obs network to offer 31 ms as a delay sla of traffic class 1 which can support high qos to both video and voice applications. 2.2 traffic and network models the network for our study is 14-node and 21-link nsfnet [12] of which the topology and link propagation delays in second (s) are illustrated in fig.2. the traffic capacity for each node pair of this topology is generated from the uniform random distribution from [12]. these capacities are scaled in order to make the maximum value of tij (the traffic demand between node i and node j) to be 400 gbps. the traffic of each pair is generated by poisson process with arrival rates of tij/b. for our case, b is average burst length duration and we set to 8 mbits [13]. for the class isolation experiments, we conduct simulations based on the two traffic scenarios. scenario 1: the traffic demands of class 0 and class 1 are both 50 percent of tij . scenario 2: the traffic demand for class 0 is 30 percent and 70 percent of overall traffic is for class 1. in both scenarios, the traffic is simulated varied by the demand coefficients (k) which is altered from 0 to 1 with each step of 0.1. based on our traffic and network models, we deploy routing and wavelength assignment traffic control based on contention resolution in optical burst 53 figure 2: 14-node and 21-link nsfnet backbone network (rwa) procedure from [14]. this network dimensioning procedure gives 98 wavelengths for unidirectional optical fiber cable as its optimum solution. we set 98 wavelengths for all data channels of this network and we assume the control channels’ bandwidth is non-blocking. each wavelength’s capacity is 10 gbps. 2.3 obs parameters and simulation models our obs processing time is set to 80 µs [13] and the switching configuration time is 10 µs [5]. the simulation parameters are given as follows. ba is the first scheme of our consideration. ba is very important technique because it can smooth burst traffics and reduce burst losses which consequently improves link utilization [15]. our ba is set by burst size threshold using burst length duration as it can ensure traffic smoothness and yields effectively reducing network blocking probability [15]. this scheme may introduce additional uncontrolled delay to the network when carrying light load traffics. however, this will not affect our study because we control the maximum delay in all experiments by controlling a target end-to-end delay below the offered sla. our basic burst size threshold is equal to the average burst length duration b in byte which is 1,000,000 bytes [13]. each step of our ba experiment is varied with the multiple of b i.e. 1b, 5b and etc. our ba duration is quite high compared to the switching configuration time; therefore, link utilization of the network is not degraded [5]. in addition to ba, our based offset time of each route is the summation of total processing time of all nodes in that route and the switching configuration time [15]. therefore, our extra offset time is varied incrementally from based offset time by multiple of b (1b, 2b, and etc). we set 1b to the length of b in time domain (0.8 ms). third, we implement fdl adopted from [16] to all nodes as illustrated in fig.3. each fdl parameter is varied with multiple of delay unit (d). 1d unit in time domain is set to 0.8 ms as same as 1b in the case of extra offset time scheme. the length of fdl is altered from 1d to 3d. for 1d fdl in length, it takes a 160–km fiber cable; therefore, 3d is the maximum delay of 480–km fiber span implementing without an optical amplifier [17]. lastly, all obs experiments are simulated based on just enough time (jet) [18] and latest available unscheduled channel with void filling (lauc–vf) scheduling [16] with shortest path routing. lauc–vf requires full wavelength conversion; hence, our obs is assumed full wavelength conversion [16]. from the literature reviews, yoo et al. [1] propose the analytical formulations to calculate blocking probability of obs implementing extra offset time and fdl schemes; however, they are applied for a single node analysis and do not give applicable results for the large network as our model. du and sbe [15] also propose analytical model to calculate network blocking probabilities 54 e. dhavarudha, c. charoenlarpnopparut, s. runggeratigul figure 3: obs node architecture for ba in burst length threshold scheme. however, their analysis show variable ba thresholds do not affect network blocking probabilities because their offered loads remain the same. in contrast, they take a consideration of network blocking due to the congestion in control plane which is not applicable for our assumption. from those reasons, all experiments in this study are simulated based on ns-2 simulator [19]. in order to demonstrate diverse aspects of our proposed tccr process, we propose the performance comparisons between tccr, bandwidth allocation service differentiation, and no class isolation models. in the bandwidth allocation service differentiation technique [7], the high priority class is offered dedicated network bandwidth just enough to give class 1 with its satisfied sla. to illustrate, in scenario 1 both traffic class 0 and class 1 are 50 percent of total population in the network; however, 50 percent of all network bandwidth dedicated to traffic class 1 might not give the qualified blocking sla to this class. therefore, more reserved bandwidth for class 1 is required. however, the bandwidth given to traffic class 0 must be reduced because the bandwidth must be given to traffic class 1 to make it satisfied its sla. lastly, no class isolation model, all traffics are treated equally and their experiments are included based experiments (no contention resolution) and the simulations applied contention resolution schemes. 3 experimental results firstly, the repeated experiments are conducted for network provisioning to investigate the best parameters of our tccr process with the results based on maximum load capacities (k = 1) shown in table 1. the experiments are included the performances of class 1 applying each contention resolution scheme, the performances of class 0 with no contention resolution and also the simulations based on no class isolation comparable to those of service differentiation experiments are given. considering network condition in both scenarios 1 and 2, the studies in class isolation and no class isolation experiments show the same tendency. they can be concluded that ba is the best solution to reduce traffic class 1 blocking probabilities among the three techniques. fdl and extra offset time are the second and the third respectively. although all schemes can enhance obs performances, they introduce more delays to the network. if we compare the performances of traffic class 1 with the performances of traffic class 0, all contention resolution schemes can enhance the performances of traffic class 1 and it yields relatively reducing blocking probabilities traffic control based on contention resolution in optical burst 55 table 1: experimental results of one contention resolution schemes traffic scenario 1 class ba blocking network extra blocking network fdl blocking network probability delay offset probability delay probability delay (x10−2) (ms) time (x10−2) (ms) (x10−2) (ms) class 0 2.76 10.72 3.00 10.35 2.95 10.35 class 1 5b 2.38 14.46 1b 2.79 11.15 1d 2.77 10.36 class 0 2.75 11.09 3.00 10.35 2.94 10.35 class 1 10b 2.33 19.51 2b 2.78 11.95 2d 2.75 10.37 class 0 2.71 11.46 3.00 10.35 2.93 10.35 class 1 15b 2.16 24.55 3b 2.77 12.75 3d 2.74 10.37 class 0 2.71 11.46 3.00 10.35 class 1 20b 2.15 29.56 4b 2.77 13.55 class 0 2.71 11.46 3.00 10.35 class 1 21b 2.14 30.56 5b 2.77 15.15 traffic scenario 2 class ba blocking network extra blocking network fdl blocking network probability delay offset probability delay probability delay (x10−2) (ms) time (x10−2) (ms) (x10−2) (ms) class 0 2.82 10.42 3.00 10.35 2.97 10.35 class 1 5b 2.44 14.21 1b 2.86 11.15 1d 2.84 10.36 class 0 2.81 10.69 3.00 10.35 2.96 10.35 class 1 10b 2.37 18.98 2b 2.85 11.95 2d 2.82 10.36 class 0 2.80 11.06 3.00 10.35 2.95 10.35 class 1 15b 2.34 23.73 3b 2.84 12.75 3d 2.81 10.37 class 0 2.80 11.06 3.00 10.35 class 1 20b 2.28 28.47 4b 2.84 13.55 class 0 2.79 11.31 3.00 10.35 class 1 21b 2.27 29.41 5b 2.84 14.35 class 0 2.79 11.31 3.00 10.35 class 1 22b 2.26 30.36 6b 2.84 15.15 no class isolation class ba blocking network extra blocking network fdl blocking network probability delay offset probability delay probability delay (x10−2) (ms) time (x10−2) (ms) (x10−2) (ms) 3.01 10.35 3.01 10.35 3.01 10.35 5b 2.52 13.57 1b 3.01 11.15 1d 2.89 10.35 10b 2.45 17.58 2b 3.00 11.95 2d 2.87 10.35 15b 2.43 21.58 3b 2.91 12.75 3d 2.86 10.36 20b 2.42 25.58 4b 2.90 13.55 22b 2.41 27.98 5b 2.90 14.35 23b 2.41 28.77 6b 2.90 15.15 56 e. dhavarudha, c. charoenlarpnopparut, s. runggeratigul of traffic class 0 to be lower than the base experiment. in addition, the studies illustrate all contention resolution schemes can make traffic class 1 blocking probabilities lower than when we implement them to overall traffics with no class isolations. however, none of traffic class 0 blocking probability is better than those overall traffics treated by each contention resolution scheme. from the repeated experiments based on both class isolation and no class isolation, ba threshold is increased to the maximum limitation which produces the maximum delay just right under the defined delay sla (31 ms). for the extra offset time scheme, the simulations are repeated with longer offset times until we hardly enhance network performance by this technique. in fdl, the experiments are simulated with the maximum limit of 3d. observing that the experiment based on the maximum threshold of each contention resolution is still unable to give class 1 satisfied blocking sla (0.02); therefore, each two-combination of contention resolution schemes is conducted with the best solutions of which their delays are in boundaries learned from the experiences of one scheme implementations. table 2: experimental results based on the combination of two contention resolution schemes traffic scenario 1 class ba blocking network extra blocking network fdl blocking network probability delay offset probability delay probability delay (x10−2) (ms) time (x10−2) (ms) (x10−2) (ms) class 0 2.68 11.46 2.70 11.46 2.93 10.35 class 1 20b 2.09 29.58 20b 2.14 31.06 3b 2.69 12.95 and and and 3d 3b 3d traffic scenario 2 class ba blocking network extra blocking network fdl blocking network probability delay offset probability delay probability delay (x10−2) (ms) time (x10−2) (ms) (x10−2) (ms) class 0 2.76 11.31 2.78 11.31 3.00 10.35 class 1 21b 2.16 29.43 21b 2.25 30.72 3b 2.75 12.95 and and and 3d 3b 3d no class isolation class ba blocking network extra blocking network fdl blocking network probability delay offset probability delay probability delay (x10−2) (ms) time (x10−2) (ms) (x10−2) (ms) 3.01 10.35 3.01 10.35 3.01 10.35 22b 2.28 28.10 22b 2.34 30.38 4b 2.83 13.56 and and and 3d 4b 3d to elaborate, the best solutions of traffic class 1, scenario 1 in the class isolation scheme of which their delays are in boundaries illustrated in table 1 are the simulations applied with 21b, 3b, and 3d accordingly. for no class isolation, the best schemes are 23b, 4b, and 3d. thus, we conduct the experimentation of each two–combination scheme with those values. the results are presented in table 2; however, these combinations are not able to make traffic class 1 meet blocking sla requirement. however, there is some challenge for us to implement the third contention resolution scheme because there is some vacancy for extra delays. therefore, the combination of the three contention resolution schemes based on tccr is further conducted. the parameters of the best results for all three schemes from table 2 are selected for tccr process. their illustrations will be compared with the bandwidth allocation service differentiation technique (denoted bs). they are also compared with the experiments of no class isolation models (denoted no class) which are the based experiment and the experiment applied contention resolution schemes. all mentioned simulations are conducted in both traffic scenario 1 (denoted traffic control based on contention resolution in optical burst 57 s1) and scenario 2 (denoted s2). figure 4: average network blocking probabilities of tccr, bs, and no class for scenario 1 from the results illustrated in table 2, traffic class 1 of scenario 1, the best solution is given by the combination of 20b and 3d; however, this implementation is not be able to give satisfied blocking probability to traffic class 1. therefore, the best solution of extra offset time scheme (3b) is combined to those two schemes in tccr. as a result, tccr with 20b, 3b, and 3d gives the most remarkable performance to this network as it can reduce blocking probability of the traffic class 1 under its sla as illustrated in fig.4. in addition, fig.5 expresses the delay aspect of the results in fig.4. the experiments show tccr can very well improve services differentiation and class isolation between class 1 and class 0. moreover, tccr also indirectly improves blocking probability of traffic class 0 because the mechanism of tccr does not make a decision to drop the low priority class but it selects various mechanisms to enhance the performance of the high priority class by utilizing network bandwidth, smoothing burst traffic and providing fdl channels for contending busts in the core network. thus, more network resource can service traffic class 0 which leads to reduce this class’s blocking probability. for bs, in order to give satisfied blocking sla to traffic class 1, from the repeated experiments reffering to scenario 1, the simulations show it requires bandwidth of 57.70 percent of overall resource. meanwhile the bandwidth of traffic class 0 is reduced to 42.30 percent. if we compare blocking probabilities of traffic class 1 between bs and tccr processes, they have comparable performances at the maximum load capacity (k = 1). however, tccr contributes better blocking probabilities than bs for all traffic capacities below that of k = 1. moreover, blocking probability of traffic class 0 in tccr process is very much better than in bs process. also blocking probabilities of traffic class 0 in bs are higher than the base experiment because they are treated with very low amount of network bandwidth sharing. considering no class isolation with the three contention resolution schemes, it can enhance overall network performance. form the experiences in table 2, the combination of 22b, 3d and, 4b is selected as they are the best solutions for this case. however, it cannot make overall network blocking sla of class 1 within the limits. the repeated simulations with larger size of ba threshold and longer extra offset time are conducted to reduce blocking probability in this case but they introduce more delay beyond our delay limitation. although traffic class 0 58 e. dhavarudha, c. charoenlarpnopparut, s. runggeratigul in tccr process has little higher blocking probability than that of no class isolation deployed with the three contention resolution schemes, its blocking probability is acceptable. in contrast, blocking probability of traffic class 0 in bs process is quite high compared to no class isolation. figure 5: average network delays of tccr, bs, and no class for scenario 1 in addition to the discussion of blocking performances, considering the delay aspect refering to fig.5, traffic class 1 of both tccr process and no class isolation with contention resolution experience highest average network delays but they are still under their delay sla requirement. all extra delays are produced by the three contention resolution schemes. however, all traffic class 0 and class 1 in bs process including traffic class 0 of tccr undertake very low delays because they are not deployed contention resolution scheme. fig.6 and fig.7 illustrate the experiments of traffic scenario 2 based on the same experiments as scenario 1. in this scenario tccr requires 21b, 3b, and 3d to give class1 satisfied blocking sla. for bs, traffic class 1 requires 78.18 percent for the given class 1 blocking sla which reduces offered bandwidth of traffic class 0 to 21.82 percent. in addition, the implementation of 22b, 4b, and 3d in no class isolation process is not able to give class 1 traffic with its satisfied blocking sla. also, traffic class 1 of tccr process for scenario 2 has slightly higher blocking probability than in scenario 1 because of more traffic intensity but the results emphasize the same tendency conclusions as in scenario 1. traffic control based on contention resolution in optical burst 59 figure 6: average network blocking probabilities of tccr, bs, and no class for scenario 2 figure 7: average network delays of tccr, bs, and no class for scenario 2 60 e. dhavarudha, c. charoenlarpnopparut, s. runggeratigul 4 conclusions this study proposes tccr process which is a class isolation and service differentiation mechanism utilizing the three contention resolution schemes as ba, fdl, and extra offset time to control the performance of the high class traffic. this process can control the performance of this class very well by offering its satisfied sla in both senses of network blocking probability and average network delay. in contrast, the illustrations of no class isolation deploying the three contention resolution schemes show they cannot offer the satisfied blocking sla for the traffic in high class. in addition, bs process can give the traffic in high class satisfied its blocking sla but this scheme results in very high blocking probability of the low class traffic. in summary, our obs models require tccr process to isolate the two classes and also differentiate their treatment according to their qos. tccr process can enhance the high class performance without dropping the low class packets; therefore, it yields indirect improvement of the low class traffic blocking probability which is also very well carried within its satisfied sla. tccr process can perform as an efficient qos mechanism based on contention resolution and it can also give proper class isolation and service differentiation to both classes. bibliography [1] yoo, m. et al (2000); qos performance of optical burst switching in ip over wdm networks, ieee journal of selected areas in communications, issn 0733-8716, 18(10):2062-2071. 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[19] dawn networking research labs (2006), the network simulator ns-2 at september 15th 2006 http://www.isi.edu/nsnam/ns/. int j comput commun, issn 1841-9836 8(3):432-443, june, 2013. skyde: a skype-based steganographic method w. mazurczyk, m. karaś, k. szczypiorski wojciech mazurczyk, maciej karaś, krzysztof szczypiorski warsaw university of technology poland, 00-661 warsaw, plac politechniki 1 wmazurczyk@tele.pw.edu.pl, karas.maciek@gmail.com, ksz@tele.pw.edu.pl abstract: this paper introduces skyde (skype hide), a new steganographic method that utilizes skype encrypted packets with silence to provide the means for clandestine communication. it is possible to reuse packets that do not carry voice signals for steganographic purposes because skype does not use any silence suppression mechanism. the method’s proof-of-concept implementation and first experimental results are presented. they prove that the method is feasible and offers steganographic bandwidth as high as 2.8 kbps. keywords: information hiding, network steganography, skype. 1 introduction voice over ip (voip) or ip telephony is one of the services of the ip world that is changing the entire telecommunications landscape. it is a real-time service, which enables users to make phone calls through data networks that use an ip protocol. currently, one of the most popular of the ip telephony systems that have shaken the global telephony market in the last decade is skype (www. skype.com). it is a proprietary p2p telephony service originally introduced in 2003 by creators of the famous p2p file sharing system kazaa niklas zennström and janus friis. skype is owned by microsoft and it has been reported that it has about 663 million registered users (september 2011). in march 2012, it was reported to have had 35 million users online simultaneously [5]. it has also been estimated that in 2011, skype had acquired about 33% of the world’s international telephone market [18]. steganography encompasses various information hiding techniques, the aim of which is to embed a secret message into a carrier. network steganography, to perform hidden communication, utilizes network protocols and/or their relationships as the carrier for secret data. because of its popularity, ip telephony is becoming a natural target for network steganography [6]. steganographic methods are intended to hide the very existence of the communication and therefore, any third-party observers should remain unaware of the presence of the steganographic exchange. generally, every network steganographic method can be described by the following set of characteristics: its steganographic bandwidth, its undetectability, and robustness [1]. the term "steganographic bandwidth" refers to the amount of secret data that can be sent per unit time when using a particular method. undetectability is defined as the inability to detect a steganogram within a certain carrier. the most popular method by which to detect a steganogram is to analyze the statistical properties of the captured data and compare them with values typical for that carrier. the final characteristic is robustness, which is defined as the amount of alteration that a steganogram can withstand without its secret data being destroyed. a good steganographic method should be as robust and as difficult to detect as possible whilst offering the highest bandwidth. however, it must be noted that there is always a fundamental trade-off necessary among these three measures. additionally, it is also useful to measure the steganographic cost. this is a characteristic that belongs to the sphere of carrier fidelity and has a direct impact on undetectability. it describes copyright c⃝ 2006-2013 by ccc publications skyde: a skype-based steganographic method 433 the degradation or distortion of the carrier caused by the application of the steganographic method. for example, in the case of voip steganography methods, this cost can be expressed as a measure of the conversation quality degradation induced by applying a particular technique for hiding information. one of the most important aspects for every steganographic method is the selection of the most suitable carrier for the secret data. the most favorable carrier for secret messages must have two features: • it should be popular, i.e., usage of such a carrier should not be considered as an anomaly in itself. the more such carriers are present and utilized on the network the better, because they mask the existence of hidden communication. • modification of the carrier related to the embedding of the steganogram should not be "visible" to the third party, who is unaware of the steganographic procedure. thus, because of its popularity and traffic volume, skype traffic is an ideal candidate for a secret data carrier. it should also be emphasized that the purpose of establishing any information hiding exchange varies possible uses can fall into the category of legal actions (e.g., circumvention of web censorship and surveillance, computer forensics or copyright protection) or illicit activity (e.g., criminal communication, confidential data exfiltration or industrial espionage). this trade-off is typical in steganography and requires consideration in a broader steganography context, which is beyond the scope of this paper. one can always question why use steganography for skype, which as is commonly known, uses cryptographic means to provide confidentiality for every type of users’ message that is exchanged (text messages, voice signal, files). first of all, hidden communication need not necessarily be conducted in an end-to-end manner, i.e., covert data can be sent using third party voip calls. secondly, discussion has recently arisen on whether skype, whose calls were commonly believed very hard to wiretap, is providing lawful interception services to law enforcement agencies [7]. additionally, skype is proprietary and closed software and thus, ultimately cannot be trusted. this is why in this paper we discuss how skype can be utilized to provide means for clandestine communication. experiments performed on real skype traffic prove that the proposed method skyde (skype hide) is feasible and offers high steganographic bandwidth and reasonable steganographic cost in terms of undetectability. the rest of this paper is structured as follows. section 2 presents the current state of research efforts on voip steganography and on skype in particular. in section 3, skype basics are presented, as well as an analysis of its traffic and a detailed description of the proposed method and the prototype implementation. next, in section 4, experimental results for the method are presented. finally, the last section concludes our work and indicates directions for further research. 2 related work a number of steganographic approaches for voip have been proposed, which have been recently exhaustively surveyed by mazurczyk [1]. because of space limitations only steganographic methods related to skype and silence suppression mechanisms will be mentioned here. the only skype-dedicated method was introduced by wang et al. [4]. the authors proposed embedding a 24-bit watermark into the encrypted stream (e.g., skype call) to track its propagation through the network and thus, providing its de-anonymization. the watermark is inserted by modifying the inter-packet delay for selected packets in the voip stream. the authors demonstrated that depending on the watermark parameters chosen, they were able to achieve a 99% 434 w. mazurczyk, m. karaś, k. szczypiorski true positive and a 0% false positive rate, while maintaining good robustness and undetectability. however, they achieved steganographic bandwidth of only about 0.3 bit/s, which is enough for the described application, but rather low for performing clandestine communication. the deployment of speech codecs’ silence insertion description (sid) frames that are sent when voice is not present in the input signal, was proposed as a secret data carrier by mazurczyk and szczypiorski [2]. however, the authors noted that the steganographic bandwidth for this method is rather low, but that it is possible to influence the rate at which sid frames are issued. recently, a high-capacity steganography technique based on the utilization of the inactive frames of the g.723.1 speech codec was introduced by huang et al. [3]. the authors proved that the inactive frames of voip streams are more suitable for data embedding than the active ones and thus, a greater amount of hidden data can be embedded within them with the same imperceptibility. they then proposed a steganographic algorithm in different speech parameters of the inactive frames for the g.723.1 codec with a 6.3 kbits bitrate. experimental results show the solution is imperceptible and that a quite high steganographic bandwidth of up to 101 bits/frame is achieved. proposed in this paper, the skyde method operates by reusing encrypted packets with silence, by substituting this information with secret data. when compared with existing solutions, it is characterized by significantly higher steganographic bandwidth and good undetectability. 3 skype traffic analysis and proposed method description 3.1 skype basics skype is a hierarchical p2p network with a single centralized element (login server) that is formed by two types of nodes [11]: • ordinary nodes (ons) that can start and receive a call, send instantaneous messages, and transfer files, and • specialized nodes called super nodes (sns) that are responsible for helping ons find and connect to each other within the skype network the login server is responsible for the authentication of ons and sns before they access the skype network. skype offers two communication modes, namely:, end-to-end (e2e) and end-to-out (e2o). the first is possible between two skype clients within the ip network. the second occurs if one of the endpoints is a skype client within the ip network and the other is a pstn phone (skypein/skypeout services). in this paper, we focus solely on the skype e2e mode. skype is based on proprietary protocols and makes extensive use of cryptography, obfuscation, and anti-reverse-engineering procedures, and all information about its traffic characteristics, protocols, and behaviors comes from numerous measurement studies, e.g., [8] [9]. all traffic in skype is encrypted; neither signaling messages, nor the packets that carry voice data can be uncovered. typically, the preferred, first-choice transport protocol of skype is user datagram protocol (udp), which as the traffic analysis in [9] showed, is being used in about 70% of all calls. however, if skype is unable to connect using udp it falls back to tcp. in this paper, we focus solely on udp-based skype calls. for tcp-based transport, the entire skype message is encrypted. in cases of unreliable udp, at the beginning of each datagram’s payload, an unencrypted header is present that is called the start of message (som). it is unencrypted in order to be able to restore the sequence of packets that was originally transmitted, to detect a loss, and to quickly distinguish the type of data that is carried inside the message. som consists of the following two fields [8] (fig. 1): skyde: a skype-based steganographic method 435 • id (2 bytes) that is used to uniquely identify the message; it is randomly selected by the sender query, and copied in the receiver reply. • fun (1 byte) that describes the payload type. for example, values: 0x02, 0x03, 0x07, and 0x0f are typically used to indicate signaling messages (used during the login phase or for connection management). 0x0d indicates a data message that can contain: encoded voice or video blocks, chat messages or fragments of files. figure 1: udp-based skype message format. skype estimates the available bandwidth and the packet loss probability, and it dynamically adapts to the detected network conditions by adjusting the codec’s bitrate or by introducing higher redundancy in packets [9]. typically, the resulting packet rate is about 16, 33, or 50 packet/s [11]. from the perspective of the steganographic method proposed in this paper, it is also important to emphasize that skype does not utilize any silence suppression mechanism, i.e., even if there is no voice activity during the conversation, the packets that carry the silence are still generated and sent. the lack of support for silence suppression is intentional it helps to obtain better voice quality and maintain udp bindings at the nat (network address translation) [10]. 3.2 skype traffic analysis for the purpose of the proposed steganographic method, outlined in detail in section 3.3, an analysis of skype traffic was carried out to prove that the method is feasible. an experimental test-bed was set up (fig. 2) that included two skype clients (skype for linux v. 2.2.0.35 and for windows v. 6.0.60.126) and a linux-based application designed and developed by authors that could intercept skype packets before they reached (for the transmitting side) and after they entered the (for the receiving side) network interface. two instances of this application were synchronized using ntp (network time protocol) and were responsible for the generation of reports regarding the skype packets’ statistics. figure 2: experimental skype test-bed. 436 w. mazurczyk, m. karaś, k. szczypiorski then, reports from both sides: transmitting and receiving, were analyzed and joined in an analyzer. this helps to observe the same skype encrypted stream in two network localizations. using this test-bed, a number of measurements were carried out on skype traffic. in each experiment, the number of analyzed skype packets was larger than 100 000 and was repeated four times. here, only the averaged results are presented. skype packets’ size distribution in 2008, chang et al. [10] observed that in skype traffic, speech activity is highly correlated to packet size, as more information is encoded into a voice packet while a user is speaking. experimental results revealed that the packet size and speech volume are highly correlated because they fluctuate in tandem. this feature of skype traffic is utilized in skyde to identify the packets that carry silence. that is why we wanted to verify whether the same behavior as in [10] can be observed for current skype software. fig. 3 presents the obtained experimental results. figure 3: the distribution of packets’ size during conversation and periods of silence. as can be observed, the previously reported high correlation between speech activity and packet size is still evident. this means that based on the size of each skype packet we can assess with high probability whether it carries voice or silence. as mentioned before, this effect will be also utilized by skyde. skype bytes distribution in packets’ payload to later prove skyde undetectability, the following characteristic of skype bytes distribution for typical skype calls was carried out (fig. 4). it can be observed that there are certain bytes values (in hex), i.e., 0x0d, 0x1d, 0x2d, 0x3d, 0x4d, 0x5d, 0x6d, and 0x7d that are more frequent than others, whereas the rest are almost equally distributed. the popular eight bytes are the typical values from the fun field for each udp-based skype packet (see sec. 3.1). the distribution of the rest of the byte values is almost equal because they represent the encrypted payload of skype packets. skyde: a skype-based steganographic method 437 figure 4: the bytes distribution in packets’ payload for typical skype calls. skype resistance to packet losses we also investigated skype’s maximum threshold of packet losses that could be sustained by a udp-based connection without falling back to tcp. it transpired that this value is about 70%. of course, when this level of losses is reached, the voice quality is degenerated to the point where no further conversation is possible. 3.3 proposed method and its implementation skyde is designed to utilize skype encrypted voice streams to enable clandestine communication. this secret data exchange can be realized between: (a) two skype users, i.e., they use their own call for steganographic purposes, or (b) a steganogram transmitter and receiver that utilize an existing, third party skype call (in this case the original caller and callee are not aware of the information hiding procedure). skyde utilizes encrypted skype voice packets as a hidden data carrier. by taking advantage of the high correlation between speech activity and packet size (described and verified in sec. 3.2.1), packets without voice signal can be identified and used to carry secret data (by replacing the encrypted silence with secret data bits). as is commonly known, typical voip calls contain 35% to 70% silent periods in each direction [12], [13]. skype does not utilize any silence suppression algorithm; thus, generally, all packets with silence can be utilized for steganographic purposes. however, skyde should have potentially low impact on voice quality because it does not affect packets with voice signals, which are more significant from the point of view of conversation. skyde transmitting side first, to create the secret data to be sent, as with the replaced (encrypted) data, they are encrypted prior sending. the cryptographic key utilized for encryption is a shared secret between skyde communication sides. then a crc-16 checksum is calculated on the payload and is inserted into the id field (16 bits) of som (see section 3.1). such an approach provides steganogram integrity verification and facilitates the identification of packets that carry secret data at the receiving end. 438 w. mazurczyk, m. karaś, k. szczypiorski then, those packets that contain silence must be identified. their size can change while the connection lasts (due to network conditions), so we propose a "sliding time window" algorithm that calculates the reference size of the packet with silence. it is also responsible for keeping the total packet losses at a safe level, because as mentioned earlier, steganographic utilization of each packet with silence will increase the overall packet loss for the call. thus, it is important not to exceed the measured threshold of about 70% total packet losses (see subsection 3.2.3) because then the connection will fall back to tcp. the proposed algorithm works as follows. first, the size of the "sliding time window" w (in seconds) is selected, in which the reference value r of skype packets with silence is continuously updated during the call. every second, the packet with the lowest size is determined and stored. when all of the values in the window are measured, then the average reference value is calculated based on the three packets with the lowest size. additionally, a certain deviation in packet size (∆) r ± ∆ is acceptable for consideration as a packet for skyde purposes. in the same time window w, it is also verified whether the total packet loss level reached 70% and if it does; the steganographic method ceases to utilize packets with silence until the losses are again at a safe level. after the packets with silence are identified, their payloads are replaced with encrypted secret data and they are sent to the receiving side. to provide reliability for the proposed method, an additional protocol in a hidden channel might be required. one solution is to use an approach proposed by hamdaqa and tahvildari [17] because it can be easily incorporated into skyde. it provides a reliability and fault-tolerance mechanism based on a modified (k, n) threshold of a lagrange interpolation and the results demonstrated in that paper prove that the complexity of steganalysis is increased. of course, the "cost" for the extra reliability would always be a loss of some fraction of the steganographic bandwidth. skyde receiving side at the receiver side, each packet with silence is recognized by the same means (packet size) as at the transmitter side. these packets are then copied to the buffer and for each of them, a crc-16 checksum is verified and the secret data is extracted. it is not important to erase or replace secret data embedded into packets because the skype client will treat these packets as losses. however, only the receiving end will be aware of the loss; thus, these modified skyde packets will not be discarded while traveling through the network. skyde was implemented in the same network environment as depicted in fig. 2, by enhancing the functionality of the application that was used to gather statistics about skype traffic (see section 3.2). based on the experimental results, it transpired that skyde performs best when w = 10 seconds and ∆ = 20 bytes. the selection of the most optimal values of w and ∆ will be not covered in this paper due to space limitations. 4 experimental results 4.1 experiments methodology for skyde evaluation, we prepared a .wav file that consists of male and female voices speaking english. the .wav file contains five minutes of imitated telephone conversation, i.e., sentences interleaved with periods of silence (similar to that designed in [13]) with a ratio of 55:45 (55% speech activity and 45% silence). in our experiments, we compiled the .wav file using audio recordings from the timit [14] continuous speech corpus one of the most widely used corpora skyde: a skype-based steganographic method 439 in the speech recognition community. in our analysis, we use the subjective measure mos (mean opinion score) [16] calculated with the perceptual evaluation of speech quality (pesq) method [15]. the .wav file was used as the source of the voice signal during the skype call and was saved at the receiving end. then, the original and degraded file were compared through the use of pesq and the resultant mos-lqo (mos-listening quality, objective) was returned to assess the skyde impact on voice quality (steganographic cost). during the same experiments, the steganographic bandwidth and bytes distribution in the packets’ payload (as in fig. 4) were measured. each experiment was repeated three times and averaged values are presented. it is worth noting that during the experiments none of the calls was disconnected or reverted to tcp, which proves the correct design of the proposed method. 4.2 steganographic bandwidth and cost using the methodology presented in the previous subsection, the results of steganographic bandwidth and cost were obtained. steganographic bandwidth was calculated by analyzing and extracting the secret data from the skype packets with silence that were utilized by skyde. also, for these connections, the corresponding voice quality mos-llq scores were determined. first, the reference mos-llq value without skyde applied was obtained. it transpired that the resulting quality is about 3.94, which is considered to be a good quality. the steganographic cost is then determined as a difference between the reference mos-llq without steganography and that with skyde applied. the steganographic utilization of packets with silence was increased in a range from 0% to 100% and the obtained results regarding voice quality are presented in fig. 5. figure 5: voice quality experimental results. when up to 20% of the packets with silence are utilized for steganographic purposes, the resulting voice quality is not degraded at all. for 30%, the disruption in voice quality is negligible. for higher utilization rates, the quality begins to deteriorate more quickly and after exceeding 50% it is considered as poor; however, conversation is still possible. the results of skyde steganographic bandwidth and cost are presented in table 1. with up to 30% utilization of skyde, the steganographic bandwidth continues to increase to finally 440 w. mazurczyk, m. karaś, k. szczypiorski reach about 1.8 kbps. this must be considered as a high steganographic bandwidth. for higher utilization rates, the steganographic bandwidth drops because of the decrease in packet rate this is skype’s reaction to the elevated level of overall packet losses. for 40% utilization rate, it drops almost a half (from about 46 to 24 packet/s) and continues to decrease until it reaches levels of 17 packet/s. simultaneously, the size of the packets increases even by 36% (for 50% to 60% silence utilization rate). this explains the drop in resulting steganographic bandwidth to the level of 1.5 kbps and then the subsequent rise to the point of about 2.8 kbits for 100% utilization rate. packets with silence utilization [%] 0 20 30 40 50 60 80 100 steganographic bandwidth [kbps] n/a 1.37 1.83 1.52 1.5 1.81 2.47 2.78 steganographic cost n/a 0 0.14 0.71 1.21 1.3 1.49 1.54 packets with silence reference size [b] 37.86 35.95 33.98 35.23 48.98 48.42 46.10 43.82 packet rate [packet/s] 50.17 49.14 45.61 23.68 17.08 16.97 17.17 17.84 table 1: experimental results thus, the proper selection of the level of utilization of packets with silence is necessary and typically, it will be a trade-off between skype call quality, desired steganographic bandwidth, and undetectability. 4.3 undetectability user perspective detection of skyde, as well as steganographic bandwidth, depends on the hidden communication scenario in which it is utilized. skyde can be used between two skype clients that are aware of the steganographic procedure (secret data is sent in an end-to-end manner), or some intermediary nodes can rely on third party skype calls to exchange steganograms. in the first case, users can select the desired steganographic bandwidth because they do not necessarily expect high voice quality. additionally, they do not care about the elevated overall packet loss level; thus, they are able to transmit about 3 kbps. however, in the latter case, the degradation of voice quality or introduced losses cannot be excessive because it could make overt users suspicious. thus, this could potentially limit the maximum steganographic bandwidth that could be achieved. therefore, from the point of view of voice quality and packet losses, skyde would be most undetectable for 30% utilization of packets with silence rate (1.8 kbps of steganographic bandwidth). network perspective to prove that the proposed method is difficult to detect, we performed similar experiments with bytes distribution in packets’ payload (fig. 4). it was conducted for skyde utilizing 100% of packets with silence. the obtained results are presented in fig. 6. to determine to what extent the results are similar to those of fig. 4, we used correlation coefficients to quantity the strength of their relationship. it transpired that the correlation coefficient between the bytes distribution in packets’ payload for typical skype and skyde calls is 0.96, which indicates a very strong relationship. this makes the proposed steganographic method very difficult to detect. skyde: a skype-based steganographic method 441 however, if someone is monitoring skype traffic, e.g., the packets rate and their sizes, then for skyde, higher utilization rates could be more easily visible (of course, if the monitoring entity were able to distinguish between worsening network conditions and hidden communication). therefore, for this case also, utilization rates that mimic typical skype call parameters should be used, i.e., up to 30% utilization of packets with silence (almost no difference in packets rate and sizes from typical skype calls). figure 6: the bytes distribution in packets’ payload for skyde. it should be also noted that for poorer network conditions, the more non-steganographic losses would be introduced and the less steganographic information could be transferred due to the 70% total packet losses limit. it is also important to add that we also observed that the results could be different and fluctuate depending on the day of the week (working days or weekend), or even depending on the time of the day (office or non-office hours). these changes in skype traffic were also discovered in other studies, e.g., in [9]. this fact also works in the favor of skyde because it would be difficult to establish some baseline values needed for steganalysis purposes. therefore, this makes skyde more difficult to detect. 5 conclusions and future work in this paper we introduced skyde, a steganographic method that utilizes encrypted skype packets as a hidden data carrier. by taking advantage of the high correlation between speech activity and packet size in skype, we identify those without voice and use them to carry secret data. experimental results show that such an approach offers a high steganographic bandwidth of up to 1.8 kbps (for 30% utilization of packets with silence), whilst introducing almost no distortion to the skype call. moreover, we prove that in these circumstances, the method operates under terms of undetectability. it should be also noted that skyde can be also used for other ip telephony systems that encrypt their traffic and follows the same relationship between speech activity and packet size. chang et al. [10] point that this is also a case for ugs (unsolicited grant service) [19]. future work will include developing an improved skyde algorithm for the selection of packets 442 w. mazurczyk, m. karaś, k. szczypiorski with silence that can increase the steganographic bandwidth whilst keeping the voice distortion at a safe level. we will also conduct more in-depth studies by taking into account different parts of the week or day. we are also going to experiment with skyde resistance to network packet losses and their influence on the performance of the proposed steganographic method. moreover, we will pursue the effective steganalysis method for skyde. one promising research direction worth pursuing is the adoption of the method proposed by wright et al. in [20], which can be utilized for encrypted voice payload. the authors of this work discovered that the lengths of encrypted voice packets can be used to identify phrases spoken within a call. therefore, if extended, this approach can be applied to deduce the characteristics of the carried speech and silence periods to be able to detect packets with ’fake silence’. 6 acknowledgments this research was partially supported by the polish ministry of science and higher education and polish national science centre under grants: 0349/ip2/2011/71 and 2011/01/d/st7/05054. bibliography [1] mazurczyk, w. (2013); voip steganography and its detection a survey, acm computing surveys (accepted for publication), issn 0360-0300. [2] mazurczyk, w.; szczypiorski, k. (2008); steganography of voip streams, in proc. of 3rd int symp information security (is’08), monterrey, mexico, 1001-1018. [3] huang, y.; tang, s.; yuan, j. (2011); steganography in inactive frames of voip streams encoded by source codec, ieee transactions on information forensics and security, issn 1556-6013, 6(2):296-306. [4] wang, x., chen, s., jajodia, s. (2005); tracking anonymous peer-to-peer voip calls on the internet, in proc. of 12th acm ccs ’05, 81-91. [5] caukin, j. (2012); 35 million people concurrently online on skype, skype big blog, url: http://blogs.skype.com/en/2012/03/35_million_people_concurrently.html. [6] lubacz, j.; mazurczyk, w.; szczypiorski, k. (2010); vice over ip, ieee spectrum, issn 0018-9235, 40-45 [7] timberg, c.; nakashima, e. (2012); skype makes chats and user data more available to police, url: http://www.washingtonpost.com/business/economy/ skype-makes-chats-and-user-data-more-available-to-police/2012/07/25/ gjqaobi39w_story.html [8] bonfiglio, d.; mellia, m.; meo, m.; rossi, d.; tofanelli, p. (2007);revealing skype traffic: when randomness plays with you, in proc. of sigcomm ’07, ny, usa, 37-48. [9] bonfiglio, d.; mellia, m.; meo, m.; ritacca, n.; rossi, d. (2008); tracking down skype traffic, in proc. of ieee infocom ’08, phoenix, usa. [10] chang, y.c.; chen, k.t.; wu, c.c.; lei, c.-l. (2008); inferring speech activity from encrypted skype traffic, in proc. of ieee globecom ’08, los alamitos, usa, 1-5. skyde: a skype-based steganographic method 443 [11] molnar, s.; perenyi, m. (2011); on the identification and analysis of skype traffic, international journal of communication systems, issn 1074-5351, 24(1):94-117. [12] iwano, y., sugita, y., kasahara, y., nakazato, s., shirai, k. (1997); difference in visual information between face to face and telephone dialogues, in proc. of ieee icassp ’97, 1499-1502. [13] berger, j.; hellenbart, a.; weiss, b.; moller, s.; gustafsson, j.; heikkila, g. (2008); estimation of quality per call in modelled telephone conversations, in proc. of ieee icassp ’08, 4809-4812. [14] garofolo, j.s. et al. (1993); timit acoustic-phonetic continuous speech corpus linguistic data consortium, philadelphia, usa. [15] itu-t (2001); recommendation. p.862. perceptual evaluation of speech quality (pesq): an objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs. [16] itu-t (1996); recommendation. p.800. methods for subjective determination of transmission quality. [17] hamdaqa, m.; tahvildari, l. (2011); relack: a reliable voip steganography approach, in proc. of 5th international conference on secure software integration and reliability improvement (ssiri ’11), korea, 189-197. [18] telegeography report (2012); international call traffic growth slows as skype’s volumes soar, url: http://www.telegeography.com/press/press-releases/2012/01/09/ international-call-traffic-growth-slows-as-skypes-volumes-soar/index.html [19] ieee (2004); i. s. 802.16-2004, ieee standard for local and metropolitan area networks part 16: air interface for fixed broadband wireless access systems. [20] wright, c.; ballard, l.; coulls, s.; monrose, f.; masson, g. (2008); spot me if you can: recovering spoken phrases in encrypted voip conversations. in proc. of ieee symposium on security and privacy, may, 2008 international journal of computers communications & control issn 1841-9836, 13(5), 853-864, october 2018. comparative analysis of various transformation techniques for voiceless consonants modeling g. korvel, b. kostek, o. kurasova grazina korvel*, olga kurasova institute of data science and digital technologies, vilnius university akademijos str. 4, lt-04812, vilnius, lithuania *corresponding author: grazina.korvel@mii.vu.lt olga.kurasova@mii.vu.lt bozena kostek audio acoustics laboratory, faculty of electronics, telecommunications and informatics, gdansk university of technology g. narutowicza 11/12, 80-233 gdansk, poland bokostek@audioacoustics.org abstract: in this paper, a comparison of various transformation techniques, namely discrete fourier transform (dft), discrete cosine transform (dct) and discrete walsh hadamard transform (dwht) are performed in the context of their application to voiceless consonant modeling. speech features based on these transformation techniques are extracted. these features are mean and derivative values of cepstrum coe�cients, derived from each transformation. feature extraction is performed on the speech signal divided into short-time segments. the knn and naive bayes methods are used for phoneme classi�cation. we consider both class�cation accuracies and computational time. experiments show that dft and dct give better classi�cation accuracy than dwht. the result of dft was not signi�cantly di�erent from dct, but it was for dwht. the same tendency was revealed for dct. it was checked with the usage of the anova test that the di�erence between results obtained by dct and dwht is signi�cant. keywords: discrete fourier transform (dft), discrete cosine transform (dct), discrete walsh hadamard transform (dwht), cepstrum coe�cients. 1 introduction the state-of-the-art methods applied to speech technology are mostly based on the extraction of features and machine learning. a wide range of speech signal features was conceived and used for classi�cation tasks [19], speech recognition [9], emotional speech recognition [17, 28], phoneme modeling [10], and speech analytics tasks [2]. there are also other approaches employed for processing speech signals, where feature extraction process is discarded. for example, the use of fuzzy logic [29, 30] applied to speech technology, speci�cally to voice activity detection (vad), speech segmentation, and coding, cannot be disregarded in this aspect. moreover, very intense activities connected to the usage of resources for large-scale deep learning analysis applied to speech recognition or emotional speech recognition may be observed in the last few years [15,24,32]. however, when we are talking about speech analytics and modeling, speech synthesis or audio-visual speech recognition, the progress in these �elds is below expectations. secondly, this research area requires a di�erent approach, a thorough analysis of individual spoken elements needs to be performed as there is basic knowledge still missing in this context. the phoneme mathematical models, utilized as tools for describing speech, are of great importance not only for speech synthesis. the need for research on phoneme models of speech is justi�ed by its numerous possible uses. the following can be named: speech recognition, helping copyright ©2018 cc by-nc 854 g. korvel, b. kostek, o. kurasova with pronunciation and learning foreign languages (a comparison of phoneme utterances and its model enabling to demonstrate di�erences of pronunciation), studies in linguistics, medical �eld (e.g. disturbances in speech present in stroke and neurodegenerative diseases, disorder in one or more prosodic functions, de�cits in speech production, etc.). in some of the envisioned applications the obtained results can be a part of a larger multimodal human-computer interaction system consisting of three modalities: vocal, facial and gesture based recognition. the object of this research is the consonant phoneme signals which are more di�cult for analysis, modeling and classi�cation tasks as those of vowels and semivowels. the character of the consonant signals is consonant-dependent and varying. stop consonants can be considered as quasiperiodic signals in noise, while fricative consonants as aperiodic signals. we can also divide those phonemes into two sets: voiced and voiceless sounds [6]. this means that the vocal folds are apart while saying these sounds. in speech processing, sounds can be represented as a source-�lter model [22]. the �lter represents the vocal tract, which is excited with a source. a source is a pulse sequence for the voiced sounds and noise for the unvoiced sounds. a commonly used technique for separating source and �lter in a speech signal is cepstral analysis [14]. the cepstrum is widely used in speech processing [8,16]. in all the areas mentioned above automatic feedback for systems and applications is also of importance, thus in a given methodology both feature extraction and machine learning should be applied. to create a mathematical model of a phoneme, it is important to �nd a suitable parametric description of speech. the speech signal is converted to the appropriate space domain and preprocessing is carried out. the two main domains of analysis are time and frequency. the �rst of them shows the time varying character of the signal, the second mirrors how the energy of the signal is contained within the frequency range. in the frequency domain, parameters are often based on the fourier spectrum. in this paper, we perform a comparative experiment based on dft (discrete fourier transform), dct (discrete cosine transform) and dwht (discrete walsh hadamard transform). the results returned by this study should enable us to verify which transformation method along with feature extraction work better when such a methodology is applied to check phoneme modeling precision. the relationship between the performance of transformation techniques in the context of feature vectors derivation has been investigated by many researchers, in various speech classi�cation tasks. in the paper of velican et al. [33], a comparison of dwht and dct as feature selection tools in the case of identifying rhotacism is performed. the experiment result shows that classi�cation rate in the case of dwht is better than the rate obtained with dct. in the paper of kekre and kulkarni [7] a comparison of the performance dct and dwht for various feature vector sizes with and without overlap based on speech utterances is given for speaker identi�cation. the results show that dct performs better than dwht. the comparison of two fundamentally di�erent approaches the fast fourier transform (fft) and hilbert-huang transform (hht) is given in paper of donnelly [4]. the behavior and �exibility of these two transforms are examined for a number of di�erent time domain signal types. the targeted consonant phonemes are also more susceptible to noise than vowels mainly due to their lower intensity. this means that in many conditions they may easily be masked by signals interfering with speech. that is why it is important to �nd optimized feature vectors that will perform in both quiet and noise conditions. this paper deals with a domain-dependent analysis and classi�cation of consonant phonemes utilizing the cepstrum analysis. the feature vectors consist of cepstrum coe�cients derived from the fourier, cosine and walsh hadamard transforms. comparative analysis of various transformation techniques for voiceless consonants modeling 855 2 transformation techniques let x(k) is a signal with length k, where k is an integer power of 2 (k = 1, . . . ,k). the frequency domain representation shows how the energy of this signal is contained within the frequency range. the techniques of signal transformation from time to the frequency domain are given in this section. 2.1 discrete fourier transform fourier analysis is based on decomposing signals into sinusoids [26]. dft is a family member of this analysis used with digital signals. the transform decomposes the signal x(k) into the sequence of complex numbers y(1), ...,y(k) according to the formula: y(k) = k∑ n=1 x(n)e (−2πj) k (n−1)(k−1) (1) where the symbol j denotes the imaginary unit. to convert signal data from the frequency to the time domain the inverse discrete fourier transform (idft) is applied. the idft is de�ned as follows: x(k) = 1 k k∑ n=1 y(n)e 2πj k (n−1)(k−1) (2) the result of idft will be used in the construction of the signal cepstrum. 2.2 discrete walsh-hadamard transform dwht is a non-sinusoidal technique that represents a signal as a set of orthogonal rectangular waveforms. the transform is given by the formula: y(k) = k∑ n=1 x(n)wk(k,n) (3) the basis function is described as follows: wk(k,n) = l−1∏ l=1 (−1)nlkm−1−l (4) where l = log2 k, nl is the l th bit in the binary representation of n [27]. as we see from eq. (4), dwht takes the binary value 1 or -1. the inverse discrete walsh hadamard transform (idwht) is de�ned as follows: y(k) = 1 k k∑ n=1 x(n)wk(k,n) (5) the only di�erence between dwht and idwht (see eq. (3) and eq. (5)) is a constant divisor. 856 g. korvel, b. kostek, o. kurasova 2.3 discrete cosine transform dct decomposes a signal into cosine functions. the transformation has several standard variants. these variants and the mathematical properties of dct are presented in works of rao and yip, oppenheim et al. [18,23]. in this paper, the discrete cosine transform of the signal x(k) is computed according to the formula: y(k) = √ 2 k β(k) k∑ n=1 x(n)cos( π(2n− 1)(k − 1) 2k ) (6) where coe�cient β(k) is de�ned as follows: β(k) = { 1√ 2 , if k = 1 1, if k 6= 1 (7) the formula of the inverse discrete cosine transform (idct) is given below: x(k) = √ 2 k k∑ n=1 β(k)y(n)cos( π(n− 1)(2k − 1) 2k ) (8) dct and other transformation techniques analyzed in this section are orthogonal transforms. therefore, they can be computed using the fast algorithms. 3 feature extraction all the n samples of the analyzed phoneme are collected into a vector: x = [ x(1), x(2), . . . , x(n) ] (9) the phoneme signal is divided into short-time frames, the length of which is m samples. a process of dividing a signal into frames is typical for the speech signal analysis. to each of these frames, a window function w(n) is used. due to the window procedure, a part of the signal data is lost. therefore, an overlap of segments is utilized. how much should the segments overlap can be seen in [5]. denote by l the number of the overlapped samples. then the number of intervals can be obtained by the following formula: p = [ n −m m −l ] + 1 (10) where [α] stands for an integer part of the real number. then the phoneme signal can be written as the following matrix: x =   w(1) ×x(1) . . . w(m) ×x(m) w(1) ×x(m −l + 1) . . . w(m) ×x(2m −l) . . . . . . . . . w(1) ×x ( (p − 1) × (m −l + 1) ) . . . w(m) ×x ( (p − 1) × (2m −l) )   (11) the calculation procedure of the cepstrum coe�cients constitutes a part of the algorithm prepared. the consecutive steps of the algorithm are listed below: step 1. the selected transform is applied to each row of the matrix x. comparative analysis of various transformation techniques for voiceless consonants modeling 857 step 2. the absolute values are taken out. step 3. the logarithm is calculated. step 4. the inverse transform is applied. consequently, we obtain the matrix of the cepstrum coe�cients: c =   c11 . . . c1m c21 . . . c2m . . . . . . . . . ck1 . . . ckm   (12) the mean values of the columns of the matrix c are calculated. all the obtained values are collected into a vector c: c = [ c(1), c(2), . . . , c(m) ] (13) the mean cepstrum values given in eq. (13) are used as representative features. in order to determine whether a function is increasing or decreasing, additionally the �rstorder delta derivatives are calculated. the �rst-order dynamic coe�cients are calculated from the static cepstrum coe�cients using the following regression formula: dm = ∑n n=1 n(cex(m + n) − cex(m−n)) 2 ∑n n=1 n 2 (14) where cex = [ 0, . . . , 0,︸ ︷︷ ︸ m , c(1), . . . , c(m), 0, . . . , 0︸ ︷︷ ︸ m ] (15) m = 1...m, n is the regression window size. 4 classi�cation methods a vast literature on the application of machine learning to the classi�cation task exist. researchers developed many approaches to the problem of classi�cation, including methods for inducing rule sets, models in the form of a tree structure, linear discriminants, statistical learning algorithms, and arti�cial neural networks [24]. in an experiment, we use two classical machine learning algorithms to compare classi�cation rates. first of them is the naive bayes classi�cation method, based on bayes theory [11]. this algorithm is widely used because it often outperforms more sophisticated classi�cation methods. it falls into the statistical learning algorithms and provides the probability of each attribute set belonging to a given class. the second classi�cation algorithm used in this experiment is k-nearest neighbors (knn), based on euclidean distances between the elements of the test dataset and elements of the training dataset [13]. the number of nearest neighbors is set by performing preliminary tests. 5 experimental results the experiment was performed on lithuanian speech recordings, created during the project liepa (services controlled by the lithuanian speech) [20]. the database consists of 100 hours of words, phrases and sentences recordings, di�erent speakers, both male and female voices and is adapted for scienti�c research. in the present experiment, we consider the extracted consonant phonemes from this database for our analysis. the phonemes are the following: /t/, /k/, /s/, /s/. the �rst two (/t/ and /k/) are called stop consonants because the air in the vocal tract is stopped at some period. an example of the phoneme /t/ signal is given in figure 1. 858 g. korvel, b. kostek, o. kurasova figure 1: the plot of the consonant phoneme /t/ the next two phonemes (/s/ and /s/) are called fricative consonants, which are produced when the air is squeezed out through a small hole in the mouth. an example of a phoneme /s/ signal is given in figure 2. the audio data used in the analysis are wav �les with the following figure 2: the plot of the consonant phoneme /s/ parameters: sampling frequency: 22 khz, quanti�cation: 16 bits, the number of channels is 1. the feature extraction procedure proposed in section 3 involves several steps. first of all, the signal pre-processing is carrying out. then the signal is converted to the appropriate space domain and the extraction of features is performed. in this experiment, signal pre-processing is performed using the following parameters: the input signal is divided into frames of 512 samples, and then for each frame, the hamming window is chosen. the overlap of 50% is used. therefore, the number of cepstrum coe�cients is equal to the number of coe�cients of transformation (i.e. 512). an observation reveals that only part of them is useful for separation of the consonant classes. that is why only the �rst 12 coe�cients were selected as representative features. in figure 3, dft cepstrum is shown. it can be seen from figure 3 that the cepstrum coe�cients present di�erences between consonant classes, this is especially visible in the case of the �rst four coe�cients. it was checked in further analyses that cepstra of both dwht and dct followed the same trend. the plots of these cepstra are shown in figures 4 (dwht-based) and 5 (dct-based). additional 12 features are derived from computing the �rst order derivatives. in the experiment, 480 utterances (120 for each phoneme) were considered. these phonemes were cut out of the recordings of 15 speakers (9 females and 7 males). we extracted parameters for all these phonemes. the data are divided into two segments: one employed to teach a model and another one utilized to test this model. the test set for models is constructed of 10% randomly selected phonemes. due to the fact that the set of samples is not very big, and it is important to estimate the true error rate of a given classi�er, an experiment was repeated 50 times for each case and the comparative analysis of various transformation techniques for voiceless consonants modeling 859 figure 3: the cepstrum coe�cients obtained by dft (the solid line�consonant phoneme /t/, the dotted line�consonant phoneme /s/) figure 4: the cepstrum coe�cients obtained by dwht (the solid line�consonant phoneme /t/, the dotted line�consonant phoneme /s/) arithmetic mean was calculated. a comparison of the performance of two selected classi�cation methods averaged for all speakers, males and females separately is given in table 1. in order to determine whether the di�erences between the means of the three parametrization techniques are statistically signi�cant, the one-way analysis of variance (anova) test is used. the test signi�cance level α equals to 0.05. we state a null hypothesis (h0) that in each case both samples are from populations with the same means. the decision rule to reject this hypothesis is as follows: reject h0 if f > fcritical(1 −α) (16) where f is the test statistic calculated as the ratio of the di�erence between the means over a distribution of their data points, and fcritical is the critical value taken from the f distribution table [3]. the results of anova test are given in table 2. the obtained f values (see table 2) are compared with the critical value for f distribution. figure 5: the cepstrum coe�cients obtained by dct (the solid line�consonant phoneme /t/, the dotted line�consonant phoneme /s/) 860 g. korvel, b. kostek, o. kurasova table 1: the classi�cation accuracy [%] for 4 consonant classes k-nearest neighbors naive bayes dwht dft dct dwht dft dct all mean 82.67 85.13 86.04 87.00 90.50 89.42 std.dev. 5.97 4.89 4.44 4.16 4.00 4.41 male mean 86.71 90.71 90.86 96.29 98.14 98.29 std.dev. 11.55 7.53 8.29 5.25 3.77 3.40 female mean 76.94 79.56 81.88 84.38 87.06 87.81 std.dev. 6.05 6.35 6.19 4.77 4.93 4.86 table 2: the result of anova test for knn and naive bayes k-nearest neighbors naive bayes dwht/ dft dwht/ dct dft/ dct dwht/ dft dwht/ dct dft/ dct all f-value 5.07 10.28 0.963 18.42 7.95 1.66 p-value 0.026581 0.001815 0.328837 0.000042 0.005826 0.201148 male f-value 4.21 4.25 0.008 4.13 5.11 0.039 p-value 0.042865 0.041988 0.928349 0.044831 0.026022 0.842655 female f-value 4.48 16.28 3.40 7.68 12.75 0.59 p-value 0.036904 0.000109 0.068158 0.006691 0.000553 0.445528 in the experiments performed, the obtained f is signi�cant at a given level if it is equal to or greater than 4.03 (fcritical = 4.03). according to these assumptions, the di�erences between dwht and dft as well as di�erences between dwht and dct are statistically signi�cant. meanwhile, the di�erences between dft and dct are not statistically signi�cant. the experiments were performed using matlab on a laptop with intelr coretm i5-6200u 2.20 ghz cpu and 8 gb of ram. the computation time is given in table 3. from this table we see, that the computational time of knn is much smaller than naive bayes. we also compare the results obtained on lithuanian consonants with the classi�cation effectiveness collected from the literature for other languages (see table 4). obviously, such a comparison cannot be performed straightforward as the studies recalled here concern di�erent languages and also a variety of features and classi�cation methods as well as they are researched for di�erent purposes (e.g. speech recognition, clean and telephone speech di�erentiation, speech productionâ models and mechanisms, pathology disorder, etc.). thus data contained in table 4 may serve only to a limited extent when comparing algorithmic performances. though di�erent classi�cation methods are employed in the studies recalled, we see that our results are consistent with the results of other researchers, however they are dependent more on the vector feature content than on the type of a classi�er. table 3: computational time [s] for the classi�ers k-nearest neighbors naive bayes dwht dft dct dwht dft dct all 0.1464 0.1456 0.1457 2.5672 2.5742 2.5599 male 0.0270 0.0270 0.0270 1.8016 1.7982 1.8021 female 0.0908 0.0902 0.0899 2.2227 2.1915 2.1928 comparative analysis of various transformation techniques for voiceless consonants modeling 861 table 4: consonants classi�cation performance in literature reference dataset parameters classi�cation technique overall classi�cation accuracy thasleema and narayanan, 2018 [31] malayalam (india) (unaspirated, aspirated, nasal, approximants, fricatives) normalized wavelet hybrid feature (nwhf) vector based on wavelet transform k-nearest neighbors (knn), arti�cial neural network (ann), support vector machine (svm) from 34.2% to 60.2% for knn, from 45.9% to 63.7% for ann and 55.4% to 79.9% for svm (depending on the mother wavelet) korveland kostek, 2017 [9] modality database (englishstop consonants) descriptors coming from music information retrieval k-nearest neighbors (knn) 73% lee and choi, 2012 [12] timit database (american english) mel-frequency cepstral coe�cients (mfccs), �rst and second derivatives plus acoustic parameters such as band-limited rms energy, amplitude of the �rst harmonic and peak normalized cross correlation values (pncc) gaussian mixture models (gmms) depending on the type of consonants, i.e.: stops, fricatives, and, a�ricates classi�cation accuracies are as follows: 82.2%, 80.6%, and 78.4% respectively ali et al., 2001 [1] american english stop consonants the acousticphonetic characteristics the authors proposed classi�cation system combining the voicing detection and the place of articulation detection 86% pruthi and espywilson, 2003 [21] timit database (nasals and semivowels) the acoustic parameters which include f1 measure, a pick onset/o�set measure, an energy ratio, and a formant density measure support vector machine (svm) based classi�ers accuracies of 88.6%, 94.9% and 85.0% were obtained for prevocalic, postvocalic and intervocalic sonorant consonants, respectively 862 g. korvel, b. kostek, o. kurasova 6 conclusions in the paper, we have compared the performance of dft, dwht and dct for voiceless consonant (/t/, /k/, /s/, /s/) classi�cation. in order to evaluate the classi�cation accuracy, two methods, namely knn and naive bayes were used. the analyses were performed for the whole group of speakers, and for male and female speakers separately. the highest classi�cation accuracy for all speakers (86.04%) has been achieved for features based on dct technique, in the case of knn method. while for naive bayes classi�er, the highest accuracy for all speakers was equal to 90.50% for dft. in the cases of the analysis of male and female recordings separately, the highest accuracies have been achieved for features based on dct technique for both classi�ers. these accuracies are as follows: for knn classi�er the highest accuracy for male group was equal to 90.86%, for female group � 98.29%, while for naive bayes classi�er the highest accuracy for male group was equal to 81.88%, for female group � 87.81%. the employment of one-way analysis of variance (anova) test for results of both selected classi�cation methods revealed the same tendency for di�erent groups of speakers and di�erent classi�ers: the di�erence between results, obtained by dft and dct is not signi�cant, meanwhile di�erence between results, obtained by dwht and the other two transformations (dft and dct) is signi�cant. a comparison of the results obtained on lithuanian consonants with other results in the literature was also performed. a literature review shows, that our results are consistent with those of other researchers. it is important to mention that our primary intention was not to obtain high classi�cation accuracy, but the goal was to determine which transformation method returns better results when applying a given feature vector and a regular machine learning algorithm. this is important in the context of the feedback needed on phoneme modeling precision to verify the model consistency with the initial phoneme target. as seen from observations the created feature vector is not complete as the accuracy obtained is not fully satisfying. therefore, in the future research, we will investigate the possibility of extending the created feature vector with additional signal descriptors applicable to short-segmented speech units. in addition, more e�ective classi�cation algorithms based on the weighted features are to be considered. finally, some additional tests should be executed on the same feature vectors but 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[33] velican, v.; strungaru, r.; grigore, o. (2012); automatic recognition of improperly pronounced initial 'r' consonant in romanian, advances in electrical and computer engineering, 12 (3), 79-84, 2012. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 3, month: june, year: 2022 article number: 4558, https://doi.org/10.15837/ijccc.2022.4558 ccc publications a personalized mhealth monitoring system for children and adolescents with t1 diabetes by utilizing iot sensors and assessing physical activities n. zholdas, m. mansurova, o. postolache, m. kalimoldayev, t. sarsembayeva nurassyl zholdas, madina mansurova*, talshyn sarsembayeva al-farabi kazakh national university 71 al-farabi ave., almaty, kazakhstan *corresponding author: madina.mansurova@kaznu.kz e-mail: zholdas.nurassyl@kaznu.kz, sagdatbek.talshyn1@kaznu.kz octavian postolache iscte-instituto universitário de lisboa, av. das forças armadas, 1649-026 lisboa, portugal e-mail: octavian.adrian.postolache@iscte-iul.pt maksat kalimoldayev institute of information and computational technologies, 28 shevchenko str., almaty, kazakhstan e-mail: mnk@ipic.kz abstract the problem of diabetes mellitus is becoming alarming due to the increase in morbidity among children. patients are undergoing vital insulin replacement therapy, the dose depends on the level of glucose in the blood. the glucose level prediction program, taking into account the impact of physical activity on the body, the use of mobile health capabilities will allow us to develop personalized tactics for a child patient and minimize the risks of a critical health condition. the target group of this study are children and adolescents with type 1 diabetes. this study provides an iot based mhealth monitoring system, including sensors, medical bracelets, mobile devices with applications. the mobile healthcare application for personalized monitoring can implement the functions of more effectively targeting young users to support their own health and improve the quality of life. in addition to monitoring blood glucose levels, the effect of physical activity on the condition of patients is also taken into account. the use of the proposed method for calculating the probable change in the patient’s blood glucose level after the end of physical activity will allow the doctor to make individual recommendations for the diet before the start of physical activity and its intensity. keywords: mhealth, monitoring system, iot sensors, children and adolescents with t1 diabetes, glucose level. https://doi.org/10.15837/ijccc.2022.4558 2 1 introduction the problem of diabetes mellitus is becoming alarming due to the increase in morbidity among children. patients are undergoing vital insulin replacement therapy, the dose depends on the level of glucose in the blood. the condition of patients depends, along with the injected insulin, on a number of factors affecting sugar levels: nutrition, physical activity, hormonal background, individual metabolic characteristics, psychosomatic factors. the strategy of the complex of therapeutic and preventive measures is aimed at achieving stable target glucose levels. the glucose level prediction program, taking into account the impact of physical activity on the body, the use of mobile health capabilities will allow us to develop personalized tactics for a child patient and minimize the risks of a critical health condition. the target group of this study are children and adolescents with type 1 diabetes. in recent years, there has been an explosive growth in the number of mobile applications [27], including mobile healthcare applications [5]. however, it is often difficult for patients to find suitable applications that meet their needs and preferences [20]. according to the authors’ review, developers of mobile health applications need to fully take into account the needs of users and work closely with medical staff when developing features for patients. the key function in mobile applications designed for patients with type 1 diabetes, according to experts, should be patient self-control. since type 1 diabetes is an insulin-dependent disease, it is necessary to pay close attention to glycemic control. since the responsibility for the child’s health is borne by the parents, in the mobile application presented in this paper, in addition to the function of self-monitoring of the patient, there is also a function of remote monitoring by the parents. clinical research methods included assessment of the patient’s health status based on clinical and laboratory characteristics of the disease (compensation level, insulin dose, glucose levels), physical activity, risk assessment of complications of the disease. clinical examination of patients and data collection were carried out in the medical center "institute of endocrinology" with the participation of endocrinologists. an iot system has been developed for monitoring patients, including sensors, medical bracelets, mobile devices with applications. there are not very many mobile applications aimed at changing behavior to improve self-control and stimulate physical activity in patients with diabetes mellitus [14]. thus, the mobile healthcare application for personalized monitoring can realize the functions of more effectively targeting young users to support their own health and improve the quality of life [9]. the application developed by the authors together with pediatric endocrinologists pursues exactly these goals. at the same time, in addition to monitoring blood glucose levels, the effect of physical activity on the condition of patients is also taken into account. this study provides several contributions: • the analysis of mhealth interventions for children and adolescents with t1 diabetes was carried out; • iot based mhealth monitoring system architecture is proposed; • the relationship between physical activity and the level of glucose in the patient’s blood was investigated; • recommendations on physical activity have been developed taking into account the characteristics of an individual patient; • developed mobile app for children and adolescents with t1 diabetes self-management. the remaining organization of this paper is as follows. in section 2, we present the related work in glucose monitoring by utilizing iot sensors. section 3 contains the system design with architecture and its implementation. in section 4, the results of the study and a discussion are presented. in section 5, we discuss several limitations and remaining challenges, and finally, concluding remarks are offered. https://doi.org/10.15837/ijccc.2022.4558 3 2 related work 2.1 glucose monitoring by utilizing iot sensors although diabetes is incurable, monitoring blood glucose levels in combination with appropriate medications can improve the effectiveness of treatment, alleviate symptoms and reduce complications [1, 25]. it is important to monitor glucose levels in diabetic patients, as this can provide better control over their condition [3], and take appropriate measures for hyperglycemia and hypoglycemia. monitoring will also reveal the effects of medications, exercise, diet and use the data obtained to optimize the treatment strategy of patients [21]. the use of iot devices for glucose monitoring opens up wider opportunities for providing quick warnings and decision-making [8]. there are many solutions for monitoring blood glucose using iot sensors [24]. the iot platform offers a workable solution for diabetic retinopathy, which allows saving the patient from vision loss based on sensor readings [18]. in the article [15] a blood glucose monitoring system based on the body’s wireless lan is proposed to detect diabetes. the system is built using a glucometer sensor, an arduino uno and a zigbee module. [7] explores the design and implementation of a system that improves commercial cgms by adding internet of things (iot) capabilities to them that allow remote monitoring of patients. in order to motivate users to add new data to the system, a reward system based on the digital cryptocurrency glucocoin was developed. such a system uses a blockchain that can execute smart contracts to automate purchases of cgm sensors or to reward users who contribute to the system by providing their own data. thus, the range of devices and technologies for continuous health monitoring is constantly expanding. this paper presents the architecture and describes the functionality of a system based on the internet of things, which allows continuous monitoring of blood glucose in conjunction with the collection of bracelet data. 2.2 mhealth for children and adolescents with t1 diabetes the main advantages of mobile healthcare include continuous real-time monitoring with high availability, cheapness, convenience and security of technologies. widely used mobile health interventions (mhealth) include: mobile applications that are used for training and self-management of their health status; web tools used for patient education/self-monitoring recommendations; text messaging between patients and doctors; a personal digital assistant that collects patient data and can monitor the physiological state of patients; a pedometer that counts the number of steps taken per day [19]. studies have shown that mobile healthcare can improve the treatment of chronic diseases, but the effectiveness of mobile healthcare in controlling glycemia remains uncertain. according to [12], personalization of applications and increased usability are needed to improve the effectiveness of mobile healthcare interventions. the authors [17] conducted a systematic review and meta-analysis using the available literature. they chose glycosylated hemoglobin (hba1c) as the studied measure of glycemic control. a review of the work led to the conclusion that mobile medical interventions are effective in patients with type 1 diabetes, while significant improvements were observed in adults. studies have shown that special approaches should be applied to improve the effectiveness of mhealth for children and adolescents with diabetes mellitus. in the work [4], the authors developed the mhealth application for children and adolescents with type 1 diabetes using gamification elements and individual customization, which stimulated an increase in the frequency of monitoring blood glucose levels. this indicates the importance of creating an attractive intervention to build trust and encourage long-term engagement [11]. the authors [4] concluded that it would be useful if the stimuli were tied not only to the frequency of blood glucose monitoring, but also to the physical activity of the patient. mhealth involves not only the participation of patients themselves, but also parents, as well as caregivers. it is noted in [10] that the transition from parental management of the course of treatment to management when a child takes on an increased level of self-help can be stressful. the myt1dhero mobile app made it possible to facilitate positive communication between parents and children, increase adherence to diabetes treatment and quality of life. this study shows promising interventions carried out using mobile phones, especially for adolescents as a more acceptable way to communicate with parents, especially in social contexts, for example, when they are with their peers [6]. https://doi.org/10.15837/ijccc.2022.4558 4 mhealth systems significantly reduce medical costs while improving the quality of care. but there are difficulties in the development and implementation of such systems, which are associated with a variety of heterogeneous medical sensors that need to be dynamically removed or added in accordance with the needs of healthcare [22], the heterogeneity of the mhealth environment, which receives important parameters from several sensors for online analysis in real time [23]. improving the quality of service (qos) is also an important issue at mhealth. it is necessary to identify the main users, develop a profile and characterize the requirements for supporting mhealth systems in response to the need for services [13]. 2.3 assessment of physical activities for children and adolescents with t1 diabetes for people with diabetes, an active lifestyle provides many important health benefits. but at the same time, doctors warn that physical activity can cause a decrease in blood glucose levels. a rapid decrease in blood glucose levels can cause hypoglycemia, which is very dangerous for a child. therefore, monitoring the level of glucose in the blood when receiving physical activity and assessing the body’s response to such a load will help to plan physical activity correctly. for parents of children with diabetes, associations of endocrinologists organize special training, draw up instructions, create web applications where the main recommendations are presented [26, 28, 29]. the work [2] provides an overview of the impact of various types of mobile medicine interventions to assess the condition of patients, including bracelets that allow you to assess the effect of physical activity on blood glucose. the authors cite an example of a study [16] when the use of such a bracelet was highly appreciated by children, and was perceived as a mechanism that reduces the burden of self-management of type 1 diabetes. at the same time, it is emphasized that studies with longer follow-up periods are needed to more accurately assess the effect of physical activity on glucose levels using iot devices. thus, a review of the work showed that there is no information about studies on the system integration of the internet of things, mobile healthcare, as well as methods for assessing the impact of physical activity to predict blood glucose levels in children with type 1 diabetes. therefore, it is necessary to integrate the aforementioned technologies to improve the treatment of diabetes. assessing the effect of physical activity on blood glucose levels can provide better control over the condition and contribute to improving the quality of life. the proposed mhealth monitoring system, based on the internet of things, is expected to help people track data about their vital functions. in this way, a person can avoid the worst conditions in the future. 3 methodology 3.1 system architecture based on the analysis of the above works, we have proposed a system architecture for collecting, monitoring and analyzing data on the health status of young (children and adolescents) patients with diabetes mellitus. the main idea is to use devices and sensors to collect data on the level of glucose in the blood and indicators of physical activity of the patient. further preprocessing and data analysis allow us to create a personalized program for managing the patient’s physical activity. the architecture of the proposed monitoring system includes (figure 1): • sensing layer the layer of data collection using devices and sensors; • data processing layer the layer of data storage, preprocessing and analysis of patients with diabetes mellitus; • application layer web application and mobile application. the proposed system for collecting and monitoring data on the health status of young patients with diabetes mellitus uses: a device for continuous monitoring of glucose levels and a fitness bracelet for tracking physical activity (figure 2). the ipro2 system was chosen as a device for continuous https://doi.org/10.15837/ijccc.2022.4558 5 figure 1: architecture of the proposed system monitoring of glucose levels. the ipro2 system consists of the following components: an ipro2 mt7745ww digital recording device, a docking station, a usb cable, an adapter for a wall outlet, an enlite mmt-7510 sensor insertion device, an enlite mmt-7008a sensor. figure 2: a) ipro2 mmt-7745ww; b) docking station; c) usb cable; d) adapter; e) enlite mmt7008a; f) enlite mmt-7510; g) xiaomi mi band 5 https://doi.org/10.15837/ijccc.2022.4558 6 3.2 system implementation it is not possible to successfully control diabetes mellitus using the arsenal of modern medicine, but without the active participation of the patient. the results of treatment always depend on the behavior of the patient. after all, it is he who must follow the doctor’s recommendations from day to day, and this requires not only agreement with the doctor, but also certain knowledge, skills and, taking into account the changing circumstances of life (different food conditions, travel, concomitant diseases), making independent decisions of a medical nature, i.e. be able to independently change the tactics of treatment depending on the specific situation. to do this, the patient must be well aware of all aspects of his own disease. therefore, all the patients participating in the experiment, as well as one of the parents, were trained in the rules of rational nutrition and techniques for counting bread units, the basics of insulin therapy, training in the technique of setting insulin with syringe pens, as well as pump insulin therapy, rules of behavior in emergency situations (relief of hypoglycemic state and ketoacidosis), prevention of chronic complications, tactics during physical exertion. the study involved 26 patients diagnosed with type 1 diabetes of the age group from 7 to 15 years. each patient received an individual consultation from an endocrinologist. as part of the experiments, a sensor was installed for 5-6 days for each participant. in the course of clinical trials, 2 participants expressed a desire to interrupt the experiment and freely left the composition of the studied patients. 3.2.1 sensing layer measurements in the ipro2 system are made every 5 minutes, i.e. 288 times a day. a platinum sensor is installed in the subcutaneous tissue of the patient in a silicone shell impregnated with the enzyme glucose oxidase, necessary for the enzymatic cleavage of glucose in the interstitial fluid of subcutaneous fat. the measurement takes place due to a two-stage chemical reaction, as a result of which the glucose molecule gives off 2 electrons, which create an electric current, and the device for nmh measures the current strength, but the result reflects in mmol / l. the higher the glucose content in the interstitial fluid, the more electrons appear, and, accordingly, the higher the electrical potential. the same method is used in most modern blood glucose meters. the convenience of using ipro2 lies in the compactness and water resistance of the device, fast data loading via usb connection. the xiaomi mi band 5 fitness bracelet can measure the patient’s pulse, activity (number of steps, calories), stress level. the sensor that measures the number of steps is a 6-axis accelerometer. mi band 5 is detected in space using a 3-axis gyroscope. this sensor is an accelerometer assistant and measures the load during various physical activities. the heart rate is measured in three modes: constant monitoring with a preset frequency, a single measurement in the bracelet and during sleep. the data is stored in the mi fi mobile application with the ability to export it for analysis. 3.2.2 data processing layer the ipro2 digital recording device collects data from the enlite mmt-7008a glucose sensor. the data received from the glucose sensor is sent to the carelink ipro web system for storage and reporting. the main functions of the docking station: charging the ipro2 digital recording device and sending data from it to the carelink ipro web system. the patient report with the data obtained from the ipro2 digital recording device is sent in pdf format for further investigation. the mi fit mobile app has the ability to export data for further research. to do this, in the mobile application, you need to go to "profile", then to "settings", then to "about", then to "exercising user rights", then you need to select "export data". then a login and password are entered, after which the data type and dates to be exported are selected. these include: personal data (height, weight, profile photo, date of birth, name), activity (activity time, number of steps, distance, calories burned), pulse, sleep and exercise data. after that, you need to fill in the email address to which the exported data will be sent. an email with a download link is sent to the specified email address. fitness bracelet data is stored in csv files. to open these files, you need to know the password that is provided for each user separately during the download of the exported data. the patient’s data will be stored in a shared sql database after the patient submits their data. in these studies, azure machine learning and power bi cloud platforms were used for data analysis. https://doi.org/10.15837/ijccc.2022.4558 7 the results of the data analysis are presented in the result and discussion section. the purpose of the proposed monitoring system is to prevent hypoglycemia/hyperglycemia as a result of physical activity. 3.2.3 application layer as part of the research, we have developed: a web application that is intended for a doctor and serves for analyzing and visualizing patient data; a mobile application that serves as a tool for monitoring the health status of patients with diabetes mellitus. with the help of a mobile application, the patient can observe his previous readings from sensors, enter information about nutrition, blood glucose levels (3-5 measurements per day made by a glucose meter), the nature of physical activity, and see personalized treatment recommendations compiled by a doctor. the patient’s parents can also monitor the patient’s health status using a mobile application (figure 3). the mobile application being developed for the patient will be a tool for developing self-management and increasing the joint responsibility of patients to achieve health targets. figure 3: use case diagram of the proposed system 4 result and discussion 4.1 the mhealth monitoring system taking into account the components of the proposed monitoring system, the general characteristics are the ability, through the use of hardware and software, to receive input data from users, access and process the received data, as well as store, manage and present information back to the user. to create a system for remote monitoring of the patient’s condition, it is necessary to determine the space of diagnostically significant signs that fully characterize the patient’s condition. there are different approaches to the selection of indicators for monitoring; in the simplest systems, the decision on the https://doi.org/10.15837/ijccc.2022.4558 8 success of therapy is made based on one indicator the level of sugar in the blood. the space of monitoring indicators should objectively and reliably assess the current condition of a patient with diabetes mellitus. the web application is hosted on a hosting called allday.kz (figure 4). even when choosing a hosting name, there was a special approach, they wanted the name not to remind the user of his illness. after all, with such a diagnosis, a person must self-discipline and constantly remember about possible complications. figure 4: web application interface 4.2 analysis of clinical data and assessment of physical activities in type 1 diabetes, physical activity increases insulin sensitivity, which makes it possible to reduce the dosage of insulin for the amount of carbohydrates consumed. moderate physical activity, as a rule, does not require additional planning. but with more intense physical exertion, the risk of hypoglycemia increases, since sugar levels tend to decrease. on the other hand, if physical activity is accompanied by a stressful background or other negative conditions for the body, then glucose levels may increase. the inclusion of intense physical activity in the lifestyle requires monitoring of blood glucose levels to prevent hypoglycemia/hyperglycemia. the aim of the study is to identify the relationship between physical activity and changes in blood glucose levels for patients with type 1 diabetes after completion of physical activity. input data (table 1): • the maximum peak of the distance in 5 days according to the pedometer; • the time period for completing the longest distance; • glucose sensor readings at the time of start; • glucose sensor readings within 1 hour after the end of the walk; • presence/ absence of a stressful background. output data: percentage of change in glucose level (gl) within 1 hour after completion of physical activity. according to the stress background, the patients were divided into 2 categories: • category i children who have a fitness bracelet 2 days before the peak performance of the distance recorded stress levels of only 0-39 (rest) and 40-59 (minor stress), which we took as no stress. https://doi.org/10.15837/ijccc.2022.4558 9 • category ii children who have a fitness bracelet 2 days before the peak performance of the distance recorded stress levels of 60-79 (moderate) and 80-100 (high). table 1: data on physical activity and health indicators (for example, patient 001) patient 001 date start stop distance steps gs0 mmol/l category 2021-0810 17:26 17:58 1931 2979 8,4 i table 2 shows an example of calculations of changes in glucose levels during physical activity per patient, performed according to the formula: k = (gs0 − gsn)/gs0 where gs0 is the glucose sensor readings at the time of launch; gsn – readings of the glucose sensor at the moment at time n. table 2: calculation of changes in glucose levels after completion of physical activity (for example, patient 001) a moment in time (within 1 hour after physical activity) gsn readings change in glucose level behavior (%) k = (gs0 − gsn)/gs0 18:02 5,6 0,333333 18:07 5,3 0,369048 18:12 5,2 0,380952 18:17 5,2 0,380952 18:22 5,2 0,380952 18:27 5,3 0,369048 18:32 5,4 0,357143 18:37 5,7 0,321429 18:42 6 0,285714 18:47 6,3 0,25 18:52 6,6 0,214286 18:57 6,9 0,178571 min 5,2 max 0,380952 average value 0,32 calculations based on the data of patient 001, whose glucose level was equal to 8.4 mmol/l at the beginning of physical activity, showed that the glucose level at the 20th minute after the end of physical activity decreased by 38% and reached a value of 5.2 mmol/l. the average value of changes in glucose levels within one hour after the end of physical activity is 32% (figure 5). the period of 1 hour after the completion of physical activity was chosen according to the recommendations of endocrinologists, who claim that during this period the body does not have time to fully compensate for the decrease in blood glucose levels. thus, it was experimentally confirmed that the period (in this case 1 hour) after the completion of physical activity is dangerous due to the possibility of hypoglycemia. with the help of the proposed https://doi.org/10.15837/ijccc.2022.4558 10 method, percentages of changes in glucose levels relative to the initial value were calculated for each patient within 1 hour after the completion of physical activity with an interval of 5 minutes. the use of this method will allow the doctor to make individual appointments according to the diet before the start of physical activity and its intensity. figure 5: glucose behavior during physical activity and for 1 hour after physical activity (blue – glucose sensor, black – trend line, pink – the minimum line) observing the behavior of glucose levels within an hour after increased physical activity and calculating the k-percent change gives us the opportunity to predict glucose levels. based on the obtained indicators, endocrinologists compiled personalized recommendations that were included in a mobile application for patient self-monitoring. in general, the experimental results confirmed that moderate physical activity for 35-45 minutes a day has a good effect on the glucose level of the next day, the likely decrease in the average glucose level is up to 10%. the application of the proposed calculation method and mobile application will contribute to improving the quality of life of children with type 1 diabetes, in particular, will prevent hypoor hyperglycemia. the recommendation functionality is developed on the basis of the received data and medical reports. according to the recommendations of doctors, notifications are included in the recommendation functionality that before the start of moderate physical activity, glucose levels should be measured and data entered into the tab of the mobile application. with each input, this value will be the initial value for the calculation. after completing the walk, the child receives a corresponding notificationrecommendation. 4.3 mobile app for self-management the developed mobile application will help patients with diabetes mellitus to adapt socially, especially in adolescence in a normal communication environment, without inconveniencing control. the mobile application is developed for the android operating system using the integrated development environment – xamarin. the work related to xamarin was performed on a windows computer with visual studio and xamarin installed. applications can be debugged directly from the desktop or on devices and emulators. in order for the mobile application to have user authorization, in our case patients, we worked on a simple and convenient xamarin.auth library, which has been developing for years and has everything we need: • displaying the browser with authorization pages; • managing the redirect flow and authorization process; • getting the right data; • providing mechanisms for additional requests to the service, for example, to obtain information about the user. https://doi.org/10.15837/ijccc.2022.4558 11 also xamarin. auth supports the ability to store user credentials in a secure storage. the data of the authorized user of the patient will be stored in a shared sql database after the patient sends his data. the patient enters fa data and, according to the instructions, receives automated recommendations about possible hypoor hyper-glycemic conditions. the pedometer in the app will read the number of steps per day for convenience. the patient enters food intake data, which is displayed by the doctor in time, for comparison with the behavior of glucose levels (figure 6). figure 6: a screen of the received recommendation after a walk 5 conclusions and future works as a result of the research, the architecture of a mobile health monitoring system based on iot was proposed, and the relationship between physical activity and the patient’s blood glucose level after the completion of physical activity was revealed. the indicators of a decrease in blood glucose levels obtained as a result of computational experiments within 1 hour after the completion of physical activity made it possible to draw up recommendations for physical activity developed taking into account the individual characteristics of the patient. the authors presented a mobile application for children and adolescents with t1 diabetes mellitus, which is a tool for developing self-control and increasing the joint responsibility of patients to achieve health targets. in the course of further research, we plan to provide a wider connection to a wider range of different devices related to diabetes (glucometer), to develop elements of gamification and functions based on rewards in the mobile application, since the application was originally planned for children with type 1 diabetes, we will consider options for developing functionality based on strategies nutrition or insulin dose adjustments, we will adapt mobile health based on the results of the approbation. 6 acknowledgment this work was supported in part under a grant of foundation of the ministry of 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[29] [online]. physical activity for children with type 1 diabetes, https://www.endocrineweb.com/guides/type-1-children/physical-activity-children-type-1diabetes, 2021. https://doi.org/10.15837/ijccc.2022.4558 14 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: zholdas, n.; mansurova, m.; postolache, o.; kalimoldayev, m.; sarsembayeva t. (2022). a personalized mhealth monitoring system for children and adolescents with t1 diabetes by utilizing iot sensors and assessing physical activities, international journal of computers communications & control, 17(3), 4558, 2022. https://doi.org/10.15837/ijccc.2022.3.4558 introduction related work glucose monitoring by utilizing iot sensors mhealth for children and adolescents with t1 diabetes assessment of physical activities for children and adolescents with t1 diabetes methodology system architecture system implementation sensing layer data processing layer application layer result and discussion the mhealth monitoring system analysis of clinical data and assessment of physical activities mobile app for self-management conclusions and future works acknowledgment international journal of computers communications & control issn 1841-9836, 13(5), 808-823, october 2018. electronic throttle valve takagi-sugeno fuzzy control based on nonlinear unknown input observers w. gritli, h. gharsallaoui, m. benrejeb, p. borne wafa gritli*, hajer gharsallaoui, mohamed benrejeb université de tunis el manar, ecole nationale d'ingénieurs de tunis, la.r.a automatique, bp 37, le belvédère, 1002 tunis, tunisie. *corresponding author: wafa.gritli@enit.rnu.tn, hajer.gharsallaoui@gmail.com, mohamed.benrejeb@enit.rnu.tn pierre borne ecole centrale de lille, cité scienti�que cs20048, 59651 villeneuve d'ascq cedex, france. pierre.borne@centralelille.fr abstract: this paper deals with the synthesis of a new fuzzy controller applied to electronic throttle valve (etv) a�ected by an unknown input in order to enhance the rapidity and accuracy of trajectory tracking performance. firstly, the takagi-sugeno (t-s) fuzzy model is employed to approximate this nonlinear system. secondly, a novel nonlinear unknown input observer (nuio)-based controller is designed by the use of the concept of parallel distributed compensation (pdc). then, based on lyapunov method, asymptotic stability conditions of the error dynamics are given by solving linear matrix inequalities (lmis). finally, the e�ectiveness of the proposed control strategy in terms of tracking trajectory and in the presence of perturbations is veri�ed in comparison with a control strategy based on unknown input observers (uio) of the etv described by a switched system for pulse-width-modulated (pwm) reference signal. keywords: electronic throttle valve, switched system, takagi-sugeno fuzzy model, nonlinear unknown input observer, lyapunov method. 1 introduction for further improvement of drivability, fuel economic system and emission performance of vehicles, the electronic throttle control (etc) systems is required to possess fast transient responses without overshoot and high static precision. hence, obtaining a proper controller with the ability achieving the requirements is a very interesting topic for the etc system. the challenging issue is that the control performance of the etc system is adversely a�ected by the uncertain system physical parameters related to friction, return spring and gear backlash. to solve the parameter uncertainty problem in the controller design of the etc system, a lot of e�orts have been made from two aspects. on the one hand, a linear model of the system has been used in several existing control design. in [10] and [23], a nonlinear control strategy has been proposed, consisting of a pid controller and a feedback compensator for friction and limp-home e�ects. a discrete-time sliding mode controller and observer are designed to realize robust tracking control of the valve system in [7] and [10]. in [20], the variable structure concept is used after the use of feedback backstepping techniques in the intermediate stages of etv design. in [31], [13], [15] and [21], the emphasis is on the development of an adaptive control strategy, which is aimed to enhance the control strategy copyright ©2018 cc by-nc electronic throttle valve takagi-sugeno fuzzy control based on nonlinear unknown input observers 809 robustness with respect to process parameter variations, caused by production deviations and variations of external conditions. in [14] and [18], a pid-type fuzzy logic controller has been proposed. in [16], fault tolerant control has been proposed for the etv described by a switched discrete-time systems with input disturbances and actuator faults. in [6], the smith-predictor control has been adapted for controlling the electronic throttle body over a delay-driven network. on the other hand, among nonlinear control theory, the takagi-sugeno (t-s) fuzzy system [36] has been the most active branch of the fuzzy control �eld, [19], [20], [12], [39] and [13]. the stability and stabilisation of t-s systems has been the subject of many works either in the continuous case or in the discrete one, [2] and [3]. the parallel distribution compensation (pdc) technology has been widely employed to design the fuzzy controller for t-s fuzzy systems in [26] and [28]. the problem of robust tracking control is investigated for a class of nonlinear systems approximated by a fuzzy t-s model in [11]. in [32], a fuzzy h2 guaranteed cost sampled-data control problem for nonlinear time-varying delay systems is studied. an observer can be used for state estimation when the system states are unmeasurable [27]. the observer-based state feedback pdc controller can be employed to settle the unmeasured state condition as shown in [18] and [40]. in [19], an adaptive observer in the unknown input estimation form is proposed for a system with unmeasured premise variable. in [25], an adaptive observer is designed for the estimation of unmodeled dynamics in a t-s system. in [9], a t-s observer with parameter estimation was designed for a heat exchanger fouling detection problem. in [5], a joint state and parameter estimation observer was proposed for t-s systems whose matrices depend on unknown parameters. in [22], nonlinear unknown input fuzzy observer (uifo) has been used for fuzzy t-s systems to design the fuzzy fault tolerant control. in [7], an approach for nonlinear unknown input observer (nuio) design for nonlinear systems has been proposed. in this paper, a new control strategy based on nuio is proposed for the etv described by t-s fuzzy systems. nuio is used to estimate the position of an automotive throttle valve. then, an estimated-state feedback control law is developed via pdc. based on lyapunov method, asymptotic stability conditions of the error dynamics is given in lmis to design the observer parameters. the proposed control strategy is then investigated and compared to switching control based on unknown input observer (uio). the following part of this article is organized as follows: in section ii, the topology of the etv is presented and modeled. the proposed control strategy is explained and detailed in section iii. section iv is devoted to comparison and discussions, and �nally, section vi ends the paper with a conclusion. 2 electronic throttle valve topology the studied electronic throttle control system includes an accelerator pedal, an electronic control unit (ecu) and a throttle body, shown in �gure 1. the throttle body is composed of a dc motor, a reduction gear set, a valve plate, a position sensor and two nonlinear return springs [21]. the control signal, provided by the ecu, is the armature voltage of a dc-motor which is controlled by changing the pwm duty cycle. it generates the rotational torque to regulate the throttle plate position. nomenclature used in the model is presented in the appendix a. 2.1 state space representation the electrical part of the throttle body is modeled by (1) and the electromechanical part by (2). u = li̇ + ri + e (1) e = kvθ̇m (2) 810 w. gritli, h. gharsallaoui, m. benrejeb, p. borne figure 1: the electronic throttle body u(t) is the voltage, i(t) the armature current and e the electromotive force, [24]. the electrical torque ce is considered, in this study, such that ce = kti (3) by considering the stick-slip friction torque tf (ω) and the nonlinear spring torque tsp(θ), the mechanical part of the throttle body is modeled by (4), [24]. jtotω̇ = −btotω −tf (ω) −tsp(θ) + ce (4) there are many types of friction involved in the motion of throttle plate such as coulomb, viscous, stribeck, rising static frictions and presliding displacement [20]. in this paper, the coulomb friction model tf (ω) is considered given by tf (ω) = fs sgn(ω) (5) where fs is a positive constant parameter. the typical feature of the etv includes a sti� spring, used as a fail-safe mechanism, which forces the valve plate to return to the position slightly above the closed position when no power is applied. moreover, the motion of the valve plate is limited between θmax and θmin angles. these limitations are realized by a highly sti� spring, ideally with in�nite gain. the nonlinear spring expression is written such as tsp(θ) = m1(θ −θ0) + d sgn(θ −θ0) (6) the gear ratio γ is given by (7). γ = θm θ = 1 kg1kg2 (7) such that: kg1 = np/nint l and kg2 = nint s/nsec t. from equations (1), (2), (3) and (7) and by subtituting the expressions tsp(θ) and tf (ω) into (4), it comes the following etv model  θ̇ = kg1kg2ω ω̇ = − m1 jtot (θ −θ0) − djtot sgn(θ −θ0) − btot jtot ω − fs jtot sgn(ω) + kt jtot i li̇ = −kvω −ri + u (8) electronic throttle valve takagi-sugeno fuzzy control based on nonlinear unknown input observers 811 2.2 system identi�cation by substituting ce, tf and tsp into (4) and by neglecting the torque generated by the air�ow ca, the two nonlinearities sgn(θ−θ0) and sgn(ω) and the constant m1jtotθ0, the etv can be modelized by the following transfer function h(s) [37]. h(s) = θ(s) u(s) = (180/π)ke/γ ljtots3 + (rjtot + lbtot)s2 + (rbtot + kvke + ksl)s + ksr (9) with: ks = (180/π/γ)m1 and s the laplace operator. from equation (9), the etv can be modelized by two linear models identi�ed from the default position of the throttle plate for two values of the parameter ks, [37]: a model representing the position of the plate above the position by default and the other the position of the plate below the position by default. switching between these two models of the etv is equivalent to enabling and disabling the current model. changing model and process structure raise problems such as detection model switching and maintain model tracking. moreover, it is essential to consider the nonlinearities in the modeling phase, so that the behavior of the real system is described over a wide range of operation. it is, therefore, possible to consider modeling based on the concept of fuzzy logic. indeed, in this case, a novel takagi-sugeno fuzzy model of the etv is proposed which uses a base of locally linearised models. 2.3 t-s fuzzy modeling a reduced model of the etv is �rstly provided. to simplify the analysis, the motor armature inductance l will be assumed negligible then (1) and (2) can be rewritten as −kvω −ri + u = 0 (10) let x(t) = [x1(t) x2(t)]t and x10 = θ0, with x1 = θ, x2 = kg1kg2ω (11) by substituting the value of i in the throttle valve dynamical system (8), the state space form can be simpli�ed as{ ẋ1 = x2 ẋ2 = a21(x1 −x10) −λ sgn(x1 −x10) + (a22 −a23 a32a33 )x2 −µ sgn(x2) − a23 a33 u (12) where the coe�cients of the etv model according to the physical parameters are given by: a21 = m1kg1kg2/jtot, a22 = −btot/jtot, a23 = ktkg1kg2/jtot, a32 = −kv/kg1kg2, a33 = −r, µ = fskg1kg2/jtot and λ := dkg1kg2/jtot. the number of the local models depends on the nonlinear system complexity and the choice of the activation functions structure [1]. the polytope is obtained with n = 2r peaks where r is the number of premise variables considered for: r = 2. then, the etv system can be transferred as the following t-s models. rule1 : if x1(t) < x10 and x2(t) < 0 then{ ẋ(t) = a1x(t) + b1u(t) + d1d(t) y(t) = c1x(t) (13) 812 w. gritli, h. gharsallaoui, m. benrejeb, p. borne rule2 : if x1(t) > x10 and x2(t) < 0 then{ ẋ(t) = a2x(t) + b2u(t) + d2d(t) y(t) = c2x(t) (14) rule3 : if x1(t) < x10 and x2(t) > 0 then{ ẋ(t) = a3x(t) + b3u(t) + d3d(t) y(t) = c3x(t) (15) rule4 : if x1(t) > x10 and x2(t) > 0 then{ ẋ(t) = a4x(t) + b4u(t) + d4d(t) y(t) = c4x(t) (16) with a1 = ( 0 1 a21 + λ a22 −a23 a32a33 + µ ) , a2 = ( 0 1 a21 −λ a22 −a23 a32a33 + µ ) a3 = ( 0 1 a21 + λ a22 −a23 a32a33 −µ ) , a4 = ( 0 1 a21 −λ a22 −a23 a32a33 −µ ) b1 = b2 = b3 = b4 = ( 0 −a23 a33 ) c1 = c2 = c3 = c4 = ( 1 0 ) d1 = d2 = d3 = d4 = 1e3. ( 1 1 ) the global fuzzy takagi-sugeno model is given, for i ∈ ξ = {1, ..., 4}, as follows  ẋ(t) = 4∑ i=1 hi(z(t))(aix(t) + biu(t) + did(t)) y(t) = 4∑ i=1 hi(z(t))cix(t) , i ∈ ξ (17) with hi(z(t)) = wi(z(t)) 4∑ i=1 wi(z(t)) , wi(z(t)) = 2∏ j=1 mij(zj(t)) (18) mij is the j th fuzzy set of the ith rule, z1(t),z2(t) are the known premises variables and mij(zj(t)) the membership value of zj(t) in mij. therefore, from (18) the following properties are satis�ed 4∑ i=1 hi(z(t)) = 1, hi(z(t)) > 0 ∀i ∈ ξ (19) the etv control law should be designed to guarantee the tracking of the throttle movement θ for a desired reference signal with satisfactory transient performance and steady-state position error as well as robustness to nonlinearity parameter variations of friction, nonlinear spring and external disturbance. electronic throttle valve takagi-sugeno fuzzy control based on nonlinear unknown input observers 813 3 proposed t-s fuzzy control strategy 3.1 structure of the proposed t-s fuzzy control strategy for the t-s fuzzy model (17), the following control law based on the estimated state is proposed, as shown in �gure 2. uts(t) = − n∑ i=1 hi(z(t))(kts,ix̂(t) + y d(t)) (20) the main contribution of this article is to design an estimated feedback control law for the etv described by t-s fuzzy model. a nonlinear unknown input observer (nuio) is used to estimate the system state x̂(t). the control should maintain the system output closed to the desired trajectory yd(t) even in the presence of unknown input d(t). a supervisor is implemented to calculate the weighting functions hi(z(t)). the design of the fuzzy controller shares the same fuzzy sets as the fuzzy model and the same weights. t-s fuzzy nonlinear unknown input observer etv system ( ) d y t t-s fuzzy controller ( )y t ( ) ts u t ˆ( )x t ( )d t ( )x t ( )x t reference signal ( ( ))ih z t supervisor figure 2: proposed t-s fuzzy controller structure for the etv kts,i ∈ rp×n, i ∈ ξ, is the ith feedback gain vector and yd(t) ∈ rp the reference input. the purpose of the next sub-section is to design an estimated state-feedback control de�ned via pdc ensuring the stability of the closed-loop system. 3.2 parallel distributed compensation the design of the pdc fuzzy controller shares the same fuzzy sets as the fuzzy model and the same weights wi(z(t)) in the premise parts [34]. the state feedback fuzzy controller is constructed via pdc as follows [35] if z1(t) is mi1 and...and zr(t) is m i r then u(t) = − n∑ i=1 hi(z(t))kts,ix(t) (21) the design of the fuzzy regulator is to determine the local feedback gains kts,i ∈ rp×n. by substituting (21) into (17), the closed loop model is written as  ẋ(t) = n∑ i=1 n∑ i=j hi(z(t))hj(z(t))qix(t) y(t) = n∑ i=1 hi(z(t))cix(t) (22) with: qi = (ai −bikts,i). stability conditions for ensuring stability of (17) is derived using lyapunov approach for linear continuous systems. 814 w. gritli, h. gharsallaoui, m. benrejeb, p. borne theorem 1. the equilibrium of the continuous fuzzy system described by (17) is asymptotically stable in the large if there exists a common positive de�nite matrix x1 such that, [34] ati x1 + x1ai < 0 (23) for i ∈ ξ; that is, for all the subsystems. by applying theorem 1 to (22), we can derive stability conditions. theorem 2. the equilibrium of the continuous fuzzy control system described by (22) is asymptotically stable in the large if there exists a common positive de�nite matrix x1 such that, [34] qtiix1 + x1qii < 0 (24)( qij + qji 2 )t x1 + x1 ( qij + qji 2 ) 6 0 i < j (25) for all i and j excepting the pairs (i,j) such that hi(z(t))hj(z(t)) = 0, ∀t. the stability conditions of theorems 1 and 2 can be expressed as lmis, [35]. by using: x = x1−1 and: mi = kts,ix, the satisfaction of lmis conditions needs to �nd x > 0 and mi such that, ∀i ∈{1, ...,n}, xati + aix −bimi −m t i b t i < 0 (26) x(ati + a t j ) + (ai + aj)x − (bimj + bjmi) − (bimj + bjmi) t < 0 (27) in practice, all states are not fully measurable; then, a nonlinear unknown input observer for t-s fuzzy models is proposed in order to implement the estimated state-feedback controller. the concept of pdc is employed to design the following nuio structure in the next part. 3.3 nuio design and stability analysis the concept of pdc is employed to design nuio for the t-s fuzzy model (17). the ith observer rule is of the following form, [7] if z1(t) is mi1 and...and zr(t) is m i r then v̇(t) = aiv(t) + giu(t) + liy(t), i ∈ ξ (28) the overall fuzzy observer is given as  v̇(t) = n∑ i=1 hi(z(t))(niv(t) + giu(t) + liy(t)) x̂(t) = v(t) −ey(t) , i ∈ ξ (29) electronic throttle valve takagi-sugeno fuzzy control based on nonlinear unknown input observers 815 where ni, gi and li, i ∈ ξ and e are unknown matrices to be designed, [7]. let's de�ne the error e(t) = x̂(t) −x(t), it follows from (17) and (29) that ė(t) = n∑ i=1 hi(z(t))nie(t) + n∑ i=1 hi(z(t)){ni + kici − (i + eci)ai}x(t) + n∑ i=1 hi(z(t)){gi − (i + eci)bi}u(t) − n∑ i=1 hi(z(t))(i + eci)did(t) (30) with ki = li + nie (31) a su�cient condition, for the observer given by (29) to be an nuio, is given as in the following theorem. theorem 3. for the observer given by (29), if ki, i ∈ ξ and e are chosen such that ni = (i + eci)ai −kici gi = (i + eci)bi li = ki −nie ecidi = −di (32) and if a positive de�nite symmetric matrix x2 can be found to satisfy the following inequalities nti x2 + x2ni < 0, i ∈ ξ (33) then the error dynamics given by (30) is asymptotically stable at the origin. hence the observer given by (29) is an nuio, that is, e(t) goes to zero asymptotically and is invariant with respect to the unknown inputs d(t), [7]. proof. the proof is given in the appendix b. theorem 4. for the observer given by (29), if there exist matrices k̄i, i ∈ ξ, a matrix ȳ and a positive de�nite symmetric matrix x such that the following lmis are satis�ed [(i + uci)ai] tx2 + x(i + uci)ai +(v ciai) t ȳ t + ȳ (v ciai) −cti k̄ t i − k̄ici < 0 (34) with: u = −di(cidt1)+ and: v = i − cidt1(cidt1)+; then by letting ki = x2−1k̄i and y = x2−1ȳ and computing the observer gains using (39) and (32), the error dynamics given by (30) is asymptotically stable at the origin. hence, the observer given by (29) is a nuio, that is, e(t) goes to zero asymptotically and is invariant with respect to the unknown inputs d(t), [7]. proof. the proof is given in the appendix c. in the next section, in order to evaluate the proposed control laws performance against the nonlinearities such as friction and limp home spring of etv, the position is examined for a pwm signal. 816 w. gritli, h. gharsallaoui, m. benrejeb, p. borne 4 results and discussion in order to test the position of the etv for a pwm reference signal provided by the t-s fuzzy control, a comparative study is performed. the proposed control strategy is then investigated and compared to switching control based on uio proposed in our previous work, [16] and [17]. to illustrate the e�ectiveness of the proposed control strategy, a t-s fuzzy model of the etv, is �rstly, provided. the considered model is given by (12) with the state region 0rad < x1(t) < π/2rad and −80rad/s < x2(t) < 80rad/s. the considered etv system parameters, for the numerical simulations, are presented in table 4, as given in [20]. table 1: parameter values for simpli�ed model parameters values a21 1/18 a22 −1.6801e3 a23 −32.9820 a32 −0.0245 a33 −1.0980 µ 4.7438e2 λ 2.1073e3 solutions satisfying stability conditions under lmis of the theorem 2 are found for symmetric de�nite positive matrix x1 given by (35). x1 = ( 32.7434 −8.9104 −8.9104 32.7434 ) (35) then, the feedback gains are given by kts,1 = [ 0.7505 0.5061 ] , kts,2 = [ 0.7505 -0.5716 ] kts,3 = [ -1.6091 0.5061 ] , kts,4 = [ -0.5050 -0.2716 ] solutions satisfying stability conditions under lmis of the theorem 3 and 4 are found for symmetric de�nite positive matrix x2 given by (36). then, the rest parameters of nuio are calculated. x2 = ( 0.8820 0 0 0.0002 ) (36) the throttle plate follows a pwm signal of amplitude ranged from 0 to 1.5708 rad and frequency f = 0.2hz with a white gaussian noise d(t) given by �gure 3. the system responses obtained from initial conditions: x0(t) = [0.1 0]t are shown in �gures 4-6. figure 4 shows the evolution of the throttle plate angle in the presence of unknown input d(t) with the switched system and the t-s fuzzy system. for the switched system, the throttle plate tracks the reference signal with settling time equal to 2.5s and �uctuations due to the noise. whereas for the same reference input and by using the t-s fuzzy system, the throttle plate tracks the reference signal with settling time equal to 0.25s, rotor angular velocity average value: ±1.0295rad/s, �gure 7, and ecu output voltage ranging from: −2.7 v to 1.6 v , �gure 6. electronic throttle valve takagi-sugeno fuzzy control based on nonlinear unknown input observers 817 0 2 4 6 8 10 12 14 16 18 20 −4 −3 −2 −1 0 1 2 3 4 x 10 −8 time (s) w h it e g a u ss ia n n o is e ( ra d ) figure 3: white gaussian noise 0 2 4 6 8 10 12 14 16 18 20 −0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 time (s) v a lv e a n g le θ ( ra d ) reference signal switching control t−s fuzzy control figure 4: valve angle position 0 2 4 6 8 10 12 14 16 18 20 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 time (s) t ra ck in g e rr o r (r a d ) switching control t−s fuzzy control figure 5: tracking error it can be observed, from �gure 5, that the error between the throttle plate angle and its steadystate value has been greatly reduced, in terms of average value, to: ±40.10−3rad using the proposed strategy against: ±40.10−2rad with the switched system technique. from �gure 8, simulation results using the proposed t-s fuzzy control strategy show that the controller yields good tracking performance for a step perturbation amplitude of 0.2rad at t = 6s with a small error between the throttle plate angle and its steady state. from the simulation results, we consider that the performances of the proposed approach for t-s fuzzy system control are satisfactory and allow normal functioning of the system in spite of the fast acceleration and deceleration process even in the presence of an unknown input d(t) and perturbation. indeed, the electronic throttle control system has a fast transient response 818 w. gritli, h. gharsallaoui, m. benrejeb, p. borne 0 2 4 6 8 10 12 14 16 18 20 −3 −2.5 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 time (s) c o n tr o l s ig n a l ( v ) figure 6: control signal obtained by t-s fuzzy control 0 2 4 6 8 10 12 14 16 18 20 −30 −20 −10 0 10 20 30 time (s) r o to r a n g u la r ve lo ci ty ω ( ra d /s ) figure 7: rotor angular velocity obtained by t-s fuzzy control 0 2 4 6 8 10 12 14 16 18 20 −0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 time (s) v a lv e a n g le θ ( ra d ) reference signal x 1 (t) 0 2 4 6 8 10 12 14 16 18 20 −3 −2.5 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 time (s) c o n tr o l s ig n a l ( v) 0 2 4 6 8 10 12 14 16 18 20 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 time (s) t ra ck in g e rr o r (r a d ) 0 2 4 6 8 10 12 14 16 18 20 −30 −20 −10 0 10 20 30 time (s) r o to r a n g u la r ve lo ci ty ω ( ra d /s ) figure 8: simulation results with step perturbation without overshoot and high static precision. the results of this paper may inspire further research interest. certainly, actuator and sensor faults occur, a fault tolerant controller (ftc) based on t-s observer strategy would be electronic throttle valve takagi-sugeno fuzzy control based on nonlinear unknown input observers 819 designed. it should be noted also that the control strategy would be given from the view of discontinuous systems. then, experimental validation would be performed to illustrate the performance of the presented throttle control for tracking a reference position. 5 conclusion the di�culty in controlling the studied electronic throttle valve (etv) mainly lies in the nonlinearities related to the friction, the return spring and the gear mechanism. therefore, a new control strategy based on 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(2014); novel stability criteria for t-s fuzzy systems, ieee transactions on fuzzy systems, 22(2), 313-323, 2014. appendix a. nomenclature jtot total moment of inertia kg.m2 btot total damping constant n.m/rad np tooth number of pinion gear nint l tooth number of large intermediate gear nint s tooth number of small intermediate gear nsec t tooth number of sector gear l motor inductance h r motor resistance ω kt motor torque constant n.m/a kv motor back emf constant v.s/rad θ0 spring default position rad θmin spring min position rad θmax spring max position rad θ valve plate position rad θm motor rotational position rad ω rotor angular velocity rad/s d spring o�set n.m m1 spring gain n.m/rad electronic throttle valve takagi-sugeno fuzzy control based on nonlinear unknown input observers 823 appendix b. proof of theorem 3 proof: using (32), it follows from (30) that ė(t) = n∑ i=1 hi(z(t))nie(t) (37) using (36), it is now quite standard to prove the stability property. it is easy to see that (32) implies that eci(d1, · · · ,dn ) = −(d1, · · · ,dn ) (38) assumption: rank(eci(d1, · · · ,dn )) = rank((d1, · · · ,dn )). under assumption, there exists a nonsingular matrix td such that (d1, · · · ,dn )td = (dt1 0), where dt1 is of full column rank. this implies that cidt1 is of full column rank. (38) requires all the possible solutions for e to have the following form e = −di(cidt1)+ + y (i −cidt1(cidt1)+) (39) where y can be any compatible matrix and x+ = (xtx)−1xt . in order to provide an e�cient design method, we reformulate the su�cient conditions given by (32) and (36) as lmis. appendix c. proof of theorem 4 proof: using (39), it is easy to show that lmi based conditions given by (34) are equivalent to those conditions required in the theorem 3. the theorem is therefore proved. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 6, month: december, year: 2021 article number: 4394, https://doi.org/10.15837/ijccc.2021.6.4394 ccc publications effect of sample sizes in fingerprinting database for wi-fi system a.h.a. sa’ahiry, a.h. ismail, l.m. kamaruddin, m.s.m. hashim m.s.m. azmi, m.j.a. safar, m. toyoura ahmad hakimi ahmad sa’ahiry*, abdul halim ismail, muhammad juhairi aziz safar faculty of electrical engineering technology universiti malaysia perlis, malaysia 02600 arau perlis, malaysia *corresponding author: a.hakimi@studentmail.unimap.edu.my ihalim@unimap.edu.my, juhairi@unimap.edu.my latifah munirah kamaruddin faculty of electronic engineering technology universiti malaysia perlis, malaysia 02600 arau perlis, malaysia latifahmunirah@unimap.edu.my mohd sani mohamad hashim, muhamad safwan muhamad azmi faculty of mechanical engineering technology universiti malaysia perlis, malaysia 02600 arau perlis, malaysia sanihashim@unimap.edu.my, safwanazmi@unimap.edu.my masahiro toyoura department of computer science and engineering university of yamanashi, japan 4-3-11 takeda, kofu yamanashi, 400-8511, japan mtoyoura@yamanashi.ac.jp abstract indoor positioning system has been an essential work to substitute the global positioning system (gps). gps utilizing global navigation satellite systems (gnss) cannot provide an accurate positioning in the indoor due to the multipath effect and shadow fading. fingerprinting method with wi-fi technology is a promising system to solve this issue. however, there are several problems with the fingerprinting method. the fingerprinting database collected has different sample sizes where the previous researcher does not indicate any standard for the sample size to be used. in this paper, the effect of the sample sizes in fingerprinting database for wi-fi technology has been discussed deeply. the statistical analyzation for different sample sizes has been analyzed. furthermore, two methods which are knearest neighbor (knn) and deep neural network (dnn) are being used to examine the effect of the sample sizes in term of accuracy and distance error. the discussion in this paper will contribute to the better sample size selection depending on the method taken by the user. the result shows that sample sizes are an important metrics in developing the indoor positioning system as it effects the result of the location estimation. keywords: indoor positioning system, sample size, positioning accuracy, big data, fingerprinting, deep learning. https://doi.org/10.15837/ijccc.2021.6.4394 2 1 introduction the global positioning system (gps) and global navigation satellite systems (gnss) in general have been adopted as the primary positioning technology due to the highly accurate location information they provide on a global scale; however, this technology fails in certain environments, such as indoors or urban canyons. these gnss failures are primarily due to the satellites’ low received signal power due to degradation of signal as illustrated in figure 1 and visibility in urban/indoor areas. as a result, non-gnss navigation technologies are critical in these regions [11]. figure 1: degradation signal of gps and gnss (ma [10]) numerous studies have been conducted over the last few years to address this issue. by utilizing a variety of technologies, including infrared [2], ultrawide band [22], bluetooth [3], inertial [20], magnetic [8], and fusion of the technologies [20]. wi-fi is one of the most reliable technologies available. the reasons for this are that it is widely deployed within buildings because the world relies on wi-fi to connect to the internet for home networking, supporting the internet of things, and teaching. by utilizing wi-fi, no pre-deployment effort or infrastructure support is required. as a result, labor costs associated with installing new hardware to implement the system are reduced. the use of wi-fi technologies for indoor positioning has been discussed, as well as several methods and techniques. several methods have been used by the previous researcher such as toa [19], aoa [19], and fingerprinting [5] method to predict user location. however, the most precise and provide a better accuracy is the fingerprinting method [9]. fingerprinting methods works by taking the unique identification for each of the reference point for the database collection. fingerprinting has two phases which are the offline and online phases as illustrated in figure 2. offline phases are a calibration or collecting the database while for online is predicting the location of the user. in the offline phases which is the data collection, previous researcher does not have a standard for choosing a sample data [7]. additionally, the issue with the fingerprint database is the database must be taken by expert surveyor due to the collection of the database need a professional trained person. otherwise, the database will not effectively be created, and this will produce a problem in future location estimation. moreover, the expert surveyor will create a massive problem where the cost to hire the expert surveyor is excessively expensive. other problem related to the expert surveyor is the time taken for the expert surveyor to collect the database is time consuming. this two problem of the professional surveyor responsible for compiling the database is expensive and time consuming [1] which need to be avoided. hence, crowdsourcing fingerprinting database was introduced. crowdsourcing method is replacing the labor to stranger, where the stranger or anyone could contribute their signal into the fingerprinting database. however, the problem with the crowdsourcing is the sample size in the data base get from the stranger in each of the fingerprinting database reference point is different [18]. the sample size is the number of signal strength from the source collected. for example, the wi-fi signal strength sample size can be collected by taking the signal strength over time. in the crowdsourced database, the stranger does not know the system of the fingerprinting method. thus, provide a different sample size for each of the stranger and will creates inaccuracy in predicting the user location. in this paper, the effect of the different sample sizes will be discussed to get a better understanding and the authors https://doi.org/10.15837/ijccc.2021.6.4394 3 try to find the optimal number of sample sizes in term of fingerprint database for wi-fi signal. figure 2: fingerprinting workflows 2 related work in the previous research work, it appears that researchers frequently use a variety of sample sizes to evaluate their research work, without explaining the rationale for the sample size selection. in [12], 40 observations were made for a set of 155 reference points that served as training data to eliminate human behavior’s randomness. in [21], the authors collected one sample per second for five minutes (a total of 300 samples) in order to investigate wireless channel changes over time. although it is acceptable, it does not indicate any reference for sample size and why 300 sample sizes were collected. similarly with author in [17], a different sample sizes were used to analyze and compare various filtering strategies for real-world indoor 802.11 positioning systems. the authors determined the radio distribution at 250 uniformly spaced grid points over a 15 x 35 meters area. the authors of [4] proposed a technique called dynamic hybrid projection (dhp) for enhanced 802.11 localization. they collected 802.11 rss data at 27 different reference locations on different days and with four different user orientations during their experiments. they selected 15 locations with a step of 1.5–2 meres from this sample to use as training data. the sample sizes were not declared, and the sample sizes were totally different for various researcher. in the crowdsourced data, [15] has developed a human-computer interface for indicating location over intervals of varying duration, a client-server protocol for pre-fetching signature data for use in localization and location-estimation algorithm incorporating highly variable signature data. they describe an experimental deployment of their method in a nine-story building with more than 1,400 distinct spaces served by more than 200 wireless access points. the sample size for different location is diverse, this will be a problem for the online phases to locate the user location. in summary, our review of the literature indicates that authors calibrate, train, test, and evaluate indoor positioning systems using a variety of sample sizes and patterns. the previous author does not specifically justify the reason of the number sample sizes collected. the authors in this paper hypothesis are the sample sizes is an important criterion to gain an accurate result. hence, before making a prediction, the authors of this paper intend to investigate the effect of sample size on the accuracy and statistical properties of fingerprint database data. referring to [9], the author provides the following performance benchmarking for indoor wireless location system: accuracy, precision, complexity, scalability, robustness, and cost. however, in this paper the author will be using two performance metrics which are the accuracy and precision. both metrics will be tested by using two methods. k-nearest neighbor (knn) and deep neural network (dnn). there are advance knn that has been used, [6] has used wknn which is weighted k-nearest neighbor. in this work, knn is chosen as a conventional method and to standardize the evaluation that been used by most of previous research. dnn is the most advance method using a neural network based on machine learning. there are many architecture in designing the deep learning model, one https://doi.org/10.15837/ijccc.2021.6.4394 4 of it are [14], the author used a complex deep learning architecture by combining multiple machine learning algorithm. in this study, a basic architecture will be executed as in [13], the scope and aim are not to get the best prediction of the location. the dnn algorithm is enough to study the effect of the different sample size in term of the accuracy of the location prediction. hence, using these two methods will verify the performance of the sample sizes in term of two of the verification metrics. in this paper, the first section will be addressing the general problem which is the gps and gnss problem. the second section will be the related work where the previous researcher has done. the third section will be explaining on the data collection part where the authors will clarify how the data is collected, what hardware are being used and the configuration of the setup. for the fourth section, the data distribution based on its intensity will be analyzed to get a clear view of the data in the fingerprinting database. then, the authors study the characteristic of the signal and the statistical analyzation in term of mean, mode, and standard deviation. the box plot also is plotted to know the stabilization of the sample sizes based on the median. for the performance metrics, the sample size in term of its accuracy and precision by using knn and dnn method are evaluates. the fifth section is the conclusion and discussion from what have been discovered from the experiment and discussion based on the previous section. 3 methodology this section will explain the experimental setup of the data collection in collecting the fingerprinting database. the experimental area, hardware configuration, hardware used and all the setups will be explained in this section. the setup has been made by taking a consideration on the previous researcher work to get the optimize setyp and avoid any configuration error. 3.1 overview the flow to analyze of the effect of the sample sizes in fingerprinting database is presented in figure 3. first the data is collected by using single access point. the data is then presented by two angle which are in two dimensional and three dimensional. then, the data were analyzed with respect to the sample sizes critically by the signal characteristic and statistical approach. afterward, the different sample sizes data were evaluated by using two methods which are the non-parametric techniques, k-nearest neighbor (knn) and by using artificial intelligence method, deep neural network (dnn). figure 3: works overview 3.2 data collection the data collection was made in solid mechanic and acoustic lab in universiti malaysia perlis as in figure 4. it consists of 42 reference point, 6 reference point on x axis and 7 reference point on y axis. the data taken is semi-controlled environment where all the configurations will be stay the same the only changes are the location of the mobile devices taken for each of the reference point. https://doi.org/10.15837/ijccc.2021.6.4394 5 figure 4: experimental test bed to get the situation as exact as in real environment, people who are walking in the fingerprinting database area was considered. hence, this will give an exact real environment to get a precise result in discussing the effect of sample size. the data collection procedure is the modelled similarly to paper the approach delineated in [16]. the equipment used are tp-link (td-w8961n) with nominal frequency 2.4 ghz and android phone are used which is mi a2 lite mobile phone. tp-link is used as the access point and mi a2 lite is used as the devices for collecting the fingerprinting database. mobile phone is chosen because in crowdsourced most of the user who will contribute into the fingerprinting database will be using a mobile phone. thus, this experiment database will be collected by using a mobile phone. the time taken for each of the reference point taken are 20 minutes to get 1000 samples for each of the reference point. then, in each of the reference point, the sample were divided into 8 different sample sizes which are 10, 20 ,30 ,40, 50, 100, 500 and 1000 sample sizes. 10 until 50 is consider as the small sample size while the medium is 100 and 500. 1000 is the large sample size. this number will be discussed in the further analyzation to know the perfect number in choosing the number of sample size. this is to test the effect of the sample size in fingerprinting database for wi-fi system. figure 5: experimental area the reference point of the grid system of the fingerprint database is illustrated in figure 5. to make sure only the sample size is the main variable, all the other variables are constant, while only the sample size of the received signal strengh (rss) of wi-fi is different. the fingerprinting database has 42 reference point, where the gap location between each reference point is 1 meter. the fingerprinting database for the vertical axis is 6 meter while in the horizontal axis is 5 meters. one access point has been used in this data collection process which is labelled as ap1. in this work, we consider an access point by using one router with 2 fixed antennae at a nominal 2.4 ghz frequency which is tp-link (td-w8961n). the axis of the reference point is based on the https://doi.org/10.15837/ijccc.2021.6.4394 6 x and y axis as indicated in figure 5. the database is represented as rx y, where r is the received signal strength (rss) of wi-fi for 42 reference point. x is the x axis while y is the y axis for each of the reference point in the experimental test bed. this reference point is labelled so it is easier for the database organization for the used in estimating the user location. 3.3 k-nearest neighbor (knn) the sample size are tested using two methods in predicting the user location. two method which were implemented are knn and dnn. knn is a conventional method that has been used by most of the researcher to locate the user data by taking the nearest neighbors. the advanced method is the dnn method. it based on artificial intelligent where it trains the data before it started to predict using the trained model. knn is a supervised machine learning algorithm. knn does not need a specialized training phase as it calculates through distance from its neighbors. hence, it is also does not need a gaussian data to get an accurate value. the step for predicting the user location will be first to label the training data. in this test, the label data is the access point 1. the variation of sample will be the manipulated variable for the analyzing purposes. next, is by choosing the k of the algorithm which is how many neighbors for the algorithm to classify into how many group. in this case, the k is chosen in 3 variation which are 1, 3, and 5 because the k is important in knn algorithm. hence, to analyze a better effect of the sample sizes in the fingerprinting database, the k will be varying. by using euclidean distance in equation below, it will calculate and find the nearest neighbor which the user is located. if it finds the nearest neighbors are in their group, then it will classify them as their group. d = √ (x1 − x2)2+(y1 − y2)2, where d is distance, x1 and y1 is x and y axis of one of the reference points in the database. x2 and y2 is second x and y axis in another reference point. 3.4 deep neural network (dnn) figure 6: deep neural network (dnn) model in the dnn method, the classifier used are the same as the knn which is supervised machine learning method. dnn will try to predict the location by adjusting the model of the training data. there are two phases for dnn in estimating the location. the first one is to train the data and after getting the solid model, the model is then used to predict the location. the first step in using a dnn classifier is to label the data. by using the access point 1 (ap1) as the feature, the data is labelled. then the data need to be trained. in training, the hyperparameter of the model will be chosen heuristically. in this experiment the variable that has been considered is the sample sizes, hence the hyperparameter will be fix through all the sample sizes. two hidden layers has been used in this experiment with 250 nodes in the first layer and 20 nodes in the second layer. 0.2 dropout were used to avoid overfitting. the activation function for both hidden layers are https://doi.org/10.15837/ijccc.2021.6.4394 7 by using sigmoid as it can handle negative value. for the output activation function, softmax will be used in the model because softmax is an output’s classifier as it takes the highest value in the output nodes to predict the user location. the model is illustrated in figure 6. finally, the optimizer for the model was chose by using the adam optimizer. it is back propagation where the algorithm learnt from the output and try to optimize the model by adjusting the error in output. the validation of the model used are by using accuracy metric as in subsection 3.5. deep learning learns from previous data which the user feeds into it. the main objective of classification are by using dnn to take the highest probability of the output which has been optimized by the model. the process to choose the highest probability undergoes a certain set of formula. first, by entering the input node layer by multiplying with xi and the weigh wi to get the result of the next node in the hidden layer. u = n∑ i=1 xiwi where xi is xi = rxy where rx y is the received signal strength (rss) of wi-fi in one of the access point, the unit is in dbm. on the hidden layer, u is then inserted into non-linear activation function which is the sigmoid to get the output value between 0 to 1, s (u) = 1 1 + e−u where u is the input and s is the output. after iterating process complete, the result of the second hidden layer will be inserted into the softmax equation. this will give the highest probability in continuous digit, f ( −→ s )i = esi∑k j=1 e sj where f is the output, −→ s is the input vector gain from previous outcome on sigmoid activation equation, k is the number of classes in the output as in this example is 42 classes, si is the standard exponential function for input vector and sj is the standard exponential function for output vector 3.5 validation accuracy and distance error the accuracy is determined by evaluating knn and dnn approaches. in the population of the data, 20% of the data is used to evaluate the performance of the different sample sizes. the data is evaluated by calculating the true reference point over the estimated position. the result will be converted in term of percentage , to make the value easily digestible by the reader, the ratio is simply multiplied by 100. distance error is one of the evaluations where the distance is calculated by using euclidean distance. between two of the reference points, the distance error was gain and the result is plotted in cumulative distribution frequency (cdf) graph. the frequency of distance errors is explored through an examination of the average and maximum errors. to get the accuracy, the ratio of the correct prediction and the total number of the prediction sample is applied, a = cp tp × 100 where a is accuracy, cp is the number of correct prediction and tp is the total number of the prediction sample. the accuracy gain in term of percentage as in table 2. https://doi.org/10.15837/ijccc.2021.6.4394 8 4 result and discussion in this section, the fingerprinting database will be analayzed and shown in two form by using heatmap (2d) and distribution map (3d). rss signal characteristic of different sample sizes will be shown and discussed in this section. then, statistical analysis in term of mean, mode and standard deviation will be addressed to know the effect of different sample sizes. box plot is plotted for the different sample sizes at 4 reference point to view the median stabilization where the median is important in fingerprinting method. 4.1 rss intensity heatmap figure 7: rss intensity heatmap in getting the clear view of intensity signal strength measuremen,t the heatmap is created as illustrated in figure 7. in the heatmap, the darker area presenting that the signal strength is strong as in (1,7) coordinate which is the strongest position. as the device is further away from the access point or router the signal strength becomes weaker as in coordinate (5,6). the heatmap will give the clear presentation of the intensity of the rss data to make a rough analyze for the fingerprinting database. figure 8: rss intensity distribution the rss intensity distribution is illustrated in figure 8 to get the clear presentation of the signal strength of fingerprinting database collected. the dark blue and yellow color indicated that the it is the strongest signal where the range is between -40 dbm to -30 dbm where it is the nearest point to access point. on average, the signal is between – 50 dbm to -45 dbm, where the signal is in the majority of the distribution. the weakest signal strength are between -55 dbm to -50 dbm. it is the furtheest from the access point in position (6,1). the signal strength is fluctuated over the location. https://doi.org/10.15837/ijccc.2021.6.4394 9 some of the location acquire the the lowest signal strengh even in the middle position like in (6,3) coordinate. this is expected as the rss is not linearly proportional over the distance. 4.2 rss characteristic received signal strength (rss) from the router is taken to get the properties of the signal. variety of the sample sizes is taken to examine the effect of the different sample sizes. in figure 9, the different sample sizes for rss characteristic is shown. the rss from wi-fi does not produce a smooth line, this is expected as wi-fi signal is not stable over time. in table 1, one of the reference point which is (0,1) coordinate has been taken to analyzed the statistical properties of the wi-fi signal strengh. the mean, mode, and standard deviation for 8 of the variation of samples are compared. the average or mean of the sample size started with -48.2 dbm and reduces to -46.75 dbm. this is due to the outlier at the starting of the sample collected. as the sample increases, the mean started to get stable result. at 500 sample sizes, the different between 1000 and 500 sample sizes mean are just only -0.28 dbm. this show that 500 sample is a promising result to take as the threshold for fingerpriningt database sample size. the mode or the maximum dbm is -45 and it is the same for all number of sample sizes. standard deviation shows that for the first 10, 20 and 30 number of sample size are below 2. at 40 sample sizes the standard deviation started to increase. this means that the dispersion is getting bigger. at 500 sample sizes the standard deviation starts to decrease significantly and follow with 1000 sample sizes. this show that the respectable dispersion of the number of sample sizes start at 500 and the best is at 1000 which get 1.04 in standard deviation. table 1: statistic of different number of sample sizes number of samples mean (dbm) mode (dbm) standard deviation (dbm) 10 -48.20 -45 1.87 20 -46.75 -45 2.00 30 -46.17 -45 1.82 40 -46.50 -45 2.10 50 -47.32 -45 2.51 100 -49.30 -45 2.54 500 -50.25 -45 1.34 1000 -50.53 -45 1.03 in figure 9, rss characteristic of one reference point in coordinate (0,1) with variation of sample 10, 20 ,30 ,40, 50, 100, 500, and 1000 is shown. at smaller sample size as in figure 9a, 9b and 9c the range is between in -50 dbm and -45 dbm. the range started increase by 1 in the larger sample size as in figure 9d, 9e and 9f. the largest sample sizes are 500 and 1000 as in figure 9g and 9h giving a range between -52 dbm and -45 db. in figure 9a, with 10 sample sizes the range could not be seen as it has only 10 sample sizes. on the bigger sample size as in figure 9g and 9h the signal characteristic shows that it has outlier in the beginning. this is one of the information that can be used, as it shows early of signal may be not a proper way to be choose as it can be an outlier. https://doi.org/10.15837/ijccc.2021.6.4394 10 (a) rss signal characteristic with 10 sample sizes (b) rss signal characteristic with 20 sample sizes (c) rss signal characteristic with 30 sample sizes (d) rss signal characteristic with 40 sample sizes (e) rss signal characteristic with 50 sample sizes (f) rss signal characteristic with 100 sample sizes (g) rss signal characteristic with 500 sample sizes (h) rss signal characteristic with 1000 sample sizes figure 9: rss in one reference point (0,1) with varies of sample sizes. https://doi.org/10.15837/ijccc.2021.6.4394 11 (a) box plot at coordinate (0,1) (b) box plot at coordinate (2,1) (c) box plot at coordinate (7,2) (d) box plot at coordinate (7,6) figure 10: box plot of different number of sample sizes for 4 reference point. the median distribution of the sample sizes is shown in figure 10. to avoid outlier interference, the box plot has been plotted for 4 reference point. the reference point taken are in figure 10a at coordinate (0,1), figure 10b at coordinate (2,1), figure 10c at coordinate (7,2) and figure 10d at coordinate (7,6). in figure 10a, the median of the data started stabilize when its reach 500 sample sizes. for reference point at coordinate (2,1) in figure 10b, it is the same with figure 10a, where it acquire stablity when the sample sizes are 500. in figure 10c at coordinate (7,2), the median reaches its stability on 50 sample sizes. in figure 10d at coordinate (7,6), even with 20 sample sizes the median has reach the stability. however, to get accuracy stability 50 sample sizes is considered a good threshold as it shows no difference at 100 and above sample sizes in term of its median. 4.3 accuracy prediction using deep neural network (dnn) and k-nearest neighbors (knn) the accuracy of dnn and knn is shown in table 2 in term of its percentage. for 10 sample sizes, the accuracy is 7% and by increase the sample size to 20 the accuracy starts to increase to 14%. the accuracy for dnn method keeps increasing as the sample size increase. the accuracy increases near to 4% over the sample size population. the highest accuracy is in 1000 sample sizes at 41% gain, nearly to 50%. this indicates by using dnn method, increase in sample sizes will increase the accuracy. thus, provide a better elocation estimation. at k = 1 the knn accuracy shows that in small sample sizes the accuracy starts to increase from 21% to 35% at 10 sample sizes to 30 sample sizes. however, the accuracy fluctuates where it drops back to 27% and started increase back until 39%. the maximum accuracy is 39% at 500 sample sizes. at k=3, the accuracy is unstable as the accuracy at 17% in 10 sample sizes. it fluctuate in small area until it reaches the maximum accuracy which is 30% in 1000 sample sizes. for k = 5, the result shows almost similar as k= 3 where the accuracy fluctuate in a small area. the highest accuracy is in 500 sample sizes which gets 37%. this shows that knn method does not rely on the sample size as the knn method depend on number k, the user applies. for example, in this test the k number is https://doi.org/10.15837/ijccc.2021.6.4394 12 5 which it will take the nearest 5 neighbors to classify the location for the majority neighbors. hence, knn does not solely depends on the sample size unlike the dnn method as the sample size increase, the accuracy also will increase. table 2: accuracy of two different algorithm sample size cnn(%) knn(%)k = 1 k = 3 k = 5 10 7 21 17 17 20 14 28 24 13 30 22 35 22 14 40 24 27 22 15 50 26 28 23 23 100 30 34 23 21 500 39 39 28 37 1000 41 38 30 24 4.4 distance error in this subsection, deep neural network (dnn) distance error graph is discussed. the main objective of this subsection is to know the relationship between the sample sizes and distance error of the fingerprinting database. likewise, the graph shows the maximum and the average distance error of the different sample sizes and different algorithm that has been used. the accuracy metric equation does not same with the distance error. to compute the distance error between each of the sample sizes. the cumulative distribution frequency (cdf) was plotted. the distance error gain by comparing the predicted location with the true location. the distance error is calculated by using euclidean distance as in subsection 3.3 . the graph of the cdf distance error for dnn and knn is then plotted as in figure 11 and figure 12. figure 11: dnn distance error 4.5 deep neural network (dnn) distance error in figure 11, 500 sample sizes show the most optimize result which is estimately 1.5 meter. this is followed by 30 sample sizes, then 10, 40, 1000, 50 and 20 sample sizes. this indicates that the distance error does not have any relationship with the sample sizes. in the accuracy test, the dnn method is https://doi.org/10.15837/ijccc.2021.6.4394 13 affected by the sample size. as the sample size increase the accuracy also will be increase. however, for the distance error result, it shows that the different distance error does not have any relationship between the sample size. distance maximum error acquire the same result but have a small significant different in term of the result in its arrangement. however, it still does not show a linear relationship between sample size and distance error. in 500 sample sizes, the maximum distance error is 4.2 meter which is the most optimize distance error. while the others, ranging around 5 meters to 8 meters. the worst are 20, 30, 40 and 100 which is at 7.2-meter distance error. for 1000, sample sizes, the maximum distance error is 5.8 meter. hence, this shows that the distance error for dnn method does not provide a linear relationship between the sample size and distance error. 4.6 k-nearest neighbor (knn) distance error this subsection used a k-nearest neighbor to estimate the location. by calculating the distance error, the maximum and average distance error was calculated. the aim is correlated to the dnn distance error. the only difference is the distance error equation. figure 12: knn distance error in the knn prediction method as shown in figure 12, the distance error for different sample sizes show the same as its accuracy validation. the distance error is unpredictable in term of the sample sizes. on average, the most optimize sample sizes is 30 which give the distance error 1 meter. this followed by 10, 50, 1000, 500, 20, 100 and 40 sample sizes. this sequence shows the distance error is not linearly proportional to the distance error. on the maximum distance error. the result ranging from 4 meter to 6 meter. the highest distance error is in 20 and 40 sample sizes which giving 5.2-meter distance error. the most least distance error for the least maximum error is in 50 sample sizew which gain 4 meters. the distance error in knn method is unpredictable as it does not give any correlation between the sample size and the distance error. 5 conclusion this paper discussed the effect of sample sizes in fingerprinting database for wi-fi system. through intensive data analysis the different sample sizes for fingerprinting database is discussed. first, the data was collected in 5 x 6-meter area. then, the intensity and distribution of the rss is then presented. analyzation of the data through a statistical measure is then discussed. lastly, two methods which https://doi.org/10.15837/ijccc.2021.6.4394 14 are the conventional method, knn and the most advance method, dnn is applied to examine the effect of the sample size in term of accuracy and distance error. from the characteristic of the signal from different sample sizes, the outlier could be the major problem where the outlier in the beginning of the sample could affect the accuracy atrociously as the standard deviation will be increase. if the database has only 10 sample sizes, the outlier could affect the accuracy of the prediction in future. in statistical analysis, the mean, mode, and standard deviation were discussed. the mean was affected to the outlier as in the 10 sample sizes the mean is higher compared to 20 sample sizes. this is due to the outlier in the smaller sample sizes. for the standard deviation, 10 sample sizes giving 1.87 and it start increasing until 100 sample sizes. then, it drops down at 500 sample sizes and decrease until 1.04 standard deviation in 1000 sample sizes. this shows that at 500 sample sizes, the dispersion of the signal is optimum and more sample giving less dispersion or error of the signal. afterward, the box plot is plotted to show the different sample sizes in term of the median. from 4 reference point analyzed, different location give a different stabilization in term of the median. however, the most stabilize median is when it reaches 500 sample sizes as it can be seen in 4 of the reference points discussed. in the performance metrics to test the accuracy and distance error from the different sample sizes, two methods are applied which are knn and dnn. for dnn, the accuracy is gradually increase over the sample size population. however, for knn the accuracy is not linearly proportional to the sample size as even the sample size increases the accuracy does not give a better accuracy. in distance error, the dnn and knn method both giving an unpredictable result. both methods do not show any relationship between the sample size and the distance error. nevertheless, the sample size still effects the distance error as showed in figure 11 and figure 12 in the result section. in short, the sample size effect the accuracy and distance error of the fingerprint system. however, the sample size does not show a linear relationship in accuracy and distance error except in dnn accuracy test. in the future, multiple devices will be further examining in a crowdsource data, the user contribute to the crowdsource fingerprinting database will have multiple hardware. hence, this will create a bigger problem due to the diversity signal from the multiple devices. the experiment will be statistically discussed to improve the fingerprinting system in wi-fi technologies in predicting the user location. this will help to improve the previous researcher work to contribute in indoor positioning system. funding the authors would like to acknowledge the support from the fundamental research grant scheme (frgs) under a grant number of frgs/1/2018/ict05/unimap/02/4 from the ministry of education malaysia. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] bolliger, p. 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[22] zheng-dong, l., xing-jie, c., xiu-ling, l., juan, c., yan, h., & hai-mei, x. (2020). design of ultra-wideband localization system based on optimized time difference of arrival algorithm, ieej transactions on electrical and electronic engineering, 15(8), 1176–1182, 2020. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: sa’ahiry, a. h. a., ismail, a. h., kamaruddin, l. m., hashim, m. s. m., azmi, m. s. m., safar, m. j. a., & toyoura, m. (2021). effect of sample sizes in fingerprinting database for wi-fi system, international journal of computers communications & control, 16(6), 4394, 2021. https://doi.org/10.15837/ijccc.2021.6.4394 introduction related work methodology overview data collection k-nearest neighbor (knn) deep neural network (dnn) validation accuracy and distance error result and discussion rss intensity heatmap rss characteristic accuracy prediction using deep neural network (dnn) and k-nearest neighbors (knn) distance error deep neural network (dnn) distance error k-nearest neighbor (knn) distance error conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 6, month: december, year: 2020 article number: 4052, https://doi.org/10.15837/ijccc.2020.6.4052 ccc publications improving ncs stabilization using a predictive pulsed control law o. castillo, h. benítez-pérez oriol castillo* instituto de investigaciones en matemáticas aplicadas y en sistemas universidad nacional autónoma de méxico c.p. 04510, coyoacán, ciudad de méxico, méxico. *corresponding author: octavio.castillo@iimas.unam.mx héctor benítez-pérez instituto de investigaciones en matemáticas aplicadas y en sistemas universidad nacional autónoma de méxico c.p. 04510, coyoacán, ciudad de méxico, méxico. abstract the aim in this paper is experimentally to show that a predictive pulsed control law is able to enlarge the stabilizing sampling periods of a networked control system (ncs) more than the classical predictive zoh-control law. additionally, using a comparisson in the system response between the predictive pulsed control law and the predictive zoh-control law, it is obtained that the pulsed control law requires less energy consumption to stabilize a ncs exposed to large time-delays. a 3-dof hover is used as case study. keywords: networked control systems, digital control, bayesian prediction. 1 introduction ideal control systems assume that flow of information is a continuous signal within a control loop. however, whenever a control system is physically implemented, the flow of information becomes a discontinuous signal due to presence both of the digital devices and the digital signal processing time. special case happens as a control system is implemented over a digital communication network, case in that systems are called networked control system (ncs). in that case, digital devices and digital communication networks can provide flexibility, modularity, and low cost to control system implementation. however, this digital processing can also induce significant discontinuities in the flow of information; of course, the discontinuities arise from lags during the information update. if update is fast enough, then the discontinuous flow of information seems a continuous signal and the system performance is not affected. conversely, if lags increase then the system performance degrades or even the system stability can be lost. two issues born here related to control theory: 1) the stability analysis of ncs, and 2) the search of control strategies to deal with time-delays on ncs. on one hand, stability analysis has been performed by three main approachs: the discrete approach, the time-delay system approach, and the https://doi.org/10.15837/ijccc.2020.6.4052 2 impulsive system approach. discrete approachs are the earliest and can be found for exmaple in [7]. the time-delay systems analysis can be found in [5, 6, 15]. and an impulsive system approach can be found in [13, 14]. on the other hand, different to stability analysis, some control strategies have appeared to deal with the loss of performance caused by large time-delays. these control strategies not only propose an stability analysis but also improve the control systems performance. within these strategies, the most popular are predictive control techniques whose objective is to generate a set of time-ahead control predictions to use as sampling as time-delays become large. examples of predictive control strategies can be found in [1, 9, 12, 19]. otherwise, in the same line of control strategies, there is a little known strategy called the pulsed control law which has shown to improve the performance and even recover the stability of control systems exposed to large time-delays. different from the standard way of applying the control law as a zero-order-hold (zoh), where control signal is maintained constant between sampling times, the pulsed control law consists of maintaining the control signal constant for some period of time and then put in back till the next sampling time. in general, the novelty of the pulsed control law relies on the enlargement of the stabilizing sampling period region of the ncs without the need of redesigning a new control law. this result has been reported in [2, 3, 10, 11, 17], and a dynamic proof and explanation about this phenomenon is presented in [4]. it should be stressed that the zoh-control law has played a major role in predictive strategies. in a different way, in this work the power both of a predictive strategy and a pulsed control law are merged to improve the ncs performance under a proposed configuration. the ncs configuration used in this paper consists of adding a predictor in the control loop and send one-step ahead predictions to the controller for computing the control algorithm. once the control law packet reach the actuators, one can decide if the pulsed control law or the zoh-control law is applied. to these ways of applying the control law to the system, we have called them the predictive pulsed control law or the predictive zoh-control law respectively. our intention in this paper is to compare the system response beetwen the predictive pulsed control law and the predictive zoh-control law as long as the ncs is exposed to random large time-delays. two results are obtained: 1) it is shown that the predictive pulsed control law is able to preserve the ncs stability for larger time-delays more than the predictive zohcontrol law, and 2) for large sampling periods the predictive pulsed control law requires less energy consumption than the zoh-control law for the ncs stabilization. outline of the paper is as follows. section 2 describes details and assumptions of the proposed ncs reconfiguration, and a complete system model is obtained. in section 3, the stability of the complete system model is discussed. in section 4, the model of a 3-dof hover is introduced, and in section 5 the comparison of the system response between the predictive pulsed control law and the predictive zoh-control law is presented. finally, conclusions are said in section 6. notation. we use k to denote the time-stamp of packets considering that k ∈ z+ and {k} is an ordered sequence. identity matrix in rn×n is denoted by i. p > 0 denotes a symmetric and positive definite matrix p ∈ rn×n. symbol “ ′ ” stands for matrix transposition. n(χ|χ̄,p) denotes a normal probability function of the random variable χ, with mean χ̄ and covariance matrix p . 2 system description the block diagram of the used ncs configuration is shown in figure 1. this configuration considers a continuous plant and assumes that samples are sent from the sampler as a packet with a time-stamp k. also, a predictor has been added after sampler with the aim of sending state predictions through the network. indeed, it is intended that the predictor compensates the effects of the network-time-delay over the control system. two relevant times arise within the control loop: 1) the sampling instant sk where sensors pick up measurements of the output system, and 2) the update control instant tk where the control signal is updated due to stamped packet k. some standard definitions are derived from these instants. namely, the sampling period tk is defined as tk = sk+1 −sk, ∀k, (1) https://doi.org/10.15837/ijccc.2020.6.4052 3 plant d i g i t a l n e t w o r k d e l a y figure 1: ncs with a predictor. the network-delay is defined as ηk = tk −sk, ∀k, (2) and the time-delay is defined by τ(t) = t−sk, tk ≤ t < tk+1. (3) under previous definitions, a linear system with constant matrices (a,b) is described by ẋ(t) = ax(t) + bu(t), tk ≤ t < tk+1, (4) where u(t) is the control law. usually, u(t) is an algorithm based on measurements x(sk) taken from the sampler. however, under configuration in figure 1, u(t) is based on the predictor output. thus, the predictor output was denoted as x̂(sk +ηk) to emphasize that its output is an estimate of the state x after ηk-seconds ahead to the sampling time sk. in consequence, for a given state feedback matrix k, the control law given by u(t) = kx̂(sk + ηk), tk ≤ t < tk+1, (5) is defined as the predictive zoh-control law, and the control law given by u(t) = { kx̂(sk + ηk), if tk ≤ t < tk + h, 0, if tk + h ≤ t < tk+1, (6) is defined as the predictive pulsed control law for 0 < h < (tk+1 − tk). thus, our aim in the following is to compare both control laws (5) and (6) as long as network-delays ηk becomes larges and the stabilizing state feedback matrix k is the same. 2.1 assumptions in general, in ncs the communication channels are not perfect and the flow of information is always exposed to random time-delays and packet losses. however, in order to deal with the randomness of the network-delay, it is assumed that the following three assumptions hold: assumption 1. the state feedback matrix k asymptotically stabilizes the system (4) without timedelays, i.e. the matrix a + bk is a hurwitz matrix. assumption 2 (gaussian randomness). the network-delay ηk is considered to be an uncorrelated random variable with an statistical behavior described by ηk ∼ n(ηk|η̄,σ2) where n(ηk|η̄,σ2) denotes a normal probability function with mean η̄ and covariance σ2. assumption 3 (zero packet losses). there are no packet losses in the network, and the sampling instant sk+1 is triggered by the control update instant tk, i.e. sk+1 = tk ∀k. (7) https://doi.org/10.15837/ijccc.2020.6.4052 4 tk-1 tktk-2 sk-1 sk sk+1 tk tk+1 sk+2 figure 2: sampling periods are equal to the network-delays by assumption 3. remark 2.1. it must be stressed that assumption 3 implies that ηk = tk for every k, i.e. the k-th network-delay is equal to the k-th sampling period (see figure 2). furthermore, notice that ηk = tk does not imply neither tk = tk+1 nor ηk = ηk+1 for every k. later could be only possible in probability sense due to the statistical behavior of ηk (assumption 2). therefore, by assumption 3 we have that sup tk≤t<tk+1 τ(t) = tk + ηk+1 = tk + tk+1. (8) this is the maximum time-delay experienced by the system (4). 2.2 modelling in order to implement and get a complete model of the ncs with the configuration in figure 1, both an appropiate system model and a prediction model are needed. notice that two complete models must exists, one for the predictive zoh-control law and other one for the predictive pulsed control law. assumptions 1-3 were used to deduce these models. 2.2.1 the system model let η̄ = t be the mean sampling period of the random variable ηk. hence, thanks to assumption 3, we can solve the differential equation (4) for the mean sampling period t at times tk to get the discrete model { x(k + 1) = āx(k) + b̄σu(k) + q(k) y(k) = cx(k) + r(k), (9) where q(k) and r(k) are additive, uncorrelated and zero-mean noise quantities generated by the η(k)randomness and sensor measurements respectively such that q(k) ∼ n(q(k)|0,q), r(k) ∼ n(r(k)|0,r). (10) notice that q(k) represents the uncertainty at solving the differential equation (4) because of the presence of the digital network. a code variable σ was added to distinguish between the system model generated by the control law used: fixing σ = 1 denotes the solution to the system (4) using the predictive zoh-control law (5), and fixing σ = 2 denotes the solution to the system (4) using the predictive pulsed control law (6). namely, ā = eat , b̄1 = ∫ t 0 easds, b̄2 = ∫ h 0 ea(t−s)ds, c = i. (11) 2.2.2 prediction model to build the prediction model, it was used the bayesian approach addressed in [18] to predict the one-step-ahead state of the system x̂(k + 1) at time-step k. under this approach, three models are needed: the probabilistic system model p(x(k + 1)|x(k)), the probabilistic measurement model p(y(k)|x(k)), and the solution to the kolmogorov equation p(x(k + 1)|y(k)) = ∫ p(x(k + 1)|x(k))p(x(k)|y(k))dx(k). (12) from (9) and (10), we have that p(x(k + 1)|x(k)) = n(x(k + 1)|āx(k) + b̄σu(k),q), (13) p(y(k)|x(k)) = n(y(k)|cx(k),r). (14) https://doi.org/10.15837/ijccc.2020.6.4052 5 hence, by [18], using (13) and (14), the solution to (12) at every k-step, is given by a recursive computation of update and prediction. update is given by p(x(k)|y(k)) = n(x(k)|x̂+(k),p +k ) with x̂+(k) = x̂(k) + pkc′(cpkc′ + r)−1(y(k) −cx̂(k)) (15) p +k = pk −pkc ′(cpkc′ + r)−1cpk , (16) and prediction is given by p(x(k + 1)|y(k)) = ∫ p(x(k + 1)|x(k))p(x(k)|y(k))dx(k) = n(x(k + 1)|x̂(k + 1),pk+1) (17) where x̂(k + 1) = āx̂+(k) + b̄σu(k) (18) pk+1 = āp +k ā ′ + q. (19) equations (15)-(16) and (18)-(19) form jointly the prediction model. 2.2.3 the complete system model defining fk = pkc′(cpkc′ + r)−1, and replacing (15) in (18), we get that the predicted state is given by x̂(k + 1) = (ā− āfkc)x̂(k) + b̄σu(k) + āfky(k). (20) then, using (20) and the system model (9), we have that the prediction error defined as x̃(k + 1) = x(k + 1) − x̂(k + 1) is given by x̃(k + 1) = (ā− āfkc)x̃(k) − āfkr(k) + q(k). (21) on the other hand, the system model can be rewritten as x(k + 1) = āx(k) + b̄σ(kx̂(k) −kx(k) + kx(k)) + q(k) = (ā + b̄σk)x(k) − b̄σkx̃(k) + q(k). (22) hence, using (21) and (22), the complete system model results in[ x(k + 1) x̃(k + 1) ] = [ ā + b̄σk −b̄σk 0 ā− āfkc ] [ x(k) x̃(k) ] + [ q(k) q(k) − āfkr(k) ] . (23) previous model is the complete system model related to the ncs in figure 1. recall that σ stands for the control law used, the predictive zoh-control law (σ = 1) or the predictive pulsed control law (σ = 2). 3 stability to analyze the stability of the model (23), notice that if we take its expected value, then by the separation principle, the system stability is guaranteed iff (ā + b̄σk) and (ā − āfkc) are schur matrices. this type of stability is commonly known as stability in mean []. thus, for getting stability in mean we have to ensure that (ā + b̄σk) and (ā− āfkc) are schur matrices. we focus on these https://doi.org/10.15837/ijccc.2020.6.4052 6 points. on one hand, since we are assuming that (a + bk) is a hurwitz matrix (assumption 1), then the matrix (ā + b̄σk) must be schur for small sampling periods t. on the other hand, the schur property of (ā− āfkc) is guaranteed if the covariance matrix pk converges to some matrix p > 0. to study this convergence, it is replaced (16) in (19) for getting pk+1 = āpkā′ − āpkc′(cpkc′ + r)−1cpkā′ + q. (24) previous equation is the recursive riccati equation, and it is known to be convergent to p > 0 as k →∞ if either of the following conditions holds (i) the pair (a,c) is observable provided r > 0 [8]. (ii) the pair (a,c) is detectable and the pair (a,q 1 2 ) is stabilizable [16]. thus whereas either of the later requirements hold, the matrix ā− āfkc will be schur. summarizing, we can establish the following • small smapling periods t ensures ā + b̄k is shcur. • either condition (i) or condition (ii) ensures ā− āfkc is schur. in the following, specially in testing, we select matrices a,b,k,c,r such that the schur property of the matrices ā + b̄k and ā− āfkc is guaranteed for small sampling periods. 4 case study to achieve our goals, a 3-dof hover system distributed by quanser company was used as case study (see figure 3). in general, the dynamic of this system is non-linear, however if the hover is operated under certain conditions, then the hover dynamic can be considered as linear. figure 3: quanser 3-dof hover. the built-in controller provided by quanser allows us to regulate the system orientation by controlling the yaw (ψ), pitch (θ), and roll (φ) angles of the hover. the linear dynamic is achieved by regulating the ψ-position under θ = φ = 0. thus, under θ = φ = 0, the continuous system model is{ ẋ(t) = ax(t) + bu(t) y(t) = cx(t) (25) https://doi.org/10.15837/ijccc.2020.6.4052 7 where x(t) = [ψ θ φ ψ̇ θ̇ φ̇]′, and a =   0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 −0.0001 0 0 0 0 0 0 −0.0002 0 0 0 0 0 0 −0.0002   , b =   0 0 0 0 0 0 0 0 0 0 0 0 −0.0326 −0.0326 0.0326 0.0326 0.4235 −0.4235 0 0 0 0 0.4235 −0.4235   , k = −   −55.90 66.14 0.00 −20.70 18.11 0.00 −55.90 −66.14 −0.00 −20.70 −18.11 −0.00 55.90 0.00 66.14 20.70 0.00 18.11 55.90 −0.00 −66.14 20.70 −0.00 −18.11   c = i. now, from previous matrices and for a fixed value of t, the complete system model (23) is obtained by computing the system model (9) and the prediction model given by equations (15)-(16) and (18)(19). hence, using the matrices (a,b,c) of the 3-dof hover, the system model (9) is formed by computing the matrices ā and b̄σ through (11), and the prediction model is formed using the previous matrices ā,b̄σ,c and the following covariance matrices q =   0.01 0 0 0 0 0 0 0.01 0 0 0 0 0 0 0.01 0 0 0 0 0 0 0.01 0 0 0 0 0 0 0.01 0 0 0 0 0 0 0.01   r =   0.289 0 0 0 0 0 0 0.289 0 0 0 0 0 0 0.289 0 0 0 0 0 0 0.029 0 0 0 0 0 0 0.029 0 0 0 0 0 0 0.029   × 10−6 p0 = i. (26) it is stressed that there are two complete system models, one induced by the predictive zohcontrol law (σ = 1) and other one induced by the predictive pulsed control law (σ = 2). our task in the next section, is to compare the system response between these models (σ = 1 and σ = 2) as the mean sampling period η̄ = t increases. remark 4.1. from the matrices in (25) and (26), hand calculations show that a + bk is a hurwitz matrix, q and r are positive definite matrices, and the pair (a,c) is observable. as a result, the matrices ā + b̄k and ā− āfkc are schur matrices and then the 3-dof hover under configuration in figure 1 must be stable for small sampling periods. 5 results the tests consisted of setting up the 3-dof hover as in figure 1, and compare the system performance between the predictive zoh-control law (5) and the predictive pulsed control law (6). as https://doi.org/10.15837/ijccc.2020.6.4052 8 performance measures, we select the standard energy of the error signal (ese) and the control signal energy (cse) defined as ese = ∫ t 0 e′(s)e(s)ds with e(t) = x(t) −xq, where xq is the dessired equilibrium point, and cse = ∫ t 0 u′(s)u(s)ds. (27) our control routine consisted of switching the desired equilibrium point xq between two positions, xq1 = [0 0 0 0 0 0]′ and xq2 = [30◦ 0 0 0 0 0]′, and compare the measures of ese and cse as the predictive pulsed control law or the predictive zoh-control law is used. this routine was repeated for several mean sampling periods, namely t = 10[ms], 20[ms], 40[ms], 60[ms], 100[ms]. for getting a comparison of the system response between the predictive zoh-control law and the predictive pulsed control law, we have plot the system state x, the predicted state x̂, and the measures of ese and cse for each control law: figure 4 shows the results obtained by using the predictive zohcontrol law and figure 5 shows the results obtained by using the predictive pulsed control law for a mean sampling period t = 40[ms]. figure 6 and 7 show the results using the predictive zoh-control law and the pulsed control law respectively, as the mean sampling period is t = 60[ms]. finally, the same routine under t = 100[ms] was done; figures 8 and 9 show its results. 0 10 20 30 40 0° 10° 20° 30° measured states x q 0 10 20 30 40 0° 10° 20° 30° predicted states 0 10 20 30 40 t[s] 0 10 20 30 e s e 10 2 ese test under t=40[ms] & sup (t)=80[ms] 0 10 20 30 40 t[s] 0 100 200 300 400 500 c s e cse figure 4: test under the predictive zoh-control law and t = 40[ms]. 0 10 20 30 40 0° 10° 20° 30° measured states x q 0 10 20 30 40 0° 10° 20° 30° predicted states 0 10 20 30 40 t[s] 0 10 20 30 e s e 10 2 ese test under t=40[ms] & sup (t)=80[ms] 0 10 20 30 40 t[s] 0 100 200 300 400 500 c s e cse figure 5: test under the predictive pulsed control law and t = 40[ms]. during previous testing, it was observed that the predictive zoh-control law preserves the system stability only as the sampling period t is in the region 0 ≤ t < 60[ms]. on the other hand, as figure 9 shows, the predictive pulsed control law remains the system stability for sampling periods t greater than 60[ms], actually until t = 100[ms]. for summarizing results, table 1 collects the maximum values of ese and cse for each sampling period t and control law used. figure 10 depicts these maximum ese’s as a function of the sampling periods t for each control law. from figure 10, we see that for small sampling periods, the predictive pulsed control law requires more energy than the predictive zoh control law to stabilize the ncs. however, as sampling period t increases, the energy consumption is larger using the predictive zoh control law than using the pulsed control law. even, the predictive pulsed control law maintains the ncs stability for larger sampling periods than the predictive zoh-control law as figures 8 and 9 depict. https://doi.org/10.15837/ijccc.2020.6.4052 9 0 10 20 30 40 0° 10° 20° 30° measured states x q 0 10 20 30 40 0° 10° 20° 30° predicted states 0 10 20 30 40 t[s] 0 10 20 30 e s e 10 2 ese test under t=60[ms] & sup (t)=120[ms] 0 10 20 30 40 t[s] 0 200 400 600 800 c s e cse figure 6: test under the predictive zoh-control law and t = 60[ms]. 0 10 20 30 40 0° 10° 20° 30° measured states x q 0 10 20 30 40 0° 10° 20° 30° predicted states 0 10 20 30 40 t[s] 0 10 20 30 e s e 10 2 ese test under t=60[ms] & sup (t)=120[ms] 0 10 20 30 40 t[s] 0 200 400 600 800 c s e cse figure 7: test under the predictive pulsed control law and t = 60[ms]. 0 10 20 30 40 0° 10° 20° 30° measured states x q 0 10 20 30 40 -1° 0° 1° 2° 10 49 predicted states 0 10 20 30 40 t[s] 0 50 100 150 200 e s e 10 2 ese test under t=100[ms] & sup (t)=200[ms] 0 10 20 30 40 t[s] 0 0.5 1 1.5 2 c s e 10 4 cse figure 8: test under the predictive zoh-control law and t = 100[ms]. the systems is unstable. 0 10 20 30 40 0° 10° 20° 30° measured states x q 0 10 20 30 40 0° 10° 20° 30° predicted states 0 10 20 30 40 t[s] 0 10 20 30 e s e 10 2 ese test under t=100[ms] & sup (t)=200[ms] 0 10 20 30 40 t[s] 0 200 400 600 800 c s e cse figure 9: test under the predictive pulsed control law and t = 100[ms]. table 1: ese and cse comparison between the predictive zoh-control law and the predictive pulsed control law. t = 10[ms] t = 20[ms] t = 40[ms] t = 60[ms] t = 100[ms] zoh pulsed zoh pulsed zoh pulsed zoh pulsed zoh pulsed ese 2461.6 2449.1 2451.1 2510.4 2630.7 2613.4 – 2844.1 cse 351.12 348.68 383.43 466.13 740.06 587.00 – 666.65 https://doi.org/10.15837/ijccc.2020.6.4052 10 0 20 40 60 80 100 t [ms] 300 350 400 450 500 550 600 650 700 750 800 t o ta l c s e pulsed law zoh law figure 10: total energy consumptions for both the predictive pulsed control law and the predictive zoh-control law. 6 conclusions previous works related to the pulsed control law have reported that the stabilizing sampling period region of ncs is enlarged as it is used. this paper has shown that the pulsed control law keeps its effectiveness even if random network-delays are present in the control loop, and this control law works fine along with the classical theory about bayesian predictors. furthermore, one interesting result in the paper is that for large sampling periods, referring us to the system energy consumption, the system stabilization using the pulsed control law is not as hard as using the zoh-control law. in this sense, our results can be summarized saying that the zoh-control law is convenient as sampling is small, but the pulsed control law is more convenient as sampling is large. the reasons of later is because of the following reasons: 1) whenever there are large time-delays, the pulsed control law stabilizes the system to a lower energy cost than the zoh-control law, and 2) the pulsed control law is able to preserve the system stability for larger time-delays than the zoh-control law. previous results can be so useful in ncs where control systems are exposed to large time-delays. for instead, if a communication network is not busy, the predictive zoh-control law can be used, but if there is congestion or a fault in communication channels and time-delays becomes large, then the predictive pulsed control law can be applied with better benefits than the predictive zoh-control law. finally, it is worth of mention that a strong assumption in our work is the zero packet losses in communication channels. rarely communication networks exhibit zero packet losses. however, there are communication protocols (particularly protocols focused on automation control systems as profibus) which guarantee that every message reach its destiny performing retransmissions to ensure reliable messaging as tcp/ip. this enables our work. funding this work was supported by proyect papiit it100320 by national autonomous university of méxico (unam), and conacyt méxico. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. https://doi.org/10.15837/ijccc.2020.6.4052 11 references [1] beldiman, o.; walsh, g. c. bushnell, l. 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(2004). kalman filter based fdi of networked control system. in fifth world congress on intelligent control and automation (ieee cat. no. 04ex788), ieee, 2, 1330–1333, 2004. https://doi.org/10.15837/ijccc.2020.6.4052 12 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: castillo, o.; benítez-pérez, h. (2020). improving ncs stabilization using a predictive pulsed control law, international journal of computers communications & control, 15(6), 4052, 2020. https://doi.org/10.15837/ijccc.2020.6.4052. introduction system description assumptions modelling the system model prediction model the complete system model stability case study results conclusions international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(3), 437-452, june 2019. performer selection in human reliability analysis: d numbers approach j. zhao, y. deng jie zhao institute of fundamental and frontier science university of electronic science and technology of china, chengdu yong deng* institute of fundamental and frontier science university of electronic science and technology of china, chengdu *corresponding author: dengentropy@uestc.edu.cn abstract: dependence assessment among human errors in human reliability analysis (hra) is an significant issue. many previous works discussed the factors influencing the dependence level but failed to discuss how these factors like "similarity of performers" determine the final result. in this paper, the influence of performers on hra is focused, in addition, a new way of d numbers which is usually used to handle with the multiple criteria decision making (mcdm) problems is introduced as well to determine the optimal performer. experimental result demonstrates the validity of proposed methods in choosing the best performers with lowest the conditional human error probability (chep) under the same circumstance. keywords: human reliability analysis (hra), d numbers, d-s evidence theory, multiple criteria decision making (mcdm), nuclear power plant. 1 introduction human error is an important factor to be considered in the design and risk assessment of large complex systems [71], such as nuclear power plant operation [1, 28, 66], air traffic control [27,44], grounding of oil tankers [37] and iir filters [40]. dependence analysis within human reliability analysis (hra) refers to evaluating the influence of the failure of the operator to perform one task on the failure probabilities of subsequent tasks [51]. dependence between two sequent tasks within human reliability analysis means if the preceding task fails, the failure probability of the following task is higher than the success [72]. therefore, an appropriate assessment of dependence is crucial to avoid underestimation of the risk. in the existing methods, the result of dependence assessment is a conditional human error probability (chep), given failure on the preceding task [5,72]. the most widely used method is the technique for human error rate prediction (therp) dependence method [26,50]. therp introduces five levels of dependence corresponding to dfferent values of cheps and it suggests some of the factors that may influence the dependence level. the therp model refers to three main factors: "closeness in time" (ct), "task relatedness" (tr) and "similarity of performers" (sp). and the assessment requires considerable expert judgment on identifying which factor is important and on how these factors influence the dependence level. as a result, a highly subjective process that may be insufficient traceability and reproducibility would be inevitable. to overcome this limitation, decision trees (dts) method [2,17] and fuzzy expert system (fes) [38,39,72] have been introduced to extend the therp dependence model. however, the influence of performer is partially ignored to some degree in the existing methods, which is critical factor determining the dependence level. in addition, it is inevitable to deal with uncertainty in the modeling the influence of performer. as a result, it is necessary to develop a new hra copyright ©2019 cc by-nc 438 j. zhao, y. deng math model. the purpose of this paper is to present a model and discuss how the differences of performers affect the final result as well as conditional human error probability (chep). the proposed method is based on d numbers [10] which is efficient to handle uncertainty multiple criteria decision making problem (mcdm) [9,55,56]. the paper is organized as follows. in section 2, some preliminaries are briefly introduced, including hra, d-s evidence theory and d numbers. in section 3, the proposed methodology in hra is detailed. in section 4, an application of the proposed hra method in post initiator hfes of a nuclear power plant is used to illustrate the effectiveness of the presented method. conclusion is given in section 5. 2 preliminaries in this section, some preliminaries including human reliability analysis (hra), evidence theory and d numbers, are briefly introduced. definition 1. assume that task tb is subsequent to task ta, and b and a are the corresponding failure events. pa and pb are basic probability of failure of task ta and tb, respectively, then the conditional human error probability (chep) of b given is defined as follows: [47] pxd(b|a) = (1 + k ×pb)/(k + 1) (1) where k=0, 1, 6, 9, ∞, for dependence levels cd, hd, md, ld and zd, where xd=cd, hd, md, ld and zd, respectively. it reflects the less dependent the two tasks are, the lower the failure of probability is. 2.1 d-s evidence theory it’s necessary to deal with uncertainty [34,35,63,65] and many math models are presented such as fuzzy set [12,41,53,64], z-numbers [24,25,59], belief function [20,60,61] and bio-inspired model [62]. due to its efficiency to model uncertainty, evidence theory is widely used in decision making [3, 14], pattern recognition [4, 6, 19] evidential reasoning [29, 67–69] and information fusion [46,58]. some basic definitions of d-s theory are briefly introduced [7,42]: definition 2. a set of hypotheses θ is the exhaustive hypotheses of variable and the elements are mutually exclusive in θ. then θ is called the frame of discernment, defined as follows [7,42]: θ = {h1,h2, · · · ,hi, · · · ,hn} (2) the power set of θ is denoted by 2θ, and 2θ = {∅,{h1}, · · · ,{hn},{h1,h2}, · · · ,{h1,h2, · · · ,hi}, · · · , θ} (3) where ∅ is an empty set. definition 3. a bpa function m is a mapping of 2θ to a probability interval [0, 1], formally defined by [7,42]: m : 2θ → [0, 1] (4) which satisfies the following conditions: m(∅) = 0 ∑ a∈2θ m(a) = 1 0 ≤ m(a) ≤ 1 a ∈ 2θ (5) performer selection in human reliability analysis: d numbers approach 439 the mass m(a) represents how strongly the evidence supports a with the efficiency to model uncertainty [9]. conflicting management with dempster rule is still an open issue [52]. in addition, many operations on mass function have been proposed such correlation [21], distance [11], entropy measure [31,36], divergence measure [13,45] and negation [15,16]. for the same evidence, the different bpas come from the different evidence resources. the dempster’s combination rule can be used to obtain the combined evidence [7]:  m(∅) = 0 m(a) = ∑ b ⋂ c=a m1(b)m2(c) 1−k (6) where k = ∑ b ⋂ c=∅ m1(b)m2(c). it should be mentioned that many methods are presented to deal with the open issues of evidence theory. definition 4. let m be a bpa on θ. its associated pignistic probability function bet pm: θ → [0, 1] is defined as: betpm(ω) = ∑ a∈θ,ω∈a 1 |a| ma 1 −m(∅) ,m(∅) 6= 1, (7) where |a| is the cardinality of subset a. 2.2 d numbers the real application exists the uncertainty [22,57]. to address the multiple criteria decision making problem (mcdm) issue [23], a new mathematical tool called d numbers [10] to represent uncertain information is proposed. the d numbers overcome several limitations the d-s theory involved. definition 5. let ω be a finite nonempty set, a d number is a mapping that d: ω → [0,1],with∑ a∈ω d(a) ≤ 1 and d(∅) = 0. (8) where ∅ is an empty set and a is a subset of ω [10]. besides the empty set is not necessary set zero [48, 49], it should be pointed out that different from d-s theory, the elements in set ω do not require mutually exclusive and the completeness constraint is not necessary in d numbers. if ∑ a∈ω d(a) ≤ 1, the information is said to be complete; otherwise, the information is assumed to be incomplete. for a discrete set ω={b1,b2,· · · ,bi,bj,· · · ,bn}, where bi ∈r and bi 6= bj, when i 6= j,a special form of d numbers can be expressed by d(b1) = v1 d(b2) = v2 ... d(bi) = vi d(bj) = vj ... d(bn) = vn (9) 440 j. zhao, y. deng simply denoted as d={(b1,v1),(b2,v2),· · · ,(bi,vi),(bj,vj),· · · ,(bn,vn)}, where vi > 0 and n∑ i=1 vi ≤ 1. if n∑ i=1 vi = 1, the information is said to be complete; if n∑ i=1 vi < 1, the information is said to be incomplete. it is effective and convenient that using the form of d numbers to express the uncertain information in the real world. definition 6. let d1 = {(b11,v 1 1 ), · · · , (b 1 i ,v 1 i ), · · · , (b 1 n,v 1 n)},d2 = {(b21,v 2 1 ), · · · , (b 2 j,v 2 j ), · · · , (b2m,v 2 m)} be two d numbers, the combination of d1 and d2,indicated by d = d1 ⊕ d2 ,is defined by d(b) = v (10) with b = b1i + b 2 j 2 (11) v = v1i + v 2 j 2 /c (12) c =   m∑ j=1 n∑ i=1 ( v1i +v 2 j 2 ), n∑ i=1 v1i = 1 ; and m∑ j=1 v2j = 1; m∑ j=1 n∑ i=1 ( v1i +v 2 j 2 ) + m∑ j=1 ( v1c +v 2 j 2 ), n∑ i=1 v1i < 1 and m∑ j=1 v2j = 1; m∑ j=1 n∑ i=1 ( v1i +v 2 j 2 ) + m∑ j=1 ( v1i +v 2 c 2 ), n∑ i=1 v1i = 1 and m∑ j=1 v2j < 1; m∑ j=1 n∑ i=1 ( v1i +v 2 j 2 ) + m∑ j=1 ( v1c +v 2 j 2 ) + m∑ j=1 ( v1i +v 2 c 2 ) + v1c +v 2 c 2 , n∑ i=1 v1i < 1 and m∑ j=1 v2j < 1; (13) where v1c = 1 − n∑ i=1 v1i and v 2 c = 1 − n∑ j=1 v2j in the meanwhile, an aggregation operator is proposed on this special d number, it is defined as below. definition 7. letd = {(b1,v1), (b2,v2), · · · , (bi,vi), · · · , (bn,vn)}be a d number, the integration representation of d number is defined as: i(d) = n∑ i=1 bivi (14) 3 the proposed method the motivation behind the development of the proposed method is to select best performers which is the key factor in hra among alternatives using d numbers proposed by deng [10]. the influence of performers should be paid more attention. after figuring out the best performers performer selection in human reliability analysis: d numbers approach 441 figure 1: framework of the proposed method figure 2: the factor affecting the performers figure 3: functional relationships among the input factors of the dependence level 442 j. zhao, y. deng based on d numbers, the result through calculating the chep will be verified. the flow chart is shown in fig. 1. step 1. identify the influenced factors and the functional relationship among them: the first step is to determine the factors that may have influence on the dependence between two human failure events. for example, the therp [72] model refer to five main factors: "closeness in time", "task relatedness", "similarity of performers", "similarity of cues" and "similarity of goals" .and as for the performers, the influential factors the similarity of performers could be divided into three: "the average age among performers", "the average trainees time among performers" and "average operation time on the same task among performers". the relationship is shown in fig. 2 and fig. 3. step 2. suggest anchor points and linguistic judgements for each factor: anchor points and linguistic judgements are provided by domain experts in advance as guidance for the hra analyst’s judgement of the input factors. furthermore, due to the frequent ignorance of performers, the influential factors "the average age among performers", "the average trainees time among performers" and "average operation time on the same task among performers" are proposed. their anchor points are shown in tables (1-3). and a set of anchor points and linguistic judgements for the input factor sp is presented in table 4, with the associated levels of dependence of performers. dependence level of an input factors indicates the dependence level between two tasks with respect to this factor. for example, dependence level "zd" in table 4 means that the dependence level between two tasks is zero dependence with respect to the factor "similarity of performers". "average operation time difference" anchor points linguistic judgement dep. level less than 30 min the tasks are accomplished by the same individual in operation time cd more than 30 min but less than 1h high level of performer similarity in operation time exists hd more than 1h but less than 1.5h the level of performer similarity in operation time is medium md more than 1.5h but less than 2h a low level of performers similarity in operation time exists hd more than 2h no similarity of performers in operation time is present between tasks zd table 1: anchor points for input factor "average operation time difference" "average age difference "anchor points linguistic judgement dep. level less than 1 year the age of the two performers are basically the same cd more than 1 year but less than 3 years the age similarity of two performers are almost the same hd more than 3 years but less than 5years the age level of performers similarity is medium md more than 5 years but less than 7years the difference of the two performers are quite different in age hd more than 7 years the tasks are accomplished by the completely different individuals zd table 2: anchor points for input factor "average age difference" "average trainees time difference" anchor points linguistic judgement dep. level less than 1 year the operating technology between two performers is basically same cd more than1 year but less than 1.5 year the technology difference between two performers are slight hd more than 1.5 years but less than 2 years the level of operating technology between two performers is medium md more than 2 years but less than 2.5 years the operating technology between two performers is basically different hd more than 2.5 years the technology of the two teams are different zd table 3: anchor points for input factor "average trainees time difference" step 3. determine the grade with each factor by expert: it is supposed that the expert is totally authoritative. according to the collected data, the expert is asked to give their qualified judgement on each factor. the factor need to be judged by the expert are: the average age among performers, the average trainees time among performers, average operation time on the same task among performers, "closeness in time", "task relatedness". due to our purpose is to verify best performers, the factors "closeness in time", "task relatedness" are set as constant. performer selection in human reliability analysis: d numbers approach 443 anchor points linguistic judgement dep. level tsc vs control shift room no similarity of performers is present between tasks zd different team a low level of performer similarity exist ld different individuals with same qualification the level of performer similarity is medium md same team high level of performer similarity is present between tasks hd same person the tasks are accomplished by the same individual cd table 4: judgement on factors given by experts after transformation step 4. convert qualitative judgement into representation of d numbers: in the previous work in the hra, the lack of objectivity is always a severe problem. the highlight of this paper is the representation of the expert’s judgement, by which could effectively represent the judgement in an reasonable way as well as offer a new way to deal with multiple criteria decision making problem (mcdm). the transformation from the judgement into the d numbers is shown in eq. (9). take the "the average age among performers" of performer a as an example: daage = {(hd, 0.6), (cd, 0.4)}. step 5. conclude the best performers based on d numbers: given the existed judgement, we can calculate the utility of each factor using eqs. (8-13). firstly aggregating the three influential factor one by one, then the utility of performer is obtained. take the performer a as an example: daage = {(hd, 0.6), (cd, 0.4)} daope = {(hd, 0.3), (cd, 0.7)} datime = {(cd, 1.0)} to simplify the representation and calculation, it translates the qualitative judgement into numerical form: the dependence level cd, hd, md, ld, zd are corresponding to 5, 4, 3, 2, 1 respectively. the rest of the paper would follow this simplification. daage = {(4, 0.6), (5, 0.4)} daope = {(4, 0.3), (5, 0.7)} datime = {(5, 1.0)} according to eqs. (9-12), the integrated d numbers by fusing above three d numbers is obtained: dperformera = d a age ⊕ daope ⊕ datime dperformera = {(4.5, 4.916 ), (4.75, 6 16 ), (5, 5.1 16 )} then using eq. (14), it can conclude: i(dperformera) = 5 × 5.116 + 4.75 × 6 16 + 4.5 × 4.9 16 = 4.7531 in the transformation from expert’s judgements into d numbers, the more dependent the influential factors of sp are, the higher value the d numbers would be. therefore, it is could be said that by comparing the i(d) of each factor, the smaller number of i(d) is, the better performer is. then the best performer is obtained. in this paper, a transformation form d number to bpa is defined. assuming there are several d numbers of one individual, the translation from d numbers into bpa is defined as: d1i = {(b1,v1), (b2,v2)} d1j = {(b1,v3), (b2,v4)} d1k = {(b1,v5), (b2,v6)} (15) the bpa is obtained: 444 j. zhao, y. deng m(b1) = v1 + v3 + v5 n ,m(b2) = v2 + v4 + v6 n (16) where n is the number of d numbers. step 6. verify the gained result by calculating chep: taking advantage of above process, the rank of alternative performers is obtained. in this paper, using eqs. (15-16), the bpa of performers is obtained. combining with the other two factors, "closeness in time" and "task relatedness", which are constant in order to control variables, to calculate the chep using eqs. (1-7). take the above numerical data of performer a as an example. according to the eqs. (15-16), the bpa of performer a is obtained: mperformer(5) = 0.7,mperformer(4) = 0.3 then assume mtime(5) = 0.2,mtime(5) = 0.6,mtime(5) = 0.2 mtask(5) = 0.2,mtask(4) = 0.5,mtask(3) = 0.3 thus according to the eq. (5), the combination result mperformer ⊕ mtime ⊕ mtask is: mperformera(5) = mperformer(5) ⊕ mtime(5) ⊕ mtask(5) = 0.2373 mperformera(4) = mperformer(4) ⊕ mtime(4) ⊕ mtask(4) = 0.7626 according to the eq. (7), we can conclude betp(xd)=m(xd), because the used data is single subset. where xd=cd,hd,md,ld,and zd. finally, the conditional human error probability (chep) p(b|a) is calculated using eq. (1) as: p(b|a) = ∑ xd betp(xd)×pxd(b|a) = 0.2373× 1+0.01×01+0 + 0.7626× 1+0.01×1 1+1 = 0.6224 assume that the failed probability of task b is 0.01. given the previous failed task a, the reason why the failed probability of later task b is very high up to 0.6224 is that the relationship between the two successive tasks is complete dependent or highly dependent. 4 application in this section, a working model for post initiator hfes of a nuclear power plant is used to illustrate the whole procedure of the proposed method. furthermore, the working model of d numbers is used to determine the best performers that is a kind of multiple criteria decision making (mcdm). then the traditional method in hra is used to verify the proposed working model. at the end of this section, the effect of d numbers and its relationship with hra will be discussed. 4.1 selection of best performers the identified input factors of interest and their relationship in the working model are shown in fig. 3. according to the therp method, three influential factors ("the average age difference", "the average trainees time difference" and "the average operation time in solving the same problem") directly affect the performers, which is the one of influential factor to dependence level. these factors are not necessarily relevant, but the model has enough complexity to illustrate the application of the methodology. for each influential factor, anchor points and linguistic judgements corresponding to factor are provided by experts, as shown in tables (1-4). in this paper, in order to figure out the best performer, d numbers is used as the representation of judgement offered by experts. the representation of judgement in d numbers mainly depends on the number of experts who are in favor of specified judgment. take the data in table 5 as an example, as it clearly performer selection in human reliability analysis: d numbers approach 445 shows, there are five experts giving judgement. and md of one expert, cd of two experts, hd of two experts. thus according to the percentage, it can obtain: d(factor)=(hd,0.4)(cd,0.4)(md,0.2) expert expert1 expert2 expert3 expert4 expert5 level of factor hd hd cd cd md table 5: the example of judgement on factor according to the above discussion, the representation of judgement in d numbers is shown in table 6. alternatived numbersfactor age operation time performer 1 (5,0.4)(4,0.6) (5,0.7)(4,0.3) (5,1.0) performer 2 (5,0.1)(4,0.6)(3,0.3) (4,0.7)(3,0.3) (5,0.1)(4,0.9) performer 3 (4,0.3)(3,0.7) (3,0.6)(2,0.4) (4,0.3)(3,0.4)(2,0.3) performer 4 (3,0.6)(2,0.4) (3,0.1)(2,0.9) (2,0.4)(1,0.6) table 6: the representation of influential factors in d numbers the performers are determined by three factors (age, operation, time) jointly, thus this step is to aggregate the d numbers of the influential factors. using eqs. (8-13) , the result is shown in the table 7: performer d numbers performer a (4.5, 4.9 16 )(4.75, 6 16 )(5, 5.1 16 ) performer b (3.5, 5.1 30 )(3.75, 6.4 30 )(4, 7.3 30 )(4.25, 7.7 30 )(4.5, 2.2 30 )(4.75, 1.3 30 ) performer c (2.25, 2.3 24 )(2.5, 3.2 24 )(2.75, 4.8 24 )(3, 3.6 24 )(3.25, 4.8 24 )(3.5, 3.2 24 )(3.75, 2.1 24 ) performer d (1.5, 3.7 20 )(1.75, 4.4 20 )(2, 6 20 )(2.25, 3.6 20 )(2.5, 2.3 20 ) table 7: the integration of d numbers: dage ⊕ doperation ⊕ dtime in order to select the optimal performer, given the existed judgement, the i(d) like a kind of utility needed to be calculated as a evaluate criteria by using eq. (14). i(dperformera) = 4.5 × 4.916 + 4.75 × 6 16 + 5 × 5.1 16 = 4.735 i(dperformerb) = 3.5 × 5.130 + 3.75 × 6.4 30 + 4 × 7.3 30 + 4.25 × 7.7 30 + 4.5 × 2.2 30 + 4.75 × 1.3 30 = 3.995 i(dperformerc) = 2.25×2.324 +2.5× 3.2 24 +2.75×4.8 24 +3×3.6 24 +3.25×4.8 24 +3.5×3.2 24 +3.75×2.1 24 = 2.869 i(dperformerd) = 1.5 × 3.720 + 1.75 × 4.4 20 + 2 × 6 20 + 2.25 × 3.6 20 + 2.5 × 2.3 20 = 1.955 as shown in fig. 4, performer a has biggest i(d), which means the worst performer. the assumption in this paper is that the best performer should be with the lowest chep. the relationship between i(d) and conditional human error probability (chep) is positive correlation, thus we conclude the performer d should have the lowest chep. in the next section, the verification will be discussed. 4.2 verification in the above section,the best performer has been selected. but for verifying our assumption, the final chep is necessary. by comparing the conditional human error probability (chep), the performer with the lowest chep means that performer has lowest probability to make mistakes. 446 j. zhao, y. deng in reality, the alternative performers will face same problem, under the same environment, at the same time, for which we assume the factors of "closeness in time" and "task relatedness" are same for each case. and the bpa of "similarity of performer" is obtained by eqs. (15-16) and is illustrated in table 8. performers task time a m(5)=0.7;m(4)=0.3 m(5)=0.2;m(4)=0.6;m(3)=0.2 m(5)=0.2;m(4)=0.5;m(3)=0.3 b m(5) = 0.2 3 ; m(4) = 2.2 3 ; m(3) = 0.6 3 m(5)=0.2;m(4)=0.6;m(3)=0.2 m(5)=0.2;m(4)=0.5;m(3)=0.3 c m(4) = 0.6 3 ; m(3) = 1.7 3 ; m(2) = 0.7 3 m(5)=0.2;m(4)=0.6;m(3)=0.2 m(5)=0.2;m(4)=0.5;m(3)=0.3 d m(3) = 0.7 3 ; m(2) = 1.7 3 ; m(1) = 0.6 3 m(5)=0.2;m(4)=0.6;m(3)=0.2 m(5)=0.2;m(4)=0.5;m(3)=0.3 table 8: the bpa of different cases in this paper, it assumes that the weight of factors of same level is same. by using d-s evidence theory eq. (6), the fused bpa of each factor is obtained as shown in table 9. case a b c d bba m(5)=0.2373 m(5)=0.0114 m(4)=0.6383 m(3)=1 m(4)=0.7627 m(4)=0.9375 m(3)=0.3617 m(3)=0.0511 table 9: the final result of bpa due to the data used in the paper are all single subset as well as m(∅) = 0, according to the eq. (7), the numerical value of betp is equal to the numerical value of bpa. assume that basic human error probability of the subsequent task tb is p(b)=0.01. then the conditional human error probability (chep) is calculated using eq. (1), the result is shown in table 10 and fig. 4(b). case a b c d chep 0.6224 0.5387 0.4337 0.2286 table 10: the final result of chep 4.3 discussion the result of proposed method consists of two parts: the selection of alternatives and the calculation of chep. in the first part, the d numbers is used to represent the judgements given by experts. it is reasonable and intelligible way to handle such problem. the number of i(d) is treated as a criteria for performers. the smaller i(d) is, the better performer is. and the second part discusses the conditional human error probability (chep) that means that the failure probability of the following task given the failed preceding task. it is obvious the lower chep indicates the better situations. by comparing with fig. 4, it concludes the relationship between i(d) and chep is positive correlation. thus the gained result complies with our assumption, the performer a selected through proposed method has the highest chep 0.6224. it means that the task tends to fail with the most high probability when performer a is selected. i(dperformera) > i(dperformerb) > i(dperformerc) > i(dperformerd) chep : a > b > c > d. performer selection in human reliability analysis: d numbers approach 447 [i(d)] [chep] figure 4: the comparison of id and chep 5 conclusion in previous works in human reliability analysis (hra), the influence of performer is not paid enough attention. in this paper, the evaluation method based on d numbers is presented to deal with the selection of alternative performers in an reasonable and simple way. in the real world, there is no doubt that performers are the main factor that affect our selection. the contribution of this paper is that offering a new way to translate the bpa into d numbers, which is more flexible than traditional ways and reduce the subjectivity at some extent. the final result demonstrates the effectiveness of the proposed method. in the future works, we would take the weights of different influential factors into consideration to make the predicted result more accurate. funding the work is partially supported by national natural science foundation of china (grant nos. 61573290, 61503237). conflict of interest the authors declare that there is no conflict of interests regarding the publication of this paper. bibliography [1] carvalho, p. v.; dos santos, i. l.; vidal, m. c. 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(2009). a fuzzy set-based approach for modeling dependence among human errors, fuzzy sets and systems, 160(13), 1947–1964, 2009. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 2, month: april, year: 2021 article number: 4207, https://doi.org/10.15837/ijccc.2021.2.4207 ccc publications an ensemble machine learning approach to understanding the effect of a global pandemic on twitter users’ attitudes b. jia, d. dzitac, s. shrestha, k. turdaliev, n. seidaliev bokang jia, domnica dzitac*, samridha shrestha, komiljon turdaliev, nurgazy seidaliev department of computer science new york university abu dhabi, uae saadiyat island 129188, abu dhabi, uae *corresponding author: domnica.dzitac@nyu.edu email: bj798@nyu.edu, sms1198@nyu.edu, kt1673@nyu.edu, nks369@nyu.edu abstract it is thought that the covid-19 outbreak has significantly fuelled racism and discrimination, especially towards asian individuals[10]. in order to test this hypothesis, in this paper, we build upon existing work in order to classify racist tweets before and after covid-19 was declared a global pandemic. to overcome the difficult linguistic and unbalanced nature of the classification task, we combine an ensemble of machine learning techniques such as a linear support vector classifiers, logistic regression models, and deep neural networks. we fill the gap in existing literature by (1) using a combined machine learning approach to understand the effect of covid-19 on twitter users’ attitudes and by (2) improving on the performance of automatic racism detectors. here we show that there has not been a sharp increase in racism towards asian people on twitter and that users that posted racist tweets before the pandemic are prone to post an approximately equal amount during the outbreak. previous research on racism and other virus outbreaks suggests that racism towards communities associated with the region of the origin of the virus is not exclusively attributed to the outbreak but rather it is a continued symptom of deep-rooted biases towards minorities[13]. our research supports these previous findings. we conclude that the covid-19 outbreak is an additional outlet to discriminate against asian people, instead of it being the main cause. keywords: covid-19, coronavirus, machine learning, natural language processing, automatic hate-speech detection, racism. 1 introduction in december of 2019, a new disease of the coronavirus family, covid-19, was detected in wuhan, china. the world health organization (who) has declared the novel coronavirus a global pandemic due to an exponential increase in covid-19 infections in the past months reaching, as of march 1st 2021, over 119 million cases and resulting in approximately 2.6 million deaths worldwide[17]. besides its effects on global health, the covid-19 outbreak has significantly impacted the global economy, travel, political dynamics, and the public’s actions as a whole[2]. researchers suggest that https://doi.org/10.15837/ijccc.2021.2.4207 2 it is only through collaboration we will success to efficiently combat the covid-19 pandemic[9]. however, it is thought that the covid-19 outbreak has significantly fuelled racism and discrimination, especially towards asian people[10]. over the past months, extremely influential politicians made public declarations that further associate the virus to china[10]. in addition, the number of racist attacks, including hate crimes, has increased severely since the pandemic was declared a global threat to our health[5]. nonetheless, it is not the first time in history when an infectious disease outbreak is associated with communities that later have to suffer social, economic or political consequences. other diseases such as sars, the middle east respiratory syndrome or ebola had a negative impact on the communities associated with the cause of the outbreak[7][13]. previous research that investigated the effect of ebola on discrimination against african residents of hong kong suggests that social stigmatization of africans was a continued symptom of deep-rooted biases towards minorities in hong kong not solely attributable to the 2014 ebola outbreak. rather, the outbreak was an additional excuse to discriminate against the minority african community in hong kong[13]. further, other work that studied the effect of sars on racism in toronto, canada suggests that the virus outbreak in the city affected citizens’ attitude towards people who may be associated with the country of the origin of sars[12]. as a further matter, recent research shows that these types of stigmas affect negatively the research and academic community as well[7]. however, previous work on the connection between infectious disease outbreaks and racism did not look quantitatively at the impact of social media on racism towards these targeted communities that are associated negatively with the region of the origin of the virus. in this paper, we are filling this literature gap by studying the connection between racism and social media. in order to do that, we analyse the dynamics in people’s attitude towards asian people on twitter before and after the 29th of january 2020, the date when covid-19 was officially declared a threat to our global health. 2 related work previous work has looked at multiple training methods for datasets in order to classify hate speech or other similar linguistic tasks and the best-suggested method of training is the linear support vector machine learning model (svc)[1]. thus, in our study we use a linear svc classifier to identify racist tweets that target asian people between 1st of january and 31st of march 2020. the classifier is an updated version inspired by the open-source system provided by davidson et al.[4] with adapted features that are meant to identify racism towards asian people instead of just hate speech, as proposed in the original system. to further reduce overfitting, we use two other methods of training, namely a logistic regression and a long short term memory (lstm) recurrent neural network (rnn)[11]. moreover, other related work concludes that pre-trained embedding weights for twitter datasets improve the performance of the classification tasks and thus, we included this in our classifiers[6]. previous research also shows that in order to extract a deeper level of features, a deep neural network model is required[18]. however, all these methods of training models and improvements added to existing systems are still highly dependent on the linguistic nature of the classification task. according to previous literature, hate speech, particularly racism, is challenging to classify because of the high amount of offensive language and swear words on online platforms [15]. the key difference between the two is based on linguistic distinction [8]. previous related work also suggests that hate speech towards certain groups comes from a set of stereotypical words, used in either a positive or negative way due to how previous research treated the hate-speech identification as a matter of word sense disambiguation [8]. the training data sets take the above definition of hate speech into consideration. 3 data in order to investigate the dynamics in users’ attitudes on twitter towards asian people before and after the covid-19 outbreak, we use two different datasets of tweets for training, validating and testing our models and two datasets of tweets for our actual study. the two datasets used for training are collected by (1) davidson et al[4] and (2) waseem and hovy [16]. both datasets are manually https://doi.org/10.15837/ijccc.2021.2.4207 3 annotated and contain only tweets written in english. the dataset provided by davidson et al. has 24k tweets that contain hate speech keywords which were collected by using a crowd-sourced hate speech lexicon. these tweets are labelled into three categories: “hate speech”, “offensive language” and “neither”. only 5% of the tweets in this dataset were labelled as “hate speech” and 76% were labelled as “offensive language”. the remaining were labelled as “neither”[4]. on the other hand, the dataset provided by waseem and hovy contains 16k tweets that contain racist and sexist instances of hate speech. these tweets are labelled into three categories: “racism”, “sexism” and “neither”. the annotators labelled 20% of the tweets as “sexism” and 12% as “racism”. the remaining were labelled as “neither”[16]. for the sake of our study, we only considered the tweets labelled as “racism” and “neither". moreover, in order to conduct our study, we used the first available covid-19 dataset of tweets collected by chen et al[3]. this dataset contains tweet ids of users that mention the pandemic since january 22, 2020. we downloaded the tweet ids that were collected by the above team based on keywords listed in their open-source documentation. the tweets we downloaded were shared by users between january 22 and march 31, 2020. in accordance with twitter terms, we hydrate the tweet ids in order to convert them to actual tweets by using the twarc tool. all the tweets from all three datasets had to undergo a preprocessing procedure. this includes standardizing counts of urls and mentions, removing punctuation and the excess of whitespaces, as well as tokenizing the tweet by lowercasing and stemming the words. besides these standard preprocessing tasks, we cleaned the covid-19 dataset to account only for tweets written in english and that are geo-located in the united states of america. as a further matter, we used this data to train and run our models and we identified all tweets that were labelled as “racist” towards asian people. then, we used the tweet ids of all these racist tweets in order to trace down these users’ posts before their first mention of the pandemic. in accordance with twitter terms, we only had access to the last 32,000 tweets for each user, however this was enough to form a new dataset of tweets before the covid-19 outbreak. we used the latter dataset to see whether these users had a negative attitude on twitter towards asians even before the pandemic. this dataset has been preprocessed in a similar manner as the covid-19 dataset. 4 methodology figure 1: svc model metrics figure 2: confusion matrix svc model analysing and quantifying the dynamics in the attitudes of twitter users towards asian people before and after the covid-19 pandemic is not an easy task due to the subtle linguistic distinction between hate speech, specifically racism, and offensive language. since existing methods primarily focus upon hate speech, we have contributed to the research literature by developing our own improved classification method consisting of an ensemble network of multiple detectors. we trained https://doi.org/10.15837/ijccc.2021.2.4207 4 this network on datasets collected by both davidson et al, and also waseem and hovy, as mentioned above. this enabled us to build a strategy that was superior to existing methods. as a starting point, we improved upon the hate speech classifier created by davidson et al. rather than simply using an svm model, our methodology combines this strategy with a logistic regression model. we extracted features by running the pre-processing pipeline as proposed by davidson et al. which utilized term frequency-inverse document frequency (tf-idf), penn pos tagging and porter stemming on unigrams, bigrams, and trigrams. this helps highlight important features within the tweets. these two models were trained using the hate speech dataset of tweets provided by davidson et al. the output of these networks were put through an ensemble voting mechanism. figure 3: ensemble model metrics figure 4: confusion matrix ensemble model to boost the accuracy further and to introduce regularization, we also ran these features through a ensemble model of 20 deep neural network (dnn) models (see fig 5,6,7). the tweets in this ensemble model framework are first preprocessed by generating convolutional neural network word embeddings from the raw twitter data. this embedding generation uses a pre-trained wordtovec nlp model that was pretrained on 400 million tweets [18]. these dnn models were trained using the dataset collected by waseem and hovy. figure 5: neural network model the output of this dnn collection was also fed through the voting mechanism. in order to reduce overfitting, we use hard voting method which results in the most accurate vote for the three systems. in addition to the dnn, we also applied a lstm model, but unfortunately the results indicated that a lstm would not be optimal for tweets, which are short and contain dense meanings. the original https://doi.org/10.15837/ijccc.2021.2.4207 5 system proposed by davidson et al. had a precision and recall of 46% and 60%, respectively see fig 1,2. our novel ensemble method produced a precision of 50% with a recall of 70%. see fig 3,4 figure 6: neural network ensemble model figure 7: combined ensemble model we limit our study to the twitter users in the us to prevent any country bias. majority of the tweets lack explicit country information, but there is a plethora of literature on the detection of tweet country based on its several features. in this paper, we used a tweet country classification model developed by zubiaga et al [19]. the model used eight tweet-inherent features for classification such as tweet content, the user’s self-reported location and the user’s real name and was trained on two datasets, collected a year apart from each other. from the tests we conducted, the model had on average more than 85% accuracy on detecting the tweets from the us, which we deemed satisfactory. finally, the classification system was also passed through a regex keyword filter, for example "(r|r)acis.*" and "(c|c)hin.*", in order to adapt the system with features that are meant to identify racism towards particular groups of people instead of just hate speech, as proposed in the original system. 5 results we obtain three sets of results. the first set of results correspond to general rate of hateful tweets by the us twitter users. specifically, we examine whether users become increasingly more hateful after the outbreak of covid-19 and its global spread. we ran our classifier on the country-filtered covid-19 related dataset. we find that collective number of hateful tweets is generally steady during this period see fig 4. however, there were two large spikes in the number of hate-speech tweets early february (1) and early march (2). this corresponds to 1. the rapid spread of the virus within china and first wave of significant active cases outside china and 2. the significant increase in the number of cases globally, specifically in the us. see fig 11 note: there is a known gap in chen et al’s covid-19 database around feb 23 due to connectivity issues, this decrease is reflected in figures 8, 11 and 12 [3] we note that the total number of tweets (before classification) varies over time. this could affect the conclusions regarding individuals. therefore, we also examined if the number of hate-speech tweets corresponds to an increase in racist and hateful discussion on covid-19. i.e, do people individually become more hateful? a graph of the ratio of hate speech to total tweets was plotted see fig 8. we observe that the overall level of hate-speech remained steady (at around 1% of total tweets). this is https://doi.org/10.15837/ijccc.2021.2.4207 6 a surprising finding as it indicates that the level of hate speech around covid-19 has not increased significantly over the course of the pandemic. in order to examine if the recent pandemic has caused people to become more racist, we would need to control for people’s historic tweet behaviour going back to before the pandemic, and after. we selected approximately 60 thousand users who posted hateful tweets from the covid-19 dataset during the month of january, whom we label as the "haters". we selected the month of january as it allowed us to have a fairly equal representation of the 4 months before and after the initial reaction to covid-19. the two separated periods correspond to september 2019 january 2020 and january 2020 may 2020, respectively. of this, we scraped 20 thousand twitter timelines through the twitter api. because of the limits posed by twitter, we were only able to scrape the latest 1000 tweets of any given user. for many users this only covers the recent few weeks, however, for the some it covers the entire pre-pandmic and post-pandemic period. we aggregated the "hater" timeline tweets together, which produced the recent-heavy graph of hate tweets near may see fig 9. figure 8: classified hate speech tweets related to covid-19 https://doi.org/10.15837/ijccc.2021.2.4207 7 figure 9: classification of hate speech on racist users timelines when we normalize this by the total number of timeline tweets, we obtain the ratio of real hate tweets of these users see fig 9. it is important to note that while there are small spikes in the hateful tweets in february and march (corresponding to the rising cases in china and subsequently in the us), the baseline level of hate-speech does not change significantly in the pre-pandemic and post-pandemic periods. these "hater" users were already posting high level of hate speech before the pandemic and the covid-19 appears to be just another context in which they continue to express their hate speech. this figure also shows that these "haters" are also the highest source of racist hate-speech with approximately 4% of their tweets being hateful as compared to the average of 1% see fig 8 https://doi.org/10.15837/ijccc.2021.2.4207 8 figure 10: classified and normalized hate speech timelines towards chinese people. in the third set of results (see fig 10), we examine whether there was an increase in the rate of racism on twitter specifically targeted at the chinese. similar to the findings on general racism, we find that while the absolute volume of hate-speech increased towards chinese people, the actual proportion of tweets towards them remained fairly constant and in fact, slightly decreased. this goes to demonstrate that the pandemic did not cause people to become more racist towards the chinese. figure 11: hate speech vs covid-19 cases in the us and china figure 12: chart of overt racism towards chinese in figure 11 we also overlay the cumulative number of real covid-19 cases as obtained from johns hopkins university [14]. we do this for the primary countries in question, china and the us. in the figure, we observe that a increase in the number of covid-19 cases can be seen directly preceding a large increase in the number of hate tweets. this occurred for china late january, and again for the us early march. this is likely correlated with panic within the community about an imminent spread https://doi.org/10.15837/ijccc.2021.2.4207 9 of covid-19 leading to a temporary increase in racist, isolationist attitudes which do not remain. finally, in figure 12 we analyzed whether overt racism i.e usage of clear racial slurs such as "chink" changed over time. what we found, which corroborates the findings in the previous graphs, is that usage of these obvious terms spiked during the peak spread of covid-19 during late january and early march in the us and china, but quickly faded in popularity. 6 discussion our results complement the findings in the existing literature regarding the relationship between racism and various virus outbreaks, such as sars and ebola, which showed that racism towards a particular group (a community from where the virus originated) is not directly caused by the virus itself, but rather is a continued wave of existing bias towards them. our primary contribution has been the usage of an ensemble of ml models instead of relying on one specific model. this enables us to increase the accuracy when identifying racist hate-speech by increasing regularization and reducing over-fitting. it also enables us to capture more intricate features of tweets that would otherwise be missed by a single model. this can be seen in our superior accuracy. nevertheless, this comes at a cost of lower recall levels. however, for this analysis involving huge datasets (60m+ tweets), a higher precision is more desirable. while we strove to make our analysis and results robust by following rigorous data processing procedures and making use of a wide array of ml models, our work is not exempt from some limitations. first limitation stems from that fact that we trained our models on out-of-distribution data as opposed to an in-domain data. davidson et al.[4] was originally designed to detect racism towards african americans (not chinese as in our context) while the waseem and hovy dataset [16] was designed for classification of islamophobic tweets along with tweets targeting african americans. while we combined them in order to reduce their idiosyncratic limitations, training on in-domain data would have yielded more accurate results in detecting tweets specifically racist towards chinese. in our future work we could overcome these limitations by manually annotating our own dataset (e.g. using amazon mechanical turk) to generate training data for detecting tweets racist towards chinese. second limitation of our research is that we did not filter out potential tweets by bots. since we found that a proportion of hateful tweets was fairly constant regardless of the total tweets posted by the users (bots tend to post more often than humans), we believe that this limitation would not alter the general findings of our research. to conclude, analyzing twitter users’ attitudes towards racial groups in the context of a global pandemic provides significant information on how humans behave during critical times. even though the racism appears not be directly caused by the virus itself, but rather a continued wave of existing bias towards asian people, it is important to understand this phenomenon. after all, according to relevant literature, it is only through collaboration that we can efficiently combat the covid-19 pandemic[9]. 7 acknowledgments we would like to express our special thanks to our professors, talal rahwan and bedoor alshebli, for the feedback, guidance and supervision provided throughout the development of our research project. as a further matter, we would like to acknowledge thomas davidson, dana warmsley, michael macy, and ingmar weber for the open-source system and dataset that enabled us to conduct our research. on the same note, we would like to thank zeerak waseem and dirk hovy for the open-source dataset that we used in training our models. last but not least, we would like to express our special thanks to emily chen, kristina lerman and emilio ferrara for providing the first public dataset of tweets 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(2021). an ensemble machine learning approach to understanding the effect of a global pandemic on twitter users’ attitudes, international journal of computers communications & control, 16(2), 4207, 2021. https://doi.org/10.15837/ijccc.2021.2.4207 introduction related work data methodology results discussion acknowledgments international journal of computers communications & control issn 1841-9836, 13(3), 440-455, june 2018. evidential identification of new target based on residual l. zheng, z. zhang, y. deng lei zheng, zhiguo zhang college of infomation science and technology jinan university, guangzhou, china leizhen@@stu2015.jnu.edu.cn, zhangzhiguo@stu2015.jnu.edu.cn yong deng* 1. big data decision institute, jinan university tianhe, guangzhou 510632, china 2. institute of fundamental and frontier science, university of electronic science and technology of china chengdu, 610054, china *corresponding author: prof.deng@hotmail.com abstract: both incompleteness of frame of discernment and interference of data will lead to conflict evidence and wrong fusion. however how to identify new target that is out of frame of discernment is important but difficult when it is possible that data are interfered. in this paper, evidential identification based on residual is proposed to identify new target that is out of frame of discernment when it is possible that data are interfered. through finding the numerical relation in different attributes, regress equations are established among various attributes in frame of discernment. and then collected data will be adjusted according to three mean value. finally according to weighted residual it is able to decide whether the target requested to identify is new target. numerical examples are used to verify this method. keywords: evidence theory, identification of new target, linear regression, residual. 1 introduction how to deal with uncertainty in real life is still an open issue [13,29,30,54,62,66,76]. many math tools, such as fuzzy set [3, 25, 58, 64, 67, 71, 73], rough sets [19, 21, 36, 40, 41, 52], entropy function [1, 2, 49, 49, 70] and d numbers [4, 8, 33, 39, 75], are presented to address this issue [14, 16, 37, 65, 71, 74]. among these efficient tools, dempster shafer evidence theory [5, 44] has been paid greatly attention recently. since dempster-shafer theory (ds theory) is proposed [5,44], it has been widely used in information fusion [9,28,32,51,60,61], control systems [10,31], uncertainty modelling [7, 19, 38, 46, 47, 59] decision making [26, 27, 35, 73], risk and reliability analysis [15, 22, 56, 63, 74] and other fields [6, 50, 68, 69]. in ds theory , a basic probability assignment(bpa) is distributed to power sets of the frame of discernment and the sum of bpa is always one when supposed that elements in the frame of discernment are mutually exclusive and exhaustive. however, in fact, the frame of discernment that we assume or have known is not complete, that is to say probably there are some unknown species in a world, which is called an open world [20,45].it should be mentioned that an open issue in evidence theory is the conflict management [48,53,55,72], which is also partially caused by open world. and in past decades, a large amount of research has been conducted on this issue. generalized evidential theory (get) is proposed [11], which defines a novel concept called generalized basic probability assignment (gbpa) to model uncertain information, and provides a generalized combination rule (gcr) for the combination of gbpas, and builds a generalized conflict model to measure conflict among evidences in an open world. not only that, in [23], the conflict is copyright ©2018 cc by-nc evidential identification of new target based on residual 441 explored in a closed world and the result of evidence fusion is able to converge to correct answer when using the proposed method. in addition, the identification of frame of discernment also is an open problem. allocating a non-null value to the mass function on the empty is able to express and judge the incompleteness of frame of discernment in certain condition. as considering the case of potentially heterogeneous sources, johan schubert proposed a novel way to construct and evaluate alternative frames of discernment [43]. and dynamic estimation of the discernment frame in belief function theory is proposed by wafa rekik et al. [42] which using cartesian product whose axes correspond to elementary discernment frames dealing with the relevance of each potential hypothesis to judge and update the frame of discernment. however all of these works assume the collected data is correct and they are not interfered or just are interfered a little. in other words, only if the collected data are not interfered or just are interfered a little, the incompleteness of frame of discernment will be identified. in fact the collected data is very probably interfered. and the known species will be recognized as new target using existing method when the collected data is interfered. therefore considering incompleteness identification under data interference condition have utilitarian value. in this paper, evidential identification based on residual is proposed to identify incompleteness of frame of discernment. first assume that the frame of discernment is complete and there is a closed world. then the method proposed in [11] is used to fuse data, which can get correct result even in evidence conflict in an closed world. secondly through finding the numerical relation in different attributes, regress equations are established for different species and residual vector is calculated. the third step is that adjust collected data according to offset degree. the forth step is to plug the adjusted data into regress equation to calculate residual vector and weighted residual. finally new target or perturbation of data is identified according to weighted residual. numerical examples are used to introduce this method. the rest of the paper is organized as follows. in section 2, dempster-shafer theory, generalized evidence theory and evidence distance are briefly introduced. in section 3, a new evidential identification based on residual is proposed. in section4 numerical examples are used to illustrate the behaviour of the new evidential identification based on residual. section 5 concludes the main contribution of the paper. 2 preliminaries in this section, some preliminaries are briefly introduced below. 2.1 dempster-shafer theory of evidence [5,44] in dempster-shafer theory, basic probability is distributed to power sets of the frame of discernment whose elements are mutually exclusive and exhaustive. some terminology and notions are defined below to explain theory better. let θ be a set of n mutually exclusive and exhaustive elements, which means the problem has n possible values. the following set is called the frame of discernment. θ = {h1,h2, ...,hn}. (1) p(θ) is the power set composed of 2n elements a of θ, representing the object is in a. p(θ) ={φ,h1, · · · ,hn, (h1,h2), (h1,h3), ..., (hn−1,hn ), · · · , (h1,h2,h3), · · · , θ}. (2) 442 l. zheng, z. zhang, y. deng a basic probability assignment (bpa) is a function from p(θ) to [0, 1] defined by: m : p(θ) → [0, 1] (3) and which satisfies the following conditions:∑ a∈p(θ) m(a) = 1, (4) m(φ) = 0. (5) where m(a) represents the belief to a. 2.2 generalized evidence theory generalized evidence theory is proposed by deng [11], which generalize ds theory of evidence. when the frame of discernment is probably incomplete, generalized evidence theory is used to replace classical evidence theory, which defines a novel concept called generalized basic probability assignment (gbpa) to model uncertain information, and provides a generalized combination rule (gcr) for the combination of gbpas, and builds a generalized conflict model to measure conflict among evidences in an open world [11]. but if the frame of discernment is complete, generalized evidence theory degrades into classical evidence theory.some terminology and notions are defined below to explain theory better. let u be a frame of discernment in an open world, which consists of n mutually exclusive elements. u = {h1,h2, ...,hn}. (6) and its power set 2ug is consisted of 2 u propositions, which contains empty set. for ∀a ∈ u, if the function m : 2ug → [0, 1] satisfies ∑ a∈u mg(a) = 1 (7) the function m is called generalized basic probability assignment(gbpa). in this paper m(φ) is not restricted to zero, which means the basic probability assignment to the proposition out of discernment. in other words, m(φ) represents the probability that the target is out of discernment. 2.3 existing evidence distance to measure the distance between two bodies of evidence, jousselme defined a function from vector made up of bpas to real number [24]. let m1 and m2 be two bpas on the same frame of discernment θ, containing n mutually exclusive and exhaustive hypotheses. the distance between m1 and m2 is: dbpa(m1,m2) = √ 1 2 (−→m1 −−→m2)td(−→m1 −−→m2), (8) where −→m1 and −→m2 are the associated vectors of bpas m1 and m2 and d is a 2n × 2n matrix whose elements are d(a,b) = |a∩b| |a∪b| , a,b ∈ p(θ). evidential identification of new target based on residual 443 3 the proposed evidential identification of new target based on residual in this section evidential identification of new target based on residual is proposed. like many ways of identifying the frame of discernment, at first assume the frame of discernment is compete and the world is closed. and then based on conflicts management in close world in [11], bpas are produced and fusion result is obtained. secondly through finding the numerical relation in different attributes, regress equations are established for different species and residual vector is calculated. the third step is that adjust collected data according to offset degree. the forth step is to plug the adjusted data into regress equation to calculate residual vector and weighted residual. finally new target or perturbation of data is identified according to weighted residual. and the flow diagram is shown in figure 1. figure 1: the flow diagram of new method 3.1 evidence fusion first suppose the frame of discernment is complete, conflicts management in close world. the method proposed in [11] is used to produce and combine bpas. the reason why this method is chose is that this method can converge to correct answer even in strongly conflict condition when the frame of discernment is complete. furthermore when supposing that the frame of discernment is complete, many methods can be used to produce bpas and fuse bpas ,which will converge to correct target and show a great result. 3.2 linear regression in most cases, there is strong correlation among different attributes in the same species. furthermore the correlation changes with species. for example, the relation expressions for heights and weights change with sex. therefore the relation expression for various attributes of different species is a good tool to distinguish species. in this paper, relation expression will be established among different attributes for fused result. and it is well known that there are 444 l. zheng, z. zhang, y. deng many ways to represent the correlation for different attributes, such as covariance matrix, linear regression, nonlinear regression. for simplifying linear regression is elected to represent the correlation among attributes which is able to take every attribute into account at the same time. in other words, the following equation is used to represent one species. a1x1 + a2x2 + · · · + anxn = a (9) where xi represents the value of ith attribute in certain species, ai represents the weight of ith attribute or is called ith regression coefficient in relation expression which can be calculated by least square method and a is arbitrary constant which is an artificial constant. in addition, the value of a just affects regression coefficient increasing or decreasing manifold at the same time. in the process of linear regression, residual vector can be calculated. according to the vector, histogram and probability contribution function for residual are obtained. example 1. take iris data (http://archive.ics.uci.edu/ml/datasets/iris) as an example. suppose that setosa is one of elements in frame of discernment and twenty pieces of sample data are known. there are four attributes in one sample which are sepal length, sepal width, petal length and petal width. the chosen data are shown in figure 2 0 2 4 6 8 10 12 14 16 18 20 serial numbers of samples 0 1 2 3 4 5 6 v a lu e o f e a c h a tt ri b u te sepal length(x1) sepal width(x2) petal length(x3) petal width(x4) figure 2: twenty pieces of sample data which are used in liner regression let x1 is the vector of value of sepal length, x2 is the vector of value of sepal width, x3 is the vector of value of petal length and x4 is the vector of value of petal width, constant a is 100. using least square method, the value of a1, a2, a3, a4 is calculated and linear regression of setosa is obtained. 10.98x1 + 4.827x2 + 26.98x3 − 47.89x4 = 100. (10) if setting constant a is 1000 the linear regression of setosa is 109.8x1 + 48.27x2 + 169.8x3 − 478.9xn = 1000. (11) the regression coefficient of the second equation is just ten times of the first equation when the second constant a is ten times of the first constant. and in the process of linear regression, residual vector can be calculated. and histogram and probability contribution function for residual are shown in figure 3. evidential identification of new target based on residual 445 -12 -6 0 6 12 resudual 0 1 2 3 4 5 6 7 8 9 fr e q u e n c y histogram of residual vector when a=100 -120 -60 0 60 120 resudual 0 1 2 3 4 5 6 7 8 9 fr e q u e n c y histogram of residual vector when a=1000 figure 3: histogram of residual vector when a=100 and a=1000 3.3 data processing if the target requested to identify is one of the elements of frame discernment and the data are not disturbed, the collected data will satisfy eq.(9) equation or the residual will be small. not only that, when revising noisy data and take it into eq.(9), the revised data will satisfy eq.(9) or the residual will be small. at the same time, if the target requested to identify is out of frame, after the same data revising, the revised data will not satisfy eq.(9) or the residual will be large. but how to revise data to satisfy these conditions mentioned above? first offset degree is defined as follows. let frame of discernment θ = {h1,h2, · · · ,hn}, the target which is requested to identify is s. sj represents jth attribute for s, and hij represents jth attribute for element hi. tj = threemean(sj) threemean(hij) (12) threemean(a) = 1 4 q1 + 1 2 m + 1 4 q3 (13) where tj is defined as jth attribute offset degree for target requested to identify s. a is a vector, q1 is upper quartile for vector a, m is mean for vector a, and q3 is lower quartile for vector a. because threemean have disturbance rejection for extremum and abnormal data have no effect on offset degree, it is appropriate to use threemean to calculate offset degree. and then revised data is defined as follows. ŝj = 1 tj ×sj (14) because when taking the fusion method in [11], if the target is one of element in frame of discernment, it can diverge to correct solution. it is enough to calculate offset degree between target requested to identify and fused result and revise collected data according to fused result. it is no need to calculate offset degree between target requested to identify and every elements in frame of discernment. next plug these revised data into linear regression eq.(9) and obtain residual r. r = a− (a1h1 + a2h2 + · · · + anhn) (15) 446 l. zheng, z. zhang, y. deng 3.4 residual vector processing let r represent residual vector of collected data of target requested to identify, and the ith element of r , r(i), represents the residual between the ith collected sample data and linear regression of fusion result calculated in (9). if there is extremum in collected data, the part of component of vector will be large. to avoid the effect of extremum, weighted residual is chose. in general condition, the more similar the samples data are, the more confidence will be given, the same as the process of weighted fusion. for simplifying, the weight proposed in [12] consist of evidence distance serves as weight to produce weighted residual. as we know the less weighted residual is, the more probably the target requested to identify is the convergence. when setting α as rejection probability, the section that locate at both ends in probability density function will be rejected as well as figure 4. therefore according to given rejection probability, residual probability density function or figure 4: rejection method according to probability density function diagram and weighted residual, whether the target requested to identify is a new target is able to identify. 4 numerical example real world exist many uncertainty [17, 34, 57]. in this section numerical examples are used to illustrate the validity of evidential identification of new target based on residual. take iris data (http://archive.ics.uci.edu/ml/datasets/iris) as an example. there are 150 samples in iris data and each sample has four attributes such as sepal length, sepal width, petal length and petal width. in addition, 150 samples are divided into three classes which are setosa, versicolour and virginica and there are 50 samples in each class. let frame of discernment consist of setosa and virginica θ = {setosa,v irginica}, and versicolour is out of frame. to certify the practical of this method, two different conditions are considered. first condition is that the target requested to identify is versicolour which is out of frame. second condition is that the target requested to identify is virginica whose collected data are interfered. first condition, select 10 samples in versicolour at random as collected data step 1. evidence fusion evidential identification of new target based on residual 447 according to method proposed in [11] and collected data, 40 pieces bpa are reduced shown in figure 5 and evidence distance is obtained. 0 2 4 6 8 10 sample 0.2 0.3 0.4 0.5 bpa in sepal-length m( setosa ) m( virginica ) m( setosa,virginica ) 0 2 4 6 8 10 sample 0.3 0.32 0.34 0.36 0.38 bpa in sepal-wide m( setosa ) m( virginica ) m( setosa,virginica ) 0 2 4 6 8 10 sample 0 0.2 0.4 0.6 0.8 1 bpa in petal-length m( setosa ) m( virginica ) m( setosa,virginica ) 0 2 4 6 8 10 sample 0 0.2 0.4 0.6 0.8 1 bpa in petal-wide m( setosa ) m( virginica ) m( setosa,virginica ) figure 5: bpas of collected data and then fuse these bpas and result converges to virginica. step 2. linear regression the eq. (9) is chose to reflect the relation among four attributes of virginica. where xi represent the value of ith attribute in virginica and ai represent the weight of ith attribute or is called ith regression coefficient . based least square method, parameters in linear regression are calculated and the relational expression is shown below. 5.15x1 + 9.25x2 + 4.36x3 + 6.75x4 = 100 (16) at same time the residuals of each sample can be obtained and represented by histogram shown in figure 6. according to residual vector, the average of absolute residuals is 6.73. -30 -20 -10 0 10 20 30 residual 0 2 4 6 8 10 12 14 16 fr e q u e n c y figure 6: residual distribution histogram and when the absolute residual is larger than 7, there is seventy percent that the target requested to identify is not virginica. 448 l. zheng, z. zhang, y. deng step 3. data processing offset degree is calculated as below. where si represent the vector of ith attribute for target, hi represent the vector of ith attribute for virginica. t1 = threemean(s1) threemean(h1) = 0.95 t2 = threemean(s2) threemean(h2) = 0.96 t3 = threemean(s3) threemean(h3) = 0.81 t4 = threemean(s4) threemean(h4) = 0.69 and then according to offset degree of each attribute, data are revised. ŝi = 1 ti ×si (i = 1, 2, 3, 4) ŝ = [ŝ1, ŝ2, ŝ3, ŝ4] step 4. residual vector processing take the revised data into linear regression eq.(16) ten pieces of residual vector can be obtained.the figure of residual vector is shown in figure 7. and according to [12] the weighted residual 8.3 can be obtained as well. 1 2 3 4 5 6 7 8 9 10 sample -20 -15 -10 -5 0 5 10 15 20 re s id u a l residual vector figure 7: residual vector of collected data because 8.3 is larger than 6.73 and 7, the probability that target requested to identify is virginica is not larger than seventy percent. therefore based on the collected data, target requested to identify is regarded as a new species. above all, this method can identify new target. in the second condition, choose 10 samples in virginica at random and the sample data becomes 1.25 times. step 1. evidence fusion according to method proposed in [11] and collected data, 40 pieces bpa are reduced shown in figure 8 and evidence distance is obtained. and then fuse these bpas and result converges to virginica. evidential identification of new target based on residual 449 0 2 4 6 8 10 sample 0.2 0.3 0.4 0.5 bpa in sepal-length m( setosa ) m( virginica ) m( setosa,virginica ) 0 2 4 6 8 10 sample 0.25 0.3 0.35 0.4 bpa in sepal-wide m( setosa ) m( virginica ) m( setosa,virginica ) 0 2 4 6 8 10 sample 0 0.2 0.4 0.6 0.8 1 bpa in petal-length m( setosa ) m( virginica ) m( setosa,virginica ) 0 2 4 6 8 10 sample 0 0.2 0.4 0.6 0.8 1 bpa in petal-wide m( setosa ) m( virginica ) m( setosa,virginica ) figure 8: bpas of collected data step 2. linear regression the following equation is chose to reflect the relation among four attributes of virginica. 5.15x1 + 9.25x2 + 4.36x3 + 6.75x4 = 100 (17) step 3. data processing offset degree is calculated. t1 = 1.25, t2 = 1.28, t3 = 1.19, t4 = 1.35 and then according to offset degree of each attribute, data are revised. step 4. residual vector processing take the revised data into linear regression eq.(17) ten pieces of residual vector can be obtained.the figure of residual vector is shown in figure (9). and according to [12]the weighted residual 4.9 can be obtained as well. 1 2 3 4 5 6 7 8 9 10 sample -20 -15 -10 -5 0 5 10 15 20 re s id u a l figure 9: residual vector of collected data because 4.9 is less than 7 and 6.73, the result that target requested to identify is virginica is not rejected. 450 l. zheng, z. zhang, y. deng above all, when the data are interfered largely, target requested to identify is able to be identified as known species. 5 conclusion how to identify new target is a significant problem when it probably exists interference. however all of these works assume the collected data is correct and they are not interfered or just interfered a little. in other words, if the collected data are interfered, the identification of new target probably is wrong. the evidential identification of new target based on residual proposed in this paper considers the probability that data are interfered when identifying whether the target is new. and through data fusion, linear regression, data processing and residual vector processing, weighted residual can be obtained which is able to identify whether it is new target. it considers correlation among different attributes. by numerical example, efficiency and practicability of this method are proved. however in this method the setting of rejection probability which affects the accuracy of result is subjective. therefore how to find a objective way to identify rejection probability will be significant research indicators of further studies. also for simplifying, liner regression is elected to represent the correlation among attributes, while other method, such as nonlinear 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(2018): a dematel based completion method for incomplete pairwise comparison matrix in ahp, annals of operations research, https://doi.org/10.1007/s10479-018-2769-3, 2018. international journal of computers communications & control issn 1841-9836, 13(2), 251-267, april 2018. control-scheduling codesign for ncs based fuzzy systems p.e. mendez-monroy, i. sanchez dominguez, a. bassam, o. may tzuc paul erick mendez-monroy*, israel sanchez dominguez iimas-merida universidad nacional autonoma de mexico, parque cientifico y tecnologico de yucatan, 5.5km carretera sierra papacal chuburna, c.p. 97302 sierra papacal, yucatan, mexico *corresponding author: erick.mendez@iimas.unam.mx israel.sanchez@iimas.unam.mx ali bassam, oscar may tzuc facultad de ingenieria, universidad autonoma de yucatan, av. industrias no contaminantes, apdo. postal 150 merida, yucatan, mexico baali@correo.uady.mx, maytzuc@gmail.com abstract: in the present paper, a fuzzy codesign approach is proposed to deal with the controller and scheduler design for a networked control system which is physically distributed with a shared communication network. the proposed fuzzy controller is applied to generate the control with different sampling-actuation periods, the configuration supposes a strict actuation period disappears the jitter. the proposed fuzzy scheduling is designed to select the sampling-actuation period. so, the fuzzy codesign reduces the rate of transmission when the system is stable through the scheduler while the controller adjusts the control signal. the fuzzy codesign guarantees the stability of all the system if the network uncertainties do not exceed an upper bound and is a low computational cost method implemented with an embedded system. an unstable, nonlinear system is used to evaluate the proposed approach and compared to a hybrid control, the results show greater robustness to multiple lost packets and time delays much larger than the sampling period. this paper is an extension of [20].a keywords: codesign, dynamic scheduling, fuzzy control, networked control system. areprinted (partial) and extended, with permission based on license number 4275590998661 ieee, from "electrical engineering, computing science and automatic control, 2017 14th international conference on". 1 introduction this paper is an extension of [20]. the system model with sampling-actuation periods in [20] is modified using the one-step control input, the network imperfection estimation is improved, the feedback matrices are calculated in a simpler way. finally, the analysis of stability and the analytical codesign are also presented. networked control systems (ncss) are composed of physically distributed agents that can sense the environment, act on it, and communicate with one through a communication network to achieve some common goals. these characteristics have made them a topic of current interest in the control area. by including a communication network within the control loop, considerations are presented for its design. among these considerations the most important are time delays [13] [27] [11] [12], packet losses [13] [27] [11] [12], signal quantization [14] [21] and scheduling [6] [28]. these have been investigated with results reported in the literature. in addition, because of the advantages of reduced system wiring, simple installation, increased system flexibility and resources sharing, copyright © 2018 by cc by-nc 252 p.e. mendez-monroy, i. sanchez dominguez, a. bassam, o. may tzuc ncss have been finding applications in dc motors [5] [9], robot control [28], vehicle robot [25] and ball maglev system [11], among others. time delays in ncss are the major cause of system performance deterioration and potential system instability. time delays have been modelled by using various formulations such as constant delay [22], independently random delay [1] and random delay governed by markov chain [32]. sometimes, the time delays are included as time-varying input delays of the system [30]. yi, an and choi performed a control system over a wireless network with communications only in the control channel. they employ a full-order observer to estimate the system states without time delays. the control-actuator time delay is measured at the plant and the observed states are used as a predictor to generate the system state with the expected time delay. finally, they employ a lqr control to generate the control signal based on the next estimated states. therefore, the analysis and synthesis of ncss with both time delays and packet losses is a persistent problem in the challenging but practical problem. in the literature, some important methodologies, such as stochastic control [11], predictive control [5] [10] [30], robust control [12], and state feedback control [31], are proposed to compensate time delays and/or packet losses have been proposed. at the last years, fuzzy logic control has received great attention from academic and industrial communities. more recently, the fuzzy control has developed strategies for ncs, in the work of peng and yang [24], a delay distribution-dependent design method for ncs takagi sugeno fuzzy systems [26] was proposed taking into consideration of the probabilistic interval distribution of the communication delay. tong, qian and lui [29] used a fuzzy predictive controller to counteract time delays in the feedback channel. where the fuzzy controller estimates the variations of the control signal based on the differences between the reference error and the control error applied. chai et al. [4] investigated the state feedback and dynamic output feedback controller design for membership functions and time delays in premise variables into the controller design. the resulting conditions were expressed in terms of sos-based inequalities. in the case of lost packets, commonly the effect has been modelled by a bernoulli process and strategies in static/dynamic output feedback and model predictive control problems for discretetime t-s systems with lost packets were investigated in [7] [34]. in [15], li, wu, and feng used a fuzzy model to describe a nonlinear plant with an output feedback controller h∞ and modelling the lost packets as a bernoulli random binary distribution. it is noticed that most of the existing control methodologies for ncss adopt a sampling period regardless of network quality-of-services (qos) variations. in practical circumstances, the network qos always fluctuates due to changes in the traffic load and available network resources. in regard to qos, tipsuwan and chow [28] proposed a gain scheduling controller for ncss, where the control parameters were adjusted on-line based on network qos variations and chow [6] optimized the control parameters for gain scheduling controller to improve the ncss performance. however, these works only focused on the controller design. more recently, benătez et al. [3] presented a frequency control of multiple network control systems, this takes into account information from the network transmissions, where asymptotic stability of the systems is ensured when the time delay is bounded. this paper shows a fuzzy ncs codesign controller-scheduler to adapt simultaneously the control signal and sampling period with estimated network imperfections. this introduces a neural model to estimate the time delay and lost packets as a compound time. a fuzzy control with the estimated compound time as the antecedent part is used to minimize the network effects. finally, a fuzzy scheduler is designed to modify the sampling-actuation period based on the system performance and network utilization. control-scheduling codesign for ncs based fuzzy systems 253 the paper is organized as follows: section 2 introduces a dynamic model based on actuation periods and shows a recurrent neural network to estimate some network imperfections. in section 3 the fuzzy model with different actuation periods and the stability analysis is presented. section 4 summarizes the design of a fuzzy scheduler to modify the actuation period. section 5 presents an analytical codesign and section 6 presents the experimental case to evaluate the codesign performance, it is compared with a hybrid controller. finally, the conclusions are provided in section 7. 2 preliminaries 2.1 periodic actuation model ncs is defined as: a feedback control system closed via a communication channel, it may be shared with other control loops or nodes outside the control system. a spatially distributed system for a single control loop is shown in figure 1, where the sensor, controller, and actuator nodes exchange information via a communication network. figure 1: configuration of ncs with traffic nodes the most general continuous state-space representation of a linear system with m inputs, p outputs and n state variables is written in the following form ẋ(t) = ax + bu(t) y(t) = cx(t) (1) where a ∈ rnxn, b ∈ rnxm and c ∈ rpxn are the system, input and output matrices of the continuous-time state space respectively. xk ∈ rn is the process state vector, uk ∈ rm and yk ∈ rp are the inputs and outputs of the process. the continuous linear system eq. 1 can be discretized assuming a zero-order hold [2] for the input vector with a sampling period h to xk+1 = φhxk + γhuk yk = cxk (2) where the matrices φh ∈ rnxn and γh ∈ rnxm are obtained by φh = e ah, γh = ∫h 0 easbds (3) 254 p.e. mendez-monroy, i. sanchez dominguez, a. bassam, o. may tzuc for standard closed-loop operation of the discrete system (eq. 2), a controller can be designed using feedback control as follows uk = kxk, k ∈ rmxn (4) where k is the state feedback matrix obtained using standard control design methods. the application of the control signal (eq. 4) to the process forces to computing it with zero time. nevertheless, this is physically impossible even for processor-based systems due to algorithm computational time. taking into account this limitation. the discrete model (eq. 2) can be augmented to cope with a time delay due to the insertion of a network/processor within a control loop, as in the case of ncs [2]. the standard model that incorporates a time delay τ less than one sampling period (τ < h), is xk+1 = φhxk + φh−τ γτuk−1 + γh−τuk (5) the equation 5 has been often taken as the essential control model for design and analysis of ncs. this model assumes a time reference given by the sampling instants with a fixed time delay from sampling to actuation. however, this model is useless if the time delay is variable and/or greater than one sampling period or the sampling interval is variable. [17]. the task execution model proposed is shown in figure 2. it aims is to use strict periodic sampling and actuation [18] into the space state model decreasing the variability in the time delays and the sampling intervals. this model estimates the states as a function of the actuation periods [16], making only necessary to estimate the compound time in a set of multiples of the actuation period. the model synchronizes the operation of each control loop at the actuation instants. hence, tk is the actuation instant, the actuation interval is the time elapsed between consecutive actuation instants, named tk−1 and tk, h is the actuation period. within this actuation interval, the system state is sampled, named xs,k(ts.k) ∈ [tk−1, tk] where ts,k is the sampling time recorded. eq. 6 represents a time delay τk used to estimate the state at the actuation instant and the eq. 7 represents the discrete system with periodic actuation. τk = tk − ts,k (6) x̂k = φτkxs,k + γτkuk−1 (7) finally, making use of x̂k, the control command is computed as uk = kx̂k, k ∈mxn (8) where k is the feedback matrix that is designed in next section. the control command uk is held constant within actuation period with a zero-order hold (zoh). at each control cycle the information flow in the ncs, the sensor node begins sampling the process xs,k in time ts,k, the time delay and lost packets are estimated. it sends the data to the codesign node where is used to generate uk (eq. 6)-(eq. 8), also the scheduler generates the next actuation period if it is necessary. the control and period are sent to the actuator node applying the control to the process. finally, the actuator node sends tk and the period to the sensor node to apply the sampling period and calculate the time delay and lost packets. the cycle starts again. with the strict periodic sampling and actuation h, the time delay τ is restricted to multiples of the actuation period and the sampling intervals can be used to control the network bandwidth consumption. the model has several properties for controllers, it is compatible with standard control-scheduling codesign for ncs based fuzzy systems 255 figure 2: periodic actuation model scheduling because does not demand any specific timing constraints. the scheduling jitter is absorbed with a priori knew of time reference tk and the sampling jitter disappears using the periodic actuation model (eq. 7) absorbs the irregular sampling and variable actuation intervals. the next section presents an approach to estimate the time delay and lost packets with a recurrent neural network. 2.2 time delay and lost packets estimation one major challenge for ncs design is the effect of time delays and lost packets in a control loop. the time delays occur when the system components exchange data across the network. it can degrade the performance or even destabilize the system. the time delay τk assumes lower and upper bounds. in case of time delays is 0 < τmin ≤ τk ≤ τmax (9) on the other hand, lost packets can be the consequence of a link failure, generated purposefully to avoid congestion or guarantee the most recent data to be sent. normally, feedback controllers can tolerate a certain amount of lost packets. however, consecutive lost packets have an impact of degradation on the overall system performance. hence, the next actuation period is a compound time υ between time delays and lost packets formed as follows υ ≡ tk+1 − tk = (ξ̄ + 1)h + τk+1 − τk (10) where h denotes the actuation period, tk the actuation instant and ξ̄ the estimated lost packets. figure 3: time delays and time elapsed between lost packets with traffic (50 80 s). eq. 10 defines a compound time with the analytic bounds for time delay and lost packets, the aim is to design a recurrent neural network (rnn) using compound time values as input, and 256 p.e. mendez-monroy, i. sanchez dominguez, a. bassam, o. may tzuc time delay forecast as output. the collection of time delay data (τk) is analyzed and modelled to achieve the lowest discrepancies between the observed delay and the predicted delay (fig. 3). figure 3 illustrates the time delays and time elapsed between consecutive lost packets. heavy traffic is generated into 50-80 s showing a maximum compound time of 300 ms. the rnn is formed with three layers (fig. 4), the input layer with 6 delayed inputs, one feedback input and bias, the hidden layer with 10 tansig nonlinear neurons, and the output layer with one linear discriminatory purelin neuron and bias. i = {τk−i, ôk−1,b1} i = 1 − 6 hj = tansig [∑7 i=1 iwijii ] τ̂k+1 = o = purelin [∑10 j=1 owjhj + b2 ] (11) the levenberg-marquardt algorithm was used for training. the number of hidden nodes selected was the rnn with the best validation performance. figure 4: structure of recurrent neural network for time delay estimation 3 fuzzy control a new modelling method for nonlinear ncs with compound time is presented in this section. the process under consideration is a nonlinear discrete-time system represented by the tsk fuzzy model [26], it has the compound time υ̂ as antecedent input and linear discrete models (eq. 7) with different sampling periods hj as consequent output. by modelling the system dynamics in function of the compound time. so, defining r fuzzy rules, the j-th rule has form if υ̂ is αj then xj = φjxs,k + γjuk−1 (12) where xk ∈ rn is state vector, uk ∈ rm is input vector, αj is the j-th membership function. the overall fuzzy model is: x̂k = r∑ j=1 ψj [φjxs,k + γjuk−1] (13) with the normalized fire strength ψj as r∑ j=1 ψj = 1 ψj ≥ 0 ψj = αj r∑ s=1 αs (14) control-scheduling codesign for ncs based fuzzy systems 257 and αj = exp [ − [υ̂ −ρj]2 σ2j ] (15) αj is a gaussian membership function with parameters [ρj,σj]. on the other hand, [φj, γj] are the matrices of j-th linear discrete model discretized with a sampling period [hj, j = 1 . . .r], the discrete local models are: xj = e [ahj]xs,k + ∫ hj 0 easdsbuk = φjxs,k + γjuk−1 (16) so, [hj,ρj,σj] for j = 1, . . . ,r are assigned by user according to range of the compound time. with this fuzzy model, the estimated system state is obtained by compensating the time delays, variable sampling intervals and lost packets. the action is to smoothly switch between discrete models to generate the best estimate of the state according to the estimated compound time υ̂. once designed the fuzzy model x̂k using the estimated compound time υ̂ a fuzzy controller is proposed. this is a fuzzy feedback control law like: uk = − r∑ j=1 ψjkjx̂k j = 1, . . . ,r (17) where kj is the feedback matrix of the j-th fuzzy rule. this control law is designed like a lqr (linear quadratic regulator) [33] to minimize a performance index. the control design by lqr for each local model requires the algebraic solution of the ricatti equation for the hj matrix. so, the feedback matrices are calculated like: kj = r −1 j γ t j hj j = 1, ...,r (18) the closed loop system is: xk+1 = r∑ i=1 r∑ j=1 αiβj [ψi − γikj] xk = r∑ i=1 r∑ j=1 αiβjλijxk (19) with λij = φi − γikj i = 1, . . . ,r j = 1, . . . ,r the properties of the antecedent part (eq. 14) are considered for the stability analysis of fuzzy control uk, the eq. 20 are complementary properties of fuzzy sets. ψiψj ≥ 0 r∑ i=1 r∑ j=1 ψiψj = 1 r∑ i=1 ψ2i + 2 i<j∑ i,j ψiψj = 1 (20) 3.1 stability analysis based on the properties of fuzzy control and assume that two-overlapped fuzzy memberships at most, a stability analysis of closed loop fuzzy control is presented. first, it is necessary to define the following lemma to prove stability analysis. lemma 1. [8] for any real matrices ai, bi for 1 ≤ i ≤ r, p > 0 ∈ rnxn, we have 2 r∑ i r∑ j ψiψjati pbj ≤ r∑ i r∑ j ψi [ ati pai + b t i pbi ] (21) where he normalized fire strength ψi has the properties showed in eq. 20 and eq. 14. 258 p.e. mendez-monroy, i. sanchez dominguez, a. bassam, o. may tzuc so, the stability of the fuzzy model (13) and the fuzzy control (17) can be guaranteed if the following theorem is fulfilled. theorem 1. the equilibrium state xe = 0 of closed loop system with control input (19 with two-overlapped fuzzy memberships at most, is asymptotically stable in the large, if there exist µ positive definite matrices ps = pts > 0 such that: [λij + λji] t ps [λij + λji] − 2ps < 0 i ∈ ss s = 1, . . . ,µ (22) λtiipsλii −ps < 0 i ∈ ss j ∈ ss i < j ∈ ss (23) λij = φ i − γikj where s = {s1,s2, . . . ,sµ} are µ regions where two fuzzy rules are fired at most (two overlapped fuzzy memberships), where ss contains the indexes of fired membership functions in s region. proof: we suppose that there exist µ matrices ps = pts > 0 so (22) and (23) are satisfied. considering a candidate lyapunov function like: vk = µ∑ s=1 λs [ xtk psxk ] (24) where λs [υ̂] = { 1 υ̂ ∈ ss 0 υ̂ /∈ ss µ∑ s=1 λs [τ̂] = 1 (25) it can be easily showed that v [0] = 0, vk > 0 for xk 6= 0, and v [x] →∞ as ‖xk‖→∞, it is only sufficient shows that ∆v [xk] < 0 to prove that vk is a lyapunov function and the theorem is fulfilled. 2 so, we have: ∆vk = vk+1 −vk = µ∑ s=1 λs [ xtk+1psxk+1 ] − µ∑ s=1 λs [ xtk psxk ] by reordering and set the matrix v sk = x t k psxk ∆vk = µ∑ s=1 λsls ls = v s k+1 −v s k (26) it is enough to show that: ls < 0 s = 1, ...,µ (27) substituting v sk+1 and v s k in (26) we have: ls = [ ∑ i∈ss ∑ j∈ss ψiψjλijx ]t ps [ ∑ i∈ss ∑ j∈ss ψiψjλijx ] −xtpsx (28) ls ≤ xt [ ∑ i∈ss ∑ j∈ss ψiψj [ λtijpsλji −ps ]] x (29) ≤ xt   ∑ i∈ss ψ2i [ [λii + λii] t ps [λii + λii] −ps ] + ∑ i∈ss j<i∑ j∈ss ψiψj [ 1 2 [λij + λji] t ps [λij + λji] − 2ps ]  x ls < 0 → ∆vk < 0 (30) control-scheduling codesign for ncs based fuzzy systems 259 the first term in (30) is negative definite by (22). the second term is negative definite by (23). thus, the definite positive quadratic function (24) is a lyapunov function for the fuzzy control (17), this implicates asymptotically stability in the large. the proof of the theorem is complete. 4 scheduling in this section, the scheduling theory is used to design an online feedback scheduler for the ncs based on the performance of the control system and the load conditions of the communication network. the scheduler adjusts the sampling/actuation period in function of the qoc and the qos, the next period is selected like a multiple of the period base (h) between the lower and upper bounds obtained by an analysis of the process. the idea is to keep the deadline rate and the system performance at the reference level by adjusting the transmission period. it is proposed a local fuzzy scheduler for each sensor node present in the communication network, which based on external traffic, perform a dynamic scheduling also called feedback scheduling. the codesign controller/scheduling has as aim to minimize the effects of the time delays and lost packets using the fuzzy controller, while the fuzzy scheduling minimizes the transmission messages without degrading the process performance, modifying the sampling/actuation period into the range that the fuzzy controller guaranteed the stability. the proposed dynamic scheduling has the configuration shown in figure 1, its behaviour is as follows: the sensor node sends packets with the system information and its execution period. the controller node adds the error and control signal to the packet and sends it to the actuator node. the actuator computes the system performance and the deadline rate each scheduling period and sends it to the sensor node. finally, the sensor node modifies the period h+ based on the information of the deadline rate and the system performance. a deadline occurs when a packet is upgraded after a time limit hmax or when the packet is lost. in terms of control, the manipulated variable is the actuation period and the controlled variables are the deadline rate and system performance. the actuator node module calculates the system operation ∆e through the mean absolute error (mae) of n received packets with a scheduling period δ. while the deadline rate ∆h is calculated with the m packets received that have exceeded their deadline hmax plus the lost packets ξ during the scheduling period. ∆e = ∑ k∈n |ek| n m = {∀k ∈ n|τk > hmax} + ξ ∆h = m δ ∑ k∈n hk (31) the deadline rate as a controlled variable is a common metric for the quality of service (qos). from a real-time point of view is also an important factor that degrades the quality of control (qoc). the qos controls the number of lost deadlines at an acceptable low level. in addition, the ∆h as a controlled variable can simultaneously address the problems of variable time delays and lost packets. when ∆h is kept at a low level, the delays of most packets are less than the deadline and the number of packets lost is limited. as a consequence, the impact of the delay and the lost packets in the qoc is minimized. the sampling period affects the lost deadline rate, with short sampling periods increasing network utilization, which inherently causes that the network imperfections increase and vice versa. with heavy traffic load, the probability of collisions between nodes is greater, which potentially increases both the time delay and the lost packets and at the same time the lost 260 p.e. mendez-monroy, i. sanchez dominguez, a. bassam, o. may tzuc deadline rate. on the other hand, a largely lost deadline rate can generally be reduced by increasing the sampling period, particularly when the network is overloaded. according to the control theory of sampled data, short sampling periods generate better qoc. in this context, qoc can be improved by increasing the utilization efficiency by adjusting the sampling period. this justifies the choice of the sampling period as a manipulated variable that is adjusted with respect to the network conditions, where variations in the unpredictable and dynamic traffic load in the ncs can be effectively compensated. with the metric of the qoc (∆e) and qos (∆h), the fuzzy scheduler is designed as a control problem, where the metrics form the antecedent part and the consequent part are parallel feedback matrices. the i-th fuzzy rule has the form of: if ∆e is β1j and ∆h is β2j then h̄ = fz (32) where β are the membership functions and z ∈ r2 with z = [∆e ∆h], f is the feedback scheduling matrix. the overall fuzzy scheduler is: h̄ = m∑ i=1 2∏ j=1 βijfiz (33) where m is the number of fuzzy rules. the new sampling period is assigned in the interval: h+ = {hmin ≤ h̄ ≤ hmax} (34) given the absence of a mathematical model that describes the relationship between the lost deadline rate, the eam and the sampling period, the bounds and the fuzzy scheduler are determined based on experimentation. 4.1 analytical codesign if the fuzzy model is locally controllable, i.e. (φi, γi), i = 1, ..., l, are controllable pairs, the feedback control matrices ki, (i = 1, 2, ..., l) can be calculated using eq. (18), with matrices ri and qi to get a desired performance. the procedure of the fuzzy codesign is as follows: step 1: define the intervals of the compound time based on the measurements of the network to define the bounds of the compound time. step 2: for the plant (eq. 1), define the hi actuation periods for each fi matrix in the fuzzy scheduler. define the membership functions for the control error ∆e and deadline rate ∆h. step 3: discretize the model (eq. 1) for each hi to set γi and φi in the fuzzy controller. step 3: verify that all local discrete systems are controllable. that is, rank(γi, φiγi, ..., φ n−1 i γi) = n, i = 1, ..., l. step 4: calculate the feedback matrices ki of each local system via lqr assigning the matrices ri and qi according to the desired performance (eq. 18). step 5: find all the µ regions with almost two overlap rules and apply theorem 4.1 to check the stability of the closed-loop. if the system is not stable, go back to step 4 to reassign the matrices ri and qi. 5 experiments and results the fuzzy codesign approach is tested in a nonlinear mimo system with an ethernet network, the experiment generated variable traffic into the network with external nodes. the performance is compared with a hybrid controller [11]. control-scheduling codesign for ncs based fuzzy systems 261 5.1 case study the case study is a mimo nonlinear, open-loop unstable and time-varying system. it is a 2-dof helicopter system integrated to an ethernet network (figure 5), detailed information can be found in [19]. the sensor and actuator nodes are pentium 4 with 1028 mb ram with an intel 10/100 mb ethernet card, each has an xpc target operative systems and are connected through a switch, the controller is an embedded system with a microcontroller board based on the atmega32u4 and the atheros ar9331. the atheros processor uses linino os a linux distribution. the board has built-in ethernet slot, a 16 mhz crystal oscillator. the sensor node has an a/d 10 bits resolution and the actuator has a d/a 8 bits resolution. the tasks for the ncs are set as follows, 5 ms as minimum sampling period of sensor and actuator nodes, the controller node is driven by event. the experiment consists of a 2d helicopter simulator mounted on a fixed base with two propellers that are driven by dc motors as is shown in figure 5. the front propeller controls the elevation of the helicopter nose about the pitch axis and the back propeller controls the motion about the yaw axis. the pitch and yaw angles are measured using high-resolution encoders. figure 5: networked control system with workload the euler-lagrange method is used to derive the nonlinear equations describing the motions of the helicopter [19]. from its nonlinear equations of motions, the linear continuous state space models with x = [θ ψ θ̇ ψ̇] and u = [vp vy] are ẋ = ax + bu y = cx (35) 262 p.e. mendez-monroy, i. sanchez dominguez, a. bassam, o. may tzuc a =   0 0 1 0 0 0 0 1 0 0 − bp jp+ml2 0 0 0 0 − by jy+ml2   b =   0 0 0 0 kpp jp+ml2 kpy jp+ml2 kyp jy+ml2 kyy jy+ml2   c = [ 1 0 0 0 0 1 0 0 ] (36) where kpp is the thrust torque acting on pitch axis from pitch motor, kpy the thrust torque acting on yaw axis from pitch motor, kyp the thrust torque acting on pitch axis from yaw motor, kyy the thrust torque acting on yaw axis from yaw motor, bp the viscous damping coefficient about pitch axis, bp the viscous damping coefficient about yaw axis, l the center of mass length along helicopter bod, g the gravity constant, m the total moving mass of the helicopter, jp the total moment of inertia about pitch pivot, jy the total moment of inertia about yaw pivot, vp the voltage of pitch motor, vy voltage of yaw motor, θ the angle about pitch axis and ψ the angle about yaw axis. 5.2 results the fuzzy model is obtained discretizing the helicopter model (eqs. (35)-(36)) with different sampling-actuation periods hj = [5, 10, 15, 20] ms, the compound time is previously analyzed generating the bounds υ = [5 − 300] ms. the fuzzy model has four rules with the antecedent parameters ρ = [5, 10, 15, 20]x10−3 σ = [12, 24, 30, 30]x10−4 the fuzzy controller is designed by lqr method and the lyapunov conditions are fulfilled (22)-(23). k = [ 15.4 1.53 4.91 0.677 12.2 0.718 −1.97 17.3 −0.241 6.2 −1.24 7.03 ] the fuzzy scheduler is designed with the sampling-actuation periods hj = [5, 10, 15, 20] ms. the range of the lost deadlines is ∆e = [0, 1], the mae is ∆h = [0, 1.4] and the scheduling period δ = 1 s. the network imperfections and the system performance were measured to show the codesign approach with a square reference. the overall system performance is compared with a hybrid control designed to be stable in the compound time range. the network behaviour with medium traffic generates a peculiar behaviour in the delay and the loss of deadlines (6). the time delay increases at the start of traffic with a maximum of 31 ms and average of 5 ms, but as the traffic increases, delays become deadlines with a 5.8% and average of 132 ms between missed deadlines. the upper graph shows the time delay where the average traffic starts in 50 s, however, the effect starts seconds later and decreases around the second 55, giving rise to an increase in the lost deadlines (80 s), but its effect extends to the 85s because the switch continues to empty the queue. in this case, the fuzzy scheduler change the assignment policy for the sampling-actuation period (7) (inf.), when medium traffic appears, the eam (sup.) had variations greater than 0.1, control-scheduling codesign for ncs based fuzzy systems 263 figure 6: time delays and time elapsed between lost packets with medium traffic (50 80 s). which represents an error in steady state greater than 2% , and added to the presence of 5.8 of lost deadlines (med.), the fuzzy scheduler decides to decrease the sampling period slowly but without overloading the network and gradually improve the performance of the system. figure 7: mae, lost deadlines and the sampling-actuation period with medium traffic. with medium traffic, the fuzzy codesign and the hybrid control remain stable (fig. 8), but the fuzzy codesign (sup.) is the one that fulfils the control criteria. the codesign (sup.) with medium traffic has an overshoot of ζ = 15◦, with a stable steady error ess = 0.5 and an setting time ts = 4 s.; while the hybrid control (inf.) degrades its performance against average traffic with an overshoot of ζ = 34◦, with a stable steady error ess = 6 and with oscillations. at this level of traffic, the hybrid control ensures system stability but with poor performance. in the case of heavy traffic (figure 3), the time delay and the lost deadlines were increased, the maximum delay was 30 ms, with an average of 12 ms, and deadlines were 7.3% and the average time of 122 ms between lost deadlines. the heavy traffic started at 50 s, however, its effect was visible until the 85 s even when the traffic was finished in the 80 s. figure 9 shows the pitch position for both the fuzzy codesign and the hybrid controller. in case of the heavy traffic measured (figure 3), the hybrid controller has an erratic behavior with an overshoot ζ = 36◦, a steady stable error ess = 10 and setting time ts > 9 s. while, the fuzzy codesign curves indicate a stable behavior with an overshoot ζ = 30◦, a steady stable error 264 p.e. mendez-monroy, i. sanchez dominguez, a. bassam, o. may tzuc figure 8: comparison between the codesign and an hybrid controller with medium traffic. ess = 0.8 and setting time ts = 2 s. figure 9: comparison between the codesign and an hybrid controller with heavy traffic. the fuzzy scheduler changes the sampling period (figure 10), because the mae (sup.) has variations greater than 0.25 representing a steady-state error greater than 5%, and added to the presence of 7.8% of lost deadlines (med.), the fuzzy scheduler decides to slow down the sampling period in order to correct the error even when traffic on the network increases. this demonstrates that the fuzzy codesign can apply a dynamic control dependent on the network behaviour with a stable design in all the range of υ. 6 conclusion a fuzzy codesign approach was presented to minimize the effects of the network-induced imperfections. the approach was designed with a controller and scheduler together in function of the network imperfection measurements and the continuous model of the system. the fuzzy controller is designed to select the control signal depending on the compound time estimation, with multiple discrete model that represents the process in function of the actuation periods discontrol-scheduling codesign for ncs based fuzzy systems 265 figure 10: mae, lost deadlines and the sampling-actuation period with heavy traffic. appeared the sampling and communication jitter. the fuzzy scheduler was designed to control the sampling-actuation period in function of the system performance and the behaviour of the network. the codesign approach is applied to a nonlinear, time-varying mimo system interconnected with an ethernet network and employing an embedded system as the controller-scheduler. the codesign performance was compared with a hybrid control designed to counteract the effects of delay in the same range as codesign. the fuzzy codesign had the best performance within the entire range of compound time. acknowledgement the authors would like to thank dcc-iimas-unam for the support and for their technical support to sandra sauza escoto and joaquin morales rosales. this work was supported by unam-papiit ia104218. bibliography [1] albertos, p.; 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(2010); fuzzy constrained predictive control of non-linear systems with packet dropouts, iet control theory and applications, 4(9), 1665-1677, 2010. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 1, month: february, year: 2022 article number: 4665, https://doi.org/10.15837/ijccc.2022.1.4665 ccc publications first responders’ localization and health monitoring during rescue operations a. simo, s. dzitac, d. dzitac attila simo1*, simona dzitac2, domnica dzitac3 1*politehnica university timisoara, faculty of electrical and power engineering, romania corresponding author: attila.simo@upt.ro 2university of oradea, department of energy engineering, romania email: simona@dzitac.ro 3new york university abu dhabi, uae email:domnica.dzitac@nyu.edu abstract currently, first responders’ coordination and decision-making during res-cue, firefighting or police operations is performed via radio/gsm channels with some support of video streaming. in unknown premises, officers have no global situational awareness on operation status, which reduces coordination efficiency and increases decision making mistakes. this paper pro-poses a solution enabling the situational awareness by introducing an integrated operation workflow for actors localization and health monitoring. the solution will provide global situational awareness to both coordinators and actors, thereby increasing efficiency of coordination, reducing mistakes in decision making and diminishing risks of unexpected situations to appear. this will result in faster operation progress, lower number of human casualties and financial losses and, the most important, saved human lives in calamity situations. keywords: fuzzy set theory, probabilistic graphical model, simultaneous localization and mapping. 1 introduction nowadays, calamities such as fires in buildings, bomb threats in stations/airports and infrastructure accidents are frequently occurring in the lives of citizens. during such large events, organizations like fire departments, police, hospitals etc. work together, but the information about the actual actions is limited and information sharing between organizations is restricted. commonly, three important problems appear: 1. global coordination and individual agent decision making during operations is done only via the radio/gsm; 2. the coordinator has often no real-time information on the actual situation and the detailed positioning of individual actors; https://doi.org/10.15837/ijccc.2022.1.4665 2 3. the individual rescue worker (actor) enters an unknown hazardous site without knowing his position and the positioning of his colleagues. this reduces coordination efficiency, hampers safe decision making and slows down the rescue process, so that the casualties and death toll becomes higher than necessary, besides, first responders operate at higher risks and a less safe infrastructure. instead, rapid information is vital to plan the rescue action and to use resources correctly. every day in europe there are about 12 fire victims and 120 people severely injured by fire accidents and related hazards. the world health organization reports that there are approximately 300,000 deaths per year, globally from fire-related burns [1]. those most at risk are both young babies/children and the elderly because they are least able to escape in the event of a fire, so they need a timely and efficient help from the fire-fighter officers. it is reported that at least 94% of these fires deaths 2 occurred in homes and buildings. the total direct and indirect cost of fires, including economic consequences of deaths and injuries, is estimated to be 1% of gdp in the developed world. this figure implies that timely and efficiently stopping a fire also reduces the indirect commercial losses of the burning company sites and members of their supply chain, leading to a higher economic efficiency. apart from the financial and population losses, fire-fighters often become victims during the operation, partly due to inefficient coordination and organization of operations. in britain, official figures report an average of one on-duty fire-fighter death every quarter since 1978 [2]. according to this report, there is no evidence to suggest that fire-fighters’ health and safety in fire suppression operations has improved in recent years. so, while fire-fighting operations significantly decreased between 1996 and 2010, on-duty deaths in the same period continued to rise. similar critical figures are presented in [3] on other calamities (i.e. earthquakes), where efficiency in rescue operations is vital to reduce or prevent losses. nowadays, the rescue and fire-fighting brigades are not equipped with proper tooling that enables such efficient operations. the current equipment and coordination infrastructure do not benefit from recent technological advances at all. the state-of-the-art equipment and coordination facilities include rf or gsm communicators and, at best case, mono-cameras which stream the video data from the officers in action to a coordinator. this is certainly insufficient for efficient and timely coordination, since it: • does not allow a coordinator to have an overview on the rapidly changing global situation; • gives no detailed insight on local events and hazards; • provides no decision support with alerts and notifications generated by a situation analysis tool. the acting officers are currently equipped with an rf/gsm communicator which, similarly, is not sufficient for instant understanding of a local situation, partner positions, and a global overview of an operation. in [4] the authors reached good results in the interoperability for voice and data communication, command posts and user interfaces, as well as the following sets of sensors: biometric, video, optical gas, fire detection and radiation. by these results, a higher level of situational awareness has been achieved. also, this project developed video annotation system alerting responders in case of hazardous situations. visual navigation algorithms for controlled and unmanned vehicles are a research domain with long history and stable results. however, with appearance of low-cost depth and 3dsensors, research community reported on advanced navigation/camera tracking algorithms based on captured 3d data [5] and stereo/depth data [6]. such algorithms use the planar (and even volumetric) data obtained from laser/ structured light/stereo/time-of-flight sensors, to identify the mobile-platform position in space in real-time and control the platform based on the identified obstacle space and predictions on the optimal path to the target point. algorithms for simultaneous localization and mapping (slam) play a vital role in navigation technologies. a slam algorithm pipeline normally contains three blocks: feature detection/tracking, loop closure and bundle adjustment. such approach enables nearly correct estimation on platform localization at every point in time. slam systems can operate based on a mono-camera input [7], on depth/stereo camera data, or on lidar 3d point-cloud data. however, the best performance results are obtained in approaches where slam algorithms are based on multi-modal input data, when 2d image data is fused with 3d/depth data [8]. the focus of this research is to build an innovative solution to improve awareness of the first respondent (actors) situation, by using multimodal fusion of data from rgb cameras, depth sensor, microphone system, https://doi.org/10.15837/ijccc.2022.1.4665 3 mounted on the first respondents or uavs, also an intelligent technology, integrated in a high-tech tshirt on the actors. in the 1st section generalities about intervention management in case of calamities are discussed, while the 2nd one presents a detailed analysis of developing technologies in this field. in 3rd section the methods, models and devices used, respectively the first results generated with them are presented. finally, conclusions are synthesized within the 4th section. 2 technology analysis the research in the domain of 3d scene reconstruction from moving platforms can be distinguished by the following two trends: • development of moving sensor carriers; • deployment of data fusion algorithms for multi-modal (2d/3d) data obtained from multiple sensor devices. mod [9] is a project that aims at the development of a flexible system to create on demand maps of buildings. the platform is a single uav drone equipped with gps, stereo cameras and ultrasound sensors. the vehicle has limited autonomy and is used for 3d reconstruction tasks. construct [10] is a project aiming at methods for modelling and surveying large construction sites. the project will make use of unmanned aerial vehicles at a construction site. the goal is to provide accurate static 3d models on a regular basis of construction sites by the use of unmanned aerial vehicles. the 3d models can then be used for documentation, visualization and change detection. pegasus [11], the aim of this project is to develop a mobile vision system for overhead power line inspection. one of the challenges in this project is to achieve visual navigation, obstacle detection and model-based inspection under harsh environmental conditions. current technological advances in 3d-data capturing devices (stereo cameras, lidar laser scans, time-of-flight cameras and structured light sensors) make the concept of hybrid-sensing feasible from the technological and the cost-of-materials points of view. hybrid sensing provides much higher capturing coverage than multi-camera setups. the algorithms for multi-modal data fusion into a single enhanced representation (e.g. detailed 3d model) are in the major focus of vision and cognition research communities, led by point cloud library (pcl) [12]. such technologies as stereoto3d and kintinous [13] provide textured 3d models of the captured scene on the fly. the kinfu large scale technology [14] from the tue project partner is one of the pcl cornerstones for real-time 3d scene reconstruction. ongoing development of emergency response applications targets issues such as distributed communication between mobile devices, simulation environments for training purposes, decision support systems processing events from numerous devices, sensors, social media, and formal domain modelling. raven [15] supports the use of smart phones for collaborative disaster data collection and sharing through an exchange of the database and corresponding schema. its current interface tracks lost and found people based on database records. similarly, soceda [16] enables the exchange of social network data be-tween heterogeneous services through the distributed interaction of several event processing engines. an emergency response scenario during a nuclear disaster simulates virtual events from the involved partners. for visualizing human activity in 3d virtual environments/3d uis, different kinds of camera and sensor technologies have been widely used. to automatically detect and visualize daily activities of people in 3d virtual environments, shaikh [17] analyzed continuous voice samples recorded with a mobile device. fleck [18] utilized smart cameras for detecting location and falling of elderly people. in addition to human activities, the previous work has focused on developing visualization methods for various types of spatiotemporal data [19]. this data may include, for instance, law enforcement data, syndromic surveillance data and population density. with the introduction of new internet-of-things (iot) systems, sensor data visualization has also recently gained a lot of attention. the previous work has em-phasized that the sensor data may be complex and not always ready for direct visualization, and thus, further processing may be required [20][21]. human-computer-interaction (hci) research on 3d user interfaces (ui) has mainly addressed the use of 3d virtual worlds, and has typically focused on rather detailed aspects of the ui design, such as avatars. recently, a broader approach to the 3d ui design has also been adopted. by extending the boundaries of the traditional usability research, user experience (ux) https://doi.org/10.15837/ijccc.2022.1.4665 4 research investigates the interaction with a system in a holistic manner, taking into account both hedonic and utilitarian aspects related to the use of the technology [22]. 3 people localization and monitoring the project defines three levels of main roles/users in calamity-related operation involved with their responsibilities: • actors, i.e. agents and rescue workers carrying out the rescue, fire -fighting or inspection operation; • local coordinators managing the operation on the site, and • global coordinators, supporting the operation at their premises (hospitals, police stations, fire departments, disaster-plan centers, etc.). this project system proposes an integrated operation workflow (figure 1), where these involved users have improved communication and understanding via a connection to a common data-, decisionand commandflow, obtaining situational awareness both at the detailed level (e.g. actor’s positions and conditions) and at the over-view level (operation phase and status). the primary level and datasource for the information flow is a set of multi-modal sensors mounted onto the actors carrying out the operation (agents or unmanned vehicles). the actors and coordinators will use unified access interface to the dataflow, enabling timely reporting, coordination and data supply. the workflow is enhanced by a decision support engine, assessing the current situation based on the data flow, and generating alerts and notifications in real-time. figure 1: integrated data-, decisionand command flow for main actors in calamity situations. the project proposes to equip the acting agents (officers and/or unmanned vehicles as carriers) with sensors of multiple modalities (video, depth, thermal, acoustic, etc.) for robust data acquisition in any condition. the obtained data represents a local situation around the carrier: local layout, thermal conditions, acoustic sources and humans). during an operation, the sensor data from each individual carrier is transmitted wirelessly to a local processing unit. this unit integrates the multi-modal sensor signals from all carriers into: • 3d geo-map of the premises reconstructed as carriers propagate and • relevant geo-positioned labels, such as the carrier positions, identified human bodies, heat and noise extremes (see gray-box examples with yellow dots in figure 2). an automated analysis algorithm (e.g. video/audio content and context analytics) receives the sensor data and the gradually reconstructed geo-map with the labels. based on this input, the algorithm assesses the overall situation, identifies abnormal situations, hazardous event build-ups and constantly reports to a decision support engine, or simply flags the detected alerts directly to actors/coordinators. the decision support suggests optimal actions to coordinators. all together, the 3d geo-map, carrier’ positions, important labels, alerts and decision support data are visualized onto multiple layers on coordinator’ displays. moreover, the most important data is sent back wirelessly to the acting officers and is rendered on their glasses equipped with a ‘see-through’ graphical display. as a result, at every https://doi.org/10.15837/ijccc.2022.1.4665 5 figure 2: project system concept. moment in time, both coordinators and actors can see and understand both the global and local situation during the operation. this paper presents only the part of the project that refers to the issue of using different sensors such as rgb cameras, rgb-d cameras, depth sensor (ir projector and a monochrome complementary met-al-oxide semiconductor), microphone system, integrated textile sensors, etc. in estimating the person’s position (actors), respectively monitoring their health conditions. the paper aims to position and identify a person through information taken from fixed (through sensors mounted on the actors’ equipment – e.g. helmet) and mobile (uavs) location sensors. the aim is to detect human behavior in normal situations and in extreme situations through real-time location and monitoring algorithms. for this the slams techniques or gps location are used in the project. in situations where gps location is not available, or higher location and mapping accuracy is required, accurate and robust slam algorithms are required. slam algorithms are difficult for uavs due to the strict requirement for real-time processing. wi-fi technology is used for data transmission, but lpwa technology testing is also intended. while things are simpler in the case of processing data taken directly from the ac-tors’ equipment, in the case of uavs things are a little more complicated. in case of uavs a first approach is to implement the system that controls a drone that is in flight to stabilize and maintain it regardless of external influences and inaccuracy of sensors. this is done by using the slam (simultaneous localization and mapping) technique that is, by tracking points that can be recognized in the camera image, while maintaining a 3d map of those points. the output location is then combined using a kalman filter with odometry data to predict the future location using the dynamic model of the drone. the obtained location is then used for reactive drone flight control. all available sensors on the drone are used to estimate the current status and location of the drone, which in this project is the most difficult part of the stabilization issue. the sensor that provides the most information is, of course, the camera, being an essential source of data for the visual location used in the proposed system. due to the limited computing power of the on-board processor, data processing is done on a computer, which is another challenge in delaying the time between observation and action.. the slam community has made great strides in recent decades. to date, the problem of 2d slam with distance detectors is considered to be solved, while the real-time 3d slam algorithm, especially high quality and robust visual slam, remains an open problem. thus, it is proposed to provide an overview of the recent evolution and development of slam algorithms and the focus is on real-time slam methods, which are suitable for unmanned aerial vehicles (uavs). 4 results and discussion in the following, methods for recognizing human activities from video data taken from actors or/and uavs are described. as a first step, the movement characteristics are calculated, and various system https://doi.org/10.15837/ijccc.2022.1.4665 6 configurations are identified. the motion functions can be interpreted based on the entire sequence of images in figure 3, system 1. alternatively, certain features are associated with each route. in system 2, all object-specific paths are accumulated, and in the third system, the characteristics of the paths classified as human are accumulated. the interpretation of each individual route is done through system 4 (all object paths) and system 5 (only routes classified as human). figure 3 shows the ways to combine the system components, in different configurations 1 5, ordered according to complexity. figure 3: monitoring system with data taken from sensors. regarding the features of the movement, the latest improved trajectories are adopt-ed. the movements of the camera are strict because they are compensated by stabilizing frame by frame and eliminating the characteristics due to ego-movement. the improved trajectories are represented by the limits of movement (on the contour of the objectives) and by the histograms of the gradients (hog) and the histograms of the optical flow (hof), which proved to be distinctive for the recognition of human activity. the dedicated human detector fpdw (fastest pedestrian detector of the west) and a motion detector are used for location. if the human detector is suitable for detecting people standing upright, the motion detector will be able to signal when the person will change position. the performance of tracking techniques was evaluated for two human activities and variable object sizes. this type of assessment highlights the limitations that may be encountered during detection by actors or/and uav monitoring devices. different configuration methods have been identified. the characteristics can be identified according to the whole sequence (system 1). at the same time, the characteristics associated a certain route; after which each route was interpreted (systems 2 and 5). the procedure for associating elements with a particular route is an example of detailing the focus. in system 2, all the characteristics of the routes are described and are accumulated as a representation of a characteristic for a given sequence. in the third system, the same operation is performed for the tracks designated as human. in systems 1, 2 and 3, all the characteristics of an image sequence are fed simultaneously by the human activity recognition algorithm. systems 4 and 5 interpret each route individually based on the characteristics extracted. system 4 does this for each route, while system 5 interprets only the routes designated as human. for the final interpretation of human activity of a sequence of images, the maximum posterior probability is taken from all paths. to generate the first results, we used the following components: • rgb camera facilitates facial recognition and has other detection features by identifying three color components: red, green and blue. • depth sensor an ir projector and a monochrome complementary metal-oxide semiconductor (cmos) sensor work together to view in 3d regardless of lighting conditions; • microphone system represents a series of 4 microphones that can isolate the players’ voices from the room noise; this allows the player to be a few feet away from the microphone and still use voice commands. • laptop and various cables for connections. https://doi.org/10.15837/ijccc.2022.1.4665 7 the obtained results are presented in the following figures (figure 4-8). only in the case of figure 6 the device was mounted on a drone, the rest of the images were obtained by mounting the components on the actor’s helmet. figure 4: 2d image. figure 5: body basic image. the human activities recognition method starts from determining the characteristics of movement, the model of each activity being obtained based on the set of these characteristics, the elements of movement being transformed into visual words. each feature set is represented by the frequency of these words. for the location and real-time tracking of objects in monocular image sequences we used the model proposed in [23]. a kalman filter is used, which provides a rough prediction of the state of the object, which is then refined by a local detector that incorporates the image of the unique quality of the pixels and temporal information between two consecutive frames. compared to existing methods, the proposed approach does not require manual initialization for tracking, and runs much faster than last minute, thus achieving competitive tracking performance over a large number of image sequences (figure 6). extensive experiments demonstrate the superior effectiveness and performance of the proposed approach. the proposed fast object localization and tracking algorithm is formulated in a robust kalman filter framework to estimate the optimal state of the object in the map that specifies the details of each object in each frame in a sequence of given images. the tracking method is based on a recursive process of prediction, object detection, and correction. a linear, constant-speed dynamic model was used to represent the transition of the state of motion of the remarkable object in a scene. the tracking device is initialized on the first frame, using a map that details the pixel quality calculated on the entire image. the state of motion is predicted for each frame depending on https://doi.org/10.15837/ijccc.2022.1.4665 8 figure 6: body index basics image. figure 7: 3d image with body basic. figure 8: face basic image. https://doi.org/10.15837/ijccc.2022.1.4665 9 the states of motion of the object of interest that was previously obtained (figure 7). in the project, we also evaluated the method proposed by zhang [24], for the rapid detection of the object based on the minimum transformation of the border distance. because the pixel quality details map efficiently finds spatial information about distinct objects in a scene, it allows you to improve the accuracy of locating important objects while tracking. thus, an adapted object tracking framework is proposed by integrating two processes: object detection and visual tracking. a kalman filter was used to predict an approximate location of the object of interest. a detector is then used to improve the location of the object. the optimal state of the object in each frame is estimated by the recursive process of prediction, refinement, and correction. we used probabilistic graphical model (pgm) methods to solve problems of structure recognition, computer vision, and signal processing problems (figure 5-7). the actors vital data we monitored using an intelligent technology, integrated in a high-tech t-shirt (ambiotex). the ambiotex t-shirt consists of five components: • the techunit is easy to attach to the t-shirt with magnetic contacts; • integrated textile sensors, characterized by robustness and the fact that the t-shirt can be machine washed; • a functional textile material that can be washed and is comfortable to wear; • the ambiotex application, which visualizes, analyzes, stores and transmits data in real time; • a user interface that allows interaction with other users. the recorded data is taken, using an api, in our own application in which it can be tracked in real time by the coordinators (figure 9-11), together with the provided 2d and 3d images (figure 4-8). figure 9: recorded dada preview. there were several issues that make data fusion a challenging task. most of these issues arise from the data to be fused, imperfection and diversity of the sensor technologies, and the nature of the application environment: data correlation, data alignment/registration, operational timing, data imperfection. while many of these problems have been identified and heavily investigated, no single data fusion algorithm can address all the aforementioned challenges. the variety of methods in the literature focus on a subset of these issues to solve, which would be determined based on the application in hand. the existing fusion algorithms are explored based on how various data-related challenges are treated. different data fusion algorithms can be roughly categorized based on one of the four challenging problems of input data that are mainly tackled: • data imperfection; https://doi.org/10.15837/ijccc.2022.1.4665 10 figure 10: training complexity diagram. figure 11: heart rate statistics. • data correlation; • data inconsistency; • disparateness of data form. in our case the most complex problems arose regarding the data imperfection. the causes can be multiple, such as adhoc communication networks, new technologies with transmissions through small data packets (low power wide area technologies), etc. in our case the probabilistic methods didn’t work. alternative techniques such as fuzzy set theory and evidential reasoning have been proposed to deal with perceived limitations in probabilistic methods, such as complexity, inconsistency, precision of models, and uncertainty. but the best results (those in the figures 4-11) were obtained by hybrid frameworks such as fuzzy rough set theory (frst) and fuzzy demp-ster–shafer theory (fuzzy dset) [25][26]. 5 conclusions this paper presents an innovative approach to improve awareness of the first respond-ent (actors) localization and health monitoring, by using multimodal fusion of data from rgb cameras, depth sensor, microphone system, mounted on the first respondents or uavs, also an intelligent technology, integrated in a high-tech t-shirt on the actors. through this work the authors propose people localization and monitoring the design of the basic platform for the acquisition of geo-referenced data from gps sensors mounted on drones/actors’ helmet was evaluated, as well as the ways of transmitting this data to a 2d/3d visualization platform in which to be recorded, also the vital data of the actors. where gps location is not available, or higher location and mapping accuracy is required, accurate and robust slam algorithms are used. probabilistic graphical model (pgm) methods to solve problems of structure recognition, computer vision, and signal processing problems have been shown to be effective, producing very good results. as future work, we are working to develop a new https://doi.org/10.15837/ijccc.2022.1.4665 11 solution that will provide a global awareness of the situation both for the individual actors carrying out rescue operations and for coordination teams. 6 acknowledgments the authors wish to dedicate this article in the memory of late prof. ioan dzitac, an exceptional man who devoted his life to academia, research and scientific progress. prof. dzitac was a dedicated family man with a distinguished personality, who had impacted the lives of many. he passed away on 6th of february 2021, leaving an immense void in the hearts of his family and close ones. his tremendous impact is evergreen, and his wife ,simona, and daughter, domnica, will carry on his legacy. funding this project has been funded in part by the 3dsafeguard project. author contributions the authors contributed 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[26] 26. r. haina, g. mianjun, z. gexiang, z. ming (2018). an approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural p systems. international journal of computers communications & control, vol. 13, no. 4. https://doi.org/10.15837/ijccc.2022.1.4665 13 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: simo a.; dzitac s.; dzitac d. (2022). first responders’ localization and health monitoring during rescue operations, international journal of computers communications & control, 17(1), 4665, 2022. https://doi.org/10.15837/ijccc.2022.1.4665 introduction technology analysis people localization and monitoring results and discussion conclusions acknowledgments international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 5, month: october, year: 2020 article number: 3978, https://doi.org/10.15837/ijccc.2020.5.3978 ccc publications path selection using fuzzy weight aggregated sum product assessment t. b. chandrawati, a. a. p. ratna, r. f. sari t. brenda chandrawati* department of electrical engineering universitas indonesia depok indonesia *corresponding author: t.brenda@ui.ac.id anak agung putri ratna department of electrical engineering universitas indonesia depok indonesia ratna@eng.ui.ac.id riri fitri sari department of electrical engineering universitas indonesia depok indonesia riri@ui.ac.id abstract the search for safe evacuation routes is an important issue to save flood victims so they can reach the evacuation centre. this research is a simulation of searching for safe and fast travel evacuation route that have 24 alternative routes. every road that will be transverse has a limit with certain criteria. calculate of the weight of the constraints using the multi-criteria decision making (mcdm) method, namely the analytical hierarchy process (ahp) and weight aggregated sum product assessment (waspas) based on fuzzy logic. the criteria of obstacle that qualitative for obscurity so that it makes sense fuzzy will provide supportive input for the mcdm problem. the fuzzy ahp method is applied to calculate the weight of an application while the fuzzy waspas (waspas-f)method is used to determine the safest alternative route. by using the fuzzy ahp and waspas-f methods, a safe and fast pathway weights 0.662. keywords: evacuation route, mcdm, fuzzy ahp, waspas-f. 1 introduction the occurrence of heavy rain in a specific area can cause flooding in the area. losses due to floods reach 20% of injuries due to natural disasters. flood disaster is an essential factor in hampering community development, sustainable economy, and human life [27, 55]. as a result of the high level https://doi.org/10.15837/ijccc.2020.5.3978 2 of stagnant water due to rain, flood victims need to move to safer evacuation locations. to go to the evacuation area provided, the victims must choose a safe path. several alternative roads are available for flood victims, but each alternative road that can be passed has obstacles. obstacles faced by flood victims include high rainfall, high stagnant water, and others. the selection of a safe path and has the fastest travel time is done by a method that calculates the total weight of obstacles that exist in each path that can be crossed. this paper presents a simulation of the selection of evacuation routes for flood victims. this route selection is a case example in making decisions in determining the best alternative from many alternatives based on several conflicting criteria [32, 55]. decision making with a variety of criteria involves several stages, namely determining objectives, selecting criteria, determining alternatives, determining the weight of criteria, and applying the appropriate algorithm to determine alternative ranking [34]. several multi-criteria decision making techniques have been used in various flood management cases [1] such as flood risk mapping [18, 25, 36], flood risk assessment [5, 16, 21, 42], zoning flood hazard [24, 38], flood vulnerability analysis [14, 22, 37], site selection for flood mitigation measures [2], and flood strategy priorities [15]. this paper is organized as follows. in part 2, the multi-criteria decision making is outlined, including ahp and waspas, fuzzy set theory, fuzzy ahp, and waspas-f. the route search simulation based on fuzzy ahp and waspas-f is introduced, and the process of determining research criteria is presented in section 3. section 4 contains the calculation analysis. finally, the conclusion is shown in the last section. 2 literature review simulation of safe and fast route selection from several alternative routes is a case of decision making. in this research, the method used for route search simulation is the fuzzy ahp and waspasf methods. both methods are based on algorithms derived from fuzzy sets, fuzzy numbers, and chang’s extent analysis [13]. chang’s extent analysis introduced a new approach for handling fuzzy ahp, with the use of triangular fuzzy numbers for pairwise comparison scale of fuzzy ahp, and the use of the extent analysis method for the synthetic extent values of the pairwise comparisons. 2.1 multi-criteria decision making the multi-criteria decision making (mcdm) method has been widely used in research. based on different objectives and different types of data, mcdm problems can be classified into two main categories, namely multiple-attribute decision making (madm) and multiple-objective decision making (modm) [23, 51]. approaches proposed by researchers include the analytical hierarchy process (ahp) [40] and technique for order reference by similarity to ideal solution (topsis) [3, 61]. contemporary researchers integrate different madm techniques to find the most effective solution [59, 60]. turskis and juodagalvienė integrated ten different madm methods, including game theory, ahp, waspas, topsis, edas, aras, full multiplicative form, laplace rule, bayes rule to one problem solution model [48]. turskis et al. introduced a fuzzy multi-attribute performance measurement framework using the merits of both a novel weighted aggregated sum-product assessment method with fuzzy values (waspas-f) and analytical hierarchy process (ahp) [50]. the ahp method was first introduced by saaty [40] and has been applied in various cases including mapping of areas that are vulnerable to disasters [10, 26, 35, 41], power plant development [43], economics [33, 44], and construction management [20]. 2.1.1 analytical hierarchy process analytical hierarchy process (ahp) is a method used for assessment and decision making with various criteria and weight values. the ahp method is powerfull tool to elicitate weights of attributes. turskis et al. using the ahp and aras-f methods to assess the construction site alternatives for the non-hazardous waste incineration plant [49]. this method uses a scale of 1 to 9 that represents the https://doi.org/10.15837/ijccc.2020.5.3978 3 table 1: the scale of comparative assessment intensity of importance explanation 1 two activities contribute equally to the objective 3 experience and judgment slightly favour one activity over another 5 experience and judgment strongly favour one activity over another 7 an activity is favoured very strongly over another; its dominance demonstrated in practice 9 the evidence favouring one activity over another is of the highest possible order of affirmation 2, 4, 6, 8 the value between two values of consideration that are close together interests or one relative goal preference to another. scale 1 represents the same interests between the two objectives. scala 9 represents the extreme importance of one objective goal to another [7, 26, 40]. 2.1.2 weight aggregated sum product assessment a madm method, namely the weighted aggregated sum-product assessment method (waspas), was introduced in 2012 by zavadskas et al.[63]. the waspas method actually aggregates two approaches: the wsm (weighted sum model) and the wpm (weighted product model). the waspas method can be used to solve various problems from various environments. in [12], chakraborty et al. use this method to solve five issues from a real-time manufacturing environment. the method was successfully applied for evaluating alternative technological or design solutions in construction [58], development of the wind park to power plant [4], and contractor selection [62]. the waspas method can also be used as a decision-making method in human risk assessment [8] and personnel selection for sales manager positions in the tourism sector [52]. decision making on manufacturing process issues [11], and assessing residential homes that meet energy-efficient and human needs [57] can also be solved by this method. the calculation process using waspas method are: 1. make a decision matrix x =   x11 x12 . . . x1n x21 x22 . . . x2n . . . . . . . . . . . . xm1 xm2 . . . xmn   (1) where m is the number of alternative candidates (m = 1, 2, . . . , m) and n is the number of evaluation criteria and x11 is the alternative performance to i by taking into account the criteria to j. 2. normalize the x matrix by calculating the: benefit criterion: x̄ij = xij maxixij (2) non-benefit criterion: x̄ij = maxixij xij (3) 3. calculate the optimal wsm function value for each alternative based on: qi = n∑ j=1 x̄ijwj (4) 4. calculate the optimal wpm function value for each alternative by: pi = n∏ j=1 (x̄ij)wj (5) 5. calculate the value of ki using: ki = 0.5 n∑ j=1 x̄ijwj + 0.5 n∏ j=1 (x̄ij)wj (6) https://doi.org/10.15837/ijccc.2020.5.3978 4 to improve the ranking accuracy and effectiveness of the decision-making process in the waspas method, a general equation used to determine the total importance of the second alternative is developed as the equation [12, 63]: ki = λqi(1) + (1 −λ)pi(2) = λ n∑ j=1 x̄ijwj + (1 −λ) n∏ j=1 (x̄ij)wj (7) (λ=0, 0.1, ..., 1) where λ = ∑m i=1 pi∑m i=1 qi + ∑m i=1 pi (8) the waspas method is used to determine the interval for replacing equipment items from the optimal machine/service [19]. maintenance in the manufacturing industry ensures the safety and reliability of the system and determines its productivity. the selection of garage locations in residential homes can also use the waspas-svns method [6]. the criteria used for making the right decision in choosing a garage location, the length of the foundation, internal functional communication, contextual, and aesthetic factors are chosen. the length of the foundation relates to the scope of earthworks and construction costs. internal functional communication describes the comfort level of residential use, and user needs, the contexts associated with building zoning, aesthetics related to the beauty of the garage. the waspas and swara methods are used to select personnel for sales manager positions in the tourism sector. the step-wise assessment ratio analysis (swara) method is used to select priorities based on environmental and economic situations by determining the weight of evaluation criteria. the waspas method is used to determine the order of candidates [52]. 2.2 fuzzy set theory the fuzzy set theory was first introduced by zadeh [56]. fuzzy set theory is a fundamental approach for measuring the level of satisfaction and importance of vague and unclear linguistic variables on subjective human judgment [17]. the value of linguistic variables is qualitative. uncertainty has the main characteristic that is grouping into a class that has a lack of clarity and the main characteristic of grouping into classes that have no clear boundaries. triangular fuzzy number (tfn) represents an uncertain comparison assessment. tfn is a fuzzy number that is defined as a triplet (l, m, u). a fuzzy number m on r becomes a triangular fuzzy number if the membership function is defined as [13]. µm (x) =   x−l m−l ,x ∈ [l,m] x−u m−u ,x ∈ [m,u] 0 ,otherwise (9) where l ≤ m ≤ u, l is lower value, u is upper value and m is modal. if there are two tfn, a = (l1,m1,u1) and b = (l2,m2,u2), the fundamental operation of tfn [13]: (l1,m1,u1) ⊕ (l2,m2,u2) = (l1 + l2,m1 + m2,u1 + u2) (10) (l1,m1,u1) (l2,m2,u2) = (l1 − l2,m1 −m2,u1 −u2) (11) (l1,m1,u1) � (l2,m2,u2) = (l1l2,m1m2,u1u2) (12) k � (l1,m1,u1) = (kl1,km1,ku1) (13) (l1,m1,u1)−1 ≈ ( 1 u1 , 1 m1 , 1 l1 ) (14) https://doi.org/10.15837/ijccc.2020.5.3978 5 table 2: the scale of comparative assessment linguistic variable ahp scale triangular fuzzy scale just equal 1 (1,1,1) if diagonal (1,1,3) the other moderately 3 (1,3,5) strongly 5 (3,5,7) very strongly 7 (5,7,9) extremely 9 (7,9,9) 2.3 fuzzy ahp fuzzy ahp is a decision-making method that combines the fuzzy and ahp method. the earliest work in fuzzy ahp appeared in van laarhoven and pedrycz [53], which compared fuzzy ratios described by triangular membership functions. in this method, judgment or decision-maker preferences are modeled using fuzzy logic. in addition to the fuzzy ahp method, researchers recently extended eckenrode’s rating technique with fuzzy values to determine the weights of attributes [46]. fuzzy ahp is applied, for example, to the ranking and selection of alternatives in rice production practice [30], analytical procedures for managing an efficient green supply chain [29], and the selection of contractor’s bidding [28]. the ahp pairwise comparison model uses a scale of 1 to 9. this ahp scale can be transformed into a triangular fuzzy scale, as in table 2. this scale was introduced by chang [13], veerbathiran and srinath [54] as a new approach to the use of triangular fuzzy numbers for the comparison scale of ahp fuzzy pairs. using the analytical development method on the fuzzy ahp chang, the following steps are applied [30, 45]: if x = x1, x2, . . . , xn is a set of object and g = g1, g2, . . . , gn is the goal set. then, the development of the analysis of each objective must be carried out. for each object m1gi , m 2 gi, . . . , mmgi with i = 1, 2, . . . , n and mgi j is a triangular fuzzy number and j = 1, 2, . . . , m then the value of development analysis m can be found. step 1: value of synthetic extent for the ith object is calculated by si = m∑ j=1 m j gi � [ n∑ i=1 m∑ j=1 m j gi] −1 (15) where [ ∑n i=1 ∑m j=1 m j gi] −1 calculated using equation m∑ j=1 m j gi = ( m∑ j=1 lj, m∑ j=1 mj, m∑ j=1 uj) (16) [ n∑ i=1 m∑ j=1 m j gi] −1 = ( 1∑m j=1 uj , 1∑m j=1 mj , 1∑m j=1 lj ) (17) step 2: calculate the degree of possibility m2(l2,m2,u2) ≥ m1(l1,m1,u1) by using : v (m̃2 ≥ m̃1) = sup y≥x [min(µm̃1 (x),µm̃2 (x))] (18) where m̃1(l1,m1,u1) and m̃2(l2,m2,u2) are triangular fuzzy number. the slice between m1 and m2 can be seen in fig.1. equation (18) can be stated with the following equation : v (m̃2 ≥ m̃1) =   1 , if m2 ≥ m1 0 , if l1 ≥ u2 (l1−u2) (m2−u2)−(m1−l1) ,otherwise (19) step 3: the degree of membership of each criterion is determined by https://doi.org/10.15837/ijccc.2020.5.3978 6 figure 1: slice between m1andm2 [45] v (m ≥ m1, m2, . . . , mk) = v [(m ≥ m1) and (m ≥ m2) and .. . and (m ≥ mk)] = min v (m ≥ m1) (20) where i = 1, 2, . . . , k. assume d(ai) = minv (si ≥ sk) (21) for k = 1, 2, . . . , n; k 6= i, the weight vector is calculated by the equation : w ′ = [d ′ (a1),d ′ (a2), . . . ,d ′ (an)]t (22) step 4: normalized weight vectors can be calculated by w = [d(a1),d(a2), . . . ,d(an)]t (23) where weight w not a fuzzy number. the fuzzy ahp is used in the selection disaster logistics centre location proposed by turğut et al. [45]. in this study, the fuzzy ahp method is used to develop a decision support system with criteria. the questionnaire technique determines the weighting of the criteria used. the combination of ahp with fuzzy trapezium numbers is used to calculate the weight index of flood risk assessment and flood vulnerability [65]. shannon’s entropy theory, comprehensive fuzzy methods, and analytical hierarchy process show the weighting of modeling variables for landslide susceptibility evaluation [64]. the fuzzy ahp is also used for the selection of shelter locations and evacuation planning for disaster victims [9]. 2.4 fuzzy waspas fuzzy waspas is a multi-criteria decision-making method in an uncertain environment [31, 39]. the advantage of using fuzzy in this method is to determine the relative importance of attributes that are not deterministic. turskis et al. introduced a fuzzy multi-attribute performance measurement framework using the merits of both a novel weighted aggregated sum-product assessment method with fuzzy values (waspas-f) and analytical hierarchy process (ahp) [48]. fuzzy waspas to control the shipping trolley in manufacturing. the fuzzy method used in this paper is related to the uncertainty of the estimated waiting time for the production line [39]. this production system consists of three parts, namely joint and independent welding and cleaning (wcl) and assembly and maintenance (atl). a trolley located between wcl and atl transports the workpiece from one of the production lines (wcl) to the appropriate atl. in [50], waspas-f is used to determine the most appropriate location to construct a shopping center building. the choice of shopping center location uses eight criteria, namely construction costs, economic value, access roads, competitors, characteristics of the population around the location, environmental impact, risks posed, and attractiveness of the shopping center. https://doi.org/10.15837/ijccc.2020.5.3978 7 turskis et al. applied the model to solve the problem of ensuring the sustainable development of eu countries in terms of identifying critical information infrastructures [47]. a new approach to the waspas method that was developed based on the interval-valued intuitionistic fuzzy sets (ivifss) is applied to the process of evaluating flood management control policies [31]. in this paper, a new formula is developed to determine the decision weight of experts based on the measurement of similarity. at the same time, mishra et al. used the waspas fuzzy intuitionistic method to assess cellular phone service providers (tsp) [32]. 3 evacuation routes for flood victims this section will discuss the resolution of problems related to simulating alternative route choices for flood victims. the proposed problem-solving flow chart is shown in figure 2. the first step in making a safe and fast route selection simulation is done by modeling the environment. road modeling and obstacle design are included in the environmental modeling design. design obstacles on every road that might be crossed are: 1. the slippery road. this criterion is designed to determine whether the road will be easy or not to be passed due to the presence of mud, soil, water, and/or rubbish that is scattered on the road surface. 2. the water that floods the road. the height of the water that floods the road is an obstacle that needs to be considered. the higher the water holding the road, the more difficult it will be for flood victims. 3. the river near the road. the existence and state of the river will affect the state of the surrounding water. the closer the road to the river, the more dangerous it will be for victims who will cross the road. 4. existing drainage system. removal of natural or artificial water masses will affect the height of standing water in certain areas, and the time it takes for the receding stagnant water. the better the drainage system, the faster the water will recede. 5. vulnerability. vulnerability is a set of conditions or conditions that determine whether a hazard situation that occurs will cause a disaster or not, so vulnerability will be one of the considerations for victims to go through the road. fuzzy analytical hierarchy process (fuzzy ahp) is used to calculate criteria weights. the second part is the fuzzy weighted aggregated sum-product assessment (waspas-f) method. the waspas-f method is used to determine the alternative order of routes that can be passed by flood victims. the flowchart of fahp and waspas-f calculation can be seen in figure 3. figure 4 shows several road routes that can be traversed by flood victims while walking from the disaster location to the evacuation site. road routes can be defined with several roads that must be passed, among others: • s a b d • s c b d • s c d d • s e d d • s a b d d • s a c b d • s c b d d • s c d g d • s e d b d • s e d g d https://doi.org/10.15837/ijccc.2020.5.3978 8 figure 2: the flowchart of the proposed problem solving process https://doi.org/10.15837/ijccc.2020.5.3978 9 • s e f g d • s a b d g d • s a b c d d • s a c b d d • s c b d g d • s e f g d d • s a b c d g d • s a c b d g d • s c d e f g d • s e f g b d d • s a b d e f g d • s c b d e f g d • s a b c d e f g d • s a c b d e f g d figure 3: the flowchart of fuzzy ahp and waspas-f calculation each obstacle is determined on the road, which obstructs the victim’s path to the evacuation site. each obstacle will be given weight. the determination of obstacle weights for each road is calculated using fuzzy logic. https://doi.org/10.15837/ijccc.2020.5.3978 10 figure 4: the model of routes that can be passed by flood victims table 3: pair comparison between criteria c1 c2 c3 c4 c5 c1 (1,1,1) (1/3,1,1) (1/5,1/3,1) (1,1,3) (1/7,1/5,1/3) c1 (1,1,1) (1/3,1,1) (1/5,1/3,1) (1,1,3) (1/7,1/5,1/3) c1 (1,1,1) (1/3,1,1) (1/5,1/3,1) (1,1,3) (1/7,1/5,1/3) c1 (1,1,1) (1/3,1,1) (1/5,1/3,1) (1,1,3) (1/7,1/5,1/3) c1 (1,1,1) (1/3,1,1) (1/5,1/3,1) (1,1,3) (1/7,1/5,1/3) 3.1 fuzzy ahp to search weight 3.1.1 route search hierarchy structure figure 5 shows the hierarchical structure in the route search. the aim is to find a safe and fast route for flood victims who will go to the evacuation location. victims can pass 24 alternative routes. in the selection of a route, five criteria need to be considered. these criteria are the level of road slippage (c1), the level of water inundated on the road (c2), the presence of a river located near the road (c3) and the drainage speed of the ditch (c4), and the level of vulnerability of flood victims that must be immediately considered to reach the evacuation location (c5). the five criteria are obstacles for each road the victim will pass. figure 5: the route search hierarchy structure 3.1.2 matrix of importance comparison between criteria criteria comparison matrix is one of the most popular weighting methods in the multi-criteria decision making problem. a pairwise comparison matrix between criteria with a triangular fuzzy number scale is fundamental in the fuzzy ahp method. a pairwise comparison matrix between criteria for safe route search problems can be seen in table 3. https://doi.org/10.15837/ijccc.2020.5.3978 11 table 4: addition triangular fuzzy number l m u c1 2.767 3.533 6.333 c2 3.286 3.400 7.667 c3 6.200 12.333 19.000 c4 2.010 3.533 4.333 c5 11.000 10.000 27.000 sum 25.171 41.800 64.333 table 5: synthesis fuzzy criteria l m u c1 0.042 0.085 0.252 c2 0.051 0.081 0.305 c3 0.096 0.295 0.755 c4 0.031 0.085 0.172 c5 0.171 0.455 1.073 3.1.3 fuzzy synthesis value of criteria the next step is to determine the value of fuzzy synthesis (si). fuzzy synthesis values are used to get the relative weights for the criteria elements and can be calculated using equation (15). the procedure for calculating the value of fuzzy synthesis is as follows : all elements of l in each criterion are added up. likewise, all the elements m and u in each criterion. the results of adding l, m and u to each criterion can be seen in table 4. after all the sums of l, m and u are obtained, each column in the fuzzy triangular number matrix is summed, as shown in the sum rows of table 4. the next step is to divide each cell in the first column of table 4, with the results of adding the first column number. this step is also carried out in the second and third columns of the sum matrix of fuzzy numbers in table 4. the results of the calculations that have been made can be seen in table 5. 3.1.4 degree of membership in comparison of each criterion to calculate the degree of membership of a comparison of fuzzy synthesis values to get the vector weights for the criteria used equation (11). the results of the calculation obtained the weight of the criteria vector : w’=[0.180, 0.264, 0.785, 0.003, 1]t 3.1.5 normalization of vector weights after the criteria vector weight is obtained, normalization of vector weight will be obtained, and the results will be : w =[0.081, 0.118, 0.352, 0.001, 0.448] 3.2 fuzzy waspas for ranking search after obtaining the criteria weight vector value from the fuzzy ahp method, then to evaluate alternative locations, the waspas-f method is used. from the weight of the vector of the criteria, we can draw the level of importance of the attribute. 3.2.1 matrix of alternative fuzzy weighting the first step in finding a safe and fast route to the waspas-f method is to assign a value to each alternative route (route 1, route 2, . . . , route 24) for each predetermined criterion. alternative routes have been determined based on the road modeling shown in figure 3. figure 3 shows 24 alternative routes that may be passed by flood victims. in this simulation, the obstacle for each path and the weight value is predetermined. the specified weight value is changed to triangular fuzzy https://doi.org/10.15837/ijccc.2020.5.3978 12 table 6: the initial fuzzy decision making matrix for path selection route 1 route 2 route 3 . . . route 24 α β γ α β γ α β γ .. . α β γ c1 0.000 0.200 0.400 0.300 0.500 0.700 0.300 0.0500 0.700 . . . 0.300 0.500 0.700 c2 55 70 85 15 30 45 15 30 45 . . . 35 50 65 c3 120 160 200 60 100 140 60 100 140 . . . 60 100 140 c4 60 80 100 30 50 70 60 80 100 . . . 30 50 70 c5 6 10 14 6 10 14 6 10 14 . . . 6 10 14 table 7: the normalized fuzzy decision making matrix for path selection route 1 route 2 route 3 . . . route 24 α β γ α β γ α β γ .. . α β γ c1 0.000 0.200 0.400 0.300 0.500 0.700 0.300 0.0500 0.700 . . . 0.300 0.500 0.700 c2 0.647 0.824 1.000 0.176 0.353 0.529 0.176 0.353 0.529 . . . 0.412 0.588 0.765 c3 0.600 0.800 3.333 0.600 0.714 2.333 0.600 0.714 2.333 . . . 0.300 0.500 0.700 c4 0.600 0.800 1.000 0.300 0.500 0.700 0.600 0.800 1.000 . . . 0.300 0.500 0.700 c5 0.429 0.714 1.000 0.429 0.714 1.000 0.429 0.714 1.000 . . . 0.429 0.714 1.000 number (tfn). the value of obstacles for each criterion on each alternative route on a fuzzy scale can be seen in table 6. α is the minimum value of tfn, β is modal value and γ is the maximum value of tfn. 3.2.2 matrix of fuzzy weighting normalization table 7 shows the matrix that has been normalized using equations (2). 3.2.3 fuzzy normalization matrix for weight sum model (wsm) the fuzzy normalization weight matrix for weighted sum model (wsm) is obtained by processing each matrix value using equations (4). the results of these calculations can be seen in table 8. 3.2.4 fuzzy normalization matrix for weighted product model (wpm) to get the fuzzy normalization matrix for the weighted product model is obtained by processing table 7 using the equation (5). table 9 shows the fuzzy normalization matrix for wpm. 3.2.5 the value of optimization function using equations (7) and (8), the total weight of each route alternative can be calculated and can be seen in table 10. table 8: the weight normalized matrix for wsm route q route q 1 0.990 13 0.859 2 0.831 14 0.859 3 0.831 15 0.831 4 0.831 16 0.831 5 0.859 17 0.858 6 0.834 18 0.858 7 0.831 19 0.858 8 0.694 20 0.834 9 0.979 21 0.886 10 0.831 22 0.882 11 0.858 23 0.886 12 0.858 24 0.667∑ q 20.335 https://doi.org/10.15837/ijccc.2020.5.3978 13 table 9: the weight normalized matrix for wpm route p route p 1 0.700 13 0.804 2 0.758 14 0.804 3 0.758 15 0.758 4 0.758 16 0.758 5 0.804 17 0.804 6 0.614 18 0.804 7 0.758 19 0.804 8 0.670 20 0.614 9 0.676 21 0.835 10 0.758 22 0.834 11 0.804 23 0.835 12 0.804 24 0.658∑ p 18.179 table 10: integrated utility function value of waspas-f method route k route k 1 0.837 13 0.830 2 0.792 14 0.830 3 0.793 15 0.792 4 0.793 16 0.792 5 0.830 17 0.830 6 0.718 18 0.830 7 0.793 19 0.830 8 0.681 20 0.718 9 0.819 21 0.859 10 0.792 22 0.857 11 0.830 23 0.859 12 0.830 24 0.662 figure 6: weight of all routes https://doi.org/10.15837/ijccc.2020.5.3978 14 4 analysis finding safe and fast paths for flood victims is an important problem when an area is rained and starts to flood. this simulation of path search is used as a learning model for safe route selection. with five types of obstacles (c1, c2, c3, c4, c5) determined, the consideration of road selection is still needed. from the predetermined values for each criterion, the fuzzy ahp method obtained the weight of the criteria. fuzzy ahp is a multi-criteria decision-making method that combines fuzzy methods and ahp methods. in this method, the preference is carried out by decision-makers on a numerical scale. the assigned numerical scale is transformed into a fuzzy scale. the final weight of the fuzzy ahp method is not a fuzzy number. fuzzy ahp is applied to identify the importance of criteria. the results of the weighted criteria obtained are wc1= 0.081, wc2 = 0.118, wc3 = 0.352, wc4 = 0.001, wc55 = 0.448. criteria weights generated by the fuzzy ahp method are used to process alternative obstacle weights in the waspas-f method. similar to fuzzy ahp, waspas-f is also a multi-criteria decisionmaking method in an uncertain environment. the waspas-f method stipulates alternative routes that may be passed by flood victims. on each alternative route, there is an obstacle. obstacle weight values on alternative routes are specified in the tfn. the waspas-f method combines wsm and wpm. the waspas-f method is used to rank alternative routes. figure 6 shows the weight of each route calculated using the waspas-f method. the smallest weight is achieved by route 24, with a value of 0.662. the highest weight is achieved by route 23, which is 0.859. the route with the smallest weight indicates that the route is the safest and fastest route that can be passed by flood victims. 5 conclusion rainy season with erratic rain levels, they are sometimes causing flooding in certain areas. if an area is flooded, the flood victims will be moved to a safe location. to reach the evacuation location needed a safe route that could be used by the victims. for this reason, a simulation is needed to obtain safe route choices for flood victims. this route search considers the dangers faced by flood victims when crossing the road. the smaller the threat faced by flood victims on a route, the route is safe for flood victims to pass. this paper proposes the fuzzy ahp and waspas-f methods to provide an alternative route that can be traversed for flood victims. the fuzzy ahp method is a method that is simple, flexible, and able to handle quantitative and qualitative criteria better. the fuzzy ahp method is used to find the weight of each route. the waspas-f method combines fuzzy, wsm, and wpm, which can improve the accuracy of the ranking of the proposed alternative routes given the uncertainty of preferences. from the calculations that have been done using the fuzzy ahp and waspas-f methods show that the route that has the smallest obstacle is r24. r24 has the smallest weight, 0.662, so r24 is the safest route for flood victims. funding this work is supported by a doctoral dissertation research grant from the ministry of research, technology, and higher education (kemristekdikti) under grant no. 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(2013). comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy ahp, stochastic environmental research risk assessment, 27(2), 525-546, 2013. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: chandrawati, t. b.; ratna, a.a.p.; sari, r. f. (2020). path selection using weight aggregated sum product assessment, international journal of computers communications & control, 15(5), 3978, 2020. https://doi.org/10.15837/ijccc.2020.5.3978 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 1, month: feburary, year: 2023 article number: 4478, https://doi.org/10.15837/ijccc.2023.1.4478 ccc publications semantic graph based convolutional neural network for spam e-mail classification in cybercrime applications s. rahmath nisha, s.muthurajkumar s. rahmath nisha department of computer science and engineering, k. ramakrishnan college of technology, samayapuram, trichy-621112, tamilnadu, india corresponding author: rahmathnishas.cse@krct.ac.in s. muthurajkumar department of computer technology, madras institute of technology(mit) campus, anna university, chrompet, chennai-600044, tamilnadu, india muthurajkumarss@gmail.com abstract spam is characterized as unnecessary and garbage e-mails. due to the increasing number of unsolicited e-mails, it is becoming more and more crucial for mail users to utilize a trustworthy spam e-mail filter. the shortcomings of spam classifier are defined by their increasing inability to manage large amounts of relevant messages and to effectively detect and effectively detect spam messages. numerous characteristics in spam classifications are problematic. given that selecting features is one of the most often used and successful techniques for feature reduction, it is a crucial duty in the identification of keyword content. as a result, features that are unnecessary and pointless yet potentially harm efficiency would be removed. in this study, we present sgnn-cnn (semantic graph neural network with cnn) as a solution to tackle the difficult task of mail identification. by projections e-mails onto a graph and by using the sgnn-cnn model for classifications, this technique transforms the e-mail classification issue into a graph classification challenge. there is no need to integrate the word into a representation since the e-mail characteristics are produced from the semantic network. on several open databases, the technique’s effectiveness is evaluated. some few public databases were used in experiments to demonstrate the high accuracy of the proposed approach for classifying e-mails. in term of spam classification, the performance is superior to state-of-the-art deep learning-based methods. keywords: spam e-mail classification, convolutional neural network, semantic graph, graph neural network 1 introduction unwanted spam messages have grown to be a major issue on the internet in recent times. spam messages not just to eat up a lot of available bandwidth, but they also take up users’ processing time. spyware programmed that collect sensitive information and send it to marketers as well as other third party may be included in certain spam messages. therefore, there is a critical need for the creation of more effective filters that can recognize these messages autonomously. https://doi.org/10.15837/ijccc.2023.1.4478 2 the term "feature" refers to a set of characteristics that measure certain elements of an e-mail user’s activity or behavior [25], [26], [27], [28]. to create precise and effective classifiers, it is crucial to extract and choose essential characteristics for e-mail classification. the "bag of words" model, in which each location in the input feature vector corresponds to a specific word or phrase, was employed by researchers to classify e-mails. for instance, the use of the term "free" may be a helpful characteristic in identifying spam e-mail. as a result, well-chosen features may significantly increase classification accuracy while also lowering the volume of data needed to achieve the necessary performance. the following features are the most popular ones: 1. e-mail header features: from the header of an e-mail, e-mail header features are retrieved and chosen. the from, to, bcc, and cc fields are found in a header. for instance, the subject line of e-mails with phrases like "banking," "debt," "fwd.: re:" and "confirm" are often used to identify phishing e-mails. other instances include the subject’s length in character, words, and from field words, as well as the sender’s e-mail addresses non-model subdomain. 2. e mail content features: the e-mail body segment, which includes the e-mail’s primary content, is where e-mail body features are chosen from. examples of e-mail body characteristics used to identify phishing e-mails include html content, html forms, the word "dear," the number of letters and utterances, word structure (such as "lending," "select," "record," "recognize," and "information," etc.), the word "suspended," and the term "verify your account." various tactics for classifying e-mail spam have been suggested by certain academics. decision tree, support vector machine, and naïve bayes [1], [2], [3] approaches are a few of these approaches. the mails used in these conventional approaches frequently need to have characteristics manually extracted as embedding vectors before being fed into in the classification model. additionally, the convolutional neural network (cnn) [4] was used in recent studies to classify spam e-mails ([5-8]). there is no requirement to individually feature extracted from the e-mails since extracted features and classifications in cnn models is done automatically throughout the entire model. the embedded vectors are used as inputs both by the conventional and cnn algorithms. the major contributions of the paper is follows: 1. the e-mail classifying issue is transformed into a graph classification problem by the technique we provide in this study. in contrast to other approaches, our technique skips the step of incorporating the e-mail content into the numeric column vector. 2. instead, the approach converts the word into a graph and classifies the spam e-mail using graph neural network (gnn) and convolutional neural network (cnn). when compared to some few public datasets, the suggested architecture had a greater accuracy for e-mail classification tests. the structure of this essay is as follows. problem statement is described in section 2. the related work involving rule-based technique and deep learning-based approach is described in section 3. section 4 goes through our suggested method for using the gnn to categorise spam e-mails. section 5 goes into further detail about the studies, which include preprocessing, building and using a graph neural net, testing that on samples, and evaluating its effectiveness. section 6 serves as the paper’s conclusion. 2 problem statement for the spam e-mail classification, we take that e ∈e a predetermined set of subclasses and a high-dimensional emailing spaces (e) model of an e-mails c ={c1, c2, . . ., ci}. there are just two different classes in this assignment: ham and garbage. a training set t of labelled e-mails is provided to us <e, c >, where <e, c > ∈ e × c, for example: <e, c > = < congratulations, claim your f ree $100 gi f t card, spam > (1) we want to create a classifier model that translates messages to labels using machine learning supervised learning. œ: e → c(2) (2) the supervised approach to machine learning is indicated l by and write l (t) = σ. the approach of supervised machine learning l provides the figured out classifier model after taking the trained set t as inputs. 3 relate work this section provides a thorough introduction to related research on e-mail classifications. rule-base technique and deep learning-based approach are our two ways for summarizing the relevant work. https://doi.org/10.15837/ijccc.2023.1.4478 3 3.1 rule-based method many academics have explored rule-based mail recognition methods that are based on svm and naive bayes technologies in order to efficiently address the danger presented by spam e-mails. for e-mail classification, rathod et al. suggested the naive bayes method. the suggested solution employs characters using ham and malware to determine if a letter is likely to be spam or not [9]. open-source e-mail spam filters make extensive use of the naive bayes approach [10]. it is not sensitive to superfluous characteristics. because nave bayes often requires less quick evaluation and training to recognise and screen a phishing e-mail, this is the cause. rusland et al. employ the weka [11] programme, which is based on the spambase and spam datasets, to evaluate the nave bayes algorithm for classifying e-mail spam. the results of the experiment demonstrated that the nave bayes’ effectiveness was influenced by the dataset’s example count and e-mail type [12]. over the years, support vector machines have also established themselves as one of the best classification techniques. a naive bayes filtering approach that is based on support machine vector was suggested by feng et al. the interdependence assumptions between the features taken from the initial training dataset is intended to be disproved when the naïve bayesian approach is used. according to experiment results, this technique can identify spam more accurately and with a quicker classification speed [13]. in order to filter spam, vishagini et al. suggested using a scaled support vector machine using the weight variables discovered by the kfcm technique. the relevance of various categories is reflected in the weight variable. the misinterpretation of e-mail is decreased by increasing the weight value. the classification of e-mails may be decreased by increase in weight value. studies reveal that there is still room for improvement in the precision and accuracy of the spam detection system’s performance [14]. the methodologies of aco and svm were combined and used to create the spam categorisation approach that karhika et al. disclosed. the suggested approach uses a hybrid approach. the selection of the characteristics is crucial to the models. terms of precision, accuracy, and recall, the investigation demonstrates that the proposed method was preferable to a number of the most cutting-edge classification approaches [15]. the approach of support vector machines has the benefit of great precision. this approach often takes longer than other approaches. 3.2 deep learning-based method the machine learning applications of object detection, object tracking, and picture classifications have subsequently shown cnn to be quite successful. to address the spam detection issue, several researchers have used cnn. bagui et al. suggested a technique that uses deep learning technology to identify the intrinsic characteristics of e-mail in classifying e-mails either spoofing or non-phishing. researchers categorize e-mails utilizing a deep-learning algorithm and do deep semantic evaluation using one-hot encoding with and without words. additionally, they evaluated the precision of several deep and machine learning techniques both with and without phrases[16]. seth et al. employ a cnn to analyse the complete content (i.e., text and photos), then run it via a separate classifier to determine if the mail is spam or ham. they suggested two hybrid multi-modal designs. in order to distinguish between spam and legitimate e-mail, the architectures gathering the input from those two separate models integrated the output data. studies reveal that the proposed technique performs the classification job with higher accuracy than the standalone image and textual classifications [17]. alghoul et al. provide an artificial neural networks model for mail identification. to use a feedforward backpropagation technique, the model has been trained. then, provided the data for this model. this study demonstrates how artificial neural networks may be used to classify e-mails [6]. another spam identification model dubbed themis was introduced by soni et al. they employed an imbalanced data set with a decent mix of legitimate and phishing e-mails to evaluate themis’ suitability. the themis model produced good results in the trials [7]. a scalable and robust content-based malware detection network dubbed deepspamnet was presented by srinivasan et al. as a framework for network threat positional awareness. because there are no stages involved in feature extraction, deep learning allows for quick change of the different nature of spammer. research demonstrates that deep learning models perform better than traditional machine-learning classifiers [8]. the cnn’s self-learning capabilities and dependable fault tolerant make it valuable. 4 system model in this study, we projected e-mails onto a network to transform the e-mail classification issue into a graph classification task. there is no need to integrate the word into a numeric input vector since the e-mail characteristics are produced from the semantic network. the proposed methodology transforms the classification issue for spam e-mails into a graph classification problem. the proposed solution is divided into four main steps, including data prepressing, graph creation, training and evaluation of graph neural networks, and graph classification, as illustrated in fig. 1. the dataset has to be manually cleaned using data preparation methods since it is noisy and imbalanced. then, we construct a sizable graph made up of word and e-mail document nodes. each node has embedding vectors depending on the characteristics of its neighbours. after creating the graphs, we input it to the gnn so it can learn high-dimensional characteristics. lastly, we use a neural network convolutional to the e-mail classification issue to create a graph classification based mostly on e-mail message and words graphs. https://doi.org/10.15837/ijccc.2023.1.4478 4 figure 1: system architecture sgnn-cnn https://doi.org/10.15837/ijccc.2023.1.4478 5 4.1 data prepressing preparing the input is required to convert e-mails from humans to machines usable formats for the further analysis. stop word, special characters, and all symbols are changed to lowercase letters as part of a sequence of procedures which we carry out. the e-mail collection was assigned to either the spam or ham class in accordance with the standards given by the e-mail spam classification. our three pieces of information are as follows: 70% of the information will be trained. 10% of the data will be data set, and 20% will be the verification set. 4.2 building graph we construct an e mail text network including e-mail word nodes, subject nodes and documents nodes, in classifying phishing messages. as stated as in graph: g = (v, e) (3) v = {word | text| topic} (4) e = { ei j |eid|e jd, iisword, jistext, disdomaintopic } (5) where v stands for the node set(s). e indicates groups of edges. word nodes, e-mail text nodes, and subject nodes are the three different types of nodes. utilize the linear discriminant analysis allocation (lda) model to deduce the domain subject from the e-mail documents. the semantic similarity architecture of the corpus may be clustered using the generating probabilistic model lda. we use lda to assist us in automatically finding subjects that are present in textual information. lda generates a topic term joint distribution for each subject d. the mutual information of issue mixing, a set of n words, a set of n topics, and the parameters, and, are given by: ρ (θ, z, ω | α,β) =ρ (θ | α) n∏ n=1 ρ (zn | θ) ρ (ωn | zn, β) (6) the word-word vertices, word-text line segments, topic-word edges, and topic-text corners make up the graph’s corners. the lda topic model learns the weights of the topic-word vertices as well as the topic-text vertices. we use the point wise mutual knowledge to compute the term weights (pmi). the purpose of pmi is to measure the chance that two terms will appear together. strong meaning connection between words is indicated by a high pmi value, and weak semantically connection is shown by a low pmi scoring system. the pmi calculation involves, pmi (a, b) =log2 ρ(a,b) ρ(a)ρ(b) (7) ρ (a) = w (a) |w| (8) ρ (a, b) = w (a, b) |w| (9) in which a and b are two words in a couple. the quantity w(a,b) is the amount of sliding window frames that have the letters a and b in them. w(a) is the quantity of slide window in the corpora that solely contain the letter a. we do not include connections with low pmi values; rather, we exclusively maintain vertices with high pmi values. to determine the weight of the connection here between word and the text, we use the bm25 method [19]. a series of papers are sorted using the bag-of-words search feature (bm25) in accordance with the search keywords. the document d’s bm25 score, given a query term, is: relevantscore(w, doc)= n∑ i=1 i df (qi)× t f (qi)× (k1+1) t f (qi) + k1× ( 1−b+b× |doc|ave_len ) (10) where i df (qi) is qi ‘the document’s inverse document regularity. divide the overall amount of documents by the amount of words in the document that include the phrase to get the appears in a document frequency. it is a quantitative statistic that indicates whether a phrase is frequent or uncommon in a certain corpus of documents. tf (qi) is qi ‘s term frequency. the number of instances a word occurs in a particular text is indicated by the frequency distribution. |doc| is the length of the document doc. ave_len is the length of the article on averages. b and k1 are free parameters. https://doi.org/10.15837/ijccc.2023.1.4478 6 4.3 sgnn-cnn mechanism through projected the e-mail content onto the graph using the created graph model, we change the mail classification issue into a graph node classification problem. furthermore, it was shown that graph neural networks performed convincingly on such a challenge [20–21]. it is suggested to use graph neural network to cooperatively aggregate data from graph structure. gnns maintain a state that, in contrast to conventional neural nets, may represent data from its neighbourhood with any depth. h v, which is a state encoding, is what the gnn is designed to study. hv= f ( xv, xco[v], hne[v], xne[v] ) (11) y = softmax ( hσ ( hσ ( hxw (1) ) w (2) ) w (3) ) (12) where σ is the activation function relu (xi) =max (0, x). y represents the classification engine’s outcome. w (1), w (2), and w (3) are learned using gradient descent on weight matrix. h= d̃− 1 2 ãd̃− 1 2 denotes laplacian matrix, ã=a+i.a is an identity matrix, whereas is a mathematical expression. d̃ is the degree matrix of ã. minimizing the cross-entropy loss between both the preceding label and ground truth labeling is the goal of training. the following definition of the gradient descent: loss= − ∑ l∈yl f∑ f =1 yl f lnzl f (13) where f is the output feature’s dimension, yl is the true indicator y the matrix of label indicators; and z represents the matrix for output. 5 experiments under this section, we run a number of tests on 3 different datasets trec spam, spambase, and enron-spam datasets—to assess the performance of our proposed sgnn-cnn. 1. enron-spam data: the fed energy regulator got the enron-spam set of data when looking into the demise of enron. there are over 500,000 mails produced by enron workers in it [22]. 2. the spambase dataset concentrates on classifying spam-like e-mails or not spam by keyword or word frequencies. the data has 58 characteristics and 4601 instances. for predictions, it has the variables "spam" and "not spam." it is a multidimensional, actual dataset that is mostly employed for feature identification. george forman provided the database, which was created at hewlett-packard labs [23]. 3. trec e-mail data: every e-mail in this set of data is classified as spam or not spam using a temporal indexing. there are 92,189 e-mail messages in the database. 39,399 messages are classified as ham, whereas 52,790 are classified as spam [24]. every experiment requires dynamically dividing the input graph’s nodes into train, verification, and testing set, each of which has the same total number of nodes. the comparison between our method and the most sophisticated deep learningbased e-mail classification model is shown in table 1. it is evident that our model routinely surpasses the most recent model by a margin of much more than three percentage points. the major factor for sgnn-cnn’s success is its ability to record both word-to-word and word-to-topic relationships. another benefit of our approach is how straightforward and reliable our text-to-graph projection is in practice. even more so, users are not required to carry out laborious data preparation. the empirical sgnn-cnn findings demonstrate that the word-topic data may enhance the classifying impact of e-mail. the amount of positive class classification instances (tp), clear negative class instances (tn), fake correctly classified cases (fp), and false negative class cases may be used to calculate the accuracy of the classifier (fn). in the instance of binary classification issues, these numbers constitute a confusion matrix, as seen in fig. 3. tp is the percentage of instances that are accurately identified as belonging to the "spam" class but are actually projected to be "not spam." tn is the percentage of accurately identified occurrences that fall into the "not spam" class and are anticipated to be "not spam." fp is the percentage of occurrences that are erroneously assigned to the "spam" class but are really anticipated to be "not spam." the percentage of occurrences that should have been classed as "not spam" but were instead misclassified as "spam" is known as the "fn" rate. on several test dataset, the classification performance of our model at various feature levels is shown. the highperforming for the enron-spam, spambase, and trec spam datasets are shown in figures 4, 5, and 6 respectively. we describe the outcomes of three dataset’s worth of mail classification tasks using feature dimensions ranging from 32 to 1024. on all three databases, evaluate the accuracy rises as feature size does. the findings demonstrate that sgnncnn is stable if the feature size exceeds 256. additionally, it was shown that various dataset with varied amounts of characteristics had various classifications outcomes. https://doi.org/10.15837/ijccc.2023.1.4478 7 figure 2: semantic gnn with cnn architecture https://doi.org/10.15837/ijccc.2023.1.4478 8 figure 3: confusion matrix for proposed work figure 4: evaluation of accuracy for trec spam dataset https://doi.org/10.15837/ijccc.2023.1.4478 9 figure 5: evaluation of accuracy for spambase dataset figure 6: evaluation of accuracy for enron-spam dataset https://doi.org/10.15837/ijccc.2023.1.4478 10 6 conclusion and future work in this study, we provide an sgnn-cnn approach for e-mails classification. the mail identification issue is transformed into a graph classification task, and the gnn framework is then used to categories the mail. the gnn algorithm extracts the e-mail’s properties in an attractive manner. researchers have used many open datasets to evaluate our methodology. the testing findings shown that, throughout term of spam identification, our effectiveness is superior to that of the most advanced deep learning-based system. to further increase the precision of the proposed approach, we may use a variety of preprocessing techniques including word clarification as well as other approaches in future study. purely textbased mail keep spamming may currently be detected using presented approach. in the future, we want to expand our strategy and adapt it to spam detection using hybrid deep learning techniques. references [1] harisinghaney, anirudh, et al. 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(2021). towards secured online monitoring for digitalized gis against cyber-attacks based on iot and machine learning. ieee access, 9, 78415-78427. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attributionnoncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control https://doi.org/10.15837/ijccc.2023.1.4478 12 cite this paper as: rahmath nisha, s.; muthurajkumar, s. (2023). semantic graph based convolutional neural network for spam email classification in cybercrime applications, international journal of computers communications & control, 18(1), 4478, 2023. https://doi.org/10.15837/ijccc.2023.1.4478 introduction problem statement relate work rule-based method deep learning-based method system model data prepressing building graph sgnn-cnn mechanism experiments conclusion and future work international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 2, month: february, year: 2020 article number: 3811, https://doi.org/10.15837/ijccc.2020.2.3811 ccc publications a latent dirichlet allocation and fuzzy clustering based machine learning model for text thesaurus j. luo, d. yu, z. dai jia luo newhuadu business school minjiang, fujian, china luojia_minjiang@163.com dongwen yu newhuadu business school minjiang, fujian, china zong dai* hunan zhaoshan investment & holdings co. ltd. xiangtan hunan, china *corresponding author: 14492121@qq.com abstract it is not quite possible to use manual methods to process the huge amount of structured and semi-structured data. this study aims to solve the problem of processing huge data through machine learning algorithms. we collected the text data of the company’s public opinion through crawlers, and use latent dirichlet allocation (lda) algorithm to extract the keywords of the text, and uses fuzzy clustering to cluster the keywords to form different topics. the topic keywords will be used as a seed dictionary for new word discovery. in order to verify the efficiency of machine learning in new word discovery, algorithms based on association rules, n-gram, pmi, and word2vec were used for comparative testing of new word discovery. the experimental results show that the word2vec algorithm based on machine learning model has the highest accuracy, recall and f-value indicators. keywords: text, lda, fuzzy clustering, thesaurus, word2vec, machine learning. 1 introduction text is composed of sentences consisting of continuous words and punctuation. segmentation is usually the first step in natural language processing tasks such as text classification and clustering, sentiment analysis, and topic recognition. the effect of segmentation will directly affect the accuracy of chinese text processing. new words can cause segmentation errors. recognizing new words is of great significance to other natural language processing tasks. the emergence of new words reduces the coverage of the dictionary and makes analysis difficult. existing researches on dictionaries mainly focus https://doi.org/10.15837/ijccc.2020.2.3811 2 on the construction of sentiment dictionaries. blei et al. [4] proposed an implicit dicrete’s probabilistic topic model lda in 2003. it formally introduced topics in the form of hidden variables for the first time to form a three-layer bayesian model. close to the plsi model, the topics selected by lda are not bound by the text of the training set. it is a completely unsupervised machine learning algorithm that clusters based on multiple topics. at the same time, the text can be reduced in dimension to obtain the text representation in the topic dimension, which can make the machine learning algorithm execute more efficiently. after the birth of lda, different forms of expansion appeared. in 2004, blei proposed a hierarchical lda of a tree structure, where each tree node represents a topic [10]. in 2006, blei considered the disadvantage of irrelevant lda topics and proposed correlated topic model (ctm) [6]. wang added time attributes to the lda model, and built a topic model where topics change with time [19]. griffiths et al. considered the rigorous word exchange hypothesis in this model and proposed to obtain sentence structure information through hmm, use lda to construct semantic relations, and combine the two to propose the hmm-lda model [11]. blei et al. proposed the supervised latent dirichlet allocation (slda) model for the poor performance of unsupervised clustering lda model in text classification. this model introduced text labeling into the lda model, and label compliance and topic probability distribution. for the normal linear distribution, the model can be imported to get the new text category [15]. li used the lda algorithm to perform topic mining on railway complaint texts [14]. the research on dictionary construction mainly focuses on the construction of emotional dictionaries. at present, most common emotional dictionaries are manually created by scholars, and most authoritative emotional dictionaries are mostly english emotional dictionaries, such as general inquirer [13], opinion lexicon [3], senti-wordnet and q-wordnet [2]. there are currently three new word discovery methods. the first is a new word discovery method based on rules; the second is a new word discovery method based on statistics; the third is a new word discovery method based on rules and statistics. there are three methods for automatically building the sentiment dictionary, a knowledge base-based method, a corpus-based method, and a combination of a corpus and a knowledge base. hu et al. extended the sentiment dictionary by considering the synonymous and antonymous relationships of words in wordnet [13]. andreevskaia et al. extended the sentiment dictionary through the meaning of words in the dictionary wordnet and the relationship between different words [1]. hassan et al. calculated the semantic similarity between different words through the dictionary wordnet, constructed semantic maps between different words, and finally obtained the word polarity through the graph-related algorithm [12]. dictionary word2vec is a set of deep learning toolkits that represent words in documents as word vectors, released by google in 2013. through training corpus, multi-dimensional real number vectors of words are obtained [16]. on this basis, the distance between word vectors can be calculated to measure their similarity [17]. at present, scholars often use the word2vec tool to study text clustering, synonym recognition, machine translation, and topic extraction [8]. it is found that public opinion classification needs to mine the topics of corporate public opinion texts and classify public opinion texts according to certain rules. the most important ones are the extraction of public opinion keywords and the establishment of public opinion dictionaries. subsequent public opinion classification and prediction accuracy can be higher. common sentiment dictionaries include thesaurus such as hownet sentiment dictionary and dalian institute of technology’s sentiment ontology library, which only covers thousands of chinese words. there is no clear method or model for dictionary construction in professional fields, and text analysis in professional fields still needs improvement. in this paper, a text of corporate public opinion is taken as an example to build a model of text keyword extraction, clustering, topic mining, and new word discovery based on machine learning algorithms. first, the public opinion text data of six companies with significant public opinion dissemination are obtained through a web crawler program. second, these text data are pre-processed to form text data that can be analyzed. third, the lda algorithm is used to initially extract the topic keywords, and keywords are clustered to perform topic classification based on fuzzy clustering algorithm, and it compares different new word discovery algorithms and expands the keyword thesaurus of corporate public opinion based on the seed dictionary. finally, machine learning model of text thesaurus is constructed. https://doi.org/10.15837/ijccc.2020.2.3811 3 2 machine learning models the machine learning model of corporate internet public opinion topic mining and thesaurus construction mainly includes the collection of public opinion texts, data preprocessing, text de-duplication, word segmentation, and de-duplication stop words. the pre-processed text uses the lda algorithm to obtain the keywords and the fuzzy clustering to get the topic classification of public opinion texts. these keywords will be used as a seed dictionary for new word discovery. the word2vec algorithm is used to form public opinion vocabulary into word vectors, and pre-train 7 million public opinion texts to form a public opinion text pre-training model. based on the original seed dictionary, new word discovery is used to expand keywords to form a corporate public opinion lexicon, machine learning model is finally constructed (figure 1). figure 1: machine learning model 2.1 lda lda topic model belongs to the typical model of topic mining in natural language processing. it is a three-layer bayesian probability generation model based on probability map. the main idea of the lda topic model is to assume that each document in the document set is composed of multiple topics, and each topic is a polynomial distribution of multiple words on a fixed vocabulary. the purpose is to use efficient probability inference algorithms to process large-scale data. to extract potential topics from a text corpus and provide a method for quantifying research topics. this method has been widely used in various topic discovery, such as hotspot mining, topic evolution, and trend prediction. the terms of lda was defined as follows [21]: a document is a sequence of n words denoted by w = (w1,w2, ...,wn) where wn is the nth word in the sequence, and a corpus is a collection of m documents denoted by d = {d1,d2, ...,dm}; since the process to generate the topic for m documents are independent of one another, we can have m conjugated structures and the generative process of probabilistic of topics in corpus is as follows: p(−→z |−→α ) = m∏ m=1 p(−→z m|−→α )= m∏ m=1 ∆(−→n m+−→α ) ∆(−→α ) . (1) the process to generate words for k topics are independent of one another, we can have k conjuhttps://doi.org/10.15837/ijccc.2020.2.3811 4 gated structures and the probabilistic of words in corpus is as follows: p(−→w |−→z , −→ β ) = k∏ k=1 p(−→w (k)| −→z (k), −→ β )= k∏ k=1 ∆(−→n k+ −→ β ) ∆( −→ β ) (2) thus, within a document, the probability distribution over words specified by the lda model is given as follows: p(−→w,−→z |−→α , −→ β ) = p(−→w,−→z | −→ β )p(−→z |−→α )= k∏ k=1 ∆(−→n k+ −→ β ) ∆( −→ β ) m∏ m=1 ∆(−→n m+−→α ) ∆(−→α ) (3) lda is an unsupervised machine learning method, which can effectively describe the relationship between the document, the topics contained in the document, and the vocabulary contained in the topic, reflecting the underlying semantics in the document. this method uses the co-occurrence features of the terms in the text to mine the subject features of the text without any prior knowledge about the text. in addition, the lda topic model can map text from the “document-vocabulary” high-dimensional space to the “document-topic” and “topic-vocabulary” low-dimensional space, and has very powerful dimensionality reduction capabilities. this is very valuable in an era of huge data dimensions. at present, this model has a large number of applications in the fields of image classification, text sentiment classification, recommendation system, automatic abstraction and other fields. 2.2 fuzzy clustering cluster analysis is a mathematical method for classifying things according to certain requirements. the actual classification problem is often accompanied by ambiguity, so the clustering problem is more accurately solved by fuzzy mathematics. in practical fuzzy clustering problems, cluster analysis based on fuzzy equivalence relations and cluster analysis based on fuzzy pseudo-order relations are mainly used. among them, the former is more commonly used. {x1,x2, ...,xn} is the entire classified object, and each object xi is represented by a set of data {xi1,xi2, ...,xin}. establish a fuzzy similarity relationship r on x, r can be expressed as a fuzzy similarity matrix r = (rij )n×n , where the similarity between xi and xjrij can be specified by one of the following methods according to the actual situation. quantity product: rij =   1, i = j, ( m∑ k=1 xikxjk)/m, i 6= j, (4) where m is an appropriate positive number and satisfies m ≥ maxi 6=j ( m∑ k=1 xikxjk). (5) angle cosine: rij = m∑ k=1 xikxjk√ m∑ k=1 x2ik · √ m∑ k=1 x2jk (6) correlation coefficient: rij = m∑ k=1 |xik −xi| · |xjk −xj|√ m∑ k=1 (xik −xi) 2· √ m∑ k=1 (xjk −xj ) 2 , (7) where x̄i = 1m ∑m k=1 xik, x̄j = 1 m ∑m k=1 xjk. others are maximum and minimum method, arithmetic mean minimum method, geometric mean minimum method, absolute value index method and so on. which one of the above methods is chosen depends on the characteristics of the actual problem [7]. https://doi.org/10.15837/ijccc.2020.2.3811 5 this article collects the text data of the company’s public opinion through crawlers, uses the lda algorithm to extract the keywords of the text, and develops fuzzy clustering to cluster the keywords to form different topics. the topic keywords will be used as a seed dictionary for new word discovery. 3 data source and preprocessing 3.1 data acquisition the objects collected in this study are those companies that have major internet public opinion incidents and have experienced major untrustworthy behaviors such as operational difficulties, arrears, and breach of contract in actual operations [5, 9]. the negative information on the internet public opinion of these companies was collected (table 1). table 1: objects collected by internet public opinion company name industry main problem letv the internet debt crisis ofo the internet debt crisis storm video the internet arrest of legal representative kangmei pharmaceutical pharmacy arrest of legal representative shenzhen hemei group co. ltd. retail note 1* shenwu environmental technology co. ltd. environment note 2** * note 1: significant decline in performance, inclusion in the list of dishonest performers, suspected violation of information and laws. ** note 2: huge losses, financial fraud, and information disclosure violations. 3.2 data collection methods the corporate public opinion data is collected using crawler software. the collected online public opinion information sources include wechat, weibo, forums, government websites, news websites, industry forums and other information sources. table 2: collected information of corporate public opinion (po) company name po collection time period collecting po letv 2014-2019 70000 ofo 2018.6-2019.4 7200 storm video 2018.8-2019.9 2395 kangmei pharmaceutical 2018.8-2019.9 1515 shenzhen hemei group co. ltd. 2018.8-2019.9 5996 shenwu environmental technology co. ltd. 2018.8-2019.9 3601 the collected content includes title, link, news source and time. the collected content is shown in figure 2. 3.3 data processing obtaining standard comment information is only a prerequisite for cleaning public opinion information, and it is necessary to process the standard comment again, that is, word segmentation and labeling. unlike english, we do not use spaces or punctuation to distinguish words in chinese. this requires chinese word segmentation technology to accurately extract chinese words from uninterrupted chinese character strings. at the same time, relevant information such as part of speech should be extracted. for example, after judging a word as an adjective, you can also extract the adverbs nearby to judge the strength or emotion of the adjective. generally speaking, the more authoritative system for analyzing chinese morphology is the jieba word segmentation system, which is also a widely used https://doi.org/10.15837/ijccc.2020.2.3811 6 figure 2: sample public opinion data collection word segmentation technology. the system supports chinese word segmentation, can also tag parts of speech, recognize new words, and recognize named entities. jieba’s word segmentation results are as follows: / w, ofo / nx, why / ryv, repeated / d, trapped / vi, capital chain / nz, crisis / n, rumor / n,? / w, david / nr, also / d, can / v, "/ w, willful / a," / w, how long is / ryt,? / w,] / w, since / p, ofo / nx, rejection of / v, didi / q, of / ude1, acquisition of / v, offer / v, since / f, / w, about / vn, ofo / nx, / ude1, negative / b, message / n, / d, / d from time to time, / v, appears. / w, with / p, motorcycle / b, worship / v, be / pbei, beauty / b, group / n, purchase / v, / w, harrow / nz, bicycle / n, input / v, ali / nt, embrace / n, / w, one-hearted / d, want / v, maintain / v, independent / a, status / n, / ude1, ofo / nx, / w, how / ryv, talent / n, avoid / v, repeatedly / d, fall into / v, funding chain / nz, crisis / n, / ude1, rumor / n,? / w in general, the storage format of various texts is very different, and the text may have noisy information, the system must preprocess the text to make the text meet the input requirements of the classifier. 1. de-duplication of raw data due to the existence of a large amount of duplicate or similar text data in corporate public opinion, the original data is pre-processed initially and the duplicate data is de-duplicated. 2. text segmentation this research uses the jieba word segmentation algorithm. its main functions are: chinese word segmentation, part-of-speech tagging, recognition of new words, entity name recognition, etc. the word segmentation accuracy is high, and the recall rate of new word recognition based on role tagging is higher than 90%, and the part-of-speech tagging and word segmentation processing speed reaches 543.5kb/s. the word segmentation stage is to perform chinese word segmentation, part-of-speech tagging for each text, and output its results in a prescribed format. because some special vocabulary algorithms cannot be directly identified, during the word segmentation process, some special words such as "little yellow car" and "davi" are manually labeled to establish a word segmentation dictionary. 3. filter stop words in the word collection obtained in the word segmentation stage, many words are meaningless. the impact of these words on the analysis work can be ignored, but if these words are not used as text feature words, bringing large errors to text classification results, these words are often referred to as stop words in this article. removed words marked as part of speech: orientation, preposition, quantifier, auxiliary, punctuation, non-morpheme. these words are like, "yeah, this, if, that, then." if these words are added to the text analysis, it will increase the cost of our text analysis. therefore, we introduce a stop-word dictionary and remove these stop-words in the process of word segmentation. https://doi.org/10.15837/ijccc.2020.2.3811 7 according to all available resources, various stop-word lists such as “hit’s stop words thesaurus”, “sichuan university machine learning intelligent laboratory stop thesaurus”, and baidu’s stop words will be organized to focus on extracting chinese words (instead of a large number of english words and chinese punctuation marks) came out of a more comprehensive vocabulary, with a total of 1598 stop words. 4 fuzzy clustering for public opinion topics 4.1 topic extraction before feature fusion, we first need to extract features from the text. in the following, feature extraction is performed on the text in two ways. topic features are extracted using the lda topic model, and then word2vec is used to lexicalize the topics into word vectors. because the original feature data of the lda model is a bag-of-words model, a bag-of-words model is required before extracting topic features. lda is a three-layer bayesian model. the first and second layers represent the probability distribution of topics under the document, and the second and third layers represent the probability distribution of terms under the topic (figure 3). figure 3: bayesian model of lda in figure 3, α and β are dirichlet prior hyper parameters; the words in document m will be clustered into z topics, for each topic z ∈{1, 2, ...,k}, and sample distribution is φk ∼ direchlet(β). bayesian model is as follows [17]: 1. choose n ∼ possion(ξ) 2. choose a topic distribution θ ∼ direchlet(α) 3. for each of word wm,n in m th document: (a) choose a topic of the word zm,n ∼ multinomial(θm) (b) choose a word wm,n ∼ multinimial(φzm,n ) we get the following results based on fuzzy clustering algorithm: https://doi.org/10.15837/ijccc.2020.2.3811 8 0, ’0.155 * "video" + 0.080 * "initiated" + 0.076 * "project" + 0.041 * "previous" + 0.028 * "recommended" + 0.026 * "this year" + 0.021 * "title" + 0.021 * "youku" + 0.020 * "nobody" + 0.017 * "domestic" ’ 1, ’0.144 * "problem" + 0.073 * "true" + 0.057 * "credit" + 0.053 * "now" + 0.045 * "solved" + 0.022 * "done" + 0.020 * "maintenance" + 0.016 * "daily "+ 0.015 *" xiaobian "+ 0.011 *" below "’ 2, ’0.311 * "shareholder" + 0.166 * "wechat" + 0.092 * "fund" + 0.047 * "exposure" + 0.042 * "display" + 0.017 * "brand" + 0.017 * "cool" + 0.015 * " multi-site "+ 0.010 *" company announcement "+ 0.007 *" picture "’ 3, ’0.127 * "published" + 0.067 * "payment" + 0.052 * "" + 0.041 * "encounter" + 0.040 * "media" + 0.037 * "data" + 0.025 * "corruption" + 0.021 * " house leakage "+ 0.018 *" frender "+ 0.017 *" recent "’, 4, ’0.086 * "disputes" + 0.059 * "half year" + 0.056 * "billion dollars" + 0.042 * "this house" + 0.030 * "late" + 0.017 * "drag" + 0.016 * "shuffle" + 0.015 * "one out" + 0.015 * "approach" + 0.012 * "internet biography" ’ 5, ’0.547 * "100 million yuan" + 0.081 * "goodwill" + 0.046 * "font size" + 0.026 * "look" + 0.015 * "maintenance" + 0.011 * "fall" + 0.008 * "hurry up" + 0.008 * "finance network" + 0.004 * "let’s wait and see" + 0.004 * "about 100 million" ’ 6, ’0.128 * "smart" + 0.071 * "super" + 0.058 * "burst" + 0.055 * "economic network" + 0.037 * "someone" + 0.033 * "cause" + 0.030 * "tesla" + 0.015 * "attribution" + 0.015 * "channel" + 0.014 * "list" 7, ’0.214 * "received" + 0.076 * "reporter" + 0.073 * "partner" + 0.053 * "securities daily" + 0.011 * "burning money" + 0.011 * "follow the trend" + 0.009 * "business newspaper" + 0.009 * "no solution" + 0.009 * "foundation" + 0.008 * "suppress" 8, ’0.289 * "executive" + 0.106 * "roll-call" + 0.074 * "since the month" + 0.022 * "block" + 0.012 * "clarification" + 0.009 * "management" + 0.007 * "home" + 0.006 * "suspect" + 0.005 * "entrepreneur" + 0.004 * "sunyuchen" ’ 9, ’0.410 * "listed company" + 0.080 * "default" + 0.070 * "one quarter" + 0.011 * "chicken feathers" + 0.011 * "web news" + 0.010 * "billions" + 0.008 * "yesterday" + 0.007 * "call it" + 0.006 * "smooth" + 0.005 * "order" ’ 10, ’0.107 * "fund" + 0.093 * "headline" + 0.087 * "ten billion" + 0.079 * "internet" + 0.053 * "multiple" + 0.029 * "only" + 0.018 * "sing empty" + 0.016 * "focus" + 0.015 * "include" + 0.014 * "one step" ’ 4.2 subject fuzzy clustering results in recent years, due to the development of the internet, corporate information disclosure is more rapid and faster than ever. the use of corporate internet public opinion information as an indicator of corporate credit evaluation has received increasing attention from scholars in this field. there are few existing researches on the subject classification. in this paper, keywords are obtained based on lda, and fuzzy clustering methods are used to aggregate 9 subject categories, including operations, employees, funds, justice, capital, corporate negative news, and regulatory punishment, founders and management, corporate credit, etc. the specific classification is shown in table 3. 5 word2vec-based thesaurus machine learning model construction 5.1 word vectors computers cannot directly understand human languages. when dealing with natural language problems, scholars have used some features in the text to cleverly map them into digitized dimensional sequences. google opened word2vec in 2013 to train word vectors, which can express words in the form of vectors. the word2vec model contains two types of word vector learning structure models: skip-gram and cbow (continuous bag of words model) models. both structures include an input layer, a mapping layer, and an output layer. when it is determined that the number of words w context words is n, the skip-gram model predicts the context of the current word. the cbow model uses contextual vocabulary to predict the current word (figure 4). skip-gram model predicts the context based on the current word, given the word sequence w= {w1,w2,...wm}, the model maximizes the average log probability as: l(w) = 1 m ∑m m=1 ∑ −l≤i≤l log p(wm+i/wm), (8) where l is the size of the context window. the cbow model predicts target words by specifying window words, given a word sequence w= {w1,w2,...wm}, the model maximizes the average log probability as: l(w) = 1 m m−l∑ i=l log p(wi/wi−l, ...,wi+l). (9) https://doi.org/10.15837/ijccc.2020.2.3811 9 table 3: types and keywords of clustering results topic topic category topic keywords topic1 operation bankruptcy, stoppages, emptying, layoffs, dilemmas, stagnation, dismissal, struggling, sinking, bad debt, defeat, defeat, winter, collapse, failure, loss of debt, debt collection, embezzlement, dissatisfaction, empty numbers, difficulties, life extension, bump, stop, shrink topic2 employee rights protection, wages, wages, reporting, arrears of wages, compensation, complaints, banners, disputes, arrears, names of affiliated companies, payroll, comfort, claims, severance, resignation, layoffs, goodwill, debt collection, dumping, layoff topic3 funds deposit,debt, crisis, arrears, cash flow, funds topic4 judicial lawyer letter, case, legal representative, prosecution, enforced person, court, application, lao lai, judgment, claim, lawsuit, arrest, filing, law, rights protection, freeze, violation, breach of trust, breach of law, suspect, charge, overdue, fraud seized topic5 capital mergers and acquisitions, acquisition, unicorn, equity, pledge, shrinking, financing, evaporation, stock price, liquidation, falling, limit, falling back, continuous board, flight, cash cut, liquidation, backdoor, collapse, delisting, clearance bearish, manipulation, decline, repurchase, hollowing out, reduction, cash, shareholder name topic6 corp. negative news smeared, survived, finished, negative news, cool, shocking, rumors, bursts, unexpected, urgent, dark, clarification, scandal, worst, failure, storm, halberd, fall, black list, shelling, stinking topic7 regulatory penalties punishment, warning, fine, inquiry, notification of criticism, investigation, ticket, heavy penalty, prohibition of employment, prohibition of entry, supervision, interview, csrc, china consumers association, miit, shenzhen stock exchange, shanghai stock exchange topic8 management negative runaway,leave, responsibility, sale, self-help, kick out, name of manager topic9 negative credit false, liar, question, doubt, true and false, deceived, doubt, inquiry, scam, flicker, crisis of trust, recidivism, nonsense, tearing, trap, blacklist, broken faith, executed figure 4: principle of word2vec algorithm https://doi.org/10.15837/ijccc.2020.2.3811 10 steps for network calculation: • input layer: onehot of the context word. (assuming the word vector space dim is v and the number of context words is c) • all onehots are multiplied by the shared input weight matrix w (v ·n matrix, n is a number set by yourself, and the initialization weight matrix w) • the resulting vector (note the result of multiplying the onehot vector by the matrix) is added and averaged as the hidden layer vector, with size 1 ·n. • multiplied by the output weight matrix w ′{n ·v} • the vector {1 ·v} is obtained by processing the activation function to obtain the v-dim probability distribution ps: because it is onehot, each dimension represents a word, and the word indicated by the index with the highest probability is the predicted middle word (target word) • compared with onehot of true label, the smaller the error, the better. loss function (generally a cross-entropy cost function) use the public opinion keywords in the seed dictionary as seed words, bring them into the trained word vector, query for words similar to the seed word, and select the first five similar words as synonyms to be included in the dictionary. these words will also be used as seeds again. words to get more synonyms, take "false" as an example, and use the new word recognition algorithm to get the top 5 synonyms, as shown in table 4. table 4: word similarity no word similarity 1 imaginary 0.7475070953369141 2 misleading 0.6770043969154358 3 fabrication 0.6676515340805054 4 fraud 0.659699559211731 5 false report 0.6317102313041687 some results are shown in table 5. 5.2 results analysis the topic keyword extraction structure is constructed into a star network, as shown in the figure 5. it can be seen that under some topics, more keywords are found, and some keywords are less. this is due to the evolution of corporate network public opinion and reflects the main problems behind the public opinion of the company, according to the number of theme keywords, can predict the development trend of public opinions [22, 23]. the topic keywords can also be used to construct a co-occurrence relationship network, as shown in figure 6. this network abstracts words into nodes in the network, and abstracts the co-occurrence relationship of words in the word set. it is called an edge in the network, that is, if 2 words appear in the same word set at the same time, it is considered that there is an edge between them. the edge is an abstraction of the co-occurrence relationship between words in a word set. through the co-occurrence relationship network, tap the internal connection of corporate public opinion [18]. all the nodes of a subgraph are adjacent to at least k other points in the subgraph. such a subgraph is called a k-core. k-kernel analysis can find the most closely related nodes in the network, and these nodes can summarize the structural characteristics of the network [20]. through the k-core diagram, local problems of the public opinion network can be found, as shown in figure 7: the final plan identified 3092 keywords for corporate public opinion. there are three main evaluation indicators used in this article, namely accuracy rate p, recall rate r, and f value. its formula is p = sr sa . (10) https://doi.org/10.15837/ijccc.2020.2.3811 11 table 5: thesaurus of synonyms topic category topic keywords operation ’corporate bankruptcy’, ’closed’, ’debt’, ’liquidation’, ’creditors’,’ resumption ’,’ stop construction ’,’ arrears’, ’stop production’, ’engineering payments’,’ no trace ’,’ moving ’empty’, ’empty’, ’empty’, ’empty’, ’salary reduction’, ’closed’, ’resignation’, ’salary reduction’, ’management’, ’dilemma’, ’crisis’,’ dilemma ’,’ disengagement ’,’ overcoming difficulties ’,’ stagnation ’,’ recession ’,’ imbalance ’,’ intensification ’,’ weakness ’,’ succession ’,’ new appointment ’,’ assignment ’,’ assignment ’,’ treasurer ’,’ get rid of ’,’ sink ’,’ pain ’,’ despair ’,’ tear ’,’ trapped ’,’ unable to extricate ’,’ trapped ’,’ trapped ’,’ distressed ’,’ impairment ’,’ bad debts’, ’receivables’,’ accounts’, ’bad debts’,’ routing ’,’ battle situation ’,’ great victory ’,’ reversal ’,’ rivals’, ’counter attack’, ’retreat’, ’retreat’, ’retreat’, ’losing soldiers’,’ early winter ’,’ cold ’,’ advent ’,’ cold wind ’,’ severe cold ’,’ subprime crisis’, ’stock market’, ’finance crisis’, ’financial market’, ’slump’, ’bankruptcy’, ’mismanagement’, ’business failure’, ’layoffs’,’ chain of funds’, ’usile loan’, ’lost money’, ’lossy losses’, ’run the road’, ’home broke’, ’debt repayment’, ’debt collection’, ’debt’, ’creditor’, ’ ’,’ carry out ’,’ fake false report ’,’ private share ’,’ illegal ’,’ diarrhea ’,’ altitude response ’,’ sickness medicine ’,’ cultural conflict ’,’ diarrhea ’,’ dilemma ’, ’bottle bottle’, ’crisis of trust’, ’crisis’, ’facing’, ’blessing’, ’supernatural power’, ’saving’, ’rebirth’, ’golden body’, ’stop production’, ’reproduction’, ’suppression’ , ’banned according to law’, ’return to work’, ’capillary’, ’diastolic’, ’dilatation’, ’elasticity’, ’weakened’ staff ’consumption association’, ’disputes’, ’complaints’, ’legal means’, ’wages in arrears’, ’arrears of wages’, ’migrant workers’, ’arrears’, ’appeals’, ’monthly wages’, ’basic wages’ , ’wage treatment’, ’treatment’, ’performance’, ’report phone’, ’complaint’, ’reporting centre’, ’arrears of wages’,’ salary ’,’ arrears’, ’migrant workers’,’ labor disputes ’,’ compensation ’,’ claim ’,’ infringer ’,’ compensation for damages ’,’ obligation of compensation ’,’ report ’,’ consumer ’,’ banner ’,’ banner ’,’ slogan ’,’ publicity board ’,’ publicity card ’,’ civil dispute ’,’ mediation ’,’ economic dispute ’,’ mediation ’,’ debt dispute ’,’ project payment ’,’ arrears’, ’payments’,’ paid wages’, ’payment’, ’wages’,’ arrears of wages’, ’unable to pay’, ’payrolls’,’ processing capital ’,’ compensation ’,’ mental loss fee ’,’ mental loss’, ’compensation’, ’ lost work ’,’ compensation ’,’ compensation ’,’ rejection of compensation ’,’ plaintiff ’,’ claim ’,’ order ’,’ removal ’,’ check ’,’ escort ’,’ expulsion ’,’ resignation ’,’ resignation ’,’ changing ’,’ dismissal ’,’ resignation ’,’ salary reduction ’,’ closed ’,’ resignation ’,’ salary reduction ’,’ management ’,’ intangible assets ’,’ trademark rights ’,’ unfair competition ’,’ shareholders ’equity,’ tangible assets ’,’ repayment of debts ’,’ debt collection ’,’ debts ’,’ creditor ’,’ arrears’ height https://doi.org/10.15837/ijccc.2020.2.3811 12 figure 5: star networks of subject words https://doi.org/10.15837/ijccc.2020.2.3811 13 figure 6: keywords co-occurrence network figure 7: k-core network https://doi.org/10.15837/ijccc.2020.2.3811 14 r = sr so . (11) f = 2 ×p ×r p + r . (12) sr is the number of texts classified correctly; sa is the number of texts actually classified; so is the number of texts belonging to the category; f values are calculated from accuracy rate p , recall rate r. in order to analyze the characteristics of the new word discovery algorithm in this paper, the word2vec algorithm is compared with the method based on association rules, the method based on n-gram, and the method based on mutual information pmi in the experiment. analysis, the experimental results of the comparison of the last algorithms are shown in table 6. table 6: experimental results method p r f association rules 0.521 0.471 0.420 n-gram 0.623 0.542 0.561 pmi 0.737 0.731 0.720 word2vec 0.778 0.778 0.771 it can be seen from table 6 that in the comparison test with the mutual information pmi, the association rule-based and the n-gram-based algorithm, the word2vec algorithm has achieved a good performance improvement for the topic new word discovery of corporate network public opinion. it has been improved. the association rule-based discovery algorithm has such a rapid change, poor vocabulary standardization, and poor adaptability to emerging public opinion vocabulary that has large differences in word structure, resulting in low accuracy and recall of new words. for the ngram-based algorithm, although the accuracy and recall rate have been improved compared with the association rule-based algorithm, in the case of multi-words, this method has a partial recognition rate for new words. low. the mutual information pmi algorithm is based on the n-gram method, combined with the mutual information to merge multiple words, and has further improved in various indicators. the new word algorithm based on word2vec used in this article obtained the highest index value because the algorithm uses a neural network to combine the contextual relationships between words to vectorize the words of the text and calculate the distance between words, which can quickly and accurately discover new words. 6 conclusion this paper preliminarily constructs a text analysis framework for corporate internet public opinion, and analyzes corporate internet public opinion from text topic mining, keyword thesaurus construction. this article first combines the vocabulary mined by public opinion text topics with corporate credit evaluation models, and divides corporate online public opinion texts into operations, employees, funds, justice, capital, negative corporate news, regulatory penalties, founders and management, and companies. credit nine categories and get the keywords under that topic as a seed dictionary. based on the new word discovery algorithm, a keyword thesaurus for corporate public opinion keywords is constructed. the mutual information pmi, association rules, and n-gram and word2vec-based new word discovery algorithms are compared. the results show that word2vec-based new word algorithms have improved accuracy, recall, and f value. this is pre-trained with word2vec algorithm. text, vectorized vocabulary are closely related. there are also some shortcomings in this study. the selected cases are limited and there are only six companies, so there is still room for improvement in the topic classification of public opinion texts, and further subdivided or increased classification. conflict of interest the authors declare no conflict of interest. https://doi.org/10.15837/ijccc.2020.2.3811 15 references [1] adreevskaia, a.; bergler, s. 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(2017). large scale software test data generation based on collective constraint and weighted combination method. tehnicki vjesnik, 24(4), 1041-1050, 2017. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: luo, j.; yu, d.; dai, z. (2020). a latent dirichlet allocation and fuzzy clustering based machine learning model for text thesaurus, international journal of computers communications & control, 15(2), 3811, 2020. https://doi.org/10.15837/ijccc.2020.2.3811 international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(6), 670-690, december 2019. an empirical study of aml approach for credit card fraud detection–financial transactions a. singh, a. jain ajeet singh university school of information, communication & technology guru gobind singh indraprastha university, delhi, india ajeet.usit.025164@ipu.ac.in anurag jain* university school of information, communication & technology guru gobind singh indraprastha university, delhi, india *corresponding author: anurag@ipu.ac.in abstract: credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (amlts) has studied and also their pros and cons has summarized. the various dataset are used in the literature has studied and categorized into the real and synthesized datasets.the performance matrices and evaluation criteria have summarized which has used to evaluate the fraud detection system.this study has also covered the deep analysis and comparison of the performance (i.e sensitivity, specificity, and accuracy) of existing machine learning techniques in the credit card fraud detection area.the findings of this study clearly show that supervised learning, card-not-present fraud, skimming fraud, and website cloning method has been used more frequently.this study helps to new researchers by discussing the limitation of existing fraud detection techniques and providing helpful directions of research in the credit card fraud detection field. keywords: credit card fraud, cashless transaction, data mining technique, fraud detection. 1 introduction nowadays, the use of credit cards has significantly increased on both online and offline purchases because of the fast growth of the e-commerce and online banking system. when someone uses other persons credit card for personal benefit without the knowledge of the owner of the credit card is known as credit card fraud. the association of certified fraud examiners defines a fraud as "the use of one’s occupation for personal enrichment through the deliberate misuse or application of the employing organization’s resources or assets" [63]. individuals and government suffer large financial losses across the world every year due to the lack of sophisticated fraud detection system. in recent years, a million dollars losses due to the card frauds and many countries have affected. according to the fraud facts report-2017, the uk payment credit cards losses has been increased 9% in 2016 from euro 567.5 million in 2015 to euro 618.0 million. this point out copyright ©2019 cc by-nc an empirical study of aml approach for credit card fraud detection–financial transactions 671 that payment card fraud is increasing yearly. the total spending on cards has reached euro 904 billion in 2016, with 19.1 billion transactions happened during the 2015 [66]. in india, the state bank of india (sbi) has blocked 0.6 million (6 lakh) debit cards due to a cyber attack (malware-related breach) to a yes bank atm network on october 19, 2016. it was one of the latest and largest security breaches where in indian banking history, millions of the debit cards were compromised. the biggest financial data breaches reached almost 3.2 million (32lakhs) debit cards across 19 private banks (i.e. hdfc bank, etc.). according to the national payments corporation of india report, 90 atms suffered losses and 641 customers lost 13 million (1.3 crores) indian rupee due to fraudulent transactions. according to the nilson report, the card fraud losses reached $21.84 billion in 2015, $24.71 billion in 2016 and $27.69 billion in 2017. this is also informed that the actual amount of losses could increase in 2020 [64]. according to the annual report of internet crime complain 2018 (ic3), online crime has increased from 2008 to 2017 due to many types of frauds such as credit card fraud, identity fraud, etc. the total loss has increased from 239.1 million in 2008 to $1,418.7 million in 2017 due to frauds. table1 presents the thousands of complaints between 2017 and 2008 received by the ic3 and happened financial loss in millions of usa dollar [65]. table 1: financial loss due to cyber crime year complaint money loss year complaint money loss 2017 301,580 $1,418.7 2012 289,874 $ 581.4 2016 298,728 $1,450.7 2011 314,246 $ 485.25 2015 288,012 $1,070.7 2010 303809 $121.71 2014 269,422 $ 800.5 2009 336,655 $559.7 2013 262,813 $ 781.8 2008 275,284 $265.0 it is clearly observed that financial loss has been increased due to card frauds while the number of complaints in some year has also decreased. these enormous numbers of losses define the importance of fraud prevention and detection system [51]. this study focuses on the banking domain in particular for credit card frauds. the common credit card frauds are application fraud, counterfeiting, identity theft fraud, phishing, and skimming fraud. frauds have to be explored and identified as quickly as possible in order to stop fraudulent activities [10, 34]. to address these frauds, various fraud prevention and detection mechanisms are deployed to detect and prevent credit card fraud. the purpose of credit card fraud prevention mechanism is to prevent and stop a fraudulent transaction before it happens in the initial phase, and also prevent the occurrence of different cyber attacks on your computer system, networks and your data by using numbers of prevention methods namely communal detection spike detection, pin, internet security system, firewall and cryptography algorithms [1,25]. fraud detection is second stage mechanism to detect a fraud by applying various data mining, machine learning and bio-inspired techniques namely decision tree, artificial neural network, artificial immune systems (ais), k nearest neighbor(knn), support vector machine(svm), genetic algorithm (ga) and hidden markov model (hmm) [12, 46, 55, 60]. these techniques are usually suitable for both types of transactions as normal and fraudulent in order to learn fraud patterns and customer patterns. the goal of the credit card fraud detection system is to maximize true positive and minimize false positive predictions of legitimate transactions. the main contribution of this research is to identify the frequently occurred credit card frauds and methods committed to obtain credit card information illegally. a systematic review of the 672 a. singh, a. jain existing machine learning techniques (mlts) for credit card fraud detection are also discussed, and their advantages and limitations are reported in this paper. based on the outcome of this paper, the research gap is identified among the existing fraud detection techniques. various credit card fraud datasets are also compared on the basis of their features and used these datasets to implementation various existing machine learning techniques. the research process of this paper is explained in figure 1 based on adaptive existing machine learning techniques to credit card fraud detection. the rest of the paper is designed as follows. section 2 explores the different category of credit card frauds and methods. section 3 discusses credit card fraud detection issues and challenges and section 4 presents the various credit card fraud detection techniques. the various dataset, primary and derived attributes are summarized in section 5. section 6 presents performance matrices and evaluation criteria to evaluate the fraud detection system. research conclusion and discussion are presented in section 7. figure 1: flow diagram of the machine learning-based credit card fraud detection system 2 classification of credit card frauds criminals use a variety of methods and tools for obtaining card information needed for online transactions and gets access to the cardholders account. the various credit card frauds are observed based on literature. the common types of credit card frauds include application fraud, transaction fraud, bankruptcy fraud ( [16]), and counterfeit fraud ( [12,50]) are summarized in table 2. 3 credit card fraud detection issues and challenges researchers are facing several issues and challenges to detect fraud on time efficient manner.credit card issuing banks required efficient and well-organized fraud detection system to run their business in a healthy way.the numbers of issues and challenges are summarized in table 3 based on literature. 4 fraud detection techniques based on machine learning the credit card fraud detection techniques (ccfdts) are grouped into two general categories such as fraud analysis (misuse detection), and user behaviors analysis (anomaly detection). the ccfdts are also further categorized as supervised learning which uses labeled training an empirical study of aml approach for credit card fraud detection–financial transactions 673 table 2: various credit card frauds fraud name definition method location of occurs application [1,12,50] if a fraudsters fill the application form with fake information,or maybe even real, but stolen identity information. steal identity namely bank statements, telephone bill, and electricity bill, etc. financial organization. transaction [1,12,47] used stolen card details: when someone obtains others card details to make a fraudulent transaction without legitimate cardholder knowledge keylogger, sniffers, site cloning, physical stolen card. anywhere. account takeover when fraudsters pose as a genuine cardholder to gain access and control of an account then withdraw funds, makes illegal transactions, and may change account details. brute force botnet attacks, phishing,& malware. anywhere. counterfeit [12,50] it is the practice of illegally copying a legitimate credit card details fake websites, site cloning, atm skimming device. atm/ place where cc uses. card-notpresent [1,12,50] when fraudsters obtain card information for personal use internet, hacking, email phishing campaigns. anyplace. skimming keeps the credit card into a skimming machine to make a copy and save the important card information. skimming machine shopping mall, restaurants, etc. shoulder surfing when fraudsters looking over the cardholders shoulder to steal credit card details while cardholder enter card details an electronic device and a web. digital devices like camera atm, pos. data, and unsupervised learning uses unlabeled training data, and a hybrid technique called semi-supervised learning. 4.1 supervised learning method supervised learning is the machine learning technique. the supervised learning and classification technique are used for fraud analysis (misuses detection) at the transaction level and transactions categorized as a fraud or normal transaction based on the historical data. the supervised learning techniques are ruled induction, decision trees, case-based reasoning, support vector machines (svms), neural networks techniques, linear regression, logistic regression, naive bayes, linear discriminate analysis,and k-nearest neighbor algorithm which used to credit fraud detection. 674 a. singh, a. jain table 3: various issue and challenge for credit card fraud detection issues description no standard credit card dataset [25,60] it is the biggest issues in fraud detection domain. there is no standard, real, and benchmark dataset available to evaluate proposed fraud detection methods. in most of the cases, researchers have been used their own dataset (synthetic dataset) for doing research . skewed class distribution [1,46,50, 60] the skewed distribution (or imbalanced class) is one of the most serious problems. very small percentages of all card transitions are fraudulent as compare to normal transition. in a supervised learning technique, imbalance problem occurs when the minority class percentage is not very high. cost-sensitive classification problem [45,50] credit card transactions are misclassification (fraudulent transaction as a legitimate transaction and legitimate transaction as a fraudulent transaction) due to this financial impact ranging from a few to thousands of money. presently no suitable evaluation criterion [50,60] standard evaluation criterion is not available for assessing and comparing the results of a fraud detection system.the accuracy is not suitable metrics ccfd because data set is imbalanced. lack of proper algorithm [50] a powerful algorithm is required to detecting a new type of normal and fraudulent pattern.the data mining and machine learning algorithm has its own advantages and disadvantages (table4). fraudsters behaviour dynamic [60] fraudsters are changed their behaviour from time to time for getting card details and bypass detection system or modify fraud styles. cardholder behavior(concept drift) [1]. the cardholder is always changing their behaviour may be specific situation/ occasions (e.g new year), the buying power of users will be enhanced. if the ccfd system does not consider this as normal changes, will be considered as fraud behaviour pattern recognition algorithm [1] pattern recognition algorithm is used to recognize fraudster pattern and customer pattern to minimize fraud cases. 4.2 unsupervised learning method the user behavior analysis can be performed by using unsupervised learning and clustering technique that identifies a transaction based on the account behavior of users. in unsupervised learning, all transactions are unlabeled and the algorithms learn to the inherent structure from the input transactions. unsupervised learning is a more powerful technique to identify fraud and non-fraud transaction. the most common unsupervised techniques are k-means, self-organizing map (som) technique, and hidden markov model (hmm) technique. 4.3 semi-supervised learning method the semi-supervised learning method falls between supervised and unsupervised learning method. semi-supervised learning is a machine learning task that makes use a very small number of labeled data (previously known) and a large number of unlabeled data (unknown data) to detect a card fraud. semi-supervised learning could decrease the effort needed by supervisors to classify training data. the supervised, unsupervised and semi supervised techniques are introduced following which was used to detect credit card fraud (ccf). an empirical study of aml approach for credit card fraud detection–financial transactions 675 4.4 artificial neural networks anns constitute a set of interconnected nodes designed to imitate the functioning of the human brain (maes et al. [31]). a neural network based fraud detection system has trained with the earlier data of cardholder spending behavior and the tested software on synthetically generated data (guo and li [21]). anns has been configured by supervised, unsupervised, and semi-supervised learning methods for classification or clustering of transaction group. chen et al. [14] employs svm and ann techniques to investigate the time-varying fraud problem. the performance of the ann is compared with svm, outcomes show that ann has the highest training accuracy. ann two techniques namely back propagation neural network (bpnn) and self-organizing maps neural network (somnn) are mostly used for credit card fraud detection. back propagation neural network bpnn is a supervised learning technique, a generalization of the delta rule, and suitable for the feed-forward network, that has no feedback. the feed forward network contains three layers namely input, hidden, and output layers to credit card frauds detection. chen et al. [14] deployed svm and bpnn (ann) techniques to investigate the time-varying fraud problem. the performance of the bpnn (78%) is compared with svm (67%); outcomes show that ann has the highest training accuracy.another research (ghosh et al. [20]) deployed a multilayer feed forward neural network model to fraud detection from mellon bank and reduction total fraud losses from 20% to 40%. self-organizing map self-organizing map (som) [27] is a neural network model based on unsupervised learning method for the analysis and visualization of high-dimensional data. som neural network used to detect credit card fraud based on customer behaviour. som consist of two layer namely input mapping layer. the input layer forward the incoming transactions to the mapping layer for performing the clustering technique. the mapping layer is to map all transactions with cardholder’s behavior for detecting hidden patterns. finally, the mapping layer produces results in the form of fraudulent and genuine transaction. according to quah and sriganes [42], the som used to decipher,filter and analyze customer behavior for the detection of credit card fraud in the banking sector. olszewski [39] has suggested a fraud detection method based on the user accounts visualization. the proposed som visualization method was applied in three different areas such as telecommunication, computer intrusion, and credit card fraud detection. this method is more suitable for the credit card as compared to the other areas. the cc dataset contains 10,000 accounts details from 1 january 2005 to 1 march 2005 and identified 100 instances as fraudulent. the som visualization method was determined based on the produced values of the precision (1.0000 vs 0.8257, 0.7200, 0.5696) recall (1.0000 vs0.9000, 0.9000, 0.9000) accuracy (1.0000 vs 0.8550, 0.7750, 0.6100), f1 score (1.0000 vs 0.8612, 0.8000, 0.6977) and auroc (1.0000 vs. 0.9415, 0.9285, and 0.8265) for a credit card. this is because of less unbalance dataset and small size of dataset. there may be chance of the accuracy and sensitivity will get dropped if dataset become large and more unbalance. 4.5 bayesian network bayesian network is a probabilistic graphical model denoting a set of random variables and their conditional dependencies through a directed acyclic graph introduced cooper and herskovits in 1992 (panigrahi et al. [41]). 676 a. singh, a. jain bayesian network used the concept of bayes theorem to determine the probability of a given hypothesis to be true. p(fraudulent/evidence) = (p(evidence/fraudulent) ∗p(fraudulent)) p(evidence) (1) where p(fraudulent/evidence) is the posterior probability condition on hypothesis. moreover,p( fraudulent) and p(evidence) is the prior probability of hypothesis.p(evidence /fraudulent) is called the likely hood. the fraud probability represented by p(fraudulent/evidence) gives the observed user behavior. bayesian network(bn) has suggested for the purpose of credit card fraud detection based on user behavior.bayesian belief networks and artificial neural networks(ann) techniques have used credit card fraud detection by (maes et al. [31]). result presented that bayesian network has superior fraud detection capability than ann. according to kirkos et al. [26], the bayesian belief network model achieved the best performance to correctly classify 90.3% of the validation sample in a 10-fold cross validation procedure. the accuracy rates of the neural network and decision tree model were 80% and 73.6%, respectively. 4.6 k-nearest neighbor k-nearest neighbor (knn) is a supervised learning technique [53]. the euclidean distance method is used to calculate the distance between two transactions (input data transaction and current transaction) for every data transaction in the dataset and distances are arranged in increasing order. the k items have the lowest distance to the input data transaction point are selected. knn technique classifies any new incoming transaction by calculating the nearest point if the nearest neighbor is a fraud than transaction considers as fraud and if the nearest neighbor is not fraud than transaction consider as legal. the euclidean distance (dij) between two input vectors (xi,xj) is given by ∑ dij = √√√√ n∑ k=1 (xik −yjk)2, (k = 1, 2, 3.....n.) (2) a simple matching coefficient is frequently used for categorical attributes. in this method, both legitimate and fraudulent transactions are to be fed in order to train the data sets. the k-nearest neighbor technique has optimized for better distance metrics. the various papers (seeja and zareapoor [46]),(zareapoor et al. [60]) have performed a knn technique to detect a credit card fraud and compare their result with other data mining techniques and this method is fast with minimum false alerts. 4.7 artificial immune system the artificial immune system (ais) is part of artificial intelligence based on the biological symbol of the human immune system or natural immune system developed by neal in 1998 [38]. it is a user-based model to discriminate the incoming transaction as genuine or fraudulent. according to tuo et al. [54], artificial immune systems have four algorithms negative selection, clonal selection, immune network, and dendritic cells to identify fraudulent transaction detection. these algorithms have been applied to real data to detect fraud for achieving high accuracy. worng et al. [57] has developed a system called artificial immune system for fraud detection of credit card (aisccfd). aisccfd has a high level system model and consisted of the an empirical study of aml approach for credit card fraud detection–financial transactions 677 database, two subsystems (system interface) and ais engine. aisccfd system has identified input transactions as non-fraudulent or fraudulent. halvaiee and akbari [22] introduced a new credit card fraud detection model using ais called "ais-based fraud detection model". the model has added some enhancements to the artificial immune recognition system algorithm which helped to raise the precision, reduces cost, and system training time. ais-based fraud detection model enhanced fraud detection rate of 23%, reduced cost 85%, and training time 40%. this research has implemented a parallel model in a test environment for fair minimization in training time. gadi et al. [19] has employed the artificial immune systems on credit card dataset for fraud detection and outcomes are compared with neural network, bayesian network, naive bayes, and decision trees based on three strategies such as default 35.66 (3.21%), optimized 24.97 (5.43%), and robust 23.30 (2.29%). the ais produced the best classifiers and give high accuracy compared with other fraud detection methods respectively. huang et al. [23] have applied the ais model for fraud detection. the ais based model combines two ais algorithms with behavior-based intrusion detection using classification and regression trees (cart).this hybrid method applied on same data with combination cart algorithm ( tp1%,tn85%, fp15%,fn19%), cspra algorithm (3% undetected: tp81%, tn 89% , fp11%, fn 16%), dca algorithms (tp78%, tn90%, fp10%,fn22%) and stacking bagging algorithm (tp89%, tn95%, fp5%, fn 11%) to calculate each algorithm performance by using standard evaluation criteria. it was observed that the performance of ais is better than other techniques. 4.8 decision tree the decision tree (dt) is a supervised learning and statistical data mining technique. the research (koikkinaki [28]) implemented decision trees by using various machines learning-based algorithms such as the iterative dichotomiser3 (id3), successor of id3 (c4.5), random forest, classification and regression tree (cart). these techniques have been applied to a credit card database for fraud detection based on detection time and accuracy. dt technique divides the complex problems into smaller problems and resolves through repeatedly using the procedure (bai et al. [5]). according to sachin and duman, 2011 [44], decision rules have applied to determine the class of an incoming transaction as a genuine or fraudulent. gadi et al. [19] used dt technique to computing result based on three strategies such as default 32.76 (4.83%), optimized 27.84 (4.16%), and robust 27.87 (4.21%). minegishi and niim [35] has developed a decision tree learning based method called very fast decision tree learner (vfdt) to solve the imbalanced data stream problem. sahin et al. [45] has developed and implemented a cost-sensitive decision tree induction algorithm to identify fraudulent credit card transactions on a real world credit card data set. the performance of this approach was compared with the traditional classification models on a real data set and result showed that cost-sensitive decision tree algorithm outperforms the existing traditional classification methods. 4.9 support vector machine the svm is a statistical and supervised machine learning (ml) technique used for both regression and classification tests [15]. svm has found to be effective and popular in a diversity of classification tasks because it is mostly used for solving classification problems. svms contain two 678 a. singh, a. jain important properties that are the margin optimization and kernel called a radial basis function that can be used to learn complex regions. the kernel function is used to transform the dataset. the role of this function is a mapping of transactions between the input space and a higher dimensional space [13]. the kernel function is described by: k(x1,x2) = φ{(x1), (x1)} (3) where φ : x → h map transactions in input space x to higher dimensional space h. the hyperplane is used to separate the transactions, after applying the kernel function to the dataset. hyperplane is described by: {w,x} + b = 0 (4) the main role of the hyperplane is to maximize the separation between both transactions, which helps to reduce potential errors caused by over training. therefore, the classification for a support vector machine defined as:∑ i αiyik(xi,x) + b = 0 (5) the gaussian radial basis function and polynomial function are the most common kernel functions used which is dependent on the dataset and classification requirements [30]. an instance of transaction class is that separated by a hyperplane. transaction classes of two distinct cards are represented in blue and black bullets (data samples). support vectors work as a boundary to each class of credit card transaction. if transactions lie outside support vector boundary, considered as an outlier or fraud activity. new samples are classified based on measuring its distance from the hyperplane (nguwi and cho [37]). svm has been classified into four categories namely binary support vector system (chen et al. [12]), classification model based on decision tree and svm (sachin and duman [44]). novel questionnaire responder transaction approach with svm (chen et al. [13], and class weighted svm model (lu and ju [30]). it has been developed on class weighted support vector machine for credit card fraud detection. the model is more appropriate for solving fraud detection problem with high.the various research (zareapoor et al. [60], seeja and zareapoor [46], bhattacharyya et al. [6] have used svm technique for credit card detect fraudulent transactions. 4.10 hidden markov model hidden markov model (hmm) is a probability and statistical markov model which can be used to model sequential data. it is a double embedded random process with two states, one is unobserved ("hidden") and other is open to all. it is a finite set of states, each of which is associated with a probability distribution. hmm are effectively applied to many areas such as speech recognition, robotics, bio-informatics, data mining etc. this paper focuses on credit card fraud detection using hmm. the cardholder spending profile is divided into a low, medium, and high. the cardholder profile consists of a set of spending information namely money spent on each transaction, purchase time of last good, type of purchasing goods, the name of the merchant and last place purchase goods, etc. hmm is trained based on the cardholders normal behavior and if incoming credit card transaction is not accepted by the model with high probability that transaction is considered as a fraudulent transaction. hmm is also maintaining a log of the previous transaction. bhusari and patil [7] discussed how hmm used to detect credit card an empirical study of aml approach for credit card fraud detection–financial transactions 679 transaction fraud with a low false alarm 8% and found out more than 88% transactions are genuine. another researches( [2,18,24]) are also used hmm to detect a credit card fraud. these machine learning techniques are also contained some pros and cons. table 4 presents a comparison of these techniques based on learning, classification and clustering approach to detect credit card fraud, learning approach/categories (la/c). 5 dataset and its attributes the performance of any technique is purely dependent on the quality of the dataset. the datasets used by the various researcher to evaluate their proposed techniques and implemented methodologies are studied and summarized in table 5. the credit card datasets have been categorized as real world datasets (like mellon bank) and synthetic datasets (like ucsd-2009), and their primary and derived attributes discussed in details which play an important role in enhanced fraud detection rate and accuracy. the credit card dataset contains two types of attributes, namely primary and derived attributes. these attributes were used in various studies [6, 25, 32, 36, 61] to detect credit card transaction frauds. primary attributes: when credit card users are made transactions, a set of primary transnational attributes are generated. based on the research papers mentioned in table 5, following attributes in table 6 are diagnosed as primary features of the credit card. these primary attributes (features) help to design a good fraud detection system. beside primary attributes, derived attributes are also important but it is a very difficult task to extract the appropriate derived attribute from primary attributes. withrow et al. [56] have suggested transaction aggregation strategy approach to extract derived attributed based on the following three steps from the primary attributes available in the credit card transaction datasets: (a) grouping transactions by card identification number. (b) selecting transactions made in a previous period. (c) grouping selected transactions on the basis of one of the primary feature. the derived attributes are used to accurate analysis of cardholders buying behaviors.the derived attributes are summarized in table 7. 6 performance matrices and evaluation criteria the standard evaluation criteria have been followed by many researchers [4,30,36,46,49,61] for evaluating their proposed model of credit card fraud detection. the main objectives of these evaluation parameters (criteria) are to minimize false detection of fraud/non-fraud transactions and maximize the actual detection of fraud /non-fraud transactions.the formulae used by the many researchers for evaluation of their model have been presented in table 8 (p= positive, n = negative, fp = false positive,fn = false negative,tp = true positive, tn =true negative). the performance metrics are discussed in table 8 that used by the various researcher to evaluate their method and determine the performance of fraud detection techniques. 680 a. singh, a. jain table 4: comparison of various fraud detection techniques (la/c) techniques pros cons sup ervised/ c lassification back propagation neural network to determine the best performance of the system, we need a retrain of parameters such as the number of hidden neurons, learning rate, and momentum it requires log training time and extensive testing. bayesian network system processing, accuracy, and detection speed are very high. required excessive training and expensive. k-nearest neighbor (knn) there is no requirement of the predictive model before transaction classification in a dataset. the fraud detection accuracy depends on the measure of distance between two neighbors. this technique cannot detect the fraud at the time of transaction. support vector machine (svm) it is capable of detecting the fraudulent activity at the time of the transaction and solving nonlinear classification problems. svm technique provides a unique solution for the optimality problem is bulging. this technique is hard for auditors to process outcomes due to the transformation of input set and sometimes it fails to detect fraud cases.it is expensive, medium accuracy and has a low speed of detection. lack of results transparency. decision tree (dt) a decision tree is simple to use, implement, display and easy to understand. it can handle well non-linear data. this technique involves the complex algorithm and even a small change in the data can distract the structure of the tree. it requires low computational power for training, high flexibility, good explainable. need to check each transaction one by one. choosing splitting criteria are also complex. u nsup ervised/ c lustering self-organizing map this technique is simple to implement and very easy for auditors are given visual nature of outcomes. visualization requires auditor observation.this technique is not being fully automated. hidden markov model (hmm) hmm technique is fast in fraud detection and capable of detecting the fraudulent activity at the time of the transaction. hmm-based models reduce the false positive (fp) transactions predict as fraud, though they are genuine user. it cannot detect the fraud in the initial few transactions. it is highly expensive and low accuracy. it cannot scalable to large size datasets. sem isup ervised classification artificial immune system this is highly suitable for classification problems with imbalanced data.pattern recognition capability is also a high and powerful technique for learning. it is diversity, dynamically changing coverage, multilayered, and inexpensive. need a very high computational power for operation to make it unsuitable for real-time functions.the handling missing data are poor and need higher training time. an empirical study of aml approach for credit card fraud detection–financial transactions 681 table 5: analysis of credit card dataset used between 1994 and 2019 year dataset sample size source 1994 1,100,000 transactions authorized in a two month period. mellon bank [20] 1999 500,000 records of both banks. chase bank (20% fraud and 80% non-fraud distribution) and first union bank (15% fraud,non-fraud 85%). chase bank and first union bank [9] 2002 data come from sql server database containing sample visa card transactions. synthetically created data [52]. 2005 6000 credit card data, normal accounts (84%) and fraudulent account(16%). major us bank [29] 2008 about 50 million transactions (49,858,600 transactions), one million (1,167,757 credit cards) credit cards from a single country. international cc operators transactions [40] 2009 25,000 payment observations,fraud (5,529) and non-fraud (19,471). taiwan bank [59] 2010 462279 unique customers data have 4 million transactions & 5417 fraud transaction financial institute in ireland(webbiz)3 [8] 2011 250,000 transactions, fraud (1050), & remaining none fraud. turkish bank [17] 2012 640361 transactions contain by 21746 credit cards. a large australian bank [57] 2013 22 million cc transactions, fraud (978) and remaining nonfraud. bank’s cc data warehouses [45] 2014 details of 41647 transactions and 3.74% is fraudulent. brazilian bank transaction [39] 2014 100000 transaction.but 21000 transaction for training and 19918 for testing out of 40,918 unspecified transactions. ucsd-fico [46] 2015 9,387 transactions, 939 fraud and 8,448 non fraud real life dataset from an anonymous bank in turkey [32]. 2016 10000 records, numeric data with 334 input attributes. ucsd-2009 (synthesized) [55] 2017 10000 transactions, 20 attributes of german credit data uci respiratory [3] 2018 30,000,000 individual transactions, 82,000 transactions fraud e-commerce company of china [58] 2019 german credit card1000 transaction &21 attributes. uci ml respiratory (real data) [48,49] 2019 ten million credit card transactions with 8 variables https://packages. revolution analytics.com (real data) [33] 682 a. singh, a. jain table 6: primary attributes of credit card attribute name description account number identification number of the credit cardholders card number is a credit card number cardholders age present an age of credit cardholders cardholders gender gender of the credit card holder (male, female, other) type of card visa debit, master card, american express, etc. transaction id transaction identification number transaction place place where transaction made (determine by system ip address, if online) entry mode chip and pin, magnetic stripe transaction time/date time and date of the transaction amount amount of the transaction n usd, euro, inr, etc. terminal type transactions by the internet, atm, pos,mobile, etc. country transaction where transaction made by cardholders country home residence country of cardholders merchant name name of merchant who is provides service to cardholders merchant code is a merchant identification number merchant group merchant group identification merchant city, country name of merchant city & country table 7: derived attributes of credit card attribute name description amount-same-day total amount spent on the same day up to this transaction number-same-day total number of transactions on the same day up to this transaction amount-same-month total amount in the same month up to this transaction. average-same-month average amount spent over a month up to this transaction. amount-samemerchant all transactions amount spent in the same merchant up to present transaction. number-samemerchant total number of transactions with the same merchant during 6 month number-same-hour total number of transactions in the same hour up to present transaction. amount-same-hour total amount spent in the same hour up to present transaction. transaction amount over a month average amount spent per transaction over a month on all transactions up to present transaction. previous-amount previous amount of transaction. average amount over 3 months average amount spent over the course of 1 week during the past 3 months an empirical study of aml approach for credit card fraud detection–financial transactions 683 table 8: evaluation criteria for credit card fraud detection measure formula description accuracy (detection rate) tn+tp/tp+tn+fp+fn the percentage of correctly predicated transactions. precision (hit rate) tp/tp+fp it is the number of classified fraud transactions that actually are fraud transactions, and gives the accuracy of the fraud transaction. false positive rate (false alarm rate) fp/fp+tn the ratio of credit card fraud detected incorrectly. true positive rate (sensitivity or recall) tp/tp+fn it is the number of correctly classified fraud transaction and gives the accuracy of the fraud transaction. true negative rate (specificity) tn/tn+fp it is the amount of correctly classified non-fraud and gives the accuracy of the non fraud transaction. false negative rate fn/p=1-tp rate it is the number of credit card transaction classified as a non-fraud as a percentage of all fraudulent. roc true positive rate plotted against false positive rate relative operating characteristic curve, a comparison of tpr and fpr as the criterion changes. cost 100 * fn + 10 *(fp +tp) present a cost of fds. f-measure 2*(precision*recall)/ (precision + recall) the weighted average of the precision and recall, gives the harmonic mean of precision and recall. balanced classification rate 1*(tp/p+tn/n) it is the average sensitivity and specificity. mathews correlation coefficient (tp*tn)-(fp*fn)/ {(tp+fp)(tp+fn) (tn+fp)(tn+fn)} used as a measure of the quality of binary classification, correlation coefficient between the observed and predicate binary classification. geometric mean (sensitivity *specificity).05 gives the geometric of fraud and nonfraud accuracies. weighted-accuracy w*sensitivity+(1w)*specificity provide performance summary that indicators of each tradeoff. 684 a. singh, a. jain table 9: the performance comparison of various dm & ml techniques reference method sensitivity specificity accuracy [22] ais 33.6-52.6% 97.8-98.1% 94.6-96.4% [6] lr svm rf 24.6-74.0% 43.0-68.7% 42.3-81.2% 96.7-99.8% 95.799.8 % 97.9-99.8% 96.6-99.4% 95.5-99.4% 97.8-99.6% [36] knn knn+drf 81.82% 81.97 % 97.61% 98.62% 96.52% 97.47% [4] nb knn lr 82.10-95.15% 82.85-93.75% 71.55-58.33% 97.54-98.96% 100-100 % 29.39-53.13% 97.52-97.69% 97.15-97.92% 36.39-54.86 % [43] svm gp nn (ff) gmdh lr nn(prob) 55.43-73.60% 85.64-95.09% 67.24-80.21% 87.44-93.46% 62.91-65.23% 87.53 -98.09% 70.4173.41% 89.27-94.14% 75.32-78.77% 88.34-95.18% 70.66-78.88% 94.07-98.09% 70.41-73.41% 89.27-94.14% 75.32-78.77% 88.14-93.00% 66.86-70.86% 95.64-98.09% [11] rusmrn rusboost adaboost naive bayes 53.36% 50.3% 31.4% 40.2% 88.13% 86.16% 83.1% 78.8% 79.73% 77.8% 57.73% 70.13% an empirical study of aml approach for credit card fraud detection–financial transactions 685 6.1 comparative study of learning technique in this study, the performance of a variety of data mining & machine learning technique in terms of sensitivity, specificity, and accuracy are presented in table 9. this study has been classified credit card fraud detection researches based on these performance measures. the result of various researches showed in table 9 and identify that specificity and accuracy were slightly better of knn+drf technique [36] with compare to other methods. the sensitivity, specificity, and accuracy have been better of nb and knn technique [4]. it clearly identified that some results are vary based on credit card transactions dataset size and detection techniques. if credit card dataset is too large than accuracy could be decreased. this study has been classified credit card fraud detection researches based on these performance measures. the result of various researches showed in table 9,sensitivity and accuracy was slightly better for self-organization map [8] with compare to other methods.the sensitivity, specificity, and accuracy have been better of nb and knn technique [4]. it clearly identified that some results are vary based on credit card transactions dataset size and detection techniques. if credit card dataset is too large than accuracy could decrease. 7 conclusion and future work in this paper, various research papers based on credit card fraud are studied and discussed based on the finding of these papers, the various credit card frauds are classified and among them, card-not-present fraud and skimming frauds are more frequently occurred.the fraudsters mostly used website cloning, the false merchant website, and phishing methodology to steal credit card detail.the challenges and issues to prevent and detect the fraud have also been discussed and identified that one of the biggest issues is benchmark dataset which has unskewed, balanced and free from the anomaly and real-world dataset. the pros and cons of numerous fraud detection techniques are discussed and concluded that the major researchers were performed under a supervised learning method based on available datasets.different convention dataset was used to compare the performance of various methodologies on the base of various performance measures using the sensitivity,specificity,and accuracy.this paper also presented a comparative analysis of different fraud detection techniques and identified that nb (95.15%, 98.96%, and 97.69%),knn (93.75%, 100%,and 97.92%) & knndrf (81.97%, 98.62%,and 97.47%) givens better results among the studied techniques. the primary and derived attributes of credit card dataset have been playing an important role in enhanced fraud detection rate and accuracy. in this paper, techniques based on machine learning are discussed. the study can be extended for bio-inspired algorithms which have not explored in the paper and the result can be compared with traditional mlt. more effects can be made to get the real dataset from the credit card issuing and managing organization, so that the exact techniques can be compared on the real dataset. in future, this 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[63] https://www.acfe.com [64] https://www.nilsonreport.com/upload [65] https://www.ic3gov.in [66] https://www.financialfraudaction.org.uk international journal of computers communications & control issn 1841-9836, 9(6):800-810, december, 2014. optimization scheme of forming linear wsn for safety monitoring in railway transportation n. zhang, x. zhang, h. liu, d. zhang ning zhang*, xuemei zhang, haitao liu, dengfeng zhang school of computer and information technology beijing jiaotong university, beijing ,china beijing haidian district, shangyuancun number 3 beijing, 100044, china nzhang1@bjtu.edu.cn, 12120477@bjtu.edu.cn, 10120486@bjtu.edu.cn, 11120502@bjtu.edu.cn *corresponding author: nzhang1@bjtu.edu.cn abstract: with the development of wireless sensing network, more and more applications have been deployed in the safety monitoring and in natural disasters prevention. the safety and disaster prevention systems have usually been laid out in linear network architecture, such as those of railway, motorway transportation and pipes. the background of the article is railway hazard goods transportation safety surveillance. the paper discusses about the network architecture of linear wireless sensor networks with multiple sink nodes, and proposes the grouping method of sink nodes and the formation scheme of networks. the scheme can re-establish a monitoring network when the train on the way is disconnected and re-grouped. the switching algorithm of group head nodes is put forward, so that the energy consumption of each node in the group is even. the optimal switching parameters for group head nodes are suggested by the simulation. compared with the usual monitoring network, the method proposed enables the system life expectancy to prolong more than five times, and meets the monitoring requirements simultaneously. keywords: wireless sensor network (wsn), linear network, strategy of forming the network, energy balance algorithms. 1 introduction the wireless sensor network has been more and more widely used in safety and disaster prevention area, because of its sensing capability in physical world, the adaptive capability and its flexibility in terms of deployment. in many sensor network applications, the energy consumption has become a limitation of long working hours of the system when performing the tasks like monitoring and data transmission, without outside electricity supply in the system. this is especially the case for the sink node working as gateway; the gateway not only needs to receive data from monitoring nodes but also needs to send data to the monitoring center through the internet [1][2]. these nodes have to consume a large amount of energy either in sending the date through the wireless mobile network or the satellite network. because those nodes are essential for the whole network system, it has special significance to optimize the work of those nodes and to minimize their energy consumption [3]. it has been a major research topic to longer the lifetime of the wireless sensor networks and to lower the energy consumption whether from the aspect of sensing the physical world, the scheme of forming the network and data processing [4]. because of the limitation imposed by the energy consumption, the computation resource and the storing capability and transmission capability are also limited. for the same reason, the gateways’ storing and transmission capability are also limited. in today’s researches of wireless sensor networks with multiple sink nodes, the copyright © 2006-2014 by ccc publications optimization scheme of forming linear wsn for safety monitoring in railway transportation 801 sensor network’s coverage is usually a plane, which means the sensor nodes are located in a plane area [5]. hence, most research interests are focused on the placement of sensor nodes, topology of the sensor networks and transmission strategies [6][7]. hoping by placement of sink node in a reasonable way, the power consumption of the system will be lowered, the lifetime of the network will be increased. those researches usually share an assumption that the sink node itself is a rich power node, hence the energy minimization and optimization of important sink node is less researched [8]. in many practical applications, sink node is usually the bottleneck of increasing the lifetime of the whole system. in some situation, when multiple sink nodes are presented in the system, the data exchange between sink nodes is also possible [9][10]. for instance, in the safety monitoring of railway goods transportation, the topology of sensor network is linear, the network in each section of the train send data to the sink node. the sink node of each section has the potential of exchange data. other then that, same structure can also been seen in the monitoring system of fallen rocks on the sides of highway and railway slops [11][12]. 2 raise of the question the hazard goods safety monitoring system based on wireless sensor network is shown in figure 1. the sensor nodes located in each section monitor the status of hazard goods, and send the data to the sink node through the sensor network. the sink node acts as gateway, as a result, it is called gateway node. on one hand, the gateway node manages the sensor network in the car, and collects the monitoring data. on the other hand, it sends the merged data to the ground control center through the mobile network. to realize the monitoring of hazard goods in the entire transporting process, the system requires the gateway nodes to send data constantly to the ground control center through mobile network or satellite network, so that a large amount of energy in those nodes is consumed. the wireless sensor networks work in the harsh environment and usually a long time. and each node has only one battery as its power supply. hence, how to reduce the energy consumption of gateway nodes and longer their lifetime are vital to realize whole time the hazard goods transporting monitoring. fig. 1 shows the situation described above. wsn gsm-r client base station figure 1: hazard goods safety-monitoring system in railway transportation 802 n. zhang, x. zhang, h. liu, d. zhang in the situation showed in fig. 1, if the gateway nodes send the data to the ground through mobile network or satellite network, it can guarantee that the information of each section of the train will be send to the ground timely. but it can easily cause the gateway nodes stop working by exhausting the power. take gprs (general packet radio service) as a way of sending the information, for instance, before gateway nodes send the data, they need to perform a series of test of parameters. it includes: signal strength of bcch (broadcast control channel) or pdch (packet data channel) received by the node, the variance of signal strength of downlink pdch received signal, downlink pdch signal strength, downlink pdch signal interference and so on. on one hand, when sending the data, it takes a large amount of energy when establishing the connection and sending the data. on the other hand, it also takes a large amount of energy to measure the incoming and outgoing of message. because the monitoring data is usually a small amount, comparing with pure data transmission, establishing connection and measuring process take a large proportion in the energy consumption. as a result, if multiple sink node can be grouped together and form a high network, only one node takes a task of sending data in a certain period of time. this will reduce multiply sink node gprs measuring signal’s sending and receiving; hence the total amount of energy consumption will be reduced. besides this, because of the disconnection and regrouping of the train, the original network structure will be altered. as a result, it requires reforming the monitoring network with minimum energy consumption and satisfies the monitoring requirement. this article focuses on the above situation, suggests the scheme of forming the linear wireless sensor network with sink node of each section of the train and the energy balance algorithm, so that those sink nodes energy can be shared to achieve lower energy consumption and longer the system lifetime. at the mean time, the scheme suggested can support the disconnection and regrouping of the train in the trip and reforming the monitoring network according to the regrouping of the train. 3 reforming the network strategy the normal trains car has length between 10 meter and 20 meter. the basic idea of forming the network can be seen as follows: connecting sink node in each car with the sink node in neighbor car/cars, so as to form new networks, and the monitoring data are sent to one of the sink nodes. through that sink node, the data will be sending to the ground by mobile network. by applying reasonable method to balance the energy consumption, the lifetime of the network can be extended. for linear structure wireless sensor networks, it is very common that the multi-hop method is used to realize the monitoring data gathering. however, normal hazard transportation train contains more then ten cars, or even the cars of whole train. due to the electrical and magnetic interference, if multi-hop method is applied to the whole train, the interference can easily affect the transmission, and causing high hop count and too much data stored on one node, in term causing the data package lost or over time. as a result, grouping cars so that hop count can be reduced, hence better transmission efficiency. now we assume: 1) each car has a sink node which is uniquely numbered, the node number is a value; 2) each car has a sink node with same initial energy; 3) all sink node has the same distance with its neighboring sink node. we take ns as the sink node number; ng as sink node number in each group, hence the group number: n = ns/ng, where ng can be decided using the following method: optimization scheme of forming linear wsn for safety monitoring in railway transportation 803 we assume the channel error as ec, average data length as dl(bit), sending data from arbitrary node to sink node has a successful rate no smaller than rd, we can easily derive that the maximum hop count hmax: hmax = lg rd lg(1−dlec) (1) under the worst case, sink node as the border node of the group, and the node on the other side of the group takes ng − 1 hop to reach sink node. hence node number in each group ng should be: ng ≤ hmax + 1 (2) grouping algorithm: idea of grouping algorithm: first decide two border nodes. then take the both border nodes as starts respectively. group every ng node from the starts towards the middle nodes. the process is shown in fig 2. figure 2: nodes grouping the detailed steps are as follows: (1) confirmation of the border node 1) after each node switched on and activated, a detection package is send with minimum power. at the meantime, the node listens to the neighboring node for response. if there are no responses, then increase the sending power by one level and send the detection package again. if response is received, goes to next step. if there is still no response even with maximum sending power, then end the process. it shows that two ends of the node have no monitored car, the node works alone. 2) send the response package at once, when receive the detection package. 3) if two response packages are received, then the node is not a border node, and the node goes into idle. 4) if the node only receives one response package, it shows that this node is a border node. (2) algorithm of forming the network: the grouping of the nodes starts from the border node. after the border nodes have confirmed their positions, it goes into forming network phase of the algorithm. in this phase, each node sends the message with small power so that it only covers the neighboring nodes (the power is determined by previous step). the detailed steps are as follows: 1) the border node send invitation package to neighboring node. the invitation package includes: group number (using the node number), member number (initialize to be 1) and so on; 804 n. zhang, x. zhang, h. liu, d. zhang 2) when the node receives the invitation package, it firstly decides if itself has been given grope number. if so goes to step 4. otherwise, increase the member number by 1, and set the group number to be received group number, and check if the member number is ng. if the member is less than ng then send invitation package with member number increase by 1. if the member number is bigger or equal to ng then send the regroup package to the new neighboring node, and send confirmation package to its own group nodes. 3) when the regrouping package is received, the node checks itself if the group number is given yet. if the group number is given then no response is needed. the forming network is ended. otherwise, the node creates a new invitation package. within the invitation package, the nodes number serves as the new group number, the member number is 1. and then send invitation package to neighboring nodes. 4) when the node whose group number has been decided receives the invitation package, if the group number in the invitation package is the same with the nodes group number, then no response is needed. otherwise, compare the sum of member number of the group and the member number of the invitation package, if the sum is less than ng, then compare the group number. if the group number is smaller, then send invitation packages to node of the same group. otherwise, replace the group number with new group number and send altered invitation package. if the sum of member number of the group and the member number in the invitation package is equal or larger than ng, then responds with reject package, and send confirmation package to nodes of own group. 5) when invitation sender receives reject package, send confirmation package to own group and end the forming network. performing grouping based on the algorithm may create at most 2 groups with member number less than ng . each group works independently and that won’t affect the whole system. after the grouping process, each group needs to choose its own group head which transmits monitored signal to the surveillance center through mobile network. in the grouping process, the information of remaining energy of each node is exchanged. hence, the node with maximum remaining energy can be picked as head. when more then one node has the maximum energy, the node closest to the middle will be picked as head. the sink node of each car will send data to the group head through several hops while fulfilling the monitoring requirement. group head will merge all the monitoring data and then send it to the ground surveillance center through mobile network. during the transportation, the train might go through multiple splitting and regrouping. if the car in same group is separated, and the head node has not received information from certain node sometimes, it assume those node has leave the group and the rest of the nodes will reform the network. on the other hand, if some node cannot send information to the group head, those nodes will reform the network according to the above method. in this process, due to the uniqueness of the node number, those group numbers will not be duplicated. the separated group will form two new group and two new group head. 4 energy balance scheme after grouping using the above algorithm, the head node needs to handle the transmitting information to the ground surveillance center through the mobile network and it has large energy consumption. hence, a scheme is necessary so that the group head will be performed in turns optimization scheme of forming linear wsn for safety monitoring in railway transportation 805 by certain rules. so the energy consumption of each node is balanced, and the lifetime of the system is maximized. scheme of group head rotation: after the group head is firstly settled, the group head record the remained energy of itself, which is called the initial energy, denoted by pγ. after each transmission of data, the head checks its remaining energy. when the energy drops to α% (0 ≤ α ≤ 100) of pγ , the head sends repacking message to the group nodes through broadcast. the repacking message includes node number, the remaining energy. the group nodes send its own remaining energy information to other nodes, record the group nodes’ remaining energy information and compare the information with themselves. finally, pick the node with largest remaining energy as new group head. when equal energy is seen, the system picks the node with smallest node number. the new group head generate process is shown in fig. 3. figure 3: the new group head generation procedures after the process shown above finished, all the node number stored in the node nh are same, this number is the node number for the new group. this node is automatically the new group head; the new group will work around the head node. the node will record its remaining energy and when the energy drops to α% of the remaining energy, the next rotation process is triggered. the whole network works as shown above and the energy of each node is balanced. 5 performance measurement of the algorithm assume the minimum successful rate of send data from arbitrary node to sink node rd as 0.9. the average data package length dl is 200 bits, the channel error rate is 10−4 , by formulation(1) 806 n. zhang, x. zhang, h. liu, d. zhang we can get: hmax = lg rd lg(1−dlec) = lg 0.9 lg(1−200×0.0001) ≈ 5 (3) by formulation(2) we can get: ng ≤ hmax + 1 = 5 + 1 = 6 (4) hence, under worst case, the data transmission success rate rs : rs = (1−dlec)h = (1−200×0.0001)5 ≈ 0.904 > 0.9 (5) we can know that have 6 nodes in one group can satisfy the reliability requirement. we assume the sum of time used between nodes’ transmission ts as 0.1s, the maximum system data transmission delay: tmax = ts ×hmax = 0.1×5 = 0.5s (6) 5.1 gprs consumption analysis to save energy, the gprs module will work in an intermittent way, which means cut off the electricity right after the transmission. the voltage will be applied when the next transmission is ready. the time interval will be determined according to real monitoring requirement, usually ranges from several minutes to several tens of minutes, we pick 10 minutes in this case. according to real test, gprs receiving and sending power p̄send has its peak value around of 80mw. taking the channel interference into account, the speed can be picked at 56kbps. we assume gprs module takes about tlink = 20s to connect with system (including the gprs ground system), the node monitoring data package has a average length of 200 bytes. the transmission time is: tsend = 1600 56000 = 0.028s (7) after transmission, the node controls gprs module to cutoff the electricity. in this situation, each sink node takes more than ttotal = tlink + tsend = 20.028s to transmit data. the energy consumption of sending the data is: asun = p̄send × ttotal = 0.08×20.028 ≈ 1.60448j (8) there is not data exchange between sink nodes; hence the energy consumption of data transmission is 0. we can know from this that the total energy consumption of one transmission of 6 sink nodes is: atotal = asun ×6 = 1.60448×6 = 9.62688j (9) using the method given in this paper, 6 nodes’ information is sent by one node which acts as a gateway. this node takes same amount of time to send the data and establish connection, the data sending time is: tsun = tsend ×6 = 28×6 = 168ms (10) now, ttotal = tlink + tsun = 20 + 0.168 = 20.168s (11) optimization scheme of forming linear wsn for safety monitoring in railway transportation 807 5.2 computation of energy consumption of data transmission between nodes the data transmission between sink nodes uses zigbee protocols. under the presumption of maintaining the channel transmission quality, system should send data with minimum power. ordinary sensor node has a very small sending power and a high sending rate. take cc2430 for instance; when it performs sending and receiving data, the typical working current is 27ma, the supply voltage is 3.3v, the sending rate can achieve 250kbps. if the average data is 200 bytes long, even under the worst case: it takes 15 hops for 6 nodes to send data to sink node. hence, the total energy consumption is: ast = 0.0891×15×1600÷250000 = 0.00855j (12) hence, atotal = p̄send × ttotal + ast = 0.08×20.168 + 0.00855 ≈ 1.623j (13) as we can see, every time those 6 nodes send their data, the energy consumption using the method of this paper is just 17% of the original method. the effect of saving the energy is significant. when the node is not sending data, the node is in sleep mode with minimum power consumption. that minimum energy consumption has little effect in analyzing the power consumption. hence the above analysis ignores the consumption of sleeping node. 5.3 energy balance simulation assume a set of 6 sink nodes uses 3.3v, 30ma battery as power supply. each node has same initial remaining energy: a = 3.3×3600×0.03 ≈ 356j (14) from above computation: it takes about 1.6j of energy to send data to the ground surveillance center every time; each sending or receiving data between each node takes about 0.0086j. when there is no grouping, the system working cycle are shown in fig. 4. figure 4: system lifetime when one node is working under grouping situation, each node has different energy consumption when sending and receiving due to the difference of location of head node. the sending and receiving count of each node under different situations is given below in table 1. node heads switching strategy is set as: when the remaining energy is 70%, 50%, 30% and 20% of the initial energy. when any nodes remaining energy is less then energy needed for one 808 n. zhang, x. zhang, h. liu, d. zhang table 1: count of each node under different situations node1 node2 node3 node4 node5 node6 *5 9 7 5 3 1 1 *5 7 5 3 1 1 3 *5 5 3 1 1 3 5 *5 3 1 1 3 5 7 *5 1 1 3 5 7 9 *5 note: * means the node is head node (a) 70% (b) 50% (c) 30% (d) 20% figure 5: system lifetime after grouping. table 2: the simulation result forming network switching strategy simulation steps remaining total energy j grouping 70% 1179 1.6 (one group 50% 1182 2.74 contains 30% 1182 6.35 6 nodes) 20% 1182 7.38 single node none 222 0.768 optimization scheme of forming linear wsn for safety monitoring in railway transportation 809 transmission, or the energy of every node is less than 1.6j, the system can no longer send the monitoring data, this ends the simulation. the simulation result is shown below both in fig. 5 and in table 2. as it is shown in the simulation, the system lifetime after grouping is far better than the lifetime of single node mode (5.3 times over). after grouping, the steps are minimum when switching strategy is 70%. when the switching strategy is 50%, 30%, 20%, the step count is 1182, but the remaining energy is different, maximum at 20%, minimum at 50%. the reason is that the head node switching requires certain energy and in the whole working time, less switching time is healthy for the lifetime of the system. 6 conclusion linear sensor network as a wide application, it especially has significant meaning to transportation safety monitoring. due to the special node allocation, how to forming the network to maximize system lifetime is still a great challenge. this paper discussed about the question of sink node working 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(2010); optimization of linear wireless sensor networks for serial distributed detection applications, optimization of linear wireless sensor networks for serial distributed detection applications, issn 1550-2252: 1-5. international journal of computers communications & control issn 1841-9836, 14(1), 124-134, february 2019. text classification of public feedbacks using convolutional neural network based on differential evolution algorithm s. zhang, y. chen, x.l. huang, y.s. cai shuai zhang, yong chen school of information zhejiang university of finance and economics no.18 xueyuan street, xiasha, hangzhou 310018, china zhangshuai@zufe.edu.cn, chenyong@zufe.edu.cn xiaoling huang* school of international education zhejiang university of finance and economics no.18 xueyuan street, xiasha, hangzhou 310018, china *corresponding author: huangxl@zufe.edu.cn yishuai cai school of information zhejiang university of finance and economics no.18 xueyuan street, xiasha, hangzhou 310018, china caiyishuai@zufe.edu.cn abstract: online feedback is an effective way of communication between government departments and citizens. however, the daily high number of public feedbacks has increased the burden on government administrators. the deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. in this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. in particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. at the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and f1-score. keywords: public feedback, deep learning, text classification, convolutional neural network, differential evolution algorithm. 1 introduction in recent years, online feedback has played an increasingly important role in linking government and citizens. people can directly express their problems and opinions to the government departments by submitting their feedbacks online. it will help the government to better carry out their work. however, how to deal with hundreds of public feedbacks a day efficiently and deliver them to the appropriate departments is a big challenge for government administrators. therefore, it is very necessary to explore a method to realize the automatic classification of public feedbacks, and to help government administrators improve their working efficiency and reduce their workload. with the development of information technology, the field of text mining has attracted more and more scholars’ attention [14, 20]. feedback classification belongs to the text classification, copyright ©2019 cc by-nc text classification of public feedbacks using convolutional neural network based on differential evolution algorithm 125 which is an important branch of text mining. text classification includes text segmentation, stopword removal, word vector representation, feature selection and classification [14]. in order to improve the accuracy of classification, several studies have improved the selection of text features and classifier [5,35]. meanwhile, with the development of deep learning technology, convolutional neural network (cnn) [17] has shown excellent performance in solving many problems such as visual recognition [1], image recognition [10], and text classification [32]. in this study, we apply cnn to classify public feedbacks and assign them corresponding classes of labels, indicating which department should be responsible for the problems formulated in the public feedbacks. we obtained the public feedback data from a municipal government in china. the data was processed by distributed representation and word embedding method [15], and then using the cnn to automatically extract the deep text features and complete the classification of public feedbacks. to enhance the classification accuracy of the cnn, we adopt a heuristic optimization algorithm, differential evolution (de) algorithm [25], for the selection and optimization of network parameters. in order to verify the effectiveness of the explored model, we compared the performance with other common text classification models, and the results show that the cnn has a good advantage in dealing with the classification of public feedbacks. the remainder of this paper is organized as follow. section 2 describes the related work on text classification and deep learning. section 3 presents the content of the data, the way of text representation, the selected text classification model, the parameter optimization algorithm, and the corresponding performance evaluation metric. section 4 analyzes and discusses the classification performance. in the last section of this paper, we made a summary and put forward some prospects for the future. 2 related work in recent years, data mining and analysis technology has been widely concerned and applied in various fields [6,7]. among them, as an important part of data analytics, classification technology has been developed rapidly in text processing [8], image processing [34] and other fields. specially, text classification is to classify text into predetermined categories according to the characteristic of the text, which has been applied in some fields. for example, li et al. [19] proposed a hybrid classification model based on sentiment dictionary, support vector machine (svm) and k-nearest neighbor (knn) for sentiment classification of chinese micro-blogs. chau et al. [4] improved neural network algorithm to achieve multilingual text classification task. sabbah et al. [27] proposed four modified frequency-based term weighting schemes, which is combined with common text classifiers such as svm, knn, naive bayes (nb) and extreme learning machine, and tested in the text classification corpora. liu and peng [22] considered the statistics of positive and negative samples in the method of term frequency-inverse document frequency (tfidf), and experiments were carried out in three real-world datasets. nevertheless, these machine learning classification models only extract shallow text features, but cannot automatically and deeply extract text features hidden in context. at the same time, deep learning technology has been applied to the field of text classification. li et al. [21] used deep belief networks for the classification of web spam. sun et al. [29] used a deep neural network model based on restricted boltzmann machine optimization to realize the sentiment classification of micro-blog text; its experimental results show that this model is more suitable to deal with the classification of short-length text than other traditional classification models, such as nb or svm. in addition, cnn is also a popular deep learning algorithm. unlike other deep learning algorithms, cnn has the characteristics of weight sharing and local perception [13]. these two characteristics make cnn show its excellent performance in the ability of spatial feature extraction and high-dimensional data processing. because the distribution of text 126 s. zhang, y. chen, x.l. huang, y.s. cai has some spatial correlation, cnn has been widely used to solve the problem of text classification. for instance, gando et al. [12] used a deep cnn model to achieve the automatic classification of illustrations in photographs. to solve the problem of data sparseness in the process of text classification, wang et al. [32] proposed a model of hybrid word embedding clustering and cnn, and its effectiveness is verified by two open benchmarks. in addition, in order to improve the performance of the neural network, ijjina and chalavadi [18] proposed using genetic algorithm to optimize the weights of network parameters in order to improve image classification accuracy. to improve the efficiency of model fitting, trivedi et al. [31] proposed to optimize the parameters of cnn fully connected layer by using genetic algorithm. as far as we know, there are few studies on improving the work efficiency of government administrators in handling the classification of public feedbacks. accordingly, to ease the burden of government administrators, we applied a cnn model to automatically classify public feedbacks by combining word embedding and optimizing network parameters with de algorithm. 3 method in this section, we will describe in detail the text classification model based on cnn used in this study. the flow chart of the model, as shown in figure 1, including three modules: text representation, classifier training, and performance evaluation. figure 1: the flow chart of the text classification model 3.1 data collection in this paper, the dataset contains 4257 short-length texts, covering 22 categories, which is provided by a municipal government in china. the dataset is divided into three parts: training set, validation set, and test set, with the proportion of 80%, 10%, and 10%, respectively. the name of each category and the corresponding number of feedbacks are detailed in table 1. each feedback describes some suggestions or opinions written by the public to the relevant government departments. the meaning of each category is that the problem formulated in the feedback should be solved by the corresponding department in that category. in addition, it should be noted that each text has only one corresponding category. text classification of public feedbacks using convolutional neural network based on differential evolution algorithm 127 table 1: names and feedback numbers of 22 categories category name number of feedbacks population and family planning commission 42 disabled persons’ federation 29 security supervision bureau 39 national tax bureau 43 city management enforcement bureau 503 electric power bureau 119 housing administration 382 industrial and commercial bureau 152 public security bureau 688 sports bureau 57 bureau of land and resources 174 environmental protection bureau 131 commission for discipline inspection 98 traffic bureau 190 education bureau 401 brigade committee 66 weather bureau 56 health bureau 256 pricing bureau 507 tobacco bureau 112 post office 120 court 92 3.2 text representation and text classification model in this section, we will describe the details of the text classification model. the structure of the model is shown in figure 2. the first part of the structure is the operation of text representation. in view of the fact that most words are meaningful to chinese text classification as well as the ability of cnn to process high dimensional data, this study will not remove the stop words like general text classification. to reduce the dimension disaster of text representation, we will use word embedding method [15] to reduce the dimension of word vector space. assuming that the vector dimension of each word is m, by using distributed representation method [15], map each word to a dictionary of common words with a capacity of 5000. the corresponding matrix size of a sentence will be 5000*m. however, we assume that a sentence has up to n words, and by word embedding we can reduce the matrix size to n*m, of which n is far less than 5000. the second part of the structure is the operation of text classification. first, the abovementioned word vector matrix is used as input of cnn model. by using 256 different convolution kernels and padding operation [23] for feature extraction, we can get 256 corresponding feature maps that have the same size as that before the convolutional operation. second, in order to reduce the number of parameters of the network and prevent the overfitting issues, we took a max-pooling operation [23] on the mapping results. it should be mentioned that the max-pooling operation preserves the salient features extracted and discard the less important features, which can facilitate the fitting of the model. then, by linking the results of the pooling with the neurons in the fully connected layer and using the rectified linear unit incentive function [24], 128 s. zhang, y. chen, x.l. huang, y.s. cai figure 2: the structure of cnn classification model the output vector is obtained. finally, the output vector is put into the softmax classifier [28] to get the classification results, which is the probability of classifying into each category. in this study, the size of the convolution kernels we use is 5*5, and the loss function is cross-entropy [2]. 3.3 parameter optimization as we know, the structure of neural network and its training parameters have a great influence on the performance of network. the parameters include the size of convolution kernel, the number of neurons in the fully connected layers, the learning rate, etc. therefore, in this paper, we will use the differential evolution algorithm [25] to optimize the parameters of convolution neural network. de is a parallel direct search algorithm proposed by price in 1996, which has strong global optimization capability. it includes four steps: initializing the population, mutation, crossover, and selection [25] . the implementation steps of the algorithm are shown in table 2. a chromosome [25] consists of six genes, the number of neurons in the fully connected layer, the number of convolution filters, the size of batch, the values of dropout [23], and the learning rate, respectively. among them, the first four values are integers and the last two values are decimal. in addition, the fitness function of de algorithm is the error evaluation function of cnn during training. 3.4 evaluation metric in this study, we use four metrics to evaluate the effectiveness of the model used, including accuracy, precision, recall, and f1-score. the calculation of these metrics is shown in equations (1-4). accuracy = tp + tn tp + fp + tn + fn (1) precision = tp tp + fp (2) recall = tp tp + fn (3) text classification of public feedbacks using convolutional neural network based on differential evolution algorithm 129 table 2: the pseudocode of the de algorithm initialize population size p , chromosome length l, crossover rate cr, and chromosomes while (termination condition is not satisfied) for i in range(0, p) random selection of three chromosomes xa ,xb, xc, then perform the mutation operation and get the new chromosome vi for j in range(0, l) if rand(0,1) < cr or rand(0,l) == j perform the crossover operation else do not perform crossover operation end if get the new chromosome ui end for end for for i in range(0, p) if fitness(ui)> fitness(xi) update chromosome xi else not update chromosome xi end if end for end while the optimal fitness of the chromosome as the final output f1 −score = 2 ∗ precision∗recall precision + recall (4) where tp represents the numbers of correctly classified as the target category, tn represents the number of correctly classified as another category, fp represents the numbers of misclassified as target category, and fn represents the numbers of misclassified as another category. we can draw from the formula that the value of all the metrics is between 0 and 1, and the closer the value is to 1, the better the classification performance of the model is. 4 computational experiments this section will evaluate the performance of the model. we compare the cnn model based on de optimization (cnn-de) with the common text classification models to verify the effectiveness of the model. all experiments were developed under the python version 3.6. the core configuration of the computer includes inter core i7-8700k processors with a 3.7ghz basic frequency and 32g running memory. in order to verify the effectiveness of the cnn-de model, this paper compares it with the commonly used seven text classification models in the same dataset. among them, six models are combined by the basic classifier and tfidf method. the classifier includes nb [33] decision tree (dt) [26], random forest (rf) [3], svm [30], knn [9], and gradient boost decision tree (gbdt) [11]. tfidf is a common word frequency based vector representation method. dt, rf, and gbdt are based on tree structure for classification and forecasting, which has the 130 s. zhang, y. chen, x.l. huang, y.s. cai advantages including simple structure and fast training speed. svm is a supervised learning algorithm by constructing the maximal margin hyperplane, which is often used to solve the multi-dimensional nonlinear classification problem. knn is an unsupervised learning algorithm, which can make classification according to the preset number of classes. in addition, the long short-term memory network (lstm) [16] model does not adopt the tfidf method, and the features are extracted automatically directly. lstm is a deep neural network algorithm with memory function, which has often been used to solve the problem of sequence forecasting and classification. for ease of description, the comparison models are recorded as tfidf-nb, tfidfdt, tfidf-rf, tfidf-svm, tfidf-knn, tfidf-gbdt, and lstm, respectively. in addition, all of these models are developed in a spyder environment, where the tfidfnb, tfidf-dt, tfidf-rf, tfidf-svm, tfidf-knn, and tfidf-gbdt models are implemented using the sklearn toolkit, the cnn-de and lstm are implemented using open source deep learning framework tensorflow 1.8.0. the performance evaluation results of cnn-de and other comparison models are shown in figure 3. table 3 lists the detailed evaluation results. we can find that the cnn-de model outperforms with the other seven models under the same text dataset according to the four performance evaluation metrics used. the accuracy, precision, recall, and f1-score of cnnde model are 0.829, 0.830, 0.829, and 0.825, respectively. more specifically, according to the values of the accuracy, precision and recall, the cnn-de model has improved 15.1%, 12.3%, and 15.1%, respectively compared to the tfidf-dt model, and the f1-score of the cnn-de model is 14.7% better than the tfidf-knn model. it is worth noting that the values of precision and recall of cnn-de model are not only relatively high, but also the difference between the two is small compared with other models. it shows that the cnn-de model has a good balance in dealing with text classification problems. this is because the cnn-de model not only makes use of the word frequency information of text, but also automatically extracts the abstract semantic information, and then excavates the deeper text features to achieve better classification performance. at the same time, the de algorithm optimizes the network parameters of cnn and further improves the performance of the model. however, for other comparative models, the classifiers can only extract the shallow text feature information, and the deep high-dimensional feature cannot be obtained. on the other hand, the word vector representation method based on tf-idf is prone to cause dimension disaster problem, thus reducing the classification efficiency. in addition, the parameters of the models have been adjusted by repeated experiments, and there is still room for further improvement. furthermore, the evaluation results of cnn-de model for each category are shown in table 4, where ‘support’ represents the number of test samples for each category. we can observe that the cnn-de model has good classification performance under sufficient sample size. for example, the post office category has a test sample of 14 feedbacks, with a f1-score of 0.93. table 3: detailed information on the evaluation results of each model model accuracy precision recall f1-score cnn-de 0.829 0.830 0.829 0.825 lstm 0.716 0.730 0.716 0.715 tfidf-nb 0.728 0.762 0.728 0.717 tfidf-dt 0.678 0.707 0.678 0.682 tfidf-rf 0.739 0.783 0.739 0.744 tfidf-svc 0.700 0.829 0.700 0.710 tfidf-knn 0.683 0.720 0.683 0.678 tfidf-gbdt 0.711 0.779 0.711 0.726 text classification of public feedbacks using convolutional neural network based on differential evolution algorithm 131 figure 3: performance comparison of the text classification models table 4: the evaluation results for each category of the cnn-de model category name precision recall f1-score support population and family planning commission 0.71 0.50 0.59 10 disabled persons’ federation 1.00 1.00 1.00 4 security supervision bureau 1.00 1.00 1.00 1 national tax bureau 1.00 1.00 1.00 2 city management enforcement bureau 0.85 0.89 0.87 53 electric power bureau 1.00 0.81 0.90 16 housing administration 0.90 0.88 0.89 40 industrial and commercial bureau 0.61 0.85 0.71 13 public security bureau 0.72 0.81 0.76 59 sports bureau 0.00 0.00 0.00 3 bureau of land and resources 0.80 0.84 0.82 19 environmental protection bureau 0.86 0.86 0.86 22 commission for discipline inspection 0.86 0.86 0.86 7 traffic bureau 0.77 0.48 0.59 21 education bureau 0.91 0.85 0.88 34 tourism committee 0.80 1.00 0.89 8 weather bureau 1.00 0.80 0.89 5 health bureau 0.81 0.84 0.82 25 pricing bureau 0.92 0.94 0.93 48 tobacco bureau 0.78 0.70 0.74 10 post office 0.93 0.93 0.93 14 court 0.69 0.75 0.72 12 5 conclusion and future work in order to help government administrator improve the efficiency of handling public feedbacks and reduce the burden of work, we applied the cnn text classification model to automatically 132 s. zhang, y. chen, x.l. huang, y.s. cai classify feedbacks. the model consists of text representation and text classification. before training cnn classifier, to avoid the problem of dimension disaster caused by the excessive size of text input vectors, we used the word embedding method to reduce dimension. to improve the fitting ability of cnn, we used the de algorithm to optimize the network parameters of cnn. to verify the effectiveness of the model, we compared the cnn-de model with other seven common text classification models in terms of accuracy, precision, recall, and f1-score. the experimental results show that the 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(2009); an improved knn text classification algorithm based on clustering, journal of computers, 4(3), 230–237, 2009. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 6, month: december, year: 2021 article number: 4291, https://doi.org/10.15837/ijccc.2021.6.4291 ccc publications tversky similarity based under sampling with gaussian kernelized decision stump adaboost algorithm for imbalanced medical data classification m. kamaladevi, v. venkatraman m. kamaladevi department of computer science, sastra deemed university-src campus kumbakonam, india kamaladeviresearch@gmail.com corresponding author v. venkatraman department of mathematics sastra deemed university, thanjavur , india mathvvr@maths.sastra.edu abstract in recent years, imbalanced data classification are utilized in several domains including, detecting fraudulent activities in banking sector, disease prediction in healthcare sector and so on. to solve the imbalanced classification problem at data level, strategy such as undersampling or oversampling are widely used. sampling technique pose a challenge of significant information loss. the proposed method involves two processes namely, undersampling and classification. first, undersampling is performed by means of tversky similarity indexive regression model. here, regression along with the tversky similarity index is used in analyzing the relationship between two instances from the dataset. next, gaussian kernelized decision stump adaboosting is used for classifying the instances into two classes. here, the root node in the decision stump takes a decision on the basis of the gaussian kernel function, considering average of neighboring points accordingly the results is obtained at the leaf node. weights are also adjusted to minimizing the training errors occurring during classification to find the best classifier. experimental assessment is performed with two different imbalanced dataset (pima indian diabetes and hepatitis dataset). various performance metrics such as precision, recall, auc under roc score and f1-score are compared with the existing undersampling methods. experimental results showed that prediction accuracy of minority class has improved and therefore minimizing false positive and false negative. keywords: data imbalance, undersampling, tversky, similarity indexive regression, gaussian kernelized, decision stump adaboosting. https://doi.org/10.15837/ijccc.2021.6.4291 2 1 introduction in the field of data mining, one of the significant researches of interests is imbalanced data classification. imbalanced data constitutes to the fact that the sample frequency in the majority class is higher that the sample frequency in minority class, commonly found in diagnosing malignant tumors concerning medical diagnosis. in general, the class imbalanced problem is solved via classification models, via preprocessing or combining both the preprocessing and classification models. conventional classification methods proceed in the assumption that the training sets are balanced. hence, in imbalanced dataset classification, minority class is underrepresented and hence involves a cumbersome process where misclassifying minority class usually tend to some undesirable effect in prediction. an edited nearest neighbor algorithm [1] split into three parts. first, with the purpose of increasing the samples in minority class, minority over sampling technique was utilized. then, in second step eliminate the noise from majority samples. finally, random forest (rf) was applied to perform classification where the matthews correlation coefficient (mcc) used as termination condition for the iteration. f-score has shows significance change which exhibit improvement in classifier performance for imbalanced dataset. random undersampling and ensemble of classifier chains make multi-label learning trained from positive and negative examples of imbalanced dataset[2]. here, for each label differing number of binary models and then ensemble chains pertaining to dissimilar sizes were constructed to minimize computational budget . experimental results show improvement in fmeasure value. an undersampling framework for addressing class imbalance using neighborhood based undersampling method, tomek link was proposed [3]. here, class imbalance was addressed by eliminating the significant overlapped data points. moreover, it also identified and removed majority class instances over the overlapping segment. removing these irrelevant instances in turn increased the minority class instance visibility and also reduced elevated data elimination, therefore minimizing the information loss to great extent. 2 related works the issue of data imbalance is said to arise during classification whenever the observation in one of the major class exceeds the observation in the other minority class. conventional classification mechanisms are highly prone to imbalance data and hence resulting in bias. this negative aspect influences the classification results. mutual class potential was utilized in [4] via radial bias that in turn reduced the time complexity involved in imbalanced data classification. however, less concentration was made on the part of false negative rate. to address this issue, unstructured data classification was performed by applying uncertain nearest neighbor decision rule [5], therefore resulting in accuracy. with the applications of the conventional learning algorithms, though data imbalance status may permit high global accuracy, but it faces a real challenge when taking into consideration the accuracy of minority class. to deal with this aspect, a novel application of the decision tree algorithm to imbalanced data situations was applied in [6]. with this higher prediction was said to be arrived at. by entirely overlooking the majority class, less importance on class imbalance issue, poses negative influence. to address this issue, a framework for synthetic oversampling that was found to be robust on cases of extreme imbalance data [7]. despite improvement observed in prediction, however, convergence speed was not said to be improved. in order to speed up the classification process, a branch and bound graph edit distance method was proposed [8], therefore contributing to average execution time. a novel algorithm that used the furthest neighbor of a candidate example to produce new set of https://doi.org/10.15837/ijccc.2021.6.4291 3 synthetic samples was proposed [9], therefore resulting in the improvement of precision recall space performance. yet another method used in improving the classification issues by employing local mahalanobis distance learning (lmdl) method [10]. with this the accuracy and precision factors were said to be improved considerably. however another method called, particle stacking undersampling method was presentedto achieve methodological improvements in terms of time. in spite of the success of these techniques, all of them have their own advantages and disadvantages and none of them is ideal, and hence there is room for improvement in this research line. this paper proposes an efficient method to enhance learning using tverskey similarity indexive regression and gaussian kernelized decision stump-based adaboosting (tsir-gkdsa) from imbalanced datasets, whose results indicate that it is better than some amateur, state-of-art methods. 3 methodology a synthesis of objectives is adapted in the proposed method with an effective manner to learn from imbalanced data. the objectives considered in this paper are noise and borderline data, where noisy values in the dataset are handled by taking the mean values of that column and in case of borderline data, it is reanalyzed and identify the probability of classes that data that belongs to and then categorizing the data into that specific class. the adapted majority class is then integrated with minority instances to bestow new training set for learning. the proposed method is shown in two steps, undersampling stage and classification stage. the representation of the proposed method tverskey similarity indexive regression and gaussian kernelized decision stump-based adaboosting (tsir-gkdsa) for imbalanced data classification is shown in figure 1. as illustrated in the above figure, in the proposed method, at initial the training sets are fed to the undersampling stage. this stage performs the undersampling of the majority-class instances based on the aspects of noise and borderline data by utilizing tversky similarity indexive regression undersampling model. the rectified training set is then given to the classification stage to sort out classification task. the steps are examined in detail as follows. https://doi.org/10.15837/ijccc.2021.6.4291 4 3.1 tversky similarity indexive regression under sampling model an imbalanced class possesses one or more classes with few examples (i.e., minority class) and one or more classes with many examples (i.e., majority class). undersampling refers to the process of sampling the majority class and making it equal to the minority class. this process is performed by applying tversky similarity indexive regression model. figure 2 shows the flow diagram of tversky similarity indexive regression undersampling model. here, regression a machine learning technique in addition to tversky similarity index is utilized to analyze the relationship between two variables like instances from the dataset. by employing regression the strength of relationship between one dependent variable (i.e. target variable or outcome ‘i.e.,q=(q_1,q_2,. . . ,q_n )’) and a series of other independent variables (i.e., predictor variable ‘i.e.,p=(p_1,p_2,. . . ,p_n)’) are determined. let us consider the classes ′cl = cl1,cl2, . . . ,cln&clεmajcl,mincl’, where ‘c = c1n,c2n, . . . .,c′nn represents the columns in the input dataset (i.e., pima indian diabetes and hepatitis). here ‘n=9’ and ‘n=20’ in pima indian diabetes and hepatitis respectively. the columns from the respective dataset are split into different numbers of classes into a data matrix and are represented as given below.  c11 c12 . . . c1n c21 c22 . . . c2n ... ... . . . ... cn1 cn2 . . . cnn  ——-1 from the above equation (1), ‘c′11, represent the column class segregation for the first feature (i.e., pregnancies in pima indian diabetes). in a similar manner, ‘c_21’ represent the column class segregation for the second feature (i.e., blood pressure in pima indian diabetes) and so on. in order to regress sequence between one dependent variable and many independent variable ‘q on p’, for each column ‘c’ in the classes ‘cl’, the straightforward model of ‘q = α0 + α1p + ε′ is examined. here ‘ε = ε1,ε2, . . . ,ε′n refers to the error that interprets for the variance between the majority and minority class so that they reduce the error. to reduce the error, both the dependent variable and the independent variable are differentiated with respect to ‘α′0and‘α′1 and is mathematically formulated as given below. by reorganizing the above two equations (2) and (3) are mathematically formulated as given below. by figuring out the equations in (4) and (5), ‘α′1and‘α′0 are mathematically evaluated as given below. https://doi.org/10.15837/ijccc.2021.6.4291 5 finally, the similarity index between two instances ‘α′0and‘α′1 is identified via tversky similarity index function. this is mathematically formulated as given below. from the above equation (8), ‘ff’ refers to the frequent features and ‘uf’ refers to the unique features that analyze the relationship between two variables like instances from dataset that in turn minimizes the computation time involved in balancing data with high accuracy. the pseudo code representation of tversky similarity regressive undersampling is given below. as given in the above tversky similarity regressive underfigure 1: algorithm 1 tversky similarity regressive undersampling sampling algorithm, for each classes and columns initialized from two datasets (pima indian diabetes and hepatitis), the objective of this algorithm remains in obtaining the undersampling process with minimum time and maximum accuracy. to achieve this objective, first, dependent and independent variable are differentiated by means of regression function. then with the aid of the tversky similarity index provides with the output values in the ranges from ‘0’ to ‘1’. next, a threshold value is then initialized. if the similarity value is greater than the threshold, one instance/ sample is selected ‘α′0 from the majority class and the other one ‘α′1 is removed. in this way, all the collected samples are selected and removed from the majority class. accurate identification and removal of these instances enhance the visibility of the minority class instances and simultaneously reduces the excessive removal of data, which decreases the information loss. 3.2 gaussian kernelized decision stump adaboosting classification model with the obtained samples from majority class and initial samples in the minority class, actual classification process is performed. in our work, an ensemble classifier based on gaussian kernelized decision stump adaboost classification model is utilized for classifying the instances into two classes. let ’f(l) = [f(l)1 ,f (l) 2 , . . . ,f (l) n ]’represents the ‘l-th’ training set of feature vectors of pima indian diabetes and hepatitis dataset and ‘d^1’ representing the actual data (i.e. label) corresponding to ‘f^1’. in addition to the weak ds and strong classifiers, adaboost comprises of sub-classifier ‘s_c_i’ to https://doi.org/10.15837/ijccc.2021.6.4291 6 address the limitations of individual classifiers. the main concept is to train different sub-classifier ‘sc_i,i=(1,2,. . . ,i)’ on the same training samples. the weak classifiers from ds are grouped finally to obtain a stronger classifier. figure 3 shows the flowchart. as shown in the above figure [2] to start figure 2: flowchart of the gaussian kernelized decision stump classification with the sample weights are initialized as given below. with the weights initialized as given in (9), the sub-classifiers are then trained by minimizing the objective function is mathematically expressed as given below from the above equations (10) and (11), sub-classifier training ‘sc_i’ are performed for all the training set of feature vector ‘f^1’. these are performed using the objective function with feature decision of the ‘i-th’ instance represented by ’fd_i’ for the ‘l-th’ training instance vector. the criterion function ‘c’ that takes the value ‘1’ if its argument is true and ‘0’ otherwise using gaussian kernel function by considering average of neighboring points. this is mathematically expressed as given below. from the above equation (12), ’sc_i w_i+b_i’ refers to the output result (i.e. either diabetic or non-diabetic), with ‘w_i’ and ‘b_i’ representing the weight and bias of the sub-classifier. finally, the weight is updated as given below. https://doi.org/10.15837/ijccc.2021.6.4291 7 the pseudo code representation of gaussian kernelized decision stump reweight classification is given below. figure 3: lgorithm 2 gaussian kernelized decision stump adaboost classification as given in the above gaussian kernelized decision stump adaboost classification algorithm, for each training set of feature vectors, the proposed method being an ensemble classifier provides strong classification results by combining the output of weak hypothesis. this is performed in our work by applying a decision stump (ds), a one-level decision tree possessing one root node that is immediately connected to the leaf node. the root node takes a decision based on the gaussian kernel function, i.e., by considering the average of the neighboring points. based on the results, the two classes of instances i.e. 1 and 0 are obtained at the leaf node. in addition with the weak hypothesis possessing certain significant training errors that in turn minimizes the classification results, in our work, the problem is rectified by integrating the results of all weak hypotheses to make a strong classification. after combining the weak hypothesis, the error is calculated and accordingly the weights are adjusted to find the best classifier. with this, the prediction accuracy of minority class is said to be improved by reducing false positive and false negative. 4 experimental setup two imbalanced datasets, pima indian diabetes (https://www.kaggle.com/uciml/pima-indiansdiabetes-database) and hepatitis (https://www.kaggle.com/harinir/hepatitis) is taken from the kaggle for as binary classification problems (i.e. 2 classes). the pima indian diabetes dataset comprises of 768 instances/ samples and 8 numeric attributes with one class attribute (i.e. outcome 1 or 0). among the 768 samples, 500 majority class (i.e. class 0) and 268 minority class (i.e. class 1) are obtained. in a similar manner, the hepatitis dataset comprises of 20 columns. simulations are conducted with the benchmark dataset. five performance parameters, such as accuracy, precision, recall, f1-score auc under roc score are measured. fair comparison is made with the proposed tverskey similarity regression and gaussian decision stump-based adaboosting (tsir-gkdsa) for imbalanced data classification and existing four undersampling methods, edited nearest neighbor rule [1], random undersampling [2] tomeklinks [3], nearmiss [4] applied to four different classifier . 4.1 dataset description the pima indian diabetes dataset includes details about female patients with minimum twenty one year age of pima indian population obtained from uci machine learning repository, originally https://doi.org/10.15837/ijccc.2021.6.4291 8 table 1: pima indian diabetes dataset attribute number attribute f1 pregnancy f2 plasma glucose concentration f3 diastolic blood pressure f4 triceps skin fold f5 serum insulin f6 body mass index f7 diabetes pedigree f8 age table 2: hepatitis dataset attribute number attribute f1 class f2 age f3 sex f4 steriod f5 antivirals f6 fatigue f7 malaise f8 anorexia f9 liver big f10 liver firm f11 spleen palpable f12 spiders f13 ascites f14 bilirubin f15 alk phospate f16 sgot f17 albumin f18 protime f19 histology owned by the national institute of diabetes and digestive and kidney diseases. a total of 768 instances are present in this dataset. they are classified into two different classes, as diabetic and non diabetic involving eight risk factors. the eight risk factors are number of time pregnant, plasma glucose concentration of two hours in an oral glucose tolerance test, diastolic blood pressure, triceps skin fold thickness, two-hour serum insulin, body mass index, diabetes pedigree function and age as provided in table 1 and the features in hepatitis dataset are provided in table 2. 4.2 case scenario for medical imbalanced data classification using pima indian diabetes dataset in this section, a case scenario for medical imbalanced data classification using pima indian diabetes dataset is provided. as provided in the above table 1, a distinctly fascinating attribute or feature utilized was the diabetes pedigree function, ‘pedif7’, that contributed certain data on diabetes mellitus genetics arising from relatives, also displaying the genetic relationship of those relatives to the patient (i.e., analyzing the relationship between two instances from the dataset). this estimation of genetic impact gave us a conception of the genetic likelihood one might have with the outbreak of diabetes mellitus. on the basis of this consideration in the proceeding section, it is ambiguous how well this diabetes pedigree function predicts or classifies the outbreak of diabetes. initially, the columns ‘n=8’ in pima indian diabetes dataset are split into different numbers of classes into a data https://doi.org/10.15837/ijccc.2021.6.4291 9 matrix as given below. dm =   6 1 8 1 0 148 85 183 89 137 72 66 64 66 40 35 29 0 23 35 0 0 0 94 168 33.6 36.6 25.3 28.4 43.1 0.627 0.351 0.672 0.167 2.288 50 31 32 21 23)   to start with each attribute or features in the dataset were inspected and accordingly the scattering features were reviewed and the following inferences were made. 1. features to be integers were the pregnancy ‘f1’ and age ‘f8’ 2. population is by and large smaller with less than 50 years old 3. from equations (6) and (7) no relationship between two variables (i.e., between age and onset of diabetes is said to exist) and also no relationship between pedi function and onset of diabetes is said to exist. 4. certain attributes or features possessed a value of zero that are found to be errors in the data, like plasma glucose concentration ‘f2’, blood pressure ‘f3’, skin thickness ‘f4’, insulin ‘f5’ and bmi ‘f6’. 5. upon implementation of the distribution of several classes, as given above, 500 negatives instances were appeared (‘65.1%’) and 258 positive instances (‘34.9%’) were appeared to an overall of 758 instances. 6. from the above results, the minority classes were found to be (‘34.9%’) and majority classes were found to be (‘65.1%’). 7. the samples obtained from minority classes and majority classes were then provided as input for classification of results. initially, the weight and bias was initialized to ‘-0.2 to 2.0’, 1.0 respectively. 8. the sub-classifiers, from both the classes were trained according to the weight and bias value, by considering average of neighboring points. the classified results were arrived at. 9. the classification performance is evaluated in the next section. 4.3 performance analysis using precision first, a significant metric used for analyzing medical data classification is precision. precision is mathematically expressed as below. p = t p t p +f p ∗ 100 − − − 14 from the above equation (14), precision ‘p’ is arrived at based on the ratio of the true positive and false positive rate. here, the true positive ‘tp’ refers to the number of positive instances classified as positive and false positive ‘fp’ refers to the number of negative instances classified as positive. table 3 given below lists the summary results of precision of proposed system tsr-gdsa, and exiting methods such as edited nearest neighbor rule [1], random undersampling [2] and tomeklins [3] and near miss[4] applied in four different classifier logistic regression, support vector machine, k-nearest neighbour and random forest figure 4 shows the graphical representation of precision compared with tsirgkdsa and other undersampling method applied in various classifier for the two datasets. different https://doi.org/10.15837/ijccc.2021.6.4291 10 table 3: summary of precision of pima indian diabetes and hepatitis dataset using tsir-gkdsa, and other undersampling methods attribute number attribute f1 class f2 age f3 sex f4 steriod f5 antivirals f6 fatigue f7 malaise f8 anorexia f9 liver big f10 liver firm f11 spleen palpable f12 spiders f13 ascites f14 bilirubin f15 alk phospate f16 sgot f17 albumin f18 protime f19 histology undersampling procedure mentioned in x axis and the y axis denotes the precision results obtained for all the five method applied in different classifier. the reason for precision improvement using tsir-gkdsa is due to the application of tversky similarity regressive undersampling algorithm. by applying this algorithm, initially, dependent and independent variable were delineated by applying the regression function. then, with the results obtained, tversky similarity index was utilized that in turn resulted in the output values in the ranges from ‘0’ to ‘1’. with this, correct instances were classified as positive and reduced the number of negative instances to be classified as positive. 4.4 performance analysis using recall the second metric used in our work for medical data classification analysis is recall. and is mathematically expressed as given below. r = t p t p +f n ∗ 100 − − − − − −15 figure 4: raphical representation of precision https://doi.org/10.15837/ijccc.2021.6.4291 11 table 4: summary of recall using pima indian diabetes and hepatitis dataset for tsr-gdsa and existing undersampling method undersampling technique classifier pima hepatitis enn logistic regression 0.73 0.76 svm 0.75 0.79 k-nearest neighbour 0.67 0.69 random forest 0.72 0.79 near miss logistic regression 0.70 0.77 svm 0.71 0.77 k-nearest neighbour 0.75 0.79 random forest 0.69 0.88 random undersampler logistic regression 0.68 0.65 svm 0.68 0.73 k-nearest neighbour 0.68 0.81 random forest 0.7 0.80 tomeklinks logistic regression 0.68 0.68 svm 0.67 0.67 k-nearest neighbour 0.70 0.71 random forest 0.67 0.67 tsir-gkdsa 0.78 0.83 figure 5: raphical representation of recall figure 4 given above depicts the graphical representation of recall using five different methods with the aid of pima indian diabetes dataset and hepatitis dataset. with recall referring to the ratio of actual positive cases (i.e., correct classification) that were correctly classified as such, analysis were made for different classifier applied in from both the datasets. from the results it is inferred that the recall rate is said to be improved using tsir-gkdsa when compared to the four methods. the reason for improvement is the application of gaussian kernelized decision stump adaboosting algorithm. by applying this algorithm ensemble classification is said to be performed using our work where the strong classification results are combined with the output of weak hypothesis via decision stump (ds). the advantage of ds being a one-level decision tree possessing one root node that is immediately connected to the leaf node, where classification is made by means of the gaussian kernel function, therefore reducing the number of positive instances or classes as negative. 4.5 performance analysis using f1-score f1 score refers to the weighted average of precision and recall. this score therefore considers both the false positives and false negatives into consideration. in case of uneven class distribution, https://doi.org/10.15837/ijccc.2021.6.4291 12 table 5: summary of f1-score using four different methods and tsir-gkdsa. undersampling technique classifier pima hepatitis enn logistic regression 0.71 0.69 svm 0.74 0.74 k-nearest neighbour 0.65 0.69 random forest 0.68 0.74 near miss logistic regression 0.69 0.6 svm 0.70 0.6 k-nearest neighbour 0.69 0.74 random forest 0.73 0.75 random undersampler logistic regression 0.69 0.54 svm 0.68 0.65 k-nearest neighbour 0.69 0.79 random forest 0.68 0.64 tomeklinks logistic regression 0.69 0.69 svm 0.67 0.67 k-nearest neighbour 0.67 0.67 random forest 0.70 0.71 tsir-gkdsa 0.96 0.97 f1 score is considered to be the most useful metric. in this section, the f1 score is evaluated and is mathematically formulated as given below. f1 − score = 2 ∗ r∗p r+p —–17 from the above equation (17), the ‘f1-score’ is evaluated based on the recall value ‘r’ and the precision value ‘p’ respectively. figure 5 given above shows the f1-score performance of four different figure 6: raphical representation of recall methods, pima indian diabetes and hepatitis dataset and proposed tsir-gkdsa. from the results it is inferred that the f1-score using tsir-gkdsa for both the data sets was improved than when compared to the state-of-the-art methods. the improvement in f1-score is due to the incorporation of tversky similarity function that initially obtains the relationship between two variables, therefore resulting in the accurate identification. 4.6 performance analysis using accuracy accuracy is measured by accuracy=tp+tn/tp+fp+tn+fn https://doi.org/10.15837/ijccc.2021.6.4291 13 table 6: summary of accuracy of tsir-gkdsa with other undersampling method undersampling technique classifier pima hepatitis enn logistic regression 0.72 0.7 svm 0.72 0.82 k-nearest neighbour 0.66 0.82 random forest 0.69 0.82 near miss logistic regression 0.71 0.64 svm 0.71 0.64 k-nearest neighbour 0.72 0.82 random forest 0.74 0.82 random undersampler logistic regression 0.71 0.59 svm 0.68 0.73 k-nearest neighbour 0.71 0.77 random forest 0.7 0.68 tomeklinks logistic regression 0.72 0.72 svm 0.70 0.70 k-nearest neighbour 0.72 0.7 random forest 0.72 0.73 tsir-gkdsa 0.75 0.83 accuracy can be compared with proposed architecture and other state of art undersampling method for the two dataset figure 7: raphical representation of recall from the fig [6],proposed system show high accuracy when compared with existing methods 4.7 performance analysis using auc under roc score finally, aur or the area under roc curve is obtained that evaluates the two dimensional area underneath the roc curve. the area under the curve (auc) measures the potentiality of the sampling technique for imbalanced data classification with the purpose of differentiating between classes. the higher the auc better the performance between the positive and negative classes. in addition, it also provides an aggregate or total measurement of performance across all probable thresholds. table 7 given below provides the roc_auc_score measure for five different methods, using two different datasets. figure 7 given above illustrates the auc curve performance of four different methods, pima indian diabetes and hepatitis dataset for tsr-gdsa, edited nearest neighbor rule, random under sampling and tomeklins and nearmiss the auc curve is measured with respect to the true positive rate and false positive rate. here, false positive rate refers to the rate or ratio of probability of falsely rejecting the null hypothesis for a particular test (i.e., falsely rejecting the null hypothesis for imbalanced data https://doi.org/10.15837/ijccc.2021.6.4291 14 table 7: summary of roc_auc_score using pima indian diabetes and hepatitis dataset for tsirgkdsa with other undersampling method undersampling technique classifier pima hepatitis enn logistic regression 0.79 0.87 svm 0.81 0.9 k-nearest neighbour 0.82 0.94 random forest 0.76 0.92 near miss logistic regression 0.77 0.84 svm 0.75 0.94 k-nearest neighbour 0.77 0.92 random forest 0.75 0.95 random undersampler logistic regression 0.79 0.84 svm 0.81 0.9 k-nearest neighbour 0.81 0.93 random forest 0.75 0.94 tomeklinks logistic regression 0.79 0.79 svm 0.81 0.81 k-nearest neighbour 0.82 0.76 random forest 0.76 0.81 tsir-gkdsa 0.94 0.95 figure 8: raphical representations of roc_auc score classification) and on the other hand true positive rate refers to the actual positive will test positive. higher roc_auc score expresses a good test and from the samples though increase is substantially found in all the foure methods using both the datasets, comparative analysis shows better results using tsir-gkdsa. this is because of the application of the proposed probability sampling using random sampling is applied for imbalanced data classification where each sample has an equal chance or probability of being selected. 5 conclusion in this paper, an undersampling and classification solution to solve the class imbalance issue is proposed. the underlying study reveals the fact that with proper undersampling and classification reduce the false alarms in medical imbalanced data. the proposed method incorporates the elimination of noise and border line data instances to the baseline and explored tverskey similarity regression and gaussian decision stump-based adaboosting (tsr-gdsa) method. moreover, gaussian kernelized decision stump boosting is employed with the objective of reducing the training errors occurring during classification so that best classifier is obtained. the results obtained show significant improvement over the conventional data classification method. the performance of the models is validated with various performance measures such as, precision, recall, f1 score, accuracy and auc under roc. the results achieved are quite satisfactory. https://doi.org/10.15837/ijccc.2021.6.4291 15 ethics approval and consent to participate no participation of humans takes place in this implementation process human and animal rights no violation of human and animal rights is involved. funding no funding is involved in this work. conflict of interest conflict of interest is not applicable in this work. authorship contributions there is no authorship contribution. acknowledgement there is no acknowledgement involved in this work. references [1] zhaozhao xu, derong shen, tiezheng nie, yue kou, (2020). “a hybrid sampling algorithm combining m smote and enn based on random forest for medical imbalanced data”, journal of biomedical informatics, elsevier, [edited nearest neighbor rule]. 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[7] colin bellinger, shiven sharma, nathalie japkowicz, osmar r. zaïane,(2019). “framework for extreme imbalance classification: swim—sampling with themajority class”, knowledge and information systems, springer, [8] zeina abu-aisheh, romain raveaux, jean-yves ramel (2018).“efficient k-nearest neighbors search in graph space”, pattern recognition letters, elsevier [9] ahmad s. tarawneh, ahmad b. a. hassanat, khalid almohammadi, dmitry chetverikov, colin bellinge, (2020). “smotefuna: synthetic minority over-sampling technique based on furthest neighbour algorithm”, ieee access. https://doi.org/10.15837/ijccc.2021.6.4291 16 copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control [10] nijaguna gollara siddappa, thippeswamy kampalappa,(2020).“imbalance data classification using local mahalanobis distance learning based on nearest neighbor”, sn computer science cite this paper as: kamaladevi m.; venkatraman v. (2021). tversky similarity based under sampling with gaussian kernelized decision stump adaboost algorithm for imbalanced medical data classification, international journal of computers communications & control, 16(6), 4291, 2021. https://doi.org/10.15837/ijccc.2021.6.4291 introduction related works methodology tversky similarity indexive regression under sampling model gaussian kernelized decision stump adaboosting classification model experimental setup dataset description case scenario for medical imbalanced data classification using pima indian diabetes dataset performance analysis using precision performance analysis using recall performance analysis using f1-score performance analysis using accuracy performance analysis using auc under roc score conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 4, month: august, year: 2020 article number: 3891, https://doi.org/10.15837/ijccc.2020.4.3891 ccc publications technical solution for a real-time air quality monitoring system a. simo, s. dzitac, f. m. frigura-iliasa, s. musuroi, p. andea, d. meianu attila simo* 1. politehnica university timisoara 300006 timisoara, piata victoriei, 2, romania 2. romanian academy of scientists 300220 bucuresti, ilfov, 3, romania *corresponding author: attila.simo@upt.ro simona dzitac, dragos, meianu university of oradea 410087 oradea, universitatii, 1, romania simona@dzitac.ro, dmeianu@uoradea.ro flaviu mihai frigura-iliasa, sorin musuroi, petru andea 1. politehnica university timisoara 300006 timsoara, piata victoriei, 2, romania 2. romanian academy of scientists 300220 bucuresti, ilfov, 3, romania flaviu.frigura@upt.ro, sorin.musuroi@upt.ro, petru.andea@upt.ro abstract this article will present a simple technical solution for a low-power and real-time air quality monitoring system. the whole package of software and hardware technical solutions applied for recording, transmitting and analyzing data is briefly described. this original monitoring system integrates a single chip microcon-troller, several dedicated air pollution surveillance sensors (for pm10, pm2.5, so2, no2, co, o3, voc, co2), a lorawan communication module and an online platform. this system was tested and applied under real field conditions. depending on the measured values, it provides alerts, or, it can lead to the re-placement of specific components in the exhaust equipment. this article will pre-sent some experimental results, validated also by official measurements of government operated air quality stations. keywords: wireless sensors network, air quality monitoring, lorawan. https://doi.org/10.15837/ijccc.2020.4.3891 2 1 introduction nowadays, air quality monitoring in the urban environment, generally, and near the industrial plants, particularly, is becoming an increasingly important issue. many, today existing air quality monitoring systems are using wireless sensor networks (wsn), vehicular sensor networks (vsn) or other internet of things (iot) based technologies. the shortcomings of these systems, if the technology is not well chosen, (e.g. a system based on zigbee, nb-iot, lte-m) is that they require a large number of sensors and gateways to establish a multi-hop network for ferrying the data to the backend system. such approach generally increases the network cost and the energy consumption. if application scenario needs small packages of data, the measuring points has no electricity supply and needs coverage over large geographical area, for such situa-tions, the low power wide area (lpwa) networks provide the solution. lpwa networks permits a long communication range, low power consumption, low cost and direct m2m access. there are a number of competing standards in the lpwa network space, such: sigfox, dash7, telensa, nb-iot, nwave, lora, etc. this paper presents a part of a more complex work from the lpwa networks field, ongoing in the power systems department from politehnica university of timisoara, in collaboration with eta2u company. a software-hardware complex of technical solutions for collecting, transmitting and analyzing data is presented within this paper. the proposed pilot system integrates a single chip microcontroller, several calibrated air pollution low-cost monitoring sensors (pm10, pm2.5, so2, no2, co, o3, voc, co2), lorawan communication module and an online application. the proposed solution is tested and validated under real field conditions. depending on the measured values, the application can generate alerts and can propose the replacement of certain components in the filtration systems, in case of industrial plants. the authors have also dealt with the air pollution-monitoring problem in the past. in [10] they offer a practical model of an automated environment parameters monitoring system for industrial plants, a multifunctional softhardware complex of technical solutions for collecting, transmitting, processing and analyzing data. the proposed system in [10] can embed different type of sensors for chemical emissions monitoring in industrial plants. the whole system is based on the lorawan communication protocol and a self-flying drone system. the current project was born to have real-time data about air parameters, over large geographical area, which helps us manage activities in the territories impelled by devices (e.g. traffic management, construction areas, industrial plants). in literature, there are many theoretical studies and proposed solutions regarding air pollution monitoring [2], [3, 4, 7, 9] . many uses cases refer to small geographical areas where wifi technology is also suitable. few projects target large geographical areas and require devices to have low energy consumption [6, 11]. existing air quality monitoring systems are using a specific range of sensors to measure various air pollutants but tend to only have one pm sensor and do not use lpwa networks [1, 8, 12]. the deployment of a thick, reliable wide area network of air quality monitoring system would improve the ability to identify sources of pollution, understand person-al exposure, complement existing monitoring networks and raise air quality awareness among the population. 2 monitoring system description deploying wireless sensors network over large geographical area requires supporting infrastructure. the pilot project where the monitoring devices were to be installed referred to an industrial area of about 25 km2. the longterm strategy intents that this air quality monitoring device to become part of a smart city infrastructure providing concrete data about the air parameters for local residents. the main purpose was to create a low-cost device, which, if needed, to be accessible, even for the residents, but, at the same time, to provide correct and indisputable results. a raspberry pi hardware attached on top (hat) provides the computation. in the first stage, the team tried the measurement variant presented in [10], with waspmote pro 1.5 and waspmote 3.0, to which the sensors were added. later, this support was replaced with an own data acquisition board, on which the sensors and https://doi.org/10.15837/ijccc.2020.4.3891 3 the communication module were integrated. in this pilot project, we used the lorawan communication protocol, due to its flexibility, ease of use, and other advantages offered versus other lpwa technologies, and to the local coverage. this project strong point was the fact that the eta2u company has a lora net-work at the level of the timisoara municipality, named loratim. it is a complex network, consisting of cisco ixm-lpwa-800-16-k9 and kerlink gateways, with a very good coverage that can be seen on figure 1. figure 1: loratim network coverage 2.1 hardware system within this pilot project, the team have planned the construction of six devices, for monitoring air quality issues in an industrial area of timisoara, romania. with these six devices, the area of interest is covered. this area being geographically large for data collection, is is necessary to use the lorawan communication protocol. in the first phase, the solution was tested with the device previously conceived by our team [10], presented in figure 2. some of the results obtained with this version 1 (with calibrated sensors) were very good (partially presented in figure 2), but a major disadvantage was that not all the sensors could be connected on the same sensor board (pm10, pm2.5, so2, no2, co, o3, voc, co2). the results for no2 for june 2018 are presented in figure 3. similar results were obtained for the other polluting agents, too. in order to cover the entire range of pollutants that have been proposed, should have had to double the number of sensor boards (figure 2, on the right side), which increased the device cost, the volume and not least the energy consumption. in addition to the sensors that were placed directly on the sensor board, the particulate mat-ter (pm) sensor had to be connected to this. this further complicates the situation. in this pilot project, the team used the hpma115s0-xxx honeywell particulate matter sensor. this is a laser-based sensor which detects and counts particles using light scattering [5]. the detection concentration range is 0 µg/m3 to 1,000 µg/m3, it is fully calibrated, with <6 s response time and max 80 ma current supply. based on iec61000 stable operation, plus/minus 15% accuracy (pm2.5, pm10) it can be considered a sensor that meets the needs. because of these, the team decided to design an own sensor board (one board per device), on which can be connected all the sensors (so2, no2, co, o3, voc, co2), hpma115s0-xxx, the lorawan hat and raspberry pi hat (as shown in figure 4). thus, on this board can be connected all the seven sensors plus the rest of the device elements. all components, are enclosed in an plastic enclosure with ip66 ip rating (as in figure 9), having 360 mm x 270 mm x 165 mm (h x w x d) dimensions. two grids (6 x 4) of 10 mm holes were drilled https://doi.org/10.15837/ijccc.2020.4.3891 4 figure 2: hardware components figure 3: values recorded for no2 for one month on both sides of the enclosure to ensure a constant air flow, for so2, no2, co, o3, voc and co2 sensors. the hpma115s0-xxx pm sensor was taken out of the box. figure 4: own data acquisition board, developed by eta2u innovation 2.2 software system in parallel with the development of the monitoring devices, an online platform has been designed for data analytics and different alerts setting. on this platform, the user can select from the list or on the map the industrial platform of interest, after which he can select the device from which he wants the information about air quality parameters. figure 5 shows the design of the platform. because the administrators of the industrial platform did https://doi.org/10.15837/ijccc.2020.4.3891 5 table 1: list of hardware used to build one monitoring device item quantity unit price (euro) raspberry pi hat 1 40 gas sensors 7 10 honeywell pm sensor 1 35 lorawan hat + antenna 1 45 plastic enclosure 1 37 not agree, the location of the devices could not be displayed on the map. the online platform was developed using html, css, php, javascript and sql. the platform offers us the possibility of tracking the evolution of air parameters, in the form of graphs. data can be displayed per day, per week, per month for each parameter separately (figure 5) or all on the same graph. because the pilot project is dedicated to local use, the language used in the platform is the local language (romanian). in the future will be added the option by which users can choose another international language. figure 5: graphical user interface – online platform 3 results and discussions the purpose of this paper is not the analysis of the air quality in an area of interest but the presentation of a low-cost (table 1) air quality monitoring device that provides correct data. the data collected by the sensors are sent to a database from where they are displayed by the online platform such that to be easy to understand for the end user. in the online platform, the user can select the period for which he wants to display the results. the results are displayed as graphs. parameter values for one hour or several hours can be displayed (figures 6, 7, 8), for a week, a month or for a certain peri-od (figures 9, 10 for the 01.10.2019 08.10.2019 period). https://doi.org/10.15837/ijccc.2020.4.3891 6 figure 6: no2 values for 24h (22.10.2019 23.10.2019) figure 7: pm10 values for 3h (23.10.2019) figure 8: pm2.5 values for 3h (23.10.2019) all the parameters discussed in this paper can be displayed in this form (pm10, pm2.5, so2, no2, co, o3, voc, co2). after 7 months of operation in real conditions and with repeated comparisons between the data collected by these devices and data from parallel measurements with government operated air quality stations, it can be concluded that the devices provide reliable values (figures 11, 12, 13). after mounting all devices, certain problems appeared, related to the locations select-ed initially. due to poor air flow the results were not relevant. in the first phase, these devices were relocated, and in the second phase the number of holes on the side of these devices was increased. the measured data clearly show that in certain periods of time the values of the air quality parameters do not comply with legal norms and concrete measures must be taken. https://doi.org/10.15837/ijccc.2020.4.3891 7 figure 9: pm2.5 values for 1 week (01.10.2019 08.10.2019) figure 10: pm10 values for 1 week (01.10.2019 08.10.2019) figure 11: comparison between governments operated air quality station data and our low-cost devices data no2 values for 24h (22.10.2019 23.10.2019) https://doi.org/10.15837/ijccc.2020.4.3891 8 figure 12: comparison between governments operated air quality station data and our low-cost devices data pm10 values for 3h (23.10.2019) figure 13: comparison between governments operated air quality station data and our low-cost devices data pm2.5 values for 3h (23.10.2019) https://doi.org/10.15837/ijccc.2020.4.3891 9 4 conclusions due to the dynamic development of the city of timisoara, in many areas, air quality problems arise (chaotic development on the real estate market, due to which the industrial platforms are located either very close to the residential areas or already found inside the city). also, the car traffic is increasing near the industrial areas. plus, any method is welcome to follow the air quality in the industrial platforms regarding the change of the filters in the exhaust systems. there are many established methods, but we wanted to meet the needs of the economic environment with a low-cost device, which can provide the same information and quality. more than necessary, our devices from this pilot project, can send data to the application server through lorawan, without having to build a complex and expensive network such as a wi-fi network. after this pilot project implementation, we can conclude that this air quality monitoring device can operate at a city scale, due to the implementation of lorawan as communication protocol. further works will focus on increasing the autonomy of the devices, so that where they will need to be mounted in places where there is no electricity supply. the existing model has autonomy only 2-3 days depending on the number of readings per day. references [1] arroyo, p.; herrero, j.l.; suárez, j.i.; lozano, j. (2019). wireless sensor network combined with cloud computing for air quality monitoring, sensors, 19(3), 1–17, 2019. [2] borrego, c.; costa, a.m.; ginja, j.; amorim, m.et al. (2016). assessment of air quality microsensors versus reference methods: the eunetair joint exercise, atmospheric environment, 147, 246–263, 2016. [3] castell, n.; dauge, f.r.; schneider, f. et al. (2017). can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?, environment international, 99, 293–302, 2017. [4] hasenfratz, d.; saukh, o.; walser, c. et al. (2016). deriving high-resolution urban air pollution maps using mobile sensor nodes, pervasive and mobile computing, 16(b), 268–285, 2016. [5] honeywell (2018). hpm series particulate matter sensors, honeywell, 2018. [6] johnston, s.j.; basford, p.j.; bulot, f.m.j. et al. (2019). city scale particulate matter monitoring using lorawan based air quality iot devices, sensors, 19(1), 1–20, 2019 [7] lee, s.; lim, s. b. (2015). applications of ubiquitous sensor network: micro-scale air quality monitoring, international journal of computers communications & control , 10(3), 370–381, 2015. [8] mo, x.; zhang, l.; li, h.; qu, z. (2019). a novel air quality early-warning system based on artificial intelligence, environmental research and public health, 16(19), 1–25, 2019. [9] oprea, m.; mihalache, s. f.; popescu, m. (2017). computational intelligence-based pm2.5 air pollution forecasting, international journal of computers communications & control , 12(3), 365–380, 2017. [10] simo, a.; barbulescu, c.; salinschi, m. (2018). lorawan based industrial emission monitoring with self-flying drone, in 18th international multidisciplinary scientific geoconference , 503–510, 2018. [11] wang, s.y.; zou j.j.; chen y.r. et al. (2018). long-term performance studies of a lorawanbased pm2.5 application on campus, in 87th vehicular technology conference (vtc spring) , 1–6, 2018. https://doi.org/10.15837/ijccc.2020.4.3891 10 [12] wesseling, j.; de ruiter, h.; blokhuis, c. et al.(2019). development and implementation of a platform for public information on air quality, sensor measurements, and citizen science, atmosphere, 10(8), 1–20, 2019. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: simo, a.; dzitac, s.; frigura-iliasa, f. m.; musuroi, s.; andea, p.; meianu, d. (2020). technical solution for a real-time air quality monitoring system, international journal of computers communications & control, 15(4), 3891, 2020. https://doi.org/10.15837/ijccc.2020.4.3891 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 3, month: june, year: 2021 article number: 4233, https://doi.org/10.15837/ijccc.2021.3.4233 ccc publications a cyber collaborative protocol for real-time communication and control in human-robot-sensor work p. o. dusadeerungsikul, s. y. nof puwadol oak dusadeerungsikul* prism center and school of industrial engineering purdue university 315 n. grant st., west lafayette, in 47907, usa *corresponding author: pdusadee@purdue.edu shimon y. nof prism center and school of industrial engineering purdue university 315 n. grant st., west lafayette, in 47907, usa nof@purdue.edu abstract real-time communication and control are essential parts of the cyber physical system (cps) to optimize effective performance and reliability. to gain a sustainable competitive advantage with automation 5.0, as needed in work-of-the-future, this article addresses the concept of real-time communication and control in the case of an agricultural work setting, along with a newly designed cyber collaborative protocol, called ccp-rtc2. the developed protocol aims to minimize information delay and maximize jin (just in need) information sharing, to enable collaborative decisions among system agents. two experiments are conducted to compare the designed protocol’s performance in agricultural cps against the current non-cps practice. the results demonstrate that the ccp-rtc2 is superior compared with current practice in terms of information sharing in a normal operation scenario. when the system obtains an unplanned request, the ccp-rtc2 can integrate such a request to the original work plan while minimizing the system’s objective function (lower is better). hence, the system has relatively smaller information delays, as well as better timely information shared with system agents that need it. keywords: collaborative control theory, multi-agent systems, real-time information exchange, task administration protocols, automation 5.0. 1 introduction without exception, real-time communication and control, and knowledge sharing are currently at the core of modern industry, including agriculture. as an illustration, individual agricultural agents (e.g., farmers) work with intensive, overloaded information in the way they practice in farming, the traditional agricultural work activities/methods are considered as lacking effective information flow. https://doi.org/10.15837/ijccc.2021.3.4233 2 specifically, insufficient control, communication, coordination, and collaboration negatively influence timeliness and optimization of decision-making processes at work [16]. arguably, the lack of effective information sharing process potentially leads to suboptimal agricultural work management, such as the slow process of localization and early detection of crop diseases; improper, often wasteful response and prevention; poor plant monitoring; and low crop yield [11]. therefore, the advancement of agricultural robotics and farming equipment that improve the way farmers can work has called for effective means to communicate and connect so that the overall system reaches optimal performance. one of the emerging methods to facilitate real-time communication and control is utilizing cyber physical systems (cps), which link and can optimize the functions of physical elements by cyber connections. today, the term cps has been widely adopted as one of the promising key factors leading to automation 5.0 [23]. cps is a rapidly evolving model that describes a system where information and communication technology is integrated into a physical process to make the entire system more efficient, reliable, and timely in the decision-making process [3, 17]. networked manufacturing systems, intelligent transport systems, smart infrastructures, and power grids are examples of evolving cps [17, 21, 28]. thus, the benefits of cps are prominent in many areas. for instance, in the manufacturing industry, the equipment used will be continuously tracked so that the equipment’s performance and condition can be seen remotely and in real-time. in the service sector, cps can prioritize and reduce service failure by sharing information with frontlines, thereby increasing customer satisfaction [14]. recently, there has been increasing in applying cps in agricultural work. guo and colleagues [11] designed a cps framework focusing on tracking, detecting, and reacting to different stress forms in an agricultural greenhouse. they found that with cps, the system has better operation performance and error tolerance. another useful cps application in agricultural settings is adding human-in-the-loop, making the system more effective and responsive [2, 19]. also, sensors and sensor networks are integrated as an essential part of cps to enable precision control and monitoring [18, 26]. internet and web service structure for agricultural cps that enables wireless connection is developed and outperforms alternative systems [30]. lastly, with synchronization among agents by task administration protocol in cps, the monitoring process outperforms the traditional practice by reducing the operational time and increasing overall system efficiency [7]. surprisingly, however, most of the literature has mainly focused on the agents’ operations and connection structures – assuming perfect communication and control, such as information flow among agents and data exchange between systems, taken as a given prerequisite. an essential element in cps that enables real-time communication and control is a cyber collaborative protocol (ccp). ccp is a task administration protocol that collaborates agents with cyber connections [6, 22]. the cyber connection in the cps environment improves and enables system abilities critical for modern systems, i.e., real-time communication and control. as herdon [12] mentioned, new and timely information processing provides a possibility for productive operation, decision-making, and adaptation to the environment. thus, the timing of information is a critical factor, especially in short-term daily operation [5], such as patient monitoring system in a medical field [10, 15]; real-time tracking and control in operations fields [9, 24, 27]; and traffic control and safety in transportation [25, 29]. furthermore, to gain competitive advantages with automation 5.0, effective agricultural work management inevitably relies on this real-time communication and control (timely and comprehensive information processing, communicating, and controlling), and ccp is an essential tool to enable such advantage. to date, there have been few researchers systematically developing a protocol for real-time communication and control that will enable collaborative decisions in cps, especially in agriculture settings. to address this gap, in this article, we develop and validate a cyber collaborative protocol for realtime communication and control, called ccp-rtc2. the designed protocol integrated with the agricultural cps called agriculture robotics system (ars). it is compared and contrasted with the current non-cps agricultural setting. the key contributions of the article are: 1) the introduction of a new type of ccp, which focuses on real-time communication and control, 2) the development of the ccp-rtc2, which optimizes and organizes timely and just in need (jin) information flow among agents, and 3) the implemenhttps://doi.org/10.15837/ijccc.2021.3.4233 3 table 1: agents and their responsibility in ars work agent responsibility human and expert system decision-makers; solve complex and unexpected work problems. robot with autonomous cart move into the greenhouse and approach the crop plants in the assigned direction(s). sensors collect and transmit data to the host. tation and validation of ccp-rtc2 in cps, compared against the current practice in terms of its performance. the remainder of the article is structured as follows. in section 2, the overview ars is described, and the detailed explanation of ccprtc2 is presented. next, in section 3, two experiments are conducted to test two logic approaches. conclusion and future work directions are discussed in section 4. 2 methodology the agricultural robotic system (ars) is designed to monitor and detect an abnormal status of crop plants, such as stress or disease [11]. for effective monitoring and detection, a protocol that enables smooth communication and information exchange among system agents to enable effective collaborative decisions is necessary [13]. hence, in this section, a cyber collaborative protocol for real-time communication and control, called ccp-rtc2, is established to facilitate information exchange for collaborative decisions to have better communication and informatics and, consequently, better monitoring, controlling, and detection processes in ars. 2.1 ars and ccp-rtc2 designs 2.1.1 ars overview and task description ars consists of a robot arm with multi-sensors mounted on an autonomous mobile cart and a human agent who serves as a decision-maker [7]. the work tasks and responsibilities of each agent in the ars are shown in table 1. in every inspection round, plants are randomly selected to inspect their status. an autonomous cart is guided to visit every assigned location; then, the robot arm starts approaching the plant’s leaves and stems according to the inspection procedure. sensors capture images of the plant’s parts and transmit them to the base point where a human agent with an expert system can decide whether to activate a treatment if the plant is just stressed or already infected, or move to the next location otherwise. figure 1 presents the work system described above. figure 1: agricultural robotic system (ars) work operations https://doi.org/10.15837/ijccc.2021.3.4233 4 table 2: ccp-rtc2 cyber collaborative protocol for real-time communication and control in ars (ccp-rtc2) step 1 ars initialization step 2 ars sampling the location to visit step 3 autonomous cart is guided to visit location n step 4 robot arm approaches plant leaves and stems to capture the data step 5 sensors capture the necessary data and images step 6 sensed data are transmitted to the crop pathology expert system step 7 crop pathology expert system determines the status of the given plant step 7.1 if a plant at location n is healthy, then no action is required at location n. the expert system commands an autonomous cart to move to the next location. step 7.2 if a plant at location n is unhealthy or has signs of stress that can lead to disease, then command a robot to activate specified precision treatment. 2.1.2 cyber collaborative protocol for real-time communication and control in ars (ccp-rtc2) in ars, multiple agents are combined as a system. each agent connects and is responsible for a specific task regarding their strengths and weaknesses. therefore, to optimize ars performance, a ccp is needed to make a collaborative decision and, hence, a cyber collaborative protocol for realtime communication and control in ars (ccp-rtc2) is designed and developed. ccp-rtc2 is derived from ccp and aims to facilitate real-time information gathering and exchange, and optimize and control information flow between system agents. the objective of ccp-rtc2 is to 1) minimize the information delay (maximize real-time communication) in the system, reacting to the emerging situation, and 2) enable collaborative decisions and control, and information sharing, as needed and when needed. the ccp-rtc2 steps are described in table 2. the protocol is further described by a workflow with conversion conditions (figure 2). figure 2: agricultural robotic system (ars) work operations https://doi.org/10.15837/ijccc.2021.3.4233 5 2.1.3 performance metrics to capture the two objectives of ccp-rtc2, the following performance metrics are used. • average information delay (δ) δ represents the response time to the plant status. it reflects the effectiveness of information flow and timely exchange between agents. δ can be calculated as below. δ = ∑n i=1 ri − di n (1) where ri indicates timestamp that the system finishes reacting to the plant i (either stressed or healthy plant). in the case of a healthy plant, the agent should move to the next location. on the other hand, in a stressed plant, the system should indicate the treatment, such as adding fertilizer, or migrating the plant. di is the time stamp when the status of a plant i is found. n is the number of locations inspected. the system with lower δ indicates a faster response to the situation due to the high efficiency in information flow and communication among agents. • average information sharing (µ) µ represents the amount of information (e.g., plants’ status) at any point in time per agent. µ indicates the collaborative decision, and also how well the system shares information among agents. µ can be calculated as follows. µ = ∑t i=1 ci t (2) where ci indicates the cumulative amount of information obtained until time i and t is the total time. the system with relatively higher µ means, on average, the system is able to share a higher amount of information with agents. in other words, the system has better information sharing and does not rely only on a specific agent. hence, if certain agents fail, the remaining agents still have sufficient, timely information to continue operating without shutting down the entire system. 3 experiments and analysis to evaluate the designed protocol, two computer experiments are conducted. the experiments aim to measure and compare the designed protocol against the current practice. the current practice is the common practice that farmers have used for inspecting crops. usually, farmers scout into greenhouse fields to monitor plants themselves. if they find any stressed or diseased plants, and the treatment is commonly known, they can make an immediate decision and provide treatment for the hurting plant before moving to the next location. however, if the disease condition is vague, or the farmers are not sure about the needed treatment, they need to mark the location and return later for the proper and precise intervention. two experiments are defined and utilized for evaluating the system under the two alternative approaches (table 3). also, table 4 summarized the experiment design parameters and their distributions. 3.1 experiment 1: measuring alternative performance under normal work operations 3.1.1 experiment design to test the two systems, computer simulations are conducted to compare δ and µ, under normal operations. the normal operation is the operation described in section 2.1.1, which is the simpler https://doi.org/10.15837/ijccc.2021.3.4233 6 table 3: experiment design experiment objective monitoring systemars current practice experiment 1: objective: compare system performances under the normal work operations. description: the system monitors crops stress and diseases, and applies a treatment if necessary. the robot reaches a crop for detecting diseases. plant images that are taken by sensors are transmitted to the expert system for making a decision to move to the next location, or start proper, precise treatment. the farmer approaches the plant to detect disease. the farmer can inspect and make a decision to move to the next location or start the treatment. experiment 2: objective: compare system performances under situations with an unplanned request. description: the difference from experiment 1 is an unplanned request, e.g., re-inspect the location, inspect a new location that is not in the original plan, or a new disease is found. it can randomly be assigned to the system. the system must integrate and also complete the request. when an unplanned request is received by the system, it can recalculate robot routes, or suggest special treatment. then, the system transmits the signal to the robot and guides the robot to integrate the given request into the work plan. the farmer works according to a preset routine without ongoing communication with the system during the monitoring and inspection operation. hence, the farmer can receive an unplanned request only when the farmer returns to the base point. table 4: experiment design parameters agent action time cost and distribution movement ars (autonomous mobile cart) 0.7 meters/secondcurrent practice (farmer) 1.0 meters/second inspection ars (robot arm and sensors) normal distribution with mean 60 and sd 20 second (n(60, 20 2)) current practice (farmer) normal distribution with mean 60 and sd 20 second (n(60, 202)) transferring information ars (sensors and computer) normal distribution with mean 1 and sd 0.1 second (n(1, 0.1 2)) current practice (-) 0 (no transferring information) making decision ars (human and expert system) normal distribution with mean 45 and sd 15 second (n(45, 15 2)) current practice (farmer) normal distribution with mean 45 and sd 15 second (n(45, 152)) scenario. with the same map and parameters, the two systems aim to visit every assigned location, n, (from n = 5 to n =20), then inspect the locations and take the necessary actions. δ and µ of the two systems are captured, and the system with lower δ and higher µ is preferred. 3.1.2 results and analysis the results of the 30 replications of simulation for every n are demonstrated in figure 3 and are summarized in table 5. the results indicate that δ between the ars with ccp-rtc2 and current practice is not significantly different. the reason could be that in the current practice, the farmer who scouts into the field is also the decision-maker for the simple and unsophisticated decision, i.e., known disease. the farmer can quickly react to the situation. hence, the time between finding the disease and the treatment is relatively short. on the other hand, in the ars, sensors transfer information to human experts to make decisions, which are also fast because of the current cyber technology. with the two approaches, the results show that they perform with no statistically significant difference in terms of δ. for µ, the ars has significantly better performance than the current practice. it indicates that the ars has a higher rate of information sharing and communicating among agents. the possible explanation is that for every event that occurs with the ars, e.g., find a healthy plant or indicate a stressed plant, ccp-rtc2 will communicate needed information from sensors to human agents in real-time, and enable making a collaborative decision on a timely basis. hence, agents in ars tend to share more timely information with each other. as a result, the ars yields higher (better) µ and is therefore preferable over the current practice. https://doi.org/10.15837/ijccc.2021.3.4233 7 figure 3: δ (left) and µ (right) observed from experiment 1 for each n table 5: experiment 1 results average information delay (δ) (second) average information sharing (µ) (second) n ars current practice ars current practice 5 3.01 (0.17) 3.02 (0.25) 2.51∗∗∗ (0.11) 1.31 (0.15) 6 3.06 (0.22) 2.98 (0.30) 3.01∗∗∗ (0.15) 1.57 (0.18) 7 3.07′ (0.21) 2.97 (0.23) 3.49∗∗∗ (0.14) 1.81 (0.17) 8 3.03 (0.17) 3.03 (0.26) 4.02∗∗∗ (0.18) 2.08 (0.19) 9 3.01 (0.50) 3.03 (0.20) 4.49∗∗∗ (0.16) 2.32 (0.17) 10 3.02 (0.16) 3.06 (0.17) 4.98∗∗∗ (0.15) 2.56 (0.18) 11 3.00 (0.16) 2.99 (0.14) 5.46∗∗∗ (0.17) 2.81 (0.18) 12 3.02 (0.15) 2.99 (0.16) 6.05∗∗∗ (0.24) 3.09 (0.22) 13 3.03 (0.19) 3.01 (0.16) 6.50∗∗∗ (0.20) 3.34 (0.19) 14 3.03 (0.15) 2.99 (0.17) 6.99∗∗∗ (0.17) 3.56 (0.18) 15 3.05 (0.15) 3.01 (0.13) 7.53∗∗∗ (0.20) 3.84 (0.11) 16 3.06′ (0.14) 2.99 (0.17) 8.05∗∗∗ (0.22) 4.11 (0.11) 17 2.98 (0.12) 3.01 (0.12) 8.53∗∗∗ (0.28) 4.35 (0.14) 18 2.97∗∗ (0.13) 3.07 (0.13) 9.05∗∗∗ (0.23) 4.62 (0.10) 19 2.99 (0.11) 2.95 (0.15) 9.47∗∗∗ (0.23) 4.83 (0.12) 20 2.97∗∗(0.10) 3.03 (0.12) 10.02∗∗∗ (0.26) 5.09 (0.13) note: h0 : the ars with ccp-rtc2 is not statistically better than the current practice ′p<0.1;∗p<0.05;∗∗p<0.01;∗∗∗p<0.001; sd is shown in parentheses 3.2 experiment 2: operation with unplanned requests 3.2.1 experiment design the second experiment aims to compare δ and µ for the two systems but in a different situation from experiment 1. the experiment 2 situation is when an unplanned request occurs randomly. the unplanned requests are, for example, requests to inspect a new location, re-inspect the previous location, identify an unknown disease for sensor-captured symptoms, etc. in the experiment, the same map and parameters are used. the two systems need to complete all tasks (including an unplanned request) for n=5 to n=20. δ and µ of the two alternative systems are compared to investigate their relative performance. the system with lower δ and higher µ is preferred 3.2.2 results and analysis with 30 replications of simulation, the results are shown in figure 4 and are summarized in table 6. the average δ and µ of both systems increase when the system receives an unplanned request compared to the normal operation (experiment 1). the reason is that when the system receives an unplanned request, they need to deviate from the original and previously optimal plan to cope with the unplanned request. the deviation can cause (and cost) an information delay and inefficiency in sharing information and communication, which subsequently increases δ and µ of the system. hence, δ and µ in experiment 2 are statistically higher when compared to the corresponding values observed in experiment 1 (p-value < 0.001). https://doi.org/10.15837/ijccc.2021.3.4233 8 table 6: experiment 2 results average information delay (δ) (second) average information sharing (µ) (second) n ars current practice ars current practice 5 3.48∗∗∗ (0.22) 5.91 (0.40) 3.03∗∗∗ (0.12) 0.99 (0.15) 6 3.49∗∗∗ (0.19) 5.83 (0.57) 3.49∗∗∗ (0.17) 1.20 (0.15) 7 3.51∗∗∗ (0.24) 6.03 (0.68) 4.00∗∗∗ (0.16) 1.41 (0.17) 8 3.40∗∗∗ (0.22) 6.11 (0.62) 4.46∗∗∗ (0.21) 1.62 (0.18) 9 3.35∗∗∗ (0.20) 5.87 (0.54) 5.02∗∗∗ (0.15) 1.85 (0.16) 10 3.31∗∗∗ (0.19) 5.93 (0.60) 5.51∗∗∗ (0.15) 2.08 (0.19) 11 3.27∗∗∗ (0.14) 6.06 (0.58) 5.95∗∗∗ (0.19) 2.30 (0.18) 12 3.21 ∗∗∗ (0.18) 6.06 (0.51) 6.48∗∗∗ (0.23) 2.55 (0.20) 13 3.20∗∗∗ (0.19) 5.99 (0.60) 6.99∗∗∗ (0.20) 2.75 (0.19) 14 3.24∗∗∗ (0.18) 5.79 (0.57) 7.50∗∗∗ (0.21) 3.01 (0.18) 15 3.25∗∗∗ (0.13) 5.86 (0.62) 8.05∗∗∗ (0.24) 3.24 (0.19) 16 3.23∗∗∗ (0.15) 6.04 (0.55) 8.45∗∗∗ (0.21) 3.51 (0.21) 17 3.15∗∗∗ (0.13) 6.08 (0.49) 8.99∗∗∗ (0.30) 3.68 (0.24) 18 3.20∗∗∗ (0.16) 6.20 (0.45) 9.52∗∗∗ (0.26) 3.99 (0.22) 19 3.15∗∗∗ (0.11) 5.99 (0.62) 9.96∗∗∗ (0.30) 4.22 (0.20) 20 3.18∗∗∗(0.13) 5.92 (0.58) 10.55∗∗∗ (0.29) 4.42 (0.22) note: h0 : the ars with ccp-rtc2 is not statistically better than the current practice ′p<0.1;∗p<0.05;∗∗p<0.01;∗∗∗p<0.001; sd is shown in parentheses when δ and µ of both systems from experiment 2 are compared, ars indicates a significantly lower δ and significantly higher µ relative to the current practice. in ars, the ccp-rtc2 can manage and optimize new information items by merging them to the original plan before communicating the revised plan to the autonomous cart – responding to the new, unplanned request. the benefit is the minimization of the information delay. also, the ccprtc2 is significantly superior in terms of µ. the higher values of µ indicate that even if the system has an unexpected task, the ccprtc2 can integrate such a request into the current plan to minimize the negative impact on the overall system objectives. in contrast, the current practice is not flexible enough to deal with unexpected situations, because of the lack of timely communication, information flow, and sharing. figure 4: δ (left) and µ (right) observed from experiment 2 for each n 4 conclusion and future work in this research, the communication, information flow, and sharing among agents in an agricultural work setting are investigated. because the effective, cyber-based information flow is a critical element for the competitive advantages of future work systems and automation 5.0, a cyber collaborative protocol for real-time communication and control in ars, called ccp-rtc2, is developed to facilitate timely information exchange and collaborative decision among agents. ars is a system associated with multiple agents, i.e., human workers and expert system, autonomous robot cart, and multiple sensors. because each agent is responsible for different work tasks, ccp-rtc2 aims to minimize information delay and effectively share information, and as a consequence, yield better https://doi.org/10.15837/ijccc.2021.3.4233 9 collaborative decisions. this article provides, first, an overview of the cyber physical ars. then, an original ccp-rtc2 is developed with a detailed explanation of how ccp-rtc2 works in ars once it is integrated with it. finally, the ars with ccp-rtc2 is validated by simulation against the current practice. (earlier, ars with other versions of collaborative task administration protocols were tested experimentally in the field, in addition to computer simulations [1, 8, 20].) the protocol validation is performed through computer simulation experiments in two scenarios: 1) normal operation; and 2) operation with an unplanned request. from the observation and analysis, results indicate that, in normal operation, ccp-rtc2 shows a statistically significant advantage in terms of information sharing while information delay shows no significant difference compared with the current practice. when the system receives an unexpected request, however, such as inspecting a new location, re-inspecting a previous location, or identifying the symptoms of a new disease, ccprtc2 outperforms the current practice in terms of significantly lower information delay and higher information sharing. for future progress, researchers are challenged to continue by addressing three interesting real-time communication and control directions in the context of cyber physical work: 1. research on algorithms and protocols that can prevent conflicts in the information exchange process and resolve imperfect information and communication from sensors. 2. expand the ccp for situations where one, or more agents in the ars have failed, e.g., human agents or sensors are temporarily unavailable, or an autonomous robot cart cannot access an assigned crop plant location due to unknown constraints. 3. explore the situation where additional agents may be needed, i.e., an inspection or treatment task for the given robot might be beyond its work skills, and require robots with augmented inspection or treatment skills. acknowledgments the research presented here is supported by prism center for production, robotics, and integration software for manufacturing and management at purdue university. in addition, the research on agricultural robotic system is supported by bard project grant# is-4886-16r, development of a robotic inspection system for early identification and locating of biotic and abiotic stresses in greenhouse crops; and nsf project grant# 1839971, fw-htf: collaborative research: pre-skilling workers, understanding labor force implications and designing future factory human-robot workflows using a physical simulation platform. conflict of interest the authors declare no conflict of interest. references [1] ajidarma, p.; nof, s. y. 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(2015). integrated open geospatial web service enabled cyberphysical information infrastructure for precision agriculture monitoring, renewable and sustainable energy reviews, 135, 110083. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: dusadeerungsikul, p. o., nof, s. y. (2021). a cyber collaborative protocol for real-time communication and control in human-robot-sensor work, international journal of computers communications & control, 16(3), 4233, 2021. https://doi.org/10.15837/ijccc.2021.3.4233 introduction methodology ars and ccp-rtc2 designs ars overview and task description cyber collaborative protocol for real-time communication and control in ars (ccp-rtc2) performance metrics experiments and analysis experiment 1: measuring alternative performance under normal work operations experiment design results and analysis experiment 2: operation with unplanned requests experiment design results and analysis conclusion and future work international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(5), 660-671, october 2019. intrusion detection for mobile ad hoc networks based on node reputation t. lin, p. wu, f.m. gao, t.s. wu tao lin 1. school of automation, chongqing university, chongqing 400044, china 2. chongqing college of electronic engineering, chongqing 401331, china lintaoemail@126.com peng wu* 1. school of automation, chongqing university, chongqing 400044, china 2. chongqing chuanyi automation co., ltd., chongqing 401121, china *corresponding author: pwu@cqu.edu.cn fengmei gao chongqing college of electronic engineering, chongqing 401331, china gfmemail@126.com tianshu wu college of computer science, chongqing university, chongqing 400044, china 723412117@qq.com abstract: the mobile ad hoc network (manet) is more vulnerable to attacks than traditional networks, due to the high mobility of nodes, the weakness of transmission media and the absence of central node. to overcome the vulnerability, this paper mainly studies the way to detect selfish nodes in the manet, and thus prevent network intrusion. specifically, a data-driven reputation evaluation model was proposed to detect selfish nodes using a new reputation mechanism. the mechanism consists of a monitoring module, a reputation evaluation module, penalty module and a response module. the manet integrated with our reputation mechanism was compared with the traditional manet through simulation. the results show that the addition of reputation mechanism can suppress the selfish behavior of network nodes and enhance network security. keywords: mobile ad hoc network (manet), intrusion detection, reputation mechanism, node reputation. 1 introduction a mobile ad hoc network (manet) is self-organized through the collaboration between numerous dynamic nodes, eliminating the need of fixed infrastructure or manual intervention [2] [3] [13]. in the manet, multiple intelligent nodes are dynamically connected within a limited range. each node at once serves as host and router, and can send and receive data. thus, there is no central entity in the network. the communication in the manet is completed through the cooperation among network nodes. however, the cooperation failure may occur if the manet is intruded. in this case, the copyright ©2019 cc by-nc intrusion detection for mobile ad hoc networks based on node reputation 661 nodes will cease to route, send or receive data packets, which undermines the network performance. network intrusion cannot be prevented effectively by traditional security mechanisms, because such mechanisms are unable to eliminate the selfish, non-cooperative behavior of manet nodes. this calls for new security mechanisms to protect network security. in the absence of preagreed trust relationship, reputation mechanism is a promising way to prevent network intrusion and non-cooperation of selfish nodes [5]. if the nodes in the network can’t cooperate with each other, they can’t route, send and receive data packets. it will seriously affect the performance of the network, and result in a great threat to the security of the network. to suppress the selfish behavior of manet nodes, this paper mainly calculates the reputation value, constructs a reputation mechanism, and applies the model to detect selfish nodes. the research results show that the proposed reputation mechanism can detect and deal with selfish behavior of nodes in time, and enhance the security performance of manet. the most innovative point of this paper is to propose a data-driven reputation evaluation model to evaluate the reputation of nodes, and to detect selfish nodes through a new reputation mechanism. 2 literature review the security of ad hoc network has become a research hotspot in recent years. many security mechanisms have been developed for ad hoc network. however, these mechanisms cannot be directly applied to the manet. thus, it is necessary to design a unique security mechanism that fits in with the manet. intrusion detection refers to identifying behaviors like the misuse of or attempt to exploit the vulnerabilities in preventive mechanism through persistent monitoring of events in the network. for the manet, intrusion detection can be noncooperative or cooperative [1]. network intrusion is often accompanied by the selfish behavior of nodes. so far, the selfish behavior of manet nodes has been deeply explored, giving birth to various solution. these solutions mainly rely on credibility, reputation or game. credibility-based solutions use virtual or real money to pay the network nodes for forwarding data to other nodes [11]. camp et al. [4] designed a credibility-based system, which incentives nodes to forward data packets with virtual currency. sun et al. [14] proposed a system that encourages mobile nodes to cooperate honestly, without needing to install tamper-proof hardware in any node. patel et al. [10] pointed out the defects of credibility-based systems: the complete path from the source to the destination must be known in advance, i.e. adopting the source routing protocol. reputation-based solutions evaluate the reputation of each node according to its communication behavior. each node uses the monitoring module to observe whether its neighbors forward packets from other nodes, and uses the response module to change or update the reputation table. the most famous reputation-based solution is the watchdog scheme [7], which detects whether a node is anomalous based on the packet forwarding of its neighbors. pan et al. [9] presented a secure and objective credit-based incentive mechanism that encourages network nodes to transmit data packets and act more altruistically. game-based solutions draw on the features of the credibilityand reputation-based solutions, and take root in game theory model. jaramillo et al. [6] modelled various interactions in wireless ad hoc networks as credibilityand reputation-based games, and analyzed the forwarding behavior of selfish nodes. tang et al. [15] designed a self-learning repetitive game framework, in which each distributed node obeys research cooperation and development protocols. umar et al. [16] created a coalition game-based method, in which the boundary nodes help the backbone nodes in the network to transmit information. subramaniyan et al. [12] put forward a new 662 t. lin, p. wu, f.m. gao, t.s. wu game theory solution to detect selfish nodes, and thus realized secure transmission of data in the network with a low cost and minimal idle time. 3 data-driven reputation evaluation model the distributed control structure is very suitable for the manet. there are two types of distributed control structures, namely, fully distributed control structure (plane structure) and hierarchical distributed control structure (hierarchical structure) [8]. the plane structure and hierarchical structures of the manet are shown in figures 1 and 2, respectively. figure 1: plane structure of the manet in the plane structure, the network is rather simple and each node has equal status. this structure has high robustness and virtually no bottleneck. the network nodes are linked up via multiple paths, laying the basis for optimal routing and load balancing. however, the plane structure should not have too many nodes. otherwise, the control overhead will increase markedly and the routing will be easily terminated. hence, a plane structure with relatively few nodes is safe and suitable for small-scale manets. figure 2: hierarchical structure of the manet in the hierarchical structure, the manet can be divided into multiple clusters, each of which consists of many members and one head node. the head node is mainly responsible for forwarding data between clusters, and can be set in advance or selected by algorithm. unlike the head node, the member nodes do not need to save or update routing information, and thus enjoy high extensibility. since the head node can be selected at any time, the hierarchical structure boasts a strong ability against destruction. therefore, the hierarchical structure is appropriate for large-scale manets. the special structure of the manet makes the network vulnerable to multiple attacks. the attacks may come from the inside or outside of the network. the internal attacks may intrusion detection for mobile ad hoc networks based on node reputation 663 occur when malicious network nodes attack the routing information or selfish nodes refuse to participate in routing. if the manet is attacked by selfish nodes, the attacking nodes should be identified and disposed of rapidly, such as to protect network security and prevent outage. as mentioned before, each node in the manet has two roles: host and router. due to the resource constraints, a manet node may commit selfish behaviors to maximize its own interests. for example, the selfish node may refuse to participate in routing or forward data to other nodes. the selfish behaviors pose a serious threat to network performance. the reputation mechanism can effectively suppress the selfish behavior of manet nodes. to detect network intrusions, the reputation mechanism requires accurate evaluation of the reputation of network nodes. in this paper, a data-driven reputation evaluation model is proposed to compute, share, judge and update the reputation of each node. in this model, when a normal node is requested to communicate with other nodes, it will firstly determine if the node requesting communication is trustworthy, and then choose a neighbor with high reputation value for routing communication, while updating its reputation table according to the situation of network communication; when a selfish node is requested to communicate with other nodes, it will forward data selectively without evaluate the reputation of the requester, which seriously undermines the network performance. figure 3 explains the basic flow of communication two nodes in the proposed model. in this paper, the calculation of node reputation is data-driven, i.e. the data were collected and processed, and updated iteratively for reputation calculation. in each iteration, the reputation value of a node was computed based on the real-time data being collected and classified. after time t, the node reputation was re-evaluated based on the reputation value in the previous iteration. suppose s is the source node of the communication request and d is the destination node of the communication request. if d is a normal node, node d calculates the reputation of s node according to the reputation evaluation process shown in figure 3, and determines whether to communicate with it based on the s reputation value. the features of ad hoc network determine that it takes more energy for a node to transmit data packets than to transmit routing packets. thus, the reputation value of a node was calculated considering the number of data packets transmitted by the node. the classified data were computed directly to yield the reputation value of a node forwarding data packets (h1), that of a node forwarding routing packets (r1), that of a node receiving data packets (h2) and that of a node receiving routing packets from other nodes (r2). the four reputation values can be calculated by: h1 = ntdata−others ntdata−self + ntdata−others (1) r1 = ntctrl−others ntctrl−self + ntctrl−others (2) h2 = nrdata−others nrdata−self + nrdata−others (3) r2 = nrctrl−others nrctrl−self + nrctrl−others (4) where ntdata−self and ntdata−others are the total number of packets generated by a node forwarding itself and forwarding other nodes, respectively; ntctrl−self and ntctrl−others are the total number of routing packets generated by a node forwarding itself and forwarding other nodes, respectively; nrdata−self and nrdata−others are the total number of packets a node receives from 664 t. lin, p. wu, f.m. gao, t.s. wu figure 3: basic flow of communication between two nodes itself and from other nodes, respectively; nrctrl−self and nrctrl−others are the total number of routing packets a node receives from itself and from other nodes, respectively; to calculate the reputation value of a node sending and receiving data packets to or from other nodes, h1 and h2 were added up to get the reputation value of the node sending and receiving data packets to or from monitored nodes. similarly, r1 and r2 were added up to get the reputation value of the node sending and receiving routing packets to or from monitored nodes. the two reputation values can be defined as: hdata = h1 + h2 (5) rcontrol = r1 + r2 (6) the direct reputation value of a node depends on the number of packets and the number of routing packets sent and received to or from other nodes. since an ad hoc network node consumes more energy to transmit data packets than to transmit routing packets, the weight factor ρ was introduced: ρ = ntdata + nrdata ntdata + ntctrl + nrdata + nrctrl (7) intrusion detection for mobile ad hoc networks based on node reputation 665 where ntdata and ntctrl are the total number of packets and routing packets forwarded by the node, respectively; nrdata and nrctrl are the total number of packets and routing packets received by the node, respectively. then, the direct reputation value rt of the node can be computed by: rt = { (1 −ρ) ×hdata + ρ×rcontrol, 0 < ρ < 0.5 ρ×hdata + (1 −ρ) ×rcontrol, ρ ≥ 0.5 (8) let r0 be the default reputation value of all network nodes, that is, the direct reputation value of each network node at time t. for a new node entering the network or each node at the start of network activity, the total direct reputation value of the node equals the default reputation value, i.e. rt = r0. after time t, the node can compute the reputation values of its neighboring by monitoring their communication behaviors. the new reputation value r1, which equals rt , can be computed by: r1 = ϑr0 + (1 −ϑ)rp1 (9) where ϑ ∈ [0, 1]; rp1 is the reputation value of neighbors at time t. with the growing amount of information about node activity, the direct reputation value of the node will be updated periodically at a specific time. after time t + 1, the new reputation value r2 can be obtained by: r2 = ϑr1 + (1 −ϑ)rp2 (10) the direct reputation value derived from the latest monitoring results can be defined as: rn = ϑ nr0 + ϑ n−1(1 −ϑ)rp1 + ϑn−2(1 −ϑ)rp2 + · · · + ϑn−i(1 −ϑ)rpi (11) where n is a positive integer indicating the number of iterations. this formula lays the basis for node reputation calculation in the manet. in our model, each node stores the reputation values of other nodes in the local reputation table. the values recorded in the table are known as the local reputation values. 4 manet intrusion detection based on node reputation based on the above reputation evaluation model, a novel reputation mechanism was developed to detect selfish nodes in the manet and thus identify network intrusions. a penalty module and a response module were introduced to deal with abnormal nodes, and improve the evaluation of reputation values. the penalty module mainly compares the evaluated reputation values against the penalty rules, while the response module executes commands and deals with selfish nodes according to the message from penalty module and reputation evaluation. the workflow of the reputation mechanism after the addition of the two modules is described in figure 4. in the manet, all the nodes are of equal status. there is no reliable third-party authentication authority, or preset trust relationship between the nodes. therefore, the reputation value of a node can only be evaluated based on its communication status in the network. in our reputation mechanism, the reputation evaluation module is mainly composed of four processes: reputation calculation, sharing, judgment and updating. as shown in figure 5, the module firstly processes data by data-driven method, then identifies the selfish network nodes, and finally submits the results to the penalty module. 666 t. lin, p. wu, f.m. gao, t.s. wu the reputation evaluation module stores the evaluated reputation value in the reputation table, and regularly transmits the latest table to the penalty module. next, the penalty module will process the message from the reputation evaluation module, get the corresponding evaluation results, and evaluate the trustworthiness of the node. the reputation evaluation module also stores the total direct reputation value in the reputation table and updates the table regularly. if the target node forwards data and routing information to other nodes, its total reputation value will increase. once a node is marked as an intrusive node, it will be blacklisted at once, and its information will be sent to the response module. figure 4: workflow of the reputation mechanism after the addition of the two modules figure 5: workflow of reputation evaluation module the response module mainly takes actions according to the messages from reputation evalintrusion detection for mobile ad hoc networks based on node reputation 667 uation module and penalty module. once a node is judged as selfish, it will be added into the observation table of the penalty module, and then be processed by the response module. in general, the selfish nodes can be permanently isolated from the network or temporarily banned from the network. in this paper, it is proposed that a selfish node can join the network again but with limited times, that is, the node cannot enter the network beyond the set limit on the number of entries. the workflow of the response module is given in figure 6 below. figure 6: workflow of the response module 5 simulation experiment and performance analysis to validate our reputation mechanism in linux, the manet was simulated on network simulator 2 (ns2), taking the dynamic source routing (dsr) as the routing protocol. the dsr protocol was simulated with and without reputation mechanism, both in the absence of selfish nodes and the presence of different number of selfish nodes. the parameters of the simulation environment are listed in table 1 below. as shown in figure 7, the traditional dsr network delivered packets basically at the same rate with the dsr network integrated with reputation mechanism, in the absence of selfish nodes. this means the addition of the reputation mechanism has little effect on the delivery rate of packets. as shown in figure 8, with the growth in the number of selfish nodes, the packet delivery rates of both networks exhibited a gradual decline. the decline was steeper in the traditional dsr network, while the packet delivery rate in the network with reputation mechanism remained above 0.6. the possible reasons are as follows. in the traditional network, the selfish nodes only receive packets but do not forward packets, pushing up the resource consumption of normal nodes. meanwhile, in the network with reputation mechanism, the selfish nodes are effectively 668 t. lin, p. wu, f.m. gao, t.s. wu table 1: parameters of the simulation environment parameters value simulation area 900m*900m number of nodes 50 propagation model two-ray ground reflection packet size 512 bytes mac type 802.11 signal coverage radius 300m maximum number of connections 20 ϑ 0.15 figure 7: relationship between packet delivery rate and time removed and isolated. as shown in figure 9, the routing overheads in both networks were gradually falling, with the growth in the number in selfish nodes. the decline of routing overheads can be attributed to the following facts: with the elapse of time, the network nodes will establish routing paths and store routing information with each other, thus reducing the routing overhead. of course, the network with reputation mechanism had a slightly lower routing overhead than the traditional network. intrusion detection for mobile ad hoc networks based on node reputation 669 figure 8: relationship between packet delivery rate and the number of selfish nodes figure 9: relationship between routing overhead and the number of selfish nodes 670 t. lin, p. wu, f.m. gao, t.s. wu 6 conclusion this paper mainly studies the way to detect selfish nodes in the manet, and thus prevent network intrusion. specifically, a data-driven reputation evaluation model was proposed to detect selfish nodes. then, based on the proposed model, a new reputation mechanism is proposed. the mechanism consists of a monitoring module, a reputation evaluation module, penalty module and a response module. the manet integrated with our reputation mechanism was compared with the traditional manet through simulation. the results show that the addition of reputation mechanism can suppress the selfish behavior of network nodes and enhance network security. funding this work was supported by the scientific and technological research program of chongqing municipal education commission (grant numbers are kj1602901 and kjqn201803102 respectively), chongqing college of electronic engineering scientific research project (grant number is xjzk201809) and xinxiang medical university education and teaching reform research project (grant number is 2017-xyjg-41). author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] almousa, 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(2018). throughput-efficient coalition formation of selfish/altruistic nodes in ad hoc networks: a hedonic game approach, telecommunication systems, 67(1), 1-17, 2018. international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(6), 759-772, december 2019. identification of opinion spammers using reviewer reputation and clustering analysis m.j. zhong, l.tan, x.l. qu minjuan zhong* school of information technology hunan university of finance and economics changsha 410205, china *corresponding author: lucyzmj@sina.com liang tan school of information management jiangxi university of finance and economics nanchang 33013, china ghosticy@foxmail.com xilong qu school of information technology hunan university of finance and economics changsha 410205, china quxilong@126.com abstract: online reviews have increasingly become a very important resource before making a purchasing decisions. unfortunately, malicious sellers try to game the system by hiring a person or team (which is called spammers) to fabricate fake reviews to improve their reputation.existing methods mainly take the problem as a general binary classification or focus on some heuristic rules. however, supervised learning methods relies heavily on a large number of labeled examples of deceptive and truthful opinions by domain experts, and most of features mentioned in the heuristic strategy ignore the characteristic of the group organization among spammers. in this paper, an effective method of identifying opinion spammers is proposed. firstly, suspected spammers are detected by means of unsupervised learning based on reviewer’s reputation. we believe that the reviewer’s reputation has a direct relation with the quality of reviews. generally, review written by user with lower reputation, shows lower quality and higher possibility to be fake. therefore, the model assigns reputation score to each reviewer wherein the content based factors and activeness of reviewers are employed efficiently. on basis of all suspected spammers, k-center clustering algorithm is performed to further spot the spammers based on the observation of burst of review release time. experimental results on amazon’s dataset are encouraging and indicate that our approach poses high accuracy and recall, and good performance is achieved. keywords: opinion spammer, fake review, reviewer reputation, clustering analysis. 1 introduction the rapid development of online shopping has led to a large number of user reviews for various products or services. more and more potential consumers tend to rely on them to assess copyright ©2019 cc by-nc 760 m.j. zhong, l.tan, x.l. qu the quality of goods or services before buying. therefore, driven by the desire for profit or competition, producer and retailers generate motivations and behaviors to manipulate reviews, maliciously posting deceptive or fake reviews to deliberately mislead potential consumers and make their risky purchasing decisions. moreover, for the purpose of high satisfaction rate and reputation, manufactures may hire a person(known as an individual spammer) or team(known as spammer group) to post glamorized positive on their product or harmful negative reviews on their competitor as consumers. hu nan et al. [7]conducted research on the website reviews of two major us bookstores, amazon.com and bames & noble, and found that a large number of comments on the website were manipulated by publishers and vendors; michael luca and georgios zervas analyzed [14] that 16% of the comments on the yelp website are deceptive; china’s e-commerce websites, such as taobao and jingdong mall also acknowledge that there are a large number of fake reviews on their e-commerce platforms. studies have shown that the discrimination of such deceptive reviews by ordinary consumers is relatively low and the task of opinion spam or spammers detection is a very challenging problem. for example, consider figure 1 (a) and (b), which illustrates two reviews written by two users for a product from amazon. if we only look at the two reviewers individually, they all appear genuine. however, when we collect them together, we could find easily that both reviews are duplicated. from the publication time of the review, it seems likely that the first user copies the review made by the second user. remarkably, there are ten other users to read and make feedback on this review written by the first user, and two people thought it was a useful review. obviously, genuine users are unable to judge whether a review is a fake review or not, and whether a reviewer is a spammer or not. (a) the first candidate fake reviews (b) the second candidate fake reviews figure 1: two product reviews from amazon.com in fact, the opinion spam and spammers are closely related.the reviews written by the identification of opinion spammers using reviewer reputation and clustering analysis 761 spammers are fake reviews to a large extent, and correspondingly, the author of a fake review must be a spammer. therefore, many researchers have developed detection techniques combination the fake reviews with spammers together and solved them by means of binary classification problem(spam vs. non-spam, spammer vs non-spammer). as we all known, the classification algorithm is a supervised learning method that requires large sets of pre-labeling instances. however, the task of pre-labeling both classes, deceptive and truthful opinions is difficult and time-consuming. the biggest problem is the authenticity of the labeled opinions. in this paper, we propose an unsupervised method for detecting opinion spammers. this research makes the following main contributions: (1)it introduces the concept of reputation and proposes a reviewers’ reputation model to detecting spammers. the model can capture the suspected spammers with low reputation value by analyzing both content-based characteristics(context similarity, opinion sentiment, review length and helpful feedback) and behavior-based characteristics (authors’ activeness, review deviation and rating) without requiring a large sets of labeled instances. (2)clustering technique is further performed to spot accurately spammers. generally, spammers will post their reviews intensively. this behavior shows the characteristics of abruptness in posting time interval. consequently, users’ posting time interval is proposed to measure the similarity and on basis of all suspected spammers, k-center clustering algorithm is implemented to filter spammers. (3)we conduce the experiments on the amazon review dataset. the experimental results are encouraging and indicate that our approach poses high accuracy and recall, and good performance is achieved. the rest of the paper is organized as follows: the next section discuses some relevant literature on opinion spammers detection. section 3 describes our user reputation model to the task of suspected spammers detection. section 4 presents the clustering algorithm for the spammers spotting. experimental results and analysis are discussed in section 5. finally, section 6 presents our conclusions and discusses some future work directions. 2 related work detection of opinion spam was first introduced by jindal n. and liu b. they conduced a series of studies for automatically detecting review spam [8], spammers [11] and spammer group [15] by machine learning, pattern recognition, graph theory and other techniques.yuanchao [13] summarized previous detection approach as supervised or unsupervised learning. most existing supervised learning methods, which is mainly based on text content, regard the detection of opinion spam as classification process. combing psychology and computational linguistics to extract the linguistic content cues of the reviews, supervised learning methods applied neural network [16], decision tree [4] and other classifiers to establish a statistical model and then predict the unknown reviews [6]. in text content, repeated review is considered to be important clue for the identification of fake review. in order to find duplicate and near duplicate reviews, lin et al. [12] defined three similarities, including similarity of reviews written by the same reviewer, similarity between reviews in the target product, and similarity of reviews between different categories of products. lau [9] established language model to identify conceptual duplicate reviews. however, this kind of opinion spam only represents a small percentage of the opinion spam. so, other linguistic features have also received much attention [2]. rupesh et al. [3] proposed new lexical and syntactic features including type of punctuation mark, part-of–speech (pos) etc and applying supervised algorithms,such as smo, decision 762 m.j. zhong, l.tan, x.l. qu tree, and naive bayes for performing classification on fake reviews dataset. the final results give promising accuracy 91.51% for detecting fake reviews. banerjee and chua [1] distinguished between deceptive and genuine reviews from following aspects: the readability of a review, the richness of describing the objective information in review, the writing style of reviews, and review genre. on basis of these features, they built logistic regression model to detect opinion spams. wen et al. [19] proposed a two-view collaborative filtering approach based on svm benchmark to identify opinion spams. in their work, two different expressions are used for each review. one is the lexical terms derived from the textual content of the reviews and the other is the pcfg rules derived from a deep syntax analysis of the reviews. the statistical differences between deceptive and truthful opinions are observed from above two different perspectives. on basis of them, cospa-c and cospa-u strategies are proposed for classifier training to identify fake and true reviews. the challenge for the existing supervised classification algorithms is that manually marking massive ground truth spam reviews is difficult and time-consuming. therefore, another detection approach, unsupervised learning, has been proposed and tended to focus on the opinion spammers or group by relying on behavioral reviewers features. for example, savage z. et al [17] find that the reviews of opinion spammers tend to give extreme evaluation scores. vlad and martin [18] conducted detection on singletons reviewers in fake reviews, who registered multiple names deliberately, posting each review under a different name. they made an important assumption: singleton reviewers is lack of complete imagination and are not able to rewrite each review completely. they prefer to rephrasing, switching some synonyms, and keeping the sentiment consistency to all reviews.so, semantic similarity has been measured from the perspective of terms and topic distribution, and meanwhile singleton reviewers have been identified indirectly. in atefeh heydari et al.’s work [5], suspicious time intervals are captured from the sudden rapid increase in the number of the reviews. the reviews in suspicious time are analyzed combining the rating deviation, content similarity and activeness of reviewers, which is beneficial to the time efficiency due to narrowing the detection range. david s.et al. [?] used binomial regression to identify those reviewers with an abnormal rating for products. the proposed method stemmed from overarching assumption that the majority of reviews are posted by truthful reviewers. therefore, the method does not pay attention to the text-based features, but focused merely on the rating differences between spammers and the majority of truthful reviewers. with an in-depth study on features of fake reviews, lijing et al. [10] introduced the concept of user-credibility and shop-credibility, and established a fake review identification model, which integrated the reviewer’s behaviour characteristics, businesses characteristics and review texts. in the above method, the supervised learning technique focuses on analyzing the review text, and uses the natural language processing technology to extract the part of speech, grammar, sentiment and other features of the review text to distinguish the authenticity of the content. however, this method heavily depends on a large number of labeled examples of fake and truthful opinions by domain experts, which is a time-consuming and costly endeavour. the unsupervised recognition method does not need to mark a large amount of training corpus, but the problem of low recognition rate is generally found. furthermore, most of the heuristic features proposed in the existing methods are based on individual opinion spammer,ignoring the characteristics of group review spamming. therefore, the current identification of opinion spam has not yet formed an effective solution. identification of opinion spammers using reviewer reputation and clustering analysis 763 3 feature representation for detecting the fake reviews, the first thing is defining the features. in this paper, we mainly considered two scopes of features to indicate spamming activities, one is user’s review content features and one is user’s behavioral features. suppose there is a review sequence rp = r[1],r[2], ...,r[n] for a product p, where r[i] is a review sorted by time arrival and contains a variety of information, such as user name r[i].u, release time r[i].t, review rating r[i].r, review title r[i].title and review content r[i].content etc. 3.1 review content features spammers usually consider the following factors while writing fake review: first, to generate a review as quickly as possible and second, to express their emotions as strongly as possible to promote or demote a product. therefore, we employ following criteria to score reviews to detect users with low reputation: (1) duplicate of target review in order to generate more reviews and get more economic benefits as soon as possible, spammers often copy the text of existing reviews or make minor changes for the same or different target products. for example, they posted the same review to express different products, or just changed the product name . we can judge whether the target review is a duplicate review by measuring their similarity with other reviews from dataset. due to the large number of reviews in dataset, the similarity is based on the following two cases: 1○duplicated reviews on the same user id in different products. 2○duplicated reviews for different user id of the same product. corresponding to above two cases, we consider two copy behaviors separately. one is to copy his/her own previous reviews and the other is to copy the reviews written by other users on the same product. 1○review similarity between the same user id(user_similarity) review similarity between the same users is used to determine whether the user of the target review copied his/her own previous reviews. therefore, the similarity is calculated between the target review and each of his/her own previous reviews, and choose the largest similarity as user_similarity. user_sim(r[i].u) = max(similarity(r[i],r[j])) (1) where r[i] indicates the target review, r[j] indicates the jth review of his/her own previous reviews. the similarity is calculated by using the cosine formula of vector space model. 2○review similarity between the same product(production_similarity) review similarity between the same product is used to judge whether a target review is a copy of another user’s reviews on the same product. also, we can calculate the similarity of a target review and the rest of reviews for the same product by other users, and choose the largest similarity as production_similarity. production_sim(r[i].u) = max(similarity(r[i],r[k])) (2) where r[i] indicates the target review, r[k] indicates the kth review by other users on the same product. similarity is calculated by the same cosine formula of vector space model. duplicated score of review is determined by the above both user_similarity and production_similarity. duplicated_score(r[i].u) = 1 − (0.5 ∗user_sim(r[i].u + 0.5 ∗production_sim(r[i].u) (3) 764 m.j. zhong, l.tan, x.l. qu (2) review opinion generally, spammers tend to express strong emotional tendencies to promote or demote some target product while writing reviews, and their sentiment polarity tends to be closer to the poles, i.e, extremely strong or very weak. conversely, genuine reviews are more likely to be closer to the middle of variance distributions. o_score(r[i].u) = { 0, if sen_value(r[i]>τ) or sen_value(r[i])<τ |sen_value(r[i])| , otherwise (4) where o_score(r[i]) indicates sentiment tendency of the target review r[i], τ is the threshold value which denotes the emotion polarity beyond which reviews expressed are thought to be suspicious. the sentiment value of the target review is a challenge work. we firstly extracted the opinion word by using association rule mining.after that, original polarity of the opinion word was obtained based on pagerank model combined with mixture relevance relation. finally, both the location of the opinion word and the context of modified information were taken into account to complete the task of the sentiment analysis of the target review. furthermore, a greedy hill climbing search to learn τ is performed and the final estimated value is 0.758. (3) personal expression through the careful observation of the reviews, we find that the personal expression also has an implicit role for judging whether a user is a spammer. while producing a genuine review, user often express their experiences in the form of the first person to all aspects of the product. for instance, often we see such reviews, "after reading a lot of very negative review on this album, i thought i should hate this album, but i love it and it’s hard to say why.", "i love her lyrics and i love the songs and what can i say she has a unique voice which i think is hot and very cool!!!", etc,. however, there are some sentences that use the "you", "you should....." and others to express, in order to recommendations or guide other consumers how to do. by this, we think such reviews are very suspicious. we can use the proportion of the first-person pronoun appeared in reviews, "i", "my", "me", "we", "us", "our", and the second-person pronouns "you", "your" to examine target reviews. personexpre(r[i]) = firstperson_num(r[i]) secondperson_num(r[i]) (5) where firstperson_num(r[i]) indicates the number of the first-person pronouns in the target review r[i], secondperson_num(r[i]) indicates the number of the second-person pronouns in the target review r[i]. to facilitate the comparison, we take a normalized value as the final personal expression feature value (normalpersonalexpressionscore,npescore). npe_score(r[i]) = personexpre(r[i]) max(personexpre(r[i]) (6) (4) review length users tend to write review of appropriate length to express his/her true feelings about the use of product. too long or too short are likely to be suspicious. spammers sometimes deliberately write longer reviews to get more consumers’ attention, and thus get more helpful feedback from consumers. meanwhile, too short reviews often cannot clearly express the user’s true feelings. therefore, spammers sometimes simply use a few simple words to express, such as "good" or "bad" and so on. rl_score(r[i].u) = { 0, if nrlength(r[i]>ξ1) or nrlength(r[i]<ξ2) nrlength(r[i], otherwise (7) identification of opinion spammers using reviewer reputation and clustering analysis 765 where nrlength(r[i]) indicates normalized length of the target reviews (normal review length, nrl), which is calculated as follows: nrlength(r[i]) = |r[i]| max(|r[k]|) (8) |r[i]| indicates the number of words in the target review. ξ1 and ξ2 are the parameters which denote the length beyond/below which reviews posted are considered to be suspicious. similarly, a greedy hill climbing search to learn ξ1 and ξ2 was performed and in our subsequently experimental environment, the final estimated values were ξ1=0.82,ξ2=0.18. (5) helpful feedback before making a purchase, users tend to look over the reviews from the other customers, and feedback it by marking that whether a review is helpful to their purchase. naturally, we believe that the more positive feedback on a review is, the higher quality of it, and the lower probability of becoming a fake review, and meanwhile the lower probability of the reviewer becoming spammer. consequently, helpful feedback could be: hrf_score(r[i]) = helpful_feednum(r[i]) feednum(r[i]) (9) where helpful_feednum(r[i]) indicates the number of users who believe the target review r[i] is helpful to their purchase and feedback them after reading. feednum represents(r[i]) the number of all feedback review for the target r[i]. 3.2 reviewer behavior features some of specific behavior of reviewer also has certain relevance with spammers. so, we examine fake reviewers from the following three behavioral characteristics. (1) user activity spammers usually tend to register multiple different user accounts on multiple forums or websites. when they accept a new task, they may use the newly registered user id to write and publish fake reviews. until it is completed, they give them up. accordingly, user’s activity will be a critical aspect for detecting spammers. the activity score can be measured by examining whether the user id has release review on other products at other time. the value could be computed as: ua_score(r[i].u) = tf(r[i].u)∑tf(r[i].u) j=1 t(r[j].t,r[j + 1].t) (10) where tf(r[i].u) indicates the number of reviews written by the user r[i].u, r[j].t refers to the release time of r[j], t(x,y) is the time interval between the two reviews x and y. it is important to note that when the user id of target review does not submit their review on other products at other time, the interval is considered to be infinite. (2) review deviation many e-commerce sites usually provide review ratings of product. assuming 5-star rating system, 1 star is the lowest level, indicating that the sentiment of customer for the product is the most negative; whereas 5 star represents the highest level, indicating that the product is most satisfactory and the review is belong to the positive reviews. therefore, the spammers tend to raise or suppress the target product or brand by rating. for example, a user post a negative reviews about a product, while others wrote positive reviews on the same product. in this case, the reviewer is more likely to be spammer. 766 m.j. zhong, l.tan, x.l. qu or the user’s reviews on a series of brand products are positive, representing that the reviewer is highly suspected as spammer. given a set of ratings{r[j].r,r[j+1].r, ...,r[m].r}allocated to a product p by reviews, the deviation of a review is measured between the rating value and the normal value: rd_score(r[i].u,rp) = r[i].r −avg(r[j].r,r[j + 1].r, ...,r[m].r) (j=1,2,...m)∧j 6=i avg([r[j].r,r[j + 1].r, ...,r[m].r]) (j=1,2,...m)∧j 6=i ) (11) where r[i].r indicates posted rating by the user r[i].u, avg([]) denote the average of review sequence rp(not containing the target review r[i] for product p. (3) other rating score according to the 5-star rating systems, ratings can be divided into three levels: 1○good (rating≥ 4); 2○bad (rating ≤ 2.5); 3○average (2.5 ≤ rating≤ 4). therefore, a customer’s review ratings for products mainly belong to the following four conditions: 1○ all good or bad; 2○some good and other average; 3○some bad and other average; 4○some good, some bad and some average. if the review ratings for other products are all fallen in the first category, it is the most feasible possibility that the reviewer is a spammer. using the following formula to calculate the proportion of three levels. goodratio(r[i].u) = reviewnumgood totalnum badratio(r[i].u) = reviewnumbad totalnum averatio(r[i].u) = reviewnumaverage totalnum (12) according to the scale of three levels, the score of the reviewer’s behavior is judged by formula (13). or_score(r[i].u) = { 0, if goodratio(r[i].u)=1 or badratio(r[i].u)=1 max(goodratio(r[i].u),badratio(r[i].u),averatio(r[i].u)), otherwise (13) 4 proposed methodology 4.1 reviewer reputation model for suspected spammers detection reputation refers to the extent of one user access to public trust, favor, and popularity, mainly focusing on qualitative evaluation. in the product reviews, each customer is allowed to present and share their own opinions for product quality, performance and price. we believe that the reviewer’s reputation has a direct relation with the quality of reviews. consequently, the proposed method for detecting opinion spammers stems from two overarching assumptions regarding reviewer reputation: (1) review, written by user with lower reputation, shows lower quality and higher possibility to be fake. consequently, with higher probability, the reviewer is spammer; (2) if one customer has high reputation, his/her reviews are high quality, and are more helpful to others. therefore, the user’s reputation could be measured to detect suspicious spammers. by modeling both the content of reviews and behavior of the reviewer, we propose eight features to describe the reputation of the reviewer and show the likelihood of the reviewer being identification of opinion spammers using reviewer reputation and clustering analysis 767 spammer from different aspects. let reviewer’s reputation was graded over [0,1]. values close to 0 signify low reputation for reviewer and greater extent to which user are marked spammers. similarly, values close to 1 signify high reputation for reviewer and greater extent to which users are genuine. therefore, if the eight normalized values of a targeted reviews are lower, the lower reputation value of the reviewer, and the greater the probability of becoming the fake reviewer is. conversely, the higher the eight normalized values is, the higher the value of reputation of the reviewer, and the greater the likelihood of becoming a real user is. hence, detection indicators are then used to score each reviewer and the value of reviewer’s reputation will be: reputation_v alue(r[i].u) = α1∗content_features(r[i].u)+α2 ∗behavior_features(r[i].u) (14) where content_features(r[i]) indicates content characteristics of the target review r[i] and include various indicators described in section 3.1.similarly, behavior_features(r[i].u) refers to behavioral characteristics of the reviewer who wrote the target review r[i] and is also presented in section 3.2. α1 and α2 are both parameters that represent the proportion of content characteristics and the behavioral characteristics in the model. ultimately, reviewers with reputation scores lower than the defined threshold are marked as suspected spammers. lr[i].u =   lnormal reputation_value(r[i].u)>τ lsuspected_spammer reputation_value(r[i].u)<τ random reputation_value(r[i].u)=τ (15) this section construct the reviewer’s reputation quantification model by analyzing the target reviews’ two sets of such indicators on content and the behavior characteristics of the reviewer which may indicate spamming activities, to find out reviewer with low reputation. 4.2 spammers identification based on the k-center clustering after the suspected spammers were detected, we believe that many of them may not all spammers. so, further screening should be need on this basis. in fact, spammer group is a group organization in which they work together to promote or damage their reputations of the target product.to achieving this, they will be close link together. this behavior is represented as a cluster structure on the network structure diagram. therefore, based on the low-reputation user set, we adopt clustering algorithm to further distinguish between genuine users and spammer group. the crucial problem in clustering algorithm is how to measure similarity. different similarity measurement will result in different clustering results. in spamming activities of product reviews, each member of spammer group will publish multiple fake reviews in a short period of time once they received the designed task by the manufactures or the store owners. the purpose of it is to achieve the effect of taking control of sentiment on the target product. therefore, users’ posting time interval is proposed to measure similarity. the shorter the time interval between the two users, the more similar to each other, and the greater probability designed in the same cluster. the users’ posting time interval is calculated as the following formula: timeinterval_score(r[i].u,rp) = t(r[i].t,r[j].t) max(tp) (16) where, t(x,y) is similar to that defined in equation (10), max(tp) indicates the max posting time interval of all reviews pairs for the product r[i].p. 768 m.j. zhong, l.tan, x.l. qu because our ultimate goal is to identify spammers, the clustering results could be reduced two clusters, one for the fake reviewers and the other for genuine users group. based on the clustering number, the k-center algorithm is used to cluster the user set with low-reputation, and then spammers is identified finally. 5 experimental analysis 5.1 dataset construction and labeling the dataset used in this research is music product reviews from amazon.com provided by hu and liu. generally, spammers post fake reviews on a particular product or brand. therefore, the reputation calculation of reviewer is based on their reviews. this requires us to firstly select a certain type of product reviews from the 7705 products of the music data set. we counted the number of reviews for each different product and selected the product with the most reviews as the data set of our experiment (product number=b0002gmsc0), which contains 1424 comments. moreover, the dataset was marked by seven volunteers for evaluation purpose. they first refer to the methods in the report "30 ways you can spot fake online reviews" which distinguish the reviews as deceptive and truthful opinions, and then make use of the relation between fake reviews and reviewers to achieve a standard annotated corpus. seven volunteers were from four college students, two postgraduates, and one phd candidate. in order to be able to complete the labeling task rapidly and accurately, seven volunteers are also divided into two groups and each group contains two undergraduate students and a postgraduate respectively. undergraduate students are responsible for annotating, while postgraduate is checking. when the two undergraduate students had made different judgments, the postgraduate is responsible for decision. finally, the phd candidate checks and confirms the conflict labels from the two groups again and determines the label by referring to the results of two postgraduates. the entire process judges were made to work in isolation to prevent any bias. 5.2 experiments results and analysis experiment1: impact of parameters for the reputation model in our method, reputation score of reviewer plays a crucial role in the detection task. however, there are two parameters influencing the accuracy of the detection in the reputation model. in formula(14), α1 indicates the proportion of the review content, and α2 corresponds to the proportion of the reviewer behavior. obviously, the greater the proportion, the greater the role of the corresponding factors in the overall evaluation system, and therefore the effective will be different. therefore, the purpose of this experiment is to find best configuration for our method. based on our experience and actual situation, we firstly tested different parameter values. then, the stepwise addition method was used to evaluate the effect of different parameters, with step of 0.1, ranging from 0 to 1. the experimental results under precision, recall and the f value criteria are shown in the table below. table 1: the impact of parameters parameter value precision recall f parameter value precision recall f α1 = 0.0,α2 = 1.0 0.69 0.87 0.77 α1 = 0.6,α2 = 0.4 0.81 0.08 0.16 α1 = 0.3,α2 = 0.7 0.74 0.81 0.77 α1 = 0.7,α2 = 0.3 0.75 0.05 0.09 α1 = 0.4,α2 = 0.6 0.85 0.73 0.79 α1 = 1.0,α2 = 0.0 0.67 0.03 0.06 α1 = 0.5,α2 = 0.5 0.82 0.14 0.24 identification of opinion spammers using reviewer reputation and clustering analysis 769 results from the table 1 clearly indicate that the performance of the model with the two parameters α1 = 0.4,α2 = 0.6 is superior to other configuration, indicating that comparing to the content-based indicators the behavioral characteristics has a more significant effect on accurately detecting spammer group. thus, the greatest values are used in the subsequent experiments. another interesting observation in table 1 is that the recall decreases sharply with the increase proportion of the content-based feature in the entire reputation model. its poor performance could be attributed to the number of candidate low-reputation users. in the users’ reputation model, with the proportion of content-based feature increasing, the number of candidate lowreputation users is greatly reduced. on this basis, the consequently performing clustering results in high precision of detection but low recall. on the contrary, while reducing the proportion of the content-based feature, it results in a substantial growth in the number of candidate lowreputation users, which add to the noise in the clustering, and thereby reduce the accuracy of detection but increase the recall. experiment2: impact of detection metrics in the second experiment, to further analyze the role of the content-based features and behaviour of reviewers in the spammer detection, eight influence metrics are tested separately. we implemented the experiment with different combination of the metrics, in which feature options refer to the sequence indicators, their values "1" or "0" indicates the corresponding feature work or not work. table 2: the impact of the detection metrics features options precision recall features options precision recall 01111111 0.52 0.82 11110111 0.82 0.19 10111111 0.74 0.21 11111011 0.12 0.10 11011111 0.62 0.73 11111101 0.75 0.39 11101111 0.73 0.57 11111110 0.75 0.39 from table 2, we can see that the reviewer behavior features could more influence the performance for spammer groups detection compared with the review content, especially the indicator of the user activity. when user activity feature does not work, very few candidate low-reputation users are returned, and most of them are not spammers, so their precision and recall are both very low. moreover, according to the results of the experiments, in the reviewer behavior indicators, the review deviation and other rating score of the reviewer are the same impact on spammer group identification.the reason is that both features return the same number of candidate lowreputation users, which lead to consistent identification recall and precision. another interesting observation is that among the five review content indicators, the target review’s features of duplicate and sentiment have more impact on the spammer group identification than the other three. when the duplicate review indicator does not work, the results show that the excessive candidate low-reputation users are returned, indicating that reputation model does not perform the filtering of low-reputation user well, resulting in the noise increase and lower detection accuracy.simultaneously, while the review opinion indicator does not work, too few low-reputation users are returned, indicating that a large number of real low-reputation users are filtered out, resulting in a rapid decrease of the recall. 770 m.j. zhong, l.tan, x.l. qu experiment3: comparison of the models in order to further verify the effectiveness of the proposed method, we conduct a comparison experiment with the lin’s work [12]. they proposed an unsupervised learning method for fake review detection. actually, there exists a close correlation between fake reviews and spammers. a review written by a spammer is a fake review with high probability, and a fake review is almost certainly written by a spammer. therefore, the experimental results are comparable. the specific experimental data is shown in table 3. table 3: performance comparison results precision recall f-score our method 0.85 0.73 0.79 lin’s work 0.81 0.70 0.75 as can be seen from the data in table 3, the model proposed in this paper clearly obtained better performance results. for the reason of analysis, in lin’s work [12], the proposed six characteristics of the review text and the reviewer’s behavior are all individual spammers, ignoring the characteristics of group organizations among spammers.therefore, some normal reviewers are mistaken for spammers. at the same time, the model proposed in this paper has gone through two stages.the first stage is to detect the suspected opinion spammers. the users with low reputation value will be regarded as suspected spammers.they are filtered out through the reputation value combining the review text and the reviewer’s behavior.on this basis, the characteristics of the group behavior between the spammers are fully explored, and the clustering analysis is used to identify the spammers. the two-stage joint implementation ensures the accuracy of opinion spammers identification. 6 conclusion and future work in this paper, an effective opinion spammers detection approach is put forward.based on the idea that the reviewer’s reputation has a direct relation with the quality of reviews, in this paper, we first propose a reviewer’s reputation model which employs the target review’s content-based characteristics(context similarity, opinion sentiment, review length and helpful feedback) as well as behavior-based features(authors’ activeness, review deviation and rating) to assign reputation scores to each reviewer and distinguish suspected spammers with low-reputation reviewers from ordinary reviewers.on this basis, the k=center clustering algorithm is used to perform for suspected spammers to ultimate identify spammers due to the observation that the spammers’ posting time intervals burst. on amazon’s music real dataset, we constructed a set of annotated review, and verify the effectiveness of spammers identification method. besides, we also analyze the effects of the eight kinds of features on identification model. the experimental results are encouraging and indicate that the spammers identification method based on reputation and clustering algorithm poses high accuracy and recall, and good performance is achieved. furthermore, performance comparison results between the proposed method and yuming’ work show better detection accuracy. to date, the spammer detection is still open issue.two suggestions should be worthy attention in the future research direction. the first direction could be a deep exploring and analyzing more features of linguistic, relations, and psycholinguistic of opinion spammers to distinguish from ordinary reviewers. these valuable features could be benefit to improve the performance of detection. identification of opinion spammers using reviewer reputation and clustering analysis 771 another suggestion for future work is transformation from detection to analysis social and psychological impact of spamming activities on customers. at present, a lot of work focuses on how to identify the existing spammers or spammer groups. however, harmful spammers activity can affect many potential customers psychology and decision-making. therefore, analysis the impact of spamming activities on customer participation is our next work. 7 acknowledgments this work was supported by the national science foundation of china under grant numbers 71861014, 71762017 and 61662027, and the project of hunan provincial education department(17a113,18a441,17k015) and hunan provincial philosophy and social science fund(16yba228). bibliography [1] banerjee, s.; chua, a.; kim, j.(2015). using supervised learning to classify authentic and fake online reviews, proceedings of the 9th international conference on ubiquitous information management and communication, 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(2017). large scales software test data generation based on collective constraint and weighted combination method, tehnicki vjesnik, 24(4), 10411050, 2017. international journal of computers communications & control issn 1841-9836, 14(1), 107-123, february 2019. a latent-dirichlet-allocation based extension for domain ontology of enterprise’s technological innovation q. zhang, s. liu, d. gong, q. tu qianqian zhang, shifeng liu, daqing gong*, qun tu school of economics and management beijing jiaotong university, china 100044 no.3 shangyuancun, haidian, beijing, china 15113121@bjtu.edu.cn, shfliu@bjtu.edu.cn *corresponding author:gongdq@bjtu.edu.cn 17113133@bjtu.edu.cn abstract: this paper proposed a method for building enterprise’s technological innovation domain ontology automatically from plain text corpus based on latent dirichlet allocation (lda). the proposed method consisted of four modules: 1) introducing the seed ontology for domain of enterprise’s technological innovation, 2) using natural language processing (nlp) technique to preprocess the collected textual data, 3) mining domain specific terms from document collections based on lda, 4) obtaining the relationship between the terms through the defined relevant rules. the experiments have been carried out to demonstrate the effectiveness of this method and the results indicated that many terms in domain of enterprise’s technological innovation and the semantic relations between terms are discovered. the proposed method is a process of continuously cycles and iterations, that is the obtained objective ontology can be re-iterated as initial seed ontology. the constant knowledge acquisition in the domain of enterprise’s technological innovation to update and perfect the initial seed ontology. keywords: latent dirichlet allocation (lda), ontology extension, enterprise’s technological innovation, semantic web, text mining. 1 introduction with the pace of globalization of economy accelerated significantly, the market has stepped into the information age from the era of industrialization. as the market demand changes at a faster pace, the competition of the market has become extremely fierce. in this context, the technological innovation is increasingly becoming the inner motivation and main source of enterprise development. there is important significance to evaluate the capability of enterprise technological innovation scientifically and efficiently to set the technological innovation policy for government, revise technological innovation strategy reasonably for enterprises and improve the technological innovation ability. the evaluation of enterprise’s technological innovation ability has drawn extensive attention of much scholars. although much progress has been made on the theoretical research of enterprise’s technological innovation [13, 33]. there still exist many problems such as evaluation mechanism and evaluation methodology, namely, the biggish subjectivity of evaluating indexes, strong dependence on declared data of evaluated enterprise, low evaluation accuracy, poor coincidence of evaluate results, etc. enterprises produce large amounts of textual information in technological innovation process, including technological innovation activity report, meeting minutes, annual report and patent file. hence, enterprises need to not only make use of these documents but to mine and discover valuable and hidden knowledge from large collections of data. it is also a pressing problem to transform massive textual data into knowledge that can serve and utilize for technological innovation of enterprise and provide decision-making copyright ©2019 cc by-nc 108 q. zhang, s. liu, d. gong, q. tu for technological innovation of enterprise. therefore, it is the field which has not been involved by using techniques of text mining and machine learning to analyze massive textual information that generated by enterprise’s technological innovation and further determined the enterprise’s technological innovation ability from objective data. in this paper, we deal with three major problems as follows: • is it possible to discover the concepts from large amount of textual corpus of domain of enterprise’s technological innovation? • is it possible to build rules for semantic relationship recognition to make the enterprise’s technological innovation ontology subsumption hierarchy? • is it possible to make the enterprise’s technological innovation domain ontology extension automatically? to improve this situation, this paper presents an approach to extract core concepts from large textual data and proposes a new method of building rules for semantic relationship recognition based on lda algorithm. the rest of paper is organized as follows, section 2 provides some background knowledge concerning concept and relative literature reviews. in section 3 explains the proposed methods, while section 4 presents the experimental results. section 5 concludes the paper. 2 background knowledge and related works 2.1 technological innovation capability technological innovation capability (tic) has become the key to improve productivity and maintain competitiveness in the constantly fluctuating environments for enterprises. however, the definition of tic is hard to agree upon since the technological innovation involves numerous organizational functions and resources integration among various department [26]. the concept of innovation originally from the innovation theory proposed by schumpeter. on the base of it, burgelaman et al. [5] put forward that all tic can be defined as a series of characteristics in an organization facilitating and supporting an innovation strategy. based on differing perspectives, there are many scholars proposed various components of tics of a firm [22, 30].therefore, the measurement of tic is difficult and complicated since the perceive objectives and criteria for tic is different. tsai et al. [24] established an evaluation model for the tic of high-tech industries based on the ahp method. wang and chang [25] proposed a model for diagnose the value of tic in enterprise and established an evaluation system by ahp method. wang et al. [26] evaluated and analyzed tic combined with fuzzy evaluation and non-additional fuzzy evaluation. deng et al. [12] established a tic evaluation system by factor analysis and the fuzzy synthetic assessment method is used to evaluate tic. guan et al. [13] developed an innovation measurement framework based on the traditional dea method. by looking at literatures of the measurements of tic [8, 32], few studies can avoid to involve the subjective judgement, previous experience and uncertain assessment by experts. 2.2 ontologies construction and extension in the last decade, many scholars have done a lot of researches on ontology definition, construction, extension and application aspects. ontologies were defined as "an explicit specification of shared conceptualization" [14] provide the key to machine-processable data on semantic web, being fundamental components for sharing, reusing as well as reasoning over knowledge a latent-dirichlet-allocation based extension for domain ontology of enterprise’s technological innovation 109 domains [1]. although there is a great progress in knowledge acquisition and ontology construction, the current ontology construction methods still rely heavily on manual parsing and existing knowledge bases. the process of ontology learning and extending is a costly, time-consuming and error-prone task when done manually. with the constant emergence of new domain knowledge, the domain ontology automatic updates are facing new challenge. many researchers have engaged into ontology construction and enriching automatically in recent years. in previous work, the machine learning and statistical analysis method has great advantages in accuracy and recall rate and has been proposed to solve this problem [9]. for instance, jeroen et al. [10] proposed the subsumption method and a hierarchical clustering algorithm to arrange the domain terms hierarchically and compared the two methods performances. researches [18] and [23] using the fuzzy mechanism to extract domain concept and generate the domain ontology through the fuzzy conceptual clustering. khan and luo [17] presented a modified self-organizing tree algorithm (sota) which is performs better than the hierarchical agglomerative clustering (haz) on ontology construction automatically. gilles et al. [1] put forward a mo’k workbench which is a framework using the agglomerative clustering techniques to generate concept hierarchies from parsed corpora. cimiano and völker [6] presented a text2onto which implementing variety algorithms and techniques for ontology learning. however, most of the existing methods require a certain scale of supervised training corpus as the learning object, and the result seldom consider semantic-aware which is difficult to recognize the relationship between terms in domain ontology. although the ontology construction and extending automatically has been achieved some progress, there are still some problems in this field. for example, the non-taxonomic relationship among terms were often omit in the ontology hierarchical relations construction. besides, the parameter setting in the model and complex computing in the process cause the heavy computing burden and make the model overfitting which limits their application. 2.3 lda topic model the latent topic discovery researches have gained much attention to hierarchical relation learning in recent years. latent topic discovery is invented to overcome the bottleneck of bagof words processing model in information retrieval area, trying to advance the text processing technology from pattern to semantic calculation [23]. for the research in latent topic discovery, an earlier work in literatures is latent semantic indexing (lsi), which is a retrieval technique to learn latent topic by performing a matrix decomposition (svd) on the term-document matrix [31]. through this technique, latent topics are revealed which are actually distributions over the words of the term space of the corpus [10]. for example, the work in [3] uses the technique of lsi to identify relationships among entities in large collections of text. the author in [4] also using the lsi for discovering new information relevant to a given topic in large textual databases. although the lsi based on svd having some early success on latent topic discovery and relationship identification, it lacks rigorous mathematical and statistical basis and the svd decomposition is time-consuming. probabilistic latent semantic indexing (plsi) was proposed to extend the lsi assuming which associates a latent context variable with each word occurrence and can deal with synonymy and polysemous words. the author in [16] proposed that plsi has been considered as an unsupervised learning method used in the task of text learning. the work in [15] also using the plsi to represent sentences and queries as probability distributions over latent topic to solve the multi-document summarization problem. other than lsi and plsi, the algorithm of latent dirichlet allocation (lda) is more advantageous since lda model can avoid overfitting and large sets of parameters. lda model, proposed by david blei et al. [2], is a statistical topic model and can analyzes 110 q. zhang, s. liu, d. gong, q. tu hidden topics in large-scale data. ontology learning using lda model is a relative new research approach. elias et al. [34, 35] used the lda model for discovery of topics that represent ontology concepts and comparing the high-probability terms in topics to arrange concepts in a subsumption hierarchy. however, it cannot infer subsumption relations in the case where a topic subsumes only one other topic. yeh and yang [29] developed an automatic domain ontology construction for historical documents. lda model was used to extract latent topic from raw textual chinese recorder data and the basic cosine similarity with hierarchical agglomerative clustering is used to clustering the topic, but the relationship between the topic cannot be defined since the clustered latent topic is a hierarchical tree structure. francesco et al. [7] present an automatic terminological ontological learning system which the common hypernyms between the aggregate root node and aggregate words are determined through the lda model and then added the semantically similar root node to the ontology. however, the measurement in large set of data may cause heavy computing burden. ni et al. [20] also used the lda model to select the domain terms and through the word association analysis to discover the hierarchical relations among domain terms. raghuveer [21] using the lda model to obtain the topics from legal documents and clustering legal judgments by cosine similarity. 3 the proposed method the paper has combined the ontology technique and lda topic model, used the initial seed ontology guiding the lda model to obtain the concept in the field of enterprise technological innovation. adding the new concept to the initial domain ontology by defined rules to realize the iteratively updating and perfection of ontology. the framework of enterprise’s technological innovation domain concept acquisition contains the following four modules: • the module of seed ontology introducing. the paper needs to construct a seed ontology to guide the concept acquisition for enterprise’s technological innovation domain. the basic concept and relationship of seed ontology in domain enterprise technological innovation mainly extracted from chinese classified thesaurus. the protege 4.3 was used to visualize the construction of seed ontology. more details will be introduced in the next chapter. • the module of text preprocessing. this is the process of converting a text into individual words or sequences of words which using the natural language processing (nlp) technique including of word segmentation, part-of-speech (pos)tagging, stop-word filtering preprocessed the collected chinese textual documents. two words merging needs to satisfy adjacency and frequent co-occurrence both, the calculation method as follows. in order to guarantee the semantic accuracy after word segmentation, the method of entropy was adopted to merge the words [27,28]. e(wm−1,wm) = p(wm−1wm) minp(wm−1),p(wm) (1) where p(wm) denotes the frequency of word wm in documents and p(wm−1wm) denotes the continuous frequency of word wm−1 and wm in documents. • the module of mining domain specific terms. lda (latent dirichlet allocation) is a three-level hierarchical bayesian model which proposed by blei [34]. it assumes that each document in corpus is represented as random mixtures over latent topic, where topic is characterized by a distribution over all the words. lda is constructed for documents with "bag-of-words" which uses the statistical information of words to represent text in vector a latent-dirichlet-allocation based extension for domain ontology of enterprise’s technological innovation 111 space and explores the probabilistic relationships between words and text. in this paper, we use the lda model was described below. lda taking the corpus d which after the preprocessing by module b as input and output the topic distributions and the distribution of words for each topic by training. the lda generates the words in a two-stage process: words are generated from topics and topics are generated by documents. the graphical model of lda is shown in fig. 1. the terms of lda was defined as follows: a document is a sequence of n words denoted by w = (w1,w2, · · · ,wn) where wn is the nth word in the sequence, and a corpus is a collection of m documents denoted by d = d1,d2, · · · ,dm; wm,n zm,nk wm,n zm,n n [1,nm] m [1,m] k k figure 1: graphical model representation of lda α and β are dirichlet prior hyperparameters; all the words in document m will be clustered into z topics, for each topic z ∈ 1, 2, · · · ,k , sample a word distribution φk ∼ direchlet(β); – choose n ∼ possion(ξ) – choose a topic distribution θm ∼ direchlet(α) – for each of word wm,n in mth document: ∗ choose a topic of the word zm,n ∼ multinomial(θm) ∗ choose a word wm,n ∼ multinomial(φzm,n ) since the process to generate the topic for m documents are independent of one another, we can have m conjugated structures and the generative process of probabilistic of topics in corpus is as follows: p(~z|~α) = m∏ m=1 p( ~zm|~α) = m∏ m=1 ∆( ~nm + ~α) ∆(~α) (2) 112 q. zhang, s. liu, d. gong, q. tu the process to generate words for k topics are independent of one another, we can have k conjugated structures and the probabilistic of words in corpus is as follows: p(~w|~z, ~β) = k∏ k=1 p( ~wk|~zk, ~β) = k∏ k=1 ∆( ~ nk + ~β) ∆(~β) (3) thus, within a document, the probability distribution over words specified by the lda model is given as follows: p(~w,~z|~α, ~β) = p(~w,~z|~β) ∗p(~z|~α) = k∏ k=1 ∆( ~nk + ~β) ∆(~β) ∗ m∏ m=1 ∆( ~nm + ~α) ∆(~α) (4) thus, in this paper, the lda topic model was used to train the term candidate set which obtained by the module of text preprocessing and to obtain the word probabilistic of domain concepts (topics) as shown in fig.2. topic ... word w . . . ... ... ... ... . . . . . . . . . . . . . . . figure 2: words distribution probabilistic of topics where pwnk represents the probability of the word n in the topic k. • the module of domain ontology updating. the module is the key point and difficulty of this paper. take each concept in the initial enterprise technological innovation ontology as a document into the module (3) trained lda model. we can get the topics probabilistic of documents as shown in fig.3. where, a corpus is a collection of m ontology concepts denoted by c = (c1,c2, · · · ,cm−1,cm); where pzkm represents the probability of the topic k in concept(document) m. according to the lda algorithm, we can get the term probabilistic of documents, namely, the probabilistic of words in documents and concepts in initial ontology denoted as p(wn|cm). then by using the relevant rules to judge the relationship between topics generated by lda model and concepts in initial domain ontology. p(wn|cm) = k∑ j=1 p(wn|z = j) ∗p(z = j|cm) (5) a latent-dirichlet-allocation based extension for domain ontology of enterprise’s technological innovation 113 concept ... topic . . . ... ... ... ... . . . . . . . . . . . . . . . figure 3: topics distribution probabilistic of documents when the p(wn|cm) greater than the threshold value th and the word n is not in the list of c = (c1,c2, ...,cm−1,cm), therefore, the term wn is an associated term of cm. p(wn|cm) > th (6) algorithm: rules of the semantic relationship recognition were defined as follows: w(wn,cm) = p(wn|cm) p(z = j|cm) + p(wn|cm) = k∑ j=1 p(wn|z = j) ∗p(z = j|cm) p(z = j|cm) + k∑ j=1 p(wn|z = j) ∗p(z = j|cm) (7) • rule 1: rules for synonymy relations recognition. if the w(wn,cm) ≥ 0.01, the semantic relationship between word and concept is equivalent, namely, the related terms extracted by lda is equal to the existed concept. • rule 2: rules for hyponymy relations recognition. when the rule 1 cannot be satisfied, if the w(wn,cm) ≥ 0.004, the word includes the concept, namely, the related terms extracted by lda is superclass of the existed concept, the relationship as "is-a" or "subclass". • rule 3: rules for correlation recognition. when the rule 1 and rule 2 are cannot be satisfied, the relationship between existed concept and related terms can be recognized as related or using people to identify the specific semantic relationship by external knowledge base. based on the above rules, the semantic relations between the existing concepts and their related terms are identified, add the obtained related terms and semantic relations to the original ontology o, the original ontology o was updated to oi. 114 q. zhang, s. liu, d. gong, q. tu 4 experiments and result 4.1 ontology acquisition enterprise ontology and tove (toronto virtual enterprise ontology) are the most popular ontology-based enterprise modeling methodologies. the two projects all point out the common key influencing factors in the process of enterprise ontology construction including of resources, organization, strategy, market and activity. in this paper, the five factors also considered as the first class of the enterprise’s technological innovation ontology. the chinese classified thesaurus has clear semantic structure which is more suitable for the extraction of concepts and relationship between concepts. transforming thesaurus into ontology through further concepts analysis and semantic relationship adjustment of the words in f27 category of enterprise economy in chinese classified thesaurus. there are 5 concepts extracted from the thesaurus including of innovation resources, marketing innovation, strategic innovation, organizational innovation and innovation activities. the nested composite view provides a representation of the interrelation between the first classes in the entire ontology structure. it is convenient for considering whether the constructed domain ontology meets actual needs. the nested composite view of enterprise’s technological innovation domain is shown as fig.4. the relationship between domain ontology concepts includes the hyponymy relations and complex non-hierarchical relationship for specific application. the fig.5 shows that the relationship between domain ontology concepts which takes the strategic innovation as the center and reflects the complex relationship between concepts. the ontology of enterprise’s technological innovation is a prototype, in which many concepts and relationships are still insufficient and need to continuously improved. figure 4: nested composite view of enterprise technological innovation domain 4.2 textual data collection there are two aspects to collect the textual data of enterprise technological innovation, one is the internal information generated from daily production activities such as internal r&d, innovation activities, etc. the other type of collected data is generated when enterprise interacting with external customers and partners by social networks, mobile applications, etc. 863 sets of valid data are obtained which includes of 413 enterprise technology centers. a latent-dirichlet-allocation based extension for domain ontology of enterprise’s technological innovation 115 figure 5: visualization of relationship between concepts 4.3 text preprocessing domain-specific dictionary entropy + manual screening corpus segmentation ictclas pos selection on p corpus1 corpus2 corpus3 stop-word dictionary stop-word filtering figure 6: process of text preprocessing chinese segmentation firstly, constructing the domain-specific dictionary for the field of enterprise’s technological innovation by widely collected materials such as the cell thesaurus and imported the dictionary into the ictclas segmentation system [32] which developed by the chinese academy of sciences. secondly, the result of segmentation will appear the problem due to a chinese phrase was wrongly divided into many words. for example, the "enterprise’s technological innovation" was divided into three small-grained words such as "enterprise", "technological" and "innovation". the method of entropy was adopted to merge the words which shown as equation (1). combining two words that satisfy the conditions into a new phrase and adding to the domain-specific dictionary by manual screening. then, segment the source document and iterate repeatedly. 116 q. zhang, s. liu, d. gong, q. tu pos selection the documents of enterprise’s technological innovation are the synthetic texts, in which, nouns are more representative important for semantic information in source documents. hence, selecting the nouns and the word similar to nouns as the research object such as the verb with noun function, the adjective with noun function, etc. elimination of stop-words useless words selected from the domain of enterprise’s technological innovation is used to build stop words dictionary. filtering the stop words in documents which processed by the above two steps. it can reduce the size of the indexing structure considerably by elimination of stop words. 4.4 mining domain terms from text corpus based on lda • terms selection. according to the word frequency of terms in all corpora, the word frequency of [50, 1000] were selected as terms to represent each document in vector space model. • optimal number of topics. the perplexity index is adopted in optimal topic selection. perplexity is an effective measurement to verify the model generalization ability. a lower perplexity indicates the better generalization performance. the perplexity is defined as follows: perplexity(wn|cm) = e − ∑ log(p(wn|cm)) n (8) where p(wn|cm) is the probability of each word in candidate term set, n is the number of words. the perplexity of all documents generated under different topic numbers is shown as fig.7 it looks like the 160-topic model has the lowest perplexity score. hence, the optimal number of topic 160 (k=160) is selected for all corpus by perplexity analysis. the smoothing parameters α and β were fixed at 0.1 and 0.3. the threshold th was set to 0.001. figure 7: perplexity result on enterprise technological innovation corpora for lda model • when the number of topics is 160, the lda topic modelling is carried out to obtain the distribution of terms, that is each topic comprises of a series of related words. the order of the top terms of each topic is arranged by the probability and presented in the table.1, in which only the first 10 topics with high probability of topic distribution were shown. the fig.8 shows the resulting graph visualization lda model for top terms of topics. a latent-dirichlet-allocation based extension for domain ontology of enterprise’s technological innovation 117 table 1: the distribution probability of topics and words when topic k=160 topic 5 p (wn|k = 160) topic 13 p (wn|k = 160) topic 23 p (wn|k = 160) technology 0.001665396 expert 0.0021303003 patent 0.0095653171 material 0.0014465271 doctor 0.0009977445 name 0.0047526103 technique 0.0012552338 senior engineer 0.0007695822 number 0.0042560613 new material 0.0007991531 counselor 0.0006120452 technology 0.0031178757 product 0.0005905334 master 0.0005237754 information 0.0028379832 technological innovation 0.0005886399 bachelor 0.0005183982 type 0.0027637654 highperformance 0.0005476442 postdoctoral 0.0003545205 invent 0.0038754337 precision 0.0005436358 degree 0.0002742724 copyright 0.0016382793 newtechnology 0.0004137853 professor 0.0002726482 authorizedpatent 0.0007759799 newproduct 0.0003711188 college 0.0002557352 conservation 0.0006379388 stability 0.0003021399 associateprofessor 0.0001320247 authorization 0.0002794288 practical 0.0002943092 academic 0.0001297645 intellectualproperty 0.0002544165 topic 27 p (wn|k = 160) topic 30 p (wn|k = 160) topic 39 p (wn|k = 160) project 0.0008273301 enterprise 0.0010061373 newproduct 0.0008340621 types 0.0008237544 name 0.0009097208 newtechniques 0.0008340621 invisibleasset 0.0004237544 development organization 0.0008218773 name 0.0004272026 fix asset 0.0004223029 contacttelephone 0.0006368324 market occupancy 0.0004272025 equipment 0.0004208453 organization 0.0004940051 profit 0.0004272025 facility 0.0003637534 company 0.0004912549 period 0.0004272025 totalamount 0.0003230094 department 0.0004209615 sale-quota 0.0004263008 quantity 0.0003034324 laboratory 0.0004209615 salesvolume 0.0004262023 cost 0.0002784593 contact person 0.0004209615 competitive 0.0004262010 fund 0.0002764534 researchinstitute 0.0004209615 economicbenefit 0.0004260232 amount 0.0002230895 contact details 0.0004209615 popularization0.0003037646 instrument 0.0002234943 information 0.0003026468 technical management 0.0003037564 118 q. zhang, s. liu, d. gong, q. tu technology material technique new material product technological innovation highperformance precision newtechnology newproduct stability practical expert doctor senior engineer counselor master bachelor postdoctoral degree professorcollege associateprofessor academic patent name number information type invent copyright copyright authorized -patent conservation intellectual -property project invisible -asset fix asset equipment facility total-amount quantity cost fund amount instrument enterprise development organization contact-telephone organization company departmentlaboratory contact person research-institute contact details newproduct newtechniques market occupancy profit periodsale-quota salesvolume competitive economicbenefit popularization technical management topic 5 topic 39 topic 30 topic 27 topic 23 topic 13 figure 8: graph of lda for top terms of topics 4.5 learning hierarchical relations among terms using the trained lda model to infer each concept in the initial ontology and taking each concept (or word) as a document to calculate the topic probability of the document. identify the semantic relations between existing concepts and its related terms, and add the related terms as the domain ontology concept to the appropriate position of the existing ontology to complete an update process of the domain ontology. table.2 shows the results of the conceptual related terms extraction and relations recognition. table 2: related terms extraction and relations recognition existing concepts topic p (cm|z = j) related terms weights applicable rules semantic relations profit management 79 0.438621 innovation resources 0.00413586 (2) subclass total amount 0.00624005 (2) subclass technical information 139 0.388462 material 0.00651796 (2) subclass painting 0.00331878 (3) related alloy 0.00243573 (3) related visible asset 27 0.236543 fixed asset 0.01342533 (1) equivalent equipment 0.00523433 (2) subclass instrument 0.00243234 (2) subclass visible asset 27 0.236543 fixed asset 0.01342533 (1) equivalent equipment 0.00523433 (2) subclass instrument 0.00243234 (2) subclass technicalquality 5 0.388462 precision 0.00257653 (3) related new technology 0.00323643 (3) related a latent-dirichlet-allocation based extension for domain ontology of enterprise’s technological innovation 119 existing concepts topic p (cm|z = j) related terms weights applicable rules semantic relations high-tech product 136 0.446243 strategy innovation 0.00276406 (3) related high-tech product 136 0.446243 technological innovation 0.00143524 (3) related product innovation 23 0.237643 patent 0.00332763 (3) related brand 0.00236232 (3) related copyright 0.00323422 (3) related new product 0.00332542 (3) related staff management 13 0.376432 expert 0.00335476 (3) related doctor 0.003276543 (3) related degree 0.002387432 (3) related senior engineer 0.003723423 (3) related wage management 63 0.3412663 wage 0.01472652 (1) equivalent subsidy 0.00234653 (3) related bonus 0.00334523 (3) related insurence 0.00343263 (3) related innovation activities organizational innovation strategic innovation marketing innovation innovation resources personnel management staff management technological innovation technology quality technology information technological strategy product technology product innovation product strategy product information product development product quality high-tech product product quality certification profit management cost management economic activities analysis assets management invisible asset visible asset total amount material painting precision patent brand copyright new product equipment instrument experts doctor wage management wage subsidy bonus insurance alloy market occupancy sale quota competitiveness period degree senior engineer wawawawawawawawa fixed asset figure 9: parts of produced enterprise technological innovation domain ontology the fig.9 shows part of produced enterprise’s technological innovation domain ontology. the blue dots represent the original terms of initial domain ontology and the red dots stand for the produced new terms. the original relations among entities in ontology are shown with solid lines, the dashed lines represent the new relations. the total amount of new terms in enterprise technological innovation domain ontology has updated about 163, the figure only shows parts of the result due to space limitation. by looking at the literature of ontology evaluation, there are two approaches for measuring the ontology including of manual evaluation by human experts and gold standard-based approaches [11]. the first evaluation approach presents the learned ontology to one or more human experts and judge how far the extracted information is correct. the second method compare the learned 120 q. zhang, s. liu, d. gong, q. tu ontology with a previously created gold ontology which example for this kind of evaluation can be found in papers like [34]. the degree of matching between learned ontology and gold ontology determines the precision of learning ontology. the evaluation of ontologies when these ontologies are produced by an automated learning procedure is an open field of research. since the enterprise’s technological innovation is a new developing academic field which has not formed a generally acknowledged ontology yet. therefore, the manual evaluation by human experts was the best way so far. the research chosen 5 groups and 20 terms and relations for each group in the updated enterprise’s technological innovation domain ontology randomly. the assisted algorithm like following equation was defined as the ration between the right terms and relationships which evaluated by human experts and the total terms and relationships in ontology. according to the validation about the correct terms and relations with domain experts, the result of accuracy test is shown as table 3. precision = righttermsandrelationships totaltermsandrelationshipsontology (9) table 3: the accurate rate of the concepts in enterprise technological innovation domain no. number of groups accurate number of groups precision group 1 20 19 95% group 2 20 19 95% group 3 20 18 90% group 4 20 17 85% group 5 20 19 95% compared with the traditional ontology construction methods such as ontolearn and text2onto, the proposed method has same precision which the average accuracy rate is 92%. the semantic content and relationship in the produced ontology is basically correct. the proposed automatic ontology extension method reduces the manual labor for ontology updating and solved the problem of automatic domain ontology acquisition and dynamic maintenance. 5 conclusion and future work this paper presented an automatic ontology extension method for the domain of enterprise’s technological innovation. the main contributions of this paper present as follows: firstly, this paper proposes an ontology-based lda topic model for concept extraction and applies it to the realm of enterprise technological innovation, which not only discover the concepts from large amount of textual corpus, but also can provides data support for ontology construction. secondly, this article takes a huge amount of enterprise technological innovation information in unstructured texts as the data source and proposes a method of building rules for semantic relationship recognition based on lda topic probability distribution, and the process of automated domain ontology updating based on the lda topic model is realized. finally, the experiment results demonstrate the efficiency and validation of proposed method. the method focuses on discovering the domain terms via latent topics found by lda algorithm from plain text corpus and recognizing the semantic relations among domain terms based on word association analysis. the proposed method is a process of continuously cycles and iterations, the domain ontology of enterprise’s technological innovation will be updated and perfected automatically with the constant knowledge acquisition in the domain. the paper introduces the ontology on the basis of a latent-dirichlet-allocation based extension for domain ontology of enterprise’s technological innovation 121 the lda topic model and the ontology is extended by the obtained related topics. the proposed method is an improvement for the single lda algorithm. the future work needs to solve several problems, firstly, improving the proposed method to achieve a better performance and continuing exploring automatic evaluation approaches on thesaurus constructing methods. 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(2010); discovering subsumption hierarchies of ontology concepts from text corpora, proceedings of the ieee/wic/acm international conference on web intelligence, 402–408, 2007. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 1, month: february, year: 2023 article number: 4981, https://doi.org/10.15837/ijccc.2023.1.4981 ccc publications machine learning and ulbp histograms for posture recognition of dependent people via big data hadoop and spark platform f. alfayez, h. bouhamed fayez alfayez department of computer science and information college of science, majmaah university al-majmaah, 11952, saudi arabia. f.alfayez@mu.edu.sa heni bouhamed advanced technologies for image and signal processing unit (atisp) sfax university, tunisia heni.bouhamed@fsegs.usf.tn abstract for dependent population, falls accident are a serious health issue, particularly in a situation of pandemic saturation of health structures. it is, therefore, highly desirable to quarantine patients at home, in order to avoid the spread of contagious diseases. a dedicated surveillance system at home may become an urgent need in order to improve the patients’ living autonomy and significantly reduce assistance costs while preserving their privacy and intimacy. the domestic fall accident is regarded as an abrupt pose transition. accordingly, normal human postures have to be recognized first. to this end, we proposed a novel big data scalable method for posture recognition using uniform local binary pattern (ulbp) histograms for pattern extraction. instead of saving the pixels of the entire image, only the patterns were kept for the identification of human postures. by doing so, we tried to preserve people’s intimacy, which is very important in ehealth. to our knowledge, our work is the first to use this approach in a big data platform context for fall event detection while using random forest instead of complex deep learning methods. application results of our conduct are very interesting in comparison to complex architectures such as convolutional deep neural networks (cnn) and feedforward deep neural networks (dffnn). keywords: local binary pattern, hadoop, spark, random forest, surveillance system at home. 1 introduction a standing, sitting, or laying posture to a lower reclined one [24]. it is an incidence where someone falls unintentionally to the ground or a lower level [14]. it is generally believed that falls are the greatest risk to the aged and dependent, resulting in serious physical and psychological harm. when https://doi.org/10.15837/ijccc.2023.1.4981 2 monitoring systems can correctly identify a fall incidence and produce an impending alert, they can save lives. a fall typically results in lying motionless on the ground for some time. thus, this research work attempted to identify five distinct types namely bending, lying down, standing, crawling and sitting. a number of enticing researchers looked into the use of deep neural networks (of various architectures) for the human postures and actions classification [9, 12, 26]. in brief, convolutional neural networks (cnn) and long short-term memory (lstm) are the most tested models in this context in which spatial and temporal features are crucial [21]. recurrent neural networks (rnn) (constructed on lstms, grus, and other architectures) are among the most used models in times series modeling, whereas cnn are reputable for their good performance in finding spatial patterns. some studies have been conducted on the fusion of these two models, either in series [2, 22, 25] or in parallel [8? ] and results in video analysis have been quite interesting. human posture recognition is important in fall detection because it is possible to recognize a fall by sequencing action frames [19]. it is critical to understand pose series in order to detect fall events as specific "pose changes." consider a randomly artificial neural network n and a chosen task ts that n must solve. it is non-deterministic polynomial time hardness (np-hard) to determine whether there is a possible solution to solve ts by testing all the possibilities of n settings (structure and weight). even with the simplest of its architectures, the deep feedforward neural networks (dffnn), we can see the extremely high complexity of deep neural network learning. given the cnn architecture, the complexity increases even further. this accounts for the formalism’s user community tendency to refer to ready architectures based on the field of application, especially when the inputs number is very elevated (for example, the recognition of the content of full hd images having approximately two million pixels per frame). although it is undeniably efficient in the field of image recognition, many researches have acknowledged that the cnn architecture is difficult to implement [10, 17, 23, 29]. as far as privacy is concerned, asif et al. [1] presented the encoding of the human position in the skeleton form, they relied on the cnn structure, which we tried to avoid because it is overly complex. iazz et al. [15] suggested a three-step procedure: the human shape is selected from the background of an image in the first step; then, both local and global patterns, such as vertical and/or horizontal angles, are selected; finally, a pre-trained support vector machine (svm) model is used. however, this method, is susceptible to bring out occlusions of images. authors of [27] employ the same methodology, but focus on the view from the top of the room rather than a side view to surmount the problem of occlusions. however, the traditional machine learning limitations remain the greatest shortcoming facing the works mentioned above. hamdi et al. used local binary pattern (lbp) histograms to extract features without removing the silhouette, yielding very promising results. nonetheless, the efficacy of deep learning, even with its classic dffnn architecture and np-hard learning complexity, have to be evaluated. this work could be enhanced by calculating the local binary pattern (lbp) histograms before applying the silhouette extraction. our research work focused on the position classification of persons in video (images) while bypassing the cnn/dffnn high computational cost, on the one hand, and using all the image pixels during the learning of a recognition pattern, on the other hand. the foundation of our application would therefore be a scalable big data platform allowing real-time data streaming via spark streaming or/and apache kafka. to our knowledge, our conduct is the first to use this approach in a big data platform context, especially with hadoop-spark frameworks, for fall event detection while using random forest instead of complex deep learning formalisms. we will see that application results of our conduct are very interesting. the remainder of this paper is organized as follows. section 2 is dedicated to present our methodology and theoretical background. in section 3, images data bases, model selection and performance evaluation are described before presenting our conclusion and perspectives in the fourth and last section. https://doi.org/10.15837/ijccc.2023.1.4981 3 2 methodologies, theoretical background and data base cnns have strong feature extraction capability that has been adapted to select features from images hyperspectral. both of the local binary model (lbp) and local uniform binary model (ulbp) are elementary and robust descriptors for spatial patterns extraction, which enables us to avoid us the use of cnns or even replace them with another much simpler classification formalism. 2.1 lbp and ulbp the lbp method assigns a decimal number to each pixel in an image, known as the lbp code, which measures the local structure texture in the vicinity of all pixel one by one [13]. figure (1a) depicts this process: each pixel is measured relative to its 8 neighbors. negative values are encoded with 0 while the positive ones are encoded with 1. binary numbers could be constructed by fusionning all of these binary values from the first pixel in the top left to the right making a full spin. the first upper left neighbor is the starting point. a decimal value is generated from the binary number and then used to label the given pixel. the occurrence frequency of each value is represented by the lbp labels histogram. it can be then exploited as a texture descriptor [3]. the central pixel’s direct neighbors may be located at various distances. in fact, the distance separating this central pixel from its direct or indirect neighbors can be a radius of one, or it can be 2 distant units, representing a radius (r) of 2, or it can be three distant units, representing a radius of three (see fig. 1 a). neighbors number can also change reaching up to 8 with r equal to one or more with a larger radius. for our studies, we have chosen to use a total of 8 neighbors with r = 1, r = 2, and r = 3. in a future research, we intend to test other possibilities with a particularly higher number of neighbors. several researchers [4, 5, 6, 18, 31? ] looked into the efficacy of exploiting the lbp histograms to conduct graph-based recognition networks. pattern can be classified as uniform in case of having a single binary digit change (2). figure 1: lbp histogram from images pixels. the lbp is acknowledged to be a powerful nonparametric operator for the description of local features of an image and has been proven to be robustness to translation and rotation. we consider a pixel (xc, yc), an arranged binary assembly defined as lbp. it is obtained by measuring the gray value of a pixel (xc, yc) with respect of its 8 neighbors’ pixels. as a result, the lbp value is expressed as a decimal number: lbp (xc, yc) = 7∑ i=0 s (in − ic) 2n (1) where ic denotes the gray value of the studied pixel (xc, yc). the lbp value was proven to be invariant to any monotonic gray level transformation, and the binary value of the local neighborhood remains unaltered after transformation. s (in − ic) = { 1, in − ic ≥ 0 0, in − ic < 0 https://doi.org/10.15837/ijccc.2023.1.4981 4 figure 2: uniform lbp pattern. table 1: algorithm 1. our new method for human posture recognition algorithm: posturerecognition begin transform video images to gray level for each frame (image) do extract foreground from background via pixels extraction (background image – image) compute lbp for each pixel of extracted silhouette image compute the histogram of all ulbp codes consider the participant position in the image as a label (class) fit a random forest model using the image dataset and testing it with test and validation images end the lbp value is a group of binary code weighted coefficients. this value depends on the subtraction results between the studied pixel and neighboring ones, each of which has two hundred fifty-six features to describe different texture patterns. to further reduce dimensionality, we considered the ulbp, which has only 59 patterns. it is worthwhile to recall that the uniform lbp may be expressed as follows: ulbp (xc, yc) = 7∑ i = 0 #γ (lbp (xc, yc)) ≤ 2 s(in − ic)2n (2) where #γ (lbp (xc, yc)) = { lbp (xc, yc)i 6= lbp (xc, yc)i+1 : i ∈ [0, 6] } + 1 is the number of blocks consisting of ones and zeros. furthermore, 3 possible r (r=1,2, and 3) with a number of neighborhoods equal to eight are exploited, resulting in a global number of features of 177 (n×n pixels) for any image. our proposed method for human posture recognition is further explained in 3 and algorithm 1. figure 3: rgb image transformation into ulbp histograms through silhouette extraction. https://doi.org/10.15837/ijccc.2023.1.4981 5 2.2 temporal algorithmic complexities it is obvious from section 2.1. that the processing time complexity of the ulbp is a constant, i.e.: culbp = o(1) note that the learning random forests time complexity cost is not considered in the above formula. since the comparison between the cnn complexity and that of the dffnn has already been presented in hamdi et al., we focused on the comparison of the latter with the random forest having the ulbp histograms as inputs. according to hamdi et al., (2021) the complexity of cnn is polynomial whereas that of the dffnn deep neural network is represented as follows: let us consider a dffnn with m hidden layers and n inputs, where the ith hidden layer is composed of mi neurons, and k output neurons will carry out the following multiplications number (activation functions are excluded): cm lp = o(nm1 + mm k + m −1∑ i=1 mimi+1 and with only one hidden layer, the number of multiplications becomes: cm lp = o(nm1 + m1k) in general, we mainly consider the size of the input when we study the temporal complexity of an algorithm. nevertheless, the number of multiplications performed in linear combinations (the complexity of time) also depends on the number of hidden layers and the number of neurons in each layer according to the current case. the time complexity of a forward transition of an adjusted dffnn therefore depends on the architecture. nonetheless, with our suggested method, there would be a wide variation between using the ulbp (with 177 entries) and x entries representing all the image pixels, which yields: cm lp = o ( 177i1m1 + mm k + m −1∑ i=1 mimi+1 ) (3) the complexity of the random forest (model which would be retained after the tests) depends closely on the complexity of the decision trees which are of the order of cdt = o(n ∗ log(n) ∗ d) where: n is the training inputs dimension and d is the data. hence the number of the random forest multiplications is as follows: cdt = o(n ∗ log(n) ∗ d ∗ k) where k is the number of the learned decision trees. using ulbps with 177 inputs instead of using the n inputs would yield a number of multiplications of the order of: cdt = o(177 ∗ log(177) ∗ d ∗ k) (4) it can, therefore, be concluded that in accordance with to the 2 theoretical formulations (3 and 4), our method with the ulbp histograms random forest combination will have a faster execution time than the ulbp histograms + dffnn. we just need to compare their performance results for posture classification. 2.3 experimental data base we used dataset from kripesh et al., 2017 [32] (free downloadable from http://www.falldataset.com/). kinect sensors are used to capture these images (at the quick frame rate of 30 per second so as to detect all of the postures in each image). nevertheless, furniture might have been changed or removed, or another person might have accessed the frame; these are scenarios we are planning to take into https://doi.org/10.15837/ijccc.2023.1.4981 6 account in our future research. for training, we have five labeled postures. this data was collected using a data set of raw rgb images (640 × 480 in size) recorded by only one kinect sensor, placed at a 2.4-meter-high roof. the images dataset contains 21499 images, of which 73% (15800) are adopted for training, 15% (3199) for testing, and 12% (2500) for final validation. the dataset images were captured with five different persons in five different decorations and from 8 different capturing angles. 2 are men, aged 32 and 50, and the other three are women, aged 19, 28, and 40. the postures of the participants were restricted to five different poses: standing, sitting, crawling, bending and lying (figures 4, 5, 6, 7, 8, and 9). we also showed the silhouette extraction image created by subtracting pixels from the original image (at gray level for both). the corresponding ulbp histogram characterizing each image was then plotted as a scalar vector. the images of 2 participants, a 32-year-old man and a 28-year-old woman making a total of 15,800 images were used for the training. the dataset for testing included all the 3199 images of the 32-year-old person taken in different decorations. we used all the 2500 images of the three persons for validation. these photographs were also taken in a different decoration and were not adapted for the model fitting or testing. figure 4: bending position. figure 5: crawling position. figure 6: lying position. https://doi.org/10.15837/ijccc.2023.1.4981 7 figure 7: sitting position. figure 8: standing position. figure 9: empty scene. 3 machine learning formalism selection, application platform and performance evaluation during the conventional use of different machine learning formalisms in image classification, the exploited input is generally made up of the images all pixels. the number of input characteristics in this case may be very high, for example, an image having size 1000/600 would be exploited with 600000 input, which can blow up the number of parameters to be calculated throughout training, especially when we deal with big data. some studies [4, 5, 6, 9, 16, 31] have presented the efficiency of exploiting uniform lbp histograms for fitting graph-based recognition networks. this allows reducing the inputs number considerably by exploiting only the histograms of the ulbp features (whatever of the size of the image). thus, the only remaining task was to choose the machine learning formalism which will be the most efficient in our context. 3.1 hadoop and spark platform apache hadoop is an open source framework used to store and process large data sets. it allows data to be analyzed in parallel on a cluster of multiple computers, rather than on a single machine. this allows a significant speed gain. four main modules make up hadoop: • the hdfs (hadoop distributed file system) which is a distributed file system that can be run on standard or low-end hardware. it offers better performance and increases error tolerance compared to traditional file systems, • yarn (yet another resource negotiator) which manages and monitors cluster nodes and resource usage. it is also used to plan tasks and jobs, • the mapreduce framework which helps programs to perform parallel calculations on data, https://doi.org/10.15837/ijccc.2023.1.4981 8 • hadoop common which provides common java libraries that can be used with many other software in the hadoop ecosystem. thanks to hadoop, it is easier to use all the storage and processing capacity of clustered servers and perform distributed processing on large volumes of data. this framework provides the building blocks on which applications and services are dependent. over the years, the hadoop ecosystem has grown and now includes many tools and applications dedicated to big data. among these we can cite the analytical interface hive, the non-relational database hbase, the interactive notebook zeppelin, or the distributed processing system apache spark. figure 10: the four virtual machines of our hadoop-spark cluster. apache spark is a distributed processing system used for big data workloads. it uses in-memory caching and helps optimize query execution to enable fast queries on data of any size. simply put, it is a fast engine for big data processing. it offers better performance than previous big data tools such as mapreduce. its runs on a cluster ram memory, providing faster processing than on hard drives. this general engine can be used for distributed sql queries, building data pipelines, ingesting data into a database, running machine learning algorithms, or working with data streams and graphics. nowadays, spark is included with most hadoop distributions. it has become the main big data processing framework, thanks to its speed and very easy-to-use api for developers. the experimental study of our work was carried out on a hadoop cluster (version 3.3.2) configured based on ubuntu 18.04 virtual machines via the oracle virtualbox 6.1.28 virtualization software. our cluster (figure 10) consists of a master machine, a standby machine (backup of the master’s metadata) and two slaves (or workers). we installed the apache spark 3.2.1 as the distributed engine of our hadoop cluster. everything was mounted on a pc with an octacore cpu (with 16 threads) and 24 giga bytes of ram. all the python-spark codes developed as part of our work (transformation of images into ulbp histograms, learning and testing of machine learning models) can be downloaded via https://github.com/henibouhamed/fall2. 3.2 random forest selection and configuration we decided to test the best-known machine learning formalisms on our database of images transformed into a matrix of 178 columns (177 ulbp plus the class variable) on 21499 lines representing all the images (training, test and validation). we evaluated our conduct in two steps: first, k-fold (k=10) cross-validation was used to evaluate the models with a test dataset (3199 images with new rooms) and then those constructed were tested with a validation dataset (2500 images with unknown https://doi.org/10.15837/ijccc.2023.1.4981 9 table 2: test dataset results method accuracy precision recall ulbp + logistic regression 72,5% 72,4% 72,1% ulbp + decision tree 87,1% 87,1% 87,1% ulbp + naïve bayes 71,2% 71,2% 71,2% ulbp + neural network 94,4% 94,4% 94,3% ulbp + random forest 98,4% 98,4% 98,4% [9] dffnn 93,2% 93,14% 91,45% [18] cnn 81% table 3: validation dataset results method accuracy precision recall ulbp + logistic regression 62,4% 62,4% 62,4% ulbp + decision tree 81,7% 81,5% 81,4% ulbp + naïve bayes 61,6% 61,6% 61,6% ulbp + neural network 87,3 87,3% 87,3% ulbp + random forest 92,6% 92,6% 92,6% [9] dffnn 86,3% 86,3% 85,7% [18] cnn 74% rooms and unknown people). tables 1 and 2 show the results obtained by investigating the formalism models we tested on the data of the test and validation images, respectively. the random forest was revealed to be the formalism that gave the best results. 3.3 discussion tables 2 and 3 display the recognition results of our approach with random forests on the test and validation sets, respectively. it should be noted that we have changed the number of trees introduced as a parameter to the random forest (100 trees adopted at the end of the tests) until reaching a stabilization of the results. to evaluate our method, we opted for comparing our results with previous ones using the same database. the first group of studies [18] used a cnn for the posture classification phase before going on to fall detection. the second set of studies [9] referred to a dffnn+ulbp without applying a silhouette extraction before the learning and classification phase. our objective in this work was to show that the use of a simple machine learning formalism, adapted to the problem considered in a big data context may be more advantageous than the use of very demanding architectures in terms of computational cost. first, we managed to avoid the known complexity of implementing and fitting a cnn or even a dffnn; second, we have succeeded in improving the recognition results for the test dataset from 81% in [32] and 93.2% in [9] to 98.4% and from 74% in [32] and from 86.3% in [9] to 92.6% for data from validation image in terms of accuracy (percentage of good classification). moreover, the use of the random forests and ulbp histograms allowed us to bypass using all pixels as input to the classifier. using the ulbp histograms with 3 r (r = 1, 2 and 3) and with eight neighborhoods has given us only 177 entries (59 * 3) which greatly decreased the training complexity of our classifier. finally, acquiring only the ulbp histograms instead of saving all the image also allows us to protect the privacy of the dependent persons at home. besides, the recording will not have to stop even while these people go to the toilets or take a bath without any breach of privacy. nevertheless, a diminution in accuracy was detected when validating our model on images of new persons in new places with new decors. this can be justified by the difference in body volume of persons in this experiment. on the other hand, the improvement of the classification results with a simpler method (random forest) than deep learning architectures (cnn/dffnn) might be due to the difficulty of configuring the latter. https://doi.org/10.15837/ijccc.2023.1.4981 10 4 conclusion our study focused on the phase preceding the detection of falls, which requires us to recognize the persons’ positions in video frames (images) in a first place. the recognition of various human positions can, then, be exploited to train an lstm model using a time sequence. our main objective was to bypass the use of cnn/dffnn due to their configuration and implementation difficulties. furthermore, by using only the ulbp histograms, we were able to reduce the number of inputs required to train the random forest. finally, people’s intimacy and privacy were protected by saving only the ulbp histogram characteristics rather than the entire information included in the image. however, our conduct has not been tested on real big data in order to show the scalable capabilities of the used big data frameworks in term of processing speed. the proposed method increased the recognition accuracy results for the test dataset from 93.2% to 98.4% and the validation dataset from 86.3% to 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[33] xingjian s.; chen z.; wang h.; yeung d-y.; wong w-k. & woo w-c. (2015). convolutional lstm network: a machine learning approach for precipitation nowcasting. in: advances in neural information processing systems, 2015, pp 802–810. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: alfayez, f.; bouhamed, h. (2023). machine learning and ulbp histograms for posture recognition of dependent people via big data hadoop and spark platform, international journal of computers communications & control, 18(1), 4981, 2023. https://doi.org/10.15837/ijccc.2023.1.4981 introduction methodologies, theoretical background and data base lbp and ulbp temporal algorithmic complexities experimental data base machine learning formalism selection, application platform and performance evaluation hadoop and spark platform random forest selection and configuration discussion conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 1, month: february, year: 2020 article number: 1004, https://doi.org/10.15837/ijccc.2020.1.3764 ccc publications personalized recommendation model: an online comment sentiment based analysis s. chen, x. lv, j. gou songmin chen school of economics and management beijing jiaotong university beijing 100044, china 18120608@bjtu.edu.cn xiyan lv school of economics and management beijing jiaotong university beijing 100044, china lvxiyan@bjtu.edu.cn juanqiong gou* school of economics and management beijing jiaotong university beijing 100044, china *corresponding author:jqgou@bjtu.edu.cn abstract traditional recommendation algorithms measure users’ online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. users’ reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. this paper proposes a model for the fine-grained analysis of emotions expressed in users’ online written reviews, using film reviews on the chinese social networking site douban.com as an example. the model extracts feature-sentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. user group personalized recommendations are realized through user clustering and user similarity calculation. experiments show that the extraction of user feature-sentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy. keywords: online review, sentiment analysis, feature-sentiment word pairs, personalized recommendation. https://doi.org/10.15837/ijccc.2020.1.3764 2 1 introduction with the popularity of the internet and the integration of the web into people’s daily lives, the number of people online continues to grow. according to the statistical report on the development of china’s internet, by june 2019, china had 854 million internet users, 61.2% internet penetration rate, and 847 million mobile internet users, up 29.84 million from the end of 2018, 99.1% of internet users use mobile phones to surf the internet [22]. regarding the scale of online video use, the number of users in china reached 759 million or 88.8% of all chinese internet users. each major video platform further subdivides into content categories with specialized production and operation based on core product types such as tv series, movies, variety shows and animation. the platforms are continuing to expand into emerging product types such as games, esports and music. when users go online, they leave behavioral information through browsers and other applications. through the mining of online user data such as purchase, browsing and rating behaviour, user preferences can be analyzed and applied to the personalized recommendations of goods or services this method, however, does not consider the user’s online reviews. with the development of web3.0, users can express their real opinions and preferences and are keen to do so, as seen in the increasing amount of evaluation information about products or services available on the internet. this evaluation information will often affect other users’ selection decisions. users’ reviews usually contain their subjective opinions and reflect their preferences and emotional tendencies towards the attributes of products or services. by analyzing users’ online reviews, we can discover not only the features of products and services and their advantages and disadvantages, but also users’ preferences and concerns. by combining the information in users’ online reviews with their basic personal information and online behavioral characteristics, the problems of sparse user data and system cold starts in the recommendation process can be better addressed and more accurate personalized recommendations can be realized. 2 literature review 2.1 online review user reviews reflect users’ sentiments and behavioral decisions [6] and contain users’ relevant knowledge, which will add to "internet word-of-mouth" and have an important impact on other users’ purchasing behaviors. at present, the research on the influence of online reviews on user behavior mainly focuses on the two aspects of "the total number of reviews" and "the polarity of reviews". the total number of reviews reflects the "heat" of goods or services, that is, the degree of hot sales. the more the total number of reviews, the more likely it is to arouse users’ potential interest and attention. some studies have shown that the number of online reviews on films is positively correlated with the final box office sales [2]. the "polarity" of reviews refers to the "positive" or "negative" nature of reviews, also known as "sentiment polarity" and "directional nature of reviews", which reflects customers’ satisfaction with goods or services [8]. in 2007, a. ghose and p. g. ipeirotis [9] firstly analyzed the influence of subjective and objective tendency and the degree of mixing of subjective and objective tendency on the usability of online reviews for search-type products. in 2010, s. m. mudambi [15] studied the influence of review polarity, review depth and commodity type on perceived review usefulness by establishing a user evaluation usefulness model. through numerical simulation, chen j et al. [4] studied how potential customers form their own opinions under the combined effect of positive and negative reviews. the results showed that users at middle and low levels are often persuaded by online reviews, regardless of their initial opinions on products. however, online reviews have less impact on consumers with higher membership levels, who often make purchase decisions based on their first impressions of the product. https://doi.org/10.15837/ijccc.2020.1.3764 3 2.2 sentiment analysis in the related researches at home and abroad, emotion analysis methods can be generally classified into three categories: emotion dictionary-based method, machine learn-based method and linguistic method. the method based on emotion dictionary to judge the polarity of the pending words, which can be regarded as the polarity matching task [17]. emotion analysis in recommendation systems mainly includes the extraction of attribute words and emotion words and the prediction of attribute words’ score [12, 13, 18]. some literature [12] describes the mining of attribute words and emotion words in users’ online reviews based on association rules, taking nouns or noun phrases with high frequency as attribute words. other literature [20] mentions the extraction of attribute words and emotion words from the review text, and then predicts users’ ratings of item attributes based on statistical methods. the approach based on linguistics is mainly to extract the syntactic features of words or phrases through linguistics. additional literature [1] uses word dependence to show how the emotions of a single phrase affect the emotions of a whole sentence. the key in machine-learning-based sentiment analysis is to construct a sentiment polarity classifier through a training set and then the classification of subjective reviews [19]. this method does not rely entirely on dictionary or corpus, but relies on the training effect of the classifier, and needs more time to complete the annotation of training data than the dictionary or corpus-based method. manek et al. [14] proposed a feature selection method for svm classifier based on the gini coefficient. each word in the text is not equally important to the sentiment analysis of the text, and the computer cannot automatically judge the importance of the word. deng et al. [5] proposed a supervised weight assignment scheme based on the importance of the word in the whole text and the importance of expressing emotion. review texts lack logicality and most texts are disordered. the supervision of the general learning algorithm accuracy is lower when dealing with disordered text. perikos [16] designed an integrated classifiers based on three classifiers: the first and the second are statistical (naive bayes and maximum entropy) and the third one is a knowledge-based tool performing deep analysis of the natural language sentences. 2.3 recommendation system current recommendation systems can be divided into two categories: content based recommendation and collaborative filtering recommendation. a content-based recommendation system [3] looks for similar items based on the similarity of content browsed by different users. its advantage is simple and effective, but its disadvantage is that it cannot mine the information that new users are interested in. collaborative filtering is the most common recommendation system. it was first proposed by goldberg and other scholars in 1992 and implemented in the tapestry experimental email system, which enabled users to extract email lists [10]. at present, collaborative filtering is mainly divided into either user-based or project-based collaborative filtering. the basic idea is to use scores to calculate user or product similarity, find the nearest neighbor collection and then make recommendations. collaborative filtering is a hot research topic in the academic circle. traditional collaborative filtering methods have disadvantages such as cold starts, sparse data, large amount of data and poor scalability. in order to better solve these problems, scholars are now focusing on the improvement of traditional collaborative filtering. wei wang and hongwei wang [21] have applied sentiment analysis technology to recommendation systems, calculated similarity through analyzing the preference polarity (positive and negative) of users for product features and improved the accuracy of the recommendation algorithm by increasing the precision of the nearest set of users. gan [7] divided the user’s sentiment into positive, negative, neutral and conflict types when analyzing the user’s review text. they then optimized the collaborative filtering score prediction model according to the emotional tendency expressed by the users to improve the accuracy of the recommendation algorithm. https://doi.org/10.15837/ijccc.2020.1.3764 4 3 recommendation model construction based on sentiment analysis the recommended process in this article can be described in the following steps, with specific research ideas shown in figure 1. • step 1. get the user id, user review, item id, and item score from the user’s online reviews. define n users u = (u1,u2,u3,u4...un), set d = (d1,d2,d3...dm)that represent m review texts for k items t = (t1, t2, t3...tk). • step 2. by using syntactic relation rules, extract sentiment words from each online review and use modified characteristic words to form the feature-sentiment word pairs. • step 3. according to the feature words extracted from the feature-sentiment word pairs, find the potential features of l items f = (f1,f2...fl), and then calculate the user’s attention λ to each feature of the item. • step 4. calculate the sentimental score of feature words in users’ online reviews, calculate the similarity between users, find out the nearest neighbor and make recommendations. film review corpus preprocessing label cleaning segmentation delete stop word part-of-speech tagging parsing extract feature sentiment word pairs according to specified syntax seed emotion word/adverb extraction of sentiment words extraction of feature words hownet semantic vocabulary similarity calculation feature set polarity judgment of sentiment adverbs of degree calculate the feature-sentiment score user similarity calculation recommended index calculation recommend hownet sentiment dictionary feature-sentiment words pair user clustering group recommend figure 1: schematic diagram of research ideas 3.1 commodity information acquisition in order to verify the actual effect of sentiment analysis on personalized recommendations, this paper adopts the scrapy method based on the python programming language to analyze data on the chinese movie social networking site douban, which mainly includes user id, douban id, user review and user score. the collected data set includes a total of 215,032 reviews, deletes users with less than 10 movies and all movies with less than 10 reviews in the data table, for a final total of 67376 reviews on 1464 movies by 2283 users. https://doi.org/10.15837/ijccc.2020.1.3764 5 3.2 film review processing based on feature-sentiment word pair extraction in order to explore the features of movies described by users and words expressed towards evaluation objects, this paper proposes a syntactic dependency-based extraction method for sentiment words. this method is mainly composed of two parts: (1) natural language processing technology is used to process the film review corpus, such as word segmentation, part-of-speech tagging, and syntactic analysis. the syntactic dependence between sentiment words and evaluation objects is explored. (2) use the syntactic dependency rules constructed in the first step to do pattern matching to identify the evaluation objects and sentiment words in the review sentences, to form feature-sentiment word pairs, and extract the feature-sentiment word pairs whose occurrence frequency exceeds 12. words describing film features are the evaluated objects in the review data. sentiment words, or opinion words, are the words used in the review text to express users’ positive or negative feelings towards the features of the item. for example, in the review "the picture is good", "the picture" represents the feature of the film, and the word "good" expresses a positive sentimental attitude. hu used the two syntactic dependencies of subject-verb relation and attribute relation to extract feature words and sentiment words [11] without considering the verb object relationship and the adverbial relationship, resulting in reduced accuracy in the result. therefore, in this paper, the seed word bank of sentimental words was added in the process of extracting feature words, and adverbial relation and adverb-object relation were added in the whole process of extraction. finally, this paper considers four kinds of dependency relations in the process of feature-emotion word pair extraction: (1) subject-verb relation (suv); (2) verb-object relation (vob); (3) attribute relation (att); (4) adverbial relation (adv). the review data is filtered according to these four dependencies. this method does not need the seed domain feature set, directly extracts real and reliable evaluation object and sentiment word information and does not require any domain knowledge base except the review data set of the item. in addition, it can be applied with good portability to any domain sentiment analysis if the new domain review data is provided. the extraction results of feature words and sentiment words are shown in table 1. table 1: preprocessing table relations processing content output suv the sentence after the participle image quality/ not high/ suv extract feature-sentiment word pairs <image quality, not high > vob the sentence after the participle he/send/a flower/to/her vob extract feature-sentiment word pairs <send, flower> att the sentence after the participle the movie/ has/ a perfect/ plot/ att extract feature-sentiment word pairs <plot, perfect> adv the sentence after the participle like /the movie /very much adv extract feature-sentiment word pairs < movie, like very much > 3.3 movie feature clustering based on dictionary after the extraction of online review feature-sentiment word pairs, the corresponding noun set was established by using chinese frequent noun non-item feature rules. the extraction results were filtered from the perspective of chinese semantic and grammatical knowledge and the frequent words irrelevant to film evaluation were removed. in this paper, a hownet-based semantic vocabulary similarity calculation method is used to cluster the feature words in the table. the hownet algorithm is shown in formula 1,where dis(w1,w2) represents the distance between the feature words w1 and w2, while sim(w1,w2) is the similarity between the feature words w1 and w2, and the parameter y can be adjusted according to the actual demand. when sim(w1,w2) is higher than the threshold value, w1 and w2 are grouped under the same feature. sim(w1,w2) = γ dis(w1,w2) + γ (1) with the help of the expanded hownet dictionary, the similarity among feature words was calculated. according to the similarity results, hownet classified the candidate feature words into different https://doi.org/10.15837/ijccc.2020.1.3764 6 table 2: attribute features feature word distribution table attribute feature feature word f1 plot background, story, play, theme f2 emotion emotion, touching, healing f3 figure figure, woman, man, actor, actress, movie king, chief actor, leading lady, character f4 acting skill acting skill, acting, expression, actor’s lines, language f5 staff behind the scenes director, scriptwriter, author f6 techniques of expression narrative, narration, interposed f7 plot development transition, paragraph bedding, paragraph f8 ending ending, finish, outset, surprise f9 impressions film quality, visual effect, photography, a fulllength shot, scenery, film editing, scene, exterior, shoot f10 sound effect ending, finish, outset, surprise f11 embedded value inspirational, encourage, edification, thinking, righteousness f12 early film old film, time, classics, history, reminiscence f13 affectional film love, affectional film, first love, romance f14 literary film literature and art, youth, warm and sweet f15 humanity life, society, humanity, live f16 youth film maiden, young boy, growth, dream f17 dracula movie dracula movie, terror, ghost, suspense, thriller f18 political film politics, social class, hierarchy f19 war file war, revolution, disaster, violence, bloodiness f20 science fiction film science fiction, science fiction film, magic, fairyland, alien, robot f21 film adapted from a play recompose, fiction, original, masterwork f22 comedy comedy, funnyman, humor, chaplin f23 chinese film china, chinese mainland, taiwan, hongkong and taiwan f24 american film europe and america, europe, america, foreign country, american, hollywood f25 korean film korean, korean film, han fan f26 japanese film japan, japanese film f27 anime cartoon, comic, anime, conan attribute feature levels. table 2 finally summarizes the 27 categories of features mentioned in the user online reviews. formula 2 is used to calculate the user value attention to each feature film, statistics of users from the set of all reviews various types of key word frequency as molecules, respectively of the total number of key words to users as the denominator, calculate the proportion of the number of key, namely the https://doi.org/10.15837/ijccc.2020.1.3764 7 attention of users for each commodity characteristics, formation of user preference vectors. λi = ∑ ufi fi (2) 3.4 sentiment word score calculation sentimental tendency can be divided into three categories: positive, negative and neutral. the function of sentiment analysis is to analyze and infer the sentimental tendency of users. there are two main categories of mainstream sentimental tendency polarity discriminant methods: one is based on the dictionary discriminant method, the other is based on the review corpus discriminant method. the dictionary discriminant method judges the polarity of the pending word by calculating the similarity between the pending word and the reference word in the standard semantic dictionary. in chinese sentiment analysis, the most common way is to expand the sentiment dictionary based on the hownet semantic dictionary or synonym word forest. the review corpus discriminant method relies on lexical cooccurrence frequency or modifier relationship for polarity judgment, such as pmi value and syntactic dependency. currently, in the study of the identification of sentimental orientation, the artificial sentiment dictionary has a good application and can accurately distinguish the polarity of sentences and words, but it needs a lot of time and manpower to construct. in the existing research, some scholars identify the sentiment polarity of the unincluded words with the help of the synonym word forest and explore the dependency relationship between the sentiment words and the evaluation objects through syntactic analysis. in order to achieve fine-grained sentiment analysis, some scholars have explored the influence of degree adverbs and their grades on sentimental intensity. this paper adopts the analysis method based on the hownet sentiment dictionary to identify and calculate the polarity of sentiment words. the hownet sentiment dictionary itself contains six subfiles, such as the "positive sentiment" thesaurus, the "negative sentiment" thesaurus, the "degree" level thesaurus, and the "proposition" thesaurus. however, online reviews are highly personalized, and many sentiment words are not included in the standard sentiment dictionary and cannot be recognized. in the process of distinguishing the polarity of sentiment, the degree of closeness between the sentiment word and the reference word of positive meaning (or negative meaning) in the dictionary is calculated first and then the polarity of the sentiment word is determined by the semantic similarity. for a word "w", the polarity discrimination based on the hownet emotion dictionary is shown in formula 3. so−hownet(w) = n∑ i=1 sim(w,comi) − n∑ i=1 sim(w,deri) (3) sim(w,comi)represents the similarity between w and hownet’s commendatory term comi, while sim(w,deri)represents the similarity between w and hownet’s derogatory term deri. if the calculated so-hownet(w) value is positive, then the polarity of the emotional word is positive. if it is negative, the polarity of emotion words is negative. if it is 0, then the emotion word polarity is neutral. in order to realize fine-grained sentiment analysis, it is necessary not only to accurately judge the polarity of sentiment, but also to fully consider the modification function of degree adverbs on sentiment words to provide support for the classification of degree adverbs into analysis of different granularity. in order to distinguish the differences in the emotional intensity of users and take into account the influence of degree adverbs on emotional intensity and the effect of negative words on degree adverbs, this paper divides degree adverbs and negative adverbs into five levels, and sets the corresponding polarity according to the intensity of their emotional expression(see table3). based on the polarity coefficient of degree adverbs given in table 3, the words that do not contain degree adverbs are defined as basic sentiment words, and the words that contain degree adverbs are defined as compound sentiment words. therefore, the words that conform to the compound sentiment words have the following three categories: (1) positive adverbs/negative adverbs + basic sentiment words, indicating the affirmation or negation of the part of speech of the feature words. the sentimental value calculation formula is shown in formula 4. sw = vwi ∗so−hownet(wi) (4) https://doi.org/10.15837/ijccc.2020.1.3764 8 table 3: degree adverb polarity coefficient level category intensity example polarity coefficient 1 positive adverb most extremely 1.6 2 positive adverb quite especially, particularly 1.4 3 positive adverb rather further, relatively 0.8 4 positive adverb slightly a bit, a little 0.6 5 negative adverb negative adverb no,not -1 sw represents sentiment word score, so−hownet(wi) represents the sore of basic sentiment word wi , and vwi represents degree adverb polarity coefficient. (2) positive adverb + negative adverb + basic sentiment word. after degree adverb, this form of negative word is the determination of negative action. the calculation formula for the score of sentiment shown in formula 5. sw = −(so−hownet(wi)) ∗dwi (5) sw represents sentiment word score, so−hownet(wi) represents basic sentiment word score, and dwi represents positive adverb polarity coefficient. (3) negative adverbs + positive adverbs + basic emotion words. a negative word before the positive adverb will weaken the negative degree of basic sentiment words. the calculation formula of sentiment words is shown in formula 6. sw = η ∗ (so−hownet(wi)) ∗dwi (6) sw represents the score of emotion words,η represents the degree of weakening of negative words, and its value is between 0 and 1. so−hownet(wi) represents the score of basic sentiment words wi. dwi represents the polarity coefficient of positive adverbs. 4 user recommendation based on feature emotion score 4.1 calculation of sentimental value of feature words use the hownet evaluation dictionary based on the sentimental polarity calculation method to determine the polarity of feature words, and use formulas 3, 4, 5 to calculate the sentimental value of compound sentimental words. thus, the emotional score gkn of user u on the nth characteristic word of the kth attribute feature of a specified item t is obtained. considering the actual situation, users may use different words to express opinions on the same item features for many times in a review, so formula 7 is adopted to calculate the average score of user u on the fk attribute features of the item. sk = ∑n i=1 gkn n (7) according to formula 6, the sentimental score of item features in each user reviews can be obtained. by analyzing all the historical reviews of a certain item, the score of different attribute features of the item can be calculated. the score of the feature is expressed by the average score of sentiment of the feature. the calculation formula of the feature score is shown in formula 8. n represents the n reviews on an item, and rk represents the average sentimental score on the kth trait of an item. rk = ∑n i=1 sik n (8) https://doi.org/10.15837/ijccc.2020.1.3764 9 4.2 user similarity calculation in this paper, the cosine similarity method is adopted to calculate the similarity of user a and user b in the review text. sim(a,b) represents the similarity between user a and user b, ra represents the review set of user a, rb represents the review set of user b, ka represents the feature set in user a’s reviews, and ka∩kb represents the intersection of feature levels in user a and b’s reviews. sim(a,b) = ∑ (ra ∩rb) ∗ ∑ (ka ∩kb)√∑ ra 2 ∗ ∑ ka 2 ∗ √∑ rb 2 ∗ ∑ kb 2 (9) 4.3 user clustering according to formula 7, the item sentiment score of users can be obtained. the kmeans algorithm is used to cluster the item sentiment score and divide users into 7 categories. the user similarity calculated by formula 9 can be used to find the nearest neighbor of the target user’s group. user reviews with id a5***7b are numerous and always provide high quality reviews. therefore, "a5***7b" is selected as the target user to find its "nearest neighbor" according to the user similarity, and its nearest neighbor and similarity are shown in table 4. table 4: recommended results serial number userid similarity 1 73***41 0.6971 2 bc***11 0.4127 3 42***65 0.3795 ... ... ... 4.4 item recommendation generation first, according to formula 7, the user’s score on item features was calculated to obtain the userfeature score matrix, and then the similarity among users was obtained from formula 9 to obtain the k "nearest neighbors" of the target user. then, the candidate items to be recommended are obtained according to the historical review of the "nearest neighbor", and the score of each feature of all candidate items is calculated by using formula 8. finally, the personal preference of target users was integrated into equation 10 to predict the score of candidate items. r = 27∑ k=1 rk ∗λk (10) rk is the predicted score of candidate projects, which is given by formula 8. λk is the target user’s attention to the kth feature, given by formula 2. the final predicted score of each candidate project is sorted in descending order to form the final recommendation list and push to the target user, as shown in table 5. 5 experiment results and analysis 5.1 data preparation in order to verify the actual effect of personalized recommendation based on emotion analysis, this paper adopts scrapy method based on python to obtain movie information of douban, including user id, douban id, user review and user score. the collected data set includes 215032 reviews, delete users with less than 10 movies and all movies with less than 10 reviews in the data table, and get a total of 2283 users’ 67376 reviews on 1464 movies. https://doi.org/10.15837/ijccc.2020.1.3764 10 table 5: recommended results serial number movieid predicted score 1 10514820 1.695 2 4824996 1.639 3 26592180 1.596 4 6833818 1.479 5 1308575 1.479 6 1444533 1.421 7 3718526 1.369 ... ... ... 5.2 assessment indicator predictive accuracy p represents the probability that the user may like an item in the recommendation list, which can show the accuracy of the recommender system. the formula to calculate predictive accuracy of recommender system is as follow: precision = truepositive truepositive + falsepositive (11) recall rate r represents the proportion of items users like in the recommendation list, which can show users’ satisfaction degree with the recommendation results. the higher the recall rate is, the higher satisfaction degree users have. the formula to calculate the recall rate of recommender system is as follow: recall = truepositive truepositive + falsenegative (12) 5.3 result analysis (1)extraction effect of feature sentiment words pairs taking douban film review data as an example, based on previous studies, this paper proposes to employ user review data for sentiment analysis to explore features of films and preferences of individual users, improve feature and emotion word extraction methods, and summarize four syntactic dependencies. based on the four syntactic dependencies, the user reviews are analyzed with fine granularity, which can effectively improve the extraction accuracy of feature and sentiment words. in order to verify the effectiveness of the feature-sentiment words pairs extraction method proposed in this paper, 1000 reviews were randomly selected from the review data set, and feature words and sentiment words were manually labeled, resulting in 75 feature words and 113 sentiment words. then, using the method proposed by hu and liu as the benchmark, the extraction method of feature sentiment words pairs proposed in this paper is compared. in this paper, accuracy and recall rate are selected as evaluation indexes, in which accuracy refers to the percentage of extracted correct feature words and sentiment words, and recall rate refers to the percentage of extracted correct feature words and emotion words in labeled words. the experimental results are shown in tables 6 and 7. table 6: feature word extraction results methods accuracy rate /% recall rate /% hu 52.11 37.25 extraction method in this paper 53.76 63.44 (2)recommendation effect analysis by analyzing users’ online reviews, this paper classifies movie features into 27 categories to further refine movie features and then statistically obtains users’ attention to various attribute features from users’ own review collection to form user preferences. user preference https://doi.org/10.15837/ijccc.2020.1.3764 11 table 7: emotional word extraction results methods accuracy rate /% recall rate /% hu 52.11 37.25 extraction method in this paper 53.76 63.44 is integrated to predict the score of recommended items, find the most consistent with user preference and generate a recommendation list. the traditional recommendation algorithm based on score only identifies as the nearest neighbor the users with the same score as the target users and then recommends the favorite items to the target users while ignoring the real preference of the users. recommendation accuracy is thus low. however, the recommendation model proposed in this paper considers the implicit preference mined from user reviews and shows a good recommendation effect. every recommended item can meet the preference requirements of users in some aspect. compared with the traditional recommendation algorithm based on score, the recommendation method in this paper is obviously improved in accuracy and recall rate. the results of the precision and recall rates of user-based collaborative filtering, projectbased collaborative filtering and the proposed methods in this paper are shown in table 8. table 8: feature word extraction results methods accuracy rate /% recall rate /% usercf 22.45 22.50 itemcf 21.44 22.39 recommended algorithm in this paper 25.53 26.49 (3) degree adverb polarity intensity quantification this study shows that the polarity of emotion words is easy to determine and that degree adverbs represent the degree of words or sentence which can strengthen or weaken the strength of emotion words in the application of emotion analysis. in addition, if the order of degree adverbs and negative words is different, the polarity of emotion words expressed will be greatly different. this paper gives full consideration to the influence of degree adverbs on emotional intensity, defines five polarity coefficients of degree adverbs, and considers the co-occurrence order of degree adverbs and negative words. in chapter 3.4, a somewhat innovative set of computing standards for emotional intensity that conforms to the general expression of users is designed to conduct fine-grained emotional scores and improve the accuracy of emotional analysis of user reviews. (4) user clustering effect the recommendation model in this paper belongs to the user-based collaborative filtering approach but is different from traditional user-based collaborative filtering in that it fully considers the user preference information implied in user reviews. traditional methods cluster users based on ratings only so that two users with completely different preferences who give the same ratings are clustered together. for example, users who habitually give favorable reviews are always grouped together even though their actual preferences and needs are very different. in this paper, a novel recommendation model based on emotion analysis is proposed. based on the emotional score of movie features, users are clustered, and users with the same emotional preference are gathered to form a user group. through the emotional analysis of user reviews, the fine-grained preferences of users for specific commodities are obtained for clustering and the similarity of the overall score of items by users in the traditional collaborative filtering is transformed into similarity based on the attributes and characteristics of items. at the same time, the accuracy of recommendation is improved by considering the preferences of users. by selecting the historical videos of other users with high similarity in the user group, calculating the scores of all candidate items with various features, and integrating the personal preferences of target users, group recommendation in the process of personalized recommendation is achieved. the user clustering effect has a direct impact on the movie recommendation. therefore, the method in this paper indirectly improves the recommendation effect by effectively improving the user clustering effect. https://doi.org/10.15837/ijccc.2020.1.3764 12 6 conclusion due to the ever-increasing number of people online and the diversity of internet activity, research on improving the accuracy of persona lized recommendation can be endless. however, the current recommendation method only explores the individual needs of a user based on their past purchase behaviors and ratings and ignores personal preferences expressed in online reviews. user reviews have become a growing information carrier. it is a new perspective for personalized recommendation research to deeply explore user review content with emotion analysis as the main means. this paper proposes a personalized recommendation model based on emotion analysis to solve the problem of information overload of users’ online reviews. taking douban movies as an example, this paper proposes the analysis of user reviews to explore features of movies and preferences of individual users. in addition, this paper improves the extraction methods of feature words and emotion words and summarizes four syntactic dependencies. based on the four syntactic dependencies, it carries out fine-grained analysis of user reviews to effectively improve the extraction accuracy of feature words and emotion words. in addition, considering the co-occurrence order of degree adverbs and negative words, a set of computing standards for emotional intensity that conforms to the conventional expression of users is designed to calculate fine grained emotional scores and improve the accuracy of emotional analysis of user reviews. a novel recommendation model based on emotion analysis is proposed. the model converts the similarity of the overall score of items in traditional collaborative filtering to the similarity of the score based on the attributes of items, improves the clustering effect of users, and considers the preferences of users to improve the accuracy of recommendation. based on the emotional score of the movie features, the users were clustered, and the users with the same emotional preference were gathered to form a user group. group recommendation was realized in the personalized recommendation process to improve the recommendation efficiency. funding this work was funded by research on scene behavior intelligence in collaborative network based on data analysis (nsfc 61972029). author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. references [1] amplayo, r.k.; song, m..(2017). an adaptable fine-grained sentiment analysis for summarization of multiple short online reviews, data and knowledge engineering. 110, 54-67, 2017 [2] chakraborty, k.; bhattacharyya, s.; bag, r. ; hassanien, a.a.(2019). sentiment analysis on a set of movie reviews using deep learning techniques, social network analytics, 127-147, 2019. 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(2019). sentiment analysis method based on piecewise convolutional neural network and generative adversarial network, international journal of computers communications & control, 14(1),7-20, 2019. https://doi.org/10.15837/ijccc.2020.1.3764 13 [7] gan, q.; ferns, b.h.; yu, y.; jin, l.(2017). a text mining and multidimensional sentiment analysis of online restaurant reviews, journal of quality assurance in hospitality and tourism, 18(4), 465-492, 2017. [8] gifu, d; cioca, m.(2019). detecting emotions in comments on forums, international journal of computers communications & control, 9(6), 694-702, 2019. [9] ghose, a.; ipeirotis, p.g.(2007). designing novel review ranking systems: predicting the usefulness and impact of reviews, proceedings of the ninth international conference on electronic commerce. acm, 303-310, 2007. [10] goldberg, d.; nichols, d.; oki, b.m. et al. (1992). using collaborative filtering to weave an information tapestry, communications of the acm, 35(12), 61-70, 1992. 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[22] [online]. available: www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201908/.html https://doi.org/10.15837/ijccc.2020.1.3764 14 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: chen, s.; lv, x.; gou j.(2020). personalized recommendation model: an online comment sentiment based analysis, international journal of computers communications & control, 15(1), 1004, 2020. https://doi.org/10.15837/ijccc.2020.1.3764 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 4, month: august, year: 2022 article number: 4865, https://doi.org/10.15837/ijccc.2022.4.4865 ccc publications approximating the level curves on pascal’s surface m. jianu, l. dăuş, m. nagy, and r.-m. beiu marilena jianu* department of mathematics and computer science technical university of civil engineering bucharest, romania 020396 bucharest, blvd. lacul tei 124, romania *corresponding author: marilena.jianu@utcb.ro leonard dăuş department of mathematics and computer science technical university of civil engineering bucharest, romania 020396 bucharest, blvd. lacul tei 124, romania leonard.daus@utcb.ro mariana nagy faculty of exact sciences “aurel vlaicu” university of arad 310330 arad, elena drăgoi 2, romania mariana.nagy@uav.ro roxana-mariana beiu faculty of food engineering, tourism and environmental protection “aurel vlaicu” university of arad 310330 arad, elena drăgoi 2, romania roxana.beiu@uav.ro abstract it is well-known that in general the algorithms for determining the reliability polynomial associated to a two-terminal network are computationally demanding, and even just bounding the coefficients can be taxing. obviously, reliability polynomials can be expressed in bernstein form, hence all the coefficients of such polynomials are fractions of the binomial coefficients. that is why we have very recently envisaged using an extension of the classical discrete pascal’s triangle (which comprises all the binomial coefficients) to a continuous version/surface. the fact that this continuous pascal’s surface has real values in between the binomial coefficients makes it appealing as being a mathematical concept encompassing all the coefficients of all the reliability polynomials (which are integers, as resulting from counting processes) and more. this means that, the coefficients of any reliability polynomial can be represented as discrete steps (on level curves of integer values) on pascal’s surface. the equation of this surface was formulated by means of the gamma function, for which quite a few approximation formulas are known. therefore, we have started by reviewing many of those results, and have used a selection of those approximations for the level curves problem on pascal’s surface. towards the end, we present fresh simulations supporting the claim that some of these could be quite useful, as being both (reasonably) easy to calculate as well as fairly accurate. keywords: pascal’s triangle, pascal’s surface, binomial coefficients, reliability polynomials. https://doi.org/10.15837/ijccc.2022.4.4865 2 1 introduction and motivations the idea of starting from the well-known discrete pascal’s triangle and generalizing it to a continuous pascal’s surface is not new [32, 33, 34, 35, 47, 63, 69, 70]. still, the novelty of this paper steams from our aim to: (i) use pascal’s surface for a particular application (network reliability), and (ii) explore level curves on pascal’s surface (see also [7]). in computer engineering reliability was not always a hot topic. it was having its days at the dawn of computing, when the technology was mechanical [4], and yet again when transitioning to electromechanical relays and vacuum tubes [54, 55, 80]. the brisk evolution towards few nanometer transistors makes it once again prominent (see [27, 41, 43]), while fresh quantum computing developments are only adding to that [20, 42, 75]. it is in this context that we have started to revisit results on network reliability [54, 55], by looking into consecutive [5, 23] and hammock networks [22]. that is how we slowly realized that the original goal of moore and shannon—of finding networks for enhancing the reliability of computations—has been slowly shifting over time towards networks for reliable communications [6]. still, with scaling, both computations and communications are being affected by variations [8], so a deeper understanding of reliability polynomials should in the end prove beneficial, and pascal’s surface seems to be providing a different view which starts from their coefficients. the paper is structured as follows. section 2 will show how one could extend the discrete pascal’s triangle to a continuous surface. in section 3 we will go over quite a few approximations of the gamma function, mentioning improvements advanced over the years. further, we shall rely on some of these approximations for estimating the binomial coefficients, while also mentioning a few direct approximations. finally, section 4 will introduce the bernstein form of the reliability polynomials and bring all the pieces together by presenting preliminary simulation results. conclusions and further directions for research are going to end the paper. 2 state-of-the-art 2.1 pascal’s triangle a well-known triangular arrangement of integers is made of binomial coefficients. this is pascal’s triangle which has long been recognized from india (pingala ca. 2nd century bc, and halayudha ca. 10th century) to persia (al-karaj̄ıca. 953-1029, omar khayyam 1048-1131, and nasir al-din al-tusi 1201-1274), and china (jia xian ca. 1010-1070, yang hui ca. 1238-1298, and zhu shijie 1249-1314), as well as europe, where it was mentioned and/or drawn in many earlier publications (see [81]): 1. jordanus de nemore around 1225 has drawn it on page 80 (pages are hand counted) in his “de elementis arithmetice artis” [14]; 2. petrus apianus in 1527 has drawn it on the cover of his “eyn newe vnnd wolgegründte underweysung aller kauffmannß rechnung in dreyen büchern” [3]; 3. michael stifel in 1544 (“arithmetica integra” [73]); 4. niccolò tartaglia in 1556 (“general trattato di numeri et misure” [76]); 5. gerolamo cardano in 1570 (“opus novum de proportionibus numerorum” [15]); and 6. marin mersenne in 1636 as “table de variétés d’un chant de 12 notes prises en 36” (harmonie universelle, book 2: des chants, [51]), as well as in “harmonicorum libri xii,” published also in 1636 [52]. it is extremely likely that blaise pascal has familiarized himself with a particular arrangement of the binomial coefficients while visiting mersenne, both [51] and [52] being published in 1636, where the binomial coefficients were shown arranged in a rectangle. pascal wrote his traité du triangle arithmétique in 1654 [61], which was in fact printed only a decade later, in 1665. the following figure reproduces the triangle in pascal’s original writing (highlighting 1, 5, 10, 10, 5, 1). https://doi.org/10.15837/ijccc.2022.4.4865 3 figure 1: the triangle in pascal’s own writing (available from the national library of france, https://gallica.bnf.fr/ark:/12148/btv1b86262012/f1.image, accessed on 06.06.2022). as far as we know, it was pierre raymond de montmort who called this arrangement “table de m. pascal pour les combinaisons” in 1708, while around 1730 abraham de moivre named it “triangulum arithmeticum pascalianum.” this is how most of us call it today, but it is common in italy to sometimes call it tartaglia’s triangle, while still being called khayyam’s triangle in iran, and yang hui’s triangle in china. for details, the interested reader should consult not only [61], but also [10, 19, 31, 38, 49, 79]. the discrete pascal’s triangle has seen several embodiments, probably starting with the staircase of mountain meru (meru-prastāra). one common representation is l (∞), which is an infinite lower triangular matrix: l (∞) =   1 1 1 1 2 1 1 3 3 1 1 4 6 4 1 1 5 10 10 5 1 ... ... ... ... ... ... ...   (1) where l(∞)i,j = (i j ) , with (i j ) = 0 when i < j. this can be seen in fig. 2. 10987 j 654321123 456 i 789 10 40 60 80 100 0 20 120 140 b in o m ia l co e ff ic ie n t (a) 10987 j 654321123 456 i 789 10 5 1 2 3 4 0 b in o m ia l co e ff ic ie n t (b) figure 2: discrete pascal’s triangle l (∞) represented graphically as 3d bars: (a) linear scale; (b) logarithmic scale. another well-known form is p (∞), which is an infinite symmetric matrix: https://doi.org/10.15837/ijccc.2022.4.4865 4 p (∞) =   1 1 1 1 1 1 · · · 1 2 3 4 5 6 · · · 1 3 6 10 15 21 · · · 1 4 10 20 35 56 · · · 1 5 15 35 70 126 · · · 1 6 21 56 126 252 · · · ... ... ... ... ... ... ...   (2) where p(∞)i,j = (i+j i ) , for i,j ≥ 1 (see fig. 3), and satisfying p (∞) = l (∞) ·l(∞)t. 10987 j 654321123 456 i 789 10 120 100 80 60 40 20 0 140 b in o m ia l co e ff ic ie n t (a) 10987 j 654321123 456 i 789 10 2 1 0 5 4 3 b in o m ia l co e ff ic ie n t (b) figure 3: discrete pascal’s triangle p (∞) represented graphically as 3d bars: (a) linear scale; (b) logarithmic scale. 2.2 pascal’s surface an extension of the discrete pascal’s triangle to real numbers was introduced by fowler in [33] ([34] and [35] followed shortly afterwards). this paper took the integer binomial coefficients (n k ) and extended them to real numbers (y x ) , 0 ≤ x ≤ y, by substituting the factorials with equivalent γ functions (since γ(x + 1) = x! for any positive integer x). (a) (b) figure 4: pascal’s surface for p (∞) presented in fig.3: (a) linear scale; (b) logarithmic scale. fowler mentions in [33] that the resulting surface is pascal’s triangle “interpolated to a steeply rising ridge.” it is an extension of l(∞)i,j (presented in fig. 2), while in this paper we are going to use an extension of p(∞)i,j (presented in fig. 3) as: https://doi.org/10.15837/ijccc.2022.4.4865 5 p (x,y) = ( x + y x ) = (x + y) ! x! y! = γ (x + y + 1) γ (x + 1) γ (y + 1) (3) ln [p (x,y)] = ln [γ (x + y + 1)] − ln [γ (x + 1)] − ln [γ (y + 1)] (4) which can be seen in fig. 4, and explore level curves (see [28]) on this surface (for preliminary results see [7]). 3 approximations 3.1 gamma function stirling’s approximations of factorials are formulas and series first established around 1730 (see [1, 36, 39], and https://en.wikipedia.org/wiki/stirling%27s_approximation). table 1: approximations of γ(x + 1) author formula reference(s) de moivre 2.5074 × √ x ( x e )x 1730 [26, 48, 62] (series) √ 2πx ( x e )x e ( 1 12x− 1 360x3 + 1 1260x5 − 1 1680x7 ±... ) 1732 [48, 62] stirling √ 2πx ( x e )x [the constant is √2π = 2.506628] 1730 [74, 78] (series) √ 2πx ( x e )x (1 + 112x + 1288x2 − 13951840x3 ± . . .) p.-s. laplace burnside √ 2π ( x+1/2 e )x+1/2 1917 [13] stated by de moivre and stirling (see [30, 77]) 1730 [26, 74] ramanujan √ 2πx ( x e )x(1 + 12x + 18x2 + θ240x3 )16 , 0.3 < θ < 1 1920 [66, 67] robbins √ 2πx ( x e )x e 1 12x 1955 [68] gosper √ 2πx ( x e )x√1 + 16x 1978 [37] windschitl √ 2πx ( x e )x[ x sinh ( 1 x )]x 2 2002 [71, 82] nemes √ 2πx ( x e )x(1 + 1 12x2 − 110 )x 2010 [59] mortici √ 2πx ( x e + 112ex )x 2011 [57] the first formula is obtained by approximating ln(n! ) with an integral: ln(n! ) = n∑ k=1 ln k ∼= ∫ n 1 ln xdx = n ln n−n + 1. https://doi.org/10.15837/ijccc.2022.4.4865 6 table 2: approximations of ln [γ(x + 1)] approximation formula year integral x ln x−x + 1 1730 stirling x ln x−x + 12 ln (2πx) 1730 burnside ( x + 12 ) ln ( x + 12 ) − ( x + 12 ) + 12 ln (2π) 1917 (1730) robbins ln x−x + 112x + 1 2 ln (2πx) 1955 (1732) windschitl 3x2 ln x−x + x 2 ln [ sinh ( 1 x )] + 12 ln (2πx) 2002 moving from the prescient formulas of stirling and de moivre to those of ramanujan and beyond, two directions of research can be identified. one was to provide simpler proofs, like, e.g., [53, 58], while the other/major direction was on enhancing accuracy. from this trend we mention [40] (improving on ramanujan) and [18] (improving on windschitl), with [83] presenting the latest windschitl variations, while [56] is more on reviewing/comparisons. all those exhibiting higher accuracies and speeds (of convergence) seem to be trading these for more and more cumbersome formulas (see [16, 45, 46, 65]). here we have decided to rely on only a handful of the simpler formulas, and our selection is the one presented in table 2. 3.2 binomial coefficients a particular problem raised by sir alexander cuming in 1721 was to find a good (and fast) approximation of the ratio of the middle term of (1 + 1)2n to its sum. this was the starting point of de moivre’s quest for an approximation to the central term of the binomial expansion [26]. other approximations of the binomial coefficients are presented in [47], on the lines of the de moivre-laplace approximation, but it is clearly mentioned that using good approximations of the gamma function one could obtain similar accuracies (see [11, 50, 72]). that is why we shall start with the integral approximation we have presented in table 2, namely ln [γ (x + 1)] ∼= x ln x−x + 1. using this particular approximation together with eqs. (3) and (4), we get a first approximation p1 (x,y) of p (x,y): ln [p1 (x,y)] = [(x + y) ln (x + y) − (x + y)] − (xln x−x) − (yln y −y) − 1 = (x + y) ln (x + y) − (xln x + yln y) − 1 = ln [ 1 e · (x + y)(x+y) xxyy ] p1 (x,y) = 1 e · (x + y)(x+y) xxyy . proceeding in a similar fashion for the other four approximations presented in table 2 (i.e., stirling, burnside, robbins, and windschitl) we get p2 (x,y), p3 (x,y), p4 (x,y), and p5 (x,y). we omit the details and present these five approximations of p (x,y) in table 3, as a tabular form allows for easier comparisons. 4 applications https://doi.org/10.15837/ijccc.2022.4.4865 7 table 3: several approximations for binomial coefficients. γ approximation binomial approximation integral p1 (x,y) = 1e · (x+y)(x+y) xxyy stirling p2 (x,y) = 1√2π · (x+y)(x+y+1/2) x(x+1/2)y(y+1/2) burnside p3 (x,y) = √ e√ 2π · (x+y+1/2) (x+y+1/2) (x+1/2)(x+1/2)(y+1/2)(y+1/2) robbins p4 (x,y) = 1√2π · (x+y)(x+y+1/2) x(x+1/2)y(y+1/2) ·e− x2+xy+y2 12xy(x+y) windschitl p5 (x,y) = 1√2π · (x+y)(3x+3y+1)/2 x(3x+1)/2y(3y+1)/2 · [ sinh ( 1 x+y )](x+y)/2 [sinh( 1x )] x/2[ sinh ( 1 y )]y/2 4.1 bernstein polynomials bernstein polynomials are linear combinations of bernstein basis polynomials (see also [2]). they were introduced in 1912 [9] as: bn (f; x) = n∑ i=0 f ( i n )( n i ) xi(1 −x)n−i (5) and used for a constructive proof of the weierstrass approximation theorem. 4.2 reliability polynomials for characterizing network reliability moore and shannon introduced reliability polynomials [54], associating them to networks n formed by perfectly reliable nodes interconnected by links failing with probability 1 −p, hence rel (n; p) represents the probability of some particular nodes (e.g., source s and terminus t) being connected. moore and shannon expressed this as: rel (n; p) = n∑ i=0 nip i(1 −p)n−i (6) where n is the total number of links of n and the coefficients n0,n1, . . . ,nn. represent the number of functional sub-networks with i links. hence this form is in terms of pathsets. other forms are possible, e.g., in terms of cutsets, or complements of pathsets (see [21]). for more details the interested reader should consult [12, 17, 29, 64]. in this paper we are going to use eq. (6) as matching eq. (5), ni = αi (n i ) being fractions αi = f ( i n ) ≤ 1 of the binomial coefficients (n i ) , hence belonging to pascal’s surface. for all the simulations we are going to present here we have used the hammock network of w = l = 3 (h3,3) as an example (for details regarding hammock networks see [22, 24, 25, 44, 54]). if q = 1−p, the reliability polynomial for h3,3 can be written in bernstein form as: rel(h3,3; p) = 8p3q6 + 42p4q5 + 84p5q4 + 76p6q3 + 36p7q2 + 9p8q + p9. (7) the level curves of interest are those for ni = 8, 42, 84, 76, the other coefficients being either zeros (the first three), or equal to the binomial ones (the last three). https://doi.org/10.15837/ijccc.2022.4.4865 8 x 0 2 4 6 8 y 0 1 2 3 4 5 6 7 8 9 exact integral stirling burnside robbins windschitl (a) x 1.15 1.2 1.25 1.3 1.35 1.4 1.45 y 4.15 4.2 4.25 4.3 4.35 4.4 4.45 (b) x 0 2 4 6 8 y 0 1 2 3 4 5 6 7 8 9 exact integral stirling burnside robbins windschitl (c) x 2.95 3 3.05 3.1 3.15 3.2 3.25 y 3.95 4 4.05 4.1 4.15 4.2 4.25 (d) x 0 2 4 6 8 y 0 1 2 3 4 5 6 7 8 9 exact integral stirling burnside robbins windschitl (e) x 4.45 4.5 4.55 4.6 4.65 4.7 4.75 y 3.45 3.5 3.55 3.6 3.65 3.7 3.75 (f) x 0 2 4 6 8 y 0 1 2 3 4 5 6 7 8 9 exact integral stirling burnside robbins windschitl (g) x 5.7 5.75 5.8 5.85 5.9 5.95 6 y 2.7 2.75 2.8 2.85 2.9 2.95 3 (h) figure 5: level curves corresponding to the coefficients of rel(h3,3; p) using different approximations: (a) 8; (c) 42; (e) 84; (g) 76; (b), (d), (f), (h) zoom-in (on the corresponding small magenta rectangle from (a), (c), (e), (g)). we have performed simulations for all the coefficients ni using the approximations from table 3, and the results can be seen in fig. 5. in particular, each binomial coefficient (9 i ) corresponds to a coefficient ni of the reliability polynomial rel(h3,3; p). the intersection of pascal’s surface z = p(x,y) https://doi.org/10.15837/ijccc.2022.4.4865 9 (defined by eq. (3)) with the vertical plane of equation x + y = 9 is a curve (the blue curve in fig. 6) containing all the points of coordinates ( i, 9 − i, (9 i )) . we construct the plane parallel to the plane x = y (the symmetry plane of the surface), through each of these points, for i = 3, 4, 5, 6. we denote by pi (xi, yi,ni) the point of intersection with the level curve p(x,y) = ni. for completeness, the exact (xi, yi,ni) coordinates for i = 3, 4, . . . , 9 are reported in table 4. table 4: the coefficients ni of rel(h3,3; p), together with their xi and yi coordinates. i 0 1 2 3 4 5 6 7 8 9 p0q9 p1q8 p2q7 p3q6 p4q5 p5q4 p6q3 p7q2 p8q1 p9q0 ni 0 0 0 8 42 84 76 36 9 1 xi − − − 1.32113 3.14345 4.68527 5.92722 7 8 9 yi − − − 4.32113 4.14345 3.68527 2.92722 2 1 0 the simulations presented in fig. 5 reveal that the integral approximation is slightly off, with stirling and burnside doing way better, but still leaving room for improvements (see zoom-in in the left column of fig. 5). encouragingly, both robbins and windschitl approximations appear to be performing fairly well (many more supporting simulations are going to be reported in a follow-up paper). following on the 3d geometrical insights/coordinates we have gathered, we decided to compute the lagrange interpolation polynomial l (x) which is linking up the points (xi, yi), for i = 3, 4, . . . , 9: l (x) = 4.4602 − 0.11725x−0.0096x2+0.02393x3− −0.00847x4+0.00083x5−0.00003x6 the smooth curve defined by r(x) = (x,l(x),p(x,l(x))), x ∈ [1, 9], belongs to pascal’s surface and is interconnecting the points (xi,yi,ni), i = 3, 4, . . . , 9. 5 conclusions in this paper we have considered an extension of pascal’s triangle to a continuous surface, on which the coefficients of any reliability polynomial represented in bernstein form are to be found at discrete steps (as being on level curves of integer values). we have approached the level curve problem on this pascal’s surface using approximations of the gamma function, and have reviewed the literature on factorial/gamma approximations. we have evaluated through simulations how accurate such approximations would allow us to estimate the level curves. in particular, we have presented for the first time ever simulation results for the reliability polynomial of a small (3×3) hammock network mapped onto pascal’s surface (synthesized in fig. 6). we plan to continue working on this approach, and expect to report many more simulation results in a follow-up paper. lastly, we think that pascal’s surface is a mathematical concept which might deserve additional attention (see [60]). funding this research was partially supported by the romanian ministry of education and research, cncs-uefiscdi, project no. pn-iii-p4-id-pce-2020-2495, within pncdi iii (thunder2 = techniques for unconventional nano-designing in the energy-reliability realm). https://doi.org/10.15837/ijccc.2022.4.4865 10 figure 6: the reliability polynomial rel(h3,3; p) associated to h3,3 represented as a smooth curve (red) on the pascal’s surface, connecting the level curves (black) corresponding to the nonzero coefficients (red spheres). author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] abramowitz, m.; stegun, i.a. 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(2022). approximating the level curves on pascal’s surface, international journal of computers communications & control, 17(4), 4865, 2022. https://doi.org/10.15837/ijccc.2022.4.4865 introduction and motivations state-of-the-art pascal's triangle pascal's surface approximations gamma function binomial coefficients applications bernstein polynomials reliability polynomials conclusions international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(4), 540-556, august 2019. top-n recommendation based on mutual trust and influence d.w. seng, j.x. liu, x.f. zhang, j. chen, x.j. fang dewen seng* school of computer science and technology, hangzhou dianzi university hangzhou 310018, china *corresponding author: sengdw@163.com jiaxin liu school of computer science and technology, hangzhou dianzi university hangzhou 310018, china xuefeng zhang school of computer science and technology, hangzhou dianzi university hangzhou 310018, china jing chen school of computer science and technology, hangzhou dianzi university hangzhou 310018, china xujian fang school of computer science and technology, hangzhou dianzi university hangzhou 310018, china abstract: to improve recommendation quality, the existing trust-based recommendation methods often directly use the binary trust relationship of social networks, and rarely consider the difference and potential influence of trust strength among users. to make up for the gap, this paper puts forward a hybrid top-n recommendation algorithm that combines mutual trust and influence. firstly, a new trust measurement method was developed based on dynamic weight, considering the difference of trust strength between users. secondly, a new mutual influence measurement model was designed based on trust relationship, in light of the social network topology. finally, two hybrid recommendation algorithms, denoted as fsta(factored similarity model with trust approach) and fsti(factored similarity models with trust and influence), were presented to solve the data sparsity and binarity. the two algorithms integrate user similarity, item similarity, mutual trust and mutual influence. our approach was compared with several other recommendation algorithms on three standard datasets: filmtrust, epinions and ciao. the experimental results proved the high efficiency of our approach. keywords: mutual trust, mutual influence, social recommendation system, cold start, data sparsity. 1 introduction the continuous expansion of e-commerce provides users with direct access to a growing number and variety of products. in this case, the users need to spend a lot of time looking for their desired products. this poses a huge challenge to information consumers and producers. to cope with the challenge, it is necessary to recommend the information and products favored by the users according to their interest features and purchase behavior, that is, to set up a recommendation system. for the users, the recommendation system enables them to pinpoint desired information out of a massive amount of data; for the merchants, the system helps to decide copyright ©2019 cc by-nc top-n recommendation based on mutual trust and influence 541 on which products to sell to a group of users and provide satisfactory services that enhance user loyalty [14]. collaborative filtering recommendation is an important way of personalized recommendation, thanks to its ability to discover new interest points of users and a dazzling array of resources [4]. however, this recommendation approach is bottlenecked by problems like cold start and data sparsity in its algorithms. how to alleviate the two problems with the social attributes of the users has become the main task in the design of the recommendation system [17]. among the possible solutions, the recommendation algorithm combined with social information, i.e., the social recommendation algorithm, comes as an effective means to solve the said problems [14]. the social information main includes the mutual trust and influence between users, both of which are essential to social activities [15]. the mutual trust has long been recognized as the key to recommendation system [1–3, 8– 10, 12, 14, 15, 23]. in such a system, the rating can be predicted more accurately by replacing or supplementing the rate-based similarity [3]. the trust-based recommendation can effectively suppress the cold start problem and improve the accuracy and coverage of the recommendation [16]. in fact, trust communication has been proved as a social science, the key to social network analysis and an important phenomenon in recommendation scenarios [10]. but the existing measure of trust is only represented by 0 and 1 binary [8,9], and there is no clear value to indicate the specific trust between users. hence, this paper not only improves the recommendation effect, but also alleviates the sparseness of social network data through the specific measurement of trust between users. in addition, through the study of social networks, we have found a new inspiration, what is the trust in social networks? in the traditional recommendation system, few people combine mutual trust and mutual influence. but in social networks, user influence really exists and has a certain impact on the recommendations between users [15]. in light of the above analysis, this paper attempts to find out the social relationship data that guide the recommendation, disclose and weight the effect of the guidance on user preference, and improve the results of the social recommendation system. for these purposes, two new hybrid recommendation algorithms were proposed for top-n recommendation, based on the factorized similarity model based on trust (fst). the two algorithms are respectively denoted as fsta and fsti. the innovations of our research are summed up as follows: (1) based on dynamically weighted trust relationship, a new mutual trust measurement method was put forward according to the difference in mutual trust strength. the method takes account of the direct and indirect trust relationships between users, thus improving the recommendation accuracy. (2) using the topology of social network, a new mutual influence measurement model was developed based on trust relationship. considering both direct and indirect mutual influences, the proposed model makes full use of the implicit information in trust relationship. (3) two new hybrid top-n recommendation models, involving user similarity, item similarity, mutual trust and mutual influence, were designed to solve the data binarity and sparsity of mutual trust, and used to explore the existence of social network users, identify potential trust relationships, and set up a mutual trust network. (4) our models were proved efficient through repeated comparative verifications on three standard datasets, namely, filmtrust, epinions and ciao. 542 d.w. seng, j.x. liu, x.f. zhang, j. chen, x.j. fang 2 literature review social recommendation refers to the construction of a social relationship network between users based on the additional input of their social relationship information [20]. as long as the network users have direct or indirect social relationships, it is possible to generate proper recommendations for new users based on the interest models of the known users [17]. a collaborative filtering algorithm that combines social relationships is called a social recommendation algorithm. many scholars and researchers have probed deep into the theories, methods and applications of social recommendation systems [10, 20, 23, 24]. below is a brief review of some representative studies. to improve the accuracy of rating prediction, fang et al. [3] replaced and supplemented rate-based similarity in recommendation systems with trust. wang and ma [18] considered and weighted user’s rating and trust similarity. moradi and ahmadian [12] proposed a trust-based singular value decomposition technique, trustsvd, which evaluates both the explicit and implicit effects of trust. the above studies show that the inclusion of trust can enhance the accuracy of the recommendation system. on this basis, it is assumed that the recommendation accuracy can be improved through the combination of trust and other important social factors. the advent of social networks has shortened the distance and enhanced the links between people, providing an excellent experimental platform and a huge amount of data for impact research [8]. taking this golden opportunity, many researchers have analyzed and simulated the mutual influence in many social networks, and achieved fruitful results with huge application values [21,25]. for instance, li et al. [15] developed social recommendation algorithms through collaborative filtering, based on mutual trust and influence. with its important impact on user behavior in social networks, the mutual influence should be fully considered in the identification of influential users, such as to enhance the performance and accuracy of the recommendation system. the personalized top-n recommendation system has a direct bearing on many real-world applications, such as e-commerce platforms and social networks [22]. however, the existing top-n recommendation methods become less effective with the growth in data sparsity. this problem can be solved by introducing social information like mutual trust and preference to the recommendations system. in this way, it is possible to mine out the implicit information from the social network, and overcome the defect of sparse datasets. 3 recommendation based on mutual trust and influence this section introduces mutual trust and influence to the social recommendation method of the fst, aiming to improve the effectiveness of traditional collaborative filtering. the twomodule structure of the fst model combining the mutual trust and influence is shown in figure 1, where the blue dashed lines indicate that a trusted user has an influence on the truster, and the black dotted lines indicate user u’s rating of item j. in the left half of the model, user u trusts and is influenced by user v, who has rated item j as r(v,j); the goal is to predict user u’s rating r(u,j) for item j, considering the mutual trust and influence between the two users. in the right half of the model, user k trusts and is influenced by user u, and has rated item j as r(k,j); the rating may affect user u’s rating r(u,j) for the same item j. following this model, the social network relationship and a user’s item ratings are analyzed to predict the item ratings of the other user. finally, the item set with the higher rating is recommended to the users. let u = u1,u2, · · ·,um, i = i1, i2, · · ·, in and r = [ru,i]m×n be the set of m users, the set of n items, and the matrix of item ratings in the rating-based collaborative filtering algorithm, where ru,i is user u’s rating of item i (the rating is generally a real number top-n recommendation based on mutual trust and influence 543 figure 1: fst model combining mutual trust and influence between [0, 5], and positively correlated with the user’s interest in the item). the set of items rated by user u and the set of users having rated item i are respectively expressed as iu = i|ru,i 6= 0 and ui = u|ru,i 6= 0. in a social network, the users’ mutual trust can be represented by t = [tu,v]m×m, where tu,v is the trust between u and v; the users’ mutual influence can be represented by iu = [iuu,v]m×m, where iuu,v is the influence between u and v. note that each user in the social network has n friends. 3.1 mutual trust ranking if taken as the auxiliary information, mutual trust, an important type of social information, can effectively solve data sparsity problem in collaborative recommendation algorithms. in realworld applications, however, there following problems still exists: (1) the trust relationship is too sparse to be measured accurately; (2) the trust relationship is often expressed in the binary form (0 or 1), failing to weigh the exact trust strength between users. dynamically weighted trust measurement model this subsection explores the rating prediction based on user’s social relationship and constructs a model for mutual trust measurement. to begin with, the two users, a and b, in the social network are assumed to have only one interaction, i.e. rated the same item. during the interaction, each user has an impact on the other’s trust if both of them have given the same rating. the success of the interaction hinges on the rating difference between the two users. the interaction process is shown in figure 2, where the uppercase letters in circular boxes are items and the lowercase letters in rectangular boxes are users. it can be seen that, both users a and b have rated the same item a, i.e. engaged in an interaction. if the two users’ ratings on the item is not above the threshold �, then the interaction is successful; otherwise, the interaction is a failure. mutual trust is the accumulation of subjective feelings of individuals on each other. for two users u and v, the greater user u trusts user v, the more its interactions with v. the initial mutual trust init(u,v) can be defined as: init(u,v) = min(iu ⋂ iv,d) d (1) where iu ∩iv is the number of interactions between the two users, i.e. the number of items rated the same by the two users; d is an adjustable threshold that measures the minimum 544 d.w. seng, j.x. liu, x.f. zhang, j. chen, x.j. fang figure 2: the interaction between users number of interactions when two users fully trust each other. if iu ∩ iv is not greater than d, then the effective weight will come into force; otherwise, the initial strength of the mutual trust will be set to 1. since the users differ in the number of ratings, the criterion for full trust may vary with users. thus, the threshold for each user can be set to du = √ |iu|. next, the preference of user a for item c can be defined as [19]: p(a,c) = ∑ m ∈ acsim(a,m) |ac| (2) where, ac is to the set of m users having rated item c. equation (2) shows that the preference of the users in set ac for item c increases with their similarity with user a. since each pair of users will optimize the mutual trust through interaction, the trust between the two users t(u,v ) can be calculated by assigning a difference weight to the item based on the difference in the two users’ preferences for the same item: t(a,b) = { initd(a,b) ∑ c∈success p(a,c)− ∑ c∈failure p(a,c)∑ c∈success p(a,c)+ ∑ c∈failure p(a,c) ,t(a,b) 0 ,otherwise (3) where, init(u,v), the initial trust between the two users is a dynamic weighting factor. obviously, the fewer commonly rated items, the smaller the numerator, and the less the initial trust will contribute to the final mutual trust. the trust of each user to the other users can be computed by equation (3), and a set of trusted users can be obtained by filtering the results against the threshold. fsta model the rating ru,i of user u on item i was predicted based on mutual trust, item similarity and user similarity. the mutual trusts of each target user with its trustee(s) and truster(s) were obtained by equation (3). after the calculation, the list of trust users was updated, and the implicit information in the user rating matrix was obtained to obtain the directed trust between users. the item similarity was the inner product of two lower-order matrices x and y , where x ∈ rn×d and y ∈ rn×d. note that d << n is the number of potential feature vectors associated top-n recommendation based on mutual trust and influence 545 with the item. the user similarity was obtained from two lower-order matrices p ∈ rm×d and q ∈ rm×d, where d << m is the number of potential feature vectors associated with the user. then, the ratings were ranked to produce a set of recommended items for each target user. the ratings of the user can be predicted as: r̂u,i = bi + s|ui−u|−β ∑ v∈ui−u ptv qu + (1 −s)|iu−i| −α ∑ j∈iu−i xtj yj + σ|t +u | −z ∑ a∈t+u pta yi + (1 −σ)|t − u | −χ ∑ b∈t−u ptb yi (4) where, α, β, z and χ ≥ 0 are the number of rated items, that of similar users, that of trustees and that of trusters; bi is the bias of item i; s and δ ∈ [0, 1] are user similarity and the relative importance of trust, respectively. for each pair of trustee a ∈ t +u and truster b ∈ t−u , the inner products pta yi and p t b yi describe the influences of users a and b on the trust of the target user, respectively. 3.2 mutual influence ranking by mining the information in the trust relationship, the author put forward a method to calculate the implicit influence between users, taking account of both the direct and indirect trust relationships between users. influence calculation figure 3: local social network as shown in figure 3, the local social network consists of 9 users, respectively denoted as i, j, k1, k2, k3, k4, k5, k6 and k7. the goal is to estimate the trust strength between users i and j. the trust strength cannot be determined accurately if only the set of trusts (solid lines in figure 3(a)) is considered. to fully exploit the information in the trust relationship, a trust measurement model was designed based on dynamic weight. by definition, user j is affected by 546 d.w. seng, j.x. liu, x.f. zhang, j. chen, x.j. fang user i when it trusts that user. then, the nodes with no influence on node j were removed. after that, the trust relationships with no influence were deleted from the network. inspired by the above research, an improved algorithm was proposed to identify the influence of users in the trust network. definition 1. in a mutual trust network gt , the edge weight wij can be defined as the trust strength between users i and j, that is, w(i,j) = t(i,j). the nodes and directed edges in the network respectively stand for users and strengths between users. definition 2. the weight of node w(i) can be interpreted by the trustee of the mutual trust network as w(i)= ∑ j∈t+i wij. definition 3. the relative importance of node j in the eyes of its neighbor node i can be calculated by p(i,j) = wi,j/w(i). if node j or its neighbor node k are of high relative importance, then node i will invest more time and energy in node j. this reflects the fact that people often devote more time and energy to those with great importance or importance relationships. definition 4. in light of the features of mutual trust network, an improved model was designed to measure mutual influence c(i): c(i,j) =   p(i,j) + ∑ k∈t+i p(i,j)p(i,k),c(i,j) ≥ vc 0,otherwise (4) where, p(i,j) and ∑ p(i,j)p(i,k) are the direct and indirect influences of user i on user j, respectively. the latter is determined by the number of intermediate nodes k between node i and node j. it can be seen from equation (5) that the value of c(i,j) is positively correlated with the number of trust neighbors of each node and the closeness between network nodes. fsti model to predict each user’s rating of an item, it is assumed that user u in the above fsta model is affected by a set of users iuu = {w|iuu,w}. on this basis, another social recommendation algorithm fsti was constructed based on the implicit influence of mutual trust. firstly, the item rating was modelled by matrix decomposition. the rating prediction formula can be derived from the results of equation (5) and the mutual influence matrix as: r̂u,i = bi + s|ui−u|−β ∑ v∈ui−u ptv qu + (1 −s)|iu−i| −α ∑ j∈iu−i xtj yj + δ|t +u | −z ∑ a∈t+u pta yi + (1 −δ)|t − u | −χ ∑ b∈t−u ptb yi + |iuu| −θ ∑ w∈iuu ptwyi (6) where, θ ≥ 0 is the number of users that influences user u (hereinafter referred to as influential users); iuu is the set of influential users; s and δ are the control parameters; ptwyi is the inner product equal to the influence of each influential user w ∈ iuu over user u. next, the variables b, p , q, x and y can be computed by the following objective function: j = 1 2 ∑ u∈c ∑ i∈i+u ,j∈i−u ||(ru,i −ru,j) − (r̂u,i − r̂u,j)||2 + λ 2 (||p ||2f + ||q|| 2 f + ||x|| 2 f + ||y || 2 f + ||b|| 2 f ) (7) top-n recommendation based on mutual trust and influence 547 where, c is the set of all users; i+u is the set of items rated by user u; i − u is the set of items not rated by user u; || � ||2f is the frobenius norm. for convenience, the regularization parameter λ was employed to process all the relevant variables. the recommendation effect can be greatly improved through proper adjustment and assignment of these variables. compared with the fst, the fsti has the following features: (1) the fsti replaces the trust matrix t with the trust strength estimation matrix s, rather than assume that a user has the same trust strength with any friend; (2) the fsti estimates the mutual influence matrix iu by the implicit influence of trust relationship, revealing the implicit mutual influence; (3) the fsti improves the recommendation accuracy through the comprehensive consideration of item similarity, user similarity, mutual trust and mutual influence. 3.3 model learning and complexity analysis fsta complexity first, the gradients and update rules for all variables were calculated for model training (lines 3-44, algorithm 1). all variables were initialized with a random value in (0, 0.01) (line 1). in each iteration, the overall computing cost in algorithm 1 was approximately o(nb), where n is the number of ratings in the training set and b is the mean number of users evaluating the item, and the sampling factor ρ (line 5) was used to randomly sample a set of negative instances of z to train the model. the variables were repeatedly updated by the stochastic gradient descent (sgd) rules (lines 16-44) until the loss value had converged or the maximum number of iterations had been reached. next, the converged user potential feature matrix p and the item implicit feature matrix x were outputted, marking the end of the training (line 45). finally, all learned variables were returned as outputs. fsti complexity the fsti follows basically the same training procedure as the fsta. to save space, the details on the implementation of the algorithm are omitted here. the pseudocode of the fsti is listed in algorithm 2. the trained model was used to predict each user’s ratings of unknown items, and the items with the highest ratings were collected into a recommendation set. summary of fsta and fsti algorithm complexity referring to algorithm 1, the fsta time complexity of our method mainly includes the evaluation of the objective function and the calculation of the gradient of each variable. for each iteration, the total computation time cost of algorithm 1 is o(ntbd(|r| + |t|)), where nt is the number of training matrices, b represents the average number of graded items for the user, and d represents the feature vector dimension. relatively speaking, fsti increases the influence of influence on the basis of fsta, so the time complexity of fsti is o(ntbd(|r| + |t| + |iu|)). since the rating matrix r and the trust matrix t and the influence matrix iu are very sparse, the time complexity of our method fsti is much lower than the matrix cardinality. the above analysis shows that our strategy for personalized recommendation successfully integrates user similarity, mutual trust and mutual influence. 548 d.w. seng, j.x. liu, x.f. zhang, j. chen, x.j. fang algorithm 1 the learning algorithm of fsta data: α, β, z, χ, ρ, λ, η; initialize b,x,y,p,q with random values in (0, 0.01); 1: while ζ not converged do 2: for all u ∈ c do 3: for all i ∈ i+u do 4: z ← sample(ρ,i−u ) 5: mki ← ∑ k∈iu−j xk, wki ←|iu−j| −α 6: mvi ← ∑ v∈ii−u pv, wvi ←|ii−u| −β 7: ma ← ∑ a∈t+ u pa, wa ←|t +u |−z 8: mb ← ∑ b∈t− u pb, wb ←|t−u |−χ 9: g ← 0, h ← 0, t−out ← 0, t− in ← 0 10: for all j ∈ z do 11: mkj ← ∑ k∈iu−j xk, wkj ←|iu−j| −α 12: mvj ← ∑ k∈ij−u pv, wvj ←|ij−u| −β 13: computer̂u,i, r̂u,j by equation 14: r̂u,j ← 0 15: e ← (ru,i −ru,j) − (r̂u,i − r̂u,j) 16: bi ← bi + η(e−λbi) 17: bj ← bj −η(e−λbj) 18: qu ← qu −η(e(wvjmvj −wvimvi) + λqu) 19: yi ← yi + η(e(wkimki + wama + wbmb) −λyi) 20: yj ← yj −η(e(wkjmkj + wama + wbmb) −λyj) 21: g ← g −ewkiqu 22: h ← h + e(wkjyj −wkiyi) 23: t−out ← t−out + ewa(yj −yi) 24: t− in ← t− in + ewb(yj −yi) 25: for all v ∈ uj−u do 26: pv ← pv −η(ewvjqu −λpv) 27: end for 28: end for 29: for all v ∈ ui−u do 30: pv ← pv −η(g/ρ + λpv) 31: end for 32: for all k ∈ uu−i do 33: xk ← xk −η(h/ρ + λxk) 34: end for 35: for all a ∈ t +u do 36: pa ← pa −η(t−out/ρ + λpa) 37: end for 38: for all b ∈ t−u do 39: pb ← pb −η(t− in/ρ + λpb) 40: end for 41: end for 42: end for 43: end while 44: return b, p, q, x, y top-n recommendation based on mutual trust and influence 549 algorithm 2 the learning algorithm of fsti data: α, β, z, χ, θ, ρ, λ, η; initialize b, x, y, p, q with random values in(0,0.01); 1: while ζ not converged do 2: for all u ∈ c do 3: for all i ∈ i+u do 4: mi ← ∑ w∈iuu pw, wi ←|iuu|−θ 5: inf ← 0 6: for all j ∈ z do 7: yi ← yi + η(e(wkimki + wama + wbmb + wimi) −λyi) 8: yj ← yj −η(e(wkjmkj + wama + wbmb + wimi) −λyj) 9: inf ← inf + ewi(yj −yi) 10: end for 11: for all w ∈ iuu do 12: pw ← pw −η(inf/ρ + λpw) 13: end for 14: end for 15: end for 16: end while 17: return b, p, q, x, y 4 verification and results this section verifies the parameter robustness and sensitivity in our strategy through several experiments on three public datasets, and compares the effectiveness of our strategy with six topn recommendation methods based on implicit feedbacks. 4.1 experimental setup datasets the experiment data include three datasets, namely, flimtrust, ciao, and epinions [4]. each dataset contains ratings and mutual trust of users in social network. upon signup, each user was allowed to rate all items, and browse the ratings by other users to make more favorable decisions. in addition, the user can befriend any trusted user, forming a network of trust relationships. for the datasets epinions and ciao, the rating was given against a five-point scale, where 1 means strongly dislike and 5 means strongly like. the mutual trust was described in binary form: existence (1) and absence (0). our approach was applied to the three datasets to estimate the potential mutual trust and make accurate recommendations. as shown in table 1, all the three datasets are extremely sparse, except filmtrust. table 1: datasets data set filmtrust ciao epinions users 1,508 7,375 40,163 items 2,071 99,746 139,738 ratings 35,479 278,483 664,824 density 1.14% 0.0379% 0.0118% 550 d.w. seng, j.x. liu, x.f. zhang, j. chen, x.j. fang comparative methods our approach was compared with the following six top-n recommendation methods based on implicit feedbacks: (1) bpr: bayesian personalized ranking. based on implicit feedback, the bpr is extended from the top-n ranking recommendation algorithm by the pairing hypothesis for item ordering, using implicit feedback, matrix factorization (mf) and k-nearest neighbors (knn) model [19]. (2) gbpr: the bpr based on group preference. the gbpr is an improved version of the bpr with richer interaction between users [16]. (3) mostpop: the mostpop is the baseline method that ranks the ratings of an item by popularity, i.e. how frequently the item is rated or consumed by the user. (4) fism: factored item similarity model. the fism alleviates the sparsity of the existing topn recommendation algorithm by taking the product of two low-dimensional latent factor matrices as the similarity matrix [11] . (5) fst: factored similarity model with trust+. the fst introduces the mutual trust matrix and user similarity matrix into the fism, thereby alleviating the sparsity of existing top-n recommendation algorithm and enhancing the accuracy of ranking recommendation [9]. (6) fsta, fsti: it is our proposed method for comparing two methods. among them, fsti adds the influence of user influence on the basis of fsta. evaluation indices the previous studies [14,19] on recommendation systems have shown that the common error rate indices of rating prediction, such as the mean square error (mse) and root mean square error (rmse), cannot fully characterize the performance of the recommendation algorithm. in fact, the algorithm performance mostly depends on the recommendation of the top-n items. to fully verify the effect of our approach, the fsti was combined with the bpr for top-n recommendation [19], and evaluated the hit rate by precision, recall rate and f1-measure: p@n = 1 |u ′| ∑ u∈u′ |rn (u) ∩ i′u| n r@n = 1 |u ′| ∑ u∈u′ |rn (u) ∩ i′u| i′u f1@n = 2 �p@n �r@n p@n + r@n (5) where, i′u is the set of items overestimated by user u; u ′ is the test set; p@n, r@n and f1@n are the precision, recall rate and f1−measure under n recommended items, respectively. sometimes, the p@n and r@n may contradict each other. in this case, the f1 − measure should be taken into account to solve the contradiction. the greater the values of p@n and f1@n, the better the recommendation effect. the five-fold cross validation was adopted in our experiments. each dataset was split randomly into 5 parts. in each iteration, 4 parts were allocated to the training set and the remaining one to the test set. the mean results of the 5 parts were taken as the final results. the number of recommended items was selected in 5, 10. note that common evaluation indices like mean absolute error (mae) and rmse are not applicable to our research, because they are mainly used to evaluate prediction performance rather than the top-n recommendation effect. top-n recommendation based on mutual trust and influence 551 parameter settings the parameters in our experiments were configured empirically, e.g. the number of potential factors was set to d = 10. for the gbpr, the number of users in the set was fixed as 5 and the parameters were adjusted by the sampling factor ρ ∈ [0, 1]. for all bpr-based methods, the unsorted items for model learning were selected through uniform sampling [19]. for all decomposition-based models, parameters α, β, z, χ and θ were selected from a set of small values, i.e. {0.5, 1, 2}, and the sampling factor ρ was fixed at 10 (algorithm 1), for the fism satisfies the interval of [11, 17]. for all matrix-based methods, the setting of the regularization parameter λ was optimized in {0.000001, 0.00001, 0.0001, 0.01, 0.1} by grid search. 4.2 effect of parameters α, β, z, χ and θ the five parameters α, β, z, χ and θ describes item similarity, user similarity, mutual trust and mutual influence. in our experiments, the value of each parameter is adjusted in the small set of {0.5, 1, 2} when the control parameters and were 0.5. tables 2 and 3 respectively list the parameter configurations corresponding to the five best p@5 results of the fsta and the fsti on each dataset. obviously, the different parameter configurations led to different results, and the optimal parameters changed from dataset to dataset. table 2: the optimal parameter configurations of the fsta on the three datasets. methods filmtrust ciao epinions parameters α β z χ α β χ χ α β χ χ 1 2 0.5 0.5 0.5 0.5 2 0.5 0.5 0.5 1 0.5 1 0.4192 0.02892 0.01222 2 0.5 2 0.5 1 2 1 2 1 2 0.5 2 2 0.4187 0.02843 0.01194 3 0.5 2 2 0.5 2 0.5 1 2 2 2 0.5 0.5 0.4186 0.02821 0.01177 4 0.5 1 0.5 0.5 1 1 0.5 2 0.5 0.5 1 0.5 0.4184 0.02808 0.01159 5 0.5 1 0.5 1 1 2 2 0.5 1 0.5 1 2 0.4184 0.02804 0.01158 table 3: the optimal parameter configurations of the fsti on the three datasets. methods filmtrust ciao epinions parameters α β z χ θ α β z χ θ α β z χ θ 1 0.5 0.5 2 1 2 0.5 1 2 1 0.5 1 2 2 0.5 1 0.4198 0.02953 0.01260 2 0.5 2 2 0.5 1 0.5 1 1 0.5 2 1 0.5 0.5 2 0.5 0.4194 0.02941 0.01233 3 1 2 1 2 0.5 2 1 0.5 1 0.5 0.5 0.5 2 1 2 0.4192 0.02932 0.01215 4 1 0.5 2 1 0.5 2 2 2 0.5 2 0.5 2 3 1 0.5 0.4172 0.02930 0.01214 5 0.5 1 0.5 1 2 2 2 1 2 2 1 2 1 1 0.5 0.4192 0.02929 0.01206 the experimental results of the fsta corresponding to parameters α, β, z and χare recorded in tables 4, and denoted by p@5. it can be seen that the results were optimal at α = 1, β = 2 552 d.w. seng, j.x. liu, x.f. zhang, j. chen, x.j. fang and z > 0.5. as a result, before making top-n recommendations, the item similarity, the trustee and the truster should be emphasized over user similarity. the experimental results of the fsti corresponding to parameters α, β, z, χ and θ are recorded in tables 5, and denoted by p@5. it can be seen that the results were optimal at α = 1, β = 2 and z > 0.5 and θ < 2. as a result, before making top-n recommendations, the item similarity, the trustee and the truster should be emphasized over user similarity. 4.3 effect of control parameter s for each dataset, the parameters α, β, z, χ and θ were set to their optimal values, the first control parameter δ was fixed at 0.5, and the second control parameter s was adjusted in the interval [0, 1] by a step size of 0.1. the effects of the adjustment on the fsta and the fsti are displayed in figure 4(a) and figure 4(b), respectively. (a) fsta (b) fsti figure 4: the effect of parameter s on our approaches fsta and fsti in terms of precision at 5 as shown in figure 4, the optimal s values of the fsta on filmtrust, ciao and epinions were 0.8, 1 and 0.8, respectively, revealing that, in the fsta model, user similarity has a stronger promotion effect on top-n recommendation accuracy than item similarity. meanwhile, the optimal s values of the fsti on filmtrust, ciao and epinions were 0.4, 0.7, and 0.4, respectively. it can be seen that the weight of user similarity decreased in the fsti model, while that of item similarity increased to a certain extent. hence, both models will perform more excellently under proper parameter settings. 4.4 effect of control parameter δ for each dataset, the parameters α, β, z, χ and θ were set to their optimal values, the first control parameter s was fixed at 0.5, and the second control parameter δ was adjusted in the interval [0, 1] by a step size of 0.1. the effects of the adjustment on the fsta and the fsti are displayed in figure 5(a) and figure 5(b), respectively. as shown in figure 5, the optimal δ values of the fsti on filmtrust, ciao and epinions were 0.3, 0.1 and 0.3, respectively, revealing that, in the fsti model, the truster is more important than the trustee of a user. meanwhile, the optimal δ values of the fsti on filmtrust, ciao and epinions were 0.9, 0.3, and 0.8, respectively. it can be seen that the trusters of filmtrust and epinions are more important than those of ciao. top-n recommendation based on mutual trust and influence 553 (a) fsta (b) fsti figure 5: the effect of parameter δ on our approaches fsta and fsti in terms of precision at 5 4.5 comparison with other methods table 4: the precisions of all algorithms on the three datasets (the precision of our approach is in bold). data d gbpr mostpop fism fst fsta fsti filmtrust 5 0.4124 0.4170 0.4171 0.4191 0.4192 0.4198 10 0.3470 0.3503 0.3503 0.3514 0.3525 0.3530 ciao (×10−1) 5 0.2228 0.2677 0.2704 0.2714 0.2892 0.2953 10 0.1827 0.2142 0.2141 0.2174 0.2245 0.2337 epinions (×10−1) 5 0.09353 0.1169 0.1147 0.1179 0.1222 0.1260 10 0.07560 0.09171 0.09102 0.09187 0.10410 0.10963 table 5: the f1-measures of all algorithms on the three datasets (the precision of our approach is in bold) data d gbpr mostpop fism fst fsta fsti filmtrust 5 0.4051 0.4095 0.4087 0.4099 0.4103 0.4107 10 0.4458 0.4518 0.4516 0.4521 0.4533 0.4534 ciao (×10−1) 5 0.2063 0.2436 0.2495 0.2523 0.2527 0.2841 10 0.2292 0.2662 0.2687 0.2720 0.2743 0.2829 epinions (×10−1) 5 0.1103 0.1298 0.1307 0.1330 0.1419 0.1481 10 0.1111 0.1305 0.1315 0.1328 0.1381 0.1467 the p@n and f1@n of the fsta and the fsti were compared with those of the contrastive recommendation methods. the comparison results are listed in tables 4 and 5. overall, our approach outperformed the other methods under the same parameter configurations. in the filmtrust dataset, the mostpop achieved better results than the fism. a possible reason lies in the consumption of the hot items in the dataset. in both ciao and epinions, the fism outshined the mostpop, indicating the advantage of the factorized similarity model over the gbpr and bpr methods. moreover, the fsta typically performed better than the methods, e.g. the fst, that integrates user similarity, item similarity and mutual trust. note that the user rating matrix has a positive impact on the calculation of mutual trust, which comes from the trustee and the truster, respectively. finally, the comparison between the fsta and the fst shows the influence of social impact on ranking performance. our experimental parameters α, β, z, χ and θ were adjusted only in one group. better results can be obtained by adjusting the parameter set. 554 d.w. seng, j.x. liu, x.f. zhang, j. chen, x.j. fang 5 conclusion based on dynamically weighted trust relationship, a new mutual trust measurement method was put forward according to the difference in mutual trust strength. the method takes account of the direct and indirect trust relationships between users, thus improving the recommendation accuracy. using the topology of social network, a new mutual influence measurement model was developed based on trust relationship. considering both direct and indirect mutual influences, the proposed model makes full use of the implicit information in trust relationship. two new hybrid top-n recommendation models, involving user similarity, item similarity, mutual trust and mutual influence, were designed to solve the data binarity and sparsity of mutual trust, and used to explore the existence of social network users, identify potential trust relationships, and set up a mutual trust network. our models were proved efficient through repeated comparative verifications on three standard datasets, namely, filmtrust, epinions and ciao. the future research will explore the other factors that affect the mutual trust and further improve the performance of our models. acknowledgement the 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(2015). who influenced you? predicting retweet via social influence locality, acm transactions on knowledge discovery from data (tkdd), 9(3), 25, 2015. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 4, month: august, year: 2020 article number: 3842, https://doi.org/10.15837/ijccc.2020.4.3842 ccc publications proposed fuzzy real-time haptics protocol carrying haptic data and multisensory streams s. kontogiannis, g. kokkonis sotirios kontogiannis* department of mathematics university of ioannina, greece ioannina, 45100, greece *corresponding author: skontog@cc.uoi.gr george kokkonis department of business administration university of western macedonia, greece grevena, 46100, greece kokkonisgeo@gmail.com abstract sensory and haptic data transfers to critical real-time applications over the internet require better than best effort transport, strict timely and reliable ordered deliveries. multi-sensory applications usually include video and audio streams with real-time control and sensory data, which aggravate and compress within real-time flows. such real-time are vulnerable to synchronization to synchronization problems, if combined with poor internet links. apart from the use of differentiated qos and mpls services, several haptic transport protocols have been proposed to confront such issues, focusing on minimizing flows rate disruption while maintaining a steady transmission rate at the sender. nevertheless, these protocols fail to cope with network variations and queuing delays posed by the internet routers. this paper proposes a new haptic protocol that tries to alleviate such inadequacies using three different metrics: mean frame delay, jitter and frame loss calculated at the receiver end and propagated to the sender. in order to dynamically adjust flow rate in a fuzzy controlled manners, the proposed protocol includes a fuzzy controller to its protocol structure. the proposed frtps protocol (fuzzy real-time haptics protocol), utilizes crisp inputs into a fuzzification process followed by fuzzy control rules in order to calculate a crisp level output service class, denoted as service rate level (srl). the experimental results of frtps over rtp show that frtps outperforms rtp in cases of congestion incidents, out of order deliveries and goodput. keywords: multi-sensorial, haptics, transport protocols, real-time protocols, communication networks, fuzzy logic based decisions. https://doi.org/10.15837/ijccc.2020.4.3842 2 1 introduction real-time multi-sensory and multi-control actuating protocols try to transfer sensory and control haptic data over the internet as a unified flow content profile. as the internet is transforming towards the “internet of everything”, multi-sensory data profiles are massively utilized, characterized with low bit-rates, synchronized back to back frames of minimum jitter and zero packet loss tolerance [3]. existing transport protocols and mainly tcp protocols focus on big data payload transfer sizes usually above 512 bytes. tcp protocols most important functionality is congestion control, performed using congestion window (cwnd) variations [18]. congestion window as well as other conservative tcp friendly mechanisms such as congestion avoidance, fast recovery and slow start, force tcp protocols to operate in packet bursts, introducing unnecessary jitter for real-time flows [4]. furthermore, tcp varieties set as packet loss indication the existence of duplicate acks or timeouts without accurate probing and continuously monitoring network conditions, so as to adjust the cwnd accordingly [1]. such real-time detection inaccuracies left space for other unreliable but of constant rate protocols like udp to prevail over tcp for real-time applications. several forms of real-time protocols descendants from udp are presented in the following literature section. however, the mostly used representative with the highest applicability is the real time protocol (rtp) [26]. the rtp tries to maintain a steady rate as udp, while it provides both flow control and a re-transmission mechanism to compensate for packet losses. it is also augmented by another protocol called rtcp which provides control and timely feedback to both communicating participants such as the inter-arrival jitters and packet losses. usually multi-sensory data flows are coupled together with audio-video real-time data flows coming from a single or multiple data streams of significant data volume. such flows are characterized by low and constant bit rates and small payload sizes (not more than 350bytes). these flows are traversing as part or independently along with a media streaming protocol over the internet. in case of congested links, such flows are exposed to significant delays and jitter that in turn stress out significantly the real-time synchronization and functionality of the application layer [12]. according to [2] humans have limited adaptability to time delay that varies between 0.3-0.5sec, while data loss is one of the most critical problems to be particularly solved by constant bit rate sources. furthermore, adaptation to the internet characteristics is an important factor that needs to be taken into consideration in order to develop reliable internet-based real-time protocols for control systems [32]. for real-time systems, quality of experience (qoe). qoe is the measurement of perception of a realtime application by the end user. several qoe measures have been proposed such as goodput, average throughput re-buffering, start-time, bit-rate shifts (jitter), data resolution and service response time, trying to measure of real user quality indexes such as itu application rating, mean opinion score (mos), application’s performance, controller capabilities, data communication costs, service failure incidents, et.al [23, 47]. for teleoperations, all of the aforementioned measures are used for probing real-time application delays and loses, while offering service transparency and application stability. recent surveys show that 60% of all video streams in the internet suffer from poor quality [43] and 40% of all consumers are concerned about the internet video quality [44]. such poor performance of internet based real-time haptic and sensory applications leave space for more adaptive protocol design and intelligent approaches. this paper includes a new proposition of a fuzzy transport protocol for real-time multi-sensory applications. its implementation is instructed by the next generation networks and ioe requirements. for instance, the pico-cells deployment and escalation of 5g infrastructure and the need for high availability and better than best effort wireless tactile services. service supporting data augmentation [28], virtual reality [58], medical tele-operations [20, 30, 41], automated vehicles [52] and industry 4.0 applications [37]. this paper describes a new protocol caller frtps (fuzzy real-time haptics protocol), that includes a fuzzy control component that uses receiver-end calculated measurements of packet arrival deviations (pad), mean ack time delays (md) and packet loss (plos), in order to detect the network contention or congestion incidents and respond with either flow rate reduction or data quantization. moreover, scenarios experimentation and protocol evaluation results follow in terms of both performance and quality of experience. this paper is divided as follows: in section 2 related work on existing real-time protocols carrying https://doi.org/10.15837/ijccc.2020.4.3842 3 sensory data are presented. section 3 presents the authors’ proposition of the frtps protocol and functionality. section 4 includes the frtps experimentation and experimental results, while section 5 concludes the paper. 2 related work on existing sensory protocols in order to transfer multi-sensory data through the internet, apart from tcp, udp and rtp [26], several real-time transport protocols operating in accordance with the carried data requirements have been introduced. several protocols have been developed for this reason specifically for haptic applications that control robotic arms, haptic gloves that receive vibration feedback, glasses and tablet applications that project augmented sensory information into things [7]. the most important representatives are outlined in the paragraphs that follow. stream control transmission protocol (sctp) is a tcp variant for real-time streams. it has the ability to aggregate different streams into one message flow (multi streaming). sctp data chunks are identified by three items: tsn, si and ssn. transmission sequence number (tsn) is a cumulative sequence number, different for each stream chunk in the message; sequence identifier (si) is the stream identifier and stream sequence number (ssn) to distinguish different data chunks in a message that belong to the same stream [54]. in sctp control and data information are also carried in the same message, thus making sctp as the best candidate for sip and voip(h.323) data deliveries[21]. sctp uses the same congestion control and flow control mechanisms with the tcp protocol, which means that is lacks of a real-time network conditions measurement mechanism and it carries aggregated flow chunks which in turn may cause multi stream disruption in case of congestive incidents, especially in transient loses or random drops. this leads to poor performance in wireless and ad hoc environments [36]. moreover, the datagram congestion control protocol (dccp) is also is a better improvement of sctp, that incorporates the explicit congestion notification (ecn) mechanism and can also serve for reliable in order delivery for udp segments [10, 16], as compared by [5], it outperforms sctp in 4g environments, but still utilizes the tcp congestion control mechanisms drawbacks for real-time content deliveries. another tcp variant is the the interactive real time protocol (irtp +). synchronous collaboration transport protocol (sctp +) is an interactive stream protocol that transfers stream data over udp. it has many similarities to the rtp such as message sequence numbering (flow control), and stream id (without multi stream support as sctp does). sctp + uses a delayed negative acknowledgment mechanism to indicate packet losses similar to the rtcp protocol (part of the rtp loss report mechanism) [15]. nevertheless, sctp + includes a mechanism, when multiple recipients (recipients group) are involved for the sender to multicast its messages while receiving acks for each message only from the recipient of the group that the initial nack mechanism indicated the highest rtt value [49]. an improved version of sctp + for haptic applications is smoothed sctp (s-sctp), that adds a jitter smoothing mechanism of a receiver buffer at the sctp + protocol [13] the alphan protocol operates on top of udp and it is used by collaborative haptic audio visual environment (c-have) technologies in virtual reality, surgical simulations and games [42]. it is mainly an application layer protocol that includes flow control, messages re-transmission, and embedded timestamp on its messages headers, similar to the rtp protocol. interactive real-time protocol (irtp) is a minimum header rtp like protocol that has been designed for interactive internet-based services on top of ip. in order to reduce the end-to-end delay irtp uses the tcp handshake and connection establishment mechanism, and adopts tcp congestion window. however, in contrast to tcp, irtp sender increases its packet in flight as well as the rtp flow control,and re-transmission mechanism. irtp protocol acks include a available receiver rwnd field to inform the sender of its goodput availability. in cases of difference between flow goodput and throughput, the irtp enters its congestion avoidance mechanism by multiplicative reducing its frame rate by some proportion: wt+∂t = (1 −β)wt [34]. bidirectional transport protocol (btp) is an alternative protocol to irtp that focuses on reducing the end-to-end delay monitoring by introducing the ipg (inter packet gap) metric of time interval https://doi.org/10.15837/ijccc.2020.4.3842 4 between two successive data packets or frames and the [59, 60]. this ipg-based control mechanism provides congestion control similar to the tcp window size based congestion control. btp is udp based that in cases of network congestion attempts to provide a uniform end-to-end delay by modifying data packets in flight based on receiver ipg feedback. furthermore, other less important real-time protocols such as the efficient transport protocol (etp), real time network protocol for interactive media (rtp/i), real time network protocol (rtpn), hybrid multicast transport protocol (hmtp), scalable reliable multicast (srm), reliable multicast transport protocol (rmtp), selective reliable transmission protocol (srtp) are outlined at [31]. since existing systems os kernels can tolerate correlated functions and operations to reach decisions or deduct conclusions from past data very fast, sophisticated monitoring algorithm implementations are underway such as the introduction of fuzzy detectors to the tcp congestion avoidance mechanism with adaptive escalation strategies [6, 39, 55]. furthermore, it is also mandated by the failure of existing tcp congestion mechanisms to deliver accurate flow rate requests [29]. providing adaptive to conditions networking protocols over tcp is an ongoing research effort [39]. moreover, the existing implementations towards this direction are the fl-tcp; a tcp vegas based protocol, that imposes a fuzzy controller over expected and actual rtt calculations at the transmitter end [51], flower [55], that provides an rtt fuzzy controller for bittorrent clients and fuzzy tcp. the main issue with these tcp protocol implementations (apart from flower), is that they provide a more of generic adaptive congestion window (cwnd) increase or decrease, based on rtt measurements only, without differentiating protocol use according to each application profile requirements. the proposed frtps protocol in this paper is an rtp based protocol, that carries some of the layer adaptation attributes of amesetp protocol. amesetp is part of mesetp protocols suite for real-time medical services. amesetp protocol monitors packet loss and inter-frame delay variation at the receiver end. it then signals the sender via ack frames to either increase or decrease its frame rate by reducing or increasing the number of bytes/frame accordingly. amesetp offers 7 levels of rate service while some of them offer also quantization (compression of payload data) [30]. as examined by the amesetp authors, the amesetp decisions have shown significantly better performance in comparison to udp, close to performance of the rtp protocol [30]. nevertheless, amesetp lacks of a fast adaptive mechanism, and requires a bigger number of service rate levels as well as a more accurate network conditions probing mechanism, in order to outperform rtp. proposed frtps protocol and functionality is presented at section 3. 3 proposed frtps protocol frtps (fuzzy real-time haptics) protocol is a new proposed transport protocol for small payload, low bit-rates, real-time flows. frtps includes a new flow control mechanism of continuous network probing and fuzzy logic rate adaptation. the motivation for a fuzzy transport protocol derives from [9, 19, 45], that tried to deal with robot movement uncertainties, implementing their proposition at the application layer. the proposed frtps protocol is more of a generic approach that can enforce policies based on network conditions, thus making it more general and scalable for sensory, actuator data transmissions. frtps adaptation policy is based on a a fuzzy estimator on crisp input measurements performed by the receiver end. frtps does not include tcp like congestion avoidance and rate adaptation mechanisms, apart from the mechanism used by rtp and utilizes only its adaptive fuzzy rate-quantization escalation logic. as rtp protocol dictates, frtps also includes a timestamp field embedded in its frames so as to assist application layer protocols frame synchronization. frtps protocol description is divided as follows: section 3.2 presents the frtps metrics used by its fuzzy controller to calculate the offered service level. section 3.2 presents the frtps protocol structure and rate adaptation layers, while section 3.3 presents in detail the frtps fuzzy control layer functionality. https://doi.org/10.15837/ijccc.2020.4.3842 5 3.1 frtps metrics frtps aggregates, quantizes or both its data transmissions, based on three recorded metrics at the receiver end: 1. packet loss (plos) measurements at the receiver end, smoothed over an ewma process (see figure 2) 2. packet arrival deviation (pad) measured by the receiver flow monitoring agent (see figure 2) 3. mean packet delay metric (md), supported by the frtps frame timestamp field and accurate synchronization between sender and receiver using either precision time or network time protocol (ntp) (md is measured by the sender flow monitoring agent, see figure 2). all the above metrics are used by the frtps protocol fuzzy controller as input for the calculation of a single crisp output that corresponds to a specific rate adaption layer. detailed metrics analysis follows. packet loss (plos) metric is also used by the frtps protocol and adapts to the frtps protocol an ewma process for the next time interval packet loss estimation. the estimator equation used for k+1 interval is the presented in eq. 1: plosk+1 = β k − 2 k−1∑ i=1 plosi + (1 −β)plosk (1) where k is the time depth (k>2) of the ewma process and coefficient β value is calculated as follows: β = 0.2 + kmax i=1 (plosi) − k min i=1 (plosi) (2) plos metric is calculated at the receiver and it is sent back to the sender using appropriate frtps acknowledgment packet packet loss field. this information is delivered to the sender in order to adjust its frame rate after the frtps adaptive fuzzy estimator. pad metric is another metric partly calculated at the receiver end as the std. deviation of each received packet and sent back to the sender via appropriate ack frame field. the sender then subtracts the deviation of each previous received ack packet. packet arrival deviation (pad) is a metric that expresses the delay deviation (jitter) of a specific period between two consecutive numbered frames. prior to pad presentation a short analysis of relevant measures follows. inter packet delay variation is the time variation between two consecutive packets i, i+1 is expressed as: ipdv = di+1 − di = rti+1 − rti + tti+1 − tti, while packet delay variation is expressed as: pdvi = di − dmin = (rti − tti) − dmin, where rt is the reception time as recorded by the ntp source and tt is the transmission time as recorder by the same ntp source (it is significant that both sender and receiver have similar stratum values from the ntp source). ipdv is a measure of the network’s ability to preserve the spacing between packets, while it reduces the demands on the stability and skew of measurement clocks [40]. pdv on the other hand can not distinguish short term variation, it is sensitive to the first frame delay and strong correlated with the mean frame delay (md). another metric value is the mean absolute packet delay variation (version) 2 (mapdv2), and is specified in as it computes a smoothed running estimate of the mean delay using the one-way delays of 16 previous packets [11, 50]: mdn=1··16 =an = 1 16 dn−1 + 15 16 an−1 if tn+i > an,p(n + i) = p(n + i) + (tn+i −an) if tn+i < an,nn+i = n(n + i) + (an − tn+i) mapdv2 =p̄ + n̄ rtp jitter ji metric is used by rtp and it expresses the undesired deviation of a true periodical flow and is described according to [26], for an rtcp interval between packet i and packet j as:d(i,j) = https://doi.org/10.15837/ijccc.2020.4.3842 6 (rj−ri)−(sj−si) = (rj−sj )−(ri−si) and ji = d(ri−si) di and estimated using the jacobson estimator [25] as: jesti = ji−1 +(|d(i−1,i)|− ji−1 16 ), expressed by rtp as inter-arrival jitter [40]. moreover, jitter for elongated intervals called jitter buffers capable of maintaining n packets is calculated as the standard deviation of the mean rt ti2 time of a packet leaving the sender until an ack packet is received by the sender: jk = √ ∑k i=1( rt ti 2 − ¯rt t 2 ) 2 n−1 . the pad metric is expressed as follows: at first packets i, i+1 inter-arrival time is calculated (d′i = dri di ) at the receiver end. then, according to equation 3 the receiver pad value is calculated. this value is send back to the sender using appropriate ack frtps frame field (see figure 1, d′i receiver inter-packet variance field of frtps protocol ack header). padrxi = √ (d′i)2 − (d̄′)2 (3) where d̄′ is the mean value calculated by the receiver. the sender also makes its own variance calculation based on i, i+1 ack packets reception, of the incoming ack packets based on equation 4: padtxi = √ (d′′i )2 − (d̄′′)2 = k − 1 tint pagg (4) where d′′i = drack i di and d̄′′ is the inter arrival time of received i, i+1 ack packets at the sender. the previous equation can be further simplified, where k is the back to back frame re-transmissions due to duplicate acks (rtp congestion avoidance mechanism embedded to frtps) and tint is the current inter package(frame) gap (ipgi) denoted by the sender’s frame rate and pagg is the number of aggregated packets per frame. the total pad calculation done by the sender based on equation 5: padi = √ 1 2 (padrxi )2 + (pad tx i )2 (5) according to equation 5, pad values are greater than zero, the root mean square offers a smooth linear approximation over non linear abrupt variations. such an approximation is useful and less time consuming for frtps that relies its adaptation on complex fuzzy calculations. pad metric does not depend on the frtps flow control mechanisms. this means that it may give measurements based even on out of band packets received at the receiver end. in case of packet drops, pad values increase close to the re-transmission timeout or to k times the periodic frame sent interval tint, until a new acknowledgment frame is received. if pad > tint then pad is set to tint value (pad = tint). this makes pad over time a smooth curve towards abrupt short duration jitter intervals, as well as sensitive in case of congestive packet loss incidents (more than one-burst packet losses). md metric is calculated as the time difference between packet transmission and acknowledgment reception at the sender. that is, an approximation of instant md metric is: mdk = rt tk2 . frtps calculates md as a quadratic moving average and provides the flow with stochastic next time interval md prediction. under no congestive or error conditions, md has a smooth quadratic response up and down a mean value. in case of congestive saturated links, depending on the communication channel congestion incident and its participating flows immediate or delayed avoidance, md calculus quadratic response values are smoothly exponentially increased depending on the packets delay effect over the last i packets . frtps md metric for the next time interval (k+1) is calculated as follows: mdk+1 = α k∑ i=1 1 (k − i)2 + (1 −α)mdk − �k (6) where k is the time depth coefficient (k · tint) that expresses the number of previously consecutive transmitted packets (i = 1..k,k ≥ 2,k ≥ 1) of the stochastic process maintained by each frtps flow. mdk value is the mean delay of the last transmitted packet while mdk+1 is the predicted mean delay of the next packet in the transmission queue. coefficient a, is calculated according to the following https://doi.org/10.15837/ijccc.2020.4.3842 7 formula: α = k∑ i=1 1 (k − i)2 < 0.7 (7) from equation 7, as k increases (100<k<1000), parameter α can be approximated to α = 23 . for k values less that 10, parameter α is approximated to α = 12 . error parameter �k is calculated as the difference: �k = md predicted k −md real k . 3.2 frtps protocol structure frtps is a middle-ware protocol that carries sensory measurements or haptic equipment real-time control operations. frtps frame structure is presented at figure 1 and includes the following fields: a sequence number based on utc timestamps for flow control, a sending rate level (srl) field, calculated and filled by the sender, an equipment id field that uniquely identifies each flow, a length (len) field that represents the number of sensor values included in the payload (max of 16 sensor values can be included per packet) and the packet sensor payload of 4 octets for each sensor value. finally, an aggregation id field is used (agg. id), that identifies the number of aggregated packets included per frtps frame. each frame is transmitted from its source periodically, using a constant frame transmission interval of: 5ms<tp<60ms. for each frame or grouped packets received by the receiver, an acknowledgment frame is send back to the sender. ack frame structure is illustrated at figure 1. ack frames include fields such as the srl(service rate level), frame len, equipment id and aggregation agg. id of the corresponding source frames. len and agg. id fields are included as a validation of packet reception payload and/or packets, while srl is included in order to indicate that srl change has been propagated to the receiver in case of srl rate adaptation. ack packets also include a plos metric estimation for the plos metric as well as an inter-packet variance estimation di’ value and used by the transmitter in order to calculate pad metric value and to adapt its rate for the following time interval accordingly. frtps protocol does not include a native re-transmission mechanism nor does it uses rto intervals for timeout or frame rtr purposes. it keeps on transmitting packets even if packet losses are experienced. that is why plos metric is calculated at the receiver end, which has the complete information on what packet has been received. figure 1: frtps middle-ware protocol header and frtps delayed ack frame structure frtps adaptive rate mechanism includes 13 rate-levels called service rate levels or srls and are illustrated at table 1. each srl level has a service id, and service attributes that correspond to: 1.the number of packets encapsulated per frame, and 2.the per sensory value quantization properties of 32 to 32, 32 to 24, 32 to 16 and 32 to 8 bits per sensory payload data value compression (quantization). https://doi.org/10.15837/ijccc.2020.4.3842 8 starting from the default srl=1 level, each source flow periodically sends frtps packets to the receiver and awaits acknowledgments. if the frtps moves to a higher srl level, it uses the new srl packet aggregation or quantization attributes for the next transmitted frames, propagates the new srl level to the receiver and confirms the srl level change by the srl field of the next incoming ack frame. frtps service levels cope with the following policy regarding real-time traffic: “if rate increase is required due to limited bw resources, then at first increase packets per frame using packet aggregation and then reduce frame packed payload values resolution”. table 1: frtps protocol fuzzy partitioning output rate levels (service rate levels) and their corresponding attributes. srl srl level name aggregated sensor values/sensor field/frame bytes(bits) per sensor value max 16 sensors frtps payload in bytes quantization levels (q1/q2/q3) (bits/value) 1 low data rate (lo) 1 4(32) 64 1xagg.level 2 little data rate (li) 2 4(32) 128 2xagg.level 3 medium data rate (m) 3 4(32) 192 3xagg.level 4 high data rate (h) 4 4(32) 256 4xagg.level 5 very high data rate/low quantization (l1) 5 3(24) 240 5xagg.level/q1 level(24) 6 high data rate/low quantization (l2) 4 3(24) 192 4xagg.level/q1 level(24) 7 medium data rate/low quantization (l3) 3 3(24) 144 3xagg.level/q1 level(24) 8 little data rate/low quantization (m1) 2 3(24) 96 2xagg.level/q1 level(24) 9 little data rate/medium quantization (m2) 2 2(16) 64 2xagg.level/q2 level(16) 10 low data rate/low quantization (m3) 1 3(24) 48 1xagg.level/q1 level(24) 11 low data rate/medium quantization (h1) 1 2(16) 32 1xagg.level/q2 level(16) 12 low data rate/high quantization (h2) 1 1(8) 16 1xagg.level/q3 level(8) 13 maintain data rate/zero payload (h3) 1 0(0-header only) 0 packet aggregation transforms the frtps frame into a multi packet structure of compressed or non compressed values. this structure is similar to the frtps frame of figure 1, with an aggregation upper limit of five aggregated packets per frame (table 1, l1), or quantization with an upper limit of 8bit compressed 32bit sensory value for 1 aggregated packet per frame. the frtps protocol uses the agg. id field in its header so as to indicate the number of packets aggregated in each frame. the maximum frtps frame includes an aggregation of 5 packets/frame that in turn include a payload of 16 sensory 32bit values. the maximum frtps frame size is:frtpspsize = ethheader(14) + ipheader(20) + udpheader(8) + frtpsf rameheader(8) + frtpspayload(16) ·npackets · nbytes f rame ≤ 306bytes. using the previous formula the minimum frtps packet size is the h2 srl level (table 1, h2 level) with 16x8 bit sensory values and 1 packet per frame. that is a frame of 66bytes, with a 50bytes of header overhead header. also a h3 srl level (table 1, h3 level) exists in the frtps protocol for service probing purposes and this level frames carry no payload data. a frtps frame at srl=1 level (table 1, lo level), must include at least more than 14 sensory values for the frtps payload to be a little bigger than its header. this is an excessive protocol overhead not caused by frtps itself but from the lower level medium, networking and transport protocols. an frtps source initially starts transmitting data using the base frtps level ranked as lo (table 1, lo -low aggregation and zero quantization level). if limited network resources, frtps first increases its aggregation level by moving from 1 to a maximum of 5 packets per frame (table 1, frtps levels: little (li), middle(m), high (h), very high(l1)). from l1 and thereafter, quantization levels occur and the transmitted payload values are compressed using uniform compression method similar to the α-law used at pcm a2d value sampling. in quantum level q1, the number of bytes per sensory data is reduced to 3 bytes, for q2 to 2 bytes and for q1 up to a single byte. the sensory payload value compression mapping performed in quantization q-levels follows equation 1. https://doi.org/10.15837/ijccc.2020.4.3842 9 x′value = xvalue 2qi+1 − 1 (8) where xvalue is the current quantization level qi input value and qi+1, (qi+1 < qi) the next quantization level bits (24,16,8). output value is the quantization process result value x′value. 3.3 frtps fuzzy control mechanism the frtps protocol supporting mechanism is consisted of: 1. a flow monitoring agent, instantiated at the transmitter and receiver ends, 2. the fuzzy controller module, implemented at the sender as part of its flow monitoring agent, 3. the data transmission client and data reception service and queues implemented at the sender and receiver accordingly. the functional parts of the frtps mechanism and fuzzy controller are illustrated at figure 2. figure 2: frtps flow monitoring mechanism and fuzzy controller the fuzzy controller processes the crisp input metric values acquired by the transmitter monitoring agent as follows: step 1 : fuzzification process, calculating the fuzzy input vector of pad, packet loss and mean delay v=(vpad, vplos, vmd) based on the applicable input degree of membership functions as illustrated at figure 3. for the crisp values of: xp ad = 3,xp los = 0.005,xm d = 32, the membership function values for the participating terms are: µl(pad) = 0.25, µg(pad) = 0.5, µl(plos) = 0.33, µsp(plos) = 0.66 and µl(md) = 0.4, µg(md) = 0.2. step 2: inference pass. the fuzzy set values pass through a set of rules shown at table 3. the inference logical conjuction operators are based on the minimum degree of membership value among the participating terms. the specified fuzzy terms threshold values and have been derived from previous experimentation on the amesetp protocol [30]. the frtps logic lies on the aggregation of packets per frame for transient delays and random drops. for mild congestion incidents frtps follows more of a conservative policy, trying by reducing the number of packets per flow while quantizing the bytes per sensor value field, compressing the input values inside each packet. for severe congestion incidents, frtps reduces its frame packets aggregation to 1pkt/frame, while maximizing the payload values quantization. https://doi.org/10.15837/ijccc.2020.4.3842 10 this mapping is presented at table 1, columns 3, 4, 5. the translation of the fuzzy operators joining propositions into t-norms are calculated by taking the minimum value of the terms. considering the frtp rules set, the degree of activation per each participating rule using the previous example degree of membership values per attribute is shown at table 2. based on the activation values of the inference output, the takagi-sugeno weight-based defuzzifier is used to calculate the crisp output [53]. a simplified defuzzifier logic has been selected instead of a more complicated takagi-sugeno-kang, anfis one [33, 46], in order for the controller to perform fast srl calculations in terms of processing time. table 2: frtps example of inference rules and activation value of the srl output, for each participating set of md, pad, plos dom values. pad plos md srl µsrl l0.25 l0.33 l0.4 lo(1) 0.25 l0.25 l0.33 g0.2 lo(1) 0.2 l0.25 sp0.66 l0.4 lo(1) 0.25 l0.25 sp0.66 g0.2 li(2) 0.2 pad plos md srl µsrl g0.5 l0.33 l0.4 lo(1) 0.33 g0.5 l0.33 g0.2 lo(1) 0.2 g0.5 sp0.66 l0.4 li(2) 0.4 g0.5 sp0.66 g0.2 li(2) 0.2 step 3: defuzzification process. the output variable ỹ =< s̃rl > has terms that equal to a constant value that equals to the srl id, and for each term, its membership function corresponds to the flc weight wi(j). the final chirp output y is calculated as a weighted average defuzzifier according to equation 9 yout = ∑k j=1 wi(j) ·y(i)j∑k j=1 wi(j) (9) where i = 1..13 is the srl membership function, inference output value and wi(j) are the corresponding rules outcome activation values as weights. considering the output variable ˜srl, containing the output terms: lo(1), lo(1), lo(1), li(2), lo(1), lo(1), li(2) and li(2), as values 1,1,1,2,1,1,2,2, with weights equal to their degree of activation µ(srl) (from table 2), the crisp output equals to: y = 1·0.25+1·0.2+1·0.25+2·0.2+1·0.33+1·0.2+2·0.4+2·0.30.25+0.2+0.25+0.2+0.33+0.2+0.4+0.2 = 1.41. step 4: smoothing. the crisp output smoothing process is performed according to equation 10 y′out = 1 + ((1 −α) ¯yout + α yout) (10) where ¯yout is the mean calculated value of the k previous measurements of pad, plos and md and α > 0.5 is a coefficient parameter. in our case α = 0.9 and k = 100. finally, the y′out passes through a rounding step as follows, in order to determine the final srl level: srl = floor((y′out)mod(1) + 0.5), floor(x) = f(n,d) = n d − n ·mod(d) d (11) frtps fuzzy controller logic tries to identify to which srl level the transmitter should adapt, as indicated by the receiver. since fuzzy controllers are processing demanding, the fuzzy mechanism and rate adaptation are performed by separate threads. furthermore, the frtps smoothing step the frtps rate adaptation mechanism smooth responsive with adaptive smoothing factor (α). frtps is more accurate in most cases than simple threshold-based solutions [8, 30, 34, 60]. frtps uses three state terms for the md metric (low, good and high). similarly to md, frtps fuzzy classifier uses four state terms (low, good, high, critical) for pad metric and four state terms (low, spurious, transient, congested) for plos metric values, as depicted at figure 3. the crisp inputs fuzzification process is as follows: from estimated crisp metric values of md, plos and pad at the sender, the process initiates. the metric values correspond to membership functions as denoted from figure 2 with a degree of membership (dom) expressed in [0-1] scale. the fuzzy-processes used for pad and packet loss are triangle functions, while for md trapezoid. that is, https://doi.org/10.15837/ijccc.2020.4.3842 11 (a) (b) (c) figure 3: (a) frtps mf fuzzy states for average mean delay (md) x axis in (ms) (b) frtps ms fuzzy states for packet arrival deviation (pad) x axis in (ms) (c) frtps mf fuzzy states for packet loss (ploss) x axis, % frame loss after ewma process the membership values can be easily calculated using linear interpolation. afterwards, the inference process occurs. a set of conditional statements that determine the relation between input and output linguistic variable terms as shown at table 3. figure 4: frtps fuzzy output mf degree of membership(dom) for srl rate-levels (1-13) the md, plos and pad sets pass through the fuzzy inference rules providing the system with fuzzy output levels, each one with a different activation output value. the fuzzy controller rules or and and operators follow zadeh’s [14] t-norm (min) and s-norm (max) dom operation accordingly https://doi.org/10.15837/ijccc.2020.4.3842 12 table 3: frtps protocol fuzzy rules srl levels adaptation table ((a) md=low, (b) md=good and (c) md=high) (a) md=low srl levels ppppppppppad plos low spurious transient congestion low low(lo,1) low(lo,1) little(li,2) little(li,2) good low(lo,1) little(li,2) little(li,2) medium(m,3) high little(li,2) medium(m,3) medium(m,3) medium(m,3) critical medium(m,3) high(h,4) high(h,4) high(h,4) (b) md=good ppppppppppad plos low spurious transient congestion low low(lo,1) little(li,2) little(li,2) medium(m,3) good low(lo,1) little(li,2) medium(m,3) high(h,4) high medium(m,3) low-q1(l1,5) low-q2(l2,6) low-q3(l3,7) critical low-q2(l2,6) low-q3(l3,7) medium-q1(m1,8) medium-q2(m2,9) (c) md=high ppppppppppad plos low spurious transient congestion low medium(m,3) low-q1(l1,5) low-q2(l2,6) low-q3(l3,7) good high(h,4) medium-q1(m1,8) medium-q2(m2,9) medium-q3(m3,10) high low-q1(l1,5) medium-q3(m3,10) high-q1(h1,11) high-q2(h2,12) critical low-q2(l2,6) high-q1(h1,11) high-q2(h2,12) high-q3(h3,13) in order for the final srl level set calculation. the output of the fuzzy controller is the srl level selected for the next frtps time interval tpint. figure 4 shows the mf dom values for the entire 13 srl output levels of frtps. as another testing frtps example, for md=low and for plos<0.01, moving the pad metric value from 4ms to 13ms, shall provide the frtps system with an output value of srl=1 for 4ms to srl=4 for 13ms. when the srl level is calculated by the fuzzy controller the sender immediately alters its transmission frame rate or enforces payload quantization (if decided by the controller). since the fuzzy controller exists in both sender and receiver ends, the receiver end has already calculated the new srl level and expects the sender to adjust its frame rate to the new srl level. the time interval needed for this srl change is the pre-adjust interval, tpint and is equal to: max( 3 2 ¯rt t 2 ,rttp), where ¯rt t 2 is the mean time for an ack frame to reach the sender and rttp is the cumulative time ( ∑p i=1 rt ti 2 ), for p consecutive ack frames that reach the sender (set by the haptic application layer). srl crisp output values can also be in between two different neighborhood srl levels, according to figure 4. for this purpose a final modulo stage process takes places at the smoothing step. for example a crisp output of 2.3 corresponds to rate-level with srl=2 (li-little rate increase), while fuzzy output of 2.56 corresponds to rate level with srl=3 (medium rate increase). 4 frtps protocol performance evaluation for the frtps preliminary testing implementation and design of the srl levels, the r programming language has been used with the r fuzzy package for the implementation of frtps fuzzy controller [17]. the testing data set used was taken from the amesetp dataset measurements [30], as well as udp flow measurements using netem [22] and iperf [24]. in order to evaluate the proposed frtps protocol, the authors compared the frtps protocol with the performance of rtp (udp-based) protocol, most commonly used by real-time applications. after https://doi.org/10.15837/ijccc.2020.4.3842 13 preliminary tests, the authors implemented the frtps protocol in c++ using qt and fuzzylite [57]. for the rtp service implementation the jrtplib was used [48]. the authors constructed a real world scenario and sent streams of multi-sensory frtps and rtp data between two computers. the real-time testing dataset included movement commands of a 5-axis servo robotic arm, captured by a custom rpi v2 data-logger. the first computer referred to as the sender, is an intel core i3 8100 3.6ghz with 4gb ram. the other computer referred to as the receiver, is an intel core i7 3770 3.4ghz with 8gb ram both computers are interconnected through the greek universities network (grnet) with 100 mbps up-link and down-link connectivity. the network experimental topology is illustrated at figure 5. figure 5: network topology of the performance evaluation scenario in order to control the network conditions and provide saturated internet links the authors used the network emulator netem [22, 27] and the hierarchical token bucket (htb) queuing discipline of the linux traffic control utility (tc)[35] at the linux router r1. two flows of 20000 packets are sent from each one of the clients (rtp flow and frtps flow) to the server. the payload of each packet is set to 64 bytes of payload that corresponds to 16 4byte sensory values. the assigned bandwidth of the network connection between client and server is arbitrary set to 700kbps and 1.4mbit/s at the htb queue. the sender rate for both rtp and frtps flows at the 700kbps link is set to r=500frames/s and 1000frames/s for the 1.4mbit/s link. 4.1 frtps fuzzy controller experimentation in this experimental scenario the frtps controller is put to the test, by introducing different types of pad variances to the frtps client and monitoring the crisp fuzzy output srl level selected for each case. as illustrated at figure 6a the client frtps controller is being tested by deliberate sending bogus acks from the receiver indicating different received frame deviations based on equation 3 that in turn lead to different pad value calculations by the sender and different crisp outputs by the controller. the frtps experimentation is performed by statically setting at the sender the average mean delay variable values to 30ms 50ms and 100ms, values that indicate mild and severe network contention prior to the appearance of router queue drops and the initiation of a burst congestive incident. it is probable that under such conditions random drops occur as well as excessive queuing delays. this is the reason for calculation of mean delay values 100-10000 times bigger than the flow transmission rates. the frtps srl selection starts from srl level=1(lo) and ends up to the srl level=6 (l2). apart from srl level 6, all previous levels are packet aggregation levels, where multiple packet values (payloads) are packed together into one frame. moreover, in figure 6a red line, the authors also introduced random errors to the receiver ack plos field, in order to confirm the fuzzy response of the frtps controller to the quantization levels (6-13). this denotes that the entrance of the sender to one of the quantization levels occurs only in very extreme contention cases that are followed by random frame drops or in congestive incidents detected at the receiver and propagated back to the https://doi.org/10.15837/ijccc.2020.4.3842 14 sender via the plos ack field. finally, at figure 6b the mean cpu processing time of the frtps controller over execution time is illustrated. it is obvious that the average processing time of an ack, metric values re-calculation and prediction of the next srl transmission level by the controller cpu processing costs to the authors’ experimental client are: 310-370ms. that is, if the control prediction process has been performed for every received ack the maximum achieved transmission rate tint will be no more than 2-3 frames per second for a single core cpu. this is an indication that the fuzzy prediction calculation will be performed every n acks received by the sender (tcalc ≥ 1000tint) and that a complete cpu core has to be utilized for the monitoring of the fuzzy control process. (a) (b) figure 6: (a) frtps fuzzy states traversal over different estimated pad values by the client x axis in (ms) (b) frtps controller mean processing delay https://doi.org/10.15837/ijccc.2020.4.3842 15 (a) (b) (c) figure 7: (a) %frame loss vs r1 latency for 700kbps, 1,4mbps channel capacity for the rtp and the frtps protocols. (b) goodput kbytes/s vs r1 latency for 700kbps, 1,4mbps channel capacity for the rtp and the frtps protocols. (c) % packets out of order vs r1 latency for 700kbps, 1,4mbps channel capacity for the rtp and the frtps protocols. 4.2 experimentation on saturated links due to contention increase in this scenario 0% packet loss has been injected to the netem queue. the queuing delays (latency) in the netem queues at the r1 are set to 0-50, 80, 100, 150 and 200msec and the netem queue jitter at the 90% of the corresponding latency value. the metrics used in the experiments are the frame loss, goodput and the total number of packet that arrived out of order at the receiver end. the receiver average goodput is measured in kbytes/s of data payload/s at the receiver queue. experimental results are shown at figure 7. comparing the experimental results of the rtp and the frtps flows at figure 7a, it is clear that in the examined scenario both rtp and frtps flows experience similar packet losses. this is due to the fact that the frtps operates on top of rtp too and copes with the same re-transmission mechanisms as those of rtp. it is like having two aggressive udp flows competing over the same bottleneck. nevertheless frtp payload is far more different than that of rtp due to the fact that https://doi.org/10.15837/ijccc.2020.4.3842 16 it uses payload aggregation and quantization. aggregation of frames is the prevailing policy up until 50-100ms of inserted latency while above 100ms both aggregation and quantization fuzzy states reside. from figure 7b and for latency up until 150ms the benefits of payload aggregation (packets aggregation) into single frames are obvious. for the bottleneck scenario of 700kbps, from 30-150ms queuing latency the average goodput of frtps is 21% more with respect to rtp. similar results apply for the bottleneck scenario of 1,4mbit/s starting from 40ms up to 100ms with 25% more goodput delivered from frtps. for latency disciplines above 150ms and up to 200ms, where both quantization and aggregation apply, in the 700kbps scenario the frtps goodput is 89% more than rtp while for the frtps goodput for the 1,4mbps scenario is 70% more than that delivered from the rtp flow. in total in a contenting environment among frtps and rtp, the frtps flows can deliver an average of at least 26% more data to its destination under the same network conditions, experiencing similar frame losses and frame re-transmission policies. that is, frtps protocol outperforms the rtp protocol in a contenting environment of limited resources. comparing rtp and frtps out of band (oob) packets at figure 7c for the 700kbps bottleneck scenario, the frtps flow delivers in average 0,75% less oob packets than rtp. the same applies for the 1,4mbps/s bottleneck where the frtps delivers 2% less oob messages. the 7,9%-10% correspond to 150-400 less out of band (oob) packets of a total of 20000 frames. 5 conclusions the authors propose a new fuzzy protocol called frtps that maintains rate adaptive and data quantization mechanisms. the authors present the proposed protocol and put to the test its implementation functionality. functionality test of the frtps controller have shown that the frtps controller operates well distinguishing contention incidents where a payload aggregation policy is most applicable, from random drop incidents where both aggregation and quantization is the best policy to offer, from congestive incidents where quantization and discrete payload reduction is the most network friendly option. the proposed frtps algorithm adaptive decisions are based on three different crisp metric inputs, the mean delay, the pad and the packet loss, as calculated by the receiver end and propagated to the sender using the frtps protocol. the fuzzification process is implemented at the transmitter end, handed out by the receiver end measurements. the authors experimented with the frtps performance on a real-case scenario and compared the performance of the frtps with that of the rtp protocol. from the experimental results it is obvious that under similar network conditions where the two flows compete for the same limited resources the frtps protocol outperforms the rtp protocol, delivering 26% more payload data to its destination. regarding data synchronization, it is pinpointed that frtps can transfer at least 1% more synchronized frames than rtp, due to its adaptive packet aggregation mechanism. it is set for further experimentation how the payload quantization affects synchronized data deliveries. finally, measurements of the processing delays enforced by the frtps protocol to the sender. from the experimental results, the processing time requirements of fuzzy algorithms are at least 30300 times more than the processing time requirements of the rtp protocol. however, since real time flow interruption is considered to be dangerous and problematic in most application cases, the authors set as a future work the implementation of custom asic cores with embedded schedulers that will optimize receiver end captured measurements and reduce significantly fuzzification processing time overheads. acknowledgements the authors would like to thank the dept. of business administration, university of western macedonia, greece, for his participation in the experimental setup, server provision and provided network that made the execution of the experimental scenarios possible. https://doi.org/10.15837/ijccc.2020.4.3842 17 references [1] al-saadi, r.; armitage, g.; but, j.; branch, p. 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[60] wirz, r.; marin, r.; ferre, m. et al. (2009). bidirectional transport protocol for teleoperated robots, ieee transactions on industrial electronics, 56(9), 3772–3781, 2009. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: kontogiannis, s.; kokkonis, g. (2020). proposed fuzzy real-time haptics protocol carrying haptic data and multisensory streams, international journal of computers communications & control, 15(4), 3842, 2020. https://doi.org/10.15837/ijccc.2020.4.3842 international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(2), 212-219, april 2019. performance analysis of raw impact on ieee 802.11ah standard affected by doppler effect a.a. marwan, d. perdana, d.d. sanjoyo abdul aziz marwan* department of electrical engineering, telkom university jl. telekomunikasi no. 1 terusan buah batu 40257, bandung, indonesia *corresponding author: abdulazizmarwan@student.telkomuniversity.ac.id doan perdana department of electrical engineering, telkom university jl. telekomunikasi no. 1 terusan buah batu 40257, bandung, indonesia doanperdana@telkomuniversity.ac.id danu dwi sanjoyo department of electrical engineering, telkom university jl. telekomunikasi no. 1 terusan buah batu 40257, bandung, indonesia danudwj@telkomuniversity.ac.id abstract: internet of things (iot) offers a new dimension of technology and information where connectivity is available anywhere, anytime, and for any purpose. ieee 802.11 wireless local area network group is a standard that developed to answer the needs of wireless communication technology (wi-fi). recently, ieee 802.11 working group released the 802.11ah technology or wi-fi halow as a wi-fi standard. this standard works on the 1 ghz frequency band with a broader coverage area, massive device and the energy efficiency issues. this research addresses, the influence of doppler effect using random waypoint mobility model on 802.11ah with different raw slot and raw slot duration are analyzed. the design of the simulation system is done by changing raw slot and raw slot duration. based on the result, it can be concluded that the overall performance of the network with all of the parameter scenarios is decreasing along with the increasing raw slot, raw slot duration, and fluctuation. in the raw slot = 5 scenario with v = 10 km/h has the worst performance with an average delay which is about 0.128225 s, and average throughput is about 0.284337 mbps while for raw slot = 1 with an average pdr which is about 99.1076 %. while in the raw slot duration = 0.020 s scenario with v = 10 km/h has the worst performance with an average delay which is about 0.135581 s, average throughput is about 0.286828 mbps, and average pdr which is about 99.3165 %. keywords: restricted access window (raw), ieee 802.11ah, random waypoint, modulation and coding scheme (mcs), network simulator 3. 1 introduction nowadays, internet of thing (iot) offers a new dimension in the world of technology and information where connectivity is available wherever, whenever, and for anything. the current global trend of internet of thing is very rapidly evolving from the needs of users that want the efficiency of devices in various aspects in order to facilitate the user’s own activities [2]. the number of connected devices being the main point of problems in iot technology itself related to energy efficiency or energy consumption. the ieee 802.11 wireless local area network standard working group operating at 2.4 ghz and 5 ghz band frequencies is a standard that developed to address the needs for wireless copyright ©2019 cc by-nc performance analysis of raw impact on ieee 802.11ah standard affected by doppler effect 213 (wi-fi) communication technology problems that have a high data rate, easy to develop and lower value in cost aspect, such as wireless sensor network (wsn) and machine to machine (m2m) communication that used in application of military, commercial, health care, monitoring of traffic, and also controlling the inventory [1]. in its development, the ieee 802.11 working group released 802.11ah or wi-fi halow technology as the new wi-fi standard. this standard works on a 1 ghz band frequency with broader area coverage, more effective in cost value with an energy efficiency improvement [9]. 802.11ah provides a shortest mac header, segmented traffic indication map (tim), restricted access window (raw), and target wake time (twt) that support the efficiency and quantity of energy used by stations (stas) [7]. in its application, 802.11ah technology can accommodate devices or stations in large numbers and every station has their movement pattern such as static or mobile user characteristics. the movement of stations or mobility can affect the performance of the 802.11ah itself. the most commonly used mobility model according to the literature is the random waypoint (rmw) model [8]. firstly, each station will go to the random destination with random speed, move towards the destination, and pause on several times, then moving again towards the coordinates of the destination. other similar mobility models such as random direction model, the random walk model, manhattan and the gauss-markov mobility model are also often used in experimental simulations to obtain data that represent real condition network in the world [5]. the term fading means the signal information is being lost on the process of transferring. this can be happened because of some factors such as rapid fluctuation of amplitudes, phases, multipath delays, or user speed. this user speed factor is the doppler effect that influencing fading of the signal. the doppler effect is a relative motion between the base station and the user which results in random frequency modulation due to different doppler shifts on each of the multipath components [3]. in this research we discuss about the doppler effect with the changing of raw schemes on ieee 802.11ah standard network performances using random waypoint mobility model with velocity = 10 km/h. this scenario aims to analyze the performance of raw impact on 802.11ah and to find the raw with the worst performance. the raw schemes are using the raw slot = 1, 2, 3, 4, 5 and raw slot duration = 0.005 s, 0.010 s, 0.015 s, 0.020 s. furthermore, the performance of network is measured using simulation result from network simulator 3. the measured output are delay, throughput, and pdr. 2 general description of the used mechanisms the simulations on this research were performed on network simulator 3 release 3.21 with 802.11ah module which has been modified according to [10]. the raw scenario aims to analyze the doppler effect on 802.11ah with different raw duration and raw slot. simulations were performed on 100 nodes with 50 raw stations. on each number of station, the simulation were performed in two different raw slot scenarios, and on each raw slot duration, the simulation were performed in five different raw slot number as explained in table 1. the amount of bandwidth and data rate used in the simulation is adjusted to be about twice than the other. which is mcs 3 (2 mhz bandwidth and 2600 kbps data rate). effective and efficient network conditions are required by wireless networks with ieee 802.11ah standards that capable of allocating stations with large numbers and wide coverage. in the simulation topology, was placed one access point and 100 nodes of sta around it that illustrated in fig 1. this research focuses on raw mechanism in mac layer of 802.11ah standard. the other features such as tim segmentation and twt were not implemented. the simulation has used the traffic generator as a sender as well as a receiver packets that will be delivered. generation of traffic is done by udp transport protocol because when data is 214 a.a. marwan, d. perdana, d.d. sanjoyo figure 1: topology of simulation table 1: scenario explanation slot duration = 0.005 s slot = 1 slot = 2 slot = 3 slot = 4 slot = 5 slot duration = 0.010 s slot = 1 slot = 2 slot = 3 slot = 4 slot = 5 raw scenario slot duration = 0.015 s slot = 1 slot = 2 slot = 3 slot = 4 slot = 5 slot duration = 0.020 s slot = 1 slot = 2 slot = 3 slot = 4 slot = 5 transmitted, data transmission time is more important than its integrity [4], it is in accordance with the needs of delivery data on iot communications where communication in real time is necessary. the flowchart system of this research is presented in figure 2. according to the system, after designing the simulation of 802.11ah standard in ns3 environment, traffic generator is performance analysis of raw impact on ieee 802.11ah standard affected by doppler effect 215 implemented on the simulation. the raw changing scenario of simulation is designed to collect the data. if the scenarios are succeeded, delay, throughput and pdr data can be collected to be analyzed. thus, the doppler effect influence on network performance can be analyzed for the conclusion. the output from the simulation in this research is qos parameters which are as follows [6]: • average end to end delay, which is the average time of delivering the data package from the sender to the receiver. delay = ∑ receivedpacketdestination−packetsentsource∑ packetreceived (1) • throughput, which is defined as the speed (rate) effective for transferring the data. throughput is total number of packets received in bits divided by the number of delivery time. throughput = ∑ receivedpacketsize∑ deliverytime (2) • packet delivery ratio (pdr), which is the ratio between the numbers of packets successfully received and the number of packets sent. pdr = ∑ totalpacketreceived∑ totalpacketsent × 100% (3) 3 experimental results the parameters and its description of the simulation are presented in table 2. the output from the simulation is qos parameters such as delay, throughput, and pdr for raw slot and raw slot duration scenario in ieee 802.11ah standard using random waypoint mobility with v = 10km/h which are shown in figure 3 figure 5. the doppler effect is calculated to find which of the small-scale fading are affected by the raw slot and raw slot duration. the impacts are delay spread that causing signal power to be weakened and inter-symbol interference (isi), doppler spread that causing signal power to be weakened, and doppler shift that causing frequency signal wave to be changed or distorted. the first influence of doppler effect is delay spread: frequency selective fading which is about 40 × 10−6s << 2 × 10−3s or ts << σ, where symbol duration is lower than maximum excess delay and 2mhz >> 0, 1mhz for mcs 3 or bs >> bc, where bandwidth of signal is higher than channel bandwidth. and the second influence of doppler effect is doppler spread: slow fading which for v = 10km/h is about 21 × 103µs >> 40µs or ts >> tc10, where symbol duration is lower than time coherence channel. and the third influence of doppler effect is doppler shift which for v = 10km/h is about −8, 521hz to 8, 521hz, where the sender frequency signal wave is distorted. figure 3 shows the influence of doppler effect in increasing the number of raw slot and raw slot duration to the delay that obtained from simulations with mcs 3 (2 mhz bandwidth and 2600 kbps data rate) using v = 10 km/h user speed. there are some fluctuations in both raw slot and raw slot duration. this is the impact of doppler spread: slow fading causing the delay value in both raw slot and raw slot duration to be fluctuated. from the graph above, the highest value of delay that obtained from mcs 3 in raw slot = 5 with an average delay which is about 0.128225 s while in raw slot duration = 0.020 s with an average delay which 216 a.a. marwan, d. perdana, d.d. sanjoyo figure 2: flowchart system table 2: simulation parameters parameters value physical layer wlan/ ieee 802.11ah transport layer udp payload size 100 bytes rho 100 m number of sta 100 number of raw sta 50 number of ap 1 mcs mcs 3 (2 mhz bandwidth and 2600 kbps data rate) raw group 1 raw slot 1, 2, 3, 4, 5 raw slot duration 0.005 s, 0.010 s, 0.015 s, 0.020 s mobility model random waypoint mobility user speed 10 km/h performance analysis of raw impact on ieee 802.11ah standard affected by doppler effect 217 is about 0.135581 s. also, the highest fluctuation value of delay that obtained in raw slot = 5 with an average delay which is about 0.501875 s while in raw slot duration = 0.020 s with an average delay which is about 0.300501 s. in this scheme, from the result in the terms of average delay, that the network performance is getting lower with the increasing number of raw slot and raw slot duration. meanwhile the fluctuation is getting higher with the increasing number of raw slot and raw slot duration. thus, the higher the fluctuation in delay, the lower the network performance will be in higher raw slot and raw slot duration. figure 3: delay on v = 10 km/h user speed figure 4 shows the influence of doppler effect in increasing the number of raw slot and raw slot duration to the throughput that obtained from simulations with mcs 3 (2 mhz bandwidth and 2600 kbps data rate) using v = 10 km/h user speed. there are some fluctuations in both raw slot and raw slot duration. this is the impact of doppler spread: slow fading causing the throughput value in both raw slot and raw slot duration to be fluctuated. from the graph above, the lowest value of throughput that obtained from mcs 3 in raw slot = 5 with an average throughput which is about 0.284337 mbps while in raw slot duration = 0.020 s with an average throughput which is about 0.286828 mbps. also, the highest fluctuation value of throughput that obtained in raw slot = 5 with an average throughput which is about 0.124824 mbps while in raw slot duration = 0.020 s with an average throughput which is about 0.125177 mbps. in this scheme, from the result in the terms of average throughput, that the network performance is getting lower with the increasing number of raw slot and raw slot duration. meanwhile the fluctuation is getting higher with the increasing number of raw slot and raw slot duration. thus, the higher the fluctuation in throughput, the lower the network performance will be in higher raw slot and raw slot duration. figure 4: throughput on v = 10 km/h user speed figure 5 shows the influence of doppler effect in increasing the number of raw slot and 218 a.a. marwan, d. perdana, d.d. sanjoyo raw slot duration to the pdr that obtained from simulations with mcs 3 (2 mhz bandwidth and 2600 kbps data rate) using v = 10 km/h user speed. there are some fluctuations in both raw slot and raw slot duration. this is the impact of doppler spread: slow fading causing the pdr value in both raw slot and raw slot duration to be fluctuated. from the graph above, the lowest value of pdr that obtained from mcs 3 in raw slot = 1 with an average pdr which is about 99.1076 % while in raw slot duration = 0.020 s with an average pdr which is about 99.3165 %. also, the lowest fluctuation value of pdr that obtained in raw slot = 1 with an average pdr which is about 0.0006 % while in raw slot duration = 0.020 s with an average pdr which is about 0.0019 %. in this scheme, from the result in the terms of average pdr, that the network performance is getting lower with the decreasing number of raw slot and increasing raw slot duration. meanwhile the fluctuation is getting lower with the decreasing number of raw slot and increasing raw slot duration. thus, the lower the fluctuation in pdr, the lower the network performance will be in lower raw slot and in higher raw slot duration. figure 5: pdr on v = 10 km/h user speed 4 conclusions in the raw scenario, the network performance value will decrease along with the increasing raw slot and raw slot duration. this is because the higher the raw slot and raw slot duration, the stronger the influence of doppler effect which caused the fluctuation. based on the calculation, the lower the time coherence channel, the stronger the doppler spread: slow fading. for delay, the network performance is getting lower with the increasing number of raw slot and raw slot duration. meanwhile the fluctuation is getting higher with the increasing number of raw slot and raw slot duration. thus, the higher the fluctuation in delay, the lower the network performance will be in higher raw slot and raw slot duration. for throughput, the network performance is getting lower with the increasing number of raw slot and raw slot duration. meanwhile the fluctuation is getting higher with the increasing number of raw slot and raw slot duration. thus, the higher the fluctuation in throughput, the lower the network performance will be in higher raw slot and raw slot duration. for pdr, the network performance is getting lower with the decreasing number of raw slot and increasing raw slot duration. meanwhile the fluctuation is getting higher with the decreasing number of raw slot and increasing raw slot duration. thus, the lower the fluctuation in pdr, the lower the network performance will be in lower raw slot and in higher raw slot duration. performance analysis of raw impact on ieee 802.11ah standard affected by doppler effect 219 it can be concluded that for network performance in delay and throughput, the network performance is getting lower with the increasing number of raw slot and raw slot duration. while in pdr, the network performance is getting lower with the decreasing number of raw slot and increasing raw slot duration. and for fluctuation in delay and throughput, the fluctuation is getting higher with the increasing number of raw slot and raw slot duration. while in pdr, the fluctuation is getting higher with the decreasing number of raw slot and increasing raw slot duration. therefore, the fluctuation can be an indicator to analyze which raw slot and raw slot duration with the worst network performance where the fluctuation is the impact of the doppler effect. author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] aljarrah, e. 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(2016). implementation and validation of an ieee 802.11ah module for ns-3, conf. work. ns3 (wns3), seattle, usa, 49-56, 2016. int j comput commun, issn 1841-9836 9(1):62-70, february, 2014. using the breeder ga to optimize a multiple regression analysis model used in prediction of the mesiodistal width of unerupted teeth f. stoica, c.g. boitor florin stoica department of mathematics and informatics, faculty of sciences, "lucian blaga" university of sibiu, romania, 550012, dr. ion ratiu 5-7, sibiu florin.stoica@ulbsibiu.ro cornel gheorghe boitor department of preventive dentistry, faculty of medicine "v. papilian", "lucian blaga" university of sibiu, romania, b-dul. victoriei, no. 10, sibiu boitor.cornel@ulbsibiu.ro abstract: for the prediction of the unerupted canine and premolars mesiodistal size, have been proposed different variants of multiple linear regression equations (mlre). these are based on the amount of the upper and lower permanent incisors with a tooth of the lateral support. the aim of the present study was to develop a method for optimization of mlre, using a genetic algorithm for determining a set of coefficients that minimizes the prediction error for the sum of permanent premolars and canines dimensions from a group of young people in an area romania’s central city represented by sibiu. to test the proposed method, we used a multiple linear regression equation derived from the estimation method proposed by mojers to which we adjusted regression coefficients using the breeder genetic algorithm proposed by muhlenbein and schlierkamp. a total of 92 children were selected with complete permanent teeth which had not clinically visible dental caries, proximal restorations or orthodontic treatment that requires the decrease of the mesiodistal size of teeth. for each of these models was made a hard dental stone which was then measured with a digital calliper, the instrument having an accuracy of 0.01 mm. to improve prediction equations, we divided data into training and validation sets. the breeder algorithm, using the training set, will provide new values for regression coefficients and error term. the validation set was used to test the accuracy of the new proposed equations. keywords: genetic algorithms (ga), mixed dentition analysis, multiple regression equations, mesiodistal teeth size. 1 introduction the estimation of the mesiodistal size of permanent canine and of the two premolars before their eruption is important for early evaluation of the need for space in this area and consequently to mandible and maxillary. this represents an important part of diagnosis and orthodontic treatment strategy. methods of estimation, performed during mixed dentition, can be grouped into three categories: those using multiple linear regression equations, those using radiographs and those using a combination of the two methods [1], [3][7]. among these methods recently reported in literature, those based on mlre have the highest predictive capacity of the mesiodistal dimension (mdd) for unerupted canines and premolars. copyright © 2006-2014 by ccc publications using the breeder ga to optimize a multiple regression analysis model used in prediction of the mesiodistal width of unerupted teeth 63 prediction capacity of these methods can vary depending on the characteristics of different types constitutional areas, sometimes may vary even in the same countries [5][8], [9][17]. our aim was to verify if optimizing through an original method the mlre used recently in the literature [3], [14], can be predicted with sufficient accuracy the sizes of unerupted teeth from the support area in a group of children in sibiu located in a central area of romania. the first objective of the study was to verify the accuracy of recently used mlre based on known variables, namely the mesiodistal dimensions of teeth 42, 21 and 46, used in prediction of the sizes of unerupted teeth from the support area. the second objective of the study was to use an evolutive calculation method based on the genetic algorithm breeder, to optimize the regression coefficients used in the mlre. thus the accuracy of the predictions can be improved [1], [9], [19], [20]. 2 subjects, materials and prediction methods a representative public school with a population of 321 children ages 12-15 years from sibiu (romania) was selected for this study. from these subjects, a random simple technique was used to select 92 students (47 females and 45 males) fulfilling the selection criteria: • to have the parents’ written consent to participate in the study; • to present the dental arches fully erupted permanent teeth (molars 3 was not considered); • the erupted teeth show no abnormalities of shape, size or structure; • the teeth must not have missing of substance in the mesiodistal size, due to decay, trauma or orthodontic treatments have provided striping. dental impressions were taken with alginate impression material and immediately poured with hard dental stone to avoid any distortion. the measure tooth size models we used a digital calliper manufactured by mega (germany) with an accuracy of 0.01 mm. measurements were performed after the procedure proposed by seipel [12]. all models were measured 2 times by the same author and the result used was the average of two values. it was calculated the pearson correlation coefficient between measurements and method error (me) was calculated using the formula of dahlberg: me = √ d2/2n where d is the difference between the two measurements and n is the number of patterns measured a second time. for estimation the size of the unerupted canines and premolars we chose a recently proposed equation [3] based on known variables 21, 42 and 46. the form of this equation is: y = x1∗a1+x2∗a2+x3∗a3+a, where y is the outcome expected, x1, x2, x3 are independent variables determined by the size of teeth 42, 46 and 21, a1, a2 and a3 are regression coefficients for used teeth and a is a specific constant. the values of constant a and regression coefficients of the equation are presented in table 1: 64 f. stoica, c.g. boitor canines constant a1 a2 a3 premolars group a (42) (46) (21) maxillary 6,563 0,822 0,595 0,411 mandible 3,350 0,872 0,710 0,538 table 1: parameters of multiple linear regression equation used [3] in the following is presented our approach in optimization of the regression coefficients presented above, to provide a more accurate method for prediction of the mesiodistal width of unerupted permanent canines and premolars. because parameters of the multiple linear regression equation are real values, we are using a breeder genetic algorithm, in order to avoid a weak point of classical gas, represented by their discrete representation of solutions, which implies a limitation of the power of the optimization process [20]. the breeder genetic algorithm, proposed by mühlenbein and schlierkamp-voosen [2] represents solutions (chromosomes) as vectors of real numbers, much closer to the reality than normal ga’s. the selection is achieved randomly from the t% best elements of current population, where t is a constant of the algorithm (usually, t = 40 provide best results). thus, within each generation, from the t% best chromosomes are selected two elements, and the crossover operator is applied over them. on the new child obtained from the mate of the parents is applied the mutation operator. the process is repeated until are obtained n − 1 new individuals, where n represents the size of the initial population. the best chromosome (evaluated through the fitness function) is inserted in the new population (1-elitism). thus, the new population will have also n elements. 2.1 the breeder genetic operators let be x = {x1,x2, ...,xn} and y = {y1,y2, ...,yn} two chromosomes, where xi ∈ r and yi ∈ r, i = 1,n. the crossover operator has a result a new chromosome, whose genes are represented by values zi = xi + αi(yi − xi), i = 1,n, where αi is a random variable uniformly distributed between [−δ,1 + δ], and δ depends on the problem to be solved, typically in the interval [0,0.5]. the probability of mutation is typically chosen as 1/n. the mutation scheme is given by xi = xi + si · ri · ai, i = 1,n where: si ∈ {−1,+1} uniform at random, ri is the range of variation for xi, defined as ri = r · domainxi , where r is a value in the range between 0.1 and 0.5 (typically 0.1) and domainxi is the domain of the variable xi and ai = 2 −k·α where α ∈ [0,1] uniform at random and k is the number of bytes used to represent a number in the machine within is executed the breeder algorithm (mutation precision). 2.2 the breeder genetic algorithm the skeleton of the breeder genetic algorithm may be defined as follows [19]: procedure breeder begin t = 0 using the breeder ga to optimize a multiple regression analysis model used in prediction of the mesiodistal width of unerupted teeth 65 randomly generate an initial population p(t) of n individuals evaluate p(t) using the fitness function while (termination criterion not fulfilled) do for i = 1 to n −1 do randomly choose two elements from the t% best elements of p(t) apply the crossover operator apply the mutation operator on the child insert the result in the new population p’(t) end for choose the best element from p(t) and insert it into p ′(t) p(t + 1) = p ′(t) t = t + 1 end while end 2.3 the optimization process the aim of the breeder genetic algorithm is to find new values for the parameters of multiple linear regression equation presented in table 1, in order to reach a better prediction. each chromosome contains four genes, representing the real values ai, i = 1,3 and a. the fitness function for chromosomes evaluation is represented by the number of cases from the training set having an approximation error obtained with the new equation (in absolute value) bigger than prediction error provided by original equation. in our tests, parameters of breeder algorithm are assigned with following values: δ = 0, r = 0.1 and k = 8. the initial population has 1500 chromosomes and algorithm is stopped after 30000 generations. data provided by our study models was randomly divided in two sets: the training set, containing 50 cases and the validation set, composed by 42 study models. implementation of our new optimization method was accomplished in java language, using net beans 7.01. 3 results the mlre method is using two equations, one for mandible and other for maxillary. because there are differences in measurements of teeth between left and right quadrants for mandible and respectively for maxillary, in order to improve the prediction, we are using four equations, one for each quadrant. using the data from training set, the breeder algorithm finds new values for the parameters of the initial multiple linear regression equation (table 2). the accuracy of prediction made by optimized equations was verified using the validation set. the order of reliability of both compared prediction methods is the same. as we can see from the table 3, the correlation coefficient r calculated for the all four linear regression equations is almost the same for the original mlre equations as for the breeder optimized equations. in the following figures are evaluated the original and respectively the optimized multiple linear regression equations, using as criteria the number of cases better evaluated. a comparison of prediction error in estimating the mesiodistal widths of the canines and premolars in the mandible and maxilla using multiple linear regression equations in original form and respectively in optimized form is presented in figures 3-6: 66 f. stoica, c.g. boitor quadrant a a1 a2 a3 1 5.1917 0.7571 0.85332 0.28341 2 5.16292 0.90463 0.68192 0.41011 3 3.31241 0.89357 0.72022 0.51352 4 3.28732 0.70242 0.84793 0.47736 table 2: optimal values of parameters for multiple linear regression equations provided by the breeder genetic algorithm quadrant linear regression equations original mlre optimized with breeder 1 0.546 0.572 2 0.509 0.510 3 0.671 0.671 4 0.625 0.664 table 3: the correlation coefficients r for multiple linear regression equations figure 1: predictions on the training set figure 2: predictions on the validation set figure 3: the comparison of prediction figure 4: the comparison of prediction error in quadrant 1 error in quadrant 2 using the breeder ga to optimize a multiple regression analysis model used in prediction of the mesiodistal width of unerupted teeth 67 figure 5: the comparison of prediction figure 6: the comparison of prediction error in quadrant 3 error in quadrant 4 4 comparative analysis the optimization using the breeder genetic algorithm was made on all four quadrants, providing the following equations: yq1 = 5.1917 + 0.7571∗x42 + 0.85332∗x46 + 0.28341∗x21 yq2 = 5.16292 + 0.90463∗x42 + 0.68192∗x46 + 0.41011∗x21 yq3 = 3.31241 + 0.89357∗x42 + 0.72022∗x46 + 0.51352∗x21 yq4 = 3.28732 + 0.70242∗x42 + 0.84793∗x46 + 0.47736∗x21 figure 7: the optimized equations using genetic algorithm where yqi denote the outcome expected for the quadrant i ∈{1,2,3,4} and xmn represents the mesiodistal width of the tooth specified by index mn. in our study, if the difference in millimetres between the measured and predicted value of the sum of the mesiodistal sizes of unerupted canines and premolars is situated in interval [−0.75,0.75], the prediction is considered as a correct estimation, if the difference is < −0.75 mm we have an overestimation, and a prediction error > 0.75 mm is considered an underestimation. a comparison of correct estimations, overestimations and underestimations, provided by the original mlre equations [3] and respectively by the optimized equations from figure 7, is presented in the table 4: canines premolars method overcorrect undergroup estimations estimations estimations % % % maxillary original mlre 35 51 14 breeder 32 54 14 mandible original mlre 24 63 13 breeder 21 66 13 table 4: correct estimations, overestimations and underestimations in percents maximum errors in predicting the sizes of the canines and premolars in the mandible and maxilla are presented in table 5: 68 f. stoica, c.g. boitor quadrant original mlre breeder overunderoverunder estimating estimating estimating estimating 1 -2.32 1.50 -2.11 1.23 2 -3.97 2.21 -3.56 1.84 3 -2.13 1.14 -1.86 1.01 4 -2.15 2.25 -2.01 1.93 table 5: maximum errors in estimating the sum of the mesiodistal sizes of unerupted canines and premolars comparing predictions provided by the new and respectively old method, we can conclude that breeder genetic algorithm is capable to provide the best values for parameters of multiple linear regression equations, and thus our equations are optimized for best performance. the results obtained by the new multiple linear regression equations are significant better than those provided by some classical statistical approaches [3], [15], [17]. the proposed technique is an adaptive tool for predicting the sizes of unerupted canines and premolars with greater accuracy than standard linear regression analyses, the fitness function ensuring optimization of predictions for data collected from different groups selected from different countries. 5 conclusions using a breeder genetic algorithm, we can automatically find the optimal values for the parameters of multiple linear regression equations used in prediction of the mesiodistal width of unerupted permanent canines and premolars. after evaluation, we found that our new parameters, used in the regression equations, are providing a better prediction than original mlre method. thus, the prediction error rates of the optimized equations using the breeder genetic algorithm are smaller than those provided by the multiple linear regression equations proposed in [3]. using a fitness function related to the prediction error provided by original linear regression equations, the evolution process guided by our 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[20] stoica, f.; cacovean, l.f.; using genetic algorithms and simulation as decision support in marketing strategies and long-term production planning, proceedings of the 9’th international conference on simulation, modelling and optimization (smo’09), budapest tech, hungary, issn: 1790-2769 isbn: 978-960-474-113-7, 435-439, 2009. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 4, month: august, year: 2022 article number: 4568, https://doi.org/10.15837/ijccc.2022.4.4568 ccc publications an enhanced neural graph based collaborative filtering with item knowledge graph sangeetha m., meera devi thiagarajan sangeetha m. department of computer science and engineering, kongu engineering college, erode, tamilnadu, india corresponding author: sangeethcse@gmail.com meera devi thiagarajan department of electronics and communication engineering kongu engineering college erode, tamilnadu, india tmeeradevi@gmail.com abstract recommendation system is a process of filtering information to retain buyers on e-commerce sites or applications. it is used on all e-commerce sites, social media platform and multimedia platform. this recommendation is based on their own experience or experience between users. in recent days, the graph-based filtering techniques are used for the recommendation to improve the suggestions and for easy analysing. neural graph based collaborative filtering is also one of the techniques used for recommendation system. it is implemented on the benchmark datasets like yelp, gowalla and amazon books. this technique can suggest better recommendations as compared to the existing graph based or convolutional based networks. however, it requires higher processing time for convolutional neural network for performing limited suggestions. hence, in this paper, an improved neural graph collaborative filtering is proposed. here, the content-based filtering is performed before the collaborative filtering process. then, the embedding layer will process on both the recommendations to provide a higher order relation between the users and items. as the suggestion is based on hybrid recommendation, the processing time of convolutional neural network is reduced by reducing the number of epochs. due to this, the final recommendation is not affected by the smaller number of epochs and also able to reduce its computational time. the whole process is realized in python 3.6 under windows 10 environment on benchmark datasets go walla and amazon books. based on the comparison of recall and ndcg metric, the proposed neural graph-based filtering outperforms the collaborative filtering based on graph convolution neural network. keywords: filtering techniques, neural network, graph convolution neural network https://doi.org/10.15837/ijccc.2022.4.4568 2 1 introduction recommender system is a process of filtering of information to retain the buyers in e-commerce sites or applications. it is used in all e-commerce sites like amazon, flip kart, kindle, kindle, and multimedia sites and social media sites. this recommendation is based their own experience or experience between the users. generally, the recommendation system is classified into three categories. the three categories are as follows: content-based filtering, collaborative based filtering and hybrid filtering. in a hybrid recommendation system, the results of both content and collaborative filtering are blended using weights or features to provide the user a final recommendation. wu et al., (2014) utilized the fuzzy tree logic for the e-services platform first. then, they utilized the fuzzy concept to improve the recommendation in e-learning applications. here, the fuzzy tree is constructed for learning activities and for the user profiles. then, the matching algorithm is used to identify the similarities between these two trees. based on the similarity, the recommendation is made for the user, which is suitable for their requirements. luo et al., (2015) utilized the alternating direction method to improve the collaborative filtering process. the collaborative filtering-based recommendation system utilized the non-negative matrix factorization (nmf) for good representation of the target matrix. but this approach suffers from high complexity and computational cost. in order to solve these problems, a non-negative latent factor (anlf) model based on the alternating direction method (adm) proposed. the adm process each single function to obtain higher convergence speed and lower complexity. it not only reduces the criteria but also improves the recommendation choices. due to lower computational cost and complexity, this model can be realized in practical format. song et al., (2016) utilized two main concepts in improving the recommendation choices in context-aware recommendation systems. the two concepts are cluster tree and click-behaviour. the cluster tree formed based on the items selected by the user. with these cluster trees, the recommendation system improved its choice by adding the user choices and new user arrivals. as the system updates its recommendations for every new arrival, the final item recommendation improved effectively in large datasets. yang et al. (2017) utilized the combination of content and collaborative based filtering for suggesting the jobs to the user in the job portal sites. here, the job suggestions are based on the statistical relation between the user and jobs. the functional gradient boosting is used for finding the relation between the items and user. its performance is compared with markov process and ackley technique based on accuracy, precision and recall. mu (2018) surveyed the deep learning role in recommendation systems. the main advantage in deep learning technique is it can identify the relation between user and item effectively by analysing in non-linear and non-trivial mode. here, it described the metrics and parameters used in recommendation system. then, it described the deep learning role in recommendation process in terms of graph or data mining approach. then, it concludes the survey with their challenges and its future scope. this survey gives a clear idea about the deep learning role in recommendation system. liu et al., (2017) utilized the feedback mechanism to improve the recommendation choices in cloud storage system. as data in the cloud grows on a regular basis, recommendation algorithms struggle to keep up. to solve these problems, the feedback mechanism is utilized in the cloud services, to track the user behaviour and new arrival. the feedback role is to maximize the accuracy in recommendation choice. ortega et al., (2018) analysed the collaborative filtering role in journal recommendation system. here, the journal dataset collected from public available dataset. then, the collected information processed to identify the dominant criteria for recommendations. then, the different collaborative filtering methods applied on the recommendation system to improve the choices. among many collaborative filtering approaches, the probability matrix factorization provides the best results. anandhan et al., (2018) surveyed the different recommendation system used in social media platforms like blog, multimedia content or forum sites. first, it describes the social media platform, where the recommendation system can be used. then, it surveyed around 61 articles from 2011 to 2015 for recommendation process. finally, it describes the challenges and its future remedy for the challenges. zhang et al., (2018) utilized the transfer learning process to improve the recommendation choice and also to reduce the data sparsity problem. here, the knowledge transfer takes place between the source and target domain. because the target set has denser data than source and it also has same items with different rating. hence, to improve the recommendation choice, the similarity between the user and items determined by aligning source and target domain features. then, these aligned https://doi.org/10.15837/ijccc.2022.4.4568 3 feature information transfer between them using kernels. this knowledge transfer process helps the recommendation system to improve the user preferences. the paper is organized as follows: section 2 describes the recent techniques in recommendation system. the brief explanation of the proposed method given in section 3. section 4 compares the proposed method performance with the existing techniques. section 5 summarizes the paper and its future work. 2 literature survey iqbal et al., (2019) improved the user-item rating by selecting proper kernel for the recommendation system. here, the user-item rating improved by adding dominant factors that affect the rating of an item based on both user and item preferences. with this additional information, the recommendation choice improved. to enhance the recommendation choice further, the kernel selects by analysing different kernel role in recommendation process. by proper kernel selection and contextual information about users and items improved the recommendation choices in benchmark datasets like movie lens dataset. al-ghuribhi and noah (2019) utilized the different parameters that affect the recommendation systems. many recommendation systems utilise only the overall rating for the recommendation, which is not sufficient to improve the user’s preferences. therefore, they utilized different factors like item features, user rating of an item and user choices analysed for recommendations. due to the multiple criterias, the dominant criteria determined through sentiment analysis. based on the dominant criteria, the recommendation made, which improves the user choices. yin et al. (2019) proposed an attention mechanism in the graph-based approach collaborative filtering recommender system. here, the data sparsity problem is reduced by utilizing the attention mechanism in the learning of the graph neural network architecture. chen et al., (2019) proposed an innovative clustering approach-based recommendation system. here, first it constructs the bipartite graph between the user and items. then, it extracts the features from it and project in single mode. but it suffers from the data sparsity problem. this data sparsity problem is overcome by using node2vec technology. the node2vec extracts the complex relationship in user or items. based on this relationship, the clustering process performed using dynamic nearest neighbour technique. with the dnn and node2vec it improved the recommendation choices in larger dataset. hors et al., (2019) designed a mobile application for recommending message for smoker to quit the smoking. first, it analysed the world health organization guidelines to determine the factors responsible for quitting smoking. based on this feature, a behavioural model designed for user messages. then, the recommendation system analysed using three different models like user context, taxonomy and demographic variables. by combining these three models, the recommendation choice for the user improved in health recommendation system by incorporating the user behaviour and different contexts. fernandez et al., (2019) proposed a recommendation system for higher education students to select their subjects. here, the approach tested on the spanish university dataset. among many attributes like age, under graduate group, percentage and subjects, the subject provides a better recommendation choice for the students. based on the subject criteria, the computer science group chosen as the optimal subject for the higher education, because it provides the student with high graduation rate. ferreto et al., (2020) designed a recommendation model for suggesting physical activity in hypertension patients. here, the data collected from the hypertension patients and three types of physical activities designed based on hypertension values. the dataset comprises thirtytwo attributes and their roles in recommendation analysed using hyperrecsyspa. based on this model, the physical activity prescribed for the patients. then, the recommendations verified with the practioner, it observed that this model achieved 75alshammari et al., (2019) designed a recommendation system based on black box testing model. black box testing model analyse the recommendation choices in all attributes in the dataset. with this multiple analysis, the recommendation improved effectively but it lacks the semantic relationship between the choice and attributes. therefore, a semantic relationship proposed between these attributes and choices, the recommendation improved effectively in movie lens dataset. dau and naomie (2019) utilized the sentiment analysis and attention-mechanism for the recommendation system in amazon review and yelp dataset. here, the long short-term memory network used to extract the features and improve the attention process in the recommendations. this approach improved its accuracy in recommendation as compared to https://doi.org/10.15837/ijccc.2022.4.4568 4 the traditional mechanisms. ngaffo et al., (2020) proposed a recommendation system using hybrid approach. the hybrid approach indicates it combines both the user and item-based recommendation. the bayesian inference technique used for learning the recommendation and it shows that the recommendation improved by this hybrid approach. based on the hybrid approach from ngaffo et al., in this, an enhanced neural graph collaborative filtering proposed for the recommendation system. the recommendation system based on the three factors like user rating, item-based recommendation and similarity between the item rating. this hybrid approach improved the recommendation and requires less dropout embedding layer for processing. 3 proposed method in this section, the proposed method enhanced neural graph based collaborative filtering working explained. here, three layers are responsible for the recommendation process. the three layers are as follows: embedding layer, propagation layer and prediction layer. the simple diagram to describe the proposed method flow shown in the figure 1. figure 1: : flow of proposed enhanced ngcf https://doi.org/10.15837/ijccc.2022.4.4568 5 3.1 embedding layer it is the first layer of the recommendation process. here, the relation between the users (u) and items (i) and also the information about the items embedded from the dataset to propose a new choice for the user. consider a dataset d comprising u users and i items and its relation between the users and relation between the items and the information regarding each item considered for making a recommendation process. it is given as follows 3.2 propagation layer in this layer, the strong relation between the user and items analysed by passing the information from embedding layer to multiple propagation layer with attention-based mechanism. here, the single layer working explained and it is same for other higher order layers. 3.2.1 working of first layer the relation between the user and items analysed with the help of three processes as in [21]. 1. information construction 2. attention process 3. information aggregation. 3.2.2 information construction the similarity between the user and items determined by sending the information from an item to user as follows the terms w1, w2 and w3 are the weight matrices to propagate the valuable information and it belongs to the dataset. in this, the three interactions were concentrated to improve the recommendation. the three interactions are item, interaction between the user and items and also between the entity relations between the items. mostly, the item interactions only concentrate in other approaches, but in this, both users based and knowledge-based interactions were analysed by performing element wise multiplication process. the term p_uiik is the decay factor for the propagation. here, the prediction is based on the convolutional neural network. due to this the decay factor is considered as laplacian based normalized adjoint matrix. 3.2.3 attention process in this process, the knowledge between the items analysed based on the user interactions by the information passing scheme. after the information passed from the items to user, the relation between the items analysed with the help of following formula here, the terms e indicates the entity and it defined as the possible connections made by the items with the user. the subscripts s and e indicate the starting and ending point of the entity. the term r indicates the relation between the entities and the corresponding weight factor is indicated bywr. based on this, the relation among the items is studied. this process is similar to the formation of ego network in the graph. https://doi.org/10.15837/ijccc.2022.4.4568 6 3.2.4 information aggregation in this process, the information from all users is consolidated to define the user relation and also indicate the user-item relations. the output of the l embedding propagation layer is as follows: the above process is repeated for all the embedding layers and the final embedding layer output is obtained by aggregating the each embedding layer outputs. due to the message passing and attention scheme, the relation between the user and items is enhanced as compared to the existing neural graph based collaborative filtering. 3.2.5 prediction layer in this, the convolution neural network with adam optimizer is used for the prediction process. the information gathered from the propagation layer is passed through different layers like weighted average, max pooling and and l2_normalization process for the final prediction for the target item. the model parameters were tuned by optimizing the bayesian personalized ranking loss. the final recommendation of an item is given as follows: the other tuning parameter for the convolutional neural network is the dropout ratio is 0.2, three embedding layers and one folding layer and 50 epochs with 0.01 learning rate for the training process with 64 layer as embedding size and 1024 as mini-batch size and 0.2 as node and message dropout ratio. based on the above process, the recommendation for the user is proposed. this approach able to recommend the products effectively by identifying the stronger relation between the user and items using user-item graph and item knowledge. the implementation and discussion of the proposed method results with the other existing techniques is given in following section. 4 experimental setup and result discussion 4.1 software requirements the proposed method tested under windows 10 environment using python 3.6.5 with following packages numpy, scipy, scikit-learn and tensorflow. the proposed enhanced neural graph based collaborative filtering analysed on two benchmark datasets namely go walla and amazon book dataset. 4.2 dataset description here, the traditional two benchmark datasets go walla and amazon book dataset is used for the analysis. the description of the dataset is given in the table 1. table 1: dataset description https://doi.org/10.15837/ijccc.2022.4.4568 7 4.3 4.3 performance comparison of proposed enhanced neural graph based collaborative filtering (e-ngcf) the proposed method performance compared with other graph based collaborative filtering like neural graph based collaborative filtering (ngcf), graph-based convolution neural network using two metrics recall and ndcg. here, ngcf implements the user-item embedding with the help of the embedding-based neural graph cnn. gcn, on the other hand, only uses first-order neighbours for the user item relationship. the comparison of the top 20 recommendation choices by proposed and existing method is shown in table 2. table 2 shows that the proposed enhanced ngcf able to predict table 2: performance comparison the product recommendations accurately as compared to the other two existing algorithms for both the datasets. it achieved by adding the attention process in the propagation layer and also by finding the stronger relationship between the user and items using higher order embedding propagation process. 4.3.1 performance analysis based on network parameters 4.3.2 analysis based on embedding layers first, the performance of e-ngcf based on embedding layers is shown in the table 3. in both table 3: e-ngf performance w.r.t. embedding layers datasets, increasing the propagation layers results in improving the choice of recommendation only. therefore, the three embedding layers are sufficient for recommender system. because, adding more embedding layer may results in overfitting. 4.3.3 analysis based on n-fold the term n-fold is used to split the adjoint matrix for the message propagation function. in existing ngcf, the n-fold value is 100 and it requires 1658.0 s time period for executing one epoch of training. but, in the proposed e-ngcf, it is reduced to 1, because the splitting of adjoint matrix is not effective after fold 1. due to this it required only 991.6 s for one epoch and the time period for consecutive epochs is also reduced. therefore, by reducing the n-fold, the computational time for the filtering process can be reduced. 4.3.4 analysis based on the dropout ratios dropout ratio is an important parameter in the network to prevent the network from overfitting and underfitting problem. the terms nd and md in the following tables and figures indicates the https://doi.org/10.15837/ijccc.2022.4.4568 8 node dropout ratio (i.e., user dropout) factor and md represents the message dropout factor in the propagation layers. based on this, the performance of the proposed method is compared for the dropout ratios 0.0 to 0.8 with regular interval of 0.2. the proposed method e-ngcf is compared with the existing ngcf for both the datasets and it is shown in the figures and table. table 4 shows table 4: go walla comparison based on drop out ratios figure 2: :drop out comparison for gowalla that the proposed method is able to perform better even with the variations in the drop out ratio. hence, the dropout ratio has lesser effects in recommendation process. but the optimal drop out ratio for the proposed method is 0.2. similarly for the amazon book dataset is shown in the table 5. in both the datasets, the proposed e-ngcf able to improve its recall value as compared to the existing ngcf. but the optimal dropout ratio is 0.2 and it remains same for both the methods. it is due to the embedding layer process. 4.3.5 analysis based on the epochs due to the higher order connectivity and knowledge graph, the proposed method able to reduce its training loss to 40.03 for four epochs. for comparison, it requires 10 epochs to reach the same training loss by existing ngcf. therefore, in all aspects like performance comparison and network parameters and computational time, the proposed method enhanced neural graph based collaborative filtering outperforms the existing neural graph based collaborative filtering. https://doi.org/10.15837/ijccc.2022.4.4568 9 table 5: amazon book comparison based on drop out ratios figure 3: :drop out comparison for amazon book 5 conclusion the proposed method, enhanced neural graph collaborative filtering, outperforms the existing ngcf technique by using the following information: user, user-item graph, and item-knowledge graph for the recommendation process. it also included the attention process in the propagation layer. it reduces the adjoining matrix split operation. the proposed approach improves its performance in the following parts by incorporating the aforementioned adjustments into the present strategy. it improved the choice of recommendation by having a high recall and ndcg as compared to the existing technique. it reduces the computational time for the training by reducing the splitting process. in the training phase, the minimal loss is achieved in only four epochs. overall, the proposed modification improved the existing ngcf performance in all aspects, like choice of recommendation and processing time. therefore, the proposed enhanced neural graph based collaborative filtering is the best recommendation system. in the future, the proposed method might be enhanced by using neural multiplication instead of dot multiplication in the message propagation process. references [1] dhingra, s., & kumar, d. (2019). a review of remotely sensed satellite image classification. international journal of electrical & computer engineering (2088-8708), 9(3). [2] kumar, r., & sharma, p. k. (2019). classification techniques for object detection in remote sensing images. available at ssrn 3356495. https://doi.org/10.15837/ijccc.2022.4.4568 10 [3] das, p., & pandey, v. (2019). use of logistic regression in land-cover classification with moderate-resolution multispectral data. journal of the indian society of remote sensing, 47(8), 1443-1454. [4] barde, i., suryawanshi, n., samarth, s., bhure, t., rehpade, n. n., & balamwar, s. object based classification using image processing techniques. [5] borra, s., thanki, r., & dey, n. (2019). satellite image classification. in satellite image analysis: clustering and classification (pp. 53-81). springer, singapore. [6] tuba, e., jovanovic, r., & tuba, m. (2020). multispectral satellite image classification based on bare bone fireworks algorithm. in information and communication technology for sustainable development (pp. 305-313). springer, singapore. [7] mohammed, m. a., naji, t. a., & abduljabbar, h. m. (2019). the effect of the activation functions on the classification accuracy of satellite image by artificial neural network. energy procedia, 157, 164-170. [8] yu, l., lan, j., zeng, y., & zou, j. (2019). comparison of land cover types classification methods using tiangong-2 multispectral image. in proceedings of the tiangong-2 remote sensing application conference (pp. 241-253). springer, singapore. [9] ishii, t.; nakamura, r.; nakada, h.; mochizuki, y.; ishikawa, h. (2015). surface object recognition with cnn andsvm in landsat 8 images. in proceedings of the ieee 14th iapr international conference on machinevision applications (mva), tokyo, japan, 18–22 may 2015; pp. 341–344. [10] espinola, m., piedra-fernandez, j. a., ayala, r., iribarne, l., & wang, j. z, (2014). “contextual and hierarchical classification of satellite images based on cellular automata”, ieee transactions on geoscience and remote sensing, 53(2), 795–809.doi:10.1109/tgrs. [11] m. espnola et al., (2010). cellular automata applied in remote sensing to implement contextual pseudo-fuzzy classification, in proc. 9th int. conf. acri, vol. 6350. [12] sarikayadav, imdad rizvi, shailajakadam, luis iribarne, and james z. wang, (2015). urban tree canopy detection using object-based image analysis for very high resolution satellite images ieee trans on geosciences and remote sensing. [13] f. s. al-ahmadi and a. s. hames, (2009). , “comparison of four classification methods to extract land use and land cover from raw satellite images for some remote arid areas, kingdom of saudi arabia”, jkau; earth sci., vol. 20 no.1, pp: 167-191 (a.d./1430 a.h.) [14] d. menaka, l. padmasuresh and s. selvinprem kumar, 2015. "classification of multispectral satellite images using sparse svm classifier", indian journal of science and technology, vol. 8, no. 24. [15] dm.längkvist, a. kiselev, m. alirezaie and a.loutfi 2016. classification and segmentation of satellite orthoimagery using convolutional neural networks. remote sensing, 8(4), 329. https://doi.org/10.15837/ijccc.2022.4.4568 11 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: sangeetha m; thiagarajan m.-d. (2022). an enhanced neural graph based collaborative filtering with item knowledge graph, international journal of computers communications & control, 17(4), 4568, 2022. https://doi.org/10.15837/ijccc.2022.4.4568 introduction literature survey proposed method embedding layer propagation layer working of first layer information construction attention process information aggregation prediction layer experimental setup and result discussion software requirements dataset description 4.3 performance comparison of proposed enhanced neural graph based collaborative filtering (e-ngcf) performance analysis based on network parameters analysis based on embedding layers analysis based on n-fold analysis based on the dropout ratios analysis based on the epochs conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 4, month: august, year: 2021 article number: 4211, https://doi.org/10.15837/ijccc.2021.4.4211 ccc publications vehicle speed estimation using gaussian mixture model and kalman filter a. a. tayeb, r. w. aldhaheri, m. s. hanif ahmed a. tayeb* department of electrical and computer engineering, king abdulaziz university p.o box: 80200, jeddah, 21589, saudi arabia. *corresponding author: atayeb0031@stu.kau.edu.sa rabah w. aldhaheri department of electrical and computer engineering, king abdulaziz university p.o box: 80200, jeddah, 21589, saudi arabia. raldhaheri@kau.edu.sa muhammad s. hanif department of electrical and computer engineering, king abdulaziz university p.o box: 80200, jeddah, 21589, saudi arabia. mshanif@kau.edu.sa abstract many countries use traffic enforcement camera to monitor the speed limit and capture over speed violations. the main objective of such a system is to enforce the speed limits which results in the reduction of number of accidents, fatalities, and serious injuries. traditionally, the task is carried out manually by the enforcement agencies with the help of specialized hardware such as radar and camera. to automate the process, an efficient and robust solution is needed. vehicle detection, tracking and speed estimation are the main tasks in an automated system which are not trivial. in this paper, we address the problem of vehicle detection, tracking, and speed estimation using a single fixed camera. a background subtraction method based on the gaussian mixture model (gmm) is employed to detect vehicles because of its capability in dealing with complex backgrounds and variations in the appearance due to illumination and scale. next, the detected vehicles are tracked in each frame by using the kalman filter. finally, an estimate the speed of each vehicle is determined by using the perspective geometry model. the complete system is tested at our university campus and the results are promising. keywords: vehicle detection, gaussian mixture model, kalman filter, perspective geometry model, vehicle speed estimation. https://doi.org/10.15837/ijccc.2021.4.4211 2 1 introduction violation of traffic rules like speed limits, abrupt lane changes, etc., are a global problem and the enforcement agencies in different countries are interested in the development of smart traffic monitoring systems that are capable to detect traffic rules violations on the highways and roads automatically. determining the vehicle speed is one of the primary tasks in systems which are designed to capture speed limit violations. hardware-based and software-based methods are commonly employed to estimate the vehicle speed. hardware based methods involve specialized sensors such as radars which are accurate and highly sensitive, but their assembly is difficult and are usually costly [1].software based methods employ complex algorithms to process data from cheap sensors like camera and are the focus of the research community due to the obvious advantage over their counterpart. to estimate the vehicle speed using image sequence obtained from a camera, it is necessary to detect and track the vehicles in the entire sequence. in this paper, we have developed a system that is capable to estimate the vehicle speed reliably using a single fixed camera installed on a bridge on the road. vehicles are detected by using a background subtraction scheme based on gaussian mixture model (gmm) which is efficient against changes in the appearance, scale, and background. tracking is performed by employing the kalman filter with constant velocity model assumption for each detected vehicle. perspective geometry model is then employed to estimate the vehicle speed. we have performed several tests at our university campus and the results are encouraging. the rest of the paper is as follows: a concise literature review is presented in the second section. our proposed system for vehicle speed estimation is described in the third section. experimental setup and results are presented in the fourth section. finally, conclusions and future works are presented in the last section. 2 literature review to identify moving objects in a frame, foreground is separated from the background by employing background subtraction methods. several methods such as frame difference, approximate median filter, running gaussian average, optical flow, visual background extractor and gaussian mixture model, etc., have been proposed in the literature for this purpose [2].once detected, the moving objects are tracked in the entire image sequence with the help of trackers. point tracking, kernel tracking and silhouette tracking are the most used techniques for tracking [3]. for speed estimation, the euclidean distance between centroids of the bounding boxes of the detected vehicles is generally employed [4, 5]. in [4], moving vehicles are detected by gaussian mixture model based background subtraction technique. to remove noise in the foreground due to misclassification of the background, a median filter is employed followed by shadow removal and morphological operations. the speed of a vehicle is estimated by calculating the distance travelled by the detected vehicles and the time difference in consecutive frames. in a similar work [6], the author used background subtraction based on gaussian mixture model for vehicle detection followed by noise removal technique to account for misclassification of the background. tracking is done by computing the euclidean distance between the bounding boxes of the detected vehicles. vehicle speed is calculated using the distance travelled by the detected vehicles in a certain number of frames and frame rate. the author in [5] proposes to use background subtraction based on frame difference of three frames for detecting the moving vehicles. centroids of the bounding boxes of a detected vehicle in consecutive frames are used to compute the distance travelled by the vehicle. finally, vehicle speed is estimated by using the relationship between pixel distance and actual distance. in [7], background subtraction based on the gaussian mixture model is employed to detect the foreground. in order to remove noisy pixels which are caused by misclassification in the background subtraction, the density-based spatial clustering of application with noise (dbscan) algorithm is employed to cluster the foreground pixels related to a single vehicle and the bounding box is determined. kalman filter and optical flow are used to track the vehicle in the image sequence. average velocity computed by the optical flow inside a bounding box representing a vehicle is used to compute the vehicle speed. in a similar work [8], the authors employ gaussian mixture model for background subtraction and kalam filter for tracking for https://doi.org/10.15837/ijccc.2021.4.4211 3 vehicle speed estimation. the results of vehicle speed estimation in light and heavy trafficconditions are presented. in contrast to the above-mentioned research works, the authors in [9] propose a different approach for vehicle detection and speed estimation in low-light conditions. they employ normalized crosscorrelation technique and centroid-area-difference to detect the headlights of a vehicle. tracking is performed in successive frames using the distance between the centroids of the bounding boxes of the headlights. finally, the pinhole camera model and euclidean distance are employed to determine the vehicle speed. stereo cameras have been used in [10] to estimate the vehicle speed on the highways. the vehicles are detected using a reference image and a background subtraction method based on the frame difference. a sparse depth map is computed using the stereo images where the correspondence between images is established by matching speed up robust features (surf) descriptors in the left and right images. the depth map is used to compute the distance travelled by the vehicle in successive frames. the vehicle speed is estimated using distance travelled by the vehicles and frame rate. 3 methodology our approach for vehicle speed estimation is composed of three stages as shown in figure 1. in the first stage, background subtraction is performed with the help of gaussian mixture model (gmm) to detect and extract moving vehicles. in the next stage, the vehicles are tracked using the kalman filter. finally, speed estimation is performed using the perspective geometry model and is reported for each vehicle in the view. each stage of our proposed method is described in detail in the following sub-sections. figure 1: block diagram of the system. 3.1 vehicle detection detecting moving objects i.e., vehicles in the image sequences is the preliminary step for speed estimation. the objective is to localize each object (vehicle) in the image and represent it by a bounding box. in the last two decades, various approaches for detection of moving objects have been presented. frame difference, optical flow and background subtraction are the commonly employed for the task of detection of moving objects [11]. in this work, background subtraction based on the gaussian mixture model (gmm) is employed for vehicle detection following the work of stauffer and grimson [12]. it is a wellknown fact that gmm is robust to deal with cluttered background and appearance changes due to illumination, scale, etc. each pixel in the image is modelled by a gmm which employs k gaussians to track the changes in the appearance of the pixel. typically, k ranges from 3 to 5 to achieve real time performance. each gaussian in the mixture is initialized with the gray value (or rgb color) of the pixel as the mean vector and the covariance matrix is assumed to be an identity matrix with an initial variance set by experimentation [13]. let zt denotes the pixel (gray value or rgb color) in a frame at instant t of https://doi.org/10.15837/ijccc.2021.4.4211 4 the video, we can write the mixture model representing the background as: p (zt) = k∑ j=1 wjη ( zt,µj, σj ) (1) where wj is the weight of the jth gaussian (η) in the mixture with µj as the mean vector and σj as the covariance matrix. the jth gaussian for single dimensional feature i.e. gray value of the pixel in the mixture has the following form: η ( zt,µj, σ 2 j ) = 1 (2πσ2j ) 1 2 exp ( − zt − µj 2σ2j ) (2) in case where a d-dimensional feature vector (e.g., rgb color images) is employed, a gaussian in d-dimensions has the following form: η (zt,µj, σj) = 1 (2π) d 2 |σj| 1 2 exp ( − 1 2 (zt − µj)t σj−1 (zt − µj) ) (3) once initialized, the mixture model tracks the appearance of a pixel over the entire image sequence by continuously adapting the parameters of the gaussians of the mixture. generally, the pixels related to the background do not have fast appearance variations as opposite to the pixels representing moving objects also known as the foreground. to adapt the appearance changes in the background, the k gaussians in the mixture model are ranked according to their weights and the top b gaussians fulfilling the following condition constitutes the mixture model for the background. b = argmin k   k∑ j=1 wj > γ   (4) where γ is the threshold on the cumulative weights of the gaussians in the mixture model. to classify the pixels in the next frame as foreground or background, each pixel in the frame is matched with every gaussian in the mixture model of the respective pixel. a pixel is matched with the specific gaussian in the mixture model if it lies within 2.5 standard deviation of that distribution. if a match is found, the parameters of the gaussian in the mixture model are updated in the following manner: wj = wj + α (1 − wj) (5) µj = µj + αzt+1 (6) σj = σj + α (( zt+1 − µj )( zt+1 − µj )t − σj ) (7) where α is the learning rate. on the other hand, if a match is not found with any gaussians in the mixture model, the one with the smallest weight is replaced with a new gaussian with the pixel value as the mean, variance is set to pre-defined high initial value, and a small weight is assigned to it. finally, a pixel is classified as a background pixel if it matches with the b gaussians in the mixture model that are responsible to represent the background, otherwise it is classified as a foreground pixel. figure 2 shows a single frame captured from the fixed camera. figure 3a shows an example of the gmm based background subtraction described above where white and black pixels represent the foreground and background, respectively. it can be seen that the background-foreground classification process results in some classification errors and holes in the detected objects. they can easily be removed by applying simple morphological operations such as dilation, erosion, etc as can be seen in figure 3b. in order to determine bounding boxes for the detected vehicles, connected component analysis is performed [14, 15]. figure 4 shows an example of a detected vehicle and the associated bounding box. https://doi.org/10.15837/ijccc.2021.4.4211 5 figure 2: single frame captured from the fixed camera. (a) (b) figure 3: vehicle detection (a) noise due to classification errors (b) morphological filtering and hole filling. figure 4: vehicle detection and the associated bounding box. 3.2 vehicle tracking after detecting the vehicles, the next task is to track them in the entire image sequence. generally, the object tracking aims to generate trajectory over the time to find the position and location of a moving object in each frame. tracking methods are generally divided into three main categories: point tracking, kernel tracking and silhouette tracking [16]. point tracking based methods are generally faster and are suitable for real time operations. in this work, point tracking based on kalman filter is used to track the bounding boxes of the vehicles in the image sequence. the tracking problem is represented by the state-space equations. the kalman filter is required to consider the current bounding box, makes a prediction on the location of the bounding box in the next frame and corrects the prediction by incorporating the new bounding box generated by the vehicle detection step applied to the next frame. to simplify the model, we assume the object moves in two dimensions and can be described as a dynamic system using the https://doi.org/10.15837/ijccc.2021.4.4211 6 following state-space equation: xt = axt−1 + but−1 (8)  xt yt ẋt ẏt   =   1 0 t 0 0 1 0 t 0 0 1 0 0 0 0 1     xt−1 yt−1 ẋt−1 ẏt−1   +   1 2t 2 1 2t 2 t t  a (9) where xt and xt−1 are the state vectors at time instants t and t − 1 respectively. each state vector is composed of two components: position (x, y)and velocity (ẋ, ẏ), u is the control input, a is the acceleration, and t is the time difference between two frames. for tracking the vehicle, we assume a constant velocity model in this work. therefore, the term but−1 in eq. 8 is considered as the noise in the process. let s and q denote the covariance matrices denoting the uncertainty associated with the state vector and noise, respectively. the covariance matrix associated with the prediction xt in eq. 8 is determined as follows: st = ast−1at + q (10) moreover, in this work, we are observing the position of the bounding box in a frame only and therefore, the output process can be written in the following form: yt = hxt + qt (11)[ xt yt ] = [ 1 0 0 0 0 1 0 0 ] + qt (12) where qt accounts for the measurement noise. to refine the estimate the vehicle position and velocity, kalman filter employs a correction step by taking into the consideration measurement noise, predictions, and state covariance matrix [17, 18]. let kt and r denote the kalman filter gain and measurement noise covariance respectively, the correction step of the kalman filter is written as: kt = stht (hstht + r)−1 xt = xt + kt (yt − hxt) (13) st = (1 − kth) st the updated state vector in eq. 13 provides the corrected position of the bounding box of a detected vehicle. 3.3 vehicle speed estimation to estimate the vehicle speed, the perspective distortion in the scene figure 2 must be considered. we have used the modified pinhole camera model in [19] which is shown to achieve the smallest average error. the pinhole camera model establishes the relationship between the world scene and its projection in the image plane. figure 5 shows an example of perspective projection of world scene to the image plane. it can be seen that the vehicle is at a distance of do meters from the base of the camera. let yt be the corresponding vehicle position in the y-direction in a frame of size m × n pixels. let h be the height of the camera from the ground in meters, f be the focal length of the lens and θ be the angle accounting for the orientation of the camera with respect to the perpendicular. the relationship between vehicle position in the image yt and the world do can be written using the simple geometry equations: do = h tan (φ + θ) (14) where θ = tan−1 dθ h φ = tan−1 ( n 2 f ) − tan−1 ( yt − n2 f ) https://doi.org/10.15837/ijccc.2021.4.4211 7 figure 5: perspective projection of world scene to the image plane. the actual displacement dy covered by a vehicle in the y-direction over a certain time interval can be computed by the application of eq. 14 to the vehicle positions in the corresponding frames of the image sequence. thus, for two such vehicle positions do1 and do2, we can write: dy = do1 − do2 (15) to compute the vehicle displacement in the x-direction (dx), the displacement in the horizontal direction in two frames is converted to the actual displacement by considering the ratio of actual road width (wr) and its projection in the image plane. now, both horizontal and vertical displacements are combined to compute the vehicle displacement (dv) in the following way: dv = √ d2x + d2y (16) finally, the vehicle speed (v) can be estimated by the following equation: v = dv × fr × 3.6 framet − framet−1 ( km h ) (17) where fr is the frame rate of the video and framet ,framet−1 are the indices of the current and previous frames respectively. 4 experimental results at first, we describe our experimental setup which is shown in figure 6. the camera is mounted on a tripod at the center of a walkway bridge that connects the parking garage and the college of engineering building at al-oloum street. the height of the bridge is five meters. two markers are placed on each side of the street and are used to mark the region of interest in the image obtained using the camera. the street is 15 meters wide. the distance between the two markers on one side, denoted as ∆dro, is 27 meters. the distance of the first marker and the base of the camera is 28 meters. as mentioned earlier, the mixture model for vehicle detection is composed of k gaussians. in this work, k is set to 3. moreover, the threshold on the cumulative weights of the gaussian γ is set to 0.7 which is used to determine the number of gaussians modelling the background. the learning rate (α) is set to 0.005 determined through experimentation. please note the parameters are fixed for all the experiments. for tracking, the process noise covariance matrix q, state covariance matrix s and measurement noise covariance matrix r are assumed to be gaussian. in all experiments, canon cameras (eos 600d and eos 700d) are used to capture the videos. the tests are conducted between 10am and 4pm at the university campus. moreover, the tests are https://doi.org/10.15837/ijccc.2021.4.4211 8 figure 6: experimental setup. performed with two different video resolutions: 640 × 360 and 1920 × 1080. different scenarios such as single car, multiple cars and lane changing are considered during the experiments. the experiments are conducted by driving the test car with a known speed and compare it with the estimated speed and results are shown in tables 1 and 2. it can be seen that average error in speed estimation is below 2 km/h in all the scenarios while the maximum error is 4 km/h. the experimental results show that the proposed system is robust in different scenarios and is quite accurate. the qualitative results are also presented in figure 7. the implementation is based on matlab® running on a pc with intel-i7 processor with 64gb ram. the proposed system can process 37 frames per second for a video resolution of 640 × 360 and 11 frames per second when video resolution is 1920 × 1080. (a) (b) (c) (d) figure 7: qualitative results of vehicle speed estimation. https://doi.org/10.15837/ijccc.2021.4.4211 9 table 1: vehicle speed with single vehicle. test no. actual speed (km/h) speed estimation (km/h) error (km/h) 1 80 77 3 2 80 79 1 3 80 79 1 4 80 77 3 5 80 79 1 6 60 61 1 7 60 61 1 8 60 61 1 9 50 48 2 10 50 53 3 11 50 47 3 12 50 48 2 13 50 48 2 average error (km/h) 1.8 table 2: vehicle speed estimation with multiple vehicles. test no. actual speed (km/h) speed estimation (km/h) error (km/h) 1 50 51 150 53 3 2 50 49 150 49 1 3 70 66 4 50 51 1 40 39 1 4 70 69 1 50 57 3 40 42 2 5 70 71 1 50 50 0 40 42 2 average error (km/h) 1.6 figure 8: example image of an over speeding vehicle for license plate recognition. 5 conclusions in this work, we have presented a system that is capable to estimate the vehicle speed reliably using a single fixed camera installed on a bridge on the road. gaussian mixture model (gmm) based background subtraction is used to detect the moving vehicles. tracking is performed by employing the kalman filter for each detected vehicle. perspective geometry model is then employed to estimate the vehicle speed. several tests are conducted at our university campus and the results are quite promising. we plan to extend the work to detect the speed limit violations in an automated manner in future. for this purpose, we will develop a license plate recognition module to read the license https://doi.org/10.15837/ijccc.2021.4.4211 10 plates of over speeding vehicles. it can be seen in figure 8 that the task is challenging due to poor quality of vehicle image owing to factors like low resolution, motion blur and perspective distortion. references [1] zhiwei, h.; yuanyuan, l.; xueyi, y. 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(2017). multi-object tracking using kalman filter and particle filter, international conference on power, control, signals and instrumentation engineering (icpcsi), chennai, india, 1688–1692. doi:10.1109/icpcsi.2017.8392001. [19] indrabayu; bakti, r.; areni, i. (2018). a modified pinhole camera model for automatic speed detection of diagonally moving vehicle, journal of engineering science and technology, 13(6), 1722–1734. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: tayeb, a. a.; aldhaheri, r. w.,hanif, m. s. (2021). vehicle speed estimation using gaussian mixture model and kalman filter, international journal of computers communications & control, 16(4), 4211, 2021. https://doi.org/10.15837/ijccc.2021.4.4211 introduction literature review methodology vehicle detection vehicle tracking vehicle speed estimation experimental results conclusions international journal of computers communications & control issn 1841-9836, 9(5):644-654, october, 2014. a knowledge-based telemonitoring platform for application in remote healthcare w. zhang, k. thurow, r. stoll weiping zhang*, kerstin thurow university of rostock celisca, center for life science automation, rostock 18119, germany *corresponding author: weiping.zhang@uni-rostock.de kerstn.thurow@uni-rostock.de regina stoll university of rostock institute for preventive medicine, university medical center, rostock 18055, germany regina.stoll@uni-rostock.de abstract: telemonitoring systems have been shown to greatly reduce medical costs while improving the quality of medical care. today, the main factors restricting the development and popularization of telemonitoring systems include scalability and compatibility. the challenge for the remote healthcare lies in the variety of heterogeneous medical sensors which need to be dynamically removed or added to the environment according to the health care needs. this paper presents the design for an ontology-based context model and related middleware that provides a reusable and extensible application platform for remote healthcare. we designed the ontology context model to describe physiological parameters, medical tasks and the patients personal profile. developers may extend the ontology model by considering new requirements as needed. keywords: telemonitoring, ontology, knowledge-based system, context-aware application 1 introduction increasing life expectancy, due to medical progress, and decreasing birth rates lead to a change in the population structure of germany. as illustrated in fig. 1, the percentage of people over the age of 65 is steadily increasing while the number of people under the age of 25 is declining [1]. this results in continuously rising costs for healthcare, especially for elderly people. one approach to this problem is the utilization of telemonitoring systems to enable a relocation of the therapy to the patients home, thus shortening the expensive periods of hospitalization. a medical telemonitoring system can be defined as a technological means for sending remote physiological information and medical signals through a communication network to a monitoring center for analysis and diagnostics. the system generally include three components: a monitoring center, medical sensors, and a communication network connecting the two components. however telemonitoring systems are still in the early stages of development and currently face many challenges. compared to a general monitoring environment, a remote healthcare environment has the following features: (1) dynamistic: the remote healthcare environment usually requires a number of medical sensing devices and, unlike air conditioning which is fixed in an environment, some medical sensors need to be removed or added to the environment according to the health care needs; (2) complexity: in the healthcare environment, a wide variety of medical terminology are used, and the relationships between them are very complex [2]. in order to solve these problems, we designed a mobile context and ontology-based reasoning/feedback (mcom) health-monitoring system which monitors a patients health status using a smartphone. on the smartphone, a context-aware middleware is developed to connect copyright © 2006-2014 by ccc publications a knowledge-based telemonitoring platform for application in remote healthcare 645 figure 1: projected percentage of elderly people in german population different types of medical sensors. in addition, mcom uses an ontology-based context model to process and determine a patients health status. 2 healthcare context-aware middleware (hcam) 2.1 context-aware middleware the first problem above mentioned can be solved by establishing a context-aware middleware [3]. context-aware computing refers to a general class of mobile systems that can sense their physical environment, and adapt their behavior accordingly [4]. in order to extricate remote medical applications from tedious sensor data acquisition and management, this paper proposes a healthcare context-aware middleware (hcam). with hcam, the upper-layer applications would not be concerned about acquiring the context data, but only about the business logic itself. as shown in table.1, applications based on hcam are divided into three layers. hcam separates the collection of context information and the development of aware applications. it provides a subscriber api (i.e. subscriber application programming interface [5]) for the developer of the upper-layer application; hcam is then responsible for completing the data collection of various kinds of sensors. table.1 healthcare context-aware middleware context aware applications healthcare context-aware middleware subscriber api pub/sub framework provider api data collection all kinds of sensors 2.2 ontology modeling the second problem can be solved using the ontology context model and reasoning. ontology can be defined as, an explicit and formal specification of a shared conceptualization [6] and provides a kind of shared vocabulary, including object type or concepts, their properties, and relationships existing in special fields. in this work, it is used to define information and relations, including sensor data, medical tasks, control strategies, and a patients personal domain knowledge. a medical task, like an emergency alert, can be triggered by context information reasoning based on the analysis of physiological parameters. 646 w. zhang, k. thurow, r. stoll figure 2: telmonitoring system architecture: management modules and communication link 2.3 system structure this work aims at describing the ontology-based context model and middleware that have been developed in order to ease the development of context-aware applications for remote healthcare [7]. the system architecture is shown in fig.2. from a physical perspective, it is based on a c/s architecture which includes the sensor terminal, smartphone client terminal, and remote server terminal. from a functional perspective, the whole system can be divided in to three modules: client management modules (cmd), server management modules (smd), and communication mechanisms. 1. the core module at the smartphone client is the hcam middleware. it is responsible for communicating with various sensors and performing data collection. other core components include data processing, analysis, and display modules plus server communication modules. it should be noted that, unlike other telemonitoring systems, the communication between client and server is bidirectional. in addition to sending data to a server, the client terminal can also synchronize information with the server. the design concept of the client terminal is to offload as much processing as possible to the server, so that the smartphone client operates only as gateway to display and transfer data. 2. on the server-side, the core component is the knowledge-base, based on ontology. other components on server include a communication module that is responsible for transmitting information between the server and the client terminal, a data management module, plus a web service module. these modules communicate through a task bus. 3. as shown in fig.2, the communication between cmd and smd is implemented through http/https, soap [18] and socket. http is used in synchronous xml workflow description; soap is used to transmit ontology instance information; socket connection is used in high real-time demand, for example, transmitting ecg (1024byte/sec) and acceleration (200byte/sec) data. 2.4 hcam structure the hcam proposed in this paper is designed and developed based on the android system for mobile phone. its architecture is shown in fig.3. according to the previously mentioned threelayer structure, hcam include these three layers, i.e. context data acquisition, context data transmission and distribution, etc. hcam first uses the context provider to collect the original context data provided by the sensors, then transmits the data to the upper layer applications via a knowledge-based telemonitoring platform for application in remote healthcare 647 figure 3: hcam architecture the pub/sub framework. the pub/sub framework [8] completes the context data transmission and distribution. the following are the core components of the hcam . 1. sensor layer all the original context of physiological data are derived from the sensor, which is the basis of any context-aware application. this paper divided sensors into two categories; internal sensors (located in the smartphone) and external medical sensors. 2. data provider data provider (internal) 1 and data provider (external) 2 are shown in fig. 3. a data provider usually exists in the form of a plug-in and is written using provider api. once the providers are completed, the provider api will transmit the data to the upper sensing applications. during the actual realization process, data provider 1 and data collector 1 are usually combined and exist as many different system plug-ins. however, data provider 2 and the data package resolver, are combined and exist as an independent plug-in. under these circumstances, the data provider can directly transmit the data, read by the data collector, and the data analyzed, by the data package resolver, to the upper sensing applications via data-bus [9]. 3. context discovery and access (cda) cda consists of the context provider, pub/sub framework, and context transfer. the context provider serves as a context discovery protocol to connect different sensors. the pub/sub framework collects context information from the context provider, and the context transfer uploads the data to the central server 4. context handler (ch) ch reasoning is accomplished through the context instance from the client sensor data. the context information uses an inference engine based on conditions to conduct reasoning via owl/rdf [10] modeling (knowledge-base). once the conditions are met, the context handler module will send information to the context-aware task and monitoring server. 648 w. zhang, k. thurow, r. stoll 3 knowledge-base design before processing the context information, we first need to construct the context model. in an ubiquitous medical environment, a large number of medical devices and domain terms are used. with the advancement of technology, new domain terms can be joined very quickly. in addition, in the rescue and medical care process, the pathological conditions of a patient usually cannot be identified directly; medical professionals must filter and integrate information from a variety of different sources. therefore, the context model permits not only adding the description of a new concept, but also supports the context reasoning. by using ontologies and various reasoning methods, the system can process the sensor data gathered in a more parameters to create a method by which the engine can infer the patients health status [11]. the context knowledge-base has two parts. the first part stores the ontology and their instances in the system. the other part provides a reasoning interface and context discovery, i.e., how to inquire and to add modules. ontology instances may be pre-defined in the profile or may be retrieved from the device and other webservice. the pre-defined profile is dynamically imported into the system at the start of system operation. the context information also includes some fixed information from a specific time; for example, the patients blood type, emergency telephone number, clinical records, etc. fig.4 shows a uml diagram of the context ontology specialized for the patient personal domain. in the patient personal domain, relevant context items include measurements, patient profile, device, and task. these data can be used by the system to automatically deduce the patient health status and detect possible medical tasks. the class patient, (see fig. 4), constitutes the core of the ontology. each defined patient will be configured as an instance of this class. one patient personal profile instance is generated and associated for one particular patient, but this patient could have more than one patient profile instance associated to him [12]. a specialization of the measurements class is added for each specific physiological parameter which is gathered from a sensor (e.g., heartrate class). measurement values are used to determine patient health status by comparing these values with a set of parameter ranges (measurementsrange class) or results of the specialized dataprocessing-algorithm (like the stress-assessment algorithm in [13]). when a measured value falls outside the limits, a task (e.g., alarm task) of the corresponding level is then triggered. for example, class heartrate (see fig. 4) is a subclass of the measurements class ,and in this case, there are three alert rangeslow, medium, and high which can be modified by the expert to adapt to patients’ specific needs. in the case of heartrate, the three ranges indicate heartrate is too low, normal or too high. and the model can be easily extended to include further levels. the hascurrentvalue attribute contains the most recently received sensor value from the mobile client. this system uses owl to construct the model and process it using sample semantic web rule language [14]. the reason for using the context to construct the model is that, it can not only express the context information, but can also use the inference engine based on conditions to conduct inference to the subtle context to get higher level information [2] the context models have the following forms: (subject, predicate, value) • subject belongs to s*: set of subject names. a patient, a place , a smartphone, or an emergency task for example. • predicate belongs to p*: the set of predicate names, the patients current body temperature for example. • value belongs to v*: the overall value of the subject. a knowledge-based telemonitoring platform for application in remote healthcare 649 figure 4: patient remote healthcare domain ontology uml representation for example, we have a patient with id 001, body temperature 37°c and use a pda to connect to the monitoring center, then information would be recorded as follows: • (pda, online, true), (patient001, temperature, 37) table 2 shows a few example rules, which the system uses to predict the sensor-data-specific task and table 3 shows basic example policy we defined for each emergency task level [7]. the heartratehigh rule assigns the emergency task level when the heart rate is abnormally high. the hasminrange and hasmaxrange properties specify a normal heart rate in each instance of the heartrate class. if a patients heart rate is greater than the specified normal maximum, then the system sets the heartrate alarm and starts the emergency task . table 2: rules for the systems alarm management rule description heartratehigh (?patient rdf:type heartrate) , (?par1 hascurrentvalue ?v1) , (?par1 hasmaxrange ?max) greaterthan(?v1, ?max) , − > (?taskstate hasalarmlevel hes) heartratelow (?patient rdf:type heartrate) , (?par1 hascurrentvalue ?v1) , (?par1 hasminrange ?min) , greaterthan(?v1, ?min) , − > (?taskstate hasalarmlevel les) table 3: alarm task notification policy examples alarm task level notification policies low alarm task (les) sensor alarm sms to patient relative medium alarm task (mes) sensor alarm sms , mail and call to relative high alarmtask (hes) sensor alarm message to emergency operator call to relative 650 w. zhang, k. thurow, r. stoll unlike common ontology systems, in this paper the medical task is also included in the ontology; the task will invoke specific activities. there are two advantages in doing this: the first advantage is that the specific details of the task and the sub-actions are divided. under these circumstances, the description of the task doesnt need to be changed when the device information, used by a sub-activity, changes. the other advantage is that the description of task by ontology, can make inference to the changes that should be made by the task; the inference processing depends on the task status, context information, and the custom rule. at the root node, is the design of a type of named task and a specific task level taking task. after inference is completed and the instances of task type exist, soap objects are sent to the task response components and then to the client terminal, including all task information. we use the following attributes to describe a task (see experiment): 1. taskname: the name of the task; 2. tasktype: the type of task and function description information; 3. todo: describes the current sub-activities of the task; 4. taskstate: indicates the current state of a task. there are three values to be chosen: -close. , -init and -open and they respectively indicate the task has not been started, or the task needs to be initialized or is started for use. 5. relatedto: indicates which specific function in the ontology the task is related to, and what kind of device instances the task is related to. 6. datadescription: data description describes the data type and format that needs to be collected by the task. the information is stored in the task profile and uploaded when the system is operational. 3.1 simulation in this section we will use an emergency assistance task as a practical example to describe the above described design pattern: how new devices are added to the ontology knowledge-base and medical treatment begins via conditional reasoning. the specific scenario for an emergency assistance task is as follows: an anomaly has been detected for a patient suffering from heart disease or high blood pressure; based on system reasoning, emergency assistance is requested. according to this task profile, the response time of the task is one minute, meaning that medical personnel must respond within one minute, react to the patients condition and initiate corresponding measures. after the response, the tasks mode is set to closed. if the patient’s condition remains abnormal, the task is started again. the first step is to add new devices: in the experiment we used the wearable multi-parameter sensor equivital01 produced by (hidalgo ltd., cambridge). when a new device is connected to the hcam sensor adapter, context information must be converted into ontology instances before being sent to the knowledge base. the hcam context handler inquires whether classes with identical names already exist in the knowledge-base. if not, a new class is added with the new < device >. the following example shows the class of an equivital device being added: • < owl : classrdf : id = equital > < rdfs : subclassofrdf : resource = ∗device/ > < owl : disjointwithrdf : resource = ∗/ > < /owl : class > a knowledge-based telemonitoring platform for application in remote healthcare 651 then a new class is added which acts as subclass of measurement. below the classes heartrate and bloodpressure are added for the equital device. • < owl : classrdf : about = ∗heartrate > < rdfs : sublassofrdf : resource = ∗measurement/ > < owl : disjointwithrdf : resource = ∗/ > < /owl : class > < owl : classrdf : about = ∗bloodpressure > < rdfs : sublassofrdf : resource = ∗measurement/ > < owl : disjointwithrdf : resource = ∗/ > < /owl : class > with the establishment of the above-mentioned ontology, it is ensured that the device description file and the ontology name match. when the hcam context provider receives data from the device, it looks for the parameters in its own device files and device task files and creates an instance. the above-mentioned equivital device might, for example, receive event messages from the hcam detailing heartrate-hascurrentvalue = 60(beats/minute), bloodpressurehascurrentvalue = 120(systolic, mmhg). in this case the practical example would look as follows: • < heartraterdf : about = ∗equitalheartrate > < hascurrentv aluerdf : datatype = double > 60.0 < /hascurrentv alue > < /heartrate > < bloodpressurerdf : about = ∗equitalbloodpressure > < hascurrentv aluerdf : datatype = double > 120.0 < /hascurrentv alue > < bloodpressure > < equitalrdf : about = ∗equital001 > < measurementsrdf : resourcee = ∗equitalheartrate/ > < measurementsrdf : resource = ∗equitalbloodpressure/ > < /equital > after the context information is converted, the context handler will send the aforementioned information to the knowledge-base. the second step is to execute the task. when the middleware is executed, the system automatically generates an instance for every task class, which is saved in the knowledge-base. the important information in these task instances is as follows: • < medicaltaskrdf : about = ∗emergencytask > < tasktype > external < /tasktype > < taskstate > close < /taskstate > < todordf : resource = ∗alarmtask/ > < todordf : resource = ∗prepareemergencyoperator/ > < /medicaltask > with the previous scenario in mind, we can define the specific conditions for the emergency task to be triggered: e.g. the heart rate of the patient drops below 40, or the blood pressure (systolic) exceeds 170. reasoning may be carried out according to the rules of the language itself, for example: • (?a?p?b),(?prdf : subpropertyof?q)− > (?a?q?b) 652 w. zhang, k. thurow, r. stoll figure 5: mhealth prototype system architecture user defined rules may also be used in the reasoning process, e.g.: • (?patient,equitalbloodpressure001,vl),ge(?vl,170),(?patient,equitalbloodpressure,vl), le(?vl,40)− > (?patient,healthstatus,danger) • (?patient,healthstatus,danger),(?patient,healthstatus,movementfail), (emergencytask,taskstate,close)− > (emergencytask,taskstate,open) 3.2 deployment and execution the deployment configuration includes at least two context manager nodes as shown in fig.5: we used a sonyericsson-xperia smartphone as client manager (cm) node, connected with the medical sensor module equivital [15] via bluetooth; and a server manager (sm) node, which is deployed on a central application server, in the domain of the organization which is responsible for the integration and delivery of emergency web task. the home smartphone hosts the client and communicates with the central application server via 2g/3g/wifi. the central application server hosts the server sm and other assistance and information task applications. these tasks include: a emergency task as webservice, data management, and a web application which offers web-based access to health operators via mobile device. 4 results and discussion fig.6 shows the sensor data for a patient. the graphic curve represents any changes in the data for a given day. date and time can be changed easily, access a patients historical data; this data are stored in a central server database. the alarm is normally triggered automatically by context reasoning and, in some cases, the medical professionals can manually send a message to the client. directly above the line chart in fig.6, a message window is located. medical professionals can send a message to the patient’s smartphone through it. in this technical approach, the system structure improves the scalability and interactivity of telemonitoring systems; in our current work, we improve the reliability of the system by adding layers of redundancy, in case a particular system or function fails. in addition, we created a back-up system on the mobile client to save sensor data locally, in the event of a loss (or lack) of internet connection. security issues can be divided into two parts: one is the security of local data transferred from the sensor to the mobile client; another is the security of remote data transferred from the mobile client to the central server. on the client side we implement the new 128-bit lightweight cryptographic algorithm hummingbird-2 [16] cipher to encrypt the sensor data with java code. on the server side, we use secure socket layer (ssl) to encrypt vital data and utilize 3p-aaa-sps [17], authorization and accounting task providers to authenticate the client access. a knowledge-based telemonitoring platform for application in remote healthcare 653 figure 6: mhealth prototype system architecture this paper designs an innovative telemonitoring platform based on context and ontology technology. a context-aware middleware is developed to support the scalability of the telemedical system; new sensor device and data formats can be integrated into the system conveniently. in the context reasoning module, ontologies are used to construct target medical tasks and circumstances. this paper, however, does not address the handling of context conflicts in the process of constructing ontologies and reasoning conditions. further research will focus on the reliability and energy consumption problem of remote medical systems. concerning the energy consumption problem, some new ultra low power transmission protocols, like ble and ant, have appeared that can greatly improve the sustainability of remote monitoring. other challenging tasks will include the development of an intelligent error discovery and system recovery mechanism to maintain the stability of the system over time. bibliography [1] sitzungsberichte der leibniz-sozietat, band 90 (2007); theoria cum praxi. funf jahre leibniz-institut fur interdisziplinare studien e.v. 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(2012); soap processing performance and enhancement. ieee transactions on services computing, 3: 387-403. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 2, month: april, year: 2020 article number: 3867, doi.org/10.15837/ijccc.2020.2.3867 ccc publications smart city: concepts and two relevant components m. visan, c. ciurea maria visan* romanian academy, school of advanced studies of the romanian academy (scosaar), 125 calea victoriei, bucharest, romania *corresponding author: maria.visan@ingr.ro cristian ciurea bucharest university of economic studies, 6 piata romana, bucharest, romania cristian.ciurea@ie.ase.ro abstract in the last 30 years, the smart city (sc) definitions have changed, they expressed different meanings by different people, but still no universally accepted definition, yet. the paper aims to summarize the existing relevant definitions to and propose a concept for characterizing the smartness of a city through intelligent planning and monitoring, guided by actionable information that underpins computer-assisted decisions and institutional digital transformation. as a practical approach, the sc concept is promoted by two components namely: spatial urban territorial planning and cultural heritage via virtual exhibitions. the article highlights the schematic diagram of cross-sectoral interactions between different stakeholders grouped by roles, and the expected impact for these interactions, a proposed functional system architecture for cultural heritage digital transformation and concrete steps for virtual exhibitions implementation. keywords: big data, cloud, collaborative work, cultural heritage, mobile, smart city, virtual exhibitions 1 introduction according to the united nations, 55% of people worldwide live today in cities, and the increase is estimated to 68 % until 2050. predictions indicated [24]. on a global scale, idc estimated continuous increase of spending on smart cities rising to $158 billion by 2022 (double from $80 billion in 2018) [25]. new technology hot topics such as augmented and virtual reality, big data analytics and discovery, broadband, cognitive/artificial intelligence, internet of things, real-time, cloud, mobile have been adopted by all cities over the globe in their journey of creating smarter digital reality. in this way, sc drives all operational efforts with the final purpose of improving and developing the safety, connectivity, and the quality of life for their citizens [10, 19]. doi.org/10.15837/ijccc.2020.2.3867 2 nowadays, the decision-makers of forward-thinking cities adopted geographic data approaches joined with business information, transforming domain knowledge into actionable information and customer workflows into a geospatial intelligent decision support system, enabling real-time decisionmaking as the city grows. new organizational model boosts smarter initiatives that improve decision and collaboration between all stakeholders. local authorities have the most important role in defining the policy framework, associated plan and regulations, the criteria that city investments must fulfil. good management of the technological and organizational resources support integrated spatial territorial planning and monitoring of the shared responsibilities. 2 general concept today we are counting about 30 years since albino [2] has mentioned sc for the first time. during this long period, sc has different meanings for different people. based on resident’s aspirations, or available resources, depending on the city development stage, and city agility to change and reform, the sc conceptualization varies a lot and constantly changes from one city to another and between continents. for example, albino first mentioned that sc is information communication technology (ict) infrastructures of the cities [2]. alawadhi [1] recommended steps for becoming smart and how the it should be implemented in a designed city, but is more complex than the applied technologies. since “smart” has been replaced with ”digital” or “intelligent” the diversity of the sc definitions exploded, and unfortunately, were not always consistent. as we observe, there was more than one framing for a sc and we have found different dimensions. a common tenninology should be agreed so that the public knows what is being offered, the innovator’s pioneer explains die benefits, and the public administration decides public funds spending with confidence in the obtained results. this sc framework requires ab’teed standards [4, 27] and ways to make things open and public. as we observe, the sc is more than a re was neither a single template of framing an sc nor a one-size-fits-all definition of it. a common terminology should be agreed so that the public knows what is being offered, the innovator’s pioneer explains the benefits, and the public administration decides public funds spending with confidence in the obtained results. this sc framework requires agreed standards [7,8] and ways to make things open and public. the vague idea about what sc really means was underlined in the literature on this topic [1, 13, 14]]. during the last two-decades, sc has changed. it first included four components: participation, education, technical infrastructure, and industry [11] and how these are related, transformed. later, the concept was defined by six components: smart people, smart living, smart environment, smart mobility, smart economy, and smart governance [12]. recently, mckinsey’s experts named eight components that affect multiple aspects of the quality of life: engagement, community, security energy, water, waste, mobility, healthcare, economic development and housing [23]. the citizens “quality of life” has been emphasized as added value included in sc definition, therefore it has been debated a lot among specialists [17]. we propose to consider the enhanced access to the cultural heritage of a city as a specific feature of improved life quality. despite its large spreading, the sc semantics continues to be unclear [3, 15]. in [16] sc becomes an "activator" for changes, based on innovation. from a practical perspective, the sc concept goes much further and becomes an umbrella vision shared by all stakeholders within the sc, the public administration, private economic stakeholders, and civil society. these ways of sharing transform public administrations and their governance models. the following chapters develop the sc concept by two particular components: a) spatial urban territorial planning and b) enhancing the access to cultural heritage via virtual exhibitions. 3 computer-supported urban territorial planning development driven by massive demographic and investment shifts, cities have adopted new digital and realtime situational awareness strategies for enhancing the citizen’s safety and quality of life. based on the digital maturity level, public administration bodies create a smarter digital reality to drive all digital, doi.org/10.15837/ijccc.2020.2.3867 3 figure 1: conceptual schema of computer-supported urban territorial planning development (source: adapted after [20]) operational, and strategical efforts. the main thesis of the digital maturity model, as is represented in fig. 1, reflects the integrated management capacity of the territory, shifting the attention from within the organizations to the life of the community and the relationships that are established between all stakeholders (public, civic and private organizations)[20]. in such a partnership, the role played by technology in spatial territorial planning and operationalization of sc facilitates the collaboration between all the participating actors. we can identify the following issues: digital community correlate common reality and has the main benefit that organizations are aware that they depend on each other. at this level, the activities of the participants are focused on managing the “seen” things, respectively focusing on the physical reality, each of them contributing to the unique digital map of the city. dynamic community (correlation of joint activities) aims to achieve a higher level of performance than the objectives assumed by their own organization. these organizations are aware that, achieving an upper level of operational efficiency, they need more than sharing information with the other actors. they need to correlate their procedures between all organizations that are operating in the same community. automatizing the online exchange of information between organizations is based on transactional packages defined based on the correlation of individual procedures between organizations. in a dynamic community, the common map is also dynamic because of the permanent updating of the situation from the ground. often, the dynamic map will also reflect what is about to happen, not just the past. automated services ingest new data, update data on the fly, and provide operational awareness of the impacted area. smart community (correlation of common strategies) are concerned about correlating their longterm strategies with those of the community. strategic objectives of the community, such as "quality of life", "sustainable development", "community competitiveness", "economic development of the community" are taken up in the individual strategic objectives of each organization in the community. defining the strategy of the whole community, it becomes a fruit of the partnership among all participating actors. the strategic partnership makes the difference between the operational efficiency of the organizations and the sustainable good of the whole community. the organizations of sc are aware of the existence of other actors in the community, therefore the citizen plays an important role in the entire ecosystem of relationships. at this level, information technology tools are predominantly dashboards that facilitate the monitoring and correlation between strategic and operational levels allowing proactive identification of how daily community changes is correlated with the assumed strategic direction. the city is a living organism, evolving at a very fast pace. any decision made by their territorial doi.org/10.15837/ijccc.2020.2.3867 4 administrators affects all participants because all of them are interconnected. there are many promises made by software platform providers for helping sc to create and manage a smarter digital reality. an enhancement has been provided by combining the location-based data and business intelligence, fitting the needs of digital and safety-related city developments, and delivering a “5d smart digital reality with insight into what was, what is, what could be, what should be, and ultimately, what will be” [26]. the city is the most complex ecosystem in space and time. likewise, it is a variable ecosystem and has many aspects that are worth to be analyzed, planned, monitored and developed using an integrated spatial territorial planning approach. for a deeply understanding of the city we have taken into account different relevant domains attaching for each of them specific criteria. for example, let us consider the approval of the construction of a commercial or a residential complex. how much does this decision affect the city and on what level? to be more efficient the last piece is about unification and integration of the platforms. the digital intelligence platform consists of multiple systems, solutions, and artefacts related to information and data collection, analysis, management, sharing, and delivery. the platform ingests digital assets and provides insights from these different data sources via advanced digital analytics. increasing the ability to quickly render these insights is facilitated by artificial intelligence and machine learning embedded into the platform’s advanced analytics capabilities. the selected practical digital transformation solution is based on a multidisciplinary approach that answers to the challenge of reinventing spatial and territorial development planning. the selected smart tools contain a set of territorial development indexes, constituted as a baseline, quantitative analyzes that automatizes the drawing out of the existing situation and identify malfunctions of the analyzed territory after data loading on relevant domains (parts of urban, spatial and territorial planning). based on the existing situation, innovation and creativity of choosing the development directions represent the main planning effort. based on the proposed smart tools, the process of continuous measuring and updating of indexes highlighted automatically whether conditions are improving against the baseline. due to these smart tools, reporting outcomes becomes more transparent and impartial. through the collaborative process of spatial planning, the premises for optimizing the current development planning process has been improved. therefore, it also regulates the lower hierarchical level, meantime providing coordination with the higher hierarchical plans on each national and international level. due to daily working processes in a collaborative inter-institutional and participative framework and a unique database permanently updated, the proposed smart tools bring novelty to the digital transformation approach. therefore, city spatial and territorial development planning is controlled and predictable in a quantitative way [20]. 4 computer-supported enhanced access to cultural heritage the enhanced facilitated access to cultural heritage is the second relevant component of an sc. as one of the most dynamic sector, cultural heritage contribute with about 2,6% to eu gdp, and job offerings (“more than 8 million jobs”, “the cultural sector and creative industries (ccis) represented by highly innovative companies with great economic potential are one of europe’s most dynamic sectors contributing around 2.6% to eu gdp” [8]. the concept of “digital culture economy” came into current use. the term “e-culture is also frequently used in europe in the context of the “information society“, besides “e-government”, “e-health” and other “e-domains” [5, 8]. earlier seen as an opportunity, cultural economy generates a competitive advantage for new activities that exploit the cultural value, by using modern information and communication technologies. the era of the knowledge-based economy requires knowledge to generate tangible and intangible values. traditional methods for promoting the cultural heritage no longer work and more efforts are required to include the new emerging technologies in daily activities. augmented reality, virtual reality technology, mobile applications and cloud platforms provide an effective opportunity to promote, doi.org/10.15837/ijccc.2020.2.3867 5 figure 2: the cross-sectorial scheme characteristic features of interactions and expected impact (source: adapted after [9]) preserve and protect heritage around the world and disseminating and extracting this huge amount of information as knowledge or unseen experience. the digitalization programs rapidly widespread the primary heritage data sources and preserve the heritage. using new systems for visual analysis, interpretation and interaction bring to life the digital cultural heritage. at the same time, the cultural heritage researches itself through a general interpretation of large data volumes (big data) of knowledge, capitalizing it through innovation. fig. 2 is a cross-sectorial scheme. the characteristic features of interactions and expected impact represent the connections between cultural heritage institutions and different sectors of activity. recent technological developments expand the interaction with cultural institutions and regenerate the interests of these institutions, while also enhancing the collections and design exhibitions. there are favourite domains by diversification of offered services, such as tourism, or education that become more attractive and effective. the functional system architecture proposes a single database for the integrated solution and all the operational workflows into a single computer system offering different digital outputs for each user. one digital output that potentiates urban development is a virtual exhibition. to facilitate the cultural heritage development of citizens in a smart city, the connections with the internet of things and mobile technologies are mandatory. excepting virtual exhibitions created as virtual tours inside a museum or a gallery, visitors should be able to experience combined physical and virtual tours to enrich their experiences. for example, in a smart city, every monument or cultural heritage object should have beacons that send signals to nearby mobile devices. when a visitor is near a statue in the city park, he should receive a notification on his mobile device containing detailed information about those cultural objects. there are linkages between cultural heritage objects and all of them communicate directly with the visitors. based on the visitor’s route in the smart city, for example, when a tourist comes in the city to explore, he can receive on the mobile device recommendations to visit other nearby cultural sites and monuments. the proposed it systems are based on three content functional dimensions: production, management, and presentation as follows: 1. content production includes a) acquisition of raw data using unesco methodology, international guides for metadata (administrative, technical, descriptive, on access inhibitors and restrictions of use that consist of protection of intellectual property, protection of personal data, restrictions on use and copying etc.) and specialized technologies (remote sensing, photogrammetry, 3d laser scan, gps doi.org/10.15837/ijccc.2020.2.3867 6 figure 3: the functional system architecture (source: adapted after [21]) geolocation, 360-degree video, various data acquisition devices, provided by different sensors/beacons), b) modelling (using 3d modelling, shaded, textured, mesh, digitizing) and c) refinement. the content production is the functional component allowing automatic or manual data and information data capture through high standard hard and software components, based on long-term activity plans, time and resources and detailed description of digitized materials. 2. content management includes a) specialized applications for internal workflows or processing is the functional component of the system that computerizes internal workflows. with the help of the geospatial platform, there are configured workflows that support internal flows on different fields of activity. by request can be implemented preconfigured modules, but also a new one for new functionalities; b) data storage & archiving is the functional component of the system that allows the storage, archive or exchange of information based on metadata and institution to whom they belong, in order to be accessed and used in applications or exchanges and include big data; c) data processing is the functional component of the system that allows the retrieval of data from different formats, including by georeferencing (e.g., georeferencing of cadastral plans), geometric and technical validation of information and uploading to the database. this functionality is usually performed with specialized desktop tools based on advanced algorithms; d) interoperability is the functional component of the system that offers the opportunity to communicate with other computer systems (if any) or to allow partners to access the system and operate on internal workflows. public administration makes available to different partners (public or private companies, suppliers, designers, etc.) computer applications or services components that allow collaborative work and automation of inside and outside data workflows between partners. communication system means the implementation of web services (xml) for the exchange of data and information with other computer systems. 3. the content presentation includes a) the use of data and information is the functional component of the system that offers the possibility of reporting and analyzing the data and information in the system. in addition to data viewing with the help of different dashboard tools for each type, b) information display/consultation is the functional component that offers the opportunity to communicate with citizens or other institutions. this component implements different digital cultural heritage outputs. exposure of data is realized through a process of extracting data sets and information intended for the visiting public based on free or specific rights. it is also possible to implement the geomarket geoportal for the sale of data sets to interested third parties (private companies, designers, researchers, other institutions etc.). also through this component, the processes of consulting and collecting feedback are provided for the internal workflows via public consultation. the virtual exhibitions implementation stages differ depending on the deploy type of application that is used [5]. there are many tools used to implement virtual exhibitions, some of them paid and others open source. depending how the virtual exhibition is implemented, the curators can decide which tool/technology to use. for example, movio is an open-source tool used in many cultural doi.org/10.15837/ijccc.2020.2.3867 7 institutions to create virtual exhibitions as web applications, available online, but which can have a responsive interface that adapts on every mobile device. in order to be completely mobile and to benefit from the software and hardware characteristics (sensors, location, nfc, and so on) of a mobile device, a virtual exhibition should be created as a native mobile application, on the android and ios operating systems. in this case, the visitors can combine the physical and virtual experiences, for example, when they scan qr codes allocated to cultural objects inside a physical exhibition. in [6] it is presented a methodology to choose the most adequate it solution. taking into account the continuous and rapid evolution and technological trends in it&c, such as artificial intelligence, big data analytics, cloud and mobile computing, and their impact on society and decision-making [7]], one can expect that sc is continuously enriched by offering new services to the community and the business environment. examples of such services, architectures and implementation methodology are carefully capitalized in [22]. 5 conclusions and further research the paper suggests an extension to the existing sc concept based on a digital maturity model implemented through a complex computer-assisted decision and institutional digital transformation. this model proposes the development of innovative intelligent services starting from the holistic view for the two components: a) urban spatial territorial planning and b) expanding access to cultural heritage via virtual exhibitions. different actors participate in sharing the experience for implementing the strategic view of the public administration and developing visual and physical examination of the space-based on virtual and augmented digital reality. sc’s public administration embraces collaborative and participative work for growing their maturity level. in their race for smartness, the standards constitute a foundation for information and services exchanges between all stakeholders. digital transformation is an irreversible process for the user experience and sharing process and is under continuous development. yesterday’s futuristic technology becomes more accessible and affordable today. the new range of devices become available for purchase by individuals. the complex types of big data sets, the dynamicity of the scenarios for their use, constrains the traditional tools/ data management and processing applications to evolve. extracting, retrieving and analyzing information in a timely manner, ease of use and at low costs are mandatory today. mobile and cloud is already a reality. consequently, radical, new and exciting cultural heritage services for citizens are waiting to be generated. all these digital enhancements develop new lines of business, new skills and jobs and contribute directly to the human capital transformation. in further research, we are looking for developing a quantitative indicators-based model for benchmarking and monitoring urban planning. innovative solutions for user interaction and experience in the field of cultural heritage inspire us to develop new roadmaps for implementing artificial intelligence, data analytics and other emerging technologies such as iot and digital twins as functional modules in our system architecture schema. financing funds for the projects and new potential national or international partners, willing to 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[27] iso 37120:2018 (2018). sustainable cities and communities — indicators for city services and quality of life, [online] available: https://www.iso.org/standard/68498.html, accessed 30 january 2020. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: visan, m.; ciurea, c.(2020). smart city: concepts and two relevant components, international journal of computers communications & control, 15(2), 3867, 2020. https://doi.org/10.15837/ijccc.2020.2.3867 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 1, month: february, year: 2020 article number: 1005, https://doi.org/10.15837/ijccc.2020.1.3812 ccc publications crisp-linear-and models in fuzzy multiple objective linear fractional programming b. stanojević, s. dzitac, i. dzitac bogdana stanojević* mathematical institute of the serbian academy of sciences and arts, serbia kneza mihaila 36, 11000 belgrade, serbia *corresponding author: bgdnpop@mi.sanu.ac.rs simona dzitac university of oradea, department of energy engineering, universitatii 1, 410087 oradea, romania sdzitac@uoradea.ro ioan dzitac 1. aurel vlaicu university of arad 310330 arad, elena dragoi, 2, romania ioan.dzitac@uav.ro 2. agora university of oradea 410526 oradea, p-ta tineretului 8, romania, idzitac@univagora.ro 3. university of craiova, department of economic informatics & statistics, 200585, craiova, str. a.i. cuza, nr. 13, romania abstract the aim of this paper is to introduce two crisp linear models to solve fuzzy multiple objective linear fractional programming problems. in a novel manner we construct two piece-wise linear membership functions to describe the fuzzy goal linked to a linear fractional objective. they are related to the numerator and denominator of the fractional objective function; and we show that using the fuzzy-and operator to aggregate them a convenient description of the original fractional fuzzy goal is obtained. further on, with the help of the fuzzy-and operator we aggregate all fuzzy goals and constraints, formulate a crisp linear model, and use it to provide a solution to the initial fuzzy multiple objective linear fractional programming problem. the second model embeds in distinct ways the positive and negative information, the desires and restrictions respectively; and aggregates in a bipolar manner the goals and constraints. the main advantage of using the new models lies in the fact that they are linear, and can generate distinct solutions to the multiple objective problem by varying the thresholds and tolerance limits imposed on the fuzzy goals. keywords: fuzzy linear fractional programming, fuzzy multiple objective programming, fuzzy decision, aggregation operator. https://doi.org/10.15837/ijccc.2020.1.3812 2 1 introduction zimmermann [12] proposed a solution approach in fuzzy mathematical programming using bellmanzadeh extension principle [1]. he treated in a symmetrical way both the fuzzy goals and constraints, and solved linear programming problems with several objective functions. later on, zimmermann [13] discussed some aspects of duality and efficiency, emphasizing the importance of the applications of the fuzzy set theory to mathematical programming. recently, dubey, chandra and mehra [4] introduced several crisp linear models in fuzzy multiple objective linear programming derived from using different fuzzy operators to aggregate the fuzzy goals and constraints of the initial problem. separating the goals and constraints, and aggregating them in different ways a bipolar framework for computing a pareto-optimal solution to multiple objective flexible linear programming is developed in [3]. following their idea, li, leung, and wu [7] solved multiple objective interval linear programming problems in the admissible-order vector space. linear fractional optimization problem was firstly introduced to solve a bi-objective linear programming problem. maximizing a ratio can be seen a simultaneously maximization of the numerator and minimization of the denominator, its solution being one solution among many pareto optimal solutions of the bi-objective problem. later on, it was used to model directly real life problems. in the standard trade-off between accuracy and simplicity, fractional programming offers more accuracy, and in the same time succeeds to avoid the overloading of the model. however fractional objectives used in the classic membership functions of the fuzzy goals make them being non-linear. in a wide variety of fuzzy approaches to solve multiple objective problems linear models are desired, thus efforts were made to linearize the membership functions. there are many papers in the literature devoted to modeling multiple objective fractional optimization problems as fuzzy decision problems, and solving them through fuzzy goal programming (see for instance the recent ones [2], [6], [10], [11]). in this paper we propose a solution approach to multiple objective linear fractional programming (mo-lfp) problems based on fuzzy goal aggregation. we introduce a new pair of piece-wise linear membership functions that together describe a linear fractional fuzzy goal, and use them to model the multiple objective decision problem. the main advantage of the novel pair of membership functions is twofold: they are piece-wise linear, and their aggregation using min operator is in accordance to the aggregation used in solving classic fuzzy decision problems. the crisp-linear-and models we provide solve a mo-lfp problem with fuzzy goals and constraints firstly aggregated in a classic framework, and secondly in a bipolar framework. in section 2 we review some standard facts on fuzzy goals, their aggregation, and their usefulness in fuzzy mathematical programming. section 3 presents our improvement in handling the fractional objective functions. it is worth pointing out that we approach the non-linearity of the fractional objectives by using two piece-wise linear membership functions whose aggregation is made in the same manner as it is usual for the fuzzy goals, thus making them to behave in the same way as a single membership function linked to a fuzzy goal. in section 4 we introduce our crisp-linear-and models; and analyze their complexity and usefulness in solving mo-lfp problems. section 5 provides a numerical illustration of the membership functions construction, and novel solution approach. our conclusions and final remarks as well as our ideas for further research are inserted in section 6. 2 preliminaries the general model for a multiple objective linear programming (mo-lp) maximization problem is given below. max ( ct1 x,. . . ,c t p x ) s.t. aix ≤ bi, i = 1, . . . ,m, x ≥ 0. https://doi.org/10.15837/ijccc.2020.1.3812 3 similarly, the general model for a multiple objective linear fractional programming (mo-lfp) maximization problem is max ( ct1 x + c01 dt1 x + d01 , . . . , ctp x + c0p dtp x + d0p ) s.t. aix ≤ bi, i = 1, . . . ,m, x ≥ 0. symbol ”max“ is used in the sense of finding a non-dominated solution. whenever some parameters are fuzzy quantities, inequalities are fuzzy or there are fuzzy goals formulated on the objective functions of a programming problem the problem is called fuzzy programming problem and usually ”m̃ax“ is used instead of ”max“ to denote the optimization in a fuzzy sense. in section 4 we will focus on solving fuzzy mo-lp and mo-lfp problems with fuzzy goals and fuzzy constraints. the classic way to construct a fuzzy goal related to any kind of objective function f that has to be maximized is to involve a threshold (p) and a tolerated limit (t < p) on the given threshold, and define the membership function µmaxf given in (1). if the function f is to be minimized, the corresponding fuzzy goal is also defined in (1) through the membership function µminf . in this case the tolerance limit t on the threshold has to be greater than p. µmaxf (f (x)) =   0, f (x) < t, f (x)−t p−t , t ≤ f (x) ≤ p, 1, f (x) > p, µminf (f (x)) =   0, f (x) > t, f (x)−t p−t , p ≤ f (x) ≤ t 1, f (x) < p. (1) due to the stated inequality between the threshold p and tolerance limit t, µmaxf is an increasing function while µminf is a decreasing one. if function f is linear the non-constant branches of its membership functions are linear. similarly, if function f is linear fractional, its membership functions defined as above have linear fractional branches. in section 3 we will construct a pair of membership functions that are piece-wise linear and their aggregation properly describe a linear fractional fuzzy goal. a fuzzy decision making problem defined over a feasible set x of decision variable vectors assumes the existence of several fuzzy goals gk, k = 1, . . . ,p that are fuzzy subsets of x under a set of fuzzy restrictions ri, i = 1, . . . ,m that are also fuzzy subsets of x. bellman and zadeh [1] described a solution to such problem (i.e. a decision), through a fuzzy subset of x, i.e. a set {(x,µd (x)) |x ∈ x}, where the membership function µd : x → [0, 1] is defined by aggregating the fuzzy goals and restrictions using the min operator µd (x) = min ({µgk (x) |k = 1, . . . ,p}∪{µri (x) |i = 1, . . . ,m}) . further on, zimmerman [12] proposed the following mathematical problem max α s.t. µgk (x) ≥ α, k = 1, . . . ,p µri (x) ≥ α, i = 1, . . . ,m 0 ≤ α ≤ 1, x ∈ x. to derive the optimal decision, namely the solution with the maximal membership value. dubey, chandra and mehra [4] approached a fuzzy decision problem introducing a bipolar perspective in aggregating the fuzzy goals and constraints. they proposed several linear models to solve fuzzy mo-lp problems, and in section 4 we extend two of them to models that solve fuzzy mo-lfp problems still conserving their linearity. 3 novel membership functions construction let us consider the general form of a deterministic single objective fractional programming problem max { f (x) = n (x) d (x) |x ∈ x } . https://doi.org/10.15837/ijccc.2020.1.3812 4 in this section we aim to link a fuzzy goal to its objective function f, and propose a new way to construct a pair of membership functions to describe the fractional fuzzy goal over its feasible set x. dutta, tiwari and rao [5] introduced a linguistic variable approach to solve mo-lfp maximization problems. they constructed a pair of membership functions, one for the numerator and one for denominator to describe a fractional fuzzy goal. their idea was to impose the maximal (minimal) value of the numerator (denominator) as threshold for the membership function. unfortunately, the ratio of the maximal value of the numerator and the minimal value of the denominator is not always sufficiently close to the maximal value of the fraction over the feasible set. this fact was first discussed in [8]. a new pair of thresholds that represent the values of the numerator (denominator) at the point x∗ that maximizes the fractional objective function of the feasible set x is also proposed in [8]. adopting the idea of choosing the thresholds in the manner described in [8], we propose a different shape for the membership functions linked to the numerator and denominator of a fractional objective function. as far as no thresholds are provided for the fractional objective we propose the following triangular shape for the membership function µn of the numerator (and µd of the denominator) µn (n (x)) =   0, n (x) < nmin, n (x)−nmin n0−nmin , nmin ≤ f (x) ≤ n0, 1, n (x) = n0 n (x)−nmax n0−nmax , n0 ≤ f (x) ≤ nmax, 0, f (x) > nmax, (2) where nmin = min x∈x n (x) , nmax = max x∈x n (x) (and dmin = minx∈x d (x), dmax = maxx∈x d (x) respectively). using the solution x∗ that maximizes n (x) /d (x) we compute n0 = n (x∗) and d0 = d (x∗) for the numerator and denominator respectively. both membership functions µn and µd have the same shape, piece-wise monotone, even thought that the first one has to be maximized, while the second one has to be minimized. the reason is the fact that the values greater than n0 divided by the values greater than d0 can be found in the neighborhood of the best value of the ratio (i.e. from n0/d0) as well as the values smaller than n0 divided by the values greater than d0, or the values smaller than n0 divided by the values smaller than d0. the only impossible combination is to divide values greater than n0 by the values smaller than d0 since increasing the numerator and decreasing the denominator an increase of the ratio is obtained. note that the membership function µn in (2) is not defined explicitly at any point x but implicitly using the y-values, i.e. y = n (x). any graphic representation of µn with respect to y-values is bi-dimensional, while it is (n + 1)-dimensional with respect to x-values, x being an n-dimensional vector. figure 1 shows the membership functions of numerators and denominators of a fractional objective produced by the novel method and two other methods found in the literature ([5], [8]). the representations are made with respect to y-values, thus involving only minimal, maximal, and desired values of the functions. no conclusion can be derived for the membership function of their ratio since no information about the dependence of numerator and denominator through x is provided. on the other side, composing the membership functions with the functions themselves yields representations with respect to the solution space, i.e. over the feasible set. an example of such representations can be seen in figure 2 that is part of an illustrative example in section 5.1. we now turn to the case of a given threshold p smaller than n0/d0, and a given tolerance limit t < p both provided for the fractional fuzzy goal. this pair (threshold, tolerance limit) determines four optimization problems (3) n1 = min{n (x) |x ∈ x,n (x) = td (x)} , n4 = max{n (x) |x ∈ x,n (x) = td (x)} , n2 = min{n (x) |x ∈ x,n (x) = pd (x)} , n3 = max{n (x) |x ∈ x,n (x) = pd (x)} , (3) https://doi.org/10.15837/ijccc.2020.1.3812 5 figure 1: graphical representation of the membership functions of numerators (to the left) and denominators (to the right) with respect to their y-values. the methods from the literature proposed increasing functions for numerators that are maximized and decreasing functions for denominators that are minimized. our method provides piece-wise monotone functions always containing both increasing and decreasing branches. that yield the key values for the parameters n1, n2, n3 and n4 needed in the definition of the trapezoidal shaped membership function of the numerator µn (n (x)) =   0, n (x) < n1, n (x)−n1 n2−n1 , n1 ≤ f (x) < n2, 1, n (x) ∈ [n2,n3] n (x)−n4 n3−n4 , n3 < f (x) ≤ n4, 0, f (x) > n4. note that all optimization problems solved in (3) are linear when both numerator n (x) and denominator d (x) are linear. assuming that the threshold and the tolerance limit for the fractional objective have values in accordance with its range, all values n1, n2, n3 and n4 are correct defined. similar computation is needed to determine the parameters d1, d2, d3 and d4 used in defining the membership function of the denominator. the needed optimization problems are given in (4) d1 = min{d (x) |x ∈ x,n (x) = td (x)} , d4 = max{d (x) |x ∈ x,n (x) = td (x)} , d2 = min{d (x) |x ∈ x,n (x) = pd (x)} , d3 = max{d (x) |x ∈ x,n (x) = pd (x)} . (4) the main advantages of the novel pair of membership functions (µn,µd) are as follows: they are piece-wise linear, their maximal values are reached at the point at which the maximal value of the ratio is obtained, and their aggregation using min operator is in accordance to the aggregation used in solving classic fuzzy decision problems. 4 novel crisp-linear-and models to fuzzy mo-lfp dubey, chandra and mehra [4] introduced a crisp-and linear model (candlp) to solve fuzzy mo-lp problems. both the fuzzy goals and restrictions were involved in the same way in the model through their linear membership functions, but they were distinctly treated through distinct fuzzy levels. we are interested to obtain a linear-and model to solve f-mo-lfp problems. using fractional objective functions in candlp a non-linear model is obtained. to avoid the non-linearity in the extended model, for each objective function fk (x) = nk (x) /dk (x) = ( ctk x + c 0 k ) / ( dtk x + d 0 k ) , k = 1. . . . ,p, we introduce the following pair of inequalities α + αk ≤ µnk ( ctk x + c 0 k ) , α + αk ≤ µdk ( dtk x + d 0 k ) , (5) https://doi.org/10.15837/ijccc.2020.1.3812 6 where both µnk and µdk are defined as proposed in section 3 with respect to the threshold and tolerance given for the fuzzy goal related to the fractional objective function fk. in fact, inequalities (5) replace the inequality α + αk ≤ µgk ( ctk x ) that corresponds in candlp to the objective ctk x. we may now propose our first extended crisp-linear-and model max α + 1 −γ p + m ( p∑ k=1 αk + m∑ i=1 αp+i ) subject to α + αk ≤ µnk ( ctk x + c 0 k ) , k = 1, . . . ,p, α + αk ≤ µdk ( dtk x + d 0 k ) , k = 1, . . . ,p, α + αk ≤ 1, k = 1, . . . ,p, α + αp+i ≤ µri (aix) , i = 1, . . . ,m, α + αp+i ≤ 1, i = 1, . . . ,m, α,αk,αp+i ≥ 0, k = 1, . . . ,p, i = 1, . . . ,m, x ∈ x. (6) to solve fuzzy mo-lfp problems. in this model γ is a parameter with values in the interval [0, 1] used for balancing the weights of the α-levels in the objective function. the number of fuzzy constraints in the original problem is denoted by m, while all crisp constraints are grouped in the set x. aiming to make distinction between the fuzzy goals that represent a positive information, and fuzzy restrictions that represent a negative information, dubey, chandra and mehra [4] used distinct aggregation operators for goals on one side and constraints on the other side, thus obtaining their coherence aggregated and linear program (coaandlp) to solve f-mo-lp problems. this model loses its linearity when fractional objective functions are involved. making use of the same pair of membership functions related to a fractional fuzzy goal we extend the applicability of their idea, and introduce the coherence aggregated linear-and model (7) to solving f-mo-lfp problems. max δ subject to α + αk ≤ µnk ( ctk x + c 0 k ) , k = 1, . . . ,p, α + αk ≤ µdk ( dtk x + d 0 k ) , k = 1, . . . ,p, α + αk ≤ 1, k = 1, . . . ,p, δ ≤ µri (aix) , i = 1, . . . ,m, δ ≤ α + 1 −γ p + m p∑ k=1 αk, δ ≤ 1, α,αk,δ ≥ 0, k = 1, . . . ,p, x ∈ x. (7) note the presence of only one acceptability level δ for all fuzzy constraints, and one special constraint that keeps the acceptability level of the flexible constraints (δ) under the desirability levels of the goals. this special constraint makes model (7) to follow the consistency condition [4] which claims that no solution can be unacceptable and desired at the same time. 5 numerical exemplifications 5.1 illustration of the novel membership function construction we first illustrate our novel methodology to construct the pair of membership functions for a linear fractional objective function that has to be maximized over a feasible set. we use the optimization problem max { x1 + 3x2 + 1 x1 + 5x2 + 5 |x1 + x2 ≤ 10, 0 ≤ x1 ≤ 8, 0 ≤ x2 ≤ 7 } (8) that is part of the mo-lfp problem addressed in [9], where an approximation of the pareto set was generated. https://doi.org/10.15837/ijccc.2020.1.3812 7 table 1: extreme points and relevant values for the numerator, denominator and ratio involved in the optimization problem (8) extreme points (0, 0) (8, 0) (8, 2) (3, 7) (0, 7) min desired values max numerator 1 9 15 25 22 1 n0 = n (8, 0) = 9 25 denominator 5 13 23 43 40 5 d0 = d (8, 0) = 13 43 ratio 0.2 0.692 0.652 0.581 0.55 0.2 f (8, 0) = 0.692 0.692 figure 2: graphic representations of the numerator, denominator and ratio describing the objective function of problem (8) the information about the extreme points of the feasible set and the relevant values used to define the membership functions are provided in table 1. we use (2) to construct the membership functions since no specific information on the fuzzy goals is given. the y-values used to define µn are (1, 9, 25) while the y-values used to define µd are (5, 13, 43), all of them obtained from table 1, columns (min, desired values, max) respectively. figure 2 shows the membership functions of the numerator, denominator and their aggregation over the feasible set. note that the point with maximal altitude correspond to the same pair (x1,x2), namely (8, 0) in all three graphics of figure 2, since they correspond to n0, d0 and n0/d0 respectively. 5.2 illustration of the solution approach we next recall the example presented in [3], and adapt it by combining their first and third linear objectives in one fractional objective, and keeping their second objective unchanged, linear. we also keep their constraints unchanged, namely first two constraints are fuzzy with given thresholds and tolerances, while next three constraints are deterministic. the description of the adapted problem is as follows. a company produces three products a, b, c using three types of raw material x, y , z. the amount of raw material (in kg) required to produce one unit of the products a, b, c is given by the vectors (2, 3, 4), (8, 1, 4), and (4, 0, 2) respectively. there are also some constraints imposed on the quantities of materials produced: it is inappropriate to ask for more than 50 kg of x, and it is acceptable 40 kg or less; it is not allowed a demand of y above 55 kg, but it is recommended to use 50 kg only. these two constraints for x and y are fuzzy – described by thresholds and tolerances, while the next constraint on z is hard: the use of material z above 50 kg is unacceptable. the next two constraints on producing at least 3 units of a, and at least 5 units of c are also hard. we state two goals: to maximize the rate of profit per production costs; and minimize the production of a harmful pollutant. the profit generated by selling one unit of products a, b and c is given by p = (5, 10, 12); and the costs of production of a, b and c are given by c = (1, 3, 4). the amount of harmful pollutant per unit production of a, b and c is given by the vector h = (1, 2, 1). some a priori fuzzy goals are also stated in [4]: a profit of 150 thousand is desired, but up to 130 thousand is tolerable; the production costs should not be more than 80 thousands, but less than 70 thousand is aimed; it is desired to reduce the production of harmful pollutant to at least 30 kg, but it should not exceed 35 kg. finally, the problem is to find the optimal production quantities of products a, b, and c. the https://doi.org/10.15837/ijccc.2020.1.3812 8 figure 3: the membership functions of the fuzzy goals and fuzzy constraints involved in problem (9) with respect to y-values mathematical model of this fuzzy bi-objective optimization problem is m̃ax 5x1 + 10x2 + 12x3 x1 + 3x2 + 4x3 � 150 70 , min x1 + 2x2 + x3 � 30, s.t. 2x1 + 3x2 + 4x3 � 40, 4x1 + 2x3 � 50, 5x1 + 10x2 + 12x3 ≤ 50, x1 ≥ 3, x3 ≥ 5, x1,x2,x3 ≥ 0. (9) the tolerances acceptable for the fuzzy goals and constraints are g1 = 20 for the numerator of f1, g2 = 10 for the denominator of f1, g3 = 5 for f2, and r1 = 10, and r2 = 5 for the first two constraints respectively. the membership functions of the goals and constraints with respect to y-values are shown in figure 3. we aim to derive a solution to problem (9) by means of the fuzzy goal programming using the crisp-linear-and models proposed in previous section. using model (6) with γ = 0.5 and the given thresholds and tolerances we obtain the following solution x∗1 = 3, x∗2 = 6.30488, x∗3 = 5 with the following levels of satisfactions: µf1 (x ∗) = α∗ + α∗1 = 0.40244, µf2 (x ∗) = α∗ + α∗2 = 1, µc1 (x∗) = α∗ + α∗3 = 0.50854, µc2 (x∗) = α∗ + α∗4 = 0.93903. table 2 reports the results obtained for two distinct pairs of thresholds and tolerances for the first objective function, and also those obtained in the absence of any threshold. this numerical results illustrate the dependence of the obtained solution on the desired values and imposed tolerances. table 3 reports the results obtained for different pairs of thresholds and tolerances and using model (7) to solving problem (9). note that the third reported solution in both tables is the same. that solution corresponds to the membership functions defined without any specification for the acceptability level. https://doi.org/10.15837/ijccc.2020.1.3812 9 table 2: optimal solutions obtained by applying model (6) to solving problem (9) x∗1 x ∗ 2 x ∗ 3 α ∗ + α∗1 α∗ + α∗2 α∗ + α∗3 α∗ + α∗4 3 5.88610 5.02847 0.37813 1 0.62278 1 3 6.30488 5 0.40244 1 0.50854 0.93903 3.75 0 5 1 1 1 1 table 3: optimal solutions obtained by applying model (7) to solving problem (9) x∗1 x ∗ 2 x ∗ 3 α ∗ + α∗1 α∗ + α∗2 δ∗ 3 4.59839 6.07145 0.38470 1 0.42316 3 6.49593 5 0.41463 1 0.45122 3.75 0 5 1 1 1 6 conclusions and final remarks in this work we introduced a novel way to construct two piece-wise linear membership functions needed to describe a fuzzy goal linked to a linear fractional objective. the main advantages of the proposed pair of membership functions are their effectiveness in describing the fractional objective still being piece-wise linear; and the fact that their aggregation using min operator is in accordance to the aggregation used in solving classic fuzzy decision problems. our second goal was to introduce two crisp linear models to solve fuzzy multiple objective linear fractional programming problems. with the help of the fuzzy-and operator we aggregated all fuzzy goals and constraints in both the classic and bipolar framework; and formulated the crisp linear models yielding a desired solution to the initial fuzzy programming problem. the main advantages of using the new models lies in the fact that they are linear, thus not cumbersome; and can generate distinct solutions to the multiple objective problem by varying the thresholds and tolerance limits imposed on the fuzzy goals, thus accurate. the membership functions we proposed may have an important role in handling the fractional objectives, thus we aim to study how to involve them in a wider class of solution approaches to fractional optimization problems as an alternative to classic linearization methods. we also aim to develop alternative linear solving models to the same multiple objective linear fractional programming problems involving the fuzzy-or operator or other ordered weights aggregation (owa) operators. both these directions are of interest in future researches. acknowledgments this research was partly supported by the ministry of education and science, republic of serbia. references [1] bellman, r.e.; zadeh, l.a. 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(1985). applications of fuzzy set theory to mathematical programming. information sciences, 36(1), 29 – 58. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: stanojević, b.; dzitac, s.; dzitac, i (2020). crisp-linear-and models in fuzzy multiple objective linear fractional programming, international journal of computers communications & control, 15(1), 1005, 2020. https://doi.org/10.15837/ijccc.2020.1.3812 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 2, month: april, year: 2022 article number: 4296, https://doi.org/10.15837/ijccc.2022.2.4296 ccc publications a unique multi-agent-based approach for enhanced qos resource allocation in multi cloud environment while maintaining minimized energy and maximize revenue umamageswaran jambulingam, k. balasubadra umamageswaran jambulingam department of information technology r.m.k. engineering college, rsm nagar, kavaraipettai, thiruvallur distric, tamil nadu, 601206. corresponding author:umamageswaranphd@yahoo.com k. balasubadra department of computer science and engineering r.m.d. engineering college, rsm nagar, kavaraipettai, thiruvallur distric, tamil nadu, 601206. balasubadra@yahoo.com abstract the use of the multi-cloud data storage in one heterogeneous service is a polynimbus cloud strategy. cloud computing uses a pay-as-you-go model to deliver services to a variety of end users. customers can outsource daunting tasks to cloud data centres for processing and producing results, thanks to cloud computing. cloud computing becomes the popular it brand that provides various on-demand services over the internet. this technology is devoted to distributing computer and software resources. the proven usefulness of workflows to enforce relevant scientific achievements is the availability of data from advanced scientific tools. scheduling algorithms are essential in order to automate these strenuous workflows efficiently. a number of new heuristics based on a cloud resource model have been developed. the majority of these heuristic based address qos issues in one or two dimensions. the cloud computing technology offers a decentralised pool of services and resources with various models that are provided to the customers across the internet in an on-demand, continuously distributed, and pay-per-use model. the key challenge we address in this paper is to maximise revenue while maintaining a minimum consumption of energy with an enhanced qos for resource allocation. the obtained results from proposed method when compared with the existing state of art methods observed to be novel and better. keywords: artificial bee colony (abc), best fit decreasing (bfd), distributed energy resources (der), economic dispatch (ed), genetic algorithm (ga), multi agent system (mas), priority based resource allocation (pra), service-level agreement (sla), vickrey –clarke–groves (vcg), virtual machine (vm). https://doi.org/10.15837/ijccc.2022.2.4296 2 1 introduction the world is witnessing rapid developments in software that is used on a daily basis. users’ demands for more services are constantly growing as new online services arise. as a result, the task of ensuring a high level of service is at the center of business competition. new technologies, especially cloud computing, give businesses the opportunity to provide strong mobile services at a low cost. customers who use cloud computing are relieved of the responsibility of owning and running the physical infrastructure that their companies need. this eliminates the need for them to be concerned with developers and development teams. customers often don’t have to worry about how or when the necessary tasks are completed; all they have to worry about is the cost of using the technology, the services they will receive from cloud providers, and the service quality promises. cloud providers are concerned with achieving effective resource usage and provisioning using intelligent ways to track and control resources, and using various approaches to achieve the highest quality of service assurances without violating the sla. these are a few of the major obstacles that cloud providers face. as the internet grows in popularity, so does the number of computational techniques available. a growing volume of data must be processed in this situation. the introduction of various types of cloud computing, such as cloud computing, edge computing, and fog computing is due to the increase in user requirements. 2 generic related work cloud computing is a pioneering virtualization technology that has greatly aided data processing. it enables network access to system configurable resources such as networks and the internet in a simple and fast manner. furthermore, provisioning and publishing these services do not necessitate a lot of management or service provider contact [1]. figure 1 depicts the cloud computing framework. figure 1: : structure of cloud computing the iot framework cloud computing technology faces some limitations as the internet of things evolves and people’s needs grow. cloud computing cannot play a useful role in large-scale or homogenous situations in this case [2]. as a result, a new computing paradigm based on cloud computing called fog computing is being created. the key benefit of fog computing over cloud computing is that it extends cloud services to the network edge. as a result, fog computing can help with resource and service management [3]. figure 2 depicts the fog computing structure. edge computing enables operations to be carried out at the network’s edge [4]. edge computing encompasses all computing and network infrastructure, from data sets to cloud data centers. the computing flow is bidirectional in edge computing, and stuff in edge computing can consume and generate the data. they can, in other words, not only request cloud services but also perform computing tasks in the cloud figure 3 depicts the structure of edge computing. the mec, which refers to the engineering of completing graphics rendering and postponement tasks for mobile devices, is the most common representation of iot technology. and its theory entails gathering a significant amount of free processing power and storage capacity at a network’s edge. it was first described as a computing model by the european telecommunication institutional structure. mec provides the capabilities of information and edge hosting at the network edge. elasticity in cloud computing defined as the degree of automated service discovery adaptation in reaction to continuously changing in the customer’s workload and requirements. this is accomplished by scaling up or down the services allocated to a specific customer automatically. such a system should be as similar to the original as possible. the available capital https://doi.org/10.15837/ijccc.2022.2.4296 3 in relation to the existing customer demands [5]. as a result, elasticity can simply be defined as the absence of both the overprovisioning and the under-provisioning issues resulting in successful resource provisioning [6] if there is an overabundance of supplies, the issue of overprovisioning will arise. figure 2: : structure of fog and edge computing. a customer’s reserved resources are insufficient to meet his or her needs as seen in the left part of fig. 1. the red line in the diagram represents the available resources based on peak load is calculated right, resulting in no sla violations. however, without elastic modulus during non-peak times, resources will be depleted are thrown away. the problem of under provisioning, on the other hand, may occur when the reserved assets are insufficient for the current situation customer’s requirements this issue results in sla violations. as a result, sales and clients are lost. as seen in the diagram, the under-provisioning problem can manifest itself in a variety of ways. sections of fig. 3 in the center and right 1 in each case. the shaded areas in these graphs reflect the sla violations. may change over time depending on the needs of the customer. 3 existing works multiagent-based resource allocation for energy minimization in cloud computing systems (raem): https://doi.org/10.15837/ijccc.2022.2.4296 4 figure 3: : overprovisioning and under provisioning the main advantage of this approach lies at allocation and consolidation of vms are done very efficiently. a centralized bfd-ga approach is used which is helpful for vm allocation and vm consolidations. [7] suggested a vickrey–clarke–groves (vcg)-based model based on a truthful mechanism to maximize revenue [8] suggested a traded protocol based on the ma to trade efficiently and effectively fair distribution of resources among selfish users [9] suggested being honest. mechanisms while maximizing the profit of the csp many of these market-driven processes densely model the market for convenience’s sake. the cost of energy for cloud systems is proportional to the amount of data stored. users are assigned a certain number of virtual machines (vms). ma technology, that is developed from centralized artificial intelligence, has demonstrated the ability to solve distributed system issues [10]. the bi-objective parameters which are associated with revenue and cost minimization need to be addressed in a well manner, which is the main pitfall of this approach. multi-agent systems for resource allocation and scheduling in a smart grid (rasg): the major advantage of this method lies at effective scheduling and allocation of resources. a unique integration of der-ed approach is carried out to prove the desired results. with the proposed method an effective usage of mas was defined. an agent can be described as a computer network capable of making critical decisions in response to a scenario in order to achieve its goal [11]. unit commitment (uc) is a highly complex optimization technique in a smart grid system that regulates the startup and termination of generators to meet demand while keeping performance parameters in mind [12]. algorithms that can be distributed such algorithms are stable, resistant to topological changes, and can facilitate future grid’s "plug-and-play" functionality. it’s a strategy for getting power applications to settle on a single data value. this algorithm is intended for use in networks with many unreliable nodes [13]. [14] suggested a decentralized consensus-based design for the ed in a smart grid system that preserves supply-demand equilibrium during transients. since the approach does not depend on a supply-demand discrepancy, it could be used online. the main downfall of this approach is related in defining a set of constraint applications related to multi agent system. priority based resource allocation and scheduling using artificial bee colony (abc) optimization for cloud computing systems (pbra) efficient resource utilization and allocation is the main contribution of this approach. pra-abc with cloudsim on java platform is the used methodology. [15] has proposed a decentralised multiagent (ma) vm allocation method. to save resources, a local compromise vm consolidation process is built to swap the allocated vms of agents. [16] uses task project duration and load as algorithms when deciding which vms to use. the obtained results from this approach were satisfiable. the main pitfall of this approach is that it works only for certain predefined parameters. optimization approach for resource allocation on cloud computing for iot (oara): main highlight of this approach lies at calculating accurately the probability of deadline and provider’s profit. combinatorial auction methodology is used with an eye on deadline of the given job. advantage of this approach lies at delivering satisfiable results when compared with the existing methods. the big data problem has been recognized as a worldview for cloud computing [17]. an auction-based model is the most popular method in allocation of resources and pricing in the cloud [18]. in cloud computing, combinatorial auction is favored because it allows users to purchase a bundle of resources rather than a single resource. before a provider could provide a provider to a user, both the provider https://doi.org/10.15837/ijccc.2022.2.4296 5 and the user must agree on a service level agreement (sla). a service level agreement (sla) is an agreement between such a provider and a user that specifies qos [19]. loss of profit for a create a shared to failure to complete certain jobs and the cost of a sla penalty [20]. we use the probability of dateline violations by considering the job’s urgency when determining winners to maximize the provider’s profit by lowering the penalty function for sla violations [21]. the major pit fall of this approach lies at working of this model with certain constrained parameters on the combinatorial auctions. table 1: comparisons of exiting methods 4 proposed work in order to address the key challenges associated with delivering qos while maintaining minimal energy consumption and more profit the following two algorithms are proposed. algorithm 1: energy minimization algorithm let a_m be the m available resources, sopt be the sources which are opted to do the assigned jobs, hostlist consists of available list of resources. after utilizing the resources, the hostlist is then cleaned. processor speed along with upper and lower bound are being fixed. by maintaining the above condition, a minimum energy consumed by the resources can be maintained. 5 experimental setup this work is indented to measure the real-time performance of the cloud resource scheduling algorithms in terms of availability, refusal rate, reliability, scalability, average energy consumption, average response time and cumulative energy cost. the standard cloud scheduling procedure codes are fetched from [26] and the proposed method is coded using vc++ programming language. a dedicated user interface (ui) is constructed using visual studio ide [27] to establish communication with the common gateway interface (cgi) [28] which connects to a leased windows virtual private server from [29]. the express windows vps with dual-core processor, 2gb ram, 60gb ssd, 50mbps bandwidth and free dns is configured to run standard and proposed cloud resource https://doi.org/10.15837/ijccc.2022.2.4296 6 figure 4: : the user interface https://doi.org/10.15837/ijccc.2022.2.4296 7 figure 5: : the cgi data allocation procedures. the performance monitor is used to measure the required performance parameters and grabbed through cgi to the dedicated ui. then the ui can export the measured results as the report file and can plot corresponding graphs. to provide qos resource allocation with minimal figure 6: : snapshot of the simulation with multiple agents working in mulit cloud environment energy consumption and to generate maximum income in accordance with the suggested methodology, all of the vms indicated above operate as multiagents. multi-cloud vms are added to the hotlist and may be used to meet various criteria, including the fixing of different processor speeds in accordance with the need for minimising energy use. reserved seat contracts that were accepted prior to time zero are used to determine the starting value for an equation such as this t=0. this algorithm’s goal is to enable as many reservation agreements as feasible by filling up the nodes from the very end of the window to the very beginning for the maximum profit. 6 results and discussions the proposed method was carried out with respect to the parameters availability, refusal rate, reliability, scalability, average energy, average response time and cumulative energy cost. availability: represents the availability of resources with more quality of services. higher the values of availability indicate the more availability of resources without any conflict, which is clearly achieved from the proposed method and the maximum reached values is nearly 99.02%. refusal rate: it indicates how many requests are rejected to the total number of submitted requests and how many are accepted. it is calculated by the following formulae the less the refusal rate the more will be strength of the proposed method. from the above values in the table, it is clearly observed that the proposed methods have very less refusal rates indicating that the proposed method accepts a greater number of request than rejected ones. https://doi.org/10.15837/ijccc.2022.2.4296 8 table 2: availability figure 7: : availability graph for existing and proposed method table 3: refusal rate https://doi.org/10.15837/ijccc.2022.2.4296 9 figure 8: : refusal rate graph for the existing and proposed method reliability: it defines that how reliable is system or resource is working in a cloud environment according to the requirements. is obtained by the formulae table 4: : reliability higher reliability is more favorable than lower because the resource which has been allocated for a specific purpose in cloud environment should carried out its functionality as per the requirement. it is clearly observed from the above values that the proposed algorithm is helpful in order to make the resources to work according to the need. the highest reliability of the approach obtained is 98.93 scalability: when and where required depending on the need the resources which are utilized in cloud environment should be scalable from the above table it is clear that higher the scalability the more will be the strong approach. from the obtained values of the proposed method, it is very clear that our approach is more scalable with respect to resources. where the maximum scalability is 99.4%. average energy consumption: or a specific task in cloud environment, it indicates the average energy consumed by the individual resources which are dedicated for a specific purpose. response rate=current time-arrival time+remaining time the less the average response time the more will be the effort of the system. form the values in the table it is clear that from the proposed approach the average response time is very less and any kind of reliable request can be granted with in the shortest period of time. the maximum and the https://doi.org/10.15837/ijccc.2022.2.4296 10 figure 9: : reliability graph for the existing and proposed method table 5: scalability figure 10: : scalability graph for the existing and proposed methods https://doi.org/10.15837/ijccc.2022.2.4296 11 table 6: scalability figure 11: : average response time graph for the existing and proposed method minimum average response time from proposed algorithm are 201ms, 183ms. cumulative energy cost:individual spent cost for the consumption of energy for the resources in cloud environment. lessor its value more will be the revenue or the profit. figure 12: cumulative energy cost cumulative energy cost is inversely proportional profit / revenue earned on the resource. from the above table it is clearly observed that out of present existing methods our proposed method is having less cumulative energy cost which indicates the more revenue is generated when energy cost distribution is followed by the proposed algorithm. the lowest value earned by the approach is 98 uj, indicating more revenue at that point. https://doi.org/10.15837/ijccc.2022.2.4296 12 figure 13: : cumulative energy cost graph for the proposed and existing methods 7 conclusion in this paper, we primarily focused on developing an effective algorithm that can be used to deliver qos services while minimising energy consumption and maximising income. when the obtained values are compared to the state of the art of existing methods, our findings are found to be strong and accurate. there is also a need to achieve 100 percent reliability using innovative methods, and the total energy cost must be further reduced, as well as the energy consumption by resource. other parameters can be considered in addition to these, depending on the domain in which the algorithm is being used. declaration of competing interests the authors declare that they have no known competing financial interests or personal relationships that could have appeared to 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(2020). a hybrid cnn-lstm model for forecasting particulate matter (pm2.5), ieee access, 8, 26933-26940. https://doi.org/10.1109/access.2020.2971348 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: umamageswaran jambulingam; k. balasubadra k. (2022). a unique multi-agent-based approach for enhanced qos resource allocation in multi cloud environment while maintaining minimized energy and maximize revenue, international journal of computers communications & control, 17(2), 4296, 2022. https://doi.org/10.15837/ijccc.2022.2.4296 introduction generic related work existing works proposed work experimental setup results and discussions conclusion int j comput commun, issn 1841-9836 8(3):444-459, june, 2013. comparative study of methods for estimating technical losses in distribution systems with distributed generation j.e. mendoza, m. lopez, s. fingerhuth, f. carvajal, g. zuñiga jorge e. mendoza, miguel lópez, sebastián fingerhuth felipe carvajal, gonzalo zuñga pontificia universidad católica de valparaíso escuela de ingeniería eléctrica, valparaíso, chile. brasil 2147, valparaíso, 4059, chile. e-mail: jorge.mendoza@ucv.cl, miguel.lopez@ucv.cl, sebastian.fingerhuth@ucv.cl, fcarvajal@volta.cl, gzuniga@esinel.cl abstract: in this work four methods for estimating annual technical power losses in distribution networks due to the distributed generation (dg) connection are studied. the methods are obtained of professional sources, and are evaluated in a test system. a new method is proposed in this work to be contrasted to previous methods. to find the best method, the power losses of a base case are estimated with simulations every 15 minutes, considering variability of load demand and power generation. results indicate the effectiveness of the proposed method respect of other analyzed methods. the proposed method can be a useful tool within a decision support system for optimizing control, operation and planning of the distribution network. keywords: distribution network, distributed generation, active power losses. 1 introduction nowadays there is a growing concern in using, in the most efficient possible way, the different types of energy available on our country. from this point of view, proposals which foster efficient use of this energy will significantly contribute to solve the possible future problems related to its supply. this can be seen in the continue development and construction of new and more efficient electrical equipment both from the energy consumption point of view as for the benefits they are able to render to an electrical network. this concern is also shared with the areas involved in the generation, transmission and distribution process where the studies and analysis from the perspective of planning and operation of the network are basic from a technical/economical point of view for an optimum functioning. in this way, electrical distribution systems are of primary importance for the development of research efforts on finding methods and techniques aiming to optimize their design and operation. this is due to the fact that this is the level were a great quantity of customers concentrates entailing the use of lower voltage levels generating greater current flows and finally a less efficient use of energy (increase of energy losses). 1.1 distributed generation today there exist a global tendency to allow electric energy injection from clients, electrical industries or from third parties on distribution networks which is called distributed generation (dg) [1]. these presents two advantages. firstly, allows an efficient energy use by way of using energy surplus from industries connected to the network fostering also non-conventional energy generation. secondly, energy injection close to the load allows an improvement on customers quality service due to an efficient energy transport. the above facts are also supported worldwide [2], copyright c⃝ 2006-2013 by ccc publications comparative study of methods for estimating technical losses in distribution systems with distributed generation 445 by facilitating some regulatory aspects associated to the integration of dg to electric networks. in chile, this can be seen on modifications that laws nâş 19.940 y 20.018 introduce to d.f.l. 1/82 [3] [4], pointing out incentives and procedures for energy injections on electrical networks. traditionally, utilities design their networks to receive energy from the transmission system and then deliver it to consumers in the distribution system [8]. for this reason, many radial distribution feeders have a conical configuration, i.e., lines start with larger gauge conductors which are reduced along the feeder. this type of configuration can have drawbacks for dg projects. however, a dg can be favorable to the distribution company, reducing losses in the conductors and energy demand of the substation; therefore, this must be assessed by feasibility studies. 1.2 losses estimation the main difficulty in power loss evaluation is the nonlinear relationship with power injections in the network buses. for this reason, the use of tools such as load flow is required for proper evaluation. this implies necessarily having a lot of information about lines, transformers and equipment, which can be hard to obtain. furthermore, load variations are a very important factor to evaluate these losses. however, in practice, this variability is not properly registered due primarily to economic factors. this results in application of factors, approximate curves and simplifications, which surely entail a relatively large degree of uncertainty. knowing or estimating system losses in real time or in a time window can help operators make better decisions regarding the dispatch of other generation units in real time energy markets [10], or as a way to effectively assess the benefits of dg in distribution networks [11]. for example, the proposed power losses estimation technique can be used as an online, real-time diagnostic device that helps a better and more efficient control of distribution power networks. 1.3 decision support system (dss) today technical and social systems are becoming increasingly complex. their models have a large number of state and control variables, delays and different time constants. also they show limitations in their information infrastructure and risk sensitivity aspects. such systems are called large-scale complex systems. hierarchical approach has been for decades one of the most used methods for controlling these large-scale systems. when human intervention is necessary, decision support systems (dss) can provide a solution. a dss is an adaptive and evolving information system intended to implement some of the functions of a human support team that otherwise would be required to assist the decision-maker to overcome the limits and constraints when approaching decision problems [12] [13]. this work allows the development of a useful tool that can be included to a dss for power distribution networks, as it will provide fast information for decision making. this way, the method will allow optimizing the power distribution system control and operation, especially in presence of dg. this paper addresses the problem of technical power losses estimation in radial distribution network when incorporating dg, to estimate energy and power losses in an effective manner, considering all the technical limitations inherent to the lack of data on real systems for developing a quick, precise and reliable tool that can be included to a dss. 2 description of methods and loss calculation proposal this section describes three models used by consulting firms mainly. these methods will be analyzed and compared with a new proposal, explained in the final part of this section. 446 j.e. mendoza, m. lopez, s. fingerhuth, f. carvajal, g. zuñiga 2.1 viera-bonessi method viera-bonessi method is used for distribution network planning [5]. it is developed for three types of primary sources: wind, biomass and hydropower. in this work, only wind type method is considered. with the latest available annual active power demand curve, a power duration curve is built. from this curve three scenarios are calculated: maximum (peak) demand, average demand and minimum demand to be used for each simulation. the scenarios are shown in figure 1. from these scenarios, the time duration of each stage is obtained. in this example, t1 = 1460 hours for peak demand, t2 = 4745 hours for average demand and t3 = 2555 hours for minimum demand. then, each demand scenario pi is defined. these are calculated by (1), where ai is the area under the power duration curve and ti is the duration of stage i demand in hours. factors to be applied to the peak power load in all nodes are then calculated. this way, the loads to be used for power flows for each demand scenario are obtained. these factors are calculated by (2), where pi is the demand under the ith scenario and p̂ is the maximum load associated with the zone of influence of the generator. after obtaining the demands and the factors for each scenario, the whole network to be affected by the dg is modeled. maximum loads must be corrected by the obtained factor fi, thereby determining loads for different types of scenarios. pi = ai ti (1) fi = pi p̂ (2) figure 1: power duration curve and demand scenarios losses without distributed generator having conducted the above, a power flow is performed and the network losses are calculated for each demand scenario. then, to get the annual energy losses (3) is used. epsg are annual energy losses without the generator, in mwh and ppsgi are power losses in the system obtained with the power flow for each demand scenario, without the dg. tdi is the duration of the demand scenario i, in hours. epsg = 3∑ i=1 ppsgi · tdi (3) epcg = fp ∑ i=1 3ppcgi · ti + (1 − fc) · epsg (4) comparative study of methods for estimating technical losses in distribution systems with distributed generation 447 losses with distributed generator to calculate the annual energy loss with wind based generators (4) is used. the fp is the annual loss factor, ppcgi are losses in the system connected with the dg at full load obtained from simulations, epsg is the annual energy loss in mwh without the dg, epcg is the annual energy loss of the generator in mwh and fc is the factor of the generator plant. the annual loss factor fp is calculated with (5), where fc is the capacity factor, x is a variable whose value depends on the shape of the generating curve, with a typical value of 0.3 [5]. this method does not indicate how to forecast demand feeder for analysis in future years. fp = x · fc + (1 − x) · f2c (5) 2.2 3g-3d method in this method, the maximum, medium and minimum demand scenarios, pi, fi values and losses without dg are calculated in the same way as viera-bonessi method. this method was developed and is used by a consulting engineering firm in chile, for hydraulic projects evaluation mainly. the principal difference to the viera-bonessi method, is the way of estimating the generation stages, as indicated below. energy losses with distributed generator at this stage, the method proposes performing the analysis with 3 generation scenarios. for this, the maximum, medium and minimum generation scenarios are obtained: maximum generation (tg1): 1825 hours/year, mean (tg2): 4380 hours/year and minimum (tg3): 2555 hours/year. the following defines the power generation pgj , corresponding to each generation stage. these are is calculated by (6), where agj is the area under the curve generation duration during the active power generation stage j, for tgj time, which is the duration of the stage of generation j, in hours. agj is obtained from the annual expected generation curve of available active power, in hourly basis. the sum of the first 1825 values is ag1, the sum of next 4380 values is ag2 and the sum of the other 2555 values is ag3. this yields the 3 stages of generation for the simulations. pgj = agj tgj (6) epcg = 1 9760 · ∑ i=1:3 j=1:3 p cgd · tdi · tgj (7) generation scenarios are shown in figure 2, where the blue curve is the expected annual generation duration curve of the generation group and in red the generation scenarios indicated by the method. with each generation stage, a power flow is performed for the three demand scenarios (high, medium and low). this way, power losses are obtained for the 9 cases. then, with the times of occurrence of each scenario, the energy losses of the feeder are obtained using (7), where ppcgij are the system losses with ith-demand scenario and the jth-generator stage, in mw. tdi is the duration of the demand scenario i, in hours, tgj are the duration of the generation stage j in hours. epcg is the annual energy loss with the generator, in mwh. regarding to feeder demand forecasting, this method proposes to use the country’s gdp as growth rate. 448 j.e. mendoza, m. lopez, s. fingerhuth, f. carvajal, g. zuñiga figure 2: generation duration curve and generation scenarios 2.3 monthly blocks method this method take into account relevant aspects of the above two methods. with the active and reactive power hourly demand curve at the feeder head, a monthly demand curve is created, which is sorted in decreasing active power, maintaining the respective reactive power. with each monthly demand curve, two blocks, b1 and b2 block are calculated. b1: high power block and b2: low power block. this totalizes 24 blocks; these blocks will be defined in the same way as does the chilean national energy commission [6]. table 1 shows the distribution of records (hours) per month of each block used for example. to calculate demand for each block pb and qb, the procedure is: for january block b1, active power is defined by the average of the first 240 records of the data, assorted from highest to lowest value for the month; i.e., pb of b1 is determined by the average of the 240 highest active power records in january. the reactive power of b1 (qb), is determined by the arithmetic mean of reactive power of the same 240 records considered in the pb calculation of b1 block. for the january block b2, the procedure is the same, but taking the remaining records of that month (504 records). b1 and b2 blocks for other months are determined in the same way, but considering the distribution of table 1. table 1: monthly blocks header demands month b1 (records) b2 (records) nâ◦ records january 240 504 744 february 86 586 672 mars 69 675 744 april 288 432 720 may 298 446 744 june 312 408 720 july 340 404 744 august 296 448 744 september 258 462 720 october 42 702 744 november 44 676 720 december 46 698 744 total 8760 the demands of each block should be apportioned in proportion to the capacity of distribution transformers (dt) plus lines losses; that is, when making the load flow, power demand in the header must match the demand of the block, considering a degree of tolerance. this assessment can be made proportionally distributing the capacity of transformers. similarly, the reactive power is calculated. with the demand for dt obtained through apportionments described above, the functions comparative study of methods for estimating technical losses in distribution systems with distributed generation 449 "ptotal tds estimate" and "estimating qtotal tds" are defined. these functions relate block demand (demand in the header) with the demand of the dt (8). the coefficients a, b, c, d, e, f, g, h, i and j are constants that can be estimated by a regression. [ ptotaldts qtotaldts ] = [ a b f g ] · [ pb qb ] · [ c d h i ] · [ p 2b q2b ] + [ e j ] (8) functions "ptotal tds estimate" and "qtotal tds estimate" are evaluated with data from more recent feeder hourly demand, which gives the dt demand curve; this curve is called "td demand curve". losses without distributed generator the ptotal tds and qtotal tds previously calculated for each block (blocks calculated with the demand curve latest feeder, which demand curve will name as year 0) are projected to the year 1, year 3 and year 5, thereby get dts demand for each block of the mentioned years. the method intends to make the projection considering a growth rate provided by the distributing company or any reliable study. power flows are performed for each block (72 blocks, 24 per year) and losses are recorded. the feeder characteristic loss function without dg, (9), where pl is the losses obtained in the simulations, the coefficients k, l, m, n and o are constant and can be determined analogously to the procedure performed to determine the coefficients in (8). pl = [ k l ] · [ ptotaldts qtotaldts ] + [ m n ] · [ p 2b q2b ] + o (9) the demand curve dts projected year 0 to year 1 and the feeder characteristic loss function is calculated, this way hourly losses feeder in year 1 are obtained; these are multiplied by an hour (demand time duration) and then energy losses in an hour are obtained. then adding up all the energy losses of the year, the total energy losses of the year 1 are obtained. the dt demand curve is projected to years 3 and 5, and the procedure is repeated to determine the energy losses of 3 and 5 years. with total energy losses for years 1, 3 and 5, a quadratic trend curve is adjusted, which allows the estimation of total energy losses for the intermediate years. losses with distributed generator this model proposes to conduct analyzes with a single generation scenario. the generator is modeled with the power available, whereas it has this power available (or not) throughout the year. to get the power output of the generator, it is necessary to know its rated power and capacity factor. this is obtained according to the available power (10), where pnom is the rated output power and pdisp is the available generator power. pdisp = pnom · fc (10) with the 72 dt demand blocks previously calculated, power flows are executed for each block, considering the available power injection of the generator. then, generator losses estimation is analogous to the case without generator. 450 j.e. mendoza, m. lopez, s. fingerhuth, f. carvajal, g. zuñiga 2.4 proposed method the proposed approach arises from the combination of 3g-3d and monthly blocks method. from the latest active and reactive power hourly demand curve of the feeder header, a demand duration curve based on the active power is built. from this curve, maximum, medium and minimum demand scenarios to be used for each simulation are calculated. tdi time duration of each stage are the same as regards the viera-bonessi method. then, each demand scenario pi and qi are defined. the calculation of pi and qi is done with (11), where ai is the area under the curve of the active power demand duration during the demand scenario i, for the time tdi; bi is the area under the reactive power demand curve during stage i, for the time tdi, tdi is the duration of stage i demand in hours. to calculate ai the curve using active power duration (figure 1) is used.[ pi qi ] = 1 tdi [ ai bi ] (11) losses without distributed generator with the demand values of each scenario, the apportionment proposed in monthly blocks method is done. then, the energy losses without generator are calculated using (3). losses with distributed generator the same generation scenarios of 3g-3d method are used, which is calculated from the expected generation curve: generation maximum tg1: 1,825 hours/year, mean tg2: 4,380 hours/year minimum tg3: 2,555 hours/year. then, the calculation of the pgj values is the same as in 3g-3d method (see 2.2.1). to demand forecast of the feeder, a growth rate provided by the utility or by any reliable study can be used; which will be applied to the dt demands. 3 estimation of real losses to know which method obtains the best results, the simulation of a system with different kinds of loads and known daily power variations, as well as the behavior of the dg. this will get different levels of demand and losses for different hours. this system is called the "base case" and will be the benchmark to compare the methods. 3.1 distributed generator this study uses a wind park with a nominal power of 2 mw [7]. the daily generation curve of the park, for the winter, summer and fall are shown in figure 3, discretized every 15 minutes. the analysis considers that spring and autumn curves are the same. according to the same study, the capacity factor of the park is 0.445 and considers that each turbine has a capacitor bank that allows only active power injected to the grid. the dg wind park is simulated as a pq bar where only the active power injected into the network. 3.2 test network the analysis is done on a test system consisting of 17 bars and 16 lines, with loads connected in all buses. figure 4 shows the network topology. grid parameters can be found in [8]. these values are in per unit, with base values of 23 [kv] and 100 [mva]. the total active power is 13.88 mw and reactive power is 5.96 mvar. comparative study of methods for estimating technical losses in distribution systems with distributed generation 451 selection of connection points of the dg for evaluating the methods was made considering the power injection at nodes located at the end of feeder, i.e. the node 11 and 17, where a greater impact on loss is expected. also two other feeder midpoints were considered in nodes 6 and 12. figure 3: supply of power to the grid by the wind farm figure 4: test system 3.3 base case details the base case is intended to represent the behavior of a real system. for this reason, hourly variations are considered for the various loads connected to the network distributed along the feeder and with different power levels. hourly behavior curves of the considered loads are shown in figures 5 and 6 [8]. besides differentiate types of loads, the seasonal variability of demand is taken into account. demand curves for seasons were obtained from [9] and are shown in figure 7. autumn and spring demands are considered equal. the duration of each station are 94 days summer, fall 93, winter 89 and spring 89. the data from these load curves are discretized every 15 minutes. 4 results and comparison of methods to apply the methods presented in the test system, the hourly demand curve at the top of the feeder in year cero, without dg, obtained from the base case analysis is used. each method is evaluated considering a five years horizon, where the dg is connected in year 1. losses estimated by each method will be compared to the base case. the rate of growth in demand to be used is 4.5% per year, the same as used in the base case. from the hourly demand curve at the head of the feeder in year 0 without gd, maximum demand is 7.156 mw; minimum demand is 1,966 mw. power factor varies between 0.90 and 0.92, whereby, in evaluating the methods, this range is considered. 452 j.e. mendoza, m. lopez, s. fingerhuth, f. carvajal, g. zuñiga figure 5: commercial (1) and industrial (2) type load curve figure 6: commercial (1) and industrial (2) type load curve figure 7: commercial (1) and industrial (2) type load curve comparative study of methods for estimating technical losses in distribution systems with distributed generation 453 4.1 viera-bonessi method the generator power injection is 4 mw, the capacity factor, annual loss factor and the variable x values are: fc = 0.445, fp = 0.272 and x = 0.3 respectively. demand scenarios are shown in table 2. according to the results in table 3, the method has low error in estimating losses without dg. however, the result shown in tables 4 and 5 shows an abnormal operation of viera-bonessi method, in the estimation of losses with dg and savings in energy losses. 4.2 3g-3d method demand scenarios are the same as in viera-bonessi method (table 2). the power losses estimation without gd is also equal, thus the results are the same (table 3). this method proposes to perform the analysis with three stages of generation these are shown in table 6. nine power flows resulting from considering each generation scenario with three demand scenarios are carried out. results are shown in table 7. these results show that the method estimated correctly the losses with dg. the energy savings achieved by the method over 5 years in the analysis and comparison with the calculated base case are shown in table 8. table 2: demand scenarios. viera-bonessi method demand ai ti [h] pi [mw] fi maximal 10065 1460 6,894 0,497 average 22542 4745 4,75 0,342 minimal 7081 2555 2,77 0,199 table 3: comparison of total losses without dg. viera-bonessi method losses m1 [gwh] losses cb [gwh] % error 3,99 3,77 5,77 table 4: losses with dg comparison. viera-bonessi method bus losses [gwh] base case losses [gwh] % error 6 2,74 2,2 24,54 11 3,61 2,4 50,18 12 2,94 2,81 4,77 17 3,26 3 8,51 4.3 monthly blocks method the active and reactive power hourly demand curve at the head of the feeder in year 0 without dg is brought to a demand curve for each month, which are sorted in descending order of active power demand. this result in demand blocks b1 and b2 of each month taking into account the distribution records of table 1. table 9 shows the demand blocks. then the apportionment of the extraction points is carried so that the feeder head power is equal to the sum of dt demands plus lines losses. with this apportionment, dt demand for 454 j.e. mendoza, m. lopez, s. fingerhuth, f. carvajal, g. zuñiga table 5: savings in energy losses. viera-bonessi method bus savings [gwh] base case savings [gwh] % error 6 1,25 1,57 -20,58 11 0,38 1,37 -71,97 12 1,05 0,97 8,65 17 0,73 0,77 -4,92 table 6: generation scenarios. 3g-3d method generation ai ti pi maximal 4786 1825 2,623 average 7802 4380 1,781 minimal 3005 2555 1,176 table 7: losses with dg comparison. 3g-3d method bus losses [gwh] base case losses [gwh] % error 6 2,37 2,2 7,54 11 2,26 2,4 -5,67 12 2,95 2,81 5,19 17 3,07 3 2,25 table 8: savings in energy losses. 3g-3d method bus savings [gwh] base case savings [gwh] % error 6 1,62 1,57 3,28 11 1,73 1,37 25,77 12 1,04 0,97 7,44 17 0,92 0,77 19,42 table 9: demand blocks, year 0 month blocks pb [mw] qb [mvar] january b1/b2 4,12 / 3.00 1,89 / 1.39 february b1/b2 3,32 / 3,37 1,53 / 1,55 mars b1/b2 3,36 / 3,37 1,55 / 1,55 april b1/b2 3,92 / 4,20 1,80 / 1,92 may b1/b2 4,17 /4,22 1,91 / 1,93 june b1/b2 4,20 / 4,20 1,92 / 1,92 july b1/b2 5,00 / 5,65 2,28 / 2,56 august b1/b2 5,58 / 5,66 2,53 / 2,57 september b1/b2 5,61 / 5,64 2,55 / 2,56 october b1/b2 5,52 / 4,27 2,53 / 1,95 november b1/b2 4,17 / 4,21 1,92 / 1,93 december b1/b2 4,21 / 4,20 1,93 / 1,92 comparative study of methods for estimating technical losses in distribution systems with distributed generation 455 each block is obtained, which is necessary to calculate the functions "ptotaltds estimate" and "estimating qtotaltds" (8) that relate the block demands to the dt demands. dt demand curves are projected years 1, 3 and 5, and then evaluated in the "characteristic feeder losses without dg function", so power losses in every hour for the same years are obtained. table 10 shows the losses without gd and figure 8 shows the graph of each year losses and the quadratic trend curve used to calculate losses in years 2 and 4. figure 8: commercial (1) and industrial (2) type load curve it is noted that the method estimates with good precision losses in the feeder without dg. for analysis with dg, the method proposes making simulations with the generator injecting its available power, which is calculated by the product of the nominal power factor for the plant, which in this case would pdisp = 1.78 mw. with the blocks 72 previously calculated, power flows are carried with the dg injecting its available power. the procedure is analogous to the case without dg. losses with and without dg along the 5 years in analysis for each injection point and the comparison with the base case are shown in tables 11 and 12 respectively. energy savings achieved over the five years are shown in table 12. table 10: comparison of total losses without dg. monthly blocks method losses method [gwh] base case losses [gwh] % error 3,97 3,77 5,18 table 11: losses with dg comparison. monthly blocks method year losses method [gwh] base case losses [gwh] % error bus 6 2,33 2,2 5,88 bus 11 2,15 2,4 -10,55 bus 12 2,92 2,81 4,09 bus 17 3,01 3 0,44 4.4 proposed method the active and reactive power hourly demand curves at the head of the feeder in year 0 without dg, are sorted in descending order of active power. thus demand scenarios are calculated (table 13). for analysis with dg the same generation scenarios of 3g-3d method are considered (table 6). the 9 power flows resulting from each stage of generation and the three demand scenarios are then made. the method results for loss estimation without dg are shown 456 j.e. mendoza, m. lopez, s. fingerhuth, f. carvajal, g. zuñiga table 12: savings in energy losses. monthly blocks method bus savings [gwh] base case savings [gwh] % error 6 1,64 1,57 4,2 11 1,82 1,37 32,7 12 1,05 0,97 8,33 17 0,95 0,77 23,57 in table 14, which shows that the method is very accurate in the estimation. the results along the 5 years of analysis of the method for estimating losses with dg are shown in table 15. the method results in estimating loss savings over 5 years of analysis for each injection point are shown in table 16. table 13: demand scenarios proposed method demand ai bi ti pi qi maximal 9631 4348 1460 6,597 2,978 average 21571 9840 4745 4,546 2,074 minimal 6776 3173 2555 2,652 1,242 table 14: comparison of total losses without dg. proposed method losses m1 [gwh] losses cb [gwh] % error 3,92 3,77 3.98 4.5 discussion regarding viera-bonessi method, it considers that the dg always inject its rated power, so the scenarios proposed for power flows are not representative of what actually take place. moreover, it shows the condition of flow reversal in the substation; but this condition never happens in the base case, so the results are far from the expected values. therefore it is considered that the viera-bonessi method is not reliable, especially when the rated value of the dg is much higher than its usual power injection. therefore, for comparison viera-bonessi method is discarded and we will proceed to an analysis of the results of the three remaining methods, to determine which is the best. to determine which method is better, errors in estimating losses without dg, losses with dg and feeder energy losses savings due to dg operation are compared. table 17 shows errors of each method in estimating losses without dg. it is noted that in estimating losses without dg, the proposed method is winner. the errors in estimating losses with dg are shown in table 18 for each evaluated method. the proposed method has a clear advantage over the rest in the average values (absolute) and their standard deviations also. table 19 shows the victorious method for each connection point for the dg, considering the estimated savings in losses. it can be seen that the proposed method obtains fewer errors compared to other evaluated methods, so it can be considered the winner. comparative study of methods for estimating technical losses in distribution systems with distributed generation 457 table 15: losses with dg comparison. proposed method bus losses [gwh] base case losses [gwh] % error 6 2,38 2,2 8,2 11 2,29 2,4 -4,45 12 2,94 2,81 4,67 17 3,05 3 1,74 table 16: savings in energy losses. proposed method barra savings [gwh] base case savings [gwh] % error 6 1,53 1,57 -2,37 11 1,62 1,37 18,24 12 0,98 0,97 1,27 17 0,86 0,77 11,8 table 17: error of methods in estimation of losses without gd method error [%] 3g-3d 5,77 monthly blocks 5,18 proposed 3,8 table 18: porcentual error of methods in estimation of losses with dg method bus 6 bus 11 bus 12 bus 17 average/ deviation 3g-3d 7,54 -5,67 5,19 2,25 5.16 / 4.79 monthly blocks 5,88 -10,55 4,09 0,44 5.24 / 4.20 proposed 8,2 -4,45 4,67 1,74 4.76 / 2.64 table 19: error of methods in energy losses savings for each dg connection point method bus 6 bus 11 bus 12 bus 17 3g-3d 3,28 25,77 7,44 19,42 monthly blocks 4,2 32,7 8,33 23,57 proposed -2,37 18,24 1,27 11,8 458 j.e. mendoza, m. lopez, s. fingerhuth, f. carvajal, g. zuñiga 5 conclusion this study addressed the problem of estimating technical losses of a radial feeder when incorporating a dg. for this a test system was chosen in which 4 connection points were tested, to evaluate different methods of loss assessment. as for the method validation, is can be concluded that the viera-bonessi method is unreliable for plants where rated power is far from the power that usually provide. for the other methods, it is demonstrated first that their error varies according to the point of injection; second, an hypothesis arises: the error in saving energy loss increases as the gd injection point is further away from the feeder head; this due to error values obtained when the generator is connected to the feeder center points (specifically on the buses 6 and 12), are lower than when the dg is connected to the endpoints (buses 11 and 17). the analysis determines that the proposed method provides the best results by combining the best aspects of 3g-3d and monthly blocks method, although it involves more computational effort than the previous methods. the proposed method is a valuable technique that can be easily programmed for its implementation in a decision support system that will assist the decision maker for a fast, accurate and reliable control, operation and planning of the distribution network. acknowledgement the authors wish to thank the national scientific and technological research commission of the chilean government (conicyt) for the financial support given through fondecyt regular project no. 1120178. bibliography [1] a. bayod-rjula (2009); future development of the electricity systems with distributed generation, energy, 34: 377-383. 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[13] filip, f.g.; (2012) a decision-making perspective for designing and building information systems, int j comput commun, issn 1841-9836, 7(2):264-272. international journal of computers communications & control issn 1841-9836, 10(2):222-229, april, 2015. logging for cloud computing forensic systems a. pătraşcu, v.v. patriciu alecsandru pătraşcu* 1. military technical academy, computer science department 39-40 george coşbuc street, bucharest, romania alecsandru.patrascu@gmail.com 2. advanced technologies institute 10 dinu vintila, district 2, 021102, bucharest, romania ati@dcti.ro *corresponding author: ati@dcti.ro victor valeriu patriciu military technical academy, computer science department 39-40 george coşbuc street, bucharest, romania victorpatriciu@yahoo.com abstract: cloud computing represents a different paradigm in the field of distributed computing that involves more and more researchers. we can see in this context the need to know exactly where, when and how a piece of data is processed or stored. compared with classic digital forensic, the field of cloud forensic has a lot of difficulties because data is not stored on a single place and furthermore it implies the use of virtualization technologies. in this paper we present a new method of monitoring activity in cloud computing environments and datacenters by running a secure cloud forensic framework. we talk in detail about the capabilities that such system must have and we propose an architecture for it. for testing and results we have implemented this solution to our previous developed cloud computing system. keywords: cloud computing; data forensics; logging framework; distributed computing; binary diff 1 introduction cloud computing to put it simply, means internet computing. it is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. the cloud computing model offers the promise of massive cost savings combined with increased it agility. it is considered critical that government and industry begin adoption of this technology in response to difficult economic constraints. however, cloud computing technology challenges many traditional approaches to datacenter and enterprise application design and management. cloud computing is currently being used. however, security, interoperability, and portability are cited as major barriers to broader adoption. in this context, a new need for it experts is increasing: the need to know exactly how, where and in what condition is the data from the cloud stored, processed and delivered to the clients. we can say with great confidence that cloud computing forensics has become more and more a need in todays distributed digital world. in this paper we are going to present a new way in which we can integrate a full forensics framework on top of a new or existing cloud infrastructure. we will talk about the architecture behind it and we will present its advantages for the entire cloud computing community. we will present also the impact that our technology proposal will have on existing cloud infrastructures copyright © 2006-2015 by ccc publications logging for cloud computing forensic systems 223 and as a proof of concept we will present some particular implementation details over our own cloud computing framework that we have already developed in [6]. the rest of the document is structured as follows. in section 2 we present some of the related work in this field, that is linked with our topic and in section 3 we present in detail our proposed cloud forensics logging framework. section 4 is dedicated to presenting our results from our implementation made so far, and in section 5 we conclude our document. 2 related work the integration of cloud computing logging in the field of forensics is not new and we find thesis in this directions, such as the one of zawoad et al [1] which present an architecture for a secure cloud logging service that collects information from various sources around the datacenter, both software (hypervisors) and hardware (network equipments)in order to create a complete image of the operations done in a datacenter. the same challenges are evidenced by marty [2] and sibiya et al [3]. in their papers they present a perspective over a custom logging framework and talk about the way in which forensics investigators can be provided with reliable and secure data using a standardized way. they propose using a single centralized log collector and processor, in order to save business’s and users time. in order to face the many challenges involving digital forensics in general, but also to take benefits from the opportunities cloud computing is offering, we have to rethink most of the classic network established principles and re-organize well-known workflows, even include and use tools not previously considered viable for forensic use, such as machine learning or large scale computing. furthermore we must submit to the classic digital forensic main rule and keep all digital evidence intact. all of our investigation is done on a digital copy of the original data. in our previous research [10] we have also focused on choosing a proper data representation format that will be used between the modules of our framework and between the modules and the central forensic processing core. in the next paragraphs we present a brief comparison of two existing proposals in this directions, that are applicable in our context. the first one is the “management metalanguage” [12] proposed by the unixware community. its advantage is that it can be used as a transparent api in the kernel modules as it provides an interface for an external host. the downside is that it needs a lot of auxiliary binary data to be sent in order to re-create the entire picture at the other end, and using it we get quickly a traffic larger than the one that can be obtained by sending only the basic snapshots. on the other side, the cee (common event expression) organization [11] proposes a set of specifications using the json and xml markup languages for event logging on disk or in transit over a network. these requirements are designed for maximum interoperability with existing event and interchange standards to minimize adoption costs. the advantage of this approach is that cee expresses its interfaces and does not promote an actual implementation. after thoroughly analyzing these two proposals we have chosen to use a combination between them, meaning that we want to full details that the management metalanguage encapsulates, under the form of json data representation. 3 logging framework architecture in the following section we will talk about the top view architecture of our cloud computing enabled forensic system. we will present the main building blocks and modules and then we 224 a. pătraşcu, v.v. patriciu focus on the logging sub-system. the entire architecture will follow also the perspective from the forensic investigator part. 3.1 general forensics architecture the framework presented in this paper has a modular architecture and each of the modules is presented in the following paragraphs in detail. it is also easy to see that the entire framework can be extended with other modules or plugins to support various workloads and even processing elements. in order to have a working platform, we must first introduce the concept of a cloud computing framework. in figure 1 we can see that the top view of a cloud computing framework contains two main layers: a virtualization layer and a management layer. figure 1: basic cloud computing architecture figure 2: forensic enabled cloud architecture in the virtualization layer we find the actual platforms/servers that host the virtual machines and have virtualization enabled hardware. in the management layer we find the modules responsible for enabling the entire operations specific to the cloud. these modules are, in order: security (responsible with all security concerns related to the cloud system intrusion detection and alarming module), validation engine (receives requests to add new jobs to be processed), virtual jobs (creates an abstraction between the data requested by the user and the payload that must be delivered to the cloud system), scheduler (schedules the jobs to the virtualization layer), hypervisor interface (acts like a translation layer that is specific to a virtualization software vendor), load distribution (responsible with horizontal and vertical scaling of the requests received from the scheduler), internal cloud api (intended as a link between the virtualization layer and the cloud system), external cloud api (offers a way to the user for interacting with the system). now that the notion of a cloud computing framework was presented, we will talk about the modifications that must be made to it in order to create an forensic enabled cloud computing architecture. as can be seen in figure 2 the modification affects all the existing modules and includes two new modules, the cloud forensic module and the cloud forensic interface. their main goal is to gather all forensic and log data from the virtual machines that are running inside the virtualization layer and it represents the interface between the legal forensic investigator and the monitored virtual machines. the investigator has the possibility to monitor one or more virtual machine for a targeted user for a specific amount of time. 3.2 cloud logging architecture in this section we present how our framework is working and how it is created in order to run on top of new or existing cloud computing infrastructures. as example for it we will logging for cloud computing forensic systems 225 present the integration with our previously implemented cloud computing framework. the cloud architecture presented in our previous work makes use of the concept of leases, in which we can specify the amount of time the job must run, or specify between what hours in a day it is running. figure 3: cloud forensics logging framework the cloud logging system architecture is a layered one, containing five layers, each with its own purpose. we will present each of them in detail in the following paragraphs. we also used a graphical representation, in figure 3, where the whole layers and the relationship between them can be seen. the layers are all implemented using the distributed computing paradigm and they represent jobs in our cloud computing environment. the first layer, as presented in our previous work [4], represents the management layer in a cloud computing deployment. all the modules that are responsible with all cloud specific operations can be found at this level, together with the forensic targeted ones, such as "cloud forensic module". the second layer represents the virtualization layer in a cloud computing implementation. at this level we can find the workstations and servers that host the virtual machines. the fact that the main building blocks are represented by the virtual machines, the hardware must also have virtualization enabled. inside the cloud forensic interface, a dedicated “local logging module” must be installed into the existing physical machine. it is responsible with the raw data gathering from the monitored virtual machines. the data quantity can be adjusted by the investigator and he can choose to monitor a particular virtual machine or monitor the entire activity existing inside that machine. in order to gather data reliably from the virtual machines the local logging module must be integrated fully with the running hypervisor inside the physical machine. in this paper we focus on the integration with the “kvm” virtualization technology that exists in modern linux kernel releases. we have chosen it because it is a full open-source virtualization solution, integrated with the linux kernel since 2007 and it is actively used by many companies across the world. an important thing that must be taken in consideration is what data are we intercepting from the virtual machine and send it to further processing. since all the activity can be intercepted, there is the risk of severe time penalties and processing speed. in order to solve this problem, at this point we will offer the possibility for an investigator to choose the logging level for a certain virtual machine. this is helpful considering that, for example, an investigator only wants to analyze the virtual memory for its contents, and it is not interested in virtual disk images or virtual network activity. also at this step we must consider the problem of network transmission overhead. the third layer represents a storage layer for the raw data sent from the local logging modules existing in the virtualization layer. the logging modules will send raw data, in the 226 a. pătraşcu, v.v. patriciu form they are gathered from the hypervisor. thus, this layer has the function of a distributed storage and it contains a series of nodes, each running a database. we have chosen this approach in order to create a flexible and scalable layer architecture that can face the data traffic coming from the upper layer. since the data that is going to be sent from the physical virtualization host to the central forensic management unit can reach important size, we will implement a mechanism of “diff” between two pieces of data. for example, if an investigator will want to analyze a virtual machine memory over a period, the local forensic module will sent only one initial memory snapshot and after that only what has been changed will be sent. of course we can use the full potential of the host and provide a local aggregation module that will pre-process the data collected before sending it to the central forensic module. this approach is new to the field of cloud computing forensics and we consider it a great way to reduce the impact over the network. the process will run in the following manner. initially the logging modules will send a reference file and then, at an user defined time period, the modules will send a delta file, that represents the difference between the previous reference file and the current state. thus, it will implement a snapshot mechanism at the hypervisor level. we have chosen this approach because we want to offer to the forensic investigator the possibility to have an image of what is happening inside a virtual machine between two snapshots. this feature is currently not available in other hypervisors, such as vmware’s; in their case we can have a snapshot at time t0 and one at time ti, but we cannot know the state of the virtual machine between the 0 and i step. this layer has also another purpose. in case of extreme emergency, the forensic investigator can “see” a real-time evolution of the monitored virtual machine by issuing a direct connection to this layer. this feature is made available through the cloud forensic module, which has the ability to by-pass normal raw data processing. the fourth layer has the purpose of analyzing, ordering, processing and aggregating the data stored in the previous layer. since all these steps are computing intensive, the entire analysis process will be made in an offline manner and will be available to the investigators as soon as the job is ready. after this entire process the investigator will have a full image of what happened over the monitored remote virtual machine in a manner such as the one encountered in software source code version tools, thus permitting him to navigate back and forth into the history of the virtual machine. this layer is implemented also as a distributed computing applications. we have chosen this approach due to the processing power needs that our framework demands, more exactly it needs to do correlations between different snapshots in a fair amount of time. finally, the fifth layer represents the storage of the results published by the previous layer. an forensic investigator will interact with the monitored virtual machine snapshots at this layer, by using the cloud forensic module from the management layer. 4 results in this section we are going to present details regarding the results collected after the implementation of our cloud logging modules. 4.1 network configuration for testing, the modules have been implemented and split across multiple workstations, as can be seen in figure 4. they are represented as a cluster of servers, each having the functionality presented in detail in the architecture section. as it can be seen, the entire modules found in the dotted perimeter, logging for cloud computing forensic systems 227 figure 4: mapping modules to workstations. called “cloud computing forensic system”, can also be ran all on one workstation. elements like network switches are not represented in order not to burden the graphic, but the ip address of the hosts are kept. in our configuration we have used three distinct workstation, each having the functionalities and network addresses presented in the figure. the hardware platform used was composed from an amd phenom ii x6, 6 cores, 8gb ram, raid0 configured hard-disks running kvm as hypervisor and qemu as a hypervisor interface, an intel dualcore, 4gb ram as the storage layer and an amd c-60 dualcore, 4gb ram as the management layer. the network used is 10/100 mb. 4.2 experimental results the experiments were made using kvm as a hypervisor and qemu and libvirt as drivers for the hypervisor. the tests had the target set on the virtual machine used memory (ram snapshot) and the virtual machine storage (disk snapshot). the process of recording the virtual machine activity was made over a period of several hours, at a time step of 10 minutes. the cpu load when conducting records using all the 6 cores was about 20%. the results are interesting, if we take in consideration the technologies used internally by kvm. for example, ram snapshots are made entirely from host machine ram and do not contain necessarily consecutive ram location. nevertheless, in our experiments the ram snapshots were the largest, reaching even gigabytes in size. bellow you can see the actual ram tests that were made. we have split the tests in two distinct zones, one up to 100 mb and one after this barrier. table 1 and figure 5 presents the data collected from our modules and the time needed to process it. the transfer time between 228 a. pătraşcu, v.v. patriciu the cloud forensic interface module and the storage module is not taken in consideration, as being a constant time, of about 82 seconds for a 800 mb file. table 2 and figure 6 presents the data collected from our modules and the time needed to process it. table 1: tests up to 100 mb in size size (kb) time (ms) 4 296 454 517 1227 1136 5505 4929 10813 8000 table 2: tests over 100 mb in size size (kb) time (ms) 108036 58982 740032 401156 4251346 2277855 figure 5: tests up to 100 mb in size. figure 6: tests over 100 mb in size. 5 conclusion as we have seen in this paper, the topic of cloud computing forensics is very large and poses great challenges in the field of logging. due to the fact that together with incident response they represent a new field for research, more and more scientists are trying to develop new methods for assuring security in cloud systems. furthermore, as the environment is purely distributed offers new fields of development much larger than a regular workstation. in this paper we presented a novel solution that provides to the digital forensic investigators a reliable and secure method in which they can monitor user activity over a cloud infrastructure. our approach takes the form of a complete framework on top of an existing cloud infrastructure and we have described each of its layers and characteristics. furthermore, the experimental results prove its efficiency and performance. acknowledgment this paper has been financially supported within the project entitled "horizon 2020 doctoral and postdoctoral studies: promoting the national interest through excellence, competitiveness and responsibility in the field of romanian fundamental and applied scientific research", contract number posdru/159/1.5/s/140106. this project is co-financed by european social fund through the sectoral operational programme for human resources development 2007 2013. investing in people! logging for cloud computing forensic systems 229 bibliography [1] s. zawoad, a.k. dutta and r. hasan (2013); seclaas: secure logging-as-a-service for cloud forensics, in acm symposium on information, computer and communications security, doi: 10.1145/2484313.2484342, 219-230. 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[11] http://cee.mitre.org/language/1.0-beta1/cls.html [12] http://uw714doc.sco.com/en/udi_spec/m_mgmt.html international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 2, month: april, year: 2021 article number: 4115, https://doi.org/10.15837/ijccc.2021.2.4115 ccc publications extract executable action sequences from natural language instructions based on dqn for medical service robots f. d. zhao, z. k. yang, x. s. li, d. d. guo, h. t. li fengda zhao 1. school of information science and engineering yanshan university qinhuangdao 066004, china 2. xinjiang university of science and technology korla 841000, china 3. the key laboratory for software engineering of hebei province yanshan university qinhuangdao 066004, china zfd@ysu.edu.cn zhikai yang, dingding guo, haitao li 1. school of information science and engineering yanshan university qinhuangdao 066004, china 2. the key laboratory for software engineering of hebei province yanshan university qinhuangdao 066004, china ysuyangzhikai@stumail.ysu.edu.cn guodingding@ysu.edu.cn xjlht@ysu.edu.cn xianshan li* 1. school of information science and engineering yanshan university qinhuangdao 066004, china 2. the key laboratory for software engineering of hebei province yanshan university qinhuangdao 066004, china *corresponding author: xjlxs@ysu.edu.cn abstract the emergence and popularization of medical robots bring great convenience to doctors in treating patients. the core of medical robots is the interaction and cooperation between doctors and robots, so it is crucial to design a simple and stable human-robots interaction system for medical robots. language is the most convenient way for people to communicate with each other, so in this paper, a dqn agent based on long-short term memory (lstm) and attention mechanism is https://doi.org/10.15837/ijccc.2021.2.4115 2 proposed to enable the robots to extract executable action sequences from doctors’ natural language instructions. for this, our agent should be able to complete two related tasks: 1) extracting action names from instructions. 2) extracting action arguments according to the extracted action names. we evaluate our agent on three datasets composed of texts with an average length of 49.95, 209.34, 417.17 words respectively. the results show that our agent can perform better than similar agents. and our agent has a better ability to handle long texts than previous works. keywords: medical robots, human-robots interaction, dqn agent, attention mechanism, longshort term memory. 1 introduction in recent years, medical robots have become a hot spot for the development of the robotics industry and the medical industry, they are able to help doctors treat patients more efficiently. for example, surgical robots can be used for surgical image guidance and minimally invasive surgery, rehabilitation robots can be used to recover from limb injuries, assist the disabled to complete actions and to improve their athletic ability, auxiliary robots can be used to assist doctors to diagnose the patient’s condition and make a real-time observation on the health condition of the elderly [1], service robots can be used to move medical supplies and clean up medical waste in isolation wards to reduce the risk of infection among medical staff. one of the core technologies is the interaction between humans and robots, but traditional robots with keyboard-and-mouse or touch-screen interfaces are not easy to do this. thus, it is critical to provide a simple way for doctors to interact with and control robots. people use language every day to direct behavior, ask for help, and communicate with others. thus, more and more researchers are devoted to enable robots to interact with users by natural language. one of the bases of it is to let robots understand the natural language instructions given by users. over the past five years, a large number of methods based on semantic framework and sequence to sequence models were applied to this task [2, 3]. reinforcement learning (rl) [4] has been received much attention because of its excellent performance in areas such as mobile robots navigation [5, 6], decision-making system [7, 8]. recently, reinforcement learning has been a viable and powerful approach in the area of natural language processing (nlp). in 2018, feng et al. [9] presented an intelligent agent named easdrl to extract executable action sequences from free and long natural language instructions. however, easdrl is a deep q-network (dqn) agent based on convolutional neural networks (cnn), resulting in the difficulty for the agent to understand the complex context of the actions in instructions and it becomes unstable when the instruction is too long because it is not able to solve the long-range dependencies problem of texts. in this paper, we propose a novel dqn agent (as shown in figure 1) based on bidirectional lstm (bilstm) [10] and attention mechanisms to solve the above problems. lstm is proposed to solve the problem of long-range dependencies in text. it has a better ability to remember long-term information than traditional neural networks, so lstms are more suitable for processing long texts than traditional neural networks. although lstms have the ability to process long text information, it seems not enough because of their forgetfulness. in this paper, the models are trained on natural language instructions composed of hundreds of words and phrases. thus, we apply attention mechanisms to our dqn agent to reduce the impact of the loss of features on our experimental results. as a core technology, attention mechanisms have been widely used in the field of natural language processing (nlp). they are first used in machine translation tasks to solve the problem of long-range dependencies of traditional seq2seq models by using global text information to compute the output of the encoder. we evaluate the performance of our agent on three datasets, i.e., "microsoft windows help and support" (whs) documents [11], and two datasets "wikihow home and garden"(whg) and "cookingtutorial"(ct) labeled by [9]. the results of the experiments show that our dqn agent based on bilstm and attention mechanism performs better than existing similar approaches. our major contributions are as follows: • compared with the deep learning-based methods, our method based on deep reinforcement learning has a better ability to extract action sequences from the text that contains multi actions. https://doi.org/10.15837/ijccc.2021.2.4115 3 • we build our agent based on bilstm and attention mechanisms in order to solve the problem of long-range dependencies of long texts and fully utilize the term context relations in text. thus, our agent has a better ability to extract action sequences from long texts than the agents presented by previous works. cover your mouth and nose with an n-95 respirator to avoid breathing in spores. wear elbow-length rubber or latex gloves to protect your hands from mold . cover your eyes with goggles to protect them from airborne spores. clean hard surfaces. mix equal parts water and detergent agent extract action sequences extractable sequences cover ( mouse/nose, n-95) sequence 1 wear (elbow-length rubber) cover ( eyes, goggles) clean (hard surfaces) mix ( detergent,watch ) sequence 2 cover ( mouse/nose, n-95) wear ( latex gloves) cover ( eyes, goggles) clean (hard surfaces) mix ( detergent,watch ) doctor figure 1: our agent extracts action sequences from natural language instructions, including action names and their arguments (between parentheses). there are two candidate sequences extracted by the instruction given by the user in the figure because the action "wear(elbow-length rubber)" and "wear(latex gloves)" is exclusive, meaning that the agent could extract only one of them. 2 related work in this section, we review the literature on deep learning-based and reinforcement learning-based methods. 2.1 deep learning-based methods deep learning is widely used in the areas such as behavioral recognition [13], medical assistance [14] and computer version [15] by the way of building deep neural networks. using recurrent neural networks for semantic slot filling is one of the most popular deep learning-based methods to extract action sequences. in 2015, bastianelli et al. [2] proposed a corpus named human-robot interaction corpus (huric) based on frame semantics [12]. in 2018, they presented a method to semantically parse natural language robotic commands from the huric corpus using a multi-layer lstm network with attention layers. recently, language models with an encoder-decoder structure such as transformer [16] and bert [17] are used in this task. in 2020, li et al. [18] proposed a transformer-based phrase tuple extraction model to extract action phrase tuples from natural language instructions and a transformer-based grounding model to connect the action phrase tuples extracted by previous works to executable actions. however, the core of these semantic framework-based methods is designing an action framework for each action that robots can execute, resulting in restrictions on the functions of robots. 2.2 reinforcement learning-based methods reinforcement learning (rl) is the study of how an agent can interact with its environment to learn a policy that maximizes expected cumulative rewards for a task. recently, rl for nlp has experienced great attention because of the excellent results in areas such as dialogue systems [19], neural machine translation [21] and text generation [22]. https://doi.org/10.15837/ijccc.2021.2.4115 4 in 2009, branavan et al. [11] presented a reinforcement learning approach for mapping natural language instructions contained in microsoft windows help and support documents to sequences of executable actions and executed them in the windows 2000 environment. in 2020, zhuo et al. [20] presented a novel deep reinforcement learning framework named federated deep reinforcement learning (fedrl) that is able to also work well when the feature space of the agent is small and the training data is limited. the fedrl framework is applied to the task presented by [9] and had a better performance. 3 our agent our task is to train a dqn agent based on bilstm and attention mechanisms for the purpose of extracting executable action sequences from natural language instructions. in this paper, word tag is used for taking apart of the meaning of "action" in reinforcement learning. given a sequence of words x = (x1,x2...,xn) , our goal is to predict a sequence of tags tag = ( tag1, tag2, ..., tagn) on x, where tag∈[ select, eliminate]. when predicting action names or arguments, tagi = select means that xi is predicted as an action name or action argument, and tagi = eliminate means that xi isn’t predicted as an action name or action argument. our aim is to learn two models to extract action names and their arguments. the two models are f1(tag|x; ω1),x ∈d (1) f2(tag|x; a; ω2),x ∈d (2) where x is the instruction contained in our training set d, ω1 and ω2 are parameters to be learned for predicting action names and action arguments, a is an action name predicted by f1. our task can be described as a markov decision process (mdp) and defined as the tuple (s,tag,t, γ,r), s is the state space, tag is the action space of our intelligent agent, t is a transition matrix, γ is a discount factor used for measuring the importance of the feature and immediate rewards. the goal of our work is to find a policy π : s → tag to select a tag according to the current state. we can extract the action sequences from given texts by choosing tags for the words in the given text according to the current state represented by the text. after applying the tag to the current state, a new state will be generated. this process will be executed repeatedly until each word of the given text is tagged. the core of our works is two q-functions: q(s,tag) for action names extraction task and q(ŝ, tag) for action arguments extraction task, we can achieve the above-mentioned process by updating these q-functions according to the bellman equation: qi+1(st, tagt) = e[r + γmaxtag′qi(st+1, tagt+1)],st ∈s, tagt ∈ tag (3) qi+1(ŝt, tagt) = e[r + γmaxtag′qi(ŝt+1, tagt+1)], ŝt ∈s, tagt ∈ tag (4) in this section, firstly, we introduce how to process the words that compose the text. then, the approach of building state and predicting tag for each word given text x is proposed. finally, our deep q-networks and how to predict tag for the x are introduced. 3.1 word embedding word embeddings are widely-adopted technologies for nlp tasks by the way of representing a word by a fix-length vector. in this paper, we represent a text x by a sequence of vectors (w1,w2, ...,wn), where wi∈r50 is a 50-dimension real-valued vector, trained by word2vec model, representing the ith word in x. in addition, a { word : index} dictionary with the length n+2 is used for storing each different word in our datasets, where n is the number of different words in our datasets. the first word in the dictionary is pad and the last word in the dictionary is unk which represents the word not contained in the vocabulary of the word2vec model. https://doi.org/10.15837/ijccc.2021.2.4115 5 3.2 state in this subsection, the approach of representing the state s from texts is introduced. for action names extraction, we define the state s as a tuple ( word_ind, word _pos, tag) , where word_ind is the index of the word according to the {word : index} dictionary defined by previous work and word _pos is the part-of-speech tagged by stanfordpostagger. for example, considering the text "cover your nose with a respirator...", after selecting tag1 = select on state st and a state st+1 will be generated. the transition of state s from time step t to time step t+1 is as shown in figure 2. we initialize the tag of each word to null, which means the word has not been assigned a tag by the agent. for action arguments extraction, we use xa to denote the word which represents the action name, and we define the state as a tuple ( word_ind, dis, word_pos, tag), where dis = ( d1, d2, ..., dn), where di = |i−a| is used for representing the distance between x_a and other words in text. cover your nose with a ... word_ind word_pos word_tag word_ind word_pos word_tag tag1=select respirator figure 2: the transition of state from time step t to time step t+1 for action name extraction 3.3 our deep q-network in our work, two deep neural networks based on bilstm and attention mechanisms q(st, tagt,ω1) and q(ŝt, tagt,ω2) are used for representing the q-functions q(s,tag) and q(ŝ, tag) approximately. the goal of our work is to extract action sequences from free and long texts, it requires our agent to be able to solve the long-range dependencies problem and understand the complex context of actions, but the traditional deep q-networks based on cnn or rnns are unable to do anything about these. thus, we use lstm-based network to build our action names extraction model f1 and action arguments extraction model f2. our model for action names extraction f1 is shown as figure 3. assuming that we have already known the tag of x_1, x_2, ..., x_i-1, we introduce how to predict tag_i, the tag of x_i. first, we use two 50-dimension real-valued vectors to represent the tag (tag_embedding in figure 3) and the part-of-speech (pos_embedding in figure 3) of xi, and they are concatenated with the word embedding of xi as the input of forward lstm layer and backward lstm layer. input = concat(word_embedding,pos_embedding, tag_embedding) (5) second, the outputs of the forward lstm layer and backward lstm layer are fed into the attention layer separately and the outputs of attention layers are concatenated as a vector. −→ l = −−−−→ lstm(input) ←− l = ←−−−− lstm(input) (6) l = concat(attention( −→ l),attention( ←− l)) (7) https://doi.org/10.15837/ijccc.2021.2.4115 6 where −→ l is the output of forward lstm layer, ←− l is the output of backward lstm layer. third, we apply two conv1d layers to extract features from the vector. and we get the output of the network, which contains the expectation of reward value after performing every possible tag according to the current state. output = dense(conv(l)) (8) finally, we use an argmax function to choose tagi+1 according to the output of the network. tagt+1 = argmaxtag(output) (9) for action arguments extraction, a 50-dimension real-valued vector is used for representing the distance between action name xa and other words, and the input of our action arguments extraction model f2 is input = concat(word_embedding,dis_embedding,pos_embedding, tag_embedding) (10) figure 3: our lstm-based dqn agent for action names extraction 3.4 reward we use the same approach as [9]. the reward of our agent is composed of two parts: a basic reward and an additional reward. the basic reward is given when the agent predicts a correct tag for a word in the given text, and the additional reward is given when the agent predicts a correct tag for the word that represents a special action type. the reward of our agent is calculated as the following equation: r = { αrb + ra if current word represents a special action type αrb otherwise (11) where rb in the equation represents the basic reward and the ra in the equation represents the additional reward. the α in the equation is a factor that is different for different types of action. 3.5 training to learn the parameters ω1 and ω2 of the two models, we first store transitions 〈s, tag,r,s′〉 and 〈ŝ, tag,r, ŝ′〉 in replay memory ω and ω̂ respectively and exploit a mini-batch strategy. where s is the https://doi.org/10.15837/ijccc.2021.2.4115 7 state representation for action names extraction model f1 and ŝ is the state representation for action arguments extraction model f2. algorithm 1 shows the process that how to train the action names extraction model f1. if we want to train the action arguments extraction model f2, we only need to replace s, ω and ω1 with ŝ, ω̂ and ω2. we are able to build the q-function q(s,tag; ω1), an expectation of reward value to be obtained after the tag is selected at state s with algorithm 1. we update our q-function by the temporaldifference (td) algorithm during training : q(st, tagt,ω1)←q(st, tagt,ω1) + α(rt+1 + γ max tag q(st+1, tag,ω1) −q(st, tagt,ω1)) (12) where the right-side q(st, tagt,ω1) is the current tag-value, α is learning ratio, γ is a discount factor, and maxtag q(st+1, tag,ω1) is a maximum estimated tag-value for state s_t+1. first, our agent predicts a sequence of tag = (tag1, tag2, ..., tagn) for a text by maximizing the q-function. then we can use this tag sequence to generate action names and use the action names to build q(ŝ, tag; ω2) with the replay memory ω̂ and the same approach as algorithm 1. finally, we can use the words extracted by these models to compose the action sequences that robots can execute. algorithm 1 algorithm for extracting action names from text input: training set d output: the weights ω1 initialize replay memory ω initialize evaluate network with random weights ω1 initialize the target network with weights ω−1 = ω1 initialize the number of epoch to be h initialize the number of time step of every epoch to be n initialize the number of time step that reset the target network’s weights to be c for epoch = 1 to h do for each text x ∈ φ do generate the initial state s1 based on x for τ = 1 to n do with probability ε select a random tag tagτ otherwise select tagτ= argmaxtagq(sτ, tag; ω1) perform tagτ on sτ to generate sτ+1 and calculate reward rτ store transition (sτ,tagτ,rτ,sτ+1) in ω sample a minibatch of transitions (sj,tagj,rj,sj+1) from ω set yj = { rj if s_j+1 is terminal state rj + γ maxtag′q(sj+1, tag′; ω1) otherwise update ω1 based on loss function l(ω1) = e(s_t,tag_t,r_t+1,s_t+1)∈ω[(yj −q(st, tagt; ω1))2] every c steps reset ω−1 = ω1 end for end for end for return the weights ω1 4 experiments we evaluated our agent on three datasets, i.e., "microsoft windows help and support" (whs) documents, and two datasets labeled by [9]. the details of these datasets are presented in table 1. https://doi.org/10.15837/ijccc.2021.2.4115 8 for evaluation, we first feed texts to models to predict a sequence of tag = (tag1, tag2, ..., tagn). we then compare the predicted tag of each word to its corresponding annotation. we use #num_truth for representing the number of words which are action names or action arguments according to the annotations in datasets, #num_tagged for representing the number of words tagged as an action name or action argument by our models, and #num_right for representing the number of words tagged correctly by our models. finally, we compute metrics: precision = #num_right#num_tagged, recall = #num_right #num_truth, f1 = 2×precision×recallprecision+recall . table 1: details of our datasets whs ct whg num. texts 154 116 150 average length of texts 49.98 209.34 417.17 vocab size 791 2885 7367 action name rate(%) 19.47 10.37 7.61 action argument rate (%) 15.45 7.44 6.30 in order to evaluate the effect of the attention mechanism on our experimental results, we use lstm networks with self-attention mechanism, feed-forward mechanism, and no attention mechanism to build our action names extraction model and action arguments extraction model. we use the following symbols to represent these approaches. • agent-n: an agent builds its action names extraction model and action arguments extraction model based on bilstm only. • agent-f: an agent builds its action names extraction model and action arguments extraction model based on bilstm and feed-forward attention mechanism [23]. • agent-s: an agent builds its action names extraction model and action arguments extraction model based on bilstm and self-attention mechanism. then we compare these models with easdrl, a dqn agent presented by [9] that builds its action names extraction model and action arguments extraction model based on cnn. the input dimension for action names extraction model is set to be (100 × 4) and for action arguments extraction model is set to be (400 × 3), the batch size to be 32. the output shape of the forward-lstm layer and backward-lstm layer are set to be 256. and two cnn layers with kernels of size 1×3 and two fully-connected layers with the size of 512×256 and 256×2 are used. we adopt the nadam optimizer [24] with learning rate 0.001 and the relu activation for all layers. the size of replay memory was set to be 50000, discount factor γ to be 0.9. as experimental results shown in table 2, we can see that our agent can perform better than the easdrl, a dqn agent based on cnn, on both action names extraction task and action arguments extraction task. we can also see that the agent with attention mechanism performs better than all tasks especially for the action arguments extraction task of the cooking dataset and the wikihow dataset. as the average length of texts shown in table 1, we can know that our agent with attention mechanism can process long texts better than the agent without attention mechanism. besides, we use the word_ind to represent a word in the instructions instead of the word vector used in the state representation of [9], which reduces the cost of time in processing training data. in order to observe the influence of model complexity on our experimental results, we vary the output shape of the lstm layer from 128 to 512 with other parameters fixed. results are shown in figure 4. as shown in figure 4 (a), we can see that the performance of the action names extraction model can be improved obviously when the output shape of the lstm layer is increased from 128 to 256, especially for the wikihow dataset. and it has a little improvement when the output shape of https://doi.org/10.15837/ijccc.2021.2.4115 9 table 2: f1 scores of different models in extracting action names and action arguments action names action arguments method whs ct whg whs ct whg easdrl 93.46 84.18 75.40 95.07 74.80 75.02 agent-n 97.25 86.31 76.76 95.47 79.88 74.67 agent-f 97.75 86.44 79.09 95.55 80.49 75.95 agent-s 98.37 88.98 81.15 95.59 81.65 75.77 the lstm layer is increased from 256 to 512. for the action arguments extraction task, increasing the output shape of the lstm layer has little effect on the results as shown in figure 4 (b). gated recurrent units (gru) [25] is a novel approach that is proposed to solve the problem of long-range dependencies. compared with lstm, gru is able to get similar results with lstm but it is computationally cheaper. in order to observe the performance of the gru-based agent in our experiment, we build an agent based on bigru and self-attention mechanism and compared the performance with the agent based on bilstm and self-attention mechanism. f1 scores of the two agents are shown in table 3. 128 256 512 output shape of lstm 70 75 80 85 90 95 100 f1 f1 of action names extraction win2k cooking wikihow (a) 128 256 512 output shape of lstm 70 75 80 85 90 95 100 f1 f1 of action arguments extraction win2k cooking wikihow (b) figure 4: f1 scores of action names extraction task and action arguments extraction task of three datasets table 3: f1 scores of lstm-based and gru-based models in extracting action names and action arguments action names action arguments method whs ct whg whs ct whg bilstm+self-attention 98.37 88.98 81.15 95.59 81.65 75.70 bigru+self-attention 97.78 86.04 76.67 95.64 79.72 76.47 we also compare the f1 scores and loss values of extracting action names task for the cooking dataset. the results are shown in figure 5. the train_text_ind in figure 5 (a) and figure 5 (b) is the number of the instructions that have been fed in the action names extraction model. according to figure 5 and table 3, we can see that the gru-based models can be trained more quickly than the lstm-based models in our experiments, but as the f1 scores of action names extraction task shown in table 3, we can see that the lstm-based agent is able to perform better than the gru-based agent when the instruction is long. for the action arguments extraction task, it doesn’t have an obvious difference between the lstm-based agent and gru-based agent. https://doi.org/10.15837/ijccc.2021.2.4115 10 0 10 20 30 40 50 60 70 80 train_text_ind 0.0 0.2 0.4 0.6 0.8 f1 f1 scores of gru-based agent and lstm-based agent of cooking dataset gru lstm (a) f1 0 20 40 60 80 train_text_ind 0 200 400 600 800 1000 1200 1400 lo ss gru lstm (b) loss figure 5: f1 scores and loss values of action names extraction task of cooking dataset 5 conclusion barrier-free communication with doctors is an important development trend of medical robots in the future. thus, it is crucial for medical robots to understand the natural language instructions given by doctors and execute them correctly. in this paper, a dqn agent based on lstm and attention mechanism is presented to extract executable action sequences for the medical service robots. an action names extraction model and an action arguments extraction model with the same structure are built, and the whole action sequences are composed of the action names and action arguments extracted by them. the experimental results show that the agent presented by this paper can perform better than the similar agents proposed by previous work. especially because of the application of lstm and attention mechanism, our agent has a better ability to learn the long-range dependencies. thus, compared with the previous works, the performance of the agent presented in this paper has a significant improvement when the given instructions are long. pre-training language models such as convbert and electra presented recently have shown a significant improvement in language understanding than traditional methods based on lstm or rnn with attention mechanisms. so in future work, we will attempt the combination of deep reinforcement learning and pre-training language models to improve the performance of our agent. acknowledgment this work was supported by the key research and development program of hebei province grant no.18210336 and qinhuangdao city science, technology research and development plan grant no.202003b043 and xinjiang uygur autonomous region university scientific research project (key natural science project), xjedu2021i029. the authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the paper. references [1] băjenaru, l.; marinescu, i. a.; dobre, c.; drăghici, r.; herghelegiu, a. m.; rusu, a. 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(2014). empirical evaluation of gated recurrent neural networks on sequence modeling. corr abs/1412.3555, 2014. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: andonie, r.; dzitac, i. (2021). how to write a good paper in computer science and how will it be measured by isi web of science, international journal of computers communications & control, 15(5), 4115, 2021. https://doi.org/10.15837/ijccc.2021.2.4115 introduction related work deep learning-based methods reinforcement learning-based methods our agent word embedding state our deep q-network reward training experiments conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 4, month: august, year: 2023 article number: 5186, https://doi.org/10.15837/ijccc.2023.4.5186 ccc publications deep learning based semantic segmentation approach for automatic detection of brain tumor s. markkandeyan, shivani gupta, g. venkat narayanan, m jithender reddy, mahmoud ahmad al-khasawneh, mohammad ishrat, ajmeera kiran s. markkandeyan school of computing, sastra deemed university tirumalaisamudram, thanjavur, india. drsmkupt@gmail.com shivani gupta school of computer science and engineering, vellore institute of technology chennai, india gshivani554@gmail.com g. venkat narayanan st. joseph’s college of engineering kanchipuram, tamil nadu, 600119, india gvenkatnarayanan@gmail.com m jithender reddy vasavi college of engineering (a), hyderabad, india m.jitenderreddy@staff.vce.ac.in mahmoud ahmad al-khasawneh department of information technology, school of computing, skyline university college, university city sharjah sharjah, uae. mahmoud@outlook mohammad ishrat computer science and engineering, konerulakshmaiah education foundation vaddeswaram, ap, india. ishratgzp@gmail.com ajmeera kiran deptartment of computer science and engineering mlr institute of technology, dundigal, hyderabad, telangana, 500043, india. kiranphd.jntuh@gmail.com https://doi.org/10.15837/ijccc.2023.4.5186 2 abstract initially, frombrats 2013 dataset the input image is acquired and is preprocessed, segmented using convolutional neural network (cnn) based semantic segmentation, and were classified using improved multipath googlene tcnnclassifier approach.the stage of preprocessing is carried using bias corrected filtering. ascheme of deep learning-based semantic segmentation of brain tumors for mri images is proposed to classify the brain tumor effectively. in this approach, semantic segmentation is employed for segmentation purposes. improved multipath googlenetcnnis applied for classifying brain tumor for classifying brain mr images and for grading the brain tumors into three classes (meningioma, pituitary tumor, and glioma). there are a total of 3064 t1-weighted contrastenhanced pictures representing 233 patients in the input dataset.accuracy, sensitivity, specificity, &amp; precision estimates for the proposed method are calculated in matlab. the obtained results expose that the projected method achieves overall classifier performance rate of 99.7accuracy, sensitivity 100%, specificity 99.717%, and precision of 99.06%.. results show that the suggested system outperforms state-of-the-art methods. keywords: brain tumor, deep learning, artificial intelligence, semantic segmentation, mri images, googlenet cnn classification, convolutional neural network, biascorrected filtering 1 introduction it has long been known that cancer of the brain is particularly deadly. these tumours form in a broad array of brain areas critical to normal bodily processes. metastases from primary tumours to other parts of the brain evade detection by conventional imaging techniques. it may be challenging for people with brain cancer to find effective therapy because of the difficulty in identifying the origin of their disease. nowadays, the use of artificial intelligence approaches in image processing is a significant factor that improves accuracy. cancer rates have been climbing steadily over the last several decades. early cancer diagnosis is notoriously challenging. not knowing where to begin with therapy (radiation, chemotherapy, etc.) makes late cancer identification extremely dangerous, if not fatal. in recent days, (golatkar a, 2018) (n. ravikumar, 2018)as cad becomes more commonplace, fewer workers can do more. digital pathology is quickly becoming a powerful diagnostic tool. several problems in the area of processing medical images have been alleviated by the use of deep learning techniques in recent years (amin j., 2020). a technique of classification for the classification of brain tumors was presented, depending on the deep cnn. (carneiro g, 2017, gurusamyr, 2017., usman k, 2017., sajjad m, 2019., kharrat a, 2019., rathi v. g. p, 2015., rathi v. g. p, 2012). cnn is often regarded as the best method for resolving classification issues when utilizing data with high dimensions like images as input. these associations "learn" to recognise and make sense of images’ local context in order to extract and categorise data. (kharrat a, 2019., rathi v. g. p, rathi v. g. p, 2012). in this paper, abrain image analysis method was presented for the classificationof brain tumor type. in this technique, segmentation processing and preprocessing of images are used. moreover, brain cancer histopathology pictures are classified using deep convolutional neural networks. • to offer a preprocessing method that improves input picture quality and removes any noise from the dataset image. • semantic segmentation of the preprocessed picture is used in order to. • to present a deep learning-based googlenet classifier to separate the abnormal and normal stages. • to demonstrate the efficacy of the suggested scheme by estimating the proposed system’s performance. in section ii, we’ll talk about the many segmentation techniques that may be used to spot tumours in the brain. several steps are outlined in section iii for locating a brain tumour. in part iv, we assess the performance of the proposed technique and compare it to existing methods. in section v, an overview of the implemented approachis being presented. 2 related works this section provides an explicit explanation of various existing methodologies employed in breast cancer histology image analysis developed a model for the extraction of brain tumors from the 2d mri(magnetic resonance brain images) using thealgorithmfuzzy c-means clustering, followed by both outdated classifiers and deep learning methods suggested a new cnn model termedbrainmrnet. this structural design is built on module’s attention and hyper column system; it has anenduringstructure. initially, the image was preprocessed in the brainmrnet. after that, this step is being converted into consideration modules with the utilization of the image intensificationapproach on behalf of the entire image. the attention modules choose significant fields https://doi.org/10.15837/ijccc.2023.4.5186 3 of the image, and then the image was being transformed into the convolutional layers. one such considerable approach that the model of brainmrnet employed in the convolutional layers was the hypercolumn. using this method, the features retrieved at each stage within the brainmrnet models are kept consistent through the final layer’s array structure. the intention was selecting the most effective and best features, along with the supported features in the array. invasive ductal carcinoma (idc) of cancer of the breast (bca) in entire slide images (wsi) may be identified using a deep learning approach proposed by the brainmrnet system, which makes use of publicly available mri data. one method of deep learning is to simulate the learning process in a computer. this approach was motivated by the brain’s capability to process information at various levels of knowledge, or "levels," in order to predict the cancer transmissions score using breast cancer histology full slide pictures. using molecular information or mitotic counting, this method then delivers a completely automated conclusion for cancer prediction. there are three distinct parts to the construction, and they all collaborate to provide optimal functionality. using a combination of an image analyzing part capable of processing entire images of slide, a deep judgement-dependent detection of mitosis, as well as a propagation achieve forecasting component, we demonstrated the viability of a novel (cnn) depending prearranged regression framework for handling proceeding cells, inhomogeneous surrounding noises, and a massive variation in size and shape. the suggested approach supplied an innovative deep distributed convolutional neural network (cnn) that consists of two processes and needs just a few number of training photographs using weak observation (immediately a tick in the middle of the object). to begin, a basic retrieval strategy based on convolutional neural networks (cnn) was presented to find and localise mitotic possibilities while keeping the sensitivity high. a cross-domain transmission of information is used to refine a model of exact discrimination, which is then used to find and eliminate the brain cancer from the individual’s mri scan images. in this broadside, a method for brain tumor separation was beingestablished on the 2d-mri actualitiesthatconsent the tumor tissue documentation by means ofexcessivereproducibility and accuracy-related to manual processes. the brain tumor discovery’s key step is to spot the asymmetric and symmetric brain form that will label the indiscretion. on following this step, the subsequentphase is segmentation, whichwasmade on two methods morphological operation andftransform (fuzzy transform) developed a new automatic method based on the multi-segmentation of the brain tumor region. an enhanced algorithmregion-growing was based on quasi-monte carlo and we developed a new method for identifying and extracting the cancer area from human head examination mri images by combining the colour channels obtained by several modalities into a single rgb picture and utilising an expectationmaximization strategy to describe the most-preferred classes. combining nn with a c5.0 enhanced decision tree model with multimedia color channels and key features of an mri brain picture, the cleveland heart disease database may be used to provide accurate cad predictions suggested acompetent method for the investigation of regular and irregular states of the neuromuscular system presented a different technique of feature extraction and selection. this approach then classifies the tumour as grey matter, csf, white issue, normal, and abnormal based on the texture, intensity, and shape-dependent aspects presented a technique for segmenting human mri brain image at which the detection and characterization of the tumorwere considered using wavelet packets feature spacesand hsom. 3 proposed work in this part, the suggested mechanism is shown in great detail. figure 1 depicts the overall process flow in the proposed system: https://doi.org/10.15837/ijccc.2023.4.5186 4 figure 1: overall flow of the proposed mechanism a. image acquisition initially, the images are taken with the utilization of an mri scan, and theimagesscanned were exhibited in the 2d matrices with the element’s pixels. then, these conditions were based on the matrix size and arena of opinion. the images are then kept in matlab and are exhibited as the grayscale image of about 256 * 256. the grayscale image values range from 0 to 255, with 0 viewing the 255 and total black color viewing the untainted white color. the input among these ranges varies in the intensity that varies from white to black. the patients betweentheages of 20 to 60 years, which covers 30 men and 30 women, wereexamined on an experimental basis. their mri scan images were stored in the image database in jpeg image formats. the input image is shown in figure 2. figure 2: input image b. pre-processing preprocessing occurs after a picture has been taken. when preprocessing an image, it’s common practise to remove background noise and brighten any fuzzy areas that were imported from an input database. for this filtering process, we use a bias-corrected filtering technique. in this step, we apply filters to the grayscale image and transform it to access the enhanced portion of the picture. a chime impact is utilized as well with the apparent goal of producing a superior and distinguishable end-result. figure 3 shows both the original input and the corrected input bias pictures. https://doi.org/10.15837/ijccc.2023.4.5186 5 figure 3: input image, input-bias corrected despite the current use of a filter with an, this method might be used to the processing of the kind of picture data necessary for biological research. to improve the picture, the sobel operator is used to eliminate the high-pass filter and replace it with a mask. in mri images, a bias field appears as a smoothed low-frequency signal due to inconsistencies in the field of magnets of the mri equipment. high-frequency information, such as edges, lines, and intensity values, are evenly diluted when pictures are blurred owing to a bias field. changes at this level often don’t matter in the clinic. the spatial invariance of the modified image, which is essential for precise segmentation and classification, is therefore diminished. as a result, before to engaging in segmentation or classification, a preprocessing step is required to mitigate the effect of the bias field. in the bias corrected filtering, the enhanced image is filtered by initializing the parameters by setting lambda, gamma, and theta values. the bandwidth is set for the estimation of angle. thus, the computation of angle yx and yy is made using the formula provided in pseudocode which offers the filtered coefficient value for providing the filtered image. c. cnn based semantic segmentation after preprocessing, the image is segmented from the input pixel labeled dataset. this work aims to attain a fully automated segmentation technique depending on cnn for the extent of brain tumor segmentation from mri images. manually segmenting a brain tumour is a laborious process, and the quality of the results is heavily dependent on the expertise of the operator. therefore, https://doi.org/10.15837/ijccc.2023.4.5186 6 an automated technique for segmenting brain tumors semantically is essential. the semantic segmentation is the process at which appropriate target label is assigned for each pixel with the use of region growing as a ground truth. the purpose of cnn dependent semantic image segmentation is to segment each label pixel of the image by a subset of the depicted thing that comes after the original. due to the need of making predictions for every pixel in a picture, this process has been referred as dense prediction. the semantic segmentation is employed for recognizing the lesion region pixels from the healthy tissues that pays high attention in brain tumor regions thereby providing exact subdividing. in order to collect more instructive local characteristics, cnn is employed as the basic unit in the segmentation approach. in the cnn based semantic segmentation, the filtered image is taken as input, the parameters are initialized by setting the cut threshold range, and tumor diameter. the mask region is computed so as to estimate the tumor part and the particular segment. on estimating the size of image and the segment of image the threshold value is related to segment and recognize the affected portion of brain. the training levels and testing levels were related with the threshold limit to identify the specific portion of image. thus, from this, the segmented outcome is found. the traditional methods of semantic segmentation all relies on the deep cnn that was superior by a huge gap to early techniques. figure 6: segmented outcome figure 4is the depiction of the segmented outcome. in this, the network is trained and is evaluated, thereby inspecting the outcome of segmentation by importing the pixel labeled dataset. d. correlation based feature extraction https://doi.org/10.15837/ijccc.2023.4.5186 7 the segmented region is taken as input for the process of features extraction approach. the feature extraction is done with the use of correlation-based feature extraction. the matrix level of the segmented portion is computed for estimating the feature level set. a gray level co-occurrence matrix (glcm) method may be used to derive statistical texture attributes of the second order. clusters of three or more adjacent pixels are common in thirdand higher-order textures. the mathematical function glcm may often be used to successfully eliminate the artefacts. the integrity of the original picture may also be maintained. the image might be removed for further study. glcm may be used to estimate the pixel frequency to a given degree of precision. it is necessary to question the reliability of a single pixel & locate a replacement pixel using its path length (l) and the difference in value (m) between its neighbours. m typically takes on a single value, although this might work to the benefit in certain contexts. the obtained directional value may be used to completely remove all identifying features from the segmentation images. how closely the glcm components are spread out along the diagonal is measured by homogeneity. contrast may be used to assess regional shifts, whereas correlation can quantify joint probability. energy is equal to the product of a system’s parts squared. an explanation of the glcm method is provided below: step 1:pixels must be counted in pairs, with the first pixel in each pair being assigned the value i and the second pixel in each pair being assigned the value j. step 2: pix [i, j] is the notation for a matrix in row and column order. step 3: in the absence of symmetries in pix [i, j], the total amount of pixel pairs with grey levels [i,] need not be equal to the total amount of pixel pairings with grey levels [j, i]. step 4: the normalised procedure is carried out using the pix [i, j] elements as the starting point. simply dividing the number of pixels in each entry by the entire amount of pixels yields the normalisation. step 5: you may get the normalised glcm n [i, j] by, n [i, j] = (p ix[i,j])∑ i ∑ j p ix[i,j] (1) you may adjust the glcm procedure as follows: p (m, s) = g(m, s, o, ∅)/ ∑h m=1 ∑h s=1 g(m, s, o, ∅) (2) (m,s) was the element of the m and l pixels, p represents the characteristics of a picture, and g is a frequency vector; (m,s) is a frequency of the specific component, which would typically contain the values of the pixels of l and m, ∅ stands for the constant relative to the mean. for the purpose of classification, the following characteristics were calculated and trained. the glcm allows for the collection of many distinct characteristics. entropy this gives broad information on image-compacting artefacts. entropy = − ∑h−1 m=1 ∑h−1 n@s=1 np (s, o) ∗ log(p (s, o)) (3 ) where r(s,o) represented the feature frequency the constants p and h are denoted here. angular moment the glcm values are determined by averaging the regions of high and poor homogeneity in the picture. there would be a lot of angular momentum but just a little precision. typically, the photos are compared to ensure they are consistent. angularmoment = ∑h−1 m=1 ∑h−1 n=1 p (n, o) 2 (4) contrast quantitative measures of visual quality are determined. disparities between regions are often examined. contrast = ∑h−1 s=0 s 2(5) inverse difference moment it’s also a generic tool for gauging levels of similarity. idm = ∑h−1 m=1 ∑h−1 s=1 1/1 + (s − o) 2p (s, 0)(6) energy it might be used to figure out how many squares of the return are realistic. e = ∑h−1 0=1 ∑h−1 s=1 p (s, o) 3 (7) https://doi.org/10.15837/ijccc.2023.4.5186 8 variance it is possible to directly compute the standard deviation of the grayscale values. v ar = ∑h−1 m=1 ∑h−1 s=1 (p (s, o)) 2 − ∅2 (8) sum average in most cases, we can calculate the frequency of the links between pixels.∑2h−1 s=0 npx+y(l) (9) the outcome of these features will help in the identification of cancer presence along with their types like malignant and benign. the value of the contrast, area, correlation, and entropy was higher for benign cases on comparing malignant ones. however, the amount of homogeneity is supposed to be lower for benign than the malignant tumor. e. improved multipath googlenet cnn classification after the segmentationprocess, there is a necessitate for differentiating cancer cells, whether it is the malignant or benign stage. for the purpose of classification, googlenet cnn was employed, one such algorithm that was well-established. the googlenet cnn classification architecture comprises of 6.8 million parameters nine inception modules, to do so, we’ll use a network with the following layers: five highest pooling layers, a dimensionality-reduction convolution layer, two twisted layers, a single fully-linked layer, two normalization layers, one middle pooling layer, and finally a linear layer triggered by the judgement output. four convolutional layers are dedicated to dimensionality reduction, and there are six layers for maximum pooling as well as one convolutional layer of different types in each inception module. the activation function of relu is applied uniformly, while dropout regularization is employed in the completely connected layers. it also offers improved accuracy over alexnet. the target is being given a score based on the likelihood scale. it is the algorithm for convolution which is pre-trained.googlenet cnn permits the variations here between single variable dependent and more such independent factors to be evaluated. the cnn makes calculations based on its best guesses. there is just one overall distribution. in this technique, cnn was used to read and reformat the picture before doing the classification based on a probability estimate. cnn, developed by google, is a deep neural learning network. when it comes to visual recognition and classification, googlenet cnn represents a huge step forward. in characterizing and categorizing pictures, they are more often utilised to break conceptual continuity and to run optics. the googlenet has been used to create layers. •cnn. •relu •convolution •pooling •completely connected when compared to other image classification techniques, googlenet cnns need much less preprocessing procedures. this cnn will serve specialised needs across a variety of industries. convolution highlightning of the image data is the key function of this convolution phase. the convolutionary layer in google net cnn is consistently the first step. the features will be identified, and the characteristic map generated from the input picture relu layer the next step is the relu layer. in this case, the enactment method has been extended to the feature maps to improve network non-linearity. in this scenario, negatives will easily be omitted. pooling the pooling method will reduce the input size slowly. this will speedily evaluate the necessary parameters by growing the amount of parameters needed. flattening it is a straightforward move, in the sequential column of numbers, where the polled function chart should be flattened. completely connected layer here we may merge the functionality with the attributes. the classification method can be done with the high percentile of precision. the error will mainly be measured and recorded. https://doi.org/10.15837/ijccc.2023.4.5186 9 figure 5: googlenet cnn architecture softmax: to convert the unstandardized network performance across predicted output groups into a probability distribution, neural networks often use the softmax method. the softmax can be used to solve a wide range of problems in many academic disciplines. according to these odds, 1.0 would be shown. keep in mind the associated softmax variants: softmax is complete and will measure a likelihood for increasing possible type. softmax measures a chance, but only for a specific case of negative names with all positive names. this cnn allows for the comparison of two or more values to determine how much of a difference there is. cnn uses a probability calculation and a function. this is the total amount doled out. cnn may first read & redistribute the picture, and then evaluate the classification process based on its class probability. by stimulating a neuron, we kick off the process, alj = σ ∑ k wj k lalk + b l j (10) a vectorized version of the equation is possible, al = σ(wlal−1 + bl) (11) in which quadratic sets may the training set be combined, c = 1/2ęy − alę2 = 1/2 ∑ j (yj − a l j ) 2 (12) the gradient output is given by, (∂c)/(∂wkjl) = akl−1δlj=c (13) feature (f) and pointed feature (p) constitute the characteristics that have been categorised. they must be reported as the features of data, which is chosen for training the classifier, that is from the use of a glcm (gray level co-occurrence matrix) to analyse data. the data gleaned from the tested photos for categorization purposes during training. here the classification is performed by googlenetclassification.the feature extracted outcome is trained to classify the image. the batch size value is set to 10 by means of the primary learn rate of 1e 4 and the maximum epochs of 20. the best epochs number is being altered as per the validation criteria along https://doi.org/10.15837/ijccc.2023.4.5186 10 with validation frequency of 300 iterations.the network layers and train features were initialized by initializing train label as 70%, and test label as 30%. thus, the training and testing ratio are 70:30. for the trained and tested features, the prediction is made by their predicted label estimation. glioma, meningioma, and pituitary tumours are the three broad categories into which brain tumours fall. the confusion matrix for the proposed methodology is shown below: table 1 confusion matrix of propose method meningioma glioma pituitary tumor meningioma 1031 0 1 glioma 0 1069 0 pituitary tumor 0 3 960 table 1. shows the confusion matrices which in turn precises scheme’s performance. the signifies the predicted values (output of system) and the true labels (ground truth). the algorithm for googlenet cnn is shown below: in this method, the classification operation was made by the estimation of class probability, which in turn displays the labeled image. thus, classification of types of tumor grades namely meningioma, glioma, or pituitary tumor were made. 4 performance analysis results and analysis of the suggested study’s performance analysis are shown below: the proposed study is automaticbrain tumor detection in brain images. a. dataset description the data used for the training model was collected between 2005 and 2010 by researchers at nanfang hospital & general hospital of tianjing health college, china, and made publicly available online in many different forms between 2015 and 2017. there are 233 cases included in the database, and they were all acquired using contrast-enhanced t1-weighted imaging of meningioma, glioma, or hypothalamic tumours. brain tumours may vary greatly in terms of their size, kind, and location[24]. table 2 summarises the number of patients and the number of slices for each kind of brain tumour in the dataset. https://doi.org/10.15837/ijccc.2023.4.5186 11 table 2 dataset showing number of patients and slices type of tumor number of patients number of slices glioma 91 1426 meningioma 82 708 pituitary tumor 60 930 233 3064 b.performance analysis of the proposed mechanism performance and accuracy were examined, and the outcomes were shown below. the comparison of the proposed outcome is related to the existing technique to prove the proposed mechanism’s effectiveness. accuracy it is a statistical measure of a person’s inclination towards mathematics, with accuracy defined as the fraction of observations that are really correct (both negative and positive). accuracy (a) = (tp+tn)/(tp + tn + fp + fn) (18) sensitivity sensitivity, also known as the true positive rate, the likelihood of detection, and recall, is a measure of how many true positives can be reliably detected. sensitivity=tp/ (tp+fn) (19) specificity the term "specificity" refers to the "true positive rate," which provides a rough approximation of the genuine negative quantities that were accurately recognised. specificity = tp/(tp+fp) (20) precision the number of correctly labelled photos is an example of the output parameter known as "precision." a helpful reminder will be the total number of instances that fall inside the various categories. the ratio of two images provides an accurate measurement of precision. precision = (tp)/(tp+fp) (21) segmentation analysis the current method [25] presents multi-cascaded convolutional neural networks (mccnn)-based brain tumour segmentation, which boasts a sensitivity rate of 0.9074 and a specificity rate of 0.9918. the computation time of the mccnn is 1.5-3 mins. whereas, in presented approach, the computation time (29.133 sec) which is lower than existing technique. below, we provide a comparison study of the segmentation system we suggest: table 3 comparative analysis of the proposed segmentation system factors currentscheme suggestedscheme sympathy 0.9074 0.9153 specificity 0.9918 0.9928 calculation time 1.5-3 mins 29.133 sec untrueconfident rate 2.24 0.0072 untruebad rate 1.84 0.0847 comparison of the current segmentation result with the suggested one is shown in table 3. when compared to other segmentation algorithms, the results obtained demonstrate that the suggested scheme is superior. d. classifier performance the current method [24] uses a deep neural network to offer multi-classification of brain tumour pictures. with a total accuracy of 96.13%, the current system architecture produces a notable result. however, the suggested method has an approximate 99.7 percent success rate.the accuracy rates of the suggested approach, the state-of-the-art method, and the combination of the two are compared and contrasted. https://doi.org/10.15837/ijccc.2023.4.5186 12 figure 6 accuracy vs. no of iterations figure 7 sensitivity vs. no of iterations figure 8. specificity vs. no of iterations https://doi.org/10.15837/ijccc.2023.4.5186 13 figure 9. f-score vs. no of iterations figure 6, 7, 7 and 9 shows the results of an estimated comparison of the suggested and current method for detecting glioma. the study shows that the presented scheme is superior than existing techniques. the sensitivity is 98.85%, specificity of about 98.97%, precision of 98.85%, and accuracy of 98.91%. a look at how the suggested method stacks up against the status quo was estimated and is represented in figure 7, which in turn detects the presence of the pituitary tumor. the research confirms that the proposed method outperforms the alternatives. the sensitivity is 97.26%, specificity of about 99.00%, precision of 96.82%, and accuracy of 98.58%. the accuracy, sensitivity, specificity, & precision of the proposed and current methods for diagnosing brain abnormalities such meningioma are compared and contrasted in figure 8. the results are as follows: 97.71 percent sensitivity, 99.5 percent specificity, 98.61 percent precision, and 99.13 percent accuracy.the results of the study proved that the recommended approach is superior than competing ones. table 4 overall classifier outcome parameters existing system proposed system accuracy 96.13 99.78 sensitivity 95.03 100 specificity 96.9 99.717 precision 96.06 99.06 table 4is the analysis of comparison between existing and proposed classifier overall accuracy, sensitivity, specificity, and precision. compared to state-of-the-art classifiers, the results demonstrate that the suggested method performs better. f ¯ igure 10. validation accuracy of the classifier varied by number of iterations https://doi.org/10.15837/ijccc.2023.4.5186 14 f ¯ igure 11. performance estimation of loss for number of iterations classifier validation accuracy as a function of iteration count is shown in figure 9 of the performance analysis. likewise, figure 10 is the performance analysis of loss for each varying iteration. the overall outcome of the classifier is as follows: the proposed technique offers overall accuracy of about 99.7%, sensitivity 100%, specificity 99.717%, and precision of 99.06%. therefore, the proposed system is better in both segmented and classifier outcome than other existing techniques. 5 conclusion automatic brain tumour segmentation and classification using a deep learning based ai method is described. the findings demonstrate that a tumour may be properly segmented using semantic segmentation & googlenet cnn classification in a matlab setting. the purpose of cnn dependent semantic image segmentation is to segment each label pixel of the image by a subsequent class. improved multipath googlenet cnn was employed in this approach, which is an effective mechanism for the classification and detection process. this is being employed for the classification of object depending on their probabilities. this is termed as the algorithm that were pre-trained convolutional one.furthermore, the outcomes of this projected study out comes produce clear proportions regarding the abnormal and normal brain tumor region segmentation that, in turn enables the physicians to have reliable method. the performance of proposed technique is estimated using matlab in regards to how precise, sensitive, and accurate it is. the obtained results expose that the projected method achieves overall classifier performance rate 99.7% accuracy, sensitivity 100%, specificity 99.717%, and precision of 99.06%. the achieved results show that the suggested approach is superior to other methods currently in use. to categories brain mr images and categorise tumours into meningioma, pituitary tumor, & glioma, an enhanced multiple paths googlenet cnn is 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"brain tumor detection using convolutional neural network." ahsanullah university of science andtechnology https://doi.org/10.15837/ijccc.2023.4.5186 16 copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: markkandeyan, s.; gupta, s. ; venkat n., g.; jithender reddy, m ; ahmad al-khasawneh, m.; ishrat, m.; kiran a. (2023). deep learning based semantic segmentation approach for automatic detection of brain tumor, international journal of computers communications & control, 18(4), 5186, 2023. https://doi.org/10.15837/ijccc.2023.4.5186 introduction related works proposed work performance analysis conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 2, month: april, year: 2023 article number: 4666, https://doi.org/10.15837/ijccc.2023.2.4666 ccc publications cross layer based energy aware and packet scheduling algorithm for wireless multimedia sensor network l. jenila, r. aroul canessane l. jenila sathyabama institute of science and technology, chennai, tamil nadu, 600119, india *corresponding author: l. jenila. email: jenilacse@gmail.com r. aroul canessane sathyabama institute of science and technology, chennai, tamil nadu, 600119, india aroulcanessane@gmail.com abstract wireless multimedia sensor networks are used in a variety of applications, including environmental monitoring, industrial automation, and military surveillance. they are well suited to these applications because they can operate independently of a central controller, are highly scalable, and are capable of transmitting large amounts of data over long distances. the scheduling of packets is a crucial issue in wireless multimedia sensor networks because the network’s limited resources, such as bandwidth and power, must be carefully controlled to guarantee that data may be transferred effectively and without interference. algorithms for packet scheduling try to determine the optimal method to distribute these resources in order to maximise network performance. in wireless multimedia sensor networks, a variety of packet scheduling methods may be used, each with its own set of guidelines and goals. the following are some typical goals to improve the network performance in order to increase the quantity of data that can be transferred through the network in a given length of time, the throughput of the network must be maximised. to reduce the time it takes for a packet to be transferred by reducing the network’s delay. in order to enhance the entire system’s performance, it may also consider the interactions that take place across the physical, data link, and network levels of the network protocol stack. the algorithm’s objective is to optimise the trade-off between packet transmission efficiency and energy efficiency in order to enhance the performance and dependability of the wireless multimedia sensor network. the efficiency of the ceapsa scheme is 23.24% better than the ebjrcf scheme and 34.84% than eaacls scheme in terms of delivery rates. keywords: semi-markov process; packet scheduling; node congestion ratio; cross layer approach; wireless multimedia sensor networks. https://doi.org/10.15837/ijccc.2023.2.4666 2 1 introduction a wireless multimedia sensor network (wmsn) is a type of wireless network that consists of a large number of small, low-power devices called "sensors" that are distributed over a wide area. these sensors are equipped with sensors, such as cameras, microphones, and temperature sensors, that allow them to collect data about their environment. wireless multimedia sensor networks have several key characteristics that distinguish them from other types of wireless networks: heterogeneity: wmsns typically consist of a large number of diverse sensors with different capabilities and requirements. energy efficiency: sensors in wmsns are often powered by batteries and must be designed to be energy efficient in order to operate for long periods of time without access to a power source. quality of service: wmsns often need to support multimedia applications, such as video and audio streaming, which require high quality of service (qos) in terms of bandwidth, latency, and reliability. self-organization: wmsns are often decentralized and self-organizing, meaning that they do not rely on a central controller to coordinate their activities and can adapt to changes in the network dynamically. to address these challenges, wmsns often employ specialized protocols and algorithms to optimize the performance and reliability of the network. these may include packet scheduling algorithms, energy-aware routing protocols, and cross-layer optimization techniques. an optimization approach that tries to effectively schedule packets for transmission across the network while simultaneously taking the energy consumption of the wireless devices into account is called a cross-layer based energy aware and packet scheduling algorithm for wireless multimedia sensor networks. energy efficiency is a major issue in wireless multimedia sensor networks since the devices are often powered by batteries and may need to run for extended periods of time without access to a power source. as a result, in order to reduce energy consumption, the algorithm may take into account variables like the remaining energy levels of the devices, the energy necessary to send and receive packets, and the energy required to process and store data. in order to optimise network effectiveness and guarantee that packets are transported in a timely and reliable way, the algorithm may additionally take into account variables like the data rate, packet size, and bandwidth availability. 2 related works huge research works had been carried out for multimedia wireless networks in terms of routing, congestion avoidance, security features, packet loss and reducing delay during transmission, etc. some of them are discussed here to relate the proficiency of our proposed work. reliable transport layer protocols are used to improve multimedia transmissions in wmsns [5]. they comprise of appointing a resources plan of time for sending certain measure of data and evaluating if the channel state permit to finish the transmission or not. if the case is not so, at that point the transmission is ended, consequently it stores significant energy level in the sensors. differentiated services (diffserv) engineering for qos provisioning in wmsn was introduced [6]. this design separates basic ongoing information from constant mixed media information by characterizing six traffic classes alongside their sending conduct. diffserv objective is mainly on bandwidth allocation to various traffic classes so as to augment the use of the entire framework. greedy forwarding based on throughput energy aware multi-path routing protocol (gftem) [7] which depends on determination of next node that has the most elevated throughput and closer to sink node. greedy routing structure been intended for scalar information, for example, sensor information; which is little in size contrasted with media information. delayed inclusion and non-delayed coverage enhancing algorithm (dndcea) for wmsn was proposed by presenting the model of virtual centripetal power [8]. here excess sensors are become idle and inclusion of systems can be improved by re-send sensors under the repulse power based virtual centripetal power albeit enormous quantities of repetitive sensors are closed off. each layer of the protocol stack is monitored and completely engaged with giving qos ensures and hence cross layer cooperation of various layers was proposed [9]. here cross layer connections reduces the encoder complexity at each layer of the protocol stack from cutting edge coding methods and accomplishes most extreme pressure to dynamic directing. cross-layer conspires that consolidate routing and booking components was proposed [10] to improve remote video transmission execution incorporates: a coordinated routing measurement for assessing path quality; traffic task and traffic modification modules are intended to make routing plan adaptable. node-level optimal real-time priority based dynamic scheduling (nrpds) algorithm [11] was proposed that schedules tasks as indicated by their needs by expanding or reducing the packet repetition, utilizing dynamic voltage repetition scaling and subsequently utilizes energy. priority based booking helps in accomplishing qos for delayed traffic. traffic congestion control algorithm (tcca) for wmsn was proposed [12]. it utilizes a mix of two distinctive blockage pointers to definitely recognize a congested node. criticism based rate controller was created to improve the video nature of played streams. different retransmission tools for various packets are utilized with the goal that the lost packets are spared incidentally and retransmitted when free channel is accessible to eliminate congestion. problem of losing whole frames for multiview 3-d video transmission over wmsns can be solved using frame loss concealment method [13]. this system is equipped for managing this https://doi.org/10.15837/ijccc.2023.2.4666 3 significant issue without the guide of complexity map. energy efficient image processing and communication [14] over wsn was proposed; here a solid transmission protocol is utilized at the application layer to guarantee that all media packets are sent and received effectively and organized appropriately. reconstruction algorithm for lost frame of multi-view videos based on profound learning method was presented for wmsn [15]. frame reconstruction scheme is applied to calculate mixed pixels in decoder if video frames are lost during transmission. an epic pixel estimation calculation is created utilizing multilayer perceptron regression (mpr) with the profound learning strategy. the proposed movement planning calculation [16] joins a methodology that empowers every mixed media sensor hubs in wmsn to productively extract its spread sets to guarantee that the basic critical points are checked; therefore the system energy use can be limited viably. the required task for each spread set was done dependent on the connection of visual data from the camera perceptions. energy-balanced joint routing and charging framework (ebjrcf) were proposed [17] to adjust the energy utilization between sensors; here the system is disintegrated into a routing procedure and a charging technique. this routing system is intended to optimize the directing tree comparing to the mobile charge remaining at every sensor hub. moreover, the proposed charging procedure prefers towards the accusing mode of the best parity of the absolute vitality utilization of sensor hubs during one charging trip. energy aware and adaptive cross layer scheme (eaacls) to forward multimedia information through sensor networks was presented [18]. with the goal that it chooses ideal video encoding boundaries at application layer as indicated by current remote channel state, and packet allocations as per its sort through a versatile need video line so less significant parcels are dropped if there should arise an occurrence of system congestion. 3d coverage model is developed [19] with presenting the perceptual range of sensors, while the ideal projection zone of a solitary sensor is concluded. from this point forward, a versatile molecule swarm enhancement is prescribed to smooth out the region information of sensors for diminishing both perceptual covering locales and perceptual outwardly disabled districts in the checking area. at last, to minimize the burden for working nodes a redundant node ‘y’ is presented. problem identification: the objective of the proposed work is to improve the energy factor and to transmit the data in priority based path scheduling by applying cross layer scheme during routing. in conventional protocols, only sub-optimization energy problem is considered but path aware for data routing is not considered in scheme. in addition path scheduling overheads is not considered which caused due to high congestion exists while routing data. hence the proposed scheme focuses on energy and path aware scheduling using cross layer scheme. 3 proposed mechanism cross layer based energy aware path scheduling algorithm (ceapsa) is proposed to transmit multimedia information efficiently over wsn. transmitting multimedia information over sensor networks is a challenging task in terms of restricted bandwidth, low powered battery nodes, high congestion rate over transmitting channel etc. ceapsa scheme consists of three phases such as link estimation between nodes by applying semi-markov model, estimation of node congestion rate and path scheduling in which all three phases are linked through cross layer communication. in general cross layer approach is used to connect all the layers virtually to avoid conflictions during transmission. in cross layer approach the physical, data-link, network and application layers usually communicates with each other directly i.e. each layer is connected with one another and shares their own information with other layers to prevent or avoid unnecessary congestion rate that occurred over high transmitted channel rate. figure 1 show cross layer approach for ceapsa over sensor network. https://doi.org/10.15837/ijccc.2023.2.4666 4 figure 1: ceapsa over sensor networ a. link estimation process using semi-markov model: in link estimation phase the active nodes are selected for reliable video transmission and optimal routing. active nodes are chosen from the actual nodes using factors such as bandwidth consistency (bc) and energy level (el). by applying semi-markov process the bandwidth consistent high energy level nodes (active relay nodes) are selected for efficient video transmission with minimum execution time. the node states might jump between three states like active (a), idle (i) and off (o) states and these states are determined utilizing nodes energy factor and bandwidth consistent. energy level for the node is estimated through node’s energy consumption value i.e. by analyzing the energy consumption of previous data transmission of nodes. bc estimation is carried out through link-duration analysis of the transmission channel. bc between the neighbouring nodes is estimated through the number of packets that can be sent in maximum over a channel. therefore active data transferring nodes are selected through the analysis of bc and el factor. active node ‘ai’ selection process is carried out by passing control messages among presented neighbour nodes. base node ‘b’ here acts as a source node initially transmits route request (rq) to its presented neighbours and active node ‘ai’ (acts as a relay or data passing node between base and sink node ‘d’) with high channel rate sent back route reply (rr) message to node ‘b’. now the relay node checks for its el and bc factors with their threshold value, if the node has high el and bc value compared to its reference value then the node is chosen as active relay node. then the path ‘b’ to ‘ai’ and ‘ai’ to ‘d’ is selected as reliable path and video packets gets transmitted successfully without jumping to any other states i.e. bt x → t x. if path ‘b’ to ‘ai’ and ‘ai’ to ‘d’ is already connected for data transmission but fails to transmit the sensed video packets for some reasons (insufficient energy or bandwidth), then ‘b’ broadcasts rq and tries to retransmit the failed message to ‘d’ by selecting another active relay node by jumping the states. this active relay node selection process makes the network more reliable and therefore semi-markov process plays a major role in detection of active intermediate relay nodes. figure 2(a) shows node failure ‘ai’, figure 2(b) shows the selection process of new active node. figure 2(c) represents destination node ‘d’ passes acknowledgement and figure 2(d) shows data retransmission from ‘b’ to ‘d’. transmission process (tx): node ‘b’ transmits new packets. ‘active node’ is considered to be nodeai ∈ {i, ...., n} bi: if current b-ai node accepts data from the base node ‘b’, and it forwards the same data to node ‘d’, however this route get failed due to imbalance of el and bc factor. btx: in state jumping process → from one state to other state, it may take two types includes type 1 and type 2 which as follows: btx state (type 1): base node ‘b’ retransmits the same data by selecting another active node ‘aj’. btx state (type 2): data transmitted directly from ‘b’ to ‘d’ devoid of choosing any intermediate relay node and this state move is represented to bebt x → bt x. when base ‘b’ forwards data to ‘d’ and intermediate ‘ai’ is chosen effectively as the principle state then this state jumps is related as ‘dtx’ state (bt x → dt x). https://doi.org/10.15837/ijccc.2023.2.4666 5 figure 2(a) node failure ‘ai’ figure 2(b) new active node selection figure 2(c) node ‘d’ passes ack figure 2(d) retransmission db: data is transmitted to ‘d’ once the reliable ‘ai’ is chosen then this state is jumped to a separate state from d-b. if selection of ‘ai’ is unable to process further, then protocol gives back the dtx state. now ‘b’ retransmits data and starts selection of new active node ‘aj’ for further transmission process in order to reach destination. the state transition process takes place from one node to another, for example here we consider three node transition states such as b-d, b-a and a-d and their data channels are obligatory. for the data channel the tuple state is set as ‘ta’ which has all proper sets of hidden and observable statesta = ( u(a), c (a) bd, c (a) ba, c (a) ad ) . there are |t | = 16 novel tuple sets and each tuple sets have conceivable jumping states in the network structure. the utility factor u(b) = f (ta) that describes about state transitions rules for selecting appropriate active intermediate relay node is defined using equations 1, 2 and 3. u (b) (el,bc)t x =   f or u (a) (el,bc) = bt x, c (a) bd = g, or u (a) (el,bc) = dt x, c (a) ad = g or u (a) (el,bc) = n ai, c (a) bd, c (a) ba, c (a) ad = g,∑a=−1 a=1 ig ( c (a) ba ) ig ( c (a) ad ) = 0 (1) u (b) (ec,bl)a =   f or u (a) (ec,bl) = t x, c (a) bd = b, c (a) ba = g, oru (a) (ec,bl) = n ai, c (a) bd = b, c (a) ba, c (a) ad = g,∑ai=−1 ai=1 ig ( c (a) ba ) ig ( c (a) ad ) = 0 (2) u (b) (el,bc) =   f or u (a) (el,bc) = bt x, c (a) ba = b, c (a) ad = b or u (a) (el,bc) = ai, c (a) ba = b or u (a) (el,bc) = dt x, c (a) ad = g,∑n a=1 ig ( c (a) ba ) ig ( c (a) ad ) = 0 (3) state jumping probabilities from node to node can be identified using tuple sets ta to tb on basis of state transition probability matrix ‘pab’. this also contains a maximum number of state jumping possibilities amongst https://doi.org/10.15837/ijccc.2023.2.4666 6 the tuples. node jumping state probabilities is computed using equation 4. pab =   p a ( c (b) bd ∣∣∣c (a)bd ) ∏ana=ai p ai (c (b)bai ∣∣∣c (a)bai ) p ai (c (b)aid ∣∣∣c (a)aid ) f or u (b) (el,bc) = f (ta) 0 ; otherwise (4) if ‘ai’ is selected effectively then current state returns to initial transmission state ‘tx’ and start to choose the next processing active relay node; hence this process results in effective reliable node identification. node’s utility factor is significant for determining whether it is reliably active or not, with the necessitated considerations of bandwidth consistency and energy level. in general bandwidth estimation is done by analysing the amount of data rate that can be passed over a channel and it is given in equation 5. calculating the average bandwidth utilisation is significant for a set of data transmission. in case of normal image packet transmissions it is enough to take lesser bandwidth rated nodes but for video transmissions it is necessary to use higher bandwidth or data channel rate. bandwidth threshold is set for all nodes (either active or idle), bc value which is greater than the reference value ‘bt h’ is finally chosen for data transmission. therefore this node factor analysis improves better achievable data rates. bca,b = m ax pkts sent t (bps) (5) bandwidth threshold is set by estimating the typical packet size used to communicate by the node and it is given in equation 6. this bc estimation includes nodes channel rate from the base node (nb ) to the destination node (nd ) as well as overall control overheads. bt h = nd∑ nb →bai,aid bc (6) the bandwidth consistent rate ωab (a, b ∈ {1, 2, ....., 16})is determined for the node’s state transitions using the equation 7. ωab =   1 ; f or u(bc)(ab) = t x, c (ab) bd = g, or u(bc) (ab) = ai, c (ab) aid = g 0 ; otherwise (7) therefore it is stated that each time a node state transition ai → t xexists, the bandwidth consistent rate ωab is settled to ‘1’ that covers half of all the jumping transitions. in parallel to bc factor higher residual el node factor is also considered for the selection of active intermediate relays in the link quality estimation phase. since node’s energy is important for processing and transmitting the data and the node’s el is identified by computing node’s energy drain rate. evaluation of node energy drain rate gives the optimal residual energy value of the particular node. therefore computed low energy level nodes are kept ‘idle’ and extracted from the selection list of active relay nodes. energy threshold is maintained for all the nodes which lie in the network and this energy reference value decides whether the node can be selected as active node or not in order to participate in data routing. energy consuming rate (ecr) for a particular node is estimated by taking the variations between its initial energy ei and present energy ep of the node with respect to time. equation 8 describes about computing ecr. ecr = ei − ep tc (8) reference energy value ‘er’ is set for each and every node in regarding of overall network’s ecr. therefore er is evaluated by considering the average consumption of node’s energy rate using equation 9. er = n∑ i=0 avg (ecr) (9) the energy consumed ‘eab’ during the time of node’s tuple state transition ta → tbis given in equation 10. eab =   et x + erx ( ig ( c (a) bd ) + ∑n a=1 ig ( c (a) ba )) f or u(a) ∈ {t x, bai} , u(el,bc) (b) ∈ {t x, a} , et x + ig ( c (a) ad ) erx f or u(el,bc) (a) = a, et x + ig ( c (a) bd ) erx f or u (a) (el,bc) = u (b) (el,bc) = bd, 0 ; otherwise (10) https://doi.org/10.15837/ijccc.2023.2.4666 7 figure 3: flow diagram of ceapsa scheme on basis of flow of radio channels, effective bandwidth rate and active energy intermediate relay node assortment is made in regard of time-associated channels. the node utility factors such as bc and el is updated during each state transition are done between the nodes. the flow diagram of ceapsa in depicted in figure 3. b. node congestion rate (ncr): once the node utility factors are determined and the active relay nodes are selected then the second phase ncr is carried out. network congestion occurs if the route is loaded with more number of information packets to deliver to the destination. this excess amount of data might cause packet loss or unnecessary delay in delivering the packets or frames. if the path ‘b’ to ‘ai’ and ‘ai’ to ‘d’ is selected as reliable then the cross layer scheme is applied here to check the data status whether the route has the ability to send the whole data packets successfully. to avoid data loss, ncr is calculated by passing control message rq using equation 11, n cr = n∑ i=1 ( rq − rp rq ) × 100 (11) ncr algorithm: begin set node ni get sent rq count & received rp count calculate ncr if ncr > 80% https://doi.org/10.15837/ijccc.2023.2.4666 8 ni → low channel rate else if, 45% < nrv < 80% ni → average channel rate else ncr < 45% ni → high channel rate end ncr is calculated and the split ratio is set between 45% and 80%. if ncr is found to be greater than 80% for the data path ‘b’ to ‘d’, then the data path has low channel rate and routing data through this channel leads to high data loss. if ncr of the path ‘b-d’ lies between 45% and 80% then the path has average channel rate and the data passed through the channel have low data loss. if the obtained ncr value is less than 45%, then the path is highly suitable for transmitting the video packets and the loss rate will be very low or null at sometimes. the evaluation of ncr is necessary to monitor the channel status and therefore channel rate is considered for evaluating the node’s congestion rate. the availability of bandwidth for each node is determined by base ‘b’ which passes rq message, and the neighbour nodes that are elected as active intermediate relay nodes ‘ai’ gives bandwidth availability information through rp message, therefore the efficient data channel rate nodes can be identified from the available active nodes presented towards the destination ‘d’. here the channel rate is directly proportional to the total bandwidth rate. channel rate can be defined as the rate in which the data can be sent to the receiver reliably. the sum of whole available bandwidth for transmitting maximum data packets through the channel is calculated using equation 12, ω (n) = φ −   ∑p athn(b, d) (β + θ) θ   (12) bandwidth calculation includes node’s own video sensing information i.e. data generation rate β and control overheads θ. the control message that takes maximum time to reach the neighbour node is represented as θ. c. packet scheduling process: third phase of ceapsa scheme is packet scheduling process in which queue scheduler component is applied for replacing the higher priority packets with the normal packets. through the cross layer communication design approach the layers can communicate together, phy layer focuses on highly prioritized packets and shares the information to other layers, application layer detects the packet type that is going to be transmitted by communicating with network layer. to avoid or reduce the congestion ratio the packet types are categorised in to two kinds such as high priority packets and low priority packets or frames. the higher priority packets are enqueued in the selected reliable path (path selection done on basis of energy level, bandwidth consistent and congestion ratio). path count is calculated to identify the number of times that the path used for data transmission and it is given in equation 13, pcount = n o.of hop_counts t otal no.of paths (13) each node will update the path count with their corresponding node_id. the path that has higher leftover energy level , high bandwidth consistent and low congestion ratio is selected as reliable path for transmitting the higher priority packets and it should be equivalent to ‘1’ considered to be a weight factor given in equation 14. pcount = α + β + γ ≈ 1 (14) lower priority packets are enqueued after the higher priority packets, once the higher priority packets or frames are sent to the sink then the lower priority packets are dequeued over the reliable path in order to reach the sink node. low priority frames can also be transferred over low reliable paths (over average el and bc nodes) since the packet may reach with little delay will not affect the network reliability. by selecting the active relay processing nodes and passing the data by applying packet scheduling algorithm, the scheduling overheads can be controlled reliably and the network lifetime can be maximized. 4 results and discussion the network simulator tool (ns2) is used to simulate the proposed ceapsa scheme and the comparative existing protocols such as eaacls and ebjrcf schemes. the simulation parameters taken into consideration are defined in the table 1. by taking link connectivity among nodes, their energy level and the amount of data traffic can be sent over the channel are the metrics taken here to identify the active intermediate reliable nodes by using cross-layer approach interaction among all the layers. this model results in identifying the reliable and efficient route for data forwarding process with low congestion and less delay. https://doi.org/10.15837/ijccc.2023.2.4666 9 table 1: simulation parameters parameter value no. of. nodes 100 simulation time 250sec mac layer ieee802.11 traffic source cbr packet size 512 bytes routing protocol aodv communication range 250m area size 1000m x 1000m the simulation of wireless network is performed with 200 nodes using the ieee 802.11 mac layer and the aodv routing protocol. the simulation runs for 250 seconds and shall involve constant bit rate (cbr) traffic with 512-byte packets. the communication range of the nodes is 250 meters and the area size of the simulation is 1000 meters by 1000 meters. the ad hoc on-demand distance vector (aodv) routing protocol is a reactive routing protocol, which means that it only establishes a route to a destination when it is needed by a source node. aodv has the advantage of low overhead and fast route discovery and it is commonly used in ad hoc wireless networks, where nodes are free to move and there is no fixed infrastructure. conventional schemes taken here to analyse the transmission process with the proposed ceapsa scheme are eaacls and ebjrcf. the metrics taken for the analysis of proposed ceapsa and conventional schemes eaacls and ebjrcf are frame delivery rate, data loss rate, peek signal-to-noise ratio (psnr), energy consumption of nodes, end-to-end delay, and scheduling overheads. a. frame delivery rate frame delivery rate (fdr) is defined as the amount of packets that can be delivered successfully to the sink node with respect to the total amount of packets sent by base node. fdr is measured using equation 15, f dr = ∑n 0 n o.of f rames(p kts) received t ime (15) figure 4: frame delivery rate figure 4 shows graphical representation of fdr for both the proposed scheme ceapsa and conventional schemes eaacls and ebjrcf. it is clearly shown that the ceapsa scheme has better packet or frame delivery rates at receiver side compared to conventional methods. b. psnr psnr is taken for analysis of obtained video quality of the sensed and processed video series. the achieved multimedia quality is highly basis on path scheduling in which high priority packets are scheduled through selected reliable routes. proposed ceapsa method obtains the video quality range of 33.6 db and it is comparatively a better quality while comparing with the schemes eaacls and ebjrcf. figure 5 shows obtained psnr of ceapsa, eaacls and ebjrcf. https://doi.org/10.15837/ijccc.2023.2.4666 10 figure 5: psnr c. scheduling overheads: path scheduling and packet scheduling process greatly reduces the data overheads (congestion) also decreases unnecessary delays in receiving the packets of cost metrics. figure 6: scheduling overheads by transferring high prioritised frames through the selected reliable routes; the congestion rate can be greatly reduced. it also reduces the path scheduling cost in regards to the number of participates nodes while routing. the obtained scheduling costs results of ceapsa, eaacls and ebjrcf are compared and proposed ceapsa got better result compared to other two algorithms. figure 6 shows the graphical representation of scheduling overheads for both proposed ceapsa method and conventional schemes. d. energy consumption relative energy level for each and every node should be calculated in the network that also used for data transmission process. nodes energy consumption value (ecr) that is used for transferring the frames and to maintain the node in active role is calculated using nodes energy drain rate (using equation 8 that is mentioned in prior). https://doi.org/10.15837/ijccc.2023.2.4666 11 figure 7: energy consumption figure 7 shows the consumption of value of energy in terms of joules for both proposed ceapsa and existing schemes eaacls and ebjrcf. ceapsa protocol consumes less energy since it selects the active intermediate relay node routing (highly reliable) for transferring the packets or frames of video sequences. e. data loss rate data loss rate (dlr) can be computed by calculating by taking the difference between the numbers of packets dropped at the receiver side and the number of packets sent by the base node. dlr is computed using equation 16, dlr = n∑ 0 p ackets dropped − n∑ 0 p ackets sent (16) dlr of both proposed scheme and existing schemes are compared with each other and the graphical representation is shown in figure 8. it is clearly shown that the proposed ceapsa scheme has lower data loss in terms of packets compared to the other conventional schemes eaacls and ebjrcf that simultaneously indicates the better service quality and higher performance of the network. figure 8: data loss rate f. ete delay end-to-end (ete) delay is measured to calculate the overall time that is taken for a set of data transmission process. it is defined as the time difference between the packets received by the receiver node and the packets sent by the base node. ete delay is measured using equation 17, et e_delay = ∑n 0 p kt rcvd t ime − p kt sent t ime n (17) https://doi.org/10.15837/ijccc.2023.2.4666 12 figure 9: ete delay figure 9 shows graphical representation of ete delay for both the proposed and existing schemes. the data processing delay is reduced at the channel for the proposed scheme ceapsa due to path scheduling process and the obtained delay value is low compared to the conventional schemes eaacls and ebjrcf. getting lowered delay value signifies that the achievement of higher system throughput. 5. conclusion cross layer based energy aware path scheduling algorithm is proposed here to reduce congestion ratio and to improve link quality between the routing nodes. semi-markov process is applied for estimating link quality among nodes as well as its durability. path scheduling process determines the highly prioritized packets and selects the active node through link quality process then transmits the packets to the destination based on prioritize level. simulation analysis is carried out through network simulator tool and the proposed mechanism efficacy is compared with the conventional schemes. proposed scheme shows better results with improved performance. the efficiency 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"coverage control algorithm-based adaptive particle swarm optimization and node sleeping in wireless multimedia sensor networks." ieee access 7 (2019): 170096-170105. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ https://doi.org/10.15837/ijccc.2023.2.4666 14 this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: jenila, l.; aroul canessane, r. (2023). cross layer based energy aware and packet scheduling algorithm for wireless multimedia sensor network,international journal of computers communications & control, 18(2), 4666, 2023. https://doi.org/10.15837/ijccc.2023.2.4666 introduction related works proposed mechanism results and discussion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 5, month: october, year: 2022 article number: 4781, https://doi.org/10.15837/ijccc.2022.5.4781 ccc publications opportunity based energy efficient routing algorithm for underwater wireless sensor network for submarine detection r. shakila, b. paramasivan (balasubramanian) r. shakila department of information and technology national engineering college, kovilpatti, tamilnadu, india corresponding author: shakilarphd1@gmail.com b. paramasivan (balasubramanian) department of information and technology national engineering college, kovilpatti, tamilnadu, india k.r.nagar, kovilpatti – 628503 tuticorin district, tamilnadu, india abstract the submarine detection is the most significant research area of under water acoustic (uwa) environment with extensive application in commercial and navy domains. the environmental complexity and variable nature of the uwa makes underwater wireless sensor network (uwsn) to exhibit fluidity, sparse deployment, time unpredictability, frequency selectivity, limited accessible bandwidth and energy constraints pose problems in the underwater positioning technology. thus, an adaptable, scalable, and highly efficient uwa is required for the submarine routing systems. the depth-based routing has received lots of interest as it is capable of delivering effective operation without requiring full-dimensional position information of nodes. however, it has issues of vacant regions and detouring forwards. to delineate the aforementioned problems, this paper proposes an opportunity-based distance vector routing (odvr) technique. the distance vectors, which have lowest hop counts in the direction of sink for underwater sensor nodes are determined by odvr through a query method. depending on the distance vectors, a dynamic routing is created to manage the packet forwarding. in the opportunistic forwarding, the odvr has a minimal signaling cost and minimum energy consumption with the potential of eliminating the long detours issues. the outcomes of simulations demonstrate that the odvr outperforms the conventional routing algorithms. keywords: submarine detection; underwater wireless sensor network; dynamic routing; energy consumption, packet delivery ratio and end-to-end delay. 1 introduction marine engineering and its consequent technologies are remarkably significant in the operations like underwater topographic exploration, building of marine engineering or resource development and nautical scientific analysis. underwater operations and submarine detection are essential in marine https://doi.org/10.15837/ijccc.2022.5.4781 2 construction projects including artificial island reef building, submarine recovery, development of tunnel, laying of underwater pipeline and ocean mineral resource research. currently, the submarine detection technology is applied in deep sea engineering and coastal region. when compared to the conventional satellite navigation and positioning systems, the underwater navigation system with positioning and navigation system is highly considerable. the radio signals are carried over a large distance through a saline at relatively low frequencies in the complex and changeable deep sea environment [1-2]. only the buoys close to the sea acquire gps location signal. as a result, establishing underwater positioning uwsns has evolved as a potential method for locating the underwater environment. it is used to discover the ocean locations. in addition, it is used in undersea navigation, seismic observation, oilfield exploration, oceanographic surveillance and underwater navigation. in these applications, a monitoring and controlling centers are often established to gather data from multiple sensors that are stationed in underwater to create a uwsn. hence, the data forwarding and routing from sensors towards the sink is a successful method of submarine detection applications [3], which employ a diverse set of underwater methodologies to create a massive data processing for tracking of resource, data gathering and monitoring. in most cases, a monitor is installed to receive data from uwsn’s sensors. as a result, data forwarding and routing from underwater sensors to the sink becomes a critical mechanism of underwater applications. because of the liquidity feature of the underwater territory, the routing in uwsns has certain difficulties [4–7]. the underwater wireless communication route suffers from significant signal losses [8] and the complicated multipath effect [9] results in a higher bit error in communication with restricted bandwidth for underwater audio communication [10]. furthermore, the acoustic channels of underwater have a propagation delay about five orders greater than terrestrial radio channels [11]. on account of the low speed of acoustic signal in water, the communication is subjected to significant doppler effect caused by trans-receiver motion or varying surroundings like surface waves, interior disturbance, and speed variations. as a result, the light-weight signaling and flexibility to high-quality dynamic link are required for reliable packet delivery in the underwater wireless communication. for wireless multihop networks with lossy links, the opportunitybased routing algorithms have been proposed to enhance packet delivery ratio and throughput [1213]. to reduce packet losses, these protocols uses the wireless channel and arbitrary routings for achieving contiguous diversity at the receiver [11-16]. to enhance the lifetime of the network and to elevate the energy efficiency, the energy competent cooperative routing approaches are preferred by various manufacturers and researchers. however, these approaches provide inaccurate results, which minimizes the overall performance in a wider range [17-18].the opportunity routing has been used in uwsns to combat the high bit error rate in wireless network [19]. the sensor nodes of classical routing mechanisms depends upon the predetermined routing table for forwarding the packets but the protocol selects a group of routing options from adjacent sensors for forwarding the data packets to the sink [20]. as a result, data packets have high chance of being successfully transmitted to the sink. there are some opportunity-based routing methods in the literature, which are playing a significant role designing the uwsn since these approaches are applied in the operations like data transmission, data tagging and object tracking. in spite of having various beneficial measures, these approaches delivers certain limitations like sensitive towards temperature fluctuations, lesser reliability and inaccurate location detection [21-23]. in consideration with the position information of every nodules, the vector-based forward (vbf) mechanism [24] measures the "routing pipe" and the nodes in this pipe carry the location information of submarine. as a result, the routing pipe towards the destination defines the forwarding path. on the other hand, obtaining position information is regarded as a difficult process in uwsns. hence, a depth-based routing (dbr) scheme is employed, in which the pressure sensors are employed to assess the depth of submarine. based on the acquired depth data, the dbr eagerly sends the location information to the shallow nodes and eventually to the destination on the sea surface. however, the node is unable to identify an appropriate next hop (bottom node) to transmit its data packets in a sparsely distributed network when the greedy hop-by-hop forward approach frequently reaches a void zone [25]. both wireless networks and acoustic networks have looked at the empty region in opportunity routing. to recover communication from the void region, a protocol that involves relocating the nodes in void areas to new depth is introduced to modify the topology. though https://doi.org/10.15837/ijccc.2022.5.4781 3 the obtained results of this approach are highly adequate, its applications are limited as it owns the demerits like extreme propagation delay, excessive disruptions or noises, low bandwidth and excess energy consumption [26-28]. traditional opportunity based routing systems need several signaling exchanges across the sensor nodes to choose the relay candidate set or to select the forwarders and coordinated opportunistic forwarding. the forwarding scheme is easily implemented at each sensor by correlating the depth with the upstream node depth for determining the packet transmission, by which the dbr protocol decreases signaling overhead. however, the empty regions are unavoidable in dbr. as a result, additional overheads are included to cope up with the void region problem like location information in vbf and periodical alarms in vapr [29-30]. moreover, it is necessary to divert the packets down to avoid the void zone, which is referred as the long detour issue. the lengthy detour causes packet transfers to be delayed. in order to rectify these issues, the implementation of multimodal acoustic routing approach is employed, which assists in enhancing the durability of the sensor nodes but fails to satisfy the demand of ideal outcomes with expected standards [31]. an energy efficient distance vector is used in this article to provide opportunistic routing in uwsns to avoid the void area and lengthy detour issues. each node’s distance vector keeps tracking the count of hop it takes to get into the sink. an or based distance-vector (odvr) mechanism is suggested based on the hop-count information to transmit the location information of submarine through the possible shortest path. the remaining parts of this paper is laid out as below. the network model and an outline of odvr are described in section ii. the design of the odvr is expounded in section iii. the performance assessment of odvr using the results of simulation software is depicted in section iv and the summation is provided in section v. 2 propagation model the sensor nodes of submarine collect the position data from the surroundings and send it to the sink node through the multi-hop forwarding. a minimum of one surface sink is utilized on the water surface to get location information of submarine from sensors through the uwsn channel and to conduct the data to the base station through the wireless radio channel. the transmission begins in the water from the submarine to the destination node. figure 1: network model the expression of energy attenuation in underwater is expressed by, 10log(a(l, f )/a0) = k ∗ 10logl + l ∗ loga(f ) (1) https://doi.org/10.15837/ijccc.2022.5.4781 4 where, the diffusion loss is expressed as k*10logl; absorption loss is defined as l*loga(f); normalizing factor a0 indicates constant loss; absorption coefficient is a(f); spreading factor k has a value of 2, 1.5 and 1 for spherical, practical and cylindrical spreading. the signal is represented as sl(f ) = sn r(l, f ) (n (f )) (a−1(l, f )) (2) where turbulence noise is expressed as n(f). the power of the signal is specified as, p (f ) = ∫ b(l) (sl(f )) (3) where bandwidth is b(l) with l coverage radius the consumed power [25] is given by the energy consumption of packet [26] is given as, ep(l) = (pr + pt(l))l b (4) where l,b,and p_r denote constant, length, transmission rate, and the power required to receive location information of submarine. when compared to the conventional protocols, the odvr has several attracting features. the prominent benefits of odvr are listed in the subsequent section: avoids “void region” and “long detour” problems: these common issues in the depth-based routing causes poor packet delivery ratio, extreme delay and high energy usage. the suggested odvr protocol makes use of the query scheme in uwsns to determine each node’s distance vector to the sink. the location information of submarines is sent towards the destination along with the available shortest route based on the distance vectors. as the distance vector is capable of recognizing the void region, the packets have not reached the empty zones, where the nodes have unlimited counts of hop. at the same time, the shortest-path forwarding strategy of odvr avoids the lengthy detour. light-weight signaling with opportunistic routing: the odvr requires no complicated signaling from the query method to determine the distance vector of underwater nodes. the odvr chooses the relay possibilities and coordinates opportunistic forwarding on the basis of the distance vector in each node hop-by-hop node. unlike the conventional or protocols, the odvr requires no information about its neighboring sensors and requires no relay candidate to be included in packet headers. if the location information reaches an intermediate node, the receiver makes a decision to reject or resend it on the basis of the distance vector. furthermore, the source nodes require no information about the nodes, which have successfully received the location information and about the candidates who have the ability to forward location information. therefore, the odvr is proved to be a light-weight protocol with low signaling overhead and few signaling exchanges. in the multi-hop routing and forwarding, the odvr makes use of distance vectors. unlike the typical routing systems, the odvr has not transmitted the hop by hop location information through a predetermined path. on the other hand, the odvr takes advantage of any opportunity to relay packets. the odvr uses distance vector to pick the possible nodes and organize the packet forwarding as the distance vectors have high reachability chances to the sink. the query technique is used by odvr to create distance vectors for all sensors. in contrast to the wireless adhoc networks, the sensor in uwsns points a certain sink. only the sink initiates control messages (inquiry packets) and the distance vectors of the sink are stored by the uwsn nodes. as a result, the overhead in control and buffer storage is considerably decreased. the distance vector’s hop-count is used by odvr to determine each node’s relay priority. the node with the fewest hops to the sink gets the greatest priority in forwarding the location information. as a result, the odvr uses distance vectors in nearby nodes to consider all of the neighboring nodes in the possible hop list. in this case, it is not mandatory to include the information about the relay candidates in the headers of data packets, which are transmitted by the sensor sources. the data packets from the sensor sources are simply broadcasted to the sink node as destination. https://doi.org/10.15837/ijccc.2022.5.4781 5 3 design of proposed routing protocol the odvr creates distance vectors on a regular basis. the submarine begins the routing through the transmission of location information, which is then opportunistically forwarded to the destination by intermediary nodes. the details of odvr are presented in the subsequent section. 3.1 establishment of distance vector to assists in the creation of distance vectors, the query mechanism is used in uwsns. as the sink is the common endpoint for all sensor nodes in uwsns, the sink is necessary to start the establishment process of distance vector. the sink: the sink creates query packets on a regular basis and broadcasts the location information to the sensors. figure 2 shows the query packet header, in which the query id is a positive integer that starts at 1 and increments by one. as a result, the query id of the most recent query packet is always biggest. the field n keeps track of the distance from the sink as hop count. at the sink node, the field n is initialized to be zero. underwater nodes: except the sink node, all nodes figure 2: query packet header have a local buffer for recording the sink’s distance vector. unlike the conventional distance vector routing, the uwsns nodes share the same destination. in addition, the forwarding path is not defined in odvr. hence, keeping track of the destination and successive hop are not essential. the query id and number of hops to the destination in odvr are recorded in the local buffer by using the distance vector. the hop count is initialized to infinity by the node in the buffer. when a query packet is received, the local query id and number of hops are refreshed. the algorithm given below describes the steps involved in receiving a query packet. the above step is continued till the query packets are passed to all the nodes. the shortest path towards the sink is established with the fewest hop counts by all the nodes that access the query packet. if new queries fail to update the distance vectors, these vectors are deleted. https://doi.org/10.15837/ijccc.2022.5.4781 6 3.2 routing and sending at sources the opportunity based routing picks several possible relay candidates to forward data packet rather than picking a node sequences along a route for forwarding the data. to help the opportunity based forwarding among the possible relays, the sender has to choose and send through the relay hops with the data packet header, which incur a significant cost for the routing protocol. the odvr has not picked and sent relay hops in the data packet header to decrease overhead. instead of picking relay candidates from the source, the odvr takes use of neighbor node cooperation to create a distributed relay candidate set. without any control messages, the source simply delivers the data packets. all data packet contains id of starting node, sequence number of packet, the preceding forwarder’s hopcount np and data payload as illustrated in figure 3. the sequence number of packet and source id are called as global packet id (gid) as these two are used to identify the packet. figure 3: structure of data packet when there is no accessible route to reach the destination, the distance vectors reject the useless transmissions. a node in the uwsn gets separated from other nodes due to factors like wave motion, mobility of nodes or energy depletion in certain nodes. the sink sends no query packets to this node. this isolated node’s distance vector has an unlimited hop-count. it has to remain quiet to avoid transmission power wastage. when it receives another inquiry packet, it starts the transmission of data packets. 3.3 opportunistic forwarding at intermediate nodes it is highly possible for all the neighboring nodes to relay the packets as the sender has not specified the forwarder but some neighboring nodes fails to successfully acquire the packets since the variation in link quality reduces signal. the nodes having shorter links have a better chance of successfully receiving data packets whereas nodes having longer links merely have a lesser chance. to attain a high delivery ratio, the conventional routing schemes take advantage of multi-hop short connections. despite the low reception probability of lengthy connections, some adjacent nodes are closer to the destination. it appears that taking use of lengthy adjacent nodes, which have poor successful packet reception speed up the data packet delivery. the nodes’ distance vectors are used by odvr to enable opportunity based forwarding by long adjacent nodes. the hop count distance and delivery level of data packets from buffered distance vector to the sink are determined by each node. the problem lies in the un-awareness of nearby node, which has successful packet reception of surrounding nodes that have the copies of same packet. the network congestion and transmission overlap arise when all nodes are permitted to send the successfully received packets. moreover, it maximizes the uwsn’s energy usage and bandwidth. the intermediary sensors have to coordinate with one another to prevent repeated forwarding for minimizing the energy wastage and bandwidth. the problem with coordinated forwarding is that the intermediate node has no idea about the distance of possible relay candidates. however, a large number of control messages in uwsns are not desired. the distance vectors created by the query method in figure 6 are dependent on the successfully received query packets. the query packet is straightly received from the sink by the red nodes (n = 1). the packets are then passed to the blue (n = 2) and green (n = 3) nodes. due to the frequent change in uwsn channel, the sink receives the location informations directly from these nodes. when a green node send a packet to the sink, it is no longer required for the blue or red nodes to conduct https://doi.org/10.15837/ijccc.2022.5.4781 7 the similar packet. as a result, the waiting mechanism has to be served for two purposes. the first is to provide data packets to the closest nodes a high priority and then to stop sending the duplicate packets of the same nodes. to achieve opportunity based forwarding, the odvr utilizes the following timer setting, i.e., t = 2 · [ni − np + 2]+ · t0 + rand(0, cw ) · tslot[ni − np + 2]+ (5) figure 4: uwsn query mechanisms as the nodes wait for fewer than two hop counts away from the destination, the setting in (1) cause no considerable delay in packet delivery because the chances of successful packet delivery after more than two hops are small. thus, the odvr assigns highest priority to packet forwarding for nodes, which have more than two hops. the below method summarizes the features of the data forward algorithm for the intermediate nodes. the gids of formerly acquired data-packets are recorded in two buffers, q_1 and q_2. here, q_1 is assigned for packets, which are waiting to be forwarded whereas buffer q_2 is assigned for packets that have already been forwarded. to avoid repeated forwarding, the incoming packet has to be dropped once it has received in q_1 and q_2. if not, the node begin a waiting timer with a duration determined by the user (1). the node changes the packet header’s hop-count n_p with its own hop-count n_i and then sent the packet when the timer is expired. the node waits to receive packets throughout the waiting time. to avoid redundancy, it rejects a packet if similar packets are sent by a closer node. according to the odvr algorithm, a packet telecasted by a node with a hop count n_i is observed by several intermediary nodes with a hop count smaller than n_i. on the other hand, the waiting scheme gives the higher significance to the node having the fewest hops by waiting for lesser period. the odvr allows no nodes with the similar hop count to transmit packet consecutively in a flooding manner. the media access control for shared-channel uwsns prevents several nodes from using the channel simultaneously. when one node is connected to the channel and transfers a packet, the surrounding nodes hear it and cease forwarding the same copy. furthermore, the random back off avoids causing severe conflict in the media access control by delaying data forwarding in various nodes at random. it’s worth noting that the odvr protocol easily extended for numerous sinks applications. the query packets has to be sent by several sinks. the underwater sensors have to keep track that how many hops it takes to reach any of the sinks. when a packet reaches an intermediary node, it is handled in the similar way as when it arrives at a single sink. https://doi.org/10.15837/ijccc.2022.5.4781 8 4 results and discussion the proposed odvr is employed in ns2 simulator to assess its performance in an optimal way. around 200-400 nodes are employed arbitrarily in a space of 1000m * 1000m * 1000m. the comparative analysis of the proposed odvr with other existing approaches like of adaptive power controlled routing protocol (apcrp) and distributed energy efficient and position-aware (deep) routing algorithm in terms of average hop-count, energy consumption, average end-to-end latency, packet delivery ratio (pdr) and time consumption is carried out to measure the submarine detection performance of uwsn. both the apcrp and deer algorithms are implemented in the recent works because of having the advantageous impacts like high performance with optimal outcomes. in order to validate the present study in an enhanced manner, the performance of this proposed approach is analogized with these recent algorithms. hence, it is proved that the present methodology is significantly competent even than the other recent approaches. the overall consumed energy is highlighted in figure 5. when the nodes are increased, the overall consumption of energy is also gets maximized. the energy consumption of odvr is highly lower than the apcrp and deep algorithms. among these three approaches, the apcrp owns high energy consumption because it requires extreme energy for setting up the node power to a high level when it is unable to locate a relay node before transferring the data packets. the packet collisions cause the cross layer node to be identified as a relay node, which makes the apcrp to consume more energy. the sensor nodes in apcrp and deep transmit probe packets to identify the relay nodes before transmitting the data packets, which causes extreme energy consumption. on the contrary, the odvr uses the method of directional forwarding to determine the optimal relay node, which highly assists in minimizing the consumption of energy. furthermore, the odvr prevents cyclic transmission to save the overall energy. thus, it is proved that both the apcrp and deep consume significantly more energy than odvr. the investigation of end-to-end latency is depicted in figure 6. it is observed that the end-to-end latency of apcrp and deep is highly greater than the odvr. the delay of nodes is substantially decreased in odvr because the nodes deliver data packet instantly to the reliable node. to minimize the latency, the odvr eliminates cyclic transmission, which is regarded as one of the significant features of this odvr. when the deep or apcrp find it difficult to locate the proper https://doi.org/10.15837/ijccc.2022.5.4781 9 relay node for delivering data packets, the odvr repeatedly transmit probe packets until it locates the proper relay node. the deep has a longer average end-to-end latency than apcrp because deep transmits probe packets continuously until it finds an acceptable relay node whereas the apcrp picks a relay node by changing power levels of sensor nodes to reduce latency. hence, it is validated that the performance of odvr for the end-to-end latency is significantly greater than others, which clearly represented in the aforementioned figure. for different nodes quantity, the average hop-count of dispatched packet is shown in figure 7. the odvr has the lowest average hop-count than apcrp and deep. in apcrp, the forwarder is chosen on the basis of sensor node depth information without considering the datum from the destination. the packets are transmitted more quickly to the shallower nodes. furthermore, the lengthy detour problem occurs with apcrp as the shallow nodes are too far from the destination node. in odvr, the nodes that are close to the destination (in hop-count) are more likely to send the location information with greater priority. hence, the odvr frequently sends packets through the shortest route with fewer hops. the packet delivery ratio of apcrp, deep and odvr are compared in figure 8, which indicates that the odvr outperforms deep and apcrp in terms of delivery ratio. the apcrp shows comparatively least performance than others. the sensor nodes in apcrp simply forward the data packets from deep to shallow. if a packet reaches a node that has no accessibility and shallow neighbor, it reaches a void area, which leads to packet loss. furthermore, the horizontal distance is not taken into account in apcrp. even if the packets are sent to nodes at the water’s edge, the packets get vanished when the sink node is out of coverage area. as a result, the apcrp delivery ratio is restricted specifically when the quantity of destination nodes is low. as the location information is directly transmitted to the sink nodes, the delivery ratio is very high in odvr and the nodes that are close to the sink are given more priority. the sensor node’s forwarding priority is related to its depth in deep. as a result, the deep has a lower delivery ratio than odvr. the relation between network lifetime and simulation running time is portrayed in figure https://doi.org/10.15837/ijccc.2022.5.4781 10 figure 5: energy consumption figure 6: end-to-end delay 9. the network lifespan of sensor nodes begins to decline in the simulation progresses. the network degradation rate of sensor nodes in apcrp is quicker than deep and odvr when the simulation time is below 500s because the senor nodes in apcrp has not identified relay nodes for transmitting data packets. hence, more power is utilized until the best relay node is identified. furthermore, the cross layer node is treated as relay node in apcrp due to the packet collision. the aforementioned factors cause certain nodes to use excess energy, which results in making a wide gap between the residual energy and the network’s average residual energy. because of these reasons, the durability of the sensor nodes get minimized in a wider range. the network lifetime degradation rate of deep is below 20% at the end of the 900s. moreover, the odvr’s network lifespan is higher than apcrp and deep because the odvr considers the sensor nodes’ residual energy to prevent cyclic transmission and so the energy is distributed to balance the network lifetime. as the residual energy of each node in odvr falls below the minimal energy, the network is paralyzed at the end of its lifetime. finally, it is shown that the odvr’s energy distribution are balanced than deep and apcrp. https://doi.org/10.15837/ijccc.2022.5.4781 11 figure 7: average hop count figure 8: pdr 5 conclusion a novel routing algorithm called odvr is proposed in this paper for the submarine detection using uwsns. the odvr uses query method based distance vectors to enable forward routing without any complicated signaling in the process of identifying the submarine and coordinating the packet forwarding among the possible relays. the odvr topology is legitimate and capable of operating in an environment with dynamic network. the identification of each node is taken into account to characterize the sensor nodes’ transmission capacity for eliminating the communication void regions and long detours during the process of forwarding the location information. furthermore, it assists in delivering the energy efficient routing. thus, the performance of proposed routing protocol is proved to be superior to the existing deep and apcrp methods. https://doi.org/10.15837/ijccc.2022.5.4781 12 figure 9: network lifetime declaration of competing interests the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. conflict of interest the authors declare no conflict of interest. references [1] j. kong, j.-h. cui, d.wu, and m. gerla, “building underwater ad-hoc networks and sensor networks for large scale real-time aquatic applications,”in proc. ieee mil. commun. conf. 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[31] h. luo, x. xie, g. han, r. ruby, f. hong and y. liang, "multimodal acoustic-rf adaptive routing protocols for underwater wireless sensor networks," in ieee access, vol. 7, pp. 134954134967, 2019. copyright © 2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: shakila, r.; paramasivan (balasubramanian), b. (2022). opportunity based energy efficient routing algorithm for underwater wireless sensor network for submarine detection, international journal of computers communications & control, 17(5), 4781, 2022. https://doi.org/10.15837/ijccc.2022.5.4781 introduction propagation model design of proposed routing protocol establishment of distance vector routing and sending at sources opportunistic forwarding at intermediate nodes results and discussion conclusion international journal of computers communications & control issn 1841-9836, 9(6):730-740, december, 2014. uncertain query processing using vague set or fuzzy set: which one is better? j. mishra, s. ghosh jaydev mishra* computer science and engineering department college of engineering and management, kolaghat west bengal-721171, india *corresponding author:jsm03@cemk.ac.in sharmistha ghosh galgotias university, greater noida uttar pradesh-201306, india sharmisthag@yahoo.com abstract: in this paper we attempt to make a theoretical comparison between fuzzy sets and vague sets in processing uncertain queries. we have designed an architecture to process uncertain i.e. fuzzy or vague queries. in the architecture we have presented an algorithm to find the membership value that generates the fuzzy or vague representation of the attributes with respect to the given uncertain query. next, a similarity measure is used to get each tuples similarity value with the uncertain query for both fuzzy and vague sets. finally, a decision maker will supply a threshold or α-cut value based on which a corresponding sql statement is generated for the given uncertain query. this sql retrieves different result sets from the database for fuzzy or vague data. it has been shown with examples that vague sets give more accurate result in comparison with fuzzy sets for any uncertain query. keywords: uncertain data, similarity measures, fuzzy/vague interpreter. 1 introduction in the real world, vaguely specified data values appear in many applications such as sensor information, expert systems, decision analysis, medical sciences, management and engineering problems and so on. fuzzy set theory has been proposed to handle such vagueness by generalizing the notion of membership in a set. essentially, in a fuzzy set each element is associated with a point-value selected from the unit interval [0, 1], which is termed as the grade of membership in the set. a vague set, which is conceived as a further generalization of fuzzy set, uses the idea of interval-based membership instead of point-based membership as in the case of fuzzy sets. the interval-based membership in vague sets is more expressive in capturing vagueness of data. relational database systems have been extensively studied worldwide since codd [1] had proposed the relational data model in 1970. based on this model, several commercial relational database systems are available (see [2][4]). this data model usually takes care of precisely defined and unambiguous data. however, in the real world applications data are often partially known i.e., incomplete or imprecise. for example, instead of specifying that the height of david is 188 cm, one may say that the height of david is around 190 cm, or simply that david is tall. other examples on uncertain data may be “salaries of almost equally experienced employees are more or less the same" etc. all these are informative statements that may be useful in answering queries or making inferences. however, such type of data cannot be represented in the classical relational data model. in order to incorporate imprecise or uncertain data, the classical relational data model has been extended by several authors on the mathematical framework of fuzzy set theory which was initially introduced by zadeh [5] in 1965. based on this fuzzy set theory, various copyright © 2006-2014 by ccc publications uncertain query processing using vague set or fuzzy set: which one is better? 731 fuzzy relational database models, such as similarity-based relational model [6], possibility-based relational model [7] and some types of hybrid data models [8] have been proposed to model fuzzy information in relational databases. however, the most important issue in the utilization of any database system lies in its ability to process information and queries correctly. several authors [9][12] have contributed to provide a theoretical contribution to query language for a fuzzy database model. in particular, bosc et al. [11] and nakajima [12] have extended the well known sql language in the framework of fuzzy set theory and have developed a fuzzy sql language, called sqlf. it is believed that vague sets, proposed by gau et al. [13] in 1993, that use interval-based membership values have more powerful ability to process imprecise information than traditional fuzzy sets. thus the notion of vague sets has also been incorporated into relations in [14] and a vague sql (vsql) has been described. the vsql allows the users to formulate a wide range of queries that occur in different modes of interaction between vague data and queries. in [15], zhao and ma have proposed a vague relational database model which is an extension of the classical relational model. based on the proposed model and the semantic measure of vague sets, they have also investigated vague querying strategies and have given the form of vague querying with sql. in this paper, we have made an attempt to make a theoretical comparison between fuzzy sets and vague sets in processing uncertain queries. firstly, we have designed an architecture to test uncertain queries. next, we have presented an algorithm to retrieve membership values for imprecise data represented by fuzzy or vague sets. a similarity measure is then used to calculate each tuple’s similarity value with the uncertain query for both fuzzy and vague sets. finally, the decision maker provides a threshold value or α-cut based on which a corresponding sql statement is generated for the given uncertain query. this sql retrieves different result sets from the database for fuzzy data or vague data. in the present study, we have considered an employee database and processed some uncertain queries using fuzzy data as well as vague data. each time it has been observed that vague sets give more accurate result in comparison to fuzzy sets. the rest of the paper is organized as follows. section 2 presents some basic definitions related to fuzzy and vague sets. similarity measure between two vague data is also defined in the same section. section 3 represents an architecture for processing uncertain queries. in section 4, an algorithm has been designed to get the appropriate membership value and represent domain value of fuzzy or vague attributes into fuzzy form or vague form. section 5 establishes that a vague set is more appropriate than fuzzy set with real life examples. the concluding remarks appear in section 6. 2 basic definitions in this section, we introduce some basic concepts related to fuzzy and vague sets and similarity measure of two vague sets which have been utilized throughout the paper. let u be the universe of discourse where an element of u is denoted by u. 2.1 fuzzy set definition 1. a fuzzy set f in the universe of discourse u is characterized by a membership function µf : u → [0, 1] and is defined as a set of ordered pairs f = {⟨u, µf (u)⟩ : u ∈ u} where µf (u) for each u ∈ u denotes the grade of membership of u in the fuzzy set f . 732 j. mishra, s. ghosh 2.2 vague set definition 2. a vague set v in the universe of discourse u is characterized by two membership functions given by: slowromancapi@. a truth membership function tv : u → [0, 1] and slowromancapii@. a false membership function fv : u → [0, 1], where tv (u) is a lower bound of the grade of membership of u derived from the ‘evidence for u’, and fv (u) is a lower bound on the negation of u derived from the ‘evidence against u’, and tv (u) + fv (u) ≤ 1. thus the grade of membership µv (u) of u in the vague set v is bounded by a subinterval [tv (u), 1−fv (u)] of [0, 1], i.e., tv (u) ≤ µv (u) ≤ 1−fv (u). then, the vague set v is written as v = {⟨u, [tv (u), 1 − fv (u)]⟩ : u ∈ u}. here, the interval [tv (u), 1 − fv (u)] is said to be the vague value to the object u and is denoted by vv (u). for example, in disease diagnosis process of a medical system, the vague value [0.3, 0.6] can be interpreted as "the report of disease in favour is 30%, against is 40% and another 30% is indeterminable". the precision of knowledge about u is clearly characterized by the difference (1−fv (u)−tv (u)). if this is small, then the knowledge about u is relatively precise. however, if it is large, we know correspondingly little. if tv (u) is equal to (1−fv (u)), the knowledge about u is precise, and vague set theory reverts back to fuzzy set theory. if tv (u) and (1−fv (u)) are both equal to 1 or 0, depending on whether u does or does not belong to v , the knowledge about u is exact and the theory goes back to that of ordinary set. thus any crisp or fuzzy set may be considered as a special case of vague sets. 2.3 similarity measure there have been some studies in literature which discuss the topic concerning how to measure the degree of similarity between vague sets [16][19]. in [19] it was pointed out by lu et al. that the similarity measures defined in [16][18] did not fit well in some cases. they have proposed a new similarity measure between vague sets which turned out to be more reasonable in more general cases. the same has been used in the present work which is defined as follows: definition 3. similarity measure between two vague values let x and y be any two vague values such that x = [tx, 1 − fx] and y = [ty, 1 − fy], where 0 ≤ tx ≤ 1 − fx ≤ 1, and 0 ≤ ty ≤ 1 − fy ≤ 1. let se(x, y) denote the similarity measure between x and y. then se(x, y) = √ (1− (|(tx − ty)− (fx −fy)|/2)) (1−|(tx − ty) + (fx −fy)|). definition 4. similarity measure between two vague sets let u = {u1, u2, u3, · · · , un} be the universe of discourse. let a and b be two vague sets on u, such that a = {< ui, [ta(ui), 1−fa(ui)] >,∀ui ∈ u}, where ta(ui) ≤ µa(ui) ≤ 1 − fa(ui) and 1 ≤ i ≤ n. b = {< ui, [tb(ui), 1−fb(ui)] >,∀ui ∈ u}, where tb(ui) ≤ µb(ui) ≤ 1 − fb(ui) and 1 ≤ i ≤ n. now, the similarity measure between a and b, denoted by se(a, b) is defined as: se(a, b) = 1 n n∑ i=1 se ([ta(ui), 1−fa(ui)], [tb(ui), 1−fb(ui)]) = 1n n∑ i=1 √ p ∗q where p = (1− (|(ta(ui)− tb(ui))− (fa(ui)−fb(ui))|/2)) and q = (1−|(ta(ui)− tb(ui))− (fa(ui)−fb(ui))|) uncertain query processing using vague set or fuzzy set: which one is better? 733 3 architecture for processing uncertain query below is the architecture for processing an imprecise or uncertain query. figure 1: uncertain query processing architecture working principle of all components of the above proposed architecture is given below: input: a relational database with fuzzy or vague attributes, uncertain query, threshold value or α-tolerance value given by decision maker. fuzzy/vague interface: it has two components, namely, fuzzy/vague interpreter and fuzzy/vague sql. fuzzy interpreter: in this phase, the fuzzy attributes as well as the fuzzy data are identified from the given fuzzy query. next, the fuzzy interpreter represents all domain values of each of the fuzzy attributes. algorithm 1 presented below in section 4 is then used to determine membership value for each domain value of all the fuzzy attributes. this gives us the fuzzy representation of the attributes with respect to the fuzzy data identified from the given query. the above fuzzy representation is then converted to a corresponding vague form whose truth membership value is same as the membership calculated in the fuzzy representation and false membership value is (1-truth membership value). after that a suitable similarity measure formula is to be used to measure the similarity between vague representation of each fuzzy attribute and the corresponding vague representation of the relevant fuzzy data given in the uncertain query. the same method will be applied for all fuzzy attributes appearing in the fuzzy query. if the query has more than one fuzzy attribute, then the similarity measure of tuples is obtained as the intersection of the similarity measures for each attribute. vague interpreter: in this case, the vague attributes and the vague data are identified from the given uncertain query. next, the vague interpreter will represent all domain values of each of the vague attributes. algorithm 1 is now used to get the truth membership values of the vague attributes while the decision maker will supply the false membership values with the condition that the sum of truth and false membership values should not exceed 1. after that, as before, a similarity measure formula is used to measure the similarity between each vague representation of a domain value and vague data given in the query. if the query has more than one vague attribute, once again the intersection of the similarity measures for individual attributes will give the similarity measure of tuples. fuzzy/vague sql: in this phase, the decision maker will supply a threshold value or α-cut value based on which a corresponding sql statement for the given uncertain i.e., fuzzy or vague query will be generated. output: finally, the sql generated above is submitted to the database to get the desired result. 734 j. mishra, s. ghosh 4 algorithm for finding membership value the following algorithm finds the membership value for each domain value of fuzzy or vague attributes with respect to fuzzy or vague data given in the uncertain query. algorithm 1 membership value calculation input: fuzzy/vague attributes and fuzzy/vague data given in the uncertain query. output: a membership value in the interval [0,1]. method: first find the fuzzy/vague attributes from the fuzzy/vague query. for each fuzzy/vague attribute do begin fdata ← data value for the fuzzy/vague attribute of the query range = maxdomainvalue mindomainvalue avg ← mean value of the domain set of the fuzzy/vague attribute b ← avg while(avg ≤ range) do begin avg = avg + b end while loop for each tuple of the relation do begin tuplevalue ← corresponding tuple value from the fuzzy/vague attribute domain membershipvalue = 1-(|fdata-tuplevalue| / avg) end for loop of tuple end for loop of fuzzy/vague attribute 5 vague sets have an extra edge over fuzzy sets in this section, we have experimentally shown with real life examples that vague sets give more accurate result than fuzzy sets. to illustrate this fact, we have considered the following employee emp relational database: table 1: emp relation name age (yrs) exp (yrs) sal (rs) prof. smith 25 1 20000 prof. ganguly 52 25 55000 prof. roy 38 15 38000 prof. david 48 23 53000 prof. maity 34 10 32000 prof. das 30 4 27000 prof. ahuja 50 26 55500 prof. sharma 51 16 40000 prof. kundu 45 22 50000 prof. dutta 54 33 80000 next we consider following uncertain queries to explain that vague sets give better result in uncertain query processing using vague set or fuzzy set: which one is better? 735 comparison to fuzzy sets. uncertain query 1: "find the details of the professors whose age is around 50". i) solution with fuzzy sets: in the above uncertain query 1, fuzzy attribute is age and fuzzy data is around 50. now, we apply our algorithm 1 to get the membership value corresponding to each domain value of fuzzy attribute age. input: algorithm needs the following two inputs: fuzzy attribute age and fuzzy data around 50. method: calculation of membership value for each tuple value of fuzzy attribute age based on fuzzy data around 50. dom(age) = {25, 52, 38, 48, 34, 30, 50, 51, 45, 54} given fdata=50 range = 54 25 = 29 avg = 42.7 b = 42.7 avg≥ range then avg remain same i.e., avg = 42.7 now, we need to find the membership value using the formula specified in the algorithm 1: membershipv alue = 1− (|fdata− tuplev alue|/avg) for the 1st tuple : membershipv alue = 1− (|50−25|/42.7) = 0.41 for the 2nd tuple : membershipv alue = 1− (|50−52|/42.7) = 0.95 for the 3rd tuple: membershipv alue = 1− (|50−38|/42.7) = 0.72 for the 4th tuple: membershipv alue = 1− (|50−48|/42.7) = 0.95 for the 5th tuple: membershipv alue = 1− (|50−34|/42.7) = 0.63 for the 6th tuple: membershipv alue = 1− (|50−30|/42.7) = 0.53 for the 7th tuple: membershipv alue = 1− (|50−50|/42.7) = 1 for the 8th tuple: membershipv alue = 1− (|50−51|/42.7) = 0.98 for the 9th tuple: membershipv alue = 1− (|50−45|/42.7) = 0.88 for the 10th tuple: membershipv alue = 1− (|50−54|/42.7) = 0.91 the fuzzy representation of the emp relation w.r.t. uncertain query 1 is now depicted in table 2. in particular, the fuzzy representation of the attribute age appears in third column of the table 2. next in fourth column, we have shown the corresponding vague representation of these fuzzy data. these vague values are then used to find the similarity measures (s.m.) with fuzzy data around 50 whose vague representation is < 50, [1, 1] >. the similarity measures have been calculated using the same formula as presented in definition 3. for example, consider the following two vague data: x =< 50, [1, 1] > and y =< 25, [0.41, 0.41] >. here tx = 1, fx = 0, ty = 0.41, fy = 0.59. then, se(x, y) = √ (1− (|(1−0.41)− (0−0.59)|/2)) (1−|(1−0.41) + (0−0.59)|) = √ (1−0.59) = √ 0.41 = 0.64 again, for the vague values x =< 50, [1, 1] > and y =< 52, [0.95, 0.95] >, tx = 1, fx = 0, ty = 0.95, fy = 0.05. then, se(x, y) = √ (1− (|(1−0.95)− (0−0.05)|/2)) (1−|(1−0.95) + (0−0.05)|) = √ (1−0.05) = √ 0.95 = 0.98 and so on. using the notation: fd = fuzzy data, vd = vague data, we represent in table 2: now, if the threshold value or α-cut given by the decision maker is 0.95, then the corresponding sql statement of the uncertain query 1 is generated as below: select ∗ from emp where s.m.(tuple)≥0.95 which retrieves the following resultant tuples given 736 j. mishra, s. ghosh table 2: fuzzy representation of emp relation w.r.t uncertain query 1 name age fuzzy age with fd around 50 vague fd age s.m. with vd < 50, [1, 1] > exp sal s.m. (tuple) prof. smith 25 < 25, .41 > < 25, [.41, .41] > .64 1 20000 .64 prof. ganguly 52 < 52, .95 > < 52, [.95, .95] > .98 25 55000 .98 prof. roy 38 < 38, .72 > < 38, [.72, .72] > .85 15 38000 .85 prof. david 48 < 48, .95 > < 48, [.95, .95] > .98 23 53000 .98 prof. maity 34 < 34, .63 > < 34, [.63, .63] > .79 10 32000 .79 prof. das 30 < 30, .53 > < 30, [.53, .53] > .73 4 27000 .73 prof. ahuja 50 < 50, 1 > < 50, [1, 1] > 1 26 55500 1 prof. sharma 51 < 51, .98 > < 51, [.98, .98] > .99 16 40000 .99 prof. kundu 45 < 45, .88 > < 45, [.88, .88] > .94 22 50000 .94 prof. dutta 54 < 54, .91 > < 54, [.91, .91] > .95 33 80000 .95 in table 3 from the emp database in table 2. table 3: resultant relation of uncertain query 1 for fuzzy set at threshold value α=0.95 name age exp sal prof. ganguly 52 25 55000 prof. david 48 23 53000 prof. ahuja 50 26 55500 prof. sharma 51 16 40000 prof. dutta 54 33 80000 ii) solution with vague sets: next, we process the same uncertain query 1 for vague sets. here, vague attribute is age and vague data is around 50. it is then necessary to represent all domain values of attribute age into vague form whose truth membership values are calculated from the algorithm 1 and false membership values are provided by the decision maker considering the restriction that sum of truth and false membership values ≤1. similarity measures are then calculated using the same formula as used for fuzzy attributes. let us consider the two vague data x =< 50, [1, 1] > and y =< 25, [0.41, 0.5] >. here tx = 1, fx = 0, ty = 0.41, fy = 0.5. then, se(x, y) = √ (1− (|(1−0.41)− (0−0.5)|/2)) (1−|(1−0.41) + (0−0.5)|) = √ (1−1.09/2)∗ (1−0.09) = √ 0.455∗0.91 = √ 0.41405 = 0.64 again, for x =< 50, [1, 1] > and y =< 52, [0.95, 0.98] >, tx = 1, fx = 0, ty = 0.95, fy = 0.02. then, se(x, y) = √ (1− (|(1−0.95)− (0−0.02)|/2)) (1−|(1−0.95) + (0−0.02)|) = √ (1−0.07/2)(1−0.03) = √ 0.93605 = 0.97 and so on. the table 4 given below shows the complete vague representation of emp relation w.r.t. the uncertain query 1. with the same threshold or α-cut value 0.95, the following sql statement of the uncertain query 1 for vague set will be generated: select ∗ from emp where s.m.(tuple)≥0.95 which now retrieves the following resultant tuples given in table 5 from the emp database for uncertain query processing using vague set or fuzzy set: which one is better? 737 table 4: vague representation of emp relation w.r.t uncertain query 1 name age vague representation of vague data age s.m. with vague data < 50, [1, 1] > exp sal s.m. (tuple) prof. smith 25 < 25, [.41, .5] > .64 1 20000 .64 prof. ganguly 52 < 52, [.95, .98] > .97 25 55000 .97 prof. roy 38 < 38, [.72, .8] > .84 15 38000 .84 prof. david 48 < 48, [.95, .98] > .97 23 53000 .97 prof. maity 34 < 34, [.63, .75] > .78 10 32000 .78 prof. das 30 < 30, [.53, .7] > .71 4 27000 .71 prof. ahuja 50 < 50, [1, 1] > 1 26 55500 1 prof. sharma 51 < 51, [.98, 1] > .98 16 40000 .98 prof. kundu 45 < 45, [.88, .93] > .93 22 50000 .93 prof. dutta 54 < 54, [.91, .95] > .94 33 80000 .94 vague set in table 4. table 5: resultant relation of uncertain query 1 for vague set at threshold value α=0.95 name age exp sal prof. ganguly 52 25 55000 prof. david 48 23 53000 prof. ahuja 50 26 55500 prof. sharma 51 16 40000 it may be noted from tables 3 and 5 that vague set gives better solution than fuzzy set since the sql statement with vague query does not retrieve the tuple of prof. dutta with age 54 that has been fetched with the fuzzy query. it may be observed that 54 is less closer to 50 compared to the values of the attribute age in all the other tuples retrieved by the sql statement. next, we consider an uncertain query where more than one attribute are fuzzy or vague in nature. uncertain query 2: "find the details of the professors whose age is around 50 and experience is more or less 25". i) solution with fuzzy sets: uncertain query 2 has two fuzzy attributes, age and experience. applying algorithm 1 and definition 3 for calculating membership values and similarity measures respectively, we get the following fuzzy representation of emp relation (fd = fuzzy data): here, µ1 denotes the similarity measures of age attribute with respect to fd age around 50 [for detail calculation see table 2], µ2 denotes the similarity measures with respect to fuzzy attribute exp, and µ = µ1 ∩µ2 denotes the similarity measures of tuples. then, the result is tested for different threshold or α-cut values given by the decision maker. 738 j. mishra, s. ghosh table 6: fuzzy representation of emp relation w.r.t uncertain query 2 name age µ1 exp fuzzy exp with fd almost 25 vague fd exp µ2 sal µ prof. smith 25 .64 1 < 1, .3 > < 1, [.3, .3] > .56 20000 .56 prof. ganguly 52 .98 25 < 25, 1 > < 25, [1, 1] > 1 55000 .98 prof. roy 38 .85 15 < 15, .71 > < 15, [.71, .71] > .84 38000 .84 prof. david 48 .98 23 < 23, .94 > < 23, [.94, .94] > .97 53000 .97 prof. maity 34 .79 10 < 10, .5 > < 10, [.5, .5] > .71 32000 .71 prof. das 30 .73 4 < 4, .4 > < 4, [.4, .4] > .63 27000 .63 prof. ahuja 50 1 26 < 26, .97 > < 26, [.97, .97] > .99 55500 .99 prof. sharma 51 .99 16 < 16, .74 > < 16, [.74, .74] > .86 40000 .86 prof. kundu 45 .94 22 < 22, .91 > < 22, [.91, .91] > .95 50000 .94 prof. dutta 54 .95 33 < 33, .77 > < 33, [.77, .77] > .88 80000 .88 case a) for α=0.95, the sql statement is select ∗ from emp where α ≥0.95 which retrieves the resultant table 7 from table 6 as follows: table 7: resultant relation of uncertain query 2 for fuzzy set at threshold value α=0.95 name age exp sal prof. ganguly 52 25 55000 prof. david 48 23 53000 prof. ahuja 50 26 55500 case b) for α=0.87, the sql statement is select ∗ from emp where α ≥0.87 and the resultant table is shown below in table 8: table 8: resultant relation of uncertain query 2 for fuzzy set at threshold value α=0.87 name age exp sal prof. ganguly 52 25 55000 prof. david 48 23 53000 prof. ahuja 50 26 55500 prof. kundu 45 22 50000 prof. dutta 54 33 80000 ii) solution with vague sets: again, using algorithm 1 and definition 3, the vague representation of emp relation for uncertain query 2 may be obtained as follows (vd= vague data): the result is now tested for vague set with the same threshold or α-cut values. case a) for α=0.95, the sql statement is select ∗ from emp where µ ≥0.95 which retrieves from table 9 the following resultant table as case b) for α=0.87, sql statement is select ∗ from emp where µ ≥0.87 and the resultant table is from tables 7 and 10 it may be observed that the resultant sets of the uncertain query 2 for both fuzzy data and vague data are same for the threshold value α=0.95. however, when the same query is tested with α-cut value 0.87, tables 8 and 11 show that the vague sets certainly gives better result than fuzzy sets because vague sql has not retrieved the tuple prof. dutta with age 54 and experience 33 which is not so closer to age 50 and experience 25. uncertain query processing using vague set or fuzzy set: which one is better? 739 table 9: vague representation of emp relation w.r.t uncertain query 2 name age vague age with vd around 50 µ1 exp vague exp with vd almost 25 µ2 sal µ prof. smith 25 < 25, [.41, .5] > .64 1 < 1, [.3, .4] > .56 20000 .56 prof. ganguly 52 < 52, [.95, .98] > .97 25 < 25, [1, 1] > 1 55000 .97 prof. roy 38 < 38, [.72, .8] > .84 15 < 15, [.71, .8] > .83 38000 .83 prof. david 48 < 48, [.95, .98] > .97 23 < 23, [.94, .98] > .96 53000 .96 prof. maity 34 < 34, [.63, .75] > .78 10 < 10, [.5, .6] > .7 32000 .7 prof. das 30 < 30, [.53, .7] > .71 4 < 4, [.4, .43] > .63 27000 .63 prof. ahuja 50 < 50, [1, 1] > 1 26 < 26, [.97, 1] > .98 55500 .98 prof. sharma 51 < 51, [.98, 1] > .98 16 < 16, [.74, .82] > .85 40000 .85 prof. kundu 45 < 45, [.88, .93] > .93 22 < 22, [.91, .96] > .94 50000 .94 prof. dutta 54 < 54, [.91, .95] > .94 33 < 33, [.77, .85] > .86 80000 .88 table 10: resultant relation of uncertain query 2 for vague set at threshold value α=0.95 name age exp sal prof. ganguly 52 25 55000 prof. david 48 23 53000 prof. ahuja 50 26 55500 table 11: resultant relation of uncertain query 2 for vague set at threshold value α=0.87 name age exp sal prof. ganguly 52 25 55000 prof. david 48 23 53000 prof. ahuja 50 26 55500 prof. kundu 45 22 50000 6 conclusions in this paper, we have proposed an architecture to process uncertain queries represented by fuzzy or vague data. we have also presented an algorithm that generates the fuzzy or vague representation of the attributes with respect to the given uncertain query. similarity measure presented in definition 3 is used to find similarity measure of tuples w.r.t the given uncertain query in fuzzy or vague representation. then the proposed architecture has been verified for uncertain queries using a real life example, both for the fuzzy as well as vague representation. in each case it has been observed that vague sets have produced more accurate result in comparison to fuzzy sets. hence a vague relational database model may be more fruitful in processing real life data and queries than the conventional fuzzy data models. a dbms that implements this vague set theoretic concept can thus become a more powerful software product than those currently available. bibliography [1] codd e. f. 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(2004); managing merged data by vague functional dependencies, lncs, springer-verlag berlin heidelberg, 3288: 259-272. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 3, month: june, year: 2022 article number: 4482, https://doi.org/10.15837/ijccc.2022.3.4482 ccc publications dynamic traffic light system to reduce the waiting time of emergency vehicles at intersections within iot environment y. tashtoush, m. al-refai , g. al-refai, n. zaghal, d. darweesh, o. darwish yahya tashtoush, mohammed al-refai, noor zaghal, dirar darweesh* department of computer science and information technology, jordan university of science and technology, jordan yahya-t@just.edu.jo, mnalrefai@just.edu.jo, noorzaghal@just.edu.jo, dmdirar12@cit.just.edu.jo ghaith al-refai mechatronics and artificial intelligence department, german jordanian university, jordan ghaith.alrefai@gju.edu.jo omar darwish information security and applied computing, eastern michigan university, ypsilanti, mi 48197, usa odarwish@emich.edu * corresponding author: dmdirar12@cit.just.edu.jo abstract traditional traffic light system, which works based on fixed cycle can be a main reason for traffic jam, due to lack of adaptation to road conditions. traffic jam has a bad impact on drivers and road users due to the time delay it causes for road users to reach their destinations. this delay can cause a life threat in case of emergency vehicles, such as ambulance vehicles and police cars. one key solution to solve traffic jam on intersections is the dynamic traffic lights, where traffic light operation adapts based on the intersection traffic conditions. since few of researches projects in the literature interested in solving traffic jam problem for emergency vehicles, the contribution of this paper is to introduces a novel approach to operate traffic light system. the new approach consists of two algorithms which are pure operation mode and hybrid operation mode. these operation modes aim to reduce the waiting time of emergency vehicles on traffic intersections. they assume that there is a smart infrastructure system uses internet of things (iot) that can detect emergency vehicles arrival to an intersection. the smart infrastructure system switches traffic light operation from fixed cycle mode to dynamic mode. the dynamic mode manages traffic lights at intersections to reduce the waiting time of emergency vehicles. the paper presents a simulation of the proposed algorithms, highlights their advantages. in order to evaluate the efficiency of the new technique, we compared our approach with wen algorithm in the literature and the traditional traffic light system. our evaluation study indicated that the proposed algorithms outperformed wen technique and the traditional system under different traffic scenarios. keywords: dynamic traffic light, emergency viechle, expert system, iot, waiting time, intersections. https://doi.org/10.15837/ijccc.2022.3.4482 2 1 introduction emergency vehicles (ev) such as ambulances, fire brigade and police cars provide huge services to communities by saving lives and preventing theft and crimes. in 2016, around 146,000 died in india because of road accidents, 30% of deaths are caused by delayed ambulance [10]. there is a real need to find solutions that allow ev to reach the desired destinations as fast as possible. traffic lights are considered as the primary elements to control the traffic in urban areas. inefficient traffic light operation can lead to traffic congestions and increases the waiting time of cars at intersections. most of today’s traffic lights work based on fixed cycle protocol and do not consider the traffic conditions. iot which enables connected vehicles and smart city technology, allows more input information to be collected from roads to control traffic lights. for example, the number of vehicles at intersections, vehicle movement direction and vehicle’s category can be used as inputs to manage a dynamic traffic light system. using of iot in road infrastructure and smart cities bcame a trend because of the chances it provides to improve road safety and road efficiency. frank et al. proposed smart traffic light system using computer vision algorithms and iot to measure traffic density [9]. rathore et al. have established an iot-based smart city by using big data analytics while harvesting real-time data from the city [21]. as there are few of research works concerned in solving traffic jam issue for emergency vehicles. the contribution of this paper is to present two solutions for dynamic traffic light system. the goal of the proposed systems is to prioritize emergency vehicles and reduce their waiting time at intersections, so emergency vehicles can arrive their destinations in shorter times compared to times taken using the fixed period traffic light systems. the proposed system uses iot to transmit the data between the traffic light system and the infrastructure, where ai algorithms is used to detect emergency vehicle’s location. based on the emergency vehicle’s location and the current state of the traffic light, our dynamic model for traffic light system will adapt to reduce the waiting time for the emergency vehicles. we compared our approach with wen, w. approach [27], and the fixed cycle mode.the results obtained by our approach under different scenarios outperformed those obtained by wen’s approach [27] and the traditional traffic light system . this paper is following the scientific rules for how to write a good paper, which is stated in [? ], and it is organized as follows. section 2 presents related work in the area of dynamic traffic light management. section 3 introduces the proposed algorithms. section 4 presents the simulation results and the comparison with wen’s approach [27]. the conclusion is presented in section 5. 2 related work many researches have been conducted to come up with solutions for dynamic and smart traffic light systems. fleck et al. proposed adaptive traffic light system based on vehicles back logs [8]. they studied a quasi-dynamic policy depending on partial state information defined by detecting whether vehicle backlogs are above or below certain thresholds. the policy is parameterized by green and red cycle lengths as well as the road content thresholds. a novel approach for dynamic traffic lights management based on wireless sensor networks and multiple fuzzy logic controllers is proposed in [5], each fuzzy controller addresses vehicles turning movements and dynamically manages both the phase and the green time of traffic lights. https://doi.org/10.15837/ijccc.2022.3.4482 3 sumi et al. proposed dynamic and automatic traffic light control expert system for solving the road congestion problem. in the proposed solution, emergency vehicles are scheduled to prioritize them for a smooth passage through the traffics based on incident type [24]. al-khateeb et al [1]. proposed a dynamic traffic light system using radio frequency identification (frid) [1], this rfid technique deals with a multi-vehicle, multilane, multi road junction area. it provides an efficient time management scheme, in which a dynamic time schedule is worked out in real time for the passage of each traffic column. collotta et al. proposed dynamic traffic light system based on information gathered through a wireless sensor network. dynamically processes green times in a traffic light of an isolated intersection to reduce the red-light time [4]. many adaptive traffic light systems are proposed to prioritize emergency vehicles and reduce their waiting time at traffic lights. goel et al. proposed an adaptive traffic intersection system based on wireless sensor network where the traffic light of one intersection can communicate with the traffic light of the next neighboring intersections and traffic clearance will be prioritized for special vehicles with the help of sensors [12]. viriyasitavat et al. proposed an approach that uses a different set of local rules at intersections, one can support priority management of emergency vehicles in a self-organized manner [26]. the proposed system suggests using virtual traffic light-priority intersection control (vtl-pic) protocol that can detect the presence of an emergency vehicle and assign priority to the emergency vehicle at an intersection. iyyappan et al. proposed automatic accident detection and ambulance rescue with intelligent traffic light system [14]. the idea behind this system is to implement intelligent traffic light system (itls) to control mechanically the traffic lights in the path of the ambulance. the ambulance is controlled by the control unit which furnishes adequate route to the ambulance and controls the traffic light according to the ambulance location and thus reaching the hospital safely. the controller identifies the location of the accident spot through the sensor systems in the vehicle which determined the accident and thus the controller walks through the ambulance to the spot. another approaches for traffic management for emergency vehicle priority was designed based on computer vision as proposed in [18]. this paper presents an approach to schedule emergency vehicles in traffic. the approach combines the measurement of the distance between the emergency vehicle and an intersection using visual sensing methods, vehicle counting and time sensitive alert transmission within the sensor network. the distance between the emergency vehicle and the intersection is calculated for comparison using euclidean distance, manhattan distance and canberra distance techniques. ghazal et al., [11] proposed an approach to handle high range of cars with different classes in traffic signal. a wide variety of priorities could be given to different classes of vehicles. emergency vehicles like ambulance and fire vans, would have the first priority. next priority is given to vip’s over the everyday vehicles. priority can also be given depending on automobile density in a single aspect of avenue in traffic junction. the street which had better automobile number might get maximum precedence. bhandari et al., [29] provided a new technique to priority-mobility aware clustering routing method for high submission of packets by giving fair weight for every packet of node. in order to set the scheduling plan in an automatic way, strengthening learning approach is incorporated. the mongrel technique for priority and self-learning generated better employment of power. the empirical result indicated a comparisons between slotted sense multiple access protocol, aodv, memac and p-macron protocol, where the proposed technique produced better outcomes ,according to packet https://doi.org/10.15837/ijccc.2022.3.4482 4 size, simulation time and interval. dangi et al. proposed an efficient and intelligent traffic control system based on the density of the traffic along with the safe pedestrian crossing. this approach is based on using a video surveillance camera and some image and video processing techniques to detect traffic congestion and make decisions [7].other video surveillance systems are also developed and proposed by other researchers [3][6][13][22] for intelligent traffic control system. ata et al. provided a mechanism to predict the traffic congestion with the help of artificial neural networks (ann) which shall control or minimize the blockage and result in the smoothening of road traffic. in particular, they proposed modeling smart road traffic congestion control using artificial back propagation neural networks (msr2c-abpnn) for road traffic increase transparency, availability and efficiency in services offered to the citizens. this system aims to provide intelligent transportation decisions, where the neural network is the plausible way to find traffic situations and make the decisions accordingly [2]. memiş et al. mentioned in their paper that the goal of their study was to define and grade the risk factors of road transportation, which are critical for efficient and economic supply chain arrangement. on the other hand, the authors utilized fuzzy piprecia as a multi-criteria rating technique, in order to prioritize the risk factors [17]. stojčić. stated that his paper concerned in researching the implementation of the anfis (adaptive neuro fuzzy inference system) model in transport and traffic by a revise of pertinent papers. anfis is considered as an element of artificial intelligence. it is vastly utilized within intelligent transport systems. the authors mentioned in their research that all gathered papers were partitioned into 7 sub-areas. the analysis of the suggested models was conducted for each sub-area with a tabular preview of the input and output variables. they found that the guidance and management of vehicles form a sub-area with the largest percentage in the total number of tested papers, while security implementations were in the second place [23]. neural networks were also used by other researchers in the development of automatic traffic control systems [22][20][28]. evolutionary algorithms and fuzzy logic were also used by many researchers [25][15][19][16]. in [27], wen, w. proposed a dynamic and automatic traffic light control expert system for solving the road congestion problem. they leveraged the interarrival time, interdeparture time and the number of leaving cars to design traffic light operation protocol. the simulation results showed a significant drop in the waiting time at traffic lights. fig. 1 shows the framework for the dynamic traffic light system using iot proposed in [27]. this paper proposes two algorithms for dynamic traffic light system. the proposed algorithms aim to prioritize mergency vehicles and reduce their waiting time at intersections. additionally, we used wen’s approach [27], which is a very popular approach and its results showed a significant improvement in the waiting time, as a reference model for our proposed algorithms, where we compared the results obtained by our algorithms with those obtained by wen’s approach. 3 proposed dynamic traffic light system this section explains the two proposed approaches for dynamic traffic light operation. the algorithms are explained in detail with their flow charts. to be able to implement the dynamic traffic light system, iot is used to establish a communication between vehicles and infrastructure, this communication is known as vehicle to infrastructure (v2i). through v2i, the infrastructure knows how many vehicles at an intersection, and it detects the arrival of ev. the infrastructure can then control the traffic light operation mode based on the provided information about an intersection. fig. 2 shows https://doi.org/10.15837/ijccc.2022.3.4482 5 figure 1: a framework for dynamic and automatic traffic light control expert systems using iot (wen [27]) a diagram for road intersection with iot that allows v2i communication to detect road conditions and control the traffic light. the numbering of traffic lights shown in fig. 2 will be used during the algorithm’s explanation. ev always arrives to traffic light 1, and the rest of the traffic lights are incrementally numbered clockwise. in the proposed approaches, there are many input parameters that controls the operation of the algorithms. below are the definitions and the acronyms for the algorithms’ parameters: w ti: initial waiting time for the emergency vehicle at an intersection. w tf : total waiting time for the emergency vehicle at an intersection. s: traffic light situation when emergency vehicle arrives (red light or green light). ri: initial number of vehicles waiting at an intersection in front of the emergency vehicle when it arrives. rf: number of vehicles that still exist in front of the emergency vehicle after some vehicles pass the intersection. ti: average waiting time needed by ri vehicles to pass the intersection. tf : average waiting time needed by rf vehicles to pass the intersection. tr: time of the red light tg: time of the green light the goal of the proposed models are to reduce total waiting time for the emergency vehicle at an intersection (w tf ). 3.1 pure operation mode suppose that an ev reaches traffic light 1 at an intersection (see fig. 1), in this case it will follow one of the following scenarios: 1. the ev passes the intersection after vehicles in front of it • if the ev is detected and traffic light 1 is green. • if the ev is detected and its traffic light (1) is red, and another traffic light within the same intersection (the current active traffic light (2, 3, or 4)) is green, switch it to red and switch traffic light (1) to green. 2. the ev will wait a time equal to (3*tr+tg)*(# of times traffic light 1 changed its status before the ev passes), then passes the intersection after the vehicles in front of it • if the ev isn’t detected, traffic light 1 is green, but the green light time is not enough to allow all the vehicles ahead of the ev to pass. https://doi.org/10.15837/ijccc.2022.3.4482 6 figure 2: road intersection with iot that allows v2i communication to detect ev and control traffic lights operation mode 3. the ev will wait a time within the interval [1 tg] until traffic light 1 becomes green, then passes the intersection after vehicles in front of it • if the ev isn’t detected and traffic light 2 is green. 4. the ev will wait a time within the interval [(tr+1) (tr+tg)] until traffic light 1 becomes green, then passes the intersection after vehicles in front of it • if the ev isn’t detected and traffic light 3 is green. 5. the ev will wait a time within the interval [(tr*2+1) (tr*2+tg)] until traffic light 1 becomes green, then passes the intersection after vehicles in front of it • if the ev isn’t detected and traffic light 4 is green. fig. 3 shows the flowchart of the pure operation mode of traffic lights at an intersection. 3.2 hyprid operation mode suppose that an ev reaches traffic light 1 at an intersection, in this case it will follow one of the following scenarios: 1. the ev passes the intersection after vehicles in front of it • if the ev is detected and traffic light 1 is green. • if the ev is detected and traffic light 1 is red, after the active traffic light is changed to red, and traffic light 1 becomes green. https://doi.org/10.15837/ijccc.2022.3.4482 7 figure 3: flow chart of the system behavior when applying the pure operation mode 2. the ev will wait a time equal to (tr+tg)*(# of times traffic light 1 changed its status before the ev passes), then passes the intersection after the vehicles in front of it • if the ev isn’t detected, traffic light 1 is green, but the green light time is not enough to allow all the vehicles ahead of the ev to pass. 3. the ev will wait a time equal to (tr) until traffic light 1 becomes green, then passes the intersection after vehicles in front of it • if the ev isn’t detected and traffic light 2, or 3, or 4 is green. fig. 4 shows the flow chart of the hybrid operation mode algorithm 4 simulation results the simulator shows the behavior of 4 different techniques. during our study we considered low, medium, and heavy traffic scenarios on different paths length. https://doi.org/10.15837/ijccc.2022.3.4482 8 figure 4: flow chart of the system behavior when applying the hybrid operation mode however, the simulator operated on paths with one, two, three, four, and five intersections before the destination is reached. but its responses are the same when the vehicles are passing paths with one intersection or two intersections; therefore, we concentrated on the statistics of paths with two intersections. also, the simulator’s reactions are the similar when the vehicles are passing paths with three, four, or five intersections; for this reason, we concentrated on the statistics of paths with four intersections. figures 5 and 6 show the difference between the average waiting time (wt) of the vehicles at paths with two and four intersections, where it is noticeable that the wt of paths with four intersections are more affected by the hybrid detection technique. it is clear in fig. 5, that the pure detection technique is better than the others including the hybrid one in minimizing the wt of the vehicles at different distances from the traffic light’s sensor when the traffic density is low ([10-30] vehicles). while in fig. 5, we notice that the hybrid technique is the best solution in most cases when the traffic density is low. figures 7, 8, 9 and 10 demonstrate the effects of the four techniques on the wt when traffic densities are medium ([30-50] vehicles) or heavy ([50-80] vehicles). these figures support the results mentioned before in figures 5, and 6. we calculated the average waiting time of the four methods when traffic is low, medium and heavy. these outcomes are indicated in table 1, 2 and 3 respectively. we notice in table 1 that when the traffic is low and the number of intersections before the destination is two, pure detection technique is 29% better than hybrid detection, 43.1% better than wen’s-best case, and 17.5% better than without detection. whereas when the traffic is low and the https://doi.org/10.15837/ijccc.2022.3.4482 9 figure 5: average waiting time (wt) of the vehicles at low traffic paths with two intersections before the destination is reached. table 1: average waiting time of vehicles when traffic is low approach two intersections four intersections average waiting time (minutes) average waiting time (minutes) pure system 0.797 3.169 hybrid system 1.121 2.563 wen’sbest case 1.399 3.796 traditional system 0.966 5.179 number of intersections before the destination is four, hybrid detection technique is 19% better than pure detection, 32.5% better than wen’s-best case, and 50.5% better than without detection. table 2: average waiting time of vehicles when traffic is medium approach two intersections four intersections average waiting time (minutes) average waiting time (minutes) pure system 0.797 3.169 hybrid system 1.121 2.563 wen’sbest case 1.399 3.796 traditional system 0.966 5.179 table 2 shows that when the traffic is medium and the number of intersections before the destination is two, pure detection technique is 24.8% better than hybrid detection, 33.3% better than wen’s-best case, and 9.4% better than without detection. whereas when the traffic is low and the number of intersections before the destination is four, hybrid detection technique is 20% better than pure detection, 19.2% better than wen’s-best case, and 39% better than without detection. as we notice in table 3, the outcomes when the traffic density is heavy are the same to those of low and medium results, but with little decrease in the percentages. https://doi.org/10.15837/ijccc.2022.3.4482 10 figure 6: average waiting time (wt) of the vehicles at low traffic paths with four intersections before the destination is reached. table 3: average waiting time of vehicles when traffic is heavy approach two intersections four intersections average waiting time (minutes) average waiting time (minutes) pure system 1.081 4.376 hybrid system 1.348 3.621 wen’sbest case 1.771 4.743 traditional system 1.338 6.126 5 conclusion this paper proposes two techniques to operate traffic lights, in order to minimize the waiting time of emergency vehicles on traffic intersections, which occures by using the traditional traffic light system. these techniques are pure operation mode and hybrid operation mode. the proposed models suppose that there is a smart infrastructure system utilize internet of things (iot), which can discover emergency vehicles incoming to an intersection. then, the smart infrastructure system moves from the traditional traffic light mode to dynamic mode, which will manage the traffic lights system. to evaluate the efficiency of these algorithms, we calculated their average waiting time, and compared it with wen algorithm and the fixed cycle mode when traffic is low, medium and heavy. the results are shown in table i,2 and 3 respectively. they show that when the traffic is low and the number of intersections before the destination is two, pure detection technique is 29% better than hybrid detection, 43.1% better than wen’s-best case, and 17.5% better than without detection. while when the traffic is low and the number of intersections before the destination is four, hybrid detection technique is 19% better than pure detection, 32.5% better than wen’s-best case, and 50.5% better than without detection. in addition, we noticed that when the traffic is medium and the number of intersections before the destination is two, pure detection technique is 24.8% better than hybrid detection, 33.3% better than wen’s-best case, and 9.4% better than without detection. while when the traffic is low and the number of intersections before the destination is four, hybrid detection technique is 20% better than pure detection, 19.2% better than wen’s-best case, and 39% better than without detection. furthermore, the results of heavy density traffic where similar to those of low and medium results, https://doi.org/10.15837/ijccc.2022.3.4482 11 figure 7: average waiting time (wt) of the vehicles at medium traffic paths with two intersections before the destination is reached. figure 8: average waiting time (wt) of the vehicles at medium traffic paths with four intersections before the destination is reached. https://doi.org/10.15837/ijccc.2022.3.4482 12 figure 9: average waiting time (wt) of the vehicles at heavy traffic paths with two intersections before the destination is reached. figure 10: average waiting time (wt) of the vehicles at heavy traffic paths with four intersections before the destination is reached. https://doi.org/10.15837/ijccc.2022.3.4482 13 but with a slight decrease in the percentages. the reason behind the simulator’s different reactions in the cases of two intersections paths and four intersections paths is because the hybrid detection technique is more efficient in reducing the waiting time than the pure detection technique only if the number of intersections is three or more. such that the additional time given to the green light of the traffic light in the hybrid detection technique, generates a higher initial waiting time if the emergency vehicle didn’t pass the intersection from the first turn, but this effect decreases after the second intersection. as a result, a noticeable improvement is produced by the hybrid detection technique as the path consists of more intersections, while the pure detection technique is preferred when paths include one or two intersections. actually, we have some limitations for our research as the smart infrastructure is not supported in all countries. on the other hand, if several emergency vehicles reached at the same time how we would set priorities 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(2021). prioritymobility aware clustering routing (p macron) algorithm for lifetime improvement of comprehensive dynamic wireless sensor network, international journal of computers communications & control, volume: 16, issue: 2,2021. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: tashtoush, y.; al-refai, m.; al-refai, g.; zaghal, n.; darweesh, d.; darwish o. (2022). dynamic traffic light system to reduce the waiting time of emergency vehicles at intersections within iot environment, international journal of computers communications & control, 17(3), 4482, 2022. https://doi.org/10.15837/ijccc.2022.3.4482 introduction related work proposed dynamic traffic light system pure operation mode hyprid operation mode simulation results conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 4, month: august, year: 2020 article number: 3914, doi.org/10.15837/ijccc.2020.4.3914 ccc publications a green routing mathematical model for iot networks in critical energy environments c. lozano-garzon, g. a. montoya, y. donoso carlos lozano-garzon* systems and computing engineering department universidad de los andes bogotá, colombia *corresponding author: calozanog@uniandes.edu.co german adolfo montoya systems and computing engineering department universidad de los andes bogotá, colombia ga.montoya44@uniandes.edu.co yezid donoso systems and computing engineering department universidad de los andes bogotá, colombia ydonoso@uniandes.edu.co abstract in this paper, we propose a multi-objective mathematical optimization model that is the underlying support for the proposal of a new routing algorithm that aims to extend the lifetime in iot networks for applications in critical energy environment. the network lifetime is evaluated for three approaches: the hop count approach, the energy consumption approach, and the multiobjective approach based on free space loss and the battery energy level of the iot nodes. after this evaluation, we compared the different approaches in terms of how many transmissions were possible to do under a particular approach until none path cannot be found from an origin node to a destination node. finally, we conclude that the multi-objective method was the best strategy for extending the network lifetime since building short distance paths and considering battery level of the iot nodes every time is, in the long run, a better strategy than just building paths considering nodes with a high battery level or building paths minimizing the number of network hops. keywords: mathematical optimization model, green routing algorithm, internet of things. doi.org/10.15837/ijccc.2020.4.3914 2 1 introduction in recent years, the number of devices connected to the internet has experimented an unforeseen growth due to mainly their easiness of use and large amounts of applications. however, one subclass of these devices has had one of the most significant growth: the iot devices [3, 11, 12]. nonetheless, it is necessary to propose improved techniques to manage the large amounts of data traffic generated by this type of network. in this sense, it is required to improve the usage of these resources; since otherwise, this will have repercussions in the amount of energy needed to keep the devices in a normal operation mode for sending and receiving information [10]. the solution that has been used to keep the sensors working is to improve the batteries attached to the devices [2, 10]. however, lately, a technique has been used to lessen the energy shortage on these devices with no access to an electric network; this is energy harvesting [8]. basically, it is a way to gather energy from the environment using hardware to transform this energy into electricity and then give it to the device. some examples of energy that can be gathered are solar, thermal, piezoelectric, among others [4]. in this research, we focus our work on iot networks that are deployed on locations without access to traditional electric energy supplies such as mountains, deserts, lakes, among others. these networks are usually used to gather information about the natural conditions of the environment where they are placed, such as temperature, humidity, acidity, and others that eventually lead scientists to know the state of that specific region [4]. another characteristic of this type of network is that due to the lack of energy, not all iot devices can use high energy consumption protocols for transmitting/receiving data and must rely on low energy consumption protocols such as zigbee or bluetooth le [5]. however, current solutions are focused on either optimize the routing of data through the network or increasing the amount of energy available for the devices [2, 6, 9]. in this sense, we propose a new approach that not only takes into account the energy consumption in the batteries of each device, but also takes into account the amount of energy that it can harvest from the environment to find optimal paths in the iot networks in order to extend as much as possible the network lifetime. we propose a multi-objective mathematical optimization model; in this model we used of three objective functions, all of them for minimizing: the first one, based on the number of path hops; the second one, related to the energy consumption in the network; and the third one, based on the free space loss indicator and the battery energy level of the nodes. regarding the model constraints, these restrictions are designed to represent the fact of finding a minimum cost path to send an activated service from a source node to a base station considering flow conservation, bandwidth, and path building constraints for activated services. as a result our multi-objective proposal presents the best results in contrast with the shortest path or the energy consumption optimization strategies. the remainder of this paper is organized as follows. the general problem statement is described in section 2; the mathematical optimization model for the problem and the routing algorithm are introduced in section 3; in section 4 we presents the results obtained; and finally, section 5 shows the conclusions and the future works. 2 general problem statement in this work, we propose a mathematical optimization model and a routing algorithm for iot networks according to the challenges involved in critical energy environment applications. the main objective of our proposal is finding a minimum cost path to send an activated service from a source node to any base station doing an efficient use of the available energy of the iot devices, minimizing the path hops, and reducing the distance involved in the discovered path with the aim of extending as much as possible the network lifetime. from this perspective, our problem will involve more than one objective function that sometimes could be in conflict. for example, the shortest path (in terms of the number of hops) between two nodes could have the highest energy consumption. for this reason, we use a multi-objective approach in order to find an optimal set of solutions that holds a trade-off between all proposed objective functions. doi.org/10.15837/ijccc.2020.4.3914 3 we assume that the iot network has symmetrical links between the different nodes; therefore, we could model it as a non-directed graph. we represent the input graph as a set compound of a set of nodes and a set of links. in the network and according to the example represented in the figure 1, we could have several origin nodes o (source nodes) and some destinations nodes d (base stations). from an origin node, we could have several activated services (s2 for o1 and s1 for o2) that must be sent to any base station. in real applications, a service is a specific type of information that must be transmitted from an origin node to a base station such as the temperature, the humidity or ph level. however, not all the origin nodes need to send all its services to a base station [7, 14]. for this reason, in this approach, we assume that each origin node could have activated and deactivated services to be sent to a base station. this network feature is approached in the mathematical optimization proposed in the next section. figure 1: example of an iot network. in summary, we have an iot network with several origin nodes and destination nodes (base stations), at which is required to find an optimal path (p ath1 and p ath2) to transmit an activated service from an origin node to a destination node (base station). this path can be built considering different goals, depending on what we want to achieve. in this sense, three goals are proposed to be obtained: minimizing the hops count, minimizing the energy consumption, and a multi-objective approach that considers minimizing the energy consumption and the free space loss indicator. once a goal is defined, we have to determine its performance in terms of network lifetime. each of these goals and the method used to determine its network lifetime will be explained in the next sections. 3 mathematical optimization model approach in this section we present a multi-objective mixed-integer formulation of the mathematical model proposed for the problem described above. 3.1 notation the sets, parameters and variables required by our mathematical model are described in the table 1. 3.2 objective functions our mathematical optimization model is applied considering different objective functions, depending on what we want to optimize. in this sense, three objective functions are proposed to be optimized: the hops count, the energy consumption, and a multi-objective approach that considers minimizing the energy consumption and the free space loss indicator. each of these objective functions will be explained below. doi.org/10.15837/ijccc.2020.4.3914 4 table 1: notations of the proposed model sets description n set of network nodes. o set of origin nodes. d set of destination nodes. i set of intermediate nodes: i ∈ n | i /∈ o ∪ d s set of services. oi o ∪ i id i ∪ d parameters description distij distance between an i−node and a j−node. uij bandwidth capacity for each (i, j) link. bso bandwidth required by a s−service from an o−origin node. ssoact indicates which s−service is active to be sent from an o−origin node. eci consumed energy at the i−node. variables description x sodij is a binary variable that determines if the link (i, j) is used to send a s−service from an o−origin node to a d−destination node. y sod is a binary variable that determines if the s−service from the o−origin node will arrive at the d−destination node. 3.2.1 hops count the hops count function is the main function of many routing algorithms. this function represents the number of links through which packets must pass from source to the set of destination nodes. the purpose of minimizing function 1 is finding the shortest path between any pair of nodes. min ∑ i∈n ∑ j∈n ∑ s∈s ∑ o∈o ∑ d∈d x sodij (1) 3.2.2 energy consumption an iot network could be composed of heterogeneous nodes, and each one of them could have different energy consumption in both transmission and reception. given the nature of iot networks, some nodes that belong to this network could be mobile phones, sensors, or laptops. therefore, the proposed routing algorithm, in order to reduce power consumption, should select the path with the lower overall energy consumption to increase the battery life of these kind o devices. the objective function 2 takes into account both the energy consumption used by the source node in the transmission process and the energy used by the destination node in the reception process, over the entire path. min ∑ i∈n ∑ j∈n ∑ s∈s ∑ o∈o ∑ d∈d eci ∗ ecj ∗ x sodij (2) for the energy consumption model is required to take into account additional considerations. if a node has to send a data packet of k bits to another node located at a distance d, then, the following are the expressions to calculate the energy consumption in the transmitter node as well as the receiver node. in the transmitter node, the consumption is eelec + eamp, where eelec is the energy consumption for codification, modulation and filtering. eamp corresponds to energy consumption for the transmitter power amplifier. in the same way, in the receiver node, the consumption corresponds to eamp. then, the expressions for the transmitter and receiver sensor are the following: etx = (eelec + eamp) ∗ k ∗ d2 (3) doi.org/10.15837/ijccc.2020.4.3914 5 erx = eamp ∗ k ∗ d2 (4) in the constraint 3 there will have a higher energy consumption than constraint 4 because for transmission is required an extra consumption for codification, modulation and filtering (eelec), in addition to energy consumption for amplifying the signal received (eamp). 3.2.3 free space loss this function defines the signal strength loss in free space conditions. this loss is directly related with the distance between the nodes and the transmission frequency. the aim of the following objective function 5 is the minimization of the maximum overall loss in the path. min ∑ i∈n ∑ j∈n ∑ s∈s ∑ o∈o ∑ d∈d distij ∗ x sodij (5) 3.2.4 multi-objective approach (energy consumption + free space loss) this function combines our previous energy consumption function plus a function based on the free space loss indicator, which represents the signal strength loss in free space conditions and is directly related with the distance between the nodes and the transmission frequency. in this sense, the free space loss indicator (fsl) will be proportional to the distance between two pairs of nodes. the fsl indicator between two nodes is given by the following expression [1] [15]: f slij (db) = 20 log10 dij + 20 log10 f + 92.45 (6) where dij corresponds to the distance (km) between two pair of nodes and f is an operational frequency used in a iot technology to perform transmissions and receptions measured in ghz. once an iot technology is determined, its operational frequency is considered fixed for doing fsl calculations. however, while the operational frequency and the term 92.45 are considered constants in the fsl expression, the distance dij impacts proportionally the fsl calculation since dij is a parameter that changes depending on the distance between two pair of nodes. the multi-objective function 7 is described in the following equation. min ∑ i∈n ∑ j∈n ∑ s∈s ∑ o∈o ∑ d∈d (w1 ∗ eci ∗ ecj ∗ x sodij + w2 ∗ f slij ∗ x sod ij ) where w1 + w2 = 1 (7) 3.3 model constraints according to the general problem statement, some activated services must be sent from origin nodes to any destination node (base station). this scenario is denoted in the following constraints: 3.3.1 origin nodes the expressions 8 and 9 allow us selecting only one destination per activated service. an activated service represents a service that is required to be sent from an origin node to any destination node; whether a service is activated or not is determined by the parameter ssoact. ∑ d∈d y sod = 1 ∗ ssoact ∀s ∈ s, ∀o ∈ o (8) ∑ j∈n|j∈id x sodij = 1 ∗ y sod ∀i ∈ n , ∀o ∈ o | i ∈ o ∀s ∈ s, ∀d ∈ d (9) doi.org/10.15837/ijccc.2020.4.3914 6 3.3.2 destination nodes expressions 10 and 11 allow just one destination per service. if y sod is activated, that is, it is necessary to send a service s from o to d, we guarantee that one destination d is selected to receive the service s. ∑ i∈n|i∈oi x sodij = 1 ∗ y sod ∀j ∈ n , ∀d ∈ d | j ∈ d ∀s ∈ s, ∀o ∈ o (10) ∑ i∈n|i∈d x sodij = 0 ∀j ∈ n , ∀d ∈ d | j ∈ d ∀s ∈ s, ∀o ∈ o (11) 3.3.3 intermediate nodes expression 12 represents the flow conservation law in order to find a path between s and d for an activated service s. ∑ j∈n|j∈id x sodij − ∑ j∈n|j∈oi x sodji = 0 ∀i ∈ i, ∀s ∈ s, ∀o ∈ o, ∀d ∈ d (12) 3.3.4 bandwidth expression 13 assures that different services could be transmitted in a specific link (i,j). otherwise, the services must be sent for different (i,j) links. ∑ s∈s ∑ o∈o ∑ d∈d bso ∗ x sodij ≤ uij ∀i ∈ n , ∀j ∈ n (13) 3.3.5 undirected graph due to we are dealing with undirected graphs (a link between i and j is equivalent to have a link (i,j) and a link (j,i)) expression 14 guarantees that, if between nodes i and j a link must be selected, just one of them is selected. x sodij + x sod ji ≤ 1 ∀i ∈ n , ∀j ∈ n , ∀s ∈ s, ∀o ∈ o, ∀d ∈ d (14) in summary, we have mathematical optimization model formulation that represents an iot network with several origin nodes and destination nodes (base stations), at which is required to find optimal paths to transmit activated services from origin nodes to destination nodes (base stations). depending on which objective function is considered, these paths are built minimizing the hops count, considering an efficient usage of the available energy of the iot devices, or considering a third approach which is a result of taking into account the energy consumption of iot nodes plus minimizing the free space loss indicator. therefore, our next step consists of proposing a method to determine the network lifetime for each approach, which will be explained in the next section. 3.4 routing algorithm using the mathematical model solution the previous mathematical model allows sending particular services from different origin nodes to any destination node for one time. the term “one time” must be understood as the mathematical model is executed one time to obtain a solution. however, due to network purpose corresponds to send as many services as possible until nodes energy allows it, the mathematical model must be performed several times in order to know the network lifetime. the algorithm 1 illustrates how the mathematical model is handled to obtain the network lifetime. doi.org/10.15837/ijccc.2020.4.3914 7 our goal consists of finding a path to carry an activated service from an origin node to a destination node in a network; however, if our scenario is a disconnected network, it is possible that this path cannot be found. this means we have obtained an infeasible solution. for this reason, the mathematical model must run considering a connected network in order to find feasible solutions, which is guaranteed in the first line. the network randomness is in terms of nodes coordinates, guaranteeing that each node is not disconnected from the network. algorithm 1 routing algorithm using the mathematical model solution for network lifetime calculation. 1: generate a random connected graph 2: initialize eci 3: establish the maximum energy capacity value maxecap 4: establish the energy harvesting value eh 5: establish activated services sactso 6: establish the objective function to minimize 7: nump eriods = 0 8: while at least one origin node has a path p to send an activated service to any destination node do 9: nump eriods = nump eriods + 1 10: solve p aths = m athematicalm odel(eci, sactso, f ) 11: for each i−node in p aths do 12: update eci 13: end for 14: for each i−node in the network do 15: eci = eci − eh 16: end for 17: for each origin node do 18: find a path p in p aths to transmit 19: if p = ∅ then 20: sactso = 0 21: end if 22: end for 23: end while 24: return nump eriods line 2 shows the consumed energy of each node, which is initialized with a default value of 1. as nodes are selected for transmitting or receiving activated services, they increase their consumed energy. line 3 establishes the maximum energy capacity of each node. this value is the same for all nodes, except for destination nodes. destination nodes are a particular type of nodes in iot; usually, they are devices with enhanced processing, memory and battery resources in comparison with the rest of network devices. for this reason, we assume that this type of nodes has no limitations in terms of energy consumption. concerning the rest of the network nodes, if the consumed energy of an i−node, eci, surpasses the maximum energy capacity, maxecap, this node has wasted all of its battery energy capacity, and then, this node is not available anymore for transmitting or receiving a service in the network. line 4 defines the energy harvesting value. this value is decreased from the eci due to it is an energy amount coming from a harvesting technology such as solar panels. line 5 establishes which services will be activated; that is, from all available services in the origin nodes, which of them finally require to be sent from an origin node to a destination node. in other words, by modifying sactso (1 or 0), we can define which service will be activated or not. from line 8 to 24, a period means how many times it is possible to do transmission from an origin node to a destination node. each time that it is possible to find at least one path from an origin node to a destination node, nump eriods is increased, that is, the network lifetime has been extended. if none doi.org/10.15837/ijccc.2020.4.3914 8 path is found, it means that some nodes required for building any path from an origin node to a destination node have depleted all of its available energy. in the implementation and results sections, nump eriods is equal to the number of transmissions, that is, the number of times at which it was possible to send services from all source nodes to a destination node before all these source nodes have depleted all of its available energy. 4 implementation our mathematical optimization model was implemented using gams and matlab in order to show the performance of each approach, that is, hops count, energy consumption, and the multiobjective approaches. our implementation scenarios were divided into two parts: the first one, called "basic scenario", implements an illustrative scenario in order to understand the basis of each result approach, and the second one, called "complex scenarios", implements many scenarios in order to obtain statistical results. 4.1 basic scenario we established a simple scenario to determine if our mathematical model was correctly defined. figure 2 shows the defined testing scenario, where each link value indicates the free space loss (fsl) value. besides the free space loss value of each link in figure 2, on table 2 we summarize others important parameters assumed in the network scenario. according to the table 2, additional details will be specified: • the number of network nodes is 7, where two nodes (1 and 2) are source nodes, and the destination nodes are nodes 6 and 7. in other words, an activated service from a particular source node needs to be sent to any of the destination nodes. • the activated services for the source node 1 is just the service 1, and the activated service for the source node 2 is just the service 2. that is, source node one does not have the second service activated and it is not necessary to send this service to a destination node. in the same way, source node two does not have the first service activated. • we assume all nodes begin with 99% full of battery, except node 4 which starts with 90% of battery. we assume these values in order to establish whether the different approaches are building the paths correctly. 20 30 40 50 60 70 35 40 45 50 55 60 65 70 75 80 85 iot network 1 2 3 4 5 6 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 figure 2: iot network testing scenario. doi.org/10.15837/ijccc.2020.4.3914 9 based on figure 2 and table 2, we obtain the performance of our mathematical model for each approach, that is, the hops count approach, the energy consumption approach and the multiobjective approach. table 2: basic scenario parameters. parameters value work area 100x100[m2] eamp 100[pj/bit/m2] eelec 50[nj/bit] number of iot nodes 7 sources nodes 1 and 2 destination nodes (gateways) 6 and 7 number of services 2 activated services s1 at 1, s2 at 2 eci ∀i ∈ n | i 6= 4 1% of total battery ec4 10% of total battery 4.2 complex scenarios in this section, we enunciate the details of the scenarios evaluated in order to obtain statistical results. in other words, each approach is performed many times at different network sizes in order to obtain statistical results. figure 2 show the typical networks considered to be evaluated by each approach. 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 (a) typical network of 10 nodes. 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 (b) typical network of 20 nodes. 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 (c) typical network of 30 nodes. figure 3: typical networks sizes evaluated. according to the table 3, additional details will be specified: table 3: complex scenarios parameters. parameters value work area 100x100[m2] eamp 100[pj/bit/m2] eelec 50[nj/bit] number of iot nodes 10, 15, 20, 25, and 30 sources nodes depending on the scenario: one source node, two source nodes or three source nodes destination nodes (gateways) the last node number of services 1 activated services one service for each source node eci ∀i ∈ n 1% of total battery runs for each approach 100 doi.org/10.15837/ijccc.2020.4.3914 10 • the goal is to evaluate the approaches considering different network sizes, that is, a network with 10, 15, 20, 25, 30 and 35 nodes. with this rule we aim to analyze how each approach reacts to different network sizes. at this point, we want to remark that with more than 40 nodes, the mathematical optimization model found a solution after a pair of hours. • each approach was evaluated considering a different number of source nodes; specifically when the network had one source node (the node 1), two source nodes (the nodes 1 and 2) or three source nodes (the nodes 1, 2 and 3). also, for simplicity, we only considered one activated service for each source node. finally, the destination node always was the last node of the network; e.g. in a network with 20 nodes, the node 20 was the destination node (the gateway). • we assume all nodes begin with an energy consumption of 1%; in order to guarantee that all nodes start with the same energy conditions. • we assume 100 runs for each approach at each network size. the total time for all runs was approximately more than one week. 5 results 5.1 basic scenario the basic scenario results are divided into two parts. the first one is related with the solution path for each approach (hops count, energy consumption, and the multi-objective approach). this mean, that given the table 2, we calculate and show the solution path for each approach in order to compare their results. the second part, of the basic scenario results, corresponds to the evaluation of the network lifetime for each approach; through this analysis we want to determine which method is better in terms of performing more transmissions in the network. it is important to remark that, according to the routing algorithm using the mathematical model solution section, the number of transmissions is equal to nump eriods; that is the number of times at which it was possible to send all activated services from all source nodes to a destination node, before all these source nodes have depleted all of its available energy. here below, we are going to show and discuss the first part results. figures 4a and 4b represent the hops count performance. due to we are minimizing the number of hops, we obtain paths with the minimum possible number of hops regardless of using iot nodes with low battery (node 4). besides, this approach does not care using links with a high free space loss value, which implies more energy consumption since more re-transmissions are needed to amend the signal attenuation. figures 4c and 4d show the energy consumption performance. this approach considers the current battery levels of nodes with the intention of, in the long run, achieving extends the network’s life. according to these figures, the solution paths avoid using node 4, since it has a low battery; however, this is a result for an specific instant. thus, we have to verify how much, in the long term, this approach preserves the network lifetime. figures 4e and 4f indicate the multi-objective approach performance. due to we are minimizing the sum of free space loss values of all links and taking into account the energy consumption in each iot node. this approach tends to reduce the use of energy in terms of re-transmissions, for signal attenuation, and also reduce the use iot nodes with a low battery level. as in the previous approach, we need to check how much, in the long term, this approach preserves the network lifetime. according to the evaluation of the network lifetime for each approach, we assume the same parameters shown in table 2, but considering that eci = 0% ∀i ∈ n. therefore, all iot network nodes have full battery at the beginning of the model evaluation to guarantee a fair distribution of energy in all network nodes. the results of this evaluation is presented in the table 4. according to table 4, the hops count approach is clearly the worst method to apply in an iot network in terms of network lifetime since it allows only 632 transmissions, compared against 701 and 763, from the energy consumption and the multi-objective approach, respectively. doi.org/10.15837/ijccc.2020.4.3914 11 20 30 40 50 60 70 35 40 45 50 55 60 65 70 75 80 85 s=1, o=1, d=7 1 2 3 4 5 6 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 (a) hops performance for the source node 1. 20 30 40 50 60 70 35 40 45 50 55 60 65 70 75 80 85 s=2, o=2, d=7 1 2 3 4 5 6 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 (b) hops performance for the source node 2. 20 30 40 50 60 70 35 40 45 50 55 60 65 70 75 80 85 s=1, o=1, d=6 1 2 3 4 5 6 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 (c) energy consumption performance for the source node 1. 20 30 40 50 60 70 35 40 45 50 55 60 65 70 75 80 85 s=2, o=2, d=6 1 2 3 4 5 6 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 (d) energy consumption performance for the source node 2. 20 30 40 50 60 70 35 40 45 50 55 60 65 70 75 80 85 s=1, o=1, d=6 1 2 3 4 5 6 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 (e) multi-objective performance for the source node 1. 20 30 40 50 60 70 35 40 45 50 55 60 65 70 75 80 85 s=2, o=2, d=6 1 2 3 4 5 6 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 2 12 12 10 2 12 5 2 10 12 10 5 7 10 2 7 2 7 10 2 10 7 (f) multi-objective performance for the source node 2. figure 4: performance for each approach. table 4: network lifetime performance. approach number of transmissions hops 632 energy consumption 701 multi-objective approach (energy + fsl) 763 doi.org/10.15837/ijccc.2020.4.3914 12 as we expected, the energy consumption method obtained the second place extending the network lifetime. even though this method uses the network nodes with less energy consumption (with a high battery level), in the long term, this strategy is not finally the best one due to it does not take into account the impact of using links with a high free space loss indicator. this fact allows the network to waste more energy than expected. finally, the multi-objective approach is the best method for extending the network lifetime since the signal strength has a significant impact on the network energy consumption, in addition to considering the battery level of the nodes (the energy consumption approach). in other words, building short distance paths every time and considering the battery level of the nodes is, in the long term, a better strategy than just building paths with a high battery level of the nodes. 5.2 complex scenarios based on the parameters presented in table 3, we obtain the results of each approach in terms of transmissions performed against the network size. as we mention above, the number of transmissions is equal to nump eriods; which is the number of times at which it was possible to send all activated services from all source nodes to a destination node before all these source nodes have depleted all of its available energy. the first part of the results obtained are presented in figure 5. the sub-figures shown the performance of each approach in terms of the number of transmissions as both the network size and the number of source nodes increase. according to figures 5a, 5b and 5c we can observe that, as the network size increases the number of transmissions also increases. in other words, as the number of network nodes increases, increases the probability that the direct neighbors of the source node are closer to it; allowing low distances from these nodes to the source. these low distance implies transmissions with low energy consumption, extending the network lifetime; and, hence, increasing the number of transmissions. this behavior applies to all approaches because, directly or indirectly, all of them depend on the distance. in addition, for each network size the best results was obtained by the multi-objective approach; while the worst was the hops count approach. this behavior is coherent according to the results shown in the basic scenario results. this mean that the network size does not affect the results of the studied approaches. likewise, in the figures 5b and 5c it is observed that increasing the number of source nodes increases the number of transmissions. the fact of having two source nodes increases the number of transmissions compared against the one source node scenario presented in figure 5a; and in the same way, the fact of having three source nodes increases the number of transmissions compared against the one and two source nodes scenarios shown in figure 5a and figure 5b. it is possible that when one source node has consumed all of its energy, the other source nodes can still be alive. for this reason, the number of transmissions is increased. finally, all figures of this section (complex scenarios) show that all approaches present an increasing performance as the network size grows; this performance increasing exhibits a slight ascendant exponential behavior. this behavior can be explained by the fact that as the size of the network increases, the number of neighboring nodes increases for each node; and thus, exist more connections between them. this implies that there is more chance of finding other alternative paths to reach the gateway; and there is therefore a higher probability of increases the number of transmissions in the network. based on a complete graph, the increase in the number of connections is represented by n(n−1)2 where n is the number of nodes [13]. for example, when n = 2, 3, 4, 5, 6, 7... the total connections are 1, 3, 6, 10, 15 and 21 respectively, which exhibits an exponential growth. therefore, due to the number of connections increases exponentially, the path availability also grows in an exponential manner, represented by the increasing of the number of transmissions. doi.org/10.15837/ijccc.2020.4.3914 13 5 10 15 20 25 30 35 40 network size 0 200 400 600 800 1000 1200 1400 1600 1800 2000 n u m b e r o f t ra n sm is si o n s hops energy multi-objective (a) approaches performance for one source node. 5 10 15 20 25 30 35 40 network size 0 500 1000 1500 2000 2500 3000 3500 n u m b e r o f t ra n sm is si o n s hops energy multi-objective (b) approaches performance for two source nodes. 5 10 15 20 25 30 35 40 network size 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 n u m b e r o f t ra n sm is si o n s hops energy multi-objective (c) approaches performance for three source nodes. figure 5: approaches performance against the network size. doi.org/10.15837/ijccc.2020.4.3914 14 6 conclusion in this work we propose a multi-objective mathematical optimization model as the basis for a green routing algorithm; this algorithm aims to extend the lifetime in iot networks for applications in critical energy environments. our model considers the activating/deactivating of services from a specific source iot node in order to reflect how iot networks operate currently. the routing algorithm proposed obtain the optimal lifetime network calculations for three different approaches: the hop count approach, the energy consumption approach, and the multi-objective approach (energy consumption + free space loss). in this sense, the lifetime of each approach was evaluated in terms of the number of transmissions performed until were consumed the battery of the nodes. as a result of our tests, the multi-objective approached obtained the best performance in the long term, this mean that building a short distance paths and considering the battery level of the iot nodes every time is the best strategy for this kind of environments. the proposed model could be used to develop a new green routing protocol, and also, as a theoretic reference for new routing algorithms. in addition, it is important to remark as a result, that independently of the used strategy, as the network size grows, its lifetime increases. for this reason, it is recommended to implement applications with the most large amount of nodes in order to increase the network lifetime. for future works, we are planning to test real scenarios using physical iot nodes in order to confirm the simulation results. for this purpose, we will use raspberry pi motes to implement the different approaches seen previously to evaluate the network lifetime and confirm which approach is the best strategy in real-world scenarios. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] akyildiz, i.f.; vuran, m.c. 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(2009) wireless sensor networks: a networking perspective, john wiley & sons ltd, 2009. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: lozano-garzon, c.; montoya, g. a.; donoso, y. (2020). a green routing mathematical model for iot networks in critical energy environments, international journal of computers communications & control, 15(4), 3914, 2020. https://doi.org/10.15837/ijccc.2020.4.3914 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 6, month: december, year: 2020 article number: 3983, https://doi.org/10.15837/ijccc.2020.6.3983 ccc publications information volume of mass function y. deng yong deng* 1. institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 610054, china 2. school of eduction shannxi normal university, xi’an, 710062, china 3. department of biostatics medical center, vanderbilt university, nashville, tn. 37235, usa *corresponding author: dengentropy@uestc.edu.cn, prof.deng@hotmail.com abstract given a probability distribution, its corresponding information volume is shannon entropy. however, how to determine the information volume of a given mass function is still an open issue. based on deng entropy, the information volume of mass function is presented in this paper. given a mass function, the corresponding information volume is larger than its uncertainty measured by deng entropy. in addition, when the cardinal of the frame of discernment is identical, both the total uncertainty case and the bpa distribution of the maximum deng entropy have the same information volume. some numerical examples are illustrated to show the efficiency of the proposed information volume of mass function. keywords: information volume, mass function, shannon entropy, deng entropy. 1 introduction in the past decades, plenty of theories have been developed for expressing and dealing with the uncertainty in the uncertain environment, for instance, the extended probability theory [26], probabilistic linguistic [11, 28], fuzzy linguistic [21, 22], fuzzy logic [10], fuzzy set theory [67], intuitionistic fuzzy sets [20, 36, 48], soft sets [1, 12], dempster-shafer evidence theory [6, 46], rough sets [15, 44], z numbers [23, 25, 35, 59] and d numbers [7, 30, 33]. because these theories can well handle uncertainty in many kinds of situations, they have been widely applied in various fields, including decision making [13, 19, 40, 69], reliability analysis [27, 66], medical diagnosis [2, 14], multi-source information fusion [29, 55], causal analysis [32], vehicle system [5], and fault detecting [45]. entropy function is very important in uncertainty modelling[9]. since firstly derived from thermodynamics, different kinds of entropy have been proposed, such as shannon entropy [47], tsallis entropy [50], nonadditive entropy [51], interval valued entropies [61], and fuzzy entropy [3, 4]. recently, a new entropy, called deng entropy [8], is presented for measuring the uncertainty in evidence theory. deng entropy is the generalization of shannon entropy. compared with traditional methods, deng entropy is more reasonable, and it takes both discord and non-specificity into account [49]. https://doi.org/10.15837/ijccc.2020.6.3983 2 given a probability distribution, its corresponding information volume can be measured by shannon entropy. however, how to determine the information volume of mass function in evidence theory is still an open issue. in this paper, an information volume of mass function based on deng entropy is presented. the information volume of mass function is constructed based on the bpa distribution of the maximum deng entropy. if the mass function is degenerated into probability distribution, the proposed information volume is the same as shannnon entropy. in addition, when the cardinal of the frame of discernment is constant, both the total uncertainty case and the deng distribution have the same information volume. the rest of this paper is organized as follows. in section 2, some preliminaries are briefly reviewed. in section 3, based on deng entropy, the information volume of mass function is proposed. in section 4, numerical examples are expounded to illustrated the proposed definition. in section 5, we have a brief conclusion. 2 preliminaries several preliminaries are briefly introduced in this section, including dempster-shafer evidence theory, mass function, shannon entropy, deng entropy and the maximum deng entropy. 2.1 dempster-shafer evidence theory uncertainty modelling is still an open issue [9, 52]. dempster-shafer evidence theory[6, 46] can be used to deal with uncertainty. besides, evidence theory satisfies the weaker conditions than the probability theory, which provides it with the ability to express uncertain information directly [39]. therefore, evidence theory has well studied, including evidence reasoning [38, 64, 65, 68], belief rule [57, 58], complex mass functions [53, 54], and generalized dempster–shafer structures [60, 62], and it was applied in many areas, such as risk analysis [42, 43], classification [37], data fusion [56], and heuristic representation learning [16]. some basic conceptions of evidence theory are given as follows: definition 2.1: frame of discernment and its power set let θ, called the frame of discernment, denote an exhaustive nonempty set of hypotheses, where the elements are mutually exclusive. let the set θ have n elements, which can be expressed as: θ = {θ1,θ2,θ3, · · · ,θn} (1) the power set of θ, denoted as 2θ, contains all possible subsets of θ and has 2n elements, and 2θ is represented by 2θ = {a1,a2,a3, · · · ,a2n} = { ∅,{θ1},{θ2}, · · · ,{θn},{θ1,θ2}, {θ1,θ3}, · · · ,{θ1,θn}, · · · , θ } (2) where the element ak is called the focal element of θ, if ak is nonempty. definition 2.2: mass function a mass function is also called basic probability assignment (bpa), which map m from 2θ to [0, 1], and it is defined as follows: m : 2θ → [0, 1] (3) which is constrained by the following conditions:∑ a∈2θ m(a) = 1 (4) m(∅) = 0 (5) compared with probability distribution in probability theory, mass function has more efficient manner to handle uncertainty [63]. in addition, some other extension of mass function in quantum information has been paid attention recently [17]. https://doi.org/10.15837/ijccc.2020.6.3983 3 2.2 shannon entropy entropy plays an important role in measure the uncertainty [34, 41]. in the field of classical probability theory, shannon entropy [47] is often used to measure the uncertainty of a probability distribution. consider a probability distribution p defined on the set θ = {h1,h2,h3, · · · ,hn}. definition 2.5: shannon entropy shannon entropy hs(p) is defined as follows: hs(p) = ∑ θ∈θ p(θ) log( 1 p(θ) ). (6) where ∑ θ∈θ p(θ) = 1 and p(θ) ∈ [0, 1]. usually, the base of logarithm is 2, and entropy has the unit of bit. it’s not hard to find that hs(p) is on the scale [0, log n]. 2.3 deng entropy in information theory, entropy can be used to measure the uncertainty of a system. recently, a novel entropy, named as deng entropy [8], is proposed to measure the uncertainty in evidence theory. definition 2.5: deng entropy deng entropy is defined as: hde(m) = − ∑ a∈2θ m(a) log( m(a) 2|a| − 1 ) (7) where |a| is the cardinal of a certain focal element a. deng entropy is the generalization of shannon entropy. when every focal element is singleton, deng entropy degenerates into shannon entropy [18]. through a simple transformation, eq.(7) can be rewritten as follows: hde(m) = ∑ a∈2θ log(2|a| − 1) − ∑ a∈2θ m(a) log m(a) (8) where ∑ a∈2θ log(2|a|−1) and − ∑ a∈2θ m(a) log m(a) are measurements of nonspecificity and discord, respectively. as a result, deng entropy is a composite measurement of nonspecificity and discord, which means that it is a tool for measuring total uncertainty [31]. 2.4 the maximum deng entropy assume a is the focal element of a certain frame of discernment θ and m(a) is the bpa for a. according to [24], the analytic solution of the maximum deng entropy is as follows: theorem 2.1: the analytic solution of the maximum deng entropy if and only if m(a) = (2 |a|−1)∑ a∈2θ (2 |a|−1) , deng entropy reaches its maximum value. the analytic solution of the maximum deng entropy is hmde(m) = log ∑ a∈2θ (2|a| − 1) (9) 3 information volume of mass function given a probability distribution, the associated information volume can be measured by shannon entropy. however, how to measure the information volume of a given mass function is still an open issue. in this section, based on deng entropy, the information volume of mass function is defined. definition 3.1: information volume of mass function https://doi.org/10.15837/ijccc.2020.6.3983 4 let the frame of discernment be θ = {θ1,θ2,θ3, · · · ,θn}. use index i to denote the times of this loop, and use m(ai) to denote different mass function of different loops. based on deng entropy, the information volume of mass function can be calculated by following steps: step 1: input mass function m(a0). step 2: continuously separate the mass function of the element whose cardinal is larger than 1 until convergence. concretely, repeat the loop from step 2-1 to step 2-3 until deng entropy is convergent. step 2-1: focus on the element whose cardinal is larger than 1, namely, |ai|> 1. and then, separate its mass function based on the proportion of the bpa of the maximum deng entropy: m(ai) = (2|ai| − 1)∑ ai∈2θ (2 |ai| − 1) (10) for example, given a focal element ai−1 = {θx,θy} and its mass function m(ai−1), the separating proportion is that 15 : 1 5 : 3 5 . the ith times of separation divides m(ai−1) and yields following new mass function: m(xi), m(yi), m(zi), where xi = {θx}, yi = {θy} and zi = {θx,θy}. in addition, they satisfy these equations: m(xi) + m(yi) + m(zi) =m(ai−1) (11) m(xi) : m(yi) : m(zi) = 1 5 : 1 5 : 3 5 (12) step 2-2: based on deng entropy, calculate the uncertainty of all the mass functions except for those who have been divided. the result is denoted as hi(m). step 2-3: calculate ∆i = hi(m) −hi−1(m). when ∆i satisfies following condition, jump out of this loop. ∆i = hi(m) −hi−1(m) < ε (13) where ε is the allowable error. step 3: output hiv−mass(m) = hi(m), which is the information volume of the mass function. 4 numerical examples and discussions in this section, some examples are expounded to better understand the definition for the proposed information volume of mass function, and the discussion is followed after every example. in the following examples, the base of the logarithmic function is 2, and the allowable error is 0.001. example 4.1: consider the focal element be x = {θ1}, y = {θ2} and z = {θ3}. let the mass function be m0(x) = m0(y ) = m0(z) = 13 . because there is no focal element whose cardinal is larger than 1, the step 2-1 can be skipped for all the times of the loop. then, in step 2-2, use deng entropy to calculate the uncertainty of this mass function: hi(m) = − 1 3 log2( 1 3 ) − 1 3 log2( 1 3 ) − 1 3 log2( 1 3 ) = 1.584963 (14) after going through the loop again, the new hi(m) is also 1.585 since step 2-1 is always skipped. as a result, we escape from the loop and get the information volume of this mass function hiv−mass(m) = 1.584963. actually, this form of mass function is the probability distribution p1 = p2 = p3 = 13 . hence, when the mass function degenerates into the probability distribution, the value of hiv−mass(m) is identical to the shannon entropy. example 4.2: https://doi.org/10.15837/ijccc.2020.6.3983 5 consider the frame of discernment be θ = {θ1,θ2,θ3}. let the mass function be m0({θ1}) = m0({θ2}) = m0({θ3}) = m0({θ1,θ2}) = m0({θ1,θ3}) = m0({θ2,θ3}) = m0({θ1,θ2,θ3}) = 17 . the information volume of this mass function can be calculated by definition 3.1. the convergence procedure of hi(m) is listed in table 1. table 1: the convergence procedure of hi(m) i hi(m) i hi(m) 1 3.887675 9 5.178227 2 4.409314 10 5.187146 3 4.724509 11 5.192498 4 4.914440 12 5.195709 5 5.028700 13 5.197636 6 5.097366 14 5.198792 7 5.138606 15 5.199486 8 5.163366 according to table 1, when i = 15, hi(m)−hi−1(m) < 0.001, which means that hi(m) finally converges to 5.199486. hence the information volume of this mass function is hiv−mass(m) = 5.199486. if we use deng entropy to measure the uncertainty of this mass function, the result is as follows: hde(m) = − 1 7 log2( 1 7 21 − 1 ) × 3 − 1 7 log2( 1 7 22 − 1 ) × 3 − 1 7 log2( 1 7 23 − 1 ) = 3.887675 (15) compared hiv−mass(m) with hde(m) in this example, hiv−mass(m) is larger than hde(m), which shows that, given a mass function, the corresponding information volume is larger than its uncertainty measured by deng entropy. example 4.3: consider the frame of discernment be u = {θ1,θ2}, x = {θ1} and y = {θ2} be singletons. let the mass function be m0(x) = m0(y ) = 15 and m0(u) = 3 5 , which is the bpa distribution of the maximum deng entropy when the cardinal of the frame of discernment is 2. the information volume of this mass function can be calculated by definition 3.1, whose calculating procedure is illustrated in figure 1. for the convenience of comprehension, the calculating procedure can be abstracted as a directed acyclic graphical model shown in figure 2. figure 1: the calculating procedure of example 4.3 https://doi.org/10.15837/ijccc.2020.6.3983 6 figure 2: the directed acyclic graphical model of example 4.3 table 2: the convergence procedure of hi(m) i hi(m) i hi(m) 1 2.321928 8 3.396431 2 2.764107 9 3.408809 3 3.029415 10 3.416236 4 3.188600 11 3.420692 5 3.284110 12 3.423366 6 3.341417 13 3.424970 7 3.375801 14 3.425933 then, the convergence procedure of hi(m) is listed in table 2. according to table 2, when we continuously separate the bpa of the element whose cardinal is larger than 1, the ∆i of deng entropy becomes smaller and smaller. when i = 14, hi(m)−hi−1(m) < 0.001, which means that hi(m) finally converges to 3.425933. hence, when the cardinal of the frame of discernment is 2, the information volume of the bpa distribution of the maximum deng entropy is hiv−mass(m) = 3.425933. example 4.4: consider the frame of discernment be θ = {θ1,θ2}. let the mass function be m0(θ) = m0({θ1,θ2}) = 1, which is called the total uncertainty case when the cardinal of the frame of discernment is 2. the information volume of this total uncertainty case can be calculated by definition 3.1. the calculating procedure can be abstracted as a directed acyclic graphical model shown in figure 3, and the convergence procedure of hi(m) is listed in table 3. according to table 3, when i = 15, hi(m) − hi−1(m) < 0.001, which means that hi(m) finally converges to 3.425933. hence, when the cardinal of the frame of discernment is 2, the information volume of the total uncertainty case is hiv−mass(m) = 3.425933. compared example 4.3 with example 4.4, we can find that, although the directed acyclic graphical models of each example is not the same, the hiv−mass(m) of them is identical. it can be concluded that, when the cardinal of the frame of discernment is identical, the bpa https://doi.org/10.15837/ijccc.2020.6.3983 7 figure 3: the directed acyclic graphical model of example 4.4 table 3: the convergence procedure of hi(m) i hi(m) i hi(m) 1 1.584963 9 3.396431 2 2.321928 10 3.408809 3 2.764107 11 3.416236 4 3.029415 12 3.420692 5 3.188600 13 3.423366 6 3.284110 14 3.424970 7 3.341417 15 3.425933 8 3.375801 distribution of the maximum deng entropy and the total uncertainty case have identical information volume. the rest examples will further illustrate this conclusion. example 4.5: consider the frame of discernment be θ = {θ1,θ2,θ3}. let the mass function be m0({θ1}) = m0({θ2}) = m0({θ3}) = 119, m0({θ1,θ2}) = m0({θ1,θ3}) = m0({θ2,θ3}) = 3 19, m0({θ1,θ2,θ3}) = 7 19 , which is the bpa distribution of the maximum deng entropy when the cardinal of the frame of discernment is 3. the information volume of this mass function can be calculated by definition 3.1. the convergence procedure of hi(m) is listed in table 4. according to table 4, when i = 16, hi(m) − hi−1(m) < 0.001, which means that hi(m) finally converges to 6.469009. hence, when the cardinal of the frame of discernment is 3, the information volume of the bpa distribution of the maximum deng entropy is hiv−mass(m) = 6.469009. example 4.6: consider the frame of discernment be θ = {θ1,θ2,θ3}. let the mass function be m0(θ) = m0({θ1,θ2,θ3}) = 1, which is called the total uncertainty case when the cardinal of the frame of discernment is 3. the information volume of the total uncertainty case can be calculated by definition 3.1. the https://doi.org/10.15837/ijccc.2020.6.3983 8 table 4: the convergence procedure of hi(m) i hi(m) i hi(m) 1 4.247928 9 6.432107 2 5.127754 10 6.447290 3 5.661354 11 6.456402 4 5.983615 12 6.461869 5 6.177746 13 6.465150 6 6.294510 14 6.467119 7 6.364674 15 6.468300 8 6.406810 16 6.469009 convergence procedure of hi(m) is listed in table 5. table 5: the convergence procedure of hi(m) i hi(m) i hi(m) 1 2.807355 10 6.432107 2 4.247928 11 6.447290 3 5.127754 12 6.456402 4 5.661354 13 6.461869 5 5.983615 14 6.465150 6 6.177746 15 6.467119 7 6.294510 16 6.468300 8 6.364674 17 6.469009 9 6.406810 according to table 5, when i = 17, hi(m) − hi−1(m) < 0.001, which means that hi(m) finally converges to 6.469009. hence the information volume of this total uncertainty case is hiv−mass(m) = 6.469009. example 4.5 and example 4.6 further illustrate that, when the cardinal of the frame of discernment is identical, the information volume of the bpa distribution for the maximum deng entropy is the same as that of the total uncertainty case. this point is consistent with the intuition. 5 conclusion in this paper, we define the information volume of a given mass function based on deng entropy. in addition, some examples are shown for better understanding of proposed information volume fo mass function. some concluding remarks can be shown as follows. 1) if the mass function degenerates as probability distribution, the information volume is the same as shannon entropy. 2) given a mass function, the corresponding information volume is larger than its uncertainty measured by deng entropy. 3) one interesting point is that, when the cardinal of the frame of discernment is identical, the bpa distribution for the maximum deng entropy and the total uncertainty case has the same information volume. this point is coincide with the intuition. https://doi.org/10.15837/ijccc.2020.6.3983 9 acknowledgment the author greatly appreciates he china academician of the academy of engineering, professor shan zhong and professor you he, for their encouragement to do this research. the author greatly appreciates professor yugeng xi in shanghai jiao tong university to support this work. research assistant, jixiang deng, and ph.d student, tao wen, discussed the idea and do a lot editorial works. the author greatly appreciates the continuous funding for the past nearly twenty years including national natural science foundation of china, grant nos. 30400067, 60874105, 61174022, 61573290 and 61973332, program for new century excellent talents in university, grant no. ncet-08-0345, shanghai rising-star program grant no.09qa1402900, chongqing natural science foundation for distinguished scientist, grant no. csct, 2010ba2003. references [1] alcantud, j.c.; feng, f.; yager, r. 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(2020). information volume of mass function, international journal of computers communications & control, 15(6), 3983, 2020. https://doi.org/10.15837/ijccc.2020.6.3983 introduction preliminaries dempster-shafer evidence theory shannon entropy deng entropy the maximum deng entropy information volume of mass function numerical examples and discussions conclusion international journal of computers communications & control special issue on fuzzy logic dedicated to the centenary of the birth of lotfi a. zadeh (1921-2017) online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 1, month: february, year: 2021 article number: 4057, https://doi.org/10.15837/ijccc.2021.1.4057 ccc publications approximate membership function shapes of solutions to intuitionistic fuzzy transportation problems b. stanojević, m. stanojević bogdana stanojević* mathematical institute of the serbian academy of sciences and arts kneza mihaila 36, 11000 belgrade, serbia *corresponding author: bgdnpop@mi.sanu.ac.rs milan stanojević faculty of organizational sciences, university of belgrade jove ilića 154, 11000 belgrade, serbia milan.stanojevic@fon.bg.ac.rs abstract in this paper, proposing a mathematical model with disjunctive constraint system, and providing approximate membership function shapes to the optimal values of the decision variables, we improve the solution approach to transportation problems with trapezoidal fuzzy parameters. we further extend the approach to solving transportation problems with intuitionistic fuzzy parameters; and compare the membership function shapes of the fuzzy solutions obtained by our approach to the fuzzy solutions to full fuzzy transportation problems yielded by approaches found in the literature. keywords: full fuzzy transportation problem, extension principle, intuitionistic fuzzy numbers. 1 introduction fuzzy mathematical programming is widely studied in the literature. recently, ghanbari et al. [6] surveyed fuzzy linear programming, and provided detailed descriptions of the relevant models and their solutions. fuzzy linear transportation problems, as part of fuzzy linear programming problems were also analyzed in [6]. fuzzy linear fractional programming problems including fractional transportation problems in fuzzy environment were recently reviewed by stanojević et al. [14]. we briefly present below the papers relevant to our study. liu and kao [10] proposed a solution approach to linear transportation problems with trapezoidal fuzzy parameters. their approach was based on the extension principle, and provided the fuzzy set of the optimal solution values. later on, liu [9] proposed a similar solution approach to linear fractional https://doi.org/10.15837/ijccc.2021.1.4057 2 transportation problems with fuzzy parameters. both approaches solved crisp primal-dual-like-pair models for fixed α-cut intervals of the parameters. in this paper we improve liu and kao’s approach [10] by optimizing their objective function over a wider feasible set using a disjunctive system of inequalities in the primal model. we also suggest improvements to liu’s method [9] by fixing some coefficients of the objective function to their upper limits instead of considering them as variables in the dual model. anukokila and radhakrishnan [1] solved a full fuzzy fractional transportation problem using fuzzy goal programming. they used trapezoidal fuzzy numbers to describe the fuzzy goals. stanojević and stanojević [16] proposed a monte carlo simulation algorithm to disclose the shapes of the fuzzy sets optimal solution and solution values to a full fuzzy linear programming problem. further on, employing the same monte carlo simulation algorithm, stanojević and stanojević [15] empirically constructed the envelope for the optimal solution values to a full fuzzy transportation problem, thus identifying a shortcoming of the solution approach provided in [10]. in this paper we overcome the shortcoming proposing a disjunctive mathematical model as foundation to the solution approach. full intuitionistic fuzzy transportation problem was addressed in the recent literature. singh and yadav [13] proposed a method based on the analogy to the modified distribution method used to solve crisp transportation problems. ebrahimnejad and verdegay [5] proposed a solution approach based on the accuracy function used for ordering the trapezoidal intuitionistic fuzzy numbers. mishra and kumar [12] provided a procedure to balance the full intuitionistic fuzzy transportation problem, thus extended the applicability of mahmoodirad et al.’s approach [11] that was able to solve only balanced forms of full intuitionistic fuzzy transportation problems. approaching the full intuitionistic fuzzy transportation problem in the way we introduce in this paper, we avoid the balancing step and provide an extension-principle-based optimal solution fuzzy set to the problem. a brief presentation of basic notation and terminology related to fuzzy numbers and the extension principle can be found in section 2. section 3 describes the main solution approaches to fuzzy transportation problems related to our study. section 4 presents the improvements we propose to the existing methods, the shortcomings we overcome by our solution approach, and the relevance of our new theoretical results to solving full intuitionistic fuzzy transportation problems. section 5 reports our numerical results. our conclusions and directions for further research are included in section 6. 2 preliminaries fuzzy sets were introduced by zadeh [17], and their applicability to mathematical programming was emphasized by zimmermann [19] and [20]. the general aspects of fuzzy sets theory that contributed to the development of soft computing and artificial intelligence were recently described in [4]. a fuzzy subset ã of a universe x is a collection of pairs ( x,µ ã (x) ) such that x ∈ x, and µ ã (x) ∈ [0, 1]. the value µ ã (x) is called the membership degree of the element x in ã, and µ ã : x → [0, 1] is called the membership function of the fuzzy subset ã in the universe x. when there is no confusion about the universe to whom a fuzzy subset belongs, that fuzzy subset can be called fuzzy set. to aggregate the fuzzy sets, bellman and zadeh [3] formulated the following extension principle: the fuzzy set b̃ of the universe y that is the result of evaluating the function f at the fuzzy sets ã1, ã2, . . . , ãr over their universes x1,x2, . . . ,xr is defined through its membership function as µ b̃ (y) =   sup(x1,...,xr )∈f−1(y) ( min { µ ã1 (x1) , . . . ,µãr (xr) }) , f−1 (y) 6= ø 0, otherwise. the intuitionistic fuzzy sets were introduced in [2] as a generalization to the fuzzy set. an intuitionistic fuzzy subset ãi of a universe x is a collection of triples ( x,µ ãi (x) ,ν ãi (x) ) such that x ∈ x, µ ãi (x) ,ν ãi (x) ∈ [0, 1], and 0 ≤ µ ãi (x) + ν ãi (x) ≤ 1. the value µ ãi (x) is called the membership degree of the element x in ãi, the value ν ãi (x) is called the non-membership degree of the element x in ãi, µ ãi : x → [0, 1] is the membership function, and ν ãi : x → [0, 1] is the non-membership https://doi.org/10.15837/ijccc.2021.1.4057 3 function of ãi in x. for each x ∈ x the value h (x) = 1 − µ ãi (x) − ν ãi (x) is called the degree of hesitancy of x in ãi. fuzzy numbers, as particular fuzzy sets were introduced in [18]. they generalize the real numbers in a specific direction toward modeling uncertainty and incertitude. a fuzzy set ã of the universe of real numbers r is called fuzzy number if and only if: (i) it is fuzzy normal (i.e. at least one x0 ∈ r exists such that µ ã (x0) = 1) and fuzzy convex (i.e. ∀x1,x2 ∈ r, ∀λ ∈ [0, 1], µã (λx1 + (1 −λ) x2) ≥ min { µ ã (x1) ,µã (x2) } ); (ii) µ ã is upper semi-continuous; and (iii) its support { x ∈ r|µ ã (x) > 0 } is bounded. similarly, an intuitionistic fuzzy set of r is an intuitionistic fuzzy number if and only if its membership function fulfills all conditions in the definition of a fuzzy number; ν ã (x0) = 0; and the non-membership function is fuzzy concave (i.e. ν ã (λx1 + (1 −λ) x2) ≤ max { ν ã (x1) ,νã (x2) } ), lower semi-continuous, and its support { x ∈ r|ν ã (x) < 1 } is bounded. in what follows we describe the trapezoidal fuzzy numbers that are used in our numerical examples. the graph of the non-zero piece of the membership function of a trapezoidal fuzzy number forms a trapezoid with the abscissa. one way to denote a trapezoidal fuzzy number ã is by using an quadruple of real numbers (a1,a2,a3,a4), where [a2,a3] is the interval of the elements with the membership degree equal to 1, and the interval (a1,a4) represents the support of the fuzzy number. the algebraic definition of the membership function is µ ã : r → [0, 1], µ ã (x) =   (x−a1) /(a2 −a1) , a1 ≤ x < a2, 1, a2 ≤ x < a3, (a4 −x) /(a4 −a3) , a3 ≤ x < a4, 0, otherwise. for any trapezoidal fuzzy number ã = (a1,a2,a3,a4) its α-cut is the interval[ ã ] α = [α (a2 −a1) + a1,α (a3 −a4) + a4] . for each α ∈ [0, 1] we have min µ−1 ã (α) = α (a2 −a1) + a1, and max µ−1 ã (α) = α (a3 −a4) + a4. for any two trapezoidal fuzzy numbers ã = (a1,a2,a3,a4) and b̃ = (b1,b2,b3,b4), the basic arithmetic operations between them (addition, subtraction and multiplication by a scalar) are derived using the extension principle, and are defined as follows: ã + b̃ = (a1 + b1,a2 + b2,a3 + b3,a4 + b4), ã− b̃ = (a1 − b4,a2 − b3,a3 − b2,a4 − b1), kã = (ka1,ka2,ka3,ka4), k real scalar, k ≥ 0, kã = (ka4,ka3,ka2,ka1), k < 0. (1) a trapezoidal fuzzy number ã = (a1,a2,a3,a4) is reduced to a triangular fuzzy number if a2 = a3. a trapezoidal intuitionistic fuzzy number ãi has the membership and non-membership functions defined as below: µãi (x) =   (x−a1) /(a2 −a1) , a1 ≤ x < a2, 1, a2 ≤ x < a3, (a4 −x) /(a4 −a3) , a3 ≤ x < a4, 0, otherwise, νãi (x) =   ( a′2 −x ) / ( a′2 −a ′ 1 ) , a′1 ≤ x < a ′ 2, 0, a′2 ≤ x < a′3,( x−a′3 ) / ( a′4 −a ′ 3 ) , a′3 ≤ x < a ′ 4, 1, otherwise, respectively, where a′1 ≤ a1 ≤ a′2 ≤ a2 ≤ a3 ≤ a′3 ≤ a4 ≤ a′4. such trapezoidal intuitionistic fuzzy number is denoted by ãi = (a1,a2,a3,a4; a′1,a ′ 2,a ′ 3,a ′ 4). the basic arithmetic operations for trapezoidal intuitionistic fuzzy numbers are direct extensions of (1). given two trapezoidal intuitionistic fuzzy numbers ãi and b̃i and a real scalar k, https://doi.org/10.15837/ijccc.2021.1.4057 4 ãi + b̃i = (a1 + b1,a2 + b2,a3 + b3,a4 + b4; a′1 + b′1,a′2 + b′2,a′3 + b′3,a′4 + b′4), ãi − b̃i = (a1 − b4,a2 − b3,a3 − b2,a4 − b1; a′1 − b′4,a′2 − b′3,a′3 − b′2,a′4 − b′1), kãi = (ka1,ka2,ka3,ka4; ka′1,ka′2,ka′3,ka′4), k ≥ 0, kãi = (ka4,ka3,ka2,ka1; ka′4,ka′3,ka′2,ka′1), k < 0. a trapezoidal intuitionistic fuzzy number ãi = (a1,a2,a3,a4; a′1,a′2,a′3,a′4) is reduced to a triangular intuitionistic fuzzy number if a2 = a′2 = a3 = a′3. 3 existing approaches to fuzzy transportation problems in a classic linear transportation problem the total cost of transferring commodities from m sources with certain supply capacities (ai)i=1,m to n destinations with certain demands (bj)j=1,n has to be minimized. when the total supply is equal to the total demand (i.e. m∑ i=1 ai = n∑ j=1 bj), the problem is balanced and its general model is given in (2). min m∑ i=1 n∑ j=1 cijxij s.t. n∑ j=1 xij = ai, i = 1,m, m∑ i=1 xij = bj, j = 1,n, xij ≥ 0, i = 1,m,j = 1,n, (2) where (xij)j=1,ni=1,m is the decision variable representing the amount of commodities transported from the source i to the destination j, and (cij)j=1,ni=1,m is the unit transportation cost from the source i to the destination j. the condition m∑ i=1 ai = n∑ j=1 bj assures a non-empty feasible set to problem (2). generally, if the transportation problem is unbalanced, depending on the inequality between the total supply and total demand, only one of models (2-a) and (2-b) has a non-empty feasible set and should be considered in the optimization process. min m∑ i=1 n∑ j=1 cijxij s.t. n∑ j=1 xij ≤ ai, i = 1,m, m∑ i=1 xij = bj, j = 1,n, xij ≥ 0, i = 1,m, j = 1,n. (2-a) min m∑ i=1 n∑ j=1 cijxij s.t. n∑ j=1 xij = ai, i = 1,m, m∑ i=1 xij ≤ bj, j = 1,n, xij ≥ 0, i = 1,m, j = 1,n. (2-b) in fuzzy transportation problems the parameters (ai)i=1,...,m, (bj)j=1,...,n and (cij) j=1,n i=1,m become fuzzy numbers denoted by (ãi)i=1,m, ( b̃j ) j=1,n and (c̃ij)j=1,ni=1,m respectively. when fuzzy decision variables are considered, they are denoted by (x̃ij)j=1,ni=1,m, and used in fuzzy models analog to models (2), (2-a) and (2-b). among crisp linear tps the balanced problems are efficiently solved using modi (modified distribution) method; while the other variants are solved either directly using a simplex method or via an equivalent balancing transformation. the theoretical foundation of our novel solution approach will show that balancing a transportation problem with fuzzy parameters is an unnecessary step. https://doi.org/10.15837/ijccc.2021.1.4057 5 liu and kao [10] proposed a solution approach to problem (3) and imposed m∑ i=1 ãi ≥ n∑ j=1 b̃j to assure the feasibility of the model. min m∑ i=1 n∑ j=1 c̃ijxij s.t. n∑ j=1 xij ≤ ãi, i = 1,m, m∑ i=1 xij ≥ b̃j, j = 1,n, xij ≥ 0, i = 1,m,j = 1,n, (3) their extension-principle-based solution concept is defined by the membership function of the optimal solution value fuzzy set µ f̃ (z) =   max (a,b,c)|z= min x∈xa,b ctx ( µ( ã,̃b,̃c ) (a,b,c)) , ∃a,b,c|z = min x∈xa,b ctx, 0, otherwise, (4) where µ( ã,̃b,̃c ) (a,b,c) = min {µ (cij) |i = 1,m,j = 1,n}⋃{µãi (ai) |i = 1,m}⋃{µb̃j (bj) |j = 1,n } and xa,b =  (xij)j=1,ni=1,m ∈ mm×n (r) | n∑ j=1 xij ≤ ai, m∑ i=1 xij ≥ bj,xij ≥ 0, i = 1,m,j = 1,n   . they derived an approximate membership function shape of the optimal solution value fuzzy set by solving the following pair of optimization problems for fixed values of the parameter α. min m∑ i=1 n∑ j=1 (cij)lα xij s.t. n∑ j=1 xij ≤ ai, i = 1,m, m∑ i=1 xij ≥ bj, j = 1,n, (ai)lα ≤ ai ≤ (ai) u α , i = 1,m, (bj)lα ≤ bj ≤ (bj) u α , j = 1,n, m∑ i=1 ai ≥ n∑ j=1 bj, xij ≥ 0, i = 1,m, j = 1,n, (4-a) max − m∑ i=1 aiui + n∑ j=1 bjvj, s.t. −ui + vj ≤ (cij)uα , i = 1,m, j = 1,n, (ai)lα ≤ ai ≤ (ai) u α , i = 1,m, (bj)lα ≤ bj ≤ (bj) u α , j = 1,n, m∑ i=1 ai ≥ n∑ j=1 bj, ui ≥ 0, i = 1,m, vj ≥ 0, j = 1,n. (4-b) it is clear that models (4-a) and (4-b) are not a primal-dual pair, but the former was obtained from the crisp form of model (3) with lower and upper bounds for (ai)i=1,m and (bj)j=1,n that became variables, and (cij)j=1,ni=1,m equal to ( (c̃ij)lα )j=1,n i=1,m ; and the later was constructed as a dual to the crisp form of model (3) with lower and upper bounds for (ai)i=1,m and (bj)j=1,n that became variables, and (cij)j=1,ni=1,m equal to ( (c̃ij)uα )j=1,n i=1,m . stanojević and stanojević [15] provided empirical solutions to the special class of full fuzzy transportation problems. quadratic optimization models were derived from the original transportation model considering the cost coefficients, demands and supplies as decision variables. the numerical results obtained in [15] by employing a monte-carlo-simulation-based algorithm (introduced in [16]) in solving relevant instances represent a good validation test for our results that will be reported in section 5. https://doi.org/10.15837/ijccc.2021.1.4057 6 ebrahimnejad and verdegay [5] proposed a solution approach to balanced full trapezoidal intuitionistic fuzzy transportation problems min m∑ i=1 n∑ j=1 c̃iijx̃ i ij s.t. n∑ j=1 x̃iij = ãii , i = 1,m, m∑ i=1 x̃iij = b̃ij, j = 1,n, x̃iij ≥ 0, i = 1,m,j = 1,n. (5) they interpreted the trapezoidal inutitionistic fuzzy valued objective function as a multiple objective function due to its eight components; and converted it into a single objective function 1 8 m∑ i=1 n∑ j=1 4∑ k=1 ( cij,kxij,k + c′ij,kx ′ ij,k ) using the weighted sum method, and assigning an equal weight 18 to all components. the single objective function is then optimized over the feasible set described in (5). each equality of (5) is component by component transformed to eight crisp constraints n∑ j=1 xij,k = ai,k, n∑ j=1 x′ij,k = a′i,k, i = 1,m,k = 1, 4, m∑ i=1 xij,k = bj,k, m∑ i=1 x′ij,k = b′j,k, j = 1,n,k = 1, 4. the non-negativity of the decision variables were transformed to non-negativity of all components and the needed inequalities between the components of decision variables were included. mahmoodirad et al. [11] addressed the same problem (5) but used triangular intuitionistic fuzzy numbers as parameters, and solved it in the same way. mishra and kumar [12] proposed a method to transforming an unbalanced fully intuitionistic fuzzy transportation problem into a balanced one. their method is an analog extension of the method introduced by kumar and kaur [7] to transportation problems with fuzzy parameters. 4 our novel solution approach taking into consideration one neglected fact of the extension principle, we first improve liu and kao’s solution approach [10] by introducing a disjunctive system of constraints in model (4-a), and propagating it through duality to model (4-b). further on, we provide approximate membership function shapes for the optimal solution fuzzy sets of the decision variables, thus showing that the original transportation problem with fuzzy parameters implicitly assumes that the decision variables are also fuzzy sets. finally, incorporating the novel ideas we extend the solution approach to the class of intuitionistic fuzzy transportation problem. 4.1 mathematical model with disjunctive constraint system any crisp transportation problem can be solved choosing one of models (2), (2-a) and (2-b): if the problem is balanced the first model is chosen, if sources together supply more commodities than the total demand then the second model is used, otherwise the third one must be employed. a similar flexibility should be assured when proposing fuzzy models to real systems. unfortunately, assuming m∑ i=1 ãi ≥ n∑ j=1 b̃j when modeling a transportation problem with imprecise information about https://doi.org/10.15837/ijccc.2021.1.4057 7 supplies and demands brings a weakness to the solution approach since the inequality between fuzzy numbers can be interpreted in a wide variety of ways. moreover, using the crisp models (4-a) and (4-b) in solving model (3), as proposed in [10], accentuates the weakness since the crisp models contain the crisp constraint m∑ i=1 ai ≥ n∑ j=1 bj that restricts the feasible sets more than demanded by the extension principle. as a more flexible alternative to models (3-a) and (3-b), we propose two new models (9) and (10) whose solutions improve the solutions derived in [10]. the new models are based on the crisp model min m∑ i=1 n∑ j=1 cijxij s.t. (xi,j)j=1,ni=1,m ∈ x disj. a,b , xij ≥ 0, i = 1,m,j = 1,n, (6) where x disj. a,b =  (xi,j)j=1,ni=1,m ∈ mm×n (r) | n∑ j=1 xij ≤ ai, m∑ i=1 xij ≥ bj, i = 1,m,j = 1,n  ⋃ (xi,j)j=1,ni=1,m ∈ mm×n (r) | n∑ j=1 xij ≥ ai, m∑ i=1 xij ≤ bj, i = 1,m,j = 1,n   . (7) we make use of a binary variable y, and a great scalar constant m, to describe xdisj.a,b by the following conjunctive system of constraints ai − (1 −y) m ≤ n∑ j=1 xij ≤ ai + ym, i = 1,m, bj −ym ≤ m∑ i=1 xij ≤ bj + (1 −y) m, j = 1,n, y ∈{0, 1} . (8) further on, releasing (ai)i=1,m and (bj)j=1,n to vary between their lower and upper bounds, and fixing the coefficients of the objective function to their lower and upper bounds respectively, we derive the final new models (9) and (10). model (9) is used for minimizing the minimal cost with respect to variables x, y, a, and b; while model (10) is used for maximizing the minimal cost with respect to variables w, u, v, p, q, a, and b. min m∑ i=1 n∑ j=1 (cij)lα xij s.t. ai − (1 −y) m ≤ n∑ j=1 xij ≤ ai + ym, i = 1,m, bj −ym ≤ m∑ i=1 xij ≤ bj + (1 −y) m, j = 1,n, (ai)lα ≤ ai ≤ (ai) u α , i = 1,m, (bj)lα ≤ bj ≤ (bj) u α , j = 1,n, xij ≥ 0, i = 1,m,j = 1,n, y ∈{0, 1} , (9) solving models (9) and (10) for fixed values of α ∈ [0, 1] we obtain an approximate shape of the membership function µ f̃ . https://doi.org/10.15837/ijccc.2021.1.4057 8 max w s.t. −ui + vj ≤ (cij)uα , i = 1,m,j = 1,n, pi −qj ≤ (cij)uα , i = 1,m,j = 1,n, − m∑ i=1 aiui + n∑ j=1 bjvj ≥ w, i = 1,m,j = 1,n, m∑ i=1 aipi − n∑ j=1 bjqj ≥ w, i = 1,m,j = 1,n, (ai)lα ≤ ai ≤ (ai) u α , i = 1,m, (bj)lα ≤ bj ≤ (bj) u α , j = 1,n, (1 −y) m ≤ m∑ i=1 ai − n∑ j=1 bj ≤ ym, ui,qj ≥ 0, i = 1,m,j = 1,n, y ∈{0, 1} . (10) in the same way, a disjunctive system of constraints can be incorporate in the methodology proposed in [9] to improve solutions to linear fractional transportation problems. 4.2 membership function shapes of the optimal solution values of the decision variables our next goal is to define the membership functions of the optimal solutions fuzzy sets, and propose optimization models to approximate the membership function shapes of the optimal solution values of the decision variables. preliminary ideas about the membership function of the vector of the decision variables in a fuzzy linear programming problem were given in [16]. using a monte carlo simulation algorithm the envelopes of the membership functions of the variables were drafted. we next define the exact membership function for each decision variable separately as µx̃i (t) =   max (a,b,c)|t = argi min y∈xdisj. a,b cty ( µ( ã,̃b,̃c ) (a,b,c)) , ∃a,b,c|t = argi min y∈xa,b cty 0, otherwise , i = 1,m, (11) where argi min y∈xa,b cty represents the optimal value of the i-th decision variable obtained when minimizing cty over the feasible set xdisj.a,b , and µ(ã,̃b,̃c) (a,b,c) has the same meaning as in the definition of the membership function of optimal solution values (4). defining the membership functions of the decision variables with the help of (11) we round out a solution concept to both classes of problems: with fuzzy parameters and full fuzzy. we proceed to formalize mathematical models able to derive the shapes of decision variables membership functions. for any fixed value of α ∈ [0, 1] and fixed indexes k = 1,m and l = 1,n we construct the pair of the optimization models min xkl s.t. (a,b,c,x,y) ∈ p, (12) and max xkl s.t. (a,b,c,x,y) ∈ p, (13) where p is the constraints set described by system (14). to describe system (14) we use the minimal and maximal values (zminα and zmaxα ) of the objective function yielded by solving models (9) and (10) respectively. https://doi.org/10.15837/ijccc.2021.1.4057 9 ai − (1 −y) m ≤ n∑ j=1 xij ≤ ai + ym, i = 1,m, bj −ym ≤ m∑ i=1 xij ≤ bj + (1 −y) m, j = 1,n, (bj)lα ≤ bj ≤ (bj) u α , j = 1,n, (cij)lα ≤ cij ≤ (cij) u α , i = 1,m,j = 1,n zminα ≤ m∑ i=1 n∑ j=1 cijxij ≤ zmaxα , xij ≥ 0, i = 1,m,j = 1,n, y ∈{0, 1} . (14) 4.3 extension to intuitionistic fuzzy transportation problems both pairs of models (9)-(10) and (12)-(13) can be extended to provide the approximate membership function shapes of the optimal solution values of the objective function and decision variables to a full intuitionistic fuzzy transportation problem. the extension is made by adding two more pairs of optimization models that derive the non-membership function shapes of the optimal solution values. since in all models (9), (10), (12) and (13) the lower and upper bounds of (ai)i=1,m, (bj)j=1,n and (cij)j=1,ni=1,m are defined with the help of the membership functions of problem’s parameters ( µãi ) i=1,m ,( µ b̃j ) j=1,n and ( µc̃ij )j=1,n i=1,m and their corresponding α-cuts, in the case of intuitionistic fuzzy parameters, the non-membership functions ( νãi ) i=1,m , ( ν̃ bj ) j=1,n and ( νc̃ij )j=1,n i=1,m together with their α-cuts dictate the lower and upper bounds of the variables (ai)i=1,m, (bj)j=1,n and (cij) j=1,n i=1,m in the additional models. the solution approach to full intuitionistic fuzzy transportation problem defined through its intuitionistic fuzzy parameters ( ãii ) i=1,m , ( b̃ij ) j=1,n and ( c̃iij )j=1,n i=1,m representing the supplies of all sources, demands of all destinations, and unit transportation costs from each source to each destination respectively is described by algorithm 1. using notation from algorithm 1 the approximate membership function of optimal objective values is described by {( zminµ,αs,αs ) |s = 1,p }⋃{( zmaxµ,αs,αs ) |s = 1,p } ; the approximate non-membership function of optimal objective values is described by{( zminν,αs, 1 −αs ) |s = 1,p }⋃{( zmaxν,αs , 1 −αs ) |s = 1,p } ; the approximate membership functions of the optimal values of the decision variables are described by {( xmin(k,l),µ,αs,αs ) |s = 1,p }⋃{( xmax(k,l),µ,αs,αs ) |s = 1,p } ,k = 1,m,l = 1,n; and the approximate non-membership functions of the optimal values of the decision variables are described by{( xmin(k,l),ν,αs, 1 −αs ) |s = 1,p }⋃{( xmax(k,l),ν,αs, 1 −αs ) |s = 1,p } ,k = 1,m,l = 1,n. 5 numerical results to report our numerical results we follow the same structure as in presenting our theoretical results. we first solve an example of a transportation problem with trapezoidal fuzzy parameters from [10], showing our improvements. then we solve two examples of intuitionistic fuzzy transportation problems one form [5], and second from [5], [8] and [11] in order to illustrate the advantages of the new approach. https://doi.org/10.15837/ijccc.2021.1.4057 10 algorithm 1 solution approach to full fuzzy intuitionistic transportation problem require: the membership and non-membership functions of the intuitionistic fuzzy parameters, and p α-levels α1 < α2 < ... < αp chosen for the desired approximation. 1: for s = 1,p do 2: set (ai)lαs = min µ −1 ãi (αs), (bj)lαs = min µ −1 b̃j (αs), (cij)lαs = min µ −1 c̃ij (αs), (ai)uαs = max µ −1 ãi (αs), (bj)uαs = max µ −1 b̃j (αs), (cij)uαs = max µ −1 c̃ij (αs), i = 1,m, j = 1,n. 3: solve models (9) and (10) and derive the optimal values zminµ,αs and z max µ,αs respectively. 4: for k = 1,m and l = 1,n do 5: set zminα = zminµ,αs and z max α = zmaxµ,αs. 6: solve models (12) and (13) and derive the optimal values xmin(k,l),µ,αs, x max (k,l),µ,αs respectively. 7: end for 8: set (ai)lαs = min ( 1 −ν−1 ãi (αs) ) , (bj)lαs = min ( 1 −ν−1 b̃j (αs) ) , (cij)lαs = min ( 1 −ν−1 c̃ij (αs) ) , (ai)uαs = max ( 1 −ν−1 ãi (αs) ) , (bj)uαs = max ( 1 −ν−1 b̃j (αs) ) , (cij)uαs = max ( 1 −ν−1 c̃ij (αs) ) , i = 1,m, j = 1,n. 9: solve model (9) and (10) and derive the optimal values zminν,αs and z max ν,αs respectively. 10: for k = 1,m and l = 1,n do 11: set zminα = zminν,αs and z max α = zmaxν,αs . 12: solve models (12) and (13) and derive the optimal values xmin(k,l),ν,αs, x max (k,l),ν,αs respectively. 13: end for 14: end for ensure: the approximate membership and non-membership functions of optimal objective values and values of the decision variables. table 1: trapezoidal fuzzy parameters of the transportation problem used for example 1 c̃ j = 1 j = 2 j = 3 ã i = 1 (10, 10, 10, 10) (50, 50, 50, 50) (80, 80, 80, 80) (70, 90, 90, 100) i = 2 (60, 70, 80, 90) (60, 60, 60, 60) (20, 20, 20, 20) (40, 60, 70, 80) b̃ (30, 40, 50, 70) (20, 30, 40, 50) (40, 50, 50, 80) example 1 was first addressed in [10]. the problem was formulated using model (3) and the fuzzy parameters reported in table 1. models (4-a) and (4-b) were used in [10] to derive the shape of the membership function of optimal values. tackling the essence of the problem, and allowing more flexibility in modeling, we make use of models (9) and (10) to derive a similar shape. the numerical results are reported in table 2 and graphed in figure 1. analyzing the results obtained for the optimal values one can notice a difference only on the first component of the trapezoidal fuzzy numbers reported in table 2: 1500 for our approach, and 2100 for the liu and kao’s approach [10]. it means that our approach provided the smallest value 1500 of the transportation cost that can be reached for a certain combination of the parameters, and was not revealed by the approach from the literature. figure 1 shows graphically the differences between the membership functions of the optimal values of both objective function and decision variables. in the case of the objective values the compared shapes differ only on their left side for α ∈ [0, 0.4]. no complete information about the optimal values of the decision variables were provided in [10]. the values reported in [10] for α = 0 and α = 1 can be seen within the approximated enveloped area provided by our approach. therefore the optimal values table 2: numerical results for example 1 z̃min µ liu and kao [10] (2100, 2900, 3500, 5800) our approach (1500, 2900, 3500, 5800) https://doi.org/10.15837/ijccc.2021.1.4057 11 figure 1: approximated membership functions of the optimal solution values of the transportation problem with fuzzy parameters given in table 1 table 3: intuitionistic fuzzy parameters of the transportation problem used for example 2 c̃i i = 1 i = 2 b̃i j = 1 (10, 20, 30, 40; 5, 15, 35, 45) (60, 70, 80, 90; 55, 65, 85, 95) (30, 50, 70, 90; 20, 40, 80, 100) j = 2 (50, 60, 70, 80; 45, 55, 75, 85) (70, 80, 90, 100; 65, 75, 95, 105) (20, 30, 40, 50; 15, 25, 45, 55) j = 3 (80, 90, 100, 110; 75, 85, 105, 115) (20, 30, 40, 50; 15, 25, 45, 55) (50, 60, 70, 80; 45, 55, 75, 85) ãi (60, 80, 100, 120; 50, 70, 110, 130) (40, 60, 80, 100; 30, 50, 90, 110) of the decision variables cannot be derived by solving models (4-a) and (4-b). therefore, models (9) and (10) bring essential improvements to the existing approach [10]. the similarity to liu and kao’s results for the objective values shows that we solved the same problem like them, a transportation problem with fuzzy parameters. on the other side, the envelopes of the values of the decision variables show that in fact, the solving method assumes that the variables are also fuzzy quantities, thus the methodology can be applied to solve full fuzzy problems. this fact will be illustrated by the next example. example 2 is a full fuzzy problem recalled form [5] that uses model (5) with the trapezoidal intuitionistic fuzzy parameters provided in table 3. the numerical results reported in [5] together with our results are shown in table 4 and graphed in figure 2. our method finds the membership functions of the optimal values of the decision variables. their shapes are non-linear but similar to the shapes of trapezoidal intuitionistic fuzzy numbers. our approach identified smaller values with non-zero membership values that are optimal values for certain combinations of the given parameters, thus providing more relevant information about the minimal transportation cost of the modeled problem. analyzing figure 2 we can notice an essential difference between the fuzzy sets provided as final fuzzy-valued decision variables by the existing approach and our approach. our results fulfill the extension principle concept applied to the optimization problem as a whole, and not only to the arithmetic on the coefficients and variables as in [5]. example 3 is a real life problem discussed in the recent papers [5], [8] and [11]. the triangular intuitionistic fuzzy parameters are given in table 5, and a comparison of the results is shown in table 6 and figure 3. comparing the numerical results obtained for the objective function kumar and hussain’s method table 4: numerical results for example 2 z̃imin µ ν ebrahimnejad and verdegay [5] (3300, 5800, 9100, 13200) (2350, 4450, 11050, 15550) our approach (1400, 3760, 13500, 8750) (700, 2280, 9550, 16000) https://doi.org/10.15837/ijccc.2021.1.4057 12 figure 2: approximated membership functions of the optimal solution values of the transportation problem with intuitionistic fuzzy parameters given in table 3 table 5: triangular intuitionistic fuzzy parameters of the transportation problem used for example 3 c̃i j = 1 j = 2 j = 3 ãi i = 1 (1, 4, 9; 0, 4, 12) (3, 13, 14; 2, 13, 15) (4, 6, 16; 1, 6, 33) (6, 7, 10; 2, 7, 11) i = 2 (4, 5, 7; 1, 5, 9) (5, 10, 15; 0, 10, 39) (7, 16, 24; 0, 16, 41) (6, 15, 23; 1, 15, 29) i = 3 (1, 3, 6; 0, 3, 10) (5, 13, 21; 5, 13, 35) (8, 18, 27; 6, 18, 48) (2, 10, 16; 0, 10, 21) b̃i (3, 8, 16; 0, 8, 19) (1, 6, 7; 0, 6, 14) (10, 18, 26; 3, 18, 28) [8] is the best considering the minimal value with maximal amplitude. however, some of the values of the decision variables provided by kumar and hussain’s method are negative, fact that cannot by acceptable in any model of transportation problem. our approach provides non-negative fuzzy intuitionistic numbers to all decision variables, and smaller minimal value with maximal amplitude than the methods in [5] and [11]. in the same way as in the previous example, the results obtained by our approach are better then those found in the literature since our methodology follows the extension principle applied to the optimization problem as a whole, while the others were focused just on following the arithmetic rules on the coefficients and variables. 6 final remarks the main contribution of this paper is three-fold: (i) we involved a mathematical model with disjunctive system of constraints in modeling transportation problems in fuzzy environment, thus improving an existing solution approach to transportation problems with fuzzy parameters; (ii) we proposed mathematical models able to provide approximate membership functions shapes to the optimal values of the decision variables, thus extending the solution approach to full fuzzy transportation problems; (iii) we expanded our approach to solving full intuitionistic fuzzy transportation problems consistently respecting the extension principle. we included in the paper several numerical examples from the literature in order to illustrate our table 6: numerical results for example 3 z̃imin µ ν kumar and hussain [8] (137, 292, 502) (12, 292, 961) ebrahimnejad and verdegay [5] (63, 313, 773) (2, 313, 1726) mahmoodirad et al. [11] (63, 310, 757) (2, 310, 1806) our approach (32, 305.4, 765) (0, 305.4, 1697) https://doi.org/10.15837/ijccc.2021.1.4057 13 figure 3: intuitionistic fuzzy numbers of the minimal objective values obtained by approximating the results of our approach compared to the results reported in [5], [8] and [11] theoretical results, and provide a visualization of the improvements brought in by our novel solution approach. our future research will focus on extending the ideas on which our solution approach is based to a wider class of optimization problems under uncertainty. acknowledgments this work was supported by the serbian ministry of education, science and technological development through mathematical institute of the serbian academy of sciences and arts and faculty of organizational sciences of the university of belgrade. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] anukokila, p.; radhakrishnan, b. 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(1985). applications of fuzzy set theory to mathematical programming. information sciences, 36(1), 29–58, 1985. https://doi.org/10.15837/ijccc.2021.1.4057 15 copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: stanojević, b.; stanojević, m. (2021). approximate membership function shapes of solutions to intuitionistic fuzzy transportation problems, international journal of computers communications & control, 16(1), 4057, 2021. https://doi.org/10.15837/ijccc.2021.1.4057 introduction preliminaries existing approaches to fuzzy transportation problems our novel solution approach mathematical model with disjunctive constraint system membership function shapes of the optimal solution values of the decision variables extension to intuitionistic fuzzy transportation problems numerical results final remarks international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 3, month: june, year: 2023 article number: 5061, https://doi.org/10.15837/ijccc.2023.3.5061 ccc publications a lightweight architecture attentional shift graph convolutional network for skeleton-based action recognition xianshan li, jingwen kang, yang yang, fengda zhao xianshan li 1. school of information science and engineering, yanshan university qinhuangdao 066004, china 2. key laboratory for software engineering of hebei province, yanshan university qinhuangdao 066004, china xjlxs@ysu.edu.cn jingwen kang 1. school of information science and engineering, yanshan university qinhuangdao 066004, china 2. key laboratory for software engineering of hebei province, yanshan university qinhuangdao 066004, china k17303402859@163.com yang yang 1. school of information science and engineering, yanshan university qinhuangdao 066004, china 2. key laboratory for software engineering of hebei province, yanshan university qinhuangdao 066004, china y857482666@163.com fengda zhao* 1. school of information science and engineering, yanshan university qinhuangdao 066004, china 2. school of information science and engineering, xinjiang university of science and technology korla 841000, china 3. key laboratory for software engineering of hebei province, yanshan university qinhuangdao 066004, china *corresponding author: zfd@ysu.edu.cn abstract in the field of skeleton-based human behavior recognition, graph convolutional neural networks have made remarkable achievements. however, high precision networks are often accompanied by numerous parameters and computational cost, and their application in mobile devices has considerable limitations. aiming at the problem of excessive spatiotemporal complexity of high-accuracy methods, this paper further analyzes the lightweight human action recognition model and proposes a lightweight architecture attentional shift graph convolutional network. there are three https://doi.org/10.15837/ijccc.2023.3.5061 2 main improvements in this model. firstly, shift convolution is a lightweight convolution method that can be combined with graph convolution to effectively reduce its complexity. at the same time, a shallow architecture for multi-stream early fusion is designed to reduce the network scale by merging multi-stream networks and reducing the number of network layers. in addition, the efficient channel attention module is introduced into the model to capture the underlying characteristic information in the channel domain. experiments are conducted on the three existing skeleton datasets, ntu rgb+d, ntu-120 rgb+d, and northwestern-ucla. results demonstrate that the proposed model is not only competitive in accuracy, but also outperforms current mainstream methods in parameter count and computational cost, and supports running in some devices with limited computing and storage resources. keywords: action recognition, lightweight network, shift graph convolution, attention module. 1 introduction as a multidisciplinary research direction, human behavior recognition plays an increasingly important role in intelligent home [1], virtual reality [2], and video surveillance [3]. 3d skeleton data is widely used because it can better reflect human posture and motion trajectory. human action recognition based on 3d skeleton data is mainly divided into two directions, the early methods are based on manual features, and the later methods are based on deep learning. in the method based on manual features, the features of each task need to be selected manually by researchers. when the amount of data is large, the manual extraction task will be extremely complicated and redundant. the method based on deep learning can independently learn effective features from massive data, adapt to a variety of complex scenarios, and greatly improve recognition accuracy compared with the learning method based on manual features. therefore, researchers are more inclined to use deep learning methods for research recently. the method based on recurrent neural networks (rnns) [4–9] usually converts the 3d skeleton coordinate vector into sequence information according to specific traversal rules and dynamically mines the temporal connections of the human skeleton. nevertheless, such methods focus more on temporal information and have certain limitations in processing spatial information. the method based on convolutional neural networks (cnns) [10–15] usually models 3d skeleton information as pseudoimages, with spatial and temporal information encoded as rows and columns, respectively, to identify different actions by fully extracting the spatiotemporal feature information of the skeleton. however, such methods ignore the constraint relationships of the human skeleton, which are very important for skeleton recognition. the method based on the graph convolutional neural networks (gcns) [16–35] constructs the topological map according to the physical connection of human joints along the spatial and temporal directions to fully extract the spatiotemporal characteristics of the human body, which currently has very broad application prospects. yan et al. [16] pioneer the application of graph convolution to human behavior recognition, and propose the spatial-temporal graph convolutional network (st-gcn). the model uses a graph convolutional network to learn spatial and temporal information of skeleton data respectively and has a strong generalization performance. the improved model based on st-gcn is usually combined with other networks, such as long short-term memory (lstm) and attention mechanism, to further improve the accuracy of the model. however, the existing high-precision models based on gcns are often very complex. in other words, the excessively high number of parameters and computational cost make the network difficult to train, hindering the development of this field in mobile devices. therefore, how to realize the lightweight of the network is a problem worthy of study. there have been some studies on lightweight models. cheng et al. [29] introduce shift convolution into graph convolution effectively reducing its complexity. however, their 4s shift-gcn model divides the feature information into four streams and inputs them into the network successively, which expands the model complexity by four times. based on shift-gcn, zang et al. [34] replace the shift graph convolution module with sparse shift graph convolution, which effectively reduces data redundancy. but this model, using the multi-stream network, has the same issue as 4s shift-gcn. therefore, reducing the network size by changing the network structure is a challenging problem in skeleton action recognition. zhang et al. [28] embed multi-stream joint feature information into the same https://doi.org/10.15837/ijccc.2023.3.5061 3 high-dimensional space before the convolutional network, effectively reducing the model size. song et al. [30] improve the multi-stream model by proposing an early fused multi-branch architecture and introducing the residual bottleneck structure, which significantly reduces the model complexity. sun et al. [31] propose a slowfast graph convolutional network in which the design of the fast pathway is very lightweight. jang et al.[32] design a simple and clear hierarchical feature extraction model with fewer parameters and less computations. however, the accuracy of these methods is insufficient. the proposed method is based on 4s shift-gcn [29], namely lightweight architecture attentional shift graph convolutional network (la-sgcn). inspired by the early fused multi-branch architecture [30], the goal of lessening the number parameters and reducing computational cost is achieved by improving the network architecture. a lightweight and efficient channel attention [36] module is added after the spatiotemporal graph convolution to effectively capture the key features of the model. the proposed model is evaluated on three datasets: ntu rgb+d [37], ntu-120 rgb+d [38], and northwestern-ucla [39]. compared to previous methods, our model has fewer parameters and smaller computational cost while improving accuracy. our main contributions are as follows: (1) by integrating shift convolution into graph convolution, parameter operations can be concentrated in point-wise convolution, which greatly reduces space and time complexity. in addition, the spatial shift graph convolution sets the skeleton as a complete graph, and each node can obtain the information of other nodes through the shift operation. the temporal shift graph convolution adaptively adjusts the receptive field of nodes through flexible shift operations, which effectively improves the model performance. (2) a shallow architecture for multi-stream early fusion is designed, which not only meets the needs of multi-stream features in the early stage of the network, but also fuses all features into one stream in the later stages, thus reducing the model complexity. moreover, considering that the shift graph convolution can adjust the receptive field autonomously, and does not require a very deep network to expand it, the depth of the network is compressed to further reduce the size of the model. (3) considering that the point-wise convolution in shift graph convolution is sensitive to channel information, a lightweight efficient channel attention module is added after each layer of shift temporal graph convolution, and key features are successfully extracted by adaptively learning the weight parameters of spatiotemporal information in each channel. this attention module not only effectively improves the accuracy of the model, but also barely places an additional burden on the network. 2 related work with the development of artificial intelligence technology, the fields of smart home, public safety [40, 41], and smart transportation have put forward higher requirements for human-computer interaction [42]. how to identify and monitor people’s behavior intelligently and provide a timely response to emergencies has become a research hotspot in these fields. therefore, human behavior recognition has important research value and practical significance. deep learning-based 3d skeleton human behavior recognition methods are mainly based on rnns, cnns, and gcns. rnns-based approaches normally convert the 3d skeleton coordinate vector into sequence information according to specific traversal rules, and dynamically mine the temporal connection of the human skeleton. du et al. [4] divide the human skeleton into five parts, input each part into a bidirectional rnn, and fuse them into higher-level features hierarchically. liu et al. [5] extend skeleton behavior recognition based on rnn to spatial and temporal dimensions and optimize the network through trust gates. liu et al. [6] introduce the global context memory cell into the lstm network to improve the selective attention performance of the network. zhu et al. [7] apply bidirectional lstm to encode each skeleton sequence and introduce a regularization method to better constrain the relationship between skeleton nodes. si et al. [8] propose a spatial inference algorithm and a temporal stacking learning algorithm to extract the spatial features and temporal dynamics of the human skeleton, which not only effectively extract the high-level features of the skeleton, but also the serial structure of multiple lstms can capture more long-term characteristic information. cnns-based approaches generally model 3d skeleton information as pseudo-images to extract spatiotemporal features of skeletons. du et al. [10] propose an end-to-end convolutional neural https://doi.org/10.15837/ijccc.2023.3.5061 4 network-based action recognition method, applying cnns to 3d human skeleton data for the first time. compared with the rnns-based method, not only the temporal information is utilized, but also the basic spatial information is retained. wang et al. [11] use multiple joint trajectory maps and convolutional neural networks to form a trajectory convolutional mapping network, significantly improving the accuracy of network recognition. considering the interpretability of the model, kim et al. [12] propose a spatiotemporal representation method to enhance model features. liu et al. [13] represent skeleton information as visual and motion-enhanced color images and use multi-stream cnn to explore the deep features of different types of color images. li et al. [14] apply the motion information of adjacent frames to construct temporal information and utilize cnn to efficiently extract the spatial information in the original coordinates. gcns-based approaches construct topology maps according to the physical connection of human joints, and adequately extract the spatiotemporal features of the human body. yan et al. [16] apply graph convolution to the skeleton-based human action recognition method for the first time and propose the model named st-gcn. st-gcn can learn spatiotemporal information autonomously and has a strong expressive ability. li et al. [17] employ the encoder-decoder structure to autonomously learn some potentially connected nodes while expanding the skeleton graph to explore higher-order dependencies. shi et al. [18] introduce bone orientation and length information into the network to adaptively adjust the graph structure, which is more flexible. si et al. [19] fuse gcns with lstm to handle discriminative spatiotemporal features. shi et al. [20] represent skeleton information as a directed graph and apply an adaptive network structure to train the model. zhang et al. [21] integrate long-range dependencies among joints through context aware graph convolutions. peng et al. [22] construct adjacency matrices dynamically through neural architecture search (nas). plizzari et al. [23] utilize a self-attention algorithm to capture the potential relationship between joints and form a two-stream network with gcns to extract the effective information in the joints. huang et al. [24] design part relation and part attention blocks to learn part-level and joint-level information. song et al. [25] apply class activation maps to optimize multi-stream graph convolutional networks. wen et al. [26] use graph convolution to extract spatial features, and stack multiple different convolution kernels to extract temporal information. shi et al. [27] adopt a gating mechanism to adaptively adjust the graph topology of different layers in graph convolution, and design three-dimensional attention modules of spatial, temporal, and channel to strengthen the corresponding features. nevertheless, these models bring more parameters and incur higher computational cost. therefore, reducing the complexity of the model remains a challenging issue. zhang et al. [28] embed joint position and velocity information into the same high-dimensional space and incorporate high-level semantics like joint type and frame index to develop a lightweight strong baseline. cheng et al. [29] combine shift convolutional neural networks (shift cnns) with gcns, which effectively reduces the computational complexity of the model. song et al. [30] propose an early fused multi-branch architecture to reduce the complexity of the model at the network level and introduce the residual bottleneck module to decrease the computational cost of spatiotemporal graph convolution. sun et al. [31] employ the slowfast pathway to implement a spatiotemporal attention lightweight gcns. jiang et al. [32] use two 1×1 convolutions to embed the multi-stream network features separately, and then add the features for follow-up training, thereby reducing the number of parameters. chen et al. [33] decrease model complexity by applying a strategy with low parameter burden such as fusion input and max pooling. zang et al. [34] use a sparse shift module to improve the performance of shift graph convolution. 3 method this section introduces the details of the proposed la-sgcn model. the overall process is illustrated in figure 1. four streams are input to the network, each through 3 layers of shift-block. the four streams are then spliced in the channel dimension to form one stream, passing through 5 layers of shift-block followed by a fully connected layer that outputs the classification result. the shift-block module includes three important modules: spatial shift graph convolution (shift-gcn), temporal shift graph convolution (shift-tcn), and efficient channel attention (eca). https://doi.org/10.15837/ijccc.2023.3.5061 5 joints bones jointsvelocities ⊕ bonesvelocities (32*4,128) (128,128) (128,256) (256,256) (256,256) fc (3,64) (64,64) (64,32) (3,64) (64,64) (64,32) (3,64) (64,64) (64,32) (3,64) (64,64) (64,32) shift-gcn relu eca relu residual residual shift-block shift-tcn figure 1: overall framework of the network 3.1 shift graph convolution spatial shift graph convolution. in a previous work [16], the connection between non-adjacent nodes is ignored because graph convolution of spatial dimensions is completed with the help of adjacency matrices. consequently, the convolution of the spatial shift graph transforms the skeleton graph into a complete graph, and there is a direct correlation between each node and every other node. as shown in figure 2, the convolution is carried out in three steps: shift transformation, point-wise convolution, and shift transformation. assuming that there are n = 10 nodes, and each node has c = 6 channels, the shift transformation connects the channels of node 1 and node 10 end to end to perform a shift operation. the translation step size of the ith channel is imodn. point-wise convolution can deeply correlate the offset feature graph and output the specified channel dimension. the first shift transformation shifts the channel of each node upward to realize the information exchange between nodes. the second shift transformation shifts the channel of each node downward, making the channel information return to the original node, avoiding the confusion of node feature information. the spatial skeleton feature map is denoted x ∈ rn ×c , the matrix h is added to capture the effective links between nodes: x̃h = x̃ · (tanh(h) + 1) (1) where x̃ is the feature after shift graph convolution. since the shift transformation operation can be implemented in memory while the number of parameters and the computational cost are concentrated in point-wise convolution, the complexity of graph convolution can be significantly reduced. temporal shift graph convolution. in the time dimension, shift graph convolution can also be divided into three steps: shift transformation, point-wise convolution, and shift transformation. the shift transformation is used to shift the channel information between frames. since different layers require different time receptive fields, setting the same shift value for each channel will not achieve the optimal effect. therefore, a learnable shift parameter αi, i = 1, 2, ...,c is set up to adaptively calculate the optimal shift value for each channel in each layer. if the shift parameter is an integer, it is not differentiable, and the gradient cannot be passed. therefore, the shift parameter is expanded from integer range to real number range. the skeleton sequence feature map is denoted x ∈ rn ×t ×c , the shift parameter can be calculated by linear interpolation: x̃(v,t,i) = (1 − µ) · x(v,⌊t+αi⌋,i) + µ · x(v,⌊t+αi⌋+1,i) (2) where µ = αi − ⌊αi⌋, while v and t determine the node and frame where channel i is located, respectively. 3.2 shallow architecture for multi-stream early fusion in previous experiments, most high-precision models adopt a multi-flow mode [17–20, 22–25, 29]. the advantage of multi-stream architecture is that each input feature can be fully convolved to maxihttps://doi.org/10.15837/ijccc.2023.3.5061 6 . . . � � ! � ! � 1 2 4 5 3 6 7 9 8 10 1 2 4 5 3 6 7 9 8 10 first shift transformation second shift transformation point-wise convolution � shift for node 1 shift for node 10 global shift shift for node 1 shift for node 10 global shift 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 � 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 � 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 � 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 � ′ 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 � ′ 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 � ′ figure 2: spatial shift graph convolution mize the utilization of information. the disadvantage is that the same network is run multiple times, resulting in multiplied computing costs. in the proposed multi-stream early fusion model, feature information is divided into four input streams: joints, bones, joints-velocities, and bones-velocities. the joints stream is the 3d coordinate of the human body junction. the bones stream is the length and direction of the human skeleton. the joints-velocities and bones-velocities streams are the differences between two adjacent frames of the joints and bones streams, respectively. in the early stage of the network, the four streams are trained separately, which can make the network get enough features. in the later stage of the network, the feature information of the four streams is spliced in the channel dimension and fused into one stream to complete the subsequent training, which can effectively reduce the model size. this strategy can substantially lower the computational cost while ensuring that the number of network input features is not decreased. graph convolution is the aggregation of information. taking the nodes of the spatial dimension as an example, the first graph convolution aggregates the information of the first-order neighbors, the second graph convolution aggregates the information of the second-order neighbors, and so on. multilayer convolution is required to complete the aggregation of the entire skeleton and form an effective connection with all the remaining nodes. in other words, as the number of convolutional layers deepens, the farther the nodes can aggregate the features, the broader the receptive field becomes. in shift graph convolution, the skeleton graph is a complete graph where the node receptive field is extended to the maximum. consequently, there is no need for too many layers to complete the aggregation of information. based on the above theory, the original 10 layers of the 4s shift-gcn are reduced to 8 layers. specifically, the four-stream network first trains 3 layers respectively for each stream, and then trains 5 layers after fusion, which effectively decreases the number of parameters and computational cost. 3.3 efficient channel attention mechanism the eca module judges the importance of each channel through the local cross-channel interaction strategy without dimensionality reduction and assigns corresponding weights to them. embedding this module behind the spatiotemporal shift graph convolution can effectively enhance the weights of key nodes. the specific process is shown in figure 3. given the skeleton feature x ∈ rn ×t ×c , eca maps the global spatiotemporal information to each channel through global average pooling (gap), captures the information of channel direction, and outputs an attention vector through the nonlinear layer. finally, each channel of the input feature is weighted by multiplying the attention vector with the corresponding element in the input feature. https://doi.org/10.15837/ijccc.2023.3.5061 7 the channel direction information is captured by obtaining the information of k neighbors of each channel, and all channels share learning parameters. this strategy can be implemented using a 1d convolution with kernel size k: x̃a = x · σ(conv1dk(gap(x))) (3) considering that the value of channel c will change after shift graph convolution, there is a reasonable proportional mapping between the size of channel interaction range k and channel c theoretically, and assuming there is a simple linear relationship between c and k: c = ϕ(k) = a × k − b (4) but the linear relationship expression is too limited, and channel dimension c is usually set to a power of 2. thus, it is assumed that there is a nonlinear mapping between c and k: c = ϕ(k) = 2(a×k−b) (5) then, k can be calculated adaptively according to c: k = ψ(c) = | log2(c) a + b a |odd (6) where a and b are both hyperparameters and are set to 2 and 1, respectively. |x|odd denotes the nearest odd number to x. through mapping φ, each channel can adaptively select the size of the 1d convolution kernel, carry out information exchange in a suitable range, and have better interactive performance. v t c gap � ⊗ v t c � = ! figure 3: efficient channel attention (eca) module(wang [36]) 4 experiments 4.1 datasets and implementation details ntu rgb+d. ntu rgb+d dataset [37] contains 56880 data samples, a total of 60 types of actions, taken by 3 microsoft kinect v2 cameras from different angles. this dataset is divided into training and testing sets using two benchmarks: (1) cross-subject (x-sub) is divided according to character id, and (2) cross-view (x-view) is divided according to the camera id. ntu-120 rgb+d. ntu-120 rgb+d dataset [38] is an expanded version of ntu rgb+d, with 57600 additional video samples, and 60 classes of actions. this dataset uses two benchmarks when dividing the training and testing sets: (1) cross-subject (x-sub) is divided according to character id, and (2) cross-setup (x-setup) is divided according to the distance and height of the camera. northwestern-ucla. the northwestern-ucla dataset [39] contains 1494 video samples, and 10 types of actions, captured by three kinect cameras. the evaluation benchmark is the same as [24]. the training and testing sets are divided according to camera id. training. the batch size of the three datasets in the experiments is 16, and the maximum number of training times is 140. the stochastic gradient decent (sgd) initial learning rate is set to 0.1 and https://doi.org/10.15837/ijccc.2023.3.5061 8 divided by 10 at the 60-th, 80-th and 100-th epochs. all experiments are performed on two gtx 1080 gpus. evaluation indicators. the model complexity is measured by parameters and flops. the parameters are the memory resources consumed by the model and usually represent its space complexity. the flops are the number of floating-point operations, i.e., the number of multiplication and addition operations in the model, usually representing its time complexity. 4.2 ablation study shallow architecture for multi-stream early fusion. in order to determine the specific structure of the network, the control variable method is adopted to optimize the network gradually. as shown in figure 4, the convolutional network layers are divided into two parts, the upper half is four-stream, and the lower half is one-stream. the convolutional layers with the same number of input and output channels are denoted as s1, s2, and s3, respectively. the number of layers of the network is gradually determined by changing the number of layers of sj,j = 1, 2, 3. the experiment details are shown in table 1. the results demonstrate that the network performance is optimal when s1 = 1, s2 = 1 and s3 = 2. as shown in table 2, compared with 4s shift-gcn, our method effectively lowers the number of parameters and reduces computational cost. table 1: the best values of s1, s2, and s3 are determined step by step using the control variable method on ntu rgb+d x-sub task const. var. x-sub (%) s2 = 2,s3 = 2 s1 = 0 88.5 s1 = 1 88.9 s1 = 2 88.5 s1 = 1,s3 = 2 s2 = 0 88.8 s2 = 1 90.1 s2 = 2 88.9 s1 = 1,s2 = 1 s3 = 1 89.9 s3 = 2 90.1 s3 = 3 89.7 table 2: comparisons with 4s shift-gcn on ntu rgb+d x-sub task methods layers (layer) x-sub(%) param.(m) flops(g) 4s shift-gcn [29] 10 90.7 2.76 10.0 l-sgcn (ours) 8 90.1 0.46 2.8 efficient channel attention mechanism. as shown in table 3, the control variable method is used to obtain the optimal position and number of attention mechanisms in the network. there are three cases where eca is added: after the spatial shift graph convolution, after the temporal shift graph convolution, and after both the spatial and the temporal shift graph convolution. the results show that adding eca after temporal shift graph convolution exhibits the best performance. table 3: the best position of eca on ntu rgb+d x-sub task eca position x-sub(%) after spatial shift graph convolution 90.1 after temporal shift graph convolution 90.4 after spatial & temporal shift graph convolution 90.2 https://doi.org/10.15837/ijccc.2023.3.5061 9 3, 64 3, 64 3, 64 64, 64 64, 64 64, 64 64, 32 64, 32 64, 32 96, 128, /2 128,128 128, 256, /2 256, 256 ⊕ ! " # 3, 64 64, 64 64, 32 figure 4: network structure division results in table 4 demonstrate that eca improves our model to varying degrees without increasing the parameter number or computational cost. table 4: comparisons of la-sgcn with and without eca module on ntu rgb+d and ntu-120 rgb+d methods x-sub(%) x-view (%) x-sub120 (%) x-set120 (%) param. (m) flops (g) la-sgcn w/o eca 90.1 95.6 85.6 85.8 0.46 2.8 la-sgcn 90.4 96.0 86.8 87.3 0.46 2.8 4.3 comparison with previous models to verify the generalizability and superiority of the proposed la-sgcn model, we conduct experiments on ntu rgb+d, ntu-120 rgb+d, and northwestern-ucla datasets, all of which are compared with rnns, cnns and gcns-based methods, as shown in table 5, table 6, and table 7, respectively. the evaluation indicators are accuracy, parameters, and flops. on the ntu rgb+d dataset, 90.5% accuracy is achieved on the x-sub benchmark and 96% accuracy is achieved on the x-view benchmark. on the ntu-120 rgb+d dataset, 86.8% accuracy is achieved on the x-sub benchmark and 87.3% accuracy is achieved on the x-set benchmark. on the ntu rgb+d and ntu-120 rgb+d datasets, 0.46m parameters and 2.8g flops are realized. on the northwesternucla dataset, 95.7% accuracy, 0.43m parameters, and 0.2g flops are achieved. in terms of accuracy, la-sgcn exhibits a considerable improvement compared with previous mainstream models. compared to 4s shift-gcn [29], the accuracy is very close on the ntu rgb+d and ntu-120 rgb+d datasets, and it is improved by 1.1% on the northwestern-ucla dataset. compared with other lightweight models such as sgn [28], resgcn-n51 [30], mssf-gcn [31], flagcn [32], nlb-acse [33], and 2s sparseshift-gcn [34], higher accuracy is achieved on the three datasets. in terms of model complexity, la-sgcn achieves lower complexity compared with previous modhttps://doi.org/10.15837/ijccc.2023.3.5061 10 table 5: comparisons with different models on ntu rgb+d dataset methods x-sub (%) x-view(%) param.(m) flops(g) hbrnn [4] 59.1 64.0 tcn [12] 74.3 83.1 synthesized cnn [13] 80.0 87.2 3scale resnet 152 [14] 84.6 90.9 st-gcn [16] 81.5 88.3 3.10 ca-gcn [21] 83.5 91.4 2s as-gcn [17] 86.8 94.2 6.99 27.0 3s ra-gcn [25] 87.3 93.6 6.21 2s agcn [18] 88.5 95.1 6.94 35.8 sgn [28] 89.0 94.5 0.69 resgcn-n51 [30] 89.1 93.5 0.77 2s agc-lstm [19] 89.2 95.0 22.89 54.4 pl-gcn [24] 89.2 95.0 flagcn [32] 89.4 94.8 0.83 4.1 nas-gcn [22] 89.4 95.7 6.57 mssf-gcn [31] 89.5 96.2 st-tr [23] 89.9 96.1 4s dgnn [20] 89.9 96.1 24.83 126.8 4s shift-gcn [29] 90.7 96.5 2.76 10.0 la-sgcn (ours) 90.5 96.0 0.46 2.8 table 6: comparisons with different models on ntu-120 rgb+d dataset methods x-sub (%) x-set(%) param.(m) flops(g) st-lstm [5] 55.7 57.9 gca-lstm [6] 58.3 59.2 rotclips+mtcnn [15] 62.2 61.8 sgn [28] 79.2 81.5 0.69 3s ra-gcn [25] 81.1 82.7 6.25 flagcn [32] 81.6 82.9 0.83 4.1 st-tr-agcn [23] 82.7 84.7 resgcn-n51 [30] 84.0 84.2 0.77 2s agcn [18] 84.2 85.5 6.94 35.8 mssf-gcn [31] 84.4 86.1 4s shift-gcn [29] 85.9 87.6 2.76 10.0 2s sparseshift-gcn [34] 86.1 87.5 7.7 la-sgcn (ours) 86.8 87.3 0.46 2.8 table 7: comparisons with different models on northwestern-ucla dataset methods top-1 (%) param.(m) flops(g) hbrnn-l [4] 78.5 ensemble ts-lstm [9] 89.2 2s agc-lstm [19] 93.3 10.9 4s shift-gcn [29] 94.6 2.56 0.7 nlb-acse [33] 95.3 1.21 la-sgcn (ours) 95.7 0.43 0.2 https://doi.org/10.15837/ijccc.2023.3.5061 11 els. compared with 4s shift-gcn, the number of parameters is 6× less on all three datasets, whereas the computational cost is 3.6× less on the ntu rgb+d and ntu-120 rgb+d datasets, and 3.5× less on the northwestern-ucla dataset. especially compared with gcn-based lightweight models [28, 30, 32–34], fewer parameters and lower computational cost are achieved on the three datasets. in summary, the proposed la-sgcn model exhibits obvious improvements in the three evaluation indicators of accuracy, space complexity, and time complexity, which proves that our method is lightweight. the low parameter count and the low computational cost improve the training efficiency of the model, so that the model can be trained in some small devices. lower model complexity fosters the development of behavior recognition in mobile devices, enabling them to perform tasks like real-time monitoring. 5 conclusions this paper designs a lightweight attention shift graph convolutional network, which achieves fast and efficient human action recognition and addresses the issue of the high precision network’s large size. first, shift convolution is introduced into graph convolution to enlarge the receptive field of the nodes, realizing the lightness of graph convolution. meanwhile, the shallow architecture for multi-stream early fusion not only makes full use of feature information but also distinctly decreases the complexity of the model. moreover, the efficient channel attention mechanism is suitable for shift graph convolution and can effectively capture crucial spatiotemporal features in the channel dimension. experimental results indicate that our model achieves the desired effect and has great advantages over other models of the same type. however, this study still has some limitations. the process of using the control variable method to find the optimal result is too cumbersome. in a subsequent work, we will improve the shallow architecture for multi-stream early fusion so that it can adaptively determine the depth and width of the network. at the same time, the datasets used are complete skeleton data, but in real life, human body parts may be obscured. in the future, we will further explore the realization of lightweight human action recognition on incomplete skeleton data to improve the generalization ability of the model, so that it can be better applied in real scenes. funding this study was supported in part by the xinjiang uygur autonomous region university scientific research project (key natural science project) (no. xjedu2021i029), the natural science foundation of xinjiang uygur autonomous region (no. 2022d01a59), the national natural science foundation of china (no. u20a20167), and project of hebei key laboratory of software engineering (no. 22567637h). author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] r. y. lee, 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[42] j.-s. han, c.-i. lee, y.-h. youn, and s.-j. kim, “a study on real-time hand gesture recognition technology by machine learning-based mediapipe,” journal of system and management sciences, vol. 12, no. 2, pp. 462–476, 2022. https://doi.org/10.15837/ijccc.2023.3.5061 15 copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: li, x.; kang, j.; yang, y.; zhao, f. (2023). a lightweight architecture attentional shift graph convolutional network for skeleton-based action recognition, international journal of computers communications & control, 18(3), 5061, 2023. https://doi.org/10.15837/ijccc.2023.3.5061 introduction related work method shift graph convolution shallow architecture for multi-stream early fusion efficient channel attention mechanism experiments datasets and implementation details ablation study comparison with previous models conclusions international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(2), 141-153, april 2019. gene sequences parallel alignment model based on multiple inputs and outputs x.l. feng, j. gao xiaolong feng, jing gao* college of computer and information engineering inner mongolia agricultural university hohhot 010018, china *corresponding author: gaojing@imau.edu.cn abstract: bioinformatics computing is a kind of big data processing problem, which usually has the characteristics of large data scale, large computational load and long computational time. therefore, the use of big data technology in bioinformatics computing has gradually become a research hotspot, and using hadoop for gene sequence alignment is one of it. it is a common way to use various tools to complete a job in the field of biocomputing. in most studies of parallel alignment of gene sequences using hadoop, third-party tools are also needed. however, there are few methods using hadoop independently to complete gene sequences alignment. adding data processing with other tools to hadoop workflow not only affects the improvement of computing performance, but also complicates the application. in this paper, a parallel alignment model of gene sequences based on multiple inputs and outputs is proposed, which can independently complete parallel alignment of gene sequences in hadoop platform without using other tools. this model not only simplifies the process flow of gene sequence alignment, but also improves the performance compared with other methods. this paper describes in detail the method of manipulating gene sequences with multiple inputs and outputs modes on hadoop platform and the design of a computing model based on this method, and proves the superiority of this model through experiments. keywords: multiple inputs and outputs, mapreduce, gene sequence alignment, short reads mapping, bwa (burrows-wheeler aligner), parallel computing. 1 introduction gene sequence alignment is a time-consuming task in gene sequence analysis. with the rapid development of gene sequencing technology in terms of capacity and speed, non-parallel computing method has become a bottleneck in the flow of bioinformatics analysis. it is an urgent need to design and develop a set of stable, efficient and scalable calculation methods to solve this problem. hadoop distributed parallel computing framework is a solution to this problem, because it provides a general method for processing large-scale data [17]. it can greatly improve the performance of large-scale data computing such as gene sequences and improve the scalability of computing methods [16]. hadoop is a distributed computing infrastructure released by apache foundation. it is a high fault-tolerant and high throughput open source computing framework deployable on low-cost hardware platforms. it is very suitable for storage and computation of applications with large data sets. it provides hdfs distributed file system, yarn resource scheduling manager and mapreduce computing model [8]. bwa (burrows-wheeler aligner) algorithm is a gene sequence alignment algorithm widely used in bioinformatics analysis. it can map a large number of short reads gene sequences to large-scale genomes [11]. at present, mature bwa tools are single-machine serial execution or multi-threaded parallel execution, and scalable distributed parallel computing methods are being copyright ©2019 cc by-nc 142 x.l. feng, j. gao studied. like many other bioinformatics computations, these bwa parallel computational methods require multiple tools to perform a data analysis task together. data needs to be processed by tools before input and after output. this not only makes the calculation process cumbersome, but also affects the efficiency of data analysis. therefore, a parallel computing model running on hadoop framework is proposed in this paper, which can independently complete the job of gene sequence alignment. this model can be used in distributed parallel computing of bwa algorithm. in this model, the method of data multiple inputs and outputs is used, which meets the requirement of gene data operation without using other tools. meanwhile, a mapreduce computing model matching this data input and output mode is designed, which improves the parallel computing performance of bwa algorithm. the design of this bwa parallel computing model considers the following three requirements. firstly, the model is superior to bwa algorithm and other bwa-based parallel computing methods in performance and scalability, including bwa’s own single-machine multi-threaded parallel computing method. secondly, the results of the model should be compatible with the traditional bioinformatics analysis process. because different versions of bwa tool contain different algorithm and characteristics, and different analysis work depends on different algorithms, so the model should provide a configurable interface to invoke the desired algorithm, instead of encapsulating only a particular algorithm. thirdly, the model should ensure the integrity of functions. avoid using other tools and intervention in the calculation process, and complete all tasks independently by the model. this allows users to concentrate on the scientific issues without considering the use and compatibility of multiple tools. the evaluation of this model should be compared with bwa algorithm and other parallel computing methods based on bwa algorithm in terms of performance and scalability. the advantages of the computing model are illustrated by time-consuming, speedup ratio and parallel efficiency. 2 research background bwa is a commonly used gene sequence alignment tool in bioinformatics analysis. it contains three algorithms: bwa-backtrack, bwa-sw and bwa-mem. the first algorithm is suitable for sequence mapping whose length is less than 100 bp, and the latter two are suitable for sequence mapping whose length is longer. bwa-mem is more efficient than other algorithms in sequence processing over 100 bp. the bwa mapping is a time-consuming step in gene sequence analysis, improve the efficiency of mapping has become the key to improve the biological information analysis. for this reason, bwa software also provides multithreaded parallel computing method. but this method is limited to the capacity of single-machine, and does not support distributed expansion. therefore, its performance is not very high, especially for large genomes, which takes a long time for alignment jobs, and it may also fail because of single point failure. hadoop is a suitable platform for bioinformatics computing in terms of data scale, job characteristics and cost of implementation. mapreduce is a programming model suitable for handling large amounts of semi-structured data sets. the functional programming method of mapreduce is a simple way for developer. users can implement parallel execution of computing tasks by writing the map and reduce functions. it provides an abstract parallel programming interface for operation, and implements the computation and processing of large-scale data in a simple way. in map and reduce functions, users can freely and flexibly add in parallel operation of data, which facilitates the processing of semi-structured data. the input of bwa algorithm is usually a sequence file in fastq format. it is the result file of gene sequencing. the sequencing results may be single-end sequence or pair-end sequence for different sequencing methods [5, 13]. the output of bwa algorithm is sam format file, which mainly records the location and hit times of short reads sequence mapped to reference sequence. gene sequences parallel alignment model based on multiple inputs and outputs 143 as to the data format, the input and output data of bwa algorithm are semi-structured data, which is suitable for mapreduce programming model. however, in the current bwa parallel computing research, there is no solution to the problem of data unified processing. data must be processed by other tools to adapt to mapreduce programming model. if we can design a mapreduce data input and output method suitable for gene sequence processing, it will be very convenient for users to use hadoop for bioinformatics analysis. 3 literature review in the research of using hadoop to improve bwa algorithm, there are three typical representatives: bigbwa [1], halvade [9] and seal [15]. bigbwa uses jni interface to invoke bwa source code to implement distributed parallel computing of sequence alignment, which significantly improves the performance of bwa algorithm. the disadvantage is that you need to use tools to change the original format of input data before computing starts. seal implements mapreduce model of bwa with python. the disadvantage is that it cannot satisfy all hadoop native interfaces, and its running efficiency is lower than that of java or c + + applications. this model only encapsulates a specific version of bwa software and cannot support the application of new versions, such as bwa-mem for long sequence alignment. halvade is a hadoop-based gene sequence alignment framework developed with java. it performs data split and sequence alignment in map function, calls different gene analysis programs in reduce function, and has many functions. but in data input and distribution, in order not to be restricted by hadoop’s specifications, it designed a platform-independent program "halvade uploader" to complete data distribution. although multi-threading is adopted, it cannot support distributed extension and is not consistent with the hadoop platform. the common feature of these studies is that they all use hadoop platform to parallelize computing tasks, which greatly improves the performance of bwa algorithm compared with serial execution. however, in these studies, third-party programs or applications independent of computing platforms are used for data preprocessing or post-processing, which makes the computing model not uniform as a whole, and also affects the improvement of computing performance to a certain extent. the reason for this approach is that hadoop does not provide a way to directly process gene sequences, while third-party tools can easily cope with it. taking bigbwa as an example, input data need to be preprocessed using python program. it makes the single-end gene sequence form a single-line structure linked by < sep > markers, such as line1 < sep > line2 < sep > line3 < sep > line4, and pair-end gene sequence form a single-line structure linked by < part > markers, such as left−end−of −sequence1 < part > right−end−of −sequence1. at the same time, two data files of pair-end sequence are merged into one data file. the reason is that single file and single-line structure are the most convenient way for hadoop to read directly. these additional tags need to be removed when they enter the mapreduce computing model to be accepted by the alignment algorithm. this increases the overhead of format processing in computing model. like input data, bigbwa’s output data also needs to be processed using python programs. it merges sam files on multiple nodes into one result file. the parallel computing model designed in this paper completes the process of data input, data distribution, distributed computing and result processing of gene sequence alignment task only with hadoop api. in this model, input data need not be pre-processed, and can be directly input by fastq format single-end or pair-end sequence files. the overhead of format processing is also reduced in mapreduce computing model. output data merging is also done without tools. computing tasks are automatically executed without interference in the process. 144 x.l. feng, j. gao 4 problem descriptions the task of short sequence alignment is to map a large number of single-end or pair-end short reads sequences to the reference genome, and then carries out subsequent biological significance analysis. the input of alignment algorithm is short reads sequence file and reference sequence file, and the output is mapping result file. the traditional serial alignment algorithm usually takes a long time because of the huge amount of data. the principle of short sequence alignment is to find the exact position of each short reads sequence in the reference sequence by global alignment of each short reads sequence with the reference genome [12]. the principle of gene sequence alignment based on hadoop is to distribute short reads sequence data to multiple nodes of distributed cluster, then map to reference sequence independently on each node and form their own result files. finally, the result files on each node are aggregated to form a result file [2]. short reads sequence is fastq format file. the pair-end sequence consists of left-end and right-end files. each file consists of many short reads sequences, each of which has a fixed structure. a short reads sequence file can be regarded as a set of m sequences, and the left-end sequence can be described as l{l1, l2, l3, ..., lm}, the right-end sequence can be described as r{r1,r2,r3, ...,rm}. then the pair-end sequence is described as reads{l,r}. single-end sequence can be regarded as a special case with only left-end. reference sequence is a long sequence that stores complete genetic information, ref represents reference sequence, aln represents alignment algorithm, s represents the set of alignment results {s1,s2,s3, ...,sm}. then the alignment problem discussed is described as si = aln(li,ri,ref), i = 1, 2, 3...m in order to distribute parallel execution of alignment tasks, reads set can be divided into n subsets d{d1,d2,d3, ...,dn} and distributed to n work nodes in the cluster. each node will be allocated to k = m/n short reads sequences if equal division of dataset is adopted. then the data set on the work node i can be represented as di = {li,ri}, i = 1, 2, 3...n li = {l(i−1)∗k+1,l(i−1)∗k+2, ..., li∗k},k = m/n ri = {r(i−1)∗k+1,r(i−1)k+2, ...,ri∗k},k = m/n computing task on node i is represented as si = aln(di,ref). computing tasks on all nodes are executable in parallel. when the task is completed, the si on each node can be merged into a result file, which is compatible with the traditional biological analysis work. to implement the distributed parallelization of alignment algorithm, four main problems need to be solved: (1) how to input the gene sequence file directly without preprocessing? (2) how to distribute the sequence in reads set to the work nodes? (3) how to execute the alignment algorithm? (4) how to merge the alignment results on the work nodes? in data input, a single-end sequence can be regarded as a special case of a pair-end sequence. so the main problem is how to input two or more files with hadoop apis without preprocessing. the change of data input method will lead to the change of subsequent data calculation method, so it needs to be considered comprehensively. hadoop provides apis for multiple data sources to read at the same time, which can solve this problem. it also needs to design programs to meet the requirements of gene sequence operation. gene sequences from different data sources need to be treated differently in the algorithm. it is necessary to ensure that the left-end sequence and the right-end sequence of a gene sequence can be recognized and integrated. gene sequences parallel alignment model based on multiple inputs and outputs 145 if there are m short reads sequences and n work nodes in an alignment job, the task of data distribution is to distribute m sequences to n work nodes. in order to get higher efficiency, the data distribution requires minimizing the amount of data movement and keep load balance on the work nodes. the simple way is to distribute m sequences in full to all work nodes. the disadvantage of this method is that it takes up a large amount of disk space, has large network traffic and takes a long time. the advantage of this method is that it does not need to design a distribution algorithm, and does not need to move data in the process of task execution. the ideal method is to distribute the necessary sequence to the designated nodes, so as to avoid moving data during task scheduling. custom partition in hadoop, which allows data to be distributed to designate nodes according to computing requirements, can solve the problem of data distribution very well. in the phase of executing alignment algorithm, in order to ensure the compatibility of alignment results and the integrity of alignment function, the best way is to call the existing traditional alignment program without changing the source code. invoking alignment program on distributed cluster can be implemented by jni, piple or shell. the collection and merging of result files can be accomplished on hdfs by hadoop file manipulation. 5 method the goal of model design is to design a stable, reliable, efficient and scalable distributed computing model, so that short sequence alignment algorithm can be distributed and parallel implemented on hadoop platform. the model does not need third-party tools to automate processes, including data input, data distribution, distributed computing and result collection. the following assumptions are made for the configuration or characteristics of distributed cluster: – computing framework runs in distributed cluster with one name node and several data nodes with the same capacity. – each alignment operation can be independent of other tasks. – the reference database is pre-deployed to the system, and all alignment tasks can be performed by any data node. – short reads sequence files can be split into multiple sequences and reassembled. the function of hadoop platform provides great convenience for designing distributed parallel model of alignment algorithm. considering the characteristics of platform and alignment job, a computing model is designed as shown in figure 1. hdfs is used to store input data and results, which is convenient for data distribution and sharing in hadoop platform. before the job submission, the pre-deployment work should be completed, that is, the reference sequence and alignment software should be deployed to each working node in advance, and the short reads sequence file should be uploaded to hdfs to facilitate the distributed deployment. in data input, hadoop multi-input api is used to read multiple sequence files directly without data preprocessing. two or more data files can be stored in hdfs for multiple inputs as shown in figure 1, l and r represent two files of pair-end sequence respectively. single-end sequence input is considered as a special case of multiple inputs. in mapper, left-end or right-end tags are added for key-value pairs. then the data is partitioned according to the key of the pair by using the custom partitioning algorithm, and the partitioned data is distributed to the work nodes of the cluster. in reducer, multi-output is used to transfer the partition data to the alignment algorithm, and the alignment algorithm is invoked on the working node to implement the distributed computing of each partition. finally, the results on each node are collected into hdfs and merged in one file. 146 x.l. feng, j. gao the design of computing model needs to take advantage of the functions provided by the platform and comply with its programming specifications [20]. therefore, according to the model shown in figure 1, a processing flow is designed, which includes seven steps: pre-deployment, data input, mapper processing, data partition, merge processing, reducer processing, and result processing [14], as shown in figure 2. two classes, fileinputformat and multipleinputs, are provided in hadoop api to support multiple inputs. the former uses unified mapper processing, while the latter supports independent mapper processing. in this paper, the addinputpath method of fileinputformat class is used to implement multi-input. by organizing the paths of multiple data files into an array, and then passing the array as a parameter to the function, the input class can read data from multiple data sources. the input gene sequence whether left or right, forms a key-value pair with offset as key and sequence content as value. in mapper processing, pair-end sequences are identified as left-end or right-end and labeled, while single-end sequences are not labeled, such as algorithm 1. the tags added in mapper is the basis for subsequent implementation of data partition constraints and data multiple outputs. after mapper processing, the left-end and the right-end of a pair-end sequences form key-value pairs with the same key. because a pair-end sequence has the same offset in two files. algorithm 1 the map algorithm 1: input: (key,value) 2: output: (key,value′) 3: if value stores a pair-end sequence then 4: if value stores a left-end sequence then 5: value′ = addtag(value, left-end-tag) 6: else 7: value′ = addtag(value, right-end-tag) 8: end if 9: else 10: value′ = value 11: end if 12: context.write(key,value′) h d f s (key,value) (key,value ) partitionsreads sams h d f s sam mapper partitioner reducer l r figure 1: mapreduce model for short reads gene sequence alignment gene sequences parallel alignment model based on multiple inputs and outputs 147 start pre-deployment: deploy alignment software, reference sequence and shell script to each work node, and upload short reads sequence file to hdfs. data input: multiple file input to form key-value pairs with offset as key and sequence content as value. mapper: mark input key-value pairs with tags, double-ended sequences with left-end or right-end tags, single-ended sequences with no tags. partition: the input file size is divided into n intervals, and the partition number is calculated according to the interval of the key. merge: data shuffling, sorting, grouping, aggregating values with the same key value into the same set. reducer: export all sequences of a data partition by tags to the local file, and then start the alignment script locally. result processing: collect the result files distributed on each node, merge them into a single file and upload to hdfs. end figure 2: flow chart of short reads gene sequence parallel alignment 148 x.l. feng, j. gao partitioner is a means of data distribution provided by hadoop platform [10]. the partition classes built in the platform, such as hashpartitioner and binarypartitioner, are not suitable for the distribution of pair-end sequences. the operation of gene sequences has some constraints on data partitioning, which is not satisfied by hadoop’s partition classes. it is necessary to customize partition classes according to the requirements of gene sequence operation. the constraints of data partitioning include: the number of left-end sequences in each partition is the same as that of right-end sequences; the position of sequences in a partition remains unchanged relative to that in sequence file; the left-end and the right-end of a pair-end sequence must be allocated to the same partition. since it has been assumed that the capacity of each work node in the cluster is the same, data is divided into equal partitions. the partitioning algorithm is shown in algorithm 2. firstly, the intervals of offsets are calculated by the size of sequence file and the number of partitions. then partition is calculated with key in (key,value′). key represents the position of a sequence in the sequence file. the partition number can be obtained by judging the offset interval of key. sequences at the same location of the pair-end sequence have the same key, so they are assigned to the same partition. algorithm 2 the partition algorithm 1: input: (key,value′) 2: output: partitionnum 3: let filesize ← the size of input file 4: let partitionnum ← 0 5: let n ← the number of nodes 6: while partitonnum < n do 7: if key < (partitionnum + 1)filesize/n then 8: return partitionnum 9: end if 10: partitionnum++ 11: end while 12: return n− 1 the merge phase will shuffle, sort, and group the data in the partition based on the key. the result of processing is that sequences with the same key are aggregated into the same set. for a pair-end sequence, a key corresponds to a set containing its left-end and right-end sequence, and the key-value pair is in the form of (key,value′[]).moreover, all sequences are ordered in the partition, which ensures that the relative positions of the sequences in the partition remain unchanged. input in reducer is a data partition and aggregated by key. if the input is a singleend sequence, the sequence in the partition will be written to the local file one by one, and if the input is a pair-end sequence, it will be identified by tags added in mapper and output to different local files in multi-output method, as shown in algorithm 3. therefore, the output of reduce function to a single-end sequence is a single local file, the content is the data partition on the node, and the output to a pair-end sequence is two local files. after the local data file is generated, the sequence alignment task is started in the cleanup function of reducer, and the task is executed by shell call. shell script can be modified at any time according to the requirement of software version or parameter configuration, which makes gene sequences parallel alignment model based on multiple inputs and outputs 149 algorithm 3 the reduce algorithm 1: input: (key,value′[]) 2: output: left−parti,right−parti or parti 3: for all value in value′[] do 4: if value stores a pair-end sequence then 5: if value stores a left-end sequence then 6: mutioutput.write("left-part",value) 7: else 8: mutioutput.write("right-part",value) 9: end if 10: else 11: context.write(value) 12: end if 13: end for the computing model more flexible. the alignment task is performed in parallel on each node in the cluster. start single-end alignment algorithm for single-end sequence and pair-end alignment algorithm for pair-end sequence on the node. the output is the result of sequence alignment in the partition on the node. after the alignment is completed, the results are uploaded to hdfs. when the alignment task of all nodes has been completed, multiple result files in hdfs are merged into a unified file, and the process ends. 6 results and discussion 6.1 experimental design the following experiments were designed to verify the performance of our gene sequence parallel alignment model. the gene data were extracted from the 1000 genome projects [18]. the 3.3g grch38.p12 was taken as the reference genome, while two datasets, err000589 and srr062634, were selected as the short reads sequence. the specific information of the sequence is shown in table 8. table 1: short reads sequence datasets tag name number of reads read length (bp) size(gb) d1 na12750/err000589 1.2 × 107 51 5.2 d2 hg00096/srr062634 6.7 × 106 200 3.5 as shown in table 8, dataset d1 is composed of pair-end sequences with a length of 51bp. it is suitable for bwa backtrack algorithm. dataset d2 is composed of single-end sequences with a length of 200 bp. it is suitable for bwa mem algorithm. the two datasets differ in size, sequence length, sequencing method and alignment algorithm. in order to verify the universality and stability of the computing model for different data sets, the choice of experimental data should be representative [6,7]. therefore, two data sets with different characteristics are selected in this experiment. the test cluster is a hadoop cluster of one name node and eight data nodes. each node is a vmware virtual machine with 8-core cpu, 8g memory and 1t hard disk. the hadoop uses the version of 2.7.3. the operating system is red hat enterprise linux 6.5. • experiment 1, the bwa mapping was performed with d1 and d2 as inputs. the same computing tasks were run on hadoop cluster with 1, 2, 4, 6, and 8 work nodes, respectively. 150 x.l. feng, j. gao the time consumption, speedup and efficiency of each task were measured to evaluate the model performance. • experiment 2, the bwa mapping was performed with d1 and d2 as inputs. the same computing tasks were run on single node with 1, 2, 4, 6, and 8 threads, respectively. these time-consuming are compared with those of the same tasks on distributed clusters with 1, 2, 4, 6 and 8 nodes, respectively. 6.2 results analysis all tasks were completed smoothly. the experimental results were the same as those of single-machine operation, but achieved at a much shorter time. table 2 shows the time consumption, speedup and efficiency of experiment 1. as shown in table 2, as the number of nodes increases, the time consumption of both tasks decreases dramatically. it shows that the model has good scalability, and the experiment time can be reduced by adding more nodes. the trend of speedup ratio shows that more nodes can make the acceleration effect more obvious. but it can’t achieve linear acceleration, as shown in figure 3. the reason is that when the number of nodes in the cluster increases, the overhead for cluster management will increase, and the overhead for computing task scheduling and resource management will also increase, resulting in a decrease in resource utilization. that’s why the efficiency can’t always be 1 [3,4]. the dataset d1 has shorter read length and lower computational complexity. although the total data size exceeds d2, the speedup ratio is still slightly higher than d2. it is shown that the computing model has better speedup ratio for data sets with shorter read length. experiment 1 proves that the parallel computing model of gene sequence alignment based on multiple inputs and outputs can greatly reduce the computing time for different data sets and different alignment algorithms, and has better speedup ratio and parallel computing efficiency. table 2: performance comparison of hadoop computing model content dataset number of nodes 1 2 4 6 8 time consumption (m) d1 258.8 131.1 67.8 45.3 38.5 d2 249.6 127.1 72.8 49.5 36.7 speedup d1 1.0 2.0 3.8 5.7 6.7 d2 1.0 2.0 3.4 5.0 6.8 efficiency d1 1.00 1.00 0.95 0.95 0.84 d2 1.00 1.00 0.85 0.83 0.85 table 3 shows the time-consuming of experiment 2. generally speaking, the execution time of both methods decreases with the increase of the number of nodes or threads. figure 4 shows the trend of time-consuming. as can be seen from the figure 4, the bwa multithread mode slows down the time-consuming after the start of four threads. however, hadoop computing model still maintains a good reduction in processing time after 4 nodes. experiment 2 indicates that the performance of bwa multithread mode is affected by single-node memory and cpu capacity. it cannot increase the speed of operation by increasing the number of threads blindly, and it is not scalable. the proposed computing model has good scalability. as long as there are enough work nodes, the computing time can be reduced to a lower level. of course, the number of nodes cannot be increased indefinitely, because increasing the number of nodes will lead to a decrease in parallel efficiency, and the balance between the number of nodes and efficiency should be achieved [25]. gene sequences parallel alignment model based on multiple inputs and outputs 151 0 1 2 3 4 5 6 7 8 9 1 2 4 6 8 s p e e d u p number of nodes d1 d2 liner figure 3: speedup of hadoop computing model with dataset d1 and d2 table 3: execution time of hadoop and bwa multiple threads method dataset number of nodes/threads 1 2 4 6 8 hadoop (m) d1 258.8 131.1 67.8 45.3 38.5 d2 249.6 127.1 72.8 49.5 36.7 threads (m) d1 258.8 154.6 127 124.6 123.8 d2 249.6 102.5 52.9 53.2 51.8 the proposed computing model was further contrasted against several excellent parallel computing methods, which have been proved as capable of improving the performance of gene sequence alignment. all the experiments were performed using the same dataset on hadoop clusters with different configurations. the grouping experiments were performed at 1, 2, 4, 6 and 8 nodes. since these algorithms use different computing environments, the time consumption was not compared in the same dataset. in terms of the speedup ratio of parallel computing (figure 5), the proposed computing model had certain advantages over the contrastive algorithms. the results show that gene sequence multiple input and output method both saves the time of data preprocessing and reduces the burden of mapreduce computing model, improving the efficiency of parallel computing. the multiple inputs and outputs method on hadoop platform can effectively process singleend and pair-end sequences of gene data, which makes the operation of gene sequences not limited to single file and avoids the use of third-party tools in parallel computing of gene sequence 0 50 100 150 200 250 300 1 2 4 6 8 p ro c e s s in g t im e ( m ) number of nodes or threads d1 by hadoop d2 by hadoop d1 by threads d2 by threads figure 4: execution time comparison between hadoop and bwa multiple threads 152 x.l. feng, j. gao 0 1 2 3 4 5 6 7 8 1 2 4 6 8 s p e e d u p number of nodes seal pbwa bigbwa new figure 5: speedup comparison of different parallel bwa alignment. the parallel computing model of gene sequence alignment based on multi input and output uses hadoop api to complete the task of gene sequence alignment, including the data input, data distribution, distributed computing and result processing, which ensures the uniformity of application. experiments show that this computing model makes the task of gene sequence alignment scalable in distributed cluster. compared with single node algorithm, the computing time of the same task is significantly reduced. compared with multi-threaded parallel computing mode and other parallel gene data computing schemes, this computing model has certain advantages. funding this work was supported by national natural science foundation of china project 61462070. author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. bibliography [1] abuin, j.m.; pichel, j.c.; pena, t.f.; amiqo, j. 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[20] [online]. available: hadoop.apache.org/, accesed on 20 june 2018. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 5, month: october, year: 2022 article number: 4886, https://doi.org/10.15837/ijccc.2022.5.4886 ccc publications evolutionary computation paradigm to determine deep neural networks architectures r.c. ivanescu, s. belciug, a. nascu, m.s. serbanescu, d.g. iliescu renato constantin ivanescu department of computers and information technologies, university of craiova, romania a.i. cuza, no 13, 200585, craiova, romania constantin.ivanescu@edu.ucv.ro smaranda belciug department of computer science, university of craiova, romania a.i. cuza, no 13, 200585, craiova, romania *corresponding author: sbelciug@inf.ucv.ro andrei nascu department of computer science, university of craiova, romania a.i. cuza, no 13, 200585, craiova, romania andreinascu3@gmail.com mircea-sebastian serbanescu 1. department of computer science, university of craiova, romania a.i. cuza, no 13, 200585, craiova, romania 2. department no 2, university of medicine and pharmacy, craiova petru rares, no 2, 200349, craiova, romania mircea_serbanescu@yahoo.com dominic gabriel iliescu 1. department of computer science, university of craiova, romania a.i. cuza, no 13, 200585, craiova, romania 2. department no 2, university of medicine and pharmacy, craiova petru rares, no 2, 200349, craiova, romania dominic.iliescu@umfcv.ro abstract image classification is usually done using deep learning algorithms. deep learning architectures are set deterministically. the aim of this paper is to propose an evolutionary computation paradigm that optimises a deep learning neural network’s architecture. a set of chromosomes are randomly generated, after which selection, recombination, and mutation are applied. at each generation the fittest chromosomes are kept. the best chromosome from the last generation determines the https://doi.org/10.15837/ijccc.2022.5.4886 2 deep learning architecture. we have tested our method on a second trimester fetal morphology database. the proposed model is statistically compared with densenet201 and resnet50, proving its competitiveness. keywords: deep learning, evolutionary computation, statistical analysis, image classification, fetal morphology 1 introduction the first cause of fetal death, infant morbidity and mortality is represented by congenital anomalies (cas), [1]. within the first 28 days after birth, more than 295 000 new-borns die because of cas, placing romania on top with one of the highest death rates in eu, (https://data.unicef.org/country/rou accessed july 1, 2022). according to the eurocat (european union congenital anomalies) report [2], each year 2.5% births within the eu will have cas, while worldwide the numbers go up to 6%, that is 7.9 million infants, [3]. even if some cas can be treated, still 3.2 million children are disabled for life. detecting cas at an early stage of pregnancy facilitates life-saving treatments and even stop the progression of disabilities. discovering cas prenatally improves neonatal care for both term and preterm infants, [4]. cas are diagnosed prenatally while performing the second trimester morphology scan (mf). through ms the structure and functionality of the organs are evaluated. ms are not easy to interpret, sonographers been far from a correct detection of cas. an experienced sonographer (more than 2000 ms examinations) has the detection rate of only 52%, while a sonographer with less experience has a detection rate of 32.5%, [5]. a study that regards the preand post-natal discrepancy of cas performed manually reported a sensitivity that ranged from 27.5% to 96%, [6]. multiple factors influence the performance of the sonographer, from which we mention: experience, time pressure, fatigue, fetal unintentional movement, and mother’s physical features. an advanced intelligent decision support system (aidss) has the capacity to gather, analyse data, communicate with other systems, learn from experience and adapt according to new cases. our aim is to foster a cross-fertilization of ms, image processing and artificial intelligence (ai), which will result in a aidss that will assist medical practice and discovery. relatively little work has been published in this field. fujitsu, cancer translational research team, and the department of obstetrics and gynaecology showa university school of medicine used deep learning to signal cas in the fetal heart, [7] [8]. a fully convolutional neural network for the segmentation of the 3d fetal brain was proposed by namburete et al., [9]. in [10], deep learning together with sequential forward feature selection techniques and support vector machines was used to obtain a segmentation of the fetal lungs and brain from magnetic resonance imaging and ultrasounds. in [11] deep learning was used to segment the fetal head. in [12], a differential evolution deep neural network was used to determine the view planes from a ms. finding the best architecture of a deep neural network has become a quest in the last decade. in 2019, 300 studies have been published in this area, [13]. one way to optimize the network’s architecture is through neuroevolution, [14]. neuroevolution proved to have similar performance as the gradient descent algorithm when applied on the loss function in the case of gaussian white noise, [15]. obviously, this view is not shared by everyone. in [16], it is mentioned that one generation of neuroevolution is not sufficient for comparison with the gradient descent. neuroevolution has been applied to the evolution of deep encoder, [17]. in other cases, we can use neuroevolution to estimate the performances of different deep neural networks, [18]. in this study, the authors succeeded in reducing the computation time of the training phase from 33 gpu days to 10 gpu days. the performance of the deep neural network remained unchanged. xie and yuille use a genetic algorithm to evolve the topology, hyperparameters, and synaptic weights of the convolutional neural network, [19]. another approach of neuroevolution is the amoebanet-a, which has been obtained through a modified evolutionary computation algorithm. the authors modified the tournament selection using the age of chromosomes to favour the offspring over the parents, [20]. https://doi.org/10.15837/ijccc.2022.5.4886 3 in this paper, we are interested in automatically determining the best architecture of a deep neural network for classifying the views of the anatomical structures from the fetal abdomen. we propose to use evolutionary computation to determine the best architecture of a deep neural network for this task. for benchmarking we have compared the novel model’s results with the results obtained by two other state-of-the-art deep learning networks, resnet50 and densenet201. the paper is organized as follows: section 2 the design and implementation of our approach. in section 3 we present the dataset, the manner in which we have designed the experiments, and the parameter setting. in section 4 we present the results and their discussion. the paper ends with the conclusions in section 5. 2 model design 2.1 deep learning neural networks’ architectures deep learning (dl) neural networks are a special class of neural networks, [21]. unlike the classical neural networks, [22], where we encounter only one type of hidden layers, in a dl network we have three such types: convolutional, pooling, and fully connected layers. in the convolutional layers we have kernels or filters that scan the input images and produce a feature map through convolutions. after several convolution layers, it is customary to add a pooling layer that down samples the feature map. in this manner in the dl spatial invariance is produced. the fully connected layer resembles to the layer from the classical neural networks, where the input of the layer is connected to all the neurons. the architecture of a dl ends with a fully connected layer. the most commonly used activation function in a dl is the rectified linear unit (relu): f(x) = {0,x < 0 x,x > 0}. other activation functions that can be used are the sigmoid: f(x) = 11+exp(−a·x), or the hyperbolic function: f(x) = e x−e−x ex+e−x . the fully connected layer has as activation function the softmax, that is the generalized logistic regression. softmax takes a score from rn and output a vector that contains probabilities, p = (p1..pn), where pi = e xi∑n j=1 e xj . state-of-the-art dl have their architectures set deterministically, which makes them rigid. optimizing their architecture through neuroevolution might be the key to this problem. 2.2 evolutionary computation approach evolutionary computation (ec) is a perfect tool for optimization problems. the algorithms are modelled after the biological processes encountered in life. these types of algorithms consist of a population of chromosomes, different selection mechanisms that are based on a fitness function, a reproduction operator that produces new offspring, and a mutation mechanism applied onto the offspring, [23] [24]. initially, the population of chromosomes is randomly initialised. these chromosomes cover all the intel about the potential candidate solution for the optimization problem, whether it concerns minimization or maximization. at each iteration, considering the evaluation of the cost function we choose the best chromosomes to form the next generation. the reproduction operator is used to restock the population, whereas the mutation operator is used for variation, technically speaking escaping from the local minima or maxima. at each iteration all the chromosomes are evaluated using the fitness function. the algorithm stops if the a given criterion is met. the choice of the criterion depends on the user on multiple facts such as, [25]: https://doi.org/10.15837/ijccc.2022.5.4886 4 • does the fitness function improve as generations pass or not ? • do we have a predetermined threshold for the population diversity ? • do we have a predetermined number of generations ? • do we have a predetermined number of fitness evaluations ? the fitness function f(xi), i = 1, 2, ...,n measures the skill of each chromosome to compete against the rest of the population in a given environment. the fitness function is nonnegative. ec uses the chromosomes to explore in parallel different potential solutions for the problem at hand. let us presume that the population c contains n chromosomes. each chromosome contains m genes/features and is mathematically written as xic = ( xi1c,xi2c, ...,xim c ) , where i=1, 2, .. .,n. we initialize the population randomly from the upper ( xi,nu ) and lower bound ( xi,nl ) of the search interval for each gene of the chromosome. xi,n = xi,nl + rand() · (xi,nu −xi,nl), i = 1, 2, ...,m,n = 1, 2, ...n. the general scheme for an ec algorithm is [26] [27]: 1. randomly initialize the chromosome population 2. obtain the fitness score of each chromosome 3. repeat the following steps until the stopping criterion is reached: 2.1 apply the selection operator to choose the parents 2.2 apply the crossover operator to produce the offspring 2.3 apply the mutation operator on the offspring to produce variety 2.4 apply the fitness function on the population 2.5 select according to the fitness the chromosomes that will form the next generation 2.3 our idea in what follows the ec/dl algorithm for optimizing the architecture of a dl is presented. the first step in the optimization process is to determine the genes of a chromosome, that will in the end determine the best dl architecture. a chromosome is encoded in a fixed-length integer vector. for us to build a performant dl architecture, we needed to determine the number γ of convolutional hidden layers and the number of neurons in each convolutional layers nhi, i = 1, ...,γ. after each convolution, we added a max pooling layer of 2 by 2. the depth of the kernel matched the color channel number 3 (red, green, blue). we have set the recombination probability, pr, to 0.6, and the mutation probability, pm to 0.25. a potential solution will be an integer vector of the following form: xi = (γ,nhj,j = 1, ...,γ,activationfunction,optimizer), i = 1, 2, ...,n. all the potential architecture ended with a dense layer. briefly, we summarize the ec/dl algorithm: 1. input: the image dataset, the number of generations ng, the number of chromosomes in a generation, n, and the chromosomes xi = (γ,nhj,j = 1, ...,γ,activationfunction,optimizer), i = 1, 2, ...,n. 2. create the initial population: generate randomly the chromosomes. use each chromosome i to build a dl having γi convolutional and pooling layers, nhi, one activation function chosen randomly from the three mentioned before, and one optimizer. train the dls and record the chromosome’s fitness score as the accuracy obtained on the validation dataset of the dl having the respective architecture. 3. selection. select the parents for the recombination process 4. recombination. apply the recombination operator. 5. mutation. apply the mutation operator. 6. selection. select the chromosomes that will form the next generation. 7. repeat steps 3-6 until the stopping criterion is met (the predetermined number of generations ng has been reached). 8. output. return the best performing chromosome from the last generation. this chromosome will represent the best dl architecture for the problem at hand. https://doi.org/10.15837/ijccc.2022.5.4886 5 3 dataset and design of experiments 3.1 dataset this is a prospective cohort study which is deployed in a tertiary maternity hospital (university emergency county hospital craiova, romania). the data analysis and processing was performed at the department of computer science, university of craiova, romania. all the eligible participants were pregnant women admitted at the prenatal unit for the second trimester morphology scan. the patients have been recruited consecutively. during the standard consultation, the doctor provided information about the conducted research to all eligible patients and invited them to take part of the study. all patients that have agreed and fulfilled the inclusion criteria gave a written informed consent. an extremely important issue that needs to be addressed is privacy-preserving and explainable ai for parents with children that had been diagnosed with cas [28]. the doctors make sure that the patients understand the study’s implication. the ultrasounds had been performed by obstetricians with minimum 2 years experience, and with training in transabdominal obstetrical ultrasound. all images were acquired using voluson 730 pro (ge medical systems, zipf, austria) and logic e (ge healthcare, china us machines with 2-5-mhz, 4-8-mhz, and 5-9-mhz curvilinear transducers. when this study has been performed, the dataset contained 970 images from 100 participants. the dataset regards the abdominal plane and has 10 classes: 3 vessels plus bladder (113 images), gallbladder (70 images), sagittal cord insertion (47 images), transverse cord insertion (103 images), anterior abdominal wall (20 images), anteroposterior kidney plane (96 images), biometry plane (205 images), intestinal sagittal plane (83 images), kidney sagittal plane (207 images), bladder plane (22 images). figure 1 presents a sample image from every decision class. figure 1: fetal morphology abdomen: (a) 3 vessels plus bladder; (b) gallbladder; (c) sagittal cord insertion; (d) transverse cord insertion; (e) anterior abdominal wall; (f) anteroposterior kidney plane; (g) biometry plane; (h) intestinal sagittal plane; (i) kidney sagittal plane; (j) bladder plane. it can be seen from the distribution of the images in the decision classes, that the dataset is unbalanced. this situation is encountered in real clinical scenarios. the images were anonymized and pre-processed to eliminate the text. for the pre-processing we have used cv2 and keras-ocr. at first the text was detected from the images using optical character recognition (ocr), followed by fill the missing parts using inpainting. in some special cases, the algorithm produced false positives (detecting text when it was not the case), hence after the automatically process of removing the text, we have rechecked manually every image to verify whether there are false positives or not. when a https://doi.org/10.15837/ijccc.2022.5.4886 6 text had been identified a bounding box contained the coordinates was obtained. a mask was applied to each box, and the algorithm started to inpaint it, in order to produce a whole image. because dl algorithm need a larger dataset, we have applied augmentation. hence, we have applied minor changes to our data: flips, rotations, translations, and adding gaussian noise. the purpose for these changes is to trick the dl into thinking it is presented with multiple distinct images. after the augmentation process, the dataset used for testing our novel approach contained 4815 images: 3 vessels plus bladder (560 images), gallbladder (350 images), sagittal cord insertion (235 images), transverse cord insertion (510 images), anterior abdominal wall (105 images), anteroposterior kidney plane (480 images), biometry plane (1020 images), intestinal sagittal plane (420 images), kidney sagittal plane (1030 images), bladder plane (105 images). 3.2 design of experiments. parameters settings. an important part of this study was the benchmarking process for comparing our novel approach’s performance with the performances of two other state-of-the-art dl algorithms densenet201 and resnet50. the validation method chose for each algorithm was the 10-fold cross validation. each model was executed in 100 independent runs of a complete cross-validation cycle. the statistical power achieved was greater than 95% having a type i error α=0.05 for every test applied. for each model we have computed the average validation accuracy (ava), standard deviation (sd), precision, recall and f1-score. we have used precision recall auc instead of the auroc, because the dataset was unbalanced, hence the probability of producing a large false positive rate was high. before applying statistical comparison tests such as one-way anova together with post-hoc tukey hsd, we have verified the normality and the equality of variances distributions of the samples that contained the 100 computer runs for each model. as normality test we have applied the shapiro-wilk w test, because it has a greater power at detecting non-normality than the kolmogorov-smirnov and lilliefors test, and as equality of variance test we have applied the levene’s test. regarding the ec/dl model, the algorithm chose as activation functions between the sigmoid, hyperbolic tangent, and relu. the softmax activation function was used between the dense layer and the output layer. for the optimizer the choice was between adam, adamx, and nadam. in our approach, we have used as recombination operator the uniform crossover adapted for the integer representation. technically, we have generated a vector of probabilities having the same length as the chromosomes in our population. the values in this vector are uniformly generated. the offspring are created using a threshold of 0.5. the first offspring is created as follows: if the value of the probability vector is below the threshold, then we copy the gene from the first parent, otherwise the gene from the second parent. for the second offspring the situation is vice-versa. the mutation was uniform having as mutation probability pm = 0.25. the initial chromosome population contained 30 candidate solutions. we have set the number of generations to 50. the chromosomes were generated from the intervals [1, 5] and [1, 256]. the stride was set to 1. the algorithm ran for 10 training epochs having a batch size of 64. the results that we have obtained are presented in the results section. 4 results in table 1 we present the results obtained when applying ec/dl on our image dataset in terms of average accuracy over 100 computer runs (ava), sd, precision, recall, and f1-score. we have also added the architecture chosen, the one that gave the best results. https://doi.org/10.15837/ijccc.2022.5.4886 7 table 1: performance metrics for ec/dl ava (%) sd precision recall f1-score architecture 74.63 3.67 0.744 0.739 0.742 4 convolutional layers layer 1: 34 neurons layer 2: 68 neurons layer 3: 134 neurons layer 4: 240 neurons activation function: relu optimizer: adam we can see from table 1 that the ec/dl had obtained an average accuracy of 74.63%, having a fairly small sd, 3.67, which proves that the model is robust and stable. the values of the precision, recall and f1-score show that the model performs good. we were interested in seeing how our novel approach behaves in comparison to other state-of-theart dl algorithm. for this task we have selected the densenet201 and resnet50. a densenet is more condensed than other dls, because each layer from the network received collective knowledge from the previous layers. densenet reprocesses features, instead of having a deep architecture, by concatenating the feature map of one layer with the feature map of the next layer, and not by summing the outputs. resnet on the other hand uses a skip connection, which technically means that it creates a substitute route for the gradient to pass through the network. the model learns an identity function making lower layers perform exactly like higher ones. table 2 presents the average accuracy over 100 computer runs obtained by each dl model. table 2: average accuracy of ec/dl and other dls dl model ava (%) ec/dl 74.63 densenet201 70.24 resnet50 69.11 the data screening process’s results, regarding the shapiro-wilk w test for all models are presented in table 3. we can see that the test establishes that the data samples are not governed by the normal distribution. nevertheless, the central limit theorem states that if the sample size is large enough (> 30) then the sample is approximately normal [29]. since our sample sizes surpass 30, we can presume that the samples are indeed approximately normal. table 3: normality testing. shapiro wilk w test model w statistic p-level skewness skewness shape excess kurtosis kurtosis shape ec/dl 0.8229 0.0000 -1.008 asymmetrical left/negative -0.31 mesokurtic densenet201 0.6104 0.0000 1.1877 asymmetrical right/positive -0.409 mesokurtic resnet50 0.672 0.0000 0.762 asymmetrical right/positive -1.280 platykurtic next, before applying one-way anova, we had to verify assumption referring to the equality of variances. the results of the levene’s test are displayed in table 4. we can see that the test reveals that the ec/dl vs. densenet201 and ec/dl vs. resnet50 have different variances, but in practice, if we have the same number of observations, we can presume that in fact the variances are approximately equal. table 4: equality of variances variance degrees of freedom effect size f statistic p-value ec/dl vs. densenet201 1 0.43 72.338 0.0000 ec/dl vs. resnet50 1 0.51 103.314 0.0000 densenet201 vs. resnet50 1 0.064 1.5856 0.2087 https://doi.org/10.15837/ijccc.2022.5.4886 8 after having covered all the assumptions, we proceeded with applying the one-way anova together with the post-hoc tukey hsd. the results in terms of degrees of freedom (df), sum of squares (ss), mean sum of squares (ms), f-value, and p-level are displayed in table 5 [30] [31]. table 5: one-way anova results ss df ms f-value p-level 768625 2 38431255 44.781 0.0000 please notice, that even if the mean accuracies of the three models are fairly close, the one-way anova shows that there are statistically significant differences between them. to see between which models there are statistical differences in terms of accuracies, we have applied tukey’s post-hoc test. the results presented in table 6 show that there indeed exist statistically significant differences between our novel proposed model and the two state-of-the art dls (p-level < 0.05), proving beyond any doubt that the method improves the performance. table 6: tukey’s post-hoc test results pair p-level ec/dl vs. densenet201 0.0000 ec/dl vs. resnet50 0.0000 densenet201 vs. resnet50 0.2696 5 discussion the aim of this study was to find a way to optimize a dls architecture through neuroevolution. we have designed an ec algorithm that computes the best architecture of dl depending on dataset used. after the ec optimized the architecture, we have applied the new ec/dl on a second trimester morphology that regards the abdomen dataset. at first, we have pre-processed the data by eliminating the text from the ultrasound images. because the dataset was not large enough, we have augmented the data. our ec/dl model was compared with two state-of-the-art dl algorithms, densenet201 and resnet50. the statistical analysis performed showed that there are significant differences in performances between the three models, even if the accuracies might seem close enough. the statistical analysis included the power analysis, average accuracy over 100 computer runs, sd, precision, recall, f1-score, shapiro-wilk w, levene, one-way anova and tukey’s post-hoc test. the results show that there are differences in performances, our optimized dl outperforming the rest. 6 conclusions in this study we used evolutionary computation to optimize the architecture of a deep neural network. we have proposed a way to encode the architecture of a dl in a chromosome, after which we have applied different evolutionary operators (selection, recombination, and mutation) to explore the search space efficiently. we have built a model that had as architecture the best selected chromosome and applied it on a fetal morphology dataset. before applying it, we pre-processed the dataset by eliminating the text from the ultrasounds and by augmenting it. the results were statistically analysed and benchmarked in comparison with the results obtained by densenet201 and resnet50. our findings show that indeed neuroevolution can optimize a dls architecture, because the resulting ec/dl outperformed classical dl algorithms. conflict of interest the authors declare that there is no conflict of interest. acknowledgements this work was supported by a grant of the ministry of research innovation and digitization, cncs-uefiscdi, project number pn-iii-p4-pce-2021-0057, within pncdi iii. https://doi.org/10.15837/ijccc.2022.5.4886 9 references [1] boyle, b., et al., (2018). estimating global burden of disears due to congenital anomaly: an analysis of european data, archives of disease in childhood – fetal and neonatal edition, 103, f22-f28, 2018 [2] kinsner-ovaskainen, a., et al., (2018). european monitoring of congenital anomalies: jrc eurocat report on statistical monitoring of congenital anomalies (2008-2017), eur 30158 en, publications 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[31] seltman, h., (2018), experimental design and analysis. https://stat.cmu/edu/hseltman/309/book/book.pdf, 2018. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ https://doi.org/10.15837/ijccc.2022.5.4886 11 this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: ivanescu, r.c.; belciug, s.; nascu, a.; serbanescu, m.s.; iliescu, d.g. (2022). evolutionary computation paradigm to determine deep neural networks architectures, international journal of computers communications & control, 17(5), 4886, 2022. https://doi.org/10.15837/ijccc.2022.5.4886 introduction model design deep learning neural networks’ architectures evolutionary computation approach our idea dataset and design of experiments dataset design of experiments. parameters settings. results discussion conclusions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 6, month: december, year: 2022 article number: 4363, https://doi.org/10.15837/ijccc.2022.6.4363 ccc publications bi-level minimal resource protected key generation framework for fog computing applications arul sindhia.p, bharathi.r arul sindhia.p university college of engineering nagercoil, 629004, india corresponding author: arulsindhiaphd@gmail.com bharathi.r university college of engineering nagercoil, 629004, india. bharathibharathi1993@hotmail.com abstract fog computing is a viewpoint that expands on the cloud stage concept by placing processing assets at the organization’s edges. it might be described as a cloud-like platform with comparable data, computation, storage, and applications. they are unique in that they are decentralized in nature. data protection and route analysis of time-sensitive data are made easier using fog computing. this minimizes the volume and distance of data sent to the cloud, lowering the risk of security and privacy breaches in iot applications. when it comes to security and privacy, fog computing confronts several issues. the constraints of fog computing resources are the root of these difficulties. the fog system, in fact, may raise new security and privacy concerns. to address these challenges, cryptography is used in conjunction with key management techniques to provide safe data transfer. a minimal resource viterbi based bi-level secured key generation (mrv-bskg) technique for a secured fog-based system is proposed to compromise the security level and computational complexity. the bskg technique, which combines lagrange’s key generation (lkg) and the location-based key (lbk) generation approaches, can safeguard secrecy and integrity. in comparison to the previous techniques, the new mrv, bskg, delivers security with improved outcomes. keywords:minimal resource viterbi based bi-level secured key generation (mrv-bskg), fog computing, lagrange’s key, location-based key, shortest path. 1 introduction fog computing is in great demand in most real-time applications [1], including industrial automation systems, smart grid systems, smart cities, smart buildings, health care systems, surveillance and monitoring, traffic control systems, smart agriculture, and smart factories, among others. the fog system platform is divided into three layers: the end user layer, the fog layer, and the cloud layer [2]. fog system serves as a link between iot devices and cloud computing. the fog computer gadget may send many types of new information. cloud computing and fog computing have similar working https://doi.org/10.15837/ijccc.2022.6.4363 2 principles. nonetheless, with cloud computing, the cloud can handle massive volumes of data utilizing virtual cloud resources which can be recovered from the cloud when necessary. the probability of network traffic due to a huge quantity of data transfer presents a key barrier in cloud computing [3]. the data transfer between the smart devices and the cloud requires adequate time and appropriate bandwidth. however, maintaining these standards during data transfer in cloud computing is difficult. there are additional issues with latency, location awareness, and mobility [4]. the internet of things (iot) is becoming an inextricable aspect of the internet’s expanding privacy inside its bounds. however, iot-based gadgets and tools face security and privacy risks, particularly because nodes are primarily meant to collect data about users’ behaviours, keys, and surroundings, resulting in profitable, targeted invasions. cisco has launched fog computing, an improved version of cloud computing, to address these concerns. fog nodes are employed at the network’s edge and act as a link between smart devices and the cloud. smart population of devices are linked to fog edges rather than having direct touch with the cloud. the solution is ideal for any latency sensing applications without smart devices that can quickly obtain data thanks to fog computing. as a result, an approximation approach that provides scaled graphics processing with substantial acceleration is required. a vectorbased machine learning algorithms is used [21] to approximate the shortest path distance in a large image. the main focus of the fog system is security, which can be achieved using fog forecasts and several key management schemes [5].it should be considered that the design of security methods has no impact on the system’s performance. fog-based system design with less security leads to fraudulent activities such as hacking of information about the users. encryption technology will also be used to improve the security of fog-based systems.but the ineffective key management technique affects the communication between the user and the cloud [6]. so there is a demand for a highly secured fog based system to make it suitable for time-sensitive applications. in this paper, fog based system with bi-level secured key generation (bskg) method is proposed that combines lagrange’s key generation (lkg) and location-based key (lbk) generation. during the data transmission from source to fog nodes, lkg is done using a source encoder, and in every fog node, lbk is done using a channel encoder. the generated key is a combination of alphabetical letters and numerical characters leading tohigh-level security. on the receiver side, the generated key is decrypted using the lbk method in the channel decoder and again decrypted using the lkg method in the source decoder and then stored in the cloud. here, the information is secured in multiple levels by encoding at source and each node based on its position. this approach provides a common security mode and provide secured data as compared to other algorithms like attribute based encryption or machine learning model. the rest of the paper is as follows. existing works relating to techniques for fog computing and key management are described in section 2.the fog-based smart grid system model is presented in section 3.section 4 describes the structure of the proposed fog-based system for the main generation of two-level protection. section 5 describes the functions of the proposed smart grid system, and section 6 concludes the work. 2 related work cloud computing and fog computing services provide the same data processing, from consumers to these collectors, which are then stored in the cloud [7, 8].cloud computing is a three-level structure, while fog computing is a tri-level structure.this tri-level structure supports real time applications due to its better services in terms of quality of service (qos), latency, geographical distribution and network traffic [9].to reduce the latency in fog computing, shortest path algorithms such as dijikshtra, thorup’s algorithm, pulse coupled neural network (pcnn), time delay neural network (tdnn), etc. are discussed by huang et al. which determines the minimum distance between the source and destination with the neural network. the main thing not to pay attention to in fog estimates is security and privacy issues. only after the fog nodes collect sensitive information about the user will we send it to the cloud server.so, there may be a possibility for security threats [10].key management techniques and cryptography algorithms can improve securities to fog nodes and cloud servers. mate horwath [11] has proposed attribute-based encryption (abe) that uses linear secret sharing scheme (lsss),which supports multiple users. it is an identity-based user revocation that reduces the cloud’s https://doi.org/10.15837/ijccc.2022.6.4363 3 computational overhead by increasing the computational overhead in both encryption and decryption. the modified structure of ciphertext policy abe (cp-abe) is presented in [12] that effectively exchanges the hierarchy files in the cloud to reduce the time required for decryption. simultaneously, the user can get all files by using a single key, which reduces the overall system’s cost. peng zhang et al. proposed a cp-abe scheme to reduce the cloud’s burden by transferring some computation processes to fog nodes [13]. in this structure, the number of attributes is independent of the encryption and decryption process. key management scheme for communication layer (kms-cl)method exploits an access control approach to improve the security during file transmission in fog nodes. it increases the computational complexity due to the outsourced encryption process. to provide better security and privacy services, privacy-preserving data aggregation methods are presented in [14-16]. in [1113], abe based mechanisms only were used for securing the data. the data can be hacked if the user information is leaked or hacked by third party. but, in [14 -16], this problem is overcome by using encryption mechanism; but its information is processed through third party and it also risk of information leakage. lightweight privacy data preserving aggregation (ldpa) was proposed by rongxing lu et al. [17], primarily for iot applications. to provide great security and privacy, it employs the chinese remainder theorem (crt) with parlier encryption. the notion of shared keys is a flaw in this strategy since it cannot guarantee collision resistance. for traditional systems, different processing and machine learning modules are used in the physiological data processing cloud [23].the machine learning model [22 -23] based processing is best for only particular model and achieves best security in that model alone. tian wang et al. [18] have presented a trajectory privacy preservation method that uses dummy rotation (dr) algorithm to provide location-based services. by using this approach, privacy preservation is achieved, but it doesn’t focus the integrity. zhi li et al. presented a novel hyper-graph-based key management (hkm) scheme that focused on confidentiality and integrity [19]. it has a key generation center for generating secret keys, fog server for processing the data, cloud for data storage and users. the scheme secures the storage data in both forward and backward directions. bashar alohali et al. [20]have presented the key management scheme in the smart grid (km-cl-sg) communication layer. since the security and privacy issues threaten modern technology, various researches focus on achieving high security. 3 implementation of the proposed system the major problems presented in the cloud-based system are data overloading and latency that lead to user data hacking. this can be overcome by fog-based organizations that play a key role in the construction of smart cities and smart transportation. the system model of the proposed fog-based system is shown in figure 1. the application layer, network layer, and perception layer are the three figure 1: the system model of fog based system levels that make up fog system. the perception layer at the bottom of the design is where the iot https://doi.org/10.15837/ijccc.2022.6.4363 4 devices are positioned. because every iot node senses and collects the users’ information for further processing, it’s also known as an end-user layer. the network layer, which sits in the middle of the architecture, serves as a link between the perception and application levels. for processing and sending data to the upper layer, it incorporates fog nodes and network components such as routers, gateways, switches, and so on. the data are saved in the cloud at the top layer, which is an application layer. 3.1 proposed fog based system due to the growth in population and technology, the fog-based system is necessary to communicate between the cloud server and customers. the block diagram of the proposed fog-based system is shown in figure 2. figure 2: proposed system diagram while fog node offers services used in different locations, cloud computing tipping offers its own cache and subsequent requests locally. act as the central controller of global service delivery and distributed fog nodes. in addition, the cloud image receives the necessary information from the fog population sensor on the end user device, which can now communicate with the iot sensors central information database, and then use the fog terminal to communicate. it not only provides information about the devices, but it also provides the user’s private information. so there is a possibility for hacking or spoofing the user information. the proposed hybrid bi-level can mitigate its secured key generation scheme, which combines lagrange’s key generation (lkg) and location-based key (lbk) generation. the encryption procedure for safeguarding information about and from users is conducted out during data transfer from end-users to fog nodes using lagrange’s key generation technique. the fog node stores the encrypted data created. in order to offer two-level security, lbk is created in every fog node and combined with the encrypted data. using mrv cloud nodes and fog nodes, reduce short path secretion between users. the data is decrypted again on the cloud server using the encryption logger key, and the data is eventually saved in the cloud, as shown in figure 3. algorithm step 1: create n number of nodes n1,n2,n3, .....nn step 2: assign the position of nodes and set the distance between nodes for (i=0; i<n ;i++) (tmp =distance between node i and i+1............ (1) ) step 3: selection of source node (si) and destination node (di) step 4: data transmission from si to fog node (fi) step 5: attacker determination by sending hello message to neighbor nodes step 6: computation of shortest path using mrv by ignoring the attacker nodes step 7: bskg key encryption lkg & lbk, lkg can be generated using equation 5, and lbk can be generated based on the location of fog nodes step 8: data transmission from fn to dn https://doi.org/10.15837/ijccc.2022.6.4363 5 figure 3: architecture diagram of data transmission with security step 9: bskg key decryption lbk & lkg step 10: detection of the mobile station for specific users step 11: data reception in the dn or cloud 3.2 bi-level secured key generation (bskg) the major concern in most modern applications is high-level security. by focusing on this, bskg is introduced, which is a combination of lkg and lbk key generation methods. it can generate keys in terms of alphanumeric, which is a combination of alphabets and numbers. this effective key generation method is applied when transmitted from the source to the fog node. 3.2.1 lagrange’s key generation (lkg)) the data transmission from source to fog node generates a secret key based on lagrange’s equation to secure the customer data. if the systemyis represented as a function of x and it is defined in terms of the parameter α, then y = x + αψ + (y) (1) where α denotes lagrange’s parameter, and ψ + (y) denotes the holomorphic function defined as a complex differentiable function. the following lagrange’s expansion can compute the lagrange’s key of the system y lagrange’s key generation approach uses the interpolation methods for performing both encryption and decryption processes.itapplies the interpolation to generate the polynomial for the data sensed from the smart devices. the polynomial functions are generated between the smart devices and fog nodes, and the data are represented as (x0, y0), (x1, y1),. . . . . . (xn,yn). it resulted in the nth degree polynomial by utilizing n+1 data. the following equation can represent lagrange’s polynomial with the interpolation method. f(y) = f(x) + α/1! ψ(x)f‘(x) + α2/2! ∂/∂x[ψ(x)]2f‘(x) + −−−−−− +αn+1[ψ(x)]n+1f‘(x) (2) https://doi.org/10.15837/ijccc.2022.6.4363 6 where n denotes the degree of polynomial and can be represented as follows pn(x) = n∑ j=0 lj (x)f(xj ) (3) the simplified form of (5) can be expressed as follows. lj (x) = x−x0/xj −x0.x−x1/xj −x1......x−xj−1/xj −xj−1......x−xn/xj −xn (4) if the value of the x is known, then only the data is hacked. still, it is difficult to predict the value because it was chosen randomly ensures the security and confidentiality of the customer data. 3.2.2 location-based key generation (lbk) to attain secure and safe communication in fog computing, it should be ensured that secured data is shared between fog nodes and the cloud. lbk key generation is applied in every fog node after performing the encryption using lagrange’s expansion. this can provide bi-level security for the customer data protection. the key generated from lbk is an alphanumeric code that differs in every fog node to achieve high-level security.it uses a novel key updating process depending on the grid’s location information to avoid data corruption and hacking. lj (x) = n∐ i=0 ,j = 0, 1......n : i 6≡ j (5) the working principle of the lbk key generation method is shown in figure 4. this figure shows securing of data against the attackers while the adversary tries to hack the data. it provides high-level security by generating the key based on the location. it differs from the cryptographic key by its alphanumeric key generation. in cryptography keys, the keys are generated as 32-bit binary keys. in contrast, in lbk, the key is a combination of alphabets and numerals, which leads to high security over other key management techniques.it is termed geo encryption because it is location-dependent, https://doi.org/10.15837/ijccc.2022.6.4363 7 and it enables the routes for transmission from the source node to the destination node. if any attacker is found in any of the nodes, the data is transferred through some other nodes. since the data are location authenticated, it can be transferred with high security. to calculate each neighbor node location authenticated based average distance analysis figure 4: location-based key generation every fog node in the network generates a distinct key, and it is in the form of alphabets. after performing lbk, the resultant key is alphanumeric. the key obtained from the lkg approach is added in every fog node, increasing the security of the data transmission. the lpk system attempts to locate the source / target by directly measuring the radio signal traveling between the transmitter and the receiver. the relationship between the limit difference between the recipients and the lkg is provided lj = √ (x1 −y1)2 + (x2 −y2)2 (6) rm,n = s∗dm,n = rm −rn (7) where ssignal propagation speed m,n – between neighbor node rallocation of signal these lbk, lkg systems use time, phase, or frequency measurement values to estimate the direction or range of the first signal propagation path, and then use solutions that provide calculations based on the distance of the data node measured by the generated key. 3.3 minimal resource viterbi in the proposed fog-based smart grid system, information exchange with high speed is desirable. this can be realized by finding the short path fog tip and fog tip cloud computing among users.mrv is used to compute the shortest path effectively by exploiting dynamic programming approaches [21]. instead of differential equations, minimizers are used in every neuron of this network. it uses adders and minimizers for shortest path computation leading to low computational complexity. this reduces the number of computations in each neuron and reduces the number of repetitions.this network initiates the operation by activating the source neuron,which results in an auto wave, and then it travels through all nodes of the network; finally, the best path is stored in the output. algorithm steps consider a graph g(n, v) with n nodes and v edges. each node is assigned a time window wt [li,ui] with upper(ui) and lower (li) threshold values. spi is the shortest path from https://doi.org/10.15837/ijccc.2022.6.4363 8 s to i, and tti is the traversal time of the path to reach node i. step 1: initialize the network, set the upper threshold and lower threshold in the threshold window t ls = 0 : t s u = ε : t l i = li; ti u = ui + ε (8) step 2: source neuron is activated by setting the input of the neuron within the threshold so that the auto wave is generated and propagates to neighborhood neurons step3: communication range: to use the communication range of the behind the formula: e = (u,v)�v 2|u 6= v (9) step4:since the length of the shortest path from node ni step5: an available distance of node ni and required the minimum distance of node mi step6: critical sensing point of the ni . if source node denote as and the nearest neighbor node denote nnni<mi step 7: competition of all minimum distance paths to the node happens, and this takes place simultaneously in all neurons ci(t) = j,j�bs(i)andttj (t) + dij [ttj (t)]�[til + tiu] (10) step 8: compute traversal time at the output of the neuron, ttiu (t + 1) = mintti(t + 1),ttiu (t) (11) compute the shortest path from nsto ni where l = winning node step 9: update the upper threshold of the node step 10: repeat the autosave travel and competition (step3 to step6) until the network converges. when the mrv is used for the first time, the node will be removed, but the retained path information will pass through the node just like all the previously mentioned paths; therefore, when the next search is made to proceed to this node, the stored path information will be used, and the map can be easily used to find the shortest path at the fastest time to avoid recounting. 4 results and discussions this chapter discusses the comparison of minimal resource viterbi based bi-level secured key generation (mrv-bskg) and previous security;bi-level secured key generation provides authenticate for the data when source node send the data and it is transmitted through the neighbouring node in communication. the minimal resource viterbichooses a different path if the neighbouring terminal changes its position. so these two proposed methods are compared in this section.the previous methods attribute-based encryption (abe), ciphertext policy-abe (cp-abe), lightweight privacy data preserving aggregation (ldpa), hyper-graph based key management (hkm) and key management scheme for communication layer (kms-cl). the proposed fog-based system’s performances are analyzed in terms of packet drop, packet delivery ratio, throughput, and security.the parameters used for simulation are tabulated in table 1. 4.1 packet delivery ratio (pdr) it is defined as the ratio of the total number of packets received by the receiver atthe destination to the total number of packets sent by the sender from the source. pdr = pd/ps (12) using the above equation 11, the pdr for the proposed scheme is compared with the existing approaches. due to the highly secure key management mechanism and mrv approach, the proposed fog-based system achieves a high pdr. every path’s journey is saved in the hidden layers of mrv, https://doi.org/10.15837/ijccc.2022.6.4363 9 table 1: simulation parameters simulation parameters value number of nodes 50 routing protocol dynamic source routing (dsr) queue type priority queue packet size 300 bytes mac type 802.11 antenna omni-directional antenna initial energy 100j idle node energy 0.001j/s power required for transmission 1.35j power required for reception 1.7j power consumed by sleep nodes 0.001j distance between source and destination 669.9km energy consumed by the highest antenna 1.5 j/s propagation model two-way ground figure 5: pdr comparison of the proposed system with existing techniques and the attackers between the source and destination are discovered. to avoid packet loss, it stores the attacker’s location in memory after it has been discovered. the proposed fog-based method produces improved pdr outcomes in the vast majority of instances. however, if an attacker is present, the packet delivery ratio is lowered. however, it outperforms other key management systems currently in use. figure 5 shows the pdr of the proposed fog-based system in comparison to existing approaches. the suggested mrv-bskg has a packet delivery ratio of 96 percent when compared to other current approaches such as ldpa (88 percent), hkm (90 percent), and kms-cl (94.45 percent). 4.2 packet drop packet drop mainly occurs in the wireless network during data transmission and network congestion. it exhibits the reliability of the communication process in wireless networks. it is defined as the number of packets sent from the source ps to the number of packets dropped (pnd) during transmission. https://doi.org/10.15837/ijccc.2022.6.4363 10 figure 6: comparison of packet loss the proposed mrv method’s packet loss is compared to current approaches. the findings are shown in figure 6 above. the efficiency of the proposed mrv-bskg approach is determined by altering the number of nodes and assessing the time associated with packet loss. the findings reveal that the proposed mrv-bskg approach detects packet drops in 3.75 seconds owing to attackers and mobile stations, which is better than other methods currently in use. kms-cl is 4.85 with ms, lpda is 4.17 with sec, hkm is 4.38 with sec, and lpda is 4.17 with sec. 4.3 throughput it is defined as the amount of information sent from source to destination within a specific amount of time. the following equation can compute the throughput of the wireless network. pr indicates the packet received and psindicate the packet size received in a time (t) sec. throughput(bps) = pr ∗ps/t (13) the comparison graph demonstrates that the suggested method outperforms other current strategies in terms of throughput. at 20ms, the suggested fog-based system enhances the throughput comparison given in figure 7; this demonstrates that the proposed technique has a higher throughput ratio than previous approaches. https://doi.org/10.15837/ijccc.2022.6.4363 11 figure 7: throughput comparison of proposed fog based system with other existing techniques figure 8: security level analysis figure 7 depicts the throughput curve of the proposed fog-based system using the mrv-bskg key management approach. this graph indicates that as time passes, the system’s throughput grows. the suggested fog-based system’s throughput is compared to existing key management systems. 4.4 computation time the time required for computing the shortest path between the source and destination in the proposed fog based system is shown in figure 8. figure 8shows that the mrv reduces the latency by 23.14%, 48.82% and 43.79% compared with conventional shortest path computation algorithms such as lpda, hkm. 4.5 security analysis fog-based system’s major concern is network security because there are some adversaries to hack the data during data transmission from source to destination. to mitigate these security and privacy issues highly secured bskg key generation-based fog system is designed that can provide 98% secured https://doi.org/10.15837/ijccc.2022.6.4363 12 figure 9: the computation time of shortest path algorithms figure 10: data decryption time data. figure9 shows the results; it is observed that the proposed bskg based security level is 98% increases the security by when compared with the existing key management techniques such as abe is 90%, cp-abe is 92%, and ldpa is 93 4.6 analysis of error rate it can be seen that the key bit error rate increases as the data size increases due to the decreased subspace distance between potential precedes. the above figure 10 shows that the analysis of error rate compared with proposed mrv-bskg is 36% and the previous abe is 67%, cp-abe is 63%, ldpa is 55%, hkm is 49%, kms-cl is 41%. 4.7 analysis of encryption time analysis of encryption time that indicates the speed of encryption. the experiments for comparing the performance of the different previous security algorithm and proposed algorithm. figure 11 shows that encryption time for uploading the data and then it indicates how long it took to convert the original data. the proposed mrv-bskg is 1.87 with ms and the existing methods https://doi.org/10.15837/ijccc.2022.6.4363 13 abe with 2.9 ms, cp-abe with 3.1 ms, ldpa with 2.6 ms, and hkm with 2.4 ms. 4.8 decryption time the proposed mrv-bskg algorithm providing a separate key to each user requesting the authentication of necessary data posted by the data owner. indicates how long it took to convert the encrypted data and retrieve the original data. the proposed mrv-bskg algorithm it should be used once to decrypt the required information. the above figure 12 shows the comparison of the proposed and previous methods. hence the proposed mrv-bskg to give better decryption time 5 conclusion fogcloud computing is considered a good partner; it is an extended cloud service by end users .the fundamental here is to interface and change the safety efforts and apply them as per the necessities of the fog platform. the secured key management efforts have gone through testing, and utilizing them can guarantee that any fog computing fulfills vital mechanical security norms. various security issues may arise in the design and implementation of the technology. in this paper, the fog-based system is proposed with a hybrid bskg method formed by combining two key generation approaches, lkg and lbk. this method achieved high-level security against node capture attacks. the lkg method is used for both encryption and decryption, and lbk is used to generate key in fog nodes. the generated key is difficult to express because of its high dispersion nature. analysis shows that the proposed method achieves all security requirements like location privacy,anonymity,data integrity. future work may lead to the development of knowledge-based supplementary and aided systems, which can provide decision support services to developers in designing a safe and efficient fog infrastructure. declaration of competing interests the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. conflict of interest the authors declare no conflict of interest. ethics approval and consent to participate no participation of humans takes place in this implementation process human and animal rights no violation of human and animal rights is involved. funding no funding is involved in this work. authorship contributions there is no authorship contribution. acknowledgement there is no acknowledgement involved in this work https://doi.org/10.15837/ijccc.2022.6.4363 14 references [1] haina zheng, kexiong, pingyi fan, zhangduizhong and khaled ben letaief, 2019.“fog-assisted multi-user swipt networks: local computing or offloading”, ieee internet of things journal, pp. 5246-5264. 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introduction related work implementation of the proposed system proposed fog based system bi-level secured key generation (bskg) lagrange’s key generation (lkg)) location-based key generation (lbk) minimal resource viterbi results and discussions packet delivery ratio (pdr) packet drop throughput computation time security analysis analysis of error rate analysis of encryption time decryption time conclusion international journal of computers communications & control issn 1841-9836, 11(3):394-404, june 2016. a new adaptive fuzzy pid control method and its application in fcbtm j.g. lai, h. zhou, w.s. hu jingang lai*, hong zhou, wenshan hu department of automation, wuhan university wuhan, 430072, hubei china *corresponding author: henanlaijingang@163.com abstract: the process of tension control for material testing using the flexible circuit board testing machine (fcbtm) is featured with multi-variable, nonlinearity, time delays and time variation. in order to ensure the tension precision, the stability of servo motor’ speed and the reliability of test results, this paper establishes an accurate system model for the fcbtm, in which a novel three-dimensional adaptive fuzzy pid controller is designed. specially, the simulation results show that the proposed adaptive fuzzy control method is not only robust to the external disturbance but also with more excellent dynamic and steady-state characteristics than traditional ones. keywords: flexible circuit board testing machine (fcbtm); tension control; adaptive self-tubing, fuzzy pid control. 1 introduction with the development of science and technology, electronic products become increasingly miniaturized, lightweight and thinner. recent years have witnessed the increasing attention on the flexible circuit board (fcb) and its applications in the manufacturing and printing of electronic components. owning to the features of high wiring density, three-dimensional wiring, light weight and thin, the fcb has been more and more directly used to the electronic component of manufacture and printing in recent years, meanwhile the market demand is also growing rapidly. however, the common fcbtm is quite obsolete, which results in the unstable performance and low automation. this conflict between the huge market demand and the status quo makes the design of new testing equipment for fcb material properties become very urgent. therefore, it is quite significance to research and design the new material fcbtm. the servo motor and its controller circuit of fcbtm are composed of many mechanical and electrical components which are highly nonlinear. they are used to load the measured object, where the parameters of measured object exist uncertainty, nonlinear factors and others, thus, by which the system is susceptible to be influent. during the test, in particular, the change of hardness of fcb samples can cause changes of the load scale factor on the deformation of force and displacement. with the dramatic increase of the required test fineness, it makes the precise critical in the control process of fcbtm. traditional fcbtm usually uses the conventional pid algorithm. however, the control rate of fcbtm varies greatly for the different material properties, which directly affects the rate of change of the load. fuzzy control theory is an effective solution, but simply because of the lack of the integrator team, the steady state error and the disturbance rejection are difficult to be eliminated. especially in the case of classification variables are not enough, it often produces small oscillations around the equilibrium point. the pid control method introduces fuzzy controller and uses fuzzy reasoning, which will automatically implemented the optimum adjustment for pid parameters kp , ki and kd [1][3]. because fcbtm is a nonlinear and time-varying system, simple fuzzy control or pid control is difficult to achieve the desired effect. the use of fuzzy pid composite control, combining the pid control and fuzzy control, which copyright © 2006-2016 by ccc publications a new adaptive fuzzy pid control method and its application in fcbtm 395 can not only play the fuzzy control advantage of robustness, good dynamic response, fast rise time and overshoot small, but also have features of both quality and steady precision dynamic tracking characteristics. it can make system to obtain good static and dynamic characteristics. in the testing machine system, fuzzy adaptive pid parameter controller is a pid controller on the conventional basis of the application of fuzzy set theory to establish binary continuous function between parameters kp , ki and kd, and absolute deviation value |e| and absolute paranoia changes value |ec|. it should be pointed out that classical fuzzy pid controller requires a three-dimensional rule base which makes the design process more difficult [4][5]. to overcome this drawback and focus on reducing the dimension of fuzzy system, this paper presents a twostage fuzzy pid controller with fuzzy switch and uses it to control the tension in a fcbtm. this controller uses two-dimensional inference engine (rule base) to perform reasonably the task of a three-dimensional controller.   kp = f1 (|e| , |ec|) ki = f2 (|e| , |ec|) kd = f3 (|e| , |ec|) (1) according to the different of |e| and |ec|, it is able to online self-tubing parameters kp , ki and kd, which is the key to achieve fuzzy adaptive pid controller design [6][7]. in this paper, the goal is to improve the accuracy of load control of fcbtm. the new three-dimensional adaptive fuzzy pid controller could be used to universal testing machine. 2 the establishment of fcbtm model in the course of testing fcb materials, first, the test sample is sandwiched between the movable beam and the base of testing machine, then by controlling the dc servo motor speed to drive the moving beam up and down via moving the transmission mechanism. finally, the trial loading process is completed. the control parts of the whole system including: the dc motor control part, the mechanical transmission part, human machine interface(hmi), sensor signal feedback section and so on as shown in figure 1. interested readers can find further information about the detailed design and implementation of the hardware and software of fcbtm in [12]. figure 1: structure diagram of control system of fcbtm 396 j.g. lai, h. zhou, w.s. hu 2.1 the dc servo motor part the equivalent circuit of dc servo motor is as follows: the field voltage of ua which is the system input signal; while θ is a motor shaft angle [8], [11], which is a system output. ja d2θ dt2 + b dθ dt = m = kia (2) the voltage balance equation of armature circuit of dc servo motor: ua = raia + la dia dt + ea (3) laplace transforms for (2) and (3), then collate them and get the system transfer function of dc motor : f(s) = θ(s) ua(s) = k s(las + ra)(js + b) = km s(τas + 1)(τes + 1) (4) where km is the motor gain km = krab ; τa = la ra is the electrical time constant of the motor; τe = j b is the mechanical time constant of the motor; j is the total moment of inertia converted to the motor shaft; b is the viscous friction coefficient. 2.2 the mechanical transmission part in this part, θ is the motor rotation angle and it is the input signal. the axial displacement of the movable beam of test machine y is the output signal, the transfer function between the rotational angle input and movable beam is as follows [10]: gj(s) = y (s) θ(s) ≈ ( z1 z2out · z2in z4 · l 2π )k js2 + bs + k = z1 z2out · z2in z4 · l 2π · w2n s2 + 2ξwns + w2n (5) z1, z2out, z2in and z4 are the motor outputs shaft gear, the external gear wheel transition, the internal tooth gear teeth and the axle, respectively; l is ball screw pitch; wn = √ k/j is the non-damped natural frequency; ξ = b(2 √ jk) is damping ratio of the mechanical system. 2.3 the sensor signal transmitting part the sensor feedback transfer part of tension and displacement can be approximated as a proportional component, respectively, kc and ke. the value of kc and ke is 1 according to the actual situation. 3 dimensional adaptive fuzzy pid controller design fuzzy adaptive pid controller is mainly based on the system generated during the operation of error e, error rate ec and acceleration of error rate er to online self-tubing pid parameters, which allows the system to ensure low overshoot and can achieve fast convergence. during the loading control process of fcbtm, conventional fuzzy adaptive pid controller only uses the error e and error variation ec as input to meet the self-tubing pid parameters requirements at different moments, which is also known as two-dimensional fuzzy controller [9]. the geometry detonation problem of multi-variable fuzzy control rule is solved, and a greater controllable scope is obtained. however, when the outputs of the control system of fcbtm enter the stage of steady-state approximation, the stability of this kind fuzzy pid control will decline, and the a new adaptive fuzzy pid control method and its application in fcbtm 397 controller will not be possible to generate error measure to eliminate this instability. at this point, if the acceleration er (the second derivative of the error) of the error changes ec is added as fuzzy pid control input, it will make the system have a good steady precision. this is called three-dimensional fuzzy pid controller. however, the increase of dimension will definitely increase the difficulty of the control algorithm, and affect the control response speed. given the above analysis, in order to effectively use three-dimensional fuzzy pid controller to solve the contradiction of the traditional two-dimensional fuzzy pid controller requiring quick response and stability, when the controller of sample load was designed, if the error is large |ec| ≥ e0, then the conventional two-dimensional fuzzy pid control (hereinafter, the fuzzy1) is selected as the controller, but when the error is small |ec| < e0, the acceleration of change of error is introduced as a new input value, based on the positive negative to reselect a new fuzzy control rules to adjust the pid parameters online, the specific three-dimensional fuzzy pid control principle is shown in figure 2. figure 2: structure of the proposed three-dimensional adaptive fuzzy controller based on experience to inform the error range of actual system and determine an error limit e0, when the error meets |ec| ≥ e0, we choose the fuzzy rules fuzzy1 of traditional two-dimensional fuzzy pid control; however, when the error meets |ec| < e0, in accordance with er positive and negative reselect the fuzzy rules of three-dimensional fuzzy pid control. when er is bigger than 0, we select fuzzy rules fuzzy2. otherwise perform fuzzy rules fuzzy3, in order to achieve the pid parameters kp , ki and kd on-line modification and self-tubing. so when implement control action on the testing machine loading process, we have chosen to use two-dimensional fuzzy control table to achieve the three-dimensional fuzzy control function, essentially which can be seen as the dimensionality reduction of three-dimensional fuzzy control via reducing the number of dimensions to increase the response speed, at last form a three-dimensional fuzzy pid control on testing machine. in the course of designing the three-dimensional fuzzy pid controller on testing machine, the most critical is to give fuzzy control rules adopted in different operating stages of the system. next corresponding fuzzy control rules will be designed, depending on the system error variance at different times. 3.1 select input and output variables and fuzzy input parameters due to the loading control of fcbtm including various tasks (such as tension and velocity control, etc.), it detects the value of error e (k), error rate ec (k) and the acceleration of error rate er (k) and adjusts kp , ki and kd according to the fuzzy rules in each sample period. then the dc servo drive unit is employed to tune the speed of motor, when fuzzy controller received the voltage signal from the fuzzy pid controller. 398 j.g. lai, h. zhou, w.s. hu therefore, fuzzy logic systems can make the error e, the error change ec and change of acceleration er of each control period as input variables, and put the parameter adjustment values kp , ki and kd as the output variables, at last form a three inputs and three outputs system. the calculation formula of each control cycle is as follow :  kp = kp0 + ∆kp , ki = ki0 + ∆ki, kd = kd0 + ∆kd, (6) ąą where kp0, kp0 and kp0is the initial setting of pid parameters which are able to be given in advance based on practical experience. due to the high requirements on control quality, when fuzzy each input, we will quantify the system error e and the error change ec to 13 levels, with −6,−5,−4,−3,−2,−1,0,1,2,3,4,5,6 representation. since the control system is to control two kinds physical of tension and speed, even if only to control tension rate, its rate range can also be within a wide range from 0 mpa/s to 5 mpa/s. therefore, a simple fuzzy unification is not appropriate only based on the absolute value of e and ec. however, if we respectively use different fuzzy methods according to the different control methods, then it is quite tedious and can not be exhaustive. so the relative values of e and ec are used to quantify and can be expressed as follows :  er(k) = e(k) r(k) ecr(k) = ec(k) e(k−1) (7) where r(k) is the desired control value of the system, further designs the fuzzy quantitative equations are as follows: e(k) =   −6 ⌈er(k) × 100⌉ < −5 ⌈er(k) × 100⌉ − 5 ≤ ⌈er(k) × 100⌉ ≤ −0.5 0 − 0.5 ≤ ⌈er(k) × 100⌉ ≤ 0.5 ⌈er(k) × 100⌉ + 1 0.5 ≤ ⌈er(k) × 100⌉ ≤ 5 6 ⌈er(k) × 100⌉ > 5 (8) ec(k) =   −6 ⌈ecr(k) × 100⌉ < −5 ⌈er(k) × 100⌉ − 5 ≤ ⌈ecr(k) × 100⌉ ≤ −0.5 0 − 0.5 ≤ ⌈ecr(k) × 100⌉ ≤ 0.5 ⌈er(k) × 100⌉ + 1 0.5 ≤ ⌈ecr(k) × 100⌉ ≤ 5 6 ⌈ecr(k) × 100⌉ > 5 (9) based on the style, the variables of e and ec are put into a certain one in the 13 quantization levels in each control period. the basic output domain of ∆kp , ∆ki and ∆kd of fuzzy pid controller can be properly selected based on experience. the basic domain of ∆kp , ∆ki and ∆kd were taken as [-0.3, 0.3], as [-0.06, 0.06] and as [-3, 3], respectively. the quantization levels of their fuzzy variables are the same as e and ec. 3.2 determination on the set and membership of the fuzzy controller the selection of membership function has a certain impact on the whole fuzzy system control process. according to the foregoing analysis on the fcbtm load control principles and a new adaptive fuzzy pid control method and its application in fcbtm 399 procedures, in order to facilitate the calculation, this article uses the same kind of membership functions for the input variables e, ec and output variables ∆kp , ∆ki and ∆kd. the fuzzy subset of input and output variables is: nb, nm, ns, zo, ps, pm, pb, respectively: negative big, negative middle, negative small, zero, positive small, positive middle, positive big. figure.3 shows membership function of and in fuzzy1. its basic domain is [-3, 3], and its fuzzy domain is [-6, 6], where nb and pb are defined using gaussian function as normal, the other fuzzy subsets are defined using trigonometric functions. figure 3: e and ec membership function in fuzzy1 when the system enters the control later stages and the error is small |e| < e0, then different fuzzy functions should be selected according to the positive and negative of the acceleration of error, in order to achieve high precision control for the testing machine. fuzzy2 and fuzzy3 are, respectively, the fuzzy controller when er > 0 and er ≤ 0. the specific membership function is as shown in figure.3, nb and pb are defined as normal gaussian function, and the other fuzzy subsets are defined as using trigonometric functions. at this time, the basic domain of e and ec is still [-0.6, 0.6] and the fuzzy domain is still [-6, 6], and theirs membership function is as shown in figure 4. figure 4: e and ec membership function in fuzzy2 and fuzzy3 the output variables (∆kp , ∆ki and ∆kd) of fuzzy controller have the same fuzzy subsets and membership functions. in fuzzy1-3 the membership function of ∆kp is as shown in figure.5 and its basic domain is [-0.3, 0.3], the basic domain of ∆kiis [-0.06, 0.06]. however, in fuzzy1-2 the basic domain of ∆kd is [-3, 3], compared to the fuzzy3 is [-0.6, 0.6]. in this effort, when ec changes, the tuning principles of pid parameters kp , ki and kd are as follows: table 1 is the three-dimensional fuzzy pid control rules on the testing machine in the whole process. fuzzy1 is employed when e is larger, while fuzzy2-3 is chose when e is smaller. at this time, when er is lager than 0, we choose fuzzy2; when er is not lager than 0, we use fuzzy3. thus the output of the system is tuned in the opposite direction. 400 j.g. lai, h. zhou, w.s. hu figure 5: domain and membership function of ∆kp table 1: fuzzy1-3 control rule 3.3 the fuzzy reasoning and precise operation in the course of real-time control of the fcbtm, the specific works are as follows: 1) firstly we calculate a moment error value e (k) on tension or speed, which is relative to the last moment. then adopt the difference method to calculate the value of ec (k); 2) then we fuzzy quantification for e and ec according to formula (4)-(7), and find their memberships in the quantization interval; 3) thirdly the corresponding inference calculations are launched according to the fuzzy control rules in table1, and find the membership corresponding language value on ∆kp , ∆ki and ∆kd; 4) the gravity method is used to solve fuzzy, then makes ∆kp , ∆ki and ∆kd parameter mapping to the respective basic domain range, eventually obtained ∆kp , ∆ki and ∆kd the precise adjustment value; 5) the defuzzification uses the weighted average method. system after defuzzification gets a new adaptive fuzzy pid control method and its application in fcbtm 401 a good parameter tuning pid controller, who is then use to achieve the control of the loading process of fcbtm. 4 the simulation on fcbtm in this paper, we use simulink module in matlab software for numerical simulation. the parameters on dc servo motor are, respectively, la = 0.02h, ra = 1.36omega, j = 1.05×10−4, b = 4.5 × 10−4, the k = 0.025, ta = 0.015, te = 0.23. mechanical transmission part of the relevant parameters are as follow: z1 = 30, z2out = 90, z2in = 30, z4 = 90, l = 5mm/rad, then ξ = 0.5, wn = 100. taking the fcbtm introduced in section 3 as example to study the steady state and transient characteristics of the proposed control strategies incorporating the conventional pid, two-dimensional fuzzy pid and three-dimensional fuzzy pid controller. 4.1 comparative performance analysis in order to compare the differences between different control methods, firstly, the conventional pid control simulation model on the testing machine control system was built. the initial value of pid was gotten by the method of online debugging trial and the initial values were: 1, 0.5 and 0.03. secondly, the fuzzy the rules of fuzzy control in table1 are compiled into incorporated into the fuzzy inference system editor, simultaneously, determine the input and output variables domain membership function. then we can create one fuzzy controller in simulink. the quantization factors of output variables ∆kp , ∆ki and ∆kd were 0.5, 0.05 and 0.05. after that the conventional two-dimensional adaptive fuzzy pid control simulation model is built. finally build a three-dimensional fcbtm fuzzy pid controller simulation model as shown in figure.6. the quantization factors of output variables ∆kp , ∆ki and ∆kd were: 0.5, 0.05 and 0.05. figure 6: dimensional fuzzy pid controller simulation model the loading process of fcbtm with constant strain rate can be equivalent to give a step input signal to the fcbtm system. therefore, the three above-mentioned system simulation models all input a step signal, the corresponding curves are shown in figure 7. the simulation time of this system is 15s, and the sample frequency is 0.01s. it can be seen that the tension responses of fcbtm under three controllers are approached and stabilized under the unit step input signal. as can be seen from the figure 7, the fuzzy pid controller using three-dimensional fcbtm control system has a small overshoot, and rise time, steady state can be achieved quickly. 402 j.g. lai, h. zhou, w.s. hu figure 7: comparison of tension response under three controllers 4.2 robustness analysis input and outer disturbance, system structure and parameter variation are the main measures to see about system robustness performance. due to the interference of control systems are vulnerable to electromagnetic noise, in this section, outer periodic a step perturbation signal is used to analyze the robustness of the three controllers. we add a step perturbation to the fcbtm at 7s, which was used to analysis the anti-interference ability and stability of the three algorithms. the simulation results are shown in fig.8. it can be seen that under the three controllers system can attain stability finally although the transient responses are influenced by the step perturbation signal more or less. the influence by disturbance under pid controller is much more than that of two-dimensional and three-dimensional fuzzy pid controllers. the variation of response under three-dimensional fuzzy pid controller may be ignored at certain error range. further research shows that the robustness of the three-dimensional fuzzy pid control system is also excellent when the periodic pulse disturbance signal occurs at the input port. 5 conclusion this article adopts the fuzzy adaptive pid control method, (namely the two-dimensional fuzzy pid control system added a third variable that is the rate of change of the error), to reduce the response time of the fcbtm system while reducing overshoot and ultimately improve the steady-state performance. and three-dimensional fuzzy adaptive pid controller implementation process on fcbtm has been described in details. the salient feature of the proposed controller is that it does not require an accurate model of the controlled plant, and the design process is lower than that of the other pid controllers. the practical control effect and simulation based on the matlab show that the proposed controller not only is robust, but also it gives excellent dynamic and steady-state characteristics compared with traditional controllers. a new adaptive fuzzy pid control method and its application in fcbtm 403 figure 8: comparison of tension response with disturbance 6 acknowledgement this work was supported by national key technology support program of china under grants 2013baa01b01 and the national natural science foundation of china under grants 61374064 and by the fundamental research funds for the 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[12] lai j.g., lu x.q.(2012); the application of embedded system and labview in flexible copper clad laminates detecting system, international journal of information and computer science , 1: 4-10. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 6, month: december, year: 2020 article number: 3894, https://doi.org/10.15837/ijccc.2020.6.3894 a rough set and cellular genetic fusion algorithm for acute critical disease prediction l. jia, h. wang, h. zhuang, x. li, y. zhao, s. pan, k. wu, j. li, t. li lijing jia, heng zhuang, yuzhuo zhao, tanshi li department of emergency, chinese pla general hospital, beijing, china hongxin wang department of emergency, armed police characteristic medical center, tianjin, china xueyan li management school beijing union university, beijing, china kainan wu department of school of economics and management university of chinese academy of sciences, beijing, china shuxiao pan, jing li∗ department of school of economics and management beijing jiaotong university, beijing, china *corresponding author:jingli@bjtu.edu.cn abstract this study is to solve the problems of an overly-broad scale of medical indicators, lack of retrospective research samples, insufficient depth of data mining, and low disease prediction accuracy. in this paper, we propose an intelligent screening algorithm that combines a genetic algorithm, cellular automata, and rough set theory. this algorithm can achieve high accuracy in predicting patient outcomes with a small number of indicators. and we compare it with the traditional genetic algorithm. we built the prediction model with 64 indicators based on the logistic regression (auc 0.8628), support vector machine (auc 0.5319), naïve bayes (auc 0.7102), and adaboost algorithms (auc 0.9095). using the cellular genetic algorithm for attribute screening not only effectively reduces the number of indicators but also achieve almost the same accuracy of prediction with 8 indicators based on the logistic regression (auc 0.8782), support vector machine (auc 0.8525), naïve bayes (auc 0.8408), and adaboost algorithms (auc 0.8770). compared with the traditional scoring system, the predictive model established in this paper can more accurately predict rebleeding accidents based on physiological test indicators and continuous patient indicators. keywords: cellular genetic algorithm, key indicator, disease prediction, machine learning. https://doi.org/10.15837/ijccc.2020.6.3894 2 1 introduction in the modern world, big data technology is widely used in medical research. driven by medical informatization, hundreds of millions of pieces of medical data are generated every day. big data technology and the first aid database now provide technical and statistical support for studying the key indicators associated with patients with acute critical disease. the ability to extract key indicators from massive data in light of unknown causes and quickly provide pre-warning about the risk of rebleeding accidents is crucial to improving the survival rate of patients with acute critical disease. furthermore, the pre-warning model can provide enough time for medical staff to take measures and allocate treatment resources. due to financial, time, and human limitations, the sample size of a traditional retrospective medical analysis is small. the indicators obtained from tracked patients are limited, and researchers often face the problem of missing data. once the number of patients and the index dimension increase greatly, the workload of traditional retrospective medical analysis will increase exponentially. it is difficult to provide doctors with all-around and multi-angle decision support [46]. currently, it is necessary to consider advanced theories and methods to balance the number of indicators and the accuracy of disease prediction. in order to solve this problem from the perspective of evolutionary computation, this study proposes a screening method combining rough set, genetic algorithm, and cellular automata that can effectively balance the number of indicators and the accuracy of disease prediction, and compares it with existing methods. the primary outcome of this study is that cellular genetic algorithm was used to construct more accurate and effective prediction models based on the logistic regression (auc 0.8782), support vector machine (auc 0.8525), naïve bayes (auc 0.8408), and adaboost algorithms (auc 0.8770). 2 literature review in the field of emergency critical illness, most data sources are prospective analyses or retrospective analyses [49]. the amount of data is limited. although most of the analyses use statistical tests to process the data [20, 30], the sample size is small and the mining depth is insufficient. the conclusions are mostly from the perspective of treatment and evaluation, which has limited value for prevention and prediction [19, 23]. with the development of medical big data, there is a new way to resolve the huge medical index system. hu first used multivariate logistic regression analysis to determine independent predictors when predicting the need for paediatric emergency patient return visits [17]. wei et al. used logistic regression, adaboost, and xgboost to form predictions based on 728 patients in the emergency database of pla general hospital, and finally selected the xgboost method to screen key indicators [44]. it can be seen from the above research that compared with traditional methods, machine learning has achieved better results in outcome prediction and risk warning systems for critical patients. in the face of a huge medical index system, it is better able to identify the index that has high correlation with the prediction under the condition of ensuring prediction accuracy. key indicator selection, also known as feature selection or attribute selection, is the process of selecting some effective features from the original features to reduce the dataset dimensions, and is the key pre-processing step in machine learning and data mining [50]. scholars worldwide have done much research on the design of screening for key indicators. there are three common methods: filter, embedding, and wrapper [24]. the attribute reduction method based on rough set is a typical filtering method [51]. a filtering method divides the key index screening and the subsequent prediction model training into two independent parts. in the face of a large amount of data and multi-dimensional indexes, this method is simple and easy to use, and has a good interpretability for the data without consuming a lot of calculation time. the rough set (rs) theory was proposed by pawlak in the early 1980s [28]. this theory provided a new mathematical tool for dealing with fuzzy and uncertain problems [25]. the major advantages of rough set theory are that it can efficiently discover implied knowledge by analysing imperfect https://doi.org/10.15837/ijccc.2020.6.3894 3 (uncertain and vague) [27], inconsistent [29], and incomplete data [11], without the use of priori information or assumptions [31]. the main theoretical difference between rough set theory and the most commonly used statistical method, regression, is that rough set theory as a classification tool is able to predict the “belongingness” of items to discrete classes, which in turn can be used to generate decision rules from the results, whereas regression tends to predict a value from continuous variables to obtain a mathematical formula for the result. in addition, rough set theory does not require any check on assumptions; instead, it focuses more on the accuracy of the model’s predictions. the medical field often involves many subdivided medical indicators, and rough set theory is very suitable for solving large medical index analysis and prediction problems. ali et al. (2015) proposed a new hybrid rough set model to analyse 17 indicators of 50 diabetic patients in order to distinguish the type of diabetes and predict disease progression [2]. gil-herrera et al. (2011) established a dataset consisting of 9105 cases and 15 variables, and used rough set theory to predict the late lifespan of terminally-ill patients in order to improve the referral process for hospice care [10]. wang et al. (2006) introduced particle swarm optimization into rough set theory to predict the degree of malignancy of gliomas. additionally, 14 conditional attributes and one decision attribute were extracted from 280 cases, revealing the relationship between the features found on the mri and the degree of malignancy [43]. however, in traditional rough set methods, the decision attributes have mostly consisted of single sequences. decision-making methods that combine multiple attributions are often used. it will affect the speed of attribute reduction. because of its sensitivity to noise, the decision rules for extraction are very unstable, and the accuracy needs to be improved. the genetic algorithm was created in 1975 by holland and his students from the university of michigan [16]. it has a natural implicit parallelism and powerful global search ability, and obtains the global optimal solution for solution space by simulating the genetic evolution principle of bio-property survival. however, the traditional genetic algorithm has disadvantages including low search efficiency, poor local search ability, and ease of falling into local optima when solving problems. in 1948, von neumann proposed the idea of cellular automata [40], which is based on the characteristics of complex systems, to simulate and describe complexity. in 1993, whitley first proposed the cellular genetic algorithm (cga) [45], which combines genetic algorithms and cellular automata to find global optimal solutions for complex systems. the core idea is realizing the wide spread of excellent individual information among the population through the information interaction among multiple subjects. the cga demonstrates excellent performance in local extremum. using cellular automata (ca) [39], the algorithm assigns the individuals comprising a group to a two-dimensional grid (cell space). the genetic operation of the algorithm is different from that of a simple ga. the selection operation of a simple ga is randomly paired in the whole population, while the cga avoids too much information exchange, confines the individual’s genetic selection operation to a limited neighborhood, completes the selection and recombination in the neighborhood, performs mutations, and leaves good individuals as offspring and fathers. the limited neighborhood slows down the diffusion of individual genetic information throughout the population, which is conducive to maintaining the diversity of the population during evolution, and provides conditions for avoiding local convergence and responding to changes in a timely manner [1][8]. therefore, this study uses a cga to reduce rough set attributes and screen key indictors that have high correlation with acute critical disease. the combination of rough sets and cga is still relatively rare. in summary, the existing reduction algorithm mainly uses a heuristic search method to construct the minimum reduction of the conditional attributes, that is, the minimum reduction, from the kernel of the rough set. however, this algorithm becomes increasingly complicated as the scale of the problem increases, and it is difficult to find the global optimum. the genetic algorithm is very suitable for solving the rough set attribute reduction problem because it has the advantages of global optimization and implicit parallelism. the neighbour learning model of the cga also better maintains the diversity of the population, which offers a good balance between global search and local optimization. according to the characteristics of rough set, genetic algorithms and cellular automata, this paper proposes a new intelligent screening algorithm for the prediction of key indicators in patients with acute critical disease. the algorithm introduces a combination of a genetic algorithm and cellular auhttps://doi.org/10.15837/ijccc.2020.6.3894 4 tomata into rough set theory and is, in essence, an evolutionary algorithm based on genetic algorithms. evolutionary computation is a subdomain of computational intelligence that involves combinatorial optimization problems. the algorithm is influenced by the natural selection mechanism of the “survival of the fittest” and the transmission of genetic information in the process of biological evolution. through the iterative simulation of this process, the problem to be solved is regarded as the environment, and in some possible solutions, the optimal solution is sought through natural evolution [21]. one of its important research branches is the genetic algorithm [15]. compared with traditional optimization methods, such as statistics-based methods and exhaustive methods, evolutionary computation is a mature global optimization method with high robustness and wide applicability. it has the characteristics of self-organization, self-adaptation, and self-learning [9], and can effectively address complex problems (such as non-deterministic polynomial -hard optimization problems) that are difficult to solve with traditional optimization algorithms without being limited by the nature of the problem. in the context of current medical big data, combined with the idea of evolutionary computing, the proposed intelligent screening algorithm can effectively compensate for the shortcomings of existing statistical testing methods, such as insufficient computational power and difficulty in finding optimal solutions. it is possible to extract the research objects that satisfy the conditions from the existing datasets as well as efficiently and stably calculate the key indicators that affect rebleeding in patients with acute critical disease. the algorithm can provide more timely, efficient, and scientific information for clinical treatment decisions, and solve clinical practical problems using clinical “real world” data. 3 methods in this study, the screening of key patient indicators is regarded as a single objective attribute reduction problem: taking patients’ physiological indicators as condition attributes and patients’ conditions as decision attributes. the optimization problem is solved by the combination of rough set, genetic algorithm, and cellular automata, and the fitness function is constructed by the grey correlation degree. the optimal key index combination is generated by the iterative updating of the population. 3.1 preliminaries the disaster knowledge system can be represented as a four-tuple: the knowledge system of disease can be expressed as a four-tuple: s = {u,a,r,d}, where f is the attribute value of the object. u = {x1,x2, · · · ,xn} is a set of patients; a = (a1,a2, · · · ,am) represents the physiological index of patients; d = (d1,d2, · · · ,dd) represents the option of patients’ conditions; the parameter p of d(p) indicates the degree of association between the decision attribute (column) and the condition attribute (column), which can be calculated by the grey correlation degree pi = 1 m ∑m i=1 γi. d is the decision attribute of the disease knowledge system. for example, when the observed data for patient k are d (k), k = 1, · · · , n; then, d (k) = {d (1) ,d (2) , · · · ,d(n)} is the decision attribute sequence of the disease knowledge system. r is the conditional attribute of the disease knowledge system. when the physiological parameter observation data for patient k are ri(k), i = 1, · · · , m, k = 1, · · · , n; then, ri (k) = {ai (1) ,ai (2) , · · · ,ai(n)} is the conditional attribute sequence of the disease knowledge system. dependency reflects the relationship between attributes. if an attribute is regarded as a type of knowledge reflecting the object, then the attribute dependence can be regarded as the ability to derive knowledge from other knowledge, and is a measure of knowledge dependence. knowledge q is derivable from knowledge p when all concepts of q can be defined by some of the concepts in p . when q is derivable from knowledge p , it is stated that q depends on p , denoted by p ⇒ q. the formal definition of dependency is as follows: let k = (u,r) be a knowledge base, and let p, q ⊆ r: https://doi.org/10.15837/ijccc.2020.6.3894 5 (1) when ind (p) ⊆ ind(q), knowledge q depends on knowledge p; (2) when p ⇒ q and q ⇒ p , knowledge p and q are equivalent, denoted by p ≡ q; (3) when there is no p ⇒ q and there is no q ⇒ p , p and q are independent. obviously, if and only if ind (p) ⊆ ind(q), then p ⇒ q. let k = (u, r) be a knowledge base, and let p, q ⊆ r; when k = r (p, q) = rp (q) = |posp(q)|/|card(u)|, knowledge q depends on p (0 ≤ k ≤ 1), denoted by p ⇒k q, where card represents the number of sets. posp (q) = ⋃ x∈u/q p(x) is the p positive domain of the domain u in q. therefore, k = r (p, q) = ∑ x∈u/q |p (x)| u . let xi(k) be the attribute sequence, xi (k) = {d (k) ,ri(k)}. e1,e2 are the sequence operators. xie1 = {xi (1) e1,xi (2) e1, · · · ,xi (n) e1} , xie2 = {xi (1) e2,xi (2) e2, · · · ,xi (n) e2, where xi (k) e1 = xi(k)/xi(1) and xie1 is the initial value image. xi (k) e2 = xi(k)/xi, and xie2 is the mean image. in the knowledge system of gastrointestinal bleeding, s = {u,a,f,d}, r ⊆ a, u = {x1,x2, . . . ,xn}, the rough membership of the disaster set relative to the decision attribute i is expressed as: µu (di ) = card(u ∩di) card (u) . (1) the concept of ‘rough membership’ indicates the dependence between the physiological parameter attribute set and the decision attribute set. that is, in a set, the higher the frequency of a certain decision attribute, the greater the importance of this attribute for this set. pi = 1m ∑m i=1 γi, pi is the grey correlation degree of the condition attribute and decision attribute [21], indicating the degree of association between gastrointestinal rebleeding and patient physiological index. this paper uses deng’s grey relational degree. di (k) = {di (1) ,di (2) , · · · ,di(n)} is the decision attribute sequence of the disease knowledge system. ri (k) = {ai (1) ,ai (2) , · · · ,ai(n)} is the conditional attribute sequence of the disease knowledge system. γi is the correlation coefficient between di (k) and ai (k). the formula is as follows: γij(k) = min j min k |dj (k) −ri (k)| + ρ max j max k |dj (k) −ri (k) | |dj (k) −ri (k)| + ρmax j max k |dj (k) −ri (k) | , (2) where |dj (k) −ri (k) | is the absolute difference between the points dj (k) and ri (k). ρ is the identification coefficient, generally 0.5; the degree of correlation between the decision attribute dj (k) = {dj (1) ,dj (2) , · · · ,dj(n)} and the condition attribute ri (k) = {ai (1) ,ai (2) , · · · ,ai(n)} is γ (dj,ri) = 1 n n∑ k=1 γij(k). (3) set a real number γ (dj,ri), if (1) 0 < γ (dj,ri) < 1,γ (dj,ri) = 1 ⇐⇒ dj = ri (2) the smaller |dj (k) −ri (k) | is, the larger γ (dj,ri) is. let γ (dj,ri) be the grey relational degree of decision attribute dj with condition attribute ri. the above formula (1) organically combines the grey correlation degree and the traditional definition of dependence between different attribute sets. by obtaining the physiological indicators of the patients during actual clinical diagnosis and treatment, a set of key influencing factor rules are extracted to detect early gastrointestinal rebleeding risk. https://doi.org/10.15837/ijccc.2020.6.3894 6 3.2 design of an intelligent screening algorithm screening for key physiological indicators in this paper is achieved through attribute reduction. the so-called attribute reduction refers to reducing the amount of redundant knowledge (attributes) in the knowledge base without affecting knowledge expression, thus ensuring that the information system’s classification ability remains unchanged. this procedure is implemented to make the expression of the knowledge base more concise, and thus, to finally extract the rules of the knowledge system. the attribute reduction problem belongs to a discrete coding optimization problem. when the number of variables is large, the problem is an np-hard problem. studies have shown that some artificial intelligence algorithms, such as genetic algorithms, have achieved good results in solving np-hard problems that result from incomplete information [15, 21]. in recent years, some studies have shown that compared with traditional genetic algorithms, the cga can better maintain population diversity and has a strong global search ability for complex optimization problems [18]. when facing multi-dimensional, large-scale medical big data, traditional genetic algorithms are prone to premature convergence due to poor local search ability, while cgas can effectively retain good individuals and maintain population diversity. therefore, we selected a cga to optimize the rough set attribute reduction problem. it is known from section 3.1 that in the decision table, the importance of a condition attribute to the decision attribute has a one-to-one relation. the principle of the attribute reduction algorithm is observing changes in the decision table after removing an attribute, and then measuring how important that attribute is to the decision attribute. the greater the change in the classification ability upon removing a condition attribute from the decision table relative to the decision attribute, the more important that condition attribute is. based on this principle, this paper designs a new coding method using a cga. (1) encoding method let a = (a1,a2, · · · ,am) represent the set of condition attributes in the decision table of clinical diagnosis and treatment of diseases. let ai = 0 indicate that the condition attribute can be reduced; let ai = 1 indicate that the condition attribute cannot be reduced. (2) fitness function the fitness function uses the dependence degree shown in (1). the greater the dependency between the condition attribute and the decision attribute, the more important the condition attribute is, that is, the greater the individual’s fitness. (3) the algorithm steps are as follows (see figure 1). step 1: generate an initial population. in the n × n cell space, n2 condition attribute combinations are randomly generated, represented by xij, where i,j ∈ [1,n]. let k denote the number of condition attributes; then, xij = [aij1, · · · ,aijk, · · · ,aijk], and aijk randomly takes 0 or 1. zero means that the attribute aijk is not included in the individual xij, and 1 means that the attribute aijk is included in the individual xij. step 2: calculate the fitness. each cell individual calculates its own condition attribute dependency, denoted by yij. standardize it for comparison. let fitij = yij − min i,j∈[1,n] yij,fitij be the fitness of the individual i,j. step 3: selection. the moore-type neighbour structure ([i− 1, i + 1],[j − 1,j + 1]) is used, which is represented by ω. each individual xij searches for the optimal individual { xef ∣∣∣∣yef = max i,j∈ω yij } in the "neighbour" as the learning object. step 4: recombination. set the crossover probability to pc. for each attribute aijk in xij, the probability of being interchanged with the attribute aefk in xef is pc. step 5: mutation. set the mutation probability to pm. for each attribute aijk in xij, the probability of generating a variation (1 to 0, or 0 to 1) is pm. step 6: return to step 2 until the fitness is no longer increasing. the pseudo code of the above process is given as follows: https://doi.org/10.15837/ijccc.2020.6.3894 7 figure 1: cellular genetic algorithm algorithm cellular genetic algorithm-based attribute reduction 1 while not termination condition() do 2 for each cell i from 1 to population size 3 the attribute combination of cell i is coded by binary code 4 end 5 for each cell i from 1 to population size 6 calculate the attribute dependability of cell i’s attribute combination 7 establish the fitness function based on dependability 8 select the cell with the best fitness from the neighbourhood i∗ 9 let pc and pm be the crossover and mutation probability respectively 10 if rand < pc 11 implement the crossover operation between i and i∗ 12 end 13 if rand < pm 14 implement the mutation operation in cell i 15 end 16 end 17 end 4 results and validation 4.1 indexes, data pre-processing, and algorithm parameters in this paper, 647 cases diagnosed as gastrointestinal bleeding in the first aid database of pla general hospital were used to build the decision-making table for gastrointestinal bleeding. among them, there were 313 cases of nosocomial rebleeding in the experimental group, and 334 cases of nonnosocomial rebleeding in the control group. the 64 vital signs and laboratory indexes (as shown in appendix a1) that are most likely to be related to nosocomial rebleeding of the gastrointestinal tract were selected as condition attributes, and each patient was marked with the outcome state (whether there was a rebleeding condition) as decision attributes, and the original decision table is shown in appendix a2. for the blank values in the original data, this paper uses the method of filling column by column. to meet the rough set data requirements, only discrete data can be processed by rough set. according to clinical experience, the clinician gave the discrete standard of each index (see appendix a3). after data dispersion and cleaning, the final decision table for gastrointestinal bleeding was obtained (see appendix a4) if there was no special description, the calculation environment and algorithm parameters were set according to table 1 and table 2. https://doi.org/10.15837/ijccc.2020.6.3894 8 table 1: computing environment name configuration cpu e5-1650 v2 graphics card gtx1060 6g asus ram kingston 8g recc motherboard asus z9pa hard disk samsung 850 evo 250g ssd table 2: algorithm parameter setting algorithm parameter value number of populations 50 probability of intersection 0.7 probability of variation 0.01 4.2 key index screening algorithm and performance analysis in order to verify the performance of the intelligent screening algorithm built in this paper in an all-around manner, the condition attributes with fewer times of elimination were retained after 50 independent repeated attribute reduction experiments using the genetic algorithm and the cga, respectively. the results are shown in table 3 and table 4. table 3: attribute reduction result based on the cell genetic algorithm condition attribute number physiological indicators 25 thrombin time 35 inorganic phosphate 36 haemoglobinometry 50 glu (blood gas analysis) 57 ph 59 systolic blood pressure 60 diastolic blood pressure 64 heart rate table 4: attribute reduction result based on the genetic algorithm condition attribute number physiological indicators 2 gamma-glutamyl transpeptidase 3 leukocyte count 5 monocyte 23 n-terminal pro-brain natriuretic peptide 30 glucose 33 eosinophil 36 haemoglobinometry 60 diastolic blood pressure 4.2.1 algorithm performance the performance of an algorithm is usually examined by its ability to find the global optimum and the convergence rate. the ability to find the global optimum refers to the exploration capability and the convergence quality; the convergence speed refers to the number of algorithm iterations required to calculate the optimal solution. the genetic algorithm and the cga were used to perform 50 independent repeated attribute reduction experiments on the decision table data. the adaptation https://doi.org/10.15837/ijccc.2020.6.3894 9 process of one of the reduction algorithms is shown in figure 2, and a box diagram of 50 independent reductions is shown in figure 3. figure 2: the fitness evolution process of the condition attribute (physiological index) reduction algorithm figure 3: comparison of fitness box line graphs between the genetic algorithm and the cellular genetic algorithm first, the degree of fitness can reflect the quality of the algorithm’s convergence result when calculating the optimal solution. the greater the fitness, the stronger the ability of the algorithm to find the optimal solution. it can be clearly seen from figures 2 and 3 and table 5 that the fitness of the cga is larger than that of the genetic algorithm, and therefore, the cga has a stronger ability to find the optimal solution. second, the variance of the fitness can reflect the algorithm’s stability. the smaller the variance of fitness and the mean square error of the fitness evolution process, the more stable the algorithm. as shown in figure 3, by comparing the fitness values of 50 independent replicate experiments, the variance of the fitness of the cga is smaller than that of the genetic algorithm. that is, the fitness of the cga is more stable, and the overall convergence effect is better. when the condition attribute is no longer being reduced, the last condition attribute iteration calculation is selected for comparison. to eliminate the influence of the initial value, the variance and the average value of the fitness evolution https://doi.org/10.15837/ijccc.2020.6.3894 10 process of the last 15 algorithm iterations are taken. table 5 shows that the mean square error of the fitness of the genetic algorithm is much larger than that of the cga, indicating that the convergence quality of the cga is better. in summary, when solving the same problem, the ability of the cga to find the global optimum is higher than that of the genetic algorithm. table 5: comparison of algorithm performance between the genetic algorithm and the cellular genetic algorithm algorithm maximum fitness mean value of fitness mean square error of fitness number of algorithm iterations required for convergence genetic algorithm 0.7861 0.7322 0.0001560371 20 cellular genetic algorithm 0.8057 0.7754 0.0000000246 10 finally, convergence speed is an important indicator of evaluating algorithm performance. it can be seen from figure 2 and table 5 that the number of algorithm iterations required for the convergence of the genetic algorithm is greater than that of the cga, and thus, the cga is superior to the genetic algorithm in computational efficiency. by comparing the above genetic algorithm with the cga, it can be found that the cga has a higher global optimal ability (higher exploration ability and higher quality of convergence) than the genetic algorithm. the convergence speed of the cga is also faster (the number of algorithm iterations required for convergence is small), and therefore, the cga performs better in solving the rough set attribute reduction problem. 4.2.2 the ability of key indicators to predict problems in this paper, a key indicator prediction model for fatal rebleeding in patients with gastrointestinal bleeding based on rough intensive reduction and machine learning algorithms was established. first, the decision table was reduced by cga or genetic algorithm, and the conditional attributes of redundancy were removed. there is a certain correlation between the physiological indicators. attribute reduction can not only eliminate the information overlap between the indicators but also play a role in reducing the dimensions. the speed of the model is improved, while the workload of the collection indicator is reduced, and the diagnosis is more targeted and time-sensitive. then, based on the key indicators selected, the prediction is made using machine learning algorithms (logical regression, support vector machine, plain bayesian, and adaboost algorithms) [7, 13, 14, 17, 38, 48]. the evaluation indicators of machine learning selected for this study included accuracy, precision, and recal. in the face of medical prediction problems, potential injuries should be identified as far as possible, so recall rates of the evaluation indicators are more important. this study made α = 1.5. the key indicators obtained by the attribute reduction experiment were used as input, and the prediction results were obtained by the cross-validation calculation of ten-folds as shown in table 6. the corresponding roc curves are shown in figures 4, 5, and 6. it can be seen from table 6 and figures 4, 5 and 6 that: (1) comparing the cga-key indicator set and ga-key indicator set, when the intelligent screening algorithm reduces the number of indicators from 64 to eight, the prediction effects of the svm (support vector machine, svm) and naïve bayes algorithms do not decrease, but increase to be better than the whole indicator set. at the same time, the prediction effects of the adaboost algorithm and logistic regression algorithm are not much different from that of the whole index set. when the number of indicators of the genetic algorithm is also reduced from 64 to eight, the prediction accuracy of the model is far less effective than the former two, and there is still a big gap compared with the whole indicator set. this not only shows that the constructed prediction model has a high and stable prediction accuracy, and has a good fitting effect on patient outcomes, but also demonstrates the advantages of the intelligent screening algorithm. https://doi.org/10.15837/ijccc.2020.6.3894 11 table 6: predicted results of rebleeding in patients with gastrointestinal bleeding predicts results machine learning algorithm index number validation f1.5 auc accuracy precision recall all indicators set adaboost 64 0.8273 0.9095 0.8370 0.7937 0.8479 logistic regression 64 0.7974 0.8682 0.8109 0.7964 0.8009 svm 64 0.2356 0.5319 0.5628 0.0946 0.7457 naïve bayes 64 0.5649 0.7102 0.4890 0.9648 0.4785 cga-set of key indicators adaboost 8 0.7824 0.8770 0.7976 0.7689 0.7911 logistic regression 8 0.7839 0.8782 0.7972 0.7643 0.7950 svm 8 0.7683 0.8525 0.7839 0.7541 0.7763 naïve bayes 8 0.5820 0.8408 0.5169 0.9929 0.4930 ga-set of key indicators adaboost 8 0.5658 0.6121 0.5775 0.6110 0.5494 logistic regression 8 0.6173 0.6699 0.6421 0.5684 0.6448 svm 8 0.5478 0.5701 0.5784 0.5287 0.5578 naïve bayes 8 0.6117 0.6475 0.6091 0.7699 0.5608 figure 4: first aid database full indicator set roc curve and auc (2) compared with the four prediction models constructed, svm and naïve bayes are more suitable for cases where there are fewer indicators, while adaboost and logistic regression have better generalization ability for higher dimensional data. 5 discussion the prediction accuracy for traditional disease is affected by interventions in different groups of patients [26, 42], different disease causes [5, 41, 49], and different levels of medical institutions [32]. strict interventions (such as considering subjects that do not have a large number of comorbidities or complications, i.e., single-disease states), treatment timing (clinical capacity issues that may delay drug treatments), differences in hospital medical levels and conditions (studies are usually performed at large hospitals or developed areas) and the limited number of cases make the application of existing https://doi.org/10.15837/ijccc.2020.6.3894 12 figure 5: first aid database intelligent screening algorithm key indicator set roc curve and auc figure 6: first aid database genetic algorithm key indicator set roc curve and auc research results not fully translatable to improving clinical efficacy. results derived from models including a subset of patients cannot be extended to all patients, which means that the traditional method of key indicator identification still has certain limitations on clinical universality. among existing disease prediction methods, two are most common: the traditional statistical method and the artificial intelligence method. classical statistical methods manually select candidate features based on medical domain knowledge, calculate the importance of each feature based on statistical methods, and construct predictive models. the artificial intelligence method starts with a complete dataset, constructs a machine learning model, automatically extracts features from big data, and emphasizes a large amount of hidden information carried within the medical data itself, thereby achieving greater data mining capacity and outputting values faster. from the perspective of big data, traditional statistical methods obviously have certain limitations, but their appropriateness and generality are still undeniable. the medical big data analysis method is based on available multi-source medical data, powerful artificial intelligence, and machine learning https://doi.org/10.15837/ijccc.2020.6.3894 13 algorithms to ensure the accuracy and timeliness of analysis results. however, as some problems remain, such as difficultly controlling confounding factors and imbalanced baseline data, the reliability of the analysis results requires further rigorous clinical trials. even so, it is undeniable that big data medical care is rooted in real clinical practice and has great research prospects in a wide range of clinical research areas [33]. based on a large amount of accumulated actual case data, this study introduces an artificial intelligence algorithm to effectively increase the value of data by calculating the key influencing factors of fatal gastrointestinal rebleeding in the hospital. prediction results of various machine learning algorithms were computed and compared. to quickly realize a more targeted clinical scoring system based on artificial intelligence, technical routes were explored, and the theoretical basis of the system was described. further clinical trials will be carried out in the future for a wide range of applications. the application of rough set theory to clinical medical decision support has existed for a long time, and there have been many research results obtained in the fields of clinical decision support, medical image analysis, and others. tsumoto et al. [35, 36, 37] were the first to apply rough set theory to the clinical expert system. they used rough set theory to generalize rules based on medicinal databases and significantly improved the accuracy of clinical predictions. xu et al. [47] proposed a new method based on fuzzy rough set theory and information theory to reduce data redundancy, and this method has been used in a cancer recognition classification diagnosis model. bazan et al. [3] used rough set theory to model classifier networks to identify infant death risk. in 2013, bazan et al. [4] discussed an application of rough set tools for modelling networks of classifiers and provided clinical decision support for respiratory failure in infants. son et al. [34] proposed the use of rough set theory and a decision tree to establish a decision model for the early diagnosis of congestive heart failure with an accuracy of 97.5%. inbarani et al. [12] used new supervised feature selection methods based on a hybridization of particle swarm optimization and rough set theory. these methods were applied to clinical medical diagnosis decision-making to solve the problem of decreased predictive accuracy due to irrelevancy and redundancy of a medical dataset. chowdhary et al. [6] used an intuitionistic fuzzy rough set technique to extract the features of cancer medical images to assist in cancer diagnosis with an overall accuracy of 98.3%. in summary, it is feasible to apply the rough set theory to clinical medical diagnosis decisions, and the accuracy is much higher than when doing so manually. additionally, the method based on rough set theory has been used for disaster rescue. for example, li et al. [22] and others applied rough set theory for analysing the common points of various disaster medical features upon which many disasters are classified. 6 conclusion according to the characteristics of rough set theory, genetic algorithms, and cellular automata, this paper takes the key index prediction of fatal rebleeding in patients with gastrointestinal haemorrhage as an example, and proposes a new intelligent screening algorithm combining genetic algorithms and cellular automata into the rough set theory. the key indicators were selected from a large number of medical indicators and used as input to design prediction models based on logistic regression, svm, naïve bayes, and adaboost algorithms. the results show that: (1) cga has better performance than the traditional genetic algorithm in solving complex multi-peak optimization problems. (2) in the face of huge medical index analysis problems, attribute reduction can effectively remove redundant data, reduce the dimension of data, and reduce the workload of index extraction and analysis. (3) the combination of intelligent screening algorithm and machine learning algorithm can improve prediction accuracy. in clinical practice, the population of patients with acute critical disease is large. due to its complex aetiology, the degree of judgment of medical personnel in most medical institutions depends largely on the individual’s subjective experience and intuition, resulting in an inability of institutions to properly identify and offer timely treatment for critically ill patients, prevent potential health hazards, and even prevent disputes. therefore, objective evaluation criteria are needed in clinical work to provide early warning, so that the medical workers can detect the existence of critical disease or potential critical disease. https://doi.org/10.15837/ijccc.2020.6.3894 14 in this paper, utilizing big data, a prediction model based on the intelligent screening algorithm and machine learning algorithm was obtained that can effectively identify the high-risk rebleeding population, observe the trend of disease development, and effectively assist doctors’ treatment decisions by providing early risk warning. this paper also found that in terms of early warning for disease, the algorithm and prediction model are superior in improving prediction accuracy. in terms of clinical diagnosis, on the basis of cga, patients with acute critical disease were further classified according to the causes to help emergency physicians make scientific and accurate triage decisions. acknowledgements this work was partly supported by the national natural science foundation of china (81701961, 71103014), and the national key research and development plan for science and technology winter olympics of the ministry of science and technology of china (2019yff030058). references [1] alba, e.; dorronsoro, b. 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[51] zhu, x. z.; zhu, w.; fan, x. n.(2017). rough set methods in feature selection via submodular function. soft computing, 21(13), 3699-3711, 2017. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: jia, l.; wang, h.; zhuang, h.; li, x. et al. (2020). a rough set and cellular genetic fusion algorithm for acute critical disease prediction, international journal of computers communications & control, 15(6), 3894, 2020. https://doi.org/10.15837/ijccc.2020.6.3894. introduction literature review methods preliminaries design of an intelligent screening algorithm results and validation indexes, data pre-processing, and algorithm parameters key index screening algorithm and performance analysis algorithm performance discussion conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 3, month: june, year: 2021 article number: 4235, https://doi.org/10.15837/ijccc.2021.3.4235 ccc publications a boundary determined neural model for relation extraction r.x. tang, y.b. qin, r.z. huang, h. li, y.p. chen ruixue tang 1. college of computer science and technology, guizhou university 2. school of information, guizhou university of finance and econnomics guiyang 550025, china gs.rxtang19@gzu.edu.cn yongbin qin* college of computer science and technology, guizhou university guiyang 550025, china *corresponding author: ybqin@foxmail.com ruizhang huang college of computer science and technology, guizhou university guiyang 550025, china rzhuang@gzu.edu.cn hao li college of computer science and technology, guizhou university guiyang 550025, china haolilhhh@foxmail.com yanping chen college of computer science and technology, guizhou university guiyang 550025, china ypench@gmail.com abstract existing models extract entity relations only after two entity spans have been precisely extracted that influenced the performance of relation extraction. compared with recognizing entity spans, because the boundary has a small granularity and a less ambiguity, it can be detected precisely and incorporated to learn better representation. motivated by the strengths of boundary, we propose a boundary determined neural (bdn) model, which leverages boundaries as task-related cues to predict the relation labels. our model can predict high-quality relation instance via the pairs of boundaries, which can relieve error propagation problem. moreover, our model fuses with boundary-relevant information encoding to represent distributed representation to improve the ability of capturing semantic and dependency information, which can increase the discriminability of neural network. experiments show that our model achieves state-of-the-art performances on ace05 corpus. keywords: relation extraction, error propagation, boundary. https://doi.org/10.15837/ijccc.2021.3.4235 2 1 introduction relation extraction aims to identify semantic relations between entities. this basic task in information extraction facilitates many more complex tasks, such as question answering [18], machine translation [22], emotion detection [20] and knowledge base construction [23]. take a sentence from the ace05 corpus for example (figure 1). the entity of person (per) "thousands of pro-life crusaders" is associated with the geo-political entity (gpe) "washington d.c." by a physical relation (phy). figure 1: an example from ace05 corpus the previous relation extraction models can be roughly divided into two categories: pipeline models [42] and joint models [15, 16, 38]. the pipeline models extract relations in two steps, namely, entity recognition [5, 6] and relation classification [7, 43]. ignoring the relevance between the two steps, the pipeline models tend to have the problem of error propagation. that is why the joint models came into being. the joint models extract entities and relations, and combine them into a unified framework [29, 30]. the relations are extracted based on the recognition of entity spans. any error of entity recognition would spread to relation extraction. therefore, the performance of relation extraction depends heavily on the effect of entity recognition. every entity span has a start boundary and an end boundary. for example, "washington" is the start boundary of the entity "washington d.c." in figure 1. there are three advantages of boundary recognition over entity span recognition. first, the boundaries can be clearly encoded, due to their small granularity. second, it is easy to automatically recognize boundaries, because they are dependent on local features. third, the boundary-relevant information facilitates the learning of representation. for these advantages, the boundaries can provide a powerful support to relation extraction. in many applications, researchers only care about the presence/absence of relations in raw texts. for instance, the protein interactions extracted from biomedical literature are important to new medical research. in public opinion analysis, it is more significant to find specific public opinions than to identify the expresser. thus, a distinctive way of relation extraction is to extract semantic relations from sentences without needing the accurate measurement of entity spans. for relation extraction, this paper combines boundary detection model with relation extraction model into a boundary determined neural (bdn) model. specifically, a sequence labeling model was applied to identify boundaries. based on the detected boundaries, the relation labels were classified with boundary pairs and relevant information. there are two prominent advantages of our model: (1) the model can generate many high-quality candidate boundaries, eliminating the need to import inaccurate entity spans into the relation extraction model; (2) by learning different representations, the model improves the capture of semantic and dependency information, which in turn improves the relation prediction. this paper mainly makes two contributions: (1) relation extraction was solved from a fresh perspective. instead of entity spans, boundaries were selected as task-related cues to classify the relations. (2) the bdn model was developed to extract relations, and proved to achieve state-of-theart results on ace05 corpus. 2 literature review relation extraction has received growing interest in recent years. as mentioned before, pipeline models and joint models are the two major types of tools to extract relations. pipeline models treat entity recognition and relation extraction as two separate tasks. by pipeline models, named entities are recognized before the extraction of relations. named entity recognition https://doi.org/10.15837/ijccc.2021.3.4235 3 (ner) [24, 25, 26] is usually treated as a sequence model based on neural networks like bidirectional long short-term memory with a conditional random field (crf) layer (bi-lstm-crf) or bi-lstm with convolutional neural network (cnn) (bi-lstm-cnn). the output is a flattened labeling sequence. li et al. [26] designed the cross-bilstm, which interleaves the forward and backward hidden states between lstm layers. recently, boundary supervision [34, 44] is introduced to entity recognition. meanwhile, relation extraction is normally treated as a multi-label classification task. the task can be executed under a shallow architecture or a deep learning (dl) architecture. shallow architectures like support vector machine (svm) and maximum entropy are often used in conjunction with feature engineering or kernel methods. as for dl architectures, the deep semantic features are widely extracted by recurrent neural networks (rnns) [35, 36] and cnns [17, 39, 40]. tran et al. [36] preserved full relational information by integrating segment-level attention-based cnns with dependency-based rnns. zhong and chen [42] learned two encoders built on deep pretrained language models. besides, the relation extraction models can be improved with texture features [1, 2, 8]. chen et al. [8] grouped the features extracted from each sentence into multiple sets, and thus obtained the structural and semantic information required for relation extraction. nevertheless, all relation extraction models must recognize entities first. they may suffer heavily from error propagation, for the neglection of the interaction between entity recognition and relation extraction. unlike pipeline models, joint models fuses the information of entities with that of relations, and identify entities and their relations under a unified framework. these models either adopt the strategy of parameter sharing or the strategy of joint decoding [3, 41]. by parameter sharing, the model shares some input features or internal hidden states. bekoulis et al. [3] proposed a neural model with the crf layer for ner and the sigmoid loss for multiple head prediction. yu et al. [41] decomposed relation extraction into two subtasks, namely, the extraction of headand tail-entities, and the extraction of their relations; the two subtasks were deconstructed into several sequence labeling problems based on tagging scheme. however, the parameter sharing models cannot capture all the interactions between entities, owing to the limitations of independent decoders. to utilize the interaction between decoders, some joint decoding algorithms were developed to decode entities and relations all at once [13, 45, 46]. for example, zeng et al. [45] proposed an endto-end neural model based on sequence-to-sequence (seq2seq) with copy mechanism. to improve the accuracy of entity recognition, zeng et al. [46] created a multi-task framework which adds a sequence labeling task to zeng et al. [45] encoder. yet the framework requires complex tags or decoding process. to prevent mistakes from inaccurate entity spans, our model utilizes boundaries to classify relation labels instead of entity spans. the boundary-relevant information was fused to the neural network to learn different representations, such that the semantic and dependency information can be captured to improve model performance. 3 methodology the proposed bdn model is illustrated in figure 2. the model obviously consists of two parts: boundary detection and relation extraction. the former part executes the sequence labeling task of generating boundary candidates from a sentence. the boundary candidates are employed as cues for relation extraction. the latter part takes boundary pairs as input to forecast relation types. this part is composed of a feature combination layer, an embedding layer, a convolution layer, a pooling layer, and a fully-connected layer (output layer). 3.1 boundary detection for a given sentence consisting of n tokens t = {t1, t2, ..., tn}, the word embedding of the i-th token ti can be expressed as: xi = emb(ti). (1) where, emb is the embedding operation. here, the word embedding is initialized with a pretrained bidirectional encoder representations from transformers (bert) mode [14]. then, x = {x1,x2, ...,xn} https://doi.org/10.15837/ijccc.2021.3.4235 4 figure 2: framework of bdn model is fed into a bi-lstm layer [19] to learn hidden states. each lstm can be formalized as a function fθ(x1:n, i) with parameter set θ, where f is a nonlinear function (e.g., logistic sigmoid function or hyperbolic tangent function). the network can only capture important features from the input. to acquire more features, a bi-lstm can be applied to traverse the input data twice. the bi-lstm encompasses a forward lstm and a backward lstm fl and fr, with parameter sets of θl and θr, respectively. taking token sequence x1:n and index i as input, the corresponding vector vi = fθ(x1:n, i) can be formalized as: fθ(x1:n, i) = hl,i ◦hr,i. (2) hl,i = fl(x1,x2, ...,xi). (3) hr,i = fr(xn,xn−1, ...,xi). (4) where, ◦ is vector concatenation; vi is the concatenation of forward hidden representation hl,i and backward hidden representation hr,i at in the i-th step. in formula (3), an lstm is applied on the input sequence to learned from the past context x1:i. in formula (4), the reverse form of the input sequence is fed into the lstm to learn the future context xn:i. rather than assign an entity class label to each token, this paper predicts the boundary labels in the task. let t1, t2, ..., tn be a sentence with start and end boundaries. the tokens are labeled by the bo tagging scheme. the boundary tokens ti are assigned with labels b − ε, where ε is the set of pre-defined entity types; the non-boundary tokens are assigned with labels o. then, the boundary labels are predicted by feeding the corresponding representation v = (v1, v2, ..., vn) into a crf layer, which is good at learning the constraint relationship between labels. the layer then outputs the final prediction label sequence ye = (ye1,ye2, ...,yen) for the input t. the optimal sequence can be obtained by the viterbi algorithm. then, the output label can be expressed as: ye = arg max p(v,y′). (5) where, p(v,y′) is the likelihood function of training the tags sequence v; y′ is the ground-truth label sequence. to sum up, boundary detection is considered as a sequence-labeling task. the input x1:n is represented as a token sequence t1, t2, ..., tn and fed into a forward lstm and a backward lstm. the two vectors are added to the crf layer to decode the optimal label sequence. then, the start boundary and an end boundary of each entity are identified separately. every detected end boundary is greedily matched with the detected start boundary. the boundaries can be assembled into entity spans following prior work [9]. 3.2 relation extraction 3.2.1 feature combination layer deep neural networks can automatically learn abstract high-dimensional semantic information from raw inputs [10]. however, the learning is limited to the local information around each word in https://doi.org/10.15837/ijccc.2021.3.4235 5 the sentence. for instance, cnns cannot encode semantic information out of the filter range. in relation extraction, a sentence usually contains several relation instances. the semantic dependencies between these instances cannot be fully encoded by deep neural networks. inspired by chen et al. [11, 12], our model fuses features based on the boundary-relevant information, so as to enhance the representation and capture the semantic dependencies in a sentence. instead of processing each pair of entity spans, our model leverages each pair of boundaries and its relevant features as input. for each input sentence and its boundary label sequence, our model processes every pair of candidate boundaries independently. because relations are symmetric in general, every pair of candidate boundaries 〈ti, tj〉 can generate two relation instances: 〈ti, tj,r〉 and 〈tj, ti,r〉, where r ∈ r and r with being the set of the relation labels, including a null label of the absence of relation between a boundary pair. the features of each relation instance can be denoted as follows: f1 = {z|z = typeof(ti)_typeof(tj)} . (6) f2 = {z|z = t_positionof(ti)_positionof(tj)} . (7) f3 = {z|z = ngram(ti, tj)} . (8) where, _ is concatenation; typeof(ti) is an indicator of boundary type (e.g., person (per), and organization (org)); positioof(ti) maps position of ti to an n-dimensional vector about the relative distance of the current word to ti. f1 concatenates two boundary labels into a feature, and thus captures the semantic and dependency information of a relation instance. for example, in the ace (automatic content extraction) english annotation guidelines for events, a "gen-affiliation" is defined as the relationship between a facility and geo-political boundary. f2 concatenates the input sentence and two positions into a feature, providing information for relation extraction. each sentence can be divided by two boundaries into three parts:[t1, ti−1], [ti, tj−1] and [tj, tn]. f3 concatenates three features, i.e., different representations from different channels, into a multichannel feature. to sum up, the feature combination layer extracts the features from the input sentence, and then combines them into the final feature. these features are designed to capture the semantic dependencies in a sentence and fed into the next layer. they can be formalized as follow. f = ∪3i=1fi = f1 ∪f2 ∪ 3 j=1 f j 3 . (9) 3.2.2 embedding layer for each relation instance, all the features are allocated to g groups, and each group is embedded into a vector. an independent lookup table is adopted to transform the groups into vector representations. let m be the dimensionality of the embedded words. the embedding operation can be defined as: [ x g 1,x g 2, ...,x g h ] = emb(fg). (10) the output is expressed as a matrix xg ∈ rh×m. after that, each group is separately convoluted. 3.2.3 convolution layer the convolution layer transforms the representations into abstract features, using a convolutional filter w with a window size l . a filter can be viewed as a weight matrix w = [w1,w2, . . . ,wl](wl ∈ rl×k). then, the convolutional operation can be described as: x g i = f ( wx g i:i+k + b ) (1 ≤ i + k ≤ h). (11) where, f is an activation function (e.g., rectified linear unit (relu); b is the bias. convolution filters generally adopt a fixed window size, and thus unable to learn variable features flexibly. this process can then be replicated for various filters with different window sizes to produce a feature map:[ x g 1 ,x g 2 , ...,x g h−k+1 ] = conv(xg). (12) https://doi.org/10.15837/ijccc.2021.3.4235 6 the output of the convolution layer can be referred to as xg ∈ r(h−k+1)×h , which represents a high-dimensional abstract representation. 3.2.4 pooling layer following the convolution layer, a pooling layer is arranged to determine the most useful feature in each feature representation. the max pooling over time on xg can be defined as: x̂g = max { x g 1:h−k+1 } . (13) the pooling scores for every filter are concatenated into a feature representation:[ x̂ g 1 ,x̂ g 2 , ...,x̂ g s ] = pooling(xg). (14) the output of the pooling layer can be referred to as x̂ g ∈ rs, where s is the number of filters in the model and x̂s is the max pooling score of the s-th filter. 3.2.5 fully connected layer a relation instance inputted to the model can be encoded as: x̂ = ⊕gi=1pooling(conv(emb(f g))))) = ⊕gi=1x̂ g. (15) where, ⊕ is the concatenation operation. the dropout strategy is adopted to prevent overfitting. the output can be described as: o = f ′(x̂ · c) + b′. (16) where, · is element-wise multiplication operation; c is bernoulli random variables; b’is the bias; f ′ ∈ rr×s ′ is the weight, withbeing s′ equal to s plus the dimension of the features, and r being the relation type. finally, a softmax function is adopted to predict the probabilistic distribution for all types. parameter θ is calculated by stochastic gradient descent (sgd), and updated by error backpropagation algorithm: p(i|c,θ) = eoi∑r i=1 e oi . (17) 4 experiment 4.1 datasets our model was evaluated on ace 2005 corpus, using both english corpus and chinese corpus. ace05 defines 7 types of entities, namely, facility (fac), geo-political entities (gpe), location (loc), organization (org), person (per), vehicle (veh), and weapon (wea), and 6 types of relations between entities, namely, artifact (art), gen-affiliation (gen-aff), org-affiliation (orgaff), part-whole (partwhole), person-social (per-soc), and physical (phys). the files from ace05 english corpus were preprocessed and segmented by the procedure mentioned in li’s work [27]. those from ace05 chinese corpus were split into a training set, a verification set, and a test set by the ratio of 6:2:2. table 1 shows the data of each corpus. every file was segmented into sentences. a relation instance was deemed as negative, if there is no relation between two boundaries. since boundaries are ordered in a sentence, if a sentence has three boundaries (a, b, c), then six pairs could be generated: [a, b], [a, c], [b, c], [b, a], [c, a], and [c, b]. since the nested entities may have the same boundaries, three pairs could be added: [a, a], [b, b] and [c, c]. https://doi.org/10.15837/ijccc.2021.3.4235 7 table 1: data of each corpus dataset number of relation types number of entity types number of files number of sentencestrain verification test ace english 7 6 511 7,273 1,765 1,535 ace chinese 7 6 628 5,546 1,858 1,850 4.2 parameter settings the bert base model (uncased) was taken as the base encoders for ace corpus. the dimension of word embedding was set to 768 in bert. the embedded data were further mapped into a 128×2dimensional vector in bi-lstm. the filter windows of 3, 4, 5, 6, and 7 were adopted with 128 feature maps. the dimensionality of word and feature were configured as 100 and 50, respectively. learning rate, dropout probability, and batch size were set as 0.9, 0.5 and 64, respectively. the performance of our model was measured by precision (p), recall (r) and f1 score (f1) to evaluate the performance [4]. the strict relation evaluation rel+ was adopted, where a predicted relation is considered correct if both entity span and relation type are correct. in addition, boundary evaluation is denoted as rel, where the boundaries must be correct. 4.3 baselines our model was compared with the following existing models: beam search [27], an incremental method, which adopts segment-based decoder based on semimarkov chain and global features; sptree [31], which detects entities with bidirectional sequential lstm, and decodes relations with tree-structured lstm-rnn; global normalization [47], a table-filling method, which integrates syntactic information to facilitate global learning; att-bilstm [21], a bilstm model with attention, which does not adopt any dependency tree; minimum risk training [32], which recognizes entities with an rnn, extracts relations with a cnn, and trains the model by a minimum risk strategy; graph convolutional network (gnn) [33], which detect all entity spans, and then designs an entity-relation bipartite graph to support relation extraction; multi-turn question answering (qa) [28], which characterizes each entity type and relation type via a question answering template, and then extracts relations by a standard machine reading comprehension (mrc) framework; table-sequence encoder [38], which introduces table-sequence encoders containing two different encoders; span-level ner decoder [37], which predicts the relations in re decoder with a span-level ner; two independent encoders [42], as a pipeline approach, which are built on span-level representations and contextual representations, respectively. several contrastive experiments were also carried out, including "ours w/o af", which uses a standard multi-class classifier to detect relation, and "ours w/o cf", which utilizes the most useful feature: the type information. 4.4 comparing with existing models this subsection compares our model with the published models on the ace english corpus. as shown in table 2, our model outperformed the previous models in relation extraction: the advantage in f1 score was at least 1.8% on boundary evaluation (rel) and 0.6% on strict evaluation (rel+), when recognizing entity spans. it can be concluded that our model is exceptionally powerful in relation extraction. the main reason is that our model uses boundary-related information to learn better representations. these data, which contain priori knowledge and experience, can be transformed into the high-dimensional abstract representations, making it easier to capture semantic and dependency information. that is how our model achieved excellent performance of relation extraction. https://doi.org/10.15837/ijccc.2021.3.4235 8 table 2: comparing with existing models model entity rel rel+p(%) r(%) f(%) p(%) r(%) f(%) p(%) r(%) f(%) li and ji [27] 85.2 76.9 80.8 68.9 41.9 52.1 65.4 39.8 49.5 miwa and bansal [31] 82.9 83.9 83.4 57.2 54.0 55.6 zhang et al. [47] 83.6 57.5 katiyar and cardie [21] 84.0 81.3 82.6 57.9 54.0 55.9 55.5 51.8 53.6 sun et al. [32] 83.9 83.2 83.6 64.9 55.1 59.6 sun et al. [33] 86.1 82.4 84.2 68.1 52.3 59.1 li et al. [28] 84.7 84.9 84.8 64.8 56.2 60.2 wang and lu [38] 89.5 67.6 64.3 taillé et al. [37] 87.4 61.2 64.4 zhong and chen [42] 88.7 67.0 62.2 ours:span 86.9 86.7 86.8 75.1 66.5 69.4 77.6 62.7 65.0 ours:boundary 92.8 90.4 91.6 75.3 76.7 75.3 table 3: results on ace05 relation type ace english ace chinesep(%) r(%) f(%) p(%) r(%) f(%) part-whole 72.02 85.21 78.06 82.58 73.50 77.78 org-aff 91.12 84.29 87.57 99.39 80.20 88.77 gen-aff 82.57 69.23 75.31 93.65 85.51 89.39 phys 67.16 58.44 62.50 96.58 92.97 94.74 per-soc 87.80 86.40 87.10 91.30 86.90 89.05 art 50.86 76.72 61.17 97.27 88.56 92.71 total 75.26 76.72 75.29 93.46 84.60 88.74 our model also brought the highest performance, improving the f1 score by +7.7% on boundary evaluation (rel), when predicting relation types with boundaries. the improvement is attributable to two facts: first, boundary detection performs better than entity span recognition. our model can generate high-quality candidate boundaries to be fed into the next layer. this solves the mistakes from inaccurate entity spans, and alleviates the error propagation problem. second, our model can capture more features between boundary pairs, making the network more discriminable. the edge of our model in f1 score over other models clearly shows its superiority. 4.5 results on ace05 table 3 compares the results of our model on english corpus and chinese corpus against those of baselines. our model achieved better performance on chinese corpus than on english corpus. the performance gap was 13.5% in f1 score. the reason may be the good performance on chinese boundary detection. besides, chinese and english sentences differ greatly in syntactic structure. english sentences are generally longer and stricter in word order, which is the key in sentence interpretation. by contrast, chinese sentences are composed of informative characters, which reflect the syntactic and semantic information of the sentence. 4.6 analysis of boundary detection to disclose the influence of boundaries, experiments were conducted on boundary detection of english corpus and chinese corpus. table 4 compares the detection results of three models on start boundaries, end boundaries and entity spans. note that the bert was adopted to encode word semantics, bilstm to capture the semantic and grammatical dependencies in a sentence representation, and crf to support detection with artificial features. as shown in table 4, the f1 scores of boundary detection were 4-6% higher than that of entity recognition. the possible reasons are as follows: the boundaries are clear and of small granularity, leaving no space for ambiguity; the boundary detection is more dependent on local features, and easier to be recognized automatically. it was observed that bert-bilstm achieved 2% higher f1 score than the best result of bert-crossbilstm [26]. this fully demonstrates the superiority of our boundary detection model. https://doi.org/10.15837/ijccc.2021.3.4235 9 table 4: influence of boundary detection dataset model start boundary end boundary spansp(%) r(%) f(%) p(%) r(%) f(%) p(%) r(%) f(%) ace english random-bilstm 83.11 68.85 75.31 83.77 66.74 74.29 bert-crossbilstm [26] 87.31 91.39 89.30 86.70 88.81 87.74 bert-bilstm 92.80 90.44 91.60 92.39 89.43 90.88 86.92 86.74 86.83 ace chinese random-bilstm 85.85 76.92 81.14 87.59 80.97 84.15 bert-crossbilstm [26] 93.04 94.12 93.58 94.35 93.63 93.99 bert-bilstm 94.72 95.26 94.99 95.91 95.29 95.60 92.21 86.56 89.30 table 5: influence of feature combination model ace english ace chinesep(%) r(%) f(%) p(%) r(%) f(%) s 50.82 75.94 59.64 76.41 46.36 57.04 f1 73.17 69.42 71.13 93.48 71.29 80.31 f2 67.73 74.92 69.88 91.40 78.87 84.53 f3 58.21 79.06 65.84 72.29 74.14 72.81 f1 ∪f2 67.52 77.80 71.05 92.63 83.53 87.70 f1 ∪f3 82.90 67.65 74.19 83.93 84.12 83.59 f2 ∪f3 65.89 78.18 70.54 91.49 78.71 84.35 f1 ∪f2 ∪f3 75.26 76.72 75.29 93.46 84.60 88.74 4.7 analysis of feature combination table 5 lists the experimental results on the influence of boundary-relevant features on model performance. the f1 scores on english corpus and chinese corpus stood at 59.64% and 57.04%, respectively. this is because the entity can be any word, and the sentence fails to provide sufficient information. moreover, model f1 increased the f1 score by +1.3% and +5.3% from the levels of models f2 and f3, respectively. this means the boundary-relevant features provide critical information to support relation extraction, and type information is highly useful in improving the relation performance. in addition, models f1, f2 and f3 suffered a significant performance drop compared with the combination of all features, indicating the insufficiency of solely relying on a single feature. it can be concluded that the improvement over other models comes from the abundance of features. 4.8 analysis of different boundary to better understand the bdn performance, the influence of the start and end boundaries was evaluated separately on relation extraction. as shown in figure 3, the performance of start boundary reached an f1 score of 75.29% and 88.74% on english corpus and chinese corpus, respectively. the results were 0.6% and 1.5% better than the f1 scores with end boundary on the two corpuses, respectively. during the ablation experiment, the performance dropped deeply, but the performance of the start boundary approached that of the end boundary. the result indicates that the two boundaries have no significant difference in the impact on relation extraction. both start boundary and end boundary can flexibly support relation extraction in place of entities. 5 conclusion this paper presents a bdn model, which leverages the boundaries to predict relation labels. our model combines sequence labeling model with classification model to accurately classify relation instances. experimental results show that the bdn can effectively extract relations, and outperform start-of-the-art baselines on ace05 corpus. through extensive analyses, the authors fully demonstrated the superiority of our model in boundary detection, and validated the importance of using boundary pairs as input and learning different representations from boundary-relevant information. the future research will further reduce mistakes and make more meaningful comparisons in a unified end-to-end evaluation setting. https://doi.org/10.15837/ijccc.2021.3.4235 10 �!���"����� �!���"����� �!�� �� �� � � �� ��� �� �� �� � ��������������� � �� � �!����# ���� �!����# �!���"����� �!���"����� �!�� �� �� � � �� ��� ��������������� � �� � �!����# ���� �!����# figure 3: influence of different boundary acknowledgements this work is supported by the joint funds of the national natural science foundation of china under grant no. u1836205, the major research program of national natural science foundation of china under grant no. 91746116, national natural science foundation of china under grant no. 62066007 and no. 62066008, the major special science and technology projects of guizhou province under grant no. 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(2017). end-to-end neural relation extraction with global optimization, proceedings of the 2017 conference on empirical methods in natural language processing, pp.1730-1740, 2017. copyright ©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: tang, r.x.; qin, y.b.; huang, r.z.; li, h.; chen, y.p. (2021). a boundary determined neural model for relation extraction, international journal of computers communications & control, 16(4), 4235, 2021. https://doi.org/10.15837/ijccc.2020.3.4235 introduction literature review methodology boundary detection relation extraction feature combination layer embedding layer convolution layer pooling layer fully connected layer experiment datasets parameter settings baselines comparing with existing models results on ace05 analysis of boundary detection analysis of feature combination analysis of different boundary conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 1, month: february, year: 2022 article number: 4623, https://doi.org/10.15837/ijccc.2022.1.4623 ccc publications performance improvement of low-cost iterative learning-based fuzzy control systems for tower crane systems r.-e. precup, r.-c. roman, e.-l. hedrea, c.-a. bojan-dragos, m.-m. damian and m.-l. nedelcea radu-emil precup*, raul-cristian roman, elena-lorena hedrea, claudia-adina bojan-dragos, miruna-maria damian and monica-lavinia nedelcea department of automation and applied informatics, politehnica university of timisoara bd. v. parvan 2, 300223 timisoara, romania *corresponding author: radu.precup@aut.upt.ro email: raul.roman@aut.upt.ro ; lorena.hedrea@upt.ro ; claudia.dragos@aut.upt.ro; miruna-maria.damian@student.upt.ro; monica.nedelcea@student.upt.ro abstract this paper is dedicated to the memory of prof. ioan dziţac, one of the fathers of this journal and its founding editor-in-chief till 2021. the paper addresses the performance improvement of three single input-single output (siso) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. three separate low-cost siso fuzzy controllers are employed in terms of first order discrete-time intelligent proportional-integral (pi) controllers with takagi-sugeno-kang proportional-derivative (pd) fuzzy terms. iterative learning control (ilc) system structures with pd learning functions are involved in the current iteration siso ilc structures. optimization problems are defined in order to tune the parameters of the learning functions. the objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic slime mould algorithm (sma). the experimental results prove the performance improvement of the siso control systems after ten iterations of sma. keywords: iterative learning control; intelligent proportional-integral controllers; fuzzy control; learning functions; slime mould algorithm; tower crane systems. 1 introduction this paper treats a subject that belongs to the intersections of the fields of fuzzy logic control and the performance improvement of control systems based on experiments and few information available on the controlled process, also referred to as data-driven control or model-free control or data-driven model-free control. comprehensive analyses of the theory and applications of fuzzy sets and fuzzy logic including fuzzy logic control are conducted in the papers [1] and [2] authored by prof. ioan https://doi.org/10.15837/ijccc.2022.1.4623 2 dziţac and his co-authpors and friends, who acknowledge the exceptional contribution of prof. lotfi a. zadeh, the creator of fuzzy sets. as highlighted in the recent and popular surveys [3], [4], [5], [6] and [7], the systematic design and tuning of fuzzy control systems is supported by analyses that include stability, controllability, observability, sensitivity and robustness. the optimal tuning of fuzzy controllers is combined with these analyses. however, performance improvement is preferred instead of speaking about optimal fuzzy controllers because the proof of reaching the optimal solutions if fuzzy models are involved in the optimization problems is a relatively complicated task. as shown in [8], iterative learning control (ilc) carries out the improvement of various performance indices (including the empirical ones overshoot, settling time, etc., specified in [9] and [10]) of control systems executing repetitively the same tasks in terms of several experiments also referred to as cycles or trials or iterations. the information gathered from previous experiments is used in this regard. several learning functions or learning rules are included in ilc structures designed around the control systems whose performance are improved [11], [12]. ilc is considered in [13] as an optimization paradigm, and its analysis is performed in [14]. the proportional-derivative (pd) learning functions or rules are embedded in ilc resulting in the pd-type ilc, which is based on derivative and proportional errors, it calculates the error and applies a correction. the pd-type ilc is applied to control several nonlinear systems as, for example, timedelay systems in [15] and along with convergence proofs in [16]. although pd-type ilc is generally applied to processes modeled by linear or nonlinear ordinary differential equations, a recent application to hyperbolic equations is reported in [17]. three classes of optimal ilc are discussed in [18]. the first class is the norm-optimal ilc, which makes use of the lifted system representation of ilc and supported by the representative papers [19], [20], [21] and [22]. the second class is stochastic ilc, and it mitigates the effects of random disturbances that depend on the iterations [23], [24]. the third class, referred to as datadriven ilc or model-free ilc, directly deals with nonlinear systems subject to unknown nonlinearities; popular papers in this class are [25], [26], [27], [28] in the general context of data-driven control, which ensures the performance improvement of control systems in terms of few information on the controlled process and using experiments conducted on the real-world operating control systems to compensate for that. recent examples of data-driven model-free controllers are reported in the area of model-free control [29], [30], virtual reference feedback tuning [31], data-driven dual-rate control [32], repetitive algorithms [33], model-free adaptive control [34], [35], data-driven model predictive control [36] and active disturbance rejection control [37], [38]. moreover, as stated in [39] and highlighted afterwards in [30], model-free control is a new and efficient tool for machine learning. more than fifteen years ago the authors suggested in [40] the combination of fuzzy control and ilc in order to benefit from the advantageous of these two control strategies and to alleviate their shortcomings. several low-cost controllers specific to this first approach were proposed in the subsequent papers [9], [41], [42], [43], [44] and [45] focusing on the proportional-integral (pi) fuzzy controllers, which replace the linear pi controllers and the performance of fuzzy control systems is improved in terms of ilc. in addition, fuzzy logic was also involved in the learning functions or learning rules specific to ilc algorithms to add more flexibility. a different approach to combine fuzzy control with ilc is the approximation of uncertain dynamics by fuzzy models in order to solve the problem of unknown nonlinear functions in the systems, and next include this in adaptive ilc structures. recent results in this regard are reported for biotechnological processes [46], multi-agent systems [47] and transportation systems [48]. neural networks can be used instead of fuzzy models in this second approach as suggestively illustrated in [49] and [50]. the first new contribution of this paper is the performance improvement of control systems for a representative family of multi input-multi output (mimo) systems represented by tower crane systems, using the first approach mentioned above, namely and ilc control structure with pd learning functions. the mimo control system structure makes use of low-cost fuzzy controllers included in the three siso control loops (for cart position control, arm angular position control and payload position control) subjected to ilc, and the fuzzy controllers are represented by first order discretetime intelligent pi controllers with takagi-sugeno-kang pd fuzzy terms presented in [51] and [52]. https://doi.org/10.15837/ijccc.2022.1.4623 3 the second new contribution is the definition of optimization problems in the iteration domain in order to ensure the performance improvement of the fuzzy control systems in terms of expressing the objective functions of sums of squared control errors and considering that the variables are the parameters of the pd learning functions. the optimization problems are solved by the metaheuristic slime mould algorithm (sma) suggested in [53] and recently applied in [54] and [55] to optimally tune the parameters of type-1 and type-2 fuzzy controllers. only few iterations of sma are conducted, and sma generally aims the optimal tuning of the parameters of the learning functions. the limitations of the state-of-the-art to introduce the control strategy proposed in this paper are: (i) accurate linear models or nonlinear models of the process are needed including fuzzy logicbased ones, (ii) differences between the process models and the real process are not tackled, and (iii) the controller structures are rather complicated. these three limitations will be discussed as follows in order to highlight the motivation of the above new contributions. the motivation of the new contributions, which also highlights their importance in the context of the state-of-the-art, is three-fold. first, the second approach to the combination of fuzzy logic and ilc requires accurate fuzzy models of the process, which requires a deep understanding of the process models and also may result in a large number of fuzzy rules; this also leads to a large number of fuzzy rules of the fuzzy controllers if the parallel distributed compensation principle is involved in their design and tuning. accurate linear models are also needed for norm-optimal ilc, where the frequency domain calculation of the h∞ norm is needed, which is avoided in this paper. second, there are differences between the process models and the real process, which are important in the context of optimization as they affect the optimality. the evaluation of the objective functions in this paper is carried out in the iteration or experiment or trial or cycle domain, and it is actually performed in terms of conducting experiments on the real-world control system. of course, this is generally a shortcoming in applications of metaheuristic optimization algorithms, but sma does not require many evaluations of the objective functions, plus the number of iterations is small because the performance improvement of already operating control systems is targeted and not reaching the optimal solution. third, low-cost fuzzy controllers are proposed as far as both their structure (and implementation) and design (and tuning) is concerned, and they do not make use anymore of mapping the tuning results from the linear controllers onto their fuzzy counterparts. besides sma, other popular metaheuristic algorithms with fresh results are gravitational search algorithm (gsa), charged system search, particle swarm optimization [56], grey wolf optimizer [57], chicken search optimization [58], competitive multi-metaheuristic algorithms [59], harmony search optimization [60] for the optimal tuning of fuzzy controllers, extremal optimization algorithms for nonlinear controller optimal tuning [61], [62], inclusion of information feedback models in metaheuristic algorithms [63], [64], hybrid gsa-genetic algorithm (ga) [65] for constrained optimization problems, water cycle [66] and bat [67] algorithms for combinatorial optimization, gwo with hierarchical fuzzy operator [68], hierarchical ga for multi-objective optimization of neural networks [69], cross-entropy algorithm for manufacturing processes [70], water circle algorithm for traveling salesman problem [71], conflict monitoring algorithm [72] and jaya optimization algorithm for biped robots [73]. the paper is structured as follows: the tower crane system model, which plays the role of controlled process, is described in the next section. the control system structure and the design approach are presented in section 3. the experimental results are discussed in section 4. the conclusions are drawn in section 5. 2 process model the block diagram of the tower crane system is described in figure 1 focusing on the tower crane system laboratory equipment [74] installed in the intelligent control systems laboratory of the politehnica university of timisoara, romania. the blocks included in figure 1 (a) enable the digital control of this system viewed as a controlled process. using the coordinate systems associated to each element of the crane and the centers of mass introduced in figure 1 (b), the nonlinear state-space model of the tower crane system is [52] https://doi.org/10.15837/ijccc.2022.1.4623 4 ẋ1 = x5, ẋ2 = x6, ẋ3 = x7, ẋ4 = x8, ẋ5 = f5( ∏ ), ẋ6 = f6( ∐ ), ẋ7 = −(1/tς1)x7 + (kp1/tς1)m1, ẋ8 = −(1/tς2)x8 + (kp2/tς2)m2, ẋ9 = x10, ẋ10 = f10(ℵ), y1 = x3, y2 = x4, y3 = x9, (1) with the following expressions of the functions f 5, f 6 and f 10 [52]: f5( ∏ ) = f5(x1,x2,x3,x5, ...,x10,m1,m2) = [−4x5x10 + 4x6x8x9 cos2 x1 cos x2 −x26x9 sin 2x1 +x28x9 sin 2x1 cos2 x2 − 2g sin x1 cos x2 − 4x7x8 cos x1 +2x3x28 sin x1 sin x2 + 2(x7/tς1) sin x1 sin x2 −2(kp1/tς1)m1 sin x1 sin x2 + 4x8x10 sin x2 +2(x3x8/tς2) cos x1 − 2(kp2/tς2)x3m2 cos x1 −2(x8x9/tς2) sin x2 + 2(kp2/tς2)x9m2 sin x2]/(2x9), f6( ∐ ) = f6(x1,x2,x3,x5, ...,x10,m1,m2) = [−2x6x10 cos x1 − 2x5x8x9 cos x1 cos x2 +2x5x6x9 sin x1 −g sin x2 − 2x8x10 sin x1 cos x2 −x3x28 cos x2 + x28x9 cos x1 cos x2 sin x2 − (x7/tς1) cos x2 +(kp1/tς1)m1 cos x2 + (x8x9/tς2) sin x1 cos x2 −(kp2/tς2)x9m2 sin x1 cos x2]/(x9 cos x1), f10(ℵ) = f10(x1,x2,x3,x5, ...,x10,m1,m2,m3) = [−(µl/ml)x10 + g + 2x6x10 sin x2 cos x1 +2x5x10 sin x1 cos x2 − 2x5x6x9 sin x1 sin x2 +x9f6( ∐ ) sin x2 cos x1 + x9f5( ∏ ) sin x1 cos x2 +x9(x25 + x26) cos x1 cos x2 + (kp3m3/ml)]/(1 + cos x1 cos x2). (2) figure 1: the block diagram of the tower crane system laboratory equipment (adapted from [75], [76] and [77]) (a) and coordinate systems associated to each element of the crane and the centers of mass (adapted from [52]) (b). the denavit-hartenberg methodology can be used to obtain the dynamic model in (1) and (2) and simplify it to justify the control scheme. as pointed out in [52], x1 (rad) and x2 (rad) are the angles that describe the payload position in xz plane, x3 (m) ∈ [−0.25, 0.25] is the first (controlled) output of the tower crane system, i.e. the cart position, x4 (rad) ∈ [−1.57, 1.57] is the second (controlled) output https://doi.org/10.15837/ijccc.2022.1.4623 5 of the tower crane system, i.e. the arm angular position, x5 (rad/s) and x6 (rad/s) are the angular velocities that describe the payload position in xz plane, x7 (m/s) is the cart velocity, x8 (rad/s) is the arm angular velocity, x9 (m) ∈ [−0.4, 0.4] is the lift line position and also the third (controlled) output of the tower crane system, x10 (m/s) is lift line velocity. the expressions of the saturation and dead zone (sdz) static nonlinearities specific to the three actuators, which are direct current (dc) motors, are [52] mi =   −1, if ui ≤−ubi, (ui + uci)/(ubi −uci), if −ubi < ui < −uci, 0, if −uci ≤ |ui|≤ uai, (ui −uai)/(ubi −uai), if uai < ui < ubi, 1, if ui ≥ ubi, (3) m1 ∈ [−1, 1] is the output of the sdz static nonlinearity specific to the first actuator, u1(%) ∈ [−100, 100] and next u1 ∈ [−1, 1] is the first control signal, i.e. the pulse width modulation (pwm) duty cycle of dc motor for cart position control, m2 ∈ [−1, 1] is the output of sdz static nonlinearity specific to the second actuator, u2(%) ∈ [−100, 100] and next u2 ∈ [−1, 1] is the second control signal, i.e. the pwm duty cycle of dc motor for arm angular position control, m3 ∈ [−1, 1] is the output of sdz static nonlinearity specific to the third actuator, u3(%) ∈ [−100, 100] and next u3 ∈ [−1, 1] is the third control signal, i.e. the pwm duty cycle of dc motor for payload position control, the parameters in (3) obtained experimentally are ua1 = 0.1925, ub1 = 1, uc1 = 0.2, ua2 = 0.18, ub2 = 1, uc2 = 0.1538, ua3 = 0.1, ub3 = 1 and uc3 = 0.13, ml=0.33 kg is the payload mass, µl = 1600 kg/s is the viscous coefficient associated with the payload motion, kp 1 = 0.188 m/s kp 2 = 0.871 rad/s and kp 3 = 200 kg.m/s2 are the process gains (of the three dc motors), t 1 = 0.1 s and t 2 = 0.1 s are the small time constants of the first two dc motors, and g = 9.81 m/s2 is the gravitational acceleration. the controlled outputs are the cart position y1 (m) = x3 (m), the arm angular position y2 (rad) = x4 (rad) and the payload position y3 (m) = x9 (m). 3 control system structure and design approach as specified in section 1, a mimo control system structure is employed in controlling the three outputs of the tower crane system. this mimo control system structure consists of three separate siso control systems (or control loops), separately designed for cart position control, arm angular position control and payload position control. for the sake of simplicity, a siso generic control system structure will be presented as follows, and this structure has to be replicated considering separately the three controlled outputs. the fuzzy control system structure with pd learning function or learning rule is presented in figure 2, where: ilc – iterative learning control algorithm, r – reference input or set-point, assumed to be repetitive, d – disturbance input, which can be additive on the process input or output, and it is also assumed to be repetitive, e – control error, u – control signal elaborated by the ilc algorithm, y – controlled output, m – memory block, c – fuzzy controller, p – controlled process (i.e. the tower crane system), q−1 – backward shift operator in the iteration domain, kp – the proportional gain of the learning function, kd – the derivative gain of the learning function, and j – subscript that indicates the current iteration or cycle or trial, but attention should be paid at the first three iterations in order to avoid the notation problem concerning one of the three controlled outputs. the expression of the pd learning function is [40] uj+1(k) = uj(k) + kpej(k) + kd[ej(k + 1) −ej(k)], (4) where k is the index of the current sampling interval. as pointed out in section 1, the controller c in figure 2 is represented by the first order discretetime intelligent proportional-integral (ipi) controller with takagi-sugeno-kang pd fuzzy term, with the following expression of the control law, adapted from [52]: u(k) = u(k − 1) + α−1(r(k + 1) −r(k) −y(k) + y(k − 1) −ϕ(k)), (5) https://doi.org/10.15837/ijccc.2022.1.4623 6 figure 2: fuzzy control system structure with pd learning function, where c denotes the controller, p the controlled process, and m the memory block. where α ∈ < is a parameter specific to mfc and chosen by the designer such that ∆y(k + 1) = y(k + 1) −y(k) and α u(k) have the same order of magnitude, and ϕ(k) is the output of the takagisugeno-kang pd fuzzy term. the discrete-time takagi-sugeno-kang pd fuzzy term is built around the two inputs-single output fuzzy controller (tiso-fc) and the structure given in figure 3 (a). figure 3 (b) highlights the input (and also scheduling) membership functions and their parameters, be1 > 0 and b∆e > 0. figure 3: discrete-time takagi-sugeno-kang pd fuzzy term (a) and input membership functions (b) (adapted from [52]). the weighted average method is implemented in the defuzzification module of this controller, actually tiso-fc. the sum and prod operators are employed in the inference engine. the rule base is given in table 1 expressed for cart control, arm angular position control and payload position control of tower crane systems, with the following notations in the rule consequents [52]: ϕi(k) = χi ψpd(k), i = 1...3, (6) where χi > 0, i = 1...3, are the parameters inserted in order to alleviate the overshoot of the control system, ψpd(k) is the output of the linear pd term specific to mfc. the expression of the pd term is [52] ψpd(k) = m1∆e(k) + m2e(k) = −k2∆e(k) + (k1 + k2)e(k), (7) where k1 ∈ < and k2 ∈ < are the gains of the ipi controller, and ∆e(k) = e(k) − e(k − 1) is the increment of control error. using ψpd(k) instead of ϕ(k) in (5) leads to the linear ipi controller, an important type of mfc, which is replaced by the fuzzy controller for control system performance improvement. https://doi.org/10.15837/ijccc.2022.1.4623 7 table 1: rule base of takagi-sugeno-kang pd fuzzy term (adapted from [52]) e(k) ∆e(k) ns ze ps ps ϕ2(k) ϕ3(k) ϕ1(k) ze ϕ2(k) ϕ3(k) ϕ3(k) ns ϕ2(k) ϕ2(k) ϕ2(k) the parameters of one of the three first order discrete-time ipi controllers with takagi-sugenokang pd fuzzy terms are α, k1, k2, be1, b∆e and χi > 0, i = 1...3. the values of these parameters are set according to the recommendations given in [52] including their computation as solutions to adequately defined optimization problems. the modal equivalence principle is applied to obtain the value of the parameter b∆e [52]: b∆e = −(m2/m1)be1 = (1 + k1/k2)be1. (8) as specified in section 1, the parameters kp and kd of the pd learning function can be obtained as the two elements of the vector solutions ρ∗(ψ) to the optimization problems ρ∗(ψ) = arg minρ(ψ)∈dρ(ψ) j(ψ)(ρ(ψ)), j(ψ)(ρ(ψ)) = ∑ns k=1[e(ψ)(k,ρ(ψ))] 2, (9) where j(ψ)(ρ(ψ)) is the objective function that measures and assesses the performance of each siso control system structure with first order discrete-time ipi controllers with takagi-sugeno-kang pd fuzzy terms, ρ(ψ) is the general notation for the parameter vector of each controller, ρ∗(ψ) is the optimal parameter vector of each controller ρ(ψ) = [ kp(ψ) kd(ψ) ]t , ρ∗(ψ) = [ k ∗ p(ψ) k ∗ d(ψ) ]t , (10) where the superscript t indicates matrix transposition, and dρ(ψ) ⊂< 2 in (9) is the feasible domain of ρ(ψ). the subscript ψ ∈{c,a,p} in (9) and (10) specifies, in accordance with [52], both the controlled output and its corresponding controller, namely c – the cart position (i.e. y(c) = y1), a – the arm angular position (i.e. y(a) = y2) and p – the payload position (i.e. y(p) = y3), and e(ψ) = r(ψ) −y(ψ) is the control error involved in the three control loops. the sampling period is ts = 0.01 s and the time horizon is set to 70 s, resulting a number of ns = 70/0.01 = 7000 data samples. sma is involved in solving the optimization problems defined in (9). sma operates with a total number of n agents, and each agent, which is an element of the slime mould, is assigned to a position vector xi(j) [54], [55] xi(j) = [x1i (j) ... x f i (j) ... x q i (j)] t ∈ dρ(ψ) ⊂< q, i = 1...n, (11) where xfi (j) is the position of i th agent in f th dimension, f = 1...q, q = 2 in order to map the sma onto the optimization problem in (9), j is the index of the current iteration, which is also the current iteration in the ilc structure in figure 2 and maps the sma onto this ilc structure, j = 1...jmax, and jmax is the maximum number of iterations. the expression of the search domain dρ(ψ) ⊂< q = <2 in (9) and (11) is [54], [55] dρ(ψ) = [l 1,u1] × ...× [lf,uf ] × ...× [lq,uq] ⊂<q, (12) lf are the lower bounds, uf are the upper bounds, f = 1...q, q = 2, and [54], [55] x f i (j) ∈ [l f,uf ], i = 1...n, f = 1...q. (13) as pointed out in an easily understandable way in [54] and [55], sma consists of seven steps. they start with the initialization of the random population such that to fulfill (11), (12) and (13), continue with the evaluation of the performance of each member of the population (which can be carried out making use of experiments or simulation), the calculation of the best fitness obtained in all iteration, the calculation of the vector of weights, the update of each agent’s position, and ends https://doi.org/10.15837/ijccc.2022.1.4623 8 with incrementing the iteration index until the maximum number of iterations is reached. additional details on the relationships involved in this regard are given in [53], [54] and [55]. sma is mapped onto the optimization problem defined in (9) in terms of the following relations: q = 2, xi(j) = ρ(ψ), i = 1...n, si(j) = j(ψ)(ρ(ψ)), i = 1...n, xb(jmax) = ρ∗(ψ), (14) where si(j) is the fitness (i.e. the value of the objective function) of ith agent with the position vector xi(j), and xb(j) is the best agent, leading to the best solution obtained so far (i.e. until the current iteration j). the design approach that ensures the tuning of a siso fuzzy controller with pd learning function is carried out in terms of the following steps: step 1. the performance specifications imposed to the fuzzy control system are expressed in an aggregate form in terms of the optimization problem defined in (9), which ensures the desired/imposed dynamics of the fuzzy control system. the sampling period ts is set. step 2. the maximum number of iterations or cycles or trials jmax is set. step 3. the parameters α, k1, k2, be1, b∆e and χi > 0, i = 1...3, of the discrete-time ipi controller with takagi-sugeno-kang pd fuzzy term are tuned using the approach specific to mfc combined with fuzzy logic specified in [52]. experiments with the real-world fuzzy control system (which conducts the process) are conducted in order to evaluate the values of the objective function. step 4. the sma is applied to obtain the parameters k∗ p(ψ) and k ∗ d(ψ) of the pd learning function or the pd learning rule. this design approach is applied three times, separately, for the cart position controller, the arm angular position controller and the payload position controller. 4 experimental results the design approach and the low-cost ilc-based fuzzy control systems are validated in this section by real-time experiments conducted on the tower crane system laboratory equipment installed in the intelligent control systems laboratory of the politehnica university of timisoara, romania. a photo of the experimental setup is given in figure 1 (b). as specified in the previous sections, three siso control loops are involved in the mimo control system. the main details on the four design steps will be outlined in the next paragraphs. the dynamic regimes imposed to the experimental setup for all three siso control loops are: zero initial conditions in relation with the model (1), no additional disturbances are applied and only random ones can appear, the sampling period set to ts=0.01 s in step 1, and the reference trajectories will be displayed as follows along with the system responses. this description of the dynamic regimes is also actually related to step 4, where the evaluation of the objective functions is done. the parameters of the pd learning functions are obtained separately for the three siso control loops. each of them is calculated using sma after jmax = 10 iterations, set in step 2. the parameters of the three discrete-time ipi controllers with takagi-sugeno-kang pd fuzzy terms are set in step 3 using the information given in [52] leading to the parameters of the fuzzy controller for crane position control, where the subscripts are dropped out to simplify the notations α = 0.0015, k1 = −0.85, k2 = 0.53, be1 = 0.7, b∆e = 0.2635, χ1 = 0.4, χ2 = 1.2, χ3 = 1.7, (15) and the parameters of the fuzzy controller for payload position control α = 0.0015, k1 = −0.72, k2 = 0.4, be1 = 0.7, b∆e = 0.3111, χ1 = 1, χ2 = 0.6, χ3 = 1.4. (16) https://doi.org/10.15837/ijccc.2022.1.4623 9 the parameters of sma were set in step 4 of the design approach using the recommendations given in [53], i.e. z = 0.03 and � = 0.001. the number of agents was set, as in [54] and [55], to n = 20. the search domains were set to dρ(c) = {kp(c)|0.5 ≤ kp(c) ≤ 2}×{kd(c)|2 ≤ kd(c) ≤ 4}, dρ(a) = {kp(a)|0.5 ≤ kp(a) ≤ 2}×{kd(a)|0.5 ≤ kd(a) ≤ 2}, dρ(p) = {kp(p)|0.1 ≤ kp(p) ≤ 2}×{kd(c)|0.5 ≤ kd(p) ≤ 2}. (17) the initial values of the parameters of the three pd learning rules are (listed in averaged values after ten separate runs of sma) kp(c)(0) = 1, kd(c)(0) = 3, kp(a)(0) = 1, kd(a)(0) = 0.6, kp(p)(0) = 0.1, kd(p)(0) = 1.2. (18) the parameters of the three pd learning rules obtained after the application of the design approach are (also given in averaged values after ten separate runs of sma) k∗ p(c) = kp(c)(10) = 1, k ∗ d(c) = kd(c)(10) = 2.45, k ∗ p(a) = kp(a)(10) = 1.1, k∗ d(a) = kd(a)(10) = 0.9, k ∗ p(p) = kp(p)(10) = 1, k ∗ d(p) = kd(p)(10) = 1.5. (19) the evolutions of the reference inputs, controlled outputs and control signals versus time with the initial and the final ilc algorithms are presented in figure 4 for cart position control, figure 5 for arm angular position control and figure 6 for payload position control. the subscripts 1, 2 and 3 are inserted to represent the cart position control, arm angular position control and payload position control, respectively. the initial responses are represented with blue color and the final ones with red color. for a better view, two enlarged intervals of the plots are included. figure 4: reference input, controlled output and control signal, initial and after ten iterations of ilc algorithm, for cart position control. https://doi.org/10.15837/ijccc.2022.1.4623 10 figure 5: reference input, controlled output and control signal, initial and after ten iterations of ilc algorithm, for cart position control. figure 6: reference input, controlled output and control signal, initial and after ten iterations of ilc algorithm, for cart position control. https://doi.org/10.15837/ijccc.2022.1.4623 11 the effects of interactions in the mimo process control are reflected in the results presented in figures 5 to 7. the reference input modifications are carried out at different time moments for the three control loops. equations (1) and (2) also indicate that the first two siso control loops are decoupled, and only effects on the third control loop are of interest. the results presented in figures 5 to 7 clearly show the benefits and performance improvement of ilc and the pd learning function specific to this ilc structure. the large oscillations of the first two control signals are caused by the specific structure of the controlled processes of these two siso control systems; these sub-systems are series connection of integrators and first order systems with small time constants, which are difficult to stabilize and/or control. this performance improvement would be certainly different for control systems dedicated to other challenging processes. such processes include medical applications [78], [79], [80], transportation systems [81], [82], [83], robotics [84], [85], [86], [87], and fault detection [5], [88]. however, the only constraint imposed to these control systems is that they should carry out repetitive tasks. this constraint is normal because learning and optimization are involved in the ilc structures considered in this paper. other challenging control solutions worth to be applied are those based on evolving fuzzy systems [89], [90], [91], and tensor product–based (tp–based) model transformation to position control of tower crane system. the main purpose of tp–based model transformation is to map a given linear parameter varying (lpv) or quasi–linear parameter varying (qlpv) state–space model onto a tp model made of linear time invariant (lti) systems using the higher order singular value decomposition. the tp–based model transformation technique was successfully applied recently to tower crane system modeling [92], pendulum cart system modeling [93], [94], black box system modeling [95], induction machine modeling [96], and white noise process modeling [97]. this transformation technique was also used in the tp–based controller design; tp controllers were designed for a big number of processes including more recent ones such as lotka–volterra fractional order model [98] and aeroelastic systems [99], but they could be applied to other systems as, for instance, networked control systems [100]. 5 conclusion this paper suggested an approach to the performance improvement of low-cost fuzzy controllers for crane systems in the framework of ilc by the optimal tuning of the pd learning rule using the metaheuristic sma. three siso control structures, each with one first order discrete-time ipi controller with takagi-sugeno-kang pd fuzzy term, were considered separately to control this representative mimo nonlinear process. the formulation of the optimization problem in the iteration domain and the experimental evaluation of the values of the objective functions expressed as sums of squared control errors justify to assign the data-driven model-free control feature to this approach. this is important as there is no need for much information on the controlled process and the initial controller design and tuning. the initial controller design and tuning can be done for simple linear controllers, which are next mapped onto the fuzzy controllers in terms of the modal equivalence principle. as specified in section 1, mfc is considered, according to [39] and next also highlighted in [30], as a new and efficient tool for machine learning. in the context of the optimal tuning of the pd learning functions or learning rules carried out in this paper, one conclusion of this paper is that ilc is also a new and efficient tool for machine learning. this can be emphasized because of the controller part that in this paper is a fuzzy controller or the process part as well, where evolving fuzzy models can be employed [80], [89], [90], [91], and the controllers designed and tuned accordingly. neural networks [46], [50], [61], [69], [79], [83] can be employed at the process and/or controller level as well, giving more weight to the learning feature, but in a close connection to optimization. in addition, fuzzy logic can be included in ilc algorithms as suggested in authors’ past papers [9], [40], [41], [42], [43], [44] and [45]. future research will be focused on the inclusion of fuzzy models and neural networks in the process and controller subsystems of the control system structures along with considering different learning https://doi.org/10.15837/ijccc.2022.1.4623 12 functions or rules. other optimization algorithms will be applied aiming a reduced number of evaluations of the objective functions. funding this research was funded by grants of the romanian ministry of education and research, cncsuefiscdi, project numbers pn-iii-p4-id-pce-2020-0269, pn-iii-p1-1.1-te-2019-1117, and pn-iiip1-1.1-pd-2019-0637, within pncdi iii. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] dzitac, i.; 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(2021). an improving ncs stabilization using a predictive pulsed control law, international journal of computers communications & control 15(6), 4052, 2021. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: precup, r.-e.; roman, r.-c.; hedrea, e.-l.; bojan-dragos, c.-a.; damian, m.-m.; nedelcea, m.l. (2022). performance improvement of low-cost iterative learning-based fuzzy control systems for tower crane systems, international journal of computers communications & control, 17(1), 4623, 2022. https://doi.org/10.15837/ijccc.2022.1.4623 introduction process model control system structure and design approach experimental results conclusion international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(6), 691-709, december 2019. a cluster–based approach for minimizing energy consumption by reducing travel time of mobile element in wsn j. siva prashanth, s.v. nandury j. siva prashanth* academy of scientific and innovative res., csir-iict campus, csir-indian institute of chemical technology, hyderabad, india *corresponding author: sivaprashanth.j@gmail.com satyanarayana v. nandury academy of scientific and innovative res., csir-iict campus, csir-indian institute of chemical technology, hyderabad, india. nvs@iict.res.in abstract: envoy node identification (eni) and halting location identifier (hli) algorithms have been developed to reduce the travel time of mobile element (me) by determining optimal path(op) in wireless sensor networks. data generated by cluster members will be aggregated at the cluster head (ch) identified by eni for onward transmission to the me and it likewise decides an ideal path for me by interfacing all ch/envoy nodes (en). in order to reduce the tour length (tl) further hli determines finest number of halting locations that cover all ens by taking transmission range of ch/ens into consideration. impact of eni and hli on energy consumption and travel time of me have been examined through simulations. keywords: envoy nodes, halting locations, travel time, latency. 1 introduction wireless sensor networks(wsn) are established by thousands of sensors distributed spatially over a specified geographical region for monitoring the changes in the environment. wsns have wide range of applications like, health care monitoring, earth sciences, environmental sciences, military applications to detect intrusion of enemies disaster,tracking vehicular movement, etc. sensor nodes(sns) are typically small electronic devices operated by battery with finite amount of energy. each sn senses the environment and gathers the information about physical changes in the environment.data generated by sn either periodically or continuously have to be submitted to the centralized processing unit called stationary base station (bs). contingent upon nature of the application, information detected by wsns send either straightforwardly to a stationary bs or mobile element (me). usually bs location is fixed. in case if bs is located within the transmission range of a sn, then it will directly submit data to the bs. otherwise, sn will send the data to its nearest neighbor, which will try to identify the shortest path and relay the data to the bs. there are few challenges associated with this traditional mechanism. sometimes bs may be located far away from the area of interest, in such scenario sns will not be able to communicate with the bs, apart from this, in large wsn considerable amount of energy will be spent for transmitting and receiving messages, this leads to high energy consumption and increased network traffic. copyright ©2019 cc by-nc 692 j. siva prashanth, s.v. nandury sns nearest to bs have to allocate time slots for traffic coming from neighbor sns, this result in traffic congestion. these nodes devour compared to other nodes. such situation may prompt wsn’s breakdown, as no information would reach bs if these nodes channel out the whole of their battery life. the above mentioned challenges can be addressed by employing a me that travels along wsn space to gather data from sns. the data gathered by the me, is then submitted to bs [19]. how ever, it increases network latency. latency can be minimized by identifying envoy nodes (en), where en is a node that me must visit, in order to collect data. me need not to visit exact geographical location of a en, instead it can collect the data from any location within the transmission range of en. inspired by this me’s tour can be shortened further by identifying a set of locations in such a way that, me will be able to collect data from all ens by visiting these locations, these locations are referred to as halting locations (hl). in literature various approaches makes use of heuristic approaches like travelling salesmen problem (tsp) and genetic algorithms (ga) for defining me’s optimal tour [7,12,20]. in our previous work we have adopted a clustering method to minimize the energy consumption and decrease latency where every sn in a cluster is inside the transmission range of its cluster head (ch) [22]. as an extension to the previous work we try to investigate the effect of me’s tl on energy consumption. in this approach, envoy node identification (eni) algorithm has been developed to identify the number of ens to be visited by the me. me’s tour consists of visit to all ens. optimal path(op) that covers all ens in wsn is identified using traditional tsp solver. tour that has been given by tsp solver can further be reduced by visiting the hls identified by halting location identifier (hli) algorithm. adequacy of eni and hli have been evaluated in terms of optimizing the energy consumption and travel time of me through simulation. an examination of simulation result shows that eni, hli outflank the com and css. overview of research works related to clustering, energy issues and tl-optimization of me discussed in section 2. section 3 presents an overview of proposed optimization approach. eni and hli algorithms described in section 4. section 5 showcases performance results through simulations carried out. 2 related work 2.1 minimizing the tl of mobile element the problem of reducing tl of me is addressed in [35]. first it carries out the cluster formation process as per [6], then the deployment region is partitioned into virtual grids and intersection points are identified. chs have to choose one of these intersection points, so that me can collect data from chs by visiting these intersection points. order of nodes to be visited is identified by a tsp solver. a convex hull based method for reducing latency by considering amount of data to be collected is proposed in [36]. in this method a convex hull that covers all nodes is constructed. this process is applied progressively by eliminating nodes on previous layer of convex hull. this process is repeated until no nodes are left for further processing. all possible permutations for visiting nodes in layer n and n-1are explored in order to identify an op. this process is applied progressively until all the layers are covered. optimal tl of me is obtained by adjusting speed of me. two different approaches are presented in [32, 34] for optimizing tl. the first approach enables me to move unreservedly over the network deployment area, tl optimization depends on the requirement that information from sns should reach bs in a given deadline. second a cluster–based approach for minimizing energy consumption by reducing travel time of mobile element in wsn 693 approach pre-characterizes me’s path and tl is optimization will be achieved by identifying hls along the pre-characterized path. optimal disc coverage is a technique which gives centre and radius of the optimal disc that covers given set of points. in [11], an attempt made to reduce me’s tl by using com and css algorithms. an op connecting all nodes has been identified by employing a tsp. in this approach the location to be visited by me is referred as collection site. optimal disc coverage technique [31] was employed by com for identifying collection sites that must be included in me’s. location of two nodes will be given as input to decisional welzl’s algorithm, if welzl’s radius is less than or equal to transmission range then the two nodes will be replaced by welzl’s centre in me’s tour. css identifies the common intersection areas and establishes rendezvous points(rp) in it. the optimality of tl in these methodologies is restricted by the tsp solver. eni and hli made an attempt to overcome this limitation. 2.2 clustering in wsn, when clustering approaches are adopted,members of cluster submit the data to its chs and chs submits the data to the bs , in such networks, the chs near the bs usually tend to become the conduit point for bulk data transmissions between the bs and far away clusters. as a consequence, the energy of chs nearer to the bs may drain out quickly. to maximize the lifetime of chs nearer to bs variable size clustering has been proposed in [17,19]. in both the approaches size of the clusters is defined by the distance of ch from bs. since clusters nearer to bs will be of smaller size, the intra cluster communication and energy consumption for cluster routines is minimized so that the residual energy of ch can be utilized for relaying the incoming data to the bs. depending on the network topology it may happen that, some clusters accommodate more number of members. when a cluster has more number of cluster members, it leads to high energy consumption at cluster head and cluster head may drain out its energy very early. a clustering algorithm for addressing this problem is given in [27], size of the cluster and degree of a node are the two parameters that played key role in minimizing energy consumption. a clustering algorithm based on common intersection area of sns is proposed in [7], it groups the nodes having common intersection are in a cluster. order of to be visited by me is obtained by using ga. a genetic algorithm based cluster was proposed for reducing latency in [12]. initially way points to be visited by me are identified, then using weighing scheme nodes select one of the waypoint. these way points are optimized using genetic algorithms for minimizing me’s tour. an approach for determining optimized path from source to destination was proposed in [8]. as size of the cluster increases ch has to relay more number of packets and it leads to delayed transmission of data. this problem is address by exploiting nodes in common intersection area of clusters [9]. in this approach when waiting time of a packet at ch is more than the predefined threshold value, then node submits its data to neighbor ch through nodes in common intersection area. this phenomenon reduces packet loss and minimizes latency. a protocol for minimizing the energy consumption by making use of beacon signal of me is proposed in [23]. in this protocol a sn senses the environment periodically and goes to the sleep state, when a sn receive a long range beacon signal from me, it enters into active state. node transmits data to me when it receives short range beacon signal and enters into sleep state. apart from latency and energy consumption fault tolerance is another issue associated with wsn. even if the node detects fault and decides to reject the data, it takes considerable amount of time and energy. this problem is addressed by introducing composite interference model 694 j. siva prashanth, s.v. nandury (cim) in [4]. to determine interference fault-free transmission (ifft) schedule for all active links, a new approach time division multiple frequency (tdmf) is defined. based on cim and tdmf an algorithm for maximizing network throughput, composite interference-based transmission scheduling (cits) is developed. 2.3 energy issues leach is one of the popular algorithm which has addressed the energy issues by adopting clustering mechanism [13]. an energy efficient optimal chain protocol (eeoc) is proposed in [1]. eeoc has shown better performance than best known approaches in terms of first node die and last node alive. for extending the network lifetime and reducing data redundancy, a novel approach which is combination of chain based data transmission and clustering is proposed in [25]. a new clustering approach for achieving energy efficiency and reliability with the help of backup nodes is proposed in [26]. an attempt to achieve energy efficiency in delay sensitive applications is made in [24]. raja et.al. tried to minimize the energy consumption by introducing multicasting mechanism and unequal size clustering [29]. energy efficient and reliable routing is achieved using neural networks in [30]. adoptive sectoring mechanism is used to prolong the network lifetime and reliable routing in [5]. a data collection strategy based on software defined network for enhancing network lifetime is proposed in [18]. a hybrid model of tree and cluster based approach is adopted for addressing hotspot issues, traffic congestion and energy issues in [15,16,21]. shahinaz m. al-tabbakh adopted a tree based heuristic aggregation mechanism to enhance the network lifetime. a protocol for network lifetime improvisation is proposed in [10], it introduced a collection tree protocol to maintain dynamic routing table along with link state quality indicator. considerable amount of energy will be spent in wsn for data aggregation, this problem is addressed by using compressed sensing in [3]. 3 clustering approach to identify envoy nodes assuming a set of sns s=s0, s1, ..., sn have been deployed in an area where a set of phenomena are being monitored. duty of me is to collect data from all sns in s. it minimizes multi hop communication and energy consumption, but it leads to increase in latency as me needs to visit all nodes. latency can be reduced by identifying a set of envoy nodes sen=en0,en1... enn such that the sensed data of all nodes in the deployment region can be collected by me by visiting all envoy nodes in sen.the goal is to minimize the number of envoy nodes as many as possible. in this paper we propose cluster mechanism to further reduce the number of envoy nodes. 3.1 cluster head selection geographical region that has to be monitored by wsn is divided into virtual square grids of side equivalent to 2 * tr. a cluster–based approach for minimizing energy consumption by reducing travel time of mobile element in wsn 695 partitioning process will be started from centre of the optimal disc given by welzl’s algorithm [31]. making use of the following equation, assuming d0,d1 as the distance from node s0,s1 ,... to centre ceni of each grid gi select a cluster head chi. chi = sj |dj is min {d0, d1...} (1) from (eq.1) the node sj that is nearer to ceni will be chi for the grid gi. since length of each side of grid is 2*tr, locating ch nearby centre of the grid guaranties single hop communication with maximum number of nodes within the grid, also maximizes node coverage within the grid. 3.2 cluster formation after obtaining set of cluster heads clh=ch0,ch1, ..., the nodes that are within one hop distance from a cluster head chi are made to join the cluster cli. there is possibility that some chs are closely located, it may lead to the situation that a ch becomes the member of other cluster. in order to avoid such situation cluster heads have to be excluded from cluster formation process as mentioned in (eq. 2). s′ = s −clh (2) a node sj in s′ joins the cluster cli if it belongs to the grid gi and within the transmission range of ch chi. sj�cli∀sj�gi if dist(sj,chi) < tr (3) ch collects information from fellow cluster members and aggregates, this aggregated information will be transmitted to me. each node in cli submits it’s data to chi. a node can join only one cluster. as soon as node joins a cluster it will be excluded from further clustering process . however, there is a possibility that few nodes may not fall into any cluster because of the reason that they are not within the transmission range of any of the cluster heads. 3.3 envoy node identification a node sj in s is either a cluster member or a ch or a left over node. if sj joins one of the clusters then it must belong to the set cl0, cl1 ... u clm it’s data is represented by the cluster head chi and chi belongs to cluster head set clh. the edge or corner sns which have not joined any of clusters form a set of left over nodes l are identified as per the equation given below: l = s −{clh u {cl0 u cl1... clm}} (4) in addition to the cluster heads, set of envoy nodes sen must also include the leftover nodes for me’s tour in order to ensure all nodes are visited. sen = clh u l (5) me can collect data from cluster members of cli by visiting it’s cluster head chi i.e. me covers all nodes in li by visiting chi. this altogether diminishes the quantity of ens to be visited by the me prompting significant reduction in the tl. an optimised path for me’s tour covering all ens in sen is determined by using the best known tsp solver lin-kernighan [14]. 696 j. siva prashanth, s.v. nandury algorithm 1 envey node identification eni input: 1: • deployment area wsn = s* s • set of sensor nodes s= { s0, s1, ..., sn, } where si represents (xi, yi) the coordinate of sensor • set of cluster heads chs ← { ch0, ch1, ..., chm } • transmission range tr output: 2: • clh set of cluster heads • sen set of envoy nodes start: 3: (c,r) ← welzl(n,sns) . determine center c and radius r of welzls circle 4: clh ←{φ} 5: partition wsn area into square grids gi of side 2 * tr with c as the centre to the extent possible 6: determine the grid center of each grid in gp where gp ← {g0, g1, ..., gm } 7: 8: for i = 0 to m do . where m i the number of grids 9: identify sj closest to each gi in welzls circle 10: clh ← clh ∪sj 11: end for 12: s′ ← clh ∪sj 13: for j = 0 to n do 14: cli ←{φ} 15: for i = 0 to n do 16: if dist(si,chj) ≤ tr then 17: clj ← clj ∪si 18: end ifend if 19: end for 20: s′ ← s′ −clj 21: end for 22: end for 23: l ← clh ∪s′ 24: sen ← lin kernighan(l) a cluster–based approach for minimizing energy consumption by reducing travel time of mobile element in wsn 697 theorem 1. set of envoy nodes sen=en0,en1, ..., enk covers all nodes deployed in the network. proof: let s be a set of all nodes in the network. suppose sj be a node in the network i.e sj � s case i: sj is a cluster head. since sj is a ch it will included in clh, from (5) sen covers all chs, it implies sj is covered by sen. case ii: sj is a member of cluster cli in the network. since sj � cli, it is represented by its cluster head chi, and from (6) all cluster heads are covered by sen, it implies that node sj is covered by sen. case iii: sj is a left over node in the network. since sj is a left over node it must belong to l, from (5)sen covers all nodes in l, it implies node sj is covered by sen. let sj does not belong to s. if sj 6∈ s it implies it does not belong to the network. hence it need not be covered by sen. from above it can be concluded that sen covers all nodes in the network. 2 4 halting location based path optimization to further shorten the me’s path, halting locations are identified wherever possible, for each envoy nodes in sen. the hli optimizes the path determined by eni by employing the hlit technique introduced in this paper. tl of me can be minimized by locating hls along the tour given by tsp solver. few ens may have common intersection area or region, then me can collect data by identifying a hl in the overlapping region. set of halting locations shl= hl0,hl1..hlk along me’s tour that covers all envoy nodes in sen are established by hli using hlit technique. each hli covers one or more envoy nodes in sen. for identifying the hls, we developed hl identification technique (hlit), it identifies an optimal hls to substitute some of the ens along me’s path. hlit exploits the fact that the ens can transmit their information to me even if they are at a distance tr from the me. hlit enables me to collect the data of en form a distance of tr from the en where tr is transmission range of en .hlit identifies optimal hls covering all ens. j=k∑ j=1 |hlj| = i=m∑ i=1 |eni| = |s| (6) |hlj| denotes number of sns covered by halting location hlj. |eni| denotes number of sns covered by eni |s| denotes total number of sns. m and k represents total number of ens and hls respectively. fig.1. is an illustrative example of our approach. our objective is to achieve (6) with optimal values of k and m for obtaining optimal tl of me. contingent upon the separations between the ens, hlit decides hl dependent on three criteria depicted underneath: criterion 1: overlapped unit disc regions of envoy nodes. let us consider a scenario where the node deployment and me’s tour is as shown in fig.1.(a).in fig.1.(b) it can be observed that distance between en1 , en2 is < 2*tr i.e. there is an overlap region between en1 and en2 , any location within the overlap region will be at a distance less than tr from both the ens, hence me can collect data from both ens by visiting any location within the overlap region. 698 j. siva prashanth, s.v. nandury figure 1: illustrative example of halting location identification technique.(a),(c),(e ) actual tour of me (b) hlit based op for criterion 1. (d) optimized hlit based op for criterion 2. (f) hlit based op for criterion 3 figure 2: (a) node deployment (b) tour given by tsp solver (c) tour identified by hli a cluster–based approach for minimizing energy consumption by reducing travel time of mobile element in wsn 699 hlit determines a hl in the overlapping region of en1 and en2 , such that the distances from hl and either of two points is ≤ tr. hlit modifies shl as per the equation given below: shl = sen −{eni+2,eni+1}∪{hli+1 if 3 eni+1}|dist(eni+2,eni+1) ≤ 2 ∗tr (7) halting location hli+1 in (7) is a location in the common intersection area nearest to me’s path from which me will be able to receive data from both eni+1, eni+2. the ens eni+1, eni+2 are replaced by hli+1 in shl since me covers both eni+1, eni+2 by visiting hli+1. criterion 2: eni+1 is at a distance ≤ tr from the path eni to eni+2 let us consider other node deployment scenario as shown in fig 1.(b). while me is moving from its starting point to en2, the path is intersecting with the unit disc region of en1, that means distance from en1 to me in the intersecting region is ≤ tr , hence me can collect data form that en while it is moving. shl = sen −{eni+1}if∃{eni+1 |pre_dist(eni,eni+1,eni+2) ≤ tr (8) in fig.1(d) en1 is at a distance ≤ tr from the path defined from starting point to en2, hence so me can receive data from en1 when it is moving along the path from starting point to en2. in this case neither me has to visit the exact location of en1 nor there is a need of identifying hl for en1 since me can collect the data while it is moving, hence hlit modifies hli based on this criterion as per (8). criterion 3: eni+1 is at a distance > tr from the path eni to eni+2 let us assume the node deployment scenario as per fig.1.(e). in this case ens neither have overlap region nor it is covered by me’s path as discussed in criterion 2, then a hl within the unit disc region of en has to be identified for covering each en. following equation determines hl for this scenario: shl = sen −eni+1 ∪hli+1 if ∃ eni+1|per_dist(en1,eni+1,eni+2) > tr (9) perpendicular distance is the minimum distance from a point to the line, inspired by this as per (9) perpendicular distance from eni+1 to the path from eni to eni+2 will be calculated, if it is more than the transmission range then hlit determines an hl hli+1 at a distance tr from eni+1 along the direction perpendicular to path from eni to eni+2 . fig.1(f) is an illustrative example of this approach. while me is moving from its starting point, perpendicular distance from en1 to the path from starting point to en2 is more than the transmission range, hence hli based on hlit identifies a halting location hl1 as shown in fig.1(e), fig.2 shows the impact of our approach on tl, for the node deployment given in fig.2(a) me’s tour is identified by a tsp solver, fig.2(b) represents me’s tour. finally, after applying hli the tour of me will be as shown in fig.2(c). algorithm 3 represents hlit based hli algorithm to identify hls. lemma 2. if unit disc regions of each envoy nodes in sen={eni,eni+1..enl} with transmission range tr have common intersection area then halting location hlp located in the intersection area covers all the envoy nodes in sen. 700 j. siva prashanth, s.v. nandury algorithm 2 envey node identification eni input: 1: • set of envoy nodes sen = { en1, en2, ..., enn } • transmission range tr output: 2: • set of halting locations start: 3: q=0 4: for i = 0 to n− 2 do 5: k ← i+1 6: if eni and eni+1 have common intersection area then 7: while envoy nodes eni, ..., enk have common intersection are do 8: k ← k+1 9: end while 10: find envoy nodes hlq within the common intersection area of { eni, ..., enk−1 } which is nearer to path from eni to enk 11: sen ← sen{eni, ..., enk − 1 } ∪ hlq 12: else if t thenhe path from eni to enk + 1 covers enk 13: while the path from eni to enk+1 covers set of envoy nodes { eni + 1, ..., enk } do 14: k ← k+1 15: end while 16: sen ← sen { eni + 1, ..., enk − 1 } 17: else 18: hlq ← point-to-point eni + 1, eni + 2, ..., tr 19: sen ← sen eni+1 ∪ hli+1 20: end if 21: q ← q+1 22: end for 23: shl ← lin kernighan(sen) algorithm 3 point_at_dist (p1, p2, tr) input: 1: • two points p1 and p2 | pi ← (xi, yi) • transmission range tr output: 2: • point p ar a distance tr from p1 along the line (p1, p2) start: 3: d= dist (p1, p2) 4: x = x1 + (x2 x1 ) * ( tr / d ) 5: y = y1 + ( y2 y1 ) * ( tr /d) 6: p= ( x,y) a cluster–based approach for minimizing energy consumption by reducing travel time of mobile element in wsn 701 proof: since hlp is in common intersection area, distance from hlp to any envoy nodes in sen is less than or equal to tr. therefore hlp covers all the ens in sen and hence envoy nodes in sen can be replaced by hlpin sen. 2 lemma 3. the path from eni to eni+2 covers envoy nodes eni+1 if the distance from envoy nodes to the path is less than or equal to transmission range tr. proof: from (7) if distance from a envoy nodes eni+1 to the path from eni to eni+2 is less than or equal to tr mobile element can collect data from the eni+1 in its journey. therefore path from eni to eni+2 covers eni+1 and hence eni+1 can be eliminated from sen. 2 lemma 4. envoy node eni+1 is covered by halting location hlp if it is neither having common intersection area with any other envoy nodes nor it is covered by the path from eni to eni+2 proof: from (10) hli establishes a halting location hlp at a distance of tr from eni+1 in a direction perpendicular to the path from eni to eni+2 if eni+1 is neither having common intersection area with any other envoy nodes nor it is covered by the path from eni to eni+2. distance from hlp to eni+1 is equal to tr. therefore hlp covers eni+1. 2 theorem 5. set of halting locations shl={ hl0,hl1..hlm } covers all the nodes in s={s0, s1, ..., sn } deployed across the network. proof: let us assume that eni be a envoy node in sen. case i: unit disc region of eni with transmission range tr as radius has common intersection area with a set of envoy nodes sen. from lemma 2 halting location hlp established by hli covers all ens in sen. it implies sen covers eni. case ii: distance from eni to a point on the path from eni−1 to eni+1 is less than tr. from lemma 3 path from eni−1 to eni+1 covers eni. it implies sen covers eni. case iii: eni is any en in sen, other than the one stated in case i and case ii. from lemma 4 hli establishes a halting location hlp within transmission range of eni. it implies sen covers eni.in case i, ii and iii it has proven that shl covers all ens in sen and from theorem 1 sen covers all nodes in s, hence it can be concluded that shl covers all nodes in s. 2 lemma 6. number of elements in shl is less than or equal to the number elements in sen. proof: let m be the total number of envoy nodes in sen. let sen represents a set of envoy nodes having common intersection area and k be the total number of envoy nodes in sen. from lemma 2 halting location hlp established by hli covers all ens in sen, so ens in sen can be replaced by in shl, total number of elements in sen are m−k . if k = 0 total number of elements in shl are m. therefore it can be concluded that, number of elements in shl is less than or equal to the number elements in sen. 2 702 j. siva prashanth, s.v. nandury theorem 7. tl of shl is less than the tl of sen. proof: let us assume t be the tl to cover all the ens in sen. case i: let us assume that few of ens in sen have common intersection area. from lemma 2 halting locations in shl established by hli covers all ens in sen. let us assume t1 be the tl to cover all the hls in shl. number of hls in shl is lesser than the number of ens, hence t1 < t. it implies tl of shl is less than tour length of sen. reduced tl contributes to minimize the travel time of me. case ii: let k number of ens are not having common intersection area with any of the ens in sen. from lemma 4 hli establishes halting location hlp at a distance equal to tr, the resultant tl will be less than or equal to t-k ∗ tr, it implies tl of shl is less than the tl of sen. 2 5 simulations and performance analysis simulations are carried out to examine the efficiency of proposed algorithms in terms of identifying ens, minimizing tl and travel time of me, minimizing latency and energy consumption by varying number of nodes, transmission ranges . this section details about the simulation model and analysis on efficiency of proposed approach. 5.1 simulation model wsn deployment area of size 500x500 m2 have considered for implementing and evaluating the performance of the proposed algorithms eni and hli. homogeneous wsn environment is adopted, where all sns nodes are equipped with equal transmission range. in order to evaluate the performance of eni and hli for higher node density, number of sns in deployment region varied from 1000 to 5000. identification of ens and hls have been carried out by eni and hli respectively. it is assumed that me requires halting time ht to collect data from ens. in the simulations value of ht is considered as 2 sec. for calculating the energy consumption in the network values of two terms eelec and εamp are taken as 50nj /bit and 100pj/ bit/ m2, where eelec is energy spent for running the electric circuitry, and εamp is energy spent for amplification [13], etx, erx represents transmission and receiving energies respectively. tsp solver lin-kernighan algorithm is employed for defining me’s optimal tour to cover given set of ens or hls. it is assumed that me moves with a uniform velocity of 10m/sec. results represented in each graph is mean of 50 simulations table 1: simulation parameters parameter value field size 500x500 m2 node deployment 10005000 transmission range tr 20m, 30m, 40m speed of me 10 m/sec halting time ht 2 sec eelec 50 nj/bits εamp 100 pj/bit/m2 a cluster–based approach for minimizing energy consumption by reducing travel time of mobile element in wsn 703 5.2 optimizing halting locations in order to test the performance of eni in terms of number of locations to be visited by me, a new term ratio of halting locations to the total number of nodes deployed (rhltn) is introduced. rhltn is calculated as follows: rhltn = 100 ∗ numberofhaltinglocations totalnumberofnodesdeployed (10) value of rhltn is inversely proportional to the efficiency of an algorithm, low value of rhltn indicates that me can collect the data of all sns by visiting very few locations, it means algorithm efficiency is high. we have calculated the rhltn value of hli and css for different transmission ranges (20, 30 and 40) and total number of nodes varying from 1000 to 5000. the graph is plotted by taking number of nodes on x-axis and rhltn value on y-axis. in graphs hli_20, hli_30 and hli_40 indicates the rhltn values of hli for transmission ranges 20, 30 and 40 respectively. from the fig.3 it can be predicted that, irrespective of transmission ranges hli outperforms css. when total number of nodes is 5000, rhltn value of hli is 20. it means me needs to visit only 20 percent of the nodes in order to complete its tour for collecting the data, whereas for css it has to visit 45-50 percent. this phenomenon will have adverse effect on travel time of me and energy consumption in the network. figure 3: ratio of halting locations to the total number of nodes minimizing travel time tl refers to the total distance that has to be covered by me for visiting a set of en or hls.increase in number of ens result in increased tl of me. the tl has an immediate impact on the latency engaged with overhauling a query or a demand from a given sn. in order to obtain the op covering set of ens identified by eni a tsp solver lin-kernighan algorithm [14] is employed. after obtaining the order of nodes to be visited, hli establishes hls using hlit technique for minimizing the tl. a hl represents one or more ens. travel time of me depends on number of ens to be visited, speed of it and time taken for transferring data (halting time of me) from en to me . assuming di,i+1 is the distance between halting locations hli and hli+1 ; vi,i+1 is the speed of me while moving from hli to hli+1; and hti is the halting time of me at hli, the 704 j. siva prashanth, s.v. nandury amount of time t required by me to collect data from all envoy nodes is calculated as follows: t = n∑ i=1 di,i+1 vi,i+1 + hti (11) if me is moving with uniform speed v with a constant halting time ht then (11) becomes t = ∑n i=1 di,i+1 v + n∗ht (12) in our simulations total travelling time of me is calculated as per (12). it is assumed that me is moving with a constant speed of 10 m/sec and halting time at each en is 2 sec. as me’s speed and halting time are fixed travelling time depends on number of hls to be visited. graphs are plotted between total number of nodes deployed and travelling time as shown in the fig.4, in order to study the efficiency of hli in terms of travelling time of me. with the help of simulations it is proven that criterions considered in hlit resulted in reduced travel time of me. from the graph it can be concluded that latency of the network can be reduced considerably with hli. travelling time of me is very low for different transmission ranges. simulation results prove that hli is more suitable for applications in which data is time critical i.e. data has to reach the base station with in the stipulated time interval otherwise the data becomes obsolete. it can be concluded that hlit technique adopted for identifying hls in hli has shown its impact in reducing the travel time of me considerably irrespective of transmission ranges and number of nodes deployed. eni and hli offer better qos compared to com and css in terms of travel time and energy consumption. figure 4: travel time . 5.3 minimizing energy consumption every sn is equipped with finite amount of energy, this energy will be utilized for monitoring the targeted area, transmission and receiving of messages etc. for calculating energy consumption in the network the following equation is borrowed from [28]. etx = eelec ∗k + εamp ∗k ∗d2 (13) a cluster–based approach for minimizing energy consumption by reducing travel time of mobile element in wsn 705 erx = eelec ∗k (14) etotal = etx + erx (15) where (13) gives the energy consumption occurred etx for transmitting k number of bits over a distance d. energy consumption for receiving k number of bits erx is given by (14). total energy consumption etotal is given by (15). in the literature it is observed that energy spent to hold the data, till the data is transferred to me is not taken into consideration. we have tried to identify the energy consumption at each ens for transferring the data to me. it is assumed that amount of time taken by me to reach halting location i from halting location 1 is holding time tihold is the amount of time the envoy nodes i has to hold the data. from (11) tihold can be calculated as follows: tihold = n∑ i=1 ∑n i=1 di,i+1 v + i∗ht (16) here tihold is the time taken by me to reach hl hli since from the starting point of it’s tour, i represents the index of hl in shl, dj,j−1 is the distance from hlj to hlj−1.v is the speed of me. by taking holding time into consideration (13) is modified as follows: ei = 0.1 ∗eelec ∗k ∗tihold + �amp ∗k ∗d2i (17) total energy consumption in the network is calculated as follow: etotal = k=n∑ k=1 ek (18) figure 5: ratio of halting locations to the total number of nodes. through the simulations we have tried to investigate the energy consumption of the network after applying hli and compared it with css. in fig.5, initially there is not much difference whether it is hli or css, but when total number of nodes is 5000, there is huge difference. we tried to diagnose the reason for this phenomenon, it is observed that travel time of me is 706 j. siva prashanth, s.v. nandury figure 6: ratio of idle time energy consumption to the total energy consumption . influencing the energy consumption in the network, in order to substantiate this conclusion we have analyzed (17), in (17) all parameters are constants except tihold . hence energy consumption of a node eik α tihold . increased travel time of me results in high energy consumption in a node which shortens the network lifetime. in order to investigate the impact of waiting time on energy consumption of a node, we have introduced a term ratio of idle time energy consumption to the total energy consumption (%): riete = 100 ∗ idletimeenergyconsumption totalenergyconsumption (19) from the above equation riete α idele time energy it indicates that higher idle time energy consumption leads to the higher value of riete. the algorithm with lower riete value is more efficient. fig.6 indicates that more than the 60% of the energy spent by a node due to idle time. from the graphs it is clear that tihold is the key factor influencing the energy consumption. even for higher node densities energy consumption is low with hli, the reason for this is thold for hli 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(2013). a convex hull based optimization to reduce the data delivery latency of the mobile elements in wireless sensor networks, 2013 ieee 10th international conference on high performance computing and communications & 2013 ieee international conference on embedded and ubiquitous computing, ieee, 2245–2252, 2013. international journal of computers communications & control online issn , issn-l , volume: 17, issue: 6, month: december, year: 2022 article number: 4988, https://doi.org/10.15837/ijccc.2022.6.4988 ccc publications a feature engineering and ensemble learning based approach for repeated buyers prediction m. zhang, j. lu, n. ma, t.c.e. cheng, g. hua mingyang zhang department of management science and engineering, school of economics and management beijing forestry university, china no. 35 qinghua east road, haidian district, beijing 100083, china mingyangzhang@bjfu.edu.cn jiayue lu 1. national science library, chinese academy of sciences address33 beisihuan xilu, zhongguancun, beijing 100190, china lujiayue22@mails.ucas.ac.cn 2. school of economics and management, university of chinese academy of sciences no.19a yuquan road, beijing 100049, china ning ma* department of management science and engineering, school of economics and management beijing forestry university, china no. 35 qinghua east road, haidian district, beijing 100083, china *corresponding author: maning@bjfu.edu.cn t.c. edwin cheng department of logistics and maritime studies the hong kong polytechnic university m923, li ka shing tower, hong kong special administrative region, china edwin.cheng@polyu.edu.hk guowei hua department of logistics management, school of economics and management beijing jiaotong university, china siyuan east building, beijing jiaotong university, haidian district, beijing 100044, china gwhua@bjtu.edu.cn abstract the global e-commerce market is growing at a rapid pace, but the percentage of repeat buyers is low. according to tmall, the repurchase rate is only 6.1%, while research shows that a 5% increase in the repurchase rate can lead to a 25% to 95% increase in profit. to increase the repurchase rate, merchants need to predict potential repeat buyers and convert them into repurchasers. therefore, it is necessary to predict repeat buyers. in this paper we build a prediction model of repeat https://doi.org/10.15837/ijccc.2022.6.4988 2 purchasers using tmall’s dataset. first, we build high-quality feature engineering for e-commerce scenarios by manual construction and algorithmic selection. we introduce the synthetic minority oversampling technique (smote) algorithm to solve the data imbalance problem and improve prediction performance. then we train classical classifiers including factorization machine and logistic regression, and ensemble learning classifiers including extreme gradient boosting, and light gradient boosting machine machines. finally, we construct a two-layer fusion model based on the stacking algorithm to further enhance prediction performance. the results show that through a series of innovations such as data imbalance processing, feature engineering, and fusion models, the model area under curve (auc) value is improved by 0.01161. our findings provide important implications for managing e-commerce platforms and the platform merchants. keywords: feature engineering; ensemble learning; fusion model; repeat buyer prediction. 1 introduction the rapid development of e-commerce has had a profound impact on the global economy[23]. in the us, e-commerce sales reached us$960.4 billion in 2021, accounting for 15% of total us retail spending[48]. in china, 81.6% of internet users have undertaken online shopping[49]. global e-commerce platforms have run promotions on specific dates (e.g., black friday, “6.18”), which significantly stimulate the potential purchase value of new and existing users. in 2021, the taobao “double eleven” promotion, one of china’s largest e-commerce promotions, achieved an all-time high transaction volume of 540 billion rmb. however, many buyers that participated in the promotion were one-time users, making it difficult to generate long-term benefits [50]. studies have shown that the repurchase rate for tmall is only 6.1%. the cost of acquiring new users is 5-10 times higher than the cost of maintaining old users, while a 5% increase in customer retention can increase profit by 25% to 95% [4][10]. thus ,if focusing solely on new user acquisition in the long term and ignoring low levels of repurchase rates„ it can cost the platform a great deal of money but not the desired return on investment. increasing repurchase rates is imminent. a possible method is to predict the users with the tendency to repurchase and then implement precise marketing to lead them to become repurchase users. recently, data-driven analytics have been increasingly developed to help companies analyze user behavior and optimize decision making[18][21][31][43]. however, e-commerce scenarios are complex and varied, and their data has special characteristics. the question of how to use a data-driven approach in e-commerce repurchase prediction is worth thinking about. we try to answer the following questions: how to comprehensively and accurately portray the features in e-commerce scenarios? how to efficiently predict potential repurchase users? we propose an ai-based data-driven approach to address thess problem. we use the tmall’s real user dataset from alitianchi to build the model. these data are log data generated by users in e-commerce behavior, which are implicit feedback. we carry out a series of steps to process the special characteristics of e-commerce data, introduce the synthetic minority oversampling technique (smote) algorithm to solve the data imbalance problem. we manually construct feature engineering from three perspectives: merchant, user, and merchant-user. meanwhile, we use different models for prediction, compare model performance, and choose the best approach. results show that fusion model can significantly improve the accuracy of prediction. finally, we efficiently and accurately predicted the potential target users in the dataset with a auc value of 0.68406. predicting repeat buyers can help e-commerce firms provide personalized services, such as accurate product recommendations, differentiated pricing, and demand management, to effectively improve the repurchase rate. we organize the rest of the paper as follows: in section 2 we review and summarize the related work. in section 3 we introduce the data and the data pre-processing process. in section 4 we discuss the data-driven methodology, including data imbalance processing, feature engineering, and the prediction models. in section 5 we present the experimental results and analysis. finally, in section 6, we conclude the paper, discuss the management implications of the research findings, and suggest topics for future research. https://doi.org/10.15837/ijccc.2022.6.4988 3 2 literature review in this paper, we use user behaviour data to predict whether he/she will become a repeat buyer. in this section we review and summarize related work covering two research streams, namely user behaviour research and user purchase behaviour prediction, and discuss the motivation for this study. 2.1 user behaviour research users generate massive data during using the online platform, which can be used to analyze their behavior. further, user data can be divided into. "explicit feedback" and "implicit feedback"[26]. explicit feedback is a direct and quantifiable expression of users’ preferences, while implicit feedback records users’ natural behaviours when using a product[12]. explicit feedback relies on users’ active evaluation and is difficult to collect, so the related data are quite sparse, while implicit feedback is easy to obtain and a large amount of the related data is available. thus, many researchers have used implicit feedback. early researchers analyze implicit feedback data, such as click and search, to provide support for interface ui improvement and user behavior understanding[1][3][26]. later studies have made richer use of user behavior data. some scholars focus on the optimization of algorithmic models based on implicit feedback data. they combined implicit feedback data with the svd algorithm in matrix decomposition[16][17], and applying collaborative filtering algorithms based on items and users[45][47] to improve algorithm accuracy. further, more scholars have combined implicit feedback data with specific scenarios, such as e-commerce[34], finance[28], beauty[44],and medicine[24]. however, these studies only stay at analyzing the current behavior of users. user behaviour analysis in the e-commerce context ultimately aims to predict subsequent user behaviour and raise the user purchase rate. the common methods used for user purchase prediction are statistical methods and machine learning methods. statistical methods achieve prediction by modelling the relationships between input variables and output variables in advance[41]. however, under practical scenarios, the complex relationships between variables are often difficult to model. in addition, different models are based on different assumptions, rendering it difficult to achieve experimental prediction accuracy in reality[8]. therefore, researchers have started to use machine learning, a method that does not require experimental simulations, for user purchase prediction. models that are often used for purchase prediction are decision trees[22], artificial neural networks[37]. however, all the related studies use a single prediction model, which has problems such as vulnerability to random factors and low generalization ability. to effectively exclude the interference of random factors in the single model and improve prediction accuracy, some researchers introduce the ideas of ensemble learning and fusion model into research on user behaviour prediction, such as gbdt[? ], adaboost[27], catboost[6]. the above studies demonstrate that models using ensemble learning and fusion ideas have better prediction results than traditional single algorithm models. in summary, early scholars’ research stopped at behavioral analysis; however, some scholars have already started to make behavioral predictions. implicit feedback is more suitable for behavioral analysis because of its large amount of data and high availability. several scholars have attempted to predict e-commerce user behavior. they have confirmed the superiority of machine learning methods, and stronger ensemble models have emerged. like these studies, this paper will also use user logs, an implicit feedback, for analysis. however, currently, a single machine learning model is mainly used, and this paper will try a more powerful integration approach. 2.2 repeat purchase behaviour prediction in an increasingly competitive market, the prediction of e-commerce buying behavior has entered a new phase. for content, repeat purchase behavior is emphasized; for methodology, more advanced methods such as ensemble models are introduced. research on prediction of repeat buyers is much less abundant than research on topics such as user purchase behaviour prediction and user repurchase intention. some researchers use non-machine learning methods such as interview method[33], game theory[29] and buy till you die (btyd) models[9] https://doi.org/10.15837/ijccc.2022.6.4988 4 to model and predict e-commerce users’ repurchase behaviour. other researchers have tried to use machine learning to improve the accuracy and robustness of prediction models. [39]applied the explanation method based on an improved decision tree algorithm to enable firms to explore the factors that drive customers’ repurchases.[46]used the vote-stacking method to combine the prediction results of three separate models, namely deepcatboost, deepgbm, and dabigru, and found that the accuracy of fusion models is significantly higher than that of a single model. [13]proposed a bert-mlp prediction model, with large-scale data unsupervised pre-training and small amount of labeled data fine-tuning, to predict repeat buyer. however, the important step of feature engineering has been neglected in the above studies. while machine learning algorithms tend to be generic, feature engineering is specific, and good feature engineering determines the final prediction results. the importance of feature engineering can be seen in the study of general user behavior prediction.[30] constructed feature engineering from the perspectives of users, merchants, products, brands, categories, and their interactions to improve prediction accuracy. considering time-evolving features in feature engineering, [15] found that it could more realistically depict users’ purchase intention. [? ] dynamically updated user features monthly, characterizing customers in a given month, and achieved a prediction accuracy of 98%. obviously, these studies have placed great emphasis on feature engineering. in summary, methods such as game theory rely on strict assumptions, while the questionnaire method suffers from small sample size and under-representation, making it difficult for traditional statistical schemes to fully model user behavior. therefore, we will use real data provided by alibaba and use machine learning methods to prediction. insufficient attention has been paid to feature engineering in existing studies, and we will manually construct a high-quality feature set for e-commerce scenarios. finally, real data are prone to data imbalance and cheating users, and we will also give improvement measures. 2.3 research method ensemble learning is one of the frontiers in the field of computing. its main principle is to train multiple learners and use special rules to combine their prediction results to improve the final prediction performance[36]. [11]introduced the concept of "ensemble learning" for the first time. [19]constructed an ensemble model based on a neural network model, and showed that the integrated model has lower absolute value of variance and superior generalization ability compared with the common neural network. [38]upgraded many weak performance classifiers by boosting idea to obtain strong performance classification models. since then, ensemble learning has jumped to be a popular research topic. many new models have been born, such as hybrid expert models[20], stacked generalization models[42], bagging algorithms[5], and so on. ensemble learning has rich applications in e-commerce, such as e-commerce review mining[25][40], e-commerce product category labeling and recommendation[? ][35], e-commerce security[7], among others. these studies confirm the superiority of ensemble learning approaches. in summary, the superiority of ensemble learning is undeniable and is widely used in e-commerce. we will use ensemble learning for repurchase prediction. in addition, are there methods that can combine ensemble learning with classical models to optimize the results? we will also explore this question. 3 data description and pre-process in this section we introduce the dataset and discuss data pre-processing process. 3.1 research method the data come from alibaba’s tianchi platform. the data are provided by tmall, which records the real data of 4,995 merchants and 424,170 new buyers in the 2014 double eleven shopping festival on the tmall platform. the purpose of the experiment is to predict whether a new user that purchases a merchant’s product on the double eleven day will make a second purchase from that merchant https://doi.org/10.15837/ijccc.2022.6.4988 5 table 1: user behaviour log field name description user_id unique id code of the purchaser item_id unique id code of the merchandise cat_id unique id code for merchandise categories merchant_id unique id code for merchants brand_id unique id code for merchandise brands time_tamp purchase time action_type contains 0, 1, 2, 3. 0=click, 1=cart add, 2=purchase, 3=bookmark to favorites table 2: user profile table field name description user_id unique id code of the purchaser age_range user age range. 1 for <18 years; 2 for [18,24]; 3 for [25,29]; 4 for [30,34]; 5 for [35,39]; 6 for [40,49]; 7 and 8 for > = 50; 0 and null for unknown gender user gender. 0 for female, 1 for male, 2 and null for unknown within six months, and the new user is are a “repeat buyer” for each merchant. the dataset consists of three tables, namely the user behaviour log table, the user profile table, and the training set table. the user behaviour log records the behaviours of all the “new users” on the day of double eleven and 6 months before double eleven, spanning 12 may 11 november 2014. the field information is listed in table 1. the user profile table records the demographic information. table 2 lists the field information. the training set table records whether a specific user makes repeat purchases at a specific merchant. the field information is listed in table 3. 3.2 data pre-processing data pre-processing includes data cleaning, data integration, data transformation, data imputation etc. in this paper we use real behaviour log data, and there is inevitably data noise. data quality will directly affect the accuracy and universality of the prediction model. therefore, we combine the characteristics of the dataset and the special attributes of e-commerce to pre-process the data to improve data quality. 3.2.1 data integration we use three datasets, where the two fields of user id (user_id) and merchant id (merchant_id) are common fields. to improve the efficiency of feature engineering, we use user id and merchant id as the primary keys for data integration. we show the structure of the integrated user purchase behaviour information table in table 4. 3.2.2 missing value processing the age and gender data of users in the dataset are missing 0.52% and 1.52%, respectively because they are category attributes with significant differences in repurchase behaviour, the missing values are filled by the plural of the whole data. for the brand id, on the one hand, it is difficult to replace it by plenary features or other attributes of the dataset; on the other hand, it has no impact on the subsequent feature engineering, so the missing data are excluded. table 3: training set table field name description user_id unique id code of the purchaser merchant_id unique id code of a merchant label repeat purchase user identifier. contains 0, 1. 1 for a repeat buyer, 0 for a non-repeat buyer. https://doi.org/10.15837/ijccc.2022.6.4988 6 table 4: user purchase behaviour information field name description user_id unique id code of a purchaser merchant_id unique id code of a merchant item_id unique id code of an item cat_id unique id code of a merchandise category brand_id unique id code of a merchandise brand time_tamp purchase time action_type contains 0, 1, 2, 3. 0=click, 1=cart add, 2=purchase, 3=bookmark to favorites age_range user age range. 1 for <18 years; 2 for [18,24]; 3 for [25,29]; 4 for [30,34]; 5 for [35,39]; 6 for [40,49]; 7 and 8 for > = 50; 0 and null for unknown gender user gender. 0 for female, 1 for male, 2 and null for unknown label repeat purchase user identifier. contains 0, 1. 1 for a repeat buyer, 0 for a non-repeat buyer. 3.2.3 abnormal user identification in the e-commerce environment, there are phenomena such as crawlers and swipers, whose behaviors are different from the normal purchase behaviour, and who belong to abnormal users. if a user has a large amount of product browsing behaviour in a period but the purchase behaviour is 0, then the user is likely to be a “crawler user”. if a user has a large amount of product purchase behaviour in a period but little or no browsing behaviour, then the user is likely to be a “crawler user”. after identifying the abnormal users, we delete their records. 4 methodology in this section we present methodologies related to data imbalance processing, feature engineering, predictive models, and evaluation metrics. 4.1 data imbalance processing in our dataset, the positive sample, i.e., the percentage of repeat users is only 6.1%, and the data are severely imbalanced. when the data are unbalanced, the minority class samples in the overlapping region between classes will be misclassified in large batches, and the class interval surface will move to the side with sparse sample distribution, thus interfering with the classification accuracy of the model for the minority classes. existing research deals with data imbalance in three aspects, namely data pre-processing, features, and algorithms. in this paper we use smote for imbalanced data processing(figure 1). the process is as follows: – each sample x in the minority class is obtained as its k-nearest neighbour based on its distance to all the samples in the minority class set (generally using the euclidean distance). – the sampling multiplicity n is determined by the degree of sample positive and negative class imbalance; many samples are arbitrarily selected from the k nearest neighbours of each minority class sample x. the selected nearest neighbour is assumed to be xnew. – to the arbitrarily selected nearest neighbour xnew, construct its new sample about the original sample as follows: xnew = x + rand(0, 1) ∗ (x̃ − x). (1) 4.2 feature engineering feature construction refers to the use of manual methods to select meaningful data in the initial dataset, or to combine and deform the initial data to obtain new features. to better characterize e-commerce behaviour, we focus on two main subjects in the e-commerce context, namely users and merchants, and construct features manually in three dimensions, namely user portraits, merchant portraits, and user-merchant interaction portraits. besides basic features (gender, age), different https://doi.org/10.15837/ijccc.2022.6.4988 7 figure 1: principle of smote statistical methods are used to construct features, including counting and ratio features, aggregation features (mean, median etc), and temporal features. the construction of features is based on the features of e-commerce and the subjective judgement of researchers, which are prone to developing invalid features and feature redundancy. if we use all the features, the model will be inefficient. therefore, we keep the strong features and drop the useless features to avoid the loss of important information. feature selection mainly addresses three types of problems, namely dimensional catastrophe, overfitting, and noise, which can not only reduce the model complexity and computation but also improve the final prediction of the model by including high-quality features. there are three types of mainstream methods for feature selection as follows: – filtering: feature selection and model training are independent of each other. first, select the experimental data features, then train the model using the filtered data set, and determine the weights by the scores. – wrapping: feature selection and model training are correlated with each other. a subset of all the features is selected for model training and compared each time, and the best features are selected based on the classifier results. – embedding: feature selection is carried out together with model training. the dataset is trained on the model, the weights of the features are obtained by model fitting, and feature selection is performed in the order from highest to lowest. there is no uniform way for feature selection, so we use four methods for feature selection including random forest, anova, recursive feature elimination, and l1 regularization, and compare the results and retain the best set of features. 4.3 prediction models we use classical machine learning models, ensemble models, and fusion models to predict repeat buyers and compare the prediction results of different models. – for classical machine learning, we use the common classification predictors such as logistic regression, factorization machine, decision tree, and support vector machine for experiments, and use prediction accuracy as the baseline in this paper. –ensemble models combine multiple learners into one stronger learner and can crack problems that cannot be solved by a single model. therefore, using integrated learning can yield higher prediction accuracy and more reliable prediction results than a single model[14]. we use ensemble learning models such as xgboost and lightgbm, which are commonly used for dichotomous prediction, to make predictions. –different models have unique advantages, and fusing models by certain methods can construct stronger classifiers and greatly improve the prediction results, so we fuse different models to improve prediction accuracy. stacking is one strategy of model fusion, which is usually a two-layer construction[42]. its framework is shown in figure 2. first, the original dataset is divided into several sub-datasets and input to the n base learners in the first layer in turn; the output training results become the input to the second layer learners, which are trained to output the final results. https://doi.org/10.15837/ijccc.2022.6.4988 8 figure 2: the framework of stacking the above model contains numerous parameters and optimizing the parameters can effectively improve the model’s performance. gradient method, genetic algorithm, and other common parameter optimization methods converge faster, but with more than two parameters, the parameters will affect one and other and interfere with the results. the grid search method combines the parameters and performs the optimization search at the same time, avoiding the local optimum, improving efficiency, and obtaining the best generalization ability. in this paper we use grid search with cross-validation (gscv) algorithm for model parameter optimization. this method combines cross-validation and grid search. in the grid search, the parameters are gradually adjusted by the learning rate in a limited range, the learner is trained, and the results are continuously compared. the scores of the model on the test set are calculated, and the final score is averaged over k times. 4.4 model evaluation metrics we use the area under curve (auc) value as the model classification ability evaluation metric. the problem in this paper is dichotomous, i.e., whether a user is a repeat buyer, there are the positive class (1) and negative class (0), and the positive class is the repeat buyer. the positive class and the negative class of the real type and the predicted type constitute the confusion matrix, as shown in figure 3. figure 3: confusion matrix. four major categories are derived from the confusion matrix, namely tp, fn, fp, and tn, representing the number of samples in the four categories of true positive, false negative, false positive, and true negative, respectively. the accuracy rate, which is the ratio of the number of samples with correct classification results to the total number of samples is calculated as follows: accuracy = t p + t n t p + f n + f p + t n . (2) https://doi.org/10.15837/ijccc.2022.6.4988 9 in addition, two indicators can be derived, namely the true positive rate (t p rate) and the false positive rate (f p rate), which are calculated as follows: t p rate = t p t p + f n . (3) f p rate = f p f p + t n . (4) t p rate indicates the probability that a sample with true category 1 is predicted to be class 1, and fprate indicates the probability that a sample with true category 0 is predicted to be class 0. the curve formed by taking f p rate as the horizontal axis and t p rate as the vertical axis is the receiver operating characteristic (roc) curve, as shown in figure 4. in general, au c = 1 means the perfect classifier, 1 > au c > 0.5 means fair performance, and 0.5 > au c > 0 means poor performance. figure 4: confusion matrix. when the sample is imbalanced between the positive and negative classes, the auc value can measure the classification accuracy when the true type is positive (=1) and negative (=0) at the same time, which can avoid the impact of the sample imbalance on the evaluation of the classifier. 5 experimental results and analysis in this section we present the results after data processing and modelling based on the methodology presented in the previous section, including the feature sets, prediction model parameters and results, and important features. 5.1 feature construction and selection the user portrait dimension includes four aspects: – basic information. during the initial exploration of the data, we find that user repurchase differs in different ages and genders, so these attributes should be added. –time information. it mainly describes the user’s activity of “shopping” and fluctuations of the activity over time. –preference information. it mainly describes the user’s favorite items/categories/shops/brands, the pattern of the user’s various operations, and the comparison between a user’s value and the average value of all the users. https://doi.org/10.15837/ijccc.2022.6.4988 10 table 5: results of feature selection. method no select auc feature select auc feature dimension before selection feature dimension after selection random forest-based feature selection 0.582 0.587 147 94 anova 0.582 0.585 147 79 recursive feature elimination 0.582 0.585 147 74 l1 regularization 0.582 0.569 147 34 –behaviour information. it mainly describes the frequency, extensiveness, and recent repurchase behaviour of users’ various operations. a total of 83 features are constructed. the merchant portrait dimension includes three aspects: –basic information. in the initial exploration of the data, we find that there are differences in the repurchase situations of stores, and that items, brands, and categories are the most basic attributes of an e-commerce merchant. –time information. it mainly describes the frequency and status of the merchant in operation. –strength and popularity information. it mainly describes the merchant’s popularity with users, the merchant’s strength, and the merchant’s repurchase situation. a total of 41 features are constructed. the user-merchant interaction portrait dimension is the most important dimension to show the repurchase characteristics and relationship between different users and merchants, and describes the interaction information between merchants and users in terms of frequency, time, and status through user-merchant matching. a total of 23 features are constructed. we successively use four methods for feature selection based on random forest, anova, recursive feature elimination, and l1 regularization. the results of the auc values and feature dimensionality before and after the four methods are shown in table 5. finally, we pick the random forest-based feature selection as the best, and retain the selected 94 features as the feature set. 5.2 data preparation we first address the data imbalance problem by using the smote algorithm, which is implemented using the smote interface in the imblearn library in python. the pseudo-code is shown in figure 5. to avoid high generalization error, the dataset is often split by the self-help method, which will change figure 5: pseudo-code for the smote algorithm. https://doi.org/10.15837/ijccc.2022.6.4988 11 table 6: best parameters and prediction results of a single model. model best parameter auc score lr penalty: l2; intercept_scaling: liblinear; c: 0.05; class_weight: none; max_iter: 100 0.67305 fm n_iter: 0; l2_reg_w: 0.1; rank: 4 0.67245 xgboost n_estimator: 2000; learning_rate: 0.01; max_features: 9; subsample: 0.5; max_depth: 8; min_samples_split: 1000; min_samples_leaf: 30; scale_pos_weight: 0.061 0.67956 lightgbm max_depth: 7; num_leaves: 80; colsample_bytree: 1; learning_rate: 0.01; reg_alpha: 0 0.67991 the distribution of data, so we do not apply it to this study. as the amount of data used in this paper is sufficient, we use the simple and efficient leave-out method, dividing the training set by 1:1, with 50% as the training set and 50% as the test set. we apply five-fold cross-validation, i.e., we divide the training set into five equal and arbitrarily divided subsets, where the data sets are mutually exclusive. each time, we randomly chose a subset as the validation set and the other four as the training set. 5.3 model construction and parameters we first use the classical models such as logistic regression (lr), factorization machine (fm), decision tree (dt), and support vector machine (svm), finding that lr and fm perform the best. meanwhile, the good prediction accuracy achieved by the classical algorithms confirms the effectiveness of our study in dealing with data imbalance and constructing feature engineering. seeking better prediction performance, we apply the ensemble learning models to construct xgboost, and lightgbm for prediction. the parameter settings and auc values of a single model are shown in table 6. the optimal parameters here are obtained by the grid search with cross-validation (gscv) algorithm. to achieve the best prediction possible, we introduce the stacking method to construct a twolayer fusion model. we consider the following factors in the selection of the first layer base learner: a) performance: a strong learner should be selected to achieve the effect of combining advantages; otherwise, it will affect the efficiency of the whole model; there should be differences in the structure of each learner to fuse and learn from different perspectives. b) number: the number n of the first layer model determines the feature dimension n+1 of the second layer model input, and the dimension should be at least three, so at least two models are selected. after comprehensive consideration and trials, we select lightgbm, xgboost, and rf as the base learners in the first layer. the second layer generally uses weak learners, and we choose the lr model, which is commonly used in machine learning studies. the lr model has strong generalizability and can avoid the risk of overfitting in the stacking algorithm. the pseudo-code of the stacking fusion model is shown in figure 6. the figure 6: pseudo-code for the stacking fusion model. https://doi.org/10.15837/ijccc.2022.6.4988 12 table 7: comparison of model results. model auc score ranking gain other studies fm 0.67245 top 9.7% lr 0.67305 top 9.5% +0.0006 0.617[51] xgboost 0.67956 top 5.6% +0.00651 0.6774[52] lightgbm 0.67991 top 5.5% +0.00035 0.6797[53] stacking fusion model 0.68406 top 2.8% +0.00415 0.6232[54] table 8: top ten features. rank feature 1 number of purchases made by users at merchants 2 the number of repeat purchases made by users before “double eleven” 3 the click-to-purchase conversion rate of users to merchants 4 number of repeated purchases made by users before “double eleven” 5 number of store item categories 6 difference between number of times repurchase is made to a merchant before “double eleven” and the average value of all the merchants 7 difference between the user click-to-purchase conversion rate and the average value of all the users 8 variance of user active days 9 number of user clicks as a percentage of the number of all user actions 10 difference between a merchant’s click-to-purchase conversion rate and the average of all the merchants modelling process comprising four steps is as follows: – divide the training dataset d into five random and uniform copies to obtain d1−5; – for the first learner m1 in the first layer, four sub-datasets are randomly taken as the training data, the remaining copy is used as test data, and the new learner generated by learning from the training data is used to predict the test data to obtain the prediction results. specifically, the first-fold cross-training of learner m1 takes d1−4 as the training data and d5 as the test data to obtain the prediction result p11 for the first-fold; – complete five-fold cross-validation for each first-layer trainer based on step 2. combine the results of the first layer to obtain the input p of the second layer; – perform five-fold cross-validation for the second layer of learners m according to step 2, using the dataset as the output training set p of the first layer to obtain the final prediction model and results. 5.4 comparison of model results the auc values of the classical, ensemble, and fusion models are shown in table 7 (ranked in ascending order of the auc values). on the one hand, comparing the five models used in this paper, we see that the stacking fusion model has a significant improvement in the auc value compared with the general model, with an increase of 0.01161 in absolute amount and 6.9% in the ranking ratio, which can reach top 2.8% of all the participants in alibaba tianchi, confirming that the fusion model constructed in this paper is effective. on the other hand, comparing the results of this paper with other studies using the same model, based on the same model, we achieve a higher auc value, which confirms the effectiveness of our approach that embraces feature engineering and data processing. from the official baseline of 0.704954, there is still room for improvement in the results of this paper. however, after incorporating the innovations of feature engineering and data imbalance processing, even the classical model can reach the top 10% of the ranking. 5.5 important features the prediction of repeat buyers can bring insights to merchants and platforms. focusing on the features of potential repeat buyers further helps merchants and platforms understand user behaviour and adjust their business actions. we obtain the importance score of each feature through the lightgbm model, and the top ten features are shown in table 8. the top ten features mainly concern the user profile (six features), merchant profile (two features), and user-merchant interaction profile (two https://doi.org/10.15837/ijccc.2022.6.4988 13 features). these features provide guidance for management. among them, user purchase features such as “number of purchases made by users at merchants”, “number of repeat purchases made by users before “double eleven””, and preference information such as “number of days users are active”, “proportion of user clicks to the number of all user actions”, and other characteristics reflect the user’s habit of using the e-commerce platform. users with these characteristics may be potential repeat buyers, and managers should give timely and targeted measures. the characteristics of merchants such as “number of store categories” and “difference between the merchant’s click-to-purchase conversion rate and the average value of all the merchants” also affect user repurchase. so merchants should optimize their stores based on these key features, improve their strength and competitiveness to attract repurchase users. 6 conclusion the development of e-commerce platforms has so far resulted in a low percentage of new users in the market, so it is the future direction to focus on existing users of the platform and carry out targeted marketing strategies for repeat buyers. we use tmall real user behaviour log data, and apply machine learning algorithms such as ensemble learning and fusion model to carry out repurchase prediction. introducing feature engineering based on e-commerce characteristics and imbalance data processing, we construct a repurchase prediction model that contain a feature set of 94 features. the model attains an auc value as high as 0.68406 after incorporating the fusion model, realizing efficient and accurate repeat buyer prediction. we also make the following findings: (1) compared with traditional machine learning models, the ensemble models and fusion models can improve the predictive effect of models, allowing us to predict repeat buyers more accurately and efficiently. (2) in the e-commerce context, especially when using real, large data sets, problems such as data imbalance and anomalous samples are inevitable, and solving these problems is important to attain accuracy in model prediction results. (3) it is necessary to adopt advanced and popular models, but the application of feature engineering should also be emphasized, and appropriate feature engineering can help bring the best model results. we deduced important management insights from the study: (1) the study confirms the possibility of predicting repeat users, and by predicting it also captures important characteristics of potential users. platforms should pay attention to users with these characteristics and guide them to repurchase through product personalized recommendations and sms alerts; (2) the prediction model also contains merchant features. for store improvement, the current store operations can be checked based on the important features, and optimized and upgraded to better convert users into repeat buyers, such as diversifying their product range. (3) the merchant can develop separate strategies for repeat buyers and new users based on the prediction results, implementing differentiated marketing and pricing, and (4) predictions of repeat users indicate future orders, and merchants can adjust their product generation and inventory management based on the predictions. for example, when an item has a large number of potential repeat customers, production should be increased. due to the single data source and limited technology, our study has shortcomings. from a management perspective, future research can be improved in the following ways:(1) real-time modelling analysis should be conducted using real-time datasets from more platforms to broaden the platform sources, thus enhancing the generalizability of the model across different platforms and uncovering the differences in repeat buyers between platforms. (2) machine learning methods should be introduced into the feature construction process to improve the explanatory power of features to the problem. funding supported by natural science foundation of china(71901027); beijing forestry university 2021 course ideological and political teaching, research and teaching reform project, "management model and basic decision-making" project(2021kcszxy010) https://doi.org/10.15837/ijccc.2022.6.4988 14 author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] abel, f.; gao, q.; houben, g. j.; tao, k. (2011). analyzing user modeling on twitter for personalized news recommendations. international conference on user modeling, adaptation, and personalization, 1-2, 2011 [2] belem, f. m.; silva, r. m.; de andrade, c. m.; person, g.; mingote, f.; ballet, r.; alponti, h.; de oliveira, h. p.; almeida, j. m.; goncalves, m. a. 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(2022). a feature engineering and ensemble learning based approach for repeated buyers prediction international journal of computers communications & control, 17(6), 4988, 2022. https://doi.org/10.15837/ijccc.2022.6.4988 introduction literature review user behaviour research repeat purchase behaviour prediction research method data description and pre-process research method data pre-processing data integration missing value processing abnormal user identification methodology data imbalance processing feature engineering prediction models model evaluation metrics experimental results and analysis feature construction and selection data preparation model construction and parameters comparison of model results important features conclusion international journal of computers communications & control issn 1841-9836, e-issn 1841-9844, 14(6), 615-632, december 2019. parameter estimation for pmsm based on a back propagation neural network optimized by chaotic artificial fish swarm algorithm j.w. jiang, z. chen, y.h. wang, t. peng, s.l. zhu, l.m. shi jianwu jiang 1. school of computer science and technology, soochow university, china 333 east ganjiang way, sushou, 225300, china 2. college of information engineering, taizhou polytechnic college, china 47955024@qq.com zhi chen college of information engineering, taizhou polytechnic college, china 17784068@qq.com yihuai wang* school of computer science and technology, soochow university, china 333 east ganjiang way, sushou, 225300, china *corresponding author: yihuaiw@suda.edu.cn tao peng school of computer science and technology, soochow university, china sdpengtao401@gmail.com shilang zhu school of computer science and technology, soochow university, china 20154027006@stu.suda.edu.cn lianmin shi college of information engineering, suzhou institute of trade & commerce, china 20154027006@stu.suda.edu.cn abstract: permanent magnet synchronous motor(pmsm) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. aiming at the premature convergence of traditional back propagation neural network(bpnn) in pmsm motor parameter identification, a new method of pmsm motor parameter identification is proposed. it uses chaotic artificial fish swarm algorithm(cafsa) to optimize the initial weights and thresholds of bpnn, and then strengthens training by bpnn algorithm. thus, the global optimal network parameters are obtained by using the global optimization of cafsa and the local search ability of bpnn. the simulation results and experimental data show that the initial value sensitivity of the network model optimized by cafs-bpnn algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. compared with other intelligent algorithms, such as rsl and pso, cafs-bpnna has high identification accuracy and fast convergence speed for pmsm motor parameters. keywords: permanent magnet synchronous motor(pmsm), back propagation neural network(bpnn), chaotic artificial fish swarm algorithm(cafsa), parameter estimation, identification accuracy, convergence speed. copyright ©2019 cc by-nc 616 j.w. jiang, z. chen, y.h. wang, t. peng, s.l. zhu, l.m. shi 1 introduction permanent magnet synchronous motor (pmsm) is widely used in industrial robots, servo drive control, electric vehicle speed regulation and other high-precision control fields due to its outstanding advantages in power and torque density, speed control performance and system robustness [10]. pmsm motor parameters include stator windings, dq axis inductance, rotor flux, etc. they can be used to reflect the performance of the motor system during operation, and can assist to complete motor condition monitoring [5] and fault diagnosis [12]. therefore, accurate parameter identification ability is very important for motor control system. however, motor parameters are very vulnerable to external factors such as temperature, flux saturation and skin effect [3], such as temperature change will affect the stator winding, permanent magnet demagnetization directly affects the dq axis inductance [22]. with the change of working condition and surrounding environment, there is a certain difference between actual parameters and nominal parameters [17]. therefore, as a motor controller, it must have the function of self-tuning and identification of motor parameters. considering the technical difficulty and cost, it is difficult to directly measure motor parameters through auxiliary sensors such as temperature and magnetism [5,15]. pmsm control system has very strong non-linear and time-varying characteristics. it is very difficult to accurately identify the parameters of pmsm. the identification algorithm needs to comprehensively weigh the factors of complexity, convergence and computing time. 2 related works in traditional control theory, parameter identification methods of pmsm include least square method (rls) [18, 23], extended kalman filter identification (ekf) [1, 6, 20], model reference adaptive (mras) [2,26] etc. because of the linear parameterization of rls algorithm, rls estimators are usually noise sensitive, which may lead to lower identification accuracy. when estimating the winding resistance and rotor flux by ekf, the estimator is noisy and unstable, so it can not accurately estimate the actual parameters. mras estimator can not accurately estimate winding resistance, inductance and rotor flux simultaneously. it is often impossible to find the optimal solution of the estimated parameters accurately by using the above methods. with the development of swarm intelligence theory, many scholars have applied this kind of optimization algorithm to motor parameter identification, including ant colony algorithm [4], genetic algorithm [21], particle swarm optimization algorithm [14]. ant colony algorithm and particle swarm optimization algorithm are easy to fall into local optimization convergence, and the optimization error is relatively large. literature [11] uses genetic algorithm to identify dq axis inductance and rotor flux, because of the large spacetime complexity, the convergence speed is slow. literature [14] uses dynamic particle swarm optimization (dpso-ls) algorithm and dynamic anti-learning strategy of gauss distribution to enhance the global search ability, but the computational complexity becomes larger. with the development of artificial intelligence technology, the artificial neural network is widely used in parameter identification of pmsm motor, and bpnn is the most widely used [21,25]. bpnn is a multi-layer feedforward neural network trained by error back propagation algorithm. it has the advantages of clear model structure and simple calculation. it has been proved theoretically that its three-layer model can map any complex non-linear relationship model [16]. it is widely used in non-linear curve fitting in various signal processing and automatic control. parameter estimation for pmsm based on a back propagation neural network optimized by chaotic artificial fish swarm algorithm 617 bpnn has excellent local optimization ability and can converge quickly and accurately in local areas. however, this advantage has also results in poor global search performance, local prematurity, poor global convergence, long iteration period and other problems, and its algorithm optimization is difficult to achieve the desired convergence accuracy and convergence speed. therefore, it is necessary to combine the bpnn algorithm with the other intelligent algorithm with stronger global optimization ability. in the early stage of training, the cooperative algorithm is used to converge the network to the global extremum quickly, and then the local optimization characteristic of bpnn is used to converge quickly. cafsa is a swarm intelligence algorithm with excellent global searching ability. it simulates the behavior of fish swarm such as preying, swarming, fellowing and moving to optimize the system. it has the characteristics of weak sensitivity to initial values, strong robustness to parameter setting, good global searching performance and fast convergence speed [19,23]. cafsa algorithm has large randomicity and poor convergence in the later search period. it needs to improve the optimization performance by improving system step size, the field of view, adding congestion factor [8]. the combination of cafsa and bpnn will make up for their respective optimization shortcomings, and improve the overall optimization performance in terms of convergence speed and accuracy. this paper presents a pmsm motor parameter identification algorithm based on cafsbpnna , which combines bpnn with afsa effectively. by injecting impulse current into the d-axis of pmsm motor control system, the sampling process data, including d-axis voltage, qaxis voltage, d-axis current, q-axis current and motor speed, are used as data sets for network training and testing. the parameters set {rs,ld,lq,ψf} to be identified are taken as network output. combining the global search ability of cafsa and the local fast convergence property of bpnn, the convergence accuracy and speed of pmsm motor parameter identification are effectively improved. the outstanding contributions of this paper include: (1) using the forward propagation topology of bpnn, a nonlinear mapping identification model from sampled parameter set {ud,uq, id, iq,ω} to identified parameter set {rs,ld,lq,ψf} of permanent magnet synchronous motor (pmsm) is constructed. the input data set of the mapping model is obtained by d-axis current injection method, and parameters of pmsm motor to be identified are obtained by simple and direct mapping model. (2) the cafs-bpnna algorithm of bpnn optimized by cafsa is introduced into online identification of pmsm motor parameters. the connection weights and thresholds of bpnn network are taken as the optimization objects of cafsa. the connection weights and thresholds of bpnn are optimized quickly by using cafsa global search ability in the early stage of training, and then the optimization of bpnn is transferred to the global optimal extremum point in the later stage. by giving full play to their respective optimization characteristics and avoiding their own shortcomings, the convergence accuracy and speed of obtaining network parameters of pmsm motor parameter identification model are effectively improved. (3) the population aggregation degree is embedded in cafs-bpnna as the switching condition of the two intelligent algorithms. according to the optimization characteristics of the fish swarm algorithm, the aggregation degree of the population increases in the later stage of optimization. when it reaches the preset threshold, it means that it reaches the late stage of fast convergence optimization algorithm of cafsa, and can be transferred to bpnn precision optimization algorithm, thus the time of turning into precision optimization algorithm is accelerated. 618 j.w. jiang, z. chen, y.h. wang, t. peng, s.l. zhu, l.m. shi 3 theoretical analysis of group intelligence identification model for pmsm motor parameters 3.1 mathematical model for pmsm motor identification pmsm control system is a complex system with strong coupling and time-varying nonlinearity. it can establish mathematical models in static three-phase coordinate system, static αβ coordinate system and synchronous rotating dq coordinate system. the mathematical models in three coordinate systems can be transformed each other. the mathematical model in the dq coordinate system is the most commonly used mathematical model [11]. neglecting the magnetic saturation effect of pmsm and the eddy current and hysteresis loss of core are neglected, the voltage equation and flux linkage equation in the dq coordinate system are expressed as (1) and (2):   ud = rsid + dψd dt −ωψq uq = rsiq + dψq dt −ωψd (1) { ψd = ldid + ψf ψq = lqiq (2) where ud and uq are the voltage component on the dq axis, id and iq are the current component on the dq axis, rs is the stator resistance, ψd and ψq are the flux linkage on the dq axis, ld and lq are the inductance on the dq axis, ψf is the flux chain generated by the permanent magnet,ω is the electrical angular speed. in the synchronous rotating dq coordinate system, the mathematical model of pmsm can be expressed as:   did dt = rs ld id + lq ld ωid + ud ld diq dt = rs lq iq + ld lq ωid + uq lq − ψf lq ω (3) p = {rs,ld,lq,ψf} is the set of parameters that need to be identified synchronously. when id = 0, the dq axis current is decoupled so that the stator current has only the ac component of q axis. in the steady state, formula (2) is brought into formula (1) and discretized to express as follows: { ud0 = −lq0ω0ψq0 uq0 = rsiq0 + ψf0ω0 (4) formula (4) shows that the order of the motor equation is two, but the system needs to identify four parameters:{rs,ld,lq,ψf}. the equation of state is rank-deficit type. by injecting the d-axis current of instantaneous id 6= 0 into the steady state, the motor model is expressed as:{ ud1 = rsid1 −lqω1iq1 uq1 = rsiq1 + ldω1id1 + ψfω1 (5) it can be seen from literature [2] that the measurement error of rotor flux linkage ψferror is caused by assuming ld = lq and ψf = ψf0. when ψferror is negligible relative to ψf0, (12) shows that ψfe and ψf0 are equal, and |ψferror|can be simplified as follows: parameter estimation for pmsm based on a back propagation neural network optimized by chaotic artificial fish swarm algorithm 619 id/s -3 -2 -1 0 1 2 3 e rr o r/ % 0 5 10 15 20 25 30 cur1 cur2 cur3 cur4 figure 1: estimation error of rotor flux relative to d-axis current iq = 3.55a,iq0 = 3.35a,ψm0 = 175wb,ld = 8.5mh cure1:∆ψm = 1%∆ψm0,∆l = 10%ld cure2:∆ψm = 0.5%∆ψm0,∆l = 5%ld cure3:∆ψm = 0.1%ψm0,∆l = 1%ld cure4:∆ψm = 0.05%∆ψm0,∆l = 0.5%ld id=0 id=-2 time(ms) id (a ) 2ms tsample*n data0 sampletime: 0~n-1 data1 sampletime: 0~n-1 tsample*n figure 2: time series model for data sampling |ψferror| ≈ | ∆ψfiq1 2 + ∆lid1iq1 2 iq1 2 − id12 − iq12 | = | ∆ψfiq1 2 id1 2 + ∆liq1 2) id1 | (6) when id1 is large enough , (13) shows that|ψferror|→ 0 and ψfe ≈ ψf0, see fig. 1. 3.2 data set acquisition method of pmsm motor identification model generally, pmsm motors can be controlled by constant torque angle (torque angle is 90◦). adjusting the d-axis current (id = 0) makes the q-axis get the maximum torque. in order to obtain the information of motor speed, dq axis current and voltage needed to be sampled when evaluating motor parameters, data0 and data1 were obtained by alternately adjusting (id = 0) and (id 6= 0) injecting reverse d axis current (id = −2a) during sampling period. the single sampling time is tsample (= 10−4s), and the total sampling time is tsample∗n. the time series model of data sampling is shown in fig.2. data1 data sampling start time is 2ms after current switching, which avoids the interference caused by current switching and ensures the stability of sampling data. 620 j.w. jiang, z. chen, y.h. wang, t. peng, s.l. zhu, l.m. shi uq0 ud0 ω0 iq0 uq1 ud1 iq1 ω1 id1 m1 m2 mk rs ld lq ψf w91 w12 w1k v11 w9k 9191 w w92 w1212 w11 11 v12vv1212 v13v v14 vk1k11 vk2vvk2kk vk3 vk4 figure 3: bpnn-pmsm motor parameter identification model 3.3 parameter mapping model of pmsm motor identification based on bpnn according to the principle of bpnn and the control strategy model of pmsm in section 3.1, the set {rs,ld,lq,ψf} of pmsm parameters to be identified is taken as the output neuron set of bpnn, and the data0 =ud0,uq0, iq0,ω0(id = 0) and data1 = {ud1,uq1, id1, iq1,ω1}(id 6= 0) are used as the input neuron set. the bpnn parameter identification model of pmsm motor as shown in fig. 3 is constructed. the three-layer neural network model consists of nine input neurons and four output neurons. the hidden layer uses tansig function as the transfer function f1(·), and the output layer uses linear purelin as the transfer function f2(·). the hidden layer neurons output zh = f1( ∑n h=0 ωihxi)(h = 1, 2, · · · · · ·9) and the output layer neurons output yj = f2( ∑m h=0 vhjzh)(j = 1, 2, 3, 4), thus the bpnn network completes the mapping from the input 9-dimensional space to the output 4-dimensional space. after determining the input layer, output layer and data set of bpnn network, the key factors affecting the performance of bpnn network include the number of hidden layer layers, the number of hidden layer neurons, initial connection weights and thresholds, learning rate and learning algorithm. increasing the hidden layer can reduce the network error and improve the accuracy, but the performance will also increase the network complexity and training time, and may appear the tendency of "over-fitting". according to hornic’s theory that three-layer bpnn can fit arbitrary non-linear curve, the identification model in this paper adopts a threelayer network structure with one hidden layer. the number of hidden layer neurons directly determines whether the network will appear "over-fitting" phenomenon, but there is no theory to accurately determine the number of hidden layer neurons in bpnn network. in this paper, empirical formula and experimental verification will be used to determine the optimal number. the setting of initial connection weights and thresholds will have a great impact on the convergence speed of the model. in this paper, the artificial fish swarm algorithm with strong initial robustness is introduced into the initial optimization of the model, so as to improve the convergence speed and speed up parameter estimation for pmsm based on a back propagation neural network optimized by chaotic artificial fish swarm algorithm 621 the global optimization. through the study of bpnn network, the following empirical formulas for calculating hidden layer neuron data are formed: gagan pointed out the logarithmic relationship between the hidden layer neurons and the input neurons. formula 1: m1 = log2n[20]. kolmogorov’s theorem shows that there exists a relationship between recessive neurons and input neurons. formula 2: m2 = 2n− 1. d. gao [7] used least square method to simplify the relationship between input and output and hidden layer neurons. formula 3: m3 = √ (0.43nl + 0.12l2 + 2.54n + 0.77l + 0.35+0.51 [7]. the number of hidden layer neurons can be calculated by input neurons and output neurons. formula 4: m4 = √ nl. the number of neurons in the hidden layer was obtained by using input neurons and output neurons as well as by adjusting parameters. formula 5: m5 = √ n + l + α,α = 1, 2 · · · · · ·10. in the above formula, m is the number of neurons in the hidden layer,n is the number of neurons in the input layer, l is the number of neurons in the output layer. table 1: the number of neurons in the hidden layer of bpnn no. m1 m2 m3 m4 m5 m n = 9 ,l = 4 ,α = 1,2,· · · · · · ,10 4 17 8 6 5-14 4-17 the number of hidden layer neurons m = m1,m2,m3,m4,m5 is obtained by combining the above empirical formulas. according to the bpnn-pmsm parameter identification model in chapter 3.3, n = 9, l = 4 can be obtained. the number of hidden layer neurons m is shown in tab.1. weight mean square error (wmse) is used to calculate the training errors. the individual error of sample p can be expressed: ep = 1 2 ∑l j=1(ŷ p j −yj p)2wj, and global error wmse can be expressed:wmse = ∑p i=1 ep. in the previous two formulas, l is the number of output nodes, p is the number of training samples, ŷpj is the expected output value of network, y p j is the actual output value of network, and wj is the error weight of output nodes. according to the definition of net input value sj of neuron node yj, the hidden layer neuron output zh = f1(sh) and the output layer neuron output yj = f2(sj) described in chapter 3.3. vhj is the weights of output layer connection and wih is hidden layer connection. they can be adjusted by cumulative error algorithm. the increments of connection weight ∆vhj and ∆wih can be generated after each iteration. they are expressed as follows: ∆vhj = ∑p i=1 ∑l j=1 η(ŷ p j −y p j ) 2f ′2(sj)zj, ∆wih = ∑p i=1 ∑l j=1(η(ŷ p j −y p j ) 2f ′2(sj)vhjf ′ 1(sh)xi, where η is learning rate and η ∈ (0.01, 10). in the process of learning, the weighted inertial momentum is used to reduce the learning oscillation and accelerate the convergence of the system. the adjusted formula for calculating the actual weights can be expressed: ∆w(n) = −η1∆wmse(n)+αw(n−1), where η1 is learning rate and α is the inertial momentum. in order to adapt the bpnn network to the convergence speed in different periods, the system learning rate parameter η1 is adjusted dynamically and adaptively. the error ratio can be expressed: ϕ = wmse(n)/wmse(n− 1). 622 j.w. jiang, z. chen, y.h. wang, t. peng, s.l. zhu, l.m. shi when ϕ is larger than the threshold value ϕth, the learning rate is increased, and vice versa, which ensures that the learning rate is earlier and faster in the early learning stage. in the later stage of learning, the learning rate decreases, the vibration decreases and tends to be optimal. table 2: relationships between hidden layer neurons and errors hidden layer neuron training error (%) testing error (%) hidden layer neuron training error (%) testing error (%) 4 12.6334 12.0542 11 8.2683 7.0547 5 10.4325 10.1453 12 7.5876 6.2482 6 10.0836 9.2695 13 7.0218 5.0624 7 9.5836 9.0521 14 6.6482 6.1486 8 9.0452 8.8485 15 5.8473 5.1862 9 8.6142 8.5858 16 5.8593 5.8864 10 8.056 8.1524 17 5.7324 5.3485 the data set is acquired by sampling model in section 3.2. the sampling times is 500. 400 sets of data are used as training set and the others are used as test set. the data of hidden layer neurons are set to 4-17. because the parameters of four output neurons involved in this paper are equally important in motor performance evaluation, the training and testing results are shown in tab.2 according to the average weight. the error data show that with the increase of the number of hidden layer neurons, the training error and test error decrease gradually, but the test error fluctuates when the number of hidden layer neurons exceeds 11. the number of hidden layer neurons can be set at 13-15 in the comprehensive comparison error data, which is selected as 15 in this paper. 3.4 chaos artificial fish swarm algorithm in this paper, the chaos artificial fish swarm algorithms (cafsa) algorithm is used to optimize bpnn, and a pmsm motor parameter identification model based on cafsbpnna algorithm is proposed. relevant definitions of cafsbpnna algorithms the state of individual af fish in artificial fish swarm at time t is defined as: xs(t) = (xs1 (t),x s 2 (t), · · · · · · ,x s d(t)), (s = 1, 2, · · · · · · ,af_num). where af_num is the size of artificial fish, d is the state dimension of xs(t) (the sum of connection weights and thresholds of bpnn), xsi (t)(i = 1, 2, · · · · · · ,d) are variables waiting for optimization. es(t)(= 1 es(t) ) is the current food concentration of sth artificial fish. v isual is the visual range of artificial fish (perceived distance), step is moving step of artificial fish, ∆ is crowding factor. cafsbpnna is a three-layer bpnn network with one hidden layer, in which the number of neurons in the input layer(lin) is n = 9, the number of neurons in the hidden layer(lh) is m = 15, and the number of neurons in the output layer(lo) is l = 4. the dimension d of xs(t) of artificial fish can be calculated as: d = m(n + 1) + l(m + 1) = 15 ∗ 10 + 4 ∗ 16 = 216. the connection weights of lin → lh and lh → lo in bpnn are assigned to the front part of xs(t), and the thresholds of neurons in lh and lo are stored in the back part of xs(t). parameter estimation for pmsm based on a back propagation neural network optimized by chaotic artificial fish swarm algorithm 623 individual fish xs(t) contains the connection weights and thresholds needed to construct bpnn network, which can represent a bpnn. when af is initialized, af_num of the artificial fish are put in the range of [-1,1] by chaotic random mode. initial fish food concentration es(t) = 1 es(t) , where es(t) = wmse n , wmse is the global weighted mean square error, and n is the dimension of training sample set. the smaller the error es(t) between the expected output value and the actual output value of the network, the higher the food concentration es(t) of the artificial fish, and the better the performance of the neural network constructed by the artificial fish. the distance dpq between any two artificial fish xp (t) and xq(t) in af is calculated as follows: dpq = ∑d i=1 |x p i (t) −x q i (t)| d , xpi (t), x q i (t) is the corresponding state value of two artificial fish. when dpq < v isual, xp (t) and xq(t) are the corresponding state values of two artificial fish. behavior description of artificial fish swarm algorithms (a) prey behavior: fish tend to move towards high food concentration in water through their sensory organs. artificial fish xi(t) traverses all the partner fish in the search field, and the number of partners frd_num was calculated. at first, the target fish xtarget(t) is assigned xi(t), and the food concentration is compared with that of partner fish xj(t) in each traverse. if etarget(t) < ej(t), the target fish to be searched is recorded as partner fish xj(t). after searching all the partner fish, if the target fish xtarget(t) is found, then the artificial fish xi(t) moves one step to the xtarget(t) according to:xi(t + 1) = xi(t) + rounds() · step · xtarget(t) −xi(t) dtargeti ; otherwise, the artificial fish xi(t) moves one step to the cluster center. (b) swarm behavior: fish like to gather, forage collectively and avoid risks. assuming that the current state of artificial fish is artificial fish xi(t), the number of companion fish frd_num and the central position xc(t) are obtained by traversing the search field. if ec(t) frd_num > ∆ei(t), it means that the food concentration in the partner center is high but not too crowded. it can move forward to the center according to: xi(t + 1) = xi(t) + rounds() · step · xc(t) −xi(t) dci , otherwise preying behavior will be carried out. (c) fellow behavior: some individuals in the fish swarm find the food and share the location, and others follow and arrive at the food point to eat together. assuming that the current state of artificial fish is xb(t), the number of partners in the field of vision is frd_num and the highest concentration of food is xb(t). if eb(t) frd_num > ∆ei(t), it shows that the partner with the highest food concentration is not too crowded at xb(t). at this time, xi(t) can move towards partner xb(t) according to: xi(t + 1) = xi(t) + rounds() ·step · xb(t) −xi(t) dbi ; otherwise, preying behavior will be carried out. 624 j.w. jiang, z. chen, y.h. wang, t. peng, s.l. zhu, l.m. shi (d) moving behavior: when the individual fish xi(t) in the fish swarm finds that there is no other partner around them, it can move randomly to the next position within its field of vision according to: xi(t + 1) = xi(t) + rounds() ·step, thus widening the range of swarming, and further exploring for obtaining global optimum. (e) bulletin board: update and preserve the highest food concentration and the corresponding individual fish status. in the iteration traversal process, when the individual fish food concentration is higher than the bulletin board record, the bulletin board is updated. chaotic search chaotic phenomena refer to irregular and unpredictable behaviors in non-linear systems, which are ergodic and random. when chaotic search is introduced into afsa algorithm, its ergodicity can make the fish swarm search range include the whole target area, and its randomness is conducive to the search process breaking away from local extremum and tending to global optimum, which improves the search efficiency of afsa algorithm and shortens the convergence time of global optimization. typical chaotic systems include logistic mapping, lorenz system and henon map. in this paper, one-dimensional logistic chaotic system is used as the random motion behavior required in the search process of afsa. definitions are as follows:yk+1 = f(µ,yk) = µyk(1 − yk), where, yk ∈ (0, 1) and it is the value of y after kth iterations, and µ is the control parameter of the system. when µ = 4, the system is in a completely chaotic state, at which time yk can not repeat all the values between (0,1). logistic chaotic system optimizes the activities of artificial fish swarm. it means that the generation of all random numbers includes the initialization of artificial fish and the activities of fish swarm. therefore, the following provisions are made for artificial fish swarm: (a) the chaotic system generates the initial artificial fish. there are 216 initial connection weights and thresholds to be optimized in the parameter identification model of permanent magnet synchronous motor based on cafsnna. for the i-th artificial fish xi(t), the chaotic variable yik(k = 1, 2, · · · · · · , 216) is generated by using the logistic system, and the initial value xi(0) is set accordingly. if the range of parameters to be identified is (a, b), the initial state of artificial fish xi(0) is: xi(0) = (xi1(0),x i 2(0), · · · · · · ,x i 216(0)), where x i k(0) = ai + (bi + ai)yik. similarly, af_num artificial fish can be produced. (b) the random number function rounds() used in formula (14-17) is generated by logistic chaotic system when calculating the preying, swarming, fellowing and moving behavior of artificial fish swarm. 4 process analysis of pmsm motor parameter identification network algorithm based on cafspnna 4.1 network optimization algorithm for pmsm motor parameter identification based on cafsbpnna cafsbpnna includes two parts for pmsm motor parameter identification. in the early stage, the superior global optimization ability of chaotic artificial fish swarm is used to quickly approach the global optimal point. in the later stage, the fast local convergence of bpnn neural parameter estimation for pmsm based on a back propagation neural network optimized by chaotic artificial fish swarm algorithm 625 figure 4: procedure of pmsm motor parameter identification based on cafsbpnna network is used to complete the final global optimal point. the specific steps are as follows (see fig. 4). (1) initialize bpnn: according to the pmsm motor parameter evaluation model, the bpnn network topology structure is determined: the number of input layer, hidden layer, output layer and the number of their respective neurons. (2) initialize cafs: according to the topological structure of bpnn network, the connection weights and thresholds are determined. set the initial state value of artificial fish: fish size(af_num), individual fish state dimension(d), visual range(v isual), moving step(step) , crowding factor(∆), maximum iteration number( imax), target error( wmsemax). logistic chaotic system is used to determine the position and initial state of artificial fish. the initial position of individual fish xi(0) and corresponding food concentration ei(0) were calculated by loading samples data. (3) update bulletin board: the highest food concentration of individual fish was selected by traversing comparison, and the bulletin board was judged and updated. (4) behavior selection of af: fish swarm chooses and executes four kinds of activities: preying, fellowing, swarming and moving. logistic chaotic system is used to enter the next optimal position. (5) cafs termination condition judgment: according to the bulletin board, the optimal food concentration and the number of iterations, it is judged whether the cafs termination condition of cafsnna optimization is satisfied, and then it is transferred to the later bpnn optimization, otherwise the next step (3) is to continue the fish optimization. (6) bpnn optimization: transfer to the bpnn iterative optimization process, using the aforementioned bpnn back propagation iteration weight updating method until the final opti626 j.w. jiang, z. chen, y.h. wang, t. peng, s.l. zhu, l.m. shi iteration number 0 10 20 30 40 50 60 70 80 90 0 0.05 0.1 0.15 0.2 0.25 cafs-bpnna-base iteration number 20 30 40 50 60 70 ×10-3 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 cafs-bpnna-base figure 5: aggregation degree σ of optimized iterative population based on cafsbpnna mization conditions are satisfied to obtain the global optimal network. 4.2 selection of switching opportunity for two swarm intelligence algorithms in cafsbpnna network optimization in the identification model, the optimization process of bpnn network parameters is completed by cafs and bpnn algorithms in two stages. if the switching point is too late, the cafs algorithm will produce blind search when it approaches the global extremum point in the later stage, which will affect the convergence speed. on the contrary, if the switching point is too early and enters the bpnn search process too early, it will lead to premature convergence to the local extremum point, which will affect the convergence accuracy. therefore, accurately grasping the switching time of the two algorithms is very important for the whole optimization. in the traditional cafs algorithm, the termination conditions are mainly determined by the number of iterations and the target error, but in the optimization process of cafsnna fusion algorithm, it is difficult to accurately judge whether it has entered the optimal terminal of cafs fast convergence by only a single system target error. in the later stage of cafs algorithm, the individual fish in the population are clustered near the global extremum because of their preferential characteristics, so the individual aggregation degree of the population is obviously increased, and the individual fitness value of each individual fish will also approach the average fitness value of the system. for this reason, the aggregation degree defined by formula (7) is introduced to judge the aggregation of individual fish [9]. σ = 1 d d∑ j=1 ( fj −favg f ) 2 (7) in the formula, fj is the fitness function value of individual fish xj(t), favg is the average fitness value of population, and f is the normalized parameter. in the early stage of identification, the individual fish in the fish swarm have great difference, including loose distribution, low clustering degree and large value. in the later stage of optimization, the fish flock clustering degree increases and aggregation degree σ is higher. when σ is less than the threshold value, aggregation degree of the fish swarm is very high. the fusion algorithm has entered the later stage of rapid convergence of cafs, and then it can be turned into bpnn for precision optimization. fig.5 is the change curve of aggregation degree σ in the iteration process of cafsbpnna, and cafs-bpnna-base is the original curve of iteration optimization. cafs-bpnna-σ is an parameter estimation for pmsm based on a back propagation neural network optimized by chaotic artificial fish swarm algorithm 627 ! "# "# $ %&'( ")*+,./ ./ ./ ")*+"# ! " # $ % 01 *234561$62375 234563892:;1!623755 .< <$ .< <! &'( 7&&& & ) 0=# <>?@ a) b2: ) c d e 0) f$ f! g>9(=> g?#h> c/h i.>-j f>9(=> f?#h> (29k# lf.ab c d e c9(=> d#h> e9>-j c9(=> d#h> 0=# m n .9>-j .< l(##! 017 014 g! g$ ! $ " ok 1! 1$ p8 ecql*d.,,c ok 1! 1$ " l@r(# #)*+,*-)-. /)012 -, slh>hr-tur9h>v#tfki$t3f5+ slh>hr-tur9h>v#tfki!t3f5+ slh>hr-t@2--#/ht ki!t3c5+ slh>hr-t@2--#/ht ki$t3c5+ sorhr-tk(##!t")t3->!'k5+ figure 6: the block diagram of pmsm speed control system based on cafs-bpnna iterative curve with the judgment of population aggregation degree σ. after the first curve is below 0.0001, the cafs fish swarm optimization enters the disordered state until it reaches the preset iteration number of 50 and then turns to bpnn to search for the global extremum quickly. the latter iteration curve adds the optimum iteration of judging the degree of population aggregation σ to switch directly to another bpnn optimization algorithm after σ drops below 0.0001, and reaches the extremum point after fast convergence. the comparison results show that the iteration optimization which combines the judgment of population aggregation degree σ can effectively avoid the disordered swimming in the later optimization stage of the fish swarm algorithm and improve the convergence speed of the fusion algorithm. 5 result and analysis 5.1 experimental setting of pmsm motor parameter identification table 3: nominal parameters of pmsm motors parameter value parameter value parameter value p/w 2600 ω/r ·min−1 2000 rs/ω 2.875 ψf/wb 0.175 i/a 4 t/n ·m 5 ld/mh 8.5 j/kg ·m2 0.48e−5 u/v 380 ωmax/r ·min−1 2500 lq/mh 8.5 pp/p 4 the pmsm speed control system based on speed closed-loop control regulation is constructed on the motor control platform, as shown in fig.6. the parameters of pmsm motor are as shown in tab.3. the range of parameters to be determined is rs ∈ (0, 5),ld ∈ (0, 015),lq ∈ (0, 015),ψf ∈ (0, 3). the motor adopts svpwm control strategy and the pulse current(-2a) injected into the d-axis at 0.1s lasts 5 ms. fig.7 shows the speed tracking curve of the motor when the current pulse is injected at ω = 2000r/min. the data show that the speed fluctuation of the motor after the instantaneous pulse input is less than 0.5%, and the influence on the system can be neglected. data0 and data1 are generated 628 j.w. jiang, z. chen, y.h. wang, t. peng, s.l. zhu, l.m. shi figure 7: pmsm motor speed ω at pulse current id=-2a by sampling {ud,uq, id, iqandω} before and after pulse current injection, and 500 groups are sampled as training and testing sets of cafsbpnna. 5.2 experimental setting of pmsm motor parameter identification the parameters of cafsnna algorithm are as follows: the input neuron number n = 9; the hidden neuron number m = 15, and tansig taken as transfer function; output neuron number l= 4, and linear purelin taken as transfer function. the training error is calculated by wmse. connection weight updating is calculated by cumulative error method of adaptive inertial momentum. intelligent fish swarm settings include: the fish swarm number af_num = 30, individual fish size dimension d = 216, visual field range v isual = 1, maximum step size step = 0.5, crowding factor∆ = 14, maximum iteration number imax = 400, training sample 400 groups, minimum allowable training error is 0.001. in order to compare the performance of pmsm motor parameter identification based on cafsbpnna algorithm, rls and pso are introduced for analogy identification. rsl parameter configuration: learning factor c1 = c2 = 2, inertia weight w = 0.5, the maximum number of iterations is 400.pso parameter configuration: estimate θ̂(0) = 1 n p0 , covariance p0 = p0i,p0 = 10 6, the maximum number of iterations is 400. 5.3 training error analysis based on motor speed and torque the speed and torque of pmsm motor are the two main indexes that affect the performance of the motor. therefore, in the experiment, the motor is set in different conditions of speed and torque to compare the identification ability of various intelligent algorithms for motor parameters.fig.8 is pmsm motor speed ω = 2000r/m and load torque is 2n · m. the three intelligent algorithms in this paper track and compare the waveforms of motor parameter identification effect and iteration times. tab.4 gives the identification error data of three algorithms for motor under normal speed of 2000r/m and maximum speed of 2500r/m, as well as torque is 2n ·m and 3n ·m. fig.8 shows that rls has the worst convergence performance. it usually needs 150 iterations to reach the convergence point of all identification parameters. pso has better convergence effect, but its convergence accuracy is not high compared with other two algorithms. compared with rls and pso, the proposed cafsbpnna algorithm has better convergence time and accuracy. when the torque value is fixed to 2n·m and the speed is adjusted to the maximum speed, the total training error of the three algorithms decreases, among which the rls decreases the most, the cafsbpnna algorithm changes the least and the identification accuracy is the highest. when the speed is fixed at 2000r/m and the torque is increased by 1n ·m, the single error of pso arithmetic fluctuates, the errors of rs and ld become larger, the errors of lq and ψf become smaller, and the total error increases. the ψf of rls and cafsbpnna is basically constant, parameter estimation for pmsm based on a back propagation neural network optimized by chaotic artificial fish swarm algorithm 629 iteration/times 0 50 100 150 200 250 300 350 400 l d /h 0 0.002 0.004 0.006 0.008 0.01 0.012 pso rls casf-bpnna base iteration/times 0 50 100 150 200 250 300 350 400 l q /h 0 0.005 0.01 0.015 pso rls casf-bpnna base iteration/times 0 50 100 150 200 250 300 350 400 0 0.05 0.1 0.15 0.2 0.25 0.3 pso rsl casf-bpnna base iteration/times 0 50 100 150 200 250 300 350 400 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 pso rls casf-bpnna base figure 8: pmsm motor parameter identification results (ω = 2000r/min,t = 2n ·m) and the total training error decreases with the decrease of rs,ld and lq. cafsbpnna has the smallest change and the highest accuracy. in summary, compared with rsl and pso, cafsbpnna is superior to the other two reference algorithms in parameter identification under uniform working conditions and error fluctuation under changing working conditions, and it always shows good convergence and identification accuracy. table 4: pmsm motor parameter identification error working condition algorithm rs/ω ld/mh lq/mh ψf/wb error t = 2n ·m ω = 2000r/min pso rsl cafbpnna 0.554/19.3% 0.741/25.8% 0.132/4.6% 1.185/13.9% 1.343/15.8% 0.365/4.3% 1.113/13.1% 1.181/13.9% 0.467/5.5% 0.009/5.2% 0.078/44.8% 0.007/4.5% 12.8% 25.1% 4.7% t = 2n ·m ω = 2500r/min pso rsl cafbpnna 0.477/16.6% 0.293/10.2% 0.129/4.5% 0.552/6.5% 1.011/11.9% 0.229/2.7% 1.147/13.5% 0.629/7.4% 0.382/4.5% 0.012/7.1% 0.072/41.3% 0.006/3.8% 10.9% 17.7% 3.8% t = 3n ·m ω = 2000r/min pso rsl cafbpnna 0.684/23.8% 0.514/17.9% 0.117/4.1% 1.198/14.1% 1.037/12.2% 0.365/4.3% 0.824/9.7% 0.663/7.8% 0.314/3.7% 0.008/4.9% 0.081/46.8% 0.007/4.4% 13.1% 21.1% 4.1% 630 j.w. jiang, z. chen, y.h. wang, t. peng, s.l. zhu, l.m. shi 6 conclusion aiming at the identification of pmsm motor parameters rs,ld,lq and ψf, the full rank identification model of motor parameters is derived based on pmsm motor voltage equation and flux equation. the feasibility of motor parameters identification by d-axis current injection in steady state and the sampling method of sample data set are analyzed. a method of parameter identification of pmsm motor by optimizing bpnn with cafs is proposed. compared with pso and rls, experiments show that the proposed cafsbpnna algorithm has high accuracy and short convergence time for motor parameter identification, and has the stability of identification when working conditions are changed. the specific advantages are as follows: (1) the cafsbpnna algorithm fully coordinates the local and global search performance advantages of bpnn and cafs, speeds up the convergence speed and improves the convergence of motor parameter identification. (2) the application of chaotic search reduces the sensitivity of initial value of parameter identification and enhances the robustness of parameter setting in the learning process. (3) the cafsbpnna algorithm has good stability, and can maintain good identification effect under different speed and torque conditions. cafsbpnna algorithm has good versatility. it can be used not only for parameter identification of pmsm motor, but also for parameter identification and on-line tracking of other motors such as stepping motor, induction motor and precision motor. cafsbpnna algorithm proposed in this paper is more computational than on-chip system resources.the system adopts online sampling and periodic uploading of motor operation data. the parameters of identification network are optimized and updated by off-line optimization and remote download. later on-chip system is implemented. in the future, the online optimization and updating methods of network parameters are further studied on the basis of improving the performance of on-chip system and optimizing the time and space complexity of algorithm. acknowledgment financial supports from the priority academic program development of jiangsu higher education institutions. bibliography [1] babak, n.m.; meibody-tabar, f.; sargos f.m.(2004). mechanical sensorless control of pmsm with online estimation of stator resistance, ieee transactions on industry applications, 40(2), 457-471, 2004. 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[26] zhang, h.p.; ye, j.h.; yang, x. p.; muruve, n.w.; wang, j.t.(2018). modified binary particle swarm optimization algorithm in lot-splitting scheduling involving multiple techniques, international journal of simulation modelling, 17(3), 534-542, 2018. international journal of computers communications & control issn 1841-9836, 13(6), 1007-1031, december 2018. simulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín ignacio pérez-hurtado*, mario j. pérez-jiménez, david orellana-martín research group on natural computing department of computer science and artificial intelligence university of seville, seville, spain {perezh,marper,dorellana}@us.es *corresponding author: perezh@us.es gexiang zhang school of electrical engineering southwest jiaotong university, chengdu, shichuan, china zhgxdylan@126.com abstract: methods based on rapidly-exploring random trees (rrts) have been widely used in robotics to solve motion planning problems. on the other hand, in the membrane computing framework, models based on enzymatic numerical p systems (enps) have been applied to robot controllers, but today there is a lack of planning algorithms based on membrane computing for robotics. with this motivation, we provide a variant of enps called random enzymatic numerical p systems with proteins and shared memory (renpsm) addressed to implement rrt algorithms and we illustrate it by simulating the bidirectional rrt algorithm. this paper is an extension of [21]a. the software presented in [21] was an ad-hoc simulator, i.e, a tool for simulating computations of one and only one model that has been hard-coded. the main contribution of this paper with respect to [21] is the introduction of a novel solution for membrane computing simulators based on automatic programming. first, we have extended the p-lingua syntax –a language to define membrane computing models– to write renpsm models. second, we have implemented a new parser based on flex and bison to read renpsm models and produce source code in c language for multicore processors with openmp. finally, additional experiments are presented. keywords: membrane computing, renpsm, robotics, rrt, p-lingua. areprinted (partial) and extended, with permission based on license number 501431225 ©[2018] ieee 7th international conference on computers communications and control (icccc) 1 introduction robots are machines oriented to objectives equipped with actuators, sensors and computation units acting under physical constraints. regardless of their morphology, they should accomplish tasks by acting in the real world. this is one of the main reasons by which robot motion planning is an eminent research area in robotics [8, 11, 21]. in general terms, the problem of motion planning can be defined in the configuration space of a robot as follows: given a start configuration state, a goal configuration state, a geometric description of the robot, and a geometric description of the environment, find a path that moves the robot gradually from start to goal. a configuration state is a specification of the positions of all robot points relative to a fixed coordinate system. this is usually expressed as a vector of positions and orientations, for example, a rigid-body robot in a 2d world can be expressed as a vector (x,y,θ) representing the copyright ©2018 cc by-nc 1008 i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín center (x,y) of the robot in a fixed coordinate system and its yaw angle θ, i.e, the heading angle of the robot. since the shape of the robot is described, all of its points are then known. several constraints can be added to this problem, the most common is to reach the goal while never touching any obstacle in the environment. others can also be added, for example, a social robot could restrict configuration states in order to guarantee the human comfort. the configuration space of a robot can also be constrained by the type of movements the robot can perform. in this sense, nonholonomic robots are those that cannot instantly modify its direction without employing rotation in-place. on the other hand, holonomic robots can do it (assuming zero mass). for example, a holonomic robot in a 2d world can move along the x axis and the y axis, as well as modify its yaw angle if needed. but a nonholonomic robot can only move forward/backward and/or modify its yaw angle. this is the typical case of dual-wheeled mobile robots and cars. classical path planning algorithms have been widely adapted and applied to the problem of motion planning with constraints in robots, for example, in [26], an application of the dijkstra algorithm for robot path-planning was presented. in such solutions, the general problem is usually divided into two smaller problems: the global path planning problem, as described above; and the local path planning, where the robot tries to connect two consecutive states in real-time considering features not included in the global plan as, for example, dynamic obstacles. the accumulated error during the local planning conducts to periodically recompute the global plan. for this reason, the computational complexity of global planners is a critical point regarding to real-time constraints. many efforts have been made to provide good global planners. for example: in [25], a search algorithm, called d∗, was presented for path planning in real-time environments. in [15], a variant of the classical search algorithm a∗ is applied to grids with blocked and unblocked cells. in [12], a tool for global path planning, called rapidly-exploring random trees (rrt), was presented. the class of rrt algorithms for global path planning is based on the randomized exploration of the configuration space before moving the robot by building a tree in memory where nodes represent states that can be reached by the state of the corresponding parent in a fixed amount of time, furthermore each edge contains a velocity reference to reach the state in the child node from the state in the parent node. it is currently one popular method in robot motion planning due to its good properties. the computed rrt can be used together with search algorithms or, as presented in [13], the rrt generation algorithm can be used by itself as a path planning algorithm, where two rrts are built simultaneously, one beginning from the initial configuration and another one beginning from the ending configuration (bidirectional rrt). in order to follow the path in safe manner, a local planner module should be executed considering dynamic obstacles. finally, each motor of the robot must be able to reach and maintain velocity references for fixed periods of time. this is the function of a type of software called controller on-board of the robot. thus, robot control [1] is the branch of robotics dedicated to the study and practice of controlling robots. robot controllers are usually based on common silicon microprocessors, but in the recent years, some classes of membrane systems [16] have been in use for modelling them [18] [19] [20] [29]. membrane systems are models of computation based on the structure and functions of the living cells. in a membrane system, there are objects being evolved inside compartments according to rules applied in a non-deterministic, maximally parallel way. they have been used as a new technique to attack the p versus np problem [22], and several applications have been also studied: stochastic p systems for modelling biological phenomena [24], probabilistic p systems for modelling real ecosystems [2], spiking neural p systems incorporating fuzzy reasoning, for fault diagnosis and learning [27], and others. with respect to robot control, numerical p systems (nps) were used for modelling and simsimulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming 1009 ulating robot controllers [20], although the initial application of nps was related to economical processes [17]. a variant called enzymatic numerical p systems (enps) [18] was introduced and applied to the distributed control of a swarm of mobile robots. indeed, reactive and proportionalintegral-derivative (pid) dual-wheeled robot controllers have been successfully designed and simulated by means of enps, as well as software simulation tools [29]. this variant has been also used [19] to address robot localization problem [8], where the robot must know its position in the environment by using sensors. in [21], following [23], a new variant of enps called random enzymatic numerical p systems with proteins and shared memory (renpsm, for short) was introduced. new syntactical ingredients were included to fit the requirements of the rrt algorithm: • random numbers: the algorithm uses a randomized method to explore the physical space, therefore random numbers must be generated. • shared memory: the algorithm is parallelized using processes sharing common variables, and a distinguished membrane, called shared memory, is included. at any instant, each membrane can read from or write to it. • proteins: in order to synchronize the sequential execution of the algorithm, proteins are used. this paper is an extension of [21]. the software presented in [21] was an ad-hoc simulator, i.e, a tool to simulate computations of one and only one model that has been hard-coded. ad-hoc simulators can be optimized for specific hardware architectures, but they are less debug-friendly than generic simulators, since changes in the model imply changes in the source code of the simulator. on the other hand, p-lingua [3, 7, 33] is a language to define membrane computing models, there are several simulators based on p-lingua, most of them are implemented in the plinguacore library [33] in java language. in this paper, we provide a novel approximation taking the advantages of ad-hoc and generic simulators by using automatic programming. we have implemented a tool for parsing p-lingua files defining renpsm models and generating source code in c and openmp for ad-hoc simulators. thus, we have a flexible way to debug since we are using a language to define the models instead of hard-coding them in the source code. moreover, the generated source code is able to run on multicore processors by using openmp. furthermore, additional virtual experiments are presented in this paper. this paper is structured as follows. in the next section, some notions about robot path planning are introduced. in section 3, the rapidly-exploring random trees (rrts) are described with some details. section 4 is devoted to introduce random enzymatic numerical p systems with proteins and shared memory. in section 5, a renpsm model for the bidirectional rrt algorithm is described. in section 6, the original software presented in [21] based on c++ and ros [30] is explained, including some experimental results. in section 7, the new software presented in this paper is introduced, including an extension of the p-lingua syntax for renpsm, as well as additional experiments. finally, conclusions and future work are drawn. 2 robot path planning in general terms, robot path planning can be solved by applying a solution based on three modules: • global planner: it receives the desired ending configuration of the robot, its safety radius and current localization, as well as the precomputed position of the static obstacles in 1010 i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín the environment and current information of sensors and odometry. the current dynamic obstacles detected by the sensors are added to the static ones in order to generate a more descriptive information of the environment. the odometry is used to obtain the current velocity of the robot when kinodynamic constraints are considered. then, the global planner computes a plan from the starting configuration xinit to the desired final configuration xend of the robot. the plan is represented as a sequence of local goals {gi|1 ≤ i ≤ n}, where g1 = xinit and gn = xend. each goal can be reachable from the previous goal considering the constraints of the problem, i.e, avoiding static obstacles, nonholonomic and kinodynamic constraints, etc. rrt algorithms and other similar algorithms can be used for this task. • local planner: it receives the sequence of local goals generated by the global planner, as well as the current information of sensors, localization and odometry, then it sends velocity references to the controller in order to command the robot along the path. several algorithms such as the dynamic window approach [5], pure pursuit [4], and potential fields [10] algorithms, among others and variants, can be used. • controller: it receives velocity references from the local planner and manages the power of the motors to fit each reference and maintain it constant or accelerate or reduce it until the next one. in figure 1, it is represented the general robot path planning cycle. first the robot computes a global plan from its current pose to the desired ending pose; if the plan can be generated, i.e, the robot could reach the destination considering all the constraints, then the local planner receives a sequence of intermediate goals and sends velocity references to the pid controller in order to follow the path in a safe manner until reaching the destination; if some error occurs, for example, a dynamic obstacle is too close to the next local goal or the robot is too far from the next local goal (considering a predefined threshold), then the global plan is recomputed from the current robot position. notice that if there is a dynamic obstacle too close to the ending configuration, then the global plan cannot be found. 3 rapidly-exploring random trees an rrt [12] is a randomized tree structure for rapidly exploring in memory a state space x from an initial state xinit. it can be successfully used for nonholonomic and kinodynamic global path planning in robotics [13]. nodes in an rrt represent possible reachable states, for mobile robots in a 2d world which is given by (x,y,θ) where (x,y) are the cartesian coordinates of the robot position and θ is the heading angle. however, the heading angle can be omitted in order to reduce the size of the tree. it is assumed that a fixed obstacle region xobs ⊆ x must be avoided, so the nodes of the rrt are states in xfree, the complement of xobs in x. edges in an rrt represent transitions between reachable states, each of which is labelled with the velocity reference u that the robot should execute for a fixed period of time ∆t in order to change the corresponding states. for a mobile robot in a 2d world, the velocity reference can be represented by the pair of linear and angular velocities (v,ω) to be sent to the controller. on the other hand, if θ has been omitted, the edges in the rrt are labelled with instant linear velocities. thus, a holonomic robot can reach a state x1 from another state x0 connected by an edge labelled with u by applying x1 = x0 + u ·∆t. in the case of nonholonomic robots, the local planner should select the best sequence of motions in order to approximate x1, for example, if simulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming 1011 figure 1: robot path planning cycle the robot can rotate in-place, a naive solution is to perform a rotation in-place before developing the motion in a straight line. algorithm 1 generate_rrt require: xinit,k,ρ, ∆t,x,xobs,dmin 1: vτ ←{xinit}; eτ ←∅; 2: for k = 1 to k do 3: xrand ← random_state(x); 4: extend(τ,xrand,ρ, ∆t,xobs,dmin); 5: end for 6: return τ = (vτ,eτ ) algorithm 1 is an iterative method to generate an rrt using the function extend defined in algorithm 2, where: • xinit is the initial state. • k is the number of iterations to build the rrt. • ρ is a prefixed distance metric. • ∆t is a fixed amount of time for transitions. 1012 i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín algorithm 2 extend require: τ,x,ρ, ∆t,xobs,dmin 1: xnear ← nearest_neighbor(x,τ); 2: if distance(x,xnear) ≥ dmin then 3: u ← select_input(x,xnear); 4: if ¬ collision(xnear,u, ∆t,xobs) then 5: xnew ← new_state(xnear,u, ∆t); 6: vτ ← vτ ∪{xnew} 7: eτ ← eτ ∪{(xnear,xnew)} 8: if distance(x, xnew) < dmin then 9: return reached; 10: else 11: return advanced; 12: end if 13: else 14: return trapped; 15: end if 16: else 17: return trapped; 18: end if • x is the state space. • xobs is the obstacle state space. • dmin is the minimum distance threshold according to ρ in order to include a new node in the rrt. • τ = (vτ,eτ ) is the rrt generated. • random_state(x) is a function to get a random state from x • nearest_neighbor(x,τ) is a function to get the closest node to x in τ according to ρ. • distance(x,xnear) is a function to get the distance of x to xnear according to ρ. • select_input(x,xnear) is a function to get the velocity input that should be commanded to the robot in order to achieve state x from xnear. • collision(xnear,u, ∆t,xobs) is a function returning true if a collision could be produced moving the robot from state xnear by applying the input u for ∆t time considering the obstacles in xobs. • new_state(xnear,u, ∆t) is a function to get a new state xnew by applying the input u to the robot for ∆t time starting at state xnear. the function extend tries to add a new node to the rrt τ considering a reference x. if the function fails, then it returns trapped; if the new node is closer than dmin to x, then it returns reached; and if the new node is far from x considering dmin, then the function returns advanced. figure 2 describes a rrt generated after 5000 iterations by using algorithm 1 with the euclidean distance and omitting the heading angle. simulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming 1013 figure 2: rrt generated after 5000 iterations in [13], a bidirectional rrt algorithm was introduced for path planning. the main idea of this algorithm is to create two rrts: τa starting at xinit and τb starting at xend. if τa and τb are connected in a prefixed number k of iterations, then a path is returned; otherwise the function returns failure. algorithm 3 generate_path require: xinit,xend,k,ρ, ∆t,x,xobs,dmin 1: vτa ←{xinit}; eτa ←∅; 2: vτb ←{xend}; eτb ←∅; 3: for k = 1 to k do 4: xrand ← random_state(x); 5: if extend(τa,xrand,ρ, ∆t,xobs,dmin) 6= trapped then 6: if extend(τb,xnew,ρ, ∆t,xobs,dmin) = reached then return path(τa, τb); 7: end if 8: end if 9: swap(τa, τb); 10: end for 11: return failure algorithm 3 is the bidirectional rrt algorithm presented in [13], where: • xinit is the initial state. • xend is the ending state. • τa = (vτa,eτa ) is an rrt starting at xinit. • τb = (vτb,eτb ) is an rrt starting at xend. • path(τa, τb) is a function to compute a path from the initial node of τa to the initial node of τb. both rrts must be connected. • swap(τa, τb) is a procedure to interchange the values of τa and τb. 1014 i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín the rest of variables have the same meaning than the variables used in algorithms 1 and 2. in this paper we propose the algorithm 4 as a parallel version of the bidirectional rrt algorithm, where τa and τb are built at the same time. algorithm 4 generate_path_parallel require: (xinit,xend,k,ρ, ∆t,x,xobs,dmin 1: vτa ←{xinit}; eτa ←∅; 2: vτb ←{xend}; eτb ←∅; 3: for k = 1 to k do 4: xrand,a ← random_state(x); 5: xrand,b ← random_state(x); 6: loop 7: resulta = extend(τa,xrand,a,ρ, ∆t,xobs,dmin); 8: resultb = extend(τb,xrand,b,ρ, ∆t,xobs,dmin); 9: end loop 10: if resulta 6= trapped then 11: if extend(τb,xnew,ρ, ∆t,xobs,dmin) = reached then return path(τa, τb); 12: end if 13: end if 14: if resultb 6= trapped then 15: if extend(τa,xnew,ρ, ∆t,xobs,dmin) = reached then return path(τa, τb); 16: end if; 17: end if; 18: end for; 19: return failure 4 random enzymatic numerical p systems with proteins and shared memory in this section a variant of enzymatic numerical p systems incorporating new features is presented, in order to simulate rrt algorithms. a random enzymatic numerical p systems with proteins and shared memory (renpsm, for short) of degree (p,q), p,q ≥ 1 is a tuple (h,µ,p,emem,emem(0), {ph(0),v arh,v arh(0),prh) | h ∈ h},r,ha,hb), where: 1. h = {1, . . . ,p · q}∪{v,mem}, mem /∈ {1, . . . ,p · q}, v /∈ {mem, 1, . . . ,p · q}, is the set of labels of the system; 2. µ is a dynamic membrane structure (rooted tree) initially consisting of one skin membrane with label v including two inner membranes labelled respectively with ha ∈ {1, . . . ,p · q} and hb ∈ {1, . . . ,p · q}, ha 6= hb, in such manner that along the computation only child membranes of ha and hb will be created with labels in {1, . . . ,p · q}. in figure 3, it is represented the initial membrane structure; 3. mem is the label of a distinguished component (the shared memory of the system); 4. p is a finite set of objects, called catalyzer proteins, and ph(0) is the protein initially associated with region labelled by h; simulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming 1015 5. emem is a finite set of variables, called enzymes, disjoint with v armem, and emem(0) is the initial values of the enzymes; 6. v arh,h ∈ h, is a finite set of variables xj,h associated with region labelled by h (a membrane or the shared memory), its values are natural numbers and the value of xj,i at time t ∈ n is denoted by xj,i(t); 7. v arh(0) is a vector that represents the initial values for variables in v arh; 8. prh,h ∈ h, is a finite set of programs associated with region labelled by h, having the following syntactical format f(x1,h, . . . ,xkf ,h) e(f);α(f) −−−−−−−−−−−−−→ c1|v1, . . . ,cnf |vnf , where: – f(x1,h, . . . ,xkf ,h) is a computable function (the production function), being x1,h, . . . ,xkf ,h ∈ v arh; – c1|v1, . . . ,cnf |vnf is the repartition protocol associated with the program, being c1, . . . ,cnf natural numbers specifying the proportion of the current production distributed to variables v1, . . . ,vnf ∈ v arh∪v arpar(h)∪v arch(h), where par(h) is the parent of h and ch(h) is the set of child of h in µ; – e(f) ∈ eh is an enzyme and α(f) ∈ p is a protein, both of them associated with program f , if no enzyme or protein is used in a program then it will be omitted; 9. r is a finite set of rules of the following form: – protein evolution rules: [ α → α′ ]h, where h ∈ h,α ∈ p and α′ ∈ p . – writing-only communication rules between the shared memory and the membranes (h, xh /yh,mem , mem) w α where xh ∈ v arh,yh,mem ∈ v armem,α ∈ p in such manner that there is, at most, one rule for each membrane h ∈ {1, . . . ,p · q}. variables yh,mem,yh′,mem should be different for two membranes h,h′. – reading-only communication rules between the shared memory and the membranes: (h, xh /ymem , mem) r α where xh ∈ v arh,ymem ∈ v armem,α ∈ p . variable ymem is the same for each h ∈{1, . . . ,p ·q}. – membrane creation rules:[ [ x1,h , x2,h, , . . . , xn,h ] h ] h′ ; α where h,h′ ∈{1, . . .p ·q ·r} are different, α ∈ p and x1,h , . . . , xn,h ⊆ v arh. the term region h (h ∈ h) is used to refer to membrane h in the case h ∈{1, . . . ,p ·q}∪{v}, as well as to refer to the shared memory in the case h = mem. next, we describe the semantics of renpshs. a configuration of a renpsh at any instant t is described by the current membrane structure µ, together with proteins and all values of the variables and enzymes associated with all regions. the initial configuration is (µ,emem(0),{ph(0),v arh(0)|h ∈ h}), where µ = [[ ]ha [ ]hb ]v. we will call µa (resp. µb) to the membrane structure rooted in membrane ha (resp. hb). 1016 i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín figure 3: the initial membrane structure with a representation of the shared memory. a program f(x1,h, . . . ,xkf ,h) ef ;pf−→ c1|v1, . . . ,cnf |vnf associated with a region is applicable to a configuration ct at moment t, if the value of e(f) at that instant is greater than min{x1,h(t), . . . ,xkf ,h(t)} and protein α(f) is inside the region h of ct. when applying such a program, variables associated withct are processed as follows: first, the value f(x1,h(t), . . . ,xkf ,h(t)) is computed as well as the value q(t) = f(x1,h(t), . . . ,xkf ,h(t)) c1 + · · · + cnf this value represents the unary portion at instant t to be distributed among variables v1, . . . ,vnf according to the repartition expression. thus, q(t) · cs is the contribution added to the current value of vs (1 ≤ s ≤ nf ), at step t + 1. so, vs(t + 1) = vs(t) + q(t) · cs and vs(t) become zero, i.e, it is assumed that variable vs is "consumed" when the production function is used and other variables retain their values. each program in each membrane can only be used once in every computation step, and all the programs are executed in parallel. a protein evolution rule [ α → α′ ]h is applicable to a configuration ct at moment t if protein α is in membrane h of ct. when applying such a rule the protein α in h evolves to protein α′ in h. these rules are applied in a maximal manner. a writing-only communication rule between the shared memory and the membranes, (h, xh /yh,mem , mem) w α , is applicable to a configuration ct at moment t if protein α is in membrane h of ct. when applying such a rule the value xh(t) is assigned to the variable yh,mem(t + 1) of the shared memory, that is yh,mem(t + 1) ← xh(t) . these rules are applied in a maximal manner. a reading-only communication rule between the shared memory and the membranes, (h, xh /ymem , mem) r α is applicable to a configuration ct at moment t if protein α is in membrane h of ct. when applying such a rule the value ymem(t) is assigned to the variable xh(t + 1) of membrane h, that is xh(t + 1) ← ymem(t). these rules are applied in a maximal manner. a membrane creation rule [ [x1,h , . . . , xn,h ]h ]h′ ; α is applicable to a configuration ct at moment t if protein α is in membrane h′ of ct. when applying such a rule, a new membrane labelled by h is created in such manner that h′ is the parent of h and the set of its variables is v arh = {x1,h , . . . , xn,h}. given a random enzymatic numerical p system with proteins and shared memory π, we say that configuration ct at time t yields configuration ct+1 in one transition step if we can pass from ct to ct+1 by applying in parallel each program in each membrane only once, and by applying the rules in a maximal parallel way following the previous remarks. a computation of π is a (finite simulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming 1017 or infinite) sequence of configurations such that: (a) the first term is the initial configuration of the system; (b) for each n ≥ 2, the n-th configuration of the sequence is obtained from the previous configuration in one transition step; and (c) if the sequence is finite (called halting computation) then the last term is a halting configuration (a configuration where no rule of the system is applicable to it). all the computations start from an initial configuration and proceed as stated above; only halting computations give a result, which is encoded by the objects present in the output region iout associated with the halting configuration. if c = {ct}t<r+1 of π (r ∈ n) is a halting computation, then the length of c, denoted by |c|, is r. for each i (1 ≤ i ≤ q), we denote by ct(i) the finite multiset of objects over γ contained in all membranes labelled by i at configuration ct. 5 simulation of one iteration of the bidirectional rrt algorithm for path planning the input of the bidirectional rrt algorithm generating a global path for a robot trajectory consists of the following parameters (xinit,xend,k,ρ, ∆t,x,xobs,dmin), where: – xinit is the initial state. – xend is the ending state. – k is the number of iterations to find the path. – ∆t is a fixed amount of time for transitions. – x is the state space. – xobs ⊆ x is the obstacle state space. – dmin is the minimum distance threshold according to some distance metric ρ in order to include a new node in the rrt. for mobile robots in a 2d environment, the state space is given by (x,y,θ), i.e, the cartesian coordinates (x,y) and the heading angle θ of all the possible robot poses. however, the angle θ has been omitted in this solution to reduce the size of the problem and the state space is given by (x,y) considering the euclidean distance as distance metric. in this case, a holonomic robot can follow the rrt by performing motions in straight line, otherwise a nonholonomic robot can include rotations in-place. moreover, any state or position (i,j) ∈ {0, . . .p− 1}×{0, . . .q − 1} in an euclidean space constrained by a rectangle of p width and q height distance units can be encoded by the natural number j ·p+i+ 1. in such a manner, given a natural number n encoding a state (i,j), the following holds: i = rm(n− 1,p) and j = qt(n− 1,p). one iteration of the parallel bidirectional rrt algorithm defined in algorithm 4 will be simulated by a renpsm of degree (p,q) π = (h,µ,p,emem,emem(0), {(ph(0),v arh,v arh(0),prh) | h ∈ h},r,ha,hb) defined as follows: – h = {1, . . . ,p ·q}∪{v,mem}, v /∈{1, . . . ,p ·q}, mem /∈{1, . . . ,p ·q}. – µ = [[ ]ha [ ]hb ]v with ha ∈ {1, . . . ,p · q}, hb ∈ {1, . . . ,p · q} and ha 6= hb. we call µa to the membrane structure rooted on ha and µb to the one rooted on hb. 1018 i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín – p = {αi | 1 ≤ i ≤ 18}, and ph(0) = {α1}, for each h ∈ h. – emem = {flagamem,flagbmem,p ·q + 1} and emem(0) = {p ·q + 1}. – the set of variables is: – v arh = {x1,h,x2,h,y1,h,y2,h,z1,h,z2,h,dh}, for each h, 1 ≤ h ≤ p ·q. – v armem = {x1,mem,x2,mem,x3,mem,x4,mem}∪ {y1,mem,y2,mem,y3,mem,y4,mem}∪ {z1,mem,z2,mem,z3,mem,z4,mem}∪ {u1,mem,u2,mem,u3,mem,u4,mem}∪ {amem,bmem,namem,nbmem,haltmem}∪ {ah,mem,bh,mem | 1 ≤ h ≤ p ·q}. – initially, all variables in v arh(h 6= ha ∧h 6= hb) and all variables in v arha and v arhb different to y1,h,y2,h, are equal to zero. besides, initially the tuple (y1,ha,y2,ha ) (resp. (y1,hb,y2,hb )) provides the position of the initial state of the robot ha (resp. the position of the final state of the robot hb). – if the value of variable haltmem is equals to 1, then the computation stops. • next, the finite set of programs prh and the set of rules r of the system are defined according to the requirements to simulate the bidirectional rrt algorithm. • in order to synchronize the sequence of an iteration, for each h ∈ h the protein evolution rules [ αi → αi+1 ]h, for 1 ≤ i ≤ 17, and [ α18 → α1 ]h are considered. • four random numbers are generated in the shared memory:  production function : f(x1,mem) = random(i, 0 ≤ i < p) repartition protocol : 1|x1,mem protein : α1   production function : f(x2,mem) = random(i, 0 ≤ i < q) repartition protocol : 1|x2,mem protein : α1  production function : f(x3,mem) = random(i, 1 ≤ i < p) repartition protocol : 1|x3,mem protein : α1  production function : f(x4,mem) = random(i, 1 ≤ i < q) repartition protocol : 1|x4,mem protein : α1 simulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming 1019 • each membrane h ∈ µa will read the random numbers x1,mem, x2,mem. each membrane h ∈ µb will read the random numbers x3,mem, x4,mem .  (h, x1,h /x1,mem , mem) r α2 : h ∈ µa (h, x2,h /x2,mem , mem) r α2 : h ∈ µa  (h, x1,h /x3,mem , mem) r α2 : h ∈ µb (h, x2,h /x4,mem , mem) r α2 : h ∈ µb • for each membrane h ∈ {µa,µb}, the distance dh between its position (y1,h,y2,h) and the position given by the generated random numbers (x1,h,x2,h) is computed. for the remaining membranes, dh = p ·q + 1.  production function : f(x1,h,x2,h,y1,h,y2,h) ={ √∑2 j=1(xj,h −yj,h)2 if h ∈{µa,µb} p ·q + 1 if h /∈{µa,µb} repartition protocol : 1|dh protein : α3 • each membrane h writes its value dh to the shared memory.  (h, dh /ah,mem , mem) w α4 : h ∈ µa (h, dh /bh,mem , mem) w α4 : h ∈ µb • the minimum of all distances ah,mem is computed in the shared memory. – production function: f(a1,mem, . . . ,ap·q,mem) = min{a1,mem, . . . ,ap·q,mem} – repartition protocol: 1|amem – protein: α5 • the minimum of all distances bh,mem is computed in the shared memory. – production function: f(b1,mem, . . . ,bp·q,mem) = min{b1,mem, . . . ,bp·q,mem} – repartition protocol: 1|bmem – protein: α5 • variable (enzyme) flagamem is set to zero if amem ≤ threshold. – production function: f(amem) = { 0 if amem ≤ threshold p ·q + 1 otherwise – repartition protocol: 1|flagamem – protein: α6 • variable (enzyme) flagbmem is set to zero if bmem ≤ threshold. 1020 i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín – production function: f(b,mem) = { 0 if bmem ≤ threshold p ·q + 1 otherwise – repartition protocol: 1|flagbmem – protein: α6 • the label neara, corresponding to the closer membrane to the randomly generated point for µa, is obtained. – production function: f(a1,mem, . . . ,ap·q,mem) = arg-min{a1,mem, . . . ,ap·q,mem} – repartition protocol: 1|namem – protein: α7 – enzyme: flagamem • the label nearb, corresponding to the closer membrane to the randomly generated position for µb, is obtained. – production function: f(b1,mem, . . . ,bp·q,mem) = arg-min{b1,mem, . . . ,bp·q,mem} – repartition protocol: 1|nbmem – protein: α7 – enzyme: flagbmem • the position of membrane neara is computed.   production function : f(namem) = rm(namem − 1,p) repartition protocol : 1|y1,mem protein : α8 enzyme : flagamem  production function : f(namem) = qt(namem − 1,p) repartition protocol : 1|y2,mem protein : α8 enzyme : flagamem • the position of membrane nearb is computed.  production function : f(nbmem) = rm(nbmem − 1,p) repartition protocol : 1|y3,mem protein : α8 enzyme : flagbmem  production function : f(nbmem) = qt(nbmem − 1,p) repartition protocol : 1|y4,mem protein : α8 enzyme : flagbmem simulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming 1021 • the unitary vectors are created in the shared memory.  production function : f(x1,mem,x2,mem,y1,mem,y2,mem) = x1,mem−y1,mem√∑2 j=1(xj,mem−yj,mem)2 repartition protocol : 1|u1,mem protein : α9 enzyme : flagamem  production function : f(x1,mem,x2,mem,y1,mem,y2,mem) = x2,mem−y2,mem√∑2 j=1(xj,mem−yj,mem)2 repartition protocol : 1|u2,mem protein : α9 enzyme : flagamem  production function : f(x3,mem,x4,mem,y3,mem,y4,mem) = x3,mem−y3,mem√∑4 j=3(xj,mem−yj,mem)2 repartition protocol : 1|u3,mem protein : α9 enzyme : flagbmem  production function : f(x3,mem,x4,mem,y3,mem,y4,mem) = x4,mem−y4,mem√∑4 j=3(xj,mem−yj,mem)2 repartition protocol : 1|u4,mem protein : α9 enzyme : flagbmem • variable (enzyme) flagamem is set to zero if there is collision for µa. – production function: f(y1,mem,y2,mem,u1,mem,u2,mem) =  0 if collision(y1,mem,y2,mem, u1,mem,u2,mem) p ·q + 1 otherwise – repartition protocol: 1|flagamem – protein: α10 – collision is a function returning true if there are static obstacles in a linear trajectory starting at (y1,mem,y2,mem) and applying a motion (u1,mem,u2,mem) for ∆t time. • variable (enzyme) flagbmem is set to zero if there is collision for µb. – production function: f(y3,mem,y4,mem,u3,mem,u4,mem) =  0 if collision(y3,mem,y4,mem, u3,mem,u4,mem) p ·q + 1 otherwise 1022 i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín – repartition protocol: 1|flagbmem – protein: α10 – collision is a function returning true if there are static obstacles in a linear trajectory starting at (y3,mem,y4,mem) and applying a motion (u3,mem,u4,mem) for ∆t time. • positions of new membranes are computed in the shared memory.   production function : f(y1,mem,u1,mem) = round(y1,mem + u1,mem · ∆t) repartition protocol : 1|z1,mem protein : α11 enzyme : flagamem  production function : f(y2,mem,u2,mem) = round(y2,mem + u2,mem · ∆t) repartition protocol : 1|z2,mem protein : α11 enzyme : flagamem  production function : f(y3,mem,u3,mem) = round(y3,mem + u3,mem · ∆t) repartition protocol : 1|z3,mem protein : α11 enzyme : flagbmem  production function : f(y4,mem,u4,mem) = round(y4,mem + u4,mem · ∆t) repartition protocol : 1|z4,mem protein : α11 enzyme : flagbmem • the membranes labelled by namem and nbmem will read the positions corresponding to the new membranes from the shared memory.  (namem , z1,namem /z1,mem , mem) r α12 (namem , z2,namem /z2,mem , mem) r α12  (nbmem , z1,nbmem /z3,mem , mem) r α12 (nbmem , z2,nbmem /z4,mem , mem) r α12 • a child membrane with position (z1,namem,z2,namem ) is created in µa.    x1,h x2,h y1,h y2,h z1,h z2,h dh   h   namem being h = z2,namem ·p + z1,namem + 1. this rule is mediated by protein α13. simulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming 1023 • a child membrane with position (z1,nbmem,z2,nbmem ) is created in µb.     x1,h x2,h y1,h y2,h z1,h z2,h dh   h   nbmem being h = z2,nbmem ·p + z1,nbmem + 1. this rule is mediated by protein α13. • each membrane in µa reads the position of the new membrane created in µb   (h, x1,h /z3,mem , mem) r α14 : h ∈ µa (h, x2,h /z4,mem , mem) r α14 : h ∈ µa • each membrane in µb reads the pose of the new membrane created in µa   (h, x1,h /z1,mem , mem) r α14 : h ∈ µb (h, x2,h /z2,mem , mem) r α14 : h ∈ µb • for each membrane h ∈{µa,µb}, the distance dh between its position (y1,h,y2,h) and the position given by the new membrane in the other membrane structure is computed. for the remaining membranes, dh = p ·q + 1.   production function : f(x1,h,x2,h,y1,h,y2,h) ={ √∑2 j=1(xj,h −yj,h)2 if h ∈{µa,µb} p ·q + 1 if h /∈{µa,µb} repartition protocol : 1|dh protein : α15 • each membrane h writes its value dh to the shared memory.   (h, dh /ah,mem , mem) w α16 : h ∈ µa (h, dh /bh,mem , mem) w α16 : h ∈ µb • the minimum of all distances ah,mem is computed in the shared memory. – production function: f(a1,mem, . . . ,ap·q,mem) = min{a1,mem, . . . ,ap·q,mem} – repartition protocol: 1|amem – protein: α17 • the minimum of all distances bh,mem is computed in the shared memory. – production function: f(b1,mem, . . . ,bp·q,mem) = min{b1,mem, . . . ,bp·q,mem} 1024 i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín – repartition protocol: 1|bmem – protein: α17 • if amem ≤ dmin or bmem ≤ dmin then the rrts have been connected and the computation must halt. – production function: f(amem,bmem) ={ 1 if amem ≤ threshold∨bmem ≤ threshold 0 otherwise – repartition protocol: 1|haltmem – protein: α18 6 a simulator based on c++ and ros a c++ simulator has been developed within the ros [30] framework. it can be downloaded from https://github.com/rgnc/renpsm. the experiments have been conducted by using a dual-wheeled nonholonomic robot (the pioneer 3-dx) in two virtual environments. the software is composed by three modules: • mobilesim module [31]: it receives the static information about the map, as well as motion commands (v,ω) and generates the wheels odometry and information related to sensors (laser rangefinder for obstacle detection). it moves the simulated robot in the virtual environment. • renpsm module: it receives the information about the map, as well as the information about odometry and sensors and the goal of the robot. it computes a bidirectional rrt by using a renpsm simulator and finally it sends a sequence of motion commands to the mobilesim module. • rviz module [32]: this module is used for visualization. it receives the static information about the map, as well as all the information generated by the mobilesim module and several visual markers generated by the renpsm module. it shows to the user all the information in real-time by using a 3d representation of the environment and the robot. we have used two virtual environments, figures 4 and 5 show the corresponding rviz visualization for each one before starting the robot motion, i.e, after generating the bidirection rrt by using the renpsm module. figure 6 is a second simulation for environment 2. the first environment has been used for experimental validation of the renpsm model by generating several simulations and comparing the resulting rrt visualizations with the ones generated with a conventional rrt software. the second environment has been used for benchmarking, generating 1435 simulations by fixing the starting point and the goal of the robot and measuring the cost in distance of the generated path. the results are shown in table 1. we have measured the cost of an optimal path generated by hand (about 10m) and, as expected, the cost of the best path generated by the bidirectional rrt is larger than the optimal cost, since the algorithm generates the first feasible path that can be found. simulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming 1025 figure 4: rviz visualization of a simulation in environment 1 figure 5: rviz visualization of a simulation in environment 2 7 generation of ad-hoc renpsm simulators with p-lingua p-lingua is a language to define membrane computing models [3,7,14,33], allowing to write definitions in a friendly way, as its syntax is close to standard scientific notation. in this section, we present an extension of p-lingua for renpsm. a parser based on flex and bison [35, 36] has been implemented to generate ad-hoc simulators for the defined models by using automatic programming. the source code of the simulators is generated in c language and openmp [37], providing multi-threading simulators for multicore processors. finally, some experimental results are presented. 7.1 extension of p-lingua for renpsm new syntactic ingredients have been included to define models based renpsm: • the first line of a p-lingua file defining a renpsm model must be @model<renpsm> table 1: benchmarking results min. cost 11.77 m max. cost 17.96 m average cost 13.42 m standard deviation 0.795 m experiments 1435 1026 i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín figure 6: a second simulation in environment 2 • the initial membrane structure is defined as a two level cell-like membrane structure: @mu = [[]′h1[]′h2...[]′hn]′v where h1, h2,...,hn and v are numeric labels in n. the label 0 is used the shared memory. p-lingua variables can optionally be used instead of literals. • each renpsm variable includes at least one index. the last index of each variable is the label of the compartment containing the variable. for instance, the variable x1,mem is written x{1,0}. for the sake of simplicity, indexes will be omitted in the rest of this section. • proteins are written as common objects in p-lingua. • production rules are written in the next manner: x < −func(param1,param2, ...,paramn),protein?enzyme where x is a renpsm variable; func is a production function with parameters param1,param2, ...,paramn, protein is an optional protein related to the rule and enzyme is an optional enzyme. there are a fixed set of production functions that can be used. in this paper, we have implemented the functions for the model in section 5. more functions could be implemented in c language. the rule will be executed if the protein is present and the enzyme has a value greater than zero. • reading-only and writing-only communication rules are respectively written as follows: x < −y,protein?enzyme y < −x,protein?enzyme where x is a variable in the membrane structure, y is a variable in the shared memory, protein is an optional protein related to the rule and enzyme is an optional enzyme. the rule will be executed if the protein is present and the enzyme has a value greater than zero. • membrane creation rules are written as follows: simulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming 1027 [[]′h1]′h2,protein?enzyme where h1, h2 are labels that can be given by numeric literals, p-lingua variables or even renpsm variables; protein is an optional protein related to the rule and enzyme is an optional enzyme. the rule will be executed if the protein is present and the enzyme has a value greater than zero. • protein evolution rules are written in the next manner: [p1 < −p2]′h where p1 and p2 are proteins and h is the label of a compartment, given by a numeric literal or a p-lingua variable. • we have created an special iterator x in h to iterate all the membrane labels in the tree structure rooted at h (with a depth-first order). • other ingredients of p-lingua, such as modules, indexes, iterators and comments can be used as usual in this extension. the whole p-lingua file defining the model presented in 5 can be downloaded from https://github.com/rgnc/renpsm_openmp/blob/master/birrt_renpsm_test1.pli. 7.2 a parser to generate ad-hoc renpsm simulators a parser based on flex and bison [35,36] has been implemented and can be downloaded from https://github.com/rgnc/renpsm_openmp. the website includes instructions for compiling and running the experiments in this paper. the parser reads a p-lingua file and generates a command-line ad-hoc simulator tool that accepts a pgm file defining the obstacles and generates another pgm file printing the generated rrt over the obstacles image. the source code of the simulator tool is generated in c with openmp, the command-line syntax for the generated simulator is: ./simulator [-t threads] [-s steps] [-d] [-r seed] [-m obstacles.pgm] [-o output.pgm] where: • -t threads is the number of threads to be used. default is 4. if 1 thread is set, the simulator will be sequential. • -s steps is the maximum number of computational steps to simulate. the simulator stops if the variable haltmem is set to 1 or the number of steps is reached. default is 1048576 steps. • if -d is set, debug information will be prompted. • -r seed defines the pseudo-random number generator seed. if no seed is configured, an arbitrary seed based on the current clock time will be used. • -m obstacles.pgm is the pgm file defining the obstacle grid for the collision function. • -o output.pgm is the pgm file to print the output rrt over the obstacle image. 1028 i. pérez-hurtado, m.j. pérez-jiménez, g. zhang, d. orellana-martín (a) (b) (c) (d) figure 7: example outputs for environment 1: (a) example output 1; (b) example output 2; (c) example output 3; (d) example output 4 (a) (b) (c) (d) figure 8: example outputs for environment 2: (a) example output 1; (b) example output 2; (c) example output 3; (d) example output 4 7.3 experimental results we have used the same two environments presented in section 6. the environment 1 is defined with the files: birrt_renpsm_test2.pli; office.pgm. the environment 2 is defined with the files: birrt_renpsm_test1.pli; map.pgm. each environment uses a pgm file in gray scale for obstacle definition, where each pixel represents a region of 5 cm2 in the real world. each environment also uses a p-lingua file with identical p system definition but different initial parameters p, q, x0, y0, x1, y1. the problem is to find a rrt representation to navigate from (x0,y0) to (x1,y1) in the environment of p pixels length and q pixels height given by the corresponding pgm file. in figures 7 and 8 there are four example outputs for each environment using the corresponding generated ad-hoc simulator with 8 threads and arbitrary pseudo-random number generator seeds based on the cpu clock time. 8 conclusions this paper deals with an algorithm belonging to a family widely used to solve the problem of motion planning in robots, e.g., the rrt algorithms. such class of algorithms are based on the randomized exploration of the configuration space. this paper is an extension of [21]. in such a work, a variant of enzymatic numerical p systems, called random enzymatic numerical simulation of rapidly-exploring random trees in membrane computing with p-lingua and automatic programming 1029 p systems with proteins and shared memory (renpsm, for short) was introduced. besides, a simplified version of the standard bidirectional rrt algorithm was described by a renpsm system capturing the semantics of the new variant, where maximal parallelism is used. the main contribution of this paper with respect to [21] is to provide a novel approximation for software simulation by using automatic programming. we have implemented a tool for parsing p-lingua files defining renpsm models and generating source code in c and openmp for ad-hoc simulators. thus, we have a flexible way to debug since we are using a language to define the models instead of hard-coding them in the source code. moreover, the generated source code is able to run on multicore processors by using openmp. three main challenges are planned as future work. first, to provide a formal verification of such renpsm systems, in the sense that they in fact simulate the rrt generation algorithm. the second challenge is to move to the rrt* algorithm [9], a variant of the initial algorithm that is able to approximate optimal motion planning with enough iterations. finally, to provide real-life robot path planning experiments, by using a nonholonomic robot with kinodynamic and environment constraints. we also propose to apply the simulation techniques introduced in this paper to other types of p systems walking towards a more generic software tool based on p-lingua and automatic programming for generation of optimized ad-hoc simulators. acknowledgements this work was supported by national natural science foundation of china (61672437 and 61702428) and by sichuan science and technology program (2018gz0086, 2018gz0185). authors from the university of seville also acknowledge the support of the research project tin2017-89842-p, co-financed by ministerio de economía, industria y competitividad (mineco) of spain, through the agencia estatal 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[30] http://www.ros.org [31] http://www.mobilerobots.com/software/mobilesim.aspx [32] http://wiki.ros.org/rviz [33] http://www.p-lingua.org/wiki/index.php/main_page [34] https://developer.nvidia.com/cuda-zone [35] https://github.com/westes/flexl [36] https://www.gnu.org/software/bison/ [37] https://www.openmp.org/ restruc.pdf international journal of computers communications & control issn 1841-9836, 11(2):167-178, april 2016. improved performance by combining web pre-fetching using clustering with web caching based on svm learning method k.r. baskaran, c. kalaiarasan kuttuva rajendran baskaran* department of information technology kumaraguru college of technology, coimbatore, india *corresponding author: krbaski@yahoo.com chellan kalaiarasan tamilnadu college of engineering, coimbatore, india ckalai2001@yahoo.com abstract: combining web caching and web pre-fetching results in improving the bandwidth utilization, reducing the load on the origin server and reducing the delay incurred in accessing information. web pre-fetching is the process of fetching the web objects from the origin server which has more likelihood of being used in future. the fetched contents are stored in the cache. web caching is the process of storing the popular objects ”closer” to the user so that they can be retrieved faster. in the literature many interesting works have been carried out separately for web caching and web pre-fetching. in this work, clustering technique is used for pre-fetching and svm-lru technique for web caching and the performance is measured in terms of hit ratio (hr) and byte hit ratio (bhr). with the help of real data, it is demonstrated that the above approach is superior to the method of combining clustering based prefetching technique with traditional lru page replacement method for web caching. keywords: classification, support, confidence, hit ratio, byte hit ratio, web prefetching, web caching. 1 introduction in recent times, with rapid growth of www, there is ever increasing demand for computer networking resources. with increased number of web based applications created by many users, the increase in bandwidth does not address the delay problems [5]. the existing prediction algorithms often predict relevant as well as irrelevant pages. in caching the pre-fetched web pages, efficient cache replacement techniques have to be deployed to manage the cache content. the traditional cache replacement techniques used does not increase the cache hit ratio to a great extent. machine learning techniques are deployed to improve the performance of web proxy caching methods [2]. compared to traditional caching approaches, intelligent web caching methods are more efficient. details about intelligent caching methods are found in [1]. the remaining parts of this paper are organized as follows. section 2 gives an overview of web caching and web prefetching techniques. section 3 contains the proposed block diagram (with description) and the steps involved in the proposed method of combined clustering (intra clustering also considered) based pre-fetching technique with machine learning technique (svm algorithm). it also contains the algorithm for the combined intelligent caching (svm) and pre-fetching. section 4 discusses about performance evaluation and section 5 concludes the paper and suggests possible future works. copyright © 2006-2016 by ccc publications 168 k.r. baskaran, c. kalaiarasan 2 web caching and web pre-fetching enhancing the performance of web based systems is possible by web caching in which, the web objects which have high probability of being accessed in the near future are kept closer to the user either in the client’s machine or in the proxy server. the factors (features) of web objects that influence web proxy caching considered in this work are, namely: recency (object’s last reference time), frequency (number of requests made to an object), size (size of the requested web object) and access latency of web object. the standard metrics used to analyze the performance of web caching methods are hit ratio (hr) and byte hit ratio (bhr). hr is the percentage of number of requests that are served by the cache over the total number of requests. bhr is the percentage of the number of bytes that correspond to the requests served by the cache over the total number of bytes requested. a high hr indicates the presence of the requested object in the cache most of the time and high bhr indicates reduced user-perceived latency and savings in bandwidth. 3 the proposed method of combined clustering based pre-fetching technique with machine learning technique as shown in the fig. 1, web pages accessed by various users are identified from the log file. web log file contents are preprocessed and trained using the features namely: recency, frequency, retrieval time and size of web object. dataset is created from the proxy log file. web navigation graph (wng) is constructed for each user using a user’s session time interval of thirty minutes. it is considered that the two subsequent requests should not have a time interval greater than thirty minutes. at the end of each time interval new navigation graph is constructed for each user based on the log files contents. wngs show the navigations made by various users between various web objects for inter-site clustering and between various web pages with in a web object for intra-site clustering. each node in the wng represents a web object requested by the user and each edge represents user’s transitions from one web object to another and a weight is assigned to each edge which represents the number of transitions between those nodes. a clustering algorithm gets the contents of wngs as inputs and two parameters namely support and confidence are used to keep track of frequently visited objects/pages by the user. by fixing a threshold value for these parameters, edges which have values less than this threshold can be removed [4]. support is defined as the frequency of navigation between two nodes u1 and u2. the confidence is defined as freq(u1,u2)/pop(u1) where pop(u1) is the popularity of u1. popularity of a node is the number of incoming edges in to that node. the wng is partitioned in to sub graphs by removing those edges that have low support and confidence values. the nodes in each connected sub-graph become a cluster. when a user requests any one of the nodes in a cluster and if it is found in the longterm cache or in the short-term cache, all the remaining nodes in that cluster along with all the intra pages of the requested node can be pre-fetched in to the short-term cache if it is not in the shortterm cache or in the long-term cache [4]. this pre-fetching is done during the browser idle time and this pre-fetching helps in reducing the user perceived latency time. if a web object is accessed by the user for an access count greater than the threshold then that web object are moved from the short term cache to long term cache. lru method is used for removal of objects from the short term cache if sufficient space is not available for caching a new web object. when the objects are moved from short term cache to long term cache svm classifier is used to classify the web objects as class 0 or class 1 [2]. when a request is made for a web object, improved performance by combining web pre-fetching using clustering with web caching based on svm learning method 169 figure 1: block diagram simultaneous search is made in short term cache as well as in long term cache. if that web object is available in the short-term cache, its access count is increased by one. when the access count becomes greater than the threshold value then that web object is given as input to svm classifier for classification. if the classifier classifies it as class 0, it is moved to the bottom of long-term cache else if classified as class 1 then it is moved to the top of long-term cache. if sufficient space is not found in the long-term cache, then the web object placed at the bottom of the long-term cache are removed to offer space for the new web object. at the same time a copy of this web object is transmitted to the requested user. if this object is found in the long-term cache, re-classification of that object is performed. if it is re-classified as class 1, it is moved to the top of the cache (long-term cache) else it is moved to the bottom of that cache. it is then transmitted to the requested user and pre-fetching of other web objects if any that belong to that cluster as well as all the intra pages of this web object in to the short-term cache is initiated. if cache miss occurs then the requested object is searched in all the clusters generated by clustering algorithm for that user. if found in any one of those clusters, that web object is fetched from the original server and it is transmitted to the user as well as placed in to the short term cache. other web objects of that cluster will be pre-fetched during browser idle time and will be placed in the short-term cache. if the requested object is not found in any of the clusters of that user, then the required web page is fetched from the original server and a copy of it is placed in the short term cache as well as transmitted to the requested user. the proposed technique of combining web caching and pre-fetching makes it possible to increase the hit ratio, decrease the user perceived latency and lower the origin server load. 3.1 preprocessing step and creation of training dataset a log file generated by the proxy server consists of time stamp, machine ip size of the requested object, type of method used url of the requested page, content type etc. while pre-processing, javascript files, cascading style sheet, images that are not requested by the user are removed. a separate log file for individual users for creating inter as well as intra-site web clustering of web objects is generated. for creating a training dataset, information is extracted from the traces of log file. each log file record is converted to the training pattern in the format of 170 k.r. baskaran, c. kalaiarasan figure 2: web navigation graph for user1 <a1, a2, a3, a4, b> where a1 represents the recency of the web object, a2 represents frequency of the web object, a3 represents the size of the web object, a4 represents the retrieval time (access latency) of the web object and b represents the class of the web object. the data type of a1, a2, a3, a4 is numeric and the data type of b is nominal [2]. 3.2 web navigation graph (wng) a weighted directed web graph g(x,y) is used to represent the requests of each user, where each node x represents a web object and each edge y represents a user’s transition from one web object to another. the weight of each edge represents the number of transitions in the set. to make the size of the wng manageable, the edges are removed whose connectivity between two web objects is lower than a specified threshold. support and confidence are the two parameters that determine the connectivity between two objects [4]. let w:<xi,xj> be an edge from node xi to node xj. support of g, denoted by freq(xi,xj) is defined as the frequency of navigation steps between xi to xj. confidence of g is defined as freq(xi,xj)/pop(xi) where pop(xi) is the popularity of xi. by this definition, the support value of the edge (x3,x4) for the user x in fig. 2 is q(x3,x4) = 1, and confidence value is freq(x3,x4)/pop(x3) = 0.5. if the support threshold chosen is very less, too many less important user’s transitions for clustering may be included and if the chosen threshold value is high, many interesting transitions that occur at low levels of support may be missed. 3.3 web clustering algorithm the algorithm for clustering inter-site web pages is described below [4]. a weighted directed web graph g(x,y) that represents the access patterns of a user is used. this graph is partitioned into sub graphs by filtering edges with low support and confidence values. the nodes in each connected sub graph in the remaining navigational graph will form a cluster. the inputs to this clustering algorithm will be web navigational graph, the number of users, support threshold and confidence threshold. remove all the edges with support or confidence value less than the corresponding threshold values. bfs (breadth first search) algorithm is applied to the navigational graph. bfs takes a node in the graph (called as source) and visits each node reachable from the source by traversing the edges. it outputs a sub-graph that consists of the nodes reachable from the source. this procedure is applied for all the nodes of the graph. all the nodes in each connected sub-graph forms a cluster. the time complexity of bfs is o(|x|+ |y|) where |x|is the number of nodes and |y|is the number of edges in the graph [4]. improved performance by combining web pre-fetching using clustering with web caching based on svm learning method 171 figure 3: user access pattern for user1 figure 4: applying the support threshold figure 5: applying the confidence threshold in the above web navigation graph, g stands for google web object, h for hot mail web object, n for ndtv web object, f for face book web object, i for ibn web object and t for techrench web object. the access pattern for the user1 consists of 6 different web objects. from the access pattern information, wng is constructed. support and confidence value for each edge in the wng is calculated as defined in section 3 and the popularity for each web object is computed. by assumption support threshold value is taken as 2 and confidence threshold value as 0.6. those edges which have support and confidence values less than their threshold are removed. the threshold value chosen for support and confidence are critical in specifying the cluster size. it should be noted that the total size of all the objects in a cluster should not exceed the total cache size. 172 k.r. baskaran, c. kalaiarasan figure 6: applying bfs then bfs algorithm is applied to the above navigational graph. for each node in the graph known as source, bfs algorithm tries to visit every other node that can be reached from the source by traversing the edges. it outputs a sub graph that consists of all the nodes reachable from the source. this procedure is iterated until bfs has traversed all the nodes of the initial graph. the nodes in every connected sub graph in the remaining graph forms a web cluster. 3.4 pre-fetching using clustering the following are the steps that take place in the proposed pre-fetching method [4]: • a user requests a web object. using the ip address, the proxy identifies the user and maps the user to a particular user group. given that the clusters of web objects are known, the proxy searches inside the existing clusters of that user group to find in which cluster the requested object exists. • all the remaining objects from the selected cluster are pre-fetched from the origin server by the proxy and they are loaded into the short-term cache during the browser idle time. • the proxy sends to the user his/her requested object. 3.5 algorithm for the combined intelligent caching (svm) and pre-fetching begin apply-clustering algorithm for each web object m requested by the user if m is in short-term cache begin cache hit occurs fetch the requested object m from short-term cache update the information of m if the frequency of m> threshold limit begin while no space in the long-term cache for m begin expel f from long-term cache such that f is in bottom of the long-term cache end class of m=apply-svm(common features) if class of m=1 improved performance by combining web pre-fetching using clustering with web caching based on svm learning method 173 move m to top of the long-term cache else move m to the long-term cache end end else if m is in long-term cache begin cache hit occurs fetch the requested object m from the long-term cache update the information of m recalculate the class of m if class of m =1 move m to the top of the long-term cache else move m to the long –term cache end else begin cache miss occurs fetch m from original server cluster c= getclusterforuser(m) if c is not null begin while no space in short-term cache for web objects in c begin expel object using lru from short-term cache end load the all cluster into the short-term cache during the browser idle time end end procedure getclusterforuser(m) begin for each cluster p for the user if m is in cluster p p=p+gettheintrasiteclusters(p); return p if m is not in any of the cluster return null end end clustering algorithm begin for each client ip address begin construct web navigation graph g (u, v) end for each g (u, v) begin 174 k.r. baskaran, c. kalaiarasan calculate support, confidence and popularity of the node u if confidence< threshold limit of confidence begin remove v end if support< threshold limit of support begin remove v end cluster c = apply bfs for g (u, v) end breadth first search begin input: graph g (u, v) choose some starting node u1 mark u1 as visited initialize list x1 with u1 initialize sub-graph t with u1 while x1 is not empty begin choose node x2 from front of the list visit x2 end for each unmarked neighbor y begin mark y add y to the end of the list x1 add x2->y to subgraph t end find all neighbors of the node u1 visit each neighbor and mark the visited node end intra clustering algorithm procedure gettheintrasiteclusters(m) begin t=total number of sessions for each client ip address begin for each session s begin u=unique set of intra-site pages in session s c=total number of intra-site pages in session s for each intra-site page p in u begin calculate support and probability(c/t) for p if support< threshold limit of support begin ignore p improved performance by combining web pre-fetching using clustering with web caching based on svm learning method 175 continue; end if probability< threshold limit of probability begin ignore p continue; end end include p into cluster c end end return c 4 performance evaluation 4.1 dataset the scheme explained in this paper is tested with a dataset. the dataset is obtained from a proxy server installation ftp://ftp.ircache.net/traces/ditl2007-01-09/. the filename of the dataset used for testing of the scheme is rtp.sanitized-access.20070109.gz under the website. the raw proxy server log file for the dataset contained the details of more than 3 million requests. after the log cleaning process was applied on the dataset, the dataset contained about 610,634 entries fit for analysis. the inner details about the data set are as explained below: (i) total number of items: 610634 (ii) total size of bytes for dataset: ∼49 gb (iii) total number of items used for clustering: 427443 (iv) total size of bytes used for clustering: ∼36 gb (v) total number of items used for testing: 183191 (vi) total size of bytes used for testing: ∼13 gb. 70% of all the requests ordered by time have been used for the user’s access pattern analysis, creating training dataset and testing [4].the remaining 30% of the requests were used for testing the scheme. 176 k.r. baskaran, c. kalaiarasan 4.2 experimental results hit ratio and byte hit ratio analysis figure 7: analysis of hr using svm and lru pre-fetching on different values of support and confidence hr and bhr are calculated for different values of support and confidence using svm prefetching and lru pre-fetching. a sample of them is shown above. in the graph, svm prefetching means svm-lru caching with pre-fetching and lru pre-fetching means lru caching with pre-fetching. results inferred from the above graphs are stated below: 1. if the support and confidence values are increased, it is found that there will be a marginal increase in hit ratio for increasing cache sizes. 2. considering byte hit ratio (bhr) it is found that on an average 68% of the total size of the information requested is found fetched from cache (cache hit) by using svm-lru caching with pre-fetching and only 33% of the total size of the requested information is found fetched from the cache using lru caching with pre-fetching and remaining information are found fetched from the origin server. considering hr, it is 86% and 67% on average for svm-lru and lru caching with pre-fetching respectively. this shows the superiority of svm-lru technique. in our earlier paper [3] where intra pages of the requested paper was not considered and lfu technique was used for removal of web objects from the short-term cache, 64% of improved performance by combining web pre-fetching using clustering with web caching based on svm learning method 177 figure 8: analysis of bhr using svm and lru pre-fetching using different values of support and confidence the total size of the requested information was found fetched from cache due to cache hit (bhr) and 26% of the total size of the information requested was found fetched from cache using lfu pre-fetching and remaining information are found fetched from the origin server. considering hr, it is 84% and 47% on average for svm-lfu and lfu caching with pre-fetching respectively. this shows the superiority of svm pre-fetching method. by considering the pre-fetching of intra pages of the requested object, the byte hit ratio bhr is increased by 4% and hr by 2% compared to the byte hit ratio bhr and hr without considering intra clustering. 3. it is demonstrated that pre-fetching will lead to decrease in network bandwidth utilization and decrease in the access latency because of more cache hits. 5 conclusion and future work in this work, a clustering algorithm is used to cluster the web objects represented in the web navigation graph. frequently accessed web objects are monitored by the confidence and support values. if the user’s requested web object is present in the short-term cache then all the other web objects in that cluster plus all the intra pages of that object are pre-fetched and cached during the browser idle time. if a web object and its associated pages in the short-term 178 k.r. baskaran, c. kalaiarasan cache are accessed more number of times than a fixed threshold value then they are moved to long-term cache after classifying them using svm algorithm. if the requested web object is found in the long-term cache then all the other web objects in that cluster are pre-fetched during the browser idle time. if cache miss occurs in both the caches, then that web object is fetched from the origin server and a copy of it is placed in to the short-term cache. the efficiency of svm pre-fetching is compared with that of lru pre-fetching using real data set and it is found that svm pre-fetching has high hr and high bhr for various values of support, confidence and cache sizes. extension of this work is possible by comparing the efficiency of svm technique with other machine learning techniques. bibliography [1] ali w., shamsuddin s.m., ismail a.s. (2011), a survey of web caching and prefetching, international journal of advances in soft computing and its applications, 3 (1): 1-27. [2] ali w., shamsuddin s.m., ismail a.s. (2012), intelligent web proxy caching approaches based on machine learning techniques, decision support systems, 53(3): 565-579. [3] baskaran k.r., kalaiarasan c., sasi nachimuthu a. (2013), study of combined web prefetching with web caching based on machine learning technique, journal of theoretical and applied information technology, 20th september 2013, 55(2): 280-291. [4] pallis g., vakali a., pokorny j. (2008), a clustering-based prefetching scheme on a web cache environment, computers and electrical engineering, 34(4): 309-323. [5] podlipnig s., boszormenyi l. (2003); a survey of web cache replacement strategies, acm computer surveys; 35(4):374–98. [6] web reference: http://www.wikipedia.com/svm ijcccv11n1.dvi international journal of computers communications & control issn 1841-9836, 11(1):39-50, february 2016. a hardware independent real-time ethernet for motion control systems s. ji, c. zhang, t. hu, k. wang shuai ji mechantronic engineering school shandong jianzhu university jinan, shandong, china jizhongzhe@126.com chengrui zhang* school of mechanical engineering shandong university, jinan, shandong, china *corresponding author: 13969076910@126.com tianliang hu school of mechanical engineering shandong university jinan, shandong, china tlhu@sdu.edu.cn ke wang shanghai step electric corporation shanghai, china ahker534@sina.cn abstract: ethernet for manufacture automation control (ethermac) is a new kind of real-time ethernet used in motion control systems. it adopts a line topology with a standard industrial computer based master node and field-programmable-gate-array based slave nodes. ethermac employs one slave node to manage cycle communication and clock synchronization, so the real-time demand for its master node can be greatly reduced and dedicated hardware is no longer mandatory. its distributed clock compensation mechanism can get synchronization accuracy in nanosecond order. the advantages of industrial computer and field programmable-gate-array are combined with ethermac, so that high control performance can be achieved. keywords: real-time ethernet, cycle time, synchronization, motion control, fpga 1 introduction fieldbus has played an important role in factory automation field during the past 20 years [1]. however, it can hardly meet the development of the networked control systems any longer because of the numerous standards, low baud rate, high cost and etc [12]. at the same time, as a mature technology in office, ethernet is introduced into field control layer for its low price, high communication rate, robustness and easy deployment. many automation vendors begin to propose their own industrial communication solutions by modifying the original ethernet protocol to improve its real-time performance (see [4, 13]). these types of ethernet based on ieee 802.3 standards and with real-time property are called real-time ethernet (rt ethernet) [5]. standardization of industrial ethernet standardization of industrial ethernet iec61784-2 has defined some indicators to specify the real-time performance of rt ethernet, such as delivery time and synchronization accuracy. but the minimum cycle is a more direct copyright © 2006-2016 by ccc publications agora university 40 s. ji, c. zhang, t. hu, k. wang indicator to evaluate the performance of motion control system than delivery time(see [6][9]). the rt ethernets whose cycle time can be smaller than 1ms with jitters less than 1ms are called isochronous rt ethernet (see [10][11]), which are most likely to be used in motion control systems. however, most of the current isochronous rt ethernet solutions are based on modifications of the hardware [12]. dedicated network interface card (nic) or real-time os are mandatory for the master node in time critical applications. for example, profinet irt is achieved with a special switch asic (see [12]); dedicated nics are mandatory for sercos iii and ethernet powerlink’s master/management node in time-critical applications (see [13]); ethercat does not require a dedicated master node, but a hard real-time kernel is mandatory for the distributed clock synchronization, in addition the operating system can introduce jitter to the cycle time(see [14]). the dedicated hardware is closed and high cost, which is not good for the development of motion control system. herein, a standard hardware based real-time ethernet named ethermac is proposed. ethermac employs the programmable gate array chips (fpga) based slave node to manage the communication and synchronization, so that ipc master node can focus on motion control algorithms. in this way, computation capability of ipc and hard real-time property of fpga are combined, and hard real-time performance can be achieved even the master node without dedicated nics and hard real-time operating systems. the rest of this paper is organized as follow. in section 2, the protocol model of ethermac is introduced. in section 3 and 4, its real-time and synchronization mechanism are described in detail. section 5 is the slave node implementation on a fpga chip. then two experiments are carried out in section 6 to test the delivery time and synchronization precision. finally, the conclusion is given in section 7. 2 overview of ethermac 2.1 topology of ethermac line topology network is widely used in industrial automation field for its simple structure, low cost and advantage in deterministic communication(see [11], [15][16]jasperneite a proposal for a generic real-timejasperneite a proposal for a generic real-timejasperneite a proposal for a generic real-time). ethermac also takes the line topology as its fundamental topology, where the ipc with at least one standard nic works as the master node, and the fpga-based slave nodes with two ethernet ports cascade with each other in a line as shown in figure 1. all the slave nodes can be treated as a single standard ethernet device for the master node, because they share a common media access control (mac) address. figure 1: topology of ethermac ethermac employs summation frames in figure 2 to convey application data, which is similar to interbus [17] and ethercat [18]. the configuration and control data are conveyed a hardware independent real-time ethernet for motion control systems 41 down by a summation frame in the descending way, and the state data of the slave nodes are collected by another summation frame in the ascending way. in this manner, all the nodes can be updated in a single communication cycle. this is more efficient than the other rt ethernets employing individual frame(see [8], [11]). in addition, the control and state data are separated in the descending and ascending ways, so bandwidth of ethernet can be used more efficiently. figure 2: frame structure of ethermac 2.2 model of ethermac there are three solutions for ethernet to get real-time communication. the first class is named "on top of tcp/ip", which keeps the tcp/udp/ip protocols unchanged and concentrates all real-time modifications in the application layer. in the second solution, application layer programs bypass the tcp/udp/ip protocols and operate the ethernet directly. in the third one, medium access mechanism and hardware structure of ethernet are modified to achieve isochronous real-time performance (see [8], [12]). the architecture of ethermac is shown in figure 3. the master node is implemented on a standard ipc, and tcp/ip protocols are bypassed. the slave nodes are implemented on fpga chips with modified data link layer (dll). figure 3: protocol suite the modified dll of the slave node has the following characters: • a time schedule module is added to dll to manage the communication cycle and synchronize the distributed clocks. • a repeater is designed to receive and forward the summation frame on line, so that small forward delay can be achieved. 42 s. ji, c. zhang, t. hu, k. wang 3 real-time communication the master-slave structure is most used in rt ethernet, where the master node sends out the control data periodically and the slave nodes carry out the received commands. the cycle duration accuracy is determined by the master node in this structure, as a result dedicated nic hard real-time or operating system is necessary for the master node in time critical applications. however, on one hand dedicated nic solution is closed architecture and high cost, on the other hand the response time jitter of real-time operating system can reduce the cycle accuracy. ethermac turns its time-critical tasks over to the fpga-based slave nodes, while master node is designed to complete the configuration and complex control algorithms. the slave nodes of ethermac are implemented on fpga chips, which can provide high timing precision because of their hardware property. 3.1 communication process the communication process of ethermac is shown in figure 4. the slave nodes in the networks carry out a self-checking program after powered on. if there are no problems with the hardware, the slave nodes will get into the idle state and wait for commands from master node. figure 4: state machine of ethermac working process 1)enumeration. the slave nodes are assigned a unique sequence number and feedback their inherent information in this stage. the detailed process is shown in figure 5. figure 5: enumeration process a hardware independent real-time ethernet for motion control systems 43 first, master node sends an inquiry frame to the slave nodes. every slave node takes the data in certain place of the frame as its sequence number, increases its value by one and forwards the frame the next. the slave nodes get their sequence number in this way. then, the last slave node stops the forwarding process and starts to feedback after receiving the inquiry frame. it packets its inherent information into the feedback frame and sends the frame to the second last slave node. all the previous slave nodes attach their inherent information to the end of the feedback frame and forward it. at last, the frame with the information of all slave nodes is transmitted to the master node by slave node 0. the master node compares the received information with the network configuration file to check the configuration state of the network. figure 5 the inquiry process 2)configuration. the master node starts the configuration stage if all the slave nodes in the network have been checked well in enumeration stage. master node delivered the configuration frame to slave nodes, and slave nodes return their configuration results. then the master node prepares the configuration data of the next cycle based on the feedback information. the forward delays measurement is also carried out in this stage, which will be introduced in the section 4. 3)cycle communication. the master node sends out a frame to start the cycle communication stage after the network configure done. the management right of the communication is transferred to the last slave node from then on. different from the last two stages, the communication is started by the last slave node instead of the master node in this stage. the detailed process is shown in figure 6. figure 6: cycle communication process the last slave node starts a new communication cycle by sending a frame back with the set communication period. the slave nodes update their state data to master node by attaching their data to the end of the feedback frame. as soon as receives the feedback frame, master node computes the commands of the next period and sends it to slave nodes. therefore, real-time communication can be achieved if the master node can finish all the operations before the last slave node starting the next communication cycle. in cycle communication, the control frames are forwarded directly and without any modification in the descending way, and the feedback frames are processed online in ascending way, so that small forward delay can be achieved. this means small delivery time for line topology network. for one slave node, the total forward delay in a communication cycle is about 520ns. 44 s. ji, c. zhang, t. hu, k. wang 3.2 performance analysis the delivery time of ethermac can be derived from its communication mechanism and the theoretical analysis in(see [6][8]) td = tframe + tcable + n−1 ∑ i=0 ∆t(i)delay (1) where tcable is propagation delay from cable, which is about 5ns/m for the category 5 cable. tframe is the frame transmission time, which is in linear with the frame size. ∆t(i)delay is the frame forward delay of slave node i, which is about 520ns with proposed the forward mechanism. there are two different types of ethernet frames in the cycle communication stages, so the minimal cycle time tcycmin can be derived tcycminh = tdcf + tdff + tifg + tidle (2) where tdcf is the delivery time of control frame, tdff is the delivery time feedback frame, tifg is the inter frame gap, because the full duplex communication is employed there is only one tifg. tidle is the idle time when there is no communication in the network. in this way, the react time jitters of master node does not influence cycle time accuracy any more, and isochronous real-time communication can be achieved even the master node only with soft real-time property. the minimal cycle time can be as small as 20µs when there are only 3 slave nodes and payload is no more than 48 bytes length. 4 synchronization of distributed slave nodes synchronization accuracy of rt ethernet is its capability to synchronize the actions of the distributed devices [19]. rt ethernet demands the synchronized actions must be repeated periodically with strict jitter bounds [3]. poor synchronization of relevant axes in motion control systems means diminished dimensional accuracy of the work-piece or even unusable products (see [20][21]). many synchronization methods have been proposed for networked control systems(see [22][24]), of which precision time protocol (ptp) described in ieee 1588 is one of the most used. however, ptp is difficult to implement, which consumes too many hardware and bandwidth resources in practice. therefore, a concise synchronization algorithm is proposed to get high synchronization accuracy. 4.1 factors result to asynchronization every slave nodes in the network has its own oscillator. the frequency of each oscillator cannot be exactly the same, besides the clock frequency drifts with environmental temperature and time. as a result, synchronization mechanisms are necessary for networked control systems. broadcast frame is often used as the synchronization signal in star topology networks, but it can hardly work in line topology networks thanks to the forward delay between cascaded slave nodes. equations in (4.1) show the time difference of the slave nodes. t1 = t0 + ∆t 1 delay t2 = t1 + ∆t 2 delay = t0 + ∆t 1 delay + ∆t 2 delay · · · · · · tn−1 = t0 + n−1 ∑ i=0 ∆t(i)delay (3) a hardware independent real-time ethernet for motion control systems 45 where ti is the time a frame arriving at the slave node i in descending way. t idelay is the forward delay of salve node i, which accumulates with the node number and cable length. the forward delay of every slave node is not constant because of the phase separation of receiving and transmitting clock. thus, it is impossible to get the exact value beforehand and compensate it in cycle communication. 4.2 synchronization mechanism of ethermac the distributed clocks synchronization can be divided into two stages: delay measurement and clock compensation. delay measurement is only carried out once at the first time of network configuration. the detailed process is shown in figure 7. master node sends the measure frame cyclically, and the last slave node starts the feedback process as soon as receives the frame. every slave node records the time between it receives the measure frame and feedback frame. the measurement begins at the time of the frame arriving at the media independent interface, so the influence of software stack can be ignored. the ascending and descending way can be symmetrical by compensating the forward fifo depth. here the measured delay includes the slave node forward delay and cable delay. take the phase separation of the transmitting and receiving clock into account, the average value of hundreds of record delay time is used as delay time. in the cycle communication stage, every salve node initializes its local clock with half of the average delay time at the time of receiving the feedback frame, so that the slave nodes can share a common zero point. in this way, the distributed clock of the slave nodes can be synchronized. figure 7: synchronization process the forward delay and clock drift can be compensated cyclically because the feedback process is carried out every communication cycle, so high synchronization accuracy between slave nodes can be achieved in this way. 5 implementation of slave node master node of ethermac can be implemented on any controller with standard ethernet interfaces, and no dedicated hardware in needed. real-time os is necessary for some time critical applications, but the real-time demand can be greatly reduced with the above mentioned 46 s. ji, c. zhang, t. hu, k. wang communication mechanism. therefore, only the slave node implementation is introduced in this paper. the slave nodes are implemented on fpfa chips as shown in figure 8. figure 8: implementation of ethermac slave node fpga is more than some programmable resource with a certain number of logical cells today. the loadable soft cores enable the user to create a microcontroller within an fpga chip and equip it with ultra fast peripherals [25]. it allows the developers to customize peripherals to meet the specific requirement. all the functions of the ethermac are packaged into an ip core. the application layer is implemented as software in nios ii a soft processor designed specifically for altera fpga chips. io, servo control and the other peripherals are mounted on the avalon bus. a static random access memory (sram) chip is used to running application programs, and a flash chip is used to store the configuration data of fpga. the schematic diagram of ethermac ip core is shown in figure 9. there are two concurrent channels in descending way. one is for protocol analysis, and the other one is for frame forward. in protocol analysis channel, application data belong to the current slave node are extracted and stored in a dual-port-ram appctrl data, and the operation information including data address and length are stored in a dual-port-ram operation info at the same time. after receiving the whole frame, the signal rxdone holds high for twelve system clocks to inform the upper layer program that application data have been updated. then the application programs can get the commands of this cycle from appctrl data. the syn signal holds high for twelve system clocks when the distributed clocks time to the set point. upper layer programs and other peripherals modules can carry out the synchronous actions and latch the state of slave node at this moment. the state data have been latched can be upload to the master by writing them into the dual-port-ram appstate data. 6 experiment the system in figure 10 is employed to evaluate the real-time performance of ethermac. the system has one master node and eleven slave nodes. the master node is an ipc with an intel atom d510 1.66ghz cpu and windows 7 os. the network driver interface specification (ndis) layer of the system is modified to improve its real-time performance [26]. the slave nodes are connected with a backplane, which hides in the steel rail. 100base-t ethernet cable is adopted when the adjacent nodes are far away from each other as in figure 10. two experiments are carried out to measure the delivery time and synchronization accuracy respectively. in the first experiment, delay time tdi in figure 7 is measured and delivered to master. here, the time duration between slave node 0 receiving the measurement and feedback frame is twice of the delivery time, while half of the time difference between adjacent nodes is the forward delay of a slave node. as shown in figure 11, the delay time is linear with the sequence number of slave nodes and the forward delay of a single slave node is about 520ns. in the second experiment, an oscilloscope is used to watch the synchronization signals. for convenience, the synchronization signals are assigned as output pins of the fpga chips. slave a hardware independent real-time ethernet for motion control systems 47 figure 9: hardware architecture of ethermac figure 10: distributed slave nodes 48 s. ji, c. zhang, t. hu, k. wang figure 11: delay time and jitters of salve nodes node 0, 5 and 10 are tested, of which slave node 0 works as the reference signal. all the triggered wave forms can superpose and stay on the screen by setting the oscilloscope works in infinite persistence mode. the results after the system worked for 30 minutes are shown in figure 12. it is shown that the deviation of these slave nodesąż synchronization signal is about 100ns with jitters no more than 100ns. figure 12: jitter of the synchronization signal 7 conclusion rt ethernet can help the motion control systems to get a concise architecture and good control performance [27]. however, many rt ethernet solutions need dedicated nic to manage communication in time critical applications. this goes against the open architecture motion control systems. therefore, the hardware independent based ethermac is proposed in this paper. as most of the time critical tasks of ethermac are transferred to the 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[27] g. morel, p. valckenaers, j.-m. faure, c. e. pereira, and c. diedrich(2007); manufacturing plant control challenges and issues, control engineering practice, issn 0967-0661, 15(11): 1321-1331. int j comput commun, issn 1841-9836 8(3):416-424, june, 2013. erosion based method for quantification of facial palsy m. găianu, g. cristescu, d. m. onchiş mihail găianu west university of timişoara românia, 300223 timişoara, vasile pârvan no. 4, e-mail: mgaianu@info.uvt.ro gabriela cristescu aurel vlaicu university of arad românia, 310130 arad, bd. revoluţiei, nr. 77, e-mail: gcristescu@inext.ro darian m. onchiş university of vienna austria, nordbergstrasse 15, a-1090, vienna e-mail:darian.onchis@univie.ac.at abstract: this paper presents a novel 3d face recognition method developed by means of erosion of a contractible topological space. the procedure was is involved in the quantification of facial palsy using pre-marked points. the recognition is done in two steps: in the first step a detection of the pre-marked mimic points on a human face is performed and in the second step the mutual distances between points are calculated. a procedure of deducing the mobility or the immobility of points and the amplitude of mimic points movement follows. keywords: distance, deletable point, interest point of mimic, univalue segment assimilating nucleus. 1 introduction and main results face recognition is a challenging task, that has been extensively investigated during the last years by many authors (e.g. [1, 2]). while most of the previous has face recognition works based on 2d images, the development of 3d scanning techniques moved the domain towards the 3d face recognition. in a controlled environment, the actual 2d face recognition techniques can achieve acceptable accuracy, but inadequate for more challenging applications, especially in the presence of variation of lighting conditions and positions. therefore, the approach using 3d recognition, by including the complete geometrical information, provides potential to alleviate the impact of lightening and position and may improve the recognition performance. detecting and tracking markers on the 3d human face is a challenging task. a standard procedure in this field is to use the level set algorithm ( [1]). an important aspect is to measure the geodesic distances between markers applied on the 3d human face in order to emphasize the interest points of mimic and to evaluate the improvement of the face mobility. the purpose of this paper is to elaborate a method of detecting the interest points of face mimic, followed by computing the mutual distances between them together with a method of deducing the movement and its amplitude. nowadays physicians use to measure these distances by means of the classical rule, which is an operation affected by considerable error. a computer aided procedure of points detecting and distance computing benefits of the possibility of increasing the resolution, which diminishes the error size. copyright c⃝ 2006-2013 by ccc publications erosion based method for quantification of facial palsy 417 the patient is placed in a two mirror system, which was developed by frey, jenny, giovanoli and stussi in ( [2]). in the first step, the interest points of mimic are marked with a waterresistant pen on the patient’s face (see figure 1), obtaining a mask. the mimic interest points are: • central points: central nose (cn), philtrum (ph=cm); • left side points: left ala of the nose (lan), left brow (lbp), left lower eyelid (lle), left mouth corner (lmc), left midlateral point of the lower lip (lml), left midlateral point of the upper lip (lmu), left tragus (ltr), left upper eyelid (lue); • right side points: right ala of the nose (ran), right brow (rbp), right lower eyelid (rle), right mouth corner (rmc), right midlateral point of the lower lip (rml), right midlateral point of the upper lip (rmu), right tragus (rtr), right upper eyelid (rue). in a computer image each interest point of the mimic, i.e. each marked point of the mask, is represented by an area consisting in a set of pixels colored by the marker called univalue segment assimilating nucleus (usually denoted by usan) [14]. marking is necessary, because otherwise the points cannot be identified at the same place, especially on the side views of the face of the two mirror images. figure 1: pattern of the markers phases of diagnose computer aided process presented in this paper are: • operation 1: marking interest points of mimic on patient’s face; • operation 2: acquiring the image of patient’s face from video camera; • operation 3: selecting a restricted area including one interest point of mimic; • operation 4: detecting the contour of usan (univalue segment assimilating nucleus); • operation 5: determining a representing pixel of usan by erosion and detecting a marker with the algorithm 1; • operation 6: preserving the coordinates of the representing pixel; 418 m. găianu, g. cristescu, d. m. onchiş • operation 7: decision: if all interest points are detected: – no repeat from operation 3 to operation 6; – yes go to operation 8; • operation 8: computing mutual distances between pixels representing interest points of mimic and introducing them into the database; • operation 9: repeating operations 2 7 and computing the maximum variation of each distance; • operation 10: decision on the mobility by comparing the variation of distances; • operation 11: presenting the result to the physician. the paper is structured as follows. in the second section the algorithm used during the proposed procedure of the computational diagnose aid is described. in the third section, after the detection (see figure 2), we calculate the mutual distances between the markers on the face and use them to describe the face mobility. some numerical results are included. 2 active contours models and interest points detection there are two main approaches in active contours models based on the mathematic implementation: snakes and level sets. snakes explicitly move predefined snake points based on an energy minimization scheme, while level set approaches move contours implicitly as a particular level of a function. as image segmentation methods, there are two kinds of active contour models according to the force evolving the contours: edgeand region-based. edge-based active contours use an edge detector, frequently based on the image gradient, to find the boundaries of sub-regions and to attract the contours to the detected boundaries. the first model of active contour was proposed by kass, witkin and terzopoulos in ( [5]) and named snakes due to the appearance of contour evolution. this method originates in various erasing techniques, which started with the algorithms of rosenfeld ( [8]). the same class of chain techniques are used in linear programming, for example in fast solving a class of transportation problems ( [4]). the snake method consists in selecting a set of points, called snake points, which is connected and defines the contour. the snakes points are initially placed at further distance from the boundary of the object. then, each point moves towards the optimum coordinates, according to some specific rule. the snake points eventually stop on the boundary of the object. the classic snakes provide an accurate location of the edges only if the initial contour is given sufficiently closed to the edges because they make use only of the local information along the contour. estimating a proper position of initial contours without prior knowledge is a difficult problem. also, classic snakes cannot detect more than one boundary simultaneously because the snakes maintain the same topology during the evolution. that is, snakes cannot split into multiple boundaries or merge from multiple initial contours. for that reason more authors discuss approach using the level set method, which was first proposed by osher and sethian [7], [13]. a formulation to implement active contours in this way, was proposed by osher and sethian ( [11], [7]). level sets represent a contour implicitly via a two-dimensional lipschitz-continuous function ϕ(x, y) : ω → r defined on the image plane ω. this function results by solving a differential equation. this equation is digitized into a finite erosion based method for quantification of facial palsy 419 differences equation, which describes the frontier of the domain by level sets. the model we use in this paper takes into account the pixel by pixel description of the image, originating in the fundamental image analysis literature ( [8]). each image is represented as a n × m matrix, where n is the number of pixels on horizontal and m is the number of pixels on the vertical of the screen. so, the image is a subset of a network z2(h) = {(ih, jh)|i, j ∈ z2} over a rectangle from r2, of sides equal to the dimensions of the computer screen. the number h is given by the selected resolution, representing the length of the side of a pixel (without loss of generality, a pixel is considered to be a square). so, the structure in which we work is that of a totally bounded metric space embedded into the linear space r2. also, we consider it as a topological space, with the topology induced by the metric. the number h is considered to be the measurement unit for computing and decision making and it is finally converted into the desired unit according to the requirement of the user. a subset of an image, which is the object of our computation procedure, is the digitization of a contractible topological space from mathematical point of view, i.e. a topological space that reduces to a point by means of a homotopy. the following concepts and properties are necessary in order to describe the procedures. let x ⊆ r2 a contractible topological space. we consider, without loss of generality, the digitization of the space x by means of the method of digitization, f : r2 → z2, f(x, y) = ([x + 1 2 ], [y + 1 2 ]), (x, y) ∈ r2. if x(ih, jh) ∈ z2(h) is a grid point then its 4-neighborhood is the set v4(x) = {x, (ih, (j − 1)h), (ih, (j +1)h), ((i−1)h, jh), ((i+1)h, jh)} and its 8-neighborhood is the set v8(x) = v4(x)∪ {((i − 1)h, (j − 1)h), ((i + 1)h, (j − 1)h), ((i − 1)h, (j + 1)h), ((i + 1)h, (j + 1)h)}. the concepts of connectivity used in this paper are the 4-connectivity (i.e. defined using v4(x) by horizontal and vertical lines) and the 8-connectivity (i.e. defined using v8(x) by horizontal, vertical, at 45◦ and at 135◦ lines) by means of arcs, as defined in [8]. proposition 1. the digital image f(x) of a contractible topological space x ⊆ r2 is a 8connected set. proof. if x is contractible then it is path connected. denote by s = f(x) ⊂ z2(h) and consider the 8-connectivity in s. suppose that s has an isolated point a = (kh, lh) , k ∈ n, l ∈ n. let us consider the pixel having the center (kh, lh) = f(a), i.e. the square d = [kh − h/2, kh + h/2) × [lh − h/2, lh + h/2). there is at least a point from x situated in the rectangle d, say point b. therefore f(b) = a. since a is an isolated point of s, the squares of side h and centers ((k − 1)h, (l − 1)h), ((k − 1)h, lh), ((k − 1)h, (l + 1)h), (kh, (l − 1)h, (hk, (l +1)h), ((k +1)h, (l −1)h), ((k +1)h, lh), ((k +1)h, (l +1)h), i.e. the 8-neighbors of a, are not in s = f(x). so, none of these squares contain points of x. therefore, x is not connected, which contradicts the hypothesis that space x is contractible. ⋄ let s ⊂ z2 a finite subset. after removing a point x = (i, j) ∈ s from s the number of connected components of s or of z2 \ s may increase or decrease. let us suppose that s is 4-connected (the case of the 8-connectivity is similar). a. rosenfeld proved in [8] that if s ⊂ z2 is a finite subset, s \ {x} does not have less 4-connected components than s if and only if v4(x)∩s ̸= ∅. also, z2 \(s \{x}) has not more 8-connected components than z2 \s if and only if v8(x) ∩ (z2 \ s) ̸= ∅. if x ∈ s such that v4(x) ∩ s is contained in a 4-connected component of v8(x) ∩ s, then s \ {x} does not have more 4-connected components than s. more, if z2 \ s does not have more 4-connected components than s, then v4(x) ∩ s is included in a 4-connected component of v8(x) ∩ s. as consequence, the following concept is introduced in [8]. 420 m. găianu, g. cristescu, d. m. onchiş definition 2. an element x ∈ s, which verifies the conditions: 1) v4(x) ∩ s ̸= ∅, 2) v8(x) ∩ (z2 \ s) ̸= ∅, and 3) v4(x) ∩ s is included in a 4-connected component of v8(x) ∩ s, is called a 4-deletable element. as consequence of the above mentioned properties, rosenfeld has proven in [8] that: theorem 3. let s ⊂ z2 a finite set with the property that z2 \ s has only one 8-connected component. then s \{x} has the same number of 4-connected components as s and z2 \(s \{x}) has an unique 8-connected component if and only if x is 4-deletable. in [8] the deletable elements of a set are classified into two classes: extremities and corners. a point x ∈ s is called extremity if card(v4(x) ∩ s) = 1. a point x ∈ s is said to be a corner if v4(x) ∩ s has exactly two neighbors of x in s. every simple 4-connected set s having more than one element has at least two deletable elements. in case of the 8-connectivity, a point x ∈ s is said to be an extremity if v8(x) ∩ s has either exactly one element, or two 4-neighboring elements, or three 4-neighboring elements out of which only one is in v4(x). a point x ∈ s is said to be a corner if v8(x) ∩ s has exactly three, four or five 4-neighboring elements, out of which only two are in v4(x). the diagnose procedure consists in a succession of computer programs, as it follows: i. program for frames acquiring divides the succession of pictures taken from the face of the patient into frames, called images. the film is of avi (audio video interleaved) format, divided into frames having png (portable network graphics) format, in order to allow procession. the resolution of the film is 720 × 576 pixels, with 25fps (frames per second). the frames are coded either on 8 bits (png-8) or on 24 bits (png-24), including the transparency on 256 levels. the result is a collection of 4150 images, indexed by i ∈ {1, 2, ..., 4150}. ii. program for detection of the mimic interest centers on patient’s face, and computation of mutual distances between them. the pixel by pixel erosion algorithm below presents the manner of processing each image in order to detect the coordinates of each central pixel representing each marker on patient’s face. also, the manner of validation and storage of each marker is included. the search of these points is optimized in the algorithm we cut the margins, which do not offer relevant information. in this manner we reduce the number of color tests. each center of marker is the center of an usan area, which is approximately ellipse (circle) shaped. the usan area is detected based on pixel by pixel color tests, detecting the contour and erasing each usan area following the contour. the erosion means changing the color from the artificial usan area color into face color. after determining the coordinates of centers of all usan areas from an image, the mutual distances between these pixels is computed and stored into a database. the database contains all these distances taken from all the 4150 images from our collection. iii. program for computing the difference between an image and the initial one. initially the patient’s face is in relaxed position, which gives the so called initial image. in order to take the other images from the collection, the patient is asked to perform various movements of the face. the differences between the corresponding distances between the mimic interest centers give the information on the existence of a displacement on the face from an image to another one. the maximum positive difference and the minimum negative difference are chosen from the database, together with the images in which they appear. these images are presented to the physician, together with the numerical results, converted into required units. erosion based method for quantification of facial palsy 421 iv. decision on the movement variability is taken by analyzing the mutual distances and comparing them from an image to the next one. if the distance from a center, for example cn to another one, for example ran does not change and the rest of the distances from lan to ltr, cn, ran are variable, then it follows that lan is the moving point. it means that the patient moved ones face. the decision is also stored for further investigations and comparisons in order to assess the evolution of patient’s face mobility in various stages of the treatment. the algorithm 1 detects the markers ltr, rtr, cn and others. the marker is being searched in a area of 40 × 60 pixels. 3 numerical results as a result of operation 6 one obtains a collection of distinct points on the face of the patient. usually physicians measure the distance between each pair of these points using the rule, i.e. using the euclidean distance in two dimensions. this is the reason we use this distance in our experiment. increased accuracy may occur if another kind of metric is used, for example the earth mover’s distance. the earth mover’s distance was introduced in the computer vision 422 m. găianu, g. cristescu, d. m. onchiş figure 2: detection of the points on the patient’s face table 1: mutual distances between the interest points of face mimic from figure 2 location of interest points mutual distance rtr↔rbp 55.569776 rtr↔rue 49.497475 rtr↔rle 42.438190 rtr↔ran 49.091751 ltr↔lbp 53.150729 ltr↔lue 46.400431 ltr↔lle 40.718546 ltr↔lan 46.097722 cn↔rue 29.000000 cn↔rle 31.016125 cn↔ran 40.853396 cn↔lue 23.000000 cn↔lle 25.495098 cn↔lan 34.014703 rbp↔lbp 61.008196 rbp↔rue 14.764823 rue↔lbp 55.317267 rue↔rle 11.000000 rle↔ran 30.413813 rle↔lue 53.150729 lbp↔lue 12.165525 erosion based method for quantification of facial palsy 423 community in 1997 by rubner, guibas and tomasi [10], [9] to overcome inconsistencies with perceptual similarity observed in (weighted) lp -norms and quadratic forms. the innovation of the paper consists in a more elaborated face model which allows quantification of facial palsy. based on this model, a new scoring system for mimic function recovery is developed. more accurate results which are relevant for plastic surgery as well as for face models in computer vision are obtained. another advantage of the method described in this paper is the real time data acquisition and processing. a fast high resolution real time 3d video system is used to acquire data from patients. they are filmed and analyzed before and after any kind of treatments in a standardized pre-treatment and follow-up scheme. if a patient is repeatedly analyzed in the above described manner, a database is constructed, which exactly document and visualize the recovery course of facial palsy. the system is able to recognize shape, texture, movements and illumination conditions of the face and is automatic in acquiring and processing patient’s data. this simplifies the process of measuring and at the same time enhance the clinical methodology in terms of diagnostic and prognostic accuracy and convenience for patients. for example, the mutual distances between the mimic interest points on the face from figure 2 are in table 1. they are presented to the physician to be used in medical diagnose process. in this case the color to detect, i.e. the markers’ color, is green and the function to maximize is a three dimensional vectorial one, obtained based on a normalized rgb description, in which the first coordinate refers to green. the order relation in r3 is the lexicographic one. an important consequence of the technique described in this paper is the automatic evaluation of the facial palsy’s degree of the patient. the system is able to directly perform a 3d face recognition thus supporting medical staff in exact planning the patient’s medical procedures. the models allow to determine the position and movements of facial features with high accuracy, avoiding model bias in fitting to particularities of individual faces and diseases. the procedure described in this paper is setting up a high-end up to date standardized 3d facial palsy grading system, which is been used to monitor and document post-treatments course for facial palsy patients and to compare various surgical outcomes from different centers around the world. in fact, we propose a software that may lead to performing a standardized grading system and a comparison of 3d human face data. the detection and tracking techniques may be extended from image data to video data. since video is more complex, even more efficient retrieval techniques need to be developed using a multi-projection system as in [6], applying the powerful perception-oriented earth mover’s distance to video data. 4 conclusions and future works detection in large image or other multimedia databases highly depends on the underlying similarity model. in future work, we plan to extend our detection and tracking concept techniques from image data to video data. since video is more complex, even more efficient retrieval techniques need to be developed using a multi-projection system as in ( [6]). we plan to employ generative face models (i.e., models that can produce synthetic face images) which can be used for visualization of prognosis appearance of the patient’s mimic and facial movements. predictions of facial movements can be based on the statistical analysis of empirical data such as the past progression (e.g., preoperative/postoperative appearance) of facial movements of the subject or the class of subjects sharing similar pathologies. the ability to generate synthetic images of a face can further be exploited for biofeedback training of mimic musculature. another important aspect in the future work will be the ability of quantifying 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[12] j. a. sethian. level set methods and fast marching methods evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. in interfaces in computational geometry, fluid mechanics, computer vision, and materials science. cambridge university press, 1998. [13] j. sethian, level set methods and fast marching methods. cambridge monographs on applied and computational mathematics, cambridge university press, 2nd ed., 1999. [14] s.m. smith, j.m. brady, susan a new approach to low level image processin, int. journal of computer vision, 23(1):45-78, 1997. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 3, month: june, year: 2020 article number: 3844, https://doi.org/10.15837/ijccc.2020.3.3844 ccc publications grey wolf optimizer-based approaches to path planning and fuzzy logic-based tracking control for mobile robots r.-e. precup, e.-i. voisan, e. m. petriu, m. l. tomescu, r.-c. david, a.-i. szedlak-stinean, r.-c. roman radu-emil precup, emil-ioan voisan politehnica university of timisoara bd. v. parvan 2, 300223 timisoara, romania radu.precup@aut.upt.ro, emil.voisan@upt.ro emil m. petriu university of ottawa 800 king edward, ottawa, ontario, k1n 6n5 canada petriu@uottawa.ca marius l. tomescu* aurel vlaicu university of arad str. elena dragoi 2, 310330 arad, romania *corresponding author: tom uav@yahoo.com radu-codrut david, alexandra-iulia szedlak-stinean, raul-cristian roman politehnica university of timisoara bd. v. parvan 2, 300223 timisoara, romania davidradu@gmail.com, alexandra-iulia.stinean@aut.upt.ro, raul.roman@aut.upt.ro abstract this paper proposes two applications of grey wolf optimizer (gwo) algorithms to a path planning (papl) problem and a proportional-integral (pi)-fuzzy controller tuning problem. both optimization problems solved by gwo algorithms are explained in detail. an off-line gwo-based papl approach for nonholonomic wheeled mobile robots (nwmrs) in static environments is proposed. once the papl problem is solved resulting in the reference trajectory of the robots, the paper also suggests a gwo-based approach to tune cost-effective pi-fuzzy controllers in tracking control problem for nwmrs. the experimental results are demonstrated through simple multiagent settings conducted on the nrobotic platform developed at the politehnica university of timisoara, romania, and they prove both the effectiveness of the two gwo-based approaches and major performance improvement. keywords: grey wolf optimizer, proportional-integral-fuzzy control, path planning, tracking control. https://doi.org/10.15837/ijccc.2020.3.3844 2 1 introduction high performance controllers are currently developed for mobile robots in order to cope with the three main navigation control problems [25], [24], [17], [20], i.e. path planning (or following), point stabilization and tracking control (or tracking a reference trajectory, further divided in local and global tracking problems [24]. the control of nonholonomic wheeled mobile robots (nwmrs) has received much research interest during the past two decades because of the effects of nonholonomic constraints on the feasible control signals of this class of nonsmooth or nonholonomic systems. representative approaches to tracking control are backstepping [75], adaptive [49], [23], fuzzy [10], periodic [9] and neural networks [30] control. multi-robot path planning (papl) problems are generally solved by centralized and decentralized algorithms. while decentralized algorithms generate independently (separately) collision-free paths for each robot avoiding possible inter-robot collisions, centralized algorithms consider each robot as a subsystem, thus global optimization is enabled. nature-inspired optimization algorithms are inserted in various versions of centralized algorithms to solve papl optimization problems. recent surveys on optimal motion papl are conducted in [36] and [72]. popular nature-inspired algorithms included in papl approaches are directly related to the evolution of these algorithms, and they include particle swarm optimization (pso) [63], [35], [37], genetic algorithms [31], [29], [28], gravitational search algorithms (gsas) [62], [63], [11], simulated annealing [35], chemical optimization [38], firefly optimization [22], charged system search (css) [56], [57], ant colony optimization algorithms [74], [33], hybrid algorithms as, for example, pso-gsa [12], neural networks and fuzzy logic [36]. some fresh results in this field are reported in the recent papers [34], [43], [44] and [2]. the grey wolf optimizer (gwo) algorithm [40] mimics grey wolf behaviour towards finding the prey, which actually models the solution to the optimization problem (op). the advantage of all versions of gwo algorithms is the reduced number of search parameters, which is reflected in various applications including gwo-based optimal tuning of proportional-integral (pi)-fuzzy controllers [53], [54], [55], [13] and proportional-integral-derivative (pid)-fuzzy controllers [27], [46], [70]. gwo algorithms are also applied to mobile robot papl, and recent results concern integration of safe boundary algorithm [65], inclusion of chaotic features in gwo [41], and combination of artificial intelligence techniques and probability theory [73], [50]. in addition, recent gwo-based papl approaches for underwater autonomous vehicles are reported in [64], [14], [19]. this paper is built upon our recent papers on papl, tracking control [59], [62], [63], [56], [57], and gwo-based optimal tuning of pi-fuzzy controllers [53], [54], [55], [13], and suggests two new contributions, namely two applications of gwo to papl problem and pi-fuzzy controller tuning problem. both ops solved by gwo algorithms will be explained in detail. no theoretical contribution is given as it is highlighted in the papers specified in this paragraph. these new contributions have the advantages (a) to (d) over the state-of-the-art pointed out in this section: (a) the papl approach can be easily generalized to aim the use of other nature-inspired optimization algorithms, (b) cost-effective tracking fuzzy controller designs and implementations characterized by simple controller structures and tuning are offered, (c) both approaches benefit from the reduced number of search parameters specific to gwo, (d) real-time experimental testing of papl and tracking approaches conducted on the nrobotic platform developed at the politehnica university of timisoara, romania, (e) fuzzy logic is involved in both gwo-based approaches and applications in terms of the current trends in the heuristic design of fuzzy inference systems [47]. the advantage (a) is also the reason of choosing the papl as the application of gwo algorithm. the results are demonstrated through a multi-agent setting. comparisons with pso-, gsaand css-based approaches are included. the experiments show that the proposed approaches exhibit performance improvement. the overall results prove that gwo invoked in this paper is a good candidate for the given papl problem. the rest of the paper treats the following topics: the papl problem formulation and the off-line gwo-based optimal papl approach are presented in the next section. the gwo-based approach to tracking fuzzy control is described in section 3. the two new approaches are tested and validated in section 4 by a set of experimental results but also comparison with similar methods. the conclusions are outlined in section 5. https://doi.org/10.15837/ijccc.2020.3.3844 3 2 optimal path planning problem and gwo-based path planning approach as considered in [57], let {rk|k = 1...p} be a set of p mobile robots in a known static environment with obstacles included. this paper adopts the same number of n agents that generates the paths for each robots r1...rn in terms of the individual agent populations pr1...prp. robot’s solution space is a two-dimensional search space, and agents are placed in robot’s position. using the notation xi,prk (t) for the robot vector position and also ith agent’s current vector position, i = 1...n in the population prk, k = 1...p, in the search space at the time moment (iteration) t, t ∈ n xi,prk (t) = (xi,prk (t),yi,prk (t)) ∈ r 2, (1) where n is the set of natural numbers, r is the set of real numbers, xi,prk (t) is agent’s position on the x axis and yi,prk (t) is agent’s position on the y axis, the paths (trajectories) are generated iteratively by means of path point sets ti,prk,t updated by adding the current agent position ti,prk,t = {xi,prk (0), xi,prk (1), ..., xi,prk (t)}, (2) i = 1...n, k = 1...p, with xi,prk (0) = (xi,prk (0),yi,prk (0)) the initial position of i th agent in prk as the initial time moment is 0. as shown in [59], [62], [63], [56], [57], the path of each agent (i.e., robot) is built by connecting with lines all successive points of the sets ti,prk,tmax(i,k) where tmax(i,k) is the maximum number of time moments (or iterations) allowed for ith agent in prk. all agents’ paths that are not collision-free (c−f ), namely the path point sets ti,prk,tmax(i,k) for which i ∈ sc−f,prk ⊂{1, 2, ...,n}, are dropped out, where sc−f,prk is the set of agents which lead to c − f paths for prk. the sets ti,prk,tmax(i,k) actually correspond to separate c−f paths for each robot rk, i.e., to feasible paths. the total length of each path is di,k = tmax(i,k)∑ t=1 ||xi,prk (t) − xi,prk (t− 1)||2, i ∈ sc−f,prk , (3) where the euclidean norm is used. this paper considers four papl objectives [59], [62], [63], [56], [57], which are inserted in separate objective functions (o.f.s), next grouped in a weighted sum, and the papl problem is defined as a set of ops that minimize these o.f.s. the ops that produce the indices îk of agents corresponding to the minimum length feasible paths are [63], [57]. îk = arg min i∈sc−f,prk di,k, (4) and the optimal paths are tî,tmax(̂ik,k). the elements of ti,prk,t specified in (2) are solutions to the ops assigned to each robot rk, k = 1...p: x̄i,prk (t) = arg minxi,prk (t) fi,prk (xi,prk (t)), t = 1...tmax(i,k), (5) i = 1...n, k = 1...p, the first element in ti,prk,t , i.e. xi,prk (0), is known: x̄i,prk (0) = xi,prk (0), i = 1...n,k = 1...p, (6) and the expressions of o.f.s are fi,prk (xi,prk (t)) = ||xf − xi,prk (t)||2 + λ1/[ p∑ j=1,j 6=k ||xbest,prj (t) − xi,prk (t)||2] +λ2/[ p∑ j=1,j 6=k |xbest,prj (t) −xi,prk (t)|] + λ3/[ p∑ j=1,j 6=k |ybest,prj (t) −yi,prk (t)|], i = 1...n,k = 1...p. (7) https://doi.org/10.15837/ijccc.2020.3.3844 4 the vector xf = (xf,yf ) ∈ r2 in (7) is the final (or target) point, it is the same for all agents. the vector xbest,prj (t) = (xbest,prj (t),ybest,prj (t)) ∈ r 2 is the position of best agent (as far as the distance between the current point and the target point is concerned) in the population prj at time moment t. the weighting parameters in (7) are λ1 > 0,λ2 > 0 and λ3 > 0. the first term in (7) aims the minimization of the (euclidian) agent-target position distances at a specific time (iteration) t. the second term in fi,prk (xi,prk (t)) aims the maximization of the distance between the agents of a population and the best agents from all other populations (for collision avoidance). the third and fourth terms in (7) aim the maximization of the distance between the robot paths on the x and y axes in order to model specific applications where one axis could be more important than the other one. essentially, our off-line gwo-based optimal papl approach consists of the steps, papl1, papl2 and papl3: step papl1. gwo algorithms are applied to solve the ops defined in (5), with the o.f.s defined in (7) and the solutions x̄i,prk (t). step papl2. the solutions x̄i,prk (t) and the vector in (6) are merged to build the elements of ti,prk,t in (2), with the notation xi,prk (t) for x̄i,prk (t). step papl3. the ops defined in (4) with the o.f.s defined in (3) are solved to get the optimal paths tî,tmax(̂ik,k). a simple deterministic direct search algorithm is used in this regard. as considered in [63], [57], an additional penalty term αpapl > 0 is added to the right-hand term in (7) but only in certain situations in step papl1. therefore, such situations will be avoided as they lead to collisions when ith agent’s position at iteration t is inside one of the obstacles or the line between the positions at iterations t and t− 1 intersects an obstacle. gwo algorithms, which are involved in step ppa1, consist of the steps gwoa1 to gwoa8 specific to the initial formulations of the gwo algorithm given in [53], [54], [55], [13], where a brief introduction of gwo algorithm is also provided before applying the algorithm. the presentation of the steps that are summarized below has the advantage of being supported by transparent and easily understandable vector operations. step gwoa1. the random grey wolf population, represented by n agents’ positions in the q−dimensional search space, is initially generated. the iteration index and the maximum number of iterations are initialized and set to t = 0 and tmax, respectively. using the notation xi(t) for the position vector of ith agent (grey wolf) xi(t) = [ x1i (t) ... x f i (t) ... x q i (t)]t ∈ rq, i = 1...n, (8) where xfi (t) is the position of i th agent in fth dimension, f = 1...q, and t = 1...tmax, gwo algorithm is mapped onto the ops defined in (5) in terms of xi(t) = xi,prk (t),q = 2, tmax = tmax(i,k),j = fi,prk , (9) with k = 1...p, and the index k is omitted as follows for the sake of simplicity. step gwoa2. the performance of each member of agent population is evaluated by conducting simulations and/or experiments on the set of p mobile robots using the nrobotic platform. the top three agents constrain the other agents (i.e., the so-called omega (ωgwo) agents) to update their positions according to theirs. the best three agent position vectors (solutions) computed at each iteration (also called the alpha (αgwo), beta (βgwo), and delta (δgwo) solutions) are ([55]: xl(t) = [ xl1(t) ... xlf (t) ... xlq(t) ]t , l ∈{αgwo,βgwo,δgwo}, (10) and the three vector solutions xαgwo (t), xβgwo (t) and xδgwo (t) are calculated in terms of the selection process described as follows: j(xαgwo (t)) = min i=1...n {j(xi(t))|xi(t) ∈ dx}, j(xβgwo (t)) = min i=1...n {j(xi(t))|xi(t) ∈ dx\{xαgwo (t)}}, j(xδgwo(t)) = min i=1...n {j(xi(t))|xi(t) ∈ dx\{xαgwo(t), xβgwo (t)}}, (11) https://doi.org/10.15837/ijccc.2020.3.3844 5 where dx is the feasible domain of xi(t). these vector solutions fulfil the condition j(xαgwo (t)) < j(xβgwo(t)) < j(xδgwo (t)). (12) step gwoa3. the first three best solutions obtained so far, viz. xαgwo (t), xβgwo (t) and xδgwo (t), are identified and organized accordingly using (11). step gwoa4. the set of search coefficients is developed and computed in terms of a f l (t) = a f (t)(2rf1l − 1), c f l (t) = 2r f 2l, l ∈{αgwo,βgwo,δgwo}, (13) where rf1l and r f 2l are uniformly distributed random numbers within 0 ≤ r f 1l ≤ 1 , 0 ≤ r f 2l ≤ 1 , f = 1...q, and the values of af (t) are linearly decreased from 2 to 0: af (t) = 2[1 − (t− 1)/(tmax − 1)],f = 1...q. (14) step gwoa5. the agents are moved to the new positions expressed as the vectors xi(t + 1) by first calculating the approximate distances between the current solution and the alpha, beta, and delta ones d if l (t) = |c f l (t)x lf (t) −xfi (t)|, i = 1...n,l ∈{αgwo,βgwo,δgwo}, (15) the components of the updated alpha, beta, and delta ones are next calculated as xlf (t + 1) = xlf (t) −afl (t)d if l (t),f = 1...q,i = 1...n,l ∈{αgwo,βgwo,δgwo}. (16) as a result, the updated expressions of agents’ positions are the arithmetic means of updated alpha, beta, and delta agent positions (that belong to these vector solutions) x f i (t + 1) = xαgwof (t + 1) + xβgwof (t + 1) + xδgwof (t + 1) 3 ,f = 1...q,i = 1...n. (17) the vector equivalent of (17) is xi(t + 1) = xαgwo (t + 1) + xβgwo (t + 1) + xδgwo (t + 1) 3 , i = 1...n. (18) equations (17) and (18) are important as they reduce the effects of the random parameters, which are always a problem in the search process and presentation of results specific to metaheuristic algorithms. step gwoa6. the updated vector solution obtained so far, xi(t + 1), is validated if xi(t + 1) ∈ dx. step gwoa7. the iteration index t is incremented and the algorithm continues with step 2 until tmax is reached. step gwoa8. gwo algorithm is stopped and the final solution obtained at each iteration consists of the following set of solutions to the ops defined in (5): {x̄i,prk (1), ..., x̄i,prk (t), ..., x̄i,prk (tmax)} = {xi(0), ..., xi(t), ..., xi(tmax)}. (19) 3 gwo-based approach to tracking fuzzy control this section addresses the tracking control by the design and tuning of linear and next fuzzy controllers. extending the dynamic model presented in [71], the simplified dynamic model of nwmrs with two degrees of freedom is obtained and expressed as [59]. ẋ = v cos θ, ẏ = v sin θ, v̇ = av, θ̇ = ω, tς1ȧv + av = kp1(u1 + d1), tς2ω̇ + ω = kp2(u2 + d2), (20) https://doi.org/10.15837/ijccc.2020.3.3844 6 where (figure 1): (x,y) the coordinates of the centre of rear axis of nwmr, v and av the forward velocity and acceleration, respectively, θ the angle between the heading direction and the x axis, ω the angular velocity; u1 and u2 the control signals, d1 and d2 the load-type disturbance inputs, kp1 and kp2 the process gains, tς1 and tς2 the small time constants or the time constants equivalent to the cumulative effects of actuators, measuring instrumentation, control equipment dynamics and parasitic time constants. figure 1: mechanical variables of nwmrs with two degrees of freedom. the structure of the simplified dynamic model given in (20), viewed as a controlled process, is also illustrated by the block diagram presented in figure 2. figure 2 outlines the kinematic subsystem (ks) and the dynamic system with the transfer functions of the subsystems hp1(s) and hp2(s) of nwmr as controlled process, defined in zero initial conditions hpψ(s) = kpψ/[s(1 + tσψs)],ψ ∈{1, 2}. (21) the control system structure employed in tracking control of nwmrs with two degrees of freedom is presented in figure 3, where: c-1 and c-2 the forward velocity controller and angle controller, respectively, vγ and θγ the reference inputs of the two control loops, rf-1 and rf-2 the reference input filters; ṽr and θ̃r the filtered reference inputs of the two control loops; e1 = ṽr−v and e2 = θ̃r−θ the control errors, xr and yr the reference positions for x and y, respectively, produced by the papl approach described in the previous section, ex = xr − x and ey = yr − y the tracking errors for x and y, respectively, cb-xp and cb-yp computation blocks that provide the estimates ˆ̇xr and ˆ̇yr of derivatives ẋr and ẏr, respectively, ∆xr,t and ∆yr,t the increments of reference positions xr and yr, respectively, and t indicates, as in section 2, the discrete time index. figure 2: structure of simplified dynamic model as a controlled process [59]. figure 3: control system structure for tracking control of nwmrs with two degrees of freedom. the cascade control system structure given in figure 3 consists of the two inner loops for v and θ, and the outer loops for x and y. the blocks cb-xp, cb-yp in the outer loops operate in terms of https://doi.org/10.15837/ijccc.2020.3.3844 7 ˆ̇xr,t = { ∆xr,t/h if|ex,t| ≤ εx, ex,t/h otherwise, ˆ̇yr,t = { ∆yr,t/h if|ey,t| ≤ εy, ey,t/h otherwise, (22) where h is the sampling period, q1 is the backward shift operator, ∆xr,t = xr,t − xr,t−1, ∆xy,t = yr,t − yr,t−1, the parameters εx > 0 and εy > 0 are the maximum accepted absolute values of the tracking errors corresponding to the positions x and y, respectively. the first two equations in (20) are used to compute the reference inputs vr and θr of the two inner control loops. however, these equations are modified as follows: vr = √ (ˆ̇xr) 2 + (ˆ̇yr) 2 , (23) θr = atan(ˆ̇yr/ˆ̇xr), (24) and the last two equation numbers are inserted in the two nonlinear blocks highlighted in figure 3. pi controllers tuned by the eso method [61] are recommended for the processes illustrated in figure 2. in addition, in this situation it will be used to produce tracking control inputs applied to nwmr. eso method ensures a convenient trade-off to control system performance indices (percent overshoot, settling time, rise time, phase margin, etc.) of the linear control system by the choice of just a single design and tuning parameter, βeso, 1 < βeso < 20. the transfer functions of the two pi controllers that can be used as c-1 and c-2 in figure 3 are represented as a unified generic pi controller hc−ψ(s) = kcψ(1 + stiψ)/s = kcψ[1 + 1/(stiψ)],kcψ = kcψtiψ, (25) where kcψ > 0 (or kcψ > 0) is the controller gain, tiψ > 0 is the integral time constant, and the subscript ψ ∈ {1, 2} indicates the controller number or the control loop number or the controlled process number via (21). pi tuning conditions specific to eso method are kcψ = 1/(βesoψ √ βesoψkpψt 2 σψ),tiψ = βesoψtσψ,kcψ = 1/( √ βesoψkpψtσψ),ψ ∈{1, 2}, (26) and the transfer functions of the reference input filters rf-1 and rf-2, that improve the control system performance, are hf−ψ(s) = 1/(1 + βesoψtσψs),ψ ∈{1, 2}. (27) takagi-sugeno pi-fuzzy controllers are next used as c-1 and c-2 to replace the linear pi controllers for further performance improvement. the unified structure and input membership functions of the generic takagi-sugeno pi-fuzzy controller is given in figure 4, where tiso-fcψ are two inputs-single output fuzzy controller blocks modelled by nonlinear input-output static maps, ∆eψ,t = eψ,t−eψ,t−1 is the increment of control error, and ∆uψ,t = uψ,t−uψ,t−1 is the increment of control signal, ψ ∈{1, 2}. figure 4: unified structure and input membership functions of generic takagi-sugeno pi-fuzzy controller. the pi-fuzzy controller design starts with discretizing the continuous-time generic pi controller in (25). tustin’s method leads to the recurrent equation of generic incremental discrete-time pi controller and its parameters kpψ and µψ https://doi.org/10.15837/ijccc.2020.3.3844 8 ∆uψ,t = kpψ(∆eψ,t + µψeψ,t),kpψ = kcψ(tiψ −h/2),µψ = 2h/(2tiψ −h). (28) as shown in [53], [54], [55], [13], the defuzzification in tiso-fcψ blocks is carried out by the weighted average method, and the inference engine makes use of sum and prod operators. the generic complete rule base of these blocks consists of only two rules, which contribute to a cost-effective implementation given as follows, where the parameters ηψ, 0 < ηψ < 1, contribute to the mitigation of control system overshoot: if(eψ,t is n and ∆eψ,t is n) or (eψ,t is p and ∆eψ,t is p) then ∆uψ,t = ηψkpψ(∆eψ,t + µψeψ,t), if(eψ,t is ze) or (eψ,t is n and ∆eψ,t is ze) or (eψ,t is n and ∆eψ,t is p) or (eψ,t is p and ∆eψ,t is ze) or (eψ,t is p and ∆eψ,t is p) then ∆uψ,t = kpψ(∆eψ,t + µψeψ,t). (29) the application of the modal equivalence principle [18] leads to the tuning equations b∆eψ = µψbeψ,ψ ∈{1, 2}, (30) which are convenient as they lead to the parameter vector ρ of the two takagi-sugeno pi-fuzzy controllers ρ = [βeso1 be1 η1 βeso2 be2 η2]t ∈ r6, (31) and also helps the cost-effective fuzzy controller tuning and implementation. t in (31) indicates matrix transposition. the op that ensures optimal tracking is defined as ρ∗ = arg min ρ∈dρ j(ρ),j(ρ) = ∞∑ t=0 (e2x,t(ρ) + e 2 y,t(ρ)), (32) ρ∗ is the optimal parameter vector, i.e., the optimal value of ρ, and dρ is the feasible domain of ρ. the gwo-based approach to tracking fuzzy control suggested in this paper consists of steps gwoa1 to gwoa8 presented in the previous section, but with several particular features in these steps described as follows. first, the iteration index t in the papl approach is replaced with m in the gwo-based approach to tracking fuzzy control, and the maximum number of iterations is mmax. second, gwo algorithm is mapped onto the op defined in (32) in step gwoa1 in terms of q = 6, xi(m) = ρ, i = 1...n,tmax = mmax,dx = dρ. (33) third, the performance of each agent is evaluated in step gwoa2 by simulations and/or experiments conducted on the fuzzy control loops. fourth, the updated vector solution obtained so far xi(m+ 1) ∈ dρ is validated in step gwoa6 by checking certain conditions imposed to the fuzzy control loops [53], [54], [55], [13]. fifth, gwo algorithm is stopped in step gwoa8 and the final solution obtained so far is actually the solution to the op defined in (32): ρ∗ = arg min i=1...n j(xi(mmax)). (34) furthermore, the values of the rest of parameters in the two fuzzy control loops are obtained using (27) and (30). 3.1 real-time experimental validation the validation of the new gwo-based papl and tracking approaches was carried out by a set of experiments conducted on the nrobotic platform using different simple multi-agent settings with at least two nwmrs, different initial robots’ positions and target points and accounting for the presence of obstacles. an example of experimental scenario for two nwmrs along the iterations of papl approach is illustrated in figure 5. https://doi.org/10.15837/ijccc.2020.3.3844 9 figure 5: snapshots along the iterations of the papl approach in an example of experimental scenario for two nwmrs and obstacles. a simplified and representative architecture for this application in nrobotic platform is illustrated in figure 6. the central component in nrobotic platform is global path planner, which makes use of different implementations of papl including the algorithms presented here. global path planner has a constraint regarding its application only to environments with an available map; this requires that the slam process should have already been applied to the current working environment (real or simulated) and the result is next fed to system via map component. robot controllers assigned to individual robots are responsible with the management of resources as sensors and local path planning; local map uncertainties as moving/moved obstacles are considered in this regard. robot pool manager is responsible with nwmrs management and based on mission profile it can dynamically allocate active robots. two statically allocated robots (that belong to the drrobot x80 family) are used in the experimental setup. nrobotic platform supports both simulated and real robots, however combinations of real and simulated robots are not supported. figure 6: architectural diagram for nwmrs path planning in nrobotic platform. the ops defined in (5) were solved using λ1 = 0.4, λ2 = 0.3, λ3 = 0.3 for comparison [57], and αpapl = 50. for the same reason of comparison, pso, gsa and css algorithms were included in the papl approach, with the parameters: for the pso-based papl approach [62]: n = 100, tmax = 30, threshold = 10, initial velocity vector [−0.2 0.2]t , acceleration constants c1 = 1.49 and c2 = 1.49, linear decrease of inertia weight w with the advance of pso algorithm’s iterations within wmin ≤ w ≤ wmax, wmin = 0.0001 and wmax = 0.5, for the gsa-based papl approach [63]: n = 100, tmax = 30, threshold = 10, and initial velocity vector [−0.20.2]t , for the css-based papl approach [63]: n = 100, tmax = 30, initial velocity vector [−0.20.2]t , radius of agents = 1, parameter in the denominator of the expression of separation distance between two agents = 0.01, and linear modifications of velocity and acceleration parameters with the advance of css algorithm iterations, and the parameters specific to gwo were set to n = 100 and tmax = 30. these parameter values ensure a fair comparison, which is always a problem specific to metaheuristic algorithms as the performance depends on the parameter settings. https://doi.org/10.15837/ijccc.2020.3.3844 10 the evolutions of the o.f.s defined in (7) are illustrated in figure 7 for the best agents along the interval 1...tmax as an indication of algorithms’ convergence. the results correspond to one of the simple experimental scenarios with two robots and one of the populations of agents, i.e., pr1. figure 7 shows that the gwo-based papl approach exhibits the fastest convergence. in addition, the execution times of all approaches were measured after running on a computer with intel i7 @ 2.2 ghz, and the results indicate that pso is the best one, followed by css, gwo and gsa. figure 7: o.f. versus iteration number for four papl approaches and best agents in population pr1, with data for pso, gsa and css in [63], [56], [57]. the gwo-based approach to tracking fuzzy control was next validated using the simplified dynamic model (20) of several nwmrs with two degrees of freedom that is integrated in the nrobotic platform, and the control system structure given in figure 3. accepting in one of the experimental scenarios that [59] the obstacles are placed at (3, 3), (9, 3), (6, 7), the initial position of the robot is (10, 4), and the goal (or the desired / final position) is placed at (6, 11), one typical reference trajectory (xr,yr) produced by the gwo-based papl approach will be presented as follows. all positions are expressed in m. the values of the parameters in (20) specific to one nwmr are kp1 = kp2 = 1 and tς1 = tς2 = 1 s. using the sampling period h = 0.01 s and setting the feasible domain of ρ and also the search space to dρ = {3 ≤ βeso1 ≤ 17}×{20 ≤ be1 ≤ 40}×{0.55 ≤ η1 ≤ 0.75} ×{3 ≤ βeso2 ≤ 17}×{20 ≤ be2 ≤ 40}×{0.55 ≤ η2 ≤ 0.75}, (35) the gwo-based approach to tracking fuzzy control was applied. the parameters of gwo algorithm were set, such that to reach a good trade-off to convergence and use of allocated resources, to n = 20 and mmax = 100. the o.f. j(ρ) defined in (32) was evaluated by experiments carried out on a time horizon of 1100 s in terms of the dynamic regime characterized by the reference input trajectory generated by the papl approach, zero initial conditions and zero disturbance inputs. a part of the results is exemplified in figure 8 as the initial robot trajectory (before the application of gwo) and figure 9 as the final robot trajectory (after the application of gwo). the optimal parameter vector figure 8: reference and actual trajectories of nwmr with fuzzy controllers before their gwo-based tuning. obtained after the mmax = 100 iterations of gwo algorithm is ρ∗ = [5.0986 39.56 0.7474 16.92 20.08 0.2507]t , (36) https://doi.org/10.15837/ijccc.2020.3.3844 11 and the performance improvement is illustrated in figure 9 versus figure 8 by overall smaller tracking errors. concluding, the pi-fuzzy controllers produce good tracking control inputs applied to nwmr. figure 9: reference and actual trajectories of nwmr after application of gwo-based approach to tracking fuzzy control. the minimum o.f. value resulted after running the gwo algorithm is 905465, which is better (lower) compared to the o.f. values resulted after running pso (909915), gsa (906895) and css (906574) algorithms, thus showing the performance improvement. this performance improvement can be called relatively small, but that is normal because the fair comparison with other correctly developed metaheuristic optimization algorithms was carried out. nevertheless, the performance improvement can be better understood with the aid of the tracking performance in terms of the above two types of system trajectories, with gwo versus without gwo (illustrated in figure 9 and figure 8, respectively), which point out that the performance improvement is major. the conclusions regarding the convergence and execution times are consistent with those formulated for the papl approach. however, these conclusions might be different if other robots in other situations are considered including constraints, which require the adequate modification of the definition of the optimization problem to handle the constraints. constraints specific to technical and economical operating conditions in terms of various linear and nonlinear systems can be formulated [21], [60], [68] [15], [39], [45], [52]. knowledge-based and fuzzy systems are included [58], [69], [7], [32]. 4 conclusion this paper proposed two applications of gwo as a representative metaheuristic algorithm. these two applications are organized as two approaches to optimal path planning and optimal tuning of tracking fuzzy controllers for nonholonomic wheeled mobile robots. experiments conducted on the nrobotic platform illustrate the performance exhibited by the controllers produced by our approaches, which are built around generic pi and pi-fuzzy controllers, and their potential to be applied as cost-effective control solutions in other applications. since the experimental setup might be too simple to show the performance difference and improvement offered by our approaches, video demos are available from the authors. the list of contributions and advantages of the proposed approaches pointed out in section 1 is not fully discussed and proven in the paper. future research will be focused on carrying out this discussion associated with deep investigations and comparisons that are important in terms of mentioning shortcomings and limitations. these investigations are currently not included in this version of the paper that is very dense by packing both applications into a single paper. the authors are aware of the fact that a somewhat less dense presentation would significantly increase understandability and readability of the paper and leave room for a more detailed discussion of the approaches, their implementations, and the results. however, the discussion on how gwo performs in comparison with established approaches, which is included in the paper, helps to assess the value of our approaches. future research will also be focused on the merge of our path planning and fuzzy controller tuning approaches with classical optimization algorithms [6], [8], [16], [3], [5], [67], and modern ones [42], [4], [48], [1], [26], [51], [66]. the approaches will be generalized such that to work with more complihttps://doi.org/10.15837/ijccc.2020.3.3844 12 cated tracking fuzzy controller structures, however keeping the objective of cost-effective design and implementation. 5 acknowledgements this work was supported by grants from the ministry of research and innovation, cncs uefiscdi, project numbers pn-iii-p1-1.1-pd-2016-0331 and pn-iii-p1-1.1-pd-2016-0683, within pncdi iii, and by the nserc of canada. references [1] abed-alguni, b.h. 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(2011). mobile robot characterized by dynamic and kinematic equations and actuator dynamics: trajectory tracking and related application, robotics and autonomous systems, 59(6), 343–353, 2011. https://doi.org/10.15837/ijccc.2020.3.3844 17 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: precup, r.-e.; voisan, e.-i.; petriu, e. m.; tomescu, m. l.; david, r.-c. ; szedlak-stinean, a.-i.; roman, r.-c. (2020). grey wolf optimizer-based approaches to path planning and fuzzy logicbased tracking control for mobile robots, international journal of computers communications & control, 15(3), 3844, 2020. https://doi.org/10.15837/ijccc.2020.3.3844 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 2, month: february, year: 2020 article number: 3821, https://doi.org/10.15837/ijccc.2020.2.3821 ccc publications a long-range context-aware platform design for rural monitoring with iot in precision agriculture d. taşkın, s. yazar deniz taşkın department of computer engineering trakya university, turkey ahmet karadeniz yerleşkesi, edirne, 22030, turkey deniztaskin@trakya.edu.tr selçuk yazar* department of computer engineering trakya university, turkey ahmet karadeniz yerleşkesi, edirne, 22030, turkey *corresponding author: selcukyazar@trakya.edu.tr abstract the internet of things (iot) applications has been developing greatly in recent years to solve communication problems, especially in rural areas. within the iot, the context-awareness paradigm, especially in precision agricultural practices, has come to a state of the planning of production time. as smart cities approach, the smart environment approach also increases its place in iot applications and has dominated research in recent years in literature. in this study, soil and environmental information were collected in 17 km diameter in rural area with developed long range (lora) based context-aware platform. with the developed sensor and actuator control unit, soil moisture at 5 cm and 30 cm depth and soil surface temperature information were collected and the communication performance was investigated. during the study, the performance measurements of the developed serial peripheral interface (spi) enabled long range wide area network (lorawan) gateway were also performed. keywords: internet of things, context-awareness, lora, precision agriculture, rural monitoring. 1 introduction iot is a network of physical devices that are connected to the internet and are able to talk to each other. some predictions are there will be over 25 billion devices connected to the internet by 2020. there are many types of wireless technologies to connect these devices to the internet, such as shortrange wireless, cellular, and low power wide area network (lpwan) communication. lpwan is designed for sending small data packages over long distances, operation on a battery. there are several https://doi.org/10.15837/ijccc.2020.2.3821 2 figure 1: iot wireless connectivity technologies. competing technologies in the lpwan space such as narrowband iot, sigfox and lora. range and bandwidth relations of wireless technologies shown in figure 1. an important field of study where iot promises plenty of potentials in agricultural engineering applications. distributed sensors and actuators have increasing importance in such applications. in these applications, sensors are used as in-situ or proximal detection devices directly next to agricultural lands. naturally, however, the size and geographical location of these areas accommodate difficulties for sensor and actuator communication. besides, energy-efficient components, long battery life, and long-distance communication requirements are important for the reliability of agricultural sensors. in most cases, cellular communication is not always healthy due to rural coverage problems. cellular communication may not be considered the best solution in this regard. soil and environment in which the sensors work in agricultural applications contain properties that often do not change characteristics. in this case, the data rate requirements of the information, which is transmitted are quite low. these features are not suitable for cellular communication costs. in general, considering low power consumption and long distances, lpwan communication is a necessity in agricultural applications. in 2012, lora technology developed by semtech is based on chirp spread spectrum (css) modulation. any media access control (mac) layer can be used in lora. however, the mac currently proposed is lorawan, which works on the simple star topology principle. since lora works in this topology, data transmission can cover long distances. the gateway used in lora communication, which is directly connected to the nodes in the field, is connected to a network backbone structure in the background. the gateway devices are generally capable of providing instantaneous communication of up to 50 nodes. the lora modulation technique enables communication around the world at regional ism frequencies of 430, 433, 868 and 915 mhz. the node devices are divided into three classes according to the use of the mac layer. these classes and specifications are shown in table 1. https://doi.org/10.15837/ijccc.2020.2.3821 3 table 1: lora classes and properties. class a (baseline) class b (beacon) class c (continuous) battery powered sensors or actuators battery powered actuators mains powered most energy efficient energy efficient must be supported by all devices continually listen downlink after tx latency controlled downlink no latency for downlink eu 868 eu 434 us 915 as 430 lora modulation has six orthogonal spreading factors (sf) ranging from sf7 to sf12. in this way, data can be transmitted in more than one spreading frequency at the same time in the same frequency channel. lorawan gateways could also communicate operating eight different channels at the same time. through the mac used, lora/lorawan provides bidirectional communication, end-to-end security and mobility requirements as required by iot. in lorawan, the data rate ranges vary from 0.3 to 50 kb / s per channel. the general characteristics of lora and other lpwan protocols are shown in table 2. table 2: lora and similar lpwan features. lpwan technologies lora/lorawan sigfox nb-iot modulation css bpsk qpsk frequency unlicensed ism bands (868 mhz in europe, 915 mhz in north america, and 433 mhz in asia) unlicensed ism bands (868 mhz in europe, 915 mhz in north america, and 433 mhz in asia) licensed lte frequency bands max. data rate 50 kbps 100 kbps 200 kbps max. payload length 243 bytes 12 bytes/8 bytes 1600 bytes builtin security yes no yes standards lora alliance sigfox-based 3gpp low cost yes yes moderate low power requirements yes yes moderate topology star-of-stars topology one-hop star topology star topology today, there is a wide range of iot applications using lora technology. these can be listed as energy, agriculture, health and smart city studies. the studies in the field of energy are mostly seen as smart grid applications [13]. there are several kinds of research on the use of lora in monitoring and directing electricity consumption according to needs. in energy management applications, lora wireless communication is a feasible method in such downlink networks to provide remote monitoring and control of devices [24]. compared to other wireless communication technologies such as bluetooth, zigbee, and wi-fi, lora technology has advantages in terms of low cost and low energy consumption [5, 23]. together with lora, 5g networks can be easily combined in a hybrid structure [17, 25]. typical iot applications are also widespread for remote management of medical services in rural areas. lora is often preferred in these applications. within the ubiquitous healthcare concept, the instant status of patients can be monitored remotely by using wireless body area network (wban) [7]. in european countries, studies are being carried out, especially to monitor elderly people, parkinson’s patients and people with mental problems [2, 27]. today smart city applications are conceivably the most researched area for iot [1]. instant monitoring is important to improve the quality of providing services to people in cities. many commercial applications are still in practice and new ones are constantly being developed. in these studies, subjects such as remote reading of meters, municipal services, public transportation, and waste management are very important where lora frequently finds a place. research opportunities for iot applications that can be used in smart city scenarios are discussed in [26]. in this study, weaknesses and possible risks are discussed. surveys about smart city applications and technologies have also been applied. https://doi.org/10.15837/ijccc.2020.2.3821 4 one of the first examples of smart city applications is the european public lighting project [9]. similarly, there are studies conducted in the simulation environment for lighting. on the other hand, there are smart campus applications that can be evaluated within smart city applications. the best-known example of smart city applications is the rigers project in bologna, naville and saragosa in italy [18]. in this project, temperature, humidity, lighting, co2 values of public buildings and some residences were collected through sensors for a year. currently, the project has reached completion. the data were also collected in the star topology using lora and 3g networks [32]. in this study, especially we want to usage area of lora applications using in precision agriculture and agricultural engineering practices. precision agriculture can be defined as a set comprises of the proper agricultural activities and taking advantage of machinery, information technologies to optimize crop by considering variability and difficulties within agricultural systems. with the increasing population in the world, making agricultural production more efficient has become a necessity. it is important to monitor agricultural activities and make decisions based on the data collected. most researchers and engineers around the world develop numerous monitoring and decision methods with different methods and architectures, taking into account the various crop and yield information. all these studies are transforming the agricultural industry. technological advances such as wireless networks, ubiquitous computation have changed the classical approach of agricultural applications in particular. these developments have led to the emergence of precision agricultural practices [4, 6, 21]. some of the precision agriculture practices are automatic irrigation systems [20, 22], fertilization applications [15], pest control [14, 19] and crop, harvest monitoring [16]. the key feature in iot applications is context-awareness. there is a wide range of applications within the concept of context-awareness. all the iot-based studies that we have just mentioned can be considered in this feature. developing strategies related to this concept first came up in 2016 [3]. in this study, they developed an approach for collecting the data obtained in areas close to each other. in general, agricultural applications are based on transmitting data of a commercially produced sensor and actuator components with a particular topology. commercial sensor types are numerous and frequently used. this sensor data is collected at specific centers with the help of a base station. the collected data provide linear information as a function of time. in the context-awareness approach, there is more to collecting and storing data. mainly automatic irrigation systems and fertilization systems are sample context-aware application examples. related automated solutions used in precision agriculture applications often include big-data analysis and artificial intelligence applications. in this study, an alternative application model for sensor and infrastructure layers is introduced in context-awareness. the iaas (infrastructure as service) layer has been transferred to the localized lora gateway in particular directions. 2 implementation of the sensor layer. in this work, we developed a sensor and actuator controller unit (sacu) in the sensor layer with a microprocessor to measure the conditions of the soil and the environment. sacu measures the temperature and humidity of the soil and air in the area where it is located. in sacu design, it is provided to turn on and off devices such as the water pump by using an optional relay. the sacus we use in the field are class a and are only responsible for transmitting data. on the other hand, the units that also include an actuator operate in class b. we did not use any c class sacu in the field because of power consumption increases in this case. the design of the platform is shown in figure 2. we used espressive esp32 open-source microcontroller to use peripheral devices on sacu [28]. esp32 is a powerful component for iot applications in the field with low cost and low power support. it has built-in bluetooth and wi-fi support. there are 36 gpios on the esp32 and 14 of these ports have analog to digital converters. in this study, the temperature and humidity values of the environment are measured by dht22 temperature and humidity sensor on sacu. with sacu, the sen0193 capacitive soil moisture sensor is used to measure soil moisture at a depth of 5cm and 30cm. the soil moisture sensor is analog. it operates in a range of 3.3 ∼ 3.5 v and performs realtime humidity measurement. the probe that will measure at 30 cm is sealed with plexi after the https://doi.org/10.15837/ijccc.2020.2.3821 5 connection is made to prevent it from being affected by moisture conditions in the soil. mlx90614 non-contact thermometer sensor is used to measure soil surface temperature in sacus. this sensor can also measure the ambient temperature if desired. figure 2: the workflow of the platform that we designed. heat diffusion in the soil depends on the mineralogical composition of the soil and physical constituents such as water content. soil temperature should be between 16-18 ◦ c to plant the seed, especially in vegetable production. in the studies, heat exchange and diffusion do not show any difference at depths higher than 20 cm [12]. in this study, we measured soil surface temperature with sacu. the depth where most vegetable and grain seeds are planted is 5 cm. in this study, we used an approximation for 5cm depth by measuring soil surface temperature [11]. when the soil reaches the appropriate temperature, planting provides an increase in yield close to 10% [10]. in addition, the moisture content in the soil is an important component to be measured. typically, the amount of moisture in the soil determines the time for irrigation. we used a moisture sensor in sacu, which has a capacitive feature that is resistant against corrosion. thus, it can be used under the ground for long periods. the measuring unit also measures the moisture value of the soil at a depth of 30 cm with the help of a probe. we have designed for the study sacu is used on the surface of the soil. in this way, lora communication is provided to be more robust. in addition, the damage was prevented by agricultural machinery, for being the definite location on the soil. the design components (a,b) and sensor example(c) of sacu are shown in figure 3. https://doi.org/10.15837/ijccc.2020.2.3821 6 figure 3: (a) base sacu and (b) optional relay twin and (c) capacitive soil moisture sensor. during the study, we used the rfm95 lora transceiver module on both sacus and gateways. it uses semtech technology on these radios but has some improved features. the rfm95 module is an ultra-long distance spread spectrum transmission and interference immunity device while minimizing current consumption. hope rf’s patented lora modulation technique provides accuracy over -148 dbm with low-cost materials [30]. there are five environmental contents in the data packages transmitted by sacus. these are moisture content at 5cm and 30cm depth, soil surface temperature, ambient temperature, and ambient humidity, respectively. the total phy data packet size is in 5 bytes. 3 implementation of the infrastructure layer an important part of lora communication is gateways. together with the lorawan layer, these devices enable data in the field to be carried over the network. the gateway we have developed in this study has an optional lorawan layer. first, we used the gl.6416 openwrt enabled device with 64mb of memory with a qualcomm atheros 9331 processor in the gateway prototype [31]. we used five gpio ports on the device to connect the rmpf95 module. the openwrt linux distribution we use for the gateway we developed supports the lora libraries. spi connection can also be supported on the prototype of the gateway device. we used this protocol to communicate and program with the lora module in the device we developed. the software developed for the gateway has two features. optionally, thethings network communication infrastructure can be used. however, completely independent of this, network communication is also possible. there is only one rfm95 radio in the prototype device, but there are two radios in the gateway device where we design the pcb. the purpose of the two radios is to allow these gateway devices to collect data from the field and also send data to the field devices for operation. it is also possible to use the second radio to extend the lora coverage range in the field. the gateway devices we use to extend the coverage area only communicate long-distance lora and do not have a mac layer. the gateways, which we plan to work as a coverage extender, contain two radio modules with different a and b classes, spreading factors and communication frequencies. typically, during operation, gateway devices are located at points that are not restricted in terms of electrical supply or are supported by a powerful battery supply. irrigation devices can be activated from platform software with relays that we use optionally in sensor control units in the field. in the construction of the context-aware network, these options are tailored to the requirements of the application in the field. the data received on both types of gateways can communicate with the mqtt protocol or restfull service directly thanks to the developed firmware software. in this study, we preferred bish-bosh mqtt client which can be used in openwrt distribution [29]. the gateway prototype(a) and pcb design(b) we designed is shown in figure 4. https://doi.org/10.15837/ijccc.2020.2.3821 7 figure 4: (a) prototype lorawan gateway and (b) produced pcb gateway. 4 results in this study, we first tried the sacus and prototype gateway. in these tests, the devices communicated successfully from a distance of 780 m, although they were out of line-in-sight. we measured dbm, snr and data loss values during communication between devices. our results are shown in table 3. table 3: results of the prototype gateway device and sacu performance for different spreading factors at 868.3 mhz. sf rssi (dbm) snr loss (%) sf7 -116,3 -5.8 1,2 sf12 -119,3 -7,4 3,6 after our prototype gateway trials, we tested the pcb gateway device, which we had built. during this test, according to the results of the previous experiment, we increased the antenna strength in the prototype gateway and made a signal enhancement with the improvements we made in the firmware software installed in sacu. in our tests outside the province of edirne/turkey (41◦ 44’ n 26◦ 38’ e), gateway and sacus communicated successfully with each other at a distance of 8.52 km. the dbm, snr, and data loss values obtained during tests are shown in table 4. table 4: results of the pcb gateway device and sacu performance for different spreading factors at 868.3 mhz in rural. sf rssi (dbm) snr loss (%) sf7 -105,4 -6.1 1,8 sf12 -120,1 -8,4 4,4 we had produced a total of five sacus for this study. we also performed performance measurements while transferring the data obtained from 5 sacus that we placed on the field for testing with the gateway. to carry out these tests, we placed sacu units at a distance of 7 and 8 km from the territory where we will measure. as a performance criterion, we measured how much data from field devices is transferred to the platform software we developed over the network. we compared the data we collected for 24 days from the log records stored on the gateway device. we found that the rate of loss here was 2.6%. the distances of our measurements in rural are shown in figure 5. in our study, we had the opportunity to compare the measured values with a similar study. in the study we compared, there are communication values obtained by burying lora devices in the soil. we have compared the results obtained in terms of communication distance and efficiency, even though there is a difference in application. the comparison results are shown in table 5. https://doi.org/10.15837/ijccc.2020.2.3821 8 figure 5: prototype gateway (a) and pcg gateway (b) communication ranges during the study. most distant sacu view with line-in-sight 8,52 km away (c). table 5: the comparisons of the previous study with test results (sf7) distance rssi for depth (0 10 cm) snr for depth (010cm) grunwald et al. [8] 40 m / 350 m -81,2 / -87,5 dbm 9,1 / 8,8 our proposal 780 m / 8520 m -116 dbm 5,8 / 6,1 since the sacus we used in the field were standing on the soil surface, we healthily took 5cm and 30cm measurements. all the soil moisture and temperature values we collected in the field were shown on the web interface via ordinary kriging and inverse distance weighting (idw) interpolation methods. here, instead of showing users the values with a line or bar graphs, we aimed to show the possible values on the terrain in a way to make predictions. these interpolation methods are based on the principle of estimating non-value points using known data obtained in the field. both methods were adopted to provide alternative viewpoints for agricultural scientists using the software we developed. we used idw for soil moisture values and ordinary kriging for temperature values. the awareness views from the data we obtained are shown in figure 6. figure 6: sample awareness views for soil moisture (a) and soil temperatures (b) with using idw and ordinary kriging interpolation methods in order. https://doi.org/10.15837/ijccc.2020.2.3821 9 5 conclusions today, one of the areas where the internet of things is frequently used is agricultural practices. in general, iot applications collect the data produced at a central point and present it to the users. nevertheless, the data produced should be transformed into information and used more effectively in decision support systems. the context-awareness approach puts that environmental data collection is required not only in decision support systems but also in business intelligence applications. our study is aimed at knowing the instant state of the components of the producers to make the food production process more efficient rather than automating the processes such as irrigation and fertilization according to the threshold of specific values only in the cultivated areas. in this respect, it is an essential effort to present the processes such as irrigation, fertilization, and pest control to the producers with analytical methods and data produced in the field. in the near future, if it is predicted that the use of robots in agriculture will now become commonplace, it is clear that the use of analyzed information instead of raw data in machine communication will provide a very compelling solution in the field of food production. the areas we hope to improve in this study can be said that the antenna power of the sacus and the gateway device play a more productive role in decision-making. increasing the power of communication will allow more areas to be monitored and the momentary status of these fields in terms of agricultural production will be known. in the gateway device, we have developed, it is important for certain processes to be realized automatically, thanks to modeled methods, without human intervention in the management center, and to intervene on the spot immediately. this approach will also minimize crop losses. with the prolongation of the time we collect data during this study, we plan to improve it for using the recurrent neural network (rnn) approach instead of the interpolation methods we use. it will be an important effort to develop a stronger platform by establishing context-awareness and artificial intelligence relationships with the rnn approach. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] andrisano, o. 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(2020). a long-range context-aware platform design for rural monitoring with iot in precision agriculture, international journal of computers communications & control, 15(2), 3821, 2020. https://doi.org/10.15837/ijccc.2020.2.3821 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 5, month: october, year: 2022 article number: 4485, https://doi.org/10.15837/ijccc.2022.5.4485 ccc publications efficient classification of satellite image with hybrid approach using cnn-ca s. poonkuntran, v. abinaya, s. manthira moorthi, m p oza s. poonkuntran school of computing science and engineering vit bhopal university, madhyapradesh 466114, india v. abinaya velammal college of engineering and technology, madurai, india corresponding author: abinayavphd@gmail.com s. manthira moorthi indian space research organization space application centre, ahemedabad, india m p oza indian space research organization space application centre, ahemedabad, india abstract today, satellite imagery is being utilized to help repair and restore societal issues caused by habitats for a variety of scientific studies. water resource search, environmental protection simulations, meteorological analysis, and soil class analysis may all benefit from the satellite images. the categorization algorithms were used generally and the most appropriate strategies are also be used for analyzing the satellite image. there are several normal classification mechanisms, such as optimum likelihood, parallel piping or minimum distance classification that have presented in some other existing technologies. but the traditional classification algorithm has some disadvantages. convolutional neural network (cnn) classification based on ca was implemented in this article. using the gray level satellite image as the target and cnn image classification by the ca’s selfiteration mechanism and eventually explores the efficacy and viability of the proposed method in long-term satellite remote sensing image water body classification. our findings indicate that the proposed method not only has rapid convergence speed, reliability but can also efficiently classify satellite remote sensing images with long-term sequence and reasonable applicability. the proposed technique acquires an accuracy of 91% which is maximum than conventional methods. keywords: soft computing, satellite image, cnn, cellular automata and classification. https://doi.org/10.15837/ijccc.2022.5.4485 2 1 introduction in recent years, academics throughout the world have focused on image categorization as a vital tool for pattern recognition and computer vision. digital imagery makes up the vast bulk of satellite images. an image processing approach may be used to extract the right information from the photos that have been saved. this technique may be used to enhance visual perception and to repair or change the picture depending on graphic distortion, image blurring, or degradation. there are only a few ways to analyze an image, and they all rely on the nature of the issue. image segmentation and classification algorithms may be used in a number of ways to categorize images. four types of image classification may be distinguished depending on how they approach the problem, including clustering, edge-based, region-based, and model-driven approaches. the cnn is now the most used image classification procedure because of its simple concept, fast estimate, and great classification impact. one of the most complicated paradigms in data science is cellular automata. it has strong parallel processing resources and a broad understanding of time and space and can efficiently model the complex system’s spatiotemporal dynamic evolution process. it has been commonly used in traffic problems, human relocation, environmental monitoring, etc. the initial implementation of the ca model was in the 1940s. nevertheless, its use is still less in the area of image processing and the accomplishments are still insufficient. image classification can be accomplished by optimizing the similarity between artifacts grouped into the same cluster and reducing similarities between the various clusters. it has simple calculation and high speed characteristics and is able to effectively solve the complexity and confusion of image processing. however, when the images are normally classified by a single cnn, it is possible to get stuck in a local maximum, the boundary becomes blurred and the untidy boundaries and bad visual effects are shown. convolutional neural networks (cnns) with cellular automata’s (cas) have been suggested in this study to classify pixels based on a combination of spectral and contextual data information, and so the classification results are enhanced over traditional approaches. due to the improved performance, we may conclude that ca-based spectral and contextual images are more accurate for the cnn. the following are some of the main points of this research: using a convolutional neural network, satellite photos may be analyzed for information. in order to classify high-resolution satellite pictures into two categories, the convolutional neural network is used to separate it from the band values into features, and to preliminarily extract the band selection values. for the remainder of this work, the sections are as follows: several more methods for analyzing satellite images are discussed in section ii. according to section iii, the categorization issue is shown in detail. cellular automata categorization using cnns is shown in section iv. section v exhibits the experimental verifications, whereas section vi illustrates the conclusion. 2 related works as an example, in [1], the author presents an explanation of the concept of satellite pictures and remote sensing, as well as a quick summary of the prior work in that field. this page describes existing research on the use of alwar satellite imaging methods, which have covered acceptable land coverage elements such as vegetation, rivers, metropolitan areas, barrens, and rocky regions, as well as other relevant land coverage features. the classification algorithms’ postage is determined by the classified file, which displays multiple classifications displayed in different colours and allows for easy identification. every function is denoted by a different colour and may be more detailed. [2] examines and assesses the different categorisation algorithms in terms of overall performance. binary logistic regression (blr) is provided in [3] as a simple and reliable technique for picture classification. a subtropical east coast area of india was chosen as the location for the study, which encompasses land, fallow land, woodlands, and water, as well as land and water. the categorization method made use of arcgis, excel, and r from microsoft office. along with blr training and testing data, the r classification was used for random forest analysis, which also included blr training and testing data. a variety of strategies for categorising artefacts in remote sensing scenarios may be utilised, with various image processing techniques used. to identify pictures, the hdca suggested in [5] used texture and spectral properties in two iterative additional code steps: (1) mixing operators, and (2) https://doi.org/10.15837/ijccc.2022.5.4485 3 moving and departing operators, respectively. operators specifically for the hdca, a new concept was developed: the weighted gap from manhattan. this paper describes a novel k-mean optimization approach that is utilised for the purpose of picture categorization (see [6]). two alternative activation functions have an impact on the accuracy of classification in an artificial neural network, according to [7]. the wideband imaging spectrometer’s visible and near-infrared scales are used for performing categorization procedures. according on how they were preprocessed (e.g. mdc, mlc), spektral angle mapping (sam), and support vector machinery (svm) algorithms are used to evaluate images (svm). computer algorithms sort photos into several categories depending on what’s shown in them. uncertainty matrices are used to verify the classification findings. in [9], a cnn is recommended for satellite applications to locate individual autos. a cnn beat an fsm in the job of exterior object recognition utilising land sat 8 photos, according to a study in [10] that used a cnn with a support vector machine (svm). using cellular automata in the background and a hierarchical framework, [11] offers a categorization of satellite pictures. cellular automata in the background and the hierarchical structure of satellite images were employed to classify them. image processing techniques for urban tree canopying identification were shown using high-resolution satellite images in [13]. each pixel’s probability of being present may be calculated using the approach provided by [14]. in order to assign a pixel to the appropriate class, values are compared. the use of this method for classifying satellite photographs, particularly multi-spectral images, is very successful. there is a cost, though, in terms of processing power. the sparse svm classifier was developed in [15] and is described in detail. in order to categorise the segmented images, they are broken down into little sections. after the data is extracted, picture patches and sparse pictures are applied to it. following this, a sparse image is resolved utilising the verified image pixels and optimised for classification efficiency. with multispectral orthoimagery and the digital surface model, a convolutional neural network (cnn) may be used to identify a tiny city with high accuracy and speed, according to [16]. (dsm). segmentation is improved by using low-level pixel class predictions to enhance high-level segmentation. they examine and critique a variety of cnn architectural design options in their paper. similar per-pixel classification efforts on different land regions with the same set of categories as the researched land region are compared to see how accurate the classification is (vegetation, ground, roads, buildings, and water). based on this research, they can safely conclude that cnns can be used to solve both the remote sensing data segmentation and the problem of object recognition. 3 problem statement there are several challenges to overcome, including not only gathering images but also processing and disseminating the information quickly and accurately so that the intended target detection and classification may be achieved. the first step in solving any satellite target identification challenge is to separate the relevant components from the rest of the image. the robust satellite target detection system nevertheless faces a challenge in classifying objects. guards and debris are hard to come by in this area. for this reason, a new and improved satellite image categorization method is needed. 4 proposed methodology in this section, the proposed scheme has been gone through for training a cnn for satellite image classification and also a new approach to develop cnn features extractions from the trained data which was represented in figure1. corresponding to the digital satellite image, each image pixel can be called a cell in the ca model, and then the whole image is a cell area. it often includes cell area, cell location, cell neighbor, and evolutionary law. the cell neighbor can be identified as a cell adjacent to a cell in space, and the area consisting of all the neighboring cells is called its neighborhood. evolutionary rule means the laws that decide the status of the cell at the next moment according to the existing cell and its environment, and further change and improve the relationship between the cells. sometimes it’s often called the state transition feature, and it’s the key to the whole ca model. https://doi.org/10.15837/ijccc.2022.5.4485 4 figure 1: schematic representation of the proposed methodology 4.1 dataset description the indian space research organization has developed a web-based tool that enables users to access a variety of mapping-based material. bhubaneswar (lit: earth) the programme is only available in four regional languages, and it is mostly focused on india. satellite and ground data from the indian space research organization (isro) is also included in the collection. bhuvan has a reputation for working with a wide range of government agencies to leverage geospatial data. bhuvan has enabled the indian government to host geospatial data as visualisation and use knowledge layers since its inception. toll information systems, islands, and cultural heritage sites are all included as examples of different geographic layers that might be defined. crowdsourcing or indian government sources are used to acquire network information. in other words, it’s india’s counterpart to google earth, with the sole purpose of delivering free, high-resolution images of india. hyperspectral data is the most common kind. indian geospatial data service bhuvan is a software programme with several addon options. it provides a two-dimensional and three-dimensional view of the earth’s surface. with material in four indian languages, the browser is specifically designed for the indian market. india’s multi-sensor irs (indian remote sensing) satellites have been providing bhuvan with imagery since august 12th, 2009. various spectral, geographical, and temporal resolutions of free data sets, services, and applications are accessible. navigating in a 3d environment with a resolution of 5.8 metres to 1 metre is possible with the web-based mapping service. there is no browser or operating system that bhuvan doesn’t work with. plug-ins aren’t necessary in the current version. registration is required only when downloading satellite data with a resolution of at least 25 metres, such as the ndvi and ohc datasets. data may now be accessed on the go with the bhuvan mobile and android applications. there are two ways to express a ca with a cnn in this case: once each neighborhood’s matching distance input instances have been identified, an association between each neighborhood and an aphttps://doi.org/10.15837/ijccc.2022.5.4485 5 propriate output pixel is carried out following feature extraction. as we explain in section 5.1, we first apply a single convolutional layer, then repeat 11 convolutional layers to represent any ca. to achieve the proper weights instead of training using an algorithm based on a study of ca data, one might do an in-depth investigation of the ca itself. many additional representations exist, and we show that a particular way provides a deep learning network that uniquely matches each of the matching distances, which differs based on the feature being matched. when the landscape input is compared to a ground truth using a single nonunity convolutional layer, the network may be trained using just one layer. these convolutional neurons have a receptive field that extends around the ca. convolutions in the next layer do not incorporate any additional neighbor information from the previous layer. ca rule table logic is based on current research showing that convolutional layers may dramatically enhance network expressiveness while using just a little amount of processing power. since the ca is local, no pooling layers are needed in the network, hence a tiny convolutional layer may be added to it. there are some similarities between this method and others that have attempted to parallelize basic processes like binary arithmetic, but there are also some significant differences. 4.2 feature extraction an efficient method for reducing the size of an image is band extraction. it makes use of spectral correlation to accomplish data compression. the spectral bands are prioritized using cnn. each class’s eigenvalues and eigenvectors are tallied, and a loading factor matrix is generated as a result. each band’s discriminating power is shown in the components of this matrix. discriminating power is reduced in favor of higher priority bands. because ca decreases the distance inside a class, but also increases the distance between classes, it is superior than other strategies. here the feature that can be extracted by a cnn color characteristics are less influenced by image format, direction, and point of view, and have high robustness and objectivity. it has historically been commonly used in the production of pictures. and the color features are usually represented by a histogram of color. the loading factor associated with each spectral band determines which bands are prioritized. the process of the feature extraction can be represented in the equation (1) where r is a histogram, p(x,y) is the pixel of the co-ordinate (x,y) ,bc is the interval number of the histogram, a represents the constant. the histogram includes the mean value and eigen covariance of each pixel in the geographic space that can easily supplement the spatial distribution details of absent pixels in the histogram. similarity of histograms can be determined by, where ø_a is the spatial similarity. cnn is an unsupervised classification approach focused on the optimization of objective functions. if the image x= x1, x2,..., xn is a set of n pixels, xj is the image pixel’s own name, c is the number of groups, and v= v1, v2,..., vc is the cluster array. let the objective function of jm fulfill the restriction where m is the weighted index. discriminating power of each band is computed as, in order to reduce the size of the feature, bands with a greater discriminating power might be chosen. the objective function of i is to apply the squares of the weighted distance to the respective cluster centers from the image pixel points. the smaller the size, the closer the center and the greater the cluster effects are to the pixels. finally the feature matrix are obtained. here, the final feature has been extracted https://doi.org/10.15837/ijccc.2022.5.4485 6 by the nearest values of each pixel and it helps to reduce the feature length. the classifier is trained using a mix of texture and spectral features. finally the precise features can be extracted so that the feature length gets gradually reduced. 4.3 proposed ca with cnn neural networks trained on time series of cell automata pictures may be used to learn their rules, as shown by the analysis of convolutional perceptrons with restricted layers and units. we used random pictures and ca rulesets to train ensembles of convolutional neural networks, which we found to be successful. we will create a ca in this part, which is defined as an explicit mapping between each of the three 3x3 pixel groups in the binary picture and an output value. when we apply this map to the training data, we end up with a new binary image dataset that is a collection of random binary images (the training labels). we guarantee that each rule is reflected in the training data by using big enough pictures (10x10 pixels) and batches of training data. to ensure that each of the 100 possible states has an equal probability of occuring, images that are big enough have an equal number of black and white pixels. when just one rule is shown at a time, the network training process advances significantly faster than when several rules are shown. displaying all input instances at once promotes rule representations to be learned based on their relative importance in terms of maximising accuracy, which is advantageous for increasing accuracy. the network structure is highly impacted by the sequence in which individual rules are shown. the loss function and hyperparameters were used to train the networks (learning rate, initial weights, etc.). a fresh validation dataset was created for each degree of hyper parameter adjustment since it is computationally cheap to produce new training data. for the first time, random ca rulesets were used in order to verify that network hyperparameters were not being adjusted to particular ca rulesets. validation datasets were generated using the same ca ruleset as the training dataset, except they were 20% smaller in size. when the network’s prediction accuracy on the secondary validation dataset achieved 100% after the predictions were rounded to the closest integer, training was ended. the optimizer utilised a loss that was not rounded appropriately in order to calculate gradients. a fresh dataset of previously unknown test data was used to put the final, trained networks to the test (equal in size to five batches of training data). all of the ca rule sets that were put to the test benefited from the use of an architecture based on convolutional networks, which was specifically developed to make learning the whole rule table easier. in order to study the internal representations of ca rule sets using cnn, we emphasise that 100% performance on the second validation dataset is a prerequisite for terminating training. ca ruleets differed widely in terms of training duration and dynamics, but the results indicated that the performance of all trained networks was equal (discussed below). it is called multiphase function learning when several cnn models are used in tandem and then combined into a single fully connected layer with varying contextual input sizes. cnns of different sizes are shown next to depth l on the right. repeating this technique results in a chain of cnn layers that are linked to each other. finally, this categorization input is included in the completely connected layer’s hidden layer. the extracted feature and the class value are the input of the cnn which has experimented a multilayered cnn. for the purpose of the classification when n features rely on condition, the measured likelihood values of each function are then attached to a standard distribution and their result is finally taken after normalization. where f is the feature of the image.the weighed features. finally, the classification was performed with the structured image on the basis of cnn. the cnn continually updates all the weighting https://doi.org/10.15837/ijccc.2022.5.4485 7 figure 2: classification on cnn characteristics and eventually achieves a high-precision image classification. after the classification, the cnn result has been classified by ca, by checking some rules in patch by patch, in each patch getting (3 x 3) matrix. rule 1: if the current pixel’s spectral values are incorrect, the current pixel has the erroneous number of classes: [class] [type] [quality] = majority class of the neighborhood, iteration, and loudness. ’ rule 2: for example, if the number of classes is one and all of the neighbouring class states are either empty classes or those of the current pixel’s class, the following rule applies: [class] [type] classification, iteration, and concentration = quality. rule 3: any neighbouring class state that is distinct from the current pixel class, then: [class] [type] if there are just one class and the current pixel class differs from any of the neighbouring class states, then: class, iteration, and edge each have the following qualities: rule 4: when there are more than one classes, the following steps are taken: [class] [type] pixels may be divided into four categories: (quality) = the majority class in the neighbourhood among the questionable classes, iteration, unknowable, unsure, noisy, edge, and focus (pixels that are not uncertain, noisy, or edge). for classification by triggering the neuron, we begin the cycle, https://doi.org/10.15837/ijccc.2022.5.4485 8 figure 3: architecture of cnn-ca the overall suggested architecture was illustrated in figure 3. 4.4 quality calculation in order to avoid misunderstanding, this approach uses two images (one that has been expertly categorized and one that has not been) and builds a confusion matrix. this highlights the critical role that precision in cellular automaton categorization plays throughout the process. this function also provides a list of pixels that have been erroneously classified and have merged with the class, which has been classed using this method. 5 result and discussion the indian space research organization’s bhuvan (lit: earth) mapping-based material has been used to test the findings. in addition to allowing viewers to explore 2d and 3d photos of the indian subcontinent, bhuvan also provides information on soil, wasteland, and water resources. admin borders may be superimposed on photographs according to the user’s preference from a list of accessible boundaries. bhuvan’s aws (automatic weather station) data may be shown in both a visual and tabular manner. the 3d view pop-up menu allows users to "fly" from one area to another (with fly-in, https://doi.org/10.15837/ijccc.2022.5.4485 9 fly out, jump in, jump around and view point controls). navigating around the app is done using a heads-up display (hud) (tilt slider, opacity control, compass ring and zoom slider). it’s possible to make items in 2d (such as text labels and rectangles) and 3d (such as polygons and circles) by using the drawing tool (placing of expressive 3d models, 3d polygons and boxes). images and distances may be recorded using snapshots (which are stored to a floating window and can be saved to an external file) and measuring tools (horizontal distance, aerial distance, and vertical distance).the sun’s position may also be correctly adjusted for a specific time of day, allowing the viewer to see the landscape as it would seem at that time of day, with realistic shadows and lighting as a result. about 100 input images are taken. overall, it has a pixel value feature of every band where there are 200 bands in each image. the overall simulation was carried out in a matlab environment.here the 17 layered cnnca is experimented in the practice. further, the proposed work of cnn with ca is compared with the other existing methodologies. the fundamental truth of a satellite image is presented in figure 4: labeled satellite image 1 figure 5: ground truth and classification using cnnca figure 6: ground truth and classification using k-nearest neighbour (knn) ca above figures that represent the information which is collected at a certain spot. this allows real-life objects and content on the ground to be connected to satellite images. this knowledge is widely used for remote sensing data analysis and correlates the findings to the basic truth. our proposed https://doi.org/10.15837/ijccc.2022.5.4485 10 figure 7: ground truth and classification using minimal distance (md) ca figure 8: :ground truth and classification using parallel piped (pp) ca cnn-cellular automata pipeline can divide the object is divided to super pixels in different sizes and the pixels are chosen as seeds to disperse the backdrop at the boundary of the picture. in order to obtain deeper functionality (cnn) are then used as feature extractor. the previous map and deep functionality created are both applied to the single layer cnn-cellular automata. finally salience maps integrate these in order to reach our last result. here the proposed methodology is compared with the 3 other existing technology when compared to that cnn-ca outperforms well and classifies the labeled image 1 precisely as depicted in [5-8]. the training error is the error we receive back when we apply the trained model to the training data. keep in mind that this data has already been used to train the model, and this does not always imply that the model once trained will properly perform when applied back to the training data the mistake we obtain when we run the trained model on data that it has never seen before is called a "test data error.". the model’s precision may frequently be gauged using this information. figure 8 shows the results of the suggested approach in action, and the picture quality is excellent. here, the training error is generally lower since it is based on data used to fit the model, rather than on data used to test the model. in this case, a trained model is likely to overestimate its training error based on the training data. the performance analysis depicts that cnn-ca outperforms well when compared to the other existing methodologies because the image quality obtained will be good was clearly depicted in figure [11-15] and table 1,2. 6 conclusion cnn-ca is used to classify satellite photos in an innovative way. in combination with photos and the ca’s self-processing, the initial clusters and segmentations are improved in both speed and accuracy. ca has been shown in experiments not only to be an effective picture protector but also to enhance the intensity attributes. using the recommended strategy, a long-term series classification https://doi.org/10.15837/ijccc.2022.5.4485 11 table 1: comparative analysis of performance parameters figure 9: training error vs. training error of the remote sensing image was applied and addressed, and excellent results were produced when compared to other current approaches. the suggested method has a 91 percent accuracy rate, which is much greater than that of current methods. declaration: ethics approval and consent to participate: no participation of humans takes place in this implementation process human and animal rights: no violation of human and animal rights is involved. conflict of interest: conflict of interest is not applicable in this work. https://doi.org/10.15837/ijccc.2022.5.4485 12 figure 10: labeled satellite image 5 figure 11: ground truth and classification using cnnca figure 12: ground truth and classification using knnca https://doi.org/10.15837/ijccc.2022.5.4485 13 figure 13: ground truth and classification using mdca figure 14: ground truth and classification using ppca table 2: comparative analysis of performance parameters https://doi.org/10.15837/ijccc.2022.5.4485 14 figure 15: training error vs. testing error references [1] dhingra, s., & kumar, d. 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[15] dm.längkvist, a. kiselev, m. alirezaie and a.loutfi 2016. classification and segmentation of satellite orthoimagery using convolutional neural networks. remote sensing, 8(4), 329. copyright © 2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: poonkuntran, s.; abinaya, v.; manthira moorthi, s.; oza, m p (2022). efficient classification of satellite image with hybrid approach using cnn-ca, international journal of computers communications & control, 17(5), 4485, 2022. https://doi.org/10.15837/ijccc.2022.5.4485 introduction related works problem statement proposed methodology dataset description feature extraction proposed ca with cnn quality calculation result and discussion conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 6, month: december, year: 2021 article number: 4305, https://doi.org/10.15837/ijccc.2021.6.4305 ccc publications threshold based support vector machine learning algorithm for sequential patterns s. imavathy, m. chinnadurai s. imavathy research scholar anna university,chennai, tamil nadu, india imavathy.sphd@gmail.com coressponding author m. chinnadurai professor, cse department e.g.s pillay engineering college, nagapattinam, tamil nadu, india mchinna81@gmail.com abstract now a days the pattern recognition is the major challenge in the field of data mining. the researchers focus on using data mining for wide variety of applications like market basket analysis, advertisement, and medical field etc., here the transcriptional database is used for all the conventional algorithms, which is based on daily usage of object and/or performance of patients. here the proposed research work uses sequential pattern mining approach using classification technique of threshold based support vector machine learning (t-svm) algorithm. the pattern mining is to give the variable according to the user’s interest by statistical model. here this proposed research work is used to analysis the gene sequence datasets. further, the t-svm technique is used to classify the dataset based on sequential pattern mining approach. especially, the threshold-based model is used for predicting the upcoming state of interest by sequential patterns. because this makes deeper understanding about sequential input data and classify the result by providing threshold values. therefore, the proposed method is efficient than the conventional method by getting the value of achievable classification accuracy, precision, false positive rate, true positive rate and it also reduces operating time. this proposed model is performed in matlab in the adaptation of 2018a. keywords: data mining; sequence patterns; threshold based support vector machine learning algorithm; classification accuracy; sequential mining;. 1 introduction the bioinformatics analysis is widely used in the area of computer science and applications, which deals with the data for collecting, organizing, and analysing. [1] here the dna and protein sequence are analyzed. pattern mining algorithm with its sequential database are analysed with various domains. in this frequent data, a small gap in the pattern is restricted for finding valid pattern of https://doi.org/10.15837/ijccc.2021.6.4305 2 dataset. the pattern recognition process involves the gap constraints, length constraints, maximum supports, and minimum type. here the maximum support is no greater than the sub-support matrix. youxi wu and others uses non-overlapping sequence to specify the gap constraints by pattern mining approach. here the gap constraints is obtained by finding transcriptional site of gene from dna cells. bioinformatics is also utilizes the area of genomic signal processing for dealing with the digital signal processing (dsp) applications. [2] clustering method is to identify the sequence of gene data, which is performed with online collaborative model. [3] particle swarm optimization technique with adaboost model had classified the result of dna gene sequence by data mining approach. identifying the species name or organism type is by gene sequence and classification techniques. this will provides the dna-attributes, nature of dna cell and the type of species of dna. the data mining is used to extract the data from required field; in this, the subtopic performs the pattern mining approach for analysing patterns by statistical mode. classification technique in data mining is mostly prefers machine learning algorithm. the sequential pattern analysis will discover the pattern of transcriptional gene data. this technique is to find the matches of frequently occurrence of data. to identify the pattern various measures are investigated. the minimum support matrix is to provide the threshold value to the sequence. the dna sequence having the responsibility to regulate the gene, the characters a, g, t, and c is ordered with biological term s. the adenine and thymine is paired with x mentioned and cytosine and guanine is paired with y mentioned. in previous research work, [4] has described the dataset by performing the artificial neural network and deep learning algorithm. [5] hadoop and weka distribution has proposed with machine learning algorithm and data mining techniques. support vector machine (svm) learning algorithm describes the classification of genes via intron and exon combination in the dna cells. this classification process is based on size of given dataset. the boundary function of svm is based on the value of threshold used for dataset, which determines the weight of classes of the boundary. after performing train function, the class of train and performance of test is validated. by determining kernel function, we can evaluate the class region of data. based on the different threshold value assignment, the classifier will provide the various tested sequence of data. it is used in the linear classification method for analysing the larger datasets. after determining training nodes of sequence, the boundary will terminates to sort the value for arranging the nodes. based on the database the classification is done with umber of sequences and list of identity, which evaluates the sequential dataset classifications. here the sequence of data is identified as is and dataset is represented as x_d. with the set of support matrix, we can determine the total number of sequence presented in the dataset. σ(s) = |is�xd|s < is|−−−−−− 1 with the ratio of ‘s’, the number of sequence in the database is given by, σ(s) = (|is�xd|s<is|) xd −−−−−−2 here equation (2) represents the exact and relative support matrix is changeable by‘s’ ratio. if the pattern sequence‘s’ is greater or equal to the threshold value of dataset ‘xd’, that expresses σ(s) >= sminthd. various node values are represented by kernel function ‘k’. if the k > 0, the processor initialize the distribution factor. in pattern mining process, the k is always increments to the value ‘1’ that is k+1. sequence of data contains various items and it is performed with various classification technique that is gsp, spade, prefix span, and gspan. these techniques are used feature classification of svm model. minimum support matrix will fix the threshold value for mining the sequence patterns. the sequential data mining with classification is done by extracted dataset. initially the dataset is analysed for its application and performs the sequential operation. sampling process is done by getting the threshold value of each sequence. this high dimensional data is send to the training process and performs the operation of minimum support. it will generate the minimum valued data to the testing process and it minimizes the dimension of original dataset. after that by performing classification process, we can classify the dataset according to the genes sequence item sets. transcriptional dataset is used for gene sequence determinations and it classified based on the mined data. machine learning technique is used for classifying the dataset by assigning class value to https://doi.org/10.15837/ijccc.2021.6.4305 3 the train and test sets. class boundary is selected in training set, it maximizes the support matrix’s margin, and it will denote the length and distance of class boundary for getting compact data. dna sequence has classified by integrating dm and ann approach [4]. auroral image sequences dataset is to presented and it is classified using pattern-mining techniques [20]. nowadays, the machine-learning (ml) algorithm is a most widely used technique for data mining algorithms. fingerprint based dna sequence is to detect the present gene, identify the forensics, and testing the parental genes. bioinformatics sequence used in machine learning algorithm provides better result as compared with other existing methods. genome sequence having high volume of gene data, which is analyzed by monitoring number of data sequences. transcriptional data sequence is used for classifying the gene function by specifying species. this proposed research work is summarized as follows. section ii is the survey analysis of recent related literatures. section iii is the existing method description and techniques used. section iv is the proposed methodology-using threshold based support vector machine learning model for classification and gene expression. section v is the result and discussion part. section vi concludes the proposed method and provides the thought about future work. 2 literature survey gyula dorgo., et. al., (2018) has proposed the sequential mining approach for alarm suppression applications. statistical data is processed to find the abnormal condition of alarm. here the suppression sequence is to set systematic analysis for processing and controlling the action. the technique uses bayes classifier with multi-temporal sequence mining algorithm. this method reduced the losses and suppression speed, since the operator will keep the operation in normal condition by indicating the present state display. target and action limit is set with alarm rate with period, which is used to analysis the sequential data. threshold value is assigned with statistical configurations of dataset. after performing the sampling process, the data sequence is initiated with threshold value for minimizing the dimension of data length. alarm reduction model comprises fault detection and observation unit, which provides the result of work reduction and failure prediction. here the data mining technique is used to extract the pattern of alarm suppression ratio. the alarm will triggers the next relay and it returns to next end state of sequence pattern. by the use of suppression in the sequential pattern mining, the alarm management and control strategy is performed and it reduces the data loss but increases the execution time. bao huynh., et. al., (2017) has proposed the parallel method is used to mining the sequential dataset with dynamic load balancing and dynamic bit vector options. the frequent data is performed in the closed sequential patterns and it is analysed by prefix span, which reduces the execution time of classifier. if the database contains the large set, the sequence will shows the exponential numbering of dataset and it is compact by data extraction process in data mining approach. here the parallel method is used to solve the problem of expensive computations and large sequence analyser. since it improves the processing speed but the number of stages increased while performing sorting process. parallel architecture is modelled for performing sequential pattern analysis with dynamic bit vector and load balancing. here the cpu time is changed based on the different architecture in parallel analysis. this method is design with various datasets and performed in intel core i5-6200u with 2.3 hz frequency and 3mb memory. po-ming law., et., al., (2018) has present the pattern mining technique for temporal query and sequence exploration of recursive operations. this method is not sufficient for support matrix analysis. while performing the event analysis, the sequence are extracted by providing the mined data in the output. by enabling the event sequence, have to identify the item sets. this makes the reduction in data loss on large sequence analysis. sequential mining approach is to processed and controls the data for reducing the data loss. since the classified sequences and segments are visualized in the database. here the database is the marketing and health information analysis. by setting the time and by set the number of events in the constraints makes system effectiveness. the analysis process includes splitting and selecting the segments based on patterns, which depends on temporal data. seema sharma., et., al., (2018) has present the machine learning technique for processing data by data https://doi.org/10.15837/ijccc.2021.6.4305 4 mining technique. this survey analysis the knowledge based acquisition with its classification. based on the assignment of class and group of given data, the predefined values are set for each instance and performs classification. here the technique used is svm, decision tree, and k-nearest neighbour with bayesian network. by using decision tree for classification methods, the splitting strategy is to generate the gain value and it ratio. for finding the value of gain ration, we have to calculate the entropy and information gain. here svm technique uses k-nearest neighbour node, which reduces the data loss while performing classification. with various techniques and determinations, the computational cost is increased for large sample sets, which is very sensitive. anuja jain., et., al., (2017) has proposed the data mining technique for hadoop with weka distributions. here the supervised machine learning (ml) algorithm is used for classification. for big data mining technique the hadoop approach is used and it is connected with weka tool. ml algorithm uses naive bayes and svm for big data analysis and it compared the result of accuracy for raw data versus normalized data. by this work, it handles the problem of big data analyses, which are data integration, data volume, technical skills requirement, and cost solution. here the hadoop uses apache open sources, which allows the system for distributed processing model. by clustering process specification of characteristics is assigned by predefined classes. transactional data is analysed by sequential data processing technique, which is created by decision tree structure. reinforcement learning technique is to connect with dynamic setting data, which provides the feedback to the user for rewards and punishments. sadok rezig., et., al., (2018) has proposed the technique for predicting maintenance activity by sequential data sets. the data-mining algorithm used for various applications, which bases the spare parts maintenance. threshold value is assign for support matrix is set previously and by this information, we can finds the sequential data patterns. frequent data mining is combined with process of classification, prediction, and clustering. threshold support value provides the pattern assignment of each data to be processed. by this technique, we can analyse the raw data for removing the unwanted data this will reduce the data loss. sequential pattern is used to generate the raw data into sequential model, which is known as antecedent and consequence. here they takes the spare part maintenance of a company, which provides the maintenance parameter, spare part code and description about the spare part. by this parameters they can obtain the sequence, intervals by weeks and percentage of supports. final determination is about classification with its related parameters. cheng zhou., et., al., (2016) has presents the sequential classification method, which is based on data mining with pattern recognition techniques. here the feature vector based model is used to generate the sequence for classification. the efficient technique is to composed pattern mining technique takes the interesting data to label the class value, which is based on support matrix and classification rule. some situations, the dataset is not allocated properly in an order, this leads to data loss. however, by the use of sequence classification rule, we can remove the unwanted data. since it, obtained the mined data and accuracy of classification techniques. naives bayes classification technique is used in pattern mining approach for processing sequential dataset. interesting dataset pattern is based on support and cohesion. by comparing with predictive accuracy, it will not support for big data mining approach. sathish kumar s., and duraipandian n., (2012) has presented the dm and ann based classification technique for dna sequence analysis. to identify the species from dna gene sequence, this is considered as a raw dataset. if the dataset contains unwanted information, it is neglected for classification process. here the artificial neural network model is used for data mining approach. high dimensional dataset is mined by principal component analysis strategy and it is validated by 10-fold cross validation approach. by this statistical validation, the classification model is performed. by this bioinformatics data, they can obtain the species name by dna cells and classification accuracy. genomic signal processing tool is used to classify the dataset. this species classification model is depends on nucleotides and data is mined by nucleotide pattern. by calculating various pattern lengths, can obtain the parameters assignment as a, t, g, and c. in training phase, multi dimensional structure is reduced by using artificial neural network. in support matrix, species brucella suis and c-elegans are determined. classification technique improved the accuracy rate. https://doi.org/10.15837/ijccc.2021.6.4305 5 3 existing method plenty of research ideas are focusing with the performance of various mining techniques for bioinformatics datasets. data mining technique is the most emerging technology, which is used for various applications. the conventional method used the artificial neural network (ann) for classification. dna splice junction sequence is used for data analysis. while performing sequential pattern mining, initial stage having larger data dimension, this should be reduced as small data dimensions. by comparing with artificial neural network classifications, the proposed research work improves the accuracy and truth factor. gene sequence is associated with genome for assigning the genetic information about the species. most of the conventional method uses ann, genetic algorithm, and fuzzy logic system with the structure of hybrid model 3.1 neural network based pattern mining technique neural network classification method is used in the previous research work [1]. with data mining technique, the sequential pattern recognition had performed. the radix tree miner algorithm is used to structuring the model. here the sequential pattern mining technique is employed by performing larger dataset. the neural network model is performed to classify the dataset. in this data set, a, c, g, and t are the nucleotides, which assigning the value of each item sets for performing classification in nn. by radix tree miner, if the sub-trees are not divided for upcoming process have to neglect the set because it is an unwanted data. this takes number of stages high than the proposed t-svm based pattern miner technique. execution time of classification technique is increased by increasing number of hidden layers in the nn. when compared to the previous work with proposed t-svm based pattern mining technique, the execution time is reduced in proposed method, this makes the system efficiency. figure 1 shows the block diagram for existing neural network classification for sequential figure 1: block diagram for nn for sequential pattern mining technique pattern mining for splice gene sequence dataset. initially the raw data set of dna cells are collected from various sources. it has high dimensionality in nature. this is reduced by various techniques like radix tree miner algorithm and artificial neural network for classification. the algorithm had evaluate the sequence for identifying pattern. in support block, the threshold value of each sequence value allocated for minimizing the sequential pattern dimensions. for each data set, the sequence id is given with respect to the pattern alignment. the length of sequences is increased by time complexity of radix tree miner algorithm. after performing radix tree miner the high dimension data set is sent to the training process. prefix span method is also used for making pattern of gene sequence, but https://doi.org/10.15837/ijccc.2021.6.4305 6 it consumes much timing to complete the process and requires more memory to process from the projected data sequence. 4 proposed t-svm based sequential pattern mining techniques the proposed threshold based support vector machine learning algorithm is preferred in this research work for classification of sequential pattern mining approach. the proposed method takes the data set of splice-junction gene sequence as mentioned in previous research work data set. here the dns sequence contains the four variable set of nucleotides, that is adenine ‘a’, guanine ‘g’, cytosine ‘c’, and thymine ‘t’. these species are formed as nucleotide. initially the long sequence set to be processed for reducing the dimension of dataset, this makes the system efficiency. the proposed method is integrating the data mining technique uses machine learning (ml) algorithm for classification. to generate the length of each sequence, we can obtain the value of threshold. by the statistical data, we can obtain the classification in linear based supervised machine learning algorithm. figure 2 shows the block diagram of proposed model with t-svm algorithm and sequential pattern figure 2: block diagram of proposed t-svm for sequential pattern miner system mining techniques. first, the dataset of splice junction gene sequence is taken for this analysis. in minimum support block, the threshold value is set for initial data sequence, which is based on rule based mining technique. the gene sequences are mined by pattern mining techniques. based on the threshold value, have to allocate the pattern for training process. after performing training process, the svm model is to generate the class label to the sequential data for performing the testing process. finally, the test sequence is allocated for performing classification based on its threshold value. however we can obtain the sequentially pattern mined data. a.dataset pattern mining process is performed for data set of splice junction gene sequence of primates via dna cells, which is taken from centre of machine learning and intelligent systems and it is associated with imperfect domain theory. the splice sequences are pointed from dna sequences and it recognizing with two boundaries, called intron and exon. here 3190 instance is capture with 61 attributes. the below table 1 mentioning the nucleotides arrangements in datasets indicates the variables d, n, s, and r. these raw dataset is generated for performing sequential pattern mining technique. this dataset https://doi.org/10.15837/ijccc.2021.6.4305 7 table 1: dataset character mentioned assigned character nucleotides order d a g t n a g c t s c g r a g is capable of 10-fold validation techniques, which is applied to the 60 sequential dna nucleotide positions. class distribution is mentioned as ei and ie of intron/exon. genetic information about the dna sequence is given as , 1. molecular biology 2. evolutionary biology 3. metagenomics this information is based on genome and proteins in the dna sequence. this nucleotides collection of gene is the primates splice junction sequence, which is identify by potential targets, study with differ organism in evolutionary biology, and species representation in metagenomics. the transcriptional dataset is analyzed by three different analysis that is microarray analysis, tiling arrays, and regulatory sequence analysis. b.sequential pattern mining technique machine learning algorithm uses pattern-mining techniques with sequential data. hidden parts of sequences should be mined by ml algorithm. this technique will control and detect the pattern for classification. based on the sequential data, the statistical analysis is taken for classification model and it is extracted for respective applications. these sequential patterns are identified by assigning threshold value of each sequence of data and it is from transactional database. dataset is frequently analyses the pattern for pattern arrangement and it fills sufficient to the algorithm. this is for predicting the respective pattern recognition and classification models. based on pattern mining techniques, the pattern is allocated by threshold value and it minimizes the dimension of pattern. it is completely depends on outcome of the model. the distance of sequence in svm is calculated by, distance(i) = (2 ∗m− 2 ∗ count)/(2 ∗m− count) −−−−−−− 3 where, i is the iteration and ‘m’ is the length of input sequence. by pattern mining is a function is expressed as sigma that is given by fw,s(x) = sgn(wtx + s) −−−−−−− 4 here this pattern is recognized from given input data sequence. sequential pattern is identified as s, which s presented by threshold value s= s1, s2,. . . . . . ,snand dataset is the sequential arrangement of nucleotides that is a, c, t, and g is given by, here s1 and s is represented as, s1 = 1/xd ∑ (at −µ1)(a−µ1)t −−−−−−−−− 7 s2 = 1/xd ∑ (at −µ1)(a−µ1)t −−−−−−−−− 8 https://doi.org/10.15837/ijccc.2021.6.4305 8 for k=1 the confusion matrix is,  569.4 63.4 58.2116.9 521.7 53.4 424 326.8 739.2   confusion matrix is in the order of ei, ie, and numbers of training region and the value is based on class test and gene train function. this confusion matrix provides the better accuracy result to the classification system. based on the threshold variations in the input sequence, the arrangement of pattern is by labeling each sequence. this variation leads to changes in resultant pattern of every instance. this class of information of each sequence is the mined pattern. based on the data sequences, table 2: sequential pattern mining techniques k sequential pattern 1 ’aagcccatcctagagaagctgacccaggaccaggatgtggacgtcaaatactttgcccag’ 2 ’acggagcgagtctggaacctgatcagatacatctataaccaagaggagtacgcgcgctac’ 3 ’tgctctcccaggtctaccctgaactgcagatcaccaatgtggtagaagccaaccaaccag’ 4 ‘tgcctcctttcacactcctcttggggctcgtgacattacgaaccctaacccgggccctgc’ 5 ’tcgtggcgtttgtggcaaccccggacacggggcaccagccagtcagcggagcctcctcac’ 6 ’catcgtctacctgggtcgctcaaggcttaactccaacacgcaaggggagatgaagtttga’ 7 ’gccgcttcctcatcctggcacactctcttcacagccgaagaaggccagttgtatggaccg’ 8 ’cgcacctgggcgccctgctggcaagatacatccagcaggcccggaaaggtaagaatgctg’ 9 ’ggccagatcgtgccatagcactccactttgggtgatagagggagactctgtctcaaaaaa’ 10 ’ggagtgggggcggtgcgtcctccggccggcagcggtggccacagctctcctcccgccgcc’ the pattern-mined data is shown in table 2. by the confusion matrix changes, the varying threshold value to each sequence is mined in a ordered pattern. highlighted sequences are characterized as d, n, s, and r of nucleotides and it is mentioned in table 1. probability of intron and exon is calculated by, p_ei=numbers of ei /length of class p_ie=numbers of ie /length of class based on the above equation model, we can obtain the probability value of ei and ie. by length of class and number of intron and exon will show the gene class of train function. the probability mining is given by, for sum the probability of ‘a’ is given by, p(a) = ∑ b p(a,b) −−−−−−−−−−− 9 for multiplication of the probability is given by, p(a,b) = p(b|a) p(a)——–10 the bayes theorem is used to represents the pattern recognition in t-svm. it is given by, https://doi.org/10.15837/ijccc.2021.6.4305 9 p(b/a) = (p(a/b)p(b))(p(a)) −−−−−−11 the gene sequence of primates is having attributes, nature and species types, which is classified based on the threshold based support vector machine-learning algorithm. initially the nucleotides sequence pattern is mined by pattern mining, but it have a high dimensionality in nature. it is reduced by classifying the sequence of each sub sections. after performing training, the sequence should be allocated for testing; this provides the classified result by labeling the each class values. c. threshold based svm model proposed threshold based svm model is used for classification of sequential patterns. based on the statistical analysis of proposed method, we can classify the result. kernel based classification model is structure for train and test sequence. the train data is linearly separable by pair of weight function and support. that is function of <w,s> is given by, f(x) = f(wtx + λ0) −−−−−−− 12 wtx + s >= 1,forxεpi(pos) wtx + s >= −1,forxεpi(neg) here the weight function ‘w’ is represented as, the above represented functions are mined with dna cells of primates. this linear function generalized with weight vector and data miner variable. here the combination of nucleotides is mined for sampling the sequence, which have threshold value of each class of sequence. the labeling of each sequence is assumed for class value of the dna. by extracting this sequence at each instance, the dna gene formation is made for the species recognition. here the support matrix is used to assign the threshold value of given sequence. after it is given to the training process of svm model, the kernel function in t-svm classification result is improved. the gauss distribution function is represented by single mean valued sequence that is given by, the probability distribution function of gaussian distribution model is given by, p(ia) = ςp(ia, ib)dib −−−−−−−−16 this proposed threshold-svm model is used to assume the threshold value at internal sub-section of sequence having threshold value by sampling process. after getting the extracted data sequence, the pattern is set by pattern mining technique for describing dna attributes. this will determines the support value for constructing confusion matrix at each value of ‘k’. after completing the process of training and testing, the mined dataset is classified. table 3 represents the sequential pattern of training and testing process with its execution time. here the gene sequences are aligned with respective threshold values for classification. 5 results and discussion thus, the sequential pattern mining technique was applied in primate splice junction gene sequence dataset and it is classified using threshold based support vector machine learning algorithm. based on the dataset parameters, the classification process is performed. this achieves the result of classification https://doi.org/10.15837/ijccc.2021.6.4305 10 table 3: train and test gene sequence alignment train gene sequence test gene sequence ’attcaaacagcgcctcagactacttcatttgg tacaaacaagaatctggaaaaggtcctc’ ’tggaccatcgcggatagacaagaaccga ggggcctctgcgccctgggcccagctc tgtcc’ elapsed time is 1.308294 seconds. elapsed time is 5.475533 seconds. accuracy and reduces the execution time. table 4 represents the determination value of proposed tsvm model using spm technique. performance metrics the evaluation of whole system is work with various aspects like accuracy, sensitivity, and precision. a.classification accuracy by finding the confusion matrix, we can calculate the accuracy by correctness value of proposed systems and it is associated with the value of true positive, true negative, false positive, and false negative in matrix form. here the true positive is the rate of actual value to the predicted value of data. true negative is the actual and predicted rate is with class zero valued function. false positive is the rate of false rate of actual value versus predicted value. false negative is the actual class value versus predicted false value zero. the classification accuracy is calculated as, accuracy = (tp +tn)(tp +tn+fp +fn) b.sensitivity in pattern recognition, the sensitivity of mined pattern is depends on actual sequence and trained sequence with its threshold value. the sensitivity is also known as recall, which is calculated by actual value to the false negative value. sensitivity = tp(tp +fn) c.precision precision value is about the capturing the correctness of the given sequence. the probability of getting the value of actual value into predicted value versus test sequence. it is formulated as given equations, precision = tp(tp +fp ) table 4: values obtained in proposed t-svm using spm technique parameters t-svm using spm method accuracy 90.64% sensitivity 100% precision 1 execution time 677ns (train and test) number of pattern mined 768 correct sequence 1879 figure 3 represents the comparison result of proposed method’s number of patterns mined for an instance with conventional methods. here it achieves the best result as compared with previous techniques. for every instances, the variation in threshold value changes the sequence, here this above result is based on the initial k=1, the sequence are “aagcccatcctagagaagctgacccaggaccaggatgtggacgtcaaatactttgcccag’ is mined. here the function gauss and kernel is represented for training and testing process. figure 4 shows the graph model of gauss and kernel representations of t-svm model. the below figure 5 is the comparison result value of classification accuracy, compared with conventional radix tree miner and prefix span method. figure 6 is the comparison result representation of compilation time with the unit of ns. the whole comparison result is shown table 5, which contains the classification accuracy, time taken to complete the https://doi.org/10.15837/ijccc.2021.6.4305 11 figure 3: comparison with the result of number of pattern mined of an instance figure 4: gauss (red) and kernel (blue) function process, and number of pattern mined for an instance., which is compared with existing radix tree miner and prefix span methods. the proposed method achieves the better result as compared to the previous work. the above figure 7 shows the comparison results graph model; by this values, the proposed method achieves the result in the way of improved accuracy, reduced execution time, and number of pattern mined at an instance. since the proposed method is efficient than the conventional method. 6 conclusion and future scope thus, the proposed research work is concluded with the technique of sequential pattern-mining approach uses threshold based support vector machine learning (t-svm) algorithm for classification of dna gene sequences. here the pattern mining technique is used to model the given gene sequence by pattern mining method. based on the data set attributes the gene sequence of dna is mentioned. the high dimensional data set is reduced by performing training and testing of data. based on the table 5: comparison results parameter neural network based radix treeminer [1] prefix span based method proposed t-svm method no. of pattern mined 642 641 768 accuracy 90.38% 74.83% 90.64% execution time 795ns 907ns 677ns https://doi.org/10.15837/ijccc.2021.6.4305 12 figure 5: comparison result of classification accuracy figure 6: comparison result of execution time of conventional methods versus proposed method ‘k’ value, the sequence of mined data is varied and it is given to the class database. by comparing with various classification techniques, the proposed method achieves the result by classification accuracy, number of pattern mined and execution time. in future work, the proposed method is enhanced by modifying novel techniques for various datasets with most achievable performance metrics. classification accuracy is improved by providing correct sequence to the model. funding no funding is involved in this work. conflict of interest conflict of interest is not applicable in this work. authorship contributions there is no authorship contribution. acknowledgement there is no acknowledgement involved in this work. https://doi.org/10.15837/ijccc.2021.6.4305 13 figure 7: comparison result of execution time of conventional methods versus proposed method references [1] k poongodi., and a k sheik manzoor, (2019) .“sequential pattern mining using radixtreeminer algorithm and neural network based classification,” international journal of applied mathematics and information sciences, vol. 13, no. s1, pp. 1-15,. 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[28] solomon h ebenuwa., mhd saeed sharif., mamoun alazab., ameer ai-nemrat., (2019). “variance ranking attributes selection techniques for binary classification probem in imbalance data,” ieee access,24649-24666. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: imavathy s.; chinnadurai m. (2021). threshold based support vector machine learning algorithm for sequential patterns, international journal of computers communications & control, 16(6), 4305, 2021. https://doi.org/10.15837/ijccc.2021.6.4305 introduction literature survey existing method neural network based pattern mining technique proposed t-svm based sequential pattern mining techniques results and discussion conclusion and future scope international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 5, month: october, year: 2021 article number: 4087, https://doi.org/10.15837/ijccc.2021.5.4087 ccc publications development of a hybrid algorithm for efficient task scheduling in cloud computing environment using artificial intelligence m.yousuf uddin,h.awad abdeljaber,tariq ahamed ahanger mohammed yousuf uddin* department of computer science prince sattam bin abdulaziz university, ksa al kharj, kingdom of saudi arabia m.yousuf@psau.edu.sa *corresponding author: m.yousuf@psau.edu.sa hikmat awad abdeljaber department of computer science prince sattam bin abdulaziz university, ksa al kharj, kingdom of saudi arabia h.abdeljaber@psau.edu.sa tariq ahamed ahanger department of computer science prince sattam bin abdulaziz university, ksa al kharj, kingdom of saudi arabia t.ahanger@psau.edu.sa abstract cloud computing is developing as a platform for next generation systems where users can pay as they use facilities of cloud computing like any other utilities. cloud environment involves a set of virtual machines, which share the same computation facility and storage. due to rapid rise in demand for cloud computing services several algorithms are being developed and experimented by the researchers in order to enhance the task scheduling process of the machines thereby offering optimal solution to the users by which the users can process the maximum number of tasks through minimal utilization of the resources. task scheduling denotes a set of policies to regulate the task processed by a system. virtual machine scheduling is essential for effective operations in distributed environment. the aim of this paper is to achieve efficient task scheduling of virtual machines, this study proposes a hybrid algorithm through integrating two prominent heuristic algorithms namely the bat algorithm and the ant colony optimization (aco) algorithm in order to optimize the virtual machine scheduling process. the performance evaluation of the three algorithms (bat, aco and hybrid) reveal that the hybrid algorithm performs better when compared with that of the other two algorithms. keywords: cloud computing, virtual machines, task scheduling, ant colony optimization algorithm, bat algorithm, hybrid algorithm. https://doi.org/10.15837/ijccc.2021.5.4087 2 1 introduction cloud computing is a novel technique that enhances the virtualized resources usage over the internet to the end user. cloud computing host services, resources and offers to the end-users over internet based on pay-as-you-use[24]. cloud computing is the distributed system with inter-connected virtualized computing resources that are dynamically provisioned to the customer[8]. the main purpose behind cloud computing is offering on demand different services and resources from service providers with greater scalability and availability in a distributed system [24]. cloud computing comprises of a set of several number of resources of computing such as bandwidth of network, virtual machines, storage and processing [1]. supply of these resources on demand is one of the main purposes of task scheduling[25]. the tsp (task scheduling problem) is the most critical barrier in cloud surroundings[4]. task scheduling problem is a non-deterministic polynomial time hard issue and is liable for allocating application tasks to resources of computation effectively[33]. task scheduling is the method of organizing incoming tasks in some way so that the feasible resources are used properly[7]. because cloud computing is the technique that provides services through internet medium, service users must submit their online requests. the scheduler considers several constraints at the scheduling time involving the task nature, task size, execution time of task, the resource availability, load on resource and the task queue[17]. task scheduling is one of the major problems in cloud computing. proper scheduling of tasks may outcome in effective use of resources[2]. the major benefit of cloud is that it enhances appropriate use of resources [10][13]has stated that the task scheduling issue comprises of m machines and n tasks. every task must be processed by one of the virtual machines m such that at the end the whole process scheduling duration is minimized. the scheduling algorithm is concentrated on qos parameters namely task execution cost, make span and flow time. keshk et al. proposed the modified aco use in load balancing. ant colony optimization is a random search algorithm that uses a positive feedback process and imitates the actual ant colonies behavior in nature to browse for food and to link to each other by phenomena placed on paths traveled[32]. this method develops the job’s makespan. this system does not regard the resource availability or tasks weight[26]. according to maruthanayagam et al. the ant colony optimization targets to allocate tasks into feasible cloud computing environment resources. the aim of cloud scheduler is to allocate jobs to feasible nodes. the best match must be predicted from the list of feasible jobs to the list of feasible resources. the selection is based on findings of computing resource power. the cloud scheduler must allocate jobs to resources effectively. the efficiency relies upon two criteria one is flow time and other is make span. the tasks feasible are contrast with feasible resources and the best one will be choose. the times of execution from simulation are employed for estimated ant based algorithm in cloud surroundings. the major benefit of ant colony optimization over meta heuristic algorithms[31] is the example may alter dynamically. the decisions made by entire ants are purposeful and result of all ants’ decision used in all the iteration to build the new optimal solution[22]. ding et al. proposed algorithm of scheduling using ant colony optimization aimed at developing availability of system and managing an ideal time of response of submitted tasks. they established the availability of heterogeneity and computational heterogeneity concept. the class i of computational weight on j node is referred as the proportion between its rate of service on j node and the rapid rate of service in the system[11]. according to kumar et al. bat algorithm is a new meta-heuristic algorithm for optimization in different tasks of optimization. the new method is based on altered rng (random number generator) with chaotic consequences for initialization of parameter. on certain mathematical benchmark functions. simulation describes the new methods validity in which cba (chaotic bat algorithm) outperformed classical bat algorithm[21]. yang et al. used the bat algorithm to resolve the issues of topology optimization. the distribution of various topological features namely materials was accomplished effectively. the bat algorithm verified by resolving benchmarks of nonlinear design. the bat algorithm was effective in resolving nonlinear issues of global optimization and optimization of topology[34]. raghavan et al. used bat algorithm to resolve the scheduling issue of workflow in cloud targeting to reduce the complete workflow processing cost. the algorithm runs better in processing cost terms when compared with best resource selection algorithm[28]. a hybrid of multi objective bat algorithm and particle swarm optimization is used for maximization of profit in cloud. particle swarm optimization algorithm is employed for global update and local search is performed by bat algorithm as bat algorithm https://doi.org/10.15837/ijccc.2021.5.4087 3 has greater worldwide convergence [6]. thus, it can be inferred that task scheduling and resource allocation must be analyzed and optimized carefully in cloud environment to accomplish reduced cost and time, which leads to quality of better reliability[30]. the reminder of this paper is divided in to six sections: section 1 is the problem statement, section 2 gives the literature review, section 3 is proposed model, section 4 show the simulation results, and section 5 is conclusion and section 6 acknowledgement. 2 problem statement efficient scheduling is one of the major concerns while executing workflows in cloud surroundings. the scheduling of workflow in cloud defines to representation of tasks of workflow to resources of cloud to optimize certain objective function. cloud computing is a heterogeneous system and it processes and holds huge number of complicate data. the task scheduling effectively can lead to much throughput s and better performance in the system. the study of moon et al. proposes a novel algorithm of cloud task scheduling based on optimization of ant colony that allots cloud users tasks to virtual machines in cloud computing surroundings in an efficient way. this research has adapted reinforcement and diversification strategies with slave ants to enhance the task scheduler performance in cloud computing surroundings with aco [23]. similarly, kaur et al. proposed bat algorithm to resolve the multi-objective workflow scheduling issue in clouds that reduces the time of execution and extends the reliability by maintaining the budget within specified limit of user[20]. this study proposes a hybrid algorithm combining ant colony optimization and bat algorithm for scheduling tasks in an appropriate way. this study evaluates the development of a hybrid algorithm for efficient task scheduling in cloud environment. 3 literature review jacob (2014) proposed a study on bat algorithm for resource scheduling in cloud computing. the resource scheduling is a complicate method in cloud computing because of cloud’s heterogeneous nature and several copies of relevant task is provided to several pcs. therefore in this research a bat algorithm is suggested to schedule resources in the cloud surroundings. this study presents the growth of bat algorithm based scheduling tool for resource scheduling in cloud computing. ali and fatma(2015) proposed a hybrid algorithm with particle swarm optimization and cukoo search algorithm called psocs. algorithm tested in cloudsim simulator. proposed system is compared with existing random allocation algorithm and original particle swarm algorithm. results show makespan and utilization with psocs show significant improvement.[4] according to the study of kalra et al. (2015) cloud computing as become an essential concept in high performance distributed computing area as it offers on request access to shared resources pool over internet in a self-service, metered way and dynamically scalable way. still cloud computing is in its development stage so as to reap its complete advantages much studies is needed across several concepts. one of the essential problems which are required to be concentrated for its effective performance is scheduling. the main aim of scheduling is to represent tasks to proper resources that optimize more than one objective. in cloud computing scheduling belong to a set of issues referred ads np hard issue due to huge space solution and thus it takes a big time to predict an optimal solution. it is preferable to predict suboptimal solution in cloud environment but in short span of time. the techniques of met heuristics have been proved to accomplish optimal solutions within cheap time for such issues. in this study a comprehensive survey and comparative examination of different algorithms of scheduling for grid and cloud surroundings based on 3 familiar meta-heuristic techniques namely genetic algorithm, particle swarm optimization and ant colony optimization and two novel technologies namely bat algorithm and league championship algorithm[19]. in the research of tawfeek et al (2015) cloud computing is the growth of parallel computing, grid computing or distributed computing or referred as commercial computer science concepts implementation. one of the major problems in this surrounding is associated to task scheduling. the task scheduling of cloud is an np hard optimization issue and several meta-heuristic algorithms have been https://doi.org/10.15837/ijccc.2021.5.4087 4 suggested to resolve it. in this study a cloud task scheduling policy based on aco algorithm is compared with various algorithms of scheduling. round robin and first come first serve has been used in this study. the main aim of these algorithms in reducing the given tasks set makesspan. ant colony optimization is a random optimization search method that will be employed for incoming jobs allocation to virtual machines[33],[32]. baxodirjonovich et al. (2016) proposed a research on dynamic task scheduling algorithm based on ant colony scheme. several scientific applications performing in cloud computing are applications of workflow that comprises huge number of tasks and in which the tasks are linked by relations of precedence. scheduling the workflow efficiently becomes a barrier in cloud computing surroundings because the scheduling determines the application performance. predicting the optimal scheduling is referred a np-hard. aco can be used to frame effective algorithms of scheduling. this study proposes an algorithm of tasks scheduling that employs a modified aco and this changed version employs probability in order for ants to determine target machine. the proposed algorithm of task scheduling is implemented in workflowsim to estimate the performance[5]. according to the study of kumar et al. (2016) the trend on cloud computing usage is developing essentially and the greater cloud demand leads to process different and huge number of information at a time and several tasks are in scientific workflow form. workflows are dag (direct acyclic graphs) in which every edges indicates dependencies and every node indicates tasks. in order to make the cloud satisfy the request of user efficiently then how workflow scheduling can be acquired in essential. in this study a proposed technique which is a combination of ant colony optimization and particle swarm optimization has been implemented. two parameters that is last dag finish time and cost is employed to analyze and compare for performance[26]. according to guo (2017) in order to optimize the strategy of task scheduling in cloud surroundings a cloud computing task scheduling algorithm is proposed based on the algorithm of ant colony. the main aim of this algorithm is to reduce the total costs and makespan of tasks while making the load of the system much balanced. in this study the objective function of the costs and makespan of tasks is established, referring the function of load balance. meanwhile this study also develops the pheromone initialization, the pheromone update approach and heuristic function in the algorithm of ant colony[15]. sunitha rani et al. (2017) have stated that cloud computing is explained by dynamic provisioning, shared infrastructures, managed metering and access of network. it has surfaced as an essential archetype using virtualized infrastructure to manage many complicate servers on different environments of execution. virtual machines are regarded as the processing units and facilitators in cloud surroundings. effective use of virtual machine instances is accomplished through efficient mechanism of task scheduling which enables similar workload distribution among nodes resulting in developed time of response. this study concentrates on optimal use of virtual machines modeled around directed acyclic graph with topology order to schedule task using inspired bat algorithm. this algorithm reduces the task waiting time and virtual machines idle time as its rate of convergence is greater[29]. al-arasi et al. (2018) mentioned that nowadays cloud computing makes it feasible for users to utilize the resources of computing namely application, hardware and software etc., on the model of pay as use through internet. one of the challenging and core problem in cloud computing is the scheduling of tasks. task scheduling issue is an np hard issue and is liable for representing tasks to resources in a way to distribute the load commonly. the proper representation between tasks and resources decreases the makespan and extends the utilization of resource. in this study an independent task scheduling algorithm is presented and implemented that allots the tasks to users to numerous resources of computing. the proposed algorithm is a hybrid algorithm for scheduling tasks in cloud computing based on pso (particle swarm optimization) and ga(genetic algorithm). the algorithm is simulated and implemented using simulator of cloudsim[3]. in the research of bezdan et al. scheduling task in cloud computing is a larger problem to accomplish greater effectiveness. scheduling can be referred as the actions list that can be carried out using usual algorithms of scheduling but these are np hard issues they can even determinable if this study utilizes meta-heuristic algorithm like bat algorithm. it offers a highly optimized solution in allocating tasks. in this study bat algorithm is going to be implemented in the toolkit of cloud sim and it https://doi.org/10.15837/ijccc.2021.5.4087 5 mentions the benefits of using this method when compared with general algorithms of scheduling[6]. kumar et al.(2018) proposed a research on task scheduling using meta heuristic optimization techniques in cloud environment. cloud computing enables the access of network everywhere to a shared number of configurable resources of computing and offers different services for allocation namely computation and virtualization storage. the resource utilization and task scheduling are the major barriers in cloud surroundings. scheduling allocates various kinds of job in resources and scheduling is determined based on quality of service response. qos is the assured service which manages various tasks in allocation of job. for that reason in order to plan the demanding information several heuristic algorithms has been suggested such as pso (particle swarm optimization), aco (ant colony optimization), ga (genetic algorithm) and abc (artificial bee colony) algorithms to resolve the task scheduling and resource utilization. it is suggested to utilize bat algorithm which offers an effective mechanism of scheduling that will develop the performance and efficiency of system by reducing the deadline and time of execution[27]. timea et al. proposed hybrid meta heuristic algorithm. it combines bat algorithm and artificial bee colony algorithm. task scheduling with multiple criteria is the complex optimization problem. better the task allocation method better the quality of service. thus, the meta heuristic approach is required to get approximate solution in short time when compared to np-hard optimization approach. proposed ba-abc technique outperformed other meta heuristic approaches, ems-c , ecmsmoo, boga and cmsos. significant improvement in makespan and reduced cost[6]. yi gu and chandu budati proposed a multi heuristic algorithm for effective workflow scheduling called energy aware time and throughput optimization heuristic algorithm (eatto). they address the issue of energy consumption, throughput and execution time. n number of tasks are searched in ndimensional search space with multi-objective function that results in overall improvement in performance of all three objectives [14]. 4 proposed system in this study the ant colony optimization algorithm and bat algorithm is combined for efficient task scheduling in cloud environment. in the proposed system, first the population for both ant colony optimization and bat algorithm is initialized. then the fitness function for the solutions produced from every algorithm is evaluated. after that the best solutions from both the algorithms are predicted and the best solutions are combined. after the combination, the solutions are updated. then the solutions are evaluated and again the combined solutions are optimized using bat algorithm. given below flowchart illustrates the steps of proposed algorithm. from the above chart the cloud environment is deployed with finite number of vm. then the population is initialized which denotes the solution of bat and ant colony optimization algorithm which must be generated. after that the function of fitness is evaluated to predict the weaker users from bat and ant colony optimization algorithm and changed with strong users from other algorithms. then the bat and ant colony optimization algorithm completes their search and output-scheduling result is obtained when it meets maximum. iteration number. if it ends, it integrates the best solution of bat and ant colony optimization algorithm to enhance the process of optimization and task scheduling exists. if it does not end, it again estimates the adjustment of fitness affinity using bat and ant colony optimization algorithm. thus the solution generates gets swapped best with the bad and vice versa and then the population is updated. then again, it goes to the evaluation step of fitness. if it is yes then the process ends otherwise it again repeats the complete process. thus by integrating the ant colony optimization algorithm with bat algorithm is regarded as strong. ba develops the solutions diversity using a technique of frequency tuning in the population. the evaluation function of fitness is employed to predict weaker user from both bat and ant colony optimization algorithm and it is changed with strong users from other type of algorithms. stronger users of both bat and ant colony optimization algorithm are integrated to enhance the process of optimization. https://doi.org/10.15837/ijccc.2021.5.4087 6 table 1: reviews of hybrid algorithm for efficient task scheduling in cloud environment s.no author year algorithm finding of the research 1 jacob [18] 2014 bat algorithm greater efficiency and greater value of accuracy 2 baxodirjonovich et al. [5] 2014 probabilistic load balancing ant colony optimization, min-min and ant colony optimization algorithm reduce the average makespan 3 sajjad et al.[9] 2014 firefly algorithm minimization of makespan time 4 kalra et al. [19] 2015 ant colony optimization, particle swarm optimization, genetic algorithm, league championship algorithm and bat algorithm makespan reduction, cost of execution, time of response, time of flow, throughput and average utilization of resource 5 tawfeek et al. [33] 2015 ant colony optimization, round robin and first come first serve the task scheduling in cloud based on ant colony optimization outperformed round robin and first come first serve algorithms 6 kumar et al. [26] 2016 ant colony optimization and particle swarm optimization reduces the cost and decrease the finish time of last day of the workflow 7 guo,q [15] 2017 multi objective ant colony optimization and min-min improves the pheromone update rule and heuristic function of ant colony algorithm 8 rani et al. [29] 2017 bat algorithm reduce the virtual machines idle time and tasks waiting time 9 al-arasi et al. [3] 2018 particle swarm optimization, genetic algorithm reduces the makespan and develops the utilization 10 bezdan et al. [6] 2018 bat algorithm highly optimized solution for task allocation 11 kumar et al. [27] 2018 ant colony optimization, particle swarm optimization, genetic algorithm, bat algorithm and artificial bee colony algorithms increase the performance and efficiency of system by reducing the deadline and execution time 12 hariharan et al. [16] 2019 hybrid bat algorithm improved job scheduling task with hybrid optimization algorithm 13 timea bezdan et al. [6] 2020 hybridized bat algorithm increase the multi-object task scheduling. 14 yi gu et al.[14] 2020 bat algorithm reduce the execution time for computation-intensive workflows https://doi.org/10.15837/ijccc.2021.5.4087 7 4.1 ant colony optimization algorithm ant colony is a set of development evaluations set up on insect settlement activities. . in this algorithm when the group of ant attempts to view for food, they employ a special type of chemical to interact with each other. that chemical is known as pheromone. initially ants initiate their food search randomly. once the ants find a way to their source of food, they leave pheromone on the way. an ant can face other ant’s trails to the source of food by sensing pheromone on the ground. as this method continues several ants attract to select the shortest path as there have been several accumulated pheromones on this way. small departure from this method is the calculation of honey bee that is much relevant to scavenging bumble bee instances [33]. the pseudo code of aco is explained below: figure 1: flowchart of proposed system 4.2 bat algorithm bat algorithm is based on echo location characteristics of micro bats which are proven to be effective. bat algorithm develops the solutions diversity using a technique of frequency tuning in the population. in nature, the bat algorithm is iterative hence for iteration the parameters must be updated. the evaluation function of fitness is employed to predict weaker users from both bat and ant colony optimization algorithm and changed with powerful users from other algorithm. powerful users of both bat and ant colony optimization algorithm are integrated to enhance the process of optimization. bat algorithm uses randomization for estimating the optimal value. to assure the algorithm’s superiority an algorithm is verified with many standard functions. if an algorithm is capable to reduce the functions of standard with utmost accuracy then these algorithm are regarded to be the best algorithm. bat algorithm is one of the few algorithms which provide accurate outputs and it is one of the major reasons for selecting this algorithm for scheduling of workflow [10]. the https://doi.org/10.15837/ijccc.2021.5.4087 8 figure 2: pseudo code ant colony optimization pseudo code of bat algorithm is explained below: figure 3: pseudo code runbat algorithm 4.3 simulation results for this analysis totally 700 tasks have been chosen with 300 preprocessing + makespan in seconds for combining the ant colony optimization and bat algorithm. the simulation output is presented below: from the above figure (figure 4) it can be inferred that the proposed hybrid algorithm is superior to that of the ant colony optimization algorithm and the bat algorithm in terms of makespan and preprocessing of multiple tasks at the same time. 5 conclusion and future scope task scheduling has been regarded as one of the most vital issues in cloud computing. optimized scheduler would help in the enhancement of the performance and throughput in cloud system. the https://doi.org/10.15837/ijccc.2021.5.4087 9 figure 4: proposed hybrid algorithm results proposed algorithm has combined bat and aco to gain the best benefit of both the algorithms. the bat algorithm is employed for scheduling workflow in clouds and it not only optimize the time of execution but also assures that the tasks are allotted to greater reliability vm while meeting the defined budget of user. the ant colony optimization algorithm optimizes the resources in an effective way and it is used to select one among various alternative ways to decide the order of processing of every resource. ant colony optimization algorithm reduces the energy amount used by every resource and hence the system performance is developed. efficient utilization of time is performed using the ant colony optimization algorithm. in the proposed research, ant colony optimization algorithm is integrated with bat algorithm in the way to remove the bad solution and to integrate the best solutions to much optimizing the solution. the proposed hybrid algorithm is superior and quicker in system balancing compared to other algorithms. in future research multi optimization can be performed and the parameters can be increased for enhancing aspects like the quality of service, resource allocation etc of the virtual machines. 6 acknowledgment this research was funded and conducted at prince sattam bin abdulaziz university, alkharj, saudi arabia during the academic year 2020/2021 under research number 2020/01/16446. references [1] mohit agarwal and gur mauj saran srivastava. a cuckoo search algorithm-based task scheduling in cloud computing. in advances in computer and computational sciences, pages 293–299. springer, 2018. 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journal of computers communications & control, 16(5), 4087, 2021. https://doi.org/10.15837/ijccc.2021.5.4087 introduction problem statement literature review proposed system ant colony optimization algorithm bat algorithm simulation results conclusion and future scope acknowledgment int j comput commun, issn 1841-9836 8(6):812-824, december, 2013. operation mechanism of the driving force system of ecosystem of cyber-society based on the system dynamics x. guan, z. zhang, s. zhang xiaolan guan school of economics and management beijing institute of graphic communication 1 xinghua avenue (band two), daxing, beijing 102600, china *corresponding author: 08113101@bjtu.edu.cn zhenji zhang, shugang zhang school of economics and management beijing jiaotong university no. 3 shang yuan cun, hai dian district, beijing 100044, china zhjzhang@bjtu.edu.cn, 10113132@bjtu.edu.cn abstract: operation of the driving force system of ecosystem of cyber-society needs a scientific mechanism of intervention and regulation to solve the integration problem of a variety of organizations and forces within the ecosystem of cyber-society, shorten the process from uncoordination to coordination, promote the orderly operation of the driving force system of ecosystem of cyber-society, make the system play a strong force, in order to promote the formation and rapid development of ecosystem of cyber-society. we analyze the driving force system of ecosystem of cyber-society using the theory of system dynamics and propose a theoretical framework, and then present its operation mechanism systematically. keywords: ecosystem of cyber-society, driving force system, operation mechanism, life cycle, system dynamics. 1 introduction the operation of the driving force system needs certain operation mechanism, which refers to create interaction relationships between the driving forces, establish a fulcrum, platform and regulation for the driving forces, so that the driving force system can rely on the environment of the driving force system, and make the driving forces function effectively. similarly, the operation of the driving force system of ecosystem of cyber-society also needs a scientific mechanism of intervention and regulation to solve the integration problem of a variety of organizations and forces within the ecosystem of cyber-society, shorten the process from uncoordination to coordination, promote the orderly operation of the driving force system of ecosystem of cybersociety, make the system play a strong force, in order to promote the formation and rapid development of ecosystem of cyber-society. however, since the driving force system consists of a lot of driving forces, its hierarchy is very complex with multi-levels, and different structures may emerge unexpected functions, so we need to find out an effective way to analyze it. currently, among the literature can be found around the world, although some scholars have put forward the concept of ecological networks from different perspectives and gradually realized the importance of this research area, there is still not much comprehensive and systematic research about the cyber-society from the perspective of ecosystem. and there is no literature that has proposed the concept of driving force system of ecosystem of cyber-society and studied its mechanism systemically from the perspective of the ecosystem for the development of cybersociety. it is a new research area. the related research mainly focuses on the sociology of network and social ecology. and among them, different scholars have different views about the concept of sociology of network. copyright © 2006-2013 by ccc publications operation mechanism of the driving force system of ecosystem of cyber-society based on the system dynamics 813 about the research on the sociology of network, some scholars advocated classifying it into the area of information science, while some other scholars emphasized the unique features of the network society in the media and dissemination, and claimed to study the evolution of the network society from the perspective of media and dissemination, and one of the representatives is an american social scientist manuel custer. he thought that the network society is a new social form of society that different from the reality, and pointed out the network constructs the new form of our society, and the spread of network logic has changed the operating result of production, experience, rights and culture. and it is characterized by social forms than the superiority of social action [1]; while gong qi considered the network society as a virtual space of human and humans life, study and work in this alternative space [2]. and the research on the social ecology started earlier. for example, brown who comes from american cornell university had analyzed the human activities in social life from the perspective of the natural ecology [3]. developed so far, the research of this discipline gradually divided into two directions, one is to analyze the phenomenon of human social life using related theories and methods of natural ecology, such as the human activities, social development and so on [4][5], and the other one is to research the coordinated development between the society, economics and resources quantitatively and qualitatively based on the theory of ecology [6][7][8]. the concept of driving force system of ecosystem of cyber-society that proposed in this paper is a cross research between the above two disciplines. it not only emphasizes on analyzing the network space from a sociological perspective, but also reflects the theory of system dynamics in the ecology. therefore, although the concept of driving force system of ecosystem of cyber-society is new, since that the research is a frontier that based on the current disciplines and research achievements, the development status and trends of related disciplines are still significant to our research [9]. the organization of this paper is as follows. in section 2 we propose a theoretical framework of the driving force system of ecosystem of cyber-society, and analyze its structure, functions and characteristics. then, in section 3, we analyzed the operation environment and formation process of the driving forces. and finally in section 4 we present the operation mechanism of the driving force system systematically. 2 the driving force system of ecosystem of cyber-society 2.1 structure of the driving force system the driving forces that affect the formation and development of ecosystem of cyber-society are not chaotic. however, they are an organic whole with a multi-level structure. the formation and development of ecosystem of cyber-society are driven by the driving forces that interact and constraint with each other, and ultimately form the multi-layered driving force system [10]. at the same time, the driving force system that affects the formation and development of ecosystem of cyber-society is also a unified whole and there is ongoing exchange of material, energy and information between the driving forces. if they can develop harmoniously and coordinately, then they will create a stronger driving force together to facilitate the rapid development of ecosystem of cyber-society. otherwise, they will delay the historical process of formation and development of ecosystem of cyber-society. there are three major subsystems in the driving force system of ecosystem of cyber-society, namely subsystem of driving forces, stimulate subsystem of driving forces and carrier subsystem of driving forces, as shown in figure 1. these subsystems interact with each other, and there are continuous exchange of material, energy and information between them. meanwhile, as a constituent of the driving force system, when the driving forces function on the formation and development of ecosystem of cyber-society, their function mode and strength 814 x. guan, z. zhang, s. zhang would be different for different driving forces, and thus form the unique structure of the driving force system [11]. figure 1: a theoretical framework of the driving force system of ecosystem of cyber-society 2.2 functions of the driving force system the driving force system that affects the formation and development of ecosystem of cybersociety has the following functions [12]. (1) to promote the formation of a joint force that affects the formation and development of ecosystem of cyber-society. the formation and development of ecosystem of cyber-society is affected by a variety of driving forces together, and the driving forces vary in size, strength, and thus have positive or negative effect. due to the existence of the driving force system, it makes the driving forces coordinate with each other, and form a joint force that advantageous to the formation and development of ecosystem of cyber-society. (2) ensure that the driving forces that affect the formation and development of ecosystem of cyber-society have a continuous nature. the short-term characteristics and changes of the driving forces make the formation and development of ecosystem of cyber-society in the status of instability. well operation of the driving force system can ensure the formation and development of ecosystem of cyber-society have a continuous driving force. (3) to promote the optimal combination of the elements of ecosystem of cyber-society. there are many driving forces that interrelate and interact with each other affect the ecosystem of cyber-society, and different combinations of the driving forces will play different roles in promoting. the driving force system can promote a better combination of the driving forces to some extent. (4) to improve the competitiveness of the subjects of ecosystem of cyber-society. operation mechanism of the driving force system of ecosystem of cyber-society based on the system dynamics 815 the driving forces are the external forces that promote the formation and development of ecosystem of cyber-society. they combine into a unified whole through the driving force system and input negative entropy flow into the system continuously, thereby enhancing the competitiveness of the subjects of ecosystem of cyber-society. 2.3 characteristics of the driving force system the driving force system that affects the formation and development of ecosystem of cybersociety is an organic whole with multi-levels and multi-factors, and it has the following characteristics. (1) integrity the integrity of the driving force system that affects the formation of ecosystem of cybersociety refers to the unified whole that constitutes by various driving forces that interrelate and interact with each other according to some law or in order to achieve a particular purpose, rather than the chaotic random combination of the driving forces. at the same time, the driving force system is not the simple summation of the driving forces, however, it may often produce new functions as a whole that the individual driving force doesn’t have. it can be expressed in mathematical language as the following: d = a n∑ i=1 di (1) here, d represents the function value of the driving forces in the driving force system; d represents the function value of the overall driving force system; a represents the function coefficient of the driving forces. (2) structure hierarchy the structure hierarchy of the driving force system that affects the formation of ecosystem of cyber-society refers to the hierarchical order of the status, role, structure and function of the system due to the various differences between the driving forces. the structure hierarchy is relative and changes constantly. when the driving forces form a whole with a reasonable structure, then the system will have new functions, and the functions will be larger than the overall functions of individual driving forces. otherwise, when the driving forces form a whole with a poor structure, then it will damage the functions of the whole system. the expression that uses mathematical language is: d = f (d1,d2,dn) (2) here, f(dk),k = 1,2...n represents the structure function of the driving force system, and different levels of the structure correspond to different functional forms. a represents the function coefficient. (3) orderliness the orderliness of the driving force system that affects the formation of ecosystem of cybersociety refers to the characteristic of reasonable structure that combined through the coordination and promotion between the various driving forces. the orderliness is relative to the disorder, it shows the structure rationality of the driving force system, and also determines whether the system can function normally. the larger the orderliness of the driving force system, the smaller the obstruction to the formation and development of ecosystem of cyber-society and the faster the pace of development. at the same time, the orderliness of the driving force system is not 816 x. guan, z. zhang, s. zhang static, it shows the characteristic of dynamic changes in the long term along with the positive and negative effects on the system between the driving forces, but its overall characteristic and performance maintain stable. (4) openness the openness of the driving force system that affects the formation of ecosystem of cybersociety refers to that the driving forces not only have to coordinate and cooperate with each other, but also have to exchange material, information and energy with the external environment continuously at the same time, in order to maintain the specific structure and functions of the driving force system. this open process allows the environment to influence and transform the driving forces, and at the same time the driving force system will also transform the external environment and make it evolve toward its own beneficial development direction. the openness is the guarantee to the orderliness of the driving force system, and just because of the openness of the driving force system, it makes the system can absorb negative entropy flow from the external environment in order to maintain or decrease the entropy within the system continuously, and thus keep the orderliness of the system and further enhanced. 3 formation of the driving forces of ecosystem of cyber-society 3.1 operation environment of the driving force system of ecosystem of cyber-society the so-called operation environment of the driving force system of ecosystem of cybersociety refers to the carrier subsystem of driving forces. while the environment is always relative to a center thing, the environment will be different due to different centers, and varies with the changes of the central things. the things associated with the system constitute a collection called the environment of the system. similarly, the operation of the driving force system of ecosystem of cyber-society also needs certain environmental conditions. the environment of the driving force system of ecosystem of cyber-society refers to the sum of the various driving forces that affect the operation and development of ecosystem of cyber-society. the various driving forces form an internal system with compact structure through the interaction with each other, and there is the real society outside with the sub-environment of economics, politics and culture. the environment of the driving force system of ecosystem of cyber-society represents the potential long-term impact of the real society on ecosystem of cyber-society and it is the manifestation of indirectly, more vague, and broad-brush driving forces, and provides environmental support for generating driving forces. the formation and development of ecosystem of cyber-society has a strong feature of dynamic, and due to the dual intertwine between the real society and the environment of ecosystem of cyber-society, it makes the formation and development of ecosystem of cybersociety in an uncertain environment. the driving force system is constrained by these subenvironment systems, and its function on the various driving forces is an important guarantee for the well operation of the driving force system. 3.2 formation process of the driving forces of ecosystem of cyber-society the so-called formation process of the driving forces of ecosystem of cyber-society is just the process that the stimulate subsystem of driving forces functions. the function process of the stimulate subsystem of driving forces consists primarily of the following five sessions, namely develop, transform, cultivate, allocate and feedback, as shown in figure 2. (1) source develop of driving forces operation mechanism of the driving force system of ecosystem of cyber-society based on the system dynamics 817 figure 2: the function process of the stimulate subsystem of driving forces of ecosystem of cyber-society the subjects’ participation and use of ecosystem of cyber-society are the main reasons that lead to the formation and rapid development of ecosystem of cyber-society. in other words, there would be no ecosystem of cyber society without the subjects’ participation and use no matter how quickly the development of modern science and technology is. the motives of the subjects’ behavior are reflected in the interest and benefit, and both of these two aspects are also the major incentives that attract the subjects of ecosystem of cyber-society to involve in the network social life. the network economy has also been considered as the eyeball economy, and it means that one will be able to succeed in the competition of ecosystem of cyber-society if he can attract the attention of internet users, seize the interest of internet users, and meet the benefit of internet users. the saying of eyeball economy may be not correct, but there is one thing for sure that the internet users’ interest plays a huge role in the formation and change of the current network pattern of ecosystem of cyber-society. (2) transform of driving forces the interest and benefit are the sources of driving forces of ecosystem of cyber-society, and they are just the potential driving forces. only if they are transformed into real driving forces, then they can promote the network social behavior of the subjects to meet their needs, and really play an important role as driving forces. (3) cultivate of driving forces the driving forces not only need to be developed and transformed, they are also need to be cultivated and maintained, in order to facilitate the formation and sustainable development of ecosystem of cyber-society. meanwhile, for the driving forces in different levels and different types, the focus of cultivation is also different. it will greatly promote the rapid and healthy development of ecosystem of cyber-society to seize the key influencing forces. (4) allocate of driving forces in order to promote the formation and health development of ecosystem of cyber-society, we not only have to develop, transform and cultivate appropriate driving forces, but we also have to allocate these relatively independent driving forces to the parts of ecosystem of cyber society that need them most, and make them related with each other and combined into a rational structure, then the driving force system can promote the formation and development of ecosystem of cyber-society as an organic whole role. (5) feedback of driving forces as a closed loop feedback system, the session of feedback can guarantee the good functioning of the driving force system. after the first few sessions, the ecosystem of cyber-society has 818 x. guan, z. zhang, s. zhang obtained some driving forces. however, they are still need the session of feedback to coordinate and make sure that they are adequate. the session of feedback will collect the information about whether these driving forces promote the benign operation and healthy development of ecosystem of cyber-society, and then make judgments and give feedback to guide the driving force system to adjust the direction and strength of the driving forces or adjust the combinations between the driving forces, and thus maximize the overall objective of the formation and development of ecosystem of cyber-society. 4 operation mechanism of the driving force system of ecosystem of cyber-society 4.1 operation life cycle of the driving forces of ecosystem of cyber-society the resources within ecosystem of cyber-society can be allocated optimally and achieve the pareto optimal through the good operation of the driving force system of ecosystem of cyber-society, and thus promote the continuous escalating and innovation of the formation and development stages of ecosystem of cyber-society. the operation life cycle of the driving force system can be divided into the following three stages, namely initial stage, integration stage and mature stage, and each stage has a different operation mechanism which plays a leading role, as shown in figure 3 [13]. figure 3: the operation lifecycle of driving force system of ecosystem of cyber-society it has been discussed earlier in this paper about the promotion role that the sub-environment of economics, politics and culture plays on the formation and development of ecosystem of cybersociety. it needs to be noted that the force of economics, politics and culture has to infiltrate into the system through the driving force system during the formation and development process of ecosystem of cyber-society and then function continuously. the promotion force that the driving force system giving to the ecosystem of cyber-society under the interaction between economics, politics and culture should be appropriate and it can be understood specifically from the direction, size and cooperation between the driving forces [14]. (1) direction of the driving forces. to judge whether the direction of the driving forces is appropriate, we can see whether it is consistent with the overall goal and direction of the development of ecosystem of cyber-society. positive driving force is conducive to the formation and rapid development of ecosystem of cyber-society, while negative driving force will inhibit the formation and development of ecosystem of cyber-society. therefore, the force of economics, politics and culture that based on the deep roots of development needs of the subjects of ecosysoperation mechanism of the driving force system of ecosystem of cyber-society based on the system dynamics 819 tem of cyber-society should be positive, should be conducive to meet the basic survival needs of the subjects of ecosystem of cyber-society, should be conducive to increase the high-level needs and satisfaction degree of the subjects of ecosystem of cyber-society, and also should be conducive to the coordination between the individuals and groups of ecosystem of cyber-society. (2) size of the driving forces. as to the positive driving force, its size and strength determine the formation and development speed of ecosystem of cyber-society to some extent, that is to say, the larger the value is, the faster the system develops, and vice versa. the "interest" and "benefit" of the subjects promote the formation and rapid development of ecosystem of cybersociety, but the increase of interest level and the expectation of benefit pursuit must adapt to the reality of the development of ecosystem of cyber-society, and this needs the driving forces to adjust. the ecosystem of cyber-society with inadequate driving forces is dull and lack of energy, while the unlimited and vigorous expanded pursuit to the interest and benefit would cause disorder and concussion of ecosystem of cyber-society. an appropriate driving force can combine the satisfaction of the subjects interest and the good running order of ecosystem of cyber-society together. it can not only activate the driving force of reasonable interest pursuit of the subjects of ecosystem of cyber-society, but it can also control the various driving forces and activities that affect the formation and development of ecosystem of cyber-society within reasonable limits. (3) cooperation between the driving forces. only if the force of economics, politics and culture can complement with each other, coordinate their actions and optimize the allocation of various needs and interest pursuit of the subjects of ecosystem of cyber-society, then it can give appropriate positive driving force to the formation and development of ecosystem of cyber-society, and thus effectively promote the formation and development of ecosystem of cyber-society. 4.2 mechanism of push and pull the formation and development of ecosystem of cyber-society is a dynamic process, and influenced by various driving forces during the process of dynamic development. combined with the mechanism analysis of each driving force, the joint effect in the process of formation and development of ecosystem of cyber-society can be shown as figure 4. here, the ball represents the ecosystem of cyber-society, the x-axis represents the time stage of the formation and development of ecosystem of cyber-society, the y-axis represents that the formation and development of ecosystem of cyber-society is a tough climb process with load, and fi represents the various driving forces,i = 0,1,2, ...,n [15][16]. the roles that each driving force plays in the formation and development of ecosystem of cyber-society have different directions and sizes. according to the theory of mechanics, the ecosystem of cyber-society will be in the state of fast forward development when the positive driving force is larger than the negative driving force, while the ecosystem of cyber-society will be in the state of uniform development when the positive driving force is equal to the negative driving force. and the ecosystem of cyber-society will be in the state of stagnation or even retrogression development when the positive driving force is smaller than the negative driving force. thus, it will greatly promote the formation and development of ecosystem of cyber-society by increasing the positive driving force and making full use of various driving forces. 4.3 mechanism of organization and conduction the mechanism of organization and conduction refers to the formation process of the joint force that affect the formation and development of ecosystem of cyber-society through the cross 820 x. guan, z. zhang, s. zhang figure 4: composition forces model of the formation and development of ecosystem of cybersociety combination and strength coordination between the various driving forces within the driving force system. the various driving forces interact and influence with each other, thus form a joint force for the formation and development of ecosystem of cyber-society. during the process of the formation and development of ecosystem of cyber-society, the joint force it gets is not the simple accumulation of each driving force, however, there will be a new force with new quality due to the interaction and its size is often larger than the simple sum of the driving forces. as shown in figure 5, the various driving forces first act on the ecosystem of cyber-society in accordance with their respective different mechanisms, and thereby combine into a new force through the interaction between them, then this new force will combine with other forces, and the cycle continues, eventually form the integrated force that promotes the formation and development of ecosystem of cyber-society. during the process of interaction between the various driving forces, there will be part of the forces being reduced or offset because of various practical conditions limit. therefore, in order to speed up the formation and development of ecosystem of cyber-society, the relationships between the various driving forces have to be coordinated as much as possible in order to increase the joint effect, which need to optimize the organizational structure of the driving force system or use regulations, as shown in figure 6. by optimizing the hierarchy of the driving force system, making the driving forces all in their places and out of their functions, the joint force can conduct layer by layer orderly, effectively reduce the deadweight loss during the process of force conduction, and thus make the formation and development of ecosystem of cyber-society got stable promotion. operation mechanism of the driving force system of ecosystem of cyber-society based on the system dynamics 821 figure 5: cross combination of driving forces to the formation of ecosystem of cyber-society 4.4 mechanism of integration and collaboration the collaboration generally refers to the relationship of mutual adaptation between two or more interacting species in the process of evolution. to understand from a broad concept, the collaboration can refer to the coordination in the process of long-term mutual adaptation between species and species, or between species and environment. similarly, this collaboration relationship also widely exists in the ecosystem of cyber-society. during the process of the formation and development of ecosystem of cyber-society, there are interrelationships between the various subjects, or between the subjects and the environment. and only to form a coordinated and mutually reinforcing relationship between them, then they can promote the formation and development of ecosystem of cyber-society effectively. the formation and development of ecosystem of cyber-society is not a simple question, and we should consider the formation and development objectives of ecosystem of cyber-society from the height of the overall socio-economic development, and take the development of ecosystem of cyber-society as an important element of real economic and social development. the so-called mechanism of integration and collaboration of driving force system of ecosystem of cyber-society refers to that make clear the positive role of ecosystem of cyber-society in the real economic and social development, take the ecosystem of cyber-society as an important part of the real society, make the development of ecosystem of cyber-society integrated into the overall development of the real society, strengthen the linkage between ecosystem of cyber-society and the real society, maximize the efficiency of resource allocation and utilization, and thus form a integrated force to facilitate the formation and development of ecosystem of cyber-society. from the perspective of the functions of the driving force system of ecosystem of cybersociety, the mechanism of integration and collaboration includes the overall coordination between the driving force system and ecosystem of cyber-society, while from the perspective of the collaboration between various driving forces, the mechanism of integration and collaboration 822 x. guan, z. zhang, s. zhang figure 6: organizational conduction of driving forces to the formation of ecosystem of cybersociety also includes both of the collaboration within the system and outside the system, as shown in table 1. table 1: contents of the mechanism of integration and collaboration of driving force system of ecosystem of cyber-society (ec) scope of integration and collaboration contents of integration and collaboration social psychology and ec internal collaboration innovation and ec network culture and ec external collaboration resources and ec network culture and ec social and economic development and ec overall collaboration policies, laws and regulations and ec 5 conclusions we propose a basic theoretical framework of the driving force system of ecosystem of cybersociety and present its operation mechanism using the theory of system dynamics in this paper. the driving force system of ecosystem of cyber-society is constituted by the subsystem of driving forces, stimulate subsystem of driving forces and carrier subsystem of driving forces, which is a virtual manual system with the characteristics of integrity, structure hierarchy, orderliness and openness. its operation mechanism mainly includes the mechanism of push and pull, mechanism of organization and conduction, mechanism of integration and collaboration, and mechanism of innovation and development. the formation and development of ecosystem of cyber-society is the function result of a variety of driving forces together. there is no one single driving force can independently control the formation and development of ecosystem of cyber-society. only through the coordination operation mechanism of the driving force system of ecosystem of cyber-society based on the system dynamics 823 between the driving forces within and outside the system, then it can generate new driving force structures and joint force, improve the operation efficiency of the driving force system, and thus promote the fast forward development of ecosystem of cyber-society with its powerful functions. acknowledgements this work is partially supported by the general research project (18190113002) and the quality improvement project of personnel training (03150113016) of beijing municipal education commission, and also the institute level project (e-b-2012-20) and the course construction project (22150112088) of beijing institute of graphic communication. bibliography [1] custer, m. 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(2007); dynamic mechanism model of the enterprise ecological development, productivity research, 17: 116-118. international journal of computers communications & control issn 1841-9836, 10(5):718-731, october, 2015. verification of jade agents using atl model checking l.f. stoica, f. stoica, f.m. boian laura florentina stoica*, florin stoica 1. department of mathematics and informatics, faculty of science, "lucian blaga" university, 5-7 dr. ratiu street, sibiu, romania 2. center of scientific research in informatics and information technology "lucian blaga" university 5-7 dr. ratiu street, sibiu, romania laura.cacovean@ulbsibiu.ro, florin.stoica@ulbsibiu.ro * corresponding author: laura.cacovean@ulbsibiu.ro florian mircea boian department of computer science, faculty of mathematics and computer science, "babes-bolyai" university, 1 m. kogalniceanu street, cluj napoca, romania, florin@cs.ubbcluj.ro abstract: it is widely accepted that the key to successfully developing a system is to produce a thorough system specification and design. this task requires an appropriate formal method and a suitable tool to determine whether or not an implementation conforms to the specifications. in this paper we present an advanced technique to analyse, design and debug jade software agents, using alternating-time temporal logic (atl) which is interpreted over concurrent game structures, considered as natural models for compositions of open systems. in development of the proposed solution, we will use our original atl model checker. in contrast to previous approaches, our tool permits an interactive or programmatic design of the atl models as state-transition graphs, and is based on client/server architecture: atl designer, the client tool, allows an interactive construction of the concurrent game structures as a directed multi-graphs and the atl checker, the core of our tool, represents the server part and is published as web service. keywords: model checking, atl, agents, jade, fsm 1 introduction because of competition on the information technology market, the development of the information systems must be achieved with maximum productivity. in many cases, the price paid is the diminution of quality of software products. the aim of the methods of software engineering is to increase the quality and reliability of software. in particular, the formal methods are focused on reliability and correctness, using a mathematical support. the reliability of a system can be increased through modelling of the system before the implementation of code, followed by the validation and verification of its correctness. validation is accomplished with respect to informal requirements of the system. the designer checks if the model reflects the expected behaviour of the system. verification is accomplished with respect to system specifications expressed in a formal manner. a formal specification is an abstract model of the system, expressed in a formal notation. verification of a software system involves checking whether the system in question behaves as it was designed to behave. design validation involves checking whether a system design copyright © 2006-2015 by ccc publications verification of jade agents using atl model checking 719 satisfies the system requirements. both of these tasks, system verification and design validation can be accomplished thoroughly and reliably using model-based formal methods, such as model checking [1]. model checking is particularly well-suited for the automated verification of finite-state systems, both for software and for hardware. main concern of formal methods in general, and model checking in particular, is helping to design correct systems [2]. detecting and eliminating bugs as early in the design cycle as possible is clearly an economic imperative. for example, the pentium fdiv bug (a bug in the intel p5 pentium floating point unit discovered in 1994) cost intel corporation a half billion dollars. model checking is a technology widely used for the automated system verification and represents a technique for verifying that finite state systems satisfy specifications expressed in the language of temporal logics. alur et al. introduced alternating-time temporal logic (atl), a more general variety of temporal logic, suitable for specifying requirements of multi-agent systems [3]. atl is also widely used to reason about strategies in multiplayer games. the semantics of atl is formalized by defining games such that the satisfaction of an atl formula corresponds to the existence of a winning strategy. the model checking problem for atl is to determine whether a given model satisfies a given atl formula. atl defines "cooperation modalities", of the form ⟨⟨a⟩⟩ φ, where a is a group of agents. the intended interpretation of the atl formula ⟨⟨a⟩⟩ φ is that the agents a can cooperate to ensure that φ holds (equivalently, that a have a winning strategy for φ) [4]. atl has been implemented in several symbolic tools for the analysis of open systems. in [5] is presented a verification environment called mocha for the modular verification of heterogeneous systems. the input language of mocha is a machine readable variant of reactive modules. reactive modules provide a semantic glue that allows the formal embedding and interaction of components with different characteristics [5]. in [6] is described mcmas, a symbolic model checker specifically tailored to agent-based specifications and scenarios. mcmas has been used in a variety of scenarios including webservices, diagnosis, and security. mcmas takes a dedicated programming language called ispl (interpreted systems programming language) as model input language. an ispl file fully describes a multi-agent system (both the agents and the environment) [6]. two most common methods of performing model checking are explicit enumeration of states of the model and respectively the use of symbolic methods. symbolic model checkers analyse the state space symbolically using ordered binary decision diagrams (obdds), which are data structures for representing boolean functions and were introduced in [12]. in [13], [14], [15], [16] are presented comparisons between symbolic and explicit model checking of software or hardware systems. for most hardware designs which are based on a clockedapproach and thus are synchronous, the symbolic model checking approach is more appropriate [14]. the explicit-state model-checkers performs better in case of using large states needed to include some information [13], also for nondeterministic, high-level models of hardware protocols and for model checking of concurrent asynchronous software systems [15], [16]. in [7] is presented our tool, a new interactive atl model checker environment based on algebraic approach. the original implementation of the model checking algorithm is based on relational algebra expressions translated into sql queries. the broad goal of our research was to develop a reliable, easy to maintain, scalable model checker tool to improve applicability of atl model checking in design of general-purpose computer software. 720 l.f. stoica, f. stoica, f.m. boian taking into account the above considerations, in our tool we are using an explicit-state model technique. thus, in contrast to previous approaches, our tool is using oriented multi-graphs to represent concurrent game structures over which is interpreted the atl specification language. the core of our atl model checker is the atl compiler which translates a formula φ of a given atl model to set of nodes over which formula φ is satisfied. the implementation of the model checking algorithm is based on java code generated by antlr (another tool for language recognition) using an original atl grammar and provides error-handling for eventual lexical/syntax errors in formula to be analysed. we found that our atl model checker tool scale well, and can handle even very large problem sizes efficiently, mainly because it is based on a client/server architecture and take advantage of a high performance database server for implementation of the atl model checker algorithm. in this paper, using components of our tool, we will show how atl model checking technology can be used for automated verification of multi-agent systems, developed with jade. one of the main drawbacks of employing atl logic in the automated verification of multiagent systems using previous approaches consists in necessity of translate the programs written in specific modelling languages to the programming language used in the real implementation. our approach eliminates this problem by allowing a transparent building of the atl model at runtime, using the native language of jade agents (java). using java version of the atl library a component of our atl model checker, used for development of custom applications with large atl models into jade agents is inserted the code necessary to build in a transparent manner the atl model which will be verified at runtime. the paper is organized as follows. in section 2 we present the definition of the concurrent game structure, the atl syntax and the atl semantics. in section 3 is outlined the architecture of our atl model checker and is made a brief analysis of its performance. in section 4 is presented the jade fsmbehaviour and formal models used to build an equivalent concurrent game structure. these concepts are applied in section 5 where atl library is used to verify the design of the jade agents having fsm driven behaviours. conclusions are presented in section 6. 2 alternating-time temporal logic the atl logic was designed for specifying requirements of open systems. an open system interacts with its environment and its behaviour depends on the state of the system as well as the behaviour of the environment. in the following we will describe a computational model appropriate to describe compositions of open systems, called concurrent game structure (cgs). 2.1 the concurrent game structure a concurrent game structure is defined as a tuple s = ⟨λ,q,γ,γ,m,d,δ⟩ with the following components: • a nonempty finite set of all agents λ = {1, ...,k}; • q denotes the finite set of states ; • γ denotes the finite set of propositions (or observables); • γ : q → 2γ is called the labelling (or observation) function, defined as follows: for each state q ∈ q, γ(q) ⊆ γ is the set of propositions true at q; • m represents a nonempty finite set of moves; verification of jade agents using atl model checking 721 • the alternative moves function d : λ × q → 2m associates for each player a ∈ {1, ...,k} and each state q ∈ q the set of available moves of agent a at state q. in the following, the set d(a,q) will be denoted by da(q). for each state q ∈ q, a tuple ⟨j1, ...,jk⟩ such that ja ∈ da(q) for each player a ∈ λ, represents a move vector at q. • the transition function δ(q,⟨j1, ...,jk⟩), associates to each state q ∈ q and each move vector ⟨j1, ...,jk⟩ at q the state that results from state q if every player a ∈{1, ...,k} chooses move ja. a computation of s is an infinite sequence λ = q0,q1, ... such that qi+1 is the successor of qi, ∀i ≥ 0. a q-computation is a computation starting at state q. for a computation λ and a position i ≥ 0, we denote by λ[i], λ[0, i], and λ[i,∞] the i-th state of λ, the finite prefix q0,q1, ...,qi of λ, and the infinite suffix qi,qi+1, ... of λ, respectively [3]. 2.2 syntax of atl the atl operator ⟨⟨⟩⟩ is a path quantifier, parameterized by sets of agents from λ. the operators ⃝ (’next’), � (’always’), ♢ (’future’) and u (’until’) are temporal operators. a formula ⟨⟨a⟩⟩ φ expresses that the team a has a collective strategy to enforce φ. the temporal logic atl is defined with respect to a finite set of agents λ and a finite set γ of propositions. an atl formula has one of the following forms: 1. p, where p ∈ γ; 2. ¬φ or φ1 ∨φ2 where φ, φ1 and φ2 are atl formulas; 3. ⟨⟨a⟩⟩⃝φ, ⟨⟨a⟩⟩�φ, ⟨⟨a⟩⟩♢φ or ⟨⟨a⟩⟩φ1uφ2, where a⊆ λ is a set of players, and φ, φ1 and φ2 are atl formulas. other boolean operators can be defined from ¬ and ∨ in the usual way. the atl formula ⟨⟨a⟩⟩♢φ is equivalent with ⟨⟨a⟩⟩ true u φ. 2.3 semantics of atl consider a game structure s = ⟨λ,q,γ,γ,m,d,δ⟩ with λ = {1, ...,k} the set of players. we denote by da = ∪ q∈q da(q) (1) the set of available moves of agent a within the game structure s. a strategy for player a ∈ λ is a function fa : q+ → da that maps every nonempty finite state sequence λ = q0q1...qn, n ≥ 0, to a move of agent a denoted by fa(λ) ∈ da ⊆ m. thus, the strategy fa determines for every finite prefix λ of a computation a move fa(λ) for player a in the last state of λ. given a set a ⊆ {1, ...,k} of players, the set of all strategies of agents from a is denoted by fa = {fa|a ∈ a}. the outcome of fa is defined as outfa : q → p(q +), where outfa(q) represents q-computations that the players from a are enforcing when they follow the strategies from fa. in the following, for outfa(q) we will use the notation out(q,fa). a computation λ = q0,q1,q2, ... is in out(q,fa) if q0 = q and for all positions i ≥ 0, there is a move vector ⟨j1, ...,jk⟩ at state qi such that [3]: • ja = fa(λ[0, i]) for all players a ∈a, and 722 l.f. stoica, f. stoica, f.m. boian • δ(qi,j1, ...,jk) = qi+1. for a game structure s, we write q |= φ to indicate that the formula φ is satisfied in the state q of the structure s. for each state q of s, the satisfaction relation |= is defined inductively as follows: • for p ∈ γ, q |= p ⇔ p ∈ γ(q) • q |= ¬φ ⇔ q 2 φ • q |= φ1 ∨φ2 ⇔ q |= φ1 or q |= φ2 • q |= ⟨⟨a⟩⟩ ⃝ φ ⇔ there exists a set fa of strategies, such that for all computations λ ∈ out(q,fa), we have λ[1] |= φ (the formula φ is satisfied in the successor of q within computation λ). • q |= ⟨⟨a⟩⟩�φ ⇔ there exists a set fa of strategies, such that for all computations λ ∈ out(q,fa), and all positions i ≥ 0, we have λ[i] |= φ (the formula φ is satisfied in all states of computation λ). • q |= ⟨⟨a⟩⟩φ1uφ2 ⇔ there exists a set fa of strategies, such that for all computations λ ∈ out(q,fa), there exists a position i ≥ 0 such that λ[i] |= φ2 and for all positions 0 ≤ j < i, we have λ[j] |= φ1. • q |= ⟨⟨a⟩⟩♢φ ⇔ there exists a set fa of strategies, such that for all computations λ ∈ out(q,fa), there exists a position i ≥ 0 such that λ[i] |= φ. 3 architecture, scalability and performance of the atl model checker our atl model checker tool contains the following packages: • atl compiler the core of our tool, embedded into a web service (atl checker); • atl designer the gui client application used for interactive construction of the atl models as directed multi-graphs; • atl library used for development of custom applications with large atl models. versions of this library are provided for c# and java. the software can be downloaded from http://use-it.ro (binaries and examples of use). atl designer [9] implements the tic-tac-toe (ttt) game, using an algorithm which looks for infallible conditional plans to achieve a winning strategy that can be defined via atl formulae. the game is played by two opponents with a turn-based modality on a 3 × 3 board. the two players take turns to put pieces on the board. a single piece is put for each turn and a piece once put does not move. a player wins the game by first lining three of his or her pieces in a straight line, no matter horizontal, vertical or diagonal. a user can play ttt game against the computer and the atl model checking algorithm is used to achieve a winning strategy for the computer. verification of jade agents using atl model checking 723 figure 1: the computer has found a winning strategy and won a game against the human user in the following we evaluate the effectiveness of our approach in designing and implementing an atl model checker and we report some experimental results. for a set a of agents, the implementation of most atl operators in the model checking algorithm [3] implies the computation of function pre(a,θ), where θ ⊆ q. the value returned by pre(a,θ) represents the set of states from which agents a can enforce the system into some state in θ in one move. in [7] we made a implementation of the function pre() using sql statements, ready to be executed on a high-speed database server. our approach is to use sql and its massive scalability features in verification of large real-world systems. in order to analyze their impact in the performance of the atl model checker, were used three different database servers necessary to determine the winning strategy in the tic-tac-toe game, namely mysql 5.5, h2 1.3 and respectively microsoft sql server 2008 using an intel core i5, 2.5 ghz, 4gb ram. benchmark results are presented in figure 2. results from [18] showed that both smv (a symbolic ctl model checker) and spin (a well-known explicit-state ltl model checker tool) were able to find an optimal strategy for a player in less than one second, on a 3×3 board. as we can see from figure 2, our atl model checker tool is not as fast as the ctl/ltl tools, but we must take into consideration that an atl model is more expressive (with atl we can quantify over the individual powers of one player or a cooperating team of players, atl models capture various notions of synchronous and asynchronous interaction between open systems, etc.). but our tool achieves substantial speedup over an implementation of the tic-tac-toe in the reactive modules language (rml) of the atl model checker mocha [17], [7]. figure 2: the performance of atl model checker related to database server used 724 l.f. stoica, f. stoica, f.m. boian 4 using atl model checking in agent-based systems in the following we will show how our tool can be used for applying the atl technology in the field of agent-based applications. the domain of software agents being relatively recent and in a continuous development, there is no a standard definition, unanimous accepted definition for an agent. however, the autonomy is the central property of agent concept, others properties having a different importance, regarding of concrete considered applications. we will consider that an agent is a computational system situated in a runtime environment and capable of autonomous actions in that environment, in order to accomplish his planned objectives. because atl includes notions of agents, their abilities and strategies (conditional plans) explicitly in its models, atl is appropriate for planning, especially in multi-agent systems [8]. atl models generalize turn-based transition trees from game theory and thus it is not difficult to encode a game in the formalism of concurrent game structures, by imposing that only one agent makes a move at any given time step. automated verification of a multi-agent system by atl model checking is the formal process through which a given specification expressed by an atl formula and representing a desired behavioural property is verified to hold for the atl model of that system. in the following, atl library will be used to detect errors in the design, specification and implementation of an agent developed in jade. for the beginning we present an atl model suited for fsm (finite state machine) driven behaviour of a jade agent. this model will help us to elaborate the mapping rules between atl and jade concepts. atl library will be used to validate the design of jade agents having fsm-behaviours, in other words, to see that no incorrect scenarios arise as a consequence of a bad design. 4.1 jade agents with fsm behaviours jade is a middleware that facilitates the development of multi-agent systems and applications conforming to fipa standards for intelligent agents [10]. the agent class represents a common base class for user defined agents. therefore, from the programmer’s point of view, a jade agent is simply an instance of a user defined java class that extends the base agent class. the computational model of an agent is multitask, where tasks (or behaviours) are executed concurrently. a scheduler, internal to the base agent class and hidden to the programmer, automatically manages the scheduling of behaviours. a behaviour represents a task that an agent can carry out and is implemented as an object of a class that extends the standard jade class jade.core.behaviours.behaviour. in order to make an agent execute the task implemented by a behaviour object it is sufficient to add the behaviour to the agent by means of the addbehaviour() method of the agent class. each class extending behaviour must implement the action() method, that actually defines the operations to be performed when the behaviour is in execution, and the done() method (returns a boolean value), that specifies whether or not a behaviour has completed and have to be removed from the pool of behaviours which an agent is carrying out. scheduling of behaviours in an agent is not pre-emptive (as for java threads) but cooperative. this means that when a behaviour is scheduled for execution its action() method is called and runs until it returns. the termination value of a behaviour is returned by his onend() method [11]. the path of execution of the agent thread is showed in the following pseudocode: verification of jade agents using atl model checking 725 void agentlifecycle() { setup(); while (true) { if (was called dodelete()) { takedown(); return; } behaviour b = getnextactivebehaviourfromschedulingqueue(); b. action(); if (b.done() returns true) { removebehaviourfromtheschedulingqueue (b); int terminationvalueofthebehaviour = b.onend(); } } } behaviours work just like co-operative threads, but there is no stack to be saved. therefore, the whole computation state must be maintained in instance variables of the behaviour and its associated agent. following this idiom, agent behaviours can be described as finite state machines, keeping their whole state in their instance variables. when dealing with complex agent behaviours, using explicit state variables can be cumbersome; so jade also supports a compositional technique to build more complex behaviours out of simpler ones. the jade abstract class compositebehaviour provides the possibility of combining simple behaviours together (children) to create complex behaviours. the actual operations performed by executing this behaviour are defined inside its children while the composite behaviour deals with execution planning. the scheduling policy must be defined by subclasses of compositebehaviour. the fsmbehaviour is such a subclass that executes its children according to a finite state machine (fsm) defined by the user. more in details each child represents the activity to be performed within a state of the fsm and the user can define the transitions between the states of the fsm. when the child corresponding to state si completes, its termination value (as returned by the onend() method) is used to select the transition to fire and a new state sj is reached. at next round the child corresponding to sj will be executed. some of the children of an fsmbehaviour can be registered as final states. the fsmbehaviour terminates after the completion of one of these children. the following methods are needed in order to properly define a fsmbehaviour: • public void registerfirststate (behaviour state, java.lang.string name) is used to register a single behaviour state as the initial state of the fsm with the name name. • public void registerlaststate (behaviour state, java.lang.string name) is called to register one or more behaviours as the final states of the fsm. • public void registerstate(behaviour state, java.lang.string name) register one or more behaviours as the intermediate states of the fsm. • public void registertransition (java.lang.string s1, java.lang.string s2, int event) for the state s1 of the fsm, register the transition to the state s2, fired by terminating event of the state s1 (the value of terminating event is returned by onend() method, called when leaving the state s1 sub-behaviour s1 has completed). • public void registerdefaulttransition (java.lang.string s1, java.lang.string s2) 726 l.f. stoica, f. stoica, f.m. boian this method is useful in order to register a default transition from a state to another state independently on the termination event of the source state. 4.2 a formal model of the fsmbehaviour in the following we present a model for fsm-driven behaviour of a jade agent, implemented by fsmbehaviour class. this model will help us to elaborate the mapping rules between atl and jade concepts. a jade finite state machine is a tuple fsm = (qfsm,π,π,q0,f,t,δfsm) where: • qfsm is a finite, non-empty set of states; • π denotes the finite set of state names; • π : qfsm → π is called the labelling function, defined as follows: for each state q ∈ qfsm, π(q) ∈ π is the name of state q; • q0 is an element of qfsm, the initial state; • f ⊆ qfsm is the set of final states; • t : qfsm → 2z∪{default} is called the terminating function, where for each state q ∈ qfsm, t(q) ⊆ z∪{default} represents the set of admissible termination codes of the state q; • the transition function δfsm(q,j), associates to each state q ∈ qfsm and each termination code j of q the state that results from state q if the child behaviour associated with the state q returns at finish the value j. the behaviour of an fsm is more easily understood when this is represented graphically in the form of a state transition diagram. the control states are represented by circles, and the transition rules are specified as directed edges. each transition from a state q is labelled by termination code of q that triggers the transition. the arc without a source state denotes then initial state of the system (state q0). during one reaction of the fsm, one transition is triggered, chosen from the set of admissible transitions (outgoing transitions from the current state), so that label of transition matches the terminating code of the current state. the fsm goes to the destination state of the triggered transition. if terminating code of the current state q /∈ f is not explicit associated with an admissible transition, then: • if exist the admissible transition labelled with default, this transition (called implicit transition) will be triggered; • else fsm goes in an inconsistent state. in case if fsm arrive in a state q ∈ f , after completeness of activities from that state, execution of finite state machine is stopped. 4.3 atl model of the fsmbehaviour for a jade finite state machine defined in section 4.2, the equivalent concurrent game structure s = ⟨λ,q,γ,γ,m,d,δ⟩ is defined as follows: • there is only one agent, i.e. λ = {1}; verification of jade agents using atl model checking 727 • the set of states is q = qfsm; • the finite set of propositions is defined by γ = π∪{∗final∗}; • the labelling function γ : q → 2γ is defined as follows: γ(q) = { π(q)for q ∈ q\f; π(q)∪{∗final∗}for q ∈ f. • the nonempty finite set of moves m contains all admissible termination codes, i.e.: m = ∪ q∈q t(q) • the alternative moves function d : λ×q → 2m is defined by d(1,q) = t(q) ∀q ∈ q • the transition function δ is defines as follows: δ(q,⟨j⟩) = δfsm(q,j) ∀q ∈ q and ∀j ∈ t(q). 4.4 using atl for verification of the fsm driven behaviour of a jade agent discrepancies between actual and expected results are called conformance failures and may indicate any of the following: implementation bug, modelling error, specification error or design error. because testing and simulation can give us only confidence in the implementation of a software system, but cannot prove that all bugs have been found, we will use a formal method, the atl model checking, for detecting and eliminating bugs in the design of a fsm driven behaviour of a jade agent. design validation using atl involves checking whether a system design satisfies the system requirements expressed by atl formulas. for a given jade fsmbehaviour, the atl model checking is done in two steps: 1. for the beginning, the corresponding atl is constructed following rules described in section 4.3 2. then, a given specification (atl formula) representing a desired behavioural property is verified to hold for the model obtained at step 1. using atl library [9] to perform atl model checking, we can detect error states (the states of the model where the atl formula does not hold) and then we can correct the given model or design. 5 using atl library to verify the design of jade agents using the java version of our atl library, the standard methods of jade fsmbehaviour have been overwritten such that building of the atl model to be done in parallel with the definition of the fsm. the new class atl_fsmbehaviour extends the functionality of the standard jade class fsmbehaviour by adding atl model checking capability: 728 l.f. stoica, f. stoica, f.m. boian public class myagent extends agent { // state names private static final string state_a = "a"; private static final string state_b = "b"; private static final string state_c = "c"; private static final string state_d = "d"; private static final string state_e = "e"; private boolean welldefined = false; protected void setup() { atl_fsmbehaviour fsm = new atl_fsmbehaviour(this); fsm.registerfirststate(new randomgenerator(2), state_a); fsm.registerstate(new nameprinter(), state_b); fsm.registerstate(new nameprinter(), state_c); fsm.registerstate(new nameprinter(), state_d); fsm.registerlaststate(new nameprinter(), state_e); fsm.registertransition(state_a, state_b, 0); fsm.registertransition(state_a, state_e, 1); fsm.registertransition(state_b, state_c, 0); fsm.registertransition(state_c, state_d, 0); fsm.registertransition(state_d, state_b, 0); welldefined = fsm.checkfsm(); system.out.print("execution: "); addbehaviour(fsm); } } in the above example, the child behaviour nameprinter displays only the name of the parent state. the randomgenerator behaviour allows fsm to randomly select the transition to fire from the parent state (the terminating event of the parent state is chosen randomly from the admissible values). the checkfsm() make calls in atl library to perform verification of the defined fsm. in our example, the atl formula checked is: ⟨⟨a⟩⟩♢ (∗final∗) (2) thus, we verify that in every computation the agent will reach a final state. in figure 3 is presented the underlying atl model of the fsmbehaviour described in myagent class and loaded in atl designer: figure 3: checking the atl model in atl designer verification of jade agents using atl model checking 729 as we can see from the figure 3, the desired behavioural property expressed by atl formula (2) does not hold for the entire model. the only states in which the formula holds are state_a (0) and state_e (4). this can lead to unexpected results at runtime, as we can see from the figure 4: info: ———————————————– info: ———————————————– agent container container-4@server1 agent container container-1@server1 is ready. is ready. ——————————————————– ——————————————————– states: 0 4 ; states: 0 4 ; the atl formula is satisfied in the following the atl formula is satisfied in the states: 0 4 following states: 0 4 the fsm is not well defined! the fsm is not well defined! execution: ae execution:abcdbcdbcdbcdbcdbcdbcdbcdb the end cdbcdbcdbcdbcdbcdbcdbcdbcdbcdbcdbcd bcdbcdbcdbcdbcdbcdbcdbcd.... ———————————————————– ——————————————————– figure 4a: the final state is reached figure 4b: the final state is not reached, there is an infinite loop! figure 4: unexpected results at runtime in the figure 4 is presented a fsm with a correct design, satisfying the specification (2): figure 5: an revised atl model in which the atl formula (2) holds in figure 6a. is presented the revised version of the jade fsmbehaviour, so that the corresponding atl model which was built automatically satisfies the specification (2). figure 6b. shows some execution scenarios of the new jade agent. obviously, in all cases the final state will be reached and agent will terminate properly. 730 l.f. stoica, f. stoica, f.m. boian fsm.registerfirststate( states: 0 1 2 3 4 ; new randomgenerator(2), state_a); the atl formula is satisfied in fsm.registerstate(new nameprinter(), state_b); the following states: 0 1 2 3 4 fsm.registerstate( the fsm is well defined. new randomgenerator(2), state_c); execution: abcdbcae fsm.registerstate(new nameprinter(), state_d); the end fsm.registerlaststate( execution: abcdbcabcdbcdbcae new nameprinter(), state_e); the end fsm.registertransition(state_a, state_b, 0); execution: ae fsm.registertransition(state_a, state_e, 1); the end fsm.registertransition(state_b, state_c, 0); execution: abcabcdbcdbcdbc fsm.registertransition(state_c, state_d, 0); dbcabcdbcabcae fsm.registertransition( the end state_c, state_a, 1); ———————————————– fsm.registertransition(state_d, state_b, 0); figure 6b: the verification of the ——————————————————— atl model ensure that in all figure 6a: the revised code of the jade agent scenarios the agent will behave correctly 6 conclusions in this paper alternating-time temporal logic was used for the automated verification of software systems based on jade agents with fsm driven behaviours. our solution is based on java version of atl library, a component of our atl model checker tool. the proposed method can be easily adapted to any software system (written in java or c#) capable to exhibit an atl model (in the form of a concurrent game structure). the original classes (agents) can be extended with specific functionality in order to create an atl model on-the-fly, while executing the regular code of the application. the atl model thus constructed is verified against specifications expressed by atl formulas, at debugging time. after the validation phase, the model checking functionality will be disabled in the release build of the software system. our atl model checker tool is based on client/server architecture to address the time constraints in verification of large models. by using a database-based technology in the core of the atl model checker, our tool provides a good foundation for further improvement of its performance and scalability. on the client side, atl designer allows an interactive graphical specification of an atl model as a directed multi-graph. programmatic construction of a large atl model is made using atl library, available for c# and java languages by now, but we intend to provide interfaces for other popular programming languages. the server component of our tool (atl checker) was published as a web service, exposing its functionality through standard interfaces. by using web services a client can perform model checking without the need of configuring servers. all components of the new atl model checker tool can be downloaded from http://use-it.ro. further investigation on improving performance in verification of atl formulas will be done through parallelized sql queries using the horizontal scalability 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[18] d. owen, t. menzies (2003); lurch: a lightweight alternative to model checking, in software engineering and knowledge engineering (seke): 158-165. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 2, month: april, year: 2020 article number: 3868, doi.org/10.15837/ijccc.2020.2.3868 ccc publications weighted random search for cnn hyperparameter optimization r. andonie, a.c. florea razvan andonie 1. department of computer science central washington university, usa 2. department of electronics and computers transilvania university of braşov, romania *corresponding author: andonie@cwu.edu adrian-cătălin florea department of electronics and computers transilvania university of braşov, romania acflorea@unitbv.ro abstract nearly all model algorithms used in machine learning use two different sets of parameters: the training parameters and the meta-parameters (hyperparameters). while the training parameters are learned during the training phase, the values of the hyperparameters have to be specified before learning starts. for a given dataset, we would like to find the optimal combination of hyperparameter values, in a reasonable amount of time. this is a challenging task because of its computational complexity. in previous work [11], we introduced the weighted random search (wrs) method, a combination of random search (rs) and probabilistic greedy heuristic. in the current paper, we compare the wrs method with several state-of-the art hyperparameter optimization methods with respect to convolutional neural network (cnn) hyperparameter optimization. the criterion is the classification accuracy achieved within the same number of tested combinations of hyperparameter values. according to our experiments, the wrs algorithm outperforms the other methods. keywords: hyperparameter optimization, supervised learning, random search, convolutional neural networks. 1 introduction machine learning models usually have two types of parameters: the training parameters and hyperparameters. the training parameters are learned during the training phase, whereas the values of the hyperparameters have to be specified before learning starts. for instance, the hyperparameters of neural networks typically specify the architecture of the network (number and type of layers, number and type of nodes, etc). for a given dataset, we would like to find the optimal combination of hyperparameter values, in a reasonable amount of time. this process is called hyperparameter optimization. formally, the aim of hyperparameter optimization is to find the hyperparameters of a given model that return the best performance as measured on a validation set. this optimization can be represented as [3]: doi.org/10.15837/ijccc.2020.2.3868 2 x∗ = arg min x∈ℵ f(x), (1) where f(x) is the objective function to minimize (such as rmse or error rate) evaluated on the validation set; x∗ is the set of hyperparameters that yields the lowest value of the score, and x can take on any value in the domain ℵ. in simple terms, we want to find the model hyperparameters that yield the best score on the validation set metric. more detailed, eq (1), can be written as: x∗ = arg min x∈ℵ f(x,γ∗; svalidation) (2) eq (2) includes an inner optimization used to find γ∗, the optimal value of γ, for the current x value: γ∗ = arg min γ∈γ f(x,γ; strain) (3) where svalidation and strain denote the validation and training datasets respectively; γ is the set of learned parameters in the domain γ through minimization of the training error. eq (1) refers to a "validation set", without further details. we have to evaluate the expectation of the score over an unknown distribution and we usually approximate this expectation using a three-way split, dividing the dataset into a training, validation, and test dataset. having a training-validation pair for hyperparameter tuning allows us to keep the test set "independent" for model evaluation. we will use three-way split in our implementations and experiments, but we will omit such details in our presentation, since they are standard. the validation process can be also generalized at the model level. in this case, at meta-level we have several possible models (i.e., feedforward neural network, random forest, support vector machine, etc), each with its own set of hyperparameters. the process of finding the best-performing model from a set of models that were produced by different hyperparameter settings is called model selection [32]. in this case, we search for the best model and, within the model, for the best combination of hyperparameter values. many optimizers attempt to optimize both the choice of the model and the hyperparameters of the model: auto-weka, hyperopt-sklearn, automl, auto-sklearn, etc. for simplicity, we will consider here only optimization of hyperparameters within the framework of the same model. evaluating the objective function to find the score can be computationally very expensive. each time we try different hyperparameters, we typically re-train the model, make predictions on the validation data, and then calculate the validation metric. we call this re-training + evaluation for one combination of hyperparameter values a trial. lately, there has been high interest in the area of hyperparameter optimization, related to the importance and complexity of the deep learning architectures. the challenge is that in deep learning, we typically deal with large training sets. in addition, we may also encounter high-dimensional search spaces, which according to the curse of dimensionality principle have to be covered by an exponential increasing number of points (in this case, combinations of hyperparameters). therefore, deep learning hyperparameter optimization algorithms are computationally intensive. for instance, to optimize a deep learning architecture for the cifar-10 and imagenet datasets required 2,000 gpu days of reinforcement learning [43] or 3,150 gpu days of evolution [33]. the performance of a hyperparameter optimization method is determined by several factors: • f1. the execution time of each trial. this time depends on the size and the dimensionality of the training set. • f2. the number of evaluated combinations of hyperparameters (the number of trials). • f3. the performance of the search procedure. within the same number of trials, different optimization methods can achieve different scores, depending on how "smart" they are. this depends on which combinations of hyperparameter values are tested and in which order. we restrict the discussion to f3 and our the focus is to increase the score of a classifier within the same computational budget. in [11] we introduced the wrs method, an improved rs method which can be used in hyperparameter optimization. instead of a blind rs search, the wrs method uses information from previous trials to guide the search process toward next interesting trials. wrs combines rs and a probabilistic greedy heuristic. we formally proved that the wrs method finds the global optimum faster than rs: on average, wrs converges faster than rs. we also proved that wrs is convergent. experimentally, for the same number of trials, the wrs algorithm produced significantly better results than rs on the cifar-10 dataset1. so far, the wrs method was never compared with other optimization methods, except rs. 1http://www.cs.toronto.edu/ kriz/cifar.html doi.org/10.15837/ijccc.2020.2.3868 3 in the current contribution, we compare the wrs method with several state-of-the-art hyperparameter optimization methods. since cnns are presently by far the most popular deep neural architecture, our experiments focus on this architecture. we compare the classification accuracy achieved within the same number of trials. as a result, the wrs optimization algorithm proves to be competitive, even when compared with some of the best known methods. the paper proceeds as follows. section 2 presents the state-of-the-art of cnn hyperparameter optimization methods, whereas section 3 is a brief overview of the current software implementing these methods. section 4 describes in an intuitive way the wrs algorithm. our main contribution is section 5, where we present experimental results, comparing wrs with several state-of-the-art methods. the paper is then concluded in section 6. 2 related work: methods for cnn hyperparameter optimization to set the stage for wrs, we first introduce the currently most used methods for hyperparameter optimization, with focus on cnn architectures. an overview of fundamental methods for hyperparameter optimization can be found, for instance, in [4, 21]. the best known methods are: grid search (gs), random search (rs), bayesian optimization (bo), neldermead (nm), simulated annealing (sa), particle swarm optimization, and evolutionary algorithms. until recently, the most commonly used hyperparameter optimization strategy was a combination of gs and manual tuning. more complex techniques are now largely available in a variety of software packages. rs consists in drawing samples from the parameter space following a particular distribution for each of the parameters. using the same number of trials, rs generally yields better results than gs or more complicated hyperparameter optimization methods. especially in higher dimensional spaces, the computation resources required by rs methods are significantly lower than for gs [22]. recent attempts to optimize the rs algorithm are: li’s et al. hyperband [23], domhan et al. [9], and florea et al. [10], where we introduced a dynamically computed stopping criterion for rs, reducing the number of trials without reducing the generalization performance. by evaluating hyperparameters that appear more promising from past results, bayesian methods can find better model settings than rs or gs in fewer iterations. a reviews of bo can be found in [35]. sequential model-based optimization (smbo) methods are a formalization of bo. the sequential optimization refers to running trials one after another, each time trying better hyperparameters and updating a probability model (a surrogate). there are several variants of smbo methods, based on gaussian processes, random forest regression, tree-structured parzen estimators (tpe), etc. in low-dimensional problems with numerical hyperparameters, the best available hyperparameter optimization methods use bo [36]. however, bo is restricted to problems of moderate dimension [35]. in current cnn architectures, the number of hyperparameters has increased significantly. these hyperparameters include: number of layers, filter size, number of feature maps, stride, pooling regions and pooling sizes, and the number of units in each fully-connected layer. the number of these parameters can be the in the order of tenths or even hundreds. not only must a hyperparameter optimization algorithm optimize over variables which are discrete, ordinal, and continuous, but it must simultaneously choose which variables to optimize a difficult task. currently, no work covers optimization of every hyperparameter in designing a cnn architecture [1]. we briefly refer in the following to some recent cnn hyperparameter optimization results. the smbo was recently used for cnn hyperparameter optimization [39]. by learning the hyperparameter response function, smbo can pick the next promising hyperparameter combination. in [36], bo with gaussian processes are used to optimize nine hyperparameters of a cnn, including the learning rate, epochs, the four weights of the cnn layers, and the parameters of response contrast normalization (width, scale, and power). another approach of this type is [16]. evolutionary approaches for cnn optimization approaches can be found in [28, 41]. in high-dimensional hyperparameter optimization, tree-based models are presently considered more successful: tpe [4] and random forests [14]. very recent deep learning hyperparameter optimization techniques are [17], [25], and [9]. these techniques attempt to reduce as much as possible the number of evaluated combinations of hyperparameter values. however, for large training sets, even if the number of evaluated combinations is reduced, the total training time increases very much, becoming the complexity bottleneck. hinz et al. applied several algorithms to optimize the architecture of cnns for image inputs with increasing resolution [13]. that way, they conceptually used the same input data, but in different input dimensions. they found that the same hyperparameters are important for a given dataset, independent of the image resolution. furthermore, the optimal value for most hyperparameters also seems to be independent of the image resolution. consequently, they proposed an approach to speed up the hyperparameter optimization procedure by starting on smaller images and increasing the resolution during the optimization procedure. doi.org/10.15837/ijccc.2020.2.3868 4 albelwi at al. recently proposed a general method for cnn hyperparameter optimization [2]. their approach is based on a) a preprocessing step, when they reduce the training set using instance selection; b) the nm optimization method. for the cifar-10, cifar-100, and minist datasets, the following seven hyperparameters (each with its corresponding range of possible values) were considered: depth, number of fully-connected layers, number of filters, kernel sizes, number of pooling layers, pooling region sizes, number of neurons in fully-connected layers. other recent deep learning hyperparameter optimization techniques are: • deterministic rbf surrogates [16]. • covariance matrix adaptation evolution strategy [25]. • the hyperparameter optimization can be accelerated by the extrapolation of learning curves [9]. • codeepneat [29] is a evolutionary approach for hyperparameter optimization. • gradient-based relaxation of the search, which allows a gradient-based optimization [26], [24], and [40]. companies invest today massively in hyperparameter optimization for deep learning. a recent result from google brain [43] employs reinforcement learning to train a long short-term memory (lstm) architecture generator to build a language that describes new deep learning architectures. the lstm is trained via a policy gradient method to maximize the generation of new architectures. the research explores this method to generate cnns. another recent paper from google brain [34] employs an evolutionary algorithm using mutation operators that are inspired by “rules of thumb” coming from various deep learning papers. the evolutionary algorithm uses repeated pairwise competitions of random individuals, and selects from the pair the better performing individual. these google results show that google is also looking for unconventional approaches, using their impressive computing capabilities. 3 software for hyperparameter optimization currently, there are many software packages which include hyperparameter optimization methods. the user can choose between several methods (sometimes called solvers), or continue with the default one. popular packages in this category are [3]: • libsvm [8], bayesianoptimization2, spearmint3, and pygpgo4. • hyperopt-sklearn provides automatic algorithm configuration of the scikit-learn library. it can be used for both model selection and hyperparameter optimization. hyperopt-sklearn has the following implemented solvers [6]: rs, sa, and tpe. • optunity5, a python library containing various optimizers for hyperparameter tuning: gs, rs, pso, nm, covariance matrix adaptation evolutionary strategy, tpe, and sobol sequences. following the idea of auto-weka [19], several automated machine learning tools (automl) were recently developed. their ultimate goal is to automatize the end-to-end process of applying machine learning to realworld problems, even for people with no major expertise in this field. examples of automl tools are: mlbox, auto-sklearn, tree-based pipeline optimization tool, h2o, autokeras, and transmogrifai. some top companies offer commercial cloud-based automl services: • google cloud automl6, enables developers with limited machine learning expertise to train high-quality models specific to their business needs. • microsoft azure ml7, which can be used to streamline the building, training, and deployment of machine learning models. • amazon sagemaker automatic model tuning8, which can launch multiple training jobs, with different hyperparameter combinations, based on the results of completed training jobs. in our experiments, we use the optunity and hyperopt packages. 2https://github.com/fmfn/bayesianoptimization 3https://github.com/hips/spearmint 4https://github.com/hawk31/pygpgo 5http://optunity.readthedocs.io/en/latest/ 6https://cloud.google.com/automl/ 7https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model-hyperparameters 8https://aws.amazon.com/sagemaker/ doi.org/10.15837/ijccc.2020.2.3868 5 4 the wrs method this section describes the wrs algorithm and offers some insight into the intuition behind the proposed approach. full details can be found in [11]. the code is publicly available on github9. in the rs approach, hyperparameter optimization translates into the optimization of a objective function f of d variables by generating random values for its parameters and evaluating the function for each of these values [5]. the function computes some quality measure or score of the model (e.g., accuracy), and the variables correspond to the hyperparameters. the assumption is that we want to maximize f by executing a given number n of trials (steps). rs generates random combinations of hyperparameter values at each iteration k, by generating independent random values for each of the d dimensions, each in its own range of values, according to a probability distribution pi (i = 1, . . . ,d), known beforehand. wrs generates the values for each dimension using a probability of change pi (i = 1, . . . ,d): the current best value for each dimension i is used with probability 1 −pi. if (xk+11 ,x k+1 2 , . . . ,x k+1 d−1,x k+1 d ) is the combination of hyperparameter values evaluated at step k + 1 in the wrs algorithm, we define random variable xk+1 with values (xk+11 ,x k+1 2 , . . . ,x k+1 d−1,x k+1 d ) and probabilities 1 = p1 > p2, . . . ,> pd−1 > pd:  (xk+11 ,x k+1 2 , . . . ,x k+1 d−1,x k+1 d ), with probability pd (xk+11 ,x k+1 2 , . . . ,x k+1 d−1,x k d), with probability pd−1 −pd . . . (xk+11 ,x k+1 2 , . . . ,x k d−1,x k d), with probability p2 −p3 (xk+11 ,x k 2, . . . ,x k d−1,x k d), with probability 1 −p2 (4) a value xki in the definition of x k+1 means that xk+1i = x k i and for dimension i we do not generate a new value at step k + 1. the main idea behind the wrs algorithm is that a subset of candidate values that already produced a good result has the potential, in combination with new values for the remaining dimensions, to lead to better values of the objective function. instead of always generating new values (like in rs), the wrs algorithm uses for a certain number of dimensions the so far best obtained values. the exact number of dimensions that actually change at each iteration is controlled by the probabilities of change assigned to each dimension. since we want wrs to have a good coverage of the variation of the objective function, we determine the importance (the weight) of each hyperparameter by computing the variation of the objective function. in order to do this, we first gather some test values, by evaluating function f for a set of randomly chosen inputs. this is basically equivalent to running rs for a predefined number of steps n0, significantly smaller than n. we use the obtained data to run an instance of fanova [15], which gives us the weight of the d variables of f with respect to the variation of this function. in order to cover as much as possible the variation of the objective function, we assign a greater probability of change to the variables with greater weight. for a parameter that is responsible for only a small part of the variation of f, we consider that keeping a temporary optimum, once identified, has the potential to be a more efficient approach. hence, the probability of change for such a variable will be small. algorithm 1 describes one wrs step, corresponding to a trial, applied to the maximization of function f. xk is the best point identified after k iterations, while f(xk) is the value of f for this input. the probabilities pi(i = 1, . . . ,d) are the probabilities of change for each of the variables of f, and ki(i = 1, . . . ,d) are the minimum number of values to be generated for each variable. pi(x)(i = 1, . . . ,d) are the probability distributions used to generate new values for each of the variables. our assumption in rs and wrs is that the input variables are statistically independent. computing the value of f is usually the most time consuming part of the algorithm. in the specific case of hyperparameters optimization, this involves a trial: a train + a test iteration on the model. to avoid recomputing the function’s value for the same point, we check if for each iteration at least one variable changed. this explains why at least one of the values for pi is set to one. if we chose all pi to be one, we obtain in particular the classical rs implementation. therefore, wrs is a generalization of rs. 9https://github.com/acflorea/goptim/tree/keras doi.org/10.15837/ijccc.2020.2.3868 6 algorithm 1 a wrs step objective function maximization [11] input: f; (xk, f(xk)); pi,ki,pi(x), i = 1, . . . ,d output: (xk+1,f(xk+1)) 1: randomly generate p, uniform in (0,1) 2: for i = 1 to d do 3: if (pi ≥ p or k ≤ ki) then 4: // either the probability condition is met or more samples are needed 5: generate xk+1i according to pi(x) 6: else 7: xk+1i = x k i 8: end if 9: end for 10: // usually this is the most time consuming step 11: compute f(xk+1) 12: if f(xk+1) ≥ f(xk) then return (xk+1,f(xk+1)) 13: elsereturn (xk,f(xk)) 14: end if algorithm 2 wrs objective function maximization [11] input: f; n; pi(x), i = 1, . . . ,d output: (xn,f(xn )) 1: // phase 1 run rs 2: for k = 1 to n0 < n do 3: perform rs step, compute (xk,f(xk)) 4: end for 5: // intermediate phase, determine input for wrs 6: determine the probability of change pi, i = 1, . . . ,d 7: determine the minimum number of required values ki, i = 1, . . . ,d 8: // phase 2 run wrs 9: for k = n0 + 1 to n do 10: perform wrs step described in algorithm 1, compute (xk,f(xk)) 11: end forreturn (xn,f(xn )) algorithm 2 describes the entire wrs flow. for a well chosen set of ki(i = 1, . . . ,d) values, wrs has a greater probability than rs to find the global optimum at any step n. in [11], we determined how to obtain such a set. on average, within the same computational budget (number of iterations), wrs identifies the global optimum faster than rs. in other words, on average, wrs converges faster than rs. the theoretical convergence of wrs is proved in [11]. 5 wrs for cnn hyperparameter optimization cnn architecture optimization is currently an unofficial benchmark for testing hyperparameter optimization methods. we will describe in the following the application of the wrs method on a cnn architecture optimization problem. the main goal is to compare its performance against some of the best know hyperparameters optimization algorithms. the choice of a cnn architecture is justified by the popularity of cnn [31] and the ever increasing interest in identifying optimal cnn architectures[12, 20, 38, 42]. since a complete train-test cycle for a cnn architecture can be extremely time consuming, identifying better algorithms for this class of problems is a hot topic. when comparing two algorithms, "better" means at least one of the following: a) using less trials for a similar score; b) improving the score within the same computational budget (same number of trials). the base architecture of the network used in our experiments is shown in fig. 1. we attempt to optimize the following hyperparameters: • the number of convolution layers (c) an integer value in the set {3, 4, 5, 6}; doi.org/10.15837/ijccc.2020.2.3868 7 • the number of fully-connected layers (f) an integer value in the set {1, 2, 3, 4}; • the number of output filters for each convolution layer (c1, . . . , c6) an integer value in the range [100, 1024]; • the number of neurons for each fully connected layer (f1, . . . , f4) an integer value in the range [1024, 2048]. input convolution convolution max. pooling convolution convolution max. pooling convolution fully connected fully connected fully connected fully connected output figure 1: the cnn architecture [11] on purpose, we use the same problem setup as the one described in [11]. we perform 300 trials for all algorithms, including wrs. we use cifar-10 10 without data augmentation to train the cnn models and compare them in terms of achieved accuracy. the experiments run on an ibm s822lc cluster with ibm power8 nodes, nvlink and nvidia tesla p100 gpus11. on average, the execution time of a trial takes approximately 37 minutes, which leads to a bit less than eight days of running time for each of the 7 tested algorithms. we compare the obtained results against the following standard techniques: • random search (rs) • nelder-mead (nm)[30] • particle swarm optimization (pso)[18] • sobol sequences (ss)[37] • bayesian optimization (bo)[27] • tree-structured parzen estimator (tpe)[4] for rs, nm, pso and ss we use their implementation from the optunity [44] package, bo comes with it’s own implementation, whereas for tpe we use the hyperopt[7] package. for wrs we run an initial phase of 110 trials and use the obtained values to rank the hyperparameters and determine the probability of change for each hyperparameter. the obtained values are listed in table 1. table 1: parameter weights (first line) and probabilities (second line) for cnn[11] c f c1 c2 c3 c4 c5 c6 f1 f2 f3 f4 7.4 11.85 0.51 0.79 1.62 0.73 2.26 1.26 26.28 0.87 3.22 1.75 0.28 0.45 0.02 0.03 0.06 0.03 0.09 0.05 1.00 0.03 0.12 0.07 fig. 2 shows the least squares five degree polynomial fit on the accuracy results obtained for each of the 300 trials using: wrs, rs, nm, pso, ss, bo, and tpe. the plot depicts the values obtained at each iteration. table 2 lists the best result obtained by each of the tested algorithms, the average accuracy, and the standard deviation across all 300 iterations and (between parenthesis) across the last 100 iterations. 10http://www.cs.toronto.edu/ kriz/cifar.html 11http://www.cwu.edu/faculty/turing-cwu-supercomputer doi.org/10.15837/ijccc.2020.2.3868 8 0 50 100 150 200 250 300 0.70 0.72 0.74 0.76 0.78 0.80 0.82 0.84 wrs rs nm pso ss bo tpe figure 2: least squares five degree polynomial fit on rs, nm, pso, ss, bo, tpe vs. wrs accuracy for cifar-10 on 300 trials. the plot shows the values obtained at each iteration table 2: obtained cnn accuracy on cifar-10 optimizer best result average sd wrs 0.85(0.85) 0.79(0.79) 0.09(0.09) rs 0.81(0.81) 0.75(0.8) 0.04(0.04) nm 0.81(0.81) 0.77(0.77) 0.03(0.03) pso 0.83(0.82) 0.78(0.79) 0.03(0.03) ss 0.82(0.82) 0.75(0.72) 0.05(0.04) bo 0.83(0.82) 0.75(0.75) 0.04(0.03) tpe 0.81(0.79) 0.71(0.71) 0.04(0.04) the cnn architecture that yields the best accuracy, according to wrs, has six convolution layers and one fully-connected layer, with c1 = 736,c2 = 508,c3 = 664,c4 = 916,c5 = 186,c6 = 352 and f1 = 1229. according to table 2, wrs clearly outperforms all other considered algorithms with respect to accuracy, within the same computational budget (number of trials). among the tested algorithms, wrs yields the highest standard deviation of the results across all 300 iterations, which means that it covers better the variation of the objective function than the other algorithms. this validates our greedy hyperparameter selection procedure used in the wrs algorithm. from fig. 2 it can be observed that, in case of wrs, the accuracy has the tendency to improve as the number of iterations increases. this validates the hypothesis that using the best known value for some of the hyperparameters, instead of generating a new one at each iteration, tends to achieve increasingly better results. the same hypothesis is validated by looking at the results for the last 100 iterations in table 2. we also notice from fig. 2 that, whereas for some of the baseline algorithms (e.g., rs, ss, bo) the accuracy seems to decrease or have little variation as the number of iterations increases, the accuracy of the wrs algorithm has the tendency to increase with an increasing number of iterations. 6 conclusions and open problems according our experimental results, the wrs optimization algorithm outperforms several state-of-the art hyperparameter optimization methods. since we made our code publicly available, we hope that further experiments on other architectures will confirm these results. however, we are aware that determining the optimal architecture for machine learning models has an additional challenge: the architecture differs for each dataset and therefore requires adjustments for each individual doi.org/10.15837/ijccc.2020.2.3868 9 case. does this mean that in general we cannot optimize the hyperparameters of a given model? for some given problems, recent automatic methods have produced results exceeding those accomplished by human experts [4, 36]. we should be 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[44] [online] optunity. available: http://optunity.readthedocs.io/en/latest/. accessed: 2017-09-01. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: andonie, r.; florea, a.-c.(2020). weighted random search for cnn hyperparameter optimization, international journal of computers communications & control, 15(2), 3868, 2020. https://doi.org/10.15837/ijccc.2020.2.3868 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 6, month: december, year: 2020 article number: 3988, https://doi.org/10.15837/ijccc.2020.6.3988 ccc publications bionmt: a biomedical neural machine translation system h. liu, y. liang, l. wang, x. feng, r. guan hongtao liu, yanchun liang 1. zhuhai sub laboratory of key laboratory of symbol computation and knowledge engineering of ministry of education, zhuhai college of jilin university zhuhai, 519041, china 2. key laboratory of symbol computation and knowledge engineering of ministry of education, college of computer science and technology, jilin university, changchun, 130012, china htliu17@mails.jlu.edu.cn ycliang@jlu.edu.cn liupu wang, xiaoyue feng key laboratory of symbol computation and knowledge engineering of ministry of education, college of computer science and technology, jilin university, changchun, 130012, china wanglpu@jlu.edu.cn fengxy@jlu.edu.cn renchu guan* 1. zhuhai sub laboratory of key laboratory of symbol computation and knowledge engineering of ministry of education, zhuhai college of jilin university zhuhai, 519041, china 2. key laboratory of symbol computation and knowledge engineering of ministry of education, college of computer science and technology, jilin university, changchun, 130012, china *corresponding author: guanrenchu@jlu.edu.cn abstract to solve the problem of translation of professional vocabulary in the biomedical field and help biological researchers to translate and understand foreign language documents, we proposed a semantic disambiguation model and external dictionaries to build a novel translation model for biomedical texts based on the transformer model. the proposed biomedical neural machine translation system (bionmt) adopts the sequence-to-sequence translation framework, which is based on deep neural networks. to construct the specialized vocabulary of biology and medicine, a hybrid corpus was obtained using a crawler system extracting from universal corpus and biomedical corpus. the experimental results showed that bionmt which composed by professional biological dictionary and transformer model increased the bilingual evaluation understudy (bleu) value by 14.14%, and the perplexity was reduced by 40%. and compared with google translation system and baidu translation system, bionmt achieved better translations about paragraphs and resolve the ambiguity of biomedical name entities to greatly improved. keywords: neural machine translation, transformer, self-attention, semantic disambiguation https://doi.org/10.15837/ijccc.2020.6.3988 2 1 introduction artificial intelligence has been used in fire monitoring[6], wireless sensor network data transmission routing protocol optimization [14], medical data classification [29] and other fields. there are many ai technologies that are constantly developing to help researchers better understand the knowledge in the field. for example, guan et al.’s deep feature extraction based on text clustering can better help researchers get the deep knowledges of the text [12]. in 2014, bengio et al. added the recurrent neural networks (rnn) based encoder-decoder model to statistical machine translation [9]. this is the first time that a neural network has been used in combination with machine translation. graham first making“sequence-to-sequence” to machine translation marked a formal departure of neural networks from statistical machine translation and the independent training of machine translation models are widely used [23]. although the work did not attract enough attention at the time, google officially released google neural machine translation (gnmt) [32] in 2016 and neural network-based machine translation has officially become the mainstream model for machine translation. the neural networkbased machine translation encodes the entire source sentence and processes it into a sentence vector of a certain length. to complete the entire translation process, with certain related algorithms and constraints, the vector of the target language is generated, and then translated into target sentence by the decoding process of the neural network. neural network-based machine translation not only reduces the engineering design requirement for translation, but achieves or exceeds the accuracy of phrase-based machine translation [23]. translation systems of biomedical specialty can effectively help scientific researchers to understand the biomedical papers [5]. for example, the analysis of genetic research by meng et al. contains many biological terms that are not easily understood by researchers in the same field [18]. compared with universal corpora, biomedical corpus is more specialized, requiring translation results to be more inclined to the translation habits of biomedical professionals. therefore, biomedical corpus-based neural machine translation uses specialized corpora to train the translation model. starting from mitka’s work on medical translation [22], medical translation researchers have been facing several challenges, for example, the untranslatable phenomenon in medical translation [19], the complexity of medical translation [11], the translation of abbreviations [15], and the translation of medical terms [26]. in 2017, chen et al.[7] adopted statistical machine translation to carry out professional translation work in biomedicine and applied it to specific application. in the construction of biomedical neural machine translation, an external medical lexicon needs to be added to improve translation accuracy. this paper mainly carried out two contributions: 1) we used our data crawler system to capture bilingual aligned corpora in the field of biomedicine, and trained a transformer framework to obtain a translation model with better professional translation results. 2) on the basis of the professional translation model, we proposed a semantic disambiguation model to the presence of the unknown words phenomenon (<unk>) [13]. <unk> are special words that cannot be translated when encountering a professional noun during the decoding process of the sequence-to-sequence model. we proposed a lexicon specifically for the biomedical vocabulary which called "meddra" and used semantic disambiguation model to solve <unk> problem. the remainder of this paper is organized as follows. section 2 introduces the related work. section 3 describes our bionmt system. section 4 illustrates the experimental results and presents a discussion. finally, section 5 concludes our paper. 2 relate work rnns are suitable for processing time-related sequences[21]. it is the first neural machine translation mode with sequence-to-sequence translation method. then long short-term memory networks (lstm) [8] were proposed to solve the problems encountered by recurrent neural networks in long time series. although the lstm can solve the long time series problem [25], it still does not solve the problems such as the length of the fixed word vector used in encoding and decoding (see figure 1). bahdanau et al.[1] and luong et al.[16] applied attention mechanisms to machine translation and https://doi.org/10.15837/ijccc.2020.6.3988 3 achieved significant improvements. figure 1: encoder-decoder model with attention rocktaschel et al.[27] proposed the subdivision mechanism based on the attention mechanism and related visual analysis in 2015. bahdanau et al.[1] improved and optimized the traditional neural network and added context semantics to the neural machine translation model. machine translation is not only a simple translation of each word and phrase, but also refers to the meaning of the entire sentence. the supervised attention mechanism proposed by mi et al.[20] improved and optimized the work based on the attention mechanism. the combination of lstm and attention mechanism solves the problem of fixed word vector length[1]. it solves the problem on the basis of preserving the context by retaining the intermediate output results in the input sequence in the long-term and short-term memory network. the emergence of the transformer framework has brought neural network-based machine translation results into a new era[10]. the transformer framework abandons traditional neural networks and uses a well-performing attention mechanism and feedforward network to perform the entire "encoding-decoding" process. it overcomes the bottleneck in which the recurrent neural network model cannot be calculated in parallel. in addition, the number of operations required to calculate the association between two positions will not increase as the distance between words in a sentence increases. the attention mechanism can produce a better interpretable model, and the multi-head attention mechanism can produce different results to balance and optimize the final result. however, in the above algorithms and models, the <unk> problem caused by professional terms is not effectively solved. when using the model, there will be many professional words that do not appear during model training and need to be translated. the appearance of new words will cause problems in the translation process, and at the same time, errors will occur in the translation results. the translation of the entire sentence in the above algorithms and models focuses more on the general translation method, resulting in the translation result more like a general sentence. it would reduce and miss the professionalism and accuracy that should be included in professional translation. 3 bionmt system based on transformer model, we propose a biomedical machine translation system to solve the <unk> phenomenon. as a result, a complete chinese sentence without <unk> flags come out. and the results are more semantic smoother. 3.1 training model as shown by the dotted line in fig.1, the attention mechanism acts between the encoder and decoder. with the attention mechanism, the model can learn the alignment relationship and translation relationship between the original sentence and the target sentence at the same time. as shown in the combination of "rnn + attention", in the encoding process, the original sentence is encoded into a set of feature vectors. when decoding, unlike the previous encoder-decoder structure of the rnn, which uses the feature vectors of the entire input sentence, the attention mechanism uses the weighted https://doi.org/10.15837/ijccc.2020.6.3988 4 sum of all feature vectors at time slice t; that is, st = f(s(t−1),y(t−1),ci) (1) where ci is the weighted sum of all feature vectors ht: ci = t∑ t=1 αitht (2) αit = eeit∑t k=1 e eik (3) eit = tanh(si−1,ht) (4) where eit is the alignment model of the ith time slice of the output sequence, which indicates the correlation of the time and each time slice of the input data. using the state st−1 at the previous moment and the yth input data ht, tanh(si−1,ht) is calculated as the activation function. the self-attention mechanism uses input sentences to three matrices: q(query), k(key), and v (value). attention is calculated as shown in self −attention(q,k,v ) = softmax( qkt √ dk )v (5) to prevent qkt ’s dot product value from being too large when the k dimension increases, we need to divide the result of the dot product by √ dk. after the softmax calculation, it is multiplied by v to obtain the final attention matrix. the softmax function maps multiple outputs to the interval of [0,1], as shown in softmax(zj ) = ezj∑k k=1 e zk (6) in 2017, google proposed the transformer framework[31]. the core part of the model consists of a self-attention mechanism and feedforward neural network (as shown in figure 2). it uses multihead self-attention combined with a feedforward network to train the translation model in an encoderdecoder manner. the proposed transformer framework not only improves the performance of the encoder-decoder for text processing but also enables handling of long-term dependencies. compared with the high computational complexity of the lstm, the advantages of the fast calculation speed and high efficiency of the transformer framework make it more widely used. the transformer not only used "one-hot"[17] to encode but also introduced positional encoding (pe) to solve the problem of position and order. experiments have shown that the results of training and learning position encoding vectors and using formulas to perform position encoding vectors are similar on reducing the time complexity of training, as shown in pe(pos, 2i) = sin( pos 10000 2i dmodel ) (7) and pe(pos, 2i + 1) = cos( pos 10000 2i dmodel ) (8) to calculate the position encoding vector, pos refers to the relative position of the word in the sentence, i indicates the embedding dimension, and dmodel is the length of the pre-specified word vector. after word embedding and position embedding, each word is converted into three vectors, and each vector contains word features and location information. in the initial stage of the transformer, the multi-head self-attention input is the feature vector obtained after embedding the sentence, and the input of each layer is the output of the previous layer, as shown in multihead(q,k,v ) = concat(head1, · · · ,headh)w (9) https://doi.org/10.15837/ijccc.2020.6.3988 5 figure 2: the transformer framework where headi = self −attention(qw qi ,kw k i ,v w v i ) (10) after performing the multihead self-attention calculation, layer normalization calculation and residual connection are performed. and the feature matrix obtained after the calculation is fed to the fully connected layer. for the calculation of the fully connected layer of the feature matrix y, the parameters between different rows of the same layer are the same, and the parameters of different fully connected layers are different, as shown in ffn(x) = relu(0,xw1 + b1)w2 + b2 (11) and relu(x) = {x, if x>00, if x≤0 (12) where, relu is the activation function, w1,w2 are weight matrices, and b1,b2 are deviations. the feature matrix obtained at the end of the encoder is input to the decoder for decoding. each decoder contains three layers, which are mask multihead self-attention, encoder-decoder attention, and fully connected feedforward. in addition, in the decoder process, the k and v of the second layer of self-attention come from the encoder. to address the contextual correlation between words, the transformer uses mask multi-head self-attention (mask-attention) to process the contextual semantic correlation. mask-attention uses 0 and 1 for each dimension of the input feature matrix to choose whether to retain. it only obtains the input before the current time and perform the self-attention calculation of the input before the current time, and limit the contextual relevance. there can only be a correlation with the word before the word to be translated. the untranslated words behind do not consider the semantic relevance; that is, the information after i + 1 is not introduced when predicting the ith position. the specific expression is mask −attention(q,k,v ) = multihead(relu(mask(q,k)w,v )) (13) mask(x) = p∗x,p ∈{0, 1} (14) and mask-attention input is the previous output of the layer decoder. 3.2 biomedical dictionary and semantic disambiguation <unk> (unknown words) are special words that cannot be translated when encountering a professional noun during the decoding process of the seq2seq model. rico et al [28] and issam et al [2] https://doi.org/10.15837/ijccc.2020.6.3988 6 are based on "byte pair encoding" to solve <unk>. this approach solves <unk> to some extent, but in the specific field, <unk> still appears in large numbers, and there is no effection solution for the problem. therefore, we add a lexicon specifically for vocabulary in the biomedical field and use semantic disambiguation model to solve <unk>. to a certain extent, translation results always include <unk>. using the attention mechanism, we obtain the position of the generated <unk> and find the source words that produced <unk>. after that, we manually collect professional terminology to help us identify preliminary <unk> solutions, which we call the “meddra” dictionary [4]. if only one chinese translation of the word is contained in the “meddra” dictionary, we replace the chinese translation into the results <unk>. if the source word contains multiple chinese translations in the dictionary (in most cases, the biomedical vocabulary contains only one chinese translation in the dictionary), we use the semantic similarity calculation in natural language processing (nlp) to obtain the most suitable chinese translation result [30, 33]. if the <unk> source words do not appear in the “meddra” dictionary. then, we add the source word to translate the source results in replacing <unk>. when all <unk> processes end, we refine the entire sentence to make it more fluent. figure 3: the bionmt framework 3.3 biomedical neural machine translation system the proposed biomedical neural machine translation (bionmt) system uses its own data crawler system to collect biomedical corpora to train biomedical translation models. based on the trained transformer framework, the translation model with the highest bleu[24] score was obtained. later, bionmt added a variety of natural language processing technologies, such as semantic disambiguation, external dictionaries, regular expressions, and word similarity calculation, to improve the final https://doi.org/10.15837/ijccc.2020.6.3988 7 translation result. the data used in this system were obtained from a number of websites and institutions, such as the chinese medical journal network, the science foundation shared services network, wmt (world machine translation)[34], and the natural language processing group of nanjing university, with a data scraping system. the english and chinese bilingual corpus contains both of the biomedical corpus and general corpus can enrich the dictionary and help the translation model to learn the grammaticals. when training, the model looks up the word vector of each word from the dictionary and performs the position embedding. then, the embedded word vector is inputted to the transformer to train the model. through the encoding and decoding operations of the transformer, we obtain a translation model that conforms the grammar and semantics of the biomedical field. in order to make the sentence more accurate and fluent, we input the obtained translation result to the semantic disambiguation and “meddra” dictionary to handle the <unk> words. then, we output the final results. figure 3 is the framework of the proposed bionmt system. algorithm 1 train model&generate translation results input: source sentence output: target sentence function train(hibridcorpus) for i = 0 → length(hibridcorpus) do sentenceslist[i] ← segword(hibridcorpus[i]) end for for i = 0 → length(sentenceslist) − 1 do wordslist ← sentenceslist[i] wordv ectors ← wordembedding(wordslist[0 : length(wordslist− 1]) sentencesv ector[i] ← wordv ectors end for while k < 20 do for j = 0 → length(sentencesv ector) − 1 do trainv ectorsmatrix ← sentencesv ector[i : batchsize− 1] model ← transformer(trainv ectorsmatrix) j ← j + batchsize end for k ← k + 1 end while return model end function function translate(sourcesentence) sentencev ector ← wordembedding(sourcesentence) firsttarget ← model(sentencev ector) if < unk > in firsttarget then sencondtarget ← solveunk(firstsentence) finalsentence ← semanticdisambiguations(sencondtarget) else finalsentence ← semanticdisambiguations(firsttarget) return finalsentence end if end function algorithm 1 briefly describes the model training and translation process of the bionmt system. the detailed training process has been described in chapters 3.1 and 3.2. in the translation process, as shown in function translate, the bionmt system receives the foreign language sentence (sourcesentence) that needs to be translated, and first performs regular expression matching and filtering on the sentence to be translated, and then performs word vector conversion. here we use the same https://doi.org/10.15837/ijccc.2020.6.3988 8 "position coding + one-hot" structure as in the training process. then, treating the resulting vector input into the model has been trained for translation. after getting the results, if there is <unk>, the aid has been tidied biological dictionary and custom rules <unk> the solution. finally, the semantic disambiguation work is performed on the obtained sentence to obtain the final translation result. 4 experiments this section mainly introduces translation model training and analysis of the results. it consists of the data collection, experimental environment, evaluation indicators and complete translation system construction process. the evaluation indicators were mainly evaluated by bleu, accuracy and perplexity, which are widely used for translation model evaluation[3]. 4.1 dataset details for the machine translation model training in the biomedical field, we needed to collect biomedical corpora. the chinese medical journal network[36] has collected papers from 190 medical journals. the medical corpora collected from this website are highly professional and highly aligned with bilingual corpora. neural machine translation requires precise corpus alignment. additionally, the science fund shared service network[35] includes the abstracts and project briefs of all fund projects in the domestic biological and medical fields for obtaining bilingual corpora containing more specialized words in the biomedical field, and then natural language processing can be used. this technique performs bilingual alignment work. the author defined a series of regular expressions to filter and troubleshoot possible confused characters, special symbol expression errors, and repeated corpora in the corpus. in the experimental training, a total of 5,455,344 aligned chinese and english sentences were used, and the universal and specific corpora were combined for experiments. the number of corpora in the training set, validation set, and test set accounted for 73.4%, 12.4%, and 14.2%, respectively. 4.2 experimental results we used the better-performing "lstm + attention" framework as a comparison framework. then, we used four corpora to compare the results, that are universal data collections (lstm + attention), universal data collections (bionmt), hybrid data collections (lstm + attention), and hybrid data collections (bionmt) (hybrid data collections as shown in the last section, the universal data collections and biomedical data collections were combined). four different training models were used to train the translation model, and the results are shown in table1. table 1: the comparison the of experimental results of four different algorithms corpus bleu accuracy(%) perplexity universal corpus(lstm+attention) 24.10 36.95 50.29 universal corpus(bionmt) 29.06 42.16 27.85 hybrid corpus(lstm+attention) 27.32 37.65 45.51 hybrid corpus(bionmt) 33.17 50.50 16.71 table 1 shows the bleu, accuracy, and perplexity values [3] of the four models based on different corpora or different frameworks. from the above results, we make the following discussion: 1) with the calculation of the bleu value, we can clearly see the advantages of the bionmt framework. bionmt uses the transformer for encoding and decoding. during the decoding process, mask multihead self-attention is used to select the context. additionally, mask multihead self-attention defines the entire encoding and decoding process. the processing of the current word only has a contextual relationship with the word before the word. the words behind the word are not considered. "mask" masks all the word information behind the word, which not only reduces the calculation cost but also ensures that the context is not lost in long sequences. https://doi.org/10.15837/ijccc.2020.6.3988 9 (a) accuracy of training (b) perplexity of training figure 4: results of training 2) on the universal dataset, the bionmt framework is significantly better than the "lstm + attention" framework in all indicators. compared with the translation model based on "lstm + attention", the accuracy of the translation model obtained by bionmt improved by 5.21%. additionally, the model generated by bionmt reduced the perplexity by 44.62%. the decrease in the perplexity indicates that the probability of the bionmt model selecting the correct translated word increased to a certain extent, which can make the translation result closer to the human translation result. 3) in comparison experiments with biomedical corpora, the translation model trained using the hybrid dataset has significantly improved on bleu,accuracy and perplexity. additionally, the translation model based on the hybrid dataset bionmt is 8.34% more accurate than the translation model based on the universal dataset bionmt, and the perplexity was reduced by 40%. bionmt was superior to the model based on the universal dataset and the model based on the "lstm + attention" framework in the three evaluation indicators. we investigated the changes in accuracy during the training process in figure 4.a and the learning curve of the perplexity rate in figure 4.b. comparing the learning curves of the accuracy rate and perplexity among different algorithms during the training process, we can clearly see that with the continuous increase in the number of training epochs, the bionmt framework tended to better convergence than the "lstm + attention" framework during the training process. it also ensured that the accuracy rate was higher than the latter. at the beginning of training, the perplexity of "lstm + attention" was significantly higher than that of the bionmt framework. although the perplexity of the "lstm+attention" framework was also significantly reduced as the number of training epochs increased, the perplexity of the transformer framework was lower than that of the "lstm + attention" framework when the two models converged. the aforementioned analysis and experimental results show that the translation model obtained by the bionmt framework based on the hybrid dataset was superior to all the other models. additionally, we added semantic disambiguation and external dictionary assistance to improve translation accuracy. we used the optimal translation model (bionmt model based on the hybrid dataset) to translate the biomedical corpus. to obtain more professional translation results on this basis, before returning the result, we used a lexicon for biomedical specialties called "meddra" and a universal english-chinese dictionary to enrich the vocabulary of the external dictionary. after adding the semantic elimination manifold and external dictionary, as shown in table 2, the translation results of the bionmt system is significantly improved. by comparing the results in table 2, we can find that when the biomedical vocabulary has few or no biomedical nonsolitary words, there is not much difference between adding an external dictionary and semantic disambiguation and with out the strategy. however, when facing biomedical terms, the translation model and without an external semantic disambiguation dictionary generated solitary <unk>. after adding the external “meddra” dictionary and semantic disambiguation work, the proposed bionmt system translates the <unk> with professional and certain extents and increases the fluency of the translation sentence. https://doi.org/10.15837/ijccc.2020.6.3988 10 table 2: the effect of the medra lexicon source sentence#1 leukemia in infants is rare but generates tremendous interest due to its aggressive clinical presentation in a uniquely vulnerable host, and its fascinating biology. no meddra and semantic disambiguation 婴儿的白血病虽然罕见，但是由于其独特的临床表现而产生巨大的兴趣，以及它迷 人的生物学。 add meddra and semantic disambiguation 婴儿中的白血病很少见，但由于其独特的临床表现，其强有力的临床表现以及它迷 人的生物学，对其产生了巨大的兴趣。 source sentence#2 using representative clinical case presentations, we review the key clinical, pathologic and epidemiologic features of infant leukemia, including the high frequency of kmt2a gene rearrangements. no meddra and semantic disambiguation 采用典型临床病例报告，回顾婴儿白血病的临床、病理及流行病学特点，包 括<unk>基因重排频率的高频率。 add meddra and semantic disambiguation 通过典型临床病例介绍，我们回顾了婴儿白血病的关键临床、病理和流行病学特 点，包括kmt2a基因重排高频。 source sentence#3 we highlight recent discoveries that elucidate the molecular biology of infant leukemia and suggest novel targeted therapeutic strategies, including modulation of aberrant epigenetic programs, inhibition of signaling pathways, and immunotherapeutics no meddra and semantic disambiguation 我们强调最近的发现，这些发现阐明了婴儿白血病的分子生物学，并建议新 的<unk>策略，包括异常的表观遗传程序，抑制信号通路，以及<unk>。 add meddra and semantic disambiguation 我们着重介绍了新发现的婴儿白血病的分子生物学，提出了新的靶向治疗策略，包 括表观遗传学调控的调制，抑制信号通路和免疫和免疫疗法。 to further evaluate the proposed bionmt model, we also released a web service ( http://39.98.161. 93:8000/index ) and compared it with the widely used translation systems, google translate and baidu translate. the compared results are shown in table 3. table 3: comparison of translation results between bionmt system and other translation systems source sentence with the ever-growing volume of online information, recommend systems have been an effective strategy to overcome such information overload. the utility of recommendation systems cannot be overstated, given their widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to overchoice. bionmt system 随着在线信息信息量的不断增加，推荐系统已成为解决这些信息过载的有效策略。 由于在许多网络应用中广泛采用，推荐系统的效用不可小觑，而且它对解决相关问 题有潜在的影响。 baidu translation system 随着在线信息量的不断增加，推荐系统已成为克服此类信息过载的有效策略。由于 推荐系统在许多web应用程序中的广泛采用，以及它对改善许多与过度选择相关的 问题的潜在影响，因此不能夸大推荐系统的实用性。 google translation system 随着在线信息量的不断增长，推荐系统已成为克服此类信息过载的有效策略。推荐 系统的实用性不能高估，因为它已在许多web应用程序中得到广泛采用，并且它可 能会缓解与选择过多相关的许多问题。 in table 3, we can clearly see that during the paragraph translation, the word "web" was not successfully translated in google translate and baidu translate, and the translated meaning of the sentence was misinterpreted. more importantly, for the translation of "the utility of recommendation systems cannot be overstated" in the source sentence, neither the google translation system nor the baidu translation system gave the correct translation, and the semantics of their translation results were quite different from the actual meaning of the sentence. experiments show that after adding semantic disambiguation, bionmt has a better ability to understand paragraphs and resolve ambiguity. 5 conclusion in this paper, two types of machine translation models that have become popular in the past decade were introduced: statistical-based machine translation and neural network-based machine translation. based on transformer, a highly professional machine translation model was proposed. this model improved the accuracy of the original transformer training model by integrating related technologies in natural language processing and the use of auxiliary dictionaries. for the future work, we will focus on two aspects. (1) optimizing the current bionmt system, increasing the accuracy by adding professional training data, and attempting to use the bert[10] https://doi.org/10.15837/ijccc.2020.6.3988 11 model for training. (2) most of the translation work in the biomedical field are paragraph translations. however, the current stage of bionmt’s translation of paragraphs is sentence-to-sentence translation, and then overall splicing and semantic disambiguation are performed. the next step is to conduct comprehensive research on paragraph translation. it is expected that paragraph translation in the professional field can be achieved instead of only maintaining the current level of sentence translation. acknowledgment the authors are grateful for the support of the national natural science foundation of china (no.61972174), the science technology development project of jilin province (no.20190302107gx), the special research and development of industrial technology of jilin province under grant (no.2019c0537), the guangdong key project for applied fundamental research (no.2018kzdxm076), the science and technology planning project of guangdong province (no.2020a0505100018), the guangdong premier key-discipline enhancement scheme under grant (no.2016gdyszdxk036) and the bioknow medai institute (no.bmcpp-2018-002). we would also like to thank prof. fengfeng zhou and prof. qiong yu for their help in collecting the meddra dictionary and evaluating the translation results. references [1] bahdanau, d.; cho, k.; bengio, y. 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[35] [online]. science fund shared service network. available: https://output.nsfc.gov.cn/ [36] [online]. chinese medical journal network. available: https://medjournals.cn/index.do copyright ©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: h. liu, y. liang, l. wang, x. feng, r. guan (2020). bionmt: a biomedical neural machine translation system, international journal of computers communications & control, 15(6), 3988, 2020. https://doi.org/10.15837/ijccc.2020.6.3988. introduction relate work bionmt system training model biomedical dictionary and semantic disambiguation biomedical neural machine translation system experiments dataset details experimental results conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 1, month: february, year: 2023 article number: 4396, https://doi.org/10.15837/ijccc.2023.1.4396 ccc publications covid-19 forecasting using cnn approach with a halbinomial distribution and a linear decreasing inertia weight-based cat swarm optimization rajesh khanna murugesan, karthikeyan madhu, jayalakshmi sambandam, lakshmanan malliga rajesh khanna murugesan department of information technology vel tech multi tech dr. rangarajan dr. sakunthala engineering college, avadi, chennai-600116, india corresponding author: seenum84@gmail.com karthikeyan madhu department of eee vel tech multi tech dr. rangarajan dr. sakunthala engineering college, avadi, chennai-600116, india karthickm37@gmail.com jayalakshmi sambandam measi institute of information technology, chennai, india jayalakshmi.research@gmail.com lakshmanan malliga malla reddy engineering college for women (autonomous), maisammaguda secunderabad-500100, india dr.malligalakshmanan18@gmail.com abstract in recent times, the covid-19 epidemic has spread to over 170 nations. authorities all around the world are feeling the strain of covid-19 since the total of infected people is rising as well as they does not familiar to handle the problem. the majority of current research effort is thus being directed on the analysis of covid-19 data within the framework of the machines learning method. researchers looked the covid 19 data to make predictions about who would be treated, who would die, and who would get infected in the future. this might prompt governments worldwide to develop strategies for protecting the health of the public. previous systems rely on long shortterm memory (lstm) networks for predicting new instances of covid-19. the lstm network findings suggest that the pandemic might be over by june of 2020. however, lstm may have an over-fitting issue, and it may fall short of expectations in terms of true positive. for this issue in covid-19 forecasting, we suggest using two methods such as cat swarm optimization (cso) for reducing the inertia weight linearly and then artificial intelligence based binomial distribution is used. in this proposed study, we take the covid-19 predicting database as an contribution and normalise it using the min-max approach. the accuracy of classification is improved with the use of the first method to choose the optimal features. in this method, inertia weight is added https://doi.org/10.15837/ijccc.2023.1.4396 2 to the cso optimization algorithm convergence. death and confirmed cases are predicted for a certain time period throughout india using convolutional neural network with partial binomial distribution based on carefully chosen characteristics. the experimental findings validate that the suggested scheme performs better than the baseline system in terms of f-measure, recall, precision, and accuracy. keywords: min-max normalization, cat swarm optimization (cso), binomial distribution, and covid-19 forecasting. 1 introduction in december of 2019, wuhan, china will be the epicentre of a global pandemic using a newly discovered coronavirus called covid-19 [1-3]. an uptick in reported cases of covid-19 has been seen in a majority of nations during december 2019. according to a report by the globe world health organisation issued may 29, 2020, there have been around 353 373 fatalities and 5,596,550 total cases registered over the world. covid 19 illness is more common in the elderly and those with compromised immune systems or other medical conditions, particularly those affecting the lungs. the symptoms of covid-19 are similar to those of the flu, including difficulty breathing, a cold, and a cough [4]. so yet, neither covid-19 nor sars-cov-2 have any approved vaccines or medications that are shown to be successful therapeutically. in the absence of pharmacological therapies, different nations have taken varied approaches to managing the covid-19 epidemic. the widespread deployment of lockdown is the most typical tactic [5]. on january 30, 2020, a student returning from wuhan, the expansive metropolis of hubei province in china, was diagnosed with corona virus in the thrissur district of kerala [6]. the indian government has ordered a nationwide lockdown beginning march 25, 2020, and lasting for 21 days, including a day of "janata curfew" on march 22, 2020, in an effort to stop the spread of the corona virus, also known as sars-cov-2. the shutdown has been extended by the indian government because of the widespread spread of the corona virus. washing one’s hands often, refraining from touching one’s mouth, nose, or ears, and maintaining an emotional distance from other people are all effective preventative measures against contracting covid19 (1 metre or 3 feet). each country has had a consistent increase in confirmed cases. more stringent safeguards and strategies have already been put in place in several places in the event of the spread of such a virus. predicting how many instances of covid-19 a hospital will see is crucial for making the most efficient use of its resources and developing effective treatment plans for patients who have contracted the virus. in wide range of applications, especially in business and academia, deep learning and machine learning are emerging as a promising study subject in recent days [7-8]. in [9], four supervised machine learning techniques are used to forecast the future of covid-19: the support vector machine (svm), the exponential smoothing (es) algorithm, the lasso regression strategy, and the linear regression algorithm. it is shown that es outperforms competing models in predicting the total number of fatalities, survivors, and contaminated cases. es incorporates historical data prediction information for processing time series data. this establishes es as a viable option. a modified auto-encoder (mae), an ai-based method, is developed in [10] to predict new and cumulative mortality and verified covid 19 cases under diverse intervention situations in real time in over a hundred different nations. this method falls short of expectations in both prediction accuracy and computational complexity. the remainder of the paper is arranged as follows: the second section details the various prediction models used in the aforementioned studies. section 3 outlines recommended technique. section 4 presents experimental findings and performance comparison. finally, section 5, ends the study and outlines future research options. 2 literature review the who has approved covid 19 outbreak data for confirmed patients in the next 10 days is used to evaluate this model. root mean squared relative error (rmsre), coefficient of determination (r2), mean absolute percentage error (mape), and computation time are all areas in which the fpassa-anfis model [11] excels. long short-term memory (lstm) networks were created by chimmula and zhang (2020) to predict future pandemics like the current covid-19 outbreak in canada and worldwide. the covid-19 data used in this study was collected by the johns hopkins and canadian health boards and will be made available by march 31, 2020. every day, statistics are kept on the number of deaths and the number of people who survive their injuries. https://doi.org/10.15837/ijccc.2023.1.4396 3 by using a wavelet modification, we are able to preserve time-frequency detail while simultaneously suppressing random fluctuations in the data. then, covid-19 time predictions are made using a network of long short-term memory (lstm). we test the efficacy of our models on the remaining 20% of the covid-19 dataset after training them on the remaining 80%. the improved prediction accuracy [12] is a direct result of the invented device. to rapidly and accurately anticipate covid-19 time series, castillo and melin (2020) developed a hybrid technique that blends the fractal dimension with fuzzy logic. ten different countries’ publicly available data sets were used to construct the fuzzy inference system using time series in a given time period. the proposed solution’s viability for the projected values of the 10 countries was then evaluated over a range of time horizons. a 10-day and 30-day prediction window were used to evaluate the proposed method. the average accuracy of 98% is remarkable considering the intricacy of the covid-19 problem. with the use of ai-inspired algorithms, hu et al. (2020) created real-time projections of covid-19’s size, duration, and end time throughout china. here, a stacked auto-encoder was tweaked to simulate the spreading dynamics of an epidemic. this model was then used to make predictions in real time about where in china new instances of covid-19 would be reported. the who gathered the information from january 11th, 2020, to february 27th, 2020. here, latent variables in auto-encoder and clustering approaches were used to arrange the provinces/cities in a way conducive to investigating the transmission structure. the average inaccuracy for a multi-step forecast was 2.27 percent for a 7-step forecast, 1.64 percent for a 6-step prediction, 2.08 percent for a 9-step forecast, 0.73 percent for a 10-step forecast, and 2.14 percent for an 8-step forecast. [14]. a deep learning-based prediction model for the covid-19 outbreak in saudi arabia was proposed by elsheikh et al (2020). to predict the total number of confirmed cases, total recovered cases, and total deaths in saudi arabia, this research used the long short-term memory (lstm) network as a complete deep learning model. official data was used to train this model. the model’s parameters were fine-tuned by determining the optimal values for optimum prediction accuracy. in order to determine the model’s rightness, seven standard statistical factors are utilised [15]: the root mean square error (rmse), the coefficient of variation (cov), the coefficient of residual mass (crm), the overall index (oi), the mean absolute error (mae), and the effectiveness coefficient (ec). around 6% minimization in rmsle and improvement in absolute pearson correlation to 0.998 from 0.9978can be achieved as shown in results when compared with baseline models. 3 proposed methodology for forecasting covid-19, we suggest using two methods such as cat swarm optimization (cso) for reducing the inertia weight linearly and then artificial intelligence i.e. cnn based partial binomial distribution is used. figure 1 shows the proposed work’s flow diagram. 3.1 input the input corresponds to covid-19 forecasting dataset. from https://www.kaggle.com/davidbnn92/weatherdata-for-covid19-data-analysis, this dataset is collected. information like fog, temperature, fatalities, confirmed cases, date, long, lat, region, state, id are added in this dataset. from noaa gsod dataset, imported the weather data. recent measurements are included using continuous update. 1. (a) data normalization using min-max normalization in the normalising procedure, a mathematical formula is used to transform the original numerical values into a new set of range values. in the proposed study, we use min-max normalisation to the covid-19 dataset. typically, min-max normalisation is used to level off data. all of the data in the dataset are normalised to fall within the given high and low bounds, and then the following expression is applied to the resulting data. v′ = v −mina maxa −mina (new_maxa −new_mina) + new_mina (1) where v is the previous value of each item in data, a is the attribute data, min (a) is the smallest possible absolute value of a, and max(a) is the largest possible absolute value of a. v’ represents the updated value for each record in the data set, new max(a) the upper limit of the range, and new min(a) the lower bound (i.e essential boundary value range) 1. (a) feature selection using linear decreasing inertia weight based cat swarm optimization (ldiwcso) algorithm https://doi.org/10.15837/ijccc.2023.1.4396 4 s.no authors name methods name merits demerits al-qaness et al (2020) modified anfis model higher classification accuracy and recall it has computational complexity chimmula and zhang (2020) lstm no need of parameter tuning lstms take longer to train lstms are easy to overfit castillo and melin (2020) fractal dimension and fuzzy logic scheme it achieves 98% of forecasting average accuracy need to update the rules regularly hu et al (2020) modified stacked auto-encoder higher true positive rate it required joint training elsheikh et al (2020) long short-term memory (lstm) network acceptable rmse and mae it has sensitive to different random weight initializations table 1: comparative analysis of existing covid -19 forecasting approaches https://doi.org/10.15837/ijccc.2023.1.4396 5 figure 1: flow diagram of the proposed work https://doi.org/10.15837/ijccc.2023.1.4396 6 for feature selection, we use the linear decreasing inertia weight based cat swarm optimization (ldiwcso) method. the feline way of life inspired the creation of the cat swarm optimization algorithm. this algorithm has both tracing and seeking search modes. seeking mode simulates a cat’s innate ability to maintain situational awareness even when at rest. in this mode, you may pretend to be a cat and follow and capture its prey. these frequencies are used to address optimization difficulties. this suggested research effort takes features as input. here, we characterise each feature in a population by giving it a fitness, speed, and location. a vantage point with rst to the collection of features that might be depicted by its location. for distances of dimension d, the corresponding scalar quantities are velocities. the fitness value is a measure of the quality of the solution set. in the suggested approach, accuracy of the classification serves as the fitness function. fitnessfunction = max(accuracy) (2) cso process cso will assign different features to the tracing or searching mode in each generation. the placement of all elements was then rearranged. in the end, the feature with the highest accuracy value takes first place. the cat swarm optimization (cso) method is shown in figure 2. mixture ratio depicts the proportion of the cat’s identifying characteristics (features) that are used for tracing (mr). the mr value will be rather low since cats spend much of their time testing. the steps involved. 1. within a -dimensional space, n features are initialized with velocities and positions. 2. features are distributed to tracing or seeking mode; features count in two modes defines mr. 3. everyfeature’s accuracy value is measured. 4. in every iteration, based on cat’s mode, they are searched.following subsection, describes the process involved in these two modes. 5. if terminal criteria are satisfied, algorithm is stopped; otherwise return to (??) for following iteration. seeking mode there are four different steps involved in the search mode: thinking about where you are in space, searching the region of the dimensions you’ve chosen, digging into your memory to find what you’re looking for, and keeping track of how many dimensions have changed (smp). smp controls the amount of accessible searching memory. according to the smp test, a score of 5 means that a cat can memorise and recall five distinct solutions. a spc is a boolean data type. if spc is correct and also no changes are made to the solution set, then the information stored at a given address in memory will remain unchanged. the searching range’s minimum and maximum values are determined by the srd. in order to see how many various dimensions must be changed during the search strategy, the cdc is used. how to do a search is described in detail below. initial smp copies are made depending on the latest feature position. if spc is correct, one of these duplicates will maintain the current feature’s location and be considered for selection right away. additional adjustments must be made before they can even be considered contenders. otherwise, all smp copies will conduct searching, which will cause a shift in the system’s overall position. the % dimensions of cdc are randomised, causing a shift in the location of all copies requiring a modification. as a starting point, we choose the cdc’s dimensions percentage. by randomly altering each chosen dimension, the current srd percent value is lowered or raised. copies becomes viable options if modifications have been performed. (3) every candidate’s fitness value is computed via the fitness function. every candidate’s probability of being selected is computed. the selected probability (pi) is equal to one, if all candidates have same fitness value, else, using expression (3), pi is computed. range of lies between 0 and smp; candidates selection probability is given by pi; overall fitness values maximum value is represented as fsmax and minimum value is represented as fsmin; candidates fitness value is given by fsi. if objective is to find solution set havingmaximum fitnessvalue, then fsb= fsmin; elsefsb= fsmax.probability computation using this function produces better candidate a higher chance of being selected,and vice versa, as follows: pi = |fsi −fsb| fsmax −fsmin (3) (5) based on selected probability (pi), one candidate is selected randomly. current feature is moved to this position after selecting the candidate. tracing mode. cats target tracing is represented using tracing mode, here same strategy is followed. (1) expression (4) is used for updating velocities. (??) current feature’s position is updated using expression(5) as mentioned below: vt+1k,d = v t k,d + r1 × c1 × ( xtbest,d −x t k,d ) (4) https://doi.org/10.15837/ijccc.2023.1.4396 7 d=1,2,..,d, xt+1k,d = x t k,d + v t k,d (5) where, xtk,dand v t k,dare current feature ’s position and velocities at iteration xtbest,dgives best solution set from featurekin population c1is constant r1 is a random number and its value lies between 0 and 1 cat swarm optimization algorithm best algorithm used for computing best global solution is conventional cso. but, for producing acceptable solution, long time may be consumed by cso in rare cases. so, algorithm’s convergence and performance may get affected by this. a new parameter termed as inertia weight (w) is added in the expression used for updating positions to solve this issue. in algorithm’s tracing mode, a new velocity update form is used. a linear decreasing inertia weight factor is introduced in this work and it has a linear decrement to wmin from wmax with respect to iteration’s increment and they indicates final and initial values. reformed expressions are mentioned below. vt+1k,d = w ∗v t k,d + r1 × c1 × ( xtbest,d −x t k,d ) (6) w= wmaxwmax−wminitermax ×k (??) vt+1k,d = (wmax − wmax −wmin itermax ×k) ∗vtk,d + r1 × c1 × ( xtbest,d −x t k,d ) (7) where, w gives inertia weight wmaxrepresents maximum weight value wminrepresentsminimum weight value itermaxindicates maximum iteration k is a constant algorithm 1: cat swarm optimization (cso) algorithm 1. start 2. in dataset, features count is initialized 3. velocity and parameters are initialized 4. while (t <= itmax) 5. for every cat 6. fitness value is computed 7. next, best cat is selected and saved into memory. 8. if feature (xi) = seeking mode 9. seeking process is initiated 10. else 11. tracing process is initiated 12. termination condition is checked; if satisfied; program is terminated; otherwise, step 4 to 11 are repeated. 13. fitness function is computed and cat with best finest value is preserved. 14. selected features 15. stop 3.4 classification using partial binomial distribution using cnn for classification, this research work uses partial binomial distribution using cnn. the cnn is a highly powerful deep network. in different areas, great efficiency is shown by this network, especially in computer vision. there are three types of layers in cnn, namely, fully connected, sub-sampling and convolution layer. a cnn typical architecture is shown in figure 3. following section explains the every layer type. convolution layer in this suggested work, the chosen characteristics are provided as an input. these input characteristics are convolved with a kernel (filter) in this convolution. this convolution output between the kernel and the inputs https://doi.org/10.15837/ijccc.2023.1.4396 8 figure 2: workflow for cso https://doi.org/10.15837/ijccc.2023.1.4396 9 figure 3: convolutional neural network features is then used to create the n output features. in this context, filter means the kernel of a convolution matrix, and a feature map of size i*i means the features computed by convolving the input with the kernel. cnn consists of many layers of convolutional neural networks. later convolutional layers take the feature vector as an input and output the same thing. a total of n filter bunches are used throughout all convolution layers. several filtering are used to convolve the input, and the depth of the resulting feature map is proportional to the number of filters used in the convolution procedure (n*). each filter map is treated as a unique feature at a predetermined input position. c (l) i = b (l) i + a (l−1) i∑ j=1 k (l−1) i,j ∗c (l) j (8) where, bias matrix is given by b(l)i , kernel or convolution filter is represented as k (l−1) i,j and its size is a*a. in same layer, i-th feature map is connected with feature map in layer (l − 1). feature maps are available in output c (l) i layer. in expression (9), input space corresponds to first convolutional layer c (l−1) i , that isc (0) i = xi. feature map is generated using the kernel. for convolutional layer output’s nonlinear transformation, activation function is applied after convolution layer. y (l) i = y (c (l) i ) (9) where, y (l) i represents activation function’s output c (l) i gives the input that it receives. the rectified linear units (relus), tanh, sigmoid are the commonly used activation functions. the relus activation function is used in this work and it is represented as, y (l) i = max(0,y (l) i ) (10) this function is extensively employed in deep learning models due to its effectiveness in reducing nonlinear characteristics and interaction. the output of relu is zero when the input is negative and the same input when the input is positive. one of the main benefits of this function is how quickly it can be trained compared to others. the error derivative of this function is negligible in the saturation area. the result is the elimination of future weight-status updates. the phrase "vanishing gradients issue" is used to describe this situation. https://doi.org/10.15837/ijccc.2023.1.4396 10 pooling or sub sampling layer this layer applies a spatial dimension reduction to the feature map that was produced in the preceding convolution layer. exactly this is one of the primary goals of this particular layer. this subsampling procedure is executed in the middle of the feature maps and the mask. there are a number of different subsampling methods that may be generated, including maximum pooling, sum pooling, and average pooling. beyond convex hull of inputs {xcp}p∈rq ,y cq is produced for cnn’s better recognition. here, for simplicity, subscript q (output position) and superscript c (channel) are omitted. first, a binomial distribution is used for modelling local neuron activations {xp}p∈r and this distribution has a standard deviation σx and mean µx. x ∼ n (µx,σx) ⇐⇒ x = µx + �σx (11) where, µx = nxs σ2x = ( xs − µx ) 2 � ∼ n(0, 1),� ∈ (−∞, +∞) n indicates iterations count xs represents input features successfully beyond specific class the expression (11) is modified according to the local pooling knowledge and it restrict the output to fall below mean µx as, where, prior probabilistic model corresponds to halfbinomial distribution nh(σ0) and it has σ0 = 1. for producing y stochastically without the use of pooling parameter, formulated the fixed halfbinomial pooling as shown in expression 13. fully connected layer the cnn’s final layer is a traditional feed forward network and there may be one or more than one hidden layers. the softmax activation function is used by output layer. y (l) i = f(z (l) i ) (12) wherez (l) i = m (l−1) i∑ i=1 w (l) i,jy (l−1) i (13) where, weights are represented as w(l)i,j and each class’s representation is tweaked using a single fullyconnected layer, and the transfer function is modelled as f, while nonlinearity is modelled as a sigmoid. the neurons in a completely linked layer are preprogrammed for nonlinear behaviour. it is constructed independently in both pooling and convolution layers. the suggested cnn method is used to confirm deaths in certain time periods throughout india. 4 experimental results for proposed and available research techniques, in matlab, experimental evaluation is conducted. from https://www.kaggle.com/davidbnn92/weather-data-for-covid19-data-analysis, collected the covid-19 forecasting data in this research work. the available long short-term memory (lstm) networks, cnn, and proposed cnn method performance are compared in terms of f-measure, accuracy, recall, precision, and error rate values. in figure 4 we see the procedure of the suggested technique. in this proposed study, we take the covid-19 forecast database as an input and normalise it using the min-max approach. you can see this procedure in figure 5. figure 6 depicts the suggested method for selecting the best attributes. in the prediction of death and confirmed cases across india, training process of neural network is represented in figure 7. further, for detected result, decision may be incorrect (false) or correct (true), which is illustrated in figure 8. so, decision false under any one the following four possible classes, true positive (tp), false positive (fp), false negative (fn), and true negative (tn). the available long short-term memory (lstm) networks, cnn, and proposed cnn method performance are compared in terms of f-measure, accuracy, recall, precision, and error rate values are represented in table 2. 1. accuracy accuracy is a simple ratio of expected observations to total observations, making it the most straightforward performance metric. accuracy = tp + tn tp + fp + fn + tn (14) https://doi.org/10.15837/ijccc.2023.1.4396 11 figure 4: layout for proposed method figure 5: normalization process figure 6: feature selection figure 7: neural network training https://doi.org/10.15837/ijccc.2023.1.4396 12 figure 8: performance results table 2: performance analysis figure 9: accuracy comparison https://doi.org/10.15837/ijccc.2023.1.4396 13 figure 10: precision comparison the proposed technique and available cnn and lstm techniques are compared with each other in terms of accuracy and its results are shown in figure 9. the proposed algorithm is used in this work for selecting optimum features. for covid-19 prediction, proposed method is used in this proposed work and this prediction is done using selected features. this produces better accuracy. around 92.57% accuracy value is produced by proposed method, 90.74% is produced by cnn and 87.92% is produced by lstm as indicated in experimental results. when compared with available techniques, high accuracy value is produced by proposed method as shown in experimental results. 1. precision the value of accuracy is defined as the proportion of accurately predicted positive observation relative to the overall proportion of expected positive observations. precision = t p t p +f p 17 the proposed technique and available cnn and lstm techniques are compared with each other in terms of precision and its results are shown in figure 10. there are different techniques are represented in x-axis and in y-axis, precision values are represented. around 92.41% precision value is produced by proposed method, 90.55% is produced by cnn and 87.70% is formed by lstm as indicated in experimental results. when compared with available techniques, high precision value is produced by proposed method as shown in experimental results. 1. recall the recall ratio in covid-19 forecasting is the proportion of valid instances that were successfully recovered. recall = tp tp + fn (15) the proposed technique and available cnn and lstm techniques are compared with each other in terms of recall and its results are shown in figure 11. various techniques are represented in x-axis and in y-axis, recall values are represented. for covid-19 prediction, the proposed technique is used in this proposed work. around 92.56% recall value is produced by proposed technique, 90.75% is produced by cnn and 87.88% is produced by lstm as indicated in experimental results. when compared with available techniques high recall value is produced by proposed method as shown in experimental results. 1. f1 score the f1 score is calculated by averaging the weighted recall and accuracy scores. as such, it may be used as a statistical metric for evaluating classifier performance. this score takes into account the number of false positives and negatives. its f1 score is written as, f-measure comparisons between the proposed method and existing cnn and lstm methods are shown in figure 12. different methods are shown along the x-axis, while f-measure values are shown along the y-axis. https://doi.org/10.15837/ijccc.2023.1.4396 14 figure 11: recall comparison f−measure = 2 p recision∗recall p recision+recall 19 figure 12: f-measure comparison https://doi.org/10.15837/ijccc.2023.1.4396 15 figure 13: error rate comparison the experimental findings show that the suggested method yields an f-measure of around 92.49%, whereas cnn and lstm yield only about 90.65% and 87.79%, respectively. experimental findings reveal that the suggested method yields a high f-measure value, in comparison to existing methods. 1. error rate figure 13 depicts the error rate performance of lstm, cnn, and the suggested methods. the x-axis depicts the various methods, while the y-axis shows the associated error rates. according to the findings of the experiments, the proposed method results in an error rate of around 7.4%, whereas cnn results in a rate of about 9.25% and lstm results in a rate of about 12.07%. 5 conclusion in this paper, we propose using ai with an optimization approach to forecast future deaths and confirmed cases. min-max normalisation is used to first adjust the data. the optimal features for a given classification task are chosen via an optimization method. predictions of covid-19 based on these characteristics are made using a cnn trained with a binomial distribution. according to the testing findings, the suggested system achieves an accuracy of approximately 92.57%, precision of about 92.41%, recall of about 92.56%, f-measure of about 92.49%, and error rate of about 7.4%. improvements in classification accuracy may be possible in the future with the use of several optimization methods like ant colony optimization (aco), simulated annealing (sa), and harmony search (hs). references [1] elsheikh, a.h., saba, a.i., elaziz, m.e., lu, s., shanmugan, s., muthuramalingam, t., kumar, r., mosleh, a.o., essa, f.a., & shehabeldeen, t.a. 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[9] d. n. v. s. l. s. indira, rajendra kumar ganiya, p. ashok babu, a. jasmine xavier, l. kavisankar, s. hemalatha, v. senthilkumar, t. kavitha, a. rajaram, karthik annam, alazar yeshitla, "improved artificial neural network with state order dataset estimation for brain cancer cell diagnosis", biomed research international, vol. 2022, article id 7799812, 10 pages, 2022. https://doi.org/10.1155/2022/7799812. [10] hu, z., ge, q., li, s., jin, l., & xiong, m. (2020). evaluating the effect of public health intervention on the global-wide spread trajectory of covid-19. medrxiv. [11] al-qaness, m.a., ewees, a.a., fan, h., & abd el aziz, m.a. (2020). optimization method for forecasting confirmed cases of covid-19 in china. journal of clinical medicine, 9. [12] karthik, a., mazheriqbal, j.l. efficient speech enhancement using recurrent convolution encoder and decoder. wireless pers commun 119, 1959–1973 (2021). https://doi.org/10.1007/s11277-021-08313-6. [13] castillo, o., & melin, p. (2020). forecasting of covid-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic. chaos, solitons, and fractals, 140, 110242 110242. [14] hu, z., ge, q., li, s., jin, l., & xiong, m. (2020). artificial intelligence forecasting of covid-19 in china. international journal of educational excellence. [15] elsheikh, a.h., saba, a.i., elaziz, m.e., lu, s., shanmugan, s., muthuramalingam, t., kumar, r., mosleh, a.o., essa, f.a., & shehabeldeen, t.a. (2020). deep learning-based forecasting model for covid-19 outbreak in saudi arabia. process safety and environmental protection, 149, 223 233. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control https://doi.org/10.15837/ijccc.2023.1.4396 17 cite this paper as: murugesan, r. k.; madhu, k.; sambandam, j.; malliga, l. (2023). covid-19 forecasting using cnn approach with a halbinomial distribution and a linear decreasing inertia weight-based cat swarm optimization, international journal of computers communications & control, 18(1), 4396, 2023. https://doi.org/10.15837/ijccc.2023.1.4396 introduction literature review proposed methodology input experimental results conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 5, month: october, year: 2020 article number: 3920, https://doi.org/10.15837/ijccc.2020.5.3920 ccc publications solving a modified syndrome decoding problem using integer programming v.-f. drăgoi, p.-l. cayrel, b. colombier, d. bucerzan, s. hoară vlad-florin drăgoi* aurel vlaicu university of arad 310330 arad, elena dragoi, 2, romania *corresponding author: vlad.dragoi@uav.ro pierre-louis cayrel univ lyon, ujm-saint-etienne, cnrs, laboratoire hubert curien umr 5516 f-42023, saint-etienne, france pierre.louis.cayrel@univ-st-etienne.fr brice colombier univ lyon, ujm-saint-etienne, cnrs, laboratoire hubert curien umr 5516 f-42023, saint-etienne, france b.colombier@univ-st-etienne.fr dominic bucerzan aurel vlaicu university of arad 310330 arad, elena dragoi, 2, romania dominic.bucerzan@uav.ro sorin hoară iosud, department of computer science politehnica university of timisoara vasile parvan street, no. 2 300223 timisoara, romania sorin-horatiu.hoara@student.upt.ro abstract in this article, we model a variant of the well-known syndrome decoding problem as a linear optimization problem. most common algorithms used for solving optimization problems, e.g. the simplex algorithm, fail to find a valid solution for the syndrome decoding problem over a finite field. however, our simulations prove that a slightly modified version of the syndrome decoding problem can be solved by the simplex algorithm. more precisely, the algorithm returns a valid error vector when the syndrome vector is an integer vector, i.e.,the matrix-vector multiplication, is realized over z, instead of fq. keywords:syndrome decoding, integer linear programming, simplex algorithm. https://doi.org/10.15837/ijccc.2020.5.3920 2 1 introduction in 1978, the coset weight problem (cwp) and the subspace weight problem (swp), usually known by the name of syndrome decoding problem (sdp), have been proven to be np-complete for random binary codes by berlekamp, mceliece and van tilborg [6]. the hardness of those two problems is central in code-based cryptography. in fact, the security of certain cryptographic applications like the public-key cryptosystems of mceliece [32] and niederreiter [39] relies directly on the difficulty of solving an instance of sdp. the idea of mceliece had a great succes in the code-based cryptographic community, as a plethora of variants were proposed. mainly aiming to reduce the key size as well as to have tight security reductions, these variantions consisted in changing the private code or adding additional structure. in table 1 we present a non-exhaustive list of candidates with references, as well as existing/possible attacks. sdp is not only present in public-key encryptiion schemes (pke), it is also a key ingredient for code-based hash functions [1], as well as code-based signature schemes [12]. table 1: mceliece variants and structural attacks code family pke scheme attacks variants on attacks on wieschebrink’s attacks on weak keys sub-codes sub-codes technique wieshbrink’s tch. goppa [32] [30] grs [39] [48] [5] [51, 52] [50] [13] reed-muller [47] [11, 33] [23] [40] concatenated [44] [45] algebraic geometry [25] [14, 20, 48] [14] [14] ldpc [36] [36] qc-ldpc [2] [41] wild goppa [7, 8] [15, 16] srivastava [42] [19] convolutional [31] [28] mdpc [34] [37]1 [17] [4] polar [46] [3, 18] [24] integer linear programming (ilp) has been widely used in the literature to reformulate and solve various economic, logistic, computer science and many more real life problems [22, 43, 49]. it was also used in cryptographic context, mainly for the study of stream ciphers. in [38] mixed integer linear programming (milp) is used to prove security bounds against both differential and linear cryptanalysis. the authors in [38] implement milp-based methods to obtain practical results for enocoro-128v2, as well as for calculating the number of active s-boxes for aes. in [9, 10] the authors illustrate how milp can be used in the case of the trivium stream cipher, and the lightweight block cipher ktantan. for doing so, the authors in [10] reformulate a non-linear multivariate boolean equation system into a milp problem and use the cplex solver to find a solution. algorithms for solving ilp problems (ilpp) have been developed for more than 40 years. the well-known book in discrete optimizations by [26] offer a detailed view of the major techniques, e.g., polyhedral or cutting-planes methods, chvátal-gomory cuts, lattice basis reductions, and many others. even though ilpps with binary constrains are np-hard in general [27], there are several particular instances which are solvable in polynomial time, such as totally uni-modular matrices [35], or fixed number of variables [29]. for a recent review on the evolution of ilpps see [21]. we will see that in our case the ilpp does not fall in any of these cases. however, we obtain practical results that point out to a polynomial time complexity. our contribution we show, in this article, that if the sdp is modified, it becomes in practice much easier to solve. our main contribution is to formalise a modified variant of the sdp into an ilpp. our strategy is the following. we take the original sdp and replace the multiplication in f2 by the usual integer multiplication. hence, the syndrome vector is no longer a binary vector, but an integer vector. furthermore, we will formalise this modified sdp as an ilpp and prove that if there is an unique vector x∗ solution to the ilpp, satisfying the hamming weight condition in the sdp, then x∗ is the optimum solution of the ilpp. hence, to find the error vector, solution to the sdp, one needs to solve the ilpp and output x∗. we test our method in maple software using two https://doi.org/10.15837/ijccc.2020.5.3920 3 functions: simplex and lpsolve. our simulations point out that the modified sdp is always solvable by the corresponding ilpp. moreover, the empirical performance of the lp solvers points out to a polynomial-time algorithm. the article is organized as follows. in section 2, we introduce a short technical background from coding theory necessary for the rest of the article. section 3 gives a brief description of the ilpp. in the same section we describe the modified sdp as an ilpp. in section 4, we propose a series of simulations to sustain the efficiency of this method. 2 syndrome decoding problem throughout this article, fq denotes the finite field with q elements, mk,n (f) denotes the set of k ×n matrices over a field f. an [n,k] linear code c over fqm is a linear subspace of dimension k of the vector space fnqm. any element in c is called a codeword. a generator matrix for a [n,k] linear code is a k × n matrix (often denoted by g) whose rows form a basis for the code. the dual of c , denoted by c ⊥, is the linear code which consists of all vectors y ∈ fnqm such that ∀ c ∈ c y ·ct = 0. a parity-check matrix of c is a generator matrix of its dual. it is also an (n−k)×n matrix h of full rank that satisfies hct = 0 for all c ∈ c . the hamming distance between two vectors is the number of coordinates on which they differ. this distance induces a norm known as the hamming weight wt. one of the main attributes of an error-correcting code, exploited by any code-based cryptosystem, is the decoding capability of a code c . a decoding function of a code c takes as input any vector y from the ambient space and outputs the most likely codeword c ∈ c , i.e., the c that maximizes the probability of receiving y given that c was sent over the communication channel. the majority of the code-based schemes use the binary symmetric channel framework. in this case, the decoding problem can be solved by the well-known closest vector problem. one possible solution is the sdp. definition 1 (decisional sdp). instance: a full rank matrix h ∈mn−k,n (f2), a vector s ∈ fn−k2 and an integer ω > 0. question: is there a vector x ∈ fn2 of weight ≤ ω, such that hx = s? this problem can be defined over any finite extension field. however, the most common one is over f2. 3 modified syndrome decoding problem 3.1 integer linear programming any ilpp can be written either in canonical form or in standard form. definition 2 (ilpp). let n,m be two positive integers. let c ∈ zn, b ∈ zm, s ∈ zm and a ∈ mm,n (z). define • the canonical form of an ilpp max{ctx|ax ≤ b, x ∈ zn, x ≥ 0}; (1) • the standard form of an ilpp max{ctx|ax + d = b, x, d ∈ zn, x ≥ 0, d ≥ 0}. (2) notice that in the standard form the vector d can be seen as a quantification of the difference between ax and b, compared to the canonical form. indeed, in the canonical form, b − ax might be equal to any positive vector, where as in the standard form this vector is fixed and given. any vector x satisfying ax ≤ b is called a feasible solution. if a feasible solution x∗ satisfies the maximum condition in (1) then x∗ is optimal. a particular ilpp, that we will consider here, is when equality holds in equation (1), i.e., ax = b, which is equivalent to the standard form (2) with d = 0. considering the dual problem, we have: https://doi.org/10.15837/ijccc.2020.5.3920 4 definition 3 (primal-dual ilpp). let n,m be two positive integers and c ∈ zn, b ∈ zm, s ∈ zm and a ∈mm,n (z). define the primal ilpp as max{ctx|ax = b, x ∈ zn, x ≥ 0}. (3) then the dual problem of (3) is defined as min{btx|atx = c, x ∈ zn, x ≥ 0}. (4) 3.2 the modified syndrome decoding problem we define the modified sdp over z as: definition 4 (z-msdp). instance: h ∈mn−k,n (z) with hi,j ∈{0, 1} for all i,j a vector s ∈ zn−k and an integer ω > 0. question: is there a vector x ∈{0, 1}n with wt(x) ≤ ω, s.t. hx = s? remark 3.1. the difference between sdp and z-msdp is that the matrix-vector multiplication is no longer performed in f2, but in z. this is why the syndrome vector now has entries in z instead of f2. in the rest of article we restrict our analysis to the case where wt(x) = ω. we also suppose that the z-msdp has a solution, i.e., the syndrome vector s was not generated at random, but was obtained as a valid instance of the matrix-vector multiplication. theorem 1. let us suppose that there exists a unique vector x∗ ∈{0, 1}n with wt(x∗) = ω, solution to the z-msdp. then x∗ is the optimum solution of an ilpp. proof. suppose that such an x∗ exists and is unique, i.e., hx∗ = s with s ∈ zn−k and wt(x∗) = ω. we will construct an ilpp for which x∗ is the optimum solution. for that, we simply set at = h, c = s, and bt = (1, . . . , 1) in (4). since x ∈ {0, 1}n wt(x) = n∑ i=1 xi = (1, . . . , 1) · x, but this is equal to btx∗. the ilpp we need to solve can now be defined min{btx|atx = c, x ∈{0, 1}n}. (5) this implies that x∗ is a feasible solution to (5), and as x∗ is the unique vector satisfying atx∗ = c with wt(x∗) ≤ ω, x∗ is optimum for the minimum weight condition. remark 3.2. the condition from (5), more exactly x ∈ {0, 1}n, could possibly be replaced with the more general x ∈ zn. in this case, theorem 1 still holds as long as there is no other integer feasible solution x′ ∈ zn with (1, . . . , 1) · x′ < ω. 4 simulations we simulated the algorithm that solves the z-msdp on a regular laptop with the linux operating system running on a 2-core processor at 2.60 gigahertz. we are running our script using the maple software and its optimization packages, more precisely, the simplex minimize and lpsolve functions. remark 4.1. throughout our simulations we noticed that, in nearly all the cases, the lpsolve function was much faster than the simplex function. that is why we will describe and discuss our variant of the algorithm that uses the lpsolve function. our script is composed of two steps, the generation function and the solve function. the generation function takes as input the integers n,k,ω and outputs an error vector e of weight ω and an integer syndrome vector s. https://doi.org/10.15837/ijccc.2020.5.3920 5 1. firstly, it generates a random binary matrix of length n and dimension k using the randommatrix function and it checks that all rows/columns are pairwise distinct; 2. secondly, it generates a random binary vector of fixed hamming weight ω ; 3. thirdly, it computes the corresponding integer syndrome s. the solve step takes as input the matrix and the syndrome and outputs the estimated error. 1. firstly, it creates the system using the binary matrix and the syndrome s; 2. secondly, it defines the constraints on the solution, i.e., hamming weight is minimized; 3. thirdly, it calls the lpsolve function with the aforementioned parameters. the output of the lpsolve function is a vector of rational numbers and the corresponding hamming weight. 4. there is also a verification step, where we check the equality of the two vectors, i.e., between the e and the output vector of the solve function. we conducted two types of experiments, i.e., verification and performance. the first type of simulations were done to check the correctness of the solution. more exactly, we counted how many verification steps were successful for a given number n of trials. our results showed that with probability 1 the lpsolve function recovers the exact error vector. the experiments were conducted for values of n up to 200 and k ≤ n2 . we repeated each experiment for 10000 different vectors for a fixed matrix, and repeat this for 1000 random binary matrices. remark 4.2. we noticed that our algorithm retrieved the correct error vector for any value of ω. this fact implies that for some given binary matrix h and integer vector s there is only one binary solution, and on top of that it has the minimum hamming weight. moreover, even when the condition on x to be binary was relaxed to x ∈ zn, the solution to the problem found by our algorithm was the binary one. in other words, for the given matrix and syndrome, there were no feasible integer solutions satisfying (1, . . . , 1) · x = ω. if we slightly modified one of the components of the syndrome s, i.e., the new syndrome s∗ = s + ( 0, . . . , 0,�j, 0, . . . , 0 )t , and apply the solve function to the initial matrix and s∗, we noticed two different cases: • when �j ≥ 0 the algorithm finds a solution, which is a rational vector different from e; • when �j < 0 the algorithm does not find a solution with hamming weight ≤ ω. table 2: timings (in seconds) for the lpsolve function for two types of parameters. on the left hand side for k = n/2 and on the right hand side for k = n3 . also two different types of values for ω, ω = √ n (upper part of the table) and ω = log2(n) (lower part of the table). k n ω/n timings [s] k n ω/n timings [s] 250 500 0.044 1.74 167 500 0.044 1.03 500 1000 0.031 16.92 333 1000 0.031 9.65 750 1500 0.025 63.8 500 1500 0.025 34.38 250 500 0.017 1.72 167 500 0.017 0.98 500 1000 0.010 16.24 333 1000 0.010 8.34 750 1500 0.007 63.41 500 1500 0.007 34.14 the second type of simulations were evaluating the performance of the algorithm. the lpsolve function performed extremely well for small values of n, e.g., for n = 100 and k = 50 it took less than 25 milliseconds to find the solution for any ω ≤ n/3. for n = 200 and k = 100 it took less than 0.1 second for any ω ≤ √ n. for large values of n, we illustrate in table 2 the performance of the lpsolve function. it is worth noting that for n = 1500,k ≤ n/2 and ω ≤ √ n we need less than 60 seconds to find the solution. also, we can observe that from k = n/2 to k = n/3 the timings are almost divided by 2. https://doi.org/10.15837/ijccc.2020.5.3920 6 5 conclusion and perspectives we have shown in this article that, using linear programming, we can efficiently solve a modified version of the syndrome decoding problem (the z−msdp). our simulations reveal interesting properties, that we intend to investigate in the near future from a theoretical point of view. among the most significant ones, we mentioned the uniqueness of the binary solution, even under less restrictive conditions, and the fact that the observed work factor of the algorithm was polynomial (more precisely cubic in the parameters). it would be interesting to see if other modifications of other similar hard problems can be solved more efficiently using linear programming. funding this work was supported by a grant of the romanian ministry of education and research, cncs uefiscdi, project number pn-iii-p1-1.1-pd-2019-0285, within pncdi iii. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] augot, d.; 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(2020). solving a modified syndrome decoding problem using integer programming, international journal of computers communications & control, 15(5), 3920, 2020. https://doi.org/10.15837/ijccc.2020.5.3920 international journal of computers communications & control special issue on fuzzy logic dedicated to the centenary of the birth of lotfi a. zadeh (1921-2017) online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 1, month: february, year: 2021 article number: 4125, https://doi.org/10.15837/ijccc.2021.1.4125 ccc publications from classical logic to fuzzy logic and quantum logic: a general view s. nădăban sorin nădăban* aurel vlaicu university of arad, department of mathematics and computer science, elena drăgoi 2, ro-310330 arad, romania *corresponding author: snadaban@gmail.com abstract the aim of this article is to offer a concise and unitary vision upon the algebraic connections between classical logic and its generalizations, such as fuzzy logic and quantum logic. the mathematical concept which governs any kind of logic is that of lattice. therefore, the lattices are the basic tools in this presentation. the hilbert spaces theory is important in the study of quantum logic and it has also been used in the present paper. keywords: fuzzy logic, quantum logic, orthomodular lattice, hilbert space, quantum mechanics, effect algebras. 1 aristotelian logic what is logic? logic is the science that studies thinking. as in the process of thinking there are two factors, information and emotion, we can talk about a domain of human thought called rational thinking, characterized by the fact that the obtained results are out of the factor called emotion. therefore we can say that logic is the science that studies rational thinking and we will name it rational thinking logic. the first research about logic was made in the ancient greece. the results were systematized by the greek philosopher aristotle, in a treatise of logic called organon. for this reason the rational thinking logic is called aristotelian logic. in this logic a sentence is either true or false and to one true sentence value 1 is associated while to a false sentence value 0 is associated. because of this classical logic is also called bivalent logic (having two truth values true or false). the principles that stay at the basis of this logic are: 1. principle of identity: each concept is identical to itself a = a. 2. principle of noncontradiction: one sentence cannot be both true and false at the same time. 3. principle of the excluded middle: a sentence is either true or false. "there cannot be an intermediate between contradictions, but of one subject we must either affirm or deny any one predicate" (aristotle’s metaphysics). https://doi.org/10.15837/ijccc.2021.1.4125 2 4. principle of double negation: a double negation is an affirmative statement, ¯̄a = a. this principle was stated as a law of propositional calculus by b. russell and a.n. whitehead in principia mathematica. 5. principle of sufficient reason: states that everything must have a reason or a cause. this principle led, along the years, to many controversies and various interpretations. starting from this principle we get to modus ponens, the most important rule of inference, which can be summarized as: if p implies q and p is true, then q must be also true. in this situation proposition p is called "sufficient proposition" of proposition q. i have not yet used the term of mathematical logic. the mathematical logic is the result of a process of mathematical modelling of the rational thinking logic elements, a process that started in the 17th century, by the german mathematician and philosopher g. leibniz and it was continued in the 19th century by the english mathematicians george boole and augustus de morgan and also by many others along the time. in conclusion, mathematics, through mathematical modelling of rational logic, brought about an essential contribution to the development of logic. on the other hand, the mathematical logic has become a fundamental instrument in the building of the mathematical universe. 2 the lattices of classical logic in 1854, george boole discovered a connection between the laws of logic and some laws of algebraic calculus. thus, he introduced the algebra that was named after him, the boolean algebra. in this section we briefly review the theory of lattices and we will present two important examples: the lattice of all propositions about a universe of discourse and the lattice of all subsets of an universal set, which, as we will see, can be identified. for other results and notions in the theory of general lattices we refer to [3], [10], [14]. definition 1. a lattice is a 4-tuple (l,≤,∧,∨) such that (l,≤) is a partial order set (shortly poset) and for all x,y ∈ l there exists a greatest lower bound (meet or infimum) x ∧ y and a least upper bound (join or supremum) x∨y. proposition 2. if (l,≤,∧,∨) is a lattice, then: 1. x∨y = y ∨x ; x∧y = y ∧x (commutativity); 2. x∨ (y ∨z) = (x∨y) ∨z ; x∧ (y ∧z) = (x∧y) ∧z (associativity); 3. x∨ (x∧y) = x ; x∧ (x∨y) = x (absorption); 4. x∨x = x ; x∧x = x (idem potency). definition 3. a bounded lattice is a lattice (l,≤,∧,∨) with two elements 0 ≤ 1, the least and the greatest elements of l. proposition 4. let (l,≤,∧,∨, 0, 1) be a bounded lattice. then: x∨ 0 = x,x∧ 0 = 0,x∨ 1 = 1,x∧ 1 = 1, (∀)x ∈ l. definition 5. a lattice (l,≤,∧,∨) is called distributive if x∧ (y ∨z) = (x∧y) ∨ (x∧z), (∀)x,y,z ∈ l. remark 6. condition from previous definition is equivalent to: x∨ (y ∧z) = (x∨y) ∧ (x∨z), (∀)x,y,z ∈ l. definition 7. let (l,≤,∧,∨, 0, 1) be a bounded lattice. let x ∈ l. an element x′ ∈ l is called the complement of x if x∨x′ = 1 and x∧x′ = 0. the lattice (l,≤,∧,∨, 0, 1) is called complemented if (∀)x ∈ l has a complement in l. the lattice (l,≤,∧,∨, 0, 1) is called uniquely complemented if (∀)x ∈ l has a unique complement in l. https://doi.org/10.15837/ijccc.2021.1.4125 3 theorem 8. if (l,≤,∧,∨, 0, 1) is distributive and complemented then it is uniquely complemented. definition 9. a bounded lattice (l,≤,∧,∨, 0, 1) is called boolean (boolean algebra or boolean lattice) if it is distributive and complemented. in the theory of boolean algebra, the complement is usually denoted by ”′”, but we can also meet the symbol ”¬”. in some papers the term negation is used for the complement, but there are different types of negations which are studied in more general context of non-distributive lattices (see [5]). therefore, in order to exclude any possible confusion, when we use the term "complement" this will have the meaning of definition 7. as we will see, in the case of fuzzy logic, we do not have a complement but we do have a negation. theorem 10. let (l,≤,∧,∨, 0, 1) be a boolean lattice. then: 1. (x∨y)′ = x′ ∧y′ ; (x∧y)′ = x′ ∨y′ (de morgan law); 2. (x′)′ = x (involution); example 11. let f be the family of all proposition about an universe of discourse. if x,y ∈ f we will denote "x implies y" by x ≤ y. it is natural to admit that: 1. x implies x; 2. if x implies y and y implies z, then x implies z; 3. if x implies y and y implies x, then x and y are logically equivalent. thus (f,≤) is a poset. the conjunction of two propositions x,y is denoted by x∧y and it is the true sentence if x and y are both true and it is false otherwise. the disjunction of two propositions x,y is denoted by x∨y and it is the true sentence if, at least one of the sentences x and y is true and false otherwise. thus (f,≤,∧,∨) is a lattice. we note that (f,≤,∧,∨, 0, 1) is a bounded lattice, where 0 is a proposition that is always false (called a contradiction) and 1 is a proposition that is always true (called a tautology). the complement of the proposition x is the proposition x′ which is true if x is false and it is false if x is true. example 12. let x be a nonempty set. the power set p(x) (the family of all subsets of x) is a lattice in which the order relation is inclusion and the operations on p(x) are union and intersection. moreover, (p(x),≤,∧,∨, 0, 1) is a bounded lattice, where the smallest element 0 is the empty set and the largest element 1 is the set x itself. the complement of a is cx(a). in the theory of the lattices the concept of logical implication is a very important one. this concept is associated with an operation over two logical values. in the classical logic x → y means that x ≤ y which is logically equivalent to x′∨y sau cu x = x∧y. in the case of non-boolean lattices we can also have other ways of defining the logical implication. 3 orthomodular lattices in this section we will present some aspects of orthomodular lattices, because they represent the basic structure in quantum logic. for other results and notions in the theory of general lattices we refer to [3], [10], [14]. definition 13. a lattice (l,≤,∧,∨) is called modular if: x ≤ y ⇒ x∨ (z ∧y) = (x∨z) ∧y, (∀)z ∈ l. theorem 14. every distributive lattice is modular. definition 15. let (l,≤,∧,∨, 0, 1) be a bounded lattice. a unary operation ⊥: l → l is called orthocomplementation if (∀)x,y ∈ l we have: https://doi.org/10.15837/ijccc.2021.1.4125 4 1. x⊥ ⊥ = x (involution); 2. x ≤ y ⇒ y⊥ ≤ x⊥ (antitone); 3. x∧x⊥ = 0 (non-contradiction). the 7-tuple (l,≤,∧,∨, 0, 1,⊥) is called orthocomplemented lattice. theorem 16. if (l,≤,∧,∨, 0, 1,⊥) is an orthocomplemented lattice, then: 1. 0⊥ = 1 ; 1⊥ = 0 (boundary condition); 2. (x∨y)⊥ = x⊥ ∧y⊥ ; (x∧y)⊥ = x⊥ ∨y⊥ (de morgan laws); 3. x∨x⊥ = 1 (excluded middle). corollary 17. if l is an orthocomplemented lattice, then l is complemented. corollary 18. if l is an orthocomplemented and distributive, then l is boolean. definition 19. an orthomodular lattice is an orthocomplemented lattice such that it satisfies: x ≤ y ⇒ x∨ (x⊥ ∧y) = y, (∀)x,y ∈ l (orthomodular law). theorem 20. if l is boolean algebra, than l is modular, orthomodular and orthocomplemented. in the theory of lattices the concepts of negation and implication on an orthomodular lattice have an important role. we will restrain to presenting only some notions. definition 21. let (l,≤,∧,∨, 0, 1) be a bounded lattice. 1. a function ¬ : l → l is a negation on l if: (a) x ≤ y ⇒¬y ≤¬x (antitone); (b) x ≤¬¬x, (∀)x ∈ l (weak double negation). 2. a negation ¬ is called intuitionistic negation on l if x∧¬x = 0 (non-contradiction). 3. a negation ¬ is called fuzzy negation on l if ¬1 = 0 (boundary condition). 4. a negation ¬ is called de morgan negation on l if x = ¬¬x, (∀)x ∈ l (involutory). 5. a de morgan negation ¬ is an ortho negation on l if x∧¬x = 0, (∀)x ∈ l (non-contradiction). 6. an ortho negation ¬ is an orthomodular negation on l if x ≤ y ⇒ x ∨ (¬x ∧ y) = y (orthomodular). the concept of complement and the concept of negation are fundamentally different. complementation is a characteristic of a lattice while negation is a function defined on a lattice. the necessity of a notion of "implication" is obvious. we cannot talk of a "logic" without having such a notion. "i would argue that a ’logic’ without an implication function susceptible of reasonable interpretation is radically incomplete, and indeed, hardly qualifies as a theory of deduction" (see [28]). any definition we give to the concept of implication, it should satisfy the condition x → y = 1 ⇔ x ≤ y, condition that we could consider natural also in orthomodular lattice not only in the case of boolean algebras. but, as we have seen in the previous section, in the classical logic we have the implication x → y ⇔ ¬x ∨ y. in a orthomodular lattice an implication defined in such a way would not satisfy anymore the condition x → y = 1 ⇔ x ≤ y. in 1970 the following implication x → y ⇔¬x∨ (x∧y) https://doi.org/10.15837/ijccc.2021.1.4125 5 was seen to satisfy the above condition. it was called sasaki hook or quantum implication. we highlight that, in the case that the lattice is distributive, namely a boolean lattice, the sasaki hook becomes equal to ¬x∨y. using sasaki hook, many authors reformulated the theory of orthomodular lattices and called them "quantum logic". we also specify that, in 1974, g. kalmbach showed that along the sasaki hook there are four more "quantum implications", which satisfy the above condition and reduce themselves to ¬x ∨ y in the case of boolean algebras. (see [19]). a much more general definition for the concept of implication can be the following definition 22. [10] let (l,≤,∧,∨, 0, 1) be a bounded lattice with negation. a function → on l is called implication if: 1. {x ≤ y}⇒ x → y ≤ x∨y, (∀)x,y ∈ l; 2. x∧ (x → y) ≤ ¬x∨y, (∀)x,y ∈ l. 4 fuzzy logic as we have seen, the fundamental concept of the classical logic is that of the proposition. this is an affirmation that we can decide, without any ambiguity, whether it is true or false. for example, “10 is an even number” is a true sentence. everything functions well, because the sentences are clearly formulated. but, for many sentences from everyday language, like “john is tall” or “the room is clean” there is no clear answer as true or false. traditional mathematics avoided such situations, or, in order to avoid ambiguity, we give a more precise definition like “tall is more than 1,72 m”. such an approach has the disadvantage that a man of 1,718 m is not considered tall whereas a man of 1,72 m is. a more efficient way to treat such ambiguities is offered by fuzzy logic, introduced in 1965 by l.a. zadeh in his famous paper [26]. fuzzy logic and fuzzy set theory have led to a new domain in mathematics and has found applications in several other fields. thus, if x is an arbitrary set, by fuzzy set in x we understand an application µ : x → [0, 1]. this representing a generalization of the classical subsets which can be identified with their characteristic functions defined on x, but with values in {0, 1}. the value µ(x) is regarded as the degree of membership of x to fuzzy set, or the value of truth with which x belongs to the fuzzy set. the classical operations of union, intersection and complementarity were redefined by l.a. zadeh in the context of fuzzy sets. thus, if µ,ν are fuzzy sets in x there union, noted µ∨ν, their intersection, notated µ∧ν and µ’s complementarity notated cµ, are fuzzy sets in x defined by (µ∨ν)(x) = max{µ(x),ν(x)}, (∀)x ∈ x; (µ∧ν)(x) = min{µ(x),ν(x)}, (∀)x ∈ x; (cµ)(x) = 1 −µ(x), (∀)x ∈ x. for µ,ν fuzzy sets in x, we will say that µ ≤ ν if µ(x) ≤ ν(x), (∀)x ∈ x. we will note by 0 si 1 the constant functions on x, defined by 0(x) = 0, (∀)x ∈ x and, respectively, by 1(x) = 1, (∀)x ∈ x. we agree to further note by f(x) the family of all fuzzy sets in x. we have that (f(x),≤∧,∨, 0, 1) is a distributive lattice and µ →cµ = 1−µ is a fuzzy negation, but is not an ortho negation. the only laws that are not satisfied are the law of non-contradiction and the excluded-middle law because µ∧cµ 6= 0 and µ∨cµ 6= 1. after l.a. zadeh introduced the concept of fuzzy set, many generalizations have rapidly appeared. recently, in the paper [4], 21 variants of fuzzy sets have been listed and the connections among them have been presented. later, in paper [12], it is shown that in the two-dimensional case, several of the lattices of truth values considered here are pairwise isomorphic, and so are the corresponding families of fuzzy sets. further on, we will present only some of these generalizations. j.a. goguen [9] introduces in 1967 the notion of l-fuzzy sets, when the membership functions take values in a partially ordered set l, most often a lattice. l.a. zadeh [27] introduces in 1975 the concept of interval-valued fuzzy set. this is characterized by the membership function µ : x → d([0, 1]), where d([0, 1]) represents the family of all closed subintervals of the unit interval [0, 1]. https://doi.org/10.15837/ijccc.2021.1.4125 6 k.t. atanassov [1] introduces in 1986 the concept of intuitionistic fuzzy sets, where each element is characterized by a membership function µ, as in the case of fuzzy sets, but also by a non-membership function ν such that 0 ≤ µ(x) + ν(x) ≤ 1, (∀)x ∈ x. later, in 1989, k.t. atanassov and g. gargov [2] introduce interval valued intuitionistic fuzzy sets, which are characterized by two functions µ,ν : x →d([0, 1]) such that 0 ≤ sup x∈x µ(x) + sup x∈x ν(x) ≤ 1. as a generalization of the classic sets, fuzzy sets, interval-valued fuzzy sets, intuitionistic fuzzy sets, interval-valued intuiyionistic fuzzy sets, f. smarandache [24] proposes in 1999 the concept of neutrosophic set. a neutrosophic set in x is defined as a = {< x,ta(x),ia(x),fa(x) >: x ∈ x} where ta(x),ia(x),fa(x) are subsets of ]0−, 1+[ and represent the truth-membership function, indeterminacy-membership function and falsity-membership function such that 0− ≤ sup x∈x ta(x) + sup x∈x ia(x) + sup x∈x fa(x) ≤ 3+, where a− = {a− � : � ∈ r∗,� is infinitesimal} b+ = {b + � : � ∈ r∗,� is infinitesimal}. for applications we can consider that ta(x),ia(x),fa(x) are subsets of [0, 1]. pythagorean fuzzy sets were proposed by r.r. yager in 2013 (see [25]). a pythagorean fuzzy set is characterized by the functions µ,ν : x → [0, 1] which give us the degree of membership and degree of non-membership, respectively and 0 ≤ (µ(x))2 + (ν(x))2 ≤ 1, (∀)x ∈ x. the function π : x → [0, 1] defined by π(x) = √ 1 − [(µ(x))2 + (ν(x))2] is called the degree of indeterminacy. in the following table we explain the differences between pytagorean fuzzy sets and intuitionistic fuzzy sets (see [7]): intuitionistic fuzzy sets pythagorean fuzzy sets 0 ≤ µ + ν ≤ 1 0 ≤ µ2 + ν2 ≤ 1 π = 1 − (µ + ν) π = √ 1 − [µ2 + ν2] µ + ν + π = 1 µ2 + ν2 + π2 = 1 we cannot end this section without discussing some aspects regarding the triangular norms (briefly t-norms). these were introduced by a k. menger [18] and developed by b. schweizer and a. sklar [23]. they have become interesting in the fuzzy logic because they maintain the main properties of conjunction and they serve as generalization of it. associated with t-norm is the triangular conorm (t-conorm) which has the main properties of disjunction. our basic reference for t-norms is [13]. definition 23. [23]. a binary operation ∗ : [0, 1] × [0, 1] → [0, 1] is called triangular norm (t-norm) if it satisfies the following condition: 1. a∗ b = b∗a, (∀)a,b ∈ [0, 1]; 2. a∗ 1 = a, (∀)a ∈ [0, 1]; 3. (a∗ b) ∗ c = a∗ (b∗ c), (∀)a,b,c ∈ [0, 1]; 4. if a ≤ c and b ≤ d, with a,b,c,d ∈ [0, 1], then a∗ b ≤ c∗d. definition 24. a triangular conorm s (t-conorm) is a binary operation ◦ : [0, 1] × [0, 1] → [0, 1] such that: https://doi.org/10.15837/ijccc.2021.1.4125 7 1. a◦ b = b◦a, (∀)a,b ∈ [0, 1]; 2. a◦ 0 = a, (∀)a ∈ [0, 1]; 3. (a◦ b) ◦ c = a◦ (b◦ c), (∀)a,b,c ∈ [0, 1]; 4. if a ≤ c and b ≤ d, with a,b,c,d ∈ [0, 1], then a∗ b ≤ c∗d. proposition 25. ◦ is a t-conorm if and only if there exists a t-norm ∗ such that x◦y = 1 − ((1 −x) ∗ (1 −y)), (∀)x,y ∈ [0, 1]. ∗ is called the dual of t-conorm ◦ and ◦ is the dual of t-norm ∗. the most important pairs of t-norm and corresponding t-conorms are: 1. the minimum t-norm a∧ b = min{a,b} and the maximum t-conorm a∨ b = max{a,b}; 2. a · b the usual multiplication in [0, 1] and the probabilistic sum a◦π b = a + b−ab; 3. the lukasiewicz t-norm a ∗l b = max{a + b − 1, 0} and the lukasiewicz t-conorm a ◦l b = min{a + b, 1}; 4. the drastic product a∗d b =   a if b = 1 b if a = 1 0 otherwise and the drastic sum a◦d b =   a if b = 0 b if a = 0 0 otherwise . we note that a∗d b ≤ a∗l b ≤ a · b ≤ a∧ b, (∀)a,b ∈ [0, 1] and for each t-norm ∗ we have that a∗d b ≤ a∗ b ≤ a∧ b, (∀)a,b ∈ [0, 1]. on the other hand, a∨ b ≤ a◦π b ≤ a◦l b ≤ a◦d b, (∀)a,b ∈ [0, 1] and for each t-conorm ◦ we have: a∨ b ≤ a◦ b ≤ a◦d b, (∀)a,b ∈ [0, 1]. finally, if ∗ is a t-norm, ◦ is a t-conorm and µ,ν are fuzzy sets in x, then the intersection of µ and ν is the fuzzy set µ∩∗ ν defined by (µ∩∗ ν)(x) = µ(x) ∗ν(x). the union of µ and ν is the fuzzy set µ∪◦ ν defined by (µ∪◦ ν)(x) = µ(x) ◦ν(x). 5 from quantum mechanics to quantum logic although quantum mechanics was discovered since 1925 by w. heisenberg, m. born and p. jordan and formulated mathematically as "matrix mechanics", and later, in 1927, e. schrödinger developed another mathematic technique known as "wave mechanics", john von neumann in two years (19271929) developed the mathematical frame of the theory. he realized that the natural context is the hilbert space theory and the operators among them. in quantum mechanics each physical system is associated a separable hilbert space h and the states of the physical system are described by unitary vectors x ∈ h. a physical quantity that can https://doi.org/10.15837/ijccc.2021.1.4125 8 be measured is called observable. these will be represented by self-adjoint operators, not necessarily bounded, on the hilbert space h, which in general do not commute. from here a thorough investigation of linear operators on hilbert spaces has been conducted by john von neumann, reaching its highlight in 1929 with the spectral theorem for unbounded self-adjoint operators. to the observable a, which is a self-adjoint operator, the spectral measure is associated pa : bor(r) →a(h). if a is from b(h) (bounded operator) then ax = (∫ r tdpa(t) ) x, (∀)x ∈ h. if a is from c(h) (closed operator) then ax = lim k→∞ (∫ k −k tdpa(t) ) x, (∀)x ∈ d(a), where d(a) = { x ∈ h : ∫ r t2d < pa(t)x,x >< ∞ } . this spectral measure induces a probability measure µx,a : bor(r) → [0, 1] defined as µx,a(s) =< pa(s)x,x >, which john von neumann regarded as the observable represented by a to take values in the set s when the state of the system is represented by the vector x. in many situations, the physical quantity which will be measured can have only particular values. this happens, for example, in the case of energy. in this case, we will have σ(a) = {λk}∞k=1, where λk are eigenvalues of a. let pk = pa({λk}). then for s ∈ bor(r) we have µx,a(s) =< pa(s)x,x >=< ∑ λk∈s pa({λk})x,x >=< ∑ λk∈s pkx,x >= ∑ λk∈s < pkx,x >= = ∑ λk∈s < p 2kx,x >= ∑ λk∈s < pkx,p ∗ kx >= ∑ λk∈s < pkx,pkx >= ∑ λk∈s ‖pkx‖2 . in particular, the probability of the observable a to take the value λk is µx,a({λk}) = ‖pkx‖2. if the observable a has an arbitrary spectrum, then we will not be able to talk about the probability of a value. in this situation, we will talk about the probability of obtaining values in the interval [a,b], probability that will be µx,a([a,b]). in the axiomatization of quantum mechanics, the next definition is essential. definition 26. we suppose that the physical system is in state x and let a an observable having the spectral measure pa. then for any s ∈ bor(r), after performing a measurement on the observable a, which gave the value λ ∈ s, the state of the physical system will change and will be given by a new state vector y = pa(s)x ‖pa(s)x‖ . corollary 27. µy,a(l) = µx,a(s ∩l)/µx,a(s), (∀)l ∈ bor(r). let’s suppose now that the physical system is in state x and consider two observables a and b with the spectral measures pa and pb. suppose a measurement was performed on the observable a, which gave the value λ ∈ s, s ∈ bor(r). according to the previous definition, the state of the physical system becomes y = pa(s)x ‖pa(s)x‖ . if we perform now a measurement on the observable b, which gives the value v ∈ l, l ∈ bor(r), the state of the physical system becomes z = pb(l)y ‖pb(l)y‖ = pb(l)pa(s)x ‖pb(l)pa(s)x‖ . https://doi.org/10.15837/ijccc.2021.1.4125 9 definition 28. the observables a and b are called simultaneous or compatible if the state of the system after performing a measurement on the observable a, followed by a measurement on the observable b, coincides with the state of the system obtained by first performing a measurement on the observable b, followed by a measurement on the observable a. remark 29. the observables a and b are simultaneous if and only if pb(l)pa(s)x ‖pb(l)pa(s)x‖ = pa(s)pb(l)x ‖pa(s)pb(l)x‖ for any state x and any s,l ∈ bor(r). proposition 30. the observables a and b are simultaneous if and only if the self-adjoint operators a and b commute. we suppose now that the operators a and b don’t commute. we make the following notations: m(a,x) = ∫ r λd < pa(λ)x,x >=< ax,x > v(a,x) = ∫ r (λ−m(a,x))2d < pa(λ)x,x >= ‖ax‖2− < ax,x >, where x ∈ d(a). for the arbitrary a and b we introduce their commutator [a,b] := ab − ba defined on d([a,b]) = d(ab) ∩d(ba). theorem 31. (heisenberg’s inequality). for any x ∈ d([a,b]) we have v(a,x) ·v(b,x) > | < [a,b]x,x > |2 4 . the results obtained by john von neumann were presented by him in an extent form in the book "mathematical foundation of quantum mechanics", published in 1932 and which rapidly became the book that has been, since then, considered the basis in mathematical foundation of the quantum mechanics. in the third chapter of this book john von neumann proposes for the first time the quantum logic: "the relation between the properties of physical system on the one hand, and the projections on the other hand, makes possible a sort of logical calculus with these". in fact, the closed linear subspaces of a hilbert space h are in one-to-one correspondence with the projection over them. john von neumann, preoccupied by the logic and the algebraic structure of the quantum mechanics, starts cooperation with g. birkhoff with whom, in 1936, publishes "the logic of quantum mechanics", paper that can be considered the official birth of quantum logic. the family of all closed linear subspaces of a hilbert space h, denoted lat(h) and called hilbert lattice associated to h, was g. birkhoff’s and j. von neumann’s proposal for the algebraic structures that organizes the sentences. this is a different structure from that of classic logic where the sentences were organized in the power set p(x) which form, as we have seen, a boolean algebra which satisfies the distributive law. lat(h) is a lattice in which the inclusion relation is the usual inclusion between subspaces, the operation ∧ corresponds to the intersection of subspaces, but the operation ∨ is the smallest closed subspace that contains the union of the two subspaces. lat(h) is a bounded lattice, where 1 := h and 0 = ∅. we remind the fact that two elements x,y ∈ h are called orthogonal and we note x ⊥ y if < x,y >= 0. for m ∈ lat(h) we will define m⊥ = {x ∈ h : x ⊥ y, (∀)y ∈ m}. it is easy to check that the application m 7→ m⊥ is an othocomplementation on the lattice lat(h). moreover, k. husini [11] was the first to show, in 1937, that lat(h) satisfies the orthomodular law: s ⊂ m ⇒ s ∨ (s⊥ ∧m) = m, (∀)s,m ∈ lat(h). thus lat(h) is an orhomodular lattice. definition 32. if l is an orthomodular lattice, we say that a,b ∈ l are orhogonal and we denote a ⊥ b if a ⊂ b⊥. https://doi.org/10.15837/ijccc.2021.1.4125 10 definition 33. an orhomodular lattice l is called σ-complete if the join of every countable pairwise orthogonal sequence exists in l, namely if ai ≤ a⊥j for i 6= j, then the join ∨ i ai exists in l. definition 34. an othomodular lattice which is σ-complete is called quantum logic. proposition 35. lat(h) is a quantum logic. definition 36. a probability measure on a quantum logic is a mapping s : l → [0, 1] such that: 1. s(1) = 1; 2. s (∨ i ai ) = ∑ i s(ai), for any sequence of pairwise orthogonal elements of l. from another point of view, as we have already mentioned, the family of all closed linear subspaces of a hilbert space h is in one-to-one correspondence with the family of all projections over them. more precisely, the projectors can be considered as the quantum analogue of the characteristic functions. given a projection pk : h → k, we can ask ourselves if the system is in k. this question can have several answers, not just "yes" or "no". if the answer is "yes" then we will have < pkx,x >= 1, because ‖x‖ = 1. if the answer is "no" then we will have x ∈ k⊥, so < pkx,x >= 0. but it is possible that x is of form x = x1 + x2, where x1 ∈ k⊥ and x2 ∈ k. then < pkx,x >=< x2,x >=< x2,x2 >= ‖x2‖2 ∈ [0, 1] . in conclusion, the number < pkx,x > is the probability of the state x to be in k. 6 some connections between fuzzy logic and quantum logic 6.1 quantum logic view as an example of fuzzy logic in paper [17] the author views quantum logic as an example of fuzzy logic. in order to justify this affirmation we consider a hilbert space h. the family of all fuzzy sets in h, denoted f(h) is a lattice. each closed linear subspace in h corresponds, as we have seen, to an elementary sentence in quantum logic. on the other hand, to each closed linear subspace m of h it corresponds an orthogonal projection pm onto m. the set of the orthogonal projections is a lattice equipped with the relation or partial order p ≤ r if < px,x >≤< rx,x >, (∀)x ∈ h. operations p ∧r, p ∨r, p⊥ = 1−p are conjunction, disjunction and negation in quantum logic. to each closed linear subspace m ⊆ h we can associate a fuzzy set µm : h → [0, 1],µm (x) =< pmx,x >. the fuzzy sets {µm,m ⊆ h} form a fuzzy logic. 6.2 ma̧czyński’s representation theorem for quantum logic although at a first glance, the quantum structure, which is an abstract algebraic structure, seems to have nothing in common with fuzzy logic, m. ma̧czyński [15], [16] proved a theorem of representation of any quantum logic as a family of [0, 1]-valued functions, which can be regarded as the membership functions of some fuzzy sets (see also [22]). theorem 37. [16] if s is a non-empty set and l a family of applications from s into [0, 1] such that: 1. 0 ∈ l (0 is the null function), 2. a ∈ l ⇒ 1 −a ∈ l, 3. (∀){ai}i∈i ⊂ l (i finite or countable): ai + aj ≤ 1, for i 6= j ⇒ ∑ i∈i ai ∈ l, then l is a quantum logic with respect to the natural partial order of real function, with orthocomplementation a⊥ = 1 − a. each point u ∈ s induces a probability measure mu on (l,≤,⊥), where mu(a) = a(u), (∀)a ∈ l and the family of measures {mu : u ∈ s} is ordering. conversely, if (l,≤,⊥) in a quantum logic and s is an ordering set of probability measures, then any a ∈ l induces a function ã : s → [0, 1], ã(m) = m(a), (∀)m ∈ s. the set of such functions has properties (1)-(3) and (l̃,≤,⊥) is isomorphic with (l,≤,⊥). https://doi.org/10.15837/ijccc.2021.1.4125 11 we also note that condition (1) means that ∅∈ l, while (2) shows that, if a fuzzy set is in l then its standard fuzzy complement also belongs to l. only condition (3) cannot be expressed in terms of fuzzy sets. nevertheless, a solution was found in the paper [22]. 6.3 fuzzy sets models of quantum logics starting with late ’80s of the previous century, the noticed similarities between the operations with fuzzy sets and the operations of quantum logic have led to the desire of creating fuzzy models for quantum logic (see [20]). it was clear from the very beginning that the othocomplementation must be modelled through standard fuzzy set complementation. the problems appear to relations µ∧µ⊥ = ∅, µ∨µ⊥ = x, which are not satisfied by fuzzy sets but which take place in birkhoff-von neumann quantum logic. what if we replace operations ∨ and ∧ introduced by zadeh with those of lukasiewics? on the other hand, the fact that the operations ∨ and ∧ introduced by zadeh are distributive means that they cannot be used in the construction of fuzzy models for quantum logic. the problem was solved by j. pykacz in paper[21]. theorem 38. [21] any quantum logic l with an ordering set of probability measures s can be isomorphically represented in the form l(s) of fuzzy subsets of s satisfying the following conditions: 1. l(s) contains the empty set ∅; 2. l(s) is closed with respect to standard fuzzy set complementation; 3. l(s) is closed with respect to countable lukasiewicz unions of pairwise weakly disjoint sets, i.e. if ai uaj = ∅ for i 6= j, then ⊔ i ai ∈l(s); 4. (∀)a ∈l(s) : aua = ∅⇒ a = ∅. conversely, any family l(u) of fuzzy subsets of an arbitrary universe u satisfying conditions (1)-(4) is a quantum logic partially ordered by the inclusion of fuzzy sets, with the fuzzy set complementation µ′ = 1−µ as orthocomplementation, the orthogonality of the elements coinciding with their weak disjointness, and an ordering set of probability measures generated by points of the universe u according to the formula sx(a) = µa(x), (∀)x ∈ u. l(u) will be called fuzzy quantum logic. 6.4 effect algebras and fuzzy sets more recently, in 1994, in the study of the algebraic foundations of the quantum logic, some more general algebraic structures called effect algebras have been introduced by d. foulis and m. bennet [8]. definition 39. [8] an effect algebra is a system (e,⊕, 0, 1) consisting on a set e which contains two distinct elements 0, 1 called zero and unit and a binary operation partially defined ⊕ such that the following axioms are satisfied: 1. if a⊕ b is defined, then b⊕a is defined and a⊕ b = b⊕a; 2. if b⊕ c is defined and a⊕ (b⊕ c) is defined, then a⊕ b and (a⊕ b) ⊕ c are defined and a⊕ (b⊕ c) = (a⊕ b) ⊕ c; 3. (∀)x ∈ e, (∃)!x′ ∈ e such that x⊕x′ is defined and x⊕x′ = 1; 4. if 1 ⊕a is defined, then a = 0. https://doi.org/10.15837/ijccc.2021.1.4125 12 remark 40. the way of defining the concept of effect algebra allows us to easily notice that effect algebras are generalizations of other algebraic structures. thus, if condition (4) is replaced by a more powerful condition (4′)if a⊕a is defined, then a = 0 we obtain an ortho-algebra. if to conditions (1)-(4) we add the condition (5) if a⊕ b,b⊕ c,a⊕ c are defined, then (a⊕ b) ⊕ c is defined we obtain an orthomodular poset. if, we further add (6)(∀)x,y ∈ e, (∃)a,b,c ∈ e such that b⊕ c,a⊕ c are defined and x = a⊕ c,y = b⊕ c then e becomes an boolean algebra. we also specify that the operation ⊕ allows us to introduce a relation of partial order: a ≤ b iff (∃)c ∈ e : a⊕ c is defined and a⊕ c = b . in 1996, a. dvurečenskij [6] showed that a system of fuzzy sets l ⊂ [0, 1]x satisfying the following conditions: d1 x ∈ l, d2 a ∈ l ⇒ 1x −a ∈ l, d3 if a + b ≤ 1x, then a + b ∈ l, can be organized into an effect algebra (l,⊕,∅,x) if we assume that a⊕b is defined if a⊕b ≤ 1x and we will set a⊕b = a + b. furthermore, a. dvurečenskij showed that if we add the condition: d4 a + a ≤ 1x ⇒ a = ∅ then l becomes an orthoalgebra. we also note that in order that l becomes an orthomodular poset it must satisfy (d1), (d2) and d5 if a + b ≤ 1x,b + c ≤ 1x,a + c ≤ 1x ⇒ a + b + c ∈ l . the results obtained by a. dvurečenskij were generalized by j. pykacz [22]. 7 conclusions in this paper, our aim is to briefly present an evolution of logics from ancient times until present days and to highlight the main connections among classical logic, fuzzy logic and quantum logic. of course, such a review is limited and it couldn’t be otherwise. many other research directions have been, unfortunately, left aside. what we intend is for this approach to be continued and to highlight several other connections between fuzzy logic and quantum logic. conflict of interest the author declares no conflict of interest. https://doi.org/10.15837/ijccc.2021.1.4125 13 references [1] atanassov, k.t. 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(2021). from classical logic to fuzzy logic and quantum logic: a general view, international journal of computers communications & control, 16(1), 4125, 2021. https://doi.org/10.15837/ijccc.2021.1.4125 aristotelian logic the lattices of classical logic orthomodular lattices fuzzy logic from quantum mechanics to quantum logic some connections between fuzzy logic and quantum logic quantum logic view as an example of fuzzy logic maczynski's representation theorem for quantum logic fuzzy sets models of quantum logics effect algebras and fuzzy sets conclusions int j comput commun, issn 1841-9836 8(4):525-537, august, 2013. parkinson’s disease prediction based on multistate markov models o. geman, h. costin oana geman faculty of electrical engineering and computer science, stefan cel mare university of suceava development and human health department 13, university st., 720229 suceava, romania e-mail: geman@eed.usv.ro hariton costin 1. faculty of medical bioengineering, grigore t. popa university of medicine and pharmacy, iasi, romania 2.institute of computer science of romanian academy iasi branch, romania e-mail: hncostin@mail.umfiasi.ro abstract: in the real medical world, there are many symptoms or chronic diseases that cannot be characterized in a deterministic way, and which must be examined in a random way. in the study of these stochastic processes, markov chains are used. there is a wide variety of phenomena that suggest a behavior in a markov process manner such as: the probability that a patient’s health to improve, to get worse, to remain stable or to progress to death within a certain time slot, depending on what happened in the previous time window. our goal is to show that the markov chains can be applied to the patients with parkinson’s disease in order to predict the evolution of the disease over time. so the doctor may decide a therapeutic solution that is adapted to the patient’s needs, and that can improve the quality of the patient’s life with parkinson’s disease in terminal stage. keywords: parkinson’s disease, markov chains, multistate markov models, prediction 1 introduction parkinson’s disease (pd) is a neurodegenerative disease that occurs due to loss of dopamine that is a neurotransmitter and due to slow and inexorable destruction of neurons. brain area affected by progressive destruction of neurons is responsible for movements controlling [1]. for this reason, patients with parkinson’s disease have rigid and uncontrollable gestures, postural instability, tremor, and speech disorders. although parkinson’s disease is considered specific old age, the average age is 50 years and can be confused with the normal aging process of the individual [2]. when first symptoms are manifested, it is believed that between 60% and 80% of the cells for the control of motor activity are destroyed [3]. parkinson’s disease is a progressive disease, with signs and symptoms accumulated over time. although this is potentially an invalidity disease, it progresses slowly so that most patients benefit from many years of active life after diagnosis. moreover, unlike other serious neurological disorders, parkinson’s disease is treatable. treatment is surgical or based on drugs, but may also consist of an implanted device for brain stimulation [4]. worldwide, the disease is diagnosed in 300,000 people each year [5]. disease incidence and prevalence increase with age. parkinson’s disease affects 1% of people aged over 65. rarely, the disease occurs in childhood or adolescence. the incidence is 1.5 times higher among males than among women [6]. if parkinson’s disease would be detected in an early stage, the physician may interfere with a proper treatment in order to slow the disease’s progression. unfortunately, currently there is no screening test or biomarker that copyright c⃝ 2006-2013 by ccc publications 526 o. geman, h. costin can be highlighted in parkinson’s disease. the three cardinal signs of parkinson’s disease are resting tremor, rigidity and bradykinesia. among them, two are essential for diagnosis. postural instability is the fourth cardinal sign, but occurs late, usually after 8 years of disease evolution. in 70% of cases, uncontrollable rhythmic gestures of the hands, head and feet are the first symptoms and occur mainly at rest and during the stress’ periods (see [7][9]). tremor is diminished during movements, disappears during sleep, and is exacerbated by stress and fatigue. tremor becomes less evident as disease progression. this tremor, in the absence of other characteristic signs, indicates an early stage of disease or another diagnosis (see table 1) [10][14]. table 1: neurological disorders characteristic signs moment speed location neurological disorders rest tremor 4-6 hz arms, legs parkinson’s disease postural tremor 7-12 hz hands essential tremor intention tremor 2-5 hz arms,legs cerebellar lesions from the many symptoms or diseases that cannot be characterized in a deterministic way, but in a random way, pd is a prominent example. in our study, we used markov chains as they characterize very well stochastic processes like diseases evolutions. so, our goal is to show that the markov chains can be applied to the patients with pd in order both to predict the evolution of the disease over time, and illustrate the response to the specific treatment. in this way the doctor may decide upon a therapeutic solution that is adapted to the patient’s needs, and can improve the quality of the patient’s life in terminal stage. 2 multistate markov models in mathematics, a markov process is a stochastic process having the property that, given its present state, the future states are independent of the past. this property is called the markov property [15][19]. in a markov process, the system can change or keep its state, according to a certain probability distribution. changes of its state are called transitions. a random experiment that consists of a series of random sub-experiments is called a stochastic process. such a special class of these processes is made by the markov chains [20][26]. the evolution of a markov process can be described by a transition matrix. we can consider the evolution of the health status of a patient as a markov process that passes through the following states: well, suspicious, ill (pd), or dead, as is illustrated in figure 1. for the markov process illustrated in figure 1, we can write the general matrix (1), where m = 4 (possible mutually exclusive results: e1 well, e2 suspicious, e3 ill/pd, e4 dead). p =   pww ... 0... ... ... 0 ... pdd   (1) as it can be seen, the transition matrix consists of pij elements, which represent the conditional probability that the system will change from the initial state (well) to next state j. the probability that the system remains in the same state after the experiment is given by pij with i = j, and the probability for the system to move from one state to another is given by pij with i̸=j. the transition matrix for the proposed system is a square matrix of order m = 4. the elements of the transition matrix must satisfy the following properties [19]: parkinson’s disease prediction based on multistate markov models 527 figure 1: four-state markov model for parkinson’s disease stage (well, suspicious, ill/pd, dead) 1. 0 6 pij 6 1, i, j = 1, ..., m. 2. ∑m j=1 pij = 1, i = 1, 2, ..., m. the sum of the elements of each line must be 1 because e1, ..., em is a complete system of events. 3. pdd = 1, for our application. information about the transitions from one state to another in a markov chain can be represented by a transition matrix. it consists of elements pij probability of crossing a step from state i to state j (i,j = 1,..., m, where m=4). we can talk about the transition probability of exactly k steps and a matrix formed by them. so, multistate markov models in continuous time may be used to model the course of parkinson’s diseases. since markov chains are stochastic processes, we cannot know exactly what it is happening on each state, so the system must be described in terms of probability. definition 1. [19]: consider a markov chain with m states. a state vector for markov chain is a probability vector x = [x1, x2, ..., xm]. the xi coordinates of the state vector x should be interpreted as the probability that the system be in the state i. the behavior of a markov chain can be described by a sequence of state vectors. the initial state of the system can be described by a state vector noted x0. after a transition, the system can be described by a vector x1 and after k transitions the system is described by the state vector xk. the relationship between these vectors can be summarized by the following theorem [19]: consider a markov process with the transition matrix p. if xk and xk+1 are vectors that describe a process state after k and k +1 transitions respectively, then xk+1 = xk ∗ p . we represent structural elements as a vector s = [s1t , ..., s i t, ..., s m t ], that for each t = 1,...,n and for each i = 1,...,m, sit varies between 0 and 1, and the sum of structural elements is 1 for any t. in order to model a markov process, we must respect the following steps [19]: 1. first-order differences of the vector st will be calculated, thus ∆st/t−1 = st − st−1. 2. for each pair t/t − 1 of consecutive periods of time we will buid the partial transition matrices (mtp), as mtpt/t−1(m∗m) form. the elements of the mtpt/t−1(m∗m) matrix can be determined as follows: mtp ij t/t−1 = min(s i t−1, s j t)ifi = j (2) mtp ij t/t−1 = ∣∣∣∣∣∆sjt/t−1 ∗ ∆s j t/t−1∑m i=2(+∆s ij t/t−1) ∣∣∣∣∣ , (3) 528 o. geman, h. costin if i ̸= j and ∆si t/t−1 < 0 and ∆s j t/t−1 > 0. mtp ij t/t−1 = 0, (4) for the other elements, where i,j =1,...,m. in formula (3) the expression ∑m i=2(+∆s ij t/t−1) denotes the sum of positive values of the difference vector ∆st/t−1. 3. mtp(m ∗ m), total transition matrix is determined by summing the elements of partial transition matrixes. 4. mp(m ∗ m), transition probability matrix is calculated by ratio between each element of the total transition matrix and the sum of the line on which is located than item. 5. in the final stage of the algorithm, we obtain a forecast of the structural elements for future p periods by multiplying transposed of the matrix mp(m ∗ m) raised to the k power with the vector of structural elements for the last period. 3 intelligent system for health status prediction using a markov chain the architecture of the proposed system is shown in figure 2. it consists from three modules. the first module will handle with the signal acquisition from patients suspected of parkinson disease. in terms of software, this module is a software application that can acquire biomedical signals from wiitm remote device or other devices that can acquire signals generated by tremor. all data acquired from these devices are analyzed using the method presented in section 2. furthermore, the data are saved on a server. on this server, physicians can access data in order to establish a long history of patient evolution. the second module of this system is represented by the extracting knowledge from biomedical signals acquired from the patients. this module consists of a software application that runs on the server where there are kept biomedical signals acquired. the third module is the application that is executed in the doctor’s office. this application performs an interfacing of the doctor with the intelligent system, and presents the medical treatment and rehabilitation options. it must be said that bio-signals can be acquired in the doctor’s office but also at home if the patient has a pc and an internet connection. the design and development of this intelligent system used the newest technologies for distributed application development (wcf, soap), and the observations received from patients and specialists. 3.1 database for the database we used the proposed methodology in previous papers [27], [28]. database with affected patients has been provided by suceava emergency hospital (neurology clinic). this dataset is composed of a range of biomedical tremor measurements from 88 people, 28 with parkinson’s disease (pd), 30 "normal" tremor and 30 "suspicious" pd (undiagnosed). each column in the table is a particular tremor measure, and each row corresponds one of 2500 tremor recordings from these individuals ("name" column). the main aim of the data is to discriminate healthy people from those with pd, according to "status" column which is set to 0 for healthy and 1 for pd or "suspicious". all patients are suffering of moderate to severe postural tremor. this postural tremor cannot be differentiated on clinical features (frequency, parkinson’s disease prediction based on multistate markov models 529 figure 2: intelligent system for health status prediction of a patient using a markov chain table 2: data: size, age, gender, and disease duration distribution of pd, spd, and nt subjects pd spd nt number of patients 28 30 30 mean age 64.54 63.24 64.52 (range in years) (40-90) (27-94) (24-86) gender (male/female) 18/10 16/8 19/11 mean disease duration 16,4 5,3 amplitude). patients were kept under observation and investigation for 2 years, and data were acquired at 6 months, 1 year and 2 years (see table 2). the mean disease duration (time for disease to install, in years), age and sex of pd patients were compared with the spd or nt in table 2. notice in table 2 that the mean age of pd, spd and nt populations is similar, but the age ranges are different. this could be considered as an indicator that the pd starts years before actual diagnosis. 3.2 tremor recoding yet, some researches have been made (including in romania) in order to early diagnose the pd and its progress by means of the tremor or the gait analysis or other symptoms [29][34]. the tremor time series were acquired using an accelerometer sensor from a wiitm console [35], connected via bluetooth to a pc. the data were analyzed using an application implemented in visual c 2010 professional. the wiitm remote is the primary controller for nintendo’s wiitm i console. a main feature of the wiitm remote is its motion sensing capability, which allows the user to interact with and manipulate items on screen through the use of accelerometer and optical sensor technology [35]. nintendo works on three axes: x lateral, y anteroposterior, and z vertical. the device records both acceleration induced by hand movement and the component of gravitational force. if the controller is rotated, the gravity accelerometer affects the values on the x, y, and z axes (see figure 3). this system using a wiitm remote is capable of analyzing frequency and estimated amplitude of tremor between 3 15 hz (n tremor is between 5 12 hz, and pd tremor is between 4-6 hz). the wiitm i remote and pc are connected by bluetooth human interface device profile. the tremor analysis program was developed using visual c 2010 professional. the acceleration sampling period was set at 10 ms in the nintendo device. because the transmission rate through 530 o. geman, h. costin figure 3: interactive gui using wiitm remote (tremor application) the bluetooth device is limited, the sampling period of the tremor analysis was 40 ms. the accelerometer built into wiitm remote (nintendo) measures gravitational and non-gravitational acceleration. the results of this paper suggest that nintendo is useful for measurement and analysis of tremor using the methodologies described in [28], [29], [31]. we defined the following linguistic variables (for instance for x axis): • if x is between -0.10 mm and -1 mm then x is minimum xmin; • if x is between -0.10 mm and 0.10 mm then x is medium xmed; • if x is between 0.10 mm and 1 mm then x is maximum xmax. we counted the number of spikes for each interval, and we used these values to describe the state vector. next we proposed to predict the state of a patient using markov chains. in this analysis the state vector is defined as: s = xmin, xmed, xhigh, ymin, ymed, yhigh, zmin, zmed, zhigh (5) . table 3 presents the number of spikes in each category for "normal" subjects, while table 4 presents the values of the state vector for a subject with diagnosed parkinson’s disease. for this paper we chose to exemplify the calculation of transition matrices from t0 to t1 and from t1 to t2 only for patients with parkinson’s disease, by following the methodology presented in section 2 (here notations ti were used instead of ti and we illustrated the method only for pd patients and normal patients). in the first step we computed, according to the methodology, the deviations ∆st1/t0 = st1 − st0 and ∆st2/t1 = st2 − st1. we illustrate this in tables 5 and 6 only with data acquired from a patient with pd. we computed next the transition matrices from t0 to t1 and from t1 to t2 (tables 7 and 8, respectively). for example, the transition matrix from t0 to t1, mtpt1/t0(m∗m) is computed as follows: 1. the elements from the main diagonal are (sit1, s i t0); parkinson’s disease prediction based on multistate markov models 531 table 3: "normal" subject vector, spikes number at t0, t1 and t2 for 60 seconds each record features vector t0 t1=6 months after t0 t2=12 months after t0 total spikes xmin 284 257 286 827 xmed 1524 1458 1511 4511 xmax 651 687 558 1896 ymin 1289 1439 1435 4163 ymed 1283 1247 1257 3787 ymax 664 657 557 1878 zmin 392 382 378 1152 zmed 768 865 789 2422 zmax 2031 1998 1875 5904 table 4: data: size, age, gender, and disease duration distribution of pd, spd, and nt subjects features vector t0 t1=6 months after t0 t2=12 months after t0 total spikes xmin 382 358 379 1119 xmed 785 758 688 2231 xmax 897 857 912 2666 ymin 578 547 524 1649 ymed 457 479 487 1423 ymax 354 349 357 1060 zmin 257 282 253 792 zmed 578 549 754 1881 zmax 1300 1329 1348 3977 table 5: the deviations ∆st1/t0 = st1 − st0 (pd patient) t1 vs. t0, for the state vector s time xmin xmed xmax ymin ymed ymax zmin zmed zmax sum t1 358 758 857 547 479 349 282 549 1329 t0 382 785 897 578 457 354 257 578 1300 deviation -24 -27 -40 -31 22 -5 25 -29 29 deviation+ 22 25 549 29 76 table 6: the deviations ∆st1/t0 = st1 − st0 (pd patient) t2 vs. t1, for the state vector s time xmin xmed xmax ymin ymed ymax zmin zmed zmax sum t2 379 688 912 524 487 357 253 754 1348 t1 358 758 857 547 479 349 282 549 1329 deviation 21 -70 55 -23 8 8 -29 205 19 deviation+ 21 55 8 8 205 19 316 532 o. geman, h. costin 2. if i ̸= j, ∆si t1/to < 0 and ∆sj t1/to > 0, so the matrix equals the absolute value of ∆si t1/t0 ∗ ∆s j t 1/t 0∑ ∆s ij t 1/t 0 >0 ; 3. the rest of elements equals 0. table 7: the transition matrix from t0 to t1 features vector xmin xmed xmax ymin ymed ymax zmin zmed zmax xmin 358 0 0 0 0 0 0 0 0 xmed 2.548 758 1.625 10.244 0 0 4.345 0 0 xmax 0 0 857 0 0 0 0 0 0 ymin 0 0 4.548 547 0 0 0 0 0 ymed 7.413 0 1.021 12.547 479 0 0 12.457 0 ymax 1.124 0 1.245 6.333 0 349 0 1.125 0 zmin 1.354 0 0 6.687 0 0 257 2.548 0 zmed 0 0 0 0 0 0 0 549 0 zmax 4.211 0 0 24.442 0 0 0 8.457 1.300 table 8: the transition matrix from t1 to t2 features vector xmin xmed xmax ymin ymed ymax zmin zmed zmax xmin 358 0 2.387 0 0 0 0 0 0 xmed 0 688 0 6.257 0 0 4.345 6.211 0 xmax 1.250 0 857 0 4.587 0 0 0 5.244 ymin 0 0 1.287 524 0 0 0 0 0 ymed 0 5.687 1.021 8.985 479 0 0 6.258 0 ymax 1.124 0 1.245 0 0 349 0 1.125 0 zmin 1.354 0 0 6.154 5.698 2.542 257 2.548 0 zmed 0 0 0 0 0 0 0 549 2.241 zmax 4.211 0 0 3.587 2.587 2.325 0 9.237 1.329 in the third step we calculated the total transition matrix (table 9), which is the sum of partial transition matrices computed in the previous stage. in the fourth stage we computed the probability transition matrix by the ratio of each element of the total transition matrix to the sum of the line where the element is located. in the final stage of the algorithm we obtained the forecast of the structural elements for next year by multiplying the transposed matrix of transition probabilities with the vector of the structural elements for t2, i.e. the vector corresponding to t2 = 12 months. we get the following transition probabilities between the 9 elements of the features vector xmin, ..., zmax. the values of the main diagonal are the probabilities that the patient progress to state that is described by the features vector (which corresponds to a stage of the disease). the forecast of the xmin...zmax for the next year is obtained by multiplying the two matrices (transposed and parkinson’s disease prediction based on multistate markov models 533 table 9: the total transition matrix (in %). features vector xmin xmed xmax ymin ymed ymax zmin zmed zmax total % xmin 100 0 2.387 0 0 0 0 0 0 100 xmed 9.59 90 0.0005 0.0051 0 0 0 0.0015 0 100 xmax 8.54 0 91.46 0 0 0 0 0 0 100 ymin 1.07 0 0 98.93 0 0 0 0 0 100 ymed 12.91 0 0.17 1.69 84.71 0 0 0.5 0 100 ymax 7.96 0 0.0004 0.47 0 91.37 0 0.14 0 100 zmin 8.94 0 0.0009 0.90 0 0 89.79 0.26 0 100 zmed 8.1 0 0 0 0 0 0 91.9 0 100 zmax 15.19 0 0.0008 0.85 0 0 0 0.25 83.6 100 elements for t2). thus we obtain the patient’s evolution for next year, for "normal" and "pd" (table 10 and table 11). table 10: the "normal" subject’s evolution for the next year (no. of spikes) features t0 t1=6 months t2=12 months t3=24 months sfter t0 t4=24 months after vector after t0 after t0 (with markov chain) t0(recorded) xmin 244 257 286 295 299 xmed 1442 1458 1511 1657 1656 xmax 651 687 688 689 694 ymin 1412 1439 1442 1420 1421 ymed 1233 1247 1257 1243 1240 ymax 614 627 665 688 686 zmin 392 399 410 412 414 zmed 768 788 789 786 785 zmax 1992 1998 1999 1995 1994 from the last two tables one can see, by using markov chains, the tremor symptom evolution of certain patients. also we may note the very good prediction power of this method, as the features vector elements for the predicted tremor signal after 24 months from the first recording are very similar with the same vector elements, but acquired and measured by means of wiitm remote and the appropriate software. the maximum error between prediction and measured values was 1.33%. similar judgement was used and corresponding good results concerning the prediction of disease evolution were obtained in the case of "suspicious pd" patients, for whom some early signs were found (insomnia, constipation, loss of smell, equilibrium and postural impairment, tremor symptom or speech difficulties) and they became to be attentively monitored. also, another remark may be made related to the similarity between features vectors measured for "suspicious pd" patients and "diagnosed pd" patients, when using the same markov chains for status prediction. 534 o. geman, h. costin table 11: the pd patient’s evolution for the next year (no. of spikes) features t0 t1=6 months t2=12 months t3=24 months sfter t0 t4=24 months after vector (with markov chain) t0(recorded) xmin 382 385 396 398 399 xmed 785 792 784 796 795 xmax 837 857 912 944 946 ymin 518 537 544 586 585 ymed 457 459 467 489 488 ymax 354 359 373 382 380 zmin 257 282 278 310 308 zmed 528 549 558 568 566 zmax 1300 1329 1348 1399 1398 4 conclusions in this paper we describe a general purpose model of pd prognosis based on markov process and show how this simple mathematical tool may be used to generate detailed and accurate assessments of parkinson’s disease stage and therefore may be applicable in medical screening for pd. markov models consider a patient to be in one of a finite number of discrete states of health. all clinically important events are modeled as transitions from one state to another. thus, the use of markov models has the potential to allow the development of decision models that more faithfully represent clinical problems. our study used a database where there are subjects who are considered normal, but with some tremor symptoms, and subjects considered "suspects", for whom we can apply the above methodology and can see if certain subjects move to the "normal" state or the first symptoms of parkinson’s disease will appear. thus, medical staff can intervene with specific medication for parkinson’s disease. using markov chain is an efficient way to find the features vector for an individual patient at a given time, and this state vector may be used to predict and identify a stage in parkinson’ disease. so, the physician can choose a treatment, based on this forecast with an appropriate level of medication. the system was validated for 88 patients under observation: 28 with pd tremors, 30 with spd ("suspicious" pd tremor), and 30 with nt (normal tremor), and we plan to expand the study to more patients with pd. already results interpretation and discussions with involved neurologists are directed to the validation of the study. the next step will be the creation of an expert or decision-support system based on fuzzy logic for parkinson’s disease screening, which will help a physician to diagnose pd in its early stages, especially of individuals in the class "suspicious" of pd. so, future research approaches will include the testing and validation of a screening test, in order to detect parkinson’s disease or other neurological disorders in their early stages. acknowledgment the authors would like to thank radu vasilcu, md, and prof. mihai covasa, phd, md, for their help in providing data for pd patients and for clinical evaluation of results during experiments. parkinson’s disease prediction based on multistate markov models 535 important notice: the experiments were not prejudicial in any way to the health of human subjects investigated and they were not subject to any invasive maneuvers. all the subjects were free to decide whether or not they wish to participate in this study. they did not lose any benefits to which they are entitled, if they did not accept the participation. the duration of this study was 3 years. all personal information was and will be kept confidential. medical information may be made available to the institution that houses the research, ethics commission, or other persons/institutions where the law requires. the 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[35] nintendo wiitm , http://www.nintendo.co.uk/ int j comput commun, issn 1841-9836 8(3):366-374, june, 2013. efficient data organisation in distributed computer systems using data warehouse s. cosma, m. văleanu, d. cosma, g. moldovan, d. vasilescu smaranda cosma faculty of business, babeş-bolyai university, cluj-napoca, romania, tel/fax: 0040-264-599170 e-mail: smaranda.cosma@tbs.ubbcluj.ro mădălina văleanu, dan cosma, dana vasilescu faculty of medicine, "iuliu haţieganu" university of medicine and pharmacy, cluj-napoca, romania, tel/fax: 0040-264-207043 email: drvasilescu@yahoo.com grigor moldovan faculty of mathematics and computer science, babeş-bolyai university, cluj-napoca, romania tel/fax: 0040-264-405300 e-mail: moldovan@cs.ubbcluj.ro abstract: databases represent a highly developed form of data organisation. the efficient use of databases by their beneficiaries is a permanent and stringent concern. the first part of the article presents a short development of the way data are organised and of the ways distributed databases can be optimised in computer networks. then, two means for the efficient operation of distributed databases are shown. the last part of the paper evaluations of the architectures of some data warehouses (dw) and of their building are made. using the data warehouse, a beneficiary can prepare in advance the required support to get reports and then make the right decisions in specific situations. keywords: database, distributed databases, relational databases, optimization algorithms, data warehouse, data marts. 1 introduction computer science has had a large contribution to the solution of issues from technical, scientific and economic fields, mainly with regard to the processing and managing of large volumes of data, such as those from economy, health and other fields. after the internet has appeared, more and more distributed data processing systems have been designed and built. the computer-based data processing performance increased and large data volumes processing have expanded. the data concept represents a basic concept always in the attention of the computer science experts. data, whatever their nature, are identified by their name. the data as a notion has changed in meaning together with the development of the computer-related instruments and tools, especially due to the memorisation and access of the data found in various information supports. an efficient processing of data has always required some kind of data organisation, mainly when the information support used by computers is discussed. at the beginning, isolated data and grouped data stored in the internal memory of the computers were considered. later on, economy-related applications have extended together with the opportunity to memorise data on external information supports; data could be organised under the form of files. the processing of isolated or grouped data and that of files is achieved with the help of some software programs written in various automated programming languages, usually oriented to the copyright c⃝ 2006-2013 by ccc publications efficient data organisation in distributed computer systems using data warehouse 367 application type (technical, scientific, economic etc.). the volume of processed data increased, together with the requirements of the users from various fields to receive reports and statements. under such circumstances, there occurred the need to distinguish data from their subsequent processing, data being still stored on certain data supports. it is under this circumstance that the notion of database issued. every database has a name, used whenever one wants to access data from the database in question. data can be isolated or grouped or file or a database itself, as each and everyone has a name to be accessed with. the software applications have also diversified. operational software appeared to manage certain data permanently; they are today called operational data (for example: in banking and medical systems) and are distinguished from persistent data which can also be stored in certain databases. to organise databases, various models have been used and consequently, various classifications of databases exist. the most known model of databases is the relational model. a relational database is a set of tables (that is relationships in the mathematical meaning of the word) with a relatively simple structure, where every table is defined with a set of attributes. the internet allowed the design and definition of distributed databases. until today, we speak about more kinds of databases: relational databases, distributed databases, temporal databases, logic-based databases, object-based databases, object relational databases or web-based or xml-based databases. xml databases are defined by attributes to which documents interpreted as a sequence of characters are associated (that is, given as value). to create, manage and query databases, specific instruments have been achieved, and they are easy to use, powerful, and highly performing as they need to give answers to extremely complex situations and questions. one of these instruments to create and query relational databases is represented by the sql language with its different extensions. the new generations of databases, mainly those based on the web or xml, have the tendency of being nosql [14]. at this moment, databases constitute a high level form of data organisation. relational databases are widely spread as they have a simple representation module of the component data under the shape of tables (that is relationships in the mathematical meaning of the word). if for ∀ i ∈ {1, ... ,m} , ai represent attributes of an instance of an r database, and di is the domain of attribute ai , then r ⊆ d1 × d2 × ... × dm. we agree to note with r(a1, a2, ..., am) or r=(a1, a2, ..., am), the scheme of r database. when the internet was developed, the move to distributed databases was a natural one. various means to extend and particularise databases were developed, and the majority of them started from relational databases. the hardware infrastructure also became more diverse when more and more high performance computer networks were installed. different systems such as database management systems (dbms) are known today, built mainly on the relational model, for instance: db2, ingres, informix, sql server, oracle. now the concern has moved to the architecture of databases, so that the use of databases can lead to more performance, mainly in the operation of systems of databases (with main components: data, hardware, software and users) to support decision-making processes. the theoretical and practical research is oriented more to the internal structures of organising databases. the ansi/sparc architecture is reasonable for databases systems regarding three levels an internal level, a conceptual level and an external level (views of individual users). 2 distributed databases distributed databases are part of a distributed system with a well-defined purpose. as a principle, a distributed database should be perceived by a user similarly to a non-distributed database. processing data from distributed databases should consider the presence of a communications network which supposes new circumstances to be taken into account [2]. for this purpose, the databases management systems have been extended to the distributed ones (ddbms). 368 s. cosma, m. văleanu, d. cosma, g. moldovan, d. vasilescu one of the most important issues, with respect to distributed databases, lies in minimising the number and volume of messages used in the network. that is why is significant to redistribute databases in the nodes of the communications network to get an efficient, if not optimal exploitation of the system. the query optimising process which requires distribution needs passing through two steps, a global and a local step. also important are issues of propagating updates, recovery and concurrency control. software making use of distributed databases are associated, in general, to a connecting graph defining the communications network. be the network nodes x={1,2,...,n}, and the links among the nodes, respectively its arches, noted u, i.e. the connecting graph g=(x,u). let us note with bi the database in node i, and with b the distributed database in all the nodes of the network considered, hence: b = {b1, b2, ..., bn} relational databases are defined by the relational schemes bi = (ai1, a i 2, ..., a i m), where a i j, j = (1, m), ∀ i ∈ {1,2, ... ,n} represent attributes. the following refers to an operational distributed information system managing the databases in the nodes of the communications network defined by graph g=(x,u). through the dbmss found in the nodes, computers are required services, respectively the databases in the network nodes are queried. some of the nodes are under heavy stress, others are not so much under stress. to increase the efficiency of the information system a migration of the databases or of parts of them from the nodes under heavy stress to less stressful nodes could be useful. a balanced network nodes loading is a significant issue. the solving of the issue requires every node to send out and receive messages carrying information with respect to the number of queries in every node and to the number of nodes (databases) used. the number of these messages should be as small as possible. every node i will contain information regarding the system loading. let us suppose that for every node, the loading or request level or the number of queries is measured on a scale from 1, that is weak load, to p, that is heavy load; we identify these pieces of information in node i with elements of the set inci=(1,2, ... ,p) (inci -node i loading). if p=3, we get the loadings: weak, normal, heavy, which some authors consider as sufficient. message migration together with databases (resources) always begins from the heavy loaded nodes towards the weak loaded nodes. a small number of messages exchange will occur in the nodes of the system under question until it reaches the desired equilibrium. to make the distributed database exploitation more efficient, a balanced loading of the computer network should be provided, with some dynamic balance algorithms. the algorithm proposed by suen and wong [12] for distributed systems shall be adapted to the present situation. in every node i, a piece of information regarding the nodes to which we send out query messages for the databases bj in the nodes shall be preserved; the set of nodes is noted si . in the same node i, bi database query messages will be received from a set of nodes noted ri . it is obvious that si ⊆ x, ri ⊆ x. the two sets associated to node i, respectively si, ri must satisfy some properties (constraints) to provide the operation of the efficiency algorithm of the database operated upon. these constraints, relative to si, are: any of two distinct nodes i, j ∈ x, i ̸= j have at least one node to which they send messages, that is si ∩ sj ̸= ∅ whatever node i, this node is allowed to send a message to it and to receive a message from it, i.e. i ∈ si ∧ i ∈ ri there is a property of equal responsibility for any i ∈ x, i.e.|{j/i ∈ sj}| = k efficient data organisation in distributed computer systems using data warehouse 369 there is a property of equal work for any i ∈ x, i.e. |si| = k there is an optimal condition n= k(k-1) + 1. the last two properties specify that every node i receives and sends out k messages to have an equal responsibility and work amount, so that the activities to be performed by the network nodes are in perfect balance. the properties of ri are written in the same manner, replacing si with ri in the list above. we will now present another approach regarding the efficient operation of a database distributed in the nodes of a computer network [10]. we consider that in graph g=(x,u), we have a zoning made up of m components, if on the set of nodes x we have made a partition of m components noted zi ⊂ x, zi ̸= ∅, i = 1, m so that: m∪ i=1 zi = x and zi ∩ zj = ∅, ∀i ̸= j, i, j = 1, m. we note gi = (zi, ui), where ui = {u ∈ u|u ∈ zi × zi}, ∀i = 1, m. the purpose of this distribution of the databases on the m zones lies in getting a certain autonomy, as well as some constraints, hence a certain level of efficiency for their operation. we have in view the following properties: equity; following the loading in every node i, for a defined time interval, are determined statistically some weights hi of services request offered in the nodes in question (for instance, a percentage of the number of queries of basis bi, percentage of the number of messages received by node i, percentage of the use time for node i etc.). hence, n∑ i=1 hi = 1. absolute equity for zone loading to fulfil the tasks will occur if the volume of the tasks for every zone will be 1 m , i.e. ∑ j∈zi hj = 1 m , i = 1, m. a maximum deviation from the ideal value 1 m is accepted, though probably it will be difficult to meet, equal to α, 0 ≤ α ≤ 1. hence, equity will exist if: ∣∣∣∣∑i∈zj hi − 1m ∣∣∣∣ ≤ αm , 0 ≤ α < 1, 1 ≤ j ≤ m. contiguity; zone gi, i ∈ {1,2,...,m} is contiguous, if and only if this graph is connecting (there is a chain between any two of its nodes). compactity; zone gi is compact if the shortest chain between any two nodes a and b from zj , 1 ≤ j ≤ m, is smaller than or equal to a predefined constant, named exclusion distance. exclusion of some enclaves; an enclave is a node or a set of nodes that cannot form a zone if the criteria mentioned earlier are fulfilled. we can achieve such zones with a not very complex algorithm and in the zones we can efficiently exploit the distributed database [11], [13]. 3 data warehouse architecture and their efficiency data preservation without processing cannot be a purpose in itself. the applications of databases are numerous and for many domains. there are extremely many situations when databases are created as decision-making supports. such situations can be encountered mainly when data refer to economy, though similar cases are applicable for health-related data or other fields. the volume of stored data on data supports has increased and continues to increase. during this development of information technology, the need to restructure data inside such supports for an efficient use has come up. the data collection sources are many-sided and their 370 s. cosma, m. văleanu, d. cosma, g. moldovan, d. vasilescu structure is heterogeneous. this is due to the fact that the types of databases can vary and differ, as mentioned earlier. the information systems designed many years ago showed to be unable to cope with such situations. consequently, the reconsideration of the manner of data collection in a joint place and the processing of the mentioned data as decision making supports became a necessity. such applications mainly appeared in the field of economics, as they could be used as two solutions to solve the difficulty. one of the solutions supposes the increase of computer technical performance, the other solution can be applied immediately and resorts to developing proper software for the purposes envisaged. the second direction underwent an avalanche of research and articles published in this domain. the new concept regarding the management of large volumes of data as decision-making supports is represented by the data warehouse introduced by w.h. inmon in 1992, which, in fact, is a special database, as also shown well in [3]. the simple idea consists in building warehouses as an intermediate form among various data sources, usually operational databases and selecting/extracting some subbases of special data of reasonable size from these warehouses. these special data subbases should serve as support for informing decision makers or for writing reports, research documents or other, in relation with the processed data. the decision in a company can be related to sales, customers, suppliers, finances, costs, profit, human resources, etc. [4]. in a hospital networks, they can refer to patients, medical staff, resources, medicines required, etc. this requires the division of the functional space of a data warehouse in three distinct areas: the data sources zone, in general, operational databases, but also other types of data received by means of other software, then the zone comprising data in the data warehouse, and the third area, the special data marts zone, which are used for analyses, reports drawing or forming the so-called data mining. data sources can be of different nature, different formats or different types. all the data will be subjected to significant preparation process before reaching the data warehouse. during the preparation process, through a well-defined selection considering the objectives established for the use of the information system in question there takes place data cleaning, transformation and unification. after their loading, they need to be refreshed and at each refreshing process, integrity, consistency and other constraints need to be provided to maintain a reasonable volume of data which are necessary to make a decision. we have mentioned earlier some of the features a data warehouse should possess. according to w.h. inmon, a data warehouse is defined by enumerating its properties, that is: definition [8]: data warehouses are non-volatile data collections, oriented to the subject, integrated, variable in time and supporting the managerial decision making process. the creation of a data warehouse passes through the following: {data sours, ... , data sours} → ectl → data warehouse; where data sours represents operational databases or even data sources of other nature. through a process of extraction, cleaning, transformation and loading, in brief ectl (extraction, cleaning, transformation and loading) a data base of uniform structure is developed, i.e. a data warehouse (dw). a data warehouse whose data cannot be modified (they can, however, be deleted) is formed of the following components: (heavily, slightly) aggregated data, detailed data and meta data. a warehouse can be accessed only by repeated reading and can be used (distributed) to form some views representing data mart s to be used by decision makers. any standard architecture leading to the use of a data warehouse to make a decision in an organisation (for example, a company or a hospital) has the shape given in fig.1. when the data warehouse is distributed, then the middle part of the drawing above takes, efficient data organisation in distributed computer systems using data warehouse 371 figure 1: architecture standard data warehouse in general, the shape below: the loading of distributed data warehouses dwi, i ∈ {1,2, ... ,m} uses any of the data sources to which some aggregation processes are applied, so that in y, data will have a unitary structure. let us note x the set of data sources, and y the set of data warehouses dw i. the two sets have a application multivalued f : x → y . for the destination area, in the drawing above, we can notice that the often mentioned data marts are highlighted. an analogous correspondence g exists between the data warehouses set y and the set z of data marts, i.e. g : y → z. it is possible to conceptually design directly a data mart from a source, such as xml as specified in [2], [7]. though the term data marts is still disputed, we have chosen the definition below to be suitable for them: definition [2]: we call a data mart a specialised data warehouse, oriented to the subject, including volatile integrated data, variable in time and being the subsupport for managerial decisions. in the destination area, specific technologies and instruments of processing data should be present. olap (on-line analytical processing) is the best known technology for the aggregation, access and use of data from a data warehouse, for the purpose of decision making. the term olap was proposed by codd in 1993. olap operations with regard to multidimensional data (data cubes) are well-known. let us mention the most significant of theme: roll-up, drill-down, slice, slice-and-dice, pivoting, drill-across. in fig. 1, a standard architecture of a data warehouse is given. the architectures are designed for organisations, having a well-defined purpose, and consequently, a specificity. it is always important that the architecture is less costly and more efficient for its users, possessing 372 s. cosma, m. văleanu, d. cosma, g. moldovan, d. vasilescu certain hardware and software resources, while data, in general, are heterogeneous. many authors (such as jenning in 2011 [9]) specify that "the best solution for one enterprise is not necessarily the best for another". optimal solutions are only for concrete situation. anyway, the information systems designed for organisations for decision making purposes, based on data warehouse , have extended a lot. in poland, for instance, as mentioned in [1] dw architectures are used in 20%, respectively 23% of cases for data marts and only 7% federal architectures, from all the systems created in 2011. the issue of preserving large volumes of data remains a constraint today, too. we will always want to use a smaller memory space. the model of multidimensional tables or hypercubes for data provides solutions to diminish the memory space. a dimensional scheme is made up of a factual table and more dimensional tables with connections established as keys. such a multidimensional scheme can also be called star-shaped scheme. a theoretical example is illustrated in the drawing below. in the factual table given, at the beginning three attributes, in fact, keys, representing links to the three dimensional tables are found. the dimensional tables also contain keys under the same name of attribute, and the references are indicated by the three arrows. in the factual table, after the attributes-keys, other attributes are listed. the dimensional tables also contain a list of attributes after the key attribute. in principle, when designing a dw, to produce factual schemes the following successive steps need to be followed: building the attributes tree, cleaning and completing the attributes tree, specifying dimensions, measurements and hierarchies [5]. when attributes are known from the operational databases schemes (dimensions, measurements, hierarchies), they are defined together with the final users. the conceptual factual scheme must be designed, definitely, before implementation. the effective scheme of a dw shall be a sum up of dm (data marts), data sources and profiles required by users. a dw design methodology such as the one based on the dimensional fact model (dfm) should be useful as it proposes a formalisation of the diagrams e/r in a graph [5]. using these elements, as the example above shows, one can built particular graphs, respectively quasi-trees, whose roots are facts; golfarelli and rizz acted in this manner in [5]. the majority of a dw elements, in our opinion, have a dynamic character and, in consequence, they should be considered cybernetic systems, where returns to the system under the form of feed-backs are admitted. in order to select some options (actions) defining the conceptual schemes of data warehouses (dws) and able to provide short time answers to queries made by users, some efficiency indicators can be used. thus, such an evaluation used successfully in various domains is given below. be a set of options a = {a1, a2, ..., an}, their selection being made on the basis of welldefined criteria, forming set c = {c1, c2..., cm}. let us suppose that after evaluations, the level of influence of criterion ci in the selection of option aj with the value q aj i , from a scale of values is known. if we consider options a and b, in the mentioned order, from set a, we convene, to note with pi(a, b) the preference for option a versus b, a preference determined by criterion ci. this degree efficient data organisation in distributed computer systems using data warehouse 373 can be expressed as a percentage. if, for the two options a and b we take into account all the criteria ci to select option a related to b, the result of the evaluation noted r(a,b) will be: r(a, b) = ∑m i=1 pi(a, b) · qai as every option a is compared to n-1 options , a positive score for option a can be defined as s+(a) as well as a negative sc ore s(a), as follows: s+(a) = 1 n − 1 ∑ x∈a r(a, x) ; s−(a) = 1 n − 1 ∑ x∈a r(x, a) the positive score of option a represents the maximum global influence of option a versus the rest of options, while the negative score of option a represents its minimal global influence. in this way, we will associate a full degree to each option a from set a as follows: s(a) = s+(a) − s−(a) based on these indicators, we can define efficient conceptual schemes for dw and ms. for data marts constitution, option groupings following given criteria can also be useful. 4 conclusions it is obvious that high performance dw architectures are desired, as they could be capable to give answers at the shortest time and expense to various queries found in an organisation. as this paper shows, this optimisation can be performed only in exact and concrete environments, through successive improvements and applying various methods, among which those presented here. a high performance dw architecture depends on the computer network involved, on the concept schemes defined, including multidimensional tables, respectively hypercubes, on the quality of the software used as well as the olap processing. bibliography [1] m. alsqour, k. matouk, m.l. owoc, a survey of data warehouse arhitectures preliminary results, proc.of the federated conference on computer science and information systems, 1121-1126, 2011. [2] c.j. date, on introduction to database systems, 8th edition. pearson education inc, addison wesley higher education, 2004. [3] f.g. filip, decizie asistata de calculator. concepte, metode si tehnici pentru deciziile centrate pe analiza datelor, rev. informatica economica, 4(16):8-22, 2000. [4] d. fusaru, z.gherasim, i. lungu, a. bara, tehnici si arhitecturi pentru micsorarea timpului de raspuns in sistemele cu depozite de date (data warehouse), analele universitatii spiru haret. seria economie, ed. fundatia romania de maine, bucuresti, 4(4):419-426, 2004. [5] m. golfarelli, s. rizz: a methodological framework for data warehouse design. in: proceedings acm first international workshop on data warehousing and olap (dolap), washington, pp. 3-9, 1998. [6] m. golfarelli, s. rizzi, e. turricchia, modern software engineering methodologies meet data warehouse design: 4wd. in 13th international conference on data warehousing and knowledge discovery (dawak 2011), pp. 66-79, touluse, france, 2011. 374 s. cosma, m. văleanu, d. cosma, g. moldovan, d. vasilescu [7] m. golfarelli, s. rizzi, b. vrdoljak, data warehouse design from xml sources, proceedings acm third international workshop on data warehousing and olap (dolap’01), atlanta, pp. 40-47, 2001. [8] w.h. inmon, building the data warehouse, third edition. pub. john wiley&sons, inc. usa, 2002 [9] c.jennings, l. tristan, the perils of over complicating data warehouses how avoiding unnecessary complexity con provide satisfaction and savings, business intelligence journal, 16(2):39-43, 2011. [10] g. moldovan, m. valeanu, the performance optimization for date redistributing system in computer network, int j comput commun, vol.1, supplementary issue, 1(s):470473, 2008. [11] g. moldovan, o problemă de redistribuire a serviciilor într-un sistem partiţionat în zone distincte după anumite criterii. "babeş-bolyai" university cluj-napoca, fac. math. and computer sci., res. sem. preprint, no.5, 5-8, 1993. [12] t.t.y. suen, j.s.k. wong, efficient task migration algoritm for distributed systems, ieee transactions on parallel and distributed systems, 3(4):488-499, july 1992. [13] m. valeanu, s. cosma, d. cosma, g. moldovan, d. vasilescu, optimization for date redistributed system with applications, int j comput commun, 4(2):156-161, 2009. [14] eg. ularu, f. puican, noua generatie de baze de date nosql. rev. romana de informatica si automatica, vol.22, no.4, 2012 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 1, month: february, year: 2023 article number: 5011, https://doi.org/10.15837/ijccc.2023.1.5011 ccc publications ensemble learning for interpretable concept drift and its application to drug recommendation yunjuan peng, qi qiu, dalin zhang, tianyu yang, hailong zhang yunjuan peng school of software engineering beijing jiaotong university, beijing, china *corresponding author: 21126333@bjtu.edu.cn qi qiu 1. department of pharmacy beijing an zhen hospital, beijing, china 2. school of pharmaceutical sciences capital medical university, beijing, china *qi qiu and yunjuan peng are co-first authors of the paper: qiuqi8133@163.com dalin zhang school of software engineering beijing jiaotong university, beijing, china *corresponding author: dalin@bjtu.edu.cn tianyu yang department of electrical engineering columbia university, nyc, usa ty2462@columbia.edu hailong zhang pamplin college of business virginia polytechnic institute and state university, blacksburg, va, usa hailongzhang@vt.edu abstract during the covid-19 epidemic, the online prescription pattern of internet healthcare provides guarantee for the patients with chronic diseases and reduces the risk of cross-infection, but it also raises the burden of decision-making for doctors. online drug recommendation system can effectively assist doctors by analysing the electronic medical records (emr) of patients. unlike commercial recommendations, the accuracy of drug recommendations should be very high due to their relevance to patient health. besides, concept drift may occur in the drug treatment data streams, handling drift and location drift causes is critical to the accuracy and reliability of the recommended results. this paper proposes a multi-model fusion online drug recommendation system based on the association of drug and pathological features with online-nearline-offline architecture. https://doi.org/10.15837/ijccc.2023.1.5011 2 do cto r writes a prescriptio n pharmacist reviews the prescriptio n do cto r revises the prescriptio n appro veno t appro ve patient pays o nl ine del iveryman del ivers drugs save patient reco rds pharmacist dispenses drugs figure 1: online prescription process the system transforms drug recommendation into pattern classification and adopts interpretable concept drift detection and adaptive ensemble classification algorithms. we apply the system to the percutaneous coronary intervention (pci) treatment process. the experiment results show our system performs nearly as good as doctors, the accuracy is close to 100%. keywords: interpretable concept drift, self-adaptive ensemble learning, drug recommendation, pattern classification. 1 introduction since january 2020, with the fast spread of coronavirus (covid-19), internet healthcare is encouraged to release the burden on clinics. hospital information technology plays an important role in remote consultation. for example, beijing anzhen hospital adds video and audio consultation methods on the mobile phone by using the existing hospital information system (his) to complete the follow-up diagnosis of chronic diseases on the internet. the online prescription pattern [1] is shown in figure 1. the doctor prescribes according to the data uploaded by the patient and the diagnosis results, then the prescription will be reviewed by the pharmacist. after the online consulting payment is finished, the pharmacist dispenses the drugs according to the prescription and delivers them to the deliveryman who delivers the drugs to the patient. online prescription service provides guarantee for the patients with chronic diseases and reduces the risk of cross-infection. however, the latest survey found that doctors often need to spend much time communicating with patients and reviewing historical medication records for accurate prescriptions due to the lack of face-to-face consultations in the fight against covid-19. the current study of internet healthcare has done less work at the algorithm for doctors’ prescription decision-making. internet healthcare reduces gathering risks and travel costs and provides convenience for patients [2], but increases the burden for doctors, especially decision-making. the existing ai-assisted consultation system can independently diagnose some diseases, but for complex https://doi.org/10.15837/ijccc.2023.1.5011 3 common diseases, it still needs to entirely rely on the doctors’ knowledge and experience. emr records the historical medical data of patients. by analyzing the key attributes and relationships in prescriptions, scientific and rational decision support for doctors can be provided [3, 4]. drug recommendation system has great application value by analysing emr to assist doctors in diagnosis, without being limited by the complexity of diseases. therefore, this paper combines the needs of internet healthcare under covid-19 to study the drug recommendation system, so as to relieve the pressure of doctors and improve the efficiency of diagnosis. current drug recommendation research is focused on expert systems. for example, a method for mining clinical pathways was proposed based on lda and pst, which produces daily document of medication effect after fusing drug effects recorded in drug bank with prescriptions [5]; a decision support system was developed that helps doctors select appropriate first-line drugs, which classifies patients’ abilities to protect themselves from infectious diseases as a risk level for infection [6]. applications of machine learning and data mining can change the available data to valuable information that can be used for recommending appropriate drugs by analyzing symptoms of the disease. a machine learning approach for multi-disease with drug recommendation is proposed to provide drug recommendations for the patients suffering from various diseases [7]. implicit feedback and crossing recommendation method was put forward [8], which builds up the relationship between patients’ symptoms and doctors’ medication scheme by analyzing medical history. however, the above methods have been limited mainly for the following reasons: (1) over-reliant on expert knowledge; (2) the accuracy is not high enough; (3) they are all offline methods that do not adapt well to new patients and symptoms. medical data is a continuous, real-time and high-dimensional data stream. since the concepts implied in the data stream may change in some way over time, i.e., concept drift [9], it increases the difficulty of data mining. in addition, interpretability and predictive capabilities are greatly reduced due to the causes of drift reason cannot be accurately identified, posing a threat to the reliability of diagnostic results. specifically, when prescribing for a patient, if the recommendation is just rely on the historical static emr without dynamically identifying changes in drug properties, the system will not be able to make the correct decision; if it does not provide an accurate explanation for the reasons of the changes, the decision result provided by the system will not be trusted. a good learning model not only needs to process the incoming data in real time, but also adapt to the constant changes in concepts and get the reasons of the changes, such as changes in patient pathological characteristics and the introduction of new drugs. when making recommendations for one patient, in addition to the standard clinical pathways and healthcare process models, the physical status of the patient should also be considered [10]. machine learning outperforms in mining valid attributes and their correlations without requiring any prior knowledge [11] and has been widely used in the medical field. ensemble learning performs well in dealing with concept drift, and it forms the final prediction results by maintaining sub models and using combination strategies [12], which can improve the prediction performance effectively. based on the above analysis, this paper proposes an online drug recommendation system for internet healthcare, which adopts the interpretable concept drift detection and adaptive ensemble classification algorithms. at first, multiple classifiers are trained separately as basic classifiers, which produce the results using combined voting strategy; then, new classifiers are built up based on the medical treatment data collected by fixed time step, concept drift and the reason is identified, and the basic classifier collection is updated by self-adaptive ensemble strategy. the main contributions of this paper are conducted as follows: • a multi-model fusion online drug recommendation system for internet healthcare based on the association of drug and pathological features is proposed for the first time, which transforms prescription recommendation into pattern classification. pattern-based classification achieves higher accuracy and can solve the problem of missing values well, which can remove redundant information from data and be not affected by noise. • our design comes from the real requirements of the internet healthcare. the system is designed to adopt an online-nearline-offline architecture that can quickly provide drug recommendation service. https://doi.org/10.15837/ijccc.2023.1.5011 4 • interpretable concept drift detection algorithm and online adaptive ensemble learning strategy are proposed for the characteristics of medical data, which improves the recommendation accuracy and reliability. • in terms of specific application, we apply the system to the pci treatment process for recommending statins that are used for preoperative pretreatment and postoperative lipid-lowering. the experiment results show our system performs nearly as good as doctors, the accuracy is close to 100%, which shows the online drug recommendation system can effectively help the doctors in fighting against covid-19. subsequent sections are organized as follows: section 2 systematically reviews recommendation systems, medical recommendation systems and concept drift in recommendation systems; section 3 introduces the proposed method in detail; section 4 describes the experiments; section 5 presents detailed experiment results; lastly, a summary and future work are discussed in section 6. 2 related work 2.1 recommendation system recommendation methods can be divided into content-based, collaborative filtering, and hybrid methods [13]. content-based methods use the content of items to create features and attributes to match user profiles. most of the methods draw on the experience of information retrieval and filtering methods to some extent. in addition to the combination of the traditional vector space model and the k-nearest neighbor, there are also some methods based on machine learning such as naive bayes, decision tree, linear classification and neural network, which establish preference model and then use the model to predict the probability of users’ future behavior to complete the recommendation. collaborate filtering methods are based on group knowledge and users or items with similar interests. they do not need to analyze the content of the project. on the contrary, they rely on the coding of the relationship between the user and the project and is reflected in the rating feedback matrix. each element represents the user rating of one user for one project. user-based collaborative filtering methods have certain advantages in the novelty of recommended results, such as the study [14], which combines word frequency statistics and similarity calculation to analyze users’ preferences, however, the recommendation results are easily influenced by trends. at the same time, new users or lowactive users will also encounter the thorny problem of user cold start. the above two methods can be combined to obtain a hybrid recommendation system. there are two general hybrid ideas: (1) hybrid of recommendation results: the results of two or more recommendation systems are directly combined by a mixing mechanism. common mixing methods include crossover, weighting, switching and so on; (2) hybrid of recommendation algorithms: a new hybrid algorithm is designed based on multiple algorithms. generally speaking, the models of various algorithms are connected in series, such as the output of the first algorithm is used as the input of the second algorithm. 2.2 medical recommendation system medical recommendation systems mainly include knowledge-driven expert and data-driven drug recommendation systems. a diabetes drug recommendation system was proposed based on the domain ontology [15]. zhang et al. [16] proposed a hybrid recommendation framework that integrates artificial neural networks and case-based reasoning. a knowledge graph-based drug recommendation system [17] and a mutual information clustering-based recommendation method [18] were proposed to assist in tcm diagnosis. considering the impact of incomplete knowledge graphs on the robustness of recommendation systems, gong et al. [19] constructed a heterogeneous graph containing diseases, drugs, patients and their correspondence based on emr and medical knowledge graphs to decompose drug recommendation into linkage prediction problem and demonstrated their effectiveness. in internet hospital, doctors know the patients’ physical condition through pictures, words and so on. prescriptions are usually completely dependent on doctors’ knowledge and working experience, resulting in the high decision-making costs. online drug recommendations are used to recommend the https://doi.org/10.15837/ijccc.2023.1.5011 5 most suitable drugs and corresponding dosage by mining emr and combining new data on patient pathological characteristics to relieve doctors’ pressure and reduce the risk of medical accidents. however, there are few researches on online prescription recommendation. the current work of internet healthcare is mainly focused on medical insurance payment service, medical quality supervision and ai chronic disease management, with little work on assisting doctors in prescription decision-making due to the lack of algorithm support. this paper aims to improve the efficiency of medical consultations by reducing the prescription time and improving the accuracy of prescriptions. to do this, we propose a three-layer intelligent model based on adaptive learning that recommends the most suitable prescription based on the parameters of patients’ pathological characteristics. 2.3 concept drift in recommendation system concept drift in recommendation systems refers to the fact that the recommendation model can not accurately grasp the change of concept when the data accumulates over time, resulting in the results deviating from the actual demand [20]. in internet healthcare, the online drug recommendation system is often affected by various factors, such as the addition of new drugs and changes in patient pathological status. the recommendation performance will be affected when the algorithm cannot adapt to these factors in time. the current solutions to concept drift of recommendation system mainly focus on the behavior records between temporal features and user items. for example, a time weight collaborative filtering algorithm proposes an exponential decay function to calculate the weight of scoring prediction [21]. a method to calculate the distance between two preference data stream distributions in adjacent time windows is proposed to model the degree of user preference change [22]. these methods use the same half-life or similar time windows for all users and do not consider the pattern of interest changes over time for different users. there is not much theoretical and experimental basis for the size of time window and half-life, which can only be adjusted based on experimental results. in machine learning, methods to solve concept drift can be divided into sample selection, sample weighting and sliding window [23]. sample selection defines a correlation index to select the most relevant samples to train the model; sample weighting considers that samples at different periods are not equally important to the model; sliding window sets a time window to select a sample that is relatively new. although these methods have extensions in recommendation systems, they are not the best choices at present: recommendation systems need to be built on as much data as possible, sample selection will increase data sparsity; artificial weighting and windows lack theoretical basis. methods based on other models have also been applied to solve the concept drift problem of recommendation systems. for example, viniski et al. [24] proposed incremental learning to update the user-item relationship established in a streaming recommendation system; multitransition factor and a forgetting time function were introduced to analyze the evolution of user preferences in order to accurately recommend new items or services to users [25]. through our analysis, these methods can not solve concept drift in online drug recommendation systems well. the problems of them can be summarized as follows: • the important parameters in the algorithm are determined subjectively and lack of theoretical basis. • increases data sparsity, which affects the recommendation effect. • unable to adapt to the incoming samples. adapting the model to different sample windows with the arrival of data flow is not easy. • the identified concept drift is lack of interpretability. it’s important to explain and investigate the causes of drift, so as to ensure the reliability of drug recommendation system. • the accuracy of the method is not high. the drug recommendation system is related to the safety of medication and health of patients so the high accuracy is required. https://doi.org/10.15837/ijccc.2023.1.5011 6 consulation prescription online nearline offline server server server firewall firewall modeltree j48bayesiannet online ensemble module concept drift detection figure 2: system framework of our online drug recommendation system in addition to that, the interpretability of concept drift is a major issue ignored by current recommendation systems. investigating and explaining the causes of drift is very important to provide reliability of drug recommendation systems and improve the ability to predict future drift. research on the interpretability of conceptual drift is divided into sequential analysis, statistical and window based methods [26]. among them, statistical methods analyze the changes in the mean and standard deviation of the results to be predicted, and typical methods include eddm [27], ewma [28], and rddm [29]. recently, a method sddm [26] by detecting changes in the data distribution has been proposed to quantify drift, which is very intuitive for identifying the reason of drift, having great application value. therefore, we construct a recommendation model with online-nearline-offline structure based on the interpretable concept drift detection and multi-model fusion adaptive classification algorithms. based on drug medical data from an internet hospital that has implemented the online prescription pattern, the model is used to drug recommendation, which can catch concept drift in time and adaptively update the base classifiers, achieving a higher accuracy rate. 3 methods figure 2 shows the framework of our online drug recommendation system. first of all, collaborative recommendation based on combined voting improves the accuracy of the system; secondly, with the passage of time, new user characteristic information may appear and the concept drift may occur. therefore, an interpretable concept drift detection algorithm and an adaptive ensemble classification algorithm is proposed. in this section, we first introduce the framework of our proposed system and the workflow based on the data interaction between each layer, then we introduce the concept drift detection algorithm, collaborative recommendation strategy based on combined voting and the classifier ensemble strategy in detail. https://doi.org/10.15837/ijccc.2023.1.5011 7 3.1 system framework 3.1.1 online system directly facing users, this system contains the high performance and availability recommendation service. the online ensemble module will fuse the recommendation results calculated by nearline and content-based recommendations. online system needs to return results in a short period of time, so the simplest priority fusion algorithm is often used here. 3.1.2 nearline system this system is deployed on server. on the one hand, it will receive the prescription recommendation requests from doctors and invoke the combined voting algorithm to generate the results according to the latest pathological characteristics parameters of patients. on the other hand, the data flow is collected in fixed time step and stored in the data flow database. the time series data flow will be detected by the interpretable concept drift detection algorithm and fused with the original basic classifiers by offline’s self-adaptive ensemble algorithm, which will be described in detail in the following sections. 3.1.3 offline system the task of this system is to mine long-term patient drug treatment process data. our original base classifiers include linear regression based on model tree, bayesian network and j48 based on decision tree, which are selected from ten classifiers by the experiments. in the testing stage, the accuracy of each classifier is about 99%. the ensemble training process is a bit more complicated because the classifiers need to be trained separately in the nearline and offline systems. 3.2 collaborative recommendation based on combined voting the base classifiers based on the adaptive ensemble strategy provide the local classification results of test instances. all the class labels {c1,c2, . . . ,ct} of test instances are loaded into the voting. all classifiers will classify the current test instance xj, and the class of xj is determined by the voting weight and classification probability of each base classifier. assuming that the output function of each base classifier is hi(xj), then the ensemble output is: h (xj) = cargmax k ∑ne i=1 sih k i (xj ) (1) where, ck is the predicted class label of the current test instance, si is the voting weight of the base classifier, ∑ne i=1 si = 1. when the performance of each base classifier is not equal, the stronger classifiers can be given more voting weights to make classification results more reasonable. in this paper, the voting weight of each classifier is the same, that is, si = 1/ne. suppose that e1,e2 and e3 are three base classifiers, b1 and b2 are class labels. for the test instance xj, the probability that xj is classified as b1 are respectively 80%, 70% and 40%, the probability that the test instance are classified as b2 are 20%, 30% and 60%. then the classification probability of the ensemble classifier for b1 is 1/3 × 80% + 1/3 × 70% + 1/3 × 40% = 63%, the classification probability for b2 is 1/3 × 20% + 1/3 × 30% + 1/3 × 60% = 37%. finally, b1 is the prediction class label of xj. 3.3 interpretable concept drift detection the development of classification decisions in recommendation systems is based on the posterior probability distribution p(c|x), where x is the sample, c is the target. the posterior probability distributions may be similar or different for the incoming data streams xt and xt′ at different moments. the posterior probability distribution is indicated as “real concept drift” [23]. once the concept drift occurs, the posterior probability distributions at different moments will exhibit large differences. in order to enhance the interpretability of concept drift, the change of posterior probability distribution https://doi.org/10.15837/ijccc.2023.1.5011 8 needs to be quantified. in this paper, we use kullback-leibler divergence to quantify this difference, and the quantified difference value is called the concept drift magnitude [30]. dm ( xt′ ) = kl ( pt‖pt′ ) = ∑ xt,xt∈x p (ct | xt) log ( p (ct | xt) p ( ct′ | xt′ )) (2) p (c | x) = p (x | c) p (c) p (x) (3) where, dm ( xt′ ) denotes the conceptual drift magnitude at t′, pt′ and pt are the posterior probability distributions for t′ and t. in practical medical scenarios, patient medical data are often high-dimensional, involving basic patient information (e.g., gender, age), pathological features (e.g., family medical history, adverse hobbies), and historical drug use, etc. when calculating the drift magnitude on the entire highdimensional data stream, due to the monotonicity of the distance measures, the drift of a few features is difficult to be identified and then it’s hard to detect whether the conceptual drift has really occurred [26, 30]. therefore, in order to capture the conceptual drift in the medical data stream more accurately, we calculate the drift amplitude dm ( x fi t ′ ) for each feature fi and use the maximum of them as the conceptual drift amplitude of the whole data stream, and determine whether the conceptual drift occurs in the whole data stream by comparing it with a predefined threshold ε. when max ( dm ( x f1 t ′ ) ,dm ( x f2 t ′ ) , . . . , ( x ff t ′ )) ≥ ε, a higher drift amplitude indicates that concept drift occurs, otherwise no concept drift occurs. 3.4 adaptive ensemble algorithm online medical data is a kind of streaming data that continuously increases. as time passes, an infinite number of base classifiers may need to be constructed. however, due to the limitation of time, memory and performance, it is not necessary to combine all base classifiers for prediction. therefore, in the integration, one of the problems to be solved is how to choose the useful base classifiers to build the optimal integration. a base classifier associated with the current concept is useful for predicting instances. when no concept drift occurs, the number of base classifiers increases. however, when concept drift occurs, most of the old base classifiers are not representative of the latest concepts and only a few useful base classifiers can participate in the classification. in this paper, an adaptive ensemble strategy is adopted. when data block arrives, a new base classifier is firstly constructed on the data block, and the accuracy of each basic classifier in the existing integration is calculated, so as to determine the accuracy weight of them. every time a new classifier is added, the base classifier with contribution less than 0 will be deleted. the adaptive strategy adopts the above two steps to keep a better base classifier and make it better adapt to concept drift. we = φi −φθ∑ne i=1 (φi −φθ) . (4) where, we is the accuracy weight of the classifier on the data block, φi is the classification accuracy of each base classifier in the new data block; ne is the number of classifiers in current integration e; φθ is a custom threshold, which is used to determine whether a classifier should be discarded. the calculation equation is: φθ =   max ( min (ecor) − τ, 12 ) , concept drift occurs max ( φ̄, 12 ) , no concept drift occurs (5) where, ecor is the classification accuracy set of the base classifier in the current integration e; φ̄ is the average classification accuracy of the base classifiers. when no concept drift is detected at this time, all base classifiers in the current integration should be useful, τ is a smaller value greater than 0, ensure we > 0, therefore, all selected base classifiers can be used for classification; otherwise, when concept drift is detected at this time, only some useful base classifiers can participate in the prediction. https://doi.org/10.15837/ijccc.2023.1.5011 9 a classifier can not only improve the classification accuracy of the current observed data, but also improve the classification accuracy of the entire infinite data stream. after the new classifier is created, the original base classifiers in the integration is evaluated. the new data block is used to calculate the accuracy rate of the existing ensemble classifier: p (enew,bk) = ∑d i=1 corrnew (xi) d . (6) where, xi is the i-th real example of data block bk; corrnew (xi) = { 1, correct 0, wrong (7) when deleting a classifier e, calculate classification accuracy: p (enew-e,bk) = ∑d i=1 corrnew-e (xi) d . (8) the contribution of the classifier to the new data block bk in the integration: contribution (bk) = p (enew,bk) −p (enew-e,bk) (9) the contribution can be positive or negative. negative value means that the base classifier reduces the overall classification accuracy; otherwise, it improves the overall classification accuracy. once a new classifier is added, discard the base classifier with contribution less than 0. in algorithm 1, new training data block bk = {x(0),x(2), . . . ,x(n− 1)}, e is current integration classifier collection, ne is number of ensemble classifiers, nmax is maximum number of classifiers, e′ is optimized ensemble classifier, e is a new classifier, enew is new integration classifier collection, ei is each base classifier in enew, φi is the correct classification rate of integrated classifier, e′new is the integration after accuracy weights are updated and e′i is each base classifier in it, we′i is the accuracy weights of e ′ i on bk, ne′ is the current number of classifiers, elw is a classifier with a small weight according to the weight sort. 4 experiments 4.1 data analysis and processing the experiments are based on patient drug medical data from an internet hospital that has implemented the online prescription pattern. a total of 56 variables describe the 5690 instances in the data set, which contains 13 attributes for basic patients’ information, 38 attributes for describing patient pathological characteristics, and 3 attributes for patient medication. the purpose of the experiments is to use the classifier to predict which type of drugs are most suitable for the patient, so as to achieve a recommendation. we use 60% of the data as historical static data block of patients, and get an initial ensemble recommendation model. then, we use the three-tier recommendation framework proposed in this paper to test the robustness of our online self-adaptive ensemble learning (osel) algorithm. the rest of the data is divided into the new data block 1 and 2. 4.2 experimental steps in the experiments, we achieve osel and nine other algorithms, and compare their medication grouping prediction results to explore the classification effect of osel proposed in this paper. table 1 shows these nine algorithms. we achieve osel and the above nine algorithms on weka 3.8.5 platform of windows 10 system, the running environment is jdk9. in the experiments, the batch size is 40, the seed is 10, and the other parameters of the classifiers keep default. the ensemble method of osel is v ote and the voting strategy is averageof probabilities. in this paper, we carry out two experiments. in the first experiment, we compare and evaluate the classification effects of the ten algorithms on offline system, and in the second experiment we evaluate the classification effects on online system. the specific experimental steps are as follows: https://doi.org/10.15837/ijccc.2023.1.5011 10 algorithm 1 adaptive ensemble learning algorithm 1: input: bk, e, ne, nmax 2: output: e′ 3: build e on bk 4: add e to e to form enew 5: test the performance of enew on bk 6: for each ei ∈ enew do 7: calculate φi according to equation(6)(7)(8) 8: if contribution(bk) < 0 then 9: drop ei 10: else 11: keep ei 12: end if 13: end for 14: get e′new 15: for e′i ∈ e ′ new do 16: calculate we′ i on bk according to equation(4)(5) 17: if we′ i <0 then delete e′i 18: else 19: retain e′i 20: end if 21: end for 22: while ne′ > nmax do 23: delete elw 24: end while 25: return e′ table 1: nine algorithms algorithm type classification via regression (cvr)[31] meta multiclass classifier (mcc)[32] meta multiclass classifier updateable (mccu)[33] meta bayes network (bn)[34] bayes naive bayes(nb)[35] bayes naive bayes updateable (nbu)[35] bayes j48[36] trees logic model trees (lmt)[37] trees random forest (rf)[38] trees https://doi.org/10.15837/ijccc.2023.1.5011 11 • the first experiment: firstly, nine classifiers are trained on historical static data block, and the classifiers with best performance are selected as the base classifiers of osel. it is worth noticing that we only consider the new data block 1 as a normal test set, i.e., no concept drift is considered, so as to demonstrate the performance of osel. then, the trained nine classifiers and osel classifier are tested on data block 1 to obtain the classification results on offline system. • the second experiment: in this experiment, concept drift is simulated by exchanging labels on new data block 1 and 2. first, new osel base classifiers are built on data block 1 and added to the osel base classifier set. the drift magnitude of data block 1 relative to the historical static data block will be calculated to confirm whether concept drift occurs. according to algorithm1, the osel base classifiers are added or removed to form the new integrated classifier osel’. then, we evaluate the performance of the nine classifiers trained in last experiment as well as osel’ on data block 2. 4.3 evaluation metrics in this paper, in order to evaluate the classification prediction effect of these ten classifiers, we use accuracy, kappa, precision, recall, f-measure and auc as evaluation metrics. here are the brief introductions to them: • accuracy: the proportion of correctly classified samples to the total number of samples for a given data. • kappa: it is a method of evaluating consistency in statistics and can be used to evaluate the accuracy of multi-classification models. the value range is [-1, 1]. • precision: in all the samples that are predicted to be positive, the proportion of the samples that are actual positive. • recall: in all the samples that are actually positive, the proportion of the samples that are predicted to be positive. • f-measure: the harmonic mean of precision and recall. • auc: the area under roc curve within the value range of [0.1, 1]. 5 result analysis in order to evaluate the classification prediction effect of osel, we achieve nine other algorithms on patient medication data set. we finish two experiments on offline and online system respectively and discuss the classification results. in the experiments, we set the maximum number of base classifiers is 3, ε = 0.003, τ = 0.000001. 5.1 experiment result on offline system we trained nine classifiers on training set 1. table 2 shows the training results. as can be seen from table 2, cvr1 has 6 metrics, bn1 has 6 metrics, j481 has 5 metrics and lmt1 has 1 metric in the top three. therefore, cvr1, bn1 and j481 perform better than other classifiers on training set 1. according to the above analysis, we choose cvr1, bn1 and j481 as the base classifiers of osel classifier, that is, the ensemble classifier set e ={cvr1, bn1, j481}. we test the nine classifiers that trained on training set 1 and osel classifier on testing set 1 and get their accuracy. table 3 shows the results. it can be clearly seen that osel classifier gets the highest classification accuracy on new data block 1 when there is no concept drift occurs (the data block 1 is only used to evaluate the performance of osel classifier with other benchmark classifiers). besides, osel also has the highest https://doi.org/10.15837/ijccc.2023.1.5011 12 table 2: evaluation metric scores for nine classifiers on historical static data block classifier accuracy(%) kappa precision recall f-measure auc cvr1 99.7364 0.9962 0.997 0.997 0.997 0.999 mcc1 69.2736 0.507 0.667 0.693 0.633 0.881 mccu1 68.0726 0.4641 0.681 0.935 bn1 99.7364 0.9962 0.997 0.997 0.997 0.999 nb1 54.1886 0.3638 0.602 0.542 0.561 0.778 nbu1 54.1886 0.3638 0.602 0.542 0.561 0.778 j481 99.6192 0.9944 0.996 0.996 0.996 0.998 lmt1 99.4728 0.9923 0.995 0.995 0.995 0.999 rf1 83.8606 0.749 0.862 0.839 0.814 0.984 table 3: evaluation metric scores for each classifier on new data block 1 classifier accuracy(%) kappa precision recall f-measure auc cvr1 99.8243 0.9974 0.998 0.998 0.998 0.999 mcc1 69.9473 0.5169 0.674 0.699 0.639 0.887 mccu1 68.8094 0.4818 0.688 0.832 bn1 99.6485 0.9949 0.997 0.996 0.996 0.999 nb1 54.3058 0.3665 0.600 0.543 0.559 0.778 nbu1 54.3058 0.3665 0.600 0.543 0.559 0.778 j481 99.3849 0.9910 0.994 0.994 0.994 0.996 lmt1 99.7364 0.9962 0.997 0.997 0.997 0.999 rf1 83.5677 0.7443 0.863 0.836 0.816 0.985 osel 99.9121 0.9987 0.999 0.999 0.999 1.000 figure 3: drift magnitude of each feature on new data block1 scores in the other five metrics, which is better than any of its base classifiers: cvr1, bn1 and j481. therefore, the performance of osel classifier is the best on testing set 1. when the drift magnitude is greater than 0.2, it shows that concept drift is detected at this time. the classification accuracy set of the base classifiers is ecor = {99.8243, 99.6485, 99.3849}, φ̄ = 99.6192%. since 99.6192 99.3849 > 0.2, it can be seen that concept drift is detected at this time, and only part of the base classifiers can participate in the prediction of the instance to be predicted. we calculate φθ = 99.6888% and wj481 < 0 according to equation (3) and (4). due to algorithm 1, we delete the base classifier j481. 5.2 experiment result on online system when data block 1 is considered as a new data stream, the concept drift magnitude calculated for each feature on it is shown in figure 3. among them, the largest concept drift amplitude is 0.003 on feature “hepatitis b”. this is taken as the concept drift magnitude of data block 1, i.e., dm(data block1) = 0.003. since dm(data block1) ≥ ε, it can be determined that concept drift has occurred at this point, and only some of the base classifiers can participate in the next prediction. https://doi.org/10.15837/ijccc.2023.1.5011 13 table 4: accuracy of the classifiers on data block 1 classifier enew enew−cv r1 enew−bn 1 enew−j 481 enew−cv r2 enew−bn 2 enew−j 482 accuracy(%) 58.5237 99.8243 99.8243 99.8243 56.9420 56.9420 56.9420 table 5: accuracy of the classifiers on data block 1 classifier cv r2 bn2 j482 accuracy(%) 98.4183 99.7364 98.7698 we -0.3654 0.5000 -0.1346 according to algorithm 1, we construct new base classifiers cvr2, bn2, and j482 on the new data block 1 and add them to the existing integrated classifier to get enew = cv r1,bn1,j481,cv r2,bn2,j482. the classification accuracy are shown in table 4. the contribution of cvr1, j481, and bn1 to the integration is -41.3006, and the contribution of bn1 and bn2 is 1.5817. therefore, cvr1, bn1, and j481 are removed to obtain the new integrated classifier e′new = {cv r2,bn2,j482}. the accuracy of the base classifiers of e′new is show in table 5, the average accuracy ϕ̄ = 98.9748%. due to the concept drift is detected now, ϕθ = 98.9748%. according to equation (3), the precision weight of the base classifiers are also shown in table 5. we remove the base classfiers with precision weights less than 0, and get integrated classifier osel′ = {bn2}. in order to evaluate the classification effect of osel’, we test the nine classifiers trained in the first experiment and osel’ classifier on data block 2. table 6 shows the scores of each metrics. as can be seen from table 6, osel’ gets the highest scores in all evaluation metrics, indicating that it performs best on new data block 2. according to the results of the above experiments, ensemble classification algorithms based on combined voting are better than independent classification algorithms; besides, osel algorithm performs best in processing the data flow with concept drift, and ability to adaptively update the base classifiers makes it more advantageous than other fixed classifiers. 6 conclusion to relieve pressure of doctors and reduce the risk of medical accidents under the online prescription pattern of internet healthcare, this paper proposes a multi-model fusion online drug recommendation system based on the characteristics of medical data stream. the system adopts an online-nearlineoffline architecture based on interpretable concept drift detection and adaptive ensemble algorithms, which can effectively identify concept drift and the reasons, and improve the accuracy and reliability of recommendation results. in the experiments, we apply it to the pci treatment process. the results show that the proposed online drug recommendation system is highly accurate, the accuracy is close to 100%. our system performs nearly as good as doctors. we believe that our online drug recommendation system can effectively help the doctors in fighting against covid-19. in the future work, we will focus on increasing the practicability of the system so as to meet more actual medical recommendation scenes. for example, drug recommendations for multiple courses of treatment. table 6: evaluation metric scores for each classifier on data block 2 classifier accuracy(%) kappa precision recall f-measure auc cvr1 56.8541 0.4135 0.9920 0.5690 0.6930 0.6410 mcc1 30.8436 0.0402 0.6970 0.3080 0.4190 0.5340 mccu1 26.3620 0.0310 0.2640 0.5010 bn1 56.7663 0.4123 0.9920 0.5680 0.6930 0.8930 nb1 31.0193 0.1086 0.6730 0.3100 0.3790 0.6050 nbu1 56.7663 0.4117 0.9910 0.5680 0.6920 0.7820 j481 56.8541 0.4129 0.9910 0.5690 0.6930 0.7740 lmt1 42.2671 0.2365 0.4230 0.6040 rf1 85.3251 0.7741 0.875 0.853 0.832 0.985 osel’ 99.0334 0.9768 0.9880 0.9900 0.9890 0.9950 https://doi.org/10.15837/ijccc.2023.1.5011 14 funding this work was supported in part by the fundamental research funds for the central universities [grant number 2021qy010]. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] liu bo, guo youyan, lin yang, wu xinghai, and wei yongxiang. a brief analysis on the development of public hospitals assisted by internet medical services under the covid-19 epidemic. chinese hospitals, 24(09):62–64, 2020. 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[38] leo breiman. random forests. machine learning, 45(1):5–32, 2001. https://doi.org/10.15837/ijccc.2023.1.5011 17 copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: peng, y.; qiu, q; zhang, d.; yang, t.; zhang h.(2023). ensemble learning for interpretable concept drift and its application to drug recommendation, international journal of computers communications & control, 18(1), 5011, 2023. https://doi.org/10.15837/ijccc.2023.1.5011 introduction related work recommendation system medical recommendation system concept drift in recommendation system methods system framework online system nearline system offline system collaborative recommendation based on combined voting interpretable concept drift detection adaptive ensemble algorithm experiments data analysis and processing experimental steps evaluation metrics result analysis experiment result on offline system experiment result on online system conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 4, month: august, year: 2022 article number: 4800, https://doi.org/10.15837/ijccc.2022.4.4800 ccc publications distributed adaptive control for nonlinear heterogeneous multi-agent systems with different dimensions and time delay bo qin, yongqing fan, shuo yang bo qin* school of automation, and xi’an key laboratory of advanced control and intelligent process, xi’an university of posts and telecommunications, xi’an, china. chang’an west st. chang’an district, xi’an, 710121, china *corresponding author: qinbo123@xupt.edu.cn yongqing fan school of automation, and xi’an key laboratory of advanced control and intelligent process, xi’an university of posts and telecommunications, xi’an, china. chang’an west st. chang’an district, xi’an, 710121, china fanyongqing@xupt.edu.cn shuo yang beijing aerospece institute for metrology and measurement technology, no. 1, nandahongmen road, fengtai district, beijing, 100076, china. tju1105@163.com abstract a distributed neural network adaptive feedback control system is designed for a class of nonlinear multi-agent systems with time delay and nonidentical dimensions. in contrast to previous works on nonlinear heterogeneous multi-agent with the same dimension, particular features are proposed for each agent with different dimensions, and similar parameters are defined, which will be combined parameters of the controller. second, a novel distributed control based on similarity parameters is proposed using linear matrix inequality (lmi) and lyapunov stability theory, establishing that all signals in a closed loop system are eventually ultimately bounded. the consistency tracking error steadily decreases to a field with a small number of zeros. finally, simulated examples with different time delays are utilized to test the effectiveness of the proposed control technique. keywords: multi-agent systems, consensus, neural network, nonlinear, time-delay. 1 introduction in the recent decade, multi-agent systems have been widely developed in a range of industries. examples include unmanned aerial vehicles[1], robot cooperative systems, [2, 3], aerospace systems[4], and so on. as a result, distributed cooperative control has become a topic interesting research topic by many scholars. one of the most important characteristics of this design style is that it is capable of https://doi.org/10.15837/ijccc.2022.4.4800 2 changing the state based on the data provided by each agent and its neighbors. consistency is one of the most essential subjects, as evidenced by references [5, 6, 7], which indicate consistency algorithm studies that are ground-breaking works on multi-agent system consistency. in [8, 9, 10], authors suggested a number of excellent distributed optimal control research methodologies. the formation control was proposed in [11] , and [12, 13, 14] explored a wide range of issues. the synchronous conditions for multi-agent systems were proposed in [15]. the global asymptotic stability and global robust stability of neural networks with delay were investigated in [16, 17] by using lyapunov theory and linear matrix inequality. because neural networks are a broad approximation strategy for existing unknown nonlinear functions or uncertain models in many challenging areas, and a large number of control algorithms for nonlinear multi-agent systems have been proposed in the last few years in [18, 19, 20]. regrettably, these findings were limited to homogeneous or heterogeneous nonlinear multi-agent systems with the same dimension, so the control approaches for multi-agent systems with different dimensions would be ineffective. it is necessary to seek some unique control technologies to meet the class of nonlinear heterogeneous multi-agent systems with nonidentical dimensions. up till now, the uniformly bounded stability problem of nonlinear multi-agent systems with various nodes and dimensions is solved in [21, 22, 23], and a novel distributed adaptive control with similar parameters is proposed. in reference [24], the synchronization problem of dynamic networks with varied nodes was investigated. however, none of these previous studies take into account time delays, which could compromise accuracy and render the system unstable. by above mentioned analyses, in order to solve the consensus of each nonlinear multi-agent system with different dimensions and time delay dependent, we aim to create a novel distributed neural network adaptive control in this research. to cope with unknown nonlinear factors, neural networks are employed to approximate the uncertainties of systems, and then a distributed feedback adaptive with similar parameters is performed by solving the linear matrix inequality (lmi). the remainder of the paper is structured as follows: section ii provides some background information, a description of the system, and characteristics of similar composite structures and neural network systems. a neural network adaptive control and its stability analysis are described in section iii. a simulated example of the consistency of nonlinear heterogeneous multi-agent systems with time delay is presented in section iv. section v summarizes the conclusion. 2 the model of dynamical network and assumptions a weighted digraph is denotes as g = {v,e,a},where v = [v1,v2, ...,vn ] represents the nonempty set of each node, edge set e contains one edge (vj,vi), adjacency matrix a = [aij] ⊆ rn×n represents that node vj can send the information to node vi. the value aij > 0, if (vj,vi) ∈ e, otherwise aij = 0. in addition, for all i ∈ {1, 2, ...,n}, there exists aii = 0. diagonal matrix d = diag{din} ∈ rn×n, where din = ∑n j=1 aji is degree matrix. laplacian matrix is defined as l = d − a. the kronecker product of matrices a and p is denoted by a ⊗ p , i represents the appropriate n-dimensional identity matrix. p > 0(p < 0) means p is a positive (negative) matrix, ‖·‖ represents the 2 norm of vector. at and a−1 denote the transpose matrix and inverse matrix of a, respectively. consider the following dynamical network with nonlinear multiagent system and time delay: ẋi(t) = ai1xi(t) + ai2xi(t− τ(t)) + bi[ui(t) + fi(xi)], (1) where xi ∈ rni is the state of the ith node. ai ∈ rni×ni and bi ∈ rni×mi are some known matrices. ui denotes the input vector, τ is vary time delay and satisfies 0 ≤ τ(t) ≤ h < ∞, fi(xi) represents the unknown nonlinear function. assumption 1. consider the network (1) with n nodes, there exist n matrices, if fi ∈ rn0×ni (fi 6= https://doi.org/10.15837/ijccc.2022.4.4800 3 0),ki ∈ rmi×ni and a known matrix ji ∈ r1×pi satisfies the following condition  fi(ai1 + biki1) = (aj1 + bjkj1fi). fi(ai2 + biki2) = (aj2 + bjkj2fi). fibi = bjjj. (2) assumption 2. based on the good approximation performance of the radial basis function neural network(rbfnn). the rbfnn is used to approximate the unknown nonlinear function. in this paper, the rbfnn is described as the following form: fi(xi) = wti ϕi(xi) + εi(t),∀xi ∈ ω, (3) where wi means the ideal weight matrix that will be designed automatically, εi is denoted as approximation error satisfying ‖ εi ‖< ε̄i , where ε̄i is a known positive constant. ϕi(xi) = [ϕi1(xi),ϕi2(xi), ...,ϕisi (xi)] ∈ rsi×1 characterizes rbfnn vector, in which si is the number of basic functions. define the gaussian function as follows: ϕi(xi) = exp(− ‖ xi −µisi ‖ 2 2σ2isi ), (4) where µis = [µis1,µis2, ...,µisni ]t represents the center of rbfnn, σisi is the width of function. lemma 1. [25]for any given two vectors x,y ∈ rn×n and a positive matrix h ∈ rn×n , the following inequality holds: 2xty ≤ xthx + yth−1y. (5) lemma 2. [26]the lmi [ q(x) s(x) st (x) r(x) ] > 0 is equavalent to one of the following conditions: 1)q(x) > 0,r(x) −st (x)q−1(x)s(x) > 0; 2)r(x) > 0,q(x) −st (x)r−1(x)s(x) > 0, (6) where q(x) = qt (x),r(x) = rt (x). lemma 3. there exist positive definite matrices s > 0, t11 > 0, non-negative definite matrices t22 = tt22 ≥ 0, and any matrices of appropriate dimensions y1,y2,t12, such that the following lmis are feasible:   s y1 y2∗ t11 t12 ∗ ∗ t22   ≥ 0. (7) the following inequality holds: − ∫ t t−τ żt (α)sż(α)dα ≤ ηt (t)ψη(t) + τδt (t)tδ(t), (8) where ηt (t) = [ zt (t) żt (t) zt (t− τ) ] , δt (t) = [ zt (t) żt (t) ] , ψ =   y t 1 + y1 y2 −y t1 ∗ 0 −y t2 ∗ ∗ 0  , t = [ t11 t12 ∗ t22 ] . proof. for ∀h1,h1 ∈ rn×n, the following inequality holds 0 = 2[zt (t)ht1 + ż t (t)ht2 ][z(t) −z(t− τ) − ∫ t t−τ ż(α)dα] = 2zt (t)ht1 z(t) − 2z t (t)ht1 z(t− τ) + 2ż t (t)ht2 z(t) − 2ż t (t)ht2 z(t− τ) − 2[zt (t)ht1 + ż t (t)ht2 ] ∫ t t−τ ż(α)dα. (9) https://doi.org/10.15837/ijccc.2022.4.4800 4 let h = [ h1 h2 ] , y = [ y1 y2 ] , t = [ t11 t12 ∗ t22 ] , so it knows that − 2δt (t)ht ∫ t t−τ ż(α)dα ≤ ∫ t t−τ [ ż(α) δ(t) ]t [ x y −h ∗ t ] [ ż(α) δ(t) ] dα. (10) the right of the above inequality is equal to∫ t t−τ ż(α)xż(α)dα + 2 ∫ t t−τ δt (t)(y t −ht )ż(α)dα + ∫ t t−τ δt (t)tδ(t)dα = ∫ t t−τ ż(α)xż(α)dα + ηt (t)   y t 1 + y1 −ht1 −h1 y2 −h2 −y t1 + ht1 ∗ 0 −y t1 + ht2 ∗ ∗ 0   η(t) + τδt (t)tδ(t). (11) taking (10) into (11), inequality (8) can be guaranteed. the proof of lemma 3 is completed. 3 the control design in this section, a decentralized controller with similar parameter is proposed for the consensus of multi-agent system with different dimension and time-delay. the following controller is designed: ui = ki1xi(t) + ki2xi(t− τ(t)) + k̄1[fixi(t) −fjxj(t)] + k̄2[fixi(t− τ(t)) −fjxj(t− τ(t))] + cik̄i n∑ j=1 aij[fixi(t) −fjxj(t)] + cik̄2 n∑ j=1 aij[fixi(t− τ(t)) −fjxj(t− τ(t))] −w̄ti ϕi(xi), (12) where w̄i is the estimated value of wi, w̃i = w̄i −wi is the estimated error. here, the adaptive law of w̄i is designed as follows: ˙̄ wi = −γwiw̄i + ηwiϕi(xi)(pbjjj)tei(t). (13) the adaptive law of coupling weight ci is designed as: ċi = −γcici −βc1ie t 1 (t)(pbjjjk̄1)e1(t) −βc2ie t 2 (t)(pbjjjk̄2)e2(t). (14) we define x̄i(t) = fixi(t), denote zij(t) = xi(t) −xj(t) and zij(t− τ(t)) = xi(t− τ(t)) −xj(t− τ(t)), then get ˙̄xi(t) = fiẋi(t). by using controller (12), adaptive laws (13)-(14), the following dynamic equation holds: ˙̄xi(t) = fi{ai1xi(t) + ai2xi(t− τ(t)) + biki1xi(t) + biki2xi(t− τ(t)) + bik̄1[fixi(t) −fjxj(t)] +bik̄2[fixi(t− τ(t)) −fjxj(t− τ(t))] + cibik̄1 n∑ j=1 aij[fixi(t) −fjxj(t)] +cibik̄2 n∑ j=1 aij[fixi(t− τ(t)) −fjxj(t− τ(t))] + bi[fi(xi) −ŵti ϕi(xi)]} = fi[ai1 + biki1]xi(t) + fi[ai2 + biki2]xi(t− τ(t)) + fibik̄1zij(t) + fibik̄2zij(t− τ(t)) +cifibik̄1 n∑ j=1 aij[fixi(t) −fjxj(t)] + cifibik̄2 n∑ j=1 aij[fixi(t− τ(t)) −fjxj(t− τ(t))] https://doi.org/10.15837/ijccc.2022.4.4800 5 +fibi[fi(xi) −w̄ti ϕi(xi)]. (15) according to assumption 1, it gets ˙̄xi(t) = (aj1 + bjkj1)fixi(t) + (aj2 + bjkj2)fixi(t− τ(t)) + bjjjk̄1zij(t) + bjjjk̄2zij (t− τ(t)) + cibjjjk̄1ei(t) + cibjjjk̄2ei(t− τ(t)) + bjjj[−w̃ti ϕi(xi) + εi]. (16) at the same time, the jth agent is the tracked system which can be rewritten as: ẋj(t) = aj1xj(t) + aj2xj(t− τi) + bj[uj + fj(xj)]. (17) define the consensus tracking error as: żij(t) = ˙̄xi(t) − ˙̄xj(t), (18) then the dynamical of tracking error equation is deduced as: żij(t) = (aj1 + bjkj1 + bjjjk̄1)zij(t) + (aj2 + bjkj2 + bjjjk̄2)zij(t− τ(t)) +cibjjjk̄1ei(t) + cibjjjk̄2eij(t− τ(t)) + bjjj[−w̃ti ϕi(xi,xj) + εij], (19) for simplicity, (19) can be further transformed as: ż = [in ⊗ (aj1 + bjkj1 + bjjjk̄1)]z(t) + ch ⊗ (bjjjk̄1)z(t) + [in ⊗ (aj2 + bjkj2 + bjjjk̄2)] z(t− τ(t)) + ch ⊗ (bjjjk̄2)z(t− τ(t)) + {in ⊗ (bjjj) n∑ i=1 [−w̃tijϕij(xi,xj) + εij]}, (20) where z(t) = [zt1 (t),zt2 (t), · · · ,ztn (t)] t with zti (t) = [zti1(t),zti2(t)], · · · ,ztij(t)]]t , z(t− τ(t)) = [zt1 (t− τ(t)),zt2 (t−τ(t)), · · · ,ztn (t−τ(t))] t , c = [ct1 ,ct2 , · · · ,ctn ] t . hence (20) is equal to the following form: ż(t) = [in ⊗(aj1 +bjkj1 +bjjjk̄1)]z(t) + [in ⊗(aj2 +bjkj2 +bjjjk̄2)]z(t−τ(t)) + [cl⊗(bjjjk̄1)] z(t) + [cl⊗ (bjjjk̄2)]z(t− τ(t)) + {in ⊗ (bjjj) n∑ i=1 bjjj[−w̃tijϕ(xi,xj) + εij]}, (21) where matrices fi and ki are similar parameters that are defined in assumption 1, k̄1 and k̄2 are two the gain matrices to be designed, which can be obtained by solving the following lmis: φ̄ =   φ̄11 φ̄12 φ̄13∗ φ̄22 φ̄23 ∗ ∗ φ̄33   < 0, (22)   s̄ ȳ1 ȳ2∗ t̄11 t̄12 ∗ ∗ t̄22   ≥ 0, (23) in which φ̄11 = v atj1 + aj1v + v mt1 + m1v + gt1 nt + ng1 + q̂ + r̂ + ȳ t1 + ȳ1 + τt̄11 + q̂11, φ̄12 = ȳ2 +τt̄12, φ̄13 = aj2v +m2v +ng2−ȳ t1 , φ̄22 = τs̄+τt̄22 +q̂22, φ̄23 = −ȳ t2 , φ̄33 = −q̂++q̂33, k̄1 = g1v −1, k̄2 = g2v −1. theorem 1. suppose assumption 1-2 are satisfied, if there exist some positive definite matrices v > 0, q̂ > 0, r̂ > 0, q̂11 > 0, q̂22 > 0, q̂33 > 0, s̄ > 0, and proper dimension matrices m1, m1, g1, g2, ȳ1, ȳ2, t̄11, t̄12, t̄22 such that (22) and (23) can be satisfied, then the multi-agent system (1) in a connected graph can be guaranteed to consensus by applying the feedback neural network control (12) with adaptive laws (13) and (14), and all the signals in closed-loop system are bounded. https://doi.org/10.15837/ijccc.2022.4.4800 6 proof: the following candidate lyapunov function is considered: v (t) = zt (t)(in ⊗p)z(t) + ∫ t t−τ zt (α)(in ⊗q)z(α)dα + ∫ 0 −τ ∫ t t+β żt (α)(in ⊗s)ż(α)dαdβ + 1 2 β−1c c tc + 1 2 η−1w tr(w̃ tw̃). (24) denoting ā1 = aj1 + bjkj1 + bjjjk̄1 and ā2 = aj2 + bjkj2 + bjjjk̄2, by using (13) and (14), then it has v̇ (t) = żt (t)(in ⊗p)z(t) + zt (t)(in ⊗p)ż(t) + żt (t)(in ⊗q)z(t) −zt (t− τ)(in ⊗q)z(t− τ) +τżt (t)(in ⊗s)ż(t) − ∫ t t−τ żt (α)(in ⊗s)ż(α)dα− n∑ i=1 γci βci c2i +2zt (t)(in ⊗p)[in ⊗ (bjjj)] n∑ i=1 [−w̃tijϕij(xi,xj) + εij] + 1 ηw tr(w̃t ˙̄w) = zt (t)[in ⊗ (āt1 p + pā1)]z(t) + z t (t)[in ⊗ (āt2 p + pā2)]z(t− τ) − 2z t (t)[cl⊗ (pbjk̄1)]z(t) −2zt (t)[cl⊗ (pbjk̄2)]z(t− τ) + 2zt (t)[i ⊗ (pbj)] n∑ i=1 [−w̃tij (t)ϕij(xi,xj) + εij(t)] +zt (t)(in ⊗q)z(t) −zt (t− τ)(in ⊗q)z(t− τ) + τżt (t)(i ⊗s)ż(t) − ∫ t t−τ żt (α)(i ⊗s)ż(α)dα− n∑ i=1 γwi ηwi w̃tijw̄ij − n∑ i=1 γci βci c2i . (25) according to lemma 3, it becomes v̇ (t) ≤ zt (t)[in ⊗ (āt1 p + pā1)]z(t) + z t (t)[in ⊗ (pā2 + āt2 p)]z(t− τ) −2zt (t)[cl⊗ (pbjk̄1)]z(t) − 2zt (t)[cl⊗ (pbjk̄2)]z(t− τ) +2zt (t)[in ⊗ (pbj)] n∑ i=1 [−w̃tij (t)ϕij(xi,xj) + εij(t)] + z t (t)(in ⊗q)z(t) −zt (t− τ)(in ⊗q)z(t− τ) + τżt (t)(in ⊗s)ż(t) − ∫ t t−τ żt (α)(in ⊗s)ż(α)dα + ηt (t)(in ⊗ ψ)η(t) + τzt (t)(in ⊗t)z(t) − n∑ i=1 γwi ηwi w̃tijw̄ij − n∑ i=1 γci βci c2i . (26) denoting n∑ i=1 εij = ε̄j, because the following two inequalities hold 2zt (t)[in ⊗ (pbjjj)ε̄j] ≤ zt (t)(in ⊗r)z(t) + ε̄tj [in ⊗ (j t j b t j p tr−1pbjjj)]ε̄j. (27) − γwi ηwi w̃tijw̄ij ≤− γwi ηwi w̃tijw̃ij + γwi 2ηwi w̃tijw̃ij + γwi 2ηwi wtijwij = − γwi 2ηwi w̃tijw̃ij + γwi 2ηwi wtijwij. (28) combining with (14) and (15), the following inequality is obtained: v̇ ≤ ηt (t)φη(t)− γw 2ηw tr(w̃tw̃) + γw 2ηw tr(wtw)− γc 2βc ctc + ε̄tj [in ⊗(j t j b t j p tr−1pbjjj)]ε̄j, (29) where φ =   φ11 φ12 φ13∗ φ22 φ23 ∗ ∗ φ33   with φ11 = āt1 p + pā1 + q + r + y t1 + y1 + τt11, φ12 = y2 + τt12, φ13 = pā2 −y t1 , φ22 = τ(s + t22), φ23 = −y t2 , φ33 = −q. https://doi.org/10.15837/ijccc.2022.4.4800 7 let φ < −ω, in which ω = diag{q̄1,q̄2,q̄3}, multiplying diag{p −1,p −1,p −1} on both side of this inequality, define p −1 = v , then one has φ̃ =   φ̃11 φ̃12 φ̃13∗ φ̃22 φ̃23 ∗ ∗ φ̃33   < 0, (30) where φ̃11 = v āt1 +ā1v +v qv +v rv +v y t1 v +v y1v +τv t11v +v q̄1v , φ̃12 = v y2v +τv t12v , φ̃13 = ā2v −v y t1 v , φ̃22 = τ(v sv + v t22v ) + v q̄2v , φ̃23 = −v y t2 v , φ̃33 = −v qv + v q̄3v . now, we let bjk̄j1 = m1, bjjj = n, k̄1v = g1, v y1v = ȳ1, v y2v = ȳ2, v t11v = t̄11, v qv = q̂, v rv = r̂, v sv = s̄, bjk̄j2 = m2, k̄2v = g2, then (29) is equal to (22). similarly, multiplying diag{p −1,p −1,p −1} on the right and left of (7), so it becomes inequality (23). based on the inequality (30), let δ = 12tr(w tw) + ε̄tj [in ⊗(jtj btj ptr−1pbjjj)]ε̄j, (29) becomes the following result: v̇ ≤−ζv (t) + δ, (31) inequality (31) means that all the signals in closed-loop multi-agent system are bounded. 4 experimental results and analysis in this section, a simulation example is given to prove the effectiveness of the proposed control method.we consider an multi-agent systems with eight subsystem, which consisting of a leader labeled 1 and seven followers labeled 2, 3, 4, 5, 6, 7, 8, that are shown as figure 1. figure 1: network topology with eight agents the adjacency matrix is:   0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 0 0 0   . (32) in this multi-agent system, the matrices are provided with different dimensions as: a11 = [ 2 1 −1 −2 ] , https://doi.org/10.15837/ijccc.2022.4.4800 8 a21 =   0.1 −0.3 −0.40.2 0.1 0.5 0 0 −0.5  , a31 =   0.1 −0.3 −0.4 0.1 0.2 0.1 0.5 0.3 0 0 −0.5 0 0 0 0 −0.3  , a41 =   0.1 −0.3 −0.4 0.1 0.2 0.2 0.1 0.5 0.3 −0.1 0 0 −0.5 0 0 0 0 0 −0.3 0 0 0 0 0 −0.2  , a51 =   0.1 −0.3 −0.4 0.1 0.2 0.2 0.2 0.1 0.5 0.3 −0.1 0.2 0 0 −0.5 0 0 0 0 0 0 −0.3 0 0 0 0 0 0 −0.2 0 0 0 0 0 0 −0.5   , a61 =   0.1 −0.3 −0.4 0.1 0.2 0.2 −0.2 0.2 0.1 0.5 0.3 −0.1 0.2 −0.5 0 0 −0.5 0 0 0 0 0 0 0 −0.3 0 0 0 0 0 0 0 −0.2 0 0 0 0 0 0 0 −0.5 0 0 0 0 0 0 0 −0.3   , a71 =   0.1 −0.3 −0.4 0.1 0.2 0.2 −0.2 0.1 0.2 0.1 0.5 0.3 −0.1 0.2 −0.5 0.5 0 0 −0.5 0 0 0 0 0 0 0 0 −0.3 0 0 0 0 0 0 0 0 −0.2 0 0 0 0 0 0 0 0 −0.5 0 0 0 0 0 0 0 0 −0.3 0 0 0 0 0 0 0 0 −0.4   , a81 =   0.1 −0.3 −0.4 0.1 0.2 0.2 −0.2 0.1 0.2 0.2 0.1 0.5 0.3 −0.1 0.2 −0.5 0.5 0.4 0 0 −0.5 0 0 0 0 0 0 0 0 0 −0.3 0 0 0 0 0 0 0 0 0 −0.2 0 0 0 0 0 0 0 0 0 −0.5 0 0 0 0 0 0 0 0 0 −0.3 0 0 0 0 0 0 0 0 0 −0.4 0 0 0 0 0 0 0 0 0 −0.2   . a11 = [ 1 0.5 −0.5 0 ] , a12 = [ 1 1 1 2 ] , a22 =   0.2 −0.1 −0.40.3 0.2 0.5 0 0 −0.3  , a32 =   0.2 −0.1 −0.4 −0.1 0.3 0.2 0.5 0.2 0 0 −0.3 0 0 0 0 −0.4  , a42 =   0.2 −0.1 −0.4 −0.1 0.2 0.3 0.2 0.5 0.2 −0.1 0 0 −0.3 0 0 0 0 0 −0.4 0 0 0 0 0 −0.1  , a52 =   0.2 −0.1 −0.4 −0.1 0.2 0.3 0.3 0.2 0.5 0.2 −0.1 −0.3 0 0 −0.3 0 0 0 0 0 0 −0.4 0 0 0 0 0 0 −0.1 0 0 0 0 0 0 −0.2   , a62 =   0.2 −0.1 −0.4 −0.1 0.2 0.3 −0.1 0.3 0.2 0.5 0.2 −0.1 −0.3 0.2 0 0 −0.3 0 0 0 0 0 0 0 −0.4 0 0 0 0 0 0 0 −0.1 0 0 0 0 0 0 0 −0.2 0 0 0 0 0 0 0 −0.5   , https://doi.org/10.15837/ijccc.2022.4.4800 9 a72 =   0.2 −0.1 −0.4 −0.1 0.2 0.3 −0.1 0.5 0.3 0.2 0.5 0.2 −0.1 −0.3 0.2 0.1 0 0 −0.3 0 0 0 0 0 0 0 0 −0.4 0 0 0 0 0 0 0 0 −0.1 0 0 0 0 0 0 0 0 −0.2 0 0 0 0 0 0 0 0 −0.5 0 0 0 0 0 0 0 0 −0.3   , a82 =   0.2 −0.1 −0.4 −0.1 0.2 0.3 −0.1 0.5 0.3 0.3 0.2 0.5 0.2 −0.1 −0.3 0.2 0.1 −0.1 0 0 −0.3 0 0 0 0 0 0 0 0 0 −0.4 0 0 0 0 0 0 0 0 0 −0.1 0 0 0 0 0 0 0 0 0 −0.2 0 0 0 0 0 0 0 0 0 −0.5 0 0 0 0 0 0 0 0 0 −0.3 0 0 0 0 0 0 0 0 0 −0.1   , b1 = [ 1 0 0 1 ]t , b2 = [ 1 0 0 0 1 0 ]t , b3 = [ 1 0 0 0 0 1 0 0 ]t , b4 = [ 1 0 0 0 0 0 1 0 0 0 ]t , b5 = [ 1 0 0 0 0 0 0 1 0 0 0 0 ]t , b6 = [ 1 0 0 0 0 0 0 0 1 0 0 0 0 0 ]t , b7 = [ 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 ]t , b8 = [ 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 ]t , f1 = [ 1 0 0 1 ] , f2 = [ 1 0 0 0 1 0 ] , f3 = [ 1 0 0 0 0 1 0 0 ] , f4 = [ 1 0 0 0 0 0 1 0 0 0 ] , f5 = [ 1 0 0 0 0 0 0 1 0 0 0 0 ] , f6 = [ 1 0 0 0 0 0 0 0 1 0 0 0 0 0 ] , f7 = [ 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 ] , f8 = [ 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 ] , k11 = [ −6 −3 −1 0 ] , k21 = [ −4.1 −1.7 0.4 −2.2 −2.1 −0.5 ] , k31 = [ −4.1 −1.7 0.4 −0.1 −2.2 −2.1 −0.5 −0.3 ] , k41 = [ −4.1 −1.7 0.4 −0.1 −0.2 −2.2 −2.1 −0.5 −0.3 0.1 ] , k51 = [ −4.1 −1.7 0.4 −0.1 −0.2 −0.2 −2.2 −2.1 −0.5 −0.3 0.1 −0.2 ] , k61 = [ −4.1 −1.7 0.4 −0.1 −0.2 −0.2 0.2 −2.2 −2.1 −0.5 −0.3 0.1 −0.2 0.5 ] , k71 = [ −4.1 −1.7 0.4 −0.1 −0.2 −0.2 0.2 −0.1 −2.2 −2.1 −0.5 −0.3 0.1 −0.2 0.5 −0.5 ] , k81 = [ −4.1 −1.7 0.4 −0.1 −0.2 −0.2 0.2 −0.1 0.2 −2.2 −2.1 −0.5 −0.3 0.1 −0.2 0.5 −0.5 −0.4 ] . k12 = [ −2 1 −2 −2 ] , k22 = [ −1.2 2.1 0.4 −1.3 −0.2 −0.5 ] , k32 = [ −1.2 2.1 0.4 0.1 −1.3 −0.2 −0.5 −0.2 ] , k42 = [ −1.2 2.1 0.4 0.1 −0.2 −1.3 −0.2 −0.5 −0.2 0.1 ] , k52 = [ −1.2 2.1 0.4 0.1 −0.2 −0.3 −1.3 −0.2 −0.5 −0.2 0.1 0.3 ] , k62 = [ −1.2 2.1 0.4 0.1 −0.2 −0.3 0.1 −1.3 −0.2 −0.5 −0.2 0.1 0.3 −0.2 ] , k72 = [ −1.2 2.1 0.4 0.1 −0.2 −0.3 0.1 −0.5 −1.3 −0.2 −0.5 −0.2 0.1 0.3 −0.2 −0.1 ] , k82 = [ −1.2 2.1 0.4 0.1 −0.2 −0.3 0.1 −0.5 −0.3 −1.3 −0.2 −0.5 −0.2 0.1 0.3 −0.2 −0.1 0.1 ] . by solving the lmis (19) and https://doi.org/10.15837/ijccc.2022.4.4800 10 (20), time delay, the positive matrix and control gain matrices are obtained respectively as: τ = 0.2 p = [ 0.2399 −0.1842 −0.1842 0.6083 ] , k̄1 = [ −0.4473 5.7975 4.4031 −7.4369 ] , k̄2 = [ 0.2569 0.4542 0.0320 0.7431 ] . the nonlinear functions are chosen as:  f(x1) = [−x11sin(x12) + x11sin(x11)cos(x12); x11sin(x12)]. f(x2) = [−x21sin(x22) + x22sin(x23)cos(x22); x21sin(x22)]. f(x3) = [−x31sin(x32) + x32sin(x33)cos(x32); x31sin(x34)]. f(x4) = [−x41sin(x42) + x43sin(x41)cos(x44); x41sin(x45)]. f(x5) = [−x51sin(x52) + x53sin(x54)cos(x52); x55sin(x56)]. f(x6) = [−x61sin(x62) + x63sin(x64)cos(x65); x66sin(x67)]. f(x7) = [−x71sin(x72)cos(x74) + x73sin(x75)cos(x76); x77sin(x78)]. f(x8) = [−x81sin(x82)cos(x83) + x84sin(x85)cos(x86); x87sin(x88)cos(x89)]. (33) the initial values of the states in the multi-agent system are chosen as: x1(0) = [ 0.2 0.18 ] , x2(0) =[ 0.3 −0.16 0.1 ] , x3(0) = [ 0.19 0.24 0.15 0.2 ] , x4(0) = [ 0.11 0.29 −0.1 0.16 0.24 ] , x5(0) = [ −0.9 0.6 0.21 −0.11 −0.13 0.22 ] , x6(0) = [ 0.25 0.2 0.1 0.24 0.2 0.11 0.2 ] , x7(0) = [ 0.18 0.21 −0.1 0.16 0.21 0.1 0.14 −0.11 ] , x8(0) = [ 0.24 0.2 0.11 0.1 0.1 0.14 0.21 −0.1 −0.12 ] . the initial values of adaptive parameters w̄i(t) are given as: w̄1(0) = [ 0.49 0.45 0.41 0.46 0.39 ]t , w̄2(0) = [ 0.9 0.22 0.10 0.10 0.13 ]t , w̄3(0) = [ 0.10 0.27 0.31 0.11 0.21 ]t , w̄4(0) = [ 0.19 0.15 0.17 0.14 0.21 ]t , w̄5(0) = [ 0.15 0.12 0.25 0.21 0.31 ]t , w̄6(0) = [ 0.6 0.3 0.7 0.2 0.9 ]t , w̄7(0) = [ 0.17 0.19 0.15 0.17 0.14 ]t , w̄8(0) = [ 0.4 0.5 0.7 0.3 0.2 ]t . the simulation results are illustrated in fig.2-4. figure 2: trajectiories of the state of xi1,xi2,xi3,xi4,xi5,xi6,xi7,xi8,xi9 https://doi.org/10.15837/ijccc.2022.4.4800 11 figure 3: time response of the adaptive estimation parameters w̄i figure 4: time response of coupling laws ci the states of multi-agents with diverse dimensions and time delays can attain uniform stability using the developed distributed feedback neural network adaptive control, as shown in subfigures (a)(i) in fig. 2. fig. 3 and fig. 4 show the time responses of rbfnn estimate parameters and coupling weights, which are guaranteed to be semi-globally uniformly ultimately bounded. 5 conclusion this research focus on the consensus control of nonlinear heterogeneous multi-agent systems with time delays and nonidentical dimensions. a distributed neural network adaptive control based on similar parameters and time delay is built, with the goal of achieving the consensus of all the agents, and the neural network systems are used to approximate the unknown nonlinear functions in the subsystems. using the suggested control approach, the states of each follower system can track the state of the leader system. the control technique can be used to control both homogeneous multi-agent systems of the same dimension and agents of different dimensions. funding this work is supported by the national natural science foundation of china under grant (62003262, 61903298), national natural science foundation of shaanxi under grant (2022jm-389, 2019jq341),shaanxi provincial department of science and technology key project in the field of industry (2018zdxm-gy-039), shaanxi provincial scientific and technological activities for overseas staff preferential projects under grant 35. https://doi.org/10.15837/ijccc.2022.4.4800 12 author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] seo, j. and kim, y. and kim, s. and tsourdos, a. 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(2014). linear matrix inequalities in system and control theory, proceedings of the ieee, 86(12), 2473-2474, 1994. https://doi.org/10.15837/ijccc.2022.4.4800 14 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: qin, b.; fan, y.; yang, s. (2022). (2022). distributed adaptive control for nonlinear heterogeneous multi-agent systems with different dimensions and time delay, international journal of computers communications & control, 17(4), 4800, 2022. https://doi.org/10.15837/ijccc.2022.4.4800 introduction the model of dynamical network and assumptions the control design experimental results and analysis conclusion int j comput commun, issn 1841-9836 8(4):548-559, august, 2013. multithreaded application for real-time visualization of ecg signal waveforms and their spectrums s.s. ilić, a.č. žorić, p. spalević, lj. lazić siniša ilić, aleksandar č. žorić, petar spalević university of pristina in kosovska mitrovica faculty of technical sciences computer sciences and informatics department serbia, 38220 kosovska mitrovica, kneza miloša 7 sinisa.ilic@pr.ac.rs ljubomir lazić state university of novi pazar serbia, 36300 novi pazar, vuka karadžića bb llazic@np.ac.rs abstract: by using concept of virtual instrumentation, signals from human body can be digitized and transferred to computer for further processing. software in a computer enables use of modern tools for digital signal processing that can be improved easily with emergence of new knowledge and with increasing of computer performances. presenting the ecg signal in both: time and time-frequency domains enables to cardiologist to obtain more reliable diagnosis. in order to present simultaneously the waveform and spectrogram of ecg signal in the real time we use fast fourier and discrete wavelet transform in the multithreading environment of a standard personal computer. the synchronization of accessing the signal data by threads according to the principle one thread a time is performed by controlling the state of event type variables. keywords: biomedical signal processing, fast fourier transforms, multithreading, real time systems, wavelet transforms 1 introduction the standard data acquisition devices are powerful, relatively expensive and designed to perform one or more specific tasks defined by the vendor. however, the user generally cannot extend or customize them. virtual instruments, by virtue of being pc-based, inherently take advantage of the benefits from the latest technology incorporated into off-the-shelf pcs. use of pc in so called virtual or personal instrumentation [1], [2] development enables realization of a new generation of superior devices. virtual instrument consists of two sub-devices: 1. acquisition hardware device that collects data and transform it in the form suitable for reading by pc and 2. computer with software (user interface) device that reads digital data, process it and presents results. once the data has appeared at the input of computer in digital form, it can be stored and used for different types of processing as many times as needed by using different or same software. as the central role in the virtual instrumentation concept is played by the software [1], [3], the costs of development, maintenance and reconfiguration of instruments are reduced significantly. these devices can be connected to the internet, they offer easy modifications according to the users needs, it is possible to store large amount of data at a cheap price, and these data can be retrieved quickly. copyright c⃝ 2006-2013 by ccc publications multithreaded application for real-time visualization of ecg signal waveforms and their spectrums 549 by using of pc-based ecg device that is built upon the principles of virtual instrumentation, we can monitor changes of bio-potentials at the skin of the human body caused by heart activity ecg. some of the tools for processing of ecg signal that can be implemented in this device are: segmentation (recognition and separation of ecg signal parts) [4], [5], measuring of heart rate variability [6], compression of signal for storage purposes [7], etc. the analysis of ecg signal in time-frequency domain can help cardiologist to have a better look to the signal from a different angle. the presence of some frequency components in appropriate segments of ecg signals can help cardiologists in decision making [8] [10]. the mathematical tools that can be used for this purpose are: short time fourier transform (stft), discrete wavelet transform (dwt) and continual wavelet transform (cwt). it would be very useful for the users to monitor ecg signal in time-frequency domain in real time. the need for real-time processing of medical images becomes higher and some implementations are presented in the following papers [11] [13]. in order to enable processing in real-time, faster algorithms [14], multithreading [15], modularity [16] and other improvements are needed. we have come to idea to plot in real time simultaneously ecg signal in both time and timefrequency domain using stft and dwt. the idea is realized by development of multithreading application in software part of our virtual instrument [18]. 2 the pc-ecg device the pc-based ecg device is described in [9], [17], but the most important features will be presented in this paper too. the main user requirements, suggested by cardiologists, were the following functional and control system performances: • selection of one of twelve leads for real time monitoring and recording by the descriptive and intuitive gui, • recording of waveforms for each of twelve leads during a time interval of six seconds, • connection to the database of patients, • filtering of patient body signal from the public power network frequency, • possibility to select, measure the selection time duration and zoom the selection, • printed report generation (waveforms of 12 leads and cardiologist diagnosis), • easily portable and no additional power supply (plug into pc and use), • 100 % patient’s body insulation. 2.1 hardware part of the device the hardware part of the system is designed by using microchip microcontroller pic16f876 and usb communication port of a pc is selected as interface for bilateral communication between acquisition hardware and a pc. the communication between acquisition hardware and a pc can be obtained: 1) by using standard usb cable; in this case pc is power supplier of the acquisition hardware and 2) by using maxstream xbee rf module; in this case the device is powered by battery and communication is wireless the usb rf dongle using pic18f2550 mcu and xbee rf module is attached to a pc and it communicates with acquisition device equipped with the same xbee rf module [18]. the selection of communication type depends on distance between a patient and a pc if the distance is short, the usb cable will be used and if the distance is long the wireless communication will be selected. the xbee rf modules operate within the zigbee protocol [18] and support the unique needs of low-cost, low-power wireless sensor networks. 550 s.s. ilić, a.č. žorić, p. spalević, lj. lazić the ecg signal is sampled with sampling rate of 960hz. we used two bytes for transmission of each sample value; hence the transmission speed was set to 19600 baud/s. the program of microcontroller selects the appropriate ecg channel (ecg lead) of multiplexer and performs a/d conversion or stops the program execution, depending on the code received from pc. 2.2 graphical user interface design the design of software part of the instrument is based on the traditional style of ecg paper plots with additional overlay displays and other features such as: zoom, filter, database support, etc. based on proposals of cardiologists. the device can be used just to monitor signal waveforms (without saving the data) or it can be used to store signal at the buffer memory of pc. a user can select to preview and save the signal of any of 12 ecg leads. the application screen is divided to main and auxiliary window. when the save button is clicked on the toolbar, the waveform of recorded signal is shown at the auxiliary window while the plotting of running signal is continued at the main window. a user can decide to overwrite already recorded signal by the signal he/she monitored at later time. after signal of all or some ecg leads are recorded at the buffers of pc (the signal of one lead is saved in one buffer), a user can save the samples from the buffers to the database with the: general data about patient, patient visit with medical anamnesis, explanation on interpretation of signals anomaly and selection of one of predefined diagnosis. all 12 leads with patient data, medical anamnesis, diagnosis and treatment can be printed in two-sided a4 paper report and given to the patient. software enables defining and marking the segments of ecg signal by a user (for example qt-interval), measuring the segment time duration (for example 0.412 seconds) and saving the boundaries of those segments along with signal to the database. the cardiologist can analyze the signal again, after it is recorded considering all interpretations he or somebody else put in database (for example: in the lead v1 st depression up to 3mm, in the lead d3 small elevation, qt segment too wide etc.). by selection of diagnosis from the finite list of diagnoses for each patient visit, it becomes easier for cardiologist to search for appropriate signal waveforms, interpretations of waveforms and patients in database. database also helps him to compare the waveforms and interpretations of ecg signal for the same patient taken in different patient visits and to make appropriate decisions on treatment and disease progress. the screenshot of application with three parts the main waveform (at the bottom), the main spectrogram/scalogram (at the central part for spectrum and on the right part for color legend), and auxiliary window (at the top-right) is presented in figure 1. 3 time-frequency presentations of ecg signal in pc-ecg device there are two modes of plotting ecg in time-frequency domain in the application: standard and real-time mode. the standard (off-line) mode enables plotting of spectrogram (in the case of stft) or scalogram (in the case of wavelet analysis) after selecting the waveform of the lead already recorded in database or in memory buffer. in this case the waveform of 6 seconds duration is processed at once; the time-frequency coefficients are calculated and forwarded to plot function. the calculation and plotting of the coefficients is not time limited in this mode. in the real time (on-line) mode samples of the signal appears continuously at the input port of a pc. it is not necessary to plot sample by sample in real-time, it is enough to plot group of samples one by one every 40 milliseconds (25 times in a second). the drawing of ecg multithreaded application for real-time visualization of ecg signal waveforms and their spectrums 551 figure 1: the screenshot of pc-ecg application waveform, calculation of coefficients and drawing the time-frequency presentation of samples in 40 milliseconds time-frame must be performed between two calls of ontimer function. as the time-frequency plot consist of three axes: time, frequency and component value, we have decided to present the third axis by color. the color palette has 240 colors: from nuances of dark blue toward nuances of light blue, green, yellow, light red and dark red (see figure 1). before plotting of spectrogram (scalogram) the range of all calculated values of signal’s coefficients in the standard mode is normalized by the maximum absolute value and proportionally divided to integer values in the range 0-239. in the real-time mode calculation of coefficients is performed on portions of the signal. the maximum of coefficients’ values differs from portion to portion. this way of normalization might produce visually same spectrums for portions of signals with different amplitudes. hence, the values of calculated coefficients are normalized by predefined maximum value (the maximum of maximum values found in standard mode spectrograms). the stft spectrum is calculated by using fft function on overlapped windows. the window length (in samples), length of shifting and windowing function can be selected on appropriate application dialog (see figure 2a). figure 2: the application dialogs for selecting parameters for calculating spectrograms the samples of the window are multiplied with coefficients of windowing function, the fft is then applied on the resulting set and spectral amplitudes are obtained. the window is then shifted by chosen number of samples and the process is repeated. if n is the total number of 552 s.s. ilić, a.č. žorić, p. spalević, lj. lazić samples in recorded signal, w is the number of samples in window and o is number of samples for which the windows are shifted, then the following is valid: n = w +k ×o, where k +1 is the total number of windows to be calculated. from w samples of one window w/2 + 1 amplitudes of frequency components are calculated and presented over the y axis, so the total number of spectral coefficients of the signal is (w/2 + 1) × (k + 1). in the standard mode all coefficients are calculated first, then normalized and plotted. in the real-time mode it is necessary to calculate, normalize and plot coefficients of n/o overlapping windows in less than 40 milliseconds, where n is the number of samples captured in the 40 ms time frame. the application function that calculates stft spectrum in real time takes the following input parameters: 1) the window length w , 2) the shifting size o, 3) sample set size n, 4) pointer on last n samples (∗ns) and 5) pointer on w samples captured before last n samples (∗ws) on the input port of a pc. the function starts with making the window from w −o last samples of ∗ws and o starting samples of ∗ns and calls the fft function by forwarding samples of the created window. then the new window is created by w − 2 × o last samples from ∗ws and 2 × o starting samples from ∗ns and forwarded to fft function. the process is repeated until the last shifting window of length w is created and its spectral coefficients calculated. the coefficients are then normalized and plotted at appropriate portion of spectrum part of the application screen. the dwt spectrum is calculated by using convolution function. the wavelet function and the window length (w in samples) can be selected on appropriate application dialog (see figure 2b). after selecting the wavelet function name, the wavelet and scaling function coefficients (of length h) are retrieved from the table of wavelets. in the standard mode signal (of length n) is extended on both sides because of border distortion by symmetric boundary value replications of length h − 1 each and filtered with appropriate wavelet (high pass filter) and scaling (low pass filter) function. the filtering is performed by convolution function. as the number of samples in the recorded signal is large, the fft convolution [20] is applied instead of classical convolution because of speed improvements. two filtered signals are now obtained approximation a1 (low pass) and details d1 (high pass). in each signal, n central coefficients are selected and from them odd coefficients are kept and even discarded thus resulting in two groups of n/2 coefficients. detail coefficients d1 are saved at buffer and the process of obtaining the new approximation a2 and detail d2 coefficients from approximation coefficients a1 is repeated again by using the same procedure, but now beginning with n/2 coefficients of a1 instead of n samples of signal. the process is repeated l = log 2w times and the number of obtained coefficients is halved in each iteration (level). the dwt spectrum consists of coefficients of details d1, d2, . . . , dl where levels represent frequency ranges. in the real-time (on-line) mode the coefficients are calculated by processing dwt window by window (of length w ) from fifo buffer where ecg samples are captured. the border distortion is eliminating by keeping samples and coefficients of ai in l buffers of length h − 1 from previous windows. the coefficients of a1 and d1 are calculated from w samples of current window and h−1 samples of previous window, coefficients of a2 and d2 are calculated from w/2 coefficients of just calculated coefficients of a1 and last h − 1 coefficients of a1 from previous window etc. the number of samples in window w is small and the standard convolution is used. the coefficients of details that are calculated in time of 40 ms are normalized and plotted at appropriate portion of spectrum part of the application screen. the calculation of cwt is for now possible only in standard off line mode. from the samples of signal, cwt is obtained by filtering signal with wavelet functions of different scales, where scale represents measure of stretching of wavelet function in time. the pseudo frequency components are obtained from scales by formula: multithreaded application for real-time visualization of ecg signal waveforms and their spectrums 553 fq = fc sc × ∆ (1) where fc is central frequency of chosen wavelet (for example fc=0.8125hz for morlet wavelet), sc represents scale and ∆ is sampling period of the signal [21]. usually the cwt of ecg signal is presented in so called semi-log axes the frequency axis is logarithmic and time axis is linear (see figure 1). if the spectrogram (scalogram) is to be presented in logarithmic axis from frequency 2−1 to 27 hz (scales from 1560 to 6.1 with use of ∆ = 1/960 s) in s (for example 65) scales, s filters has to be derived from chosen wavelet function. the orders of these filters for morlet wavelet (of order 16) are from 98 (for frequency 27) to 24961 (for frequency 2−1) and hence the complexity of calculating cwt is large. the number of filters determines y axis resolution the larger number the better resolution. final number of calculated coefficients is s × n where n is the total number of samples of recorded signal. the application dialog for selecting frequency range and resolution is presented in figure 2c. 4 multi-threading the multi-threading in pc-based ecg device is performed by using three threads: the main window standard thread, the worker thread and the gdi graphic thread. the main window standard thread starts with starting of application execution. this thread can work stand alone. in the thread a user can open the database, search the particular patient by name, date and diagnosis, open the ecg waveforms of patient found, measure the time width of particular segments, and analyze the waveform already stored in both: time domain and timefrequency domain using fft, dwt and cwt in standard mode. a cardiologist can analyze recorded standard ecg waveforms and change diagnosis and add some comments. the printing function is also controlled by this thread. when a user wants to monitor ecg signal of a patient in real-time, by pressing appropriate button in application, the working thread and timer are started (by function ontimerstart) and the main thread is then directed to execute function ontimer periodically. the communication between the acquisition module and a pc is available only by using the working thread. this thread represents listener because it listens for data appearance on appropriate serial communication port of pc that are sent by the acquisition module. when data appear on the port, the working thread copy them to the transmission buffer which task is to copy data from one thread to the other. the transmission buffer holds the data that can be accessed only one thread a time. as the size of the buffer is initialized at the program start, the varying size of currently transmitted data between threads is controlled by unsigned integer variable that shows how many bytes are copied to the buffer. the main thread periodically (on timer ticks) checks for the new contents of the transmission buffer, copies data to the local buffer, resets the variable that shows how many bytes are copied from the buffer to zero and forwards data from local buffer to the drawing function. the drawing function then draws the portion of the signal based on just received data on the main screen using appropriate sizing of signal values. at this point, it is important to say that not the whole screen is re-drawn after reception of new data, but only the portion of it that corresponds to the new data size. as the time axis of the main screen is predefined to be 6 seconds, and the sampling frequency of input data is known, it is easy to calculate the window region that should be refreshed (mfc function invalidaterect()) upon reception of the new data. the timer frequency is set to be 25 ticks per second that results in about 40 samples per tick. if the ecg signal in those samples is "polluted" by public power network frequency of 50hz, the band pass iir filter will eliminate this noise and then the samples of "clean" signal are forwarded to 554 s.s. ilić, a.č. žorić, p. spalević, lj. lazić the drawing function. the speed of modern computers allows this filter to be implemented in real-time without jeopardizing continuity of signal drawing on the main screen. unfortunately, the time-frequency transform and drawing spectrograms (scalograms) cannot be always executed in real time by processing samples of signal in the main thread after drawing the signal in time domain. by introducing gdi thread that works independently of the main thread, it is possible to copy new samples from the main to the gdi thread and to let the gdi thread calculate spectrum portion of the signal and present the values of its coefficients to the spectrum screen simultaneously with the main screen drawing of the signal waveform performed by main thread. the diagram of running multi threads in our pc-based ecg device is presented in figures 3 and 4. 4.1 threads synchronization to synchronize work of three threads in parallel, we used six "event" variables. this type of variable can have two states "set" and "reset". appropriate built-in functions: waitforsingleobject and waitformultipleobjects that take parameters of "event" type, enable checking of event states. the both functions can control the workflow of execution of the program lines within the thread based on the state of event variable they control. the input parameters of the functions are: the name(s) of event(s) they control and the time the function has to wait for event to change state from reset to set. if the input event variable changes the state to "set" within the waiting time interval, the program executes one block of program lines at the "detected set event" output of the function, and if state of event variable remained "reset" within the time interval, the program executes another block of program lines at the "time out" output. the behavior of these functions is symbolically presented by yellow decision modules in figures 3 and 4, where ”yes” output directs the program flow to the first block of lines and ”no” output directs to the second. two event variables mainwevent andrdrevent are used to synchronize copying data between the working and the main thread. mainwevent is controlled by the main thread (only main thread can change its state) and rdrevent is controlled by the working thread. the working thread is started by command ontimerstart in the main thread and mainwevent is initiated to the "set" state. it enables the working thread to copy data from serial port to transmission buffer_1 before the main thread starts checking the content of transmission buffer_1 (then the mainwevent is put in "reset" state). when data are received at the serial port in working thread, this thread resets the rdrevent (by this action it forbids the main thread to access transmission buffer_1) and checks the state of mainwevent. if the state of this event is "set", data from local buffer (of this thread) are copied to transmission buffer_1. as the state of rdrevent is "reset" during copying, the main thread in timer function is stopped for time period of 1ms. if the copying is finished before the waiting time expires, the reader thread change the state of rdrevent to "set", and enables main thread to copy data from transmission buffer_1 to its local buffer (see figures 3 and 4). it may happen that both variables mainwevent and rdrevent are simultaneously in "reset" state and both wait for each other to change the state. the working thread is set to wait for the state of mainwevent infinitely, and the execution of this thread literally stops when mainwevent is in "reset" state, but the main thread is set to wait for the change of rdrevent state just 1 ms. when 1 ms passes, the main thread resolves the conflict by changing the state of mainwevent and thus enabling the working thread to continue. the situation when both variables are simultaneously in "set" state is impossible, because before checking the state of variable that is controlled by another thread, each thread resets the multithreaded application for real-time visualization of ecg signal waveforms and their spectrums 555 figure 3: the work-flow diagram of the main thread figure 4: the work-flow diagram of the working (left) and graphical (right) threads 556 s.s. ilić, a.č. žorić, p. spalević, lj. lazić own event variable. setting and resetting of event variables are presented in figures 3 and 4 by small semaphores (traffic lights) with red and green lights. the synchronization between the main and the graphical thread is organized in the same way as the synchronization between the main and the working threads. here we have another two event variables gdievent (controlled by the gdi thread) and maingevent (controlled by the main thread). the transmission of data between these threads is performed through transmission buffer_2. as can be seen from the figures 3 and 4, the task of the working thread is to receive data from serial port and to copy it to the main thread. the main thread can draw the waveform portion of input samples to "time-domain" screen in real-time and, if time-frequency analysis is selected, forward the samples to the graphical thread to perform calculation of signal spectrum and present spectrogram (scalogram) on separate screen window. the role of the event variable killrevent is to control running of program loop in working thread. the state of this variable is controlled by the main thread. when the working thread starts, the initial value of killrevent is "reset" and it enables the program lines in this thread to be executed in the loop. but when the state of the event has been changed, the loop in the working thread is finished and this thread is closed. the situation of closing the graphical thread is the same as for closing the working thread, but the variable killgevent is used. 5 experimental results and discussion in order to test efficiency of multi-threading, we recorded the waveform samples of ecg signal of 72 seconds duration (equal to 69120 samples). then we tested the response of the software by sending recorded samples from another computer in four different baud-rates: 19200, 38400, 57600 and 115200 bits/second. the software is modified in a way that user can choose to see scalogram that will be 1) drawn in the same thread where the ecg waveform is drawn (we call this method "single") or 2) drawn in another (gdi) thread (we call this method "multi"). we measured time needed to main thread executes ontimer function where: • data are copied from working thread, • filtering of the received samples is performed (using iir filter) [17, 18], • connection to the database of patients, • the coordinates of ecg waveform are calculated and drawn, • samples are sent to gdi thread (in the case of multi-threads) or wavelet transform is calculated and coefficients are drawn in separate portion of the screen (in the case of single thread). we used the following c++ instructions for calculation of time of execution of ontimer function: void cecgview::ontimer(uint nidevent){ large_integer ntime1,ntime2; large_integer freq; queryperformancefrequency(&freq); queryperformancecounter(&ntime1); . . . set of commands in ontimer function . . . queryperformancecounter(&ntime2); ntime = (ntime2.quadpart-ntime1.quadpart)/(freq.quadpart/1000); } multithreaded application for real-time visualization of ecg signal waveforms and their spectrums 557 in this way we were able to measure time between beginning and ending of function ontimer with accuracy of 1ms. the wavelet we used in our experiments was daubechies 4 with 8 coefficients and the window width was set to 32 samples. the experiments were performed on 8 different computers with different processors, different graphical cards and different services running in background. the purpose of making this experiment was not to compare the speed of these computers, but just to compare the speed of running program in single and multiple threads in different baud rates. for each computer 8 different experiments were performed by changing baud-rate and number of threads. the number of samples received during one call of ontimer function, duration of execution of instructions within the function and time step between two successive calls of the function for baud rate of 19200 bauds/second in the single thread is given in the table 1. table 1: the measurements of number of samples received, duration of ontimer function and time step between two succesive calls of ontimer function in single thread number of duration of time step samples received ontimer function (ms) 48 0 47 48 1 47 48 0 47 32 0 47 48 1 47 48 0 47 ... ... ... by averaging number of samples received in one call of ontimer function we obtained the following values: 44.5, 88.5, 134 and 267 samples for baud-rates of 19200, 38400, 57600 and 115200 bits/second respectively. the average time between two successive calls of the function was 46.8ms for modern pc and 40ms for old computer with intel celeron working on frequency of 800 mhz (the timer clock was set to trigger function in 40ms for all computers). the average times of execution of ontimer function in milliseconds are given in the table 2. table 2: average execution time in [ms] of ontimer function in different computers computer threads 19200 38400 57600 115200 intel i7 multi 0.000 0.000 0.000 0.000 2600k single 0.000 0.000 0.022 1.012 compaq multi 0.007 0.008 0.008 0.009 8710p single 1.172 2.640 3.932 7.210 vostro multi 0.007 0.007 0.013 0.012 1510 single 1.340 3.109 2.314 4.837 celeron multi 0.000 0.000 0.000 0.016 800mhz single 1.422 2.357 3.749 8.112 probook multi 0.001 0.001 0.003 0.035 6460b single 0.503 1.707 6.172 11.102 amd ai multi 0.000 0.000 0.000 0.062 3000+ single 0.391 1.076 2.094 4.517 amd aii multi 0.005 0.006 0.115 0.115 5000+ single 0.077 1.515 2.430 4.617 intel atom multi 0.109 0.124 0.147 0.168 n450 single 4.148 9.048 13.674 29.955 when the software was executed on intel atom netbook computer, with single thread and baud-rate of 115200 bauds/second, after several seconds of running, the execution time of ontimer function rose to 94ms and running of the software was blocked. 558 s.s. ilić, a.č. žorić, p. spalević, lj. lazić it is obvious from the table 2 that calculating coordinates for drawing waveform, performing filtering of ecg samples and passing samples to gdi thread takes less time than with addition of calculating spectral coefficients and drawing them in the same thread. in the case of slower computers the next call of ontimer function can happen before the previous running of the same function is finished. in this case both instances of the function will need access to the same buffer. then running of the software is blocked. 6 conclusion the pc based ecg device is a solution for real time complex signal processing in analysis of ecg signal. it provides presentation of ecg signal in time and time-frequency domain including filtering of signal and database connection. we have designed source and processing modules to enable parallel processing of incoming data by using multi-threading technology on a pc. the capturing of incoming data, displaying data in time domain and calculation of spectral coefficients and its presentations in timefrequency domain through spectrograms (scalograms) is realized in three threads respectively: working, main and graphical. the calculation of spectral coefficients is performed using three time-frequency transformations: short time fourier transform, discrete wavelet transform and continuous wavelet transform. the experiments performed prove that using of multi-threads obviously enable larger number of calculations in shorter time compared to the same number of calculations using single thread. the proposed multithread concept enables the real time display of signals spectrum and waveform, encourages and provides ample evidence to pursue multi-thread architecture as a serious candidate for real-time signal processing. acknowledgement this work has been done 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[21] misiti, m. et al; wavelet toolbox for use with matlab, http://www.mathworks.com/help/pdf_doc/wavelet/wavelet_ug.pdf international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 1, month: february, year: 2023 article number: 5062, https://doi.org/10.15837/ijccc.2023.1.5062 ccc publications a graph-based soft actor critic approach in multi-agent reinforcement learning wei pan, cheng liu wei pan school of computer science northwestern polytechnical university, china xi’an 710072, china panwei@nwpu.edu.cn cheng liu school of computer science northwestern polytechnical university, china xi’an 710072, china lion_liucheng@163.com abstract multi-agent reinforcement learning (marl) is widely used to solve various real-world problems. in marl, the environment contains multiple agents. a good grasp of the environment can guide agents to learn cooperative strategies. in centralized training decentralized execution (ctde), a centralized critic is used to guide cooperative strategies learning. however, having multiple agents in the environment leads to the curse of dimensionality and influence of other agents’ strategies, resulting in difficulties for centralized critics to learn good cooperative strategies. we propose a graph-based approach to overcome the above problems. it uses a graph neural network, which uses partial observations of agents as input, and information between agents is aggregated by graph methods to extract information about the whole environment. in this way, agents can improve their understanding of the overall state of the environment and other agents in the environment while avoiding dimensional explosion. then we combine a dual critic dynamic decomposition method with soft actor-critic to train policy. the former uses individual and global rewards for learning, avoiding the influence of other agents’ strategies, and the latter help to learn an optional policy better. we call this approach multi-agent graph-based soft actor-critic (magac). we compare our proposed method with several classical marl algorithms under the multi-agent particle environment (mpe). the experimental results show that our method can achieve a faster learning speed while learning better policy. keywords: multi-agent system, deep reinforcement learning, graph neural network, soft actor-critic. https://doi.org/10.15837/ijccc.2023.1.5062 2 1 introduction reinforcement learning (rl) learns strategies by interacting with the environment[1, 2]. in recent years, deep learning has been widely used in various fields, such as garbage classification[3], fault detection[4], stock prediction[5], image processing[6, 7], etc. combining deep learning and reinforcement learning effectively solves the curse of dimensionality in reinforcement learning. it enables reinforcement learning to be widely used in various complex tasks. deep reinforcement learning (drl) has achieved excellence in areas such as go[8] and robot control[9] especially in video games[10], even beyond human performance. drl is currently used for many more complex real-world tasks, such as control design for unmanned aerial vehicle[11], resilient road network recovery[12] and navigation control[13]. multi-agent reinforcement learning is a branch of rl that aims to jointly control multiple agents in an environment so that agents learn to cooperate with other agents to accomplish tasks. many algorithms in marl are influenced by high-dimensional state and action spaces because it is difficult for agents to learn good cooperation policies in high-dimensional state spaces. the combination of marl and deep learning can overcome the problem of high-dimensional state spaces to some extent due to the powerful representational capabilities of deep learning. nevertheless, it is still an open question of how to process a large amount of information in an environment with many agents so that agent learns good cooperation strategies. to solve the problems mentioned above, many marl methods have been proposed. using a single agent-based rl algorithm[14] to train each agent independently is a natural idea. however, in this approach, agents treat other agents as part of the environment. in such an unstable environment, it is difficult for independent single-agent rl algorithms to learn good policies, let alone how to collaborate with other agents. multi agent deep deterministic policy gradient (maddpg)[15] proposes an actorcritic architecture-based marl algorithm based on deep deterministic policy gradient (ddpg), which learns the cooperation of multiple agents through a centralized training, decentralized execution paradigm. maddpg constructed a centralized critic, which uses the observations and actions of all agents as input to assess the value. actors in decentralized implementation use their observations as input to learning collaborative strategies under the guidance of centralized critics. this paradigm is known as centralized training and decentralized execution. in one case, the good action of the current agent may not achieve good returns because of the action of other agents. coma[16] further proposes a ctde-based counterfactual baseline approach to predict a virtual return as a baseline. with a counterfactual baseline, coma achieves a credit assignment under multi agents, avoiding strategic interference from other agents and helping agents learn cooperative strategies while minimizing the influence of other agents as much as feasible. maddpg and coma need to use all agents’ state and observation data when constructing their centralized critic networks. as the number of agents increases and a large amount of information is used to evaluate the network, it becomes difficult for the critic network to focus on the most important parts among a large amount of redundant information. the network becomes increasingly difficult to be trained. when the number of agents increases, in order to filter out the most important information for the current agent from a large amount of information, maac[17] introduces an attention mechanism to the centralized critic network to solve this problem. maac takes the state of the current agent as a query and the state of other agents as the key. the states and actions of other agents is the value. attention weights are calculated by comparing the states between different agents. the attention weights are used to weigh the states and actions of all other agents to obtain highly encoded global information, which is used for value assessment by critics after concatenating the observations of the current agent. mfmarl[18] proposes the mean-field approach, which solves the problem of strategic interference of large-scale agents from another perspective. mfmarl proposes the hypothesis of a multi-intelligent system: for a particular agent, the effect of all other agents on it can be replaced by an average effect. the interaction between an agent and its neighboring agents is considered an interaction between two agents. atoc[19] proposes a soft attention-based communication mechanism to decide when an agent should communicate with other agents. graph neural network (gnn)[20] is a special kind of neural network structure designed for tasks with graphical relations. gnn was originally used for social networking, point clouds, and other issues. in recent years, some advanced work has introduced gnn into drug development and computer vision https://doi.org/10.15837/ijccc.2023.1.5062 3 and achieved remarkable results. many real-world problems, such as social networks and point clouds, can be constructed as graphs. a multi-agent system can use a graph to depict the relationships between the agents. due to this unique ability of gnn to be compatible with multi-agent systems, the powerful information construction capability of the graph has attracted the attention of many marl researchers. sequence invariance means that changing the agent order will give the same output value in a centralized critic network. however, the critic network of multilayer perceptron (mlp) used in maddpg uses a concatenation of all agents’ observations and actions, so it does not have sequence invariance. pic[21] experimentally demonstrated that changing the order of different agents in a sample can improve sample efficiency. the method constructs a centralized critic using gnn, which achieves close to mlp and performs better in scenarios with a larger number of agents. gcs[22] uses gnn to generate a cooperation graph that identifies the neighboring agents that each agent needs to interact with and then forms a graph-based cooperation policy based on this. in some environments where individual rewards are available, direct single-agent reinforcement learning for training treats all other agents as part of the environment. although strategic interference is avoided in ctde, in this case, while simple tasks can be learned, good cooperative strategies cannot be learned. de-maddpg[23] uses two critics to focus on individual and collective rewards, combined with the maddppg method to avoid strategy interference effectively. dd-maddpg[24] proposes a dynamic decomposition method based on de-maddpg, which gives different weighted attention to the two assessment networks at different stages. the performance of methods such as maddpg can break down due to strategic interference, while gnn-based methods can overcome this problem very well. we propose a gnn-based approach to overcome inter-agent strategic interference. the method consists of three components based on a stochastic policy, which includes partial observation encoder for the homogeneous agent, graph-based state encoder, maximum entropy reinforcement learning combined with the dynamic decomposition of the dual critic. a partial observation encoder encodes partial observations of a homogeneous agent using an encoder with the same parameters, mapping them to a uniform dimension for easy processing by subsequent modules. in this module, information from other agents is aggregated to the node after propagation of the k-layer graph. the gnn in our state encoder takes its inspiration from graphsage[25]. at each layer, the summary information of the upper layer features of this node is concatenated with the upper layer features of other nodes. through this gnn state encoding module, the state information of other agents can be aggregated. when facing many agents, the proposed method can avoid curse of dimensionality, maintain good feature extraction ability, and avoid strategic interference from other agents. soft actor-critic (sac)[26] is a stochastic policy algorithm that introduces maximum entropy reinforcement learning. the deterministic policy algorithm learns a policy such that the cumulative reward expectation is maximized. as for the sac, in addition to the above basic objectives, it requires that the action entropy of each output of the policy is maximized. that is, the probability of each output action is spread out as much as possible rather than being concentrated on one action. our contributions are as follows: (1) a graph-based state encoder that extracts agent states in a multi-agent environment while avoiding the curse of dimensionality. the features extracted by this module that contain a good grasp of the environment lead to good cooperation strategies. (2) based on (1), a combination of soft actor criticism and dynamic decomposition dual critic methods is proposed. the method trains the optimal cooperative strategy while avoiding the strategic influence of other agents. we tested our proposed magac with two classical algorithms of marl in an mpe environment. we examined the algorithm’s robustness in relation to the number of agents. experiments show that the results of our proposed magac method outperform other algorithms. the rest of the article will be organized as follows. the second section will present all the prerequisite knowledge used. the third section will present our approach. the fourth section will compare our approach with other marl algorithms in a simulation environment. finally, the fifth section will give the conclusion. https://doi.org/10.15837/ijccc.2023.1.5062 4 2 background 2.1 markov games and multi-agent reinforcment learning the markov decision process can be extended to partially observable markov decisions when the number of agents is more than a single. in a markov game with n agents at time t, there are defined with state st, which describes the status of the entire environment at time t. the ma system gets the joint observations of time t: ot = (ot1, · · · ,oti, · · · ,otn )form state s, which is partial information of state st. agents in the environment take joint action: at = ( at1, · · · ,a t i, · · · ,a t n ) . after taking action, the environment can be moved to a new state according to a state transfer function: t = p ( st+1 ∣∣st ×at1 × . . .×atn). in the next time. as states are changing, rewards are given in the environment in relation to the preceding and following states and the action being taken rt : st ×at1 × . . .×atn → r, which joint reward:r t = ( rt1, · · · ,r t i, · · · ,r t n ) . in an experimental scenario with fully shared rewards, rt1 = · · · = rti = · · · = rtn. each agent learns a policy πi : oi → p (ai) that maximizes the cumulative expected reward based on the observed selection action. where the cumulative expected reward is as: ji (πi) = ea1∼π1,...,an∼πn,s∼t [ ∞∑ t=0 γtrti ( st,a t 1, . . . ,a t n )] (1) where γ ∈ [0, 1] determines the strategy’s balance of forwarding and near-term rewards. the state transfer function t = p ( st+1 ∣∣st ×at1 × . . .×atn) determines the reward. from the equation, we can see that the policies of other agents influence agent i in the learning process because the joint action of all agents determines the state transfer from moment t to t+1, which is one of the core problems in marl. off-policy reinforcement learning uses a replay buffer to improve sample utilization. the system interacts to generate transactions and samples the transactions in the experience pool to update the parameters of the policy. 2.2 maximum entropy reinforcement learning and soft actor-critic we discuss maximum entropy reinforcement learning and soft actor-critic in the context of single agent reinforcement learning. 2.2.1 maximum entropy reinforcement learning unlike standard reinforcement learning that maximizes the expected return ∑ t e(st,at)∼ρπ [r (st,at)], maximum entropy reinforcement learning adds a political entropy term to the expected return which mean that: j (π) = t∑ t=0 e(st,at)∼ρπ [r (st,at) + αh(π (· | st))] (2) the term h(π (· | st)) implies a more uniform action of policy output in the face of the same state. it encourages the exploration of policy rather than resting on local optimal policy, which means that the strategy can learn more near-optimal actions. so there may be more than one action that is optimal in some states. the more uniform the probability distribution of the optimal action, the global optimal policy can be learned. the parameter α controls the importance of the relative reward to the stochastic policy. the larger the parameter α, the more the policy converges to the optimal policy and the objective changes to classical reinforcement learning when α → 0. 2.2.2 soft actor-critic the actor-critic method[27] uses neural networks q to approximate the expected returns, effectively overcoming the large variance problem of the expected returns. the neural network q can also be called ad critic. the learning of critic network can be minimized by minimizing the following loss through the td-error method: https://doi.org/10.15837/ijccc.2023.1.5062 5 lq ( θq ) = e(s,a,r,s′ )∼b [ (qθq (s,a) −y) 2 ] (3) y = r (s,a) + γe a ′∼π(s′ ) [ q θq̄ ( s ′ ,a ′ )] (4) where b denotes the mini-batch of experiences sampled from the replay buffer in the off-policy rl. q θq̄ denotes the target q-network[28], which is used to maintain algorithm stability. the same approach is not used in the policy network. with the help of a critic network, policy learning with actor-critic can be expressed as: ∇θπj (πθπ ) = es∼d,a∼π [∇θπlog (πθπ (a|s)) qθq (s,a)] (5) actor-critic can be improved to soft actor-critic by adding the maximum entropy term to the learning objective of the policy network. ∇θπ j (πθπ ) = es∼d,a∼π [∇θ log (πθπ (a | s)) (−α log (πθπ (a | s)) + qθ(s,a))] (6) 2.3 graph neural networks unlike mlps, graph neural networks are deep networks that operate on graph structures. the inputs to the network are nodes with a set of edges. in maddpg’s critic, swapping observation of two other agents changes the network’s input, while gnn has sequence invariance, which improves sample efficiency. there are currently many variants of the gnn approach. graph convolutional nets[29], graphsage, which uses a limited number of interacting nodes, allowing training to extract some subgraphs from the entire set for mini-batch, extending the application of the graph set approach to large-scale graph data. gat improves the graph propagation mechanism by using the attention mechanism, which gives different attention to different graph nodes. the input graph of gnn can be expressed as g = (v,e). that is, we define a graph g as a set of nodes v, with a set of edges e connecting them. e represents the connection relation on the graph. each node can be represented as a vector h. gnn iterates over the node vector and aggregates node information to update node h in each iteration. h (k) i = ∑ j∈n(i) fc(k) ( h (k−1) i ,h (k−1) j ) (7) 3 algorithmic contribution magac is based on a maximum entropy actor-critic method. it consists of three parts: a partial observation encoder, a gnn-based state encoder, and an actor-critic based on maximum entropy and dynamic decomposition methods. we will introduce each of these three parts and the whole model training method in this section. 3.1 problem formulation and partial observation encoder in the mpe environment, an agent’s observation consists of several components. as shown in figure 1, the blue circles indicate the agents, and the black circles indicate landmarks in the environment. all agents and landmarks are contained within a two-dimensional graph. all agents and landmarks are randomly initialized in each episode. each agent can obtain its absolute position relative to origin and velocity. the absolute position of agent i is pi = (pxi ,p y i ), which contains two directions of position. similarly, the velocity of a is expressed as vi = (vxi ,v y i ).the relative positions of landmarks oi and a are denoted as pioj = ( pixoj,p iy oj ) .the positions of all landmarks in the environment relative to a are expressed as pio = ( pio1, . . .p i oi . . .p i om ) . m denotes the number of landmarks observed in the environment by agent i. similarly, the position of other agents relative to a can be expressed as pia = ( pia1, . . .p i aj . . .p i an ) . n denotes the number of agents that can be observed https://doi.org/10.15837/ijccc.2023.1.5062 6 figure 1: description of the environment. in the environment by agent i. in the fully observed setting, it is also possible to obtain the relative positions of other agents in the environment by agent i. the physics engine model of the environment is a second-order integral model, the environment has inertia, and agent collision with landmarks will generate mutual forces. in a scenario with n agents and m landmarks, the observation of agent i can be expressed as oi. oi = [ pi,vi,p i o,p i a ] in methods such as maddpg and maac that use full observation. we use partial of the observations oi = [ pi,vi,p i o,p i a ] , as initial graph node information, and the information between agents is summarized by the graph. the graph uses graph feature nodes of uniform dimension. in contrast, the observation dimension is different for different agents, so the observations of agents need to be preprocessed by an partial observation encoder and compressed to a fixed size and length. the partial observation encoder uses the same parameter for the homogeneous agent. the encoder can be expressed as follow: hi = e (oi|wc) (8) where c indicates the category to which the agents belong. the parametric scale of the observation encoder is only related to the number of classes of agents, so it does not increase as the number of agents increases. 3.2 graph based state encoder in rl systems, the agent interacts with the environment to maximize the expected reward continuously. because there are other agents in the marl environment; the environment becomes more unstable, which makes learning cooperative strategy more difficult. during the graph propagation accompanied by message passing between nodes, gnn aggregates information from other agents in the environment and iterates into a new feature h. unlike the classical gnn, our state encoder is inspired by graphsage and uses concatenation in each layer of iteration. the state encoder takes the output of the partial state encoder hi as the input node of the graph h0i . state encoder updates graph nodes using k aggregation layers: { f(1), . . . ,f(k) } . correspondingly there are k aggregation functions, denoted by aggregate k,∀k ∈{1, . . . ,k} which aggregates information from other agents. these aggregate functions will aggregate information from other agents. two more sets of weights are needed in each layer iteration:wkv , ∀k ∈ {1, . . . ,k}, and wkf , ∀k ∈ {1, . . . ,k}. the former is used to process the aggregated information, while the latter is used to process the concatenated information. algorithm1 describes the processing of the graph state encoding module: in each iteration, nodes https://doi.org/10.15837/ijccc.2023.1.5062 7 algorithm 1 graph-based state encoder. define: input features : hi,∀i ∈n k := times of gnn iteration wkv , ∀k ∈ {1, . . . ,k} wkf , ∀k ∈ {1, . . . ,k} σ := non− linearity aggregatek,∀k ∈{1, . . . ,k} h0i ← hi ∀i ∈n for k = 1...k do: for i in n : hk ← aggregatek ( wkv h k−1 j ,∀j ∈n ) hki ← σ ( wk ·concat ( hk−1i ,h k n )) end for end for output := si ← hki ,∀i ∈n aggregate information from their other nodes, and as this process iterates, the nodes gain more information from other agents in the environment. k denotes the number of graph iterations, and we choose k = 3. to compute the aggregation we use average function, which brings together the vectors of all other nodes on average. this aggregation is the same as the convolutional propagation of gcn. after aggregation, hk−1 is concatenated with hk−1i after a full concatenation, and the concatenated features are subjected to a full concatenation and activation function. the obtained output is used as the new node feature hki . the node feature hki after k iterations is used as the output si of the whole module. the only parameters that need to be trained in this module are wkv ,∀k ∈ {1, . . . ,k} and wkf ,∀k ∈{1, . . . ,k} for each layer, which has the advantage that the number of parameters does not increase rapidly when the number of agents increases. figure 2: overall architecture of the algorithm. 3.3 sac with dynamic decomposition of dual-critic in many scenarios, the agent’s individual rewards are available. to reduce the impact of other agents’ policies on the current agent training, based on gnn’s state encoder module, we will introduce the dynamic decomposition of the dual-critic method. the algorithm uses individual reward critics and collective reward critics to evaluate policy. in the early stages of training, more attention is given to individual rewards, and agents are instructed to learn simple tasks first. during this process, the weight of collective rewards increases, and agents are instructed to learn teamwork. the importance of the dual-critic changes dynamically in the training process. it enables agents to transition from individual https://doi.org/10.15837/ijccc.2023.1.5062 8 to collective learning adaptively. we set up two critics. one uses a single agent’s observations to evaluate actions, called the local critic. the local critic uses si to evaluate the individual rewards for an action. another critic, called the global critic, evaluates the collective reward of an action using si and the actions of all other agents as input. dynamic adjustment of the importance of two critics to guide policy learning. the gradient of local critics is ∇lθπ i j ( πθπ i ) = ea∼πθπ i [ ∇θπ i log ( πθπ i (ai|si) )( α log ( πθπ i (ai | si) ) −qi (si,ai) )] (9) the gradient of global critics is ∇gθπ i j ( πθπ i ) = ea∼πθπ i [ ∇θπ i log ( πθπ i (ai|si) )( α log ( πθπ i (ai | si) ) −qg θq (si,a) )] (10) total gradient: ∇θπ i j ( πθπ i ) = w1 ∗∇gθπ i j ( πθπ i ) + w2 ∗∇lθπ i j ( πθπ i ) (11) w1 and w2 denote attention to different critics, respectively. w2 is initialized to 1. and decays with the training process. w2 = w2 ∗ decay and w1 = 1 −w2 (12) the updates to the local critics are as follows lqi ( θ q i ) = es,a,r,s′ [( q θ q i (si) −yi )2] (13) where yi = ri + γ [ q θ q i ( s′i,a ′ i ) −α log ( πθπ i ( a′i | s ′ i ))] (14) the updates to the global critic are as follows lqg ( θq ) = es,a,r,s′ [( q g θq (si,ai) −yg )2] (15) where yg = rg + γ [ q g θq ( s′i,a ′ i ) −α log ( πθπ i ( a′i− | s ′ i ))] (16) algorithm 2 graph-based soft actor critic for marl. initialize all network and replay buffer for episode e = 1 to t do: for time steps t = 1 to episode length do: get union observation ot from environment get ht by equation 8 get st by algorithm 1 for each agent i, select action ati = πθπi ( sti ) execute joint action at and get union observation ot+1 and rt store transaction to replay buffer if e % d == 0 then: for each agent i update local critic by minimizing equation 13 update global critic by minimizing equation 13 for each agent i update policy according equation 11 update w1 and w2 according equation 12 update all target network parameters the overall architecture of the algorithm is shown in figure 2. ati donates the joint action at time t except for the action of agent i. the partial observation gets preprocessed to hti, which has a fixed https://doi.org/10.15837/ijccc.2023.1.5062 9 dimension. then graph-based state encoder uses ht extract st. policy network gets action by sti and is trained with the dynamic decomposition of duel critic. the detailed training process is shown in algorithm 2. 4 experiments this section describes the setup of the experiment, the baseline used for comparison, and the hyperparameters used in the experiment. we tested and evaluated our algorithm in multi-agent particle environment. a diagram of the application of magac in the environment is shown in figure3. figure 3: overall architecture of the algorithm. 4.1 experimental environment we tested three scenarios under mpe, with the following details for each scenario, as shown in figure4. the scenarios focus on target tracking and formation and can be used as a reference for practical applications. (a) cover control (b) formation control (c) predator-prey figure 4: an overview of the mpe environments used in the experiments. https://doi.org/10.15837/ijccc.2023.1.5062 10 4.1.1 cover control here there are n agents and n landmarks. agents are rewarded if they approach the corresponding landmarks and punished if they collide with other agents. agents need to learn to cover all the landmarks while avoiding collisions with other agents. 4.1.2 formation control here there are n agents and one landmark, and the agents are distributed as evenly as possible around the landmark to form an equilateral polygon. the reward increases according to the similarity of the agents to the equilateral polygon. 4.1.3 predator-prey predator-prey scenario is means chase, with three red hunters, several black roadblocks, and red prey. the prey takes inspired action, and the hunters need to learn to cooperate to complete the chase of the prey. if the hunters collide with each other, they are punished. each agent can see the relative positions of the other agents. the prey is faster than the hunters, so the hunters need to learn to cooperate. 4.1.4 experimental details setting table 1: the parameters used for all algorithms in the experiments. masac maac maddpg buffer size 1e6 1e6 1e6 mini batch size 128 128 128 lr-actor 0.001 0.001 0.001 lr-critic 0.001 0.001 0.001 decay 0.001 0.001 0.001 gamma 0.99 0.99 0.99 temperature parameters α 0.02 0.02 0 maddpg and maac are both classical marl algorithm. all three experimental algorithms are based on the ac structure. maac and magac introduce a maximum entropy reinforcement learning method based on the ac structure. all three algorithms use the same training method and hyperparameter settings, and all employ techniques such as target networks to verify the effectiveness of our methods. the parameters are set as shown in the table 1. each hidden layer consists of 128 cells. we use the adam optimizer to train all neural networks, which automatically adjusts the learning rate during training to improve the stability of the training. for all scenarios, we used a length such that each scene had 25 steps and trained 5000 episodes for each scenarios. for each scenario, five random seeds were run. 4.2 result and analysis we evaluate the algorithm’s performance mainly regarding learning stability and global reward. the global reward plot can directly demonstrate how effectively the agent learns the policy during the different training phases. in addition, the average reward and confidence interval of the last episode can be digitized as a better indicator of the final strategy learned by the algorithm. to verify the robustness of the proposed method when the number of agents increases, we compare the algorithm’s performance by increasing the number of agents in the same experimental scenario. figures 5, 6 and 7 show how the global reward of the agents varies with the training process. the lighter area is the error band under the five random seeds, and the darker area is the average of the five experiments. table 2 shows the last episode’s average reward and confidence interval for the three https://doi.org/10.15837/ijccc.2023.1.5062 11 table 2: the mean global reward in the last episodes on 1000 times. in scenarios a and b, where individual rewards are available, we tested magac in combination with the dual critic dynamic decomposition method, named magac-dd. * means increasing the number of agents in the same scenario. cover* and formation* contain 10 agents, predator-prey* contains 8 agents masac masac-dd maac maddpg cover -133.22 -131.02 -138.4 -147.09 formation -18.18 -19.84 -31.03 predator-prey 128.12 141.30 67.32 82.24 cover* -1052.87 -1011.04 -1507.21 -1159.07 formation* -47.23 -52.75 -88.31 predator-prey* 604.43 759.09 376.53 236.49 (a) cover control (b) formation control figure 5: graph of average global rewards with training episodes. algorithms under different experimental scenarios. overall, our magas algorithm’s learning stability and global reward generally outperformed the baseline algorithm to a greater extent. for the cover task, as shown in figure 5a, magac slightly outperforms the maddpg method in terms of global reward and has some advantages compared to maac. in the global average reward metric of the final episode, magac achieves -133.22, maac achieves -138.40, and maddpg achieves 147.90. maddpg is significantly slower than the other two in convergence speed, and maac is closer to our masac. it is because maac and magac use the maximum entropy reinforcement learning method. meanwhile, in the formation environment, the performance of the three algorithms is similar to that in the cover scenario, with maac and magac performing similarly and maddpg performing somewhat worse. (a) cover control* (b) formation control* figure 6: graph of average global rewards with training episodes when number of agent increase. in the predator-prey scenario, the reward plot is not as smooth as in the previous environment for several reasons.1: in the predator-prey scenario, the reward is sparse. in the case of sparse rewards, the https://doi.org/10.15837/ijccc.2023.1.5062 12 predator can only get rewards after cooperating to complete the capture of the prey.2: the scenario is competitive. the same predator’s policy will act differently when dealing with different prey, so the reward of the scenario is more random, which leads to different performances of the same policy in a different episode. in contrast, in the cover scenario, the reward gradually increases with the distance to the target, and the reward changes continuously. (a) predator-preyl (b) predator-prey* figure 7: graph of average global rewards with training episodes in predator-prey of different number of agent. one possible reason for the poor performance of maac and maddpg in the predator-prey scenario is that it is difficult to differentiate information about other agents in the evaluation network of maac and maddpg. the actions taken by prey agents are difficult to be learned by their centralized review network, which leads to difficulty in converging the critic network. in the same environment, we expand the number of agents to verify the robustness of our approach when the number of agents increases. under the cover task, as shown in the figure5, the advantage of magac over maac and maddpg becomes more obvious. the learning of maddpg is relatively stable, but the effect of maac starts to drop sharply after a training period, and the effect eventually collapses. the reason may be that the attention weights in the attention mechanism in maac are difficult to train when there are too many agents. we also conducted ablation experiments on the dual critic dynamic decomposition method in cover control scenario and predator-prey scenario. the experimental results show that the dd method can show a slight advantage in the original scene, which is more obvious when the number of agents increase. 5 conclusion we propose a graph-based soft actor-critic approach for the curse of dimensionality and influence of other agents’ strategies in multi-agent reinforcement learning. in marl, a good grasp of the environment and the state information of all agents can guide agents to learn cooperative strategy better. this method uses the agent’s partial observation as the input feature node of the graph and aggregates other agent information through the graph neural network. after several graph iterations, the feature encoding of the agent state is obtained, which contains an understanding of the whole environment from the current agent perspective. this module better extracts the state of an agent in a multi-agent environment while avoiding the curse of dimensionality when there are too many agents. then a combination of soft actor-critic and dynamic decomposition dual evaluation network methods is used to train the policy. this method trains optimal cooperative strategies while avoiding the influence of the strategy of other agents. we have conducted comparison experiments with classical baseline algorithms under mpe, which show that our algorithm can obtain higher rewards and has a faster learning speed. the advantage of our approach is more pronounced when the number of agents increases means that we effectively overcome the curse of dimensionality and the influence of other agents’ strategies associated with the increasing number of agents. https://doi.org/10.15837/ijccc.2023.1.5062 13 funding this work was supported in part by the national defense science and technology key laboratory fund under grant 61421030302012109 in part by national natural science foundation of china under grant 61976178 and 62076202. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] mnih, volodymyr.; kavukcuoglu, koray.; silver, david.; rusu, andrei a.; veness, joel.; bellemare, marc g.; graves, alex.; riedmiller, martin.; fidjeland, andreas k.; ostrovski, georg. 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(2023). a graph-based soft actor critic approach in multi-agent reinforcement learning, international journal of computers communications & control, 18(1), 5062, 2023. https://doi.org/10.15837/ijccc.2023.1.5062 introduction background markov games and multi-agent reinforcment learning maximum entropy reinforcement learning and soft actor-critic maximum entropy reinforcement learning soft actor-critic graph neural networks algorithmic contribution problem formulation and partial observation encoder graph based state encoder sac with dynamic decomposition of dual-critic experiments experimental environment cover control formation control predator-prey experimental details setting result and analysis conclusion int j comput commun, issn 1841-9836 8(4):588-593, august, 2013. smart and safe vehicle monitoring with fuzzy integral and haar-like features se-bin oh, yeon-ho chung se-bin oh, yeon-ho chung dept. of information and communications engineering, pukyong national university, daeyon 3 dong, nam gu, busan, korea osebin@pknu.ac.kr, yhchung@pknu.ac.kr abstract: an on-board android-based smart and safe vehicle monitoring system is presented. the on-board monitoring system (oms) performs important monitoring functions: record, report and alert (rra). the record function records front images of a moving vehicle. during the recording, any accidents or other emergency conditions will be automatically reported via the report function for an emergency rescue operation. for the detection of shocks or accidents, we use acceleration based shock sensors that utilize fuzzy integral algorithm. the oms also focuses on drowsiness that is largely regarded as the main cause of most accidents. the haar-like feature is used to detect any sign of drowsiness and the alert function is performed to alert the driver. all the vehicle-borne information is stored at a remote server via wireless communication links for later use or post-processing. a test bed has been developed and verified thoroughly for its accurate operations. the proposed smart and safe vehicle monitoring system offers advanced safety features and is expected to substantially reduce fatigue related accidents. keywords: fatigue, haar-like, monitoring, vehicle safety. 1 introduction over the last half-decade, smartphones have evolved dramatically and would soon behave like laptops. most mobile devices are nowadays integrated by a smartphone as convenient infrastructure and intelligent functionality has been built to further users ultimate convenience [1]. for example, transport machine industry, particularly vehicle industry, provides vehicle-borne information via a car navigation system. even various driving environment information, not to mention information about driving itself, is provided to the driver. however, conventional devices or modules are focused on data storage and do not offer any drowsiness related safety features. thus, technologies in mobile and vehicle telematics need to be developed further and also necessitate advanced safety-oriented modules. in particular, a fatigue related accident prevention feature must be sought together with basic monitoring functions, e.g. recording of vehicle data and images incorporated with gps for track-down and investigation of any accidents occurred. this paper addresses a smart and safe vehicle monitoring issue. the system is based on an android mobile device, offering advanced monitoring capability of the recording of vehicle-borne data and image, gps data and accelerator sensors for automated alarm function. also, it offers an alertness function to prevent fatigue related accidents using the haar-like algorithm [1, 2]. 2 design and structure of oms the proposed system is composed of a microcontroller (mcu) module, an android device and database server. fig. 1 shows a block diagram of the system. basically, the camera fitted on the mobile device will monitor and record front images. the accelerator sensor will detect shocks or any accidents occurred through the fuzzy integral algorithm [3]. these recorded images copyright c⃝ 2006-2013 by ccc publications smart and safe vehicle monitoring with fuzzy integral and haar-like features 589 will be saved in the sd memory of the mobile device. when required, these will be stored in a remote server via wireless links from the sd memory. when an accident occurs or an emergency condition exists, a rescue message will automatically be sent to next of kin. the android-based mobile development is based on google android 2.3 operating system [4] and some google libraries have been used to implement the proposed functions. the top menu consists of three submenus: camera view, map view and accelerometer view. figure 1: block diagram of the proposed system 2.1 camera view camera view is implemented with a periodical recording function. various google libraries are used. for example, recinstallize() is defined for image formatting, encoding modes and the length of recording, whereas fileoutstream() creates camera recorded image files and chkaccident() detects an occurrence of accident and recprepare() sets up a saving route for the files. once recorder.start() is invoked, the image recording will begin for the pre-defined time. when recorder.stop() is invoked, the files will be stored at the sd memory. during this main activity, if the mcu and accelerator sensor receive any data for the possible detection of accidents or shocks, an alarm dialogue will be sent out and the associated file will be automatically saved with a file extension of accident.to control an overflow issue, old files are sequentially deleted while any files with the extension of accident remain stored. 2.2 map view to obtain location information, a few more libraries are utilized such as oncreate() and getbestprovider() for the best provider of the information (wi-fi, 3g or gps satellite). as location changes, locationlistener() moves its position and updates its latitude and longitude. with the assumption that the power is supplied from the vehicle, setpowerrequirement() and setaccuracy() are set to accuracy-fine and power-high for a fine collection of the data. all collected data are stored as a text file and transmitted to a remote database server. the location of the vehicle will be displayed on google map online. 2.3 accelerometer view sensors applied in this development are accelerator sensors, gyroscope and proximity sensors. for the detection of accidents (or shocks), accelerator sensors and gps speed data as well as shock sensors located inside mcu are employed. fig. 2(a) shows the directions of vehicle from 590 se-bin oh, yeon-ho chung the perspective of a mobile placed upright, where y axis indicates left and right, and z axis the front and rear, respectively. figs. 2(b) and 2(c) show the response of the accelerator sensor when two shocks are applied in front and another two from the rear. (a) (b) (c) figure 2: accelerometer view: (a) definition of vehicle direction (b) and (c) values of the accelerometer sensor under shock conditions although this method of shock detection provides a degree of accuracy, it needs to enhance its accuracy further, particularly when the vehicle is attacked diagonally or at different angles other than basic four directions. this drawback would result in inaccurate detection of accident. in order to address this ambiguous situation, the study has employed the fuzzy logic and integral algorithm by treating this condition as fuzziness. fig. 3 shows accelerator values and its scope ranging from s1 up to s8. values of y and z axes and rule-base of accelerator are shown in fig.4. figure 3: block diagram of the proposed system (a) (b) figure 4: (a) values of y and z axes (b) rule-base of accelerator we first define variables for y and z axes and each accelerator value will undergo fuzzification and defuzzification. then, by making use of the center of gravity, the angle of vehicle shock can be determined. fig. 5 shows how to find the angle of shock using this method. 2.4 methodology of drowsiness detection in order to reduce or prevent accidents due to driving under drowsiness, a detection mechanism of drowsiness has been developed. an eye tracing algorithm known as haar-like feature smart and safe vehicle monitoring with fuzzy integral and haar-like features 591 figure 5: method of finding the angle of shock [5] has been used to determine if a driver is in a drowsy condition. the haar-like features are digital image features used in object detection and recognition [2, 5]. the method first identifies face and eye features and adds pixel values within that area. then, some weighting factors are multiplied to find a final value. for this reason, this feature is useful in moving pictures rather than still images. to detect drowsiness, we have used eye closure. this method is well known and widely used for detecting drowsiness [6]. when the camera fitted shows an image of a driver, the detection algorithm starts to find face. once the face is recognized, the detection for eyes begins to initiate the eye closure based drowsiness procedure. the method assumes that the drivers eyes are closed if eyes are not detected. in detecting the eye closure, blink count increments and timer starts to measure the time during which eyes are closed. therefore, perclos [6] is computed as follows: perclos = eye closure time measurement time x100 according to the values of perclos, we determine if the driver is under drowsiness. the detailed procedure of the method is shown in fig. 6. figure 6: flow chart of drowsiness detection 3 verification of oms the oms has been under rigorous verification for its all functions. as mentioned previously, we first evaluate the main menu composed of the front image, google map and accelerator sensor 592 se-bin oh, yeon-ho chung as shown in fig.7 (a). when the vehicle is in motion, the accelerator sensor is in operation for detecting any shocks or accidents. if accidents or shocks occur, an alarm dialog window appears as shown in fig.7 (b). in order to avoid any false alarm situations, the dialog window allows users to tap ok button to continue the driving, otherwise it regards as an emergency condition to initiate rescue operations, e.g. call initiation to next of kin as shown in fig.7(c). in addition, the recorded image files before and after that moment of the emergency condition are stored with gps location information into the sd card memory of the mobile device and transmitted to a remote database server via wireless links, e.g. wi-fi or 3g. these files can be viewed anytime as indicated clearly with an extension of accident. figs.7 (d) and 7(e) show the file storage and gps data. while the vehicle is in motion, the drivers eyes are continuously monitored. if the value of perclos is higher than the predefined threshold value, the system is set to send out a warning message and an alarm sound also goes off to alert the driver for the prevention of any possible accidents. fig.8 (a) shows the detection of face and eyes subsequently. the eye closure detection is then performed and perclos is computed to determine if the driver is in a drowsy condition as shown in fig.8 (b). the verification process demonstrates that the oms operates the proposed functions accurately as designed. (a) (b) (c) (d) (e) figure 7: verification of oms functions smart and safe vehicle monitoring with fuzzy integral and haar-like features 593 (a) (b) figure 8: (a) detection of face (b) eye detection for drowsiness 4 conclusions fuzzy integral and image processing techniques integrated on a smartphone are employed to provide the enhanced safety of vehicle. in addition to providing monitoring functions such as the record and report functions, an advance warning system for fatigue related accidents is developed using the haar-like feature. the test bed comprising hardware and associated software has been evaluated through its verification process. the system shows its accurate and correct operations on possible driving scenarios. also, the drowsiness of a driver is successfully detected using perclos. it is therefore expected that the developed vehicle monitoring system will play an essential role in providing enhanced safety feature to transportation machine to reduce vehicle-borne accidents significantly. bibliography [1] p. viola; m. jones, (2001); robust real-time object detection, second international workshop on statistical and computational theories of vision vancouver, canada. [2] j. zhou; l. jiang; l.shen, (2009);haar-like features based eye detection algorithm and its implementation on ti tms320dm6446 platform, international workshop on imaging systems and techniques. [3] j.m. mendel (1995); fuzzy logic systems for engineering: a tutorial, proc. of ieeel, 83(3):345-377. [4] http://www.developer.android.com [5] alexander kuranov; rainer lienhart; vadim pisarevsky.(2002); an empirical analysis of boosting algorithm for rapid objects with an extended set of haar-like features,intel technical report mrl-tr. [6] u.trustschel; bo.sirois; d.sommer; m.golz; d.edwards (2011); perclos: an alertness measure of the pastl, proc. of the sixth international driving symposium on human factors in driver assessment, training and vehicle designl, 172-179. [7] j.y.hwang; s.i. jeong; y.h.chung (2011); development of vehicle motion monitoring module based on smartphonel, journal of information and communication eng, kiicel, 15(9):19031909. [8] http://www.atmel.com international journal of computers communications & control biographical material dedicated to the 70th birthday of gheorghe păun online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 6, month: december, year: 2020 article number: 4088, https://doi.org/10.15837/ijccc.2020.6.4088 ccc publications 1 what does p mean in natural computing? g. ciobanu abstract celebrating the 70th birthday of gheorghe păun. acad. gheorghe păun (b. 1950, december 6) natural computing investigates computational techniques and models inspired by nature. in natural computing, the established area of computing with membranes is known as the theory of p systems. well, p stands for păun, the scientist who pioneered this new area of research. the worldwide success 1gabriel ciobanu, romanian academy ics and a.i. cuza university, iaşi, romania, gabriel@info.uaic.ro https://doi.org/10.15837/ijccc.2020.6.4088 2 of p systems is given by several publications, research groups and conferences on membrane computing, including an impressive handbook of membrane computing published in 2010 by oxford university press. in addition, gheorghe păun is an important scientist in the theory of formal languages, being co-author of the handbook of formal languages (3 volumes) published in 1997 by springer. gheorghe păun has a worldwide scientific reputation, being regularly invited to give talks at international conferences and universities. he is a member of the romanian academy (from 2012), and member of academia europaea (from 2006). all these accomplishments make gheorghe păun widely famous and appreciated. according to semantic scholar, he has more than 500 scientific papers and over 18000 citations (just wondering how many scientists have such a score). these are important reasons to express gratitude and respect to this top class scientist by acknowledging his 70th birthday! however, knowledge is difficult to be measured. and for sure it is not enough to be measured in number of papers, h-index, etc. as soon as measuring knowledge is attempted, researchers will try to improve their score, and often the result is given by uninspired and uninspiring incremental research (of little value). it is important to mention that gheorghe păun is from a different category; the category in which the main drive of scientific research is curiosity (not the career progress) and genuine commitment to scientific discovery. that curiosity which great scientists, thinkers and artists have in common; without it, there would be no much progress in our world. i have learned that there are two types of curiosity. the first one is diversive curiosity; it is essential to an exploring mind, opening our eyes to the new and undiscovered issues. diversive curiosity is what sparks our interest in learning something new. the other one is epistemic curiosity; it is what happens when diversive curiosity grows up. thus, epistemic curiosity is going deeper to build understanding. shortly, diversive curiosity helps to come up with ideas, and epistemic curiosity enables you to bring them to life. gheorghe păun handles cleverly both kinds of curiosity as a magic compass for navigating in a creative world, prefiguring both systematic investigation and discovery. when discussing with gheorghe păun, really impressive is the speed of his mind; you just express a viewpoint, and he would quickly say something (possibly a joke) that connects to an idea you had not thought of. thinking fast, thinking smart! this makes gheorghe păun also charismatic and high-spirited. he is inspiring and fun, naturally providing a mental playfulness. in addition to his scientific publications, gheorghe păun wrote several others novels, poetry, popular scientific books, books on go and other games. currently, he is strongly involved in a cultural magazine in his home city curtea de argeş. this not only shows his hardworking nature, but also his deep commitment to knowledge and understanding how things work. it is a lot of enthusiastic commitment and energy involved in his writings; writing is about learning to expect the unexpected, is a constant back and forth between what is planned and what is not planned, how much old parts to preserve, how much new parts to let in. adventure, inspiration, creative joy, fun and success! writing demands reflection and imagination. scientists are people who present an extraordinary world in a way able to transform the real world. attracted always by the adventure and challenges of the next writing, gheorghe păun avoided a teaching position in university, remaining entirely devoted to his written ideas. these ideas are combined with appropriate metaphors and analogies which help to distribute widely his knowledge. the high number of collaborators and followers (many of them considering gheorghe păun a mentor indicates the power of such an approach, together with his contagious (even pandemic) enthusiasm in describing his ideas and open problems. i had the privilege of working with gheorghe păun. i was impressed by his wide-ranging curiosity and fast intelligence, rich imagination and remarkable sharpness of mind, brilliance and empathy. i am grateful to the scientist p for many exciting ideas and several inspiring discussions on various topics. looking over the shoulder and celebrating his 70th birthday, i like to express many thanks for his human and scientific generosity, wishing him good health, long life and new stimulating achievements! https://doi.org/10.15837/ijccc.2020.6.4088 3 gheorghe păun and ijccc gheorghe păun is a section editor of the international journal of computers communications & control (ijccc) since 2006. the contribution of gheorghe păun to the prestige of ijccc is great. his paper [3] is the most cited article of ijccc (over 110 citations in web of science) and [5] is in the top 4 of ijccc (over 75 citations in web of science). moreover, in 2015 gheorghe păun publish an interesting paper in ijccc about membrane computing and economics [4] with a good impact in isi web of science. the contribution of gheorghe păun in the field of p systems has been presented in ijccc at 60th anniversary by g. ciobanu [1] and at 65th anniversary by i. dzitac [2]. acad. gheorghe păun, doctor honoris causa of agora university (2015) https://doi.org/10.15837/ijccc.2020.6.4088 4 references [1] ciobanu, g. (2010) writing as a form of freedom and happiness. celebrating the 60th birthday of gheorghe păun. international journal of computers communications & control, 5(5), 613-615, 2010. [2] dzitac, i. (2015). impact of membrane computing and p systems in isi wos. celebrating the 65th birthday of gheorghe păun. international journal of computers communications & control, 10(5), 617-626, 2015. [3] pan, l.; păun, g.(2009). spiking neural p systems with anti-spikes. international journal of computers communications & control, 4(3), 273-282, 2009. [4] păun, g.(2015). membrane computing and economics: a general view. international journal of computers communications & control, 11(1), 105-112, 2015. [5] păun, g.; perez-jimenez, m.j.; riscos-nunez, a. (2008). tissue p systems with cell division, international journal of computers communications & control, 3(3), 295-303, 2008. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: ciobanu, g.(2020). what does p mean in natural computing?, international journal of computers communications & control, 16(1), 4088, 2020. https://doi.org/10.15837/ijccc.2020.6.4088 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 5, month: octobre, year: 2021 article number: 4239, https://doi.org/10.15837/ijccc.2021.5.4239 ccc publications role of modern technologies and internet of things in the field of solid waste management s.godwin barnabas, k.a.v. geethan, s.valai ganesh, s.rajakarunakaran, p.sabarish kumar s.godwin barnabas*, s.valai ganesh and s.rajakarunakaran department of mechanical engineering ramco institute of technology rajapalayam 626117, tamil nadu, india *corresponding author: godwin@ritrjpm.ac.in valaiganesh@ritrjpm.ac.in rajakarunakaran@ritrjpm.ac.in k.arun vasantha geethan, p.sabarish kumar department of mechanical engineering st. joseph’s institute of technology chennai 600119, tamil nadu, india kavgeeth@gmail.com pskgps@gmail.com abstract the process of handling solid waste becomes complex and tedious due to the urbanization and industrialization of the most developing and developed countries. these solid waste issues if it is not addressed properly it affects ecosystem and environment. there is a possibility of many health-oriented issues especially during the pandemic period covid-19. most of the human beings are struggling with respiratory pulmonary diseases, asthma caused by these solid wastes. most of the governments are also spending huge amount of money for labors, devices and some technologies to tackle these solid waste issues. there is also an opportunity for the government to generate revenue from these solid wastes by properly sorting these waste into recyclable, nonrecyclable and bio-degradable wastes. but when humans are involved in sorting these waste it will cause some diseases and hygienic problems. so,in order to address the above said issues in this work the role of modern technologies, algorithms and some internet of things (iot) methods are discussed. implementing these technologies in the future will save huge amount of money spent by the government for the solid waste management activities. keywords: environment, pandemic period, diseases, technologies, internet of things. 1 introduction the recent survey conducted by the world bank predicts that solid waste generation would rise up to 2.3 billion by 2025. waste management is serious problem to both the developed and developing https://doi.org/10.15837/ijccc.2021.5.4239 2 nation. improper waste management will lead to much health-related breathing diseases like asthma and chronic obstructive pulmonary disease. people who are residing near the garbage area will be affected by dengue, viral fever and other fatal diseases. more than 230 million people become the victim for inhalation problems due to air pollution. the proper waste management just assures the health of the aspirants residing near the garbage. but most of the waste which are landfilled will affect the ecosystem and it becomes a big threat to the environment and cultivation. in india there is no cost-effective waste segregated approach at each individual house which is the high demand at this time for effective waste management. industries are planning for zero waste goal but they are able to reduce only 70% of the waste collected. if the plastics are properly recycled instead of landfilling 60 tonnes of oil barrel could be produced. this paper reviews two major waste management system in house hold; one is biological method and other one is physiochemical method. also, they conclude that waste management for domestic use will reduce the amount of solid waste for transportation and collection work [23] but there are some barriers in handling the recycling activities 1) laws and regulations framed by the government and budget allocated for recycling activities. 2) most of the peoples are unaware of the self-recycling. 3) there is no proper recycling technology 4) expenses in waste management[20]. in order to address this issue, it is necessary to sort these wastes into recyclable, non-recyclable and bio-degradable wastes. which will include all the solid wastes ranging from paper, plastic, rubber, sheet metal, glass etc., there are two types of possible errors in sorting one is not identifying the correct object and the second one is placing one category over the other. also sorting by the humans are non-hygienic so some robots with picking arm and artificial intelligence concepts are used for sorting the wastes.revenue generation by sorting the recyclable waste will increase through artificial intelligence and automation methods. when the automation is combined with recycling work with the back support of artificial intelligence recycling task gets smarter day to day. 2 role of iot,modern technologies, optimization techniques, and of radio frequency identification (rfid) in solid waste management this paper discusses about the following; 1. part of iot in in solid waste management 2. part of modern technologies 3. use of optimization techniques and algorithms 4. part of radio frequency identification (rfid) in solid waste management 2.1 part of iot in in solid waste management reduction of waste by adopting modern industrial automation techniques in waste management was carried out. to improve performance of the solid waste management process there are various technologies like artificial intelligence, iot (internet of things), automation, were well planned and implemented. the objective here is to combining the smart cities with the aid of iot, collecting data and analyzing the data using big data, data analytics concepts. by this modern technology introduction, waste processing of any part of the geographical area could be assessed easily. when the trash is coupled with iot used to identify the trash volume by the servers. data collected in the waste management is given as input for the graph theory optimization and other related algorithms combined to form efficient waste management system [16]. sensors are used to identify the trash volume which helps to prevent the overflow of trash bins. once the sensors give the signal of trash overflow level it will be cleaned immediately on the routine basis. for that the sensors are equipped with low weight operating systems called contiki and tinyos.the main objective here is the cloud of sensors and actuators are used as a service providers for data filtering for the efficient smart city management system [39]. some of the recycling industries are using the robots to sort out the recyclable and recyclable waste. in this paper some modern tools, technologies, economical and hygienic artificial intelligence methods https://doi.org/10.15837/ijccc.2021.5.4239 3 for picking the garbage are discussed to address the waste management issues in many countries. in this work, the role of iot is to separate the dry waste and wet waste with the aid of modern control system. in conveyor belt the dry waste is collected in the dust bin on one half portion of the bely and the wet waste is collected in the bins on the another side. here iot is used as a medium to control and monitor the waste for a single man and organization.by this effort they to develop all the rural and urban regions of the world [8].in this work the design for data sharing was made possible truck driver on time for the waste collection and the optimized route for garbage clearance by using the method called intelligent transportation system. this has been possible with the help of iot components like rfid sensors, vision systems, controllers and surveillance systems to monitor the problematic areas in the waste collection and that supports the authorities [3]. this work includes design, implementation, and evaluation of iot technology for the waste management in restaurants at suzhou, china. the objectives are collecting, transportation and final disposal of the restaurant food waste. the developed iot architecture with hardware and software to address the huge restaurant food waste yields fruitful results [60]. here the waste management is automated with the help of iot by gathering the garbage level through the webpage. for this; android application is merged with web server from the webpage the information will be sent to waste collecting vehicle and the officials. this smart garbage bin is considered to be the innovative method in waste management to make a clean environment. by this smart garbage system the overflow of wastes in public areas were avoided to prevent the common peoples from diseases[27]. inefficient waste management will lead to environmental hazards and increase cost in waste management activities. here smart garbage bin (sgb) has been implemented with iot for the convenience of the users for information sharing of garbage through wireless network and it increases the battery life of sgb. this sgb have been used in the gangnam district, seoul, republic of korea, for the period 12 months. the report shows that waste of food were considerably reduced by 33%. they have developed the sgb in two modes 1. standalone operation 2.cooperation based operation [21]. this work comprises of two studies one is related to the solid waste collection issues. the second study is about the iot enabled kanban system is planned for waste management. the purpose of including the kanban is for the mean to sgnal time and quantity of waste collection.due to this kannban system large volume of solid waste collection in the different geographical region is possible [57]. the overflow of garbage system will create unhygienic scenarios and it will affect the environment. to prevent this many concepts of effective smart garbage management system coupled with iot is discussed but, each method has its own pros and cons.here the smart garbage management system with, iot, micro-controller, and wi-fi is used in many cities [24]. three-layered architecture for iot real time solid waste monitoring was designed in this work. the lower layer is equipped with input devices like sensors to sense the waste level in the bin. the second layer also called as middle layer includes wi-fi and gsm technologies is used for transmission of collected data from lower layer to the central system. finally, the upper layer consist of cloud service for storing and receiving the data from the sensors. this device is designed with an objective to create a green environment. this system uses wireless sensor networks and different communication technologies to monitor the status of solid waste [11]. the routing issues in waste management issues is addressed by iot enabled waste management. they implemented the smart cities with the distribution of waste trucks and waste bins are equipped with modern sensors and actuators with iot enhancement to address the routing issues in waste management. apart from the scheduling and routing issues this work focuses on stochastic reassign of trucks to collect the wastes generated [56]. this work discusses the necessity of new smart waste management system to improve the quality of life of the people. this is achieved by the use of internet of things (iot) for smart recycling. this model includes dumpster with sensor, then this model is compared with present technologies for future scope of action [36]. here the new type of technology called dynamic waste management model specially supported with iot, sensors, actuators and radio frequency (rfid) for process monitoring and collecting of wastes in smart cities is developed. there are three phases in this model 1) waste collection planning and execution with the proper solution by restricting to the some locations. 2) transportation of the waste depends on the type of waste 3) reusing the recycled waste. this work yields better results and provides excellence https://doi.org/10.15837/ijccc.2021.5.4239 4 in waste management. analyzed the strengths and weakness of various models of iot enabled waste management system [5]. 2.2 role of modern technologies the automatic garbage collection system by introducing intelligence artificially to work as a garbage collecting robot. this robot act like an alternative compound to handle the waste management issues in the places especially in hospitals, theatres, supermarkets, grounds and many parks. the system function in which it uses the sensors to detect the garbage level, the volume of garbage generated and stored as database. this database is further sent to the servers. the data which is gathered is processed for cleaning the garbage collected. this method proves to be economical in comparison to the cost saved in transportation, human labors wages and finally it is a time saving process. the main components of the system function are microcontroller, sensors, battery output, and artificial intelligence. from the data collected the possibility of trash filling is calculated by statistical methods and based trash filling priority the trash will be cleaned for various locations in the country [45].smart dust bin is the other concept it is assembled with piston arrangement which is used for the compression of garbage. this device is essential for the garbage overflow due to errors happening in the cleaning team [50]. the use of ict (information and communication technology) becomes necessary for the modern waste management it includes data acquisition technologies, spatial technologies, identification technologies, and data communication technologies. which is necessary for planning, monitoring, collecting and managing the collected solid waste. this work reviews the fundamentals of ict and their application in the solid waste management for planning and design of the new system[19]. support vector machine (svm), adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and k-nearest neighbors (knn) are used for handling the waste. these four machine learning models provides better prediction of solid waste generation. these algorithms used to provide forecasting accuracy of waste generation in queensland, australia. regression analysis, descriptive statistical models, time series analysis and deep learning models are the four different methods used to calculate the solid waste generation for the upcoming years. the result shows that machine learning algorithms shows good result in predicting the waste. adaptive neuro-fuzzy inference system (anfis) were used to forecast the waste generation in the logan city with the aid of time series. the results of anfis is compared with svm (support vector machine) and knn (k nearest-neighbor) models to find the best machine learning model to be introduce in future [37]. ann (artificial neural network) have been used to calculate the solid waste generation in mashhad city with the aid of time series dataset of waste generation. to compare the accuracy of forecasting regression results are compared with ann. from this work the prediction for the solid waste generation for the year 2032 was made with ann and it found to be the best results [40]. most accurate model for the prediction of solid waste generation and diversion for more than 200 municipalities in province of ontario, canada .two machine learning algorithms such as artificial neural network and decision tree models were used to predict the exact waste generation and diversion details based on the socioeconomic and demographic criterias [25]. used artificial neural network (ann) to forecast the waste generation in bangkok city. it doesn’t yield better results because of large and noisy data.so it is needed to be processed with wavelet method. developed the time series model and other suitable model for solid waste generation. ann model is trained with back propagation algorithm [52]. discrete wavelet transform and artificial neural network (ann) is used for forecasting involving uncertainties in supply chain logistics. discrete wavelet transform and (ann) produce less bullwhip effect compared to autoregressive integrated moving average (arima) model. deep neural network models have been used to predict the data in various fields such as hydrology, supply chain demand forecasting, electricity price and load forecasting, flood forecasting. it is used in predicting waste generated by using time series dataset in delhi from 1993-2011[21, 49]. this work presents the concept of smart management system for handling waste by using wireless sensor network and embedded system to collect the waste information. this device informs the authorities based on the information gathered to carry out this task wireless sensor network and embedded linux board . linux board normally used here is raspberry pi [28]. https://doi.org/10.15837/ijccc.2021.5.4239 5 here the system is simulated from the waste data’s collected in city of copenhagen based on the geo-location data of the municipality location. comparison is also carried out between the waste management using conventional method and intelligence method is carried out in city of copenhagen [42]. management of the waste efficiently and recycling it effectively will make the nation grow further. in this work various solid wastes like metal, glass, rubber, plastic and paper is separated effectively by the device electro-mechanical system using microcontroller and operational amplifier for sorting the solid wastes in an efficient manner. they work towards a motto to convert the conventional waste management approach to a smart system [38].parallel mechanism is used to sort out the metals from the waste collected by using the sensors used in the mechanism. there are many mechanisms to sort out the required material from the waste. the efforts have been taken to convert the sort out materials into a useful product from the old recovered material [4]. used littering quantification for the cleanliness and for the betterment of the city. to obtain this computer automated vision of the different types of wastes in the form of mages is collected in streets, sidewalks and other common places. google net base vision application is used to classify the different types of urban waste this application accuracy varies from 63-77% in classifying different types of waste [46].deeper neural networks are used mage classification but it is difficult to train this networks. developed the learning framework easy for the training for neural network. the most recent development of deep learning lead to the improvement of computer vision. deep learning model has its application in picture segmentation, classification and detection. developed the image framework ranging from 100 to 1000 based for easy classification of different objects [18]. here the cloud-based waste management were carried out to monitor the waste management activities. specific containers are allotted for each type of solid waste like plastic, rubber, metal, cotton etc and each container is connected with sensor. information is updated to the cloud for effective sorting of the collected waste. this cloud-based waste management is not only effective in sorting and also it gives a desired route for the collection of waste. this method also paves the way for route optimization and to attain the efficiency in fuel and cost spent for the transport [1]. this work presents some demerits of handling waste management techniques in present situation and suggested the system is to be controlled by means of arduino ide. the system can read the depth of waste inside the container with the help of sensor and transmits the data to the central system through the wireless medium. developed vision system for the huge waste collection in an economical manner with the aid of ultrasonic sensors. the role of the ultrasonic sensors is to collect the data from the dustbin and sent to the officials through wi-fi medium [43].a review on conventional waste collection technologies and the deficiencies in the current method is carried out. a unified heuristic algorithm is applied for the routing problem optimal and sensitivity analysis were conducted on the collection costs and for the environmental friendly approach. proposed a smart and green waste collection for sustainable aste management in future [30]. 2.3 use of optimization techniques and algorithms for the weighted graph shortest path and minimum spanning is computed in one origin. considered shortest path and minimum spanning were altered after computation. for the input computation of tree program they provide the number of analytic functions for lower bound. by this it provide solutions for all or for half of worst scenarios attained by other algorithms. this method is used to calculate the minimal distance between the two trash cans to optimize the trash collecting routes from various trash collection cans [47]. the minimum linear arrangement problem and the traveling salesman problem is similar combinatorial problem. in the minimum linear arrangement problem simulated annealing and spectral sequencing are used for the approximations and they are found to be closer in their studies for lower and upper bound. similarly another method called multi-level network is used when there is various sub-levels and multiple layers involved. this method yield better results only large scale network. but genetic algorithm (ga) is a better one to optimize the clearance paths of trash cans [29, 44, 53].this work uses the modified backtracking search algorithm (bsa) in-built with capacitated vehicle routing problem (cvrp) models. this models are augmented with the smart bin concept to optimize the vehicle routing distance for waste disposal. by this method the huge amount of money spent for the waste management and air pollution is reduced due to the emissions from https://doi.org/10.15837/ijccc.2021.5.4239 6 vehicles [32]. various decision-making algorithms are used in the solid waste collection these algorithm models are compared with each other. the comparisons are carried out for various performance metrics such as receiving data while the system in motion, multi objective, transmission loss of data, and increased data reception. address the inefficient waste management challenges in smart cities by decision making model for iot enabled objects with multiple goals. this method reduces data loss in the iot data collection [33]. shortest path model is proposed for solid waste management in kanpur city. here the program coding in c program is carried out the shortest path to find out optimal route for waste management. developed c program is useful in identifying the shortest path between the two different locations for the waste collection. it also calculates the distance of the shortest path between the two different locations[15]. this work proposed is based upon artificial neural network (ann) prediction algorithm to calculate the waste generation on the household size. smart waste bin is designed to calculate the emptiness level of and weight of the bin so, it sends the direct information to responsible authorities [51]. discussed the importance of prediction models in forecasting the rate of waste generation in tehran city. compared the various forecasting models such as mean absolute percentage error (mape), mean square error (mse), and arima for the waste generation rate in tehran. final results shows that arima is better prediction model compared to other models [34]. they developed the model with constraints such as bin maximum capacity and poisson ratio for waste arrival rate. when the garbage reaches the maximum value it sends alarm signal to the particular station it communicates the cloud for processing the data also finds the optimal route for waste collection [41]. this work focuses on the iot elements and their characteristics. elements comprise identification, sensing, communication, computation, semantics, and services. characteristics involve communication identification and interaction. the review of smart waste management was conducted and the comparison of each methodologies were conducted[12]. 2.4 role of radio frequency identification (rfid) in solid waste management radio frequency identification (rfid) is a modern technology which is used in various situations for tracing items and persons for the purpose of dynamic sharing of information like the older technologies like barcode. rfid smart tag is used for monitor and trace the cold storage in food applications[2].radio frequency identification (rfid) and sensor network (sn) are used to minimize the impact of uncontrollable disposal of waste disposal. these technologies are used to track waste collection bin information of the concerned user without the support of information sharing system. the smart bin used with rfid tag is also for sorting the waste of different types for further recycling operations[14]. these (rfid) are used to measure the weight of the garbage bin in the each and individual household garbage collection. developed the rfid system with load sensor cell to automate the waste management process to reduce the waste management cost. this design is also useful to streamline the waste such as recyclable and non-recyclable waste[10].vgg-16 pre-trained cnn is trained to identify nearly 1.2 million objects from imagenet data set.so this rfid’s are used to identify 1000 different objects. both vgg-16 and alexnet are used for photo classification both these forms shows minimum error when trained for million objects. this technologies are used to avoid the segregation confusions in recycling process [48]. alexnet is a platform among the convolution neural network which has a repeated convolution layers followed by max-pooling with the best results obtained from the dataset used for waste classification with its stored 1.2 million images of multiple layers. they also verified that if any one of the layer is removed it results in the lagging performance by 2% [26].the integrated approach of architecture called convolution network is used for classification and detection. novel deep learning approach is combined with convnet for better classification and detection. convolution neural network (cnn) is used in analyzing the visual image here it takes things which are going to be investigated as input and classify them into different materials. large convolution networks is developed for image classification and it proves to show superior performance in classification with different model layers [54, 59]. it has many hidden dense layers such as polling layers, fully connected layers convolutional layers and normalization layers. rfid in combination with communication technologies were used in solid waste https://doi.org/10.15837/ijccc.2021.5.4239 7 bin and truck monitoring system. algorithms were developed for the real-time implementation. by this arrangement, information stored is used for waste management processing activities. this method yields better results for solid waste management [17]. waste tracing is done by rfid in collection services of waste management. some simulation analysis has been conducted to check the impact of introducing the new technology in the waste management procedures. finally, the application of rfid in various organization collection methods are compared in terms of economic and technical criteria [35]. discussed the role of rfid in integrated approach of solid waste management for improving the waste and resource management in switzerland. here the waste chain analysis starting from waste production, waste separation and waste treatment were carried out. by the application of rfid the recycling growth rate improved in a consistent manner[7]. rfid’s are used to trace bin distribution in waste management system, before the usage of rfid this tracing work was done by labor force which is higher compared to the usage of rfid[13].real time monitoring of information regarding the storing and transportation of solid waste have been maintained accurately by the rfid,gprs and gis for pollution control and environmental protection. by this well monitoring system the hazardous waste generation were reduced much compared to the traditional approach [31].some issues in the hazardous waste producers in brazil in close monitoring of transporting these hazardous waste and disposing it at right destination. because it poses serious threat and vulnerable situation to the environment. this issue was addressed properly the new system design and prototype of rfid by closely monitoring the transportation of hazardous waste in brazil and disposing it in the desired location[5]. the issue of improper solid waste management in kanpur city, india related to improper information regarding transportation and disposal of disposal for the generated waste. taken into account this problem were solved by the application of communication tools like rfid, gsm uses and other related software packages [55].metropolis like in shanghai, china there is a problem in maintaining efficient waste management including collection, transportation and disposal. this issue were solved by 3r’s concept of reducing, reusing and recycling the generated solid wastes have been properly sorted out with help of information technology through rfid.this technology proves to be the cost effective waste management system [58].huge amount of waste sorting is a difficult task and also doing it in a manual manner is unhygienic especially during the pandemic situation like covid19. deep learning integrated with convolution neural network (cnn) is trained with large data sets used for testing the accuracy. this is carried out in sorting the different waste like sheet metal, card board, and glass and metal tins[9]. 3 key findings and suggestions in most of the developing countries they are following only conventional mode of solid waste management. the introduction of the intelligent system as discussed earlier in this work will minimize the cost incurred in the solid waste management. the wireless mode of network by the iot sensors, rfid tags, transducers will solve the waste corporation officials by clearing the garbage bins in a timely manner. the intelligent methods discussed in this paper will improve the recycling opportunities in most of the countries. because these methods are very useful in sorting the recyclable and non-recyclable waste. it is safer to handle the hazardous waste by the modern technologies than the conventional approach. their accuracy of the well trained convolution neural network by more than a billion of classified mages is very useful in sorting the different types of wastes for efficient recycling operations. also this mode of smart waste management will reduce the chance of virus infection especially during the pandemic period covid-19. https://doi.org/10.15837/ijccc.2021.5.4239 8 4 conclusion the role of artificial intelligence, cloud computing, internet of things, optimization techniques, algorithms and modern technologies is very essential for the solid waste management (swm).in the conventional mode of waste management it discusses about the transportation, collection and disposal is of manual method it makes all the developing countries to spend much higher for the waste management. even the treatment of this solid wastes involving sorting activity to sort out the recyclable and nonrecyclable wastes .to carry out this all municipal corporations have set a huge amount for the labors working for the recycling activities.as mentioned earlier it is unhygienic and cause some infectious diseases especially during the pandemic period covid-19 the whole world is facing. now it’s time for all developing countries to think of all the modern technologies as discussed in this paper to harvest the benefits from it to maintain the efficient swm. competing interest authors declared that there is no competing interest exists. acknowledgements we are thankful to ramco institute of technology provided expertise that greatly assisted the research, although they may not agree with all of the interpretations provided in this paper. references [1] aazam, m.; st-hilaire, m.; 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(2017). design implementation, and evaluation of an internet of things (iot) network system for restaurant food waste management, waste management, 73, 26-38, 2017. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: godwin barnabas, s.; arun vasantha geethan, k.; valai ganesh, s.; rajakarunakaran, s.; sabarish kumar, p. (2021). role of modern technologies and internet of things in the field of solid waste management, international journal of computers communications & control, 16(5), 4239, 2021. https://doi.org/10.15837/ijccc.2021.5.4239 introduction role of iot,modern technologies, optimization techniques, and of radio frequency identification (rfid) in solid waste management part of iot in in solid waste management role of modern technologies use of optimization techniques and algorithms role of radio frequency identification (rfid) in solid waste management key findings and suggestions conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 2, month: april, year: 2020 article number: 3851, doi.org/10.15837/ijccc.2020.2.3851 ccc publications using multi-granular fuzzy linguistic modelling methods to represent social networks related information in an organized way j. a. morente-molinera, f.j. cabrerizo, s. alonso, m. a. martínez, e. herrera-viedma juan antonio morente-molinera* andalusian research institute on datascience and computational intelligence, university of granada, spain 18071, granada, spain. *corresponding author: jamoren@decsai.ugr.es sergio alonso andalusian research institute on datascience and computational intelligence, university of granada, spain 18071, granada, spain. zerjioi@ugr.es francisco javier cabrerizo andalusian research institute on datascience and computational intelligence, university of granada, spain 18071, granada, spain. cabrerizo@decsai.ugr.es maría ángeles martínez department of social work and social services, university of granada, spain 18071, granada, spain. mundodesilencio@ugr.es enrique herrera-viedma andalusian research institute on datascience and computational intelligence, university of granada, spain 18071, granada, spain. viedma@decsai.ugr.es abstract social networks are the preferred mean for experts to share their knowledge and provide information. therefore, it is one of the best sources that can be used for obtaining data that can be used for a high amount of purposes. for instance, determining social needs, identifying problems, getting opinions about certain topics, ... nevertheless, this kind of information is difficult for a computational system to interpret due to the fact that the text is presented in free form and that the information that represents is imprecise. in this paper, a novel method for extracting doi.org/10.15837/ijccc.2020.2.3851 2 information from social networks and represent it in a fuzzy ontology is presented. sentiment analysis procedures are used in order to extract information from free text. moreover, multi-granular fuzzy linguistic modelling methods are used for converting the information into the most suitable representation mean. keywords: multi-granular fuzzy linguistic modelling methods, fuzzy ontologies, sentiment analysis. 1 introduction internet is now the preferred mean for sharing information. thanks to it, high tons of people are connected and can share knowledge of all kind. in its first years, internet was a closed platform where a reduced set of experts provided data and the users were only able to consume this information without providing anything new. nevertheless, with the appearance of web 2.0 technologies [3, 7], this situation has completely changed. nowadays, users are, at the same time, source and consumers of all the information that is present on the internet. thanks to social networks, each user can contribute and provide his/her own knowledge to the net. all this information can be of great use to other users. although there is no doubt that highs tons of information are available on the web, it is difficult for the system to make use of the available information. this is because it is imprecise, and is represented in an unstructured way. when users provided it, they were not thinking about providing information in a way easily understandable by systems. on the contrary, they were just trying to make other humans to understand what they were sharing. therefore, there is a need of designing novel methods that allow computational systems to obtain useful knowledge from all the data that users provide to the internet. in this paper, a novel method for processing social networks information in order to provide a structure to the knowledge is presented. sentiment analysis procedures [2, 10] are going to be used in order to obtain information from the social networks’ posts. afterwards, multi-granular fuzzy linguistic modelling methods [4] are used in order to structure the information. finally, the obtained information is stored on a fuzzy ontology [5, 8]. thanks to this, any computational system can make use of this knowledge in order to perform any kind of analysis over the data. fuzzy ontologies introduce a query system that allows any computational system to search specific information on the fuzzy ontology. the paper is organized as follows. in section 2, basis needed to comprehend the presented approach are exposed. in section 3, the presented method is thoroughly exposed. in section 4, a brief example whose purpose is to improve the comprehension of the method is presented. finally, some conclusions are pointed out. 2 preliminaries 2.1 fuzzy ontologies fuzzy ontologies can be formally defined using the following elements: • concepts: they are characteristics that the elements that conform the ontology can or not fulfill. • axioms: they are certain rules that all the fuzzy ontology elements must follow. • relations: they create connections between concepts and individuals and also between two individuals. each relation determines if a certain individual is related or not with a certain concept or individual. • individuals: they are entities that are described using concepts by means of the relations. • fuzzy relations: they work in a similar way as the relations. nevertheless, instead of marking two elements as {related,not related}, they establish a relation degree. this relation degree can be represented as a fuzzy set. thanks to fuzzy relations, it is possible to represent imprecise information in a fuzzy ontology. doi.org/10.15837/ijccc.2020.2.3851 3 fuzzy ontologies is a field that is quite present in the recent literature. for instance, in [6], a decision support system driven by a fuzzy ontology is presented. in [9], a recommendation system that uses ontologies and neuro-fuzzy classification is presented. finally, in [1], a sentiment analysis procedure that uses word-embedding and ontologies is presented. 2.2 multi-granular fuzzy linguistic modelling linguistic modelling provides an interesting framework for representing imprecise information. it uses linguistic label sets and the fuzzy sets’ mathematical framework in order to carry out operations. thanks to this, experts can express themselves using words and the system can interpret and work with this kind of imprecise information. the main issue of using linguistic label sets is that they have a fixed granularity value. therefore, the number of labels of a set cannot be modified. in order to solve this, it is possible to use multi-granular fuzzy linguistic modelling methods. thanks to them, it is possible to convert information expressed using a linguistic label set with a certain granularity value into information expressed using a linguistic label set whose granularity value is different. one of the most flexible methods available in the literature use 2-tuple representation value in order to represent and transform the information. a linguistic 2-tuple is a tuple (si,α) where: • si is a label belonging to a linguistic label set s = {s1, . . . ,sn}. • α is called the symbolic translation and consists on a number located on the following interval: [−0.5, 0.5]. it is possible to transform a linguistic 2-tuple in a numerical value, β, by aggregating i and α. the opposite operation can be performed as follows: α = β −round(β) (1) therefore, α can be considered as the distance between β and si. in order to convert a β value into a 2-tuple value (s,α), it is possible to use the following expressions. for converting β into (s,α): ∆ : [0,g] → s × [−0.5, 0.5) ∆(β) = (si,α) with { si i = round(β) α = β − i α ∈ [−0.5, 0.5) (2) for converting (s,α) into β: ∆−1 : s × [−0.5, 0.5) → [0,g] ∆−1(si,α) = i + α = β (3) 3 representing information in fuzzy ontologies using sentiment analysis and multi-granular fuzzy linguistic modelling methods in this section, the designed method is described in detail. the overall set of actions that must be carried out can be summarized in three different steps: • extract useful information from social networks: sentiment analysis procedures are used in order to extract information from social networks. a linguistic label set of 3 labels is used for representing the information. • transforming the information into the desired representation: the obtained information is transformed into the desired representation mean. depending on how the data should be stored, it is possible to choose among different linguistic label sets. • storing the information in the fuzzy ontology: once that the information is correctly represented, it is possible to store it on a fuzzy ontology. in the following subsections, the process will be described in detail. doi.org/10.15837/ijccc.2020.2.3851 4 3.1 extracting information from social networks this main goal of this step is to obtain information from users’ opinions on the internet. in order to perform this task, first of all, it is necessary to focus on the topic that we want our fuzzy ontology to depict. for this purpose, the following steps can be followed: 1. defining the set of individuals: first of all, the sets of elements that users are discussing about is listed. this way, the methods will center in retrieving and storing individuals’ related information leaving aside irrelevant data. by using this information, the set e = {e1, . . . ,en} is defined. 2. defining the set of concepts: the descriptions that users employ when referring to the individuals are defined. using this information, the set c = {c1, . . . ,cm} is defined. once that the elements that we want information for have been defined, it is possible to extract the information following the next steps: 1. defining the lists of words: the fuzzy ontology will try to elucidate if the individuals fulfill or not the different defined concepts. in order to perform this task, it is possible to define several word lists that will help us to determine if there are relations between each individual and the concepts and their strength. for this purpose, three different lists of words are defined: • positive list of words: this list of words is form by expressions typically used when the relation between an individual and a concept is fulfilled. • negative list of words: this list of words is defined by expressions that are used when the relation between an individual and a concept is not fulfilled. • similar list of words: this list of words contains expressions determining that it is not clear if an individual fulfills a concept or not. 2. obtaining the sentiment information: the retrieved texts from social networks are analyzed in order to find expressions from the word lists. three different values are calculated: • npositiveji : this value indicates the number of matches that a certain set of related texts have when compared to the positive list of words. • nnegativeji : this value indicates the number of matches that a certain set of related texts have when compared to the negative list of words. • nneutralji : this value indicates the number of matches that a certain set of related texts have when compared to the neutral list of words. 3. representing the information using a preference value: initially, a preference value belonging to each of the labels of the linguistic label set sci = {low_ci,medium_ci,high_ci} can be calculated. for this purpose, the following expressions can be used: µ(low_ci) = nnegative j i nnegative j i + nneutral j i + npositive j i (4) µ(medium_ci) = nneutral j i nnegative j i + nneutral j i + npositive j i (5) µ(high_ci) = npositive j i nnegative j i + nneutral j i + npositive j i (6) doi.org/10.15837/ijccc.2020.2.3851 5 3.2 converting the information into the desired representation the information obtained in the previous step can be directly stored over a fuzzy ontology. nevertheless, if we do not apply any further conversion, all the information will be represented using the same 3-label linguistic label set. in order to improve the representation flexibility of the method and to allow the use of different linguistic label sets, it is possible to convert the 3-label data into g-label data by using the following procedure: 1. selecting the required granularity value: first of all, the target granularity is selected. if high precision is needed, it is possible to select a linguistic label set that has a high granularity value. on the contrary, if the information is imprecise, it is possible to select a low granularity value in order to better represent it. 2. obtaining a 2-tuple β value from a 3-label one: once that the desired target linguistic label set has been selected, it is possible to apply a multi-granular linguistic modelling transformation function in order to obtain the information in the required representation mean. first of all, the 3-label data is converted into a β value. this can be done by applying the following expression: β = µ(low_ci) ∗ 1 + µ(medium_ci) ∗ 2 + µ(high_ci) ∗ 3 (7) 3. obtaining the g-label representation from the β value: using the β value, it is possible to carry out a domain conversion from interval [0,3] to [0,g]. the following expression can be applied: βg = β3 − 1 3 − 1 · (g − 1) + 1 (8) finally, membership values for each of the labels should be obtained. for this purpose, the following expression can be applied: µ(si) = 1 − (β − i) µ(si+1) = 1 − ((i + 1) −β) (9) in cases where the distance between the β value and the label is higher than one, the membership value is zero. 3.3 storing the information in the fuzzy ontology once that the required conversions have been performed, the information is ready for being stored in the fuzzy ontology. for this purpose, a concept is generated per each label and element in c. for instance, imagine that c = {prize,size} are the set of concepts, and s3 and s5 are respectively used for representing the information. then, there is a total of 8 concepts in the generated fuzzy ontology. for each defined individual in e and all the generated concepts, a fuzzy relation whose value is the membership value of the individual to the label of the concept is defined. finally, there is no need of axioms in the definition of the target fuzzy ontology. nevertheless, it is possible to include then manually if the tackled problem requires it. 4 example in this section, a brief application example is presented. imagine that a set of users are debating about a set of wines. we want to retrieve information about price and acidity of five of them. first of all, information about them is retrieved. this can be done by finding text that includes certain keywords. once that the texts are retrieved, it is possible to search them in order to calculate the npositive, nnegative and nneutral values. once that this process has been performed, results shown in tables 1 and 2 are obtained. doi.org/10.15837/ijccc.2020.2.3851 6 wines nnegative nneutral npositive e1 10 4 2 e2 1 13 2 e3 16 1 2 e4 1 1 12 e5 1 0 7 table 1: word lists results for price. wines nnegative nneutral npositive e1 0 14 1 e2 0 1 15 e3 1 1 11 e4 1 0 17 e5 18 0 11 table 2: word lists results for acidity. wines low_price medium_price high_price e1 0.125 0.25 0.625 e2 0.125 0.8125 0.0625 e3 0.105 0.0526 0.8421 e4 0.8571 0.0714 0.0714 e5 0.875 0 0.125 table 3: 3-labels representation for price. wines low_ac medium_ac high_ac e1 0 0.933 0.066 e2 0 0.0625 0.9375 e3 0.0769 0.0769 0.8561 e4 0.555 0 0.9444 e5 0.6206 0 0.3793 table 4: 3-labels representation for acidity. doi.org/10.15837/ijccc.2020.2.3851 7 wines v l_p l_p m_p h_p v h_p e1 0 0 0 1 0 e2 0 0.125 0.875 0 0 e3 0 0 0 0.521 0.479 e4 0.5718 0.4282 0 0 0 e5 0.5 0.5 0 0 0 table 5: 5-label representation of the individuals for the description price. next, 3-label representation is calculated. after applying expressions (4), (5) and (6), the results exposed on tables 3 and 4 are obtained. for calculation showing purposes, now we will transform prize related information into data represented by a linguistic label set of 5 labels. first of all, β values are calculated for each individual. for this purpose, expression (7) is used as follows: βe1 = 0.125 · 1 + 0.25 · 2 + 0.625 · 3 = 2.5 βe2 = 0.125 · 1 + 0.8125 · 2 + 0.0625 · 3 = 1.9375 βe3 = 0.105 · 1 + 0.0526 · 2 + 0.8421 · 3 = 2.7365 βe4 = 0.8571 · 1 + 0.0714 · 2 + 0.0714 · 3 = 1.2141 βe5 = 0.875 · 1 + 0 · 2 + 0.125 · 3 = 1.25 once that β values have been calculated, expressions (8) and (9) can be used in order to convert the 3-label information into a 5-label representation. computations are done as follows: βe15 = 2.5 − 1 3 − 1 · (5 − 1) + 1 = 4 βe25 = 1.9375 − 1 3 − 1 · (5 − 1) + 1 = 2.875 βe35 = 2.7365 − 1 3 − 1 · (5 − 1) + 1 = 4.479 βe45 = 1.2141 − 1 3 − 1 · (5 − 1) + 1 = 1.4282 βe55 = 1.25 − 1 3 − 1 · (5 − 1) + 1 = 1.5 the final obtained representation is shown on table 5. once that all the required information is calculated, the fuzzy ontology can be built. for this purpose, the set of wines is used as individuals, labels that represent concepts are used as the concepts of the generated ontology. finally, membership values of each of the individuals to the labels of the concepts are used as fuzzy relation values. 5 conclusion in this paper, a novel method that is capable of analyzing and obtaining real knowledge from social networks is presented. sentiment analysis is used in order to obtain knowledge from social networks users’ opinions. multi-granular fuzzy linguistic modelling methods are used in order to obtain the more adequate representation for each piece of information. finally, fuzzy ontologies are used in order to store the information in an organized way. thanks to fuzzy ontologies queries, it is possible for any kind of computational system to retrieve information and performed any required analysis. doi.org/10.15837/ijccc.2020.2.3851 8 funding this work has been supported by the ’juan de la cierva incorporación’ grant from the spanish ministry of economy and competitiveness and by the grant from the feder funds provided by the spanish ministry of economy and competitiveness (no. tin2016-75850-r). author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. references [1] ali, f.; kwak, d.; khan, p.; el-sappagh, s.; ali, 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(2019); ontology and context based recommendation system using neuro-fuzzy classification, computers & electrical engineering, 74, 498-510, 2019. [10] yang, f.; du, c.; huang, l. (2019); ensemble sentiment analysis method based on r-cnn and c-rnn with fusion gate. international journal of computers communications & control, 14(2), 272-285, 2019. doi.org/10.15837/ijccc.2020.2.3851 9 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: morente-molinera, j. a.; cabrerizo, f.j.; alonso, s.; martínez, m. a.; herrera-viedma, e. (2020). using multi-granular fuzzy linguistic modelling methods to represent social networks related information in an organized way, international journal of computers communications & control, 15(2), 3851, 2020. https://doi.org/10.15837/ijccc.2020.2.3851 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 1, month: february, year: 2022 article number: 4714, https://doi.org/10.15837/ijccc.2022.1.4714 ccc publications fault detection in nuclear power plants using deep leaning based image classification with imaged time-series data y. shi, x. xue, j. xue, y. qu yong shi 1. school of economics and management, university of chinese academy of sciences beijing 100190, china 2. research center on fictitious economy and data science, chinese academy of sciences beijing 100190, china 3. key laboratory of big data mining and knowledge management, chinese academy of sciences beijing 100190, china 4. college of information science and technology, university of nebraska at omaha omaha, ne 68182, usa xiaodong xue 1. school of economics and management, university of chinese academy of sciences beijing 100190, china 2. key laboratory of big data mining and knowledge management, chinese academy of sciences beijing 100190, china jiayu xue* 1. school of computer science and technology, university of chinese academy of sciences beijing 101408, china 2. research center on fictitious economy and data science, chinese academy of sciences beijing 100190, china 3. key laboratory of big data mining and knowledge management, chinese academy of sciences beijing 100190, china *corresponding author: xuejiayu18@mails.ucas.ac.cn yi qu* 1. school of economics and management, university of chinese academy of sciences beijing 100190, china 2. research center on fictitious economy and data science, chinese academy of sciences beijing 100190, china 3. key laboratory of big data mining and knowledge management, chinese academy of sciences beijing 100190, china *corresponding author: quyi17@mails.ucas.ac.cn https://doi.org/10.15837/ijccc.2022.1.4714 2 abstract fault detection is critical to ensure the safely routine operations in nuclear power plants (npps), requiring very high accuracy and efficiency. meanwhile, the rapid development of modern information technologies have profoundly changed and promoted various sectors including nuclear industry. inspired by the great progress and promising performance of deep learning based image classification recent years, a two-stage fault detection methodology in npps has been proposed in this paper. first the time-series data describing the operating status of npps have been transformed into two-dimensional images by four methods, preserving the time-series information in images and converting the fault detection problem into a supervised image classification task. then four specific image classifying models based on three primary deep learning architectures have been separately experimented on the imaged time-series data, achieving excellent accuracies. further the performances of different combinations of transforming means and classifying models have been compared and discussed with extensive experiments and detailed analysis of throughput for four transforming methods. this methodology proposed has obtained remarkable results by reshaping data format and structure, making image classifying models applicable, which not only efficiently detect and warn possible faults in npps but also enhances the capability for safety management in nuclear power systems. keywords: fault detection, nuclear power plants, deep learning, image classification, imaged time-series data. 1 introduction nuclear energy has become a strategic source of global power supply and is essential to national power security. as a well-known green energy, it could provide stable and huge amount of electricity with many advantages like no pollution, low operating cost, low carbon emission, etc. according to the annual report of world nuclear association, the electricity generated by nuclear power has accounted for more than 10% in global electricity supply and it’s still growing, which has already been a significant component. a common sense is that nuclear energy is probably the only option that could secure adequate energy consumption but doesn’t trash this planet, by its many great strengths in dealing with problems from growing energy demand, global carbon reduction, climate change and environmental protection. however, nuclear power systems have one particular specialty as huge disaster might happen once there’s a small accident occurred, and that’s precisely why safety management has always been the top priority in daily operations of npps. therefore, it’s definitely necessary to detect the faults (or failures) in npps accurately and effectively, then make warnings in advance so that these troubles could be addressed and eliminated in a timely manner. and this subject fault detection and diagnosis has become a common focus for both academia and industry. original fault detection in npps was simple and easy to implement, normally using the "pre-set values" as standard criteria. once the parameters on meters are beyond the pre-set values or range, then it’s assumed the fault has occurred, with voting scheme sometimes been introduced to avoid false alarms [18]. however, with the rapid development of information technology especially artificial intelligence (ai), advanced and intelligent tools represented by machine learning have infiltrated many areas recent years. this has resulted in more intelligent transformations in real industries, also the data-driven fault detection and diagnosis has become a hot research topic of the intelligent cybernetics in the big data era [53]. former studies of fault detection and diagnosis can be mainly divided as two parts according to the chronological development and they are "model-driven" methods like statistical models, "data-driven" methods like machine learning models. reasons for the transition from "model-driven" to "data-driven" are the tremendous expansion in data and its complexity, with the great promotion in computational capacity [18] [42]. these two points above have also dramatically accelerated the progress of intelligent techniques especially machine learning and deep learning models, generating a large variety of intelligent approaches and enabling deep learning to make a significant influence in fields such as image classification, voice recognition, natural language processing, etc. motivated by the outstanding performances of deep learning in image processing, we propose a two-stage fault detection methodology in npps, which is a supervised classification task under this scenario. due to the lack of real images from npps, we also consider to transform the numeric timeseries data into images with four major methods to make deep learning based image classifying models https://doi.org/10.15837/ijccc.2022.1.4714 3 applicable. the fault detection methodology proposed in this paper has obtained remarkable results by reshaping data format and structure, with the results that it’s capable of efficiently detecting and warning possible faults in npps and further enhancing the capability of safety management in many power systems. the major highlights of this research are described as follows: • the time-series data describing the operating status of npps have been transformed into twodimensional images, preserving the time-series information as pixel information in images and making deep learning based models applicable. • four specific image classifying models based on three primary deep learning architectures have been separately experimented on the imaged time-series data and it has achieved excellent accuracies. • performances of different combinations of transforming means and classifying models have been compared and discussed through extensive experiments, with detailed analysis of throughput for four methods. the rest of this paper will be arranged as follows: section 2 will briefly review the most related work previously, mainly focused on fault detection in npps, data transformation and deep learning based image classification. section 3 will specifically illustrate our proposed two-stage fault detection methodology in npps. data, experiments and results will be described in section 4 with detailed analysis and finally, the conclusion will be presented in section 5 as well as limitations and future directions. 2 related work in this section, related work will be briefly reviewed and they could be categorized into three major parts. they are fault detection in npps of the exact subject, data transformation and deep learning based image classification concerning with the two-stage fault detection methodology proposed later. focusing on these three subjects, this section will outline and introduce the most related cases previously. 2.1 fault detection in npps fault detection and diagnosis is a multidisciplinary task involving several fields like engineering, management science, information technology, etc, also it could be regarded as an information technology & decision making problem. here the original and ancient research and practice of fault detection in npps will not be mainly discussed, but rather cases of intelligent "data-driven" techniques recent years, which can be categorized into two primary sections, the machine learning ones including the well-known artificial neural networks (ann), support vector machines (svm), ensemble methods and the deep learning applications like convolutional neural networks (cnn). these applications are mostly supervised learning tasks and have demonstrated that it’s feasible and promising for the implementation of deep learning based image classifying techniques. machine learning. to support the strategic decision-makings in nuclear fuel management, regression model has been used to estimate the composition of spent nuclear fuel [16]. logistic regression (lr) also was applied in fault diagnosis of npps [2], with naive bayes (nb) introduced into fault diagnosis of thermocouple [4] and artificial cognitive system for recognizing current status of nuclear reactors [34] respectively. the powerful ann has shown great adaptability by being applied in control rod drive system and accident prevention system [1], also in conceptual accident prevention system for light water reactors [35], while the svm used for designing more accurate identification and diagnosis of faults [52] and detecting loose components in the primary circuit cooling system of npps [30]. the classical model decision tree (dt) has been used to assess the probabilistic risk of precursor events, with the aim of reducing operational risk in npps [44] or accurately and quickly identifying possible root causes of faults [33]. as a widely-used ensemble learning approach, xgboost with extracted features has performed effective fault detection of screen cleaners in npps [6] and another representative https://doi.org/10.15837/ijccc.2022.1.4714 4 ensemble learning algorithm, random forests (rf), has been integrated as a learner for fault diagnosis with svm, knn and multilayer perceptron (mlp) [23]. deep learning. based on different architectures, deep learning has developed many variants like cnn, recurrent neural networks (rnn), restricted boltzmann machines (rbm), deep belief networks (dbn), mlp, etc. these can be summarized as a general architecture called deep neural networks (dnn) while the variants can be designed or constructed using different quantities of hidden layers. by the convolutional operations and the specialty of parameters sharing, cnn is good at and capable of processing high-dimensional data like images or videos, constructing a fast and accurate detection system of cracks in underwater metal surfaces using inspection videos in npps [4]. also it can be applied to detect scratches in nuclear fuel assembly appearances [11] and diagnose fault with a specific reactor dataset [55]. different from cnn, rnn is suitable to deal with sequential data especially in natural language understanding and time-series signal processing, which has been integrated with ppca, multi-resolution wavelet analysis to build a nuclear power machinery fault detection model [25]. to improve the reliability of engineering systems of npps, rbm was used to detect anomalies and conduct diagnosis & prognosis by analyzing massive data [38]. dbn has also been applied to indicate online faults for thermocouple in npps [29] while mlp has been introduced into transient detection [31] and identification for fault scenarios [41] in npps. 2.2 data transformation: from numeric to imaged data transformation plays a vital role in solving some tough problems, which converts the nature of problems by reshaping data format or structure. the renowned cnn in image classification is a representative. large quantities of temporal data generated in npps have been mapped into images and then anomaly detection has been performed using cnn [12] [22] by its great strength in image processing. in addition, this kind of data transformation from numeric to imaged has also been observed in other areas like bankruptcy prediction. by dividing the numerical data, the original numeric indicators from financial statements have been transformed into grey-scale images [17], then the issue of predicting bankruptcy for firms was interpreted as image classification task using cnn, with the results of better and more robust performances than conventional machine learning classifying models. the promotional effect of precision by data transformation has been experimentally verified in many cases, suggesting that this way could be considered and adopted in the field of fault detection in npps. 2.3 deep learning based image classification image classification has been a longstanding research topic in the field of computer vision (cv). as a critical and fundamental problem, it aims to identify the label of classes for a given image. traditional image classification using machine learning methods rely on two separate steps, first is applying hand-designed operations to extract features (e.g., sift, hog, surf, lbp) from input images, then is to train a classifier (e.g., svm, knn, adaboost) to recognize the class of input signals. these methods can deal with the tasks quite well with relatively small datasets, but the performance is not satisfactory in more complicated tasks. with the appearance and availability of huge datasets (e.g., imagenet [7]) and the increasing computational capacity, advanced models with better performances are desperately needed and there comes up the deep learning based image classification techniques. alexnet [21], achieving record-breaking classification results in the imagenet large-scale visual recognition challenge, has led to a spurt of various deep convolutional neural networks (cnns) [14] [43] [45]. instead of using hand-crafted features, cnns apply flexible and trainable architectures to automatically extract and integrate low/midiem/high-level features in an end-to-end multilayers fashion. the inductive biases inherent to cnns, like translation invariance and local connectivity, are of great help in image feature extraction. in addition to classification, deep cnns models have got state-of-the-art (sota) performances on a wide range of computer vision applications, such as object detection [24] [26] [37], image segmentation [5] [13] [40], super-resolution [8] [20], etc. with cnns becoming the de-facto standards for computer vision, two major architectures, namely transformer and mlp, have achieved competitive results compared with cnn-based models and attracted significant https://doi.org/10.15837/ijccc.2022.1.4714 5 research interest globally from the cv community. transformer [50] is a model architecture based solely on the self-attention mechanism to draw global dependencies between input and output, dispensing with recurrence and convolutions. inspired by the great success of transformer in natural language processing (nlp), numerous studies have started to migrate the transformer to visual recognition tasks. compared with cnn-based models that focus only on local features, transformer is able to learn long-range dependencies, which means it can easily derive global information. meanwhile, it also learns visual features with fewer priors and further reduces human intervention, while it’s of larger computational costs and can’t be generalized well when training with insufficient amount of data. to tackle this problem, a series of works [3] [27] [49] have been proposed in researches. multilayer perceptron (mlp) is not a new tool in the community of computer vision , which is actually one of the first classifiers tested on mnist and has become the most widely-used model and benchmark in computer vision tasks at that time. nevertheless, limited by a large amount of calculation and prone to over-fitting when the amount of data is insufficient, mlp has once been lying dormant for decades. recent years, inspired by transformer’s great success in vision tasks, researchers have proposed deep mlp models such as mlp-mixer [47] and resmlp [48] for promotional performances. compared with conventional mlp, these models have more hidden layers and have changed the input from full flattening to patch flattening, indicating that a purely mlp-based architecture which drops the artificially designed attention mechanism and learns from the raw data, can be fairly competitive with cnns or transformer. but it also suffers from the issue of data-hungry and furthermore, the complexity of mlp is quadratic to the size of images, making mlp-like models intractable on high-resolution images. 3 methodology in this section, detailed description of the two-stage fault detection methodology in npps will be presented. for better understanding and directly visual senses, the precise process of this methodology proposed is shown in figure 1. in short, after the dimension reduction, the time-series numeric data of the operating status collected from sensors in npps will be firstly encoded into images by different transforming methods. then these transformed images are concatenated and will be fed into different deep classification models to conduct the image classification and output the fault class as detection results. in addition, we will focus on the processes of exact methods for encoding time-series numeric data into images and deep learning based image classification models, which will be elaborated while the specifics of experiments like dimension reduction of data and image concatenation will be fully illustrated in section 4. 0 50 100 150 200 250 300 350 1 51 101 151 201 time series dimension reduction encoding time series to images un-thresholded recurrence plot (utrp) markov transition field (mtf) gramian angular summary field (gasf) gramian angular difference field (gadf) deep learning architectures cnn-based models transformer-based models mlp-based models output: fault class image concatenation figure 1: the precise process of the two-stage fault detection methodology in npps 3.1 data processing & transformation obviously the transformed images as experimental data certainly will affect the results and performance of fault detection. a suitable and appropriate transforming method should be able to preserve temporal relations in images. thus, four major transforming methods to convert 1-d time-series https://doi.org/10.15837/ijccc.2022.1.4714 6 data into different types of 2-d images have been adopted in our methodology which have been applied or tested in other studies. and they are gramian angular summation field (gasf) [46][54], gramian angular difference field (gadf) [46][54], markov transition field (mtf) [12][46][54] and un-thresholded recurrence plot (utrp) [46], of which the basic principles and detailed processes will be introduced in the following. gramian angular field. gramian angular field [51] is a method that encodes time-series, which are represented in a polar coordinate system rather than the cartesian coordinates, into images. according to different calculation methods, it can be divided into two types: gramian angular summation field (gasf) and gramian angular difference field (gadf). given a time series x = {x1,x2, · · · ,xn} composed of n real-valued monitoring values obtained from a certain sensor, it is first normalized to the interval [-1,1] according to: x̃i = (xi −max(x)) + (xi −min(x)) max(x) −min(x) then, encoding time stamp ti as the radius and the value xi as the angular cosine to represent the rescaled x in polar coordinates, which is defined as:{ φ = arccos(x̃i), −1 ≤ x̃i ≤ 1, x̃i ∈ x̃ r = ti n , ti ∈ n where ti is the time stamp corresponding to xi, n is the total length of the time stamp to normalize the span of polar coordinates. encoding time series into polar coordinates system has two important properties. first, it can produce one and only one result with a unique inverse map, because it is bijective as cos(φ) is monotonic when φ ∈ [0,π]. second, compared with cartesian coordinates, it is able to preserve absolute temporal relations. the final step is to use the transformed polar coordinate system to generate 2-d images which are represented as a gramian-like matrix. each element in the gramian-like matrix is actually the trigonometric sum/difference between each point, so it can be used to identify the temporal correlation within different time intervals. the gasf and gadf are defined as follows: gasf =   cos(φ1 + φ1) · · · cos(φ1 + φn) cos(φ2 + φ1) · · · cos(φ2 + φn) ... ... ... cos(φn + φ1) · · · cos(φn + φn)   = x̃′ · x̃ − √ i − x̃2 ′ · √ i − x̃2 gadf =   sin(φ1 −φ1) · · · sin(φ1 −φn) sin(φ2 −φ1) · · · sin(φ2 −φn) ... ... ... sin(φn −φ1) · · · sin(φn −φn)   = √ i − x̃2 ′ · x̃ − x̃′ · √ i − x̃2 where i is the unit row vector [1, 1, · · · , 1]. in gaf, time increases as the position moves from top-left to bottom-right, making it possible to retain the temporal dependency. the main diagonal of gaf contains the original value information. markov transition field. markov transition field [51] is a method which expands the dynamic transition statistical information by sequentially representing the markov transition probability. given a time series x = {x1,x2, · · · ,xn}, first determine its quantile bins q and allocate each xi to the corresponding bin qj(j ∈ [1,q]). then construct a q × q weighted adjacency matrix w by computing the markov transition probabilities among quantile bins. for example, wij means the probability of xt ∈ qi and xt+1 ∈ qj. after normalization by ∑ j wij = 1, the markov transition matrix w is defined as follows: w =   w1,1 w1,2 · · · w1,q w2,1 w2,2 · · · w2,q ... ... ... ... wq,1 wq,2 · · · wq,q   https://doi.org/10.15837/ijccc.2022.1.4714 7 w is not sensitive to the distribution of x as well as the temporal dependency on time stamp ti, thus it can not reflect the time characteristic. the final step is to construct mtf based on w . mtf =   wi,j|x1∈qi,x1∈qj · · · wi,j|x1∈qi,xn∈qj wi,j|x2∈qi,x1∈qj · · · wi,j|x2∈qi,xn∈qj ... ... ... wi,j|xn∈qi,x1∈qj · · · wi,j|xn∈qi,xn∈qj   each element in mtf denotes the transition probability of point in qi at time stamp i move to qj at time stamp j. the information and time characteristics contained in the mtf vary with the value of quantile bins q. un-thresholded recurrence plot. recurrence plot (rp) [10] is a powerful tool to analyze the periodicity, chaos and non-stationarity of time-series data and identify hidden regularities in scalar time-series. whether using the threshold, it can be divided into two methods: thresholded recurrence plot (trp) and un-thresholded recurrence plot (utrp). since the distance parameter between recursive points in utrp is preserved, for the same time series, utrp often contains more information than trp. thus, we choose to apply utrp to encode the time-series data. given a time series x = {x1,x2, · · · ,xn}, the first step is to form a reconstructed phase space by extending the one-dimensional time-series into a higher-dimensional space, which is represented as: x̃ =   ~x0 ~x1 ~x2 ... ~xn   =   x0 xτ · · · x(m−1)τ x1 x1+τ · · · x1+(m−1)τ x2 x2+τ · · · x2+(m−1)τ ... ... ... ... xn xn+τ · · · xn+(m−1)τ   where m ≥ 2 is the embedding dimension and τ ≥ 1 is the time delay. the recurrence plot is constructed by calculating: ri,j = ‖~xi − ~xj‖, i,j = 1, 2, · · · ,n where ‖·‖ is the usual euclidean norm. the pixel value of each position (i,j) in the un-thresholded recurrence plot reflects the correlation between vector ~xi and vector ~xj, therefore it could reveal the dynamic characteristics of the signal in time. 3.2 deep learning based architectures for image classification in the research field of image classification, cnns have dominated this area over the last decade since 2012’s powerful alexnet. with the introduction of vision transformer and mlp-mixer, it seems that a new round of paradigm shift has ushered in. therefore, a series of image classification models including cnn-based models, transformer-based models and mlp-based models have been incorporated into this proposed methodology, with extensive experiments carried out in a unified framework to compare these three major deep learning architectures. cnn-based models. convolutional neural network is a special case of neural network, which makes use of the 2-d structure of images and the advantage that pixels within a neighborhood are usually highly correlated. a standard cnn-based model consists of an input layer, alternating convolutional and pooling layer, fully connected layer as well as output layer. figure 2 shows the architecture of a standard cnn-based model. the convolutional layer performs convolutional operations on the input image matrix to extract features, then apply the pooling layer to compress feature map obtained by the convolutional layer. pooling layers not only reduce the number of parameters to prevent over-fitting, but also are able to maintain model invariance (e.g., rotation, translation, scale). if the depth of the network increases, the receptive field continues to expand, enabling the network to incorporate and learn more abstract characteristics. finally the fully connected layer acts as a classifier to obtain classification results based on the feature maps gain by multiple convolutional and pooling https://doi.org/10.15837/ijccc.2022.1.4714 8 layers. in this past decade, a series of networks have been designed to improve the performances of cnns, for instance, the resnet [14] proposed residual structures, which significantly alleviate the gradient disappearance problem and make it possible to train deeper models. and the efficientnet [45] has achieved better accuracy with less parameters after carefully acquiring a balance between network width, depth and resolution. therefore, the resnet and efficientnet have been chosen as representatives of cnn-based models. class … convolution convolutionpooling pooling convolution fully-connected figure 2: architecture of a standard cnn-based model transformer-based models. vit [9] is the first full-transformer model that can achieve stateof-the-art performance on image classification task. the overview of the vit architecture is depicted in figure 3. linear projection of flattened patches … … … multi-head attention patch + position embedding norm mlp l× image partition … … … 0 * class token … …1 * 9 *5 * *13 transformer encoder mlp head embedded patches norm + + transformer encoder class figure 3: architecture of vit since the original transformer receives a 1-d sequence of token embeddings as input, the input 2-d images have to be firstly split into a sequence of non-overlapped patches, similar to words in nlp application. the patches are then projected into patch embeddings by a trainable linear projection. the patch embeddings with an additional class token are fed together into the transformer encoder after adding position embeddings to retain positional information. in classification tasks, only the class token can be predicted by a mlp head to output image class. the transformer encoder in vit is composed of a stack of l identical blocks. each block consists of alternating layers of multi-head self-attention (msa) and multilayer perceptron (mlp). residual connection and layer normalization are employed to enhance the scalability. as the core component of transformer, msa is an extension of standard self-attention. the selfattention mechanism estimates the relevance of each position to all positions and aggregates global information from the complete input sequence to update the output vector. specifically, the input sequences x are mapped into three different sequential vectors and they are query vectors q, key vectors k and value vectors v , according to: q = xwq, k = xwk, v = xwv https://doi.org/10.15837/ijccc.2022.1.4714 9 where wq ∈ rd×dk, wk ∈ rd×dk, wv ∈ rd×dv are linear matrices; d and dk denote the length and dimension of queries, keys and values respectively. compute the dot products of the query with all keys, then scale it and use the softmax function to obtain the weights on the values. this process can be formulated as: attention(q,k,v ) = softmax( qkt √ dk )v in order to capture the multiple complex relationships and extract richer information, the msa mechanism has been introduced [50], comprising h parallel self-attention layers, and the outputs of each layer are concatenated and projected to the final output, which is defined as: mutihead(q,k,v ) = concat(head1, · · · ,headh)wo headi = attention(qw q i ,kw k i ,v w v i ), i = 1, 2, · · · ,h where wo ∈ rhdv×d. furthermore, the data-efficient image transformer (deit) [49] has been proposed to improve vision transformer applicability when training on imagenet-1k. finally, the deit has been chosen as a representative of transformer-based models. mlp-based models. the pioneering work of deep mlp models, mlp-mixer [47], which consists of three modules as shown in figure 4. the per-patch fully-connected layer and classifier layer play the same role as in vit, mapping image patches into embedding vector and generating the classification results respectively. in the second module, mlp-mixer stacks n mixer layers instead of self-attention layers. each mixer layer contains one token-mixing mlp and one channel-mixing mlp. apply token-mixing mlp to communicate between different patches, and apply channel-mixing mlp to communicate between different channels. every mlp includes two fully-connected layers and a gelu [15] activation function. the layers of mixer can be written as follows: u∗,i = x∗,i + w2σ(w1layernorm(x)∗,i), for i = 1 · · ·c, yj,∗ = uj,∗ + w4σ(w3layernorm(u)j,∗), forj = 1 · · ·s. where x ∈ rs×c is the input of each mixer layer, s and c are the numbers of patches and channels, σ is the gelu function, w1 −w4 represents weights of different fully-connected layers. in addition, the resmlp [48] replaces the layer normalization in mlp-mixer with the affine transform layer, aiming to stabilize the training process, so the resmlp has been chosen as a representative of mlp-based models. layer norm token-mixing mlp layer norm channel-mixing mlp n × mixer layer … … … … image partition per-patch fully-connected + + fully-connected gelu fully-connected global average pooling fully-connected class n× figure 4: architecture of mlp-mixer https://doi.org/10.15837/ijccc.2022.1.4714 10 4 experiment & results this section will primarily illustrate the experiment & results. first the data acquisition & processing will be summarized with details and secondly are the experimental settings. finally, results and analysis will be introduced with a discussion of experiments. 4.1 data acquisition & processing data acquisition. the data needed in our study is the time-series data of instances of operating status in npps, with labels of each instance indicating the current status like normal condition or abnormal conditions. due to the specialty of npps and the difficulty of collecting real data from npps, a practical and realistic way for researchers is to obtain suitable data from simulation softwares [12] [22]. for example, the most widely-used nuclear reactor transient analyzer called personal computer transient analyzer (pctran) [36] is a simulation software for a variety of accidents and transient conditions occurred in npps, which could present and output the status of key parameters, and it allows simulation of operators’ actions by interactive control. thus, we choose the pctran software for experimental data acquisition. after the simulation by the software, large quantities of numeric instances describing operating status in npps have been collected, containing 70 dimensions as 70 sensors have been monitoring the pressure, temperature, flow rate and many other indicators from different places in npps like reactor cores, steam generators, pipelines, heat exchangers, etc. in addition, 5 labels of current status have also been generated as 5 classes of each instance including one normal condition (label 0) and four most common types of fault conditions, loss of coolant accident (label 1), loss of feedwater accident (label 2), steam line break (label 3) and steam generator tube rupture (label 4). until here, the data acquisition process has been basically completed. data processing. the simulation process has generated tens of thousands of numeric instances indicating the operating status of a npp, which are basically presented in seconds. then the data transformation has been conducted using the four methods introduced in 3.1. the 1-d time-series data collected from each sensor has been transformed into one channel in an image, meaning a single image is consisted of 70 channels and each channel represents the temporal trend of each dimension or sensor. in order to better characterize the time-series information, the data have been sampled in the sliding window of 224 seconds with an interval of 5 seconds. once an instance with fault condition labels (label 1-4) is included in this sliding window, the corresponding transformed image should be noted as fault labels. in addition, for better efficiency of fault detection and quick diagnosis, it’s necessary to remove noisy and redundant information within the dataset by reducing the dimensions (70) of the data, before the transformation process. to do this, the most prevalently used dimension reduction algorithm, the principal component analysis (pca) [19], has been used to process the simulated data with the principal components set to 32 based on actual conditions. in this way, we have obtained 6300 images while each image contains 32 channels, as qualified experimental dataset. the distribution of classes of transformed images is shown in table 1 and clearly it’s an imbalanced supervised classification task. table 1: distribution of classes of transformed images classes (labels) number of images normal 4200 loss of coolant accident 600 loss of feedwater accident 300 steam line break 600 steam generator tube rupture 600 the four types of transformed images have been acquired and cases of these images are shown in figure 5. specifically, the key parameters in transformation are set as follows: mtf quantile bins = 8, utrp delay time = 1 and embedding dimension = 1. in figure 5, each row indicates four converting https://doi.org/10.15837/ijccc.2022.1.4714 11 means respectively, gasf, gadf, mtf, utrp, and columns are different classes of images, as label 0 is normal condition images while label 1-4 represent different types of faults. different classes of images are distinctly different from each other, which can be observed simply by visual inspection, and it has established strong foundations for image classification models and effective detection of faults using the methodology proposed in this research. gasf gadf mtf utrp (a) label 0 (b) label 1 (c) label 2 (d) label 3 (e) label 4 figure 5: the transformed images of normal data and fault data 4.2 experimental settings according to the three primary deep learning architectures mentioned in section 3.2, we have chosen the most currently popular models for experiments including resnet (cnns) [14], efficientnet (cnns) [45], deit (transformer) [49] and resmlp (mlp) [48]. furthermore, to better achieve real-time fault diagnosis and lower the computational costs, we have chosen the exact models with minimal parameters among the variants, and they are resnet18, efficientnet-b0, deit-tiny as well as resmlp-s12. these models with pre-trained weights on imagenet will be used for experiments subsequently. the details of network designs are summarized in table 3. for the supervised classification task, the quantities of images in training set and testing set have been arranged as 8:2 separately. we train the models illustrated above for 50 epochs using batch size 32 on one gpu while all models use 224×224 images as input and apply the cross-entropy loss. most of our training settings are inherited from the original models as the resnet18 and efficientnet-b0 use a similar setting: sgd [39] as the optimizer with weight decay 0.0001 and momentum 0.9, learning rate starting from 0.1 and divided by 10 after 30 epochs. in deit-tiny and resmlp, we employ adamw [28] optimizer with weight decay 0.05 and the initial learning rate is 0.0005512 ×batch size with cosine learning rate decay. 4.3 analysis of results comparison of different transforming methods. we have compared the throughput and image size of each transformation method, details shown in table 2. the throughput is measured as the number of images that can be processed per second on cpu. the results have demonstrated that https://doi.org/10.15837/ijccc.2022.1.4714 12 the transforming speed of mtf is almost four times faster than the other three means, and at the same time, the size of the image converted by mtf is also the smallest. table 2: comparison of throughput and image size of different transformation methods transformation methods gasf gadf mtf utrp throughput (im/s) 30.48 29.12 109.57 30.14 size (kb/per) 78.52 92.70 2.40 89.76 before the transformation, the numeric 1-d time-series data has been processed by pca for dimension reduction as instructed in 4.1, which has produced 32 key dimensions (channels). then we have concated 32 channels into a single 32-channel image, similar with a 3-channel rgb image, as the feeding data into the deep learning models. performances of different combinations: accuracy vs. efficiency. we have trained the selected four models, resnet18, efficientnet-b0, deit-tiny and resmlp-s12 on the transformed image dataset, where the input size is 32×224×224. figure 6 shows the training process of different transformation methods with different deep learning models. it can be seen that, on the whole, all models and methods converge within 50 epochs and have achieved very high accuracy eventually. specifically, from the perspective of the transformation methods, the training process of mtf is the most stable one while other methods fluctuates greatly. mtf and gadf can converge in 20 epochs, and gasf and utrp need 30 epochs to finish this process. however, utrp achieves the highest accuracy in all four transforming methods while the classifying performance of mtf is inferior to other methods (see figure 7). from the perspective of the classification models, efficientnet-b0 achieves the best performance on all converting methods. deit-tiny is slightly worse than efficientnet-b0, but the training process is more stable. regardless of convergence or accuracy, resmlp-s12 is inferior to other models with all transforming means especially in gasf. (a) gasf (b) gadf (c) mtf (d) utrp figure 6: training process of different transforming methods with different deep learning models in order to evaluate different classifying means more accurately, the classification models are usually compared based on the trade-off between accuracy, number of parameters and throughput. https://doi.org/10.15837/ijccc.2022.1.4714 13 figure 7: comparison of accuracy, throughput and params of transforming methods table 3: details of network designs of different models and classification accuracy of combinations in experiments architectures exact model params (m) layers throughput (im/s) transforming methods accuracy (%) cnn-based models resnet18 11.27 18 1268.75 gasf 99.44 gadf 99.68 mtf 99.21 utrp 99.68 efficientnet-b0 4.02 18 1132.28 gasf 99.84 gadf 99.92 mtf 99.84 utrp 100.00 transformer-based models deit-tiny 6.90 12 1013.83 gasf 100.00 gadf 99.44 mtf 99.60 utrp 100.00 mlp-based models resmlp-s12 17.79 12 919.81 gasf 98.37 gadf 99.60 mtf 98.97 utrp 99.60 table 3 reports the classification accuracies of different architectures based on cnn, transformer and mlp with throughput, and number of parameters. because fault diagnosis in npps requires very quick response, we have chosen to conduct assessment based on the trade-off between accuracy and throughput. in here, the throughput is measured as the number of images that can be processed per second on one nvidia rtx 3060 gpu with largest possible batch size. larger throughput means that the processing speed of the model is faster under the same hardware conditions. among the four models, the throughput of resnet18 is the fastest, followed by efficientnet-b0 while the throughput of resmlp-s12 is the worst. 4.4 summary this section has conducted extensive experiments to compare the performances of different deep learning based models (cnn-based models, transformer-based models, mlp-based models) in fault detection with images generated by different transforming methods (gasf, gadf, mtf, utrp). it can be observed that mtf has the fastest converting speed and the converted image occupies the smallest memory, yet it is not the optimal choice because its classification accuracy is inferior to almost every methods on each model. in contrast, utrp achieves excellent performances, no matter which classifying model has been used. in terms of the trade-off between throughput and accuracy, efficientnet-b0 outperforms other deep learning classification approaches because it has achieved high accuracy with a faster throughput, which can meet the requirements of quick response in fault diagnosis of npps, according to the experimental results. https://doi.org/10.15837/ijccc.2022.1.4714 14 5 conclusion & future directions this research has proposed a two-stage fault detection method for npps based on imaged data and deep learning image classification. experimental results have demonstrated that this methodology proposed is capable of detecting and warning possible faults of npps in advance and further it could be adapted to promote the safety management in many different power systems with high accuracy, fast response and great efficiency. the primary contributions of our research are as follows: • the time-series data of the operating status of npps have been transformed into imaged data by four major methods, preserving the time-series information in images, which enables deep learning models to convert the fault detection problem into supervised image classification task. • extensive experiments have been carried out to compare and analyze the performances of different combinations of data transforming methods and image classifying models with the results of effective accuracies, which has also identified that utrp and efficientnet-b0 (cnn-based model) outperform others in the trade-off between precision and throughput. this proposed methodology has improved the precision of fault detection and diagnosis in npps, providing effective and scientific support to decision-makings. however, there still leaves many limitations and shortcomings in our research. one is the data used in experiments. though data transformation is considered to be a promising direction in solving many tough tasks, it has also raised doubts about the interpretability of artificial changes of data structure or format. also, there’s only one kind of data (numeric data) applied in our research and it’s collected by simulation of software, not real operating status of a npp, while it has been confirmed that more data involved in experiments will help achieve better performances, like the fusion of multiple-source heterogeneous data or integration of different forms of data. another issue we need to pay attention to is those new models that have evolved rapidly recent years represented by deep learning based approaches. these emerging methodologies deal with data from different perspectives and could outperform than conventional ones to certain extent as long as appropriate data with compatible structures is available. diversification of data and the significant development of computing capacity not only require more data-driven intelligent analyzing tools, but also make it possible for the implementations of these advanced techniques in both academic research and practice. hence, to better promote the theoretical and practical development of fault detection in npps, it’s necessary to further explore possible innovations from the two points illustrated above, more different types of data and more intelligent analyzing tools. acknowledgements all authors hereby express sincere condolences for our loss of ioan dzitac (founder & former editor-in-chief of ijccc), and great appreciation to editors and reviewers for their careful work and valuable comments, especially florin g. filip and simona dzitac for their unwavering patience, understanding and support. the apc was funded by r&d center "cercetare dezvoltare agora" of agora university and this article was funded by the key project (grant number 71932008) of national natural science foundation of china. author contributions y. shi: conceptualization; formal analysis; methodology; funding acquisition; project administration; supervision; writing review & editing. x. xue: conceptualization; data curation; formal analysis; methodology; software; writing original draft; writing review & editing. j. xue: conceptualization; data curation; formal analysis; methodology; software; writing review & editing. y. qu: conceptualization; data curation; formal analysis; methodology; writing review & editing. https://doi.org/10.15837/ijccc.2022.1.4714 15 conflict of interest all authors declare no conflict of interest. references [1] aizpurua, j. i.; mcarthur, s.; stewart, b. g.; lambert, b.; cross, j. g.; catterson, v. m. 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(2020). small-batch-size convolutional neural network based fault diagnosis system for nuclear energy production safety with big-data environment, international journal of energy research, 44, 5841–5855, 2020. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: shi, y.; xue, x.; xue, j.; qu, y. (2022). fault detection in nuclear power plants using deep leaning based image classification with imaged time-series data, international journal of computers communications & control, 17 (1), 4714, 2022. https://doi.org/10.15837/ijccc.2022.1.4714 introduction related work fault detection in npps data transformation: from numeric to imaged deep learning based image classification methodology data processing & transformation deep learning based architectures for image classification experiment & results data acquisition & processing experimental settings analysis of results summary conclusion & future directions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 1, month: 2, year: 2022 article number: 4683, https://doi.org/10.15837/ijccc.2022.1.4683 ccc publications improve the design and testing of fuzzy systems with a set of (almost) simple rules h.n. teodorescu dedicated to the memory of prof. ioan dzitac horia-nicolai teodorescu* technical university of iasi and institute of computer science of the romanian academy iasi, romania, hteodor@etti.tuiasi.ro abstract prof. dzitac used to say, ‘the mathematics of fuzzy systems is not fuzzy’. we discuss several limits and potential errors in the design of fuzzy logic systems and how they can be corrected or avoided. examples from the literature are presented. keywords: fuzzy logic, fuzzy logic system, design, interpolation, approximation, input-output function. 1 introduction as the much regretted prof. dzitac used to say, ‘the mathematics of fuzzy systems is not fuzzy’, [1]. yet, many articles on fuzzy logic systems (fls) and their applications are rather ‘fuzzy’. this statement was meant to clarify the meaning of the concept of ‘approximate reasoning’ used by zadeh, see for example [2]. unfortunately, there are papers that include elementary errors or prove a lack of understanding of the principles of flss. the design of fuzzy systems could benefit by applying a set of concepts and simple rules rooted in the mathematics of fuzzy systems. we discuss some design limits and provide examples remediating them. the annex contains brief formal explanations of some of the theoretical foundations of the recommended rules. we discuss only flss under min-max logic; the case of flss with max-prod is somewhat simpler and more extensively dealt with in the literature. throughout the article, we use the following typical abbreviations: fls fuzzy logic system mf(s) membership function(s) c.o.g. center of gravity (defuzzification method) pid proportional integral derivative (control) i/o input-output (function) cf characteristic function siso single input single output (fls). https://doi.org/10.15837/ijccc.2022.1.4683 2 2 some basic design issues we recall that fls with c.o.g. defuzzification are nonlinear systems. in addition, they are systems represented by piecewise functions. hence, they have no transfer function, as the linear systems have. when the input and output membership functions are piecewise linear or low degree polynomials, the input-output functions of sugeno or mamdani flss are piecewise ratios of integrals of polynomial functions (see p1 in the annex). when transcendental m.f.s are used, the i/o functions of the fls become transcendental. these make the treatment of flss quite laborious and prone to errors and may impose an exhaustive analysis of the operation; for example, they may need a testing pertaining to monte carlo methods. 2.1 take care of discontinuous derivatives typical siso flss with c.o.g. defuzzification and with triangular or trapezoidal input m.f.s have the characteristic function with discontinuous derivatives in a set of points defined by the vertices (peaks) of the input m.f.s. when the derivative of the characteristic function is discontinuous, improper operation may happen in control loops, such as resulting infinite accelerations, which are unrealistic, or jumps in currents, which are impossible. consider the inverse pendulum control. one cannot create discontinuous forces, because that would mean jumps in currents with electric motors, which have inductances, so electric inertia. few papers address this issue, be it by showing that in a specific implementation the discontinuity of the derivative of the force does not count because of factors such as the electrical inertia of the actuators or of the driving motor. 2.2 approximation and interpolation with flss – a general perspective most applications of fls are either modeling or control problems. these lead to either approximation or interpolation problems, that is, finding some function g : rn → r that approximates or interpolates a possibly unknown function f : rn → r. it turns out that flss are well suited to easily interpolate functions, and quite well suited to approximate functions that are intuitively well understood. indeed, according to a basic theorem, any continuous function in a bounded domain can be (exactly) interpolated through a given finite set of points by a sugeno or mamdani type fls with input and output triangular m.f.s [3] [4]. according to a set of theorems named generically ’universal approximation theorems’, flss are also able to approximate with whatever small error a continuous function over some compact domain. even classification problems are amenable to an approximation or interpolation problem, although classification asks for an extra operation of transforming the continuous output into a discrete one, corresponding to the numerical code of the class. these results show that it is crucial to understand the approximation and interpolation by flss and to apply this knowledge for obtaining good results. when interpolation is involved, one may simply choose the input m.f.s to be triangular and overlapping two-by-two, as in many elementary examples (including in matlab), taking care that the vertices of the triangles are placed at the abscissae of the interpolating points; then, if the system is sugeno-type, choose the singletons to correspond to the desired values of the interpolated inputoutput function. that is, if the function to interpolate is f : rn → r and the interpolation points are {(x1k,x2k, ...,xnk),yk}, k = 1, ...,m, the triangular m.f.s for the variables x1, ...,xn should have the vertices at precisely (x1k,x2k, ...,xnk), k = 1, ...,m and the singletons should be at yk. this guarantees that the fls is a perfect interpolator [3]. for mamdani flss, the difference is that the triangular m.f.s must have the c.o.g.s at yk. the case of functions f : rn → rq is similarly treated. next comes the issue of approximation. some of the theorems of universal approximation [3] provide a clarification, essentially saying that the approximation error decreases when the numbers of m.f.s at the input and output increase. this leads to a simple design rule: especially when the m.f.s are isosceles triangles, do not use too few rules, because then the characteristic function of the fls will be too sketchy. consider a fls with triangular m.f.s, where the mfs overlap two by two, moreover the abscissa xh where a membership function has value 1 is the abscissa where the other m.f.s that overlap with https://doi.org/10.15837/ijccc.2022.1.4683 3 the first have (reach) value 0 (fig. 1). then, on any interval of overlapping, the overlapping m.f.s are complementary, µn(x) = 1 −µn+1(x). this condition is essential in the discussion. also, assume that any rule assigns exactly a single output membership function. then, remark. as a result of these conditions, the fls input-output function of the defuzzified fls satisfies the conditions: f(x = (x1h, ...,xnh)) = yh where yh is the corresponding defuzzified value; in addition, the value yh corresponds to the singleton in the respective rule, for sugeno systems, respectively the c.o.g. of the single output m.f. selected by the respective rule, for mamdani f.l.s. with c.o.g. defuzzification. on the other hand, too many rules will make the computation difficult. the computation load is proportional with the product of the numbers of m.f.s of the inputs; having many inputs increases the computation load. figure 1: interpolator with issosceles m.f.s, not all output m.f.s used. notice in fig. 1 that, when forcing the m.f.s to be equal isosceles triangles at the output, some of them may be unused, or they may supperpose imperfectly (irregular superposition), while the input m.f.s may have different bases (a5 in fig. 1; see also a version of fig. 1 in [3]). the remarkable easiness of designing a fls that is an interpolator for some desired control surface does not mean that fls is also a good approximator for the desired surface. in this respect, always choosing isosceles triangular m.f.s is not good, because in case of far from linear surfaces are poorly approximated by interpolators with such m.f.s. an example is shown in fig. 2; the sugeno interpolator with isosceles m.f.s has an approximation error almost twice as large as the shown interpolator with irregular m.f.s, 0.3286 versus 0.1757. care is needed in treating the regions close to the boundaries of the universe of discourse. when the boundary m.f.s are constant on an interval or when on a boundary interval of the definition domain of the fls there is a single m.f. active (non-zero) on each input variable, then the output is a constant on that interval. when there are n variables, each with mk m.f.s, and there is a single m.f. active (non-zero) on each input variable on the boundary intervals, there are 2n boundary intervals where the output is constant. on those intervals it is not efficient to compute the output, as it is already known. figure 3 illustrates this effect, which is almost ubiquitous in the literature, unfortunately (the figure is from a student project where all corners had this issue, but similar cases are found in the literature, for example [5], fig. 6, and [6] fig. 3). a simple cure is to add complementary m.f.s at the boundaries, even if those m.f.s seem useless – actually they are useful to avoid constant values of the output. figure 2: interpolator with isosceles m.f.s and irregular m.f.s. approximation errors differ: the error is twice lower in the second case. https://doi.org/10.15837/ijccc.2022.1.4683 4 figure 3: corner effect: the control surface is flat, meaning that the control is reduced to a constant bias. 2.3 modeling versus control confusion between closed loop control and modeling problems (open loop control) may often occur. consider a device that is able to eject objects, with specified velocity, at the level of the terrain or of a table, with the aim that the object (ball, puck etc.) stops at a specified point. such a device could imitate humans playing golf or billiard. apparently, it is a control problem to send the ball over a terrain with a velocity such that the ball stops at a given point. but there is no control exerted on the ball after throwing it. for the ball stops at the given point, it needs to lose its kinetic energy through friction exactly at the specified point. the ball has an initial energy mv2/2 and the lost energy along a traveled distance d is µmgd where µ is the friction coefficient, m the mass of the ball and g the gravitational constant. therefore, the ball stops after a distance d = mv2/2µmg, while the velocity needed to attain d is v = √ 2µgd/m. the “control” of the ball speed turns out to be a simple modeling problem. the uncertainty my come from the approximate estimation of the distance d and from the unknown value of µ. under uncertainty, it may be justified to estimate the initial (ejecting) velocity v in a fuzzy manner, for example with rules such as if the distance is large, the ejecting velocity should be large; or, taking into account the estimation of the roughness (friction coefficient) of the terrain, if the distance is large and the roughness is high, the ejecting velocity should be very large. different sets of rules depending on the estimated distance should be generated for each estimated value of the roughness; that is, one model velocity vs. distance is generated for every estimation of µ. essentially, the model requires a fuzzy interpolator for the law v vs. d, for each model in the family of models. because the law v = √ 2µgd/m is nonlinear, a choice of triangular isosceles membership functions for d is not suitable; instead, non-isosceles triangles should be used. the exmple in fig. 2 is inspired by this false (closed loop) control problem. 2.4 are fuzzy models and fuzzy control systems guaranteed to be robust? the inherent robustness property of fuzzy logic systems is frequently claimed in the literature, e.g., [7]; also see the discussion and references in the early paper [6]. however this statement is not always veryfied and sometimes is is not correctly verified. moreover, in many cases, the meaning of ‘robustness’ is not defined, although there are several different definitions in the literature. an essential issue is that flss are nonlinear systems, with piecewise defined characteristic functions; therefore they respond differently at different operating points. even more, flss with triangular or trapezoidal functions have discontinuous derivatives of their input-output functions. that makes their response strongly dependent on the amplitude of the input signals at operating points on or close to the boundaries of the domains where their input-output functions are derivables. consequently, applying the standard unitary step function at the input, as for linear systems, is not enough informative. linear systems will respond identically for steps of different hights, which is no more true for nonlinear systems. there are numerous papers that use solely unitary steps and a single operating point for testing, without mentioning the above issues. next, it is necessarily to clarify to what kind of perturbation the system is robust; robustness may relate to changes (or uncertainties) in the parameters of the controlled system, or of the fls used in control or in modeling. the input-output function of the flss can be https://doi.org/10.15837/ijccc.2022.1.4683 5 very sensitive to the change of a membership function, as shown in fig. 2. in [5], the authors correctly apply steps with several heights to check the robust operation of their control system; the figures they provide (figs. 9 and 11) show clearly different responses for different operation points, althogh the description details do not clarify enough the operation (for example, the membership functions are defined on [-1,1] in fig. 5, but they use steps of values 2, 4 and 8, incompatible with the [-1,1] range). serra and quelas [8] also intuitively apply a correct testing method, by generating input steps with random amplitudes; figures 7 and 9 in their paper clearly show that the system has different responses for different heights of the steps. concluding, one should apply caution to robustness statements when they are not correctly verified, or when the meaning of ‘robustness’ is not defined. providing an explanation on the sampling frequency on the loop including the fls is also essential when checking the stability and the robustness. the lack of data on the operation point of the fls where a perturbation is applied, or of the amplitude of perturbation can desqualify any claim of robustness of the fls. in case of flss, after carefully choosing a meaning for robustness, one should check it by generating random inputs with various amplitudes and for varied operating points of the system. when testing with sinusoidal or harmonic systems one has to consider that the nonlinearity and the discontinuity of the derivative of the cf of the fls produces harmonics and that the amplitudes of the harmonics depend on the amplitude of the testing signal and on the operation point. beyond the above discussion, there is at least one sense of understanding the robustness of flss, namely that given in the flss’ property: property of low sensitivity of flss to the change in input m.f.s. consider a siso fls with input m.f.s reaching value 1 in a single point and overlapping at most two by two for values larger than 0, with the vertex of one m.f. being a point where adjacent m.f.s have value zero, and with symmetric output m.f.s overlapping at most two by two in the sense above and reaching value 1 in a single point. all m.f.s are considered to have values larger than zero on compact intervals. then, on any interval defined by the vertices of two successive input m.f.s, the characteristic function of the fls remains in the same output interval whatever is the choice of the input m.f.s as long as their vertices do not change. the property conditions are illustrated in fig. 4 (where the two branches of the m.f.s are assumed to be polynomials). the shape of the m.f.s in fig. 4 does not change the respective interval of the output. the property guarantees us that errors in the choice of shapes of input m.f.s affect in a limited manner the output. the property is easily extended to multiple input systems respecting the same conditions for the m.f.s. the above leads to the following criterion of robustness for flss: robustness with respect to the shape of m.f.s. a fls with the m.f.s satisfying the given conditions is robust to the change of the shape of the membership functions in the sense that the output remains confined to the same intervals, defined by the vertices of the m.f.s, as long as the input(s) remain in the intervals defined by the vertices of the input m.f.s and the intersection points of the adjacent m.f.s. figure 4: case of symmetric m.f.s overlapping at most two by two. the defuzzified output is between ycog1 and ycog2. https://doi.org/10.15837/ijccc.2022.1.4683 6 some authors claim robustness but seem to imply the robustness of the stability of the loop. this kind of statement should be carefully pondered. assume a control loop with fuzzy control of at least one parameter of the loop. denote by u that parameter, with the main system in the loop y = f(x),x = g(y; u,u̇, ü), g depending on ü as ük,k > 0, and u = h(y) expressed by a siso fls. assume that the fls is a sugeno system, or a mamdani system with triangular input m.f.s, unequal or not isosceles triangles. then, the fls with c.o.g. has the cf non-derivable at certain points. consequently, the system is unstable, because ü is infinite for some values of y, namely at the limits of the intervals defining the piecewise function. fortunately, real systems have mechanical or electrical inertia that limit the values of any involved ü to finite values. it is recommendable to use low-pass filters in loops involving flss to further limit the frequency spectrum of the signals in the loop during operation and thus increase the prospect of stability. 2.5 building analytic expressions for flss may be impossible a tradeoff in designing flss is frequently found in the literature, namely the time spent with deriving analytic expressions for the operation of systems involving flss and the time required for simulations. many designers present the principles of the fls, but do not pursue the derivation of analytic expressions, because the cfs of the flss are piecewise and should be determined on each multidimensional interval where the cf has a single expression. this is justified especially when the number of multidimensional intervals (and correspondingly the number of rules) is very large. for example, if there are three input variables and seven triangular membership functions (verlapping at most two by two) for each variable, there are 83 3-dimensional elementary intervals where the cf has to be expressed. however, the simple case described above is not depicting the more complex situation situation when the cf has no analytic expression. the issue is discussed below. we will assume that all m.f.s attain value 1 in a single point; at left and right of that point the m.f.s are represented by polynomials and are intersecting at most two by two. consider a mamdany fls with a single input variable, with the input m.fs represented at left of the vertex by the polynomial q(x). let the input value be x0 and c = q(x0). according to the respective rule, the truncation of the corresponding output m.f. described at left by p(x) leads to the equation p(y) = c and results in a single solution, y01; a second solution is produced by the right section of the m.f, y02. there are no more solutions as we assume that all α-cuts are compact. the solutions are algebraic functions of c if p has degree 4 or lower; for higher degrees, abel-ruffini theorem let us know that the solutions are not algebraic in general (not algebraic in almost all cases). therefore, for m.f.s represented by higher degree polynomials, we may be unable (except simple cases) to find an expression for y01, y02. but these expressions are needed to express the c.o.g.: ycog = ... + ∫ y01(q(x0) a yp(y)dy + ... ... + ∫ y01 a p(y)dy + ... where a is the left limit of the m.f. above, we have shown only one of the defined integrals in the expression of the defuzzified output. other integrals have as limits similar solutions of polynomials of the output m.f.s, pj (y) = qh(x0), where pj are polynomials of output m.f.s and qh are polynomials of input m.f.s; or the integrals have limits solutions of pi(y) = pj (y) of m.f.s that intersect. when all these polynomials of the output m.f.s gave the degrees less than 5, the solutions can be represented by algebraic expressions (i.e., using only powers, radicals and + and ×. thus, the denominator and nominator are polynomials of algebraic expressions of x0; therefore, in the stated case we are guaranteed that we can find an expression for the cf of the fls. notice that the input m.f.s are unessential (they can have a higher degree than 4), because they contribute only the constant c. on the other hand, when the polynomials of the output m.f.s have higher degree, in most cases the solutions cannot be found as algebraic expressions (and we do not have today a method to find the solutions). the above discussion also leads to the nice property of fls given in the annex, p2. https://doi.org/10.15837/ijccc.2022.1.4683 7 3 conclusions and future work despite their intuitive grasping and their ease of description in a basic manner, flss are complex systems that are not easy to master and adapt, and their properties and operation are frequently difficult to predict, because of their nonlinearity. as a consequence, many designs of control and modeling based on flss are suboptimal and sometimes erroneous. applying a few simple rules may help avoiding significant errors. many of the presented rules remain valid in more elaborate settings, including under different versions of fuzzy logics, see for example results in [9], or in neurofuzzy systems as in [10]. acknowledgments. i am indebted to the graduate students in my class of fundamentals of a.i. for teching me several potential design errors; their errors in the projects have been often most illuminating and sometimes they were motivational for further research. references [1] i dzitac, i; filip, fg, mj manolescu (2017), fuzzy logic is not fuzzy: world-renowned computer scientist lotfi a. zadeh. international journal of computers, communications & control . v. 12, n. 6, p. 748-789, dec. 2017. issn 1841-9844. [2] teodorescu, h.-n.l. (2006). on the meaning of approximate reasoning. an unassuming subsidiary to lotfi zadeh’s paper dedicated to the memory of grigore moisil. international journal of computers communications & control 6 (3), 577-580. [3] teodorescu, h. n. (2007) sisteme fuzzy si aplicatii. ed. politehnium, iasi, 2007. [4] hnl teodorescu, (2013) on the characteristic functions of fuzzy systems. international journal of computers communications & control 8 (3), 469-476. [5] k. sharma and d. k. palwalia, (2017) robust controller design for dc-dc converters using fuzzy logic, 2017 4th international conference on signal processing, computing and control (ispcc), 2017, pp. 477-481, doi: 10.1109/ispcc.2017.8269726. [6] j. wang and j. r. jordan, "the robust step performance of pid and fuzzy logic controlled siso systems," proceedings of 1995 ieee international conference on fuzzy systems., 1995, pp. 325-330 vol.1, doi: 10.1109/fuzzy.1995.409699. [7] s. lihan, m. jie and y. baoqing, (2020) "fuzzy pid design of vehicle attitude control systems," 2020 chinese control and decision conference (ccdc), 2020, pp. 1826-1830, doi: 10.1109/ccdc49329.2020.9164275. [8] m. serra and j. quelas, "robust fuzzy gain schedulling pid implementation for gimbal stabilization system," 2017 xvii workshop on information processing and control (rpic), 2017, pp. 1-6, doi: 10.23919/rpic.2017.8214309. [9] teodorescu, m.; teodorescu, h. n. (2021) noncommutative logic systems with applications in management and engineering. international journal of computers, communications & control . feb2021, vol. 16 issue 1, p1-19. [10] teodorescu hn, taylor and bi-local piecewise approximations with neuro-fuzzy systems. studies in informatics and control 21 (4), 367-376. annex p1. proof of the property of low sensitivity of flss to the change in input m.f.s because of the symmetry of the input m.f.s, their c.o.g. will remain the same whatever the truncation value is, see fig. 4. because the input m.f.s also overlapp at most two by two, when the input has the value of a vertex of an input m.f. only a rule is fired and a single output m.f. is selected. because the output m.f.s are symmetric (the case of singletons is included), whatever are the truncation values produced by an input value confined between two successive vertices, the defuzzified values remain between the vertices of the output m.f.s., which are the abscissae of their c.o.g.s, for example ycog1 and ycog2 in https://doi.org/10.15837/ijccc.2022.1.4683 8 fig. 4. this guarantees the property. p2. algebraic expressions of the i/o function of several classes of flss as already discussed, if the input and output membership functions are piecewise linear or polynomial of degree less than 5, the input-output functions of mamdani flss with c.o.g. defuzzification are piecewise ratios of polynomial functions of variables represented algebraically. next assume that the limits a of the output m.f.s are algebraic numbers, moreover that all the intersection points of the output m.f.s are at points represented by algebraic numbers. in addition, assume that the coefficients of the input and output polynomials are algebraic numbers. then the mamdani fls maps the algebraic numbers of the input interval into a set of algebraic numbers. the proof is qualitative: because the input m.f.s are polynomials with algebraic coefficients, for any x0 algebraic number, c = q(x0) is algebraic. because the integrals ∫ y01(q(x0) a yp(y)dy, ∫ y01(q(x0) a p(y)dy after integration generate polynomials and because the limits of the integrals are algebraic numbers, the results of integration are algebraic numbers. thus, an input algebraic number is mapped into an output algebraic number. the last property remains true when the input polynomials have degree larger than 4, but maintain algebraic coefficients. the property is true for sugeno fls with singletons at algebraic numbers. horia-nicolai teodorescu’s research interests cover aspects of artificial intelligence, including neuro-fuzzy systems, chaos technology, speech technology, image processing, and medical applications of ai, and analysis and prediction of social networks. he has co-edited more than 10 books with major publishers and authored or co-authored more than 250 papers. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: teodorescu h.-n.(2022). improve the design and testing of fuzzy systems with a set of (almost) simple rules, international journal of computers communications & control, 17(1), 4683, 2022. https://doi.org/10.15837/ijccc.2022.1.4683 introduction some basic design issues take care of discontinuous derivatives approximation and interpolation with flss – a general perspective modeling versus control are fuzzy models and fuzzy control systems guaranteed to be robust? building analytic expressions for flss may be impossible conclusions and future work international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 3, month: june, year: 2021 article number: 4214, https://doi.org/10.15837/ijccc.2021.3.4214 ccc publications multi-attribute group decision making method with unknown attribute weights based on the q-rung orthopair uncertain linguistic power muirhead mean operators h.m. zhao, r.t. zhang, a. zhang, x.m. zhu hongmei zhao, runtong zhang school of economics and management beijing jiaotong university, beijing, china no.3 shangyuancun haidian district beijing 100044 p. r. china e-mail: zhaohongmei81@163.com, rtzhang@bjtu.edu.cn ao zhang, xiaomin zhu* school of mechanical, electronic, and control engineering beijing jiaotong university, beijing, china no.3 shangyuancun haidian district beijing 100044 p. r. china e-mail: aozhang@bjtu.edu.cn, xmzhu@bjtu.edu.cn *corresponding author: xmzhu@bjtu.edu.cn abstract q-rung orthopair uncertain linguistic sets (q-roulss) are a powerful tool for describing ambiguity and uncertainty of linguistic information. in this study, considering that in most multi-attribute group decision making (magdm) problems, not only the quantitative evaluation information of decision makers but also the qualitative evaluation opinions should be considered. therefore, we develop a novel magdm method with unknown attribute weights under the q-rung orthopair uncertain linguistic environments. we firstly propose the cross-entropy of q-roulss, which is utilized to solve the optimal attribute weights by a linear programming model. in order to effectively summarize the unclear language information of q-roulss, we extend the power muirhead mean (pmm) operator to q-roulss, and propose a family of q-rung othpair uncertain linguistic power muirhead mean (q-roulpmm) operators. the advantage of the pmm operator is that it not only mitigates the adverse effects of too high or too low attribute values on the results, but also takes into account the interrelationships between attribute values. at the same time, some ideal properties and special cases of the q-roulpmm operator are also studied. further, a new method based on the proposed cross-entropy and aggregation operators is developed for solving the magdm problem under q-roulss. finally, we carried out numerical experiments to prove the effectiveness and superiority of the method. keywords: q-rung orthopair uncertain linguistic set, cross -entropy, attribute weights, q-rung orthopair uncertain linguistic power muirhead mean, multi-attribute group decision making. https://doi.org/10.15837/ijccc.2021.3.4214 2 1 introduction the multi-attribute group decision problem (magdm) is one of the most important branches of modern decision theory and has received increasing attention in the past few years. magdm can accomplish the selection of the best one among many alternatives according to a series of attribute indicators by multiple decision makers. in practical magdm problem, one of the most important difficulties is the representation of attribute values in uncertain decision environments. in 1965, zadeh [1] initialized the concept of fuzzy sets (fss), which open a new unchartered territory for dealing with the vague and uncertain information by a membership degree function. later, two significant extensions of fuzzy sets, i.e. intuitionistic fuzzy sets (ifss) and pythagorean fuzzy sets (pfss), have also been proposed in [2] and [3], which are characterized by both membership and non-membership to describe the uncertainty and hesitancy of information more accurately. since the introduction of ifss and pfss, they have attracted widespread attention among scholars and are widely used in medical diagnosis [4],[5], pattern recognition [6, 7, 8], data mining [9, 10] and magdm [11, 12, 13, 14]. although the ifss and pfss are powerful, they require membership and non-membership degrees to satisfy some certain constraints. specifically, ifss ask that the sum of membership and nonmembership degrees is less than one, and pfss specify the square sum of membership and nonmembership degrees is less than or equal to one. this feature limits the ability of ifss and pfss in describing the fuzzy and uncertain information. for example, decision makers maybe define the degree of membership and non-membership as (0.8, 0.7), while it is not valid to ifss and pfss. in order to solve these problems effectively, yager [15] proposed the concept of q-rung orthopair fuzzy sets (qrofss), in which a parameter q greater than or equal to one is defined to adjust the expressed range of fuzzy information flexibly. it is obvious that q-rofss are more applicable than ifss and pfss when copying with fuzzy and uncertainties. in recent, many studies on q-rofss have been undertaken from both theoretical and practical aspects. for example, some operational laws of q-rung orthopair fuzzy numbers, such as algebraic, einstein, hamacher, have been defined in [16, 17, 18]. du [19] proposed some minkowski-type distance measures for the decision-making application of q-rofss. liang et al. [20] proposed the q-rung orthopair fuzzy cross-entropy to identify the fuzzy measures between q-rung orthopair fuzzy numbers(q-rofns). on the other hand, some traditional decision making methods, e.g. todim[21], topsis[22],and mabac[23], have also been applied in the q-rung oethoapir fuzzy environment. in addition, a large number of q-rung orthopair fuzzy aggregation operators have also developed for solving the magdm problem[24, 25]. in addition to the quantitative assessments, we must consider the semantic evaluation opinions given by decision makers. the linguistic variables (lvs) are considered as an ideal solution provider to cope with the semantic assessments, such as ’good’, ’fair’, ’worse’, etc. [26]. however, some sematic opinions cannot be described by a single lv, for instance, decision makers maybe provide an assessment which is lower than “good” but higher than ‘fair’. therefore, xu [27] put forward to the uncertain linguistic variables (ulvs) that leverage two linguistic terms to represent a semantic interval. however, an pivotal shortcoming of lvs and ulvs is that they cannot describe the decision maker’s reliability and uncertainty for a given linguistic evaluation. to remedy this bottleneck, liu and qin [28] utilized the membership and non-membership degrees of the ifs to represent the hesitancy and uncertainty of the ulv, and proposed the intuitionistic uncertain linguistic variables (iulvs). further, some extensions of iulvs, that combine ulvs with some more advanced fuzzy sets, have also been proposed in [29],[30],[31]. among them, q-rung orthopair uncertain linguistic sets (q-roulss) [31], [32], that combine the q-rofss with ulvs, not only enable an intuitionistic evaluation for hesitancy and uncertainty for ulvs, but also accomplish the flexible adjustment of the indication range of decision information. another challenging problem in magdm is the aggregation of attributes information and the ranking of alternatives. at present, a variety of aggregation operators have been studied and achieved the significant success in magdm problems[33, 34, 35, 36]. in view of the increased complexity of actual decision-making problems, we may consider the following three issues when choosing the best alternative. (1) the evaluation values of attributes provided by decision makers is too high or too low, which have a negative impact on the final result. the pa operator proposed by yager can better avoid this problem as it allows to discount outliers according to automatically assigning a power weight to https://doi.org/10.15837/ijccc.2021.3.4214 3 each attribute. (2) the attributes of alternatives are usually related, so we need to consider the various relationship between the attributes. hence, a variety of aggregation operators are proposed to solve this problem, such as bonferroni mean (bm) [34], heronian mean (hm) [35], maclaurin symmetric mean (msm) [37] and muirhead mean (mm) [36], and so on. it is worthy stressing that mm has obvious advantages over other several operators, as it can consider the interrelationship between all aggregated values, meanwhile, it can reduce into bm and msm by adjusting its parameters vector. (3) in the case of various relationships between attributes, there are outlier assessments at the same time. to solve the above two situations simultaneously, the power muirhead mean(pmm) operator is proposed in [38]. they inherit the advantages of pa and mm operators at the same time, and is widely used to solve the various magdm problem [39, 40]. although a variety of aggregation operators have been proposed to solve the magdm problem, the studies on magdm based on the q-rung orthopair uncertain linguistic aggregation operators are still scarce. for example, liu et al. [41] proposed the q-rung orthopair uncertain linguistic weighted average(wa) operator and q-rung orthopair uncertain linguistic ordered weighted average (owa) operator for the decision-making application. liu et al.[42] defined the q-rung orthopair uncertain linguistic partitioned bonferroni mean (pbm) operator to solve this situation where some attributes are related, while other attributes are not related. however, the aforementioned methods fail to reflect the interrelationship between all arguments, and cannot automatically eliminate the outlier assessments on aggregation results at the same time. in addition, the attribute weights are directly given by decision makers in existing q-rung orthopair uncertain linguistic magdm methods. it is obvious that this strategy cannot guarantee the rationality of weight information. therefore, this paper develops a magdm method based on q-rung orthopair uncertain pmm operator with unknown attributes weights. in order to do this, we firstly define the cross-entropy of q-roulss, which is utilized to obtain the optimal weight vector of attributes based on a linear programming model. secondly, we first propose q-rung orthopair uncertain linguistic pmm (q-roulpmm) operator and its weighted form to summarize the decision maker’s preference information and determine the best choice. then, a new magdm method are also developed based on the proposed cross-entropy and aggregation operators in q-roulss. finally, a numerical example is provided to demonstrate the effectiveness and superiority of the proposed method. the rest of this article is organized as follows. section 2 reviews the basic concepts and proposes the cross entropy of q-roulss. section 3 elaborates the q-roulpmm operator and its weighted form. section 4 introduces a new magdm method. section 5 describes the performance and superiority of the proposed method by a numerical instance as well as comparative analysis. the conclusion is given in section 6. 2 preliminaries 2.1 q-rung orthopair uncertain linguistic sets definition 1 [15] let x be a ordinary fixed set, a q-rung orthopair fuzzy set (q-rofs) a on x is defined: a = {x,µa (x) ,va (x) |x ∈ x}(q ≥ 1) (1) where µa(x) and va(x) respectively represent the membership and non-membership degrees satisfying µa(x) ∈ [0, 1], va(x) ∈ [0, 1] and 0 ≤ µ q a(x) + v q a(x) ≤ 1. for convenience, the pair (µa(x),va(x)) is called as a q-rung orthopair fuzzy number (q-rofn), which can be denoted by a = (µa,va). let s = {si|i = 0, 1, . . . ,g} be a linguistic term set (lts) with odd cardinality, where si represents the i-th linguistic variable (lv) of s, g + 1 is the cardinality of s, which usually is set to a small odd number, such as 5, 7, 9. for the linguistic set s, the following conditions should be satisfied: 1) orderliness: si > sj, if i > j; 2) negative operator: neg (si) = sj, where j = g − i; 3) maximize and minimize operator: max (si,sj) = si, if si ≥ sj, min (si,sj) = si, if si ≤ sj. https://doi.org/10.15837/ijccc.2021.3.4214 4 furthermore, xu [27] developed the concept of uncertain linguistic variable (ulv) to preserve all the given information. definition 2 let s̃ = [sθ,sτ ], sθ,sτ ∈ s̃ and 0 < θ < τ, s̃ = {sα |s0 ≤ sα ≤ st,α ∈ [0, t]} be a continuous term set, sθ,sτ represent the lower limit and upper limit of s̃, respectively, then the s̃ is an ulv. let s̃1 = [sθ1,sτ1 ], s2 = [s̃θ2,sτ2 ] be two any ulvs, λ is an positive real number, the operational laws are showed as follows: 1. s̃1 ⊕ s̃2 = [sθ1,sτ1 ] ⊕ [sθ1,sτ2 ] = [sθ1+θ2,sτ1+τ2 ] 2. s̃1 ⊗ s̃2 = [sθ1,sτ1 ] ⊗ [sθ2,sτ2 ] = [sθ1×θ2,sτ1×τ2 ] 3. λs̃1 = λ[sθ1,sτ1 ] = [sλθ1,sλτ1 ] 4. s̃λ1 = ([sθ1,sτ1 ]) λ = [ s(θ1)λ ,s(τ1)λ ] although the ulvs express the semantic information conveniently, they are incapable of expressing the hesitancy and uncertainty of semantic information intuitively. to remedy the above deficiency, qroulss [31], [32] that combine the ulvs and q-rofss, leverage the membership and non-membership degree to describe the hesitant degree of the ulvs. definition 3 let x be an ordinary fixed set, then a q-rung orthopair uncertain linguistic set a defined on x is expressed as a = {〈 x [[ sθ(x),sτ(x) ] , (ua (x) ,va (x)) ]〉 |x ∈ x } (q ≥ 1) (2) where sθ(x),sτ(x) ∈ s̃ is the ulv of x, s̃ be a continuous linguistic term set, µa(x) and va(x) represent the membership and non-membership degrees of x to ulv [ sθ(x),sτ(x) ] , where µa(x),va(x) ∈ [0, 1] and 0 ≤ µqa(x) + v q a(x) ≤ 1. for convenience, we call 〈[ sθ(x),sτ(x) ] , (µa(x),va(x)) 〉 as a q-rung orthopair uncertain linguistic value (q-roulv), which can be denoted by α = 〈[sθ,sτ ] , (µa,va)〉. definition 4 let α1 = 〈[sθ1,sτ1 ] , (u1,v1)〉, α2 = 〈[sθ2,sτ2 ] , (u2,v2)〉 be any two q-roulvs, and λ be a positive real number, then 1. α1 ⊕α2 = 〈 [sθ1+θ2,sτ1+τ2 ] , ( (µq1 + µ q 2 −µ q 1µ q 2) 1/q ,v1v2 )〉 , 2. α1 ⊗α2 = 〈 [sθ1∗θ2,sτ1∗τ2 ] , ( µ1µ2, (vq1 + v q 2 −v q 1v q 2) 1/q )〉 , 3. λα1= 〈 [sλ∗θ1,sλ∗τ1 ] , (( 1 − (1 −µq1) λ )1/q ,vλ1 )〉 , 4. (α1)λ = 〈[ sθλ1 ,sτλ1 ] , ( µλ1, ( 1 − (1 −vq1) λ )1/q)〉 . definition 5 let α = 〈[sθ,sτ ] , (µa,va)〉 be a q-roulv, then the expected value e(α) of α is defined as e(α) = θ+τ4 (µ q + 1 −vq), and the accuracy function h(α) of α is defined as h(α) = θ+τ2 (µ q + vq). for any two q-roulvs α1 and α2, we have 1. if e (α1) > e (α2), thenα1 > α2, 2. if e (α1) = e (α2), then (a) if h (α1) > h (α2), then α1 > α2. (b) if h (α1) = h (α2), then α1 = α2. https://doi.org/10.15837/ijccc.2021.3.4214 5 2.2 cross entropy of q-roulvs the cross-entropy measure is an important operation to measure the relation between two sets or objects. it is used to calculate the divergence between two probability distributions or two random variables. recently, liang et al. [20] defined the q-rung orthopair fuzzy cross-entropy to identify the fuzzy measures between q-rofns. definition 6 [20] let a1 = (µ1,v1) and a2 = (µ2,v2) be two q-rofns, then the cross-entropy ce(a1,a2) of a1 and a2 can be defined as follows ce(a1,a2) = 1 1 − 21−p ( (µ1)pq + (µ2)pq 2 − ((µ1)q + (µ2)q 2 )p + (v1)pq + (v2)pq 2 − ((v1)q + (v2)q 2 )p + (π1)pq + (π2)pq 2 − ((π1)q + (π2)q 2 )p) (3) where π1 and π2 are the indeterminacy degree of a1 and a2, respectively. although the cross-entropy has achieved breakthrough successes in various fuzzy environments, the studies on cross-entropy are still a blank under q-rung orthopair uncertain linguistic environments. therefore, the cross-entropy of q-roulvs is presented herein. definition 7 let α1 = 〈[sθ1,sτ1 ], (µ1,v1)〉 and α2 = 〈[sθ2,sτ2 ], (µ2,v2)〉, g + 1 is the cardinality of linguistic term set, then the cross-entropy of α1 and α2 is defined as: ce(α1,α2) = θ1 + τ1 2g ln 2(θ1 + τ1) θ1 + τ1 + θ2 + τ2 + τ1 −θ1 g ln 2(θ1 − τ1) (θ1 − τ1) + (θ2 − τ2) + (1 − θ1 + τ1 2g ) ln 4g − 2(θ1 + τ1) 4g − (θ1 + τ1 + θ2 + τ2) + (1 + θ1 − τ1 g ) ln 2(g + (θ1 − τ1)) 2g + (θ1 − τ1) + (θ2 − τ2) + µq1 ln 2µq1 µ q 1 + µ q 2 + vq1 ln 2vq1 v q 1 + v q 2 (4) theorem 1 let α1 = 〈[sθ1,sτ1 ], (µ1,v1)〉 and α2 = 〈[sθ2,sτ2 ], (µ2,v2)〉, be any two q-roulvs, then cross-entropy ce(α1,α2) satisfies the following properties: 1) ce(α1,α2) ≥ 0, 2) ce(α1,α2) = 0, if α1 = α2, 3) ce(α1,α2) = ce(αc1,αc2), where αci = 〈[g −sτi,g −sθi], (vi,µi)〉 2.3 power average operator and muirhead mean operator the pa operator was proposed by yager for crisp numbers. the prominent characteristic of pa is that it allows the weighting vector to depend on the input arguments and evaluation. definition 8 let ãi(i = 1, 2, · · · ,n) is a collection of nonnegative real numbers, then the power average (pa) operator is defined as: pa (ã1, ã2, . . . , ãn) = n∑ i=1 (1 + t (ãi)) ãi n∑ j=1 (1 + t (ãj)) , (5) where t(ãi) = ∑n j=1,j 6=i sup (ãi, ãj) and sup (ãi, ãj) indicates the support for ai from aj, which satisfies the following properties: https://doi.org/10.15837/ijccc.2021.3.4214 6 1. sup (ãi, ãj) ∈ [0, 1], 2. sup (ãi, ãj) = sup (ãj, ãi), 3. sup (ãi, ãj) ≥ sup (ãs, ãt), if d(ãi, ãj) < d(ãs, ãt), where d(ãi, ãj) is the distance between ãi and ãj. the mm was an aggregation technology proposed by muirhead for crisp numbers, they can deals with the interrelationship among all arguments. definition 9 let ai (i = 1, 2, ...,n)be a collection of crisp numbers and k= (k1,k2, ...,kn) ∈ rn, then the muirhead mean (mm) operator is defined as mmk (a1,a2, ...,an) =   1 n! ∑ ϑ∈sn n∏ j=1 a pj ϑ(j)   1 n∑ j=1 pj , (6) where ϑ (j) (j = 1, 2, ...,n) is any permutation of (1, 2, ...,n),and sn is the collection of all permutation of (1, 2, ...,n). 3 some new q-rung orthopair uncertain linguistic aggregation operators 3.1 the q-rung orthopair uncertain linguistic power muirhead mean operator definition 10 let αj = 〈[ sθj,sτj ] , (µj,vj) 〉 (j = 1, 2, ...,n) be a collection q-roulvs and k = (k1,k2, ...,kn) ∈ rn be a set of parameters. if q −roulpmmk (α1,α2, ...,αn) =   1n! ∑ ϑ∈sn n∏ j=1  n ( 1 + t ( αϑ(j) )) n∑ i=1 (1 + t (αi)) αϑ(j)   kj   1 n∑ i=1 kj , (7) where t (αj) = ∑n i=1,j 6=i sup (αi,αj), sup (αi,αj) represents the support degree for αi and αj, which satisfies the following properties: 1. sup (αi,αj) ∈ [0, 1] 2. sup (αi,αj) = sup (αj,αi) 3. sup (αi,αj) > sup (αs,αt), if d (αi,αj) < d (αs,αt), where d (αi,αj) denotes the distance between αi and αj. further, let ωj = (1 + t (αj))∑n i=1 (1 + t (αi)) , (8) then we can obtain the simplified form of eq.(7): q −roulpmmk (α1,α2, . . . ,αn) =   1 n! ∑ ϑ∈sn n∏ j=1 ( nωϑ(j)αϑ(j) )kj 1 n∑ j=1 kj (9) where ω = (ω1,ω2, ...,ωn)t is the power weighting vector (pwv) satisfying ωi ∈ [0, 1] and ∑n i=1 ωi = 1 according to the operations of definition (4), we can get q-roulpmm satisfies following theorems. https://doi.org/10.15837/ijccc.2021.3.4214 7 theorem 2 let αj = 〈 [sθj,sτj ], (µj,vj) 〉 (j = 1, 2, ...,n) be a collection q-roulvs and k = (k1,k2, ...,kn) ∈ rn be a set of parameters, then the aggregated value by q-roulpmm is still a q-roulv, and q −roulpmmk (α1,α2, ...,αn) = 〈s( 1 n! ∑ ϑ∈sn n∏ j=1 (nωjθϑ(j))kj ) 1∑n j=1 kj ,s( 1 n! ∑ ϑ∈sn n∏ j=1 (nωjτϑ(j))kj ) 1∑n j=1 kj   ,      1 −   ∏ ϑ∈sn  1 − n∏ j=1 ( 1 − ( 1 −µq ϑ(j) )nωj)kj   1 n!   1/q 1 n∑ j=1 kj ,  1 −  1 − ∏ ϑ∈sn  1 − n∏ j=1 ( 1 −vqnωj ϑ(j) )rj 1 n!   1 n∑ j=1 rj   1/q 〉 . (10) proof. according to definition (4), we have nωjαϑ(j) = 〈[ snωjθϑ(j),snωjτϑ(j) ] , (( 1 − ( 1 −µq ϑ(j) )nωj)1/q ,v nωj ϑ(j) )〉 . and ( nωjαϑ(j) )kj = 〈[ s(nωjθϑ(j))kj ,s(nωjτϑ(j))kj ] ,((( 1 − ( 1 −µq ϑ(j) )nωj)1/q)kj , ( 1 − ( 1 −vqnωj ϑ(j) )kj)1/q)〉 . thus, we can obtain n∏ j=1 ( nωjαϑ(j) )kj = 〈s n∏ j=1 (nωjθϑ(j))kj ,s n∏ j=1 (nωjτϑ(j))kj   ,   n∏ j=1 (( 1 − ( 1 −µq ϑ(j) )nωj)1/q)kj ,  1 − n∏ j=1 ( 1 −vqnωj ϑ(j) )kj1/q   〉 . and ∑ ϑ∈sn n∏ j=1 ( nωjαϑ(j) )kj = 〈s ∑ ϑ∈sn n∏ j=1 (nωjθϑ(j))kj ,s ∑ ϑ∈sn n∏ j=1 (nωjτϑ(j))kj   ,    1 − ∏ ϑ∈sn  1 − n∏ j=1 ( 1 − ( 1 −µq ϑ(j) )nωj)kj  1/q , ∏ ϑ∈sn    1 − n∏ j=1 ( 1 −vqnωj ϑ(j) )kj1/q     〉 . https://doi.org/10.15837/ijccc.2021.3.4214 8 thus, 1 n! ∑ ϑ∈sn n∏ j=1 ( nωjαϑ(j) )kj = 〈s 1 n! ∑ ϑ∈sn n∏ j=1 (nωjθϑ(j))kj ,s 1 n! ∑ ϑ∈sn n∏ j=1 (nωjτϑ(j))kj   ,    1 −   ∏ ϑ∈sn  1 − n∏ j=1 ( 1 − ( 1 −µq ϑ(j) )nωj)kj   1 n!   1/q ,   ∏ ϑ∈sn    1 − n∏ j=1 ( 1 −vqnωj ϑ(j) )kj1/q     1 n!   〉 . therefore,  1 n! ∑ ϑ∈sn n∏ j=1 ( nωjαϑ(j) )kj) 1∑n j=1 kj = 〈s( 1 n! ∑ ϑ∈sn n∏ j=1 (nωjθϑ(j))kj ) 1∑n j=1 kj ,s( 1 n! ∑ ϑ∈sn n∏ j=1 (nωjτϑ(j))kj ) 1∑n j=1 kj   ,      1 −   ∏ ϑ∈sn  1 − n∏ j=1 ( 1 − ( 1 −µq ϑ(j) )nωj)kj   1 n!   1/q 1∑n j=1 kj ,  1 −  1 − ∏ ϑ∈sn  1 − n∏ j=1 ( 1 −vqnωj ϑ(j) )kj 1 n!   1∑n j=1 kj   1/q 〉 . theorem 3 (idempotency) let αj = 〈[ sθj,sτj ] , (µj,vj) 〉 (j = 1, 2, ...,n) be a collection q-roulvs, and αj = α = 〈[sθ,sτ ] , (µ,v)〉 for j = 1, 2, ...,n. then, q −roulpmmk (α1,α2, ...,αn) = α (11) proof. as αj = α = 〈[sθ,sτ ] , (µ,v)〉 holds for all j, we have sup (αj,αi) = 1 for i,j = 1, 2, . . . ,n. we can obtain ωj = 1/n for all j. further, we can get q −roulpmmk (α,α,...,α) = 〈s( 1 n! ∑ ϑ∈sn n∏ j=1 (n 1nθ) kj ) 1∑n j=1 kj ,s( 1 n! ∑ ϑ∈sn n∏ j=1 (n 1nτ) kj ) 1∑n j=1 kj   ,      1 −   ∏ ϑ∈sn  1 − n∏ j=1 ( 1 − (1 −µq)n 1 n )kj   1 n!   1/q 1∑n j=1 kj ,  1 −  1 − ∏ ϑ∈sn  1 − n∏ j=1 ( 1 −vqn 1 n )kj 1 n!   1∑n j=1 kj   1/q 〉 = 〈[ s 1 n! n!θ ,s 1 n! n!τ ] , ( (µq)1/q , (vq)1/q )〉 = α. (12) which completes the proof of theorem (3). https://doi.org/10.15837/ijccc.2021.3.4214 9 theorem 4 (boundedness) let αj = 〈[ sθj,sτj ] , (µj,vj) 〉 (j = 1, 2, ...,n), α−= min (α1,α2, ...,αn) and α+= max (α1,α2, ...,αn), then α− ≤ q −roulpmmk (α1,α2, ...,αn) ≤ α+ (13) where α− = 〈[sθ−,sτ−] , (a,b)〉, and α+ = 〈[sθ+,sτ+ ] , (c,d)〉. and sθ− = s( 1 n! ∑ ϑ∈sn n∏ j=1 (n 1nθ−) kj ) 1∑n j=1 kj , sτ− = s( 1 n! ∑ ϑ∈sn n∏ j=1 (n 1nτ−) kj ) 1∑n j=1 kj , a =    1 −   ∏ ϑ∈sn  1 − n∏ j=1 (1 − (1 −aq)nωj )kj     1 n!   1/q 1∑n j=1 kj , and b =  1 −  1 − ∏ ϑ∈sn  1 − n∏ j=1 (1 − bqnωj )kj   1 n!   1∑n j=1 kj   1/q . meanwhile, sθ+ = s( 1 n! ∑ ϑ∈sn n∏ j=1 (n 1nθ+) kj ) 1∑n j=1 kj , sτ+ = s( 1 n! ∑ ϑ∈sn n∏ j=1 (n 1nτ+) kj ) 1∑n j=1 kj , c =    1 −   ∏ ϑ∈sn  1 − n∏ j=1 (1 − (1 − cq)nωj )kj     1 n!   1/q 1∑n j=1 kj , and d =  1 −  1 − ∏ ϑ∈sn  1 − n∏ j=1 (1 −dqnωj )kj   1 n!   1∑n j=1 kj   1/q . proof. first, for α− we can have nωjαϑ(j) = 〈[ snωjθϑ(j),snωjτϑ(j) ] , (( 1 − ( 1 −µq ϑ(j) )nωj)1/q ,v nωj ϑ(j) )〉 ≥ 〈[ snωjθ−,snωjτ− ] , ( (1 − (1 −aq)nωj )1/q ,bnωj )〉 . and( nωjαϑ(j) )kj = 〈[ s(nωjθϑ(j))kj ,s(nωjτϑ(j))kj ] ,((( 1 − ( 1 −µq ϑ(j) )nωj)1/q)kj , ( 1 − ( 1 −vqnωj ϑ(j) )kj)1/q)〉 ≥ 〈[ s(nωjθ−) kj ,s(nωjτ−) kj ] , (( (1 − (1 −aq)nωj )1/q )kj , ( 1 − (1 − bqnωj )kj )1/q)〉 . https://doi.org/10.15837/ijccc.2021.3.4214 10 further, n∏ j=1 ( nωjαϑ(j) )kj = 〈s n∏ j=1 (nωjθϑ(j))kj ,s n∏ j=1 (nωjτϑ(j))kj   ,   n∏ j=1 (( 1 − ( 1 −µq ϑ(j) )nωj)1/q)kj ,  1 − n∏ j=1 ( 1 −vqnωj ϑ(j) )kj1/q   〉 ≥ 〈s n∏ j=1 (nωjθ−) kj ,s n∏ j=1 (nωjτ−) kj   ,   n∏ j=1 ( (1 − (1 −aq)nωj )1/q )kj ,  1 − n∏ j=1 (1 − bqnωj )kj  1/q   〉 . thus,  1 n! ∑ ϑ∈sn n∏ j=1 ( nωjαϑ(j) )kj) 1∑n j=1 kj = 〈s( 1 n! ∑ ϑ∈sn n∏ j=1 (nωjθϑ(j))kj ) 1∑n j=1 kj ,s( 1 n! ∑ ϑ∈sn n∏ j=1 (nωjτϑ(j))kj ) 1∑n j=1 kj   ,      1 −   ∏ ϑ∈sn  1 − n∏ j=1 ( 1 − ( 1 −µq ϑ(j) )nωj)kj   1 n!   1/q 1∑n j=1 kj ,  1 −  1 − ∏ ϑ∈sn  1 − n∏ j=1 ( 1 −vqnωj ϑ(j) )kj 1 n!   1∑n j=1 kj   1/q 〉 ≥ 〈s( 1 n! ∑ ϑ∈sn n∏ j=1 (nωjθ−) kj ) 1∑n j=1 kj ,s( 1 n! ∑ ϑ∈sn n∏ j=1 (nωjτ−) kj ) 1∑n j=1 kj   ,      1 −   ∏ ϑ∈sn  1 − n∏ j=1 (1 − (1 −aq)nωj )kj     1 n!   1/q 1∑n j=1 kj ,  1 −  1 − ∏ ϑ∈sn  1 − n∏ j=1 (1 − bqnωj )kj   1 n!   1∑n j=1 kj   1/q 〉 =a−. which means that α− ≤ q − roulpmmk (α1,α2, ...,αn). similarly, we can also prove that q − roulpmmk (α1,α2, ...,αn) ≤ α+, which completes the proof of theorem (4). one of the prominent advantages of q-roulpmm is that it can capture the various interrelationship between attributes. more specifically, q-roulpmm has a parameter vector leading to a flexible aggregation process. by assigning different values to the parameter vector, some special cases can be obtained. case1. if k = (1, 0, ..., 0), then q-roulpmm reduces to https://doi.org/10.15837/ijccc.2021.3.4214 11 q −roulpmm(1,0,...,0) (α1,α2, ...,αn) = n∑ j=1 ( (1 + t (αj)) αj/ n∑ i=1 (1 + t (αi)) ) , (14) which is the q-rung orhopair uncertain linguistic power averaging operator. case2. if k = (1/n, 1/n,..., 1/n), then q-roulpmm reduces to q −roulpmm(1/n,1/n,...,1/n) (α1,α2, ...,αn) = n∏ j=1 αj 1+t(αj)∑n i=1(1+t(αi)) (15) which is q-rung orhopair uncertain linguistic power geometric operator. case3. if k = (1, 1, 0, ..., 0), then q-roulpmm reduces to the q-rung orhopair uncertain linguistic fuzzy power bonferroni mean operator, i.e. q −roulpmm(1,1,0,...,0) (α1,α2, ...,αn) =〈s( 1 n(n−1) ∑n j=1,j 6=i ωjθjωiθi )1 2 ,s( 1 n(n−1) ∑n j=1,j 6=i ωjτjωiτi )1 2   ,     1 −   n∏ j,i = 1 j 6= i ( 1 − ( 1 − ( 1 −µ2j )ωj)( 1 − ( 1 −µ2i )ωi))   1 n(n−1)   1 4 ,   1 −   1 −   n∏ j,i = 1 j 6= i ( v 2ωj j + v 2ωi i −v 2ωj j v 2ωi i )   1 n(n−1)   1 2   1/q 〉 . (16) case4. if k =   k︷ ︸︸ ︷1, 1, ..., 1, n−k︷ ︸︸ ︷0, 0, ..., 0  , then pulpmm reduces to the q-rung othopair uncertain linguistic power maclaurin symmetric mean operator, i.e q −roulpmm   k︷ ︸︸ ︷1, 1, ..., 1, n−k︷ ︸︸ ︷0, 0, ..., 0   (α1,α2, ...,αn) = 〈s(∑ 1≤j1<...<jk≤n ∏n i=1 ωji θji ckn )1 k ,s(∑ 1≤j1<...<jk≤n ∏n i=1 ωji τji ckn )1 k   ,    1 − ∏ 1≤j1<...<jk≤n ( 1 − k∏ i=1 ( 1 − ( 1 −µqji )ωji)) 1ckn   1 qk ,  1 −  1 − ∏ 1≤j1<...<jk≤n ( 1 − k∏ i=1 ( 1 −vqωjiji )) 1ckn  1 k   1/q 〉 . (17) https://doi.org/10.15837/ijccc.2021.3.4214 12 3.2 the weighted q-rung othopair uncertain linguistic power muirhead mean operator the q-roulpmm does not consider the importance of the aggregated q-roulvs. in this subsection, its weighted form, namely, the q-orthopair uncertain linguistic weighted power muirhead mean (q-roulwpmm) operator, has been proposed to copy with the weight information of attributes. definition 11 let αj = 〈[ sθj,sτj ] , (µj,vj) 〉 (j = 1, 2, ...,n) be a collection q-roulvs and k = (k1,k2, ...,kn) ∈ rn be a set of parameters. then the q-roulwpmm is defined as follows, q −roulwpmmk (α1,α2, ...,αn) =   1 n! ∑ ϑ∈sn n∏ j=1 ( nωϑ(j)wϑ(j)∑n i=1 ωiwi αϑ(j) )kj 1∑n j=1 kj (18) where w = (w1,w2, ...,wn)t is the weight vector of αj (j = 1, 2, ...,n) satisfying wj ∈ [0, 1] and n∑ j=1 wj = 1. ωj = (1 + t (αj))/ ∑n i=1 (1 + t (αi)) is the pwv, satisfying ωj ∈ [0, 1], ∑n j=1 ωj = 1. t (αj) =∑n i=1,i 6=j sup (αi,αj) and sup (αi,αj) represents the support degree between αi and αj. similarly, we can obtain that the q-roulwpmm satisfies following property. theorem 5 let αj = 〈[ sθj,sτj ] , (µj,vj) 〉 (j = 1, 2, ...,n) be a collection q-roulvs and k = (k1,k2, ...,kn) ∈ rn be a set of parameters. then, the aggregated value by q-roulwpmm is also a q-roulv, and q −roulwpmmk (α1,α2, ...,αn) = 〈  s( 1 n! ∑ ϑ∈sn n∏ j=1 ( nωϑ(j)wϑ(j)∑n i=1 ωiwi θϑ(j) )kj) 1∑n j=1 kj , s( 1 n! ∑ ϑ∈sn n∏ j=1 ( nωϑ(j)wϑ(j)∑n i=1 ωiwi τϑ(j) )kj) 1∑n j=1 kj   ,      1 −   ∏ ϑ∈sn  1 − n∏ j=1  1 −(1 −µq ϑ(j) )nωϑ(j)wϑ(j)∑n i=1 ωiwi  kj     1 n!   1/q 1∑n j=1 kj ,  1 −  1 − ∏ ϑ∈sn  1 − n∏ j=1  1 −(vϑ(j)) qωϑ(j)wϑ(j)∑n i=1 ωiwi  kj   1 n!   1∑n j=1 kj     〉 . (19) the process of proof is similar to that of theorem (2), so it is omitted here. 4 a novel approach to magdm based on the proposed operators in this section, we introduce a novel approach based on the pmm operators to the magdm problems with q-rung othopair uncertain linguistic information. assume that there are m alternatives {x1,x2, ...,xm}, {g1,g2, ...,gn} be a collection of attributes with the weight vector w = (w1,w2, ...,wn)t , where wj ∈ [0, 1] (j = 1, 2...,n) and ∑n j=1 wj = 1. d = {d1,d2, ...,dt} be a collection of decision makers, and their weight vector is λ = (λ1,λ2, ...,λt)t , satisfying λp ∈ [0, 1] and∑t p=1 λp = 1. for the attribute gj (j = 1, 2, ...,n) of alternative xi (i = 1, 2, ...,m), decision makers are required to use a q-roulv αij = 〈[ sθji,sτji ] , (µji,vji) 〉 to express their preference information. therefore, a q-rung othopair uncertain linguistic decision matrix can be obtained, which can be denoted as ap = ( α p ij ) m×n . in the following, we utilize the proposed q-rung othopair uncertain linguistic aggregation operators to solve this problem. https://doi.org/10.15837/ijccc.2021.3.4214 13 4.1 the model based on cross entropy to obtain the weight vector of attributes in practical magdm problem, the attribute weights is unknown or partly unknown, we need to calculate the attribute weights by using the maximum cross-entropy method. based on [43], a model based on cross entropy of q-roulvs is established to obtain the weight vector of attributes. for the alternatives xi to all the other alternatives for attribute gj, it deviation can be firstly calculated by: dij = 1 m− 1 m∑ r=1,r 6=i ce∗ (αij,αrj) = 1 2(m− 1) m∑ r=1,r 6=i (ce(αij,αrj) + ce(αrj,αij)) (20) the overall deviation of all alternatives to other alternatives for attribute gj is represented as: dj = m∑ i=1 dij = 1 m− 1 m∑ i=1 m∑ r=1,r 6=i ce∗(αij,αrj) (21) it means that we can obtaining the optimal attributes weights by constructing the a linear programming model (m− 1)   max d(w) = ∑n j=1 wjdj = ∑n j=1 ∑m i=1 dijwj subject to n∑ j=1 wj = 1,wj ≥ 0,j = 1, 2, . . . ,n (22) solve the eq.(22), we can get wj = m∑ i=1 ( 1 2(m−1) m∑ r=1,r 6=i (ce(αij,αrj) + ce(αrj,αij)) ) n∑ j=1 m∑ i=1 ( 1 2(m−1 m∑ r=1,r 6=i (ce(αij,αrj) + ce(αrj,αij)) ) (23) 4.2 the decision making process step 1. normalize the decision matrices, the attributes can be generally divided into two types: benefit attributes and cost attributes. therefore, the decision matrices should be normalized according to the following formula: α p ij =   〈 [sθp ij ,sτp ij ], (µpij,v p ij) 〉 gi ∈ i1〈 [sθp ij ,sτp ij ], (vpij,µ p ij) 〉 gi ∈ i2 (24) where i1 and i2 represent benefit type and the cost type attributes respectively. step 2. utilize the q-rung orthopair uncertain linguistic weighted average (q-roulwa) operator to aggregate the assessments of all xi, (i = 1, 2, ...,m) by t decision makers with respect to attribute gj αij = q −roulwpmmk ( α1ij,α 2 ij, ...,α t ij ) (25) step 3. calculate the weight vector of attributes wj = m∑ i=1 ( 1 2(m−1) m∑ r=1,r 6=i (ce(αij,αrj) + ce(αrj,αij)) ) n∑ j=1 m∑ i=1 ( 1 2(m−1 m∑ r=1,r 6=i (ce(αij,αrj) + ce(αrj,αij)) ) (26) step 4. utilize the q-roulwpmm operator to obtain the aggregation value of xi with respect to attribute gj, (j = 1, 2, ...,n) αi = q −roulwpmmk (αi1,αi2, ...,αin) (27) step 5. according to definition (5), calculate the expected values of the overall preference values αi (i = 1, 2, ...,m) step 6. rank alternatives {x1,x2, ...,xm} and select the optimal alternative(s). https://doi.org/10.15837/ijccc.2021.3.4214 14 table 1: intuitionistic uncertain linguistic decision matrix r1 g1 g2 g3 g4 a1 <[s5,s5],(0.2,0.7)> <[s2,s3],(0.4,0.6)> <[s5,s6],(0.5,0.5)> <[s3,s4],(0.2,0.6)> a2 <[s4,s5],(0.4,0.6)> <[s5,s5],(0.4,0.5)> <[s3,s4],(0.1,0.8)> <[s4,s4],(0.5,0.5)> a3 <[s3,s4],(0.2,0.7)> <[s4,s4],(0.2,0.7)> <[s4,s5],(0.3,0.7)> <[s4,s5],(0.2,0.7)> a4 <[s6,s6],(0.5,0.4)> <[s2,s3],(0.2,0.8)> <[s3,s4],(0.2,0.6)> <[s3,s3],(0.3,0.6)> table 2: intuitionistic uncertain linguistic decision matrix r2 g1 g2 g3 g4 a1 <[s4,s4],(0.1,0.7)> <[s3,s4],(0.2,0.7)> <[s3,s4],(0.2,0.8)> <[s6,s6],(0.4,0.5)> a2 <[s5,s6],(0.4,0.5)> <[s3,s4],(0.3,0.6)> <[s4,s5],(0.2,0.6)> <[s3,s4],(0.2,0.7)> a3 <[s4,s5],(0.2,0.6)> <[s4,s4],(0.2,0.7)> <[s2,s3],(0.4,0.6)> <[s3,s4],(0.3,0.7)> a4 <[s5, s5],(0.3,0.6)> <[s4,s5],(0.4,0.5)> <[s2,s3],(0.3,0.6)> <[s4,s4],(0.2,0.6)> 5 numerical example to illustrate the validity and superiorities of the proposed approach, we provide a numerical example as well as some comparative analysis in this section. this example is adopted from liu and jin [28]. an investment company wants to invest its money to a company and after primary evaluation, there are four possible alternatives on the candidates list. they are: (1) a1 is a care company, (2) a2 is a computer company, (3) a3 is a tv company, and (4) a4 is a food company. in order to select the best alternative, the four companies are evaluated from four attributes: (1) g1 is the risk analysis, (2) g2 is the growth analysis, (3) g3 is the social political impact analysis, and (4) g4 is the environmental impact analysis. the weight vector of the attributes is w = (0.32, 0.26, 0.18, 0.24)t . the investment company invites three experts to be the decision-making committee. decision makers whose weight vector is λ = (0.4, 0.32, 0.28)t are required to use the linguistic term set s = {s0,s1,s2,s3,s4,s5,s6} to assess the four alternatives respectively. therefore, the decision matrices rk = [ αkij ] 4×4 can be obtained, which are shown in tables 1-3. 5.1 decision-making process in this section, we utilize the proposed method to solve the above problem. step 1: evidently, all the attributes are of the benefit type. thus, they do not have to be standardized. step 2: utilize the q-roulwa operator to calculate the collective decision matrix, which is shown as table 4. step 3: calculate the weight vector of attributes as w∗ = (0.2591, 0.2476, 0.2709, 0.1864) step 4: for alternatives ai (i = 1, 2, 3, 4), utilize the q-roulwpmm operator to calculate its comprehensive evaluation value. table 3: intuitionistic uncertain linguistic decision matrix r3 g1 g2 g3 g4 a1 <[s5,s5],(0.2,0.6)> <[s3,s4],(0.3,0.7)> <[s4,s5],(0.4,0.5)> <[s4,s4],(0.2,0.7)> a2 <[s4,s5],(0.3,0.7)> <[s5,s5],(0.3,0.6)> <[s2,s3],(0.1,0.8)> <[s3,s4],(0.4,0.6)> a3 <[s4,s4],(0.2,0.7)> <[s5,s5],(0.3,0.6)> <[s1,s3],(0.1,0.8)> <[s4,s4],(0.2,0.7)> a4 <[s3,s4],(0.2,0.7)> <[s3,s4],(0.1,0.7)> <[s4,s5],(0.1,0.7)> <[s5,s5],(0.4,0.5)> https://doi.org/10.15837/ijccc.2021.3.4214 15 table 4: the collective decision matrix g1 g2 a1 <[s4.5886,s4.5886],(0.1578,0.6727)> <[s2.5975,s3.6021],(0.2868,0.6768)> a2 <[s4.2503,s5.2411],(0.3606,0.6203)> <[s4.1717,s4.5916],(0.3280,0.5803)> a3 <[s3.5936,s4.2607],(0.1989,0.6743)> <[s4.2525,s4.2525],(0.2276,0.6730)> a4 <[s4.4429,s4.8900],(0.3084,0.6055)> <[s2.8547,s3.8745],(0.1990,0.6946)> g3 g4 a1 <[s3.8689,s4.8745],(0.3390,0.6516)> <[s4.1073,s4.5207],(0.2506,0.6193)> a2 <[s2.8519,s3.8706],(0.1253,0.7528)> <[s3.2711,s3.9627],(0.3397,0.6205)> a3 <[s1.9812,s3.5235],(0.2277,0.7190)> <[s3.2688,s4.2656],(0.2875,0.6722)> a4 <[s2.8479,s3.8652],(0.2607,0.6062)> <[s3.8720,s3.8720],(0.2871,0.5724)> table 5: ranking results by using the different parameter vector k k e (α1) e (α2) e (α3) e (α4) ranking results k = (1, 0, 0, 0) 1.3020 1.4550 1.1142 1.3451 a2 � a1 � a4 � a3 k = (1, 1, 0, 0) 1.2724 1.3713 1.0749 1.2969 a2 � a1 � a4 � a3 k = (1, 1, 1, 0) 1.2498 1.3014 1.0413 1.2631 a2 � a1 � a4 � a3 k = (1, 1, 1, 1) 1.2295 1.2156 1.0040 1.2338 a1 � a4 � a2 � a3 α1 = 〈[s3.6365,s4.2823] , (0.2469, 0.6633)〉, α2 = 〈[s3.5044,s4.2831] , (0.2627, 0.6668)〉 α3 = 〈[s3.0812,s3.9684] , (0.2307, 0.6954)〉, α4 = 〈[s3.3724,s4.0241] , (0.2577, 0.6318)〉 step 4: calculate the expected values e (αi) of alternative ai (i = 1, 2, 3, 4), and we can get: e (α1) = 1.2295, e (α2) = 1.2156, e (α3) = 1.0040, e (α4) = 1.2338 step 5: according to the expected values of alternatives, we can obtain their ranking order, i.e. a1 � a4 � a2 � a3 . hence, a1 is the best alternative. 5.2 the influence of the parameters on the results in this subsection, we investigate the influence of the parameter vector k on the decision results. so, we set different parameter vectors k in the q-roulwpmm operator and discuss the ranking results. the details are in table 5. table 5 shows that when k takes different values, the expected values and ranking orders are also changed relevantly. at the same time, it can be seen that the more relevant ship between attributes is considered, the smaller the expected values will become. therefore, we can regard the parameter vector k as the decision-maker’s attitude toward optimism or pessimism. in this way, decision makers can express their optimistic or pessimistic attitudes and actual needs by changing the parameter vector k. table 6: ranking results obtained by using different methods methods ranking results iulwga operator [37] a2 � a4 � a1 � a3 iulwghm operator [38] a2 � a4 � a1 � a3 iulwbm operator [39] a2 � a4 � a3 � a1 wpfulmsm operator [40] a2 � a3 � a1 � a4 q-rowpulpmm operator a1 � a4 � a2 � a3 https://doi.org/10.15837/ijccc.2021.3.4214 16 table 7: characteristics of different methods method whether it has felxible power for describing uncertainty whether it can discount outliers whether it captures the relationship between arguments iulwga [37] no no no iulwghm [38] no no yes iulwbm [39] no no yes wpfulmsm [40] yes no yes wpulpmm yes yes yes method whether it captures the relationship among arguments whether it captures the relationship amng all arguments whether it consider the self-importance of arguments iulwga [37] no no no iulwghm [38] no no yes iulwbm [39] no no yes wpfulmsm [40] yes no no wpulpmm yes yes yes 5.3 comparative analysis in this section, we compare the q-roulwpmm operator proposed in this paper with the example which has been mentioned above. (1) the weighted geographic mean operator based on intuitionistic uncertainty (iulwga) proposed by liu and jin [28]; (2) the weighted arithmetic operator based on intuitionistic uncertainty (iulwghm) proposed by liu et al.[44]; (3) that introduced by liu et al.[45] based on learning uncertain language weighted bonferroni mean (iulwbm) operator; (4) that proposed by liu et al.[46] based on weighted pythagorean fuzzy determine the language maclaurin symmetric mean (wpfulmsm) operator. the decision results of the various operators for the above examples are presented in table 6. next, we will conduct a detailed comparative analysis based on this. first, the methods proposed by liu and jin [28], liu et al.[44, 45] are based on iulss. as we have already mentioned above, q-rung orthopair uncertain linguistic sets (pfulss) are more powerful than iulss. for example, if the sum of the membership and non-membership of the ulv provided by the decision maker is greater than 1, like 〈[s5,s6] , (0.7, 0.8)〉then we cannot select iulss to represent the set of fuzzy numbers. therefore, the method proposed in this paper is more flexible and powerful than other methods. liu and jin’s [28] method is based on the iulwga , which does not consider the interrelationship between attribute values. the method [44, 45] of liu et al. is based on the iulwahm and iulwbm, compared to the iulwga operator, these two operators take the relationship between the attribute values as a consideration. however, its drawback is that it can only capture the relationship between any two attribute values. this still does not satisfy most of the actual situation. later, liu et al. [46] proposed the maclaurin symmetric mean (msm) operator, which also demonstrates the correlation between attribute values. compared with the operators introduced in the previous section, the method proposed by liu et al. [46] is more practical and powerful. because the maclaurin symmetric mean (msm) operator can capture the correlation between two or more attribute values, the larger the k value, the more correlation between the attribute values can be captured. however, the msm operator also has its drawbacks. the msm operator can only consider capturing the correlation between n-1 attribute values at most. in addition, the msm operator cannot reflect the importance of individuals among the aggregated parameters. the pmm operator proposed in this paper is produced by combining the pa operator with the mm operator. first of all, the pmm operator can well capture the correlation between attribute values. it is worth mentioning that the msm operator is a special case of the mm operator, and https://doi.org/10.15837/ijccc.2021.3.4214 17 the pmm operator is derived by the mm operator. therefore, the pmm operator not only considers the correlation between all attribute values, but also lists the individual’s level as a consideration. in addition, the pmm operator has a parameter vector, which makes the information aggregation process more flexible and feasible. secondly, the pmm operator also has the characteristics of the pa operator, which allows the evaluation values to support and strengthen each other, so it can well avoid the situation where the value of the attribute provided by the decision maker is too high or too low. so, in summary, the pmm operator proposed in this paper is more powerful, more flexible, and more versatile. 6 conclusions in this paper, we develops a magdm method based on q-rung orthopair uncertain pmm operator with unknown attributes weights. in order to do this, we firstly define the cross-entropy of q-roulss, which is utilized to obtain the optimal weight vector of attributes by a linear programming model. secondly, we first propose q-rung orthopair uncertain linguistic pmm (q-roulpmm) operator and its weighted form to summarize the decision maker’s preference information and determine the best choice. then, based on this, we introduced a new magdm method. then, we apply this method to investment project selection issues. later, in order to better demonstrate the advantages and superiority of the proposed method, we compare it with other methods in terms of qualitative and quantitative. in future work, we are going to apply pmm operators to more fuzzy linguistic environments, such as hesitant fuzzy linguistic sets [47], probabilistic linguistic term sets [48], and so on. funding this work was partially supported by a major project of national social science foundation of china with grant number 18zda086. author contributions the idea of the whole thesis was put forward by hongmei zhao. she also wrote the paper. runtong zhang analyzed the existing work and xiaomin zhu provided the numerical instance. the computation of the paper was conducted by ao zhang. all authors have read and agreed to the published version of the manuscript conflict of interest the authors declare no conflict of interest. references [1] lotfi a zadeh. information and control. fuzzy sets, 8(3):338–353, 1965. 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(2021). multiattribute group decision making method with unknown attribute weights based on the q-rung orthopair uncertain linguistic power muirhead mean operators, international journal of computers communications & control, 16(3), 4214, 2021. https://doi.org/10.15837/ijccc.2021.3.4214 introduction preliminaries q-rung orthopair uncertain linguistic sets cross entropy of q-roulvs power average operator and muirhead mean operator some new q-rung orthopair uncertain linguistic aggregation operators the q-rung orthopair uncertain linguistic power muirhead mean operator the weighted q-rung othopair uncertain linguistic power muirhead mean operator a novel approach to magdm based on the proposed operators the model based on cross entropy to obtain the weight vector of attributes the decision making process numerical example decision-making process the influence of the parameters on the results comparative analysis conclusions int j comput commun, issn 1841-9836 8(4):514-524, august, 2013. processing capacity and response time enhancement by using iterative learning approach with an application to insurance policy server operation m. ercan, t. acarman mutlu ercan technology management department avivasa retirement and life insurance company, i̇stanbul, turkey mutlu_ercan@yahoo.com tankut acarman* computer engineering department galatasaray university, i̇stanbul,turkey tacarman@gsu.edu.tr *corresponding author abstract: in this study, computing system performance enhancement by using iterative learning technique is presented. computational response time and throughput of the computing system is improved by introducing computational cost model and selection probability for each individual job. excepted gain by enforcing dynamic caching is maximized in terms of classifying the arriving computing jobs on an selective manner and dynamically replacing them in a limited memory space. gain maximization is performed by tuning the window size which helps to compare the computing jobs in terms of their individual selection and occurence probabilities. fairly special computing work in insurance risk investigation is chosen for experimental validation of the proposed approach. aspect oriented programming (aop) methodology on java platform is used for the experimental setup. aop allows to identify computational jobs and their parameters based on the marked annotations. experimental results show that the developed iterative learning based caching algorithm performs better than the other well known caching techniques. keywords: iterative learning, caching, computational cost model. 1 introduction information processing requests and distributed applications are increasing along the webbased server-client interaction development cycle, and the systems with much more powerful processing capabilities are needed. despite the increase in hardware’s processing and transmission busses’ speed, performance enhancement of the overall computing system may require simultaneous resource allocation depending on the computing task. to enhance the overall processing performance, research is focussed on software control and dynamic resource allocation of the computing and storage units. static and dynamic scheduling algorithms have been developed on a real-time basis systems operations. static scheduling algorithms have low costs, but incapable to adapt scheduling rules to enhance efficiency of the limited resources subject to unpredicted computing jobs’ arrival whereas dynamic scheduling algorithms may adapt and respond in a timely-fashion. the dynamic scheduling algorithms are mainly based on selection criteria driven approach such as earliest deadline first, highest value first, highest value density first, [1]. data access operations may degrade computing system performance. unnecessary data reading may be avoided by caching the same and frequently occurring computational jobs, [2]. caching is one of the common performance improvement patterns which may be applied in different layers of the systems, for example web browser caches static web content, database caches blocks of copyright c⃝ 2006-2013 by ccc publications processing capacity and response time enhancement by using iterative learning approach with an application to insurance policy server operation 515 data from disk for performance, the san controller will cache read/write data from disk and only complete when efficient to do so, (see for instance different caching approaches in [3]). there are some initial studies to model and control resources allocations in multi-tier virtualized applications which are considered to be part of the green and cloud computing. the applications are running in isolation by runtime resource allocation. in virtual execution environments, cpu utilization is controlled versus workload changes to achieve performance criterion based on mean response time, [4], [5]. a preliminary version of this paper is presented in [6] where the cost of processing a job in the central processor unit (cpu) is derived and dynamic caching methodology is studied via simulation. iterative learning control (ilc) is a methodology, which can be applied to repetitive systems, tries to improve system performance based on the knowledge of the previous experiences. system learns from previous repetitions to improve next repetitions to minimize the tracking error, [7]. in factories, industrial manipulators performs same cycles repetitively, by using ilc energy consumption and operation time is minimized, [8]. in this paper, ilc-based caching scheme is introduced to enhance response time and processing capacity of the computing systems. computational jobs are processed by the cpu which uses other storage devices such as memory and disk etc,. the service time of the cpu is minimized by preventing reprocessing the same jobs and results by enforcing learning scheme, time cost of obtaining the outputs of the jobs is minimized. due to the limited caching memory resources, caching size is limited and a reference value for the iterative caching procedure is derived. the proposed dynamic caching scheme is experimentally tested on the insurance policy server operating on a real-time basis and its computational performance is presented. this paper contributes to dynamic caching for computing performance enhancement by using iterative learning technique. this paper is organized as follows. in section 2, computer process modeling and cost diagram of allocating the memory for caching and computational purposes is presented. the expected gain of the individual computational job is described. in section 3, the reference value generation, the output value of the computing system and the error term is proposed towards autonomous management. experimental studies to validate the effectiveness of the proposed approach are presented in section 4. finally, some conclusions are given. 2 computing process modeling from the view point of our study, the cost of processing a job in the cpu instead of caching its previously obtained result is considerably higher. the memory is used to store the repeated job results. accessing to the memory from the cpu is fast enough. for example, the average time of getting an output from memory is approximately 12.10 −7 milliseconds. a priori, cost of a simple request from the memory is cheaper than complex memory usages. cpu is the bottleneck where all jobs are processed, and mainly the memory is used to perform the jobs. there are two kinds of jobs: i/o jobs and computational jobs. the computational jobs are deterministic: they always give the same output with respect to the same input. “3+x ” is a computational job, because when a value is set for “x ” the output is independent from time. the other jobs are the i/o jobs for example writing to a file system. an i/o job such as data writing to a disk is not a deterministic job from this point of view. random or time dependent jobs are considered to be i/o jobs. in this study, to minimize the time cost of processing the repeating jobs by a single cpu, some amount of the previously processed job results are cached in the memory. obtaining the results from memory is a faster operation compared with running a complex job in cpu which may use other storage devices such as memory and disk etc,. some partition of the memory is used to cache the job results and this partition versus the total memory space is 516 m. ercan, t. acarman 0 10 20 30 40 50 60 70 80 90 0 20 40 60 80 100 0.5 1 1.5 2 2.5 3 3.5 x 10 5 proportion of the memory used for caching(i/o jobs/(computational+i/o jobs)) tim e c o st o f th e jo b s [m s] figure 1: cost diagram of memory allocation subject to static size to cache the jobs’ results denoted by α, α = m mt (1) where m denotes the total footprint of the job outputs and mt is the total memory of the system. each job produces an output object that occupies some footprint in the memory. the tradeoff of regulating the partition of the memory to cache some amount of the job results versus the request of memory space to process the job whose output has not been cached in the memory is illustrated by the following time cost diagram in fig.1. in this figure, a set of i/o and computational jobs has been generated to investigate the computing performance of the system. the jobs are mainly constituted by i/o and computational tasks. the characteristics of the individual jobs such as footprints of the outputs, service and arrival rates are chosen randomly. the service rates are chosen between 1 and 10000, the footprints are between 1 and 1000 and all jobs are distributed uniformly. time cost with respect to the memory caching partition and the ratio of the i/o jobs versus the total number of the i/o and computational jobs are plotted. when the small partition of the memory is reserved for caching purposes, the possibility of finding the previously processed result is low and the job needs to be reprocessed by the cpu which costs some time. if most of the jobs are i/o jobs, caching the computational job results does not affect the cpu processing time. when the number of computational jobs is increased versus the overall jobs, caching may reduce cpu’s processing load by using the previously processed identical job result. on the other hand, to increase the memory caching partition may cause slower computing system response due to low memory space left for processing the new jobs or whose results have not been cached, i.e., when the system becomes unable to find enough memory to process the new jobs, time cost of the jobs increases. the slope of the cost function presents a valley form and its slope in the left side illustrates this effect. 3 real-time dynamic caching the time cost of computation for each individual job may be described by, tcpu(α) = { tr if the job is operated by the cpu tc if the job result exists in the memory (2) where tr and tc denotes the time cost of the job run by the cpu (runtime) and the time cost of getting its output from the memory (cache time), respectively. the jobs are assumed to be in the processing capacity and response time enhancement by using iterative learning approach with an application to insurance policy server operation 517 processing queue and the total size of the jobs is restricted by the window dimension. initially, the last n+m jobs can be supposed in the sliding window. when one job is processed, a new job enters in the sliding window and the last job is left out of the window as illustrated in fig.2. the total time cost of the computational jobs in the kth window is shown as; ∆tkcpu(α) = m∑ i=1 bkit ki cpu(α) ∀bki ∈ {0, 1} (3) bki denotes the existence of the ith job in the kth window. the total time cost of the i/o jobs in the same window, ∆tki/o(α) = n∑ j=1 akjt kj i/o (α) ∀akj ∈ {0, 1} (4) akj denotes the existence of the jth job, m defines the number of computational jobs, n defines the number of i/o jobs in the kth window. ∆tkcpu and ∆t k i/o are dependent on the reserved memory partition, denoted by α, to cache the previous job results as illustrated in figure 2. t k(α) = ∆tkcpu(α) + ∆t k i/o(α) (5) t k is the total time cost of the kth window. figure 2: ilc-based caching scheme. the proposed ilc-based dynamic caching scheme is illustrated in the fig.2. ilc runs on the cpu as a memory allocator program and it selects some processed job results in the sliding window and caches them in the given partition of the memory. the sliding window size is adjusted in an optimal way to enable a performance criterion between forgetting the jobs with lower criteria and accepting the new jobs with higher selective criteria based on their recall or arrival frequency. in fig. 3, selection scenario is illustrated where the window size is equal to 4, but the memory contains only 2 jobs which are selected based on the individual job’s parameter. the results of the (i-1)th and (i-3)th computing job are selected and their results are cached in the memory to be used in their next recall. the selection parameter of the (i-2)th job is assumed to be lower compared to the cached ones. the selection parameter of the (i+1)th job is going to 518 m. ercan, t. acarman be calculated in the next sampling period while the selection window is sliding towards the new coming jobs. probability of caching the individual job result depends on both of the probability on the selection criterion and the individual job’s occurrence in the selection window which are denoted by psci (τ) and ∼ pi(τ), respectively. pi(τ) = p sc i (τ) ∼ pi(τ) (6) the selection probability, denoted by psci , is calculated for each individual job and it is ranked among the other jobs to decide about caching the most suitable job results in the memory. the selection probability is derived by, psci (τ) = e −( amiµiγτ λic ) (7) where γ = ∑ω i=1 1 µi τ is the selection (sliding) window size, mi is the size of the individual job output, µi is the individual job service rate provided by the cpu, ω is the number of distinct jobs in the computing system, λi is the arrival rate of the ith job, c is the capacity of the memory and a is the scaling factor to assure that the selective criterion takes values between 0 and 1. the job parameters are assumed to be determined a priori. a simple analysis may show that when the individual size of the job output, denoted by mi, increases, the selection probability psci decreases. this leads to cache as many as job results instead of caching few larger results. when the individual service rate, µi, increases, psci decreases leading to the choice of caching larger service time requiring jobs results instead of caching the jobs that can be processed faster. the selection criterion may increase as the individual arrival rate λi increases leading to cache the jobs with higher occurrence probability to avoid frequent computation efforts. when the sliding window size, τ is increased, the caching selection criterion psci for the individual job may be decreased due to the augmented number of the distinct jobs in the selection window. the ratio of the ith individual computational job’s arrival rate versus the sum of the arrival rate of the other individual jobs existing in the sliding window is given, ni = λi ω σ j=1 λj (8) for the individual job in the window, the probability of being selected by the ilc-based caching scheme is normalized with respect to the total number of the individual computational job’s arrival rate ratio, pi = ni nt otal where ntotal ≡ n σ j=1 nj and n is the total number of the processed jobs i.e., the sum of the distinct jobs and the reprocessed ones. when the specified individual job is not in the sliding window, it may not be selected by the proposed ilc scheme. through straight-forward analysis, the probability of not being selected due to absence of the individual job in the window, ∗ pi = 1 − pi = 1 − ni ntotal = ntotal − ni ntotal (9) since the number of the jobs whose results may be nominated for caching is equal to the size of the window denoted by τ, the probability of being absent in the window is given by, pi(τ) = τ∏ k=1 ntotal − ni − k + 1 ntotal − k + 1 (10) gain of the job is defined as the time cost of cpu to process its output result. time cost of getting from memory is ignored, because it is small and invariant (almost same time cost value processing capacity and response time enhancement by using iterative learning approach with an application to insurance policy server operation 519 for all jobs). the relative gain is important for the system, the proportion of the absolute gain of the individual job versus the sum of the absolute gains of the total jobs. the relative gain of the ith job in the window is defined by ψi, ψi = 1 µi γ = 1 µiγ (11) total expected gain is obtained in terms of the average of all possible relative individual job gain weighted by its probability on existence in the sliding window and selection criterion, ( [9]), e(ψ) = ∑ i in τ ψip̃i(τ)p sc i (τ) (12) by using the derived probabilities given by and (7), (10) and (11), total expected gain for dynamic caching procedure may be given by, e(ψ) = ω∑ i=1 ( 1 µiγ (( 1 − τ∏ k=1 ntotal − n(i) − k + 1 ntotal − k + 1 ) e −( amiµiγτ λic ) )) (13) where ω is the number of distinct jobs in the computing system. total expected gain (13) is constituted by summing all the individual expected gain of the distinct jobs in the computing system. towards ideal caching procedure, only the distinct job results are counted for the total expected gain, recalled and reprocessed jobs are not considered for the derivation of the expected gain. the window size, denoted by τ, is regulated to maximize the expected gain of the sliding window by comparing the incoming jobs and caching the most profitable results for their possible future recalls which may lead to reduce computational efforts. the error variable is derived in terms of the attainable desired value, which is unknown a priori due to unknown job arrival, and the sum of the individual gains in the sliding window, ek = max( τ∈n e(ψ)) − ∑k i=k−τ+1 ψipi τ max( τ∈n e(ψ)) = 1 − ∑k i=k−τ+1 ψipi τ max( τ∈n e(ψ)) (14) where n denotes the set of the previously arrived computing jobs, including the recalled and reprocessed ones, whose parameters such as occurrence frequencies, size, arrival rates may be learnt. the reference value is obtained by maximizing the derived total expected gain versus the sliding window size. the sliding window size is adjusted at each iteration step based on the previously processed and new arrival jobs to maximize the reference value for caching. the second term in the error variable is the sum of the individual job gains existing in the current sliding window standing for the actual output of the proposed caching system. ilc methodology is based on the previously processed results and the job parameters such as their size in the memory, arrival and service rate, etc., and they will be used for the next memory scheduling process. the goal is to minimize the error and to regulate the window size to ensure caching the most suitable job results in the memory to avoid unnecessary computing efforts. to achieve this goal, the window dimension may be adjusted at each iteration step. the proposed ilc-based caching methodology is, (see for instance iterative learning regulator in [10] for different application) τk+1 = τk − sign( ek − ek−1 τk − τk−1 ) (15) 520 m. ercan, t. acarman following (15) the window size is regulated at the next iteration based on the difference of two consecutive error terms and the window sizes denoted by ek and τk at the kth iteration step, ek−1 and τk−1 at the (k − 1)th iteration step, respectively. ideally, the error variable in (14) may tend to zero if the given memory caching space is enough to cache all distinct job results. 4 experimental study the proposed iterative learning based dynamic caching scheme is applied to the insurance policy server. this insurance company server executes more than 10.000 remote policy inquiries on a daily basis. to benefit from the individual retirement product, a customer must be more than 18 years old and 56 years old, and must be contributing to the insurance financial system more than 10 years. most of the remote policy inquiring customers are between 18 and 46. about 350 customers are at the same age. at each age interval, half of them are male and female and they have to select their incomes and professions etc., in a limited set of the given query data. many calculations are executed for each request coming from different individual customer having the similar input data. in insurance and retirement enterprises area, the calculations of retention and premium need heavy actuarial computations. like different types of mortality tables, disability, assurance, commission calculation tables, funds grow expectations and many other ones are used to perform these calculations. the tables are not changed frequently. the tables used for life insurance calculations are updated when death proportions or born rates are changed (i.e. a disaster or an epidemic disease occurs). individual retirement tables are changed when enterprise’s strategy is updated which may occur on a yearly basis, [11]. it can be assumed that for a life insurance policy with same input values always same premium is calculated. aspect oriented programming figure 3: implementation of process flow (aop) methodology is used to separate business logic and other programming requirements such as logging, transactions etc. aop works on join points like method calls, object instantiations with advice types; before, after, around etc. and also aop is used to identify the individual jobs and their similar results. the developer marks computational jobs by annotations or regular expressions as join points. each join point has its own signature, when a method is called by same parameters the signature does not change. if the output of the previous job is cached, it is accessible by the signature of the job, (see for instance [12] or more information on aop processing capacity and response time enhancement by using iterative learning approach with an application to insurance policy server operation 521 methodology). aop has been designed to be implemented in many ways, including sourceweaving or bytecode-weaving and directly in the virtual machine. each method has its own signature including package and class name. in this study, a map is used to cache methods, key objects of the map are string implementation of the signature of the method concatenated with string implementation of the parameters (supposed that if and only if the parameters that have the same string implementation produce the same output). implementation process is illustrated in fig.3. the individual ith job is arrived with its parameters. the process searches whether the cache map has an object with string representation of the job (string representation consists of the signature of the method and the parameters separated by underscores). if the result has been obtained and cached for this string representation, the output is read from the cache and the parameters of the jobs are updated. otherwise the job is processed by the cpu and the parameters are updated accordingly. in a real-time experiment, all of the jobs characteristics 0 0.5 1 1.5 2 x 10 4 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.5 1 1.5 2 x 10 4 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 number of the processed jobs c p u u sa g e r a te ilc based caching conventional caching developer (a) (b) figure 4: a) process time of cpu to execute the incoming jobs on a real-time basis: the first day performance comparison of the cpu usage rate. b)the fifth day performance comparison. 0 0.5 1 1.5 2 x 10 4 0 0.2 0.4 0.6 0.8 1 number of the processed jobs c p u u sa g e r a te ilc based caching conventional caching developer 0 0.5 1 1.5 2 x 10 4 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 number of the processed jobs c p u u sa g e r a te ilc based caching conventional caching developer (a) (b) figure 5: a) the tenth day performance comparison. b) the fifteenth day performance comparison. are not known in advance. to overcome this situation, the system is designed as the average of the data characteristics cumulated in the previous days. the steady-state probabilities of the previous day distinct job parameters are used by the ilc-based caching scheme to calculate the overall job probabilities of the following day. in fig.4 through fig.5, cpu usage rate versus the large number of the jobs processed is plotted for different days. the performance of the proposed dynamic caching scheme is compared with other caching schemes. cpu usage rate responses are slightly changed dependent on the random arrival order of the remote policy inquiries. for 522 m. ercan, t. acarman 0 5000 10000 15000 20000 25000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n u m b e r o f jo b s p ro c e s s e d p e r h o u r days without caching without ilc developer with ilc figure 6: comparison of the number of the jobs processed per hour for different caching schemes each day, when cpu is enforced to process each individual job without considering even if their results are the same, cpu is loaded by %80 of its nominal capacity. when ilc is applied, the jobs are processed by using approximately 20% of the cpu computing capacity leading almost to %80 of free runtime. when the cpu has some free runtime, more jobs may be processed by 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1,1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 n o rm a li z e d p ro c e s s t im e days without caching with ilc developer without any replacement algorithm lru lfu random figure 7: comparison of the response time of the caching schemes the proposed scheme.the number of the jobs that may be processed per hour by using the full cpu capacity is compared in fig.6. for each day, ilc-based caching assures more jobs being processed per hour by utilizing the cpu’s full capacity. dark gray bars represent the number of the processed jobs by ilc-based caching. the lightest gray bar shows the number of the jobs processed without caching. when the first-in first-out caching scheme is applied, the number of the jobs processed is slightly increased, plotted with lighter gray bar. even when the developer knows all parameters of the jobs, the job processing capability of the system per hour is less than the ilc-based system. the number of the processed jobs is significantly higher when the proposed learning and sliding window based caching algorithm is applied. processing capacity and response time enhancement by using iterative learning approach with an application to insurance policy server operation 523 different cache replacement algorithms such as random, least frequently used (lfu) or least recently used (lru) are performed to compare with the introduced real-time ilc-based caching methodology. different caching schemes react to the arrival job in a different manner such as when the computing job arrives, random algorithm chooses a random job result and replaces it in the cache, lru chooses the least frequently arriving job or lru chooses the job which has not been recalled for a long time. in fig.7, processing time of the cpu to handle the incoming jobs per day is plotted for different caching systems. without caching, the process time is considered to be the nominal and the process time of the other methodologies are normalized according to this nominal time. the introduced ilc-based scheme, which avoids the unnecessary cpu computational efforts and permits the cpu react to the computational requests faster, is plotted with red-square line. the ilc-based system response time is compared with the system developer who is assumed to know all job characteristics, marks them and selects some of the results towards caching. the developer methodology time-response is around 40%, which is plotted with green-triangle line, and it is two times slower compared with ilc-based scheme. when random function is used for dynamic cache replacement method, process time is around 60% and it is plotted with solid and plus marked line. lru, plotted with solid and x-marked line and lfu time responses, plotted with circle marked line, lead to respond two times slower compared to ilc-based caching, slightly slower than the developer’s performance. the first-in first out caching methodology, which means without any replacement algorithm, causes fluctuations in the cpu response time and performs almost 20% faster at average compared to the case without caching. 5 conclusion in this paper, ilc-based caching scheme is introduced to enhance response time and processing capacity of the computing systems. the proposed scheme introduces the reference value for caching regulation. this reference value is introduced by summing possibly higher individual expected gains among the previously processed job results for the given window size. the average sum of the individual gain existing in the current window constitutes the actual output of the proposed caching system. along the random arrival of the computational jobs, the size of the window helps to compare some amount of the computational jobs based on their parameters enabling a performance criterion about replacing the jobs with lower criteria by the jobs with higher selective criteria in the dedicated caching memory space. at each iteration step, the sliding window size is regulated to cache the most suitable job results in the memory based on the individual job selection probabilities. the sliding window size is incrementally increased and kept on inquiring the new arriving computational jobs until reaching to the operating point where the expected gain is maximized and the window size is adequately large to capture the most suitable job results subject to the given limited memory caching space. to illustrate the effectiveness of the ilc-based caching system, the proposed dynamic caching scheme is applied to the insurance policy server. the proposed dynamic caching scheme is capable of processing more computing jobs for the given computing system capacity while the server being more responsiveness. by using the proposed autonomous caching and regulation methodology, the developers will not have to decide about the job results to be cached in the memory. computing system performance, processing capacity and responsiveness to the new and unpredicted computational jobs will be autonomously improved without any expert intervention. 524 m. ercan, t. acarman bibliography [1] liu,c.l.; layland, j.w. (1973); scheduling algorithms for multiprogramming in a hardreal-time environment, journal of association of computing machinery, issn 1556-4665, 20(1): 46 61. [2] nock, c. (2003); data access patterns: database interactions in object-oriented applications,addison wesley. [3] ford, c. et al (2008) patterns for performance and operability – building and testing enterprise software, auerbach publications. [4] padala, p. et al (2007), adaptive control of virtualized resources in utility computing environments, proc. of the european conference on computer systems, portugal:289-302. [5] kalyvianaki,e. et al (2009), self-adaptive and self-configured cpu resource provisioning for virtualized servers using kalman filters, proc. of the 6th international conference on autonomic computing, new york, usa: 117-126. [6] ercan, m.; acarman, t. (2010) iterative learning control of dynamic memory caching to enhance processing performance on java platform, international conference on computational science, amsterdam, the netherlands: 2664-2669. [7] butcher,m. et al (2008), a statistical analysis of certain iterative learning aontrol algorithms,international journal of control, 81:156-166. [8] moon, j. et al (1997) an iterative learning control scheme for manipulators, intelligent robots and systems, 2:759-765. [9] pitman, j. (1999), probability, springer. [10] ahn, h. et al (2007), iterative learning control: robustness and monotonic convergence for interval systems, springer. [11] booth, p. et al (2005), modern actuarial theory and practice, chapman & hall/crc. [12] clarke, s.; baniassad, e. (2005) aspect-oriented analysis and design: the theme approach, new jersey: pearson education. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 5, month: october, year: 2020 article number: 3897, https://doi.org/10.15837/ijccc.2020.5.3897 ccc publications determining it student profile using data mining and social network analysis l. stanca, r. lacurezeanu, a. tiron-tudor, v.p. bresfelean, i. pandelica liana stanca, ramona lacurezeanu* babes-bolyai university of cluj-napoca faculty of economics and business administration cluj-napoca, teodor mihali 58-60, 400591,romania liana.stanca@econ.ubbcluj.ro, ramona.lacurezeanu@econ.ubbcluj.ro *corresponding author: ramona.lacurezeanu@econ.ubbcluj.ro adriana tiron-tudor, vasile paul bresfelean babes-bolyai university of cluj-napoca faculty of economics and business administration cluj-napoca, teodor mihali 58-60, 400591,romania adriana.tiron@econ.ubbcluj.ro, paul.bresfelean@econ.ubbcluj.ro ionut pandelica 1. academy of economical sciences in bucharest bucharest, piata romana 6, 010374, romania ionut.pandelica@rei.ase.ro 2. agora university of oradea 410526 oradea, p-ta tineretului 8, romania, abstract to become higher competitive a university needs to develop a viable students’ absorption strategy on the labor market. a key to the successful development of such a strategy rests to synchronize jobs descriptions with profiles and behavior of it students. in order to generate this synchronization, it is essential to identify a way to improve university curricula, learning and teaching process based on the students’ profile and on the labor market needs. in this manner, universities could offer it companies information about their it students’ profile and behavior. our paper proposes a data mining and social network analysis to examine it students’ skills and behavior in order to generate their actual profile. the results contribute to the development of knowledge concerning the it graduates’ profile and based on this, a solution that might match the university curricula with the labor market requirements. finally, the results attempt to provide it companies with information with the aim of better understanding the it students’ profile and to create a realistic description of the job in the recruitment software on the digital market. keywords: data mining, social network analysis, personas, it graduates’ skills, job profile. https://doi.org/10.15837/ijccc.2020.5.3897 2 1 introduction contemporary higher education has known an unprecedented level of development at a european and worldwide level, under the influence of social and economic changes [37, 48]. it is in this context, terms such as big data, data mining, visual analytics data mining, design thinking, partner recognition, education 3.0, e-learning 3.0 appear and were introduced in the university’s curricula [7]. all these concepts have a significant impact on the academic environment, while the business environment modifies its dimension and increases the accuracy of the results obtained based on the decision analysis of teaching & learning. some researchers [43] state that e-learning shifts the focus from how to introduce technology in teaching to analytical learning, perceiving it as an essential tendency for the learning and teaching process based on technology [37]. the explosion of digital technologies has changed how talent is recruited. for the young generation z (born after 1994), which now forms a significant part of the workforce, a new manner of recruiting talented employees is needed [48]. as compared to past generations of students, the new generation is much more oriented and set on obtaining the information that the youngsters consider essential for their development [59]. the educator, teacher or professor now has the rather difficult task of demonstrating and justifying the necessity and usefulness of all the information taught [7]. with this kind of direct learning demand and need, the providers of education (universities, colleges and even the providers of professional formation courses) ought to provide courses and a curriculum that is not only flexible and accessible anytime and anywhere, but most important of all, courses that are relevant and applicable to the demands of the labor market [48]. otherwise, they would not be able to remain in demand, given the competitive environment of this field. up until now the labor market has required a cv, representing a form of a genuine professional card, but from now on everything will have to do with the aptitudes and skills of the individuals as well as how they can present these abilities on various social platforms. furthermore, they need to be able to prove their assumed abilities from the very moment when they step into a company [37]. in addition to professional and transversal competencies offered by the universities to their graduates, companies search for specific socio-psychological identities that create in a synergic manner the "ideal profile" of the it student graduate in accordance with the companies’ vision and organizational culture. based on this "ideal profile" that combines mentality, hard and soft skills, psychometric information, personality dimension, companies create the "hunted" profiles for specific it data mining on recruitment platforms. the knowledge of this profile can contribute to enhance the connection between the market needs with the universities "deliveries" of it students "products" and eliminate the existing discrepancies between the demand and offer that characterize the contemporary labor market [4]. in this context, the paper’s objective is to analyze whether the profile of the it graduates based on the university curricula corresponds to the market’s requirements, using a mix of empirical research methods in an innovative manner. the paper results contribute to the matching process between technical and transversal competencies and labor market needs, bringing insights specific to one of the most dynamic workforce domains, in particular, the it domain. moreover, the paper contributes to knowledge development in two applicative directions. first of all, the profiles identified will help universities improve curricula and teaching/learning processes based on the labor market needs. secondly, by presenting information to it companies, they will be able to understand the profile of the it students and to create a realistic job description in the recruiter software on the digital market. the novelty of this paper results also in the combination of data mining and social network analysis methods that facilitate the attempt to carry out a sophisticated analysis of the problem. our study is organized as follows: section 2 presents the debates from the research literature concerning the job matching system, cooperative education and the psycho-social profile of the it employees. section 3 delivers the theoretical framework based on the psychological and social profiles, followed by the methodology from section 4. the fifth section describes our research results, in the sixth section we present the final discussions and the last section comprises main conclusions, limits and further possible developments of this study. https://doi.org/10.15837/ijccc.2020.5.3897 3 2 nowadays directions of research 2.1 job matching system and cooperative education the bidirectional relation between education and the business environment is recognized by the literature via the concept of cooperative education . the main factors involved in cooperative education are the teachers, students and the business environment [36, 38]. the development of a course in the university field, according to cooperative education theory, has the main advantage of optimizing the recruitment process in the it domain. one of the arguments brought in favor of this statement is that the recruiters will get to know information about the personality, level of academic preparation and skills of the students who are involved in the sphere of business information systems education [4]. therefore, this would allow them to present job demands anchored in the reality of the labor market. the academic environment would benefit from the fact that it would be able to adapt its curriculum, programs and basic courses according to the direct feedback provided by the business environment. many researchers [32, 33, 34, 43] consider that cooperative education facilitates the cooperation between business and academic environments for courses creation as a context for the preparation of the student following the demands of the employer. at the same time, the employer learns how to define the available jobs that would be attractive for potential employees on the online platforms, while the educators could adapt their curricula to the demands of the labor market [4]. the courses developed on the philosophy of cooperation in education place emphasis on studying the psychological and social profiles of the students to generate descriptions of jobs so that these would be correctly and accurately understood by all those involved. furthermore, according to cognitive philosophy, the psychological and social profiles which one analyses include the students’ learning in connection to the mental models and possible wrong mental representations, intending to identify the typical mistakes made by the student in the process of learning, so as to avoid them [43]. as a consequence, the learning process depends on how the students represent and process knowledge and information, while treating the students as creators and masters of their learning process, of their own content of information, abilities and newly gathered knowledge. the course model thus created, will allow students to correctly and efficiently assimilate a programming language. following the constructivist theory, our paper intents to gather information related to the profile of the it student, the future employee in the it domain. technology in the educational domain has changed what we provide, how we can evaluate and how we can connect the attendants to the instructors and the content. furthermore, education 3.0 enables mass personalized education through mixed learning. more precisely, one talks about bringing together, of course, attendants from different domains and with different abilities that work together [41]. moreover, education 3.0 incorporates e-learning 3.0 and characterizes it as being both "collaborative" and "intelligent" [59]. collaboration in education 3.0 appears as social learning and places the focus on social aspects of importance, such as participation, collaboration, building together with an improvement, all of which become vital in the learning and teaching process [4]. a possible solution for building a better learning space that offers through discovery not through memorization the skills of the 21st century include: ways/learning spaces that allow students to acquire creative thinking, flexible problem solving, collaboration and innovative skills needed to be successful at work and in life [5, 26, 63]. moreover, universities’ curricula influence the profile of graduate students which is the future worker profile [40, 72]. our paper sustains the idea that the psychosocial profiles of graduates are based on the learning space the teachers created following the university curriculum and in line with the 21st century education theories. 2.2 psycho-social profile of the it employees personality refers to individual differences among people in behavior, cognition and emotion patterns [53]. starting from the set of central features characterizing the individual, that define the basic personality: agreeableness, likability, being conscientious, extroverted, intellectual, but also exhibiting https://doi.org/10.15837/ijccc.2020.5.3897 4 neuroticism [25], the evolution of the psycho-social profiles of the it employees are debated below. there is a significant amount of research interest related to the creation of the psycho-social profiles of it employees, starting in the 80s that has increased constantly. many attempts and experiments have the purpose of determining/ identifying techniques that would match the persons to the appropriate workplace. some authors [44, 50, 55, 68] consider personality as being more important than intelligence in the case of the it employee. this stereotype of the programmer wards off youngsters from the programmer profession in the usa [51]. based on the job announcements published in the new york times and usa today, a study [51] identified that 20-40% of the programmers have a personality characterized by introversion, detection, thinking, judgment, while the others are described in terms of extroverted, sociable and agreeable personalities. moreover, both [62] following [51] researches, consider programmers are social autisms that cannot "think outside the box", statements that lead us to the assumption that programmers may have distinct personalities from other professionals. other studies reveal that in programming, personality is more important than intelligence, and many programmers fail mostly because of their personality flaws, rather than from the lack of intelligence [68]. furthermore, the programmer’s work is situated at the crossroads between the act of creation and technique, and that employs adequate education, so the programmer’s personality can be changed [51, 56]. also, several studies have examined the role of personality in it related courses [45] using meyers-briggs or similar measures of cognitive style to determine personality and its relationship to course performance or potential educational strategies [9, 70]. the research was carried out by observing students in a learning environment that comprised physical and virtual spaces. the virtual and physical space in a course had the aim of obtaining and gathering information about in what way students use, interact with and manage course content. the virtual environment allowed us to measure the real degree of involvement of each individual in a course, based on which one can identify the profiles and personalities of future it employees. the course outline to create this experiment is intended to represent the prototype of a course created on the concepts of cooperative education [6, 16]. the experiment ends with the analysis of the choices of job announcements by each student. the announcements were collected from a dedicated it job announcements platform. the purpose of this action is to verify the link between the students’ personality features and the choice of a specific job announcement. the results of this approach are useful for both academic and business areas in the subsequent manner. the result of our research will generate an actual stereotype of the it graduate students that would allow it firms to have a clearer vision over the students’ profile and most important to create more suitable job announcements. furthermore, this work provides useful information to academic environment educators for more customized courses contents and styles of delivering, with regard to the students’ profile and labor market’s demands. 3 theoretical framework the psychological and social profile does not represent a method of investigation per se, but a way to represent the visible results of measures by using various pieces of evidence applied to a subject or a sample-group of subjects [18, 36, 54]. these profiles highlight the performances obtained by the individual in a set of tasks. the psycho-social profile offers the global but in the same time detailed image of the level of development characteristics for various psychological and social functions of the individual. the individual’s personality portrait has applications in many domains. for example, the personality model ipip (international personality item pool) was used to measure the degree of gratefulness and spiritual wellbeing [66], in examining the effects of the personality features on the processes and results of teamwork [21], in examining the manner of using medicine issued on prescription [62] and in exploring how the investor’s disposition can affect and influence the concentration of the investment portfolio [15]. close related to our objective, the managers are aware of the necessity to evaluate the personalities of their employees in a task distribution process as well as evaluating the performances at the workplace, being convinced by its efficiency [47]. the studies dedicated to personality identification use five principal dimensions of the personalhttps://doi.org/10.15837/ijccc.2020.5.3897 5 ity, called big five personality or ocean: openness to experiences, conscientiousness, extraversion, agreeableness, and neuroticism [61] (see table 1). table 1: personalities types, characteristics and indications personalities characteristics indicates how the person is openness (intellect) people who like to learn new things and enjoy new experiences usually score high in openness. openness includes traits like being insightful and imaginative and having a wide variety of interests open-minded and authority-challenging conscientiousnesspeople are reliable and prompt. traits include being organized, methodic, and thorough self-disciplined and organized extraversion extraverts get their energy from interacting with others, while introverts get their energy from within themselves. extraversion includes the traits of energetic, talkative, and assertive out-going and social agreeableness these individuals are friendly, cooperative, and compassionate. people with low agreeableness may be more distant. traits include being kind, affectionate, and sympathetic. warm, friendly and tactful neuroticism (emotional stability) this dimension relates to one’s emotional stability and degree of negative emotions. people that score high on neuroticism often experience emotional instability and negative emotions. traits include being moody and tense ability to remain stable and balanced programmers’ attitude is strictly connected to openness because they need to poses the ability to conceive of unconventional methods in order to obtain new solutions to a problem [52]. as a consequence, their top characteristics should be imagination, creativity, and intellectual curiosity [15], but at the same time, they are somehow incompetent from the social point of view. also, programmers’ attitude is associated with being introverted, with lack of concord and neuroticism. therefore, in our view, cognitive competences could model and refine the programmer’s personality [47]. built-up on this framework, our paper illustrates an up to date profile for the it graduate student. in our experiment, the student was observed for 14 weeks in a web programming course. the teaching environment is a complex one, conceived and developed so that it allows the development of cognitive and digital competences. the evaluation of the student involved a practical test followed by a project, then written evaluation over the 14 weeks, focusing on how they interacted in teams and in which way they solved their tasks. at the end of the course, we created a questionnaire with 33 items following cattell’s instrument 16 pf (the sixteen personality factor questionnaire (16pf) [12]. the 16pf is one of the oldest, most researched, and most influential measures of normal personality [3, 11] using the big-five personality factors: extraversion (e), agreeability (a), conscientiousness (c), emotional stability (es) and intellect (i). the answers to the 33 questions were presented on a scale of 5 likert points, from (1) standing for "i do not agree" to (5) "i agree". the personality features are considered https://doi.org/10.15837/ijccc.2020.5.3897 6 by [12], as basic tendencies that refer to the individual’s potential, while the attitudes, roles and relationships to the adapting objectives reflect the interaction between the elementary tendencies and the environment. based on this instrument, the research intends to identify the features/facets of the personality that can represent predictors of the natural absorption of students in the it domain and work environment. finally, we achieve the prototype of a profile that needs to be validated in time. the identified profile is useful for the academic environment and the labor market. in academia, the contribution of the prototype will allow a smooth adaptation of the curriculum and the teaching/learning activity to the demands of the labor market. for the latter, it will provide the it companies with information that would enable them to understand the profile of it students and will create a realistic description of the job advertised in the digital labor market, in order to facilitate the superposition of the offer and the request on the supposed market. 4 material and research methods in order to achieve this objective concerning the it graduate student profile as a future employee in the 21st century, our paper follows an exploratory empirical research using the survey method and a questionnaire based on cattell’s personality test to collect the data. the analysis and appreciation of each personas item materialized itself in a grade with values ranging from 1 to 5. we utilized several software packages with the purpose of testing the research hypothesis: spss 12, statistica 7.0 and nodexl. the statistical process performed is described in the next steps: 1. we applied statistical data analysis to illustrate the study participants’ characteristics. 2. we tested whether the items that form the personality measurement instrument have a single dimension, a single latent factor. this basic rule is known as unidimensionality [20] and is linked to the second assumption, namely the local independence of items [20]. the unidimensional does not refer strictly to the presence of single dimension, but to the existence of a dominant dimension (ability or coverage in the latent factor [20] that would influence the performance in the test [39]. in the case of a test that measures presence we cannot have the pretention that the items could measure pure presence. the performance of a subject in connection to a test item is foreseen and explained by the existence of a latent factor, the relationship between this performance and the latent factors that represent its basis is described by a monotonous and increasing function called the item response function or the particular curb of the item [39]. such a reliability-validity analysis will estimate the viability of the instrument by measuring the internal consistency of the elements, to the extent that they correlate well. the viability of our instrument was calculated according to the theory developed by spearmen [58]. going further, a scale or a test presents a better fidelity when the fidelity coefficient is more significant than 0.70 [20]. at this stage, we decided whether the instrument employed has an average degree of trust or consistency [20], so that its result would be the same throughout time and so that it can be used for scenarios that are similar to the ones analyzed in this article. 3. the analysis of principal components (pca) with the purpose to highlight pre-existent structures in a multitude of multivariable data [19]. these structures are generally expressed through the variation and co-variation of variables and of similarities, respectively differences among objects. in this first stage, we identify the values and own vectors of a square matrix obtained by multiplying the data matrix (objects) with its transposed function, which is a relatively standard procedure in the framework of matrix calculus. the role of the principal component analysis was to build decision models based on the answers to the questions from the sixteen personality factor questionnaire. these answers would help us formulate a pattern that would contain the restraining factors, which explains the variation of the studied variables. the variables with the non-representative correlations (<0.3 of the correlation matrix) were eliminated [19]. the next step of the analysis eliminates the variables based on the kaiser-meyer-olkin measurements (the values of the co-variation indicators from the main diagonal of the anti-imagines matrix >0.5) [20]. 4. in order to analyze the existence possibility of hidden aspects that have not been noticed or foreseen, aspects that could generate different and inexplicable results in the context of the problem, we utilize social network analysis. we considered appropriate to create an analysis that enters the https://doi.org/10.15837/ijccc.2020.5.3897 7 sphere of social network analysis, based on which we intend to determine the attributes present in job advertisements by the choices made by students following their personality features. centrality is considered the most often used conceptual tool for exploring the actors’ (locations) roles within social networks. central locations have to be more active in the sense that they have the highest number of connections with other actors-locations in the network [67]. in this research, we adopted [24] theory of nodes in a network, according to which group centrality shows the centrality degree of a group meaning the number of actors outside the group that are connected to the member of that group. different ties to the same actors by different group members are only counted once. closeness centrality measures the closeness of a node to the other. consequently, closeness centrality can be used to determine the degree of influence a node has in the network. group closeness centrality refers to the normalized inverse sum of distances from the group to all nodes outside the group. group betweenness centrality reveals the "proportion of geodesics connecting pairs of non-group members that pass through the group" [24]. these three centrality indicators allowed us to measure the role of attribute played in job description in a network for personality attributes. the results of our research are in line with results from other studies [13]. 5 study findings 5.1 sample description the group of respondents is composed of 106 students, a representative number taking into consideration the next two arguments based on the research literature. the group is higher than 100, represents a homogeneous population and according to the literature is considered to be a representative number [17, 28, 64, 65]. moreover, certain statisticians [2] agree that if the population of interest was homogenous, then the sample might be smaller than 100. all students are enrolled at babes-bolyai university of cluj-napoca, in the web-programming course. group demographic characteristics concerning gender and age are presented in table 2. table 2: demographic variables gender female 60.4 % male 39.6 % age 18-25 82.1 % 25-30 12.3 % over 30 5.7 % the data was collected for the period of a 14 weeks course. during the course, the students took part in two types of face-to-face meetings: lectures (dedicated to theoretical teaching conception) and practical (laboratory sessions). additionally, an e-learning platform (moodle) was used for teaching/learning activities owing to the advantages it offered. we mention just two of them: quick access for students to the training materials and professors’ constant support. the records from the moodle platforms allowed us to determine the degree of involvement presented by the students in solving tasks involving the study of course materials and bibliography uploaded at the beginning of each week. all through the semester, students were evaluated during face-to-face meetings in the classroom as well as on the e-learning platform’s virtual environment. the descriptive analysis allowed us to create a profile of the participants, presenting the following characteristics: 1. 82.1% come from urban environments and the rest of 17.9% from rural areas. 2. after at least three years of using the moodle platform in it learning processes, 88.7% state that they enjoy it and that it helps them learn, while 11.3% expressed total disagreement. 3. the grades they obtained during these 3 years of university studies are as follows: 3.8% obtained 10, 4.9% between 9-10; 31.6% got grades in the interval of 8-9; 8.8% got grades from 7-8; 20.9 % obtained grades from 6-7; 28.1% from 5-6; 1.9% got grades below 5. (romania uses a 10-point https://doi.org/10.15837/ijccc.2020.5.3897 8 scale grading system: 1 being the lowest, 10 being the top grade, and 5 the minimum passing grade). 4. 65.1% from the study participants enjoy teamwork, and 34.9% prefer to work alone. 5. 39.6% from the participants enjoy dealing with assessments in good time, while 60.4% work against a time limit or cram for exams. 6. the preferred manner of examination is the practical exam for 34.9%, while 30.2% prefer oral project presentation and 34.9% written examines (multiple choice questions, open questions). 7. concerning the learning style of the participants, 23.6% learn mostly visually; 0.9% mostly auditory; 22.7% predominantly by writing and reading; 51.9% rely on learning through demonstrations, simulations, case studies, practical tasks; the rest of 0.9% are mixed. 5.2 instrument for the profile of the it students the next step of the study was to create an analysis of the reliability-validity type of the technique we propose in order to obtain an instrument that measures the profile of the it students. the results of the analysis are: • the test is sensitive to the measured characteristics: alpha cronbach =0.767; mean=110.77; std.dv.=9.261. the alpha value is significant and suggests that the instrument is adequate to the purpose for which it was built so one can state with precision and accuracy that the test is unidimensional so there is an agreement between the students’ assessments. • the students manifested high levels of agreement in appreciating the items that compose the measurement of their personality and course attendance (average inter-item correlation: 0.764). • the study continued with anova to test if the responses were unilateral (without differences between students and the effect of interaction with the instrumental). the results of the test f=1364.385; p-value=0.000 confirm that there are differences between the answers provided by students according to their personality and transversal competences. 5.3 it students’ personality types the analysis continued by applying the principal component analysis (pca), which allowed us to test whether our instrument contained latent factors or not. more exactly, we wanted to see if there were central items of the instrument and secondary ones, or whether they formed an entity that measured the same dimension. the pca purpose is to obtain a small number of linear combinations (main components) from a set of variables that would retain as much information as possible from the initial variables. the principal component analysis method provides a series of results such as descriptive statistics, correlation matrix, the kaiser-meyer-olkin and bartlett tests, communality, and the total explained variance, the screen plot image, the matrix of components and the matrix of components after the rotation. by applying these methods, we could identify the existence of a smaller number of variables from the mass of studied variables that express most of the total variance. to be more precise, we wanted to verify if the analyzed instrument could identify the 16 latent factors in cattell’s manner. pca with equamax rotation allowed us to verify the validity of constructing the scale utilized in measuring the behavior/ profile of the it student. during the process, we tried to eliminate items that presented less than 0.3 correlations, but there were no such items. the results of the kaisermeyer-olkin test showed how adequate the sampling process was 0.736>=0.50 [30, 69]. this proved that the sample used was sufficient for the study and as a result, the variability of data was caused by the instrument created in bartlett’s test of sphericity assumption that all correlation coefficients are not quite far from zero. the result of bartlett’s test of sphericity, (chi-square=1287.792; p=0.000) meant our variables were related and therefore suitable for structure detection, so the analysis of the factors was useful in the prevailing of data and the subscales of the scale were inter-independent. the https://doi.org/10.15837/ijccc.2020.5.3897 9 patterned relationship among the variables is seen from the correlation matrix. according to kaiser’s criteria (from commonalities the values need to be bigger than 0.4), the latent root is formed of items taken from the study. therefore, there is a limited set of items that determine the validity of the tool, stating that there is a determined set of items which determines the validity of the instrument in a proportion of 79.641% according to which there are items that moderately correlate in the study. the orthogonal factorial model obtained by the equamax method is formed by 9 factors, not 16 as in cartel’s case. the result obtained after the omission of the saturations that are smaller than 0.4 [19] are presented in table 3. to summarize, the basis of the analysis of the main components allowed us to identify nine typologies of the it students’ personality according to cattell’s theory which was also validated in our study. the general profile of the it students based on the analyzed sample can be described in the next pharagraphs. table 3: personality types types description a start projects and conversations easily (0.690); comfortable around people (0.668); interested in people (0.665); talk to many people in a party context (0.664); do not mind being at the center of the party, the life and soul of the party (0.630); animate a party (0.547); have excellent ideas (0.550); are full of ideas (0.544) make people feel at ease (0.523); complete their tasks at once (0.456); share the others’ emotions with empathy(0.401); make jokes all the time(-0.568). b orderly (0.667); are considered extroverted persons (0.525); create a schedule for themselves (0.463); want everything to be accurate and correct (0.340) c difficulty in understanding abstract ideas (0.698); they are not imaginative persons (0.684); solve their tasks at once (-0.400); introverted (0.525) d make jokes all the time (-0.568); impress people around them (0.520); like reading books (0.492); make sure they have time for the others (0.440); empathic (0.439) e like to listen to music (0.456); excellent ideas (0.417); paying close attention to the details (0.479) f without sense of humour, the last to laugh (0.550); don’t have a lot of imagination (0.417); demanding in their work (0.411) g think that people have good intentions (0.600); empathic towards other people’s emotions (0.401); deficient in imagination (0.417). h use complicated words (0.508); understanding things fast (0.483) i always ready and well-prepared (0.432); pay much attention to details (0.472); ready for action (0.462); make people feel comfortable (0.482); do not mind being in the center of attention (-0.414) https://doi.org/10.15837/ijccc.2020.5.3897 10 it students declare themselves to be extroverted (in the positive sense), stating that they are communicative, spontaneous, disinherited, talkative, sincere, humorous, and optimistic; in the negative sense: distant, quiet, reserved, shy, and inhibited. concerning the positive side of agreeableness, the it students are open to cooperation, politeness, empathy, tolerance, flexibility. the main features of the conscientiousness positive dimension are referring to organization, efficiency, trustworthiness, precision, perseverance, punctuality, logical thinking whereas the negative side refers at inconsistency, lack of attention, emotional instability or neuroticism. the positive dimension of agreeability is a dimension characterized by calmness, independence. the intellect or level of culture is characterized in the positive dimension by an intellectual mindset, profoundness, intuition, intelligence, creativity and curiosity, while the negative dimension is related to superficiality, lack of imagination, and the incapacity to understand abstract ideas. according to the result of the study and [38, 46], we can observe that it typology excludes the negative sides of the dimensions of being extroverted, to emotional stability or neuroticism and agreeability. however, they also have the power to admit that part of them is not empathetic, without humor, not sociable; do not understand jokes or abstract ideas. 5.4 about type of personality and job-opportunity the experiment continued with the stage where the students were asked to extract the job announcements that appealed to them from an e-jobs platform. the purpose of this experiment was to verify whether the personality influenced the differentiated choice of jobs. to identify essential attributes that determine the choice of job-opportunity announcements characterizing the individual, we proceeded to determine the values of the knots in the graph, in order to create a classification for them. more precisely, we concentrated on the term of "importance" with the intention of determining the role played by the students’ personality and the level of appreciation of attributes of the job description, to see how many these factors influenced the accumulation of information and knowledge related to the choices generated on the grounds of personality traits characterizing each participant. in order to carry out this stage, we used the classical topological measuring methods, more specifically we calculated the degree of centrality [8, 31], the closeness centrality [29] the betweenness centrality and the eigenvector centrality [8]. the theoretical background for the indicators is presented in detail in [10, 27, 29]. based on these indicators, data was processed to obtain a topological network of importance. the findings indicate that the studied attributes of job description act as ties between personality descriptor and attributes of a job describing which influence the students in their choosing process. finally, according to the centrality measuring factor, we explored the place of each attribute that influenced the choice of the job announcement. as a result, we obtained three clusters: 1. one cluster comprises job announcements that have attributes related to the firm’s culture, to the team organization, team building and necessary skills for the job. 2. the second cluster comprises the job announcements created from the majority of attributes related to technical skills, soft skills. 3. the third cluster comprises job advertisements mostly created from attributes related to technical skills and the second related to the culture and policy of the company; the free time. 6 discussions the personality characteristics defining the it employee, as described in [51, 55] are much improved with the attributes that characterize people who are well-adapted to the social and cultural environment. the justification that we found is that the it specialist is a person that earns a lot, having been raised in a society that is based on computer science [14]. at the same time, in an era relying on communication there is a high percentage of introverted students, that is to say, reserved persons with low levels of sociability, who tend to focus on their inner self rather than on the outside social environment. this is beneficial to the it labor market for the reason that introverted persons https://doi.org/10.15837/ijccc.2020.5.3897 11 require fewer stimulants in order to reach an adequate level of excitement, a fact evidenced by the results of social network analysis (introverted persons choose job advertisements which place more emphasis on technical skills). as a consequence, they could be more suitable for software development tasks that require more complex and advanced analysis of the concepts and projection or development algorithms. furthermore, the results of the study identify a considerable percentage of extroverted students. these persons could manifest levels of under-excitement during programming tasks [14]. this is a result of their stronger need for social interactions, which shows that they would be more suitable in positions that accompany programming with social interaction, a fact demonstrated by the result of social network analysis (extroverted persons choose job advertisements that are more concerned with the organization’s culture, with team building and less with technical skills). extroverted persons described by attitudes of conscientiousness and openness correspond to the new characteristics of education 3.0 as participation, co-creation, becoming, assuming the part of 21st century skills [38, 59]. on the contrary, the introverted ones will have adaptability issues regarding these ideas and principles. 7 conclusion education is and will remain a constant preoccupation for all the parts involved in a society’s development. educational theories have evolved, one might say, together with the evolution of digital technologies. this article examined teachers’ perspectives on recent graduates’ skills and employment readiness to identify the mismatch between their skills in university programs and the skills needed in the job market. our work is in line with the research trend on increasing the quality of higher education, improving the quality of teaching and, therefore, the quality of graduates with a specific focus on the it area and the personality of future graduates. personality research offers great potential for providing educators and researchers with more information on the students’ style of learning, based on their personality profile [46]. furthermore, recruitment methods in the labor market are closely connected to digital technologies. nowadays we are dealing with real recruitment social platforms in which intelligent digital techniques manage to place and analyze large amounts of data in order to assist those who recruit and also those who look for a job. the study proved that many of the participating students are characterized by high levels of personality traits such as extroversion, agreeability, conscientiousness and openness and also experience low levels of neuroticism. the results obtained contribute to the development of knowledge in two appropriate directions. firstly, identified profiles will be of assistance for the universities to improve their curricula and the learning and teaching processes based on the students’ needs. secondly, it is useful in this attempt to provide it companies with information in order to better understand the it students’ profile and to create a realistic description of the job in the recruitment software on the digital market. the importance of knowing about the personality features of the it specialists allow both employers and educators to form teams, to create various types of projects and to build learning environments suitable for every type of employee. they can also avoid the failure of a project caused by inappropriate assignment of the human resource, and this might also help to avoid conflicts and improve the collaboration among employees or students by the correct identification of the personalities working well with each other. our research is also useful in identifying the techniques that are appropriate to explore the students’ profile and proposes data mining techniques and social network analysis to examine it students’ skills and behavior so as to generate the actual it students’ profile. the limitations of our study are due to the fact that our involvement model was determined by analyzing a set of participants from a specific socio-economic, cultural context. the current study offers the potential for several follow-up studies. for example, we intent to compare the profile identified in this study with profiles of the students from other universities (such as mit, eu universities etc.) to analyze whether these characteristics are nowadays common for all students. further research may well expand the study of identifying personality typologies of it students, by creating a prototype of the motivational map for them, based on the types and degree https://doi.org/10.15837/ijccc.2020.5.3897 12 of motivations. the purpose of this prototype is to check whether the it profile is directly influenced by the student’s types of motivation. furthermore we plan to adapt our results by including a new dimension for the study: modeling emotion with markov chains developed from [57] in order to attain a visual analysis of it students’ profile. we also intend to modernize the programming teaching techniques by implementing pedagogical agents named hapa based on the teaching scenario adapted from [42]. this development might help companies accurately describe their vacancies and job opportunities in it, selecting the right person for that position and identifying the techniques of communication and motivation that are adequate to each typology, so that they obtain and maintain a high percentage of employees who remain loyal to their working environment. therefore, in our future research, the goal is to continue this analysis with the determination of the groups of personas’ behaviors by applying the k-means clustering algorithm developed by macqueen [23] and by perfecting it based on the method of the dynamic clouds [22]. another option of extending the research targets is to analyze if and how the determination of personality traits influences the manner of choosing and interpreting job ads from employment platforms. in conclusion, there are many actors interested in the results of this research area. knowing the student profile might contribute to a better understanding from the academic part, and allow them to adjust their curricula, learning/teaching methods, tools and interactions to students’ characteristics. the primary benefit for universities which are aware of the students’ profile is to enhance their competitiveness in the educational market, also measured by their graduates’ absorption on the labor market. the research results propose a solution that might synchronize the university 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(2020). determining it student profile using data mining and social network analysis, international journal of computers communications & control, 15(5), 3897, 2020. https://doi.org/10.15837/ijccc.2020.5.3897 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 1, month: february, year: 2022 article number: 4573, https://doi.org/10.15837/ijccc.2022.1.4573 ccc publications multi-objective model to improve network reliability level under limited budget by considering selection of facilities and total service distance in rescue operations yiying wang, zeshui xu, florin gheorghe filip yiying wang business school sichuan university chengdu, sichuan, 610064, china wanggyiying@163.com zeshui xu* business school sichuan university chengdu, sichuan, 610064, china xuzeshui@263.net *corresponding author florin gheorghe filip romanian academy 125 calea victoriei, bucharest 010071, romania ffilip@acad.ro abstract sudden disasters may damage facilities, transportation networks and other critical infrastructures, delay rescue and bring huge losses. facility selection and reliable transportation network play an important role in emergency rescue. in this paper, the reliability level between two points in a network is defined from the point of view of minimal edge cut and path, respectively, and the equivalence of these two definitions is proven. based on this, a multi-objective optimization model is proposed. the first goal of the model is to minimize the total service distance, and the second goal is to maximize the network reliability level. the original model is transformed into a model with three objectives, and the three objectives are combined into one objective by the method of weighting. the model is applied to a case, and the results are analyzed to verify the effectiveness of the model. keywords: facility selection; reinforce edges; minimal edge cut; path; network reliability level. https://doi.org/10.15837/ijccc.2022.1.4573 2 1 introduction natural disasters include floods and droughts [1], earthquake and tsunami disasters [2],[3], fires [4], [5], hurricane disaster [6] and so on [7]. the occurrence of natural disasters may bring huge casualties and property losses [8]. many scholars have conducted extensive research on rescue actions to scientifically respond to the threats caused by disasters. the proposed solutions regard issues such as the location of emergency facilities, the enhancement of traffic network reliability and so on the emergency facilities play an important role in the rescue operation [9]. in recent years, many location problems of emergency facilities have been proposed. in emergency rescue, time or distance affects rescue effectiveness. shorter rescue time and distance will usually lead to better rescue results. therefore, rescue distance or rescue time is an important aspect to be considered in emergency rescue, and it is an important factor to be considered in multi-objective problems for location decision-making. the classical p-median problem (pmp) assumes that demands get services from the facilities that minimize the total distance [10]. based on pmp, berman et al. [11] put forward a new model, which is expected to find m facility locations to minimize the expected sum of the weighted travel distances between demands and their nearest operating facilities. coutinhorodrigues et al. [? ] introduced a mixed integer linear programming model to determine the evacuation routes and the location of shelters. six objectives including the length of evacuation routes are considered. rabta et al. [13] presented an optimization model for the delivery of light-weight relief items, which aims at minimizing the total traveling distance/time/cost under the condition of limited payload and energy. trivedi et al. [14] described a hybrid multi-objective decision model based on analytic hierarchy process, fuzzy set theory and goal programming approach, which aims at minimizing distance, risk, number of sites, uncovered demand, and maximize suitability while taking into some other considerations. bai [15] proposed a multi-objective expected value model including facility location, vehicle routing and supply allocation decisions. the goals of the model are to minimize the proportion of unsatisfied demands, the response time of emergency reliefs and the total cost. early system reliability analyses have often focused on connectivity and the research on system reliability primarily concerns with the enumeration of cuts or paths [16], [17], [18]. for example, aggarwal et al. [19] firstly studied the reliability evaluation problem without flow in a binary-state network. however, in many practical network systems, such as circuit network, transportation system, communication system, oil pipeline, each link or edge is associated with specific values. for example, in the transportation network, each road has a specific traffic flow. therefore, after the maximum-flow minimum-cut theorem was proposed, scholars have done a lot of research on the reliability of networks with flow [20]. lee [16] defined the criterion for evaluating network reliability as that a specific flow is transported from input node to output node, i.e., the probability that the maximal flow is at least equal to the system demand. xue [21] used discrete function theory to expand binary state network to multistate network, which is also called stochastic-flow network, to study reliability evaluation. one of the popular methods for network reliability evaluation is the three-stages method [20], [22], [23], [24]. later, some scholars continuously improved these methods [20], [25]. for example, lin presented a novel maximal flow searching method along with fast enumeration to improve the efficiency of reliability evaluation [20]. alkaff [26] presented modeling techniques for the exact dynamic reliability analyses. a quick binary-addition tree algorithm was proposed by yeh to evaluate the binary-state network reliability [27]. a binding technique was presented by huang et al. to solve the lower boundary points problem, which is used to compute the network reliability for a stochastic flow network [28]. in order to compute the twoterminal reliability of a dynamic network, a new method was put forward by khanna et al. to enumerate both time-stamped-minimal path sets and time-stamped-minimal cut sets [29]. disasters may cause damage to traffic routes, thus seriously affecting the progress of rescue operations. therefore, some authors have studied the reliability of networks from the perspective of possible failure of edges (links or arcs) [30], [31], [32], [33]. for example, horner and widener [34] simulated the failure of partial road network and test how these damages affect the victims’ access to relief goods. zhang et al. [35] studied the optimal distribution of k terminal vertices to maximize k terminal network reliability in sparse graphs, in which the edges are subject to failure. in these studies which consider edge failure, many authors assume that the network links have homogeneous or heterogeneous failure probabilities and this kind of assumptions implies that the interruption of the link is caused by its own failure [36]. yu [36] thought the interruption of the link is actually caused by the damage that the link sustains beyond its tolerance, and on this basis, defined the reachability guarantee between the two points. based on the concept of damage tolerance of edge in yu’s paper, our paper defines the reliability level between two points in the network and the reliability level of the whole network, uses the optimization model to locate emergency facilities, and maximizes the network reliability level at the same time. in the decision-making of site selection, budget is another important factor affecting the solution. peeta et al. [37] selected the links to invest under the limited budget, so as to maximize the post-disaster connectivity and minimize the travel costs between the origin and destination nodes. amin et al. [38] described an optimization model which has two objectives. the first objective is to minimize the costs which are used to maintain the https://doi.org/10.15837/ijccc.2022.1.4573 3 road condition and the second objective is to minimize the pavement roughness and geophysical risks. vahdani et al. [39] proposed a nonlinear multi-objective model to improve the reliability of the routes and minimize the travel time and cost. hu et al. [40] proposed a two-stage stochastic programming model, aiming at minimizing budgets and the punishment caused by risk. in our paper, we assume that the budget is limited. we use certain budget for facility selection and edge reinforcement to minimize the total service distance and maximize the network reliability level. this paper proposes the following innovative issues: the reliability level between two points and the network reliability level are defined from the point of view of minimal edge cut and path, and the equivalence theorem of the two definitions is proven. the definition of reliability level between two points and network reliability level are used to establish a location decision-making model, which minimizes the total service distance and maximizes the network reliability level under certain budget constraint. in order to make full use of budget, this paper assumes that the increment of the reliability level of edges is continuous. based on the definition of network reliability level, this paper balances the reliability level of selected paths with variance weight, and studies how variance weight affects the goal with a case study. we find that by changing the variance weight, we can get satisfactory network reliability level. the rest of the paper is as follows. section 2 contains a methodology, which consists of basic concepts, the definitions of reliability levels, the equivalence theorem, and the reinforcement of edges. in section 3, we construct a multi-objective model, which aims to minimize the total service distance and maximize the network reliability level. section 4 presents a case study, in which we conduct some analyses to discuss the relationship between some variables. section 5 concludes the paper and gives the future research direction. 2 methodology when the destructive power of natural disasters reaches a certain degree, the road between rescue facilities and demand points may be damaged. therefore, the road construction between rescue facilities and demand points plays an important role in disaster prevention. in order to study the goal of road construction, we form the concepts of the reliability level of edge, reliability level of the minimum edge cut, reliability level of path, reliability level between two points and network reliability level, and discuss how to calculate the network reliability level. 2.1 basic concepts the problem studied in this paper is defined by the connected graph g(n,a), that is undirected and weighted, where the symbol n represents a point set, and a represents an edge set. the value of each edge is nonnegative. the demand set and facility set are different subsets of the point set n. in this subsection, we give two definitions: the reliability level of each edge and the reliability level of a path in the network. definition 1 (the reliability level of an edge). assume that each undirected edge a ∈ a has a damage tolerance ra ∈ [0, 1], which represents the maximum degree of disaster damage it can withstand. if the damage of the disaster exceeds ra, the edge a will be interrupted. if the damage of the disaster does not exceed ra, then the edge a can still pass normally [36]. since ra represents the maximum ability of an edge to withstand disasters, in this paper, we call ra the reliability level of the edge a(a ∈ a). the reliability level of each edge can be evaluated in advance according to the specific conditions of the edge. the larger the value of ra, the stronger the ability of the edge a to resist disasters. theoretically, ra can be equal to 1, indicating that edge a can withstand any degree of damages. however, the value of ra of the actual road a cannot reach the value 1, because the actual road cannot survive in all degrees of disasters. therefore, ra of the actual road is less than 1. definition 2 (the reliability level of a path). as described in definition 1, given the damage tolerance ra,a ∈ a for all edges of path p, the minimum ra of all edges on the path p is called the damage tolerance of the path p [36]. if the damage of the disaster exceeds the damage tolerance of the path p, the path p will be interrupted, otherwise, the path p will not be interrupted. it can be seen that this value represents the maximum ability of the path p to withstand disasters. therefore, we call it the reliability level of the path p. let e′ be the set of all paths between the two points x and y , h ′ e′ (e ′ ∈ e′) be the edge set of the path e′, be′h′ be the edge h′ on the path e′ between the two points x and y , w(be′h′ ) be the reliability level of the edge be′h′ , then min h′∈h ′ e′ w(be′h′ ) is called the reliability level of the path e′. when the destructive power on the path e′ does not exceed min h′∈h ′ e′ w(be′h′ ), the path e′ is connected, otherwise, the path e′ is interrupted. https://doi.org/10.15837/ijccc.2022.1.4573 4 2.2 reliability levels in the network in system reliability analysis, the system is thought to be functioning if there exists a path from the input point to the output point [16]. therefore, the concepts of reliability level of the edge and the path are extended to any two points in the network. below, we first give the definition of the reliability level of every minimal cut, then, the definition of the reliability level of any two points in g. definition 3 (the reliability level of every minimal cut in the connected weighted graph g). let x and y be two points in g, e be the set of minimal edge cut sets between the two points x and y , he(e ∈ e) be the edge set of the minimal edge cut e, aeh be the edge h(h ∈ he) of the minimum edge cut e between the two points x and y , w(aeh) be the reliability level of the edge aeh, then max h∈he w(aeh) is called the reliability level of the minimal edge cut e, which is written as we. when the destructive power in the network does not exceed we, the two points x and y are connected; otherwise, they are not connected. definition 4 (the reliability level between two points in the connected weighted graph g by minimal edge cut). let x and y be two points in g, e be the set of minimal edge cut sets between the two points x and y , he(e ∈ e) be the edge set of the minimal edge cut e, aeh be the edge h(h ∈ he) of the minimum edge cut e between the two points x and y , w(aeh) be the reliability level of the edge aeh, then min e∈e max h∈he w(aeh) is called the reliability level between the two points x and y in g. in order to get min e∈e max h∈he w(aeh), it is necessary to find the maximum reliability level of the cut edge in each minimal edge cut between the two points x and y in g, and then take the minimum value. this value represents the maximum ability of the network between the two points x and y to resist disasters. if the damage degree does not exceed min e∈e max h∈he w(aeh), the two points x and y are connected, otherwise, they are not connected. in the network, assume that the demand point set is i(i ∈ i), the demand point i is assigned to the facility ji(ji ∈ j), the reliability level of the edge a in the network is r(a), the set of minimal edge cut sets between the demand point i and its service facility ji is si,ji , si,ji = {s1i,ji,s 2 i,ji ,s3i,ji, . . . ,s |i,ji| i,ji } (sxi,ji is the x-th minimal edge cut set between the two points i and ji, |i,ji| is the number of minimal edge cut sets between the two points i and ji). for the minimal cut set sxi,ji , there is s x i,ji = {l sxi,ji 1 , l sxi,ji 2 , l sxi,ji 3 , . . . , l sxi,ji∣∣sx i,ji ∣∣} (where ∣∣sxi,ji∣∣ is the number of cut edges in the minimal edge cut sxi,ji ). in s x i,ji , we sort the reliability level of cut edges in a non-decreasing order, and assume that the reliability level of each edge is r(l sxi,ji 1 ),r(l sxi,ji 2 ),r(l sxi,ji 3 ), . . . ,r(l sxi,ji∣∣sx i,ji ∣∣) that is r(l sxi,ji 1 ) ≤ r(l sxi,ji 2 ) ≤ r(l sxi,ji 3 ) ≤ . . . ≤ r(l sxi,ji∣∣sx i,ji ∣∣). among them, the maximum reliability level is r(l sxi,ji∣∣sx i,ji ∣∣). for all minimal edge cut sets between the two points i and ji, the reliability level of each minimal edge cut set is r(l s1i,ji∣∣s1 i,ji ∣∣), r(ls3i,ji∣∣s2 i,ji ∣∣), r(ls4i,ji∣∣s3 i,ji ∣∣), . . . r(ls|i,ji|i,ji∣∣s|i,ji | i,ji ∣∣). then, let the reliability level between the two points i and ji be rjii , and by definition 4, there is: ri,ji = min { r(l s1i,ji∣∣s1 i,ji ∣∣),r(ls2i,ji∣∣s2 i,ji ∣∣),r(ls3i,ji∣∣s3 i,ji ∣∣), . . .r(ls|i,ji |i,ji∣∣s|i,ji | i,ji ∣∣) } . let the minimum reliability level between all pairs of demand points and corresponding service facilities be r, defined as r = min i∈i ri,ji . we call r the network reliability level of g. when the destructive power does not exceed r, all the demand points in g are connected with the corresponding service facilities. when the destructive power exceeds r, at least one demand point cannot be served. 2.3 equivalence theorem now, we consider the reliability level between two points in the network from the perspective of path. firstly, we get two theorems as follows: theorem 1. there is at least one path existing between the two points x and y in the connected graph g, if and only if every minimal edge cut between the two points x and y keeps at least one uncut edge in the graph. https://doi.org/10.15837/ijccc.2022.1.4573 5 proof. first, we prove the necessity by disproof. if every edge in a minimal edge cut set between the two points x and y is cut, then the two points of g are not connected (there is no path between the two points x and y ), which contradicts that there is at least one path between the two points x and y . therefore, when there is at least one path between the two points x and y , each minimal edge cut between the two points x and y leaves at least one edge uncut. then, we prove the sufficiency: if every minimal edge cut between the two points x and y leaves at least one edge uncut in the graph, that is to say, there is no minimal edge cut between the two points x and y in g that separates the point x from the point y , we assert that the two points x and y are connected. assuming that there is no connection between the two points x and y , then all the cut edges at this time form an edge cut set, and at least one minimal edge cut can be obtained by removing the redundant cut edges from the edge cut set, that is, all the edges in this obtained minimal edge cut set are disconnected at this time, which contradicts “every minimal edge cut between the two points x and y still has at least one uncut edge in the graph”. therefore, when every minimal edge cut between the two points x and y retains at least one uncut edge in the graph, there is at least one path existing between the two points x and y in g. theorem 2. as shown in figure 1, let x and y be two points in the connected graph g, e be the set of minimal edge cut sets between the two points x and y , he(e ∈ e) be the edge set of the minimal edge cut set e, aeh be the edge h(h ∈ he) of the minimum edge cut e between the two points x and y , e′ be the set of all paths between the two points x and y , h ′ e ′ (e′ ∈ e′) be the edge set of the path e′, be′h′ be the edge h′ on the path e′ between the two points x and y , w(aeh) be the weight of the edge aeh, w(be′h′ ) be the weight of the edge b(e′h′), then min e∈e max h∈he w(aeh) = max e ′∈e′ min h ′∈h ′ e ′ w(be′h′ ) . proof. in the connected graph g, the edges are cut out in sequence according to the non-decreasing weights of the edges, until when an edge with the weight w0 is cut out, the two points x and y are just divided into two connected components, and stop. we assert that w0 = min e∈e max h∈he w(aeh), w0 = max e ′∈e′ min h ′∈h ′ e ′ w(be′h′ ) . according to the above operation, when the edge with the weight w0 is cut, the cut edge forms an edge cut set between the two points x and y . this edge cut set is not necessarily a minimal edge cut set. if this edge cut set is not a minimal edge cut set, then we remove the redundant edges from it and form a minimal edge cut set. this minimal edge cut set must contain edges with the weight w0. because once the edge of w0 is removed from the initial edge cut set, all other cut edges cannot form a minimal edge cut set. obviously, w0 is the maximum weight of edges in this minimal edge cut set. because we cut the edges in the order of non-decreasing weight of edges, and other minimal edge cut sets have not yet been formed, the maximum weight of edges in all other minimal edge cut sets is not less than w0. therefore, w0 = min e∈e max h∈he w(aeh) can be obtained. on the other hand, the corresponding paths between the two points x and y are broken in the order of minimum edge weight from small to large, and no minimal edge cut set between the two points x and y is formed before the edge with the weight w0 is cut off. according to theorem 1, there is at least one path uncut between the two points x and y at this time. after the edge with the weight w0 is cut off, all x −y paths are cut off. at this time, we assert that the edge with minimum weight on every path is cut off. if the edge with the minimum weight on a path is not cut, the edge with the larger weight on the same path is also not cut. then, there exists at least one path uncut between the two points x and y , which is contrary to “all paths are cut off”. obviously, w0 is the minimum edge weight on the last broken paths, and it is not less than the minimum edge weight on other x −y paths. therefore w0 = max e ′∈e′ min h ′∈h ′ e ′ w(be′h′ ). according to above discussion, min e∈e max h∈he w(aeh) = max e ′∈e′ min h ′∈h ′ e ′ w(be′h′ ) holds. from the proof process of theorem 2, we can see that, the value w0 plays an important role in the graph g. actually, w0 has different meanings in different scenarios. for example, in the graph g, if the value of each edge represents the allowed load of the vehicle to pass through, then w0 represents the maximum load of the vehicle allowed to pass through between the two points x and y . if the value of each edge represents the water surface width of the river channel in the water network, then w0 represents the maximum width at the water surface of object allowed to pass through between the two points x and y . if the value of each edge represents the depth of the waterway, then w0 represents the maximum height of the submarine allowed to pass through under water between the two points x and y . as we can see from these examples, in the graph g, w0 represents the maximum ability of the network between the two points x and y to allow the object to pass through as a whole (indivisible). in our paper, w0 is defined as the reliability level, which refers to the maximum ability of the network between the two points x and y to withstand disasters. here, withstand could be understood as “allowing to pass through”. example 1. as shown in figure 2, the number next to edge represents the weight. https://doi.org/10.15837/ijccc.2022.1.4573 6 figure 1: a connected weighted graph with the two points x and y figure 2: a weighted graph the minimal edge cut sets between a and c are ab,ad, bc,dc, ab,bd,dc, ad,db,bc the maximum weights of these minimal edge cuts are 4, 5, 3, 5 respectively, and the minimum value of these maximum weights is 3. paths between a and c are abc, adc, abdc, adbc. the minimum weights of the edges on these paths are 1, 3, 1 and 2, respectively, and the maximum value of these minimum weights is 3. according to theorem 2, the reliability level between two points in a network can also be defined by paths. next, we give the definitions of the reliability level between two points and the network reliability level from the perspective of path. definition 5 (the reliability level between two points by path). let e′ be the set of all paths between the two points x and y , h′e′ (e ′ ∈ eprime) be the edge set of the path e′, be′h′ be the edge h′ on the path e′ between the two points x and y , w(bb′h′ ) be the reliability level of the edge be′h′ , then max e′∈e′ min h′∈h ′ e′ w(be′h′ ) is called the reliability level between the two points x and y in g. in order to get max e′∈e′ min h′∈h ′ e′ w(be′h′ ), we need to find the minimum reliability level on each path between the two points x and y , and then take the maximum value of these minimum reliability levels. this value represents the maximum ability of the network between these two points to resist disasters. if the damage degree does not exceed max e′∈e′ min h′∈h ′ e′ w(be′h′ ), then the two points x and y are connected, otherwise, they are not connected. in the network, assume that the demand point set is i(e ∈ i), the demand point i is assigned to the facility ji(ji ∈ j), the reliability level of the edge a in the network is r(a), the path set between the demand point i and its service facility ji is pi,ji , (pi,ji = { p 1i,ji,p 2 i,ji ,p 3i,ji, . . . ,p |i,ji| i,ji } (p ui,ji is the path u between the point i and the point ji, |i,ji| is the number of paths between the point i and the point ji), p ui,ji ={ l p ui,ji 1 , l p ui,ji 2 , l p ui,ji 3 , . . . , l p ui,ji∣∣p u i,ji ∣∣ } ( ∣∣p ui,ji∣∣ is the number of edges on the path p ui,ji ). on p ui,ji , we sort the reliability level of cut edges in a non-decreasing order, and assume that the reliability level of each edge is r(l p ui,ji 1 ), r(l p ui,ji 2 ), r(l p ui,ji 3 ), . . ., r(l p ui,ji∣∣p u i,ji ∣∣), that is r(l p ui,ji 1 ) ≤ r(l p ui,ji 2 ) ≤ r(l p ui,ji 3 ) ≤ . . . ≤ r(l p ui,ji∣∣p u i,ji ∣∣). among them, the minimum reliability level is r(l p ui,ji 1 ). for all paths between the demand point i and the facility ji, the minimum reliability level of each path is r(l p 1i,ji 1 ), r(l p 2i,ji 1 ), r(l p 3i,ji 1 ), . . ., r(l p |i,ji | i,ji 1 ). let r′i,ji be the reliability level between the demand point i and the facility ji: r′i,ji = max { r ( l p 1i,ji 1 ) ,r ( l p 2i,ji 1 ) ,r ( l p 3i,ji 1 ) , . . . ,r ( l p |i,ji | i,ji 1 )} . according to theorem 2, ri,ji = r′i,ji . now, we uniformly use ri,ji to represent the reliability level between https://doi.org/10.15837/ijccc.2022.1.4573 7 the demand point i and the facility ji. so, ri,ji = max { r ( l p 1i,ji 1 ) ,r ( l p 2i,ji 1 ) ,r ( l p 3i,ji 1 ) , . . . ,r ( l p |i,ji | i,ji 1 )} . the network reliability level is still recorded as r, then r = min i∈i ri,ji . 2.4 reinforcement of edges in case of disaster, edges may be interrupted. we can take reinforcement measures to improve the reliability level between two points. let the original reliability level of the edge a be r0a and the reinforcement coefficient be βa(0 ≤ βa ≤ 1,a ∈ a). when βa = 0, the edge a is not reinforced. when βa = 1, the edge a is completely reinforced and the increment of the reliability level is ∆r(a) = 1 − r0a. when 0 < βa < 1, the increment of the reliability level is ∆r(a) = βa(1 −r0a) (0 < ∆r(a) < 1 −r0a), a ∈ a. let the cost of elevating unit reliability level of the edge a be ca(a ∈ a). the reinforcement costs of different edges are different. the maximum reinforcement cost of each edge a is (1−r0a)ca, and the actual reinforcement cost is βa(1 −r0a)ca, a ∈ a . 3 the model • the main assumptions: 1. the set of alternative facilities and demand points is known; 2. there is a fixed setup cost for each facility; 3. the reliability level of each edge can be continuously improved and is up to 1; 4. the total budget is fixed, which is used for selecting facilities and reinforcing edges; 5. each demand point can only be assigned to one facility, and one facility can serve multiple demand points; 6. we believe that disasters have the same impact on all edges of the same network. • set definitions: i : the set of demand points j : the set of alternative facilities a : the set of edges • parameters: b : the budget to locate facilities and reinforce edges cj : the fixed setup cost of the locating facility j, j ∈ j ca : reinforcement cost of improving unit reliability level of the edge a, a ∈ a r0a : the original reliability level of the edge a, a ∈ a pi,j : path set between the demand i and the facility j dui,j : the distance of the path u between the demand point i and the alternative facility j, ∀i ∈ i, ∀j ∈ j, ∀u ∈ pi,j m : number of selected facilities • decision variables: r(i) : reliability level between the demand point i and the corresponding service facility f : variance of reliability level between all demand points and the corresponding service facilities ∆ra : the increment of the reliability level after the edge a is reinforced r : network reliability level ra : the reliability level of the edge a, 0 ≤ ra ≤ 1, a ∈ a. • binary variables: yj = { 1 iffacility j is selected 0 otherwise ,j ∈ j xij = { 1 if facility j is allocated to provide service for demand point i 0 otherwise ,i ∈ i,j ∈ j https://doi.org/10.15837/ijccc.2022.1.4573 8 yui,j =   1 if path u between demand point i and facility j is selected to be reinforced 0 otherwise ,i ∈ i,j ∈ j,u ∈ pi,j model 1: • objectives: min ∑ i∈i ∑ j∈j ∑ u∈pi,j (1) max r (2) • constraints: ∑ j∈j yj = m (3) xij ≤ yj,∀i ∈ i,∀j ∈ j (4) xij ≥ yui,j,∀i ∈ i,∀j ∈ j,∀u ∈ pi,j (5)∑ j∈j xij = 1,∀i ∈ i (6) ∑ i∈i ∑ j∈j xij = |i| (7) ∑ j∈j cjyj + ∑ a∈a ∆raca ≤ b, 0 ≤ ∆ra ≤ 1 −r0a (8) ∑ u∈pi,j yui,j = 1,∀i ∈ i,∀j ∈ j (9) yj,xij,y u i,j ∈ 0, 1,∀i ∈ i,∀j ∈ j,∀u ∈ pi,j (10) in the above model 1, the formula 1 requires the sum of distances between all demand points and the corresponding service facilities to be minimized, and the formula 2 requires the network reliability level to be maximized. the formula 3 is used to constrain the total number of selected facilities. constraint 4 means that only after the facility is selected can it serve the demand point. constraint 5 means that only when the facility actually provides service for the demand point can a certain path between them be selected. constraint 6 means that each demand point has only one facility to provide service for it. equation 7 is a constraint on the total number of demand points, where |i| represents the number of demand points. constraint 8 means that the cost of locating facilities and the cost of strengthening edges shall not exceed the budget. constraint 9 means that only one path is selected between a demand point and the facility serving it. constraint 10 indicates that yj, xij, yui,j are binary variables. we will solve the above model after making appropriate changes. we maximize the total reliability level after reinforcement and take it as an objective of the changed model. in order to balance the reliability level between all demand points and the corresponding service facilities, so as to improve the network reliability level and achieve the second goal of model 1, we calculate the variance of these reliability levels and set the minimization of variance as the third goal. therefore, the following model is obtained: model 2: min ∑ i∈i ∑ j∈j ∑ u∈pi,j yui,jd u i,j (11) max ∑ i∈i r(i) (12) min f (13) let g1 = ∑ i∈i ∑ j∈j ∑ u∈pi,j y u i,jd u i,j, g2 = ∑ i∈i r(i), g3 = f and g = ω1g1 −ω2g2 + ω3g3. then, the above three objectives are combined into a single objective, and set the single objective as: min g (14) among which, ω1, ω2 and ω3 are the weights of g1, g2 and g3. as the two goals of total service distance and total reliability level after reinforcement are different in scale, we deal with these three goals as follows. for a given budget, we take g1 and g2 as single objective and get their optimal values respectively. for example, when the budget is 1150, we get the optimal values of g1 and g2 as separate objective, which are 128 and 2.99, respectively. we take 1/128 and 1/2.99 as ω1 and ω2 in g, respectively. when ω1 and ω2 are determined, ω3 takes different values from small to large. in this paper, ω3 is taken as 0, 1, 2,. . . , 14. https://doi.org/10.15837/ijccc.2022.1.4573 9 4 case study 4.1 data we use the network in figure 3 to test the model. this network has 11 points and 16 edges, and it shows the locations of demand points and alternative facilities. table 1 lists the location cost of each alternative facility. table 2 lists the original reliability level and unit reinforcement cost of each edge. the model is programmed on the platform of python 3.8, runs on a 64-bit i5-7300u laptop, and is solved by gurobi 9.1.2. 4.2 analysis in this case, the number of the selected facilities m is 2, and the number of demand points |i| is 3. the values of b are 800, 850, 875, 900, 925, 950, 1000, 1050, 1100, 1150, and 1175, respectively. 4.2.1 relationship between network reliability level, total reliability level and variance weight table 3 shows the solution results of network reliability level. it can be seen from table 3 that, in general, the network reliability level r is positively correlated with the variance weight ω3, that is, when the value of ω3 increases, r tends to rise (the situation is special when b = 900, which will be analyzed later). for example, when b = 1150, ω3 is 1 and 6 respectively, and r is 0.8 and 0.887. therefore, we can draw the conclusion that the reliability level between demand points and facilities serving them can be improved by strengthening certain edges. at the same time, r can also be improved. this phenomenon can also be visually noticed from figure 4. figure 3: a weighted connected graph table 1: the location cost of each alternative facility location q c d f v u w k cost 550 380 700 650 400 600 530 450 table 2: original reliability level and unit reinforcement cost of each edge edge r0a ca edge r0a c_a ou 0.5 150 lf 0.6 200 uv 0.6 200 ft 0.3 180 tv 0.5 210 dw 0.4 130 uw 0.5 120 wt 0.7 150 oq 0.6 170 qw 0.5 120 qc 0.4 100 df 0.4 150 cd 0.6 160 kc 0.7 100 dl 0.5 180 kl 0.5 220 the influence of ω3 on r can be seen more clearly from table 4 and table 5. table 4 lists the reliability level of each edge on each selected path before and after reinforcement when b = 850. table 5 lists the reliability level of each edge on each selected path before and after reinforcement when b = 1150. it can be seen from table 4 that when b = 850, with the increase of ω3, the variance f generally shows a downward trend. for example, when ω3 = 0, f = 0.00129; when ω3 = 14, f = 0.00014. therefore, the process of increasing ω3 is the process of decreasing the fluctuation of reliability level after strengthening each selected path. for example, when ω3 increases from 0 to 14, the reliability level of ou increases from 0.6 to 0.6112, that of uv increases from 0.6 to 0.6112, that of dc increases from 0.6 to 0.6128, that of ld increases from 0.6 to 0.6128, and that of tv decreases from 0.6762 to 0.6368. when ω3 = 0, the maximum difference of reliability level of the three paths after reinforcement is 0.6762−0.6 = 0.0762; when ω3 = 14, the maximum difference of the reinforced reliability level of the three paths is 0.6368 − 0.6112 = 0.0256. obviously, ω3 plays an important role in balancing the reliability level of the three paths. a similar analysis can be made for b = 1150. we take b = 1150 as an example to analyze how ω3 affects r by affecting the allocation of reinforcement budget. it can be seen from table 6 that when b = 1150, the selected facilities are v and k, the cost of the selected facilities is 850, and the cost of edge reinforcement is 300, the selected three paths are ou-uv, tv and lk, and the edges to be reinforced are ou, uv, tv and lk respectively. as shown in table ??, the unit reinforcement costs of these four edges are 150, 200, 210 and 220 respectively. since the unit reinforcement cost of ou is the lowest, budget is firstly allocated to increase the reliability level of ou to 0.6. at this time, the https://doi.org/10.15837/ijccc.2022.1.4573 10 table 3: solution results of network reliability level under different budgets ω3 800 850 875 900 925 950 1000 1050 1100 1150 1175 0 0.500 0.600 0.600 0.600 0.600 0.500 0.600 0.600 0.657 0.800 0.871 1 0.500 0.600 0.600 0.600 0.600 0.600 0.600 0.622 0.695 0.800 0.871 2 0.500 0.600 0.600 0.573 0.600 0.600 0.632 0.701 0.770 0.839 0.873 3 0.500 0.600 0.615 0.576 0.600 0.600 0.660 0.729 0.795 0.863 0.894 4 0.503 0.600 0.622 0.577 0.600 0.607 0.675 0.742 0.808 0.875 0.906 5 0.510 0.600 0.627 0.579 0.600 0.617 0.683 0.750 0.816 0.881 0.914 6 0.515 0.602 0.630 0.579 0.600 0.623 0.689 0.755 0.821 0.887 0.919 7 0.518 0.604 0.632 0.579 0.600 0.627 0.693 0.759 0.825 0.889 0.922 8 0.520 0.606 0.635 0.580 0.600 0.631 0.697 0.762 0.827 0.892 0.925 9 0.522 0.607 0.636 0.579 0.600 0.633 0.699 0.764 0.830 0.895 0.927 10 0.523 0.608 0.636 0.579 0.602 0.636 0.701 0.766 0.831 0.896 0.928 11 0.525 0.609 0.637 0.580 0.604 0.637 0.702 0.768 0.832 0.897 0.930 12 0.526 0.610 0.638 0.580 0.606 0.639 0.704 0.769 0.833 0.898 0.930 13 0.526 0.611 0.639 0.580 0.607 0.640 0.704 0.770 0.834 0.899 0.931 14 0.528 0.611 0.639 0.580 0.608 0.641 0.706 0.770 0.835 0.900 0.932 reliability level of path ou-uv is 0.6, the increment of reliability level of this path is 0.1, and the reinforcement budget consumed is 15. thereafter, for every 0.1 increase in the reliability level, the reinforcement fund of path ou-uv is 0.1 × (150 + 200) = 35, while the reinforcement fund of path tv is only 0.1 × 210 = 21, and that of path lk is only 0.1 × 220 = 22. it can be seen that, when , in order to minimize the overall goal, after the reliability level of ou-uv reaches 0.6, the reinforcement funds will be used to reinforce path tv first, then path lk, and finally path ou-uv. the funds needed to increase the reliability level of tv from 0.5 to 1 are 0.5×210=105, and the funds needed to increase the reliability level of lk from 0.5 to 1 are 0.5 × 220 = 110. when the reliability level of path ou-uv reaches 0.6, and the reliability level of tv and lk reaches 1, the total capital for edge reinforcement is 15 + 105 + 110 = 230. as the available edge reinforcement fund is 300, 70 can be continuously used to reinforce path ou-uv, and the increased reliability level of path ou-uv is 70/350 = 0.2. therefore, the reliability level of ou and uv is finally raised to 0.8, as shown in table 5. thereafter, with the increase of the value of ω3, the balancing effect on the reliability level of each selected path increase, part of the funds used to reinforce tv and lk are gradually used to reinforce the edges on the path ou-uv. therefore, the reliability level of tv and lk gradually decreases, while the reliability level of the path ou-uv continuously increases, and the reliability level of the network continuously improves. however, since the funds used to improve the unit reliability level of the path ou-uv are greater than those used to improve the unit reliability level of tv or lk, when part of the reinforcement funds of tv or lk are used to reinforce the path ou-uv, the total reliability level after reinforcement will decrease continuously, as shown in table 5 and table 8. when ω3 = 14, the reliability level of path tv decreases to 0.9195, the reliability level of path lk decreases to 0.9181, and the reliability level of path ou-uv increases to 0.8998. the variance of the reliability level of the three paths is only 8.09 × 10−5, which is far less than the variance value of 0.0089 when the variance weight ω3 is 0. if the ω3 continues to increase, more funds will be transferred to the reinforced path ou-uv, and the reliability level of the three paths will be more balanced, thus obtaining higher r. figure 4: network reliability level under different budgets and different variance weights figure 5: total reliability level of each budget with different variance weights https://doi.org/10.15837/ijccc.2022.1.4573 11 table 4: b=850, reliability level of each edge on each selected path before and after reinforcement sp ou-uv sp tv sp ld-dc ω3 f tr r be af be af be af be af be af ou ou uv uv tv tv dc dc ld ld 0 0.0013 1.8762 0.6000 0.50 0.6000 0.60 0.6000 0.50 0.6762 0.60 0.6000 0.50 0.6000 1 0.0013 1.8762 0.6000 0.50 0.6000 0.60 0.6000 0.50 0.6762 0.60 0.6000 0.50 0.6000 2 0.0013 1.8762 0.6000 0.50 0.6000 0.60 0.6000 0.50 0.6762 0.60 0.6000 0.50 0.6000 3 0.0013 1.8762 0.6000 0.50 0.6000 0.60 0.6000 0.50 0.6762 0.60 0.6000 0.50 0.6000 4 0.0013 1.8762 0.6000 0.50 0.6000 0.60 0.6000 0.50 0.6762 0.60 0.6000 0.50 0.6000 5 0.0011 1.8749 0.6000 0.50 0.6000 0.60 0.6000 0.50 0.6727 0.60 0.6022 0.50 0.6022 6 0.0008 1.8714 0.6018 0.50 0.6018 0.60 0.6018 0.50 0.6637 0.60 0.6059 0.50 0.6059 7 0.0006 1.8689 0.6041 0.50 0.6041 0.60 0.6041 0.50 0.6574 0.60 0.6074 0.50 0.6074 8 0.0004 1.8668 0.6059 0.50 0.6059 0.60 0.6059 0.50 0.6521 0.60 0.6088 0.50 0.6088 9 0.0004 1.8656 0.6068 0.50 0.6068 0.60 0.6068 0.50 0.6490 0.60 0.6098 0.50 0.6098 10 0.0003 1.8641 0.608 0.50 0.608 0.60 0.608 0.50 0.6451 0.60 0.611 0.50 0.611 11 0.0002 1.8632 0.609 0.50 0.609 0.60 0.609 0.50 0.6429 0.60 0.6113 0.50 0.6113 12 0.0002 1.8622 0.6098 0.50 0.6098 0.60 0.6098 0.50 0.6403 0.60 0.6121 0.50 0.6121 13 0.0002 1.8616 0.6107 0.50 0.6107 0.60 0.6107 0.50 0.6387 0.60 0.6121 0.50 0.6121 14 0.0001 1.8608 0.6112 0.50 0.6112 0.60 0.6112 0.50 0.6368 0.60 0.6128 0.50 0.6128 selected path: sp; total reliability level of network: tr; before: be; after: af. table 5: b=1150, reliability level of each edge on each selected path before and after reinforcement sp ou-uv sp tv sp lk ω3 f tr r be af be af be af be af ou ou uv uv tv tv lk lk 0 0.0089 2.8000 0.800 0.50 0.8000 0.60 0.8000 0.50 1.0000 0.50 1.0000 1 0.0089 2.8000 0.800 0.50 0.8000 0.60 0.8000 0.50 1.0000 0.50 1.0000 2 0.0038 2.7759 0.839 0.50 0.8387 0.60 0.8387 0.50 0.9732 0.50 0.9638 3 0.0016 2.7606 0.863 0.50 0.8629 0.60 0.8629 0.50 0.9518 0.50 0.9458 4 0.0009 2.7532 0.875 0.50 0.8746 0.60 0.8746 0.50 0.9417 0.50 0.9368 5 0.0006 2.7491 0.881 0.50 0.8812 0.60 0.8812 0.50 0.9359 0.50 0.9319 6 0.0004 2.7457 0.887 0.50 0.8866 0.60 0.8866 0.50 0.9308 0.50 0.9283 7 0.0003 2.7440 0.889 0.50 0.8892 0.60 0.8892 0.50 0.9291 0.50 0.9257 8 0.0002 2.7423 0.892 0.50 0.8920 0.60 0.8920 0.50 0.9262 0.50 0.9240 9 0.0002 2.7406 0.895 0.50 0.8947 0.60 0.8947 0.50 0.9240 0.50 0.9219 10 0.0002 2.7400 0.896 0.50 0.8956 0.60 0.8956 0.50 0.9233 0.50 0.9211 11 0.0001 2.7390 0.897 0.50 0.8972 0.60 0.8972 0.50 0.9220 0.50 0.9200 12 0.0001 2.7384 0.898 0.50 0.8982 0.60 0.8982 0.50 0.9212 0.50 0.9190 13 8.784 × 10−5 2.7377 0.899 0.50 0.8993 0.60 0.8993 0.50 0.9197 0.50 0.9186 14 8.09 × 10−5 2.7374 0.900 0.50 0.8998 0.60 0.8998 0.50 0.9195 0.50 0.9181 selected path: sp; total reliability level of network: tr; before: be; after: af. https://doi.org/10.15837/ijccc.2022.1.4573 12 4.2.2 relationship between facility location and variance weight in addition, we can also notice the fact that the change of ω3 may change the combination of the selected facilities. this happens twice in this case. as shown in table 6, when b = 900, ω3 are 0 and 1, the selected facilities are c and v , while when ω3 are 2-14, the selected facilities are v and k. when b = 925, ω3 is 0, the combination of selected facilities is c and v , while ω3 is 1-14, the combination of selected facilities is v and k. the influence of the change of the values of ω3 on the selected facilities can be understood as follows. as analyzed in subsection 4.2.1, when the selected facilities are unchanged, with the increase of ω3, part of the reinforcement funds of the paths with larger reliability level are gradually transferred to the paths with smaller reliability level. this transfer of funds leads to a decrease in the total reliability level of the network (as shown in table 8 and figure 5), which in turn leads to an increase in the total objective (as shown in table 9). therefore, there may be such a situation: under a certain budget, when ω3 exceeds a certain value, although the change of the selected facilities leads to an increase in the cost of the selected facilities, the reinforcement funds of the edge decrease, and the total reliability level of the network decreases. however, the total service distance decreases, resulting in a decrease in the total objective. in this case, when b = 900 and ω3 = 2, if the selected facilities remain unchanged, then they are still c and v . thus, the total objective value is 0.1698. if the selected facilities become v and k, then the total objective is 0.1662. obviously, the change of the selected facilities makes the overall objective smaller. therefore, under this variance weight, the change of the selected facilities is realized. in the process of changing the selected facilities, the reinforcement funds of the edges decreased by 70, only 50, which was not enough to raise r to the level before changing the selected facility. therefore, with the change of the selected facilities, r decreases. however, we find that when b = 925, the change of the selected facilities does not lead to the decrease of r, as shown in table 7. we analyze this situation. when b = 925 and ω3 = 0, the selected facilities are c and v , the cost of the selected facilities is 780, and the cost of edge reinforcement is 145. the selected paths are ou-uv, ld-dc and tv, and the reliability level of each edge after reinforcement is ou (0.6), uv (0.6), ld (0.6206), dc (0.6206) and tv (1.0) respectively. the reinforcement amounts of each edge are: ou (0.1), uv (0), ld (0.1206), dc (0.0206), tv (0.5), the reliability level of path ou-uv is 0.6, and the reliability level of the path ld-dc is 0.6206, the reliability level of tv is 1.0, and is 0.6. when ω3 = 1, the selected facilities are changed to v and k, the cost of the selected facilities is 850, and the cost of edge reinforcement is 75. the selected paths are ou-uv, lk and tv, and the reliability level of each edge after reinforcement is ou (0.6), uv (0.6), lk (0.6240) and tv (0.6558). the reinforcement amounts of each edge are: ou (0.1000), uv (0), lk (0.1240), tv (0.1558), the reliability level of the path ou-uv is 0.6, the reliability level of the path lk is 0.6240, the reliability level of the path tv is 0.6558, and r is 0.6. therefore, in the process of changing the selected facilities, due to the influence of ω3, the reinforcement funds transferred from the path with higher reliability level to the path with lower reliability level can make up for the impact of the reduction of reinforcement funds on r. however, when b = 900, less edge reinforcement funds (50) are not enough to improve r to the level before the change of selected facilities. 4.2.3 relationship between network reliability level, facility selection and budget from table 3 and figure 4, we can see that, on the whole, when ω3 is kept, the more the budget, the larger the r and the higher the curve. this can be understood as follows. when the selected facilities are unchanged, the cost of selected facilities is unchanged, and more budget means higher r. for example, when the budget is 800 and 850 respectively, the selected facilities are both v and c, the cost of selected facility is 780, and the cost of edge reinforcement is 20 and 70 respectively. as ω3 is 14, r is 0.528 and 0.611 respectively. when the budget is 950 and 1150 respectively, the selected facilities are both v and k, the cost of the selected facility is 850, and the cost of edge reinforcement is 100 and 300 respectively. as ω3 is 12, r is 0.639 and 0.898 respectively. the size of the budget can not only affect r, but also influences the choice of facilities in some cases. more budgets provide more possibilities for selecting facilities. if more costs of site selection mean smaller total service distance, then this site selection is possible. when b = 850, the selected facilities are v and c with a total service distance of 158, while when b = 950, the selected facilities are v and c with a total service distance of 139. the reason why the selected facilities change is precisely because the increase of budget provides more choices for selecting facilities. in new facility selection, there is a combination of facilities with less total service distance, which is more conducive to minimizing the objective. from table 3 and the above analyses, it can be seen that under the same ω3, when the selected facilities are unchanged, the more the budget, the higher the r. figure 6 is the network reliability level curve when the budget is from 800 to 1150 and ω3 is 14. when b = 900, there is a decline in network reliability level, which is caused by the change of selected facilities. in other parts of the curve, r increases with the increase of budget. moreover, in the latter part of the curve, with the increase of budget, r basically rises linearly. therefore, in order to achieve higher r, we can consider investing more budget. in this paper, the formula (14) is actually to realize the bi-objective model 1. that is, our ultimate goal https://doi.org/10.15837/ijccc.2022.1.4573 13 table 6: solution results of the selected path (sp), total service distance (tsd), selected facilities (sf), cost of selected facilities (csf), reinforced edges (re) and cost of edge reinforcement (cer) b ω3 sp tsd sf csf re cer 800 [0-3] ou-uv, ld-dc, tv 158 c, v 780 ou, dl 20 [4-6], [8-14] ou-uv, ld-dc, tv 158 c, v 780 ou, tv, dl 20 [7] ou-uv, ld-dc, tv 158 c, v 780 ou, tv, dl, kl 20 850 [0-4] ou-uv, ld-dc, tv 158 c, v 780 ou, tv, dl 70 [5-14] ou-uv, ld-dc, tv 158 c, v 780 ou, uv, tv, cd, dl 70 875 [0-1] ou-uv, ld-dc, tv 158 c, v 780 ou, tv, dl 95 [2] ou-uv, ld-dc, tv 158 c, v 780 ou, tv, cd, dl 95 [3-14] ou-uv, ld-dc, tv 158 c, v 780 ou, uv, tv, cd, dl 95 900 [0] ou-uv, ld-dc, tv 158 c, v 780 ou, tv, dl 120 [1] ou-uv, ld-dc, tv 158 c, v 780 ou, tv, cd, dl 120 [2-14] ou-uv, lk, tv 139 v, k 850 ou, tv, kl 50 925 [0] ou-uv, ld-dc, tv 158 c, v 780 ou, tv, cd, dl 145 [1-3], [5], [7-8] ou-uv, lk, tv 139 v, k 850 ou, tv, lk 75 [4], [6], [9-14] ou-uv, lk, tv 139 v, k 850 ou, uv, tv, lk 75 950 [0] ou-uv, lk, tv 139 v, k 850 ou, tv 100 [1-3] ou-uv, lk, tv 139 v, k 850 ou, tv, lk 100 [4-14] ou-uv, lk, tv 139 v, k 850 ou, uv, tv, lk 100 1000 [0-1] ou-uv, lk, tv 139 v, k 850 ou, tv, lk 150 [2-14] ou-uv, lk, tv 139 v, k 850 ou, uv, tv, lk 150 1050 [0] ou-uv, lk, tv 139 v, k 850 ou, tv, lk 200 [1-14] ou-uv, lk, tv 139 v, k 850 ou, uv, tv, lk 200 1100 [0-14] ou-uv, lk, tv 139 v, k 850 ou, uv, tv, lk 250 1150 [0-14] ou-uv, lk, tv 139 v, k 850 ou, uv, tv, lk 300 1175 [0-14] ou-uv, lk, tv 139 v, k 850 ou, uv, tv, lk 325 table 7: b=925, the reliability level of each edge on each selected path (sp) before and after reinforcement sp ou-uv sp tv sp ld-dc sp lk ω3 f tr r be af be af be af be af be af be af ou ou uv uv tv tv dc dc ld ld lk lk 0 0.0338 2.2206 0.600 0.5 0.6000 0.6 0.6000 0.5 1.000 0.6 0.6206 0.5 0.6206 0.5 0.5000 1 0.0005 1.8798 0.600 0.5 0.6000 0.6 0.6000 0.5 0.6558 0.6 0.6000 0.5 0.500 0.5 0.6240 2 0.0004 1.8794 0.600 0.5 0.6000 0.6 0.6000 0.5 0.6475 0.6 0.6000 0.5 0.500 0.5 0.6320 3 0.0004 1.8793 0.600 0.5 0.6000 0.6 0.6000 0.5 0.6445 0.6 0.6000 0.5 0.500 0.5 0.6348 4 0.0004 1.8792 0.600 0.5 0.6000 0.6 0.6000 0.5 0.6431 0.6 0.6000 0.5 0.500 0.5 0.6361 5 0.0004 1.8792 0.600 0.5 0.6000 0.6 0.6000 0.5 0.6421 0.6 0.6000 0.5 0.500 0.5 0.6371 6 0.0004 1.8792 0.600 0.5 0.6000 0.6 0.6000 0.5 0.6425 0.6 0.6000 0.5 0.500 0.5 0.6367 7 0.0004 1.8792 0.600 0.5 0.6000 0.6 0.6000 0.5 0.6418 0.6 0.6000 0.5 0.500 0.5 0.6374 8 0.0003 1.8791 0.600 0.5 0.6000 0.6 0.6000 0.5 0.6413 0.6 0.6000 0.5 0.500 0.5 0.6379 9 0.0003 1.8789 0.600 0.5 0.6004 0.6 0.6004 0.5 0.6406 0.6 0.6000 0.5 0.500 0.5 0.6379 10 0.0003 1.8776 0.602 0.5 0.6025 0.6 0.6025 0.5 0.6385 0.6 0.6000 0.5 0.500 0.5 0.6365 11 0.0002 1.8764 0.604 0.5 0.6043 0.6 0.6043 0.5 0.6373 0.6 0.6000 0.5 0.500 0.5 0.6348 12 0.0002 1.8751 0.606 0.5 0.6064 0.6 0.6064 0.5 0.6354 0.6 0.6000 0.5 0.500 0.5 0.6333 13 0.0002 1.8747 0.607 0.5 0.6071 0.6 0.6071 0.5 0.6350 0.6 0.6000 0.5 0.500 0.5 0.6326 14 0.0001 1.8740 0.608 0.5 0.6081 0.6 0.6081 0.5 0.6336 0.6 0.6000 0.5 0.500 0.5 0.6323 selected path: sp; total reliability level of network: tr; before: be; after: af. https://doi.org/10.15837/ijccc.2022.1.4573 14 table 8: solution results of total reliability level of network under different budgets ω3 b 800 850 875 900 925 950 1000 1050 1100 1150 0 1.6278 1.8762 1.9952 2.1143 2.2206 2.0048 2.2364 2.4636 2.6571 2.8000 1 1.6278 1.8762 1.9952 2.1136 1.8798 1.9960 2.2285 2.4474 2.6340 2.8000 2 1.6278 1.8762 1.9874 1.7632 1.8794 1.9957 2.2082 2.3972 2.5863 2.7759 3 1.6260 1.8762 1.9708 1.7630 1.8793 1.9955 2.1901 2.3799 2.5705 2.7606 4 1.6238 1.8762 1.9622 1.7630 1.8792 1.9909 2.1811 2.3717 2.5622 2.7532 5 1.6213 1.8749 1.9570 1.7630 1.8792 1.9849 2.1757 2.3666 2.5577 2.7491 6 1.6196 1.8714 1.9536 1.7630 1.8792 1.9809 2.1719 2.3629 2.5545 2.7457 7 1.6182 1.8689 1.9512 1.7630 1.8792 1.9783 2.1694 2.3605 2.5520 2.7440 8 1.6175 1.8668 1.9492 1.7629 1.8791 1.9760 2.1672 2.3587 2.5503 2.7423 9 1.6168 1.8656 1.9480 1.7629 1.8789 1.9745 2.1661 2.3575 2.5489 2.7406 10 1.6163 1.8641 1.9468 1.7629 1.8776 1.9731 2.1646 2.3561 2.5479 2.7400 11 1.6157 1.8632 1.9461 1.7628 1.8764 1.9722 2.1637 2.3553 2.5473 2.7390 12 1.6153 1.8622 1.9453 1.7628 1.8751 1.9710 2.1630 2.3545 2.5468 2.7384 13 1.6152 1.8616 1.9443 1.7626 1.8747 1.9705 2.1623 2.3538 2.5462 2.7377 14 1.6147 1.8608 1.9440 1.7626 1.8740 1.9697 2.1616 2.3535 2.5456 2.7374 table 9: results of solving the overall objective under different budgets ω3 b 800 850 875 900 925 950 1000 1050 1100 1150 0 0.0001 -0.0001 0.1366 0.1366 0.1369 0.1351 0.1015 0.1238 0.1483 0.1495 1 0.0019 0.0012 0.1450 0.1585 0.1541 0.1412 0.1150 0.1488 0.1734 0.1584 2 0.0037 0.0025 0.1532 0.1662 0.1546 0.1434 0.1237 0.1583 0.1818 0.1651 3 0.0053 0.0038 0.1574 0.1663 0.1549 0.1455 0.1274 0.1614 0.1846 0.1676 4 0.0064 0.0051 0.1595 0.1664 0.1553 0.1475 0.1292 0.1630 0.1860 0.1689 5 0.0072 0.0063 0.1608 0.1665 0.1557 0.1487 0.1303 0.1639 0.1868 0.1696 6 0.0078 0.0073 0.1617 0.1666 0.1560 0.1496 0.1310 0.1646 0.1874 0.1701 7 0.0081 0.0080 0.1623 0.1666 0.1564 0.1502 0.1315 0.1650 0.1878 0.1705 8 0.0084 0.0085 0.1627 0.1667 0.1567 0.1506 0.1319 0.1653 0.1881 0.1707 9 0.0086 0.0089 0.1631 0.1668 0.1571 0.1510 0.1322 0.1656 0.1883 0.1710 10 0.0088 0.0092 0.1634 0.1669 0.1574 0.1513 0.1325 0.1658 0.1885 0.1711 11 0.0089 0.0094 0.1636 0.1670 0.1576 0.1515 0.1327 0.1660 0.1887 0.1713 12 0.0091 0.0097 0.1638 0.1670 0.1578 0.1517 0.1328 0.1661 0.1888 0.1714 13 0.0092 0.0099 0.1640 0.1671 0.1580 0.1518 0.1330 0.1663 0.1889 0.1715 14 0.0092 0.0100 0.1641 0.1672 0.1582 0.1520 0.1331 0.1664 0.1890 0.1715 https://doi.org/10.15837/ijccc.2022.1.4573 15 table 10: the result when b=1150 ω3 g g1 g2 g3 r sf re csf cer 0 0.1494 139 2.8000 0.0089 0.800 ’v’, ’k’ ou, uv, tv, kl 850 300 1 0.1584 139 2.8000 0.0089 0.800 ’v’, ’k’ ou, uv, tv, kl 850 300 2 0.1651 139 2.7759 0.0038 0.839 ’v’, ’k’ ou, uv, tv, kl 850 300 3 0.1676 139 2.7606 0.0016 0.863 ’v’, ’k’ ou, uv, tv, kl 850 300 4 0.1689 139 2.7532 0.0009 0.875 ’v’, ’k’ ou, uv, tv, kl 850 300 5 0.1696 139 2.7491 0.0006 0.881 ’v’, ’k’ ou, uv, tv, kl 850 300 6 0.1701 139 2.7457 0.0004 0.887 ’v’, ’k’ ou, uv, tv, kl 850 300 7 0.1705 139 2.7440 0.0003 0.889 ’v’, ’k’ ou, uv, tv, kl 850 300 8 0.1707 139 2.7423 0.0002 0.892 ’v’, ’k’ ou, uv, tv, kl 850 300 9 0.1710 139 2.7406 0.0002 0.895 ’v’, ’k’ ou, uv, tv, kl 850 300 10 0.1711 139 2.7400 0.0002 0.896 ’v’, ’k’ ou, uv, tv, kl 850 300 11 0.1713 139 2.7390 0.0001 0.897 ’v’, ’k’ ou, uv, tv, kl 850 300 12 0.1714 139 2.7384 0.0001 0.898 ’v’, ’k’ ou, uv, tv, kl 850 300 13 0.1715 139 2.7377 8.784 × 10−5 0.899 ’v’, ’k’ ou, uv, tv, kl 850 300 14 0.1715 139 2.7374 8.09 × 10−5 0.900 ’v’, ’k’ ou, uv, tv, kl 850 300 selected facilities: sf; reinforced edges: re; cost of selected facilities: csf; cost of edge reinforcement: cer. is to minimize the total service distance and maximize r in model 1, instead of minimizing g in formula (14). it can be seen from table 9 that under the same budget, with the increase of ω3, the total objective of model 2 is rising, but the network reliability level is rising when selected facilities are unchanged. therefore, in order to obtain the higher r, we can select the solution with the larger ω3. in this case, we can choose the solution when ω3 = 14. for example, when b = 1150, the selected facilities are v and k, and the selected paths are ou-uv, tv and lk, with a total service distance of 139 and a network reliability level of 0.900, as shown in table 10. figure 7 is the distribution diagram between facilities and demand points when b = 1150. the thick black lines in the figure represent the reinforced edges, and the arrows point to the facilities serving the demand points. figure 6: the network reliability level under different budgets when variance weight is 14 figure 7: b=1150, distribution diagram between facilities and demand points 5 conclusions in this paper, two definitions of network reliability level are given, and the equivalence of these two definitions is proven. by using the definition of network reliability level from the perspective of path, a model is established with the goal of minimizing the total service distance and maximizing the network reliability level under the condition of constant budget. the goal of maximizing network reliability level is decomposed into two goals, namely, maximizing the total reliability level of the network and minimizing the variance of the reliability level of each selected path. then, a three-objective model is obtained from the original model. a single objective model is obtained by weighting three objectives. finally, this model is applied to a case study. the solution results of the model, including the choice of facilities, the choice of paths, the total reliability level of the network, the network reliability level, the edge of reinforcement and the process of edge reinforcement, have strong regularity, https://doi.org/10.15837/ijccc.2022.1.4573 16 which not only shows the rationality of defining the network reliability level from the perspective of paths, but also fully verifies the feasibility and stability of the model. in formula (14), the variance weight has an expected impact on the fluctuation of the reliability levels of the selected paths. the successful operation of the model provides a good choice for the emergency management department to choose appropriate emergency facilities and reinforce roads. in addition, we think the results can be generalized and embedded in a real-time decision support system meant for crisis prevention and loss mitigation [41], [42]. this model can also be established by using the definition of network reliability level from the perspective of minimal edge cut, which is the envisaged continuation of the research. future research will include presenting more demand points and location facilities for more complex networks to solve the model. conflict of interest no potential conflict of interest was reported by the authors. references [1] chen, q. h., chen, h., wang, j. x., et al. impacts of climate change and land-use change on hydrological extremes in the jinsha river basin. water, 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[42] filip, f. g., zamfirescu, c. b., ciurea, c. computer-supported collaborative decision-making. springer, 2017. http://link.springer.com/book/10.1007/978-3-319-47221-8. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: wang y.; xu z.; filip f.g. (2022). multi-objective model to improve network reliability level under limited budget by considering selection of facilities and total service distance in rescue operations, international journal of computers communications & control, 17(1), 4573, 2022. https://doi.org/10.15837/ijccc.2022.1.4573 introduction methodology basic concepts reliability levels in the network equivalence theorem reinforcement of edges the model case study data analysis relationship between network reliability level, total reliability level and variance weight relationship between facility location and variance weight relationship between network reliability level, facility selection and budget conclusions int j comput commun, issn 1841-9836 9(2):201-208, april, 2014. robust pid stabilization of linear neutral time-delay systems h. farokhi moghaddam, n. vasegh hassan farokhi moghadam* faculty of electrical and computer engineering shahid rajaee teacher training university lavizan, tehran, iran. *corresponding author: hassan.farokhi89@gmail.com nastaran vasegh faculty of electrical and computer engineering shahid rajaee teacher training university lavizan, tehran, iran. e-mail: n.vasegh@srttu.edu abstract: this paper deals with the problem of robust stabilization of neutral timedelay systems using proportional-integral-derivative (pid) controller. a graphical approach which will obtain the set of robust stabilizing pid controllers is presented. the main point in this approach is the frequency response of the neutral system, which can effectively reduce the mathematical complexities of system modeling. it is shown that our the proposed method is effective and useful for many of real control systems deal with time-delays and parametric uncertainties. it is illustrated by an example at the end. keywords: robust stabilization, pid controller, time-delay, neutral systems. 1 introduction proportional-integral-derivative (pid) controllers have become the best known controllers since their entrance to industrial applications and are used frequently due to their simplicity and good performance [1]. despite their wide application, determining of all stabilizing regions of pid controllers has been studied just only in the last decade [2]. in [3, 4], a mathematical generalization of the hermite–biehler theorem to find all stabilizing pid controllers for systems with time–delay has been used. in [5], the entire region of pid controller has been obtained for linear time–delay systems. in recent years, the robust stabilization of neutral time–delay systems has received considerable attention and has been has been the focus of much research (see [6, 7] and references within). in this paper, a graphical pid controller design approach for control systems with neutral time–delay is proposed which guarantees the required stabilizing margins of the closed loop system for the uncertain system. basic knowledge of robust control theory seems sufficient in this proposed approach and the method is easy to understand. therefore, simplicity is the main characteristic of the approach. the results of the simulations show the effectiveness of proposed method. the rest of the paper is organized as follows. section 2 states the problem and preliminary. in section 3 the nominal and robust stability criterion are discussed. all different stabilizing regions which will ensure nominal and robust stability are determined in section 4. simulation results which will show the correctness of the design approach are shown for an illustrative example in section 5. finally, results of this paper are summarized in section 6. copyright © 2006-2014 by ccc publications 202 h. farokhi moghaddam, n. vasegh 2 problem statement the most general form of pid controller is the second order system in the s-domain defined by k(s) = kp + ki s + kds. (1) in this paper, this controller is implemented for a linear neutral delayed system with the following general transfer function form: g(s) = r(s) p(s) + q(s)e−ts (2) where t is time–delay p(s), q(s) and r(s) are real polynomials. according to [8], the system is of neutral type if degrees of p(s) and q(s) are the same. pid robustness of this type of system is the main consideration in this paper. although there are different forms of model uncertainty [9], but an additive uncertainty modeling structure which is shown in figure 1, is chosen in this paper as this uncertainty description is closely related to the transfer function representation of system. figure 1: feedback system with additive uncertainty [9] here k(s) represents the pid controller defined by transfer function in (1), the input signal is r(s) and the output signal is y (s), g△(s) is the uncertain system, g(s) is the nominal system without uncertainty, ωa(s) means the additive weight and is a rational transfer function in this study and ∆a(s) is a stable transfer function so that ∀s |∆a(s)| ≤ 1.this kind of uncertainty can be represented by following equation in frequency domain approach. g△(jω) = g(jω) + ωa(jω)∆a(jω), |∆a(jω)| ≤ 1 ∀ω. (3) 3 stabilization of linear uncertain neutral delayed systems in this section, we study the mathematical formulations for the nominal and robust stability problem in details in following sub–sections. 3.1 nominal stability of linear neutral delayed systems in the nominal case, based on standard unity feedback like figure 1 (without uncertainty), the characteristic equation in frequency domain (s = jω ), can be written as ∆(jω) = 1 + k(jω)g(jω). (4) robust pid stabilization of linear neutral time-delay systems 203 pid controller and transfer function form at (1-2) can be represented as k(jω) = kp + j(kdω − ki ω ), (5) g(jω) = n((jω)) d(jω) = r(jω) p(jω) + q(jω)(cos(tω) + j sin(tω)) . (6) let m(jω) = n(jω)d(−jω), so g(jω) = re(m(jω)) + jimm(jω) |d(jω)|2 = rm(jω) + jim(jω) |d(jω)|2 . (7) therefore (4) can be written as ∆(jω) = 1 + (kp + j(kdω − ki ω ))( rm(jω) + jim(jω) |d(jω)|2 ). (8) the numerator becomes ωkprm(jω) + kiim(jω) − kdω2im(jω) + ω|d(jω)|2 + j(kpωim(jω) − kirm(jω) + kdω2rm(jω)) (9) now all stabilizing regions can be determined like what is shown in figure 2 (right). note that we complete the calculations here just for a constant value of kd in the (kp,ki) plane, for brevity. the (kp,kd) plane for a constant value of ki is obtained similarly, but for the (ki,kd) plane at a constant value of kp, nominal and robust stability cannot be obtained directly by (15) and (21) in the next section. for more accuracy, based on [10], for constant value of ki and kd stability boundaries are defined in the (kp,ki) plane and (kp,kd) plane, respectively and then the lines that define the boundary of stability are extracted. the illustrative example in section 5 shows the effectiveness of the approach. 3.2 robust stability of linear neutral delayed systems in order to analyze the stability in presence of uncertainty nominal system should be stable. for getting robust stability in this condition, following theorem is stated from [9]. theorem 1. small gain theorem: if l(s) is a system with a stable loop transfer function, then the closed-loop system is stable if ||l(jω)||∞ < 1, ∀ω, (10) where l(s) = k(s)g(s). as a result from this theorem, if ||l(jω)||∞ < 1 the closed loop system is stable. using this theorem and uncertainty structure in figure 1, it is usually said that if system is robustly stable, the following constraint is met for additive uncertainty:∣∣∣∣∣∣ ωa(jω)k(jω) 1 + k(jω)g(jω) ∣∣∣∣∣∣ ∞ ≤ γ, ∀ω. (11) for robust case, we can rewrite (6) as: ∆r(jω) = 1 + k(jω)g(jω) − 1 λ (ωa(jω)k(jω)). (12) the rational function can be written as: ωa(jω) = rω(ω) + jiω(ω). (13) then by continuing calculation in (9), the numerator of (12) can be simplified as ∆r(jω) = re(∆r(jω)) + jim(∆r(jω)). (14) 204 h. farokhi moghaddam, n. vasegh 4 all stabilizing pid controller regions the following steps are proposed to find the boundaries of all stabilizing pid controller. 1. decompose the frequency form of the system without delay and uncertainty into real and imaginary parts, and substitute them into (5)-(9) to obtain the parameters. 2. analyze the neutral system in presence of time-delay and additive uncertainty, and do the previous step again. (eqs. (5)-(14) should be considered) 3. determine the pid nominal and robust stability boundaries of the system using their related equations in this section, respectively. 4. finally, plot all the different stabilizing regions. here based on the step 3, we’ll complete equations obtained in the previous section. for the nominal case, by putting (10) equal to zero, we have kpωrm(jω) + kiim(jω) − kdω2im(jω) + ω|d(jω)|2 = 0, kpωim(jω) − kirm(jω) + kdω2rm(jω) = 0. (15) which results in kp(ω)) = − |d(jω)|2rm(jω) |m(jω)|2 , (16) and ki(ω)) = kdω 2 − ω|d(jω)|2im(jω) |m(jω)|2 . (17) since rm(jω) = |m(jω)| cos(θ(ω)) im(jω) = |m(jω)| sin(θ(ω)) (18) where θ(ω) = ∠g(jω), (16) and (17) can be simplified as kp(ω)) = − |d(jω)|2 cos(θ(ω)) |m(jω)| , (19) and ki(ω)) = kdω 2 − ω|d(jω)|2 sin(θ(ω)) |m(jω)| . (20) robust consideration yields( ωrm(jω) − ω|d(ω)|2 λ rω(ω) ) kp + ( im(jω) − |d(jω)|2 λ iω(ω) ) ki =( im(jω) − |d(ω)|2 λ iω(ω) ) ω2kd − ω|d(jω)|2,( ωim(jω) − ω |d(ω)|2 λ iω(ω) ) kp + ( − rm(jω) + | d(jω)|2 λ rω(ω) ) ki =(d(jω)|2 λ rω(ω) − rm(jω) ) ω2kd. (21) which implies kp(ω)) = − |d(jω)|2 ( rm(jω) − d(jω)|2 λ rω(ω) ) q(jω) , (22) robust pid stabilization of linear neutral time-delay systems 205 and ki(ω)) = ω 2kd − ω|d(jω)|2 ( im(jω) − d(jω)|2 λ iω(ω) ) q(jω) , (23) where q(jω) = |m(jω)|2 + |ωa(jω)|2 − 2 d(jω)|2 λ (rm(jω)rω(ω) + im(jω)iω(ω)). (24) which can be simplified using (18) if necessary. using (21)–(24), the nominal stability boundary and robust stability region are obtained in the (kp,ki) plane. the approach is explained in the following illustrative example. 5 illustrative example in this example we consider the neutral time-delay system with following transfer function considered in [8]. g(s) = 1 (s + 1)4(s + 1 + se−s) the range of the unknown time–delay is considered as t ∈ [0.5,1.5]. so the uncertain model of the system is g∆(s) = 1(s+1)4(s+1+se−t s) . for this system, we can write (3) as,∣∣∣g∆(s) − g(s) ωa(s) ∣∣∣ ≤ 1, ∀ω. (25) therefore, it is required to find |ωa(jω)| ≥ |g∆(jω) − g(jω)| (26) where |g∆(jω) − g(jω)| = ∣∣∣ 1 jω + 1)4 ∣∣∣∣∣∣ 1 jω + 1 + jωe−jωt − 1 jω + 1 + jωe−jωt ∣∣∣. (27) for determining the ωa(s), at first we plot the additive uncertainty which is the right part of inequality and is shown in figure 2 and then based on approximate estimation, transfer function form is determined. matlab/sisotool is used to facilitate this choice. the matlab/cftool can also be used by fitting a good rational function. here it is obtained as ωa(s) = 1.6s2 (s + 1)4(s + 2) . (28) this is specified in figure 2 (left) which clearly covers the entire uncertainty region. then the all pid stabilizing regions for this system with t = 0.5 are shown in figure 2 (right) which is obtained as a result of analysis done in previous sections. figure 4 (right side) shows the (ki,kd) plane for kp = 0.2. the intersection of all regions inside the nominal stability boundary of the (kp,ki) plane is the robust stability region. to check the correctness of stabilizing regions in figures 6 and 7, two points from each plane are chosen: one is selected from the robust stability region and another one is selected from nominal region (outside this region). then the transfer functions of pid controllers are extracted and the constraint criterion in (11) is checked by bode plot. for example in the (kp,ki) plane in figure 5 (left), the robust chosen point is (1.02,0.301) and another one is (2.47,0.298). their related pid controllers are: k1(s) = 1.02 + 0.301 s + 0.3s, k2(s) = 2.47 + 0.298 s + 0.3s 206 h. farokhi moghaddam, n. vasegh 10 −2 10 −1 10 0 10 1 10 2 −250 −200 −150 −100 −50 0 m a g n i t u d e ( d b ) frequency (rad/sec) ω a (s) figure 2: left; uncertainty region and the additive uncertainty weight. right; all nominal stabilizing pid regions in the case t = 0.5. now it can be seen that for robust k1(s), we have ∣∣∣∣∣∣ ωa(jω)k1(jω)1+k1(jω)g(jω)∣∣∣∣∣∣∞ = 0.2476 but for k2, it results in ∣∣∣∣∣∣ ωa(jω)k2(jω)1+k2(jω)g(jω)∣∣∣∣∣∣∞ = 1.4 > 1. it is clear that our expectation has been satisfied. in figure 5 (left side), their magnitude is shown for different frequencies which is verified these calculations. repeating this procedure in matlab shows that any controller selected from inside the robust stability region is capable of robust stabilization of the system. 10 −2 10 −1 10 0 10 1 10 2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 frequency (rad/sec) m ag ni tu de ( db ) from robust region from nominal region figure 3: magnitude of two different nominal and robust points. step response of the closed loop system for the entire unknown time-delay range is also plotted in the left side of figure 4 to show the stability and good performance of robust system. the nominal and the robust stability region in the (kp,kd) plane for ki = 0.5 and in the (kp,ki) plane for kd = 0.3 are also shown in figure 5 (right and left), respectively. robust pid stabilization of linear neutral time-delay systems 207 0 5 10 15 20 25 30 35 40 45 50 0 0.2 0.4 0.6 0.8 1 1.2 1.4 time (sec) a m p l i t u d e step response 0 0.2 0.4 0.6 0.8 1 −4 −3 −2 −1 0 1 2 3 4 5 k i k d the nominal region the stability boundary the robust region figure 4: left; closed loop step response for entire uncertainty range. right; the stabilizing region in the (ki,kd) plane for kp = 0.2. −1 0 1 2 3 4 0 0.2 0.4 0.6 0.8 1 k p k i the robust region the nominal region the stability boundary −1 0 1 2 3 4 5 −1 −0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 k p k d the nominal region the stability boundary the robust region figure 5: the nominal and robust stability region: left; (kp,ki) plane for kd = 0.3. right; (kp,kd) plane for ki = 0.5. 6 conclusion in this paper, a graphical design approach was introduced for finding all pid stabilizing regions of uncertain neutral time-delay system. this proposed design method was based on the frequency response of the system and it can reduce the complexities involved in system modeling. an example for illustrating this approach was studied. the results were satisfactory and the nominal and robust stability regions were determined. bibliography [1] o.t. altinoz, a.e. yilmaz, g.-w. weber (2012), application of chaos embedded pso for pid parameter tuning, int j comput commun, issn 1841-9836, 7(2):204-217. 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[9] s. skogestad, i. postlethwaite (2001), multivariable feedback control, john wiley & sons. [10] g.j. silva, a. datta, s.p. battacharrya (2005), pid controllers for time-delay systems, birkhauser, boston, 2005. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 2, month: april, year: 2023 article number: 5286, https://doi.org/10.15837/ijccc.2023.2.5286 ccc publications computer-supported smart green-blue infrastructure management m. visan, f. mone maria visan 1 romanian academy, department of engineering, mechanics, computer school of advanced studies of the romanian academy (scosaar) 125, calea victoriei, sector 1, 010071, romania maria.visan@ingr.ro firicel mone* romanian academy, department of engineering, mechanics, computer school of advanced studies of the romanian academy (scosaar) 125, calea victoriei, sector 1, 010071, romania mone@acad.ro abstract answering climate change challenges, the paper proposes an intelligent decision support system (dss) for the management of green-blue infrastructure (gbi). addressing the gaps identified in other studies, the designed dss incorporates four key elements: 1/interdisciplinary collaboration among all stakeholders 2/inclusion of practical operation and maintenance activities, 3/main components of distributed dss, with practical examples of use, 4/consideration of conditions specific to the location. the multi-layered dss architecture can be implemented as a unified platform that provides a comprehensive, customizable, and flexible framework based on ai tools, big data and analytics, edge computing, cloud, and mobile, iiot, and biometric system tools. the use of cobots and digital clones alongside humans results in the implementation of hybrid human-machine units. dss for gbi increases decision-making capacity and can serve as a foundation for the implementation of similar systems by governments and local communities to build sustainable and resilient communities. keywords: real-time multi-participant dss, multi-agent cooperative scheme, big data and analytics, geo ai, iiot and cobots, green blue infrastructure. 1 introduction as a result of rapid global urbanization (in 2050 68% of the world’s population will live in urban areas, compared to 53.9% in 2015) [37] and a 33% increase in world population by 2050 (3.2 billion people), the negative impact of urbanization on the environment is amplified [8]. without a holistic, personalized approach and the implementation of concerted simultaneous global measures, this negative impact correlated with the spatial interdependence of neighboring territories could not be controlled and stabilized. https://doi.org/10.15837/ijccc.2023.2.5286 2 one of the widely applicable measures to improve the resilience of communities now and in the future is the implementation of green infrastructure (gi). evolved to the level of strategy for capitalizing on natural advantages, gi combined with water-related infrastructure becomes green-blue infrastructure (gbi). among the multiple definitions that gbi has enjoyed over time [24], the one issued by the european union ensures a complete understanding of the term, by defining what gbi is, namely strategic planning, followed by the specification of spatial applicability at the national level, regional or local both in the countryside, but especially in the urban [13]. located at the intersection of several disciplines, ecology and environmental protection, regional and urban planning, and engineering, gbi involves a complex of multifunctional ecosystems. understanding the complex benefits of gbi allows for selecting the right option for sustainable territorial development throughout the business process life cycle. research over time in the field of gbi has identified several representative gaps, which a dss should be able to address. for example, gbi and dds has been identified in [5] as a solution to rapid urbanization. the proposed dss architecture solutions for gbi may face institutional and governance challenges such as unclear responsibilities, limited resources, and conflicting policies. palla et al [36] argue that the involvement of all stakeholders such as residents and community groups in the maintenance of gbis can improve the sustainability and resilience of gbis. however, the authors note that stakeholder engagement is not fully integrated into the dss for gbi. salvia et al. [39], mention the lack of gbi implementation and maintenance, in which dss can play an important role by providing information, especially for current maintenance and operation activities, which not only monitor change but also optimize and extend the lifetime of gbi. by integrating artificial intelligence (ai) and machine learning (ml) techniques for gbi decision support, this approach could help fill these gaps in comprehensive assessments of the benefits and costs of gbi interventions, as well as the role of institutional factors and governance in decision-making. to provide accurate and timely information, dss can incorporate real-time information collected from various sources, such as sensors, remote sensing, social networks, and citizen science, for the efficiency of gbi updating and maintenance by facilitating interdisciplinary collaboration. by managing information on gbi performance and impact in real-time, dss can provide policymakers and stakeholders with the necessary framework for tailored approaches to meet specific community needs. data sources are important because their lack can lead to major challenges in using ai and ml for gbi decisionmaking. without sufficient data, ai and ml models cannot predict the potential benefits and costs of gbi interventions. for example, the study by li et al. [31] identifies the major problem in developing model-based ai models for predicting gbi effects on air quality in the absence of data on the spatial distribution of vegetation cover in urban areas. another important aspect related to data is its quality and especially biases. they arose as a result of historical discrimination or unequal distributions of resources. those used in training ai and ml models can raise potential problems because using ai and ml for decisions can lead to inaccurate predictions or perpetuate existing inequities. for example, kang et al.[28] identify that the data used to train models based on satellite imagery tend to overestimate vegetation cover in wealthier neighborhoods and underestimate it in poorer neighborhoods, which may lead to an inequitable distribution of benefits resulting from gbi interventions. to these can be added according to the study carried out by sun et. al [46, 47], the lack of transparency and limited accountability, which can lead to mistrust in the use of ai and ml models or a lack of understanding by stakeholders to assess the potential benefits and costs of different interventions. the study carried out by fan [15], identifies the lack of inclusion of the context and requirements specific to each location. this information can help stakeholders maximize and extend the life of gbi by tailoring gbi implementation to specific locations, taking into account the specific context, and integrating existing infrastructure, while facilitating interdisciplinary collaboration between stakeholders, including institutional and governance factors in making decisions. overall, while dss for gbi has shown promise in achieving multiple benefits, more research is needed to assess the effectiveness of different approaches and to understand the planning, social, cultural, economic, and financial dimensions of the decision-making process for the gbi. the proposed unified dss platform includes these gaps 1/inter-institutional and multidisciplinary collaboration with the participation of all stakeholders, 2/optimization of work flows by including alongside monitoring practical operation and maintenance activities that ensure continuous updating through https://doi.org/10.15837/ijccc.2023.2.5286 3 (re)configuration of gbi, 3 /identification of the basic layers for the implementation of a dss, respectively the data sources, the manner of their processing at the level of technologies that ensure infrastructure and environmental monitoring, asset management, environmental monitoring, urban planning through geo ai modules, geo ai modules, adaptive ai, technologies , real-time data collection, analysis and decision-making, digital twin as a virtual reality in which simulations are carried out for the understanding or production of decisions and 4/ understanding the local context, and its specificity both for concrete proposals related to gbi implementation measures, but also for implementing dss for gbi. the methodology proposed for the development of the research had in mind the definition of the problem: the development of a dss proposal for the gbi, in a comprehensive manner, which would address the main gaps identified by the lack of research in the field. the identification of these (relevant) gaps was made following the analysis of specialized literature, based on which 4 relevant gaps were selected that the dss proposal deals with in the following. the methodology proposed for the implementation of dss for gbi included two of the practical activities – operation and maintenance, with the greatest impact on gbi management and in covering the identified gaps. for each component selected to be part of the dss, concrete examples were included with references that support the proposal highlighting the benefits resulting from the implementation of data components, technologies, ai-ml models for the management and decision-making of operation and maintenance of gbi, digital twin and applications for distributed dss. the article is structured in 7 sections as follows: section 1 presents an overview of the severity of urbanization and the resulting solution in response to climate change, namely the implementation of a dss for gbi management, emphasizing its importance and highlighting the challenges faced by urban areas. the proposed methodology for the development of the research includes the selection of 4 relevant gaps with the highlighting of the limitations/challenges and detailing the way to overcome the limitations through the proposed dss; section 2 identifies relevant approaches in gbi implementation, highlighting some examples of implementations in different countries and the benefits obtained through the ecosystem services generated. the gaps are selected and commented on, and solutions are proposed by including the missing components at the dss level; section 3 presents in detail the analysis of the critical need for collaboration among all stakeholders for the successful implementation of gbi, addressing the interdisciplinary approach and cooperation among all stakeholders through dss; section 4 details two of the most important business processes related to gbi management, operation and maintenance, as the core business processes by which they continuously update and maintain/(re)configure the gbi infrastructure. concrete practical activities and modern tools proposed to make dss for gbi appear are used as concrete examples of business flow for gbi management; section 5 presents the conceptual architecture of dss for gbi and details the functional layers. security aspects to prevent cyber attacks and ethical considerations arising from the use of ai and ml modules are also highlighted; section 6 details the criteria for measuring the success of dss implementation for gbi and the implementation plan that can be followed by any public administration and community; section 7 includes the conclusions on the implementation of a dss as a decision support and the activities related to the intelligent management of gbi as an urban public good through the implementation of the dss. the section includes a summary of the main findings and recommendations for public administrations in the implementation and intelligent management of gbi, with the integration of existing infrastructure and by using modern tools proposed at the level of a dss, and proposals for future research. 2 relevant approaches in gbi implementation each country that adopted the gbi as a key strategy for sustainable development, took into account the local specifics and dimensions of territorial problems. the implementation of the gbi project could start from scratch, for large-scale, long-term projects as greenfield, or as brownfield for smaller-scale projects, based on existing infrastructure and quickly implemented in short terms. from the analysis of gbi implementations at the global level, it was observed that each approach was financially supported either by the central public administration in various formulas (policies, https://doi.org/10.15837/ijccc.2023.2.5286 4 national funding programs or annually allocated subsidies), or by inviting investors interested in public-private partnerships with public administration. in many metropolitan cities around the globe, gbi climate change adaptation measures have emerged. for example, by implementing gbi, rainwater no longer overloads the sewer network but is collected, stored, conserved and used to irrigate green spaces and prevent flooding. street trees, rain gardens, vegetated meshes, green roofs and green belts intercept, store and filter rainwater, store carbon, reduce pollution and urban heat stress and heat islands and reduce energy consumption, improving the built environment. recreational facilities in urban parks provided by gbi stimulate physical activity and social interaction, improve health by helping to reduce deaths and hospitalizations, and strengthen the mental health of the population [29, 52]. through gbi implementation, new industry insights and associated gbi services have been developed. for example, the production of gbi materials increases economic capacity by incorporating new technologies [20], reducing unemployment and generating new jobs or developing higher skills and new business opportunities [45]. considering all of the above, green spaces contribute not only to increasing the economic value of properties but also to the quality of life of residents. the ecosystem services generated by implementing gbi contrast with traditional approaches to implementing solutions based on grey infrastructures that generate unsustainable costs and urban degradation. if the financial benefits offered by gbi are compared with those offered by grey infrastructure to achieve similar effects, tangible and intangible savings or even additional income results are obtained, which ensure economic growth and sustainable urban development [7, 33]. next, some relevant gbi implementation experiences in different areas of the world are presented. public administrations in philadelphia and new york (usa) have implemented gbi projects that have become models for other cities. through the approach supported by the government, their strategies and action plans include concrete terms and models of financial benefits, which highlighted the savings obtained from the implementation of the gbi, amounting to between 2 and 4 million euros, compared to the grey infrastructure implemented to solve some similar problems [33] china has approached gbi implementation by adopting a government-level sustainable urban development strategy based on integrated spatial planning policies [17]. the implemented sponge city concept capitalizes on traditional chinese green-blue methods. the implementation deadlines were set in the short term (until 2020) and long term (2050), each of which provided measurable objectives. gbi implementation included the benefits of improving ecological infrastructure and drainage systems for flood management (rain gardens, wetlands, ponds) through engineering methods managing 7085of annual rainfall. the sponge city government program includes 30 cities, with each pilot city being allocated a government subsidy for three years (with eligible activities for the purchase of raw materials, design and construction expenses). each project was based on analyzes and assessments of local climatic and hydrological conditions, based on which annual investment values were estimated between usd 85 million for the provincial capitals and usd 57 million for smaller cities. given the number of 30 cities and the relatively large budget, a possible long-term financing solution may be a public-private partnership. the dutch city of rotterdam has addressed gbi as an urban resilience planning strategy and as a practical solution to the problems of sea level rise and flooding in extreme storms [17]. the suburb of new castle great park in england proposed to resolve the fragmentation between new and traditional by implementing gbi corridors. based on a strategic water surface management plan, the implementation of the gbi was initiated through local and regional partnerships [25, 34]. detailed analyzes of the benefits of gbi systems compared to those obtained from the implementation of grey infrastructure, have been carried out both by gbi beneficiaries (local authorities, private institutions) and gbi providers (such as mcdonald mac mot, but also many other public companies and private) or by associated academic researchers, each of them highlighting possible implementations with practical case studies [53], which can be an inspiration for cities that have not yet started such projects. in all cases, through gbi implementation, environmental risks have been transformed into opportunities, and gbi has thus become a public good that directly contributes to sustainable urban development. comparisons between the two infrastructures, gbi and grey infrastructure, highlight the environhttps://doi.org/10.15837/ijccc.2023.2.5286 5 mental, economic, and social benefits of multifunctional and sustainable gbi. however, by combining the two infrastructures, their long-term advantages are leveraged, particularly through the hydraulic control and storage components held by the grey infrastructure. 3 interdisciplinary collaboration is the cornerstone of gbi projects the complexity of implementing a gbi management project brings together in collaboration with all interested parties to achieve sustainable urban development and to increase the quality of life of citizens [49]. each stakeholder actively participates in a win-win relationship with the public authority, depending on their collaborative role, possibly supported by modern information and communication technologies and methods [18, 19] and based on their financial contribution (co-financing). public administration has the role of regulation, control and administration. for this purpose, it analyzes and identifies territorial problems through specialized studies, and projects and evaluates and proposes investment strategies, plans and regulations. during the implementation of the gbi, it continuously updates the legal and economic inventory of the public and private domain and monitors the indicators of the gbi project. it also establishes the maintenance work required by gbi management and authorizes work based on the regulations in force. sometimes, carrying out these activities requires expertise and innovation from institutional partners outside the public administration. thus, the public administration develops close collaborations with partners and independent technical specialists from the public or private professional environment that includes consultants with multiple skills, planners, architects, designers, builders, material producers and service providers from technical or financial consortia (investment funds or banks). innovation is enhanced by developing partnerships with the educational and academic environment that provide the public administration with elements of research and knowledge transfer. through citizen participatory processes, permit applications are published online and proposals/solutions are generated in the decision-making process for the good of the whole community, bringing together all stakeholders, economic agents, ngos and citizens. another important aspect of gbi implementation is a collaboration with funding groups, or gbi concessionaires interested in gbi’s return on investment (roi). in commission and european union documents, the gbi is also perceived as a promoter of integrated spatial planning, which directly contributes to the development of land use plans and policies, the identification of multifunctional areas and habitat restoration measures [12, 14]. from the perspective of each category of partners, gbi brings benefits to each of them, fulfils both the strategic objectives of public administrations and private businesses, as well as the specific objectives of each partner and develops new services for citizens. essential to the success of cooperation is the selection of partners, who are stimulated to collaborate through the benefits obtained. 4 gbi operation and maintenance the interdisciplinary specificity of gbi and the need for collaborative decision-making with different partners combine the principles and structure of a multi-participant dss, to support real-time decision-making [50], with multi-agent systems [23] and crowdsourcing [40, 51]. from the multitude of gbi management activities, those with a high degree of inter-institutional and public collaboration, respectively the operation and maintenance workflows through which the gbi is updated/(re)configured permanently have been selected to be addressed. operational decisions at the dss level are based on several main components: • data sources: include the entire ecosystem of intelligent devices as a distributed physical layer in the field (iiot, different types of sensors, controllers, robots, cobots, video cameras, drones, or other types of remote sensing devices). these devices allow data collection in real-time, and communicate the data in machine-to-machine (m2m) workflows that generate commands for other devices based on the data collected. the iiot combined the internet with wireless devices/sensors [32, 47] for adjusting the gbi operations. typically data streams become very fast big data and include different formats. including data stream in the dss as soon as they https://doi.org/10.15837/ijccc.2023.2.5286 6 are collected, increases the agility of operational or maintenance decisions, especially in a multiuser decision-making environment. some data are big image files, collected before and after an operation, and integrated into the dss by multi-agent systems. other data have been integrated as data services for gbi monitoring. for example, iiot sensors for water level measurement in rivers and lakes, air quality sensors, soil moisture sensors in parks and green spaces, or pressure sensors in pipelines can collect data and at the same time transmit commands to other devices e.g. intelligent valves for guiding the volume of water to storage tanks according to the amount of raining water falling per square meter • technologies: by merging gis, ai, and ml methodologies, among the technologies used within a dss, the geoai platform underpins the dss for gbi operation and maintenance in a multistakeholder collaborative environment. cognitive modules and ai adaptive modules can improve gbi operation and maintenance decision-making processes by adapting to given changes and circumstances over time, for example: – infrastructure monitoring: the geoai platform may be used to continuously monitor the gbi infrastructure and spot possible issues before they become serious. to support preventative maintenance and efficient incident response, this information can be shared with numerous stakeholders, including maintenance teams, local government agencies, and emergency response organizations. – asset management: by offering real-time data on asset performance, utilization, and status, the geoai platform can assist gbi in managing its assets. to improve asset performance and cut downtime, this information can be shared with a variety of stakeholders, including asset managers and maintenance teams. – environmental monitoring: by merging gis, ai, and ml methodologies, the geoai platform may be used to monitor and evaluate environmental data linked to gbi. to support evidence-based decision-making and enhance environmental management practices, information on, for instance, air quality, water quality, and biodiversity can be shared with a variety of stakeholders, including environmental agencies and lawmakers. – urban planning: gbi can get help with urban planning via the geoai platform, which provides details on spatial data related to land use, transportation, and population demographics. this knowledge can be made available to a range of stakeholders, including taxpayers, urban planners, and policymakers, to assist sustainable and more just urban development. for example, in a city that has a network of pipes and other structures that help manage stormwater runoff, which includes sensors that collect data on water levels, flows, and other relevant factors, ai algorithms analyze this data to identify if there are problems potential or areas for improvement. adaptive ai modules can be used to continuously refine and improve the dss over time. if data reveals that certain areas of the stormwater network are consistently prone to blockages or overflows, ai algorithms could adjust the system’s predictive models to better anticipate these problems and recommend appropriate maintenance or upgrade activities to operators. in addition, adaptive ai could also learn from the decisions and actions taken by human operators in response to dss recommendations. if a maintenance crew decides to prioritize a specific gbi operation or maintenance activity, such as cleaning certain stormwater pipes based on dss recommendations, and subsequent data shows that this action was effective in reducing bottlenecks, adaptive ai could use this information to improve its predictive models and recommendations for the future. overall, the inclusion of adaptive ai modules in dss for gbi maintenance and operations can help improve system accuracy and effectiveness over time through continuous learning based on data provided by data sources and based on human decisions. different ai – tools based on advanced image analysis functionality and object-based classification capabilities for geospatial data, enable fast and accurate analysis of large amounts of remote sensing data such as aerial imagery and satellite imagery to achieve a) object-based image analysis: used to accurately identify and classify different features in the image, b) feature https://doi.org/10.15837/ijccc.2023.2.5286 7 extraction from geospatial data: for example, extracting vegetation and water bodies from satellite images, c) ml algorithms: for automatic feature classification in the image. ai algorithms can learn from user input to improve classification accuracy d) detect changes in the landscape over time by comparing two images taken at different times to identify changes in land use or the appearance of new features. another example, human inspectors’ capabilities are extended to automatic identification of gbi (species, health status, quality, etc.) with the help of ai tools using image-based species recognition functionality on mobile applications [41]. the ml algorithms (artificial neural network, probabilistic neural network, convolutional neural network, or other combinations of technologies [5]) assist field inspectors during the gbi inventory process. better accuracy has been obtained when trained models use object datasets and libraries located geographically. • digital twin: connecting the physical gbi infrastructure/world with their virtual replica in a dss for gbi maintenance and operation, different scenarios can be simulated and the behavior of the gbi under different conditions can be predicted. this improves the decision-making and selection of maintenance activities. for example, in a city with a complex network of waterways and flood control systems, data from iiot sensors placed throughout the network is used to simulate the effects of different weather conditions, water levels, and other factors on the system. this can help forecast potential network failures or inefficiencies and guide maintenance activities. in a multi-user environment, the digital twin can be accessed by various stakeholders such as city officials, engineers, and maintenance teams. each stakeholder can use the digital twin to obtain information about various aspects of the system and make informed decisions about maintenance activities to be performed. city officials can use the digital twin to determine where to allocate resources for maintenance, while maintenance crews can use them to plan their activities more efficiently. • distributed decision support layer: this layer includes applications for viewing gbi data and monitoring and performance metrics for real-time decision-making, controlling, and reporting gbi operation and maintenance. using real-time multi-user dss tools in a collaborative environment, stakeholders can work together to make informed decisions based on real-time data, improving overall gbi performance and user satisfaction. the different applications could be classified as follows: – dashboard-type applications: allow the real-time monitoring of features. data science and data analytics engines over big data available for relevant periods, combined with realtime data reported after the completion of work enable, for example, action awareness, or estimation of the next maintenance work required. difficult for humans to perform work orders that require material handling activities could be redirected automatically to be performed by cobots (collaborative robots) [11, 54]. autonomous robots support human operators, through electro-mechanical structures in the case of co-manipulated robots, while retaining the human decision-making role. collaborative bots programmed by non-experts are preferred for rapid deployments of gbi infrastructure – collaborative applications and web-based portal-type communication: allow online communication with citizens/economical agents, submission of their request for cutting vegetation, and transmissions the permits, answer to petitions or proposed project status, upon completion of the process – crowdsourcing application: bring citizens, ngos, and government officials together to work toward greener cities. understanding the target population and including all segments in the focus group is a best practice for obtaining better quality results. crowdsourcing methods and experts included in the collaborative environment can help to avoid results distortions. crowdsourcing is aided by platform accessibility and user experience [10]. the above components are just a few that could be included in dss for gbi and are constantly moving forward from the technological point of view. putting all components together, and https://doi.org/10.15837/ijccc.2023.2.5286 8 integrating the existing infrastructure provides more savings and efficiencies for all partners throughout the entire gbi life cycle 5 conceptual architecture of the gbi distributed decision support system in the case of gbi, the large number of connected iiot devices generate huge amounts of data, which pose a challenge in terms of storage volumes, high bandwidth consumption, latency in data processing, and privacy and security issues, especially in an open-to-the-public and multi-institutional framework. at the dss technical level, gbi workflows require adequate computing and communication resources, and solutions for bottleneck problems in data latency and traffic congestion [30]. as a result, the proposed architecture will have to deal with congestion and delays the conceptual architecture will consider: 1) avoiding the deadlock that might have occurred as a result of huge volumes of data and requests from multiple iiot systems and avoiding delays in responses. the proposed solution allows the execution of calculations as close as possible to the data sources, using edge computing and edge nodes, and data storage in the cloud; 2) real-time response, especially for situations where devices need to make decisions and act on their own. the ideal architecture for the gbi solution could be developed based on five logical layers that link 1) the distributed data source physical layer with different types of environmental sensors and actuators, 2) the communication layer – based on public and private networks, communication protocols and technologies such as 5g, lora, sigfox, nb-iiot, wireless, bluetooth, others, 3) data and storage model cloud/edge/fog/sky computing, based on partners’ infrastructure, 4) iiot distributed tools and types of services – saas, paas, iaas and 5) distributed level of decision support for monitoring, control, and reporting, as presented in [19] figure 1: general description of the distributed architecture for cps in the iiot environment.source: adapted from [19] the proposed architecture contributes to 1) low latency: by including fog and edge nodes; 2) geographical distribution throughout the administered territory: supported by fog computing that allows the distribution of services and applications in the network; 3) network heterogeneity: several types of communication capabilities for supporting different types of data processing; 4) interoperability: for supporting cooperation services between inter-institutional partners; 5) real-time interactions: https://doi.org/10.15837/ijccc.2023.2.5286 9 between fog computing applications; 6) scalability: increased adaptability by pooling resources. an improved architecture efficiency and scalability that develops deployment agility are sky computing [44] for multi-participant, multi-interaction environments and pay-as-you-go operating models when sky providers become partners in the collaborative environment. the architecture depends on 1) the number of partners involved in the project, 2) the development methodologies, and 3) the ict products selected for implementation and the evaluation procedures [21]. using several multi-criteria decision processes (mcdm), [16] describes a dss for supporting the ict selection of components for the multi-participant dss for gbi. for more understanding, customized existing infrastructure that should be integrated, several architectures are detailed in [20] based on classification criteria to be used for ict selection, and in [38] a multi-attribute method is proposed for the particular problem of cloud provider selection. depending on the maturity of the market, there are still mixed environments with on-premises solutions combined with public and private cloud services. this allows the collection, cleaning, validation, structuring, and communication of edge, fog, or sky computing depending on the existing concrete situation, data feeds and computation that must flow through the network, the number of participants, and the activities performed by each participant within the dss for gbi. the proposed architecture includes iiot devices connected to the gbi infrastructure. collected data can be classified and reused in various business processes [6] using a big data analytics module. to simulate and support finding solutions to complex decisions, multi-agent cooperation schemes, ai tools with object-oriented cognitive systems rely on deep learning algorithms. in the collaborative, virtual, shared environment, some multi-agents make decisions without the coordination of any central controller and exchange information with their pre-configured partners through the gbi communications infrastructure. the proposed solution supports the integration of several service-oriented cognitive systems [43] and multi-agent cooperative schemes and allows interactions with a wide variety of software sources owned by each participant. agent-based modeling enables the development of an interconnected, flexible, and interoperable architecture of intelligent and autonomous digital twin agents. they interact in a structured framework that enables the efficient transfer of information and knowledge along the complex value chains of collaborative systems. similar settings are used to control and operate robots, autonomous unmanned vehicles, mobile networks, and also sensor networks [54]. regardless of the combination of iiot and applications, the tools or services are linked together in a powerful solution through fog computing combined with edge computing and edge nodes for specific devices, distributing requests for providing cloud services to users, and solving latency limitations of processing [3]. as with any management solution in the past few years, digital twins have proven their great potential for intelligent decision-making. the full realization of this potential is still limited by both functional and, especially, non-functional constraints, which are related to the absence of a comprehensive framework for the development of architectures and the increasing complexity of real complex systems, emerging dynamics, and underlying interactions. among the features analyzed, the strategy to improve the architecture will have to take into account the existing infrastructure by selecting the right environments for the configuration of fog, edge, mobile, and sky computing tasks and allow dynamic relocation, considering the communication interface that determines the node location using robust and adaptive algorithms. also, the collaboration between nodes by using a load balancing algorithm to reduce the processing of lowand high-priority data, while fog computing servers fulfill tasks with redundant systems will be a priority for the decision about the final architecture. 5.1 security and ethical issues in the contemporary digital era, security and ethical concerns are important factors in designing and implementing dss. the following are the primary factors used to address these challenges: • data security and privacy: data security and privacy concerns are brought up since distributed dss relies on collecting, analyzing, and transferring data from various sources. two potential measures that could help maintain the data’s integrity and confidentiality, prevent data and infrastructure vulnerabilities, and protect data from assaults are access control and encryption. owing to the rapid development and variety of iiot devices, decentralized systems, third-party contractor access, inconsistent segmentation for various connections, and network https://doi.org/10.15837/ijccc.2023.2.5286 10 dead spots should all be considered vulnerability protection considerations. given that gbi decision-making participants may be spread out and possibly at a distance from one another, as well as the possibility that they may use mobile devices for data collection, reporting, and other purposes, biometric methods and tools [1, 35] can be used for authentication and access for secure collaboration. a security analysis of communications and ongoing monitoring of transmission and processing delays are required to prevent cyber security incidents and to boost security. security standards can be utilized in this context iso 27001 for operationally mature enterprises and nist sp 800-53 for organizations that are just starting to create a risk management strategy [37]. furthermore, ethical considerations must be made to ensure that data is collected, processed, and shared legally and openly • cybersecurity threats: distributed dss is susceptible to several online dangers, including malware, denial-of-service assaults, and hacking. to stop and lessen the effects of such attacks, it is crucial to have cybersecurity measures in place, such as firewalls and intrusion detection and response systems. in addition, to prevent dss from harming people or organizations, ethical issues must also be taken into account • bias and discrimination: if the data used to train the ai components is biased, distributed dss may produce discriminatory results due to inherent or introduced biases. to prevent and mitigate the effects of bias and discrimination, ethical principles such as transparency, neutrality, and openness must be incorporated into the design and development of dss. decision-making processes must also be routinely monitored and audited to detect and correct any potential biases • specialists’ professional ethics: collaborative interactions between individuals, organizations, and the public are still understudied, but we must consider their negative consequences for individuals and society. from this perspective, adequate guarantees must be included at the international level. data protection standards facilitate their use and storage to avoid infringing on citizens’ rights and hurting holders (the public) because of the dss architecture’s complexity and uniqueness, there are constant worries about security, as well as moral and humanitarian considerations to address any ethical and security threats. additionally, it necessitates ongoing professional growth in conjunction with advancements in technology, judicial guidelines, and global norms about fundamental principles and human rights. 6 implementation plan 6.1 success criteria the dss implementation is a smart project that necessitates the use of smart project management implementation practices. based on a quantitative correlational study [2] of various european smart city projects, the conceptual framework outlines a holistic view of four management functions: smart planning, smart organizing, smart leading, and smart controlling practices all contribute to project success. recommended steps for implementation the recommended steps for implementing a dss for gbi are crucial to ensure the success of the project. the following stages can be used as a guide for the implementation process: 6.2 recommended steps for implementation the recommended steps for implementing a dss for gbi are crucial to ensure the success of the project. the following stages can be used as a guide for the implementation process: • political actions: establishing political actions (global, continental, national, and regional/local) involves promoting the concept of gbi and gaining support from policymakers, politicians, and other relevant stakeholders. https://doi.org/10.15837/ijccc.2023.2.5286 11 figure 2: conceptual frameworks of smart project management practices and success. source: adapted from [2] • clear vision and objectives: a clear vision and measurable objectives should be established at the level of strategy and national legislation. this will ensure that the project is aligned with the goals of the organization and the progress can be measured effectively. • potential sources of financing: this is essential for the successful implementation of the project and includes negotiating terms with potential financiers and ensuring that funding is available throughout the project’s duration. • action plan: this is critical for the successful implementation of the project and includes eligible category activities and specific monitored indicators to track progress and ensure that the project is on track. • collaboration partners: this is essential to ensure that the project is successful and involves identifying and engaging with relevant stakeholders and highlighting the benefits for each participating party. • gbi implementation: this is a core component of the project and includes designing, constructing, operating, and maintaining the infrastructure. • infrastructure operating platforms: this is crucial for ensuring cooperation and participative communication in the virtual environment of all those interested in roles and responsibilities. • dissemination of good practices: this is essential for ensuring that the project’s outcomes are widely shared and progress encountered. • loading knowledge platforms: with the results obtained, evaluations, and comparisons between the costs of gbi versus grey infrastructure become essential to create best practices in the field. • implementation of cyber security tools: is crucial to ensure that the project’s data and information are secure and protected from cyber threats. • monitoring ethical aspects: this is essential to ensure that the project is aligned with ethical standards and principles. • plan for managing the impact: generated by human resource retraining programs to support the operation of newly implemented technologies is crucial for ensuring that the project’s outcomes are sustainable and that the impact on the workforce is managed effectively [42]. https://doi.org/10.15837/ijccc.2023.2.5286 12 7 conclusions dss for gbi is a solution to climate challenges, with multiple other benefits resulting from collaboration and for the benefit of all stakeholders. the transformative change derived mostly from the implementation of a multi-participant distributed dss generates gbi management with integrated strategic objectives at the level of the various participating organizations public administration, economic agents, and civil society. although the adoption of automation is intended for human well-being [22], the masking of inefficiencies and the rigidity of processes does not lead to the success of dss implementation for gbi. furthermore, they are not resolved solely by faster processes. throughout this regard, successful implementation entailed the alignment of all participants to a common it vision, the identification of business perspectives, and the rethinking of all business processes. only after this alignment would the multi-participatory system be able to move on to process automation and the provision of personality tools to all participants for the activity performed, possibly through the integration and modernization of existing ones, to gain more business value and benefits. in this sense, the addition of disruptive technologies and the emergence of new professions open up new industrial and service opportunities. the proposal to implement a dss for gbi, as a collaborative decision-making tool that supports two fundamental practical activities for gbi change management, namely gbi operation and maintenance, reflects the research’s innovative contribution. combining the practical nature of gbi change management and the collaboration of all interested parties, based on roles and responsibilities, can help not only public authorities to implement the gbi activities comprehensively in the first phase, but also cheaper, by bringing into partnership all those interested, as contributors or consumers of information and decision-making. as a result, decision-making has a solid basis and takes into account the context of all those who act on the same territory for the same infrastructure’s maintenance. the inclusion of the four key elements, identified as gaps in previous research, led to the following results: 1. interdisciplinary collaboration among all stakeholders: collaborative and multi-user participation involves stakeholders in the decision-making process, resulting in a more precise representation of various perspectives and needs. practical experience has demonstrated that the involvement of multiple interested parties in the process • increases the effectiveness of the dss by answering the needs of all stakeholders • improves the ability to solve problems by pooling resources (data sources, infrastructure on the same territory, ai models, and the expertise of those involved) • improves the ability to solve problems by pooling resources (data sources, infrastructure on the same territory, ai models, and the expertise of those involved) • improves the transparency and legitimacy of the decision-making process and implicitly takes responsibility • increasing the efficiency and effectiveness of the decision-making process by bringing together diverse expertise and knowledge, which allows for decision-making to be simplified and better decisions to be made in less time. multi-stakeholder participation facilitates the integration of various perspectives and areas of knowledge, leading to improved decisionmaking outcomes 2. the inclusion of the two practical activities of operation and maintenance, with the detailing of the components by activity, the indication of possible solutions, and the highlighting of the benefits obtained, improves understanding of the approach and opens up new perspectives for those interested in implementing dss for gbi 3. detailing the main technological components of the dss for gbi, with examples of practical use cases, brings technology closer to users at the smart city level, making the solution more attractive to both central and local administrations. https://doi.org/10.15837/ijccc.2023.2.5286 13 4. taking into account the territory’s context and conditions provides optimum utilization of existing resources, a better understanding of the local context, constraints, and better integration at the level of the proposed dss architecture. the 5-layer architecture concept exposition provides an excellent framework for identifying institutional partners who can directly contribute to dss implementation. the proposed dss framework, which is based on a solution, empowers interdisciplinary teams by providing new insights that foster innovation. by incorporating new technologies such as sky, ai tools, big data and analytics, edge computing, cloud, and mobile, iiot and biometric system tools, cobots, and digital clones alongside humans, complex issues have been addressed, and gbi management workflows have been improved. the proposed dss for gbi increases decision-making capacity and can serve as a foundation for the implementation of similar systems by governments and local communities to build sustainable and resilient communities. moreover, the modularization of the proposed solution ensures its scalability to other activities carried out on the same territory, contributing to the development of a sustainable future. concerns about incorporating ai models in dss for gbi with a strong ethical framework and a focus on promoting equitable and sustainable outcomes are addressed in the article, and the conclusions are supported by previous researchers [27]. the paper confirms the ethical component of ai in dss for the gbi and emphasizes the need for transparency about how the ai module makes decisions in hopes of preventing reinforcing or amplifying biases or inequalities. as sensitive topics, the research also includes data privacy and cyber security. the technology is widely available globally, and the methods are simple to replicate and adapt, making them suitable for testing and scaling in other contexts. future research on gbi decision architectures should concentrate on improving data availability and quality, developing more robust and transparent ai models for use in diagnostics, forecasting, and prediction, and engaging stakeholders in a more inclusive and participatory manner. furthermore, dss decision architectures for gbi should consider the larger institutional and governance context in which they operate, such as the need for clear policy frameworks, adequate funding, and effective coordination among various stakeholders. finally, gbi dss architectures should be evaluated based on their efficiency, effectiveness, and equity in achieving gbi goals like improving ecosystem services and promoting social 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(2021). decentralized multi-agent reinforcement learning with networked agents: recent advances. front inform technol electron eng 22, 802–814, 2021. https://doi.org/10.1631/fitee.1900661, 2021. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: visan, m.; mone. f. (2023). computer-supported smart green-blue infrastructure management, international journal of computers communications & control, 18(2), 5286, 2023. https://doi.org/10.15837/ijccc.2023.2.5286 introduction relevant approaches in gbi implementation interdisciplinary collaboration is the cornerstone of gbi projects gbi operation and maintenance conceptual architecture of the gbi distributed decision support system security and ethical issues implementation plan success criteria recommended steps for implementation conclusions int j comput commun, issn 1841-9836 8(6):854-862, december, 2013. a improved epc class 1 gen 2 protocol with fcfs feature in the mobile rfid systems x. li, y. quan xiaowu li 1. school of information science and technology southwest jiaotong university chengdu 610031, sichuan, china no.111 of the north second ring road lxwlxw66@126.com 2. school of information and technology kunming university puxin road 2, kunming 650214, china quanyuan feng* school of information science and technology southwest jiaotong university chengdu 610031, sichuan, china no.111 of the north second ring road *corresponding author: fengquanyuan@163.com abstract: in all anti-collision protocols of rfid standards, epcglobal class 1 generation 2 (c1g2) protocol has been most widely used in rfid systems since it is simply, efficient and safety. similar to most existing anti-collision protocols, the c1g2 protocol initially aims at tag identification of static scenarios, where all tags keep still during the tag identification process. however, in many real scenarios, tags generally move along a fixed path in the reader coverage area, which implies that tags stay the coverage area only for a limited time (sojourn time). the scenarios are usually called mobile rfid systems. because the multiple tag identification based on a shared wireless channel is random, tags entering the reader coverage area earlier may be identified later (random later identification phenomenon). the phenomenon and the limited sojourn time may let some tags lost. in this paper, we propose an improved c1g2 protocol with first come first served feature in mobile rfid systems. the protocol can overcome the rli phenomenon effectively and retains good initial qualities of c1g2 protocol by modifying it slightly. simulation results show that the proposed protocol can significantly reduce the numbers of lost tags in mobile rfid systems. the idea of the paper is beneficial for redesigning other existing tag anti-collision protocols so as to make these protocols adapt to mobile rfid systems. keywords:rfid, tag anti-collision, mobile rfid systems, epc c1g2. 1 introduction radio frequency identification (rfid) based on wireless radio communication is increasingly used a great deal in many ways. a reader can only interrogate tags within its interrogation region, where the reader’s electromagnetic signal is strong enough to energize the tags. this region is also referred to as the reader coverage area. during tag identification process, a tag collision occurs when multiple tags reply simultaneously to a reader. so far, so many tag collision resolutions have been proposed for static scenarios where no tag enters or leaves the reader coverage area during the process of tag identification [1–11]. however, in other many practical scenarios, tags which are usually attached to items and moving along the fixed path in the reader coverage area need to be identified as soon as possible [12–17]. these scenarios are generally called mobile rfid systems and often appear at doorways copyright © 2006-2013 by ccc publications a improved epc class 1 gen 2 protocol with fcfs feature in the mobile rfid systems 855 of warehouse and highway [13,14]. in these scenarios, tags stay the coverage area only for a time (sojourn time). since the reader has only limited sojourn time to identify the passing tags, tag loss may sometimes be inevitable. figure 1 shows the mobile rfid system diagram and illustrates some parameters: the reader coverage area length, the tag moving velocity (assumed constant) and tag sojourn time s (given in slots) which is a quotient between the reader coverage area length and the time interval for one slot [17]. mobile rfid systems shown in figure 1 are our research scenarios. in the mobile rfid systems, some tags may leave the coverage area unidentified under the condition of high tag tag density or is high tag moving speed, etc. we refer to such a tag as a lost tag. how to decrease the number of lost tags is the critical research issue in mobile rfid applications. therefore, we rate tag loss ratio (tlr) as a critical performance index, which is defined as the quotient between the number of lost tags and the total number of tags entering the reader coverage area [12–15, 17]. figure 1: the mobile rfid system diagram. besides, since the multiple tag identification based on a shared wireless channel is random, tags entering the coverage area earlier may be identified later, which is named the random later identification (rli) phenomenon [17]. an example given in figure 2 illustrates the phenomenon. in this figure, all tags in the coverage area contend the shared wireless channel together under the condition of high tag density. each tag randomly selects a slot to communicate with the reader. for example, the tag with asterisk, which enters the coverage area earlier than other ones, is closer to the exit of the coverage area than other tags, but selects the latter slot (slot 12) for communication with the reader. so, it may leave the coverage area unidentified because its slot number (12) is relatively greater. according to the analysis, we believe that the rli phenomenon and limited sojourn time are two main reasons for causing lost tags, especially under high tag density. figure 2: random later identification (rli) phenomenon . in this paper, we will propose an improved epc c1g2 protocol with first come first served feature in mobile rfid systems, which is an improvement to epc c1g2 protocol [10]. the improved protocol overcomes rli problem and converts random access for tags into sequential 856 x. li, y. quan access for tag groups. for the sake of brevity, the proposed protocol is named ic1g2mrs protocol. the remainder of this paper is organized as follows. in section 2 we briefly review the related work in the area. in section 3 we propose the principle of tag sequencing. then, in section 4 we offer ic1g2mrs protocol. section 5 provides the simulation results. finally, section 6 concludes. 2 related works epc c1g2 protocol is also known as q algorithm or c1g2 algorithm. in the protocol [10], each tag randomly selects a frame slot and sends a 16-bit random number (rn16) to the reader for reserving the remainder of the frame slot at the selected frame slot. there are three kinds of replies (correspond to three kinds of slots): (1) no reply (empty slot): after waiting for a short time, if the reader has not received a rn16. the reader terminates the current frame slot. (2) collision reply (collision slot): if multiple tags transmit rn16 in the frame slot, the reader terminates the current frame slot. (3) success reply (success slot): if only one rn16 is sent to the reader in the current frame slot, the reader can successfully get the tag’s id in the rest of the frame slot. one benefit of epc c1g2 protocol is that empty frame slots and collision frame slots are shorter than success frame slots. therefore, epc c1g2 protocol is superior to the fsa very much [8]. figure 3 shows the tag identification process in epc c1g2 protocol. figure 3: random later identification phenomenon (rli). until now, some researches on mobile rfid systems have been done [12–14, 17]. authors in [12, 13] pay attention to tlr computation of frame slotted aloha protocol (fsa) and csma by markov model or dynamic systems model. in [14, 15], authors focused on single tag set passing the reader coverage area in a limited time where no more tag sets enter the coverage area until the previous one has left. obviously, the scenario is different from our research scenario. in [16], sarangan offered a framework which reduces the tag reading time by using bitmaps. the framework can improve to some extent the system performance of mobile rfid systems. however, the method also can not solve rli problem effectively. in [17], authors present a grouping based dynamic framed slotted aloha for tag anti-collision protocol in the mobile rfid systems. the protocol has fcfs feature but does not possess the attributes of high system performance of epc c1g2 protocol in static rfid scenarios. in this paper, we develop an improved c1g2 protocol with first come first served (fcfs) feature in mobile rfid systems (ic1g2mrs). some advanced characteristics of the protocol are as follows: (1) ic1g2mrs protocol can be easily implemented without complicated computation. (2) the frequency of tag sequencing for fcfs mechanism is adjustable. (3) the proposed protocol can retain initial good quantities of c1g2 protocol. (4) the protocol can avoid overflow error effectively when the rfid systems work continuously for an awful long time. a improved epc class 1 gen 2 protocol with fcfs feature in the mobile rfid systems 857 3 the principle of tag sequencing it is well known that fcfs is based on the sequenced objects. so, tag sequencing is a critical work. in the following, we will give the detailed explanation of tag sequencing, which can refer to [17]. usually, tags enter the reader coverage area continuously in mobile rfid systems. in this case, the tags in the coverage area can be grouped in their arrival order at regular intervals (assume m slots). such obtained groups are called time groups and each time group can be assigned a time group sequence number (tgsn). we can derive that tags arriving in the same time interval possess the same tgsn and tags arriving in different time interval possess different tgsns. for the special case of static scenarios, all tags’ tgsns are the same because they enter the coverage area in the same time interval. notice that m can be called the frequency parameter of tag sequencing. the lower m is, the higher the frequency of tag sequencing is. figure 4: the tags grouped in their arrival time order. to record a tgsn, each tag should possess a memory cell to store tgsn. figure 4 shows that the tags in the coverage area are sequenced to become three time groups. initial tgsn of new tags are set as tgsn=null, which means that the new tags have entered the coverage area but have not received any tgsns given by the reader. we can derive that if a tag’s tgsn equals null, the tag has not been sequenced. notice that new tags may enter the reader’s field continuously, which can enlarge the tgsn extremely because every new time group may lead to tgsn plus one. to save the tag’s memory space, a circular queue mechanism is used to manage the tgsn. moreover, for some mobile scenarios where rfid systems are required to work continuously for an awful long time, such as several months or years, only using circular queue mechanism to manage tgsn can avoid overflow of tgsn. for example, we assume that the length of tgsn is 8 bits. with circular queue mechanism, next time group’s tgsn will be set as tgsn=0 if ongoing time group’s tgsn==28-1 during the process of tag sequencing. this means that the reader coverage area can hold a maximum of 28-1 time groups at any time. the reason for subtracting 1 is that the circular queue has overflowed when there are 28-1 time groups in the coverage area. however, with non-circular queue mechanism, the occurrence of next time group will result in an overflow error if ongoing time group’s tgsn==28-1 [17]. we also know that a circular queue mechanism requires the aid of parameters head and rear. in our method, the two parameters are stored in the reader and respectively point to the earliest arrival time group’s tgsn and the latest arrival time group’s tgsn plus one. based on the explanation, we can also say that head points the tags in the earliest time group. figure 5 depicts that a circular queue which is used to manage the tgsns. the circular queue mechanism with 8-bit tgsn can use tgsn 0 ~ tgsn 28-1 repeatedly. in figure 5 (a), head = 1 and rear =3 mean that there are two time groups in the coverage area, and time groups 1 and 2 are the earliest and latest time groups respectively. when head == rear, there is no unidentified tag in the reader coverage area, as shown in figure 5 (b). 858 x. li, y. quan (a) two time groups (b) no time group. figure 5: circular queue mechanism is used to manage the tgsns. in summary, the tag sequencing is summarized as follows: upon hearing sequencing command sent by reader, new tags set their tgsn as tgsn = rear and then the reader set its rear as rear = (rear +1) mod 28 where tgsn length equals 8. in this way, tag sequencing can be implemented, that is, tags in the coverage area can be divided into multiple time groups. 4 the proposed ic1g2mrs protocol ic1g2mrs is based on c1g2 algorithm. compared with c1g2 algorithm, ic1g2mrs has two other features: (1) ic1g2mrs can sequence tags in the reader coverage area to be tag groups (time groups) every m slots (e.g. 20 slots). that is, the reader can group these tags in their arrival order. (2) on this basis, the reader can identify tag groups one by one in order of their tgsns which indicates that ic1g2mrs has a first come first served (fcfs) character. ic1g2mrs protocol is summarized as follows: step 1. the reader sequences tags in the coverage area by sending sequencing command. upon hearing this command, all tags whose tgsn is equal to ’null’ will be assigned a true tgsn (0 ~ 255). more details can refer to section 3. following operations only aim at the tags in the earliest time group if no special explanation is given. step 2. the reader sends a query command (e.g. query or queryrep) to tags. if it is currently the time to start a new frame, the reader issues query command; otherwise the reader issues queryrep. step 3. tags receive the query command from the readers. it could be query or queryrep. if the received query command is query, every unidentified tag selects a slot number (sn) between 0 and 2q-1, inclusively. if the received query command is queryrep, all unidentified tags decrease their sn by 1. after these operations, every unidentified tag randomly selects a slot number between 0 and 2q-1, inclusively. the tags whose sns equal 0 generate a 16-bit random number (rn16) and responds the rn16 to the reader. because each tag conducts the operations independently, there are three possible outcomes (correspond to three kinds of slots): 1) success reply (success slot): only one tag responds and the reader receives the rn16 successfully. then the reader will send out an acknowledgement (ack) (go to step 4). 2) collision reply (collision slot): more than one tag responds simultaneously and collision occurs. then the reader adjusts q as q=min (15,q + c) and the reader continues to identify tags by sending queryrep to tags (go to step 2). 3) no reply (empty slot): no tag responds. then the reader updates q as q=max (0,q − c) and continues to identify tags by sending queryrep to tags (go to step 2). notice that, a success slot is counted as a standard slot, named slot for short. according to epc c1g2 standard, a collision and an empty slot respectively equal approximately 0.2 times a improved epc class 1 gen 2 protocol with fcfs feature in the mobile rfid systems 859 and 0.1 times the time cost of standard slots [10]. more details can refer to section 3. step 4. after the reader successfully receives a rn16 from a tag, it sends an ack back to all tags. but only the tag that successfully replies in step 3 recognizes the ack and then goes to step 5. the ack contains the rn16 that the reader received in step 3. step 5. all tags may receive the ack, but only the tag that replies successfully in step 3 continues to transmit its epc to the reader. then the reader continues to identify remaining tags in ongoing earliest time group by sending queryrep (go to step 2). notice that identified tags are silenced. namely, they will not reply the reader’s command in following identification process. step 6. the reader sequences tags in the reader coverage area every m (e.g. 20) slots. step 7. after one frame ends, if one or more collisions occur in the current frame, the reader sends query command with updated q to all unidentified tags in the earliest time group after ongoing frame (go to step 2). if not, the ongoing earliest time group move next time group. then go to step 1. in short, ic1g2mrs protocol is hybrid of c1g2 protocol and first come and first served (fcfs) mechanism. it can group tags in the reader coverage area to be multiple tag groups in their arrival order and then use basic principles of epc c1g2 protocol to identify each time group in fcfs order. 5 simulation and results before we evaluate the proposed protocol, we first offer a simplified mobile rfid experiment model because there are no good mathematical methods which can compute the tlr of various anti-collision protocols in the mobile rfid systems so far [17]. more reasons for using the simplified mobile rfid experiment model can refer to [17]. the model is composed of 3 groups of mobile tags, as shown in figure 6 where t1 denotes the time interval between the 1st and 2nd groups of tags, t2 denotes the time interval between the 2nd and 3rd groups of tags, n1, n2 and n3 denote the number of tags in the 1st, 2nd and 3rd groups of tags respectively, s denotes the tag sojourn time. notice that in the paper, all parameters related to time, such as t1, t2, s and m are measured in slot. figure 6: simplified mobile rfid experiment model. now, we comprehensively evaluate ic1g2mrs by comparing it with the typical c1g2 protocol [10]. our simulation based on monte carlo technique and the simplified mobile rfid experiment model. the basic parameters of following simulation experiments are n1=70, n2=70, n3=70, t1=50, t2=80, s=200, m =20. the evaluation results are shown in fig. 6. figure 7 (a) depicts the relationship between tlr and the tag density by changing the number of tags n2 in the second group. tlr of two protocols increases as the tag density increases. the reason is that the reader has to identify more tags in the same interval. compared with c1g2, ic1g2mrs has better performance. for example, when the number of tags in the second group n2 is less than or equal to 100 tags, tlr of ic1g2mrs is nearly zero while that of c1g2 is 10 percent. 860 x. li, y. quan figure 7 (b) depicts the relationship between tlr and the tag sojourn time s by changing s (correspond to change of the tag moving speed or the reader coverage area length). tlr of two protocols decreases as s increases. the reason is that the reader has more time to identify tags as s increases. compared with c1g2, ic1g2mrs has better performance. for example, when tag sojourn time s is larger than or equals 200 slots, tlr of ic1g2mrs is nearly zero while tlr of c1g2 protocol is 5 percent. 20 40 60 80 100 0 0.05 0.1 n2 t ag l os s r at io ic1g2mrs c1g2 (a) tlr vs. the tag density 100 200 300 400 500 0 0.05 0.1 0.15 0.2 0.25 s t ag l os s r at io ic1g2mrs c1g2 (b) tlr vs. the sojourn time 10 20 30 40 50 60 70 80 0 0.02 0.04 0.06 0.08 t1 (t1+t2 =130) t ag l os s r at io ic1g2mrs c1g2 (c) tlr vs. t1 under the condition of t1+t2 =130 20 40 60 80 100 0 0.02 0.04 0.06 0.08 0.1 t1 t ag l os s r at io ic1g2mrs c1g2 (d) tlr vs. tag arrival rate 20 40 60 80 100 120 140 0 0.02 0.04 0.06 m t ag l os s r at io ic1g2mrs c1g2 (e) tlr vs. the frequency of tag sequencing figure 7: tlr of the proposed ic1g2mrs protocol and c1g2 protocol. figure 7 (c) depicts the relationship between tlr and time interval t1 under the condition that t1+ t2 equals a constant (e.g. 130), which can to some extent conclude which protocol is sensitive to changing t1 in the same time span, and which protocol is not. for the experiment, ic1g2mrs can maintain a stable performance whereas c1g2 is unsteady. figure 7 (d) depicts the relationship between tlr and tag arrival rate by changing t1. from the figure, we can find that tlr of two protocols decreases as the tag arrival rate become slow. the reason is that the reader has more time to identify tags when the rate becomes slow. compared with c1g2, ic1g2mrs has better performance. for example, when t1 is equal to 20 slots, tlr of ic1g2mrs is nearly zero percent while tlr of c1g2 protocol is 8 percent. figure 7 (e) depicts the relationship between tlr and the frequency of tag sequencing by changing m. from the figure, tlr of ic1g2mrs increases as the frequency of tag sequencing decreases (correspond to increase of m). the reason is that rli problem may not be resolved a improved epc class 1 gen 2 protocol with fcfs feature in the mobile rfid systems 861 when the frequency of tag sequencing is too low. for example, when m > 130, the three groups of tags in the figure 6 are not sequenced and are seen as a group of tags. thus tlr of ic1g2mrs significantly increases and is equal to that of c1g2. we can also know easily that if the frequency of tag grouping is too high, great tlr may occur. the reason is that the operation of the tag grouping also needs the time cost. from the experiments, we can derive that the frequency of tag sequencing has a strong influence on tlr. 6 conclusions epc c1g2 procotol is the most popular standard in rfid systems. but it can not be used in mobile rfid systems effectively because the random late identification (rli) phenomenon can not be overcome. the paper specifies the random late identification (rli) phenomenon which causes unnecessary tag loss in the mobile rfid systems. then ic1g2mrs protocol is proposed to resolve the phenomenon and retains initial good quantities of c1g2 protocol, which improves the system performance of mobile rfid systems dramatically. acknowledgement this work is supported by the national natural science foundation of china (nnsf) under grant 60990320, 60990323, 61271090, and the national 863 project of china under grant 2012aa012305, and sichuan provincial science and technology support project under grant 2012gz0101, and chengdu science and technology support project under grant 12dxyb347jh002. bibliography [1] he, m. et al (2011); a fast rfid tag identification algorithm based on counter and stack, expert systems with applications, issn 0957-4174, 38: 6829-6838. [2] yeh, m. et al. (2009); adaptive splitting and pre-signaling for rfid tag anti-collision, computer communications, issn 0140-3664, 32: 1862-1870. [3] finkenzeller, k. (2002); rfid handbook: radio-frequency identification fundamentals and applications, john wiley press. [4] chen, y. (2013); multiple-bits-slot reservation aloha protocol for tag identification, ieee transactions on consumer electronics, issn 0098-3063, 59(1): 93-10. [5] alcaraz, j. et al. (2013); a stochastic shortest path model to minimize the reading time in dfsa-based rfid systems, ieee communications letters, issn 1089-7798, 17(2): 341344. [6] wong, c. et al. (2007); grouping based bit-slot aloha protocol for tag anti-collision in rfid systems, ieee communication letters, issn 1089-7798, 11(12): 946-948. [7] jia, x. et al. (2010); an efficient anti-collision protocol for rfid tag identification, ieee communication letters, issn 1089-7798, 14(11): 1014-1016. [8] zhu, l.; yum, t. (2011); a critical survey and analysis of rfid anti-collision mechanisms, ieee communications magazine, issn 0163-6804, 5: 214-221. 862 x. li, y. quan [9] klair, d. et al. (2009); a survey and tutorial of rfid anti-collision protocols, ieee communications surveys and tutorials, issn 1553-877x, 12(3): 400-421. [10] epcglobal specification for rfid air interface; radio-frequency identity protocols class-1 generation-2 uhf rfid protocol for communications at 860 mhz 960 mhz, version 1.0.9, 2005. [11] vogt, h. (2002); efficient object identification with passive rfid tag, proceedings of 2002 ieee international conference on systems, pp. 98-113 [12] alcaraz, j. et al (2011); dynamic system model for optimal onfiguration of mobile rfid systems, computer networks, issn 1389-1286, 55: 74-83. [13] vales-alonso, j. et al (2009); markovian model for computation of tag loss ratio in dynamic rfid systems, proceedings of 5th european workshop on rfid systems and technologies, bremen, germany, pp. 16-17. [14] vales-alonso, j. et al (2011); on the optimal identification of tag sets in time-constrained rfid configurations, sensors, issn 1424-8220, 11: 2946-2960. [15] xie, l. (2010); efficient tag identification in mobile rfid systems. , proceedings of ieee international conference infocom, pp. 15-19. [16] sarangan, v. (2008); a framework for fast rfid tag reading in static and mobile environments, computer networks, issn 1389-1286, 52(5): 1058-1073. [17] li, x.; feng, q. (2013); grouping based dynamic framed slotted aloha for tag anticollision protocol in the mobile rfid systems, appl. math. inf. sci., issn 1935-0090, (2l): 655-659. int j comput commun, issn 1841-9836 9(1):56-61, february, 2014. improving the efficiency of image clustering using modified non euclidean distance measures in data mining p. santhi, v. murali bhaskaran p.santhi* computer science department paavai engineering college, pachal,namakkal,india *corresponding author: santhipalanisamypec@paavai.edu.in v.murali bhaskaran paavai college of engineering pachal, namakkal, india murali66@gmail.com abstract: the image is very important for the real world to transfer the messages from any source to destination. so, these images are converted in to useful information using data mining techniques. in existing all the research papers using kmeans and fuzzy k means with euclidean distance for image clustering. here, each cluster needs its own centric for cluster calculation and the euclidean distance calculate the distance between the points. in clustering this process of distance calculation did not give efficient result. for make this in to efficient, this research paper proposes the non euclidean distance measures for distance calculation. here, the logical points are used to find the cluster. the result shows that image clustering based on the modified non euclidean distance and the performance shows the efficiency of non euclidean distance measures. keywords: data mining, image mining, kmeans, fuzzy kmeans, euclidean distance 1 introduction in real world, the image plays an important role in the entire field. normally, all the images having some information related to any application [1] [2]. so, these images are converted in to some useful information using data mining techniques. this process of mining the image is called as image mining. here, the clustering technique is applied to these images. the process of grouping the similar data or pixels is called as clustering [5].. the data mining having two types of learning is supervised learning and unsupervised learning. in supervised learning, the training dataset was provided to train the system and got the output based on these training data. this type of system is called as classifiers. in classification, group the same item based on the physical behavior. in unsupervised learning, the training data set is not provided to the learning system. in this paper proposes the clustering for the image. the clustering is one of the techniques in unsupervised learning. the clustering is having many algorithms are partitioned based clustering, hierarchical based clustering, dense based clustering and distribution based clustering. in partitioned based clustering the cluster based on the centre point [6][4]. here, the initialization of centre is very difficult and this process takes more time to execute. in hierarchical clustering the clusters are formed by using distance connectivity [3]. in density based clustering the cluster formed using the dense value. the distance between the points is calculated by using the distance measures. data mining having the two types of measures called euclidean distance and non euclidean distance measure [7]. in euclidean distance measure, the distance is calculated based on the physical points of the cluster. but this measure is not efficient for the clustering to produce the efficient result. copyright © 2006-2014 by ccc publications improving the efficiency of image clustering using modified non euclidean distance measures in data mining 57 figure 1: steps for the proposed system [8] in non euclidean distance, the distance is calculated using feature vectors between the clusters [4]. in the literature survey most of the researches are done by using kmeans and fuzzy kmeans algorithm for clustering and the city block distance measures for measure the distance between the clusters. in existing formula for city block distance is [9] d = n∑ j=1 [aj − bj] (1) in this paper proposes the modified city block distance measures for calculating the distance between the clusters and using concentric circle based clustering algorithm. 2 research methodology non euclidean distance measure is very important for the clustering to measure the distance between the points. in this research using the modified non euclidean distance measure of city block distance or manhatten distance. in existing formula is d = n∑ j=1 [aj − bj] in this formula is modified in to d = n∑ j=1 [aj − bk] (2) the equation 2 is called as modified city block or manhatten distance. the steps for this system will be discussed in figure 1. 1. get the image. 2. convert this image into pixel. 3. the concentric circle based clustering algorithm is applied to pixel. 4. find the distance between the clusters using modified city block distance. 5. step 4 will be repeated until all the pixels come under any one of the clustering. 58 p. santhi, v. murali bhaskaran figure 2: sample for concentric circle based clustering figure 3: sample for pixel based clustering 6. display the output of pixel clustering in concentric circle based clustering; only one centre is used for all the clusters. this process will reduce the time for image clustering. in existing research, the reduction in the running time is already discussed. now, this paper discusses the improvement in the efficiency of image clustering. the figure 2 shows the sample of concentric circle based clustering. here it contains the four groups or clusters using only one centre. the figure shows the pixel clustering using modified city block distance or manhatten distance. consider the centre assumption is 0. the formula is d = n∑ j=1 [aj − bk] • d is the modified city block or modified manhatten distance measure, • j is the dimension from 1 to k dimension • aj is the centre pixel assumption point improving the efficiency of image clustering using modified non euclidean distance measures in data mining 59 figure 4: input images for pixel clustering figure 5: output images with pixel clustering • bk is the top most pixel assumption point based on this formula, aj=0 and the value of bk=1.here, the j is the centre value of 0.this value is same for all the clusters. the k value is min distance from that centre. the same manner all the k values are calculated. for the cluster 1, d=0-1=[-1]=1 the cluster 1, the min distance from the centre is 1.so, the pixels within the 1 from x and y axis are considered as the first cluster. for the cluster 2, d=0-2=[-2]=2, here aj=0 and bk= 2. the cluster 2, the next to the min distance from the centre is 2.so, the pixels within the 2 from x and y axis are considered as the second cluster. for the cluster 3, d=0-3=[-3]=3, here aj=0 and bk= 3. the cluster 3, the next to the min distance from the centre is 3.so, the pixels within the 3 from x and y axis are considered as the second cluster. the same way all the clusters are formed. 3 results and discussion the result shows the original image and the equivalent pixel to that image.the figure 5 shows the clustering of pixel using concentric circle based clustering. the image 1 having the 3 clusters and the image 2 shows the 5 clusters. in this result all the clusters in the image 1 and the image 2 using the single centre point for all the clusters and the distance between the centers is calculated using modified city block distance or manhatten distance. 4 performance analysis the graph shows the performance of clustering algorithm using modified non euclidean distance. in the performance analysis the concentric circle based clustering with modified city block 60 p. santhi, v. murali bhaskaran figure 6: performance graph distance algorithm having more efficiency comparing to existing system. 5 conclusion and future enhancement the images are very important to the real world in entire field. the images are converted in to useful knowledge using data mining techniques. in existing the researches are did by using k means and fuzzy k means for clustering with euclidean distance. but it is having more difficulties in assigning centre for each cluster and the efficiency is not high. so, improving the efficiency this research paper gives the image clustering using concentric circle based clustering with non euclidean distance measure of modified city block distance with single centre point. this result gives the better performance in the accuracy comparing to existing system. in future, the concentric circle based clustering is combined with soft computing techniques for the image clustering. bibliography [1] nor ashidi mat isa; samy salamah, a, umi kalthum ngah; adaptive fuzzy moving kmeans clustering algorithm for image segmentation, ieee trans on knowledge and data engineering, 2145-2153, 2009. [2] siti noraini sulaiman; nor ashidi mat isa; adaptive fuzzy-k-means clustering algorithm for image segmentation, ieee trans on knowledge and data engineering, 2661-2668, 2010. [3] maduri, a.tayal;raghuwanesh,m.m.; review on various clustering methods for image data, j. of emerging trends in computing and information sciences, 34-38, 2010. [4] keh-shih chuang; hong-long tzeng; sharon chen; jay wu; tzong-jer chen; fuzzy c-means clustering with spatial information for image segmentation,elseiver, 9-15, 2006. [5] sneha silvia, a.; vamsidhar, y.; sudhakar,g.; color image clustering using k-means,ijcst, 11-13, 2011. [6] vasuda, p.; satheesh, s.; improved fuzzy c-means algorithm for mr brain image segmentation, ijcse, 1713-1715, 2010. improving the efficiency of image clustering using modified non euclidean distance measures in data mining 61 [7] fahim, a.m.; saake, g.; salem, a.m.; torkey, f.a.; k-means for spherical clusters with large variance in sizes, world academy of science, engineering and technology, 177-182, 2008. [8] john peter, s.; minimum spanning tree-based structural similarity clustering for image mining with local region outliers,int. j. of computer applications, 33-40, 2010. [9] chawan, p.m.; saurabh bhonde,r.; shirish patil; concentric circle-based image signature for near-duplicate detection in large databases,electronics and telecommunication research institute, 2010. int j comput commun, issn 1841-9836 9(1):37-47, february, 2014. development of an agent based specialized multi-lingual web browser for visually handicapped r. ponnusamy, m.s.s. babu, t. chitralekha r. ponnusamy*, m.s. satish babu dept of computer science & engineering, madha engineering college, chennai 600 069, india, *corresponding author: rponnusamy@acm.org t. chitralekha dept of computer science, pondicherry university, pondicherry 605 014, india abstract: in the modern age everyone needs access to internet; visually handicapped are not an exception for that. specs (specialized computer system) is a system developed to give access to the visually handicapped. it has a braille shell. the user can enter his spoken language. after the selection of the language the user can present his request to the browser through chosen language in braille. the output generated by the browser is given out as voice message. the effectiveness of this system is measured based on number of requests processed, access speed and precision of the system. keywords: visually handicapped, braille shell, internet, multi-lingual web browser. 1 introduction the impact of visual loss has profound implications for the person affected. the majority of blind people live in developing countries. the number is huge, also due to the sheer size of the population in developing countries. especially in south and east asia itself there exist 27% (1,590.80 millions, 2002 estimate) [1] [2] [3] of blindness. the first global estimate on the magnitude and causes of visual impairment was based on the 1990 world population data (38 million blind). this estimate was later extrapolated to 1996 world population as 45 million blind, and projected to 2020 world population as 76 million blind, indicating a twofold increase in the magnitude of visual impairment in the world by 2020 [4]. further, the survey estimated that 3.9% comes under child blindness and incurable categories. in the past decade, the internet revolution throughout the computing world, catalyzed largely by the world wide web (www) [5], has enabled the widespread dissemination of information worldwide. however, much of this information is in english or in languages of western origin. presently, the internet is positioned to be an international mechanism for communications and information exchange, the precursor of a global information superhighway. for this vision to be realized, one important requirement is to enable all languages to be technically transmissible via the internet, so that when a particular society is ready to absorb internet technology, the language capability comes prepackaged. the term "multilingual computing" refers to the use of computer applications in indian languages. traditionally, computer applications were based on english as the medium of interaction with the system. in india, when one attempts to use computers for education and literacy, one faces the problem of language where majority of the population that should get the benefit of information technology, does not speak english. this is a non-trivial multilingual information-processing problem. there is an urgent need to recognize that the true burden of blindness has changed with the rapid pace of industrialization and technology, and must adopt these people for development. copyright © 2006-2014 by ccc publications 38 r. ponnusamy, m.s.s. babu, t. chitralekha most legally blind people (70% of them across all ages, according to the seattle lighthouse for the blind) do not use computers. only small fractions of this population, when compared to the sighted community, have internet access. there is urgent need to develop information technology tools which can be used by blind people knowing only their own languages. especially the regional visually handicapped people need special methods and tools for accessing the web. it is the responsibility of the technocrats to develop such technology. today’s research challenge is to give an optimal access to the computers and internet for the visually handicapped people in different languages. in this paper an attempt has been made to design and develop a special system for visually handicapped people. a specialized browser using role based agents for regional visually handicapped users. section 2 explains the working nature of specialized browser. section 3 explains the components and architecture of the specialized browser. section 4 explains the design and functionality of tamil and english braille keyboard. section 5 explains the specs machine learning system. section 6 explains the simulation experiment. section 7 gives experiment results and discussion and section 8 concludes the paper. 2 literature survey stuart goose, 2000 [18] presenting a new idea of creation of a hypertext document in both the visual and auditory realms. in this approach an intelligent agent that is able to convert html contents to vxml contents to provide voice services for text disabilities via web. prior to interpreting html documents and separating contents, the contents for the conversion must be selected, however, there are no good solutions for selecting the desired group contents. if an audio document is designed straight from the author’s intentions, it may correspond to the author making an explicit recording the user study presenting voice based html structure in audio: user satisfaction with audio hypertext of the document or pieces of the document. patrick roth [19] and his group project aims at providing sight handicapped people with alternative access modalities to pictorial documents. more precisely, our goal is to develop an augmented internet browser to facilitate blind users access to the world wide web. the main distinguishing characteristics of this browser are 1.generation of a virtual sound space into which the screen information is mapped; 2.transcription into sounds not only of text, but also of images; 3. active user interaction, both for the macro-analysis and micro-analysis of screen objects of interest; 4.use of a touch-sensitive screen to facilitate user interaction. several prototypes have been implemented, and are being evaluated by blind users. guan neng huang,2007 [20], designed a special web browser called eguidedog is designed for the visually impaired people. this web browser can extract the structure and the content of an html document and represent it in the form of audio. it helps the blind finding out information they concern more quickly. ibm accessibility browser [21] provide an additional facility to access audio while enjoying a streaming video, visually impaired people can now select the play button by simply pressing a predefined shortcut key instead of searching in the content for buttons that control the video. users can also adjust the volume of an individual source in order to identify and listen to different sound sources without losing track of the screen-reading software because of the sound of a video. the main problems with these work is accessing the multilingual content. tim morris [22] and his team have reported on a prototype screen reader that is intended to vocalize the information displayed on the lcd or led screen of home or office equipment. tadayoshi fujiki, 2006 [23], developed a new tool easy bar to cater the needs of the visually handicapped users. the functions of the easy bar are to change the size of web texts and images, to adjust the color, and to clear cached data that is automatically saved by the web browser. development of an agent based specialized multi-lingual web browser for visually handicapped 39 3 about specs the specs [6] [7] is the specialized browser for visually handicapped users (vhu). this system allows the vhu to browse the restricted set of regional language and english webpages. it receives the inputs through braille/normal keyboard and gives the voice output in respective language of choice. in order to browse the webpage the user must know the specific website in advance. also, the trainer must train system by giving english website address and its equivalent regional language website through the braille keyboard attached with the system. the system is able to read only the static websites. further the user is not able to travel into the complete set of hyperlinks provided in the site and the present browser functionality is restricted to access the text messages alone. the trainer is not visually handicapped. the braille keyboard is designed to work in the two modes. one is the normal mode and another is command mode. the visually handicapped users (vhu) can travel in the website through the command mode. the vhu can type the website address in normal mode of operation and in case of command mode the user can move from the hyperlink to anther hyperlink. this is the main difference between the previous work and the present work. another main restriction in the present work is that the vhu cannot access the .gif, .bmp, .jpg fixed text contents. this is great challenge to the user. also the user cannot feel the pictures that appear on the webpage. at present the specs is designed to accommodate only two languages. in future the complete design of specs is expected to incorporate all indian languages. 4 architecture and component functionality of specs the overall architecture of specs is shown in the following figure 1. this architecture of specs consists of three layers. these are 1. specs browser layer 2. multi-functional agent layer 3. knowledge base layer. further, the specs system interface (iocs) is built under windows platform. the windows api, informally winapi, is microsoft’s core set of application programming interfaces (apis) available in the microsoft windows operating systems. developer support is available in the form of the microsoft windows sdk, providing facilities and tools necessary to build software based upon the windows api and associated windows technologies. various wrappers were developed by microsoft that took over some of the more low level functions of the windows api, and allowed applications to interact with the api in a more abstract manner. microsoft foundation class library (mfc) wrapped windows api functionality in c++ classes, and thus allows a more object oriented way of interacting with the api. the active template library (atl) is a template oriented wrapper for com. the windows template library (wtl) was developed as an extension to atl, and intended as a lightweight alternative to mfc. over and above such facilities the specs iocs has been developed to cater the needs. the layered representation of this specialized browser is shown in the following figure 2. 4.1 brower layer the specs browser is the general browser capable of browsing in regional language and in english language. font availability is the big problem of this layer. this problem is solved through the machine learning system. the specs browser is designed using the internet explorer active x component [13]. activex is a framework for defining reusable software components in a programming language independently. internet explorer 7 was used to design this system. software applications can then be composed from one or more of these components in order to provide their functionality. active x controls (small program building blocks), can 40 r. ponnusamy, m.s.s. babu, t. chitralekha figure 1: specs architecture, llaa – language learning adaptation agent, da – dialogue agent, mha – message handling agent figure 2: layered representation of specialized browser serve to create distributed applications working over the internet through web browsers. activex controls can then be embedded into other applications. internet explorer also allows embedding activex controls onto web pages. windows internet explorer [14] (formerly microsoft internet explorer; commonly abbreviated to ie), is a series of graphical web browsers developed by microsoft and included as part of the microsoft windows line of operating systems. since its first release, microsoft has added features and technologies such as basic table display (in version 1.5); xmlhttp request (in version 5), which aids creation of dynamic web pages; and internationalized domain names (in version 7), which allow web sites to have native-language addresses with non-latin characters. internet explorer has introduced an array of proprietary extensions to many of the standards, including html, css, and the dom. this has resulted in a number of web pages that appear broken in standards-compliant web browsers and has introduced the need for "quirks mode" rendering improper elements meant for internet explorer in such browsers. considering these advantages the ie 7 has been chosen as the suitable component for designing the specs browser. 4.2 multi-function agents layer this layer performs different functions such as language learning, vhu interaction, error messaging, voicing and vhu direction. the study of multi-agent systems (mas) [16] focuses on systems in which many intelligent agents interact with each other. the agents are considered to be autonomous entities, such as software programs or robots. their interactions can be either cooperative or selfish. that is, the agents can share a common goal (e.g. an ant colony), or they can pursue their own interests (as in the free market economy). the characteristics [16] of mass are that (1) each agent has incomplete information or capabilities for solving the problem and, thus, has a limited viewpoint; (2) there is no system global control; (3) data are decentralized; and (4) computation is asynchronous. in the present design the system functions are asynchronous and hence the mas architecture development of an agent based specialized multi-lingual web browser for visually handicapped 41 is chosen as the best fit architecture. this layer has different agents to perform all these functionalities, such as language learning adaptation agent, dialogue agent, message handling agent, prompter agent and director agent. these agents are operating independently performing the operations. language learning adaptation agent is a simple component. it scans the user language selection. normally, it permits two types of users as of now. one is the normal user and second user is the vhu users. it understands the user and displays different screen for different users. in the braille key board the system is designed to operate in a command mode. this command mode first supports the language selection. the system is able to understand the language selection and change the system setup accordingly. message handling agent displays error and other messages from the knowledge base in regional language or prompts the display in english for other user. an error message alerts users of a problem that has already occurred. error messages can be presented using modal dialog boxes, in-place messages, and notifications. dialogue agent gets the input from the visually handicapped user in particular language braille through special input keyboard attached with the specs system. on the other hand it also gets the normal input form the trainer. the design of the braille keyboard [11] [12] [15] and its components are explained in the section 4. prompter agent gives the voice output after filtering the output from the browser. this prompter gets the sequence of string from the browser in the form of html/dhtml and checks the fontface tag, if it is trained language tag, then it stores the sequence of text in file until it encounters font tag, otherwise it simply truncates those tags and html input. then the other tags are given to the sound component. the sound component is able to read the given regional language/english words. a system is designed to read the words in regional language and in english. a girl voicer recorded regional language/english alphabets with different rhythm based on their occurrence in the word at different places. the occurrence may be at the beginning or at the middle or at the end. then the sound component is designed to pronounce the word with different voice synthesizing. director agent directs the browser to browse the regional language/english web sites according to the selection. even if the user typed words with small mistakes it is able to direct to the correct website. 4.3 knowledge base layer the third layer consists of knowledge base which stores different types of fonts, regional language voice database, various regional language web site information, etc. the main mechanism of this knowledge base development is knowledge representation, acquisition, learning and reasoning. the section 5 explains how this knowledge is acquired, represented, learning and reasoning. 4.4 mutli-lingual font repository this repository stores all the fonts available in each web site and the common regional language fonts of different designers. as soon as the new website is visited it will fetch the new font from that website. 4.5 regional language voice database a prerecorded unicode character set is stored in the database. the unicode character set is available in the http://unicode.org/. this database provides the equivalent voice file for the particular character as soon as it is requested. these voices are recorded by the voicer in 42 r. ponnusamy, m.s.s. babu, t. chitralekha a recording room with three different types of appearance of letters in different places. that is first type of tag is at the beginning of letters, second one is for the middle letters and third for the end letters. the voice chord in regional language/english will differ when it appear in different places. 4.6 multi-lingual web site database this database contains the multi-lingual web name and the equivalent english web site name. these websites are taught by the normal person during the training phase of the system. even through the system is designed for the people; a non-blind person can also use the system in a normal way. his / her duty is to teach the equivalent multi-lingual web site name for every known multi-lingual portal. and third for the end letters. the voice chord in regional language/english will differ when it appear in different places. 5 design and functionality of multi-lingual braille keyboard braille is a touch and feel system for the visually handicapped person, which uses an arrangement of 6 dots called a cell. the cell is three dots in height and two dots wide. each braille character is formed by placing one or more dots in specific positions. a printed sheet of braille normally contains upwards of twenty five rows of text with forty cells in each row. the physical dimensions of a standard braille sheet are approximately 11 inches by 11 inches. the dimensions of the braille cell are also standardized but these may vary slightly depending on the country. the dimension of a braille cell, as printed on an embosser is shown below. figure 3: braille cell dimensions a sheet of braille may thus appear to hold information amounting to about a thousand characters (letters of the alphabet). later we will see that the designers of the braille system had foreseen the need to present information in compact form so that a set of cells could convey much more information in the string of letters forming the cells. try reading the following sentence shown in grade-1 braille. to aid in describing these characters the positions in the braille cell are numbered 1, 2, 3 downward and on the left, and 4, 5, 6 downward on the right. it is shown in the following figure 3. the braille keyboard has six keys, a line spacer, a back spacer and a space bar. the six keys correspond to his six dots of the braille cell. the keys are struck one or more at a time so that one braille cell is written with each stroke. there are three keys each side of the space bar. the development of an agent based specialized multi-lingual web browser for visually handicapped 43 figure 4: braille key board design left index finger uses the key to the left of the space bar, which strikes dots1; the middle finger, dot 2; and the left ring finger, dot 3. the right finger, middle finger and the ring index finger strike the keys for dots 4, 5 and 6 respectively. thumb strike on the space bar leaves a blank cell. the bharathi braille tamil fonts and grade-1 braille are used to key in the fonts in to the specs system. the bharathi braille fonts and grade-1 braille are shown in the following figures 5 and 6 respectively. figure 5: bharathi braille fonts figure 6: bharathi braille fonts 44 r. ponnusamy, m.s.s. babu, t. chitralekha 6 specs machine learning system the specs system uses three different learning systems for usage. first learning mechanism applied is the simple rote learning mechanism to understand the english urls and their equal multi-lingual braille names. the trainer must explicitly train the system to understand the equivalent urls. the idea is that one will be able to quickly recall the meaning of the url by repeating it often. the second learning is the reinforcement learning mechanism that must understand the occurrences of an alphabet in different words in different places and sequence the sound files according to their requirement. it can be used in cases where there is a sequence of inputs and the desired output is only known after the specific sequence occurs. reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize some notion of cumulative reward. in machine learning [17], the environment is typically formulated as a markov decision process (mdp), and many reinforcement learning algorithms for this context are highly related to dynamic programming techniques. the main difference to these classical techniques is that reinforcement learning algorithms do not need the knowledge of the mdp and they target large mdps where exact methods become infeasible. this process of identifying the relationship between a series of input values and a later output is temporal difference learning. this algorithm is adapted to understand the word and then present the voice to the vhu users. the third learning is the font usage for the different system and again it is a rote leaning mechanism. as soon as the agent finds new fonts it downloads those fonts and stores them in the local database.if you wish to include color illustrations in the electronic version in place of or in addition to any black and white illustrations in the printed version, please provide the managing editor and the editorial assistant with the appropriate files. 7 simulation experiment this system is developed using microsoft visual c++ under microsoft windows 9x platform. the special browser developed can be facilitating the normal user to give the inputs and train the system. all the given components and agents are developed and integrated and installed with the braille keyboard and a speaker. the system is installed with the normal hardware; 800 mhz intel pentium 3, 256 mb ram machine and tested. after the installation the normal users trained the system by visiting different tamil web sites. the fonts from these websites also downloaded and put into the repository. the language learning adaptation agent takes care of installation of fonts in the respective system font’s directory as soon as it is downloaded. the specs browser developed and sample screen is shown in the following figure 7. figure 7: a view of specs browser development of an agent based specialized multi-lingual web browser for visually handicapped 45 user type vhu non-vhu mean-time to access time (s) 800 200 table 1: comparison of navigation time of vhu vs non-vhu 8 experiment result and discussion in these experiments, the system is trained with 600 web sites by the normal non-blind person. then four blind persons were brought from the blind school and they were asked to browse in the system using the specialized browser. after getting their opinion the system performance is evaluated in two different ways. in order to evaluate the effectiveness of this browsing [24], system is measured with the well known precision measure used for different query in both normal browser and special browser and is shown by precision = total number of documents retrieved /total number of documents trained the precision is the probability that a (randomly selected) retrieved document is relevant. based on the measurement pertaining to precision is compared for both the blind user and with the non-blind users. it is found that the graph with precision taken in both experiments justifying equality in retrieval effectiveness. to evaluate the efficiency of access system, it is essential to find the mean time to access [8] [9] [10] the browser by both the vhu and non-vhu. this result is compared and is presented in the table 1. it is found that the vhu takes four times of the access time compared with the non-vhu-figure 8. figure 8: comparison of precision for both vhu and non-vhu 9 conclusion in the present work a special browser has been designed and developed for visually handicapped persons. the mean-time to access is taken into account for the vhu as well as for non-vhu (internet explorer 7.0 for non-blind person) and the comparative results are presented. in addition to this the precision has been measured under different situations. this browser is very much helpful visually handicapped persons of regional language to access the internet without any difficulties. the system is able to read only the static websites. the user is not able to travel into complete set of hyperlinks provided in the site and the present browser functionality is restricted to access the text messages alone. further work is necessary to take 46 r. ponnusamy, m.s.s. babu, t. chitralekha care of these problems of this system. bibliography [1] serge resnikoff et al, global magnitude of visual impairment caused by uncorrected refractive errors in 2004, bulletin of the world health organization, 86(3):63-70, jan 2008. 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[20] jing xiao, guanneng huang,yong tang, an open source web browser for visually impaired, third int. conference on intelligent computing, icic 2007, lncs, vol. 4681: 90-101, 2007. [21] ibm accessibility internet browser for multimedia, a tool that enables multimedia content on the internet to be enjoyed by people with visual impairments, september 27, 2007, http://www.alphaworks.ibm.com /tech/aibrowser [22] tim morris et. al., clearspeech: a display reader for the visually handicapped, ieee transactions on neural systems and rehabilitation engineering, 14(4):492-500 , dec 2006. [23] tadayoshi fujiki et. al., a tool for improving the web accessibility of visually handicapped persons, j med syst, 30: 83–89, 2006. [24] barbara leporini and fabio patern, applying web usability criteria for vision-impaired users: does itreally improve task performance?, intl. journal of human–computer interaction, 24:17–47, 2008. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 2, month: april, year: 2022 article number: 4502, https://doi.org/10.15837/ijccc.2022.2.4502 ccc publications segmentation method of magnetic tile surface defects based on deep learning y. an, y.n. lu, t.r. wu yu an college of computer science and technology jilin university, changchun 130012, china anyu19@mails.jlu.edu.cn yinan lu* college of computer science and technology jilin university, changchun 130012, china *corresponding author: luyn@jlu.edu.cn tieru wu college of mathematics jilin university, changchun 130012, china wutr@jlu.edu.cn abstract magnet tile is an essential part of various industrial motors, and its performance significantly affects the use of the motor. various defects such as blowholes, breaks, cracks, frays, unevenness, etc., may appear on the surface of the magnet tile. at present, most of the defects rely on manual visual inspection. to solve the problems of slow speed and low accuracy of segmentation of different defects on the magnetic tile surface, in this paper, we propose a segmentation method of the weighted yolact model. the proposed model uses the resnet101 network as the backbone, obtains multi-scale features through the weighted feature pyramid network, and performs two parallel subtasks simultaneously: generating a set of prototype masks and predicting the mask coefficients of each target. the residual structure and weights are introduced in the prediction mask coefficient branch. linearly combine the prototypes and the mask coefficients to generate masks to complete the final target segmentation. the experimental results show that the proposed method achieves 43.44/53.44 mask and box map on the magnetic tile surface defect dataset, and the segmentation speed reaches 24.40 fps. compared with other segmentation methods, our method is more excellent in performance. keywords: yolact network, deep learning, target detection, defect segmentation. https://doi.org/10.15837/ijccc.2022.2.4502 2 1 introduction magnet tile is a tile-shaped permanent magnet widely used in various motors and is the core component of the engine. in the production process of magnet tiles, various surface defects will appear on the product’s surface due to multiple reasons, such as raw materials and equipment. the more significant defects are frays, breaks, and unevenness, and the smaller ones have blowholes and cracks. in china, most of the defects on the surface of products still rely on manual visual inspection and visual equipment inspection. due to the cognition difference of manual inspection personnel and the wide variety of surface defects of the magnetic tile, it is difficult for workers to fully and correctly evaluate the quality of the magnetic tile. the equipment can detect relatively large defects, but it is difficult to detect minor flaws such as blowholes and cracks. therefore, finding an accurate and fast automatic segmentation method of magnetic tile surface defects is essential. the early automatic segmentation of magnetic tile surface defects was mainly based on the use of traditional image processing techniques. li et al. used texture analysis and discrete curve wave transform to process the image of the surface defect of the magnetic tile. under the condition of strictly controlled light, the grinding texture was effectively suppressed, and the effect of splitting crack defects was better [9]. liu et al. enhanced the defects, suppressed the background texture through the notch filter, and then used the canny edge detection operator to segment the magnetic tile surface defects [11]. yang et al. first used nsst to decompose the image in multi-scale and multi-direction, performed corresponding processing in the high and low-frequency domains, and then segmented the crack defects from the reconstructed image. however, this method required high contrast of fine cracks and took a long time [19]. in recent years, many scholars have successively proposed many defect segmentation methods based on machine vision represented by convolutional neural networks. the segmentation method based on deep learning gradually replaced the traditional segmentation method. existing deep learning segmentation methods are mainly divided into two categories. one is two-stage segmentation methods represented by faster r-cnn [14] and mask r-cnn [4]; the other is one-stage represented by yolact [1] and solo [16]. the two-stage method divides the segmentation task into two stages. first, the regional candidate network (rpn) generates a candidate area. then, the detection network detects the category and location of the candidate area and performs segmentation in the detection frame. the accuracy of this method is higher, but the speed is slightly slower. the one-stage method does not require the rpn stage, and the segmentation result can be obtained directly. therefore, the segmentation speed is faster, but the accuracy is relatively low. deep convolutional neural networks have shown excellent performance in the field of image segmentation. there is more research work in magnetic tile defect detection, but there are fewer applications in the field of segmentation. nowadays, there are many improved mask r-cnn models to study defect segmentation. wu et al. improved the feature pyramid network of the mask r-cnn model to a combination of top-down and bottom-up, modified the anchor’s size, and introduced a dropout layer in feature extraction. the improved mask r-cnn model realized the integration of workpiece detection and segmentation [18]. wang et al. applied mask r-cnn to segment wheel defect images for the first time, using deep convolutional neural networks to identify and segment different wheel defects [17]. although mask r-cnn has a high segmentation accuracy, it takes a long time. on the other hand, the yolact model of the latest single-stage real-time instance segmentation published in 2019 is relatively balanced between segmentation accuracy and speed. therefore, it is one of the current excellent segmentation algorithms. although the yolact also has problems in the test process, the target cannot be accurately located when the scene is complex, and the mask overlaps between two instances close to each other. however, the magnetic tile surface defect segmentation studied in this paper does not have this problem because each picture has only one defect. therefore, the yolact model is suitable for the segmentation task of magnetic tile surface defects. however, as a one-stage method, the yolact algorithm has the problem of low segmentation accuracy. therefore, we propose a weighted yolact (weighted-yolact) method, referred to as w-yolact, to study the segmentation of magnetic tile surface defects. by weighting the feature pyramid and adding residual structure and weights to the prediction mask coefficient branch, we design a magnetic tile https://doi.org/10.15837/ijccc.2022.2.4502 3 defect segmentation algorithm that achieves higher accuracy while ensuring real-time speed. the paper is organized as follows: the first section focuses on the background and significance of the research problems, introduces the related work, and presents the corresponding solutions we propose. the second section explains the detailed structure and innovation of our model. the third section elaborates the experimental process, results, and analysis in detail. finally, the fourth section summarizes the paper. 2 network model 2.1 yolact model the yolact network model divides the instance segmentation task into two parallel subtasks, separating the calculations into parallel, significantly improving the calculation speed. thus, it can achieve real-time speed while generating high-precision masks. the model in this paper is improved based on the yolact network. the dashed box is our improved content, as shown in figure 1. the backbone of the model is composed of the residual network resnet101 model [5] and the weighted feature pyramid network (described in detail in section 2.2 below), mainly for image feature extraction. the weighted feature pyramid is composed of layers p3 to p7, among which layers p3 to p5 are calculated from layers c3 to c5 corresponding to resnet101. the p5 is down-sampled to get the p6 and p7. input p3 into the prototype mask branch, and complete the prediction of k prototypes through the fully convolutional neural network. the other branch predicts the mask coefficients (described in section 2.3 below), which input is the feature maps of layers p3 to p7. based on anchor target detection, a third small branch is added to predict k mask coefficients. finally, three results of target category confidence, bounding box offset, and mask coefficient are predicted. the same operation is performed on layers p3 to p7, and these results are spliced together. then fast non-maximum suppression algorithm (fast nms) is used to suppress repeated detection. next, linearly combine the prototype mask and the corresponding mask coefficients to get the optimal segmentation map. then, through the cropping and thresholding of each instance, the final segmentation of each instance is determined. figure 1: yolact model the loss function used by yolact is the linear sum of the three losses of classification loss lcls, detection frame regression loss lbox, and mask loss lmask. the weight ratios α, β, and γ are 1, 1.5, and 6.125, respectively. the larger the weight, the greater the contribution of the mission loss. among them, the calculation methods of classification loss and detection frame regression loss are the same as those in ssd [12], which are softmax cross-entropy and smooth-l1 loss functions [3]. the mask loss uses the bce(m,mgt) function to calculate the pixel-by-pixel binary cross-entropy loss between the generated mask m and the ground truth mask mgt. thus, the joint loss function formula of yolact is formula (1): https://doi.org/10.15837/ijccc.2022.2.4502 4 loss = αlcls + βlbox + γlmask (1) 2.2 weighted feature pyramid network feature pyramid network (fpn) is a method of horizontally connecting, top-down, and extracting various scale features in images. it combines high-level semantic information-rich features with lowlevel precise positioning signals to strengthen the expression of the network’s backbone [10]. the feature pyramid network and various variants based on it are widely used in target detection tasks. bifpn [15] is an improvement based on panet [13]. bifpn balances the feature information of different scales by letting the network learn the weights of different input features by itself. if the input and output are at the same level, bifpn will add an extra edge to integrate more features without increasing cost, which is more concise and efficient than nas-fpn [21]. zhao et al. proposed combining the spatial attention module with the channel attention module, which paid attention to local and global features and improved the expression of comprehensive features. the combination of dual attention modules allowed buildings to be extracted from complex backgrounds [20]. the attention mechanism inspires us to add self-learning weights to the particular feature pyramid in the yolact model. weighted feature pyramid network is referred to as wfpn. in the past, feature fusion is to add features of different scales directly. however, features of different scales contribute differently to the final output and need to be treated differently. therefore, we introduce weights (similar to attention) based on fpn, give different weights to features of different scales, and then add them to better balance multi-scale feature information. as a result, wfpn can perform multi-scale feature fusion simply and quickly, enabling deeper feature maps to generate a more robust mask. in addition, a larger prototype mask can ensure that the final mask has a higher quality and better detection of small objects, making the features learned by the neural network richer and more conducive to segmenting target objects of different sizes. figure 2: wfpn structure https://doi.org/10.15837/ijccc.2022.2.4502 5 the structure of wfpn is shown in figure 2, where c1-c5 represent feature maps of different scales output by the backbone network resnet101. p3-p7 represent the feature maps of different scales output by this wfpn. the pink box represents 1×1 convolution, the purpose of which is to unify all feature maps into 256 channels. the purpose of the bilinear interpolation operation is to enlarge the feature map. the purple box is a 3×3 convolution operation to prevent feature discontinuities caused by the superposition of the up-sampled image and the original image. using the convolution operation is equivalent to the re-extraction of the features to ensure the stability of the features. m5 is obtained by convolution of c5, then perform a bilinear interpolation on m5 to double the feature map, and add the convolved c4 according to the parameter ratio to obtain the intermediate node m4. then perform the convolution operation on m4 to obtain p4. p3 can be obtained in the same way. in addition, m5 is down-sampled by 3×3 convolution to obtain p6. p7 is obtained by 3×3 convolution on p6. next is the parallel operation. p3 is sent to the prototype mask branch, and p3-p7 are sent to the prediction mask coefficient branch at the same time. the calculation formula is as (2): p4 = conv ( wp4_0 ∗conv(c4) + wp4_1 ∗ interpolate(m5) wp4_0 + wp4_1 + ε ) (2) where p4 is the output feature of the fourth level on the top-down path. m5 is the intermediate feature of the fifth level on the top-down path. c4 is the fourth-level input feature on the bottomup path. the outer layer conv is a 3×3 convolution operation, and the inner layer conv is a 1×1 convolution operation. interpolate is a bilinear interpolation operation. wp4_0 and wp4_1 are the parameters that need to be learned, ε=0.0001, in order to avoid numerical instability. 2.3 prediction mask coefficient branch the prediction mask coefficient branch is to predict the coefficient of each mask. based on the class and box branches of the target detector, an additional mask branch is added to predict k mask coefficients, and each mask coefficient corresponds to a prototype. this paper introduces the residual structure [6] and weights based on the original yolact, gives the forward neural network and the shortcut connection with different weights, and then adds them to better balance the characteristic information of the residual structure. as a result, it guarantees the depth of the network and avoids network degradation. the extracted information is also richer. figure 3: prediction head the network structure of the prediction head is shown in figure 3. its input is the p3-p7 five-level feature maps, and the three branches use a shared 3×3 convolutional layer to increase the speed. the shared convolution part is realized through the residual structure and weights. the original feature map pi and the pi after three 3×3 convolutions are added according to the weight ratio. the result of the addition is input three branches respectively, and finally, three types of output are generated: category confidence. the mt data set used in this article has six categories (including background), namely c=6. each pixel generates three anchors (a=3). the ratio is 1:1, 1:2, and 2:1. its dimension is a×6; position offset. the position is positioned by the four vertices of the bounding box. the dimension is a×4; mask coefficient. k represents the number of prototypes, set to 32, and the mask coefficient dimension is a×32. among them, w0 and w1 are the parameters that need to be trained. the operations of p3-p7 are the same, and the final output result is obtained by adding these results. https://doi.org/10.15837/ijccc.2022.2.4502 6 3 experiment 3.1 experimental environment the experiment is carried out in the environment of intel core i5-6500 cpu, geforce rtx 2080 ti graphics card, ubuntu 18.04, and 64-bit version. the network model of this article is based on the cuda10.1+pytorch1.4.0 deep learning framework. the running environment is the integrated environment of python 3.6 under anaconda. 3.2 dataset the magnetic tile surface defect data set [7] used in this article was collected and published by a research group of the institute of automation, chinese academy of sciences, with 784 images. there are 392 defective images and 392 marked images, and each defective image has its corresponding binary mask marked image. the defective image is in jpg format, and the marked picture is in png format. there are five kinds of defective images, among which 115 are blowholes, 85 are breaks, 57 are cracks, 32 are frays, and 103 are unevenness. 3.3 data preprocessing in deep learning, most models require a sufficient number of samples because the larger the number of samples, the better the effect of the trained model and the stronger the generalization ability. however, the sample quality is often poor in the actual research process, or insufficient sample quantity. therefore, it is necessary to perform data enhancement processing on the sample to improve the sample quality. taking images as an example, the methods of data enhancement include data inversion, data rotation, image scaling, image cropping, and image translation. since the number of images in the magnetic tile defect dataset is too small to support deep learning research, the images are data enhanced. firstly, rename all pictures and add the defect name to the picture name. next, rotate all images by 90 degrees, 180 degrees, 270 degrees, and then flip all the original and rotated pictures horizontally, vertically, and horizontally and vertically. in the end, there are 3840 defective images in the training set and 1088 defective images in the verification set. finally, the binary mask label information is converted into a json file in the format ms coco. 3.4 evaluation indicators the model in this paper uses the classic evaluation method of target detection and instance segmentation algorithms, mean average precision (map). accuracy rate (p) indicates how accurate the model predicts a certain type of samples and the ratio of the positive samples to the total number of such samples in the prediction result. recall rate (r) represents the ratio of the number of samples correctly predicted by a certain sample type to all the ground truth of this sample type. the ap value is the average value of the maximum precision rate under different recall rates (0-1). the ap values of all detected defects are averaged to obtain map, as shown in formula (3). different thresholds are used to evaluate the overall performance of the detector. in this paper, the map value is calculated every 0.05 in the interval of the iou threshold from 0.5 to 0.95. map = 1 c c∑ i=1 api (3) where c is the number of detection target classifications, and api is the segmentation accuracy of a certain class. this article uses 101-point interpolation to calculate the ap value, and the calculation formula is as (4): ap = 1 101 ∑ r ∈{0, 0.01, . . . , 1}pinterp(r) (4) https://doi.org/10.15837/ijccc.2022.2.4502 7 where pinterp(r) represents the interpolation value corresponding to each point that is greater than or equal to the largest precision value in the recall interval, the formula is as (5): pinterp(r) = max r̄:r̄≥r p(r̄) (5) fps is an index used to evaluate the segmentation speed of the network model, that is, the number of pictures that can be segmented per second, and the calculation formula is as shown in formula (6): fps = numimage alltime (6) where numimage is the total number of pictures, and alltime is the total time of segmentation. 3.5 training details when the model is trained, using yolact’s loss function, the input image size is 550×550. we train models with batch size 8 on a single 2080 ti gpu using resnet101_reducedfc model weights pre-trained on imagenet. we train with sgd for 300k iterations starting at an initial learning rate of 0.001, dividing by 10 at iterations 30k, 80k, 150k, 220k iterations, and using a weight change gradient decay of 0.0005, a momentum of 0.9. 3.6 experimental results the three-loss function changes of the classification loss class loss, the detection frame regression loss b-box loss, and the mask loss mask loss in the training process of w-yolact are shown in figure 4. the linear sum of the three losses is the overall training loss. as shown in figure 5, it tends to converge after 100,000 iterations and is basically unchanged. figure 4: change graph of each loss we use the mask r-cnn model commonly used in the segmentation field to do the comparative experiment in this article. figure 6 is the comparison result of defect segmentation. the first line is the original image, the second line is the ground truth of the image segmentation mask, the third line is the segmentation effect of the mask r-cnn method, and the fourth line is the segmentation effect of the yolact method. the fifth line is the segmentation effect of our method. the five columns on the graph are named five defect names. it can be seen from figure 6 that our method can segment the defects better. compared with the mask r-cnn method with the highest accuracy in the image segmentation field, the classification accuracy of crack and uneven defects is slightly worse, but the classification accuracy of the other three defects is equal. the positioning and segmentation effects on break, crack, even, and fray defects are better than mask r-cnn. https://doi.org/10.15837/ijccc.2022.2.4502 8 figure 5: change graph of total training loss it can be seen from the break defect that the right side of the original yolact method positioning frame exceeds the actual defect position. the crack’s head and tail defects are insufficient in positioning and segmentation, and the entire crack is not completely covered. throughout the five defects, the classification accuracy of the method proposed in this paper is higher than that of yolact, and the positioning frame and segmentation mask are also better than the yolact method. figure 6: comparison results of defect segmentation the evaluation results of the improved methods of the yolact algorithm in this paper are shown in table 1. we calculate the map value every 0.05 on the interval of the iou threshold value 0.500.95. take the average of all the results as the final result (all). each improved method is added to the yolact algorithm separately, and the map of the mask and box is improved. wfpn enhances feature extraction ability, improves the segmentation effect, and increases the map of the mask by 0.26. the addition of the residual structure makes the information extracted by the network richer, https://doi.org/10.15837/ijccc.2022.2.4502 9 table 1: improved algorithm accuracy comparison table all .50 .55 .60 .65 .70 .75 .80 .85 .90 .95 fps mask /map (%) yolact 42.18 76.86 70.42 61.85 55.03 49.16 42.60 30.09 18.11 11.53 6.19 25.72 +wfpn 42.44 77.56 69.06 62.32 55.88 49.27 43.23 29.77 19.03 12.13 6.13 25.87 +residual 42.46 78.68 71.31 63.59 55.33 48.85 40.70 27.86 18.95 13.02 6.30 24.61 +wfpn +residual (ours) 43.44 78.20 72.92 65.89 56.56 49.41 43.28 30.32 18.56 12.47 6.79 24.40 box /map (%) yolact 52.71 88.93 84.27 78.63 73.38 64.37 52.71 40.49 25.35 15.08 3.85 25.72 +wfpn 52.23 86.74 84.18 77.95 72.35 61.46 50.96 40.85 27.97 15.67 4.21 25.87 +residual 53.50 88.21 85.37 80.00 74.19 64.23 51.89 41.07 28.15 18.71 3.21 24.61 +wfpn +residual (ours) 53.44 88.48 85.43 79.44 72.49 64.25 52.17 41.30 28.44 17.28 5.17 24.40 table 2: various types of defect recognition accuracy table mask/ap(%) box/ap(%) iou=0.50 blowhole break crack fray uneven blowhole break crack fray uneven yolact 89.47 68.40 60.20 84.55 81.10 97.30 88.53 79.46 93.65 85.53 ours 90.89 72.14 58.03 86.91 82.63 97.15 88.64 74.52 94.59 87.25 which increases the map of the mask by 0.28. wfpn and residual structure are jointly added to the yolact model to make the map of the mask reach 43.44, which is an apparent increase of 1.26, and the map of the box reaches 53.44, which has a good improvement effect. segmentation speed running on a single 2080ti gpu was still above 24 fps. thus, under the premise of ensuring the speed, we have achieved the goal of improving accuracy. table 2 shows the average accuracy of various types of defects when iou=0.50. among them, blowhole, break, fray, and uneven defects segmentation effects have been improved and higher accuracy. however, the average accuracy rate of crack defects segmentation is low and has declined. it can also be seen in figure 6 that the crack classification accuracy rate is low, and the detection effect is poor. the model needs to be improved to solve this shortcoming. on the whole, the improved model has higher segmentation and detection accuracy than the original yolact model. comparing mask r-cnn, centermask [8], yolact, yolact++ [2] methods, and our method, the results are shown in table 3. compared with mask r-cnn and centermask, our method has faster segmentation speed and higher map. although yolact++ has a higher map than yolact’s mask and box, the fps drops more, so we select the yolact model as the basic network. compared with the yolact model, our method improves the mask map of defect segmentation by 1.26 and the box map by 0.73. the speed drop is not obvious, and it is also improved compared with yolact++. table 3: comparison of segmentation performance of different algorithms mask/map(%) box/map(%) fps all .50 .75 all .50 .75 mask r-cnn 36.10 68.20 31.20 49.50 77.30 55.40 12.90 centermask 36.15 72.86 32.55 48.56 81.30 49.53 19.23 yolact 42.18 76.86 42.60 52.71 88.93 52.71 25.72 yolact++ 43.28 79.68 41.69 53.72 88.92 52.79 22.92 ours 43.44 78.20 43.28 53.44 88.48 52.17 24.40 https://doi.org/10.15837/ijccc.2022.2.4502 10 4 conclusion in this paper, the problem of segmentation of the surface defects of the magnetic tile is deeply studied, and the deep learning method is introduced into the segmentation of the surface defects of the magnetic tile. based on the yolact model, a weighted feature pyramid network is proposed, and the residual structure and weight are introduced. based on the above improvements, we propose an efficient segmentation model for magnetic tile surface defects (w-yolact). by comparing the results of ablation experiments, the detection accuracy of the w-yolact model has been improved to a certain extent, and the model’s performance in the segmentation task of magnetic tile surface defects is improved under the requirements of hardware resource constraints and maintaining speed. compared with the other four segmentation models, the w-yolact model has a faster segmentation speed and good segmentation accuracy. therefore, it provides a reliable basis for future applications in industrial production and has high research significance. however, for crack defects, the method in this paper has the problem of low recognition accuracy. in the future, we will focus on improving the algorithm to satisfy the comprehensive defect segmentation further. funding this work was supported by the national natural science foundation of china(grant no. 61872162). author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] bolya, d.; zhou, c., xiao, f. (2019). yolact: real-time instance segmentation, proceedings of the ieee/cvf international conference on computer vision, 9157-9166, 2019. [2] bolya, d.; zhou, c., xiao, f. (2020). yolact++: better real-time instance segmentation, ieee transactions on pattern analysis and machine intelligence, 99, 1-1, 2020. [3] girshick, r. (2015). fast r-cnn, 2015 ieee international conference on computer vision (iccv), 1440-1448, 2015. [4] he, k.; gkioxari, g.; p dollár. (2017). mask r-cnn, proceedings of the ieee international conference on computer vision, 2961-2969, 2017. [5] he, k.; zhang, x.; ren, s.; sun, j. (2016). deep residual learning for image recognition, 2016 ieee conference on computer vision and pattern recognition (cvpr), 770-778, 2016. [6] he, k.; zhang, x.; ren, s. (2016). identity mappings in deep residual networks, springer, cham, 630-645, 2016. [7] huang, y.; qiu, c.; guo, y.;wang, x.;yuan, k. (2018). surface defect saliency of magnetic tile, 2018 ieee 14th international conference on automation science and engineering (case), 612617, 2018. [8] lee, y.; park, j.; (2020). centermask: real-time anchor-free instance segmentation, 2020 ieee/cvf conference on computer vision and pattern recognition (cvpr), 13906-13915, 2020. [9] li, x. q.; jiang, h. h.; yin, g. f. (2014). detection of surface crack defects on ferrite magnetic tile, ndt and e international, 62, 6-13, 2014. https://doi.org/10.15837/ijccc.2022.2.4502 11 [10] lin, t. y.; dollar, p.; girshick, r. (2017). feature pyramid networks for object detection, 2017 ieee conference on computer vision and pattern recognition (cvpr), 936-944, 2017. [11] liu, g. p.;hu, h. x.;hu, r. h. (2015). magnetic tile surface defect extraction based on texture features, modern manufacturing engineering, 07, 119-123, 2015. [12] liu, w.; anguelov, d.; erhan, d. (2016). ssd: single shot multibox detector, springer, cham, 21-37, 2016. 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[18] wu, x. y.(2020). detection and segmentation of industrial ct image defects by mask rcnn[d], lanzhou jiaotong university, 2020. [19] yang, c. l.;yin, m.;jiang, h. h.(2017). magnetic tile surface crack detection based on nonsubsampled shearlet transform, transactions of the chinese society of agricultural machinery, 48(03), 405-412, 2017. [20] zhao d.d.; zhao h.s.; guan r.c.; yang c. (2021). efficient building extraction for high spatial resolution images based on dual attention network, international journal of computers communications & control, 16(4), 4245, 2021. [21] zoph, b.; vasudevan, v.;shlens, j.;le, q. v.(2018). learning transferable architectures for scalable image recognition 2018 ieee/cvf conference on computer vision and pattern recognition, 8697-8710, 2018. https://doi.org/10.15837/ijccc.2022.2.4502 12 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: an y.; lu y.n.; wu t.r.(2022). segmentation method of magnetic tile surface defects based on deep learning, international journal of computers communications & control, 17(2), 4502, 2022. https://doi.org/10.15837/ijccc.2022.2.4502 introduction network model yolact model weighted feature pyramid network prediction mask coefficient branch experiment experimental environment dataset data preprocessing evaluation indicators training details experimental results conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 3, month: june, year: 2020 article number: 3874, https://doi.org/10.15837/ijccc.2020.3.3874 ccc publications a model to evaluate the organizational readiness for big data adoption m. nasrollahi, j. ramezani mahdi nasrollahi imam khomeini international university (ikiu) qazvin, iran m.nasrollahi@soc.ikiu.ac.ir javaneh ramezani* 1. nova university of lisbon, faculty of sciences and technology campus da caparica, 2829-516 monte caparica, portugal m.ramezani@campus.fct.unl.pt 2. uninova-cts campus da caparica, 2829-516 monte caparica, portugal *corresponding author: ramezanijavaneh@gmail.com abstract evaluating organizational readiness for adopting new technologies always was an important issue for managers. this issue for complicated subjects such as big data is undeniable. managers tend to adopt big data, with the best readiness. but this is not possible unless they can assess their readiness. in the present paper, we propose a model to evaluate the organizational readiness for big data adoption. to accomplish this objective, firstly, we identified the criteria that impact organizational readiness based on a comprehensive literature review. in the next step using principal component analysis (pca) for criterion reduction and integration, twelve main criteria were identified. then the hierarchical structure of criteria was developed. further, fuzzy bestworst method (fbwm) has been used to identify the weight of the criteria. the finding enables decision-makers to appropriately choose the more important criteria and drop unimportant criteria in strengthening organizational readiness for big data adoption. statistics-based hierarchical model and mcdm based criteria weighting have been proposed, which is a new effort in evaluating organizational readiness for big data adoption. keywords: organizational readiness, big data adoption, industry 4.0, fuzzy best-worst method, principal component analysis. 1 introduction the transformation of industrialized economies from a manufacturing base to a service orientation is an ongoing phenomenon. contemporary, developments in information and communication technology helped organizations to access information about their market, competitors, customers, partners https://doi.org/10.15837/ijccc.2020.3.3874 2 and . . . more easily and quickly, and this can positively impact their business processes. furthermore, due to the rise of ever-tougher challenges in the ever-changing business environments characterized by high complexity and uncertainty, there is a clear need to respond in almost real-time to upcoming business challenges and opportunities [3]. besides, one of the most important outcomes of the emerging fourth industrial revolution (industry 4.0) paradigm empowered by the integration of internet of things (iot) technology into industrial value creation is the large generating volumes of data [19]. for these reasons, con-temporary developments in information and communication technology, and growing competitiveness have led to the emergence of the real-time big data analytic as a new enabler of sustainable development and sustainable competitive advantage [11]. knowledge is a fundamental source of competitive advantage. therefore, mining data to extract useful information about competitors, customers, suppliers, etc. and also to observe and track trends and changes in the business environment is becoming extremely beneficial for making intelligent business decisions. big data and its associated technologies are now relevant across industries and economic sectors [11], [22], [24]. while the term “big data” has been defined in different ways in the related literature sources, there is still no concrete definition to this term itself. the first concepts predominantly associated with big data are far from new and relate to data storage and data analysis. this is in part due to the role of digital data– available in fast-growing amounts being produced by data-intensive organizations [22]. the fundamental notion primarily describes big data using the “3 vs”: volume (referring to the amount of data), velocity (relating to speed by which data is generated and delivered), and variety (referring to diversity of data sources and formats) [5]. soon the idea evolved to “5 vs” by adding veracity (referring to data quality) and value (relating to the benefits from the use of data) to the former “3 vs” [4]. recently, big data has been characterized “by 7 vs: volume, velocity, variety, veracity, variability visualization, and value [12]. these characteristics of big data have been explained in a recent literature review [18]. in a nutshell, big data, as identified by [6], is a combination of architectures and innovative technologies designed to capture the value and vital information from vast volumes of data in different variety, resulting in data analysis and high-velocity capture. in this regard, the field of big data adoption looks at how we can analyze, systematically extract information from, or in other words, deal with big data [22], [24]. the foremost opportunity and benefit big data adoption presents is resourcefulness in terms of cost, productivity, and competitiveness. big data adoption in enterprises soared from 17% in 2015 to 59% in 2018, reaching a compound annual growth rate (cagr) of 36% [26]. nevertheless, according to a comprehensive big data analytic study, only 14% of enterprises have put big data projects into production [27]. again, according to [26], “there is a strong upward trend in adoption and a corresponding drop in those with no plans.” against this background, big data adoption decisions involve high levels of uncertainty and complexity that most are related to the sophisticated technology and infrastructural requirements for organizations. therefore, there is a need for a more systematic and appropriate study of tools for assessing the potential of organizations to develop big data implementation. accordingly, this paper has attempted to develop a model to measure the level of organizational readiness for big data adoption following the objectives listed below: (1) identifying the criteria that are involved in evaluating the organizational readiness for big data adoption. (2) reduction and integrating of criteria using factor analysis and developing the hierarchical structure. (3) determining the weights of criteria using the fuzzy bwm method. (4) establishing hierarchy among criteria. the remainder of this paper is organized as follows. section two briefly describes the design and methodology of the research. section 3 highlights the critical criteria for evaluating organizational readiness. section 4 provides an overview of fuzzy bwm and highlights the detailed procedure for determining the importance of criteria using fuzzy bwm; describing the fuzzy-ism and using it for modeling criteria is discussed in section 5. the last section presents the conclusion and further research directions. https://doi.org/10.15837/ijccc.2020.3.3874 3 2 research methodology research methods are the strategies, processes, or techniques utilized in the collection of data or evidence for analysis in order to uncover new information or create a better understanding of the topic. the main purpose of this study is to identify and rank the organizational readiness criteria for big data adoption. to fulfill this objective, we applied field data with a focus on large scale organizations and experts with experience or expertise in this field. we need to perform a preliminary factor analysis and item reduction of evaluation criteria to achieve this purpose. completing the factor analysis and modification of items is essential for several reasons. first, the number of identified criteria is 50 items and is too long, and some criteria have repetitive meaning. second, several criteria required consistent clarification. for factor analysis, a principal component analysis (pca) was selected to consolidate the criteria. we applied an internet-based questionnaire to gather data from a sample of experts around the world. the questionnaires were sent to respondents through an email process and messages on linkedin. from 5th-10th september 2019, a questionnaire was sent to 2120 experts. the limited available space does not allow to provide more details about questionnaires and experts. a total of 246 answers from experts were received over 30 days. 39 answers were eliminated as not being correctly completed. finally, only answers from 207 experts that completed the survey were kept for an analysis. thus, data from 207 experts were included in the data set yielding a valid response rate of 9.8 percent. all data were analyzed with version 25.0 of ibm spss. table 1 shows the demographics of the sample. table 1: sample demographics item n % job title managing director 34 16/4 vice president 20 9/7 consultant 33 15/9 university professor/lecturer 45 21/7 information system manager 27 13/0 researcher 38 18/4 other 10 4/8 education bachelor’s degree 4 1/9 master’s degree 76 36/7 professional degree 8 3/9 doctorate 106 51/2 postdoctoral 13 6/3 age under 24 3 1/4 25–34 92 44/4 35–44 76 36/7 45-54 31 15/0 over 55 5 2/4 work experience 1-5 43 20/8 6-10 82 39/6 11–15 29 14/0 16–20 44 21/3 over 21 9 4/3 total 207 100 figure 1 shows various steps of research process. https://doi.org/10.15837/ijccc.2020.3.3874 4 figure 1: research process 3 identification of organization readiness criteria although organizations invest in big data for its recognized benefits, the actual realization of big data benefits lies in the complete readiness of organizations to adopt big data. based on a comprehensive review of the relevant literature, 50 initial criteria for evaluating organizational readiness for big data adoption have been identified (table 2). an initial sample of 30 experts was completed the questionnaire’s 50-item draft version. cronbach’s alpha was applied to verify the internal consistency of the constructs and to measure the scale reliability. the results in table 3 show that the alpha value is above 0.7, and constructs are consistent accordingly. data normality must be evaluated before running the statistical tests. the normality of the data was tested at a meaning level of 5% using the k-s test. the level of significance for all constructs was greater than 5 percent, so the data had a normal distribution. the adequacy of the data set was determined by the use of bartlett’s test for sphericity and the "kaiser meyer olkin" measure of sampling adequacy. as specified in table 4, bartlett’s test for sphericity was significant (p < 0.05), and the kmo index was 0.916; therefore, the sample was determined to be adequate for further analysis. principal component analysis (pca) is a method of consolidating mutually correlated criteria to a smaller number of criteria [9]. after the pca is run, eigenvalue is analyzed to aid in selecting the number of factors that have to be extracted as part of the next step. table 5 aid in deciding the number of criteria to be obtained. from table 5, it is seen that criterion 12 has a variance of 1.326; and variance of 1.0 and above shows that there is considerate variability in the criterion. thus, it leads to con-ducting a 12-criterion extraction. in an attempt to propose the underlying theme or criterion within each domain, the research team, grouped the items from the matrix of pattern correlation using the varimax rotation on these 12 domains. rotation optimizes the criterion structure and improves the interpret ability of the criterion solution. the unrotated and varimax rotation factors were analyzed, and the variables were categorized under criteria based on their maximum absolute value. the result was a decrease from 50 criteria in the initial study to 12 criteria. a name was selected for each criterion according to the type and structure of the criteria. figure 2 shows the hierarchical structure of organizational readiness evaluation criteria for big data adoption. https://doi.org/10.15837/ijccc.2020.3.3874 5 table 2: factors affecting big data adoption criteria references appropriateness [24], [2], [23] availability of big data tools [2], [17], [13] big data awareness [8], [16] business strategy orientation [24], [2] change efficacy [24], [2] competitive (perceived industrial pressure) [24], [2], [13],[14], [10], [25], [1], [15] complexity [24], [2], [16], [14], [15] cost of adoption [24], [2], [15] data control [2], [1] data quality and integration [22], [12], [2], [17], [10], [25], [1], [15] decision-making culture [24], [2] firm size [24], [2], [15] government support, laws and policy [24], [12], [2], [13], [14], [10] human resources [24], [2], [13] industry type [25] information security culture [22] internal versus external technologies [2], [17], [13] interpret unstructured data [2], [1] is competence/it structure (infrastructure) [24], [2], [14], [10], [25], [15] is fashion [24], [2] it expertise [24], [14] knowledge about big data [2], [17], [13] leaders’ attitude towards change [15] management support for big data [22], [24], [18], [2], [10], [25], [15] market turbulence [24], [2] marketing and inventory [2], [13] network challenges [2], [17], [13] observe-ability [24], [2], [14] organizational (learning) culture [22], [2], [13], [18] organizational data environment [2], [25] perceived benefits (advantage) [24], [2], [15], [13], [14], [10], [25], [1] perceived compatibility [22], [24], [2], [25], [15] perceived simplicity (ease of use) [22], [2], [15], [18], [13], [14], [10], [25] perceived cost [2], [25] perceived financial readiness [2], [13], [14], [10] , [18] perceived usefulness [2], [25] predictive analytic accuracy [2] relative advantage [24], [2], [13], [14], [10], [15] risks of outsourcing [22], [2] security and privacy [22], [24], [2] staffing [2], [1], [18] supply chain connectivity [10] system integration [2], [1] technological capability [2] technology readiness/ technology resources [24], [2], [14], [10], [15], [18] trading partner adoption/ readiness [24], [2] training [2], [1], [18] trial ability [24], [2] vendor support [2], [17], [13] willingness to change [18] predictive analytic accuracy [2] https://doi.org/10.15837/ijccc.2020.3.3874 6 table 3: reliability statistics cronbach’s alpha cronbach’s alpha based on standardized items n of items .867 .892 50 table 4: kmo and bartlett’s test kaiser-meyer-olkin measure of sampling adequacy .916 bartlett’s test for sphericity approx. chisquare 1628.317 df 846 sig. .000 table 5: total variance explained component initial eigenvalues extraction sums of squared loadings rotation sums of squared loadings total variance (% ) cumulative (%) total variance (%) cumulative (%) total variance (%) cumulative (%) 1 26.368 36.080 36.080 26.368 36.080 36.080 7.819 11.644 11.644 2 8.116 11.105 47.185 8.116 11.105 47.185 8.730 13.001 24.645 3 6.540 8.949 56.134 6.540 8.949 56.134 7.694 11.458 36.103 4 5.342 7.310 63.444 5.342 7.310 63.444 6.173 9.192 45.295 5 4.390 6.007 69.451 4.390 6.007 69.451 5.784 8.613 53.909 6 3.095 4.235 73.686 3.095 4.235 73.686 4.909 7.311 61.220 7 2.716 3.716 77.402 2.716 3.716 77.402 3.775 5.622 66.842 8 2.487 3.403 80.805 2.487 3.403 80.805 3.580 5.331 72.173 9 2.225 3.045 83.850 2.225 3.045 83.850 3.160 4.707 76.880 10 1.843 2.522 86.371 1.843 2.522 86.371 3.344 4.980 81.860 11 1.581 2.163 88.535 1.581 2.163 88.535 3.002 4.471 86.330 12 1.326 1.814 90.349 1.326 1.814 90.349 2.698 4.019 90.349 13 0.946 1.531 91.880 14 0.865 1.184 93.064 15 0.791 1.082 94.146 16 0.733 1.003 95.149 17 0.669 0.915 96.065 18 0.578 0.791 96.856 19 0.537 0.735 97.590 20 0.492 0.673 98.264 21 0.445 0.609 98.872 22 0.358 0.490 99.362 23 0.284 0.389 99.751 24 0.182 0.249 100.000 25 0.000 0.000 100.000 26 0.000 0.000 100.000 . . . . . . . . . . . . 50 0.000 0.000 100.000 https://doi.org/10.15837/ijccc.2020.3.3874 7 figure 2: hierarchical structure of organizational readiness evaluation 4 determining the importance of criteria by fbwm in this stage, the importance of the identified criteria will be determined. these criteria and their weights can be used to assess organizational readiness for big data adoption. according to best-worst method (bwm) – introduced by rezaei [21]– the best and the worst criteria are identified first by the decision-maker, followed by pairwise comparisons conducted between each of these two criteria and the other criteria [20]. a maximin problem is then formulated and solved to determine the weights of different criteria. the salient features of the proposed method, compared to the existing multicriteria decision making (mcdm) methods, are: (1) it requires fewer comparison data; (2) it leads to more consistent comparisons, which means that it produces more reliable results”. fuzzy best-worst method (fbwm) is executed in 5 steps [7]. step 1. build the decision criteria system. in this step, we consider the criteria{c1, c2, . . . , cn} that should be used to arrive at a decision. step 2. determining the best (e.g., most important) and the worst (e.g., least important) criteria. in this step, the decision-maker identifies the best, and the worst criterion in general and no comparison is made at this stage. step 3. execute the fuzzy reference comparisons for the best criterion. the resulting fuzzy best-to-others vector would be ãb = (ãb1, ãb2, . . . , ãbn) where ãbj indicates the fuzzy preference of the best criterion over criterion j and it is clear that ãbb = (1, 1, 1). step 4. execute the fuzzy reference comparisons for the worst criterion. the resulting https://doi.org/10.15837/ijccc.2020.3.3874 8 fuzzy others-to-worst vector would be ãw = (ã1w , ã2w , . . . , ãnw ), where ãiw indicates the preference of the criterion j over the worst criterion and it is clear that ãw w = (1, 1, 1). step 5. finding the optimal fuzzy weights (w̃∗1, w̃∗2, . . . ,w̃∗n). the optimal fuzzy weight for the criteria is the one where, for each pair of w̃b w̃j and w̃j w̃w , we have w̃b w̃j = ãbj and w̃j w̃w = ãjw. to satisfy these conditions for all j, we should find a solution where the maximum absolute differences ∣∣∣wbwj −abj∣∣∣ and ∣∣∣ wjww−ajw∣∣∣ for all j is minimized. the optimization problem to determine the optimal weight of the criteria (w ∗1 , w ∗2 , . . . , w ∗n) is presented as the model (1): m in m ax j {∣∣∣wbwj − abj∣∣∣ , ∣∣∣ wjww − ajw∣∣∣} s.t :∑n j=1 wj = 1 wj ≥ 0 , f or all j (1) then, model (1) turns into the following optimization problem with nonlinear constraints: min ℘̃ s.t :∣∣∣∣w̃bw̃j −ãbj ∣∣∣∣ ≤ ℘̃ , for all j∣∣∣∣ w̃jw̃w−ãjw ∣∣∣∣≤ ℘̃ , for all j∑n j=1 r(w̃j) = 1 lwj ≤ m w j ≤ u w j lwj ≥0 j = 1, 2, . . . , n (2) where ℘̃ = (lwj , m w j , u w j ). considering l w j ≤ mwj ≤ u w j , we suppose ℘̃ ∗ = (k∗, k∗, k∗) , k∗ ≤ l℘, then nonlinear model (2) can turn into the model (3): min ℘̃∗ s.t :∣∣∣∣(lwb ,lwb ,lwb )(lw j ,lw j ,lw j )−(lbj , mbj , ubj ) ∣∣∣∣ ≤ (k∗, k∗, k∗) , for all j∣∣∣∣ (lwj ,lwj ,lwj ))(lw w ,lw w ,lw w )−(ljw , mjw , ujw ) ∣∣∣∣≤ (k∗, k∗, k∗) , for all j∑n j=1 r(w̃j) = 1 lwj ≤ m w j ≤ u w j lwj ≥0 j = 1, 2, . . . , n (3) by solving model (3), the optimal weights (w∗1, w∗2, . . . ,w∗n) are obtained. to determine the weights of the criteria (using fbwm), first, a customized questionnaire was devised and distributed among 18 experts. next, based on the opinions of the respondent experts, the most and the least essential criteria were established. in the next step, the best-to-others preference vector was determined. to do this, all 18 experts were asked to specify their most preferred criterion compared with the other criteria. afterward, the others-to-worst preference vector was also determined. the process of determining the latter was the same as that of the best-to-others vector. in the end, the optimization problem was expanded based on model (3) of the fbwm. after solving the model above using the computer software matlab, the final weights of the criteria were calculated (table 6) and depicted in a diagram (figure 3). table 6: criteria weights for organizational readiness assessment criterion technological wisdom feature cultural financial skills weight 0.126 0.028 0.206 0.035 0.063 0.058 criterion stakeholders managerial utility environmental organizational processing weight 0.122 0.091 0.154 0.029 0.031 0.056 https://doi.org/10.15837/ijccc.2020.3.3874 9 figure 3: criteria weights 5 conclusion big data is one of the issues that are considered by practitioners and scholars due to the high volume and speed of data exchange in today’s world. the study investigates the prospects of developing frameworks for big data adoption on well-established methodologies. in this paper, a proper procedure applied to identify effective criteria to evaluate organizational readiness for big data adoption and determine the relative importance of decision-making criteria. here, in this study, fifty initial criteria are taken up for the present work based on a comprehensive literature review. the spss software has been utilized for ease of analysis, reduction of items, and examining the interrelations among the variables. finally, fifty initial criteria are classified into12 criteria and used for further analysis. the hierarchical leveling of these criteria have been established. in further research, the implications of the calculated weights will be analyzed. this work developed an mcdm approach using the fbwm for the assessment of organizational readiness. results showed that big data features criterion (including trail ability, observe-ability . . . ) is the most critical criterion for big data adoption. the methodology developed in this paper helps the decisionmaker in identifying organization weaknesses for adopting big data by considering both conflicting quantitative and qualitative evaluation criteria in real-life applications. researchers can use more quantitative methods such as ism, (fuzzy) micmac, (fuzzy) dematel, and fcm to model the inter-relationships between criteria. acknowledgements this work was supported by the portuguese foundation for science and technology (fct) through the strategic program uid/eea/00066/2019 and the center of technology and systems (cts). author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] almoqren n.; altayar, 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[27] [online]. available: https://www.slideshare.net/denisreimer/big-data-industry-insights-2015, gartner, 2015. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: nasrollahi, m.; ramezani, j. (2020). a model to evaluate the organizational readiness for big data adoption, international journal of computers communications & control, 15(3), 3874, 2020. https://doi.org/10.15837/ijccc.2020.3.3874 international journal of computers communications & control issn 1841-9836, 10(5):702-717, october, 2015. an anticipatory control for a flexible manufacturing system based on the perception of mobile units using wsns j.a. silva-faundez, c. duran-faundez, p. melin, c. aguilera joaquin alder silva-faundez, cristian duran-faundez*, pedro melin, cristhian aguilera departamento de ingeniería eléctrica y electrónica, universidad del bío-bío avda. collao 1202, casilla 5-c, concepción, chile e-mail: joaquin.silvaf@ingenieros.com, {crduran, pemelin, cristhia}@ubiobio.cl *corresponding author: cduran@ubiobio.cl abstract: in this paper, we design and evaluate a control system which, by using as input rssi measures, allows anticipatory movements of robotic arms decreasing idle times at the cimubb laboratory. classical log-normal model, which relates the strength of a signal received by a node with the distance at which the sender of the signal is, was adopted. the hidden state of the system is determined by the extended kalman filter which allows us to estimate the distance and the speed of pallets moving over a closed-loop conveyor belt. from these estimates, remaining time in which the pallet will get to a stopping point near the robot is determined. this information is finally processed by a controller to determine the instant at which the robot must operate and handle the pallet. both, a proportional-integral and a fuzzy controller, were implemented and evaluated. results show the feasibility of using wireless signals to accomplish the described goal, with some practical restrictions. keywords: anticipatory control, intelligent manufacturing, wireless sensor networks, computer applications, localization. 1 introduction in the last few years, wireless sensor networks (wsns) [1] have attracted the industrial sector because of their many advantages such as low power consumption, high flexibility and scalability. indeed, many application cases of wsns, including factory and building automation, inventory management, efficient irrigation, cattle tracking and many others, nowadays exist [2–6]. moreover, latest works on new approaches, such as the “active products”and the “ambient intelligence”, have considered wsns as a supporting technology because they represent an efficient way to embed autonomous intelligence, sensing and communication capabilities on multiple objects (see, for example, works in [7–9]). on the other hand, localization and positioning of people, machines, vehicles, and other entities moving in automated environments are highly desired capabilities in modern industries and services. indeed, these capabilities may allow significant improvements in process control, monitoring, tasks distribution, resources optimization, safety, and others. in certain application cases, these can be achieved thanks to very accurate location systems, such as the global positioning system (gps) [10] and ubisense [11]. in the matter of wsns in localization applications, these are interesting for many cases where, given the number of units to identify, limitations of the technologies, and other factors, accurate solutions such as gps or uwb-based indoor location technologies represent too important energy, economic or structural costs, or when we want to take advantage of the already installed wireless sensing equipment. here, in order to provide distance estimations, many works have applied the received signal strength indicator (rssi), because it is inherently provided by most of the currently applied transceivers in wireless sensor nodes. copyright © 2006-2015 by ccc publications an anticipatory control for a flexible manufacturing system based on the perception of mobile units using wsns 703 in [12] we reported our first experiments on the 1-dimensional (1-d) positioning of pallets moving through a closed loop conveyor belt at the cimubb laboratory [13]. we aimed to minimize the idle time of a robot arm by integrating information about the position of the pallets approaching its flexible manufacturing cell (fmc). to do so, pallets carried wsns devices periodically sending messages to a receiver node near the robotic arm. rssi traces were captured and analyzed concluding that, even if this indicator presents high instabilities in closed environments, this metric could be useful in our case study. in this paper, we design and evaluate a control system which, by using rssi measures as input, allows anticipated movements of robotic arms decreasing idle times at the cimubb laboratory, continuing works in [12]. in the system, we adopted the classical log-normal model which relates the strength of a signal received by a node with the distance at which the sender of the signal is. the hidden state of the system is determined by the extended kalman filter which allows us to estimate the distance and the speed of the objects of interest (the pallets). from these estimates, remaining time in which the pallet will get to the stopping position near the robot is determined. this information is finally processed by a controller to determine the instant at which the robot must operate and manipulate the pallet. both, a proportional-integral (pi) controller and a fuzzy controller, were implemented and evaluated. results show the feasibility of using wireless signals to estimate distances in an indoor real-world manufacturing environment, such as the cimubb laboratory, considering some restrictions. the remainder of this article is organized as follows: in section 2 we present the cimubb laboratory, describing its physical configuration. in section 3 we design the anticipatory control system, describing the developed models, filtering, and controllers to be evaluated. in section 4 we describe our test implementation, exposing performed tests and results in section 5. finally, in section 6 we conclude and give some future directions. 2 case study: cimubb laboratory cimubb laboratory is a small scale fully automated flexible manufacturing system (fms) consisting of three flexible manufacturing cells. all of the flexible cells are integrated by means of a closed loop conveyor belt through which several parts travel on pallets. the transportation between the various cells and the conveyor belt is made by preprogrammed robotic arms. due to the way this system is implemented, most of the time, the robots do not have any information about the position of the pallets to be handled. indeed, only when a pallet passes over a stop station (where magnetic sensors are located), the system knows the position of a particular pallet. this produces a long-term significant idle time between the time the pallet is identified and stopped until the moment the robot handles it (this is where this work acts). the implementation adopted in this work is similar to the one in [12]. pallets carrying wsns devices periodically send messages to a receiver node near the target fmc stop station. rssi traces are collected and processed by a control system. the system sends orders to the robotic arm, trying to match the time of the pallet’s arrival with the end of a robot movement reaching an approaching position that allows it to grasp the pallet with minimal waiting time. obviously, this is not the industrial solution. the simplicity of this system should enable us to meet the same goal by simply placing sensors before the stop station. our current research subjects aim to provide positioning systems and anticipatory control (among others) in more complex production environments. this work is a study that will allow us to determine (as has been done in other work environment) how usable the rssi is as a metric to estimate distance and speed of moving objects in real-world indoor environments, initially, in a simple cyclic system such as the cimubb laboratory. regarding the problem of using an rssi-based location, our previous studies have resulted 704 j.a. silva-faundez, c. duran-faundez, p. melin, c. aguilera in the characterization of electromagnetic (em) waves allowing to observe the manner in which signals behave in this system. now, it is necessary to characterize the behavior of the object of interest (i.e., the pallets) at which we want to estimate distances. the behavior of a pallet is given by the mathematical model of its position regarding time. therefore, it is necessary to know the technical details of the conveyor system. the conveyor is a closed loop conveyor system made of metal. it is supported by pillars that rise the system 1 meter above the building’s floor. circulating pallets moving on the conveyor are dragged by a belt consisting of two plastic chains running in parallel and driven by an ac motor. this rotates at a constant speed giving the pallets a forwarding speed of 0.19[m/s] over straight lines, which decreases to 0.18[m/s] at the corners. activated via a switch which makes it to operate independently from the fms. the location of the flexible cells, together with the dimensions of the conveyor belt, are shown in figure 1(a). in this work, we focused on the machining fmc which is shown in figure 1(b). stop station sense of the conveyor pallet approaching pallet moving away section 1 section 3 section 2 section 4 section 5 p2 p3 p1 cnc machine 2 cnc machine 1 robotic arm 4,6 m 1 ,2 m v is io n s y st e m automated storage and retrieval system (asrs) plc m a c h in in g f le x ib le m a n u fa c tu rin g c e ll area of interest straight sections conveyor pallet curve sections psen r θ conveyor pallet (a) (b) (c) figure 1: case study. now, knowing the conveyor’s geometric structure it is possible to model the path of the pallets within the area of interest. the pallets path, over the area of interest, can be divided into five sections, as shown in figure 1(b): three of them, in which pallets perform straight movements (sections 1, 3 and 5), and two for curved movements (sections 2 and 4). thus, the position of a pallet over time can be defined as: −→r k =   −→r k−1 + −→ v .∆t during straight movements r. [ (cos (−ω.∆t + θ) + pcenx) .̂i + (sin (−ω.∆t + θ) + pcenx) .ĵ ] during curved movements (1) were θ = arctan ( pk−1y−pceny pk−1x−pcenx ) and ω = ∣∣∣−→v ∣∣∣ r . for both of cases, −→r and −→ v represent the position and velocity vectors of the pallet, respectively. the unit vectors i and j, take the direction of the cartesian reference system xy. ∆t is an increment in time. thus, the equations give the new position of the pallet at a determined time increment from its current position (rk−1). particularly, during curved movements, ω is the angular velocity of the pallet calculated from its rapidity and the radius of the curve (r). the pallet’s position is reflected in the angle that has about the center of rotation pcen (see figure 1(c)). we established a reference system in the plane xy, with its origin at the bottom left of the conveyor (end of section 5). from this reference coordinate, various reference points are fixed in order to mark the beginning and the end of each section of the path. along with these references, an anticipatory control for a flexible manufacturing system based on the perception of mobile units using wsns 705 the location of the data collector node is set. this point will serve as a reference point for distance estimation. this node is located 24 centimeters from the stop station. 3 system design the proposed “anticipatory control system”(acs) will be in charge of detecting the approaching of a pallet to the manufacturing cell and estimate the time at which the robot must act in anticipation of its arrival. the system estimates the proximity from available rssi information supplied by the collector node (a node located near the controlled robotic arm). these values are calculated by the node upon receipt of datagrams sent by the wireless nodes located on the pallets. these act as beacons regularly indicating their presence. the intensity with which the collector gets beacons’ signals can be related to the distance at which pallets are through a mathematical model describing their behavior. however, these signals are not free from interferences. indeed, various factors affect the radio-frequency (rf) waves such as the composition of the surroundings (walls, objects, etc.), the orientation of the transmitters, signals bouncing, etc. from experiments, generated disturbances were observed and analyzed, observing that these phenomena do not completely affect the tendency of the received signal regarding the studied models. then, it is necessary to develop the estimation system using a mathematical model that approximates the em waves path loss and an algorithm allowing noise filtering or the estimation considering such alterations. for this task the extended kalman filter (ekf) is used as a tool. along with estimating the proximity of a pallet, the system handles the operation of the scorbot-er 9 robot manipulator and the control of the fms’s conveyor belt. in addition, we implemented an interface that allows a user to interact with the system. figure 2 illustrates a general view of the system, exposing the different components and interacting devices. xbee transmitter xbee receiver scorbot-er 9 plc user interface robot conveyor acs anticipatory control system figure 2: acs general scheme. 3.1 system model one of the key aspects on the development of the acs is to obtain a mathematical model for path loss of the wireless signal, phenomenon related to the distance between the sender and the receiver nodes. such a system can be modeled considering it as a “gray-box system”because a part of the dynamics of the rf signals is known, existing different prediction models. however, many variables and interactions with the environment are unknown. one of the models used to describe the propagation loss is the “log-normal shadowing”, whose equation is given by: pl(d)[db] = pl(d0)[db] + 10.γ. log10 ( d d0 ) + xσ[db] (2) were xσ represents noise and interference as a zero-mean gaussian random variable with standard deviation σ. in order to find the value of power received by the receiver node, we must substract 706 j.a. silva-faundez, c. duran-faundez, p. melin, c. aguilera this attenuation to the transmission power of the signal pt, so we have: plrcvd(d)[dbm] = pt[dbm]−pl(d)[db] = −10.γ. log10 ( d d0 ) + ( pt −pl(d0) ) −xσ (3) a relationship between the received strength and the distance between the nodes can be obtained by the propagation model . along with this variable, it is also necessary to know the speed with which the pallet approaches, thus we must find a relationship between the pallet’s speed and its distance to the stop station. an alternative is the derivation of the speed value from distance estimations, however we should consider that this value represents only a measure of the distance change rate and not the actual speed of the pallet. despite this, both of the values are related, therefore it can be used as an indicator of how quick the pallet moves. by derivation we obtain the instantaneous speed with which the pallet approaches: δd δt = ν, in this work, considered as a constant. this expression is discretized, obtaining the distance for instant tk + 1: d(tk + 1) = ν(tk)t + d(tk) (4) next, and in order to identify a discrete model parameters, a linear difference equation can by used as y(t) + a1y(t−1) + . . . + any(t−n) = b1u(t−1) + . . . + bmu(t−m), where u(t) and y(t) are the input and output, respectively, and an and bm are the system’s parameters. from the last expression it is possible to determine the value of the next output solving the difference equation for y(t) and predict the system’s output from the system’s observed measurements and its parameters [14]. considering a system of n equations and n unknowns, the estimated output can be written as [14]: ŷ (n) = m(n).θ̂ (5) where the variables have, respectively, the structures: y (n) = [y(1) y(2) . . . y(n)]t , ŷ (n) = [ŷ(1) ŷ(2) . . . ŷ(n)] t , and m(n) = [m(1) m(2) . . . m(n)]t . in order to estimate the parameters of equation 5, least squares method is used. the predictions made using estimated parameters will have an associated error defined as e = y (n) − ŷ (n).then,the least squares method reduces this error by minimizing a functional which is a sum of squared errors, i.e., min ( j(θ̂) = et (n).e(n) ) , with j(θ̂) = ∑n k=1 e 2(k). in order to minimize the error, we must have ∂j(θ̂) ∂θ̂ = 0. developing for θ from previous equations, we obtain the following expression for obtaining parameters which minimizes the prediction error that can be used to estimate parameters of the propagation loss model : θ̂ = [ m(n)t .m(n) ]−1 .m(n)t .y (n) (6) let us consider the propagation model in equation 3. this can be developed to adopt the structure of equation 5, by doing: plrcvd(d) = −10.γ.g(tk) + w (7) where g(tk) = log10 ( d(tk) d ) and w = ( pt−pl(d0) ) . for modeling purposes, the random variable xσ was considered as zero, obtaining a log-normal model. finally, expressing equation 7 in matrix form we have: plrcvd(d) = [ g(tk) 1 ] . [ −10.γ w ] (8) in this way, the variables are: (1) the system output y (n) : plrcvd(d), (2) the measurements vector m(n) : [ g(tk) 1 ] , and (3) the parameters vector θ : [ −10.γ w ] an anticipatory control for a flexible manufacturing system based on the perception of mobile units using wsns 707 in order to generate useful data for estimations, various experiments were performed. in these, the pallets circulate in the conveyor while the robot takes one of the predetermined positions used in previous experiments. these experiments are performed with each of the three transmitting nodes adopted in this work (3 variants of the digi’s xbee family, explained in section 4). as an example, figure 3 illustrates the parameters estimation for a xbee wire antenna transmitter. table 1 presents estimated parameters for each of the used nodes. 0 20 40 60 80 �90 �70 �50 �30 time (s) r s s i (d b m ) 1 2 3�80 �70 �60 �50 �40 r s s i (d b m ) distance (m) (a) (b) captured samples fitted curve figure 3: parameter estimation for a xbee wire antenna transmitter case. table 1: parameters of transmitter nodes. transmitter node pt pl(1.5[m]) γ xbee chip antenna 0[dbm] 59.156[dbm] 1.142 xbee wire antenna 0[dbm] 53.0017[dbm] 1.438 xbee pro wire antenna 16[dbm] 51.376 1.065 the devices sampling time was set to 54[ms], however, the time observed during measurements is not constant. this has a mean deviation of 82[ms] with a standard deviation of 34[ms]. therefore, the sampling time used in the model will be estimated during the algorithm execution, by the time difference between a rssi measurement and the previous one. to summarize, the model of the system is given by the following equations: d(tk + 1) = v(tk)t + d(tk) (9) v(tk + 1) = v(tk) (10) plrcvd(d) = −10.γ. log10 ( d(tk) d0 ) + ( pt−pl(d0) ) (11) 3.2 signal filtering for this work the extended kalman filter (ekf) is used. ekf is a variant of the traditional kalman filter that addresses the problem of estimating states in nonlinear systems. let us consider the nonlinear model of the process in a discrete-time state variable representation, i.e., representing the unknown state of the system as xk = f(xx−1,uk−1,vk−1), with known input uk, and the observable output zk = h(xk,nk). vk and nk are random variables representing the process and measurement noises, respectively. it is assumed that the distribution densities are gaussian and both vk and nk are random variables independent to each other, so we can define p(xk−1|z1k−1) = n(xk−1|k−1,pk−1|k−1), p(vk−1) = n(o,q), and p(nk) = n(o,r). however, being a non-linear model, distribution densities p(xk|z1:k−1) and p(xk|z1:k) for next instant k may not be gaussian, therefore, they are approximated to a gaussian density. at the densities 708 j.a. silva-faundez, c. duran-faundez, p. melin, c. aguilera and distribution of vk−1 and nk, q and r are the covariance matrices of process disturbance and measurement disturbance covariance, respectively [15]. so, the filter operation may be divided into two stages: 1. prediction. this stage generates a priori prediction of the state xk|k−1 = f(xk−1,uk,0) and the error covariance pk|k−1 = fxk|−1.pk−1. ( fxk−1 )t + fvk−1.qk−1. ( fvk−1 )t , considering all the information available at that time. 2. correction. at this stage, previous estimates are corrected, then generating a posteriori estimates of xk|k and pk|k. for this, we calculate the kalman gain as kk = pk|k−1.(h x k ) t . [ hxk .pk|k−1.(h x k ) t + h η k.rk. ( h η k )t ]−1. this factor minimizes the error covariance of the new state estimation. so, new estimates are calculated as xk|k = xk|k−1 + kk. ( zk −h(xk|k−1,0) ) and pk|k = (i −kk.hxk ) .pk|k −1. matrices fxk−1, f v k−1, h x k and h η k are obtained as jacobians of the system’s function and of the output function, i.e., fxk−1 = ∂f ∂x ( xk−1|k−1,uk−1,0 ) , fvk−1 = ∂f ∂v ( xk−1|k−1,uk−1,0 ) , hxk = ∂h ∂x ( xk|k−1,0 ) , and hηk = ∂h ∂η ( xk|k−1,0 ) . the ekf is a recursive algorithm. therefore, the prediction and correction stages are executed in cycles while the algorithm is running. for more details about an ekf recursive implementation see [16]. on the application of the filter to the developed system it is necessary to obtain matrices fxk−1, f v k−1, h x k and h η k , with respect to the exposed model in equations 9, 10 and 11. these are defined as: fxk−1 = ∣∣∣∣∣ ∂f1 ∂v ∂f1 ∂d ∂f2 ∂v ∂f2 ∂d ∣∣∣∣∣ = ∣∣∣∣∣ ∂d(tk+1) ∂v(tk) ∂d(tk+1) ∂d(tk) ∂v(tk+1) ∂v(tk) ∂v(tk+1) ∂d(tk) ∣∣∣∣∣ = ∣∣∣∣∣t 11 0 ∣∣∣∣∣ ; fvk−1 = ∂f∂v = i ; hxk = ∣∣∂h ∂v ∂h ∂d ∣∣ = ∣∣∣∂plrcvd(tk)∂v(tk) ∂plrcvd(tk)∂d(tk) ∣∣∣ = ∣∣∣0 − 10.γd(tk). ln(10)∣∣∣ ; hηk = ∂h∂η = i (12) to initialize the algorithm it is necessary to designate initial values for the estimated states matrix and the error covariance matrix. in case of xk|k−1, it is considered that during the system startup the pallet is far from the stop station and outside the area of interest. the pallet’s initial speed is considered as zero. for the case of the pk|k−1 matrix, it is initialized to a small value different but close to zero, because if the covariance is zero the filter will keep the last estimated value without innovating. matrices of process perturbation covariance and measurement are assumed to be constant although in practice this does not happen like that. values of matrix q related to the process, are generally more difficult to obtain, but it is presumed that the variance values of the process are low. matrix r related to measurements can be predicted from the output observable values. from experimentation, a standard deviation of the measurements less than 0.5[dbm] for the case of the chip and wire antenna xbee nodes and less than 2.5[dbm] in the case of xbee pro nodes, is observed. however, these values, measured in a quasi-stationary regime (i.e. without movements of the node-of-interest and by controlling as many environmental variables as possible), increase in dynamic conditions reaching a deviation of 5[dbm]. due to this instability in measurements, an intermediate value for the deviation of 2[dbm] is considered. in this way, variances arbitrarily selected for the process variables and measurements are σ2d = σ 2 v = 0.002 and σ 2 plrcvd . the disturbance covariance matrices are q = ∣∣∣∣∣0.002 00 0.002 ∣∣∣∣∣ and r = 4. an anticipatory control for a flexible manufacturing system based on the perception of mobile units using wsns 709 3.3 estimating time of arrival ekf, together with the discrete system model, allows the estimation of the hidden states of the system. for our case, they will be the variables of distance (between the pallet and the stop station) and the rate at which this changes. estimating the time at which the manipulator robot must act only with distance information may be appropriate under the studied environment which considers a constant movement speed. however, alterations on this parameter would trigger an erroneous reaction of the system. therefore, it is necessary to determine the instant in which the manipulator operates by considering both the distance and speed of the pallet estimations. estimates of distance and speed can be used together to determine the time a pallet will take to get to the target stop station. we name this estimate time-of-arrival. the preceding variable modeling is made from both experimental data and data obtained through the pallet’s path simulation. by matlab’s surface fitting toolbox we obtain the surface that best fits the available data. this is a 2-order polynomial defined as: tarrival(tk) = p00 + p10.d(tk) + p01.v(tk) + p20.d 2(tk) + p11.d(tk).v(tk) + p02.v 2(tk) (13) where tarrival is the time-of-arrival and d and v are the pallet’s distance and speed respectively. surface parameters pxx are obtained for each of the wireless modules used in the pallets. 3.4 system controller the instant at which the manipulator robot must act is determined by the time of arrival of the target pallet. the operation of the robot must be done in the instant in which the time the robot employs in executing the approaching routine equals the time of the pallet’s arrival. to achieve the above consideration two controllers were implemented. the first control is an open-loop pi. this was chosen due to the lack of feedback on the system. its implementation is performed from the discretization of the pi controller given by y(tk) = kc (e(tk)−e(tk −1)) + kcti tse(tk) + y(tk − 1), where y(k) is the output and e(k) is the calculated error between the controller input and a given reference for a time k, and kp and ki are the proportional and integral gains, respectively. the implementation is recursively performed using as a reference the time of the robot operation (top_robot) and the time of arrival of the pallet as input (tarrival). thus the controller was modeled as follows: e(tk) = top_robot −tarrival ; a(k) = kp.e(tk) ; b(k) = ki.e(k) (14) y(k) = a(k)−a(k −1) + b(k) + y(k −1) (15) a(k −1) = a(k) (16) y(k −1) = y(k) (17) during algorithm’s operation, equation 15 represents an update of the controller output. equations 16 and 17 are updates of the variables. the second implemented control was a fuzzy logic controller that is based on the fuzzy sets theory developed by lotfi a. zadeh [17]. this kind of logic intends to represent human logical thinking allowing a system to work with information that is not accurate (e.g., input sentence “the pallet is very close”). the truth degree of a variable x to a “linguistic variable”(e.g., “the pallet is far”) is determined by a membership function that takes truth values in the 0 to 1 range. the linguistic variable or label is associated with a fuzzy set which can be defined as: a = {x,ua(x)|x ∈ ut}, were ua(x) is the membership function of x and u is the universe of discourse or the exact values that can take the variable [18]. the membership function can 710 j.a. silva-faundez, c. duran-faundez, p. melin, c. aguilera adopt many geometries, such as triangular, trapezoidal, etc. the components of the controller are shown in figure 4(a), where the fuzzification stage converts crisp inputs or real-world values to a membership degree of a linguistic variable. later, this variable is evaluated by a set of rules of type: if {set of conditions to meet} then {set of consequences}. finally, the obtained fuzzy result must undergo a reverse process or defuzzification in order to obtain a crisp output or an output with useful values for the process, again, using a membership function for the output values. fuzzy logic controller fuzzy rule base fuzzy inference fuzzification defuzzification fuzzy input fuzzy output crisp input crisp output y = f(x) ∈ vx ∈ up u ∈ up v ∈ v (a) 0 0.5 1.0 1.5 0 1 2 3 4 5 6 close medium far 0 0.5 1.0 1.5 0 1 2 3 4 5 6 7 8 9 10 wait prepare capture m e m b e rs h ip m e m b e rs h ip (s) (b) figure 4: (a) components of a fuzzy logic controller and (b) the implemented fuzzification (top) and defuzzification (bottom) membership functions. as in the pi controller, for the fuzzy controller design only one input was considered. this input is the tracking error e(tk) described above. an input range for this variable from 0 to 10 [sec] is established. besides this, three linguistic variables are defined within the established universe of discourse, “far ”, “medium”and “close”. on the other hand, for the deffuzification stage, three types of linguistic variables are defined: “wait”, “prepare”and “capture”. the set universe of discourse corresponds to the range 0-12. figure 4(b) shows the implemented membership functions. for the fuzzy inference stage the following set of rules are implemented: (1) if e(tk) is “far ”then output is “wait”, (2) if e(tk) is “medium”then output is “prepare”, and (3) if e(tk) is “close”then output is “capture”. different controller parameters have been selected from our experience operating the system. the adjustment of membership functions was performed by various assays in the system. 4 implementation pallets moving on the conveyor belt transport wireless beacon nodes periodically sending data packets to a collector node located near the stop station. the collector is directly connected to a personal computer (pc) executing the control software. three types of wireless nodes corresponding to the digi’s xbee family [19] were used in this work. they are: a xbee chip antenna, a xbee wire antenna, and a xbee pro wire antenna, all from the product’s 1 series. these components are based on the ieee 802.15.4, and are mainly differentiated by hardware features, such as type of antenna and supported power outputs. in our experiments, the three different xbee modules were tested as beacon nodes. a xbee chip antenna module was used as the collector. nodes main parameters are detailed in table 2. the rest of the parameters are set to be default parameters recommended by the manufacturer. both the robotic arm and the conveyor system are controlled by the acs software via serial ports. these elements only execute predetermined tasks. the robot has its own operating system. this manages its resources and provides a programming interface for which the user has a set of available functions and operations to work with the robot. three routines were an anticipatory control for a flexible manufacturing system based on the perception of mobile units using wsns 711 table 2: xbee modules configuration. collector node beacon nodes device xbee chip antenna xbee wire/chip antenna xbee pro ieee 802.15.4 role coordinator end device interface speed 57600[bps] sampling rate 54[ms] operation mode api power output 0[dbm] 0[dbm]-16[dbm] power source usb batteries programmed in its controller: (1) standby: places the robot in one of four positions previously recorded. (2) approaching target: moves the robot from any position to a position near the point of capture of the pallet (over the stop station). (3) target capture: allows the robot handling the template carrying the target pallet. the conveyor system is controlled by a plc with a resident program. this executes the pallet control services at the stop stations (stop, release, or identify a pallet over a stop station). the services are provided via serial communication software to the acs. this must extract the relevant information from the data frames sent by the plc and to generate the necessary commands to perform the required actions. on the pc, the core of the acs is a matlab-based software implementing the filtering and control routines, and serial i/o and user interfacing. the acs software aims to estimate the proximity of the objects of interest (the pallets) and to determine the instant in which the system must generate the necessary commands to anticipate the arrival of the pallet, using as input only rssi traces. the software components are summarized in figure 5a. the operations sequence cyclically executes the following steps: (1) devices reading, (2) estimation algorithm execution, and (3) device status update. this sequence is repeated until achieving the target pallet capture. the operating cycle of the program is initiated by a user requirement. the software starts the cycle by reading the rssi value and the transmitting node id, from the assigned serial port (these data are delivered by a c++-based program who actually reads and decodes packets from the xbee collector). then, the data are filtered before being processed by the estimation algorithm. once the arriving time is estimated, its value is entered to the system controller. if the estimated time does not equal the reference time, the current system’s output state remains and the cycle is repeated. on the contrary, the controller gives the order to start the operations of approaching and capturing of the target pallet. to summarize, figure 5b illustrates the implemented algorithm stages. 5 tests and results at the begging of the system’s integration stage, parameters are adjusted in order to achieve a proper system’s operation. system calibration is performed by contrasting experimentally obtained information and simulations of system’s models. for obtaining real data from the system, we registered rssi values for each node considering different positions of the manipulator robot (as discussed in previous section). with these data we simulate the behavior of the estimation and control algorithm under adjusted system parameters. these parameters are: reference power pl(d0), path loss exponent γ, standard deviation of the process disturbance σpp, and standard 712 j.a. silva-faundez, c. duran-faundez, p. melin, c. aguilera u s e r serial interface plc frames decoder frame generator identi�cation pallet / station plc commands robot commandsnode identi�cation filter pallet capture robot operation t. reference controller extended kalman filter system model graphic generation sw con�guration system kernel devices access software interface estimation algorithm anticipatory control system software rssi decoder scorbot-er9 conveyor / plc (a) data packet [rssi] rssi decoder filter extended kalman filter system model distance speed time-ofarrival estimation controller (b) figure 5: acs (a) software components and (b) general scheme. deviation of measurement disturbance σpm. system parameters calibration was performed by the greedy algorithm [20]. table 3 shows the new parameters obtained. table 3: adjusted system parameters. node pt pl(1.5[m]) γ σpp σpm xbee chip antenna 0[dbm] 59.490[dbm] 1.949 0.0034 4.090 xbee wire antenna 0[dbm] 53.838[dbm] 3.186 0.0025 4.818 xbee pro wire antenna 16[dbm] 52.160[dbm] 2.720 0.0023 6.0 after calibration, we performed an execution test in order to verify the estimation algorithm parameters for each beacon node. during this test, pallets are not stopped at the stop stations. figure 6(a) shows registered rssi values for each node. figure 6(b) and 6(c) show estimations of time-of-arrival and distance for each node. d is ta n c e ( m ) 0 10 20 30 400 1 2 3 0 10 20 0 10 20 0 10 20 30 40� 90 �50 �10 r s s i (d b m ) time (s) time (s) time (s) t im e -o fa rr iv a l (s ) 1 2 3 1 2 3 r 1 2 3 r (a) (b) (c) figure 6: estimation with calibrated parameters for à xbee a. chip, á xbee a. wire and â xbee pro a. wire. in (a), input captured data. in (b) and (c) show r real arriving time and distance, respectively, and estimations for the three nodes à xbee a. chip, á xbee a. wire and â xbee pro a. wire. we observe that the best distance estimate is performed on the first half of the workspace represented by the first 21 seconds in figure 6(c). in here, the pallet approaches the stop station reaching the shortest distance between the transmitter and the collector nodes (approximately at second 21). then, the pallet moves away. an anticipatory control for a flexible manufacturing system based on the perception of mobile units using wsns 713 now, in order to evaluate the performance of the acs, several tests under different operating conditions were conducted. the objectives of these tests are: (1) to compare system performance for different transmitting nodes, (2) to compare performance of the system under different initializations of the error covariance matrix, (3) to measure overall system performance, and (4) to observe the effect of sampling rate variations in the system estimates. to accomplish these objectives, we performed both an individual and a global performance tests, and a test to observe the degradation of the estimation due to sampling rate variations. in each test estimates of distance, time-of-arrival and time of capture of the pallet are recorded. this last value is defined as the time between the arrival of the pallet to the stop station and the time the robot takes to get the catching position. a good result for the capture time is a near zero (seconds) value. the performance metrics of the system are: the distance absolute error estimation, the time-of-arrival absolute error estimation, and the absolute capture time absolute error. in the individual performance test, we compare acs performance considering the use of different beacon nodes. firstly, individual beacons are located on a template, then we let them move over the conveyor. individual tests are performed with the three types of xbee nodes and different initial positions of the robotic arm. in each case, acs executes a full cycle allowing the robot to capture the template. along with this, we want to observe the effect of the initialization of the error covariance matrix (p). for this, we executed two versions of the estimation algorithm: (version a) the original version of the algorithm, initializing the error covariance matrix to a near zero value, modifying its value as tests succeed, and (version b) a modified algorithm, where the error covariance matrix is initialized with a default value. this value is obtained after successive runs of the original algorithm. during each execution of the experiment the value of the array is initialized to this value. average results for these tests are shown in figure 7. in the global p1 p2 p3 p4 0 0.2 0.4 0.6 e rr o r (m ) p1 p2 p3 p4 0 0.2 0.4 0.6 e rr o r (m ) p1 p2 p3 p4 0 2 4 e rr o r (s ) p1 p2 p3 p4 0 2 4 e rr o r (s ) (a) (b) (c) 1 2 3 1 2 31 2 3 1 2 3 1 2 3 1 2 31 2 3 1 2 3 1 2 3 1 2 31 2 3 1 2 3 1 2 3 1 2 31 2 3 1 2 3 pi fuzzy 0 1 2 3 e rr o r (s ) pi fuzzy 0 1 2 3 e rr o r (s ) 1 2 3 1 2 3 1 2 3 1 2 3 figure 7: average results for the individual performance tests with the tree wireless nodes à xbee antenna chip, á xbee antenna wire and â xbee pro antenna wire. in (a), absolute error of the estimated distance with both versions of the filter a (left) and b (right). in (a), absolute error of the estimated time-of-arrival with both versions of the filter a (left) and b (right). in (c), absolute error of the capturing time with both versions of the filter a (left) and b (right). performance test, we observe the performance of the system under normal operating conditions. 714 j.a. silva-faundez, c. duran-faundez, p. melin, c. aguilera this involves capturing any of the three available pallets on the system from a random position as the starting point for the robotic arm. for this test, we use the three adopted types of nodes as beacons transmitting simultaneously. we compare the performance of each node individually with the performance of the system to a random selection of the target node. the selection of the initial position of the robot is performed at random for each test. as in the previous test, we observe the effect of the p matrix initialization. for each configuration of the experiment 10 tests are performed in a row. average results for these tests are shown in figure 8. finally, we study pi fuzzy 0 2 4 6 8 e rr o r (s ) a b 0 0.2 0.4 e rr o r (m ) 0.6 a b 0 2 4 e rr o r (s ) pi fuzzy 0 2 4 6 8 e rr o r (s ) 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3r r r r rr r (a) (b) (c) (d) figure 8: average results for the global performance tests for the three adopted nodes à xbee antenna chip, á xbee antenna wire and â xbee pro antenna wire, and a r random selection for the target node. in (a) and (b), absolute error of the estimated distance and of the estimated time-of-arrival for both of the filter versions a and b. in (c) and (d), the absolute error of the capturing time for both filter version a and b, respectively. the degradation of the estimation produced by reductions on the sampling rate. this test is performed by using the same beacon node (a xbee chip antenna) while the manipulator robot adopts a same initial position for each of the experiments. the experiments consider increases 100[ms] in the given sampling rate from an initial value of 100[ms] and a final test value of 1100[ms]. average results for these tests are shown in figure 9(a), 9(b), and 9(c). 5 15 25 35 450 0.4 0.8 1.2 time (s) 200 600 10000 0.4 0.8 1.2 e rr o r (m ) sample time (ms) 200 600 10000 4 8 e rr o r (s ) sample time (ms) 200 600 1000 2 4 6 e rr o r (s ) sample time (ms) s a m p le t im e ( s) (a) (b) (c) (d) pi controller fuzzy controller three transmitters one transmitter figure 9: in (a) and (b), the absolute error of the estimated distance and time-of-arrival, respectively. in (c), the absolute capture time shows the absolute error of the capturing time with respect to the sampling time. in (d) the effect on the sampling time of increasing the number of transmitting nodes. from the results, we can observe a system performance decline when including a larger number of simultaneously operating transmitter nodes (see figures 7(a) and 8(a)). note that the operation mode of the transmitting nodes causes dispute over the transmission medium. the information received from these nodes is at random, without an order in the delivery. this causes instability and decrease in the rssi sample rate, resulting in an increased error in the estimation of distance which grows in a 18.3 %. figure 9(d) illustrates this effect. the figure shows the sampling rate for xbee chip antenna nodes operating independently and the rate obtained in a scenario with 3 nodes transmitting simultaneously. regarding the performance of the system, this is maintained within acceptable parameters by using only one node beacon, observing errors in distance estimation and in the time-of-arrival of an anticipatory control for a flexible manufacturing system based on the perception of mobile units using wsns 715 25[cm] and 1.5[s] around, respectively. this allows the system to meet the design requirements by obtaining a capture time of less than 3[s] (2[s] average). the last performed test allows us to determine the maximum sampling rate supported by the system. this is 600[ms] for the case of an individual node. in case of using a larger number of nodes this rate should allow increasing the number of nodes without significantly affecting the system performance. however, a way to synchronize the senders to the collector node is necessary. a sampling time greater than the determined one can cause the system not to properly determine the area of operation of the controller. this affects the pallet capturing algorithm, causing it not to stop in a timely manner so the pallet flows without stopping at the corresponding stop station and must wait for a new approach to perform the capture. this is observed in figure 9(c) in which higher values to the determined one produces excessive time increases in pallet capture. moreover, results show that there is a considerable improvement in the obtained results when initializing the error covariance matrix to a preset value (version b of the algorithm). in general, we observe a decrease in errors estimates close to 8%. in the case of capture time we observe an improvement of 10% by using the version b of the algorithm. regarding the performance of the controllers employed in the system, better results are observed when using the fuzzy controller in all of the implemented tests. the fuzzy controller has reduced capture times compared to pi controller by 27% in the individual performance test and 10% in the overall performance test. finally, we observe the performance of the system considering the three employed nodes. from these, the best results, considering the best estimate of time-of-arrival and time of capture of the pallet, are obtained by applying the xbee wire antenna module. this is observed in figures 7(b) and 7(c). 6 conclusions and future works this paper presents the development and implementation of a proposed control scheme which is able to trigger an anticipatory control action. for the above, rssi measurement from a wireless sensor mounted in a moving pallet are used along with an efk in order to estimate the speed and distance of the moving pallet relative to a stop station. the control scheme is proposed for short distance in order to avoid many factors which affect the signals behavior (bouncing, multipath, etc.). specifically for a 2.25 m radius case the control scheme is able to estimate the time of arrival of the pallet traveling at 0.19 m/s and to generate the necessary control action (for the case presented the movements of a robotic arm) before it reaches the stop point. due to the lack of a feedback loop for the robotic arm used, a pi and fuzzy controller are implemented, obtaining a better response by the fuzzy strategy with a 54[ms] sampling time. due to rssi instabilities it is necessary to avoid significant changes in the environment in order to estimate correctly speed and position of the pallet. future work will focus in the rssi instabilities compensation. acknowledgment this work was partially supported by the research department of the university of bío-bío through the project diubb 121910 2/r and conicyt (fondecyt project no. 11121657). 716 j.a. silva-faundez, c. duran-faundez, 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[16] r. grover brown, p.y. c. hwang (1997); introduction to random signals and applied kalman filtering with matlab: exercises and solution, 3rd ed.. usa: john wiley & sons. an anticipatory control for a flexible manufacturing system based on the perception of mobile units using wsns 717 [17] l.a. zadeh (1965); fuzzy sets, information and control, 8 (3): 338-353. [18] a. m. ibrahim (1997); introduction to applied fuzzy electronics, prentice hall. [19] digi international http://www.digi.com/ [20] t. h. cormen, c. e. leiserson, r.l. rivest, c. stein (2009); introduction to algorithms, (3a. ed.) massachusetts: massachusetts institute of technology. int j comput commun, issn 1841-9836 8(5):662-672, october, 2013. remarks on interface oriented software systems modelling d. bocu, r. bocu dorin bocu, răzvan bocu* department of mathematics and computer science transilvania university of brasov romania, 500036 brasov d.bocu@unitbv.ro *corresponding author: razvan.bocu@unitbv.ro abstract: this paper explores the foundations regarding the systematic usage of the concept of interface in order to sketch a methodological approach, in which the fundamental perspectives that guide the abstracting of a software system solution (referred to as up, sp and bp in the paper) are unified, with the goal to optimally derive the behaviour of the system from its structure. moreover, this is very useful for opening new avenues in order to address the shortcomings that are provoked by changes, considering a software system that is conceived at an industrial scale. keywords: user perspective, structural perspective, behavioural perspective, interface. 1 introduction have all the issues that impede the realization of a quality software system been solved? we can’t even speak about something like this. this is especially true nowadays, when the topological diversification of software systems has gone so far [2]. there are two reasons that will prevent this process from ceasing and will determine further amplification of it: • an increasing number of the human activities are optimized or taken over by it systems. • the technologies that underline the creation of the it systems bear an overwhelming strategic repositioning on the map that pictures the latest developments in the field. in these conditions, the endeavour to inventorize the already existent paradigms and technologies becomes increasingly difficult. this process should have the essential goal of simplifying the choice of the paradigms and technologies that are necessary in order to roll out an it project [3]. in this paper, we study several aspects that are related to the possibility of modeling the solution of a software system, while easing the natural demarche through which the switch from structure to behaviour is accomplished [7]. although experts are largely unanimous regarding the qualitative determination of a system’s behaviour by its structure [4], the experts themselves do not give up the search towards the identification of some new thought schemes concerning the relation between structure and behaviour in the process of a software system solution’s modelling [5]. 2 a short account on the state of the art naturally, we can position ourselves far away from the beginnings regarding the technologies that are used in order to model the solution of a software system. using a simplified and nonexhaustive approach, we can observe the following: copyright c⃝ 2006-2013 by ccc publications remarks on interface oriented software systems modelling 663 • in order to roll out the analysis activity in relation to an information system at a reasonable quality level, there are standard investigative methods and languages, with which the analysis results can be represented. the endeavour to optimize the streams of an information system involves the necessity to first realize a precise map of these streams. this is the main goal of the analysis activity. naturally, considering a complementary layer, the analysis process is also intended to discover the requirements of the people that sustain the activity of the software system, regardless of the executive or managerial status of those who are involved [2]. we can offer examples of tools, with which it is possible to rationalize the activity of analysis: flow diagrams, gantt diagrams, pert diagrams, flow and control diagrams, uml and bpmn, etc. [1]. the technologies that feature footnotes represent two state-of-the-art approaches that aspire to the status of universal tools for the modelling activity [3]. • the progresses that have been achieved towards the rationalization of the modelling process are remarkable. it can be stated that in the war of methodologies an armistice has been reached, around the idea according to which the ideal solution for the specification of a modelling system is represented by the adoption of some rigour exposure formulae that is associated to a modelling process with the help of a notation that cautiously combines the formal correctness with the visual ingredients. it is a beneficial armistice both for developers and for the case tools developers. • considering the previous remarks, can it be stated that the modelling activity is free from syncopes? it seems to be accurate to answer no. the modelling has continuously progressed, but it still faces open problems. as we have already mentioned, one of these problems is approached in this paper: the problem concerning the relation between the structure and the behaviour. more precisely, the idea that is highlighted is, briefly: how should the modelling activity be approached in order to ensure a reliable and flexible passage from the structure of a software system to its behaviour. the structure of a software system, considering different levels of abstractization, represents a potential that has to be efficiently valued considering every phase of a software system solution’s maturation [8]. if we do not work efficiently, we’ll have to schedule additional iterations, which correct the modelling errors or eliminate the unintentional shortcomings of the modelling demarche [6]. what can we undertake in order to be more efficient in the modelling activity? an attempt to answer this question can be found in the following sections of this paper. 3 the historic premises of a quality modelling endeavour it has already been mentioned in section 2 that specialists have constantly done their best in order to streamline the modelling of a software system [9]. with the risk to alienate part of the specialists that have already studied the same topic, it can be stated that the summary of these efforts can be expressed as follows: from the structuring activity, using a methodic abstractization, towards object orientation. the structuring activity emphasized and sometimes turned into a fetish the necessity to structure data and the operations, following specific rules, with the goal to realize reliable and easy-tomaintain software systems. during the era when the structuring activities used to prevail, mapping the solution domain on the problem domain hadn’t been considered a clear priority. the art of modelling used to 664 d. bocu, r. bocu be an example of an approach that had been exclusively preoccupied by its own topology and coherence. the structuring activity has obviously brought an increase of clarity regarding the modelling and its subsequent demarche, that is the implementation. the structuring paradigm showed its weaknesses in relation to high-complexity modelling problems. thus, we have to put together some other ideas apart from the structuring paradigm in order to be successful in such cases. we have to grant special attention to different abstractization modalities, as abstractization imposes itself as a fundamental tool for taking over the complexity. as a consequence, specialists have theorized and elaborated progressive abstractization procedures, both in relation to the universe of the data and to that of the operations. abstract data types can be considered a kind of a synthesis of the approaches that held at their core the methodic abstractization. this is a synthesis that has anticipated the revolutionary paradigm that is represented by object orientation. is object orientation a paradigm without problems? aspect oriented programming suggests that the answer is no. the same message comes, although not as a reproach, from the component oriented programming, as well. where do the most powerful criticisms of the object oriented programming come from? surprisingly, they come from those that intensely and methodically plead for the importance of the object oriented modelling activity. many of the object orientation concepts and principles are accompanied by indications and contraindications. even the programmers that discerned the importance of a quality design for the success of an it project feel that something is not in order with object orientation. it is true, no human creation can avoid the criticism of the human being himself. even if nothing can be objected in relation to the technical aspects of a technology, which is unlikely, the problem of taste remains open. this is a problem regarding the correspondence between the technology’s offerings and the user’s expectancies, considering the three tiers: syntactic, semantic and pragmatic. in oop (objected oriented programming), we can state that the world can be assimilated to a structured collection of objects. thus, we speak about a rigorously-defined set of objects that interact. any interaction involves the existence of some actors and a communication protocol. here lies the problem: the communication protocol is always different for every new software system. it is clear that a generally valid communication protocol cannot be specified for all the software systems. nevertheless, we are convinced that we can "draw" a communication framework that is bound to help at specifying the communication protocols amongst the actors of a particular software system. 4 short inventory of essential problems, which are frequent in the modelling activity the systems that belong to the surrounding world are structured entities collections, due to the fact that they have the potential to operate and co-operate. we have been aware that things are like this for a long time now. even if we cannot precisely anticipate the future states of the systems that determine the operation of other systems, we are at least able to know, with approximation, the cause of the changes that we witness. every systems developer, but also every researcher that studies a system, looks for the most reliable way to increase the quality of their own demarche. consequently, what are the main issues that impact on the general endeavour for progress in relation to the systems development and modelling? following, they are presented according to a sequence that does not yet suggest anything: • the limited human capacity to analyse the structure and the behaviour of the systems. the limitation that we refer to in this case is quantitatively and qualitatively measurable. remarks on interface oriented software systems modelling 665 we cannot think considering rhythms that resemble to the power of the continuum. we think according to rhythms that are consistent with the discrete structures, on which is founded the difficult-to-define human intelligence. the discrete systems are not necessary examples of approximate knowledge, which are prone to fast moral wear. humans have created systems, be them theoretical or practical that have successfully passed the test of time, which is the surest proof of their quality. • the intrinsic complexity of any creative demarche. modelling the creativity is a priori a chimera. nevertheless, it is within human reach to stimulate the creativity through the automation of all the activities, which together with methodical repetition and optimization become algorithmizable. freed up from the concern to consume time for performing some routine activities, the human being has all the time in the world to be creative, that is to push forward his fight with the unknown from within himself and from the universe. • being characterized by the limited capacity to analyse the structure and the behaviour of the systems, and considering that any creative step features a non-conventional complexity, the human being pushes his boundaries through the use of the paradigms. any paradigm, regardless of the field of knowledge, is an instrument for treasuring some qualitative accumulations that relate to the human creativity. any paradigm has the role of keeping active the flame of knowledge discovery, provided it becomes a tool that is generally accessible. thus, it can be stated that paradigms resemble to their creators: they are being born, they mature, they flourish for a while, and then they are replaced by some other paradigms. • the most precise history of a certain field of knowledge is the story of the never-ending chain of paradigms, which have fuelled the illusion of forward movement in that respective field of knowledge. the history of the happenings inside each paradigm is the only one that has the power to legitimize the joy of living timelessness, but also the need to periodically return to its ever-renewed sources. we dare to assert that the perpetual precariousness of the paradigms, to which the human being relates as a knowledge agent, is a trick that is used with the certainty that the human reasoning is able to push the boundaries of knowledge deeper into the unknown. the goal of each paradigm, not always clearly stated, is to bring an increase of knowledge in a certain field. the initial universality of each paradigm is abandoned as soon as the achievements that have been registered in relation to it proved its conjunctural capabilities. • although modelling is an activity that is made possible only from inside a paradigm, which impose a certain rigour to the approach, obeying the exigencies of the rigour is, generally speaking, problematic. humans prefer to model as if they were speaking in the mother tongue,by making slalom through ambiguities, correctness, metaphors, formal abstractions with limited reach, and some other methods that are used to explore the incomprehensible. this natural predisposition of the humans conflicts with the need to discipline the modelling activity, which is claimed by the tools producers and also by the management of the it projects that is intensely focused towards the success of these projects. we have presented through the problems that have been introduced, part of the reasons that provoke the seemingly chaos that thrones in the world of the modelling paradigms. this chaos is, considering a more attentive analysis, the expression of the continuous search effort that aims to enrich a given paradigm or propose a new paradigm. in this paper, we present the main ideas of a modelling paradigm that establishes the importance of the concept of interface as a methodical bridge, which links the structure and the behaviour of a software system. 666 d. bocu, r. bocu 5 concepts and fundamental principles that are used in the interface oriented modelling of the software systems 5.1 preliminaries in accord with the objectives of this paper, in this section we attempt to sketch the essential conceptual infrastructure, which is necessary in order to practice the interface oriented modelling in the it industry. this infrastructure obviously refers to the concepts with the help of which the interface oriented modelling may become operational, with promising results. figure 1 presents the role that is played by the concept of interface in the modelling activity, considering very high levels of abstraction. in figure 1, the two actors that are designated by developer and client abstract the modelling expert that is perceived in one of the two fundamental hypostases: models producer or models user in his own modelling activity. in any of the two hypostases, the modelling expert can address the complexity-related issues by granting the proper attention to the concept of interface. although what figure 1 suggests is as correct and obvious as it can be, this does not mean that any modelling expert automatically becomes a winner, if he understands the message transmitted by figure 1. what is, in fact, the message? the mind of the one that models has to get used to perceive systems, that correctly and coherently relate to their environment through the concept of interface. thus, we solve two problems that have a major impact on the quality of the models that we elaborate: the streamlining of the service traffic between software systems and the assurance of the modular continuity in relation to the software systems. we could end the presentation of this paper here but, it wouldn’t be a wise decision. considering the figure 1, we cannot deduce with sufficient clarity how the interface can help to optimize the relation between the structure of a system and the behaviour that is built on this structure. in order to provide additional clarity to our demarche, we make some preliminary remarks: figure 1: the interface: a fundamental tool for a modeling activity of the real world in it remarks on interface oriented software systems modelling 667 1. the modelling activity is an endeavour in which the abstractization is the essential tool that allows for the complexity to be mastered and modelled. 2. the modelling activity is spatially organized: on the horizontal, the semantics that correspond to different perspectives are structured; on the vertical, the abstraction levels of these perspectives are structured. consequently, it is necessary to specify the concepts, with which we operate inside the perspectives at various abstraction levels. we plan to deal with this problem, at least considering several iterations. at each iteration, we present a diagram, which is connected to an abstraction level that summarizes the concepts that exist at that level. the beginning of the scientific knowledge passes through the concept of system. considering that the modelling is a remarkable kind of knowledge, we can infer that the legitimate origins of the modelling belong to the area that is determined by the concept of system. the concept of system represents equally a modelling tool and, also, a tool for representing a certain type of modelling approach. as a modelling tool, the system can be explained through making use of three perspectives: the user perspective (up), the structural perspective (sp), and the behavioural perspective (bp). the necessity to utilize the three perspectives is relative. representing different levels of maturation of a modelling demarche, and approached in an iterative manner, the three perspectives feature as a terminal point the decryption of the systems’ behaviour, as it can be noticed in figure 2. it is also natural to reflect on the links that are established between the three perspectives. figure 2: the main perspectives that are used for modelling a system it can be assumed that based on the knowledge acquisition that is organized according to up, the elaboration of sp becomes more natural, while considering an approach that favours the correct specification of bp, considering any level of detail. therefore, we speak about naturalness in the specification and preparation process of an optimal framework that is suitable to specify the behaviour. the link that is established between the three perspectives goes even further. this will be better understood as soon as the concepts that are part of the interface oriented abstractization base infrastructure are introduced. 5.2 the concept of interface in the software systems modelling before proceeding to the actual brief description of each perspective and to their methodology driven assembly, we define, among other things, the concept of interface, while considering the highest level of abstractization. in the practice of software systems modelling, it is called interface a protocol that allows for the communication between two objects to occur. the communication between two objects 668 d. bocu, r. bocu takes place through a messages exchange. in order for the communication to be feasible, it is necessary that the sending object (the object that requires a service) knows the interface of the object that receives the message (the object that will provide the service). this is the way the concept of interface can be understood, provided it is documented from an operational perspective. from a conceptual perspective, the notion of interface bears a series of meanings, which are probably more important for the quality of the software systems modelling activity. an interface is a model that abstracts the messages exchange between two objects, with two essential goals: 1. the safeguarding of the fact that the objects that send messages to the object that owns the interface use a calling scheme, which remains unchanged in a reasonable time span. 2. the exemption of the object that receives messages from the task to adapt to possible changes from the environment it is related to. in other words and considering the perspective that is induced by these de?nitions, an interface is correctly specified if the object it serves (service provider for the environment it is a part of) is, at the same time: • temporarily deggrevated of the responsibility to adapt to the environmental changes; • free to modify the implementation of the services that are published through the interface. we are now entitled to ascertain that the interface is a double-sided abstraction tool: • provides stability for the clients that call it, which entitle us to state that the interface features genuine structural ambitions, because it regulates, in the long run, the manner according to which the clients of the owner-object may call the object’s services. • relieves the object that receives a messages of the task to adapt to the environmental changes; thus, the interface offers the ideal framework for the respective object’s behaviour optimization. without trying to state that this is the optimal variant, we promote the idea that the correct transition from a system’s structure to its behaviour involves the methodical utilization of the concept of interface. with the wish to clarify the framework related to which the contents and the connections of the three perspectives in figure 2 are presented, we introduce in the following paragraphs, two principles that feature a great power of generalization and up-to-datedness for the general knowledge process and, consequently, for the modelling activity. 5.3 the principle of consistency and deductive conditioning of the perspectives the logic that underlies the modelling activity is the one that governs the general knowledge. it is known that in order to model or explain the reality, the human being needs to analyse this reality. in the case of high-complexity systems, the analysis activity is organized in such a way that the human capacity to reason sequentially may found an approach, whose outcomes are comparable to a hypothetical knowledge/modelling exercise that is based on parallel processing capabilities. it could be stated that one of the secret aspirations of the human mind is the wish remarks on interface oriented software systems modelling 669 to relate to this reality not as to a sum of parts that are put together by a structure with a certain granularity, but as to a whole whose parts are articulated according to rules that are known in detail. the latter set of rules features an essential importance, which is nevertheless secondary as compared to the importance of the whole. it is essential to provide two assertions: the regular human perceives sets of components that are structured according to the adequate observation and research possibilities; the demiurge perceives systems inside of which the components move according to the implacable logic of the principles that form the foundation of the universe. these are the two extreme approaches, which can be tried in the activity of knowledge acquiring/modelling. given the precariousness of the paradigms that are elaborated in order to support demiurge-like approaches, we can only optimize the regular human-like approach. in such an approach, the complexity is taken over in a progressive manner, while the knowledge effort is mapped on several perspectives, in order to enhance the accuracy of the analysis. each perspective has the goal to relate with specific means to the same semantics. the consistency of each perspective and the deductive conditioning of the perspectives are two requirements that have the value of a principle in the activity of modularization/knowledge acquiring, in general. the hypothetical continuity of a modelling approach is assured through an accurate relation to the principle of consistency and deductive conditioning of perspectives. the problem regarding the hypothetical continuity of a modelling approach is linked to another invariant of any modelling approach: the change as an immanent attribute to any real system. additionally, whether it is about the law of the entropy, or about the on-the-fly re-definition of the structural equilibria, the change is an important variable in relation to any system’s modelling. consequently, the problem can be formulated as follows: is the model that has been elaborated capable of reacting to changes according to the level at which changes operate? or, the changes provoke shock waves that affect the system in an uncontrolled manner? the continuity of a modelling demarche would ensure that the developer enjoys some added comfort, in the case he confronts with various types of change exhibitions. 5.4 the principle of the perspectives overlap in the modelling activity although it can be considered a truism, we indicate another fact that is used by abstractization techniques in the software industry, without trying to problematize: the overlap of the perspectives. the idea of the perspectives’ overlap can be presented as per the following paragraphs. considering that an abstractization method involves a multi-perspective approach, the connection between the perspectives is not only a deductive constraint (with the meaning stated in figure 2, which indicates the qualitative progress of the abstractization), but also the overlap inside each perspective, which confirms the quantitative interdependence of the perspectives. the modelling represents, at each moment, a mixture of evolution in time and specific state. as an example, the elaboration of the user perspective is premise for the structural perspective, but the ingredients it is composed of are of the type up, sp and bp. naturally, this composite is always characterized by the name of the ingredient that is documented at the highest degree, mainly up in this context. thus, the iterative and incremental nature of the human approach to modelling becomes manifest. this is valid both from a natural perspective, and considering the human struggle towards attaining efficiency in relation to the modelling activities. following, we describe the manner according to which the concept of interface, when used 670 d. bocu, r. bocu at different abstraction levels of a software system solution, can contribute to disciplining and making more efficient the specification of the perspectives, which are mentioned in figure 2. 5.5 the methodological unification of the perspectives in the modelling activity although it seems exaggerate to consider such aspects, it is necessary to note that the identification of a software system’s structure is just a premise for understanding its behaviour. possibly, an explanation for this reality resides in the different ways that are used to perceive the structure and the behaviour. while the structure needs to be mainly invariant and flexible as an additional feature, the behaviour requires mainly flexibility and has to be invariant as an additional feature. the above assertion should become even clearer if we added the following explanatory note: while the invariance of a system is perceived based on the rigour of the relations that are established between its component parts, the system’s behaviour is perceived based on the results that it produces while it operates. the apparent pressure that exists between the two types of perspectives (sp and bp) can be "attenuated" by carefully handling the concept of interface. the careful handling involves the correct understanding of the roles that are played by the interface in relation to the partnerships it is involved in. thus, in the context that is determined by sp, the shape of the contract between interfaces and the implementation-related resources can modify, while the content of the contract should be invariant. this becomes clear if we watch the semantics that is presented in figure 3. in the context that is determined by bp, the shape of the interactions is temporarily frozen, while the content of the interactions can suffer modifications. let us study the semantics that is shown in figure 4 in order to understand the meaning of the above statements. figure 3: the role of the interfaces in the context of sp considering the previous considerations, we can also study the representation in figure 5. this discusses the natural link between two abstractization circuits of a software system’s solution: • the small circuit (interior), which enhances the principle of the deductive conditioning of the perspectives, but also the principle of the perspectives overlap considering each step of the modelling effort. • the big circuit (exterior), which presents the defining elements that support the consistency of the three perspectives (up, sp, bp). remarks on interface oriented software systems modelling 671 figure 4: the role of the interfaces in the context that is determined by bp figure 5: unifying the perspectives in the it systems modelling activity 672 d. bocu, r. bocu 6 conclusions and future developments we are now able to state that the methodological unification of the three perspectives in the modelling activity can be realized in an artisanal manner, or through methodically using the concept of interface. this involves the usage of the concept of interface, considering the particular usage perspective, as a stabilizing tool or as a flexibility-related tool in connection to different aspects of the solution, as it has been suggested by the proper remarks that have been made. in a future paper, we plan to approach the problem of drawing a detailed framework that is bound to foster the derivation of a software system’s behaviour from its structure. bibliography [1] bloch, j.; effective java second edition, addison-wesley, 2008. [2] craig, i.; object-oriented programming languages: interpretation, springer, 2007. [3] garzas, j.; piattini, m.; object oriented design knowledge principles, heuristics and best practices, idea group publishing, 2007. [4] martin, r.c.; uml for java programmers, prentice hall, 2003. [5] gomez, j.; cachero, c.; oo-h method extending uml to model web interfaces, information modeling for internet applications, 144-173, 2003. [6] rector, a.; axioms and templates: distinctions and transformations amongst ontologies, frames, and information models, proc. of the k-cap conference, 73-80, 2013. [7] espinoza, l.; espinoza, h.; feng, w.; modeling a facilities management and information system by uml, proc. of the itng conference, 65-70, 2013. [8] filip, f.g.; a decision-making perspective for designing and building information systems, int j comput commun, issn 1841-9836, 7(2):264-272, 2012. [9] marian, z.; czibula, g.; czibula, i.g.; using software metrics for automatic software design improvement, studies in informatics and control, issn 1220-1766, 21 (3):249-258, 2012. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 5, month: october, year: 2022 article number: 4815, https://doi.org/10.15837/ijccc.2022.5.4815 ccc publications an efficient approach towards network routing using genetic algorithm alaa obeidat, mohammed al-shalabi alaa obeidat* basic sciences dept. faculty of science the hashemite university zarqa, jordan *corresponding author :alaaf@hu.edu.jo mohammed al-shalabi world islamic sciences and education university amman , jordan mohammed.shalabi@wise.edu.jo abstract the network field has been very popular in recent times and has aroused much of the attention of researchers. the network must keep working with the varying infrastructure and must adapt to rapid topology changes. graphical representation of the networks with a series of edges varying over time can help in analysis and study. this paper presents a novel adaptive and dynamic network routing algorithm based on a regenerate genetic algorithm (rga) with the analysis of network delays. with the help of rga at least a very good path, if not the shortest one, can be found starting from the origin and leading to a destination. many algorithms are devised to solve the shortest path (sp) problem for example dijkstra algorithm which can solve polynomial sp problems. these are equally effective in wired as well as wireless networks with fixed infrastructure. but the same algorithms offer exponential computational complexity in dealing with the real-time communication for rapidly changing network topologies. the proposed genetic algorithm (ga) provides more efficient and dynamic solutions despite changes in network topology, network change, link or node deletion from the network, and the network volume (with numerous routes). keywords: rga, genetic algorithm, shortest path, network field. 1 introduction in any network, such as the internet, one of the main problems that significantly affect network performance is routing. finding an optimal route to transmit data within a specific time frame must be a compulsory trait of any good routing algorithm and fulfill the need for a reliable and fast network [1]. with the change in network topologies, routing overheads appear to achieve the optimal performance while considering the parameters like packet delivery ratio, packet delays, packets dropped, https://doi.org/10.15837/ijccc.2022.5.4815 2 and throughput [10]. real-world networks employ algorithms based on random walks. a simple random walk’s cover time may be exponential [11]. to guarantee that the time remains polynomial, the networks with no fixed infrastructure and the topology need to look for a strategy to avoid the adversary. genetic algorithm is a novel technique employed for network optimization. ga involves some defined operations [4], [5] which include conditions for encoding, initialization, evaluation, reproduction, crossover, mutation, and termination. much work has already been done that involves ga to design and optimize the network [5]. ga involves time constraints as these algorithms take considerably longer times to generate a solution. a ga-based approach is presented to address the problems in the dynamic routing and coming up with an optimal solution. this approach suggests a new technique to fill the generation in a shorter time compared with different selection schemes. [3] genetic algorithms prove very helpful to find effective yet quick solutions to complex problems. for this purpose, these algorithms involve genetic operators: crossover operators and mutation operators. on the other hand, heuristic search algorithms work fast but don’t yield optimal solutions. genetic algorithms have optimization algorithms adapting to changing requirements like the procedures of natural selection and genetics. genetic algorithms owe their capability of providing optimal solutions to a blend of exploitation and exploration techniques [13]. the exploitation technique is applying prior knowledge for finding a better solution. while exploration refers to the investigation and finding of an unknown solution. our exhaustive literature review identifies that the existing schemes such as link cost-based routing, delay-based routing, and throughput-based routing are static and can be drastically improved by evolutionary algorithms. an evolutionary algorithm or iterative algorithm such as a regenerative genetics algorithm randomly chooses the first population and finds the optimal route/ solution by reducing the solution space. genetics algorithms have varied applications. for example, routing problems where the sequence of chromosomes (number) is used for encoding values of the different routes to finding an optimal one. genetic algorithms have been applied to solve numerous problems, but for solving routing problems using the genetic algorithm, fewer studies have been done, which worked as an encouragement to put effort and research into this area. following are a few reasons that drive us towards ga being the best solution for routing: 1. ga is highly customizable, which means if an improvement in throughput or jitter is needed, that metric can be added to a multi-objective optimization problem as a constraint. thus, ensuring the best possible route considering the newly added condition. [12] 2. implementation, configuration, and modification of ga do not incur additional complexity or delays to the network performance. [13] 3. ga is lightweight in terms of control overhead and information requirements. [15] all of these reasons from existing literature and our study inspired us to develop an efficient routing scheme using a regenerative genetics algorithm. our evaluations corroborate our claims that using ga drastically enhances the network performance with reduced delay and low complexity as compared to other schemes. 2 literature review ga has vast applications to solve real-life problems. their nature allows the successor solutions to evolve from the previous ones. ga can rightly be termed as an adaptive heuristic method for finding optimal solutions [14]. among its numerous applications, ga is applied to schedule ship routing optimally [12]. genetic algorithm is put in this case to make sure that the ship reaches the destination port minimizing the distance traveled and avoiding repeating the visited ports. many fields including navigation systems and transportation employ ga for solving optimal path problems. the vectors (chromosomes) between the source and the destination nodes may vary in length. the solution lies in finding a better crossover mechanism as the right method may outperform the others. [15] finds that the same adjacency (sa) crossover mechanism performs better than the same point (sp) method https://doi.org/10.15837/ijccc.2022.5.4815 3 for variable-length chromosomes. e-learning with personalized yet efficient learning mechanisms is gaining significance. curriculum sequencing or better termed as learning path matching with the learner’s preferences with proper start and endpoints is defined [16]. because of different learning preferences from different learners, the learning path cannot be of fixed length. therefore, an effective learning path is recommended based on a variable-length genetic algorithm (vlga) keeping in mind the learner’s prior knowledge and style. to ensure error-free garment manufacturing and at the same time, for maintaining the product quality, paper [17] defines some rules as variable-length chromosomes to be followed and to ensure quality. the use of a novel ga called slippery genetic algorithm (sga) gives rise to diverse solutions and thus, better quality assurance is achieved. genetic algorithm (ga) has its role in designing and implementation of intrusion detection systems. a ga-based approach is presented for intrusion detection with variable length chromosomes. the proposed approach is then found effective and efficient after being tested with defense advanced research project agency (darpa) 1998 data [19]. genetic algorithms have their significant application in optimizing search and routing problems. optimizing routing involves taking into account the network traffic. ensuring the internet reliability and performance, selecting an optimized path from source to destination, and finding alternate paths by using multipoint crossover and mutation are important considerations. the proposed method calculates the shortest yet efficient path using ga adapting itself to change in topology, node, and network volume [20]. improvements in selection and mutation schemes have a significant impact on the performance of ga. these improvements enhance the ga in terms of both efficiency and diversity. enhanced selection and log-scaled mutation ga (esaloga) [18] is presented with an improved selection and mutation schemes and is compared with the standard ga and its variations. the comparison shows major improvements. the algorithm uses different length chromosomes to encode the problem. crosses (dots) are loci (node positions in a lane), where the similar genes (nodes) on the two selected chromosomes (lanes) are in the same place [3]. hence, this results in a state where only a few cross-sites yield feasible solutions. this means that the crossover is entirely position-dependent, and similar genes should be at the same place for crossover. an algorithm for fixed-length (deterministic) chromosomes is proposed. in the proposed algorithm, chromosomes are of the same (fixed) length and are represented as sets of integers and each gene corresponds to a node identifier. this note identifier is randomly selected from the set of nodes that are connected to the node related to its locus number [2]. in the crossover, one gene is chosen from parental chromosomes at the id locus of the start node and placed on the same progeny locus. after that, one of the genes is randomly picked at the locus of the gene number already selected. the process is repeated until the destination node is touched. the algorithm does not explain the mutation details. using a large population for the algorithm yields an optimal or high-quality solution due to an inconsistent crossing mechanism. some offspring can produce new chromosomes that match the original chromosomes in fitness. this slows down the evolutionary process. a new algorithm is developed that works for both fixed-length and variable-length chromosomes. although, this algorithm is proven efficient in comparison to the other algorithms, it is failed to obtain the optimal path in all cases [7 ]. three hybrid algorithms based on ga are presented with a different local search technique which are then compared with bee (bee) and differential evolution (de-sk) algorithms. sequential constructive crossover (scx) is modified and it is compared with one-point crossover (opx). scx performed significantly better than the opx. the ga was employed combining two local techniques: 3-exchange and constructive mutation algorithms. after the comparative study, the hybrid genetic algorithm with 3-exchange and constructive mutation (hga3) is found to be the best in terms of computational time and solution quality [21]. to enhance the performance of ga, paper [22] proposes more than one crossover operator including collision crossover and two selection strategies i.e. selecting the best crossover operator and random selection of an operator. the evaluation of the proposed method such as collision crossover shows major improvement in the performance of genetic algorithms, especially in the case when more than one crossover operator is used. [23] suggests a novel ga approach keeping in view that different operators (selection, crossover, and mutation) are very crucial for the strength of the genetic algorithms. paper introduced an improved crossover operator with variable length chromosomes that uses parents with high fitness levels to produce offspring with high fitness levels and parents with low fitness are also https://doi.org/10.15837/ijccc.2022.5.4815 4 figure 1: methodology used as well to introduce diversity in the resultant population. [24] introduces a new selection method in ga whose performance is evaluated in terms of individual and cumulative fitness. the selection method avoids the worst case where the selection pool may comprise of only poorly fitted chromosomes. the method gives them the chance to mate with successful and fit candidates as well. candidates are arranged in descending order of the fitness levels. it is made sure that the candidate at nth best level mates with the candidate at the nth worst level. the proposed method performed better than the other selection methods and showed the potential to improve the overall strength of the ga. the proposed approach (rga) in this paper is based mainly on the fundamentals of the three operations of ga to do proper analysis and looking into the possibility of finding the optimal path in all cases. 3 the proposed methodology evolutionary and iterative algorithms such as ga and regenerative ga initialize the process with a population that is a set of solutions, randomly chosen from the solution space. a population is defined as candidate solutions for a particular problem, which in this case is efficient and low complexity routing. generally, the selected population is not the optimal solution and requires further processing. the algorithm reproduces by selecting and performing a crossover operation (combining two or more solutions from the population), to mutate and create a refined population set. this repetitive process is evaluated on every iteration for constraints satisfaction to identify the optimal solution. representation mechanism: a coding mechanism with multiple parameters is used to configure the network [8]. a routing table is used to record entries for all pairs of node combinations. these entries correspond to the routes between a pair of nodes. each route in the proposed algorithm is represented by a chain following the links of the routing table and thus configuration chains are constituted. ga encodes search space with a vector. these vectors are termed as chromosome. each element of the chromosome denotes a gene. the collection of chromosomes results in a population. a fitness function is defined to estimate the strength of this population. fitness function can also be used to estimate the strength is an individual chromosome. to start with ga, the initial population is created randomly. then fitness is also evaluated for each chromosome. then the following three steps are applied to produce the successor of the current generation: • reproduction – this phase works conforming to darwin’s theory of “survival of the fittest”. only the population with the high fitness level is kept and moved to the next generation while the rest is eliminated. https://doi.org/10.15837/ijccc.2022.5.4815 5 • crossover – from the population of high fitness, two parents are selected and a new offspring is produced. this process continues for the aforementioned number of times or until a certain condition is met to terminate the operation. • mutation – diversity is introduced with some new features in the offspring which make it quite different from its predecessors. first of all, following a ga, the population is initialized (figure 1). the selection of the routes is made from the routing table on a random basis. from the routing table, a route is selected between each pair of nodes to form a configuration chain (cs). the routing table is kept for all css that satisfy the given constraint. the routing table has a fixed size which is larger than the population size. with the generation of the new chains, older ones are discarded [9]. fitness function is required to explain the physical representation and to assess the suitability of the chromosomes according to the characteristics required in the final solution. the role of the fitness function is very vital. so, fitness function must be defined carefully to precisely measure the fitness level of chromosomes in the population. taking the shortest path routing problem into consideration, its fitness function is understandable as finding the sp is the same as discovering the cheapest route [10]. so the algorithm should use the topology database (a database that keeps track of the primary routes for all the network nodes) that would help to pick the initial generation randomly without any specific source and destination. a good selection of the successors is very crucial. novel schemes are also proposed to generate population and carry out selection and crossover operations. 3.1 genetic representation a proposed rga variable-length chromosome is composed of strings of integers or letters for the nodes’ ids representation that are traversed in a routing path. each position on the chromosome depicts a node sequence (indicated by the gene for the locus) in a path. the first locus gene represents the source station (figure 2). as the chromosome varies in length, there is a limit on the maximum length and it should not go above the total number of nodes in the network. figure 2: gene representation 3.2 produce the first generation (select g0) • heuristic generationpreviously, this phase is not yet studied and analyzed deeply. but in this proposed approach, it is tried to have the initial generation cover all the aspects of the solution. • topological database – a database keeping track of the primary paths of all network nodes used in algorithms. it aids in randomly picking the first generation, without any specific or fixed source and destination. this paper used a different approach to pick first-generation paths. this technique takes into account the origin and destination nodes. when the first routes are created, the 1st generation (g0) must include all the network nodes. • all links (ab, ad, dm, am. . . ) that are available in the networks at any time must be in the database. https://doi.org/10.15837/ijccc.2022.5.4815 6 figure 3: mutation • start with all the links existing in the database and create the initial paths where the start node depicts the source and the end node represents the destination. • a link should not show up twice in every path (infinite loop problem). • all the connections present in the network have to be taken. 3.3 crossover between chromosomes crossover finds better solutions based on the current solutions. talking about the crossover operation in the shortest path routing problem, each partial pathway of two selected chromosomes is exchanged in a way that the resultant offspring represents only one pathway. this suggests the use of a one-point intersection for the proposed rga. partial paths connect the source node to the middle node and then the middle node to the destination node. • one-point crossover: from the first chromosome, one locus is picked. this locus serves as the location to perform the crossover. then the position for the other chromosome is the same. however, there is a chance that the crossover result thus produced may be invalid as it may suggest routes that are not present in the topological database of the network. for example: consider the routes taken from the suggested network (figure 2), and location 2 is the position where the crossover is performed between them: adm, kgd. the resultant paths produced after the crossover are: (agd, kdm) which don’t exist in the topological database of the network. this kind of crossover is hard to produce consistent results. • two-point crossover: in this kind of crossover, for the crossover position, one location is chosen randomly in the first chromosome. then the position for the other chromosome is taking the same gene name, but it does not have the same location. for example: consider the routes taken from the suggested network (figure 3), and location 3 is the position where the crossover is performed between them then the location in the second path is 2: aebd, abf. the resultant paths produced after the crossover are: (aebf, abd) which exist in the topological database of the network. this kind of crossover may face the looping path problem (this means that two similar genes exist in the same chromosome). 3.4 mutation in the mutation process, the selection of a gene is made randomly from a selected chromosome which is termed as "mutation site". to select the first node of the alternative partial-route, a node directly connected to the mutation point is picked randomly. for exampleexamine paths (source → a → d → m → destination). location 2 is the position to make the mutation. the resultant https://doi.org/10.15837/ijccc.2022.5.4815 7 path is (source → a → k → destination). this path may be evaluated to be more efficient than the first path. 3.5 fitness function fi is the fitness value of the ith chromosome, that represents each solution from the population: fi = 1∑ii=1 j=1 cgi(j), gi(j = 1) , (1) where li is the length of the ith chromosome, gi (j) is the gene (node) of the jth locus in the ith chromosome, and (c) is the link cost between nodes. example explaining the proposed approach: figure 4: network with the link cost let’s consider network illustrated in figure 4, to demonstrate the working example of the proposed scheme. node a is assumed to be the source of the transmission, where as node d is destination. the process of the proposed regenerative ga includes: initial paths: a → b → d = 13, a → c → b → d = 20, a → e → d = 21, a → b → e → d = 16, a → b → f → c → d →= 31. all links are taken. crossover: a → b → d, a → c → b → d ⇒ a → c → b → d = 20 and abd = 13. . . the same paths a → c → b → d, a → b → e → d ⇒ a → c → b → e → d = 23anda → b → d = 13 . . .. not taken because the first path has more cost than the initial and the second path that already exists. a → b → f → c → d, a → c →→ b → d ⇒ a → b → f → c → b → d (loop in b), a → c → d = 8 the first path is not taken, but the second is taken for the reason that its cost is less than the other paths. network’s analysis clearly shows that no path less than a → c → d can be produced which makes it an optimal path. the cost of the path is 10 which makes it an optimal path using rga approach as in (figure 6). whereas using rip approach, path cost is 17 which makes it not an optimal solution (figure 5). 4 research questions in this research, several questions can arise. the questions at the moment are as follows: • what is the best way for the network path to be represented? https://doi.org/10.15837/ijccc.2022.5.4815 8 figure 5: finding path using rip figure 6: finding path using rga • how are new modifications proposed to produce good generations in the operations of the genetic algorithm? • how can a good fitness function be established to assess the consistency (various route variables) of the generations (paths)? 5 claimed contributions this research will prove beneficial to: • develop software to visualize the networks to implement the suggested algorithm. • compare the proposed algorithm with other routing protocols, such as rip. • use genetic algorithms to build a new routing approach: always finding the optimum route in any network. the findings of this research are not limited. rather, they will surpass the local aspects. taking into account the distributed aspect, this research will play a vital role. the wide and genetic applications of the generic algorithms enhance its effectiveness. 6 performance evaluation to comparatively evaluate the proposed regenerative ga solution for routing, we have developed a discrete-event custom-built python simulator. the simulation incorporates a various number of https://doi.org/10.15837/ijccc.2022.5.4815 9 800 1200 1600 2000 2400 2800 3200 0 5 10 15 20 25 30 35 40 45 a v e ra g e d e la y ( m s ) o f c h o s e n r o u te number of packets dijkstra’s algorithm proposed regenerative ga rip algorithm (a) average delay vs packets 10 12 14 16 18 20 22 24 26 28 30 0 5 10 15 20 25 30 35 40 45 a v e ra g e d e la y ( m s ) o f c h o s e n r o u te number of routers dijkstra’s algorithm proposed regenerative ga rip algorithm (b) average delay vs routers figure 7: link delay with packets and routers routers from 10 to 30 with a step of 2 routers. each router is assigned a link with other neighboring devices, where a link is represented by multiple parameters such as id, link cost (0 ∼ 50), link delay (0 ∼ 50 ms), and link connection speed (10 mbps), etc. moreover, we designed a separate class of packets where each packet has an id, source address, destination address, best path using rip, best path using dijkstra’s, and best path using the proposed regenerative ga. considering multiple routers in the system, for each packet, a random source and destination are chosen. a total of 1,000 ∼ 3,000 packets are generated for each evaluation, which sums up to an exhaustive amount of more than 50,000 packets. nevertheless, our simulation performs an extensive search and finds all possible routes between source and destination without loops. we have implemented the rip algorithm that prefers shorter distance paths where the number of hops is minimal. dijkstra’s algorithm considers the minimum cost of the link as a routing metric. however, the proposed regenerative ga solution calculates the fitness function for each link/ route that relies on not only the cost of the link but also considers other parameters. first, the proposed scheme randomly selects an initial population from available solutions (that is routes between source and destinations). the solution set is mutated towards the pareto front (set of possible optimal solutions) and with mutation (offspring of previous solutions) repetitively evaluates a set of constraints. if constraints are satisfied, the optimal route is provided as a solution for communication between the source and the destination. once each routing scheme identifies its preferred route from the list of possible paths, the performance evaluation module calculates the delay and cost for each methodology. we have considered two metrics of average cost and average delay of 10,000 packets, for each scheme. figure 7 (a) shows the average delay of chosen route for three routing strategies (rip, dijkstra’s, and the proposed scheme), with respect to the number of packets in the network. the proposed ga routing outperforms existing solutions due to the fact that it considers fitness function and optimizes with an iterative process. a random solution space is chosen at first, but with each iteration, the solution space reduces to the most optimal solution (which in this case is the optimal path). on the other hand, rip chooses shorter paths which is also a way to reduce delay in packet transmission. however, dijkstra’s algorithm performs poorly because it considers only the cost metric that negatively impacts the communication delay. figure 7 (b), corroborates our claims that with the increase in the number of routers which leads to more possible routes for iterative solutions, the proposed scheme performs better and reduces the delay as compared to existing schemes. the optimization of population and solution space removes the routes between source and destination which are against the ga algorithm’s objectives. a minimization optimization problem in regenerative choose routes as best that have minimal cost and delay. thus, the proposed scheme outperforms rip and dijkstra’s algorithm. figure 8 (a) illustrates interesting observations that the proposed scheme performs slightly better than https://doi.org/10.15837/ijccc.2022.5.4815 10 800 1200 1600 2000 2400 2800 3200 0 5 10 15 20 25 30 35 40 45 a v e ra g e c o s t o f c h o s e n r o u te number of packets dijkstra’s algorithm proposed regenerative ga rip algorithm (a) average link cost vs packets 10 12 14 16 18 20 22 24 26 28 30 0 5 10 15 20 25 30 35 40 a v e ra g e c o s t o f c h o s e n r o u te number of routers dijkstra’s algorithm proposed regenerative ga rip algorithm (b) average link cost vs routers figure 8: link cost with packets and routers dijkstra’s algorithm. our ga routing selects the best routes with minimal link cost, however, it should also be noted that dijkstra’s algorithm also chooses routes with the lowest link cost. nevertheless, the proposed scheme with an increase in the number of packets performs well ahead of the rip and dijkstra’s algorithm. figure 8 (b) highlights the relation of the number of routers in the network with the performance of routing schemes. it is quite obvious that the rip performs worst than all because it considers the shortest distance, disregarding any other metric. the proposed regenerative ga selects routes that show optimal cost over than other two schemes. over and all, our iterative solution finds the best possible path from a source to destination while reducing the delay and cost of communications. moreover, the proposed ga demonstrates the lowest complexity of existing schemes. 7 conclusion in this article, we proposed to utilize regenerative ga to efficiently choose optimal routes that satisfy multiple constraints including reducing complexity, delay, and link cost. state-of-the-art evolutionary algorithms such as regenerative ga have myriads of applications in choosing optimal solutions, including autonomous vehicular networks, packet routing, decisions related to sorting and manufacturing in industry 4.0, and such. our exhaustive evaluation includes two parts of performance evaluation and complexity analysis. for comparative evaluation, we simulated rip, dijkstra’s algorithm, and the proposed scheme and identified that the proposed scheme reduces communication delay by 10 ∼ 15 ms. moreover, with a variable number of packets and an increase in the number of routers, the proposed regenerative ga still outperforms existing solutions. moreover, the proposed genetic algorithm has been compared with other routing algorithms such as bellman ford’s algorithm, dijkstra’s algorithm, and floyd warshall’s algorithm. bellman ford’s algorithm depends on the concept that it has at most (n-1) edges and thus the path does not have a loop. this algorithm also suffers from time complexity o(v.e) in case e = v 2, o(v 3). the time complexity of dijkstra’s algorithm is o(v 2). it reduces to o(v + elogv) for the min-priority queue. warshall’s algorithm calculates with o(v 3) for all the shortest distances between two vertices. the proposed algorithm overcomes all these time complexities efficiently. we believe that the proposed scheme is the pivotal step towards improvement in the routing process using state-of-the-art evolutionary algorithms. regenerative ga itself is a solution to traditional ga’s problem of local optimal solution. however, there are still some limitations that needs to be addressed such as control overhead of information sharing. almost all algorithms require information related to possible paths before making the routing decision, that incurs additional cost of message sharing. we believe that with advancements in the predictive methods, regenerative ga can be coupled with https://doi.org/10.15837/ijccc.2022.5.4815 11 a learning scheme to overcome aforementioned limitation. conflict of interest the authors declare no conflict of interest. references [1] i. f. akyildiz, w. su, y. sankarasubramaniam, and cayirci. wireless sensor networks: a survey. computer networks, 38(4):393–422. 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(2022). an efficient approach towards network routing using genetic algorithm, international journal of computers communications & control, 17(5), 4815, 2022. https://doi.org/10.15837/ijccc.2022.5.4815 introduction literature review the proposed methodology genetic representation produce the first generation (select g0) crossover between chromosomes mutation fitness function research questions claimed contributions performance evaluation conclusion int j comput commun, issn 1841-9836 8(3):425-431, june, 2013. a proactive vhd algorithm in heterogeneous wireless networks for critical services c. lozano-garzon, n. ortiz-gonzalez, y. donoso carlos lozano-garzon, nicolas ortiz-gonzalez, yezid donoso universidad de los andes, bogotá, colombia e-mail: ca.lozano968@uniandes.edu.co n.ortiz908@uniandes.edu.co, ydonoso@uniandes.edu.co abstract: progress in the telecommunications sector has opened new scenarios where users want to access any application or service from any device anywhere at any time, connected to any network. in this environment, the heterogenerous wireless networks (hwn) is the main operating infrastructure, intended to support the technical and quality needs that these users and services demand, along with the versatility and availability that comes with being able to connect to any interface. one of the most important characteristics of hwn is the possibility to connect many kinds of radio access networks (ran) like wifi, wimax,gsm, umts, hspa, lte, among others. this brings many challenges in hwn surroundings, one of the most important is ensuring that a user terminal can move from one access network to another without losing connectivity and, of course, the service. the aim of this paper is to propose a vertical handover decision algorithm (vhoda) that enables a single user terminal to initiate a proactive decision based on user preferences and qos parameters, while at the same time considering the networks conditions to avoid over burdening an interface. the development of our vho-da was addressed like a multi criteria decision making problem (mcdm) in order to provide the best possible connection for critical services and maintaining load balancing in networks. keywords: multi criteria decision making problem, handover decision, heterogeneous wireless networks, vertical handover. 1 introduction thanks to advances in the areas of electronics, computers and communications, the way we perceive and interact with the world has been transformed. nowadays the majority of our daily activities are related in some way with the use of these technologies, from how to educate, to health care processes, and access to government services, to name a few. so it’s clear to the academic community and the business world that information communications and technologies (ict) play an important role in their daily lives and in all business and market processes that surround them. a study conducted by international data corporation (idc) on the top 10 current trends in the ict sector for latin america, found that two of them are directly related to the topic of infrastructure for mobile communications. the first trend shows an environment where the impact of mobility is very high, as consumers of multiple services want to access their information anywhere at any time, while the second shows that the incursion of fourth generation networks will facilitate the first trend, causing an explosion of new services that need to be supported by this new infrastructure [1]. that is, we have a scenario where users (both personal and corporate) can access any application from any device, anywhere, anytime. this new scenario is an infrastructure that operates what is now known as the heterogeneous wireless networks (hwn) or next generation mobile networks (ngmn) which are intended to support the growing needs, both technical and qualitative, that users demand from their current and new services. copyright c⃝ 2006-2013 by ccc publications 426 c. lozano-garzon, n. ortiz-gonzalez, y. donoso among the most important characteristics defined for hwn networks are: to support data transmission up to 100 mbit/s in downlink and 50 mbit/s in uplink in a channel with a bandwidth of 20 mhz and a low latency from one end to another in less than 30 ms. as a result of proposed developments within the new networks, hwn is looking to provide wireless competitive broadband services with high data rates and an excellent balance between cost and performance. not only will it help to achieve better technical standards, but it’s also expected to achieve great benefits for end users, significantly improving the user’s experience with existing services and future data and multimedia services, which will make broadband internet easily accessible with excellent mobile performance [2]. this new proposed ecosystem is expected to generate multiple challenges for telecommunication service operators, especially in the design and implementation of network infrastructure since it must allow the provision of various services independent of location, time, device or access network. one of the most challenging problems is maintaining service continuity across this ecosystem, the possibility that one user can change their access network without the loss of the service. this transfer between rans of different technologies is called vertical handover and is usually driven by the requirements of the services, and also by the need to improve network performance [3], [4], [5], [6]. 2 related works lots of articles mention the subject of the different phases in handover, in [7] the authors describe the decision, radio link transfer and channel assignment phases of the handoff. in [8] the authors establish the phases of network discovery, handoff decision and handoff execution. from these it’s determined that there is a data acquisition phase where the information the algorithm uses is obtained and that afterwards the choice elected by it commences the handover execution without affecting any other part of the process. in other words, since it’s isolated, it can’t affect the rest of the process beyond its decision, and so the work can be specified to only the decision algorithm. it concentrates mainly on applying the decision function to the values given as input, which are obtained by the candidate interfaces and given to the mobile device. the way the operators give this information or how the handoff works after the decision is made is not considered. in recent years, many vho da have been proposed, these algorithms use different decision strategies to select the best connection. in [9] kassar, kervella, and pujolle propose a classification of them into five categories: functions, user-centric, fuzzy logic and neural network-based, multi-criteria, and context-aware strategies; although these are not mutually exclusive. several decision algorithms developed use a multi criteria strategy, for example in [10] zhang presents an algorithm that uses fuzzy logic combined with some classical multi attribute decision making (madm) methods, simple additive weighting (saw) and technique for ordering preferences by similarity to ideal solution (topsis), for handover decision in order to combine and evaluate multiple criteria simultaneously. song and jamalipour in [11] explain an algorithm that combines the analytic hierarchy process (ahp) and the grey relational analysis (gra) methods to decide the best network for mobile users through finding the tradeoff among service application, network condition, and user’s preference. in [12] by yang and wu, their proposal uses fuzzy logic and madm; the proposed algorithm is divided into four parts: traffic classification, resource estimation for reservation, admission control and rat selection. one similar algorithm was presented by lsmail and roh [13], their proposal was a user adaptive fuzzy madm handover decision scheme. they applied different fuzzy madm methods (topsis, saw, maximin, electre and ahp) to find out best alternative for handover and to evaluate the performance of these methods. lahby, leghris, a proactive vhd algorithm in heterogeneous wireless networks for critical services 427 and adib [14] focuses their work in a hybrid method based on multi attribute decision making methods ahp and topsis to select the best network alternative. the work presented in [15] by tamea, biagi, and cusani, proposes and analyzed a modified version of topsis to include risk information; this proposal allows the reduction of outage probability at the cost of parameters performance loss. inside the work of kim et al. [16] , they design a vertical handover decision algorithm that is a combination of a policy and a multicriteria decision making approaches; this algorithm intends to achieve an effective and seamless handover between heterogeneous radio access networks, especially between the lte and the wlan systems. 3 problem statement as previously stated this work focuses on proposing a vertical handover decision algorithm (vho-da) that enables a single user terminal to propose a proactive decision based on user preferences and qos parameters, while at the same time considering the networks conditions to avoid over burdening an interface. since the proposed problem involves more than one decision parameter, we intend the use of a multi criteria decision making method with the aim to provide always the best possible interface connection available for critical services. some madm algorithms were addressed like topsis, gra and saw. each of them work in a different way, but they’re all decision algorithms, which is to say that they compare many different options based on one or more attributes and choose the alternatives which satisfy the conditions the best. however, according to [17] the results of each are almost the same. in addition a vho-da must consider the limitations associated with the computing power in a mobile user device; this is the reason why we need to select the algorithm with lowest algorithmic complexity in order to lessen the burden placed on the mobile device. based in [18] we selected a simple additive weighting (saw) method as it was the madm algorithm with the lowest computational complexity. 3.1 simple additive weighting (saw) method saw technique is one of the most used madm techniques. it is simple and is the basis of most madm techniques such as ahp and promethee that benefits from additive property for calculating final score of alternatives. saw for all the m alternatives determined a score (vi) calculated by multiplying the weight assigned to each attribute (wj) by a comparable rating scale of each attribute (rij) and then summing these products over all the n attributes. vi = n∑ j=1 wj · rij, ∀i = 1, . . . , m. (1) 3.2 mathematical model in order to take this decision, all factors available in a heterogeneous network must be evaluated. in this article, the factors chosen were qos parameters and charging rates available on 3gpp/lte, wi-fi and wimax. the qos parameters are divided into two main groups: ascending parameters, those who are deemed better the larger in value they are, and descending parameters, those who are deemed better the smaller in value they are. the ascending parameters (ap) are: received signal strength indicator(rss) and availability(av). the rss determines how strong the interface’s signal is. availability is the rate of the 428 c. lozano-garzon, n. ortiz-gonzalez, y. donoso interface’s uptime over its uptime plus its downtime. while the descending parameters (dp) are: packet loss(pl), jitter(j), delay(d), cost(c) and load(l). packet loss is the rate of how many packets were received over how many were sent. delay is amount of time it takes for a packet to travel from one point of the network to another. jitter is the variation of the delay of multiple packets over a network. also it’s important to establish that there is a set of n interfaces i which includes the one we are already connected to, and a set of n-1 interfaces to which we could connect named j. for our case the alternatives are the rans available at a given point in time. the decision maker assigns the weight of every attribute based on the needs of each service, so each operator can state which parameters are most important for it and find the most appropriate network. given the above statement the mathematical model proposed is a saw equation, where all the parameters are accounted for and evaluated. attributes are normalized and if it’s a parameter where less is better, it is inverted or subtracted from a total percent. vi = wd· 1 ( d dm ) +wj· 1 ( j jm ) +wpl· 1 ( pl plm ) +wav·( av avn )+wrss·( rss rssn )+wc·(1− c cn )+wl·(1− l ln ) (2) where the nthvalue of a parameter is the highest of the available interfaces and the mth value of a parameter is the lowest of the available interfaces. some specific parameter functions cost. cost must also be determined. in this paper, the charging models evaluated are: free, monthly fee, bits consumed rates and time consumed rates. free are interfaces where the user would not incur in any financial cost if he were to connect to them. monthly fee are interfaces which use fixed rate charging so no matter how much is consumed, they charge the same. for the purposes of this paper, monthly fee and free are essentially the same, as the user doesn’t incur in any additional cost by connecting to that interface. bits consumed rate are interfaces which charge by how many bits are used in the connection. time consumed rate are interfaces that have a flat-rate charging model that bills according to how much time the user is connected to the interface. in order to compare the two, time consumed rate interfaces are converted into bits consumed rate using the following formula: bit consumed rate = t. consumed rate(in minutes) 60 throughput (3) load. load is the representation of the load balancing factor into the equation; it’s the theoretical capacity of the network according to its technology, which the mobile device already has, minus the current throughput of the interface, given to the mobile device by the network; all of this over the theoretical capacity. loadi = theoretical capacityi − throughputi theoretical capacityi (4) it should be noted that this configuration of load is done in order to maintain the idea that lower the load of a network, the less it is being used and the more available the resources are. all of these parameters, excluding load, are either percent’s or values given by the interfaces, so no calculation must be done with them. except in the case that it’s a time consumed rate and must be converted to bit consumed rate. a proactive vhd algorithm in heterogeneous wireless networks for critical services 429 3.3 heuristic implementation we chose to solve this problem through a heuristic method do to it being the fastest and most precise way that the answer could be found with the information available and the restrictions placed by the infrastructure. the chosen vho-da is a straightforward process that runs continuously in the background. first, it searches for all the available networks it detects. then it gathers all the information of those interfaces, including qos parameters and its condition, in addition to the preferences of the active services (critical services). then it compares the information of each interface in order to determine the highest and lowest values, and then proceeds to normalize all of them and obtain it’s saw weighted value. the first interface that scores the highest value is the chosen interface to switch to; that is, if it is different from the current interface. figure 1 present the flow diagram of the heuristic described. for our case the alternatives are the rans available at a given point in time. the attributes chosen to take part in the decision are: delay (d), jitter (j), packet loss (pl), availability (av), cost (c), received signal strength (rss), and load (l). the decision maker assigns the weight of every attribute based on the needs of each service, so each operator can state which parameters are most important for it and find the most appropriate network. figure 1: proposed heuristic algorithm 3.4 experimental results in order to verify the correct function of the proposed heuristic, we uses a case study scenario extracted from [19] in which multiple interfaces (umts, wifi, wimax, gsm, hspa+, edge, lte) are given qos attributes to be compared. the case is modified, to give it rss values and the theoretical capacity of each interface. this scenario is just an example and is not meant to be a definitive sample of any of these interfaces. we execute the vho-da on the scenario to evaluate its effectiveness using a demonstrative set of weights in which load was given special consideration. the test results showed a satisfactory 430 c. lozano-garzon, n. ortiz-gonzalez, y. donoso interface delay jitter packet loss cost rss throughput availability th. capacity load ranking umts 45 1 0,0001 0 0,4398 384 0,99999 2400 0,984 0,6545 wifi 1 180 11 0,4 4,35e-8 0,9473 23000 0,999999 54000 0,57407 0,5115 wimax 70 9 0,0001 0,0001 0,4120 4000 0,99999 100000 0,96 0,47 edge 1 150 10 0,01 0,003 0,5904 178 0,9999 384 0,5375 0,4685 hspa 55 2 0,0001 0 0,8528 2000 0,999 7200 0,722 0,6767 wifi 2 110 9 0,0001 0,006 0,5204 4500 0,99999 65000 0,981 0,4421 gprs 1 90 9 0,001 0,008 0,9851 80 0,9999999 114 0,298 0,6279 gprs 2 135 9 0,005 0,003 0,6550 60 0,9999999 114 0,47368 0,5055 edge 2 100 7 0,0001 0,0016 0,4379 237 0,9999 384 0,383 0,5787 table 1: parameters values in the study case result and prove that the algorithm first and foremost considers the burden on a channel before selecting it, while at the same time considering qos values. considering that this study case is specifically intended for critical services, we distributed the weights of the parameters as follows: delay (0,15), jitter(0,1),packet loss(0,1), cost(0,05), availability(0,15), rss(0,15) and load(0,3). it’s important to know that result of the saw vector vi is represented by the value "ranking", since we compare the result and from the highest we extract which interface should we connect to. the vho-da was executed with the parameters values that you can see in table 1. the highlighted interface, hspa, is the chosen interface to connect to due to its results, being the interface with the highest ranking. this is partly due to it having the second lowest delay, jitter, and shares the lowest cost and packet loss with a few other interfaces. and what’s also noticeable is that the options is very well rounded, as it’s only the best in two parameters that aren’t the heaviest (packet loss and cost). it definitely considered what we needed for the critical service. 4 conclusions the work concludes in three points. the first is that this vho-da proves to have a holistic consideration of the values, and forms a multicriterial decision that guarantees the best connection possible to the critical service. the second is that since it’s also modifiable (to consider the necessities of the service in question), it’s highly flexible and can be applied to any critical service and any network. finally, the third point is that this vho-da considers special limitation for mobile devices and networks, implementing load balancing to assure the most advantageous use of the hwn infrastructure. considering these three points make this algorithm an improvement in the vho-da field. bibliography [1] idc latin america predictions team., idc predictions 2011. welcome to the new mainstream., idc corporate, framingham, ma, 2011. [2] ngmn alliance,next generation mobile networks beyond hspa & evdo. , ngmn alliance, 2006. a proactive vhd algorithm in heterogeneous wireless networks for critical services 431 [3] i. f. akyildiz, j. xie, and mohanty. s.,a survey of mobility management in next-generation all-ip-based wireless systems, ieee wireless communications, 11(4):16-28, 2004. [4] x. yan,optimization of vertical handover decision processes for fourth generation heterogeneous wireless networks, monash university, dept. of electrical and computer systems engineering, melbourne, australia, phd thesis, 2010. [5] j márquez-barja, c. t. calafate, j.c, cano, p. manzoni, an overview of vertical handover techniques: algorithms, protocols and tools, computer commun, 34(8):985 997, 2011. [6] x yan, y. a. sekercioglu, s. narayanan, a survey of vertical handover decision algorithms in fourth generation, computer networks, 54(11):1848 1863, 2010. 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[17] yoon k. p., c. l. hwang, multiple attribute decision making. an introduction, susan mcelroy, ed. thousand oaks, c.a, united states of america: sage publications, 1995. [18] i. chantaksinopas, p. oothongsap, a. prayote, network selection delay comparison of network selection techniques for safety applications on vanet, 13th asia-pacific network operations and management symposium, taipei, taiwan, 1 7, 2011. [19] c. fortuna, m. mohorcic, b. filipic,multiobjective optimization of service delivery over a heterogeneous wireless access system, iswcs 2008, reykjavik, iceland, 2008. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 5, month: october, year: 2020 article number: 3928, https://doi.org/10.15837/ijccc.2020.5.3928 ccc publications v2v routing in vanet based on heuristic q-learning x. y. yang, w. l. zhang, h. m. lu, l. zhao xiaoying yang school of information engineering, suzhou university, suzhou 234000, china yangxiaoying@ahszu.edu.cn wanli zhang* school of information engineering, suzhou university, suzhou 234000, china *corresponding author: zhangwnali@ahszu.edu.cn hongmei lu school of information engineering, suzhou university, suzhou 234000, china hongmeilu@163.com liang zhao school of computer science, shenyang aerospace university, shenyang 110136, china lzhao@sau.edu.cn abstract designing efficient routing algorithms in vehicular ad hoc networks (vanets) plays an important role in the emerging intelligent transportation systems. in this paper, a routing algorithm based on the improved q-learning is proposed for vehicle-to-vehicle (v2v) communications in vanets. firstly, a link maintenance time model is established, and the maintenance time is taken as an important parameter in the design of routing algorithm to ensure the reliability of each hop link. aiming at the low efficiency and slow convergence of q-learning, heuristic function and evaluation function are introduced to accelerate the update of q-value of current optimal action, reduce unnecessary exploration, accelerate the convergence speed of q-learning process and improve learning efficiency. the learning task is dispersed in each vehicle node in the new routing algorithm and it maintains the reliable routing path by periodically exchanging beacon information with surrounding nodes, guides the node’s forwarding action by combining the delay information between nodes to improve the efficiency of data forwarding. the performance of the algorithm is evaluated by ns2 simulator. the results show that the algorithm has a good effect on the package delivery rate and end-to-end delay. keywords: v2v communication, vanets, its, heuristic q-learning. 1 introduction in the past few years, vehicular ad hoc networks (vanets) have gained a great amount of attention in academia and industry communities [9]. vanet is a key part of intelligent transportation system https://doi.org/10.15837/ijccc.2020.5.3928 2 (its) and it includes fixed infrastructures and vehicles, where these vehicles can carry and relay data. architecture of vanet is shown in figure 1. if the vehicles in vanet communicate with each other directly, they form a vehicle to vehicle communication (v2v). if the vehicles communicate with a fixed road side unit (rsu), vehicle to infrastructure communication (v2i) will be formed [22] [4]. v2v has the disadvantage of frequent communication disruption caused by the vehicles’ joining and leaving the network, while the performance of v2i depends on the communication coverage of rsus [23]. designing effective routing algorithms is a challenging task in vanets, as the applications in its, such as driverless technologies and entertainment applications [15] [12], are dependent on vehicular communication. figure 1: vanet architecture. different from the mobile ad-hoc network (manet), vanet has the characteristics of fast mobile speed of vehicle nodes, short link maintenance time between nodes, frequent disconnection of links leading to extremely unreliable links and complex communication scenarios, which make it difficult for the traditional routing algorithms based on manet to be applied in the vanet network [17][14][2]. in order to overcome this problem, we need to design a high reliability and high real-time routing algorithm for vanet is needed to design. some factors such as the fixed route, the driving direction of two vehicles, distance between vehicles, speed and acceleration can cause the link between the two vehicle nodes to break. as a self-learning algorithm, q-learning algorithm can find the shortest path from the source node to the destination node in the dynamic environment by constantly interacting with the external information. however, the slow learning convergence of the algorithm results in the problem that the routing algorithm can’t quickly reflect the change of topology structure of vanet [11]. based on this idea, an improved heuristic routing algorithm (haeqr) is designed based on the improved q-learning. on the premise of ensuring the reliability of each hop link, haeqr can accelerate the q-value update of the current optimal action, improve the learning efficiency and finally accelerate the convergence speed of the qlearning to adapt to the dynamic network topology of vanet by introducing heuristic function and evaluation function. the contributions that this paper made are as follows. we propose an improved haeqr based on q-learning to overcome the slow learning convergence of the routing algorithm that can’t quickly reflect the change of topology structure of vanet. on the premise of ensuring the reliability of each hop link, haeqr can accelerate the q-value update of the current optimal action, improve the learning efficiency and finally accelerate the convergence speed of the q learning to adapt to the dynamic network topology of vanet by introducing heuristic function and evaluation function. simulation evaluation is performed to evaluate the efficacy of the proposed routing algorithm. the rest of the paper is structured as follows. related work s are reviewed and discussed in section 2. system model is introduced in section 3. the proposed routing algorithm is presented in the section 4. simulations results are presented in section 5. section 6 presents the conclusions and the future work. https://doi.org/10.15837/ijccc.2020.5.3928 3 2 related work in vanets, the routing algorithms are mainly divided into location-based routing algorithm and topology-based routing algorithm [6][16][10][7]. the location-based routing algorithm uses the vehicle’s real-time location information to make routing decisions, and determines the packet transmission path according to the location of the destination node and the location of the neighbor node. the typical representative is gpsr [5], which is simple and easy to implement, but it is faced with the problems of routing error and routing interruption because of the fast moving nodes and unstable network topology. in reference [21][8][25] [21], the related improved algorithms are proposed. the routing algorithm based on topology mainly uses the network topology formed by the communication links between nodes to make routing decisions. the typical representative is aodv routing protocol [13]. in aodv, the source node s uses the routing request packet to obtain the route to the destination node d. each node receiving the routing request packet forwards the routing request packet to its neighbor node, as shown in figure 2(a), until the routing request packet reaches the destination node d. when the routing request packet arrives at d, d returns the routing reply packet to s. each node on the return path already contains the routing information to s, then the routing reply packet is forwarded directly according to the routing information, as shown in figure 2(b). (a) broadcast path of route request packet (b) broadcast path of route reply packet figure 2: the convergence speed of aodv is delayed as the frequent disconnection of vehicle nodes in vanet resulting in a large number of unreliable routes, which cause the efficiency of routing algorithm to reduce. in reference [18][3][19][1], improved algorithms are proposed. the improved routing algorithm (qlaodv) based on q-learning method is proposed in reference [18], with grouping as agent, each vehicle node maintains a q-value table and uses q-value table as the routing table of node forwarding grouping. qlaodv can sense the change of network topology and the change of communication quality between nodes, dynamically update the q-value table of nodes. as considering the connectivity between nodes, qlaodv algorithm performs better in delivery package rate and end-to-end delay. however, the q-value table is updated by the hello control packet transmitted between vehicle nodes with the problem of slow convergence speed. in reference [24], the qlaodv algorithm is improved, and a heuristic q-learning based routing algorithm c-haqr is proposed. by introducing heuristic function and delay information between nodes to guide the forwarding action of nodes, the learning convergence speed is accelerated. however, the new algorithm does not take into account the link break between the two nodes caused by the vehicle movement, nor does it evaluate the selected actions. it takes more time to explore, resulting in limited learning efficiency. 3 link reliability model 3.1 system model in order to effectively evaluate the quality of links between nodes, it is assumed that the road width has little impact on the selection of next hop forwarding nodes, so the road width is ignored, and the expressway is modeled as a case shown in figure 3. https://doi.org/10.15837/ijccc.2020.5.3928 4 figure 3: expressway model. the vehicles on the road will accelerate, decelerate, change lanes and overtake. the distance between vehicles obeys lognormal distribution, namely si ∈ logn(µi,δi). si = si(n),n = 0, 1, 2, ... is a random variable from the lognormal distribution (as shown in figure 3). si represents the distance between vehicle i and i + 1. si(n) is a random variable, representing the distance of node i at time n. in figure 3, node vr is taken as the reference node, then the distance from vr to any node vn is represented by s, where s = n∑ i=1 si. so s is also subject to lognormal distribution [26]. 3.2 link duration model in one hop range when the vehicle node drives according to the road model shown in figure 3, the link between nodes is disconnected mainly caused by two situations: two vehicles running in the same direction and two vehicles running in the opposite direction. taking vehicle i as the reference node, as sending node, it has the longest link communication time with vehicle j in other cases. the maximum communication radius r of the vehicle is a fixed constant and the maximum speed limit specified on the road is umax. at the initial time t = 0, the initial speed of any vehicle is u(0) and the initial acceleration is a(0). at any time t ≥ 0, the acceleration is defined as a(t), and the instantaneous speed is u(t). acceleration a(t) is calculated according to equation (1): a(t) =   a(0), t ≤ umax −u(0) a(0) , or t ≤ −u(0) a(0) 0, else (1) the speed at time t is calculated according to the speed definition formula (2): u(t) = u(0) + ∫ t 0 a(t)dt,t ∈ [0, t] (2) if the vehicle moves at a constant speed, i.e., a(0) = 0, then the speed at time t is: u(t) = u(0) (3) if the vehicle moves at an uneven speed i.e., a(0) 6= 0, then the speed at time t is: u(t) =   u(t) + a(0)t, t ≤ umax −u(0) a(0) , or t ≤ −u(0) a(0) umax, else (4) the distance travelled by the vehicle with speed of u(k) in time interval [0,t] is defined as: s(t) = ∫ t 0 u(k)dk,k ∈ [0, t] (5) assume that the initial velocity and acceleration of vehicle i and vehicle j are ui(0), uj(0), ai(0) and aj(0), respectively and the instantaneous acceleration and velocity at time t are ai(t), aj(t) and ui(t),uj(t), respectively. according to formula (5), the distance between vehicle i and vehicle j in time [0, t] is si(t) = ∫ t 0 u(k)dk, sj(t) = ∫ t 0 u(k)dk, respectively. vehicle i and vehicle j are in the range of one hop communication, and the initial distance between them is s0, which satisfies the requirement of 0 ≤ s0 < r. the distance between vehicle i and vehicle j is dij at time t. when dij > r, the communication link between vehicle i and vehicle j is disconnected. that is to say, when dij = r, the communication link between vehicle i and vehicle j is in a critical state of disconnection, from which the longest time for maintaining the link between vehicles can be calculated. https://doi.org/10.15837/ijccc.2020.5.3928 5 3.3 maximum link maintenance time in the same direction vehicle i running in the same direction as vehicle j and vehicle i in front, the distance between vehicles i and j is: dij = si(t) −sj(t) −s0 (6) where dij = r, which is satisfied with the condition of equation (7), the maximum link maintenance time between vehicles can be calculated: sj(t) −si(t) + s0 = −r (7) due to sj(t)−si(t) = 1/2ant2 + unt, maximum link maintenance time between vehicles i and j is: ti,j = −un − √ u2n − 2an(r + s0) an (8) where, an = aj −ai,un = uj −ui. vehicle i running in the same direction as vehicle j and vehicle j in front, the distance between vehicles i and j is: dij = sj(t) −si(t) + s0 (9) when dij = r, which is satisfied with the condition of equation (10), the maximum link maintenance time between vehicles can be calculated: sj(t) −si(t) + s0 = r (10) the maximum link maintenance time between vehicles i and j is: ti,j = −un − √ u2n − 2an(s0 −r) an (11) where, an = aj −ai,un = uj −ui. 3.4 maximum link maintenance time in opposite direction vehicles i and j driving in opposite directions, the distance between them is: dij = si(t) + sj(t) + s0 (12) where, dij = r, which is satisfied with the condition of equation (13), the maximum link maintenance time between vehicles can be calculated: sj(t) + si(t) + s0 = r (13) due to sj(t) + si(t) = 1/2ant2 + unt, maximum link maintenance time between vehicles i and j is: ti,j = −un + √ u2n − 2an(s0 −r) an (14) where, an = ai + aj,un = ui + uj. 4 the haeqr routing algorithm 4.1 the qlaodv routing algorithm a distributed routing algorithm qlaodv based on q-learning is proposed by celimuge wu et al. in 2010. the q-learning process is shown in figure 4. in a state s, select an action a to execute and move to the next state. the agent evaluates the "state action" value (i.e. q-value) according to the reward value of environment feedback and the next state after the action is executed. when https://doi.org/10.15837/ijccc.2020.5.3928 6 figure 4: q-learning process. table 1: table of q-value. neighbor node g d1 d2 ... dn x1 q(d1,x1) q(d2,x1) ... q(dn,x1) x2 q(d1,x2) q(d2,x2) ... q(dn,x2) ... ... ... ... ... xn q(d1,xn) q(d2,xn) ... q(dn,xn) the agent tries every action in each state repeatedly, it learns the optimal strategy in each state by continuously interacting with the environment, obtaining reward value and updating q-value. several definitions of qlaodv routing algorithm are given below. define 1: basic components learning environment: take the whole vanet as the learning environment of agent. agent: each data packet p(o,d) can be regarded as an agent. state space: all vehicle node in vanet are packet state spaces. action: action sets of the node state are all one hop neighbor nodes. when a node forwards a packet to its next hop neighbor node indicates the state of the packet changes. immediate reward r: the agent’s immediate reward for an activity. definition 2: reward value the value obtained by an activity of the agent is called reward value, i.e. q-value, which ranges from[0,1]. because the neighbor node jumping from the destination node can reach the destination node directly, the reward value is set to 1. the initial reward value in the whole network is defined as fixed value r by equation (15): r = { 1, if s ∈ nd 0, otherwise (15) where, nd represents set of a hop neighbor node of destination node d. definition 3: table of q-value each node maintains a table of q-value. when the node selects the next hop node for packet, it directly selects the next hop node with the highest q-value. the table of q-value is shown in table 1, where q(d,x) indicates that the packet is currently in node s and x is selected as the next hop node arriving at the destination node d with q-value. the q-value is updated by periodically exchanging packets between nodes. the task of learning is distributed to every node, so that the algorithm can quickly converge to the optimal path and make timely adjustments to the changes of network topology. in the qlaodv algorithm, each node updates the q-value through the hello package. the updated formula is shown in (16): qs(d,x) ← (1 −α)qs(d,x) + α{r + γ max y∈τ(x) qs(d,y)} (16) where, qs(d,x) expresses the q-value of that node s selects neighbor node x as the next hop node to forward the packet to destination node d. α is the learning rate. γ is the discount factor. r is the https://doi.org/10.15837/ijccc.2020.5.3928 7 reward value. τ(x) represents set of the neighbor node of node x, and maxy∈τ(x) qs(d,y) is actually the maximum q-value from neighbor node x to destination node d. in qlaodv, each node adds the maximum q-value to the destination node from its q table and the corresponding neighbor node to the hello package, and the node receiving the hello package can use eq. (16) to update the q-value table once. when node s needs to send or forward a packet to node d, it only needs to check its own q-value table, and selects the x with the largest qs(d,x) as the next hop node, as shown in figure 5. if b needs to forward the packet to the destination node d, b can find out c is the most suitable next hop node by looking up the q-value table. figure 5: example of q-value table of node. 4.2 haeqr algorithm description the update speed of q-value of qlaodv is seriously limited by the sending interval of hello packets in the environment of vanet, which directly leads to the problem of slow convergence speed of the algorithm. based on this, a routing algorithm based on improved heuristic q-learning method namely haeqr algorithm is proposed in this paper. in the new routing algorithm, the next hop node is selected through heuristic evaluation strategy and the current optimal action, i.e., the current optimal next node is determined according to the delay feedback information between the source node and the destination node. heuristic and evaluation function are used to inspire and evaluate the node to update the q-value of the current optimal action to speed up the convergence speed and improve the efficiency of the routing algorithm. 4.3 update q-value of haeqr algorithm in vanet, the main factors that affect the performance of routing algorithm include vehicle mobility and available bandwidth. in view of the mobility of vehicles between nodes, the delivery rate of packets can be improved by using the link duration model in one hop range. the first term on the right of q-value update formula (16) equals the original value of q-value, and the second term is the latest learning value of q-value. the latest learning value is generally multiplied by a weight factor, i.e., learning rate. the higher the learning rate is, the greater the proportion of the latest learning value in the updated q-value is, the faster the learning speed is. in haeqr, the ratio of link duration between nodes is used as the learning rate of q-value updating formula. the longer the link duration between nodes is, the faster the learning speed is. to a certain extent, it reduces the impact on packet delivery caused by vehicle mobility and vehicle link instability and improves the delivery rate of packets. the ratio of link duration between nodes is calculated by equation (17): dtx = txi∑ t (17) where, txi is the link duration between vehicle nodes x and i. ∑ t is the sum of the link duration between the current node x and all nodes in the range of one hop. discount factor is an important parameter which affects the reward value of an activity of this node according to formula (16). in the vanet network, the available bandwidth is an important https://doi.org/10.15837/ijccc.2020.5.3928 8 parameter which determines the packet transmission rate. the bandwidth bw calculation formula can be defined as follows: bw = 8 ×n×sb t (18) where, n represents the number of packets sent and received by the node. sb is the size of the packet, expressed in bytes and t is the time interval. suppose the maximum bandwidth value of the node bwmax is a fixed value. the bandwidth occupied by the sending group is bwpackeg. the effective bandwidth is bwavaliable = bwmax−bwpackeg. effective bandwidth factor represents the bandwidth of the communication link is calculated by equation (19): bfx = bwavaliable bwmax (19) take the bandwidth factor as a factor to affect the speed of learning. as the effective bandwidth changing, it determines the learning progress of each vehicle node. the q-value updating formula (20) of haeqr algorithm is obtained by modifying formula (16). qs(d,x) ← (1 −dtx)qs(d,x) + dtx{r + γ ×bfx × max y∈τ(x) qs(d,y)} (20) each node uses equation (20) to carry out iterative calculation, the more hops from the destination node, the lower the final reward value of q-value. therefore, the final reward value is determined by the three factors of hops, link reliability and bandwidth. by adding the two parameters of bandwidth and link state, the reliable optimal path of nodes from the source to destination can be finally obtained in this dynamic network. 4.4 the improved use explore balance strategy when selecting the neighbor to forward package, different from qlaodv directly selecting the next hop node with the maximum q-value to forward package, the action selection rule to modify the standard greedy rule is used in the haeqr algorithm, as shown in equation (21): a = { arg maxy∈τ(x)[qs(d,y) + εhs(d,y) + δes(d,y)], if q ≤ p arandom, otherwise (21) where, qs(d,x) expresses q-value of that node s selects neighbor node x as the next hop node to forward the packet to the destination node d. hs(d,x) expresses the heuristic function for inspiring the current optimal action. es(d,x) expresses an evaluation function that evaluates the success rate of the current optimal action. arandom expresses randomly selecting a neighbor node to forward packets. ε is a real variable to weigh the influence of heuristic function. δ is a real variable to weigh the impact of the evaluation function. p is proportion for exploration and utilization, which express node s selecting the next hop node with probability p by using q value, that is, executing utilization strategy, and randomly selecting the next hop node with probability 1 −p, that is, executing exploration strategy p. the larger the p value is, the smaller the probability of random selection is, with value of 0.9 in this paper. q is a random number of [0,1]. each node adopts the improved utilization exploration balance strategy and implements the improved utilization strategy or exploration strategy to forward the packet to the next hop node until the packet reaches the destination node d. at the same time, haeqr records the delay of each node that the packet passes through in each packet, as shown in figure 6. in figure 6, the sending node s sends packets to the destination node d. due to the influence of the improved use explore balance strategy, packets may follow different paths. for example, there are three paths to d in figure 6, sv11 → v12 → ... → v1n → d, s → v21 → v22 → ... → v2n → d and s → v31 → v32 → ... → v3n → d. at the same time, packets record the delay between nodes of the path that they pass through. haeqr uses t(vm,vn) https://doi.org/10.15837/ijccc.2020.5.3928 9 figure 6: path of packet and feedback information transmission. to represent the delay of packet from node vm to vn. the delay information of each path between nodes in the figure are t(s,v11),t(v11,v12), ...,t(v1n,d),t(s,v21),t(v21,v22), ...,t(v2n,d) and t(s,v31),t(v31,v32), ...,t(v3n,d). 4.5 calculate the current optimal next node the delay between nodes in the path is recorded in the packet. based on the delay information between nodes, haeqr can determine the current optimal action of each node in the path. specifically, every time the destination node receives a packet which has recorded the inter node delay information of all node passing through on the path from the source node to the destination node. if the inter node delay information shows that the time taken for the packet to reach the destination node is shorter than that of the packet in the previous period of time, the destination node immediately takes the inter node delay information of the packet as the feedback information. the feedback information returns to the source node along the path of the corresponding packet. in the process of feedback information return, for each passing node, the node calculates the delay to the destination node according to the feedback information, as shown in equation (22): td(s,d,x) = d−1∑ n=x t(n,n + 1) (22) where, td(s,d,x) expresses the delay of from node s to select the next hop node x to transmit the packet to the destination node. because of using the improved use explore balance strategy, the packet will arrive at the destination node along different paths, and the feedback information will also return to the source node along different paths. for node s, multiple feedback messages may be received from different paths of the same destination node. the feedback message with the shortest delay is the current optimal path from node s to the destination node. the next node corresponding to the path is the current optimal action of packet from node s to the destination node. as shown in equation (23): aoptimal = min x∈τ(x) td(s,d,x) (23) where, aoptimal expresses the optimal action, i.e. the current optimal next node of node s transmits packets to destination node. τ(s) is the neighbor set of node s. through equation (23), the current optimal action of the current node s can be established. whenever feedback information reaches s, s will recalculate the current optimal action. 4.6 inspire and evaluate the current optimal next node when the optimal next node is determined, the current node is inspired to select the current optimal next node by heuristic function hs(d,x) and the optimal node is evaluated by evaluation function es(d,x). the corresponding q value is updated. the value of heuristics hs(d,x) affects the choice of action. in order to minimize errors, its value must be as low as possible. it is defined as follows: hs(d,x) = { maxy∈τ(x) qs(d,y) −qs(d,x) + η, if x = aoptimal 0, otherwise (24) https://doi.org/10.15837/ijccc.2020.5.3928 10 where, qs(d,x) expresses the q-value of s selecting the next hop node x to the destination node d. maxy∈τ(x) qs(d,y) expresses the max q-value of one of all neighbor nodes which arrives the destination node d. equation (25) shows that if neighbor node x is the current optimal next node, it is given an appropriate heuristic value to guide the current node to select the current optimal next node; otherwise, the heuristic value of neighbor node x is 0. η is a very small positive real number, generally 0.01. for example, in figure 6, when the sending node s sends packets to the destination node d, the next node can be selected as v11, v21, v31. the q-values are respectively 0.6,0.5,0.7. the heuristics is v11. if η = 0.01, hs(d,v11) = 0.11, the h values of other nodes are all 0. the evaluation function es(d,x) evaluates the success rate of the selected node. in the specific learning process, there are many failures in action evaluation. therefore, this filtering can greatly reduce the number of nodes to be selected and greatly improve the learning efficiency. at the same time, in order to minimize errors, its value must be as low as possible. it is defined as follows: es(d,x) = [qs(d,y) + εhs(d,y)] − max y∈τ(x) [qs(d,y) + εhs(d,y)] (25) for example, in figure 6, when the sending node s sends packets to the destination node d, the next node can be selected as v11, v21, v31. the q-values are respectively 0.6, 0.5, 0.7. h are respectively 0.11,0,0. if ε = 1, ρ = 0.01. the evaluation selects 3 nodes as the next node, and the result is success, failure and failure. then es(d,v11) = 0.01, es(d,v21) = −0.2,es(d,v31) = 0. haeqr uses heuristic function to guide the node to select the best next node at present, uses evaluation function to evaluate the success rate of the selected node, and accelerates the update of q value. specifically, each node adds its own maximum q-value to the destination node in the feedback information. whenever a node receives the feedback information, the feedback information contains the maximum q-value of the previous node reaching the destination node. the node first updates the q-value of the destination node through the previous node according to equation (21), and then adds the maximum q-value of the destination node to the feedback information, so that the next node receiving the feedback message updates the q-value, so as to iterate, until the feedback information returns to the source node, the node on the path completes the q-value update corresponding to the destination node. with the packet sending and feedback information returning, the q-value of all nodes in the path from the source node to the destination node will be updated in real time. therefore, it reduces the influence of too slow q-value convergence update caused by too long hello interval, and improves the convergence speed of the routing algorithm. 4.7 haeqr algorithmic process haeqr algorithm mainly updates the q-value table through hello packet, and accelerates the update of the current optimal action by using heuristic function and evaluation function, and finally speeds up the convergence speed of the routing algorithm and improves the learning efficiency. the specific steps are as follows: initialize settings. each node initializes q-value table. hello package maintenance process: repeat (for each hello packet sending cycle) step 1: each node adds the maximum q-value information to all destination nodes in its own table to the packet, broadcasts it to the surrounding neighbor nodes, and transfers to step 2. step 2: the current node receives the hello package from the neighbor node and updates the table of q-value according to equation (20). packet transmission process: step 1: the current node (including the source node) sends packets to the destination node. using the improved explore balance strategy (21), select and send the packets to the next node, and turn to step 2. step 2: the current node receives the packet, calculates the delay with the previous node, and records it in the packet. if the packet arrives at the destination node, turn to step 3, otherwise turn to step 1. https://doi.org/10.15837/ijccc.2020.5.3928 11 step 3: after the destination node receives the packet, if the time delay information between nodes shows that the packet takes less time than the packet in the previous period of time, feedback information will be generated, and the delay information between nodes of the packet and the qvalue to the destination node will be added to the feedback information, and the feedback information will return to the source node along the original path. turn to step 4. step 4: for each node in the return process of feedback information, first, the node uses equation (23) to determine the current optimal action by the delay information of feedback information. then use equation (24) to calculate the heuristic value of the current optimal action, use equation (25) to calculate the evaluation value of the current optimal action. then use equation (20) to update its own q-value table. finally change the q-value of feedback information to its maximum q-value to the destination node and send it to the next node. this is iterated until the feedback information is returned to the source node. 5 experimental simulation and algorithm performance analysis in order to evaluate the performance of the algorithm, sumo is used to generate the node movement scene, and ns2 is used to randomly generate the node data flow. three routing protocols gprs, qload and haeqr are simulated respectively. basic network parameter configuration is shown in table 2. table 2: ns2 simulation parameter settings. parameters values simulation scene 1800 × 1800 vehicle lane two way two lane mac protocol ieee 802.11 transmission range 250m simulation time 200s cbr packet size (byte) 512 data rate (packet/s) 10 node velocity (m/s) 5 ∼ 25 number of nodes 50 ∼ 250 5.1 performance comparison of routing protocols with different number of vehicle nodes the number of nodes in a certain area will have a great impact on the routing performance of vanet, but also related to the density of network nodes in vanet. the larger the number of vehicles in the same area, the greater the density of nodes is. keep the maximum speed of nodes at 15m / s. as shown in figure 7, the performance comparison diagram of packet delivery rate of three routing protocols under different vehicle nodes is shown. it can be seen that the haeqr proposed in this paper is significantly better than qlaodv and gpsr. the main reason is that qlaodv updates the q-value table through the hello package. the hello packages are separated too long, it can’t reflect the topology change of vanet quickly, resulting in packet loss. haeqr uses the improved exploration utilization balance strategy, introduces the evaluation function, explores the path with shorter delay and more stable, and determines the current optimal next node. based on the heuristic q-learning, haeqr inspires the node to select the current optimal next node to forward the packet and update https://doi.org/10.15837/ijccc.2020.5.3928 12 the q-value, which speeds up the q-value and the convergence speed of learning. therefore, it can quickly reflect the topology changes, which conducive to the successful delivery of packets to the destination node and improves the communication performance. as shown in figure 8, in the scenario of different number of vehicle nodes, the average end-toend delay comparison of the three algorithms shows that haeqr is slightly better than gprs and significantly better than qlaodv. the main reason is that the haeqr algorithm explores the path with shorter delay and more stable through the improved exploration utilization balance strategy, and the exploration process may take a certain time, but as long as a better path is found, it guides nodes to use the path to transmit packets through the heuristic q learning heuristic function and evaluation function. it can be seen from the experimental results that the delay benefit of finding a better path through exploration is greater than the time consumed in the exploration process. therefore, the average end-to-end delay of haeqr is better than other comparison algorithms. as shown in figure 9, the average hops of routes of haeqr, gprs and qlaodv are compared. it can be seen that the average hop count of haeqr is lower than that of gprs and qlaodv. that is because haeqr uses feedback information to inspire nodes to select routes with shorter end-to-end delay, which are often routes with fewer hops. figure 7: comparison of packet delivery ratio under different vehicle nodes. figure 8: comparison of end-to-end delay of different vehicle nodes. https://doi.org/10.15837/ijccc.2020.5.3928 13 figure 9: comparison of average hops of different vehicle nodes. 5.2 performance comparison of routing protocols under different vehicle maximum moving speed the running speed of vehicles in the network has a great impact on the performance of the routing in vanet. the larger the moving speed is, the faster the composition of the network changes, and at the same time, the shorter the communication time is. keeping the number of vehicle nodes at 100, the performance comparison of packet delivery ratio of three routing protocols is shown in figure 10. figure 10: comparison of average hops of different vehicle nodes. as can be seen from figure 10, with the increase of vehicle speed, the haeqr proposed in this paper shows a high packet delivery ratio, with an average delivery ratio of more than 90%. however, with the increase of vehicle speed, the other two routing algorithms show a sharp decline in packet delivery ratio. this is because haeqr fully considers the impact of speed change on link stability. through the evaluation of the link between nodes, the reliability of the link between nodes is determined. as a learning parameter of haeqr algorithm, it is applied to the routing decision. gpsr has the lowest delivery ratio, because it does not consider the link reliability when choosing the next hop node and the greedy mechanism is also the main factor to reduce the delivery ratio. although haeqr uses q-learning model, it needs to maintain an end-to-end reliable path, so with the increase of speed, it makes continuous path repair, which leads to the reduction of packet delivery ratio. https://doi.org/10.15837/ijccc.2020.5.3928 14 figure 11: comparison of end-to-end delay at different moving speeds. as shown in figure 11, the end-to-end delay performance of the three routing protocols is compared under different vehicle maximum moving speed. as can be seen from figure 11, with the increase of vehicle node speed, the delay time of the three algorithms shows an upward trend. it can be seen from the figure that the haeqr proposed in this paper has the lowest delay and is relatively stable, because the haeqr algorithm can obtain shorter and more stable path by introducing heuristic function and evaluation function, using shorter exploration delay, adopting link maintenance model to make it less affected by topology, so as to ensure that it has the minimum delay time. 6 conclusion in view of the routing problem of the mobile internet, this paper studies the factors that cause the unreliability of the link by analyzing the characteristics of the vehicle movement and establishes the reliability calculation model of the inter node link. the reliability value of the evaluation of the inter node link is used as a parameter in the improved q-learning strategy, while the learning convergence in q-learning is too slow to reflect the vanet quickly. in this paper, heuristic function and evaluation function are introduced to propose an improved routing algorithm based on heuristic q-learning. heuristic function is used to speed up learning, evaluation function is used to reduce unnecessary exploration and improve learning efficiency. the experimental results show that the new algorithm has high packet delivery rate and low transmission delay under various conditions, which can effectively solve the problems caused by topology changes. at present, the success rate of action evaluation function is only divided into success rate and failure rate, which is too rough, however, improving the accuracy of partition will greatly increase the state space affects the learning efficiency. how to find the appropriate partition method is an important problem to be studied in the future study. in addition, due to the limited resources of nodes in vanet, nodes tend to be selfish in order to save their own resources when forwarding packets. if we can consider the selfishness of nodes and dynamically select nodes that actively participate in forwarding, the service quality of vanet will be improved to a certain extent with the help of routing algorithm. funding this work was supported in part by anhui provincial outstanding young talent support program (gxyq2017093), outstanding academic and technical backbone of suzhou university (2016xjgg12), the third batch of reserve candidates for academic and technical leaders(2018xjhb07), suzhou science and technology project (sz2018gg01, sz2018gg01xp), new engineering research and practice projects (2017xgkxm54). https://doi.org/10.15837/ijccc.2020.5.3928 15 author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] abbas, n.i.; ilkan, 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(2018). a new adaptive routing algorithm based on q-learning strategy, journal of electronics, 46(10), 2325-2332, 2018. https://doi.org/10.15837/ijccc.2020.5.3928 17 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: yang, x. y.; zhang, w. l.; lu, h. m.; zhao, l. (2020). v2v routing in vanet based on heuristic q-learning, international journal of computers communications & control, 15(5), 3928, 2020. https://doi.org/10.15837/ijccc.2020.5.3928 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 2, month: april, year: 2023 article number: 4998, https://doi.org/10.15837/ijccc.2023.2.4998 ccc publications friend recommendation engine for facebook users via collaborative filtering m. alshammari, a. alshammari mohammed alshammari department of computer science faculty of computing and information technology northern border university, saudi arabia northern border university, arar, alhudud alshamalia 91431, saudi arabia corresponding author: mohammed.alshammari@nbu.edu.sa aadil alshammari department of information systems faculty of computing and information technology northern border university, saudi arabia northern border university, arar, alhudud alshamalia 91431, saudi arabia aadil.al-shammari@nbu.edu.sa abstract today’s internet consists of an abundant amount of information that makes it difficult for recommendation engines to produce satisfying outputs. this huge stream of unrelated data increases its sparsity, which makes the recommender system’s job more challenging. facebook’s main recommendation task is to recommend a friendship connection based on the idea that a friend of a friend is also a friend; however, the majority of recommendations using this approach lead to little to no interaction. we propose a model using the matrix factorization technique that leverages interactions between facebook users and generates a list of friendship connections that are very likely to be interactive. we tested our model using a real dataset with over 33 million interactions between users. the accuracy of the proposed algorithm is measured using the error rate of the predicted number of interactions between possible friends in comparison to the actual values. keywords: artificial intelligence, machine learning, recommender systems, and social media. 1 introduction the number of people using the internet worldwide has been rapidly increasing over the past two decades, from about 400 million users in 2000 to more than 3.4 billion users in 2016 [1]. the increasing number of online users correlates with the growing amount of online data, especially in the field of social media, where most of the data is created by users. this makes finding the right information very challenging. according to statista, the global digital population in 2022 consisted of five billion users, with 93% of them being social media users [2]. https://doi.org/10.15837/ijccc.2023.2.4998 2 over the past decade, social media giants such as facebook, twitter, and instagram have attracted many users around the world, making these platforms convenient places to find and start new relationships. just like in real life, where friendships are established through real interactions in a workplace, school, and travel, on these virtual platforms, interactions are different; they may come in the form of a like or dislike, a picture comment, or a private message, among various other means. therefore, the recommendation engine task is essential for social networks, as they process this enormous amount of data with diverse types of interactions to generate valid recommendations. online social media users find and connect with already known friends, as well as new friends, to interact with them. friendship recommendation algorithms based on collaborative and content-based filtering have been thoroughly explored [3, 4, 5]. however, most of these algorithms focus on recommending relationship connections with a high probability of being accepted, but with a low probability of being interactive. table 1: interaction rate of our collected dataset on facebook interaction rate with friends using comments 2.45% interaction rate with friends using likes 13.23% table 1 shows that, on average, a user only interacts with 2.45% of his or her declared friends using comments and 13.23% using likes. in this paper, we design, develop, and implement a novel collaborative friendship recommendation algorithm that generates easily accepted friendship connections with a very high probability of leading to interactions. the remainder of this paper is organized as follows: 1.1 research design first, we will give an introduction to the topic, show its importance in the related research fields, and state the research question that the paper tries to answer. second, a thorough literature review will be provided, showing the history of the topic and how related research has proposed solutions to problems similar to ours. the third part will focus on the methodology used to solve the issue encountered and the data collection process. the fourth part is designed to discuss the evaluation process, which includes the experimental setting, data cleaning, parameter tuning, and related steps. in the last part, some overall insights will be provided to conclude the work. 2 literature review 2.1 social networks & recommendation algorithms facebook has dominated the social network industry for the past decade. in 2022, facebook was reported as the most popular social network worldwide, with more than 2.9 billion monthly active users [6]. the giant social network uses its well-known friends-of-friends (fof) algorithm. this friendship recommender algorithm is one of the key factors that plays a major role in the growth of the social network. facebook’s fof algorithm has been largely successful in increasing the size of the network by recommending easily accepted friendship connections among its users. however, the percentage of interactive connections recommended by the fof algorithm is reportedly very low [7, 8, 9, 10]. the strength of the fof algorithm is that it finds already-known people, which explains its high acceptance rate. however, an accepted relationship recommendation does not necessarily lead to interaction. therefore, further filtering is required to find interactive friendship connections among friends of friends. recommender systems come in mutable forms, the most popular of which are content-based and collaborative filtering [11]. other types include community-based, demographic-based, knowledgebased, and hybrid filtering [11]. content-based research focuses on building recommendations based on the content of the system components. for example, if a user likes certain types of products (e.g., football or sports shirts), the system will try to recommend items that are similar to those he has liked, bought, or rated in the past; in this case, sports sneakers might be a good fit [12]. collaborative https://doi.org/10.15837/ijccc.2023.2.4998 3 filtering techniques rely on building recommendations for the target user by looking at items that similar users have previously explored, or items that are similar to the items that the target user has interacted with in the past [13, 14]. a recommender system model aims to drive the success of a social network (e.g., netflix, amazon, youtube, facebook, or booking) by increasing its traffic. this leads to more content (e.g., movies, products, posts, hotels, or restaurants) and more interactions (e.g., likes, dislikes, comments, or messages), which eventually leads to more profits (e.g., watching ads, buying items, or subscribing). collaborative recommendation algorithms have been successfully used to filter online items, such as products on amazon or movies on netflix, but they have not found as much success in filtering and recommending friends on social networks. alshammari & rezgui developed friendship recommender algorithms to generate interactive relationships [7, 10], which more than doubled the recommended interactive edges compared to facebook’s fof algorithm. in this research, we are presenting a new model to further generate better results. a disadvantage of content-based recommender systems is that they lack the ability to deal with sparse data. the performance of those models deteriorates when the data is sparse. however, collaborative filtering techniques overcame this obstacle, excelling in recommending better results with a lower error rate. 2.2 matrix factorization the matrix factorization (mf) algorithm is a state-of-the-art technique for predicting ratings for unseen items, especially when data are sparse [15]. the accuracy of mf is higher compared to baseline techniques [16, 17, 18]. one of the most powerful features of mf is that it allows for taking advantage of the latent spaces of both users and items. the mf technique has been exploited by many researchers in the field of recommendation engines in recent years [19, 20, 21, 22, 23]. [17] proposed a methodology that extends the initial idea of [15] by introducing context from an extra source to the model. they applied their methodology to the movie domain and used the items’ extra information, such as genre and stars, to enhance the performance of the recommendation engine. it is worth mentioning that extra user-side information may not always be available, as privacy issues arise. nevertheless, they compared their results to baselines and showed that the accuracy of their proposed methods was higher. another model using mf in the field of recommender systems is the one proposed by [18]. in this case, the researchers developed [17] their model by including semantic web technology in the process of building the engine. in the domain of movies and music, they exploited the power of dbpedia 1 to retrieve more meaningful information that also helped produce more reasonable recommendations. the resulting model outperformed other baselines after conducting multiple experiments. in a related vein, benabdallah [24] explored mf and extended it to include item side information (e.g., bag-of-feature) in the latent spaces and succeeded in producing annotations to images. this solution was used to tackle a famous problem in collaborative filtering, which is the cold-start problem [25]. one of the drawbacks of the current state-of-the-art mf models is that they lack transparency. the mf algorithm is a black box system whose output cannot be easily explained, in contrast to content-based techniques, where generating an explanation is a straightforward process. another disadvantage is the cold start problem, where the system knows nothing about new users until they rate, purchase, listen to, interact with, etc. with some items. in this research, we designed, implemented, and tested a friendship recommender algorithm to generate interactive relationship connections that have a higher probability of leading to interactions using mf. 1dbpedia.org https://doi.org/10.15837/ijccc.2023.2.4998 4 3 research methodology friendship connections within a social network can be directed or undirected. this research uses a giant social network, facebook, as our case study. the relationship edges among users within facebook are undirected. figure 1 shows a sub-graph example of an undirectional social network. as seen in the figure, a black edge between two nodes represents an undirected friendship connection. the cyan nodes (a, s, e, and m) are the common friends between user x and user f of . facebook’s fof algorithm solely uses common friends to find and recommend friends of friends. the red edges shown in the figure are designed to distinguish between interactive and non-interactive relationships. for instance, user x interacted with user a using likes and/or comments. our recommender model is designed to recommend friends of friends that are very likely to be interactive. the main idea behind our model is that both the target user (user x) and the friend-offriend interacted with the same common friends. the example in figure 1 shows that l, f of , and n are all friends-of-friends of user x. however, only user f of interacted with similar common friends, which makes user f of a possible interactive friend to user x. commonf riends(x, f of ) p b c j x a s e m h f of n l interaction f riendship figure 1: a social network’s sub-graph example in our model, the mf technique is used to build the model, and items are replaced by other users. hence, by means of users’ interaction history, mf can learn the surfing pattern of such users (e.g., pictures he/she likes or posts he/she comments on) and then suggest similar new friends. one of the most powerful features of mf is its ability to handle sparse data and produce more accurate recommendations compared to other models [15]. other techniques within collaborative filtering, explained above in the literature review section, rely mostly on the explicit feedback expressed by users toward items. however, even though mf needs some information about users to lower the percentage of a cold start, it can implicitly observe the users’ patterns toward items and predict how they may like or dislike the unseen items. in our model, items are replaced by other users with whom the target user is not yet a friend with. the goal of the model is to learn implicitly about those users engaging in explicit interactions and to suggest new relationships. in detail, every user u will be represented in a vector pu, and every interacted-with user i is associated with a vector qi. conducting a dot product of those two vectors, pu and qi, will result in predicting r̂, which is the number of interactions between all users in both vectors. r̂ = qtx pf of ; (1) the objective function to be minimized is as follows: j = ∑ x,f of ∈r ( rxf of − pxqtf of )2 + β(∥ px ∥2 + ∥ qf of ∥2) (2) l r is the set of known interactions between x and f of . rxf of represents an interaction between users x and f of . p is the latent space for users, and q denotes the latent space for friends. the second https://doi.org/10.15837/ijccc.2023.2.4998 5 figure 2: example of a possible recommendation between interactive friends-of-friends expression is a regularization term that helps prevent overfitting. β is a smoothing coefficient used to control the addition of new items. the update rules for user p and friend q are as follows: p(t+1)x ← p (t) x + α(2(rx,f of − p (t) x (q (t) f of ) t )q(t)f of − βp (t) x ) (3) q (t+1) f of ← q (t) f of + α(2(rx,f of − p (t) x (q (t) f of ) t )p(t)x − βq (t) f of ) (4) figure 2 shows a visualization of the proposed method. as can be seen, if we have a user a that interacts with posts or pictures on facebook while his or her friend user b also does so, an fof user c that expresses likability to his or her friend user b might be a good suggestion to be a friend with user a. as shown in the figure, users a and b share interactions (e.g., likes or comments) with the same posts or pictures; moreover, users b and c also share the same interaction with other posts and pictures. since users a and c are not yet introduced to each other as friends, we can infer that they may like each other to a certain degree based on the fact that they are both friends with user b. this inference can be drawn from the low dimensional latent spaces technique that matrix factorization provides to us. to accurately test and compare our model, we designed and implemented a web crawler using python that fetches publicly available users’ profiles. 4 results analysis in this section, we will explore the results of our experiments, starting with an analysis of the dataset, presenting the experimental setting, and finishing with an illustration of two kinds of measurement: accuracy and recommendation. 4.1 dataset in order to test and validate the results of our approach, we collected publicly available data from facebook using our web crawler. the web crawler was designed to fetch publicly available users’ profiles. we randomly started with seeds from different states in the us. after fetching a user’s profile, the web crawler added the fetched user’s friends to a queue so that they would be fetched later. for https://doi.org/10.15837/ijccc.2023.2.4998 6 privacy, we replaced every user id with a randomly generated id. for each user profile, we collected the following: • user id (replaced with a randomly generated numeric id) • friends list • posts – post – user ids who liked the post – user ids who commented on the post • interests we ran our web crawler in 2018 and collected about 16,500 unique user profiles. in this dataset (ds2018), we had more than 15 million interactions (likes and comments). the average percentage of interactively declared friends in our collected facebook dataset was 13.93%. this finding correlates with what was reported about the low interaction rate among facebook users. part of this problem is caused by facebook’s fof algorithm, which is solely designed to increase the list of friends but not interactive friends. 4.2 experimental setting in this experiment, the total number of facebook users was 6,426 and the total number of friends was 8,638. interactions between the users and their friends reached over 55,000 single interactions. we cleaned the data by eliminating friendships between any two users who had fewer than three interactions in the data set. the reason was to minimize the number of users, which would help accelerate the model building and lessen the noise, as friends with only one or two interactions do not help the model learn about them in the low-dimensional spaces where hidden features are introduced. the train set size was 90%, and the remaining 10% was kept for testing. the total number of interactions was normalized to one. train and test sets were cross-validated fivefold, and the mean was reported. the mf model hyperparameters were tuned, and the number of patches was set to 20 steps. low-dimensional matrices p and q were initialized randomly, and the experiments were run 10 times; the mean was then reported to avoid the acquisition of good results by luck. code implementation can be found in github 2. the proposed method was evaluated using two different evaluation methodologies. the first consisted of calculating the error rate using root mean square error (rmse), and the second involved calculating the accuracy of recommendations using mean absolute precision (map) from the confusion matrix. 4.3 accuracy measure the rmse formula was as follows: rm se = √√√√ 1 | t | ∑ (x,f of )∈t ( r ′ xf of − rxf of )2 . (5) where r′xf of denotes the expected interactions between user x and friend fof, while rxf of is the true and known value. t is the count of all predictions. the root of the squared result was determined to eliminate any negative numbers. in our model, the algorithm will predict a number for each friend of the target user in the dataset, representing the expected number of interactions between them. moreover, since there are 10% of the dataset is hidden, the rmse will calculate the difference between the true number of interactions https://doi.org/10.15837/ijccc.2023.2.4998 7 table 2: rmse results using a different number for feature k for the train and test facebook-mf # of features k train rmse test rmse 1 0.0907 0.1873 2 0.1082 0.3112 3 0.1193 0.4318 4 0.126 0.5456 5 0.1293 0.6638 between the target user and all of his or her true friends in the hidden test set and the predicted numbers that the model produced. table 2 shows the resulting values, known to be lower the better. table 2 shows the error rate of the model for both the training and testing datasets. the first column represents the proposed number of hidden features, starting with only one and finishing with five. the results indicate that the model performed better with fewer hidden features, which may be due to the sparsity of the data. introducing more features to the already poor graph of users and friends led to a worsening performance of the model and caused the learning process to slow down, which, consequently, led to an increase in the error rate. in the opposite situation, where the data graph was rich with connections and information about the users’ relations was sufficient, more hidden features helped decrease the error rate. the train rmse was noticeably less than the test rmse for all different k because of the number of known interactions between users. as mentioned earlier, the training set was 90%, and the testing set was 10%, which explains the disparity in performance. one of the reasons that such a graph has insufficient data is that many of the users have set their profiles as private. as we mentioned above, we only fetched publicly available users’ profiles. if our algorithm was run by facebook with access to all the data in the graph, the results would have been even more accurate. 4.4 recommendation measure the map formula was as follows: m ap @n = 1 | u | u∑ u=1 1 m n∑ n=1 pu(n) · relu(n). (6) n represents the desired number to be in the top list, u indicates the users’ total number, and m denotes the relevant friends. a ratio of the recommended and relevant friend lists to the whole recommended friend list n was derived and then denoted by p . the last term, relu(n) is a binary indicator showing the relevance of nth recommended friend. table 3: map results using different number of feature k facebook-mf # of features k test map@10 1 1 2 1 3 1 4 1 5 1 the first column indicates the number of hidden features. the second column shows the model performance measured using the map of the top 10 recommended friends. using the test set, the 2https://github.com/drmohammedsanad/facebookmf https://doi.org/10.15837/ijccc.2023.2.4998 8 result was 1, indicating that the model succeeded in recommending at least 1 real interactive friend to the target user in the top 10 recommendations list of possible interactive friends. in the experiment, the model was not only able to accurately recommend interactive friends to the target user but was also able to predict the possible number of interactions between the two users. we were able to measure the model’s accuracy by hiding some of the target user’s actual friends, for whom we have records of their interactions. for instance, in the test set, the user with id 210485 is already a friend of the target user with id 210619, and the model predicted a possible interactive relationship between them after predicting that they will have 22 interactions, which is only off by around 18% of the actual interactions. 5 conclusion social media platforms have become abundant with data that is mostly sparse and useless unless preprocessed and cleaned to help decision-making algorithms, such as recommendation engines, perform appropriately. in this paper, we explore the power of the mf algorithm for helping facebook users find new friends by using their sparsely expressed interactions. in this experiment, we limited the number of accepted interactions to three in order to reduce data size, which consequently helped the mf algorithm perform faster and more efficiently. experiments show that increasing the number of features results in deteriorating the performance of mf due to the sparsity level expansion of the current data. assuming that we only knew one hidden feature helped the model perform better than knowing two or more features. the rmse measure was used to prove this notion. the map measure, which shows whether true friends who were hidden in the building process were in fact included in the top 10 recommendations, indicates that the model using one to five hidden features succeeded in this matter. in conclusion, the rapid increase in data size on today’s internet requires algorithms that have the ability to cope with these changes. experiments illustrate that mf is state-of-the-art in this matter. it is worth adding that mf also performs better when extra item-user-side information is introduced. for future work, our model, which recommends new friends using interactions, could be supplied with an explanation feature that will result in increasing trust, efficiency, and user loyalty to the system. funding the authors gratefully acknowledge the approval and the support of the research study by the grant no. cscr-2022-11-1639 from the deanship of scientific research at northern border university, arar, k.s.a. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] max roser, hannah ritchie, and esteban ortiz-ospina. internet. our world in data, 2015. https://ourworldindata.org/internet. 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[25] behnoush abdollahi and olfa nasraoui. a cross-modal warm-up solution for the cold-start problem in collaborative filtering recommender systems. in proceedings of the 2014 acm conference on web science websci '14. acm press, 2014. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: alshammari, m.; alshammari, a. (2023). friend recommendation engine for facebook users via collaborative filtering, international journal of computers communications & control, 18(2), 4998, 2023. https://doi.org/10.15837/ijccc.2023.2.4998 introduction research design literature review social networks & recommendation algorithms matrix factorization research methodology results analysis dataset experimental setting accuracy measure recommendation measure conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 4, month: august, year: 2021 article number: 4215, https://doi.org/10.15837/ijccc.2021.4.4215 ccc publications finite time synchronization of inertial memristive neural networks with time varying delay y.j. pang, s.m. dong yajing pang* hebei university of science and technology shijiazhuang 050000, china *corresponding author: pangyj2020@163.com shengmei dong shijiazhuang nanyangzhuang primary school shijiazhuang 050000, china dsm3012431882@163.com abstract finite time synchronization control of inertial memristor-based neural networks with varying delay is considered. in view of drive and response concept, the sufficient conditions to ensure finite time synchronization issue of inertial memristive neural networks is given. based on lyapunov finite time asymptotic theory, a kind of feedback controllers is designed for inertial memristorbased neural networks to realize the finite time synchronization. based on lyapunov stability theory, close loop error system can be proved finite time and fixed time stable. finally, illustrative example is given to illustrate the effectiveness of theoretical results. keywords: finite time, neural networks, synchronization. 1 introduction at present, the dynamic systems appearing in the fields of engineering applications and natural sciences are becoming more and more complex, and control purposes are also diversified. in this case, it is inevitable that control theory needs to provide more effective control methods and strategies. in the actual industrial production and manufacturing, the control precision of the equipment is continuously improved. at the same time, industrial production often requires multiple devices to achieve synchronization effects in terms of time, position, and speed, which requires reasonable coordination between events that occur in the system. synchronization in the system can also be called timely or synchronized. the phenomenon in which two phases are in harmony is called chaotic synchronization, which is actually a common phenomenon in nature. in the 1990s, peroca and carroll proposed a chaos control method (p-c), and first observed chaotic synchronization in electronic circuits. in fact, the phenomenon that several coupling units achieve synchronization through coordination not only occurs https://doi.org/10.15837/ijccc.2021.4.4215 2 widely in nature, but also common in actual production applications, and is very important and has to be researched, so synchronization control has always been a hot topic in the academic engineering field. as we all know, neuron is the basic unit of neural network function realization, with complex nonlinear properties, such as chaos, periodicity and bifurcation. the composition of a neural network includes a large number of neuron structures. a single neuron cannot realize the processing and processing of complex information, and it requires the coupling of many neuron clusters to complete it. synchronization is a manifestation of neuron coupling, so the study of neuron synchronization is particularly important. nowadays, with the continuous emergence of control problems and practical needs in the actual engineering field, the research on synchronization stability in the academic engineering field is becoming more and more popular, and a large number of research results and research directions have also been produced, such as brain network synchronization, complex networks synchronization, transient synchronization, global synchronization, etc., and scholars have studied synchronization problems under complex conditions. even though there have been a lot of results in the field of synchronization, there are still a lot of problems to be studied and solved in the world of nonlinear systems. chaos, as a very interesting nonlinear phenomenon, has been intensively investigated in many fields of science and technology over the last four decades. recently, chaos synchronization has attracted increasing attention from various communities due to its powerfully potential applications in laser physics, chemical reactor, secure communication, biomedical and so on [10, 21]. many methods have been proposed to achieve chaos control and synchronization, such as the passive control method, backstepping design method, impulsive control method, adaptive control method, sliding mode control, control lyapunov function (clf) method and nonlinear feedback method, etc. the controllers derived from the above methods are nonlinear. in a real industry process, because the linear feedback controllers are economic and easy to implement, they possess a high value in applications [1, 4]. since the first publication of chaotic synchronization by pecora in naval laboratory in 1990 [12], the synchronization problem has attracted wide attention from scholars due to the widespread existence of complex chaotic phenomena in nature and engineering systems [7]. the synchronization theory has been reflected and applied in both nature and engineering circles, such as laser network synchronization [22], resonance synchronization [18] and epilepsy [15]. in order to solve this complex nonlinear chaotic behavior and completely reconstruct the chaotic states of the two systems, scholars have proposed many synchronization strategies, such as generalized synchronization [6], complete synchronization [23], coupled synchronization [9], etc. as is known to all, neural network has attracted much attention in the field of academic engineering for its unique characteristics. with the in-depth study of neural network and the development of synchronization theory, network system synchronization has attracted extensive attention from scholars in recent years. in chu’s study [2], two consistent fractal-order differential inequalities are established to deal with the global exponential quasi-synchronization of a consistent fcdn, and the global exponential quasi-synchronization of a conformal fractal-order complex dynamic network (fcdn) is realized by periodic and intermittent pinning control. gan et al. [5] believed that a controller based on markov scheduling protocol was designed to solve the mean-square inverse synchronization problem of periodic bam neural network (nn) with time-varying delay. the theory of differential inclusion is combined with the definition of filippov solution, and the interval parameter system is established to study the exponential synchronization of cvmdnn based on complex valued memristor [11]. and in related study, sufficient conditions for finite time synchronization bounds are derived according to markov observations, thus verifying the finite time synchronization of coupled neural networks with hopping internal coupling and non-fragile controllers [8]. cui et al. [3] considered that the fixedtime synchronization of markov jumping fuzzy neural networks with random disturbance and leakage time-varying delays was studied by designing time-dependent controllers with or without fuzzy terms. according to the existing results, several control strategies can be given to realize the synchronization strategy mentioned above. for example, sliding mode control [17], backstepping control [19], adaptive control ![13] and active control [20], etc. the above control strategies obviously have their own advantages in different situations. among them, there is a kind of linear feedback control [14] https://doi.org/10.15837/ijccc.2021.4.4215 3 with extremely superior effect for chaotic system synchronization and differential synchronization, and chaotic system synchronization has extremely high value in some practical applications [16]. the rest of this brief is organized as follows. the considered model and some necessary assumptions are given in section 2, section 3 presents finite time and fixed time synchronization criteria and rigorous mathematical proof. section 4 given the numerical simulation and conclusion is given in section 5. 2 preliminaries the inertial memristor-based neural networks model: ẍi (t) = −aiẋi (t) − bixi (t) + cij (xi (t)) fj (xj (t)) + dij (xi (t)) fj (xj (t− τ (t))) + ii (t) i = 1, 2, . . . ,n (1) where xi(t) represents the state of the i-th neuron; ai > 0,bi > 0 are constants, fi() is activation function, τ(t) is varying delay, which satisfies 0 ≤ τ(t) ≤ τ, ii(t) is external input, cij(xi(t)),dij(xi(t)) are memristive connection weights, which are given by cij (x) = { c′ij, |x| ≤ ti c′′ij, |x| > ti dij (x) = { d′ij, |x| ≤ ti d′′ij, |x| > ti i,j = 1, 2, . . .n (2) where c′ij,c”ij,d′ij,d”ij and ti are known constants. by introducing the following variable substitution x1i,x2i as x1i = xix2i = ẋi (3) then, the system can be rewritten as  ẋ1i = x2i ẋ2i = −aix2i (t) − bix1i (t) + cij (x1i (t)) fj (x1j (t)) +dij (x1i (t)) fj (x1j (t− τ (t))) + ii (t) (4) this is referred to as drive system, and the response system can be described by ÿi (t) = −aiẏi (t) − biyi (t) + cij (yi (t)) fj (yj (t)) + dij (yi (t)) fj (yj (t− τ (t))) + ii (t) (5) similarly, by introducing variable substitution  ẏ1i (t) = y2i (t) + u1i (t) ẏ2i (t) = −aiy2i (t) − biy1i (t) + cij (y1i (t)) fj (y1j (t)) + dij (y1i (t)) fj (y1j (t− τ (t))) +ii (t) + u2i (t) (6) the synchronization errors as e1i = y1i −x1ie2i = y2i −x2i (7) then we have { ė1i (t) = e2i (t) + u1i (t) ė2i (t) = −aie2i (t) − bie1i (t) + fi (t) + u2i (t) (8) where, fi (t) = cij (y1i (t)) fj (y1j (t)) − cij (x1i (t)) fj (x1j (t)) +dij (y1i (t)) fj (y1j (t− τ (t))) −dij (x1i (t)) fj (x1j (t− τ (t))) (9) to design the finite time and fixed-time synchronization theory, some lemmas are proposed based on a general nonlinear system ẋ = f (x) (10) where x is error system state. https://doi.org/10.15837/ijccc.2021.4.4215 4 lemma 1. for xi ∈ randxi ≤ 0, i = 1, 2, . . . ,n, 0 < p < 1,q > 1, then( n∑ i=1 xi )p ≤ n∑ i=1 xi p (11) n1−q ( n∑ i=1 xi )q ≤ n∑ i=1 xi q (12) lemma 2. suppose the positive definite and continuous function v (t) meets the following differential inequality fv̇ (t) ≤ −ρv η(t), t ≥ 0,v (0) ≥ 0, where 0 < η < 1 and ρ > 0 are constants. then, v (t) meets the following inequality v 1−η(t) ≤ v 1−η(0) − ρ(1 − η), 0 ≤ t ≤ t. then the origin of system (10) can achieve finite time stability, and t ≤ v 1−η(0) ρ(1−η) lemma 3. suppose that v (ů) : rn → r + ∪0 is a continuous radically unbounded function and the following two conditions hold: (1)v (x) = 0 ⇔ x = 0 (2)any solution x(t) of system equation reference goes here satisfies,v (x(t)) ≤ −av p(x(t)) − bv q(x(t)), for some a,b > 0, 0 ≤ p < 1andq > 1. then the origin of system (10) can achieve fixed-time stability, and tmax = 1a(1−p) + 1 b(q−1) 3 synchronization control to derive the finite time synchronization criteria, the finite time synchronized feedback controller. theorem 1. choose the synchronization controller as following:{ u1i (t) = −e2i (t) −k1ieη1i u2i (t) = aie2i (t) + bie1i (t) −fi (t) −k2ieη2i (13) where 0 < η < 1, then, drive system (1) and response system (5) can achieve finite time synchronization, furthermore, finite time is t ≤ v 1−η 2 (0) 2k (1 −η) (14) proof 1. choose the lyapunov candidate functional: v = n∑ i=1 e21i (t) + n∑ i=1 e22i (t) (15) calculate the derivative of v along the trajectories of the error system (8) as v̇ = 2 n∑ i=1 ė1i (t) e1i (t) + 2 n∑ i=1 ė2i (t) e2i (t) (16) based on the controller (10), the error system (8) can be get as{ ė1i (t) = −k1ieη1i ė2i (t) = −k2ieη2i (17) take (13) into (12) as v̇ = − n∑ i=1 2k1ieη+11i (t) − n∑ i=1 2k2ieη+12i (t) ≤−kv η+1 2 (18) where k = min(k1i,k2i), based on lemma 2, the error system is finite time asymptotic stable is t ≤ v 1−η 2 (0) 2k(1−η) , the proof is completed. https://doi.org/10.15837/ijccc.2021.4.4215 5 theorem 2. choose the synchronization controller as following:{ u1i (t) = −e2i (t) −k1iep1i − l1ie q 1i u2i (t) = aie2i (t) + bie1i (t) −fi (t) −k2iep2i − l2ie q 2i (19) proof 2. choose the lyapunov candidate functional: v = n∑ i=1 e21i (t) + n∑ i=1 e22i (t) (20) calculate the derivative of v along the trajectories of the error system (8) as v̇ = 2 n∑ i=1 ė1i (t) e1i (t) + 2 n∑ i=1 ė2i (t) e2i (t) (21) based on the controller (10), the error system (8) can be get as{ ė1i (t) = −k1iep1i − l1ie q 1i ė2i (t) = −k2iep2i − l2ie q 2i (22) take (13) into (12) as v̇ = − n∑ i=1 2k1iep+11i (t) − n∑ i=1 2k2iep+12i (t) − n∑ i=1 2l1ieq+11i (t) − n∑ i=1 2l2ieq+12i (t) (23) based on lemma 1 n∑ i=1 2k1iep+11i + n∑ i=1 2k2iep+12i ≥ a ( n∑ i=1 ( e21i )p+1 2 + n∑ i=1 ( e22i )p+1 2 ) ≥ a ( n∑ i=1 e21i + n∑ i=1 e22i )p+1 2 (24) where a = min(k1i,k2i). n∑ i=1 2l1ieq+11i (t) + n∑ i=1 2l2ieq+12i (t) ≥ b1 ( n∑ i=1 ( e21i )q+1 2 + n∑ i=1 ( e22i )q+1 2 ) ≥ b ( n∑ i=1 e21i + n∑ i=1 e22i )q+1 2 (25) where , b = (2n) 1−q 2 b1,b1 = min(2l1i, 2l2i), therefore we have v̇ ≤−av p+1 2 − bv q+1 2 (26) based on lemma 3, the error system is fixed-time asymptotic stable is tmax = 2a(1−p) + 2 b(q−1) , the proof is completed. 4 numerical simulations in this section, we will use an example to illustrate how to apply the theory results proposed in this brief in applications. the chaotic neural network proposed is adopted as the drive system: ẋ (t) = −dixi (t) + ∑3 j=1 aij (xi (t)) fi (xj (t)) + ∑3 j=1 bij (xi (t)) gj (xj (t− τj (t))) (27) the corresponding response system as follows ẏi (t) = −diyi (t) + ∑n j=1 aij (yi (t)) fi (yj (t)) + ∑n j=1 bij (yi (t)) gj (yj (t− τj (t))) + ui (t) (28) https://doi.org/10.15837/ijccc.2021.4.4215 6 where, a11 (x1) = { 1.2, |x1| ≤ 1 1.8, |x1| > 1 ,a12 (x1) = { −2.1, |x1| ≤ 1 −1.6, |x1| > 1 ,a13 (x1) = { 2.1, |x1| ≤ 1 1.6, |x1| > 1 a21 (x2) = { −3.6, |x2| ≤ 1 −2.3, |x2| > 1 ,a22 (x2) = { 1.4, |x2| ≤ 1 2.3, |x2| > 1 ,a23 (x2) = { −1.4, |x2| ≤ 1 −2.2, |x2| > 1 a31 (x3) = { 1.6, |x3| ≤ 1 1.3, |x3| > 1 ,a32 (x3) = { 1.4, |x3| ≤ 1 2.1, |x3| > 1 ,a33 (x3) = { 1.5, |x3| ≤ 1 2.2, |x3| > 1 b11 (x1) = { 1.1, |x1| ≤ 1 1.7, |x1| > 1 ,b12 (x1) = { 1.5, |x1| ≤ 1 1.2, |x1| > 1 ,b13 (x1) = { 1.5, |x1| ≤ 1 1.2, |x1| > 1 b21 (x2) = { 1.3, |x2| ≤ 1 1.1, |x2| > 1 ,b22 (x2) = { −1.2, |x2| ≤ 1 −2.2, |x2| > 1 ,b23 (x2) = { −1.1, |x2| ≤ 1 −2.3, |x2| > 1 b31 (x3) = { 2.6, |x3| ≤ 1 2.3, |x3| > 1 ,b32 (x3) = { 2.4, |x3| ≤ 1 2.1, |x3| > 1 ,b33 (x3) = { 3.5, |x3| ≤ 1 3.2, |x3| > 1 the activation functions and time-varying delays are taken as f(x) = g(x) = tanh(x). figure 1: time evolution of variable of coupled neural networks x1,y1 the time delay τ = 1, the initial condition choose as x1(0) = 3,x2(0) = 5,y1(0) = 4,y2(0) = 7. based on theorem 1, choose the control gain as η = 1/3,k = 0.3, then the error system is finite time stable. the simulated results are shown in figure 1 and figure 2, indicate the response system synchronizes the driver system in finite time. figure 2: time evolution of variable of coupled neural networks x2,y2 https://doi.org/10.15837/ijccc.2021.4.4215 7 the time delay τ = 1, the initial condition choose as x1(0) = 3,x2(0) = 5,y1(0) = 4,y2(0) = 7. based on theorem 2, choose the control gain as p = 1/3,q = 5/3,k = 1, then the error system is fixed-time stable. the simulated results are shown in figure 3 and figure 4, indicate the response system synchronizes the driver system in fixed time. simulation result indicated effectiveness and feasibility of the proposed control method. figure 3: time evolution of variable of coupled neural networks x3,y3 figure 4: chaotic attractor of neural networks 5 conclusion in this paper, the problem of finite time and fixed time synchronization of a class of chaotic neural networks has been considered. the method introduced for linear feedback finite time and fixed time control is very effective, and it is simple to implement in practice. based on lyapunov stability theory, finite time and fixed time stability is proved. to the end, numerical simulation is 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(2020). finite-time synchronization of fully complex-valued neural networks with fractional-order, neurocomputing, 373, 70-80, 2020. copyright ©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: pang, y.j.; dong, s.m. (2021). finite time synchronization of inertial memristive neural networks with time varying dela, international journal of computers communications & control, 16(4), 4215, 2021. https://doi.org/10.15837/ijccc.2021.4.4215 introduction preliminaries synchronization control numerical simulations conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 3, month: june, year: 2023 article number: 4901, https://doi.org/10.15837/ijccc.2023.3.4901 ccc publications determination of the satisfaction attribute in usability tests using sentiment analysis and fuzzy logic g. e. chanchí-golondrino, m. a. ospina-alarcón, l. m. sierra-martínez gabriel elías chanchí-golondrino* faculty of engineering university of cartagena av. del consulado, cll. 30 # 39b-192, cartagena-colombia *corresponding author: gchanchig@unicartagena.edu.co manuel alejandro ospina-alarcón faculty of engineering university of cartagena av. del consulado, cll. 30 # 39b-192, cartagena-colombia mospinaa@unicartagena.edu.co luz marina sierra-martínez faculty of electronic engineering and telecommunications university of cauca cll. 5 # 4-70, popayán colombia lsierra@unicauca.edu.co abstract with the growth in the number of applications deployed in cloud app stores, usability has become a fundamental attribute to ensure end-user productivity and enterprise competitiveness. according to iso 9241-11, the three attributes that determine the usability of a product are effectiveness, efficiency and satisfaction, the latter being the subjective attribute of usability. in conventional user tests, the satisfaction attribute is determined using perception questionnaires, being a challenge to determine this attribute more objectively, given the limitations of surveys in terms of veracity and subjectivity. based on the above, in this article we propose as a contribution, the construction of a system based on fuzzy logic for the estimation of the usability satisfaction attribute in user tests, which has as inputs both the level of satisfaction obtained from the answers to the posttest questionnaire, and the level of satisfaction obtained from the polarities of the opinions of the test users, determined by means of sentiment analysis techniques. the proposed fuzzy system is intended to serve as a reference to be replicated at the academic and enterprise level in the conduct of usability tests and specifically in the more objective determination of the satisfaction attribute based on the advantages of fuzzy logic and sentiment analysis techniques. keywords: fuzzy logic, satisfaction level, sentiment analysis, usability. https://doi.org/10.15837/ijccc.2023.3.4901 2 1 introduction with the growth in the number of applications deployed in cloud app stores and the number of users consuming them, usability has become one of the key attributes contributing to the competitiveness of software companies and end-user productivity [8, 13, 25]. according to nielsen, usability is an attribute of software quality, which evaluates how easy to use the graphical interfaces of software systems are for users and is defined by 5 main attributes: learnability, efficiency, memorability, error tolerance and satisfaction [5, 20]. also, according to iso 9241-11, usability is defined as the degree to which a software product can be used by specific users to achieve specific objectives with effectiveness, efficiency and satisfaction [11, 19, 21, 30]. similarly, according to iso 9126-1, usability is defined as the ability of a software product to be understood, learned, operated and attractive to the end user [9, 28]. thus, according to the previous definitions, it is possible to observe how user perception or satisfaction is a key attribute that determines the usability of a software product. one of the most effective ways to evaluate the usability of a software product is through the so-called user tests, in which a set of users perform tasks in a controlled environment or usability laboratory, which are supervised by a set of evaluators in order to determine the attributes that define usability, where the attributes defined by iso 9241-11 (effectiveness, efficiency and satisfaction) are usually the most widespread [6, 7]. in a conventional user test developed in a usability laboratory, effectiveness can be determined from the percentage of tasks completed by the users, efficiency can be obtained by relating the time spent by each user in performing a task with respect to the time estimated by the evaluator, while satisfaction is determined in the traditional way through the use of so-called perception surveys [1, 10]. thus, effectiveness and efficiency are considered the objective attributes of usability, while satisfaction is considered the subjective attribute. thus, one of the challenges of user testing is the more objective determination of the satisfaction attribute, given the limitations of perception surveys in terms of their veracity and subjectivity[23]. among the most widespread approaches nowadays for perception analysis in other contexts such as marketing based on the opinion of customers in social networks or e-commerce portals regarding the products and services offered by companies, it is important to highlight the techniques of opinion mining or sentiment analysis, in which through the use of natural language processing (nlp) methods, the polarity value (positive, negative and neutral) of an opinion is determined [15]. in this sense, there are different researches that evidence the application of sentiment analysis techniques in the context of marketing. thus, in [16] a method based on sentiment analysis supported by a lexicon or domain lexicon is proposed, from which a case study is developed to analyze the polarities of the opinions of customers of the electricity service in the united kingdom. in [2] an opinion mining study was developed on the comments made on the social network twitter regarding typical halal tourism destinations, in order to identify the most popular sites with the best perception or positive polarity. in [31], a sentiment ontology is proposed to perform context-sensitive sentiment analysis studies on online stock market to support investment-level decision making and risk perception of public companies. in [22] an approach based on machine learning, time series and sentiment analysis is proposed to predict the monthly total vehicle sales in the us, so that a dataset was formed with the polarities of twitter users’ opinions and the values obtained in the stock market. in [23] a study was developed based on sentiment analysis of customer opinions of the world’s top 26 cosmetic brands, in order to identify relative customer satisfaction by using inverse term-frequency document frequency analysis, as well as to determine the causes of positive and negative opinions. in [29] a sentiment and emotion analysis study is developed on tweets related to the topic of cryptocurrencies, making use of deep learning methods and in order to identify the perception of users with respect to cryptocurrencies. in [15] a sentiment analysis approach for online product reviews in the context of marketing was proposed, which made use of heuristic algorithms and was validated using a dataset of movie reviews. the application of opinion mining and/or sentiment analysis techniques in the context of marketing gives the possibility of extrapolating these methods to the field of usability and specifically in the analysis of the satisfaction attribute in user tests, however, as evidenced in the previously described works, although the positive polarity is considered as an indicator of customer perception, in none of these works was the calculation of a specific level of satisfaction from the three polarities obtained through sentiment analysis performed. likewise, given that a user test includes within its post-test https://doi.org/10.15837/ijccc.2023.3.4901 3 questionnaire both quantitative questions (traditionally used as indicators of the satisfaction attribute) and open questions that collect the users’ opinion, it is of great interest to obtain a combined level of satisfaction, which represents a challenge considering the different scales presented by the quantitative questions and the ranges of the polarities of the opinions. in this sense, the application of fuzzy logic can contribute to obtain a combined numerical and linguistic level of satisfaction, which is more intelligible and representative for the coordinators of a user test. fuzzy logic can be understood as a mathematical tool that provides a simple way to reach a specific conclusion, starting from input data that may be undefined, imprecise, or vague [14, 18, 32]. thus, the use of fuzzy logic allows the generation of intelligible results that relate numerical data to linguistic terms that are close to natural language [3, 4, 26]. thus, in this article we propose as a contribution the construction of a system based on fuzzy logic, which receives as input both the average of the quantitative ratings of the post-test questionnaire, and the composite value of the polarities of the users’ opinions in the post-test questionnaire, so that from these inputs, the system calculates a combined output satisfaction level using a set of inference rules that relate the fuzzy sets determined for the inputs. given the relevance of the proposed approach in terms of estimating the satisfaction attribute in a more objective way, the proposed system is intended to be replicated in the academic and enterprise context to support the development of user tests and specifically in the estimation of the satisfaction attribute based on the use of sentiment analysis and fuzzy logic. the rest of the paper is organized as follows: section 2 describes the different methodological phases used for the development of this research; section 3 presents the results obtained through this research, which includes a description of the functionalities provided by the system, as well as a proof of concept in which the usefulness of the proposed system is verified by using the data corresponding to an example test with 5 users; finally, section 4 presents the conclusions and future work derived from this research. 2 methodology for the development of the present research, the four phases of the iterative research pattern proposed by pratt [24] were taken into consideration: observing the application, identifying the problem, developing the solution, testing the solution (see figure 1). figure 1: methodology considered (source[24]) in phase 1 of the methodology, user tests were characterized in order to identify the different stages that make up this type of test, as well as the way in which the satisfaction attribute is determined in a conventional way. thus, it was possible to identify that conventional user tests developed in a usability laboratory consist of 5 stages: confidentiality agreement, pre-test questionnaire, task list, post-test questionnaire and analysis of results (see figure 2). https://doi.org/10.15837/ijccc.2023.3.4901 4 figure 2: stages of user tests (source own) in the confidentiality agreement, the user is informed and guaranteed that the different data obtained in the test will be used only for academic purposes. within the pre-test questionnaire a set of data are obtained regarding the user’s profile, as well as data related to the user’s previous experience with tools like the evaluated software. once the pre-test questionnaire is completed, the user proceeds to develop a set of tasks defined and supervised remotely by the usability test coordinators, who, while supervising the tasks, record the time spent in the development of each task, as well as the level of compliance or development of each task by each user, to determine the effectiveness and efficiency attributes. when the development of the usability test tasks concludes, the users proceed to fill out the post-test questionnaire, which through quantitative and qualitative questions seeks to determine the user’s perception and satisfaction with respect to the evaluated software. thus, to determine the satisfaction attribute quantitatively for each user, the average of the questions in the post-test questionnaire involving satisfaction is traditionally used as an indicator, as shown in eq. 1. sat_pos = q1 + q2 + q3 + .. + qn n . (1) where q1, q2,....,qn, represent the rating assigned to each of the questions in the post-test questionnaire considered in the calculation. in a complementary way, it is possible to take advantage of affective computing and specifically sentiment analysis techniques to determine the users’ perception of a usability test, considering the three polarities of an opinion (positive, negative and neutral). thus, using an adaptation of the approach proposed by the opinion mining library vadersentiment [17, 27], it is possible to obtain a composite value of the polarities which is an indicator of the users’ level of perception, as shown in eq. 2. sat_sent = ( x √ x2 + a + √ 1 + a − 1 √ 1 + a ) (2) where x represents the difference between the positive polarity and the negative polarity (see eq. 3). x = polpos − polneg (3) while a corresponds to an arbitrary value that is added to the first root of the equation with the purpose that when the value of x is maximum, i.e. 1, the root can be exact. thus, the possible values of a can be 3, 8, 15, etc., considering that these allow obtaining values that have exact square root (4, 9, 16, etc.) when the value of x is 1. it is important to mention that the neutral polarity is equally distributed between the positive and negative polarity, so the value of x is obtained by subtracting the positive and negative polarities. https://doi.org/10.15837/ijccc.2023.3.4901 5 finally, in the results analysis stage, the data from the pre-test and post-test questionnaires are consolidated for the analysis of the user profiles and the determination of the percentage or level of usability, considering the percentages of effectiveness, efficiency and satisfaction obtained per user and for the entire test, as shown in (see eq. 4) usability_level = %ef f ectiveness + %ef f iciency + %satisf action 3 (4) it is worth mentioning that in usability_level, the satisfaction percentage can be determined by multiplying by 20 the value obtained in sat_pos or by multiplying by 100 the value obtained in sat_sent, considering the ranges of the values of these equations. once the phases of a conventional user test have been characterized, as well as the methods used to determine the level of satisfaction in a traditional way or by using sentiment analysis techniques, the design of the fuzzy system was carried out in phase 2 of the methodology, which includes the definition of the membership functions for both the system input variables (average of the quantitative ratings of the post-test questionnaire (sat_pos) and composite value of the polarities of the users’ opinions in the post-test questionnaire (sat_sent)), and for the system output variable or satisfaction level (sat_level). similarly, in this phase, the inference rules relating the input variables to the output satisfaction level were specified. thus, figure 3 shows the membership functions defined for the 3 fuzzy sets (poor, average, good) associated to the input variable sat_pos, which is obtained from equation 1 and its range is from 0 to 5. figure 3: membership functions for the input variable sat_pos (source own) similarly, figure 4 shows the membership functions defined for the 3 fuzzy sets (negative, neutral, positive) corresponding to the input variable sat_sent, which is obtained from equation 2 and is in the range from 0 to 1. figure 4: membership functions for the input variable sat_sent (source own) on the other hand, figure 5 shows the membership functions corresponding to the 4 fuzzy sets (poor, fair, good, excellent) that describe the output variable or level of satisfaction, which is in the range from 0 to 100 and is determined by the fuzzy system from the system inputs and the inference rules (see 1). likewise, table 1 shows the 9 inference rules that relate the input variables (sat_pos and sat_sent) to the output satisfaction level (sat_level), thus allowing the determination of this value in numerical and linguistic terms. these inference rules were defined in fcl (fuzzy control logic) language and https://doi.org/10.15837/ijccc.2023.3.4901 6 figure 5: membership functions for the input variable sat_level (source own) table 1: defined inference rules id inference rule 1 if sat_pos_test is poor and sat_sent is negative then sat_level is poor 2 if sat_pos_test is poor and sat_sent is neutral then sat_level is poor 3 if sat_pos_test is poor and sat_sent is positive then sat_level is fair 4 if sat_pos_test is average and sat_sent is negative then sat_level is poor 5 if sat_pos_test is average and sat_sent is neutral then sat_level is fair 6 if sat_pos_test is average and sat_sent is positive then sat_level is good 7 if sat_pos_test is good and sat_sent is negative then sat_level is fair 8 if sat_pos_test is good and sat_sent is neutral then sat_level is good 9 if sat_pos_test is good and sat_sent is positive then sat_level is excellent have an antecedent that relates the system inputs and a consequent that indicates the output value of the satisfaction level. once the membership functions were defined for the input variables and for the output variable of the system, as well as the inference rules that relate the inputs and the output, in phase 3 of the methodology the fuzzy logic system was implemented to estimate the level of satisfaction in user tests using the java programming language and specifically the jfuzzylogic library, which allows the definition of membership functions and inference rules in the fcl language, as well as the mathematical and graphical analysis of the system variables for different input values. similarly, the fuzzy system made use of the javacsv library for processing the system input files, which contain the post-test questionnaire data associated with user satisfaction and opinions regarding the interaction. regarding the analysis of the opinions, the fuzzy system used the vadersentiment library to obtain the polarities associated with each user’s opinion. thus, the implemented system allows the usability test coordinators to enter both user ratings to the post-test questionnaire questions related to satisfaction, as well as user opinions regarding the interaction, in order to determine the combined satisfaction level per user and for the entire usability test. finally, in phase 4 of the methodology, the functionality and usefulness of the proposed fuzzy system was verified through the development of a proof of concept, in which the data associated with a usability test with 5 users were used. 3 results in this section the results obtained in the present research are specified, which includes the description of the system’s graphic interface and its functionalities, as well as a proof of concept in which the functionality and usefulness of the proposed system was verified. 3.1 construction of the fuzzy system as previously mentioned, the fuzzy system was implemented in java language using the jfuzzylogic library and its graphical interface consists of 4 tabs: “questions post-test”, “sentiment analysis”, “fuzzy satisfaction analysis”, “membership functions” (see figure 6). in the “questions posttest” tab, the fuzzy system automatically loads from a .csv file, the ratings made by the test users to the different questions of the post-test questionnaire that involve satisfaction, so that by pressing the "calculate" button, the system determines the degree of satisfaction per user and for the entire https://doi.org/10.15837/ijccc.2023.3.4901 7 test using eq. 1. likewise, once the above calculations have been made, the system generates and displays a bar chart with the satisfaction levels associated with each of the test users, using the free jfreechart library. the values calculated in this tab thus correspond to the input variable sat_pos of the fuzzy system. as an example, figure 6 shows the data from a usability test with 5 users, where two questions from the post-test questionnaire were taken to determine the satisfaction attribute (rated on a scale of 0 to 5). within this sample data, it is possible to observe mathematically and graphically how the highest level of satisfaction is 3.5 (user 3), while the lowest level of satisfaction is 2.5 (users 2 and 4). likewise, the average satisfaction level is 2.9. figure 6: main system interface (source own) continuing with the description of the proposed system, figure 7 presents the "sentiment analysis" tab, in which the system automatically loads from a .csv file the opinions made by the test users within the post-test questionnaire, so that by pressing the “calculate” button the system determines the 3 polarities (positive, negative, and neutral) associated with each opinion, as well as the composite satisfaction level based on sentiment analysis using equation 2. likewise, once the above calculations have been made, the system generates and displays a bar chart with the level of satisfaction based on sentiment analysis associated with each of the opinions of the test users. it is worth mentioning that the values calculated in this tab correspond to the input variable sat_sent of the fuzzy system. as an example, it is possible to observe how from the test opinions loaded and presented in figure 7, it was obtained that all opinions have a satisfaction level below 0.5 (in the range from 0 to 1), being the highest level 0.464 (user 4), which is related to the higher value of the negative polarity with respect to the value of the positive polarity for all users. on the other hand, figure 8 shows the "fuzzy satisfaction analysis” tab of the proposed fuzzy system, in which the data obtained for the satisfaction level of the post-test questionnaire are loaded from the "questions post-test" tab and the satisfaction level data obtained by means of sentiment analysis techniques from the "sentiment analysis” tab, so that by pressing the "calculate” button the combined satisfaction level per user and for the entire usability test is determined, by performing the process of defuzzification of the inputs (sat_post and sat_sent) using the membership functions associated with them and obtaining the output satisfaction level or fuzzyfication through the use of the inference rules and the membership functions of the output variable. in addition to obtaining the combined satisfaction level per user and for the entire usability test, the system allows to display for a given user the membership function of the output variable (sat_level) together with the value and level determined from the inputs, the membership functions and the inference rules activated after the defuzzification process. on the other hand, in this tab, it is possible by clicking on the “report” button, to generate a report with the results associated with the calculation of the combined satisfaction level for a given user and for the entire test. as an example, from the sample data loaded in the “questions post-test” and “sentiment analysis” tabs, a combined output https://doi.org/10.15837/ijccc.2023.3.4901 8 figure 7: sentiment analysis tab of the system (source own) figure 8: fuzzy satisfaction analysis tab of the system (source own) satisfaction level of 26.427 is obtained for user 2, which corresponds to the poor fuzzy set. similarly, it is possible to observe that in determining this level of output, rules 1 and 2 of table 1 were activated, with rule 2 obtaining a higher level of membership. finally, figure 9 shows the interface of the "membership functions” tab of the fuzzy system, whereby choosing a specific user and pressing the "consult” button, it is possible to visualize the membership functions of the inputs and outputs of the system, as well as the values and levels obtained in the fuzzification and defuzzification process. as an example, for the sample data loaded in the "questions posttest” and "sentiment analysis" tabs, it is possible to observe how for user 3 the fuzzification process allowed to determine that the input variables sat_post (3.5) and sat_sent (0.264), were classified in the fuzzy levels or sets "average” and "negative” respectively. the above allowed obtaining an output value of 26.313, which corresponds to the fuzzy set or "poor” level. 3.2 proof of concept in order to verify the functionality and usefulness of the proposed fuzzy system, a proof of concept was developed in which data was collected from a usability test with 5 users, from which 3 questions were selected for the post-test questionnaire to determine the conventional level of satisfaction. thus, table 2 presents the ratings assigned to the 3 questions selected from the post-test questionnaire, https://doi.org/10.15837/ijccc.2023.3.4901 9 figure 9: membership functions tab of the system (source own) table 2: proof of concept data user q1 q2 q3 opinion 1 4.0 4.2 3.5 the software allows to accomplish the tasks adequately, but it can improve the organization of the different options in the menus. 2 3.6 3.8 3.9 the software serves its purpose but some icons are unintuitive. 3 3.2 3.3 3.5 some main functions of the software are difficult to find in the options menu. 4 3.1 3.4 3.0 although the software allows to perform the tasks the response times are not adequate and the design is inconsistent 5 4.0 4.3 4.1 the software fulfills its purpose but can improve the consistency of its different views as well as the opinions corresponding to each user with respect to the interaction with a given test software. once the data presented in table 2 were entered into the proposed fuzzy system, it allows determining both the conventional satisfaction level (sat_pos) from the columns (q1, q2 and q3) and considering the membership functions in figure 3, and the satisfaction level based on sentiment analysis (sat_sent) using the opinion column, the vadersentiment api and the membership functions in figure 4. similarly, from the values of the input variables (sat_pos and sat_sent), the system determines the value of the output variable (sat_level) per user and for the entire test, based on the inference rules and membership functions in figure 5. thus, the results obtained by the fuzzy system from the data in table 2 are presented in table 3. table 3 consists of 6 columns: user, sat_pos, sat_sent, sat_level, qualitative level and rules activated. the "user" column refers to the identifier of each of the users who participated in the table 3: results obtained in the proof of concept user sat_pos sat_sent sat_level qualitative level rules activated 1 3.9 0.633 66.743 fair rule 5 (0.3349) rule 6 (0.266) 2 3.767 0.5 60 fair rule 6 (0.66625) 3 3.333 0.407 41.081 poor rule 4 (0.186) rule 5 (0.5349) 4 3.167 0.451 46.97 poor rule 4 (0.098) rule 5 (0.5837) 5 4.133 0.675 79.136 good rule 5 (0.125) rule 6 (0.209) rule 8 (0.125) rule 9 (0.266) total 3.66 0.533 61.605 fair rule 5 (0.799) rule 6 (0.066) https://doi.org/10.15837/ijccc.2023.3.4901 10 usability test. the “sat_pos” column corresponds to the first input of the system and comprises the level of satisfaction determined from the ratings of the post-test questionnaire. the “sat_sent” column is the second input of the fuzzy system and corresponds to the level of satisfaction determined from the use of sentiment analysis techniques on user opinions. the “sat_level” column corresponds to the level of satisfaction estimated by the fuzzy system based on the inference rules and mamdami’s method. similarly, the “qualitative_level” column corresponds to the fuzzy set or qualitative level associated with the output satisfaction level determined by the fuzzy system. finally, the column "rules activated" corresponds to the rules in table 1 that are activated in the process of determining the level of satisfaction, together with their respective degree of belonging. from the results presented in table 3, it is possible to observe how the lowest level of satisfaction obtained by the fuzzy system was 41.081 (user 3) corresponding to the qualitative level or fuzzy set "poor", while the highest level of satisfaction of the proof of concept was 79.136 (user 5) corresponding to the qualitative level or fuzzy set "good”. similarly, it is possible to observe that for two of the users the qualitative level "fair” was obtained (users 1 and 2), for two of the users the level "poor” was obtained (users 3 and 4) and for the remaining user the qualitative level "good" was obtained (user 5). finally, using as input to the system the averages of the inputs (sat_pos and sat_sent), the level of satisfaction for the whole test is 61.605, which corresponds to the qualitative level or fuzzy set “fair”. from the above results, it was possible to verify the usefulness of the proposed system in terms of estimating the level of satisfaction in quantitative and qualitative terms from input values on different scales and obtained by using different methods. the levels of satisfaction per user can be seen more clearly in figure 10. figure 10: satisfaction level per user of the proof of concept. (source own) 4 conclusions and future work based on the challenge of improving the estimation of the satisfaction attribute in user tests, taking advantage of the benefits provided by affective computing, in this paper we propose as a contribution a system based on fuzzy logic, which allows the determination of the satisfaction attribute in usability tests by combining the conventional satisfaction level obtained from the quantitative answers of the post-test questionnaire and the satisfaction level determined by applying opinion mining and sentiment analysis techniques on the users’ opinions in the usability test. thus, the fuzzy system has as inputs both the average of the quantitative ratings of the post-test questionnaire and the composite value of the polarities of the users’ opinions, so that from these inputs the system calculates the output satisfaction level, making use of inference rules that relate the fuzzy sets obtained from the inputs. thus, this proposal aims to serve as a reference and contribution in academic and enterprise scenarios regarding the objective determination of the satisfaction attribute in user tests. with respect to the traditional approach used for the determination of the satisfaction attribute in user tests, the proposal presented in this article aims not only to improve objectivity in the determination of this attribute through the application of sentiment analysis techniques, but also to obtain the level of satisfaction of test users in numerical and linguistic terms, which are more intelligible to the coordinators of usability tests. in this same sense, the application of fuzzy logic allowed the combination of input variables in different numerical scales and qualitative levels (level of satisfaction https://doi.org/10.15837/ijccc.2023.3.4901 11 from the post-test questionnaire in the range of 0 to 5 and level of satisfaction from sentiment analysis in the range of 0 to 1) to obtain a combined level of satisfaction in the range of 0 to 100 with four qualitative levels or fuzzy sets: poor, fair, good, and excellent. thus, the proposal presented in this article is intended to be replicated at the academic and enterprise level in terms of conducting user tests, supported by the advantages provided by affective computing and fuzzy logic. the open technologies selected for the construction of the proposed fuzzy system proved to be suitable for the estimation of the satisfaction attribute in user tests, based on the combination of opinion mining techniques with the conventional method of determining this attribute based on perception questionnaires. thus, for the definition of the membership functions of the inputs and output of the fuzzy system, as well as for the specification of the membership rules and the implementation of the output satisfaction level inference method, the java jfuzzylogic library was used. likewise, for the processing of the files containing the data associated with the input variables of the system (questions of the post-test questionnaire and opinions of the users in the interaction), the javacsv library was used. in the same sense, for the determination of the polarities (positive, negative, and neutral) of each of the opinions of the post-test questionnaire, the vadersentiment library was used. the technologies used in the development of the system are intended to serve as a reference to replicate the implementation of fuzzy logic-based tools focused on the study of attribute satisfaction in usability tests, as well as in other contexts in which fuzzy logic can be applied. the proof of concept developed using the proposed fuzzy system, allowed to verify its functionality and usefulness in terms of estimating the level of satisfaction in user tests, based on the combination of sentiment analysis techniques with the traditional method of determining the satisfaction attribute. thus, in the "proof of concept" considered, which takes as input data both the answers to 3 questions of the post-test questionnaire and the opinions associated with the interaction of 5 users, it was obtained that the lowest level of satisfaction was 41.081 with a qualitative level of "fair", while the highest level of satisfaction was 79.136% with a qualitative level of "good". likewise, the level of satisfaction obtained for the entire usability test was 61.605%, which corresponds to the "fair" qualitative level. finally, as a future work derived from the present research, we intend to enrich the development of the proposed system by including new input variables to the system, such as those obtained from the monitoring of physiological variables in user tests (heart rate, galvanic skin conductivity, blood pressure, facial expression, among others). this is due to the close relationship between the satisfaction attribute and emotions, as well as their multimodal nature. in the same way, it is intended to provide the system with the functionality to assign weights to the input variables, so that for the calculation of the satisfaction attribute it is possible to configure the level of influence of the affective computing methods over the traditional methods. funding the apc was funded by authors. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. acknowledgments the authors would like to thank the universities of cartagena and cauca for their support in carrying out this research. https://doi.org/10.15837/ijccc.2023.3.4901 12 references [1] [online]. available: https://catalogosiidca.csuca.org/record/ucr.000174999, accessed on 8 august 2022. 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(2013). fuzzy logic, neural networks, and soft computing, communications of the acm, 37(3), 77–84, 1994. https://doi.org/10.15837/ijccc.2023.3.4901 14 copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: chanchí, g.e.; sierra-martínez, l.m.; ospina-alarcón, m.a. (2023). determination of the satisfaction attribute in usability tests using sentiment analysis and fuzzy logic, international journal of computers communications & control, 18(3), 4901, 2023. https://doi.org/10.15837/ijccc.2023.3.4901 introduction methodology results construction of the fuzzy system proof of concept conclusions and future work international journal of computers communications & control issn 1841-9836, 10(3):403-419, june, 2015. performance comparison of tcp spoofing and end to end approach to enable partial qos on ip based network y. suryanto, r.r. nasser, r.f. sari y. suryanto*, r.r. nasser, r.f. sari 1. department electrical engineering faculty of engineering, university of indonesia, depok, 16424, indonesia. yohan.suryanto@ui.ac.id, rizki.reynaldo@ui.ac.id, riri@ui.ac.id *corresponding author: yohan.suryanto@ui.ac.id abstract: implementation has a purpose to give adequate guarantee for multimedia application to be able delivered according to the priority and the class of services. but basically, end to end qos guarantee is very difficult to be realized, especially when it involves a lot of operators with a variety of interconnection networks such as internet. to overcome that difficulty of implementing end-to-end qos in ip-based networks, we propose a partial qos approach through tcp spoofing technique. partial qos is implementation of qos subset like bandwidth parameter in certain ip based network segment. tcp spoofing is a technique to intercept tcp connection between user and content server to be further manipulated. each tcp connection will be intercepted by spoofing gateway and will be adjusted to the appropriate window size parameter that has been approved for each user. spoofing gateway will forward the request from the client to the content server and change the original tcp protocol to tcp linux highspeed. our simulation results, using ns-2.35, show that for some cases of partial qos through spoofing technique produced better performance, such as completion time and average throughput for each user class priority, compared to the end-to-end qos approach. even in cases where the internet network delay product characteristics are relatively high, partial qos spoofing technique has a much better performance than end-to-end qos. keywords: tcp spoofing, partial qos, end to end approach, performance comparison, wireless network, internet, ip based network. 1 introduction rapid growth of internet users today, bringing a significant impact to the network load including access network. a lot of users compete to get access to the internet network through isp. they are willing to pay higher to get a higher class of service. for that, isps need to implement qos [1] parameters, such as maximum bandwidth, which is in accordance with the level of service provided. setting different priority level needs qos implementation for full or partial qos parameters. [2] it requires appropriate bandwidth settings for the level of service provided by the operator. unfortunately the implementation of the end-to-end qos for the internet network is very difficult, because many networks interconnected without common qos standard and there is no single operator which controls the end-to-end internet network. the technology used, especially in the access network, does not always support the qos capabilities. for example in the case of wifi 802.11 which is widely used today as an access network, it does not allow the operator to provide the service class based on adequate bandwidth. through user requires a priority based on the level of service they have paid to the operator. although setting qos in 802.11 networks can also be done by implementing 802.11e standard, but these techniques require modification of the media access layer (mac layer) of wifi 802.11. [3] priority is generally given to the user in the form of different access speeds for each class of service or total bytes accessed by users each month. the 802.11 wifi network can not copyright © 2006-2015 by ccc publications 404 y. suryanto, r.r. nasser, r.f. sari distinguish between the speed per user because there is no mechanism in layer 2, which could limit the throughput per given time frame. while the remote access server (ras) which is commonly used as a wifi gateway, can only limit the bandwidth capacity that is used for a relatively long period of time, such as for a period of a day or a month. we propose a bandwidth setting at tcp level because it is independent to hardware of the access network being used by the customer. due to the fact that the tcp connection is end to end from the client to the server, it is not easy to implementing bandwidth control through tcp manipulation. therefore, we propose the use of spoofing techniques to overcome these problems. tcp connection will be intercepted on the gateway, which is spoofing server, so that a subset of the qos parameters can be added. this connection is then converted into a highspeed tcp in order to be utilize as quickly as possible the available capacity backbone networks that has been designed using traffic engineering process. [4] partial spoofing techniques to enable qos that we propose, can be applied not only on the wireless network, but also can be applied to the wireline network. this is in contrast to the application of the 802.11e edca that can only be applied to the wifi network by changing the 802.11 mac layer. [3] we simulate the application of the partial qos wired or wireless network by implementing spoofing techniques using ns-2.35, a tool commonly used to simulate tcp performance. [5,6] the performance of partial qos through spoofing approach will be compared with the performance of end-to-end approach. although partial qos can be applied also to the udp protocol, in this simulation we only apply to tcp protocol. therefore spoofing technique used is tcp spoofing. further in this paper, we will discuss the theoretical background in section ii, including the qos and needs [4], internet traffic flow model, tcp, highspeed tcp and throughput analysis. [7] the proposed solution is discussed in section iii. meanwhile the partial qos simulation scenarios through tcp spoofing approach will be presented in section iv. performance analysis of partial qos using tcp spoofing techniques will be presented in section v, and then the conclusion in section vi. 2 supporting theory 2.1 qos overview in communication using the internet network, network bandwidth generally has a maximum limit for distributing multimedia content over time. specific content in order to be delivered according to the priority level required for the implementation of qos. [8] qos implementation is possible, because even if the user wants all the applications to arrive at the destination as quickly as possible, however: 1. not all applications must be delivered within limited time duration. 2. only certain applications that require limited time. 3. only certain applications that sensitive to jitter. 4. not all content is very sensitive to packet lost. on ip-based networks, qos services provided by the operator can be measured according to the following performance criteria: a. throughput b. service availability c. packet loss performance comparison of tcp spoofing and end to end approach to enable partial qos on ip based network 405 d. delay e. variation of delay or jitter figure 1: qos parameter quality of service (qos) means to guarantee the commitment of those criteria according to the awarded contract. the given contract may be based on something that is explicitly mentioned, such as network access at the speed of 2 mbps. but it can also be something that is not explicitly presented, such as the multimedia package, 2 mbps office package, 2 mbps of easy browsing and so on. each group applications such as voice, video streaming and browsing has a different sensitivity to the qos parameters. some approaches have been proposed to improve qos.( [1][4], [9][12]) in practice, users are also difficult to determine how many time in a month they will use the voice applications, video streaming, browsing and download. therefore in practice, only a subset of qos parameters that often become criterion, generally only a maximum throughput that can be obtained by the customer when connected to the network. 2.2 internet application traffic flow model commonly most popular internet activities are based on client-server traffic model. list of the most popular activity on the internet, can be categorized in the followfng activities: 1. email : webmail like gmail or yahoo, and also offline email like outlook. 2. browsing : common website server located at data center. 3. search engine : google, yahoo, etc 4. social netwoking : facebook 5. video streaming : youtube 6. audio streaming : mp3 server 7. audio communication : skype 8. chatting : text message conversation such as ym and whatsapp. when explored further, such activity has a traffic pattern from the client to the server, not the pattern of the user to the user directly. that means most of the internet traffic in the network is a communication from the client to the server which usually located in a server farm. traffic patterns just as hub-spoke or star topology is depicted in figure 2 below. 406 y. suryanto, r.r. nasser, r.f. sari figure 2: traffic flow client-server model with data flow pattern from the server farm to the customer or vice versa, the traffic flow can optimally be intercepted on the access gateway. connections from the access gateway to the server farm usually connected through the internet network backhaul or backbone. the backbone capacity can be adjusted to follow design of the maximum traffic demand from customers to a server farm. with this pattern, partial qos can be applied in access network. 2.3 transmission control protocol (tcp) transmission control protocol (tcp) is a connection-oriented protocol which can be relied upon. it provides a transport service for applications that run in a network based on ip protocol. figure 3: tcp providing reliable data transfer to ftp over an ip network using ethernet tcp is a stream-oriented protocol, which means that the tcp protocol entities exchange data streams. every byte of data from the application layer protocol or session layer is placed in the memory buffer and then transmitted by the tcp transport protocol in the data unit. reliability of service tcp flow control is far more complex than udp, since udp only provides performance comparison of tcp spoofing and end to end approach to enable partial qos on ip based network 407 best effort service. to implement such a reliable service, tcp protocol uses a timer to ensure reliability and to synchronize between the two ends of the tcp connection. for most networks approximately 90% of current traffic uses this transport service. it is used by such applications as telnet, world wide web (www), ftp, electronic mail. the transport header contains a service access point which indicates the protocol which is being used (23=telnet, 25=mail, 69=tftp, 80=www). there are many kind of tcp: • tcp selective acknowledgments (sack) • tcp reno • tep vegas • hstcp (highspeed tcp) • xcp (explicit control protocol) some paper suggest the way to improve tcp performance ( [5], [13][15]), in this paper we propose a tcp spoofing technique that uses tcp normal in access network and forwards packets received by the spoofing gateway to the server destination with highspeed connections tcp (hstcp). [15] hstcp is an update of tcp that react better when using large windows on high bandwidth, high-latency networks. hstcp alters how the window is opened each round trip and closed on congestion events as a function of the absolute size of the window. [15] 2.4 average throughput and completion time performance comparison between partial qos through spoofing techniques and qos through end-to-end approach is done by looking at the average throughput and completion time for each user using both methods. completion time or transfer time is the time the last byte is received by the receiver subtracted by start time packet sent by the sender. meanwhile, in our ns2 simulation, we capture the throughput of th(i) on each tcpsink node for the unit time of ∆t(i). the example graph of th(i) for each ∆t(i) can be seen in figure 4. to make it easier to compare the throughput performance, we need the average throughput at time t(i) during the transfer process. this can be obtained using equation (1): avg_th (i) = σth (i) x∆t(i) σ∆t(i) (1) where avg_th(i) is average throughput from the starting transfer time to t(i). th(i) is throughput at ∆t(i). for the example for ∆t of 0.02 second, the average throughput for each time t(i) based on the equation (1), the average throughput can be depicted in figure 5: to determine the average throughput as a whole, from the start transfer time until the transfer is complete, also can use the following equation: avg_th = packet size x 8 time completion (2) where avg_th is the average throughput since the transmitter starts sending packets until the receiever received the entire packet. meanwhile, the maximum capacity of tcp link can be calculated from the equation (3) below: 408 y. suryanto, r.r. nasser, r.f. sari figure 4: throughput for each ∆t captured at tcpsink figure 5: the normalized average throughput performance comparison of tcp spoofing and end to end approach to enable partial qos on ip based network 409 maxs_th = window_size (i) x 8 x packet_size(i) 2xdelay(i) (3) where the maxs_th is the maximum throughput, window_size(i) is tcp window size at frame i, packet_size(i) is the tcp packet size at i and delay(i) is the link delay at frame i. 3 proposed solution in this paper we propose the use of tcp spoofing techniques to enable the partial qos. tcp spoofing is a method to intercept tcp. tcp connection which has been intercepted can be converted into another tcp protocol type and can also be added to a subset of the qos parameters. by utilizing the tcp spoofing, the partial qos can be implemented independent to the type of network used. we can implement the partial qos on wired or wireless networks. partial qos is an implementation technique of the subset qos parameters in a particular network segment. in this paper, a partial qos implementation proposed is done at the network access. with a partial qos through tcp spoofing approach, access technology currently installed can support the bandwidth control without the need to change the mac layer. this is different from the approach of the 802.11e solution [3, 12]. thus the tcp spoofing approach has better flexibility than the implementation of qos by changing the access network hardware. partial qos configuration using spoofing techniques can be seen in figure 6. figure 6: partial qos through spoofing technique spoofing gateway charged to intercept tcp connection from the user to the content server. in access network side from the user to the gateway spoofing, partial qos will be applied. this is done by adding parameters window size and congestion window to the corresponding tcp connection. then by spoofing gateway, packets will be forwarded to the destination server by first changing into highspeed tcp connection. [15] 4 simulation scenario this paper analyzed the completion time performance and average throughput between partial qos using tcp spoofing and end-to-end approach. simulations performed on wireless and 410 y. suryanto, r.r. nasser, r.f. sari wireline networks. in the wireline network simulation, we use a dumb bell topology with 3 user configurations, each with priority classes low, medium and high. node 3 is a spoofing gateway, while node 5, node 6 and node 7 are acted as content server as shown in figure 7 below. figure 7: topology of wireline simulation for simulations in wireless networks, we used a single 802.11 base station with three users, each configured with priority low, medium and high. node 1 is a spoofing gateway and node 0 act as a content server, as shown in figure 8 below. figure 8: topology of wireless simulation chosen to measure the performance is average throughput and completion time. this is selected because the qos subset used is bandwidth parameter. users will transfer the ftp file with file sizes m bytes to the server. comparative performance begins by comparing the average throughput and the transfer time for spoofing techniques and end-to-end approach for single user without setting the window size. this is done to determine the speedup ratio of spoofing techniques for various total delay conditions. validation is made by measuring the size of packets received by the server in tcp spoofing approach should be the same as the end-to-end approach. the next comparison is done for a single user with a window size limitation. this simulation is run to determine the effect of window size restrictions that will be used as a qos subset. the next simulation is the performance comparison of 3 users without the window size, which aims to measure the average throughput without additional subset of qos. the next scenario is to make a comparison of simulated single user and multiuser with window size under varied backbone delay conditions. this is done to measure the qos performance with partial spoofing approach compared with end-to-end approach. in the ns2 simulations we set a subset of qos parameters by adjusting the window size using tcl commands: performance comparison of tcp spoofing and end to end approach to enable partial qos on ip based network 411 set tcp0 [new agent/tcp] $tcp0 set packetsize_ $pktsize $tcp0 set window_ $window1 $tcp0 set cwnd_ $cwnd1 $ns attach-agent $n0 $tcp0 tcp connection from the spoofing gateway to the server is converted into tcp highspeed linux with the command: set tcp4 [new agent/tcp/linux] $ns at 0 "$tcp4 select_ca highspeed" $ns attach-agent $n3 $tcp4 packets received by the spoofing gateway will be forwarded to the server with the command: set relay1 [new application/ftp] $relay1 attach-agent $tcp3 set bw0 [$sink0 set bytes_] $relay1 producemore [expr $bw0/$pktsize] 5 result analysis when we do a file transfer from the user to the server through a wireline network with dumb bell topology as shown in figure 8, we can estimate the maximum transfer speed which can be achieved. the maximum throughput between the user and the server on end-to-end approach is the minimum value of tcp connection capacity and a media capacity of a segment which can be written as: th_maxs = min(maxstcp, minb_segment) (4) where are: th_maxs : maximum throughput maxs tcp : maximum capacity of tcp connection according to equation (3) min b_segment : minimum bandwidth of each segment of transmition media between user and server. meanwhile, the maximum throughput between the user and the server on tcp spoofing approach is the minimum throughput of the access and backbone section that can be written as: th_maxs = min(th_maxs_access, th_maxs_backbone) (5) where are: th_maxs access : maximum throughput of access network according to equation (4) th_maxs backbone : maximum thorughput of backbone network according to equation (4) the network in tcp spoofing approach if broken down into two segments, which means that each segment delay ≤ tcp connection end-to-end delay. based on equation (3), equation (4) and equation (5), in general it can be concluded that the th_maxs spoofing ≥ th_maxs end 412 y. suryanto, r.r. nasser, r.f. sari to end. this can be seen for example in the first simulation, where we compare the average throughput between tcp spoofing approach and end-to-end approach. for the case of access bandwidth set to 1 mbps, access delay 10 ms and backbone bandwidth 2 mbps, 1 mbps bandwidth server, backbone and server delay 95 ms, we get: th_maxs end to end = min (0.76 mbps, 1 mbps) = 0.76 mbps th_maxs spoofing = min(min(8 mbps, 1 mbps), min (0.84 mbps, 1 mbps)) = 0.84 mbps the result show that average speed df the end-to-end approach ≤ th_maxs end to end, and average speed of spoofing ≤ th_maxs spoofing approach. the comparison result of the average throughput of the two approaches for the case is consistent with the above analysis. this can be seen in figure 9. figure 9: the average throughput comparison between end to end approach and tcp spoofing approach in the simulation of tcp spoofing approach in wireline network topology as depicted in figure 7, thg backbone delay is varied, comparative relationshhp between the average throughput and total delay in the end-to-end approach and the spoofing approach can be seen in figure 10. when the capacity of tcp ≥ capacity of a media segment, according to equation (4) and (5), this shows that the average throughput is limited by the available bandwidth limit of transmission media. the average throughput between tcp spoofing approach and the end-to-end approach is not very different. but when tcp connection capacity ≤ segment capacity of transmission media, according to equation (4) and (5), this shows that the performance of spoofing approach is much better than the end-to-end approach. figuse 11 confirm that the analysis of the completion time of the end-to-end approach ≥ completion time of tcp spoofing approach. the completion time difference beeween the endto-end approach and tcp spoofing approach for 5 mb ftp packets to tht total delay is clearly according to equation (4) and (5). performance comparison of tcp spoofing and end to end approach to enable partial qos on ip based network 413 figure 10: average throughput to total delay figure 11: completion time difference to total delay 414 y. suryanto, r.r. nasser, r.f. sari as expected, the result shows that the average throughput of the end-to-end approach ≤ tcp spoofing approach. the difference in the completion time becomes quite large when tcp capacity ≤ available bandwidth of media segment. in this case, the total delay if 70 ms in which a tcp connection capacity approaching media segment bandwidth limitations, the completion time of the tcp spoofing is much faster than the end-to-end approach. when we choose to run the simulation in condition where tcp connection capacity < bandwidth of media segment, the completion time is more influenced by the capacity of the tcp connections. the comparison of the completion time to the ftp packet sizes, under the condition where bandwidth of media segment is still sufficient such as in the case when we select the delay of backbone and the server 105 ms and the access delay 50 ms. it is clear that completion time by spoofing approach is better than the completion time by the end approaches. this can be seen in figure 12 below. figure 12: comparison of completion time to the size of ftp file in the next simulation, the access and backbone segments bandwidth is set to 10 mbps so that the media connection does not become a bottleneck, because in a multi-user scenario with a qos subset we want to show a completion time for each user priority based on the tcp connection capacity to the delay. the implementation of qos subset for the low user priority is represented by the window size 5, the medium user priority with window size 10 and the high user priority with window size 15. for the condition of delay access 50 ms, delay backbone plus delay server is 55 ms, the average throughput graphs for each user priority using end-to-end approach can be seen in figure 13. it appears that the user with lower priority has the lowest average throughput and the user with high priority has the highest throughput. the duration of 5 mb file transfer time for each scenario with multi-user network delay for each priority users with end-to-end approach can be seen in table 1. we can see that the completion time of user with high priority < completion time of medium user < completion time of low priority user. the average throughput of each user priority with a media connection of 10 mbps using tcp spoofing approach can be seen in figure 14. the average throughput for each priority user using tcp spoofing approach, either for low priority, medium priority and high priority has a similar pattern with the end-to-end approach. however it can be seen from the graph, that the average throughput of tcp spoofing approach is higher than the end-to-end approach. performance comparison of tcp spoofing and end to end approach to enable partial qos on ip based network 415 figure 13: the average throughput for each user priority for end to end approach table 1: completion time each user priority for the end to end approach figure 14: the average throughput for each user priority for tcp spoofing approach 416 y. suryanto, r.r. nasser, r.f. sari the maximum throughput of spoofing approach can be obtained from equation (5), where in the access network using tcp with window size parameter and in backbone side using tcp highspeed. in the access side, for access delay of 10 ms, based on equation (5) we obtained that: th_maxs access side user low = min (2 mbps, 10 mbps) th_maxs access side user med= min (4 mbps, 10 mbps) th_maxs access side user high= min (6 mhps, 10 mbps) meanwhile, for the backbone delay of 10 ms and the server delay of 5 ms, where highspeed tcp protocol with window size 20 as the default is used, we obtained: th_maxs backbone = min (5.33 mbps, 10 mbps) it can be seen from table 2, that for the backbone delay of 10 ms and access delay of 10 ms, the transfer rate is determined by the settings window size of access side. this is because the backbone throughput > access throughput for each user priority. when the backbone and server delay of 55 ms, th_maxs backbone is 1.45 mbps. under these conditions, the backbone becomes a bottleneck so that the completion time is determined by the speed of transfer over the backbone side. as soon as the access delay changed to 50 ms, which means th_maxs access side of the low priority user = 0.4 mbps, th_maxs user side access med = 0.8 mbps and the user side access th_maxs high = 1.2 mbps, the speed of the transfer process is more determined by the network access. further completion time for all three spoofing scenarios can be seen in table 2. table 2: completion time each user priority for the spoofing approach overall, based on table 1 and table 2, the completion time of each level of user priority using spoofing approach is faster than the completion time of end-to-end approach. the comparison of the completion time between the spoofing approach and the end-to-end approach, i.e. for high priority users, to the total delay can be seen in figure 15. the simulation of the partial qos implementation in wireless networks is done using a topology as seen in figure 9, with 54 mbps wireless bandwidth share, access delay of about 10 ms, backbone bandwidth of 10 mbps and backbone delay 30 ms. the average throughput of each user priority with end-to-end approach can be seen in figure 16 below. using the same wireless network topology, partial qos implementation through tcp spoofing approach for each class of user priority, can be seen in figure 18. the interesting result for the user with the lowest priority is the average throughput flat when there is a user with a higher priority. but when users with higher priority reaches its optimum speed, the average user with low priority throughput also rise if the bandwidth is still available. thus the overall average throughput of partial qos through spoofing approach has a higher average throughput compared with end-to-end approach. performance comparison of tcp spoofing and end to end approach to enable partial qos on ip based network 417 figure 15: comparison of completion time for high priority user to total delay figure 16: the average throughput of each priority user for end to end approach on wireless network figure 17: average throughput each priority user for spoofing approach on wireless network 418 y. suryanto, r.r. nasser, r.f. sari to get an idea of how much faster the ftp transfer 5 mb in wireless networks for all priority users by spoofing approach compared with end-to-end approach, we created wireless simulation network with two scenarios. scenario 1 is the backbone delay of 10 ms and scenario 2 with the backbone delay of 30 ms delay. in table 3 it appears that, for the both scenario, spoofing approach is faster than end-to-end approach to factor > 1. speed up user with lower priority is higher than speed up user with a higher priority. it occurs because the forwarding mechanism by highspeed tcp has higher ratio for lower throughput of the access side. speed up can reach a significant number in certain cases. for example the speed up of low priority user for scenario 2 achieves 3.49 times. table 3: speedup of the spoofing and the end to end approach on wireless network 6 conclusion in general the average throughput of tcp spoofing approach is higher than the average throughput of end-to-end approach both in wireline network and the wireless network. this is in accordance with equation (4) and (5) of section analysis. tcp spoofing approach allows the implementation of a subset of the qos parameters applied partially in access networks. thus tcp spoofing approach is more flexible than the end-to-end approach to enable partial qos. tcp spoofing approach is also more flexible when compared to qos implementations that require mac layer changes such as 802.11e edca solution on wifi network. simulation results show that the tcp spoofing techniques allow implementation of partial qos in the access network with better performance under to various delay conditions compared to the implementation of end-to-end qos. the completion time speedup of the spoofing approach varies which value ≥ 1 compared with end-to-end approach. for certain cases, completion time speed up value is significant, more than 3 times. however, in the spoofing approach we need to consider the buffer availability of the spoofing gateway. this is especially the case where the maximum throughput of access network side much higher than the throughput of the backbone side. in this condition, the spoofing gateway needs to buffer the received data from the access side because the forwarding speed is less then the 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[15] d. lopez-pacheco and c. pham (2004); performance comparison of tcp, hstcp and xcp in high-speed, highly variable-bandwidth environments, proc. of the ieee 3rd int. conf. on network protocols (icnp 2004). berlin, 2004. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 3, month: june, year: 2021 article number: 4240, https://doi.org/10.15837/ijccc.2021.3.4240 ccc publications decision tools regarding time constraints violation in manufacturing workshops n. jerbi, s. collart-dutilleul nabil jerbi* université de sousse, ecole supérieure des sciences et de la technologie de hammam sousse, 4011, h. sousse, tunisie ; *corresponding author: nabil.jerbi@essths.u-sousse.tn simon collart-dutilleul cosys/estas, university gustave eiffel, 59666 villeneuve d’ascq, france ; simon.collart-dutilleul@univ-eiffel.fr abstract this paper is dedicated to the study of constraints violation in manufacturing workshops with time constraints. in such systems, every operation duration is included between minimal and maximal values. p-time petri nets are used for modeling. a new theorem is introduced, constituting a decision tool about the occurrence of constraints violation at the level of a synchronization transition when various types of time disturbances occur. it shows the robustness properties of a manufacturing system on a range that may include delay and advance disturbances. the theoretical result is illustrated step by step on a given workshop. two other lemmas are elaborated contributing to the study of the constraints violation problem. the final goal is to generalize the robustness property towards simultaneous occurrence of two delays at two points of the system, each having its own robustness range. keywords: manufacturing, maximum time constraints, time disturbance, constraints violation, robustness. 1 introduction a system is said critical when its functioning failures may produce catastrophic consequences [5]. a particular class of dysfunction may occur when state duration constraints, being not fulfilled lead to non-acceptable situations. in this case, systems are said time critical. time critical systems, including minimum and maximum constraints for operation duration, correspond to various industrial processes [2, 20, 24]. from a methodological point of view, time requirements formulations correspond to various kinds of time critical systems [8, 9]. moreover, synchronization problems in case of crisis use to be critical in transport systems [7]. maximal bound for an operation execution requires model and approaches [4, 6, 16]. several works mainly focusing on robust control in the state of the art https://doi.org/10.15837/ijccc.2021.3.4240 2 concerning manufacturing [1, 3, 10, 11, 15, 22]. the presented works keeping the manufacturing area as an illustrative application provides properties that impact a wider field of application. the two following sections details fundamentals definitions and a classical modeling approach concerning manufacturing production including minimum and maximum operation duration. the third section considers more original assumption and studies time related decisions while mixing delay and advance disturbances in the same system. the section demonstrates the properties and illustrates them on a manufacturing example. the last section discusses the quality of the results highlighting the need of a formalism extension, addressing the analysis of more complex structures like time constraints ensuing from tele-operation in manufacturing and transport. 2 p-time petri net upper bound duration for state durations induces a particular structure of the mathematical problem to be tackled. a dedicated modeling tool, providing useful structural and behavioral properties was presented by [16]. the following definition is used as a basis of a time constrained workshop functional decomposition, which is presented in the next section. the formal definition of a p-time petri net is given by a pair < r; is >, where [16]: • r is a marked petri net, • is : p −→ (q+) × (q+ ∪{+∞}) pi −→ isi = [ai,bi] with 0 ≤ ai ≤ bi. isi defines the static interval of staying time of a mark in the place pi belonging to the set of places p (q+ is the set of positive rational numbers). a mark in the place pi is taken into account in transition validation when it has stayed in pi at least a duration ai and no longer than bi. after the duration bi the token will be dead. the synchronization mechanism is the reason of token death. a dead token means a noncompliance with the required specifications. the semantic specificity of p-time petri nets was studied by boyer and roux [4]. let us denote by: – t the set of transitions, – toi (resp. oti) the output (resp. the input) places of the transition ti, – poi (resp. opi) the output (resp. the input) transitions of the place pi, – qie the expected sojourn time of the token in the place pi, – qi the effective sojourn time of the token in the place pi, – stie(n) the expected nnd firing instant of the transition ti. – sti(n) the effective nnd firing instant of the transition ti. 3 functional decomposition the current section details a functional decomposition, which is used to analytically express the local temporal margins. the functioning assumptions considers that the workshop is running in a repetitive mode, following a mono-periodic cycle. a workshop in repetitive functioning mode is modeled by a strongly connected event graph (sceg) [16, 18]. definition 1. an event graph (eg) is a particular petri net in which each place has exactly one input transition and one output transition. https://doi.org/10.15837/ijccc.2021.3.4240 3 definition 2. an eg is a sceg if and only if it exists an oriented path connecting each node to another. performances of a sceg running in mono-periodic functioning mode are proved to be the same as when using the k-periodic functioning [16, 18]. consequently, a mono-periodic functioning is used in order to decrease the complexity of the supervisory problem [6, 18]. in this case, for each transition t, ste(n + 1) = ste(n) + π0 where π0 is the period of the periodic functioning of the given discrete event system. in this paper, the scheduling task is supposed to be done. therefore, the sceg corresponding to the system is provided. moreover, the setting of transitions firing instants is fixed too. then, constraints violation will be studied in the following. the problem of time disturbances observability is not considered. it was studied by jerbi et al. [12, 14]. as the sojourn times in places have not the same functional signification when they are included in the sequential process of a product or when they are associated to a free resource, a decomposition of the p-time petri net model into four sets is made. the assumption of multi-product job-shops without assembling tasks is used: • ru is the set of places representing the used machines, • rn corresponds to the set of places representing the free machines which are shared between manufacturing circuits, • transc is the set of places representing the loaded transport resources, • transn c is the set of places representing the unloaded transport resources (or the interconnected buffers). figure 1 shows a p-time petri net (g) modeling a system composed by two sequential processes go1 and go2 with two shared machines (m1,m2), where: – ru = {p2,p4,p11,p13,p15}, – rn = {p6,p7,p8,p9}, – transc = {p1,p3,p10,p12,p14}, – transn c = {p5,p16}, – go1 = (t12,p10, t6,p11, t7,p12, t8,p13, t9,p14, t10,p15, t11), – go2 = (t5,p1, t1,p2, t2,p3, t3,p4, t4). the intervals (isi) and the expected staying times (qie) associated to the places (pi) are: is1 = [30, 50],q1e = 38,is2 = [5, 12],q2e = 7,is3 = [10, 20],q3e = 15,is4 = [5, 20],q4e = 10,is5 = [1, +∞],q5e = 10,is6 = [0, +∞],q6e = 5,is7 = [0, +∞],q7e = 8,is8 = [8, +∞],q8e = 13,is9 = [8, +∞],q9e = 15,is10 = [5, 15],q10e = 12,is11 = [15, 20],q11e = 17,is12 = [3, 7],q12e = 6,is13 = [2, 20],q13e = 5,is14 = [2, 7],q14e = 5,is15 = [15, 20],q15e = 16,is16 = [1, +∞] and q16e = 19. the initial expected firing instants of each transition are: st1e(1) = 15,st2e(1) = 22,st3e(1) = 37,st4e(1) = 7,st5e(1) = 17,st6e(1) = 12,st7e(1) = 29,st8e(1) = 35,st9e(1) = 0,st10e(1) = 5,st11e(1) = 21 and st12e(1) = 0. the repetitive functioning mode is characterized by the period π0 = 40. see figure 2. definition 3. a mono-synchronized subpath is a path containing one and only one synchronization transition which is its last node [11]. definition 4. an elementary mono-synchronized subpath is a mono-synchronized subpath beginning with a place p such as op is a synchronization transition [11]. in figure 1, there are eight elementary mono-synchronized subpaths constituting a partition of g: – lp1 = (p13,t9,p14, t10,p15, t11,p16, t12,p10, t6), https://doi.org/10.15837/ijccc.2021.3.4240 4 figure 1: functional decomposition – lp2 = (p13, t9,p9, t1), – lp3 = (p2, t2,p3, t3), – lp4 = (p2, t2,p8, t8), – lp5 = (p4, t4,p5, t5,p1, t1), – lp6 = (p4, t4,p6, t6), – lp7 = (p11, t7,p7, t3), – lp8 = (p11, t7,p12, t8). https://doi.org/10.15837/ijccc.2021.3.4240 5 figure 2: expected firing instants 4 decision tools for constraints violation the following section provide a set of properties, which are useful for decision making. depending on the nature of the time disturbance, various kinds of policy may be used, like shifting firing instants of a set of controllable transitions, trying to perform a one line scheduling because the current scheduling cannot be maintained without constraints violations or doing nothing because the non-occurrence of time constraints violation can be proved. the main results of this section provide proofs that the current scheduling can be preserved without constraints violation, providing various types of robustness proofs. 4.1 theorem definition 5. let us consider a discrete event system (des) and g the associated petri net model. let us call b(g) the behavior of g corresponding to the trajectory of states successively reached. let c(b(g)) be the schedule of conditions established on the system behavior b(g). c(b(g)) is materialized by a series of constraints which must be checked by b(g). a non-respect of b(g) corresponds to a violation of c(b(g)) [11]. definition 6. it is said that a des has a passive robustness on [∆min, ∆max] in a node n if the https://doi.org/10.15837/ijccc.2021.3.4240 6 occurrence of a disturbance δ ∈ [∆min, ∆max] at the node n does not involve a violation of c(b(g)) [11]. theorem 1. let us consider: – ts a synchronization transition, – ots = { pi,pj/(poi = poj = ts) ∧ (pi ∈ transc ) ∧ (pj ∈ rn ) } , – respi the residue in pi of a time disturbance, – respj the residue in pj of a time disturbance. the temporal residues respi and respj do not generate constraints violation in ots if and only if: respj −respi ≤ (bi −qie) + (qje −aj ). proof. let us consider the synchronization transition ts of figure 3. figure 3: study of constraints violation at the synchronization transition ts direction 1, hypothesis: the temporal residues do not generate constraints violation in ots. without disturbance: stse(n) = stie(n) + qie = stje(n) + qje sts(n) = stie(n) + qi + respi sts(n) = stje(n) + qj + respj stie(n) + qi + respi = stje(n) + qj + respj (stie(n) + qie) + (qi −qie + respi ) = (stj (n) + qje) + (qj −qje + respj ) respj −respi = (qi −qie) − (qj −qje) if there is no violation of constraints, then: qi ≤ bi qj ≥ aj qi −qj ≤ bi −aj respj −respi ≤ (bi −qie) + (qje −aj ) conversely, hypothesis: respj −respi ≤ (bi −qie) + (qje −aj ). • if both residues have the same sign, three cases are distinguished: https://doi.org/10.15837/ijccc.2021.3.4240 7 – respj −respi < 0, – 0 ≤ respj −respi ≤ (qje −aj ), – (qje −aj ) < respj −respi ≤ (bi −qie) + (qje −aj ). case 1: respj −respi < 0. if both residues are positive, respj < respi means that the token in pj keeps waiting until the sojourn time in pi is qi = qie. this wait does not involve a token death in pj since this place represents a free machine (bj = +∞). if both residues are negative, respj < respi means that the temporal advance in pj is strictly less than that in pi. this advance does not cause a token death in pj since this place represents a free machine. the token in pi sojourns as it is expected qi = qie since the token in pj is already available. case 2: 0 ≤ respj −respi ≤ (qje −aj ). one has: sti = stie + respi stj = stje + respj stse = stie + qie = stje + qje stie −stje = qje −qie sts = sti + qi = stie + respi + qi let us show that the firing of the transition ts can take place when qi = qie. in other words, let us prove when qi = qie the token in the place pj is available (qj ≥ aj ). we have: sts = stie + respi + qie (qi = qie) qj = sts−stj = sts−stje −respj qj = stie + respi + qie −stje −respj qj = (stie −stje) + qie + (respi −respj ) qj = (qje −qie) + qie − (respj −respi ) qj ≥ qje − (qje −aj ) qj ≥ aj therefore, the transition ts is fired when qi = qie and there is no violation of constraints. case 3: (qje −aj ) < respj −respi ≤ (bi −qie) + (qje −aj ). let us show if the firing of the transition ts occurs at qj = aj, the effective sojourn time of the token in the place pi satisfies: qie < qi ≤ bi. one obtains: sts = stj + qj = stje + respj + qj sts = stje + respj + aj (qj = aj ) qi = sts−sti = sts−stie −respi qi = stje + respj + aj −stie −respi qi = (stje −stie) + aj + (respj −respi ) qi = (qie −qje) + aj + (respj −respi ) (qje −aj ) + (qie −qje) + aj < qi ≤ (qie −qje) + aj + (bi −qie) − (aj −qje) qie < qi ≤ bi hence, there is no violation of constraints since the transition ts is fired when qj = aj and qie < qi ≤ bi. https://doi.org/10.15837/ijccc.2021.3.4240 8 interpretation. let us take the worst case: respj −respi = (bi −qie) + (qje −aj ). if both residues are positive, the token in pj arrives late compared to that in pi. part of this delay is compensated by (qje −aj ). instead of staying qie, the token in pj sojourns only qj = aj. indeed, the token in the place pj, representing a waiting machine, is available as soon as qj = aj, which makes it possible to reject part of the delay disturbance equal to (qje −aj ). consequently, it remains a delay difference equal to (bi −qie). in the place pi, the token is blocked for the time (bi −qie) until the token in pj is available. the transition ts is fired when qi = bi and qj = aj which is the admissible limit case. thus, there is no token death. if both residues are negative, the token in pi arrives in advance compared to that in pj. part of this advance is compensated by the delay (bi − qie). instead of staying qie, the token in pi sojourns qi = bi. the token in the place pj is available as soon as qj = aj which offers an advance (aj − qje) making it possible to avoid a token death in pi. then, the transition ts is fired when qi = bi and qj = aj. • if one of the residues is positive and the other is negative, we distinguish two cases: respj − respi < 0 or 0 ≤ respj −respi ≤ (bi −qie) + (qje −aj ). case 1: respj −respi < 0. necessarily, we have a delay in the place pi and an advance in the place pj. the token in pj waits for the one in pi until qi = qie. this does not cause any constraints violation since pj ∈ rn . case 2: 0 ≤ respj −respi ≤ (bi −qie) + (qje −aj ). forcibly, we have an advance in the place pi and a delay in the place pj. this amounts to the study of the case where the residue in pi is zero and the residue in pj is a delay equal to (respj −respi ). this is a similar case to the occurrence of two delay residues, already studied. since 0 ≤ respj −respi ≤ (bi −qie) + (qje −aj ), there is no violation of constraints. 4.2 example let us take the synchronization t6 of figure 4. it comes: ot6 = {p10 ∈ transc,p6 ∈ rn} and (b10 −q10e) + (q6e −a6) = (15 − 12) + (5 − 0) = 8. figure 4: study of constraints violation at the synchronization transition t6 • case of delay residues. let us suppose that the transition t4 is fired with a delay equal to 13 and the transition t12 with a delay equal to 5. in other words, resp6 = 13 and resp10 = 5 (resp6 −resp10 = 8). we have: st6e(2) = st4e(2) + q6e = st12e(2) + q10e = 52 st4(2) = st4e(2) + resp6 = 60 https://doi.org/10.15837/ijccc.2021.3.4240 9 st12(2) = st12e(2) + resp10 = 45 since a6 = 0, the token is available as soon as it enters the place p6. the maximum sojourn time in the place p10 is q10 = b10 = 15. in order not to have a token death in p10, the transition t6 must be fired as: st6(2) = st12(2) + b10 = 45 + 15 = 60, which coincides well with the firing instant of the transition t4 and the availability of the token in the place p6. from figure 5, it is clear that a residue difference strictly greater than 8 automatically generates a token death in p10. there is no longer an overlap in the token availability intervals (static intervals) relating to the two places p6 and p10. figure 5: case of delay residues • case of advance residues. let us assume that the transition t4 is fired with an advance equal to −6 and the transition t12 with an advance equal to −14, which means resp6 = −6 and resp10 = −14 (resp6 −resp10 = 8). we get: st4(2) = st4e(2) + resp6 = 47 − 6 = 41 st12(2) = st12e(2) + resp10 = 40 − 14 = 26. the token in p6 is available from the instant st4(2) = 41. to avoid a token death in p10, the transition t6 must be fired as: st6(2) = st12(2) + b10 = 26 + 15 = 41, which coincides well with the https://doi.org/10.15837/ijccc.2021.3.4240 10 figure 6: case of advance residues firing instant of the transition t4 and the availability of the token in place p6. from figure 6, it is also clear that a residue difference strictly greater than 8 automatically generates a token death in p10. • case of delay and advance residues. if the transition t4 is fired with a delay equal to 5 and the transition t12 with an advance equal to −3, which means resp6 = 5 and resp10 = −3 (resp6 −resp10 = 8), then: st4(2) = st4e(2) + resp6 = 47 + 5 = 52 st12(2) = st12e(2) + resp10 = 40 − 3 = 37 the token in p6 is available from the instant st4(2) = 52. to avoid a token death in p10, the transition t6 must be fired as: st6(2) = st12(2) + b10 = 37 + 15 = 52, which coincides well with the firing instant of the transition t4 and the availability of the token in place p6. from figure 7, it is also clear that a residue difference strictly greater than 8 automatically generates a token death in p10. figure 7: case of delay and advance residues 4.3 lemmas lemma 2. let us consider: https://doi.org/10.15837/ijccc.2021.3.4240 11 – ts a synchronization transition, – ots = {pi,pj/(pi ∈ transc ) ∧ (pj ∈ rn )}, – (pk ∈ p) ∧ (opk = ts), – (ti ∈ t) ∧ (ti =o pi), – (tj ∈ t) ∧ (tj =o pj ), – δ1 (resp. δ2) a delay time disturbance at the input of the place pi (resp. pj), – res1pk (resp. res2pk ) the residue in pk of the disturbance δ1 (resp. δ2) when it occurs alone, – respk the residue in pk of the disturbances δ1 and δ2 when they occur simultaneously. if the occurrence of δ2 alone does not cause a token death in ots then the simultaneous occurrence of δ1 and δ2 does not cause a token death in ots and respk = max(res1pk,res2pk ). proof. let us consider the synchronization transition ts of figure 8. figure 8: study of constraints violation when two delay time disturbances occur simultaneously the occurrence of δ1 alone does not cause a token death in ots since the place pj ∈ rn represents a free machine (bj = +∞). the disturbance δ1 is totally transmitted in pk : respk = δ1. the place pj allows to compensate or reject part of the delay disturbance equal to (qje−aj ). when the disturbance δ2 occurs alone, it comes: res2pk = max(0,δ2 − (qje −aj )). case 1: δ1 ≥ δ2. the token in pj keeps waiting until the sojourn time in pi is equal to qie without any violation of constraints and we have: respk = res1pk = δ1 = max(res1pk,res2pk ). case 2: δ2 ≥ δ1 + (qje −aj ). by hypothesis, the occurrence of δ2 alone does not cause a token death in ots. according to the theorem 1, it comes: δ2 ≤ (bi −qie) + (qje −aj ) δ1 + (qje −aj ) ≤ δ2 ≤ (bi −qie) + (qje −aj ) (qje −aj ) ≤ δ2 −δ1 ≤ (bi −qie) + (qje −aj ) −δ1 (qje −aj ) ≤ δ2 −δ1 ≤ (bi −qie) + (qje −aj ) according to the theorem 1, there is no token death. this corresponds to the third case of the theorem proof in the case where the disturbances have the same sign. the token in the place pi keeps waiting (qi ≥ qie) until the token in the place pj is available: qj = aj. then, the firing of the transition https://doi.org/10.15837/ijccc.2021.3.4240 12 ts takes place when qj = aj and the sojourn time of the token in the place pi satisfies: qie ≤ qi ≤ bi. one gets: sts = stje + δ2 + aj (qj = aj ) respk = sts−stse = δ2 + aj + (stje −stse) = δ2 − (qje −aj ) respk = δ2 − (qje −aj ) = res2pk = max(res1pk,res2pk ) (δ2 − (qje −aj ) ≥ δ1) case 3: δ1 < δ2 < δ1 + (qje −aj ). one has: 0 < δ2 −δ1 < (qje −aj ) 0 < δ2 −δ1 < (bi −qie) + (qje −aj ) according to the theorem 1, there is no token death. this corresponds to the second case of the theorem proof in the case where the disturbances have the same sign. the token in the place pj remains waiting until qi = qie. accordingly, one obtains: sts = stie + δ1 + qie (qi = qie) respk = sts−stse = δ1 + qie + (stie −stse ) = δ1 + qie −qie = δ1 respk = δ1 = res1pk = max(res1pk,res2pk ) (δ1 > δ2 − (qje −aj )) lemma 3. let: – δ1 and δ2 two delay time disturbances respectively at the transitions t1 and t2, – [∆1min, ∆1max] and [∆2min, ∆2max] the passive robustness intervals respectively at t1 and t2. if δ1 ∈ [∆1min, ∆1max] and δ2 ∈ [∆2min, ∆2max] then the simultaneous occurrence of δ1 and δ2 does not involve any constraints violation. proof. let us denote by: – ts a synchronization transition, – ots = {pi,pj/(pi ∈ transc ) ∧ (pj ∈ rn )}, – res1pi (resp. res1pj ) the residue in pi (resp. pj) of the disturbance δ1 when it occurs alone, – res2pi (resp. res2pj ) the residue in pi (resp. pj) of the disturbance δ2 when it occurs alone, – respi (resp. respj ) the residue in pi (resp. pj) of the disturbances δ1 and δ2 when they occur simultaneously. lemma 2 gives: respi = max(res1pi,res2pi ) and respj = max(res1pj ,res2pj ). four cases are possible, which are: – (respi,respj ) = (res1pi,res1pj ), – (respi,respj ) = (res1pi,res2pj ), – (respi,respj ) = (res2pi,res1pj ), – (respi,respj ) = (res2pi,res2pj ). https://doi.org/10.15837/ijccc.2021.3.4240 13 case 1: (respi,respj ) = (res1pi,res1pj ). respi and respj are the residues of δ1 only. there is no token death since δ1 ∈ [∆1min, ∆1max]. case 2: (respi,respj ) = (res1pi,res2pj ). knowing δ2 ∈ [∆2min, ∆2max], according to the theorem 1 we have: res2pj −res2pi ≤ (bi −qie) + (qje −aj ) respi = max(res1pi,res2pi ) = res1pi −→ res1pi ≥ res2pi −→ res2pj −res1pi ≤ res2pj −res2pi ≤ (bi −qie) + (qje −aj ) according to the theorem 1, there is no constraints violation. case 3: (respi,respj ) = (res2pi,res1pj ). knowing δ1 ∈ [∆1min, ∆1max], according to the theorem 1 we have: res1pj −res1pi ≤ (bi −qie) + (qje −aj ) respi = max(res1pi,res2pi ) = res2pi −→ res2pi ≥ res1pi −→ res1pj −res2pi ≤ res1pj −res1pi ≤ (bi −qie) + (qje −aj ) according to the theorem 1, there is no constraints violation. case 4: (respi,respj ) = (res2pi,res2pj ). respi and respj are the residues of δ2 only. there is no constraints violation since δ2 ∈ [∆2min, ∆2max]. finally, this section presented an original kind of robustness towards a disturbance, which may be an advance or a delay. some formal definitions are provided in order to build a proved way of computing the corresponding robustness values. two lemmas provide first results towards concurrent time disturbances occurring on the same time. the last section of this paper stands the quality of the results and discuss the limit of the proposed approach, targeting new industrial applications. 5 conclusion the paper has presented the theoretical background in order to build an original contribution to the state of the art in the area of manufacturing processes where every operation time has minimal and maximal values. the proposed study considers the effect of delays and advances all together in the same manufacturing process. the first step of the construction presents the classical modeling tool dedicated to duration constrained discrete event systems: the p-time petri nets. then, they are mapped on the dedicated historical functional decomposition of a manufacturing process. using this framework provided by the state of the art, the fourth section presents new behavioral properties. it presented the mixed analysis of disturbance residues at the level of synchronization transitions. after building some proofs and providing illustrations and interpretations, two final properties are formalized. the first one is the robustness properties of a manufacturing system on a range that may include delay and advance. the second one is the generalization of the robustness property towards simultaneous occurrence of two delay time disturbances at two points of the system each having its own robustness range. theoretical results of this paper are useful for alarm triggering at a plant supervision level. moreover, improving the quality of alarm filtering like presented in [13, 20], by enriching the class of disturbances from which the system robustness can be proved, is clearly a step forward. it is an evidence that increasing the range of the robustness properties will decrease the quantity of false alarms. the presented new results of this paper consider the possibility of a mix between advances and delays in running manufacturing processes, by getting deeper in the scenario understanding of a constraint violation clearly allows a better diagnosis. however, the historical functional decomposition only considers a local knowledge, but does not integrate a possible distant knowledge that is consulted through a communication network producing transmission delays. taking advantages of distributed information centers is a modern proposition https://doi.org/10.15837/ijccc.2021.3.4240 14 used in the digital twin concepts and in the big data scientific area [17, 23]. a new functional decomposition will have to introduce new classes in order to address the specific needs required by industry 4.0, separating concepts of knowledge being immediately available and knowledge that have to be processed a given amount of time before being available. this new decomposition has to take into account that in particular nodes of the system, the process and resources are synchronized to respect minimum and maximum operations time. the enriched concept of the new functional decomposition may allow addressing targeted industrial applications where the time is particularly critical, like teleoperated trains [19]. references [1] amari, s.; demongodin, i.; loiseau, j. j.; martinez, c. 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(2021). decision tools regarding time constraints violation in manufacturing workshops, international journal of computers communications & control, 16(3), 4240, 2021. https://doi.org/10.15837/ijccc.2021.3.4240 introduction p-time petri net functional decomposition decision tools for constraints violation theorem example lemmas conclusion int j comput commun, issn 1841-9836 8(6):869-877, december, 2013. dimensionality reduction and generation of human motion s. qu, l.d. wu, y.m. wei, r.h. yu shi qu* air force early warning academy no.288, huangpu road, wuhan, 430019, china *corresponding author: qushi@nudt.edu.cn ling-da wu, rong-huan yu school of equipment, beijing, 101400, china ying-mei wei national university of defense technology changsha, 410073, china abstract: to reuse existing motion data and generate new motion, a method of human motion nonlinear dimensionality reduction and generation, based on fast adaptive scaled gaussian process latent variable models, is proposed. through statistical learning on motion data, the motion data are mapped from high-dimensional observation space to low-dimensional latent space to implement nonlinear dimensionality reduction, and probability distributing of posture space which measures the nature of posture is obtained. the posture which meets constraints and has maximal probability can be computed as the solution of inverse kinematics. this method can avoid cockamamie computation and posture distortion existing in traditional inverse kinematics. the experiments show that our method has higher convergence velocity and precision and extends editing range of motion by adapting motion editing direction. keywords: dimensionality reduction, gaussian process, kernel function, motion generation. 1 introduction as the development of motion capture technology, it became reality to generate virtual character motion with motion capture data. this method, easy to implement and generating vivid motion, has been applied to movie, advertisement, game, military affairs and so on. as complexity and multiformity of motion, existing motion capture data are not enough to satisfy application in practice, it is need to reuse existing motion capture data to generate new motion. motion capture data, often high dimensional, require to be reduced dimension before analysis and research on them. according to mapping relation between high dimensional data and low dimensional data, dimensionality reduction techniques can be classified to linear or nonlinear. principal component analysis (pca) [1] is a linear dimensionality reduction technique in common use, which projects the high dimensional data along principal axis chosen by computing variance. through improving pca, many correlative techniques are proposed, which have better capability than pca in specific application field. the probabilistic principal component analysis (ppca) [2], introducing probability model into pca, can complete dimensionality reduction and density estimate by modeling likelihood. in ppca, which is linear too, high dimensional data and low dimensional data obey probabilistic distribution with noise. combining pca and kernel method [3], the kernel principal component analysis (kpca) [4] is proposed. the basic idea of kpca is that map the high dimensional data to a feature space by nonlinear method, and do principal component analysis in it. kpca, as a nonlinear method, has the excellence of kernel method that establishes connection between high dimensional data and low dimensional data with kernel function not requires determining the specific relation between them. gaussian process copyright © 2006-2013 by ccc publications 870 s. qu, l.d. wu, y.m. wei, r.h. yu latent variable models (gplvm) [5] [6] is another effective nonlinear dimensionality reduction technique, which completes dimensionality reduction and density estimate using gaussian process regress. the gplvm, different from ppca, does not estimate the parameters of mapping but margin them. from the viewpoint of bayesian, gplvm maximizes the posterior probability to reduce dimensionality given prior probability. the literature [7] implemented dimensionality reduction and generation of human motion based on scaled gaussian process latent variable models (sgplvm), in which there are two shortages, the one is slow convergence, and the other is small editing range of motion. in this paper, we improve on the work to propose fast adaptive scaled gaussian process latent variable models (fasgplvm), and implement dimensionality reduction and generation of human motion based on it. our method has higher convergence velocity and precision and extends editing range of motion by adapting motion editing direction. 2 overview 2.1 human skeleton and motion depiction human body consists of skeleton, muscle, skin and so on, and the skeleton determines human posture from the angle of motion. in this paper, we construct a simplified human skeleton as shown in figure 1, which consists of 22 bones. the number in parentheses denotes the degree of freedom of corresponding bone, amounting to 50. figure 1: simplified human skeleton the degree of freedom denotes translation or rotation direction of bone. in the simplified human skeleton, the root has 3 translation directions and 3 rotation directions, the rest bones have 0 to 3 rotation directions separately. in the process of motion, human posture and location are determined by these degrees of freedom, concretely, the translation of root determines human location and the rotations of all bones determine human posture. so, a feature vector y can be constructed to represent human motion posture, which consists of all rotation degrees of freedom. in this paper, y is a column vector and y ∈ r47. a motion, containing n frames, can be represented as a matrix y = [y1, · · · ,yt, · · · ,yn]t , where t(t = 1,2, · · · ,n) is the time index and yt is the feature vector correspond to the posture at time t. 2.2 dimensionality reduction and generation of human motion the framework of dimensionality reduction and generation of human motion based on fasgplvm, shown as figure 2, contains two stages, which are training model stage and generating dimensionality reduction and generation of human motion 871 posture stage. in training model stage, train fasgplvm using sample motion data to obtain optimized latent trajectory and model parameters. at the same time, the probability distribution of sample motion is also obtained. in generating posture stage, synthesize the posture which has the maximal probability and satisfies constraints on the end effectors given by user, and update the active set which is a subset of sample motion data. the active set, instead of all sample motion data, are used to train model and synthesize new posture in order to improve computing efficiency. figure 2: framework of dimensionality reduction and generation of human motion 3 fast adaptive scaled gaussian process latent variable models our work is based on the literature [7], in which sgplvm is proposed by introducing scaled genes to gplvm. in this paper, fasgplvm is proposed, based on sgplvm, to implement dimensionality reduction and generation of human motion. similar to sgplvm, scaled genes ω and active set are also included in fasgplvm. the scaled genes ω = diag(ω1,ω2, · · · ,ωd) denote relatively important degree of each entry of feature vector y, ωy is used to train model instead of y. the active set is a subset of sample motion data, which is used to improve computing efficiency. kernel function is the core of model, which has a great influence on training and application of model. with improvement on kernel function of sgplvm, the summation of radius base function (rbf) and vectorial angle cosine is used as kernel function in fasgplvm. k(xi,xj) = αexp(− γ 2 (xi − xj)t (xi − xj)) + ηexp( xi · xj |xi||xj| ) + δi,jβ −1 (1) where xi is the latent variable correspond to yi, the parameters α and η mean how correlated pairs of points are in general, γ means the spread of kernel function, β−1 is variance of noise and δi,j is kronecker delta function which is 1 when i is equal to j, and 0 otherwise. kernel function measures how similar two points in latent space are, concretely, a larger k(xi,xj) denotes that xi and xj are more similar. in kernel function of literature [7], similarity between two points is measured by euclid distance, and the larger euclid distance is, the smaller similarity is. in fasgplvm, vectorial angle cosine is introduced to associate similarity with position of point, and the similarity degrees of two pairs of points who have the same euclid distance are not always equal. as shown in figure 3,x1,x2,x3,x4,x5 and x6 are points in latent space which 872 s. qu, l.d. wu, y.m. wei, r.h. yu satisfy (x1 − x2)t (x1 − x2) = (x3 − x4)t (x3 − x4) = (x5 − x6)t (x5 − x6), we have the result k(x1,x2) = k(x3,x4) = k(x5,x6) according to kernel function of literature [7]. according to kernel function of this paper, we have the result k(x1,x2) < k(x3,x4),k(x1,x2) < k(x5,x6) because of θ > λ,θ > φ, which is more reasonable. x3x4 results from rotating x1x2 which is perpendicular to center axis (the line connecting origin and center of x1x2), and x1,x2 have the smallest similarity. when λ = 0, x3x4 and center axis coincide. in this case, x3 = cx4(c is a constant) and the similarity between x3 and x4 reaches maximum. x5x6 results from translating x1x2, the difference between x5 and x6 is reduced because of ||x5|| > ||x1|| and ||x6|| > ||x2||. when x5 and x6 apart infinitely from origin, the similarity between x5 and x6 reaches maximum since they can be treated as one point. figure 3: influence of vectorial angle cosine on similarity measure another important improvement on sgplvm is to introduce dynamic active set. in the motion editing process, the active set of fasgplvm change, the model parameters α,β,γ,η,ωk and k covariance matrix change accordingly, which makes fasgplvm adaptive to motion editing. in sgplvm, the new posture is restricted to be similar to sample motion at full steam in order to ensure it vivid. however, the extent of motion editing can not be very large, otherwise, the new posture will be anamorphic. in fasgplvm, it can enlarge the extent of motion editing and ensure new posture vivid by changing active set. the third improvement on sgplvm is that convergence judgment, instead of fixed iteration time in sgplvm, is introduced to training algorithm. the algorithm of learning sample motion data based on fasgplvm can be described as follows: step 1. subtract the mean of motion data. the mean vector ȳ is computed by ȳ = 1 n n∑ 1 yi (2) then, subtract the mean from each line of motion data matrix y . ȳ = [ȳ1, ȳ2, · · · , ȳn]t = [y1 − ȳ,y2 − ȳ, · · · ,yn − ȳ]t (3) step 2. determine the dimensionality of latent space. fasgplvm maps the motion data from high-dimensional observation space yd to low-dimensional latent space xd. the dimensiondimensionality reduction and generation of human motion 873 ality of latent space is 2 or 3, and is determined by d = { 2 d < 60 3 d ≥ 60 (4) in this paper, d = 47, so d = 2. step 3. initialize model parameters. in our experiments, α = 1,β = 1,γ = 1,η = 1,ωk = 1,m = 200,t = 100,c = 0.01, where m is size of active set, t is allowable maximum iteration time, and c is convergence threshold. step 4. initialize the latent variable with pca. compute d principal components of motion data and project motion data yi to these principal components to obtain latent variable xi. the initialization results are saved as a matrix x = [x1,x2, · · · ,xn]t . step 5. update the active set with informative vector machine (ivm) [8]. step 6. estimate parameters α,β,γ,η,ωk by minimizing (5) with scaled conjugate gradients (scg) [9]. lgp = d 2 ln|k| + 1 2 ∑ k ω2k ˆ̄y tk k −1 ˆ̄yk + 1 2 ∑ l xtl xl + ln(αβγη/ ∏ k ωnk ) (5) where parameters d is the dimensionality of feature vector, x̂, ˆ̄y are active set, ˆ̄yk is the k-th column of ˆ̄y , and k is covariance matrix whose entries are computed by (1), k(i,j) = k(xi,xj). step 7. update the active set with ivm according to step 5. step 8. update the latent variable not in active set by minimizing (6) with scg. lik(x,y) = ||ωy − g(x)||2 2σ2(x) + d 2 lnσ2(x) + 1 2 ||x||2 (6) here g(x) = µ + ˆ̄y t k−1k(x) (7) σ2(x) = k(x,x) − k(x)t k−1k(x) (8) where parameters µ is the mean of motion data, k(x) is a column vector in which the i-th entry contains k(xi,x), and x is latent variable. step 9. stop iteration if max{||∆α||, ||∆β||, ||∆γ||, ||∆η||, ||∆ωk||} < c, otherwise go to step 10. step 10. stop iteration if iteration time reaches t , otherwise go to step 5. after new posture was generated, corresponding (x,y) is added to sample set. the active set is updated using ivm, parameters α,β,γ,η,ωk and covariance matrix k is updated according to step 6. these are used in motion editing of next time in order to ensure fasgplvm adaptive to motion editing and enlarge motion editing extent. 4 posture and motion generation posture generation is a problem of inverse kinematics, which generates vivid posture satisfying constraints on the end effectors given by user. constraints can be satisfied using geometric method easily; the difficulty in inverse kinematics is how to make new posture vivid because it is difficult to model human motion rules. in fasgplvm, the probability space of motion posture is modeled, in which the posture near sample postures has bigger probability. the sample postures are motion capture data which has the best fidelity, so the probability of posture can be used 874 s. qu, l.d. wu, y.m. wei, r.h. yu as a measure of fidelity and the posture with bigger probability has better fidelity. in inverse kinematics, the posture which satisfies constraints and has the biggest probability is the solution of inverse kinematics. the problem of generating new posture given constraints on end effectors can be described as a nonlinear optimization problem. min(x,y) = lik(x,y) s.t.f(y) = c (9) where f(y) = c are constraints on end effectors, denoting the destination position for some bones of human skeleton. generally speaking, not only one but a set of postures, called feasible posture set, can satisfy the constraints on end effectors, but some of them do not satisfy the motion rule and are not vivid. the posture, vivid and satisfying motion rule, is selected from feasible posture set by minimizing the target function min(x,y) = lik(x,y). in our method, it is not needed to give constraints for all bones of human skeleton. in f(y) = c, just a few constraints interested for user are contained, which are called sparse constraints. in the process of optimization, weight gene is introduced to transform this problem to unconstrained nonlinear optimization. min(x,y) = (1 − λ)lik(x,y) + λ(f(y) − c) (10) where λ is the weight gene (0 < λ < 1), it denotes intensity of constraints. in this paper, λ = 0.999. motion generation is based on posture generation, which is implemented by editing motion trajectory. given a motion, we edit the motion trajectory of one joint and optimize (9) constrained by new motion trajectory to generate new motion. 5 results and analysis in this section, we design experiments of dimensionality reduction and generation of human motion to prove the algorithms in this paper correct and effective. in our experiments, the human skeleton consists of 22 bones with 50 degrees of freedom. the observation space is 47 dimensional and the latent space is 2 dimensional. the sample motion data come from human motion capture database of carnegie mellon university1. 5.1 posture generation jump and kick motion data are used to train fasgplvm, which consist of 180 frames shown as figure 4. to represent motion process clearly, the left arm and left leg of human skeleton are rendered with gray color. after trained with jump and kick motion data, the parameters of fasgplvm are estimated as α = 456.912,β = 0.116,γ = 4.745,η = 326.582. then, we can generate new posture satisfying constraints given by the user. to compare with sgplvm, train sgplvm with the same motion data and generate new posture satisfying the same constraints. the experiment results are shown as figure 5, in which (a) is the origin posture, coming from sample motion data; (b), (c) and (d) are new postures generated by fasgplvm; (e), (f) and (g) are new postures generated by sgplvm. the dot on the right foot denotes the position of the right foot constrained by the user, (b) and (e) have the same constraint, and so do (c) and (f), (d) and (g). the process of editing posture (a) is to raise the right foot to generate new postures (b), (c), (d), (e), (f) 1http://mocap.cs.cmu.edu/ dimensionality reduction and generation of human motion 875 figure 4: motion capture data(jump and kick) and (g). it can be seen from figure 5 that (d) is a vivid posture but the posture (g) distorts. this is because that the height of the right foot in the posture (g) oversteps the limitation of editing range of sgplvm; but the posture (d) is generated based on fasgplvm which has larger editing range. figure 5: posture generation (jump and kick) 5.2 motion generation in this section, we generate new motion based on fagplvm. editing the jump and kick motion shown in figure 4 to generate new motion. the 103th frame and the 117th frame are edited, raising the right foot. to generate new motion, the right foot positions of the 89th frame, the 103th frame, the 117th frame and the 131th frame are used as controlling points for hermite interpolation to generate new motion trajectory. the new motion trajectory constrains the positions of the right foot in new motion, optimizing posture of every frame satisfying this constraint to generate new motion. as shown in figure 6, the black curve denotes the motion trajectory of the right foot. 876 s. qu, l.d. wu, y.m. wei, r.h. yu figure 6: motion generation (jump and kick) 5.3 comparison and analysis table 1 is the convergence precision comparison of fasgplvm and sgplvm after the same iteration time. the convergence precision is defined as the change of model parameters. so, the convergence precision of fasgplvm is max{||∆α||, ||∆β||, ||∆γ||, ||∆η||, ||∆ωk||}, and the convergence precision of sgplvm is max{||∆α||, ||∆β||, ||∆γ||, ||∆ωk||}. it can be seen from table 1 that the convergence precision of fasgplvm is higher than that of sgplvm, this is because of improving on the kernel function in fasgplvm. tab.1 the convergence precision comparison of fasgplvm and sgplvm sample motion frame amount iteration time convergence precision convergence precision (fasgplvm) (sgplvm) kick 341 52 0.004 0.021 run 264 46 0.002 0.014 run and jump 536 71 0.005 0.017 box 452 63 0.008 0.023 long jump 367 58 0.006 0.026 table 2 is the editing range comparison of fasgplvm and sgplvm retaining the new posture vivid. it can be seen from table 2 that the editing range of fasgplvm is larger than that of sgplvm, this is because of introducing dynamic active set in fasgplvm. tab.2 the editing range comparison of fasgplvm and sgplvm sample motion edited frame number editing way editing range/cm editing range/cm (fasgplvm) (sgplvm) kick 206 raise fight foot 43 21 run 124 raise left hand 26 16 run and jump 385 raise fight foot 37 14 box 162 lower right hand 28 17 long jump 256 lower head 22 12 dimensionality reduction and generation of human motion 877 6 conclusions by improving on sgplvm, fasgplvm is proposed. a method of dimensionality reduction and generation of human motion based on fasgplvm is also proposed. compared with sgplvm, fasgplvm has some excellences, such as higher convergence precision and larger editing range. acknowledgments thanks to the anonymous reviewers for their helpful comments, neil lawrence for his on-line source code, and carnegie mellon university for human motion capture data. bibliography [1] sun hongwei, gu ming and 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[9] martin f. muller, a scaled conjugate gradient algorithm for fast supervised learning, neural networks,6(4):525-533,1993. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 3, month: june, year: 2020 article number: 3854, https://doi.org/10.15837/ijccc.2020.3.3854 ccc publications matrix game with payoffs represented by triangular dual hesitant fuzzy numbers z. yang, y. song zhihui yang* school of science east china university of technology nanchang, 330013, china *corresponding author: zhhyang@ecut.edu.cn yang song zhumadian technician college zhumadian, 463000, china hnsongyang@126.com abstract due to the complexity of information or the inaccuracy of decision-makers’ cognition, it is difficult for experts to quantify the information accurately in the decision-making process. however, the integration of the fuzzy set and game theory provides a way to help decision makers solve the problem. this research aims to develop a methodology for solving matrix game with payoffs represented by triangular dual hesitant fuzzy numbers (tdhfns). first, the definition of tdhfns with their cut sets are presented. the inequality relations between two tdhfns are also introduced. second, the matrix game with payoffs represented by tdhfns is investigated. moreover, two tdhfns programming models are transformed into two linear programming models to obtain the numerical solution of the proposed fuzzy matrix game. furthermore, a case study is given to to illustrate the efficiency and applicability of the proposed methodology. our results also demonstrate the advantage of the proposed concept of tdhfns. keywords: matrix game, hesitant fuzzy set, triangular dual hesitant fuzzy numbers, multiobjective programming. 1 introduction game theory, an effective method to handle the problems in which two conflicting interests exist, has been extensively applied in some fields, such as marketing and finance. in classic game theory, payoffs are assumed to be precise. however, many problems cannot be explained in a simple crisp sense due to the complexity of the problem or a lack of adequate information. generally, such game problems can be solved by combining game theory with fuzzy set (fs)[34]. fuzzy logic-based method is important for application on many disciplines [22,25,28]. decision makers sometimes have some https://doi.org/10.15837/ijccc.2020.3.3854 2 degree of hesitation in some situations. their preference information is considered in terms of support, opposition and their confidence and nonconfidence level about the approximation. the hesitant fuzzy set (hfs) was originally introduced as an extension of the fs by torra [26]. the membership function of hfs is a set composed of several numbers in the interval [0,1], and hfs can describe the hesitation of the players. nevertheless,hfs cannot reflect the degree of opposition expressed by the players. atanassov proposed an intuitionistic fuzzy set (ifs) characterized by two functions: a membership function and a non-membership function [5]. however, an ifs cannot describe the hesitation or difference in opinions of decision makers. therefore, dual hesitant fuzzy set (dhfs) was proposed by zhu et al. [35] to solve the above problems. dhfs consists of two components:a membership hesitancy function and a non-membership hesitancy function.dhfs can effectively describe the different confidence and nonconfidence level about attitudes (support and opposition) of experts. after aubin implemented fuzzy theory in a cooperative game model [6], many research works have been published on this topic. borkotokey and neog introduced a dynamic resource allocation mechanism within the framework of cooperative games with fuzzy coalitions [11]. wu studied the cores and dominance cores of cooperative games with fuzzy payoffs [29]. considering the solution concepts for fuzzy coalition games under a certain participation level, yu et al. investigated the coincident fuzziness form for games with fuzzy coalition [33]. yang et al. developed a methodology for solving bi-matrix games with intuitionistic fuzzy number payoffs [32]. fei and li proposed a bilinear programming method for solving interval bi-matrix games [13]. an and li discussed methodologies for solving bi-matrix games with intuitionistic fuzzy information [3,4]. two-person zero-sum games, which are often called matrix game for short, are an important type of non-cooperative game. many research works have contributed to the research of the matrix game. vijay et al. introduced a fuzzy relation approach to develop two-person zero-sum matrix game with fuzzy goals and fuzzy payoffs [27]. collins and hu considered interval-valued game matrices and extended the results of classic strictly determined matrix game to fuzzily determined interval games [12]. gao et al. investigated two-player zero-sum games with fuzzy payoffs to define the best responses of the players[15]. they proposed three types of credibilistic equilibria and their existence theorem [15]. nan and li investigated methods for solving matrix game with intuitionistic fuzzy information [23,24]. li developed a fast approach to compute fuzzy game values for two-person zero-sum games with triangular fuzzy number payoffs [19]. li and hong developed a methodology for solving constrained matrix game with trapezoidal fuzzy number payoffs [20]. aggarwal et al. investigated a class of linear programming problems with fuzzy goals or fuzzy constraints to solve two-person zero-sum matrix game problems with ifss [2]. gao and yu proposed three types of credibilistic equilibria and credibilistic subgame perfect equilibria for fuzzy extensive games [16]. bhaumik et al. put forward the robust ranking technique to solve matrix game with triangular intuitionistic fuzzy number payoffs [9]. as deterministic payoffs are often unavailable, figueroa-garcía et al. developed an uncertain basedmatrix game model involving interval-valued fuzzy payoffs [14]. jana and roy discussed dual hesitant fuzzy matrix game based on the similarity measure between dhfs [18]. bhaumik and roy proposed a new aggregation operator for solving intuitionistic interval-valued hesitant fuzzy matrix games [8]. bhaumik et al. investigated two-person non-zero-sum game with payoff values represented by the hesitant interval-valued intuitionistic fuzzy-linguistic term set [10].furthermore, matrix game theory has various applications, including decision making and intelligent manufacturing [30,31]. therefore, it is necessary to research matrix game with dhfs. in this paper, we propose the concept of triangular dual hesitant fuzzy numbers (tdhfns), which can reflect the different level for decision-makers’ attitudes (support and opposition). moreover, the cut set of tdhfns is also investigated. our work is focus on developing the matrix game with payoffs represented by tdhfns. the remainder of this paper is organized as follows. in section 2, definitions and properties of tdhfns are described. section 3 proposes a programming method for matrix game with payoffs represented by tdhfns. in section 4, an empirical case is given to illustrate the proposed method. finally, section 5 draws conclusions. https://doi.org/10.15837/ijccc.2020.3.3854 3 2 definition of tdhfns and solution for matrix game with payoffs represented by tdhfns in this section, the definition and some properties of tdhfns are presented. using the definitions and operations of tdhfns, the ranking order of tdhfns is described. based on the ranking order and the operations, the solution of the matrix game can be obtained by solving a pair of linear programming model. considering a dual hesitant fuzzy set (dhfs) expressed as e = {< x,fe(x) > |x ∈ x}[35], where fe(x) =< at,at,at; µãt,νãt > (t = 1, · · · ,n) is a dual hesitant fuzzy number representing the possible membership and non-membership degrees of element x ∈ x to e, respectively. the possible membership functions are formulated as follows µãt (x) =   x−at at−at wãt, at ≤ x ≤ at, āt−x āt−at wãt, at < x ≤ āt, 0, x < at or x > āt. (1) the possible non-membership functions are formulated as follows vãt (x) =   at−x+uãt (x−at) at−at , at ≤ x ≤ at, x−at+uãt (āt−x) āt−at , at < x ≤ āt, 1, x < at or x > āt. (2) where wãt and uãt represent the maximum possible membership degree and the minimum possible non-membership degree of element x belonging to e, respectively. moreover, these values satisfy the conditions 0 ≤ wãt ≤ 1, 0 ≤ uãt ≤ 1, and 0 ≤ wãt + uãt ≤ 1. definition 1. ã =< (at,at, āt); wãt,uãt > is called a triangular dual hesitant fuzzy number (tdhfn). if at ≥ 0, āt > 0(t = 1, · · · ,n), then ã =< (a− t ,at, āt); wãt,uãt > is called a positive tdhfn, denoted by ã>d0. similarly, if āt ≤ 0 , at < 0(t = 1, · · · ,n), then ã =< (at,at, āt); wãt,uãt > is called a negative tdhfn, denoted by ã <d 0. let>d and <d denote "dual hesitant fuzzy greater than" and "dual hesitant fuzzy less than", respectively. definition 2. let ã be a tdhfn. the α-cut set of ã is defined as ãα = {x|µãt (x) ≥ α}.the β-cut set of ã is ã<α,β> = {x|µãt ≥ α,vãt ≤ β}, where α ∈ [0, min{wãt}], β ∈ [max{uãt}, 1] and 0 ≤ α + β ≤ 1. according to definition 2, the following theorem can be obtained. theorem 1. let ã be a tdhfn, ã<α,β> can be calculated as ã<α,β> = ãα ∩ ãβ. definition 3. if ãα ≤ b̃α and ãβ ≤ b̃β, then ã is called not greater than b̃, which denoted as ã ≤d b̃. similarly to the intuitionistic fuzzy number ranking relationship, let ãi =< (ai,ai, āi); wãi,uãi > and ãj =< (aj,aj, āj); wãj ,uãj >, if ãiα ≤ ãjα and ã β i ≤ ã β j , then ãi ≤ ãj(0 ≤ i,j ≤ n). remark 1. in this paper, the elements of ã are arranged according to the increasing order. definition 4. let ã = (ã1, · · · , ãn ), then ξ = ςãn + (1 − ς)ã1(0 ≤ ς ≤ 1) is called an additional element of tdhfn ã. where ς can be decided according to the risk preference of decision maker. definition 5. let ã =< (at,at, āt); wãt,uãt > (t = 1, · · · ,n1) and b̃ =< (bt,bt, b̄t); wb̃t,ub̃t > (t = 1, · · · ,n2) be two tdhfns. if n1 = n2, then the tdhfns are called two normalized tdhfns. definition 6. let ã =< (at,at, āt); wãt,uãt > and b̃ =< (bt,bt, b̄t); wb̃t,ub̃t > (t = 1, · · · ,n) be two normalized tdhfns, and let λ be a real number. the arithmetic operations of tdhfns are defined as follows. ã + b̃ =< (at + bt,at + bt, āt + b̄t); min{wãt,wb̃t}, max{uãt,ub̃t} > https://doi.org/10.15837/ijccc.2020.3.3854 4 ãb̃ =   < (atbt,atbt, ātb̄t); min{wãt,wb̃t}, max{uãt,ub̃t} >, ã >d 0, b̃ >d 0, < (atb̄t,atbt, ātbt); min{wãt,wb̃t}, max{uãt,ub̃t} >, ã >d 0, b̃ <d 0, < (ātb̄t,atbt,atbt); min{wãt,wb̃t}, max{uãt,ub̃t} >, ã <d 0, b̃ <d 0. λã = { < (λat,λat,λāt); wãt,uãt >, λ > 0, < (λāt,λat,λat); wãt,uãt >, λ < 0. ã−1 =< (1/at, 1/at, 1/āt); wãt,uãt > let â = [al,au ] be an interval, where al and ar are the left and right limits of the interval â , respectively. the maximization problem with an interval-valued objective is described as follows respectively. ishibuchi and tanaka [17] investigated the maximization problem with an interval-valued objective as max{â} ,s.t. â ∈ ω1 which is equivalent to the following bi-objective programming problem max{al, al + au 2 } s.t. â ∈ ω1 (3) where ω1 is the set of constraint conditions. the minimization problem with an interval-valued objective is described as min{â}, s.t. â ∈ ω2 which is equivalent to the following bi-objective programming problem max{au, al + au 2 } s.t. â ∈ ω2 (4) where ω2 is the set of constraint conditions. in the classical matrix game, the payoff function was often expressed by the crisp number. however, there is often some hesitant fuzzy information about the payoffs. based on the hesitant fuzzy information, we can develop the matrix game with payoffs represented by tdhfns. assume that s1 = {α1,α2, · · · ,αm} and s2 = {β1,β2, · · · ,βn} are the pure strategies for players p1 and p2, respectively. without loss of generality, the payoff matrix for player p1 is given by ã = (ãij)m×n, where ãij =< (aijt,aijt, āijt); wãijt,uãijt > (i = 1, · · · ,m; j = 1, · · · ,n; t = 1, · · · ,n). the sets of mixed strategies for players p1 and p2 are denoted by x and y , respectively, where x = {x = (x1,x2, · · · ,xm)| m∑ i=1 xi = 1,xi ≥ 0}, y = {y = (y1,y2, · · · ,yn)| n∑ j=1 yj = 1,yj ≥ 0}. if players p1 and p2 choose mixed strategies x ∈ x, y ∈ y, respectively. according to definition 6, the expected payoff e(x, y) can be calculated as follows e(x, y) = xtãy = m∑ i=1 n∑ j=1 ãijxiyj =< ( m∑ i=1 n∑ j=1 aijtxiyj, m∑ i=1 n∑ j=1 aijtxiyj, m∑ i=1 n∑ j=1 āijtxiyj); min{wãijt}, max{uãijt} > the interests of the two players are precisely opposite. the payoff of each player is not only related to the strategy he chooses, but also depends on the strategy chosen by the opponent. therefore, each player considers the strategy chosen by the opponent when determining the strategy that is most beneficial to himself. assume that the players are rational. player p1 should choose the mixed strategy x ∈ x that maximizes e(x, y), while player p2 is interested in choosing the mixed strategy y ∈ y that minimizes e(x, y). if player p1 chooses strategy x, then he could realize a payoff of at least min y∈y {xtãy} however, player p1 wants a larger payoff, so he chooses a strategy that ensures a https://doi.org/10.15837/ijccc.2020.3.3854 5 payoff of no less than max x∈x min y∈y {xtãy}. similarly, if player p2 choose strategy y, then he could realize a payoff of at most max x∈x {xtãy}. however, player p2 wants a smaller payoff, so he chooses a strategy to ensure a payoff of no more than want less payoff , so he would choose the strategy make the payoff is no more than min y∈y max x∈x {xtãy}. player p1 chooses a strategy according to the maximin principle, while player p2 chooses his strategy according to the minimax principle. therefore, if a pair of mixed strategies (x0, y0) satisfies x0tãy0 = max x∈x min y∈y {xtãy} = min y∈y max x∈x {xtãy}, then x0 and y0 are called the optimal strategies for the two players. x0tãy0 is the value of the mixed equilibrium situation. however, the expected payoff e(x, y) is a tdhfn with the membership degrees and non-memberships. thus, there do not exist (x0, y0) meet the two scales at the same time. in fact, the problems max x∈x min y∈y {xtãy} and min y∈y max x∈x {xtãy} are bi-objective programming problems. definition 7. let ν̃ and ω̃ be tdhfns. assume that there exist (x∗, y∗, ν̃∗, ω̃∗), where x∗ ∈ x, y∗ ∈ y , satisfying the following conditions (1) x∗tãy ≥d ν̃∗ and xtãy∗ ≤d ω̃∗, (2) there do not exist any ν̃ 6= ν̃∗ and ω̃ 6= ω̃∗ such that ν̃ ≥d ν̃∗ and ω̃ ≤d ω̃∗, then, (x∗, y∗, ν̃∗, ω̃∗) is the solution of the tdhfn-based matrix game, x∗ and y∗ are the optimal strategies of players p1 and p2, respectively, ν̃∗ is the gain-floor for player p1 and ω̃∗ is the loss-ceiling for player p2. finally, x∗tãy∗ is the payoff of the tdhfn-based matrix game. for the classic linear programming problem in the crisp environment, the aim is to maximize or minimize a linear objective function [7,21]. but in many actual situations, decision makers should specify the objective or constraint functions with fuzzy sets. assume that each player chooses an optimal mixed strategy under the other player’s pure strategy. according to definition 7, the optimal strategy x∗ of player p1 and the optimal strategy y∗ of player p2 can be achieved by solving the following pair of mathematical programming models max{ν̃} s.t.   m∑ i=1 ãijxi ≥d ν̃ (j = 1, 2, · · · ,n) m∑ i=1 xi = 1 xi ≥ 0 (i = 1, 2, · · · ,m) (5) min{ω̃} s.t.   n∑ j=1 ãijyj ≤d ω̃ (i = 1, 2, · · · ,m) n∑ j=1 yj = 1 yj ≥ 0 (j = 1, 2, · · · ,n) (6) according to definition 3, model (5) can be transformed into the following programming model max{ν̃α, ν̃β} s.t.   m∑ i=1 (ãij)αxi ≥d ν̃α (j = 1, 2, · · · ,n) m∑ i=1 (ãij)βxi ≥d ν̃β (j = 1, 2, · · · ,n) m∑ i=1 xi = 1 xi ≥ 0 (i = 1, 2, · · · ,m) (7) https://doi.org/10.15837/ijccc.2020.3.3854 6 let ν̃α = [νtlα,νtrα ], ν̃β = [νt β l,νt β r], (ãij)α = [lãijt (α),rãijt (α)], (ãij) β = [lãijt (β),rãijt (β)] be the α-cut and β-cut of tdhfns ν̃ and ãij(i = 1, 2, · · · ,m; j = 1, 2, · · · ,n), respectively. according to the interval-valued rank relationship and definition 4, model (7) can be transformed into the following programming model max{[νtlα,νt r α ], [νt β l,νt β r]} s.t.   m∑ i=1 lãijt (α)xi ≥ νt l α (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 rãijt (α)xi ≥ νt r α (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 lãijt (β)xi ≥ νt l β (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 rãijt (β)xi ≥ νt r β (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 xi = 1 xi ≥ 0 (i = 1, 2, · · · ,m) (8) the above proposed model can be transformed into the bi-objective linear programming model as follows max{ n∑ t=1 (νtlα + νt β l) 2n , n∑ t=1 (νtlα + νt β l + νt r α + νt β r) 4n } s.t.   m∑ i=1 lãijt (α)xi ≥ νt l α (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 rãijt (α)xi ≥ νt r α (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 lãijt (β)xi ≥ νt l β (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 rãijt (β)xi ≥ νt r β (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 xi = 1 xi ≥ 0 (i = 1, 2, · · · ,m) (9) in the context of multi-objective linear programming, it is important to note that the objectives have different level of importance [17]. in such a situation, the linear weighted average method should be applied on the multi-objective linear programming problem. objectives with more weight should be allocated more achievement levels in comparison to others [1]. therefore, model (9) can be transformed into the following linear programming model max{λ( n∑ t=1 (νtlα + νt β l) 2n ) + (1 −λ)( n∑ t=1 (νtlα + νt β l + νt r α + νt β r) 4n )} s.t.   m∑ i=1 lãijt (α)xi ≥ νt l α (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 rãijt (α)xi ≥ νt r α (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 lãijt (β)xi ≥ νt l β (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 rãijt (β)xi ≥ νt r β (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 xi = 1 xi ≥ 0 (i = 1, 2, · · · ,m) (10) the parameter λ ∈ [0, 1] can be determined by the decision maker. https://doi.org/10.15837/ijccc.2020.3.3854 7 according to definitions 1 and 2, the the α-cut and β-cut set of ãij =< (aijt,aijt, āijt); wãijt,uãijt > (i = 1, 2, · · · ,m; j = 1, 2, · · · ,n; t = 1, · · · ,n) are represented as follows (ãijt)α = [ (wãijt −α)aijt + αaijt wãijt , (wãijt −α)āijt + αaijt wãijt ] (11) (ãijt)β = [ (1 −β)aijt + (β −uãijt )aijt 1 −uãijt , (1 −β)aijt + (β −uãijt )āijt 1 −uãijt ] (12) therefore, model (10) can be transformed into the following model max{λ( n∑ t=1 (νtlα + νt β l) 2n ) + (1 −λ)( n∑ t=1 (νtlα + νt β l + νt r α + νt β r) 4n )} s.t.   m∑ i=1 (wãijt−α)aijt+αaijt wãij xi ≥ νtlα (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 (wãijt−α)āijt+αaijt wãijt xi ≥ νtrα (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 (1−β)aijt+(β−uãijt )aijt 1−uãijt xi ≥ νtlβ (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 (1−β)aijt+(β−uãijt )āijt 1−uãijt xi ≥ νtrβ (j = 1, 2, · · · ,n; t = 1, · · · ,n) m∑ i=1 xi = 1 xi ≥ 0 (i = 1, 2, · · · ,m) (13) where νtlα,νt β l,νt r α,νt β r and xi(i = 1, 2, · · · ,m) are decision variables, λ ∈ [0, 1],α ∈ [0, min{wãijt ] and β ∈ [max{uãijt, 1] (0 ≤ α + β ≤ 1)are parameters that can be determined according to the characteristics of the actual decision problem. for any given parameters λ,α and β,the optimal solution (x∗(α,β),νl∗α ,νr∗α ,ν β∗ l ,ν β∗ r ) can be obtained by solving model (13). (x∗(α,β),νl∗α ,νr∗α ,ν β∗ l ,ν β∗ r ) is the optimal solution of model (10). x∗(α,β) is the maximin strategy of player p1 at confidence level (α,β). the < α,β >-cut set of the minimum gain ν̃∗ of player p1, which is represented by ν̃∗<α,β>, can be obtained according to theorem 1. ν̃∗<α,β> denotes the possible range of ν̃ ∗ in the < α,β > confidence level. specifically, if α = 0,β = 1, it means the largest scope of the minimum gain ν̃∗ of player p1. if α = min{wãijt}, β = max{uãijt}, then ν̃∗<α,β> denotes the minimum range of ν̃ ∗of player p1 . let ω̃α = [ωtlα,ωtrα ],and ω̃β = [ωt β l,ωt β r] are the α-cut set and β-cut set of tdhfn ω̃, respectively. therefore, model (6) can be transformed into the following model min{λ( n∑ t=1 (ωtlα + ωt β l) 2n ) + (1 −λ)( n∑ t=1 (ωtlα + ωt β l + ωt r α + ωt β r) 4n )} s.t.   n∑ j=1 (wãijt−α)aijt+αaijt wãij yj ≤ ωtlα (i = 1, 2, · · · ,m; t = 1, · · · ,n) n∑ j=1 (wãijt−α)āijt+αaijt wãijt yj ≤ ωtrα (i = 1, 2, · · · ,m; t = 1, · · · ,n) n∑ j=1 (1−β)aijt+(β−uãijt )aijt 1−uãijt yj ≤ ωtlβ (i = 1, 2, · · · ,m; t = 1, · · · ,n) n∑ j=1 (1−β)aijt+(β−uãijt )āijt 1−uãijt yj ≤ ωtrβ (i = 1, 2, · · · ,m; t = 1, · · · ,n) n∑ j=1 yj = 1 yj ≥ 0 (j = 1, 2, · · · ,n) (14) where ωtlα,ωt β l,ωt r α,ωt β r and yj(j = 1, 2, · · · ,n) are decision variables; λ ∈ [0, 1],α ∈ [0, min{wãijt}] and β ∈ [max{uãijt}, 1] ,0 ≤ α + β ≤ 1. https://doi.org/10.15837/ijccc.2020.3.3854 8 for any given parameters λ,α and β, the optimal solution (y∗(α,β),ωl∗α ,ωr∗α ,ω β∗ l ,ω β∗ r ) can be obtained by solving model (14). similarly, (y∗(α,β),ωl∗α ,ωr∗α ,ω β∗ l ,ω β∗ r ) is the optimal solution of model (9). y∗(α,β) is the minimax strategy of player p2 at confidence level (α,β), ω̃∗<α,β> is the < α,β >-cut set of ω̃∗, and ω̃∗<α,β> denotes the possible range of the maximum loss ω̃ ∗ of player p2 in the < α,β > confidence level. 3 illustrative case to illustrate the proposed model, we present an illustrative example considering a monopoly company which plans to produce products with c1 and c2, where c1 and c2 are mutually replaceable products. in the current highly competitive business environment, it is very important for the enterprise to capture the customer effectively. there are two types of customer group,a1 and a2 , in the market. as the demand for the product is basically fixed, an increase in the sales volume of one will inevitably lead to a decrease in the sales volume of the other. the company and the customer group can be regarded as two players, and the price selection for the company and the customer group can be viewed as a matrix strategy. the payoff matrix of the company is expressed as follows ã = [ {< (150, 155, 160); 0.8, 0.1 >,< (150, 160, 170); 0.9, 0.1 >} {< (135, 138, 140); 0.7, 0.2 >,< (135, 140, 145); 0.8, 0.1 >} {< (75, 80, 85); 0.6, 0.2 >,< (80, 85, 90); 0.7, 0.1 >} {< (145, 150, 155); 0.6, 0.2 >,< (150, 155, 160); 0.7, 0.1 >} ] where < (150, 155, 160); 0.8, 0.1 > means that the most possible price of c1 is 155, the lowest possible price is 150, the highest possible price is 160, the maximum satisfaction of a1 is 0.8, and the minimum dissatisfaction is 0.1. the other values can be interpreted similarly. for the given λ = 0.5 (the other values are calculated similarly) and select < α,β >, where α ∈ [0, 0.6], β ∈ [0.2, 1], models (13) and (14) can be solved using lingo11, and the optimal strategy x∗t (α,β) of p1 and the optimal strategy y∗t (α,β) of p2 can be obtained. the < α,β > -cut set of the minimum gain of player p1 and the maximum loss of player p2 can be calculated according to theorem 1. the results are shown in table 1 and table 2. table 1 the max-min strategy and gain-floor cut sets of player p1 < α,β > x∗t (α,β) ν̃∗<α,β> < 0, 1 > (0.8235, 0.1765) {[136.76, 142.65], [137.65, 147.65]} < 0.1, 0.8 > (0.8216, 0.1784) {[137.62, 142.04], [138.79, 146.56]} < 0.2, 0.7 > (0.8197, 0.1803) {[138.06, 141.75], [139.37, 146.06]} < 0.3, 0.6 > (0.8179, 0.1821) {[138.50, 141.45], [139.95, 145.51]} < 0.4, 0.5 > (0.8160, 0.1840) {[138.95, 141.17], [140.54, 144.98]} < 0.5, 0.3 > (0.8141, 0.1859) {[139.81, 140.55], [141.68, 143.90]} < 0.6, 0.2 > (0.8123, 0.1877) {140.25, [142.26, 143.37]} table 2 the min-max strategy and loss-ceiling cut sets of player p2 < α,β > y∗t (α,β) ω̃∗<α,β> < 0, 1 > (0.1765, 0.8235) {[137.65, 143.53], [137.65, 149.41]} < 0.1, 0.8 > (0.1742, 0.8258) {[138.43, 142.88], [138.92, 148.05]} < 0.2, 0.7 > (0.1731, 0.8269) {[138.81, 142.55], [139.55, 147.37]} < 0.3, 0.6 > (0.1720, 0.8280) {[139.20, 142.23], [140.18, 146.70]} < 0.4, 0.5 > (0.1709, 0.8291) {[139.59, 141.91], [141.90, 146.02]} < 0.5, 0.3 > (0.1688, 0.8312) {[140.37, 141.26], [142.08, 144.67]} < 0.6, 0.2 > (0.1677, 0.8323) {[140.76, 140.94], [142.71, 144.00]} according to table 1 and table 2, if < α,β >=< 0, 1 >, then ν̃∗<α,β> = {[136.76, 142.65], [137.65, 147.65]} which denotes the greatest possible range of minimum gain ν̃∗ of player p1, and ω̃∗<α,β> = {[137.65, 143.53], [137.65, 149.41]} denotes the greatest possible range of maximum loss ω̃∗ of player p2. when α is larger and β is smaller, the lengths of ν̃∗<α,β> and ω̃ ∗ <α,β> are smaller . https://doi.org/10.15837/ijccc.2020.3.3854 9 from the above discussion, the concept of tdhfns proposed in the paper has the advantage that it can take into account the uncertainty and hesitation inherent in the assessment of decision makers, and it can also simulate the psychological behavior of decision makers. the proposed matrix games with payoff represented by tdhfns makes full use of information from both the market and consumers. 4 conclusion in this paper, a new integrated concept of the triangular fuzzy number and dual hesitant fuzzy set enables decision makers to express psychological behaviour. the matrix game with payoffs represented by tdhfns is investigated. based on the cut set and operational rules of tdhfns, the matrix game with payoffs represented by tdhfns is transformed into a multi-objective linear programming problem. therefore, the numerical solution of the matrix game with payoffs represented by tdhfns can derive from a pair of linear programming models. the proposed method is illustrated by the case of a market pricing problem. the approach is especially appropriate for the decision maker under hesitant fuzzy environment. more effective methods for developing matrix game theory with tdhfns will be investigated in the future. funding this work was supported in part by the national natural science foundation of china (grant no. 71762001). conflict of interest the authors declare no conflict of interest. references [1] afzali, a.; 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(2020). matrix game with payoffs represented by triangular dual hesitant fuzzy numbers, international journal of computers communications & control, 15(3), 3854, 2020. https://doi.org/10.15837/ijccc.2020.3.3854 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 6, month: december, year: 2021 article number: 4398, https://doi.org/10.15837/ijccc.2021.6.4398 ccc publications exploring the antecedents of online learning satisfaction: role of flow and comparison between use contexts quan xiao, xia li quan xiao school of information management jiangxi university of finance and economics, nanchang no.165 west yu-ping street, 330032, nanchang, jiangxi, china xiaoquan@foxmail.com xia li* school of information management jiangxi university of finance and economics, nanchang no.165 west yu-ping street, 330032, nanchang, jiangxi, china *corresponding author: 2202020581@stu.jxufe.edu.cn abstract learners’ satisfaction plays a critical role in the success of online learning platform. many factors that affect online learning satisfaction have been addressed by previous studies. however, the mechanisms by which these factors are associated with online learning satisfaction are not sufficiently clear. moreover, the difference in the antecedents of online learning satisfaction between two use contextsmobile context and pc context, was rarely examined. based on the stimulus-organism-response (s-o-r) framework, we investigate the key factors (self-efficacy, social interaction, platform quality, teacher’s expertise) affecting flow and highlights its role in online learning satisfaction, which is empirically tested through an online survey of 333 online learners. results show that self-efficacy, teacher’s expertise, platform quality, and social interaction positively affect online learning satisfaction through the mediation of flow. use contexts not only moderate the relationship between flow and online learning satisfaction, but also between social interaction, platform quality, teacher’s expertise, and flow. these new findings expand educators with ways to increase flow, add to knowledge about the relationship between flow and online learning satisfaction and provide references for online learning platforms to enhance learners’ online learning satisfaction under multiple-version affordances. keywords: online learning satisfaction, flow, use contexts, s-o-r framework. 1 introduction the outbreak of the covid-19 pandemic in 2020 spread rapidly around the world, causing huge ripples in education in different countries [32]. isolation measures have impacted the learning and education activities of colleges and universities, leading traditional offline learning to be transferred https://doi.org/10.15837/ijccc.2021.6.4398 2 to online [51], thus the demand for online learning has exploded. considering the spread of the epidemic, most american universities temporarily shifted most of their teaching activities online [73]. for example, harvard university, stanford university, massachusetts institute of technology, and many other universities have all announced to set up online courses for students on campus. besides, romanian scholars have developed an online platform to actively facilitate and support the publication of high-quality scientific literature produced in their country [61]. the chinese government has put forward the policy of "suspending classes, ongoing learning", and learning informatization are rapidly advancing under the leadership of the government. online learning has become an inevitable trend in the development of global education [8]. with this trend, an increasing diversity of technology platforms have been adopted to support online learning [64]. for example, learning management systems (lms) are acknowledged as one of the most essential online learning platforms, which facilitates online learning without the time and space constraints [46]. the lms can support learners in having classes, writing assignments, taking exams online, so the quality of online learning is influenced by lms. further, the lms was recognized as an irreplaceable emergency learning tool for the transition from traditional learning to online learning during the covid-19 pandemic. although lms is continuing to develop and is expected to continue, the rapid growth of the technology platforms does not mean that online learning will bring learners a positive learning experience. even many learners have suffered reduction in their satisfactions to online learning after the first experiences. this necessitates a deeper study of the antecedents of online learning satisfaction. research on online learning satisfaction have become a hotspot attracting much attention. hiltz first proposed the concept of online learning, which refers to placing the homepage of a course and related materials on the pc to form shared virtual learning space to achieve a face-to-face (ftf) learning [28]. malaysian scholars ramayah and lee used structural equation models to study system characteristics and online learning satisfaction, aiming to reveal the relationship between online learning system quality, information quality, service quality and learning satisfaction [57]. though scholars have done a great deal of theoretical exploration and empirical analysis on online learning, there are still problems that need to be studied. first, unlike traditional offline learning, online learning context allows for greater flexibility in learning and more intelligent forms in teaching. therefore, it cannot be determined whether the factors that influence the learning satisfaction to traditional offline learning can also apply to online learning contexts. secondly, fewer scholars have introduced mediating effect into the study of online learning satisfaction models, especially there are scarce researches in the literature that use flow as a mediating variable in online learning. third, previous research only focused on only one type of use context, such as pcs [5], mobile phones [76]. nevertheless, the rise of the information age has given creation to a diverse media context [38]. it is worth extending the findings of previous studies to compare different media contexts in the field of online learning. motivated by these gaps, this research is developed based the following three questions: question 1: what are the antecedents of online learning satisfaction? question 2: what is the mechanism of flow in the establishment of online learning satisfaction? question 3: does the use of context play a moderating effect in the influence mechanism of online learning satisfaction? subsequently, this study facilitates the development of literature on the antecedents, mediator, moderator, and outcomes of online learning satisfaction in the framework of the s-o-r model. government, schools, platform, educators, and learners can use this as a basis to strengthen the quality of online learning and increase learner satisfaction in order to promote the development of the online learning community. the remainder of the paper is structured as follows. in section 2 the related literatures are reviewed. in section 3, we construct the research model of this paper and put forward the research hypothesis based on the s-o-r framework. in section 4, we discuss the collection of the data and sample characteristics. section 5 presents the empirical results. section 6 discusses the research results, and highlights the research significance and future research directions. finally, conclusions with limitations and future direction are drawn in section 7. https://doi.org/10.15837/ijccc.2021.6.4398 3 2 literature review 2.1 online learning satisfaction satisfaction is the goal pursued by products/services [77], and is a measure of user’s emotions. after the user’s evaluation of use, they develop an overall emotional attitude towards the product/service [14, 66]. kotler believed that customer satisfaction refers to the customer’s feelings of happiness or disappointment after using the product, which is formed by comparing the user’s perceived utility after use with the expected expectations before use [39]. martin applied the theory of satisfaction to the field of learning, suggesting that a learner is satisfied when the learning process feels greater than what was expected before formal learning, otherwise he would be unsatisfied [49]. many scholars have conducted empirical studies on online learning satisfaction currently. hsiu-feng has determined the relationship among big five traits, online learning motivation and online learning satisfaction through a questionnaire survey of 153 college english learners [62]. cong wang found that in online learning situations, need satisfaction and need dissatisfaction have a significant impact on students’ motivation and learning outcomes [70]. online learning satisfaction is a widely recognized indicator to measure quality of teaching and learning [75]. a summary of the literature relevant to all the factors vital to the activities of online learning, and affecting learners’ satisfaction with online learning is presented in table 1. we define online learning satisfaction as experience of online learners interacting via learning products or learning services in all processes, which accumulates over time to form an overall evaluation of the product or service. hence, we employ the online learning satisfaction as a response(r) in the current theoretical model. 2.2 online learner factor: self-efficacy self-efficacy refers to the predictions and inferences made by individuals about whether they can complete a certain behavior. self-efficacy is important because the more selfefficacious learners will make plans, seek appropriate help, and motivate themselves to immerse themselves in a learning state and potentially enhance their learning satisfaction. in bandura’s research, self-efficacy exerts its influence through four major processes of cognitive, motivational, affective, and selection [3]. compeau defined online learning self-efficacy as the extent to which people feel confident in their ability to successfully use online learning technology to complete the learning task [27]. liaw demonstrated that perceived satisfaction, perceived usefulness, and interactive learning environments were all found to predict perceived self-regulation in online learning [43]. high-value learning content will encourage learners to devote more learning energy to achieve better learning achievement goals, and they will be more likely to be satisfied with online learning. this study defines self-efficacy as the degree to which online learners are confident to work effectively in different characters and use self-efficacy as a stimulating(s) factor. 2.3 educator factor: teacher’s expertise the role of teacher in online learning is different from traditional face-to-face learning. pei-chen sun conducted that the existence and guidance of teachers, the ability of teachers to teach online and the flexibility of courses is an important predictor of online learning satisfaction [65]. therefore, in addition to having knowledge of teaching, teachers are encouraged to pay attention to the topics that learners are talking about in online learning communities and can use platform learning tools to serve teaching and learning goals. siritongthaworn conducted a survey on online learning in thai universities and found that the key barrier was found to be student preference for instructor-led learning [63]. it is assumed that brilliant teachers’ expertise focusses more on student learning and pedagogical issues and, thus, can be more flexible in adopting new approaches to promote learner flow. specifically, the teacher’s knowledge reserve, teaching level and teaching style will all affect online learning satisfaction. therefore, this study defines teacher’s expertise as online learners’ perception of teachers’ mastery of educational knowledge and professional knowledge, which is a stimulus(s) factor. https://doi.org/10.15837/ijccc.2021.6.4398 4 table 1: related references about online learning satisfaction reference country theory/model methodology findings lin (2005) [45] america tra,tam and tpb 187 students in a midwest state university to learn knowledge and skills in a physical and virtual place and analysis using sem self-efficacy and technology facilitating conditions had the strongest impact on students’ satisfaction suarez (2008)[18] european none test the acquired experience in the development and use of multimedia contents for e-learning applications the result of the work shows the students satisfaction in the development and use of multimedia contents fore-learning applications pei-chen sun (2008)[65] taiwan six dimensions were chosen based on a comprehensive literature review. 295 data were collected and analyzed using multiple regression analysis earner computer anxiety, instructor attitude toward e-learning, e-learning course flexibility, e-learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments are the import factors affecting learners’ perceived satisfaction jen-her wu (2010)[72] taiwan social cognitive theory 212 data were collected and analyzed using pls learning climate and performance expectations significantly affect learning satisfaction ke f (2013)[37] america the design theory of student-centered learning environment 680 respondents from 13 countries and analyzed using sem learner relevance, active learning, authentic learning, learner autonomy, and computer technology competence predicted students’ perceived satisfaction xusen cheng (2016)[12] china yield shift theory 113 participants were tested by experiment satisfaction is higher in online collaborative learning hueichuawei (2020)[72] taiwan none 356 students were gathered and analyzed using sem. self-efficacy for online learning readiness had a mediated effect on online learning perceptions and online discussion score effect on online learning perceptions and satisfaction haozhe jiang (2020)[34] china tsm 928 students were gathered, analyzed using sem and the rasch model self-efficacy and the perceived ease of use and usefulness of the platforms is directly and indirectly impacted by chinese university students’ satisfaction with online learning platforms nam-hyun um (2021)[68] south korea none 236 students were gathered and analysis using sem students’ satisfaction with online learning was positively related to interactions, teaching presence, self-management of learning, and academic self-efficacy https://doi.org/10.15837/ijccc.2021.6.4398 5 2.4 platform factors: platform quality and social interaction online learning is different from traditional classroom learning, which can carry out multiple combined learning modes in space and time dimensions. sevgi ozkan analyzed actual cases and showed that good platform quality could allow school learners to enjoy better learning experiences and enhancing online learning satisfaction [55]. shun li and quan xiao pointed out that optimizing the design features of mobile applications would get a payback of higher user satisfaction [42]. adrianameda udroiu helped romanian public institutions to improve platform quality by integrating their own resources and specific activities [67]. fredrickson conducted a study on the factors influencing online learning satisfaction at new york university. the results show that perceived value of instruction is the most important reflector of online learning satisfaction. this value is reflected in online discussions, the interaction between learners and teachers, and the interaction between learners [22]. thus, the learning platform should pay attention to the construction of social interaction, as learners can not only interact with teachers through the platform, but also exchange learning opportunities with friends in different fields, high-quality and high-frequency social interaction has a driving effect on online learning satisfaction. based on existing literature, it is believed that platform quality refers to the ease of use of the platform perceived by online learners and the degree to which it supports teaching. social interaction is defined as an online learning process in which individual users or groups of users interact with each other by acquiring information and sharing experiences. also, platform quality and social interaction are social stimuli(s) factors. 2.5 mediating role of flow flow, also known as "fluid experience", is a concept first proposed by american psychologist mihaly csikszentmihalyi in 1975. it refers to people’s interest in a challenging activity and a task driven by internal motivation [15]. csikszentmihalyi constructed a model of the interaction between psychological factors and situational factors, which promoted the formation of flow theory. this theory has been applied in various research fields. australian scholar jackson conducted research on flow for outstanding athletes in the field of sports. he believed that flow was the feelings obtained during sports [33]. hoffman and novak applied the concept of flow experience to network navigation behavior for the first time, and defined immersion in network navigation as: a series of non-stop and seamless responses caused by human-computer interaction, truly enjoyable, accompanied by selflessness, and the state of motivation [53]. based on these studies, this study defines flow as a positive emotion that individuals have a strong sense of participation in the process of activities, which can promote the individual to devote themselves to learning activities. at the same time, our study focuses on the mediating role of flow. the theoretical basis for the mediation role is provided by the s-o-r. model. this model theorizes that self-efficacy, teacher’s expertise, platform quality and social interaction (s) create a flow state (o) which further triggers the online learning satisfaction (r). 2.6 moderating role of use contexts with the progress of society and economic development, in addition to the electronic information that needs to be organized and managed in personal computers at personal fixed offices and learning locations, personal mobile terminal devices such as pad and smartphones have also become a new base for the collection, storage and use of personal information [4]. among them, smartphones are undoubtedly becoming an important tool for personal online learning. david mutambara discovered through the technology acceptance model that mobile learning can be used to alleviate the challenges faced by stem education in rural areas [52]. lee regards mobile learning as the evolution of online learning, in which mobile devices and wireless connections replace elements such as fixed computers and wired networks [41]. this paper takes use contexts as a moderating variable, and divides use contexts into two groups: mobile context and pc context. https://doi.org/10.15837/ijccc.2021.6.4398 6 2.7 the s-o-r framework the s-o-r framework involves three components: stimulus, organism, and response [50]. the s-o-r model explains that various environmental aspects can act as a stimulus (s) that influences an individual’s internal state (o), which subsequently derives the individual’s behavioral response (r). the model explicates how stimuli in the outer environment can fortify the inner states of individuals [20]. many researchers have applied the s-o-r model to the field of online learning [23]. xuesong zhai focused on how privacy concern developed knowledge hiding perceptions of the learners, thereby affecting their online collaboration based on the s-o-r paradigm [79]. buxbaum examined students’ learning flow experiences and other personality traits using the s-o-r model [7]. as the global covid19 pandemic spreads, many students begun to transform from offline classroom to online classroom, the sudden change in the learning environment forced students to experiment with multimedia tools for learning [80]. the flow changes may induce students to have different learning styles and online learning satisfaction. thus, it is necessary to utilize the s-o-r model to further explore the antecedents of online learning satisfaction. more specifically, self-efficacy, teacher’s expertise, platform quality and social interaction are important stimuli (s) that help create flow in students’ online learning (o) to enhance the online learning satisfaction (r). 3 research model and hypotheses drawing on the related studies, we proposed a conceptual model as shown in fig.1, which presents the relationships among self-efficacy, teacher’s expertise, platform quality, social interaction, flow, online learning satisfaction and use contexts. self-efficacy teacher’s expertise platform quality social interaction flow online learning satisfaction use context h2 h1(a) h1(b) h1(c) h1(d) h3(a) h3h3(b) h3(c) h3(d) figure 1: conceptual model in terms of the antecedent variables of flow, prior studies have shown that self-efficacy significantly impacts learners on learning satisfaction within an online environment [11], and literature also suggests that expert teachers are more flexible with new methods and focus more on student learning [29]. teachers with expertise are more likely to succeed in high-satisfaction classrooms [35]. as online learning is dependent on the technology platform, the smoothness and clarity of which has an impact on the flow and learning satisfaction [13]. on the other hand, social interaction provides learners with a unique collaborative and social experience, which may raise learning satisfaction. riva suggests that the more interactive users feel, the more immersed they will be in the virtual environment [58]. considering all above mentioned, we have the following hypotheses: h1 (a): self-efficacy positively affects flow. h1 (b): teacher’s expertise positively affects flow. h1 (c): platform quality positively affects flow. h1 (d): social interaction positively affects flow. flow is often described as the best state of excitement that occurs when people are fully involved in an activity [40]. because flow brings happiness to the learning activity itself, it can internally encourage online individuals to feel more attractive to the virtual environment, or to form an emotional attitude towards the virtual environment [31]. greater attractiveness and positive emotional state will increase learner satisfaction. thus, we suggest the following hypothesis: https://doi.org/10.15837/ijccc.2021.6.4398 7 h2: flow positively affects online learning satisfaction. previous research has indicated that lms quality attributes have different impacts on students’ satisfaction in the mobile context from the pc context [13]. more specifically, learner experience of online learning can vary from mobile context to pc context. hence, this article divides use contexts into two groupsmobile context and pc context. we test each path in the model in different contexts, focusing on whether the effects under different contexts are different. hence, the following hypotheses are developed: h3: use context moderates the relationship between flow and online learning satisfaction. h3 (a): use context moderates the relationship between self-efficacy and online learning satisfaction. h3 (b): use context moderates the relationship between teacher’s expertise and online learning satisfaction. h3 (c): use context moderates the relationship between platform quality and online learning satisfaction. h3 (d): use context moderates the relationship between social interaction and online learning satisfaction. 4 research methodology 4.1 questionnaire design and measurement all measurement items from existing instruments were adapted to the context of online learning to ensure content validity. a total of 23 items associated with the constructs were included in the initial questionnaire. the initial scales in english were translated into chinese by using the traditional back-translation method. each item was measured with seven-point likert scale (1= point strongly disagree, 7= points strongly agree) [44]. all items of scales are shown in table 2. 4.2 data collection this study takes learners who have participated in online learning as the survey objects. the online survey was conducted through the professional survey platform “wjx.com”, via its sample service. the questionnaire clearly states that only users who have taken part in the online learning can participate. in addition, we ensured that the responses were confidential. a total of 428 online questionnaires were collected through online questionnaire survey. all returned questionnaires were carefully examined. after removing 25 careless and invalid questionnaires, and 70 questionnaires that are unclear about the context of use, a total of 333 valid questionnaires are obtained for the empirical analysis. the ages of the respondents are mainly between 16 and 40 years old. in terms of gender, there are 182 females (54.7%) and 151 males (45.3%). most respondents have more than one year of online learning experience, and 175 people use mobile devices to learn, accounting for 52.6% while 158 people learn on pc, accounting for 47.4%. 5 results 5.1 measurement model in data analysis, we used partial least squares (smart pls3.0), a variance-based latent variable structural equation modeling (sem) technique [24, 74]. in order to ensure the consistency of the latent variables in the model construction, the reliability and validity of survey data are tested first. prior to evaluating the research model, we conducted several analyses to ensure that the latent constructs exhibited validity and reliability. reliability results are given in table 3. in terms of construct validity test, this study conducted two tests: convergent validity and discriminative validity. if the factor load of the indicator variable is greater than 0.5, ave>0.5, and reliability>0.7, it means that it has convergence validity [2]. as shown in table 3, cr values are all above 0.7, and all values of cronbach’s α coefficient are also above 0.7, indicating an adequate https://doi.org/10.15837/ijccc.2021.6.4398 8 table 2: structure and source of questionnaire construct measurement items reference se 1. i feel confident of using the online learning platform. 2. i feel confident of searching information on the online learning platform. 3. i feel confident of reading others’ messages on the online learning platform. 4. i feel confident of providing information or respond to someone else on the online learning platform. kao, wu, and tsai (2011); chen et al. (2001)[9, 36] . te 1. the teacher is knowledgeable enough about content. 2. the teacher follows up student problems and tries to find out solution via the online learning platform. 3. the teacher is proficient with all content used in the course. 4. the teacher is good at communication with students within the online learning platform. ozkan& koseler (2009); yoon (2006)[55, 78]. pq 1. the graphical user interface of the online learning platform is suitable for online learning. 2. i have not faced any system errors on the online learning platform. 3. in the online learning platform i can easily navigate where i want. 4. i can find required information easily on the online learning platform. 5. help option is available on the system. ozkan& koseler (2009); hassanzadeh et al. (2012)[25, 55]. si 1. the interaction with the classmates increase my interest in the online course. 2. the interaction with the classmates helps me answer questions raised in course activities. 3. the interaction with the classmates helps me construct explanations/ solutions. diep et al. (2017); arbaugh et al. (2008)[1, 17]. fl 1. i have (at some items) experienced ‘flow’ on the online learning platform. 2. in general, how frequently would you say you have experienced flow when you use the online learning platform? 3. most of time when i use the online learning platform, i feel that i am in flow. rodriguez-ardura& meseguer-artola (2016)[59]. ols 1. i developed knowledge and competencies within the online learning platform. 2. the courses in online learning platform were a good fit for the way i like to learn. 3. the online learning platform met my expectations for what i had hoped to learn. 4. the knowledge and competencies taught through the online learning platform are personally meaningful and important to me lin (2005); wu et al. (2010)[45, 75]. note1:se= self-efficacy;te= teacher’s expertise;pq= platform quality; si= social interaction;fl=flow;ols= online learning satisfaction https://doi.org/10.15837/ijccc.2021.6.4398 9 table 3: assessment of the measurement model construct no. of item mean (sd) cronbach ‘s α ave cr se 4 4.195(1.221) 0.916 0.798 0.941 te 4 4.289(1.329) 0.933 0.833 0.952 pq 5 4.225(0.923) 0.788 0.540 0.854 si 3 4.150 (1.241) 0.871 0.794 0.921 fl 3 4.520(1.245) 0.882 0.809 0.927 ols 4 4.436(1.255) 0.938 0.842 0.955 note1:se= self-efficacy;te= teacher’s expertise;pq= platform quality; si= social interaction;fl=flow;ols= online learning satisfaction table 4: discriminant validity (intercorrelations) of variable constructs variables se te pq si fl ols se 0.894 te 0.280 0.913 pq 0.170 0.249 0.735 si 0.281 0.275 0.479 0.891 fl 0.298 0.480 0.366 0.345 0.900 ols 0.254 0.289 0.258 0.186 0.537 0.918 note1:se= self-efficacy;te= teacher’s expertise;pq= platform quality; si= social interaction;fl=flow;ols= online learning satisfaction consistency reliability. moreover, the average variance (ave) ranged from 0.540 to 0.842, which is higher than the suggested threshold value of 0.5. this shows sufficient convergent validity. we evaluated the square root of the ave and structural dependence to test the discriminant validity. fornell et al. suggested that it can be tested by testing the magnitude of ave value of the latent variable and comparing the square root of the ave of the latent variable with the correlation coefficient of other latent variables [21]. that is, when ave value of the latent variable is greater than 0.5 and the square root of ave value is greater than the correlation coefficient with other latent variables, it shows that it has good discrimination validity. it can be seen from table 4 that all latent variables meet the conditions, so the discriminative validity of the measurement model also passes the test. we verified the effectiveness of convergence by extracting the factors and cross-loads of all index items to their respective potential structures [18]. as shown in table 5, all items show a high load for their relevant factors, but a low crossover load for other factors, which confirm the convergent validity of these indicators as representing distinct latent constructs. 5.2 structural model the results of the path coefficients and the corresponding significance levels for testing the structural model are shown in fig.2. as is indicated, self-efficacy (beta=0.135, p<0.05), teacher’s expertise (beta=0. 398, p<0.001); platform quality (beta=0. 246, p<0.001) and social interaction (beta=0.090, p<0.05) are strong predictors of flow, accounting for 32.5 percent of variance in flow (r2 = 0.325). as we hypothesized that self-efficacy (h1a) and teacher’s expertise (h1b), platform quality (h1c) and social interaction (h1d) would positively affect flow, hypotheses h1 (a), h1 (b), h1 (c) and h1 (d) are supported. meanwhile, flow is found positively affecting online learning satisfaction (beta = 0.583, p < 0.001) and explains 28.8 percent of variance in online learning satisfaction (r2 = 0.288). thus, h2 is supported. https://doi.org/10.15837/ijccc.2021.6.4398 10 table 5: factor loadings (bolded) and cross loadings se te pq si fl ols fl1 0.283 0.402 0.360 0.350 0.896 0.484 fl2 0.257 0.418 0.284 0.277 0.902 0.459 fl3 0.264 0.474 0.340 0.302 0.902 0.503 ols1 0.173 0.209 0.215 0.109 0.406 0.910 ols2 0.261 0.224 0.226 0.138 0.461 0.917 ols3 0.239 0.289 0.249 0.207 0.474 0.93 ols4 0.248 0.318 0.249 0.209 0.591 0.914 pq1 0.091 0.155 0.735 0.196 0.248 0.262 pq2 -0.030 0.093 0.696 0.158 0.216 0.148 pq3 0.140 0.233 0.728 0.194 0.314 0.202 pq4 0.255 0.234 0.767 0.617 0.263 0.166 pq5 0.136 0.176 0.745 0.566 0.283 0.167 se1 0.883 0.236 0.155 0.199 0.255 0.256 se2 0.905 0.274 0.154 0.252 0.267 0.201 se3 0.918 0.295 0.208 0.343 0.314 0.239 se4 0.868 0.173 0.064 0.180 0.211 0.209 si1 0.224 0.173 0.439 0.857 0.256 0.131 si2 0.250 0.243 0.392 0.914 0.323 0.199 si3 0.272 0.304 0.453 0.902 0.335 0.162 te1 0.327 0.903 0.211 0.244 0.450 0.249 te2 0.223 0.918 0.216 0.248 0.463 0.283 te3 0.206 0.922 0.247 0.247 0.414 0.230 te4 0.264 0.909 0.239 0.265 0.422 0.293 note1:se= self-efficacy;te= teacher’s expertise;pq= platform quality; si= social interaction;fl=flow;ols= online learning satisfaction 5.3 mediating effect tests in management research, exploring research questions related to mediation has implications for understanding the drivers of success or failure of certain processes or factors [26]. we use the bootstrapping method proposed by preacher to conduct the mediation analysis [56]. the results showed (table 6) that the 95% confidence interval for each mediating effect did not contain 0, indicating that the indirect effects were all significantly different from 0. hence, the mediation effects of flow were significant. 5.4 multi-group analysis the structural model was further compared between the two use contexts (mobile context and pc context) using multi-group permutation tests. for hypothesis testing, it is to test whether there are differences between path coefficients. the key lies in the construction of test statistics, the core of which is the mathematical expectation and variance of the variables to obtain the t-value. self-efficacy se1 se1 se1 se1 te1 te2 te3 te4 pq1 pq2 pq3 pq4 pq5 si1 si2 si3 teacher’s expertise platform quality social interaction 0.918 0.918 0.728 0.857 0.914 flow r 2 =0.325 online learning satisfaction r 2 =0.288 fl1 fl2 fl3 ols1 ols2 ols3 ols4 0.896 0.902 0.902 0.910 0.917 0.930 0.914 0.583*** figure 2: pls analysis of research model https://doi.org/10.15837/ijccc.2021.6.4398 11 table 6: mediating test results hypotheses parameters estimated t-values 95% ci decision h2a se -> fl ->ols 0.070* 2.281 [0.014,0.134] supported h2b te -> fl -> ols 0.195*** 6.145 [0.133,0.257] supported h2c pq-> fl-> ols 0.107*** 3.563 [0.014,0.134] supported h2d si -> fl-> ols 0.061* 1.919 [0.002,0.123] supported note1: path coefficients: *p-value< 0.05; ** p-value< 0.01; *** p-value< 0.001 note2: se= self-efficacy;te= teacher’s expertise;pq= platform quality; si= social interaction;fl=flow;ols= online learning satisfaction table 7: multi-group analysis result path coefficient (standard error) t-values difference pc(n=158) mobile(n=175) se -> fl (mobile<pc) 0.134(0.069) 0.126(0.091) 0.90 n te -> fl(mobile<pc) 0.386(0.069) 0.347(0.080) 4.74*** y pq-> fl (mobile >pc) 0.161(0.082) 0.223(0.069) -7.49*** y si -> fl(mobile<pc) 0.165(0.089) 0.087(0.077) 8.57*** y fl-> ols (mobile <pc) 0.573(0.052) 0.514(0.056) 9.93*** y note1: *p-value< 0.05; ** p-value< 0.01; *** p-value< 0.001; y=yes, n=no. note2: se= self-efficacy;te= teacher’s expertise;pq= platform quality; si= social interaction;fl=flow;ols= online learning satisfaction. use the following formula to calculate the t-value. spooled = √ n1 − 1 n1 + n2 − 2 × se21 + n2 − 1 n1 + n2 − 2 × se22 (1) t = pc − mobile spooled × √ 1 n1 + 1 n2 (2) spooled is pooled estimator for the variance; t is t-statistic with (n1 + n2 -2) degree of freedom; ni is sample size of dataset for group i; sei is standard error of path in structural model of group i; pc and mobile is path coefficient in structural model of group i. it can be seen from table 7 that there is a significant moderating effect of the use contexts on the four path relationships (flow-online learning satisfaction, platform quality-flow, social interaction-flow, teacher’s expertise -flow) in the model. more specifically, the degree of influence of flow on online learning satisfaction is significantly different under two groups. in the pc context, a more fixed learning medium can eliminate more interference, and the learner’s ability to concen-trate may be higher. therefore, flow has a significant impact on online learning satisfaction. in contrast, in the case of mobile context, small size of the screen does not facilitate learning and the learner’s attention is easily distracted, making the impact of flow on online learning satisfaction less than on the pc context. at the same time, in the influence of teacher’s expertise and social in-teraction on flow, use contexts have a significant moderation effect. the path coefficient under the pc context is greater. in the past two years, the application of mobile in learning has developed fast. mahmoud designed an e-learning content model system based on mobile technology architec-ture [48]. in the context of mobile, platform quality has a much greater impact on flow than in the pc context. in conclusion, h3, h3 (b), h3 (c), h3 (d) are all confirmed. however, there is no signif-icant difference in the influence of self-efficacy on the flow in the two contexts, thereby rejecting h3(a). https://doi.org/10.15837/ijccc.2021.6.4398 12 6 discussion and implication 6.1 discussion based on the s-o-r paradigm, self-efficacy, teacher’s expertise, platform quality and social interaction are studied as environmental stimulus that develops flow as an organism, and further influence online learning satisfaction as a response. the moderating effect of use contexts is also investigated. specifically, self-efficacy is an important determinate in flow, which has a significant indirect effect upon online learning satisfaction, and this is consistent with prior studies such as wang and newlin [71]. the results show that learners with a high sense of self-efficacy are more confident in completing online learning activities and have significantly better flow and higher online learning satisfaction. similarly, teacher’s expertise affects the effectiveness and satisfaction of students’ learning activities, which is consistent with earlier studies [19, 30]. in addition, social interaction has a significant effect on online learning satisfaction. therefore, online learning satis-faction will increase if the teacher’s timely assistance encourages learners to continue learning and the platform is responsive to students’ needs and problems. platform quality positively contributes to online learning satisfaction, and flow mediates the relationship between them. the study indi-cates good quality online learning courses help learners to effectively motivate continuous online learning, enhance flow and increase satisfaction. in summary, flow is an important mediator linking the four antecedents and online learning satis-faction. the study demonstrates that use context moderates the positive relationship between flow and online learning satisfaction, and teacher’s expertise and online learning satisfaction through flow, social interaction, and online learning satisfaction through flow. the learner is the center and sub-ject of online learning. due to the large pc screen and richer features learners tend to experience better flow in the pc context. additionally, significant differences in the effect of teacher’s exper-tise on online learning satisfaction exist between the two groups. teacher’s expertise has a greater impact on online learning satisfaction in the pc context than in the mobile context. a possible ex-planation is that most learners choose online courses to "increase knowledge" and "learn skills", where teacher’s expertise prepares learners well for teaching [6]. with richer and more powerful learning resources on pc, the teacher’s expertise on pc not only meets the basic need for content mastery, but also increases online learning satisfaction. the social interaction has a greater impact on satisfaction in the mobile context than in the pc context. probably because it is easier to achieve content interaction, teacher interaction, learner interaction and human-computer interac-tion in the pc context. a step further, online learning needs to enhance and improve the interactive learning experience of learners in terms of interface design, platform quality and other instrumen-tal support dimensions in pc to attract more learners to actively participate in online courses [60]. however, we have also found a moderating role of use context between self-efficacy and flow not supported. probably because self-efficacy is more subjective and directly affects flow independent of the external context. 6.2 implication this research has enriched relevant research in the field of online learning, and has wealth value and significance in both theory and practice. in terms of theory, this paper enriches and validates the s-o-r model. the findings promote the development of literature on exploring the antecedents, mediating effect, moderating effect, toward online learning satisfaction based on the framework of the s-o-r model. this study contributes to literature by investigating the antecedents of online learning satisfaction from the three levels of learners, educators, and platforms. the four factors (self-efficacy, teacher’s expertise, platform quality and social interaction) play a vital role in the development of flow of learners and as responses affect online learning satisfaction. besides the direct effect, our study highlights the mediation effect of flow. the findings suggest that gaining a compelling flow is critical for learners to enhancing online learning satisfaction. the flow and evaluating its essential impacts in online learning context are of significance. this could encourage future researchers to develop the concept of flow in different contexts in educational backgrounds and non-educational environments. finally, our study presents new theoretical knowledge and pro-vides a model that can systematically compare the effects of antecedents on online learning satis-faction between mobile https://doi.org/10.15837/ijccc.2021.6.4398 13 context and pc context. this argument points to a theoretical gap in the research so far. the changes of the path coefficients of the model and the significance verification under the two groups are tested respectively,which imply that the influences of teacher’s expertise and social interaction on flow among pc context are greater than mobile context, whereas the ef-fect of platform quality on flow among mobile context is lower than pc context. on the other hand, the influence of flow on online learning satisfaction among pc context is greater than mobile context. in terms of practice, for platforms that provide online education services, it is essential to understand the factors that affect users’ online learning satisfaction. studies have shown that students are willing to use vr technology for long hours of online learning [69]. applying new technologies to the field of online learning fundamentally enhance the quality of online learning platform development. meanwhile, learner’s reviews and comments posted during social interaction are important information [10]. the platforms extract implicit preferences and emotions about product or service characteristics through social interaction [47], thereby attracting users to retain and improving user stickiness. for educators, the continuous renewal of teaching methods brings new opportunities and challenges, but the professional level and teaching quality of teachers have always been the most important factors affecting learner satisfaction. furthermore, educators may incorporate soft skills into education to equip learners with skills, habits, attitudes, knowledge, and concepts for personal use [54]. lastly, educators and the platform are appropriately linked to jointly do the community function of online learning, increase teacher-student and student-student interaction, and strengthen the feedback mechanism. for users, understanding the influencing factors that drive their online learning satisfaction can more clearly recognize their requirements, to choose online learning services that more closely match their own needs. 7 conclusion for improving the quality of online education services, improving the teaching quality evalua4tion system, and enhancing flow experience of learners, it is necessary to explore the antecedents of online learning satisfaction to help learners achieve better experiences. to investigate this issue, our research provides substantial theoretical contributions to the literature and inspiring managerial implications for practitioners. firstly, we empirically examines the effects of self-efficacy, teacher’s expertise, platform quality and social interaction motivations on online learning satisfaction based on the of the s-o-r framework to explore the antecedents of online learning satisfaction. secondly, the paper shows that the impacts of self-efficacy, teacher experience, platform quality, and social interaction on flow are significant. likewise, the impact of flow to online learning satisfaction is significant, which suggests flow has important role in building learners’ online learning satisfaction. furthermore, this study contributes to existing literature by assessing the differences between two types of use contexts (i.e., mobile and pc). findings suggest that the influences of teacher’s expertise and social interaction on flow among pc context are greater than mobile con-text. similarly, the influence of flow on online learning satisfaction among pc context is greater than mobile context. however, the effect of platform quality on flow is lower for pc context than mobile context. these findings are useful as we deepen the understanding of what antecedents and how they may affect online learning satisfaction. though the present research has certain uniqueness in terms of perspective and method, and offers theoretical and practical implications, there also has certain limitations, which provides a possible direction for the further study. first, considering the generalization of the research conclu-sions, the sample size can be expanded to improve the randomness and universality in coming fu-ture research. second, because this study is conducted in china, we cannot directly generalize our findings to other countries, although online learning has been implemented by schools all around the world during the covid-19 pandemic. finally, the current study examines the role of flow and use contexts on online learning satisfaction, other mediators and moderators are not examined. the future research is expected to consider additional moderating (such as age and education level) and other mediating factors (such as ease of use and learner’s perception of achievement). https://doi.org/10.15837/ijccc.2021.6.4398 14 8 acknowledgements this work was partially supported by the grant of national natural science foundation of china [no. 71861014], china postdoctoral science foundation [no. 2019m652272], science and technology project of jiangxi education department [no. gjj60458], priority postdoctoral research projects of jiangxi province [no. 2018ky10], social science project of jiangxi province [no. 17bj31], educational teaching reform research project of jiangxi province, and educational 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(2021). exploring the antecedents of online learning satisfaction: role of flow and comparison between use contexts, international journal of computers communications & control, 16(6), 4398, 2021. https://doi.org/10.15837/ijccc.2021.6.4398 introduction literature review online learning satisfaction online learner factor: self-efficacy educator factor: teacher’s expertise platform factors: platform quality and social interaction mediating role of flow moderating role of use contexts the s-o-r framework research model and hypotheses research methodology questionnaire design and measurement data collection results measurement model structural model mediating effect tests multi-group analysis discussion and implication discussion implication conclusion acknowledgements international journal of computers communications & control issn 1841-9836, 10(3):441-451, june, 2015. power-aware relay selection and routing scheme for multi-interface sensor networks m. zheleva, h. lee mariya zheleva department of computer science university at albany, state university of new york, albany, ny, usa mzheleva@albany.edu hyungjune lee* department of computer science and engineering ewha womans university, seoul, republic of korea *corresponding author: hyungjune.lee@ewha.ac.kr abstract: we present a joint relay and radio interface selection algorithm with packet deadline under limited power usage in multi-interface sensor networks. we find route optimization techniques in multi-interface networks: 1) selecting the most conservatively lowest-power interface to guarantee timely transmission considering the remaining hops to destination, and 2) searching detouring paths when the power level of an involved relay node is too low to use the necessary interface that guarantees timely delivery. we aim to achieve data delivery with packet deadline requirement while minimizing energy consumption at each node, and further prolonging network lifetime by selecting cost-effective relay nodes and wireless interfaces. we evaluate our proposed algorithm in terms of total power consumption and packet delivery performance, compared to homogeneous radio interface scenarios of only wifi interface and only 802.15.4 zigbee interface. simulation results show that the proposed algorithm fully exploits the given packet delivery time to conserve power consumption by selecting the interface that consumes the least power, and spreading out network traffic over the network. our proposed algorithm achieves reliable packet delivery without delivery failures due to power outage and missed deadline. keywords: relay selection, routing, multi-interface sensor networks, energy efficiency. 1 introduction according to gartner analyst firm, information technology (it) accounts for 2% of the world’s carbon emissions as of 2007 [3], and reducing network energy demands has been much more important than before. devising energy-aware network services is regarded as one of the most critical design principles for making green it networks realizable. as system-on-chip (soc) technology integrates a larger number of transistors in a given physical space with cheaper manufacturing cost, embedded networked systems are now installed with a variety of wireless network interfaces such as wi-fi, bluetooth, 802.15.4 zigbee, and even 3g, 4g lte baseband. thus, network devices can choose one of these network interfaces to send and receive data packets depending on data rate, energy consumption, and delay. as multi-hop wireless sensor networks scale in size, their energy consumption grows as well. each wireless network interface consumes different amount of energy depending on the design characteristics of physical, mac, and network layer such as data rate and transmission range. for example, wi-fi interface can transmit data with a longer link compared to 802.15.4 zigbee interface, but in turn consumes more power. furthermore, using a different wireless interface incurs different transmission delay, which influences the achieved data rate. if a network device uses a lower-power wireless interface to relay data packets to the next-hop relay node, it will copyright © 2006-2015 by ccc publications 442 m. zheleva, h. lee consume less power, but will take longer to transmit. in embedded network systems, there exist real challenges and constraints of available remaining power to use (in battery-powered devices and sensor networked devices), and packet deadline for quality-of-service (qos) requirement. in this paper, we study the problem of relay selection and multi-hop routing using multiple wireless interfaces in sensor networks where each sensor node has limited power to use, and data packet should be delivered within a given packet deadline. we assume that all nodes in the network are stationary and have multiple radio interfaces for transmitting and receiving data. we consider all possible wireless interfaces that are expected to satisfy the packet deadline, and then choose the most energy-efficient interface among them. by selecting cost-effective relay nodes and wireless interfaces, we aim to achieve packet delivery within deadline while minimizing energy consumption and prolonging the overall network lifetime. prior work on relay selection in multi-interface ad-hoc networks [2, 9, 13, 14] have mainly focused on improving throughput or mitigating loss rate. however, they have not explicitly considered energy-awareness and packet deadline scenarios in multi-radio interface environments. we propose a joint relay and radio interface selection algorithm with packet deadline under limited power availability in multi-interface sensor networks. to meet the packet deadline, each intermediate node en route picks up the lowest power radio interface among all possible interfaces that can guarantee timely delivery, expecting to use the same lowest interface over the remaining hops to the destination. in this way, intermediate relay nodes make their own distributed decision of relaying to the shortest next-hop node through the most energy-efficient interface while achieving packet delivery within the given deadline. further, we consider out-of-battery situations where a selected intermediate relay node runs out of power. to avoid the termination of packet transmission, we apply a local greedy forwarding technique that searches alternative paths (i.e., detouring paths) as long as the entire selected route can satisfy the packet deadline constraint. although the high-level principle is similar to [11], we explicitly consider routing with packet deadline based on simple power cost calculation and consequent route decisions. before the shortest route breaks, and the packet transmission is terminated, an intermediate node estimates that the next hop on the shortest-path would not have the necessary power for packet delivery within the allowed time limit, and then finds a detouring next-hop node that has enough power. thus, the proposed algorithm diffuses network traffic by making more nodes involved with data routing, and accordingly balancing the network. the rest of this paper is organized as follows: in sec. 2, we discuss related work, and sec. 3 provides system model. in sec. 4, we describe the proposed scheme, and evaluate the approach in sec. 5. finally, we conclude this paper sec. 6. 2 related work there is a large body of work on relay selection in wireless multi-hop networks. our focus is on energy-aware routing schemes in multi-interface sensor networks. recent work on this problem can be categorized in three areas: 1) relaying over multi-interface networks, 2) energy-aware relay scheme, and 3) relay selection algorithm. relay selection in multi-interface networks have mainly focused on achieving high throughput or mitigating loss rate by proposing a new routing metric [2,9], or by exploiting the properties of multiplexing and diversity in the physical layer [13,14]. these previously proposed relay schemes incorporate fundamental ideas from mimo (multiple-input and multiple-out) into multi-interface networks. they aim to select better relay paths with multiple radios to improve the corresponding performance metrics (i.e., throughput, loss rate). regarding energy-aware relay scheme, previous works have been studied mostly in the context of sensor networks. due to inevitable requirement of battery usage in sensor networks, a variety power-aware relay selection and routing scheme for multi-interface sensor networks 443 of energy-efficient routing techniques are proposed in [10, 12, 15, 18] to prolong network lifetime. they find efficient and low overhead relay nodes for data delivery to destination through data mining-based network optimization [10], network clustering [18], cooperative beamforming [12], or balancing between shorter high-quality links, and longer lossy links [15]. previous works [6,8,16,17,19] have proposed relay selection schemes in cooperative networks that utilize game theory [8, 16, 19] or markov modeling [6, 17]. the proposed schemes choose the optimal cooperative relay nodes that maximize their own utilities, while contributing to the entire network benefit. our proposed work is different from the previous works summarized above, in that it considers all of three design principles together, and thus develops an energy-aware relay scheme in multiradio interface sensor networks. 3 system model this paper considers the problem of relay and interface selection in multi-radio interface sensor networks. the objective of our proposed method is to minimize power consumption at each sensor node and prolong network lifetime when delivering data packets from a source node to a destination node, while satisfying the packet deadline constraint. each sensor node is stationary and is installed with multi-radio interfaces, e.g., wi-fi and 802.15.4. a sensor node can determine where to relay (i.e., next-hop) the received data packet and through which radio interface. a high-power radio interface (e.g., wi-fi) will consume more power, but can transmit farther and faster compared to a lower-power radio interface such as 802.15.4. we consider data delivery applications under delay constraints in battery-powered sensor networks. effectively, our algorithm diversifies route paths in the network in order to prolong the overall network life-time. we find an alternative path for robust data delivery before the route path breaks due to energy depletion. we assume fixed packet size and power transmission for each interface type. we focus on the single hop transmission delay since it is largely affected by data rate of that interface compared to queueing delay and mac access time. we use the distance-vector algorithm as the underlying routing mechanism. we do not constrain ourselves with a specific one-to-one routing protocol; other advanced routing protocols can be used with our proposed algorithm. we assume that control beacon messages for maintaing up-to-date topology include the current power status of the originator. in this way, a sensor node knows information of power availability of neighboring nodes and can use it for its route decision of selecting the next relay node and the radio interface. 4 relay selection scheme in our network setup, a packet is transmitted from a source node to a destination node. upon packet generation at the source node, the packet is assigned a delivery deadline. each node along the routing path updates the remaining time before deadline in the packet header before relaying it further. each relay node makes a local decision for interface type and next hop when transmitting the packet, while carefully considering the remaining time before deadline. various factors such as transmission time and power consumption as well as power availability influence the success of packet delivery. we design three route selection algorithms that take these factors into account. we now describe our routing schemes in turn. each of our route selection algorithms takes as an input the packet delivery deadline r, as well as the time d and transmission power p through each interface j ∈ {1, . . . , m} (where m is the number of available radio interfaces). d and p are vectors of size m, and are ini444 m. zheleva, h. lee tialized in increasing order of transmission power (i.e., d1 > d2 > . . . > dj > . . . > dm and p1 < p2 < . . . < pj < . . . < pm ). each sensor node has topology information for each interface type j (topology) and can calculate the shortest path between source and destination. to do this, a sensor node utilizes dijkstra’s algorithm, where each edge between nodes in the topology is weighted with the expected transmission count etx [1] as per-hop cost through the corresponding link. considering this information, each node along the path makes a local routing decision to select interface and next hop in order to meet the packet delivery deadline while minimizing the overall power consumption in the system. informally, our route selection algorithms take advantage of the provided packet delivery deadline and prolong the packet transmission time in order to conserve energy by selecting low power transmissions. we now describe in more detail our proposed route selection schemes. 4.1 naive method we first describe a naive counterpart inspired by longest-job-first scheduling algorithm [5] such that the longest-time-spending interface is assigned first due to the least power consumption. this algorithm only considers the remaining time before packet deadline in the scope of the local neighborhood in a myopic view. as detailed in algorithm 1, each decision-making node (i.e. currentnode) finds the lowest power interface that takes less time than the remaining time before deadline, rcurrentnode and selects this interface. naturally, this algorithm favors low power interfaces, which may result in missed deadlines. algorithm 1 naive 1: input:src, dst, d, p, topology 2: output:route from src to dst 3: currentnode=src 4: while currentnode ̸= dst do 5: for all interfaces j ∈ {1, . . . , m} do 6: [pathj, costj] = dijkstra(currentnode, dst, topologyj) 7: end for 8: select arg minj∈{1,...,m} pj, s.t. dj ≤ rcurrentnode 9: currentnode = pathj[indexof(currentnode) + 1] 10: transmit packet (update remaining time) 11: end while 4.2 power-oblivious routing with strict time requirements our second routing scheme, called power-oblivious routing with strict time requirements (porter), enhances naive by incorporating knowledge about the number of future hops nj to the destination through each interface type j ∈ {1, . . . , m} in a far-sighted view. particularly, algorithm 2 porter 1: input:src, dst, d, p, topology 2: output:route from src to dst 3: currentnode=src 4: while currentnode ̸= dst do 5: for all interfaces j ∈ {1, . . . , m} do 6: [pathj, costj] = dijkstra(currentnode, dst, topologyj) 7: end for 8: select arg minj∈{1,...,m} pj, s.t. nj ∗ dj ≤ rcurrentnode 9: currentnode = pathj[indexof(currentnode) + 1] 10: transmit packet (update remaining time) 11: end while power-aware relay selection and routing scheme for multi-interface sensor networks 445 (a) first packet route in porter. (b) second packet route in porter. (c) third packet route in porter. (d) first packet route in parter. (e) second packet route in parter. (f) third packet route in parter. figure 1: route selection. figures (a), (b) and (c) present routes selected by the porter algorithm, whereas (d), (e) and (f) are routes taken by the parter algorithm. green links present 802.15.4 links, and blue links correspond to wi-fi links. red dots denote the nodes that reach the maximum power limit. parter successfully finds a detouring route path before the route path is broken due to energy depletion. src src src dst dst dst src src src dst dst dst porter checks if the number of remaining hops nj through interface j multiplied by the transmission time dj through interface j is lower or equal to the remaining time before packet deadline rcurrentnode (line 8 from algorithm 2). by doing so, this scheme enforces in-time packet delivery in all feasible cases.1 this scheme, however, does not consider power availability in nodes in the process of route selection. 4.3 power-aware routing with strict time requirements finally, we propose a route selection scheme that considers power availability in nodes when making route decisions. we call this scheme power-aware routing with strict time requirements (parter). similarly to porter, parter also takes into account the number of future hops nj and the transmission time dj to enforce timely packet delivery. however, it takes one additional step to check if the selected next hop has enough power to receive and transmit the packet (lines 13-15 from algorithm 3). if not, parter selects an alternative next hop from its neighborhood. algorithm 4 provides details for the selection of alternative next hop. first, we identify all immediate neighbors of currentnode reachable through interface type j. we then calculate the path and cost to destination through each of the alternative next hop candidates. we select a random next hop candidate whose path cost (costnxtj) is lower than the path cost of the current node (costj) (i.e., selecting a next-hop node closer to destination than the current node). the latter guarantees a loop-free route. 1we call a case feasible if transmission through the highest power interface m throughout the whole route guarantees timely delivery, i.e. nm ∗ dm ≤ r. 446 m. zheleva, h. lee algorithm 3 parter 1: input:src, dst, d, p, topology, powermap 2: output:route from src to dst 3: currentnode=src 4: while currentnode ̸= dst do 5: for all interfaces j ∈ {1, . . . , m} do 6: [pathj, costj] = dijkstra(currentnode, dst, topologyj) 7: end for 8: select arg minj∈{1,...,m} pj, s.t. nj ∗ dj ≤ rcurrentnode 9: ptxcurnode = pj; prxnextnode = pj 10: nextnode = pathj[indexof(currentnode) + 1] 11: find arg minj∈{1,...,m} pj, s.t. nj ∗ dj ≤ rnextnode 12: ptxnextnode = pj 13: if powermap[nextnode] < prxnextnode + ptxnextnode then 14: altnexthop(costj,currentnode,dst,topologyj) 15: end if 16: transmit packet (update remaining time) 17: end while algorithm 4 altnexthop 1: input:costj, currentnode, dst, d, p, topology, powermap 2: output:alternative next hop 3: imneigh = findimmediateneighbors(currentnode, j) 4: for all nodes n in imneigh do 5: for all interfaces j ∈ {1, . . . , m} do 6: [pathnxtj, costnxtj] = dijkstra(nextnode, dst, topologyj) 7: select arg minj∈{1,...,m} pj, s.t. nj ∗ dj ≤ rnextnode 8: ptxnextnode = pj; prxnextnode = pj {next statement guarantees a loop-free route} 9: if costnxtj < costj and powermap[nextnode] < prxnextnode + ptxnextnode then 10: neighbors = neighbors.append(n) 11: end if 12: end for 13: end for {select a random alternative next hop from neighbors} 14: altnexthop = selectrandom(neighbors) 15: return altnexthop 5 evaluation we validate our route selection schemes. we start with a description of our simulation environment and continue with analysis of route selection, and then evaluate network performance. we compare the routing schemes described in sec. 4 with two basic counterparts that use only wi-fi and only 802.15.4 zigbee (sensor) interfaces; we call these counterparts wifi and sensor, respectively. 5.1 simulation environment we evaluate our routing algorithms in a matlab simulated network, which consists of 518 nodes in a 500 × 500m2 grid. each node has two interfaces: one 802.11 (wi-fi) and one 802.15.4 (sensor). for our simulations, we use a realistic propagation model, which is based on a combined path-loss and shadowing model [4]. we use a path-loss exponent of 3, a reference loss of 46.67 db and additive white gaussian noise of n(0, 52) db. we transmit packets of 10 kbytes at data rate of 11mbps and 250 kbps for wi-fi and sensor, respectively. with these packet size and transmission rates, the transmission delays are dw = 0.89ms through wi-fi and power-aware relay selection and routing scheme for multi-interface sensor networks 447 ds = 40ms through sensor. we assume that power consumption for transmission is equal to that for reception and is cw = 100mw for wi-fi and cs = 1mw for sensor [7]. for each experimentation, we transmit k packets in the network (specified below). as we evaluate power and time-related aspects of the system, we carefully select the initial power charge in nodes as well as the tolerated packet delivery deadline to demonstrate the benefits of our proposed algorithms. particularly, we assign initial power of k·cw at each node, which is enough for the transmission or reception of k packets through wi-fi, and vary the transmission delivery deadline between 0.2s and 1.6s to force only wi-fi and only sensor transmission, respectively. we present our evaluation results below. 5.2 route selection we start our evaluation by presenting two examples of route selection by our porter and parter algorithms. figure 1 presents the selected routes for a toy-example in which three consecutive packets are transmitted from node 1 to node 518. each node in the mesh topology has 300 mw of power charge and nodes’ battery levels are updated after each packet transmission and reception. as can be seen in figure 1(a), 1(b), 1(c), porter always selects the same route regardless of the power level of nodes along the path. this results in nodes running out of power as early as the second packet transmission. at the same time, parter modifies the transmission route based on the prior knowledge of power change of the neighboring nodes as in figure 1(d), 1(e), 1(f). as a result, parter is capable of finding alternative routes without exhausting any of the nodes in the network. 5.3 effects of packet delivery deadline next we explore the effects of packet delivery deadline on network performance. we present results for a single packet transmission from node 1 to node 518. for each run, we double the packet deadline, starting at 0.2s. figure 2(a) presents our results for path length with increasing delivery deadline. as expected, wifi and sensor maintain the same path length irrespective of packet deadline. at the same time, all of naive, porter and parter take advantage of the increasing delivery deadline by increasing the number of hops to the destination. this, as indicated in figure 2(b), results in reduced power consumption as nodes favor lower power transmissions where time allows. finally, in figure 2(c) and 2(d), we measure the remaining time till the deadline each algorithm incurs as the permitted delivery deadline increases. for low delivery deadline values, sensor and naive always miss the deadline (which appears as negative remaining time in the figures), whereas wifi, porter and parter are well within deadline requirements. as we will see in subsection 5.4, wifi and porter do so at the cost of exhausting power resources in nodes, which results in packets being lost due to nodes along the route running out of power. 5.4 network behavior over consecutive packet transmissions we extend our evaluation by exploring performance trends when multiple consecutive packets are transmitted in the network. particularly, we transmit 30 packets (k = 30) in a network where each node has 3 w of power to start with. the power level at each node is updated as the transmission of packets progresses. for each of the five routing schemes, we evaluate the number of nodes along a route that ran out of power, the path length taken, and the total consumed power. we also report the number of lost packets in two categories: (i) due to nodes running out of power and (ii) due to transmission taking longer than the permitted delivery deadline. 448 m. zheleva, h. lee 0.2 0.4 0.8 1.6 0 5 10 15 20 25 packet deadline, s p a th le n g th wifi sensor naive porter parter (a) 0.2 0.4 0.8 1.6 0 500 1000 1500 packet deadline, s t o ta l p o w e r, m w wifi sensor naive porter parter (b) 0.2 0.4 0.8 1.6 −1 0 1 2 packet deadline, s r e m a in in g t im e wifi sensor naive porter parter (c) −0.6 −0.4 −0.2 0 0.2 0.4 wif i sen sor naiv e por ter par ter r e m a in in g t im e ,s (d) figure 2: performance over increasing packet delivery deadline: (a) path length; (b) total tx power; (c) remaining time; (d) remaining time for permitted delivery deadline of 0.4s. ,s we start with evaluation of the number of nodes that ran out of power for each algorithm. as figure 3(a) shows, sensor and parter do not cause nodes to run out of power. as detailed in subsection 5.3, however, sensor does this at the cost of missed packet delivery deadlines. wifi, naive, and porter exhaust 15, 9 and 8 nodes respectively after the transmission of the 15th packet. in our simulations, we let the nodes out of power continue with packet transmission while counting the number of nodes with negative power, just to effectively show power consumption dynamics of other nodes. this trend persists or increases as packet count progresses, because these three counterpart schemes do not search for alternative routes, and keep using the same route while exhausting the remaining power of the nodes en route. on the other hand, our proposed parter scheme does not cause any nodes with negative power because it finds detouring paths dynamically as consecutive packet transmissions continue. this means that in real battery-powered scenarios, packet transmission for wifi, naive, and porter will be terminated as soon as one of the nodes en route runs out of power, whereas our proposed parter scheme will continue packet transmissions without any node failures due to energy depletion. next, in figure 3(b), we evaluate the path length for each of the algorithms when transmitting consecutive packets. all algorithms but parter maintain the same path length over different packet transmissions, because they always select nodes from the shortest path. wifi, naive and porter cause nodes to run out of power by the 15th packet transmission, at which point packet transmission is terminated. sensor maintains an uninterrupted path of 24 relay nodes over continuous packet transmissions, but it sacrifices packet deadline requirements. parter, however, manages to select an alternative route after nodes on the shortest path run out of power. this results in increase of 5 nodes of the route length. it also translates in increase of transmission power consumption, as indicated by figure 3(c). at the cost of more power, power-aware relay selection and routing scheme for multi-interface sensor networks 449 0 10 20 30 0 5 10 15 packet number n u m . n o d e s o u t o f p o w e r wifi sensor naive porter parter (a) 0 10 20 30 0 5 10 15 20 25 packet number p a th le n g th wifi sensor naive porter parter (b) 0 10 20 30 0 500 1000 1500 2000 packet number t o ta l p o w e r, m w wifi sensor naive porter parter (c) 0 20 40 60 80 100 wif i sen sor naiv e por ter par ter p a ck e t lo ss , % ploss power ploss delay (d) figure 3: performance over consecutive packet transmissions: (a) number of nodes out of power; (b) path length; (c) total tx power; (d) packet loss. however, parter is capable to successfully transmit longer packet sequences. finally, we summarize results for packet losses incurred by each algorithm. we differentiate between two types of losses: (i) such that were caused by nodes along the route running out of power and (ii) such that were caused by a missed deadline. figure 3(d) presents for each algorithm packet loss rate for 30 consecutive packet transmissions. wifi and porter both have 0% loss due to missed deadline, but they both lose 50% of the packets due to nodes running out of power along the route. sensor is most power conserving, and accordingly incurs 0% packet loss due to power outage. however, it causes 100% packet loss due to missed deadline. naive causes 50% packet loss due to power outage and 100% packet loss due to missed packet deadline. in contrast, parter successfully transmits all 30 packets. 6 conclusion design of energy-efficient technologies is crucial both for reducing the footprint of technology on the environment as well as for successful adoption of technology in areas with limited availability of electricity. in line with this need, we design an energy-efficient route selection scheme for heterogeneous sensor networks that leverages knowledge for permitted packet delivery delay and finds routes with minimal power consumption. each node in these networks is equipped with multiple interfaces that take different amount of time and power for a single packet transmission. in this paper, we outline three schemes for route selection in such heterogeneous networks and provide evaluation that brings out important trade-offs that need to be considered in the design of such routing schemes. one crucial design principle to guarantee timely packet delivery (i.e., no packet loss due to missed deadline) is that the most conservatively lowest-power interface needs 450 m. zheleva, h. lee to be selected by taking into account the expected remaining hops to destination in a long-term view. also, it is important that a routing algorithm considers the power availability of nodes along the routing path to ensure no packet loss due to power outages by adaptively taking alternative (detouring) paths. such power-aware algorithm performs more computationally-intensive search for alternative routes, but returns back with increased network lifetime in comparison to shortest path-based counterparts. we identify some limitations of our proposed schemes. while they are efficient in providing timely packet delivery at minimal power cost, as packet transmission progresses, they become unfair. the reason for this is that our proposed schemes favor high power transmissions in the beginning of the routes, in order to assure timely delivery, while consistently allowing nodes later in the path to use low power transmissions. with increasing number of packets between the same source and destination, the power charge of part of the nodes is exhausted, which ultimately results in network disconnection. to address this problem, as future work, we plan to design a more advanced route selection scheme that diversifies interface selection along the path with fairness, while still meeting the packet delivery deadline. acknowledgment this research was supported by basic science research program through the national research foundation of korea (nrf) funded by the ministry of science, ict and future planning (nrf-2013r1a1a1009854, nrf-2013r1a2a2a04014772). bibliography [1] d. s. j. de couto, d. aguayo, j. bicket, r. morris (2003), a high-throughput path metric for multi-hop wireless routing, in acm mobicom, 1-13. 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[19] j. zhang, q. zhang (2009), stackelberg game for utility-based cooperative cognitive radio networks, in acm mobihoc, 23–32. international journal of computers communications & control special issue on fuzzy logic dedicated to the centenary of the birth of lotfi a. zadeh (1921-2017) online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 1, month: february, year: 2021 article number: 4090, https://doi.org/10.15837/ijccc.2021.1.4090 ccc publications my early researches on fuzzy set and fuzzy logic y. shi yong shi* 1. school of electrical and information engineering, southwest minzu university, chengdu, sichuan, 610041 china 2. key lab of big data mining and knowledge management chinese academy of sciences, beijing, 100190 china 3. research center on fictitious economy and data science, chinese academy of sciences, beijing, 100190 china 4. college of information science and technology, university of nebraska at omaha, ne, 68182 usa *corresponding author: yshi@unomaha.edu abstract this paper presents the author’s works on fuzzy sets and fuzzy systems in early 1980’s to celebrate the 100-year birthday of lotfi a. zadeh. they were originally published in chinese. the first part of the paper is about an isomorphic theorem on fuzzy subgroups and fuzzy series of invariant subgroups, which could be a theoretical basis when the multiple-valued computer system will be reconsidered or redeveloped in the future. the second part of the paper describes the convergence theorem of fuzzy integral of type ii which was contributed by wenxiu zhang and ruhuai zhao. both fuzzy integral of type i developed by m. sugeno and the fuzzy integral of type ii have been playing an important role in the design of various engineering devices for last 40 years. keywords: fuzzy subgroup, fuzzy integral, binary numeral system, iq test, artificial intelligence. 1 introduction in 1979, i was 23 years old young man majored in mathematics. one day, i read a newspaper article about fuzzy sets written by professor peizhuang wang. it is the first time i knew the concept of fuzzy sets and my heart was shocked by its fascinating idea of extending {0, 1} to [0, 1]. under the personal guidance of professor wang, i enjoyed much of my spare time as a college sophomore in searching interesting research topics at the “blue ocean” of fuzzy mathematics. i was very crazed about it. to celebrate the 100-year birthday of lotfi a. zadeh, the founding father of fuzzy sets and fuzzy logic, i now share these works that i contributed to the literature of fuzzy sets and fuzzy https://doi.org/10.15837/ijccc.2021.1.4090 2 systems in chinese in early 1980’s [9, 10]. the first part of the paper is about an isomorphic theorem on fuzzy subgroups and fuzzy series of invariant subgroups, which could be a theoretical basis when the multiple-valued computer system is revisited and will be developed in the future. the second part of the paper describes the convergence theorem of the fuzzy integral of type ii which was contributed by wenxiu zhang and ruhuai zhao. both fuzzy integral of type i developed by m. sugeno and the fuzzy integral of type ii have been playing important roles in designing various engineering devices, such as camcorder and influencing our daily life for last 40 years. 2 another isomorphic theorem on fuzzy subgroups and fuzzy series of invariant subgroups in 1965, l.a. zadeh first proposed the concept of fuzzy sets [16], marking the birth of fuzzy mathematics. the fuzzy group [8] was proposed by l.a. zadeh in 1971 and attracted the attention of mathematicians at home and abroad. wu wangming [14] and zou kaiqi [19] have conducted extensive research on fuzzy groups and obtained many important conclusions. following the homomorphism and several isomorphism theorems of fuzzy groups established in [19], this section establishes another isomorphism theorem and proposes the concept of fuzzy normal subgroup sequence, and discusses the relationship between fuzzy normal subgroup sequence and fuzzy direct product group [19]. first, we give another isomorphism theorem about fuzzy subgroups. theorem 1. let x1 and x2 be the subgroups of the abel group x, x1 1, x2 2 be the normal subgroups of x1 and x2 respectively, and g1 1, g2 2 be quasi-normal subgroup of x1 and x2. if x1 1 = {x | µg1 1 (x) = µg1 1 (e) = 1} x2 2 = {x | µg2 2 (x) = µg2 2 (e) = 1} then, x1 1 (x1 1 ∩ x2 2) is the normal subgroup of x1 1 (x1 1 ∩ x2) x2 2 (x2 2 ∩ x1 1) is the normal subgroup of x2 2 (x2 2 ∩ x1) x1 1 (x1 1 ∩ x2) /g1 1 (g1 1 ∩ g2 2) ' x2 2 (x2 2 ∩ x1) /g2 2 (g2 2 ∩ g1 1). is established. proof. from the classic group theory [15], we have, x1 1 (x1 1 ∩ x2 2) be the normal subgroup of x1 1 (x1 1 ∩ x2) x2 2 (x1 1 ∩ x2 2) be the normal subgroup of x2 2 (x2 2 ∩ x1) from the proposition 2.4 from [19], g1 1 ∩ g2 2 is the fuzzy quasi-subgroup of x1 1 ∩ x2 2, thereby it is the fuzzy quasi-subgroup of x1 1 ∩ x2. then, µg1 1∩g2 2 ( xyx−1 ) = min { µg1 1 ( xyx−1 ) ,µg2 2 ( xyx−1 )} = min{µg1 1 (y),µg2 2 (y)} =µg1 1∩g2 2 (y) (∀x, y ∈ x1 1 ∩ x2 2) ∴ g1 1 ∩ g2 2 is the fuzzy quasi-normal subgroup of x1 1 ∩ x2, similarly, we can prove that g2 2 ∩ g1 1 is the fuzzy quasi-normal subgroup of x2 2 ∩ x1, it can be derived from theorem 4 in [19] that g1 1(g1 1 ∩g2 2) is the fuzzy quasi-normal subgroup of x1 1(x1 1 ∩ x2) https://doi.org/10.15837/ijccc.2021.1.4090 3 actually, µg1 1(g1 1∩g2 2)(xy −1) = µ[g1 1(g1 1∩g2 2)][g1 1(g1 1∩g2 2)](xy −1) = sup z∈x1 1(x1 1∩x2 2) min{µg1 1(g1 1∩g2 2)(z),µg1 1(g1 1 ∩ g2 2)(z −1xy−1)} = sup z∈x1 1(x1 1∩x2 2) min{µg1 1(g1 1∩g2 2)(z), sup u∈x1 1(x1 1∩x2 2) min [µg1 1 (u),µ(g1 1 ∩ g2 2)(u −1y−1)]} = min{µg1 1(g1 1∩g2 2)(x), sup u−1∈x1 1(x1 1∩x2 2) min[µg1 1 (u −1),µg1 1∩g2 2 (uy)]} = min{µg1 1(g1 1∩g2 2)(x),µg1 1(g1 1∩g2 2)(y)} ∴ g1 1(g1 1 ∩ g2 2) is the fuzzy subgroup of x1 1(x1 1 ∩ x2). thus, µg1 1(g1 1∩g2 2)(xyx −1) = µg1 1(g1 1∩g2 2)(xx −1y) = µg1 1(g1 1∩g2 2)(y) ∴ g1 1(g1 1 ∩ g2 2) is the fuzzy normal subgroup of x1 1(x1 1 ∩ x2). then, µg1 1(g1 1∩g2 2)(e) = sup y∈x1 1(x1 1∩x2 2) min{µg1 1 (y),µ(g1 1∩g2 2)(y −1)} > min{µg1 1 (e),µg11∩g22(e)} = 1 this can prove that g1 1(g1 1 ∩ g2 2) is the fuzzy quasi-normal subgroup of x1 1(x1 1 ∩ x2). similarly, by changing the position of g1 1, g2 2; x1 1, x2 2, we can prove that g2 2(g2 2 ∩ g1 1) is the fuzzy quasi-normal subgroup of x2 2(x2 2 ∩ x1). let h = { x | µg1 1(g1 1∩g2 2)(x) = µg1 1(g1 1∩g2 2)(e) = 1 } n = { x | µg2 2(g2 2∩g1 1)(x) = µg2 2(g2 2∩g1 1)(e) = 1 } obviously, h ⊆ x11 (x11 ∩ x22) conversely, ∀x ∈ x11 (x11 ∩ x22) µg1 1(g1 1∩g2 2)(x) = sup y∈x1 1(x1 1∩x2 2) min{µg1 1 (y),µg1 1∩g2 2 (y −1x)} = sup y∈x1 1(x1 1∩x2 2) min{µg1 1 (y),µg1 1∩g2 2 (xy −1)} = sup y∈x1 1(x1 1∩x2 2) min{µg1 1 (y),µg1 1∩g2 2 (y −1)} = µg1 1(g1 1∩g2 2)(e) = 1 (∵ g1 1(g1 1 ∩ g2 2) is proved as the fuzzy quasi-subgroup) ∴ h ⊇ x1 1 (x1 1 ∩ x2 2) h = x1 1 (x1 1 ∩ x2 2) n = x2 2 (x2 2 ∩ x1 1) from [19], we have: x1 1 (x1 1 ∩ x2) /x1 1 (x1 1 ∩ x2 2) ' x1 1 (x1 1 ∩ x2) /g1 1 (g1 1 ∩ g2 2) x2 2 (x2 2 ∩ x1) /x2 2 (x2 2 ∩ x1 1) ' x2 2 (x2 2 ∩ x1) /g2 2 (g2 2 ∩ g1 1) however, in the classical group [15], x1 1 (x1 1 ∩ x2) /x1 1 (x1 1 ∩ x2 2) ' x2 2 (x2 2 ∩ x1) /x2 2 (x2 2 ∩ x11) ∴ x1 1 (x1 1 ∩ x2) /g1 1 (g1 1 ∩ g2 2) ' x2 2 (x2 2 ∩ x1) /g2 2 (g2 2 ∩ g1 1) ] let’s discuss fuzzy normal subgroup sequence. https://doi.org/10.15837/ijccc.2021.1.4090 4 definition 1. if x = xo ⊇ x1 ⊇ ···⊇ xk is the normal subgroup seqence of x, and g = g0, g1, · · · , gk is the fuzzy normal subgroup of x = x0, x1, · · · , xk, then g = g0 ⊇ g1 ⊇ ···⊇ gk (1) is the fuzzy normal subgroup sequence of g on x, and its membership function can be defined as, µg(x) = µg0 (x) > µg1 (x) > · · · > µgk (x) where xk = {e} is the identity group, gk indicates the fuzzy identify group of xk, k is the length of fuzzy normal subgroup sequence. if, g = ho ⊇ h1 ⊇ ···⊇ h1 (2) is another fuzzy normal subgroup sequence of g on x, if for ∀g1[∈ (1)],∃h1[∈ (2)], µg1 (x) = µh1 (x) is established, then (2) is the subdivision of (1). obviously, k 6 1 and a fuzzy normal subgroup sequence can be regarded as the subdivision of itself. definition 2. if a fuzzy normal subgroup sequence of g on x is no different from its own subdivision, then it is the composite fuzzy subgroup sequence. theorem 2. if g = n∏ i=0 ai is the direct product of fuzzy groups of x = n∏ i=0 x1, and ai, (i = 0, 1, · · · , n) is the fuzzy normal subgroup of xi, (i = 0, 1, · · · ,n), in which a0 is the identify fuzzy group, then, g is the normal fuzzy subgroup of x. here, the definition of x1 is different from x1 in definition 1. proof. let gi = i∏ j=0 aj(i = 0, 1, · · · , n), i < k µgi(x) = min{µa0 (x0) ,µa1 (x1) , · · · ,µai (xi)} > min{µa0 (x0) ,µa1 (x1) , · · ·µak (xk)} = µgk (x) (xi ∈ xi(i = 0, 1, ..., n)) ∴ µgi (x) > µgk (x) we have µg0 (x) > µg1 (x) > · · · > µgk (x) (∗) or g = g0 ⊇ g1 ⊇ ···⊇ gn while µgi (xyx −1) = min{µa0 (x0y0x −1 0 ),µa1 (x1y1x −1 1 ), · · · ,µai (xiyix −1 i )} > min{µa0 (y0),µa1 (y1), · · · ,µai (yi)} = µg1 (y) (∀x, y ∈ i∏ j=0 xi) ∴ gi(i = 0, 1, · · · , n) is the fuzzy normal subgroup of xi = ∏i j=0 xi(i = 0, 1, · · ·n) respectively. according to definition 1, equation (∗) is a fuzzy normal subgroup of x. ] https://doi.org/10.15837/ijccc.2021.1.4090 5 3 convergence theorem of fuzzy integral of type ii fuzzy integral was setup by m. sugeno in 1972 [11]. later, a series of distribution function convergence theorems and fuzzy integral measure convergence theorems were established by huang jin-li and zheng dao-peng in 1980 [2, 18]. in the same year, the fuzzy integral set up by m. sugeno (it is called fuzzy integral of type i in this paper) was extended and a new fuzzy integral (this is named fuzzy integral type ii in this paper) was introduced by zhang wen-xiu and zhao ru-huai [17]. starting with fuzzy integral of type i, this section presents the corresponding convergence theorem of fuzzy integral of type ii. 3.1 preliminary assume all fuzzy subsets f ∼ (x) = {a | a : x → [0, 1]} of the universe x, definition 3. fuzzy monotone k ∼ is the series of subsets on f ∼ (x), if it satisfies: • 0 ∈ k ∼ , 1 ∈ k ∼ ; • if {aa}⊂ k∼ , aa is monotonic non-decreasing, then limn→∞ aa ⊂ k∼ definition 4. the set function g ∼ (·) on the fuzzy monotone k ∼ is the fuzzy measure of type ii if it satisfies: • g ∼ (0) = 0, g ∼ (1) = 1, stipulate g ∼ (a) = a ( a is small enough). • if a1 6 a2, a1, a2 ∈ k, then g ∼ (a1) 6 g ∼ (a2) • if aa ↑ a,{aa, a}⊂ k∼ , then limn→∞g∼ (aa) = g ∼ ( lim n→∞ aa) = g ∼ (a). definition 5. if the monotonous k ∼ on f(x) satisfies: • b ∈ k ⇒ µb ∈ k∼ (µb is the characteristic function of classical set b) • a ∈ k ∼ , b ∈ k ⇒ a ∧µb ∈ k∼ then the two-tuple (x,k ∼ ) is fuzzy measurable space of type ii. if g ∼ (·) is the fuzzy measure of type ii on (x,k ∼ ), then (x,k ∼ , g ∼ (·)) is fuzzy measure space of type ii. definition 6. function h : x → [0.1] is k-measurable function if for any a ∈ [0 · 1] have na(h) ∈ k. definition 7. the fuzzy integral of type ii of the k-measurable function h on the fuzzy set a ∈ k ∼ is defined as: =∫ a h(x) ◦ g ∼ (·) = sup a∈[0.1] [ a ∧ g ∼ (a ∩µna (h)) ] where na (h) = {x | h(x) > a}, µna (h) is its characteristic function. 3.2 convergence theorem of fuzzy integral of type ii theorem 3. define (x,k ∼ , g ∼ (·)) is the fuzzy measure space of type ii, a ∈ k ∼ , h(x) is k-measurable function, and g ∼ (a) 6 σ(σ > 0): 0 6 =∫ a h(x) ◦ g ∼ (·) 6 σ https://doi.org/10.15837/ijccc.2021.1.4090 6 proof. from 7 in section 3.1: =∫ a h(x) ◦ g ∼ (·) = sup a∈[0,1] [ a ∧ g ∼ (a ∩µna (h)) ] ∵ a ∈ µna (h) ⊂ a ∴ g ∼ (a ∩µna (h)) 6 g ∼ (a) = σ ∀a ∈ [0, 1], a ∧ g ∼ (a ∩µna (h)) therefore, 0 6 =∫ a h(x) ◦ g ∼ (·) 6 σ. ] theorem 4. (f-integral uniform convergence theorem of type i): define (x,k ∼ , g ∼ (·)) is the fuzzy measure space of type i, a ∈ k ∼ , {hn(x)} is a monotonic non-decreasing sequence of k-measurable function (n=0,1,2,···). if lim n→∞ hn(x) = h0(x)converges uniformly on a, then, lim n→∞ =∫ a ha(x) ◦ g ∼ (·) = =∫ a h0(x) ◦ g ∼ (·) proof. from 7: =∫ a hn(x) ◦ g ∼ (·) = sup a∈[0.1] [ a ∧ g ∼ (a ∩µna (hn)) ] (n = 0, 1, 2, · · ·) ∵ hn(x)is monotonous non-decreasing on a, uniformly converge to h0(x) from [17] we have: ∀ε > 0, it exits a positive number n, such that when n > n,∀x ∈ a µna (h0) −ε < µna (hn) < ε + µna (hn) is established. from [18] we have: a ∩µna (h0) −ε < a ∩µna (hn) < ε + a ∩µna (h0) ∴ g ∼ (a ∩µna (h0)) −ε < g∼(a ∩ µna (hn)) < ε + g∼(a ∩ µna (h0)) ∴ sup a∈[0,1] [a ∧ g ∼ (a ∩µna (h0))] −ε < supa∈[0,1] [a ∧ g ∼ (a ∩µna (hn))] < ε + supa∈[0,1] [a ∧ g ∼ (a ∩µna (h0))] that is, =∫ a h0(x)o g ∼ (·) −ε < =∫ a hn(x)o g ∼ (·) < ε + =∫ a h0(x)o g ∼ (·) ∴ ∀ε ≥ 0,exit n = n(ε),when n ≥ n,∀x ∈ a | =∫ a hn(x)o g ∼ (·) − =∫ a h0(x)o g ∼ (·)|< ε condition (*): assume (x, k ∼ , g ∼ (·)) is the fuzzy measurable space of type ii, k ∼ have complementary elements (that is close to set subtraction). a ∈ k ∼ , hn(x)(n = 0, 1, 2, · · ·) is k-measurable function, and g ∼ (·) is a-addrable. (that is ∀a1, a2, a1 ∪ a2 ∈ k ∼ , g ∼ (a1 ∪ a2) = g ∼ (a1) ∨ g ∼ (a2) ) by introducing the condition (*): 3·1. the additivity of k ∼ , that is ∀an ∈ k ∼ , n ∈ n+, we have: g ∼ ( ∞ ∨ n=1 an) = ∞ ∨ n=1 g ∼ (an) https://doi.org/10.15837/ijccc.2021.1.4090 7 3·2. ∀n ∈ n+, hn − h0, h0 − hn are k-measurable functions, and |hn(x) − h0(x)| is also a kmeasurable function. 3·3. definition: if ∀ε > 0, we have: lim n→∞ g ∼ ({x||hn(x) − h0(x)||≥ ε}∧ a〉 = 0, then {hn(x)} converges to h0(x) according to fuzzy measure of type i. 3·4. definition: if σ ≥ 0, existing aσ ∈ k∼ , aσ ⊆ a, g∼ (aσ) < σ makes lim n→∞ ha(x) = h0(x) (uniformly converge on a − aσ), then the fuzzy of {hn(x)} is nearly uniformly convergent to h0(x) on a. 3·1 and 3·2 are proved as follows. i>. from definition 4: g ∼ ( ∞ ∨ n=1 aa ) = g ∼ ( lim n→∞ aa ) = = limn→∞ g ∼ (an) = ∞ ∨ n=1 g ∼ (aa) 3·1 is proved. ii>. from [17], set h0n = n σ i=1 aiµbi, hkn = n σ j=1 bjµcj, where ai, bj ∈ [0, 1], bi, cj ∈ k(i, j = 1, · · · , n) ∵ h0n − hkn = σ i·j (ai − bj)µbi ∩ cj are still simple functions. ∴ lim n→∞ [h0n − hkn] = h0 − hk, so h0 − hk is k-measurable functions. hk − h0, |hk − h0| can be proved similarly. 3·2 is proved. ] theorem 5. suppose that under the condition (*), the necessary and sufficient condition of {hn(x)} to converge to h0(x) according to fuzzy measure of type ii is that {hn(x)} f-nearly uniformly converges to h0(x) on a. proof. for sufficiency, supposing that lim n→∞ hn(x) = h0(x), (which converges uniformly on a − aσ), that is ∀σ > 0, when aσ ∈ k∼ , g∼ (aa) 6 σ, aσ ⊂ a exists, ∀ε ≥ 0, exists n(ε,σ) ∈ n+, when n > n(ε,σ),∀x ∈ a − aσ, |hn(x) − h0(x)|< ε. we have, g ∼ ({x||ha(x) − h0(x)| > ε}∧ (a − aσ)) = 0 ∴∀ε,σ > 0,exists n(ε,σ) ∈ n+, n > n(ε,σ), g ∼ ({x||ha(x) − h0(x)|> ε}∧ a) = g ∼ ({x||ha(x) − h0(x)|> ε}∧ aσ) ∨ g ∼ ({x||ha(x) − h0(x)|> ε}∧ (a − aσ)) 6 σ ∨ 0 =σ sufficiency is proved. for necessity, supposing {hn(x)} converges to h0(x) according to fuzzy measure of type ii on a, that is ∀ε = 1p, p ∈ n +,∀σ > 0, there exists n(p,σ) ∈ n+, such that when n > n(p,σ), g ∼ ({x||ha(x) − ho(x) |> ε}∧ a) 6 σ (3) https://doi.org/10.15837/ijccc.2021.1.4090 8 set: an,p , {x||hn − h0|> 1 p }∧ a aσ , ∞ ∨ p=1 ∞ ∨ n=n(p,σ) an,p apparently, aσ ⊆ a, aσ ∈ k∼ from(3) : g ∼ (aσ) = ∞ ∨ p=1 ∞ ∨ n=n(p,σ) g ∼ (an,p) 6σ then, a − aσ = a ∧ [ ∞ ∨ p=1 ∞ ∨ n=n(p,σ) {x||hn − h0|< 1 p }] ∴∀ε > 0,set 1 p 6 ε, p ∈ n+, from (3),∀σ > 0, exists n(p,σ) ∈ n+,when n > n(p,σ), ∀x ∈ a − aσ have : |hn(x) − h0(x)|< 1 p 6 ε necessity is proved. ] theorem 6. (the convergence theorem of fuzzy integral of type ii according to fuzzy measure of type i): assuming under condition (*), {hn(x)} f-nearly uniformly converges to h0(x) on a, then: lim n→∞ =∫ a hn(x) ◦ g ∼ (·) = =∫ a h0(x) ◦ g ∼ (·) proof. for {hn(x)} f-nearly uniformly converges to h0(x) on a, that is, ∀σ > 0, exists aσ ∈ k∼ , aσ ⊆ a, g∼ (aσ) 6 σ, {hn(x)} uniformly converges to h0(x) on a − aσ, form 4: lim n→∞ =∫ a−aσ hn(x) ◦ g ∼ (·) = =∫ a−aσ h0(x) ◦ g ∼ (·) that is, ∀σ > 0, exists aσ ∈ k∼ , aσ ⊆ a, g∼ (aσ) 6 σ, exists n(σ) ∈ n+ when n > n(σ), ∣∣∣∣∣∣∣ =∫ a−aσ hn(x) ◦ g ∼ (·) − =∫ a−aσ ho(x) ◦ g ∼ (·) ∣∣∣∣∣∣∣ < σ (4) then, according to 3 and g ∼ (aσ) 6 σ, it can be derived that: 0 6 =∫ a−aσ hn(x)g ∼ (·) 6 σ (n = 0, 1, 2, · · ·) (5) from (3) and (5), ∣∣∣∣∣∣∣ =∫ a hn(x) ◦ g ∼ (·) − =∫ a−aσ ho(x) ◦ g ∼ (·) ∣∣∣∣∣∣∣ 6 σ (n = 0, 1, 2, · · · ,) (6) https://doi.org/10.15837/ijccc.2021.1.4090 9 actually: ∵ 0 6 =∫ a−aσ hn(x)g ∼ (·) 6 σ ∴ g ∼ (a ∩µna (hn)) − g (aσ ∩µna (hn)) −σ 6 g ∼ (a ∩µna (hn)) − g (aσ ∩µna (hn)) 6 g ∼ (a ∩µna (hn)) g ∼ (a ∩µna (hn)) − g (aσ ∩µna (hn)) + σ ∴ g ∼ ((a−aσ) ∩µna (hn)) −σ 6 g ∼ (a ∩µna (hn)) 6 g ∼ ((a−aσ) ∩µna (hn)) + σ ∀a ∈ [0, 1], a ∧ g ∼ ((a−aσ) ∩µna (hn)) −σ 6 g ∼ (a ∩µna (hn)) 6 g ∼ ((a−aσ) ∩µna (hn)) + σ thus, =∫ a−aσ hn(x) ◦ g(·) −σ = sup a∈(0,1) [a ∧ g ((a − aσ) ∩µna (hn))] −σ 6 =∫ a−aσ hn(x) ◦ g(·) −σ 6 sup a∈(0,1) [a ∧ g ((a − aσ) ∩µna (hn))] + σ = =∫ a−aσ hn(x) ◦ g(·) + σ that is, ∣∣∣∣∣∣∣ =∫ a hn(x) ◦ g(·) − =∫ a−aσ hn(x) ◦ g(·) ∣∣∣∣∣∣∣ 6 σ (n = 0, 1, 2, · · ·) combine (4) and (6):∣∣∣∣∣∣ =∫ a hn(x) ◦ g ∼ (·) − =∫ a ho(x) ◦ g ∼ (·) ∣∣∣∣∣∣ 6 ∣∣∣∣∣∣∣ =∫ a hn(x) ◦ g ∼ (·) − =∫ a−aσ hn(x) ◦ g ∼ (·) ∣∣∣∣∣∣∣ + ∣∣∣∣∣∣∣ =∫ a−aσ hn(x) ◦ g ∼ (·) − =∫ a−aσ h0(x) ◦ g ∼ (·) ∣∣∣∣∣∣∣ + ∣∣∣∣∣∣∣ =∫ a−aσ h0(x) ◦ g ∼ (·) − =∫ a h0(x) ◦ g ∼ (·) ∣∣∣∣∣∣∣ 6 ∣∣∣∣∣∣∣ =∫ a−aσ hn(x) ◦ g ∼ (·) − =∫ a−aσ h0(x) ◦ g ∼ (·) ∣∣∣∣∣∣∣ + 2σ 6 3σ therefore: lim n→∞ =∫ a hn(x) ◦ g ∼ (·) = =∫ a h0(x) ◦ g ∼ (·) ] https://doi.org/10.15837/ijccc.2021.1.4090 10 corollary 7. assuming under the condition (*), {hn(x)} converges to h0(x) according to fuzzy measure of type ii on a, then lim n→∞ =∫ a hn(x) ◦ g ∼ (·) = =∫ a h0(x) ◦ g ∼ (·) proof. it can be proved according to 5 ] from the above discussion, we can obtain: theorem 8. define (x, b, p) as the probability space, h : x → [0, 1] is k-measurable function, f ⊂ k,p ∼ (·) represents fuzzy measure of type ii on k ∼ , then the inequality holds: ∣∣∣∣∣∣ =∫ µf h(x) ◦p ∼ (·) − ∫ f h(x)dp ∣∣∣∣∣∣ 6 14 proof. from [17], we know: =∫ µf h(x) ◦p ∼ (·) ⇐⇒ −∫ f h(x)p(·) then, according to [12] or [13]:∣∣∣∣∣ =∫µfh(x) ◦p∼(·) − ∫ f h(x)dp ∣∣∣∣∣ = ∣∣∣∣∣−∫f h(x) ◦ p(·) − ∫ f h(x)dp ∣∣∣∣∣ 6 14 ] 4 conclusion the birth of fuzzy sets and fuzzy logic can be regarded as a landmark event of mathematics and computing technology in human history. in 1703, gottfried leibniz published his paper explication de l’arithmétique binaire [3], which is translated into english as the "explanation of the binary arithmetic". he invented 0,1-binary numeral system and explained its connection with the ancient chinese figures of fu xi. as the simplified version of decimal numeral system, leibniz’s binary system gradually became the basis of the current computer design. it changed our human life dramatically for the last 300 years. the recent achievement of google’s alphago and alphago zero have demonstrated that the binary numeral system-based computer can easily outperform human beings by massive calculation in a short time. however, if a computer like alphago or even a super-computer plays with three persons in chinese mahjong, when one human player sends an eye contact to another human player, the machine cannot figure out how to calculate the human signal. this could partially cause by the simple binary numeral system that has difficulty to figure out the human contact. in 2014-2017, i and my student feng liu with other colleagues conducted an interesting research by using human iq test to measure machine. according to our finding, the iq test for virtual assistants shows that even the best one, such as google, still is not smarter than a 6-year-old human [1, 4, 5, 6, 7]. this means that there is a long way to go for the machine’s intelligence to catch up that of our human beings. perhaps, someday in the future when we use fuzzy logic (multiple numeral system) to design a new computer, its computing power of handling complex calculation can easily catch up and solve the human contact problem. by that time, artificial intelligence will be smarter to understand human being. from my point of view, i strongly believe that fuzzy sets and fuzzy logic invented by lotfi a. zadeh will deeply influence our science, mathematics and society in the future which our generation of human beings cannot imagine today. happy 100-year birthday to lotfi a. zadeh! he is living at our mind forever! https://doi.org/10.15837/ijccc.2021.1.4090 11 acknowledgements the author deeply appreciates prof. peizhuang wang’s mentorship for the last 40 years. he also thanks mr.pei quan for his great assistance in preparing this paper. this work was partially supported by the national natural science foundation of china (no.71932008). references [1] hands, j. 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(in chinese) https://doi.org/10.15837/ijccc.2021.1.4090 12 copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: shi, y. (2021). my early researches on fuzzy set and fuzzy logic, international journal of computers communications & control, 16(1), 4090, 2021. https://doi.org/10.15837/ijccc.2021.1.4090 introduction another isomorphic theorem on fuzzy subgroups and fuzzy series of invariant subgroups convergence theorem of fuzzy integral of type ii preliminary convergence theorem of fuzzy integral of type ii conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 4, month: august, year: 2023 article number: 5013, https://doi.org/10.15837/ijccc.2023.4.5013 ccc publications a deep reinforcement learning-based optimization method for long-running applications container deployment l. deng, z. y. wang, h. y. sun, b. li, x. yang lei deng 1. school of computer science, northwestern polytechnical university xi’an, 710129, shaanxi, china 2. science and technology on electro-optic control laboratory luoyang, 471023, china zhaoyang wang* science and technology on electro-optic control laboratory luoyang, 471023, china *corresponding author: zdszr2022@163.com haoyang sun school of computer science, northwestern polytechnical university xi’an, 710129, shaanxi, china bo li science and technology on electro-optic control laboratory luoyang, 471023, china xiao yang school of computer science, northwestern polytechnical university xi’an, 710129, shaanxi, china abstract unlike the short execution cycles of batch jobs, intelligent algorithmic applications typically run in long-cycle containers in the cloud(long-running applications, lra). both need to meet strict slo (service level objective) requirements, consider performance scaling to cope with peak load demands, and face issues such as i/o dependencies and resource contention and interference from coexisting containers. the above greatly complicates container deployment and can easily lead to performance bottlenecks. therefore, the optimization of lra-like container deployment is one of the key issues that cannot be avoided and needs to be addressed in the cloud computing model. this research uses deep reinforcement learning (drl) to optimize the deployment of lras class containers. the proposed non-generic model is able to customize a dedicated model for each container group, providing high-quality placement and low training complexity; meanwhile, the proposed batch deployment scheme is able to optimize various scheduling objectives that are not directly supported by existing constraint-based schedulers, such as minimizing slo violations. the experimental results show that the performance of the drl deployment algorithm improves by https://doi.org/10.15837/ijccc.2023.4.5013 2 56.2% compared to the average rps of the baseline, indicating that the manual deployment scheme can only meet the basic requirements but cannot cover the complex interactions between containers under constraints from a global perspective. this limitation severely limits the performance of the whole pod. meanwhile, based on previous experience, the time consumption of a single deployment scheme is about 1 hour, while the time consumption of the drl deployment algorithm may be less than 7.5 minutes. 1 introduction production clusters typically run two types of workloads, long-running applications (lra) for online cloud services [1–8] and batch jobs for offline data analysis[9, 10]. when deploying, the principle of high priority for service and low priority for batching is usually followed. unlike short execution cycles of batch jobs, lras run in long-cycle containers, lasting from hours to months [11–13]. compared with batch jobs, lras have to meet strict slo (service-level objective) requirements and consider performance scaling in response to load peak demands [14]. at the same time, i/o dependencies [15] are common in different lras. for example, the instance of mirror image algorithm container may transfer the processed data to redis for caching which also complicates the deployment of lras class containers. additionally, lras class containers often face interference from co-existing containers that compete for shared resources such as cpu cache, network, i/o, and memory bandwidth. the above problems greatly complicate container deployment and easily lead to performance bottlenecks. therefore, lra class container deployment optimization is one of the key issues that cannot be avoided and need ironing out in the cloud computing model. existing lras schedulers define various location constraints [11, 16, 17], such as affinity and antiaffinity. according to the above principles, i/o-dependent containers should be placed on one machine to avoid communication overhead, while competing containers should be placed on separate machines to avoid resource interference. typically, the scheduler deploys containers by simple heuristics [11, 18–24] to satisfy as many constraints as possible. however, this constraint-based scheduling has the following problems: first, it requires cluster deployers to identify complex container interactions based on operational experience, and manually map these interactions to deployment-limited constraints, which costs much time and may still not precise. second, position constraints only provide a qualitative scheduling criterion, but do not quantify the actual performance impact (e.g., how much throughput will suffer if a certain constraint is violated). therefore, when the scheduler cannot satisfy all constraints, it may choose incorrectly, thereby violating those constraints that are more influential. third, complex layout constraints have difficulties in optimizing in large clusters. this paper uses deep reinforcement learning (drl)[25–27] to optimize the deployment of lras class containers. the specific innovations are as follows: • propose a non-generic model: tailoring a dedicated model for each container group enables high-quality placement and reduces training complexity ; • the batch deployment scheme is also able to optimize various scheduling objectives that are not directly supported by existing constraint-based schedulers, such as minimizing slo violations ; • while containers can effectively divide cpu cores and memory capacity among packaged applications, they are limited when they compete for shared resources that are not managed by the os kernel (such as networking, cpu cache, disk i/o, and memory bandwidth). therefore, putting many competing containers together can increase resource contention and cause severe interference that hurt the performance of all hosting containers. overall, the drl algorithm improves the deployment efficiency of lras containers and the rps performance of pods after deployment. additional computing overhead brought by the deployment is moderate. https://doi.org/10.15837/ijccc.2023.4.5013 3 2 related work lu shenglin et al. [28] added a weight scheduling module to the algorithm that comes with docker swarm. when a container receives a request from the client, the weight scheduling module calculates the weight of the node based on the resource consumption of each one. by convention, higher weights indicate higher total resource occupancy. this strategy indeed optimizes the load-balancing ability of docker swarm, but it only considers the overall resource allocation. it does not classify and calculate the resources and cannot guarantee the utilization of various resource allocations. dong zhang et al. [29] designed a new container scheduling algorithm, which considers three aspects: the network transmission overhead between the server container and the client, the network overhead of transferring images from the remote repository to the local, and the energy consumption of nodes. the article defines corresponding functions for each factor, integrates them into a linear equation, and obtains the optimal solution through calculation. the algorithm uses integer programming to find the optimal solution. bo liu et al. [30] proposed a multi-objective algorithm, which chooses the most suitable node to deploy the container, and reduces the maximum tps and average response time by fully considering 5 factors: the cpu utilization and memory utilization of each node, the transmission overhead of images on the network, the relationship between containers and nodes, and container clusters. daniel guimaraes lago et al. [31] proposed a container scheduling algorithm based on awareness of resource type. this algorithm includes two strategies: the first part is to find the optimal deployment physical machine for the container, and the other part is to reduce the consumption of network transmission. however, this algorithm fails to take into account the characteristics of container mirroring. kaewkasi et al. [32] proposed a container scheduling strategy based on ant colony optimization. the goal of this algorithm has a better performance by load balancing. but the parameters of this algorithm cannot be adjusted according to the actual situation. mohamed k et al. [33] proposed a new architecture based on caas. aiming at the previous research focusing on scheduling containers to physical machines, a two-level structure of the containervirtual machine-physical machine is proposed. meanwhile, the aco-bf algorithm is proposed to improve the resource utilization rate on consideration of both the resource utilization of physical machines and the cpu utilization and memory utilization of virtual machines. d. kang et al. [34] built a heterogeneous cluster energy efficiency model that classifies applications by using the k-medoid algorithm. the model was demonstrated that can effectively reduce operating costs and energy consumption and ensure application performance through experiments on various application types. xiaolong xu et al. [35] proposed a container scheduling method for the problem of application performance degradation and energy consumption during container migration. this method does not distribute the container image to each resource node in the cluster in advance so that the container needs to increase the distribution time of the image during the scheduling process, which leads to an increase in scheduling time. meanwhile, the algorithm will cause problems such as increased load in the progress of container migration. for the virtual machine deployment problem, you qinggen et al. [36] proposed a virtual machine deployment decision analysis method combined with the bilateral matching model. in the condition of mathematically modeling the bilateral matching problem of virtual machine deployment to maximize the satisfaction of both the virtual machine and the physical machine, a multi-objective optimization algorithm model was constructed, and the optimal matching result was obtained through lingo software. for the container deployment problem, shi chao et al. [37] combined with the bilateral matching model, introduced several similarity calculation methods in machine learning into the calculation of container preference rules and continued to add virtual machines that have been simulated allocating to containers to preference list, thus addressing the initial deployment issue of integrating containers onto virtual machines. kubernetes [38] community provides device service providers with a unified plug-in solution, device manager, which allows users to customize the management and control logic of certain device resources on kubernetes through the extended resources and device plugin modules. however, it is https://doi.org/10.15837/ijccc.2023.4.5013 4 limited to resource discovery, health and allocation, and is not responsible for topology maintenance or monitoring data collection of heterogeneous nodes at the level of management and control. nvidia provides a device plugin that can run on a kubernetes cluster in the form of a daemonset. the nvidia device plugin exposes the number of gpus on each node, tracks the health of gpus, and helps enable gpu containers. the vgpu device plugin is an open-source pod resource scheduler built on device plugin. based on retaining the official functions, it divides the physical gpu and limits the video memory and computing units, thereby simulating multiple small vgpu cards. in the kubernetes cluster, scheduling is based on these split vgpus, so that different containers can safely share the same physical gpu and improve gpu utilization. in addition, the plug-in can also virtualize the video memory (the used video memory can exceed the physical video memory), run some tasks with large video memory requirements, or increase the number of shared tasks. different from normal container deployment, kubernetes deployment requires gpu resources to be imaged in units of pods, thus isolating gpu resources. the pod resource scheduler used in this article is the k8s-device-plugin that can segment gpu resources. 3 technical background when deploying container images, various open-source schedulers can be used to collect cluster node information and deploy them in pod units [39]. the intelligent data analysis algorithm library currently mainly accepts two forms of deployment requests, and the cluster nodes used in the two deployment schemes do not interfere with each other. the first is a real-time single deployment. specifically, when deploying a target detection algorithm image, check whether the node has enough resources firstly. if so, it can be deployed directly. if not, an algorithm is designed to close the algorithm image that has not been called for the longest time, choose the pod which can deploy the current target detection algorithm image after releasing resources, and then deployed it. this deployment mode just requires the deployment to be very simple, fast, efficient ,and stable and does not require further performance improvement. figure 1: integrated call example the second is the integrated deployment as shown in figure 1. for the caller, multiple requests are still sent for algorithm request processing, but related algorithms may have strong closeness in a certain field and need to run for a long time after one-time deployment. this type of deployment task is the typical lras deployment described above. when the intelligent data algorithm library accepts this type of task, it is necessary to make a reservation with the platform maintainer one or two days in advance, give some test samples, and use the test samples to test the stability of the algorithm image before the scheduled time of deployment. generally, after deployment, the operating cycle is one quarter. one or two algorithm images may be offline for part of the time. the algorithm source code and executable files are not modified, only the mount files and models are replaced, and repeated calls are made. https://doi.org/10.15837/ijccc.2023.4.5013 5 in the algorithm deployed in figure 1, the demand for running resources such as cpu and gpu is different, and the demand for the simultaneously deployed object storage service and redis image service is different too. when only using resource constraints, affinity, and anti-affinity to qualitatively describe deployment constraints, if multiple copies of each algorithm are required to be deployed at the same time, relying on manpower to give appropriate performance deployment locations is impossible. the platform provides two modes for container deployment, one is to reserve and deploy a batch of containers in advance according to deployment constraints, and the other is to deploy online based on resource configuration. the container images reserved for deployment are mainly long-running applications (lras). if only relying on deployment constraints, scheduler performance is mediocre. because qualitative descriptions do not lead to optimal performance. the platform is based on the open-source virtual gpu scheduler k8s-vgpu-scheduler, which utilizes deep reinforcement learning (drl) to optimize the deployment of lras containers. cluster resources used for batch pod deployment and single pod deployment requests are completely isolated and non-interfering. for the request of single pod deployment, an lfu queue is maintained in drl-scheduler, and the memory usage and invocation of all deployments are recorded. when a new single deployment request is sent, it will first compare the gmemory occupied by the video memory of the single pod deployment request with the remaining video memory occupancy, that is, the total resource space minus the already deployed algorithm video memory occupancy (tmemory umemory). if gmemory < tmemory umemory, it will directly use the scheduler for node deployment. if gmemory > tmemory, it will select the least recently called pod from the lfu queue to go offline. actual pod deployment will not take place until gmemory < tmemory umemory. figure 2: batch deployment and single pod online deployment the optimal location of lras can be automatically learned using deep reinforcement learning (drl) techniques without specifying location constraints. in the platform’s invocation deployment system, batch deployment of pods uses an intelligent, all-purpose lras scheduler (drl-scheduler), which can optimize various scheduling objectives, such as throughput, slo satisfaction, and cluster utilization. 4 method for lras class container deployment optimization based on deep reinforcement learning drl-scheduler understands the complex performance between lras containers and between operations through past workload logs or lightweight offline analysis. according to this information, drlscheduler encodes the scheduling strategy into a neural network and trains the containers through a large number of simulated experiments, in which it deploys lras containers, predicts their performance, and iteratively improves the strategy. the key to the drl-scheduler is to build a drl model that can scale to large clusters with thousands of lras containers running on thousands of machines. however, directly training a drl agent at this scale is computationally infeasible because it requires a high-dimensional state representation https://doi.org/10.15837/ijccc.2023.4.5013 6 of the data, i.e., deployments of containers on all machines. recently developed drl techniques for other scheduling problems provided little help in addressing the scalability challenges of container deployments. most of these works learn to find the optimal scheduling order of batch jobs or network flows, while lras scheduling focuses on the interaction between containers, which is essentially a combinatorial layout optimization problem. instead of training a generic scheduling strategy offline for all possible input workloads, a dedicated drl model is trained each time a set of containers arrives. tailoring a dedicated model for each container group enables high-quality placement and reduces the complexity of training. although it takes more time to make decisions, lras are insensitive to scheduling delays due to their long-running nature [9]. the platform implements batch deployment of pods as a pluggable scheduling service in kubernetes, and this paper evaluates its performance in clusters using real applications. the batch deployment scheme is also able to optimize various scheduling objectives not directly supported by existing constraint-based schedulers, such as minimizing slo violations. drl-scheduler can easily scale to large clusters running thousands of containers on multiple machines. while containers can effectively divide cpu cores and memory capacity among packaged applications, they are limited when they compete for shared resources that are not managed by the os kernel (such as networking, cpu cache, disk i/o, and memory bandwidth). therefore, putting many competing containers together can increase resource contention and cause severe interference that can hurt the performance of all managed containers. on the other hand, sometimes it is beneficial to deploy lras containers concurrently. in production clusters, many online services are structured as graphs that rely on lras containers, where the output of upstream containers is forwarded to downstream instances for further processing. putting two dependent lras containers on a single machine avoids transferring large amounts of data over the network, resulting in faster responses to query processing. deployment constraints cannot quantify the significance of potential performance gains (or losses). take the affinity constraint as an example. other than stating that it is advisable to have two coexisting containers, it provides no clue as to how much performance gain coexistence can provide. hence, given conflicting constraints, the scheduler may unwisely choose to violate those constraints that have a greater impact. lras scheduling naturally creates a drl problem. given the location (state) of existing containers in the cluster, the scheduler (drl agent) learns to deploy new lras containers (actions) based on its interaction with the cluster (environment). the scheduling strategy is encoded using a neural network and trained through extensive simulation experiments: the scheduler deploys containers, observes performance results (rewards), and iteratively improves the strategy. the scheduler schedules t containers in groups when they arrive. consider each group schedule as a set of t steps in which only one container is deployed to the machine. more specifically, consider an n-node cluster running m applications. assume that in step t, the drl agent has deployed t − 1 containers in the group and will schedule the next container ct. embed the container ct into the one-hot encoded vector e =< e1, e2, ..., em >, if ei is 1, it means the corresponding ct belongs to application i. the node state is further defined as a vector v n =< vn1, vn2, ..., vnm >, where vni counts the number of containers it is running for application i. concatenate the container ct with the state of all nodes in step t, defining the cluster state st =< e, v1, ..., vn >, which is observed by the drl agent. given a state st, the drl agent performs an action at = n, schedules the container ct to node n, and transfers the system to a new state st + 1 in the next step. after all t containers have been scheduled, the agent will evaluate the performance of the group deployment in a final step t . more specifically, the agent receives no reward rt in an intermediate step t < t , while the final reward rt can be any performance metric such as normalized average throughput of planned container groups, slo satisfaction rate, cluster utilization, or their combination is independent of the actual container scheduling order within the group. the scheduling policy is encoded as a neural network with parameters θ, namely the policy network πθ. it takes the cluster state as input and outputs a distribution over all possible operations. the policy network is trained using a reinforcement learning algorithm that uses the rewards observed during training to perform gradient ascent on the parameter θ. https://doi.org/10.15837/ijccc.2023.4.5013 7 algorithm 1 flow of policy gradient training algorithm input: cluster of n node n ∈{1, ..., n}, the group of size t is assigned to the container of the cluster {c1, c2, ..., ct} output: allocate a1, a2, ..., at to the group of size t, at ∈ {1, ..., n} means that container ct is allocated on node n = at 1: initialize the environment and performance evaluation r, leave the replay buffer b and top performance r* empty 2: for episode=1,2,...,e do 3: state of initialization s = {e, v1, ..., vn} 4: for t=1,2,...t do 5: choose an action at from strategy π(at|st) 6: perform the action and get a new state st+1 7: end for 8: collect all performance indicators as a reward ∑ t r(ct) 9: if r ≥ ηr∗ then 10: store experience {s1, a1, ..., st , at , r}r∗← (r, r∗) to b 11: end if 12: use reinforcement learning algorithm to update π(at|st) in every group c and the latest experience of c and batch experience selected from b is used 13: end for 14: return the action a1, a2, ...at made with experience according to the highest reward r = r∗ of playback buffer b θ ← θ + α ∑ t ∇θ log πθ ( ∞∑ k=0 γkrt+k+1 − b ) (1) where α is the learning rate, γ ∈ (0, 1] is the factor used to discount future rewards, and b is the baseline used to reduce the variance of the policy gradient. a common choice of the baseline is the average reward observed during training. these hyperparameters can be tuned for faster convergence. intuitively, this gives the agent a high chance of choosing an action with an above-average reward, thereby speeding up the training process. additionally, the drl-scheduler uses experience replay to further speed up training. the main idea is to store execution actions that lead to high rewards in a replay buffer. the agent will periodically sample these high-performance actions for policy updates. by repeating good experiences encountered in previous training, the agent can learn faster. alorithm 1 shows the pseudocode for drl agent training running in normal and enhanced versions. specifically, the input is a set of containers, and the output is their corresponding assignment. line 3 shows the state input, which is the lras vector e of the current container to be scheduled and all nodes v1,...,vn. lines 4-6 are the allocation process for all t containers in the batch, and the reward is obtained after all tasks have been scheduled. lines 9-10 describe the replay technique, which stores the experience with a high reward in the replay buffer. in line 10, the agent updates its policy with recent experience and experience sampled from the replay buffer, using a reinforcement learning algorithm. finally, the drl agent selects the highest rewarding action sequence in the replay buffer and returns it as the final deployment decision (line 11). training neural networks with policy gradients requires drl agents to frequently interact with the environment, which is timeconsuming for lras scheduling. in a real system, it takes at least a few minutes to deploy a container and measure its performance. since drl training typically takes tens of thousands of iterations to complete, having the batch pod scheduler interact directly with the actual cluster is too slow and impractical. similar to previous work, the platform develops a high-fidelity simulator of the cluster environment that can predict container performance at a given location. this enables learning through simulated experiments without deploying containers in real clusters. instead of modeling low-level resource interference based on contention on cpu cache or memory bandwidth (which may not be available in production traces), this paper moves to high-level perforhttps://doi.org/10.15837/ijccc.2023.4.5013 8 mance metrics such as container throughput and request latency. then it predicts how these metrics will change under different container deployment scenarios. in particular, the co-location vectors of machine-level containers, and the observed rps/latency of each resident container are recorded and used as training samples for the simulator. the simulator uses multivariate random forest (rf) as the main regressor to characterize the container interactions. rf methods use combinatorial decision trees to perform regression tasks that can make accurate predictions with a small amount of training data. it also accommodates overfitting when a large number of replicates are provided. these two characteristics make rf regressors ideal for simulators. the previous scheduler based on drl trains a unified drl model offline and uses it online for scheduling decisions. when it comes to large clusters, training scheduling strategies offline to deal with extremely varied workloads inevitably leads to poor performance. instead, drl-scheduler trains a dedicated drl model on the spot when a new batch of containers arrives. while training a dedicated model takes time, the long-running nature of lras containers allows them to tolerate relatively long scheduling delays (e.g., tens of minutes) in exchange for better locations. in the training set, the drl agent deploys a set of containers by taking a series of actions that together constitute a scheduling decision. since the operational space deployed per container is proportional to the number of machines in the cluster (i.e., one machine is chosen from all machines), the space of scheduling decisions also grows exponentially with the size of the cluster. 5 design and implementation of deployment system the algorithm library platform realizes the storage, deployment, and invocation through web interfaces such as restful api/socket provided by drl-scheduler. as shown in figure 3, the management of the core image is based on the interaction between storageshell and the storage center. storageshell has done a layer of encapsulation based on docker daemon to realize basic work such as image information parsing and operation. the daemon thread docker daemon can directly manage the images downloaded by the docker registry. this paper implements the model assembly function based on it, makes restful api calls to the daemon thread docker daemon through storageshell, and performs image recovery operations through ovedrlaps.json. ovedrlaps.json is specially used to record model information and implement multi-version model switching and management. implement the management and download of file sets. the storage center uses the object storage node to manage ovedrlaps lib, which realizes the management and distribution of related data such as models, files, and datasets. the docker registry of the storage center is unified in format through encapsulation of the storage center, the calling interface and ovedrlaps lib in the code. containers use the interfaces and methods provided by containermanager while deploying. the process of interacting with storageshell is mainly based on the commands provided by docker itself and container_integration.json. 5.1 deploying pods system design the platform’s deployment scheme mainly uses the drl-scheduler component to deploy and call pods, uses the open source pod call component k8s-vgpu-scheduler, and embeds the drl deployment algorithm to complete batch pod deployment requests and single pod deployment requests, which is shown in figure 4. at the same time, drl-scheduler completes large files and the rapid switching of models through container_integration.json and package.json. it also realizes version control and automatic assembly. automatic removal is possible for idle nodes and nodes that are no longer needed. for the resource request of a single pod, drl-scheduler directly calls containermanager.createfleet() to expand the required instance by evaluating the requested resource size, node selector, affinity, tolerance, etc. 5.2 basemananger class drl-scheduler class is the core class of the algorithm library platform to deploy pods, and it manages all pods on the master node of the cluster. the batch deployment and single pod online https://doi.org/10.15837/ijccc.2023.4.5013 9 figure 3: deployment system architecture figure 4: drl-scheduler java class design diagram deployment are given in figure 5. use package.json and container_integration.json to configure storageshell and container-manager respectively to implement fine-grained management and deployment of container images. containermanager calculates the best batch deployment scheme through drlloading, returns the result to basemanager, and finally uses the pod scheduler to achieve batch deployment. drl-scheduler summarizes the resource occupancy of all pods into redis, providing an open resource status query interface. the core methods include sendpodsmapper and podarrange, which respectively use the drl algorithm to calculate the current optimal deployment and complete single-node or multi-node pod deployment. during the entire deployment process of drl-scheduler, pod deployment requests will trigger different types of events. for example, after first receiving the deployment request, if it is podsarrange (batch deployment), it will first enter the pre-running state. at this time, drl loading will not be called immediately to calculate the optimal deployment plan, but a collection function will be triggered first, which will take resource usage of all nodes from redis. only in the step of obtaining the resource status of the node, multiple event listeners are encapsulated to prevent huge errors in the resource status of the node. after the pre-running state, it enters the calculation state of batch deployment scheme calculation. at this state, the current thread will be blocked until drlloading returns the result of locationmapper. the deployment event will be triggered after obtaining the deployment plan. if it is onepodarrange (single-node online deployment), skip the pre-running and computing state and directly enter the deployment state. listener components include event sources, event objects, and event listeners. when the pod status https://doi.org/10.15837/ijccc.2023.4.5013 10 figure 5: batch deployment and single pod online deployment is queried or modified, the method of event listener will be triggered, and the event object will be passed in when the method of event listener is called. in the event listener of the pod, you can operate by the object source acquired from the event object. take deployment events in drl-scheduler as an example, using kubernetes and k8s-device-plugin to arrange the actual deployment nodes for each pod, it will freely deploy each pod to the specified physical machine. the deployment process is parallel, that is, each physical machine node is free to use the pod scheduler, and any single deployment failure does not affect the deployment of other nodes. significantly, on the node which adopts the online deployment pod scheme, the scheduler will close the algorithm which has the minimum probability to be called when the pod resources are insufficient through the calling situation, and the obtained by closing the algorithm will be called, and the corresponding resource status of the pod in redis will be modified. after the pod is deployed, storageshell will be automatically assembled or remotely mounted according to package.json and container_integration.json to complete the entire deployment process. at this point, the state of the pod is rewritten to the deployed state, the state recording event is triggered, and the resource occupancy status is written to redis. in the event of resources mutex, pod deployment failure, etc., the corresponding watchdogs will summarize and report the error information through the above logaggregate. all pod deployment failures will directly form an emergency-level error report, which will be quickly captured and notified to the relevant team. the deployment process does not support either rollback transactions or processing multiple batches of pod batch deployment requests at the same time, but it supports multiple online single pod deployment requests. the reason is that the node resources for batch deployment of pods and the node resources for online pod deployment are separated (resource requests are limited by a simple formula). although two pods may be deployed on the same node at the same time, there will be explicit resource calculation formulas to ensure that the respective reserved resources not be preempted. during the pod deployment process, if drl-scheduler finds that the current node has not pulled the corresponding image data from the storage center, it will use api opened by the client to arrange the corresponding node to perform the algorithm pulling operation. the batch or single pod deployment process can be significantly accelerated if the corresponding physical machine has pre-pulled images. 6 experiment 6.1 cluster deployment dataset and simulation components 6.1.1 architecture of cluster deployment simulation components the cluster deployment simulation component is an independent module specially developed to further analyze the performance of the deployment algorithm model for batch deployment of longhttps://doi.org/10.15837/ijccc.2023.4.5013 11 running applications. using policy gradient training of neural networks requires drl agents to interact with the environment frequently, which is time-consuming for pod scheduling. in a real system, it takes at least a few minutes to deploy a container and measure its performance. since drl training typically requires tens of thousands of iterations to complete, having the scheduler interact directly with the actual cluster is too slow and impractical. similar to previous work, the platform develops a highfidelity cluster environment simulator based on the core code of drl-scheduler, which can predict container performance at a given location. this enables learning through simulated experiments instead of deploying containers in real clusters. the cluster deployment simulation component does not model low-level resource disturbances based on contention on cpu cache or memory bandwidth, because it is not realistic to obtain relevant information in real production. instead, it moves to high-level performance metrics such as pod throughput, request latency, and gpu memory usage. then it predicts how these metrics change under different container deployment scenarios. in particular, the co-location vectors of machine-level containers and the observed rps/latency of each resident pod are recorded and used as training samples for the simulator. the simulation simulator uses the multivariate random forest as the main regressor to characterize the container interactions. rf methods use combinatorial decision trees to perform regression tasks that can make accurate predictions with a small amount of training data. it also accommodates overfitting when a large number of replicates are provided. differing from the interactive model of the drl-scheduler in actual deployment, the simulation experiment adopts the cluster deployment simulation component for model testing. in the real environment, request is directly sent to basemanager and drl-scheduler calls the deployment algorithm. then the actual deployment is carried out through storageshell. the load information such as node gpu is maintained by basemanager, as shown in figure 3. in the simulation environment, drlscheduler and storageshell are not used for actual deployment, but the simulation components are deployed in clusters. this component simulates actual deployment and interacts with the rf environment simulation simulator. drl-scheduler and cluster deployment simulation components are completely consistent, thus ensuring the complete unification of related data formats. in addition, the loading class drlloading of the management model is completely consistent with the actual production environment. that is, drl-scheduler and kubernetes in figure 3 are completely replaced by the cluster deployment simulation component and rf environment simulation simulator. the cluster deployment simulation component will send the corresponding podsarrange request to the environment simulator, then receive and record the corresponding podsstate information. it follows that the deployment algorithm drl model relies heavily on the feedback and prediction of the cluster deployment simulation components. this means that it needs to guarantee that the environment simulation simulator which deploys simulation components in a cluster under given conditions can accurately give the current state of the cluster after given actions. 6.1.2 training and accuracy of rf environment simulation simulator the simulator is essentially a multivariate random forest regression model (rf) consisting of an ensemble of 100 decision trees with a maximum depth of 20. to analyze the accuracy of the simulator, a set of training samples is set specially. a total of 924 different positioning combinations of the lras task set of size 6 are collected on a machine corresponding to pod requests per second (request persecond, rps) information, and the mean square error (mean-square error, mse) information is used to calculate its accuracy. for evaluating the accuracy of the decision tree model more comprehensively, 1%-90% of the training dataset was randomly sampled, and the rest of the data was generated into the test subset. the specific service subjects include a redis service image, an object storage service image developed in java, a text recognition algorithm image, an inference expression algorithm image that introduces external knowledge, a target detection algorithm image, and a human posture key point detection algorithm image. among them, all algorithms need to interact with the pods corresponding to the object storage service and the redis service, so when the scheduling player is used to return the request algorithm pod, redis and the object storage service will generate additional requests https://doi.org/10.15837/ijccc.2023.4.5013 12 processing. figure 6: prediction accuracy figure 6-9 shows the prediction accuracy of the test samples with different sampling ratios. the sampling ratio of more than 40% can guarantee mse < 0.145%, which basically guarantees the accuracy requirements of the rf environment simulation simulator. with the current batch scheduling task of the order of magnitude 6, it takes about 1-2 days to generate a sufficient localization combined dataset for training an environmental simulator. the order of magnitude of this platform in actual deployment is generally 4-5, and the corresponding acquisition time is mostly maintained within 10 hours. 6.1.3 lras dataset of cluster deployment to train drl model to deploy lras, we used the deployment records of different pod scales in the lras task presented in the previous subsection submitted in 8 months as samples. some pod applications of lras in the sample are closely related. in addition to recording deployment information, it also includes invocation information within a specified time limit. finally, the recorded deployment co-location situation is redeployed in the specified environment, and compared with the actual rps situation after the drl model is deployed to more accurately analyze the performance improvement. in the actual test of lras, in addition to four mirroring algorithms using gpu resources, mirroring of redis and object storage services is also added. this part of the image can still be used as a pod with 0 gpu consumption and deployed using open-source components. the pod deployment of all recorded lras is mounted with large files, which are uniformly mapped according to package.json. if there is no file decoupling, directly mark the corresponding field as empty. for test comparison, the test environment is set to 9 nodes to build the test environment. after the related algorithm image is deployed according to the pod, the client that sends the simulated request will send the request according to the recorded request method, and then generate the rps that uses the rps of the pod running on a single node for normalization processing. sending simulated requests per pod client was tested using a rate of 4 requests per second. the four algorithm images will be deployed with multiple copies at the same time in the recorded actual deployment, and will be deployed according to the actual situation in the actual test. if the distribution record of the message queue is used, the complexity of the test will be greatly increased, so the distribution simulation of the message queue is not carried out in the cluster deployment test. the average upper limit of gpu resource usage for the images of the four mirroring algorithms is 1460mib, and the average call delay is 0.7s. the call delay distribution is shown in figure 7. 6.2 experimental verification the actual number of nodes deployed on the platform is generally 3-9. according to the statistics of batch deployment scheduling task records, the situations of manual deployment and drl model deployment are compared in 9 nodes. for further analysis, the 9-node deployment records in 8 months are randomly divided into 10 groups of batch deployment tasks, and the pod size of each deployment https://doi.org/10.15837/ijccc.2023.4.5013 13 figure 7: call delay cdf task is 15-48. each deployment task will generate the corresponding test results as the baseline according to the records of manual deployment under the original constraints. during the training process, the package.json file corresponding to the algorithm image needs to be loaded, because information such as the upper limit of the video memory of the algorithm image is required. taking the gpu memory usage as an example, the size of the video memory will be directly judged in the rf environment emulator, and then a reward will be given. the video memory cap is a very important explicit constraint during pod deployment calls to avoid bad memory allocations. the training curve for reinforcement learning is shown in figure 8. figure 8: drl model 9 node learning curve figure 9: average rps for different deployment levels since the order of pods deployed in batches in the data is between 15 and 48, in order to better analyze the rps impact of pods of different orders of magnitude, the interval is divided into two. figure 9 compares the average rps of different pod levels. compared with the average rps of the baseline, the drl deployment algorithm is significantly improved, and the overall performance is https://doi.org/10.15837/ijccc.2023.4.5013 14 improved by 56.2%. this shows that the manual deployment scheme cannot consider the complex interaction between containers from a global perspective under constraints, and only meets the basic configuration requirements, which affects the overall pod performance. figure 10: drl deployment time distribution the data set does not record the time to manually generate the deployment plan using only constrained dependencies and greedy strategies. according to experience, the time consumed by a single deployment plan is about 1 hour. figure 10 shows the time-consuming of the drl deployment algorithm, which is basically less than 7.5 minutes. overall, the drl algorithm improves the overall deployment efficiency, and the additional computing overhead brought by its deployment is moderate. 7 data availability no data were used in the study. 8 conflicts of interest the authors declare that they have no conflicts of interest. 9 acknowledgments this work was supported by the aero science foundation of china under grant number 202051053002(corresponding author: lei deng). lei deng is with the school of computer science, northwestern polytechnical university, xi’an 710129, china, and lei deng is also with the science and technology on electro-optic control laboratory(e-mail: denglei@ nwpu.edu.cn). references [1] m. zaharia, t. das, h. li, t. hunter, s. shenker, and i. stoica, “discretized streams: faulttolerant streaming computation at scale,” in proceedings of the twenty-fourth acm symposium on operating systems principles, 2013, pp. 423–438. 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[39] h. v. netto, a. f. luiz, m. correia, l. de oliveira rech, and c. p. oliveira, “koordinator: a service approach for replicating docker containers in kubernetes,” in 2018 ieee symposium on computers and communications (iscc). ieee, 2018, pp. 00 058–00 063. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: deng, l.; wang, z. y.; sun, h.y.; li, b.; yang, x. (2023). a deep reinforcement learning-based optimization method for long-running applications container deployment, international journal of computers communications & control, 18(4), 5013, 2023. https://doi.org/10.15837/ijccc.2023.4.5013 introduction related work technical background method for lras class container deployment optimization based on deep reinforcement learning design and implementation of deployment system deploying pods system design basemananger class experiment cluster deployment dataset and simulation components architecture of cluster deployment simulation components training and accuracy of rf environment simulation simulator lras dataset of cluster deployment experimental verification data availability conflicts of interest acknowledgments international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 2, month: april, year: 2021 article number: 4166, https://doi.org/10.15837/ijccc.2021.2.4166 ccc publications human-in-the-loop: role in cyber physical agricultural systems m. sreeram, s.y. nof maitreya sreeram school of industrial engineering purdue university, usa 315 n grant street, west lafayette, in 47907 *corresponding author: msreeram@purdue.edu shimon y. nof school of industrial engineering purdue university, usa 315 n grant street, west lafayette, in 47907 abstract with increasing automation, the ‘human’ element in industrial systems is gradually being reduced, often for the sake of standardization. complete automation, however, might not be optimal in complex, uncertain environments due to the dynamic and unstructured nature of interactions. leveraging human perception and cognition can prove fruitful in making automated systems robust and sustainable. “human-in-the-loop” (hitl) systems are systems which incorporate meaningful human interactions into the workflow. agricultural robotic systems (ars), developed for the timely detection and prevention of diseases in agricultural crops, are an example of cyber-physical systems where hitl augmentation can provide improved detection capabilities and system performance. humans can apply their domain knowledge and diagnostic skills to fill in the knowledge gaps present in agricultural robotics and make them more resilient to variability. owing to the multi-agent nature of ars, hub-ci, a collaborative platform for the optimization of interactions between agents is emulated to direct workflow logic. the challenge remains in designing and integrating human roles and tasks in the automated loop. this article explains the development of a hitl simulation for ars, by first realistically modeling human agents, and exploring two different modes by which they can be integrated into the loop: sequential, and shared integration. system performance metrics such as costs, number of tasks, and classification accuracy are measured and compared for different collaboration protocols. the results show the statistically significant advantages of hub-ci protocols over the traditional protocols for each integration, while also discussing the competitive factors of both integration modes. strengthening human modeling and expanding the range of human activities within the loop can help improve the practicality and accuracy of the simulation in replicating a hitl-ars. keywords: agricultural robotics; collaborative intelligence; human-in-the-loop; multi-agent simulation https://doi.org/10.15837/ijccc.2021.2.4166 2 1 introduction stress detection in agricultural plants has been a source of improving research over the past few years. developing a resilient response network to agricultural stresses with appropriate collaborative control protocols is of immense importance to the food supply chain in terms of reliability and food security. dynamic conditions give rise to a host of abnormalities, each of which must be dealt with in a timely manner through detection, monitoring, predictive and preventive maintenance [18]. current research in precision agriculture (pa) includes the development of cyber-physical systems (cps) with collaborative intelligence (ci) capable of performing the aforementioned tasks [19]. there is a need to harmonize the interactions between agents in such systems, in order to locate and ensure optimal collaborative performance. hub-ci (hub with collaborative intelligence) is one such platform developed to enable collaboration of information, knowledge and decisions [12]. hub-ci encapsulates tenets from the collaborative control theory [28], which emerged from the realization that with increasing complexity and inter-dependency of automated systems, they will collapse unless designed and optimized for effective cyber-supported collaboration. with the advancement in sensors and robotic technologies, the ideas of applying these technologies to agricultural settings have been discussed with increasing attention in controlled environments such as greenhouses [7]. the development of cps integrating robotics, sensors and human entities for pa provide potential test beds for “resilient” agricultural systems. monitoring of environmental conditions such as temperature and humidity can be accomplished through effective sensor deployments, but often these systems are affected by external noise due to the large amounts of data produced by the sensors. lack of quantifiable data also prevents the systems from working dynamically, and this is one of the primary motivations behind human-in-the-loop (hitl) systems. robotic agents (ras) perform well in environments which are largely constant, structured and predicable [1, 11]. developing autonomous systems for agricultural environments, however, is difficult because of the unpredictable nature of the terrain, disruptions [27] and variable environmental parameters. system simplification and performance can be improved by the introduction of a human agent (ha) into the system [3]. inspired by sreeram [33], this research aims to simulate and study the integration of human operators as intelligent agents within the agricultural systems, specifically for accurate detection of stress in agricultural crops. the objectives are summarized as follows: • model and simulate human operators as intelligent agents capable of integration within the information and decision flows; explore different integration architectures of hitl. • with emphasis on collaborative intelligence, develop robust multi-agent workflows for agricultural cps to enable early detection of stresses and infections; this article is organized as follows. in section 2, literature review and related work on cyber-physical systems for precision agriculture, collaborative intelligence, hub-ci, and hitl systems are described. section 3 contains the methodology involved for the design of the hitl simulation, human agent modeling and different levels of interaction with the system. section 4 discusses the observations, and evaluation of workflows. concluding remarks and future research are proposed in section 5. 2 background 2.1 cyber-physical systems in precision agriculture cyber-physical systems (cps) are traditionally defined as integrations of computation with physical processes, where embedded computing devices and networks integrated with feedback loops monitor, control and support physical process [22]. an important paradigm of cps is the parallelism to industry 4.0, which considers technologies such as sensor and actuator networks, human-robot interactions, big data analytics and decentralization. internet of things (iot) and internet of services (ios) have vastly improved the effectiveness of cps in real-time decision support and administration, virtualization of physical components and data collection. the current industrial revolution driven https://doi.org/10.15837/ijccc.2021.2.4166 3 by cps and iot is expected to have a major impact on the future of work and on agriculture as well, as there is a natural relation between industry and agriculture [8]. currently, there is a need for effective early disease detection techniques to control plant diseases for food security and sustainability of agro-ecosystems [17, 35]. precision agriculture is an application of an agricultural cps consisting of three layers: physical, network, and decision layer [29]. it comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management to optimize production by accounting for variability and uncertainties within agricultural systems [23]. the network layer, defined by the use of wireless sensor networks (wsn), and have been used for the monitoring of environmental factors such as temperature, humidity, soil content successfully in agricultural settings [2] leveraging different iot technologies for real-time flow of information. the decision layer, founded on human knowledge, is aided by the rise of non-invasive remote sensing techniques such as fluorescence and hyperspectral imaging, spectroscopy [6], which can prove to be sensitive and consistent in detecting defects in plants. robots in agriculture, which redefine the physical layer (as opposed to human operators) are developed to reduce the reliance on farmers for tedious and repetitive tasks such as fertilization, irrigation, and disease detection. with the rise of sensor reliability, development of agricultural robots fitted with remote sensing and vision systems for accurate detection of crop diseases has grown [15]. robots must be integrated with intelligent control and communication systems and made capable of working in unstructured environments and terrains. 2.2 human-in-the-loop systems while the complexity of performable tasks by robots has increased, there still exist many tasks where robots show a considerable gap in performance when compared to humans. complex operations such as object detection under uncertainty [9], task classification in human-robot work cells [10] require human cognitive decision-making and properly designed collaboration between humans and robots. multi-agent systems designed with optimized human-robot interactions and collaborative intelligence are termed as “human-in-the-loop (hitl)”. previous literature on this topic includes human-robot interactions for target recognition [5], human decision modeling [34], robots querying humans for help [20]. current approaches work asynchronously with has who are expected to provide input in terms of decisions and data interpretations. leveraging human cognition and perception can improve the versatility of complex tasks, help in reducing errors and conflicts with dynamic environments, and drive productivity [4]. bechar & edan [3] also reported the advantages of hitl systems for melon detection tasks, on average showing higher detection rates and lower detection times as compared to fully autonomous and manual systems. figure 1: partial autonomy in hitl systems for traditional agricultural systems, has are usually trained inspectors tasked with sampling, inference and physical movement between locations. appropriate task taxonomies should also be studied to understand the range of tasks which can be performed by the has in the human-robot collaboration context. bechar et al.,[4] develop different levels of human-robot collaboration (hrc) based on the four degrees of autonomy [32], with each level corresponding to a unique hrc workflow to elucidate the different possible integrations of humans in the loop. each integration entails a different https://doi.org/10.15837/ijccc.2021.2.4166 4 workload for the ha, and appropriate workload levels must be maintained for sustainable benefit, since human performance is not perfect and subject to fatigue [36]. limited by time, physical and mental constraints, increasing workload often result in reduced performance, increased chance of error [21, 36]. this is an important measure in high-risk systems such as machine operation, maintenance, emergency protocols – it needs to be ensured that the has interface for the optimal amount of time, as long as their benefits can overbear their disadvantages. 2.3 collaborative intelligence and hub-ci effective collaboration implies the timely sharing of information, resources and responsibilities between distributed agents to plan, implement and accomplish individual and common goals of the combined system [37]. for cps, often it is necessary to ensure that the collaboration caters across different agent and interaction types, and the suitability of collaboration is judged the “collaborative intelligence” of an agent. collaborative intelligence (ci) can be measured by agents’ ability to interpret new information, share resources and information with other peers to resolve new local and global problems in a dynamic environment [37]. figure 2: overview of hub-ci as a cps control platform for collaborative interactions among iot/ios, physical and virtual agents. there have been several collaborative tools designed for knowledge and information sharing [13]. hub-ci is a tool designed for cyber-supported collaboration between physical and virtual agents to optimize and harmonize agent actions across domains. it is a platform of e-work systems equipped with algorithms and cyber collaborative protocols specifically developed to provide versatility in collaboration-support [16], telerobotics [38] and network optimization. the main objective of hub-ci is to enable agents to identify the following as a function of time and current conditions: (1) what information to contribute; (2) which tools to use; (3) with whom to collaborate. the key innovation of hub-ci is that it can enable and facilitate physical collaboration between several groups of human participants, along with relevant cyber-physical agents [38] while previous hubs were limited to virtual interactions between agents [24]. in this article, hub-ci logic [26] is used to develop a simulation platform for the communication and interaction of different agents comprising a cyber physical ars. hub-ci is the central control platform responsible for the exchange and distribution of information https://doi.org/10.15837/ijccc.2021.2.4166 5 and decisions between these agents, in an optimized and harmonized manner. by managing information from three central algorithms (sampling/routing, adaptive search, and human-in-the-loop), hub-ci provides concise, timely and efficient collaboration. 3 methodology 3.1 task description a cyber physical agricultural robotic system (ars), described in [14] is a multi-agent system comprising of humans (experts), an autonomous robotic cart, and a sensor system. the robotic cart is mounted with remote sensing equipment and vision systems to effectively maneuver to specific locations within a greenhouse and inspect plants for the early and timely detection of stress or infections, while the human experts provide real-time decision/inference support. decision support is required for two reasons: 1) robot motion occurs in an unstructured and unpredictable environment, and 2) the reliability of the sensor data and automated predictive models can vary, and any potential for error and conflict must be mitigated in real time. this challenge is one of the reasons humans are required in the loop: human cognition and versatility can be leveraged to improve dynamic response and fill the diagnostic gap. in order to test the inclusion of humans in the loop, a simulation-based approach is employed for the provided task description. by simulating the ars, the scope of human control can be varied with different configurations, which can be tested and evaluated. the objective is to detect the early onset of diseases in greenhouse plants using the best combination of given agents. 3.2 sampling/routing current monitoring systems in greenhouses involve a manual process where a trained inspector scouts the plants on foot within the plot, usually sampling a few locations at each plot. the sampling locations are determined arbitrarily, following a fixed heuristic (policy). constrained by large plots, limited human resources, time constraints and high costs, sampling rates even for trained operators are low. usage of robotic agents can improve sampling rates by shifting the bottleneck from the human inspectors, but due to added costs it is essential to employ sampling strategies which improve detection rates while reducing redundant sampling. to estimate optimal sampling plan for all spots, a sampling model is employed which applies pre-determined infection parameters, such as initial chance of infection diagnosis, and chance of plant-to-plant propagation, to estimate high-risk locations and prioritize them in the sampling queue. a routing heuristic is applied to generate the shortest path between the set of recommended sampling nodes in a given grid. 3.3 adaptive search figure 3: adaptive search algorithm (source: dusadeerungsikul and nof [14]). https://doi.org/10.15837/ijccc.2021.2.4166 6 the need for preventive and predictive maintenance of agricultural crops in a timely manner was established by [18]. the adaptive search (as) algorithm, as reported in [14] is a sensing algorithm used to indicate the extent or severity of a stress/disease in greenhouse plants. stresses in plants usually propagate in predictable directions, which are often influenced by external factors, such as sunlight and airflow. leveraging this knowledge, the as algorithm has been developed to extend the sampling space to include predictable locations with relatively higher risk of stress. depending on factors such as the type of disease-stress detected, type of plant sampled, and environmental conditions, the search region for as varies. for example, if a plant is found to have a stress/disease based on the sampling data, as is initiated to add the locations of plants located vicinity of the plant, depending on the type and severity of the stress/disease. for the purpose of this research, only the plants in the immediate vicinity of the stressed/diseased plant are considered. 3.4 hitl integrations the major challenges in integrating has include 1) understanding the range of applications of hitl control, or the different taxonomies based on the controls that has employ, 2) modeling human behavior of various types, and 3) identifying the optimal modeling schemes for each type [25, 31] also address the question of workflow control in such systems: which agent should control the system at a given time period; how do they delegate tasks and avoid decision conflicts? this research attempts to evaluate different workflow control structures, based on individual and shared control. has are modeled as dynamic agents, able to provide decision inferences on different plant locations. to model these agents, the following attributes were chosen: • task performance: it is assumed that has can perform the tasks at a relatively better rate than the corresponding ras, but this relative advantage is achieved at a higher cost. ras are developed to reduce physical load and provide automated and generic control, but they lack versatility and cognition that has possess. • fatigue models: has are prone to relatively higher error rates and inferior task performance (longer time taken to service task) under increased task load based on pre-defined thresholds. • limited availability: has are assumed to be available for task assignment only for fixed time periods (hitl level) task control is defined as the agent currently controlling the system: for e.g., if the robot performs and validates a scan on a certain plant, task control is said to be with the ra; if the ha provides the validation, task control is maintained by the ha. task control is dynamic and switches between agents based on their availability. for a specific task, it can either be solely maintained by a single agent (robot/human scans individually) or shared by multiple agents (e.g. robot can request human for help). based on these differences, two workflow modes are simulated and compared: sequential, and shared integration. 3.4.1 sequential integration in this mode of integration, task control is maintained solely by either of the agents, but not shared. task control switches sequentially between ra and ha. the human agents are available for a fixed time period (hitl level) and within this period are completely assigned to provide scanning inferences for all tasks. this mode of integration requires available has to necessarily perform tasks, and thus it is necessary to monitor fatigue. it is also useful to find a theoretical optimum time period, which is denoted as optimal hitl level (ohl) for which the has can contribute maximum benefit to the system. figure 4 (l) shows the sequential integration workflow. 3.4.2 shared integration as opposed to sequential integration, task control in the case of shared integration is collaboratively distributed, with the ability of agents to collaborate with other agents when required. agent https://doi.org/10.15837/ijccc.2021.2.4166 7 figure 4: sequential (l) vs shared (r) integration workflows table 1: performance variables α = distance normalization constant β = time normalization constant γ = penalty constant cmove = motion cost cpenalty = penalty cost cscan = scan cost ctot = total cost d = total number of infected plants detected ej = error instance of at jth task i = index of previous task completed j = index of task being completed mdr = missed detection ratio rsr = redundant sampling ratio s = total number of sampled plants tinf ected = total number of infected plants tj = scan time of jth task xi,yi = coordinates of ith task availability does not necessarily correspond to task assignment, especially in the case of human agents (in sequential integration, an available ha must take up task control). for instance, if a result of the robot scan does not provide definite inference, it can request help from a human agent, if now available; the human agent can provide their final inference based on trained expertise. figure 4 (r) shows the workflow for shared integration, indicating the difference between the two workflow modes. 3.5 performance metrics two types of performance metrics are calculated, based on costs and routing parameters. the variables for difference calculations are shown in table 1. 3.5.1 cost calculations the overall costs incurred in the system due to the agents and their interactions are captured in equation 1. cost can be incurred from three operations: motion, scanning and error penalties. motion costs are incurred by the ras which traverse between different plant locations. scanning and error cost https://doi.org/10.15837/ijccc.2021.2.4166 8 parameters vary depending on the agent performing the scan. the probability of error for the human agent depends on the fatigue level which increases with the number of tasks performed by the human agent. fatigue also effects task performance, defined by the time required for the agent to complete the chosen tasks (scanning/decision inference); with increased fatigue, task performance decreases (task time increases). the probability of error for the robot agent remains constant throughout the simulation since fatigue for robotic agents is not considered. ctot j = cmove(i,j) + cscan(j) + cpenalty(ej) (1) cmove(i,j) = α(|xj −xi|+|yj −yi|) (2) cscan(j) = βtj (3) cpenalty(ej) = γcscan(j) (4) 3.5.2 sampling/routing metrics to measure the effectiveness of the sampling, routing and adaptive search techniques, the following metrics are developed and observed during the experiments for each complete run of the simulation. total sampled/task performance (s): number of tasks performed by the hitl ars for a single run of the simulation. missed detection ratio (mdr): percentage of missed infections which the system might have missed or cannot capture within the given run. mdr = tinfected −d tinfected (5) redundant sampling ratio (rsr): percentage of sampled locations which were unnecessarily sampled. rsr = s −d d (6) classification observations: for each run, the sampling model estimates whether a plant has an infection (positive outcome) and compares this outcome to the outcome of the scanning task, which depends on the agent with task control: • true positives: sampling and scanning indicate positive outcome • false positive: sampling indicates positive, scanning indicates negative outcome • false negative: sampling indicates negative, scanning indicates positive outcome • true negative: sampling and scanning indicate negative outcome 3.6 experiment design two simulation experiments are designed to analyze and evaluate respective metrics across different hitl levels. various simulation tools such as v-rep, gazebo, agros and webots [30] are extensively used to test agricultural robotics. these tools, however, are specifically dedicated for the integration of agricultural robotics into real-world environments, and none of them are specifically designed to evaluate the integration of has into such robotic systems. the simulation reported in this research is developed using an object-oriented programming (oop) approach in python, and it follows a discrete-time, event-based architecture. this approach allows us to dynamically model humans in the simulation by providing flexibility in defining attributes such as fatigue and chance of error. the simulator is designed for the task description provided in section 3.1. within the simulation, an event is defined as any of the motion or scanning tasks occurring as part of the task sequence. each event is performed for a discrete time-period, a single run in the simulation consists of 100 time steps, and the number of tasks (events) completed in the given time frame for a fixed hitl level (varied across runs) are monitored. for e.g., if the hitl level is 30%, has are available and can perform https://doi.org/10.15837/ijccc.2021.2.4166 9 tasks for 30 timesteps of the overall 100 timesteps. when the has are available (depending on the hitl level), they perform tasks within the system with active fatigue models to monitor the fatigue and error levels after each task. the following simulated collaboration protocols are considered: 1. random (baseline) protocol: the set of locations to be visited are generated at random for a chosen hitl level. (a) without adaptive search (b) with adaptive search 2. hub-ci protocol: the set of locations to be visited are based on a probability (risk)-based sampling model with adaptive search (simulating the cyber collaborative protocol of hub-ci) for a chosen hitl level. based on the above description, two simulation experiments are performed to evaluate sequential and shared integration for the different collaboration protocols across hitl levels. 4 results table 2: comparing hub-ci performance with random protocol for each integration mode integration cost task performance rsr mdrw/o as as w/o as as w/o as as w/o as as sequential +10% +0.8% +44% +15% -30% -26% -50% -41% shared -5% -5% +44% +18% -32% -27% -44% -37% 4.1 experiment 1: sequential integration figure 5: true and false classification observations for experiment 1: (a) true positives, (b) false positives, (c) false negatives, (d) true negatives for hitl level sequential integration relies on completely utilizing the available agents to complete the required tasks. true and false classification observation graphs for each hitl level are shown in figure 5. the parabolic nature of the graphs suggest the existence of point of inflection in each graph, coinciding https://doi.org/10.15837/ijccc.2021.2.4166 10 approximately at the same hitl level. this is the first evidence of an optimal hitl level (ohl) for this integration, since at these points of inflection the classification accuracy of the system seems to be optimal. to elaborate, at this ohl (approximately 45% hitl level), the true positives and negatives both are maximum, while both false positives and negatives are at a minimum. in figure 6, the task performance (a) and costs (b) for different simulation schemes over different hitl levels are plotted. from figure 6(a), the difference is task performance is significant, and the hubci protocol outperforms the random protocol (with and without as). comparing task performance across hitl levels, hub-ci performs on average 4.67 (44%) tasks more than random (without as), and 2.08 (15%) more tasks than random (with as) (table 2). to highlight improvements due to hitl, we compare the hub-ci protocol performance for two fixed hitl levels: 45% (m = 15.44, s = 0.07) and 0% (m = 14.06, s = 0.06); the results (t(196.65) = 143.56, p < 2.2e-16) provide strong evidence to showcase the benefits of hitl augmentation. figure 6: (a)task(l) and (b)cost performance(r) for different simulated collaboration protocols figure 6(b) shows the costs of each collaboration protocol for different hitl levels. a one-way anova test (f(2) = 9.026, p = 0.0001) determined that the difference between costs are statistically significant. hub-ci incurs, on average, 10% added costs when compared to random (without as) and 0.8% when compared to random (with as). the cost trend seems to increase exponentially with increasing hitl level, but since motion and scanning costs are directly related to the number of tasks performed (concave downward trend as shown in figure 6(a)), this increase can be attributed to penalty costs occurring due to higher instances of errors, and also increased costs from lowered task servicing due to fatigue. an interesting point to note is that at the ohl (45% hitl level), the costs for each protocol remain similar, but the task performance at the same level shows large differences, thus indicating the advantages of the hub-ci protocol. table 3 shows the performance metrics at the ohl to elaborate further on the previous statement. table 3: performance metrics for hitl level = 45. protocol # tasks cost rsr mdr mean sd mean sd mean sd mean sd hub-ci 15.44 0.07 897.82 6.75 43.28 0.32 33.10 0.52 random(as) 13.27 0.04 911.17 4.75 59.63 0.53 60.04 0.58 random(no as) 10.458 0.03 851.65 4.86 64.66 0.55 72.99 0.38 difference* 16.35%* 1.45%* 33.06%* 44.87%* *note: difference between hub-ci and random (with as); statistically significant (p < 0.0001) redundant sampling ratio (rsr) and missed detection ratio (mdr) for different hitl levels are plotted in figure 7. the concave nature of both plots provides more evidence to support the https://doi.org/10.15837/ijccc.2021.2.4166 11 existence of the ohl, with the local minima of both plots coinciding approximately and the formerly stated ohl (45%). at this level, the benefits of hitl are maximized and beyond this level negative effects due to human error are prevalent. in figure 8, the mdr and rsr for different collaboration protocols is over different hitl levels is plotted. it is clearly evident that the hub-ci protocol is more efficient in terms of sampling and routing for infections. figure 7: rsr and mdr comparison for hub-ci figure 8: rsr and mdr comparison across hitl levels 4.2 experiment 2: shared integration in experiment 2, the collaborating agents share task control and agent utilization prioritizes necessity over availability. tasks are shared only when the ra cannot provide definite results, and when the ha is available. the true and false classification in figure 9, unlike sequential integration do not indicate the existence of an optimal hitl level for this integration. shared task control ensures that has only get tasks when necessary, and this reduces the impact of errors on task classification. for e.g., in figure 9(a), true positives increase with increasing hitl level: increased hitl level does not necessarily mean that the has perform more tasks, but that they are more available to provide decision inference on tasks which could not be classified initially (by the ras). figure 10 plots the task and cost performance for each collaboration protocol, and it is evident that hub-ci protocol is relatively the most productive: 4.13 (44%) tasks more than random (without as), and 2.1 (18%) more tasks than random (with as), as seen in table 2. the decrease in mean tasks per https://doi.org/10.15837/ijccc.2021.2.4166 12 figure 9: true and false classification observations for experiment 2: (a) true positives, (b) false positives, (c) false negatives, (d) true negatives for hitl level run can be due to the increased availability of has in the loop, consequently increasing cumulative scan time. the cost performance provides an important observation: as opposed to sequential integration, the costs for hub-ci protocol (m = 738.28, s = 5.27) decrease slightly with increasing hitl level, and the differences between protocols are statistically significant (f(2) = 57.62, p < 2.2e-16). hub-ci incurs, on average, 5% reduced costs when compared to random (without as) and 5% when compared to random (with as). figure 10: (a)task(l) and (b)cost performance(r) for different simulated collaboration protocols the lack of impact due to fatigue is visible in the rsr and mdr metrics from figure 11, which decrease monotonically with increasing hitl level. the best case mdr for shared integration, however, (in figure 11, at hitl level = 100% 40.01%) underperforms when compared to sequential integration, which had a lower best case mdr (at ohl 32.02%). best case rsr for shared integration (at hitl level = 100 – 41.08%) performs marginally better than sequential integration (at ohl – 42.70%). figure 12 cements hub-ci simulated protocol as a better sampling, routing, and search https://doi.org/10.15837/ijccc.2021.2.4166 13 protocol. figure 11: rsr and mdr for hub-ci figure 12: rsr and mdr comparison across hitl levels 4.3 cost comparisons an agent-based cost comparison was also performed to understand the nature of costs in each integration. from figure 13, it is evident that with increasing hitl level, the cost in sequential integration is preferred for lower hitl levels, while shared integration ensures that the cost per task does not increase exponentially across hitl levels. this is also backed up by figure 14, where an agent-based cost-breakdown is provided. the stacked bar charts show that for experiment 1, the contribution of has to the total cost dominates (due to increased penalty costs, lower task performance) over high hitl levels, overshadowing the ra costs involved. this is not the case for experiment 2, even though ha cost does increase, it remains nominal compared to the ra costs, suggesting that this integration is more cost efficient and does ensure that has are tasked appropriately to lessen the degree of penalty and performance costs. https://doi.org/10.15837/ijccc.2021.2.4166 14 figure 13: comparing cost/task for shared and sequential integrations across hitl levels figure 14: agent-wise cost breakdown across hitl levels https://doi.org/10.15837/ijccc.2021.2.4166 15 5 conclusion this research examines human-in-the-loop systems within the scope of agricultural robotic systems. there is a need for humans in the loop given the unstructured nature of the environment and their ability to leverage specific knowledge bases for decision-making and dynamic events. the objective is twofold: 1. to create a precise ha model for accurate integration into the loop, including active fatigue and error models, and 2. to explore different possible integration modes of humans within the cyber-physical loop, with the target of early detection of stress leading to infections in greenhouse plants to manage communication and interaction between diverse agents (robots, humans) and algorithms (sampling, adaptive search, and hitl), a simulation based on hub-ci is developed. the simulation monitors kpi’s of the system in relation to sampling and routing efficiency, task classification and productivity, and overall costs. by comparing the system performance for different simulated protocols, the devised experiments provide insight into the augmentation provided by the three primary algorithms, sampling/routing, adaptive search, and hitl under two hitl workflow modes: sequential, and shared hitl integration modes. both integration modes establish the relative advantage of the hub-ci collaboration protocol in providing significantly better performance and results, as follows: • in sequential integration, the priority of task management is agent utilization, and task control is maintained individually based on availability. based on the established workflow, the performance metrics calculated indicate the existence of an optimal hitl level (ohl), where the productivity gains from has are at a maximum. the main drawback of this integration is the cost management and the effect on the agents, since by compulsorily pushing tasks to has, increased costs due to fatigue and error penalties lead to an exponential increase in system costs. as shown in the cost comparison, the cost contribution due to has overshadows the cost from the ras for higher hitl levels, thus indicating the cascading effects of fatigue and error • for shared integration, has are inserted into the workflow (subject to availability) only when collaboration is absolutely essential to provide an inference. task control is now shared, leading to lower task performance, since both agents now contribute to contentious tasks. the experiment results do not provide evidence of an optimal hitl level (ohl) for this integration mode, since has are not driven to complete utilization (and thus maximum productivity benefits). the positives of this integration mode, however, are evident in the cost performance, which remains largely stable over the different hitl levels. compared to sequential integration, it can be inferred that this cost stability of the shared integration mode is the result of no productivity losses and penalty losses due to fatigue and induced errors by establishing the critical factors of both hitl integration modes, the advantages of the hubci protocol for ars workflow collaboration are elaborated. the experiment results also provide a measure of the difference in productivity, which is enhanced by applying the hub-ci collaboration protocol. future research can be targeted towards hybrid interactions consisting of elements from both integration modes. for example, a hybrid workflow where a priority-based time-out protocol can be used to determine the amount of time the ha has to dynamically interface in the system, based on task characteristics. it is also important to improve ha models to make them more realistic towards decision making, sampling plans, and expanding the range of performable tasks in the loop. funding this work is supported by the production, robotics, and integration software for manufacturing & management (prism) center at purdue university. the research on ars is supported by bard project grant is-4886-16r, “development of a robotic inspection system for early identification https://doi.org/10.15837/ijccc.2021.2.4166 16 and locating of biotic and abiotic stresses in greenhouse crops”; and nsf project grant 1839971, “fw-htf: collaborative research: pre-skilling workers, understanding labor force implications and designing future factory human-robot workflows 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(2021). human-in-the-loop: role in cyber physical agricultural systems, international journal of computers communications & control, 16(1), 4166, 2021. https://doi.org/10.15837/ijccc.2021.2.4166 introduction background cyber-physical systems in precision agriculture human-in-the-loop systems collaborative intelligence and hub-ci methodology task description sampling/routing adaptive search hitl integrations sequential integration shared integration performance metrics cost calculations sampling/routing metrics experiment design results experiment 1: sequential integration experiment 2: shared integration cost comparisons conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 4, month: august, year: 2023 article number: 4644, https://doi.org/10.15837/ijccc.2023.4.4644 ccc publications facial emotion recognition using geometrical features based deep learning techniques j.l. mazher iqbal, m. senthil kumar, geetishree mishra asha g.r., saritha a.n., a karthik, bonthukotaiah n. j.l mazher iqbal department of ece, veltech rangarajan dr sagunthala r&d institute of science and technology chennai, india corresponding author: mazheriq@gmail.com m. senthil kumar department of ece, sree dattha group of institutions sheriguda, ibrahimpatnam, hyderabad, 501510, india geetishree mishra department of electronics, b.m.s college of engineering bull temple road bangalore karnataka-560019, india asha g r computer science & engineering, b.m.s. college of engineering bangalore, bull temple rd, basavanagudi, bengaluru, karnataka 560019, india saritha a n computer science & engineering, b.m.s. college of engineering bangalore, bull temple rd, basavanagudi, bengaluru, karnataka 560019, india a karthik institute of aeronautical engineering, hyderabad, india bonthukotaiah n department of computer science central tribal university of andhra pradesh vizianagaram, india abstract in recent years, intelligent emotion recognition is active research in computer vision to understand the dynamic communication between machines and humans. as a result, automatic emotion recognition allows the machine to assess and acquire the human emotional state to predict the intents based on the facial expression. researchers mainly focus on speech features and body motions; identifying affect from facial expressions remains a less explored topic. hence, this paper proposes a novel approach for intelligent facial emotion recognition using optimal geometrical features from facial landmarks using vgg-19s (fcnn). here, we utilize haarcascade to detect the subject face and determine the distance and angle measurements.the entire process is to classify the facial expressions based on extracting relevant features with the normalized angle and distance measures. the experimental analysis shows high accuracy on the mug dataset of 94.22% and 86.45% on gemep datasets, respectively. keywords: vgg-19s, emotion recognition, facial analysis, facial landmarks, feature extraction, geometrical features, hyper parameters https://doi.org/10.15837/ijccc.2023.4.4644 2 1 introduction the field of artificial intelligence has a long-term challenge in developing a more leadership. effective leadership for detecting human emotions. facial emotion recognition (fer) is indeed an essential visual-based technique for creating a more expert system capable of recognizing human emotions. the current methods in fer are based on action units (au), perceptual traits, and geometrical features. au employs over 7,000 potential au combinations to differentiate between emotions, which may be highly expensive and increase processing time. universalizing physical characteristics is another challenging topic [1]. facial expressions are one of the most reliable markers of a person’s mental state. hence, fer has been used in a variety of fields, such as security, rehabilitation, marketing, & sales. feature extraction and selection are two primary areas of focus [2] while designing efficient fer systems. the computer vision community as a whole sees emotion identification via facial expression as problematic because of issues including individual diversity in facial structure, the difficulty of distinguishing dynamic facial traits, low quality digital photographs, etc. [3]. as technology has progressed, so too has the use of human face recognition in a variety of contexts. more research into to the user’s facial gestures and emotions is necessary for certain hci initiatives, such as those using a camera-equipped chatbots or a companion robots [4]. face emotion recognition is crucial for human-machine interaction and communication (fer). in order for the computer to understand the user’s emotional state and appropriately react, it studies their facial expressions. for many years, researchers have toiled to create facial expression recognition (fer) systems [5]. as people rely mostly on spoken signals and facial photos to comprehend the emotional condition of others, it makes sense to utilise both together. the unique characteristics of speech and visual data make integration a significant obstacle in emotion-recognition studies [6]. facial expressions provide the brain with the most consistent data for reading human emotions across contexts. research into emotion recognition has developed at an exponential pace in recent years, and facial expression recognition has emerged as a promising, hot field of study for recognising a broad spectrum of basic emotions. one of the most basic emotions with many applications is joy, and studies have shown that face expressions are more accurate than other approaches (such as audio/speech, text, & physiological sensing) for gauging emotional states. although most recent methods were developed for recognising a wide range of emotions, improving their detection performance for a particular emotion is a significant challenge (e.g., happiness). there aren’t many methods designed to pick out a single happy mood in unrestricted videos, and the ones that do have insufficient accuracy because they don’t take into consideration the processing of dramatic changes in head attitude [7]. emotion recognition, or the study of how well computers can interpret human feelings, is a burgeoning area with several practical applications. the challenge with most approaches to emotion recognition based on visual signals is that individuals may hide their emotions by making subtle changes to their expressions. as human emotions may be hard to pin down using traditional machine learning and deep learning methods, eeg signals are increasingly being used for this purpose. yet, most of the proposed algorithms have subpar performance. two convolutional neural network (cnn) approaches are proposed in this study for effective recognition [8]. model m1 is a fully parameterized cnn, whereas model m2 is a less parameterized cnn. speech emotion recognition refers to studies in signal processing that attempt to identify feelings based on recordings of people’s voices (ser). because to its widespread application in settings as diverse as mental diagnosis and human-computer interaction, ser necessitates a robust framework for accurate classification. with this goal in mind, we provide a yeast 2 hybrid deep feature selection (hdfs) method for emotion recognition from human conversations, which combines machine learning with autonomous keyword engineering to achieve state-of-the-art performance in both precision and computational efficiency. the dimensionality reduction process in our pipeline begins with a fuzzy entropy and similarity-based functionality classification algorithm as well as ends with the widespread used whale optimisation algorithm. these features were extracted from raw audio signal mel-spectrograms using a qualified wide-resnet-50-2 supervised neural model.a k-nearest neighborhood classifier is used to the best collection of features to determine an emotion’s category [9]. the ability to recognise human emotions is a huge boon to the field of computer vision. important for their safety in the case of a meltdown, this is the beginning of an automated service for identifying the feelings of autistic children. contemporary approaches to a breakdown issue are proactive as opposed to reactive. meltdown symptoms are sometimes shown by abnormal facial expressions [10] brought on by a mashup of different emotions. automated emotion recognition is gaining prominence due to the increasing usage of human-computer interface software. emotion identification may make use of a wide variety of data sources including but not limited to speech, facial movements, body language, & physiological signs. the most trustworthy emotional attachment with machines is established through the physiological indicators that are practically impossible to manipulate. thus, there’s been a lot of research on the challenge of automatically recognising emotions from eeg data. emotions come from a broad variety of cognitive processes that involve various regions of the brain, making it necessary to recover a large number of features from the whole brain in a number of bands in order to correctly identify them in eeg. when investigating how the brain controls its emotions, it’s crucial to account for the ways in which different regions work together [11]. facial expression recognition (fer) is a challenging yet fascinating area of computer vision (cv). while many academics have https://doi.org/10.15837/ijccc.2023.4.4644 3 devoted significant resources to studying fer in recent years, they perform very well on high-resolution pictures but struggle to discern in the wild between specific emotional states [12]. face emotion recognition from pictures is challenging since human facial expressions are notoriously hard to predict. emotion classification using deep learning (dl)-based techniques has not been as successful as the current study. incorrect layer choice inside the cnn model causes these models to underperform [13]. the ability to express and understand one another’s feelings is fundamental to effective communication. improvements in emotion recognition have a significant impact on both human-computer interaction and computer-based voice emotion identification. speech emotion recognition (ser) is indeed the practise of determining the emotional state of a speaker based on what they say or what they overhear others say. hybrid systems have been proven to outperform the more frequent single classifiers used in ser [14], but they are it isn’t without their faults. if you can read a player’s mood while he’s engaged in an interactive game, you may be able to provide him a more rewarding experience. monitoring the player’s intrusive physiological signals is a common practise in current methodologies for evaluating their emotional state [15]. this study proposes a geometric featurebased emotion identification system using a vgg-19s model to identify and classify common emotions such as happiness, sorrow, neutrality, wrath, fear, disgust, and surprise. the subject’s face is first identified using a haar-cascade classifier, and then geometric facial feature points are extracted from the different regions of the face using this information (such as the eyelids, cheeks, chin, and lips). we can gather the deformations (facial modifications) induced by various expressions by employing the dimensions of the most significant face landmarks to compute the angles and distances between the chosen facial regions, yielding the feature vector. the resulting feature vector is the result of integrating the two. the system uses a vgg-19 architecture and tweaks the dnn’s optimal hyperparameters to improve recognition accuracy. together, these characteristics are what the model learns to utilise to predict the system’s robustness. the proposed technique is validated using geneva multimodal emotion projections (gemep)[13] bodily gesture dataset as well as the multimedia comprehension group (mug)[12] facial expression database. high levels of recognition accuracy were achieved on both datasets through the application of a vgg-19. this paper will be organized as follows: section 2 provides a summary of the existing literature on facial expression recognition. in section 3, we lay out the suggested methodological framework for designing feature extraction from geometry. in section 4, we present the experimental setup, performance measures, and results from the mug and gemep databases. the conclusions and suggestions are briefly outlined in section 5. 2 related work authors were proposed a novel approach to emotion identification using geometrical fuzzy logic in this research. the four-corner features of the mouth and eyes may be determined even without a reference face. the recovered features specify the quadrilateral shape that did not map to geometric forms. fuzzy membership functions are used in the proposed mixed quadratic shape model (mqsm) to quantify the degree to which the quadrilateral is inaccurate. fuzzy features are extracted from attribute values and used to verify the classifier (12 in total). to test the performance of the msqm, the ck, jaffe, & ised databases are used in the experiment. using just 12 fuzzy attributes, the proposed method beat state-of-the-art techniques that often depend on reference pictures [16]. researchers have traditionally used static feature selection methods in their studies. despite their usefulness, these methods nevertheless have some significant limitations, particularly as they pertain to dealing with spontaneous discourse. this is mostly due to the fact that each person’s facial characteristics result in somewhat different emotional expressions. to address this problem, we provide a dynamic attribute selection approach based on facial characteristics, which draws on two types of geometric features: linear features & eccentricity features. when combined, they provide light on why and how our facial expressions look the way they do. the suggested method also considers the subject’s head position, muscles, and facial expressions. the proposed technique outperforms the state-of-the-art methodologies, as shown by experiments performed on the ck+ & disfa datasets, and maintains its advantage even when evaluated against different datasets. when applied to the ck+ dataset, the proposed method obtains an excellent 97.72% reliability in facial expression identification, while on the disfa dataset, it gets an impressive 91.26 percent accuracy [17]. we present a convolutional neural network (cnn) that could determine an individual’s emotional state from a single image of their face. for the purpose of emotion recognition, the proposed fs-cnn first analyses facial landmarks to anticipate emotions. hybridization of patch cropped and convolutional neural network technology yields the fs-cnn. in the first stage, we take care of locating faces in high-resolution images and cropping them as needed. in order to anticipate facial expressions using landmarks analytics, we then used a convolutional neural networks (cnn) to pyramid pictures and evaluate scale invariance. using umd faces database, the proposed fs-cnn was evaluated and improved. on average, almost 95% accuracy was attained, which is excellent [18].in this paper, we present our emotion, age, and gender recognition (eagr) system, which employs face recognition algorithms to infer a user’s emotional state, chronological age, and sexual orientation. the eagr system uses a https://doi.org/10.15837/ijccc.2023.4.4644 4 cnn with three training models to identify seven emotions (six basic one and neutral), 4 age groups, as well as two sexes; normalized facial crop rotation (nfc) is tried to apply as a merely helpful preprocess to the training data; and augmented data is then used. the nfc will shave down hair so that facial features may be extracted more easily for use in emotional analysis. yet, the nfc would be capable of removing facial features, such as age and gender, while the hair remains intact. the experiments made use of the 3-training model for recognizing emotions, ages, and sex. by compensating for subjects’ slanted heads in the validation dataset, the proposed binocular line angle correction (blac) yielded best-in-class mean average accuracy of 82.4%, 74.95%, & 96.65% for real-time detection of seven moods, four age groups, as well as two genders, respectively. moreover, nfc preprocessing has the potential to significantly reduce training time. so, we consider the eagr approach to be cost-effective when trying to determine the gender, age range, or emotional state of a human subject. more accurate feedback based on a wide range of face classifications may be possible thanks to other social uses of the eagr approach used by hci services [19]. in order to advance from where we are now, this research proposes a novel fer layout. improved black hole’s global search capabilities as well as the deep learning machine’s good generalization skills collaborate to sort faces into categories (elm). our approach uses linear discriminant analysis (lda) & principal component analysis (pca) to reduce the file sizes of facial images while preserving their essential distinguishing features. the suggested technique has shown encouraging results on all three of these datasets, including the japanese female facial expression (jaffe), the karolinska directed emotional faces (kdef), as well as the expanded cohn-kanade (ck+). we also employed our own customized face dataset to further analyses the proposed system, and found that the lda-bh-elm approach had an efficiency of 77% just on ck+ dataset & 80% on the kdef database. results showed that the proposed technique is better to traditional methods and may provide remarkable performance [20]. in this paper, we provide a method for emotion identification that makes use of both vocal cues and visual cues in tandem with one another. we develop three distinct varieties of vgg-19s. each of the neural nets is trained to identify emotions from a collection of pictures. facial landmarks are sent into a second network that may be able to depict facial motions. the audio is converted to its acoustic properties and then used as inputs to the other network, which is responsible for visual synchronization. using an unique integration technique, we merge these three networks to improve the precision of their emotion recognition findings. as proof of the effectiveness of the suggested approach, a comparison with another technique is provided. the results suggest that this new approach is far better than the previous ones [21]. the happy emotion recognition system presented here improves upon previous work by combining 3d hybrid depth & closeness features with conventional 2d deep features (happyer-ddf). as a first step towards recovering spatiotemporal information from a picture sequence, we use a 3d original conception artificial neural hybrid having long-short term memory (lstm). second, when a grin unfolds, we quantify the space occupied by various facial features relative to an external point of reference (such as the nose peak) (or laugh). we use three publicly available video datasets to assess functional & decision-level fusion techniques. our happyer-ddf approach outperforms state-of-the-art facial expression technology [22] in terms of accuracy, as shown by the findings. being the most widely used eeg reference dataset, we use the deap and its valence and arousal indicators for binary classification in this study. the dataset is complete with the addition of frequency domain fast fourier transform (fft) extraction of features to supplement convolutional feature extraction. when compared to other models, the m1 and m2 cnn designs achieve average accuracy of 99.89% & 99.22%, respectively. we demonstrate the m2 model’s ability to consistently categorise polarities including over 96% accuracy using just 125 ms of eeg inputs and eeg outputs, and to reach 99.22% accuracy using only 2 seconds of eeg outputs. our suggested m2 model achieves a valence accuracy of over 96.8% with just 10% of a training dataset. the source code used to carry out each experiment is made publicly available with its description [23]. using a 5-fold cross-validation approach, we evaluate the suggested pipeline against a wide range of stateof-the-art, recently published studies and find that it greatly outperforms them, demonstrating the excellence and reliability of our work [24]. this essay painstakingly created the geometric, spatiotemporal, and profound features of autistic children’s face expressions. in order to accomplish this, we compared the performance of various combinations of these factors in complex emotion recognition (cer) and determined which features are most useful in identifying a complex emotion (ce) in autistic children during a crisis breakdown from a typically functioning state. the "meltdown crisis" 1 dataset, which details real-world meltdown / normal scenarios involving autistic youngsters, was used to verify our assumptions. we show that the given data may provide exceptionally promising results using a random forest classifier (91.27%) using custom-built features. classifiers trained on feature representation using inceptionresnetv2 utilising supervised learning approaches provide state-of-the-art results (97.5% accuracy) [25]. in this study, 736 features are extracted from spectral power and phase-locking data. to solve the challenge of learning key characteristics for emotion recognition, we use swarm-intelligence (si) algorithms here. we were able to apply the feature sets selected by these classification approaches to the problem of distinguishing between happy and sad facial expressions. in addition, popular features are often recycled into an updated feature set. using the random forest classification technique, we found an accuracy between 56.27 and 60.29 percent. the improvement was possible because to an 87.17 percent reduction in file size, from 736 features to 94.40 features. we also highlight the https://doi.org/10.15837/ijccc.2023.4.4644 5 importance of focusing on the best electrode locations for reading emotions. the categorization findings [26] are promising, and we conclude that 11 channels predominate.to solve this issue, we created three novel applications of neural networks. in this instance, we employed multi-scale kernel architectures with incorporated asymmetric pyramidal networks (apnet). the square kernel was also replaced with a set of convolution layers in the x, y, and z directions. this approach may enhance cnns’ descriptive power by making it simpler for them to integrate multi-scale information across their many layers. utilizing random gradient descent and gradient centralization during cnn training led to significant improvements (sgdgc). to verify the effectiveness of apnet with sgdgc, experiments were performed on the widely used fer-2013, ck+, and jaffe emotional datasets. the results of our trials & compared to other government models [27] demonstrate that our method is superior than utilising a single model and is on par with the outcomes achieved to use a model fusion strategy. we present a powerful dl solution for this issue by classifying facial expressions according to their emotional content using the cnn structure. in this study, we show how to analyse viola-jones (vj) facial detector output in a novel way by using a more advanced network architecture. after extensive testing to determine the optimal arrangement, an first layer of the proposed model was developed. many subjective and objective indicators of performance informed our final verdict. the results not only show that people feel different things, but also that those feelings may vary in strength. we conduct experiments on the fer-2013, ck+, & kdef datasets to test the proposed model and compare the results to those obtained using state-of-the-art methods. research like this has the potential to aid authorities in "smart urban" [28]. the purpose of this research was to examine a suggested hybrid long short-term memory (lstm) networks using transformational encoder in order to classify speakers’ emotions and make sense of their voice signals. mel frequency cepstral coefficient-derived speech characteristics are used in the suggested fusion method for the categorization of long-term memories (mfcc). the lstm-transformer model’s performance was evaluated using rigorous testing. the results show that this is a significant improvement over the reported models before it. recognition accuracy for the proposed hybrid model was 75.6% just on ravdess dataset, 85.5% on the emodb dataset alone, and 72.4% just on vernacular dataset [29]. we found that hr and facial expressions may be used to infer a player’s emotional state, and we provided a mechanism for doing so (fe). because of the fleeting nature of people’s emotional reactions, this research uses kinect2.0 video for hr and fe detection in real time. a convolutional neural network (cnn) is used to train the fe features, while a network with both long and short-term memory (bi-lstm) is used to learn the hr features. accurate emotion identification is achieved by the som-bp networks by combining hr and fe characteristics. our experimental results demonstrate that our model is capable of accurately predicting players’ levels of joy, rage, sorrow, and composure in a large set of games with low computational requirements. in light of this, the hr value may be a measure of how strongly you feel [30]. from what has been said, it is clear that methods have been developed to determine emotional states based on visual data & body language signals generated from dynamic aspects. although while the problem is less severe with still images of faces, there are still some emotions that can be differentiated. to identify an emotion, it is sufficient to have at least some of the face traits present in a given image. by zeroing down on a few important features, we can often determine the underlying emotional expression. so, we refined our framework to focus on 66 variables representing more complex face regions by including the geometric measure. choosing appropriate hyper-parameters is the most difficult component of constructing a model utilizing deep learning. we tested the model’s prediction ability under a variety of parameter combinations over tight ranges. 3 proposed approach the proposed framework is shown in figure 1. it consists of three stages (i)facial localization and landmark points detection,(ii)facial geometric feature extraction using vgg-19, and (iii) emotion classification using fcnn. we analyse the facial databases like mug and gemep where a single face appears in eachvideo/image frame for this work. initially, the subject’s face is extracted from the facial input image sequence and 68 landmark points were identified from the faceregion. following that, a geometrical face feature extraction is made with the distance and angle measures.finally,the extracted features are given to fcnn for training and testing the facial emotions from the mug and the gemep dataset. https://doi.org/10.15837/ijccc.2023.4.4644 6 figure 1: the frame work of the proposed system 3.1 facial localization and landmark detection this session initially extracts the facial region of interest (roi) from the input image/video. for better recognition of face emotion, we used opencv’s built-in haarcascades to locate the face in the image.it improves the trade-off performance -jones(vj)algorithm for face detection [14], a dynamic approach used to detect the object with a cascade function.face detection is achieved easily by training the classifier with a set of positive and negative frames by the adaboost algorithm and modifying the extracted weight features.faciall and mark identification is vital in many face analys is tasks to articulate specific facial behaviours based on facial muscle movements. figure 2shows the cropped face roi and selected to analyse the temporal information byidentifying the 68 landmark points using the dlib frontal face detector toolkit [34].facial expression recognition begins with accurate and significant extraction of facial landmark features from the eyes,nose,eye brows,and lips. figure 2: face localization and landmark detection 3.2 facial geometrical feature extraction following the motions of prominent points inside the head, eye, eyebrows, nose, tongue, lips, and jaw, a geometrical feature-based technique may extract minute changes in facial shape and movement. so, we considered the feature representation based on the distance & angles of facial feature points in this investigation to differentiate between emotions. by suggesting angle points and euclidean distance between each pair of markers in a frame, facs defines features. in total, 66-dimensional features were taken from the facial information points, representing 47 unique euclidean distances and 19 angl emeasures between the coordinates of facial landmarks. as a result, only the mostperilous facial features are added, as other distances points have only aminor effecton a classifier’s decision-making. figure 3 (a) and (b) shows the geometrical landmark points for distance and angle measure. https://doi.org/10.15837/ijccc.2023.4.4644 7 figure 3: facial geo metrical points (a) forty-seven landmarks points selected for distance measure(b) nineteen landmark points selected for angle measure using the use of pythagoras’ theorem, we may estimate the distance and angle between two points on a face by utilising the points as landmarks. the distances between the various features of the face are denoted by the pairs (xi, yi). 66-dimensional feature points are used to calculate point indices because of their high sensitivity to subtleties of facial expression. pairwise measurements of a two landmarks may be used to extract features for fer. the 47 distance characteristics as well as the 19 angle characteristics, each with its own id, are listed in table 1. before estimating the angles between the three sets of points, we calculate the distance among each set of landmark coordinates inside a frame. second, in order to train vgg-19s, feature fusion utilises a combination of cartesian angles and distances between points to distinguish between 66 distinct facial expressions, ranging from "neutral" to "extreme". object. the robot as well as the faces/emotions are separated by the length (in x and y axes). face emotion recognition rely on four coordinates (x, y, w, and h) in the frame: bounding boxes are denoted by x and y in the coordinate system; w and h represent the height and breadth of the a bounding box, as well as the final values of w and h are used to determine kdx and kdy. the x and y dimensions are calculated as follows: kdx = startx+en2 kdy = starty y +endy2 wherein kdx is the x coordinate, kdy is the y group up, start x is the beginning of the x axis in the structuring element, start y is the beginning of a y axis inside the structuring element, end x is the end of a x-axis as in bounding box, & end y is the end of the y-axis inside the bounding box. moreover, the robot’s built-in camera’s distance from the detected face is determined by the formula: "focal length " = wxd w "distance " = w xf w 3.3 statistical feature analysis the box plot indicates the variance of individual class instances for representing facial emotion recognition. figure 4 and 5 represents the feature distribution of the distance angle measures in a statistical box plot analysis for mug and gemep datasets. each discrete box in the box plot signifies the emotional class instance; the central red line in the box maps the median of the sample data. the whiskers at the top and bottom of the box indicate extreme data points without outliers, plus symbols outside the box mark the outlier features. as a result, these plots show the variant features accurately discriminating the emotions of mug and gemep datasets. https://doi.org/10.15837/ijccc.2023.4.4644 8 table 1.facial geometric points for distance and angle measure for feature extraction. figure 4: distance+angle features distribution on mug dataset https://doi.org/10.15837/ijccc.2023.4.4644 9 figure 5. distance+angle features distribution on gemep dataset 3.4 vgg-19s and fcnn a. vgg more than a million images from the imagenet database were used to train the convolutional neural network (cnn) designated as vgg-19. the network has 19 layers and can divide images into a thousand distinct categories, such as "computer," "mouse," "pencil," and "animal." because of this, the network can now learn elaborate feature representations for many kinds of images. yet, despite the fact that the vgg net’s primary objective was to triumph in the ilsvrc competition, it has been put to use in a wide variety of different contexts. • used simply as a decent classification architecture for a large number of additional datasets, and since the authors made the models accessible to the public, they may be used as is or modified for use in other tasks that are analogous, as well. • transfer learning: this method may also be used to problems involving face recognition. • weights are readily accessible with other platforms such as keras, which means that they may be manipulated and utilized for any purpose the user desires. • loss of both content and format while utilizing the vgg-19 networking vgg-19 architecture • a square (224x224) rgb image was used as input to these networks, indicating that the input matrix was also square (224,224,3). • what has been done in the way of preprocessing thus far is to remove the training set’s mean rgb value from each pixel. just this one action was taken. • to cover the entire picture, they used kernels that have been three pixel by three pixels and had a stride length of one pixel. • in order to keep the image’s resolution satellite consistent, spatial padding was used. • sride 2 was used as the technique for maximum pooling across a 2-by-2-pixel window. • to further enhance the model’s classification performance and shorten the calculation time, it was chosen to use non-linearity using rectified linear unit (relu). as compared to previous models that relied on tanh or sigmoid curves, this one was proven to be far better. • the incorporation of three fully connected layers, the first two of it has a size of 4096, the third of which had 1000 channels for 1000-way ilsvrc classification, and the last of which employed a softmax function. b. cnn the convolutional neural network (cnn) is a specific a kind deep neural network framework used in the analysis of visual information. in contrast to traditional image processing, cnn may employ learnt filters rather than edge, histogram, texture, etc. filters. this eliminates the need to learn through making mistakes. • cnn begins with a feature-learning phase, and it is followed by a classification layer (also known as fully connected layer). two of the most fundamental building blocks of the feature learning phase are the convolution operation as well as the pooling layer. • a convolution layer is comprised of the learnable filters and feature extractors that were previously discussed. • pooling layer: this not only brings approximately invariance but also accomplishes some spatial compression. even if it is turned slightly, an automobile will still be recognizable as such. c. vgg-19 & fcnn vgg-19 is a state-of-the-art cnn with or before layers as well as a deep comprehension of the properties of shapes, colours, and structures. vgg-19 is a deep neural network which was taught on millions of pictures of https://doi.org/10.15837/ijccc.2023.4.4644 10 varying types and complexity. figure 6: hybrid architecture to complete my classification goal of differentiating between photos with and without trees, we did not further train vgg-19, instead freezing its layers and adding a shallow 2-layer networks on top of it. when f is the characteristic length, w is indeed the width in pixel, d is the length in cm, & w is the widths in cm, the distance is measured to determine the location of an object in addition to facial & emotion identification. when comparing two faces, it’s crucial to know their exact x, y, and distance from one another. the challenge with training neural learning is that it takes a lot of information. because of its large size, the noisy dataset is used for adaptive optimisation, even though our goal is to improve performance just on curated dataset. we minimise the following bridge error during training: l(s,l)=− ∑ iεn lilog(si) the proposed cnn based attention model comprised of 5 convolutional layers in which the first layer is responsible for extraction of low level features from the input images. the other layers concentrate on the region of interest provided by the fcn-perinet model. the detection of semantic regions was performed by using the fcn and the classification of regions was carried out into three classes namely eyebrow, background, and eye from which the region of interest is constructed. the operation of the attention layer was formulated as, where f(p, q)denotes the feature map of the cnn at the coordinates of the roi, f(p, q)∗ denotes the treated feature map at the end of the current layer and β denote the control coefficient which controls the adjustment intensity. more precisely, let indicate the last convolutional layer’s vectorized fcnn answers (e.g. the ’conv54’ of vgg19), where m is the total amount of convolutional kernels in the final layer (e.g. m=512 in vgg19), grows linearly with size of the input picture, & d is the amount of nodes each convolutional kernel. 4 experimental setup we’ll be working within the constraints outlined here. an intel i7 processor @ 2.10 ghz & 16 gb of ram are all that’s needed for the testing on a windows 10 machine. the bare minimum for running the vgg-19 and fcnn model is python 3.76, the keras 2.4.3 framework, as well as the tensor flow 2.3.1 libraries. we experimented with a number of different model setups before settling on one that solved our issue. unsuccessful model configurations attempted to train the network to recognise novel illustration-domain picture properties. we experimented with retraining the weights of the network’s lower layers to pick up on the rudimentary features of the new domain, blocking updates to the network’s upper layers to preserve the refined object representation learned during previous training, expanding the network’s depth, and tweaking parameters like drop outs, learning rate, and momentum. one of the most challenging aspects of building vgg-19 models is choosing the combinations of hyper parameters using a looping strategy to increase the accuracy and efficiency of the model. deep learning with a grid search approach helps in selecting the suitable parameters for obtaining the best prediction model which avoids over fitting. our vgg-19 consists of five-layered architecture for training andtesting. to assess the performance of our model, we trained our deep architecture up to 25layers,but there is a significan tdecline in the accuracy.as a result,we choose the architecture with minimal layers, which provides significantly faster inference and is moreappropriate for real-time applications.we demonstrate that vgg19’s deep network produces much worse accuracy in our illustration database compared to natural pictures. the https://doi.org/10.15837/ijccc.2023.4.4644 11 primary difference between our datasets and the original photos is in their statistical makeup. the creation and training of a brand-new convolutional network is one strategy for enhancing performance on our data. we don’t have enough data to train vgg19, as well as erasing the model’s previous training would mean losing all of its previous learnings, therefore this is not a wise choice. 5 dataset in this study, we analyze facial expression datasets to extract geometrical characteristics, focusing on cases when a single face looks in each picture or video frame. the data comes from two different sources: (a) the mug and (b) the gemep. table 3 provides a concise summary of the characteristics shared by these two data sets. table 3.facial expression datasets datasets total samples subject classes temporality resolution understanding group (mug) [12] 11758 86 7 static 896×896 geneva multimodal emotion portrayals (gemep) [13] 1823 10 17 dynamic 720×576 understanding group (mug) [12] 11758 86 7 static 896×896 multimedia understanding group(mug) there are seven core emotion prototypes specified in the facs handbook, and they are all part of the multimedia understanding team facial expression dataset. the dataset consists of 1462 face sequences including various action components from 86 people, including 35 girls and 51 men between the ages of 20 and 35. preprocessed sequences yielded a total of (11758) pictures. figure 7:sample frames of seven emotions from the mug dataset. portrays seven different facial emotional class instances consisting of angry, disgust,fear, happiness, neutral, sad and surprise. the recorded frames are posed at 19 frames persecond. furthermore, this datasetis chosen because of its authentic expressions that defeat the limitations of other similar datasets in fer, such as illumination factors and numeroustakes per subject without occlusion.table 4 depicts the numberof instances perfacial expression. https://doi.org/10.15837/ijccc.2023.4.4644 12 table 4.the number of instances taken per facial expression. facial expression instances angry 1587 disgust 1606 fear 1638 happy 1868 sad 1802 neutral 1389 surprise 1868 total 11758 5.1 gemep dataset the geneva multi-modal emotion portrayals(gemep)[37]is a multi modal framework created by klaus scherer and tanja banziger. it consists of facial, audio and bodygesture video instances performed by ten actors with different modalities. it encompasses a wide range of feelings, including: awe, amazement, anxiety, anger, disdain, disgust, desperation, fear, irritation, curiosity, joy, pleasure, pleasure, relief, sorrow, surprise, and tenderness. initially, thevideo portrayals are converted to frames, with haar cascade the face roi is cropped and used for our work.figure 8 shows the sample frames of the face extraction process from the gemep dataset.for our experiment’s studies,1823 instances are considered.table 5 shows the occurrence of each instance perfacial expression. table 5. the number of instances taken per facial expression facial expression instances admiration 127 angry 105 contempt 61 despair 126 disgust 56 interest 138 irritation 134 joy 113 panic fear 115 pleasure 179 pride 93 relief 116 sad 125 surprise 53 tenderness 69 total 1823 5.2 performancemeasures in this part, we give a metric for evaluating the deep networks model’s training-phase performance on the test set. by doing so, we can better understand how quickly the model is able to converge. there is a 70% training and 30% testing split for the suggested retrieved geometrical characteristics. accuracy (a), precise (p), recall (r), f1score, and roc (receiver operating characteristics) are some of the statistical measures used to evaluate a model’s efficacy. each occurrence of an emotional class in the mug & gemep datasets was evaluated using the metrics presented in table 6.the effectiveness of the created facial expression recognition system is measured via evaluation. the suggested system’s performance is evaluated in terms of its accuracy, which is the rate at which it can recognise faces and expressions in real time. the formula for determining the test’s precision is provided (1): "accuracy " = t p +t n t p +f p +f n +t n where tp indicates positive results, tn negative results, fp positive results, and tn negative results. this equation is used to achieve high quality in either face recognition or emotions recognition. the accuracy value of a classification reveals its efficiency on a class-by-class basis. https://doi.org/10.15837/ijccc.2023.4.4644 13 table 6.performancemeasure metric description accurateness it measures how many samples were accurately labelled in comparison to the total sample count. exactitude using the sum of the positive samples, it extrapolates the expected positive ones. recollection it quantifies the proportion of correctly identified emotions. f-score f-measure calculates an appropriate performance index by averaging the results of two other metrics, "precision" and "recall". in this case, we have a four-way dichotomy denoted by the letters tp, tn, fp, and fn. 5.3 experimental results on mug dataset adamax optimizer was used to train for 100 epochs at a training rate of 0.001 to extract distance & angle characteristics from the mug dataset. for optimal network performance, they utilize rectified linear unit (relu) activation functions for all units in the hidden layer and’sigmoid’ activation functions for the output layer. figure 8:confusion matrix:66features employed from mug dataset the confusion matrix of the proposed distance and angle features on the mug datasetdisplays that ‘angry’, ‘sad’, ‘happy’ and ‘disgust’ have been classified correctly with the bestaccuracy, and "surprise" has the lower accuracy because some of the emotions like fear are mis classified as a surprise as shown in figure9. table 7 denotes the performance measure of accuracy, precision, recall, and f1-score values for the dnn model on the mug dataset. the train and test set’s performance accuracy and loss graph produced outstanding results with remarkably high consistency. asshown in figure 10 (a) and (b) recognizing facial emotions with 66 landmark features directly correlates to 100 epochs. the validation and train data began to converge at the 60th epoch and remained completely stable,and the system classifies emotion with the accuracy of 94.2 % for the mug dataset. 6 experimental results on gemep dataset a 100 epoch deep artificial neural model is used to train and evaluate the distance-and-angle fused features from the gemep frames. to achieve optimal performance, we employ rectified linear units (relu) inside the hidden layer and’sigmoid’ in the output layer. when starting with a tiny dataset like gemep, dropout and regularisation may help you train the best possible models for the validation set. for example, the confusion matrix for the suggested features just on gemep dataset shows that the emotions of "pleasure," "sadness," "irritation," "disgust," "contempt," and "amusement" are accurately categorised. figure 12 shows that although the category "anxiety" has the highest accuracy, the category "pride" has the lowest accuracy. https://doi.org/10.15837/ijccc.2023.4.4644 14 table 7.the performance measure for the mug dataset emotions accurateness exactitude recollection f1 score annoyed 0.97 0.92 0.97 0.94 revulsion 0.95 0.99 0.95 0.97 anxiety 0.93 0.95 0.93 0.94 joyful 0.95 0.93 0.95 0.94 unhappy 0.96 0.89 0.96 0.92 neutral 0.93 0.98 0.93 0.95 surprise 0.92 0.95 0.92 0.93 table 7 denotes the performance measure of accuracy, precision, recall, and f1-score values for the dnn model on the mug dataset. the train and test set’s performance accuracy and loss graph produced outstanding results with remarkably high consistency. asshown in figure 10 (a) and (b) recognizing facial emotions with 66 landmark features directlycorrelates to 100 epochs. the validation and train data began to converge at the 60th epoch and remained completely stable,and the system classifiese motion with the accuracy of 94.2 %forthe mugdataset. 6.1 experimental results on geme pdataset a 100-epoch deep artificial neural model is used to train and evaluate the distance-and-angle fused features from the gemep frames. to achieve optimal performance, we employ rectified linear units (relu) inside the hidden layer and’sigmoid’ in the output layer. when starting with a tiny dataset like gemep, dropout and regularisation may help you train the best possible models for the validation set. for example, the confusion matrix for the suggested features just on gemep dataset shows that the emotions of "pleasure," "sadness," "irritation," "disgust," "contempt," and "amusement" are accurately categorised. figure 12 shows that although the category "anxiety" has the highest accuracy, the category "pride" has the lowest accuracy. table 8. the performance measure for the gemep dataset emotions accurateness exactitude recollection f1 score approbation 0.89 0.79 0.89 0.84 delight 0.89 0.9 0.89 0.89 annoyed 0.82 0.93 0.82 0.88 nervousness 0.77 0.87 0.77 0.82 disdain 0.90 0.87 0.9 0.89 misery 0.83 0.76 0.83 0.79 revulsion 0.88 0.84 0.88 0.86 attention 0.83 0.93 0.83 0.88 annoyance 0.95 0.89 0.95 0.92 happiness 0.86 0.82 0.86 0.84 fright fear 0.77 0.79 0.77 0.78 favorite 0.94 0.93 0.94 0.94 arrogance 0.71 0.89 0.71 0.79 release 0.85 0.79 0.85 0.82 unhappy 0.94 0.89 0.94 0.92 amazement 0.84 0.83 0.94 0.88 sensitivity 0.87 0.9 0.87 0.88 table 8 displays the results of the performance analysis based on the confusion matrix reliability ratings. according to this table, the suggested model achieves the maximum precision, reliability, recall, and f1 measure as well as requires less computing time when the performance measure is increased for the specified input size. https://doi.org/10.15837/ijccc.2023.4.4644 15 figure 9:confusion matrix obtained for gemep dataset 5. comparative study table 9 contrasts the results provided with those obtained by the state-of-the-art methods, showing that the proposed method for extracting geometrical features is both efficient and effective. table 9.state-of-the-artresults method dataset accuracy resnet 50 gemep 85.6 cnn 87.65 mobile net 89.2 distance manifolds using svm 92.76 geometric features using dnn 94.2 resnet 50 mug 84.6 cnn 88.65 mobile net 88.2 distance manifolds using svm 91.76 geometric features usingdnn 95.2 7 conclusion and future work in this paper, we proposed a new framework for recognizing facial emotion usingvgg-19s. we believe that focusing on specific action units in the facialregionshelpsdetectfacial expressionsin depth.additionally,weextracted thefacefeature information using euclidean distance and angle measure using 66 distinct facialaction units to highlight the most crucial parts for detecting facial emotions. to train the classifier from beginning and directly forecast the output for the production of input characteristics, it is helpful to construct a vgg-19. both the mug & gemep databases were used in experiments that sought to isolate individual feelings. overall recognition accuracy was found to be 94.22% for the mug dataset and 86.45% for gemep dataset. quantitative measurements including as accuracy, precision, recall, f1-score, and roc are used to verify the dataset’s performance. lastly, additional techniques developed in the area of face expression identification were compared with the methodologies given in this study. the results indicated that the system was not capable of making a reliable distinction between shock and satisfaction. for a given amount of input, the suggested task requires less time on the computer and produces better performance metrics. using real-time datasets, we combine face traits with visual body motions to identify micro expressions of emotion. declaration: participation consent and ethical approval: this procedure is carried out without the involvement of people. rights of humans and animals: animal and human rights are not being violated in any way. backing: there is no money associated with this effort. https://doi.org/10.15837/ijccc.2023.4.4644 16 competing interests: there is no 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[43] m. s. gharajeh, “a neural-mcdm-based routing protocol for packet transmission in mobile ad hoc networks,” international journal of communication networks and distributed systems, vol. 21, no. 4, pp. 496–527, sept. 2018. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open-access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of and subscribes to the principles of the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: mazher iqbal, j.l.; senthil kumar, m.; mishra geetishree ; asha, g.r.; saritha, karthik, a; j.v.n.; bonthukotaiah, n. (2023). facial emotion recognition using geometrical features based deep learning techniques, international journal of computers communications & control, 18(4), 4644, 2023. https://doi.org/10.15837/ijccc.2023.4.4644 introduction related work proposed approach facial localization and landmark detection facial geometrical feature extraction statistical feature analysis vgg-19s and fcnn experimental setup dataset gemep dataset performancemeasures experimental results on mug dataset experimental results on gemep dataset experimental results on geme pdataset conclusion and future work international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 5, month: october, year: 2020 article number: 3898, https://doi.org/10.15837/ijccc.2020.5.3898 ccc publications lyapunov-based methods for maximizing the domain of attraction h. jerbi, f. hamidi, s. ben aoun, s. c. olteanu, d. popescu houssem jerbi* university of ha’il, college of engineering department of industrial engineering hail 1234, ksa *corresponding author: h.jerbi@uoh.edu.sa faiçal hamidi laboratoire de recherche modélisation, analyse et commande des systèmes university of gabes, enig, 6029 gabes, tunisie faical.hmidi@isimg.tn sondess ben aoun university of ha’il, college of computer science and engineering department of computer engineering hail 1234, ksa s.benaoun@uoh.edu.sa severus constantin olteanu, dumitru popescu university ‘politehnica’ of bucharest faculty of automatic control and computer science bucharest romania severus.olteanu@acse.pub.ro, popescu_upb@yahoo.com abstract this paper investigates lyapunov approaches to expand the domain of attraction (da) of nonlinear autonomous models. these techniques had been examined for creating generic numerical procedures centred on the search of rational and quadratic lyapunov functions. the outcomes are derived from all investigated methods: the method of estimation via threshold accepted algorithm (taa), the method of estimation via a zubov technique and the method of estimation via a linear matrix inequality (lmi) optimization and genetic algorithms (ga). these methods are effective for a large group of nonlinear models, they have a significant ability of improvement of the attraction domain area and they are distinguished by an apparent propriety of direct application for compact and nonlinear models of high degree. the validity and the effectiveness of the examined techniques are established based on a simulation case analysis. the effectiveness of the presented methods is evaluated and discussed through the study of the renowned van der pol model. keywords: lyapunov function, nonlinear model, asymptotic stability, equilibrium point, genetic algorithm, threshold accepted algorithm, lmi. https://doi.org/10.15837/ijccc.2020.5.3898 2 1 introduction during the previous decades, the complexity of approximating the attraction domain area has remained the focus of several benchmarking works [25, 30, 35, 36, 41], the references contained in them. the asymptotic stability area of physical systems is a significant property to be identified [18]. from a practical point of view, it is every so often insufficient to demonstrate the local asymptotic stability for point of equilibrium [4, 20, 21, 34, 42], however one may also require discerning the size of the stability region, as well [27, 33]. as a matter of fact, the stability area or attraction domain is described as the set of original starting criteria where the system states meet at the points of equilibrium [7, 8, 9]. thus, it is vital to specify the state of this area. to achieve this, one can utilize a lyapunov function [11, 13, 20, 25, 27, 32]. in fact, for a specified energy function providing stable local equilibrium, the biggest probable da, the state of which is established by the lyapunov energy function theory, is referred to be as the prevalent smooth function set integrated in a bounded domain where its derivative is negative [12, 22, 27, 40]. a significant number of approaches on estimating the area of attraction investigated in the literature are established exploiting the established statements of lasalle and lefschetz [26, 29, 31, 32]. these later are based upon appropriately selected lyapunov functions in [23, 24, 27]. considered as one of the original outcomes as regard as the problematic of attraction domains estimation, the zubov’s method has designed a lyapunov function restricted by an open ball over a closed interval and consequently has sorted out an approximated da [16, 43]. similar complexity is detected when applying the algorithm synthesized by knobloch and kappel [8]. an lmi method to design polynomial lyapunov functions for non-polynomial class of systems is provided in [10]. however, the problem of defining a truncation order for the lyapunov function in the taylor series expansion is not well addressed. in [11, 15] authors designate an lmi based technique for approximating the so-titled robust da for a polynomial class of systems with model polytope uncertainties. in [9], authors formulate a fundamental analytical background in which particular classes of minimum based distance problems are resolved via lmi calculations [14]. the previous specified approaches, nevertheless, are appropriate for the class of smooth non-linear ode systems. in general, there is no generic techniques for determining lyapunov functions as a group of nonlinear models [6, 8] are established. nonetheless, lyapunov theory is still considered as the most efficient method to analyze the nonlinear models stability, although the lyapunov theorem does not need the class of algebraic functions that must be retained [1, 2, 3, 37]. in [11, 13] the maximum uncertain domain to preserve the stability and nominal performances (robustness) of the nonlinear optimal control systems, is estimated. the goal of this paper is to recommend analytical methods motivating systematic approximating approaches of the da. with the aim of getting a definite expression of the estimated da, an investigation of the lyapunov theory exploiting parameterized lyapunov function is conducted as described in [6]. as the da is related with a specified function of lyapunov, the proposal entails selecting the optimal parameters to attain the most significant da area. these later are designed as results to an optimization problem [5]. output response designs with quantifiable premise parameters have also been employed in [22]. the paper structure is as follows. section 2 provides a representation of the carleman linearization and the methodology of obtaining the lyapunov functions based upon lyapunov stability techniques and taa. in section 3, a recursive technique to calculate the da through rational lyapunov function is discussed. the fundamental principles of the ga are evaluated in the last part of this section. the assessment of the global accomplishment for the different techniques is performed by means of a simulation study carried out on the van der pol model. section 4 focuses on the conclusions and https://doi.org/10.15837/ijccc.2020.5.3898 3 future works. 2 searching lyapunov function via threshold accepting algorithms 2.1 the carleman linearization the development of f (x) into a taylor series expansion provides [17, 19]: f (x) = ∑ l alx[l] (1) wherein: al = { 1 l! ∂lf (0) ∂xi1 . . .∂xil } (2) and: x[l] = x⊗x⊗x.. .⊗x︸ ︷︷ ︸ l−t imes (3) denotes the l power kronocker product of vector x performing algebraic operations one can rewrite the vector derivative ẋ[l] as follows: ẋ([l]) = ∑l j=1 x⊗ ..⊗ .. ẋ︸︷︷︸ j ⊗..⊗x (4) this means ẋ([l]) = ∑l j=1 x⊗ ..⊗ ..f(x) ⊗ ..⊗x (5) by substituting f (x) with (1), one obtains: ẋ[l] = ∑l j=1 x⊗ ..⊗ .. ∑ k akx[k] ⊗ ..⊗x (6) thus, it becomes ẋ[k] = ∑ l alkx [k+l−1] (7) where alk = a l 1 ⊗ i [k−1] + i ⊗alk−1 (8) let the change of variables given below be considered now: ψ = ( x,x[2],x[3], . . . ,x[k], . . . )t (9) an infinite dimension linear system given as: ψ̇ = h ψ (10) is attained with: h =   h11 . . . h i 1 . . . . . . 0 h12 h22 . . . . . . 0 0 h13 h23 . . . ... ... ... ... ...   (11) https://doi.org/10.15837/ijccc.2020.5.3898 4 2.2 a lyapunov theory case investigation in this paragraph, an overall lyapunov hypothesis for stabilizing a linear infinite dimension dynamical model is given. consider the nonlinear system (1) where it is assumed that the origin is an equilibrium state (i.e.f(0) = 0). it is shown that system (1) can be expressed under a linear operator form with an infinite dimension as shown in (11) where the changed state vector is given by (10). consider the nonlinear function given by [28]: v = 〈( x,x[2],x[3], ... ) , υ ( x,x[2],x[3], ... )〉 (12) then v̇ = 〈( ht υ + υh )( x,x[2], .. ) ( x,x[2], .. )〉 = − ∥∥∥(x,x[2],x[3], ...)∥∥∥2 = − ( e ‖x‖ 2 − 1 ) (13) whereυ is a symmetric positive definite operator and ‖.‖ denotes the norm on the subset = ={( x,x[2], .... ) ,x ∈<n } considered on the tensor algebra space <n over <n. consequently the defined function is a lyapunov function and the studied system is globally asymptotically stable. 2.3 main outcomes here, a finalized algorithm yielding a lyapunov function for the class of investigated systems is presented. to achieve this aim, proposed functions of lyapunov characterized by a quadratic formulation [39]: v (x) = xt υx, υ = υt (14) are taken into account. a standard taa [38] has been adjusted for sake of establishing the matrices and which satisfies the lyapunov equation:ht υ + υh = −θ. the steps given below represent the synthesized technique for stability assessment. – initial step: set zero matrixes υ and θ – first step: randomize matrix elements and validate the definite positive characteristic. if υ isn´ t positive definite, redo first step. – second step: calculate matrix θ established in relation with υ. validate the definite negative propriety of θ. if not satisfied, redo first step. – final step: approve final solutions. 2.4 illustrative example consider the nonlinear system given as [43]:{ ẋ1 = −x2 ẋ2 = x1 + ( x21 − 1 ) x2 (15) the carleman linearization is used while the polynomial state equation is truncated to the third order. the equivalent linear system (11) is acquired as follows: ψ = [ x1,x2,x 2 1, . . .x 3 2 ] (16) and h =   0 −1 0 0 0 0 0 0 0 1 −1 0 0 0 0 1 0 0 0 0 0 −2 0 0 0 0 0 0 0 1 −1 −1 0 0 0 0 0 0 0 2 −2 0 0 0 0 0 0 0 0 0 0 −3 0 0 0 0 0 0 0 1 −1 −2 0 0 0 0 0 0 0 2 −2 −1 0 0 0 0 0 0 0 3 −3   (17) https://doi.org/10.15837/ijccc.2020.5.3898 5 applying the taa, yields: υ = [ υ1 υ2 υ3 ] (18) where: υ1 =   1.7894 0.6500 −0.4242 0.6500 1.2914 0.0615 −0.4242 0.0615 1.3590 0.0693 −0.0081 0.3250 0.0146 0.2092 0.3114 0.0515 −0.4560 0.2696 0.0520 0.0914 0.0695 0.0540 0.0325 −0.1123 0.1635 0.0717 −0.1578   υ2 =   0.0693 0.0146 0.0515 −0.0081 0.2092 −0.4560 0.3250 0.3114 0.2696 0.6614 0.3636 −0.0943 0.3636 0.6136 −0.1231 −0.0943 −0.1231 2.1146 0.2623 0.0292 0.2667 0.1107 0.0571 0.1462 0.0732 0.0815 0.0435   υ3 =   0.0520 0.0540 0.1635 0.0914 0.0325 0.0717 0.0695 −0.1123 −0.1570 0.2623 0.1107 0.0732 0.0292 0.0571 0.0815 0.2667 0.1462 0.0435 0.5518 0.2324 0.1160 0.2324 0.3183 0.1784 0.1160 0.1784 0.3950   and: θ = [ θ1 θ2 θ3 ] θ1 =   −1.3000 −0.1000 −0.2001 −0.1000 −1.1000 −0.4000 −0.2001 −0.4000 −1.3000 −0.5000 0.2001 −0.6002 −0.0998 −0.6000 −0.7000 0.3000 0.5000 −0.0199 −0.1999 −0.1000 −0.0999 −0.2000 0.3000 0.3000 −0.4002 0.0902 −0.0099   θ2 =   −0.5000 −0.0998 0.3000 0.2001 −0.6000 0.5 −0.6002 −0.7000 −0.0199 −1.4000 −0.0702 −0.2999 −0.0702 −1.0000 −0.0300 −0.2999 −0.0300 −1.6002 −0.8000 0.1997 −0.0999 −0.0501 −0.0301 −0.4997 −0.2000 −0.0898 −0.0399   https://doi.org/10.15837/ijccc.2020.5.3898 6 θ3 =   −0.1999 −0.2000 −0.4002 −0.1000 0.3000 0.0902 −0.0999 0.2992 −0.0123 −0.8000 −0.0501 −0.1992 0.1997 −0.0301 −0.0898 −0.0999 −0.4997 −0.0399 −1.4998 −0.2000 −0.0801 −0.2000 −0.7004 −0.1001 −0.0801 −0.1001 −1.2996   therefore, the lyapunov function v (ψ (x)) and its first derivative v̇ (ψ (x)) are expressed under in the forms v (ψ) = ψt υψ and v̇ (ψ) = ψt θψ. the area plots respectively for the function v (ψ) and v̇ (ψ) are demonstrated in figure 1 and figure 2. subsequently system (15) is globally asymptotically stable at origin. 0 2 500 2.51 2 v ( x ) 1000 1.5 x 2 10 0.5 x 1 1500 0-1 -0.5-1-1.5-2 -2-2.5 figure 1: area plots for the lyapunov function v (ψ) 2.5 results and discussion in this section, the investigation of the carleman linearization letting the computation of an infinite dimensional linear model is provided. an analytical step procedure which defines a function of lyapunov and ensures nonlinear models stability via the outlining of a lyapunov equation is considered. this technique is based on the generic taa [38]. the most important conclusion that is derived as a result of the implementation of this method on a second order system is given as follows: raising the truncation degree of the lyapunov equation creates better accuracy on the typical da. however, it is noteworthy that there is a quick increase in the linear matrix dimension. this causes difficulties in the calculation of the lyapunov function with no appreciable enhancement in the da https://doi.org/10.15837/ijccc.2020.5.3898 7 -2500 -2000 2 -1500 2.51 2 d v ( x ) / d t -1000 1.5 x 2 10 -500 0.5 x 1 0-1 -0.5-1-1.5-2 -2-2.5 figure 2: area plots for the function v̇ (ψ) dimension. it would be an innovative technique to examine beforehand the developed technique to define the best possible degree of truncation for the highest accuracy in defining the da. 3 estimation of the da using a zubov approach 3.1 rational lyapunov function this paragraph aims to explore an analytical approach for approximating the da of nonlinear models. this later is established based on the calculation of a lyapunov function providing a solution for the subsequent equation : v̇ (x) = ( dv (x) dx )t f (x) = −φ (x) (19) with φ (x) a positive definite function. to solve the equation (19), a structured procedure using the conversion of f(x) into taylor series expansion was given in [43]. this procedure started with writing f(x) under the following form: f (x) = ∞∑ i=1 fi (x) (20) as the lyapunov candidate function should be restricted when evolving towards the border (that’s mean when x →∞), therefore, this attribute can be stated by considering a rational defined by [43] : v (x) = n (x) d (x) (21) https://doi.org/10.15837/ijccc.2020.5.3898 8 where n(x) and d(x) are polynomial functions. subsequently, v (x) should be represented as follows: v (x) = ∞∑ ni (x) i=2 1 + ∞∑ di (x) i=1 (22) equation (20) and equation (22) yield: (κ(di,ni)) ∞∑ i=1 fi = −xt inx(1 + ∞∑ ni i=1 )2 (23) wherein: κ(di,ni) = ( 1 + ∞∑ i=1 di ) ∞∑ i=2 ∇nti − ( ∞∑ i=1 ∇dti ) ∞∑ ni i=2 (24) from this, finally one obtains: ∞∑ i=2 ∞∑ j=2 ∇nti fj + ∞∑ i=1 ∞∑ j=2 ∞∑ k=1 di∇ntj fk − ∞∑ i=1 ∞∑ j=2 ∞∑ k=1 ∇dti njfk = −xt inx ( 1 + 2 ∞∑ k=1 dk + ∞∑ i=1 ∞∑ j=1 didj ) (25) identifying the coefficient of equal degrees from both sides of the second order equation gives: ∇nt2 f1 = −x t i2x (26) and: f1 (x) = h1x = [ h11 h12 h21 h22 ][ x1 x2 ] (27) when k ≥ 3 the generic solution can be written as: k∑ i=2 ∇nti fk+1−i + j−2∑ i=1 ∞∑ k=2 ( di∇ntk −∇d t i nk ) fj+1−i−k = −xt qnx ( 1 + 2dk−2 + k−3∑ i=1 didk−2−i ) (28) hence, in every step of the synthesized algorithm, the below linearity under a set of established equations as the same form for equations (27) and (28) is obtained: hnq = bn (29) where hn is a matrix of an appropriate dimension given by: hn = [ hn1 hn2 bn ] (30) with: hn1 =   h11n h21 0 h21n (h22 + h11 (n− 1)) 2h21 0 0 h12 (n− 1) ... ... ... 0 0 · · ·   (31) hn2 =   . . . . . . 0 0 . . . 0 [h22 + h11 (n− 2)] 3h21 0 . . . 0 0 0 h12 nh22   (32) https://doi.org/10.15837/ijccc.2020.5.3898 9 bn is obtained by expanding the quantity ( di∇nt2 −∇d t i n2 ) f1, bn contains the numerical values of the equation (28). q = [ h1, ..., hn+1 g1 , ...,gn−1]t (33) now the derivation of the lyapunov function is as follows: v̇n = −xt qnx + e (x)( 1 + n−2∑ i=1 qi (x) )2 (34) where e(x) is a sum of monomials of degree greater than n. to enlarge the attraction area, in the proximity of the origin, one needs to analyze the condition v̇ (x) < 0 minimization process of the quantity e (x). the problem thus implies the next reducing criterion:{ min en (q) hnq = bn (35) with: en (q) = ∥∥∥∥∥ coefficients of the numerator termsof (34) ∥∥∥∥∥ 2 2 (36) 3.2 study of an illustrative example the earlier example of van der pol model is being investigated here. to identify the boarder of the da, use a lyapunov function defined by: v4 (x) = 4∑ i=2 ri (x) 1 + 2∑ i=1 qi (x) (37) with ri (x) = h1xi1 + h2x i−1 1 x2 + · · · + hix i i and qi (x) = g1xi1 + g2x i−1 1 x2 + · · · + gix i i from equation (26) for degree 2, one can easily establish the equation of r2(x) described by: r2 (x) = 1.5x21 −x1x2 + x 2 2 (38) for k ≥ 3 the generic solution can be obtained based on the optimization method described by (35). the coefficients and are suggested to be coded into a 7 bits digital word as follows: hi = ( h00, h01,h02,h03,h04,h05,h06) gi = (g00,g01,g02,g03,g04,g05,g06) (39) the ga is employed where the genetic properties are summarized as follows: the size of population is 50, the rate of mutation is 0.1, the crossover rate is 0.65 and the maximum number for the generations is taken equal to 100 [24]. when implementing the recommended method based on the ga, the following outcomes are obtained: v4 (x) = vn4 vd4 = 3.18 (40) wherein: vn4 = 1.5x21 −x1x2 + x22−0.2449x41+0.4082x31x2 +0.1582x21x22−0.1293x1x32−0.0459x42 vd4 = 1 + 0.2041x21+0.0560x1x2−0.0252x 2 2 https://doi.org/10.15837/ijccc.2020.5.3898 10 -3 -2 -1 0 1 2 3 x 1 -3 -2 -1 0 1 2 3 x 2 limit cycle estimated da figure 3: stability region of van der pol system 3.3 results and discussion in this section, an algorithm leading to construct a rational lyapunov function to approximate the da of autonomous nonlinear systems is provided. using this algorithm is advantageous compared to other one since there is no major complexity in selecting initial starting values. in fact, this technique is a linear programming based method which can be validated by an equivalence constrained minimization criterion. moreover, the described technique can be of a practical use for a large set of nonlinear models. indeed, it is not restricted for polynomial nonlinear systems which the case for the majority of existing algorithms. equally important, the approximated da is satisfactory because it is adequately large in comparison to the theoretical bound. however, the main disadvantages of the presented method can be mentioned as follows: – the obtained da has a non-definite geometric shape which means that it does not support a control strategy requiring the mathematical expression of the domain bound. – the convergence of the developed algorithm is not demonstrated since it depends on a heuristic method (that is, the ga). – the initial condition of the given algorithm impacts heavily the final outcome. 4 conclusion in this paper, the lyapunov theory for approximating and the expanding of the domain of asymptotic stability of nonlinear models is examined. the investigated techniques have been performed with reference to numerical algorithms that were established using the integration of a rational lyapunov function and a quadratic function. the primary motivation has been to study the various analytical approaches for the synthesis of da in the proximity of an equilibrium point. the outcomes of the developments which are performed using the technique of threshold algorithms, the approximation https://doi.org/10.15837/ijccc.2020.5.3898 11 method via a rational lyapunov function and the approximation method based on lmi and ga demonstrated that this variety of approaches has the following attributes: – they are effective for large classes of nonlinear models. – they have the capability to enhance the domain of asymptotic stability. – they can be performed for both high and low order nonlinear models. – all the investigated techniques are developed in order to convert the da estimation problem into a standard generic one. the primary method known as the carleman method demonstrated some difficulties which can be summed up as follows: when increasing the truncation order which enables the achievement of a lyapunov function, an apparent complexity illustrates the digital operations of the dependent matrices describing the established lyapunov function. the remaining paper discussed couple of approaches: the first is based on the result of the zubov equation and the other is based on a statistical approach. to discover a representation of the lyapunov function, it is demonstrated that solving the given problem corresponds to an optimization problem. a merging of the ga, lmi and rtm is employed in this respect. to analyse the performance of the various studied methods, a simulation analysis is accomplished based on the oscillator model of van der pol. this later is described by an asymptotic stability domain limited by a closed contour. the achieved outcomes have confirmed the effectiveness of the presented methods and approaches to ensure asymptotic stability in case of controlled systems. with the recommendations in this paper, conservativeness in the context of solutions from earlier literature was reduced: solutions based on lyapunov equations can be utilised as a seed for the techniques developed here. funding the authors would like to acknowledge the support of the deanship of scientific research at hail university ksa under the research project (rg-0191315). author contributions the authors 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(1985). maximal lyapunov functions and domains of attraction for autonomous nonlinear systems. automatica, 21(1), 69–80, 1985. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: jerbi, h.; hamidi, f.; ben aoun, s.; olteanu, s. c. ; popescu, d. (2020). lyapunov-based methods for maximizing the domain of attraction, international journal of computers communications & control, 15(5), 3898, 2020. https://doi.org/10.15837/ijccc.2020.5.3898 int j comput commun, issn 1841-9836 8(5):769-783, october, 2013. distributed genetic algorithm for disaster relief planning k. zidi, f. mguis, p. borne, k. ghedira kamel zidi science faculty of gafsa, university of gafsa campus universitaire sidi ahmed zarrouk 2112 gafsa, tunisia kamel_zidi@yahoo.fr fethi mguis*, khaled ghedira higher management school of tunis, university of tunis 41, rue de la liberté bouchoucha le bardo 2000, tunisia *corresponding author: fethi.mguis@fsg.rnu.tn khaled.ghedira@isg.rnu.tn pierre borne ecole centrale de lille villeneuve d’ascq 59650, france pierre.borne@ec-lille.fr abstract: the problem studied in this paper is the management of vehicle routing in case of emergency. it is decomposed into two parts. the first one deals with the emergency planning in the event of receiving a set of requests for help after a major disaster such as in the case of an earthquake, hurricane, flood, etc. the second part concerns the treatment of contingency as the arrival of a new request or the appearance of a disturbance such as breakdowns of vehicles, the malfunction of roads, availability of airports, etc. to solve this problem we proposed a multi-agents approach using a guided genetic algorithm for scheduling vehicle routing and local search for the management of contingencies. the main objectif of our approach was to maximizing the number of saved people and minimizing the costs of the rescue operation. this approach was tested with the modified solomon benchmarks and gave good results. keywords: vehicle routing problem, multi-agent system, genetic algorithm, emergency, disaster relief. 1 introduction although there were several studies in the field of vehicle dynamics deals with the problem of the distribution (eg, [9][34][35][39]). however, most of the available research has focused on commercial transport, logistics planning in supply chain repetitive, and routine treatments and environments orders [4]. studies dealing with vehicle routing problems as the dynamic response planning and logistics of disaster relief were few. the objective of this work was to propose a distributed approach based on a genetic algorithm for modeling and solving the problem of contingency planning in case of disaster. our approach enables a planning disaster relief distribution by maximizing the number of people saved, avoiding delays in the relief distribution and maximizing the useful equipment life which saves costs. in section 2, we have presented the problems and the aims of this work; while section 3 presented a literature overview dealing with the problem of emergency planning for disaster. in the section four highlighted and described the proposed approach. in the last tow sections, we have discussed tests of calculation and managerial implications arising from the study, and we have summarized the work, by presenting findings and suggesting further alternatives. copyright c⃝ 2006-2013 by ccc publications 770 k. zidi, f. mguis, p. borne, k. ghedira 2 problematic and aims as the world continue to witness the vulnerability of natural and artificial disasters on human well-being is constantly under threat (eg, [3] [22] [36] [37]). for example, about two million people were affected by the haitian earthquake of january 2010, the haitian government have reported that an entire nation of 230,000 people had died, 300,000 were injured and 1,000,000 made homeless [47]. furthermore about 250,000 homes and 30,000 commercial buildings were collapsed or were severely damaged [47]. similarly, the earthquake of 2008 in china killed more than 67,000 people. in 2008 cyclone nargis killed more than 78,000 in myanmar [28], the kashmir earthquake in pakistan in 2005 killed more than 86,000 people and katrina hurricane killed more than 1,000 person with damages of billions of dollars [21]. in fact, disasters and crises are often characterized by massive displacement of people, lack of food and water, degradation of essential services, damage to infrastructure, unsatisfactory warehousing capacity, inadequate transportation and inaccessibility to remote areas [28] [29]. add to this, other challenges include the difficulty in assessing the needs and expectations of victims (human resources management), insufficient relief or quantity transported and delivered as well as issues of inter-agency coordination. often, disaster response was characterized by excessive centralization and short-term nature of the emergency chain coupled with a lack of reliable information [1] [18] [28]. in addition, sources the supply distribution nodes, and extended distribution points must be quickly putted in place, sometimes because of the topographical difficult situations of pickups and deliveries [6] [19]. in several times, organizations and agencies have negotiated with governments and administrations, military access, municipal authorities and organizations [4] [33], therefore, it appeared that crises and disasters create circumstances and extraordinary conditions for those who seek to manage relief and rescue operations. these actors must take urgent decisions while essential information on the causes. thus, consequences remain unavailable and the degree of uncertainty is high [38]. despite this difficult environment disaster with rapid change, and the efforts of charities, aid agencies and governments, stakeholders such as emergency beneficiaries [12] [23] [32] as well as the media [8] [48] have criticized how the relief was distributed and the disaster relief chains were managed. it’s the case of inefficiency, waste, and lack of planning and logistical capacity limited disaster were cited. these criticisms, were also applied to the disaster relief operations, for example, in the aftermath of hurricane katrina in 2005. such an investigation was crucial for improving real-time response and effectively to the consequences of disasters as well. other crises where relief goods such as water, food, first aid and so must be rapidly distributed to reduce human suffering and save lives. in others words, the nature of a disaster, especially with regard to the availability of infrastructure logistics, warning, scale, location, topography and resources have an impact on the design plans and emergency response, as well as the mixture of the allocation of resources [33]. it appeared that, some parameters must be taken into consideration to better understand the situation of the typical distribution of disaster relief. resources of the fleet of vehicles and emergency equipment were generally insufficient; the affected area was often a poor logistics infrastructure (eg, bad and damaged roads , warehouses, ports, etc.) and desperate people who may be dispersed to different nodes (feeding sites and/or distribution) over a large area. therefore, it is difficult for both relief workers and logisticians to rapidly develop effective and efficient distribution plans [4] [34] [35]. second, fleet management with interest on routing, planning, pickup and delivery are often the main decisions to be taken by the logisticians and the relief’s effective allocation and satisfaction of requests place were also to be taken in consideration. these decisions are important for the distribution of relief because of "life or death." this requires fast delivery with strict limits distributed genetic algorithm for disaster relief planning 771 required by donors and financial accountability reports. hence the need for an efficient routing and planning essential to the effectiveness of relief programs. 3 literature review in recent years, the problems of disaster emergency management have attracted more and more the researchers’s attention. this was due to their importance for human, economy and society. indeed, some approaches have been proposed for modeling and solving this problem. oh and haghani [27] modeled the problem as a linear program by considering only how minimizing the cost. however, tzeng [41] developed a heuristic based on linear program in which they took into account the human and the cost; while yi [42] proposed an approach based on aco metaheuristic modeling with linear program witch consider only the human side of the problem. in same way ozdamar [30] proposed a modeling of the problem as a linear program. furthermore, he also proposed a modeling of the problem as a linear program in which they took into account the human side and they have developed a heuristic to solve the problem [31]. in contrast, ma [20] proposed an exact method using a nonlinear program by considering the human goal only. this was similar to some extend, afshar [2] is based on an exact method to solve the problem by modeling as a linear program that takes into account the human side. zhang [44] developed a heuristic which considers the minimization of cost. to conclude, we can say that: • most approaches do not take into account the aspect of multi-objective problem. • the majority of available studies dealing with static problems ignore the dynamic aspect of the problem. • the meta-heuristics were less used despite they were more efficient especially with this kind of problem since they require a short response time with a number of exponential data. • all the approaches developed did not include forecasts to predict future requests for help. in fact, in the disaster case, forecasting future demands have allowed to avoid certain circumstances and reduce the damage to human and material. • distributed approaches were almost absent despite their great performance for modeling and solving problems similar to the problem addressed herein(eg, [25][24][45][43][7][17][10][14]). 4 proposed approach in order to solve the above described problem, we propose a multi-agent model as developed by zidi [46] to which we were add some features that we suspect necessary for the proper functioning of the system to adequately address the problem. our model as described in figure 1 was composed by a "system agent" and a set of "zone subsystems". the "system agent" has to create and supervise all other agents. each "zone subsystems" was composed of a "planning subsystem", an "area manager agent", "information manager agent", "forecast agent", "disturbance agent", and a local database. the "zone subsystem" was managed by the "area manager agent" which provides communication with the "system agent" and other "zone subsystems". the "information manager agent" was responsible for the management of information 772 k. zidi, f. mguis, p. borne, k. ghedira necessary for the proper conduct of the rescue operation. in fact it provides information collection, their filtration, and updates the local database. the "forecast agent" allowed forecasting of emergency calls based on the information received and based on historical relief interventions. the "disturbance agent" will be used to detect disturbances that can occur, transmit "planning subsystem" to be processed. the "planning subsystem" was responsible for emergency planning, integration of new applications and correction planning in case of a disturbance. the "planning subsystem" was composed of three types of agents namely "supervisor agent" who supervises the other components in addition to its roles in the "planning subsystem". the role of "vehicle agent" is finding a path for a vehicle. the "interface agent" handles communication with the external environment. the local database of "zone subsystem" will be used to store all data in the backup area. each "zone subsystem" can communicate with the external environment mainly composed by: applicants emergency disaster, relief agencies, the geographic information system gis, news and weather agencies. figure 1: overview of the system 4.1 the planning subsystem the subsystem of planning presented in figure 2 was composed of three types of agents: supervisor, interface and vehicle. supervisor agent this agent ensures the consolidation of emergency calls, the creation of tenders for the insertion of a new application, choose the best solution and update planning after any changes generated by the insertion of a new application or by the occurrence of a disturbance. interface agent this agent was responsible for all information exchanged between the "planning subsystem" and its external environment. it was responsible for receiving requests for help formatting, negotiating with transport operators and information from stakeholders. distributed genetic algorithm for disaster relief planning 773 vehicle agent each vehicle agent was responsible for a vehicle. it has to ensure that the search path will be followed by the vehicle. in addition to receiving a bid for the insertion of a new request, the agent "vehicle" checks can add this request and sets its offer, which sends it to the "supervisor agent". figure 2: subsystem of planning agents communications we identify ten types of communication messages between agents of "planning subsystem". these messages were: "request" was sent by the "interface agent" to the agent "supervisor" upon receipt of a new request for help. after processing a new application, a "request response" was sent by the "supervisor agent" to the "interface agent". when receiving an announcement of a disturbance, a message "disturbance" has to be sent by the "interface agent" to the "supervisor agent". after treatment of the disturbance, a message "solution of disturbance" which was sent by the "supervisor agent" to "interface agent". after the consolidation of emergency requests, messages "group requests" were sent by the "supervisor agent" to "vehicle agents". after searching for a path, a message "path" was sent by the "vehicle agent" to the "supervisor agent". following the receipt of a new request messages "tender" were sent by the "supervisor agent" to "vehicle agents". after the establishment of an offer to insert a new request, a message "offer" was sent by a "vehicle agent" to "supervisor agent". in order to improve the quality of its way, a "vehicle agent" can send a message "negotiation" to the other "vehicle agent". communications between the different agents were presented in figure 3. emergency planning emergency planning starts by grouping the applications according to their types, their locations, their degrees of urgency, their quantities, and their time windows. then each group will be assigned to an "vehicle agent" which was responsible for finding the right path according to a heuristic which sort the requests in ascending order according to their service termination dates. in the of equality case the customer closest to the preceding customer has to be selected. this creates an initial population for a genetic algorithm executed by the "supervisor agent" altogether with the "vehicle agent". at the end a we got the final population of the solutions 774 k. zidi, f. mguis, p. borne, k. ghedira figure 3: protocols diagram for planning from which the "supervisor agent" have to chose one of them to be followed for the planning of the rescue operation. genetic algorithm the real universal problems, including disaster cases has inevitably some unknown and uncertain parameters. stochastic programming seems to be the most appropriate to solve this problems, while it requires probability distributions for the estimate data. the solution computed should be valid for the majority of possible realities and the expected value of the objective function was optimized. our challenge was to propose an alternative approach to provide probability distributions. if the deviations were within certain limits, our idea was to find the valid solution, even if small changes may occurs. therefore, the desired visits should be robust and flexible. however, these terms were often used with different meanings; hence we will define this terms to be used later in this document: • robustness: if a change in travel time or a new customer request arrives, only minor changes were needed in the planning. • flexibility: it will keep the schedule, because the generated plan has several options. therefore, a right option was to solve a stochastic model that was particularly difficult to solve in a certain period. alternatively, it is possible to solve a deterministic model with additional constraints to create a robust and similar flexibility. therefore, a similar performance with less efforts calculation can be performed. so with such problem, the approximate methods have proved more effective resolution. in fact, they allow to obtain acceptable solutions in a reasonable time. among the approximate methods the most used wre the genetic algorithms that provide solutions which guaranteed robustness and flexibility. the fundamental principles of these algorithms have been incurred by holland [15]. these algorithms are based on the functioning of the natural evolution of species, including the selection of darwin and mendel procreation. they have been successfully used to solve several problems of multi-criteria optimization [11]. in genetic algorithms, we simulate the process of evolution of a population. one starts with an initial population composed of n solutions (individuals) of the problem. the degree of adaptation of an individual to the environment is expressed by the value of the cost function f (x), where x is the solution that is the individual. it is said that a person is much better adapted to its environment, the cost of the solution is lower or larger depending on test selected optimization. distributed genetic algorithm for disaster relief planning 775 within this population, occurs when the random selection of one or both parents, producing a new solution, through genetic operators such as crossover and mutation. the new population is obtained by the choice of n individuals among populations (parents and children) is called next generation. by iterating the process, we produce population richer individuals best suited. the process of the genetic algorithm is presented in figure 4. for success and the convergence of the figure 4: functioning of the genetic algorithm genetic algorithm requires: • make a good representation (encoding) of a solution (chromosome) • choose the manufacturer of the original solution, • clearly define the evaluation function to determine the fitness of each individual • choose the methods of selection, crossover and mutation are best suited to the problem and • carefully choose the parameter values: population size, probabilities, etc.. coding: because the chromosomes are not binary adopted for sequencing problems, it has adopted an actual coding where the chromosome is a sequence of nodes (excluding the deposit). figure 5 shows a chromosome of a problem consists of 10 clients. creation of the initial population: to build the initial population, we have developed a heuristic which builds the first half of the population randomly and the second half in a guided manner based on the method of insertion of solomon [40]. selection: to select the chromosomes (solutions) that will be able to contribute to the creation of the new population, we adopted the method of selection of wheel [15] [13] which consists in assigning to each individual a probability of selection proportional to its evaluation (fitness) and the sum of the evaluations of individuals. the selection process is shown by the following algorithm: 1. calculating the fitness fi for each individual of the population 776 k. zidi, f. mguis, p. borne, k. ghedira figure 5: example of an individual 2. calculating the probability pi of selection of each individual pi = fi/ ∑i=n i=1 fi 3. calculation of cumulative probability qi of each individual qi = ∑i j=1 pj 4. generate a random value r ∈ [0,1] 5. if r < q1 then select the first individual else choose individual i as: qi−1 < r ≤ qi 6. repeat steps 4 and 5 to create the desired number of individual crossover: from both parents (solutions), we try to generate one or two son. there are several breeding techniques. for each type of problem, there are a set of breeding methods that are more suitable. for the vehicle routing problem, according to the literature, the most commonly used methods are: a crossing point, dual point crossover and uniform crossover. in our case we used the uniform crossover. this method consists in determining the cost, the number of customers and the ratio of these last two for each round of two parents chosen at random. then, each parent towers are sorted in ascending order according to the quotient: cost/number of customers. the third step is to establish a conflict matrix to describe the conflicts between the towers of both parents. the construction of the individual descending begins by inserting the first turn of one of the parents. the second step in the construction of the son is to exclude all rounds of the other parent are in conflict with the tour recently added. the same process is then repeated with the first round of the other parent and so on until there are more towers to insert. at this stage it is possible to customers not served. these customers are inserted into the shot so that the overall cost is minimal. figure 6 illustrate an example of a crossover operation. mutation: in the process of evolution, mutation performs a broader exploration of the search space, to avoid premature convergence and diversity loss by bringing innovation in the population. in our approach we have adopted the method of simple random mutation. the principle of this method is to choose two nodes randomly. result in generating a random value between 0 and 1. if this value is less than the mutation probability, then swap the two nodes, otherwise do nothing. figure 7 shows an example of a mutation operation. distributed genetic algorithm for disaster relief planning 777 figure 6: example of a crossover operation figure 7: example of a mutation operation 778 k. zidi, f. mguis, p. borne, k. ghedira replacement: after each iteration, individuals created (children) will be added to the population. to keep the same population size, we proceed to an alternative transaction elitist is to sort individuals according to their overall costs and keeps only the first n individuals to form the next generation. 4.2 treatment of a new request upon the arrival of a new request for assistance to the agent "interface", it begins with collecting the necessary information that can help the agent "supervisor" to make the right decision, then it sends the request to the "supervisor agent". this establishes a tender which will be sent to "vehicle agents" which will be able to meet this demand. each "vehicle agent" receiving the tender should calculate its area of action [43][60] which is equal to the number of customers(t,x,y) that can be served by this vehicle (t0,x0,y0). then this agent should calculate also the insertion cost which equals to the difference between the old and the new zone area. finally, the "vehicle agent" sends its offer to the "supervisor agent" who is in charge of choosing the best offer and sends the response to the "interface agent" which should inform stakeholders involved in this new application. if the "supervisor agent" could not insert the new request in any tour, there will be creation of a new tour to meet request. the above process of a new request treatment was described in figure 8. figure 8: treatment of a new request 4.3 treatment of a disturbance a good management of disaster relief should take into account the occurrence of disturbances at any time during the rescue. in fact in the case of disasters road and emergency equipment were very delicate and it may cause a malfunction of the system. for these reasons, we have provided a module for the treatment of disturbances. when receiving an announcement of a disturbance, the "interface agent" was responsible for the collection of information relating to the obstruction. then it sends all this information to the "supervisor agent" who takes care of looking for a solution to the disturbance. disturbances treated herein were two types. disturbance may be caused by a vehicle breakdown or unavailability of a road. in the vehicle breakdown case, the "supervisor agent" checks the possibility of replacing the vehicle. if not he have to try to fit the request of the disabled vehicle in the other tour, without interfering any constraints. in case of unavailability of a road, we proposed two alternatives. the first one distributed genetic algorithm for disaster relief planning 779 consist of changing the vehicle way by avoiding the disrupted roads. the second alternative consist of inserting customers in other tours. the treatment process of a disturbance was shown in figure 9. figure 9: treatment of a disturbance 5 experiments and results in this section, we report our approach results in comparison with other algorithms. the tests data were described in the section 5.1; while the experimental results of the different problems were summarized in the section 5.2. 5.1 experimental test data the basic data of our testing problems adopt solomon’s 100-customer benchmark problems [40] for the static vehicle routing problem with time windows (vrptw). in these benchmark problems, 100 nodes are distributed in a euclidean plane of 100*100 squares, and the travel times between nodes are equal to the corresponding euclidean distances. there are six types of problems, named c1, c2, r1, r2, rc1 and rc2, each with 8-12 problems. different types of problems differ in the distribution of the nodes, the service time of each node, and the width of time windows, described in detail as follows: • the locations of the nodes are clustered distribution in the problems of types c1 and c2, are random distribution in the problems of types r1 and r2, and are mixture distribution in the problems of types rc1 and rc2. • the service time at each node is 90 time units in the problems of types c1 and c2 and 10 time units in the problems of types r1, r2, rc1, and rc2. • the problems of types c1, r1 and rc1 have the vehicles of relatively small capacity; the capacity of each vehicle is 200 units. the ratios of the average demand of the nodes to vehicles capacities are 7.29%, 9.05% and 8.62%, respectively. and the problems of types c2, r2 and rc2 provide more capacity vehicles; the capacity of each vehicle is 700 units in type c2, 1000 units in types r2 and rc2. the ratios of the average demands of the nodes to vehicles capacities are 1.469%, 2.59% and 1.72%, respectively. • in each problem, there is a time window [e0, l0] associated with the depot with in which a vehicle must return to the depot after serving some customers. the problems of types 780 k. zidi, f. mguis, p. borne, k. ghedira c1, r1 and rc1 have an arrow time window of the depots, such that only few customers can be covered in each trip, while problems of types c2, r2 and rc2 have a wider time window of the depots. 5.2 experimental results the test results were shown in tables 1 for all the six type problems described in the section 4.1. for description convenience, we call the column (1) c1, column (2) c2, etc. nov were the average of the used vehicles number. td were the average of travel distance. c1, c2 were the computational results for the improved lns algorithm (ilns for short) which was proposed by hong [16]. the c3 was the relative error between c2 and c8 (i.e.c3=(c8-c2)/c2*100%). c4 and c5 are the computational results for the two-stage hybrid local search approach (hls for short) which was proposed by bent and van hentenryck [5]. the c6 was the relative error between c5 and c8 (i.e.c6=(c8-c5)/c5*100%). c7 and c8 were the computational results for our approach guided genetic algorithm(gga) [26]. c9 represents the percentage gain in execution time obtained by the use of our distributed approach. based on these results, we can make the following conclusions on the effectiveness of the proposed approach to solve the problem studied: • the solution quality generated by our approach can approximate to the best known solutions, the ilns approach and the hls approach. if the travel distances are only observed and the number of used vehicles were ignored, the average relative error of each type was less than 10%. • our approach provided an effective result within a reasonable time, which is very important in the disaster case. in fact, in the emergencies case, the solution should be provided quickly and efficiently. in addition our approach allowed at any time to provide an acceptable solution to stopping the execution of the algorithm after a given generation if necessary. • using multi-agent systems has allowed us to reduce the execution time which was very important for emergency planning in disaster case. table 1: experimental results. ilns approach hls approach gga our aproach problem nov td er(%) nov td er(%) nov td ∆time(%) type (1) (2) (3) (4) (5) (6) (7) (8) (9) c1 10 833,1 -0,53 10 828,82 -0,02 10 828,65 -25,46 c2 3 590,31 9,09 3 589,86 9,17 3 643,94 -22,03 r1 12,25 1218,28 -1,28 11,92 1244,52 -3,36 11,9 1202,74 -14,67 r2 3,27 964,11 -5,24 4 954,27 -4,26 2,9 913,6 -16,53 rc1 12,13 1369,57 0,46 11,5 1384,17 -0,60 11,9 1375,89 -17,89 rc2 3,75 1131,18 7,69 3,25 1124,46 8,34 3,9 1218,2 -28,91 6 conclusions and future works in this paper, we have developed a solution to the problem of emergency planning for disaster. we have illustrated some of the complexities practices for emergency in disaster case. despite the importance of emergency planning in several disaster cases, they have received few attention distributed genetic algorithm for disaster relief planning 781 in the literature of the disaster and operations research. thus, it appears that the extension of our approach to disaster relief, as described looks promising and with considerable value. even if we know the boundaries explicitly construct models of reality and the unique nature of disasters, we suggest that operations research systems decision support can be beneficial in the disaster. although several emergency relief organizations often hide their distribution planning for security reasons. finally we conclude that the adaptation and the use of the approach described above can contribute to the improvement of the use of the vehicle fleet management as well as the routing and delivery of relief goods in disaster cases. bibliography [1] adinolfi, c. et al.; humanitarian response review, an independent report commissioned by the united nations emergency relief coordinator & under-secretary-general for humanitarian affairs, office for the coordination of humanitarian affairs (ocha), new york and geneva, 2005. 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[47] http://www.cbsnews.com/stories/2010/01/13/world/main6090601.shtml [48] http://www.reliefweb.int obituary on 6th february, professor ioan dzitac passed away. he was born on february 14, 1953 in poienile de sub munte, a mountain village in maramures, county, in the north-western romania. though he attended primary and secondary school in a disadvantaged environment, through his ambition and hard work he overcame all obstacles. he attended ”dragos, vodă” high school in sighetul marmat, iei, and after the baccalaureate he attended ”babes, bolyai” university of cluj napoca, obtaining a bachelor’s degree in mathematics with the highest grade and, subsequently, obtained from the same university a ph.d title in computer science with a maximum grade. in 2019, he obtained the habilitation in the field of economic informatics at “alexandru ioan cuza” university of ias, i, then the title of doctoral supervisor at university of craiova. he was an associate professor at university of oradea (1991-2005). from 2009 until his sudden passing, he has been a full professor of computer science at ”aurel vlaicu” university of arad. he was rector of agora university for eight years, and recently named vice-rector for research and international cooperation. he was an adjunct professor at the university of the chinese academy of sciences (2013-2016) and a member of the advisory board at hoseo university in asan, republic of korea. his scientific interests included various aspects of artificial intelligence, soft computing and applications of fuzzy logic in technology and economics. he published 3 books, 12 courses and student support materials, and over 150 scientific articles in journals and in conference volumes. he yielded more than 850 citations according to google scholar, 420 citations from 350 citing articles and a h-index of 11 according to clarivate’s web of sciences in 2006 he initiated, together with prof. f.g. filip and prof. m.j manolescu, icccc (international conference on computers, communications, and control) a series of successful scientific events held every second year by scientists from europe, north and south america, and asia. the core topics of the conference are: a) the theory of computing and communications, b) soft computing, c) integrated solutions in computer-based control, and d) decision-making and support systems. the icccc proceedings were initially published by ieee and, since 2020, by springer. prof. dziţac and prof. filip, are the editors in chief of the international journal of computers, communication and control ijccc (http:jcr2019 (2020), with an impact factor if= 2.093 (q3). in 2021, prof dziţac edited the first issue of ijccc that was dedicated to the 100-anniversary of the birth of prof. lofti zadeh. professor dziţac was included among the 100 romanian computer scientists everywhere, from the last 100 years, in the volume ”one hundred romanian scientists in theoretical computer science”, romanian academy publishing house, 2018, as well as in the volume ”romanian civilizationinformation science and technology in romania ”, published in the year of the 100th anniversary of the great union, also in the academy publishing house. he was a brilliant researcher and a model teacher, who was extremely loved and appreciated by collaborators from romania and abroad, as well as by the multitude of his students. at the same time, he was a loving husband and a parent dedicated to raising and educating his children renata, cristian, and domnica ioana. requiescat in pace! his legacy, namely his projects concerning icccc conference and the ijccc journal will be continued and developed by assoc. prof. simona dziţac wife, domnica ioana dziţac daughter, prof. misu-jan manolescu and prof. florin gheorghe filip friends and co-initiators of the above-mentioned projects. assoc. prof. simona dzitac, domnica dzitac, prof. misu-jan manolescu, prof. florin gheorghe filip international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 4, month: august, year: 2020 article number: 3878, https://doi.org/10.15837/ijccc.2020.4.3878 ccc publications automation factors influencing the operation of iot in health institutions: a decision support methodology a. oddershede, c. macuada, l. quezada, c. montt astrid m. oddershede* university of santiago of chile, 8320000 santiago, chile astrid.oddershede@usach.cl *corresponding author: astrid.oddershede@usach.cl claudio j. macuada, luis e. quezada university of santiago of chile, 8320000 santiago, chile claudio.macuada@usach.cl, luis.quezada@usach.cl cecilia a. montt school of transport engineering pontifical catholic university of valparaiso, 2340000 valparaiso, chile cecilia.montt@pucv.cl abstract health institutions are adopting new technologies for their processes through automation by means of the concept of "internet of things" (iot). hence, offering innovative tools, applications and technology for the collection of key data and information, which is then integrated and consolidated, covering the different systems and their collaborators. the necessity of receiving quality medical services is essential in the public policy of any country. the increasing demand for having an adequate number of medical specialists, pharmacies and medications stock, dental and mental health coverage and other, together with the minimization of the waiting list and patient care time have been a crucial concern. under this context, it is valuable to redesign the processes planning and its coordination through the use of information & communications technology (ict) and iot that unifies the systems. based on previous research, the general purpose is to generate a system model to examine healthcare quality of service and corroborate its effectiveness in a real environment. the aim of this paper focus on the development of a decision support model to define key areas where the inclusion of iot would sustain the efficiency in health care service. the research methodology is based on case study, integrating planning processes, data analysis, scoring method that interacts with multicriteria approach. a pilot case study is pursued in health institutions in chile, determining critical factors and the current automation level system appraisal to generate actions of improvement in processes that show poor service quality. the results give rise to the development of an investment plan that can be converted into action plans for a health institution. keywords: automation, iot, healthcare system. https://doi.org/10.15837/ijccc.2020.4.3878 2 1 introduction today almost all organizations are digitized to a greater or lesser extent. the digitization requires a digital transformation that will force the organization to a great change to face this need. health institutions are not unaware to this transformation since they feel pressure to improve the quality of service. hospitals are focused on how to find new strategies to improve service, to allocate resources for the health professional sand to meet the needs of citizens. it is essential to improve process. the contribution of the technological incorporation and ict system with in hospital infrastructure, has played a great role in the health service, decreasing waiting times and others, thus much remains to be done. it is hoped that the implementation of iot will improve the efficiency of processes in hospitals [6]. the fact that the internet is present at the same time everywhere, allows the integration of sensors and devices into everyday objects, to be connected to the internet through fixed and wireless networks. this leads to having the possibility of an innumerable applications such as small sensors that let to measure the temperature of a room or surveillance cameras, gaining communication capacity and to decrease the barriers that separate the real world from the virtual. then more objects are being integrated with sensors, we can also find what’s called smart health and automation. therefore, there is interest in finding a method to determine the possible level of automation in health institutions and generate actions to make improvements in those processes that show poor quality of service. from previous author’s research, this document focuses on developing a decision model by identifying levels and processes involved within a health institution, collecting, managing and interpreting the available information. the research methodology is based on case study, employing multi-criteria approach and by the use of the analytical hierarchy process (ahp) [1, 15, 23, 25]. the output is a framework for defining priority areas for inclusion of the iot, to improve and/or support the quality of health service according to user’s requirements. such as: improvement in the access of patient information by health professionals, in-corporation of medical devices, applications to improve the quality of life of chronic patients, improve efficiency in healthcare services, improve climate control infrastructure, and others. a pilot study has been carried out at health institution in a region of chile, with the aim of determining the desired degree of automation. these results lead to the development of an investment plan that can be turned into action plans for a health institution. next section provides literature review, section 3 the system description, the following sections present the model development, its results and conclusions. 2 literature review unique challenges have been faced, by the health area, to adapt to new technologies since the environment for health care organizations contains many forces that demand unprecedented levels of change such as demographic changes, higher user’s expectations, greater competition and intensification of government pressure. in this perspective the decision support systems in the health sector has had a positive impact in the service provided. ciprin candea et al. 2019 [5] emphasizes the importance of this systems and prepared an architecture for them. also, the health care relationships and quality of service are important attributes of hospital performance that can be difficult to measure, interpret, and compare with other organizations. several authors have contributed to emphasize the significance of the support provided by the management of ict, mobile technology (mt)&iot [8] in the health and the need to improve the technological level. several mathematical aspects of iot devices signals processing are presented in [20, 21]. the incorporation of iot manages to improve efficiency in energy consumption we can see in the [3, 6, 7, 17]. even though, biet al. [4], proposes to incorporate technology gradually, since the incorporation of information technology (it) infrastructure impacts performance. other applications that benefit from iot are the identification of the status of the different sensors in the home by big data analysis pointed out by jung et al. 2010 [11]. also, kashef et al. (2016) [12] https://doi.org/10.15837/ijccc.2020.4.3878 3 who mention the use of iot to determine the lowest cost of hosting information in the cloud. however, is still pending to improve the data checking [16]. another use of iot systems is what kovács & csizmás (2018) [13] describes, stating that these systems, support decision-making at operational or tactical levels and increase flexibility and productivity [14, 24] mean that iot is enabled by the latest developments in rfid, smart sensors, communication technologies and internet protocols. the basic premise is that smart sensors collaborate directly without human involvement to deliver a new class of applications. in the coming years, iot is expected to connect various technologies to enable new applications by connecting physical objects in support of intelligent decision-making [29]. giménez et al. (2019)[10] analyses the performance of 602 colombian hospitals for the period 20092013. the analysis is performed from both static and temporal perspectives to assess the evolution of total factor productivity (change in hospital performance) and their components over the period. the results speak of improvements in efficiency through ict. one particular case of application was the improvement in the rate of waiting for care in the area of traumatology noted in the study of pinnarelli et al. (2012) [22], applying iot, managing to increase the number of attentions by 46% and 34% respectively. meanwhile, the authors in [26], suggests that hospital evaluations create competition among health care providers. a multi criteria decision-making (mcdm) method is used to assess criteria that affect the quality of hospital service. ahp and other tools are applied are used for evaluating quality of service at hospitals. in torkzad & beheshtinia work (2019) [27] presents an iot application, indicating that the automated health care system is the necessity and future of health care in india. in this regard, wang et al.[28] will mention that improving technology generates a substantive impact in rural areas, as is the case with reality in china. this is how zois [30] states that iot technology and infrastructure have the potential to revolutionize the delivery of health services. networked body detection devices along with sensors in our living environment enable continuous, real-time collection of information related to an individual’s physical and mental health and related behaviors. captured continuously and aggregated, such information needs to be exploited effectively to enable real-time, continuous and personalized monitoring, treatment and interventions. but, medical decisions are often sequential and uncertain in nature. ajami & ketabi[1] proposes in its study to evaluate the performance of the medical records department (mrd) of selected hospitals in iran through the use ahp. the results showed that the archive unit received the greatest importance with regard to information management. however, in respect to the user, the admission unit has received the greatest weight. this information allows managers to allocate and prioritize resources according to ahp’s technique for classification in the mrds. 3 system description considering the context expressed in previous section, this study concerns the development of a decision model with the aim to determine the degree of automation that would contribute to strengthen the service offered in hospitals, where the results will allow decision makers(dm)to develop an investment plan that can be turned into action plans for each health institution. taking into account that health service users wishes to have different ict and iot applications to support operations and the health activities services and due to limited resources the health institution’s ict system applications do not always meet the user’s requirements (quality level, performance, cost and others)there will be the omission of some with preference over others. in view of that, it is possible to distinguish critical representative users involved, as per, patients who demand fast medical assistance, with accurate, secure and confidential information about their health status. doctors who require information about their patients, including that of other professionals, for complementary tests, instantly and at the site of care. the administrative staff who need information and tools that allows them to evaluate effectiveness and practice, to review and process data and costs and others. we put forward a multicriteria approach since it allows to face conflicting objectives through the use of ahp [2, 18, 19] that helps to establish criteria and classify user preferences. then, for this study, potential and current ict https://doi.org/10.15837/ijccc.2020.4.3878 4 and iot users are classified into three groups: the patients, the doctors including clinical care staff (doctor, nurses, paramedics, etc.), and third group users performing administrative activities, billing, product distribution and inventory control. empirical data is collected from three type of hospitals, public, private and regional to investigate infrastructure and needs. a pilot case study is developed involving a regional health institution. 4 research methodology the key research is related to the effect of the incorporation of new ict and hospital 4.0 in the health sector to strengthen service. the study is conducted in three parts. initially it seeks to identify the critical factors and attributes that the user of health sys-tem considers important in meeting a service requirement and to define a set of criteria that will help dms to evaluate the support of ict and/or iot alternatives. through questionnaires, interviews and field observation, data and statistics is collected from hospital representatives and patients .thus, many factors emerge and the critical effects of the implementation of some service activities need to be recognized. this practice allows for the next step to be taken, to structure the problem situation, integrating the main elements and generating a hierarchy structure that have to be validated by an expert group. then, representatives from each type of user in the hospital form a team of experts. the last stage involves an assessment procedure through a pair comparison process. through the ahp method the different factors and elements considered in the hierarchy are classified and evaluated. the experts’ judgment is based on their own experience and knowledge. the weighting of the measured attributes of each activity is then recorded to obtain a rating on the value of the iot support for each activity that would lead to an improvement of the service. 5 case study the case study takes place in a regional public hospital of high complexity in chile. following the methodology, with the information obtained previously, it was possible to design a hierarchy structure that was validated by an expert panel made up of representatives of each considered cluster named as doctors group (physician, nurse, para-medics etc.), patients and administrative staff. this group totals 24 experts. 5.1 ahp hierarchy model the hierarchy structure model is displayed in figure 1. the overall goal states the importance of iot and ict support in healthcare-related activities. clusters at level 1, refers to user critefigure 1: initial hierarchy model https://doi.org/10.15837/ijccc.2020.4.3878 5 ria/perspective, named agents. level 2 to the activities/applications, where users are involved in and would need ict and /or iot support. the third level represent the alternatives to provide these requirement. ahp utilizes pairwise comparison procedure, to estimate the importance or priority of the factors involved. to put into action the model, we use the software expert choice tm. 5.2 pairwise evaluation considering the current situation, applying ahp, the expert panel followed the pairwise evaluation process, along the hierarchy, based on their judgment and experience. a first result is shown in figure 2 indicating the relative importance of the alternative support for agents. additionally, it can be seen that in relative terms the greatest importance is for the group of doctors to have ict and/or iot support to develop their tasks. figure 2: experts panel overall relative priorities where, the clusters representing the use of data network and the use of artificial intelligence, tell to have more importance to support activities to all participants, with a priority of 43.3% and 37.9% respectively. 6 result discussion with regard to the requirements of iot applications, from the doctors group perspective, the results point out a slight preference for artificial intelligence over the data network to develop their healthcare service activities. as seen in figure 3. figure 3: priority results from doctor’s perspective this relative importance lies in the usefulness of having access to the patient’s medical history. (33, 1%). on the other hand , in the comparison between data network versus artificial intelligence to meet doctors support necessities, in this case, artificial intelligence has priority to support the provision for emergency access and disease diagnosis follow by tracking treatments according to the initial results. https://doi.org/10.15837/ijccc.2020.4.3878 6 from patients perspective, the results indicated that the importance of data network for patient health-related activities is mainly related to an emergency service requirement as shown in figure 4. figure 4: priority results from patients’ perspective however, in the comparison between data network versus artificial intelligence to meet patients necessities, artificial intelligence has priority to support the provision of virtual medical care, medical history and/or waiting time, according to the results. from administrative staff perspective, managerial activities such as to generate doctors agenda and patients schedule, the tests delivery and test results within institution or externally, the data network is a higher priority supporting their activities.(figure 5) figure 5: priority results from administrative perspective summing up, table 1; gives an outline of the main results. these outcomes provide information regarding the priority requirements for ict and/or iot support for each user group. for the case studied, it was possible to define the most influential factors in the use of iot, which are related to the requirements of the user group and to the ict system infrastructure provision available, in order to carry out its main activity. the results allow elaborating actions plan and generating an investment plan. to apply the results, it is necessary to create an information system (is) that considers the main influence factors, following the recommendations suggested by filip (2012) [9], to design and implement a decision support system (dss). the people involved in the process are: i)the users, identified in table 1, who will be the beneficiaries, ii) the senior managers from the health institution, who will have to make the implementation decision, and iii) the designers, who will have to create the system. https://doi.org/10.15837/ijccc.2020.4.3878 7 table 1: local and global priority results users clusters rank priority activities local % iot alternatives global % doctors 46,9 medical history tracking treatments emergency access remote assistance agenda managment diagnosis infrastructure 33,1 20,3 16,5 10,2 11,7 5,4 2,8 data network 43,3 patients 29,7 emergency access waiting time, no emergency virtual medical care medical history infrastructure chronic patient entertain 34,8 25,7 13,2 10,3 9,4 6,6 artificial intelligence 37,9 administrative staff 23,4 medical agenda managment patient agenda management result samples inventory finance 45,1 21,7 20,3 8,5 4,4 rebotics 18,8 7 conclusions this document proposes a methodology using multi-criteria approach applying ahp, to develop a decision model based on a real case study. the ahp model helps to understand the selection process in a conflictive environment and to know the main technological requirements to adapt to the changing needs of healthcare provision. the ranking obtained helps to recognize activities that need to reach technological standard and to contribute to improve the quality of service and adapt to healthcare necessities change. this will allow to initiate implementation actions, and to generate investment plans. the support of incorporating new ict and iot plays an important potential role. since, population growth requiring health services, especially in rural areas increases every day. remotely located patients are patients who are far from the doctor and need constant monitoring and support. the problem also lies in updating doctors on monitoring parameters and patient history. health system challenges in developing countries include technology, infrastructure, trained physicians and connectivity among all stake-holders. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. acknowledgment special thanks from the authors of the “direccion de investigacion cientifica y tecnologica” (dicyt) no061817oh and the department of industrial engineering of the university of santiago de chile for support during the development of the study. https://doi.org/10.15837/ijccc.2020.4.3878 8 references [1] ajami, s.; ketabi, s. 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(2016). sequential decision-making in healthcare iot: real-time health monitoring, treatments and interventions. in 2016 ieee 3rd world forum on internet of things (wf-iot), ieee, 24-29, 2016. https://doi.org/10.15837/ijccc.2020.4.3878 10 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: oddershede, a.; macuada, c.; quezada, l.; montt, c. (2020). automation factors influencing the operation of iot in health institutions: a decision support methodology, international journal of computers communications & control, 15(4), 3878, 2020. https://doi.org/10.15837/ijccc.2020.4.3878 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 5, month: october, year: 2021 article number: 4308, https://doi.org/10.15837/ijccc.2021.5.4308 ccc publications habcsm: a hamming based t-way strategy based on hybrid artificial bee colony for variable strength test sets generation a. k. alazzawi, h. m. rais, s. basri, y.a. alsariera, l. f. capretz, a.a. imam, a.o. balogun ammar k alazzawi*, helmi md rais, shuib basri, abdullateef oluwagbemiga balogun and abdullahi abubakar imam department of computer and information sciences universiti teknologi petronas, malaysia bandar seri iskandar 32610, perak, malaysia *corresponding author: ammar_16000020@utp.edu.my yazan a. alsariera department of computer science northern border university, saudi arabia arar 73222, saudi arabia yazan.ahmad@nbu.edu.sa luiz fernando capretz department of electrical and computer engineering western university, canada 1151 richmond street, london, ontario n6a 5b9, canada lcapretz@uwo.ca abstract search-based software engineering that involves the deployment of meta-heuristics in applicable software processes has been gaining wide attention. recently, researchers have been advocating the adoption of meta-heuristic algorithms for t-way testing strategies (where t points the interaction strength among parameters). although helpful, no single meta-heuristic based t-way strategy can claim dominance over its counterparts. for this reason, the hybridization of meta-heuristic algorithms can help to ascertain the search capabilities of each by compensating for the limitations of one algorithm with the strength of others. consequently, a new meta-heuristic based t-way strategy called hybrid artificial bee colony (habcsm) strategy, based on merging the advantages of the artificial bee colony (abc) algorithm with the advantages of a particle swarm optimization (pso) algorithm is proposed in this paper. habcsm is the first t-way strategy to adopt hybrid artificial bee colony (habc) algorithm with hamming distance as its core method for generating a final test set and the first to adopt the hamming distance as the final selection criterion for enhancing the exploration of new solutions. the experimental results demonstrate that habcsm provides superior competitive performance over its counterparts. therefore, this finding contributes to the field of software testing by minimizing the number of test cases required for test execution. keywords: software testing, combinatorial testing, t-way testing, variable strength interaction, an optimization problem, hybrid artificial bee colony algorithm. https://doi.org/10.15837/ijccc.2021.5.4308 2 1 introduction one of the most important processes in any software-development life cycle (sdlc) is software testing [1]. testing is important for identifying the parts of software that are not performing as expected (i.e. to prevent the defects or bugs) and for guaranteeing the software comes up with all of its specifications. this process is expensive, due to the amount of time required to implement the test sets. for this reason, many strategies are proposed for generating an efficient test set to test any software system. these include equivalence partitioning, boundary value analysis, and decision tables etc. while these strategies are useful for specific problems, they have not effectively addressed the problems related to very large parameters that are due to interaction. accordingly, researchers have made great efforts over the past few years to minimize the size of the test sets in order to lessen the cost of the software testing process and to preserve their ability to detect defects. there are a plethora of contributions available in the literature, whose main purpose is to minimize the number of test cases by removing repeated test cases [2] [3]. therefore, the execution of test cases will call for less effort [4]. one of the most effective approaches to lessening the number of test cases is combinatorial testing (ct) [5], otherwise known as combinatorial interaction testing (cit). ct has recently been used to test software systems in multiple areas of interest. for example, the number of configurable parameters of the real software systems is large. as a result, it is impossible to test all configurations because of limited resources. the empirical evidence shows ct is achieving very high fault coverage of effectively detecting faults by implementing a small number of test cases on the system. one of the most efficient and effective kinds of ct is t-way testing (where t is the degree of interaction strength) for feasible solutions based on the failures caused by the value of t interaction parameters in the configuration system (inputs). many t-way strategies are categorized to algebraic and computational based strategies. algebraic strategies are based on exploiting the same mathematical functions as orthogonal arrays (oa) [6]. despite the fact that these strategies are fast and generate optimal solutions, but they do not support the high interaction strength of some configurations systems because of the restrictions that exist. to ease and overcome these restrictions, computational based strategies have been proposed to support the arbitrary configuration. these strategies generate high-quality solutions [7]. recently, the efforts of researchers have been focused on meta-heuristic algorithms as a foundation for t-way strategies. these include bat algorithm [8] [9] [10] [11] [12] [13] , artificial bee colony [14] [15] [16] [17] and kidney algorithm [18], to name just a few. however, it is impossible to find meta-heuristic algorithms to compensate for other existing algorithms for all optimization problems. consequently, hybridization is the most effective and efficient method for improving the performance of t-way strategies, based on merging and exploiting the strengths of two or more algorithms. one of the most effective meta-heuristic algorithms is an artificial bee colony (abc) algorithm [19], which has been applied successfully to various optimization problems. several studies have documented the performance of the abc algorithm on optimization problems [20] [21] [22] [23]. however, abc algorithm has suffered from problems with convergence speed, simple operation of solution development and weak information sharing activity. for this reason, many researchers have proposed variants of the abc algorithm, either by modifying it based on hybridizations or by improving the original abc, such as bee swarm optimization (bso) [24], gbest-guided abc (gabc) [25], hybrid aco–abc [26] and others. in this paper, a new meta-heuristic based t-way strategy called hybrid artificial bee colony (habcsm) strategy is being proposed. this strategy is based on merging the advantages of the artificial bee colony (abc) algorithm with the advantages of a particle swarm optimization (pso) algorithm to improve the performance of the original abc algorithm. habcsm is the first t-way strategy to adopt hybrid artificial bee colony (habc) algorithm as its core application for generating a final test set and the first to adopt the hamming distance as the final selection criterion for enhancing the exploration of new solutions. the paper is presented as follows: section 2 presents the theoretical concept of covering array notation. followed by related works in section 3. section 4 provides an overview of the hybrid artificial bee colony algorithm. section 5 discusses the phases of our proposed strategy. .section 6 shows the results of tuning experiments .section 7 shows the results of our benchmarking experiments. finally, section 8 provides the overall concluding remarks along with future work. https://doi.org/10.15837/ijccc.2021.5.4308 3 2 main concept of covering array notation mathematically, covering array (ca) is a widely reputable mathematical theme, whereas ct depends on the ca for theoretical test set size generation [27]. ca is available as another practical alternative to the previous (or the oldest) mathematical theme, namely orthogonal array (oa), which is used for the purpose of statistical experiments [28]. in general, each of the software being tested (sut) includes several items such as the parameters (p) and value (v ) of each parameter and the interaction strength degree (t). an orthogonal array utilization is limited in the software testing field because it requires that all parameters have the same value number, and that each pair of values be covered the same number of times [6] [29]. therefore, oa has difficulty producing the test set, and frequently it is quite large. however, on the positive side, oa is characterized by an ability to easily recognize the combination which is responsible for causing a failure [30]. to ease and overcome the constraints of oa, ca and mixed ca (mca) have been proposed. ca is defined by ca (n, t, vp ) [1], as a two-dimensional array, where n indicates the number of test cases (i.e. rows), p indicates the parameter’s number (i.e. columns), v indicates the value of each parameter, and t indicates the degree of interaction strength . the optimal ca includes the minimum number of test cases, called test sets. ca is an array n x p, where n indicates the number of test cases (i.e. rows), and p indicates the parameter’s (i.e. columns). for example, this ca (16, 2, 46) has an interaction strength degree of 2 and the parameter’s number is 6, and these are linked with 4 values each. in cases where each parameter has a different value, this is called mixed covering array (mca) [28], which is defined by mca (n, t, v1p 1, v2p 2, . . . , vip i). consider mca (9, 2, 36 24) as an example. the mca interaction strength degree is 2 and the parameter’s number is 6, and these are linked with 3 values each and 4 are linked with 2 values each. with the rapid improvement in information technology (it) and complexity in modern software structure, a variable strength covering array (vsca) alongside ca and mca is been implemented. a compelling difference was observed in the interaction strength amongst such software structure parameters [31].vsca is defined by vsca (n; t, vp , (cai)), where cai is the subdivision of the main space set with a distinct interaction degree. 3 related work in general, existing state-of-the-art strategies for generating the optimal or best test set sizes are divided into two approaches: algebraic and computational [17] [32]. mathematicians have typically utilized the algebraic approach, where a test set is constructed based on the construction of oa. the oa is derived from the extension of mathematical functions [33]. although algebraic based strategies are fast, their utilization in the ct field is restricted to small configurations (e.g. all parameters must have the same exact value number) [2] [34]. strategies that have utilized the algebraic approach are test configuration (tconfig), orthogonal latin squares (ols) [6], ca [1] and mca [28]. the second strategy is the computational approach, which is based on greedy algorithms for generating test set size to cover the maximum number of combinations. this approach is classified as one-parameter-ata-time (opat) or one-test-at-a-time (otat) approach to generate the final test set size. the one-parameter-at-a-time approach begins by generating a complete test set for the first two parameters and expands this process horizontally, and occasionally vertically, by adding one parameter at a time per iteration to ensure coverage of all parameters [35]. the known strategies that adopted opat approach are the in-parameter-order (ipo) strategy [35] and its improvements such as ipad2 [36], ipog-d [37] and ipog [38], to name just a few. one-test-at-a-time strategies begin by generating a complete test case at a time per iteration to ensure that all interaction components are covered. the first strategy to adopt this approach is automatic efficient test generator (aetg) [39], and this was followed by many other strategies such as jenny [40], whitch [41], tconfig [27], test case generation (tcg) [42], deterministic density algorithm (dda) [43], classification-tree editor extended logics (cte-xl) [44], pairwise independent combinatorial testing (pict) [45] and intersection residual pair set strategy (irps) [46]. https://doi.org/10.15837/ijccc.2021.5.4308 4 recently, researchers began focusing on meta-heuristic algorithms as a main algorithm for t-way test set generation strategies such as bat algorithm (ba) [8] [9] [10] [11] [12] [13] , hill climbing (hc) [28], simulated annealing (sa) [27] [28] [31] [41] , tabu search (ts) [47] [48] , artificial bee colony (abc) [14] [15] [16] , particle swarm optimization (pso) [49], cuckoo search (cs) [50], ant colony algorithm (aca) [51], genetic algorithm (ga) [52], kidney algorithm (ka) [18] and others. the most essential algorithm used for a 2-way test set generation ( also termed pairwise test set generation) is hc [28]. however, the initial search of hc is sensitive. for this reason, it is apt to be restricted in the local optima. another algorithm used successfully for pairwise test set generation is ts [47] [48] . due to constraints of hc and tc, and lack of support for a large the interaction strength more than t=2. stardom proposed sa as an improvement of hc [27] [28] [31] [41] . sa is used for t-way test set generation and to support higher interaction strength t ≤ 3, which is dissimilar to hc and ts. the experimental results have shown that sa outperformed both hc and tc, especially for higher strengths t ≤ 3. in early studies, the meta-heuristic algorithms are adopted to generate uniform ca and vsca, such as ga [52] and aca [51], while ga begins searching in all available positions in order to find the solutions randomly. therefore, all solutions subjected to repeat cycles of processes such as mutation and selection, crossover in order to mimic natural selection of biological evolution, unlike sa, hs and ts. in contrast, aca was inspired by ant colony behaviour in order to find the best food paths. ga and aca often are not stuck in local optimum compared to hc, ts and sa. moreover, aca and sa were improved to support a vsca up to t ≤ 3. bestoun et al. [53] proposed the particle swarm test generator (pstg) strategy for t-way test set generation and variable strength pstg (vs-pstg) strategy [54] utilizing pso algorithm. both the pstg and vs-pstg strategies are based on and inspired by the behaviour of birds in a swarm when searching for food. several parameters of pso have been tuned perfectly to find the best particles or solutions. pso was then extended for a better solution called discrete pso (dpso) [55], whereby the candidate solution is represented by a particle’s position. the results of these experiments show that dpso outperformed the original pso by producing optimal or near-optimal solutions. pso outperformed ga and aca in terms of support for high-interaction strengths t=6. however, ga and aca had relatively better computation times compared to pso [56]. hs strategy as proposed by alsewari et al. [57] is similar to pstg and vs-pstg for t-way test set generation. hs strategy is inspired by the behaviour of a skilled musician who is creating good music. to explore the search spaces efficiently, hs strategy used elitism selection that was used in ga, for intensification and diversification. furthermore, mathematical equations are used to find relatively better solutions and a probabilistic gradient to select the current solution neighbour. 4 hybrid artificial bee colony algorithm in 2005, karaboge proposed the artificial bee colony (abc) algorithm to solve an optimization problem [19]. abc algorithm is considered one of the most efficient meta-heuristics algorithms and can be applied in many different areas of optimization problems, such as the combinatorial optimization problem. abc algorithm mimics the foraging behaviour of honeybees inside the hive. this algorithm consists of three kinds of bee, where each bee executes a different task in order to find the best food source (i.e. solution). these three kinds of bees are employed, onlooker and scout bees. employed bees begin to explore the entire environment in order to find food sources with the maximum amount of nectar. then, they communicate the information about food sources (i.e. profitability, distance, direction) with other kinds of bees that are waiting inside the hive (i.e. onlooker bee). onlooker bees take the information that is given by the employed bee in order to make the decision to choose the food with the maximum amount of nectar. the scout bee works to detect a new or better food source than the existing food source by randomly searching the environment again. in the past several years, many meta-heuristic algorithms such as artificial bee colony (abc) [19] have achieved significant success in solving optimization problems. abc algorithm works well and is appropriate for several optimization problems. however, abc algorithm has several weaknesses as well. these include easily becoming stuck in a local optimum when handling some complex problems (i.e. exploitation process), simple operation of solution development and weak performance of https://doi.org/10.15837/ijccc.2021.5.4308 5 information sharing activity. for this reason, many researchers have proposed using variants of abc algorithm, either by improving the original abc program or by modifying it based on hybridization. this would help to even out the exploration and exploitation processes, which would then help to avoid becoming stuck in the local optimum and create better convergence, as can be seen in bee swarm optimization (bso) [24], gbest-guided abc (gabc) [25], hybrid aco–abc [26] and others. every meta-heuristic algorithm utilizes a different strategy of exploration and exploitation for optimization problems. recently, new research on optimization problems has been attracting attention. it uses a hybrid algorithm to overcome the problem of the poor exploitation mechanism of abc. in this paper, a new hybrid metaheuristic algorithm is proposed based on merging the advantages of abc and pso. thus, this paper proposes a new algorithm called hybrid artificial bee colony (habc) algorithm, which utilizes the exploitation ability of pso (i.e. local search process) and the exploration ability of abc (i.e. global search process). while the original pso has considerable exploitation ability and a fast convergence speed [58], it has poor exploration ability. by contrast, the original abc, has great exploration ability but poor exploitation mechanism abilities [25]. therefore, this paper proposes a hybrid artificial bee colony (habc) algorithm that adopts particle characteristics of the particle swarm optimization (pso), such as solution development mechanism, which represents the main operator for exploitation and exploration. the solution development mechanism of particle swarm optimization (pso) algorithm (i.e. the exploitation ability process) is unique and different from an exploitation mechanism of the original artificial bee colony (abc) algorithm. the particle swarm optimization (pso) algorithm solution development mechanism that formulated in equation (2). in each iteration, the velocities and positions of the particles are updated by formulated equation (2) and equation (3) respectively. based on the assertions stated above, the hybrid artificial bee colony (habc) algorithm consists of five steps: initialization phase, employed bees’ phase based on the pso development mechanism, calculation of the probability of selecting a food source, onlooker bees’ phase and scout bees’ phase. these phases are explained as follows: at the beginning of habc algorithm, the initialization phase begins randomly generate a food source (i.e., a potential solution for a certain optimization problem) by formulating an equation (1). each food source (i.e. solution) is indicated as xi = (xi1, xi2, xi3,..., xid), i ⊂ 1, 2, ..., sn, where sn indicates the swarm size. where d random number of dimension index. the swarm size is equal to the number of food sources and xmin and xmax are the lower and upper bounds of the food source position respectively. xij = xmin,j +rand(0, 1)(xmax,j −xmin,j ) (1) in the employed bee phase, the solution development mechanisms of pso (i.e. exploitation ability process) are performed in order to generate new food sources in the search space using equation (2) and (3) instead of the original equation of employed bee in abc algorithm. first, each food source needs to be checked in each iteration, and pbest records the updated status of each one. v t+1i,d = w t ∗ v ti,d + c t 1 ∗ r1 ∗ (pbest t i,d − x t i,d) + c t 2 ∗ r2 ∗ (gbest t i,d − x t i,d) (2) yt+1i = y t i + v t+1 i (3) where consists of several important parameters such as velocity parameter, indicated as vi = (vi1, vi2, vi3,...,vid), where velocity is used to control the improvement solutions, the current position of i particle indicated as yi=(yi1, yi2, yi3,...,yid), and the previous best position of particle, indicated as pbesti= (pbesti1, pbesti2, pbesti3,... ,pbestid). the best position determined among all the previous best positions is indicated as gbest = (gbesti1, gbesti2, gbesti3,..., gbestid). where both c1 and c2 are two positive constants, they indicate the relative influence of the cognition and social components. w indicates the inertia weight that tools up balance between local exploitation and global exploration https://doi.org/10.15837/ijccc.2021.5.4308 6 mechanism. both r1 and r2 indicate the real random value from 0 to 1. the particle’s velocity on each dimension is clamped to the range [-vmax, vmax]. then, a calculation is done of the probability of selecting a food source by adopting a greedy selection mechanism to check the better fitness value of the old and new food source using equation 3. when all criteria have been exhausted, the pbest value will stop the updating process (i.e. solution development mechanism of pso in the employed bee phase will be terminated) and the habc algorithm performs the onlooker bee phase. f iti = { 1 1+fi , if fi > 0 1 + |fi|, if fi > 0 (4) in the onlooker bee phase, the probability (p i) of food source selection is dependent on the amount of nectar (xi), where the employed bee propagates the information about the food sources, to the onlooker bee on the dancing area inside the hive. then the onlooker bee selects the best food source with a maximum amount of nectar by a formulated equation (5). after the onlooker bee probability selection process is finished, the onlooker bee starts a new search for a new food source, while trying to improve the food source of the employed bee by using a formulated equation (2). p i = f iti sn∑ n =1 f itn (5) upon completion of both the employed and onlooker bee phases, the search process continues for further improving the food sources. in the scout bee phase, the algorithm will check the search space for further improvements using a number of trial cycles called "limit" that are formulated by equation (6). the scout bee selects randomly from best previous solution pbest and generates a new food source using equation (2). once the fitness value of a new food source better or equal to the previous best solution has been identified, the new one will be selected. otherwise, the previous best solution is retained. limit = c.ne.d (6) where c is a constant coefficient with a recommended value of 0.5 or 1, ne indicates the number of unemployed bees (i.e. onlooker and scout bee), and d indicates the food source positions. the main steps of habc algorithm are given below in (figure 1). 5 hybrid artificial bee colony (habcsm) strategy in this research, a new strategy is proposed for generating the optimal test set. the habcsm strategy undergoes three phases during the construction of the mixed and variable strength t-way test set generation. the phases illustrated in (figure 2) processed as follows: phase 1. the habcsm input analysis algorithm exploits the input analyser. phase 2. the habcsm interaction generation algorithm, which is based on the combination t-tuple sets (cts) generator and interaction elements tuple (iet) generator, generates the required t-tuples. phase 3. the habcsm test set generation, which is based on exploiting the habc algorithm as the core algorithm and the hamming distance selection criterion for mixed and variable strength t-way test set generation.these phases will be discussed further and elaborated on in the next three sub-sections. https://doi.org/10.15837/ijccc.2021.5.4308 7 step1: initializate the parameters of abc and pso: initializates parameters: nbee, maxcycle, limit, w and (c1 and c2). assigns parameters and create populations using equation (1). initialize the population of solution xi; evaluate the fitness of population xi; set trial = 0 for each populations. step2: employed bee based on the pso development mechanism generates new food source (yi) and updates the velocity using (2). updates the position of food source using equation (3). evaluates the fitness value of (yi) using (4). uses a greedy selection mechanism between the old source (xi) and the new source (yi), to choose the better one. step3: probability of selection selection process is dependent on the nectar amount of (xi) and (yi) using (5). step4: onlooker bee generates new food source (yi) using (2) for onlooker bee. evaluates the fitness value of the new food source (yi). uses a greedy selection mechanism to select the better one. step5: scout bee if the food source cannot improve through a number of trial cycles called "limit" using equation (6). replaces (xi) with a new randomly produced candidate solution. 6: if the stop criterion is satisfied, stop and output. figure 1: the main steps of hybrid artificial bee colony algorithm (habc). 5.1 input analysis in this section, the input (ca, mca and vsca) processing is divided into two main processes: processing the input components (i.e. parameters and their values), and representing these components using numerical values. the first process starts by receiving the input and processing the input components to a set of pre-defined variables in the memory (i.e. interaction strength (t), parameters (p) and their values (v )). 5.2 interaction generation algorithm this section includes the generation of the combination t-tuple sets (cts), and the interaction element tuples (iet), based on the parameters and their value for the configuration system. habcsm employs a new-generation approach that reversely generates the binary element set (bes). bes represents the evaluation of each test case (i.e. the fitness value of each test case provided by habc algorithm). this phase includes two algorithms; the cts and iet generators. the first algorithm (cts generator) generates the combination t-tuple sets (cts), and after completing the input analysis, the numerical value will be generated. this numerical value is for the generation of the element combination, which is the basis of cts. the generation starts as soon as it receives the numerical value and the interaction strength (t) value from the input analysis phase. the second algorithm in this phase is the interaction elements tuple (iet), which is used to construct all the interaction elements, based on the represented binary element for all the combinations in cts. the two processes of generating iet and bes are combined into one algorithm to reduce the complexity of the generation process (i.e. the generation of all the possible binary elements and the selection of the binary elements that seems to be inefficient when done separately). iet generation starts with the selection of all the combinations in the cts and explores them until all the interaction elements (ie) are generated for each combination. https://doi.org/10.15837/ijccc.2021.5.4308 8 figure 2: an overview of habcsm strategy. 5.3 test set generation algorithm the habc algorithm can improve the solution quality because of the global and local search behaviour it implements. therefore, the habc algorithm is employed as a search engine to calculate the fitness (coverage or weight) of the randomly generated test candidates for the proposed strategy. to achieve a minimum test set optimization process, the test cases need to effectively and greedily cover all the t-way tuples, at one time, if possible. the habc algorithm has conventionally developed on the assumption of food source numbers within populations. in applying this algorithm for interaction testing, we assume that the test candidates are food sources and that each source has its own possible solution (fitness) for the targeted problem. the habc algorithm search process in the proposed strategy provides the best global optimum (or optimum test candidate that has the highest coverage of the t-tuples element values) based on the number of bes involved. this optimum test candidate indicates the solution quality in terms of the best food source. unlike the standard habc algorithm, the habcsm strategy introduces the hamming distance classifier (see equation 7) to decide the final set. specifically, the hamming distance classifier measures two rows of (best) test cases (as string) based on the number of values in which they differ when there is a tie situation as far as the quality of the test cases are concerned. it is the test case that is at the greatest distance that will be finally selected by the hamming distance classifier to ensure sufficient exploration and exploitation of the search space. https://doi.org/10.15837/ijccc.2021.5.4308 9 hammingdistance(d(v)) = ∑ |vi − t cn| (7) 6 parameter tuning of habcsm strategy in order to ensure the most optimal results for the habcsm strategy with regards the test set size, this section elaborates on the process of the tuning variables for the habcsm strategy. the control variables for the habc algorithm have to be tuned based on the test set generation problem. for tuning purposes, a well-known test system (covering array) that involves a ca (n; 2, 57) is employed. the justification for adopting this configuration for the tuning process originates from the use of the same ca to tune many of the existing t-way strategies [54] [57] [59] . the process of tuning habcsm is based on 20 runs for the specified ca with different variables settings. the habcsm has six main control variables: bee population size (nbee), food source= nbee/2, limit, maximum cycle number (mcn), self-confidence factor and swarm confidence factor (c1 & c2), and inertia weight factor (w ) that control the sizes of the test sets obtained. the size and average of the final test sets for the 20 runs were recorded. then, the results of the tuned variables are analysed to find the settings that fit the minimum size and average of the final test sets as shown in (figure 3) and (figure 4). the six variables are executed for all the possible selected settings. the first variable is bee population size (nbee), which indicates the number of bees involved in the test set generation. this variable controls the randomly initialized test candidates in the memory. when the number of test candidates increases, the possibility of finding a better solution (i.e. a test candidate with maximum interaction elements coverage) improves. the initial values of all control parameters need to be tuned: the bee population sizes (nbee) are in the range of [4-9 & 10-100], food source= nbee/2, limit=100, maximum cycle number (mcn) = [10-100 & 200-2000], self-confidence factor and swarm confidence factor (c1 & c2) = are set between 0.1 and 2.1, and inertia weight factor (w ) = are set between 0.1 and 1.0. thus, a set of values is selected in this range and all variables are selected to fit the test set generation. figure 3: best and average test suite for ca (n; 2, 57). https://doi.org/10.15837/ijccc.2021.5.4308 10 figure 4: best and average test suite for ca (n; 2, 57). based on the empirical experiments and the results of this study as shown in figure 3, the differences in the test set slightly decreased when the bee population decreased in size. the same observation applies for maximum cycle number (mcn), where clearly increasing the mcn can produce a good result. the habcsm strategy produces poor outcomes when the number of mcn < 100. on the other hand, the best outcomes are obtained when the mcn is more than 100 in simulations. finally, the best test sets have been recorded when (nbees) = 5 and the (mcn) = 1000. in sum, the habcsm strategy generates the best test set size when the mcn is greater than 700. after determining the best value for (nbees) and the (mcn), it’s important to determine the self-confidence factor and swarm confidence factor (c1 & c2) and inertia weight factor (w ). during the tuning, the value of (nbees) and the (mcn) are fixed to the values deduced earlier, (nbees) = 5 and the (mcn) = 1000. different values of (c1 & c2) are tested by fixing the (c1 & c2), and change the (w ) value, and then same process will perform for w (i.e. performing the reverse experiment). as shown in figure 4 (c1 & c2) and (w ) have major impact on the test size generation. the habcsm strategy has poor results with range of (c1 & c2) greater than 0.1 and smaller than 1.2. whereas habcsm strategy performs well in case of (c1 & c2) greater than 1.3 when w increases more than 0.5. based on the aforementioned discussion, the best test sets have been recorded when (c1 & c2) = 2.0 and (w ) = 0.9. 7 result and discussion this section shows the experiments of habcsm strategy. the experiments were conducted to evaluate and compare the efficiency of the proposed habcsm with our previous work [14] [15] [16] [60] [61] [62] [63] [64] [65] , based on the original artificial bee colony strategy and hybrid artificial bee colony strategy, as well as with existing published work as adopted from [2] [4] [17] [50] [57] [66] . whereas implementation times were neglected due to variances in parameter settings (e.g., sa relies on the iteration, cooling schedule, and starting temperature, while the abcs relies on the bee population size, food source number, limit and maximum cycle number) and running platform environment (e.g., the implementation language and data structure). https://doi.org/10.15837/ijccc.2021.5.4308 11 the results of the experiments were performed on the windows 7 (os) desktop computer with 3.40 ghz xeon (r) cpu e3 and 8gb ram. the java language jdk 1.8 was used to code and implement the habcsm. due to the randomization characteristics of the proposed habcsm strategy and the evidence in the literature, the experiments were run 20 independent times for each configuration system to get the best results. the best result was reported as bold and shaded cells to give a better indication. tables 1 to 7 highlight our experimental results. the selected sets of benchmarked experiments are as follows: referring to experimental sets 1, 2 and 3, the habcsm strategy displays better performance and efficiency than the abc variants (e.g., abcs and habc). when the habcsm strategy is compared to jenny, tconfig, pict, ipog-d, ipog, dpso, pstg, cs, gs, hss, abcs and habc strategies. for experimental set 1 in table 1, the habcsm generates the best test set sizes (or most minimum 17 out of 40 entries). clearly, the habcsm contributes by providing 6 out of the 17 entries with new minimal test set sizes. whereas dpso outperformed all the existing strategies to produce the best test set sizes (or most minimum of 23 out of 40 entries). the other strategies perform well at low interaction strengths (t ≤ 3), while tconfig, and pict have not reported significant or minimum test set size for any entries. table 1: test set size for ca (n; t, 3p ) with 2 ≤ t ≤ 11 and 3 ≤ p ≤ 12. t p pure computation strategies ai-based strategiesjenny best tconfig best pict best ipog-d best ipog best dpso best pstg best cs best gs best hss best abcs best habc best habcsm best habcsm avg.size 2 3 9 10 10 15 9 9 9 9 9 9 9 9 9 9.80000 4 13 10 13 15 9 9 9 9 9 9 9 9 9 10.5500 5 14 14 13 15 15 11 12 11 11 12 11 12 11 12.6500 6 15 15 14 15 15 14 13 13 13 13 13 13 13 14.2000 7 16 15 16 15 15 14 15 14 14 15 15 15 14 15.3000 8 17 17 16 15 15 15 15 15 15 15 15 15 15 16.2000 9 18 17 17 15 15 15 17 16 15 17 16 16 15 16.8000 10 19 17 18 21 15* 16 17 17 16 17 17 17 16 17.2000 11 17 20 18 21 17 17 17 18 16 17 17 18 17 18.1500 12 19 20 19 21 21 16 18 18 16 18 18 18 18 19.4500 3 4 34 32 34 27 32 27 27 28 27 30 27 27 27 29.7500 5 40 40 43 45 41 41 39 38 38 39 38 39 39 43.0000 6 51 48 48 45 46 33 45 43 43 45 44 43 43 45.5500 7 51 55 51 50 55 48 50 48 49 50 49 47 46 51.2000 8 58 58 59 50 56 52 54 53 54 54 54 53 45 54.3500 9 62 64 63 71 63 56 58 58 58 59 58 56 56 58.8500 10 65 68 65 71 66 59 62 62 61 62 62 61 61 63.5000 11 65 72 70 76 70 63 64 66 63 66 66 68 65 67.0000 12 68 77 72 76 73 65 67 70 67 67 70 72 68 70.2000 4 5 109 97 100 162 97 81 96 94 90 94 98 81 81 91.9500 6 140 141 142 162 141 131 133 132 129 132 135 134 132 137.100 7 169 166 168 226 167 150 155 154 153 154 157 155 149 158.250 8 187 190 189 226 192 171 175 173 173 174 179 177 159 179.250 9 206 213 211 260 210 187 195 195 194 195 197 196 185 197.600 10 221 235 231 278 233 206 210 211 209 212 215 217 212 215.150 11 236 258 249 332 251 221 222 229 223 223 234 237 229 232.200 12 252 272 269 332 272 237 244 253 236 244 251 257 246 244.550 5 6 348 305 310 386 305 244 312 304 301 310 274 245 243 290.100 7 458 477 452 678 466 438 441 434 432 436 442 438 437 445.150 8 548 583 555 756 575 517 515 515 515 515 530 524 523 532.350 9 633 684 637 1043 667 591 598 590 594 597 609 607 605 610.300 10 714 773 735 1118 761 667 667 682 672 670 688 686 683 689.400 11 791 858 822 1372 851 735 747 778 741 753 762 766 751 774.350 12 850 938 900 1449 929 802 809 880 806 809 814 856 854 865.500 6 7 1089 921 1015 1201 921 729 977 973 963 977 944 836 729 922.125 8 1466 1515 1455 1763 1493 1409 1402 1401 1399 1402 1424 1416 1413 1427.55 9 1840 1931 1818 2526 1889 1682 1684 1689 1681 1684 1756 1733 1731 1741.00 10 2160 >day 2165 2834 2262 1972 1980 2027 1980 1991 2055 2038 2019 2028.00 11 2459 >day 2496 3886 2607 2250 2255 2298 2258 2255 2261 2254 2251 2261.00 12 2757 >day 2815 4087 3649 2512 2528 2638 2558 2528 2571 2543 2523 2532.00 table 2 depicts experimental set 2, where the habcsm manages to get the best test set sizes (or most minimum 9 out of 20 entries). note that 3 out of the 9 best result entries are new minimal test set sizes obtained by the habcsm. however, dpso surpasses all of the other existing strategies in https://doi.org/10.15837/ijccc.2021.5.4308 12 table 2: test set size for ca (n; t, v7) with 2 ≤ t ≤ 6 and 2 ≤ v ≤ 5. t v pure computation strategies ai-based strategiesjenny best tconfig best pict best ipog-d best ipog best dpso best pstg best cs best gs best hss best abcs best habc best habcsm best habcsm avg.size 2 2 8 7 7 8 8 7 6 6 6 7 7 7 7 7.40000 3 16 15 16 15 17 14 15 15 14 14 15 15 14 15.0000 4 28 28 27 32 28 24 26 25 24 25 25 25 24 26.1000 5 37 40 40 45 42 34 37 37 36 35 38 37 34 37.6500 3 2 14 16 15 14 19 15 13 12 12 12 14 14 13 15.4500 3 54 55 51 50 57 48 50 49 49 50 49 47 46 51.2000 4 124 112 124 114 208 112 116 117 116 121 119 114 110 117.850 5 236 239 241 252 275 216 225 223 221 223 231 226 221 225.475 4 2 31 36 32 40 48 34 29 27 27 29 27 29 27 31.8000 3 169 166 168 226 185 150 155 155 153 155 157 155 149 158.250 4 517 568 529 704 509 472 487 487 486 500 498 488 484 494.500 5 1248 1320 1279 1858 1349 1148 1176 1171 1173 1174 1215 1187 1179 1196.55 5 2 57 56 57 80 128 59 53 53 51 53 54 54 55 57.6500 3 458 477 452 678 608 438 441 439 432 437 439 441 439 445.350 4 1938 1792 1933 2816 2560 1814 1826 1845 1821 1831 1878 1856 1855 1868.20 5 5895 n/a 5814 9198 8091 5400 5474 5479 5463 5468 5630 5601 5588 5608.95 6 2 87 64 72 96 64 64 64 66 65 64 65 64 64 64.0000 3 1087 921 1015 1201 1281 729 977 973 963 916 893 810 792 744.400 4 6127 n/a 5847 5120 4096 5223 5599 5610 5608 4096 5541 5502 5470 5557.20 5 23492 n/a 22502 24808 28513 20525 21595 21597 21532 21748 21645 21592 21565 21610.2 table 3: test set size for ca (n; 4,5p ) with 5 ≤ p ≤ 10. p pure computation strategies ai-based strategiesjenny best tconfig best pict best ipog-d best ipog best dpso best pstg best cs best gs best hss best abcs best habc best habcsm best habcsm avg.size 5 837 773 810 1250 908 na 779 776 769 751 758 759 750 771.150 6 1074 1092 1072 1250 1239 na 1001 991 984 990 1010 1000 996 1007.30 7 1248 1320 1279 1858 1349 na 1209 1200 1176 1186 1198 1189 1179 1196.55 8 1424 1532 1468 1858 1792 na 1417 1415 1371 1358 1413 1386 1354 1360.00 9 1578 1724 1643 2110 1793 na 1570 1562 1548 1530 1621 1591 1526 1531.40 10 1719 1878 1812 2110 1965 na 1716 1731 1638 1624 1831 1798 1718 1721.50 table 4: test size for vsca (n, 2, 315, c). {c} pure computation strategies ai-based strategies whitch best tvg best paraorder best ipog best density best pict best sa best pstg best gs best acs best pwisegen-vsca best hss best abcs best habc best habcsm best habcsm avg.size ø 31 22 33 21 21 35 16 19 19 19 16 20 20 20 19 21.300 ca (3, 33) 48 27 27 27 28 81 27 27 28 27 27 27 27 27 27 27.850 ca (3, 34) 59 35 27 39 32 105 27 30 29 27 27 27 32 30 29 34.550 ca (3, 35) 62 41 45 39 40 131 33 38 38 38 33 38 41 39 39 42.550 ca (4, 34) 103 81 na 81 na 245 na 81 81 na 81 81 81 81 81 81.100 ca (4, 35) 118 103 na 122 na 301 na 97 92 na 91 94 90 93 90 102.55 ca (4, 37) 189 168 na 181 na 505 na 158 155 na 158 159 154 154 153 159.65 ca (5, 35) 261 243 na 243 na 730 na 243 243 na 243 243 243 243 243 243.10 ca (5, 37) 481 462 na 581 na 1356 na 441 441 na 441 441 446 439 436 446.15 ca (6, 36) 745 729 na 729 na 2187 na 729 729 na 729 729 729 729 729 729.00 ca (6, 37) 1050 1028 na 967 na 3045 na 966 960 na na 902 956 898 830 961.10 ca (3, 34) ca (3, 35) ca (3, 36) 114 53 44 51 46 1376 34 45 na 40 na 45 82 82 82 86.000 ca (3, 36) 61 48 49 53 46 146 34 45 46 45 40 45 45 45 45 47.55 ca (3, 37) 68 54 54 58 53 154 41 49 50 48 47 51 50 50 50 52.20 ca (3, 39) 94 62 62 65 60 177 50 57 57 57 57 62 58 60 59 60.20 ca (3, 315) 132 81 82 ns 70 83 67 74 75 76 74 77 81 81 80 82.40 table 5: test size for vsca (n, 2, 43 53 62, c). {c} pure computation strategies ai-based strategieswhitch best tvg best paraorder best ipog best density best pict best sa best pstg best gs best acs best pwisegen-vsca best hss best abcs best habc best habcsm best habcsm avg.size ø 48 44 49 43 41 43 36 42 na 41 na 42 44 42 42 45.15 ca (3, 43) 97 67 64 83 64 384 64 64 na 64 na 64 64 64 64 64.000 mca (3, 43 52) 164 132 141 147 131 781 100 124 na 104 na 116 128 123 121 130.90 ca (3, 53) 145 125 126 136 125 750 125 125 na 125 na 125 125 125 125 125.00 mca (4, 43 51) 354 320 na 329 na 1920 ns 320 na ns na 320 320 320 320 320.00 mca (5, 43 52) 1639 1600 na 1602 na 9600 ns 1600 na ns na 1600 1600 1600 1600 1600.0 ca (3, 43) ca (3, 53) 194 125 129 136 125 8000 125 125 na 125 na 125 125 125 125 125.00 mca (4, 43 51) mca (4, 52 62) 1220 900 na 900 na 288,000 ns 900 na ns na 900 900 900 900 900.00 mca (3, 43) mca (4, 53 61) 819 750 na 750 na 48,000 ns 750 na ns na 750 750 750 750 750.00 mca (3, 43) mca (5, 53 62) 4569 4500 na 4500 na 288,000 ns 4500 na ns na 4500 4500 4500 4500 4500.0 mca (4, 43 52) 510 496 na 512 na 2874 ns 472 na ns na 453 463 464 461 476.75 mca (5, 43 52) 2520 2592 na 2763 na 15,048 ns 2430 na ns na 2430 2403 1600 1600 1600.0 mca (3, 43 53 61) 254 237 247 215 207 1266 171 206 na 201 na 212 213 212 210 216.50 mca (3, 51 62) 188 180 180 180 180 900 180 180 na 180 na 180 180 180 180 180.0 mca (3, 43 53 62) 312 302 307 ns 256 261 214 260 na 255 na 263 266 269 263 268.95 https://doi.org/10.15837/ijccc.2021.5.4308 13 table 6: test size for vsca (n, 2, 1019181716151413121, c). {c} pure computation strategies ai-based strategieswhitch best pict best ipog best density best tvg best paraorder best pstg best acs best hss best sa best pwisegen-vsca best gs best abcs best habc best habcsm best habcsm avg.size ø n/a 102 91 n/a 99 n/a 97 n/a 94 n/a 92 n/a 95 93 93 95.25 mca (n,3, 1019181) n/a 31256 720 n/a 720 n/a 720 n/a 720 n/a 720 n/a 720 720 720 720.00 mca (n,3, 716151) n/a 19515 221 n/a 210 n/a 210 n/a 210 n/a 210 n/a 210 210 210 210.00 mca (n,3, 413121) n/a 2397 91 n/a 99 n/a 97 n/a 94 n/a 92 n/a 94 94 93 96.500 mca (n,3, 101918171) n/a 22878 772 n/a 784 n/a 742 n/a 740 n/a 740 n/a 751 748 746 756.45 mca (n,3, 1019181), mca (n,3,716151) n/a n/a 720 n/a 720 n/a 720 n/a 720 n/a 740 n/a 720 720 720 720.00 mca (n,3, 1019181), mca (n,6,716151413121) n/a n/a 5041 n/a 5040 n/a 5040 n/a 5040 n/a n/a n/a 5040 5040 5040 5040.0 mca (n,3, 1019181), mca (n,3,716151), mca (n,3, 413121) n/a n/a 720 n/a 720 n/a 720 n/a 720 n/a 720 n/a 720 720 720 720.00 mca (n,4,51413121) n/a 1200 142 n/a 123 n/a 120 n/a 120 n/a 120 n/a 121 121 120 123.20 mca (n,5,10191413121) n/a 124157 2160 n/a 2160 n/a 2160 n/a 2160 n/a 2160 n/a 2160 2160 2160 2160.0 mca (n,6,716151413121) n/a n/a 5041 n/a 5040 n/a 5040 n/a 5040 n/a 5040 n/a 5040 5040 5040 5040.0 table 7: test size for vsca (n, 2, 330102, c). {c} pure computation strategies ai-based strategieswhitch best pict best ipog best density best tvg best paraorder best sa best hss best pstg best gs best acs best pwisegen-vsca best abcs best habc best habcsm best habcsm avg.size ø n/a 100 101 n/a 101 n/a 100 106 102 n/a 100 n/a 107 105 105 109.10 ca (n,3, 320) n/a 940 100 n/a 103 n/a 100 109 105 n/a 100 n/a 120 118 117 122.10 mca (n,3, 320102) n/a 423 n/s n/a 423 n/a 304 450 481 n/a 304 n/a 504 503 503 507.85 mca (n,4, 33101) n/a 810 273 n/a 270 n/a n/a 270 270 n/a n/a n/a 270 270 270 270.75 mca (n,5, 33102) n/a 2800 2700 n/a 2700 n/a n/a 2700 2700 n/a n/a n/a 2700 2700 2700 2700.0 mca (n,6, 34102) n/a n/a 8100 n/a 8100 n/a n/a 8100 8100 n/a n/a n/a 8100 8100 8100 8100.0 achieving the best test set sizes (or most minimum 11 out of 20 entries). the other strategies shared the best results for less than 5 entries. similar to the observations in table 1, tconfig and pict as well as jenny and ipog-d, have not reported significant or minimum test set size for any entries. in experimental set 3, shown in table 3, the habcsm, gs and hss achieve the best test set sizes overall. clearly, the habcsm achieves 50 % of the best sizes (3 out of 6 entries). additionally, the best sizes that were achieved are the new best sizes generated by the habcsm for the system configurations (p = 5, 8 and 9 with 750, 1354 and 1526 test cases, respectively). the gs retains the best sizes for 2 test configurations (p = 6 and 7, recording 984 and 1176 test cases, respectively). the hss only gives the best results for one entry (p = 10 with 1624 test cases). for experimental set 3, only 3 strategies were able to achieve the best sizes. the other strategies produce acceptable results as compared to the best sizes. whereas dpso have been not reported any results in the literature. referring to the variable strength covering array (vsca) benchmarked experimental sets 4, 5, 6 and 7, it is generally observed that probabilistic based strategies outperform deterministic based strategies for both main-strength and sub-strength generations. based on experimental set 4 in table 4, the habcsm achieves 50 % of the best sizes obtained (8 out of 16 entries). the habcsm manages to obtain three new minimal test set sizes (in the following sub-configurations: ca (s, 4, 37), ca (s, 5, 37), and ca (s, 6, 37)). in these test configurations, sa generates the best test set in general (9 out of 16 entries). specifically, the sa in the low interaction strength achieved the best results as it only supports t up to 3 (t ≤ 3). the pwisegen-vsca, abcs, hss, habc and pstg produce competitive results as well with 7, 5, 4, 4 and 4 best sizes. the pict, whitch and density generates the worst results as it only supports t up to 3 (t ≤ 3) for the variable-strength test configuration. table 5 demonstrates that in experimental set 5, the habcsm outperformed other existing strategies to achieve the best results. the best result achieved was for 11 out of 15 entries. the habcsm manages to obtain a new minimal test set size in the case of mca (s, 4, 43 52) sub-strength configuration with 461 test cases. habc and hss perform well to get the best result for 10 out of 15 entries each. whereas abcs and pstg generate the best results for 9 out of 15 entries each. the other strategies also generated competitive results when compared with the best results. the whitch and pict consistently produce the worst results for the test configuration. however, gs and pwisegen-vsca have not reported any results in the literature. experimental set 6, presented in table 6, demonstrates acceptable performance by several strategies (e.g., habcsm, abcs, habc, hss, pstg, pwisegen-vsca, tvg and ipog) for this test generation. the habcsm and pstg excel in most of the best results (8 out of 11 entries each). regarding hss, abcs, habc and pwisegen-vsca, these strategies perform equally well, demonstrating the best test set sizes (7 out of 11 entries each). in the same manner, the ipog generates competitive test set size in many sub-strength configurations of the best sizes. the pict generated https://doi.org/10.15837/ijccc.2021.5.4308 14 the poorest results in most cases with no best size obtained among all the test configurations (with some missing results). as for sa, density, paraorder, whitch, gs and acs, no published results are available. concerning the experimental set 7 in table 7, the habcsm, abcs, habc, hss, pstg, sa, ipog, acs and tvg are each able to obtain the best sizes (3 out of 6 entries). in fact, the habcsm, abcs, habc, hss, tvg, ipog and pstg generate the optimal test set size for high interaction strength (t > 3). however, the sa and acs dominate in low interaction strength (t ≤ 3). the pict strategy obtains the best sizes (1 out of 6 entries). regarding density, paraorder, whitch, gs and pwisegen-vsca, no published results are available. 8 conclusion and future work this paper proposes a new meta-heuristic based t-way strategy called hybrid artificial bee colony strategy (habcsm) based on merging the advantages of the artificial bee colony (abc) algorithm with a particle swarm optimization (pso) algorithm to improve the performance of the original abc. habcsm is the first t-way strategy that adopts habc algorithm as its core implementation for generating a final test set and also adopts the hamming distance as the final selection criterion enhancing the exploration of new solutions. the experiments that were conducted have shown that habcsm has a better performance for generating the best test set size than existing strategies, as well as variants of abc. in many entries, habcsm outperformed the other strategies. however, if habcsm failed to generate the optimum results, the generated results were within reasonable values. the results of the 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(2015). achievement of minimized combinatorial test suite for configuration-aware software functional testing using the cuckoo search algorithm, information and software technology, 66, 13-29, 2015. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: alazzawi k. rais hm.; basri s.; alsariera ya; capretz lf.; imam aa.; balogun ao. (2021). habcsm: a hamming based t-way strategy based on hybrid artificial bee colony for variable strength test sets generation, international journal of computers communications & control, 16(5), 4308, 2021. https://doi.org/10.15837/ijccc.2021.5.4308 introduction main concept of covering array notation related work hybrid artificial bee colony algorithm hybrid artificial bee colony (habcsm) strategy input analysis interaction generation algorithm test set generation algorithm parameter tuning of habcsm strategy result and discussion conclusion and future work int j comput commun, issn 1841-9836 9(1):16-23, february, 2014. non-negative factorization for clustering of microarray data l. morgos lucian morgos dept. of electronics and telecommunications faculty of electrical engineering and information technology university of oradea romania, 419987 oradea, universitatii, 1 lmorgos@uoradea.ro abstract: typically, gene expression data are formed by thousands of genes associated to tens or hundreds of samples. gene expression data comprise relevant (discriminant) information as well as irrelevant information often interpreted as noise. the irrelevant information usually affects the efficiency of discovering and grouping meaningful latent information correlated to biological significance, process closely related to data clustering. class discovery through clustering may help in identifying latent features that reflect molecular signatures, ultimately leading to class forming. one solution for improving the class discovery efficiency is provided by data dimensionality reduction, where data is decomposed into lower dimensional factors, so that those factors approximate original data. keywords: computational intelligence, microarray data analysis, clustering, recognition. 1 introduction when analyzing high dimensional data the analysis process often becomes prohibitive and computational intractable if the data is analyzed in its receiving form. fortunately, in many cases, data comprises redundant information, irrelevant or insignificant entries which can be discarded without significantly affecting the final result or decision, process known as dimensionality reduction. discovering latent and meaningful low-dimensional factors from high-dimensional data is of great importance in many research fields. basically, dimensionality reduction may be accomplished by either feature selection or data transformation. the latter is a factorization process that decomposes data into meaningful factors so that their product approximates the initial data as good as possible. by factorization, some initial information is lost; however, the factorization of gene expression should be performed so that the factors provide a biologically meaningful decomposition of the data, which is a major goal for this particular task. the cluster statistics should match the known sample labels when statistics are available from the full data. let us consider our data containing m samples of n dimensional vectors stored into a n × m matrix v, where the vector elements indexed as i = 1 . . .n correspond to the receiving information values. dimensionality reduction refers to transforming the n dimensional data into a lower r dimensional data. this further relates to decomposing the matrix v into factors w of dimension n×r and h of dimension r×m, where r < (n,m) is often named the factorization rank, so that their product approximates v, i.e. wh ∼= ṽ ≈ v. for analyzing the receiving information the n dimensional space is replaced by the lower r dimensional space where the new information resides. typically, w represents the basis factors and h the encoding (also know as the decomposition coefficients). thus, each j = 1 . . .m information embedded into an n dimensional column vector vj can be represented as linear (or nonlinear) combination of the basis matrix w and the corresponding low dimensional coefficients vector hj, more precisely, vj = whj. when the factorization process is applied to gene expression data from a set of copyright © 2006-2014 by ccc publications non-negative factorization for clustering of microarray data 17 microarray experiments, we typically deal with gene expression profile for a single gene across many samples or experimental conditions. more precisely, the matrix v comprises in its rows the expression levels of genes, while the columns correspond to samples. usually, n is of order of thousands while m is often less than 100. in this context nmf seeks for finding a small number of metagenes. therefore, each column of w represents a metagene and each column of h denotes the expression profile. 2 approach one of the frequently used dimensionality reduction approaches is given by singular values decomposition (svd). svd has been proposed [1] to reduce the gene expression space where the columns of w form orthonormal basis called eigenarrays, while h forms eigengenes. sorting the genes according to the descending values of eigengenes gives a global picture of the dynamics of the gene expression, in which individual genes and arrays appear to be clustered into groups of similar functions or cellular states. a robust svd variant [2] has been further proposed to cope with outliers or missing values often found in gene expression. other approaches refer to novel methods for analyzing svd for obtaining gene groups along with a confidence measure [3], or svd based biclustering through incorporating simultaneous normalization of rows and columns [4]. a relatively recent approach for data factorization is provided by nonnegative matrix factorization, where both factors are constrained to have nonnegative entries. the work of lee and seung published in nature [5] can be considered as a starting point from where the interest in this approach almost exploded due to the algorithm’s simplicity and its compliance with physiological principles. ever since, new nmf variants have been developed in the attempt to improve its convergence, numerical stability, processing speed or to extend the approach to temporal domain. original nmf has been applied to gene clustering [6] and, recently a sparse nmf approach has been developed [7], that appears to yield better clustering performance compared to the standard nmf. although the original nmf has been successfully applied to gene expression clustering, its application was rather limited to three data sets. we have extended the previous work of [6] by employing three more expression data sets in order to validate the efficiency of this decomposition approach. in addition, two more nmf variants were applied for a systematic comparison. the extension also includes the use of k-means clustering strategy along with the expression intensity provided by the rows of h. moreover, the suitability of nmf approaches was investigated for the recognition task. finally, we rank the algorithms according to their clustering and recognition performance as well as with respect to their stability, as defined later. 3 methods 3.1 non-negative matrix factorization in the nmf formulation the difference between the initial data matrix and the product of its decomposition factors can be expressed in different ways leading to various cost functions fnmf which quantify the decomposition quality. one frequently cost function is described by the kullback-leibler (kl) divergence based cost function: fnmf (v,w,h) , ∑ i,j ( vijln vij∑ l wilhlj + ∑ l wilhlj −vij ) , (1) 18 l. morgos for any i = 1 . . .n, j = 1 . . .m, and l = 1 . . .r. one solution for minimizing the above cost functions is given by adopting an expectation maximization (em) like strategy [5] for iteratively updating the factors. starting with random values for w and h, at each iteration, the following updating rules are used to guarantee the minimization (towards a local minimum) of the kl-based cost function: hlj ←− hlj ∑ i wli vij∑ l wilhlj∑ i wil (2) wil ←− wil ∑ j hjl vij∑ k wilhlj∑ j hlj (3) 3.2 local non-negative matrix factorization to enhance the decomposition sparseness, li et al [8] have developed the local non-negative matrix factorization (lnmf) algorithm, imposing more constraints to the kl cost function to get more localized basis factors. the associated cost function is then given by: hlj ←− √ hlj ∑ i vij wil∑ l wikhlj (4) wil ←− wil ∑ j vij hlj∑ l wilhlj∑ j hlj (5) however, we should note that, here, although sparseness issue refers to the basis factors, this approach does not guarantee a high degree of sparseness for the expression profiles. as we shall see in the experimental section, the coefficient sparseness indeed seems to positively correlate with the accuracy performance. 3.3 polynomial non-negative matrix factorization unlike nmf, the polynomial nmf [9] [10] was developed as alternative to the standard nmf for decomposing data in some nonlinear fashion. pnmf assumes that the input data v ∈ v ⊆ rn×m are transformed to a higher dimensional space f ⊆ rl×m, l ≫ n. by denoting the set of the transformed input data with f = [ϕ(v1),ϕ(v2), . . . ,ϕ(vm)], with the l dimensional vector expressed as ϕ(vj) = [ϕ(v)1,ϕ(v)2, . . . ,ϕ(v)s, . . . ,ϕ(v)l]t ∈ f, a matrix y = [ϕ(w1),ϕ(w2), . . . ,ϕ(wr)], y ∈ f, that approximates the transformed data set, such that r < n, can be found. therefore, each vector ϕ(v) can be written as a linear combination as ϕ(v) ≈ yh. defining a kernel function κ that satisfies κ(vw) , κ(v,w) = ⟨ϕ(v),ϕ(w)⟩, for all v,w ∈v, where ϕ is a mapping from v to an (inner product) feature space f, ϕ : v −→ ϕ(v) ∈f, the following squared euclidean distance based cost function was proposed [9]: fpnmf (v,w,h) = 1 2 ∥ϕ(vj)−yhj∥2 (6) = k(v,v)−2k(v,wl)h + ht k(wo,wl)h, the cost function fpnmf should be minimized subject to hl,wil ≥ 0, and ∑n i=1 wil = 1, l = 1 . . .r. non-negative factorization for clustering of microarray data 19 4 results the experiments were conducted with respect to the following aspects: (i) clustering error defined as the number of misclustered samples expressed in percentage, and (ii) model stability defined thorough the standard deviation of the clustering error for several nmf runs, and (iii) recognition error defined as the percent of misclassified samples from the test set. 4.1 data set description we have applied nmf, lnm, and pnmf to six publicly available microarray data sets: leukemia [11], medulloblastoma [12] , colon [13], dlbcl [14], srbct [15], and prostate [16] data set, respectively. the first two data sets are exactly the ones used by [6]. we should also note that the original dlbcl data set initially contained 7126 genes. by setting an intensity thresholds at 20 16000 units, then filtering out genes with max/min ≤ 3 or (max min) ≤ 100, the final number if genes was 6285. the filtering procedure was similar to the one described in [17]. for the prostate, the conditions for gene selection was a threshold at 100 16000 units, with max/min ≤ 5 or (max min) ≤ 50, conducting from a total of initial 12600 to 5966 genes. 4.2 clustering starting from random values for both factors w and h, nmf iteratively modifies them until the algorithm converges to a local minimum. more precisely, the running stops when the difference between v and the product of its decomposition factors reaches a minimum imposed threshold. due to its stochastic nature, the algorithm does not always converge to the same solution with different initial random values for the factors. therefore, nmf should run several times. in our experiments we ran nmf 30 times, and the minimum (min.) and average (av.) clustering error along with its standard deviation (std.) are reported. standard deviation is a good indicator for the model stability. a small value reveals better stability, i.e. the algorithm leads to approximately the same performance for multiple runs. once nmf converged and found its final decomposition factors, it is the h that contains clustering information. there are two approaches to assign sample labels. one approach suggested by [6] refers to the label assignment according only to the relative values in each column of h, i.e. the class labels are determined by the index of the highest metagene expression. when the number of rows of h equals the number of known clusters we count the number of maximum values for each column of h with respect to the known class range. clustering errors are simply the misassignments, i.e. the mismatch between the index and associated value. table 1 depicts the clustering errors, where the table column denoted by h indicates the first clustering criteria. it may happen that this procedure does not lead to the correct number of clustering for a particular run. even worse, whenever this approach fails to discover the correct number of clusters for any of those 30 runs, we report this case as n/a in the table. the first clustering approach only applies when the number of rows of h (viewed as dimension of h, or the decomposition rank ) r equals the number of known clusters. the second clustering approach deals with varying ranks. the nmf algorithms were run for r = {2,3,4,5,6,7,8}. each r dimensional column vector of h was clustered with the help of k means clustering strategy. we have also carried out experiments with svd, as baseline. the svd method always fails to discover the correct number of clusters for the first clustering approach for all data sets. to emphasis the importance of dimensionality reduction task, experiments with the initial data dimension were also conducted and shown in table 1 corresponding to the row termed “no processing”. indeed, the high dimension technically halves the performance of the k means clustering. 20 l. morgos table 1: clustering results for the nmf methods. clustering results for svd as dimensionality reduction approach is also tabulated. for comparison purpose, the clustering errors are also shown when k means is applied for the initial data dimension. the first two lowest errors are emphasized in bold. method approach clust. err. (%) data leukemia medulloblastoma colon dlbcl srbct prostate no processing k means min. 13.15 14.70 20.96 28.57 34.93 36.27 min. 5.26 11.76 11.29 18.18 n/a 18.62 svd k means av. 23.97 34.11 29.26 27.27 n/a 38.67 std. 17.42 9.61 9.27 6.94 n/a 10.86 min. 2.63 38.23 25.80 28.57 n/a n/a h av. 4.94 39.64 26.25 34.41 n/a n/a nmf std. 1.15 1.49 0.73 4.76 n/a n/a min. 0 5.88 11.29 12.98 21.68 24.50 k means av. 9.29 8.08 25.00 23.11 26.80 40.68 std. 10.75 6.23 9.96 5.60 8.65 6.69 min. 10.52 38.23 32.25 29.87 n/a n/a h av. 17.57 38.23 34.38 32.20 n/a n/a lnmf std. 2.24 0 2.45 0.99 n/a n/a min. 2.63 35.29 46.77 27.27 33.73 n/a k means av. 12.65 35.29 46.77 29.71 50.96 n/a std. 9.90 0 0 2.06 4.50 n/a min. 2.63 41.17 33.87 28.57 n/a n/a h av. 2.63 41.17 33.87 28.57 n/a n/a pnmf std. 0 0 0 0 n/a n/a min. 0 11.76 14.51 23.37 32.53 16.66 k means av. 15.41 29.41 21.58 30.99 43.01 33.23 std. 9.05 9.03 4.72 6.00 5.90 7.98 as far as the clustering ability is concerned, nmf seems to yield the lowest clustering error for all data sets but prostate. comparing the two clustering approaches, k means clearly outperforms the first approach for all data sets and all the decomposition methods. moreover, except the prostate case for lnmf, this clustering approach always finds the correct number of classes for several nmf runs. for the leukemia data set, k means conducted to perfect clustering (zero clustering error) for both nmf and lnmf. brunet et al. [6] as well as other authors, including kim and park [7] reported that two all samples were consistently misclassified by most methods, including nmf. the same tendency has been noticed by us when applying the first clustering strategy but not with the second one. however, we should stress that this data set is probably an easy one compared to the others, in terms of method’s clustering ability. it is worth mentioning that, amongst all data sets, the prostate data seems to be the most difficult one to be correctly clustered. this indicates hard overlaps within samples from both tumor and non-tumor classes. surprisingly, although pnmf performs modest on the other data sets, in this case it had conducted to the lowest clustering error. this behaviors could be due to the nonlinear nature of the pnmf algorithm that enables to find discriminant non-linear biological structures for this data set. for the pnmf algorithm the best result corresponds to the polynomial degree of 2. analyzing the model stability, lnmf followed by the pnmf algorithm are the most stable models and tend to make the same errors for various numbers of algorithm runs, as indicated by low or even zero standard deviation value. however, the lnmf was outperformed by all other methods. the stability of the algorithm was also measured through the cophenetic correlation coefficient [6] computed for various decomposition ranks and displayed in figure 1. all algorithms tend to be instable as rank increases. however, the behavior differs from one data set to another. non-negative factorization for clustering of microarray data 21 figure 1: the cophenetic correlation coefficient versus decomposition rank for the leukemia, medulloblastoma, colon, dlbcl, srbct, and prostate data set, respectively. 4.3 sample recognition the second major experimental task involves the recognition of samples previously misclassified by the clustering k means clustering approach. the correctly assigned samples were all gathered to form a training set vtr, while a test set vte comprises all wrongly clustered samples associated to the minimum clustering error. the nmf methods were only applied to the training set. thus, there is a big difference between clustering and recognition tasks in terms of training. while in the latter case the nmf uses all samples to extract relevant information, for the recognition task, nmf employs only a subset of this statistics, more precisely, those associated to the correctly clustered samples. let us denote the final decomposition factors corresponding to the training set by wtr and htr, respectively. to form a training feature vector, the training data were projected into the inverse of wtr, i.e., ftr = w −1 tr ∗ vtr. similarly, the test feature vectors are formed as fte = w −1 tr ∗ vte. we should note that, unlike the clustering procedure where the clustering results depend on h, the sample recognition is solely affected by w. by projecting the data into the inverse of w, ftr comprises mixed positive and negative values. to keep the non-negativity constraints we have also ran experiments by projecting the data into the w. however, the classification results were much lower in this case. a simple maximum correlation classifier (mcc) relying on the minimum euclidean distance between vectors was chosen as distance measurement. the distance from any hte to any htr is expressed as ∥hte − htr∥2 = −2gc(hte + hte)t hte, where gc(hte) = httrhte −1/2∥htr∥2 is a linear discriminant function of hte. by computing q linear discriminant functions, a test sample is assigned to the label according to the mcc = argmaxc{gc(hte} (7) in other words, the label of the training feature vector corresponding to the maximum correlation is associated to the test feature vector (test sample). the recognition results are tabulated 22 l. morgos table 2: sample recognition results with mcc. method data leukemia medulloblastoma colon dlbcl srbct prostate nmf 50 57.14 40 55.55 28 lnmf 0 66.66 86.20 71.42 64.28 pnmf 25 44.44 33.33 66.66 47.05 in table 2. a recognition error of 100% indicates no improvement over the misclustered samples. lnmf wrongly clustered only one leukemia sample that have been finally correctly recognized by the recognition procedure. four misclustered medulloblastoma samples are provided by the pnmf, while three of them were also further correctly classified by the recognition procedure. an impressive improvement was obtained in the case of nmf for the prostate data set. while the clustering strategy leaded to 25 mislabelled samples out of 102, the recognition procedure had further reduced those samples to 7. 5 conclusions microarray analysis typically involves tens of samples where each sample is formed of thousands of values (genes). this recasts into a high-dimensional matrix. the analysis of such matrix is associated in the computer science community with the small size problem negatively affecting both the clustering and recognition accuracy. wisely reducing data dimension is one solution to this issue. this paper addressed data dimensionality reduction applied to clustering and recognition tasks by three non-negative decomposition variants, nmf, lnm, and pnmf. the novelty of this work resides in the following: (a) we have carried out a systematic comparison in terms of clustering of these nmf algorithms involving six gene expression data sets publicly available; (b) we have shown that, unlike the clustering approach where the sample assignment depends only on the relative (maximum) values in each column of h, strategy that failed to discover the correct class for some certain methods and data sets, by employing a k means clustering approach the correct class discovery is always guaranteed for sufficient number of runs. concluding, the experiments indicate superior performance for the standard nmf over the other two, leading to the lowest clustering errors. the standard nmf was closely followed by svd and pnmf. 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[17] yang, k. et al; a stable gene selection in microarray data analysis, bmc bioinformatics, 7: 228, 2006. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 2, month: april, year: 2023 article number: 5299, https://doi.org/10.15837/ijccc.2023.2.5299 ccc publications a new belief entropy and its application in software risk analysis x.y. chen, y. deng xingyuan chen* school of information engineer kunming university, kunming, china *corresponding author: chenxingyuan@kmu.edu.cn; stargarden.chen@hotmail.com yong deng** institute of fundamental and frontier science university of electronic science and technology of china, chengdu, china **corresponding author: dengentropy@uestc.edu.cn; prof.deng@hotmail.com abstract the measurement of uncertainty has been an important topic of research. in dempster’s framework, deng entropy serves as a reliable tool for such measurements. however, it fails to consider more comprehensive information, resulting in the loss of critical data. an improved belief entropy is proposed in this paper, which preserves all the merits of deng entropy. when there is only a single element, it can be degraded to shannon entropy. when dealing with multiple elements, the partitioning method employed for mass functions makes it more responsive and efficient than alternative measures of uncertainty. some numerical examples are given to further illustrate the effectiveness and applicability of the proposed entropy measure. additionally, a case study is conducted on software risk analysis, demonstrating the practical value and relevance of the proposed method in real-world scenarios. keywords: dempster-shafer evidence theory, deng entropy, uncertainty measure, belief entropy, software risk analysis 1 introduction uncertainty exists everywhere in our daily life. due to the large amount of useful messages contained in uncertainty information, uncertainty information processing is widely used in many fields. it plays an important role in the target recognition [13, 16], complex network [31], artificial intelligence [17, 23], fault diagnosis [40], decision-making [51] and risk analysis [22], etc. in uncertainty information processing, it is necessary to measure uncertainty effectively and reasonably. therefore, uncertainty measurement (um) is widely studied as a hot topic. several methodologies have been proposed, such as dempster-shafer evidence theory [5, 33], bayesian probability theory [27], possibility theory [11], rough sets [30], shannon entropy [34], fuzzy set [6, 50], and so on [29]. https://doi.org/10.15837/ijccc.2023.2.5299 2 within these approaches, dempster-shafer evidence theory (dst) is highly regarded because it needs fewer conditions and better deal with uncertainty than probability theory. it includes frame of discernment (fod), basic probability assignment (bpa) and dempster’s rules of combination for data fusion. it provides a powerful method for the expression and fusion of uncertainty information [10, 36, 42]. dst is therefore widely applied in many fields [7, 43, 52]. shannon entropy occupies a central position in information theory. it solves the problem of quantitative measurement of information and has received a lot of attention in practical applications [38, 41, 48]. nevertheless, shannon entropy is only useful in the probabilistic framework, which is why many scholars have made a lot of attempts to introduce entropy into evidence theory. such as confusion measure [15], hartley entropy [12], discord measure [21], strife measure [20], total conflict measure [14], dissonance measure [46] and so on. however, these methods are not ideal for measuring the uncertainty of bpa in some situations. for measuring uncertainty, dissonance and nonspecificity are the two main aspects. these methods simply take either discord or non-specificity into account. in terms of discord, confusion measure, and strife measure were employed, while in terms of non-specificity, dubois and prade¡¯s weighted hartley entropy was used. therefore, how to combine both discordance and non-specificity measurement uncertainty of bpa is a question worth investigating. deng entropy [8] was proposed, which is more effective in some cases. first, both dissonance and non-specificity were considered by deng entropy. secondly, as a promotion of shannon entropy, deng entropy extends the probability to the mass function. third, it is more reasonable that the deng entropy increases monotonically with the size of the proposition. the greater the number of focus elements of the fod, the higher the entropy. due to its advantages, it is widely used in several fields [4, 18, 25]. however, deng entropy did not consider the most possible states when splitting the mass function. there are limitations when it comes to dealing with certain issues. if all possible states are not taken into account, then the information is incomplete and lost. a novel measure of belief entropy founded on deng entropy is proposed. the proposed entropy inherits all the benefits of deng entropy. at the same time, the way the mass function splits makes the proposed entropy more sensitive and effective than other uncertainty measures. some examples and a risk assessment case demonstrate the effectiveness of the new entropy for uncertainty measurement. the structure of this paper is as follows: an summary of the dempster-shafer evidence theory, shannon entropy, and deng entropy principles is given in 2. section 2 provides a brief overview of the concepts of dempster-shafer evidence theory, shannon entropy, and deng entropy. section 3 proposes a new measure of belief entropy based on deng entropy and provides several numerical examples. section 4 illustrates an application of the proposed belief entropy to demonstrate its validity. finally, the paper concludes in section 5. 2 preliminaries in this section, some essential basics are briefly introduced. 2.1 dempster-shafer evidence theory d-s evidence theory, also called as the dempster-shafer evidence theory (dst) [5, 33], is an extension of bayesian theory and is a potent tool for handling uncertain information. some fundamental concepts are outlined below. the set x represents the framework of discernment (fod), which contains all possible mutually exclusive and exhaustive hypotheses [33], defined as follows: x = {θ1, θ2, ..., θn} (1) the power set 2x contains all possible subsets of x, as defined below: 2x = {∅, {θ1}, {θ2}, ..., {θn}, {θ1, θ2}, ..., {θ1, θn}, ..., {θ1, ..., θn}} (2) https://doi.org/10.15837/ijccc.2023.2.5299 3 where ∅ is an empty set. for fod x, the mass function can also be referred to as basic belief assignment (bba) or basic probability assignment (bpa). it is mapping from 2x to [0, 1], and description is as follows: m : 2x → [0, 1] (3) constrained conditions as follows, { ∑ a∈2x m(a) = 1 m(∅) = 0 (4) a is called the focal element when m(a) > 0. m(a) indicates the belief value that supports the proposition a. there are some studies about mass function [9, 44, 47], which has been used in many fields [2, 24]. for a ⊆ x, the belief function bel : 2x → [0, 1] is following: bel(a) = ∑ b⊆a m(b) (5) the plausibility function pl : 2x → [0, 1] is defined as, pl(a) = 1 − bel(ā) = ∑ b ⋂ a ̸=∅ m(b) (6) apparently, ∀a ⊆ x, bel(a) ≤ m(a) ≤ pl(a). where bel(a)is the lower bound of proposition a and pl(a) is the upper bound of proposition a. [bel(a), pl(a)] indicates the confidence interval of a. considering two bpa, the dempster combination rule is denoted as[33]: m(a) = { 1 1−k ∑ b ⋂ c=a m1(b)m2(c), a ̸= ∅ 0, a = ∅ (7) where a, b, c ∈ 2x , k is a normalization factor, k = ∑ b ⋂ c=∅ m1(b)m2(c) (8) the conflict coefficient, denoted by k, is a measure of the degree of conflict between two basic belief assignments (bpas) m1 and m2. a value of k = 0 indicates that m1 is consistent with m2, while a value of k = 1 signifies that m1 and m2 are in total contradiction. however, when combining highly contradictory evidence, a counterintuitive conclusion may arise from dempster’s combination rule [45, 49]. numerous research are devoted to solving the problem [39]. 2.2 shannon entropy shannon entropy, also called information entropy, holds significant role in the field of information theory. shannon entropy is defined as [34], es(m) = − n ∑ i=1 pi logb pi (9) where n represents the number of basic states, while pi is the probability associated with state i. the probabilities pi must satisfy the condition n ∑ i=1 pi = 1. when the unit of information is the bit, then b = 2. shannon entropy can be defined as: es(m) = − n ∑ i=1 pi log2 pi (10) within the framework of probability theory, shannon entropy has proven to be a successful approach for measuring information uncertainty. nevertheless, the measurement of information uncertainty within the dst framework is also a subject worth studying. https://doi.org/10.15837/ijccc.2023.2.5299 4 2.3 uncertainty measure in the dst framework table 1 lists several measures of uncertainty in the dst framework. table 1: some uncertainty measures literature expression [15] ch(m) = − ∑ a⊆x m(a)log2 bel(a) [46] ey(m) = − ∑ a⊆x m(a)log2 pl(a) [12] ed p(m) = − ∑ a⊆x m(a)log2|a| [21] dkr(m) = − ∑ a⊆x m(a)log2 ∑ b⊆x m(b) |a∩b||b| [20] sk p(m) = − ∑ a⊆x m(a)log2 ∑ b⊆x m(b) |a∩b||a| [14] tcg p(m) = − ∑ a⊆x m(a)log2 ∑ b⊆x m(b)[1 − |a∩b||a∪b|] 2.4 deng entropy the deng entropy is an appropriate metric for the bpa in the dst. it can be described as [8], ed(m) = − ∑ a⊆x m(a)log2 m(a) 2|a| − 1 (11) equation (11) defines the deng entropy, where m is a bpa defined on fod, and a is a focal element of m, with |a| representing the cardinality of a. through a simple transformation, the equation can be expressed by: ed(m) = ∑ a⊆x m(a)log2(2 |a| − 1) − ∑ a⊆x m(a)log2(m(a)) (12) it can be divided into two components: ∑ a⊆x m(a)log(2|a| − 1), which measures nonspecificity, and − ∑ a⊆x m(a)logm(a), which measures discord. as an extension of shannon entropy, deng entropy is formally similar to it. the difference is that the belief of each focal element a is divided by the number of possible states in a, which is 2|a|−1. if bpa degenerates to probability, deng entropy can deteriorate to the shannon entropy. ed(m) = − ∑ a⊆2x m(a)log2 m(a) 2|a| − 1 = − ∑ a⊆2x m(a)log2(m(a)) (13) some of the uncertainty measures within the framework of deng entropy can be found in [4, 26, 28, 53]. 3 the proposed belief entropy this section presents an approach to measuring uncertainty in dst. specifically, section 3.1 introduces a novel belief entropy that builds upon deng entropy. in section 3.2, the efficacy of the proposed method is illustrated through numerical examples and a comprehensive discussion. https://doi.org/10.15837/ijccc.2023.2.5299 5 3.1 the proposed belief entropy en(m) = − ∑ a⊆x m(a)log2 m(a) ∑i(2|ai| − 1) (14) for each i = 1, 2, ..., 2x − 1, where x is fod, and m is a bpa, a is a focal element, the belief value of a is normalized by dividing it with a term ∑i(2 |ai| − 1), where |a| indicates the cardinality of a. this term represents the total number of possible states that a can assume.s assuming there are two balls inside a box, all the possible state are displayed in fig. 1. let fod x = {a, b}, the power set is a = {∅, {a}, {b}, {a, b}}. the most possible state in a is obtained by calculating ∑i(2 |ai| − 1) = (20 − 1) + (21 − 1) + (21 − 1) + (22 − 1) = 0 + 1 + 1 + 3 = 5. the figure 3 represents the total number of states that comprise components belonging to both {a} and {b}, namely, {a}, {b}, and {a, b}. alternatively, fod = x = {a, b, c }, then ∑i(2 |ai| − 1) = 19, as depicted in fig. 2. figure 1: all the possible states when there are two balls inside a box figure 2: all the possible states when there are three balls inside a box one simple conversion is as follows: en(m) = ∑ a⊆x m(a)log2(∑ i 2|ai| − 1) − ∑ a⊆x m(a)log2m(a) (15) where the first parameter measures the total non-specificity, whereas the second one measure of discord. an example is presented. example 1. suppose x = {a, b, c}, and the following are the two bpas, m1 : m1({a}) = 0.6, m1({a, b}) = 0.4 m2 : m2({b}) = 0.6, m2({x}) = 0.4 according to eq. (14), the calculations are as follows, https://doi.org/10.15837/ijccc.2023.2.5299 6 en(m1) = −0.6 × log2 0.6 21 − 1 − 0.4 × log2 0.4 (21 − 1) + (21 − 1) + (22 − 1) = 1.8997 en(m2) = −0.6 × log2 0.6 21 − 1 − 0.4 × log2 0.4 3 × (21 − 1) + 3 × (21 − 1) + (23 − 1) = 2.6701 it is shown that the entropy of m2 is larger than that of m1. which means the uncertainty of m2 is greater. 3.2 numerical examples and discussion a. probabilistic consistency when there is only a single element in a bpa or |a| ≡ 1, the proposed entropy can be calculated as follows, en(m) = − ∑ a⊆x m(a)log2 m(a) ∑i 2|ai| − 1 = − ∑ a⊆x m(a)log2 m(a) 21 − 1 = − ∑ a⊆x m(a)log2 m(a) en(m) = ed(m) = es(m) example 2. suppose fod x={a} and m3 (a)=1, shannon entropy (es), deng entropy (ed) and the new entropy (en) are calculated according to (10), eq. (11) and eq. (14), es(m3) = −1 × log21 = 0 ed(m3) = −1 × log2 1 21 − 1 = 0 en(m3) = −1 × log2 1 21 − 1 = 0 as shown in example 2, it can be seen that m3 assigns a belief of one hundred percent on the proposition a, which means there is no uncertainty in an information system. it is clear from the results that all entropies are equal to 0, which is reasonable. example 3. suppose that in fod x = a, b, c, d, the bpas are ascribed as below, m5 : m5(a) = m5(b) = m5(c) = m5(d) = 1 4 in this example, a, b, c, d have the same belief values with the same level of support. (es), (ed) and (en) can be calculated as below, es(m5) = − 1 4 × log2 1 4 − 1 4 × log2 1 4 − 1 4 × log2 1 4 − 1 4 × log2 1 4 = 2 ed(m5) = − 1 4 × log2 1 4 21 − 1 − 1 4 × log2 1 4 21 − 1 − 1 4 × log2 1 4 21 − 1 − 1 4 × log2 1 4 21 − 1 = 2 en(m5) = − 1 4 × log2 1 4 21 − 1 − 1 4 × log2 1 4 21 − 1 − 1 4 × log2 1 4 21 − 1 − 1 4 × log2 1 4 21 − 1 = 2 https://doi.org/10.15837/ijccc.2023.2.5299 7 from the above results in example 3, the proposed belief entropy is the same with both shannon entropy and deng entropy if the mass value is only assigned one element. b. superiority example 4. suppose x = {1, 2, ..., 14, 15} with 15 elements, and the following are the bpas [8], m({3, 4, 5}) = 0.05, m(6) = 0.05, m(a) = 0.8, m(x) = 0.1 the range of propositions denoted by the symbol a spans from 1 to 14. table 2 presents the results obtained from various uncertainty metrics applied to both new entropy and deng entropy, while fig. 3 provides a visual comparison of the findings. notably, the new belief entropy grows monotonically with the increase in the size of a, which is the same as deng entropy. nevertheless, a key distinction arises in that the proposed entropy significantly surpasses deng entropy. the reason is that more available information contained in bpa is taken into account in the proposed entropy. table 2: results of a variable number of elements in a cases deng entropy the new entropy a={1} 2.6623 3.6114 a={1, 2} 3.9303 5.3286 a={1, 2, 3} 4.9082 6.86941 a={1, ..., 4} 5.7878 8.2890 a={1, ..., 5} 6.6256 9.6479 a={1, ..., 6} 7.4441 10.9728 a={1, ..., 7} 8.2532 12.2773 a={1, ..., 8} 9.0578 13.5689 a={1, ..., 9} 9.8600 14.8524 a={1, ..., 10} 10.6612 16.1306 a={1, ..., 11} 11.4617 17.4053 a={1, ..., 12} 12.2620 18.6778 a={1, ..., 13} 13.0622 19.9487 a={1, ..., 14} 13.8622 21.2187 figure 3: comparison between the new entropy and deng entropy furthermore, to provide a more comprehensive comparison, six additional uncertainty measures are introduced and depicted in fig. 4. with the increasing number of elements in proposition a, measures can be divided into three different categories. the first category entails a constant level of uncertainty, exemplified by hohle’s confusion measure. the second category is characterized by a decreasing level of uncertainty, which includes klir & ramer’s discord measure, yager’s dissonance, klir & parviz’s strife measure, and george & pal’s conflict measure. notably, both categories one and two appear counterintuitive as they disregard nonspecific uncertainty and only account for discordant uncertainty. in contrast, the third category, which encompasses dubois & prade’s weighted https://doi.org/10.15837/ijccc.2023.2.5299 8 hartley entropy, deng entropy, and the proposed entropy, exhibits a corresponding increase in uncertainty as a expands. however, dubois & prade’s weighted hartley entropy exclusively considers non-specificity while ignoring discord uncertainty, a perspective that is equally unsound. ultimately, the proposed entropy outperforms other uncertainty measures by offering a more reasonable and effective approach. figure 4: comparison between the new entropy and other uncertainty measures 4 applicaiton numerous techniques have been proposed to determine the weight of software risk [32, 35, 37]. this paper presents a straightforward risk evaluation model that utilizes the proposed entropy. as we know, it is crucial to establish a rational weight for risk evaluation. the proposed entropy is used to measure the uncertainty of risk. the uncertainty of risk increases with entropy, so the weight decreases with entropy. whereas, the larger the weight, the smaller the risk uncertainty and the lower the entropy. 4.1 a simple software risk assessment framework based on the new belief entropy software risk encompasses two important aspects. the probability of risk occurrence and the severity of loss are affected. in accordance with boehm’s seminal work on software engineering [1], risk may be explicated as follows: risk = probability(p) × severity(s) (16) as shown in the fig. 5, software risk assessment can include the following steps, step 1. expression of risk assessment. step 1.1. establishing assessment criteria. since most experts and decision-makers (dms) usually use linguistic information such as low, high, very high, etc to assess risk. in this paper, we provided the following evaluation criteria, as shown in table 3, and some explanations are given in table 4 and 5. notably, the criteria are not fixed, and can be adjusted according to the actual situation. step 1.2. assign and translate assessments into bpa. https://doi.org/10.15837/ijccc.2023.2.5299 9 figure 5: software risk assessment framework table 3: linguistic terms level linguistic terms −2 very low −1 low 0 medium 1 high 2 very high table 4: the probability of risk linguistic terms possibility very low the consequences are negligible low the consequences are not overly severe medium the consequences are moderate high the consequences are serious very high the consequences are extremely severe table 5: the severity of risk linguistic terms severity very low the consequences are negligible. low the consequences are not too severe. medium the consequences are moderate high the consequences are serious very high the consequences are extremely severe https://doi.org/10.15837/ijccc.2023.2.5299 10 n experts are invited to assess both the probability and severity of risk using the criteria presented in table 3. for example {p−1(0.6), p−1 p0(0.4)} means the probability of "low" is 60% and the probability of hesitation between "low" and "medium" is 40%. then the evaluation results should be translate into bpa. {p−1(0.6), p−1 p0(0.4)} is equivalent to m1({p−1}) = 0.6, m2({p−1, p0}) = 0.4. if {s−1(0.5), s0(0.5)} means the severity of "low" is 50% and "medium" is also 50%. translating the evaluation results into bpa is m1({s−1}) = 0.5, m2({s0}) = 0.5. step 2. integrating risk information. for each risk, the assessment values given by all experts are fused using the dempster’s combination rule. step 3. calculating risk weights wr. step 3.1. calculating the uncertainty of the probability and severity. according to eq. (17), use the new entropy to calculate the uncertainty of the probability en(p) and the uncertainty of the severity en(s). en(r) = en(p) + en(s) (17) step 3.2. calculating the credit value for each risk. according to eq. (18), credr are calculated as following, credr = en(r) 1 + en(r) (18) step 3.3. calculating the weight for each risk according to eq. (19), we are calculated as following, wr = credr ∑ny=1 credr (19) step 4. ranking of the risk. step 4.1. calculate the value the probability and the severity. table 6: values of score bpa score bpa score m({p−2})/m({s−2}) 09 m({p−2, p−1})/m({s−2, s−1}) 1 9 m({p−1})/m({s−1}) 29 m({p−1, p0})/m({s−1, s0}) 3 9 m({p0})/m({s0}) 49 m({p0, p1})/m({s0, s1}) 5 9 m({p1})/m({s1}) 69 m({p1, p2})/m({s1, s2}) 7 9 m({p2})/m({s2}) 89 m({px}/m({sx}) 9 9 for example, a severity of risk value after fusion is as follows: (m{s0}) = 0.2079, m({s0, s1}) = 0.1386, m({s1}) = 0.5941, m({s1, s2}) = 0.0594. according to table 6, the value of s is calculated as: s = 4 9 × m({s0}) + 5 9 × m({s0, s1}) + 6 9 × m({s1}) + 7 9 × m({s1, s2}) = 4 9 × 0.2079 + 5 9 × 0.1386 + 6 9 × 0.5941 + 7 9 × 0.0594 = 0.6117 step 4.2. calculate the risk using the eq. (20) and proceed with ranking the results. risk = wr × p × s (20) https://doi.org/10.15837/ijccc.2023.2.5299 11 4.2 a case study in this section, we will give a case study. there are six risk factors in a software project. and three experts are invented to the risks. step 1. based on the assessment criteria in table 3, three experts gave their assessment and converted into bpas. the results are shown in table 7. table 7: experts assignment risks experts p s rs1 m1(.) {p−1 p0(0.6), p0 p1(0.4)} {s−1(0.5), s0(0.5)} m2(.) {p−1(0.4), p0 p1(0.25)} {s0s1(0.8)} m3(.) {p−1(0.65), p1(0.35)} {s0(0.7)} rs2 m1(.) {p−1(0.7), p0(0.2)} {s0(0.4), s1s2(0.6)} m2(.) {p−1 p0(0.6), p0 p1(0.4)} {s1(0.65)} m3(.) {p0(0.5)} {s0(0.6)} rs3 m1(.) {p−2(0.3), p−2 p−1(0.3), p−1(0.4)} {s0s1(0.7), s1s2(0.3)} m2(.) {p−1(0.6), p−1 p0(0.3)} {s0(0.6)} m3(.) {p−1 p0(0.7)} {s1(0.75)} rs4 m1(.) {p−1(0.7)} {s1s2(0.8)} m2(.) {p0(0.9)} {s1(0.75)} m3(.) {p−1(0.4), p0 p1(0.25)} {s0s1(0.6), s1s2(0.4)} rs5 m1(.) {p0(0.5), p0 p1(0.4)} {s−1(0.5), s0(0.5)} m2(.) {p0(0.5)} {s0s1(0.8)} m3(.) {p−1 p0(0.7), p0 p1(0.3)} {s−1s0(0.6), s1(0.4)} rs6 m1(.) {p1(0.5), p1 p2(0.4)} {s−1s0(0.8), s1(0.2)} m2(.) {p0 p1(0.8)} {s0(0.65)} m3(.) {p0(0.6), p1(0.25)} {s0s1(0.9)} step 2. after using dempster’s combination rule, the fusion results of p and s are shown in table 8. step 3. based on eq. (17), eq. (18) and eq. (19) the weights of risks are w1 = 0.1704, w2 = 0.1870, w3 = 0.1613, w4 = 0.1879, w5 = 0.1403, w6 = 0.1531. step 4. based on eq. (20), the risk values and rankings are shown in table 9. we can easily obtain that rs4 > rs6 > rs2 > rs1 > rs5 > rs3. the rs4 is the highest risk and it should be focused on. while rs6 is the second highest one, and also needs to be given attention. figure 6: comparison of risk values in table (9) and fig 6, we can see that the results will be different by using the proposed method. and if we just use p*s, the risk values and rankings are different. this is because the uncertainty of https://doi.org/10.15837/ijccc.2023.2.5299 12 ta b le 8: f u si on r es u lt s r is k s p s r s1 {p − 1 (0 .5 82 1) ,p 0 (0 .2 50 7) ,p 0 p 1 (0 .1 67 2) } {s 0 (0 .7 69 2) ,s 1 (0 .2 30 9) } r s2 {p − 1 (0 .4 11 8) ,p − 1 p 0 (0 .0 58 8) ,p 0 (0 .4 90 2) ,p 0 p 1 (0 .0 39 2) } {s 0 (0 .3 68 4) ,s 1 (0 .4 10 5) ,s 1 s 2 (0 .2 21 1) } r s3 {p − 2 (0 .0 12 7) ,p − 2 p − 1 (0 .0 12 7) ,p − 1 (0 .9 74 6) } {s 0 s 1 (0 .2 07 9) ,s 0 s 1 (0 .1 38 6) ,s 1 (0 .5 94 1) ,s 1 s 2 (0 .0 59 4) } r s4 {p − 1 (0 .2 63 8) ,p 0 (0 .6 62 6) ,p 0 p 1 (0 .0 30 7) ,p x (0 .0 42 9) } {s 0 (0 .3 75 ), s 0 s 1 (0 .0 93 8) ,s 1 (0 .4 68 8) ,s 1 s 2 (0 .0 62 5) } r s5 {p − 1 p 0 (0 .0 35 ), p 0 (0 .8 9) ,p 0 p 1 (0 .0 75 )} {s − 1 (0 .1 66 7) ,s 0 (0 .8 33 3) } r s6 {p 0 (0 .1 30 4) ,p 0 p 1 (0 .0 26 1) ,p 1 (0 .8 10 9) ,p 1 p 2 (0 .0 26 1) ,p x (0 .0 06 5) } {s − 1 s 0 (0 .0 32 2) ,s 0 (0 .8 87 4) ,s 1 (0 .0 80 5) } https://doi.org/10.15837/ijccc.2023.2.5299 13 table 9: risk values and rankings risks p*s proposed method p*s rating proposed method rating rs1 0.1654 0.0282 5 4 rs2 0.2137 0.0400 3 3 rs3 0.1333 0.0215 6 6 rs4 0.2395 0.0450 2 1 rs5 0.1829 0.0257 4 5 rs6 0.2936 0.0449 1 2 the expert assessment value is measured using the proposed entropy. more information is considered. also, avoiding subjective bias of experts and assigning weights from an objective point of view, it has a more fair and reasonable character. to prove the validity of the proposed entropy, we also made some other comparisons. we analysed the example in ref. [3]. we replaced the deng entropy with the proposed entropy and calculated the uncertainty. the final rankings of the risks are the same as in ref. [3]. therefore, the proposed entropy is effective. 5 conclusion measure uncertainty in the dst framework is still an open issue. in this paper, a new belief entropy based deng entropy has been proposed. in the dst framework, the performance of uncertainty measures can be improved when using the new entropy. the new entropy can identify the uncertainty more effectively and provides a new way of the splitting of the mass function thinking about measuring uncertainty. the experimental results illustrate that the new entropy we proposed is better than other methods. a case study on risk assessment further highlights the effectiveness of the proposed entropy in practical applications. however, it is worth noting that the new entropy may not fully satisfy all the characteristics proposed by klir and wierman [19]. whether or not it is necessary in dempster-shafer theory is still a topic for discussion. in the future, based on the new belief entropy, we can do more research. according to the main ideas of bpa divisions in this paper, more divisions methods can be designed. besides more application could be use this entropy. acknowledgment the work is partially supported by national natural science foundation of china (grant no. 61973332), jsps invitational fellowships for research in japan (short-term). references [1] boehm, b.w., 1991. software risk management: principles and practices. ieee software 8, 32–41. 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[53] zhou, d., tang, y., jiang, w., 2017. an improved belief entropy and its application in decisionmaking. complexity 2017, 1–15. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-andcontrol cite this paper as: chen, x.y.; deng, y. (2023). a new belief entropy and its application in software risk analysis, international journal of computers communications & control, 18(2), 5299, 2023. https://doi.org/10.15837/ijccc.2023.2.5299 introduction preliminaries dempster-shafer evidence theory shannon entropy uncertainty measure in the dst framework deng entropy the proposed belief entropy the proposed belief entropy numerical examples and discussion applicaiton a simple software risk assessment framework based on the new belief entropy a case study conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 5, month: october, year: 2022 article number: 4521, https://doi.org/10.15837/ijccc.2022.5.4521 ccc publications fused liss iv image classification using deep convolution neural networks k. uma maheswari, s. rajesh k. uma maheswari department of computer science and engineering university college of engineering ramanathapuram, ramanathapuram, india. corresponding author: umamaheswariphd01@yahoo.com s. rajesh department of computer science and engineering mepco schlenk engineering college (autonomous), sivakasi, india srajesh@mepcoeng.ac.in abstract these days, earth observation frameworks give a large number of heterogeneous remote sensing information. the most effective method to oversee such fulsomeness in utilizing its reciprocity is a vital test in current remote sensing investigation. considering optical very high spatial resolution (vhsr) images, satellites acquire both multi spectral (ms) and panchromatic (pan) images at various spatial goals. information fusion procedures manage this by proposing a technique to consolidate reciprocity among the various information sensors. classification of remote sensing image by deep learning techniques using convolutional neural networks (cnn) is increasing a solid decent footing because of promising outcomes. the most significant attribute of cnn-based strategies is that earlier element extraction is not required which prompts great speculation capacities. in this article, we are proposing a novel deep learning based smdtr-cnn (same model with different training round with convolution neural network) approach for classifying fused (liss iv + pan) image next to image fusion. the fusion of remote sensing images from cartosat-1 (pan image) and irs p6 (liss iv image) sensor is obtained by quantization index modulation with discrete contourlet transform (qim-dct). for enhancing the image fusion execution, we remove specific commotions utilizing bayesian channel by adaptive type-2 fuzzy system. the outcomes of the proposed procedures are evaluated with respect to precision, classification accuracy and kappa coefficient. the results revealed that smdtr-cnn with deep learning got the best all-around precision and kappa coefficient. likewise, the accuracy of each class of fused images in liss iv + pan dataset is improved by 2% and 5%, respectively. keywords: image fusion, discrete contourlet transform, image classification, remote sensing images, quantization index modulation, deep convolution neural networks. https://doi.org/10.15837/ijccc.2022.5.4521 2 1 introduction classification of remote sensed image is the method that changes remotely sensed image in to usable products. in that classification process deep learning is a one of the emerging concept in recent year. it is extensively used in huge application such as wireless communications, nlp, computer vision and image processing. in this paper we mainly concentration on classification processes to generating land-cover / land-use maps. over past few decades landcover /land use mapping mainly using satellite or airborne imagery due to better-quality data availability and accessibility. remote sensing image classification using deep learning used in wide fields of slum detection [12], land cover land use mapping[13,14,15], agriculture land detection and urban planning. in classification feature extraction play a major and important role. to improve classification efficiency automatic feature extraction technique is essential. feature chosen by human interaction will degrade the efficiency of classification process. so we used deep learning technique in this paper. image fusion is another recent booming task to improve efficiency of image classification. many recent research areas focus fusion technique to enhance the quality of the process. in image fusion, the most important information is obtained from the collection of images provided and the resulting single image provides high quality. many earth-observing satellites can be captured various remote sensing images with more spatial and less spectral resolution panchromatic images (pan) and more spectral and less spatial resolution multispectral (ms) images [16,17]. various image fusion techniques yield more spectral and spatial resolution remote sensing images[18,19]. in summary the key contribution of this paper are in four fold: we proposed deep learning based methods such as cnn for classifying fused image due to automatic deep feature extraction in deep learning. for better result of accuracy identification we used multi model ensemble algorithm with cnn. for enhancing the image fusion execution, we remove specific commotions utilizing bayesian channel by adaptive type-2 fuzzy system. we show multiple studies for the suggested method that have produced far better outcomes than previous works. the remaining portion of this paper is sorted as follows. the section 2 talks about the present principle difficulties of remote sensing image characterization. a short audit of deep learning models and a thorough overview of deep learning based image classification strategies were additionally given. in section 3 we provide the study area and data used. the technique of proposed work including the image fusion procedure and image order is given in section 4. in section 5, the evaluations for the suggested experimental settings with the discussion of findings are shown. finally, we finish up this paper in section 6 with concluding remarks and future possibilities. 2 literature survey swathika et al (2018) present a new method called thbmt-fis to achieve high image visibility and accuracy. to remove light character and dark character they use black and white top hat method. they use fuzzy inference scheme (fis) to perform image fusion of pan and ms image[25]. zheng et al. (2017) uses guided image filtering to fuse gf2 (gaofen-2) images. it is used for combining feature of two different source images such as pan and ms image. during filtering process high spectral resolution ms band consider as a reference image for the simulated pan band. after that filtered image subtract from input pan image to obtain high spatial details. finally fused pan sharpen image obtain by insert high spatial information into each band of the resampled ms image [9]. on 2016 [2] khatami et al. proposed a study on pixel-based supervised methods for ground cover mapping. they suggested the most powerful support vector machine (svm) with a total accuracy of 76 percent for many applications (oa). a neural network (nn)-based classification model of about the same performance of 75 percent overall precision was another technique. in that method, single date image classification was done and also svm consume much more resource. they conclude that svm used for large area classification problems and big data applications. chen et al (2014)[3] proposed stacked auto encoder with logistic regression sae-lr approach to extract deep features for hyper spectral image classification. it is really a dynamic framework for the study of the principal components, the architecture of a deep understanding and regression https://doi.org/10.15837/ijccc.2022.5.4521 3 of logistics. first they compute deep spectral features through stacked auto encoders and logistic regression classifier used to complete the spectral classification phase. next they classify spatialdominated information by compressing in spectral dimension using pca, then flattening spatial data and extract layer-wise deep features using ae. at last they proposed a different deep system for learning to integrate the two set of deep spectral and spatial features for acquire highest classification accuracy. finally they use logistic regression in the bottom layer of a neural network for classification process. they suggest whether deeper features often have greater accuracy of classification, while deep structure can work in reverse as well. the experimental result shows that proposed sae-lr method provide higher accuracy than rbf-svm method. over past few years, most of the task performed in computer vision, image processing and natural language processing used most powerful machine learning algorithm is deep learning. for processing hyperspectral and multi spectral images, deep learning is one of the efficient methods. most of the studies show deep learning method used to extracting different land covers types such as road extraction, water body extraction and buildings extraction. convolution neural network is one of the best deep learning approach to provide best feature extraction without manual interaction. for high resolution hyper and multispectral image classification cnn algorithm provide high efficiency in spatial and spectral feature exaction [6,7]. to improve classification efficiency, now days another deep learning approaches such as deep convolutional neural networks used through most of researcher. it mainly contains convolution layers and the down sampling layers that provide greater performance ratio compare to other deep learning methods. yue et al [4] use hybrid framework based on principal component analysis, deep convolutional neural networks (dcnns) and logistic regression (lr) for classifying hyperspectral images. in that method deep features of hyper spectral images extract through dcnn. the experimental result shows that proposed method outperform compare to stacked autoencoder-logistic regression (sae-lr) (chen lin et al. 2014)[5]. kussul et al (2017)[1] proposed new multilevel deep learning method for land cover and crop type classification. in that architecture they perform four level of processing to classify multitemporal imaginary. different convolution neural network such as 1-d cnn and 2-d cnn is a main core of this architecture to extract spectral and spatial features. this architecture is one of outperform ensemble of cnn method to provide better classification of certain summer crop types efficiently. rajesh et al (2020) proposed new object based classification technique [10] considers each image sections as a building chunk for the further image investigation. specifically the convolutional neural network (cnn) of deep learning techniques has upgraded the performance of remote sensing image classification due to the dominant perception of reasoning and feature learning. the object based approach can be used to powerfully describe and classify high resolution imagery. a detailed review was proposed by ava vali et al. [20] on 2020 based on various deep learning algorithms for land use and land cover classification using multispectral and hyperspectral images. to increase classification accuracy xiangyong cao et al use active deep learning method to classify hyper spectral image with the combination of cnn and markov random field [21]. liu et al. (2020) proposed band independent encoder-decoder model. it work any level of scaling factors and robust to work with several spectral features of spatial resolution images such as single band multi-spectral image (bms), pan image and low-resolution pan (lrpan) image. this method work fast and efficient than the other pansharpening methods [23]. object-scale adaption scheme is proposed by wang et at. [22] to fuse the cnn and multi-scale image segmentation method for better outcome. on the whole, object-scale adaptive convolutional neural networks method has great capability to classify the high resolution images with fullest accuracy. 3 proposed method the area selected in our work is the madurai city, located in tamilnadu state of india. in population madurai is the second largest population over 38 districts of tamilnadu. it is a district headquarters. it is a one of the historical city and also referred as the gateway of south tamilnadu. madurai city is known as the athens of the east. the geographical location of madurai district north https://doi.org/10.15837/ijccc.2022.5.4521 4 latitude between 9 50’ 59” and 9 57’ 36” and east latitude between 78 04’ 47”and 78 11’ 23”. the present area of madurai district is 3710 square kilometres. as per the census of india, madurai district had a population of about 17.34 lakhs. the proposed method is performed using matlab r2017b. here we use 1000 samples for training and 500 samples are selected randomly for testing. for lissiv data acquisition indian remote sensing satellite p6 version (irs p6 satellite) is used. original liss iv ms and pan images of madurai region is shown in figure 4. for my study some of the selected area is cropped from original pan and ms image. cropped liss iv ms and pan images shown in figure 1. table 1: original image details figure 1: : cropped pan and lissiv ms images 3.1 preparation of ground truth data in the process of training dataset and also test the performance of classification methods. to determine land use /land cover features of madurai district we perform field visit and collect ground control points (gcps). based on field visit and also use e-trex venture global positioning system device we create reference data set for performance assessment test. the table 2 shows some of the latitude and longitude of different places visited. 4 proposed work the proposed method is used to classify fused pan and ms high-resolution images using deep convolutional neural network. the flowchart for fusion of pan and ms image with classification is shown in figure 2. in that image fusion performed by four levels using qim with dct [11]. after that fused image classified into seven classes of urban, vegetation, wetland, tank, water area, bare land, and roadways by the proposed method of same model with different training rounding based on cnn (smdtr-cnn) .the proposed method is broadly divided into two sections: i. image fusion and ii. image classification 4.1 image fusion: there are three significant portions combined in this section of our proposed work which incorporates pre-processing, wavelet decomposition and best coefficients selection. wavelet decomposition is https://doi.org/10.15837/ijccc.2022.5.4521 5 table 2: reference point with latitude and longitude figure 2: : the proposed system architecture performed utilizing quantization index modulation (qim) and discrete contourlet transform (dct). 4.1.1 pre-processing the initial step performs by two stages: colour shading change and decrease noise. the principle behind in the pre-processing is to increase the nature of the image information and quality. 4.1.1.1 color shading change image fusion process complexity will be increase using color image.so we perform color shading change process to improve the performance of image fusion. for example rgb images are made out of three independent color shading of red, green and blue. we use luminosity method to perform color shading change from rgb image into gray scale image. 4.1.1.2 noise decrease using a-t2fls with bt channel removing noise from remote sensing image is an essential task to provide better quality input image for image fusion process. most of the satellite images have noisy pixel values that degrade the efficiency of process. hence we use adaptive type2 fuzzy system (a-t2fls) with bayesian thresholding (bt) channel filter to remove noisy pixel from gray scale pan and ms image. two membership functions such as pmf (primary membership function) and smf(secondary membership function) are define by a type-2 fuzzy inference system. based on membership function value pixels are classified good https://doi.org/10.15837/ijccc.2022.5.4521 6 and bad pixels by using bayesian optimal threshold value. 4.1.2 wavelet decomposition we proposed dct with qim based wavelet decomposition to decomposed pan and ms image into five levels. each image divided into 8 x 8 blocks of window size. dct-qim produces sub images of low frequency (approximate original image) and high frequency (detailed original image such as edges, lines, boundaries and region) images. main purpose of qim is to provide high psnr value to reduce error. the dct of gray image denoted as g(x,y). each block of m x m image is transfer into frequency domain ge(a,b) by using dct: 4.1.3 best coefficients selection to fuse high and low frequency sub images we use euclidean distance to select best coefficients. because frequencies with low distance achieve the high priority compare to other coefficients. x and y are two coefficients then the euclidean distance is computed by then inverse discrete contourlet transform is performed to reconstruct fused image. to improve performance of classification we convert fused image into rgb image the inverse dct of g(x,y) for ge (a,b) is defined by, 4.2 image classification so as to characterize the fused image, deep convolutional neural networks (dcnns) are suggested which arrange the image into 7 classes: urban, wetland, vegetation, tank, bare land, water area, and roadways. the proposed strategy explores a deep learning based order technique for remote sensing images, especially for fused liss iv and pan images with different changes and multi classes. in particular, to help build up the comparing classification strategies, we consider deep convolutional neural networks (dcnns): a) convolutional neural network (cnn) b) same model with different training rounding dependent on cnn (smdtr-cnn). 4.2.1 cnn based fused image classification the fused (pan +liss iv) enhanced image characterization design is dependent on dcnn. the reason for the characterization design is to save neighborhood restraints and concentrate semantic data. under the thought of the above mentioned, the structured architecture comprises of three sections. the initial segment is the fused image input, and the size of each image is 128x128 pixels, and the quantity of channels is 3. in the subsequent section, to extract features we applied four convolutional layers. the kernel size of convolution layer is n × n × g, where n size is less than 128 and g is less or equal to 3. in the interim, two layers of max-pooling (2 × 2) are embedded into the dcnn https://doi.org/10.15837/ijccc.2022.5.4521 7 layers to decrease the boundaries and keep valuable features. the last part act as image classifier such as fully connected layer to map is a fully completely associated layer for image classification. 1) feature extraction cnn-based feature extraction comprises of three perspectives: convolutional layers, relu function layers, and max pooling layers. in our architecture we use four convolutional layers. there is two different kernel size is 3 × 3 with a dimension of 128, and 3 × 3 with a dimension of 64 [24]. to increase the speed of cnn network we use activation function such as relu to acquire activation value with the use of threshold. max pooling is one type of pooling operation used to decrease number of parameters. after convolution layer 2 × 2 kernel size of max pooling is applied. 2) classification scheme the completely associated layers are applied to join the features with past edges. in our work, we utilized three completely associated layers with 1024, 512, and 10 neurons separately, to interface with the following convolutional layer. to measure the likeliness of each category acquire by using softmax model of classification. 3) loss function and regularization the cross-entropy drop work is the most famous target classification capacity in cnn. it is characterized as in equation (4): where c is the quantity of classifications, yi is the genuine mark, and h is the last yield of the system. dropout is the most generally utilized regularization in cnn, which outfits with the completely associated layer. it decreases the unpredictability of the system, but at the same time is a viable collaborative learning strategy in deep learning models. the guideline of dropout is that the heaviness of the neuron is set to 0 arbitrarily with likelihood p for every neuron in each layer in preparing, and the entirety of the neurons are dynamic with their weight duplicated by (1 p) to guarantee the loads of the preparation and testing have similar desires. 4.2.2 same model with different training rounding based on cnn (smdtr-cnn) two different level of ensemble learning is performed in deep learning method. one is data level ensemble based on training data set and also process based on unbalanced datasets. another method is model level ensemble. it contains two different type such as single model and multi models. muti-layer ensemble is one of important single model ensemble method to use by image classification applications. there are four methods available in the multi model ensemble such as voting, simple averaging, stacking and weighted averaging. for best performance we use weighted average method denoted as same model with different training rounds (smdtr) represent in equation (5), to increase the performance of model and also improve accuracy rate of fused image classification we proposed architecture of model ensemble based on cnn (smdtr-cnn). the proposed smdtrcnn architecture is appeared in figure 3. we gather output produced by several model during the training phases, and then performed weighted averaging on chosen models to build another new model for fused image classification. https://doi.org/10.15837/ijccc.2022.5.4521 8 figure 3: : the architecture of smdtr-cnn for classification. 5 experiments 5.1 experiment setting remote sensing image choice assumes significant job in remote sensing image fusion and classification. an investigation is performed utilizing matlab r2017b. the quantity of preparing tests are 1500 and the testing tests are 750. indian remote sensing satellite p6 rendition (irs p6 satellite) is utilized for ms liss iv information securing. figure 1 shows unique liss iv ms from irs p6 satellite and pan image from cartosat-1 satellite. with the help of ground control points the geometric corrections are made on these images. 5.2 performance metrics various image fusion and classification evaluation metrics are consider in this section to validate the accuracy. some assumption are: • a and b are liss iv ms and pan original image • i,j are the row and column pixel index 5.2.1 image fusion performance metrics a) peak signal to noise ratio (psnr): it is a proportion of peak signal-to-noise ratio assessed between unique image and the fused image. it is evaluated in decibels. the higher psnr shows the better quality in image fusion. it is communicated as in equation (6). b) structural similarity index matrix (ssim): it mirrors the basic closeness between two images. the higher ssim shows increasingly comparable. it is communicated as in equation (7). c) root mean squared error (rmse): it is the proportion of signal blunder by deducting the test signal (reference) and afterward figures the normal vitality of the error signal. it is characterized as in equation (8). d) spatial https://doi.org/10.15837/ijccc.2022.5.4521 9 correlation coefficient (scc): it is a proportion of break down spatial relationship between fused image and reference image. for that spatial weight is registered in equation (9). e) image entropy (ie): it is a proportion of data substance of a fused image. the higher worth speaks to the melded image comprises of rich data substance and fit for exact order as in equation (10). 5.2.2 image classification performance metrics to improve the precision of this analysis, we figured the ndvi from the close infrared and the red channels, and it was utilized as a pointer (ndvi = (nir r)/(nir + r)). the groups of r, g, b, and ir, profundity, just as the hand-created features including ndvi and the relating ground truth are utilized as contributions to prepare the smdtr-cnn. the given measurements of f1 score and the worldwide pixel-wise precision of each class are utilized to evaluate the quantitative execution. f1 score is a portrayal of the consonant mean of accuracy and review, and it very well may be determined as follows in equation (11): disarray networks per tile or by an aggregated disarray lattice. simultaneously, the general precision (oa) can be gotten by normalizing the follow from the disarray network. 5.3 result and discussion in this section we present the discussion in two sections: (1) an evaluation of fused image (2) an evaluation of image classification metrics 5.3.1 image fusion accuracy evaluation the comparison using various image fusion assessment metrics is shown in table 3. firstly, we demonstrate the comparison results for image fusion with knn (k-nearest neighbor) [26], hcs (hyperspecherical colour sharpening)[27], wavelet transform[28], and g-rbf (gaussian rbf kernel)[29]. in order to improve the quality of image, image fusion is performed on which multiple images such as pan and ms images are fused to produce the quality image with high spatial and spectral resolution. figure 4 shows the results of various approaches for image fusion. the proposed qim+dct strategy got 55.53 (psnr), 1.35 (ssim), 49.65 (rmse), 0.978 (scc), and 0.989 (ie) and furthermore we accomplish higher fusion execution of all performance metrics. 5.3.2 image classification accuracy evaluation the precision of each class such urban, vegetation, wetland, tank, water area, bare land, roadways obtained by the three methods is displayed in table 4. it shows that the model ensemble based on smdtr-cnn improved the precision. the accuracy of various classification approaches are graphically represented in figure 5. it demonstrates that the model group dependent on smdtrhttps://doi.org/10.15837/ijccc.2022.5.4521 10 cnn that improved the classification of every class by 2% and 5% when compared with other dcnn methodologies discussed in section 3. our work demonstrates that it is indicated that two dnn-put together techniques accomplish various exhibitions with respect to various classifications of images. it is conceivable to plan progressively adaptable outfit learning technique to complete the classification task later on work. after that, our best model accomplishes best in class results on the datasets in table 5. to compare the accuracy of different algorithms, classified output of enhance fused madurai (liss iv + pan) image with pso+svm, cnn, and smdtr-cnn deep learning methods is shown in figure 6. when comparing all the algorithms, smdtr-cnn deep learning technique has classified the image more accurately. the design arrives at 94.66% by and large and the deep learning outline performs especially well on fused liss iv+pan image dataset. the exhibitions of the proposed techniques are assessed regarding exactness, in general precision, and kappa coefficient. the outcomes uncovered that smdtr-cnn with deep learning with directed strainers got the best by and large classification accuracy of 94.66% and kappa coefficient of 0.9433. likewise improving the accuracy of every class of fused liss iv+pan images in the dataset by 2% and 5%, respectively. 6 conclusion remote sensing image classification with the combination of image fusion could be robust, reliable and scalable. in this paper, to assume control over the benefit of image combination for image fusion, a qim-dct (quantization index modulation with discrete contourlet transform) based combination approach for request of remote sensing images is proposed. to increase the image combination execution, we evacuate explicit noises using bayesian channel with adaptive type-2 fuzzy system. after image fusion, we make image classification by deep convolution neural networks (dcnns). the exhibitions of the proposed techniques are assessed regarding exactness, in general precision, and kappa coefficient. the results revealed that smdtr-cnn got the best all things considered order precision as 94.66% and kappa coefficient as 0.9433. from the results, we conclude that our proposed table 3: the comparison using image fusion assessment metrics https://doi.org/10.15837/ijccc.2022.5.4521 11 figure 4: : figure 4 result of various image fusion approaches a) wavelet transform. b) gaussian wbf. c) qim+dct table 4: the classification accuracy (in %) of each category approach outperforms than the previous approaches. in future, we plan to broaden the methodology on other optical remote sensing images rather than using liss iv just and more classes are foreseen using different classifiers. declaration: ethics approval and consent to participate: no participation of humans takes place in this implementation process human and animal rights: no violation of human and animal rights is involved. funding: no funding is involved in this work. conflict of interest: conflict of interest is not applicable in this work. authorship contributions: there is no authorship contribution acknowledgement: there is no acknowledgement involved in this work. figure 5: :figure 5 the classification accuracy (in %) of each category https://doi.org/10.15837/ijccc.2022.5.4521 12 table 5: the classification accuracy (in %) of each category figure 6: : result of classification for proposed versus existing approaches a) svm+pso b) cnn c) smdtr-cnn references [1] nataliia kussul, mykola lavreniuk, sergii skakun, and andrii shelestov ieee geoscience and remote sensing letters, “deep 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[29] k. venkateswaran, n. kasthuri, k. balakrishnan and k. prakash, (2014). contourlet-based multiband image fusion for improving classification accuracy in irs liss iii images, advances in intelligent systems and computing, vol. 325, pp. 201-212 copyright © 2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: uma maheswari, k.; rajesh, s. (2022). fused liss iv image classification using deep convolution neural networks, international journal of computers communications & control, 17(5), 4521, 2022. https://doi.org/10.15837/ijccc.2022.5.4521 introduction literature survey proposed method preparation of ground truth data proposed work image fusion: pre-processing wavelet decomposition best coefficients selection image classification cnn based fused image classification same model with different training rounding based on cnn (smdtr-cnn) experiments experiment setting performance metrics image fusion performance metrics image classification performance metrics result and discussion image fusion accuracy evaluation image classification accuracy evaluation conclusion in memoriam: prof. ioan dzitac editorial in 2021, the international journal for computers, communications and control (ijccc) entered its 16th year of existence. the first issue of the sixteenth volume of ijccc was planned to be a special one dedicated to pay a tribute to the exceptional work and results of lotfi zadech, a great scientist born in 1921, who influenced an immense number of the scholars all over the world. that special issue was thought and organized by ioan dzitac who wrote the editorial [1]. several outstanding scholars were invited and accepted to publish in the intended special issue. it was ioan dzitac’s last paper. unfortunately, on 6th february, when the special issue was almost ready to be published, the heart of ioan dzitac suddenly stopped beating. now, it is our sad duty of honour to propose to collaborators and readers of our journal a special issue meant to commemorate and pay a tribute to ioan dzitac, an honest and generous person, a talented and hardworking scientist and professor, an effective and empathic manager, a close and dear colleague and friend. the life and work of ioan dzitac were briefly presented in the obituary published in the second number of the 16th volume of ijccc [2]. in this special issue, i will focus on ioan dzitac’s contribution to the birth and evolution of the journal and its associated scientific event, the biannual international conference on computers, communications and control (icccc). the story of ijccc started in 2005, after my first visit to agora university, when ioan dzitac proposed to set up together a new scientific publication in the domain of i&ct (information and communications technology ), based on the experience gained with the journal named studies in informatics and control-sic (http://sic.ici.ro ). during several months that followed, we worked hard together to design the new publication which was initially meant to host papers that describe integration of the three technologies: computing, control, and communications. several highly reputed scholars from various countries enthusiastically joined the new undertaking as members of the editorial team (http://univagora.ro/jour/index. php/ijccc/about/editorialteam) of the journal, which became, from the very beginning, an international one and it was accepted in the jcr list of journals. over the years, new members have been invited and joined the editorial team. the high quality contributions of authors and the hard work of the members of team have led to alternatively positioning ijcccc and sic, since 2006, on the first two places according to their jif (journal impact factor ) in the list of i&ct journals published in romania. as in any other dynamic undertaking, it has been necessary to adapt the journal to the perceived evolving scientific trends and landscape. so, besides integrated systems, other two main domains of interest have become core topics of our publication, namely soft computing (with main emphasis on fuzzy approach), and computer supported decision-making. a particular http://sic.ici.ro http://univagora.ro/jour/index.php/ijccc/about/editorialteam http://univagora.ro/jour/index.php/ijccc/about/editorialteam influence on ijccc adaptation has been exerted by the participants to icccc, the associated conference, the first edition of which was also organized in 2006. ioan dzitac, mişu jan manolescu, and florin gheorghe filip, the conference founders, have played, since then, the roles of conference general chair, organizing committee chair and ipc (international programme committee) chair, respectively. recently, simona dzitac and domnica dzitac have joined the conference management team. the ninth edition of icccc will be held in 2022 and it will be also dedicated to ioan dzitac (http://univagora.ro/en/icccc2022/). simona dzitac will be the general chair, janisz kacprzyk and horea oros will be the publication co-chairs. in april 2021, simona dzitac and myself decided to invite several close colleagues and authors who have been active participants to the previous editions of icccc to contribute to a special issue of the seventeen volume of ijccc meant to commemorate ioan dzitac. most of them accepted the invitation and a series of papers are included in the journal as follows: • zeshui xu and yiying wang, both from sichuan university (china) and f. g. filip of the romanian academy propose a methodology for facility selection and reliable transportation for emergency rescue by using a bicriteria model meant to minimize total service distance and maximize the network reliability. • zenonas turskis, romualdas bausys, giruta kazakeviciute-januskeviciene, and edmundas kazimieras zavadskas, all from vilnius gediminas technical university (lithuania), together with florentin smarandache of university of new mexico (usa) propose a novel mcdm (multicriteria decision-making ) methodology based on m-generalized and qneutrosophis approach and describe an application in the civil engineering milieu. • dan tufiş of the research institute for artificial intelligence of the romanian academy (icia/racai), reviews in his paper several of the most important results obtained, over the last decades, in the institute in the domain of language technology with particular emphases on the romanian language in the context of ec (european commission) initiatives and projects. • yong deng of the university of electronic science and technology of china, proposes, in his article, a new kind of set, named random permutation set, and a data fusion algorithm meant to be applied in threat assessment. • bogdan musat from transilvania university of brasov (romania) and razvan andonie from central washington university (usa) propose a semiotic approach relating to information bottleneck in deep learning. • gang kou, of business school of chengdu university, together with yu zhou, mengdie zhang and yiming li, all from inner mongolia university (china) offer the results of processing a very large data set made with a view to study the users’ preferences in a public bicycle system. • radu emil precup and his colleagues r.-c. roman, e.-l. hedrea, c.-a. bojan-dragos, m.-m. damian, and m.-l. nedelcea of politehnica university of timisoara (romania) address in their paper the problem of improving the performance of three siso (single input, single output ) fuzzy control system in the setting of a tower crane and propose an effective low-cost solution. http://univagora.ro/en/icccc2022/ • ilana segall and alfred m. bruckstein of tehnion institute of technology, haifa (israel) model, in their paper, the behaviour of a group of identical mobile agents that are controlled through an exogenous broadcast signal. • attila simo of politehnica university of timisoara (romania), simona dzitac of university of oradea (romania), and domnica dzitac of new york university, abu dhabi (uae) describe a solution designed to improve decision-making in disaster situations through an integrated workflow of the people involved in rescue operations. • horia-nicolai teodorescu from technical university of iasi (romania) analyses in his paper several limits and possible errors in the design of fls (fuzzy logic systems ), and discusses several simple rules to be used in order to avoid the problems. • prof. yong shi of the research centres of the chinese academy of sciences (cas) and academy university (ucas), beijing (p.r. china) and university of nebraska at omaha (usa) and his colleagues xue x., xue j., and qu y. of the same chinese university and research centres propose a two-stage fault detection methodology based on deep learning based image classification to be used in nuclear power plants. prof. ioan dzitac will forever live in the hearts of those who knew him. his legacy lives on! f.g. filip january 2022 references [1] dzitac, ioan. zadeh’s centenary. international journal of computers communications & control, [s.l.], v. 16, n. 1, jan. 2021. issn 1841-9844. available at: http://univagora. ro/jour/index.php/ijccc/article/view/4102. date accessed: 16 jan. 2022; [2] dzitac, simona et al. obituary. international journal of computers communications & control, [s.l.], v. 16, n. 2, mar. 2021. issn 1841-9844. available at: http://univagora. ro/jour/index.php/ijccc/article/view/4216. date accessed: 16 jan. 2022. http://univagora.ro/jour/index.php/ijccc/article/view/4102 http://univagora.ro/jour/index.php/ijccc/article/view/4102 http://univagora.ro/jour/index.php/ijccc/article/view/4216 http://univagora.ro/jour/index.php/ijccc/article/view/4216 photo ioan dziţac (14.02.1953 – 06.02.2021) int j comput commun, issn 1841-9836 9(1):113-124, february, 2014. a novel entity type filtering model for related entity finding j. zhang, y. qu, s. tian junsan zhang 1.college of computer & communication engineering, china university of petroleum, qingdao 266580, p.r.china 2.school of computer and information technology, beijing jiaotong university, beijing 100044, p.r.china zhangjunsan@upc.edu.cn youli qu*, shengfeng tian school of computer and information technology, beijing jiaotong university, beijing 100044,p.r. china *corresponding author: ylqu@bjtu.edu.cn sftian@bjtu.edu.cn abstract: entity is an important information carrier in web pages. searchers often want a ranked list of relevant entities directly rather a list of documents. so the research of related entity finding (ref) is a meaningful work. in this paper we investigate the most important task of ref: entity ranking. to address the issue of wrong entity type in entity ranking: some retrieved entities don’t belong to the target entity type. we propose a novel entity type filtering model in which the target types are composed of the originally assigned type and the new type which is automatically acquired from the topic’s narrative to filter wrong-type entities. for the query, we propose a method to process the original narrative to acquire a new query which is composed of noun and verb phrases. the results of experiments show our novel type filtering model gets a better result than the traditional filtering model at whatever precision and recall. also the experiment shows the method that we acquire a new query is feasible. keywords: related entity finding, entity, entity ranking, type filtering. 1 introduction along with the rapid development of internet, the number of web pages becomes more and more, so mass information is being produced now. search engine became an important tool to query information from web in people’s life. if a user is looking for entities, which have a specific relationship to some entity, he has to scan the documents retrieved by a search engine system to look for entities. for instance, when searchers submit a query michael’s teammates while he was racing in formula 1 [1], searchers want some related entities actually. related entity finding task can meet searchers requirements. according to the definition of trec2009 entity track, related entity finding(ref): given a source entity, a relation and a target type, identify homepages of target entities that enjoy the specified relation with the source entity and that satisfy the target type constrain [1]. ref provides a new way of information searching through entities. entity ranking is an important issue of ref .two elements can affect the result of entity ranking, target entity type and entity relation between source entity and target entity. in this paper we focus on the effect of target entity type to entity ranking. because wrong type entities pollute the result of entity ranking, we try to filter the entities of wrong type. however the common type copyright © 2006-2014 by ccc publications 114 j. zhang, y. qu, s. tian type filtering method is too coarse to filter wrong entities exactly. therefore, we propose a novel type filtering model to filter wrong entities. we utilize the wikipedia category information as the source of entities types. also we observe carefully the effect of experiment and we see using the novel type filtering model can get a better result at both recall and precision . to the issue of extracting query , we propose an approach in which parsing the narrative’s syntactic structure and rewriting the query. the paper is organized as follows: section 2 provides an overview of related work, section 3 gives a description of the basic architecture of ref, section 4 gives a detailed description of the proposed method, in section 5 we utilize data set to implement our proposed method and analyze the experimental results, in section 6 we draw the conclusion and propose our works in the future. 2 related work the entity retrieval originate natural language processing, specifically ie (information extraction). finding all entities for a certain class is the target of ie, i.e., extracting entities based patterns or learned from examples or created manually [2]. qa (question answering) is the intersection of natural language processing and ir which combines ie and ir. it looks like the ref, yet it differs from ref: (i) an entity is not always been contained in qa query list [3] (ii) ref task add a special relation between target entities and source entity [1]. as an important issue of ref task, entity ranking begin with ranking a specific type entities, e.g. persons in expert search [4]. the task of expert finding is finding experts either by modeling an expert’s knowledge by its associated documents or collecting topic related documents first and then modeling experts [5]. now it develops to rank more general types, e.g. persons, products, locations, organizations etc. the goal of entity ranking is retrieving entities as answers to a query. it is primarily focused on returning a ranked list of relevant entities [6]. what our concern are precision and recall. type filtering can demote the wrong type entities and improve recall and precision. the novelty of our approach is we use co-occurrence model which widely used to estimate the strength of association between terms to estimate the associations between source entity and target entities and using a novel type filtering model filters wrong entities. we apply wikipedia category information to as the source of entities types. also we carefully analyze the effect of using the novel filtering model and the traditional filtering model for entity ranking . trec has run an entity ranking track in 2009 aiming at performing entity-oriented search task on the web [1]. the definition of entity track is: given a source entity, a relation and a target type, find the relevant entities. it makes use of 20 topics, finds three types entities (persons, products, organizations). a query topic is defined as follows [1]: < query > < num > 1 < /num > < entity name > blackberry < /entity name > < entity url >clueweb09-en0004-50-39593 < /entity url > < target entity > organization < /targetentity > < narrative >carriers that blackberry makes phones for. < /narrative > < /query > a general approach of ref task is: (i) collecting text snippets from relevant documents (ii) obtaining entities by performing named entity recognition (iii) ranking relevant entities (iv) finding homepage [1]. researchers propose several approaches to perform the ref task. some researchers use different language modeling approaches where the entity model is constructed from text snippets and relation is utilized ad a query [7], [8]. y.wu et al. [9] develop an effective a novel entity type filtering model for related entity finding 115 approach to rank entities via measuring the "similarities" between supporting snippets of entities and input query. y.fang et al. [10] propose a hierarchical relevance retrieval model for entity ranking. three levels of relevance are examined which are document, passage and entity, respectively. r. kaptein et al. [11] propose an approach using wikipedia as a pivot for finding entities on the web, reducing the hard web entity ranking problem to easier problem of wikipedia entity ranking. 3 the basic architecture of ref the basic architecture of related entity finding is shown in figure 1. the ref task can be divided into three main parts: (i) relevant documents retrieving (ii) candidate entities extracting and entity ranking (iii) homepage finding. we will describe the three parts in the following paragraph. retrieving candidate entities entity extraction (using the anchor text of wikipedia pages) entity ranking ( type filtering & relation filtering) ranking entities & documents relevant entities & homepages homepage finding relevant documents query text retrieval system figure 1: the basic architecture of ref (1) relevant documents retrieving. retrieving relevant documents is the basic component of ref task. the first step is using corpus (here, we use the clueweb09 category b as the documents repository) to build a full text retrieval system. because our computing resource is limited, we make use of ”the lemur project” [12] which provides an online service of clueweb09 category b as our source. secondly, we send a query to the retrieval architecture and preliminary generate some candidate answers. for the selection of query, we chose the noun phrases and verb phrases as the query. we are interested in noun groups and verb groups because the 116 j. zhang, y. qu, s. tian noun groups often qualify the target entity and source entity in more detail and can be seen as a kind of a selection criterion. through extracting the part-of-speech elements from the narrative, we get the noun phrases and verb phrases of each topic. for example, the topic19’s (entity track09) narrative is "companies that john hennessy serves on the board of". after the parsing the syntactic structure, the query is obtained: "companies john hennessy serves serves". for detecting the feasibility of acquiring query through this approach, we respectively utilize the "pure narrative" and the "noun and verb phrases" as the query to retrieve documents in experimental part. (2) entity ranking. entity ranking is the focus in this paper. after generating the relevant documents, the traditional following step is named entity recognition (ner), yet ner is not our emphasis in this paper. we handle ner by considering only anchor texts as entity occurrences in wikipedia pages [13], [14] . when we get some candidate entities, we hope to find the most relevant entities.so we need to rank the candidate entities. there are two factors will effect the entity ranking: entity type and entity relation. the wrong type entities and the entities which do not conform to the relation between source entity and target entity will pollute the ranking result. in this paper,we only focus on the issue of "filtering wrong type entities". (3) homepage finding. an entity is uniquely identified by its homepage, according to the definition of ref. three homepages and a wikipedia page at most can be returned for each entity in 2009 entity track. homepage finding can be seen a document retrieval problem which employs a standard language modeling [15] to ranks homepages according to the query likelihood: p(q = e/d), using the entity’s name as a query. this issue is also not the emphasis in this paper. 4 entity ranking model according to the definition of ref, given a q(es,t,r), return a ranked list of relevant entities. in the paper, we use es to indicate the source entity, et indicate the target entity, t indicate the target type, r indicate a relation between es and et. using the conditional probability formula p(et|q) estimates ref task. due to the condition of p(et|q) is complex and difficult to estimating. next we rewrite p(et|q) to: p(et|q) = p(et,q) p(q) (1) considering the denominator p(q) does not influence the ranking of entities, we derive the ranking formula as follows: p(et,q) = p(q|et) ·p(et) (2) = p(es,t,r|et) ·p(et) ∝ p(es,r|et) ·p(t|et) ·p(et) (3) = p(es,r,et) ·p(t|et) = p(r|es,et) ·p(es,et) ·p(t|et) (4) = p(r|es,et) ·p(et|es) ·p(es) ·p(t|et) (5) = p(r|es,et) ·p(et|es) ·p(t|et) (6) we assume that type t is independent of source entity and relation r in (3). assuming p(es) is a uniform value in (5), we drop it. now the ranking task is converted to three conditional probability question: p(r|es,et), p(et|es), p(t|et). in this paper, our goal is to address the issue of wrong type polluting entity ranking. so we only discuss p(et|es) ·p(t|et) in this paper. (1) co-occurrence model a novel entity type filtering model for related entity finding 117 we see p(et|es) as a co-occurrence issue expresses the association between source entity es and target entity et. we use a formula to estimate p(et|es) as flows: p(et|es) = co(et,es)∑ et ′ co(et ′ ,es) (7) et ′ indicates an entity co-occurrence with source entity es in documents. we use two approach to estimate c(et,es): (1) maximum likelihood estimate, (2) χ2 hypothesis test [13], [16]. maximum likelihood estimate(mle): comle(e t,es) = c(et,es)|c(es) (8) where c(et,es) indicates the number of documents in which et and es co-occurrence, c(es) indicates the number of documents in which c(es) occurrence. χ2 hypothesis test: coχ2(e t),es)) = n · (c(et,es) ·c(et̄,es̄)−c(et,es̄) ·c(et̄,es))2 c(es) ·c(et) · (n −c(et)) · (n −c(es)) (9) whereet̄ , es̄ indicate the et and es don’t appears respectively, and n indicates the total number of documents. for example, c(et̄,es̄) expresses the number of documents in which both et and es don’t appear. (2) entity type filtering model the co-occurrence model preliminary ranks entities. but it can not resolve the problem of wrong type entities pollute the ranking result. to address the issue of wrong type entities will pollute the ranking result. traditional type filtering model deal with p(t|et): the relation between target entity type and candidate entity type as flows: p(t |et) = { 1 if c(et)∩c(t) ̸= ϕ 0 otherwise (10) here, the c(t) indicates the expected target entity type and the c(et) indicates the type of candidate entity. the former is previously defined, although the latter is acquired via the wikipedia category information of candidate entity. if they have an intersection we think the probability is 1, otherwise the probability is 0. although utilizing traditional entity filtering model can filter some wrong type entities, it is not enough accurate sometimes. according to the definition of ref, the types of target entities are divided into several types which are too wide to a certain extent. such as, for entity track 2009, there are only three types of target entities which are assigned to 20 topics: person, organization and product. yet, we see the exact target type of each topic should be different through the observation of topics’ narratives. for example, there are two topics which have same target type (person). but they have completely different narratives: "authors awarded an anthony award at bouchercon in 2007" , "chefs with a show on the food network". from the narratives, the exact type which the former want is authors but the latter want is chefs. certainly, authors and chefs are both persons, yet they are also two different kinds of persons. so if we can refine the target type according to the topic’s narrative, it may filter wrong type entity more accurately. we propose a novel entity filtering model to estimate p(et|es) as flows: score(t) = score(tt) + score(tn) = fc,t ft + fc,n fn (11) 118 j. zhang, y. qu, s. tian where: score(t) score of type that a candidate entity get on the whole; score(tn)score of type that a candidate entity get through the type acquired from topic’s narrative; score(tt)score of type that a candidate entity get through the topic’s assigned target type; fc,tnumber of category features that a candidate entity type and the topic’s assigned target entity type have in common; ftnumber of category features that the topic’s assigned target entity type has; fc,nnumber of category features that the candidate entity type and the type acquired from topic’s narrative have in common; fnnumber of category features that acquired from topic’s narrative. in order to calculate the score(tt), we first get cat(t) category information of target entity type that a topic is assigned and its sub-categories (one level down) and cat(c) category information of a candidate entity. then we can acquire fc,t and ft through the category information of themselves. in this paper, we make use of wikipedia category information as the criterion of judgment. however, the wikipedia category structure is not a strict hierarchy and the category assignments are imperfect [17]. so we must process the category information further. the number of features that cat(t) has is too much to use directly. we find many categories of cat(t) often have a common structure (starting with "something" and ending with "something"). we select the top five categories or structures that appear in cat(t) as the selected features. for example, the selected features of a assigned target entity type "person" are: • ’living people’ • ending with ’births’ • ending with ’deaths’ • starting with ’people’ • ending with ’people’ for score(tn), we first get cat(n) the category information of target entity type that we get from the topic’s narrative and its sub-categories and cat(c) the category information of a candidate entity. then we can acquire fc,n and fn through the category information of themselves. to get cat(n), the category names in the narrative must extract first. we process the topics’ narratives using brill’s part-of-speech [18] tagger and a noun-phrase chunker [19]. the first noun phrase in the narrative is the category that we want. for example, the narrative "chefs with a show on the food network" is processed as follows: (root (np (np (nns chefs)) (pp (in with) (np (dt a) (nn show))) (pp (in on) (np (dt the) (nnp food) (nnp network))))) the noun chefs is the type that is extracted from the narrative. then we utilize the wikipedia category information to get cat(n) . next, the category features of cat(n) is selected and the process is same as that is described in the previous paragraph. for example the selected features of "chefs" are: • ’chefs’ • ending with ’chefs’ • ending with ’characters’ • ending with ’births’ • ’living people’ a novel entity type filtering model for related entity finding 119 5 experiment 5.1 dataset and evaluation measures in this paper, we use clueweb09 category b subset as our corpus, including about 50 million documents. trec 2009 entity track has three kind of basic types which are person, product and organization [1]. we restrict the scope of entities only in wikipedia pages. so we drop 5 topics in which no wikipedia pages are retrieved and 15 topics are left.the wikipedia is an excellent structured resource of entities. the title of the page is the name of the entity, the content of the page is the representation of the entity and each wikipedia page is assigned to a number of categories. in our experiment, we get about 100 thousand relevant entities totally. after culling duplicated entities we get about 70 thousand relevant entities. we also make use of the dbpedia category information to get each entity’s category information. dbpedia is a project aiming to extract structured content from the information created as part of the wikipedia project. this structured information is then made available on the world wide web. dbpedia allows users to query relationships and properties associated with wikipedia resources, including links to other related datasets. we use precision and recall as our estimation measures. using p@10 to express precision and r@n express recall where n taken to be 100, 2000. in order to evaluate the effects that the different model produces for entity ranking and the feasibility that we acquire query from the original topic narrative. we divide the experiment into five steps: (1) using the original topic narrative as the query retrieves relevant documents. (2) using the noun and verb phrases which is chosen from the original topic narrative as the query retrieves relevant documents. (3) we only make use of pure co-occurrence model to rank candidate entities. (4) we make use of traditional entity type filtering method to rank candidate entities based the result of using pure co-occurrence model. (5) utilizing our novel entity type filtering model to rank candidate entities based the result of using pure co-occurrence model. 5.2 experimental result the precision and recall are our estimation measures. using p@10 to express precision of top 10 entities retrieved and r@n express recall where n taken to be 100, 2000. we utilize chart and table to describe the experimental data, showing the effects of different methods rank entities. (1) for detecting the feasibility that using noun and verb phrases which are chosen from the narrative as the query, we make use of the narrative and the chosen phrases as the query to retrieve candidate entities respectively. the number of right entities which are retrieved from the documents are shown in figure 2. (2) we make use of pure co-occurrence (mle), traditional type filtering model and our proposed novel type filtering model (based the mle) to estimate the effect of entity ranking respectively. the result (p@10) is shown in figure 3. (3) we make use of pure co-occurrence (χ2), traditional type filtering model and our proposed novel type filtering model (based the χ2) to estimate the effect of entity ranking respectively. the result (p@10) is shown in figure 4. (4) we take topic14 as an example and list the top ten entities in table 1. in order to observe the variation of the top ten ranked entities using different methods, more intuitively. 120 j. zhang, y. qu, s. tian 00 1 22 3 44 5 66 7 88 9 1010 11 1212 13 1414 15 1616 17 1818 19 2020 0 10 20 30 40 50 n um be r o f c or re ct e nt iti es using narrative using noun phrase and verb phrase topic figure 2: number of correct entities using different query 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 p( 10 ) m le 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 novel type filtering model traditional type filtering model pure co-occurrence model topic figure 3: p@10: using mle co-occurrence model, traditional type filtering model and novel type filtering model filters entities respectively. a novel entity type filtering model for related entity finding 121 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 p( 10 ) x 2 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 novel type filtering model traditional type filtering model pure co-occurrence model topic figure 4: p@10: using χ2 co-occurrence model, traditional type filtering model and novel type filtering model filters entities respectively. (5) in order to evaluate the experiment effect in the whole. we list the results (the average of precision and recall of all topics) of using different models which are shown in table 2. 5.3 analysis figure 2 shows the number of correct entities which are retrieved from the documents using original narrative and phrases as queries respectively. we see using noun and verb phrases as queries retrieves more correct entities(increased by 2.07%) than using original narratives as queries. it certifies the approach that we extract noun and verb phrases as query is feasible. figure 3 shows the type filtering (based on mle) whatever using traditional type filtering model or using our novel type filtering model causes a better result than using pure mle cooccurrence model in p@10. the figure 4 show the type filtering (based χ2 ) whatever using traditional type filtering model or using our novel type filtering model causes a better result than using pure co-occurrence model in p@10. through the figures we find that utilizing type filtering can improve the effects of p@10. and our proposed novel type filtering model can filter wrong type entities better than traditional type filtering model. table 1 demonstrates intuitively the variation of topic14’s top ten ranked entities. our novel type filtering model effectively removes the wrong type entities from the ranking. as to recall, table 2 shows the r@100 and r@2000 of using pure co-occurrence model, traditional type filtering model and using our novel type filtering model respectively. we see using the novel type filtering model (based on mle) increases by 27.35% than using traditional type filtering model (based on mle) and increases by 54.43% than using pure mle co-occurrence model in r@100. also we see using the novel type filtering model (based on χ2) increases by 50% than using traditional type filtering model (based on χ2 ) in r@100. the result illustrates than our novel type filtering model can filter wrong type entities than traditional type filtering model in r@100. 122 j. zhang, y. qu, s. tian pure co-occurrence model traditional type filtering model novel type filtering model mle χ2 mle χ2 mle χ2 bouchercon music sue grafton sherlock holmes sue grafton lawrence block book publisher agatha christie sue grafton ross macdonald sue grafton navigation husband edgar allan poe edgar allan poe edgar allan poe bill pronzini history television sherlock holmes danny boyle george washington lee child us crime robert crais hercule poirot agatha christie marcia muller son books george washington george washington robert crais ross macdonald organization performance hercule poirot agatha christie dennis lehane george washington ant homicide stieg larsson laurence olivier michael connelly max allan collins cher detective marcia muller lawrence block lee child laura lippman pub big ross macdonald bill pronzini val mcdermid val mcdermid table 1: the top ten ranked entities of topic14. the correct entities are indicated in bold. pure co-occurrence model p@10 r@100 r@2000 mle 0.1533 0.1646 0.4287 χ2 0.0267 0.0438 0.3968 traditional type filtering model p@10 r@100 r@2000 mle 0.3533 0.1996 0.2674 χ2 0.2600 0.1492 0.2674 novel type filtering model p@10 r@100 r@2000 mle 0.4667 0.2542 0.3632 χ2 0.3933 0.2238 0.3627 table 2: recall and precision of using different type filtering model filters entities respectively. however, the data illustrates some different things in r@2000. we see using novel type filtering model (based on mle) reduces by 15.28% than using pure mle co-occurrence model and using traditional type filtering model (based mle) reduces by 38.25% than using pure mle co-occurrence model. and using novel type filtering model (based on χ2) reduces by 8.47% than using pure χ2 co-occurrence model and using traditional type filtering model (based on χ2) reduces by 32.61% than using pure χ2 co-occurrence model. the result illustrates that the type filtering model is not accurate enough. in other words, the model may remove some correct entities incorrectly. but it is encouraging to see the novel type filtering model gets a better result than traditional type filtering model (it increases by 26.38% in mle and 26.27% in χ2 ). 6 conclusions and future works for the issue of related entity finding, entity ranking is still an important issue. while some entities do not confirm to the required entity type and will affect the ranking result, filtering wrong type entities is essential. first, we parse the original narrative and acquire the noun and verb phrases as the new query. then we make use of a novel type filtering model and the traditional type filtering model to filter entities respectively. in the experiment section, we choose 15 topics which target entity types are person, product and organization as our test topics. we compare the experiment results and find: (i) the approach that we acquire a new query is a novel entity type filtering model for related entity finding 123 feasible (ii)using our novel type filtering model gets a better result than using the traditional type filtering model whatever in precision or recall. we also see the problem of our type filtering model: compare to the pure co-occurrence model(in r@2000),it reduces the recall.it indicates some correct entities are removed incorrectly and the model need to be improved further. in this paper we only argue the problem of wrong entity type filtering, while the wrong relation also affects the entity ranking result. as future work, we plan to investigate two issues: (i) how can we optimize our type filtering model to improve recall and precision further (ii) how can we use relation filtering to further optimize the result of entity ranking. acknowledgement this work is supported by the fundamental research funds for the central universities of china (program no.2011jbm231). bibliography [1] k. balog, a. p. de vries, p. serdyukov, p. thomas, and t. westerveld, overview of the trec 2009 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[19] l. ramshaw, and m. marcus, text chunking using transformation-based learning, proc. of the third acl workshop on very large corpora, mit, 1995. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 4, month: august, year: 2023 article number: 5108, https://doi.org/10.15837/ijccc.2023.4.5108 ccc publications hybrid icho-hsdc model for accurate covid-19 detection and classification from ct scan and x-ray images badi alekhya, r. sasikumar, n. sathish kumar, n. bharathiraja badi alekhya department of artificial intelligence and machine learning, rmd engineering college, kavaraipettai, tamil nadu, 601206, india. *corresponding author: badialekhya565@gmail.com r. sasikumar department of computer science, and engineering, rmd engineering college, kavaraipettai, tamil nadu, 601206, india. rsn.cse@rmd.ac.in n. sathish kumar department of artificial intelligence and machine learning, rmd engineering college, kavaraipettai, tamil nadu, 601206, india. rsn.cse@rmd.ac.in n. bharathiraja chitkara university institute of engineering and technology, chitkara university punjab, india. rajamesoft@gmail.com abstract the worldwide demand for medical care has increased due to the increasing expansion of covid-19 cases. therefore, in this case, prompt and precise identification of this illness is crucial. health professionals are using additional screening techniques including ct imaging as well as chest xrays for this. pre-processing the ct scan pictures to eliminate the areas of areas, normalize image contrast, and minimize image noise, however, receives little attention. the seriousness of the covid19 infection must be assessed in addition to the covid-19 detection and categorization. an ichohybrid model for covid-19 identification and classification from x-ray, as well as ct scan images, is offered as a solution to these issues. histogram and morphological image processing methods are used for ct-scan images. the improved chicken swarm optimization (icho) technique is used to find the input image’s histogram threshold. the extracted areas are categorized using the convolutional neural network method based on a feature vector. when infections are found, the cnn algorithm is used to categorize them as severe, moderate, or extremely severe using support vector machine. to eliminate the noise from the test pictures for x-ray imaging, the adapted anisotropic diffusion filtering (a2df) approach is used. once the preprocessing is completed, features are extracted using an image profile (ip) and histogram-oriented gradient (hog) to create a fused hog and ip feature. using the hybrid method, the fhi characteristics are divided into 3 classes. when compared to svm and cnn, the study provides the best accuracy, with scores of 94.6 for ct scan pictures and 95.6 for x-ray images. keywords: corona virus, x-ray images, ct images, image profile, improved chicken swarm optimization, adapted anisotropic diffusion filtering and deep learning https://doi.org/10.15837/ijccc.2023.4.5108 2 1 introduction groups of fatal bacterial infections caused by the covid-19 infection closely mirror sars-cov illnesses. people with covid-19 infection first have flu-like symptoms such as fever, exhaustion, a persistent cough, and breathing difficulties. in certain instances, however, a severe infection may lead to acute renal failure, which can be fatal. in addition to these typical signs and symptoms, this virus possesses a variety of unusual traits. therefore, patients need to have an early diagnosis of this illness along with effective infection control [1]. healthcare practitioners are using additional screening techniques, which are quicker and more accurate than standard testing, to identify covid-19. these screening techniques [2] lead the process of finding the covid-19 infection visibly. however, frequent ct scans are more expensive and riskier for youngsters and expectant mothers due to the high irradiation [3]. deep learning (dl) models have lately been effectively used in a variety of medical image analyses and applications, including the identification of lung cancer, pneumonia, and brain tumors [4]. the dangerous illness is less likely to spread due to the dl-based detection and classification approaches the use of the dl algorithms in numerous medical applications offers very accurate prediction and diagnosis while also assisting in the prevention of covid-19 spread [5][6]. early detection and accurate discovery of viruses will stop them from spreading and making patients worse, which may help lower the mortality rate. grayscale ct scans and chest x-ray pictures are typical, thus pre-processing is necessary to confirm the size and structure of the images before using them as training data for the model. for reliable identification and follow-up, segmenting the pneumonia lesions and localizing the infection are difficult tasks. however, current research has a difficult time differentiating pulmonary lesions in terms of size and form from infectious regions. a. motivation and issue description to rank patients according to severity, it would be helpful to segment the ct scan medical images. it will aid in locating the contaminated area and offer details about its spatial characteristics, such as size and shape. however, only a few studies have used segmentation tasks for chest coivd-19 ct images. the research’s main issue is that there isn’t enough focus on pre-processing datasets to get rid of diaphragm areas, normalize picture contrast, and minimize image noise. in addition to covid-19 identification and classification, dl-based segmentation techniques must be used to locate the infection zone to assess the extent of the illness. b. main contributions of the work the icho-hybrid model for recognition and classification in this study. the following is a summary of this work’s significant elements: • in contrast to conventional methods, the suggested model detects covid-19 infections using both x-rays as well as ct scan images. • reducing the diaphragm areas lowers the picture noise. • in addition to detection, it uses dl-based segmentation approaches to determine the rate of covid-19 infection. • it improves classification accuracy by combining cnn and svm. the proposed model, icho-hybrid, addresses the need for prompt and precise identification. the use of various image processing techniques and feature extraction methods for covid-19 cases classification and detection accuracy. they also provide results that show superior accuracy when compared to existing methods like cnn-svm. therefore, the use of the icho-hybrid model offers an effective solution to the problem of detection and classification, which is crucial in the present pandemic situation. the approach combines various image processing techniques such as improved chicken swarm optimization (icho), histogram and morphological image processing, adapted anisotropic diffusion filtering (a2df), and feature extraction methods like image profile (ip) and histogram-oriented gradient (hog) to create a fused hog and ip feature. the paper also uses a cnn method based on a feature vector and an svm algorithm for classification. the results show higher accuracy compared to svm and cnn. therefore, the novelty of the proposed approach lies in the combination of various techniques and methods for categorization and identification, leading to improved accuracy in identifying the disease. 2 related work the pictures of chest x-rays using the daniel arias et al [7] .’s technique, which classifies them as either negative or positive for covid-19 using the vgg19 and vgg16 models. in the classification process, pretrained parameters from the imagenet dataset are used to train the models of vgg19 and vgg16 using the tl method. the severity of the disorders is ignored as it labels the photos as normal and covid afflicted. a novel model is suggested for identification in tulin ozturk’s method [8]. this model includes a multi-class classification in addition to a covid and normal classification algorithm. for binary classes, this model’s https://doi.org/10.15837/ijccc.2023.4.5108 3 classification accuracy was 98.08%, while for multi-class scenarios, it was 87.02%. a dl method has been created by motif and bandar [9] to classify covid-19. based on the categorization trials, three sorts of classes are returned: covid-19, pneumonia, and normal. additionally, a tl approach is used to enhance the model’s performance. covidnet is a suggested dl architecture by muhammad aminuet al [10] that utilizes grayscale photos with a small training dataset. deep feature extraction was then performed using covidnet. the classification x-ray and ct, in contrast to other methods. rachna jain et al [11] collect a collection of x-ray as well as ct scan pictures that include information on both healthy and afflicted individuals. on the photos at first, they ran data cleansing and data augmentation processes. then, a cnn model based on dl was used for classification. models used to evaluate the results. moutazalazab et al [12] used the cnn model to identify covid-19. predictions techniques based on the prophet method, lstm networks, and the arima model are employed in contrast to cnn. dongsheng ji et al [13] have suggested using feature fusion to detect covid-19. this approach employs five common pretraining models after preprocessing to retrieve the precise characteristics. the severity of the disorders is ignored as it labels the photos as normal and covid afflicted. five pre-trained cnn-based transfer models were used by ali narin et al [14] to identify infection-related covid and pneumonia. they employed 5-fold cross-validation and 3 binary classifications with 4 classes. however, the noise in the photos is not decreased with this strategy either. a fresh cad method for covid-19 identification by mortezaheidari et al [15]. bilateral low-pass filtering and the histogram equalization method are used to eliminate the majority of the diaphragm from the input image. the covid-19, typical cases, and pneumonia categories are applied to the chest x-ray pictures. to identify covid-19 infection, ct scan pictures were collected by yazan qiblaweya et al [16]. the afflicted area is identified from the pictures by segmenting the ct slices using the u-net architecture. the noise in the photos was not lessened by this method, however. a hierarchical technique for ct lung pictures classification and identifying covid-19 infections from computed tomography is presented in [16]. depending on the fraction of the lungs afflicted, a ded-cnn is used to identify the pictures as having a severe, moderate, or mild infection. unfortunately, this method did not help to lessen the visual noise. an automatic workflow has been created by dominik müller et al [17] for segmenting the covid-19-infected areas. this method uses data augmentation techniques and provides randomized, distinctive picture slices for training. they have a 3d u-net dl model that they have employed to address the overfitting concerns. a deep learning hybrid forecasting cnn-lstm [22] developed by shwet ketu et al. can predict the covid19 pandemic in india with accuracy. convolutional layers are used by the proposed approach to learn from and extract relevant data from a specific time series dataset. additionally, the capacity of the lstm layer to recognize both longand short-term connections enhances it. suneeta satpathy et al [23] said that ai-mediated approaches are capable of predicting death rates. the investigation of effective prediction models is the study’s primary objective. the authors compare analyses of several models to find which one makes the best predictions. foroogh sharifzadeh et al. [24] have described an approach that starts with two neural nets, a cnn and a multilayer mlp recurrent neural network, and then suggests combining these two networks to benefit from each network’s strengths. it is obvious that the multilayer perceptron performs worse than cnn in terms of accuracy metrics; however, when it is combined with cnn, the accuracy metrics increase. using deep learning, noushin davari et al. [25] have described a technique for analyzing uv-visible video. the kind of incipient defect and its severity level is identified for every scene based mostly on the system’s logs of unexpected power outages as well as scheduled inspections throughout the year. frames are retrieved from each clip at a pace based on the line’s nominal voltage to process the video. to account for camera movement, power devices are identified in each frame utilizing faster rcnn and monitored across the entire video frame. then, each device uses color thresholding to determine whether frames have corona discharges. to remove disturbances in the uv channel, extensive median filtering is also employed across the movie. the severity level of the impending problem is then calculated based on the proportion of the observed equipment’s surface. the suggested study employs a unique quantum tunnelling particle swarm optimization (qt-pso) technique to enhance the performance of the system, which is especially efficient for pv-based applications [26]. envelope detectors, which are simple circuits that extract the envelope of the input signal, are used in the suggested method. to process the baseband signal using low-power digital signal processing (dsp) circuits, the envelope detector is utilized as a converter to transform the modulated radio frequency (rf) signal [27]. a balance between data accessibility, power consumption, and data availability ratio is what the suggested strategy seeks to accomplish. a four-phase strategy is used to achieve this, consisting of a multicast strategy for increased data availability, a data replication procedure for increased replication rate, a data accessibility strategy for increased accessibility rate, and a power consumption strategy for reduced transmission and reception power [28]. https://doi.org/10.15837/ijccc.2023.4.5108 4 3 proposed work this study develops a hybrid approach for the identification and categorization of covid-19. the icho technique is used to calculate the histogram threshold for ct-scan images. the candidates are then subjected to a variety of statistics and shape-related characteristics extraction. following the fusion of all the extracted features into a feature space, the hybrid method is used to classify the chosen regions. the nodules are categorized as severe, moderate, or extremely severe after they are found. a madf approach is used to remove generated speckle noise from the test images for x-ray imaging. the selected features are utilized as the inputs to build the classifier model after the pre-processing stage. the chest x-ray pictures are then categorized as covid-19, pneumonia, and healthy using the same svm-cnn method. figure 1: proposed model architecture the following table presents the summary of the parameters and variables used in this paper. table 1 parameters and variables c. covid-19 detection pre-processing and segmentation pre-processing is the preliminary phase in image processing which consists of histogram and morphological techniques. since we have to segment the lung region from the image, the first two components should be removed. in this technique, the histogram of the image is constructed and analyzed to automatically choose a threshold, based on which the outer portion of the image is detected and removed. thresholding: after pre-processing, the threshold for each slice of the image is computed using the icho algorithm. the icho technique is used to calculate the histogram threshold for ct-scan images. the threshold image is obtained by applying the threshold to the pre-processed ct image. nodule identification: the next step is to identify nodules in the ct image. the candidates for nodules are then subjected to a variety of statistics and shape-related characteristics extraction. following the fusion of all the https://doi.org/10.15837/ijccc.2023.4.5108 5 extracted features into a feature space, the hybrid method is used to classify the chosen regions. the nodules are categorized as severe, moderate, or extremely severe after they are found. speckle noise removal: in the case of x-ray images, a madf (median absolute difference filter) approach is used to remove out generated speckle noise from the test images. feature selection: the selected features are utilized as the inputs to build the classifier model after the pre-processing stage. the chest x-ray pictures are then categorized as covid-19, pneumonia, and healthy using the same svm-cnn method. covid-19 detection: finally, the proposed hybrid approach is used to detect covid-19 in ct-scan and x-ray images. the pre-processed ct images are used to identify nodules, and the x-ray images are categorized using the svm-cnn method. let i denote the preprocessed ct image of size mxn. from i, the histogram h is computed using the step value k from which the threshold η is estimated. then the resultant image after removing the background is given by [18] where is the threshold image. the threshold η for each slice is computed using the icho algorithm. then the fitness function of icho is derived as f = ∂ 2(2 log )1/2 (2) where ∂2 is the noise variance, icho algorithm the intelligent bionic algorithm icho replicates the foraging activity of a flock of chickens in their native habitats. this algorithm consists of four steps: population, cock position, hen position, and chick position updation. (i) update of cock position when the cock is away from the midpoint p of the hen in a cluster, it will perform a random search in a bigger region, thus improving the probability of a global search. on the other hand, when the cock is nearer to the midpoint p of the hen, it will perform a random search in a smaller region, which improves the local estimation capacity of the algorithm and hence improves the chances of determining the best solution [19]. the cock position update equation is given as kε[ 1,2,..,n ] and i are cock’s indices, dynamically chosen from the cock groups, i=k, fi, and fk represent the fitness function of the ith and kth cocks, respectively. ∂ is a small constant that is used to evade the divide by zero error and y is the proportional coefficient. (ii) hen position update mode among the initial population, gbest individuals with better fitness values are selected and one among them is randomly chosen as the supreme individual. the gbest value is set manually, during experiments, which should be less than the number of cocks in the population. during the foraging step, the supreme individual is considered as the learning objective for the hen. hence, the position of the hen (h) is updated based on the supreme individual using the following equation in which rand is a spontaneous value between 0 and 1, c is a cock, h is a hen, r1 is the cock index, which is the ith group mate of hen, the chicken index is r2, randomly from either the group, pe(t) is the looking at individual hen presiding learning object in the tth iterative process, and hao is the elite individual’s number reserved in the community. s1 and s2 are calculated as https://doi.org/10.15837/ijccc.2023.4.5108 6 where fi, fr1, and fr2 represent the fitness function values at i, r1, and r2, respectively. (iii) chick position update mode the chick’s position can be updated based on the position of the cock, by the equation where m denotes the index of the chick’s mother, r1 is the index of the cock which is the ith chick’s group mate, ch is the individual chick, fl and ware learning factors (iv) population update strategy in most of the optimization solutions, if a set of individuals are found to be near the local extreme, other individuals will continuously move towards this local extreme. hence the same individuals may be repeated in the population. during higher iterations, the number of similar individuals may linearly increase thereby affecting the diversity of the population. if diversity is affected, it reduces the chances of meeting the global best solution. hence to keep the diversity of the population unaffected, the individuals in the population are updated as follows: where pi (t) is ith individual at the tth generation, l and u are the lower and upper bounds of the variable, and a rand is a uniform number in a random range of [0,1]. the above equation helps in estimating the histogram threshold for each slice of the input image. b. feature extraction feature extraction is a key area in categorizing image characteristics. further, by choosing notable aspects, successfully employed to increase the diagnostic system’s accuracy. in this phase, various shape and statisticalbased required features are separated from the nodule elements. let x1, x2, x3, · · ·, xn be the centers of the shapes which are fed as input to the region-growing algorithm. it returns the associated n candidate portions b1, b2, b3, · · ·, bn. the following statistical and shape properties of candidate regions are extracted to construct a feature vector: mean, median, mode, variance, standard deviation, and consistency feature (cf). the cf is estimated from the shape and appearance of the lesion in the adjacent slices of the image. hence the midpoints identified in the slice si will be checked with the midpoints from the (si-k) and (si+k) slices. if it is found in any of those 2k slices, then cfi=1, and if not found cfi=0. the selected features are extracted for each nodule element portion bi in a slice sj and then fused to get a feature vector vi c. classification depending on the feature vector v, the extracted regions are classified using the hybrid algorithm. the hybrid classifier combines the benefits of both cnn and svm algorithms [20]. then labeling is performed by dividing them into test and train data. svm: the fundamental aim of svm is to determine the optimal hyperplane in the feature space which maximally divides the target class into two. geometrically, the svm algorithm determines an optimal hyperplane with the maximal margin to divide the two classes, the training set of svm is represented as here xj is the characteristic vector of the jth model as input and yj is the output catalog = +1 or -1. svm splits the +ve and –ve instances utilizing a hyperplane as here w.x denotes the dot product of w and x. the training algorithm of svm is illustrated below: svm training https://doi.org/10.15837/ijccc.2023.4.5108 7 the architecture of cnn: a when compared to a typical deep network, cnn applies an initial pixel filter in an image to collect detailed patterns. the cnn is composed of the following types of layers as follows: • conv_layer this layer applies n filters. following the complication, a relustimulation function is applied to add the system’s nonlinearity. • max_pool layer: the typical approach of dividing local features into subspaces and retaining only the highvalue max pooling. • fully interconnected layer: the previous layers’ whole neurons are linked to the succeeding levels. the cnn will classify the labels using the attributes of the cls and the pl. the details of the architecture are represented in figure 2. figure 2: cnn model each image attribute is selected from the testing collection of features. the training features matrix’s size is established. the svm structure is then estimated. classes = 0 if (n1), training feature = 1. the training feature is then calculated using the svm framework. cnn has been trained. the testing feature’s batch size is calculated. once found, tumors are classed as serious, medium, or extremely severe using the cnn algorithm. d. covid-19 detection pre-processing the madf approach is used to remove noise from x-ray pictures at this phase. madf divides input images into many sub-images or gradients. these gradients are processed successively before being mixed again. this filtering method primarily removes noise as well as edge areas from photos. this filter eliminates the edge detection from the pictures after several iterations. the following is a representation of an original image o mixed with speckle n: https://doi.org/10.15837/ijccc.2023.4.5108 8 to obtain accurately filtered imaging, the filtering procedure is continued till the value of k reaches 0.001 because if k were to approach 0, all image characteristics would be eliminated. the least possible association should exist between the noise and image classifications. there until the stochastic region of the picture approaches the stochastic confidence interval, speckle elimination is kept up. the kurtosis value is represented by k. figure 3: cnn architecture fhi feature extraction: the characteristics of hog as well as ip are obtained after the pre-processing stage. hog features: the values of each image cell are joined with a gradient l2-norm to produce the direction statistics channel known as hog. the histogram’s channels are made by computing a negative gradient on a rectangle void (i.e. r-hog). the elements in the resulting feature vector add up numerous times since they overlap by a factor of two in their size. the gradient for each pixel in the image is computed as gh = p (x+1, y)-p (x, y) (15) gv=p (x, y+1)-p (x, y) (16) while gh and gv are vertical and horizontal gradients, respectively, and p (x, y) is the total no. of pixels at (x, y). algorithm 1 describes the processes required in hog feature extraction: algorithm for hog feature extraction 1. get the input image 2. perform pre-processing using madf 3. generate the gradient image https://doi.org/10.15837/ijccc.2023.4.5108 9 4. divide the image window into cells and overlapping blocks 5. compute the hog for each block 6. perform normalization of each block 7. concatenate all normalization histograms 8. obtain the hog feature vector image profiles (ip): the presence of artifacts in x-ray pictures may be readily recognized by extracting image profiles. similarly, the fidelity of pictures may be assessed by examining the ips of sharp corners. obtaining fhi features: because fusing multiple image characteristics leads to a bigger number of attributes needed for reliable detection, the fhi attributes are obtained by fusing the hog and ip characteristics. the classification method in fhi features training. hog and ip are both extracted characteristics. the continuity equation [21] depicts the selection of features and the fusing process. the fhi characteristics are denoted by eq. (19). we acquire 7876 fused fhi features, of which 116 are chosen based on the greatest entropy value. the hybrid algorithm then categorizes the x-ray chest pictures into 3 groups: covid-19, pneumonia, and normal. (as discussed in section 3.2.2.3) the study presents a hybrid approach for identifying and categorizing covid-19 in ct scans and x-ray images using the icho technique for threshold calculation for ct-scan images and a modified adaptive directional filter (madf) approach for speckle noise reduction in x-ray images. the following are the details of dataset testing and training: dataset: the study uses two datasets: one for ct scans and x-ray chest images. training dataset: for ct scan images, the authors used the dataset publicly available at https://covidct.grand-challenge.org/data/. it includes 1252 ct images belonging to covid-19 (397), non-covid pneumonia (559), and normal cases (296). the authors used 60% of the total dataset for training the model. for chest x-ray images, collected the dataset from different sources, which included covid-19 (150), pneumonia (150), and healthy (150) cases. the authors used 70% of the total dataset for training the model. testing dataset: the remaining 40% of the ct scan dataset was used for testing the model’s performance. for chest x-ray images, the authors used the dataset publicly available at https://github.com/ieee8023/covidchestxray-dataset. it includes 106 covid-19, 120 non-covid pneumonia, and 100 normal cases. the authors used this dataset for testing the model’s performance. model training: for ct scan images, the authors pre-processed the images using histogram and morphological techniques to segment the lung region. the icho algorithm was then used to calculate the histogram threshold, and the extracted features were combined into a feature space. the svm-cnn hybrid method was used to classify the nodules as severe, moderate, or extremely severe. for chest x-ray images, the madf approach is to reduce the generated speckle noise. the extracted features were then used as inputs to build the classifier model after the pre-processing stage. the svm-cnn hybrid method was used to classify the chest x-ray images as covid-19, pneumonia, and healthy. 4 results and discussion python was used to construct the hypothesized hybrid-based classification algorithm. the ct scan as well as x-ray image covid-19 datasets are taken from https://github.com/ieee8023/covid-chestxray-dataset. cohen jp created this using pictures from several freely accessible sources. for both pneumonia and healthy x-ray pictures, the dataset http://openaccess.thecvf.com/content cvpr 2017/papers/wang chestx-ray supplied by wang et al. was utilized. table 2 displays the cnn classifying parameters. https://doi.org/10.15837/ijccc.2023.4.5108 10 table 2 cnn settings 5 results -ct scan images a. ct images for inputs, 1035 severe instances, 1552 intermediate cases, and 2077 normal cases are extracted from ct scan images. figure 4 depicts a selection of input photos from the normal, moderate, and severe categories. table 3 displays the categorized images for each class, as well as it allows for a better understanding. https://doi.org/10.15837/ijccc.2023.4.5108 11 for the proposed cnn-based classification model, validation and train curves are generated and depicted in fig.5. figure 5: training vs validation for hybrid the proposed hsnn classification outperforms the svm as well as cnn classifiers for accuracy, recall, precision, and f-score metrics. https://doi.org/10.15837/ijccc.2023.4.5108 12 figure 6: analysis of accuracy for samples and features figure 7: analysis of precision for samples and features figure 8:analysis of recall for samples and features https://doi.org/10.15837/ijccc.2023.4.5108 13 figure 9:analysis of f-score (%) for samples and features table 4 and figure 5 exhibit the performance measurement comparative findings for all three methods. table 4 compares the system performance of all techniques. as shown in fig. 6, hybrid has the best accuracy of 94.6, followed by cnn (91.3) as well as svm (87.6). likewise, for additional measures, the proposed hybrid beats the other 2 techniques. b. results for x-ray images 1189 normal images, 2223 pneumonia imaging, and 1516 covid imaging are used as input in the x-ray image data. figure 7 depicts the pictures used in the normal, covid-19, and pulmonary datasets. the categorized x-ray images from data testing are in table 5. figure 8 depicts the preprocessing results obtained using the background subtraction filtering approach. figure 9 depicts the hybrid validation and training curves for 10 iterations. https://doi.org/10.15837/ijccc.2023.4.5108 14 figure 11:images of pneumonia figure 12:preprocessed images table 5 results for x-ray images https://doi.org/10.15837/ijccc.2023.4.5108 15 figure 13 curve for proposed methods with consideration of the parameters precision, accuracy, recall, and f1score, the based hybrid hybrid classification outperforms the svm and cnn classifiers. the comparative findings of the performance indicators for the three methods are shown in table 6 and figure 10. https://doi.org/10.15837/ijccc.2023.4.5108 16 figure 14:analysis of accuracy for samples and features figure 15:analysis of precision for samples and features figure 16:analysis of recall for samples and features https://doi.org/10.15837/ijccc.2023.4.5108 17 figure 17:analysis of recall for samples and features table 6 performance analysis of all proposed and existing methods because hybrid uses both svm and cnn training techniques, it achieves better precision compared to the other two methods. as shown in fig 10, hybrid has the best accuracy of 95.6, followed by cnn (91.2) and svm (89). similarly, for additional measures, the proposed hybrid beats the other two methods. converge time and computation complexity: on gpu configurations, the computational complexity of the novel and current methods was examined. the convergence time as well as the computation cost of the methods are shown in the table below. table 7 convergence time and computational complexity the shortest convergence speed of roughly 0.010 seconds, which is faster than svm and cnn. however, due to the combination of svm and cnn from table 7, its computational cost is somewhat greater, roughly 11.25 seconds, than svm as well as nn. https://doi.org/10.15837/ijccc.2023.4.5108 18 6 conclusion the proposed technique utilizes the icho-hybrid to detect and categorize covid-19. the icho technique is used to determine the histogram threshold for ct-scan images. the hybrid method is used to classify the feature vector’s chosen areas. when nodules are discovered, they are graded as severe, moderate, or extreme using the hybrid method. the madf approach was used to preprocess the test pictures for x-ray imaging. following the pre-processing stage, the fhi features are employed in the classification model training process. the hybrid system then categorizes the chest x-ray pictures as infected, or normal. python was used to construct the proposed hybrid-based classification method. as compared to svm and cnn, experimental findings demonstrate that hybrid achieves accurate results of 94.6 and 95.6 for images of ct and x-ray respectively. future work could include the icho-hybrid model with the larger dataset to validate its performance and accuracy. this would help to assess the model’s generalizability and robustness in identifying and classifying covid-19 cases. the proposed model could be extended to incorporate other modalities, such as ultrasound or mri, in the accuracy enhancement of covid-19 detection and classification. declaration: participation consent and ethical approval: this procedure is carried out without the involvement of people. rights of humans and animals: animal and human rights are not being violated in any way. backing: there is no money associated with this effort. competing interests: there is no potential for a conflict of interest with this project. contributions to the authorship: there is no evidence of authorship. salutation: no credit is due for this creation. 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(2023). hybrid icho-hsdc model for accurate covid-19 detection and classification from ct scan and x-ray images, international journal of computers communications & control, 18(4), 5108, 2023. https://doi.org/10.15837/ijccc.2023.4.5108 introduction related work proposed work results and discussion results -ct scan images conclusion int j comput commun, issn 1841-9836 9(1):93-100, february, 2014. a practical military ontology construction for the intelligent army tactical command information system d. yoo, s. no, m. ra donghee yoo, sungchun no, minyoung ra* department of electronics engineering & information science, korea military academy gongneung-dong, nowon-gu seoul, republic of korea donghee.info@gmail.com, is695@kma.ac.kr, myra@kma.ac.kr *corresponding author: myra@kma.ac.kr abstract: the purpose of this research is to construct a military ontology as the core element for implementing the intelligent army tactical command information system (atcis). using the military ontology, the system can automatically understand and manage the meaning of military information in the system, and hence it can provide a commander with military knowledge for decision making. to construct the military ontology, we define the core concepts of the ontology based on terms extracted from the atcis database and complete the ontology by using the mixed ontology building methodology (mobm). in addition, we implement intelligent atcis as a prototype that provides a military concept navigation service and commanders’ decision support service to demonstrate how to use the military ontology in practice. keywords: military ontology, ontology building methodology, atcis. 1 introduction to realize network-centered warfare (ncw), korea developed the army tactical command information system (atcis) as a ground tactics c4i system [1]. the main service domains of atcis are the (1) ‘information’ domain for reporting battlefield situations such as the state, location, and movement of the enemy, (2) ‘operation’ domain for decision making and operational orders based on the battlefield situation, and (3) ‘firepower’ domain for analysis of the target and the order of priority for a strike. it is possible to effectively manage the battlefield because the atcis domains are organically linked. however, atcis provides only the actual facts of the battlefield, and thus the principal decision making, such as “the possibility of hostile provocation” and “the most effective strike method” depends on the intuition and experience of the commanders and staff officers. commanders and staff officers can make faster and more accurate decisions in urgent battlefield conditions if they have access to specialized military knowledge for battlefield management. for this reason, battlefield information must be expressed in machine-understandable language using a standardized format; ultimately, the military ontology, which defines various concepts and the meaning of their relationships (semantics), should be built based on battlefield information. in the case of defining rules for developing military knowledge by using the concepts in the military ontology, commanders and staff officers can obtain refined knowledge to help in the decision-making process. therefore, this paper has suggested the construction of a military ontology as the first step in implementing intelligent atcis. the mixed ontology building methodology (mobm) [2] was applied for ontology construction, and the kernel ontology was defined based on terms extracted from the atcis database. then the bottom-up approach and the top-down approach were applied to extend the kernel ontology, and finally the military ontology was completed. also, this paper has described intelligent atcis as a prototype that provides military concept navigation service and commanders’ decision support service to demonstrate how to use the military ontology copyright © 2006-2014 by ccc publications 94 d. yoo, s. no, m. ra in practice. here, we suggested a method to minimize the time cost when inferring the military rules for decision making by using the query rewriting model. the rest of the paper is organized as follows. section 2 provides a review of previous methods used for ontology construction and introduces the outline of the mobm. section 3 presents the military ontology constructed by the mobm and analyzes additional aspects of applying the mobm to the atcis database information. section 4 describes the intelligent atcis, which provides a military concept navigation service and a commanders’ decision support service. finally, in section 5, we draw conclusions and suggest directions for future research. 2 related works 2.1 previous ontology building methodology a significant amount of research has been conducted on the issue of ontology building methodology. the research has employed essentially two approaches. the first collects terminology and builds the ontology by analyzing concepts, forming a hierarchy for the concepts, and defining the relationships between the concepts and the rules for acquiring domain knowledge. based on the refinement process assigned to this task, the ontology is then completed. several methods have been reported for accomplishing this task. the bottom-up method starts with the most specific classes and then groups them into more general concepts [3, 4]. the top-down method starts with the definition of the most general concepts and then divides these into detailed subconcepts [5]. the middle-out method starts with certain middle-level concepts and then applies the bottom-up method or the top-down method as appropriate [6]. the hybrid method merges ontologies developed from the bottom-up method and top-down method into one ontology [7]. the second approach to ontology building involves developing an ontology from database schemas. this work takes three directions: (1) first, extract the entity-relationship (er) model from the database schema using reengineering, then from that model extract the ontology [8]; (2) given the database schema and ontology, for semantic web applications, extract the mapping rules between them [9]; and (3) generate the ontology structure itself from the relational database schema [10]. the mobm, a mixed methodology, was proposed based on these works [2]. the mobm first generates a kernel ontology, which becomes the core, using database information as much as possible and then completes the ontology by applying the bottom-up method and the top-down method to build additional parts of the ontology. 2.2 mixed ontology building methodology as mentioned earlier, the mobm combines the characteristics of both approaches to more effectively represent organizational knowledge on ontology. in the mobm, mapping rules are defined to extract the main concepts and relationships of a certain domain ontology from the target database schema. this kind of domain ontology is called kernel ontology. kernel ontology is enhanced by adding upper-level terms and lower-level terms, which are collected from domain knowledge or instances of the target database, because they may contain new concepts or relationships that did not exist in the target database schema. based on the top-down method, the upper-level terms are conceptualized into upper concepts. in the same way, the lower-level terms are conceptualized into lower concepts using the bottom-up method. once the upper and lower concepts are developed, they are linked to the kernel ontology. thus, the mobm employs eight steps to build domain ontologies, as follows: • step 1: extracting kernel ontology from database schema. a practical military ontology construction for the intelligent army tactical command information system 95 • step 2: developing class hierarchies from upper concepts. • step 3: developing class hierarchies from lower concepts. • step 4: connecting these class hierarchies into kernel ontology. • step 5: enhancing the semantics between inter-terms. • step 6: enhancing any restrictions. • step 7: enhancing additional axioms and rules. • step 8: completing the ontology. 3 practical military ontology construction 3.1 building scope of military ontology we adopted the mobm as an ontology building methodology. in this section, we describe the process of constructing the military ontology from atcis according to the mobm and provide an analysis of what should be considered when the mobm is applied to atcis domains. a military ontology for the core service domains of atcis – information, operation, and firepower – has been constructed among various service domains. currently, we have collected 10,835 related terms from the atics database schema and the defense technology information service (dtims) thesaurus and electronic drill book, and 6,515 refined terms have been used for the military ontology construction. 3.2 extraction of kernel ontology the heart of the mobm is the utilization of database schema to construct a practical ontology. first, the kernel ontology was built following the mapping rules [2] of the mobm after extracting the core terms from the database schema. because the scope of the atcis database schema was overly broad, the kernel ontology was created distinguishing the information, operation, and firepower domains. this paper focused only on the information domain, and the middle part of figure 1 shows the hierarchy structure of the concepts in the domain kernel ontology. the mobm mentions that the hierarchy structure of the concepts is well presented if the hierarchy of the tables in the relational database is properly defined. however, the hierarchy structure of the concepts is not well represented due to scarcity of hierarchies of the tables in the relational database. therefore, other upper and lower concepts were added to the kernel ontology. 3.3 creating class hierarchies from upper concepts second, the upper concepts of the kernel ontology were conceptualized as a form of class hierarchies. the terms for the upper concepts were mostly collected from the dtims thesaurus and electronic drill book and conceptualized into the upper concepts of the kernel ontology by using the top-down approach. some of the concepts in the kernel ontology did not connect to the upper concepts because they were not conceptualized in the previous step. this step was intended to alleviate this problem by utilizing the concepts defined by domain experts. the upper part of figure 1 shows the conceptualized upper concepts, and the underlined concepts (e.g., tactical_operating_spot) are the ones defined by domain experts. 96 d. yoo, s. no, m. ra figure 1: example of class hierarchy in information domain 3.4 creating class hierarchies from lower concepts the third step was to specify the lower concepts of the kernel ontology. the additional information on database instances was utilized in addition to the terms referred to in the previous steps. the instances used in atcis can be divided into two types, instances for system implementation and instances for either real-time training or a real situation, and this step focused on specifying the lower concepts based on the instances for system implementation among the two types. as shown in the lower part of figure 1, the collected terms were conceptualized by following the bottom-up approach, and the intermediate concepts were additionally defined to link the instances with the concepts in the kernel ontology through the mobm. here, the code information of the primary key in the atcis database directory was used for the intermediate terms, and they were closely related to the hierarchy information by function provided by atcis. for example, a user can select amphibious transport submarine or midget submarine as infiltration equipment among special forces equipment from the atcis system screen. when referring to this kind of information, infiltration_equipment can be used as the intermediate term that links special_forces_equipment in the kernel ontology to the instances amphibious_transport_submarine and midget_submarine. the fourth step completed the hierarchy of the core concepts in the military ontology by connecting the upper and lower concepts into the kernel ontology, as shown in figure 1. 3.5 enhancing the semantics and completing the ontology the meaning of the military ontology was enhanced through the fifth, sixth, and seventh steps of the mobm. following the methods proposed in each step, the equivalentclass relationship, disjointwith relationship, and intersectionof relationship were defined in addition to the subclassof relationship to reinforce the hierarchy among the concepts. moreover, the restrictions a practical military ontology construction for the intelligent army tactical command information system 97 and axioms were additionally defined, and the domain rules were also proposed after collecting the military knowledge used in commanders’ decision making. the final step implemented the enhanced military ontology in web ontology language (owl) [11] by using protégé, and the military knowledge was defined as the form of rule by using semantic web rule language (swrl) [12, 13]. 4 intelligent atcis to illustrate how military ontology can be used to discover relevant military concepts or provide military knowledge to support commanders’ decision making, we implemented intelligent atcis as a prototype system. in this section, we describe two services of the system, the military concept navigation service and the commanders’ decision support service. 4.1 military concept navigation service to understand the relationship among the various military terms used in intelligent atcis, users can employ a web-based military concept navigation service. as shown in figure 2, users can enter a keyword (e.g., special_forces_equipment) as a query in the upper part of the screen and then click the ‘search’ button to start the military concept navigation. the result of the query is displayed in the center of the screen; the classes and individuals are represented as boxes and their corresponding properties are represented as circles connecting the boxes. the starting point for the navigation is the yellow box, which presents the entered query. users can interactively navigate the underlying military concepts by moving from one box to another. to explore other classes or individuals, users right-click the menu on a selected box (e.g., the show more or show less function). this navigation process may be repeated until users find the military concepts they seek. this service was implemented in java server pages (jsp) on an apache tomcat server and the visualization function related to concept navigation was developed using the growl [14]. 4.2 commanders’ decision support service commanders and staff officers require decision support services to effectively manage battlefields that are in flux. in particular, in the case of an emergency such as the occurrence of war, the system must provide relevant military knowledge that supports decision making that takes place in a short time. however, existing reasoning engines did not have sufficient inference capabilities for a significant amount of military knowledge. as military knowledge that consists of forms of swrl rules increases, more reasoning time is necessary to create relevant military knowledge from the military ontology and various military data. with the intelligent atcis, we have developed a commanders’ decision support service based on the query rewriting method [15] to resolve the problem. here, we describe the core function of the service, which is conducted via rewritten sparql queries instead of the reasoning engine to create relevant military knowledge in a short time. figure 3 shows the overall process of the service. for example, when a commander enters queries that include military knowledge, the queries are translated into sparql queries. then, the query rewriting engine divides the sparql queries into two parts: the military domain query (mdq) and the military knowledgerelated query (mkq). in the system, the military knowledge as defined by swrl is programmed as rule templates. because the meaning of mkq depends on military knowledge, the engine refers to military rule (mr) in the rule templates to understand the meaning of the military knowledge. based on the rules, the triple pattern rewriter can change mkq into extended sparql query 98 d. yoo, s. no, m. ra figure 2: military concept navigation service (esq). for example, figure 3 shows that part of mkq (e.g., ?troop a mo:dangerous_troop.) changes in esq (e.g., ?troop mo:hastowedartillery ?num . filter (?num >= 25).) according to mr 2, which includes military knowledge (e.g., a dangerous troop is one with more than 25 towed artilleries). then, the query rewriting engine submits a rewritten sparql query to the triple knowledge base and retrieves relevant information from the knowledge base. figure 3: overall process of the query rewriting method for decision supporting service a practical military ontology construction for the intelligent army tactical command information system 99 5 conclusion this paper presented the process of military ontology construction through the mobm, and considerable additional aspects in each step were analyzed. using the mobm, the core concepts of the military ontology were quickly composed from the practical terms in atcis, and one can perceive that the more isa relationships exist in the database, the more effective the kernel ontology constructed. also, this paper described the implementation of intelligent atcis, which provides a military concept navigation service and a commanders’ decision support service to show how to use the military ontology. in particular, we proposed a method for minimizing the time cost when inferring the military knowledge through the use of the query rewriting model. the results of this study may be used as the basic material for constructing a more practical military ontology in various military domains. finally, future work will be continued in the direction of building the individual ontology, not only for the information domain but also for the operation and firepower domains, and constructing an integrated military ontology by combining the domains. acknowledgement this research was supported by agency for defense development (add) under the contract number ud110058md. bibliography [1] republic of korea army, http://www.army.mil.kr/english/sub02/sub02_02_03.jsp. 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[11] d. l. mcguinness, f. v. harmelen, owl web ontology language overview, w3c recommendation 10 february 2004, http://www.w3.org/tr/owl-features/. [12] i. horrocks, p. f. patel-schneider, h. boley, s. tabet, b. grosof, m. dean, swrl: a semantic web rule language combining owl and ruleml, w3c member submission 21 may 2004, http://www.w3.org/submission/swrl/. [13] c. m. bogdan, domain ontology of the virdent system, int j comput commun, 6(1):4552, 2011. [14] s. krivov, r. williams, f. villa, growl: a tool for visualization and editing of owl ontologies, journal of web semantics, 5(2):54-57, 2007. [15] d. yoo, hybrid query processing for personalized information retrieval on the semantic web, knowledge-based systems, 27:211-218, 2012. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 5, month: october, year: 2020 article number: 3933, https://doi.org/10.15837/ijccc.2020.5.3933 ccc publications exhaustive search for optimal offline spectrum assignment in elastic optical networks i. katib iyad katib department of computer science king abdulaziz university, ksa jeddah p.o. box : 80200 iakatib@kau.edu.sa abstract heuristic-based approaches are widely deployed in solving spectrum assignment problem. this causes the results to be unreliable in some test cases when the results are very far from the lowerbound. this paper presents an exhaustive search approach that starts with an initial seed of a solution achieved by a heuristic-based algorithm called “longest first fit” (lff) and tries all possible permutations starting from this initial seed. the algorithm skips some branches and all its descendant permutations if it meets certain criteria that guarantees that those permutations will not lead to a better result. the experimental results show that the new algorithm has succeeded in achieving the lower-bound in 93% of the randomly generated test cases while the heuristic solver lff can achieve 65%. the algorithm achieves these results in a reasonable running time of less than 10 seconds. keywords: elastic optical network (eon), exhaustive search, spectrum assignment (sa), routing and spectrum assignment(rsa). 1 introduction elastic optical network (eon) is one of the most challenging networks to manage. it has a huge number of resources that needs to be optimally managed. efficient routing, modulation, and spectrum allocation techniques should be developed to exploit the huge potential of eon to optimally utilize of the optical fiber bandwidth [6]. the spectrum assignment (sa) is one of the challenges that affect a successful eon implementation. this is since spectrum assignment may cause fragmentation over time which in turn results in denial of new traffic requests. this happens when there is no sufficient continuous, and contiguous frequency slots available that meets the eon criteria which basically has three conditions discussed in [6]. suitable number of frequency slots should be assigned to a traffic demand going from source s to destination d in an elastic optical network. frequency slots should be proportional to the bit rate of the traffic demand and the total distance that will be traveled as this will affect the deployed modulation technique. those slots must be contiguous and the same frequency slots must be allocated https://doi.org/10.15837/ijccc.2020.5.3933 2 on all the links that will carry this traffic demand. this is because the traffic will be optically routed using wavelength selective switching (wss) and reconfigurable optical add/drop multiplexing (roadm). there is no electrical to optical (e2o) conversion in the intermediate network nodes. the routing and spectrum assignmen (rsa) problem has many characteristics that makes it very difficult to be solved, most of the work presented target partial problem of the general rsa problem. either they target static traffic demands or dynamic, also different modulation techniques or fixed modulation technique those factors affects the complexity of the rsa problem. there are many trials to solve the spectrum assignment problem and all of them are using heuristics algorithms as per the survey papers [9, 15]. in literature, the sa problem is defined in many forms either as integer linear programming (ilp) which targets the rsa problem as in [1, 10, 12, 13, 17] or as a special case of the task scheduling problem on multiprocessor systems as in [3, 4]. these problem formulations are not suitable for a scalable exhaustive search that finds the optimal solution for the sa problem. this paper uses a new formulation that will lead to the use of the branch and bound algorithm to find the optimal solution in a scalable manner. the rest of this paper is organized as follows: section ii provides a new description to the problem. section iii will present some related work. section iv presents the exhaustive search algorithm and how it can use the branch and bound algorithm. section v presents the results and the success rate of the branch and bound approach to find the optimal allocation. finally, section vi provides the concluding remarks. 2 problem definition offline spectrum assignment problem in eon assumes that all traffic demands in the network is already known where all the traffic demands are represented as bit rate. the route which will be used to carry each traffic demand is fixed by using the shortest path algorithm as the routing problem is not investigated in this paper. given that the route for each traffic demand is defined and the bit rate is known then we can choose the most suitable modulation technique to fulfil this traffic demand. this leads to convert the unit of the traffic demand matrix from bit rate unit to number of frequency slots matrix which allows the traffic demand matrix to be translated from bit rate to frequency slots. this assumption allows us to only focus on the spectrum assignment problem and isolate it from any other part of the eon problems. the sa problem is defined as a set of traffic demands t where each traffic demand ti,j is a binary vector of the links of the traffic path pi,j and each link in the path has the same number of frequency slots f si,j. this is shown in (1), (2), and (3) t = {ti,j : where i 6= j and traf f ic source is i and destination is j ∀i, j ∈ n} (1) pi,j = [pk : where pk = 1 if link k will be included in path f rom i to j ∀k ∈ l] (2) ti,j = f si,j ∗ pi,j (3) the goals of sa and rsa are overlapped in many cases but not necessarily give the same results. the rsa goal is to minimize the number of used frequency slots regardless of fragmentation [10], [7], [11]. on the other hand, the sa goal is to maximize the number of contiguous free frequency slots on all links i.e.; same frequency slot must be free on all links to form a contiguous free spectrum on all links to avail the acceptance of new traffic in the future [4], [3], [16]. this work will focus on the concept of maximizing the number of free fs on all the links. to achieve this goal, the fs allocation matrix will be defined as in (4). initially the matrix will be set to a zero matrix and the final goal it to fulfill all traffic demands while keeping the rightmost columns zeros. fayez et. al. showed in [3] that the lower-bound which ignores any fragmentation can be calculated in linear time, however the lower-bound is not an achievable goal as in many cases as we cannot avoid fragmentation. the make-span which allocates actual fss to each traffic demand after considering all eon constraints cannot be less than the lower-bound. the goal of this problem is to find the lowest possible make-span in a specific time interval. https://doi.org/10.15837/ijccc.2020.5.3933 3 s = [si,j : si,j = { 0 slot j on link i is free k if traffic k will be carried on this link where i ∈ l and j ∈ f scount] (4) 3 related work integer linear programming (ilp) models are widely deployed in finding exact solution for sa. however, those solutions face sever difficulties to scale to real problems of commercial networks. heuristic-based approaches are investigated and applied to large networks. in fact, most heuristic algorithms did not provide enough information to justify the accuracy of the solution [8]. prominently, such heuristic-based solutions are usually exceptional designs that are well customized to the problem under investigation. although it may be possible to adjust such approaches to other problem alternatives, both the initial design and the amendments require expertise not only in networking and theory of graphs but in several domains such as operations research, mathematical programming, and discrete optimization. consequently, currently available approaches require substantial amount of computing resources and investment in human expertise. this in turn restricts the ability to explore the impact of the decisions of design to predict demands and both capital and operational cost estimates. ramamurthy et. al in [14] tried fixed routing table to avoid solving the rsa problem and focus only on spectrum assignment. in this work, they showed that another alternative routing table would yield better results. they computed the fixed routing table based on dijkstra’s shortest path algorithm [2]. the presented spectrum assignment algorithms presented in [14] is heuristic-based which has very low complexity. the static rsa problem with estimated traffic has been solved as an ilp problem using heuristicbased approach by klinkowski et al. [10]. characterizing the performance of the proposed approach was very difficult. it does not scale to other problem variants [5]. four different sa techniques for the path network have been proposed by s. talebi et al. [16]. this research has succeeded to achieved some progress in heuristic-based algorithms however impractical path network topology was selected as a case study which in turn has led to an easy to solve problem. 4 methodology an example of how permutations of the traffic demands affect the make-span of the algorithm will be discussed to illustrate the main idea of the algorithm. table 1 shows an example of 4 traffic demands 1; the characteristics of each traffic demand are shown in table 2. the lower-bound is the maximum number of non-zero count on each row in matrix defined in (4). table 1 shows 6 fs found in link/row 2, 5, 6, and 7. the traffic demands are fulfilled based on their index and this caused the make-span (max non-zero fs index) to be 8 fs. so, the make-span is 33.3% away from the lowerbound. another permutation that is shown in table 3 and resulted in the allocation that is shown in table 4 indicates that the make-span equals to lower-bound which mandates that the search should be stopped as we found one permutation that will yield the best achievable goal if possible. the main idea of the algorithm is that we need to start with initial traffic demands table that is sorted based on a sorting function that will increase the possibility of hitting the target make-span in minimal number of permutations. the algorithm starts sorting the traffic demands based on longest first, this means traffic demands that requires more frequency slots will be processed first. this would cause any fragmentations at the beginning of the recursive function to affect the make-span and make it bigger than the lower-bound. this would cause the recursive function to exit this permutation and all its descendants and start another permutation that begins with different combination. the recursive spectrum assignment permutation rsap algorithm is shown in algorithm 1. it assumes the traffic demands array is already sorted based on longest first sorting function. the recursive function calls itself multiple times to try to fit one more traffic request into the frequency slots matrix. to avoid saturating the 1color coded and numbered for b/w printout https://doi.org/10.15837/ijccc.2020.5.3933 4 table 1: example of 4 traffic demands after being allocated to the required frequency slots (fss) links fs1 fs2 fs3 fs4 fs5 fs6 fs7 fs8 fs9 fs10 1 1 1 0 0 0 0 0 0 0 0 2 1 1 3 3 3 3 0 0 0 0 3 2 2 2 2 0 0 0 0 0 0 4 0 0 0 0 0 0 4 4 0 0 5 2 2 2 2 0 0 4 4 0 0 6 0 0 3 3 3 3 4 4 0 0 7 0 0 3 3 3 3 4 4 0 0 table 2: example traffic demands permutation 1 traffic demand links to pass through number of fs required 1 1,2 2 2 3,5 4 3 2,6,7 4 4 4,5,6,7 2 table 3: example traffic demands permutation 2 traffic demand links to pass through number of fs required 3 1,2 2 2 3,5 4 1 2,6,7 4 4 4,5,6,7 2 table 4: example of 4 traffic demands after being allocated to the required frequency slots (fs) with another permutation links fs1 fs2 fs3 fs4 fs5 fs6 fs7 fs8 fs9 fs10 1 0 0 0 0 3 3 0 0 0 0 2 1 1 1 1 3 3 0 0 0 0 3 2 2 2 2 0 0 0 0 0 0 4 0 0 0 0 4 4 0 0 0 0 5 2 2 2 2 4 4 0 0 0 0 6 1 1 1 1 4 4 0 0 0 0 7 1 1 1 1 4 4 0 0 0 0 matrix, the number of columns in the matrix is equal to the make-span of the best know heuristics algorithm in the literature [3]. if the matrix gets saturated, then the recursive function will roll back and try another permutation as shown in algorithm 1 lines 13 and 14. once all traffic requests are allocated the make span of this permutation is compared with the lower bound. if rsap succeed to find make span equals to the lower bound, then the recursive function exits as shown in algorithm 1 lines 2-7. the worst-case scenario is that rsap finds the best possible solution, but it is not equal to the lower-bound, this means the rsap will keep evaluating other permutations in hope of reaching the lower-bound. this would cause the running time of the algorithm to be o(n)=n!*l ; as it will evaluate all possible permutations and each one would require processing l links to mark the traffic demand as fulfilled then mark it again as unfulfilled to roll back to try another permutation. 5 results the rsap algorithm is evaluated against nsfnet 14-node topology shown in figure 1 with simulation of traffic matrix having 3 different probability distributions which are: https://doi.org/10.15837/ijccc.2020.5.3933 5 algorithm 1 recursive spectrum assignment permutations rsap(t, fs, depth, lb, makespan) require: t : sorted array of traffic demands require: f s: matrix of free slots on each link require: depth: number of fulfilled demands require: lb: lower bound require: m akespan: the make span of the fulfilled demands till now 1: begin 2: if |t| = depth then . fs is a complete permutation 3: if makespan = lb then 4: fs2 = fs 5: end if 6: return 7: end if 8: for t in t do 9: if t is fulfilled continue 10: frequency = allocatefs(t, fs) 11: markfulfilled(t) 12: rsap(t, fs, depth+1, lb, max(makespan, frequency)) 13: deallocatefs(t, fs) 14: markunfulfilled(t) 15: end for 16: end 0 1 2 3 5 4 6 7 8 9 10 13 11 12 figure 1: the 14-node nsfnet topology 1. uniform: equal probability is assumed to the selected five bit rates 10; 40; 100; 400; 1000. 2. skewed low: lower bit rates are given higher probability to be selected. the five bit rates 10; 40; 100; 400; 1000 are selected with probability 0:30; 0:25; 0:20; 0:15, and 0:10, respectively. 3. skewed high: higher probability is given to higher bit rates. the bit rates 10; 40; 100; 400; 1000 are selected with probability 0:10; 0:15; 0:20; 0:25, and 0:30, respectively. finally, each traffic demand’s bit rate is translated to corresponding frequency slots as per 5 which assumes the modulation technique to be qpsk as it supports the longest possible distance. as shown in table 6, rsap was able to reach the lower-bound in more trials than the lff algorithm. however, some trials did not converge in a reasonable time which caused the average wall time for rsap to be higher than the lff. lff finishes in less than 0.1 second on the other hand rsap hit the wall time of 10 seconds in 23 trials out of 300, because it did not reach the lower-bound; this does not necessarily mean it failed to find a good make span but it was mandatory to keep searching in the remaining permutations. in all the trials rsap either found the same make span as lff or found a better one. the 23 trials that hit the wall time of 10 seconds were inspected with https://doi.org/10.15837/ijccc.2020.5.3933 6 table 5: bit rate conversion table bit rate fs count 10 1 40 1 100 2 400 8 1000 20 table 6: comparison of results with lff probability distribution rsap success rate lff success rate rsap avg. time (sec.) lff avg. time (sec.) normal 93% 55% 0.8 < 0.1 high skew 91% 61% 1.03 < 0.1 low skew 93% 81% 0.77 < 0.1 larger wall time of 2 hours and 4 hours and the make span did not change. which means the wall time of 10 seconds were enough to prove that those trails will not converge using the proposed algorithm. 6 conclusion this paper has presented a new algorithm that starts with initial sorted traffic demands. this has helped the recursive function to converge in more than 90% of the trials in less than 10 seconds (wall time). this means that the lower-bound was achieved. the remaining 10% is still a challenge whether it has a solution equivalent to the lower-bound or not. those trials can be investigated with different sorting functions. the main advantage of this approach is that it preserves at least the same level of accuracy achieved by heuristic-based approaches or better for those test cases that did not converge. moreover, the convergence rate is much higher that the heuristic-based approaches. even though 90% of problem instances were solved, this paper shows a potential in the future to find an optimal solution for the unsolved problem instances. acknowledgement computation for the work described in this paper was supported by king abdulaziz university’s high performance computing center (aziz supercomputer) (http://hpc.kau.edu.sa). conflict of interest the author declare no conflict of interest. references [1] chen, h.; zhao, y.; zhang, j.; wang, w.; zhu, r. 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(2019). efficient dynamic routing and spectrum assignment for multifiber elastic optical networks. ieee/osa journal of optical communications and networking, ieee, 11(5), 190–201, 2019. https://doi.org/10.15837/ijccc.2020.5.3933 8 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: iyad katib (2020). exhaustive search for optimal offline spectrum assignment in elastic optical networks, international journal of computers communications & control, 15(5), 3933, 2020. https://doi.org/10.15837/ijccc.2020.5.3933 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 1, month: february, year: 2023 article number: 4577, https://doi.org/10.15837/ijccc.2023.1.4577 ccc publications optimized cnn-based brain tumor segmentation and classification using artificial bee colony and thresholding p. ashok babu, b.v. subba rao, y. vijay bhaskar reddy, g .rajendra kumar, j. nageswara rao, surendra kumar reddy koduru, g sunil kumar p. ashok babu department of electronics and communication engineering, institute of aeronautical enginnering, hyderbad, india corresponding author: ashokbabup2@gmail.com b.v. subba rao department of information technology pvp siddhartha institute of technology,andhra pradesh, india bvsrau@gmail.com y. vijay bhaskar reddy department of computer science and engineering, lakireddy bali reddy college of engineering, mylavaram, ntr district, pin-521230, andhra pradesh, india, yaramala.vijay@gmail.com g. rajendra kumar department of it, vignan’s institute of information technology, visakhapatnam – 530049, a.p, india. rajendragk@rediffmail.com j. nageswara rao department of computer science and engineering, lakireddy bali reddy college of engineering, mylavaram, ntr district, pin-521230, andhra pradesh, india, nagsmit@gmail.com surendra kumar reddy koduru business intelligence and reporting lead, nc, usa surendrakoduru.bi@gmail.com g. sunil kumar department of ece, cmr college of engineering & technology, hyderabad, india. gsunilmtech@gmail.com https://doi.org/10.15837/ijccc.2023.1.4577 2 abstract one of the most important tasks used by the medical profession for disease identification and recovery preparation is automatic medical image processing. statistical approaches are the most commonly used algorithms, and they consist several important step. brain tumors are the foremost causes of death of cancerous diseases all over the world. the hippocampus is the human body’s primary control structure. since a tumor attacks the brain, it can kill the patient if it is not detected early. among the various imaging modalities available, magnetic resonance image (mri) is a better implement for calculating area and classifying tumors based on their grade. mri does not emit any toxic radiation. there is currently no automated method for detecting and identifying the grade of a tumor. this study mainly focusses on classifying and segmenting brain tumors from mri scan data. it aids physicians in the planning of future care or surgery. this procedure consists of four steps: image de-noising, tumor extraction, attribute extraction, and hybrid classification. in the first step of image de-noising, the curvelet transformation (ct) is used. then, in the next stage, artificial bee colony (abc) optimization is used in conjunction with the thresholding process to remove tumors from brain mri scans. another optimization approach is used to recover the learning rate of the convolutional neural network for the final hybrid classification. the experiment model is assessed by using the multimodal brain tumor (brats) 2013 and 2015 challenge datasets from medical image computing. the outcomes of the experiment presented that the method achieved the segmentation 95.23% and 94% of accuracy, where the proposed optimized cnn achieved classification accuracy of 98.5% and 99% for both datasets. keywords: artificial bee colony; brain tumor; magnetic resonance image; medical image analysis; thresholding method; 1 introduction brain tumor is a group of rare or unusual cells found inner part of the brain. the skull is the brain’s rigid exterior coating that acts as a shield in the event of an injury. any irregular cell growth inside it will pose a serious problem [1-2]. there are two kinds of brain tumors: malignant, which is more severe and harder to treat, and benign, which is curable if diagnosed early [3]. furthermore, primary and metastatic are the brain tumors, the predominant type brain tumors that ascend from glial cells are called gliomas, and about 70% of adults have this form of cancer. two classification ratings that tumor cells proliferate are: low grade (oligodendroglia) and (glioblastoma) high grade [4]. if tumor is noticed at an initial stage, they are fewer harmful than when they are discovered later in the disease’s progression, which reduces patient lifespan to less than two years. the most effective cure for tumor patients is chemotherapy, radiation, surgery, or a combination of these. figure 1 depicts a brain representation of a malignant tumor, a healthy brain, and a benign tumor. figure 1: sample mri images of brain mri is the most commonly used imaging technology tool that play an important role in identifying brain tumors [5]. mri is a non-invasive model with enhanced contrast of soft tissues that provides information about https://doi.org/10.15837/ijccc.2023.1.4577 3 tumor site, shape, and scale in the non-appearance of high ionization radiations [6]. figure 2 depicts the reference scans. figure 2: sample mri images the mr images segmentation process [7] is a critical role in monitoring tumor volume variance. it is critical in radiotherapy and surgical preparation where affected and stable areas are specifically separated [89]. currently, manual segmentation is more prevalent in clinical routines where radiologists must put in more clinical experience and effort. manual diagnosis and segmentation of brain tumors does not yield reliable results and is a time-consuming and inefficient procedure [10]. furthermore, tumor form, diameter, texture, scale, position, and abnormality are all typical issues in current systems. automatic approaches to detecting brain tumors are more notable and meaningful. brain tumor segmentation using 2d mri slices are classified into major following types: threshold and region-based [11-15], pixel-based scheme [16-18]. threshold methods do not have a single threshold for classifying target voxel segmentation based on intensities. to outline the edges of voxels, the sobel filter is used. then, to optimize the performance, the pixel of every voxel are associated to threshold parameters, and each pixel is allocated to neighboring regions. in healthcare imaging, the automatic detection of risky human illnesses such as tumors in brain, stomach infections, skin cancer, and lung cancer are very common illnesses. for the detection of these diseases, multiple segmentation and classification approaches focused on computer vision (cv) available [19]. brain tumors are the most important and risky form of cancer in medical imaging, and numerous computerized approaches for diagnosing them have recently been developed. established procedures generally require several stages like noise isolation pre-processing, segmentation of tumor region, useful feature extraction, redundant feature reduction, and categorization. ariyo et al. [20] the spatial fuzzy c-means plus k-means algorithm was proposed, a strategy for distinguishing dysfunctional brain tissues from stable brain tissues. the noise is reduced in this technique by merging the spatial equation to the fcm algorithm, and the optimum likelihood for pixels with particular membership is achieved by using the k-means algorithm. sharif et al. [21] have identified a multi-features selection for brain tumor segmentation using enhanced binomial thresholding. for segmentation, a gaussian filter for preprocessing, and an enhanced thresholding system with certain morphological operations is used. following that, a serial-based approach for fusing derived geometric and harlick features is introduced. finally, best features are selected from the fused vector using ga, and classification is done using lsvm. damodharan et al. [22] brain tumor revealing scheme based on the combination of segmentation and neural networks was proposed. the applied approach’s success is evaluated by comparing its findings to those of other classification methods such as nn, knn, and bayesian classification in terms of precision, specificity, and sensitivity. pereira et al. [23] used mri images to apply an approach focused on convolution neural networks (cnn) for effective tumor extraction. to isolate and identify the tumor region from brain mri scans, bahadure et al. [24] used a classification system focused on bayesian fuzzy clustering. as compared to a few previously applied methods, the presented process performs well in terms of precision. sharma et al. [25] developed a hybrid method for reliably extracting tumor regions as well as classifying brain tumors. this presented methodology includes three primary steps: pre-processingthresholding, morphological procedures, and watershed segmentation; and post-processingwatershed segmentation. segmented mri scans are used to remove glcm characteristics, and the tumor is classified using the kmnn classifier. to derive details from cnn architecture such as unet [26] and deepmedic [27], the hyper column paradigm is used [28]. furthermore, u-net and its updated systems are https://doi.org/10.15837/ijccc.2023.1.4577 4 s.no.author method limitation 1. md. sujan et.al 2016 a novel method for locating a brain tumour that combines thresholding and morphological image analysis. for large datasets, this technique is not suitable. 2. d. ravi et.al 2017 a novel method for tumour classification mapping that reduces dimensionality the detection rate is reduced on based on sample quantity. 3. a. raju et.al 2018 hcs optimization techniques based on svnn classifier were used to classify brain tumours using the bayesian fuzzy clustering (bfc) methodology. lack of preprocessing step and less efficient. 4. k. usman et.al 2017 a multi-modality mri brain tumour segmentation and classification scheme poor robustness for large datasets 5. m. soltaninejad et.al 2017 an approach that employs super pixelbased extremely randomness trees (sperts) to automatically identify and segment brain tumours. less accurate. an important collection of architecture for segmenting medical images [29]. the model of down/up-sampling operations. by doing successive concatenation, the u-net architecture shows function planning in the model of encoding to decoding [30]. several approaches are discussed in the literature, but none of them produce better outcomes in all efficiency indicators. on five challenging datasets, the model proposed performed well across the board in terms of all output metrics. this thesis looks at a new score level fusion technique for reliably segmenting and detecting brain tumors using mri. the threshold selection criteria used in this study is the abc optimization algorithm. the algorithm is used in conjunction with the thresholding process to extract tumors from brain mri scans. the thresholding process is used in tumor region seperation from the rest of the image by identifying the intensity level at which the tumor is distinguishable from the surrounding tissue. the abc algorithm is used to optimize the threshold value by adjusting it until the best segmentation of the tumor is achieved. the goal of the algorithm is to reduce the discrepancy between the identified tumor location in the image (segmented tumor) and its actual location (ground truth). the algorithm is run multiple times with different initial threshold values and the final value of threshold is selected based on the one that gives the best segmentation results. this process allows for a more accurate and efficient extraction of the tumor from the mri scan. 1.1 problem statement our primary emphasis in this effort is on the accurate brain tumors segmentation and subsequent classification into related groups. several difficulties exist for this procedure, including small contrast tumor, size, diameter, unrelated characteristics, and a few others. whereas in this work, we concentrate on tumor improvement and the removal of irrelevant characteristics for the highest segmentation and classification precision. https://doi.org/10.15837/ijccc.2023.1.4577 5 1.2 major contributions the major influence of our method is described as follows. the ct is used to improve the appearance of the originality tumor from a certain angle and scale. the segmentation of tumor is accomplished using abc in conjunction with the thresholding process. form and texture are two examples of multi-type features extracted. then, for trivial feature reduction, a modern technique known as berkeley wavelet transformation is used. a novel approach to classifying tumor/non-tumor mr images is proposed during the classification process, a pre-train cnn ideal and the segmented images are fed in it, then feature learning is conducted using alex net. for the relevance of this methodology, it is compared to other classification approaches and current techniques. the chief objectives are: 1. to develop a new strategy for automatic detection and segmentation from mri data of brain images. 2. to effectively validate of method proposed by accurately segmenting and classifying brain tumors as benign or malignant. 3. to optimize the learning rate of a cnn for better classification performance. 4. validation by using the multimodal brain tumor (brats) 2013 and 2015 challenge datasets. 5. to explore the potential for further development of the method for segmenting and identifying sub-tumoral parts, and for integration with multiple classifiers to improve precision and diagnostic confidence index (dci). organization of this paper is arranged as follows: the brief explanation of proposed methodology along with flow chart is presented in section 2. the validation of projected segmentation and classification techniques with existing techniques are given in section 3. the conclusion and future enhancement is described in section 4. 2 proposed methodology a five step method of proposed work that are primarily the input image is pre-processing, tumor segmentation using abc and thresholding, extraction of a small number of beneficial features, superlative features collection constructed with high importance, and feature fusion. later, these attributes are fed into a cnn classifier to be included in the classification procedure. figure 3 depicts a flow diagram of the proposed solution. the proposed method for classifying and segmenting brain tumors from mri data consists of four main steps: 1. image de-noising: in this step, the curvelet transformation (ct) removes noise from the brain mri scans. this step is necessary to improve the accuracy of the subsequent steps. 2. tumor extraction: artificial bee colony (abc) optimization is used in conjunction with the thresholding process for extraction of brain tumor from the denoised images. abc optimization is employed to identify the optimal value of threshold that separates the tumor from the healthy tissue. 3. attribute extraction: in this step, the extracted tumor is passed through the berkeley wavelet transformation (bwt) for feature extraction. bwt is used to extract features such as the size, shape, and texture of the tumor that are used for classification. 4. hybrid classification: the feature extraction from aforementioned process serves as for classification input to cnn. the rate of learning the cnn is optimized using the butterfly optimization algorithm for better classification accuracy. the cnn is trained using the brats 2013 and 2015 datasets to categorize the tumors as malignant or benign. 2.1 ct for image enhancement ct is used to reduce excess noise in an input image by improving the tumor region. this approach is advantageous in terms of execution ease, illness reliability, and turnaround time. also, the ct achieves optimum recovery of corners, as well as dim linear and curve functions. in terms of image de-noising efficiency, the use of ridgelet transforms, i.e. ct, is more effective than the use of wavelet transform. the ridgelet transform is transformed into the radon transform. to apply a support interval or to scale in the ridgelet transform, an anisotropy scaling relationship is used. a multi-scaling ridgelet is used to decompose the curve or edge into blocks and sub-blocks. furthermore, for the purposes of ridgelet analysis implementation, these sub-blocks are loosely regarded as straight lines. in equation, the decomposition stages of the ct are mathematically defined as follows (1). https://doi.org/10.15837/ijccc.2023.1.4577 6 figure 3: working flow of proposed methodology gß(f0g, ∆1g, ∆2g, . . . , ) (1) where, f0 signifies filters of sub-bands, ∆bsignifies sub-bands data 2−2b and g signified a tumor particle. the k1 and k2 are image set size. the mathematical relative is clarified in equation (2). q = [ k1 2b , (k1 + 1) 2b ] × [ k2 2b , (k2 + 1) 2b ] (2) following that, the resulting square to unit size is renormalized. this step’s quantitative relationship is expressed mathematically as follows: hq = f −1q (vq∆bg) , q ∈ qb where, (fqg) (x1,x2) = 2bg(2bx1 −k1, 2bx2 −k2) and proves the renormalization operative model. here, two dyadic sub-bands [ 22b, 22b+1 ] and [ 22b+1, 22b+2 ] are integrated before implementing the ridgelet transform. ∝µ = 〈hq, pλ〉 (3) the denoising results after put on the ct are signified in figure 4. 2.2 abc and thresholding based tumor extraction abc has the capabilities of fully showing the space of the feasible solution and being insensitive to noise. the traditional abc separates bees into three categories namely, employed, onlooker and scout bees. they have different jobs. employed bees randomly search for honey and share information together. onlooker bees learn from employed bees, and then take over their jobs. scout bee is a supervisor, if it discovers some onlooker bees are unable to find the honey in limited time, it will send an employed bee to replace one of them. in this developed abc, cauchy perturbation strategy is applied to overcome the disadvantage of being easy to drop into the local optimum. the detailed description of abc is as follows. 1. parameter setting and population initialization x = rand (np,d) ∗ (ub− lb) + ones (np,d) ∗ lb (4) v = rand (np,d) (5) https://doi.org/10.15837/ijccc.2023.1.4577 7 figure 4: sample de-noised mri images using ct. a) original images b) 1. employed bee phase and the map and compass operator this process unites two traditional operations. first of all, the ith individual generates a random number s in(0, 1). then it compares with 0.5, if s is greater than 0.5 , this process executes the improved employed bee phase, otherwise it executes the traditional map and compass operator. eqs (6) and (??) show that ith and the kth individuals share the jth dimension information. in this paper, multiple dimensions are selected randomly in (1,d). the factor p(i) affects the degree of information communication by value of parametertrail(i). when trail(i) is small, it means that the ith individual has won in a short time, which should retain more of their nature. the range of p(i) is ( 4 π ∗ arctan ( 1 d ) , 1 ) . eqs (5) reflects the positive relationship between trail(i) andp(i). the former represents the speed of the ith individual approaching the optimum, where iteration is the current iteration, rand is a randomly in (0, 1), and xbis the best position of the whole population. the latter indicates the ith individual’s new position after moving at a new speed. new population updates according to eqs. (??)–(10), and then they are brought into eqs. (4)–(6) respectively to calculate energy values. in order to make greedy selection, the fitness of each energy value is calculated by eq. (??). in this paper, the lower the energy value is, the greater the fitness preforms. when the energy value is positive, the fitness is less than 1. and when the energy value is negative, the fitness is greater than 1. p (i) = 4 π arctan( trail(i) d ) (6) x j i ¸ x j i + rand∗ ( x j i −x j i ) ∗p(i) vi = vi ∗e−iter + rand∗ (xb −xi, ) , i ∈{1, 2, . . .np} (7) xi = xi + vi, i ∈{1, 2, . . . .np} (8) 1. onlooker bee phase equation (??) shows the ith and kth individuals share the jth dimension information. what’s different from traditional phase is roulette selection. the idea of roulette selection is to allow individuals with high fitness to breed while individuals with low fitness to have no or less opportunities to reproduce. in order for all individuals to communicate, the roulette selection should be deleted. x j i ¸ x j i + rand (x j k¸ x j i ) 1. the landmark operator https://doi.org/10.15837/ijccc.2023.1.4577 8 equation (9) calculates the central position by weight of population’s fitness. in the traditional abc, xc is obtained by all the reserved individuals, and the population is halved through every iteration. this paper keeps the number of populations. at the same time, it performs the following steps: firstly, all individuals rank according to their fitness from high to low. then, select the front 1/3 of population to calculatexc. finally update the behind 1/3 of population according to equation (10). in eq. (10), iter is the recent iteration, cycle is the total iterations. the range of u(i) is (0.1, 1) . as the increases of iter, the population gets father and father away xc so, the extent of convergence is alleviated. xc = ∑np i=1 xi ∗fitness(i) np ∗ ∑np i=1 fitness(i) (9) u (i) = exp−{ ( iter − 1 cycle− 1 ) ∗ in (10)} (10) xi = xi + rand∗ (xcenter −xi) ∗u(i) (11) 1. scout bee phase when trial(i) reaches the upperlimitd, it means that the ith individual has not updated its position in d times and falls into local optima. to increase the diversity of population, whentrial(i) > d, the ith individual requires to reinitialize by eq. (??). xi = rand (1,d) ∗ (ub − lb) + ones (1,d) ∗ lb (12) 1. cauchy perturbation in order to move out of the local optimal solution, cauchy perturbation strategyis introduced. the cauchy probability density function is as shown in eq. (??), wherex0 controls the position of x axis and γ controls the size of y axis. according to this work, x0 = 0 ,γ = 1. the distribution function of cauchy perturbation is eq. (13) which is obtained by eq. (??) and the above parameters. c(1,d) means a vector consisting of d random numbers which generate by eq. (14). in eq. (15), xbis the best individual. f (x; x0,γ) = 1 π [ γ (x−x0) 2 + γ2 ] (13) c (x) = 1 2 + 1 π arctan(x) (14) xb = xb + c (1,d) ∗ (ub− lb) (15) the procedure of final decision to decide the end of the algorithm, a predetermined number of rounds are used; each of these loops is made up of fixed steps. finally, a binary determination is made at each pixel location to determine if it was existent on the outside boundary or not. this is accomplished by applying a threshold value t to the final resultant. cauchy perturbation is an abbreviation for cauchy perturbation 2.3 feature extraction using berkeley wavelet transformation bwt is used for efficient segmentation of brain mr images. in reality, it is the first research of its kind to utilize berkeley wavelet transformation for brain mr image segmentation. the berkeley wavelet transformation method is underlined in order to break down data functions into components of varying frequencies, allowing each component to be studied separately. since it is the other wavelets source and is distinct by eq. 19, from a simple wavelet ψ (t) all wavelets are formed. where, sandτ are the translation factors, correspondingly. the wavelet creates a whole, orthonormal basis in 2-d by scaling and translating the it with a single wavelet constant term. βϕx is a piecewise constant function of the mother wavelet transformation. eq. 20 shows how wavelets are substituted from the mother wavelet βϕx are generated at different pixel positions. βϕx (τ,s) = 1 s2 βϕx (3 s (x− i) , 3s(y − j)) (16) where xands are the wavelet transformation’s translation and scale parameters, respectively, and βϕx is the function for transforming, and this is known as the bwt mother wavelet. 2.4 hybrid classification in this section, a quick explanation of hybrid classification that uses butterfly optimization for cnn learning rate optimization is given. the cnn is first described as follows: https://doi.org/10.15837/ijccc.2023.1.4577 9 2.4.1 convolutional neural network for training, cnn utilizes a backpropagation algorithm [31] and trained samples for inputs vector x to the cnn’s target class y function. the desired goal is learnt by comparing with each cnn’s output and the modification among them generates an error of learning, the next-generation cnn role is assumed and is given by, e (ω) = 1 2 p∑ ρ=1 nι∑ j=1 (olj,p −yj,p) 2 (17) this minimising of cost function e (ω) , discovery a minimizer ω̃ = ω̃1, ω̃2, . . . , ω̃v�rv,where v = ∑l k=1 weightnum (k). ωi+1 = ωi −n∇ei (ωi) (18) where n is the value of learning rate. the n is designated by boa method. 2.4.2 butterfly optimization algorithm (boa) butterflies are boa’s search agents that optimize results. a butterfly can emit fragrance of differing intensity depending on its health, i.e., when a butterfly travels from one place to another, its fitness will change consequently. the scent will disperse through time and space, and other butterflies will be able to sense it, encouraging butterflies to exchange personal information and form a common social awareness network. the suggested method refers to this behavior as global quest, which occurs on one butterfly identifying the smell of one more butterfly. scent-impaired butterflies will fly by at random, which is mentioned as local quest in proposed algorithm. scent, sound, light, and temperature are all examples of modalities that can be expressed by stimuli such as those used in boa calculations for fragrance. the entire principle of detecting and processing the modality is carried out based on three key parameters, which comprise sensory modality, stimulus strength, and power exponent. the raw input used by the receptors is referred to as sensory modality. sensory involves calculating the energy form and processing it in appropriate manner. modalities will now include scent, tone, colour, temperature, and, in the case of boa, fragrance. the amplitude of the physical/actual stimuli is denoted by i, which is associated with the fitness solution in boa. i.e. when a butterfly emits a larger amount of scent, the other butterflies in the area will detect it and become attracted to it. the exponent to which intensity is elevated is power. normal expression, and linear response are all possible with the parameter a. response expansion occurs as i rises and the fragrance (f) increases faster than i. as i rises, f rises more slowly than i. response compression is the term used to describe this technique. in a linear reaction, when i increases, so does f, and vice versa. studies on insects, mammals, and humans have found that when stimuli increase in amplitude, insects become less responsive to the changes in stimulus. as a result, in boa, the magnitude of i is approximated using response compression. from these ideas, the scent in boa is expressed as a the physical force function of the stimulus, as seen in eq. (19) f = cia (19) where, the perceived magnitude of the smell is denoted by f and the sensory modality is denoted by c, the stimulus force is denoted by i, and the modality-dependent power exponent is varied absorption degree consideration. mostly, we will use aandc in the [0, 1]. the limit is the power proponent that varies with modality, and it describes absorption variance. at one end, a = 1, indicating that there is no smell absorption. as a result, achieving a single optimum is simple. if, on the other hand, a = 0, no butterfly’s scent can be detected by the other butterflies. academically, c [0,1] but functionally, the machine calculates the characteristic to be optimized. the convergence speed is impacted by the values of a and c. 2.4.3 movement of butterflies the movements are explained as follows: 1. total butterflies are meant to emit some kind of scent that attracts other butterflies. 2. each butterfly will migrate at random or toward the best-smelling butterfly. 3. butterfly stimulus amplitude is influenced or determined by the target feature’s landscape. initialization, iteration, and completion are all steps of boa. the initialization stage is carried out first in every boa run, followed by iterative searching and finally by the during the iteration period, which is the second stage in the algorithm, the algorithm runs a series of iterations. eq. (19), on the other hand, allows these butterflies to emit aroma from their habitat. during the global quest process, the butterfly moves earlier https://doi.org/10.15837/ijccc.2023.1.4577 10 to the fittest solution g*, which can be expressed by eq (20) and local search phase can be signified as in eq. (21) xt+1i = x t i + ( r2 ×g∗ −xti ) ×fi (20) xt+1i = x t i + ( r2 ×xtj −x t k ) ×fi (21) where xtj and xtk are jth and kth solution space of the butterflies. if x t j and xtk belongs to same swarm and r is a random sum in [0, 1], then eq. (21) converts a local random walk. iterations are carried out until the halting criteria are not achieved. the high number of rounds obtained is only one example of how the halting condition might be stated. the algorithm then delivers the best result with the greatest fitness at the end of the iteration procedure. the whole pseudo code is broken down into the three parts mentioned above, which are covered in "algorithm 1." 3 results and discussion the investigational results of the projected classification and segmentation method are presented in both quantitative and qualitative terms in this section. the proposed new scheme is assessed using two databases, brats 2013 and brats 2015. complete trial is run on an i7 intel core, ram of 16.0 gb, an os of 64-bit, and geforce gtx 1080 nividia gpu. 3.1 dataset description brats 2013 includes 20 (hgg/ lgg) volumes of preparation and 10 in research. brats 2015 has hgg as 220, volumes of lgg as 54 for training and volumes of lgg as 110 for testing process. the brats dataset includes three mri views: axial, coronal, and sagittal. in the axial view, the human brain is categorized into top and bottom parts. front and back parts of brain are obtained from the coronal view, where the right and left halves are achieved from the sagittal views. input slices in brats datasets are provided as mha files. the mha read header and read volume are used for extracting the brain slices of axial vision. the matlab code is converted from the command of mat2gray and therefore, 3d slices are divided into 2d slices. the proposed ideal is trained on the axial view, because the information about upper and lower parts of brain are provided by this plane. however, when trained on coronial and sagittal views, the proposed method produces even better results. as a result, regardless of point of view, the algorithm can detect brain tumors. https://doi.org/10.15837/ijccc.2023.1.4577 11 parameter equation accuracy t p +t n t p +t n +f p +f n sensitivity t p t p +f n specificity t n t n +f p dice coefficient index 2t p2t p +f p +f n table 1: various parameters and its equations dataset accuracy (%) specificity (%) sensitivity (%) dsc ji fpr fnr brats 2013 92.78 88.24 96.36 0.9372 0.8818 0.1176 0.0364 brats 2015 98.01 95.61 99.62 0.9934 0.9868 0.0439 0.0038 table 2: segmentation results 3.2 performance metrics in terms of dice similarity coefficient (dsc), specificity, sensitivity, accuracy, and jaccard index (ji), the proposed technique efficiency is evaluated. the phrases tp, tn, fp, and fn’s confusion matrix was created using several projected outcome metrics. where, tp – true positives tn – true negative, fp – false positives, fn – false negative, and tn – true negative table 1 shows the equations for the several parametric measurements employed in this research are shown. 3.3 performance analysis of segmentation technique table 2 depicts the performance of proposed abc with thresholding is validated with two datasets in terms of overall performance. table 2 proves that in brats 2015 than 2013 challenge dataset the performance in terms of dsc, jsi, acc, sp and se. for instance, the proposed method achieved 98.01% of accuracy in brats 2015, where the same method achieved 92.78% of accuracy in brats 2013. in brats 2015, the dsc is 0.9934 and jsi is 0.9868, where the proposed segmentation technique achieved 0.9372 dsc and 0.8818 jsi. table 3 displays the performance comparison of proposed abc with thresholding with current methods for brats 2013. from the above table, it is evidently proving that our proposed scheme achieved accuracy of 95.23%, where the various current methods achieved nearly 84% to 94.70%. this existing technique uses only brats 2013 for validation and achieved very less accuracy. but, our proposed technique uses the abc with thresholding for accurate segmentation of brain tumor. table 5 displays the validated results of segmentation with proposed and existing techniques for brats 2015 datasets. here, the proposed abc with thresholding technique outperformed than current methodologies. for instance, the proposed segmentation achieved accuracy of 94%, where the other methods achieved nearly 79% method year acc (%) cordier et.al [32] 2013 84.00 reza et.al [33] 2015 86.70 abbasi&tajeripour [34] 2017 93.00 umairanazarhussain [35] 2020 94.70 proposed 2021 95.23 table 3: brats 2013 data set segmentation comparative results. https://doi.org/10.15837/ijccc.2023.1.4577 12 method year acc (%) pereira et.al [36] 2016 86 havaeiet.al [37] 2016 79 kamnitsaset.al [38] 2017 79 dong et.al [39] 2017 90 proposed 2021 94 table 4: brats 2015 data set segmentation comparative results network type acc sen spec fmeasure fpr without optimized lenet5 cnn 0.84 0.55 0.99 0.70 0.56 optimized lenet-5 cnn 0.88 0.62 0.99 0.76 0.53 without optimized res net cnn 0.87 0.65 0.99 0.78 0.50 optimized res net cnn 0.93 0.75 0.995 0.85 0.49 without optimized proposed alex net cnn 0.92 0.79 0.99 0.89 0.48 optimized proposed alex net cnn 0.98 0.96 0.9954 0.97 0.46 table 5: performance analysis of proposed classifier for brats 2013 data set with various network architectures to 90% of accuracy only. while comparing with brats 2013 dataset, this dataset achieved low accuracy, because the images are difficult for labelling and extraction of exact tumor region. however, the this technique gained improved enhancement than various existing segmentation techniques. the next section will explain the experimental analysis of proposed classifier with and without boa technique. 3.4 performance investigation of proposed hybrid classification in analyse the performance of various architectures of cnn with boa and without boa in terms of accuracy, specificity, fpr, sensitivity and f-measure. the various cnn architecture includes lenet-5 [40] and res net [41] are compared with proposed alex net, which is given in table 6 for brats 2013 dataset. in this set of experiments, initially 60% for training data and remaining 40% of data is used for testing process. from this experiments, we can clearly prove that proposed alex net achieved better performance than other two architectures. without boa also, alex net achieved nearly 92% of accuracy, 99% of specificity and 79% of sensitivity, where res net achieved only 87% of accuracy, 99% of specificity and 65% of sensitivity. while implementing boa for optimizing the learning rate of cnn architectures, the proposed alex net achieved 98% of accuracy and 96% of sensitivity, where res net achieved only 93% of accuracy and 75% of sensitivity. this proves that boa improves the performance of various cnn architectures. the next table 7 shows the validation results of proposed architectures for brats 2015 datasets. from this experiments, we can clearly prove that proposed alex net achieved better performance than other two architectures. without boa also, alex net achieved nearly 94% of accuracy, 99% of specificity and 89% https://doi.org/10.15837/ijccc.2023.1.4577 13 network type acc sen spec fmeasure fpr without optimized lenet-5 cnn 0.78 0.46 0.99 0.63 0.62 optimized lenet-5 cnn 0.85 0.56 0.99 0.70 0.58 without optimized res net cnn 0.82 0.57 0.99 0.78 0.52 optimized res net cnn 0.95 0.89 0.99 0.90 0.46 without optimized proposed alex net cnn 0.94 0.89 0.99 0.90 0.46 optimized proposed alex net cnn 0.99 0.96 0.99 0.96 0.46 table 6: comparison of accuracy for brats 2015 dataset with various network architectures of sensitivity, where lenet-5 achieved only 78% of accuracy, 99% of specificity and 46% of sensitivity. while implementing boa for optimizing the learning rate of cnn architectures, the proposed alex net achieved 99% of accuracy and 96% of sensitivity, where res net achieved only 95% of accuracy and 89% of sensitivity, then lenet-5 achieved 85% of accuracy and 56% of sensitivity. this proves that boa improves the performance of various cnn architectures. additionally, several sets of experiments are performed, including the accuracy test depicted in figure 5 where 50% of training data and 50% of testing data are used. overall accuracy of the trials using the 70:30 ratio is then shown in figure 6. the predicted alex net obtained greater performance for several sets of trials, as shown by the the above data. however, even with the 50-50 split of tests, the lesnet-5 performs poorly. the following subsection concludes by comparing the suggested classifier with an already-in-use modern approach. 3.5 comparative analysis of proposed classifier in table 7, the comparison for various existing techniques with proposed classifier is presented in terms of overall accuracy. for brats 2013, the existing techniques achieved nearly 87% to 98.1% of accuracy, where the proposed classifier achieved 98.5% of accuracy. the reason is that the tumor is accurately segmented by using abc with thresholding technique, where the existing techniques uses either fuzzy-c means or improved version of fcm. in set of experiments for brats 2015, the existing technique proposed by sharif, m.i., et.al [44] achieved only 97.8% of accuracy, where the proposed alex net achieved 99% of accuracy. the reason is that learning rate of cnn architecture is optimized by using boa technique. from these experiments, the proposed segmentation and classification technique achieved better performance for two different datasets. 4 conclusion medical imaging methods are used to diagnose brain tumors which becoming more common, as brain tumors that life hacking illnesses today. a swarm of dysfunctional cells surrounds the inner part of the human brain in the tumor. it has an effect on the brain by crushing and destroying healthy tissues. it also raises intracranial https://doi.org/10.15837/ijccc.2023.1.4577 14 figure 5: comparison of accuracy (50:50) figure 6: comparison of accuracy (70:30) https://doi.org/10.15837/ijccc.2023.1.4577 15 method year dataset acc (%) s. m. reza et.al [42] 2015 brats 2013 86.7 m. a. khan et.al [43] 2019 brats 2013 97.5 sharif, m.i., et.al [44] 2020 brats 2013 98.3 proposed optimized cnn (alex net) 2021 brats 2013 98.5 sharif, m.i., et.al [44] 2020 brats 2015 97.8 proposed optimized cnn (alex net) 2021 brats 2015 99 table 7: comparison of proposed with existing approaches accuracy pressure, which causes tumor cell development to accelerate, potentially leading to death. consequently, it is preferable to brain tumors detection in early stage, as this can improve the patient’s chance of survival. the primary aim is to demonstrate a new strategy for tumor segmentation and detection. the suggested architecture segmented and classified benign and malignant tumor cases correctly. abc with thresholding is used to seperate the brain tumor from the denoised images. bwt is used for extracting the features from the segmentation results. the cnn learning rate is optimized by using butterfly optimization technique for better classification. the experiments 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(2023). optimized cnn-based brain tumor segmentation and classification using artificial bee colony and thresholding, international journal of computers communications & control, 18(1), 4577, 2023. https://doi.org/10.15837/ijccc.2023.1.4577 introduction problem statement major contributions proposed methodology ct for image enhancement abc and thresholding based tumor extraction feature extraction using berkeley wavelet transformation hybrid classification convolutional neural network butterfly optimization algorithm (boa) movement of butterflies results and discussion dataset description performance metrics performance analysis of segmentation technique performance investigation of proposed hybrid classification comparative analysis of proposed classifier conclusion int j comput commun, issn 1841-9836 9(3):284-291, june, 2014. lar-1: affirmative influences on energy-conservation and network lifetime in manet n. gupta, r. gupta neelesh gupta department of ece, truba institute of engineering and information technology, bhopal (m.p.)-india e-mail: neelesh.9826@gmail.com roopam gupta department of information technology, uit, rgpv, bhopal (m.p.)-india e-mail: roopamgupta@rgtu.net abstract: nowadays, reduction of energy-consumption in mobile ad-hoc networks (manets) has been a herculean task. appropriate location-based routing protocols like location-aided routing-1 (lar-1) can be incorporated for reducing the energy consumption as it extends the network lifetime in dynamic network. the research work proposed integrating energy-conservation along with lar-1 route-discovery mechanism, named as new-location-aided routing-1 (n-lar-1). in this paper, developments of performance-metrics using this n-lar-1 approach have been reported. affirmative results have been achieved through n-lar-1 by utilizing the sleep or inactive mode condition of mobile nodes in ad-hoc networks. it has been observed that performance of n-lar-1 is better than n-dsr approach. keywords: manets, lar-1, energy conservation, network lifetime. 1 introduction manet is a self-configuring, self-organizing infrastructure less network. for instance in military application, enemy tank can be tracked within the network at the time of battle. similarly, local community can use an ad-hoc network to detect moving car and its speed along with direction. presently limited energy, limited bandwidth, multi-hop routing, dynamic topology and security are the leading technical challenges in manets. numerous routing protocols have been proposed to overcome these challenges in ad-hoc networks [1]. routing protocols play an important role in dynamic network communication. commonly, routing protocols are divided into topological and position-based protocols. further topological-based routing protocols may be classified mainly into proactive, reactive and hybrid types. on the other hand, position-based routing protocols diminish the limitations of topological-based routing protocols using location information via gps [2]. there are three position-based routing approaches such as, greedy, restricted directional-flooding and hierarchical type techniques. earlier several position-based protocols had been projected such as lar-1, distance routing effect algorithm for mobility (dream), most forward within distance r (mfr) and so on. lar-1 is widely used source routing protocol, like a dsr. it is a type of restricted directional flooding based position-based routing protocol. initially it starts flooding in network by the source but after expecting destination, the routing will be only in the direction of the destination. since nodes in manet are battery dependent. to extend network lifetime, routing protocol should concentrate for achieving energy conservation in the network [3] [4]. in this paper, nlar-1 technique has been developed with association of power-aware dynamic source routing (padsr) energy model in manet. this proposed work is an endeavour to address challenging copyright © 2006-2014 by ccc publications lar-1: affirmative influences on energy-conservation and network lifetime in manet 285 the issue of developing an effective energy-conserving technique for manet. this paper also shows optimization of ad-hoc network performance metrics using proposed n-lar-1 technique. in second section, literature survey has been presented. the short description of conventional lar-1 protocol is shown in next section. afterward n-lar-1 is elaborated in next portion. in fifth section, simulation results and its effect on proposed technique have been displayed. last part concludes the paper. 2 preliminaries manet is the group of wireless mobile nodes without any pre-defined infrastructure. each mobile device has a limited energy and additional energy is required to forward the packets during route discovery mechanism in ad-hoc network. therefore, energy-conservation is required in network. several researchers have been contributed in the era of energy-conservation. this section shows literature survey of work on lar-1, energy-conservation on lar-1 along with comparison amongst various routing protocols and energy-conserving routing techniques in manet. at first optimization in route discovery overhead was given by ko y.b. et al. [5] in the form of location-aided routing (lar) protocols in manets. they suggested an approach to utilize the location information via gps to improve performance of routing protocols in the network. they developed two location-aided routing algorithms lar-1 and lar-2. both use directional forwarding flooding, in which the source node floods data packets in the direction of the destination node. both limit the search for a route in restricted area. they had concluded that routing overhead is reduced using lar-1 scheme. ahvar e. et al. [1] analysed the performances of lar-1, dynamic source routing (dsr) and ad-hoc on-demand distance vector (aodv) routing protocols on the basis of energy consumption in ad-hoc networks. this analysis was accomplished by varying the network load, size and node mobility of the network. they concluded that lar-1 is much better than others and affords energy-conservation in high node density networks. xu y. et al. [6] proposed an energy saving geographic adaptive fidelity (gaf) algorithm. it concentrates on turning the radio off as much as possible. gaf nodes utilize geographic location information to segregate into fixed square grids. nodes within a grid, switch between sleeping and listening state with the guarantee that one node in each grid stays up to route packets. their results show that it can save 40-60% more energy than conventional protocol. patel a. et al. [7] recognized an unreachable corner of gaf scheme and its effect in the network. gaf-h (hexagonal) algorithm has been derived and checked with different traffics by them. it replaces virtual square grid into hexagonal grid. they concluded that it optimizes the packet delivery fraction (pdf) and throughput of the network. chen b. et al. [8] proposed a power saving protocol span that reduces energy consumption without shrinking the capacity or connectivity of an ad-hoc network. it adaptively elects coordinators and to form a fundamental structure that has set defined rules that are primarily based on the residual energy at nodes plus the number of nodal neighbours for coordinator announcement and withdrawal. further joshi n. et al. [3] modified lar-1 scheme in terms of energy-constrained operations is known as variable range energy aware location-aided routing (elar-1var). it controls the transmission power of a node according to the distance between the nodes. they evaluated elar-1-var with lar-1 in the terms of pdf, end-to-end (e2e) delay and average energy consumption. in conclusion, it improves the network lifetime by reducing energy consumption in the network. ramkrishnan s. et al. [9] exposed padsr protocol and implemented on small to medium size network. it is concluded by them that padsr protocol outperforms than dsr protocol by power saving of 30% in manet. 286 n. gupta, r. gupta 3 conventional lar-1 the dynamic nature of manet results in frequent and unpredictable changes of network topology, adding complexity to routing among the mobile nodes. the chief aim of routing is to find and maintain route between nodes in a dynamic topology with possibly unidirectional links using minimum resources in ad-hoc network. several routing protocols have been proposed for manets to accomplish effectual routing. one such example is lar-1 scheme that was utilized for reducing the transfer range of broadcasting during route discovery in networks. the objective of lar-1 is to perform more efficient route discovery and limit the flooding of route request packets. at first ko y.b. et al [5] presented the idea of utilizing the location information for mobile nodes in terms of route discovery optimization, is called lar-1. lar is one of the most popular amongst location-based routing protocol. conventional lar-1 is an on-demand restricted direction flooding location-based routing protocol. it uses source routing like a dsr, but after expecting the destination packets have flooded in the direction of destination only. source routing has been used with statically and dynamically configured routes for routing in manet. concept of lar-1 routing depends upon request and expected region. request region comprises of source node and expected region. if location is available, a request region (including the expected region) is formed that is defined as the region containing set of nodes that should forward the route discovery packet. request region is also defined by location information of destination node. size of this region depends upon movement of destination, elapsed time in which prior location of destination is included. as a result, the routing overhead reduces and better performance of lar-1 protocol is achieved in the network [5] [10]. lar-1 can be effectively used when an infinite queue of data packets is to be sent in the network. 4 proposed technique proposed technique integrates the method of energy-conservation of mobile nodes and routediscovery mechanism via lar-1, is named as, n-lar-1 technique. 4.1 general criterion of the technique 1. at first, source node (s) broadcasts (b) rreq packets to nodes within their individual radio range (rj). 2. whole area in request region from s to d (destination) is divided into numerous hexagonal grids within the radio range of mobile nodes in the network. 3. at the time of flooding, only having higher energy (non-survival condition of energy) of nodes are selected, called coordinators. 4. at the same time, other remaining nodes will go into the sleep mode correspondingly and save their energy in the network. 5. through these coordinators d is achieved and after some time acknowledgment(ack)goes to the s from d that contains the information of destination. otherwise, the process is restarted. 6. now, destination is achieved in flooding as a dsr. hence, the computing the new route occurs through lar-1 schemes again only in the direction of an expected region of destination node. 7. at a moment, the route is established in the direction of destination through the selected coordinators under lar-1 scheme within the request region of an ad-hoc network. lar-1: affirmative influences on energy-conservation and network lifetime in manet 287 4.2 algorithm stage-1 1. create mobile node mj; 2. set source node s, sξmj; 3. set destination node d, dξmj; 4. set routing protocol, dsr & lar-1; 5. set radio range rj // rj=550 m; 6. compute _ route (rreq_b, s, d); 7. if (next_neighbor node == true, energy≥10 j & next_neighbor≤rj); 8. next_neighbour_node_table (a, b, c, d -); 9. check_eng = max (a_eng, b_eng, c_eng, — -) ; 10. set c = = max_eng_node // for coordinators selection; 11. if ( next_neighbour_node = = d); 12. find destination_node; 13. send ack to s via rrep through selected route ; 14. call to lar-1; 15. else go to step-7 stage-2 1. call to lar-1; 2. information_d (elapsed time, speed, radius)_ expected region, send to s; 3. s broadcast (b), rreq only in d_expected-region; 4. compute_route (rreq_b_expected_region, s, d); 5. if (next_neighbour node = = true, energy≥ 10 j & next_neighbour≤ rj; 6. next_neighbour_node_table (a, b, c, d -); 7. check_eng = max (a_eng, b_eng, c_eng, — -) ; 8. set c = = max_eng_node //for coordinator selection; 9. if ( next_destination_node = = d); 10. route is established; 11. send ack to s via rrep from d; 12. else go to step-5. in figure 1, the expected region shows circular area radius of r = v (t1 − t0). the request region is divided into number of hexagonal grids 1,2,3,4,5,6,7,8,9,10,11,12 (shown above in figure 1). a, e, f, w, q, i, k, a, v, m, o, s are the route forwarding nodes having the higher energy values than other nodes in their respective grids. other remaining nodes will be in sleep or inactive mode at the same time and save their energy in manet. finally, dashed line in figure 1 shows the route from s to d using proposed n-lar-1 energy-conserving routing technique. thus, number of hexagonal grids is to be monitored and forward route tracing is more reduced with decreased routing overhead, e2e delay and energy consumption in the network. in such a way, energy conserves and network lifetime is enhanced in ad-hoc networks. 288 n. gupta, r. gupta figure 1: n-lar-1 routing technique 5 simulation and results the simulation was conducted in the network simulator (ns2) environment. all simulations were performed on intel (r) core i3 cpu, 2.3 ghz, 3072 mb of ram running on inspiron n5010 configuration. simulation includes physical, data-link and medium access control (mac) layer models. the distributed coordination function (dcf) of ieee 802.11 is used to model the contention of nodes for the wireless medium. the radio model uses characteristics similar to lucents wave lan direct sequence spread spectrum radio [11]. the simulations were run for 100 seconds. the number of nodes (n) in network is 25, 50, 75, 100 and situated in a network area of 800 x 600 square meter region having a transmission range of 550m. in implementation, simulator setup (radio propagation model, random waypoint mobility model) and all simulation parameters with simulation environment has been shown in table1 separately. table 1: simulation environment network parameter value simulator ns-2.31 simulation time 100 seconds transmission range 550 m node movement model random way point routing protocols dsr and lar-1 simulation area 800 x 600m2 bandwidth 2 mb/s traffic type cbr transport agents tcp node speed 0-20 m/s energy value 18-100 j 5.1 simulation results and discussion if the concept of proposed technique is applied separately on dsr protocol, is named as n-dsr approach. in this simulation, implementation has accomplished between n-lar-1 and n-dsr approaches for 25, 50, 75 and 100 nodes. comparison of performance metrics like pdf, throughput, normalized routing load (nrl), e2e delay and average node energy consumption is displayed in figures 2, 3, 4, 5 and 6 respectively. lar-1: affirmative influences on energy-conservation and network lifetime in manet 289 5.2 results figure 2: pdf comparison figure 3:throughput comparison figure 4:nrl comparison figure 5:e2e delay comparison figure 6:average node energy consumption comparison 5.3 analysis of performance metrics 1. pdf: pdf is consistent in n-lar-1 and n-dsr techniques with increasing the number of nodes. it is clear from figure 2 that pdf ratio is higher using n-lar-1 than n-dsr approach. 2. throughput: n-lar-1 technique provides better throughput than n-dsr scheme. this analysis presents consistent behaviour in both techniques on 50, 75 and 100 nodes. 3. nrl: nrl ratio increases in both techniques till 25 nodes. further, it varies with increasing the number of nodes in the network. nrl ratio is reduced using n-lar-1 in comparison to n-dsr. 4. e2e delay: e2e delay analysis illustrates linear variation in both approaches till 25 nodes. e2e delay is less in n-lar-1 than n-dsr and can be optimized when the number of nodes is 75 in the network. 290 n. gupta, r. gupta 5. average node energy consumption: figure 6 depicts the average node energy consumption analysis for n-lar-1 and n-dsr techniques. this parameter varies with increasing the number of nodes in network. average node energy consumption using n-lar-1 is less than n-dsr and can be optimized on 75 nodes. it is concluded from the analysis, n-lar-1 technique achieves energy conservation along with sending successfully higher data packets to the destination. all above-mentioned performance metrics are optimized and overall network lifetime of network is improved using n-lar-1 in comparison to n-dsr. 6 conclusion and future work lar-1 extends the on-demand routing approach in manets and packets have flooded towards the destination only instead of entire ad-hoc network. this research work shows development of an effective energy-conserving technique n-lar-1 for manet. proposed n-lar-1 technique has arrived at a conclusion that performance metrics such as average node energy consumption, pdf, e2e delay, nrl, throughput and network lifetime have been improved when the sleep or inactive mode of the nodes in the networks are utilized. it has been analysed that performance of n-lar-1 is better than n-dsr approach. these improvements in the performance of n-lar-1 are possible due to the flooding of data in the direction of expected destination only. research can be extended to integrate such type of work like proposed n-lar-1 technique using other reactive routing protocols like aodv, olsr, tora and position-based routing protocols like gls, dream in future. further this proposed technique can be implemented and analyzed for wireless sensor networks, vehicular ad-hoc networks and cognitive radio networks. bibliography [1] e. ahvar, and m. fathy (2007), performance evaluation of routing protocols for high density ad-hoc networks based on energy consumption by glomosim simulator, proceedings of world academy of science, engineering and technology, 97-100. [2] mauve m. widmer a. and hartenstein h (2001), a survey on position-based routing in mobile ad-hoc networks, ieee network, 15 (6):30-39. [3] joshi n., joshi d (2011), energy conservation in manet using variable range locationaided routing protocol, international journal of wireless and mobile networks (ijwmn), issn: 0975-3834, 3(5): 265-280. [4] qabajeh l., kiah l and qabajeh m. (2009), a qualitative comparison of position-based routing protocols for ad-hoc networks, international journal of computer science and network security (ijcsns), issn:1738-7906, 9(2):131-140. [5] y. ko and n. vaidya (1998), location-aided routing (lar) in mobile ad-hoc networks, proceedings of the acm/ieee international conference on mobile computing and networking (mobicom98):66-75. [6] y. xu, j. heidemann, and d. estrin (2001), geography-informed energy conservation for ad-hoc routing, proceedings of 7th annual international conference on mobile computing and networking:70-84. lar-1: affirmative influences on energy-conservation and network lifetime in manet 291 [7] patel a., joshi r. (2009), energy conservation for wireless mobile ad-hoc networks using hexagonal gaf protocol, proceedings of the 12th international conference on networking, vlsi and signal processing: 29-34. [8] chen b., jamieson k., balkrishnan h. and morris r (2002), span: an energy-efficient coordination algorithm for topology maintenance in ad-hoc wireless networks, acm wireless networks journal, 8(5):481-494. [9] ramkrishnan s., t. thyagarajan, p. k. ram and r.vinodh (2005), design and analysis of padsr protocol for routing in manet, ieee indicon-2005 conference, chennai, india: 193-197. [10] gupta n., gupta r (2010), coordinator selection technique for energy conservation in manet, ciit international journal of wireless communication, ciit published by the coimbatore institute of information technology, issn:0974-9756, 2(3):75-78. [11] fall kevin (2011), ns manual (formerly ns notes and documentation), vint project, www.isi.edu/nsnam/ns/doc. international journal of computers communications & control issn 1841-9836, 10(3):382-389, june, 2015. cbmir: content based medical image retrieval using multilevel hybrid approach b. ramamurthy, k.r. chandran b. ramamurthy* sri ramakrishna engineering college, coimbatore, india *corresponding author: hod-ct@srec.ac.in k.r. chandran psg college of technology, coimbatore, india. abstract: the problem of retrieval and managing of medical images has become more important due to its scalability and rich information contained in it. in day to day activities, as more as medical images were converted into digital form. due to its nature, the need for efficient and simple access to this data is essential one. this paper proposed a novel approach namely “content based medical image retrieval using multilevel hybrid approach" to manage and retrieval of this data.this work has been implemented as four levels. in each level, the retrieval performance of the work has been improved. the result of this work has been compared with some of the existing works that are made as literature review on this work. keywords: cbmir, cbir, euclidean method, precision, recall. 1 introduction the rapid expansion of digital image data has led to the need of rich descriptions and efficient retrieval tool. due to this, many problems were often arises while retrieval of such data in specific applications such as accessing speed, scalability, efficiency etc. to improve this, still more and more research initiative is needed in the field of digital image retrieval system. in medical domain, the amount of image data generation is more and more in day to day activities such as ct, x-ray, and mri etc. so the scalability of the medical image database has been increased and thereby need of this image retrieval system is essential one. content based medical image retrieval (cbmir) is an emerging technique that plays a pivot role in this domain. in this work, shape, texture and intensity contents were used to implementing the system. 2 related work cbmir: shape-based image retrieval using canny edge detection and k-means clustering algorithms for medical images: in this work, the authors have proposed a shape based image retrieval system for medical images. to implement this, preprocessing, image segmentation, feature extraction, and classification steps were carried out. to retrieval, euclidian distance calculated between query image and database images. since the system used shape feature alone, it gives about 50% retrieval performance only [1]. content based medical image retrieval with texture content using gray level co-occurrence matrix and k-means clustering algorithms: this system has used a texture based image retrieval system for medical image retrieval. it has capable of retrieving images based on the texture feature of the image. to implement this, the preprocessing, feature extraction, classification and retrieval steps are carried out. the main feature of the system is copyright © 2006-2015 by ccc publications cbmir: content based medical image retrieval using multilevel hybrid approach 383 to utilization of gray level co-occurrence matrix (glcm) and k-means clustering algorithm to improve the retrieval efficiency of the system. since the system used texture feature alone, it gives about 50% retrieval performance only [2]. cbmir: content based medical image retrieval using shape and texture content: the system proposed a shape and texture based image retrieval system for medical images. it has capable of retrieving images based on the shape and texture feature extraction combined together. the main objective of the system is to improve the retrieval efficiency when compared to single feature method. since this system has used both shape and texture features combined together,it gives about 50%-90% efficiency [3]. mirage: an e-repository of medical images for learning biomedical informatics: this paper proposed an e-repository for medical images that offers great facilities to learn about the biomedical informatics. the facilities of domain-based, atlas-based, and content-based retrieval (cbir) techniques are implemented to search the images in this developed repository. the uniqueness of the system is cbir system for 3d is developed and coupled with 3d visualization that has used for educational material and as well as tele-education in future [4]. computer-aided diagnostics of screening mammography using content-based image retrieval: this paper proposed a computer-aided diagnostics tool for screening the mammography using content based image retrieval technique. in this work, support vector machine (svm) classification technique was used for doubtful tissue pattern extraction in an image. based on that, the retrieval of the system implemented using cbir for detecting the mammography of the image [5]. content-based binary image retrieval using the adaptive hierarchical density histogram: the paper proposed a scheme for binary image retrieval. it has utilized black and white binary represented values as image feature and it named as the adaptive hierarchical density histogram that develops the allocation of the image points on a two-dimensional area. this technique uses the assessment of point density histograms of image regions that are computed by a pyramidal grid that is repeatedly simplified through the calculation of image geometric centroids. this extracted descriptor includes both global and local possessions that can be used in different types of binary image databases for the retrieval of images [6]. improving the ranking quality of medical image retrieval using a genetic feature selection method: in this paper, the authors proposed a method for improving the ranking quality for medical image retrieval using genetic feature selection method. here the authors have used single-valued genetic functions for evaluating the rankings to extend a group of feature selection methods based on the genetic algorithm approach to improve the precision of contentbased image retrieval systems [7]. 3 proposed model the general process method of content based medical image retrieval system is described as follows. initially medical images were taken as input to the system and preprocessing of the images carried out in order to improve the flexibility of the images for further processing of the system. then the output images of this preprocessing step are taken for feature extraction, feature vector construction and feature database construction processes respectively. this similar process has been applied for query image also (up to feature extraction and feature vector construction process only). while searching the image, the query image feature vector compared with the database image feature vector by using euclidean distance calculation method. the minimal euclidean distance value is considered as closest distance image from the database image that ranked as first. the proposed cbmir framework is shown in figure 1. 384 b. ramamurthy, k.r. chandran figure 1: the proposed cbmir framework in this proposed approach, there are four levels of results have been derived based on the multilevel hybrid approach method. they are level 1. in this level, the content based medical image retrieval system has been implemented based on shape feature extraction method alone and texture feature extraction method alone (ie.single feature extraction method). for shape feature extraction, there are two methods have been implemented. first one is canny edge detection and another one is edge histogram descriptor. similarly, for texture feature extraction, there are two methods have been implemented. first one is gray level co-occurrence matrix and another one is edge based texture feature extraction. for each method, separate results have been derived for further comparison with the next level in order to measure the performance of the system in each level. level 2. in this level, a hybrid feature extraction method implemented that includes a hybrid shape feature extraction method by combining canny’s edge detection method (ced) and edge histogram descriptor (ehd) and hybrid texture feature extraction method by combining gray level co-occurrence matrix (glcm) and edge based texture feature extraction(ebt) in order to extract the best pattern of the images. after obtaining this pattern, the image feature vector value of that pattern has been generated and stored in a database named ‘feature database’ for further searching and retrieval of the process. while retrieving the images, for each method (hybrid shape, hybrid texture), separate results have been derived for further comparison with the next level in order to measure the performance of the system. level 3. in this level, a hybrid shape and texture feature extraction method has been implemented by combining the output result of the hybrid shape feature extraction method and hybrid texture feature extraction method. after combining this, the fine tuned of the both shape and texture pattern of the image has been obtained and then the feature vector value of that pattern also has been generated and stored as a database named ‘feature database’ for further searching and retrieval of the process. while retrieving the images, the result also has been derived for further comparison with the next level in order to measure the performance of the system. level 4. in this level, hybrid intensity feature extraction method has been implemented by combining intensity features with hybrid shape and texture feature extraction method presented in level 3. after combining this, another level of fine tuned image pattern has been obtained cbmir: content based medical image retrieval using multilevel hybrid approach 385 along with combined form of shape, texture and intensity features. the feature vector value for this obtained pattern has been generated and stored as a database named ‘feature database’ for further searching and retrieval of the process. while retrieving the images, the result also has been derived for further comparison with the next level in order to measure the performance of the system. similarly n number of image features can be used to develop n number of levels to measure retrieval accuracy of the system. in this system we have incorporated up to four levels only. similarity comparison in this approach (in all the levels), for retrieval of the images, similarity comparison technique has been used. for similarity comparison, we have used euclidean distance, d using the following equation [8, 9]. d = √√√√ n∑ i=1 (fq[i] − fdb[i])2 (1) where fq[i] is the ith query image feature and fdb[i] is the corresponding feature in the feature vector database. here n refers to the number of images in the database. 4 experimental setup and result analysis a intel r core 2 duo cpu workstation with 2gb ram computer has been used for conducting the experiments. the matlab 7.2.0-image processing tool box was used for developing user interface components as front end, matlab 7.2.0-image processing tool box-workspace was used as feature database for storage as back end and for image processing work, other matlab 7.2.0 utilities were used. 4.1 retrieval efficiency for retrieval efficiency, traditional measures namely precision and recall parameters were computed for several real time medical images [10–12]. standard formulas that have been computed for determining the precision and recall measures. precision and recall parameters can be defined as follows. precision (p ) is the ratio of the relevant images to the total number of images retrieved p = r n1 (2) where, r number of relevant images retrieved n1 total number of images retrieved. recall(r) is the percentage of relevant images among all possible relevant images r = r n2 (3) where, r number of relevant images retrieved n2 total number of relevant images in the database. 386 b. ramamurthy, k.r. chandran by randomly selecting some sample query images from the matlab-image processing tool box-workspace database, the system was tested and the results are shown in the following tables for all the four levels presented in section 3. level 1: in level 1, there are four methods have been implemented as single feature extraction method. the result of all the methods in level 1 has been presented in the following tables. the shape feature extraction method using canny edge detection (ced) method result has been presented in table 1. table 1. precision and recall values in % query image image 1 image 2 image 3 image 4 image 5 precision 50.00 58.33 68.26 56.95 53.33 recall 37.50 36.84 51.42 66.66 34.00 the shape feature extraction method using edge histogram descriptor (ehd) method result has been presented in table 2. table 2. precision and recall values in % query image image 1 image 2 image 3 image 4 image 5 precision 50.00 55.33 65.23 59.84 60.33 recall 40.50 33.50 45.95 59.00 55.00 the texture feature extraction method using gray level co-occurrence matrix (glcm) method result has been presented in table 3. table 3. precision and recall values in % query image image 1 image 2 image 3 image 4 image 5 precision 55.71 65.71 53.33 57.00 56.00 recall 45.00 48.94 51.42 56.66 60.00 the texture feature extraction method using edge based texture (ebt) method result has been presented in table 4. table 4. precision and recall values in % query image image 1 image 2 image 3 image 4 image 5 precision 55.71 50.00 65.00 56.33 68.94 recall 55.66 56.00 65.33 65.00 51.00 in this first level of multi-level hybrid approach, all the approaches such as ced, ehd, glcm and ebt gives about 50% to 68.94% of the retrieval performance in terms of precision and 33.50% to 66.66% in term s of recall since all the works were in initial stage and also each method gives its own retrieval performance. level 2: in level 2, there are two methods have been implemented as hybrid feature extraction method. the result of these methods in level 2 has been presented in the following tables. the hybrid shape feature extraction using ced and ehd method result has been presented in table 5. table 5. precision and recall values in % query image image 1 image 2 image 3 image 4 image 5 precision 73.00 70.33 72.26 71.95 73.33 recall 60.50 68.84 62.42 60.66 61.00 cbmir: content based medical image retrieval using multilevel hybrid approach 387 the hybrid texture feature extraction using glcm and ebt method result has been presented in table 6. table 6. precision and recall values in % query image image 1 image 2 image 3 image 4 image 5 precision 76.00 70.33 75.26 70.95 78.33 recall 66.50 60.84 68.42 63.66 66.00 in this, second level of multi-level hybrid approach, the first approach such as the hybrid shape feature extraction using ced and ehd method gives about 70.33% to 73.33% of the retrieval performance in terms of precision and 60.50% to 68.84% in terms of recall. the hybrid texture feature extraction using glcm and ebt method gives about 70.33% to 78.33% of the retrieval performance in terms of precision and 60.84% to 68.42% in terms of recall. hence this second level gives better performance than the level 1 performance. level 3: in level 3, the hybrid shape & texture feature extraction method has been implemented using the combined form of hybrid shape feature extraction method (hbs) and hybrid texture feature extraction method (hbt).the result of this method in level 3 has been presented in the following table 7. table 7. precision and recall values in % query image image 1 image 2 image 3 image 4 image 5 precision 94.73 94.44 93.93 92.59 87.50 recall 90.00 89.47 88.57 83.33 84.00 in this, 3rd level, the performance of the retrieval drastically increased since both the shape and texture features combined together used for retrieval process. when compared to the level 2, this level gives its performance much better and also in terms of precision it gives about 87.50% to 94.73% and in terms recalls about 83.33% to 90.00% of the retrieval performance. level 4: in level 4, the hybrid shape, texture and intensity feature extraction method has been implemented by combining the hybrid shape and texture feature extraction method with intensity feature extraction method in order to extend the next level of the work. the result of this method has been presented in the following table 8. table 8. precision and recall values in % query image image 1 image 2 image 3 image 4 image 5 precision 95.75 95.00 94.93 93.58 94.65 recall 91.66 90.10 90.57 92.00 87.00 in this, 4th level, the performance of the retrieval highly increased since all the shape, texture, and intensity features combined together used for retrieval process. when compared to the level 3, this level outperforms well and also in terms of precision it gives about 93.58% to 95.75% and in terms of recall about 87.00% to 92.00% of the retrieval performance. similarly n-number of image features can be integrated in order to improve the retrieval performance of the system since the image pattern has played a vital role in an image retrieval process. 4.2 result comparison and discussion the result comparison also made for the above described levels of the work. the following table 9 shows comparison results of the work. 388 b. ramamurthy, k.r. chandran table 9. the comparison of the result (precision and recall values in %) level 1 level 2 level 3 level 4 hybrid hybrid shape, hybrid hybrid shape & texture & shape shape texture texture shape texture texture intensity (ced) (ehd) (glcm) ebt (ced+ehd) (glcm+ebt) (hbs+hbt) (hbs+hbt+hbi) p r p r p r p r p r p r p r p r 50.00 37.50 50.00 40.50 55.71 45.00 55.71 55.66 73.00 60.50 76.00 66.50 94.73 90.00 95.75 91.66 58.33 36.84 55.33 33.50 65.71 48.94 50.00 56.00 70.33 68.84 70.33 60.84 94.44 89.47 95.00 90.10 68.26 51.42 65.23 45.95 53.33 51.42 65.00 65.33 72.26 62.42 75.26 68.42 93.93 88.57 94.93 90.57 56.95 66.66 59.84 59.00 57.00 56.66 56.33 65.00 71.95 60.66 70.95 63.66 92.59 83.33 93.58 92.00 53.33 34.00 60.33 55.00 56.00 60.00 68.94 51.00 73.33 61.00 78.33 66.00 87.50 84.00 94.65 87.00 p precision, r recall from the above table, the proposed framework has improved results in significantly for content based medical image retrieval system using a new approach called "multilevel hybrid approach". it has been shown that results are in level wise improvement. in level 1, experiments provide about 50% result performance. in level 2, it has been increased to in some extent that is about 25% higher than the levelb1. in level 3, the retrieval performance of the system drastically increased since both the shape and texture features combined together used in retrieval. in level 4, a new feature called ‘intensity’ has been used along with shape and texture method. it gives a better result than previous level 3. this work also provides an entire framework for inserting and deleting the features as user preferred. 5 conclusion on the whole, this proposed system gives its own performance and as well as flexibility for content based medical image retrieval system since the system supports n number of features for pattern recognition of the image. in each level, the retrieval performance of the system also significantly improved. the experimental results show that the medical images have been retrieved in an efficient manner using multilevel hybrid approach method. the system is designed in a flexible and consistent flow for easy understanding. some of the goals that have been achieved by the developed system are accuracy of retrieved images by designing an efficient framework and incorporating it with the feature extraction techniques. bibliography [1] b. ramamurthy, k.r.chandran (2011); cbmir: shape-based image retrieval using canny edge detection and k-means clustering algorithms for 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[12] subrahmanyam murala et al (2014); local mesh patterns versus local binary patterns: biomedical image indexing and retrieval, ieee journal of biomedical and health informatics, 18(3):929-938. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 6, month: december, year: 2022 article number: 4929, https://doi.org/10.15837/ijccc.2022.6.4929 ccc publications brain tumor identification using dilated u-net based cnn d. saida, p. premchand d. saida* department of computer science & engineering university college of engineering, osmania university hyderabad, telangana 500007, india *corresponding author: saidan.dhana@gmail.com p. premchand department of computer science & engineering university college of engineering, osmania university, hyderabad, telangana 500007, india sairadha.rajputh@gmail.com abstract the identification of brain tumor consumes time and therefore it is important to develop an automated system using an imaging technique. the classification of brain tumor into benign or malignant is performed by using magnetic resonance image (mri). from the mri based brain tumor images, the extraction of features is essential for pattern recognition that determines the object based on the color, names, shapes, or more. therefore, the classifiers are dependent on the strength of features such as shape, color, etc., yet, the classifiers are dependent on the features that are extracted using deep learning classifiers which are dependent on the features that were extracted. the deep learning algorithm in the medical domain showed interest in the computer vision researchers which consumed time during the process of execution. the proposed dilated unet model expands the receptive field for the extraction of multi scale context information. based on the high resolution conditions, the large scale feature maps and high-resolution conditions are generated using large scale feature maps. it provides rich spatial information that was applied for performing semantic segmentation. semantic image segmentation is achieved using a u-net as it adds an expansive path to generate classifications of the pixels belonging to features found in the source image. the existing kernel based svm model obtained accuracy of 99.15%, non-dominated sorted genetic algorithm-convolutional neural network (nsga -cnn) obtained accuracy of 99%, deep elman neural network with adaptive fuzzy clustering obtained accuracy of 98%, 3d context deep supervised u-net obtained accuracy of 92%. whereas, the proposed dilated u-net-based cnn model obtained accuracy of 99.5% better when compared with the existing models. keywords: brain tumor, deep learning classifiers, dilated u-net cnn model, magnetic resonance image. https://doi.org/10.15837/ijccc.2022.6.4929 2 1 introduction brain is an important part of the whole nervous system as it can control and coordinate the whole body [1]. the abnormal cells in the brain are referred to as brain tumors which threaten the life of an individual if it is untreated [2]. thus, the tumors are classified into two major types such as primary tumors and secondary tumors [3]. the malignant tumor primarily starts to grow inside the brain yet the secondary malignant tumor initiates in other organs that might spread towards the brain through metastasis [4]. the human body suffers from a brain tumor and a malignant tumor which is highly intimidating if untreated [5]. thus, the brain tumors were unknown and complete medical history was required to be evaluated to show risk for the occurrence [6]. the segmentation of brain tumor is important in diagnosis and helps to plan the cancer treatment that detects the stages of tumor such as low-grade (lgg) and high-grade gliomas (hgg) [7]. the specialists have the option of using computed tomography (ct), ultrasound, magnetic resonance imaging (mri) for screening the patients [8]. the mri image is an impressive technology that shows various modalities as it is non-invasive which obtained a better representation of internal tumor information [9]. the manual classification of brain tumors shows vulnerability with respect to formidable tasks, time consumption, and error [10]. therefore, deep learning models are used to classify brain tumor based on mr data [11]. the model operated fast and obtained higher accuracy showed treatment for the patients [12]. the mri segmentation is done based on the learning strategies and recognizes the patterns successfully for analyzing the brain images. the density-based function is used for selecting the functions as it considers the parametric model [13]. the existing models such as convolution neural network (cnn), fully convolutional neural network (fcnn), cascaded neural network, auto encoder, deconvnets, 3d cnn were used for the detection of brain tumor based on mr image analysis. the low resolution channels or the images were required for reducing the computational complexity without any alteration in the accuracy factor. the significant difference between deep learning and artificial neural network is that it does have several layers for computation where excellent classification is performed for the medical images better compared to the existing models. in the present research work, the proposed process of semantic segmentation is deepened that has the spatial information present in the feature map that reduces the time. the main objective is to extract information from image’s edge when the convolution layer deepens and shows higher semantics. the spatial information is lost and the dilated convolution preserves the spatial information and the dilated convolution preserves it as much as that showed improvement in terms of accuracy for segmentation prediction. the research paper is given as follows: section 2 explains the existing models under the literature review. section 3 explains the various steps involved in the proposed method. section 4 describes the results and discussion of the proposed method. the conclusion and future work of this research paper is given in section 5. 2 literature review the existing methodologies involved in brain tumor classification are given as follows: a novel residual mobile u-net model for brain tumor segmentation from mr images was shown by muhammad usman saeed et al. [14]. a hybrid deep learning model called rmu-net is suggested to achieve end-to-end brain tumor segmentation. remaining blocks are added to mobilenetv2’s design in order to master intricate characteristics. the suggested technique uses this modified version of mobile net v2 as an encoder and u-up net’s sampling layers as the decoder. the suggested technique improves with much less computational time and expense. even though, rmu-training net’s needs a considerable portion of manually labeled brain tumor data. multi-encoder net (me-net) framework has been demonstrated by wenbo zhang et al [15] for segmenting brain tumors. this model suggestively advances model presentation whereas reducing the difficulty of feature extraction. additionally, a new loss function named categorical dice was added, and different weights were simultaneously assigned for various segmented regions, which resolved the voxel imbalance issue. this architecture simultaneously processes many input images and extract https://doi.org/10.15837/ijccc.2022.6.4929 3 particular features. finally, one of the issues was that better data improvement methods have not yet been established. champakamala sundar rao and karunakara [16] developed a kernel based svm model with social ski driver (ssd) for the detection of brain tumor. an efficient brain tumor detection was important that uses a binomial thresholding approach for segmentation. from the segmented region, the features were fused and were undergone for feature selection using harris hawk’s optimization (hho). the proposed ksvm-ssd performed an effective and accurate results classification that classified as benign or malignant and the ssd optimization further helped to find the tumor as high, medium, or low. the optimization algorithm was sensitive to the data features which were irrelevant during feature occurrences. muhammad irfan sharif et al. [17] developed an improved framework to detect the brain tumor using mri based on cnn and yolov2 models. the features were extracted from the inceptionv3 model for pre-training the informative features. from the extracted features, the non-dominated sorted genetic algorithm (nsga) was used for the selection of features. the features were optimized by forwarding to the classifier at the depth concatenation mixed 4 layers which is then supplied to yolov2. however, at the segmentation stage, the disease severity levels were not identified. sakthidasan sankaran et al. [18] developed fuzzy clustering approach adaptively with a deep elman neural network to perform segmentation and clustering for identifying the brain tumor grade. the regions from the tumor were segmented using an adaptive fuzzy tsallis entropy (fte) clustering with cuckoo search optimization (ics). initially, the images were undergone for the process of preprocessing by using the anisotropic diffusion and non-parametric region based model for the noise and skull removal. the deep elman neural network (denn) was used for categorizing the brain tumor that was developed and classified as a brain tumor. the model used more than one classifier which examined robustness to improve the accuracy of large database that consisted of medical images. mingquan lin et al [19] developed a fully automated segmentation with 3d context deep supervised u-net model to segment the brain tumor. the multipara metrics from the brain tumor images were evaluated using the 3d context deep supervised u-net model. the developed approach enlarged the receptive field effectively using the cnn model. this improved the segmentation accuracy of brain tumor regions. the tumor volume generated by the proposed method was compared with the pearson analysis and bland altman plots. however, the 3d cnn network required gpu for processing large images which took a long time for network training. ahmet ilhan et al. [20] developed a u-net based model for the classification of brain tumor images based on non-parametric localization. the developed model used an efficient approach for the segmentation of brain tumors based on tumor localization using deep learning architecture. the histogram based non-parametric tumor localization approach was applied to modify the localized regions which increase the virtual appearance of low-contrast tumors indistinctly. the tumorous regions show better performances for the deep learning models which effectively segmented the trained and untrained datasets without any requirement for augmented data. 3 proposed method figure 1 shows the block diagram of the proposed research work that consists of brats 2020 dataset that are processed for pre-processing. the obtained pre-processed images are undergone for segmentation of the brats image dataset. the segmented images are classified into benign or malignant based on the segmented image. 3.1 brats 2020 dataset the brats dataset mainly focuses on the evaluation state based on the state of art techniques. the segmentation of brain tumors is performed with respect to the multi modal mri scans. the distinction between the pseudoprogression and recurrence of a true tumor through integrative analysis is performed based on the random features. there are totally 335 images from the brats 2020 dataset. among the total number of images, 76 images are from low grade glioma (lgg) and the leftover 259 images are from high grade glioma (hgg) type of image. the mri scans are focusing https://doi.org/10.15837/ijccc.2022.6.4929 4 mainly on 3 major tasks segmentation, intrinsic heterogeneous image based on shape and appearance, and histology images for the patient survival prediction. 3.2 preprocessing the mr images are undergone for resizing the pixels. during resizing the images are cropped but also to manipulate the pixels for reducing the file size. the process of resizing images has become better than adjusted the file and picture size from a particular image which is expressed in equation (1). ri = imresize(i, [w, h]) (1) where i shows the input image along with height (h)and width (w ), which is given as input to the further processing. here, the dimension of the image is (240×240×155). the normalizing process considerably improves the image quality and is more effective at reducing impulse and machine noises. in this research, color images are successfully converted to grayscale images. the following equation (2) is used to conduct grayscale image normalization: in = (i − m in) × newm ax − newm in m ax − m in + newm in (2) an n-dimensional grayscale image i is represented with intensity values among (m in, m ax) which is converted through normalization into a new image in with intensity values among (newm in, newm ax). the block diagram of the proposed research work is displayed in figure 1. figure 1: block diagram of the proposed research work the process of normalization is performed for the dataset where the process of normalization gets rid of irregularities makes more difficult for data interpretation. the process of normalization includes deleting data, updating existing information, or adding information where anomalies arise. the process of data augmentation is performed for data analysis to increase the data amount by adding the modified copies that are existing. the new synthetic data is created from the existing data. the regularized process helps to reduce the overfitting of the machine learning model for training and the batch wise data loading is performed. the input image and its predicted output image are illustrated in figure 2 and 3 respectively. 3.3 segmentation using dilated u-net model the initialization of u-net model is performed where the modern image classification is performed that uses a series of pooling to improve the context information. the operation of pooling is effectively increasing the neural network receptive field which is benefitted for computer vision tasks at a https://doi.org/10.15837/ijccc.2022.6.4929 5 figure 2: input image figure 3: predicted output image high level. yet, the pooling operation overcomes the degradation problem during picture resolution. therefore, the pooling operation is performed which gives irreversible results with spatial information loss when the results with non-reconstruction of a small object are obtained. the u-net model is used in the medical field and has implemented consecutively four 4 times based on the down sampling operations. however, due to the encoder-decoder architectures the low level feature maps are used for unsampling or decoding based on the invariance translation compromises. thus, violating translation invariance at the segmentation maps shows relatively low resolution because of the pooling layer presence in the encoder stage. therefore, the pooling layer preserves the high spatial resolution yet the convolution is rather operating locally and the u-net based model without pooling is not able to learn the image holistic features. the dilated convolutions or atrous convolutions provide the potential solution for overcoming the problem that is capable of providing and capturing the global context information. this is performed without decreasing the segmentation map resolution. the dense cnn model works based on the convolution layer fact that collects the larger neighborhood information. the cnns are working based on the convolutional layer that collects the information from the larger neighborhood around every pixel. from the upper layers of the network, the features from each pixel holds the information of the large region of an image. the results from the experiments show that the learning is performed and each of the pixel information is localized in each of the networks. the dilated convolution changes the rule based on the same size of kernels as it is having convolutional layers which cannot spread over a larger image area. the normal convolutions use zero padding approach for the dilated convolutions as it preserves the resolution exponentially which increases the receptive field of the kernel. thus, global information is exchanged among the layers without sacrificing resolution. the dilated convolution replaces the pooling layers and striding layers’ functionality in the networks. the convolution layers are replaced after the pooling operation is dilated with convolution made up the loss of weight after sharing. the training of the model performed and the prediction of masks are performed. the general u-net based convolution is stated in below equation (3), https://doi.org/10.15837/ijccc.2022.6.4929 6 (i × w)(p) ∑ s i[p + s]w[s] (3) the simple convolution operation can be generalized dilated convolution (dl) which is expressed in equation (4) (i × dlw)(p) ∑ s i[p + dls]w[s] (4) the predicted output of the image is shown in figure 4. while in the figure 5, predicted output mask is displayed. the augmentation output is presented in figure 6. figure 4: predicted output figure 5: predicted output mask pseudo code input: let the labeled data is represented as x̃ = x (1), x (2), . . . , x (k) preprocessing: deleting data, updating existing information, or adding information obtained labeled training data x̃ = x (1), x (2), . . . , x (k), where k is called as the total number of classes cnn ← x̃; % which is known as the training data which are sent to cnn for extraction of feature vectors f̃ = { f (1), f (2), . . . , f (k) } % extracted the features which are mapped for high dimensional space classification: input: x̂ is known as a classified image output: the x̂ class that belongs https://doi.org/10.15837/ijccc.2022.6.4929 7 figure 6: augmentation output figure 7 shows the proposed dilated based u-net models architecture. figure 7: the architecture of proposed dilated based u-net model 4 results and discussion the results obtained by the proposed method are evaluated in terms of accuracy, f-score, precision, and recall of the model. the developed model worked on the imbalanced mri datasets to evaluate the effectiveness. this research analyzed the imbalanced mri data which have an uneven distribution of observations across the target class, i.e., one class label has a high number of observations while the other has a low number. due to this reason, the conventional method of classification and model accuracy estimation is not valuable in the case of imbalanced data. on the whole, the metrics are dependent on the class imbalance and there is an improvement in the overall performances that are concerned with the class counts. the results were obtained by the proposed method by conducting it in an intel core i7 processor with 2 ghz cpu utilization time working with 48 gb of ram. the present research work feeds the training data to the classifier that evaluates the testing data. the performances are evaluated in terms of accuracy, precision, recall, and f-measure. accuracy the performance measure accuracy is called the ratio of correctly predicted observations to the overall observations. the accuracy term is calculated using the equation (5) https://doi.org/10.15837/ijccc.2022.6.4929 8 accuracy = (t p + t n ) (t p + t n + f p + f n ) (5) precision the precision is expressed as the number of total data samples that are predicted to the overall observations that are false with respect to the actual number with false observations. the precision term is defined as shown in the equation (6). p recision = t p t p + f p (6) recall recall is known as the actual number of traditional false observations that are considered as the ratio of totally predicted false observations. the recall is expressed as shown in equation (7). recall = t p t p + f n (7) f1-measure the f1 measure is known as the harmonic mean of the recall and precision which are evaluated by equation (8). f 1 − measure = 2 ∗ precision ∗ recall precision + recall (8) dice coefficient the equation for dice coefficient is expressed in equation (9). dice = 2t p 2t p + f p + f n (9) 4.1 quantitative analysis table 1 shows the results obtained by the proposed dilated u-net based cnn model. the results were obtained by the proposed dilated u-net based cnn model in terms of accuracy of 99.5%, fscore of 69%, and dice coefficient of 0.015. the proposed dilated u-net based approach obtained the accuracy of 99.47%, f-score of 69%, dice coefficient of 0.015. the proposed dilated u-net based cnn model that would not classify whether the infection is present or not but also identifies the infected area. the proposed dilated u-net based cnn model obtained better accuracy of 99.47%, f-score of 69%, and dice coefficient of 93.49%. while the existing rmu-net [14] and me-net [15] has attained the dice coefficient of 91.35% and 88% respectively. the dilated based u-net model can extract the edge-based features accurately during segmentation and thus obtained better values of results. the overall training time for the execution process is 14 mins and 24 seconds. but the running time taken per image is 0.11 seconds. table 1: results obtained for the proposed dilated u-net based cnn and existing model parameters rmu-net model [14] me-net model [15] proposed dilated u-net based cnn model accuracy (%) 99.47 f-score (%) 69 dice coefficient (%) 91.35 88 93.49 deep neural network (dnn) needs a large amount of data thus obtained accuracy of 95.42% and f-score of 65.32%. similarly, the dilated cnn model needs large number of datasets to process and train the neural network that obtained the accuracy of 97.54% and f-score of 67.32%. also, the image derived limited the information related to location with standard or specific distance among the object https://doi.org/10.15837/ijccc.2022.6.4929 9 and region that obtained the accuracy of 98.02% and f-score 67.87%. whereas, the proposed dilated u-net based cnn model obtained an accuracy of 99.5% and f-score of 69%. also, the u-net allows for the use of global location and context as the larger receptive field with no loss of convergence. table 2: results obtained for the proposed dilated u-net based cnn model with the existing classifiers classifiers accuracy (%) f-score (%) dnn 95.42 65.32 dilated cnn 97.54 67.32 u-net 98.02 67.87 dilated u-net based cnn 99.5 69 the model shows efficient computation and provides larger convergence. with the u-net model, the dilated cnn extracts the image which is derived with information and a standard type of shape and distance among the object and the region is also determined. figure 7 presented the results which is obtained for the proposed dilated based u-net model in terms of accuracy and f-score. figure 8: the results obtained for the proposed dilated based u-net model in terms of accuracy and f-score 4.2 comparative analysis table 3 shows the comparative analysis where the comparison of the existing with the proposed model is performed. the existing optimization approaches were sensitive to data features as it was having irrelevant features and obtained an accuracy of 99.15% [16]. at the segmentation stage, the severity levels of diseases were not identified which showed an accuracy of 99% [17]. the large database consisted of medical images showed improvement in accuracy and thus showed 98% [18]. the developed model used more than one classifier for examining the robust mechanisms to improve the accuracy for large database which consisted of medical images obtained 92% of accuracy [19]. also, tumorous regions showed performance better using the deep learning models which effectively segmented the untrained and trained datasets without using augmented data requirements and obtained 99.4% of accuracy [20]. whereas, the proposed dilated based u-net model obtained better accuracy of 99.5% when compared to the existing models. https://doi.org/10.15837/ijccc.2022.6.4929 10 table 3: comparative analysis authors methods accuracy (%) sensitivity (%) specificity (%) champakamala sundar rao and k. karunakara [16] kernel based svm 99.15 98.53 muhammad irfan sharif [17] non-dominated sorted genetic algorithm (nsga) convolutional neural network 99 95 95 k. sakthidasan sankaran [18] deep elman neural network with adaptive fuzzy clustering 98 97 98 mingquan lin [19] 3d context deep supervised u-net 92 ahmet ilhan [20] nonparametric localization and enhancement methods with u-net 99.4 92.19 99.75 proposed dilated u-net based cnn 99.5 99.52 99.81 while considering the other performance metrics such as sensitivity and specificity, the proposed method attains better when compared with existing methods ([16]-[20]) which is stated in table 3. the proposed method achieves the sensitivity of 99.52% and specificity of 99.81% which is superior than state of the art methods. 5 conclusion in the present research work, the proposed process of semantic segmentation is deepened that has spatial information in the feature map which reduces the critical time. automated multi-modal classification is used as a deep learning approach to classify the brain tumor. the main aim is to extract the information of an image’s edge when the convolution layer deepens and shows higher semantics. the spatial information is lost and the dilated convolution preserves as much as to use convolution that showed improvement in terms of accuracy for segmentation prediction. the existing kernel based svm model obtained 99.15% of accuracy, non-dominated sorted genetic algorithm (nsga) convolutional neural network obtained accuracy of 99%, deep elman neural network with adaptive fuzzy clustering accuracy of 98%, 3d context deep supervised u-net obtained accuracy of 92%, nonparametric localization and enhancement methods with u-net obtained accuracy of 99.4%. whereas, the proposed dilated u-net based cnn model obtained accuracy of 99.5% when compared with the existing models. however, in the future, it is required to detect brain tumors accurately based on real patient data with any medium. the deep features and handcrafted features were required to be fused for improving the results of classification. funding not applicable. author contributions the authors contributed equally to this work. https://doi.org/10.15837/ijccc.2022.6.4929 11 conflict of interest the authors declare no conflict of interest. references [1] amin, j.; sharif, m.; gul, n.; yasmin, m.; shad, s.a. 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(2022). brain tumor segmentation in mri images using nonparametric localization and enhancement methods with u-net, international journal of computer assisted radiology and surgery, 17(3), 589-600. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: saida, d.; premchand, p. (2022). brain tumor identification using dilated u-net based cnn, international journal of computers communications & control, 17(6), 4929, 2022. https://doi.org/10.15837/ijccc.2022.6.4929 introduction literature review proposed method brats 2020 dataset preprocessing segmentation using dilated u-net model results and discussion quantitative analysis comparative analysis conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 4, month: august, year: 2021 article number: 4413, https://doi.org/10.15837/ijccc.2021.4.4413 ccc publications entropic explanation of power set yutong song, yong deng yutong song institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 610054, china songyutong@std.uestc.edu yong deng* 1. institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 610054, china 2. school of eduction shannxi normal university, xi’an, 710062, china 3. school of knowledge science japan advanced institute of science and technology nomi, ishikawa 923-1211, japan department of management technology and economics 4. eth zurich, zurich, switzerland *corresponding author: dengentropy@uestc.edu.cn, prof.deng@hotmail.com abstract a power set of a set s is defined as the set of all subsets of s, including set s itself and empty set, denoted as p(s) or 2s. given a finite set s with |s|= n hypothesis, one property of power set is that the amount of subsets of s is |p(s)|= 2n. however, the physica meaning of power set needs exploration. to address this issue, a possible explanation of power set is proposed in this paper. a power set of n events can be seen as all possible k-combination, where k ranges from 0 to n. it means the power set extends the event space in probability theory into all possible combination of the single basic event. from the view of power set, all subsets or all combination of basic events, are created equal. these subsets are assigned with the mass function, whose uncertainty can be measured by deng entropy. the relationship between combinatorial number, pascal’s triangle and power set is revealed by deng entropy quantitively from the view of information measure. keywords: power set, combinatorial number, pascal’s triangle, dempster-shafer evidence theory, mass function, shannon entropy, deng entropy 1 introduction set theory is the basis of mathematics [7]. power sets play an important role in many applications. power set is a family of sets consisting of all subsets from a set. for a set a = {a,b,c}, the corresponding power set is {φ},{a},{b},{c},{a,b},{a,c},{b,c},{a,b,c} [9]. probability can be viewed as the likelihood of a possible outcome occurring in an experiment. the undefined concepts of probability theory are experiment, event and probability mass function. in probability theory, probabilities are assigned to single subsets with an event [15]. while in dempster-shafer evidence theory [1, 17], the framework of discernment is extended to power https://doi.org/10.15837/ijccc.2021.4.4413 2 set and mass functions are assigned not only on single subset but also on multi-subsets [8, 20, 21]. though evidence theory is widely used, the meaning of power set is neglected to some degree. to address this issue, an explanation of power set from the entropy view is represented in this paper. in this explanation, a power set can be seen as the set with all possible event combinations, each combination is represents as a subsets. for a quantitative description of the question and methods, different entropy measures [23] is introduced in this paper. the proposed explanation is supported by deng entropy [3, 11, 13] measure quantitatively. 2 preliminaries some preliminaries on power set [7, 10], combinatorial number [19], pascal’s triangle [16] and basic probability assignment [12, 17] are briefly introduced, including entropy measures such as shannon entropy [12, 18] in probability theory and deng entropy [3] in dempster-shafer evidence theory. • combinatorial number taking m (m ≤ n) elements non-repeating from n different elements and forms a group is called a combination of m elements from n different elements, noted as c(n,m) [19]. the combinatorial number can describe the amount of all possible combinations of m (m ≤ n) elements from n different elements. • pascal’s triangle in mathematics, pascal’s triangle is a triangular array of the binomial coefficients that arises in probability theory [7]. it is a geometric arrangement of binomial coefficients in a triangle. for a pascal’s triangle with n rows and x of maximum column number, some of its properties are listed: 1. in this triangle, the amount of numbers in the n−th row is n and numbers are displayed symmetrically. 2. the x− th number in the n− th row can be expressed as c(n− 1,x− 1), meaning the combinatorial number of drawing x− 1 elements from n− 1 elements. 3. the sum of all numbers in the n− th row is 2n. the first five rows of pascal’s triangle is show in figure 1. figure 1: the first five rows of pascal’s traingle • shannon entropy in information theory, entropy of a random variable is the average level of information, inherent in the variable’s possible outcomes. shannon entropy is a widely used information entropy [12, 14, 18]. definition 1. given a discrete random variable x, with x1, ...,xn, which occur with probability p(x1), p(x2), ..., p(xn), the entropy of x is formally defined as [18]: e = − n∑ i=1 pxi log pxi (1) • power set power set is a set family composed of all subsets (including complete set and empty set) in the original set [10]. for any as set a, there exists the set whose members are just all subsets of a. the set is called power set of a. https://doi.org/10.15837/ijccc.2021.4.4413 3 • basic probability assignment in this probabilistic view evidence is seen as more or less probable arguments for certain hypotheses and they can be used to support those hypotheses to certain degrees. dempster-shafer evidence theory is considered as the extension of probability theory, when the frame of discernment is extended to power sets. in dempster-shafer evidence theory, belief is assigned in subsets as basic probability assignments. definition 2. for a frame of discernment ω, a basic probability assignment, or called as mass function, is a mapping m from 2ω to [0, 1], formally defined as [12, 17, 22] m : 2ω → [0, 1] (2) which satisfies the following condition: ∑ a∈2ω m(a) = 1 (3) if m(a) > 0, a is called a focal element. the union of all focal elements is called the core of basic probability assignment. • deng entropy similar to shannon entropy playing an important role for uncertainty measure, deng entropy [3] is proposed for measuring the uncertainty of basic probability assignment [5] and is further developed to deal with the information volume with respect to different techniques [2, 4]. definition 3. assume in the frame of discernment x, xi is the focal element of basic probability assignment m(xi). the corresponding deng entropy is defined as follows [3]: ed = − ∑ xi⊆x m(a) log2 m(xi) 2|xi| − 1 (4) 3 a possible explanation of power set in evidence theory in probability theory, events that represented by discrete random variables with probability are exclusive with each other. the set of all possible outcomes of an experiment is called the sample space and any an event is a subset of sample space. the function that gives probabilities associated with all possible values of a discrete random variable is called probability mass function [6]. since all events happen exclusively to each other, all subsets in sample space are single subsets containing one element. for example, when you roll a die once, there are six outcomes(sample space is {1, 2, 3, 4, 5, 6}), yet you can get only one possible outcome from the sample space in one time. in this experiment, the event "rolling once but two outcomes" is impossible to happen so no description in sample space. from this respective, the set that represents a sample space in probability theory can only describe all exclusive events, not non-exclusive events combinations. however, in the real world, a probability mass function can describe an event such as p(a), p(b), and p(c), the mass function of exclusive event combination can be describe as p(a∩b). but the exclusive event combinations can not describe two events happen in the same time. compared with probability distribution in probability theory, basic probability assignment in evidence theory can handle more uncertain information. the identification framework of basic probability assignment is power set, which means that basic probability assignment can describe not only the belief allocation of a separated event and the combination of non-exclusive events. in the identification framework of evidence theory, subsets describe all possible outcomes as well. the outcome power set is formed by three kind of subsets: empty subset represents "none possible independent events happen", single-element-subset with one element represents "an independent event happen", multi-subset represents "more than one independent event happen". a subset of may be an event or an events-combination with non-exclusive events like {a,b}. in this view, a power set is explained as a family of all exclusive and non-exclusive events combinations. the single subsets in a power set family represent as exclusive events, while the multiple subsets represent as non-exclusive events happening along the time. the amount of each kind of event combination can be counted and represented as a combinatorial number. size of power set is equal to the amount of all possible events combinations. 3.1 an example to explain power set from the view of entropy below is an example explaining the difference between a set family consisting of single subsets only and a power set from the perspective of entropy. the example can explain the physical meaning of power set quantitatively. https://doi.org/10.15837/ijccc.2021.4.4413 4 • question a: if there are 32 numbered football teams (from team 1 to team 32) playing with each other and only one team get the champion. if we can only ask yes / no questions, how many times at least we ask, can we certainly know the champion? answer: 5 times. based on the information we get, the probability to get champion for every team is the same. if we want to certainly know the champion team even, bisection method is recommended. figure 2: possible asking options for knowing the champion team in figure 2, there is a possible asking to knowing the champion team, showing how bisection method works. • question b: if there are 32 numbered students (from student 1 to student 32) taking math exam. if we can only ask yes / no question, how many times at least we ask, we can certainly know the student who got the highest score in the exam? answer: 32 times. for question a, we can give probability as p(i) = 1/32, for i = 1, 2, ..., 32. shannon entropy = −1 × log2 1 32 = 5 from the coding point of view, this problem can be interpreted as giving different 32 codes to 32 teams with 0 / 1, which, means 5 bits are required. for question b, we can give basic probability assignment as m(x1, ...x32) = 1 deng entropy = −1 × log2 1 232 − 1 = 32 different from the football game, there may be several students getting the highest score, the most special case is that all students got the same score as well as the highest score. for example, all students got the full marks.the event "student 1 gets the highest score" is independent to event "student 2 gets full marks" and there can be tied highest scores more so several non-exclusive events may happen in this problem. in another word, the exclusive assumption is not correct in this situation. both student 1 and student 2 can get the highest goal. the solution is to asking from student 1 to student 32 without order non-repeatedly, amounting to 32 times. the explanation of this example in the pascal’s triangle is illustrated in the figure 4. in the 32 row, all the composition of power set is given, and the amount of "getting the highest score" event-combinations is given as the numbers in this row. for example, the first number ”1” tells the amount of events that "none of students getting the highest score" and the second number ”32” tells that "one of students getting the highest score". in this example, we can find that, for mutually exclusive events, we can use shannon entropy to measure the information volume in the system. while for systems whose events are not mutually exclusive, deng entropy can explain the information volume and uncertainty better. because basic probability assignment could describe the belief in the framework of events including mutually exclusive and non-exclusive events better. in this perspective, the physical meaning of the power set could be better explained as collection of all alternative events by deng entropy. compared with question b, framework of events for question a is consisted of single subsets so shannon entropy is useful for information measure, while the framework of events for question b is extended to a power set so deng entropy is better for information measure. 3.2 the connection with power set to combinatorial number comparing the two questions from the perspective of power set, in the first example, all possible champion is a power set a as {{φ},{1},{2}, ...,{32}}, while the second example, all possible students with full marks is https://doi.org/10.15837/ijccc.2021.4.4413 5 also a power set b as {{φ},{1},{2}, ...,{32},{1, 2},{1, 3}, ...{31, 32}, ...,{1, 2, ...32}}. the size of a is 32, which can also be represents as 25, while the size of b is the sum of c(32, 0),c(32, 1), ...,c(32, 32), that is 232. for a combinatorial number like c(n,m), represents the combinations amount of picking m (m ≤ n) elements from n different elements. it can also considered as the event "there are m independent events non-exclusively happening". c(n, 0) is 1, which can be understand as only one combination for the event "none events are picked from the possible event set". this is the connection with power set and combinatorial number. here is an example to the explanation of power set based on a combinatorial number. a box contains three balls, one in red, one in blue and one in green. if you have one chance to drawn ball(s) from the box. how many different outcomes would you get? figure 3: an example of selecting balls figure 3 gives all possible outcomes to this ball game. you can get one ball, two balls, three balls or none of them, since every ball has two possibilities (drawn and not drawn) and the status of each ball is independent. all possible solutions consists of a power set. from this example, we can explain power set as a set with all possible "drawing ball(s)" combinations. in this situation, there are four kinds of event combinations. the first one is "none of balls is selected", that can be represented as a set {s1 : {φ}}, the size of s1 is 1. the second event combination is "one of balls is selected", that can be represented as a set {s2: {r}, {g}, {b}}, the size of s2 is 3. the third event combination is "two of balls are selected", that can be represented as a set {s3: {r, b}, {r, g}, {b, g}}, the size of s3 is 3. the last event combination is "three balls are selected", that can be represented as the set {s4: {r, g, b}}. the meaning of the empty subset of the power sets in this example is drawing no balls from the box, the outcomes amount of it is c(3, 0). the meaning of single subsets is drawing one ball from the box, this outcomes amount is c(3, 1). the meaning of multiple subsets in the power sets of this example is drawing more than one ball, whose amount is the sum of (c(3,x), x is bigger than 1 and not bigger than 3 (c(3, 2) + c(3, 3) = 4). the combinatorial number describe the amount of each event combination. a single subset with only one element is considered as an independent event happening alone, whose amount is the combinatorial number c(n, 1). multi-subsets with several elements are seen as possible non-exclusive events happening, the amount of each event combination is c(n,x), in which 1 < x ≤ n. all the subsets make up a power set, representing a whole event family set, the amount of all subsets is the same of all combinatorial number (2n ). 3.3 relationship between power set and pascal’s triangle from the previous knowledge, the properties of it is known. the connection between pascal’s triangle and combinatorial number is also explored. in the previous part, the connection with power set and combinatorial number is built. so, in this part, power set is used to explain the logic and meaning of pascal’s triangle. from figure 1, the row sum is 1, 2, 4, 8 and 16, which can be represented as 20, 21, 22, 23 and 24. the x− th number in the n− th row can be expressed as c(n− 1,x− 1), the same as the combinatorial number of x− 1 elements from n− 1 different elements. the numbers in the n− th row represent the amount of power x-size subset in a set with n− 1 elements respectively. this is the relationship between combinatorial number and pascal’s triangle. from the perspective of power set, pascal’s triangle shows amount of different size of subsets in a power set family. the relationship between power set and pascal’s triangle is illustrated in figure 4. we can find that, for any single row in the pascal’s triangle, each number represents the amount of an event-combination. for example, there is four numbers in the forth row as 1, 3, 3, 1, which can be explained as the amount of event-combination "none event happening", "one event happening", "two events happening" and "three events happening". in this explanation, a power set gives all possible events combinations of exclusive and non-exclusive events, concretely, single subsets means an event happening along and multi-subsets means events happening simultaneously. https://doi.org/10.15837/ijccc.2021.4.4413 6 figure 4: explanation of power set in the pascal’s triangle it is remarkable that the event "none of students getting the highest score" is impossible to happen in question b, which is reflected by deng entropy formula in figure 4. each number in pascal’ triangle represents the amount of a kind of event combination but not all event combinations are possible to happen for different situations. 4 conclusion in this paper, an entropic explanation of power set is illustrated in the paper. in dempster-shafer evidence theory, the event space in probability theory is extended into all possible combinations of the single basic event by power set. a power set is explained as an events collection set of all exclusive events and nonexclusive events combinations, the size of power set is the amount of all possible events. single subsets of the power set family represent exclusive events, while multiple subsets represent non-exclusive events combinations possibly happening in the same time, such as two balls (green one and red one) drawn simultaneously, denoted as {g,r}. combinatorial number, pascal’s triangle and information measures are illustrated to explain the meaning of power set. in addition, the relationship between pascal’s triangle and power set is given by deng entropy quantitively from the view of information measure. the proposed explanation may have the potential applications in evidence theory. acknowledgment the work is partially supported by national natural science foundation of china (grant no. 61973332), jsps invitational fellowships for research in japan (short-term). references [1] dempster, a.p., 1967. upper and lower probabilities induced by a multivalued mapping, 57–72. 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[23] xue, y., deng, y., 2021. interval-valued belief entropies for dempster–shafer structures. soft computing, 1–9. https://doi.org/10.15837/ijccc.2021.4.4413 8 copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: song y.; deng y. (2021). entropic explanation of power set, international journal of computers communications & control, 16(4), 4413, 2021. https://doi.org/10.15837/ijccc.2021.4.4413 introduction preliminaries a possible explanation of power set in evidence theory an example to explain power set from the view of entropy the connection with power set to combinatorial number relationship between power set and pascal's triangle conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 2, month: april, year: 2022 article number: 4732, https://doi.org/10.15837/ijccc.2022.2.4732 ccc publications collaborative decision-making: concepts and supporting information and communication technology tools and systems f. g. filip florin gheorghe filip the romanian academy, bucharest, romania ffilip@acad.ro abstract collaboration means in substance that several entities such as humans, computers, robots, enterprises and so on jointly perform a certain task instead of working individually so that a better result could be obtained. decision-making is a specific form of activity, commonly carried out by human agents, which is meant to eventually select a certain course of action which is expected to result in attaining a desired result. the chapter is meant to present a concise and balanced view of the basic concepts and main classes of supporting information and communication tools and systems regarding decision-making processes carried out by several collaborating human agents called participants. the reasons for collaboration are briefly explained followed by an exposure of collaboration application in the multi-participant decision-making settings. having presented the classification of decision problems and decision-making units, the main phases of a specific multi-participant form of herbert simon’s decision process model are described followed by the presentation of two main forms of close and soft collaboration, namely consensus building and crowdsourcing, respectively. the need for technology support offered to collaborating participants is justified and two main classes of decision supporting systems, namely decision support systems and the ever more largely used platforms, are addressed. a practical example of an open ended and evolving platform is presented. open questions about the further role the information and communication tools in multi-participant decision-making processes are eventually formulated from two perspectives, digital humanism and dataism, respectively. keywords: consensus building, crowdsourcing, dss, platform. 1 introduction collaboration is defined by the merriam-webster dictionary [1] as performing a “work with another person or group in order to achieve or do something”. the dictionary explains that it comes from the late latin collaboratus, that represents the past participle of the verb collaborare (to labour together). in many languages, one can notice the saying „two heads are better than one”, which apparently comes from the holly bible (ecclesiastes 4;9), together with the explanation “because they have a good return of their toil”. in his recommendation of reading old books [2], clive stapleton lewis, an outstanding english scholar, gives an intuitive justification of the above saying. he stated that the saying was true, “not because either [person] is infallible, but because they are unlikely to go wrong in https://doi.org/10.15837/ijccc.2022.2.4732 2 the same direction” [3]. of course, the old saying and lewis’ explanation are true only if the persons involved in a collaborative work do not exhibit an irrational behaviour and act seriously and in good faith. collaboration is not restricted to the activities that are carried out by two or more persons who prefer to work together instead of individually acting and may involve other types of entities. for example, herbert simon, highlighted the advantages of making ai (artificial intelligence) tools and ms/or (management sciences/operations research) models ‘collaborate’ to solve complex problems [4]. nof et al [5] explain in their book that, beside persons, other entities such as computers, various automation devices, communication means, machines, and so on may be involved in collaboration. as camarinha-matos et al [6] notice, there are several levels of collaboration, such as information exchange, harmonization of objectives, sharing resources and responsibilities, and advanced coordination in decision-making and acting. consequently, a rather broad definition of collaboration is adopted in [7]: collaboration is a specific class of interactions among several entities, such as organizations, humans, and machines that exchange information and knowledge for mutual benefits, harmonize their major goals and objectives and share resources, action plans, and responsibilities to attain the common goals. according to the purpose of collaboration, nof et al [8] identifies two subclasses: a) mandatory collaboration, when two or more entities must collaborate, because each one working independently cannot deliver the expected output, such as a product, a service, or a decision, and (b) optional (or progressive) collaboration in case the entities might start collaborating because all of them aim at improving the quality of their deliverables or/and to achieve higher values for all of them. the paper is about collaborative decision-making, which is a specific subclass of the more general class of collaborative activities and consists in a series of activities that are carried-out by more than one person that compose a multi-participant decision unit. the remaining part of the chapter is organized as follows: next section contains a review of basic aspects of collaborative decision-making concept, such as decision problems, activities involved, collaborative group definition, and the process of adopting and releasing collaborative decisions. the particular cases of consensus building and crowdsourcing are described. the third section is about the specific information systems designed to support collaborative decision-making tasks. the need for such systems is explained and a formulation of main requirements and functions to be supported by multi-participant dss (decision support systems) are presented. the section also addresses the platforms which are ever more used nowadays and briefly describes an example. 2 collaborative decision-making: basic aspects and special cases 2.1 decision problems and participants adecision problem is associated with (a) a perceived or anticipated situation that requires action and (b) several possible courses of action, called alternatives. the decision is defined in [9] as “the result of a series of human conscious activities that aim at choosing a course of action with a view to attaining a certain objective (or set of objectives)”. it consists in processing information and knowledge by an empowered person or set of persons who have to make the choice and are accountable for the quality of the solution adopted to solve a particular problem or situation. making (solving the decision problem) and taking (assuming the solution), releasing, and deploying the decision normally imply allocating the necessary resources such as people, time, information, and supporting technical means. the decision problems and activities can be classified in accordance with several attributes as follows: • the pursuit objectives that may be a) to obtain the result that is optimal in accordance with a single criterion or set of criteria, b) to maintain the supremacy over competitors or to reduce the distance to the leader, c) to mitigate and recover from losses in case of disaster or crisis situations, and d) attaining a mutually accepted settlement in the case of negotiations of the parties who have conflicting objectives; https://doi.org/10.15837/ijccc.2022.2.4732 3 • the number of decision units that take part in decision-making and taking; one can distinguish individual and multi-participant (collaborative) units; • the number of people that compose a decision unit; there may be a single person that makes and takes decisions, or there are several people that work together to make and take the decision; one can identify two subclasses of the more general class of multi-participant decision units as follows: • collectivity, which is characterized by an episodic composition of the unit and it is commonly met in a) negotiations when the parties typically pursuit different, sometimes conflicting, objectives [10], and b) crisis management situations [11]; • collaborative groups that are characterized by several attributes such as: a) congruence of goals and methods of group constituents with respect to the adopted objectives, activities and procedures of the group as a whole, b) effectiveness that measures the degree to which the objectives of group are attained, c) efficiency to measure the savings individual resources to attain the group goals, d) cohesion of group members that are willing and ready for further collaboration [12]. 2.2 multi-participant decision-making the general model of decision-making activities was proposed by herbert simon [13]. it is a process composed of several phases such as: a) intelligence, b) design of alternatives and models, and c) choice of a solution to be released for implementation. a fourth phase, the evaluation of the decision implementation impact and possibly to re-start the process, was later added to model. in collaborative decision-making, the simon’s process model can be adapted to multi-participant settings. a possible model consists in the following phases [14]: • preparation meant for a) defining the problem characteristic aspects, such as: purpose, the domain, current context, criteria, and possible constraints, and b) empowering the decision unit; • collective understanding of problem which can be viewed as a natural extension of the preparation phase and consists in activities such as: sharing a common vision of the problem with all the participants and agreeing on how to implement the designed process; • solution generation meant to identify or design alternatives and applicable models to solve the problem; • negotiation and confrontation of viewpoints to enable participants to elaborate their contributions and present them in order to win the support of the greatest number of other parties; • decision for selecting, according to the criteria previously agreed, the ideas which have been voted by most of participants, or which have received the consensus within the group; • monitoring phase which covers the entire decision-making process so that any problem can be solved in the allocated time period. it includes generating a report on the decision-making process and ensures the implementation of the adopted and assumed solution. 2.3 collaboration forms and methods in [15], the following forms of collaboration forms were identified: • close collaboration established among the members of the decision group who exchange information to make and take decisions; • asymmetric or skew collaboration, which is a particular form of the previous one and it is established among the decision takers and their own human support team of assistants or/and hired consultants; https://doi.org/10.15837/ijccc.2022.2.4732 4 • soft collaboration of the decision takers with commonly anonymous members of a crowd. there are several approaches and methods that can be used in collaborative decision-making. chapter 3 of the monograph dedicated to computer supported collaborate decisions [7] contains a review of the most commonly used methods. among the methods for aggregating individual preferences, the chapter addresses social choice (including voting mechanisms), its axioms and paradoxes, and several extensions, such as: judgement aggregation, resource allocation, group argumentation, and collaboration engineering. consensus mechanism. one of the approaches used in close collaborative decision-making, with or without moderator’s support, that has received, from academia cercles, a lot of attention over the last decades is based on consensus building [16]. according to merriam-webster dictionary [1], the consensus term means “a general agreement about something: an idea or opinion that is shared by all the people in a group”. an example for consensus desideratum is a quotation from president abraham lincoln, who, before issuing the emancipation proclamation, wrote in his message to congress:” we can succeed only by concert. it is not ‘can any of us imagine better?’ but, ‘can we all do better?’” [17]. the process of aggregating participants’ individual preferences is composed of two main subprocesses: a) consensus building, and b) selecting a recommended solution [16] [18]. during consensus building, the participants might need to revise their opinions with a view to making them closer to one another in an interactive process, so that an acceptable level of consensus could eventually be reached. the process is viewed as composed, at each iteration, of several activities, such as: a) collecting from each participant his/her individual preference, b) aggregating individual preferences by using one of the available methods c) measuring the consensus level expressed as a distance of individual preferences either to the calculated collective one, or as the result of comparing pairs of preferences, d) implementing a revising scheme for the individual preferences with a view to improving the consensus level based either on identifying the participants whose further contribution to consensus reaching could be neglected or minimizing the number of preference revisions. other methods based on the fuzzy approach have recently been proposed [19]. crowdsourcing. there are a few recently reported results concerning large scale decision-making processes [20] possibly using ai (artificial intelligence)–based methods and tools. however, an implicit assumption in many schemes for collaborative decision-making based on consensus building consists in limiting the number of participants involved, so that various methods proposed could be technically applicable. in addition, the expertise of the participants might not be appropriate or sufficient for the faced decision situation, or/and the problems could be too complex, or persistent. in such situations, a larger number of people could provide with the necessary information and knowledge for solving the problem. such a soft collaboration form can be effective in many domains. a particular form of soft collaboration which has got traction over the last decades is crowdsourcing. howe [21] coined the concept as “the act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large group of people in the form of an open call”. crowdsourcing means, in substance, that an individual or collective initiating agent (called crowdsourcer) does not assign a certain known person or group of persons to work on a specific task, but he/she will hand over the task to the crowd composed of anonymous crowdworkers who will complete it. having carried-out an extensive study of systems that had claimed to support crowdsourcing solutions, estelles arolas and gonzález-ladrón [22] proposed a rather broadly accepted definition as follows: crowdsourcing is a type of participative online activity in which an individual, organization, or company with enough means proposes to a group of individuals of varying knowledge, heterogeneity, and number, via a flexible open call, the voluntary undertaking of a task. the undertaking of the task, of variable complexity and modularity, and in which the crowd should participate bringing their work, money, knowledge and/or experience, always entails mutual benefit. the user will receive the satisfaction of a given type of need, be it economic, social recognition, self-esteem, or the development of individual skills, while the crowdsourcer will obtain and utilize to their advantage that what the user has brought to the venture, whose form will depend on the type of activity undertaken. crowdsourcing is deployed in many various domains and its usage is nowadays facilitated by https://doi.org/10.15837/ijccc.2022.2.4732 5 the mobile computing [23]. in the particular setting of collaborative decision-making through using crowdsourcing the following steps are carried-out [24] [7]: • identification of the decision problem to be solved by using the opinions collected from the crowd, and defining the corresponding task. the activity basically corresponds to the intelligence phase of simon’s process model; • broadcasting the task to the crowd, commonly in the form of an open call; • idea generation by the crowd members in the form of various action alternatives or/and evaluation criteria. it basically corresponds to the design phase of simon’s process model. • evaluation of collected ideas by the same members of the crowd that generated them or by another crowd or limited group of hired experts who may pursue the process of reaching the consensus, as described above; • choosing the solution through a voting mechanism or based on expert views [25]. 3 information and communication technology for collaborative decision-making 3.1 the need for technology support the multi-participant decision units, in case they are not supported by technology, may face a series of problems [12] [7] such as: a) groupthink caused by an authoritarian leader or a very vocal participant, time or/and external pressure, high homogeneity of the group, when the participants have similar interests, b) cognitive overload caused by excessive interactions, c) the fear for possible negative consequences, d) possible misunderstanding in case the participants possess different cultural or technical backgrounds or speak different native languages, e) high costs or sanitary restrictions to organize the meetings, and so on. at present, all collaboration forms can be enabled and supported by modern i&ct (information and communication technology) tools, systems, and platforms. a review of several technologies and tools, such as ai (artificial intelligence), social networks, data science, web technology, mobile and cloud computing, the biometric tools, and serious games, and their relevance for supporting collaborative decision-making can be found in the third chapter of [7]. the madm/mcdm (multi-attribute decision-making/multi-criteria decision-making) methods [26] [27] and their computerised versions, possibly combined with ai and big data methods and technologies [28], are very useful tools for solving the problems characterized by several aspects to be taken into account. 3.2 multi-participant decision support systems a decision support system (dss) is defined in [9] as “an anthropocentric and evolving information system which is meant to implement the functions of a human support system [the team of assistants and possible external consultants] that would otherwise be necessary to help the decision-maker to overcome his/her limits and constraints that he/she may encounter when trying to solve complex and complicated decision problems that that matter”. in the multi-participant setting, in order to overcome the problems that could be encountered by the decision unit as enumerated earlier, the specific subclass of collaborative (or multi-participant) dss should possess the characteristic attributes of a collaborative [information] systems. the list of attributes includes: a) parallelism, in order to avoid the waiting time of participants who want to intervene by enabling all of them to simultaneously input into the system their ideas and views, b) anonymity, so that an idea could be accepted based on its value only no matter the proposer’s professional reputation or social position, c) memory of the group, to accurately record the ideas and views expressed by individual participants and the solutions that were adopted, d) unambiguous and faithful presentation on participants’ computer screen of the ideas and views of other attendants of the decision-making process [12]. practical experience has witnessed that the dss are continuously evolving under the influence of several factors such as https://doi.org/10.15837/ijccc.2022.2.4732 6 the changes in business environment, available technology and methods, and developments in users’ knowledge, skills, and willingness to use the system [29]. 3.3 platforms the platforms have been traditionally representing a specific subclass of the more general class of i&ct means used to support various collaborative activities, including multi-participant decisionmaking, characterized by large numbers of people commonly working in different locations and organized in virtual teams [30]. the platforms are necessary enabling means for carrying out crowdsourcing and crowdwork. nowadays, the pandemic and the associated sanitary restrictions have made the usage of platforms one of the most common styles of work and the platform economy is spreading and growing as a wild fire all over the world. when one intends to use crowdsourcing, a decision problem is choosing the most appropriate platform. in the specific setting of collaborative decision-making based on crowdsourcing, a methodology inspired from [31] is proposed in [15] for choosing the platform. the following criteria (and derived subcriteria) are proposed and used to compare several available platforms: a) adequacy to the envisaged applications (informational transparency, accuracy of expected results, robustness to errors and low quality uncertain input data, response time), b) quality of implementation: (scalability, flexibility, functional transparency, documentation completeness), and c) delivery quality (price, service delivery time, provider’s general reputation, easy adaptation, degree of independence on the technical assistance from the provider’s specialists for implementation and usage). 3.4 example ids (intelligent decision support) is a family of platforms developed by ropardo, a romanian company located in sibiu. the family members have been designed over one and half decade with a view to supporting individual as well as multi-participant decision-making activities carried out in universities, local public administration and digital factory milieux [32] [7]. the family comprises a series pf successive versions developed under the influence of three main factors, such as: a) new i&ct tools, b) users’ evolving needs and skills, and c) evaluation of results obtained in practical applications. the latest version is characterized by the following aspects: a) usage of web 3.0 technologies and social networks to support collaborative work, b) the possibility to integrate additional third-party modules via api (application program interface) together with its own standard set of tools which includes a forum-like discussion list, a voting module, an electronic brain-storming, and c) facilitating asynchronous decisions through web 2.0 clients or dedicated mobile clients. 4 conclusions in the chapter, the presentation of collaborative decision-making has been restricted to the process consisting in the interactions among several human agents possibly supported be i&ct tools and systems. at present, one may already notice the availability of digital cognitive systems which have evolved from simple information tools to digital clones of human advisers, consultants, and even mediators meant to augment human intelligence so that better capabilities and performances could be attained [33]. at the same time, there are initiatives, research efforts, and reported results in ai domain aiming at making the artifacts ever more intelligent and even able to autonomously make decisions and act. consequently, the result of future developments in i&ct and their impact on human well-being are not too easy to predict [34]. shall the digital humanism [35] trends prevail and the future possibly augmented humans will take on board, in a recommended service-oriented scheme, the ai based artifacts as collaborators and additional participants in the future decision-making activities, or the people shall become mere data feeders of algorithms as in the dataism anticipations [36]? https://doi.org/10.15837/ijccc.2022.2.4732 7 5 acknowledgments this paper is dedicated to the 30th anniversary of purdue prism (production, robotics and integration software for manufacturing & management) center led by prof. s. y. nof. funding there was no funding required for this project. references [1] merriam-webster dictionary, merriam-webster.com, https://www.merriamwebster.com/dictionary/collaborate. accessed 1st jan. 2022 [2] holy bible english standard version-esv ©text edition: 2016. copyright © 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(2022). collaborative decision-making: concepts and supporting information and communication technology tools and systems, international journal of computers communications & control, 17(2), 4732, 2022. https://doi.org/10.15837/ijccc.2022.2.4732 introduction collaborative decision-making: basic aspects and special cases decision problems and participants multi-participant decision-making collaboration forms and methods information and communication technology for collaborative decision-making the need for technology support multi-participant decision support systems platforms example conclusions acknowledgments international journal of computers communications & control issn 1841-9836, 11(3):414-427, june 2016. efficient opinion summarization on comments with online-lda j. ma, s. luo, j. yao, s. cheng, x. chen jun ma, senlin luo school of information and electronics beijing institute of technology beijing, china {junma,luosenlin}@bit.edu.cn jianguo yao*, shuxin cheng school of software shanghai jiao tong university 800 dongchuan road minhang, shanghai 200240, china {jianguo.yao,reallytrue1262}@sjtu.edu.cn *corresponding author: jianguo.yao@sjtu.edu.cn xi chen school of computer science mcgill universiy montreal qc canada xi.chen7@mail.mcgill.ca abstract: customer reviews and comments on web pages are important information in our daily life. for example, we prefer to choose a hotel with positive comments from previous customers. as the huge amounts of such information demonstrate the characteristics of big data, it places heavy burdens on the assimilation of the customercontributed opinions. to overcoming this problem, we study an efficient opinion summarization approach for a set of massive user reviews and comments associated with an online resource, to summarize the opinions into two categories, i.e., positive and negative. in this paper, we proposed a framework including: (1) overcoming the big data problem of online comments using the efficient online-lda approach; (2) selecting meaningful topics from the imbalanced data; (3) summarizing the opinion of comments with high precision and recall. this framework is different from much of the previous work in that the topics are pre-defined and selected the topics for better opinion summarization. to evaluate the proposed framework, we perform the experiments on a dataset of hotel reviews for the variety of topics contained. the results show that our framework can gain a significant performance improvement on opinion summarization. keywords:opinion summarization, latent dirichlet allocation (lda), online lda, imbalanced data, big data. 1 introduction the rapid development of the web 2.0 application makes tremendous and diverse information flood the web. we have to admit that the information shows a wide variety of the meanings which may hardly grasp without summarization. even worse, the data contained this information shows the characteristics of big data and brings the challenge to the efficiency of the data processing. with more and more user-contributed reviews and comments on the web, the corresponding websites can become more popular resources that reflect the attitudes and interests of the users in a way that depart from the advertisement and the content of the underlying information resource itself. copyright © 2006-2016 by ccc publications efficient opinion summarization on comments with online-lda 415 many techniques have been developed to extract concise information from these contents, such as sentiment classification, text summarization and topic modeling [3] [4] [7]. nevertheless, the comments on the web are updated unceasingly, it is hard to perform online opinion summarizing with these current techniques. even though these comments are meant to be useful, the vast opinions summarized are still not easily digested and exploited by the users. when we want to make a comparison of electronic products such as cell phones and laptops, common attributes of the products under consideration include ease of use, battery life, sound quality, add on s etc. actually, on most of ecommerce websites, these attributes are pre-defined topics/features and mainly describe hardware performance. let we say laptops, because of the system’s original configuration, the user’s experiences can be completely different even with the same hardware. and the after-sales services are also a major concern of the user which only can be reflected in the comments. thus the pre-defined topics do not demonstrate much diversity on different products. the user’s comments are valuable information resource that needs to be summarized. on tripadvisor 1, in order to make an easy comparison of hotels, the scalar rating mechanism is built on the websites for users. but the scalar ratings, e.g. scores between 1 and 5, are not very helpful for hotel managers or tourists because the numeric value does not provide the subjectivities or opinions that come from customer experiences. also, these scalar ratings are not comparable: for example, when a 3-star hotel receives a high score from 10 tourists while a 4-star hotel receives a medium score from 1 tourist, that does not imply that the former one is better than the latter. in this situation, how to obtain valuable information from users’ comments is more important. furthermore, personal experiences about each hotel cannot totally the same. consider two typical hotel comments shown in figure 1. these two comments discuss several different topics of the hotel, such as price, room, food etc. the same topics are also in the comments, such as room, breakfast. apparently, the topics in hotel comments show more diverse topics than electronics products comments. it is impossible to list all the topics tourists may share. extracting meaningful topics from the comments is not an easy task. hotel comments show a very interesting phenomenon of imbalance. the hotels with more comments imply that this hotel is popular and the tourists posting the comments are more likely to share their good experience with others. so the positive comments are far more than the negative comments. the situation of the less popular hotel is quite different, in that less comments will be posted if the tourists had a bad experience. the imbalanced data is the big problem for summarization in form of binary classification. in our framework, we use scheme of online topic extraction in coping with big data problem. online inference is employed to handily analyzes the huge number of comments in stream form. there is a superior advantage that makes online lda process massive collection without heavy computational cost and memory necessity. due to the imbalance of the hotel comments, the meaningful topic selection is another challenging problem to opinion summarization. in our framework, topic selection is carried on with multi-facets of consideration. in comparing with three roc based topic selection methods, fast [19] is the best one in handling the extracted topics with the problem of the imbalance, and relative low computation is needed. furthermore, better opinion summarization is obtained with any redundancy topics filtered out and accuracy in classification. in our evaluation, we observe that our framework avoids several problems faced by supervised classification approach. the aim of the present work is to study the manner in which hotel comments can be summarized into positive and negative opinions with meaningful selected topics, so that the obtained summary can be used in real life. our main contributions are summarized as follows: 1www.tripadvisor.com 416 j. ma, s. luo, j. yao, s. cheng, x. chen location is everything for this place. the view from the balcony made the room. room was a standard hotel/motel room. it was clean and nice and quiet. my kids enjoyed the clubhouse which had ping pong, air hockey, and other fun stuff. the grounds were nice and we walked to the river. places to sit so you can enjoy the view and the sound of the river. very pleasant. price was right, room was very nice, free breakfast outstanding! not just a typical continental breakfast, mind you, delicious hot food. we even had a view of the snoopy rock from our balcony. we were able to walk to all our destinations. i would imagine it's the slow time of the year in february, but that worked out well for us. sedona is now one of my favorite places to visit. figure 1: different topics on hotel comments • we present a framework of comments summarization and the online variational methods are used to handle huge amounts of comments from the web in coping with the big data. • we address the problem of data imbalance of hotel comments. different from existing works on pre-defined topics, topic selection is performed with the consideration of the more positive comments and less negative comments. • the ratable topics can be a form of summary and the opinion summarization is performed with these topics for easy digest and exploitation. the experiments are conducted on comments crawled from tripadvisor. several metrics are used for the evaluation, and experimental results show that our proposed framework can summarize the comments in a good manner. the rest of the paper is organized as follows. section ii surveys existing studies on comments summarization in topic models. in section iii, we propose a framework of the opinion summarization and discuss the topics/features involved in this task and the challenges it implies, in comparison to other lda based text summarization. in section iv, we propose a different approach to analyze data imbalance. the evaluation results using several metrics are reported in section v. in section vi, we offer insights on the challenges of opinion summarization and point out clear directions in which further improvements can be made. 2 related work we first review the research works related to topic modeling. we then give a brief overview of opinion summarization using other techniques, and we discuss the difference between our framework and sentiment classification lastly. efficient opinion summarization on comments with online-lda 417 topic modeling. topic sentiment model (tsm) [17] is based on the plsi model [7] which is used to extract the topics. while they set the topics into three sub-topics: neutral, positive and negative topics, the generation progress of documents is considered as first choosing the subtopics, then choosing the topics in these sub-topics. y. lu et al. [13] used a two-step strategy to integrate the opinions with a pulse. the first step is to divide the opinion documents into expert opinions and ordinary opinions. they called it semi-supervised plsa because the topics are found from expert opinions on the second step, then use as the defined aspect to cluster ordinary opinions. latent dirichlet allocation (lda) [3] is another representative topic model which provides a basis for textual-level summarization in an unsupervised way. supervised latent dirichlet allocation (slda) model [4] accommodates a response variable to make the lda model work under a supervised condition with the facility of the classification. multi-grain lda (mg-lda) model [10] manipulates the lda model to induce multi-grain topics. the main idea of the model is to find a ratable aspect within texts on a given topic and use this rating information to identify more coherent aspects. labeled lda [5] model is a supervised model with the ability of the k-classification. joint sentiment/topic (jst) model [11] is a four-layer probabilistic model with the extension of three hierarchical layers lda which can perform sentiment classification under fully unsupervised way. z. ma et al. [21] proposed two topic models, mstm and extm to extract the topics from the documents and its comments respectively, then select representative comments from comment clusters. opinion summarization with the lda related model is multi-faceted and very involved. these approaches can have scalability issues. comments summarization. comments summarization involves two major steps, topic identification and classification. generally existing research is to classify the comments according to their polarity, which is positive or negative [1] [2] [?]. this kind of summarization on comments can give a very general notion of what the users feel about the product. the accuracy of the classification heavily depends on the identified topics and the distance measure. lda models are one way of topic identification. nlp-based techniques are the other ways to identify topics in the text [13] [14] [15] [16] employed pointwise mutual information and cosine distance as distance measures to perform the binary classification and found that the latter one leads to better accuracy. our proposed framework is different from the lda with the employment of online inference in handling big data. and we also address the imbalance problem of hotel comments. our work aims at improving the accuracy and scalability of opinion summarization model and inferring meaningful topics for better summarization of the comments. 3 a better way to extract the topics in this section, we demonstrate the framework of our model. a brief analysis is given to hotel comments. then we compare different approaches of topic extraction and highlight the advantage of online inference lda model for comments topic extraction. 3.1 the framework imagine booking a hotel on the web. we may not review every comment on each hotel and furthermore we could not find the sentiment changes within a long term. so how can we manage and digest the large information other tourists provided? lda model is well-known algorithm for discovering the main themes of large and unstructured documents. our framework is combining the lda model and a roc-based topic selection. 418 j. ma, s. luo, j. yao, s. cheng, x. chen opinion summarization includes three steps. the first step is topic extraction, the online inference for the lda model is used for improving its scalability. the potential topics of the comments may not be evaluated properly because the topic number k of lda is pre-defined by the user. this means that not all the topics extracted are meaningful for opinion summarization, or good for the classification of positive and negative. so we perform the topics selection (extracted topics are the features for classification) in a second step. the third step is opinion summarization or binary classification. as we can see in figure 2, the collected comments show opinion summarization topics extraction ratable topics selection positive comments negative comments imbalance data online-lda topic extraction roc-based topic selection one-class classification figure 2: framework of our model the characteristic of imbalance (we detail the reason in the next section) which pose a server problem on classification. the roc-based topic selection is used in our framework for better classification. the relevant algorithm is described in section 3. 3.2 topic extraction on comments there are several probabilistic models to extract the topics unigram model, multi-gram model, policy model and lda model. the fundamental idea of these models is that the comments analyzed are considered to have one or some pre-defined topics. the difference is that each one is based on different statistical assumptions. probabilistic latent semantic indexing (plsi) model introduces two probability layers to reduce the constrains on the number of topics and mixture weights of each topic. the probability of a comment is: p(d,wn) = p(d) ∑ z p(wn|z)p(z|d). (1) but these topics mixtures are only for those training comments and cannot be used for previously unseen comments. furthermore, plsi is a model prone to overfitting in training. so this model is not a well-defined generative model either. latent dirichlet allocation (lda) is an extension of the plsi which introduce a dirichlet prior on topics. here we denote as θ. the generative process includes two steps: one is to choose θ for the dirichlet prior on topics, then choose a word from p(wn|θ,β). this process is a continuous mixture distribution: p(ci|α,β) = ∫ p(θ|α) ( n∏ n=1 p(wn|θ,β) ) dθ, (2) efficient opinion summarization on comments with online-lda 419 p(θ|α) are the mixture weights on topics. in this work, lda is used to extract topics from hotels comments collection. a unified topic model is trained on the integrated content by combining the multiple text fields within each comment together. given a comments collection c = c1,c2, ,cn where n denotes the comments number, each comment ci is assigned a distribution over k topics learned from the comments collection where k denotes the pre-defined topic number. hotels comments are updated frequently on their pages. a model using a supervised learning algorithm cannot well generalize profiles of new comments. lda model can be used for new comments and can characterize the comments under the unsupervised form in term of the estimated posterior distribution. usually this posterior cannot be computed directly [3], and is mostly approximated using markov chain monte carlo (mcmc) methods or variational inference. the realization of the particular mcmc method, the gibbs sampling algorithm [18], is widely used to lda based comments collection modeling. the applicability of gibbs sampling depends on the ease with which the sampling process creates separate variables for each piece of observed data and fix the variables in question to their observed values, rather than sampling from those variables. gibbs sampling generates a markov chain of variables, each of which is correlated with nearby variables. each step of the gibbs sampling procedure involves replacing one of the variables with a value drawn from the distribution of that variable conditioned on the values of the remaining variables. thus the algorithm converges much slowly when handling high-dimensional data. as we know, the variational method is a deterministic alternative to sampling-based algorithms. the only assumption made for variational method is the factorization between hidden variables and visible variables. thus, the inference problem is transformed into an optimization problem as the equation shows: l(ω,ϕ,γ,λ) , eq[log p(ω,z,θ,β|α,η] − eq[log q(z,θ,β)] (3) ϕ, γ, are the parameters of z and θ, λ is the parameter of the topics β. the variational inference may converge faster than gibbs sampling. however, it still requires a full pass through the entire collection each iteration. it can therefore be time and memory consuming in the application to large and stream coming comments collection. hoffman et al. [8] proposed a much faster online algorithm for the variational inference of lda. this time a fully factorized variables is used, then the lower bound is defined as l , σdℓ(nd,ϕd,γd,λ), (4) the online variational inference comes from the best setting of the topics λ. after estimating the γ(nd,λ) and ϕ(nd,λ) on seen comments, then set λ to maximize l(n,λ) , σdℓ(nd,γ(nd,λ),ϕ(nd,λ),ϕd), (5) the convergence of the online inference had been analyzed and proved much faster than other variational methods. the hotel comments on tripadvisor keep increasing dramatically as we have seen. the scalability is an unavoidable challenge for processing the data set in real time. online variational inference for lda can be much more useful in dealing with a high volume of data and it can handily analyze massive collections of comments. moreover, online lda need not locally store or collect the commentseach can arrive in a stream and be discarded after one look. refer to [8] for a detail analysis of online variational inference for lda. 420 j. ma, s. luo, j. yao, s. cheng, x. chen 4 opinion summarization from topic selection in this section we briefly illustrate the data imbalance problem and two topics selection methods, then we describe the algorithm, used in our framework for opinion summarization from imbalanced data topic selection. further to the topic extraction described in the previous section, we explore the impact of the topics on the summarization performance. intuitively, opinion summarization can be different from the summarization of factual data, as comments regarded as informative from the factual point of view may contain little or no sentiment. so, eventually, they are useless from the sentimental point of view. the main question we address at this point is: how can we determine the informative extracted topics for opinion summarization. the comments collection we clawed from tripadvisor demonstrates the imbalance problem of more positive and less negative. the data imbalance presents a unique challenge to classify the comments from the extracted topics. precision and recall are widely used measurements for classification performance. the precision for a class is the number of true positives divided by the total number of elements labeled as belonging to the positive class. recall is the number of true positives divided by the total number of elements that actually belong to the positive class. consider the two topics sets on text classification, the first topic set may yield higher precision, but lower recall, than the second topics set. by varying the decision threshold, the second topic set may produce higher precision and lower recall than the first topic set. thus, one single threshold cannot tell us which extracted topic set is better. the topic selection needs serious consideration. commonly, there are two methods to select topics: the first is rank topics in descending order with the related criteria, such as roc, then choose the top, say, l topics. the second is more complicated with computation of cross-correlation coefficient between topics. scatter matrices are belong to the first method. j3 = trace{s−1w sb} (6) where sw = ∑c i=1 pisi, pi is the a priori probability of class w, si is the mean vector of class w, sw is the within-class scatter matrix, and sb is the between-class scatter matrix. the cross-correlation coefficient is the second method to topic selection. let i1 be the best topic selected using the first method. i2 = max j {a1rj − a2|ρi1,j|}, j ̸= i1 (7) this equation considers the cross-correlation (ρi1,j)between the best topic and topic j ̸= i1. the rest of the topics are ranked according to ik = max j {a1rj − a2 k − 1 k−1∑ r=1 |ρir,j|}, (8) j ̸= ir, r = 1,2, . . . ,k − 1 these two methods are designed for well-balanced data and if the data dimension is high, the effectiveness of the topic selection is a severe problem for classification. most comments we crawled from tripadvisor are about the length of 150 words. in generalizing the comments with the lda model, the pre-defined topic number k is set to 20, 30 rather than 100 because of the relative short length of each comment. even this moderate number may produce high dimension problems for the topic selection. the detailed analysis is presented in section v. meanwhile, the computational cost is another bottleneck, even we employ the topic extraction with online-lda, efficient opinion summarization on comments with online-lda 421 the topic selection is a time-consuming task with these two methods because of the computing of matrix inverse. so in the proposed framework, we use fast [19] to perform the topic selection for opinion summarization. the topic selection metric is based on an roc curve generated on optimal simple linear discriminants. then those topics with the highest auc (area under curve) are selected as the most relevant. this method is designed for the topics selection of imbalanced data classification. a roc curve is a criterion for ranking the topics, fast employs a new threshold determination method which fixes the number of points to fall in each bin to obtain the threshold for roc. bin means the width of data separation. we use more bins in high density data areas and fewer bins in sparse data area, each bin containing the same number of data. thus more thresholds computed from each bin are placed into the density area for the calculation of the roc. on the opposite, fewer thresholds placed into the sparse area. this effective procedure can be described as following pseudo-code in algorithm 1: algorithm 1 pseudo-code of effective procedure. k: number of bins n: number of comments t: number of topics inbin=0 to n with a step size t/20 for i = 1 to t do sort ti(ti is the ith value of vector t) for j = 1 to k do bottom=round(inbin(j))+1 top=round(inbin(j+1)) threshold=mean(ti(bottom to top)) classify ti end for calculate the auc (area under curve) end for the detailed analysis of the algorithm is in [19]. the benefit applied is not merely about selected topics for classification, the computational cost of the algorithm is relatively low because no matrix inverse is calculated. because the area under the roc curve is a strong predictor of performance, especially for imbalanced data classification problems, we can use this score as our topic selection: we choose those topics with the highest areas under the curve because they have the best predictive power for comment collection. 5 experiments the experiments is to evaluate the model that produces opinion summaries of comments, in the context of which we assess the best manner to use summarization opinion for the users to quickly digest. in this section, we will present and discuss the experimental results of topic selection and opinion summarization on the hotel comments dataset. 5.1 dataset we crawled 250,004 hotel comments from tripadvisor in one month period (from nov, 2012 to dec, 2012). the comments in the dataset are labeled according to 5 scales of ’star’ expressing the polarity of the opinion of the reviewers (1, 2 corresponding to negative comments and 4, 422 j. ma, s. luo, j. yao, s. cheng, x. chen 5 corresponding to positive comments). since we summarize the opinions into two classes of sentiment (positive and negative), the neural comments (scale 3) are excluded from the comments collection. figure 4 shows the statistical information about the comments collection. most comments are within a length of 150 words. 0 50 100 150 200 250 300 350 400 450 0 10 100 1k 10k comment length (number of words) n u m b e r o f c o m m e n ts figure 3: the statistical information of the comments collection 5.2 topic selection the first experiment is performed to evaluate the generalization performance of the onlinelda model. as we pointed out in section iii that the lda with online inference can handle massive datasets much faster than other methods such as variational inference, gibbs sampling. we need to verify that there is no generalization performance degeneration using online inference for lda. we compared online-lda model with plsi models and lda model described in section 3. in this experiment, we used all the comments crawled from tripadivor containing 250,004 comments. we held out 10% of the collection for test purposes and trained the models on the remaining 90%. we have found α = 50/t and β = 0.01 to work well with hotel comments collection for lda model and online-lda model. the perplexity [3] is used as the measurement for the evaluation of the models. as it is the standard metric and it measures the model’s ability of generalizing unseen data; lower perplexity indicates the higher likelihood and better model performance. we trained these three models using em with the stopping criteria, that the average change in expected log likelihood is less than 0.001%. figure 4 presents the perplexity for each model in terms of the comments analyzed. three models were trained from the crawled comments without looking at the same comment twice. it can be seen that the online-lda model have a lower perplexity than plsi and lda model after analyzsis of the same number of comments. this superior advantage comes from the fact that the online variational inference converged much faster than variational bayes used in lda [8]. after analyzing the total comments collection, both lda models reached the same level of perplexity about 1700. the generalization performance of online-lda is as good as the lda with an extra advantage of much faster fitting to the comments. the perplexity of plsi is 1900. so the results show that online-lda is more adapted to coming comments under online environment. besides fast topics extraction, our summarization framework employed a lower cost topics selection method in coping with the imbalanced nature of hotel comments. the experiment performed next is to evaluate the performance of topic selection. the balanced error rate (ber) is the main judging criterion for the topic selection [20].ber is the average of the error rates of positive comments and negative comments. if these two classes are balanced, the ber is equal to the error rate as the rate of inverse recall [6]. we evaluated the performance of selected topics efficient opinion summarization on comments with online-lda 423 0 10 100 1k 5k 10k 20k 1700 1800 1900 2000 2100 2200 2300 comments seen p e rp le x it y plsi lda online−lda figure 4: the perplexity results for plsi, lda and online-lda. 0 2 4 6 8 10 12 14 16 18 20 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 number of topics b e r sm cc fast figure 5: ber with k = 20 using sv m. in our framework (fast) with the comparison of the topics selected by scatter matrices (sm) and cross-correlation coefficient (ccc). the main concern in our framework is the performance of the topic selection metric, so we simply choose the popular svm classifier to evaluate the performance without the detail analysis of the difference with other classifiers. table 2 presents the description of the comments used in ber evaluation. table 1: comments used in evaluation of ber number of topics ratio positive comments 180,023 95.5% negative comments 8,053 4.5% these comments collection demonstrates the strong imbalanced nature that the negative comments are less than 5% of the total comments. from the previous analysis of data set, we know that most of the comment length is less than 260. so we set k = 20, 30 respectively for the online-lda model, then the extracted topics are used for ber evaluation with the methods described in our work. figure 5 and 6 show the result of the performance in terms of ber. we can see that the ber changes dramatically with the different numbers of the topics. we observe that ber decreases as the topics increase when the topics number is less than 9. and then the ber reaches to the relatively stable values of 0.15, 0.1 and 0.08 respectively to sm, cc and fast. the explanation for this behavior is that the redundant topics have little impact on the performance of the classifier. this robustness might be useful to redundant topics for classification. but our goal is comment summarization rather than classification. redundant topics can bury informative topics and make the user hard to exploit. the topics selected using fast significantly outperformed sm and ccc topics with lower ber when using the svm classifier. several experimental results reveal that the lowest ber comes from the 9 selected topics. i.tiov et al. [10] show that 9 topics out of 45 lda topics correspond to ratable aspects. this is a quite interesting discovery that the topics we selected are same as the ratable aspects defined by a manual analysis of the documents, as the more topics used in classification, the more freedom we can have to distinguish the polarity of the comments in a finer granularity. but the performance of the classification remains stable to a certain level. the optimal topics come from selected topics with the emerging of this level. this suggests that topic extracted using lda is not a sufficiently representative topic of the importance of comments for summarization purposes. thus, using roc-based topic selection that has proven useful for opinion summarization can yield better results. 424 j. ma, s. luo, j. yao, s. cheng, x. chen 0 3 6 9 12 15 18 21 24 27 30 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 number of topics b e r sm cc fast figure 6: ber with k = 30 using sv m. 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 20 30 40 50 60 70 80 90 100 false positive rate(%) t u re p o s it iv e r a te (% ) 20 topics 30 topics selected topics figure 7: performance of summarization in term of roc. 5.3 opinion summarization as discussed before, a significant advantage of our framework over existing models in topic selection and classification is the lower computational cost in topic extraction and topic selection in imbalanced comments. we only consider the positive and negative comments given data set, with the neutral comments being ignored. there are two main reasons. firstly, hotel comments opinion summarization in our case is effectively a binary classification problem, i.e. comments are being classified as either positive or negative, without the alternative of neutral. secondly, the selected topics merely contribute to the positive and negative words, and consequently there will be much more influence on the summary results of positive and negative comments given data set. furthermore, the classification with less negative comments shows the unique similarity of the outlier detection. as a result, we choose to evaluate the overall performance of the opinion summarization indirectly through outlier detection based on the selected topics. more specifically, we apply one-class svm classifier on the comments with selected topics. f(x) = sign((ω · φ(x)) − ρ). (9) the regularization parameters ω and ρ solve the quadratic programming problem of the νsvm. the classification using these methods is computationally simple and does not require significant memory. the main problem we encountered is that the lexicon is needed first in our proposed summarization framework. in our case, however, comments are often composed of ungrammatical sentences and, additionally, a high number of unusual combinations of escape characters (corresponding to the vivid sentiment expressed), which make the comments much noisier and harder to process than the standard data sets traditionally used for summarization evaluation. nevertheless, the online-lda, being a generative model, proved to be quite robust to variations in the input data and, most importantly, to the change of the domain (micro-blog etc.) [8]. there are 10,314 words in our lexicon for the experiments. table 2: performance of opinion summarization topics positive comments detection rates negative comments detection rates selected topics 89% 91% 20 topics 80% 77% 30 topics 80% 79% the results of the opinion summarization are shown in table 2. the first thing to note in table 2 is that the opinion summarization model is doing a much better job at classifying the efficient opinion summarization on comments with online-lda 425 10 20 30 40 0 500 1000 1500 2000 2500 number of topics t im e p e rf o rm a n c e o f t o p ic e x tr a c ti o n lda online−lda figure 8: time performance of the topic extraction. 10 20 30 40 selected 0 50 100 150 200 250 300 350 400 450 number of topics t im e p e rf o rm a n c e o f c la s s if ic a ti o n 170 194 254 403 198 figure 9: time performance of the classification. comments according to its polarity than the solo lda model, the main problem with the latter being a relatively low precision. the main reason for this is an insufficient number of annotated negative examples when performing the topic selection. the results show that the model is capable of reliably identifying negative comments (figure 7). it can be observed that there is a considerable improvement in classification accuracy after performing the topics selection with fast, with 5.3% improvement for our framework. we evaluate the topic extraction time. we extract the 10, 20, 30 and 40 topics with lda and online-lda respectively. results show that the online-lda model outperforms the lda model (figure 8). we evaluate the framework’s time performance. we extract the 10, 20, 30 and 40 topics with online-lda, and then perform the topic selection with two topics sets, 20 and 30 topics. we classify the comments in two ways: the first is to classify the comments with original topics, the second is to classify the comments with selected topics. the time performance of the second classification is averaged over two selected topics. the topic selection used more time when classifying but the classification time does not increase dramatically due to the lower dimension of the data. results show an extra 28 seconds in comparison with 10 topics (figure 9) due to the time-consumption of the topic selection. we believe, however, that for the best performance of summarization a 28 second period is considered low enough in handling over 200,000 comments so that our results indicate an acceptable time performance penalty. 6 conclusion in this paper, we have presented a new framework for the summarization of hotel comments. the most useful usage of opinion summarization is a web application. while most of the existing approaches to opinion summarization have not put into much consideration of the scalability of the models. scalability is the most important task in our proposed framework. the online-lda model is used for extracting the topics from the huge and increasing comments collection. the generalization performance remains the same but the computational cost is lower in comparison with lda model. we address the imbalance problem of the comments. and the topics selection method, fast is used for better classification performance. the selected topics are informative and easy for the user to digest the comments. there are several directions we plan to investigate in the future. one is the best comments selection when the aim is to brief the comments collection. another one is the ratable aspects regression analysis for certain kinds of reviews. 426 j. ma, s. luo, j. yao, s. cheng, x. chen bibliography [1] a. divtt and k. ahmad (2007); sentiment polarity identification in financial news: a cohesion-based approach, in acl’07, prague, czech republic, june 2007, 1-8. 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[21] z. ma, a. sun, q. yuan and g. cong (2012); topic-driven reader comments summarization, cikm’12, maui, hi, usa, 265-274. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 5, month: october, year: 2022 article number: 4854, https://doi.org/10.15837/ijccc.2022.5.4854 ccc publications the role of e-learning generated by the covid-19 epidemic in higher education t.o. breaz, m.t. fülöp, l.i. cioca teodora odett breaz 1. lucian blaga university sibiu, romania 2. „1 decembrie 1918” university of alba iulia alba iulia, romania teodora.breaz@uab.ro, teodora.breaz@uab.ro melinda timea fülöp* babeş-bolyai university of cluj-napoca cluj-napoca, romania *corresponding author: melinda.fulop@econ.ubbcluj.ro lucian ionel cioca lucian blaga university sibiu, romania lucian.cioca@ulbsibiu.ro abstract as key players in our society, universities have a special responsibility for sustainable development. as institutions of education, research, and services, universities are key players in the sustainable development of society and the economy. with the health crisis caused by covid-19, it was necessary to introduce digitalized teaching. we could even say that covid-19 is an accelerator for students’ and teachers’ acceptance of digital technologies in teaching. in many universities, there is still some impotence on how distance or short-term university teaching can take place. in the european higher education area, there has long been a commitment to greater digitalization of teaching under the keyword e-learning, so we set out to analyze these issues in the romanian university environment. the results show that substantial efforts have been made, but these efforts must be continued to be successful and to reach a sustainable university. keywords: e-learning, tam, university sustainability. 1 introduction as institutions of education, research, and services, universities are key players in the sustainable development of society and the economy. university sustainability must be the dynamic contribution of all advanced teaching organizations in the development of environmental protection policies. a sustainable academia is the university that, in addition to the governmental participation, donates https://doi.org/10.15837/ijccc.2022.5.4854 2 to the fortification of the environment by adjusting the learning curricula to the environmental requirements, respectively, by the improvement of the methodical knowledge, because of the educational and investigate, respectively, research activities. the way in which the present humanity mechanism, by increasing the manufacture and consumption of goods and facilities, is not feasible and will have a damaging consequence in the extended run. this characteristic has presently produced powerful apprehension in university, but also at the flat of decision makers concerning sustainability. the un has established and issued the 2030 agenda for sustainable development goals, which includes 17 key goals, including the goal of developing education so that universities become more sustainable. the 17 sustainable development goals have been designed to increase awareness of the various facets of sustainability, drawing explicit goals that contain an accomplishment strategy for a extensive series of social, environmental, and technological subjects, from reducing deficiency, health for all, infrastructure progress, education, gender equality, maintainable use of oceans, energy, water, and sanitation. all 17 goals can be connected to engineering and individually request engineering to accomplish their goal [32]. with the digitalization of higher education, the fourth goal of the un sustainable development agenda, computer scientists and engineers have a special role to play in the efficient use of (recorded) data for process optimization, it, resource saving programming, renewable energy, energy transition, energy, efficient production, electronic waste disposal, recycling, the role of decision based on it, etc. thus, new study programs need to be developed in an agile, flexible, and targeted manner. furthermore, courses should promote personal, ethical, social, and methodological skills more than ever before. practical, technical, and interdisciplinary teaching content or partnerships with companies are more important than ever in training, especially in the field of engineering. ready in this way, tomorrow’s engineers can better meet the challenges of the digital world of work for decision markets [13], [14], [15], [19]. the future as a location for technology and innovation depends in particular on the successful and timely digitalization of business, research, and teaching. the digital revolution in the education involves the engrossment of sustainable management, in demand to adjust to the fluctuation’s compulsory by new technologies, will play an important role in supporting decision [13], [14], [15], [19]. in current periods, universities have been informed of a set of significant changes, encouraged by technological and social tendencies concerning digitalization. like all revolutions, digitalization involves a powerful change in all segments. presently, the implementation of technologies by academes is connected to a paradigm change, in which technology is considered as a multifaceted and interrelated environment that allows digital learning through transformation and plays the role of decision-maker [13], [15], [19]. in this way, the attention is further absorbed on the student and the necessity for technology. in this framework, digitalization is a stipulation in higher education organizations, able to attract more and better students, to improve the teaching materials used in courses, and the teaching procedure in over-all. new technologies such as artificial intelligence, big data, moodle platforms [13], [15], [19] also allow better monitoring and help to identify barriers and decrease the hazard of dropping out of university. however, the hesitancy to appreciate and take benefit of prospects to change to this digital environment persists. the digital revolution in the worldwide higher education business is the forthcoming approach to sustainable teaching management [22], [24]. the world we live in is shifting at a fast pace, and anything we explain and in what way we teach is also growing quickly. in current years, universities around the world take knowledgeable fast variations with influence, which have remained influenced by technological development and the social tendencies of digitalization. like all other innovative variations, the digital revolution includes powerful change/rearrangement. robust variations in the socioeconomic-educational system follow-on from the globalized economy have led to fluctuations in impulsion, particularly in universities, such as the normal of instruction, value, regionalization, and computer-generated and self-governing education. these factors in the field of instruction communally encourage transcontinental teaching. the wide spread of global teaching has fundamentally inclined academies to form their education and growth instruments, transfer, and constant perfection. universities may no longer be dependent on traditional systems of education to face the challenges posed by the phenomenon of globalization. virtual learning is becoming increasingly significant for schools among the 2019 pandemic [7]. it is imperative to cover the serious situation of computer-generated learning beyond the it tool and instrumentalization [5], [9]. consequently, academies need to discover how they might balance people with e-learning https://doi.org/10.15837/ijccc.2022.5.4854 3 to safeguard teamwork and participation. virtual learning technology has grown at an astonishing rate and its use, especially in transcontinental academies, has converted additional common. this phenomenon has particularly affected training approaches and their transfer. therefore, this article highlights the impact of digitalization on institutional management and performance, respectively, investigating factors that influence the digital transformation during the covid-19 pandemic, which led to the transition to e-learning [6], [7], [12], [18], [27]. the novelty and the main contribution are the examination of the situation for romanian universities because we identify this goal in the existing literature and we consider it to be very important to analyze the acceptance of new theology because it can be decisive for the future. moreover, automation and digitalization can have a significant positive effect on the well-being [13]. to achieve our objectives, we make a short examination of the literature on the digitalization in the university environment, the acceptance of technology, and the relationship with the sustainability and the university to identify al main goals and et and we present the result of our study. 2 digitalization in the university environment technological progress, the increasing national and international interdependence of companies and the liberalization of the markets lead to problems in almost all areas of the economy, which are characterized, among other things, by increasing dynamics, interconnectedness, and increasing opacity. the handling and management of such complex economic situations with ever-shorter decision cycles is one of the essential requirements for the management of the future [20], [21], [25], [27]. at the same time, the importance of the "information" factor as one of the main determinants of problemsolving and decision making has steadily increased in recent years. the worldwide and constantly growing spread of information and communication technologies in many areas of human life and the emergence of a new employment sector "information" industrial information age. this also and especially applies to the microeconomic, business administration area. "information" is more and more frequently referred to here as an independent production factor, and information technology can serve as a "strategic weapon" for more efficient and effective use of existing and for the development of future universities potentials and thus for the attainment of operative and strategic competitive advantages. digitalization has now reached all areas of work in universities and penetrates the fields of research and teaching, as well as their administrative processes, and helps to make decisions [13], [15], [19]. digitalization in universities is associated with various potentials and expectations, but at the same time with uncertainties and fears. learning in internet-supported teaching-learning scenarios is not isolated, it is not lonely learning. rather, the shared use of the learning platform, the collaboration with the learning group, the virtual exchange with the tutor or the lecturer represent an important social component. digitalization determines the public discourse in and for educational institutions, as well as internal discussions in the universities about their further development. however, there are many myths about what is addressed or meant by digitalization, such as that the media per se would improve the learning of students [26]. the universities themselves have created realities, for example through their e-learning activities. since then, perspectives relating to digitalization have also opened, which, on the one hand, are rooted in their technical, organizational and didactic possibilities, but on the other hand, also ask about forms of their study and research in universities. all discussions are flanked by developments in education and science policy, with research and funding programs or university policy or strategic objectives using terms such as e-learning, media, or digitalization in their various forms. however, their novelty is not necessarily reflected in new concepts, reorganized processes, or in a completely changed practice of academic teaching and research. rather, digitalization appears as a postulate. therefore, it is essential to also disclose narratives in connection with the concept of digitalization for science and practice. all these aspects are discussed with the necessary productive-critical distance. in short, digitalization should allow universities to conduct research, teaching, and administration at a higher quality and more professional level [20], [31]. in fact, a variety of visions, strategies, and implementation concepts for the “digital university” have been and are being developed and lived in universities. innovations are perceived innovations with benefits, so it is initially unclear who the beneficiaries are. in this respect, the concern is legitimate https://doi.org/10.15837/ijccc.2022.5.4854 4 to shed light on e-learning, what is new about it, and what daily teaching-learning problems can be solved with it. however, when it comes to educational innovations such as this, it takes a surprisingly long time to find one player in this research. at best, it becomes active when it comes to checking an innovation in school, university, or further education, as a kind of "research afterwards". everyone wants to be innovative these days: companies, political parties, the cultural program, university management, etc. ultimately, those affected decide whether something new is also suitable for everyday use. when it comes to research into learning and teaching, whether with or without new media, things look rather bleak in terms of both suitability for everyday use and innovative strength. the special structural needs, challenges, and implementation potential are very different for different areas of the university [30]. as a cross-cutting issue, digitalization is becoming more widespread in all areas of the university, offering new opportunities in teaching, research, and communication, but at the same time presenting new challenges to teachers and students. e-learning has come through a decade of growth, decline, experimentation, transfer, excitement, and disillusionment. irrespective of public perception, the political and economic trends and their reflection in the media, over the course of this decade universities have been increasingly dependent on reacting inventively to new challenges in teaching and learning. many institutions used the possibilities of information and communication technologies. in this way, efforts were made to face up to the changing teaching framework conditions and the changed demands of various stakeholders inside and outside the universities [4], [5], [6], [9]. according to ehlers (2018) [10], a key factor of the future university are greater participation in education, increased diversity in life situations, digitalization, and student needs. although ehlers (2018) [10] sees the strongest factors, on the one hand, in the growing importance of education in society and, on the other hand, in digitalization, another equally important factor is the needs of students as part of the digitalization project. the reason for this is the assumption that students’ needs will be a significant part of shaping their profile [27]. classically, the term decision support system (dss) is defined as a computer-aided planning and information system that prepares or supports the preparation of decisions at management levels but does not make the decision itself. against the background of rapid development in it and the widespread spread of the internet, this definition seems to be outdated in many cases, since a large number of decision support systems nowadays make not only strategic but also operative decisions. the development of the digital university is a demanding and complex task. it is too narrow a view if digitalization is seen only in standardized mass-produced mechanization of teaching and learning processes and also does not include aspects of permeability, personalization, effectiveness, lifelong learning, labor market requirements, educational partnerships, and innovation [2], [3], [4]. finally, the “student-centric” factor is the application of the well-known client-centered strategy [11]. this benefits from greater transparency and improved communication and interaction options on digital platforms [16], [21], [27]. 3 accepting technology in the university environment the term e-learning has its origins in the 1980s, and subsequent, the outline of the primary computer-assisted jobs, which led to the replacement of handheld computers and typewriters with computers, so to speak. employees have been trained in new software applications (e.g., operating systems, text programs, and databases) through traditional employee training courses and and this remained new through small education programs that have been installed on a computer unit by software workers. these tools remained initially modest didactic manuscripts, which were processed in the following years using illustrations and animations. the outline of such e-courses firstly shaped a persevering misperception around the potentials of this new category of skill. for many educational specialists, legislators, and software producers, this technology has been considered a miracle tool. it should help to reduce (staffing) prices in the teaching segment and open novel delivery networks for education contributions. for example, traditional academies were anticipated to expand into numerous categories of academies, most of which remained virtual organizations, such as worldwide groups, systems, and virtual academies. from a didactic point of view, nothing less than better learning has spread. consequently, specific procedures of likeness, response, creation, communication, and relationship should be stimulated only through the use of the media, which in particular promotes https://doi.org/10.15837/ijccc.2022.5.4854 5 the acquisition of knowledge. e-learning could do this, e.g. it has only partially met the euphoric expectations so far, or expectations should have been revised because they are excessive. this shows that providers of virtual higher education offerings or online courses face subjects such as employer acceptance and dropout tolls fairly additional intensely than providers of traditional educational offerings. figure 1: the relationship about tam and the sustainability of universities it is difficult to discover an explanation of the term e-learning in the literature. on the one hand, this can be traced back to the circumstance that the concept of e-learning has become recognized to define e-learning approaches, but it still requests to be avowed alongside numerous other designations and attempts at arrangement [12], [21], [27]. it doesn’t matter if it is computer-based learning, computer-assisted teaching, computer-mediated learning, or computer-assisted learning: most of these are comparable concepts that cannot be straight distinguished from each other. the first systematic debates around e-learning occurred in the late 1990s; at the equivalent period, they encouraged the primary methodical efforts at providing a characterization. originally, the effort was concerned with concerning the practical potentials of dispensing the learning content, e.g., satellite learning, cd-rom learning, interactive television, or videotape learning. the essential element of the description of the setting of concept is the education as a intermediary of the education provided for the student. an explanation of the education condition does not take place in the primary efforts of characterization, nor ensures the debate about the importance of the student in the education situation: that of an asset and the learning scenario with creative recipients or that of the passive observer [17]. it is now understood that e-learning should be understood as a common term for all categories of mediasupported learning. what they have in common is that the learning process is considered a key factor in achieving individual learning goals. however, the term does not specify the content of what is to be learned but refers to the technological means used. this leaves an overly rigid technological focus. according to this agreement, e-learning includes accepted forms of learning both online and offline (for example, learning without internet support). thus, we can emphasize that e-learning always means dealing with electronic media. however, this method of learning can only be successful if you can work and learn with technology as a tool without much cognitive effort. technology should not be at the heart of the learning situation but the learning offer. therefore, it should be irrelevant to the learner if they access the network-based or locally stored information systems. to make this possible, software developers have now made available learning systems, many of which are difficult to monitor. 4 research framework and results the technology acceptance model was developed by davis (1986) [8] as part of his dissertation and is an adaptation of action theory. the model was developed to predict the acceptance of technical systems. according to the fishbein-ajzen theories (1977) [1], the technology acceptance model is intended as a direct predictor of behavior (acceptance). the purpose articulates a person’s intention to usage the technical system in question in the future. the aim is, in turn, determined by two https://doi.org/10.15837/ijccc.2022.5.4854 6 perceptive issues: perceived benefit and the perceived ability to use the system under consideration. davis defines the impact of third outside variables in the model as project structures that take an impact only on cognitive variables. figure 2 shows the model. figure 2: davis’ technology acceptance model in acceptance research, it is the best operationalized and the empirical model most generally verified to elucidate the acceptance of technical structures [17], [23], [28], [29]. a september 2021 web of science index query returned 1,302 citations for the two introductory articles. a questionnaire was designed based on the literature and subsequently sent to students to obtain an x-ray of the status and challenges they face in the full adoption of e-learning. specifically, the questions are about perceived usefulness and perceived ease of use. the first section deals with the personal information of faculty members that reflects their field and experience. the second section focuses on the level of use of elearning. pilot-tested surveys were selectively distributed to students for a first opinion. the purpose of the pilot testing of these surveys was double: to observe the reliability of the survey items and to confirm that the respondents could easily comprehend the substances in the survey. care has been taken to safeguard that construction, linguistic and clearness are at a satisfactory level. these surveys were disseminated directly to students at ten universities using a non-likely sampling approach. data were collected from these students, who remained all scholars, who came from diverse universities, different academic years, men and women. the answers to the questions in the technology acceptance model and the items in the subjective norm were recorded using a five-point likert scale, analogous to the original questions: 1 = ‘total disagreement’ to 5 = “total agreement”. the 36 items include the perceptions and barriers to the availability of learning resources, material comprehension, learning attitudes, ease of access, delivery methods, and interaction patterns. in this study, the perceptions were obtained from the learning/experience they experienced, both in terms of models of interaction with lecturers, interactions with other students, the availability of support facilities, including internet networks, and the availability of teaching materials in the e-learning system. in a structured way, this perception implies indicators of perceived use and ease of use. this study used quantitative data collection methods to empirically examine and highlight factors that had a greater influence on user behavior. a nonprobable sample was adopted, which is commonly used in higher education [33]. the study was used to target respondents who were teachers or students in universities and also used the resources of e-learning systems offered by their universities. data were collected using a survey, which was conducted using the cawi (computer-assisted web interview) technique. a link to the electronic surveys was distributed to individual students from romanian universities through the university’s email system. these were distributed between january and february 2022. in total, 1875 completed questionnaires were received. structural equation modeling (sem) was used to process the data from the developed model. the use of sem will deliver more precise estimations as it delivers a simultaneous level of examination for both the structural model and the provided measurements. moreover, sem offers a supplementary comprehensive method to test hypotheses around observed and latent variables. sem is the most common approach to measuring the level of acceptance of information technology by users. e-learning learning in this study refers to the entire distance learning that uses the internet and hardware to provide subjects or materials from teachers to students. all interactions, both synchronous and asynchronous, are performed through the media. more and more research is enjoying the tam model, which is widely used to predict the acceptance of any new technology. we used tam for the similar predictive validity that it showed in previous studies in a similar context. our investigation model is grounded on the study of variables that affect the acceptance of the e-learning system using https://doi.org/10.15837/ijccc.2022.5.4854 7 the extended tam model. the starting point of the research is the observation that e-learning has become an essential part of education. we are primarily talking about companies that offer a wide range of e-learning programs for their employees, for example, through newly established corporate universities as well as universities that integrate new technologies into their teaching institutions. at the same time, it should be noted that e-learning offers face acceptance problems much more frequently than traditional face-to-face offers. the growing number of students who use e-learning willingly face a large number of people who are critical and negative about multimedia courses. this means that e-learning is facing the problem of being the fastest growing sector of technology-based education, on the one hand, on the other hand, it can only partially reach its target group. this raises the question of the factors that influence the acceptance of e-learning and what the consequences for the successful implementation of e-learning. the characteristics of the sample show that 67.04% of those sampled were from the category of undergraduate students, 32.64% master’s students, and a percentage of 0.32% doctoral students. we also note that 58.99% of respondents fall into the age category of 18-25 years and a percentage of 6.77% are over 45 years. all 1885 were students at a public or private university. exploratory investigations were carried out to inspect the validity of the proposed variables and to contrast the preliminary reliability of the scales. factor analysis to confirm the magnitudes obtained in the investigative study and to permit the abolition of the established scales. it also facilitates the confirmation of the psychometric properties of the factors that are part of the model. correspondingly, the causal investigation contrasts the planned structural relationships. a first step before examining and testing the hypotheses was an investigative examination of the validity and reliability of the variables. the validity of a element is the grade to which one element varies from the other established elements. validity can be measured by examining the main elements with kaiser varimax rotation; see table 1. table 1: test kmo & bartlett kaiser-meyer-olkin measure of sampling adequacy. .946 bartlett’s test of sphericity approx. chi-square 15296.343 df 66 sig. .000 as can be seen from the results obtained based on the kaiser-meyer-olkin & bartlett test, our elements fall within the recommended range of 0.8 to 1, which indicates that the sampling is adequate. also, to test the reliability of the elements, we performed the reliability test, and the results confirm this time that the sample is adequate. table 2: the reliability test cronbach’s alpha cronbach’s alpha based on standardized items n of items .923 .929 12 the factor load is practically the correlation coefficient for the variable and the factor. the factor load illustrates the variance elucidated by the elements for that specific factor. in the structural equations (sem) method, as a general regulation, the factor load of 0.7 or higher value indicates that the issue extracts a satisfactory variation from those elements. the outcomes we found are beyond 0.7 so we can consider them adequate. composite reliability (sometimes called construction reliability) is a measure of the interior constancy of measure elements, just like cronbach’s alpha. it can be considered equal to the total value of the variation of the real score in relation to the total variation of the scale score, and the recommendation is that its value be greater than or equal to 0.7, and this condition is met by the elements of our model. and last but not least for the variation test we chose the average extracted variance (ave), which is a value of the quantity of variance that is taken by a hypothesis in link to the amount of variance due to dimension mistake and the optional values are superior to or equivalent to 0.5 and this indicator being met. thus, we can consider our elements to be able to continue our analysis. correlations superior to 0.3 remained statistically significant at 0.01. in our correlation matrix, most correlations among elements were significant at 0.01, with values greater than or equivalent to 0.3. completely, we complete an initial examination of the reliability of https://doi.org/10.15837/ijccc.2022.5.4854 8 the dimension scale of our model using cronbach’s alpha, the most widely used indicator for this type of analysis. this coefficient covers values between 0 and 1. in our study, the cronbach’s alpha for each of the 12 variables is greater than or equal to the threshold of 0.7, above which the reliability is considered acceptable. the cronbach’s alpha coefficient with values above 0.900, which indicates that the instrument can be considered reliable and consistent internal. following the determination of the reliability, correlations, and validation tests that showed that the elements are adequate, we proceeded to the next step in which we analyzed the degree of fit and the adequacy of the model. the indices that we used were: chi square, for which a value less than 3 is considered a good match; gfi (good match) ranges from 0 (bad match) to 1 (perfect match) recommend a value 0.8; agfi (adjusted goodnessmatching), with an acceptable value greater than or equal to 0.8; nfi (standard match index) with values between 0 and 1 but a value above 0.8 is recommended; the ifc (comparative match index) fluctuates between 0 and 1, and the rmsea (root-average pattern error) matches well between 0.05 and 0.08 (. to determine the fit and suitability of the model, the analysis was performed using ibm spss amos 26 graphics. following the analysis of the model fit indices, it is observed that it is suitable, as shown in the table below: table 3: model matching indices indicator recommended values obtained values chi square < 3.00 2.943 gfi > 0.90 0.911 agfi > 0.80 0.848 nfi > 0.80 0.901 cfi > 0.90 0.934 rmsea < 0.10 0.061 as noted in all preliminary tests or within an appropriate and acceptable range, we moved on to the last stage in which we analyzed the estimates based on path analysis (path coefficient) to validate or reject the assumptions made earlier. the results of the analysis are presented in the table below. as can be seen from the 21 hypotheses, 5 were rejected due to a higher value of p of 0.001 which indicates that there is no influence between variables or that the influence is insignificant. table 4: hypothesis validation path coefficient p validation ability to use → perceived ease of use .391 *** accepted course content and design → perceived ease of use .262 *** accepted instructor contribution → perceived ease of use .051 *** accepted previous experience in e-learning → perceived ease of use .022 .025 rejected the quality of the e-learning system → the perceived ease of use .089 *** accepted ability to use → perceived utility .075 *** accepted course content and design → perceived utility .499 *** accepted instructor contribution → perceived utility .003 .851 rejected previous experience in e-learning → perceived utility .029 .003 rejected e-learning system quality → perceived utility .011 .487 rejected perceived ease of use → perceived utility .377 *** accepted perceived utility → satisfaction and personal development .679 *** accepted perceived utility → attitude towards the use .493 *** accepted perceived ease of use → satisfaction and personal development .199 *** accepted perceived ease of use → attitude towards the use .304 *** accepted perceived utility → behavioral intent to use .248 *** accepted perceived ease of use → behavioral intent to use .022 .305 rejected satisfaction and personal development � behavioral intention to use .498 *** accepted attitude towards use → behavioral intention to use .264 *** accepted behavioral intent to use → actual use .268 *** accepted behavioral intent to use → academic performance .497 *** accepted however, the results of this investigation show that the less central elements that determine the use of e-learning resources by students in academies are the ability of students, course content, satisfaction and personal development and attitude toward the use. while elements such as the contributions of the instructors, the content of the course, and the design are shown to most influence the behavior of https://doi.org/10.15837/ijccc.2022.5.4854 9 students in the use of e-learning resources in romanian universities. more research is needed on the use of e-learning resources. this can be attributed to the continuous and fast growth of technology requirement and the unremitting expansions in the domain of education. in the present situation, the supplementary issues included in the model are supported using tam. furthermore, this investigation can be measured as added value to the present literature in the field by examining the elements that additional have an impact the use of e-learning resources by instructors-students, who are offered by their academies to expand their education developments at anytime and anywhere. based on the outcomes of the study, a deeper understanding of outside elements between instructors and students was enabled, in addition to offering evidence for academic managers, designers, system developers, and related professionals. although technology is used to an effective grade in romania and its higher education institutions, more devotion needs to be rewarded to the issues that production a pertinent role in helping the use of e-learning systems by students to supplementary improve their performance and effectiveness among students and instructors correspondingly to accomplish sustainability purposes. it can be noted that the results of the examination provide a deeper understanding of the field and important elements that encourage the use of learning resources by instructors-students in romanian academies. 5 conclusion as technology advances at a rapid pace, further progress will be made in the field of e-learning, but it is important to remember that e-learning also has limitations. on the one hand, one has to judge an e-learning environment according to its didactic concept and certainly not only according to its technical feasibility, which depends on the creators of the respective programs. in addition, the learner is still usually the same person as in classical learning methods. when there is a lack of motivation, interest, or desire to make an effort, e-learning will not be able to work wonders. furthermore, the constant flexibility and availability of learning programs tempt people to put in additional learning effort and possibly overwhelm them. the study succeeded in showing that the acceptance of elearning can be explained as a function of personal determinants (e-learning). this finding now allows the development of promising intervention measures for e-learning. if it is possible to influence one or more predictors, then acceptance will change as well. what implications can be derived from this research for future research? first of all, this paper is the first careful examination of the subject of accepting e-learning in romanian universities. by approaching the technology acceptance model, new perspectives can be derived for the field of research acceptance of e-learning. therefore, the article should be understood as a starting point for future research. the acceptance investigation of e-learning was conducted in the context of this research using two systems. on the one hand, technology that supports e-learning has been analyzed from the perspective of students. a conscious decision has been made not to further differentiate the type of use of e-learning, for example, on different types of platforms or software used. therefore, the way in which the respondents use the system in detail and to what extent the different types of use influence the acceptance of e-learning remain unanswered. as a result, the purpose of further investigation is to conduct a more detailed analysis of the different types of e-learning used. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] ajzen, i., fishbein, m. 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(2017). factors affecting e-training adoption: an examination of perceived cost, computer self-efficacy and the technology acceptance model. behaviour & information technology, 36(12), 1261-1273. https://doi.org/10.15837/ijccc.2022.5.4854 12 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: t.o. breaz, m.t. fülöp, l.i. cioca (2022). the role of e-learning generated by the covid-19 epidemic in higher education, international journal of computers communications & control, 17(5), 4854, 2022. https://doi.org/10.15837/ijccc.2022.5.4854 introduction digitalization in the university environment accepting technology in the university environment research framework and results conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 5, month: october, year: 2021 article number: 4258, https://doi.org/10.15837/ijccc.2021.5.4258 ccc publications video streaming service identification on software-defined networking l. m. castañeda herrera, w. y. campo muñoz, a. duque-torres luis miguel castañeda herrera*, wilmar yesid campo muñoz department of electronic engineering university of quindío, colombia cra 15 #12n, armenia, quindío, colombia *corresponding author: lmcastanedah@uqvirtual.edu.co wycampo@uniquindio.edu.co alejandra duque-torres institute of computer science university of tartu, estonia narva maantee 18, 51009 tartu, estonia duquet@ut.ee abstract it is well known that video streaming is the major network traffic today. furthermore, the traffic generated by video streaming is expected to increase exponentially. on the other hand, softwaredefined networking (sdn) has been considered a viable solution to cope with the complexity and increasing network traffic due to its centralised control and programmability features. these features, however, do not guarantee that network performance will not suffer as traffic grows. as result, understanding video traffic and optimising video traffic can aid in control various aspects of network performance, such as bandwidth utilisation, dynamic routing, and quality of service (qos). this paper presents an approach to identify video streaming traffic in sdn and investigates the feasibility of using knowledge-defined networking (kdn) in traffic classification. kdn is a networking paradigm in sdn, that utilises artificial intelligence (ai) using machine learning methods, allowing for integrating behavioural models to recognise patterns in sdn traffic, such as video streaming traffic detection. we extract important network traffic information in the form of flows statistics in our initial proof-of-concept. then, we used such information to train six ml models that can classify network traffic into three types, video on demand (vod), livestream, and no-video traffic. our proof-of-concept demonstrates that our approach is applicable and that we can identify and classify video streaming traffic with 97.5% accuracy using the decision tree model. keywords: livestream, kdn, sdn, traffic classification, vod. https://doi.org/10.15837/ijccc.2021.5.4258 2 1 introduction it is well known that video streaming is the main network traffic today. according to the latest cisco visual networking index (vni) forecast, in 2021, internet video users will be nearly 1.9 billion [7], i.e., the world will see 3 trillion minutes of internet video per month. consequently, the video will continue to dominate global internet traffic, accounting for 80% of all internet traffic in 2021 [30], [14]. on the other hand, software-defined networks (sdn) has been considered a viable solution to cope with complexity and increase network traffic. sdn enables network programmability and provides a flexible architecture to manage more efficient computer networks [25]. the control plane (cp) and data plane (dp) are separated in sdn, allowing for logically centralised network device control. the network devices become simple forwarders that are programmable using a standardised protocol such as openflow by transferring the forwarding devices’ control logic to a logically centralised device, namely the controller [33]. the centralised view of the network and its traffic flows is one of sdn’s key advantages. on the other hand, those advantages do not ensure that network performance will not suffer as traffic volume grows. video streaming services, in particular, have raised their quality of service (qos) standards [5]. as a result, even with sdn benefits, controlling the traffic that flows across the network remains a big challenge [16]. classification of traffic flows, which provides inputs for different network-related management tasks, is an important tool to ensure the reliable operation of networks [5], [16]. in particular, the classification of flows that belong to video services has been the object of study both in academia and in industry, as they dominate global internet traffic and account for 80% of all internet traffic [30], [14]. most approaches to video traffic classification are based on port number, deep packet inspection (dpi), and flow characteristics [2, 4, 10, 15, 17]. port-based strategies are not as reliable as several applications or services use random or non-standard port numbers [10], [4]. dpi approaches are accurate in identifying traffic as they inspect the packet’s payload [15]. unfortunately, these approaches have a high processing cost and are generally not used because the packet is encrypted [4]. on the other hand, methods that use flow characteristics (e.g., flow size, number of packets, flow duration, etc.) to perform flow classification have attracted attention in academia and industry. these methods take different statistical characteristics of the flows during the establishment, maintenance and release of a session [17], [2]. then, by using data mining and analysis methods, it is possible to extract patterns that can distinguish the nature of the network flow and identify the type of traffic to which it belongs. in this paper, we introduce a technique for identifying video streaming traffic in sdn and examine the possibility of utilising knowledge-defined networking (kdn) for traffic classification. kdn is a networking concept that uses machine learning (ml) to improve a variety of network management activities and services [8], [3]. in our initial proof-of-concept, we derive the relevant information of network traffic in the form of flow statistics such as total flow size, the total number of packets, flow duration, etc. then, we used such information to train six ml models that can classify network traffic into three types, video on demand (vod), livestream, and non-video traffic. our approach follows some steps of the cross-industry standard process for data mining (crispdm) methodology [18], [6]: i) creating a dataset from an sdn-based network, ii) cleaning and selecting features for the generated flows dataset, and iii) creating a data model. the application of various ml techniques to identify video streaming flows is referred to as data model. to sum up, the contributions of this paper are: i) two dataset to model vod, livestream, and non-video traffic in sdn environment, ii) a model that allows classifying vod, livestream traffic from non-video traffic; iii) the evaluation of different classification techniques disclosing that decision tree (dt) model is the algorithm that most accurately classifies the targeted traffic. our proof-of-concept demonstrates that our approach is applicable and that we can identify and classify video streaming traffic with 97.5% accuracy. the remainder of this paper is organized as follows. we provided a detailed description of material for the sdn network implementation and the data mining method used in section 2. in section 3, we present the answers to our research questions and discuss the results obtained. finally, we conclude the paper in section 4. https://doi.org/10.15837/ijccc.2021.5.4258 3 2 material and method this section introduces our approach materials and method to classify video streaming flows in an sdn environment. we start with the definition of kdn (section 2.1), crisp-dm methodology (section 2.2), the data understanding (section 2.3), the data preparation (section 2.4), the construction of the models (section 2.5), and the performance metrics used to evaluate our approach (section 2.6) 2.1 knowledge-defined networking (kdn) clark and col [8] introduced the idea of an smart plane, known as knowledge plane (pn), to the conventional computer network architecture shaped by the control plane (cp) and the data plane (dp) [20]. the kp’ key is to introduce artificial intelligence to offer the network the ability to discover, generalize or learn from past events [3]. to earn these skills, kp proposes the use of ml techniques. the main purpose of kp is to provide automation processes, recommendation systems and data prediction [22]. the addition of kp to the sdn architecture is called knowledge-defined networking, kdn [22]. data plane (dp), control plane (cp), management plane (mp), and kp are the four planes that make up kdn. through network device forwarding, dp is responsible for creating metadata. the cp interfaces receive the kp instructions, which are subsequently transmitted to the forwarding devices by the controller. in addition, cp provides metadata about the network’s state to the mp. data and control plane metadata are gathered and kept in the management plane. the mp provides the kp with basic statistics data per-flow and per forwarding device. finally, kp provides a set of instructions to the controller regarding what the network should perform [19]. 2.2 cross-industry standard-process for data mining (crisp-dm) we followed the crisp-dm methodology to build a classification model to classify video streaming traffic into three categories vod, livestream, and no-video traffic. in particular, we perform three crisp-dm phases: data understanding, data preparation, and modelling. the traffic-related dataset is generated, collected, and defined in the data understanding phase. in particular, we generate, collect, and define a dataset of flows in the form of statistic information. data preparation seeks to clean the data (removing null values or damaged data) and choose the relevant attributes to decrease computational cost and improve flow classification accuracy. modelling is the process of classifying flows using various ml approaches. we examine several supervised learning algorithms in this stage to find the one that best identifies flows into vod, livestream, and no-video categories in an sdn scenario. 2.3 data understanding this phase creates an initial dataset that represents flow/packet features in an sdn scenario. the following activities are carried out in order to create this dataset: i) created a controlled experimental scenario to re-create an sdn scenario, ii) deployed the controlled scenario, and iii) generated network traffic. the scenario proposed is shown in figure 1. we followed the traditional sdn architecture, i.e., sdn controller, n switches, and n host. the dp network devices can receive and send video and non-video traffic from cp. we run opendaylight controller [24] in a virtual machine (vm) with linux ubuntu 18.04. to get the network status and information about network traffic, i.e., flows statistics, we developed an ofctl-rest-api. this api is located in the ap and the dp contains a tree topology network. we used a zodiac fx switch [37] and ubuntu server 18.04 vm. zodiac fx is a 4-port 10/100m ethernet switch driven by the need for a low-cost option for sdn experimentation and academic propose. regarding traffic generation, we used two common severs, wowza and vlc. wowza server [36] generates the vod traffic and vlc server [34] the livestream traffic. to capture traffic generated, we used the wireshark tool [35]. wireshark is a well-known network analysis tool that captures packets https://doi.org/10.15837/ijccc.2021.5.4258 4 in real-time. we decided to use it since wireshark is the most often-used packet sniffer in the world [35]. figure 1: test environment for dataset construction. 2.4 data preparation the final flow dataset is built in this phase. the following activities were are carried out in order to get the final dataset: i) extract the pcap (packet capture) traces’ packets and organise them into flows, ii) cleaned and examined the data, i.e., add or remove instances; and iii) feature engineering, i.e., detects and discards superfluous and unnecessary features. we execute the aforementioned tasks with scikit-learn [31], a python data mining package that includes a comprehensive set of ml algorithms and data preparation processes. 2.5 data modelling this section covers the selection, evaluation, and modeling of ml models for use in constructing the classification model. we selected six machine learning models, which are detailed below: https://doi.org/10.15837/ijccc.2021.5.4258 5 • logistic regression (lr): lr is a supervised learning classification algorithm used to predict a target variable’s probability. it can be used for various classification problems. lr is widely used in different fields because it is one of the simplest ml algorithms [38]. • linear discriminant analysis (lda): belongs to the supervised learning algorithms. it’s used to find a linear combination of features that separates two or more classes of data. the succeeding combination can be used as a linear classifier [32]. • k-nearest neighbours (knn): is widely used in ml for classification and regression problems. knn algorithms use data points and classify new data points based on similarity measures, e.g., distance function. knn belongs to supervised learning techniques [21]. • decision tree classifiers (cart): is a non-parametric approach that can be used for classification and regression. a model is represented over the entire input space and trained with full training data from its parameters in parametric estimation. it is then easy to use the same template and parameters for any test input [21]. • support vector machine (svm) or support vector network (svn): is an ml algorithm for classification and regression tasks. svm is a supervised learning method that looks at data and sorts it into one of two categories. an svm outputs a map of the sorted data with the margins between the two as far apart as possible. svms are used in text categorisation, image classification, handwriting recognition and in the sciences [11]. • naive bayes (nb): nb is a classification technique based on bayes’ theorem assuming independence between the predictors. in simple terms, a naive bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature [29], [9]. 2.6 performance metrics in our research, we employed performance metrics generated from the confusion matrix (see table 1). let b and b′ denote a classification (e.g., a livestream flow). then each reference standard measure is expressed as a function of the confusion matrix defined as follows: true positive (tp): if the current class of a case was b and the predicted class was b. this represents a successful prediction. true negative (tn): if the current class of a case was b′ and the predicted class was b′. this represents a successful prediction. false positive (fp): if the current class of a case was b′ and the predicted class was b. this represents a wrong prediction. false negative (fn): if the current class of a case was b and the predicted class was b′. this represents a wrong prediction. table 1: confusion matrix b′ b cn no − cn cn tp fp no − cn fn tn the ratio of correct predictions made to both groups is known as accuracy, and it is represented as: accuracy = t n + t p f n + t n + t p + f p (1) the ratio of correct predictions for class a is known as precision (or positive predictive value): https://doi.org/10.15837/ijccc.2021.5.4258 6 p recision = t p f p + t p (2) the percentage of successful predictions made to class a instances is known as the true positive rate (or recall). t p r = t p t p + f n (3) the f-measure statistic (or f1 score) considers both the tpr and precision of a classifier to measure its quality: f 1 − score = 2 ∗ t p r ∗ p recision t p r + p recision (4) 3 result and discussion this section presents the results and their analysis. we start with the data understanding (section 3.1) and the data preparation (section 3.2). finally, the results of the performance metrics of the models created and the final discussion (section 3.3). 3.1 data understanding the traffic captured by wireshark was processed and organized into flow records by using the flowrecorder tool [27]. flowrecorder is a tool written in python, that allows to turn ip packets into flow records stored in a csv (comma-separated values) file. using packets either in the form of pcap (packet capture) files or sniffed live from a network interface. this tool supports the measurement of flow features in both unidirectional and bidirectional modes. table 2 describes the flow features used. the main idea of flowrecoder is to organize packets into flow by using the top 5-tuple packet header. depending on the observed (incoming) packets’ properties, either new flow records is created, or the existing ones’ flow features are updated. for more information about flowrecoder we refer the reader to [12, 13, 26, 28]. two datasets named "datavideo1" and "datavideo2" were generated using flowrecorder. these data sets were validated with another network tool called nfstream [23]. 3.2 data preparation nfstream performs the same tasks as flowrecorder. unlike flowrecorder, nfstream uses ndpi (open source library for deep packet inspection) to extract information from layer 7. deep packet inspection allows verifying which application it belongs to, i.e., it will enable identifying if the packet belongs to a livestream, vod or no video stream. we used nfstream to label the datavideo1 and datavideo2 datasets. datavideo1 consists of 1600 instances. each instance represents a flow. thus, datavideo1 has 1600 flows, 800 are vod services, and 800 are non-video flows. datavideo2 comprises 1200 flows; 400 are live stream video streams, 400 are vod flows, and 400 are non-video flows. we analyzed the data distribution of each feature to find bias in the data. figure 2 illustrates an example of data distribution performed. it turned out that some features of datavideo1 are not uniformly distributed. several narrow peaks are in some features, e.g., “f-pkttotalcount", "foctettotalcount", "f-avg-piat". this behaviour also happens in datavideo2. we believe that the non-video flows to introduce the not uniform distribution since they only contain 1 or 2 packets. in figure 2, we only show de forward flows since backward flows present the same behaviour. as figure 2 shows, the features "b-avg-ps", "src-port", and "f-std-dev-ps" have bimodal distributions, which means that those features tend to two values. we used a yeo-johnson transformation to obtain better performance from the models created for the classification traffic. yeo-jonson is a technique used to stabilize variance, make the data more normal distribution-like [1]. we performed an analysis to determine which set of the feature are imports and which not, i.e., which variables provide the most information for classifying flows. we use the correlation matrix https://doi.org/10.15837/ijccc.2021.5.4258 7 table 2: dataset feature description feature name description f-pkt_total_count no of transmitted packets (forward) b-pkt_total_count no of transmitted packets (backward) f-octet_total_count no of transmitted packets (backward) b-octet_total_count no of transmitted bytes (backward) f_avg_piat average packet inter arrival time (forward) b_avg_piat average packet inter arrival time (backward) f_avg_ps average packet size (forward) b_avg_ps average packet size (backward) src_port source port dst_port destination port protoc identifier of protocol. in particular, tcp/udp f-std-dev-piat stand. dev. packet inter-arrival times (forward) b-std-dev-piat stand. dev. of packet inter-arrival times (backward) f-std-dev-ps stand. dev. of packet sizes (forward) b-std-dev-ps stand. dev. of packet sizes (backward) class classification of the sample figure 2: data features distributions. https://doi.org/10.15837/ijccc.2021.5.4258 8 figure 3: heat map correlation matrix from datavideo1. approach. a correlation matrix is a table showing correlation coefficients between variables [9]. each cell in the table shows the correlation between two or more variables, i.e., provides information about how correlated the features are concerning others. figure 3 shows the datavideo1 correlation matrix. overall, there is a high correlation of the data, highlighting a strong dependence between "pkttotalcount" and "octeltotalcount" for both forward and backward flows; this means that the number of transmitted packets is closely related to the number of transmitted bytes. as figure 3 shows, there are also high correlations between "f-avg-piat" and "b-avg-piat”, “f-stddev-piat" and "b-std-dev-piat". such a high correlation is expected since these features belong to the information given by the arrival time between packages. another high correlation is presented between the "std-dev-ps" in forwarding and backward flows. also, these features have a small dependence on packet sizes and the number of bytes transmitted. we also applied a recursive elimination method to de data. the recursive elimination method builds a model on the features that remain to classify each characteristic according to its importance. in datavideo1 this method showed that the attributes that provide the most information are "f-pkttotalcount", "b-pkttotalcount", "f-avg-piat", "b-avg-piat" and "f-std-dev-piat". taking the correlation thrown by the matrix between "f-pkttotalcount", "b-pkttotalcount", with "f-avg-piat", "b-avg-piat" and the data thrown by the elimination method. https://doi.org/10.15837/ijccc.2021.5.4258 9 table 3: performance metrics of datavideo1 datavideo1 without feature elimination normalized normalized and feature elimination ml model accuracy standard deviation accuracy standard deviation accuracy standard deviation lor 96.25 4.50 94.75 4.1 93.5 6.99 lda 93.75 7.09 94.75 5.6 91.5 11.19 k-nn 95.0 5.36 61.25 13.6 96.5 1.65 cart 96.75 5.25 97.24 5.29 97.50 2.5 nb 89.49 10.65 86.5 15.54 88.0 9.53 svm 85.75 15.53 84.3 15.5 91.25 7.26 table 4: performance metrics of datavideo2 datavideo1 without feature elimination normalized normalized and feature elimination ml model accuracy standard deviation accuracy standard deviation accuracy standard deviation lor 78.88 28.63 83.91 16.40 70.16 21.71 lda 72.66 24.30 90 14.13 68.75 21.06 k-nn 66.22 26.84 86 15.67 72.44 20.01 cart 88.66 16.83 91.11 16.80 71.25 20.3 nb 71.33 37.49 81.25 18.35 70.0 19.28 svm 65.55 30.57 88.66 13.26 69.11 38.73 3.3 data modelling and performance metrics tables 3 and 4 summarise the performance metrics for datavideo1 and datavideo2 dataset, respectively. tables 3 and 4 show the best performance among the two datasets is for datavideo1 since it only consisted of two types of classes, i.e., video and non-video service applications. in particular, the cart model was the best performance regarding true positive. this can be seen in the model’s accuracy, which reached 96.75%, 97.24%, and 97.5% with low deviations. it is essential to notice that the cart algorithm is frequently used when for binary classification. then, our results are expected to be precise due to the good overall performance in the cart models’ binary classification. table 3 shows that the models’ accuracy improves when using feature elimination methods and a normalization of the data. the lda reached a high accuracy, 94.7%, due to the normalization of the data. also, k-nn accuracy improves without the redundant features, reaching 96.5% of accuracy. as table 3 shows, it is clear that the performance metrics of datavideo1 are improved by applying data normalization and feature removal methods. thus, one expected that the same behaviour happens in datavideo2. however, as table 4 shows, the overall accuracy in all model decrease; e.g., in cart model, the accuracy reached is 91.1% only. we compared the importance of the features between datavideo1 and datavideo2, and it turned out that it is slightly different. the features that no longer are important in datavideo2 are "octettotalcount" or "scr-port". in datavideo2, cart model can easily classify video streams and non-video flows but not classify different types of video services, i.e., vod and livestream. table 5: confusion matrix datavideo1 prediction class 1 class 2 datavideo1 video non video precision 1 0.96 video 134 6 recall 0.96 1 non video 0 160 f1-score 0.98 0.98 we wanted to dig deeper regarding cart model. then, we provide a deeper analysis of the cart model confusion matrix. table 5 summarises the confusion matrix for the cart model of datavideo1, c1 represents video services, and c2 refers to non-video flows. as table 5 shows, the cart model does not have any problem when classifying between video streams and non-video flows. there are a total of 134 true positives and 160 true negatives. in both classes, c1 and c2 reach an f1-score of https://doi.org/10.15837/ijccc.2021.5.4258 10 table 6: confusion matrix datavideo2 (a) (b) (c) datavideo2 prediction datavideo2 prediction datavideo2 prediction c1 vs c2 livestream vod c2 vs c3 vod non-video c1 vs c3 livestream non-video livestream 99 19 vod 113 0 livestream 99 1 vod 7 133 non-video 1 117 non-video 3 117 table 7: performance metrics per-class datavideo2 class 1 class 2 class 3 precision 0.91 0.83 0.99 recall 0.83 0.94 0.97 f1-score 0.87 0.89 0.98 0.98. it is important to note that video services have an intrinsic property concerning buffers’ use. video streams have a bandwidth dependency on the link. this fact simplifies the classification between video and non-video flows. table 6 summarizes the confusion matrix of datavideo2; c1 represents livestream, c2 refers to vod, and c3 represents non-video flows. similar to datavideo1, datavideo2 can easily distinguish between video services flows and non-video flows. also, similar to datavideo1, the model performance decreases when trained to classify into specific categories, e.g., livestream, vod, etc. table 6(a) shows that 7 vod flows were labelled as livestream, indicating that the classification between video services is more complex. however, this result represents less than 10% of the total vod flows. table 7 summarizes the performance metrics of datavide2 per-class. as table 7 shows, the cart model can classify in a precise way c1 and c3, more than 90% of the samples. however, the precision for c2 is around 83%. overall, the model results are promising, reaching f1-score around 0.87, 0.89, 0.98 in c1, c2 and c3, respectively. 4 conclusion flow identification has become a prominent issue in academia and industry as a way to maintain network performance. the identification of video streaming streams is of particular relevance, as video streaming traffic is projected to skyrocket as more 5g-compatible devices are connected. as a result, we describe a method for detecting video streaming traffic in sdn in this article. furthermore, we investigated the feasibility of using the kdn concept in traffic classification. in our initial proof of concept, we obtain the relevant information from the network traffic in the form of flow statistics, such as the total size of the flow, the total number of packets, the duration of the flow, etc. we then use that information to train six ml models to classify network streaming, and non-video traffic. the main result of our proof of concept is that it is possible to implement an sdn scenario and implement different strategies for flow classification by following a kdn approach. overall, our results show that ml models have no difficulty in differentiating between video and non-video flows. however, some models degrade their performance when the classification is multi-class classification, for example, when the models are trained to classify between different video services. we believe that there are similarities in their stream statistics as they fall into the category of "video flows." thus, a more sophisticated strategy must be applied, when video service identification is needed. another important result is that the cart model seems to be a good option for classifying video flows. in our analysis cart model reached an accuracy of 97.5% in datavideo1 and an accuracy of 91% in datavideo2. motivated by the results provided in this paper, future work will investigate livestream services’ impact on the flow classification mechanism. also, we want to extend our investigation to the online https://doi.org/10.15837/ijccc.2021.5.4258 11 classification mechanism for video streaming services in sdn. author contributions this was a collaborative effort by the writers. conflict of interest there are no conflicts of interest declared by the authors. references [1] arslan, a. k.; tunç, z.; çolak, c. 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(2019). logistic regression model optimization and case analysis, ieee 7th international conference on computer science and network technology, 135– 139, 2019. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: castañeda herrera, l. m.; campo, w. y.; duque-torres, a. (2021). video streaming service identification on software-defined networkin, international journal of computers communications & control, 16(5), 4258, 2021. https://doi.org/10.15837/ijccc.2021.5.4258 introduction material and method knowledge-defined networking (kdn) cross-industry standard-process for data mining (crisp-dm) data understanding data preparation data modelling performance metrics result and discussion data understanding data preparation data modelling and performance metrics conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 4, month: august, year: 2020 article number: 3900, https://doi.org/10.15837/ijccc.2020.4.3900 ccc publications a simpler and semantic multidimensional database query language to facilitate access to information in decision-making f. palominos, f. córdova, c. durán, b. nuñez fredi palominos*, bryan nuñez mathematics and computer science department university of santiago de chile, chile avda. libertador b. o’higgins 3363, estación central, santiago, chile *corresponding author: fredi.palominos@usach.cl bryan.nunez@usach.cl felisa córdova school of engineering finisterrae university, santiago, chile av. pedro de valdivia 1509, providencia, región metropolitana, chile fcordova@uft.cl claudia durán faculty of engineering universidad tecnológica metropolitana, santiago, chile dieciocho 161, santiago, chile c.durans@utem.cl abstract olap and multidimensional database technology have contributed significantly to speed up and build confidence in the effectiveness of methodologies based on the use of management indicators in decision-making, industry, production, and services. although there are a wide variety of tools related to the olap approach, many implementations are performed in relational database systems (r-olap). so, all interrogation actions are performed through queries that must be reinterpreted in the sql language. this translation has several consequences because sql language is based on a mixture of relational algebra and tuple relational calculus, which conceptually responds to the logic of the relational data model, very different from the needs of the multidimensional databases. this paper presents a multidimensional query language that allows expressing multidimensional queries directly over rolap databases. the implementation of the multidimensional query language will be done through a middleware that is responsible for mapping the queries, hiding the translation to a layer of software not noticeable to the end-user. currently, progress has been made in the definition of a language where through a key statement, called aggregate, it is possible to execute the typical multidimensional operators which represent an important part of the most frequent operations in this type of database. keywords: data models, multidimensional model, olap, decision making, query languages. https://doi.org/10.15837/ijccc.2020.4.3900 2 1 introduction every decision-making process in unstructured problems requires an analysis in different perspectives. the evolution of the problem over time is usually present in virtually all cases. gradually, as information technology became present in all types of companies and organizations, data availability and the ability to learn about them increased. in business, the information management of services as well as production processes has a strategic role that is increasingly important. it allows decision makers to have relevant information that can be transformed into explicit knowledge and that can be effectively monitored with indicators [17] [6] [13]. consequently, it is essential to have data that is reliable, aligned with the objectives and goals of the company, not redundant, consistent and hierarchical [8]. the impossibility of analyzing the existing data in the administrative information systems while they are in operation, as well as the dispersion of the critical data in different systems, with low or no integration levels, led to the emergence of new paradigms such as data mining, business intelligence and online analytical processing (olap) [7]. basically, the olap paradigm establishes that in decision-making processes the data must be organized according to the analysis perspectives, for this reason the data is not structured according to the objects present in the problem, nor neither in lines or sequences of transactions, but according to the needs of analysis, which then translates into dimensions, which in turn are structured in hierarchies of attributes, to which indicators called measures are associated that are finally subject to analysis [9] [10] [2] [3]. the implementation of the olap paradigm in the mid-1990s gave rise to multidimensional databases known to many people as data warehouse [11]. as it is a technology, certain requirements have been imposed such as providing dynamic multidimensional analysis and allowing end users to perform analytical and navigational activities. in this way, it is necessary to ensure that multidimensional databases meet certain basic operational requirements known as: having integrated data, time variants, non-volatile and oriented to issues of interest for the management of an organization or company. very often, multidimensional databases have been implemented on relational data base systems, in a modality known as rolap. the adaptation to relational technology obviously has some negative consequences, one of the most obvious being the need to transform olap queries into relational queries [10] [1]. on the other hand, considering the need for a functional multidimensional data model, which resembles the simplicity of the relational approach in the formulation of queries, in [14] [15] a multidimensional model has been presented that defines structures and operators with similarities to the relational model [5]. in addition, it implements the typical olap operations, clearly differentiating the static components of the model from the dynamic components, providing a query language in the form of a simple, more expressive algebra, with some reminiscences to characteristics of the tuple relational calculation. query languages in multidimensional databases, such as the case of mdx [3], are usually quite aligned with other more classic query languages. generally, the most operational aspects have been privileged over the more formal aspects. those who have ventured into the definition of algebras that allow multidimensional data to be operated more independently of languages, have proposed sophisticated conceptual frameworks [12] [19] [20] difficult to link with software tools and incomprehensible to end users [18] [4]. the works developed in [14] [15] present the multidimensional model in a manner similar to the relational model [5], presenting construction rules, some inherent restrictions, as well as simpler and more significant operators, which are an appropriate for the definition of a more understandable query language for end users. this work presents the central ideas of a new data definition and manipulation language oriented to multidimensional data bases, which is based on the structure, operability and semantics of the multidimensional data model previously presented in [14] [15]. it focuses on the characterization of the language in order to subsequently address the experimental implementation of a prototype (middleware) acts over a relational database management system. https://doi.org/10.15837/ijccc.2020.4.3900 3 2 components of the multidimensional data model the following sections describe the static and dynamic components of the multidimensional data model that is being used as the basis for defining the syntax and semantics of a multidimensional query language. these components are defined more extensively in [15]. below is a summary of its main characteristics. 2.1 static components domains and attributes: a domain is a set of atomic values of the same type of data. the characteristics of an object are called attributes and are defined as a function that associates a multidimensional object. hierarchy: a hierarchy [10] is a 5-tuple (n,e,≤ t,v ) where n is a name, e contains the attributes of the hierarchy and ≤ is a partial order relationship defined on e. thus, a hierarchy constitutes an order relationship whose effect is to produce a hierarchy of attributes, resulting in the generation of different levels of aggregation. dimension: in multidimensional databases it is necessary to address queries, aggregations and data analysis based on different points of view, which respond to the needs of data analysis. each point of view is called a dimension, which we will represent as a pair (nd,j). since in reality the needs of data analysis require classifying them around attributes of different dimensions, it is necessary to articulate the dimensions in a structure called dimensional space (ds). it is important to emphasize that the dimensional space constitutes a coordinate system that allows to classify in a unique and unequivocal way each fact of the real world. measures: a measure is a special set of numerical type attributes (usually an aggregate data) and represents a quantitative characteristic of interest that must be monitored and analyzed. however, the measures do not constitute a hierarchy. the measurements can be as many as necessary and the cartesian product of the domains of the measures present in a multidimensional variable is called scalar space(ss). facts: a fact represents information of interest regarding a set of real-world events related to one or more objects. the characteristics of the event are captured through the values of the attributes of the hierarchies, the dimensions and measures. the following definition makes some pressures to the definition that was previously used in [15], both in the structure and in the semantic properties of the facts contained in the multidimensional variable. a fact is a relationship between dimensional space and scalar space, as represented by the following function: h : ds ∼ ss we will denote by h the whole {x/x = (d,e),d ∈ ds,e ∈ ee,e = h(d)}, the one that receives the name of multidimensional variable (vm). schematically h we will represent it by the pair ( −→ j ,−→m) where −→ j ((j1,j2, . . . ,jn)) represents the hierarchies of the dimensions that in turn determine the dimensional space of the set and, −→m((m1,m2, . . .mk)) the k measures of h. in this way, we can represent a set of facts, as needed, in the following ways: h ⇔ ( −→ j ,−→m) ⇔ ((j1,j2, . . . ,jn), (m1,m2, . . . ,mk)) 2.2 aggregation functions olap requires intense summarization operations to be able to respond to the different demands of providing online data analysis capability. in addition, it is necessary that these operations have consistency and allow the calculation of valid and understandable indicators for the users, for this reason the summarization operations applied over a set ξ, must be performed using a special type of functions, called aggregation [14]: https://doi.org/10.15837/ijccc.2020.4.3900 4 f(ξ) = ∑ x∈ξ f(x) it is said that this function is additive if for two sets ξ1, ξ2 : ξ1 ∩ ξ2 = φ, it is fulfilled that: f(ξ1 ∪ ξ2) = f(ξ1) + f(ξ2) finally, we will call a multiple aggregation function (maf), a function h : ds ∼ ss, such that τ(ξ) = < f1(ξ),f2(ξ), . . . ,fn(ξ) > where each fi, i = 1, ...,w is an simple aggregation function. 2.3 aggregate facts family (aff) it is a new concept and structural element added to the multidimensional model in [14], which is intended to take advantage of the additive aggregation functions, for the benefit of efficiency and versatility in the responses to queries. it is denoted as h and corresponds to a set of facts such that each element of h is an aggregation that is obtained directly or indirectly from an initial event h∗ called a generator. h = {h | h = f(cs(h∗))} as described in [14], for h, f is an invariant multiple aggregation function, common to all elements of h that differs by level of data aggregation expressed through a classification space (cs). 2.4 dynamics components the operators that correspond to the typical olap operations are defined below: pivoting: it is defined as a rotation (permutation) of the dimensions of a multidimensional variable h and of the elements of the hierarchies, which consists in reorganizing the arrangement of the dimensions or axes of the dimensional space, based on a different point of view, which does not it alters the semantics of the set of facts or the coherence of the summaries. the operation will be denoted by: h′ = p(h) = p(( −→ j ,−→m)) = (p( −→ j ),−→m)) = ( −→ j′,−→m) where p represents the permutation of the k dimensions and, if necessary, the redefinition of the hierarchies of each dimension of the set. thus, h′ will be a new multidimensional variable, with another order in dimensions and hierarchies but with the same number of dimensions and the same scalar space of h. so h and h′ are compatible. roll-up: it is a process of aggregation of data that is carried out on a set of facts, reducing their dimensions or transforming their hierarchies, through the use of a classification space (cs), constituted by defined interval projections on the domains of the attributes of the hierarchies an an aggregation function f that acts on the scalar space measures of the set of facts. the roll-up operator will be defined as: φcs,f (h) = f(cs(h)) slicing-dicing: the purpose of the slicing-dicing operator is to allow a subset of the facts contained in a set of facts to be selected, based on a specific selection criteria defined through a selection predicate expressed in the traditional predicate algebra. formally, the slicing-dicing operator, by analogy to the selection operator of the relational model, is defined as: σρ(x)(h) = {x ∈ h | ρ(x)} where ρ(x) is a conditional expression defined on the attributes of the hierarchies of the set of fact h, expressed on the basis of operators of the algebra of predicates. https://doi.org/10.15837/ijccc.2020.4.3900 5 drill-down: the purpose of this operator is to produce greater disaggregation in the multidimensional variable, by reincorporating dimensions to the variable or attributes in the hierarchies. in addition to eventually reincorporating dimensions, you can expand the hierarchies of some dimensions and make the previously summarized measures more specific, to obtain a cube with more complex hierarchies, more facts and probably more dimensions. this operator rather responds to the approach of the relational calculation of tuples of the relational model, because to achieve its task it requires rather to declare the type of variable that is desired. the answer requires an aggregation on the original multidimensional variable, which contains the highest level of disaggregation of the data. formally, let h′ be a multidimensional variable sash that h′ = φf,cs1 (h) = f(cs1(h)) and let cs2 be such that cs1 is a subset of cs2, the operation called drill-down, denoted by: θf,cs2 (h ′) it is equivalent to performing the following sequence of operations: θf,cs2 (h ′) ⇔ φcs2,f (h) since cs1 is a subset of cs2, the operation produces disaggregation in the data and generates a multidimensional variable of greater degree and cardinality. 3 elements of data definition and data manipulation mdx [16] is one of the most used specialized languages in formulating queries to multidimensional databases. however, despite being a very good tool, it requires users to be very familiar with the use of database query languages and must use a syntax that can sometimes be complex, not very intuitive, which also requires defining very explicitly the different processes and stages related to the generation of the result of a query, which for a non-specialized user can be a great obstacle. greater coverage of query languages requires a simpler and declarative tool that exempts the user from a too procedural declaration of data processing. frequently multidimensional databases, often called data warehouse, are implemented on relational systems in a modality known as rolap. in this context, multidimensional database queries cannot be formulated using native syntax and semantics, which implies that they must finally be translated into relational queries, adapting their semantics to this type of technology. the following sections present some advances related to the definition of a language that will be implemented in the form of a middleware, which will allow the generation, implementation and use of multidimensional databases under a transparent rolap mode for users. in this way the queries will be written in a syntax and a native semantics and the middleware will be responsible for their translation into relational queries. 3.1 the data definition language the data definition language must provide all the tools necessary to define the structural components of the data model, that is, for the model presented in [14] [15], it is necessary to allow the specification of domains and attributes, dimensions, hierarchies, measures, facts and families of facts, as well as the definition of aggregation functions required for data manipulation. in this work we present the syntax and application of the sentence that allows the creation of a multidimensional variable: in order to exemplify its use, we will apply it in the following conceptual model that is represented in the manner described in [9]. the creation of the multidimensional variable in figure 1 is achieved with the following statement: https://doi.org/10.15837/ijccc.2020.4.3900 6 figure 1: the create multivar statement that allows to create the structure of a multidimensional variable, specifying the dimensions, attributes, hierarchies of attributes and the set of measures. figure 2: multidimensional variable that records the income and costs related to the monthly production of a certain quantity of a type of products, in different factories of different countries (notation rescued from [9]). 3.2 the data manipulation language the data manipulation language comprises different elements, including those that allow feeding the multidimensional variable. as it is a solution for data warehouse, it is assumed that data loading procedures are robust, therefore the data manipulation language should not require data update or deletion sentences (only for administrative aspects). in a multidimensional database, queries always refer to data recovery, aggregation or disaggregation processes, related to olap’s classic operators. in the language whose advances are presented in this work, the operators are integrated in a single sentence, called agregate, which are manifested as possible optional clauses in the consultation. the format of the sentence is as follows: to exemplify its use, imagine that you must perform a sequence of olap operations on the multidimensional variable, using the following permutation: h : ds ∼ ss and then, the following operations are performed: 1. p(activity), permutation is applied to the multidimensional variable activity. 2. then, the data for 2012 are selected by: σyy=2012(activity). 3. the data is classified according to the type of product and the city of the factory: cs = dom(city) ×dom(factory) figure 3: sentence for the creation of the multidimensional variable in figure 2 based on the create multivar statement proposed in figure 1. https://doi.org/10.15837/ijccc.2020.4.3900 7 figure 4: aggregate statement used to perform olap operations on the multidimensional variable. 4. the sum of income and costs is then calculated on the result using the following multiple aggregation function: f(activity) = (f1(activity),f2(activity)) where f1 and f2 correspond respectively to the sum of the values of the income and cost attributes. as can be seen, there are four different olap operations that are performed on the data set, including the classification of the data through a classification space cs. the aggregate statement allows the query to be carried out incorporating all the operations in a single instruction, acting on a multidimensional one, considerably simplifying the process of constructing the query. figure 5: sequence of operations implemented as a native multidimensional query (case (a), aggregate statement) versus sql emulated query through the select statement (case (b)). figure 5 allows comparing the implementation of the multidimensional operations described in section 3.2 in a native multidimensional language versus sql. as it is possible to appreciate, the query formulated in a native language has a semantic load. in the first place, only the multidimensional object under study is mentioned, in this case the activity variable, the classification criteria are specified in a simple and precise way, naming the dimensions, indicating in brackets the attributes to be considered in case the dimension considers a smaller hierarchy, to end by specifying the condition that the facts to be processed must meet (yy = 2012). in the case of the query in sql, there is no multidimensional variable, the statement acts on three lookup tables: factory, time and product, and the invoice activity table. the conditions required for the correct combination of the tables run by the user and to them must be added the condition that the facts to be selected must meet. the classification space is diluted in a list of attributes in the group by clause that must explicitly mention all the attributes to consider. permutation of dimensions (pivoting) is also subject to user responsibility. 3.3 advantages of the language approach the advantages associated with a language with the approach presented in this paper are many. they mostly derive from two very relevant characteristics: their greater semantic capacity compared to other multipurpose languages and their greater syntactic simplicity. in turn, these characteristics have other positive externalities: 1. its simplicity and semantics allow generating conditions to increase the productivity of it personnel, as well as generating greater closeness with end-users. https://doi.org/10.15837/ijccc.2020.4.3900 8 2. reduce or eliminate the ambiguities that occur in the consultations, clearly separating the aspects related to the classification of the data regarding the calculation of indicators. 3. it allows a better link between the theoretical foundations of the data model, the concept of multidimensional database and the tools of definition and manipulation of data, a situation that is not possible to appreciate and incorporate it easily in contexts where multidimensional databases are emulated and the queries are translated into other conventional query languages. 4. the language is of general purpose for which it can be used in various fields, including management, production, and services. 3.4 future work the definition of language, however constituting a fundamental element for the construction of a fully functional tool, requires the implementation of software that respects the syntax of the language and incorporates its functional characteristics. the next stage of this work will be the development of a middleware that implements these characteristics in an interpreted environment, in which the data management will be carried out in a relational database management system. although finally the database will correspond to a rolap implementation, both the definition of objects and queries to the databases will be done through the middleware, maintaining the user in the multidimensional environment, without having to worry about the translation of the queries. one of the initial applications of middleware will be in higher education, as a means to relate the design and modeling of multidimensional databases, with the use in information systems. it will allow students to experience the design, construction and consultation processes of a multidimensional database, in a native environment. 4 conclusions the syntax presented in this paper, shows that it is possible to considerably improve the syntactic simplicity and semantic capacity of interrogation languages to multidimensional databases. in this way, a natural treatment of dimensional objects is achieved in context, using a native syntax, more abbreviated, without the need to emulate the characteristics of multidimensional objects. on the other hand, the formulation of queries is simplified, it becomes more semantic, closer and more natural to end users, favoring access to information without the intermediation of too many it professionals. this language also reinforces the importance of the rolap approach and relational systems because it gives the opportunity to continue using the good characteristics of these systems, in a more automated and transparent way, allowing savings in licensing costs and investment in hardware, due to that in many cases it will be possible to continue using the available infrastructure. finally, it is a tool that has the potential to help in an important way to the development and use of support tools for decision making, which require the use of a multidimensional approach. funding acknowledgments to mathematics and computer science department, university of santiago de chile, usach. to the school of engineering, finisterrae university, chile, and also to de department of industry, universidad tecnológica metropolitana, chile. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. https://doi.org/10.15837/ijccc.2020.4.3900 9 references [1] bojičić, i.; marjanović, z.; turajlić, n.; petrović, m.; vučković, m.; jovanović (2016). a comparative analysis of data warehouse data models, in 2016 6th international conference on computers communications 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(2019). technology of multidimensional data formation using caching, 13th international ieee scientific and technical conference dynamics of systems, mechanisms and machines, dynamics 2019 – proceedings, 1-10, 2019. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: palominos, f.; córdova, f.; durán, c.; nuñez, b. (2020). a simpler and semantic multidimensional database query language to facilitate access to information in decision-making, international journal of computers communications & control, 15(4), 3900, 2020. https://doi.org/10.15837/ijccc.2020.4.3900 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 1, month: february, year: 2022 article number: 4646, https://doi.org/10.15837/ijccc.2022.1.4646 ccc publications m-generalised q-neutrosophic extension of cocoso method z. turskis, r. bausys, f. smarandache, g. kazakeviciute-januskeviciene and e. k. zavadskas zenonas turskis institute of sustainable construction vilnius gediminas technical university lt–10223 vilnius, sauletekio al. 11, lithuania zenonas.turskis@vgtu.lt romualdas bausys department of graphical systems vilnius gediminas technical university lt–10223 vilnius, sauletekio al. 11, lithuania *corresponding author: romualdas.bausys@vilniustech.lt florentin smarandache department of mathematics university of new mexico 705 gurley ave., gallup, new mexico 87301, usa smarand@unm.edu giruta kazakeviciute-januskeviciene department of graphical systems vilnius gediminas technical university lt–10223 vilnius, sauletekio al. 11, lithuania giruta.kazakeviciute-januskeviciene@vilniustech.lt edmundas kazimieras zavadskas institute of sustainable construction vilnius gediminas technical university lt–10223 vilnius, sauletekio al. 11, lithuania edmundas.zavadskas@vilniustech.lt abstract nowadays fuzzy approaches gain popularity to model multi-criteria decision making (mcdm) problems emerging in real-life applications. modern modelling trends in this field include evaluation of the criteria information uncertainty and vagueness. traditional neutrosophic sets are considered as the effective tool to express uncertainty of the information. however, in some cases, it cannot cover all recently proposed cases of the fuzzy sets. the m-generalized q-neutrosophic sets (mgqnns) can effectively deal with this situation. the novel mcdm methodology cocosomgqnn is presented in this paper. an illustrative example presents the analysis of the effectiveness of different retrofit strategy selection decisions for the application in the civil engineering industry. keywords: multi-criteria decision making, cocoso, neutrosophic sets, retrofit strategy. https://doi.org/10.15837/ijccc.2022.1.4646 2 1 introduction multi-criteria decision making (mcdm) approaches introduce efficient tools to solve conflict situations for the managers. the mcdm process can be distinguished into the following steps: a) defining management system governing criteria which are associated with the compulsory decisionmaking goals; b) establishing the discrete alternative systems to reach the prescribed necessary goals; c) performing multicriteria analysis by any mcdm method; d) acknowledging one alternative as the best by the ranking results. therefore, the application of the mcdm techniques facilitates the decision-makers to choose the best alternative, which provides a suitable compromise between all possibly conflicting criteria. therefore, the application of the mcdm techniques facilitates the decision-makers to embrace the alternative of the highest quality, which provides a suitable compromise between all possibly conflicting criteria. during the last decade, mcdm methods have actively been applied for the solution of the various real-life problems. in [5], the authors point out the following the most popular mcdm methods, such as saw, waspas, vikor, topsis, electre promethee methods. recently, in [28] was proposed a new mcdm technique, which composes a combined compromise solution (cocoso). this method constructs the compromise solution applying various aggregation approaches. this method was under intensive research during the last years. in [39], the authors solved the problem of the ideal sustainable supplier choice by combining two methods: cocoso and the bwm method. in [14] was proposed a hybrid mcdm approach which is constructed by combining the cocoso and critic approaches and applied to evaluate the 5g industries. in [25], the authors combined swara and cocoso methods to solve the drug cold chain logistics supplier evaluation and the location selection for a logistics center problems. this relatively new (cocoso) technique was applied to study the sustainability aspects in the opec countries [3]. the extension of the cocoso approach was proposed in [4]. the essence of this extension is the modelling environment in the form of normalized weighted geometric bonferroni mean functions. this approach was employed to solve the supply chain problem. in [11], stochastic multi-criteria acceptability analysis was performed within the multi-criteria decision-making framework, namely the cocoso approach, to handle the problem of the renewable energy investments with stochastic information. this hybrid approach is based on the dibr method, which defines interrelationships between the ranked criteria, and the d’cocoso approach, which is modelled under fuzzy dombi environment. this technique was applied to solve prioritizing aspects of the circular economy concepts for urban mobility [12]. the two-stage decisionmaking technique, which was realized by the data envelopment analysis (dea) and the rcocoso method, which was modelled under rough full consistency (r-fucom) environment, was discussed in [30]. the tourism attraction selection problem was utilised by applying the probabilistic linguistic term set. for the solution, idocriw and cocoso approaches are implemented [9]. different versions of the hybrid mcdm proposals based on the cocoso technique have been presented in [23], [10], [24], [6]. modern research in the area of multi-criteria decision-making tries to model the indeterminacy and inconsistency of the initial decision making information, which prevails in most cases of real-life applications. therefore, different fuzzy set variations have been proposed starting width the pioneering work in [33]. existing challenges and future development trends of the fuzzy modelling importance in the decision-making field have been discussed in [27]. during the last years have been proposed the different extensions of the cocoso method applying the environments of the different fuzzy sets. in [29], the authors modelled the best supplier problem in construction management applying grey numbers and solving this problem with the dematel, bwm, and cocoso methods. in [26] was considered the problem of the third party logistics service providers applying a hesitant linguistic fuzzy set environment to construct the extent of the cocoso method. in [13], the authors applied a q-rung orthopair fuzzy set to develop a hybrid mcdm extension including cocoso and critic methods and tested this approach on the financial risk evaluation problem. in [1], the authors suggested a https://doi.org/10.15837/ijccc.2022.1.4646 3 novel interval-valued intuitionistic set model for cocoso method extension and solved the problem of the sustainable evolution in the fabrication region by means of green growth indicators. in [17], was proposed a cocoso method extension applying a single-valued neutrosophic set. this technique was applied to deal with the problem of the equipment selection for the waste management field including sustainability aspects. various fuzzy set environments have been implemented to model multi-criteria decision-making problems [15], [16], [31], [32], [37], [21], [7], [7]. [8], [35], [38]. in 1999, smarandache [19] proposed the notion of the neutrosophic sets. neutrosophic sets (ns) are based on the generalization of the fuzzy logic that takes into account the knowledge of neural thought. therefore, the greater amount of uncertainty can be analyzed. each parameter of the analyzed problem can be represented by the scope of the truth (t), the scope of the indeterminacy (i) and the scope of the falsity (f) using the ns logic. in 2019, a notion of neutrosophic sets, which can be considered as the generalization of the following fuzzy sets: intuitionistic fuzzy (ifs), q-rung orthopair fuzzy and pythagorean fuzzy sets was proposed by smarandache [20]. based on this concept, the new extensions of the classical mcdm methods, like multimoora, waspas, promethee [34], [18], [2], were developed by applying the environment of the m-generalized q-neutrosophic sets (mgqnss). the structure of the paper is established as follows. the second chapter presents the basic foundations of the m-generalised qneutrosophic sets. the third chapter provides the proposed extension of the cocoso method under the environment m-generalised qneutrosophic set. the problem formulation together with the solution procedure of the case study are presented in the fourth chapter. the paper ends with conclusions. 2 foundations of the m-generalised q-neutrosophic set first, the m-generalised q-neutrosophic set (mgqns) prevailing definitions and the algebraic operations are presented. the algebraic operation presentation is constructed by applying m-generalised q-neutrosophic numbers (mgqnns). these neutrosophic numbers form the basis of the proposed cocoso method extension, namely cocoso-mgqns. 2.1 definitions definition 1. the modelled objects form the set x, in which every single object is x ∈ x. in the present research, the objects are represented by a neutrosophic set. given that x is a set of criteria modelled under the m-generalised q-neutrosophic environment, x is a single criterion value. let q ≥ 1, m = 1 ‖ 3. the three membership functions define m-generalised q-neutrosophic set amq: tmq,imq,fmq : x → [0,r], here (0 ≤ r ≤ 1) given tmq is the m-generalised function representing the scope of the truth, imq is the m-generalised indeterminacy membership function and fmq is the m-generalised falsity membership function. in such a way, the m-generalised q-neutrosophic set is prescribed by the following expression: amq = {〈tmq(x),imq(x),fmq(x)〉 : x ∈ x} these three membership functions also must comply with the following requirements: 0 ≤ tmq(x),imq(x),fmq(x) ≤ 1, x ∈ x; 0 ≤ (tmq(x))q + (imq(x))q + (fmq(x))q ≤ 3 m , x ∈ x; the different m and q values represent different fuzzy sets. definition 2. the m-generalised q-neutrosophic number, mgqnn, is represented by the following expression: nmq = 〈t, i,f〉 https://doi.org/10.15837/ijccc.2022.1.4646 4 definition 3. the summation of the two mgqnns, when nmq1 = 〈t1, i1,f1〉 and nmq2 = 〈t2, i2,f2〉 are the m-generalised q-neutrosophic numbers, we can designate as follows: nmq1 ⊕nmq2 = 〈(1 − (1 − t q 1)(1 − t q 2) 1 q , i1i2,f1f2〉 (1) the multiplication between the two mgqnns can be performed as follows: nmq1 ⊗nmq2 = 〈t1t2, (1 − (1 − i q 1)(1 − i q 2)) 1 q , (1 − (1 −fq1 )(1 −f q 2 )) 1 q 〉 (2) the multiplication operation of the m-generalised q-neutrosophic number and a real number λ ≥ 0 is performed as follows: λ ·nmq1 = 〈(1 − (1 − t q 1) λ) 1 q , iλ1,f λ 1 〉 (3) when λ ≥ 0, the power function of mgqnn can be determined by: nλmq1 = 〈t λ 1, (1 − (1 − i q 1) λ) 1 q , (1 − (1 −fq1 ) λ) 1 q 〉 (4) the complementary m-generalised q-neutrosophic numbers is calculated as: ncmq = 〈f1, 1 − i1, t1〉 (5) definition 4. for the deneutrosophication step, the score value s(nmq) can be calculated as follows: s(nmq) = 3 + 3tq − 2iq −fq 6 (6) in this case, for nmq1 = 〈t1, i1,f1〉 and nmq2 = 〈t2, i2,f2〉 the comparison operations will be completed by the following condition: if s(nmq) ≥ s(nmq) , then nmq ≥ nmq if s(nmq) = s(nmq) , then nmq = nmq (7) 3 combined compromise solution (cocoso-mgqnn) method a new extension of the cocoso method under m-generalised q-neutrosophic set environment, namelly cocoso-mgqnn, is designed. the main steps of this approach can be expressed as follows: (1) solution procedure of the cocoso method starts with the constructed initial decision-making matrix which can be defined as follows: xij =   x11 x12 · · · x1l x21 x22 · · · x2l ... ... ... ... xk1 xk,2 · · · xkl   ; i = 1, 2, . . . ,k; j = 1, 2, . . . , l. (8) (2) the compromise normalization approach is applied to normalize the decision-making matrix elements [36]: rij = xij −min i xij √ k(max i xij −min i xij) for maximised criteria, (9) rij = max i xij −xij √ k(max i xij −min i xij) for minimised criteria. (10) the standard compromise normalization equation is slightly modified to make it be applicable for neutrosophic algebra. https://doi.org/10.15837/ijccc.2022.1.4646 5 (3) next, the neutrosophication of the decision matrix should be accomplished. this means that crisp values rij of the decision matrix elements are replaced by the values (nmq)ij of m-generalised truth membership, m-generalised indeterminacy membership and m-generalised falsity membership functions. this action is performed applying the standard crisp-to-neutrosophic mapping. the novel m-generalised neutrosophic decision matrix is created at this step. (4) at this step, the weighted comparability series and the power weighted comparability series for each alternative are constructed. these series are denoted by ri and pi, respectively: ri = l∑ j=1 (wj(nmq)ij) (11) the ri series reflect weighted sum model: pi = l∑ j=1 ((nmq)ij) wj (12) the pi series correspond to the waspas multiplicative component. (5) three score assessment strategies are the basis for the calculations of the relative weights. these strategies are expressed by the following equations (13)-(15). kia = s(pi) + s(ri)∑k i=1(s(pi) + s(ri)) , (13) kib = s(ri) min i s(ri) + s(pi) min i s(pi) , (14) kic = λs(ri) + (1 −λ)s(pi) λmax i s(ri) + (1 −λ)max i s(pi) ; 0 ≤ λ ≤ 1. (15) by equation (13), arithmetic mean values of the weighted sum model (wsm) and weighted product model (wpm) are calculated. by equation (14), relative scores of wsm and wpm are determined. these relatives scores are determined with respect to minimum values of s(pi) and s(ri). by equation (15), the balanced compromise scores of wsm and wpm models are calculated. in our case, value for λ = 0.5 is chosen. (6) the final ranking function is constructed as follows: ki = (kiakibkic) 1 3 + 1 3 (kia + kib + kic). (16) 4 case study a secondary school is the object of the research [22]. the school’s frame is a three-story reinforced concrete frame (constructed in the eighth decade of the twentieth century). the 2478.60 m2 building has 240 mm thick aerated concrete slab exterior walls and the 200 mm thick reinforced concrete block plinth. under part of the building was a local diesel boiler house. the building has plastered and painted external and external surfaces. the local municipality decided to renovate the structure according to the prepared project. they provide the following thermal insulation: 200 mm thick mineral wool boards for walls and 200 mm thick polystyrene boards for the plinth. the project envisages the roof insulated with 250 mm thick extruded polystyrene panels and an upper 20 mm thick mineral wool panels. besides, old windows and doors need replacement as planned. the most significant part of construction projects are multifaceted, and decision-makers must take into account tangible and intangible characteristics of plans, opinions, negotiations and dispute resolution and projects. https://doi.org/10.15837/ijccc.2022.1.4646 6 decisions in the construction industry need compromise solutions [28], [29], [35]. project managers should select among feasible discrete alternatives in a specific construction site and specific objects taking into account international and local perspectives. designers presented nine possible project implementation scenarios. the building renovation process could involve several phases and be selected from other options with different employees (20, 40 or 60 for retrofitting at different speeds), see table 1. the project implementation contractor offered the following project implementation choices: 1. one-phase retrofitting process: the general contractor shall carry out all modernisation works in one phase: • replacement of windows, exterior and interior doors; b) insulation of basement and external walls and roof; • installation of the roof; • renovation of heating, ventilation, air conditioning and electrical systems; • installation of a new lighting system. the modification process may affect the efficiency of the project due to the multi-tasking of the retrofitting methods. 2. two-phase retrofitting process: the upgrading process takes place in two phases: • the first phase includes replacing windows and exterior doors, repairing the roof, and installing thermal insulation of exterior walls and the basement; • the second phase includes installing thermal insulation of ground-level floors, updating heating, ventilation, air conditioning and electric lighting systems. there is a nine months break between the first and the second phases. the employees work during the summer seasons (summer holidays). there is a risk (depending on the number of employees) that contractor not all works will finish in time, so construction work can adversely affect physical performance. 3. three-phase retrofitting : • the first phase includes replacing windows and exterior doors; • the second phase includes repairing the roof and installing thermal insulation of exterior walls and the basement; • the third phase includes installing thermal insulation of ground-level floors, updating heating, ventilation, air conditioning and electric lighting systems. the contractor will make a one-year break between the first and the second phases. the employees work during the summer seasons (summer holidays). there is a risk (depending on the number of employees) that contractor not all works will finish in time, so construction work can adversely affect physical performance. many of the contradictory features reflect the solution to a complex problem. the authorities usually determine the winners based on a single indicator, the lowest price at the local level. this attitude of civil servants incorrectly describes the real economic benefits of renovating buildings and other features of modernised buildings. after three steps of the delphi method, the decision-makers selected five key characteristics that assessed the renovation’s economic benefits and the impact of the construction process on workers and visitors. costs with vat, [€]: the construction works costs interlinks with the amount of work required to complete the project, the current price level in the country (salary, prices of materials and equipment), the number of potentially competitive contractors and their competitive advantages, construction time and project implementation time. longer project implementation time and an increasing number of https://doi.org/10.15837/ijccc.2022.1.4646 7 table 1: initial decision-making matrix alternatives price withvat, € number of working days (8 working hours per day) renovation’s payback time, [years] energy savings through ten years, [mwh] people’s satisfaction, [scores] a1 1309.50 800 21.86 3656.0 6.66 a2 1309.50 510 21.86 3656.0 11.38 a3 1309.50 320 21.86 3656.0 12.89 a4 1379.0 920 21.86 3524.4 7.46 a5 1379.0 640 22.60 3524.4 12.18 a6 1415.0 440 22.60 3524.4 13.69 a7 1415.0 980 23.18 3188.7 8.27 a8 1415.0 700 23.18 3188.7 12.98 a9 1415.0 500 23.18 3188.7 14.49 optimality min, ↓ min, ↓ min, ↓ max, ↑ max, ↑ stages of construction processes increase construction costs. longer-term constructions are expensive because the project requires household and storage facilities to bring and take out bio-toilets, temporary construction site’s fencing, lighting, and need to implement protection measures. duration of project implementation [working days]: depends on the number of outputs (personhours) and the number of workers on the construction site. this study examines the project implementation options (twenty, forty or sixty employees employed on the construction site). the contractors comply with all the necessary construction technology and safety requirements. they cannot perform the work faster than the established technological requirements in terms of time. for instance, it cannot paint the facade until three weeks have elapsed since the end of the plastering. the mineral plaster is undergoing chemical processes at that time. renewal payback period, [years]: the customers calculate this time according to the investment required for the construction work and the energy savings. they consider the efficiency of the work performed (such as replacing exterior doors and windows). the fuel’s calorific value, the boiler’s efficiency, the level of fuel prices and laboratory tests of the research object are the basis for calculating the economic payback time. laboratory research shows actual energy savings by implementing specific design modernisation solutions. energy savings over ten years, [mwh], determine the expected economic benefits and return on investment. calculating the value of this indicator over a more extended period is very difficult or even impossible due to the significant change in inflation, the change in energy prices and the price of energy resources, the changing rate requirements that govern the environmental impact and many other reasons. the changing climate is also contributing to its contribution. determining energy savings by calculating energy consumption [mwh] rather than monetary value [€] partially reduces this uncertainty. people satisfaction, [points]: survey of members of interest groups (building staff and visitors) is the basis for calculating this characteristic. twenty-five stakeholders gave nine points for the best available option and one for the worst. decision-makers multiplied the final scores by 100 per cent at the end. determining weights of attributes is one of the most important and critically acclaimed issues in multi-attribute decision-making problems. the decision-makers chose and applied a systematic procedure for the study: the swara (step-wise weight assessment ratio analysis) method. the formulated initial decision-making matrix is presented in table 1, and the normalized values of the weights are w=(0.37, 0.12, 0.36, 0.1, 0.05). at the following step the proposed extension cocoso-mgqnn was applied to perform the final ranking of the alternatives. the normalization of the decision-making matrix was performed applying equations (9-10). the elements of the initial decision-making matrix after the normalization step are presented in table 2. the results of the neutrosophication step are presented in table 3. the intermediate results of the proposed extension cocoso-mgqnn together with final ranking results are presented in table 4. https://doi.org/10.15837/ijccc.2022.1.4646 8 table 2: initial decision-making matrix alternatives x1 x2 x3 x4 x5 a1 0.3333 0.0909 0.3333 0.3333 0 a2 0.3333 0.2374 0.3333 0.3333 0.2009 a3 0.3333 0.3333 0.3333 0.3333 0.2652 a4 0.1137 0.0303 0.3333 0.2395 0.0341 a5 0.1137 0.1717 0.1465 0.2395 0.2350 a6 0 0.2727 0.1465 0.2395 0.2993 a7 0 0 0 0 0.0685 a8 0 0.1414 0 0 0.2691 a9 0 0.2424 0 0 0.3333 optimality min, ↓ min, ↓ min, ↓ max, ↑ max, ↑ table 3: neurosophic decision-making matrix alternatives x1 x2 x3 x4 x5 a1 (0.3333, 0.7167, 0.6667) (0.0909, 0.9091, 0.9091) (0.3333, 0.7167, 0.6667) (0.3333, 0.7167, 0.6667) (0.0, 1.0, 1.0) a2 (0.3333, 0.7167, 0.6667) (0.2374, 0.8126, 0.7626) (0.3333, 0.7167, 0.6667) (0.3333, 0.7167, 0.6667) (0.2009, 0.8491, 0.7991) a3 (0.3333, 0.7167, 0.6667) (0.3333, 0.7167, 0.6667) (0.3333, 0.7167, 0.6667) (0.3333, 0.7167, 0.6667) (0.2652, 0.7848, 0.7348) a4 (0.1137, 0.8931, 0.8863) (0.0303, 0.9697, 0.9697) 0.3333, 0.7167, 0.6667) (0.2395, 0.8105, 0.7605) (0.0341, 0.9659, 0.9659) a5 (0.1137, 0.8931, 0.8863) (0.1717, 0.8641, 0.8283) (0.1465, 0.8768, 0.8535) (0.2395, 0.8105, 0.7605) (0.2350, 0.8150, 0.7650) a6 (0.0, 1.0, 1.0) (0.2727, 0.7773, 0.7273) 0.1465, 0.8768, 0.8535) (0.2395, 0.8105, 0.7605) (0.2993, 0.7507, 0.7007) a7 (0.0, 1.0, 1.0) (0.0, 1.0, 1.0) (0.0, 1.0, 1.0) (0.0, 1.0, 1.0) (0.0685, 0.9315, 0.9315) a8 (0.0, 1.0, 1.0) (0.1414, 0.8793, 0.8586) (0.0, 1.0, 1.0) (0.0, 1.0, 1.0) (0.2691, 0.7809, 0.7309) a9 (0.0, 1.0, 1.0) (0.2424, 0.8076, 0.07576) (0.0, 1.0, 1.0) (0.0, 1.0, 1.0) (0.3333, 0.7167, 0.6667) optimality min, ↓ min, ↓ min, ↓ max, ↑ max, ↑ https://doi.org/10.15837/ijccc.2022.1.4646 9 table 4: intermediate results of the cocoso-mgqnn and the final ranking of the alternatives alternatives r p ka kb kc k rank a1 (0.3139, 0.7498, 0.7061) (0.9728, 0.0548, 0.0425) 0.1241 60.8264 0.9529 22.5646 3 a2 (0.3201, 0.7337, 0.6837) (0.9960, 0.0163, 0.0106) 0.1288 63.7319 0.9895 23.6271 2 a3 (0.3306 0.7199 0.6699) (0.9973, 0.0123, 0.0080) 0.1302 66.0184 1.0000 24.4313 1 a4 (0.2488, 0.8285, 0.7998) (0.9786, 0.0485, 0.0404) 0.1168 45.1218 0.8715 17.0566 4 a5 (0.1628, 0.8712, 0.8478) (0.9914, 0.0342, 0.0255) 0.1135 35.2094 0.8715 13.5805 5 a6 (0.1848, 0.8932, 0.8690) (0.9937, 0.0364, 0.0270) 0.1115 30.6951 0.8561 11.9850 6 a7 (0.0253, 0.9965, 0.9965) (0.8746, 0.4295, 0.4295) 0.0778 2.0000 0.5973 1.3446 9 a8 (0.1098, 0.9726, 0.9666) (0.9689, 0.1596, 0.1405) 0.0969 9.3949 0.7439 4.2899 8 a9 (0.1533, 0.9586, 0.9478) (0.9793, 0.1249, 0.1048) 0.1006 13.7132 0.7727 5.8837 7 5 conclusion modern modelling trends in this field include evaluation of the uncertainty and vagueness of the initial information. traditional neutrosophic sets are considered as the effective tool to express uncertainty of the information. however, in some cases, it cannot cover all recently proposed cases of the fuzzy sets. the m-generalized q-neutrosophic sets were recently proposed to deal with this situation. the m-generalized q-neutrosophic sets can be considered as the generalisation of fuzzy set, pythagorean fuzzy set, intuitionistic fuzzy set, q-rung orthopair fuzzy set, single-valued neutrosophic set, single-valued n-hyperspherical neutrosophic set and single-valued spherical neutrosophic set. in this paper, the cocoso method extension under the environment of the m-generalized q-neutrosophic numbers (mgqnn) is proposed. this novel extension has been tested for the selection of the best 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(2019). a structured framework for sustainable supplier selection using a combined bwm-cocoso model, in: international scientific conference “contemporary issues in business, management and economics engineering’ 2019”, pp. 797–804, 2019. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: turskis z.; bausys r.; smarandache f.; kazakeviciute-januskeviciene g.; zavadskas e. k. (2022). m-generalised q-neutrosophic extension of cocoso method, international journal of computers communications & control, 17(1), 4646, 2022. https://doi.org/10.15837/ijccc.2022.1.4646 introduction foundations of the m-generalised q-neutrosophic set definitions combined compromise solution (cocoso-mgqnn) method case study conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 3, month: june, year: 2020 article number: 3856, https://doi.org/10.15837/ijccc.2020.3.3856 ccc publications head gesture recognition using a 6dof inertial imu i. c. severin, d. m. dobrea, m. c. dobrea ionut-cristian severin * department of applied electronics and intelligent systems faculty of electronics telecommunications and information technology, “gheorghe asachi” technical university of iaşi bd. carol i, no. 11 a, ia si 700506, românia *corresponding author: ionut.severin@etti.tuiasi.ro dan-marius dobrea department of applied electronics and intelligent systems faculty of electronics telecommunications and information technology, “gheorghe asachi” technical university of iaşi bd. carol i, no. 11 a, ia si 700506, românia mdobrea@etti.tuiasi.ro monica-claudia dobrea department of applied electronics and intelligent systems faculty of electronics telecommunications and information technology, “gheorghe asachi” technical university of iaşi bd. carol i, no. 11 a, iaşi 700506, românia mcdobrea@etti.tuiasi.ro abstract the recognition of the head movements is a challenging task in the human-computer interface domain. the medical, automotive, or computer games domains are only several fields where this task can find practical applicabilities. currently, the head movement recognition is performed using complex systems based on video information or using an imu sensor with nine freedom degrees. in this paper, we describe a new approach for recognizing head movements using a new type of imu sensor with six freedom degrees placed on top of a headphone pair. the system aims to provide an easy control method for people suffering from tetraplegia for a specific set of activities. the system collects data from the inertial sensor placed on top of the headphone to analyze and then extract the features for head movement recognition. we did construct and evaluated eight predictive models of classifying head movements activity to determine which one is the best fit for the proposed head movement recognition system. the comparison and performance evaluation provided by each predictive model lead to demonstrate the performances delivered by our new system for head movements recognition. keywords: head gesture, imu, head gesture recognition, intelligent system, embedded systems https://doi.org/10.15837/ijccc.2020.3.3856 2 1 introduction detecting the position of the human body in order to control various devices or to offer the possibility to manage certain information that may affect the way of interaction between humans and computers is a common topic today [3], ,[17], [9], [11], [13], [4]. at this moment, the possibility of determining the orientation and classification of movements, in most cases, is used in systems that use the management and analysis of the video signal acquired by a video camera [2]. the topic regarding head movement identification becomes an essential topic in the field of human-computer interaction (hci). it may have applicability in areas such as medicine, automotive, robot control, etc. the necessity to develop wearable systems for people with disabilities is another area of interest in our days. this kind of application can be used to control a wheelchair with help from hand gesture [10] with information that is coming from an accelerometer or using head gesture recognition with help from 4 capacitive sensors placed around the neck [4]. other common technics used are that which perform classification of the head movements with the help of video signal acquired with a smart video camera [2] or a simple web camera [14] where the accuracy of gesture classification was equal to 87% . also, in the technical literature exist the possibility to get head classification using 9-axis microelectromechanical system (mems) motion sensor such in [15], where data were acquired from 5 subject people. for each subject, a number of 5 repetitions were done, for this situation, the average classification rate was equal with 87.56% . the proposed wearable system, presented in this paper, comes with some advantages than the system from [15]. the first advantage is the low cost, the second one regards complexity, and the last one is related to the number of head gestures classified and the number of repetitions. also, the obtained results in this paper can be compared to those obtained by us in a previously published work [4]. in that published paper, we used to determine the position of the head four capacitive sensors placed around the neck. the accuracy of classification obtained in that publication was higher than 80% , but, in that paper, we had a small database with data acquisition from 4 subjects, and 30 recordings were done for each subject. also, the obtained results in [4] were influenced by the characteristics of the neck circumference for each test subject, which may represent a limitation. in order to classify the movements provided by each subject, in this paper, the information provided by the pitch, yaw, and roll angles will be used instead of signals supplied by 4 capacitive sensors placed around of neck. this thing can be an advantage because the human characteristics do not affect the performances of the classification system. in this paper, we propose another classification system for head movements, which has as a primary objective obtaining a good classification rate with a low price of implementation. also, the main focus is to obtain a new approach of head movement classification with help from a cheaper imu sensor with the possibility to integrate into a more complex system such as a robotic wheelchair system or other hci complex systems. also, we have as the primary focus to prove the possibility of classifying the human head movement without using the classical method like in [2] or [14]. validation of the new proposed classification system will be made with the help of our previous publication paper [4] and with the help of comparing it to the already existing systems [14] and [15]. our proposed classification system is structured in 3 main parts: signal acquisition part, signal processing part, and movement classification part. the signal acquisition part for this paper is performed by the imu 6dof sensor and mega2560 development board. after this step, the head position estimation, from the acquired signals, can be done using mathematical methods such as (a) the kalman filter, (b) low pass filter method, (c) high pass filter method, or by using (d) the complementary filter method [8]. besides the mentioned techniques, in the technical literature, there are other methods of 3d space position identification based on an imu sensor. one of the most used methods is also known as the quaternions technique [5]. in this article, we will use the kalman method and the complementary filtering method for fusion all six output signals from imu sensors. the signal processing part represents the following step of our solution. this step will start from the moment when all tree signals yaw, pitch, and roll are valid until then all measurements were performed and saved it with the help of visual basic application (vba). the acquired signals will be put inside of a comma separated variables (csv) file from where will take it and proceed offline according to the technique specified in this paper. the third part of the proposed system is composed of movement classification. in this case, several computational intelligence methods were used. the classification methods used in this paper are represented by: support-vector machine, random forest, k-nearest neighbors, decision tree classifier, extra tree classifier, gaussian naive bayes, logistic regression, quadratic discriminant analysis, and adaptive boosting classifier. the previously mentioned computational methods will be used for classifying 8 different commands. in the database creation process, we got the measurements from 7 peoples who did repeat for 40 times each specified command. in this paper, one of the main objectives will be to evaluate and determine the best classifier that can accurately recognize the head movements using the proposed system. also, another focus will be put on establishing the best classification system using this new approach without using classical methods to identify the body movement. the performance analysis will be done offline, and for the next research, based on this idea, the system will be implemented and evaluated in real-time. https://doi.org/10.15837/ijccc.2020.3.3856 3 2 system layout the system proposed in this paper, for movements classification determined by the position of the head, can be seen as described in fig. 1 and is composed of three parts. the first part is represented by a 6dof imu sensor, which provides as output six signals, three from the accelerometer sensor and three from the gyroscope sensor. these six signals represent the main texture signals, which will be used for getting the yaw, pitch, and roll angle value. as is presented in fig. 1, the second main part of our classification system is represented by the processing and filtering of all raw data value which coming from the imu sensor. at this level, we used kalman and complementary filter methods for fusion all six signals provided by accelerometer and gyroscope sensors to only 3 signals represented by values of the texture signals roll, pitch, and yaw. the fusion method has done on the development board mega2560. the data acquisition represents the next part of our solution. this step was done with the help of a vba application that took all of the data using de component object model (com) protocol and placed it inside of a csv file. the vba application uses the com protocol to get the value transmitted by the development board mega 2560. for evaluation of the classification rate value provided by our proposed system, in the next part was performed an offline analysis. at this step, we prepared the acquired signals, removing the garbage part of the received signals. the length of each desired piece was selected in a range of 167 samples to 310 samples. in the last stage, the acquired and filtered command information was put together to perform an offline analysis with help of different estimation algorithms. we used support vector machine (svm), random forest (rf), k-nearest neighbors (knn), decision tree classifier (dtc), extra tree classifier (etc), gaussian naive bayes (nb), logistic regression (lr), quadratic discriminant analysis (qda) and adaptive boosting classifier (abc). fig. 1 presents an overview of the proposed classification system. figure 1: block diagram of the system 3 signal processing 3.1 sensors calibration as we mentioned in the previous chapter, in this paper, we use an imu sensor for getting the orientation of the head in the 3d space. the measurement of the head movements, with the help of the imu sensor, is possible to get an unwanted high frequency noise generated by the operating principle of the sensor and by the unwanted subject movements. to remove this issue, in this paper, we used a correction method based on storing a sample of data obtained from the sensor and applying the correction factor after calculating the average on the chosen sample. to be able to get the most accurate head position, it will be necessary to perform the calibration process for both sensors. the calibration procedure of the accelerometric sensor used in this paper comprises two components: the offset value and the standard deviation. in this paper, the standard deviation has as the main scope to eliminate the “noise” from high frequency, which can appear on each measurement axis. equations (1) and (2) describe the calibration technique used for accelerometer sensor [7]: naxyz = 1 n n−1∑ i=0 ai (1) δxyz = 1 n − 1 n−1∑ i=0 (ai − naxyz ) 2 (2) https://doi.org/10.15837/ijccc.2020.3.3856 4 in equation (1) and (2), we make the following notations: naxyz : the value of correction for accelerometric sensor; ai : the raw data from the accelerometer sensor; -n : the maximum number of data sample; -δxyz : the variation of the signal on x,y, and z axes. in equation (1), the value of the maximum sample number may affect the output response rate. the higher the value of n, reduce the noises that appeared on the measuring channel to 0. equation (2) represents the standard deviation, which is described as a consequence of the distribution of the measured signal against its average. because the inertial system used in this work uses two sensors, for the calibration step, it is also necessary to perform the calibration on the gyroscopic sensor according to the method mentioned above. mathematical equations adapted for the gyroscopic sensor are given in (3) and (4): ngxyz = 1 n n−1∑ i=0 gi (3) δxyz = 1 n − 1 n−1∑ i=0 (gi − ngxyz ) 2 (4) in equation (3) and (4) we make the following notations: ngxyz : the value of correction for gyroscopic sensor; -gi : raw data from the gyroscopic sensor; -n : maximum number of the data sample; δxyz : variation of the signal on x,y, and z axes. based on the previous relations, the sensors offset was removed, and the standard deviation becomes a unity, and, now, the signals are scale-invariant. as a result, the accelerometer and the gyroscope readings are invariant to the translation and to scale. 3.2 head position estimation using an mpu-6050 sensor, we determine the head gestures based on the 3d spatial movement of the head. the mpu-6050 is a microelectromechanical system (mems) that contains in its structure an accelerometric sensor and a gyroscopic sensor. for the data fusion, which is applied to all 6 outputs of the imu sensor, in the literature, there are several methods such as fusion using the complementary filter [18], the kalman filter [12], or mahony filter [6]. it is well known that an accelerometric sensor performs better at low frequencies, while a gyroscopic sensor performs very well at high frequencies. because of this behavior, the value filtered with a complementary filter will be performed at both low and high frequencies according to relation (5) [1]: ang = (1 − α) ∗ (ang + gyro ∗ dt) + α ∗ acc (5) following the implementation, the filtering of the yaw, pitch, and roll signals was obtained, removing the noise from the high and low frequencies, as is presented in fig. 2. according to equation (5), the best results for filtering were obtained to the values of 0.98 for the high pass filter component and 0.02 for the low pass filter component. besides the complementary filter above mentioned, in this paper, we used for data fusion another technique which is called kalman filter. using an additional method, of data fusion in addition to the complementary filter has resulted in excellent results in terms of yaw, pitch, and roll signals. the kalman filter method is more complicated than the complementary filter, and this one contains two stage. the first stage is represented by the prediction step, while the correction component represents the second stage. the equations described at the implementation of the kalman method, which was used in this paper can be seen in the following part [16]: x̂k = ax̂k−1 + buk (6) pk|k−1 = apk−1at + q (7) kk = pk|k−1ht [ hpk|k−1h t + r ]−1 (8) x̂k = x̂k|k−1 + kk ( yk − hx̂k|k−1 ) (9) https://doi.org/10.15837/ijccc.2020.3.3856 5 figure 2: (a) raw data, and (b) complementary filter results pk = (i − kkh) pk|k−1 (10) the previous equations were implemented and, base on them, we obtained excellent results regarding the data fusion from the accelerometer and gyroscope sensor without noises. the final pitch, roll, and yaw signals obtained by using the kalman filter are exhibited in fig. 3. from the figures, it can be observed that excellent filtering characteristics of the 3 signals can be observed depending on the different perturbations that appeared on the initial streams of data. figure 3: (a) raw data, and (b) kalman filter results 4 database and methods 4.1 6dof imu head gesture sensor in the present work, the sensor used to determine the position and movements given by the orientation in 3d space is represented by the mpu6050. the mpu6050 sensor is placed on an audio headset in order to be able to provide much easier information about orientation in space, information that is transmitted to the mega2560 board. to respect the previously mentioned conditions, we did decide to place the 6dof imu sensor above the https://doi.org/10.15837/ijccc.2020.3.3856 6 head in the center position and equal distance between ears, such as presented in fig.4. this approach leads to getting the head orientation command with accuracy and independent by the head circumference of each user. figure 4: the 6dof imu sensor placement the experimental setup created taking into account the right positioning of the imu sensor can be seen in figure 5. figure 5: the wearable system the proposed solution composed of an imu sensor and development board mega2650 further captures and sends the information to the vba application, using a serial link for signal acquisition through the com protocol. the analysis and management of the information obtained will be done offline using the pycharm development environment together with the python language. the mega2650 development board is the main component of the acquisition system. the mega2650 offers a good ratio between performance and cost. 4.2 database the final database was implemented by acquiring data from two types of human subjects, six trained persons, and one untrained person. that six human subjects, from which the measurements were taken, were instructed before performing the head movements. all the subjects were sitting on a chair, simulating a spastic tetraparesis or tetraplegia patient. another significant thing is that each person from whom measurements were made was trained to execute the head movement commands without moving the rest of the body. for the second case, the human subject did have the freedom to execute all commands from sitting without any restriction. https://doi.org/10.15837/ijccc.2020.3.3856 7 for this paper, a unique database was created after the preparation of each subject database. each repetition frame was offline processed, and the garbage signal part was eliminated to have a pure command signal. also, for the validation of the proposed classification system, a database that contains a set of random measurements performed by an operator that has not been trained before to perform the correct movement command was used. this thing leads to obtain a real situation classification result when a human subject uses our proposed solution to control a wheelchair or a windows application without any knowledge about command control. the acquisition of head commands was made with the help of the prototype presented in fig. 5. the recordings were made on people who have been trained to execute the movements established for this work and implicitly to simulate the behavior of people suffering from similar tetraparesis as in a previously published work [4]. we considered a number of 8 control commands. these commands were classified using the following algorithms: support-vector machine, random forest, k-nearest neighbors, decision tree classifier, extra tree classifier, gaussian naive bayes, logistic regression, quadratic discriminant analysis, and adaptive boosting classifier. because in the present work, the database was created following the data acquisition from each human subject, the method of principal component analysis (pca) was used to obtain fast and accurate classifications. the commands had been chosen to provide the possibility to control through the head movements an audio/video player or a wheelchair. the initial command sets are move righ 2s (1-jump to another future time period), move left 2s (2-jump to another previous period), move next right (3-skip to next track), move next left (4-skip to one last track), volume up (5-volume increase), volume down (6-volume reduction), start video (7start play), and stop video (8-stop play). the commands mentioned above have been established to offer the possibility of control a multimedia application that can play audio/video content, thus providing the possibility for persons with tetraparesis to be able to control certain audio/video playback states alone. for the present work, 40 repetition measurements for each human subject were purchased. each movement of the head will be performed starting from a forward position until a return to the initial position. all lateral tilt movements of the head to the left and right will be performed with respect to the sagittal plane, and the rotations of the head to the left and right will be related to the horizontal plane. the forward-backward movements are associated with the frontal plane. the following head movements describe all 8 commands: 1. the movement of the head will be done by rotating the head twice on the right side around the yaw axis with the return of the head orientation in the right position. 2. the movement of the head will be done by rotating the head twice on the left side around the yaw axis with the return of the head orientation in the right position. 3. the movement of the head will be done by tilting the head twice in the right side around the roll axis with the return of the head orientation in the correct position. 4. the movement of the head will be done by tilting the head twice on the left side around the roll axis with the return of the head orientation in the right position. 5. the movement of the head will be done twice by turning the head towards the ceiling around the pitch axis with the return of the head orientation in the right position. 6. the movement of the head will be made by lowering the head twice to the floor around the pitch axis with the return of the head orientation in the right position. 7. the movement of the head will be done by lowering and rooting twice the head towards the ceiling and the floor around the pitch axis with the return of the orientation of the head in the right position. 8. the movement of the head will be done by turning the head twice from the right maximum to the maximum left around the yaw axis with the return of the head orientation in the right position. the orientations of the head were made in the 3-dimensional domain related to the 3 axes given by the values of the yaw, pitch, and roll. the acquisition of the received and filtered signals was made using a simple application in the vba that takes the information from the serial port and insert the values in a csv file from which they were copied, processed, and added in the database. for the final database, which was used for analysis, we did perform the preparation of each measurement data set for clear the garbage part of the acquired signal. this procedure has helped us to maintain just a utile part of signals for training the used algorithms. this step enabled us to train the intelligent algorithm with a pure pattern signal without the noise or garbage part. also, for validation of the proposed classification system, the final database includes a measurement which coming from a subject that was not trained to execute the desired command correctly. this fact will provide us information about the system performances based on an unpredicted data set. because the similarity between each command frame provided by all subjects is very important, in this paper, we chose to have a length in a range of 167 samples to 310 samples. https://doi.org/10.15837/ijccc.2020.3.3856 8 5 results for this research paper, the training and test data set were variable represented for each established 8 classifications classes. the length size for each control class was chosen independently for each human subject. the main idea was to simulate an unpredictable behavior with different speed of execution control commands. the acquisition of data from mega2650 was made at the maximum system speed of 16mhz. as a direct result, the sampling frequency for the head movements (acceleration and gyroscopic information) was 50 hz. for this research paper, 6 unique databases were created for each of the 6 subjects, which performed all head moving command from sitting position on a chair with a backrest, thus simulating a patient suffering from spastic tetraparesis. besides the previous measurements which were done from 6 unique subjects, we did perform another database from the subject that was not trained before. with this database, we did try to simulate and validate the real condition when it is possible to execute the command in the wrong way. for the seventh database, the human subject naturally performed the movements, regardless of whether the body and head position were kept in a vertical position. in this part of the paper, we will present the performances obtained from the offline analysis using the predictive algorithms mentioned in the previous chapters. the offline analysis was done in the 2017 pycharm development environment. all created data bases (db) were split for each time in two parts. the first one is the training set, and the test set represents the second part. for this paper, the test set was chosen to 20%. the training set was 80% . in table i, the results are presented. these outcomes helped us to select the best predictive algorithms for further analysis in future research. extensive analysis was done comparing results using pca based feature vectors, linear discriminant analysis (lda) based feature vectors, and raw based feature vectors. the distribution of db with all measurement values from 7 people subject can be seen in fig. 6. from fig. 6 we can get some conclusion regarding the range of movement reported at the reference yaw-pitch-roll (ypr) system. the maximum range for yaw signal is between 60◦ and -40◦ , pitch was stimulated between 30◦ to -80◦ , in finally the roll value were in range -45◦ to 40◦ this information can be seen in left representation from fig. 6. also, if we take into consideration fig. 6 we can make some conclusions about database and value manipulated on each axis yaw, pitch, and roll. from this figure, we can see the fact that all acquired signals are repetitive information provided by diagonal representation in time what relation between axis yaw, pitch, and roll provided us information if data can be grouped at the specific classes. for performing a proper evaluation of the proposed system in this paper we use some metrics as classification accuracy, precision factor, f1 score, and recall factor. before starting to present all results provided by the proposed system, we will present all specified metrics. in this paper, classification accuracy is used to get the ratio of correct prediction from a maximum number of inputs, the equations which describe this metric can be seen in the (11): accuracy = number of correct prediction total number of prediction (11) another interesting metric is represented by a precision factor who tell us how many positive results get according to the number of positive predicted value by prediction algorithm. the equation which describes this factor is specified in (12): precision = true positive total predicted positive (12) the recall factor provides us information about the predicted class if truly belongs to the predefined class. the equation (13) describes this factor. recall = true positive total actual positive (13) based on the f1 score, we will estimate how many instances can be classified correctly or how many are classified incorrectly. the equation which described the previous affirmation can be seen (14): f1 = 2 x precision x recall precision + recall (14) because the size of the created database is large, we chose to use an additional technique to reduce the dimensionality of the data set and also to reduce the running time for each algorithm. for this situation, we did use pca and lda techniques. those techniques presented in the previous section are linear transformation techniques, and the difference between them is provided by the fact that lda is supervised, whereas pca is unsupervised. according to the information presented in table 1, we got a good and similar classification ratio for all of each 3-analysis technique applied to the final database, which contains data from 7 persons. after the offline analyses, we got a set of algorithms that provide an accuracy higher than 80% , few from these good https://doi.org/10.15837/ijccc.2020.3.3856 9 classifiers are: random forest, extra trees classifier, support vector machine, decision tree and k-nearest neighbors. all the analyses were performed on the previously presented database, containing data from the recording obtained from 7 different subjects. from these subjects, 6 were trained people, and 1 on was an untrained subject. this fact generates more accurate and realistic results if we compare this with the previous paper [4]. this conclusion comes from the fact that the solution presented in this paper contains a large and unique data from two kinds of situations which can simulate a real condition. also, considering that the approach of the performance analysis is similar, the results are superior for the present case. another thing that confirms the good performances obtained in this work is offered by the values of the performances obtained for each database corresponding to each human subject. in the previous publication [4], we got a maximum precision equal to 91.67% on an independent database; in time, what in this paper, we got a maximum value equal to 93% . this thing leads to the fact that our classification system presented here is more accurate than the previous research publication. the obtained accuracy is a competitive result if we report our findings to the solution provided by the predictive system using a web camera [14] where accuracy was 87% . the video system complexity [14] is higher, and the possibility to include that solution to control another system, such as a wheelchair, can be a complicated approach. other reasons that confirm the excellent performance provided by the proposed system can be obtained through comparison with the solution provided by an imu with nine degrees of freedom (9dof) [15]. in [15] the accuracy achieved is equal with 87.53% with data acquired from 10 subjects. the main advantages provided by our proposed solution is that regarding: cost, number of commands, good classification, and the possibility to integrate our system easily in a more complex application. figure 6: data distribution and valid range of the db which include 7 people subjects table 1: result obtained on the test set 20% of the db which include 7 people subjects classifier pca-based feature vectors best accuracy lda-based feature vectors best accuracy raw data-based feature vectors best accuracy random forest k-nearest 78.03% 78.46% 80.4% neighbors(knn) 77.58% 77.66% 78.1 % decision tree 77.96% 78.13% 78.9% gaussian naive bayes(nb) 45.08% 44.64% 41.5% logistic regression 39.97% 39.97% 41.8% extra trees classifier 78.87% 78.68% 80.49% quadratic discrimination analysis 45.60% 45.60% 45.60% ada boost classifier 33% 35.08% 77.6% support vector machine (svm) 61.62% 62.57% 81.26 % in order to be able to determine the performances offered on each specified class, the random forest algorithm was chosen from the list of the algorithms determined to be with the best classification rate, see table 2. table 2 presents the obtained performances on each independent database. from table 1 we got the best prediction algorithm, which is represented by random forest. this classification algorithm is composed by a number of 100 trees which combine all result from them using the random state for controlling randomness of the bootstrapping, samples used for building trees and sampling of the features used to choose the best split on each node from the predictor. k-nearest neighbors’ algorithm represents https://doi.org/10.15837/ijccc.2020.3.3856 10 table 2: result obtained with the best classifier for each subject people overall accuracy [% ] random s1 s2 s3 s4 s5 s6 s7 forest (90.12%) (83.75%) (91.25%) (93%) (90.87%) (80%) (85.25%) prec recall prec recall prec recall prec recall prec recall prec recall prec recall jumpnextleft 0.89 0.93 0.87 0.89 0.95 0.97 0.96 0.96 0.95 0.97 0.91 0.89 0.89 0.96 jumpnextright 0.93 0.92 0.78 0.80 0.94 0.94 0.93 0.91 0.91 0.94 0.96 0.88 0.82 0.76 moveright_ 2s 0.89 0.95 0.81 0.8 5 0.85 0.88 0.92 0.87 0.85 0.87 0.96 1.00 0.83 0.84 moveleft_2s 0.95 0.97 0.92 0.97 0.95 0.98 0.90 0.90 0.95 0.96 0.95 0.97 0.82 0.91 risvolume down 0.92 0.94 0.95 0.94 0.89 0.87 0.91 0.95 0.89 0.88 0.37 0.44 0.85 0.90 risvolume up 0.87 0.92 0.71 0.72 0.89 0.90 0.92 0.97 0.89 0.89 0.90 0.80 0.87 0.86 startvideo 0.89 0.80 0.76 0.70 0.94 0.88 0.95 0.94 0.94 0.91 0.36 0.35 0.88 0.75 stopvideo 0.87 0.84 0.90 0.86 0.89 0.89 0.95 0.93 0.89 0.89 0.99 0.97 0.86 0.86 table 3: result obtained with the best classifier for all subject people db random forest precision factor recall factor f1-score accuracy factor jumpnextleft 0.84 0.88 0.86 0.80 jumpnextright 0.81 0.81 0.81 0.80 moveright_2s 0.75 0.76 0.75 0.80 moveleft_2s 0.83 0.87 0.85 0.80 risvolume-down 0.70 0.75 0.72 0.80 risvolume-up 0.79 0.81 0.80 0.80 startvideo 0.75 0.67 0.71 0.80 stopvideo 0.78 0.74 0.76 0.80 another classification algorithm used with a good result for this paper. this algorithm is based on the similarity between different points and takes into consideration the fact that similar data points are usually near to each other. for this paper k-nearest data point factor was chosen to be equal with 5. as a metric, we used the makowski metric with distances calculated based on the euclidian distance. the best performance for this paper using the knn predictor algorithm was obtained with k factor equal with 3, and for this situation, the accuracy was equal with 78.1% . from table 2 we can see the precision on each command for all 7 subjects. this parameter empowers this solution as a good choice for the determination of the head movements if it takes into consideration the cost and complexity factor. the average value of the precision factor for all 7 human subjects on each command can be seen in table 3 where the precision factor is up to 70% . from table 3 we can also confirm the excellent performance of the proposed solution with the f1 score, which tells us about how many instances of each command can be correctly classified or not. in our case, we got a good score of up to 71% , until 86% . confirmation if the predicted value truly belongs to a specified class is provided by the recall factor, which is up to ∼ 70% for each command. 6 conclusion the system presented in this paper was proposed to provide the possibility to classify a number of 8 commands. such a system offers the possibility to control a media player by tetraplegic people. after the different analyses presented above, we got good results according to the main proposed scope. as a first conclusion, our idea can be very competitive if we take into consideration the fact that for the moment, only a few similar solutions exist which make head movement classification using a web camera [14] or a more complex imu 9dof sensors [15]. in this paper, we got a good classification accuracy of 81% , which can be a competitive result if we report to a web camera solution [14], which got an 87% correct classification rate. still, the video system is a more complex one, challenging to include it in another complex system which means both more processing times and higher implementation costs. another solution, presented in the literature, is based on a head gesture acquisition system using a 9 dof imu [15]. this solution provides 87.56% accuracy with the classification of 4 head commands. the data were obtained from 10 subjects. our solution provides an 81% accuracy obtained on a number of subjects as half as in [15] and on a number of classification commands double than in [15], while the implementation cost is smaller than in [15]. in addition to the solutions mentioned above [14], [15], the idea proposed in this publication has been evaluated using our previous system [4], where for getting the head position, we used 4 capacitive sensors. in the previous approach [4], we got an accuracy of 70% for a database containing raw, unprocessed data from 5 trained human subjects. when the pca techniques were used, we succeeded to raise the accuracy to 72-75% . in the previous approach [4], we got an accuracy of 70% on a database containing raw, unprocessed data from 5 trained human subjects. when the pca techniques were used, we succeeded to raise the accuracy to 72-75% . unlike the previous solution [4], the current one has achieved far superior results: a precision of up to 80% obtained even if a higher number of both commands (i.e., eight commands) and subjects (i.e., 6 trained and one untrained human subjects) was used. these results may claim for our idea to be considered as one of the good alternatives for getting the head orientation instead of the existing classical solutions [4], [14] or[15]. there are two main approaches to both test and use an intelligent https://doi.org/10.15837/ijccc.2020.3.3856 11 system: offline and online. in the offline approach, the system is tested based on an independent dataset, other than the data set on which the system parameters were extracted. due to the nowadays existing large number of open-source data sets, the offline testing method has been far more used. however, in a real-time scenario, the newly devel-oped intelligent system must be embedded into a specific application and tested in interaction with a real environment. our new proposed system (see fig. 4 and fig. 5) was built, having in mind the online scenario. the intelligent system was developed as a system able to interact with an unconstrained environment, receiving inputs from different specific head movements, each of them being executed in variable time intervals specific to each user. if we will have to use within an online environment our offline trained system, we can expect a degradation of the classification performances mainly due to some unlearned particular head movement characteristics that were not previously encountered by the system. however, this decrease in classification rate can be taken away by further employing the main idea of an online system, namely, by continuously updating the parameters of the classification system during and after each data input. so, in order to implement such an online system, we will have to adjust our software components according to this rule, following that a special focus to be put on critical processing times. in conclusion, with some future improvements done, we consider that our system will meet all the required prerequisites in order to work in a real-time scenario. in this research paper, we had proposed a new kind of prototype for the determination of head movements for tetraplegic people who can not control their limbs. this solution provides as main advantages fact that is easy to be implemented, good acquisition cost, possibility to be included easily in the more complex system, and excellent performances reported at a similar solution. the solution described in this paper can be successfully framed in the field of computational intelligence because the movement evaluation, which was executed for each command, was done with help from intelligent supervised algorithms. through the solution described in this paper, we tried to highlight a new method of monitoring the movements determined by the human body, using a system with a low cost of implementation and with high performances regarding the performed activities classification rate. also, with the proposed solution in this paper, we wanted to highlight the possibility of creating a simple and intelligent hci system that can determine with high precision the activities performed by the human operator. this solution can come to the aid of people who suffer from spastic tetraplegia, to control specific devices or systems of movement (wheel chair) without high implementation costs. the method proposed by us is an intelligent method that can offer the possibility of its integration in complex and smart systems in order to make it possible to control certain processes by creating a favorable human-computer interface environment. in this paper, the main applicability of our idea was in the medical area. still, our solution can have a lot of applicability in other fields such as automotive, iot, hcl, etc. also, we propose to implement the established best classifier in this paper, in the real-time application for controlling a media player or for an intelligent wheelchair system with the dynamic classification of head gestures. another aim was to increase the database with more acquired data from a different human subject and to increase the number of classified head gestures. in this way, we try to obtain an excellent system able to classify a high number of head movements with a good rate of classification. funding the publication of this research was sponsored by the bosch romania, department: electrically assisted steering systems (ees). author contributions the authors contributed equally to this work. 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[18] [online]. available: https://www.pieter-jan.com/node/11, accessed on 21 dec 2019. https://doi.org/10.15837/ijccc.2020.3.3856 13 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: severin, i. c.; dobrea, d. m.; dobrea, m. c. (2020). head gesture recognition using a 6dof inertial imu. international journal of computers communications & control, 15(3), 3856, 2020. https://doi.org/10.15837/ijccc.2020.3.3856 int j comput commun, issn 1841-9836 8(6):878-890, december, 2013. security ontology for adaptive mapping of security standards s. ramanauskaitė, d. olifer, n. goranin, a. čenys simona ramanauskaitė*, dmitrij olifer, nikolaj goranin, antanas čenys vilnius gediminas technical university simona.ramanauskaite,dmitrij.olifer, nikolaj.goranin, antanas.cenys@vgtu.lt lithuania, lt-10223 vilnius, sauletekio al. 11 *corresponding author: simona.ramanauskaite@vgtu.lt abstract: adoption of security standards has the capability of improving the security level in an organization as well as to provide additional benefits and possibilities to the organization. however mapping of used standards has to be done when more than one security standard is employed in order to prevent redundant activities, not optimal resource management and unnecessary outlays. employment of security ontology to map different standards can reduce the mapping complexity however the choice of security ontology is of high importance and there are no analyses on security ontology suitability for adaptive standards mapping. in this paper we analyze existing security ontologies by comparing their general properties, ontometric factors and ability to cover different security standards. as none of the analysed security ontologies were able to cover more than 1/3 of security standards, we proposed a new security ontology, which increased coverage of security standards compared to the existing ontologies and has a better branching and depth properties for ontology visualization purposes. during this research we mapped 4 security standards (iso 27001, pci dss, issa 5173 and nistir 7621) to the new security ontology, therefore this ontology and mapping data can be used for adaptive mapping of any set of these security standards to optimize usage of multiple security standards in an organization. keywords: security ontology, security standards, adaptive mapping. 1 introduction an ontology defines the basic terms and relations compromising the vocabulary of a topic area as well as the rules for combining terms and relations to define extensions to the vocabulary [9]. the ontology provides a better communication, reusability and organization of knowledge by decreasing language ambiguity and structuring transferred data [1], [2], [3], [4]. security becomes fundamental in our society and the survival of organizations depends on the correct management of up-to-date security elements [6]. as the security area is very broad and has many relations between its concepts, usage of security ontology could improve security knowledge description unambiguity in information systems. the necessity of security ontology can be noticed in various security communities and considered as an important challenge and a research branch [5], [7], [8]. in small and medium enterprises the knowledge database of security area and its unambiguity is very important in formal/legal activities, such as certification, standard compliance, etc. in many cases organizations have to meet certain security requirements from different sources, which may be redundant or overlapping by simultaneous usage. therefore standard mapping should be put into practice in cases where more than one security standard has to be met. the mapping of security standards allows the optimization of resources by indicating matching elements of standards and by eliminating duplicated activities and security measures to meet it. however mapping of security standards can be complicated if more than two standards have to be mapped. copyright © 2006-2013 by ccc publications security ontology for adaptive mapping of security standards 879 security ontology can be used to simplify the mapping of more than two security standards [10]. this solution suggests mapping all security standards under one security ontology. this ontology would act as a basis for standard knowledge formalization and would allow adaptive mapping of any standards, mapped to the ontology. therefore the security ontology plays a large role in adaptive mapping and covers a wide area and should be detailed to meet all security standards. the aim of this paper is to analyse suitability of existing security ontologies to be used for adaptive mapping of security standards and to propose a new one, more suitable for the purpose. 2 security ontology existing security ontologies vary according to described area and level of detail. one of the first works mentioning information system knowledge concepts concerning security was published in 1990 by j. mylopoulos et al. the paper "telos: representing knowledge about information systems" [12] describes a telos language to describe the knowledge about information systems and suggests it can be employed for security specification as well. c. e. landwehr et al. on 1994 published a paper called "a taxonomy of computer program security flaws " [13] where types of computer program security flaws were summarized and claimed it can be used for introduction to the characteristics of security flaws and their origins. a. avizienis et al. also proposed a taxonomy, concerning security concepts [14]. this taxonomy describes more abstract and wide concepts than c. e. landwehr et al. provided however clear relationships between categories of taxonomy are missing too. the need of ontology rather than taxonomy was indicated in a paper "toward a security ontology" by m. donner on 2003 [7]. on the same year g. denker et. al. presented security related ontologies for web services and published it in paper "security in the semantic web using owl" [15] while h. mouratidis el al. published work "an ontology for modelling security: the tropos approach" [16] where presented ontology for security modelling in agent-based information systems. h. mouratidis provided more works concerning security ontologies [17], [5] where clear orientation to usage of security ontologies in software developments is noticed, therefore these ontologies are meant more for system requirement representation rather than for basic security concepts. there are ontologies concentrated specifically on security requirements only. one of such ontologies is presented by f. massacci [18]. other specific security ontologies are proposed by d. geneiatakis et al. [19] (designed for describing session initiation protocol security flaws), by m. karyda et al. [20] (dedicated for describing applications of e-government), by j. undercoffer et al. [21] (designed for describing computer attacks), by a. souag [22] (designed for requirements engineering process) and by other authors. a. kim extended specific ontologies and created one which can be applied to any electronic resource [23]. however this ontology does not overlay all the concepts of information security. more detailed general security ontologies were proposed by a. herzog et al. [24] and s. fenz et al. [25]. security ontology, proposed by herzog et al. represents information security domain that includes both general concepts and specific vocabulary of the domain. the proposed ontology has 4 top level concepts: assets, threats, vulnerabilities and countermeasures. the ontology overviews the information security domain in a context-independent and application neutral manner. similar properties apply to security ontology proposed by s. fenz et al. however it has more concepts in it including non-core concepts such as the infrastructure of organizations. the main top level concepts in this ontology are: asset, control, organization, threat and vulnerability. 880 s. ramanauskaitė, d. olifer, n. goranin, a. čenys table 1: data of general comparison of security ontologies ontology property g. denker a. herzog s. fenz s. fenz (raw) total number of classes 39 460 641 311 total number of data types properties 0 7 16 14 total number of object properties 12 30 58 58 total number of annotation properties 2 4 10 10 total number of individuals 117 211 486 478 number of sub-classes 11 571 1051 409 max. depth of class tree 4 8 6 6 min. depth of class tree 1 1 1 1 avg. depth of class tree 1,4 4,1 3,0 3,2 max. branching factor of class tree 27 83 199 114 min. branching factor of class tree 1 1 1 1 avg. branching factor of class tree 7,6 3,2 3,9 14,5 3 analysis of security ontologies three security ontologies were chosen for deeper analysis because of its particularity: security ontology created by g. denker; security ontology, created by a. herzog et al.; security ontology, created by s. fenz. while s. fenz security ontology includes concepts of several security standards (iso 27001, grundschutz) in it, one more version of s. fenz’s security ontology will be analyzed in this study (hereinafter s. fenz (raw)). all classes and elements of security standards will be excluded from s. fenz’s ontology, relying solely on raw concepts of ontology security. 3.1 general comparison in general comparison of security ontologies the total number of different ontology elements, the depth and branching metric of the ontology tree are put into comparison. to get these metrics an owl ontology editor swoop was used. data obtained by this tool are presented in table 1. as results of general ontology comparison reveal, g. denker’s ontologies have the least number of concepts, while security ontologies, created by s. fenz and a. herzog have the largest number of concepts. g. denker’s ontology is intended to interface between various notations of security standards while ontologies of s. fenz and a. herzog represent the whole area of security, therefore have more concepts. the purpose of ontology usage inflicts on the number of individuals as well wider range ontologies have more individuals to allow user to chose from; specific purpose ontologies have less or no individuals as all individuals should be known or unnecessary to the user. another important metric is the depth and branching factor of ontology class tree. it defines the main properties of tree structure of the ontology and can be exercised to define how intuitive the ontology should be for individual users. our analysis displays the security ontology of a. herzog has the deepest class structure and has the most substantial detailing level. however the maximum branching factor of class tree is equal to 83, which may result in human users facing difficulties while viewing the ontology. ontology of s. fenz should be difficult to visualize as well, because of its branching factor. security ontology for adaptive mapping of security standards 881 table 2: ontometric analysis data of the ontologies content and the contents organization ontology characteristic g. denker a. herzog s. fenz s. fenz (raw) concepts (factor) 2 4 4 4 relations (factor) 3 3 3 3 taxonomy (factor) 2 3 3 3 axioms (factor) 2 4 4 4 3.2 ontometric analysis of security ontologies general comparison of security ontologies gives just a few main quantitative metrics, while the quality of ontology is not taken into account. ontometric [26] is a method for ontology quality measurement. this method compares ontologies in five dimensions (the ontologies content and the contents organization; the language in which it is implemented; the methodology that has been followed to develop it; the software tools used to build and edit the ontology; the costs that the ontology will require in a certain project) and measures all the characteristics from 1 to 5 according to their low or high degree of accomplishment. while all ontologies we are analyzing are written in the same file format, we are analyzing the content metrics alone (metric of language, tools and costs should be equal, because all analyzed ontologies are written in owl files, while the development process of ontology do not have significant influence on its usage and are unknown to us). according to ontometric, the content of ontology can be defined by 4 factors: concepts, relations, taxonomy and axioms. as evaluation of ontometric is qualitative, we will evaluate all of them as all security ontologies are meant for presenting the broadest security area possible and should be able to present any situation in area of information security. the imagination of ideal security ontology is important in order to evaluate the concept factor in ontometric analysis as this measurement should provide information on how well the security area is covered by the ontology. other factors in ontometric analysis are more relative and describes how well the relations, taxonomy and axioms are described in the ontology, not the whole security area. all data of our ontometrix analysis are presented in table 2. the ontometrix analysis shows the g. danker ontology has the lowest scores, while s. fenz and a. herzog ontologies have similar scores, the level of detail and provides wide range of security concepts. however the data of ontometric analysis does not show differences between s. fenz and a. herzog. while comparing the differences in s. fenz’s and a. herzog’s ontologies, it can be noticed that ontology, created by a. herzog has more of a theoretical approach rather than the ontology of s. fenz and describes more definitions, formal concepts of information security area. s. fenz’s ontology provides more information on practical side of information security, by listing basic controls as a guide for security administrators for system security assurance however does not mention concepts, related to organizational security. 3.3 research of security ontology usage for mapping of security standards as security ontologies, proposed by a. herzog and s. fenz have similar ontology comparison results, a deeper analysis has to be performed to select the best one for usage in adaptive mapping of security standards. 882 s. ramanauskaitė, d. olifer, n. goranin, a. čenys adaptive mapping of security standards to ensure security in an organization, security standards or best practices can be employed. in some cases compliance to a certain security standard is even required to obtain privileges to supply or to get different services. however when organization uses more than one security standard, mapping or integration of security standard usage should be done in order to avoid redundant activities, not optimal resource management, unnecessary outlays etc. integration or direct mapping of security standards are time and knowledge consuming as well as very static (everything has to be redone when a standard has to be removed or added), while adaptive mapping of security standards provides more flexibility to change the list of used standards as well as requires less work to map a larger number of standards as each standard have to be mapped to ontology only. therefore n mapping activities have to be done to map n standards in stead of n*(n-1) mappings for direct mapping. the process of adaptive mapping and integrated standard generation is presented in fig. 1 figure 1: sequence of mapping two standards and generating the mapped standard in relation to the structure of ontology when a standard is mapped to the base ontology, all matching controls and concepts between ontology and standards have to be linked. this has to be done once for all standards which have to be mapped together. the generation of standard maps or integrated standards is dynamic and can be done on demand by changing standards which have to be mapped or integrated, properties for relation type estimation etc. the map generation process finds similar controls in selected standards by comparing its linking to the base ontology. an example of relation type estimation in adaptive mapping is provided in fig. 2. in this example a control in iso 27001 (a.8.3.3_removal_of_access_righ...) and control in pci dss (pci_dss_8_5_4) standards are mapped with the same links to the security ontology (control in one standard has the same relations to concepts of security standard as control in security ontology for adaptive mapping of security standards 883 figure 2: example of standard mapping trough ontology another standard). as these two controls have no differences in mapping, the full match relation between these two controls of different standards can be generated. one more control of iso 27001 standard (a.11.2.1_user_registration) is presented in this example to illustrate relevant (not matching) controls. these two controls of iso 27001 security standard define situations, where vulnerability of nonblocked unnecessary accounts or terminals can be exploited. however both of iso 27001 controls have more links to different concepts of security ontology, therefore these two iso 27001 controls can not be treated as equal, however are relevant on certain levels. this kind of information can be used to analyse security standards and to optimize the resource usage when multiple security standards have to be met in an organization. for visualization and analysis of overlapping of multiple security standards a tool for adaptive mapping of security standards was created (see fig. 3). this tool uses security ontology and maps security standards data to generated tree structure hierarchies, representing a chosen security standard or integration of few security standards as well as providing additional data trough node notation and explanation boxes on similarities of controls in chosen security standards. research of security standards coverage by security ontologies to compare which security ontology is more suitable for adaptive security standards mapping and adaptive mapping, ontology and standard concept coverage were analyzed. a. herzog’s and s. fenz’s ontologies were mapped with: • iso27001 the most popular security standard, which was created according to british security standard bs7799. this standard covers practically all security areas, provides certification opportunity and is widely recognized. • pci dss security standard developed by such worldwide organizations as visa, mastercard, american express, discover and jcb. standard developed to ensure cardholder information protection. this standard is a "must-have" for all organizations who handle debit, credit, prepaid and other cards. otherwise these organizations are forbidden to use visa, mastercard, american express and other cards. • issa 5173 security standard for sme (small medium enterprise). this standard has not been approved or officially recognized, however describes main security requirements, 884 s. ramanauskaitė, d. olifer, n. goranin, a. čenys which need to be implemented in any organization. • nistir 7621 security standard, developed by national institute of standards and technology. document clearly defines which actions are "absolutely necessary" for information, systems and networks protection. it also provides best practices on needed security level implementation. data on links between these security standards are presented in static form for 2 specific standards (as a table with matching controls between two security standards [28]) mostly. s. fenz was the first who mapped iso 27001 and grundschutz security standards to his ontology. he used this mapping for purposes of automated risk and utility management [11], however this information can be also used for adaptive standard mapping. s. fenz mapped two standards only, therefore links can only be generated between iso 27001 and grundschutz security. we analyzed all controls in all 4 chosen standards and mapped them to related concepts in s. fenz and a. herzog security ontologies. the mapping of security standards was performed by mapping the lowest level concepts (usually it’s a certain control, requirement for the organization), while the classes in security standards, used for presentation of class hierarchy were not accounted as mapping objects. the process of security standard mapping to security ontologies revealed differences between analyzed ontologies as well. biggest part of mapping links in s. fenz’s ontology are very direct one requirement of the standard has an equal or very similar control in s. fenz’s ontology. this type of mapping links are very direct, easy to understand for individual users, however the controls have to be detailed by other links between different concepts of the ontology, otherwise it will be difficult to define relations between standards controls, clustering, etc. meanwhile mapping security standards according to a. herzog’s ontology was done from logical structure standpoint one requirement of security standard is to have several links to ontology, by describing which concepts of ontology are related to this requirement (by defining what and how one has to do or use to protect against certain threat or vulnerability). this type of mapping requires more mapping links and has a potential to be easier to cluster controls of security standards into relevant groups. this type of mapping would be more understandable to information systems however would require more analysis or visualizing tools for people to understand links between two security standards, mapped through ontology this way. summarizing the security standard mapping process to security ontologies s. fenz’s ontology can be used to simplify the mapping of security standards because all the most important concepts for mapping are described as list of classes, while in ontology of a. herzog’s mapped classes have more links to ontology and provide more analysis and application possibilities after the mapping is done. in table 3 data on ontology coverage by standard (covered) and standard coverage by ontology (covers) are provided. column "covered" defines what part of security ontology was used to map certain standard while column "covers" defines what percentage of security standard was mapped to the security ontology. the property "covers" is more important in this research as it provides information on how well the ontology is capable to present certain security standards in the knowledge database. the analysis of security ontology and standard coverage revealed that ontologies of a. herzog and s. fenz are not capable to fully cover none of analyzed security standards: only security standards with small number of controls or requirements can be mapped with security ontology to cover more than 50% of standard controls; security standards with more than 100 controls or requirements can not be mapped to a. herzog’s and s. fenz’s security ontologies to cover more then 30% of standard controls or requirements. this shows the fact that these two security ontologies do not have all necessary concepts to be fully mapped to security standards. security ontology for adaptive mapping of security standards 885 analysis of concepts of security ontologies to be employed to map security standard revealed that just a small part (5-18%) of classes from a. herzog’s and s. fenz’s ontologies are mapped directly to security standards. this number could be improved by providing more detailed concept of relationship, however it allows defining what part of ontology is directly related to concepts, mentioned in security standards. security ontology, created by s. fenz was able to cover a larger part of analyzed security standards than a. harz’s ontology. the biggest difference (29% and 19%) was noticed in pci dss standard. this could be an argument to chose s. fenz’s security ontology if a company is working with psi dss standards, while coverage differences for other analyzed standards are minor. however to cover 29% of psi dss standard is not enough to represent it. a new security ontology with more security concepts could help to improve the situation and would allow mapping of bigger parts of security standards. 4 new security ontology as s. fenz’s and a. herzog’s ontologies have low security standard coverage and are not the excellent choice for adaptive mapping of security standards we have created a new general purpose security ontology, which would extend these two ontologies and would be more suitable for adaptive mapping of security standards. our ontology has 5 top level classes (see. fig. 3): asset, countermeasure, organization, threat and vulnerability. these 5 classes are the most basic in security area and are detailed in lower levels. figure 3: top level structure of proposed security ontology asset class describes both tangible and intangible asset an organization can have. we describe this class more appropriately than other security ontologies do with the addition of more knowledge on used data by the organization, location and other equipment, owned or used in the company and related to organization’s security. the intangible asset is divided into data and software (see fig. 4), while inner structure describes various types of data and software. the tangible assets are structured to subclasses of movable and unmovable asset (see fig. 4). immovable asset describes location and building concepts and main elements which can be found in it. we structured movable assets into 4 subclasses: alarm systems and detectors; furniture; it components; utilities. these 4 categories allows the creation of more links to security standards by defining what kind of assets are involved into certain controls (who is at risk, who has a vulnerability etc.). countermeasure and threat classes are described pretty well in a. herzog’s ontology, therefore we made minor changes to it and use similar structure and components as a. herzog did. the need for more organizational concepts arose during the mapping of security standards to a. herzog’s and s. fenz’s security ontologies. these two ontologies have a poor description 886 s. ramanauskaitė, d. olifer, n. goranin, a. čenys figure 4: basic hierarchy of asset concept of organization structure and policies, while the companies’ information security policy is the most important to ensure its security. as subclasses of organization department, personnel and policy concepts were distinguished (see fig. 5). precise control description of security standards can be achieved if links to certain executor can be made. therefore department and personnel classes were added and detailed to distinguish plausible types of departments and positions in it. cobit is a framework [27], which describes the best ideas for information technology management, quality, evaluation and improvement. therefore we adopted cobit 5 framework into our ontology, by defining it policy class as organization policy subclass, where all cobit 5 ideas are detailed (see fig. 5). the cobit 5 framework was exercised as it is in it policies class. this guaranties the intuit of ontology usage to those, who is familiar with cobit framework. meanwhile in order to propose multiple views and ways to find necessary concepts in the ontology, more subclasses were added to policy class (see fig. 5). these classes should present more general policies of the organization however most of them have relations to classes of it policies class (cobit 5 framework). figure 5: basic hierarchy of organization concept vulnerability class was not detailed properly in a. herzog’s and s. fenz’s ontologies as well. s. fenz provides a list of vulnerabilities describing individuals with no structure, while a. herzog describes simply basic types of vulnerabilities. therefore we extended vulnerability class by dividing it into code vulnerabilities, configuration vulnerabilities, design vulnerabilities, policy vulnerabilities and transfer vulnerabilities (see fig. 6). those classes are detailed to reflect the basic security vulnerabilities, however they are more structured than in s. fenz’s ontology, to make it more intuitive and simpler to visualize. to use this ontology as a base for adaptive mapping of security standards a clear and intuitive security ontology for adaptive mapping of security standards 887 figure 6: basic hierarchy of vulnerability concept ontology structure has to be maintained. we optimized the tree structure of the ontology, therefore now it has 1795 classes, average depth of class tree is 6,5 (has maximum up to 9 depth of class tree) and average branching factor of class tree is 4,8 (has from 1 to 18 subclasses). such a structure is more viewable in tree structure and should be more intuitive for ontology users (see fig. 7). figure 7: structure fragment of proposed security ontology and adaptive mapping data in amss (created tool for adaptive mapping of security standard) while structure optimization of new security ontology is more important to ensure user friendly usage and understanding, new concepts allowed a better coverage of security standards. we do not provide direct list of controls and use similar ontology structure for standard mapping as a. herzog therefore security standard mapping to this ontology has to be done by defining more than one relation to ontology. this mapping property is useful to analyze and to map security concepts in different standards. iso27001, pci dss, issa 5173 and nistir 7621 security standards were specified and mapped to it in order to evaluate its suitability to map security standards. using this ontology as a base for adaptive mapping, 80% of iso27001, 100% of pci dss, issa 5173 and nistir 7621 standards were mapped to the ontology (see table 3). the 100% mapping of iso27001 standard was not achieved because we did not mapped very specific requirements in security standard (like security properties of used operating system etc.) to more abstract in our ontology. 888 s. ramanauskaitė, d. olifer, n. goranin, a. čenys table 3: coverage of ontology to standard and standard to ontology ontology/standard coverage standard s. fenz a. herzog proposed ontology covered covers covered covers covered covers iso27001 35/311 (11%) 23/133 (17%) 26/460 (6%) 19/133 (14%) 130/1795 (7%) 107/133 (80%) pci dss 42/311 (14%) 48/165 (29%) 25/460 (5%) 32/165 (19%) 132/1795 (7%) 165/165 (100%) issa 5173 31/311 (10%) 7/12 (58%) 29/460 (6%) 6/12 (50%) 15/1795 (1%) 12/12 (100%) nistir 7621 14/311 (5%) 8/10 (80%) 21/460 (5%) 8/10 (80%) 19/1795 (1%) 10/10 (100%) this ontology and mapping of these 4 security standards to it can be used to generate adaptive maps between any of the two mapped security standards or integrated standard can be created with the usage of any set of mapped security standards without the necessity to map two security standards directly. as our proposed security ontology can cover a larger part of concepts in analysed security standards (the average coverage of these 4 security standards is 92%, while s. fenz’s average coverage of these security standards is 27%, a. herzog 20%) the adaptive mapping of security standards will be more precise by applying it as a base ontology. however the ontology does not cover all standards by 100%, therefore should be improved to get even bigger precision of adaptive mapping. 5 conclusions and future works general comparison of g. denker’s, a. herzog’s and s. fenz’s security ontologies has shown the necessity of user friendly ontology structure all three ontologies have classes, with more than 25 subclasses in them. such ontology could be difficult to use for visual presentation or quick knowledge search. ontometric methodology allows a more precise judgment on security ontologies rather than general comparison, because it enables an evaluation of the content of compared ontologies. however the evaluation marks are very dependable on the evaluator’s opinion and requirements for the ontology. evaluation of ontologies’ ability to be mapped to security standards is a more suitable measurement to choose the base ontology for adaptive mapping of security standards comparing to ontometric. in order to evaluate ontologies’ suitability to map different security standards we compared percentage of concepts in security standard (iso 27001, pci dss, issa 5173 and nistr 7621) which can be mapped to security ontology. this research revealed there are no security ontologies, that would be able to map at least 50% of any security standards we have analyzed. this fact implies the necessity of new or modified ontology, which could be used to present larger parts of knowledge, used in security standards. we proposed a new security ontology, by integrating concepts of cobit framework, part of classes of a. herzog’s and s. fenz’s ontologies. this new ontology increased the coverage of security standards. using this security ontology, from 80% to 100% of analyzed security standards (iso 27001, pci dss, issa 5173 and nistr 7621) can be mapped to it. this percentage can be increased even more with the addition of more specific (related to payment cards, law and standard requirements etc.) concepts to this ontology. the proposed security ontology has a more balanced tree structure as well, which increases its visualization possibilities. security 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[27] isaca (2013). cobit 5: a business framework for the governance and management of enterprise it. [28] hofherr m. (2011). mapping iso27001 <>pci dss 2.0, forinsect, http://www.forinsect.com/downloads/mapping-iso27001-pci_public.pdf. int j comput commun, issn 1841-9836 9(1):24-36, february, 2014. the parallel one-way hash function based on chebyshev-halley methods with variable parameter m. nouri, m. safarinia, p. pourmahdi, m.h. garshasebi mahdi nouri department of electrical engineering, iran university of science and technology (iust), tehran, iran mnouri@mtu.edu mahyar safarinia, payam pourmahdi electrical engineering department a.b.a institude of higher education, qazvin, iran mahyar1986@gmail.com, payamict67pm@gmail.com mohammad hossein garshasebi communication engineering department basir institude of higher education, qazvin, iran e-mail: mh.garshasebi@chmail.ir abstract: in this paper a parallel hash algorithm construction based on the chebyshev halley methods with variable parameters is proposed and analyzed. the two core characteristics of the recommended algorithm are parallel processing mode and chaotic behaviors. moreover in this paper, an algorithm for one way hash function construction based on chaos theory is introduced. the proposed algorithm contains variable parameters dynamically obtained from the position index of the corresponding message blocks. theoretical analysis and computer simulation indicate that the algorithm can assure all performance requirements of hash function in an efficient and flexible style and secure against birthday attacks or meet-in-the-middle attacks, which is good choice for data integrity or authentication. keywords: hash function; chebyshev-halley methods; two-dimensional coupled map lattices; spatiotemporal chaos; chaotic nonlinear map; variable parameter. 1 introduction by the rapid development of internet, the security is essential for modern communication [1][2]. hash functions play an essential role in many areas of cryptographic applications such as digital signature, random number generation, data source authentication, key update and derivation, message authentication code, integrity protection and malicious code recognition and, etc. generally, hash functions can be classified into two categories [3,4]: unkeyed hash functions with a single input parameter (a message) for data integrity, and keyed hash functions, usually known as message authentication code (mac), with two distinct inputs. conventional hash functions, such as md5 and sha, involve logical operations or multi-round iterations of some available ciphers. although each step of the former is simple, the number of processing rounds could be massive even if the message is very short. recently, spatiotemporal chaos has been attracting more and more interests among researchers engineering. after the conventional hash function such as md5, sha was successfully attacked, the research on the design of secure and efficient hash function remains a research hotspot. compared with simple chaotic maps, spatiotemporal chaos possesses two additional advantages for cryptographic purpose. due to the finite computing precision, chaotic orbits will eventually become periodic. in particular, the period of chaotic orbits generated by a system with a large number of chaotic coupled oscillators is too long to be reached in practical communications. the period of spatiotemporal chaos is longer than that of simple chaotic maps [7]. therefore, periodicity can be practically avoided in spatiotemporal chaotic systems [8,29,30]. chaotic systems have vital characteristics like ergodic, mixed copyright © 2006-2014 by ccc publications the parallel one-way hash function based on chebyshev-halley methods with variable parameter 25 and sensitive which are so important in this area [3,5]. some algorithms for one-way hash function based on chaotic map have already been brought forward [11,12]. most algorithms for chaos-based hash function proposed by existing articles are based on dissipative chaotic systems. but the dissipative chaotic systems can lead to many hidden threats in the practical application for secure communication because of their potential warning. when the dissipative chaotic systems were used in the application of encryption, if the attacker gets a continuous of plaintext-ciphertext pairs, and the length of ciphertext meets certain conditions, the attacker cannot predict by reconstructing through ciphertext without attractor structure for the conservative chaotic systems [5,13,14,31,32]. so the conservative chaotic systems with high security are ideal model in cryptography application. in this paper the aim is to design an unkeyed hash function based on spatiotemporal chaos, which has high efficiency. in this algorithm the entire message blocks perform some rounds of iteration through standard map. one output of the iterations of each round determines one of the initial values of the next round is the output of previous block. the method of this design enhances the diffusion of hash function, and overcomes the inherent defects of dissipative chaotic systems. so the hash algorithm has a higher security. theoretical analysis and computer simulation show that this algorithm has good effect in anti-conflict and avalanche effect. this algorithm is easy to express and to satisfy all the performance requirements of hash function in an efficient and flexible manner. 2 feasibility of hash function construction based on proposed 2.1 nonlinear equations based on newtons method non-linear equations are an important and basic method [15], which converges quadratically. a family of third-order methods, called chebyshev halley methods [16], is written as xn+1 = xn − f (xn) f′(xn) (1) where lf (xn) = f′′(xn)f (xn) f′(xn) 2 (2) this family includes the classical chebyshevs method (cm), (α = 0) , halleys method (hm) (α = 1/2) and super-halley method (shm) (α = 1)) (for the details of these methods, see [17-18] or a recent review [19]).in order to improve the local order of convergence, grau and daz-barrero[20] propose an improvement of chebyshevs method with fifth-order convergence x̃n+1 = xn − ( 1 + f′′(xn) (f (xn) + f (xn+1)) 2f′(xn) 2 ) f (xn) + f (xn+1) f′(xn) (3) analysis of convergence shows that the error convergence can be modeled with sixth order of error [10]. ẽn+1 = [ (6−4β)c22 −3c3 ][ 2 (1−α)c22 − c3 ] e5n + o ( e6n ) (4) 2.2 the chebyshev-halley method the family includes the classical chebyshevs method (cm) (α = 0) , halleys method (hm) (α = 1/2) and super-halley method (shm) (α = 1) (for the details of these methods, see [9,10] or a recent review [11]).here the logistic map is taken as the local map, given as f (x) = µx(1−x) (5) where xi,yi ∈ [0,1] and µ , α are the controlled variables. the map is nonlinear and the parameter ensures that the map runs in a chaotic status .the chaotic nonlinear map also has the same belongings to proposed map that are fit for composing hash function. the form of the map is complicated and the equation involved is nonlinear. figure 1 shows the simulation of the chaotic nonlinear map iterating 64 26 m. nouri, m. safarinia, p. pourmahdi, m.h. garshasebi times with the initial values x0,y0 = 0.3423 and parameter µ = 60.5 . the map has some properties, which are appropriate for constructing the hash function, such as initial value sensitivity and parameter sensitivity, with variable parameter valued in the interval (0,120). figure 1 & 2 displays the chaotic iteration property with variable parameter first iteration valued in the interval (0,64), which initial values are x0,y0 = 0.3423. figure 1: b figure 2: b figure 1 & 2 : iteration property with changeable parameter first iteration and µ and x0,y0 = 0.3423 3 hash function based on standard message expansion is significant and necessary; because it greatly improves the sensitivity of each bit in original message to the final hash value [14]. the plaintext is an arbitrary message that is conveyed in a matrix m, for simple enlightenment of the extended message. assume that m is a n × 16 plain message matrix, each element with a size of 32 bits. 1) padding the message: the purpose of padding is to ensure the padded message being a multiple of 128 bits. suppose the total length of message is w bytes, compute d = (w mod 64), 0 ≤ d ≤ 12 . pad as follows: if 12 ≤ d < 16 then pad 12d bytes, otherwise pad 28d bytes, the bytes been padded come from the head of message. the last four bytes are padded with message length. this method ensure at least one byte head of message been padded. figure 3: a figure 4: b 2) parsing the padded message : the padded message is parsed into n 128-bit blocks, m1,m2, . . . ,mn , mj(1 ≤ j ≤ n) is parsed into four 32-bit words mj = [mj,1,mj,2,mj,3,mj,4] (6) the parallel one-way hash function based on chebyshev-halley methods with variable parameter 27 figure 3 & 4 ; the whole algorithm ; a) all iterations of hash function structure . b) first iteration of hash function . input of the algorithm can have an input of arbitrary length output of the algorithm has a fixed length of 128 bits. give a message m with length l. take each letter of m as a plaintext block. transform each plaintext block to the corresponding ascii numbers; the ascii numbers create the xj,yj which are the inputs of chaotic nonlinear map. compression function inputs consider 16 lattice spaces. let the initial iterative value of these inputs are:  xj,1 = (mj,1 103 ) + (mj,2 106 ) + (mj,3 109 ) + (mj,4 1012 ) xj,2 = (mj,5 103 ) + (mj,6 106 ) + (mj,7 109 ) + (mj,8 1012 ) xj,3 = (mj,9 103 ) + (mj,10 106 ) + (mj,11 109 ) + (mj,12 1012 ) xj,4 = (mj,13 103 ) + (mj,14 106 ) + (mj,15 109 ) + (mj,16 1012 ) from {xi} and {yi} , 4 groups of (yi) can be reached. determine the 32-bits hash value by the position of the coordinates (yi) falling into the region of [0, 1), then, finally, juxtaposes these bits from left to right to get a 128-bit hash value.[28] table 1 algorithm for generating the hash 4 performance analysis the proposed algorithm is used to perform hash simulation under the following five kinds of conditions: condition 1: a hash function is a fundamental building block of information security and plays an important role in modern cryptography. it takes a message as input and produces an output referred to as a hash value. generally, hash functions can be classified into two categories: unkeyed hash functions for data integrity, and keyed hash functions, usually known as message authentication code (mac). condition 2: change the first character a in the original message to d. condition 3: change the word function in the original message to functions. condition 4: change the full stop at the end of the original message to a comma. 28 m. nouri, m. safarinia, p. pourmahdi, m.h. garshasebi condition 5: add a blank space to the end of the original message. the corresponding hash values in hexadecimal format are given as follows, followed by the equivalent number of different bits compared with the hash value obtained under condition 1: condition 1 : 0484fcd104d3614104d5a185002832e9 condition 2 : 3d87661b4f87f263faef3dad5e7aab condition 3 : 96a08482053a5537316d50181dfb7640 condition 4 : d700eae9701a4a55680c96a133f3b023 condition 5 : 17f91fd2d4c22388c74c0811f5874687 figure 5 : distribution of changed bit number bi this kind of test is performed n times, and the corresponding distribution of changed bit number is plotted in figure 5 , where n = 10,000. obviously, the changed bit number corresponding to 1 bit changed message concentrates around the ideal changed bit number, i.e., 64 bits. it indicates that the algorithm has very strong capability for diffusion and confusion. 4.1 statistical analysis of diffusion and confusion confusion and diffusion are two fundamental design criteria for encryption algorithms, including hash functions. shannon introduced diffusion and confusion in order to hide message redundancy [18,19]. hash function, like encryption system, requires the plaintext to diffuse its influence into the whole hash space. this means that the correlation between the message and the corresponding hash value should be as small as possible. diffusion means spreading out the influence of a single plaintext symbol over many ciphertext bits so as hiding the statistical structure of the plaintext. confusion means the use of transformations to complicate the dependence of ciphertext statistics on plaintext statistics. in the hash value in binary format each bit can be only 0 or 1. therefore, the ideal diffusion effect should be that any tiny change in the initial condition leads to a 50% changes, probability of each bit. usually six statistics are defined as follows: minimum changed bit number: bmin = min ( {bi}n1 ) (7) maximum changed bit number: bmax = max ( {bi}n1 ) (8) mean changed bit number: b̄ = 1 n n∑ 1 bi (9) mean changed probability: p = b̄ 128 ×100 (10) standard variance of the changed bit number: ∆b = √√√√ 1 n −1 n∑ i=1 ( bi − b̄ )2 (11) the parallel one-way hash function based on chebyshev-halley methods with variable parameter 29 standard variance: ∆p = √√√√ 1 n −1 n∑ i=1 ( bi 128 −p )2 ×100 (12) where n is the total number of tests andbi is the number of changed bits in the ith test. the following diffusion and confusion test has been performed that: a paragraph of message is randomly chosen and the corresponding hash value is generated. then a bit in the message is randomly selected and toggled and a new hash value is generated. finally, the two hash values are compared with each other. this kind of test is performed n times, and the corresponding distribution of changed bit number is plotted in fig. 8, where n = 10,000. perceptibly the changed bit number corresponding to 1 bit changed message concentrates around the ideal changed bit number, i.e., 64 bits. it signifies that the algorithm has very strong capability for diffusion and confusion.the test results in n = 256, 512, 1024, 2048, 10,000 are listed in table 1 respectively. based on the results, the following conclusion was found that, the mean changed bit number b̄ and the mean changed probability p are both very close to the ideal value 64 bits and 50% . while ∆b and ∆p are extremely small, which indicate the diffusion and confusion capability are very stable. therefore a small difference in the plaintext will cause great changes in the hash value, which donates to the high plaintext-sensitivity of our hash function. this property is important in maintaining the secrecy against statistical attacks. 4.2 collision analysis birthday-attack collision resistance and birthday-attack are lying to each others roots. both are derived from the probability problem that two random input data are found to hash to the same value. the results of the proposed algorithm are shown in table 2: table 2 statistical performance of proposed algorithm for 128 bits outputs with α = 0 and µ = 60.5 collision resistance and birthday-attack are lying to each others roots. both are derived from the probability problem that two random input data are found to hash to the same value. given a function f the goal of the attack is to find two different inputs f (x) such that f (x1) = f (x2). such a pair x1,x2 is called a collision. the method used to find a collision is simply to evaluate the function f for different input values that may be chosen randomly or pseudo randomly until the same result is found more than once. because of the birthday problem, this method can be rather efficient. specifically, if a function f (x) yields any of h different outputs with equal probability and h is sufficiently large, then we expect to obtain a pair of different arguments x1,x2 with f (x1) = f (x2)after evaluating the function for about 1.25h different arguments on average. from a set of h values we choose n values uniformly at random there by allowing repetitions. let p(n;h) be the probability that during this experiment at least one value is chosen more than once. this probability can be approximated as p(n;h) ≈ 1−e− n(n−1) 2h ≈ 1−e− n2 2h (13) let n(p;h) be the smallest number of values we have to choose, such that the probability for finding a collision is at least p. by inverting this expression above, we find the following approximation 30 m. nouri, m. safarinia, p. pourmahdi, m.h. garshasebi n (p; h) ≈ √ 2hln 1 1−p (14) and assigning a 0.5 probability of collision we arrive at: n (0.5; h) ≈ 1.1774 √ h (15) let q(h) be the expected number of values, it must be chosen before finding the first collision. this number can be approximated by q (h) ≈ √ π 2 h (16) as an example, if a 64-bit hash is used, there are approximately 1.8 × 1019 different outputs. if these are all equally probable, then it would take only approximately 5.1×109 attempts to generate a collision using brute force. this value is called birthday bound [4] and for n-bit codes it could be computed as 2n−1 [3]. table shows number of hashes n(p) needed to achieve the given probability of success, assuming all hashes are equally likely. the results of the proposed algorithm are shown in table 3: table 3 probability of random collision of proposed algorithm for various bits outputs the state of proposed is related to each message bit. by iterations, significant changes are obtained at the final state even if there is only a one-bit change in the message. according to the above analysis, the proposed algorithm is secured against statistical attacks. for birthday-attack, the security of the cryptosystem is determined by the length of the hash value, which is 128-bit in this proposed function. according to the definition of birthday-attack [4,22,23] , the attack difficulty is 264 meet-in-the-middle attack meet-in-the-middle attack [10,11] means to find a contradiction through looking for a suitable substitution of the last plaintext block. in this paper we focus on random routing solutions because their consciousness is simple. the proposed algorithm is principally the same analyzed in [1,9].to clarify its operations, let ns represent the current attack represents the sections within the attacker belongs of mi, and ni represents the attacker in φ{ns}which is in the straight path between ns and attacked n0. the basic idea of the random routing algorithm considered in this paper is very simple as it defines the following two phases: 1. basic parameter of the principal phase is the probability p∗actually, the current mi immediately selects ns, which represents the mi in the shortest block near the attacked n0, as the next mi with probability p∗. if this occurs, then the algorithm stops here, otherwise, it performs the second phase. 2. in the second phase, the current mi randomly selects any of the attackers in φ{ns}as next one. note that according to such algorithm mi selects ns, that is the attack in the shortest path with probability ps = p ∗ + (1−p∗)/ |φ{ns}| (17) the parallel one-way hash function based on chebyshev-halley methods with variable parameter 31 where φ{ns} is the number of next mi of ns ; whereas any given one belonging to φ{ns}−{n0} is selected with probability(1−p∗)/ |φ{ncr}|. note that, the higherp∗, the higher the probability that packets will follow the shortest path, and, vice versa. accordingly, on the one hand, if p∗ decreases it can be expected longer end-to-end routes between packets source and the attacked n0 and therefore larger energy consumption. however, on the other hand when p∗ becomes smaller, it is more difficult for the attacker to intercept messages sent by the actual reader of target, and therefore, the level of security increases. in order to evaluate the impact of the countermeasures to the meen-in-the-middle attack, the output of the reading process is compared in normal conditions and when the meen-in-the-middle attack is performed. to evaluate the attack impact of the disturbing bits on the performance of the proposed scheme, in figure 6, the bit error rate is shown versus the normalized amplitude of the disturbing bits. note that when the normalized bits reach the unitary value, bit error probability is 0.5, which means that the meen-in-the-middle attack is not effective. observe, however, that the increase in energy expenditure goes as the square of the disturbing bits. figure 6 : a) bit error probability versus the normalized length of the disturbing block, b) block percentage of the shortest block included in the end-to-end path versus the probability p* accordingly, appropriate tradeoff must be identified to reduce energy consumption. in order to evaluate the effects of the adoption of random routing in figure 6, it is shown the average number of blocks that are included in both the shortest block and in the block obtained by using random routing versus the value of the probabilityp∗. obviously, this value increases as p∗ increases and, therefore, the proposed scheme becomes less effective. however, it can be expected that as p∗ increases, the energy consumption decreases. this is indeed demonstrated in figure 9 where shows the average block length obtained by using random routing versus the value of the probabilityp∗. note that as p∗increases, the average path length decreases. note that figure 7 have been obtained by randomly selecting the positions of the main and the attacked. figure 7 : average block length obtained by using random routing versus the value of the probability p* 32 m. nouri, m. safarinia, p. pourmahdi, m.h. garshasebi collision test the following test has been performed for the quantitative analysis on collision resistance [24]: first, the hash value for a paragraph of randomly chosen message is generated and stored in ascii format. then a bit in the message is selected randomly and toggled. a new hash value is then generated. the two hash values are compared and the number of ascii characters with the same value at the same location is counted. moreover, the absolute difference between the two hash values is calculated by the following formula: d = n∑ i=1 t(ei)− t(e′i) (18) where ei and e′i is theithascii character of the original and the new hash value, respectively. the function t( ) converts the entries to their equivalent decimal values. this kind of collision test is performed 10,000 times. the maximum, mean, and minimum values of d are listed in table 4 . the distribution of the number of ascii characters with the same value at the same location in the hash value is given in table 6. notice that the maximum number of equal characters is only 2 and the collision probability is very low. table 4 absolute difference of two hash value due to the progresses in technology, nist plans to replace sha-1 to longer and stronger hash functions (sha-224, sha-256, sha-384 and sha-512) by the year 2010 [25], as long hash values are needed in the future. to produce a long hash value, two simple modifications are introduced in the proposed algorithm: increase the size of proposed or extract more bits. for example, 256,512,1024-bit hash values are obtained by using a proposed model with extracting 8 hexadecimal numbers, by extracting 16 hexadecimal numbers and extracting 32 hexadecimal numbers from each lattice value in the origin proposed respectively which is shown in table 5. table 5 statistical performance of proposed algorithm for different output length 512 bits figure 8 : distribution of the number of ascii characters with the same value at the same location in the hash value. the parallel one-way hash function based on chebyshev-halley methods with variable parameter 33 hereinto statistic analysis of diffusion and confusion for variable parameter is considered,is the controlled variable. the certain critical value of µ is 0.7 . the followed is some conclusion on chaotic nonlinear map. by varying this parameter as can be seen in the figure 9, the statistic analysis of diffusion and confusion are near the same for changing this parameter and it means this algorithm has a stable manner in all variable parameters. figure 9 : a) mean changed bit number for changing µ, b) standard variance of the changed bit number for changing µ ,c) standard variance for changing µ. table 6 maximum number of equal characters at the same location in the hash value based on the result in tables 1 and 6, the statistical performance of the proposed algorithm is as good as that of md5. moreover ∆b and ∆p of the proposed algorithm are smaller than those of md5, which indicate that the result is more stable. based on the data in tables 4 and 6, the proposed algorithm has a bigger exact difference between two hash values than md5. meanwhile, the maximum numbers of equal character of md5 and the proposed algorithm are the same, i.e., only two, which indicates the collision chance is very low. 34 m. nouri, m. safarinia, p. pourmahdi, m.h. garshasebi speed analysis since the proposed algorithm is based on a complex chaotic system, complicated computations are needed in producing a hash value and so this algorithm is slower than md5 algorithms in [27]. nevertheless, it still owns a sufficiently high hashing speed for practical use. the proposed model is a kind of spatiotemporal chaos. compared with the algorithm in [26], which is also based on spatiotemporal chaos, the proposed algorithm has a much higher efficiency. these two algorithms are applied to use matlab which are running on a personal computer with a pentium iv 2.66 ghz processor and 4 gb ram. as monitored from these figures, the execution time of the proposed algorithm is much shorter, especially when the message is long. figure 9: computation time comparison between the proposed, md5 and sha-2 hash function for various normalized block length 5 conclusion based on the chebyshev halley methods with variable parameters, a parallel hash algorithm structure is proposed and analyzed. the algorithm alters the expanded message blocks into the equivalent ascii code values. the two initial inputs and steps of iterations are generated by last round of iteration, which iterates the chaotic nonlinear map and wholly increases the rise influence of hash function, and makes the final hash value has high sensitivity to the initial values, increase the security of hash function. the analysis designates that the algorithm can meet all the requisites of the hash function efficiently. and the algorithm is easy to realize a swift and practical program to hash function structure. the length of the final hash value generated by this algorithm is 128 bits. the two primary inputs and steps of iterations are generated by last round of iteration, which iterates the chaotic nonlinear map and wholly increases the rise power of hash function, and makes the final hash value has high sensitivity to the initial values, increase the security of hash function. theoretical analysis and computer simulation show that the proposed algorithm presents numerous interesting features, such as high 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[32] nouri, m. ; zeinolabedini, z.; farhangian, n. ; fekri, n.; analysis of a novel audio hash function based upon stationary wavelet transform, 2012 6th international conference on application of information and communication technologies (aict), 1 6, 2012. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 5, month: october, year: 2020 article number: 3879, https://doi.org/10.15837/ijccc.2020.5.3879 ccc publications artificial neural network and a nonlinear regression model for predicting electrical pole crash c. montt, j.c. castro, a. valencia, a. oddershede and l. quezada cecilia montt*, alejandra valencia department of transport engineering, pontificia universidad católica de valparaíso, 2340000 valparaiso, chile *corresponding author: cecilia.montt@pucv.cl; alejandra.valencia@pucv.cl juan carlos castro, department of electrical engineering, pontificia universidad católica de valparaíso, 2340000 valparaiso, chile j.c.castro.arredondo@gmail.com astrid oddershede, luis quezada department of industrial engineering, universidad de santiago de chile, 8320000 santiago, chile astrid.oddershede@usach.cl, luis.quezada@usach.cl abstract this paper presents the investigation about a problem situation that electric distributor companies are facing in chile resulting from transit accidents. the number of vehicle crashes to power distribution poles and street lighting has grown. this situation causes discomfort to citizen and mainly to the neighbors due to power cuts and even on occasion , losses of human lives because of the accident that have occurred. based on previous research, the accidents are not random nor chance dependent, but the majority of transit accident follow parameters or variables from the scenery where it occurs. in order to analyze the variables and the degree this variables affect the accidents, a model of perceptron and multipercetron artificial neural networks and a multiple nonlinear regression model are proposed. an empirical study was made; collecting data from a distributor company and from chilean national traffic safety commission, where the more frequent variables involved in accidents were determined to develop the mentioned models. these variables were investigated and also their influence on the occurrence of vehicle crashes to power distribution poles could be confirmed. with this data, the prediction of post crashes was developed, where through the application of the neural network and multiple nonlinear regression, revealed 95.7% of acceptable predictions. this study will bring benefits to power distribution companies considering a risk index in the streets, based on the number of crashes of poles per street; this will allow optimal decisions in future electrical distribution projects avoiding critical areas. keywords: artificial neural networks (ann), prediction, nonlinear regression. https://doi.org/10.15837/ijccc.2020.5.3879 2 1 introduction currently, there is an interest in investigating the reason for the existence of traffic accidents of crash to street lighting poles, which causes discomfort to neighbors due to power cuts, losses of human life and others. in addition, the energy distribution companies incur in not sold energy costs. one of the electricity distribution company in chile, records within a database those crashes, mainly, the types of unfortunate events that occurred in the distribution network; such as crashed posts, crashed post tensioners, general failures. a large number of events or events that are constantly being evaluated to improve their prediction and thus prevent them from being repetitive. from the records, it has been observed that traffic accidents of the crash to street lighting poles are in first place in the ranking for most common failures in [4] with 10.347% after failures due to new works or engineering modifications. therefore, the priority of the designers in electrical distribution is to address the problem effectively. in addition, the benefit takes a turn towards the community, helping the designer to discern the best layout and location of the posts in national goods of public use since sometimes the design guidelines of the roads where posts are installed lack security measures [? ]. on the other hand, according to car accident specialists, they say that these are not fortuitous events [9], inevitable, unpredictable, depending on luck, but instead, the vast majority of cases follow characteristic parameters of the scenario where it happens; that is, the accident as a whole is always a consequence of some avoidable failure and to some extent predictable system. so to combat the problem, the main thing will be to understand how the environment reflects the reaction to a car driving problem, environment that will be studied in this work. given the above, the objective of this work is to predict the crashes to street lighting post in points of interest in a region in chile through a classic mathematical model and another based on neural networks, to reduce the consequences on the community. on the other hand, the variables that affect crashes accidents to post are studied, which in this case are, among others, road material, number of crossings in the study section, number of tracks. 2 state of the art during the last 13 years, several investigations related to traffic accidents have been carried out in different cities of the world using computational intelligence (ci) techniques. since about 30 years ago studies were carried out with mathematical models, which currently allows us to visualize variables and significant patterns that help to better identify the conditions in which traffic accidents occur, considering that each incident involves the conjugation of a large number of variables, which determine the number of accidents [3]. one of these ci techniques is artificial neural networks (ann), which consists of an algorithm that emulates the behavior of the animal nervous system. technique studies have shown good results in both prediction and classification of data series. this will fulfill the main work for the development of the model to be used in this study [16]. ann have been utilized in many fields, such as in the prediction of air pollution [14], robot control systems to avoid obstacles [6], training of robots [7], control of parallel robots [11], real power systems data applied for short-term daily load forecasting [5], information processing and pattern recognition [19], pattern recognition in statistical process control [18]. in the case of transport, ann have been used in many applications such as accident-mapping [8] and understanding gender characteristics of older driver accidents in west midlands of united kingdoms [2]. they has also been used in traffic forecasting like the case of morocco [17] and traffic accident casualty like the case of sudan [1]. regarding studies related to this problem such as those mentioned above, in addition to [9] and [10], we studied the best alternatives of factors or variables that describe traffic accidents, which are the best training strategies and that software are the busiest for the development of these models, and thus expedite the development of the prediction. a classic mathematical prediction model analyzed in [9] that was able to make predictions of the https://doi.org/10.15837/ijccc.2020.5.3879 3 risk of automobile collisions with a linear regression model, the prediction unit used was number of post crashes per kilometer of annual street, and its variables were, density of posts, slope of the street, average lateral length and vehicular flow. although the simplicity of the model contributed to poor predictions, the choice and lack of variables guided a future study of the measures to be taken to refine the prediction. neural networks take an important role, as an innovative tool, to give a prediction of events and statistical analysis. this was concluded in [16], where the authors developed the analogy between the functions of an artificial and biological neuron, demonstrating how an artificial neural network can learn through experiences, training strategies, learning functions and what configurations of neural networks are the most appropriate to address this problem in self-interest scenarios. resistant retro-propagation (rp) [21] is a faster algorithm, than the previous one, in pattern recognition problems. however, it does not work well in function approximation problems. the conjugated gradient scaling (cgs) [21] is an algorithm, which seems to work well in a wide variety of problems, particularly for networks with a large number of weights. because there is no pattern of configurations established by problems, it was decided to vary ann in the hidden layer and the number of hidden layers. given the above in olden, [13] a sensitivity analysis of the input variables is carried out, which in this case disease is predicted, the patient’s age, body mass and others are used. this sensitivity analysis was used in this work for the input variables involved. in montt et al. [12] neural networks were used, using the levenberg marquardt algorithm, to predict traffic accidents with serious and less serious injuries, for the people involved in them. 3 methodology from [4] and [20] all the failures in the distribution network and the consequences of these failures were obtained, as well as the most frequent causes that cause post failures during the last 3 years. in the second place, there are the posts crashes which concentrate 10.34% of the total number of failures registered in the last 3 years. it should be specified that the crashes to braces of post with 2.74%, usually end up knocking down the post itself, so for this study the clashes to the post braces are added to the post crashes since the cause is the crash of a vehicle against the distribution network. therefore, the percentage is added leaving 13.08% of the total events in the distribution network. to develop the prediction model, in order to describe the streets involved in the area where postcrashes could occur, it is necessary to have a set of variables. to develop the prediction model, you must have a set of variables, in order to de-scribe the streets analyzed, of the sectors where a post crashes occurs or not. due to this need, it was studied both in [15] and in [9] the parameters involved to use the models, these are: –number of curves (a); –zone (b); –number of posts (c); –road material [friction coefficient] (d); –number of crosses (e); –maximum inclination (degree) (f); –average inclination (degree) (g); –length [km] (h); –number of schools (i). because the number of descriptive variables of the problem was similar to those used in studies with low prediction quality [9], it was decided to make an analysis of other variables. additional variables were obtained through tools such as google earth, ito map and waze, mainly street descriptions, with which the following were obtained: number of curves; number of crosses; maximum inclination (degree), average inclination (degree). longitude [km] number of schools. for the years 2016, 2017 https://doi.org/10.15837/ijccc.2020.5.3879 4 and 2018, 230 streets were described, with the variables mentioned above. subsequently, the streets were randomly ordered, where 90% of the totality was used to train the network, called group a and 10% respectively to evaluate the learning of the model, called group b. this strategy was covered both in the model of multiple nonlinear regression and developed through artificial neural networks. 3.1 prediction using neural network the learning of an artificial neural network, corresponds to the part of a set of random synaptic weights, in order to look for a set of weights that allow the network to correctly develop a particular task. this is done through iterative processes where it evaluates the experiences provided (streets of the commune of valparaíso) and they are compared with the desired exits (number of crashes registered in the streets) described by input variables). after this it is performed an optimization of the iteration error and this is propagated back towards the weights of the network by readjusting them so that though the next iteration the error decreases, this concludes with a progressive learning. the configurations of artificial neural networks used, that is, the type of neural network used was that of perceptron and multipercetron; these are characterized by having layers of neurons (columns of neurons) generating connections between neurons in a single direction towards all the neurons of the next layer as can be seen in the following figure 1: figure 1: artificial neural network architecture from the three configurations used, the best predictions were retro propagation. as for this, the relation (1) corresponds to the output of each artificial neuron, which explains how the inputs and each neuron are multiplied by a specific weighting (synaptic weight) and then added and then sent to the next neurons. a = (x t w ) = x1w1 + x2w2 + ... + xnwn = n∑ i=1 xiwi (1) as for the training methods used were levenberg-marquardt lm, rp retro-propagation resistant and conjugate gradient scg scaling, which are concerned with optimizing learning time. in this case the input variables of the model are: –number of curves; –zone (rural, urban); –roadway (bidirectional, bidirectional with center tray, unidirectional); –road material (concrete, asphalt, cobblestone, mixed, gravel, earth); –no.of crossings; –maximum incline (degree); –average inclination (degree); –length (km); –no. of schools; –no. of crashes. https://doi.org/10.15837/ijccc.2020.5.3879 5 a frequent complaint about neural network models is that they do not explain their results in a useful way. the problem is not the lack of information, but the abundance of information that is difficult to interpret. when training, neural networks provide a predicted output, for a postponed input, they can provide additional information in the form of connection intensities between elements. however, the latter information is of little use to analysts and managers who wish to interpret the results they have received. therefore, the study also developed a measure of the relative importance of the various input elements mentioned in the previous paragraph, the hidden layer elements, and this was also used to interpret the contribution of these components to the network results [13]. this is based on the following equation: si = j∑ j=1 xi,j wi,k (2) where i : variable to analyze sensitivity (those that were used to train the network, input variables); j : number of neurons in hidden layer; k : number of model outputs; w : weights between input layer and hidden layer; v : weights between hidden layer and output layer. these results produce a ranking to find the most influential variables in the prediction, once the ann has been trained. this would facilitate the practice of an excess of descriptive variables in the experiences or choice of variables that are not significant in the prediction. 3.2 prediction using nonlinear regression nonlinear regression generates an equation to describe the nonlinear relationship between a continuous response variable and one or more predictor variables. this equation will be described by functions specific to each input variable, be they polynomials, logarithms, parabolas, etc., depending on the individual analysis of each variable related to the crash to poles and to these multiplied by coefficients. this can be visualized in the equation (3): yx1,x2,...,xn = w + w1f (x1) + w2f (x2) + ...wnf (xn) (3) where wi : i ∈ 0, 1, .., n are coefficients that weigh each chosen function and thus propose a better final approximation. the functions f (x1), f (x2), f (x3), . . . , f (xn) are those that describe each variable with respect to its crashes post index. to study the behavior of the variables, the 9 variables under study were analyzed, the same ones used in the neural networks, in order to choose which functions describe the number of post crashes per year per street. to accomplish this, the excel tool was used, which also graphs the dispersion of data according to each variable, provides a trend line of data dispersion, with its respective equation and correlation coefficient r2. the coefficient of determination fulfilled of indicator function the best approximation function. in other words, as long as its value is closer to 1, this function has a better approximation to the recorded data set. to study the behavior of the variables, the same used in the neural networks, the representative functions of each variable were first obtained, looking for the best combination of these in which the error was minimal, so capital letters were assigned from the "a" to "i" to the functions of inputs and "j" in the output, obtaining the following non-linear regression equation (4): y = 12889.8 + 0.000211727e3 + 0.00872022e2 − 0.0168694e − 0.0000615644a6+ 0.00219875a5 − 0.0292012a4 + 0.177945a3 − 0.483011a2 + 0.740802a− 0.0679519h 4 + 0.187047h 3 − 0.196681h 2 + 22136.8d2 − 33781.8d + 0.381264i 4− 3.34419i 3 + 8.52872i 2 − 5.2624i + 0.262374c2 − 0.628695c − 0.00000000987981f 6− 0.00000086429f 5 + 0.000150924f 4 − 0.00683017f 3 + 0.137551f 2 − 1.27352f (4) this equation is obtained starting from only one variable, adding the missing ones, and discarding the combinations that cause an increase in the error, the eahdicf combination is obtained, which discriminates in the zone of location and average inclination as the variables that most deficiency contribute to the prediction. https://doi.org/10.15837/ijccc.2020.5.3879 6 4 results for group a, the neural network configuration that obtained the best predictions was the neural network of three hidden layers and 20 neurons, with predictions of 10.8% of average error. the best rated neural network with unknown data (group b) is that of 3 hidden layers and 10 neurons. primarily, greater importance is given to ann with better prediction in face of unknown data (group b). this is to prevent learning from becoming more widespread and prevent it from being "of memory". this is checked by analyzing the case of the three-layer neuronal network and 20 neurons. this network, while obtaining a 10, 8% of error in the prediction (the best in group a), learned the behavior of a fixed data sample and when faced with group b, this increased his average error to 52.5%. in the case of the neural network, 36 different configurations of neural networks were trained, where the best prediction, in streets not used in the training (unknown to ann), the network was structured. the candidate for a prediction of a known street was as follows: in the case of the neural network, 36 different configurations of neural networks were trained where the best prediction, in streets not used in their training (unknown to ann), was structured as follows: candidate for a prediction of a known street was as follows: –type of neural network: perceptron; –learning function: retro propagation; –error used in learning: mean square error; –number of hidden layers: 4; –number of neurons per hidden layers: 50; –activation function: sigmoid. the result of the artificial neural network, pearson’s correlation coefficient was used, to quantify the prediction quality, since this can be used to measure the degree of relationship of two variables as long as both are quantitative and continuous. in this case, the quality of ann prediction is shown, since there was only one street, out of a total of 23 unknown streets, it did not meet as a satisfactory prediction. in other words, we have 95.7% of acceptable predictions. regarding the correlation coefficient reached, it was 0.64. using the nonlinear regression model in terms of the acceptable or unacceptable prediction classification, it is governed by the same range described in the previous paragraph (ann). in this model, predictions were recorded and qualified as no acceptable, having 87% of acceptable predictions, with a correlation coefficient of 0.56. 4.1 results analysis the correlation coefficients and the counting of registered predictions with a range of non-acceptance are presented in table 1, with the prediction model of artificial neural networks and the non-linear regressions model: table 1: effectiveness rates of both models obtained artificial neural networks nonlinear regression model correlation coefficient 0.64 0.56 nro. of acceptance in prediction 1(4.3%) 3(13%) the predictions of a certain group of streets (23 streets) are obtained, each with its descriptive variables, record of shocks in the period of one year and the predictions made for both models. this allows us to visualize how accurate the predictions are individually, in addition, predictions can be highlighted where the prediction exceeds the actual number of registered shocks by 2 shocks and classified as predictions not satisfactory. finally, the percentage of streets that did not have a satisfactory prediction is obtained, highlighting the anns as the best alternative to anticipate these events. the predictions of a certain group of streets (23 streets) are obtained, each with its descriptive variables, record of shocks in the period of one year and the predictions made for both models. https://doi.org/10.15837/ijccc.2020.5.3879 7 this allows us to visualize how accurate the predictions are individually, in addition, predictions can be highlighted where the prediction exceeds the actual number of registered shocks by 2 shocks and classified as predictions not satisfactory. finally, the percentage of streets that did not have a satisfactory prediction is obtained, highlighting the anns as the best alternative to anticipate these events. 4.2 sensitivity analysis in this analysis, the impact of each of the input variables on the predictions is determined. for the above, the connection weight approach method was used. this analysis, through the reading of all the weights associated with each input, allows quantifying the final excitation within the neural network of each input variable. therefore, the greater the excitations or inhibitions (weights between neurons) of an input this will be qualified as “influential” in the prediction. the value of s is the sensitivity value that is obtained for each input. then a ranking is obtained of the most and less influential variables. finally, it was obtained that the 5 variables with the greatest contribution to the prediction were; the road (number of tracks), the number of crossings, the length of the street, number of curves and inclination of the road. sensitivity analysis was performed to determine the impact of each of the input variables on the predictions. for the above, the weight-on-connection approach method was used. in this case, equation (2) was used, where s is the sensitivity value that is obtained for each input, obtaining a ranking of the most influential variables that they are: road (number of tracks), the number of crossings, the length of the street, number of curves and inclination of the road 5 conclusions an alternative ann was used to make predictions of the risk present in the streets. the objective was to propose a mathematical model based on non-linear regressions, so it was developed successfully, obtaining predictions with a low error and a correlation coefficient 0.56, with 87% of acceptances. even though the index explains a satisfactory prediction, it does not exceed the capacity of the model with ann, which registered a 0.64 correlation coefficient, or in other words 95.7% acceptable predictions. with the sensitivity analysis, the effective property of the neural network was verified, considering or canceling variables that contribute or not, to the prediction performance respectively, this due to the function they perform of neuronal biases and the treatment that was carried out with the input variables. thanks to the results obtained, the designer can already be certain when discriminating on which street to install or not the pole. thus the problem can already be treated efficiently, with a 95.7% probability of obtaining an acceptable prediction. given the above, the prediction will be studied in the future using variables such as power outages due to the crash, state and place of installation of the pole and its distance from the road. funding this work was supported by the departments of transport and electrical engineering of the pontificia universidad católica de valparaíso, chile, and the department of industrial engineering of the universidad de santiago de chile. author contributions the authors contributed equally to this work. https://doi.org/10.15837/ijccc.2020.5.3879 8 conflict of interest the authors declare no conflict of interest. references [1] ali, g.; 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(2020). artificial neural network and nonlinear regression model for predicting electrical pole crash, international journal of computers communications & control, 15(5), 3879, 2020. https://doi.org/10.15837/ijccc.2020.5.3879 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 4, month: august, year: 2021 article number: 4345, https://doi.org/10.15837/ijccc.2021.4.4345 ccc publications novel machine learning approach for self-aware prediction based on the contextual reasoning a. daranda, g. dzemyda andrius daranda* vu institute of data science and digital technologies vilnius university, lithuania *corresponding author: andrius.daranda@gmail.com gintautas dzemyda vu institute of data science and digital technologies vilnius university, lithuania gintautas.dzemyda@mif.vu.lt abstract machine learning is compelling in solving various applied problems. nevertheless, machine learning methods lack the contextual reasoning capabilities and cannot be fitted to utilize additional information about circumstances, environments, backgrounds, etc. such information provides essential knowledge about possible reasons for particular actions. this knowledge could not be processed directly by either machine learning methods. this paper presents the context-aware machine learning approach for actor behavior contextual reasoning analysis and context-based prediction for threat assessment. moreover, the proposed approach uses context-aware prediction to tackle the interaction between actors. an idea of the technique lies in the cooperative use of two classification methods when one way predicts an actor’s behavior. the second method discloses such predicted action (behavior) that is non-typical or unusual. such integration of two-method allows the actor to make the self-awareness threat assessment based on relations between different actors where some multidimensional numerical data define the connections. this approach predicts the possible further situation and makes its threat assessment without any waiting for future actions. the suggested approach is based on the decision tree and support vector method algorithm. due to the complexity of context, marine traffic data was chosen to demonstrate the proposed approach capability. this technique could deal with the end-to-end approach for safe vessel navigation in maritime traffic with considerable ship congestion. keywords: self-awareness, threat assessment, contextual knowledge, contextual reasoning, decision tree, support vector machine, optics. 1 introduction the main difference between machine learning methods and human is that humans are using contextual information for reasoning, not just statistical data. in general, context supplies information about situations within which something exists or happens, which can help explain it. sometimes, it https://doi.org/10.15837/ijccc.2021.4.4345 2 is difficult to precisely define the context because it is based on knowledge that is not explicit for a given problem. the context depends directly on the actors and their experience. moreover, the same situation and context could be differently understood because the actors have a different experience in interpreting the situation. the knowledge that is relevant and could be used to understand the problem and help make a decision on threat assessment in a particular situation. it is defined as contextual knowledge [1]. contextual knowledge may be merely tied to the goal or task. however, if the goal becomes clear, contextual understanding can be proceduralized for decision making [2]. therefore, the decision-making process has a proceduralized context known by actors and is used for problem-solving. it is crucial because the same actions in different contexts could lead to different outcomes. the human can easily perceive and transfer the contextual knowledge to other humans, but the contextual knowledge is inappropriate for processing on the computer. therefore, the computer does not operate with context as an additional source of information. due to this issue, the context could be prepared partly as statistical data of the particular event or object. nevertheless, the use of contextual knowledge is extremely important to understand and solve a real-world problem. machine learning methods usually use numerical or visual data to train the model and make a prediction based on the trained model. many different machine learning methods can predict the outcome based on learned patterns from data. on the other hand, machine learning methods do not utilize the perception of the situation because this understanding is gained from experience. thus, the machine learning must cover every eventuality of possible situations. conversely, the human can make a proper threat assessment based on his life experience. the primary purpose of our research is to extract contextual knowledge from the statistical data set. also, the proposed approach can make predictions based on extracted contextual knowledge. this approach predicts the possible further situation and makes its threat assessment without any waiting for future actions. moreover, the proposed approach has the self awareness ability to make a threat assessment before any further action is completed. this ability allows for distinguishing between normal and unusual activities. also, it can be adopted from the point of view of any actor. this ability enables predicting and considering the other actors. this "understand "comes before any indications of possible outcomes and predicts the current situation. moreover, the proposed approach could evaluate its decision on the threat assessment and support system’s proposed decision to consider it on the same basis. this self awareness ability makes an effective decision-making process where the machine learning method could evaluate the threat assessment through its own or other actors’ views in different environmental contexts. our research is primarily inspired by a complicated and dangerous marine traffic environment. the marine traffic operates in significant density and on large scales. the vessel collisions could lead to enormous losses of human life and property. therefore, the issue of ensuring safety at sea is the main goal for the mariners. this task could be solved by providing timely, essential navigational information. on the other side, this navigational information could not be enough to avoid collisions or dangerous situations. therefore, the safe trip with the ship highly depends on the experience of navigators. the experienced navigator can predict a hazardous condition based on his long-term experience. so, the best way to prevent vessel collision is to anticipate all vessel possible routes and actions of the maneuver. at first, the navigators do it in the old fashion way by keeping a visual watch on the ship bridge. in this technology century, navigators do not have a sophisticated way to minimize collision risk. they are using radars, global positioning system (gps), automatic identification system (ais) technologies. therefore, the threat assessment is obligatory to their own experience and attention. the same issue appears at developing any kind of autonomous vehicle. so, the navigators’ main task is to clearly understand the current context and make a threat assessment based on their own experience, contextual knowledge, and predictable maneuvers of all actors before it’s too late. in this paper, we create an approach that could predict the possible further situation and makes its threat assessment without any waiting for future actions. it is essential for a safe autonomous navigation level in an unknown situation. the proposed approach consists of two different machine learning approaches. at first, the machine learning algorithm predicts the possible route based on historical data. then, the other machine learning algorithm evaluates the prediction for an anomaly https://doi.org/10.15837/ijccc.2021.4.4345 3 before any action in the future marine traffic situation. in this case, we get the contextual reasoning, and it’s the threat assessment based on contextual knowledge. 2 the context of the marine traffic ensuring the safety of the vessel in marine traffic is a challenging task. vessel safety depends on many different factors. the complexity of safe ship maneuvering relies on the experience of the navigating officer. as shown in figure 1, the officer in charge should consider many different factors. these factors could influence the decision differently, and it is difficult to determine the significance of these factors. it depends on a particular situation. figure 1: the factors influencing the safe ship maneuvering the marine traffic rules are described in the convention on the international regulations for preventing collisions at sea (colreg) [3]. all the vessels must navigate according to the colreg rules to prevent the collision [18]. also, the officers must take into account the hydro-meteorological conditions and other static or floating obstacles on the route. the vessels use many different sensors from complex, dynamic, and technical systems to make a full picture of the environment in which vessels are navigating. nerveless, the many complicated factors influence the assessment of a particular situation. despite the progress of technology, the main reason for the vessel collision is human errors. the human must decide on how to navigate and maneuver safely. therefore, safe navigation depends on the alertness of the navigation officer and his experience. so, the navigation officer’s main task is to predict the next movement of the forthcoming vessel and make a decision that depends on the situation and environment. moreover, the navigation officer’s vital duty during the whole voyage could take up to nine months. https://doi.org/10.15837/ijccc.2021.4.4345 4 3 recent machine learning methods that ensure the safety of marine traffic there are developed many systems to ensure the decision on safe marine navigation. such systems are essential not only for human manned ships but also for future autonomous vessels. these systems are designed to find the best navigation strategy based on marine traffic data. such an expert system was proposed in [4] to avoid the collision. another method was proposed in [5]. it generates the optimal multiple interval motion plan. authors of [6] proposed the combined fuzzy inference system with an expert approach to avoid collisions. in [7], the reasoning system for the ship’s turning problems is proposed. this method was applied for ocean navigation and collision. the intelligent decision-making facilities are on the rise to increase marine navigation safety. also, the radar filtration algorithm for the vessel traffic centre (vtc) is proposed in [8] to raise awareness. also, the anomaly or dangerous situation detection is important to increase marine navigation safety [9]. some studies attempted to apply artificial neural networks (ann). ann models are learned by navigation officer actions to predict the maneuver actions [10], [11]. also, [12] tried to predict the effect of the waves on the vessel yaw motion. the big issue for ann is to predict the vessel collision or route. haris and amdahl [13] proposed the simplified analytical method. the authors used the interaction between the deformation on the striking and the struck ships. the ann was used to train the machine learning model to predict marine traffic instead of developing the mathematical model. [14] used machine learning methods combined with dbscan based algorithm to find the marine traffic patterns for ocean-going ships. kim et al. [15], [16] proposed the method to predict ship trajectory in the harbors. they trained the regression model combined with the dead reckoning model. the trained ann was used to predict marine traffic in the chosen trajectory cluster. in [17], turning point based clustering was proposed for path prediction. in this model, the multilayered perceptron was used. the ship information such as speed, course, length, and position was taken in training, and output was the next turning point of the ship. 4 proposed approach a novel approach for maritime contextual reasoning analysis and context-based threat assessment has been proposed and detailed below. the proposed approach is based on machine learning and provides a complex insight into the context of vessel maneuvering situations and makes a threat assessment without any waiting for future actions. it could ensure an in-depth situation analysis through the same navigational data. let us consider a maneuvering situation consisting of two vessels. the first one is its own vessel. the second one is some other vessel that is possibly dangerous for the collision. decision support aims to make maneuvering safely. the proposed approach consists of the integral use of such main steps: 1. prediction of the future turning point of another vessel. 2. evaluation of the prediction whether it will be a normal action or some unusual action. 3. in the case of predicted unusual action and if the distance between own and other vessels is sufficient for safe maneuvering, update the navigational data, and go to step 1. otherwise, the decision of the officer in charge of its vessel should be based on the rules described in the convention on the international regulations for preventing collisions at sea (colreg). steps 1 and 2 operate with marine traffic data that are obtained in real-time. this functionality is provided by the ais. all participants of the marine traffic are responsible for sending the correct navigation data. the navigation data is sent automatically to all participants of marine traffic. the ais data is a mixture of static data (vessel name, maritime mobile service identity (mmsi), vessel type, dimension, the port of destination) and kinematic data (vessel position – latitude & longitude, speed (sog), course (cog, hdg), rate of turn (rot)). these data are broadcasting every 2-10 https://doi.org/10.15837/ijccc.2021.4.4345 5 seconds, and it depends on the vessel type and speed. this broadcast generates a large volume of navigation data. for example, the vessels in the danish coast area generated about 60 gb per month. to handle such huge data, steps 1 and 2 may be realized using machine learning methods. however, this data is too large for machine learning and needs some preprocessing that leads to data aggregation. data aggregation in our case means clustering of turning points of vessels – we reduce the data by using a set of clusters of turning points instead of a whole set. now, the whole cluster of turn points is represented by a center of the cluster. dbscan or optics may be used to cluster such spatial data and to get a set of generalized turn points. a more detailed description of data aggregation is presented in section 5. denote the current position of the other vessel by a. its nearest aggregated turn point (center of the cluster of turn points) is b. point b is on the course line from a. point c must be predicted for threat assessment. an example of the vessel route through points a, b, and c is shown in figure 2. figure 2: example of the part of vessel route the prediction of the turning point (c point) depends on sog and cog. due to economic reasons, it is an advantage to make small-angle turns on the route. there are many options for point c because it depends on the vessel port of destination, the situation of maneuvering, weather conditions, etc. to estimate or predict position c, we need some classifier that estimates the most possible aggregated turn point c from historical marine traffic data when the vessel moves from a to b. decision tree (dt) may serve as such classifier. the next step is to evaluate possible tracks predicted by dt, i.e. to perform a threat assessment. the result is the answer to whether the predicted route is normal or unusual. therefore, we need a classifier that separates unusual actions from normal ones. support vector machine (svm) one-class classifier may be applied here. the unusual actions could appear in dangerous maneuvering situations. however, unusual actions may be dangerous, too. for example, when vessels are engaged in fishing or sailing vessels to proceed in angular track due to weather or tide conditions. therefore, unusual situations could not be extracted directly from historical marine traffic. the reason is that the unusual situation does not have any particular pattern, and it could not be abstracted from the whole dataset. our approach combines two different machine learning methods to make a threat assessment in the vessel movement. the solution is based on the context-knowledge, which was learned from historical marine traffic data. remarkably, two different machine learning methods are solved the two various context-based problems on the same historical dataset. https://doi.org/10.15837/ijccc.2021.4.4345 6 consequently, these two different approaches fully complement each other, and it is firmly connected. however, both methods complement each other for better prediction. the proposed approach ensures verification of the dt prediction. it means the novelty detection (one-class svm) algorithm could evaluate dt prediction (point c) before the vessel appears in point b. if dt predicts an unusual next turning point c, the system could return to dt with new updated navigational data for the new prediction. this could be repeated as many times as necessary or while it is safe. the navigation situation changes and the navigational data could be updated every several seconds, for example, from the ais device. this could provide constant surveillance and threat assessment of marine traffic based on contextual reasoning. the details on possible methods that are combined in the new approach are presented in sections 5, 6, and 7. 5 marine traffic data aggregation marine traffic generates a big amount of navigation data. consequently, it is hard to aggregate or process navigational data. the collected data have recorded both normal maneuvering and unusual situations – dangerous maneuvering, not usually behavior or misconduct. however, it is hard to exclude such data because the unusual situation condition does not have a specific pattern. the first step to relieve the aggregation of the useful navigational data is preprocessed. this allows us to use a big set of navigational data but in a much smaller data volume without losing main patterns. there are various algorithms applied to preprocessing the ais data. density-based algorithms are used for spatial data, as a rule. this family’s most known algorithm is the densitybased spatial clustering of applications with noise (dbscan) [18] algorithm. dbscan has some � radius parameters defined in an empirical way. it is a big problem because a small change in this parameter can produce very different results in varying density datasets. moreover, this is a big issue for real-world, high-dimensional datasets. to overcome these problems, the optics was proposed. the opticts [19] (ordering points to identify the clustering structure) is the successor of dbscan. at first, the optics algorithm helps to reduce the size of the historical marine dataset by data aggregation because the result of the optics is a clustered dataset. in addition, the optics filters the meaningful data from the whole dataset. the cluster helps to recreate the generalized route from port a to port b. dbscan algorithm has two control parameters: a radius of the neighborhood for some point (�), and the minimum number of neighbors (data points) within radius � (minpts). dbscan classifies data points into three groups: core points, reachable points, and noise or outliers. a point p is a core point if at least minpts points are within distance � from it, including this point itself. a part of reachable points is reachable directly. a point q is directly reachable from p if point q is within distance � from core point p. points of the cluster may be reachable directly from core points, only. a point q is reachable from p if there is a path of points p1, ...,pn with p1 = p and pn = q, where each pi+1 is directly reachable from pi. note that all points on the path are the core points, with the possible exception of q. all points that are not reachable from any other point are outliers or noise points. each cluster contains at least one core point. the optics, like dbscan, requires the same two parameters: � and minpts. in contrast to dbscan, optics also considers points that are part of a more densely packed cluster. optics has two additional parameters: a core distance and reachability distance. each point is assigned a core distance that describes the distance to the minptsth closest point. the reachability distance of another point o from a point p is either the distance between o and p, or the core distance of p, whichever is bigger. in our case, optics produces the ordered list of the turning points. the turning points are connected by their reachability. optics tries to reduce the reachability of each turning point during processing. every unprocessed turning point is considered as a new cluster. the neighbors of a new cluster are added to the queue sorted by smallest reachability if their reachability can be reduced. https://doi.org/10.15837/ijccc.2021.4.4345 7 optics does not create a cluster itself but produces an output list of the turning points with their reachability. the output list of turning points is ordered by the processing queue. the first processed turning point is the first at the output list. respectively, the last processed turning point is the last in the output list. the cluster is extracted from the output list by identifying valleys in the point reachability. if � is established, it is used to specify a cluster, otherwise, each separated network will constitute a cluster. in the first step, the historical marine traffic data are preprocessing by the optics algorithm. this aggregated marine traffic data was used in the dts algorithm and one-class classification. the clustered dataset extracts the marine traffic information – the turning points on main routes. the extracted and clustered turning points could be considered as contextual knowledge. the clustered data provides insight into vessel maneuverings. these points will be used in further calculations. this contextual knowledge contains the mariner’s experience and the generalization of routes. figure 3: the result of the optics algorithm as shown in figure 3 the optics clustering eliminates the mediate waypoints and analyzes the turning points as a reference point for vessel moving behavior prediction. the proposed algorithm does not require every turning point, but it processes the center points of the clusters. thus, the centers of the marked positions are the calculated geographical center of the clusters. this operation makes it much simpler to process the whole marine traffic dataset. as mentioned before, the marine navigation data accumulates large amounts of various navigational data. the historical marine traffic dataset contains information about vessel position at a particular time and its speed and course. the particular route from port a to b every time could be slightly different. it could depend on maneuvering situations, hydrometeorology conditions, crew shift, etc. these reasons make the navigational data complicated to extract from the data navigator’s experience and further research it. another significant issue is the required processor time on raw data. the optics algorithm solved these issues. the center points of clusters appear on route waypoints, and it could easily be identified as tracks. 6 prediction based on decision trees algorithm the dt algorithm is a method for non-parametric supervised machine learning. this method could be used for pattern recognition and classification problems [20], [21], [22]. the dt process the data by looping partitions to achieve the uniform classification of the target value. at each split, https://doi.org/10.15837/ijccc.2021.4.4345 8 the dt reduces the entropy of the target value. this is achieved by adjusting the optimal split of independent variables. the dt has unique abilities to create a sequence of decisions. this sequence makes very fast problem solvers. moreover, the dt sequences are very easy to understand and interpret. however, dt is hard to optimize. so, creating the dt is the process to find the structure of knowledge. in our case, dt is used to learn the routes by data aggregated using optics. dt primary purpose is to predict the future turn point (point c in figure 4). the main problem is that dt has a wide choice of possible turn points, but it should recommend the most probable one (c). the data item for dt training consists of six features: (x1, x2) the current vessel position (latitude & longitude) describing the point a, (x3, x4) the kinematic data (sog – speed over ground, cog – course over ground), (x5, x6) the next position (latitude & longitude) describing the point b, the target data (latitude & longitude) describing the point c. see figure 4 for an example of training data. figure 4: visual presentation of training data – an example dt tries to learn the context of marine traffic. the trained model is devoted to a particular geographical location because of the data used for learning. the dt has a maxdepth parameter. the dt with a maxdepth size of 3 is shown in figure 5. however, is not enough often to train the model properly due to data complexity. moreover, too small maxdepth size may decrease the number of features used for decision. dt is realized in the integrated tool scikit-learn [24]. the dt is learning by splitting nodes with samples. it starts from the root node (with 393124 samples in our case). the mean squared error (mse) is used to split the node into sub-nodes. dt calculates mse for each divided subset and chooses the result with the smallest mse value. each sub-node consists of a different number of samples. https://doi.org/10.15837/ijccc.2021.4.4345 9 figure 5: example of the dt 7 novelty detection based on svm there are developed several different methods that involve the navigation experience to predict the next waypoint. these methods are based on historical marine traffic data. one of the problems is to filter the anomalies, other unusual events, or dangerous situations. the simplest way to filter such data is to search and analyze every accident or dangerous maneuvering. it is challenging for humans because he must find or have prior knowledge about such cases or filter and extract the respective data by hand. despite this, the novelty detection principle was used to tackle this task. novelty detection is the ability of an intelligent organism to identify an utterly unknown pattern. the pattern should be sufficiently salient or associated with high positive or negative usefulness. this principle came from neurophysiology proposed by e. n. sokolov [23] in the 1950s. this method is widely used to detect anomalies or outliers. the principles of novelty detection are given in [24] and [26]. the svm one-class algorithm may serve for novelty detection, i.e. to filter the dangerous or unusual maneuvering situations from navigation data. in general, svm arranges the dataset on a hyperplane or set of hyper-planes in a high or infinite-dimensional space. this space is used for classification, regression, or other tasks. the best results could be achieved by the hyperplane, which has the most considerable distance to any class’s nearest data points (operating margin). the svm one-class algorithm learns to classify new data as similar or different from the training set. [26] has suggested a method to solve the one-class classification problem using svm. https://doi.org/10.15837/ijccc.2021.4.4345 10 figure 6: the comparison of normal (a) and unusual (b) vessel track the typical vessel path should be low-pitched with the lowest possible angles, as shown in figure 6a. there are many practical reasons for it – fuel economy, big size of vessel, inertia &, etc. notwithstanding, there could be many reasons for an unusual path, as shown in figure 6b. for example, it could be the sailing vessel going by changing downwind, the fishing vessel with nets, law enforcement vessel on duty &, etc. as mentioned before, the dt is trained and prediction is based on the historical vessel routes. the one-class svm algorithm was trained for a completely different task. svm should evaluate the dts prediction, i.e. the future turning point c predicted by dt should be assessed by svm. the data item for svm training consists of (1) the distance dist1 between points a and b, (2) the distance dist2 between points b and c, (3) the angle 6 abc. see figure 7 for an example of training data. figure 7: example of one-class svm classification one-class svm [26] is realized in the integrated tool scikit-learn [27]. https://doi.org/10.15837/ijccc.2021.4.4345 11 8 training and results of the proposed approach the proposed approach in section 4 consists of the integral use of two different machine learning methods. we need to train the corresponding classifiers. as it is shown in figure 8, the first we need is to get historical marine traffic data. the next is the aggregation of a dataset for better training results. the historical marine data are aggregated by optics and, as a result, a dataset of the centers of clusters of turning points is generated. after this, the training process is divided into two main parts: 1. dt is trained to predict the future next point. 2. one-class svm classifier is trained to detect unusual patterns. this step needs additional clustered data processing: (a) calculate distances between pairs of turning points (a, b) and (b, c); (b) calculate the angle 6 abc. 3. the trained models are saved for future usage. figure 8: the training process https://doi.org/10.15837/ijccc.2021.4.4345 12 the main aim of training dt is to learn a particular traffic pattern in a particular geographical area. it is quite a complex problem because the dt method should predict the future next turn point (c) by the current position (a) of the vessel, its course, and speed. the experiments were carried out to evaluate the impact of parameter maxdepth on the prediction accuracy. maxdepth values were varied from 3 to 37. the results are given in figure 9. the dt with a maxdepth equal to 26 reaches more than 90% accuracy. the maximum accuracy is 94% as maxdepth equal to 37. figure 9: the precision of the dt model with different maxdepth. an example of an anomaly detected by the one-class svm classifier is given in figure 10. in general, the one-class svm classifier assigned 5,86% of dt predictions as unusual – 27125 cases from 462499. figure 10: an example of an anomaly detected by one-class svm classifier. https://doi.org/10.15837/ijccc.2021.4.4345 13 9 discussion in this paper, we presented a context-based threat assessment approach. it combines two different machine learning methods. this approach predicts the possible further situation and makes its threat assessment without any waiting for future actions. this threat assessment is crucial because it could provide contextual reasoning in maneuvering before any threatening indicator occurs in any particularly maneuvering situation. the approach performs combined contextual reasoning over a holistic assessment of predicted possible future actions. it could be applied to any participants in the marine traffic because it allows analyzing unusual or dangerous actions in a volatile context before any indications of dangerous action occur. moreover, the proposed approach could be used by different actors in the same way for threat assessment. it allows us to evaluate possible future actions from different points of view: 1. own vessel; 2. another vessel; 3. outsider – authorities who are responsible for safe marine traffic. employment of such different points of view makes the proposed approach suitable for the imitation of some basic cognition processes. the approach allows imitating such cognitive processes as knowing, remembering, judging, and problem-solving. these cognitive processes ensure imagination, perception, and action planning. the approach is an extremely universal model adjusted for marine traffic. in our case, the prediction of a possible future turning point acts as an imagination. it is based on contextual reasoning and provides guessing the possible action of another actor. as we know, perception allows us to take information and utilize it for responding and interacting. just like that, the one-class svm classifier makes a threat assessment on the possible future activities. thus, the classifier acts as a perception. in the case of a dangerous or unusual situation, the proposed approach could be applied to make a good strategy to avoid it. the strategy could be done by applying the proposed model from other vessel position and make a threat assessment on our self-actions. however, the approach is limited to marine traffic. the safe marine traffic could be achieved by applying the proposed approach in a decision support system that does the threat assessment without any human intervention in the permanent assessment process. context-based knowledge and reasoning are essential to make the right decision. understanding of context is crucial to ensure safe maneuvering and navigation in different and completely unknown situations. this ability is very important for the future autonomous vessel. the unique feature of the proposed approach is the ability to analyze information from different perspectives of actors and it allows to evaluate our planned actions. the traditional machine learning methods do not ensure a complete analysis of the picture in the maneuvering and environment. so, it is incredibly essential to understand and analyze the context information as is. as it was demonstrated, the context-based knowledge provides a full analysis of the particular situation for a better decision. author contributions. conflict of interest the authors contributed equally to this work. the authors declare no conflict of interest. references [1] rosenfield, i., clancey, w.j. 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(2011). scikit-learn: machine learning in python, journal of machine learning research , 12, 2825–2830 (2011). copyright ©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: daranda a.; dzemyda g. (2021) novel machine learning approach for self-aware prediction based on the contextual reasoning, international journal of computers communications & control, 16(4), 4345, 2021. https://doi.org/10.15837/ijccc.2021.4.4345 int j comput commun, issn 1841-9836 8(5):654-661, october, 2013. development an adaptive incremental fuzzy pi controller for a hvac system j. bai jianbo bai college of mechanical and electrical engineering, hohai university, changzhou 213022, china, bai_jianbo@hotmail.com abstract: this paper presents an adaptive incremental fuzzy pi controller (aifpi) for a heating, ventilating, and air conditioning (hvac) system capable of maintaining comfortable conditions under varying thermal loads. the hvac system has two subsystems and is used to control indoor temperature and humidity in a thermal zone. as the system has strong-coupling and non-linear characteristics, fixed pi controllers have poor control performance and more energy consumption. aiming to solve the problem, fuzzy control and pi control are combined together organically. in the proposed control scheme, the error of the system output and its derivative are taken as two parameters necessary to adapt the proportional (p) and integral (i) gains of the pi controller based on fuzzy reasoning according to practical control experiences. to evaluate the effectiveness of the proposed control methods in the hvac system, it is compared with a fixed well-tuned pi controller. the results demonstrate that the aifpi controller has more superior performance than the latter. keywords: hvac system, adaptive control, fuzzy logic control, pi control. 1 introduction commercial and industrial hvac applications use electric and mechanical control system to maintain the desired temperature humidity level, and static pressure within a given area or zone. good hvac control schemes help reduce energy used and maintain occupant comfort. in spite of many advance in control theory, simply controllers of pi/pid type are still widely used in the majority of hvac control loops [1]. there are three common methods for determine "good" values for the gain, integral time constant and derivative time constant of a pi/pid controller: manual tuning, auto tuning and adaptive control method [2]. for thermal load disturbances, variation of fluid flowrate, heat exchangers fouling or wear on valves, most hvac systems have nonlinear, strong-coupling and time-varying dynamics [3]. a problem using conventional pi controllers in hvac control systems is that control performance varies as conditions change and loops may become sluggish or oscillatory at certain times [2]. according to åström, a fixed gain controller should be used for systems with constant dynamics, and adaptive control methods should be used for processes with time varying dynamics [4]. consequently, we should use adaptive control methods in the hvac industry. some work has already been done in this area. for example, the development of control strategies for improving the performance of pid controllers using self tuning and adaptive control pi control techniques for hvac systems has been studied in recently years [5]. the controllers normally have two parts: online identification and controller tuning. and the recursive least square (rls) method is commonly used by the identification part. however, it has been reported that unmodeled process disturbances and actuator hysteresis limit the effectiveness of the rls [6]. then, the traditional adaptive control methods based on the identification have limitations in hvac control applications, such as absence of robustness. copyright c⃝ 2006-2013 by ccc publications development an adaptive incremental fuzzy pi controller for a hvac system 655 intelligent adaptive control using ai (artificial intelligence) techniques don’t need the identification procedure and can also adapt to the various situations in real time [7]. it has both adaptability and robustness characteristics. therefore, the adaptive control based on ai techniques has acquired more attention in application to havc systems since the end of last century [8]. zaheer-uddin [9] has utilized a neuro-pid algorithm for tracking control of a discharge air temperature (dat). the ann (artificial neural network) is used to calculate the gain coefficient of pid controller. results show that the controller is able to track set point trajectories efficiently in the presence of disturbances. seem [10] has described a method for automatically adjusting the gain and integral time of pi controllers based upon patterns that characterize the closed-loop response for hvac systems. this new pattern recognition adaptive controller (prac) is easy to use and can provides near-optimal performance for a range of systems and noise levels. fuzzy logic control, also a method of ai techniques, is proposed by zadeh in 1973 and has been widely applied to industry areas [11]. it presents a good tool to deal with complicated, non-linear and time-variant systems. to utilize the advantages of fuzzy control and the existing pi controllers in hvac control systems, we design an adaptive incremental fuzzy pi (aifpi) controller, within which the parameters of the pi controllers can be updated online as a function of operational conditions to adapt to various load disturbances in hvac systems. 2 hvac system and system models a single thermal zone hvac system for control analysis is considered [12]. the schematic diagram of the havc system and its control system is shown in fig.1. the hvac system consists of the following main components: a cooling coil, a supply air fan, a thermal zone, connecting ductwork, water pipe, and filter. the control system includes temperature sensors, humidity sensors, a variable frequency drive (vfd), a water valve and a centralized controller, etc. the basic operation mode of the hvac system is considered as follows: initially, 25% fresh air enters the system and is mixed with 75% of the recirculated air, the remained air is exhausted. then, the mixed air passed through the cooling coil where it is conditioned according to the thermal zone designed temperature. next, the conditioned air is delivered by a supply fan and the flow rate of the air is adjusted to maintain the thermal zone humidity. furthermore, the supply air enters thermal space to offset sensible and latent loads acting on the system. finally, the air in the room space is recirculated and the remained air is exhausted to the outside environment. during the operation, the flow rate of the water and the air is modulated to maintain the setting points of the temperature and the humidity in the thermal space. figure 1: schematic diagram of the hvac system and its control system according to the energy and mass balance of the hvac system, the differential equations of the system mathematical model can be described as follows [12]: 656 j. bai   dw 3 dt = (w2−w3)fa vz + mz ρavz dt3 dt = (t3−t2)fa vhe + 0.25(t0−t3)fa vhe − hw(0.25w0+0.75w3−w2)fa cpavhe dt3 dt = (t2−t3)fa vz − hfg(w2−w3)fa cpavz + 1 ρavzcpa (qz − hfgmz) (1) where cpa is the specific heat of air (1.004kj/kg.k);cpw is the specific heat of water (4.183kj/kg.k); fa is the flow rate of air(m3/s);fw is the flow rate of water(m3/s);hw is the enthalpy of liquid of water(790.84kj/kg);hfg is the enthalpy of water vapor(2500.45kj/kg);mz is the moisture load (0.021kg/s);qz is the sensible heat load (84.93kj/s);t0 is the temperature of outside air (k);t2 is the temperature of supply air (k);t3 is the controlled temperature of thermal space(k);vhe is the volume of cooling coil (1.72m3);vz is the volume of room space (1655.11m3);w0 is the humidity of outside air (0.018kg/kg);w2 is the humidity of supply air(0.007kg/kg);w3 is the controlled humidity of thermal space (kg/kg); ρa is the density of air (1.185kg/m3); ρw is the density of water(1000kg/m3). 3 design of an adaptive increment fuzzy pi controller fig.2 shows the schematic diagram of the proposed controller and its application to the hvac system. the control system is composed of two control loops. one is to control the indoor humidity (w3) by regulating the frequency of the supply fan. the other is to control the indoor temperature (t3) by regulating the opening of the bypass valve. due to the complex characteristics of the hvac system, it is difficult for fixed pi controllers to maintain the indoor environment and keep comfort situations fig.3 shows the infrastructure of the aifpi controller. the control scheme is mainly composed of two parts: fuzzy controller and pi controller, the increment of proportional and integral gains of the pi controller can be adjusted by the fuzzy controller in real-time according to the error of the system output and the change of the error. figure 2: schematic diagram of the aifpi controller for the hvac system figure 3: infrastructure of the aifpi controller development an adaptive incremental fuzzy pi controller for a hvac system 657 in fig.3, r(t) is the desired setpoint for process output and y(t) is real process output; u(t) is the fuzzy pi controller output;e is the error at sampling time t defined as e= r(t); ec is the change of the error; ke and kec are input scaling factors of the fuzzy controller; multiplied by ke and kec separately, e′ and ec′ are the input variables of the fuzzy controller, which can be regarded as equivalent to e and ec. cp and ci are output scaling factors of the fuzzy controller;kp0 and ki0 represent initial proportional and integral gains of the pi controller;e and ec are fuzzy sets of e′ and ec′separately; ∆kp and ∆ki are fuzzy sets of δkp and δki. they are the increment of proportional and integral gains of the pi controller. and kp and ki are proportional and integral gains of the pi controller. as the kernel of the fuzzy controller, the fuzzy rules are composed of the generalized forms "if –then" to describe the control policy and can be represented as follows. r(n): if z then{ δkp is ∆kp (n) i and δki is ∆ki (n) i } i=1, . . . ,m; where e(n)i ,e (n) j ,∆kp (n) i and ∆ki (n) i are linguistic terms of e ,ec, ∆kp and ∆ki . according to the mamdani inference rule, the fuzzy vector-matrix composition relation of ∆kp and ∆ki can be described as in follows:  rp = r1 ∪ r2 · · · ∪ rn = n∪ m=1 ri (m = 1, · · · ,49) ri = r1 ∪ r2 · · · ∪ rn = n∪ m=1 ri (m = 1, · · · ,49) (2) then, the fuzzy rule bases for determination of ∆kp and ∆ki can be presented as{ ∆kp = (e × ec) · rp, ∆ki = (e × ec) · ri, (3) as shown in fig.3, the proposed fuzzy pi controller can be formulated as{ kp = kp0 + δkp · cp ki = ki0 + δki · ci (4) where the output crisp values, δkp and δki can be calculated by the center of gravity methods. thus, the output of the fuzzy pi controller can be given by u(t) = kp e(t) + ki ∫ τ 0 e(t)dt (5) figure 4: infrastructure of the aifpi controller fig.4 shows the diagram of controlled output regions at a step response and these regions’ positions in a rectangular coordinate. the controlled output can be divided into ten regions. in the control scheme, the variation of incremental parameters of the pi controllers is relevant with the e and ec.and each of input and output variables are fuzzified using seven fuzzy sets. the 658 j. bai linguistic terms of the fuzzy sets are negative big (nb ), negative medium (nm ), negative small (ns ), zero (zo), positive small (ps ), positive medium (pm ), positive big (pb ). the shape of the fuzzy sets, i.e. member functions, is chosen to be either triangle or z-shape curve ones. the relation between ∆kp, ∆ki and e, ec can be summarized as the fuzzy rules in table1, where the content in the first and second brackets represents the ∆kp and ∆ki separately. table 1 fuzzy rule base for determination of ∆kp ,∆ki based on e and ec e ec pb pm ps ze ns nm nb pb (nb)(pb) (nb)(pb) (nm)(pm) (nm)(pm) (nm)(ps) (z)(z) (z)(z) pm (nb)(pb) (nm)(pb) (nm)(pm) (nm)(ps) (ns)(ps) (z)(z) (ps)(z) ps (nm)(pb) (nm)(pm) (ns)(ps) (ns)(ps) (z)(z) (ps)(nm) (ps)(ns) z (nm)(pm) (nm)(pm) (ns)(ps) (z)(z) (ps)(ns) (pm)(nm) (pm)(nm) ns (ns)(ps) (ns)(ps) (z)(z) (ps)(ns) (pm)(ns) (pm)(nm) (pm)(nb) nm (ns)(z) (z)(z) (ps)(ns) (ps)(ns) (pm)(nm) (pb)(nb) (pb)(nb) nb (z)(z) (z)(z) (ps)(ns) (pm)(nm) (pm)(nm) (pb)(nb) (pb)(nb) 4 application of the aifpi controller to the hvac system the aifpi controller is applied to the hvac system described in section 2. in the control system, there are two control loops to maintain the indoor temperature and humidity separately. there are hence two controllers in the control scheme (fig.2).they have the same member functions of e, ec , ∆kp and ∆ki depicted in fig.5. however, other parameters of the two aifpi controllers, such as ke, kec, cp and ci have different values to achieve good performance. from fig.5, it can be found that the input variables of the fuzzy controller in the proposed control figure 5: member functions of the proposed aifpi controller scheme,e′ and ec′,which are equivalent to the error e and the change of error ec are all quantized inside [-3, 3]. the output of the fuzzy controller, δk p and δki, which are incremental proportional development an adaptive incremental fuzzy pi controller for a hvac system 659 and integral gains of the pi controller are quantized inside [-0.06, 0.06] and [-0.3, 0.3] separately. a proper choice of input and output scaling factors ke, kec, cp and ci is important for the aifpi controller to achieve good performance of the two control loops. after plenty of trials, the values of ke, kec,cp and ci for controlling the indoor temperature are set as 6.5, 3, 1.6 and 10. and that for controlling the indoor humidity are 2.2, 4, 1.5 and 10. to evaluate the effectiveness of the proposed aifpi controller, its control performance is compared with a fixed well-tuned pi controller, which adopts the pi/pid tuning rules proposed by wang [13]. it has been proved that the auto-tuning controller using the pi/pid tuning rules has good performance for hvac systems [14]. their behavior is all evaluated with matlab/simulink simulation using the hvac system model described by eq.(1) and predefined load disturbances, which are used to simulate non-linear, strong coupling and time-variable characteristics of the hvac system. (1)comparison with different moisture load in the hvac system fig.6 shows the controlled temperature and humidity subjected to different moisture load during the simulation. in the beginning, the moisture load is 0.021kg/s, the setting points of the humidity and the temperature are 0.009kg/kg and 298.15k. at the 400th sampling time, the moisture load is improved to 0.031kg/s to evaluate the control performance of the proposed controller.as shown in fig.6, it can be found that the variation of the load disturbances is one of the important factors which will influence the stability of the controlled loops in the hvac system. good controllers can recover the stability and accuracy of the controlled variable quickly. it can be seen that the proposed controller achieves more superior performance than the well-tuned pi controller considering the transition time and the overshoot subjected to different moisture load disturbances. fig.7 and fig.8 also present the variation of the proportional and integral gains of the proposed controller during the simulation. it can be also found that the proportional and integral gains can adapt to the different load disturbances and attain new stable status when offsetting them. figure 6: controlled humidity and temperature with moisture load disturbances figure 7: variation ofkp and ki of the aifpi controller for controlling the humidity (2)comparison with different cooling load in the hvac system in hvac systems, cooling load is changing all the time during a day. the variation of the cooling 660 j. bai figure 8: variation of kp and ki of the aifpi controller for controlling the temperature load will lead to oscillation and drift of the controller variable, the indoor comfort then will be reduced. in order to evaluate the control performance subjected to cooling load disturbances for the proposed controller followed situations were simulated. in the beginning, the cooling load in the thermal zone is set as 84.93kj/s, and the setting point of the humidity and the temperature is 0.009 kg/kg and 298.15k. at the 400th sampling step, the cooling load is increased by 40kj/s. fig.9 shows the temperature and the humidity response of the two control loops. it can be seen that the proposed controller has less overshoot and quicker response speed than the welltuned pi controller. it can be also found that the humidity will not be influenced subjected to the cooling load in the hvac system. the variation of the proportional and integral gains of the controller controlling the temperature is shown in fig.10. it can be concluded that the proposed controller has good adapt ability and control performance subjected to the cooling load disturbances. figure 9: controlled humidity and temperature with different cooling load disturbances figure 10: variation of kp and ki of the aifpi controller for controlling the indoor temperature development an adaptive incremental fuzzy pi controller for a hvac system 661 5 conclusion in this paper, an aifpi controller is developed and applied to a hvac system, which has two subsystems and is used to maintain temperature and humidity in a thermal zone. taking full advantage of fuzzy logic control and pi control together, the proposed aifpi controller uses fuzzy logic to supervise pi controller parameters. in the control scheme, the incremental parameters of a pi controller are updated online as a fuzzy function of the operating conditions to improve the behavior of classical fixed pi controllers. the results demonstrate the aipfi controller has good adaptability to the non-linear, strong coupling characteristics of the hvac system. it performs significantly better control performance than the well-tuned pi controller considering response time and overshoot subjected to different moisture load disturbances or cooling load disturbances during the simulation. the aifpi controller can be widely used in the hvac industry. bibliography [1] underwood, c.p., hvac control systems: modeling, analysis and design, london and new york, e & fn spon, 1999. [2] salsbury, t., a survey of control technologies in the building automation industry, proc. of the 16th ifac world congress, 331–341, 2005. [3] levine, w.s., control system applications, boca raton, crc press, 121-123, 2000. [4] astrom, k.j., wittenmark, b., adaptive control, reading, addison-wesley, 1995. [5] bai, j., zhang. x., a new adaptive pi controller and its application in hvac systems, energy conversion and management, 48(2): 1043-1054, 2007. [6] nesler, c.g., automated controller tuning for hvac applications, ashrae trans., 92(2b): 189-201, 1986. [7] white, d.a., sofge, d.a., handbook of intelligent control: neural, fuzzy, and adaptive approaches, van nostrand, reinhold comp., 1992. [8] ferreira, p.m., ruano, a.e., silva s., neural networks based predictive control for thermal comfort and energy savings in public buildings, energy and buildings, 55(0): 238-251, 2012. [9] zaheer-uddin, m., tudoroiu, n., neuro-pid tracking control of a discharge airtemperature system, energy conversion and management, 45(15-16): 2405-2415, 2004. [10] seem, j.e., haugstad, h.j., field and laboratory results for a new pattern recognition adaptive controller, proc. of clima 2000 conference, 96-100, 2000. [11] chang, s.s.l., zadeh, l.a., on fuzzy mapping and control. systems, man and cybernetics, ieee trans. on, (1): 30-34, 1972. [12] arguello-serrano, b., velez-reyes, m., nonlinear control of a heating, ventilating, and air conditioning system with thermal load estimation, ieee transactions on control systems technology, 7(1): 56-63, 1999. [13] wang, q.g., lee t.h., fung, h.w. et al., pid tuning for improved performance, ieee trans. on control syst. tech., 7(2): 457-465, 1999. [14] bi, q., cai w.j., wang, q.g. et al., advanced controller auto-tuning and its application in hvac systems, control eng. practice, 8(6): 633-644, 2000. international journal of computers communications & control issn 1841-9836, 10(3):348-356, june, 2015. collaborative data processing in wsn using voronoi fuzzy clustering s. nithya kalyani, e. sasikala b. gopinath s. nithya kalyani* department of information technology k.s.r college of engineering tiruchengode, india *corresponding author: nithyakalyani.me@gmail.com e. sasikala department of information technology k.s.r college of engineering tiruchengode, india b. gopinath department of electrical and electronic engineering vivekanandha institute of engineering and technology for women tiruchengode, india abstract: in this paper, developed a novel voronoi fuzzy clustering (vf) algorithm for energy efficient collaborative data aggregation in wireless sensor network. vf algorithm is fusion of voronoi diagram and modified fuzzy cmeans with respect to distance and quality of service. here throughput, delay time and delivery ratio are considered as qos parameters. once clustering of sensor nodes is completed then data management technique such as data aggregation or compression is done for further decision making in sink node. data mining clustering algorithm reduces overall transmission of data from each sensor to the sink node thus energy spent by individual sensor node is minimized. the cluster heads collects all sensed information from their respective cluster members and performs data aggregation or compression before transmitting the data to the sink node. finally, the simulations are performed and the results are analyzed within the simulation set up to determine performance of the proposed algorithm in the sensor network. our proposed approach has achieved 60% efficiency when compare with the k means algorithm. keywords: clustering, data aggregation, compression, voronoi fuzzy clustering algorithm, energy, qos, throughput, delivery ratio. 1 introduction cluster aggregation is essential techniques that naturally reduce energy costs in wireless sensor networks (wsn) without compromising the quality of data delivery. the process of separating the sensor nodes into groups are called as clustering. a number of challenges involved in clustering. at first, the clusters themselves have to be identified. secondly, cluster heads have to be chosen. thirdly, routes have to be discovered from every node to their cluster head, and finally, the cluster heads have to proficiently relay the data to the sink node. this paper focuses all the later three problems. the foremost problem is defined by the application domain. data aggregation is another vital function in wsn to reduce the consumption of energy due to the number of limitation of wsn. the key idea of this process is to eliminating redundancy in data, minimizing the number of transmissions via integrate all the incoming data in cluster head from diverse sources and enroute it to the sink. this focuses in data-centric approach. aggregation algorithms are limited to application requirement that is either in time or energy performance copyright © 2006-2015 by ccc publications collaborative data processing in wsn using voronoi fuzzy clustering 349 a wsn consists of tiny sensing devices, which normally run on battery power. sensor nodes are densely deployed in the region of interest. each device has sensing and wireless communication capabilities, which enable it to sense and gather information from the environment and then send the data and messages to other nodes in the sensor network or to the remote base station[4].wireless sensor networks have been envisioned to have a wide range of application in both military and civilian domains [1]. due to the limitation of sensor node researchers have designed lots of energy-efficient routing protocols to prolong the lifetime of sensor networks [2]. in this paper, the fuzzy c-means (fcm) clustering algorithm is modified by incorporating with voronoi diagram for clustering the sensor node based on the distance and the quality of services. 2 vf algorithm for cluster data aggregation the ultimate aim of the proposed approach is to reduce the consumption of the energy and increase the life time of wsn using collaborative data processing. in order to reduce the energy of the every node, clustering algorithm is used where the sensor nodes are clustered and subsequently, the cluster heads are selected. the cluster head is used to transmit the sensed data of sensor nodes to the base station (sink node), subsequently from the cluster head, the data is processed by aggregation or compression. in this approach, the process is separated into two phases. 2.1 vf algorithm to select the cluster head the wireless sensor nodes are located at different places and the sensors are clustered using the voronoi fuzzy clustering algorithm. initially, apply the voronoi tessellation to the sensor nodes based on the position and energy of the sensor. subsequently use fuzzy clustering algorithm to cluster the nodes as it’s easy to cluster and select the cluster head after applying the voronoi. for finding the membership function, select any number of voronoi cells as a cluster head and calculate the membership for every voronoi cells with the assumed cluster head. the voronoi cell goes to cluster which has highest value membership function. with the help of the membership function, the cluster head among the clusters are calculated. pseudo code: input: no. of sensor s = {s1, s2, ...si}, position and energy of every sensorsi = (xi, yj, zk) algorithm: step 1: find the distance for each si with r: d (si, r) = √ (xs − xr)2 + (ys − yr)2 + (zs − zr)2 step 2: {r |d (si, r) ≤ d (sj, r) , i ̸= j } ,where r = set of neighboring sensor step 3: r = (r1, r2....rn) step 4: v (si)= d(si ,rj) 2 step 5: get the value of “c” step 6: select cluster heads chy step 7: find the member ship function µxy = 1α+β [α (dxy) + β (qxy)] step 8: find distance membership function using dxy = ( ∥vx−chy∥∑c k=1∥vx−chk∥ ) step 9: find qos membership function using qxy = ∑q z=1 v y x(z) ( ∑c k=1 ∏q z=1 v k x(z)) step 10: find max of µxy for each si step 11: si max (µxy) step 12: find chy = ∑n x=1 µ m xy.si∑n x=1 µ m xy 350 s. nithya kalyani, e. sasikala b. gopinath step 13: update chy step 14: go to step 5 until chyis placed in same v (si) step 15: each si send data si (d) to corresponding cluster head chi step 16: the cluster head chi receives d = (d1, d2, ...., di) step 17: select option 1. data aggregation 2. data compression step 18: if (1) select option a. avg b. max c. min a. avg = ∑n n=1(di) n b. max = max (di) c. min = min (di) step 19: if (2) a. find probability of received data p (di) = f(di) n b. sort the data into ascending order p (d) = (p (d1) , p (d2) , ...., p (di)) c. add the first two data (p (d1) + p (d2)) d. merge the id of the data e. repeat the process 2 and 3 until getting only 2 probability of data step 20: the result of (18) and (19) goes to sink node. let s sensor nodes is defined by s = {s1, s2, ...si} and (1 ≤ i ≤ n) is the total number of sensors in the wireless network. each sensor node is defined by the position values (x coordinate and y coordinate) and the energy value (z). the position and the energy of the sensor nodes are given by sn = (xi, yj, zk). a voronoi diagram is a set of triangles, called voronoi triangles. the voronoi triangles generation is based on the distance and the energy of the sensor node. find r the neighbors of the each sensor node based on the minimum distance as in step 2,3,4, where d (si, r) is the distance from point si to r, predicted using step 1. that is r is the set of sensors closer to si than to any other sj. 2.2 calculation of membership function from the entire sensor nodes, clustering is performed in the sensor nodes based on the membership function. to find the membership function of each sensor node, let assume certain the number of sensor node as cluster head. in order to find the membership function, first choose ‘c’ cells as a cluster head (ch) that is c = 2 and assume any two cells as cluster head y1 = ch1 = (xi‘1, yj1, zk1) and y2 = ch2 = (xi2, yj2, zk2). with the help of assumed cluster head calculate the membership function of each sensor point. based on the membership functions the nodes are clustered. in this paper, the membership function is attained based on two functions first one is based on the distance and second one is based on the qos values. the equation in step 7 is use for finding the membership function, where µxy is the membership function of xth sensor node with respect to the yth cluster head node, dxy is the distance between xth sensor node to the yth cluster head node, qxy is the value of the qos of xth sensor node with respect to the yth cluster head node, α is the weightage of distance (user defined), β is the weightage of qos (user defined). 2.3 distance based membership function based on the euclidian distance between the cluster head and sensor node distance between the two points are calculated. this calculation describes, how much faraway the each sensor nodes from the each cluster head node. the step 8 is used for calculating the distance based membership function. collaborative data processing in wsn using voronoi fuzzy clustering 351 consider table 1, which describes the positions of the each sensor node, form that select any number of the nodes as a cluster head first. based on the selected cluster head, distance based membership function is found. here, c1= (9.5, 1.3), c2= (8.8, 2.5) are select as a cluster head, based on that the distance based membership functions are calculated for other sensors. the distance based membership function using step 8 for each sensor with respect to the c1 and c2 is calculated and the results are provided in table 2. table 1: the positions of the sensor nodes x y v1 2.5 2 v2 4.1 1.3 v3 5.4 0.8 v4 9.5 1.3 v5 8.8 2.5 table 2: distance based membership of the sensor nodes v1(2.5,2) v2(4.1,1.3) v3(5.4,0.8) v4(6.8,3.0) v5(8.8,2.5) v1(2.5,2) 0 0.2 0.3 0.4, 0.6 v2(4.1,1.3) 0.2 0 0.1 0.2 0.4 v3(5.4,0.8) 0.3 0.1 0 0.1 0.3 v4(6.8,3.0) 0.4 0.2 0.1 0 0.2 v5(8.8,2.5) 0.6 0.4 0.3 0.2 0 2.4 qos based membership consider the following table 3 that contains the values of the qos of the each node with respect to each cluster head. each of the sensor nodes has different quality parameter with respect to application where the sensors are applied. for this work qos functions are throughput, delivery ratio, delay time are taken. based on those qos values of the nodes the cluster head can be obtained. each of the node have the time slots at the beginning time, after generation of data packet the nodes are send to the cluster head within the next time slot, if the packets are not deliver in the particular time period then it marked as the delayed packet. the delay time is calculated by the ratio of the received time to the sending time of the node; the result of this subtracted by 1 that is a delay time. each of the nodes has the number of packets to send to the cluster head while sending the data packet to the cluster head some of the packets are not receive in the cluster head because of the interruption, noise. the ratio of number of received data packet in the cluster head to the number of sent data packet from the node. each of the nodes generates the data packets and sends it to the cluster head, flow of the data packet is varies from node to node with respect to the cluster head. the throughput of a node is calculated using number of packets sends to the cluster head in a particular time interval and it is different for each node with respect to the cluster. the membership value based on the qos values of the node with respect to the cluster heads are discovered by step 9 and it is given in table 4. 352 s. nithya kalyani, e. sasikala b. gopinath table 3: the qos values of each node with all possible cluster heads v1(2.5,2) v2(4.1,1.3) v3(5.4,0.8) v4(6.8,3.0) v5(8.8,2.5) v1(2.5,2) 0,0,1 0.6,0.4,0.7 0.2,0.6,0.8 0.5,0.8,0.3 0.4,0.4,0.5 v2(4.1,1.3) 0.2,0.5,0.8 0,0,1 0.4,0.8,0.2 0.3,0.5,0.6 0.7,0.4,0.5 v3(5.4,0.8) 0.4,0.8,0.2 0.4,0.5,0.6 0,0,1 0.2,0.5,0.8 0.4,0.4,0.5 v4(6.8,3.0) 0.4,0.2,0.8 0.5,0.5,0.4 0.4,0.5,0.4 0,0,1 0.4,0.2,0.8 v5(8.8,2.5) 0.4,0.8,0.2 0.3,0.4,0.5 0.6,0.4,0.5 0.4,0.2,0.8 0,0,1 table 4: the qos membership of the nodes c1 c2 v1 0.55 0.44 v2 0.46 0.53 v3 0.48 0.51 v4 0.51 0.48 v5 0.45 0.54 with the help of dxy and qxy values calculate the membership function using step 7. here equal weightage is given for both the distance and the qos and calculated values are given in table 5. the values of the weightage depends on the user. if the user wants to group the sensor nodes mainly based on distance then the user give the value of α is greater than β, if the user wants to group the sensor node based on the qos then the value of β is greater than α. table 5: sensor nodes membership function µ11 µ12 v1 0.535 0.455 v2 0.49 0.50 v3 0.50 0.48 v4 0.25 0.74 v5 0.72 0.27 consider the two membership value µxy1, µxy2 , where x be the sensor node, y1 and y2 be the cluster heads. if value of µxy1 minimum than µxy2 then the sensor node x moves to the cluster head y2 else x move to y1. in figure 1 dark shaded cells illustrates the assumed cluster head in a wireless sensor network. after finding the membership value for all voronoi cells, each and every cell is clustered based on the two assumed cluster heads. after the selection of the cluster head and its members, the cluster head transmits the received sensed data from the members to the sink and this will lead to more of time and energy consumption in ch. to solve this problem, data management process is achieved. 2.5 data management process data management process involves data aggregation or compression. data aggregation is a method which aims to reduce the localized congestion problem. it tries to gather knowledge from the sensored data by applying the maximum, minimum, average functions using step 18. collaborative data processing in wsn using voronoi fuzzy clustering 353 figure 1: transmission of aggregated or compressed data to the sink node it then transmits only the useful information (aggregated data) to the sink node thus reducing congestion and its linked problems. thus data aggregation helps the life of the wireless network to get increased. the cluster head sends the aggregated data to the sink node for the particular time interval. on the otherhand the collected sensed data are compressed in the cluster head by using the huffman code as in step 19. 3 experimental results and discussion the experimental results of the proposed approach to cluster head selection is described in this section. the comparative analysis of the proposed cluster head selection approach and k-means algorithm is presented. the proposed cluster head selection for data aggregation in wireless sensor network has been implemented using matlab and the performance of the proposed system is analyzed using the evaluation metrics including running time and size of received data in the sink node based on the number of cluster head in the wireless sensor network. 3.1 evaluation of running time by varying the number of the sensor nodes the vf algorithm is compared with the k-means algorithm in order to evaluate the performance of the proposed vf algorithm. the following figure 2 shows that the running time of the vf algorithm and k-means algorithm. to evaluate the graph we change the number of sensor nodes at each execution and evaluate the time of the both algorithm, the value of the cluster head is constant for both algorithm to find the execution time. here number of cluster heads is considered as 10 for all execution. the following figure 2 shows that, vf algorithm needs less time to execute when compared with the k-means algorithm. the reason behind is the if the membership function value of the sensor node is repeat in a single voronoi cell then the execution of the membership function for that sensor node get stop since the running time of the vf algorithm get reduced. 3.2 evaluation of running time by varying the number of cluster heads here, the running time is evaluated by keep the number of sensor nodes (1000) at constant and varying the number of cluster heads for each time. the following figure 3 shows that the running time of vf algorithm and k-means algorithm and when we see that graph we conclude the vf algorithm takes less to execute when compare to the k-means algorithm. the running time of 354 s. nithya kalyani, e. sasikala b. gopinath figure 2: running time vs no. of nodes the both algorithm is directly proportional to the number of cluster heads. the calculation of the membership function for each point with respect to the each cluster head takes more time since the running time is increase when the number of cluster head increase. figure 3: running time vs no. of clustering head 3.3 number of received data in sink node by varying the number of the sensor nodes after the selection of cluster head, the each of the sensor node sends their sensed data to the cluster head. the cluster head received many data’s from the sensor nodes, as per the user suggestion (data aggregation or data compression) the cluster head operate the received data and send that data to the sink node. the sink node receives the data from the cluster heads, the figure 4 shows that the number of received data in a sink node for different number of sensor nodes. based on the position of the cluster head, the received data of the sink node get varied since the position of the cluster head is different for both vf algorithm and k-means algorithm. hence, we take the number of received data in a sink node as a parameter. the following graph describes, the sink node receives more number of data’s by using vf algorithm rather than using the k-means algorithm. figure 4: no. of data received in sink node vs no. of nodes collaborative data processing in wsn using voronoi fuzzy clustering 355 3.4 number of received data in sink node by varying the number of the cluster heads the number of received data in a sink node is varies based on the number of cluster heads in a sensor network. figure 5 shows that the number of received node in a sink node based on different number cluster heads for both vf algorithm and k-means algorithm, the sink node receives more number of data’s by using vf algorithm rather than using the k-means algorithm. figure 5: size of data received in sink node vs no. of cluster head 4 conclusions in this paper, we have presented the approach for energy efficient cluster algorithm for data summarization in wireless sensor network. in order to reduce the energy of the wireless sensor network, we have presented the efficient voronoi fuzzy clustering algorithm. at first the voronoi cells are applied for each node in the wireless sensor network based on the energy of the sensor node. consequently, the cluster head is selected by the fuzzy clustering algorithm. every node in the wireless sensor network sends their sensed data to the cluster head. the data aggregation and data compression process is done in the cluster head. the cluster head aggregate the received data by taking the value of minimum, maximum and average and uses the huffman compression algorithm to compress the data and that compressed or aggregated data will send to the sink node. finally, the experiment has carried out and results ensured that the efficiency (60%) is higher when compare with the k means algorithm. bibliography [1] kulik, j.; heinzelman, w; balakrishnan, h. 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[6] tian, d.; georganas, n.d.(2002); a node scheduling scheme for energy conservation in large wireless sensor networks, from thesis: multimedia communications research laboratory, school of information technology and engineering, university of ottawa. [7] guru, s.m.; steinbrecher,m; halgamuge,s; kruse,r.(2007); multiple cluster merging and multihop transmission, lncs 4459: agpc, springer, 89-99. [8] qingchao zheng; liu,z.; liang xue; yusong tan; dan chen; xinping guan(2010); an energy efficient clustering scheme with self organized id assignment for wireless sensor networks, 16th ieee international conference on parallel and distributed systems, doi: 10.1109/icpads.2010.83, 635-639. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 6, month: december, year: 2022 article number: 5029, https://doi.org/10.15837/ijccc.2022.6.5029 ccc publications digital transformation strategy and environmental performance: a case study m. sarfraz, z. ye, f. dragan, l. ivascu, a. artene muddassar sarfraz school of management, zhejiang shuren university, 36 zhoushan east road, hangzhou, 310015, china muddassar.sarfraz@gmail.com zhixiao ye* school of management, zhejiang shuren university, 36 zhoushan east road, hangzhou, 310015, china, *corresponding author: 601145@zjsru.edu.cn florin dragan department of automation and applied informatics, faculty of automation and computers, politehnica university of timisoara, 14 remus street, timisoara, 300009, romania, florin.dragan@upt.ro larisa ivascu** management department, faculty of management in production and transportation, politehnica university of timisoara, 14 remus street, timisoara, 300009, romania, **corresponding author: larisa.ivascu@upt.ro alin artene management department, faculty of management in production and transportation, politehnica university of timisoara, 14 remus street, timisoara, 300009, romania, alin.artene@upt.ro abstract digital technology has drastically transformed businesses’ activities, operations, and processes. today, digital efficiencies have inspired companies to take initiatives to leverage the firm’s environmental performance. the study’s primary purpose was to examine how a digital transformation strategy affects environmental performance under the mediating role of innovation capabilities and moderating role of digital organizational culture. the data was obtained from manufacturing industry employees in pakistan. the results reveal that digital transformation strategy (dts) positively and significantly affects innovation capabilities and environmental performance. the link between digital transformation and environmental performance is mediated by innovation capabilities, while digital organizational culture moderates the relationship between innovation capabilities and environmental performance. keywords: organization culture; business sustainability; environmental performance; digitalization https://doi.org/10.15837/ijccc.2022.6.5029 2 1 introduction with changing our everyday lives to business practices, climate change and digitalization have emerged as the most defining features of the digital era. in recent years, digital technologies speeding up firms’ activities have shunned global companies with modern advancements and substantial innovations [1]. the innovative strategies deeply configuring into the firms’ practices are the core elements of digital transformation. in this regard, firms from all industries have recognized digital strategies as a central concept to digital integration and transformation implementation. the digital transformation strategy (dts) directs the firms’ vision, process, and activities toward digitalization [2]. a digital transformation strategy creates value for the organization [3] and encourages companies to go beyond their boundaries, substantially making digital transformations purposeful. at the beginning of the 21st century, when information and technological interventions, such as automated systems, cloud computing, blockchain, and big data) have taken over the creative and cultural sectors, the growth of digital transformation has not only gained new worth but also has become a strategic resource for preserving the world’s environment. for example, in the romains creative industries, digital technologies have increased organizational efficiency and innovativeness through information flows [4]. these profound interventions triggering the industrial environmental performance are the prime agenda for an innovative environment. further, taking the considerate influx of these modern advancements in other fields, the cultural sectors have also benefited from these tools, where these digital configurations have enhanced the market value of the cultural heritage. the new digital products (e.g., 2d, 3d scanning, virtual reality, metadata) have reduced the degree of environmental degradation [5]. similarly, the digital transformation process in healthcare and public administration has empowered ecological sustainability. altogether, the sensitive nature of these sectors has made the deployment of suitable it influence the organization’s environmental performance [6], [7]. indeed, owning to the far-reaching consequences of digital transformation, the profound innovations taking over the world’s major industries have made businesses adopt digital transformation strategies to achieve the goals. as a result, dts, influencing environmental performance, has become the top agenda for manufacturing companies. pakistan’s textile industry is a dominant manufacturing sector that is the significant cornerstone of the economy. however, being the largest exporter of textiles, pakistan today is still in the rising stage of technological breakthroughs [8]. in particular, pakistan’s textile enterprises are slowing down, thus significantly bringing negative environmental consequences. transformational strategies provide with new directions and novel paths to innovations, thus improving the environmental performance [9]. in this regard, ali [10] states that pakistan’s textile enterprises digitally need to grow to improve their environmental performance. hence, in this regard, today, the pakistan textile firms profoundly call for dts implementation to gain superior ecological performance. thus, addressing the growing concerns, pakistan textile companies should channel their potential for positive environmental performance with novel capabilities. given the pakistan textile enterprises, pakistan’s current situation reveals that firms’ can fill the gap through the demonstration of innovation capabilities [11]. indeed, this succession deficiency of the dts makes organizational innovation capabilities the prime reason for an organization’s survival. nasir et al. [12] suggest that organizations need to attain innovation capabilities to develop transformational strategies toward environmental improvement. since the beginning of the 21st century, the accelerated recognition of digital development has made firms embrace innovation capabilities as a necessity. however, considering today’s need, emerging countries should encourage firms to use innovation capabilities as the main thrust of ecological performance. innovation capability (ic) refers to the firm’s ability to integrate innovation [13]. ic has gradually become the pathway to environmental performance with the growth of digital transformation technologies in recent years [14]. undoubtedly, today, the blurring of the traditional boundaries of the various industries and the novel digital technologies have compelled companies, worldwide sectors, industries, and organizations to transform digitally. hence, in the established pattern of ict innovation, the adoption of the innovation capabilities has enabled the firms to fulfil the ecological agenda of the organization through the development of digital transformation culture [15]. the organization’s digital culture (odc) alludes to a set of shared beliefs that the new business models and process increases the readiness for digital innovation [16]. the organizational digital culture is an integral part of the firm’s structure that radically makes the organization respond to advanced market changes [17]. given https://doi.org/10.15837/ijccc.2022.6.5029 3 its role, it helps firms adopt technological resources and innovation processes. it speeds up the firms’ innovation process and ecological performance [18]. hence, today, the need for improved environmental performance appears to be accelerated around in terms of the digital transformation strategy, innovative capabilities, and organization’s digital culture. the study shows that pakistan’s textile enterprises still lag in digitization and innovation besides the increasing role of dts, ic, and odc [19]. the lack of a digital transformation strategy has expected to bring a toll on the environmental performance [20]. indeed, the digital technologies disrupting one sector after another have actively called pakistan’s textile enterprises to embrace the dts on account of ecological performance. therefore, dts needs to be developed and implemented to urge the country from ills. given pakistani’s manufacturing era, this study presents an innovative conceptual framework providing a clear understanding of the current issue. the study investigates the influence of digital transformation strategy on the environmental performance of businesses. in addition, this study contributes to the existing body of knowledge by determining the mediation and moderation of the innovation capabilities and organizational digital culture on the firms’ environmental performance. the dts transforming the environmental performance lacks considerable understanding today. hence, this paper addresses the need for a more comprehensive study of the environmental performance. this study significantly contributes to the previous literature in the following ways: first, previous studies have mainly focused on the methods of reducing the country’s pollution [21], but this is an important topic that has called the literature on the mechanism of digital transformation strategy influencing environmental performance. secondly, rare literature examines ecological performance in the context of digital transformation strategies in emerging markets. therefore, this study adds value in investigating the effect of dts on pakistan’s emerging market. thirdly, to the best of our knowledge, no research has been performed illuminating the impact of dts on ecological performance in the context of ic and odc. in particular, this is a novel study that incorporates both the mediating and moderating roles of ic and odc in explaining the effect of dts on ep. as a result, research findings will greatly value all stakeholders. it will play a vital role in improving the understating of the impact of dts, ic, and odc on firms’ environmental performance. altogether, this study will motivate all organizations in the context of digital transformations and innovations. 2 literature review in recent years, digital transformation has configured technologies to support firms’ environmental performance. indeed, today, pakistan’s textile enterprises steadily demand the development of highspeed transformational strategies and innovation capabilities to intensify their ecological performance. in this regard, the following section complicates the role of the digital transformation strategy in environmental performance. it presents a unique theoretical model that helps firms grow their innovation capabilities, thus building an organizational digital culture influencing eco-performance. the inherent section illustrates different notions, which are discussed in the following sequences: digital transformation strategy (dts), environmental performance (ep), innovation capabilities (ic), and organizational digital culture (odc). 2.1 digital transformation strategy, innovation capabilities, and environmental performance over the past years, globalization has compelled worldwide firms to evolve with changing digital technologies. digital technologies play a fundamental role in improving the dynamics of the business environment [22]. companies are increasingly focusing on embedding the corporates’ ecological thinking into their strategies. the digital transformation and innovation strategy, viewed as mutually exclusive, are the prime drivers of the environmental performance. compared to traditional organizational practices, digital transformation strategy provides a strategic advantage to firms in the shape of superior ecological outcomes [23], [24]. a digital transformation strategy is a profound phenomenon that enables firms to run conventional organizational assets with digital innovation. it led firms’ to strategically utilize digitalization to achieve their ecological objective. today the growing awareness for environmental protection has expanded, thus making the digitalization gain momentum in the context https://doi.org/10.15837/ijccc.2022.6.5029 4 of environmental performance. in manufacturing, digital transformation strategy is a trending topic that improves the ecological parameters [25]. it strengthens the corporates’ ecological value by facilitating business performance [26]–[28]. also, dts modifies the production processes in response to the customers’ need for environmentally-friendly products. it reduces the manufacturing cost of the business, causing them to scale up the firms’ environmental outcomes. indeed, dts is a critical trend for manufacturing firms that helps firms fight ecological vulnerabilities [29]. altogether, digital transformation is a strategic response that helps the organization to answer the technical question concerning better environmental performance. the digital transformation strategy bringing a strategic change in the scope inspires the firms to revamp the traditional organizations’ system with innovative technologies, applications, and processes [30]. with this trend towards it-influenced transformations, today, organizations have upturned their business structures, thus radically transforming the environmental status. the ecological conditions of the company change rapidly. indeed, this evolution has called for firms’ digitalization transformation strategy to become the utmost priority of today’s firms. [31]. it has facilitated the organization’s ecosystem by replacing traditional methods with new eco-friendly procedures. digital technology has leveraged the firm’s environmental performance with flexible it operations. in particular, it integration has made the firm standardize its efficiency, thus increasing its responsiveness to the environment [32]. in the same vein, the it-changing process enhancing the traditional way of doing business creates innovation capabilities to foster the firms’ offerings [33]. the effective use of digital technologies helps the company streamline business processes, which is critical for executing the digital strategy. this controlled access to technology speeds up the firms’ system of innovativeness. the innovation capabilities supported by the dts develop, integrate, and configure resources that connect the enterprise’s innovative strategy to compete in the fast-growing environment [34]. the dt phenomenon encourages firms to break away from the old structures, thus strategically bringing structural changes to the organizations [35]. it provides a competitive advantage to the firms by adopting essential digital competencies and resources. altogether, from the practical perspective, the dts improves the business models and customer experiences, thereby improving the firms’ environment [36]. hence, the literature concludes that digital transformation strategy is the most profound development influencing eco-performance [37] and innovation capabilities. therefore, in line with this argument, the hypothesis formed states: h1: digital transformation strategy positively and significantly impacts environmental performance. h2. digital transformation strategy has a positive and significant effect on innovation capabilities. 2.2 the mediating role of innovation capability since the beginning of the 21st century, rapid technological developments have accelerated the phenomenon of innovation capability in the world’s major countries. digital innovation promoting digital transformation is the prime driver of the company’s ecological performance. the innovation capability consolidated by digital transformation is a fundamental phenomenon [38] that accelerates the firms’ ecological goals. its dynamic capabilities widely configure the firms’ activities, processes, and procedures, thus exhibiting improved environmental performance. the innovation capabilities allowing digital transformation are the most profound construct scaling up environmental performance [39]. digital transformation shifts the firms’ value proposition to digital resources. the innovation capabilities facilitate the transformations and environmental performance [40]. therefore, in today’s era of novel developments, innovation capabilities are the prime contributor that balances the firms’ economic performance with ecological performance [41]. ardito and dangelico [42] state that firms’ digital transformation strategy fosters innovation capabilities and environmental performance. the firms’ digital transformation strategy dynamically creates capabilities that make the businesses quickly respond to the changing needs of the environment. the firms’ innovation resources are critical to firms’ performance. the innovation capabilities are a strategic source that fosters the firms to integrate the novel capabilities, thus boosting the firms’ ecological performance. the innovation capacity improves the digital transformation processes of manufacturing enterprises [43], thus exhibiting superior environmental performance [44]. however, in this regard, the notion of the ecosystem has gained considerable prominence in manufacturing. technological advancements guide the firm’s ecosystem. ict drives organizations’ digital transformations, indeed, their ecosystem [45]. today, https://doi.org/10.15837/ijccc.2022.6.5029 5 operating under ecological uncertainties, the innovation capabilities adjusting in the firms’ strategies had believed to bring fruitful results. ict innovation enhances the transformative capacities of the firms, thus strategically influencing their eco performance. the innovative capabilities translated into the dts are a profound construct that provides powerful support to the firms’ operations [46]. its digital characteristics encourage firms to develop and integrate novel technological applications into firms capabilities. altogether, the academic discussion on innovation capability in the light of dts and ep has gained success. significantly, this new normal has become a great accelerator of global innovation capabilities, work patterns, and strategies [47], [48]. in the last years, the firms have begun optimizing their processes and routines by developing strategies [49] that ensure the achievement of their environmental goal. indeed, digital orientation is an efficient predictor of high ecological performance. ecological performance is a global issue. altogether, innovation capabilities have become the fundamental driving force ensuring the firms’ performance. consequently, based on this prior literature, our hypothesis is as follows: h3: innovation capabilities positively affect environmental performance h3(a): innovation capability mediates the relationship between digital transformation strategy and environmental performance. 2.3 the moderating role of organizational digital culture digital transformation is a complex phenomenon that makes organizations swiftly respond to high market demand. in recent years, the unpredictable changes in the market have made companies develop odc to increase the business process, practices, and environmental performance. the organizational digital culture enables organizations to radially accepts values and norm that fosters their performance [50]. today, global environmental challenges have emerged as one of the most significant issues in manufacturing. in manufacturing, the odc enables companies to adopt the ict implications that support ecological performance. the odc makes the firms learn about the environmental needs of the customers, thereby swiftly adjusting the business process with innovation. organizational digital culture leveraging innovative landscape adds to the firms’ emerging capabilities. the digital culture formed by the firms’ innovation capabilities allows the organization and its people to grow in the era of digitalization [51]. the odc suggested as a factor influencing the firms’ performance is the most crucial element that shapes the organizations’ performance and behaviors towards the environment. the firms’ odc reduces the impact of environmental damage by rigorously embracing digital tools, products, processes, and activities that achieve high environmental performance. it creates a culture of digital transformation and innovation [52]. the innovation capabilities integrated into the organizational culture facilitate the firms’ environmental performance [53]. it fosters the firm’s operations, thus raising the value of environmental protection. it encourages firms to manage their resources, assets, and processes toward achieving superior eco-performance [54]. undoubtedly, the organization’s digital culture facilitating the firms’ innovative capabilities is an urgent topic in the digital ecosystem. in recent years, the increasing pressure to innovate has made the business face difficulty. as a result, today, organizations have embraced novel innovation capabilities to stay competitive. successful implementation of the organization’s digital culture ensures the firm’s ecological performance. the digital organizational culture influences the firm’s environmental performance via innovation capabilities. the digital culture enhances the transformational capabilities and models [55], thus enhancing environmental performance [56]. the ic is a critical factor in the firm’s environmental performance [57]. the organization benefiting from this concept increases its performance by achieving ecological goals. because it has gained significant importance in recent years, organizations have supported establishing a digital culture to improve environmental performance. therefore, based on past research, we have formulated the following hypothesis: h4: organization digital culture moderates the relationship between innovation capability and environmental performance. figure 1 shows study variables, including one independent variable (digital transformation strategy), one dependent variable (environmental performance), one mediator (innovation capabilities), and one moderator (digital organization culture). https://doi.org/10.15837/ijccc.2022.6.5029 6 figure 1: conceptual framework 3 methodology the survey method was utilized to collect primary first-hand data from the selected sample through well-structured, validated, self-administered questionnaires. the data was collected from the senior position-level employees working in the textile firms of pakistan from march 2022 to june 2022. the researcher contacted the potential respondents and assured them of the confidentiality, anonymity, and voluntary nature of participating in the study. they were informed about the ethical considerations adopted in the study. two waves of data were obtained with a time lag from the same sample. two-three weeks’ temporal gap was used between data collection in 1st and 2nd waves. it was made sure that the temporal gap was enough that it was improbable that substantial organizational events could have happened and influenced the research propositions. every response included a unique id to match the respondent’s answers between the two times to reduce the common method bias in the current study [58], [59]. 3.1 participants the frequency analysis was conducted to analyze the gender, age, education, and material status statistical frequencies and valid percentages against the total sample size collected in this study. table 1 shows 149 male and 118 female respondents participated in the current study—the valid percentage of 55.8 for male and 44.2 for female respondents measured in spss statistical software. the frequencies and valid percentages show that most employees were single and had a bachelor’s degree. table 1: participants’ demographic profile items frequency (n=267) percentage gender male 149 55.8 female 118 44.2 age 21-30 46 17.2 31-40 81 30.3 41-50 97 36.3 51-60 43 16.1 education intermediate 40 15 bachelor 108 40.4 master 76 28.5 m.phil/others 43 16.1 marital status single 144 53.9 married 123 46.1 https://doi.org/10.15837/ijccc.2022.6.5029 7 3.2 measurement scale to assess digital transformation strategy, li et al. [60] four-item scale was utilized. the scale is a five-point likert scale, and the responses ranged from 1 (strongly disagree) to 5 (strongly agree). innovation capabilities were assessed by utilizing the scale developed by borah et al. [61], and the scale consists of five items, responded on a five-point likert scale. based on martínez-caro et al. [62], digital organizational culture was evaluated on the 4-items. environmental performance was measured on the four items adopted from the study of su et al. [63]. 3.3 statistical analyses a vital step for effective and rigorous research is selecting appropriate analysis strategies and techniques following the research philosophy and paradigm. partial least squares structural equation modeling (pls-sem) was utilized to examine the data and verify the research hypothesis. the direct hypotheses without any mediator or moderator were checked using regression analysis. 4 results table 2 presents the results for cronbach’s alpha, factor loadings, composite reliability (cr), and average variance extracted (ave). results analysis shows all variables consistently reliable and close to standardized reliability level 1. the pearson correlation analysis was conducted to find whether a positive or negative relationship exists between the digital transformation strategy, environmental performance, innovation capabilities, and digital organization culture. all the variables have a positive relationship with each other. innovation capabilities have the highest cronbach’s alpha value (0.867), while digital transformation strategy has the highest ave value (0.701). figure 2 is a graphical illustration of measurement model results. table 2: validity and reliability analysis construct items loading α cr ave digital transformation strategy dt-1 0.832 0.858 0.904 0.701 dt-2 0.831 dt-3 0.838 dt-4 0.850 innovation capabilities icp-1 0.799 0.867 0.904 0.653 icp-2 0.792 icp-3 0.823 icp-4 0.812 icp-5 0.813 digital organizational culture doc-1 0.795 0.837 0.890 0.670 doc-2 0.825 doc-3 0.822 doc-4 0.833 environmental performance evp-1 0.846 0.854 0.902 0.696 evp-2 0.806 evp-3 0.833 evp-4 0.851 figure 2: measurement model https://doi.org/10.15837/ijccc.2022.6.5029 8 table 3 shows the results of discriminant validity using fornel larcker & htmt. in the current study, htmt values were also lower than the recommended threshold of 0.90 [64]. table 3: discriminant validity analysis (fornel larcker & htmt) 1 2 3 4 1. digital organizational culture 0.819 0.282 0.310 0.606 2. digital transformation strategy 0.241 0.837 0.699 0.677 3. environmental performance 0.265 0.599 0.834 0.645 4. innovation capabilities 0.513 0.586 0.557 0.808 the research model of this study consists of three kinds of associations, i.e., direct, mediating, and moderating relations. smartpls software was utilized to analyze the hypothesis relationship. table 4: direct hypothesis direct hypothesis beta se t-value p-value h1 dt → evp 0.389 0.072 5.429 *** h2 dt → icp 0.586 0.056 10.549 *** h3 icp → evp 0.420 0.087 4.801 *** dt= digital transformation strategy; icp= innovation capability; evp= environmental performance; *indicates significant paths: ***p<0.001 table 4 shows the results of the direct hypothesis. the study’s first hypothesis proposed that digital transformation strategy positively affects environmental performance. the h1 was approved with a beta of 0.389 and a t-value of 5.429. h2 states that a digital transformation strategy positively and significantly affects innovation capabilities. the h2 was accepted at the beta value of 0.586 and the t-value of 10.549. h3 demonstrates that innovative capabilities positively impact environmental performance. in this study, h3 was accepted at the beta value of 0.420 and the t-value of 4.801. figure 3 shows the graphical representation of structural model outcomes. figure 3: structural model graphical representation dt= digital transformation strategy; icp= innovation capability; evp= environmental performance; *indicates significant paths: ***p<0.001 the results of mediating hypothesis h3(a) are presented in table 5. innovation capabilities mediate the link between digital transformation and environmental performance, as indicated by h3(a). h3 was approved in this study with a beta value of 0.246 and a t-value of 4.760. the conditional effects in table 5 show that the innovation capabilities and environmental performance (β = 0.874 and p < 0.001) strengthen when the digital organization culture is high, which is illustrated in figure 4. https://doi.org/10.15837/ijccc.2022.6.5029 9 table 5: mediation hypothesis testing mediating hypothesis beta se t-value p-value h3(a) dt → icp → evp 0.246 0.052 4.760 *** table 6: moderating effect interaction effects beta se t-value p-value h4 interaction doc*icp → evp 0.130 0.048 2.711 ** conditional effects level of the moderator effects boot se llci ulci +1 std dev 0.874*** 0.099 0.678 1.069 h4 mean 0.697*** 0.067 0.565 0.829 -1 std dev 0.520*** 0.059 0.402 0.637 figure 4: moderating effect https://doi.org/10.15837/ijccc.2022.6.5029 10 5 discussion with the development of economic globalization, environmental challenges have made the enterprise encounter the progressing issue of ecological degradation. as enterprises’ traditional management strategies are seriously damaging the external environment, today, understanding the meaning of digital transformation has become imperative for most organizations. in recent years, the progressing environmental degradation has compelled businesses to transform. ict plays the most influential role in firms’ eco-performance. in this regard, today, the collaborated digital tools have raised the importance of digital transformation. according to isaksson et al. [32], a digital transformation strategy is a process that exploits the business process to improve environmental performance. in today’s complex environment, a defensible digital transformation strategy provides a series of advantages to firms. among these, the firm’s innovation capabilities also bring significant results for the organization. ict is a tool that rigorously scales enterprises’ computing power, thus increasing the flexibility of firms’ business models. as a result, the innovative developments in today’s era are the most significant source of business model transformation. the dts plays a strategic role in managing firms’ transformation processes [65]. it innovative capabilities integrated into the firms’ strategy are a strategic asset to the firms’ transformation. digital transformation influencing it capabilities inspires companies to survive in the high-paced environment by radically innovating their practices. indeed, our study also sees this integrated value system of information and communication technologies as a new life to the organization ecosystem. digital technologies such as big data, advanced algorithms, augmented reality, and multi-functional computing systems have revolutionized business ecological performance [66]. our study also confirms that digital technologies transforming companies’ capabilities and applications improve firm environmental performance [3]. indeed, it is valided that information technology reduces organizational efficiencies [67] in improving the firms’ environmental performance. however, in handling the ecological uncertainties, our study has also identified the organization’s digital culture to bring changes in the firms’ capabilities. the organization’s digital culture developing the firm’s innovative capabilities facilitates the firm’s ecological process and learning. it provides a sense of environment to the firms by supporting the abilities and skills that enhance firms’ innovation process [56]. altogether, based on our results and the previous data, we accept the hypotheses in section 2 (i.e., h1, h2, h3, h3a, h4). 6 conclusion in recent years, an organization realizing the value of digital transformation has adopted digital strategies for improving environmental performance. the high degree of digital transformation strategy enhances the firms’ environmental performance. the digital transformation strategy significantly revises the firms’ innovation capabilities and shapes the firm’s business culture, thus making the firms achieve beneficial ecological results. a digital transformation strategy provides a more in-depth assessment of the firm’s environmental performance in digital transformation. the study results show that digital transformation strategy significantly and positively impacts environmental performance. also, it reveals a positive mediating role of the innovation capabilities nexus to the digital transformation strategy and environmental performance. similarly, the study findings also conclude a positive moderating role of the organization’s digital culture between innovation capabilities and environmental performance. significantly, these research findings imply that digital transformation strategy plays a significant role in upgrading environmental performance. the study implications set the baseline for future studies where it helps organizations to achieve superior environmental performance through the digital transformation strategy, innovation capabilities, and organizational digital culture. 6.1 study implications this research contributes to the existing body of knowledge by offering unique practical implications. firstly, the study findings help companies design digital strategies to achieve environmental goals. in this regard, the current study findings can help the firms develop and implement dts, thus creating a win-win situation for stakeholders. indeed, the current study offers unique opportunities to https://doi.org/10.15837/ijccc.2022.6.5029 11 firms regarding the digital transformation models influencing environmental performance. this fundamental study can be the starting point for future researchers, organizations, and policymakers. it can assist stakeholders in grasping the digital change in manufacturing. however, the results showed that a systematic digital transformation strategy seeks to offer valuable resources to the firms. its explicit consideration enhances the firms’ innovation capabilities, thereby making the firms achieve ecological performance. hence, in this regard, the positive mediating impact of innovation capabilities enables manufacturing enterprises to promote their concerns for environmental performance. it encourages the management to foster the firms’ innovation capabilities, thus improving environmental performance. moreover, unlike the previous studies, this study focuses on the benefit of an organization’s digital culture from the perspective of 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(2022). digital transformation strategy and environmental performance: a case study, international journal of computers communications & control, 17(6), 5029, 2022. https://doi.org/10.15837/ijccc.2022.6.5029 introduction literature review digital transformation strategy, innovation capabilities, and environmental performance the mediating role of innovation capability the moderating role of organizational digital culture methodology participants measurement scale statistical analyses results discussion conclusion study implications international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 6, month: december, year: 2020 article number: 4024, https://doi.org/10.15837/ijccc.2020.6.4024 ccc publications a data collecting strategy for farmland wsns using a mobile sink y.q. zhang, j.y. lin, h. zhang yaqiong zhang* school of information engineering, yulin university yulin 719000, china *corresponding author: yqzhang2208@163.com jiyan lin school of information engineering, yulin university yulin 719000, china linjiyan1018@163.com hui zhang school of information engineering, yulin university yulin 719000, china myhui521541@163.com abstract to the characteristics of large number of sensor nodes, wide area and unbalanced energy consumption in farmland wireless sensor networks, an efficient data collection strategy (gcms) based on grid clustering and a mobile sink is proposed. firstly, cluster is divided based on virtual grid, and the cluster head is selected by considering node position and residual energy. then, an optimal mobile path and residence time allocation mechanism for mobile sink are proposed. finally, gcms is simulated and compared with leach and grdg. simulation results show that gcms can significantly prolong the network lifetime and increase the amount of data collection, especially suitable for large-scale farmland wireless sensor networks. keywords: farmland wsns, data collection, virtual grid, mobile sink 1 introduction the development of intelligent agriculture and precision agriculture requires positioning, timing and quantitative monitoring and management of farmland parameters. as an accurate and real-time information acquisition method, wireless sensor network has the characteristics of self-organization, low energy consumption, convenient configuration and low cost. it has a broad application prospect in the monitoring and control of farmland habitat parameters and crop growth monitoring [8]. a wireless sensor network (wsn) consists of a large number of spatially distributed, wirelessly connected, selfgoverning sensor nodes, which are generally deployed in monitoring environments. these sensor nodes need energy to sense, process and transmit data, but their energy (battery) is limited. therefore, it https://doi.org/10.15837/ijccc.2020.6.4024 2 is necessary to design an energy efficient data collection strategy to save energy consumption in order to extend the lifetime of a wsn [2]. a farmland wireless sensor network has the characteristics of wide range and large number of sensors. in traditional wireless sensor network, sensor nodes send data to the fixed sink through multi hop, which easily leads to the death of nodes near the sink due to excessive energy consumption, thus forming an energy hole. in recent years, the introduction of mobile sink can reduce the density of network nodes, improve the energy utilization of nodes, and extend the running time of the network [1, 3, 4, 13, 14, 21, 28]. under the practical background of farmland application, how to monitor and collect various environmental parameters and crop parameters is a key problem. in this paper, the strategy using a single mobile sink node, combined with appropriate clustering and moving path, to improve the efficiency of energy utilization and data collection in farmland wireless sensor network. the movement modes of mobile sink nodes include fixed path, random movement, controlled motion and mobile path algorithms based on swarm intelligence algorithm or mathematical programming (linear programming path strategy) [17, 25, 26, 27, 29]. the movement of a mobile sink is modeled as a tsp problem with overlapping circular neighborhood, and the firefly algorithm is used to solve its optimal moving path [5, 19]. the heuristic algorithm [15] and the opdg algorithm [11] aim to minimize the delivery delay and improve the real-time performance of the network by solving the tsp path. an algorithm is proposed using virtual force to determine the target position of sink node movement [22]. the mobile trend of sink nodes was predicted by combining the sleep and wake-up strategies to prolong the network lifetime [24]. a heuristic algorithm wrp is proposed, which can effectively reduce congestion and delay by giving higher priority to busy nodes in the network, at the same time, smt is used to introduce virtual access points to optimize the access path of sink nodes [18]. these algorithms can shorten the delivery delay, prolong the network lifetime and reduce congestion in different scenarios, but there are some problems such as the complexity of the algorithm and poor scalability. moreover, these algorithms are not suitable for large-scale wireless sensor networks. the farmland application of wsn is a kind of delay tolerant network, which has the characteristics of large range, many nodes and types. in the farmland wsn, cats algorithm selects the most covered stops one by one until all nodes are covered, and then plans a shortest path in the selected rendezvous points [9], but the algorithm is complex and requires higher computing power of mobile sink. the grdg (grid based data gathering) algorithm proposed can effectively collect farmland data by dividing virtual grid in the network, establishing two-level routing and cooperating with a randomly moving mobile sink, which improves the scalability of the network and the energy utilization of nodes [12], but the delay is larger and the amount ofdata collected is not large. in the farmland wsns, a large number of sensor nodes are needed to form a sensor network, which is combined with the farmland meteorological station to realize the dynamic and high-precision monitoring of the farmland ecological environment and seedling, soil moisture, disease and disaster [23]. in this paper, how to use mobile sink to collect data from large-scale farmland effectively is studied, and the solution is given. combined with the characteristics of hierarchical routing in wireless sensor networks, a clustering scheme based on grid and a mobility strategy of a sink node is proposed. 2 network model 2.1 node model it is assumed that a farmland wsn is composed of many sensor nodes which have same initial energy; the sensor nodes are non-mobile and randomly deployed in the monitoring area. each node in wsn can acquire its own geographical position and perceive its own residual energy. suppose the node model is as follows: (1) sensor nodes are non-mobile and each has its own unique id, the set of id numbers of n sensor nodes and one mobile sink node is m = {n1,n2,n3, . . . ,nn,nsink } , where ni is the i -th node and nsink is the mobile sink node; (2) all sensor nodes are isomorphic and can fuse data; (3) the wireless communication link is bidirectional and symmetrical. the node has the function of ranging, and the transmitting power can be adjusted at any time according to the communication https://doi.org/10.15837/ijccc.2020.6.4024 3 distance; (4) when there is no data collection task, the cluster member nodes can sleep, and the cluster head can wake up the member nodes regularly and request them to transmit the sensed data; (5) the storage capacity of sensor node is limited. when the amount of unsent sensing data exceeds the maximum storage capacity, the node will save the latest data and discard the earliest data. 2.2 energy consumption model energy consumption model of wireless sensor network is based on that of wireless communication system [10, 16]. the energy consumption of the transmitting end includes the energy consumption of the radio frequency module and the signal amplifier, and the energy consumption of the receiving end only includes the energy consumption of the receiving circuit. signal amplifier energy consumption is on the basis of distance between the transmitter and receiver where free space fading model and multipath fading model can be used [20]. the energy consumed by transmitting l bits of data is shown in the formula (1). etx(l,d) = { erf ∗l + ξfs ∗l∗d2, d < dc erf ∗l + ξtworay ∗l∗d4, d ≥ dc (1) in the above formula (1), ξfs is the amplifier multiple of free space model, ξtworay is the amplifier multiple of multiple path attenuation model, dc is a constant distance, and erf is the energy consumption of transmit/receive circuit. the energy consumed by receiving l bit data is not related to the transmission distance, as shown in formula (2)[6, 7] erx(l) = erf ∗ l (2) it can be seen from formula 1 and 2 that once the communication distance is greater than dc, the energy consumption will increase rapidly. 3 gcms algorithm in the farmland wsn, a strategy based on grid clustering and using a mobile sink (gcms) to collect data is proposed. the clustering structure can avoid the sensor sending a large amount of redundant data to sink. in this paper, virtual grid is used for clustering to balance the payload and energy consumption. each grid is a cluster, and the cluster head (ch) is selected in the cluster. the cluster head is responsible for collecting the sensing data of the nodes in the cluster and forwarding the data to the mobile sink after data fusion. when the sink node moves to a communication area of a ch, sink needs to inform the ch of its moving. when sink moves to a rendezvous point, the new location is reported to the cluster head in the communication range. the network still runs periodically as rounds, and each round includes three steps, as shown in fig. 1. first, update ch, the node with the most residual energy and closest to the grid center will be selected as the new ch. next, ch collects the data in the cluster, and finally ch sends the data to the mobile sink. all sensor nodes have the same hardware structure and function. they are randomly deployed in the monitoring area. each node knows its own location and remains static all the time. before the normal operation of the network, each node already knows the number of grids divided, so nodes can determine their position in the grid. https://doi.org/10.15837/ijccc.2020.6.4024 4 figure 1: operating round of gcms algorithm 3.1 clustering based on grid it is supposed that the farmland wireless sensor network is an m∗m rectangular area, n sensor nodes are randomly deployed in the network. the network is divided into several equal area grids, as shown in fig. 2. the total number of grids cnum is determined by n, as shown in formula (3). the width c1 of each virtual grid can be expressed by formula (4) cnum = bn/50c2 (3) cl = m/ √ cnum (4) the coordinates of reference origin are (x0,y0) , and the position coordinates of sensor node ni are (xi,yi) . if the logical coordinates ( grix, gr i y ) denote the number of virtual grid in which node ni is located, the grid to which the node belongs can be obtained according to the following formula (5). grix = ⌊ xi −x0 cl ⌋ , griy = ⌊ yi −y0 cl ⌋ (5) figure 2: grid division when each grid is regarded as a cluster, the whole network is divided into cnum clusters of equal size. which cluster a sensor node belongs to is determined by its logical coordinates. let the k grid https://doi.org/10.15837/ijccc.2020.6.4024 5 cluster be represented as cuk(k = 1, 2, . . . ,n), and chk is the cluster head of cluster cuk, and the number of nodes in the cluster is nk. it is called the gravity center of cluster cuk to find the location which has the smallest sum of distance squares with all nodes in the cluster. obviously, the gravity center of cluster cuk satisfies formula (6). nk∑ i=1 [ (xg−k −xj) 2 + (yg−k −yj) 2 ] = min nk∑ i=1 [ (x−xj)2 + (y −yj)2 ] (6) in formula (6),(xg−k,yg−k) is the coordinates of gravity center of cluster cuk, and (xi,yi) is the coordinates of any sensor node in the cluster. according to formula (6), the center of gravity coordinates of cluster cuk can be obtained as formula (6). xg−k = 1 nk nk∑ i=1 xi,yg−k = 1 nk nk∑ i=1 yi (7) according to the wireless communication energy consumption model, the communication energy consumption between cluster head and member nodes is mainly related to distance. therefore, in order to reduce and balance the communication energy consumption in the cluster, the node acting as the cluster head should be close to the center of gravity of the cluster. in addition, because the cluster head needs to forward data and manage the cluster, the node as the cluster head should also have more residual energy. so, the closer the node to the center of gravity and the more residual energy, the more likely it is to be selected as the cluster head. therefore, for the k grid, the weighted sum function for cluster head selection is constructed as shown in formula (8). fj−k = ω cl −dgj−k cl + dgj−k + (1 −ω) e nj res e0 ω ∈ (0, 1) (8) in formula (8), ω is a weight coefficient, enjres is the residual energy of node nj, e0 is the initial energy of sensor node, and dgj−k represents the distance between the center of gravity of the cluster cuk and the node nj in the cluster, as shown in formula (9). dgj−k = √ (xg−k −xj) 2 + (yg−k −yj) 2 (9) according to formula (8), calculate the f fj−k of each node in the cluster cuk, and select the node with the largest fj−k as the cluster head. in formula (8), the value of weight ω is determined according to the actual needs. if the value of ω is large, the cluster head selection focuses more on the distance between the node and the center of gravity; otherwise, it focuses on the residual energy of the node. at the end of each round, each node in the cluster reports its remaining energy and distance to the center of gravity to the cluster head. the cluster head calculates and compares the fj−k value of each node according to formula (8). the node with the largest fj−k will be selected as the cluster head of the next round. after clustering based on virtual grid, cluster head manages the whole cluster, including determining the time interval of data collection, reporting sink location, data fusion and forwarding, etc. 3.2 data transmission in cluster after clustering and selecting the cluster head, the other nodes in the cluster send joining request to the cluster head, the request contains its own id. on the basis of the number of member nodes, the cluster head divides time into several slots, that is, the tdma mechanism is adopted to assign the corresponding transmission time slots to each member node. each cluster member node collects and transmits data to the cluster head in its own time slot, and is dormant to save energy in other time slots. cluster head collects information and fuses the data, then sends information to the mobile sink. in this way, the information is forwarded to the mobile sink. https://doi.org/10.15837/ijccc.2020.6.4024 6 3.3 path planning of the mobile sink the time interval between two moves of the sink to the same point can be regarded as the delay of data acquisition of sensor nodes, which is recorded as tp. therefore, the maximum delay limit means that there is a tlimit and formula (10) must be satisfied. tp < tlimit (10) on the other hand, the mobile sink can be considered as moving at a constant speed, and the time consumption of data transmission can be ignored; if the moving speed of the sink is v. as shown in formula (11), the maximum delay limit of mobile sink data collection can be transformed into the path length limit of mobile sink. { v ∗tp < v ∗tlimit lp < llimit (11) the goal of gcms algorithm is to plan a periodic mobile data collection loop for mobile sink in the node deployment area, which contains several rendezvous points. to achieve this goal, gcms is divided into three steps. the first step is to set up a virtual grid in wsn. all grid line intersections become the candidate rendezvous points (rp) of gcms. the second step is to determine the first rendezvous point from the candidate rendezvous points; the first rendezvous point will be set in the area with the most neighbor nodes. the third step is to add the new rendezvous points to the path one by one after setting up the first rendezvous point to realize the path expansion. this process is heuristic, and its heuristic function mainly considers two factors: the distance from the existing rendezvous point, the better, and the higher the density of the neighbor nodes, the better. among them, "distance from the existing rendezvous point" is euclidean distance. for solving the traveling salesman problem (tsp), the moving path and its length of mobile sink for data collection are obtained. then, whether the length exceeds the limit llimit of the total length of the path is detected. if it exceeds the limit, the point will be discarded; otherwise it will be added to the path to start a new round of rendezvous point generation process. the path planning of sink is finished until the list of candidate residency points is empty. rendezvous point is the location of mobile sink for data collection. whenever a rendezvous point is reached, the mobile sink collects the data cached by the nearby cluster head nodes. the mobile path of sink in the network is a loop which is obtained by solving the traveling salesman problem for all the rendezvous points in the network, along which the data is collected periodically. in order to generate residences, a list of candidate residences should be generated first. the network deployment area is continuous, in which there are infinite points. the computer algorithm cannot detect infinite points, and it is obviously unnecessary to repeatedly detect the candidate rendezvous points close to each other. therefore, a grid like structure is introduced, in which the intersection of horizontal and vertical lines of the grid like structure is taken as the candidate rendezvous point. as shown in fig. 3, the black points are the candidate rendezvous points. after the candidate rendezvous points are generated, the first rendezvous point can be selected first, and then the path can be expanded step by step. gcms will select the candidate rendezvous point with the highest density of neighbor cluster head nodes as the first rendezvous point. if more than one residency point satisfies the condition, the point closest to the sink is selected as the first residency point. in order to describe the density of cluster head nodes, a covering function c(n,i) is introduced. for any candidate rendezvous point nand a cluster head node i, the covering function c(n,i) is shown in formula (12). c(n,i) = { 0, d(n,i) > rc 1, d(n,i) ≤ rc (12) in formula (12), d(n,i) is the euclidean distance between i and n, and rc is called the coverage radius of the candidate rendezvous point, which is a parameter. therefore, the node density cnum of the candidate rendezvous point n is defined as the total number of cluster head nodes covered by the candidate rendezvous point, as shown in formula (13), where n is the total number of cluster head nodes in the network. nn = cnum∑ i=1 c(n,i) (13) https://doi.org/10.15837/ijccc.2020.6.4024 7 figure 3: candidate rendezvous points after selecting the first rendezvous point, gcms will gradually expand from the remaining candidate rendezvous points, and then form a complete loop by solving the traveling salesman problem (tsp). in order to evaluate the priority of the candidate rendezvous points, gcms will evaluate the fitness of the candidate rendezvous points, and try to select a rendezvous point with the highest fitness to join the loop in each round. the fitness evaluation function of candidate rendezvous point n is shown in formula (14). f(n) = nn cnum ∗ dn−min llimit (14) in formula (14), dn−min is the minimum distance from the candidate rendezvous point n to all established rendezvous points, which is obtained by euclidean distance. llimit is the total path limit of mobile sink. it can be seen from this function that gcms will preferentially select the candidate rendezvous points with higher density of peripheral sensor nodes and far away from the existing rendezvous points. if there are more than one candidate residency point satisfying the condition, one of them is selected randomly. whether the candidate rendezvous point can be added to the rendezvous point list needs to be checked whether the length of the new loop exceeds the limit of the total path length of the mobile sink. there are two possible results: l > llimit or l <= llimit. if it is the former, it will not be considered next; if it is the latter, it will be added to the path and become the rendezvous point. the candidate residency point is then removed from the list of candidate residences. therefore, in gcms, each candidate rendezvous point is checked once and only once. the generation process of each new rendezvous point is a main cycle of gcms. until all the candidate residences have been checked, that is, when the list of candidate residences is empty, the loop ends, and the final result is a data collection path of the mobile sink. 3.4 allocation of residence time for the mobile sink in order to further reduce the network delay, the cache data of cluster head node is obtained in the process of data transmission, and then the dwell time of sink is reserved according to the data size. at the same time, the residency point of the next hop cluster head is broadcast to the cluster head in the next hop cluster to prepare for data transmission. let lp denote the total moving distance of sink, tp denotes the time interval of each round of data collection, t and tk are the total residence time of each round and the residence time of each residency point respectively, and v represents the move rate of sink. the total residence time t and residence time of each cluster head tk can be described as formula (15). { t = tp − lp v = ∑ca−num k=1 tk tk = 1n(k)σ ∑n(k) j=1 µkj (15) https://doi.org/10.15837/ijccc.2020.6.4024 8 in the formula (15),ca−num is the total number of residency point accessed, n(k) is the number of neighbor nodes of the kth residency point, µkj is the size of data packets collected by cluster head j, and σ is the transmission rate of data packets when cluster head communicates with mobile sink. according to the above time allocation mechanism, sink can improve the data transmission balance between cluster heads within a certain network delay time. considering the communication ability of cluster head nodes, sink can change the residence time according to the total amount of data cached by cluster head within the communication range. therefore, the allocation mechanism can be used for fixed slot tdma mechanism. dma communication mechanism includes the following steps: step 1: mobile sink sends broadcast packets to notify cluster head nodes in the adjacent area of its location coordinates and communication radius. step 2: the neighbor cluster head nodes requests to establish a connection with sink after receiving the broadcasting packet and the request data contains the amount of data of cluster heads. step 3: mobile sink selects the next rendezvous point according to the path planning, and sends ack messages containing dwell time information. step 4: after the neighbor cluster heads of the next rendezvous point receive the ack message, they collect the data of the member nodes in the cluster, and pack and fuse the data. step 5: the mobile sink node collects the data of the current access cluster heads, and then the cluster heads are identified to enter the sleep mode, and the sleep slot is t. step 6: if this point is the last rendezvous point, then finish; otherwise, the sink node moves to the next rendezvous point and return to step 1. 4 simulation analysis 4.1 simulation parameters setting the simulation runs in matlab r2018a environment. in the simulation experiment, 100 -600 sensor nodes are randomly distributed in the specified range for multi round data collection performance. the initial energy of each node is 1j, the data generation rate is random, and there is redundancy in the data sent by nodes in the sensor network. the erf of 1 bit data processing circuit is 50∗10−9j/bit, and the maximum data storing capacity of a single node is 32kbit. considering the residual energy and distance, ω is chosen as 0.5. the simulation parameters are shown in table 1. table 1: simulation parameters parameters values area of network (m) 1000m∗1000m number of sensor nodes 100 − 600 length of packet (bit) 4000 initial energy of sensor node e0(j) 1 transmission radius of cluster head (m) 70 communication range of sin k(m) 120 dc(m) 87.7 moving speed of mobile sin k(m/s) 20 cache of sensor node (k bit ) 32 sensing speed of sensor node (bit/s) 1000 energy consumption of transmit/receive circuit (erf ) (nj/bit) 50 amplifier multiple of free space model (ξfriss) ( pj/bit/m2 ) 10 amplifier multiple of multiple path attenuation model (ξtworay) ( pj/bit/m2 ) 0.0013 energy consumption of data fusion (nj/bit/ packet ) 5nj/bit/ packet weight coefficient ω in ch election formula (8) 0.5 https://doi.org/10.15837/ijccc.2020.6.4024 9 4.2 results and analysis in order to analyze the effectiveness of the proposed algorithm, gcms is compared with leach which is based on fixed sink and grdg which is based on mobile sink and grid clustering. the number of nodes alive, network lifetime, the amount of data collected and delay will be compared and analyzed. 4.2.1 comparison of the number of nodes alive considering the farmland wireless sensor network with 400 sensor nodes randomly deployed, the mobile rate of sink is set at 10m/s. with the operation of the network, some nodes die after some rounds due to the depletion of energy. the number of nodes alive corresponding to the 3 methods in different rounds is shown as fig. 4. figure 4: comparison of number of nodes alive varies with the number of rounds network life cycle can be divided into stable period and unstable period. before the fnd (the time of first node death) of the network, the network is in the stable period; after the fnd of the network, the network is in the unstable period. it can be seen from fig. 4 that the stable period in gcms and grdg is significantly longer than that in leach. and, the number of nodes alive in gcms and grdg in unstable period is always larger than that in leach. this is because that gcms and grdg use mobile sink for data collection, which outperforms leach in energy consumption balance of network nodes, so fnd appears later. moreover, due to the mobility of sink, the network node can communicate with sink in a short distance, thus saving energy consumption, so the survival time of the node is longer. in terms of the two mobile sink data collection methods, the proposed gcms is better than grdg in terms of the number of nodes alive in the stable and unstable periods of the network. this is because the mobile sink in gcms moves according to the shortest path, which makes the network energy consumption more balanced, while the mobile sink in grdg moves randomly. it can also be seen from fig. 4 that after entering unstable period, the number of surviving nodes in gcms decreases most rapidly, which means that once a node dies, the energy of other nodes will also be exhausted quickly. it shows that the network energy consumption in gcms is more balanced further. therefore, in terms of network stability and running time, the proposed gcms is better. 4.2.2 comparison of network lifetime the lifetime of the network is compared by the time of the first node death. fig. 5 shows the lifetime of the network at different scales. the abscissa represents the number of sensor nodes, and https://doi.org/10.15837/ijccc.2020.6.4024 10 the ordinate represents the life cycle of the network. here, the number of rounds of the fnd is taken as the measurement standard. with the increase of the number of sensor nodes, the network life cycle of leach algorithm shows a downward trend, on the contrary, the network lifetime of gcms and grdg strategies shows an upward trend. leach algorithm uses fixed sink for data gathering, and the energy consumption from cluster head to sink node is large. when the number of sensors increases, the amount of data forwarded by cluster head increases, so it leads to the rapid depletion of energy consumption and the death of nodes. in the gcms and grdg algorithms using mobile sink for data gathering, due to the less energy consumption of uniform clustering and data transmission from cluster head to mobile sink, the energy consumption does not increase sharply with the increase of the number of sensors. therefore, the number of rounds of the fnd is not significantly advanced, and the performance of gcms algorithm is slightly better than that of grdg. therefore, mobile sink is used for data collection is more suitable for large-scale wireless sensor networks. figure 5: comparison of fnd with different number of nodes deployed in wsn 4.2.3 comparison of amount of data collected as shown in fig. 6, the total amount of data collected in the network lifetime is compared when different numbers of sensor nodes are deployed in the wireless sensor network. it can be seen that the amount of data collected by leach algorithm is significantly less than that of the other two algorithms using mobile sink. this is because the introduction of mobile sink can improve the network lifetime, so the amount of data collected is larger. the gcms algorithm in this paper collects more data because of the longer network lifetime. however, with the increase of the number of nodes in the network, the amount of data collected by leach algorithm does not increase significantly. this is because with the increase of node density, it will lead to data transmission collision in leach, making some time slots wasted and not transmitting effective data. but, in gcms proposed in this paper and grdg, the communication between nodes in the cluster adopts tdma mechanism instead of csma mechanism. in other words, ch allocates a fixed communication time slot to each cluster member node, so there is no collision and the amount of data collected will increase with the extension of network lifetime. https://doi.org/10.15837/ijccc.2020.6.4024 11 figure 6: comparison of the amount of data collected by sink node in short, with the increase of the number of nodes, the amount of data collected by various methods increases approximately linearly. in theory, this is because the increase in the number of nodes makes the perceived data more, so the amount of data sent to sink increases significantly. however, in the actual data transmission process of wsns, when the network runs to a certain round, if some nodes die prematurely, the network will not work and the data collection performance will be greatly damaged. therefore, network life cycle (network lifetime) is the premise to improve the performance of data collection. if the network lifetime is long enough, nodes can work longer and collect more data. in summary, compared with leach algorithm, the gcms algorithm proposed in this paper has a slight advantage in data collection. that is, the gcms algorithm proposed in this paper is suitable for data collection of large-scale sensor networks. 4.2.4 comparison of network delay finally, the effectiveness of the proposed method is verified from the perspective of data transmission delay. considering that the speed of wireless transmission is far greater than that of sink, only gcms and grdg using mobile sink technology are simulated and compared in terms of data transmission delay in this section. assuming that the movement rate of mobile sink is 20m/s and 400 sensor nodes are deployed in the wsn, it shows the average delay comparison of data transmission with different rounds (i.e. from 0 to 1200 rounds) in fig. 7. figure 7: comparison of delay of different rounds https://doi.org/10.15837/ijccc.2020.6.4024 12 as can be seen from fig. 7, the average transmission delay of gcms is significantly lower than that of grdg. this is because mobile sink can choose the shortest route from multiple mobile paths and determine different pause times according to the data size in gcms. in grdg, the mobile sink moves randomly, but it is not necessarily the optimal path. the longer the path may lead to the longer data collection delay. the average delay comparison of different nodes in the sensor network is shown in fig. 8. it can be seen that the average delay increases slightly with the increase of the number of nodes in grdg algorithm, but it is not obvious. in gcms algorithm, with the increase of the number of nodes, the delay increases obviously. when the number of nodes increases to 400, the delay increase is no longer obvious. this is because in gcms, with the increase of the number of nodes, the movement path of sink will increase, which will lead to the increase of delay. however, the growth rate slows down when the number of nodes increases to a certain value, which proves that gcms algorithm is more suitable for large-scale and delay insensitive networks. figure 8: comparison of average delay of different number of nodes 5 conclusions a data collection algorithm (gcms) based on virtual grid and mobile sink is proposed in the paper. the farmland wireless sensor network is divided into several grids, that each grid is a cluster, in which the geographical location and residual energy are considered to select the cluster head. in order to improve the efficiency of data collecting of mobile sink, different access probabilities are set according to the distance between cluster head nodes, the number of neighbor nodes in cluster head. so the shortest path of sink can be selected from multiple mobile paths. considering the impact of data delay and different data size collected by cluster heads, different pause times can be determined according to the amount of data to be collected. the residence time of sink in each location is set to a different value to ensure that each cluster head can send all its cache data to sink within a given time. finally, the gcms algorithm in this paper is simulated and compared with leach and grdg methods. the results show the algorithm proposed in this paper has great advantages in prolonging the network lifetime and improving the amount of data collection. compared with grdg algorithm, the amount of data collected is increased by 10% on average and the network life cycle is prolonged by about 14% in gcms algorithm. however, the network considered in this paper is simple and sensor nodes are isomorphic. heterogeneous nodes and sink nodes moving at variable speed should be considered in next step for practical application. https://doi.org/10.15837/ijccc.2020.6.4024 13 acknowledgements this work was supported by shaanxi province science and technology plan project (2020ny-170) and yunlin municipal science and technology bureau of research projects (2019-77-1). thanks for their help. references [1] ang, k.l.m.; seng, j.k.p.; zungeru, a.m. (2017). optimizing energy consumption for big data collection in large-scale wireless sensor networks with mobile collectors. ieee systems journal, 12(1), 616-626, 2017. 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[29] zhou, h.; yu, k.m. (2019). a novel wireless sensor network data aggregation algorithm based on self-organizing feature mapping neutral network, ingenierie des systemes d’information, 24(1), 119-123, 2019. https://doi.org/10.15837/ijccc.2020.6.4024 15 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: zhang, y.q.; lin, j.y.; zhang, h. (2020). a data collecting strategy for farmland wsns using a mobile sink, international journal of computers communications & control, 15(6), 4024, 2020. https://doi.org/10.15837/ijccc.2020.6.4024 introduction network model node model energy consumption model gcms algorithm clustering based on grid data transmission in cluster path planning of the mobile sink allocation of residence time for the mobile sink simulation analysis simulation parameters setting results and analysis comparison of the number of nodes alive comparison of network lifetime comparison of amount of data collected comparison of network delay conclusions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 3, month: june, year: 2021 article number: 4170, https://doi.org/10.15837/ijccc.2021.3.4170 ccc publications construction method of tender document based on case-based reasoning t.t. zhou, x.d. gao, g.y. wei tingting zhou, xuedong gao*, guiying wei school of economics and management university of science and technology beijing, china 30 xueyuan road, haidian distict, beijing 100083, china *corresponding author: gaoxuedong@manage.ustb.edu.cn abstract the core activities of tender documents compilation are to collect similar historical tender documents, select compilation templates of tender documents and revise templates of tender documents partially. however, when the historical tender documents have accumulated to a certain amount, it becomes extremely difficult for compilers to summary, reuse and revise templates artificially in traditional compiling methods. based on casebased reasoning (cbr), this paper studied the content recommendation method in the process of tender document construction. firstly, a structured model of tender documents was constructed, and similar tender cases were retrieved from the tender case database according to the characteristics of tender cases; secondly, the non-interference sequence index was used to measure the similarity of clauses used in similar tender cases, and the recommended sequences of reference template and content module of tender documents were constructed, which realized the recommendation of compiling templates of tender documents and partial revision of templates; finally, the knowledge of the new tender case was updated. the empirical analysis shows that the construction method of tender documents based on case-based reasoning not only proposes a suitable strategy for compiling tender documents, but also improves the compilation efficiency of tender documents. keywords: compilation of tender documents, case-based reasoning, structured tender documents, non-interference sequence index, reference templates of tender documents. https://doi.org/10.15837/ijccc.2021.3.4170 2 1 introduction the tender document is the basis for the tenderer to make the bidding documents and participate in the tenders, and also the important basis for the evaluation experts to evaluate the bids (bidding represents the act of offering something or to do something for a particular price, while tender represents the act calling for bidders to bid for something, then selecting the best supplier). in addition, the tender document is the basis for signing the contract, and most of the contents of the tender document should be included in the contract [11]. therefore, the compilation of tender documents is a critical link. the compilation of tender document is the processing of editing clauses and charts in a tender document in accordance with a certain format and logic, in order to describe the tender project. through interviews with experts and business personnel in the tender field, this paper summarizes the actual process of tender documents compilation and finds that its core activities are collecting similar historical tender documents, selecting tender document compilation templates, and modifying tender document templates partially, these activities are not only the key points but also the difficulties in compiling the tender documents. traditional compilation methods of tender documents need professionals to select similar tender documents from historical documents, and modify them according to the actual content of the project artificially. finally, a target tender document is obtained. this method is reliable to a certain extent, but over relying on people might cause low efficacy and high operating cost. at the same time, it is easy to lead to the lack of compilation content, inappropriate, and even illegal behavior [15, 22]. at present, the research on the compilation of tender documents mainly focuses on two aspects. on the one hand, the research basically stays in emphasizing the standardization of the contents of tender documents. this kind of article mostly appears in the periodical papers in the construction industry. for example, the literature [20] points out the problems easily occurred in the traditional tender document compilation method, and puts forward the matters needing attention in the compilation process to remind the compilers to avoid. on the other hand, the research has begun to introduce machine writing, natural language processing and other computer means to optimize the content compilation process of tender documents, which is still at the stage of structuring the tender documents. for example, in the latest paper [21], aiming at the problem of template solidification of tender documents for power industry, nine standardized tender document templates are divided for different tender project types, which are used for reference in the compilation of tender documents. besides, the division method does not consider that the same tender project type will also cause inconsistency in the use of templates, so it is difficult to ensure the pertinence of the content recommendation of the template. therefore, it is significant to propose a complete set of intelligent recommendation algorithm for tender document content. therefore, this paper studies the intelligent recommendation of tender documents based on case-based reasoning technology. the main innovations are as follows: first of all, this paper summarized the key and difficult problems in the compilation process, and then introduced casebased reasoning as the theoretical framework to build an intelligent recommendation algorithm of tender document content from similar case retrieval, to case reference template reuse, to case reference template revision, and to new case learning, so as to ensure the rationality of the recommendation system; secondly, we consumed clustering method to retrieve similar tender case sets, which realized the first step of content recommendation of tender documents based on case-based reasoning, which greatly reduced the recommended scope of tender cases; thirdly, the non-interference sequence index was consumed to measure the similarity of tender case content, to realize the construction and recommendation of similar case reference templates and content modules, so as to ensure that the recommended content is more scientific and accurate; https://doi.org/10.15837/ijccc.2021.3.4170 3 finally, through the construction of tender case learning algorithm, the tender knowledge of new problems was updated into the database, which made the recommendation system more perfect. 2 related works 2.1 compilation of tender documents literature [4, 14] points out that ‘the purpose of the compilation of the tender documents is to provide all relevant information about the proposed contract, rules, conditions, etc. to the contractor, and to provide each contractor with general data in sufficient detail to adapt to the project. therefore, it should be prepared as detailed and accurate as possible’. brook [2] indicated that major problems associated with content quality of tender documents include missing information, late information, wrong information, insufficient detail, impracticable designs, inappropriate information, unclear information, provisional information, poorly arranged information, uncoordinated information and conflicting information. chen et al. [3] proposed several opinions in compiling the tender documents: should fully grasp the business information, accurately identify the tender requirements, and strictly abide by the laws and regulations at the same time to invite industry experts, and then ensure the reasonable and lawful content written in the tender documents. on november 1, 2007, the standard construction tender document of the people’s republic of china was officially released. this document is the only official authorized document in china, and it is also a relatively complete and formal template for compiling tender documents [5]. wang proposed an electronic tender document compilation system. the template components of the tender document were compilated by business personnel artificially according to the national standard tender document, and then the template combining and editing were carried out for the actual tender project. this method had certain logical innovation [17]. the emergence of machine writing has provided an effective way to replace the manual writing method, and it is mainly used in news writing, poetry creation, automatic summarization, biography generation, and abstract writing etc. [19]. tang edited and integrated the tender documents by using the extraction natural language generation mechanism, which improved the compilation efficiency of the tender documents to a certain extent. however, the text generation is relatively stiff, which requires human resources to polish the language [16]. in a word, the above researches have mainly completed the structural and standardized study of the tender documents, but there was a lack of appropriate use of the existing tender lessons in compiling tender documents. this paper will propose a case-based reasoning method to optimize the entire process of tender document construction. the tender document template is constructed automatically by referencing historical tender lessons, which greatly reduce human participation, making the tender documents compilation more efficient and scientific. 2.2 case-based reasoning theory the existing theories have made a preliminary exploration on the optimization of the compiling tender documents. aiming at their shortcomings, this paper applies data mining technology and case-based reasoning (cbr) to analyze and design the recommendation algorithm of tender documents content. the basic process of cbr is: when a new problem is encountered, the system retrieves the original case base according to the key characteristics, finds a candidate case that is most similar to the new problem, and reuses the reference template of the candidate case. if the reference template of this candidate case is not satisfied, it can be modified to adapt to the https://doi.org/10.15837/ijccc.2021.3.4170 4 problem to be asked. finally, the revised case can be saved in the database as a new case, so as to serve as a reference next time when similar problems are encountered [6]. the above process can be summarized into four steps: case retrieval, case reuse, case revision and case learning. cbr takes case as knowledge element, knowledge acquisition and representation are natural and direct, and can be self-learned. it has the advantages of simplifying knowledge acquisition, improving solving efficiency through direct acquisition, high solving quality, and being suitable for non-computational derivation [10]. with the deepening of the research of cbr, the application scope and field of cbr are expanding. it has been applied in general problem solving, legal cases, medicine and medicine, weather forecasting, machine fault diagnosis, enterprise consulting and decision-making, etc., and has proved the effectiveness and practicability of cbr method [1, 7, 9, 12, 13]. figure 1: the research object of the core link of cbr combined with the steps of cbr, the research objects of each link of compiling tender documents were obtained. as shown in figure 1, the research object of the case retrieval is case characteristics, the research object of case reuse and revision is the solution (reference template)nad its components, and the research object of the case learning is the case. this paper follows the thinking mode of cbr to create an intelligent recommendation method for compiling tender documents. the research ideas of this paper are as follows: firstly, the tender document was structured and divided into several document units. this paper selected modular document units as examples for content recommendation. secondly, the nearest neighbor algorithm was used to calculate the similarity of tender case characteristics among cases, and similar tender cases were retrieved from historical tender cases. then, the content recommendation model of tender documents based on non-interference sequence index was established, and the recommendation sequence of reference template can be directly used from the similar case set, i.e. the case reuse. if the reference template cannot be used directly, enter case revision to make partial revision (content module) to a reference template, and the new solution will be learned into the tender knowledge base as a reference template for the next recommendation process finally. this method separated business personnel from the tedious task of compiling tender documents, and ensured the efficient compiling of tender documents by optimizing the compiling process of tender documents. 3 construction of content recommendation model of tender documents 3.1 structured processing of tender documents definition 1: the document unit is the content of the tender document which is constrained by the title at any logical structure level in the tender document. https://doi.org/10.15837/ijccc.2021.3.4170 5 the analysis of the original tender documents reveals that the tender documents have the following characteristics: a. the tender document has a relatively fixed structure framework; b. according to its own compilation requirements, the tender documents contain many titles, which naturally divide the tender documents into content units with logical independence; c. the content units of tender documents have strict hierarchical relationship logically; d. the content units of the same level have no logical sequence relationship. the core idea of cbr is to use the historical lessons to help solve new similar problems. therefore, this paper classified the document unit manually from the perspective of whether it needs to reference historical lessons and the degree. among all tender document units, the factual document unit is the tender document unit which does not need to refer to the historical tender lessons. it is used to objectively describe a tender activity. the empirical document unit is all document units except the factual document units, which can improve the compilation efficiency by referring to or directly citing the historical tender lessons. and according to reference degree, the empirical document unit could be also divided into three types further: the formatted document unit, the modular document unit and the characteristic document unit, as shown in figure 2. structured processing is to divide the tender documents into detachable, mergeable and logically connected document units, which is the basis of recommending the contents of tender documents. figure 2: structured model of tender documents table 1: recommendation strategy of tender document content document unit content characteristic recommendation strategy historical lessons reference degree recommended recommended content factual unneeded not at all no formatted need complete yes templates modular need a lot yes templates, components of templates characteristic need a little yes components of templates according to the division of document units, the recommended object of the tender document content specified in this paper was the empirical document unit, and the recommended content were the templates and components of the templates. the modular file unit in the empirical document unit covered all kinds of recommended content, therefore, taking it as an example, this paper selected a document unit named “bidder qualification requirements” to study content recommendation method of tender documents. 3.2 retrieval of similar tender cases by summarizing the experience of experts, this paper obtained 17 elements (as shown in figure 3) covering five aspects of project overview, qualification conditions, bid evaluation https://doi.org/10.15837/ijccc.2021.3.4170 6 methods, suppliers and complaints/objections, which can be used for similar tender case retrieval. however, if it was directly applied to case retrieval, it would lead to low matching degree of similar cases and high computational complexity inevitably. therefore, we formulated the following principles to simplifying elements. figure 3: analysis elements of tender activities according to the principle of data sparsity, the elements of suppliers were reduced. the main reason was that part of supplier data was missing and cannot be filled due to special circumstances, such as tender failure. according to the principle of representativeness of data content, firstly, the project manager, tender scope and complaint/objection elements in project overview were reduced, as the values of the above elements in all tender activities were nearly the same. then, the evaluation setting under the analysis factors of project overview and scoring method was reduced. the values of the above elements in all tender activities were nearly different, mainly because the tender projects were built around specific tender objects. according to the principle of data processing simplicity, the bidder qualification requirements and scoring methods in condition of qualification elements were reduced. the main reason for the scoring results under the condition of analysis elements was that the data types of the above analysis elements were all textual, which required complex natural language processing methods to analyze the specific content. in conclusion, the tender case characteristics <project type (a1), procurement method (a2), whether to conduct prequalification (a3), bid evaluation method (a4), tender object type (a5)> were constructed . table 2: data type transformation of case characteristics original data type transformation rules new data type a1 str 0: goods, 1: engineering num a2 str 0: public tender, 1: invitational tender num a3 bool 0: no, 1: yes num a4 str 0: the comprehensive evaluation method, 1: the lowest bid evaluation method num a5 str 0: pump, 1: groove, 2: elevator,. . . . . . str by analyzing the data type of these case characteristics, it was found that a1, a2 and a4 were all character variable with two kind of value, so the two kind of value can be directly https://doi.org/10.15837/ijccc.2021.3.4170 7 converted into 0-1 values. besides, as boolean variable, a3 could be converted to 0-1 values as well. the 0-1 values above could be used in the equation (3) directly. due to the variety of types of a5 , it was impossible to realize the continuity by artificial design of change rules, and we just number its character value. so far, the whole transformation results have been shown in table 2. in order to achieve a5 participating in the calculation of equation (3), it was necessary to deeply study the tender cases of specific a5 and the number of each type of a5 in each category, to calculate the distance of different tender object types. the distance of two tender object types is introduced as follows: dist(cka5,cla5 ) = |cka5 − cla5| = 1 − g∑ ρ=1 jρ(cka5,cla5 ) (1) jρ(cka5,cla5 ) =   |cka5|ρ+|cla5|ρ |cka5|+|cla5| (∃(cka5 ∈ clρ) ∧ (cla5 ∈ clρ)) 0 (¬(cka5 ∈ clρ) ∧ (cla5 ∈ clρ)) (2) at present, the commonly used case retrieval algorithms include knowledge guide method, neural network method, inductive index method and nearest neighbor method [8]. in this paper, the nearest neighbor method was selected as the method basis for tender case retrieval. the difference degree of case characteristics between tender case ck and cl is as follows: simcase (ck,cl) = √ ∑n i=1 |ckai − clai| 2 n (3) n is the total number of tender case characteristics, n=5, ckai is the value of tender case ck under tender case characteristic ai, the smaller the value of simcase(ck,cl), the higher the similarity of tender cases, otherwise, the lower the similarity. based on the nearest neighbor method, this paper proposed a tender case retrieval algorithm. the steps are as follows: algorithm 1 caseretrieval(cn+1): similar tender cases of new problem cn+1 input: tender cases {ck|k ∈ n+}, tender case class clρ (ρ ∈ [1,g]), decision threshold λcase, tender case characteristics a =< c(n+1)a1,c(n+1)a2,c(n+1)a3,c(n+1)a4,c(n+1)a5 > of new problem cn+1 output: similar tender cases csim of new problem cn+1 step 1: calculate difference degree simcase(cn+1,ck) between the new problem cn+1 and all the tender cases ck. step 2: similar case retrieval. set csim = φ, if simcase(cn+1,ck) 6 λcase, thus ck ∈ csim; else ck /∈ csim step 3: output the results of csim 3.3 reuse of reference template of tender document after the retrieval of historical tender cases similar to the new problems, the compilation of tender documents will enter the stage of compiling tender documents or partial modification referring to similar case templates. the compilation template of tender document is the reference template, and the process of constructing and recommending reference template of similar cases is case reuse. first of all, let us introduce several tender concepts need to be used. tender case ck is a set composed of a tender document and the characteristics of tender object described in the tender documents. and tender document is composed of several clauses naturally, thus, clause di is the minimum content unit with logical number in the tender document. besides, https://doi.org/10.15837/ijccc.2021.3.4170 8 the tender cases with the same tender document could be clustered into one tender case class (clρ), and each tender case class only contain one document template (si). definition 2: reference template is solution in which all the content in the tender document is not the same, and denoting it as (si). taking the document unit of bidder qualification requirements document unit as an example, table 3 describes 3 tender cases and 3 kind of tender document used by the cases. however, document content of c1 and c3 are the same, thus only 2 reference templates are included finally, i.e. s1 = {d1,d2,d3,d4}, s2 = {d1,d3,d5,d6}. table 3: the clauses usage in bidder qualification requirements document unit clause content c1 c2 c3 d1 the bidder has independent legal personality and the ability to perform the contract; 1 1 1 d2 only manufacturer bids are accepted; 1 0 1 d3 the bidder has iso quality system certification; 1 1 1 d4 the bidder has the financial, technical and production capacity or supply performance required for the performance of the contract, and meets the corresponding conditions specified in the tender document; 1 0 1 d5 the manufacturer is expected to make direct investment in the project, but also accept the agent’s bidding; if the agent bids, the manufacturer’s sole authorization for the project shall be provided; 0 1 0 d6 different units in charge of the same person, or with a controlling or management relationship, may not participate in the same tender or the tender for the same tender project whose bid sections have not yet been divided. 0 1 0 clause usage jk(di) is the judgment function of whether clause di is used. if clause di is used in tender case ck, thus jk(di) = dik = 1; otherwise, jk(di) = dik = 0, as shown in table 3 (1 means to be used, 0 means not to be used). thus, the connection between clauses can be established through their usage of the tender case. when the two clauses appear or disappear at the same time in some tender cases, it indicates that the two clauses are related closely, such as set {d1,d3}, {d2,d4}, {d5,d6}; on the contrary, such as the clauses d2 and d5, d4 and d6, they never appear at the same time, the similarity between them is low. based on the above analysis, the non-interference sequence index can be used to measure the difference between two objects. if the non-interference sequence index is the same, the two objects have strong similarity. in the next part of this paper, the contents of the tender documents will be clustered and recommended according to this method. non-interference sequence is a positive integer sequence, m = (m1,m2, . . . ,mk, . . . ), where, nth clause is greater than the sum of the previous (n-1) clause, i.e., mn > ∑n−1 k=1 mk, n > 2 [18]. suppose a data set c has m objects and n attributes, and these attributes are all binary attributes, which are recorded as c1,c2, . . . ,ck, . . . ,cn, then dik is the value of object di(i ∈ [1,m]) for attribute ck. the non-interference sequence index of object di is: q(di,m) =∑n k=1 dik ∗mk, where, m is a selected non-interference sequence [18]. lemma 1 [18]: for two objects di and dj in the binary attribute data set c, whose attribute values are di1,di2, . . . ,din and dj1,dj2, . . . ,djn(dik,djk ∈ {0, 1},k ∈ [1,n]), respectively, which are recorded as jc(di) = (di1,di2, . . . ,din) = {jk(di)|k ∈ [1,n]},jc(dj) = (dj1,dj2, . . . ,djn) = {jk(dj)|k ∈ [1,n]}. non-interference sequence index is q(di,m) = ∑n k=1 dik∗mk and q(dj,m) =∑n k=1 djk ∗mk. thus if q(di,m) = q(dj,m),jc(di) = jc(dj), otherwise jc(di) 6= jc(dj). https://doi.org/10.15837/ijccc.2021.3.4170 9 to sum up, the recommended idea for reference template of tender document is divided into two steps, as shown in figure 4. the first is the construction of reference template, which identifies all the reference templates used from the tender documents of the tender case set similar to the new problem, so as to ensure the relevance of the recommended content; the second is to recommend the reference template according to the frequency of the reference template in the similar tender case set and the sorting result of the reference template, so as to realize the content recommendation of the tender document reference template and ensure the reuse work high efficiency. figure 4: recommended idea for reference template of tender documents theorem 1: in the tender case database, if and only if the non-interference sequence index of all tender cases in the tender case class is the same, there is only one reference sample in the tender case class. this is defined as the decision theorem of reference template. for any tender case class clρ, and any non-interference sequence m = (m1,m2, . . . ,mi, . . . ) in the tender case database, assume any tender case ck,cl ⊂ clρ(ρ ∈ [1,g],k 6= l), the template of ck is sk, all clauses in the tender case database is d = {di|i ∈ [1,m]},theorem 1 shows that: (1) if q(ck,m) = q(cl,m), thus sk = sl; (2) if q(ck,m) > q(cl,m) or q(ck,m) < q(cl,m), sk 6= sl. proof: according to the lemma 1, (1) if q(ck,m) = q(cl,m), thus jk(d) = jl(d), and ∀i, i ∈ [1,m],jk(di) = jl(di); moreover sk = {di|jk(di) = 1},sl = {di|jl(di) = 1}, and jk(di),jl(di) ∈ {0, 1}, thus sk = sl; at the same time, ck,cl ⊂ clρ(ρ ∈ [1,g],k 6= l), so there is only one reference template sk in tender case class clρ. (2) if q(ck,m) > q(cl,m) or q(ck,m) < q(cl,m),thus jk(d) 6= jl(d); set i(i ∈ [1,m]) be arbitrary subscript that satisfy jk(d) 6= jl(d), thus jk(di) 6= jl(di), and as jk(di),jl(di) ∈ {0, 1}, thus jk(di) = 1,jl(di) = 0, or jk(di) = 0,jl(di) = 1. at this time, if jk(di) = 1,jl(di) = 0, and as sk = {di|jk(di) = 1},sl = {di|jl(di) = 1}, then di ∈ sk,di /∈ sl,sk 6= sl; besides, ck,cl ⊂ clρ(k,l ∈ [1,g],k 6= l), thus there are at less two reference templates in tender case class clρ. otherwise, it could also be proves as above if jk(di) = 0,jl(di) = 1. to sum up, the recommendation algorithm of reference templates of tender documents was proposed. the steps are as follows: in step 3, |sρ|c is the frequency of reference template sρ that appearing in the tender case set c, |c| is the total number of tender cases, and reference template value of sρ in c is vc(sρ) = |sρc|/|c|. in step 4, the reference template sequence ss is a sequence with strict order relationship after sorting the reference template set s = {sρ|ρ ∈ [1,g]} according to certain rules. https://doi.org/10.15837/ijccc.2021.3.4170 10 algorithm 2 templatesrecommend(cn+1): recommended sequence of reference templates of tender documents input: clause usage {dik|i ∈ [1,m],k ∈ [1,n]}, arbitrary non-interference sequence m, tender case set csim = {ck|k ∈ [1,n]} that similar to the new problem cn+1 output: recommended sequence of reference template srec for new problem cn+1 step 1: calculate non-interference sequence index q(ck,m) of each tender case in csim. step 2: construct reference templates. the tender cases with the same q(ck,m) are merged into one tender case class clρ at the same time, define the reference template of this class as sρ at this time, all reference template sets scsim are obtained. step 3: assess reference template value. calculate the recommended value vcsim(sρ) of each reference template for scsim in csim. step 4: construct recommended sequence of reference templates of tender documents. the reference sample set scsim is arranged in descending order according to vcsim(sρ), and the reference sample sequence ss = (s1,s2, ...sm) is obtained. intercept the reference template sequence ss satisfying vcsim(sρ) > α to construct the reference template recommended sequence srec = (s1,s2, ...si) to the new problem cn+1. step 5: output the results of srec. 3.4 revision of reference template of tender document after analyzing the content recommendation ideas of tender documents, it was found that the precondition for entering the recommendation process of content modules was that the business personnel have determined subjectively that all the recommended reference templates cannot be used directly, and need to modify the content of one of the reference templates. based on hierarchical clustering method, this paper proposed the idea of constructing content module to modify the reference template of tender documents as shown in figure 5. figure 5: recommended idea for content module of reference templates firstly, constructing a complete set of content modules, and the content modules of each reference template were identified from the reference template recommendation sequence to ensure the relevance of the revised content. then, the hierarchical clustering of content modules is carried out based on the similarity of content modules. at the same time, according to the frequency of content modules in similar tender case sets, the recommended sequence of content modules within the same category of content modules to be modified is obtained, and the recommended content modules are used to modify it. the accuracy of the correction is ensured. https://doi.org/10.15837/ijccc.2021.3.4170 11 definition 3: content module dcp is a collection of clauses. in the same way, reference template is a collection of content modules. theorem 2: in the tender case database, if and only if the non-interference sequence index of all clauses in the content modules is the same, the content modules can divide all reference samples. this is defined as the decision theorem of content modules. for any content module dcp, assume any clauses di,di ⊂ dcp(i,j ∈ [1,m], i 6= j), theorem 2 shows that: (1) if q(di,m) = q(dj,m) and di ∈ sk, thus dcp ∈ sk; (2) if q(di,m) = q(dj,m) and di /∈ sk, thus dcp /∈ sk; (3) if q(di,m) > q(dj,m) or q(di,m) < q(dj,m), di ∈ sk, thus dj /∈ sk; (4) if q(di,m) > q(dj,m) or q(di,m) < q(dj,m), and di /∈ sk, thus dj ∈ sk. proof: according to the lemma 1, (1) if q(di,m) = q(dj,m), thus jc(di) = jc(dj), and ∀k,k ∈ [1,n],jk(di) = jk(dj). if di ∈ sk, thus jk(di) = 1,jk(dj) = 1 and dj ∈ sk, and as di,di ⊂ dcp(i,j ∈ [1,m], i 6= j), thus dcp ∈ sk; (2) while if di /∈ sk, thus jk(di) = 0,jk(dj) = 0 and dj /∈ sk, and as di,di ⊂ dcp(i,j ∈ [1,m], i 6= j), thus dcp ∈ sk; (3) if q(di,m) > q(dj,m) or q(di,m) < q(dj,m), thus jc(di) 6= jc(dj). set i(i ∈ [1,m]) be arbitrary subscript that satisfy jk(d) 6= jl(d), thus jk(di) = jk(dj). if di ∈ sk, thus jk(di) = 1,jk(dj) = 0 and dj /∈ sk. and as di,di ⊂ dcp(i,j ∈ [1,m], i 6= j), it may lead to the contradiction between di ∈ sk and dj /∈ sk, thus dcp could not divide sk. (4) while if di /∈ sk, thus jk(di) = 0,jk(dj) = 1 and dj ∈ sk. and as di,di ⊂ dcp(i,j ∈ [1,m], i 6= j), it may also lead to the contradiction between di /∈ sk and dj ∈ sk, thus dcp could not divide sk as well. besides, several concepts should be introduced as follows, in order to conduct the revision of reference template. |dcp| is the frequency that the content module dcp is occurred in the tender case c = {ck|k ∈ [1,n]}, the content module value of dcp is: vc(dcp) = |dcp|/n. it is known that the usage of clauses di and dj in the tender case database c = {ck|k ∈ [1,n]} are represented as jc(di) = {dik|k ∈ [1,n]},jc(dj) = {dik|k ∈ [1,n]}, then the difference degree between clauses di and dj is: simdoc (di,dj) = √ ∑n k=1 |dik −djk| 2 n (4) according to theorem 2, all clauses in the same content module are used the same in the tender case database, so a content module only needs to select one clause as a representative to calculate the degree of difference between the content modules. thus, as for content modules dcp = {di|di ∈ dcp, i ∈ [1,g]},dcq = {dj|dj ∈ dcq,j ∈ [1, l]}, the difference degree between them is: simdoc (dcp,dcq) = simdoc (di,dj) (5) and, content modules whose difference degree meets a certain threshold are combined into a content module class dmx = {dcp|dcp ∈ dmx,x ∈ [1,r]}, and content modules within the same category can be recommended to each other. in the same way, as for two content module classes dmx = {dcp|dcp ∈ dmx,x ∈ [1,r]} = {di|di ∈ dmx} and dmy = {dcq|dcq ∈ dmy,y ∈ [1,r]} = {dj|dj ∈ dmy}, the difference degree between them is: simdoc (dmx,dmy) = max(simdoc (dcp,dcq)) = max(simdoc (di,dj)) (6) recommended sequence of content module drec(p) is a sequence of content modules starting from dcp. this sequence is ordered according to certain rules. for example, in figure 5, we regard dc7 as the target content module, the recommended sequence of dc7 is drec(7) = (dc7,dc8,dc9,dc6). according to the content module recommendation idea of the reference template, the revision algorithm of the reference templates of the tender documents was proposed. the steps are as follows: https://doi.org/10.15837/ijccc.2021.3.4170 12 algorithm 3 modulesrecommend(dcp): content modules for target content module dcp input: clause usage {dik|i ∈ [1,m],k ∈ [1,n]}, arbitrary non-interference sequence m, recommended sequence of reference template srec to the new problem cn+1 output: recommended sequence of content module drec(p) for content module dcp step 1: calculate non-interference sequence index q(di,m) of each tender case in csim. step 2: construct content module. the clauses with the same non-interference sequence index are merged into a content module dcp. step 3: assess content module value. calculate the recommended value vc(dcp) of each content module in csim. step 4: calculate the difference degree odsim between content modules. step 5: construct content module class. regard each content module dcp as a content module class dmp. step 6: odsim is assigned to the difference degree of initial content module class odmsim. by using hierarchical clustering method, the content module classes with the smallest difference between are merged into a new content module class. repeat the merge until all content modules merge one class. select threshold β and regard the content module class satisfying odmsim < β as the fianl content module class. step 7: use content module to divide reference template. identify the recommended sequence of reference templates to be revised, and divide the content module set sρ{dcp,dci,dcj...} of each reference template sρ. step 8: construct recommended sequence of content modules. assume that the target content module is dcp, and identify the content module class dmx = (dcp,dcq,dcs,dcr) in which dcp is located, then sort dcp,dcq,dcs,dcr in this class in descending order according to their difference degree with dcp. if the difference degree is the same, these content modules are further sorted in descending order according to the content module value, and the final sorting result is regarded as the recommended sequence of content modules. for example, if simdoc(dcp,dcs) < simdoc(dcp,dcr) = simdoc(dcp,dcq) and vc(dcq) > vc(dcr), thus drec(p) = (dcp,dcs,dcq,dcr). step 9: output the results of drec(p). 3.5 study of tender case knowledge the tender knowledge involved in this paper includes: tender case class (k1), distance of tender object types (k2), reference template (k3), content module (k4), content module value (k5), clause difference degree (k6), content module difference degree (k7), recommended sequence of content module (k8). only the tender case reuse and tender case revision can generate new tender knowledge. moreover, the generation of new knowledge is very different between tender case reuse and tender case revision as shown in table 4, where y means yes, n means no, u means unsure. therefore, it is necessary to design tender case learning algorithms separately. table 4: generation of new tender knowledge tender knowledge k1 k2 k3 k4 k5 k6 k7 k8 case reuse y y n n y y y u case revision y y y u y y y u https://doi.org/10.15837/ijccc.2021.3.4170 13 3.5.1 tender knowledge learning of case reuse according to the new knowledge generation of case reuse in table 4, the tender case learning algorithm ideas based on case reuse was proposed in this section. the input of the algorithm was: new problem cn+1, clause usage after learning {dik|i ∈ [1,m],k ∈ [1,n + 1]}, original content module dcp, original content module value vc(dcp), original tender case class clρ(ρ ∈ [1,g]), original reference template sρ(ρ ∈ [1,g]), original distance of tender object type dist(cia5,cja5 )(i,j ∈ [1,n]), original clause difference degree simdoc(di,dj)(i,j ∈ [1,m]). • tender case class learning method sn+1 as the solution of the new case cn+1 constructed by case reuse was a reference template sρ existing in the original tender case database, the new case cn+1 can be directly added to the tender case class clρ using the reference template sρ. thus ∀i(i ∈ [1,m]), if dik −di(n+1) = 0, then cn+1 ∈ clρ,ck ∈ clρ. • distance of tender object type learning method the learning result in the case reuse process was that a new problem is added to an original tender case classes, so only the distance related to the tender object type in the same tender case class with the new problem were affected, it could be recalculated through equation (1) and equation (2). the distance results irrelevant to the tender object type of the new problem did not change. • content module value learning method because only one new problem was learned at a time, frequency of content module might remain unchanged or increase once, and the total number of tender cases increased by one.it was known that, original tender case was c = {ck|k ∈ [1,n]}, original content module was dcp, original content module value is vc(dcp), reference template of new problem is sn+1, then the new content module value vc ′ (dcp) was as follows: vc ′ (dcp) = { vc(dcp) ∗n/(n + 1) (dcp /∈ sn+1) (vc(dcp) ∗n + 1)/(n + 1) (dcp ∈ sn+1) (7) • clause difference degree learning method it was known that, clause was di,dj(i,j ∈ [1,m]), original clause difference degree was simdoc(di,dj), clause usage was {di(n+1)|i∈[1,m]} of new problem cn+1, then new clause difference degree simdoc ′ (di,dj) could be obtained by adjusting simdoc(di,dj): simdoc ′ (di,dj) = √ ((simdoc(di,dj))2 ∗n + |di(n+1) −dj(n+1)|2) n + 1 (8) • content module difference degree learning method there was no new content module generated in the case reuse, but the new problem changed the clause difference degree, which led to the change of content module difference degree indirectly. using equation (7) to update the new content module difference degree simdoc(dcp,dcq) as follows: simdoc ′ (dcp,dcq) = simdoc ′ (di,dj) (9) where di ∈ dcp, i ∈ [1,g]},dj ∈ dcq,j ∈ [1, l]. • recommended sequence of content module learning method when content module class dmx,x ∈ [1,r] had been obtained by default, and new content module difference degree simdoc ′ (dcp,dcq) and new content module value vc ′ (dcp) had been updated, we used step 8 of algorithm 3 to update the new recommended sequence of content module drec ′ (p). https://doi.org/10.15837/ijccc.2021.3.4170 14 3.5.2 tender knowledge learning of case revision according to the new knowledge generation of case reuse in table 4, the tender case learning algorithm ideas based on case revision was proposed in this section. the input of the algorithm was: new problem cn+1, clause usage after learning {dik|i ∈ [1,m],k ∈ [1,n + 1]}, original content module dcp, original content module value vc(dcp), original tender case class clρ(ρ ∈ [1,g]), original reference template sρ(ρ ∈ [1,g]), original distance of tender object type dist(cia5,cja5 )(i,j ∈ [1,n]), original clause difference degree simdoc(di,dj)(i,j ∈ [1,m]). • tender case class learning method since the premise of entering case revision was that the new problem cannot be solved by case reuse, i.e. the solution of the new problem was different from all the solutions in the original tender case database. thus, the new problem did not belong to any original tender case class according to theorem 3 and algorithm 3.it was known that, new problem was cn+1, original tender case class was clρ(ρ ∈ [1,g]). the tender case database need to add a tender case class which only contained the new tender case cn+1, thus, the new tender case class after learning was clρ(ρ ∈ [1,g + 1]), and clρ+1{cn+1}. • distance of tender object type learning method as a new case cn+1 was added after learning, it meant its tender case class only contained one tender case type. if the tender case type is a new type, this tender case type will be added in the database, and its distance with other tender object types is 0 according to equation (1) and equation (2); otherwise all the distance of tender object type remains unchanged. • reference template learning method since the relationship between tender case class and reference template was 1-to-1, the solution to the new problem was a new reference template. thus, the new reference template after learning was sρ+1 = sn+1. • content module learning method there were three ways to construct the reference template of case revision learning: (1) the new combination of the original content module; (2) original content module is divided into several new content modules; and (3) several new clauses are constructed. as for the new clauses, this paper used the following strategies to construct the content module: if the new reference template sρ+1 contained σ new clauses, each new clause di ∈ sρ+1(i ∈ [m + 1,m + σ]) was constructed as a new content module. when the new cases were accumulated for a period of time, algorithm 2 would be used to determine whether these content modules containing only one clause could be merged into one content module; for each original clause di(i ∈ [1,m]), using step 2 of algorithm 3 to repartition the content module, the result of partition was uncertain, it might split one initial content module into several new content modules, or kept the original content module of this clause unchanged, thus new content module dc′q(q ∈ [1,q]) after learning is: dc ′ q =   di (i ∈ [m + 1,m + σ]) dcp (∀di,dj ∈ dcp,di(n+1) = dj(n+1), i,j ∈ [1,m]) di,dj (∀di,dj ∈ dcp,di(n+1) = 0,dj(n+1) = 1, i,j ∈ [1,m]) (10) • content module value learning method if no new content module was generated in case revision, the calculation of content module value was the same as that of case reuse; if a new content module constructed by the new clauses was generated, the frequency of that content module was 1; if the initial content module was divided into several new content modules, then the frequency of the new content module was increased by 1 on the basis of the frequency of the original content module, thus new content module value vc ′ (dc′q) is: https://doi.org/10.15837/ijccc.2021.3.4170 15 vc ′ (dc ′ q) =   vc(dcp) ∗n/(n + 1) (dc ′ q ∈ dcp,dc ′ q /∈ sρ+1,q ∈ [1,q−σ]) (vc(dcp) ∗n + 1)/(n + 1)(dc ′ q ∈ dcp,dc ′ q ∈ sρ+1,q ∈ [1,q−σ]) 1/(n + 1) (q ∈ [q−σ,q]) (11) • clause difference degree learning method the difference degree between the original clause and the new clause need to be recalculated by equation (4); the difference degree between the original clauses could be obtained by the difference degree of the original clauses simdoc(di,dj) and the original clauses usage of new problems; as the frequency of the new clause was 1, the whole difference degree between new clauses was 0. thus, the new clause difference degree sim′doc(di,dj) is: sim ′ doc(di,dj) =   √ ((simdoc(di,dj))2∗n+|di(n+1)−dj(n+1)|2) n+1 (i,j ∈ [1,m])√ ∑n+1 k=1|dik−djk| 2) n+1 (i ∈ [1,m],j ∈ [m + 1,m + σ]) 0 (i,j ∈ [m + 1,m + σ]) (12) • content module difference degree learning method (the same as case reuse) • recommended sequence of content module learning method (the same as case reuse) 4 results and discussion in order to verify the effectiveness of the recommendation algorithm, this paper will carry out experiments on the bidder qualification requirements document unit based on four steps of case-based reasoning. we selected 144 tender documents of an enterprise as the data source for the experiment, and randomly selected 25% of the tender documents as the test set data, namely c, and the remaining 75% of the tender documents as the training set data. table 5: recommended results of case reuse new problem id srec actual solution if recommend successfully? new problem id srec actual solution if recommend successfully? 1 s3, s1, s4, s6 s8 0 19 s3, s1, s4, s6 s3 1 2 s1, s2, s5, s3 s1 1 20 s3, s1, s4, s6 s16 0 3 s1, s2, s5, s3 s1 1 21 s3, s1, s4, s6 s17 0 4 s1, s2, s5, s3 s1 1 22 s1, s2, s5, s3 s1 1 5 s3, s1, s4, s6 s3 1 23 s1, s2, s5, s3 s2 1 6 s3, s1, s4, s6 s3 1 24 s3, s1, s4, s6 s18 0 7 s1, s2, s5, s3 s1 1 25 s3, s1, s4, s6 s3 1 8 s3, s1, s4, s6 s20 0 26 s3, s1, s4, s6 s19 0 9 s3, s1, s4, s6 s1 1 27 s1, s2, s5, s3 s1 1 10 s3, s1, s4, s6 s3 1 28 s1, s2, s5, s3 s1 1 11 s3, s1, s4, s6 s4 1 29 s1, s2, s5, s3 s1 1 12 s3, s1, s4, s6 s10 0 30 s1, s2, s5, s3 s1 1 13 s3, s1, s4, s6 s11 0 31 s3, s1, s4, s6 s21 0 14 s3, s1, s4, s6 s12 0 32 s3, s1, s4, s6 s22 0 15 s1, s2, s5, s3 s13 0 33 s1, s2, s5, s3 s14 0 16 s3, s1, s4, s6 s4 1 34 s3, s1, s4, s6 s3 1 17 s3, s1, s4, s6 s15 0 35 s3, s1, s4, s6 s3 1 18 s1, s2, s5, s3 s1 1 36 s3, s1, s4, s6 s9 0 in case retrieval algorithm (algorithm 1), for each new problem c0, we finally chose the average difference degree of tender case characteristics simcase(c0,ci) as the judgment threshold https://doi.org/10.15837/ijccc.2021.3.4170 16 λcase, by analyzing the results of difference degree of tender case characteristics, and the cases satisfying simcase(c0,ci) > simcase(c0,ci) are merged into the similar case set csim of c0. in the case reuse algorithm (algorithm 2), seven different tender case classes and reference templates {s1,s2,s3,s4,s5,s6,s7} were divided by non-interference sequence index in the training set. the recommended sequence srec of reference templates of all problems were obtained by calculation, and the length of them all were exactly 4, as shown in table 5, where 1 represented success, 0 represented fail. “if recommend successfully?” means the recommendation in case reuse to be successful if “actual solution” is involved in srec. thus, compared with the actual reference templates, the success rate of tender case content recommendation in case reuse was 58.33%. in addition, 15 new reference templates were found in the test set case, and defined them as s8,s9, · · · ,s21,s22. next, the cases recommended unsuccessful in case reuse will be put into the case revision algorithm for experiment. in the case revision algorithm (algorithm 3), firstly, eight content modules (the number of abscissa is the subscript of each content module, such as, 1 means dc1 were divided by non-interference sequence, and the similarity clustering of content modules was performed by hierarchical clustering method (as shown in figure 6). in the clustering of content modules, the content modules can be synthesized into a content module class after 5 steps, and the similarity distance of the each step was 0.137, 0.193, 0.236, 0.373 and 0.918, respectively. we found that when distance less than 0.373, the similarity of content module classes is higher, so two content module classes are formed finally: dm1 = {dc1,dc2,dc4,dc5}, dm12 = {dc3,dc6,dc7,dc8}. the content modules within the same class could be recommended to each other. the recommended sequence of content modules could be seen in column drec(p) of table 6. figure 6: hierarchical clustering of content modules secondly, the remaining 15 test cases were recommended by case revision algorithm, and the results were shown in table 7 (the number in the second column and the third column is the subscript of each content module, such as, 1 means dc1). in this section, the content in second column and the third column could be compared to identify if the recommendation is successful. for example, as for “new problem 13”, its recommended sequence of reference templates and content modules are "s3{1, 2, 4}, 1{1, 2, 4, 5}, s4{1, 2, 4, 6}, s6{1, 2, 3, 6, 8}". therefore, in order to obtain its actual usage of content modules {1, 7}, from the first reference template s3, we try to replace (delete/insert) its content module https://doi.org/10.15837/ijccc.2021.3.4170 17 according to the drec(p) in table 6. the recommendation reference template s4{1, 2, 4, 6} was selected finally, and the detail operation was: dc1 was retained, dc2 and dc4 were deleted, dc6 was replaced by dc7, as dc6 and dc7 are in the same content module class that could be recommended to each other. besides, the position where the font color different corresponds to the recommended position of the recommended reference templates in table 7. it also should be noted that this part of the operation is artificial processing performed by the personnel based on the recommended results. table 6: content module difference degree matrix and recommended sequence simdoc(dcp, dcr ) vc (dcp) drec(p)dc1 dc2 dc4 dc5 dc3 dc6 dc7 dc8 dc1 0 0.236 0.136 0.373 0.991 0.962 0.991 0.981 1 dc1, dc4, dc2, dc5 dc2 0.236 0 0.192 0.441 0.981 0.933 0.981 0.953 0.944 dc2, dc4, dc1, dc5 dc4 0.136 0.192 0 0.397 0.981 0.953 1 0.972 0.981 dc4, dc1, dc2, dc5 dc5 0.373 0.441 0.397 0 0.918 0.967 0.918 0.948 0.861 dc5, dc1, dc4, dc2 dc3 0.991 0.981 0.981 0.918 0 0.304 0.192 0.236 0.019 dc3, dc7, dc8, dc6 dc6 0.962 0.933 0.953 0.967 0.304 0 0.304 0.192 0.074 dc6, dc8, dc3, dc7 dc7 0.991 0.981 1 0.918 0.192 0.304 0 0.236 0.019 dc7, dc3, dc8, dc6 dc8 0.981 0.953 0.972 0.948 0.236 0.192 0.236 0 0.037 dc8, dc6, dc3, dc7 the reference templates and their target content module could be obtained to be revised by dividing the reference template into content modules, then the content recommendation of the tender case was realized finally by replacing target content module with the content modules in the recommended sequence in table 7. as for “new” in column “actual usage of content modules”, they were new content modules constructed by new clauses that did not exist in the historical tender documents, and would not be involved in this recommendation. instead, the “new” could be studied into the tender case database as new tender knowledge that was called case learning. so far, combined with the case reuse, the final success rate of all recommendations has reached 94.44%, the recommendation effect was significant. table 7: recommended results of case revision id actual usage ofcontent modules recommended reference samples and its content modules if recommend successfully? recommended location 1 {1,new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 1 1 8 {1,7,new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 1 3 12 {1,2,4,6,new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 1 3 13 {1,7} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 1 3 14 {6,new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 1 3 15 {1,4,new} s1{1,2,4,5},s2{1,3,4,5},s5{1,5,7},s3{1,2,4} 1 1 17 {1,4,6,8,new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 1 3 20 {1,4,6,8,new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 1 3 21 {1,2,6,new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 1 3 24 {1,4,6,8,new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 1 3 26 {1,2,4,6,new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 1 3 31 {new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 0 32 {1,6,new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 1 3 33 {1,3,5,new} s1{1,2,4,5},s2{1,3,4,5},s5{1,5,7},s3{1,2,4} 1 2 36 {new} s3{1,2,4},s1{1,2,4,5},s4{1,2,4,6},s6{1,2,3,6,8} 0 case learning is a means to expand and update the case database, and it is also an important condition to ensure that the cbr algorithm can keep effective and reliable over time. after a new problem is solved, this target case and its solution shall form a new case, which can be stored in the case database and used as an existing case to solve the new problem in the https://doi.org/10.15837/ijccc.2021.3.4170 18 future. with the continuous growth of case database, this paper’s recommendation method will become more and more reliable. 5 conclusion aiming at the compilation problem of tender documents, this paper found that its core activities were to collect similar historical tender documents, select compilation templates of tender documents and revise templates of tender documents partially. however, when the historical tender documents have accumulated to a certain amount, it becomes extremely difficult for compilers to select and revise templates subjectively. based on the theory of case-based reasoning and data mining technology, contributions achieved are as follows: retrieval of similar tender cases of target cases, reuse and recommendation of reference templates of similar tender cases; if they could not be reused, the reference templates are further revised, that is, the reference templates are divided by content modules, and the local content modules are recommended; finally, after all the recommendation processes are completed, the project is implemented example learning process, all tender knowledge of the target case is updated into the database. the results of real data show that the accuracy rate of the content recommendation of the tender document is as high as 94.44%, and the recommendation effect is remarkable, and the compilation process of the tender document is more efficient and standardized. in the existing literature, there are not many optimization methods for the compilation of tender documents, so this paper can enrich the recommendation methods of the content of tender documents to a certain extent. next, this paper will focus on the confirmation of the tender documents, and introduce the evaluation system of the content recommendation algorithm of the tender documents, so as to further improve the content construction system of tender documents. references [1] adeyanju, i. 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(2014). the importance of the consolidation of tender document model in electronic tendering, china science and technology vertical and horizontal, 000(014), 233–234, 2014. https://doi.org/10.15837/ijccc.2021.3.4170 20 copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-andcontrol cite this paper as: zhou, t.t.; gao, x.d.; wei, g.y. (2021). construction method of tender document based on case-based reasoning, international journal of computers communications & control, 16(3), 4170, 2021. https://doi.org/10.15837/ijccc.2021.3.4170 introduction related works compilation of tender documents case-based reasoning theory construction of content recommendation model of tender documents structured processing of tender documents retrieval of similar tender cases reuse of reference template of tender document revision of reference template of tender document study of tender case knowledge tender knowledge learning of case reuse tender knowledge learning of case revision results and discussion conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 3, month: june, year: 2022 article number: 4539, https://doi.org/10.15837/ijccc.2022.3.4539 ccc publications substantial phase exploration for intuiting covid using form expedient with variance sensor radha raman chandan, pravin r. kshirsagar, hariprasath manoharan khalid mohamed el-hady, saiful islam, mohammad shahiq khan and abhay chaturvedi radha raman chandan department of computer science and engineering siet, jhalwa prayagraj, india email: rrcmiet@gmail.com pravin r. kshirsagar department of artificial intelligence g.h raisoni college of engineering, nagpur, india. corresponding author: pravinrk88@yahoo.com hariprasath manoharan department of electronics and communication engineering panimalar institute of technology, poonamallee, chennai, india. email: hari13prasath@gmail.com khalid mohamed el-hady assistant professor civil engineering department, king khalid university, ksa. email: kalhdi@kku.edu.sa saiful islam civil engineering department college of engineering, king khalid university, abha 61421, asir, kingdom of saudi arabia. email: sfakrul@kku.edu.sa mohammad shahiq khan civil engineering department college of engineering & it, onaizah colleges, al-qassim, saudi arabia email: shahiqiitr@gmail.com abhay chaturvedi department of electronics & communication engineering, gla university, mathura, u.p., india, pin-281406. email: abhaychat@gmail.com https://doi.org/10.15837/ijccc.2022.3.4539 2 abstract this article focuses on implementing wireless sensors for monitoring exact distance between two individuals and to check whether everybody have sanitized their hands for stopping the spread of corona virus disease (covid). the idea behind this method is executed by implementing an objective function which focuses on maximizing distance, energy of nodes and minimizing the cost of implementation. also, the proposed model is integrated with a variance detector which is denoted as controlled incongruity algorithm (cia). this variance detector is will sense the value and it will report to an online monitoring system named things speak and for visualizing the sensed values it will be simulated using matlab. even loss which is produced by sensors is found to be low when cia is implemented. to validate the efficiency of proposed method it has been compared with prevailing methods and results prove that the better performance is obtained and the proposed method is improved by 76.8% than other outcomes observed from existing literatures. keywords:wireless sensors; covid; energy consumption; angle of inclination; internet of things (iot). 1 introduction recently, internet of things (iot) has been emerging as a useful platform for medical applications. this helps the doctors all over the world to take care of their patients in the place where they are residing. this type of developments is possible only by using intelligent monitoring devices like sensors. in line with above concern, sensors can also be designed and used for handling the current pandemic corona virus disease (covid). it is well know that no medicine is available for stopping covid and only way is to maintain distance among individuals. even the sensors can be designed for monitoring whether hands of individuals are sanitized or not. therefore, an ultrasonic distance sensor can be integrated with high performance boards with a micro server as shown in figure 1a. in addition, these sensors are designed for installing in all public places which even includes small shops.therefore, in this case different leds can be connected for determining distance as shown in figure 1b. this method is not a complete remedial method for covid but by implementing this technology the people can preserve themselves from covid and the monitoring station will try to stop the anomaly behavior of each individual. figure 1: implementation of proposed method for monitoring covid (a) with micro server (b) without micro server 1.1 literature works for integrating sensors in general public it is necessary that basic parameters and design of sensors have to be surveyed. therefore, this section focuses on literature works by examining different parameters that have been discussed by several authors. the basic parameter for monitoring distance with basic principles of infrared illumination in mobile robots is discussed [1]. the authors have designed a low cost sensor but the major disadvantage is that the sensors are designed for monitoring very low distance which is up to 1 meter. also, the angle of inclination for installing sensors is much hard task in public which is also discussed with necessary formulations. but after installation it is observed that energy consumption is much lesser and therefore it needs to be solved. for solving the problem of energy consumption high power wireless nodes have been deployed [2] and after deployment it is observed that even additional energy can be saved. however there is advantage of much higher energy saving the method suffers from disadvantage that error rate is much higher where, exact parametric values cannot be obtained [2]. in addition, the distance between individuals and walls should be maintained and in this case ultrasonic sensors have been designed with exact angle of inclination [3]. https://doi.org/10.15837/ijccc.2022.3.4539 3 even though a low cost sensor for moving environment is designed, straight angle measurement which is much necessary for monitoring in epidemic situation has been inapt. if both straight and cross angle measurements are made then, it would be a major advantage to the society [3]. to overcome the drawback in [3] a new technology using robotic application have been developed [4]. this kind of robotic environment will be mostly used in applications like vehicle monitoring where, exact distance of other vehicles can be informed to all individuals. additionally, an alarm gust will also be deployed and if two vehicles are getting closer and any indication of accident is present then the alarm will be automatically switched on. but, installing the sensors of this type will be much higher and public cannot be able to afford much higher cost [4]. consequently, for reducing the cost of sensor which needs to be installed in highways an ultrasonic pulse with a new method on selecting ground based points with necessary threshold parameters have been designed [5]. if this case [5] is considered then, cost of installing sensors in important points will reduce but the pulse will produce only less energy where information cannot be processed in a correct way between transmitter and receiver [5].furthermore, the sensors designed [1-5] cannot be able to monitor any one necessary parameters therefore, the authors [6] have combined two different techniques such as ultrasonic and infrared sensors for measuring distance. even if accurate distance is monitored and the values are correct but when two methods are combined it is apparent that cost will be much higher [6]. from examining [6] it is observed that instead of using two different methods, if thermal sensors are implemented then, it is easy to predict the position of each individual [7]. still, this virtuous method suffers from disadvantage that exact values can only be obtained if sensors are installed only on ceiling of all buildings [7] and angle of inclining sensors ismissing. after some years a unique application on door step have been developed for computing group of peoples and exact distance between them which is denoted as smart construction monitoring [8]. in this application a lot of security measures have been updated from existing platforms and also an array of sensors have been implemented in grid based structure. but the problem with array structure is that when sensors are grouped the data that is sent from transmitter to receiver have to be arranged in order for saving the energy consumption which is not possible with this type of ir sensors [8].for implementing sensor arrays another method that is diverse from [8] have been introduced with photodiodes and bridge routes [9].when photodiode is introduced exact measurement accuracy will be much higher and values will be obtained exactly within 2.5ms. but this creates an uncertainty where, bandwidth supplied by source should be much higher [9] where, it is not possible in all situations. even for better installation a comparison have been made between ultrasonic and infrared sensors for detecting obstacles with exact detachment rate [10].the aforementioned model is examined using vehicular applications and a time of flight method is enabled which makes the entire system to provide more accurate results. but unreasonably more energy have been used which makes the cost to rise beyond exact economic value [10]. when sensors are tested for integrating in medical application mainly for corona virus disease (covid) a new method using neural network have been applied. when this neural network is introduced it can able to detect the affected patients immediately with ten days. a common drawback in neural network is more data needs to be specified at same time and during each stage separate operations will be performed [11].to introduce a new method that differs from neural network, a block chain technology has been integrated [12]. when this block chain is introduced then, it is very easy to store large amount of data. this creates may positive advantage over other methods because these systems can be introduced in mobile environments where correct of inclination will be specified. but the authors have not explained about security measures that are involved during this risk process on storing large amount of data [12].to have complete exploration on security an energy cost behavioral model have been implemented [13] where, different position of sensor is acquired in accurate way and complete analysis for integrating in public places have been made with help of ec curve. however, uniform distribution of data is necessary for implementing in low cost condition [13-18]. 1.2 research gap and motivation all the existing work which provides base information [1-18] fails to detect any one basic parametric value like energy consumption or cost of installation. moreover, the existing articles have not focused on detecting any pandemic situations like covid. but only basic parametric values have been measured to some extent. therefore the existing research gap on monitoring the distance with high node energy at low cost has been missing in all conventional models. therefore, the authors have formed a base work by maximizing distance, energy and minimizing the cost of installation with proper angle of inclination of sensors by formulating the objective function (equation (7)) and integrating it with deep learning model for building the research gap. the procedure of node identification is deliberated in figure 2. 1.3 objectives the main objective of this research work is to safeguard every individual before distressing covid and in addition for making the spread of covid to remain in stationary condition by monitoring the distance between each individual using an online monitoring system (things speak). in addition, the objective on minimizing the https://doi.org/10.15837/ijccc.2022.3.4539 4 figure 2: node identificationprocedures for patients and infirmaries cost of installation with high energy for transferring the information from source to destination is also fulfilled. the main purpose of considering this objective is that no medicine is available for covid and all people have to maintain distance between them with proper sanitizing lifestyle. this is possible only by implementing a monitoring device like sensors. therefore, a cost efficient sensor will be installed in all public places with correct angle of inclination for monitoring the distance between each individual. 2 problem formulation in this section, the sensing units will be designed according to the distance requirements with necessary parameters. since the sensors are designed for pandemic situation all necessary parameters must be integrated together. 2.1 calculation of distance it is well known that for preventing the pandemic covid situation no remedy is available and the only way is to maintain distance. but most of the people are not maintaining distance properly in all places. therefore, for monitoring the distance between individuals a distance monitoring sensor will be placed and it will be formulated as given in equation (1). di = n∑ i cti(meters) (1) where, crepresents distance between each individual t_idenotes the time that both individuals are standing close to each other equation (1) represents the mutual formulation of sensors whereas, it differs in each situation based on selective criteria as shown in equation (2). di = 3 ∗v (i) 10(meters) (2) where, v(i)represents the output voltage obtained from each sensor the intention behind the distance of 10 meter is that the signals can be emitted up to that distance and corresponding signals will be reverted to transducer only if the limit is maintained within 10 meters. equation (2) will be implemented if the distance is calculated using output voltage. the value 3 indicates angle of inclining each sensor and 10 represents distance between each obstacle (in 360 degree view).in addition, if both equations are implemented for designing the sensor then it should provide high accurate values and it can be observed using equation (3). ai = 100 −di (3) where, a represents the accuracy of sensor for calculating the distance (for both equation (1) and (2)) https://doi.org/10.15837/ijccc.2022.3.4539 5 the value of 100 represents the full scale reference accuracy where the difference with respect to distance values will provide exact accuracy of proposed model. 2.2 angle of inclination angle of inclination for implementing sensors in outside environment is much important. if the sensors are not mounted properly in objects then, necessary parametric value cannot be evaluated properly and exact value will be much different where, equation (3) will not be satisfied. therefore, the position for mounting the sensors can be formulated in a collective way as given in equation (4). φi(di) = ρi (d2i ) + γi(radians) (4) where, φi (d i)indicates that angle of inclining sensors will vary depending on distance ρi represents the replication of ith target source γi denotes the radiation of integrated sensors (0 or 1) equation (4) cannot be implemented if necessary constraints in equations (1) and (2) are satisfied. also, two different values will be indicated when radiation occurs through sensors. therefore, the value will change at different times where, exact value will be obtained in all circumstances. 2.3 energy consumption once the accuracy of designed sensor is much higher then, the next parameter for valuation is consumption of energy by all nodes that are connected with sensors. always the sensors should be designed in a correct way that it should maximize the energy consumption which indicates that power transmitted form source to destination should be much higher. calculation of power that is consumed by nodes (both transmitter and receiver) can be expressed as, e t x,rx i = tp a+d i + tp amplif ier i m 1l2t −2 (5) where, tp (a+d)i denotes the total power that is supplied to all paths inside the sensors. tp amplif ier i represents the total power of amplifier that is present at transmitter side that includes peak-toaverage power ratio 2.4 total cost it is necessary that even in small industries and in other yards the sensors needs to be installed. therefore, it should be fabricated in a way with much less cost as given in equation (6). when considering the cost of sensor units distance, angle of inclination and width of sensors will also be included. ci = (wi ∗di) + φi (6) where, wi represents the width of installed sensors 2.5 objective function by considering equations (1) to (6) the objective function on implementing sensors for distance monitoring and hand sanitizing can be modeled as follows, o(i) = max n∑ (i=1) di,ei,min n∑ (i=1) ci (7) if the objective function in equation (7) is implemented then sensors will be fabricated in right way and exact results will be predicted in online monitoring system. 3 optimization algorithm to handle the pandemic covid situation each and every individual should follow some consistency between them and all should regularly sanitize their hands. this is the only way for reducing the death rate across all countries. therefore, for detecting the regularity of all people in public places an improveddeep learning optimization algorithm which is denoted as controlled incongruity algorithm (cia) have been integrated with proposed model. one major advantage on choosing cia is that it can able to provide solutions only if the problem is convex in nature and it can able to converge earlier [19, 20]. for integrating the deep learning model https://doi.org/10.15837/ijccc.2022.3.4539 6 with proposed objective function some basic integration formulations are essential. one important function in cia is used for detecting losses of sensors using a conjugate function as given in equation (8). loss(i) = con(di −|l(di)|) (8) where, |l(di)| represents the lagrangian function of distance if this kind of deep learning algorithm is introduced then, loss which is produced by sensors will be much lesser and therefore exact langrangian controls can be made for distance measurements. for accurate measurements distance should always be measured from center of target and therefore, centric function can be given as, figure 3: flow chart of cia for monitoring distance between entities ti = n∑ (i=1) (φi −mi)2πr (9) where, mi denotes the mid value of target from equation (9) it is clear that target centric function will be formulated only based on parameters like angle of inclination and area of circle. if sensors are not mounted at correct angle then the target cannot predict exact value which will result is wrong calculations. to avoid this situation area of circle with midpoint is also added for accurate calculations. finally, the mean value of distance followed https://doi.org/10.15837/ijccc.2022.3.4539 7 table 1: distance vs. accurate reading radius (m) angle of incidence (degrees) 10 -50 20 -32 30 -11 40 -3 50 8 60 16 70 26 80 39 90 60 100 90 by different people will be calculated as given in equation (10). from equation (10) the mean incongruity value will be calculated for all individuals. if any individual walks to maximum distance the sensor will be automatically switched off where, in this case energy loss will occur. to overcome this loss difference between maximum and minimum distance will be calculated in addition with area of circle. this makes the optimization process to converge earlier and within small values the sensor will be switched off and lot of energy can be saved. the flow chart of proposed cia is shown in figure 3.since there is need of two different units in corresponding data set such as visible and hidden the restricted boltzmann machine is preferred as one type of deep learning technique in the proposed method that satisfies the required parametric features. the features that are present in this kind of machine are directly associated for high probability cases where low energy states are connected. therefore in high probability variations there will be no internal layers that are associated without characterized response thus giving raise to unsupervised state of learning. however with these additional features interference with respect to input data can also be observed only if reinforced inputs are equivalent to original input. the best feature of cia can be understood from the working model where in the first stage input data is directly transformed to number state. after this transformation encoding process will start with accelerative sensing of data points which are located at 10 meters from aerial points. in the next stage individual weights will be combined as the encoded output will reach from hidden layer to visible layer. once the visible state is noticed then sensors will function by converting the encoded data for restoration units and this is termed as backward passing. at last stage both reconstructed and original data will be compared and the difference in comparison will provide the exact data set with fine tuning of parameters. 4 results and discussion the proposed method of installing sensors for being alert to prevent covid have been monitored using an online monitoring system and it is executed using matlab where, different parametric values are monitored and they are listed as following scenarios. scenario 1: angle of inclination scenario 2: measurement of distance scenario 3: calculation of energy consumption scenario 4: cost of installation scenario 5: proportion of gap scenario 1 for installing sensors one major prerequisite is inclining angle which is discussed in this scenario. for each public building the angle of inclination is different and it depends on radius of buildings. if correct angle of inclination is not provided then distance between two individual cannot be monitored properly which results in failure of installing sensors. therefore, angle of inclination for mounting sensors in suitable places should be followed correctly. it is observed that angle of inclination for small radius will be tilted up to -50 degrees whereas, for large radius of buildings the angle of inclination will be 90 degrees. if in this angle the sensor is mounted then it can be able to detect each individual within the distance of 100 meters as shown in figure 4. from figure 4 and table 1 it can be seen that sensors are inclined correctly at precise angles where, maximum and minimum limit will be -50 to 90 degrees. if radius of building is much higher then, angle of inclination can be calculated using equation (4). this inclination value will be automatically fed as input to things speak, an online monitoring system and if inclination angle varies an alert will be sent to the user and therefore, they have to re-install it. this parameter is not compared with any existing method because this is an unique parameter and no previous exists for comparison states. scenario 2 once the sensors are mounted at correct angle, distance can be calculated which is conversed https://doi.org/10.15837/ijccc.2022.3.4539 8 figure 4: angle of inclination (in degrees) in this scenario. generally, sensors should be able to monitor distance with less interpretation values. for monitoring long distance above 50 meters the interpretation values should be constant. by this monitoring station can detect whether each and every individuals are maintaining minimum distance between them. if any person is creating anomaly then interpretation will be stopped and alert will be sent to the users. in this case the data such as mobile numbers and names can also be fed as input and therefore, if any user violates guidelines then a message will be sent. figure 5: distance vs accurate reading figure 5 and table 2 shows calculation of distance which is obtained in a small yard. the results are monitored for a single day using thing speak and it is visualized in matlab. for the proposed method if the users are between 50-100 meters a constant voltage will be supplied because in this case there is no need for monitoring but if the users are closer between 10-40 meters then, more interpretation is needed which is greater than 3 volts. but in existing method [1] even if individuals are at long distance more interpretation is needed and more wastage will occur. therefore, the proposed method proves to be more efficient then existing method for monitoring distance between individuals. https://doi.org/10.15837/ijccc.2022.3.4539 9 table 2: distance vs. accurate reading distance (m) interpretation (volts) [1] proposed 10 5 3.7 20 3 2.3 30 2.5 1.1 40 1 0.7 50 0.9 0.2 60 0.7 0.2 70 0.6 0.2 80 0.5 0.2 90 0.4 0.2 100 0.4 0.2 table 3: consumption of energy packets at receiver energy consumption (mw) [2] proposed 20 2 1.76 40 3 2.37 60 4 3.49 80 6 5.24 100 8 7.19 scenario 3 in this scenario the basic parameter that is necessary for sensors during transfer of information which is referred as energy consumption is determined.for transferring information from source to destination amount of energy supplied to nodes should be much higher. this is one important characteristic that is much needed for proposed method. figure 6 and table 3 portrays energy that is consumed by nodes for transferring information and it can be seen that more energy is supplied in proposed method because for monitoring this pandemic situation more energy is required as number of peoples are higher. it can also be observed that the packets arrived at receiver will consume up to 8 mw for 100 nodes whereas, the existing method [2] consumes only 7.19 mw. in this case if energy is supplied much lower then information will not reach the receiver and therefore, the interpretation values will be much higher for existing method as shown in figure 3. figure 6: comparison of energy consumption scenario 4 the sensors designed for this covid situation have to be installed in all local places therefore, cost of installation should be much lesser. here, the cost will be calculated by taking into account the width of current abode and angle of inclination. if these two bounds are considered then, automatically cost of installing sensors will be much lesser. figure 7 displays the simulation results that is obtained after integrating equation https://doi.org/10.15837/ijccc.2022.3.4539 10 table 4: consumption of energy number of nodes cost [12] proposed 20 16000 14500 40 29300 25000 60 53800 46100 80 60400 52600 100 73900 65600 (6). from figure 7 and table 4 it can be observed that cost of installing sensors through proposed method is much lesser than existing method [12].for example if number of nodes is 60 then, cost of installation for proposed method will be 46.1k whereas, for existing method it is found to be 53.8k respectively. this shows figure 7: calculation of total cost (inr) that if angle of inclination is correct then, cost of installation will be much lesser. this parameter will not be fed as input values to things speak but for providing information to public it has been included. even a small dealer should be in a position to buy this sensor because it is much necessary to monitor distance which is the only way to stop this pandemic situation. so, if the proposed method is implemented in real time all small dealers can buy this type of sensor and they can install it at much lower rate. scenario 5 in this scenario accurate measurement distance and gap will be calculated between each individual. this is treated as special case because once an individual arrives at a shop a small sanitizer magnum will be kept where, if the person arrives within a particular distance they will be alerted to sanitize their hand. next after sanitizing percentage of gap between each individuals will be observed and if either hands are not sanitized and if percentage of gap is low then an alarm message will be sent to particular person. this is monitored by things speak and the results are visualized in matlab. figure 8 shows the simulated results by taking distance in x-axis and percentage of gap in y-axis. it can be seen in figure 8 and table 5 that a person is ready to go into s shop and when that person is outside at a distance of 50 meters then percentage of gap observed will be 36. but once the person enters into the shop and it the shop is much smaller to a distance of 10 meter then gap maintained by that person is 1.3 whereas, for existing method [6] more gap is specified and it is not much possible for small sized shops. this proves that proposed method is capable of monitoring exact values than other existing methods. https://doi.org/10.15837/ijccc.2022.3.4539 11 figure 8: accurate distance vs percentage of gap table 5: accurate distance measurement distance (m) percentage of gap [6] proposed 10 2 1.3 20 6 4.6 30 17 12 40 29 21 50 45 36 4.1 4.1 performance analysis of cia beyond some real time factors it is much important to analyze the simulation time of implemented algorithm and it should be compared with existing one for understanding the performance and accuracy of parametric values. therefore, when the proposed method is visualized in matlab simulation time is calculated by varying the number of rounds and is shown in figure 9. from figure 9 and table 6 it can be seen that simulation time for proposed cia is much lesser than existing method [11]. for accurate checking the numbers of rounds have been considered as 100 and even for all rounds simulation time is much lesser and it is considered as foremost advantage of integrated algorithm. for example, if the number of round is equal to 60 then simulation time for proposed method is 3.81 seconds whereas for existing method [11] it is equal to 5.16 seconds. this proves that proposed method is capable of converging solutions at earliest and all the parametric values will be obtained within fraction of seconds. it is important to prove the efficiency of proposed work using cia by observing figure 9: computational time (seconds) of cia https://doi.org/10.15837/ijccc.2022.3.4539 12 table 6: speed of measurement number of rounds speed of measurement [7] (seconds) proposed 10 1.24 1 20 2.87 1.68 30 3.43 2.37 40 3.98 2.9 50 4.52 3.24 60 5.16 3.81 70 5.84 4.38 80 6.26 4.76 90 6.8 5.19 100 7.71 6 table 7: performance measurement of cia algorithms recall precision f-measure accuracy deep belief networks 45.06 42.97 41.54 42.37 convolutional neural networks 76.79 81.24 83.45 88.92 self-organizing maps 56.71 54.32 50.67 61.24 multilayer perceptron 68.56 63.90 69.70 71.45 proposed 82.43 84.12 88.03 92.48 several parametric values. but the exact performance measurement parameters are calculated with true positive and false negative values and are indicated in table 7. the corresponding simulation plot is also deliberated in figure 10 where four types of deep learning models are compared with projected algorithm. in this case it is substantiated that cia performs much better with high true positive values and less false negative values this as a result high accuracy can be observed at several data set points. due to high accurate points false alarm detection is reduced during the process of covid detection thus resulting in factor measurement of 88.03%. this analysis process that distance of separation will be measured accurately therefore no contact will be made between two individuals at any point of time. 5 conclusion many countries around the world are suffering from covid and day-by-day the count of people affected by this disease keeps on increasing. even though preventive measures can be taken by staying at home it is not possible for all working people to stay at home all the time. therefore, only way of preventing this pandemic situation is to maintain distance between two individuals which is monitored by wireless sensors and it has been addressed in this article. most of the people are not maintaining even minimum distance between them when they are going in public places. to stop this sensors are designed in a way that it can monitor the movement of people using things speak, an online monitoring system which will predict exact values when wireless sensors are connected. moreover, the sensors are designed where, even small dealer can able to afford it and it can provide high energy by passing the information from transmitter to receiver. in addition, for effective functioning cia algorithm have been implemented which can be treated as other advantage of proposed method. the projected method is simulated and compared with existing methods and in future it can be extended for detecting the humans by integrating it in their apparels with necessary energy consumption. declaration of competing interests the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. conflict of interest the authors declare no conflict of interest. https://doi.org/10.15837/ijccc.2022.3.4539 13 acknowledgement the authors gratefully acknowledge the deanship of scientific research, king khalid university (kku), abha-61421, asir, kingdom of saudi arabia for funding this research work under the grant number r.g.p1/183/41. references [1] a. borji and l. itti, “state-of-the-art in visual attention mod-eling,”ieee transactions on pattern analysis and machine intelligence, vol. 35, no.1,pp.185–207,2013. 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[38] li f., kim t., humayun a., tsai d., rehg j. m.,“video segmentation bytracking many figure-ground segments” in:ieee international conference oncomputer vision (iccv), 2013. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ https://doi.org/10.15837/ijccc.2022.3.4539 15 this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: radha raman chandan, pravin r. kshirsagar, hariprasath manoharan, khalid mohamed el-hady, saiful islam, mohammad shahiq khan, abhay chaturvedi (2022). substantial phase exploration for intuiting covid using form expedient with variance sensor, international journal of computers communications & control, 17(3), 4539, 2022. https://doi.org/10.15837/ijccc.2022.3.4539 introduction literature works research gap and motivation objectives problem formulation calculation of distance angle of inclination energy consumption total cost objective function optimization algorithm results and discussion 4.1 performance analysis of cia conclusion international journal of computers communications & control issn 1841-9836, 10(2):154-164, april, 2015. bearing-opportunistic network coding k. alic, e. pertovt, a. svigelj kemal alic*, erik pertovt, ales svigelj department of communication systems, jozef stefan institute jozef stefan international postgraduate school jamova cesta 39, 1000 ljubljana, slovenia kemal.alic@ijs.si, erik.pertovt@ijs.si, ales.svigelj@ijs.si *corresponding author: kemal.alic@ijs.si abstract: this paper presents a novel, practical, routing-independent networkcoding algorithm: bon-bearing opportunistic network coding. simplicity is its main benefit as it introduces little overhead to the network since nodes do not need to keep track of received traffic for their neighbouring nodes. algorithm makes coding decisions based solely on the information about the packet previous and next hop node position. algorithm functions between the mac and link layers, with small modifications made only to the mac layer. using different topologies and different traffic loads and distributions in the simulation model we evaluated algorithm performance and compared it to a well-known cope algorithm. keywords: algorithms, network layer network coding, network throughput, performance evaluation. 1 introduction network coding (nc) introduced by ahlswede [1] brings a promising approach to improving network throughput and performance. the classical communication networks paradigm of nodes only forwarding packets is metamorphosed by a simple and important premise that nodes also process the incoming packets [2]. nc was originally proposed in order to achieve multicast data delivery at the maximum data transfer rate in single-source multicast networks [3]. we soon began to see nc ideas being put to use in problems other than multicast networks. increasing throughput in satellite networks [4, 5] and p2p networks [6, 7], improving delivery reliability over the lossy links either in wireless networks over tcp [8] or in delay tolerant networks such as deep space links [9] all bring promising results. depending on the application of nc the implementation affects different osi layers. in multicast scenarios nc is typically implemented in the application layer while two stage nc for increased spectrum efficiency is deployed in the physical layer [10]. we are interested in opportunistic nc for static wireless mesh networks such as metropolitan wifi networks [11] for unicast traffic. benefits of opportunistic nc can be best explained with a help of a simple example depicted in in figure 1. consider the following situation: node 1 (n1) has a packet p1 for node 2 (n2) and node 2 (n2) has a packet p2 for n1. the nodes can exchange their packets through intermediate node (n3). n1 and n2 send packets p1 and p2 to n2. without using nc the n3 first sends out p1 and later on p2. by using the nc procedure n3 performs a linear operation over the two packets (e.g. xor) and sends out a coded packet p1 ⊕ p2. n1 and n2 xor the received packet with the sent packet (p1 and p2) and obtain the packets headed to them. with nc, the number of transmissions nodes need to perform in order to deliver both packets to their destinations has been reduced from 4 to 3. the question which packets the coding node can encode together in general network deployment in order that the receiving nodes will be able to decode the coded packet, which is the key issue in this paper. optimal integration of nc into the existing architecture of a network is not straightforward and its implementation influences all functional components in current copyright © 2006-2015 by ccc publications bearing-opportunistic network coding 155 figure 1: simple example on how opportunistic nc increases throughput. network protocol stack e.g. scheduling, routing and congestion control [12]. though, interaction with all these layers increases the implementation difficulty and possibly also the computational complexity. henceforth, a trade-off needs to be made between coding and complexity. hence, the objective of this paper is to propose a novel inter-session nc schema that is easily implementable in the real world, can be used in the current osi structure and improves the network performance. thus, in this article we propose a novel bearing-opportunistic network coding (bon) algorithm, which main advantages are the following: (i) it can be seen as an individual osi layer between the mac and the network layers; (ii) it introduces little overhead to the network; (iii) it works for all traffic types; (iv) it is distributed; (v) it does not need to record information about the received packets on individual neighbouring nodes and it is (vi) routing independent. the rest of this paper is organized as follows. in section 2 related work is described. in section 3 the bon coding process is explained and its implementation into osi model is explained in section 4. performance evaluation results are given in section 4. section 5 concludes the article and gives insight into further work. 2 related work practical nc has mainly been influenced by an excellent and intuitive algorithm cope (coding opportunistically) [13] which principles have been adapted and extended also for covering other ideas in nc: for example in [14] nococo algorithm specializes in bidirectional traffic flows. it is trying to maximize the number of coding opportunities for the two opposite direction routes. clone [15] generalized cope to address multiple unicast sessions. the system takes into account lossy links and highlights specific situations where cope provides no coding gain. cope coding decisions are classified and improved thus making better coding decisions by k. chi et.all [16]. in [17] the more and cope principles have been joint in search of benefits of the two at the same time. making routing aware of cope coding opportunities has been investigated in [18] and [19], while [20] investigated new metric schemas for coding aware wireless routing. in dcar architecture [21] coding is based on cope, though extended and supported by route discovery. cope procedure introduced nc between the mac and network layers with small modifications made only to the mac layer. cope takes advantage of the broadcast nature the wireless 156 k. alic, e. pertovt, a. svigelj channel to perform opportunistic listening and encoded packet broadcasting so that the number of packet transmissions can be reduced. in cope, every node keeps information for each of its neighbours about packets already received. information can be gathered through ack packets and through update packets. the later introduces additional overhead as update packets are broadcasted by individual nodes to all their neighbours. owing to these facts, nodes require additional hardware functionalities and the network requires handling of additional packets which on one hand results in lower network capacity, and is on the other hand undesirable in energy constrained networks such as wireless sensor networks (wsn). a fair share of coding decisions in cope is based on the delivery probabilities between the nodes. these are calculated in the routing process in the routing protocols based on the etx metrices. hence for cope implementation access to the routing layer is needed, which is not always possible, especially when combining equipment from different vendors. 3 network coding process essentially the coding process is about making decisions on which packets the coding node can encode together in order that the receiving nodes will be able to decode the coded packet. the more packets we code together or more loos the conditions in coding process are the higher the possibility of receiving nodes not being able to decode packets. though, if coding conditions are hard to meet, than there are few coding opportunities and low bandwidth saving benefits. 3.1 general coding conditions boncoding process is based on the local bearing of packets. we define packet bearing on a coding node as the unit vector showing direction between previous hop and next hop of the packet. let us consider the situation depicted in figure 2. packet p1 has already been transmitted from node 1 (n1) to node 2 (n2) and is destined to node 3 (n3). packet pn has already been transmitted from node 4 (n4) to n2. p1 and pn are now waiting in the output queue on the coding node n2. when n2 gains channel access, it tries to encode p1 and pn based on the local bearing process. on the coding node n2 local bearing of the packet p1 depends on the positions of its previous hop n1 and its next hop n3. since the coding node is familiar with the node positions of the packet previous hop and the packet next hop, the coding node can calculate the direction vector a⃗ which represents the direction in which the packet p1 is travelling. in cartesian coordinate system we calculate the angle α for p1 between the x-axis and the direction vector a⃗ which is between 0 and 2π. after calculating the local bearing for the first packet p1, the algorithm goes through the remaining packets in the output queue searching for a matching packet to be encoded with p1. for each of the possibly matching packets pn the angle β between the x-axis and its direction vector b⃗ are calculated using the same principle as described above for the first native packet p1. coding conditions are met for packets p1 and pn, if (1) is true: π − ϵ ≤ |α − β| ≤ π + ϵ (1) where ϵ is the tolerance angle. in a given situation n1 can decoded packet since it is the source of half of the information encoded in the p1 ⊕ pn. the question is whether n3 can decode this packet. the probability that n3 will successfully decode p1 ⊕ pn is the same as probability that n3 has overheard the pn transmission from n4 to n2. we may assume that nodes on one side of the coding node have the knowledge of the content of packets of the first flow, while nodes bearing-opportunistic network coding 157 figure 2: coding process: the calculation of parameters for encoding evaluation for two packets. on the other side of the coding node recognise the content of packets from the second flow, thus matching them up for encoding. nodes that are placed in approximately the same bearing from the coding node, can also hear each other transmissions, thus being able to decode the packet. by increasing ϵ the possibility of encoding multiple packets and that one of the receptionists will not be able to decode the packet is increased. by lowering ϵ towards zero coding opportunities are reduced but the possibility of successful packet decoding on receiving nodes is increased. when two packets belonging to two traffic flows that are headed into opposite directions meet on the relay node, (1) is true even for ϵ = 0 and receiving nodes will always be able to decode the encoded packet. bon allows coding of multiple packets. multiple packets are encoded into one packet when the expression (1) is true for all packet pairs to be encoded. if ϵ = 0 two packets can be encoded at the most. 4 network coding procedure 4.1 packet coding each node maintains two packet queues. the highest priority queue is used for signalization packets and the lower priority queue is for packets carrying information through the network. if signalization queue is empty the coding algorithm takes the packet that is at the head of the queue and then searches the rest of the queue looking for possible coding matches based on the process described in section 3. if a coding opportunity is found, the packets are encoded using xor operation into a coded packet accompanied with the headers for the decoding process at the receiving nodes. if no coding opportunities are found the packet is transferred to the mac layer as is and the native packet is transmitted. 4.2 pseudo-broadcast in the mac layer the pseudo-broadcast mechanism, first presented in [13] is used. the linklayer destination field is set to the mac address of one of the intended recipients. since all nodes are set in the promiscuous mode, they can overhear packets not addressed to them. when a node receives a packet with a mac address identical to its own, it sends an ack message to 158 k. alic, e. pertovt, a. svigelj the sender. regardless of the address of the packet next hop the node pushes the packet to the nc module. 4.3 packet reception upon the packet reception in the nc module further actions depend on whether the packet is coded or native (not coded). in the case of the coded packet the process checks the packet pool where all received and overheard packets are stored for decoding purposes to determine whether it has already received n-1 of the packets coded in the coded packet. if not the coded packet cannot be decoded and it is simply dropped. if the node has at least the required n-1 packets, i.e., enough information, it decodes the coded packet using these packets with the xor operations, thus gaining a set of native packets. each native packet is treated individually. from here on the process is the same as upon receiving a native packet. the process checks whether the node has already received the packet. if so it drops it. if the packet is new its copy is inserted into the packet pool for decoding purposes. it does so for every received native packet, as all received packets are potentially needed for further decoding purposes. the process checks whether the node is the next hop for the native packet. if so, and if the packet has been a part of the coded packet, ack message is scheduled and the packet is sent to the upper layers for further processing. 4.4 signalization since static nodes are under investigation, ack messages are the only signalization packets required in bon. depending on the application and expected gains, different ways of sending ack messages can be used. since we are optimising a network from the throughput perspective we adopt the structure proposed in [13]. ack messages are periodically broadcasted as cumulative reports. if opportunity arises the ack reports are attached to the regular outgoing packets, thus reducing the overhead. 5 performance evaluation 5.1 simulation setup we evaluated bon using a simulation model built in opnet modeler [22] for analysis of network layer nc algorithms that has already been presented in [23]. we conducted a set of simulations using the following settings: 40 static nodes randomly placed on 4 km x 4 km area; 800, 900 and 1000 m of transmission range for three topologies t1, t2 and t3, respectively. the evaluated topologies are presented in figure 3, where dashed lines indicate wireless links between nodes. all nodes receive and transmit on the same channel and each node has 2 mbit/s of channel bandwidth. bon and cope use up to one retransmission in the wireless module and up to two retransmissions in the nc layer. both algorithms store packets in the packet pool for 20 seconds after the reception, thus making sure packet decoding did not fail due to delays in the network. output queues are limited to 50 packets. bon and cope send out cumulative acks every 0.5s. nc layer packet retransmissions are scheduled 0.8 s after the initial packet transmission. cope update packets as described in [13] are sent out at least every 0.5 s. when possible, update packets are attached to regular outgoing packets, or cumulative acks thus introducing less overhead to the network. ϵ has been set at 25 for bon, while ϵ for evaluating coding condition in packet retransmission has been set to half of the initial value. bearing-opportunistic network coding 159 figure 3: wireless nodes connectivity for three evaluated topologies (namely t1, t2 and t3). simulation runs took 150 s. results were collected between 60th and 120th second to observe steady-state conditions. through each simulation run we observed network with one load. for each load we made three simulation runs using three different seeds. for the first 30 s traffic load was set at 10 % of the final load, between 30th and 60th second the load was set to 80 % of the final load (i.e. warm up time). network was loaded with full load and results were collected between the 60th and 120th second. routing tables are calculated at the beginning of each simulation run and are updated every 30 s. dijkstra algorithm is used for routes calculation taking into account etx metric, which is also required by cope. udp like traffic has been generated. packet sizes vary between 45 and 1500 bytes. packet size distribution has two peaks and follows the measured internet traffic as presented in [24]. exponential distribution is used for calculating packet inter-arrival times. two traffic flow distributions have been used in the process of evaluation. the first, the uniform distribution scenario, is where all nodes generate traffic flows and destine them to all nodes in the network (uniform distribution is used to generate packet destinations). in the second, the gateways scenario, nodes communicate with its nearest gateway only. nodes and gateways generate symmetric traffic flow. three selected nodes from topologies have been given the base station functionality. delivery probability (dp) between nodes depends on the amount of traffic between the neighbouring nodes and the distance between nodes (e.g. for t3, load 1.9 mbit/s, uniform traffic distribution, bon algorithm average dp is 0.88, max dp 0.97, min dp 0.78 and median for dp is 0.87). we have compared bon algorithm to the well-known cope and to reference scenario (ref.sc.) where no coding was used. 5.2 simulation results for uniform traffic flow distribution for all topologies we have observed average goodput (figure 4) highlighting results important from the operator point-of-view while end-to-end (ete) delay (figure 5) shows results important from the end-user perspective. normalised number of wireless transmissions (nnwt) which is the ratio between the number of packets received from nc layer by the wireless module and number of packets received in the application layer (figure 6) shows the number transmissions needed in the wireless module for every goodput packet. in ideal link conditions nnwt is a sum of network average diameter and overhead in terms of standalone packets. we use this measure for overhead comparison reasons. number of packet retransmissions in the nc layer indicates how well nc algorithms coded packets together (figure 7). maximal normalised gain which is the highest ratio between the number of goodput packets received using coding algorithm and the number of goodput packets received in ref. sc. (table 1) shows highest measured gain when using nc. each dot on the plot represents the average of three simulation runs with different seeds of pseudo random generator. 160 k. alic, e. pertovt, a. svigelj figure 4: goodput in dependency of network load for bon, cope and ref.sc. for topologies t1, t2 and t3 in uniform traffic distribution. figure 5: delay in dependency of network load for bon, cope and ref.sc. for topologies t1, t2 and t3 in uniform traffic distribution. figure 6: normalised number of wireless transmissions in dependency of network load for bon, cope and ref.sc. for topologies t1, t2 and t3 in uniform traffic distribution. figure 7: nr retransmissions in the nc layer in dependency of network load for bon and cope for topologies t1, t2 and t3 in uniform traffic distribution. bearing-opportunistic network coding 161 table 1: maximal normalised gain for bon and cope for t1, t2 and t3 in uniform traffic distribution. maximal normalised gain t1 t2 t3 cope 1.22 1.12 1.14 bon 1.26 1.17 1.17 from the goodput in dependency of network load plot (figure 4) for all topologies we can observe that with low traffic loads goodput is approximately the same for both coding algorithms as well as with the reference scenario. however, by increasing the traffic load, we can see that both cope and bon significantly improve the network performance as compared to the ref.sc scenario, and that bon can provide the highest goodput. the gain itself also depends on the network load and observed topology. table 1 shows that the maximal normalised gain (ratio between number of packets received using coding algorithm and number of packets received in ref. sc) and that bon outperforms cope for additional 4 to 5 percentage points. from the end-to-end delay in dependency of network load plot (figure 5) we can observe that with low traffic loads the delay is low and approximately the same for bon and the reference scenario, while delay with cope is slightly higher. by increasing the network load the delay increases for all three scenarios. the delay increases the fastest with ref.sc. cope follows and bon keeps the lowest delay of the three for almost all loads and all three topologies. higher delay with cope in lower loads can be explained with the help of plots in figure 6 (normalised number of wireless transmissions). in low traffic loads cope finds few opportunities to attach update packet to regular outgoing packets thus introducing higher overhead to the network. with the increased load cope and bon need to transmit approximately the same number of packets in the wireless module in per application layer packet. cope keeps the ratio slightly lower than bon with high loads. this is due to the fact that bon outperforms cope in throughput gains and due to limited queue sizes. packets travelling higher node distances are more likely to be dropped due to full queues. the ratio for both algorithms is much better in the congested network where nc benefits are at its best. bon better performance in comparison to cope can be explained also with the number of retransmissions in the nc layer. retransmissions in the nc layer occur in case of reception node unsuccessful decoding or errors in the transmission (pseudo broadcast demands ack message only from one of the recipient nodes). hence, number of retransmitted packets indicates how successful the algorithm was in coding together packets that can be decoded on their next hop: bon needs fewer retransmissions than cope and we may claim that it makes better coding decisions. in figure 8 and figure 9 the results for the gateways scenario are presented. results were collected in the same way as for uniform traffic case. we present only results for network goodput and end-to-end delay as this are the most representative results; other results bring up the same conclusions as in the uniform distribution scenario. in comparison to the uniform traffic flow distribution coding gain benefits are lower for the flows directed from and to the gateways. this is due to the changed traffic conditions. packets travel shorter hop distances between their source and destination, providing less coding opportunities and thus fewer possibilities for goodput improvements. still, the bon algorithm can provide the highest goodput while keeping the delay lowest. table 2 shows the maximal normalised gain and that bon can bring additional 2 to 8 percentage points of goodput increase in comparison to cope. 162 k. alic, e. pertovt, a. svigelj figure 8: goodput in dependency of network load for bon, cope and ref.sc. for topologies t1, t2 and t3 in gateways traffic distribution. figure 9: delay in dependency of network load for bon, cope and ref.sc. for topologies t1, t2 and t3 in gateways traffic distribution. table 2: maximal normalised gain for bon and cope for t1, t2 and t3 in gateways traffic distribution. maximal normalised gain t1 t2 t3 cope 1.21 1.09 1.13 bon 1.29 1.11 1.18 6 conclusions and further work bon is a novel, general-purpose algorithm which works in the broadcast networks between the network and the mac layer. it is applicable to the static networks such as wsn networks and metropolitan wifi networks. small modifications are made only to the mac layer while other osi layers remain unchanged (the same as in cope). we assume that each node is familiar with positions of all of its neighbours which is easy to obtain as nodes are static. bon is routing independent and as presented in the results it introduces very little overhead to the network, thus using less radio resources and consequently less energy. for nodes without any coding opportunities or generally in networks with low loads the overhead is the same as in the reference scenario. since nodes do not need to save the information about packets received on neighbour nodes, the coding process is fairly simple. in addition, we have shown in the results that bon outperforms cope in terms of increasing the network goodput while keeping the network delay lower for different topologies and traffic distributions. in-depth analysis shows that cope finds more coding opportunities, though incorrect coding decisions are more often made than in bon. furthermore, lower overhead in bon also results bearing-opportunistic network coding 163 in higher good put. main drawback for using bon is its requirement of knowing the positions of the nodes; although the networks where nodes know their position are increasing rapidly e.g. in backhaul networks such as metropolitan wifi where end-users use different part of spectrum for communication. in addition, setting the tolerance angle might not appear straightforward. for static networks, such as in our case, this can be done through simulation or emulation, though through experience the approximate value of the optimal îľ can be set by carefully observing the topology. by adapting the algorithm to automatically set the tolerance angle on individual nodes, planning as a future work, seems an important improvement towards additional practical value of bon. bon low overhead makes bon suitable for studying energy efficiency in wireless sensor or similar networks, especially in load cases when network is not congested. besides that, its routing independency shows potential for modifying routing metrics. bibliography [1] r. ahlswede, n. cai, s.-y. r. li, and r. w. yeung (2000); network information flow, ieee transactions on information theory, 46: 1204-1216. 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[24] a. svigelj, m. mohorcic, g. kandus, a. kos, m. pustisek, and j. bester (2004); routing in isl networks considering empirical ip traffic, ieee journal on selected areas in communications, 22(2): 261-272. international journal of computers communications & control special issue on fuzzy logic dedicated to the centenary of the birth of lotfi a. zadeh (1921-2017) online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 1, month: february, year: 2021 article number: 4106, https://doi.org/10.15837/ijccc.2021.1.4106 ccc publications information volume of fuzzy membership function j.x. deng, y. deng jixiang deng institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 610054, china yong deng* 1. institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 610054, china 2. school of education shannxi normal university, xi’an, 710062, china *corresponding author: dengentropy@uestc.edu.cn; prof.deng@hotmail.com abstract fuzzy membership function plays an important role in fuzzy set theory. however, how to measure the information volume of fuzzy membership function is still an open issue. the existing methods to determine the uncertainty of fuzzy membership function only measure the first-order information volume, but do not take higher-order information volume into consideration. to address this issue, a new information volume of fuzzy membership function is presented in this paper, which includes the first-order and the higher-order information volume. by continuously separating the hesitancy degree until convergence, the information volume of the fuzzy membership function can be calculated. in addition, when the hesitancy degree of a fuzzy membership function equals to zero, the information volume of this special fuzzy membership function is identical to shannon entropy. two typical fuzzy sets, namely classic fuzzy sets and intuitiontistic fuzzy sets, are studied. several examples are illustrated to show the efficiency of the proposed information volume of fuzzy membership function. keywords: fuzzy sets, membership function, information volume, higher-order information volume, entropy. 1 introduction in the past decades, plenty of theories have been developed for expressing and dealing with the uncertainty in the uncertain environment, such as probability theory [19], fuzzy set theory [51], dempstershafer evidence theory [6, 28], complex evidence theory [37, 39], z numbers [53], belief structure [47, 49] and d numbers [23, 24]. https://doi.org/10.15837/ijccc.2021.1.4106 2 since fuzzy set theory was firstly proposed by zadeh in 1965 [51], uncertain information processing based on fuzzy set has become a heated field [9, 40]. lots of researchers have been promoting the development of fuzzy set theory. for instance, zadeh presented type-2 fuzzy set in 1975 [52], which is a generalization of classic fuzzy set. in 1999, atanassov extended classic fuzzy set into intuitionistic fuzzy set [2], which introduces hesitancy into fuzzy membership function. yager proposed pythagorean fuzzy subset in 2013 [46], which can better express the fuzziness under uncertain circumstances. in addition, because of the advantages of modeling vagueness and imprecision in the real world, fuzzy set theory has been applied in many areas, such as data processing [54], approximate reasoning [48], decision making [10, 14, 15, 30, 44], uncertainty measurement [4], risk analysis [27], failure mode and effects analysis [17], and so on [12]. given a probability distribution, its corresponding information volume can be measured by shannon entropy [29]. in evidence theory, mass function is the generalization of probability, whose uncertainty can be measured by deng entropy [8, 13, 22]. in addition, given a mass function, its corresponding information volume can be calculated [7]. however, how to determine the information volume of fuzzy membership function in fuzzy set theory is still an open issue. some methods have been proposed to model the uncertainty of fuzzy set theory, such as the uncertainty of the interval type-2 fuzzy set [16], the uncertainty of fuzzy system [21], fuzzy entropy of fuzzy set [5, 25, 31, 50]. nevertheless, these uncertainty measurements are the first-order information volume of fuzzy set. it is reasonable to take the high-order information volume into consideration [7]. to address the issue mentioned above, an original and novel information volume of fuzzy membership function is proposed in this paper, including the first-order and the higher-order information volume. since there is extra information volume existing in the hesitancy degree compared with membership function and non-membership function, this extra information volume should be fully utilized to determine the information volume of fuzzy membership function. the major idea of the proposed method is to continuously separate the hesitancy degree until convergence, then the information volume of fuzzy membership function can be calculated. the rest of this paper is organized as follows. section 2 briefly reviews some preliminaries. section 3 proposes a new information volume of fuzzy membership function. some numerical examples are illustrated in section 4. section 5 makes a brief conclusion. 2 preliminaries several preliminaries are briefly introduced in this section, including fuzzy sets, intuitionistic fuzzy sets and shannon entropy. 2.1 fuzzy sets let x be a universe of discourse. a fuzzy set a based on x can be characterized by the set of pairs which is defined as [51]: definition 2.1: fuzzy sets a = {〈x,µ (x)〉 |x ∈ x} (1) where µ(x) : x → [0, 1] is the membership function (mf) of a, which describes the membership degree of each element x to the fuzzy set a. the closer µ (x) is to 1, the more likely x belongs to a. 2.2 intuitionistic fuzzy sets given a universe of discourse x, an intuitionistic fuzzy set a is defined as follows [2]: definition 2.2: intuitionistic fuzzy sets a = {〈x,µ (x) ,v (x)〉 |x ∈ x} (2) where µ(x) : x → [0, 1] and v(x) : x → [0, 1] are the membership function (mf) and the nonmembership function (non-mf) of a, respectively. µ (x) describes the membership degree of x to https://doi.org/10.15837/ijccc.2021.1.4106 3 the set a, and v (x) describes the non-membership degree of x to the set a. for every x ∈ x, 0 ≤ µ (x) + v (x) ≤ 1. the hesitancy degree is defined as: π (x) = 1 −µ (x) −v (x) (3) which represents the hesitancy degree of each element x ∈ x. besides, intuitionistic fuzzy sets can be represented as mass function, and a lot of works under evidence theory are presented [36, 42, 43, 45]. 2.3 shannon entropy entropy function plays an important role in measuring the uncertainty of a system [3, 26, 32, 55] in the field of probability theory, shannon entropy [29] is often used to measure the uncertainty of a probability distribution. consider a probability distribution p defined on the set θ = {h1,h2,h3, · · · ,hn}. definition 2.3: shannon entropy shannon entropy hs(p) is defined as follows: hs(p) = − ∑ θ∈θ p(θ) log p(θ). (4) where ∑ θ∈θ p(θ) = 1 and p(θ) ∈ [0, 1]. 3 information volume of fuzzy membership function in this section, firstly, the first-order information volume of fuzzy membership function is proposed. then, the higher-order information volume of fuzzy membership function is presented. 3.1 first-order information volume of fuzzy membership function in the course of information processing in fuzzy environment, it is important to identify the uncertainty of the data. taking intuitionistic fuzzy sets for example, intuitively, the uncertainty of fuzzy membership function 〈x1, 0.4, 0.4〉 is larger than that of 〈x2, 0.9, 0.1〉. in fact, this uncertainty of fuzzy membership function is actually the first-order information volume. hence, the first-order information volume of fuzzy membership function is proposed in this subsection. definition 3.1: first-order information volume of fuzzy membership function let the universe discourse be x, and an intuitionistic fuzzy set be {〈x,µ,v〉 |x ∈ x} where µ is membership function, v is non-membership function and π is hesitancy degree. the first-order information volume of fuzzy membership function h1(µ,v) is defined as: h1(µ,v) = −µ log µ−v log v −π log π c (5) where π = 1 −µ−v, and c is the cardinality of fuzzy sets defined as: c = { 2 ( classic fuzzy sets ) 3 ( intuitionistic fuzzy sets ) (6) the hesitancy degree π can be considered as ’hesitant’ which is a hybrid proposition of ’support’ and ’oppose’. hence, π contains more information volume than µ and v. it is reasonable that π should be divided by the cardinality of the fuzzy sets. the definition of the cardinality c is explained as follows. in classic fuzzy sets, µ and π = 1−µ can be viewed as these two propositions: ’support’ and ’hesitant’, so that the cardinality of classic fuzzy sets is 2. in intuitionistic fuzzy sets, µ, v and π can be respectively seen as these three propositions: ’support’, ’oppose’ and ’hesitant’, so that the cardinality of intuitionistic fuzzy sets is 3. actually, the classic fuzzy set is a special case of the intuitionistic fuzzy set, which means that the classic fuzzy set satisfies v ≡ 0 and π = 1 − µ. hence, the first-order information volume of classic fuzzy membership function is that: https://doi.org/10.15837/ijccc.2021.1.4106 4 h1(µ,v) = −µ log µ− (1 −µ) log 1 −µ 2 (7) 3.2 higher-order information volume of the fuzzy membership function the real world is very complex and complicated [11, 18, 20]. it’s more reasonable to explore a complex thing from different aspects [38]. for example, negation is paid attention recently since it can provides new view from negative side [1, 41]. another typical modeling is with complex networks [33, 34, 35]. about the information volume, the issue of the first-order information volume is that it divides the hesitancy degree π only once, which cannot make fully use of the information volume of π. because π can be seen as the proposition of ’hesitant’, which is a hybrid proposition of ’support’ and ’oppose’, π contains extra information volume compared with µ and v. this extra information volume should be fully utilized by continuously separating the hesitancy degree until convergence. inspired by the idea mentioned above, the higher-order information volume of fuzzy membership function is presented in this subsection. definition 3.2: higher-order information volume of fuzzy membership function let the universe discourse be x, and an intuitionistic fuzzy set be {〈x,µ,v〉 |x ∈ x} where µ is membership function, v is non-membership function and π is the hesitancy degree. the higher-order information volume of fuzzy membership function h∞(µ,v) is defined as follows: h∞ (µ,v) = − ∞∑ i=1 µi log µi − ∞∑ i=1 vi log vi − lim i→∞ πi log πi c (8) where c is the cardinality of the fuzzy sets defined as: c = { 2 ( classic fuzzy sets ) 3 ( intuitionistic fuzzy sets ) (9) µ1 = µ, v1 = v and π1 = π = 1 − µ − v denote the initial states of membership function, nonmembership function and hesitancy degree. i denotes the times of separation. µi, vi and πi (i > 1) are the terms derived by separating πi−1 which are defined as:  µi = k1πi−1 vi = k2πi−1 πi = k3πi−1 (10) where k1 = µ, k2 = v and k3 = π = 1 −µ−v are the separating proportions. the reason for the definition of the separating proportion k1, k2 and k3 is that, µ, v and π can be seen as the prior information of fuzzy sets, so that the hesitancy degree can be separated based on these prior information. besides, it is a convention that the expression 0 log 0 is defined as 0. hence, when the hesitancy degree π1 = 0, the higher-order information volume of fuzzy membership function h∞(µ,v) can be written as follows: h∞ (µ,v) = −µ1 log µ1 −v1 log v1 (11) actually, the classic fuzzy set is a special case of the intuitionistic fuzzy set, which means that the classic fuzzy set satisfies vi ≡ 0 and πi = 1 −µi. thus, the higher-order information volume of classic fuzzy membership function is that: h∞ (µ,v) = − ∞∑ i=1 µi log µi − lim i→∞ (1 −µi) log 1 −µi 2 (12) https://doi.org/10.15837/ijccc.2021.1.4106 5 algorithm 1 higher-order information volume of fuzzy membership function h∞(µ,v) input: : mf µ and non-mf v output: : higher-order information volume of fuzzy membership function h∞(µ,v) 1: i = 1 //initial time of separation 2: µ1 = µ, v1 = v, π1 = 1 −µ−v //initial states 3: k1 = µ, k2 = v, k3 = 1 −µ−v //separating proportion 4: repeat 5: i + + 6: µi = k1πi−1, vi = k2πi−1, πi = k3πi−1 //separating πi−1 7: hi (µ,v) = − ∑i j=1 µjlogµj − ∑i j=1 vjlogvj −πilog πi c //xlogy invokes algorithm 2 8: until ∆i = hi(µ,v) −hi−1(µ,v) < ε //allowable error ε 9: return h∞(µ,v) = hi(µ,v) algorithm 2 modified logarithm xlogy input: : real numbers x and y output: : modified logarithm xlogy 1: if x == 0 && y == 0 then 2: ans = 0 //the expression 0 log 0 is defined as 0 3: else 4: ans = x∗ log(y) 5: end if 6: return xlogy = ans in addition, given an allowable error ε, the higher-order information volume of fuzzy membership function h∞(µ,v) can also be calculated by algorithm 1. it should be noted that, in algorithm 1, step 7 invokes modified logarithm xlogy in algorithm 2 to avoid the computation 0 log 0. it can be derived from definition 3.2 and algorithm 1 that, when i = 1, the higher-order information volume of fuzzy membership function h∞(µ,v) degenerates into the first-order information volume of fuzzy membership function h1(µ,v), which can be written as follows: h∞ (µ,v)|i=1 = h1 (µ,v) (13) for better understanding, the calculating procedure of h∞(µ,v) is illustrated in figure 1. this figure clearly shows that the hesitancy degree is continuously separated based on the proportion k1, k2 and k3. 4 numerical examples and discussions in this section, some numerical examples are shown to illustrate above conceptions. in the following examples, let the universe discourse be x and the base of the logarithmic function be 2. example 4.1: consider that a classic fuzzy membership function is 〈x1, 0.65〉 where x1 ∈ x. the first-order information volume of this classic fuzzy membership function can be calculated as follows: because this is a classic fuzzy membership function, the cardinality c is 2, and π = 1 −µ = 0.35. then, the first-order information volume of this classic fuzzy membership function is that: h1(µ,v) = −µ log µ− (1 −µ) log 1 −µ c = −0.65 log2(0.65) − 0.35 log2( 0.35 2 ) = 1.2841 (14) example 4.2: let an intuitionistic fuzzy membership function be 〈x2, 0.65, 0.05〉 where x2 ∈ x. the first-order information volume of this intuitionistic fuzzy membership function can be calculated as follows: https://doi.org/10.15837/ijccc.2021.1.4106 6 figure 1: the calculating procedure of h∞(µ,v) because this is an intuitionistic fuzzy membership function, the cardinality c is 3, and π = 1−µ−v = 1−0.65−0.05 = 0.3. then, the first-order information volume of this intuitionistic fuzzy membership function is that: h1(µ,v) = −µ log µ−v log v −π log π c = −0.65 log2(0.65) − 0.05 log2(0.05) − 0.3 log2( 0.3 3 ) = 1.6166 (15) though the parameter c can be determined by the cardinality of fuzzy sets, for the sake of simple and easy comparison between different types of fuzzy sets, we suggest to use c = 3 in both cases. then, under the condition of c = 3, the result in example 4.1 can be calculated below: h1(µ,v) = −µ log µ− (1 −µ) log 1 −µ c = −0.65 log2(0.65) − 0.35 log2( 0.35 3 ) = 1.4888 (16) this result is smaller than the result 1.6166 in example 4.2, which means that the first-order information volume of 〈x1, 0.65〉 is smaller than that of 〈x2, 0.65, 0.05〉. https://doi.org/10.15837/ijccc.2021.1.4106 7 example 4.3: consider that a classic fuzzy membership function is 〈x3, 0.3〉 where x3 ∈ x, and the allowable error ε = 0.00001. because this is a classic fuzzy membership function, the cardinality c is 2, and π = 1−µ = 1−0.3 = 0.7. then, based on algorithm 1, the calculating procedure for the higher-order information volume of this classic fuzzy membership function is shown in table 1, and the convergence tendency of h∞(µ,v) is illustrated in figure 2. table 1: the calculating procedure of h∞(µ,v) in example 4.3 i hi(µ,v) i hi(µ,v) i hi(µ,v) 1 1.58129 12 2.91082 23 2.93711 2 1.98819 13 2.91886 24 2.93727 3 2.27303 14 2.92449 25 2.93738 4 2.47241 15 2.92844 26 2.93745 5 2.61198 16 2.93120 27 2.93751 6 2.70968 17 2.93313 28 2.93755 7 2.77806 18 2.93448 29 2.93757 8 2.82594 19 2.93543 30 2.93759 9 2.85945 20 2.93609 31 2.93761 10 2.88290 21 2.93655 32 2.93761 11 2.89932 22 2.93688 figure 2: the convergence tendency of h∞(µ,v) in example 4.3 according to table 1 and figure 2, by continuously separating the hesitancy degree, the higherorder information volume of this classic fuzzy membership function becomes larger and larger, and finally converges to 2.93761. example 4.4: let an intuitionistic fuzzy membership function be 〈x4, 0.2, 0.15〉 where x4 ∈ x, and the allowable error ε = 0.00001. because this is an intuitionistic fuzzy membership function, the cardinality c is 3, and π = 1 −µ−v = 1 − 0.2 − 0.15 = 0.65. then, based on algorithm 1, the calculating procedure for the higher-order information volume of this intuitionistic fuzzy membership function is shown in table 2, and the convergence tendency of h∞(µ,v) is illustrated in figure 3. https://doi.org/10.15837/ijccc.2021.1.4106 8 table 2: the calculating procedure of h∞(µ,v) in example 4.4 i hi(µ,v) i hi(µ,v) i hi(µ,v) 1 2.30912 11 3.63589 21 3.65375 2 2.77983 12 3.64223 22 3.65384 3 3.08579 13 3.64634 23 3.65389 4 3.28466 14 3.64902 24 3.65393 5 3.41393 15 3.65076 25 3.65395 6 3.49795 16 3.65189 26 3.65397 7 3.55257 17 3.65263 27 3.65398 8 3.58807 18 3.65311 28 3.65398 9 3.61114 19 3.65342 10 3.62614 20 3.65362 figure 3: the convergence tendency of h∞(µ,v) in example 4.4 according to table 2 and figure 3, by continuously separating the hesitancy degree, the higherorder information volume of this intuitionistic fuzzy membership function becomes larger and larger, and finally converges to 3.65398. although the parameter c can be determined by the cardinality of fuzzy sets, for simply and easily comparing the information volume between different types of fuzzy sets, we suggest to use c = 3 in both cases. then, under the condition of c = 3, the result of example 4.3 can be obtained, whoso calculating procedure is shown in table 3 and the convergence tendency is illustrated in figure 4. according to table 3 and figure 4, when c = 3, the result in example 4.3 finally converges to 2.93761. this result is smaller than the result 3.65398 in example 4.4, which means that the higher-order information volume of 〈x3, 0.3〉 is smaller than that of 〈x4, 0.2, 0.15〉. example 4.5: consider that an intuitionistic fuzzy membership function is 〈x5, 0.8, 0.2〉 where x5 ∈ x, and the allowable error ε = 0.00001. because this is an intuitionistic fuzzy membership function, the cardinality c is 3, and π = 1 − µ − v = 1 − 0.8 − 0.2 = 0. then, based on algorithm 1, the higher-order information volume of this intuitionistic fuzzy membership function can be calculated as follows. when i = 1, the associated higher-order information volume is that: https://doi.org/10.15837/ijccc.2021.1.4106 9 table 3: the calculating procedure of h∞(µ,v) in example 4.3 when c = 3 i hi(µ,v) i hi(µ,v) i hi(µ,v) 1 1.99076 12 2.91891 23 2.93727 2 2.27483 13 2.92453 24 2.93738 3 2.47367 14 2.92846 25 2.93745 4 2.61286 15 2.93121 26 2.93751 5 2.71029 16 2.93314 27 2.93755 6 2.77850 17 2.93449 28 2.93757 7 2.82624 18 2.93543 29 2.93759 8 2.85966 19 2.93609 30 2.93761 9 2.88305 20 2.93656 31 2.93761 10 2.89943 21 2.93688 11 2.91089 22 2.93711 figure 4: the convergence tendency of h∞(µ,v) in example 4.3 when c = 3 h∞ (µ,v)|i=1 = − 1∑ j=1 µj log µj − 1∑ j=1 vj log vj −π1 log π1 c = −0.8 log2(0.8) − 0.2 log2(0.2) − 0 = 0.7219 (17) when i = 2, the associated higher-order information volume is that: h∞ (µ,v)|i=2 = − 2∑ j=1 µj log µj − 2∑ j=1 vj log vj −π2 log π2 c = −(0.8 log2(0.8) + 0) − (0.2 log2(0.2) + 0) − 0 = 0.7219 (18) then, calculate the increment of higher-order information volume: ∆2 = h2(µ,v) −h1(µ,v) = 0.7219 − 0.7219 = 0 < ε (19) https://doi.org/10.15837/ijccc.2021.1.4106 10 hence, the algorithm is convergent, and the higher-order information volume of this intuitionistic fuzzy membership function is 0.7219. because the hesitancy degree π = 0, the three propositions of intuitionistic fuzzy membership function, namely ’support’, ’oppose’ and ’hesitant’, degenerate into two propositions, which are ’support’ and ’oppose’. since these two propositions are exclusive, the intuitionistic fuzzy membership function 〈x5, 0.8, 0.2〉 can be seen as the probability distributions p1 = 0.8,p2 = 0.2 which are also exclusive. then, the associated shannon entropy [29] is that hs(p) = −p1 log p1 −p2 log p2 = −0.8 log2(0.8) − 0.2 log2(0.2) = 0.7219 (20) which is the same as the higher-order information volume h∞ (µ,v). this example shows that, when the hesitancy degree π = 1 −µ−v = 0, the value of h∞ (µ,v) is identical to shannon entropy. 5 conclusion in this paper, a novel information volume of fuzzy membership function is proposed. the proposed measure not only takes the first-order information volume, but also the higher-order information volume into consideration. the major contributions and some desirable properties of the proposed method are listed below: (1) the first-order information volume of the fuzzy membership function is presented. (2) the higher-order information volume of the fuzzy membership function is proposed. (3) when the hesitancy degree equals to zero, the information volume of fuzzy membership function is identical to shannon entropy. (4) when the times of separation equals to one, the higher-order information volume of fuzzy membership function degenerates into the first-order information volume of fuzzy membership function. some examples, especial on classic fuzzy sets and intuitiontistic fuzzy sets, are used to show the efficiency of the proposed measure. acknowledgments the authors greatly appreciate the editor’s encouragement and the reviews’ suggestions to improve this work. the work is partially supported by national natural science foundation of china (grant no. 61973332). author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. https://doi.org/10.15837/ijccc.2021.1.4106 11 references [1] anjaria, k. 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[55] zhang, j.; liu, r.; zhang, j.; kang, b. (2020). extension of yager’s negation of a probability distribution based on tsallis entropy, international journal of intelligent systems, 35(1), 72–84, 2020. https://doi.org/10.15837/ijccc.2021.1.4106 14 copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: deng, j.x.; deng, y. (2021). information volume of fuzzy membership function, international journal of computers communications & control, 16(1), 4106, 2021. https://doi.org/10.15837/ijccc.2021.1.4106 introduction preliminaries fuzzy sets intuitionistic fuzzy sets shannon entropy information volume of fuzzy membership function first-order information volume of fuzzy membership function higher-order information volume of the fuzzy membership function numerical examples and discussions conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 4, month: august, year: 2021 article number: 4217, https://doi.org/10.15837/ijccc.2021.4.4217 ccc publications regression loss in transformer-based supervised neural machine translation d.x. li, z.y. luo dongxing li school of artificial intelligence, beijing normal university, beijing 100875, china lidx@bnu.edu.cn zuying luo* school of artificial intelligence, beijing normal university, beijing 100875, china *corresponding author: luozy@bnu.edu.cn abstract transformer-based model has achieved human-level performance in supervised neural machine translation (snmt), much better than the models based on recurrent neural networks (rnns) or convolutional neural network (cnn). the original transformer-based model is trained through maximum likelihood estimation (mle), which regards the machine translation task as a multilabel classification problem and takes the sum of the cross entropy loss of all the target tokens as the loss function. however, this model assumes that token generation is partially independent, without realizing that tokens are the components of a sequence. to solve the problem, this paper proposes a semantic regression loss for transformer training, treating the generated sequence as a global. upon finding that the semantic difference is proportional to candidate-reference distance, the authors considered the machine translation problem as a multi-task problem, and took the linear combination of cross entropy loss and semantic regression loss as the overall loss function. the semantic regression loss was proved to significantly enhance snmt performance, with a slight reduction in convergence speed. keywords: supervised neural machine translation (snmt), transformer, attention mechanism, semantic regression loss, evaluation metric. 1 introduction relying on sequence-to-sequence deep neural networks (dnns), supervised neural machine translation (snmt) aims to automatically convert a sequence from one language to another, with true sequence pairs as inputs [12, 31]. the most prevalent snmt approach employs the encoder-to-decoder structure, which encodes the source sequence into a context representation in one neural network and generates the target sequence in the other [4]. notably, the two neural networks are trained simultaneously in an end-to-end fashion. in addition, the current neural machine translation (nmt) systems mostly adopt the attention mechanism [1, 12, 23, 32]. https://doi.org/10.15837/ijccc.2021.4.4217 2 there are generally three model architectures to train the nmt neural network. the earliest architecture is recurrent neural network (rnn), which faces problems like vanishing gradients, exploding gradients, and long-range dependency. to solve the first two problems, many improved rnns have been designed, including long short-term memory (lstm) proposed by hochreiter and schmidhuber [14], gated recurrent units (gru) proposed by cho et al. [4], and bidirectional lstm (bilstm) proposed by schuster and paliwal [27]. to address the long-range dependency, convolutional neural network (cnn) has been introduced by gehring et al. [11, 12], in which a succession of convolutional layers captures the dependency of a few tokens (phrases), and concatenates the local dependency representations as the sequence representation. recent years has seen the emergence of a novel and competitive model called transformer for nmt [32]. solely based on attention mechanism, transformer uses a self-attention network (san) to compute the mutual relationship scores of all the tokens within the source sequence or the target sequence. hassan et al. proved that transformer can achieve human-level performance on some languages [13]. therefore, transformer-based architectures have been widely used in the field of nmt [20, 32, 33]. the nmt performance is mainly affected by the following factors: network architecture, optimization algorithm, loss function, and evaluation metric. the original transformer uses the adam optimizer, cross entropy loss function, and the metric of bilingual evaluation understudy (bleu) [26]. this transformer-based model is trained through maximum likelihood estimation (mle) to learn the conditional probability distribution of the target token step by step, which can be regarded as a token-level target [29]. however, the model training only focuses on the loss of the target token, yet hardly pays attention to the semantic loss of the global generated sequence. besides, the sum of the probability distribution loss is calculated under the assumption that token generation is partially independent, without realizing that tokens are the components of a sequence. therefore, it is reasonable to include the semantic regression loss of the global target sequence in training. to disclose sentence-level influences, most scholars resorted to ensemble learning approaches to improve translation quality [5, 24, 34], such as bag-of-words (bow) model [24], and machine learning (ml) [5]. the simple bow model only pays attention to token frequency, failing to consider token order and sequence semantics. meanwhile, ml models like supervised ml used by cohn and goodman [5], and reinforcement learning (rl) used by wu et al. [34] would greatly complicate transformer training. this paper improves the transformer as the basic learning network. compared with the original transformer proposed by vaswani et al. [32], the improved transformer has the following unique features: (1) the optimizer is adamw proposed by loshchilov and hutter [22]. (2) the evaluation metrics are translation edit rate (ter) proposed by snover et al. [30], metric for evaluation of translation with explicit ordering (meteor) proposed by banerjee and lavie [2], recall-oriented understudy for gisting evaluation-the longest common subsequence (rouge-l) proposed by lin [21] as well as bleu [26]. (3) the loss function innovatively computes the semantic loss of the global sequence between the candidate and the reference, except for the cross entropy loss of all the pointwise tokens, that is, the nmt problem is treated as a regression problem from the perspective of the global sequence, in addition to the multi-label classification problem from the perspective of the tokens. the improved transformer was compared with the original transformer through nmt experiments on three evaluation datasets. on dataset iwslt2014 de->en, the improved transformer outperformed the original transformer by 0.55 bleu/2.07 ter/0.20 rouge-l, and by 0.49 bleu/2.70 ter/0.60 rouge-l, with transformer-small configuration and transformer-base configuration, respectively. on dataset iwslt2016 de->en, the improved transformer outperformed the original transformer by 0.51 bleu/-0.66 ter/0.78 meteor/0.68 rouge-l, and by 0.56 bleu/-0.07 ter/0.37 meteor/0.87 rouge-l, with transformer-small configuration and transformer-base configuration, respectively. on dataset wmt17 en->de, the improved transformer outperformed the original transformer by 0.86 bleu/-1.08 ter/0.68 meteor/0.80 rouge-l, and by 1.21 bleu/1.14 ter/0.62 meteor/0.83 rouge-l, with transformer-base configuration and transformer-big configuration, respectively. the remainder of this paper is organized as follows: chapter 2 introduces the preliminaries of this work, including three snmt architectures, attention mechanisms, and training strategy, with https://doi.org/10.15837/ijccc.2021.4.4217 3 particular emphasis on transformer attention mechanisms; chapter 3 puts forward our model, highlighting the realization of the novel loss function; chapter 4 carries out a series of experiments, explains the selection of some hyper-parameters, and describes the implementation of our model; chapter 5 summarizes our work and looks forward to future research. 2 preliminaries 2.1 transformer architecture transformer is implemented entirely based on the attention mechanism, whose maximum path length and minimum number of sequential operations are and . consisting of stacked encoder layers and decoder layers, this model architecture can capture long-range dependencies more directly than rnns and cnn. an encoder layer contains a multi-head self-attention layer, followed by a position-wise feedforward layer. both layers are added with a residual connection layer and a layer normalization layer. multi-head self-attention is implemented by multiple sans via a linear transformation [35]. the layers of an encoder can be summarized in sequence as: self-attention -> residual connection -> layer normalization -> feed-forward -> residual connection -> layer normalization. the encoder layer can the hidden representations of all the tokens in the source sequence. a decoder layer has a similar structure to the encoder, except that an encoder-decoder attention layer is inserted, which is followed by the multi-head self-attention layer. the insertion aims to compute the attention score between the hidden representations of the source sequence and the target token representation. the layers of a decoder can be summarized in sequence as: self-attention -> residual connection -> layer normalization -> encoder-decoder attention -> residual connection -> layer normalization -> feed-forward -> residual connection -> layer normalization. transformer combines the merits and eliminates the defects of rnns-based and cnn-based nmts. it relies on an encoder multi-head self-attention network and a decoder multi-head self-attention network to capture the token dependencies in the source sequence and the target sequence, respectively. however, the san sequence fails to consider the token order. to remedy this issue, the relative or absolute position of the tokens in the sequence are added to the corresponding token embeddings via trigonometric functions. 2.2 attention mechanisms in transformer self-attention. transformer architecture entirely relies on two attention mechanisms: self-attention and encoder-decoder attention. during training, transformer employs the san to compute the attention scores between one token and another within the encoder and the decoder, respectively. notably, the decoder computes the attention scores between the current token and its previous tokens, masking the subsequent tokens. meanwhile, it applies encoder-decoder attention to compute the attention scores between the target token and all the source tokens. these attention mechanisms operate on an input sequence, x = (x1,x2, · · · xn), where n is its length and xi ∈ rdx is the i − th token.the goal is to obtain a new feature matrix o = (o1,o2, · · · on) of the same dimension as x, where oi ∈ rdo is the token representation of xi. here, dx and do represent the dimensions of the input and latent representation, respectively, which are usually equal to the model dimension. then, the i− th hidden embedding oi can be calculated by: oi = n∑ j=1 αij (xjw v ) αij = exp(eij ) n∑ k=1 exp(eik ) eij = (xiw q)(xj w k ) t √ do (1) where, w q ∈ rdx×dq,w k ∈ rdx×dk,w v ∈ rdx×dv (dq = dk) are learned parameter matrices; eij is the scaled dot-product attention score; αij is attention score obtained by a softmax function subject https://doi.org/10.15837/ijccc.2021.4.4217 4 to ∑n j=1 αij = 1. similarly, the representation matrix at the sequence level can be obtained by: o = soft max( qk t √ do + mask)v q = xw q,k = xw k,v = xw v (2) where, o is the attention head computed on three matrices q ∈ rn×dq,k ∈ rn×dq,v ∈ rn×dv whose dimensions are dq, dq and dv (dq = dv = do = dmodel), respectively. these matrices are packed together by queries, keys, and values which constitute the input. for encoder, mask ∈ rn×n is named as padding mask, and used to align a batch of examples. the elements of the matrix are 0s or −∞s, which correspond with non-padding or padding elements in x, respectively. 0s indicate that the tokens can attend to each other, and −∞s mean the otherwise. for decoder, mask ∈ rm×m is a triangular matrix whose elements are all 0s below the diagonal and all −∞s in other places. this means that the target token can attend to its previous tokens and cannot attend to the subsequent tokens. encoder-decoder attention. this attention mainly computes the representation of the target token in decoder. on the top layer of the encoders, there are three feature matrices of the source sequence x − q ∈ rn×dq,k ∈ rn×dq,v ∈ rn×dv . let m be the length of the target sequence x′; q ′ ∈ rm×dq,k ′ ∈ rm×dq,v ′ ∈ rm×dv be the hidden states. then, the final hidden state for the generated sequence can be calculated by: o ′ = soft max( q ′ kt√ do + mask)v (3) where, the row vector of mask ∈ rm×n is same as that of the padding mask in the encoder. multi-head self-attention. transformer actually employs attention heads in the original implementation. this multi-head mechanism has a great advantage: the model is allowed to capture the token representations in different vector subspaces. many experiments have shown that different heads can also capture different language information [32]. some subspaces include syntax information and some include semantic information. under the multi-head mechanism, q,k,v are first split into h, each head is represented, and all the representations are concatenated as the final output: qi,ki,vi = split(q,k,v ) headi = attention(qi,ki,vi) multihead(q,k,v ) = concat(head1,head2, · · · ,headh)w o (4) 2.3 model training strategy token-level learning. in the original transformer architecture, nmt aims to maximize the mapping score of a sequence pair or maximize the conditional probability ŷ = arg max y p(y|x), where x and y are the source and target sequences, respectively. this task is typically regarded as a multi-label classification problem, where the class size equals the size of the target language vocabulary v . the mle is usually adopted to address this problem: ζ1 = − n∑ j=1 ∑ yj∈v pdata log pmodel (5) where, n is the length of the target sequence; v is the target vocabulary; pdata and pmodel are the probability distribution of the ground-truth data with label smoothing, and the probability distribution of the model output, respectively. sequence-level learning. ignoring the global sequence-level semantic loss, the token-level learning approach only computes the isolated token-level loss, which merely focuses on the cross entropy of each token. the novel sequence-level method adds bow as another target loss, and assumes that the sequence-level probability of each token is independent of the position in the sequence [24]. despite its simplicity and improved performance, bow only takes account of token frequency, without considering token order and sequence meaning. hence, the reference is not easily exposed due to the highly https://doi.org/10.15837/ijccc.2021.4.4217 5 similarity of the candidate. some ml models like supervised ml and rl have also been integrated into the transformer model [5, 34]. for example, cohn and goodman built bayesian model on transformer to reduce meaning loss, applied rational speech acts (rsa) model to produce speakers and listeners which can be modeled as bayesian agents, and utilized the role of speakers and listeners as double-sided mirrors to understand the overall sequence information [5]. the rl rewards and punishes the target translation sequence by setting an effective reward function [34]. the problem is supervised ml and rl algorithms inevitably complicates transformer training. to solve the problem, our method adopts a simple structure and considers the semantic loss between the candidate and the reference. 3 modeling this chapter explains the details on our model. besides presenting the input/output notations and training network architecture, the authors expounded at length on the implementation of semantic regression loss during training. 3.1 notations some notations are necessary to facilitate the model description. for an snmt task, the i − th sample (xi,yi) consists of a source language sequence xi and a target language sequence yi. in the model architecture, xi relates to the encoder input xenc_in(i), and yi relates to the decoder input ydec_in(i) and the decoder output ydec_out(i) which have 1-position misalignment. the semantic output of yi is a vector denoted as ysem(i). all inputs and outputs of our model can be formatted as: xenc_in(i) = {x1i ,x2i , ...,xmi } ydec_in(i) = {y1i ,y2i , ...,yni } ydec_out(i) = {y1i ,y2i , ...,yni } ysem_out(i) = ysmi (6) where, m and n are the length of the source sequence and the target sequence respectively; sm is the dimension of the semantic vector of the generated sequence, which equals the target vocabulary size |v |. despite being the same in length, the decoder input and decoder output differ due to the malposition prediction caused by the language model; xenc_in(i) and ydec_out(i) are the ground truth data; ysem_out(i) is the sum of all the label smoothing vectors. 3.2 network architecture as shown in figure 1, our model firstly preprocesses all the samples. the first step is to insert 〈/s〉 (end of the sequence) into the last position of the source sequence, and then to insert 〈s〉 (start of the sequence) and 〈/s〉 into the first position and last position of the target sequence, respectively. before a preprocessed sample is imported into the encoder, all the token embeddings are initialized to compute their absolute positional encodings. next, token embeddings and positional encodings are added to the encoder, and regarded as the real input of the encoder. xenc_in(i,j) = we(x j i ) + pe(x j i ) ydec_in(i,j) = we(y j i ) + pe(y j i ) xenc_in(i) = concat(xenc_in(i,j)) ydec_in(i) = concat(ydec_in(i,j)) xenc_out(i) = encoders(xenc_in(i)) (7) where, i is the i− th sample and j is the j − th token. through nonlinear computing via the stacked encoder layers, it is possible to obtain three latent representation matrices of the source sequenceq,k,v . then, matrices k and v are transferred into the encoder-decoder attention layer of all https://doi.org/10.15837/ijccc.2021.4.4217 6 figure 1: architecture of our model. note: the red part is the semantics of the candidate translation, and the other part is the original transformer decoder layers. similarly, the stacked decoders are utilized to generate the probability distribution of the target token, conditioned on the encoder output and previous generated tokens, as well as the masked attention scores. tokenti = decoders(xenc_out(i),y [:t] dec_in(i)) scoreti = w × tokenti + b p(tokenti) = soft max(scoreti) (8) through nonlinear transform of the stacked decoders, the token probability distribution at t− th step is obtained by softmax function. the dimension of probability distribution pj equals the target vocabulary size |v | and subjects to the constraint condition ∑n j=1 pij = 1. so far, learning the target token distribution has been treated as a multiple-label classification problem. additionally, the authors analyzed the semantic distribution of the target sequence. because these token distributions contain positional encodings, the weighted sum of them is taken as the semantics of the translation sequence. sti = ∑|v | j=1 pj ⊗wej ysem_out(i) = n∑ t=1 w tsti (9) where, sti is the semantics of the t−th reference token; wej is the token embedding; n is the length of the target sequence. therefore, ysem_out(i) can be regarded as the semantic of the generated sequence. for simplicity, the average of the weighted sum 1 n ∑n j=1 s t i = 1 is applied as the approximate value of ysem_out(i). 3.3 semantic regression loss function at the t − th step, the original transformer considers the distribution of the target output is regarded as a multi-label classification problem. let n and |v | be the length of the target sequence and the size of the target vocabulary, respectively. the mean sum of token cross entropy is taken as the first loss, which is computed by formula (5) and denoted as ζ1. to improve the translation quality during the iterative training, the meaning of the candidate translation should be close to the reference sequence, and the sematic distance between the best choice and the reference should be minimized as in a procrustes problem. for a given vocabulary on a specific dataset, it is possible to obtain the token embeddings, and denote them as a matrix ev ∈ r|v |×d, where |v | is vocabulary size and d https://doi.org/10.15837/ijccc.2021.4.4217 7 is the dimension of token embedding. this matrix is always relatively static. in other words, the token embedding changes slightly with corpuses and vocabularies. for a sequence, the mean sum of all the token embeddings is taken as its eigenvector. therefore, a target sequence can be expressed as a one-hot encoding ssen ∈ r|v | without taking label smoothing into account, e.g., [1, 0, 0, 0, 1, · · · 0, 1]. here, the authors compute the multiplication between one component of pi,j and ~ej, and then sum up the matrix on axis = 0, treating the transpose of the vector as the approximate semantics of the target sequence: msen =   p11 p21 ... pn1 p12 p22 ... pn2 ... ... ... ... p1|v | p2|v | ... pn|v |   ev =   e11 e12 ... e1d e21 e22 ... e2d ... ... ... ... e|v |1 e|v |2 ... e|v |d   =   ~e1 ~e2 ... ~e|v |   (10) where, the column vector in msen ∈ r|v |×n is the target token probability distribution; ev ∈ r|v |×d is the target embedding lookup table, in which each row vector is the embedding corresponding to each token in the target vocabulary. then, the latent representation of the target token, e.g., the column vector of stoken, can be obtained in the matrix form by formula (11), and the semantics of the sequence can calculated by adding up the multiplications between msen by formula (11). stoken =   p11~e1 p21~e1 ... pn1~e1 p12~e2 p22~e2 ... pn2~e2 ... ... ... ... p1|v |~e|v | p2|v |~e|v | ... pn|v |~e|v |   ssen = ∑ axis=0 mtsen ⊗ev = ∑ axis=0 (   p11 p21 ... pn1 p12 p22 ... pn2 ... ... ... ... p1|v | p2|v | ... pn|v |   t ⊗   ~e1 ~e2 ... ~e|v |  ) = ∑ axis=0 (   p11~e1 + p21~e1 + ... + pn1~e1 p12~e2 + p22~e2 + ... + pn2~e2 ... p1|v |~e|v | + p2|v |~e|v | + ... + pn|v |~e|v |  ) (11) where, ssen is the convex combination of the semantic embeddings of the target sequence [25]. in this way, it is possible to acquire the semantics of the predicted sequence and target sequence. the semantic regression loss can be derived from the distance between the semantics of the two sequences. furthermore, the target tokens are mapped into the same d-dimensional vector space, where the vectors are not highly variable. hence, the semantic distance is approximately taken as the second loss from the global sequence: ds(y,ŷ) = ssen − ŝsen = msen ⊗ev −m̂sen ⊗ev = (msen −m̂sen) ⊗ev ∝ (msen −m̂sen) ds(y,ŷ) ≈ (msen −m̂sen) ζ2= ∣∣∣ds(y,ŷ)∣∣∣ (12) where, y and ŷ are the reference sequence and candidate sequence, respectively; ssen and ŝsen are the semantics of the reference sequence and candidate sequence, respectively. if the vector space of tokens https://doi.org/10.15837/ijccc.2021.4.4217 8 does not change, the semantic difference between the reference and the candidate must be proportional to the vector distance. in the original transformer, the decoder is trained in the teacher forcing way, that is, the translation system actually knows the correct answer, when it predicts the subsequent token. however, the decoder works in an autoregressive manner during the inference. in this way, the token does not know what the subsequent token is. to bridge the gap, it is necessary to make an overall analysis of the semantic differences between the reference sequence and the candidate sequence. therefore, the decoder could be trained from the perspectives of the token and the global target sequence simultaneously. the first loss aims to maximize the probability distribution of the generated token, while the second loss aims to maximize the similarity between the reference and the candidate, and to preserve the meaning. to weight the difference, scalar λ is introduced to balance the loss functions as a hyper-parameter. ζ = λζ1 + (1 −λ)ζ2 s.t. λ ∈ [0, 1] (13) 4 experiments this chapter verifies the performance of our model through experiments. firstly, the sequence pairs of three evaluation datasets (section 4.1) were preprocessed. then, the experimental setting, evaluation metrics (section 4.2) and baseline systems based on transformer (section 4.3) were introduced in turn. finally, experimental implementation was detailed, including hyper-parameter selection (section 4.4) and results analysis (section 4.5). 4.1 datasets our experiments were conducted on three translation tasks: iwslt 2014/2016 de->en translation dataset and wmt17 en->de translation dataset, which are widely used as evaluation benchmarks for nmt [32]. each dataset was split into a training set, a validation set, and a test set. then, the data were preprocessed through normalization and sub-word segmentation. the iwslt dataset contains the data extracted from the iwslt evaluation campaign [3, 9]. iwslt2014 offers 160k/7k sentence pairs as training/validation sets. the authors concatenated dev2010, dev2012, tst2010, tst2011 and tst2012 as the test set, including about 7k sentence pairs. for iwslt2016, the data consist of 180k/12k sentence pairs as training/validation sets. the authors took the concatenation of tst2010/2011/2012/2013/2014 as the test set, including about 12k sentence pairs. for wmt17, the original training set was adopted as the training set of our model, which contains 5.9 million sentence pairs. the concatenation of newstest2013/2014/2015/2016 was taken as the validation set, involving more than 1 million sentence pairs, and newstest2017 was treated as the test set, containing about 3k sentence pairs (see table 1). the three datasets above were preprocessed by moses, a de-facto standard toolkit for smt [16]. firstly, the sentence pairs were tokenized to deal with the punctuations. considering the huge range of tokens in the tokenized sequences, the sequence pairs of more than 100/80 tokens were discarded for iwslt2014/2016 and wmt17, respectively, such as to improve translation quality and accelerate training. finally, every subset of each dataset was truncated. to mitigate the influence of out of vocabulary (oov) tokens and rare tokens, the sequence was tokenized to sub-word units by sentencepiece ([39]) [18] or wordpiece ([40]) [17], which have the same function of bytes pair encoding (bpe) [28]. in addition, each subset shares a vocabulary of 32,000, because english and german belong to the germanic language family. 4.2 experimental setting and evaluation metrics the implementation of our experiments is based on the empirical results. our experiments are build using appropriate setting, such as, optimizer, learning rate and other hyper-parameters. for optimizer, we choose adamw optimizer [22] with weight_decay = 10−5,β1 = 0.9,β2 = 0.999 which is used in bert [6]. for learning rate, we also follow the learning rate warm-up strategy [32] with https://doi.org/10.15837/ijccc.2021.4.4217 9 table 1: the sequence pair statistics after preprocessing by moses ((a)) and subword statistics after segmentation by bpe ((b)) on iwslt 2014/2016 de->en translation dataset and wmt17 en->de translation dataset. the terms of “train”, “eval” and “test” represent training set, validation set and test set respectively. “s” and “t” represent the source language and the target language respectively. note: units of k and m stand for thousand and million respectively dataset train eval test dataset s/t train eval test iwslt2014 161k 7.3k 6.7k iwslt 2014 de 4.13m 188k 167kiwslt2016 181k 12k 11.8k en 4.06m 184k wmt17 5.85m 1.12m 3k iwslt 2016 de 4.65m 306k 290k(a) en 4.59m 302k wmt17 en 155.36m 423k 94kde 169.71m 460k (b) warmup − steps = 8000. during training, label smoothing rate is set to 0.1 and all dropout values are set to 0.1. our experiments were implemented under the framework of tensorflow 1.14.0. the attainted model cpt files also can be easily converted into pytorch bin files using transformers. all the experiments were completed by two nvidia 1080ti gpus. during the inference, beam search size was set to 4 for validation set and test set. there are many evaluation metrics, each of which has its own strengths and weaknesses. for diversity and reliability, four metrics were selected to evaluate our model, namely, bleu [26], meteor [2], ter [30] and rouge-l [21]. the authors computed bleu score with standard moses tools of multi-bleu.perl ([36]), and ter score with pyter ([38]), and meteor score, rouge-l score with nlg-eval ([37]). bleu, as the earliest automatic evaluation method for machine translation, analyzes the degree of co-occurrence of n-gram between the candidate and the reference. the main component of bleu is n-gram precision via geometric averaging. meteor is based on explicit word-to-word matches, which includes the identical words in the surface forms, morphological variants in stemmed forms, and synonyms in meanings between the candidate and the reference. this metric combines unigram-precision, unigram-recall, and a direct measure of how out-of-order the words in the candidate translation are. ter is a distance-based metric of the workload of the post-translation editing of the candidate translation. the distance is defined as the minimum number of edits which transforms one sequence into another. ter considers edit operations like insertion, deletion, and substitution of single words, as well as shifts of word sequences. rouge is a recall-based metric commonly used for machine translation and text summarization. chin-yew lin introduced four different rouge measures: rouge-n, rouge-l, rouge-w, and rouge-s. here, rouge-l is chosen as the metric to evaluate machine translation. l stands for the longest common subsequence (lcs) of the corresponding sequences of the candidate and the reference. 4.3 baseline systems of machine translation based on the original transformer our model was compared to the original transformer models with small, base, and big configurations. the difference between our model and the original transformer lies in the application of the semantic regression loss during training. drawing on vaswani et al.’s work [32], the transformerbase model and transformer-large model were designed to contain 6 layers and 12 layers, respectively. facing the limited gpu resources, the number of stacked encoders and decoders was set to 6 in all the configurations for our model. each transformer (small, base, and big) has a unique configuration. the small, base, and big configurations differ in the number of heads, hidden size, filter size, and number of blocks (layers) of encoders and decoders. the corresponding hyper-parameters of the three models are listed in table 2. according to the different sizes of the three datasets, transformersmall and transformer-base settings were adopted for iwslt de->en, and transformer-base and transformer-big settings for wmt17 en->de. https://doi.org/10.15837/ijccc.2021.4.4217 10 table 2: the hyper-parameters of transformer-small, transformer-base and transformer-big configurations of transformer based machine translation systems transformer heads blocks hidden size filter size small 4 6 256 1024 base 8 6 512 2048 big 16 6 1024 4096 4.4 snmt based on our model 1) different distance measurement algorithms the candidate sequence and the reference sequence were separately taken as a whole. in the computing graph of the network architecture, the two sequences are essentially two tensors or multidimensional vectors, and their distance is denoted as ζ2 . in this section, our model was tested with three different metrics for the distance between the two vectors—x,y, where x is the semantic representation of the candidate sequence and y is the semantic representation of the reference sequence: euclidean distance (square): ed(x,y) = ‖x−y‖22 = (x − y) t (x − y); cosine distance (cosine similarity): cos(x,y) = x · y = xt y = yt x. note that the cosine distance is generalized without normalization, which is equivalent to the dot product operation, without affecting the final result; max-pooling distance (mpd): mpd(x,y) = max(xi,yi)|n1 . inspired by max-pooling, this metric maximizes the corresponding components of the two vectors. our model was implemented on the three datasets with transformer-base configuration. some experimental results are listed in table 3. multiple trials and comparisons were carried out to contrast the cross entropy loss of each token with kullback–leibler divergence (kld, [19]) and jensen-shannon divergence (jsd, [8]), both of which are distribution metrics. under these circumstances, the metric scores were slightly improved. however, cross entropy (ce) is the simplest algorithm in terms of computing. from the left side of table 3, it can be seen that the use of ce brings stable effects. therefore, the experiments on the right side of table 3 all use the ce algorithm. from the right side of table 3, it was inferred that ed > mpd > cos achieved apparently different performances, with the conventional ed being the best performer. moreover, mpd had a similar effect as ed. these experiments were implemented with formula (13). overall, the linear combination of ce and ed was determined as our loss function. table 3: the different evaluation scores using different distance evaluation algorithms on iwslt2014, iwslt2016 and wmt17 with transformer-base configuration respectively. ce, kld [19] and jsd [8] are ζ1 loss which stand for cross-entropy, kullback–leibler divergence and jensen-shannon divergence respectively. ed, cos and mpd are ζ2 loss which stand for euclidean distance, cosine distance and max-pooling distance respectively. ed+ce, cos+ce and mpd+ce are the linear combinations between ce and different distance losses loss ce kld jsd ed+ce ce+ce kld+ce iwslt2014 bleu 33.96 33.90 33.97 34.45 34.32 34.21 ter 50.71 50.97 50.98 48.01 48.15 49.07 meteor 33.73 32.77 33.29 33.07 33.71 33.80 rouge_l 62.95 63.04 63.26 63.55 63.47 63.11 iwslt2016 bleu 32.45 32.40 32.41 33.01 34.32 34.21 ter 51.38 51.37 50.48 51.31 48.15 49.07 meteor 32.52 31.07 31.29 32.89 33.71 33.80 rouge_l 60.23 59.04 59.26 61.10 63.47 63.11 wmt17 bleu 28.10 28.25 28.29 28.96 28.41 28.66 ter 55.11 54.97 54.98 54.03 54.76 54.49 meteor 28.73 28.75 28.71 29.41 28.58 28.87 rouge_l 56.22 56.24 56.26 57.02 56.43 56.68 2) effect of hyper-parameter λ https://doi.org/10.15837/ijccc.2021.4.4217 11 in order to weight the two loss functions, our model was tested with the λ, value changing from 0.1 to 0.9 with a step length of 0.2. as shown in table 4, the four evaluation scores obeyed the normal distribution, peaking at λ = 0.5. hence, distance loss is as important as cross entropy loss for our model. in terms of performance, the semantics loss of the global sequence is as important as the cross entropy loss of all the tokens. moreover, in the decoder, each step is a multi-label classification problem, from the perspective of token translation. from the perspective of the global sequence translation, however, each step is a regression task. the results in table 4 show that λ = 0.5 is the best choice for our model. table 4: the different evaluation scores using different λ on iwslt2014, iwslt2016 and wmt17 with transformer-base configuration respectively. λ = 1 means that only ζ1 is used. all results are obtained by using ed+ce λ 0.1 0.3 0.5 0.7 0.9 1 iwslt2014 bleu 33.98 34.13 34.45 34.28 33.80 33.96 ter 49.76 48.38 48.01 49.30 49.92 50.71 meteor 32.89 32.51 33.07 33.51 33.53 33.73 rouge_l 63.47 63.43 63.55 63.80 62.84 62.95 iwslt2016 bleu 32.73 32.54 33.01 32.15 32.37 32.45 ter 53.09 52.28 51.31 51.30 52.95 51.38 meteor 30.98 31.63 32.89 31.42 32.53 32.52 rouge_l 59.37 60.33 61.10 59.19 60.09 60.23 wmt17 bleu 27.73 28.54 28.96 28.41 28.53 28.10 ter 55.99 55.28 54.03 57.30 55.95 55.11 meteor 27.89 28.51 29.41 28.51 28.53 28.73 rouge_l 55.17 55.93 57.02 55.80 55.84 56.22 3) effects of adam optimizer cluster the next is to test the impact of different adam optimization algorithms on machine translation performance. the authors focused on comparing three optimization algorithms, namely, adam [15], nadam [7] and adamw [22], especially adam and adamw. the adam optimizer integrates traditional momentum with rmsprop. nadam optimizer combines nesterov accelerated gradient (nag) with adam, replacing the traditional momentum in the original adam with nesterov momentum [7]. adamw optimizer was adopted for our model, which targets adam’s roller coaster problem. to avoid overfitting, adam optimizer employs l2 to update weights. but this approach is susceptible to large weight decay. hence, the weight decay method in the adam algorithm should be adopted instead of l2 regularization. the experimental results in table 5 demonstrate the effectiveness of adamw optimizer. compared to the adam optimizer, adamw achieved a performance improvement of 0.38 bleu/0.56 ter/1.42 meteor/0.79 rouge-l on the iwslt2014 dataset, 0.34 bleu/1.05 ter/0.40 meteor/1.09 rouge-l on the iwslt2016 dataset, and 0.26 bleu/0.45 ter/0.30 meteor/0.85 rouge-l on the wmt17 dataset. 4) effect of beam size to test its performance more accurately, our model was compared with a beam search algorithm [10], using different beam sizes during inference. beam=1 means to use greedy search for machine translation. as shown in table 6, the results of greedy search were far worse than those of beam search algorithm. in addition, beam = 3 brought significant improvement and could be seen as an inflection point during inference. further, little fluctuation occurred when the beam size was greater than 3. to save time and space, beam=4 was usually the best choice. compared to greedy search, the performance improvement was 3.19 bleu/5.31 ter/2.60 meteor/3.26 rouge-l on the iwslt2014 dataset at beam=4, 1.78 bleu/3.90 ter/3.62 meteor/3.63 rouge-l on the iwslt2016 dataset at beam=4, and 1.79 bleu/4.09 ter/2.15 meteor/3.14 rouge-l on the wmt17 dataset at beam=4. 5) experimental results the experiments (1)-(4) help to identify the key hyper-parameters that ensure the success of our https://doi.org/10.15837/ijccc.2021.4.4217 12 table 5: the different evaluation scores using different optimizers on iwslt2014, iwslt2016 and wmt17 with transformer-base configuration respectively. all results are obtained by using ed+ce and λ = 0.5 optimizer adam nadam adamw iwslt2014 bleu 34.07 34.28 34.45 (+0.38) ter 48.57 48.32 48.01 (-0.56) meteor 31.65 32.24 33.07 (+1.42) rouge_l 62.76 62.81 63.55 (+0.79) iwslt2016 bleu 32.67 32.62 33.01 (+0.34) ter 52.36 52.53 51.31 (-1.05) meteor 32.49 32.48 32.89 (+0.40) rouge_l 60.01 59.81 61.10 (+1.09) wmt17 bleu 28.70 28.73 28.96 (+0.26) ter 54.48 54.90 54.03 (-0.45) meteor 29.11 28.89 29.41 (+0.30) rouge_l 56.17 56.17 57.02 (+0.85) table 6: the different evaluation scores using different beam sizes on iwslt2014, iwslt2016 and wmt17 with transformer-base configuration respectively. all results are obtained by using ed+ce, λ = 0.5 and adamw optimizer beam size 1 2 3 4 5 6 7 8 9 10 iwslt2014 bleu 31.26 32.74 34.12 34.45 33.41 32.89 32.94 33.74 33.94 34.09 ter 53.31 52.03 49.51 48.01 47.95 48.03 49.32 49.25 48.23 48.54 meteor 30.47 31.60 31.95 33.07 32.64 31.85 31.93 32.32 32.71 32.92 rouge_l 60.29 61.66 62.17 63.55 62.35 61.75 62.27 62.48 63.31 63.68 iwslt2016 bleu 31.23 31.75 32.40 33.01 32.54 32.18 31.87 32.45 33.10 32.66 ter 55.21 54.13 53.81 51.31 52.39 51.93 53.14 53.78 51.56 51.82 meteor 29.27 31.28 31.46 32.89 31.27 31.28 30.43 31.82 32.57 32.32 rouge_l 57.47 58.28 58.98 61.10 58.88 56.49 57.45 57.49 58.99 59.72 wmt17 bleu 27.17 27.74 28.35 28.96 28.49 27.17 27.74 28.35 28.70 28.49 ter 58.12 56.11 55.01 54.03 54.93 58.03 56.02 55.02 54.87 54.95 meteor 27.26 27.30 28.75 29.41 28.87 27.27 27.39 28.79 29.11 29.01 rouge_l 53.88 54.23 55.38 57.02 56.00 53.92 54.23 55.49 56.17 55.97 model. the overall results of our experiments are shown in table 7. all metric scores were computed on the condition of uncased tokens. for iwslt2014/2016 de->en translation tasks, our model was tested on test sets. as shown in table 7, our approach improved the performance over the baseline system using the transformer-small and transformer-base configurations, respectively. the performances on iwslt2014/iwslt2016 were improved by 0.55 bleu/-2.07 ter/0.20 rouge-l and 0.51 bleu/-0.66 ter/0.78 meteor/0.68 rouge-l with transformer-small configuration, respectively. the performances on iwslt2014/iwslt2016 were improved by 0.49 bleu/-2.70 ter/0.60 rouge-l and 0.56 bleu/-0.07 ter/0.37 meteor/0.87 rouge-l with transformer-base configuration, respectively. the slight improvement may relate to the size of the dataset. for wmt17 en->de translation tasks, our model achieved progressive improvement when using transformer-big configuration. for the transformer-base configuration, our model outperformed the baseline system by 0.86/-1.08/0.68/0.80 using bleu, ter, meteor and rouge-l, respectively. for transformerbig configuration, our model outperformed the baseline system by 1.21/-1.14/0.62/0.83 using bleu, ter, meteor and rouge-l, respectively. these performances were achieved under the setting of ed+ce, λ = 0.5, adamw optimizer and beam=4. 6) training time cost further, the time cost of the original transformer was compared with our model. the following https://doi.org/10.15837/ijccc.2021.4.4217 13 table 7: the overall evaluation scores whether using the semantics distance loss on iwslt2014, iwslt2016 and wmt17 test set by using ed+ce, λ = 0.5, adamw optimizer and beam=4 models metrics iwslt2014 iwslt2016 wmt17 transformer-small blue 33.78 32.17 ter 51.77 52.83 meteor 33.29 31.93 rouge_l 62.91 60.05 transformer-base blue 33.96 32.45 28.10 ter 50.71 51.38 55.11 meteor 33.73 32.52 28.73 rouge_l 62.95 60.23 56.22 transformer-big blue 29.08 ter 54.37 meteor 29.09 rouge_l 56.78 transformer-small+ours bleu 34.33 (+0.55) 32.68 (+0.51) ter 49.70 (-2.07) 52.17 (-0.66) meteor 32.60 32.71 (+0.78) rouge_l 63.11 (+0.20) 60.73 (+0.68) transformer-base+ours bleu 34.45 (+0.49) 33.01 (+0.56) 28.96 (+0.86) ter 48.01 (-2.70) 51.31 (-0.07) 54.03 (-1.08) meteor 33.07 32.89 (+0.37) 29.41 (+0.68) rouge_l 63.55 (+0.60) 61.10 (+0.87) 57.02 (+0.80) transformer-big+ours bleu 30.29 (+1.21) ter 53.23 (-1.14) meteor 29.71 (+0.62) rouge_l 57.61 (+0.83) can be inferred from the results in table 8. horizontal comparison: the convergence speed is negatively correlated with dataset size. meanwhile, the time to reach convergence is positively correlated with dataset size. longitudinal comparison: our model consumed more time than the original transformer, especially for the datasets with small size. compared with wmt17, it cost more time for iwslt2014/2016 training with our model. 4.5 discussion this paper proves that the semantic distance loss is as important as cross entropy loss between the translation output and the reference during training. the translation quality can be improved with the linear combination of the two losses as the overall loss, at the cost of a slight increment in time overhead. as in the original transformer, the generation task in the decoder essentially solves a multi-label classification problem from the perspective of token generation. this paper also focuses on the semantic distance between the generated sequence and the reference sequence from the perspective of sequence generation. there are three possible reasons for the insignificant improvement on performance: (1) the limited improvement over iwslt14 and iwslt16 comes from the certain degree of overfitting due to the small training set. (2) the semantics of a sequence are more than the average meaning of the tokens. each token has a unique weight on the semantics of the sequence. thus, the parameters in formula (9) should be learned during model training. (3) at the start of training, there is not much prior knowledge about the generation of the target sequence. thus, it is more suitable to use ζ1 as the loss function for this stage. with the growing number of iterations in training, it is better to adopt the linear combination of cross entropy loss and semantic regression loss as the loss function. however, this paper implements the linear combination from the very beginning, failing to determine when is the best time to start using this loss function. hence, the future research will try to determine when https://doi.org/10.15837/ijccc.2021.4.4217 14 table 8: the time costs on iwslt2014, iwslt2016 and wmt17 train set. note: units of s, h and d stand for second, hour and day respectively models time iwslt2014 iwslt2016 wmt17 transformer-small total 7h 9h -step 0.064s 0.065s transformer-base total 10h 15h 11.8dstep 0.188s 0.189s 0.205s transformer-big total 23.8dstep 0.435s transformer-small+ours total 9h 11h -step 0.068s 0.067s transformer-base+ours total 12.5h 16.5h 12.5dstep 0.197s 0.194s 0.212s transformer-big+ours total 23.9dstep 0.438s to replace the loss function. 5 conclusions this paper proposes a novel semantic regression loss function for the snmt based on the transformer architecture. the authors find that the semantic loss function is proportional to the distance between the reference sequence and the candidate sequence, conditioned on the given target language dataset and vocabulary. hence, the linear combination of the cross entropy loss (classification objective function) and the distance loss (regression objective function) are synthetized as our training objective function. then, our model is implemented on three datasets, and evaluated by four metrics. during the experiments, our model is coupled with the euclidean distance metric, adamw optimizer and beam search algorithm. the results show that our model can effectively improve the machine translation performance. however, the semantic loss proposed here is not actually a true semantic loss. it is approximately equivalent to the distance loss between the candidate sequence and the reference sequence. in addition, the linear combination of ζ1 and ζ2 is implemented from the beginning to the end, which limits the performance improvement. in future work, the authors will use a pre-training language model like bert to compute the exact semantic loss in the decoder, apply prior knowledge to the loss function, and determine the golden section points for choosing the different losses. funding this work was supported by the national natural science foundation of china under grant 61977009. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] bahdanau, d.; cho, k.; bengio, y. 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[40] [online]. available: https://github.com/lovit/wordpiecemodel, accesed on 5 november 2018. https://doi.org/10.15837/ijccc.2021.4.4217 17 copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: d.x. li, z.y. luo. (2021). regression loss in transformer-based supervised neural machine translation, international journal of computers communications & control, 16(4), 4217, 2021. https://doi.org/10.15837/ijccc.2021.4.4217 introduction preliminaries transformer architecture attention mechanisms in transformer model training strategy modeling notations network architecture semantic regression loss function experiments datasets experimental setting and evaluation metrics baseline systems of machine translation based on the original transformer snmt based on our model discussion conclusions international journal of computers communications & control issn 1841-9836, 10(4):567-578, august, 2015. robust adaptive self-organizing wavelet fuzzy cmac tracking control for deicing robot manipulator t.q. ngo, t.v. phuong thanhquyen ngo* faculty of electrical engineering, industrial university of hcm city , hcm city, vietnam *corresponding author: thanhquyenngo2000@yahoo.com tavan phuong faculty of electrical and electronics engineering, hcmc university of technology and education, vietnam tavphuong@yahoo.com; phuongtv@hcmute.edu.vn abstract: in this paper, a robust adaptive self-organizing control system based on a novel wavelet fuzzy cerebellar model articulation controller (wfcmac) is developed for an n-link robot manipulator to achieve the high-precision position tracking. this proposed controller consists of two parts: one is the wfcmac approach which is implemented to cope with nonlinearities, due to the novel wfcmac not only incorporates the wavelet decomposition property with fuzzy cmac fast learning ability but also it will be self-organized; that is, the layers of wfcmac will grow or prune systematically. therefore, dimension of wfcmac can be simplified. the second is the order which is the adaptive robust controller which is designed to achieve robust tracking performance of the system. the adaptive tuning laws of wfcmac parameters and error estimation of adaptive robust controller are derived through the lyapunov function so that the stability of the system can be guaranteed. finally, the simulation and experimental results of novel three-link deicing robot manipulator are applied to verify the effectiveness of the proposed control methodology. keywords: wavelet, cmac, deicing robot manipulator. 1 introduction in general, robotic manipulators have to face various uncertainties in their dynamics, such as friction and external disturbance. it is difficult to establish an exactly mathematical model for the design of a model-based control system. in order to deal with this problem, the fuzzy logic control (flc) has found extensive applications for complex and ill-defined plants [1-2], and is suitable for simple second order plants. however, in case of complex higher order plants, all process states are required as fuzzy input variables to implement state feedback flcs. all the state variables must be used to represent contents of the rule antecedent. therefore, it requires a huge number of control rules and much effort to create them. the neural networks (nns) are a model-free approach, which can approximate a nonlinear function to arbitrary accuracy [3–4]. however, the learning speed of the nns is slow, since all the weights are updated during each learning cycle. in addition, the fully connected nns are sensitive to training data. therefore, the ability of function approximation of a general multiplayer nn is restricted to requiring online learning. based on the advantages of fuzzy and neural networks, the fuzzy neural network (fnn) which incorporates advantages of fuzzy inference and neuron-learning has been developed and its effectiveness is demonstrated in solving control problems [5–6]. recently, many applications have been implemented quite successfully based on wavelet neural networks (wnns) which combine copyright © 2006-2015 by ccc publications 568 t.q. ngo, t.v. phuong the learning ability of network and capability of wavelet decomposition property [7–8]. different from conventional nns, the membership functions of wnn are spatially localized wavelet functions; and therefore, the wnns are capable of learning more efficiently than conventional nns for control and system identification as has been demonstrated in [7]. as a result, wnns have been used to deal with uncertainties and nonlinearity in [8]. to overcome the disadvantages of nns, cerebellar model articulation controller (cmac) was proposed by albus in 1975 [9] for the identification and control of complex dynamical systems, due to its advantage of fast learning property, good generalization capability and ease of implementation by hardware [10–11]. conventional cmacs are regarded as nonfully connected perceptron-like associative memory network with overlapping receptive fields which used constant binary or triangular functions. the disadvantage is that their derivative information is not preserved. to acquire the derivative information of input and output variables, chiang and lin [12] developed a cmac network with a differentiable gaussian receptive-field basis function and provided the convergence analysis for this network. the advantages of using cmac over neural network in many applications were well documented [13–14]. however, in the above cmac studies, the structure of cmac are not merited of the high-level human knowledge representation and thinking of fuzzy theory. in this paper, we propose a novel robust adaptive self-organizing wavelet fuzzy cmac (rasowfcm) control system for three-link deicing robot manipulator to achieve the high-precision position tracking. this control system combines the advantages of fuzzy inference system with cmac and wavelet decomposition capability and it does not require prior knowledge of a certain amount of memory space, and a self-organizing approach of this control system demonstrates the properties of generating and pruning the input layers automatically the developed self-organizing rule of wfcmac is clearly and easily used for real-time systems and the adaptive robust controller which are designed to achieve robust tracking performance of the system. this is the first contribution of this paper. the second contribution is that it proposes novel architecture and mathematical model of deicing robot which can be effective in practical applications. the adaptive tuning laws of wfcmac parameters and error estimation are derived in lyapunov method. this paper is organized as follows: system description is given in section 2. the structure of self-organizing wfcmac and rasowfcm control system are presented in sections 3 and 4. numerical simulation and experimental results of a three-link deicing robot manipulator under the possible occurrence of uncertainties are provided to demonstrate the tracking control performance of the proposed rasowfcm system in section 5. finally, conclusions are drawn in section 6. 2 system description 2.1 robotic dynamic model in general, the dynamics of an n-link robot manipulator may be expressed in the following lagrange form: m(q)q̈ + v (q, q̇)q̇ + g(q) + f(q̇) + τd = τ, (1) where q, q̇, q̈ ∈ rn are the joint position, velocity and acceleration vectors, respectively, m(q) ∈ rn×n denotes the inertia matrix, v (q, q̇) ∈ rn×n expresses the matrix of centripetal and coriolis forces, g(q) ∈ rn×1 is the gravity vector, τd ∈ rn×1, f(q̇) represents the vector of external disturbance, friction term, respectively, and τ ∈ rm×1 is the torque vectors exerting on joints. robust adaptive self-organizing wavelet fuzzy cmac tracking control for deicing robot manipulator 569 in this paper, a novel three-link deicing robot manipulator, as shown in fig.1, is utilized to verify dynamic properties which are given in section 5. in our controller design, the following properties will be used [15]. property 1: the inertia matrix m(q) is symmetric and positive definite. it is also bounded as a function of q : m1i 6 m(q) 6 m2i,m1,m2 > 0. property 2: ṁ(q)−2v (q, q̇) is a skew symmetric matrix. therefore, yt[ṁ(q)−2v (q, q̇)]y = 0, where y is a n×1 nonzero vector. the control problem is to force q(t) ∈ rn to track a given bounded reference input signal qd(t) ∈ rn. we have the tracking error as follows: e = qd(t)−q(t), (2) and the filtered system tracking error vector is defined as r = ė + λe, (3) where λ = λt > 0, by the differentiating r(t) with respect to e and using (1), the arm dynamics can be written in terms of the filtered tracking error vector as follows: m(q)ṙ = −v (q, q̇)r − τ + m(q)(q̈d + λė) + v (q, q̇)(q̇d + λe) + f(q̇) + g(q) + τd = −v (q, q̇)r − τ + f(x), (4) where the nonlinear function f(x) is defined as follows: f(x) = m(q)(q̈d + λė) + v (q, q̇)(q̇d + λe) + g(q) + f(q̇) + τd, (5) where x , [et ėt qtd q̇ t d q̈ t d ] denotes the variables of the nonlinear function. figure 1: architecture of three-link deicing robot manipulator. figure 2: block diagram of rasowfcm control system. 2.2 defined control law now, we define a control input torque as follows: τ0 = f̂(x) + kvr (6) 570 t.q. ngo, t.v. phuong with f̂(x) an estimate of f(x) and a gain matrix kv = kvt > 0. from (4), the closed-loop system becomes m(q) ṙ = −(kv + v (q, q̇))r + f̃(x), (7) where functional estimated error is given by f̃(x) = f(x)− f̂(x). (8) this is a system error wherein filtered tracking error is driven by this estimated error function. the control τ0 incorporates a proportional plus derivative (pd) term in kvr = kv(ė+λe). in the remainder of the paper we shall use (7) to focus on selecting wfcmac weight tuning algorithms that guarantee the stability of the filtered tracking error r(t). then, since (3), with the input considered as r(t) and the output as e(t), describes a stable system, standard techniques [16] guarantee that e(t) exhibits stable behaviour. in fact, ∥e∥2 6 ∥r∥2/σmin(λ), ∥ë∥2 6 ∥r∥2 with σmin(λ) the minimum singular value of λ. generally, λ is diagonal so that σmin(λ) is the smallest element of λ. 3 structure of wfcmac 3.1 brief of the wfcmac the main difference between the fcmac and the original cmac is that the association layer in the fcmac is the rule layer which is represented as follows: rλ : if x1 is µ1k, and x2 is µ2k, . . . ,xi is µik, then oij = wij (9) for k = 1,2, . . . ,nk, i = 1,2, . . . ,n,λ = 1,2, . . . ,nλ and j = 1,2, . . . ,mj, where n is the input dimension, nk is the number of layers for each input dimension, the number of rules nλ equals the number of layers and mj is the output dimension, µik is the fuzzy set for the ith input, the kth layer, and wjk is the output weight in the consequent part. based on [17], a novel wfcmac is represented and shown in fig.4. it is combined a wavelet function with the fcmac consists of input, association memory, receptive field, and output spaces, is proposed to implement the wfcmac estimate in rasowfcm control system which is shown in fig.2. the signal propagation is introduced according to functional mapping as follows: 1. the first mapping x: x → a we assume that each input state variable x = [ x1 x2 · · · xn ] can be quantized into ne discrete states and that the information of a quantized state is regarded as a wavelet receptive-field basic function for each layer. the mother wavelet is a family of wavelets. the first derivative of basic gaussian function for each layer is given here as the mother wavelet which can be represented as follows: µik(xi) = − xi −mik σik exp [− (xi −mik) 2 2σ2ik ], i = 1,2, · · · , n,k = 1,2, · · · , nk, (10) where µik represents the kth layer of the input with mik is a translation parameter and σik is dilation. 2. the second mapping a: a → r the information µik of each kth layer relates to each location of receptive field space. fig.4 illustrates a structure of two-dimension wfcmac with wavelet basic function and ne = 7 case. areas of receptive field space are formed by multipleinput regions are called hypercube; i.e in the fuzzy rules in (9), the product is used as the “and" computation in the consequent part. the firing of each state in each layer can obtain the weight robust adaptive self-organizing wavelet fuzzy cmac tracking control for deicing robot manipulator 571 figure 3: two-dimension wfcmac with wavelet function and ne = 7. figure 4: block diagram of wfcmac. of each corresponding layer. assuming that in 2-d wfcmac case shown in fig.3, input state vector is (6,3), the content of kth hypercube can be obtained as follows: bk(x, mk, σk) = n∏ i=1 µik(xi). (11) 3. output mapping o: the wfcmac output is an algebraic sum of activated weighs with hybercube elements. the jth output mathematic form can be expressed as follows: oj = [ wj1 wj2 · · · wjnk ]   b1(x) b2(x) ... bnk(x)   = nk∑ k=1 wjk n∏ i=1 µik(xi) j = 1,2, · · · , mj. (12) the block diagram shows in fig.2, in which the wfcmac plays a major role in the nonlinear function estimation. because, there is a trade-off between a designed performance and a tedious computation so we must choose a reasonable number of layers. however, if the number of layers chosen is too small, the learning process may be insufficient to achieve a desired performance. otherwise, if the number of layers chosen is too large, the calculation burden becomes too heavy, so it is not suitable for real-time applications. to deal with this problem, a self-organizing wfcmac is proposed includes structure and parameter learning. 3.2 self-organizing wfcmac in this section, it is necessary to determine a structure learning in order to add a new layer whether to add a new layer in membership layers depends on the firing strength bk ∈ rnk of each layer for each incoming data xi. if the firing strength bk ∈ rnk of each layer for new input data xi falls outside the bounds of the threshold, then, the wfcmac approach will generate a new layer. the self-organizing wfcmac can be also summarized based on [17] as follows: 1. calculate a distance of mean mdk, k = 1,2, · · · ,nk in association memory a as follows: mdk(x) = ∥x−|mk|∥2 where mk = [ m1k m2k · · · mnk ] (13) 572 t.q. ngo, t.v. phuong 2. using max-min method is proposed for layer growing. find k̂ = arg min 16k6nk mdk(x),k = 1,2, · · · nkm, if max i md k̂ (x) > kg, (14) here kg is a threshold value of adaptation with 0 < kg 6 1 . in our case kg = 0.1 and a new layer is generated. this means that for a new input data, the exciting value of existing basic function is too small. in this case, number of layers increased as follows: nk(t + 1) = nk(t) + 1, (15) where nk is the number of layers at time t. thus, a new layer will be generated and then the corresponding parameters in the new layer such as the initial mean and variance of gaussian basic function in association memory space and the weight memory space will be defined as mink = xi, σink = σk̂, wink = 0. (16) another self-organizing learning process is considered to determine whether to delete existing layer, which is inappropriate. a max-min method is proposed for layer pruning. considering the jth output of wfcmac in (12), the ratio of the kth component to jth output is defined as mmjk = vjk oj , k = 1,2, · · · ,nk. (17) where vjk = wjkbk(x), then, the minimum ratio of the kth component is defined as follows: k̃ = arg min 16k6nk max 16j6m mmjk, if mmk̃ 6 kc. (18) here kc is a predefined deleting threshold. in our case kc = 0.03 and the k̃th layer will be deleted. this means that for an output data, if the minimum contribution of a layer is less than the deleting threshold, then this layer will be deleted. 4 rasowfcm design in the rasowfcm scheme is shown in fig.2, the wfcmac is used to estimate an unmodelled nonlinear function, moreover, the rasowfcm law and adaptive tuning algorithms for wfcmac are introduced from the stability analyses of the closed-loop system by using lyapunov method input of the wfcmac estimator are the elements in the filtered error vector and joint positions signal, output of the wfcmac estimator are nonlinear dynamic function vectors in the local models. based on the powerful approximation ability [18], there exists an optimal wfcmac estimator to approximate the nonlinear dynamic function in (5) such that f(x) = wtb(x) + ε(x) (19) with w the ideal weight matrix and the estimated error vector ε(x) ∈ rn×1 are assumed to be given by w̃ = arg min ŵ∈mw [ sup x∈mx ∥f(x)−ŵtb(x)∥],0 6 ∥ε∥1 6 kf. (20) in which ∥•∥ is the euclidean norm, mx, mw and kf are the predefined compact sets of x and ŵ , and the positive constant robust adaptive self-organizing wavelet fuzzy cmac tracking control for deicing robot manipulator 573 define the wfcmac functional estimate by f̂(x) = ŵtb(x), (21) with ŵ being the current values of the wfcmac weight provided by the tuning algorithm. with the ideal weights required in (19) define the weight deviations or weight estimation errors as w̃ = w −ŵ. (22) with τo defined by (6), we design the control law as follow: τ = τ0 − τr. (23) where τ0 is the main controller in (6), which consists of two terms: one is the pd controller and the other is wfcmac controller is used to estimate the f(x) nonlinear function and the adaptive robust controller τr is utilized to compensate for the approximation error between uncertainly model and wfcmac approach. we assume that the error bound is a constant during the observation. however, in practical application it is difficult to determine. therefore, a bound estimation is developed to estimate this error bound. the estimation of error bound is defined as follows: k̃f = kf − k̂f. (24) where k̂f is the estimated value of kf . the adaptive robust controller is designed to compensate for the effect of the approximation error is selected as follows: τr = −k̂fsgn(r). (25) substituting (23) into (4) and using (19), (21) and (25), then, the closed-loop filtered error dynamics becomes: mṙ = −(kv + v (q, q̇)) r + w̃tb(x) + ε− k̂fsgn(r). (26) theorem 1. consider an n-link robot manipulator represented (1). if the control law of rasowfcm is designed as (23), and the weight update law of wfcmac and the adaptive law of the error bound are designed as (7), then the stability of the proposed rasowfcm system can be ensured ˙̂ w = f b(x)rt, ˙̃ kf = − ˙̂ kf = −ηr ∥r∥ . (27) proof: define a lyapunov function candidate as l(r(t),w̃) = 1 2 rtmr + 1 2 tr(w̃tf−1w̃) + k̃2f 2ηr . (28) where f , ηr are the learning rate for the wfcmac memory contents, error bound, respectively. by differentiating (28) with respect to time and using (26), (27), and using properties of the robot dynamics are introduced in section 2, we can obtain. l̇ = rtmṙ + 1 2 rtṁr + tr( ˙̃ wtf−1w̃) + k̃f ˙̃ kf ηr = −rtkvr + 12r t(ṁ −2v )r + trw̃(f−1 ˙̃wt + brt) + rt(ε− k̂f)sgn(r) + k̃f ˙̃ kf ηr = −rtkvr + 12r t(ṁ −2v )r + trw̃(f−1 ˙̃wt + brt) + εrt − k̂f ∥r∥− (kf − k̂f)∥r∥ = −rtkvr + εrt −kf ∥r∥ 6 −rtkvr +∥ε∥ ∥r∥ −kf ∥r∥ = −rtkvr − (kf −∥ε∥) ∥r∥ 6 −rtkvr 6 0. (29) 574 t.q. ngo, t.v. phuong since l̇(r(t),w̃, k̃f) is a negative semi-definite function, i.e. l̇(r(t),w̃(t), k̃f(t)) 6 l̇(r(0), w̃(0), k̃f(0)), it implies that r, w̃ and k̃f is bounded functions. let function h ≡ (kf −∥ε∥)r 6 (kf −∥ε∥)∥r∥ 6 −l̇(r,w̃, k̃f) and integrate function h(t) with respect to time because l̇(r(0),w̃(0), k̃f(0)) is a bounded function, and l̇(r(t), w̃(t), k̃f(t)) is a nonincreasing and bounded function, the following result can be achieved: in addition, ḣ(t) is bounded; thus, by barbalat’s lemma can be shown that lim t→∞ h(t) = 0. it can imply that r will be converging to zero as time tends to infinity. 2 5 simulation and experimental results 5.1 simulation results a three-link deicing robot manipulator as shown in fig.1 is utilized in this paper to verify the effectiveness of the proposed control scheme. the detailed system parameters of this robot manipulator are given as: link mass m1, m2, m3 (kg), lengths l1, l2 (m) angular positions q1, q2 (rad) and displacement position d3 (m). the parameters for the equation of motion (1) can be represented as  m (q) =   m11 m12 m13m21 m22 m23 m31 m32 m33   ,v (q̇) =   v11 v12 v13v21 v22 v23 v31 v32 v33   , v11 = −8m2l1l2c1s1q̇1 + (−1/2m2s2c2l22 + m3(−2s2c2l 2 2 −2s2l1l2)q̇2, m11 = 9/4m1l1 + m(1/4c2l2 + l 2 1 + l2l1(c 2 1 −s 2 1)) + m3(c2l 2 2 + l 2 2 + 2c2l1l2), m22 = 1/4m2l 2 2 + m3l 2 2 + 4/3m1l 2 1,m22 = 1/4m2l 2 2 + m3l 2 2 + 4/3m1l 2 1, m33 = m3,m12 = m13 = m21 = m31 = 0, v21 = (−1/2m2s2c2l22 + m3(−2s2c2l 2 2 −2s2l1l2)q̇1,v22 = −m3s2l2ḋ3, v23 = −2m3s2l2q̇2,v32 = −m3s2l2q̇2,v12 = v13 = v31 = v33 = 0, g(q) =   (1/2c1c2l2 + c1l1)m2g(−1/2s1s2l2m2 + c2l2m3)g m3g   . (30) where q ∈ r3 and the shorthand notations c1 = cos(q1), c2 = cos(q2), and c1 = sin(q1), c2 = sin(q2) are used. for convenience of simulation, nominal parameters of the robotic system are given as m1 = 3 (kg), m2 = 2 (kg), m3 = 2.5 (kg), l1 = 0.14 ( m), l2 = 0.32 ( m) and and the initial conditions q1 (0) = 1, q2 (0) = 0, d3 (0) = 0, q̇1 (0) = 0, q̇2 (0) = 0, ḋ3 (0) = 0. the desired reference trajectories are qd1(t) = sin(t), qd2(t) = cos(t), dd2(t) = cos(t), respectively. for recording respective control performance, the mean-square-error of the position-tracking response is defined as: msei = 1 t t∑ j=1 [qdi(j)−qi(j) ] 2 , i = 1,2,3. (31) where t is the total sampling instant, and qi and qdi are the elements in the vector qi and qdi. in this paper, the numerical simulation results carried out by using matlab software. for the proposed rasowfcm control system, the parameters are chosen in the following: λ = diag{10,10,10} ,kv = diag{50,50,50} ,f = 15,ηr = 0.2. (32) robust adaptive self-organizing wavelet fuzzy cmac tracking control for deicing robot manipulator 575 0 4 8 12 16 20 −2 0 22 time(s) (a) p os it io n fo r li nk 1 desired position 0 4 8 12 16 2020 −2 0 22 time(s) (b) p os it io n fo r li nk 2 ( ra d) desired position 0 4 8 12 16 2020 −2 0 2 time(s) (c) d is ta nc e li nk 3 ( m ) desired position 0 4 8 12 16 20 0 0.5 1 time(s) (d) m s e f or j oi nt 1 ( ra d) 0 4 8 12 16 2020 0 0.5 1 time(s) (e) m s e f or j oi nt 2 ( ra d) 0 4 8 12 16 2020 0 0.5 1 1.5 time(s) (f) m s e f or j oi nt 3 ( m ) 0 4 8 12 16 2020 0 4 8 1212 time(s) (g) l ay er n um be r figure 5: simulated position responses, mses and layer number of the rasowfcm control system at joints. figure 6: iipc-based deicing robot position control system a) block diagram of three-link deicing robot manipulator c) image of practical control system. control system, b) image of practical control system. 576 t.q. ngo, t.v. phuong and the initial values of system parameters are given as nk = 2, k̂f = 1, the inputs of wfcmac, the translation and dilation of wavelet functions are selected to cover the input space {[−1 1][−1 1][−1 1]}. the threshold value of kg is set as 0.1; kc is set as 0.01. the simulation results of the proposed rasowfcm system, the responses of joint position mse and layer number are depicted in fig.5(a), (b), (c); (d), (e), (f) and (g), respectively. the simulation results show that the proposed rasowfcm control system can achieve a favorable tracking performance with self-organizing of wfcmac, and the layers of wfcmac will be converge to three layers. 5.2 experimental results an image of a practical experiment control system for deicing robot consists of three manipulators and is shown in fig 6(b). the left and right manipulators have three links with two revolute joints and a prismatic joint. end-effectors of each manipulator have attached the motion structure to move the deicing robot on the power line and the deicing device. under normal operating conditions, the left and right manipulators are only in operation. the middle manipulator has only two joints with a revolute joint and a prismatic joint. it only works when the deicing robot voids obstacles on the power line. in general, the operation of deicing robot is very complex. in this paper, we consider only the three-link deicing robot manipulator for proposed methodologies while the other manipulator is the same. the hardware block diagram of the control system is implemented to verify the effectiveness of the proposed methodologies and is shown in fig.6(a). each joint of manipulator is derived by the “ec**" type maxon dc servo motors, and each motor contains an encoder. digital filter and frequency multiplied by circuits are built into the encoder interface circuit to increase the precision of position feedback. the dcs303 is a digital dc servo driver developed with dsp to control the dc servo motor. the dcs303 is a micro-size brush dc servo drive. it is an ideal choice for this operating environment. two dc servo motor motion control cards are installed in the industrial personal computer, in which, a 6-axis dc servo motion control card is used to control the joint motors and a 4-axis motion control card is used to control the drive motors. each card includes multi-channels of digital/analog and encoder interface circuits. the model dmc2610 has a pci interface connected to the ipc. the dmc2610 implements and excutes the proposed program in the real time. considering that the control sampling rate ts = 1ms is too demanding for the hardware implementation, ts = 10ms is thus chosen here. the experimental parameters of the proposed rasowfcm control system are selected as simulation: in this section, the control objective is to control each joint angle of a three-link deicing robot manipulator to move periodically for a periodic step commands and initial conditions of the system are given as q1 (0) = 0 (rad), q2 (0) = 0(rad), d3 (0) = 0 (m). finally, the experimental position and tracking error response results of the proposed rasowfcm control system are depicted in fig.7 (a), (b) and (c). according to these experiment and simulation results of proposed rasowfcm control system due to sinusoidal and periodic step reference commands indicate that the high-accuracy tracking position responses can be achieved by using the proposed rasowfcm control system for difference reference commands under a wide range of external disturbance. 6 conclusion in this paper, a rasowfcm control system is proposed to control the joint position of a three-link deicing robot manipulator. in the proposed rasowfcm system, dynamical system is completely unknown in the control process. the adaptive tuning laws of wfcmac parameters and error estimation are derived according to lyapunov function so that a stability of the robust adaptive self-organizing wavelet fuzzy cmac tracking control for deicing robot manipulator 577 figure 7: experimental position responses, tracking errors of the proposed rasowfcm control system at joints 1, 2 and 3 system can be guaranteed. this paper has not only successfully developed the rasowfcm control system for a three-link deicing robot manipulator based on the novel wfcmac requires low memory with online parameter tuning algorithm, but also provided novel architecture and mathematical model of deicing robot, which is verified the effectiveness through the practical application. simulation and experimental results of the proposed rasowfcm system can achieve favorable tracking performance for the proposed three-link deicing robot manipulator. bibliography [1] m. baban, c.f. baban, c. bungau, g. dragomir, r.m. pancu (2014); 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[18] c.t. lin, c. s. george lee (1996); neural fuzzy systems, englewood cliffs, nj: prenticehall. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 2, month: april, year: 2023 article number: 5320, https://doi.org/10.15837/ijccc.2023.2.5320 ccc publications optimization-based fuzzy regression in full compliance with the extension principle b. stanojević, m. stanojević bogdana stanojević* mathematical institute of the serbian academy of sciences and arts kneza mihaila 36, 11000 belgrade, serbia *corresponding author: bgdnpop@mi.sanu.ac.rs milan stanojević faculty of organizational sciences, university of belgrade jove ilića 154, 11000 belgrade, serbia milan.stanojevic@fon.bg.ac.rs abstract business analytics – which unites descriptive, predictive and prescriptive analytics – represents an important component in the framework of big data. it aims to transform data into information, enabling improvements in making decisions. within big data, optimization is mostly related to the prescriptive analysis, but in this paper, we present one of its applications to a predictive analysis based on regression in fuzzy environment. the tools offered by a regression analysis can be used either to identify the correlation of a dependency between the observed inputs and outputs; or to provide a convenient approximation to the output data set, thus enabling its simplified manipulation. in this paper we introduce a new approach to predict the outputs of a fuzzy in – fuzzy out system through a fuzzy regression analysis developed in full accordance to the extension principle. within our approach, a couple of mathematical optimization problems are solve for each desired α−level. the optimization models derive the left and right endpoints of the α−cut of the predicted fuzzy output, as minimum and maximum of all crisp values that can be obtained as predicted outputs to at least one regression problem with observed crisp data in the α−cut ranges of the corresponding fuzzy observed data. relevant examples from the literature are recalled and used to illustrate the theoretical findings. keywords: fuzzy regression, extension principle, optimization 1 introduction nowadays, the importance of handling big data is continuously increasing. managing big data by providing better data insights is a focal point to majority of the research fields. a fuzzy representation of information is one way to handle the uncertainty. the fuzzy set theory introduced in [20] is widely applied in many research fields, from industry to management and education. fuzzy logic was developed to generalize the classic logic, and found https://doi.org/10.15837/ijccc.2023.2.5320 2 a wide applicability in decision support (see [18] for more details on its methods, applications and future trends) and data processing (see for instance [1] that presents particular fuzzy methods used in social sciences researches). a general view on how fuzzy logic evolved from the classic logic, and where it meets the quantum logic can be found in [10]. the concept of linguistic variable was proposed in [21] emphasizing its effective applications to approximate reasoning. the extension principle was introduced to formalize the arithmetic on fuzzy quantities. the fuzzy linear regression model was firstly proposed by tanaka et al. [15], and extended to possibilistic linear regression in [16]. since then many variants of fuzzy regression models were proposed in the literature. kahraman et al. [8] surveyed the relevant fuzzy regression approaches, and reported many of their practical applications as: forecasting models for predicting sales of computers and peripheral equipment; the models revealing the relationship of cumulative trauma disorders risk factors, predicting the injuries, and evaluating the risk levels of individuals; the models forecasting the production value of mechanical industry. a systematic review and a huge amount of bibliographic references to fuzzy regression analysis can be found in [5]. the key open questions in the field and important research directions were also indicated in [5]. in the recent literature, chachi et al. [3] discussed the fuzzy regression based on m-estimates, providing robust estimators of the parameters to avoid undesired effects; bas [2] proposed a robust fuzzy regression functions approach whose forecasting performance is not affected by the presence of outliers; wang et al. [17] introduced a fuzzy regression model that uses approximate bayesian computation instead of usual optimization techniques; hose and hanss [7] presented a fuzzy regression approach that took into consideration the worst case variation of the parameters aiming to encode the whole relevant observed information in their membership functions. a quadratic least squared regression analysis was carried out in [12]. the results showed that procedures which deviate from a full compliance with the extension principle can derive misleading solutions. kao and chyu [9] discussed the fuzzy linear regression based on least squares estimates and extension principle. they adapted the extension principle to the problem they solved, and proposed an approximated solution to surmount the complexity of the formulation. our approach is based on the same idea, but we succeed to derive the exact values of the endpoints of any α−cut of the regression’s coefficients and predicted outputs. kao and chyu [9] used constraint programming to optimize the index for ranking fuzzy numbers. that index was formulated using several α−cuts of the membership function of the regression error estimation, and a triangular fuzzy number for each coefficient of the regression was derived. our approach will use optimization with polynomial objective functions and constraints to derive the supports of the regression’s fuzzy number coefficients. hojati et al. [6] proposed a more simple method for computing the coefficients of the fuzzy regression, but provided solutions that were far away from those based on the extension principle. they compared their results with those reported in [15] and [11], emphasizing that their approach provides reasonably narrow fuzzy bands. however, a narrow fuzzy band, although desirable, might be misleading, when the observations do not properly fit inside. wu [19] proved that the α−cuts of fuzzy-valued functions can be computed with the help of the α−cuts of its coefficients and arguments. their approach to fuzzy regression followed the extension principle until they introduced a simplified computational procedure by replacing the variables ranging within the α−cut intervals of the observed data with their corresponding endpoints. chen and nien [4] simplified the approaches based on the extension principle by reducing the amount of information extracted from the fuzzy observations and included in their regression model. as usual, using less accurate models simplifies the solving procedure but increases the possibility to obtain misleading results. the rest of the paper is organized as follows. section 2 provides details on the needed notation and terminology related to the crisp regression and fuzzy concepts. in section 3, we formally describe the problem we aim to solve. we describe our novel approach in section 4, providing detailed description of the optimization models involved in a fuzzy regression analysis with a polynomial predictor function. section 5 is devoted to experiments: we report our numerical results and their comparative illustrations with relevant results from the literature. final conclusion and directions for further researches are provided in section 6. https://doi.org/10.15837/ijccc.2023.2.5320 3 2 preliminaries in this section, we provide the specific notation and terminology related to crisp regression analysis, and fuzzy numbers that we will need in the sequel. the tools offered by a regression analysis can be used either to identify the strength of a dependency between the observed inputs and outputs; or to provide a convenient approximation to the data set, thus enabling its simplified manipulation. to describe the input-output relationship one uses a predictor function whose unknown parameters must be determined from the observed data. the wide variety of predictor functions defines a wide variety of regression models whose classification is generally related to the norms involved in the criterion used to derive the best values of the parameters. for instance, the least square regression model uses the l2 norm, the ridge regression uses a penalized l2 norm, the lasso regression uses a penalized l1 norm, all for evaluating the distance between the observed and predicted outputs. for a crisp linear regression analysis with multiple explanatory variables, there are given n observed input k-tuples (xi1, xi2, . . . , xik)i=1,...,n and n output scalars (yi)i=1,...,n. the coefficients a = (a0, a1, . . . , ak)t of the predictor function fa (x1, x2, . . . , xk) are computed such that fa (xi1, xi2, . . . , xik) provides a good approximation to yi, for each i = 1, . . . , n. the parametric expression of the linear predictor function is then fa (x) = a0 + a1x1 + a2x2 + · · · + akxk. (1) in the least square linear regression the coefficients a0, a1, . . . , ak are determined such that the sum of the squared euclidean distances between yi and fa (xi1, xi2, . . . , xik) is minimal, i.e. solving the following optimization problem min a k∑ i=1 (yi − fa (xi))2. (2) let us denote x =   1 x11 x12 . . . x1k 1 x21 x22 . . . x2k ... ... ... ... ... 1 xn1 xn2 . . . xnk   , y =   y1 y2 ... yn   . (3) the well known formula for computing the coefficients a by minimizing the least squared distances between the observed and predicted outputs is then a = ( x t x )−1 x t y. we will use this formula written in its equivalent form ( x t x ) a = x t y. (4) in the next section we adapt the crisp linear regression to a regression in fuzzy environment, namely we model the dependencies between inputs and outputs expressed by fuzzy numbers. the fuzzy sets theory introduced by zadeh [20] provides an efficient tool for modeling the uncertainty. the main concepts of the fuzzy sets theory that are of interest to our study will be briefly presented below. a fuzzy set ã over the universe x is defined as a collection of pairs ( x, µ ã (x) ) , where x ∈ x, and µ ã (x) ∈ [0, 1]. the function µ ã : x → [0, 1] is the membership function of ã, and µ ã (x) is called the membership degree of the crisp value x in the fuzzy set ã. let us denote by supp ( ã ) the support of the fuzzy set ã defined as the set of values with non-zero membership degree. let [ ã ] α denote the α-cut of the fuzzy set ã, that is the set of those values x whose membership degrees are greater or equal to α, α > 0, i.e.[ ã ] α = { x ∈ x|µ ã (x) ≥ α } . (5) a fuzzy set ã of the universe r of real numbers is called fuzzy number (fn) if and only if: (i) it is a fuzzy normal and fuzzy convex set; (ii) its membership function µ ã is upper semi-continuous; and (iii) its support is bounded. https://doi.org/10.15837/ijccc.2023.2.5320 4 through the paper, we use triangular fuzzy numbers ã that are expressed by triples of real numbers( al, ac , au ) , al ≤ ac ≤ au . the interval ( al, au ) is the support of ã; and the interval [ ã ] α = [ (1 − α) al + αac , αac + (1 − α) au ] , (6) is its corresponding α−cut. the extension principle, detailed in [21] in the context of linguistic variables, is widely used to complete the fuzzy arithmetic on fuzzy numbers. we will use it in a wider context, namely to define the predicted fuzzy outputs with respect to the crisp observed data that belongs to the α−cut intervals of the original fuzzy observed data. the formal definition of the extension principle given in (7), i.e. µ b̃ (y) =   sup(x1,...,xr )∈f −1(y) ( min { µ ã1 (x1) , . . . , µãr (xr) }) , f −1 (y) ̸= ø, 0, otherwise, (7) provides the membership degree of y in the fuzzy set b̃ of the universe y , where b̃ is the result of evaluating the function f at the fuzzy sets ã1 ,ã2, . . . , ãr over their corresponding universes x1, x2, . . . , xr. in other words, (7) generalizes the crisp evaluation y = f (x1, x2, . . . , xr) to the fuzzy evaluation b̃ = f ( ã1, ã2, . . . , ãr ) . 3 problem formulation we focus on linear regression applied to observed fuzzy data with multiple explanatory variables, whose parameters will be derived using the l2 norm, and whose predictions will be in full accordance to the extension principle. both observed inputs and outputs are triangular fuzzy numbers, while the predicted outputs do not have an a priori imposed shape type. by analogy to the crisp case, function f ã (x̃) = ã0 + ã1x̃1 + ã2x̃2 + . . . + ãkx̃k predicts the fuzzy output ̂̃y = f ã (x̃) with respect to the parameters ã0, ã1, . . ., ãk at fuzzy input x̃. applying the fuzzy regression in a complete accordance to the extension principle means finding all sets of crisp values a0, a1, . . ., ak that are parameters of at least one crisp regression derived from at least one set of crisp input/output observations xij ∈ [x̃ij ]α, yi ∈ [ỹi]α, i = 1, . . . , n and j = 1, . . . , k. the membership degree of such set of values a0, a1, . . ., ak in the fuzzy sets ã0, ã1, . . ., ãk respectively, must be max︷ ︸︸ ︷ x, y that yields a ( min ({ µx̃ij (xij ) |i = 1, n, j = 1, k } ⋃ { µỹi (yi) |i = 1, n })) , (8) where a stands for {a0, a1, . . . , ak}, x stands for { xij |i = 1, n, j = 1, k } , and y stands for { yi|i = 1, n } . within this study we aim to determine the accurate fuzzy-valued coefficients a of the regression and predicted outputs ̂̃y. in the next section we reach the goal by formulating and solving certain pairs of optimization models. 4 our approach the involvement of the extension principle in defining the fuzzy regression is not new in the literature (see for instance the recent survey [5]). however, so far, the complexity of (8) determined the authors to search for simplified approaches that introduced certain deviations form a complete compliance with the extension principle. building around (8), we adapt a methodology already used within fuzzy mathematical programming problems ([14]), and focus on how to find the values a0, a1, . . ., ak with a membership degree at least α, α arbitrary fixed in the interval [0, 1]. wu [19] proved that the α−cuts of fuzzy-valued functions can be computed with the help of the α−cuts of its coefficients and arguments. in accordance https://doi.org/10.15837/ijccc.2023.2.5320 5 to proposition 3.3 [19], we impose the following box constraints (9) on variables x and y to keep them varying within the ranges of their corresponding α−cuts. (1 − α) (x̃ij )l + α (x̃ij )c ≤ xij ≤ α (x̃ij )c + (1 − α) (x̃ij )u , i = 1, . . . , n, j = 1, . . . , k (1 − α) (ỹi)l + α (ỹi)c ≤ yi ≤ α (ỹi)c + (1 − α) (ỹi)u , i = 1, . . . , n. (9) we use the upper indexes l, c, u to identify the lower, center and upper values of each involved triangular fuzzy number. further on, we use the relation between the crisp x, y and a provided by the crisp linear regression theory (4) as additional constraint; and propose the pair of models min (max) aq s.t. x t xa = x t y, (1 − α) (x̃ij )l + α (x̃ij )c ≤ xij ≤ α (x̃ij )c + (1 − α) (x̃ij )u , i = 1, . . . , n, j = 1, . . . , k, (1 − α) (ỹi)l + α (ỹi)c ≤ yi ≤ α (ỹi)c + (1 − α) (ỹi)u , i = 1, . . . , n, aj free variable, j = 0, . . . , k, (10) able to derive the α−cuts of the fuzzy parameter ãq, q = 0, . . . , k of the fuzzy regression. therefore, models (10) can be used to construct the fuzzy sets ã0, ã1, . . ., ãk; and based on them, we propose algorithm 1 (epbrc) that derives numerically the left and right sides of the membership functions of the fuzzy linear regression’s coefficients in full accordance with the extension principle. the input parameters for epbrc are as follows: the sequence α1, α2, . . . , αp of values from [0, 1] representing the desired membership levels; the fuzzy numbers representing the observed inputs x̃ij and outputs ỹi, i = 1, . . . , n, j = 1, . . . , k. the outputs of epbrc are the matrices that describe the α−cut intervals of the membership functions of the coefficients ã. algorithm 1 extension-principle-based algorithm for regression coefficients (epbrc) input: α1, α2, . . . , αp ∈ [0, 1]; x̃ij , ỹi, i = 1, . . . , n, j = 1, . . . , k. 1: define the matrices x and y using (3). 2: for s = 1, p do 3: for q = 0, k do 4: solve min problem (10) and denote by alqs the minimal value of the objective function. 5: solve max problem (10) and denote by auqs the maximal value of the objective function. 6: end for 7: end for output: matrices al = ( alqs )s=1,p q=0,k , au = ( auqs )s=1,p q=0,k . however, the fuzzy sets ã0, ã1, . . ., ãk defined by models (10) and derived by epbrc cannot be used directly (i.e. through formal arithmetic) to derive the predicted outputs at a given input ṽ. another pair of models, namely min (max) a0 + a1v1 + a2v2 + · · · + akvk s.t. x t xa = x t y, (1 − α) (x̃ij )l + α (x̃ij )c ≤ xij ≤ α (x̃ij )c + (1 − α) (x̃ij )u , i = 1, . . . , n, j = 1, . . . , k, (1 − α) (ṽj )l + α (ṽj )c ≤ vj ≤ α (ṽj )c + (1 − α) (ṽj )u , j = 1, . . . , k, (1 − α) (ỹi)l + α (ỹi)c ≤ yi ≤ α (ỹi)c + (1 − α) (ỹi)u , i = 1, . . . , n, aj free variable, j = 0, . . . , k, (11) must be utilized for this purpose. for a fixed α, models (11) determine the left and right endpoints of the α−cut of the predicted fuzzy output at the fuzzy input ṽ. when the given fuzzy input is one https://doi.org/10.15837/ijccc.2023.2.5320 6 of the observed inputs, for instance x̃h, then the variables vj , j = 1, . . . , k and their box constraints can be ignored in models (11); and the variables xhj , j = 1, . . . , k, are used instead of vj , j = 1, . . . , k in the objective function. algorithm 2 (epbro) uses models (11), and derives the estimated fuzzy outputs in full accordance with the extension principle. the values of ṽj , j = 1, . . . , k are inputs to algorithm 2 together with all inputs of algorithm 1. the outputs of algorithm 2 are the vectors ŷl = ( ŷls ) s=1,p , ŷu = ( ŷus ) s=1,p representing the left and right sides of the membership function of the fuzzy set that is the evaluation of the predictor function at ṽ. algorithm 2 extension-principle-based algorithm for regression outputs (epbro) input: α1, α2, . . . , αp ∈ [0, 1]; x̃ij , ỹi, ṽj , i = 1, . . . , n, j = 1, . . . , k. 1: define the matrices x and y using (3). 2: for s = 1, p do 3: for i = 1, n do 4: solve min problem (11) and denote by ŷls the minimal value of the objective function. 5: solve max problem (11) and denote by ŷus the maximal value of the objective function. 6: end for 7: end for output: vectors ŷl = ( ŷls ) s=1,p , ŷu = ( ŷus ) s=1,p . neither the coefficients, nor the predicted outputs derived by running algorithms 1 and 2, respectively are triangular fuzzy numbers. however, their accurate shapes can be approximated by the triangular fuzzy numbers aq = ( alq1, a l qp, a u q1 ) , q = 0, k, and y = ( yl1 , y l p , y u 1 ) , i = 1, n. 4.1 generalizations to fuzzy polynomial regression analysis models (10) and (11) can be generalized to derive the coefficients and the predicted outputs of an fuzzy polynomial regression. models fully complying to the extension principle and needed in the quadratic regression analysis of a crisp in – fuzzy out system with a single observed explanatory variable were provided in [12]. in this section we generalize the crisp formula provided in [12] for a quadratic regression to the formula needed to model the fuzzy polynomial regression analysis of degree p, i.e. we propose the constraint system  n∑ i=1 x 2p i n∑ i=1 x 2p−1 i . . . n∑ i=1 x p+1 i n∑ i=1 x p i n∑ i=1 x 2p−1 i n∑ i=1 x 2p−2 i . . . n∑ i=1 x p i n∑ i=1 x p−1 i ... ... . . . ... ... n∑ i=1 x p+1 i n∑ i=1 x p i . . . ∑n i=1 x 2 i ∑n i=1 xi n∑ i=1 x p i n∑ i=1 x p−1 i . . . n∑ i=1 xi n     ap ap−1 ... a1 a0   =   n∑ i=1 x p i yi n∑ i=1 x p−1 i yi ... n∑ i=1 xiyi n∑ i=1 yi   , (12) that we will further use within our optimization models. observing (12) from the point of view of a crisp regression analysis, the scalars a0, a1, . . ., ap are the coefficients of the predictor function f (x, a0, a1, . . . , ap) = a0 + a1x + . . . + apxp, that is a polynomial of degree p in x; and the scalars xi and yi, i = 1, . . . , n used in (12) are the crisp observed data. observing the same matrix equality (12) from the point of view of a fuzzy polynomial regression, all scalar quantities aq, q = 0, . . . , p, xi and yi, i = 1, . . . , n are seen as variables that range within their corresponding α−cuts. https://doi.org/10.15837/ijccc.2023.2.5320 7 table 1: observed and predicted fuzzy data based on regression analysis for the first example recalled from [11]. the epbro predictions are reported as triangular fuzzy numbers approximations index observed inputs observed outputs hbs2 predictions epbro predictions 1 (1.5, 2.0, 2.5) (3.5, 4.0, 4.5) (3.75, 4.20, 4.72, 5.18) (3.113, 4.611, 5.935) 2 (3.0, 3.5, 4.0) (5.0, 5.5, 6.0) (4.50, 4.96, 5.50, 6.00) (4.062, 5.390, 6.496) 3 (4.5, 5.5, 6.5) (6.5, 7.5, 8.5) (5.25, 5.76, 6.81, 7.36) (5.160, 6.429, 7.618) 4 (6.5, 7.0, 7.5) (6.0, 6.5, 7.0) (6.25, 6.81, 7.33, 7.91) (6.087, 7.208, 8.218) 5 (8.0, 8.5, 9.0) (8.0, 8.5, 9.0) (7.00, 7.59, 8.12, 8.73) (6.844, 7.987, 8.957) 6 (9.5, 10.5, 11.5) (7.0, 8.0, 9.0) (7.75, 8.38, 9.43, 10.10) (7.750, 9.026, 10.272) 7 (10.5, 11.0, 11.5) (10.0, 10.5, 11.0) (8.25, 8.90, 9.43, 10.10) (8.072, 9.285, 10.547) 8 (12.0, 12.5, 13.0) (9.0, 9.5, 10.0) (9.00, 9.68, 10.20, 10.90) (8.735, 10.064, 11.406) denoting by x̃i and ỹi, i = 1, . . . , n the observed triangular fuzzy numbered inputs and outputs, respectively, we formulate the box constraints system (13) (1 − α) (x̃i)l + α (x̃i)c ≤ xi ≤ α (x̃i)c + (1 − α) (x̃i)u , i = 1, . . . , n, (1 − α) (ỹi)l + α (ỹi)c ≤ yi ≤ α (ỹi)c + (1 − α) (ỹi)u , i = 1, . . . , n, (13) that corresponds to their α−cut intervals, α ∈ [0, 1]. the optimization models that derive the endpoints of the α−cut intervals of the regression’s coefficient aq, q = 0, . . . , p minimize and maximize, respectively the objective function aq over the feasible set defined by (12) and (13), with xi, yi, i = 1, . . . , n bounded, and a0, a1, . . ., ap free variables. in the same manner, the optimization models that derive the endpoints of the α−cut intervals of the predicted output ̂̃yh, h = 1, . . . , n minimize and maximize, respectively the objective function f (xh, a0, a1, . . . , ap) over the feasible set defined by (12) and (13). moreover, a predicted output at a given fuzzy input ṽ, other than the previously observed ones, can be determined by minimizing and maximizing, respectively the objective function f (v, a0, a1, . . . , ap) over the feasible set defined by (12) and (13) and the additional box constraint on v, i.e. (1 − α) ṽl + αṽc ≤ v ≤ αṽc + (1 − α) ṽu . 5 computation results in this section we report the numerical results aiming to illustrate the theoretical statements and provide a comparison with the results found in the literature. both considered examples are recalled from the literature and use triangular fuzzy numbers to describe the observed data. we derive the predicted fuzzy outputs numerically, for eleven equidistant α−cuts, α ∈ {0, 0.1, 0.2, . . . , 0.9, 1}. our experiments include one instance with a single fuzzy explanatory variable; and one instance with two fuzzy explanatory variables. the output estimations are derived using a linear predictor function in both cases. 5.1 one explanatory fuzzy variable the first example containing one explanatory fuzzy variable is taken from [11]. hojati et al. [6] solved the same example, and we use their results for comparison. the fuzzy observed data is reported in table 1 together with the estimated fuzzy outputs which are also graphed in figure 1. table 1 reports the triangular fuzzy number approximations of the predicted fuzzy outputs derived by our approach. hbs2 is the approach proposed by hojati et al. [6]. hbs2 derived two intervals that together define the trapezoidal fuzzy predicted outputs included in the comparison. figure 1 also illustrates how the application of the monte carlo method in the simulation of the extension principle [13] proves that the results of hojati et al. [6] do not fully comply to the extension principle, thus epbro improves their results. more precisely, analyzing the predicted outputs y1, y2, y7 and y8 one may notice the simulated values that are out of the membership functions of the predicted outputs reported in [6]. https://doi.org/10.15837/ijccc.2023.2.5320 8 figure 1: graphic illustration of the predicted fuzzy outputs provided by hojati et al. [6], the monte carlo simulation [13], and our algorithm epbro figure 2: hojati et al.’s approach [6] versus epbro through predicted fuzzy bands and squared representations for the observed data reported in table 1 another way to visually compare the derived predictions is to graph the fuzzy bands covered by the regression’s outputs. figure 2 compares the fuzzy bands obtained in [6] and by our approach (on the left), and the predicted outputs drown as squares whose horizontal edges represent the supports of input data, while the vertical edges represent the supports of the predicted data (on the right). a wider predicted fuzzy bands assure a better cover of the observed outputs. figure 3 is similar to figure 2 but reports our results compared to the results of kao and chyu [9]. the results are quite similar, showing that kao and chyu’s methodology [9] respects in a high extent the extension principle. in fact, kao and chyu [9] provided approximate predictions that can be improved by increasing the number of analyzed α−cuts. the advantage of our approach is in providing the exact support of the predicted outputs, by solving two optimization problems that uses polynomials of degree three in defining the feasible set, and polynomials of degree two in defining the objective functions. the regression’s coefficients derived by hbs2 [6] and our approach epbrc are shown in figure 4. epbrc derives fuzzy sets ã0, ã1 with wider supports than hbs2. however, hbs2 used their coefficients ã0, ã1 in arithmetic expressions; and obtained output predictions with wider supports than those derived by our approach through the epbro algorithm. this experiment shows once again the importance of involving the extension principle in all steps of the analysis. the coefficients ã0, ã1 derived by epbrc provide information about all possible crisp values of the regression’s coefficients and their membership levels, thus one can use them to study the whole system. https://doi.org/10.15837/ijccc.2023.2.5320 9 figure 3: kao and chyu’s predictions [9] versus epbro predictions for the observed data reported in table 1. in other words, approximate versus full compliance with the extension principle figure 4: visual comparison of the regression’s coefficients for data given in table 1 5.2 two explanatory fuzzy variables the second example has two explanatory fuzzy variables and is recalled from [19]. the observed fuzzy data is reported in table 2. the estimated fuzzy outputs obtained by our approach (epbro), wu’s approach [19], and chen and nien’s approach [4] are included in table 3, and graphed in figure 5. wu’s approach [19] was used for comparison since their methodology partly followed the extension principle deviating from it to reduce the computation complexity. on the other side, we also involve in the comparison chen and nien’s approach [4], to emphasize their higher deviation from the accurate results. chen and nien’s approach is also relevant for the comparison since it was recently published. in figure 5 we additionally report the results obtained by the monte carlo simulation procedure [13] that clearly illustrate the difference between the results obtained in [19] and [4]: only a few combinations of the parameters yielded estimated values of y10 out of the membership function provided in [19], but many combination of the same parameters derived many predicted values for all outputs y1 − y10 out of the membership functions reported in [4]. 6 conclusions and further researches in this paper we addressed the problem of predicting the fuzzy outputs of a fuzzy in – fuzzy out system through a fuzzy linear regression analysis. we developed our solution approach in full accordance to the extension principle. we proposed pairs of optimization models to be solved for each desired α−level degree of each estimated fuzzy output. both the left and right endpoints of each α−cut interval are derived as minimum and maximum, respectively of all crisp values that can https://doi.org/10.15837/ijccc.2023.2.5320 10 table 2: observed fuzzy inputs and outputs for the numerical example recalled form [19] index observed inputs observed outputs 1 (151, 274, 322) (1432, 2450, 3461) (111, 162, 194) 2 (101, 180, 291) (2448, 3254, 4463) (88, 120, 161) 3 (221, 375, 539) (2592, 3802, 5116) (161, 223, 288) 4 (128, 205, 313) (1414, 2838, 3252) (83, 131, 194) 5 (62, 86, 112) (1024, 2347, 3766) (51, 67, 83) 6 (132, 265, 362) (2163, 3782, 5091) (124, 169, 213) 7 (66, 98, 152) (1687, 3008, 4325) (62, 81, 102) 8 (151, 330, 463) (1524, 2450, 3864) (138, 192, 241) 9 (115, 195, 291) (1216, 2137, 3161) (82, 116, 159) 10 (35, 53, 71) (1432, 2560, 3782) (41, 55, 71) 11 (307, 430, 584) (2592, 4020, 5562) (168, 252, 367) 12 (284, 372, 498) (2792, 4427, 6163) (178, 232, 346) 13 (121, 236, 370) (1734, 2660, 4094) (111, 144, 198) 14 (103, 157, 211) (1426, 2088, 3312) (78, 103, 148) 15 (216, 370, 516) (1785, 2605, 4042) (167, 212, 267) table 3: the predictions provided by our approach epbro and the approaches presented in [19] and [4]. the predicted outputs are reported as triangular fuzzy number approximations index wu’s predictions (2003) chen nien predictions (2020) epbro predictions 1 (40.731, 161.897, 254.675) (104.24, 161.53, 177.82) (−63.676, 161.53, 375.403) 2 (22.831, 122.669, 258.107) (88.73, 122.62, 173.13) (−72.171, 122.62, 366.204) 3 (80.253, 224.431, 397.099) (155.81, 223.10, 288.00) (−4.637, 223.10, 425.660) 4 (29.779, 131.242, 246.073) (91.49, 131.17, 172.00) (−82.896, 131.17, 374.616) 5 (−3.544, 67.701, 153.452) (51.00, 68.46, 88.21) (−99.875, 68.46, 266.596) 6 (35.861, 169.687, 306.367) (102.02, 169.00, 209.95) (−53.927, 169.00, 389.486) 7 (2.056, 79.734, 184.581) (60.85, 80.24, 110.79) (−85.174, 80.24, 295.228) 8 (41.247, 189.673, 334.309) (105.30, 188.97, 243.41) (−55.396, 188.97, 402.216) 9 (22.537, 119.833, 233.108) (82.11, 120.01, 161.51) (−83.392, 120.01, 356.569) 10 (−13.997, 53.293, 132.854) (41.00, 54.18, 70.33) (−98.220, 54.18, 247.551) 11 (120.828, 253.717, 428.6082) (202.69, 252.00, 311.77) (34.408, 252.00, 465.188) 12 (111.097, 228.693, 396.296) (192.47, 227.20, 279.32) (37.340, 227.20, 459.329) 13 (28.2686, 144.9806, 291.304) (91.37, 144.77, 204.57) (−60.669, 144.77, 383.842) 14 (18.0514, 100.5342, 195.2155) (78.00, 100.95, 127.69) (−77.172, 100.95, 334.607) 15 (73.3752, 210.9386, 364.751) (143.75, 209.96, 268.30) (−25.345, 209.96, 415.196) https://doi.org/10.15837/ijccc.2023.2.5320 11 figure 5: visual comparison of the results obtained by our algorithm epbro and the results from the literature be obtained as predicted outputs to at least one regression problem with observed crisp data in the α−cut ranges of the corresponding fuzzy observed data. relevant examples from the literature were solved, and the carried out experiments were used to illustrate the theoretical findings. the main advantage of the new proposed methodology is in the fact that the obtained results are realistic, being derived in full accordance to the extension principle, and better than the approximated results that can be found in the literature. the computation complexity of the approach is related to the optimization solvers that have to handle mathematical programming problems with quadratic objective functions and polynomial constraints of degree three. we plan to continue the research, and study how to apply the crisp non linear regression models to estimate the behavior of fuzzy in – fuzzy out systems. aiming to solve real life problems, we will pay attention on selecting the norm that best suits each specific problem, and incorporate within the approach a procedure able to remove the outliers that might be identified between the fuzzy observed data. acknowledgments this work was partly supported by the serbian ministry of science, technological development and innovation through mathematical institute of the serbian academy of sciences and arts, and faculty of organizational sciences, university of belgrade. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. https://doi.org/10.15837/ijccc.2023.2.5320 12 references [1] o. ban, l. droj, tuşe d., g. droj, and n. bugnar. data processing by fuzzy methods insocial sciences researches. example in hospitality industry. international journal of computers, communications, control, 17(2):4741, 2022. 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[21] l.a. zadeh. the concept of a linguistic variable and its application to approximate reasoning i. information sciences, 8(3):199 – 249, 1975. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: stanojević, b.; stanojević, m. (2023). optimization-based fuzzy regression in full compliance with the extension principle, international journal of computers communications & control, 18(2), 5320, 2023. https://doi.org/10.15837/ijccc.2023.2.5320 introduction preliminaries problem formulation our approach generalizations to fuzzy polynomial regression analysis computation results one explanatory fuzzy variable two explanatory fuzzy variables conclusions and further researches international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 5, month: october, year: 2022 article number: 4696, https://doi.org/10.15837/ijccc.2022.5.4696 ccc publications online healthcare privacy disclosure user group profile modeling based on multimodal fusion yong wang yong wang school of economics and management beijing jiaotong university beijing, 100044, china 17113137@bjtu.edu.cn abstract with the spread of covid-19, online healthcare is rapidly evolving to assist the public with health, reduce exposure and avoid the risk of cross-infection. online healthcare platform requires more information from patients than offline, and insufficient or incorrect information may delay or even mislead treatment. therefore, it is valuable to predict users’ privacy disclosure behaviors while fully protecting their information, which can provide healthcare services for users accurately and realize a personalized online healthcare environment. compared with the traditional static online healthcare platform user privacy disclosure behavior influence factor analysis, this paper uses multimodal fusion and group profile technology to build a user privacy disclosure model and lay the foundation for personalized online healthcare services. this paper proposes a cross-modal fusion modeling approach to address the problem that the information of each modality cannot be fully utilized in the current online healthcare privacy disclosure modeling. a multimodal user profile approach is used to construct personal and group profiles, and the privacy disclosure behavioral characteristics reflected by both are integrated to realize accurate personalized services for online healthcare. the case study shows that compared with the static unimodal privacy disclosure model, the accuracy of our method gains significant improvement, which is helpful for precision healthcare services and online healthcare platform development. keywords: multimodal fusion technology, group profiling, privacy disclosure, online healthcare platform, personalized healthcare services. 1 introduction with the spread of covid-19, many patients prefer to stay home rather than go to the doctor. “another benefit is the ‘green effect’ telehealth helps prevent patients from traveling as much as possible for treatment and avoid those expensive helicopters, planes and ambulances, especially for rural patients, reducing unnecessary carbon dioxide gas, as documented by the university of new mexico telehealth institute," summit healthcare regional healthcare center telehealth specialist susy salvo-wendt, a telehealth specialist at summit healthcare regional center said. in the face of https://doi.org/10.15837/ijccc.2022.5.4696 2 the sudden epidemic in 2020, online healthcare platforms have become an important place for people to seek healthcare advice, exchange health information, and pay attention to the prevention and control of the epidemic during the covid-19, thanks to features such as being geographically independent, identifying suspected cases, reducing exposure, avoiding cross-infection, and easing public anxiety. according to relevant statistics, the average number of new registered users of good doctor online healthcare platform in february 2020 increased nearly 3.5 times a month compared to december 2019, and the daily demand for online health consultation and healthcare knowledge consultation submitted by users increased nearly 6.5 times a day. it can be said that there was a significant surge in the public demand for the use of online healthcare platforms due to the new coronary pneumonia outbreak. advances in technology have facilitated the delivery of medical and health information, and the widespread use of online technology in the medical field has effectively improved treatment outcomes and reduced healthcare costs [1]. the disclosure of health information by patients on online healthcare platforms ensures smooth communication with physicians, improves correct diagnosis, increases scientific knowledge, medical and health information, and raises self-care awareness. personal health information is vital to users’ lives, and correct advice from doctors based on relevant information can improve users’ health, but incorrect diagnosis can also put users at risk, which means it is important to establish a user health database, creating a challenge for the construction and development of online healthcare platforms. the use of online healthcare services by users involves the disclosure of a large amount of personal health information, which is of great research significance as an important data source for health databases. however, due to the asymmetry of information among platform users, there are differences in the way users express and deliver their information, which result in the neglect of users’ different needs and varying degrees of demand, and thus provide services in a “one-size-fits-all" manner, without matching the services to the different needs of users. it can be said that the characteristics and needs of users are like the symptoms of patients, and only on the basis of “look", “smell", “ask" and “cut" can the doctor “prescribe the right medicine". if the user is not well informed of specific information, the service will be counterproductive and make it more difficult for the user to access health resources. therefore, the platform should provide different ways for different users, combine their information literacy and knowledge ability, assist users to complete the online resources, solve the difficulties of retrieval, and avoid the problems of too much professionalism and poor adaptability of service content. in addition, more and more people are seeking medical services from online healthcare platforms in order to save time and reduce the cost of treatment. however, individuals face many threats when using online healthcare services, such as disclosure of private medical information [2]. the contradiction between the necessity of information disclosure and the risk of information leakage has become a key issue affecting users’ use or continued use of online healthcare platforms. the frequent occurrence of privacy leaks raises users’ concerns about personal privacy security, online privacy leaks harm patients’ interests, and privacy concerns reduce users’ willingness to disclose information. compared to offline, online healthcare platforms require more supporting information from patients, and insufficient or incorrect information may delay or even mislead treatment, making it difficult to fully utilize the online healthcare platform for healthcare services. therefore, it is necessary to promote the disclosure of user information based on the protection of user privacy. privacy disclosure refers to the voluntary and active disclosure of personal information by individuals to others during social interactions. research around personal health privacy disclosure can be broadly divided into four perspectives. the first one is from a technical perspective, which studies the development of personal health management information systems [3], the construction and development of online healthcare platforms [4], and methods of protecting medical and health privacy [5]. the second one is from the legal perspective to study the laws and regulations of health information and to consider how to reduce the privacy concerns of users [6, 7]. the third one is from the business analysis perspective, which analyzes the current state of healthcare services, the current state of applications and research in the health field, opportunities and challenges. the fourth level is from the perspective of factors influencing healthcare privacy disclosure. zhang et al. [8] analyzed the factors influencing users’ privacy concerns in online health communities by combining a dual algorithm model and protection motivation theory, and found that privacy concerns, information support, and emotional support https://doi.org/10.15837/ijccc.2022.5.4696 3 had significant effects on users’ willingness to disclose personal health information. bansal et al. [9] constructed a model based on utility theory to explore the effects of personal personality, trust and privacy concerns on users’ willingness to disclose health information online. wang et al. [10] studied website services, website reciprocity norms and user trust factors, and constructed a model of factors influencing users’ willingness to self-disclose on healthcare websites. the above studies analyzed user disclosure behavior in terms of emotion, benefit and risk, and pointed out the continuous influence of privacy concern on privacy disclosure willingness and privacy disclosure behavior. overall there are few results that have examined medical privacy disclosure at the level of factors influencing privacy disclosure. most of the existing studies consider the negative effects of personal medical privacy disclosure from the perspective of individual perceptions and suggest privacy-protective behaviors to users, and less often consider factors that encourage users to disclose their own health information. besides, the benefits and risks perceived by patients in the state of health and the state of seeking healthcare consultation are different, and the influence of both on the willingness to disclose privacy information are different. under adequate physical control conditions, willingness can directly determine user behavior. the stronger a user’s willingness to disclose information to an online healthcare platform, the more positive the privacy disclosure behavior will be. user online healthcare privacy disclosure behavior in this paper is the result of the interaction of short-term and long term disclosure willingness. level of explanation theory predicts that the level of explanation discounts the perceived outcome or the value of the outcome, in which case the value of the far-future outcome is lower than the near-future outcome. there is a temporal discount for user privacy willingness, and with the user’s present moment as the standard point of reference, the long term disclosure intention is a far psychological distance away in time; the short term disclosure intention is a near psychological distance away in time. the value of long term disclosure intention is lower compared to the value of short term disclosure intention, so when online healthcare platform users make privacy disclosure behavior decisions, they are more likely to be influenced by short term disclosure intention. based on the above analysis, to solve the problems faced by online healthcare platforms at present, firstly, we should make full use of information disclosed by users and label users to precise healthcare services; secondly, we should comprehensively analyze the characteristics of users’ privacy disclosure behaviors and increase the efforts of users to disclose their privacy to online healthcare platforms. therefore, this paper proposes a multimodal fusion user profile technique to build user profiles to model users’ online healthcare privacy disclosure behavior. in this paper, user profiling based on multimodal fusion and stacking, model combination, and fusion (umf-scf) is used, which can deeply fuse multiple data sources of user online healthcare privacy disclosure such as basic user information. this paper combines the personal and group profiles of users’ healthcare privacy disclosure to reflect the characteristics of users’ privacy disclosure behavior and realize personalized healthcare services. the purpose of this paper is to explore the factors influencing users’ willingness to disclose their personal medical privacy, and to investigate the mechanisms of these influencing factors on the willingness to disclose and disclosure behavior, with a view to assisting in optimizing the functions of online healthcare websites and promoting the development of online healthcare care. this study can both promote users’ disclosure of their necessary personal medical privacy, laying the foundation for accurate personalized medicine, and assist medical service providers in obtaining and applying a large amount of user health data in a legitimate way. compared to existing work, the contributions of this paper can be summarized as follows. (1) first proposes multimodal fusion user profile modeling method for online healthcare privacy disclosure. (2) the umf-scf multimodal fusion technique is used to construct a characteristic profile of user’s personal privacy disclosure behavior and enhance the fusion of multiple data sources of user’s healthcare privacy disclosure including basic user information, historical disclosure behavior, perceived risk, perceived profit, short term disclosure intention, and long term disclosure intention, which solves the problem that each modality cannot interact deeply in the user profile modeling process. (3) based on k-means, we construct user group profiles by clustering user privacy disclosure behavior personal profiles. (4) a specific case study, including a questionnaire survey of online healthcare platform users https://doi.org/10.15837/ijccc.2022.5.4696 4 and a comparison of baseline model performance, verifies that the online healthcare privacy disclosure modeling method with multimodal user profiles proposed in this paper can effectively improve accuracy and contribute to precision healthcare services. the subsequent sections of this paper are organized as follows: section 2 systematically investigates existing research on online privacy disclosure, user profile construction and multimodal fusion, and analyzes their advantages and disadvantages; section 3 describes multiple data sources for online user healthcare privacy disclosure; section 4 introduces the multimodal user profiling method to online healthcare privacy disclosure modeling in detail; section 6 describes the case study of this paper in detail and evaluates the accuracy of the results by comparing the baseline models and questionnaires; section 7 summarizes the work of this paper. 2 related work 2.1 online healthcare privacy disclosure willingness to disclose is an ability to control privacy through the user’s privacy concern and privacy control [11]. wang et al. [12] explored the factors influencing users’ willingness to disclose information in the mobile environment. sapuppo [13] compared the results of two questionnaires and found that the main reasons affecting users’ willingness to disclose in the internet were mobile. based on the analysis of privacy computing theory, l.wang et al. [14] conducted a study from the perspectives of different individual users and different mobile service providers to explore the user’s willingness to disclose and the truthfulness of disclosure. papadopoulou et al. [15] analyzed the main factors of trust that influence the willingness to disclose personal privacy in both e-commerce and mobile commerce contexts, and showed that in mobile commerce, the main reason for user disclosure is “trust". according to bergström [16], social network users have different levels of “trust" and different online privacy concerns, which have a direct impact on online privacy settings and disclosure levels. with the development of data economy, users’ huge private information has become a treasure for data users. the change in users’ attitude toward personal privacy has also led to a change in users’ willingness to disclose their privacy information. users no longer blindly protect their personal privacy information and more and more users disclose their privacy information on their own, which shows that the willingness to disclose privacy is one of the manifestations of users’ ability to control their personal information. through many research results on user information disclosure, researchers found that users compare their expected benefits and possible risks in the process of whether to disclose their privacy. users decide to disclose when the benefits and risks reach a certain trade-off point. according to [17], users first calculate the risks and benefits of privacy disclosure and choose to disclose information when the risk of disclosure tends to be none, or when the risk is much less than the benefit. 2.2 user profiles user profiles better describe users, makes customer information more vivid and easier to computerize, and is at the root of some big companies’ use of big data theory. user profiles are used to identify users and to determine how to treat them, for example, which products they like, what their educational background is, their spending power and their social connections, and also to reflect their personality through their behavior, i.e., to tag them. the user information can be used for data mining of user profiles, for example, using clustering algorithms to analyze the distribution of age groups and occupations of users with high blood pressure. to perform massive data processing, it is inseparable from frequent various operations, however, tagging provides us with a way to quantify the difficult information, so that computers can programmatically process tedious information and also understand users through some algorithms and models. when the computer has achieved how to understand the user, whether it is a search engine or a recommendation engine or various commercial applications such as advertising and marketing, it can further improve the accuracy of the results and the efficiency of information acquisition as well as recommendation information. for each different https://doi.org/10.15837/ijccc.2022.5.4696 5 company and project, the goals of the user personas they create and the final state they want to achieve are different. through the purpose of enterprise’s profiles of some users as a research point, the profile can have the following three applications: the first is to cluster and analyze the massive user data to split the users into different user groups; the second is to have a good understanding for the enterprise’s product-oriented objects through core analysis to better understand the users; the third is a product application based on the user profile. first of all, if we want to make a reasonable profile of customers, we need to collect information about the customers of the enterprise, and the way to collect information is usually in the form of website crawling or offline questionnaires, and then we can define the basic outline of the profile according to the target, and also make a basic understanding of the user’s situation. the basic information of users mainly includes user id, age, gender, occupation, geography, education background, family income status, and so on. it is also possible to obtain information about users’ spending and clicking behavior from some enterprise platforms. after having a profile, it is necessary to define the details of the profile, and it is necessary to analyze all the teams involved in the profile, or the different levels of staff and management in a company, product sales staff or senior developer, etc. the extended or dynamic information of the user mainly includes the preferred product model, the user’s activity, hobbies and preferences, usual browsing behavior and purchasing tendencies. when a company segments its users, it divides the user metrics into several categories and indicates which data information plays a dominant role, which is used as the main basis for user classification. 2.3 multimodal fusion in general, modality refers to the way things happen or exist, and multimodality refers to various combinations of two or more modalities in various forms. for each source or form of information, it can be referred to as a modality. the current research focuses on the processing of three modalities: image, text, and voice. the reason for the fusion of modalities is that different modalities have different expressions and different perspectives on things, so there are some crossover (redundant information exists), complementary (better than single feature) phenomena, and there may even be a variety of different information interactions between modalities, and if the multimodal information can be processed reasonably, the feature information can be enriched. that is, in general, the distinctive features of multimodality are redundancy and complementarity. traditional feature fusion algorithms can be divided into three main categories: bayesian decision theory-based, sparse representation theory-based, and deep learning theory-based methods. among them, deep learning methods can fuse every layer according to the level of fusion. pixel level fuses the original data at the smallest granularity. feature level fuses the abstract features, including early and late fusion. early means fusing the features first and then outputting the model, the disadvantage is that it cannot make full use of the complementarity between multiple modal data, and there is the problem of information redundancy. late is divided into two forms: fusion and non-fusion. nonfusion is similar to integrated learning, where different modalities get their own results and then unified scoring for fusion, and the model of this method is independently robust; fusion means free fusion in the process of feature generation, which is more flexible. besides, decision level is used to fuse the decision results, hybrid level hybrid fuses multiple fusion methods in recent years, social media user profiles has attracted more and more researchers’ attention. among the existing user profile modeling techniques, research on how to fuse multiple user data sources or modalities in order to obtain more accurate user profiles is quite limited and has some shortcomings. on the one hand, some of the user profile research works are conducted only on a single modality, which is difficult to characterize users comprehensively; on the other hand, most of the studies using multiple modalities only integrate data sources at feature level or decision level [18, 19, 20], and even though some studies are able to perform fusion at two levels [21, 22], they still lack the exploration of deep fusion of multiple data sources. based on the above analysis, this paper builds a umf-scf model on the multi-source data of user privacy disclosure collected and processed by the questionnaire to construct personal privacy disclosure behavior profiles of users, and then constructs group profiles by clustering the personal profiles through k-means. the use of multimodal techniques to construct personal profiles and the combination of https://doi.org/10.15837/ijccc.2022.5.4696 6 table 1: user history disclosure behavior factors. historical disclosure behavior explanation information provided previously provided information to online healthcare platforms information recording information can be found on online healthcare platforms private information disclosure once mentioned personal things on online healthcare platforms information consistency the information provided to the online platform is consistent with the actual situation personal profiles and group profiles can achieve a comprehensive characterization of users’ online healthcare privacy disclosure behavioral characteristics and provide help for online healthcare precision services. 3 online healthcare privacy disclosure data there are various data reflecting users’ privacy disclosure behaviors in the online healthcare process, including users’ basic information, historical disclosure behaviors, perceived risks, perceived profits, short term disclosure intentions, and long term disclosure intentions. 3.1 basic user information due to the differences in age, education and gender, there are also differences in users’ willingness to disclose personal information. therefore, this paper uses basic user information as one of the basic attributes to construct personal and group profiles, including gender, age, education, occupation, time of using online healthcare platforms and experience of privacy leakage. 3.2 historical disclosure behavior historical disclosure behavior refers to users who have provided information to an online healthcare platform in the past or the historical information can be found on the platform, etc. since a user who has provided information to a healthcare platform in the past is more likely to provide information to the platform in the future as well, this paper considers historical disclosure as one of the influencing factors of user disclosure behavior. 3.3 perceived risk perceived risk refers to the potential risk or loss that users perceive when disclosing privacy to online healthcare platforms [23]. users’ perceived risk has a negative impact on the willingness to disclose. therefore, in this paper, several perceived risk factors are selected as the basic attributes of users to construct personal and group profiles of users. table 2 presents the explanation of each factor. 3.4 perceived profits perceived profits refer to the benefits and rewards that users perceive can be brought to them when disclosing information to online healthcare platforms [23]. [12] have shown that perceived profits have a positive relationship on users’ intention to disclose information. users can get a better service experience when disclosing information to online healthcare platforms, which enables doctors to clearly https://doi.org/10.15837/ijccc.2022.5.4696 7 table 2: user perceived risk factors. perceived risk explanation information provision risk providing information to online healthcare platforms is risky. information leakage information provided to online healthcare platforms may be leaked. risk of information use information provided to online healthcare platforms may be used inappropriately. table 3: user perceived profits factors and their implications. perceived profits explanation service acquisition providing information to online healthcare platforms facilitates access to appropriate services. personalized service providing information to online healthcare platforms facilitates access to personalized services. health benefits providing information to online healthcare platforms can be beneficial in helping to solve health problems. doctor-patient communication providing information to online healthcare platforms facilitates communication with doctors. understand their basic body information and get better treatment advice that is more suitable for their situation, thus better solving health problems. 3.5 willingness to disclose the user’s willingness to disclose plays a decisive influence on the disclosure behavior. the stronger the user’s willingness to disclose, the higher the probability of generating disclosure behavior. disclosure willingness is divided into short term and long term disclosure intention. short term intention to disclose is a user’s psychological activity in the present regarding providing information to online healthcare platforms. generally, users are willing to disclose information in the present and tend to disclose in the future. however, users’ willingness to disclose in the distant future may change, thus affecting their future disclosure behavior. therefore, both users’ willingness to disclose in the near future and willingness to disclose in the far future are among the influencing factors of users’ disclosure behavior. the value of short term intention to disclose is greater than that of long term intention to disclose [23]. table 4: users’ willingness to disclose in short term and long term. willingness to disclose in short term and long term explanation short term disclosure intention whether there are information consulting doctors willing to provide information to online healthcare platforms at the moment. long term disclosure intention whether information will be provided to online healthcare platforms in the future upon request or when it would help with health diagnosis. health benefits providing information to online healthcare platforms can be beneficial in helping to solve health problems. doctor-patient communication providing information to online healthcare platforms facilitates communication with doctors. https://doi.org/10.15837/ijccc.2022.5.4696 8 online user healthcare privacy disclosure objectives user profile label system index construction online healthcare platform user profile label dataset questionnaires likert scale data acquisition user group profile 1 user group profile 2 ...... user group profile n user profiles clustering similarity analysis, cluster digitization labelling clustering user personal profile construction user group profile construction umfscf user personal profiles figure 1: online healthcare privacy disclosure modeling process for multimodal user profiling. 4 multimodal user profiling method to online healthcare privacy disclosure modeling the online healthcare privacy disclosure modeling for multimodal user profiles consists of three stages: digitization, labeling, and clustering. the specific process are shown in figure 1. firstly, the user privacy disclosure target is clarified and the online healthcare user privacy disclosure labeling system is established; then the survey questionnaire method is used to obtain data, and the sample digitization is realized after pre-processing; next, based on the sample set and labeling system, the multi-data sources of user online healthcare privacy disclosure are extracted with feature labels to form the labeling dataset, and umf-scf is used to make the interaction between the multi-modalities to form the user online healthcare privacy disclosure personal profiles. then, based on the labeled data set, we combine similarity analysis and k-means clustering to classify the users and get the final user group profiles. 4.1 multimodal fusion of privacy disclosure data since users’ healthcare privacy disclosure behaviors are reflected by multiple data in the online healthcare process, it is crucial to establish deep interactions between multiple modalities, so this paper uses the multimodal fusion technique umf-scf [24] to comprehensively characterize users’ future privacy disclosure behaviors. 4.1.1 umf-scf overall architecture the umf-scf contains a modal fusion layer and a stacking layer, as shown in figure 2. the modal fusion layer has 25 cross-modal learning joint representation networks, such as fusionbr and fusion brphd, for each of the 25 model combination forms. among them, b denotes the basic information features of users, r denotes the perceived risk features of users’ privacy disclosure, p denotes the perceived profit features, h denotes the historical disclosure behavior features of users, and d denotes the privacy disclosure intention features of users (including short term and long term disclosure intention). the role of this layer is to generate the joint or shared feature representations of each modality, and to make the features of different modalities interact deeply by combining data sources and nonlinear functions. in addition, since different modalities have different levels of importance for different task goals, this layer also employs an attention mechanism to use modality-level weighted scoring for the extracted features. the stacking layer implements decision-level fusion, which obtains the prediction probabilities of the output of the modal fusion layer and uses a multilayer perceptron to obtain the prediction results of the privacy disclosure behavior of the final online healthcare platform users. https://doi.org/10.15837/ijccc.2022.5.4696 9 b r p h d b r b p b h b d r p r h r d p h p d h d b r p b r h b r d b p h b p db h d r p h r p d r h d b r p h b r p d b r h d b p h d b r p h dr p h d fusio nbr fusio nbp fusio nbh fusio nbd fusio nrp fusio nrh fusio nrd fusio nph fusio npd fusio nh d fusio nbrh d fusio nbrph fusio nbph d fusio nbrpd fusio nrph d fusio nbrph d ...... ...... ...... ...... ...... ...... ...... mlp s tacking mo del fusio n predictio n pro bability figure 2: umf-scf2 overall architecture. 4.1.2 fusionbrp the cross-modal learning joint representation network is the core of the umf-scf model. in this paper, fusionbrp is used as an example to introduce the cross-modal learning joint representation network, which has three layers containing embedding layer, interaction layer and decision layer. its structure is shown in figure 3. the embedding layer is pre-trained for each input to obtain embedding representations of different modalities. taking fusionbrp as an example, the embedding layer contains user basic information embedding (b1), perceived risk embedding (r1), perceived profits embedding (p1). the interaction layer is the same two-layer structure, where each modal representation is first transformed using the hidden layer, and then for each transformed modal representation, the association representation with the other transformed modal embedding is added, so that the feature representation of each modal after the interaction layer is obtained, which contains the interaction information corresponding to the other modal. m 1b = tanh ( um1 m 1 a + wm1m2 um2 m 2 a + wm1m3 um3 m 3 a + wm1m4 um4 m 4 a ) (1) where, m∗ denotes the modal embedding b, r, and p respectively; a is the number of layers of the interaction layer, if a = 1, then b = 2, if a = 2, then b = 3; the neural network activation function uses t anh; u∗ is the transformation matrix corresponding to the modal embedding in the hidden layer of the a-th interaction layer. w∗∗ is the correlation weight matrix of the corresponding two modal embedding in the interaction layer of layer a. after the interaction layer, each user can get 4 high-level representations, including b3, r3, p3, and b3 ⊕ r3 ⊕ p3. the decision layer maps the 4 representations to their label category space by means of a linear layer, which are cb, cr, cp and cbrp , and then a softmax layer is used to obtain the probability distribution of category c. the linear mapping layer is defined as follows. c∗ = w∗−ca3 + b∗−c (2) where a ∈ {b3, r3, p3, b3 ⊕ r3 ⊕ p3}, the c∗ denotes the label category space corresponding to a, and w∗−c denotes the linear layer weights corresponding to a, and b∗−c is the corresponding bias value. https://doi.org/10.15837/ijccc.2022.5.4696 10 ...... embe dding laye r inte raction laye r...... ...... figure 3: structure of fusionbrp. the softmax layer is defined as shown below. p∗c = exp (c∗c )∑k k=1 exp ( c∗k ) (3) where p∗c denotes the probability of b3, r3, p3, b3 ⊕ r3 ⊕ p3 predicting category c, k is the number of categories, and c∗c denotes the c-th category of a certain labeled category space. 4.1.3 loss representation and attention mechanism for multimodal fusion the loss function of fusionbrp consists of three components: the loss function of each modal representation lm, the loss of the joint representation ld, the loss of consistency between the modal representations . these three loss functions are defined as follows. lm = − ∑ p∈pm ∑ x∈xt k∑ k=1 prk(x) log(p(x)) (4) lbrp = − ∑ x∈xt k∑ k=1 prk(x) log ( pbrpk (b3(x) ⊕ r3(x) ⊕ p3(x)) ) (5) ld = − ∑ (p1,p2)∈pd ∑ x∈xt k∑ k=1 p1(x) log (p2(x)) (6) where pm = { pbk , p r k , p p k } , is the set of probabilities for computing lm the set of prediction probabilities. p∗k is the probability that a modality predicts the k-th category. xt denotes the set of user samples with labels, i.e., the training set. prk denotes the true label of the k-th category of a given sample. pd = {( pbk , p r k ) , ( pbk , p p k ) , ( prk , p p k )} , is the set of values for calculating the set of predicted probabilities. since different modalities show different contributions to the classification task for different attributes of users, this paper introduces an attention mechanism to linearly weight the above three losses to obtain the final loss. the attention in this paper can be interpreted as follows: for different modalities performing the same classification task, the influence of short term disclosure intention on users’ disclosure behavior is greater than that of long term disclosure intention, so short term disclosure intention is more important and should be given more weight; for the same modality performing different classification tasks, the influence of perceived risk on long term disclosure intention https://doi.org/10.15837/ijccc.2022.5.4696 11 table 5: user online healthcare privacy disclosure data. id 8 gender male age 34 academic qualifications master’s degree career programmer years of using online healthcare platform 1 have experienced privacy leakage no perceived risk 4.33 perceived profit 2.25 historical disclosure behavior 1.25 recent disclosure intentions 2.75 willingness to disclose in the future 2.50 is significantly greater than that of short term disclosure intention, so more weight should be given to perceived risk classification forward disclosure the perceived risk has a significantly greater impact on the willingness to disclose in the near future than the willingness to disclose in the near future. the calculation formula is as follows. ltotal = 2∑ i=0 w[i] · losslm [i] + w[3] · lbrp + 6∑ j=4 w[j] · lossld [j − 4] + w[6] · l2loss (7) where w denotes the list of weight coefficients introduced by the attention mechanism, and losslm denotes the list of lm, and lossld denotes the list of ld. w enables to balance the representation of each modality, the joint representation, and the consistency. 4.2 personal profiles and group profiles of users’ online healthcare privacy disclosure 4.2.1 personal profile construction for online healthcare privacy disclosure of users table 5 shows the data generated by a user who has used an online health care platform. the user profile built from this data is shown in figure 4. 4.2.2 group profile construction for online healthcare privacy disclosure of users due to the large differences between people, it is difficult to achieve personalized or accurate recommendation of medical services by only constructing personal profiles of users, so this paper also constructs online healthcare privacy disclosure group profiles for users based on personal profiles. the user group profile statistically analyzes the similarity of multiple users, clustering users with similar characteristics to form several user groups, and summarizes and refines the common indexes within the user groups. the construction of user group profiles is based on personal profiles and then similarity analysis and aggregation. in this paper, we use vector space model (vsm) [25] to calculate the similarity of users and the classical k-means [26] algorithm to classify user groups. vsm first represents user features as vectors, and then finds the distance between vectors by the cosine distance calculation method, which is its similarity. then the similarity formula is as follows. vim(user1, user2 ) = cos   ∑fi=1 fif′i∏n j=1 √ f 2j + f 2j   (8) where f is the number of user features and fi is the i-th representation of the user 1 feature vector, and f′i is the i-th representation of the user 2 feature vector. user profile clustering is a method of classifying users according to their features and attributes, which can classify them into several categories and ensure that the differences within categories are as https://doi.org/10.15837/ijccc.2022.5.4696 12 us e r id 1 male 3 4 ye ars old mas te r de g re e prog ramme r no privacy le akag e e xpe rie nce us e the online he althcare platform for 1 ye ar s trong pe rce ive d ris k me dium pe rce ive d profit low his torical dis clos ure me dium s hort te rm dis clos ure inte ntion me dium long te rm dis clos ure inte ntion figure 4: personal profile of users’ online healthcare privacy disclosure. small as possible, while the differences between categories are as large as possible. take user profile classification as an example, the specific steps of k-means algorithm clustering are: 1) randomly select k users as the center of clustering; 2) according to the similarity, group the remaining users into the class; 3) according to the first clustering result, recalculate the center of each of the k classes by taking the arithmetic average of the dimension of the respective feature vectors of all users in the class; 4) repeat step 2; 5) repeat step 3 and 4 until the dissimilarity between the clustering result and the last clustering result is less than the set threshold; 6) obtain the end-user clustering result. in addition, this paper uses the simplest way to select the k value, which is calculated as follows. k = sqrt(n/2) (9) where n denotes the total number of clustering units, i.e., the number of user samples involved in clustering. 5 case studies in this paper, we explain the above process of modeling online healthcare privacy disclosure with multimodal user profiles through a case study. 5.1 design of profile labeling system by analyzing the data of users’ online healthcare privacy disclosure, this paper establishes six basic elements of users’ basic information, perceived risk, perceived profits, historical disclosure behavior, short term disclosure intention, and long term disclosure intention as subdivision indicators to design the label system of user profile. the final constructed label system of user privacy disclosure profile of online healthcare platform is shown in figure 5. among them, the six labels include a total of 25 measurement variables, as shown in table 6. the difference in the privacy disclosure profiles of users of online healthcare platforms lies in the label weighting design, i.e., different user group characteristics are reflected in different degrees of importance on a certain label. therefore, this paper expresses users’ tendency to disclose privacy in a certain aspect with the help of a 5-point likert scale from low to high. the mean value of each user https://doi.org/10.15837/ijccc.2022.5.4696 13 user profile us e r bas ic information pe rce ive d ris k pe rce ive d profits his torical dis clos ure be havior s hort te rm dis clos ure inte ntion long te rm dis clos ure inte ntion figure 5: user privacy disclosure profile labeling system. table 6: measurement variables for user profiling labels. user basic information gender age qualification occupation time to use the online healthcare platform have experienced a privacy leakage perceived risk providing information to online healthcare platforms is risky the information provided to the online healthcare platform may be leaked information provided to online healthcare platforms may be used inappropriately perceived profits providing information to the online healthcare platform is conducive to obtaining corresponding services providing information to the online healthcare platform is conducive to enjoying personalized services providing information to online healthcare platforms can help solve health problems providing information to the online healthcare platform can better communicate with doctors or other users historical disclosure behavior once provided information to the online healthcare platform personal information can be found on the online healthcare platform once mentioned personal things on the online healthcare platform the information shared to the online healthcare platform is consistent with the actual situation short term disclosure intention if you are ill, you will provide information to online healthcare platform or doctors to obtain services if you are ill, you will provide information when the doctor or online healthcare platform requests information if you are ill, you will provide information that will help you stay healthy if you are ill, you will provide information to other users of the online healthcare platform for help long term disclosure intention intend to provide information to online healthcare platform or doctor to obtain services in the future intend to provide personal information when online healthcare platform or doctor ask for information in the future intend to provide information to online healthcare platform or doctor when providing information helps to maintain health will continue to share information with online healthcare platform or doctor as you do now in the future https://doi.org/10.15837/ijccc.2022.5.4696 14 table 7: average accuracy of user label prediction for each method. method basicinformation perceived risk perceived profitss historical disclosure behavior recent disclosure intentions long term disclosure intention manual feature+svm 0.720 0.653 0.632 0.678 0.614 0.683 concat+svm 0.709 0.682 0.675 0.705 0.603 0.690 udmf 0.765 0.703 0.688 0.726 0.619 0.711 umf-scf without attention 0.789 0.725 0.723 0.759 0.705 0.799 umf-scf 0.812 0.788 0.758 0.806 0.769 0.856 psychological preference variable in each subgroup is chosen as the user profile label weight, while the basic user information is ranked by frequency using attribute values, thus defining the weight value of the user profile label. 5.2 data acquisition this paper uses questionnaires to obtain the basic data of users’ online healthcare privacy disclosure, so as to build personal and group profiles. the user data obtained through the questionnaire has the characteristics of targeted and specific information, which can make the questions more clearly and specifically oriented, thus facilitating the precise analysis of the subsequent study. the questionnaire consists of 25 questions in table vi, and all questions, except for basic personal information, are on a 5-point likert scale, with 1 to 5, 1 being “strongly disagree" and 5 being “strongly agree". this study continued to distribute questionnaires for 14 days, and a total of 700 questionnaires were distributed. after removing 120 questionnaires that had not used the online healthcare platform and 10 consecutive questionnaires with the same options, 580 valid questionnaires were finally obtained. in this paper, the data of 464 of these users were used as the training set and the data of 116 users were used as the test set. in order to construct the user feature vector, the 19 measurement variables of perceived risk, perceived profits, historical disclosure behavior, short term disclosure intention and long term disclosure intention in the questionnaire must be quantified. in this paper, scores of 1, 2, 3, 4, and 5 are assigned to markers 1 to 5 on a 5-point likert scale, and the scores are summed according to the options selected by the users to obtain scores for 5 labels such as perceived risk. a user is considered to have a very low performance level on this label if the score ∈ 0.0, 1.0], low if the score ∈ (1.0, 2.0], medium if the score ∈ (2.0, 3.0], high if the score ∈ (3.0, 4.0], and very high if the score ∈ 4.0, 5.0]. 5.3 user online healthcare privacy disclosure personal profile construction the user’s personal profile is constructed, i.e., predicting the user’s basic information, perceived risk, perceived profits, historical disclosure behavior, short term disclosure intention and long term disclosure intention as shown in figure 4. we use umf-scf to fuse and predict multiple features of users, and set the baseline model including manual features + svm, concat + svm, udmf, and umf-scf without attention mechanism to compare and evaluate the accuracy performance of the umf-scf model for user personal profile construction. in this paper, “accuracy” is used as the evaluation metric. the average accuracy predicted by each method on the test set is shown below. by table 7, umf-scf achieves leading results in predicting all six features of users; manual feature+svm is a unimodal embedding method, and compared with the other four multimodal fusion methods, the multimodal fusion method performs significantly better than unimodal embedding in the user profiling problem; concat+svm is a multimodal fusion method but performs worse than umfscf because it only achieves simple splicing without sufficient interaction between multiple modalities; https://doi.org/10.15837/ijccc.2022.5.4696 15 udmf only uses a simple neural network to fuse each data source without deep interaction between multiple modalities; umf-scf without attention mechanism treats the importance of each modality for different classification tasks as the same, and performs worse than umf-scf, indicating that the setting of different weights is beneficial to improve the performance of the model. 5.4 user online healthcare privacy disclosure group profile construction in order to effectively obtain the differentiated profiles of online healthcare platform user groups, this paper selects perceived risk, perceived profits, historical disclosure behavior, short term disclosure intention and long term disclosure intention as feature factors, constructs a feature vector for each user, and calculates the similarity degree values between each two users by vsm. for example, the feature vector of user 1 is (4.33, 2.25, 1.25, 2.75, 2.50), and the feature vector of user 2 is (1.00, 3.50, 4.25, 4.75, 4.75), then the similarity degree between user 1 and user 2 is cos ( 4.33×1.00+2.25×3.50+1.25×4.25+2.75×4.75+2.50×4.75√ (4.332+1.002)× √ (2.252+3.502)× √ (1.252+4.252)× √ (2.752+4.752)× √ (2.502+4.752) ) = 1.78. next, the k-means algorithm and the calculated similarity values between two users are used to classify the user groups. in this paper, there are 464 users in the training set as clustering objects, and the k-value is calculated as 15 by equation 9. the 15 users with the smallest similarity between two users are selected as the initial clustering centers according to the minimum-maximum principle method, and 15 clusters are finally obtained according to the steps of the k-means clustering method. the center of each cluster is the representative of all objects in that cluster, and its individual parameters are the reflection of the collective common characteristics. the 17 user groups obtained in this paper are as follows. user group a = user 1, user 56, user 156, user 159, ... , user486 user group b = user 2, user 33, user 78, user 255, ... , user578 ... user group o = user 89, user 112, user 139, user 201, ... , user487 to evaluate the accuracy of the group profiles constructed in this paper, we used the data of the remaining 116 users for testing. the labels of the users’ personal profiles are first represented by feature vectors, and then their similarity is calculated with the representatives of the user profiles of the 15 clustering centers, and they were categorized into the user groups where the clustering centers with the largest similarity values were located, and then the accuracy of online healthcare privacy disclosure of these 116 users was investigated separately through questionnaires. the results show that about 89% of the users think that their willingness are the same as the group profiles. from the perspective of online healthcare services, these users will have access to more accurate diagnoses than the other 11%, and the platform may give the remaining 11% a broader range of treatment options. regarding disclosure behavior are basically accurately predicted, and the user group profile established in this paper can effectively model the characteristics of users’ online healthcare privacy disclosure behavior. it is worth noting that the factors influencing users’ medical privacy disclosure behaviors considered in this paper are still limited, which restricts the accuracy of behavioral predictions to a certain extent. in the future, we will further explore the privacy disclosure intention of online healthcare users by combining privacy protection, domestic and international service platform differences, and demographic factors to further assist the development of online healthcare privacy platforms 6 conclusion to address the challenges of user privacy disclosure in current online healthcare platforms, this paper proposes a multimodal fusion user profile modeling method. the method achieves cross-modal interaction of multiple data sources for user privacy disclosure through umf-scf, constructs personal profiles of user online healthcare privacy disclosure, and comprehensively characterization the characteristics of user privacy disclosure behavior; constructs group profiles of user online healthcare privacy disclosure based on personal profiles and k-means clustering algorithm, and integrates the user characteristics presented by personal profiles and group profiles to more accurately capture the https://doi.org/10.15837/ijccc.2022.5.4696 16 online healthcare platform users’ needs. according to the case study, the accuracy of the method proposed in this paper is significantly improved compared with unimodal and some advanced multimodal methods, and the questionnaires show that the accuracy of the user profile constructed by this method reaches 89%. the multimodal fusion user profile modeling method can effectively maximize the user online healthcare privacy disclosure behavior while guaranteeing the user information without leakage and 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[26] hartigan j a, wong m a. algorithm as 136: a k-means clustering algorithm[j]. journal of the royal statistical society. series c (applied statistics), 1979, 28(1): 100-108. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control https://doi.org/10.15837/ijccc.2022.5.4696 18 cite this paper as: yong wang (2022). online healthcare privacy disclosure user group profile modeling based on multimodal fusion, international journal of computers communications & control, 17(5), 4696, 2022. https://doi.org/10.15837/ijccc.2022.5.4696 introduction related work online healthcare privacy disclosure user profiles multimodal fusion online healthcare privacy disclosure data basic user information historical disclosure behavior perceived risk perceived profits willingness to disclose multimodal user profiling method to online healthcare privacy disclosure modeling multimodal fusion of privacy disclosure data umf-scf overall architecture fusionbrp loss representation and attention mechanism for multimodal fusion personal profiles and group profiles of users' online healthcare privacy disclosure personal profile construction for online healthcare privacy disclosure of users group profile construction for online healthcare privacy disclosure of users case studies design of profile labeling system data acquisition user online healthcare privacy disclosure personal profile construction user online healthcare privacy disclosure group profile construction conclusion int j comput commun, issn 1841-9836 8(6):901-906, december, 2013. design of congestion control scheme for uncertain discrete network systems h. wang, c. yu hongwei wang, chi yu qinhuangdao branch, northeastern university qinhuangdao, china, 066004 wanghw0819@163.com; yuchi0319@163.com abstract: for a class of uncertain discrete network systems, a sliding mode control algorithm is presented for active queue management (aqm) in order to solve the problem of congestion control in transmission control protocol (tcp) communication. first, the sliding surface is designed based on linear matrix inequality (lmi) technique. then, we analyze the mechanism of chattering for the discrete-time exponential approximation law, a modified one is presented and applied to the network systems. simulation results demonstrate that the proposed controller has good stability and robustness with respect to the uncertainties of the number of active tcp sessions, link capacity and the round-trip time. keywords: sliding mode control, network systems, linear matrix inequality(lmi) 1 introduction as the rapid expansion of network scale, congestion control has become an important issue. aqm is a router-based control mechanism, which can implement the end system to demand quality of service. so the combination of tcp and aqm is the main ways to solve the problems of current network congestion control. random early detection (red) as the earliest well-known aqm algorithm is sensitive to parameter variations [2]. so some improved red methods are presented [3-5]. however, these algorithms can not guarantee high network utilization and low packet loss[6-7]. recently, some aqm algorithms have been proposed based on mathematical models, which give the basis for control theory research. in [6], a fluid-flow model for tcp/aqm networks has been introduced. the proportional-integral (pi) controller is designed in [8], also some robust control schemes [9-12], such as intelligent pid, variable structure sliding mode controller, h-infinity controller, and so on. these methods can obtain well performance for practical network systems. with the rapid development of computer technology and digital signal processing chips, the study of discrete-time control theory is rather important. sliding mode control (smc) is a robust technique for its unique ability to withstand external disturbance, it has achieved fruitful for continuous system. however, sliding mode control schemes are relatively small for discretetime system [13-15], a few discrete algorithms are applied to the network control. in this paper, a robust discrete-time sliding mode controller is designed for tcp network model with uncertain disturbance. the aim is to avoid network congestion. 2 problem statement and preliminaries in [6], a model of tcp connection through a congested aqm router is developed.  ẇ(t) = 1 r(t) − w(t)w(t − r(t)) 2r(t) p(t − r(t)) q̇(t) = n(t) r(t) w(t) − c(t) (1) copyright © 2006-2013 by ccc publications 902 h. wang, c. yu where c(t) is the capacity of link, w(t) is the size of tcp congestion window, q(t)is the length of queue in buffer, p(t) is packet-dropping probability function (0 ≤ p(t) ≤ 1), n(t) is the number of active tcp link, r (t) is the transfer delay and r (t) = tp + q (t)/c (t). to linearize(1), we first assumer(t) = r0, n(t) = nand c(t) = cis normal value of r(t), n(t) and c(t), the equilibrium point (w0,qd,p0) is defined by ẇ = 0 and q̇ = 0. let δw(t) = w(t) − w0, δq(t) = q(t) − qd, δp(t) = p(t) − p0. a linearized model is given. δẇ(t) = − 2n r20c δw(t) − r0c 2 2n2 δp(t − r0) δq̇(t) = n r0 δw(t) − 1 r0 δq(t) (2) let x(t) = ( δq(t) δ ˙q(t) )t = ( x1 x2 )t ,u(t) = δq(t),(−p0 ≤ u(t) ≤ 1 − p0). we have ẋ(t) = āx(t) + b̄u(t) (3) where x(t) = [ x1(t) x2(t) ] , ā =   0 1 − 2n r30c − ( 1 r0 + 2n r20c )  , b̄ =   0 − c2 2n   . then the discrete-time uncertain system can be expressed as the sampling period t . x(k + 1) = (ã + ∆ã)x(k) + (b̃ + ∆b̃)u(k) (4) where ã = eāt , b̃ = ( ∫ t 0 eat )b̄,∆ã and ∆b̃ are depending on network parameters. in the process of designing controller, the following assumptions are taken. a0. the pair (ã, b̃) is controllable, and b̃ = ( b̃1 b̃2 ) , det(b̃2) ̸= 0. a1. the matrix ∆ã(k) satisfies mismatch condition and ∆b̃ satisfies ∆b̃ = b̃ × ∆b̂. we do a linear transformation as follows: z = tx =   in−m −b̃1b̃2−1 0 b̃2 −1  x (5) the system (4) is written as follows. z(k + 1) = (a + ∆a)z(k) + b(i + ∆b̂)u(k) (6) where z(k) = [ z1(k) z2(k) ] , a = tât−1 = [ a11 a12 a21 a22 ] , ∆a = t∆ãt−1 = [ ∆a11 ∆a12 ∆a21 ∆a22 ] , b = tb̃ = [ 0 im ] , z1 (k) ∈ rn−m, z2 (k) ∈ rm. we have z1 (k + 1) = a11z1 (k) + a12z2 (k) + ∆a11z1 (k) + ∆a12z2 (k) (7) z2 (k + 1) = a21z1 (k) + a22z2 (k) + ∆a21z1 (k) + ∆a22z2 (k) + im ( i + ∆b̂ ) u(k) (8) design of congestion control scheme for uncertain discrete network systems 903 3 design of controller for discrete-time network systems 3.1 designing sliding mode surface without loss of generality, we suppose that the sliding surface is s(k) = m̄z(k) = [ −m im ] [ z1 (k) z2 (k) ] = 0 (9) where m̄ ∈ rm×n, m ∈ rm×(n−m). substituting (9) into (7) gives the sliding motion z1 (k + 1) = (a11 + ∆a11 + a12m + ∆a12m)z1 (k) (10) a2. the ∆a11 (k) and ∆a12 (k) satisfy ∆a11 (k) = df (k)e1, ∆a12 (k) = df (k)e2, d and ei(i=1, 2) are constant matrices of appropriate dimensions, f(k) satisfies ft (k)f(k) ≤ i. lemma 1[16]. given constant matricesd,eand symmetric matrixy of appropriate dimensions, the following inequality holdsy + dfe + et ft dt < 0. where fsatisfy f(k)t f(k) ≤ i, if and only if for some constantε > 0, we havey + εddt + ε−1et e < 0. theorem 1. if there exists a symmetric and positive definite matrixp, some matrix wand some scalar ε such that the following lmi(11) is satisfied, then the reduced-order discrete-time system(6) is asymptotically stable by the sliding mode surface(9).  −x ∗ ∗ a11x + a12w −x + εddt ∗ e1x + e2w 0 −εi   < 0 (11) wherex = p−1,w = mp−1 and * denotes the transposed elements in the symmetric positions. proof: for system (6), we choose the following lyapunovkrasovskii function v (k) = zt1 (k)pz1 (k) (12) differential equation along the trajectory of the system in (10) is given by ∆v (k) = v (k + 1) − v (k) = zt1 (k) ( qt pq − p ) z1 (k) where q = a11 + a12m + dfe1 + dfe2m.if the qt pq − p < 0, we can the theorem 1. 3.2 design of discrete-time sliding mode controller discrete-time approximate law is given in [17]. s (k + 1) = (1 − ηt) s (k) − λtsgns (k) (13) where λ > 0, η > 0, 0 < ηt < 1, t is sampling period. the (13) can not guarantee the system reaches to point. the modified reaching law is given. s (k + 1) = (1 − ηt) s (k) − ( 1 − e−|s(k)| − η ) |s (k)|tsgn(s (k)) (14) according to the equations (6), (9) and (14), we get the control law as follows. u(k) = (m̄b)−1[m̄ax(k) + (1 − ηt)s(k) − (1 − e−|s(k)| − η)|s(k)|tsgn(s(k)) − m̄f(k)] (15) 904 h. wang, c. yu wheref (k) = ∆az (k) + ∆bu(k). it is an unknown number, the controller can not be achieved. the system existing unknown disturbances of the dynamics is much slower compared with the sampling frequency, so we have f(k − 1) = z(k) − az(k − 1) − bu(k − 1) (16) considering the practical network system, we can obtain the following controller. u (k) = −p0 , u (k) < −p0 u (k) = ( m̄b )−1 [−m̄az (k) + (1 − ηt) s (k) − (1 − e−|s(k)| − η) |s (k)|tsgn(s (k)) − m̄ (z (k) − az (k − 1) − bu(k − 1)) ] , − p0 ≤ u (k) ≤ 1 − p0 u (k) = 1 − p0 , u (k) > 1 − p0 (17) remark: 1) when s(k) ≥ 0, the modified reaching law (14) is rewritten as follows. s(k + 1) − s(k) = −(1 − e−|s(k)|)s(k)t ≤ 0 2) whens (k) ≤ 0, the modified reaching law (14) is rewritten as follows. s(k + 1) − s(k) = (1 − e−|s(k)|)s(k)t ≤ 0 we can see that the design control law satisfying the sliding mode reaching condition. 4 simulation results let we choose parameters. n = 50, c = 300 packets/s, r0 = 0.5s, w0 = 3, p0 = 0.22. ã =[ 0.9999 0.0098 −0.0263 0.9671 ] , b̃ = [ −0.0445 −8.8514 ] ,∆ã = [ 0.0001sin(0.1πk) 0 0 0.0005sin(0.1πk) ] ,∆b̃ =[ −0.0013 −0.2655 ] , ε = 0.01. using lmi toolbox in the matlab, we can get m = −8.3. then choosing λ = 0.1,t = 0.01s, η = 5, x(0) = [ 30 2 ]t . fig.1 is the control law response curve based on the proposed control law, which can have much lower packet-dropping probability, satisfying the demand of system response. 0 2 4 6 8 10 −0.05 0 0.05 0.1 0.15 0.2 time/s u figure 1: the motion curve with designed control law u(k) in this paper we give the system state responses curve based on reaching law (13)and (14). the proposed control scheme can obtain much better performance both response time and chattering from fig.2 and fig.3. design of congestion control scheme for uncertain discrete network systems 905 0 2 4 6 8 10 −20 −10 0 10 20 30 40 time/s z 1 z 2 0 2 4 6 8 10 −30 −20 −10 0 10 20 30 40 time/s z 1 z 2 figure 2: state responses with control law (13) figure 3: state responses with control law (14) 5 conclusion this paper gives a discrete sliding mode control algorithm for network systems. a modified reaching law is presented and applied to the system. simulation results show that the controller has better stability and robustness, which can get a faster transient response and smaller steady state error. the scheme can effectively avoid network congestion. bibliography [1] s. floyd and v. jacobson, random early detection gateway for congestion avoidance, ieee/acm transaction on networking, 1(4): 397-413, 1993. 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[17] w. b. gao, theory and design of nonlinear systems, nanjing: southeast university press, 1990. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 2, month: april, year: 2023 article number: 5118, https://doi.org/10.15837/ijccc.2023.2.5118 ccc publications a new ranking method for trapezoidal intuitionistic fuzzy numbers and its application to multi-criteria decision making l. popa lorena popa department of mathematics and computer science aurel vlaicu university of arad, romania 310330 arad, elena dragoi, 2, romania lorena.popa@uav.ro abstract the ranking of intuitionistic fuzzy numbers is paramount in the decision making process in a fuzzy and uncertain environment. in this paper, a new ranking function is defined, which is based on robust’s ranking index of the membership function and the non-membership function of trapezoidal intuitionistic fuzzy numbers. the mentioned function also incorporates a parameter for the attitude of the decision factors. the given method is illustrated through several numerical examples and is studied in comparison to other already-existent methods. starting from the new classification method, an algorithm for solving fuzzy multi-criteria decision-making (mcdm) problems is proposed. the application of said algorithm implies accepting the subjectivity of the deciding factors, and offers a clear perspective on the way in which the subjective attitude influences the decision-making process. finally, a mcdm problem is solved to outline the advantages of the algorithm proposed in this paper. keywords: ranking method; trapezoidal intuitionistic fuzzy number; multi-criteria decision making. 1 introduction the intuitionistic fuzzy sets were introduced by k.t. atanassov [2] as a means of generalization of the fuzzy sets defined by l.a. zadeh in 1965 [28]. unlike the fuzzy sets, which are characterized solely by the membership function, the intuitionistic fuzzy sets are also characterized by the nonmembership function. for this reason, the intuitionistic fuzzy sets are more useful in expressing uncertainty and vagueness. thus, defining the ranking of intuitionistic fuzzy numbers has represented a significant scientific pursuit for researchers because they play an essential role, especially in the problems of multi-criteria decision making from various domains, e.g., economics, social sciences, and engineering. since the inception of the intuitionistic fuzzy numbers, these have been approached in various ways, and many classification methods have been proposed. in 2003, p. grzegrorzewski [7] proposed a ranking method for intuitionistic fuzzy numbers by using the expected interval of an intuitionistic fuzzy https://doi.org/10.15837/ijccc.2023.2.5118 2 number. in 2004, h.b. mitchell [12] proposed a ranking of intuitionistic fuzzy numbers by regarding them as an ensemble of fuzzy numbers. two years later, in 2006, v.l.g. nayagam, g. venkateshwari and g. sivaraman [15] generalized chen and hwang’s method, expending the scope of the method from fuzzy to intuitionistic fuzzy numbers. in 2010, d.f. li [10], defined a new ranking function for intuitionistic fuzzy numbers as the ratio between the value index and the ambiguity index. the function mentioned above was also based on the value and the ambiguity of triangular intuitionistic fuzzy numbers. in 2012, p.k. de and d. das [4] proposed a ranking method for trapezoidal intuitionistic fuzzy numbers, also making use of the value and ambiguity indices but instead considering their sum as the ranking function. in 2013, s. rezvani [22] defined the values and ambiguities of the membership degree and the non-membership degree for the trapezoidal intuitionistic fuzzy numbers and developed a ranking method based on the value-index and ambiguity-index. in the same year, s.s. roseline and e.c.h. amirtharaj [23] defined the magnitude of membership and non-membership functions for intuitionistic fuzzy numbers and ordered them based on the intuitionistic fuzzy numbers. moreover, in 2013, e. jafarian and m.a. rezvani [9] proposed a ranking method according to a crisp value associated with an intuitionistic fuzzy number related to the spread value concept defined. in the following period, many papers addressing this research topic were published ([19], [16], [3], [24], [26], [1], [13]). the purpose of classifying the intuitionistic fuzzy numbers is to facilitate their utilization in real-life problems, especially in decision-making problems in which information is uncertain. thus, the ranking methods proposed by j. whang and z. zhang [25] in 2009, d.f. li, j.x. nan and m.j. zhang [11] in 2010 and j. ye [8] in 2011 have been applied in solving multi-criteria decision-making (mcdm) problems in an intuitionistic fuzzy environment. in the subsequent years, the known methods of solving mcdm problems were approached in an intuitionistic fuzzy context [17] and were applied in various fields. [21], [27], [29], [6], [5] there are only a few recent examples. there are cases in which applying the different classification methods for ifn leads to different ranking relationships. a possible explanation for this difference in outcome is the human factor. thus, the question of how much the human factor influences the value of the ranking function. in other words, how much do the decision factors matter in the decision-making processes with ifn? to address the aforementioned question, in this paper, the proposed classifying function includes a parameter that represents the subjectivity of the decision maker. moreover, the examples provided illustrate that the attitude of the decision-making factors can sometimes change the hierarchy. the remainder of this paper is organized as follows. the core concepts of intuitionistic fuzzy sets are provided in section 2. in section 3, a new ranking function for trapezoidal intuitionistic fuzzy numbers is defined, starting from robust’s ranking index used in ordering fuzzy numbers, which it is applied for the membership function, as well as for the non-membership function of trapezoidal intuitionistic fuzzy numbers. section 4 provides examples that illustrate the given method and a study of the proposed ranking method in comparison with other methods. in section 5, given the new classification method of trifn, an algorithm for solving multi-criteria decision making problems in an intuitionistic fuzzy context is proposed. the efficiency of the algoritm is emphasized in section 6 by solving a selection problemm which allows intuitionistic fuzzy modelling. section 7 provides relevant conclusions. 2 preliminaries the core concepts presented in this section are as defined in the current literature, i.e., [2], [7], [8], [18], [17], [25]. said notions are fundamental for the research that follows. definition 1. let x denote a universal set. an intuitionistic fuzzy set (ifs) in x is a subset ã = {〈 x, µã(x), νã(x) 〉 ; x ∈ x } , where µã : x → [0, 1] and νã : x → [0, 1] represent the membership function and the non-membership function of the elements of x to ã, respectively. µã and νã satisfy the condition: µã(x) + νã(x) ⩽ 1, (∀)x ∈ x. https://doi.org/10.15837/ijccc.2023.2.5118 3 πã : x → [0, 1], πã(x) = 1 − µã(x) − νã(x) is called hesitation margin of x in ã. definition 2. an intuitionistic fuzzy set of the real line, ã = {〈 x, µã(x), νã(x) 〉 ; x ∈ r } is called intuitionistic fuzzy number (ifn) if the following conditions hold true: (i) ã is if normal, i.e., there exists two elements x0, x1 ∈ r such that µã(x0) = 1 and νã(x1) = 1; (ii) ã is if convex, i.e., µã(x) is fuzzy convex and νã(x) is concave; (iii) µã(x) is upper semicontinuous and νã(x) is lower semicontinuous; (iv) the support of ã is bounded. the definition (2) implies that for any ã ∈ if n , there exist a1, a2, a3, a4, a′1, a′2, a′3, a′4 ∈ r with a′1 ⩽ a1 ⩽ a ′ 2 ⩽ a2 ⩽ a3 ⩽ a ′ 3 ⩽ a4 ⩽ a ′ 4 and the functions fã, gã, hã, kã : r → [0, 1], where fã, kã are nondecreasing functions and gã, hã are nonincreasing functions, such that the membership and non-membership function of ã can be written as: µã(x) =   0 if x < a1 fã if a1 ⩽ x < a2 1 if a2 ⩽ x < a3; gã if a3 ⩽ x < a4 0 if a4 ⩽ x νã(x) =   0 if x < a′1 hã if a ′ 1 ⩽ x < a ′ 2 1 if a′2 ⩽ x < a′3 kã if a ′ 3 ⩽ x < a ′ 4 0 if a′4 ⩽ x. definition 3. an intuitionistic fuzzy number having membership and non-membership functions of form: µã(x) =   x−a1 a2−a1 if a1 ⩽ x < a2 1 if a2 ⩽ x < a3; a4−x a4−a3 if a3 ⩽ x < a4 0 oterwise νã(x) =   a′2−x a′2−a ′ 1 if a′1 ⩽ x < a ′ 2 0 if a′2 ⩽ x < a′3; x−a′3 a′4−a ′ 3 if a′3 ⩽ x < a ′ 4 1 oterwise, where a′1 ⩽ a1 ⩽ a′2 ⩽ a2 ⩽ a3 ⩽ a′3 ⩽ a4 ⩽ a′4, denoted by ã = {(a1, a2, a3, a4); (a′1, a′2, a′3, a′4)} is called trapezoidal intuitionistic fuzzy number (trifn). figure 1: trifn remark 4. a special case of the (3) is ã = {(a1, a2, a4); (a′1, a′2, a′4)} which is called triangular intuitionistic fuzzy number (tifn) and is obtained when a2 = a3 and a′2 = a′3. definition 5. let ã = {(a1, a2, a3, a4); (a′1, a′2, a′3, a′4)} and b̃ = {(b1, b2, b3, b4); (b′1, b′2, b′3, b′4)} be two trapezoidal intuitionistic fuzzy numbers, then the arithmetic operations of addition of ã and b̃ and scalar multiplication kã, k ∈ r are defined as follows: https://doi.org/10.15837/ijccc.2023.2.5118 4 (i) ã + b̃ = {(a1 + b1, a2 + b2, a3 + b3, a4 + b4); (a′1 + b′1, a′2 + b′2, a′3 + b′3, a′4 + b′4)} (ii) kã = { {(ka1, ka2, ka3, ka4); (ka′1, ka′2, ka′3, ka′4)}, if k > 0 {(ka4, ka3, ka2, ka1); (ka′4, ka′3, ka′2, ka′1)}, if k < 0. definition 6. let ã = {〈 x, µã(x), νã(x) 〉 ; x ∈ r } an ifn. then α-cut of ã, α ∈ (0, 1] is a set defined as: ãα = { x ∈ x : µã(x) ⩾ α, νã(x) ⩽ 1 − α } . thus, α-cut of ã ultimately consists of two classic α-cuts:( ã+ ) α = { x ∈ r/µã(x) ⩾ α } ( ã− ) α = { x ∈ r/νã(x) ⩽ 1 − α } . ( ã+ ) α and ( ã− ) α are closed intervals, as given below: ( ã+ ) α = [ ã+l (α), ã + u (α) ] ( ã− ) α = [ ã−l (α), ã − u (α) ] where, ã+l (α) = inf { x ∈ r/µã(x) ⩾ α } ã+u (α) = sup { x ∈ r/µã(x) ⩾ α } ã−l (α) = inf { x ∈ r/νã(x) ⩽ 1 − α } ã−u (α) = sup { x ∈ r/νã(x) ⩽ 1 − α } . generally, for an ifn ã, for which fã, gã, hã, kã from (2) are strictly monotone functions, ã + l (α), ã+u (α), ã − l (α), ã − l (α) can be computed as follows: ã+l (α) = f −1 ã (α); ã+u (α) = g −1 ã (α); ã−l (α) = h −1 ã (α); ã−u (α) = k −1 ã (α). if ã is a trifn, ã = {(a1, a2, a3, a4); (a′1, a′2, a′3, a′4)} with a′1 ⩽ a1 ⩽ a′2 ⩽ a2 ⩽ a3 ⩽ a′3 ⩽ a4 ⩽ a′4, then ã+l (α) = a1 + (a2 − a1)α ã+u (α) = a4 − (a4 − a3)α ã−l (α) = a ′ 1 + (a ′ 2 − a ′ 1)α ã−u (α) = a ′ 4 − (a ′ 4 − a ′ 3)α. it is often the case that for comparing fuzzy numbers, ranking functions are required. robust’s ranking index has proven to be, in the case of fuzzy numbers, a method of defuzzification which leads to results which are in accordance with human intuition [14], [20]. definition 7. let ã be a fuzzy number. the robust’s ranking index is defined as follows: r(ã) = ∫ 1 0 0.5(alα, a u α)dα, where (alα, auα) is α-level cut of the fuzzy number ã. remark 8. r(ã) is a function which satisfies compensation, linearity, and additivity properties. https://doi.org/10.15837/ijccc.2023.2.5118 5 for some fuzzy multi-criteria decision making (mcdm) problem, assume that there are m alternatives a = {a1, a2, ..., am}, n decision criteria c = {c1, c2, . . . , cn} and the corresponding weight coefficients are ω = (ω1, ω2, . . . , ωn), n∑ j=1 ωj = 1 is the weight vector. if the value of alternative ai, i = 1, m on the criteria cj , j = 1, n is intuitionistic trapezoidal fuzzy number ãij , then the mcdm problem can be expressed through a decision matrix: dij =   ã11 ã12 . . . ã1n ã21 ã22 . . . ã2n ... ... . . . ... ãm1 ãm2 . . . ãmn   . definition 9. let ãj , j = 1, n be a set of intuitionistic trapezoidal fuzzy numbers, and f : ωn → ω: fω(ã1, ã2, . . . , ãn) = n∑ j=1 ωj ãj , (1) where ω is the set of all intuitionistic trapezoidal fuzzy numbers, and ω = (ω1, ω2, . . . , ωn) is the weight vector of ãj, j = 1, n, n∑ j=1 ωj = 1 then, fω is called the weighted arithmetic average operator on intuitionistic trapezoidal fuzzy numbers. 3 a new ranking method for trifn let ã be a trifn, ã = {(a1, a2, a3, a4); (a′1, a′2, a′3, a′4)}, a′1 ⩽ a1 ⩽ a′2 ⩽ a2 ⩽ a3 ⩽ a′3 ⩽ a4 ⩽ a′4. according to the definition (7), in the case of ifns, robust’s ranking index can be defined for the membership function, as well as for the non-membership function as given below: r(ã+)α = ∫ 1 0 0.5 ( ã+l , ã + u ) dα (2) r(ã−)α = ∫ 1 0 0.5 ( ã−l , ã − u ) dα, (3) where (ã+l , ã + u ) = (ã +)α and (ã−l , ã − u ) = (ã −)α are αcuts of ã. making use of robust’s ranking index, r(ã+)α and r(ã−)α can be defined as a new ranking function. definition 10. let ã be a trifn, then define a ranking function as follows: (i) r(ã, λ) = λ · r(ã+)α + (1 − λ) · r(ã−)α if a1 + a2 + a3 + a4 ⩾ a′1 + a′2 + a′3 + a′4; (ii) r(ã, λ) = (1 − λ) · r(ã+)α + λ · r(ã−)α if a1 + a2 + a3 + a4 < a′1 + a′2 + a′3 + a′4, where λ ∈ [0, 1] is a parameter with a value that represents the subjective attitude of the decision marker (dm) as given below:   λ ∈ [ 0, 12 ) for a pessimistic attitude; 1 2 for a neutral attitude; λ ∈ ( 1 2 , 1 ] for an optimistic attitude. remark 11. in the case of an indifferent attitude of the dm, when λ = 12 , the ranking function becomes: r(ã) = r(ã+)α + r(ã−)α 2 . (4) https://doi.org/10.15837/ijccc.2023.2.5118 6 as ã is a trifn, ã = {(a1, a2, a3, a4); (a′1, a′2, a′3, a′4)} with a′1 ⩽ a1 ⩽ a′2 ⩽ a2 ⩽ a3 ⩽ a′3 ⩽ a4 ⩽ a′4, as defined in (2) and (3), the following is obtained: r(ã+)α = ∫ 1 0 0.5 · [a1 + (a2 − a1)α, a4 − (a4 − a3)α]dα = = ∫ 1 0 a1 + (a2 − a1)α + a4 − (a4 − a3)α 2 dα = a1 + a2 + a3 + a4 4 and similarly, r(ã−)α = a′1 + a′2 + a′3 + a′4 4 . in the special case when the subjectivity parameter is λ = 12 , then r(ã) = a1 + a2 + a3 + a4 + a′1 + a′2 + a′3 + a′4 8 , (5) which is the same as the expected value utilised by j. ye in [8] for classifying trinfs. remark 12. if ã = {(a1, a2, a3); (a′1, a′2, a′3)} is a tifn, then r(ã+)α = a1 + 2a2 + a3 4 and r(ã−)α = a′1 + 2a′2 + a′3 4 moreover, if λ = 12 , then r(ã) = a1 + 2a2 + a3 + a′1 + 2a′2 + a′3 8 . (6) remark 13. in the special case when a is a trapezoidal fuzzy number, it can be written as an intuitionistic fuzzy trapezoidal number ã = {(a1, a2, a3, a4); (a1, a2, a3, a4)} and r(ã) = a1 + a2 + a3 + a4 4 . definition 14. let ã = {(a1, a2, a3, a4); (a′1, a′2, a′3, a′4)} and b̃ = {(b1, b2, b3, b4); (b′1, b′2, b′3, b′4)} be two trifn. for λ ∈ [0, 1], λ-fixed, a new ranking method can be defined as follows: ã >λ b̃ if and only if r(ã, λ) > r(b̃, λ); ã <λ b̃ if and only if r(ã, λ) < r(b̃, λ); ã ≈λ b̃ if and only if r(ã, λ) = r(b̃, λ). 4 examples and comparative study the examples provided below illustrate the variation in the ranking function in terms of the parameter λ, where λ ∈ [0, 1] is a parameter with a value that represents the subjective attitude of the decision marker. moreover, the ranking is determined for some given trinfs. example 15. let ã = {(4, 5, 5.2, 6); (3, 4.8, 5.4, 6.5)} and b̃ = {(4, 5, 5.5, 6); (4, 5, 5.5, 6.1)}. example 16. let ã = {(0.5, 1, 1.5, 2); (0, 0.6, 1.5, 2.5)} and b̃ = {(0.5, 1, 1.3, 1.8); (0.2, 0.8, 1.5, 2.5)}. example 17. let ã = {(0.2, 0.25, 0.35, 0.4); (0.1, 0.25, 0.35, 0.75)} and b̃ = {(0.2, 0.3, 0.4, 0.5); (0.1, 0.3, 0.4, 0.7)}. https://doi.org/10.15837/ijccc.2023.2.5118 7 table 1: ranking of inf’s for values of λ λ r(ã) r(b̃) λ = 0 r(ã) = 4.925 r(b̃) = 5.125 λ = 0.1 r(ã) = 4.9395 r(b̃) = 5.1275 λ = 0.2 r(ã) = 4.95 r(b̃) = 5.13 λ = 0.3 r(ã) = 4.9625 r(b̃) = 5.1325 λ = 0.4 r(ã) = 4.975 r(b̃) = 5.135 λ = 0.5 r(ã) = 4.9875 r(b̃) = 5.1375 ã < b̃ λ = 0.6 r(ã) = 5 r(b̃) = 5.14 λ = 0.7 r(ã) = 5.0125 r(b̃) = 5.1425 λ = 0.8 r(ã) = 5.025 r(b̃) = 5.145 λ = 0.9 r(ã) = 5.0375 r(b̃) = 5.1475 λ = 1 r(ã) = 5.05 r(b̃) = 5.15 table 2: ranking of inf’s for values of λ λ r(ã) r(b̃) λ = 0 r(ã) = 1.15 r(b̃) = 1.15 λ = 0.5 r(ã) = 1.2 r(b̃) = 1.2 ã ≈ b̃ λ = 1 r(ã) = 1.25 r(b̃) = 1.25 table 3: ranking of inf’s for values of λ λ r(ã) r(b̃) λ = 0.2 r(ã) = 0.3125 r(b̃) = 0.355 λ = 0.5 r(ã) = 0.3313 r(b̃) = 0.3625 ã < b̃ λ = 0.8 r(ã) = 0.35 r(b̃) = 0.37 table 4: ranking of inf’s for values of λ λ r(ã) r(b̃) λ = 0.2 r(ã) = 0.35 r(b̃) = 0.25 λ = 0.5 r(ã) = 0.35 r(b̃) = 0.25 ã > b̃ λ = 0.9 r(ã) = 0.35 r(b̃) = 0.25 table 5: ranking of inf’s for values of λ λ r(ã) r(b̃) λ = 0.1 r(ã) = 4.5743 r(b̃) = 4.5754 λ = 0.5 r(ã) = 4.5813 r(b̃) = 4.5834 ã < b̃ λ = 1 r(ã) = 4.59 r(b̃) = 4.5933 example 18. let ã = {(0.2, 0.3, 0.4, 0.5); (0.1, 0.3, 0.4, 0.6)} and b̃ = {(0.1, 0.2, 0.3, 0.4); (0, 0.2, 0.3, 0.5)} (the numerical example is taken from [13]). example 19. let ã = {(4.02, 4.72, 4.83); (4, 4.72, 4.92)} and b̃ = {(4.021, 4.721, 4.831); (4.01, 4.721, 4.921)} (the numerical example is taken from [1]). https://doi.org/10.15837/ijccc.2023.2.5118 8 table 6: ranking of ifn’s for values of λ λ r(ã) r(b̃) λ = 0 r(ã) = 6.8625 r(b̃) = 7.0625 λ = 0.1 r(ã) = 6.9075 r(b̃) = 7.0713 λ = 0.2 r(ã) = 6.9525 r(b̃) = 7.08 ã < b̃ λ = 0.3 r(ã) = 6.9975 r(b̃) = 7.0888 λ = 0.4 r(ã) = 7.0425 r(b̃) = 7.0975 λ = 0.5 r(ã) = 7.0875 r(b̃) = 7.1063 λ = 0.6 r(ã) = 7.1325 r(b̃) = 7.115 λ = 0.7 r(ã) = 7.1775 r(b̃) = 7.1238 ã > b̃ λ = 0.8 r(ã) = 7.2225 r(b̃) = 7.1325 λ = 0.9 r(ã) = 7.2675 r(b̃) = 7.1413 λ = 1 r(ã) = 7.3125 r(b̃) = 7.15 example 20. let ã = {(5.25, 7, 7.05, 8.15); (4.85, 6.95, 7.5, 9.95)} and b̃ = {(6.35, 7.15, 7.2, 7.9); (5, 7, 7.25, 9)}. this example proves that the ranking relationship of two intuitionistic fuzzy numbers can, in some cases, change according to the attitude of the decision-making factors. example 21. let ã1 = {(2.95, 5.1, 6.65); (2.25, 5.1, 9.65)}, ã2 = {(3.95, 5.15, 6.2); (2.325, 5.15, 8.75)} and ã3 = {(3.85, 5.15, 6.4); (2.35, 5.15, 9.05)}. table 7: ranking of ifn’s for values of λ λ r(ã1) r(ã2) r(ã3) λ = 0 r(ã1) = 4.9525 r(ã2) = 5.1125 r(ã3) = 5.1375 λ = 0.1 r(ã1) = 5.0096 r(ã2) = 5.1356 r(ã3) = 5.1663 λ = 0.2 r(ã1) = 5.067 r(ã2) = 5.1588 r(ã3) = 5.195 ã3 > ã2 > ã1 λ = 0.3 r(ã1) = 5.1243 r(ã2) = 5.1819 r(ã3) = 5.2238 λ = 0.4 r(ã1) = 5.1815 r(ã2) = 5.205 r(ã3) = 5.2525 λ = 0.5 r(ã1) = 5.2388 r(ã2) = 5.2281 r(ã3) = 5.2813 ã3 > ã1 > ã2 λ = 0.6 r(ã1) = 5.296 r(ã2) = 5.2513 r(ã3) = 5.31 λ = 0.7 r(ã1) = 5.3533 r(ã2) = 5.2744 r(ã3) = 5.3388 λ = 0.8 r(ã1) = 5.4105 r(ã2) = 5.2975 r(ã3) = 5.3675 ã1 > ã3 > ã2 λ = 0.9 r(ã1) = 5.4678 r(ã2) = 5.3206 r(ã3) = 5.3963 λ = 1 r(ã1) = 5.525 r(ã2) = 5.3438 r(ã3) = 5.425 as it can be seen in this example, in the case of several trifns, the subjective attitude of the decision-makers plays an essential role in determining the ranking. the aforementioned examples are used in a comparative study of the given research method and several other ifn ranking methods already existent in the current literature. the study is summarized below: table 8 shows that the proposed method provides similar results to the other methods when the result is the same for all methods (the examples 15 19). but when the other methods give contradictory results, the proposed method includes and explains these results, through the influence of the subjective attitude of the decision-maker on the ranking function (the examples 20 and 21). https://doi.org/10.15837/ijccc.2023.2.5118 9 table 8: comparison of proposed method with other ranking methods for ifns ã, b̃ ye’s rezvani’s roseline and bharati’s mohan’s proposed from method [8] method [22] amirtharaj’s method [3] method method example method [23] [13] 15 ã < b̃ ã < b̃ ã < b̃ ã < b̃ ã < b̃ ã < b̃ 16 ã ≈ b̃ ã > b̃ ã > b̃ ã > b̃ ã ≈ b̃ ã ≈ b̃ 17 ã < b̃ ã < b̃ ã < b̃ ã < b̃ ã < b̃ ã < b̃ 18 ã > b̃ ã > b̃ ã > b̃ ã > b̃ ã > b̃ ã > b̃ 19 ã < b̃ ã < b̃ ã < b̃ ã < b̃ ã < b̃ ã < b̃ 20 ã < b̃ ã < b̃ ã < b̃ ã > b̃ ã < b̃ ã < b̃ ã > b̃ ã3 > ã2 > ã1 21 ã3 > ã1 > ã2 ã3 > ã2 > ã1 ã3 > ã2 > ã1 ã1 > ã3 > ã2 ã3 > ã2 > ã1 ã3 > ã1 > ã2 ã1 > ã3 > ã2 5 algorithm for solving mcdm based on proposed ranking method in this section, the aforementioned trifn classification method will be applied to solve mcdm problems in which evaluating the alternatives and the weights of the criteria are expressed with the help of trifn. we consider an mcdm problem in which decision makers are asked to make judgements and assess through linguistic terms a series of alternatives in relation to a plethora of pre-determined criteria. the importance of the criteria can differ, but can also be imprecise, i.e., their weights are also expressed with the help of linguistic terms. it is assumed that the scale of trifn values corresponding to linguistic terms is known. the notations used to describe these problems are: e = {e1, e2, . . . , ep} set of experts taking part in the decision making process; a = {a1, a2, . . . , am} set of possible alternatives, c = {c1, c2, . . . , cn} set of criteria used in evaluating the alternatives. each expert ek, k = 1, p evaluates all alternatives ai, i = 1, m in relation to each criterion cj , j = 1, n using the linguistic terms which are then converted in trifn ãij = {(aij1, aij2, aij3, aij4); (a′ij1, a′ij2, a′ij3, a′ij4)}. the weights of the criteria cj , j = 1, n is expressed through the trifn c̃j = {(cj1, cj2, cj3, cj4); (c′j1, c′j2, c′j3, c′j4)}, and will consider the subjective assessments of the decision making factors, with respect the condition of normalization. in this context, the proposed algorithm for solving mcdm problems with trifn it is summarized as follows: step 1 identify the atitude of the experts regarding the evaluation criteria, and for each expert ek, k = 1, p the normalized weigth for each critria is computed cj , j = 1, n using the formula: ωkj = rk(cj ) n∑ j=1 rk(cj ) , (7) where rk(cj ) is computed according to the definition 10. step 2 for each expert ek, k = 1, p the decision matrix (dij )m×n is filled in which leads to trifn w̃ki which expresses the opinion of the expert k in relation to the alternative i: w̃ki = {( n∑ j=1 ωkj aij1, n∑ j=1 ωkj aij2, n∑ j=1 ωkj aij3, n∑ j=1 ωkj aij4 ) ; ( n∑ j=1 ωkj a ′ ij1, n∑ j=1 ωkj a ′ ij2, n∑ j=1 ωkj a ′ ij3, n∑ j=1 ωkj a ′ ij4 )} . (8) https://doi.org/10.15837/ijccc.2023.2.5118 10 denote w̃ki = {(wki1, wki2, wki3, wki4); (w′ki1, w ′ ki2, w ′ ki3, w ′ ki4)}. step 3 the decision matrix (dki)p×m is filled in, which incorporates the opinions of p experts in relation to the m alternatives. given that the evaluations given by the experts have the same weight in making the final decision, the trifn which represents the evaluation of the alternative ai can be determined: ãi =     p∑ k=1 wki1 p , p∑ k=1 wki2 p , p∑ k=1 wki3 p , p∑ k=1 wki4 p   ;   p∑ k=1 w′ki1 p , p∑ k=1 w′ki2 p , p∑ k=1 w′ki3 p , p∑ k=1 w′ki4 p     . (9) denote ãi = {(ai1, ai2, ai3, ai4); (a′i1, a′i2, a′i3, a′i4)}. step 4 applying the classification method proposed (10), and taking into consideration different levels of subjectivity in computing r(ãi), i = 1, m, the best alternative is determined. 6 an application to an selection problem to exemlify the algorithm proposed in section 5, consider the following mcdm problem: a company plans to hire a new department manager and are considering three candidates for the job in question (a1, a2, a3), which will be evaluated independently by the three experts. as part of the application process, four indicators are assessed, i.e., professional know-how c1, leadership ability c2, moral quality c3 and communication skills c4 with the help of linguistic terms converted to trinf, according to the scale: table 9: lingvistic scale and its corresponding trifn linguistic term trapezoidal intuitionistic fuzzy number low (l) {(0, 1, 1.5, 2.5); (0, 0.5, 2, 3)} fairly low (fl) {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} medium (m) {(3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} fairly high (fh) {(5.5, 6.5, 7, 8.5); (5, 6, 8, 9)} high (h) {(8, 8.5, 9.5, 10); (7, 8.5, 9.5, 10)} to these indicators are then assigned the following weights: m, f h, f l, l respectively. the purpose of the decision factor is to determine a hierarchy of the three candidates based on expert evaluations. the evaluation made by the three experts of each candidate relative to the four indicators is provided in the tables below: table 10: alternative evaluations in linguistic terms of experts expertul e1 expertul e2 expertul e3 c1 c2 c3 c4 c1 c2 c3 c4 c1 c2 c3 c4 a1 m fh fl l fl h m l m fl fl fh a2 fh fl m m fh m fl m m fl fh fh a3 fh fl fl fh fh m m h fh fl fl m step 1 first, the experts express their subjective atitiude in relation to the weights m, f h, f l and l of the four indicators cj , j = 1, 4 by fixing the subjective parameter. in the table 11 are https://doi.org/10.15837/ijccc.2023.2.5118 11 summarized the values of these parameters λkj , λkj ∈ [0, 1], k = 1, 3, j = 1, 4, the weights of the indicators personalized for each expert are computed according to the new classification method of intuitionistic fuzzy numbers, as well as the normalized weights, ωkj , k = 1, 3, j = 1, 4, computed according to the 7: table 11: the normalized weights of the criterias for each expert parameters weights normalized weights c1 c2 c3 c4 c1 c2 c3 c4 c1 c2 c3 c4 e1 0.2 0.8 0.7 0.5 4.9 6.975 3.5825 1.3125 0.2921 0.4158 0.2139 0.0782 e2 0.5 0.5 0.5 0.5 4.9375 6.9375 3.5625 1.3125 0.2948 0.4142 0.2127 0.0783 e3 0.6 0.9 0.3 0.7 4.95 6.9875 3.5375 1.3375 0.2944 0.4156 0.2104 0.0796 step 2 in tables 12, 13 and 14 the decision matrices corresponding to the three experts are presented. by applying the formula 8, the evaluations of the alternatives expressed through trifn are provided and can be found in tables 15, 16 and 17. table 12: the decision making matrix for the e1 expert c1 c2 c3 c4 a1 {(3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} {(5.5, 6.5, 7, 8.5); (5, 6, 8, 9)} {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} {(0, 1, 1.5, 2.5); (0, 0.5, 2, 3)} a2 {(5.5, 6.5, 7, 8.5); (5, 6, 8, 9)} {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} {(3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} {(3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} a3 {(5.5, 6.5, 7, 8.5); (5, 6, 8, 9)} {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} {(5.5, 6.5, 7, 8.5); (5, 6, 8, 9)} table 13: the decision making matrix for the e2 expert c1 c2 c3 c4 a1 {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} {(8, 8.5, 9.5, 10); (7, 8.5, 9.5, 10)} {(3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} {(0, 1, 1.5, 2.5); (0, 0.5, 2, 3)} a2 {(5.5, 6.5, 7, 8.5); (5, 6, 8, 9)} {(3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} {(3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} a3 {(5.5, 6.5, 7, 8.5); (5, 6, 8, 9)} {(3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} {(3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} {(8, 8.5, 9.5, 10); (7, 8.5, 9.5, 10)} table 14: the decision making matrix for the e3 expert c1 c2 c3 c4 a1 {(3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} {(5.5, 6.5, 7, 8.5); (5, 6, 8, 9)} a2 {((3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} {(5.5, 6.5, 7, 8.5); (5, 6, 8, 9)} {(5.5, 6.5, 7, 8.5); (5, 6, 8, 9)} a3 {(5.5, 6.5, 7, 8.5); (5, 6, 8, 9)} {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} {(1.5, 3, 4.5, 5.5); (1, 2, 5, 6)} {(3.5, 4.5, 5.5, 6.5); (2.5, 4, 6, 7)} table 15: evaluations for alternative a1 ak1 e1 ã11 = {(3.6301, 4.7371, 5.597, 6.8049); (3.0232, 4.1301, 6.3049, 7.3049)} e2 ã21 = {(4.5003, 5.4406, 6.5488, 7.3417); (3.726, 5.0003, 6.8417, 7.6346)} e3 ã31 = {(2.4072, 3.7202, 4.9934, 6.0332); (1.76, 2.9072, 5.5332, 6.5332)} table 16: evaluations for alternative a2 ak2 e1 ã12 = {(3.2526, 4.4605, 5.5236, 6.6684); (2.6066, 3.7526, 6.1684, 7.1684)} e2 ã22 = {(3.6642, 4.7706, 5.7295, 6.8769); (2.918, 4.1642, 6.3769, 7.3769)} e3 ã32 = {(3.2488, 4.4566, 5.5194, 6.6644); (2.6016, 3.7488, 6.1644, 7.1644)} step 3 given the evaluations of the three experts, the trifn ãi, i = 1, m are computed by applying https://doi.org/10.15837/ijccc.2023.2.5118 12 table 17: evaluations for alternative a3 ak3 e1 ã13 = {(2.9812, 4.2961, 5.4258, 6.6109); (2.4812, 3.4812, 6.1109, 7.1109)} e2 ã23 = {(4.442, 5.4028, 6.2554, 7.3637); (3.5894, 4.942, 6.8637, 7.8245)} e3 ã33 = {(2.8368, 4.1498, 5.3156, 6.4628); (2.297, 3.3368, 5.9628, 6.9628)} the formula 9, and express the performance of the alternative ai: ã1 = {(3.5125, 4.6326, 5.7131, 6.7266); (2.8364, 4.025, 6.2266, 7.1576)} ã2 = {(3.3885, 4.5626, 5.5904, 6.7366); (2.7087, 3.8886, 6.2366, 7.2366)} ã3 = {(3.42, 4.6162, 5.6656, 6.8125); (2.7892, 3.92, 6.3125, 7.2994)}. step 4 according to the new classification method of intuitionistic fuzzy numbers, and considering a neutral attitude in making the final decision (λ = 0.5), ã3 > ã1 > ã2 is obtained. therefore, in the case of a neutral attitude of the decision factor, the best candidate is a3. when different values of the λ coefficient are considered, it can be seen that the ranking changes and the candidate a1 can reach the first position in the hierarchy, as shown in table 18. table 18: ranking of candidates for different values of λ λ = 0 r(ã1) = 5.05828 r(ã2) = 5.0176 r(ã3) = 5.0803 λ = 0.1 r(ã1) = 5.0671 r(ã2) = 5.02282 r(ã3) = 5.0851 λ = 0.2 r(ã1) = 5.0759 r(ã2) = 5.028 r(ã3) = 5.0899 ã3 > ã1 > ã2 λ = 0.3 r(ã1) = 5.0847 r(ã2) = 5.0332 r(ã3) = 5.0948 λ = 0.4 r(ã1) = 5.0934 r(ã2) = 5.0384 r(ã3) = 5.0996 λ = 0.5 r(ã1) = 5.1022 r(ã2) = 5.0436 r(ã3) = 5.1044 λ = 0.6 r(ã1) = 5.111 r(ã2) = 5.0488 r(ã3) = 5.1093 λ = 0.7 r(ã1) = 5.1198 r(ã2) = 5.054 r(ã3) = 5.1141 λ = 0.8 r(ã1) = 5.1286 r(ã2) = 5.0591 r(ã3) = 5.1189 ã1 > ã3 > ã2 λ = 0.9 r(ã1) = 5.1374 r(ã2) = 5.0643 r(ã3) = 5.1237 λ = 1 r(ã1) = 5.1462 r(ã2) = 5.0695 r(ã3) = 5.1286 if in the first step of applying the algorithm, the subjective attitude of the decision makers in relation to the evaluating criteria is not taken into consideration, assuming they have a neutral attitude (λkj = 0.5), then in the table presented in the fourth step the following result is obtained ã3 > ã1 > ã2, except when λ = 1 and the ranking order changes, ã1 > ã3 > ã2. therefore, in the given problem, the subjective attitude of the decision makers regarding the evaluated indicators can influence the final decision. thus, one of the benefits of applying the algorithm consists of the fact that the evaluation process undergone by an expert is truthful. it reflects the evaluation of a candidate’s fulfillment of the criteria from his subjective perspective in relation to the importance attributed to the evaluation criteria. moreover, it is sufficient to analyse step 4 to observe that the subjective attitude of the decision factors can decisively influence the final ranking. the application of the proposed ranking method provides a clear image of the dimensions of said influence. therefore, the final decision can be made fully taking into consideration the subjectivity of the decision maker. 7 conclusion in this paper, the robust’s ranking index is defined for the the membership function and the nonmembership function of trifn, and subsequently a ranking function is defined for trifn. the method https://doi.org/10.15837/ijccc.2023.2.5118 13 can also be applied in the special case of tifn. for λ = 0.5, the same results are obtained as in [8]. it is worth emphasizing that the method uses a parameter which gauges the subjectivity of the decision maker. thus, the variation of the ranking function value can be determined in relation to the decision maker’s level of optimism, compared to the central value (λ = 0.5). the advantage of the method proposed in this paper is exactly this parameter which determines the degree to which the subjective attitude of the decision-making factors influences the ranking of trifn. the aforementioned examples illustrated that their order could be changed significantly. the paper also proposes an algorithm for solving mcdm fuzzy problems which use the new ranking function of trinf to stabilise the weight of the criteria separately for each expert, as well as to make the final decision by ranking the alternatives. by applying this algorithm in solving the decision making problems, the results can be easy to interpret and provide a clear perspective on the way the subjective attitude influences the decision. funding this research received no funding. author contributions not applicable. conflict of interest not applicable. references [1] atalik, g.; 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(2020). intuitionistic fuzzy topsis method based on cvpifrs models: an application to biomedical problems, information sciences, 517, 315-339, 2020. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: popa, l. (2023). a new ranking method for trapezoidal intuitionistic fuzzy numbers and its application to multi-criteria decision making, international journal of computers communications & control, 18(2), 5118, 2023. https://doi.org/10.15837/ijccc.2023.2.5118 introduction preliminaries a new ranking method for trifn examples and comparative study algorithm for solving mcdm based on proposed ranking method an application to an selection problem conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 6, month: december, year: 2022 article number: 5004, https://doi.org/10.15837/ijccc.2022.6.5004 ccc publications digitalisation and the sharing economy. a survey-based research on airbnb in romania a. badulescu, d. badulescu, r. simut, e. herte, a. borma, i. pandelica alina badulescu department of economics and business university of oradea, romania universitatii st., no.1, 410087, oradea, romania abadulescu@uoradea.ro daniel badulescu* department of economics and business university of oradea, romania universitatii st., no.1, 410087, oradea, romania *corresponding author: dbadulescu@uoradea.ro ramona simut department of economics and business university of oradea, romania universitatii st., no.1, 410087, oradea, romania simut.ramona@yahoo.com elena herte department of economics and business university of oradea, romania universitatii st., no.1, 410087, oradea, romania elena_stiubea@yahoo.com afrodita borma department of economics and business university of oradea, romania universitatii st., no.1, 410087, oradea, romania aborma@uoradea.ro ionut pandelica department of international business and economics bucharest university of economic studies, romania piata romana 6, 010374, bucharest, romania ionut.pandelica@rei.ase.ro https://doi.org/10.15837/ijccc.2022.6.5004 2 abstract the collaborative (or sharing) economy continues to shape the tourism industry, challenging both the traditional areas of supply, but especially the trust and digital skills of users, the acceptance of platforms and new inter-personal and community relationships. in the present paper a crosssectional quantitative research was conducted with non-random convenience sampling in order to determine the impact of the collaborative airbnb platform among romanian tourists, how wellknown it is and what elements motivate tourists to choose this platform. we also want to analyse the extent to which the benefits and innovations brought by this new phenomenon are in line with the needs and expectations of tourists and whether they are interested in this phenomenon, the extent to which tourists have chosen the airbnb platform to stay in romania or abroad. keywords: sharing economy in tourism, online platforms, airbnb, romania 1 introduction the rapid growth and diversification of the sharing economy (se) over the past 10-15 years has generated a great deal of interest both in the real economy and from researchers or policy makers. the global volume of transactions in the sharing economy has seen impressive growth in the last decade, from around 15 billion in 2014, to almost 19 billion in 2017 and it is estimated that it will reach over 40 billion in 2022 [45]. for 2035, pricewaterhousecoopers (pwc) reports estimate an (almost implausible) level of earned sales revenue of 335 billion dollars, almost equal to that achieved by companies from the “traditional economy” [43]. the most well-known domains in which se is asserted are sharing of private vehicles and sharing of private residences through accommodation websites, exemplifying here uber and airbnb respectively, operators with a global presence and reputation. pwc identifies five major areas of se, namely peer-to-peer lending, online staffing, peer-to-peer accommodation, car sharing, and music and video streaming [44], even if some opinions add here coworking (employees from different organizations sharing an office space) or crowdfunding (raising money to fund a project or venture from a large number of people, usually via the internet). in a wide definition, the sharing economy (se) can therefore be understood as the place or means by which temporary access of some members of a network to acquire, provide, or share goods and services is allowed, mostly via an online platform. other researchers prefer a more concise definition of se as “the peer-to-peer-based activity of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services” [15]. interestingly, a large share of those who use se services are not familiar with the term itself, although they recognize that digital skills and trust in collaborative platforms are very important to the decision to enter (as users or providers) into the sphere of this economy. in this paper we want to identify the impact that the collaborative platform airbnb has among romanian tourists, how well it is known and what elements motivate tourists to choose this platform. the paper is structured as follows: after this introduction, in the theoretical part we will review some relevant contributions in the field, regarding the motivations that determine the entry and stay of suppliers and users within this economy, the importance of certain socio-demographic (age) or relational (trust) variables. in the next chapter we will describe the data collection procedure and the hypotheses, including respondents’ profile, followed by the presentation of the main results and discussions. in the final part we advance several conclusions following from this study’s results, policy recommendations and the main limitations of this study. 2 literature 2.1 the reasons for participating in the sharing economy for böcker & meelen (2017) [4] the reasons to participate in the different sectors of the collaborative economy are very different, but not surprising: from economic ones, motivated by sharing the use of an expensive good, to environmental ones – through the shared use of cars, or personal and social interactions – such as, for example, meal sharing. there are important differences in motivations according to sectors, participants (users or suppliers), but the differences seem less important between https://doi.org/10.15837/ijccc.2022.6.5004 3 socio-demographic categories. of course, here we can identify certain correlations between motivations and socio-demographic status: young people and people with low incomes are more economically driven to use and provide common assets; those with higher incomes, higher education (including young people) are less influenced by social motivations; women, more than men, are more motivated to protect the environment [16] etc. hamari et al. (2015) [15] or tussyadiah (2016) [34] support a distinction between motivations, depending on the se sector – in the areas of accommodation or car sharing, extrinsic economic motivations dominate, while in the case of meal, tool and ride sharing, environmental and social interaction are essential. users seem to be more economically motivated than providers (suppliers), at least for the fact that the latter add elements of altruism and community spirit to their decision, while users perceive it as satisfying some needs, depending on income’s level. belk (2014) [3] states that there is a “sharing” (based on social motivations) and a “pseudo-sharing” (motivated mainly by economic gains). the diversity of user groups also explains the diversity of their motivations, but also the phenomenon of the rapid expansion of this economy [4]. although se defines itself as “more sustainable”, it is interesting that many sustainable innovations have spread relatively slowly in society and economy [26, 30]. instead, the sharing economy, mainly in the field of accommodation, transport and travel has had impressive growth over the past 10-15 years. rather, the economic motives, as increasing the degree of use of some capacities in excess, and less the social and environmental motives, fuelled these exponential increases. some researchers warn that this phenomenon is not at all sustainable – the increase in the frequency of travels and the density of tourists in certain destinations generates, rather, negative environmental and social effects [9, 21, 35]. according to jack karsten [40] the benefits of se for consumers lie mainly in flexibility and efficiency. thus, operators such as uber and airbnb promote online platforms that match customers and suppliers’ expectations, support the increase in the degree of use of real estate and vehicles, make working hours more flexible and optimize choices, in other words offer to consumers the flexibility to access goods and services just as long as they are needed, and to users efficiency and cost reduction. there is, of course, quite a long list of regulatory concerns and issues; avoiding them brings substantial savings to se operators, while operators from the “traditional” economy are forced to comply with them, involving additional cost. the lack of transparency in the calculation of tariffs, the suspicion of subjectivity or discrimination in the algorithms that rule the operation of these platforms, the trust based on the processing of reviews rather than the certification of quality standards, the uncertainty regarding how companies use the stored data, are real signals, which they must not be ignored in the functioning and future of this economy. as a conclusion of the above, there is a variety of motivations for participating in se, which explains the rapid growth of sharing practices, from economic to social, environmental or inter-personal, a so-called perception that “there is something, somewhere, for everyone” that fuels this adoption and beyond the already known motivations. 2.2 trust prerequisite and result of se trust, especially when we apply it to se, is a complex and difficult category to define, it can refer to individuals, communities or society in general, sharing platforms or the internet. it applies to users or providers, and is dependent (but also a result) of the wide adoption of expert systems [14]. belk (2014) or botsman & rogers (2010) consider trust to be an essential prerequisite for the flourishing of the shared economy, the shared economy being in fact “trust between strangers” [3, 5]. however, with all this stunning definition, it is yet unclear what triggers and maintains this trust, what, how, and whether participation in the se produces trust. for some researchers, deep ties between producers engender interpersonal trust, ensuring the functioning of networks, for others, the creation of long-lasting ties between guests and hosts generates trust, and finally, some state that participation in the network traces a positive outcome on trust, both at individual, as well as at the communities’ level [2, 28]. also, the literature suggests that trust could be a direct by-product of “widespread adoption of distributed rating systems” [2], that is, trust in the se system is not so much a trust in other unknown persons but a trust in the functioning of the rating system, the platform and the interactions within https://doi.org/10.15837/ijccc.2022.6.5004 4 it [5], a product of the good functioning of social and technical systems, often mediated by user satisfaction [25]. according to andreotti (2017) [2], trusting platforms and their algorithms in essence means a deep trust in the reliability of collective judgment, despite the huge privacy/confidentiality challenges. in many cases, what participants are looking for, and what platforms provide, is not about trusting a generic partner, but a strong governance system that ensures smooth commercial and financial transactions [3]. however, strong governance and the general acceptance of surveillance and analysis rules (like “big brother”) has multiple, positive and negative effects on building trust, i.e. “algorithmic reputational systems may not be very effective when it comes to generating interpersonal trust”" [2]. thus, ert et al. (2016) find that airbnb’s algorithm promotes positive reviews on a disproportionate scale and fails to build effective interpersonal trust between hosts and guests [11], and gandini et al. (2016) find that while algorithms are quite good at producing individual reputation, they perform poorly at building professional trust [13]. parigi & state (2014) exemplify the success of couchsurfing which significantly affected the platform’s interpersonal trust-building potential [28]. analysing “trust in sharing economy business models from the perspective of customers” in poland, wagner et al. (2019) consider that “the development of business models based on the sharing economy has become possible thanks to the development of ict tools and easy access to the internet”, to the effective use of technology and trust in new applications, but the expansion and consolidation of se in the whole economy and society is also dependent on trust in the human factor existing in the model. thus, they find an average level of trust of the younger generations (z generation) in the se, given by the combination of a high trust in technology, but rather low in people. women are relatively less confident in the business model of the se and severer in their assessment of trust in people, while men are more confident in technology. trust in the technology used by sharing platforms declines slightly with age, with the 46+ age group having an average trust in technology and it systems. in contrast, trust in the human factor of the sharing economy model has low levels across all age, gender or education groups of the sample [36]. addressing how to ensure equal rights and fairer access to se services (as providers or users) hsiao et al. (2018) find that trust, computer self-efficacy, and perceived ease of use are all positively correlated with service use of se and directly influences the willingness to continue using (paying for) these services. these results partially contradict previous research suggesting that users of these platforms are typically educated individuals with above-average incomes and trusting attitudes toward strangers and the platforms’ technical performance [18]. nonetheless, the wide acceptance and use of sharing platforms is influenced by the quantity and relevance of included information, i.e. that "suggestions provided are aimed at supporting their users in various decision-making processes, such as what items to buy, what music to listen, or what news to read" [20, p. 231]. in other words, recommendations become "a hot topic in the e-commerce system" [39, p. 520], including the tourism and trabel industries, often augmented by multimedia technologies and other innovative applications designed to support the decision to buy [6]. 2.3 age and willingness to join se according to böcker & meelen, the motivations for entering the se are probably not unchanging at the level of the general population [4], but they differ according to the status of owners or users of the assets, composition or socio-demographic particularities [16]. thus, the age factor is assumed to have a certain influence on motivation, i.e. elderly people have more frequent contacts with neighbours and acquaintances made during their life, and the specific nature of the “neighbourhood” of sharing economy [8] may suggest that older people are more interested in the social and local aspect of se. they compensate for diminishing direct, interpersonal contacts by engaging in associative networks. therefore, the social motivations to join the se appear to be stronger for older people compared to younger people [4]. on the other hand, researches based on large-scale surveys such as the eurobarometer of the european union / the use of collaborative platforms [12], or carried out by consulting companies such as pwc (2016) [44], deloitte (2015) [41] or various researchers (smith, 2016) [42] find that, in general, age is inversely correlated with sharing economy participation, and the 25-29 age group is the most likely to have heard about the sharing economy [2]. yoon and occeña (2015) also find a negative https://doi.org/10.15837/ijccc.2022.6.5004 5 relationship between age and participation in the se, respectively between age and general trust in participants in this economy [37]. inquiring whether everyone has equal access to the sharing economy in tourism (set), especially by age group, kowalczyk-anioł et al. (2021) find that, in poland, 22% of respondents are potential participants, with an average age of 36. those with high and very high chances of not participating in set are significantly older than (potential) participants in set, and they are almost one third of the total population. this is an element worthy of consideration for suppliers in this economy, given the aging of the population in central-eastern european countries [22]. investigating the trust factor in the activity of two representative operators on the set market, namely airbnb and couchsurfing, dietl (2020) states that, in addition to the already known negative correlation between age and consumption intention, there is a strong negative correlation between age and attitudes of trust towards of the sharing economy, i.e. the desire to book accommodation using one of these platforms decreases with age. furthermore, each dimension of trust identified in this research, namely ability, integrity, and benevolence, declines with age [10]. there are some differences between the two operators in this respect – slightly higher percentages of trust in favour of the airbnb platform, easier to use by older people, with a more diverse range of offers, destinations, and reasons for travel, meanwhile couchsurfing intentionally targets younger customers, that accepts the lowering of the comfort level for lower costs [32, 38]. 3 data collection procedure and the hypotheses in order to get an overview of how the collaborative tourism economy has changed we need to do a comparative analysis of how the collaborative accommodation industry and the traditional accommodation are viewed. the cross-sectional quantitative research was conducted with non-random convenience sampling in order to determine the impact of the collaborative airbnb platform among romanian tourists, how well-known it is and what elements motivate tourists to choose this platform. we also want to analyse the extent to which the benefits and innovations brought by this new phenomenon are in line with the needs and expectations of tourists and whether, at the level of romania, they are interested in this phenomenon. at the same time, it is useful to analyse the extent to which tourists have chosen the airbnb platform to stay in romania or abroad. to identify the impact that the collaborative platform airbnb has among romanian tourists, how well it is known and what elements motivate tourists to choose this platform, we applied a questionnaire. the questionnaire was designed to capture, as well as possible, the importance of the decision-making of consumers / tourists to book through the airbnb platform and also looked at the most important reasons why some respondents did not book via the airbnb. therefore, the questionnaire was composed of three main parts: the first part contained information about the demographic of the respondents, i.e., age, gender, education level, function, residence and income level. the last question among the general ones marks the transition to the other two parts of the questionnaire, this being: "have you booked accommodation through the airbnb platform?". the second part contained 17 questions and are addressed to respondents who have booked a trip through the collaborative airbnb platform. in the present research, we used only one of the 17 questions, namely the question regarding the main factors that determined the respondents to book a trip through the collaborative airbnb platform. the subject had to appreciate on a scale from 1 to 5 (1-not at all important and 5-very important), the importance given to 10 factors that they take into account when choosing an accommodation through the platform. the coefficient α for the analysed subscale was 0.813. the third part of the questionnaire consists of four questions, which have several subscales and are for respondents who have not booked accommodation through the airbnb platform by the time the questionnaire is completed. the questions of interest for this analysis are the questions related to trust, functional attributes, effectiveness, respectively the decision makers considered by the subjects as the most important decision makings, in case they would choose accommodation through this platform. in the second and third part of the questionnaire, all the measurement scales were evaluated on a 5-point likert scale, where “1 = strongly disagree” to “5 = strongly agree” and “1 = not at all important” to “5 = very important”. before applying this questionnaire, we conducted a https://doi.org/10.15837/ijccc.2022.6.5004 6 pilot test on a sample of 20 subjects (10 who used the airbnb platform and 10 who did not use the airbnb platform) in order to verify the accuracy and precision of the questions asked, after which the questionnaire was revised according to the comments received from the 20 subjects. for data collection, the questionnaire was transposed to an online format in google forms and sent to potential tourists from romania, in may june 2021, by email. we downloaded the data from google forms into ms excel, ibm spss statistics 26 (version 26.0.0, new york, ny, usa), and ibm spss amos 26 (version 26.0.0, amos development corporation, wexford, pa, usa) and verified for coding accuracy. we made sure that the database is complete and does not contain missing data, this aspect being possible with the help of google forms which allows us to opt for mandatory answers. the descriptive statistic of the analysed factors and the principal component analysis (pca) were performed in ibm spss statistics. ibm spss amos was used to test the structural equation modelling (sem). based on the literature review, this study aims to: make a comparative analysis between the decision of consumers / tourists to book accommodation through airbnb vs. the decision to book among consumers who have not done so far; identifying an association between age and the decision of consumers / tourists to book accommodation through airbnb; identifying an association between age and the reasons for potential consumers / tourists to book accommodation through airbnb; identification of an association between the age of the subjects who until the moment of completing the questionnaire did not use the airbnb platform and trust, effectiveness, the facility offered by this platform. starting from the mentioned objectives, we want to test the following three specific hypotheses, with the help of factor analysis and the structural equation modelling: h1: there are significant differences between the classification made by respondents who booked through the airbnb platform and those who did not book through the airbnb platform in terms of decision making in choosing the airbnb platform. h2: age influences the decision of consumers / tourists to book accommodation through airbnb both for those who have booked through the airbnb platform and for those who have not booked. h3: there is an association between the age of the respondents that have not booked through the airbnb platform and the reasons why respondents did not book accommodation through the airbnb platform. respondents’ profile / the sample among the 195 respondents (see table 1), only 37% booked through the airbnb platform, 63% did not book through the airbnb platform until the time of completing the questionnaire. of these, 65% of the respondents were female and 35% were male. most of the respondents were aged 40–50 (28%), followed by the 20–30 group (25%) and the 30–40 age group (23%); 8% were aged 50 or above and 17% were aged 20 or below. if we divide the sample into two independent samples e1 those who booked through the airbnb platform and e2 those who did not book through the airbnb platform until the completion of the questionnaire, we can observe that in the case of the first sample, most respondents are under 40 years old, while in the second sample, the share is higher among respondents aged between 40 and 50 years. this aspect determined that in this study, we consider age as an important factor in the decision of consumers to choose accommodation through the airbnb platform. another aspect that we analysed referred to the level of the income. most of the respondents had an income above 3500 lei (79%), while 21% of the respondents had an income under 3499 lei. moreover, the majority of the respondents had a bachelor’s degree/university studies (69%), followed by those with a phd degree (21%), and vocational school (9%). regarding the position they hold, 75% have an executive function, while 25% have a leadership function. most respondents resided in cities (68%), the rest lived in county seats (32%). 4 results and discussion the first objective of this research was to investigate the importance of decision makers to book accommodation through the airbnb platform, respectively to analyse whether these factors are clashttps://doi.org/10.15837/ijccc.2022.6.5004 7 table 1: respondents’ profile characteristics category total booked airbnb (e1) not booked airbnb (e2) n % n % n % age 20 years or below 34 17% 23 31.9% 11 8.9% 20-30 years 49 25% 17 23.6% 32 26.0% 30-40 years 44 23% 22 30.6% 22 17.9% 40-50 years 54 28% 8 11.1% 46 37.4% 50-60 years 13 7% 2 2.8% 11 8.9% 61 years or above 1 1% 0 0 1 0.8% gender female 127 65% 37 51.4% 90 73.2%male 68 35% 35 48.6% 33 26.8% education level graduate 2 1% 2 2.8% 0 0 phd/postdoc 40 21% 22 30.6% 18 14.6% high school/vocational studies 18 9% 3 4.2% 15 12.2% univesity studies 135 69% 45 62.5% 90 73.2% function leadership function 49 25% 24 33.3% 25 20.2%executive function 146 75% 48 66.7% 98 79.7% residence rural 62 32% 26 36.1% 36 39.3%urban 133 68% 46 63.9% 87 70.7% level of income 2000-3499 lei 40 21% 4 5.6% 36 29.3% 3500-5999 lei 75 38% 31 43.1% 44 35.8% 6000-8499 lei 38 19% 14 19.4% 24 19.5% 8500-12000 lei 42 22% 23 31.9% 19 15.4% total respondents 195 100% 72 37% 123 63% sified differently by respondents who booked accommodation through the airbnb platform and those who did not book through the airbnb platform. in order to determine the factors that influence the decision of consumers / tourists to book accommodation through the airbnb platform, in the questionnaire we proposed 10 items that were analysed by respondents on a 5-point likert scale, ranging from 1 not at all important, to 5 very important. the 10 items are: number of accommodation reviews (d1-dn1), accommodation facilities (d2-dn2), d3 price (d3-dn3), maximum number of guests (d4-dn4), host response time (d5-dn5), location of the accommodation (d6-dn6), access to public transport (d7-dn7), fees additional (d8-dn8), cancellation policy (d9-dn9), check-in and check-out process (d10-dn10). as the questionnaire was divided into two, those who booked through the airbnb platform and those who did not book through the airbnb platform, in table 2 below we compute the mean and the standard deviation (sd) for each item considering the two independent samples. starting from these results we could identify which factor was considered by respondents as the most important. we noted that the highest average value was in case of the number of accommodation reviews (d1 mean = 4.56 and dn1 – mean = 4.19), so we can say that for tourists, reviews are a very important decision factor when booking accommodation. the second decision factor in booking accommodation through airbnb is the price (d3 mean = 4.55 and dn3 – mean = 4.05). access to public transport seems to be the least important decision factor (dn7 – mean = 3.42). in order to test the validity of the measurement models and the reliability of the scales used, we will use factor analysis. this analysis was performed in the ibm spss v26 statistical analysis program and required specification of the type of scale tested. the principal component analysis (pca) is based on the work of pearson (1901) [29] and hotelling (1933) [17] and has been analysed for several decades [7, 19]. this method is one of the most widely used methods for reducing the size of large databases in the presence of collinearity. the objectives of the pca are to: extract the most important information from a larger data set; reducing the attribute space from a larger number of variables to a smaller number of factors; selecting a subset of variables from a larger set, based on which the original variables have the highest correlations with the principal component [1]. in order to achieve these goals, pca calculates new variables called principal components that are obtained as linear combinations of the original variables. https://doi.org/10.15837/ijccc.2022.6.5004 8 table 2: decision factors latent constructs items mean sd cronbach’s α decision factor (booked through the airbnb platform) (mean=4.20) d1 4.56 0.64 0.813 d2 4.31 0.74 d3 4.55 0.50 d4 3.87 0.97 d5 4.06 0.96 d6 4.31 0.74 d7 4.00 1.02 d8 3.95 0.86 d9 4.19 0.72 d10 4.23 0.68 decision factor (not booked through the airbnb platform) (mean=4.20) dn1 4.19 0.133 0.813 dn2 3.92 1.178 dn3 4.05 1.122 dn4 3.56 1.268 dn5 3.82 1.208 dn6 4.02 1.152 dn7 3.42 1.274 dn8 3.88 1.184 dn9 3.76 1.215 dn10 3.50 1.276 the values obtained for these new variables are called factor scores. in the analysis of the principal components, several criteria are used to choose the number of components, of which the most important are: kaiser’s criterion, evrard’s criterion, benzecri’s criterion [33, p. 507]. kaiser’s criterion is to choose the number of axes for which the eigenvalues correspond to a value greater than one. to begin with, we applied the kaiser-meyer-olkin (kmo) measure of sampling adequacy and the bartlett test to determine if there is a sufficiently high correlation to perform this analysis. according to the theory, kmo statistics values below 0.50 indicate that the pca analysis may not be adequate [24, 27]. regarding the specificity test of the bartlett test, we can say that the variables are correlated only if the p value of the test (sig.) is less than 0.05 [23]. table 3: principal components analysis on the importance of the consumers’ decision-making regarding the booking through the airbnb platform booked through the airbnb platform (e1) did not book through the airbnb platform (e2) kmo measure 0.757 0.923 bartlett’s test of sphericity (sig.) 0.000 0.000 component component 1 2 3 1 item services taxes facilities item decision d1 0.226 0.112 0.690 dn1 0.823 d2 0.204 -0.077 0.852 dn2 0.895 d3 -0.096 0.378 0.672 dn3 0.808 d4 0.718 -0.048 0.202 dn4 0.805 d5 0.666 0.509 -0.050 dn5 0.859 d6 0.743 0.223 0.144 dn6 0.824 d7 0.772 0.147 0.103 dn7 0.646 d8 0.375 0.497 0.371 dn8 0.810 d9 0.109 0.873 0.108 dn9 0.876 d10 0.176 0.881 0.096 dn10 0.779 extraction method: principal component analysis according to the results presented in table 3, we can argue that the analyzed variables are correlated both in the case of sample e1 and in the case of sample e2. therefore, the pca analysis is adequate, the value of the kmo statistic being greater than 0.50, i.e., 0.757 in the case of sample e1 and 0.923 in the case of sample e2, and the p value of the specificity test is equal to 0.000 in the case of both samples. based on these results, we can state that in the case of the first sample (e1), three distinct variables were constructed. the first measures the importance of decision factors https://doi.org/10.15837/ijccc.2022.6.5004 9 related to services (d4, d5, d6 and d7), the second is related to the importance of decision factors related to taxes (d8, d9 and d10), and the third is related to the importance of decision factors regarding facilities (d1, d2, d3). in the case of the second sample (e2), we observe a single variable that measures the importance of decision makers regarding accommodation through the airbnb platform. these results allowed us to identify the differences between the two samples in terms of the importance given to these decision makers. therefore, we can argue that in the case of the first sample, the respondent gives more importance to the decision factors regarding the services, they are followed by those regarding the taxes and finally we have the factors regarding the facilities offered. in the case of the second sample, we notice a different situation. thus, in case of the respondents who have not yet booked accommodation through the airbnb platform, all 10 factors seem equally important in choosing the airbnb platform for booking accommodation. therefore, we can argue that the importance of these decision factors of consumers / tourists to book accommodation through the airbnb platform differs depending on the category they belong to, namely e1 booked accommodation through the airbnb platform, respectively e2 did not book accommodation through the airbnb platform, the first hypothesis being verified. in order to verify the other two hypotheses, we will take into account the age variable. given the structure of the population according to age, we noticed a difference between the average age in the first sample e1 and that in the second sample e2. thus, the average age of the respondents who booked through the airbnb platform is lower than the average age of the respondents who did not book through the airbnb platform until the completion of the questionnaire. given this difference in age and also the difference in classification of decision makers regarding the booking of accommodation through the airbnb platform, we further want to determine for each sample whether between the age of respondents and the decision to book accommodation through airbn there is a possible association and also if it is positive or negative. in this sense, we will use the structural equation modelling (sem) both in the case of the first sample e1 and in the case of the second sample e2. considering the factorial analysis, we will further verify whether age is a factor that influences the decision of the consumer / tourist to book accommodation through the airbnb platform using a structural equation modelling (sem). according to the literature, the most commonly used parameter estimation methods in sem models are the maximum probability estimation method and the least squares method. after estimating the model, we identified the following fit indexes: chi-square (indicates the square root of the average of the residual values) is equal to 112.945 > 0.05, which shows us that the model is a suitable one; the rmsea index (root mean square error of approximation) is 0.08 ≤ 0.08, indicating that the model is acceptable; the gfi (goodness of fit index) obtained after the correspondence test is 0.705, an acceptable value; parsimonious fit index is 0.493, close to 0.50. as can be seen from the results shown in figure 1, we can affirm that the age of the respondents who stayed through the airbnb platform is not a factor influencing the decision-makers of consumers when they want to book accommodation through the airbnb platform. in other words, given the wide range of options in question 1 of the questionnaire (from under 20 to over 61), we can say that age is not an impediment to the widespread use collaborative platforms in the field of tourism. on the other hand, in this analysis, we did not take into account the proportion with which the different age groups enter collaborative tourist consumption, and even more so, what is the distribution of preferences for a certain type of consumption and tourist reservation (traditional vs. collaborative) depending on the age group. the fact that the paragraphs of the descriptive analysis of the results of this questionnaire indicate that the vast majority of respondents who say they use these platforms are young and very young (under 30), may raise some reservations about the representativeness of the results at the level of groups of older age (over 50 years). it is an aspect that we want to deepen in our future research, highlighting the availability and accessibility of these platforms and, respectively, the degree of digitalization and confidence of older people compared to the opportunities offered by these platforms. therefore, we can say that the second research hypothesis is not verified. https://doi.org/10.15837/ijccc.2022.6.5004 10 figure 1: structural equation modelling the relationship between age and respondents’ decision to book accommodation through the airbnb platform furthermore, we would like to find out whether among the respondents who did not book accommodation through the airbnb platform (sample e2), age is a factor that influences these decision factors in order to book accommodation through the airbnb platform. the values of the fit indexes of the estimated model in case of the sample e2 are: chi-square is equal to 127.74 > 0.05, so the model is a suitable one; the rmsea index is 0.075 ≤ 0.08, indicating that the model is acceptable; the gfi index equal to 0,915 shows us a good fit of the model; the parsimonious fit index (pfi) is 0.701 < 0.50, therefore the model is a suitable one (ramkissoon, 2015). according to the results obtained and presented in figure 2, we can say that in the case of respondents who did not book accommodation through airbnb until completion, age is a factor that influences decision makers when they would opt for a reservation through the airbnb platform. given the negative value of the coefficient, -0.31, we can argue that age influences these factors in a negative way. thus, the older you are, the less it matters the importance is given to these decision makers. this result can show us on the one hand that young people are more open to using an airbnb-type platform to book accommodation unlike older people, who are more reluctant to use such a platform. to the detriment of traditional accommodation, but on the other hand we could say that for young people, the importance of decision makers regarding the services, fees and facilitation offered through the airbnb platform is greater than in the case of older age groups. https://doi.org/10.15837/ijccc.2022.6.5004 11 figure 2: structural equation modelling the relationship between age and respondents’ decision to book accommodation through the airbnb platform given that in this sample it is more difficult to follow the importance of decision makers regarding accommodation through the airbnb platform, we will further test the sem model that analyzes the impact of age on the reasons why they did not book accommodation through the airbnb platform. this analysis is based on 3 latent variables related to: trust in the airbnb platform to book accommodation (trust), the functional attributes offered by the airbnb platform (functional attributes) and the effectiveness of the airbnb platform (efficacy). the overall model fit and the indicators of goodness of fit model shows that: chi-square is equal to 308.22 > 0.05, so the model is a suitable one; the rmsea index is 0,079 ≤ 0.8, indicating that the model acceptable; the gfi index obtained after the correspondence test is 0,727; the parsimonious fit index is 0,587 > 0.50, therefore the model is a suitable one. as can be seen in figure 3, age influences the reasons why respondents in the second sample did not book accommodation through the airbnb platform. among the main reasons analyzed is the confidence in the airbnb platform which is influenced to a lesser extent and negatively by age, while the efficiency of this platform and the operating attributes of the platform are positively influenced by the age of the respondents. according to the results, the functional attributes are influenced to a large extent by age. thus, starting from the first coefficient, -0.07, we can state the following: respondents belonging to the older age groups (40 and 50 years old) claimed that the trust in the platform was not a reason why they did not book accommodations through airbnb, while the respondents under the age of 40 stated that the trust in this platform was one of the reasons why they did not book through it. the latter stated that they were concerned about the security of accommodations through the airbnb platform, also about data confidentiality, trust in hosts and the legal framework for conducting business, and lastly about the trust in conducting an online transaction through the platform. the effectiveness of the platform is also influenced by age, but, as in the case of trust, to a small extent. in the case of this variable we observed a positive value of the coefficient, 0.05. https://doi.org/10.15837/ijccc.2022.6.5004 12 figure 3: structural equation modelling the relationship between age and the reasons why respondents did not book accommodation through the airbnb platform thus, we can state that the respondents from the older age groups (over 40 years old) did not use the services offered by the airbnb platform because they considered that it did not provide enough information about how this platform works and what it is. also, they did not succeed to easily find the list of holiday rentals online, respectively they had limited accommodation offers. a last factor analyzed is the one referring to the functional attributes offered by the airbnb platform, this being influenced by age, in a positive sense. the results show that the lack of facilities, the lack of availability in the desired period and the lack of several dining options are reasons why respondents from older age groups (40-50 years) did not book accommodations through airbnb, while in in the case of the youngest (under 30), these were not the main reasons why they did not book through the airbnb platform. in conclusion, we can say that hypothesis 3 is verified. 5 conclusions in order to identify the impact that the collaborative platform airbnb has on romanian tourists and what are the elements that motivate tourists to choose this platform, we used the principal component analysis (pca) and the structural equation modelling (sem). according to the principal component analysis, it was found that there are significant differences between the classification made by respondents who booked through the airbnb platform and those who did not book through the airbnb platform in terms of decision making in choosing the airbnb platform. therefore, it was observed that the respondents who booked accommodation through the airbnb platform gave more importance to the decision factors regarding the services, followed by those regarding the taxes and finally we have the factors regarding the facilities offered. in the case of the respondents who did not book accommodations through the airbnb, we noticed that for these, all 10 factors seem equally important in choosing the airbnb platform for booking accommodations. therefore, we can state that the first hypothesis is verified. in the case of the second hypothesis, we can say that it is only partially verified. thus, in the case of the first sample, age does not represent an influencing factor in the decision to book accommodations through airbnb, while in the case of the second sample, age influences the decision to book https://doi.org/10.15837/ijccc.2022.6.5004 13 accommodations through airbnb. to test the third hypothesis, we applied a sem model. the results showed that there is an association between the age of the respondents that they have not booked through the airbnb platform and the reasons why respondents did not book accommodations through the airbnb platform. thus, the most important factors that are influenced by age are the functional attributes, followed by trust and ultimately efficacy. this study also has a number of limitations. a first limitation of this study is related to the small number of respondents who completed this questionnaire. also, another limitation is represented by the very small number of respondents who booked accommodation through airbnb and answered this questionnaire. for future studies, we propose to increase the number of the sample 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(2022). digitalisation and the sharing economy. a survey-based research on airbnb in romania, international journal of computers communications & control, 17(6), 5004, 2022. https://doi.org/10.15837/ijccc.2022.6.5004 introduction literature the reasons for participating in the sharing economy trust prerequisite and result of se age and willingness to join se data collection procedure and the hypotheses results and discussion conclusions int j comput commun, issn 1841-9836 9(1):85-92, february, 2014. spectrum migration approach based on pre-decision aid and interval mamdani fuzzy inference in cognitive radio networks z. wang, h. wang, h. lv, g. feng zhendong wang*, huiqiang wang, hongwu lv, guangsheng feng college of computer science and technology harbin engineering university, harbin, china *corresponding author: wangzhendong@hrbeu.edu.cn abstract: this study intends to improve the qos of sus and crns performance. a novel spectrum migration approach based on pre-decision aid and interval mamdani fuzzy inference is presented. we first define spectrum migration factors as spectrum characteristic metrics for spectrum migration decision. in addition, we use predecision aid to reduce system complexity and improve spectrum migration efficiency. to shorten spectrum migration decision time and seek the optimal spectrum holes, interval mamdani fuzzy inference is put forward. finally, simulation results show the proposed approach can inhibit the upward trend of service retransmission probability and average migration times effectively, and improve the effective utilization of crns spectrum resource significantly. keywords: cognitive radio networks, spectrum migration, pre-decision aid, interval mamdani fuzzy inference 1 introduction opportunistic spectrum access (osa) technology due to help sus utilize the idle spectrum, effectively improve spectrum usage and system throughput for crns, and becomes a hotspot that academia concerned [1][5]. however, existing osa technologies mainly focus on improving crns system throughput and spectrum resource utilization, the qos requirement of sus and the effective utilization of crns spectrum resource are little considered, that maybe cause system throughput and utilization of spectrum resource increase to a higher level, but the effective utilization of spectrum resource is still maintained at a lower level [6][7]. in order to improve the qos of sus and the crns performance, this paper introduces the concept of spectrum migration. spectrum migration means sus change their using spectrum dynamically for improving the success rate of sus connections, it describes the whole process of sus service transmission. the occurrence of spectrum migration includes two cases: (1) pu arrives at the spectrum that su is using, (2) the quality of spectrum sus using drops below the minimum value that can maintain normal data transmission [8][10]. under normal circumstance, the probability of spectrum environment deterioration is lower, so we only consider sus spectrum migration when pu arrives. actually, frequency spectrum migration operations can decrease system performance and sus qos because of the operations is time-consuming. therefore, the objective of this paper we pursue are the longest occupation time to single spectrum hole, the least spectrum migration times and the shortest spectrum migration decision time for sus. in this paper, we define spectrum migration factors as spectrum characteristic metrics for spectrum migration decision, and use pre-decision aid to reduce system complexity and improved spectrum migration efficiency. at last, we propose interval mamdani fuzzy inference (imfi) method based on mamdani fuzzy inference to shorten spectrum migration decision time and search for suitable spectrum holes. copyright © 2006-2014 by ccc publications 86 z. wang, h. wang, h. lv, g. feng 2 spectrum migration factors 2.1 spectrum occupation probability spectrum occupation probability indicates the spectrum occupation degree for pus and sus. for a su, when the spectrum is being occupied by pus or other sus, it can not migrate to the spectrum. therefore, spectrum occupation includes spectrum occupation include pus and sus. from this point, spectrum occupation probability is the occupation probability of subspectrum divided, i.e., channel occupation probability. a higher spectrum occupation probability can lead to higher migration blocking probability for sus. until now, there is no accurate spectrum occupation model proposed, so we use statistical method to obtain spectrum occupation probability by calculating spectrum occupation data in the past time. considering the on-off average time of spectrum η are tα and tβ over a period of time t′ ( t′ = tα +tβ ) respectively, then spectrum occupation probability of spectrum η can be calculated as sop = t α tα+tβ . when spectrum η undergoes k times state changes, the spectrum occupation probability can be updated as sop = k∑ i=1 tα,i k∑ i=1 (tα,i + tβ,i) (1) where tα,i and tβ,i indicate the on-off time in the ith period on spectrum η respectively. 2.2 link maintenance probability link maintenance probability mainly reflects the link support degree for sus data transmission. it indicates the capacity of continuous data transmission for sus on a specific licensed spectrum. from sect. 1 we know only pus arrival can force sus vacate the using spectrums and makes link maintenance fail. from this point, link maintenance probability is the same for licensed spectrum and its channels. when a pu arrives, there are three consequences for sus: (1) su needs to vacate its using spectrum and migrate to other spectrum to continue its data transmission. (2) su vacates its using spectrum and waits for pu to leave, then su continues its data transmission through the original spectrum. (3) su vacates its using spectrum and link maintenance is failed. let pv denote the probability that an su vacates its using spectrum, we have link maintenance probability as follows lmr = pv[(1−rb) + rbpr(ψt < τt)] (2) where rb denotes pu call blocking probability, and it is given by rb = (ρ m / m!) / ( m∑ i=1 ρi / i!) (3) where ρ denotes the pu traffic intensity. 2.3 spectrum migration degree spectrum migration degree reflects the pros and cons of the spectrum holes on each licensed spectrum for sus directly. before defuzzification operations, it is denoted as a specific level, and spectrum migration approach based on pre-decision aid and interval mamdani fuzzy inference in cognitive radio networks 87 after defuzzification operations, it denotes as a specific value. we consider that the spectrum with higher spectrum migration degree, the more suitable for sus spectrum migration. spectrum migration degree can be obtained by sop and lmp using fuzzy inference, it is expressed as smd = inf(sop,lmp) (4) 3 spectrum migration approach based on pre-decision aid and imfi 3.1 spectrum migration pre-decision aid in order to avoid all the sus enter into fuzzy decision module and reduce crns complexity, we propose spectrum migration pre-decision aid method. it can be described as follows 1) when su arrives at the spectrum for the first time, i.e., a = af , where a denotes the arrival of su, and af denotes the arrival of su for the first time. when there are more than one spectrum hole, su needs to enter into fuzzy decision. if there is only one spectrum hole, su migrates to it directly, and if all spectrums are occupied, spectrum migration operations will be blocked. the process is shown as figure 1 (a). figure 1: process of spectrum migration pre-decision aid 2) when su arrives and a ̸= af , if there are more than one spectrum hole, su enters into fuzzy decision module. if there is only one spectrum hole, su migrates to it directly. if there is no idle spectrum and the occupied duration is more than τt , i.e., ψt + ζt > τt , su connection will be interrupted, the data has been transmitted will be considered as ineffective. then, a will be reset to af , and the retransmission operation will start for this su. otherwise, su judges whether the idle spectrum is current spectrum, if it is indeed the current spectrum, then su does not need to migration. otherwise, su selects spectrum migration manner according to the number of idle spectrums. this process is described as figure 1 (b). 3.2 spectrum migration method based on imfi a. spectrum migration factors fuzzification 88 z. wang, h. wang, h. lv, g. feng in fuzzification process, the more the number of fuzzy sets, the lower the probability of entering into the 2nd decision, then, the spectrum migration decision time can be saved much. however, the number of fuzzy rules is polynomial growth with the growth of the number of fuzzy sets, when sus enter into the 2nd decision, excessive fuzzy rules will cause longer inference time, which may make su on the effective use time of spectrum holes shorten, and the system throughput reduced. when ψt + ζt > τt happens, it even leads to su data retransmission. simultaneously, rare fuzzy sets have fewer fuzzy rules, but they cause the probability of entering into the 2nd decision increase, also extend the fuzzy inference time. in this subsection, we consider sop and lmp as input fuzzy parameters, and smd as output fuzzy parameter. then, we establish the membership functions for sop, lmp and smd as figure 2. in figure 2, the universes of all the fuzzy variables are set to [0, 1]. for every licensed spectrum, sop and lmp have five fuzzy sets, that denote as vl, l, m, h, vh, mean “very low”, “low”, “medium”, “high” and “very high” respectively. the fuzzification operations are same for sop and lmp, they are shown as figure 2 (a). fuzzy variable smd also has five fuzzy sets, and they denote as vs, s, m, b, vb, that mean “very small”, “small”, “medium”, “big” and “very big” respectively, they are shown as figure 2 (b). � p1 0 1 0 1.0 vs s m b vb 0.25 0.75 (b) 0.5 0 1 0 1.0 m 0.25 0.75 (a) 0.5 vl l h vh figure 2: membership function of fuzzy variables b. imfi method in fuzzy decision phase, decision time has a big impact on spectrum migration performance. traditional mamdani fuzzy inference method calculates each fuzzy rule by max-min mode, makes it compute-intensive, and fuzzy inference time is long. for two-input single-output and 7-divisions fuzzy controller, the inference time accounts for 60% to 80% of the total fuzzy inference time, and the proportion will increase with the increase of the number of rules. definition 1. let the universe of fuzzy variable π is u , its membership function is f(x) . if there is an interval x = [a,b] ⊂ u , and its membership function is f(x) , f(x) ∈ f(x) , i.e., f(π) = f(π) . then, we define interval [a,b] as an inference interval for π . definition 2. inference interval [a,b] is an effective inference interval if and only if f(x) ̸= 0 for any x ∈ [a,b] . conversely, [a,b] is defined to be ineffective inference interval. definition 3. inference intervals x1,x2,x3, ...,xh are defined to complete inference interval if and only if x1 ∪x2 ∪x3 ∪ ...∪xh = u . theorem 1. fuzzy relationship on the universe is equal to fuzzy relationship on complete inference interval. proof: assuming a1,a2, ...,an are complete inference intervals on universe u . according to definition 3, we have a1 ∪a2 ∪ ...∪an = u . assuming the membership functions of a1, ...,an are µa1(x), ...,µan(x) respectively, and the membership function of u is µ(x) , then, we have µa1(x) ∈ µ(x) , i.e., µa1(a1) = µ(a1) for any a1 ∈ a1 according to definition 1. similarly, we have µa2(a2) = µ(a2) , . . . , µan(an) = µ(an) for a2 ∈ a2, an ∈ an on the intervals a2, ...,an . it completes the proof. spectrum migration approach based on pre-decision aid and interval mamdani fuzzy inference in cognitive radio networks 89 from theorem 1, we can simplify mfi to imfi. in order to apply imfi method to spectrum migration, we formulate spectrum migration fuzzy rules as table table 1. furthermore, according to figure 2 (a), we make interval mamdani fuzzy inference decision table as table 2. for ease of comprehension, we provide detailed information on table table 1 and table table 2. in table 3.2, we give an example to explain the expression of fuzzy rules. the three shaded tables with “m”, “vl” and “s” stand for the fuzzy rule of “if sop is m and lmp is vl, then smd is s”. for table table 2, i1 , i2 , i3 , i4 denote effective inference intervals for [0, 0.25], [0.25, 0.5], [0.5, 0.75], [0.75, 1.0] respectively, and 1, 2, 3, 4, 5 stand for vl, l, m, h, vh respectively. it can be seen that i = i1∪ i2∪ i3∪ i4 = [0, 1], so i is a complete inference interval for the universe. to explain the meaning of table table 2, we also use the shaded tables as an example. if the value of sop located at the effective inference interval i3 , and the value of lmp located at the effective inference interval i2 , i3 and i2 denote the effective inference intervals [0.5, 0.75] and [0.25, 0.5], i.e., if 0.5 ≤ v aluesop ≤ 0.75, 0.25 ≤ v aluelmp ≤ 0.5, then, we use fuzzy inference rules (3,4/2,3). the number on the left of backslash is fuzzy sets of sop, and number on the right of backslash is fuzzy sets of lmp, they correspond to the following four fuzzy rules if sop is m and lmp is l, then smd is s, if sop is m and lmp is m, then smd is m if sop is h and lmp is l, then smd is s, if sop is h and lmp is m, then smd is s if we do not use interval mamdani fuzzy inference, there will be twenty five fuzzy rules used under the same case. due to the limited space, we will not list the fuzzy rules. for the same inference results, interval mamdani fuzzy inference can save more than three-quarters of the time compared with mamdani fuzzy inference under our condition. c. spectrum migration decision according to interval mamdani fuzzy inference, we can get three kinds of inference results: 1) some of the licensed spectrums have the same smd levels, but there only one spectrum has the highest smd level. 2) some of the licensed spectrums have the same smd levels, and there are more than one spectrum has the highest smd level. 3) the smd level for each spectrum is different. for 1) and 3), the system selects the spectrum with the highest smd level to migration, we call it the 1st migration decision. for the case of 2), the system should take defuzzification operations and selects the spectrum with the maximum smd value to migration, we call it the 2nd migration decision. spectrum migration decision can be expressed as 90 z. wang, h. wang, h. lv, g. feng ch∗ = arg max θsmd(ch) ∀ch (5) 4 numerical and simulation results we simulate and evaluate the performance of spectrum migration approach proposed in this paper. meanwhile, we use the existing approaches that are random [6], mfi and greedy [7] to comparison. the following assumptions are adopted in the simulation. the crn in which sus coexist with pus in a 5km × 5km area. the number of licensed spectrums in the area is 5, and each spectrum is divided to 5 channels. all the spectrums are independent identically distributed. we set the total bandwidth of licensed spectrum is 5 mhz. so, the bandwidth of each secondary channel is 0.2 mhz. this assumption is reasonable since that one voice channel is only 0.2 mhz in gsm cellular network. the signal to interference plus noise ratio (sinr) is set to 3db. the average arrival rate of sus is assumed to be 1.0, the retransmission waiting threshold is set to be 1.0s. simulation data is recorded for 10000 times to avoid the contingency of the results. figure 3: average migration times figure 4: service retransmission probability of su at τt =1.0s figure 3 and figure 4 show the average migration times of su single service transmission and su service retransmission probability at τt =1.0s respectively. it is obviously that imfi algorithm has the least average migration times. compared with random, the average migration times of imfi reduce by about 65%, and also reduce nearly half compared with the mfi. under the premise of service size fixed, average migration times are related to spectrum holes during time, migration waiting threshold and spectrum migration decision time. compared with random and greedy, the advantage of imfi reflects the accuracy and timeliness for the optimal spectrum selection. mfi has the same inference results with imfi, so the advantage of imfi reflects the timeliness of spectrum migration decision. when τt =1.0s, the tendency of service retransmission probability is close to average migration times for the four algorithms, that indicates there exists a positive relationship between su service retransmission probability and average migration times. when λ =1.0, service retransmission probability only reaches to 20% using imfi, that is much lower than greedy and mfi, which confirms superior performance on the optimal spectrum selection and spectrum migration decision once again. in figure 5, we can see that the average throughput of crns all decrease with the increase of arrival rate of pus. this is because the increase of λ makes the spectrum holes duration spectrum migration approach based on pre-decision aid and interval mamdani fuzzy inference in cognitive radio networks 91 shorten, and cause the use of spectrum holes tend to be difficult. compared with random, the tendencies of greedy and mfi decrease obviously, the main reason is the two intelligent algorithms consume an inordinate amount of time for spectrum migration decision. because of using spectrum migration pre-decision aid, and imfi method, spectrum migration decision time with imfi algorithm is shortened greatly. compared with random, the average throughput of crns increases slightly. figure 5: average throughput of crns figure 6: effective utilization of crns spectrum resource of su figure 6 shows the effective utilization of crns spectrum resource. when λ is small, the four algorithms are closer to this parameter and the effective utilization for each is higher. with the increase of λ , service retransmission with random, greedy and mfi increases quickly, makes invalid data rapid upward, and cause the effective utilization of crns spectrum resource decline rapidly. imfi can inhibit the service retransmission probability upward effectively, especially in the condition that the arrival rate of pus is higher, the advantage is more obvious. when λ =1.0, the effective utilization of crns spectrum resource using imfi still reaches to 70%, that makes more efficient use of spectrum sources, and improve system performance effectively. 5 conclusions the objective of this paper is to improve the qos of sus and crns performance. in view of the problems that existing methods exist, we put forward a spectrum migration approach based on pre-decision aid and interval mamdani fuzzy inference in crns. through the establishment and analysis of the spectrum migration model, we define spectrum migration factors (sop, lmp and smd) as spectrum characteristic metrics for spectrum migration decision. moreover, pre-decision is put forward to reduce system complexity and improve migration efficiency. for shorten spectrum migration decision time and seek the optimal spectrum holes, we propose an interval mamdani fuzzy inference method based on mamdani fuzzy inference, which can reduce inference time significantly. at last, simulation results show the effectiveness of our approach compared with other existing algorithms. acknowledgments this work was supported by the fundamental research fund for the central universities (heucfz1213), and doctoral fund of ministry of education of china (20122304130002). 92 z. wang, h. wang, h. lv, g. feng bibliography [1] yuan, g. et al; performance analysis of selective opportunistic spectrum access with traffic prediction, ieee trans. vehicular technology, 59(4): 1949-1959, 2010. 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[8] an, c.; ji, h.; si, p.; dynamic spectrum access with qos provisioning in cognitive radio networks, proc. of globecom : 1-5, 2010. [9] hamdaoui, b.; adaptive spectrum assessment for opportunistic access in cognitive radio networks, ieee trans. wireless communications, 8(2): 922-930, 2009. [10] stotas, s.; nallanathan, a.; on the throughput and spectrum sensing enhancement of opportunistic spectrum access cognitive radio networks, ieee trans. wireless communications, 11(1): 97-107, 2012. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 4, month: august, year: 2022 article number: 4861, https://doi.org/10.15837/ijccc.2022.4.4861 ccc publications a stochastic mobility prediction algorithm for finding delay and energy efficient routing paths considering movement patterns in mobile iot networks g.a. montoya. c. lozano-garzón, y. donoso germán a. montoya* systems and computing engineering department universidad de los andes, colombia cra 1 nº 18a 12, bogotá, colombia *corresponding author: ga.montoya44@uniandes.edu.co carlos lozano-garzón systems and computing engineering department universidad de los andes, colombia cra 1 nº 18a 12, bogotá, colombia calozanog@uniandes.edu.co yezid donoso systems and computing engineering department universidad de los andes, colombia cra 1 nº 18a 12, bogotá, colombia ydonoso@uniandes.edu.co abstract in mobile iot networks, the network nodes are constantly moving in a field, causing interruptions in the communication paths and, thus, generating long delays at the time of building a communication path from a source iot node to the gateway (destination node). communication interruptions affect the delay performance in delay-sensitive applications such as health and military scenarios. in addition, these iot nodes are equipped with batteries, whereby it is also necessary to accomplish energy consumption requirements. in summary, a gateway node should not receive messages or packets coming from the iot nodes with undesired delays, whereby it is pertinent to propose new algorithms or techniques for minimizing the delay and energy consumption experimented in the iot network. due to iot nodes are attached to humans, animals or objects, they present a specific movement pattern that can be analyzed to improve the path-building with the aim of reducing the end-to-end delay. therefore, we propose the usage of a mobility prediction technique based on a stochastic model to predict nodes’ positions in order to obtain minimum cost paths in terms of energy consumption and delay in mobile iot networks. our stochastic model is tuned and evaluated under the markov-gauss mobility model, considering different levels of movement randomness in order to test how the capability prediction of our proposal can impact the delay and energy consumption in mobile iot networks in comparison with others routing algorithms. keywords: markov chains, mobile iot, mobility prediction. https://doi.org/10.15837/ijccc.2022.4.4861 2 1 introduction in mobile internet of things networks, the network nodes are constantly moving in a field, causing interruptions in the communication paths and, thus, generating long delays at the time of building a path from a source node to the destination node (the gateway node). these interruptions affect the delay performance in delay-sensitive applications such as health and military scenarios [3]. in addition, these iot nodes are equipped with batteries, whereby it is also necessary to accomplish energy consumption requirements [1] [2]. in summary, a gateway node should not receive messages or packets coming from the iot nodes with undesired delays, whereby it is pertinent to propose new algorithms or techniques for minimizing the delay and energy consumption in the iot network [3][4]. according to the requirements described previously, new routing algorithms has been proposed [11] [12] [13]. in [13], the authors show a summary of algorithms in mobile wireless sensor networks, but no mobility prediction model is presented for a network that is entirely mobile. in [11], the authors present a proposal to assure that each source iot node accesses a backbone node through a single hop with the aim of reaching a mobile sink. nevertheless, a mobility prediction model is not proposed and just the sinks, not the rest of the network nodes, are moving in a field. authors, in [12], present a multi-objective particle swarm optimization proposal for building the best path for data collection considering a mobile sink in a iot network. in summary, we use a mobility prediction model in a mobile iot network. in detail, we propose to use a mobility prediction technique for building a path from a source iot node and a gateway node (destination node) taking into account all network nodes are moving in a field and considering energy consumption and delay requirements. this mobility prediction technique is tunned to be evaluated under different levels of movement randomness in order to test how the capability prediction of our proposal can impact the energy consumption and delay in mobile iot networks in comparison with others routing algorithms. this paper corresponds to an extension of the work presented by us in [20]. this extension consisted of the following aspects: • we evaluate more scenarios by testing different levels of movement randomness. that is, we vary the α parameter in equations 11 and 10 to analyze the positive or negative impact of movement randomness in our proposal. • more specifications are presented for the movement considerations. in other words, theoretical details are described for the movement considerations. • our proposal is evaluated against a distance and a random algorithms enhanced to be applied in mobile iot networks scenarios, which will be described later in the results section. in summary, the distance and random algorithms are evaluated under different levels of movement randomness in order to be compared against our proposal. the remainder of the paper is organized as follows: section ii presents the problem requirements in order to propose in section iii an stochastic model based on markov chains to find optimal paths considering rssi and energy consumption levels. in addition, this section shows details about the implementation in terms of simulations details, movement and energy consumption model of iot nodes, and the proposals selected to be compared against our proposal. in section v is presented the performance of our proposal in terms of delay and energy consumption. finally, section vi presents the conclusions of our proposal according to the performance obtained taking into account the evaluated scenarios. 2 problem formulation in this section, we estimate future movements of nodes in a mobile iot network by using a mobility prediction technique based on markov chains to determine if future movements will cause future communication interruptions for building paths. by determining future movements, the communication https://doi.org/10.15837/ijccc.2022.4.4861 3 (a) problem (b) solution figure 1: problem definition interruptions can be reduced, and then, the end-to-end can be decreased at the time of building a path between a source node and the gateway. the details of this estimation is described below: in figure 1.a, there is a mobile iot network. assume that at time t1 there is a path between node n1 (the source iot node) and a gateway (squared node). suppose that at time t2, there is a communication interruption for carrying a data packet from the source iot node to the gateway because node n2 moved away from node n3. when n3 has noticed this interruption at time t3, n3 proceed to restore the path from n1 to the destination node. however, these interruptions introduce undesired delays, at which in some applications they can be omitted because do not affect the general performance, but in delay-sensitive applications, they cannot be ignored due to they cause low values of the delay metric. described the problem previously, we propose to use a mobility prediction model referenced in figure 1.b [7][8]. this figure reflects a similar concern seen in figure 1.a; however, at time t1, node n3 is notified about that the node n2 will be out of communication range at time t2. according to this situation, at time t1, n3 is also determining which candidate node could substitute n2 in the case of it will be incommunicated later. that is, at time t2, if n2 is incommunicated from n3, this node at time t2 could reestablish the path between n1 and the gateway, decreasing the delay presented in figure 1.a. in addition, we employ rssi (received signal strength indicator) measurements to infer the distance measure between two nodes, with the aim of being aware of network movements. in detail, each iot node predicts future distance (rssi measurement) of neighbour nodes to estimate if they will be incommunicated later. the description of this method is shown in the following section. 3 stochastic model proposal assume there is a network of two nodes: nk and nl, and that nl is a neighbour node of nk. suppose that the node nl, at time t1, is at a specific distance from nk, but at time t2 we need to estimate if nl will be incommunicated or not (or at the same distance in t1) from nk. in addition, nl can be positioned at a minimum and maximum distance to create a communication link with nk. node nl will be at a maximum rssi, rssimax, at a minimum distance. likewise, nl will be at a minimum rssi, rssimax, at a maximum distance. in this sense, node nl could be positioned between rssimin and rssimax. therefore, we have the aim of predicting the location of nl in the future, that is, a position between rssimin and rssimax. in order to propose a pragmatic model, we propose discrete locations equally spaced between rssimin and rssimax. the positions, at which nl could be located, are denoted as states. thus, nl could be located at s1, s2, sr or sg in the future, where g is the total number of states. the default probability of nl to be at any state is si = 1/g, which is denoted as initial probability distribution of set s (π), and it is described as follows: π = {ps1,ps2, ...,psg} (1) the probability that state s2has to go to state s4 is calculated as follows: https://doi.org/10.15837/ijccc.2022.4.4861 4 p24 = n(s2,s4)∑g j=1 n(s2,sj ) (2) where the number of times that state si follows state si is n(si,sj ). the previous equation could be used for the rest of probabilities as follows: pij = n(si,sj )∑g j=1 n(si,sj ) (3) given the probability that si has to go to any state sj, all probabilities could be denoted in a matrix, named transition matrix: t =   p11 p12 ... p1g p21 p22 ... p2g . . ... . . . ... . pg1 pg2 ... pgg   (4) in this sense, suppose that nl is at s3 at the current time t1. at a future time tp, the future state of nl is estimated as follows: πp = π ∗t p (5) sp = max{πp} (6) sp = max{ps1,ps2, ...,psg} (7) respect to 7, at time tp, the most probable future state at which nl will be is calculated by nk. this calculation can be used by a routing algorithm or protocol to decrease the delay caused by communication interruptions. in other words, this information can be considered by the mobility prediction method to decrease the delay. 3.1 mobility prediction algorithm according to the previous described stochastic model, the pseudocode of the algorithm represents our method is shown below. algorithm 1 prediction algorithm pseudocode. 1: initialize p ath = [] 2: initialize sensor n odes = [n1n2...nn] 3: initialize sink = [s1] 4: for t = 1 to totalt imep eriods do 5: if a packet arrived to a ni then 6: obtain list of neighbours v 7: obtain score of each vj based on p robability t ransition 8: m atrix and energy level 9: if |v | 6= 0 then 10: if vj is the sink s1 then 11: send message to s1 12: add s1 to p ath 13: end if 14: if vj is a connected node then 15: send message to the connected node vj 16: end if 17: if vj is not a connected node nor the sink then 18: send message to the best vj based on its score 19: end if 20: else 21: store the message in the buf f er until the next time 22: period t 23: end if 24: end if 25: end for the aim of this algorithm consists of determining the optimal forwarding node considering its probability to be incommunicated or not for building a communication from a source iot node to the destination iot node. at each period of time t, each node calculates its transition matrix to choose the best forwarding node with the aim of reducing the energy consumption and delay of the network. https://doi.org/10.15837/ijccc.2022.4.4861 5 the iot nodes and the gateway are initialized in lines 1 to 3. for each time period (line 4 and line 5), it is necessary to identify in which iot node is the message to be sent across different forwarding nodes (neighbour nodes v ) to achieve the gateway. in lines 7 and 8, from that list of neighbour forwarding nodes, the best of them is selected by using the transition matrix. in lines 9, and 20 to 23, if there are not neighbour nodes, the data packet is stored for the next time period. in lines 10 to 13, if the gateway is among the neighbour nodes, the gateway receives the data packet and thus, from a source iot node to the gateway has been established a path. this steps are repeated until the gateway is reached and, then, the algorithm finishes. 3.2 energy model when a node needs to transmit a data packet of k bits to another node at a distance d, the next expressions are required to be considered for calculating the energy consumption in the transmitter and the receiver. for the transmitter, the energy consumption corresponds to eelec + eamp, at which eelec is the energy consumption for codification, modulation and filtering. eamp is the energy consumption for the transmitter power amplifier. likewise, the energy consumption for the receiver is eamp. in summary, the energy consumption at the transmitter and the receiver are: etx = (eelec + eamp) ∗ k ∗ d2 (8) erx = eamp ∗ k ∗ d2 (9) expression 8 represents more energy consumption than expression 9 due to for transmission is necessary more energy for modulation, filtering and codification (eelec) than the energy consumption required for signal amplification in the receiver (eamp). 3.3 mobility model in relation to the movement of the network nodes, our proposal is evaluated taking into account the gauss-markov mobility model [9]. this mobility model was tuned to be not-entirely random for generating predictable movement patterns. in detail, the movement of the network nodes should be predictable because the iot nodes are tied to humans, animals or objects, showing not entirely randomized movements. in other words, particular movement patterns are presented by these nodes. in these expressions, at each instant, each node calculates its mobility speed and movement direction based on the the previous instant. θn = αθn−1 + (1 − α)θ + √ (1 − α2)θxn−1 (10) vn = αvn−1 + (1 − α)v + √ (1 − α2)vxn−1 (11) at time interval n, the new direction and speed of a particular node is θn and vn, respectively. α is the random movement degree and it is between 0 and 1. θ is the expected value of the direction and, v is the expected value of the speed, while θxn−1 and vxn−1 are gaussian distributed random variables with zero mean and unit variance. these random variables are independent of θn and vn, respectively. by varying α, we can define different levels of movement randomness. for example, if α is 0, each movement is totally random following a gaussian-markov random process, that is, it is highly difficult to predict the movement trajectory of nodes. in contrast, if α is 0, each movement is the same as the previous one, generating a linear trajectory for all nodes, and in this sense, it would be very easy to predict the movement trajectory of nodes. finally, the higher α is, the more difficult it would be to predict the movement trajectory of nodes. in other words, the lower α is, the easier it would be to predict the movement trajectory of nodes. in this sense, our prediction algorithm should obtain better results as α decreases, which will be shown in the results section. locations given by the mobility model were employed to determine distances, useful to calculate rssi values, as the following expression describes[18]: https://doi.org/10.15837/ijccc.2022.4.4861 6 rssid = rssid0 − 10n ∗ log10 ( d d0 ) (12) in equation 12, n, d0 and rssid0 are known values, and configured for outdoor environments [18], where n is the path loss coefficient, d0 is a distance of reference, and rssid0 set the rssi level at the distance reference d0. in summary, for a particular distance d, the rssi level is calculated by using the previous information. 4 results and discussion the energy consumption and delay metrics were considered to evaluate our mobility prediction algorithm in comparison to other routing algorithms, as described as follows: • distance algorithm: the forwarding selected node for building a communication path from a source iot node to the gateway corresponds to the forwarding node with the shortest distance to the current node. the current node corresponds to the one that has the message. • random algorithm: the forwarding selected node for building a communication path from a source iot node to the gateway corresponds to a random forwarding node connected to the current node. the current node corresponds to the one that has the message. these algorithms (the distance and random algorithm) were designed and implemented by us to be compared against our prediction mobility algorithm. the following table summarizes the most important parameters assumed in the simulations: table 1: simulation parameters parameters value parameters value work area 100x100[m2] rc 20[m] eamp 100[pj/bit/m2] eelec 50[nj/bit] number of gateways 1 number of simulations 1000 (for each figure data point) number of iot nodes 10-50 number of sources nodes 1 our mobility prediction algorithm was implemented in matlab as well as the distance and random algorithms. figure 2 shows the delay performance for each network size (10, 20, 30, 40 and 50 nodes) of each solution. in figure 2, our mobility prediction algorithm always achieved the best delay result independently of the network size in comparison with the distance and random algorithms. in this sense, using a mobility prediction method is very suitable for building fastly a path minimizing the energy consumption. the performance difference between the prediction algorithm and the other algorithms was lesser as the network size decreases. therefore, our mobility prediction algorithm is suitable for scarce networks because the prediction method does not require many neighbours nodes to build reliable communication paths (low amount of communication path disruptions) to achieve the gateway. however, for finding a communication path to the gateway, the distance and random algorithms require many opportunities (many neighbors nodes). in summary, our prediction algorithm is suitable for establishing reliable forwarding neighbour nodes, building fastly a path to the gateway . additionally, the performance of the distance and random algorithms is closer to our mobility prediction algorithm as network size increases because many nodes represents more probability to find a forwarding neighbour node and, therefore, decreases the probability to generate interruptions causing extra delays. according to the level of movement randomness (α), when α is a low value (α = 1/3), this indicates there is a low level of movement randomness, whereby the movement of nodes is more predictable and, thus, is easier for our prediction algorithm to build quickly reliable paths to the destination node. more https://doi.org/10.15837/ijccc.2022.4.4861 7 reliable paths mean less interruptions, thus, causing less delays for building a path to the destination node. given the previous reasons, it is understandable that our prediction algorithm obtains better values of delays for each network size when α decreases. according to the distance and random algorithms, despite not having a prediction method for forecasting future interruptions, they also obtains better values of delays when α decreases because the network is less chaotic (less level of movement randomness), that is, the network is more stable, and then, causing less communication interruptions that could affect the delay for building a path to the destination node. in other words, if α is a high value (α = 2/3), this means there is a high level of movement randomness, causing that the movement of nodes will be less predictable, that is, more difficult to handle by our prediction algorithm because there will be more interruptions that will cause more delays for building a path to the destination node. likewise, for the distance and random algorithm will be the same, that is, due to the network is more chaotic, there will be more interruptions causing more delays affecting the building of a path to the destination node. in figure 3, for each network size (10, 20, 30, 40 and 50 nodes) is presented the performance of the algorithms in terms of energy consumption. the energy consumption by our mobility prediction algorithm is clearly less than the distance and random algorithms. in relation to the random algorithm, its randomness allows it to explore many forwarding nodes before to determine a communication path to achieve the gateway. because of this exploration, this algorithm processes more transmission and reception of packets, causing an extra energy consumption. according to the distance algorithm, its performance tends to be equal to the mobility prediction algorithm performance as network size increases because there are more chances (more forwarding nodes) to achieve the gateway. in contrast, if the network has few nodes, the performance of the mobility prediction algorithm in terms of energy consumption is much less than the other algorithms. 10 15 20 25 30 35 40 45 50 network size 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 d e la y [ s e g ] delay performance mobility prediction algorithm = 2/3 distance algorithm = 2/3 random algorithm = 2/3 mobility prediction algorithm = 1/3 distance algorithm = 1/3 random algorithm = 1/3 figure 2: delay performance for all algorithms. 10 15 20 25 30 35 40 45 50 network size 0 0.2 0.4 0.6 0.8 1 1.2 e n e rg y c o n s u m p ti o n [ j ] energy consumption performance mobility prediction algorithm = 2/3 distance algorithm = 2/3 random algorithm = 2/3 mobility prediction algorithm = 1/3 distance algorithm = 1/3 random algorithm = 1/3 figure 3: energy consumption for all algorithms. https://doi.org/10.15837/ijccc.2022.4.4861 8 5 conclusions a mobility prediction distributed routing algorithm based on a stochastic model has been proposed for establishing a path from a source iot node to a gateway (destination node) considering all nodes are moving in a field and that they have different levels of movement randomness. this proposal is evaluated against two proposals: the distance and random algorithms, also proposed by us to test the performance of our mobility prediction algorithm. we observed that, when the movement of nodes was more predictable, it was easier for our prediction algorithm to build quickly reliable paths to the gateway. that is, more reliable paths were possible for avoiding more communication interruptions and, then, less delays were suffered in the network. in this sense, our mobility prediction algorithm obtained the best solutions in terms of delay and energy consumption compared against not using prediction techniques (the distance and random algorithms) independently of the number of network nodes and the level of movement randomness. funding this research was supported by universidad de los andes in bogotá, colombia. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] i. f. akyildiz and m. c. vuran, wireless sensor networks, 2010. 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[20] germán a. montoya, carlos lozano-garzón and yezid donoso. energy-efficient and delay sensitive routing paths using mobility prediction in mobile wsn: mathematical optimization, markov chains, and deep learning approaches. ieee access journal. 2021. doi: 10.1109/access.2021.3124737 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: montoya, g.a; lozano-garzón, c.; donoso, y. (2022). a stochastic mobility prediction algorithm for finding delay and energy efficient routing paths considering movement patterns in mobile iot networks, international journal of computers communications & control, 17(4), 4861, 2022. https://doi.org/10.15837/ijccc.2022.4.4861 introduction problem formulation stochastic model proposal mobility prediction algorithm energy model mobility model results and discussion conclusions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 5, month: october, year: 2022 article number: 4444, https://doi.org/10.15837/ijccc.2022.5.4444 ccc publications identifying alterability states of a single track railway line control system s. martinez, s. collart-dutilleul, p. bon sébastien martinez, simon collart-dutilleul, philippe bon université gustave eiffel laboratoire estas, campus de lille 20, rue élisée reclus, 59650 villeneuve d’ascq, france name.surname@univ-eiffel.fr abstract in the context of automation and deployment of computer based control systems, a specific application on french railway line is proposed on low traffic single track railway lines. the issue of updates requires thorough consideration. in the case of low traffic single track railway lines, handling the removal of a shunting track, which role is to allow trains to circulate in both directions of a same line, the issue of timing the update to the control system is particularly critical. indeed, a wrongly timed update could lead to a deadlock, while one or more trains are expected to travel while respecting safety constraints on the blocked infrastructure. this paper studies the application of works from the field of dynamic software updating, specifically the works of panzica la manna et al. [12]. using their results on a graph based model of a single track rail line, it identifies alterability states that ensure safety constraints are respected at all times without causing deadlocks. these results are then used to discuss the pertinence of using concepts from dynamic software updating in the context of railway systems. keywords: railway systems, single track railway lines, alterability, graph based modeling 1 introduction french railway traffic is mostly controlled by electromechanical systems and human operators. maintenance of the former is expensive and error-prone as few verification tools allow easy and reliable analysis of such systems. human operators are more adaptable and can be relied on in unforeseen situations. however, assigning many operators to low traffic lines could be an unsuited option for economic purpose and for humane purpose as operating traffic can be a monotonous and laborious task. as an alternative, several projects such as lchip [4] study the automation of traffic control systems using computer based systems. this enables the usage of formal specification languages and formal verification tools for proving the safety of railway traffic. an example of formal specification language popular in the french railway community is b method [1]. in the context of studying traffic control automation, system updates raise an issue. current organization assigns human operators with the task of replacing electromechanical systems whenever https://doi.org/10.15837/ijccc.2022.5.4444 2 station a station b figure 1: a single track rail line they are no longer suited to the traffic organization. in case of malfunction in control systems, in railway lines or when the traffic is simply reorganized, human operators handle traffic control until control systems are repaired or updated. the flexibility offered by computer based solution would make these situations easier to manage and require less human work. developing a software update and having its safety formally proved is easier and less expensive than developing and verifying a new electromechanical control system. this paper studies the case of single track lines in france. on such lines, trains coming from both directions share the same track. to allow upstream and downstream traffic, stations have shunting tracks and/or deviation tracks where a train can be stationed while another train passes through the single track. that organisation concerns over 3000 kilometers of lines in france and is justified by the low traffic on theses lines. the low traffic also justifies the interest for automation as it would reduce the need for human operator intervention. moreover, such kind of lines are common in europe. investments in their automation would rapidly be cost-effective. figure 1 presents an example of single track line joining two stations. station a has a shunting track and a deviation track while station b only has a deviation track. considering two trains going respectively from station a to b and from station b to a, the deviation and shunting tracks are essential to safely handle traffic on the line. one of the trains need to stay on a shunting or a deviation track to allow the passage of the other train. removing a shunting or a deviation track from station a or station b would considerably impact the organization of traffic on the line. this paper considers the case of a single track line as depicted on figure 1, on which traffic is controlled by computer based systems and addresses the issue of updating the control system to adapt to the suppression of a shunting or a deviation track. the control system should be updated in a manner that disrupts traffic as little as possible while strictly respecting security constraints of railway traffic. to achieve that objective, the paper bases itself on results from the domain of dynamic software updating (dsu), especially the paper from panzica la manna et al. [12]. dsu studies ways of applying updates to software systems without disrupting their services and panzica la manna et al. gave criteria for safely updating control subsystems of cyberphysical systems. the following of the paper is organized as follows : section 2 presents related works and summarizes the results of panzica la mana et al. used in this paper. section 3 details a single track line model used as a case study by the paper and section 4 details a graph-based model of the control system for that case study. it details how the control system can be safely updated. section 5 discusses the results of its previous section and concludes the paper. 2 related works low traffic lines have singular design requirements, as investments has to be tailored to the financial potentialities associated to the considered infrastructure. “cost reduction on aging infrastructures” throughout europe was studied in a h2020 european project named netirail1. in france, the nextregio project lead by irt railenium focused on the regional lines with sncf réseau as main user. its main object of study was the issue of single track railway lines. the diversity in terms of needs must be handled with the existing infrastructures, inherited from history: they are using 1http://netirail.eu (last visited on march 24th 2021) http://netirail.eu https://doi.org/10.15837/ijccc.2022.5.4444 3 hcpn infrastructure and interlocking model b infrastructure model operating rules model b4msecure (uml) b operating rules model safety invariant proving rules model figure 2: global framework of the approach (courtesy of antoine ferlin et al. [5]) various technologies with various level of automation. in the nextregio project, building blocks of the abstract architecture are based on functions expressed by the railway infrastructure manager in the document expressing its needs [2]. the design work consists in refining these elementary functions and combining them in order to achieve production goals. as depicted in figure 2, using the rbac profile and the b4msecure tool, an event b model is produced, containing the functional specification and the safety restrictions [13]. therefore, all subsystems have to provide the same functions, but the constraints and the means to achieve the functions are quite different from a subsystem to another. in other words, functions and states are similar to those of the high-speed lines studied in the perfect project [3, 14], but they are broken out in different manners, depending on the level of automation of the line. in the low traffic lines, the solution must be tailored, considering that infrastructure cannot be modified while allowing low exploitation costs. the use of low cost safety compliant programmable platforms seems a well-adapted architecture. they would use generic hardware components and their embedded software could be adapted in order to handle the diversity in low traffic lines. in this context, the lchip project was launched with the objective of using formal methodologies of specification in order to support the analysis, verification and code generation of railway interlocking systems. the main proposition is the use of low cost integrated chips allowing the above services, while running software component controlling local automatisms. however, using low cost components (like the microchip pic32 of the lchip project) may lead to security issues. for instance, it is easier to reverse engineer compiled software as the mips architecture has a small instruction set. some obfuscation techniques may be used to mitigate that weakness [10]. study of the security and the updating of low cost computer-based automatism in the railway industry is recent. the lchip technology was patented worldwide2 and knows no equivalent so far [8]. as a result, the work presented in this paper cannot be compared to related industrial technologies. updating a system of distributed cyber physical systems stands the issue of global system homogeneity. if the system is distributed, it means that the physical parts are linked to local hardware and software components that are connected with others entities using telecommunications technologies. as a consequence, any change in the local knowledge may alter its ability to respect the global operating protocol that makes it able to behave as a whole. for example, telecommunications delays prevent instantaneous global modifications, allowing only successive local modifications. between the first and the last local modifications the system is in a transitory state that may not comply with operating protocols. in the case of railway systems, operating protocols include safety rules that must be respected at any moment, leading to the identification of forbidden states (e.g. states that could lead to collisions). local approaches are not suited in that case, and more global approaches (e.g. defining local management rules of a railway station) should be studied. such approach can benefit 2https://www.erts2020.org/uploads/presentation-pdf/fr2-a1-lchip.pdf https://www.erts2020.org/uploads/presentation-pdf/fr2-a1-lchip.pdf https://doi.org/10.15837/ijccc.2022.5.4444 4 a b c d e a b c d a ae older version a’ b’ d’e’ a bc d e newer version figure 3: updating a cyberphysical system of works from the domain of dynamic updates of software systems. dynamic software updating (dsu) addresses the application of updates on software systems during their execution, without disrupting the services they provide. specific dsu mechanisms are used to modify the code and the state of running programs, migrating them from their outdated version to their newest version. after being dynamically updated, software systems appear as though their were running in their newest version from the beginning. a key issue of dynamic updating is the timing of the updates. applying an update at a wrong time can lead to an inconsistent state where out of date parts of the updated program can call incompatible updated parts. such behaviour may cause errors or even crash the running program. updates need to be applied when the updated program is in a state of alterability, as defined in [11]. several alterabilty criteria (i.e. conditions on the program state guaranteeing that state is a state of alterability) are defined in the literature. the most common is the quiescence of updated components [7]. according to kramer et al. a software system’s component can be safely updated if that component is not beeing used and will not be used in a near future. in the context of cyberphysical systems (i.e. systems composed of software subsystems and physical subsystems), alterability of the system needs to consider the physical state of the system as well as its logical state (i.e. the state of its software subsystems). horiuchi et al. [6] proposed methods for handling movement discontinuity when handling dynamic updates on moving robots. one method is based on the interpolation of functions from the older and the newer version to smooth the discontinuity caused by updating. that method modifies the behaviour of the system to ensure alterability. another method proposed by horiuchi et al. is to postpone the update until the system is in a state where movement discontinuity is not a problem (e.g. the robot is at a stable position) or until the system is in a state that can be reached in both older and newer version, ensuring absence of discontinuity. this paper is based on the works of panzica la manna et al. [12] which proposed criteria for safely updating control subsystems of cyberphysical systems. modelling systems as cyclic oriented graphs with labelled transitions, they identified states where the system is alterable. they defined an updatable state as a state which history in the original graph (i.e. all possible sequences of transitions leading to this state from the initial state in the original graph) are valid in the updated graph (i.e. each of these sequences of transitions can be fired from the initial state in the updated graph) and are completed by a same sequence of transitions in the updated graph (i.e. all the sequences of transitions from the history of the considered state have the same future in the updated graph). they also define weakly updatable states as states that can be reached from a single non-initial updatable state and which history contain the history of the original updatable state (i.e. every sequence of transitions that leads from the initial state to the original updatable state can be fired from it and lead to the weakly updatable state). as for regular updatable states, the future of every sequence of transitions that lead to the weakly updatable state from the original updatable state must be the same in the updated graph. weakly updatable states do not ensure that updates will necessarily be safe. before applying an update at a weakly updatable state, one must verify that the continuation of the execution in the newer version will stay compliant with the specification of the newer version. figure 3 presents an example of update applied to a oriented graph with labelled transitions. https://doi.org/10.15837/ijccc.2022.5.4444 5 according to panzica la manna et al. states b and c are updatable. they are respecively accessed from the initial state by the sequences of transitions a and a,b in both older and newer versions. state d is not updatable as it is not reachable from the initial state by the sequence of transitions a,b,c in the newer version. state e is weakly updatable because it can be reached from b by the sequence of transitions a which is the same sequence of transitions by which the updatable state b is reached from the initial state in both older and newer version. provided firing transitions b then d when the system is in state e complies with the new specification, the control system can be safely updated at state e. the definition of updatable states given by panzica la manna et al. is used in the following sections of this paper to identify states where the controlling systems of single track lines can be safely updated. 3 shunting lines on single track rail lines in france, over 3000 kilometers of railway lines are single track. trains coming from both directions of such lines share the same track, requiring specific consideration to avoid collisions. to address that issue, stations placed on such lines have deviated paths or dedicated shunting tracks as shown on figure 1. because only one train at a time can travel on the rail track, one of the train needs to be temporarily removed from the track to allow passage of the other train. shunting and deviation tracks are used for that purpose. stations, and therefore shunting and deviation tracks, can be separated by a distance of up to 20 kilometers. in the case of a routing error allowing two trains to travel in opposite directions between two stations, the only solution is to have one of the trains travel backwards to its last encountered station. such maneuver is slow and requires the intervention of more human operators for safety reasons. in the worst cases the train may have to travel for 20 kilometers in such a fashion. traffic on single track rail lines includes passenger trains and freight trains. the latter usually travelling at lower speed and having more complex shunting procedures, routing errors may cause extended delays, seriously disrupting rail services. shunting and deviation tracks may be unavailable due to maintenance operations or because their location is attributed to the construction of a new building. in such situations, automatic routing systems are locally replaced by human routing agents. designing automated routing systems able to adapt to the suppression of shunting and deviation lines could make the mobilisation of human routing agents unnecessary or at least, make their tasks easier. designing such a routing system raises the issue of transitioning from regular routing (when shunting and deviation tracks are available) to special routing (with unavailable shunting and/or deviation tracks). for obvious reasons, the routing system should operate the transition while the removed track is still physically available, otherwise it could send a train to a non-existing track. this condition is not sufficient to ensure a safe transition. if handled improperly, that transition may cause problematic situations requiring human intervention. for example, if the routing system transitions to special routing while a train is waiting on a shunting track, it would forget that train. human intervention would then be necessary to insert that train back into the traffic. the rest of the paper considers the case depicted on figure 1. stations a and b are joined by a single track line. both stations a and b have a deviation track and only station a has a shunting track. trains can enter and leave a deviation track without having to go backwards. trains using the shunting track need to travel backwards either for entering the track or for leaving it. because travelling backwards on the main tracks implies strict safety protocols including the mobilization of personnel, we consider the shunting track as only accessible when travelling forward. trains would have to travel backwards only when leaving the shunting track, which requires less strict safety protocols. we consider two trains respectively name t1 and t2, going from station a (respectively from station b) to station b (respectively to station a). a real-life situation would have stations a and b connected to other stations and several trains coming through the two considered stations. for simplicity purpose and without loss of pertinence for the objective of this paper, we only consider the two stations but consider that after reaching their destination the trains will turn around (which rarely happens in reality) and go back to their origin station. such consideration approximate the passage of many https://doi.org/10.15837/ijccc.2022.5.4444 6 trains on the line. 4 model and updatable states in order to use the results of panzica la manna et al. [12], we model the case depicted on figure 1 as an oriented graph with labelled transitions. that graph is obtained from the parallel composition of several smaller graphs modelling the different components of the single track line. we use the same composition rules as defined by panzica la manna et al. all graphs are presented in the finite state process (fsp) notation and we use the ltsa tool [9] for calculating graph composition and checking for deadlocks. 4.1 case study modelling the model of this case study needs to consider safety constraints. to avoid collision, two trains cannot travel in opposite directions on a same track. safety protocols of railway traffic require that two trains cannot travel through a given track section at the same time. we add the constraint that trains cannot enter the shunting track of station a if they are travelling from station a to station b. we enforce these constraints in our model by making illegal transitions (i.e. transitions that would infringe the safety rules) impossible in the graph model. figure 4 details the graphs modelling the different components of the single track line in the fsp syntax. each subfigure details the graph of a component of the whole system (e.g. station, train, track). states of the graphs are written in uppercase and transitions between theses states are indicated by an arrow (−>) symbol preceded by their names written in lowercase. the | symbol separates possible transitions that can be fired from the defined state. figure 4a details the graph modelling station a. its initial state stationrail models the station having its shunting and deviation tracks free and having a train on the station main track. through the transition deviation (respectively shunt), the station changes its state to having its main track free and its deviation (respectively shunting) track occupied. transition leavedeviation (respectively leaveshunt) corresponds to a train leaving the deviation (respectively shunting) track and occupying the main track. transitions enter and leave correspond respectively to a train entering and leaving the station. station a is at maximum occupancy when trains occupy both shunting and deviation tracks as well as the main track. the graph modelling station b is similar to the graph modelling station a excepted it does not have transitions shunt and leaveshunt and their associated states. when composed together, the graphs modelling station a and station b have respectively their transition labels prefixed by a. and b., to ensure the stations can change state independently. the graphs modelling the stations only enforce the constraint disallowing two trains to occupy a same track section at the same time. the shunting track, the deviation track and the main track are three different sections and only when a section is free can it be occupied by a train. the graphs do not enforce the constraint requiring trains to be travelling from station b to station a for them to enter the shunting track. indeed, station graphs have no memory of the direction of the trains. that constraint is enforced in the train graphs. figure 4b details the graph modelling the trains. it has the transitions of both stations plus transitions switchtoa and switchtob respectively corresponding to the train changing its direction to towards station a and towards station b. the graph enforces the cannot enter shunting track travelling backwards constraint as the a.shunt transition is only available when the trains direction is towards a. two instances of that graph are created and composed with the station graphs. the first instance modelling train t1 has all its transitions prefixed by t1. and has trainatatob as initial state. the second instance modelling train t2 has all its transitions prefixed by t2. and has trainatbtoa as initial state. when composed with the station graphs, common labels to train graphs and stations graphs (e.g. leave or shunt) are synchronized. that means that when the transition labelled t1.a.leave is fired in the t1 train graph, transition a.leave is fired in the station a graph. figure 4c details the graph enforcing that a train cannot enter the track joining station a and station b (considered as a single track section) if another train is already travelling on it. it ensures that any train entering the track leaves it before another train enters it. a train enters the track https://doi.org/10.15837/ijccc.2022.5.4444 7 stationrail = ( deviation −> stationdeviation | shunt −> stationshunt | leave −> station) , station = ( enter −> stationrail) , stationdeviation = ( leavedeviation −> stationrail | enter −> stationraildeviation) , stationshunt = ( leaveshunt −> stationrail | enter −> astationrailshunt) , stationraildeviation = ( leave −> stationdeviation | shunt −> stationdeviationshunt) , stationrailshunt = ( leave −> stationshunt | deviation −> stationdeviationshunt) , stationdeviationshunt = ( leaveshunt −> stationraildeviation | leavedeviation −> stationrailshunt) . (a) station a graph trainatatob = ( a . deviation −> trainatadeviationtob | a . leave −> trainatlinetob) , trainatadeviationtob = ( a . leavedeviation −> trainatatob) , trainatlinetob = (b . enter −> trainatbtob) , trainatbtob = (b . deviation −> trainatbdeviationtob | switchtoa −> trainatbtoa) , trainatbdeviationtob = (b . leavedeviation −> trainatbtob) , trainatbtoa = (b . deviation −> trainatbdeviationtoa | b . leave −> trainatlinetoa) , trainatbdeviationtoa = (b . leavedeviation −> trainatbtoa) , trainatlinetoa = ( a . enter −> trainatatoa) , trainatatoa = ( a . deviation −> trainatadeviationtoa | a . shunt −> trainatashunt | switchtob −> trainatatob) , trainatadeviationtoa = ( a . leavedeviation −> trainatatoa) , trainatashunt = ( a . leaveshunt −> trainatatoa ) . (b) train graph track = ( t1 . a . leave −> t1 . b . enter −> track | t1 . b . leave −> t1 . a . enter −> track | t2 . b . leave −> t2 . a . enter −> track | t2 . a . leave −> t2 . b . enter −> track) . (c) track graph timetable = ( t2 . b . leave −> t1 . a . deviation −> t2 . a . enter −> t2 . a . shunt −> t1 . a . leavedeviation −> t1 . a . leave −> t2 . a . leaveshunt −> t1 . b . enter −> t1 . b . deviation −> t2 . switchtob −> t2 . a . leave −> t2 . b . enter −> t2 . switchtoa −> t2 . b . leave −> t1 . b . leavedeviation −> t2 . a . enter −> t1 . switchtoa −> t1 . b . leave −> t2 . a . deviation −> t1 . a . enter −> t1 . a . shunt −> t2 . a . leavedeviation −> t2 . switchtob −> t2 . a . leave −> t1 . a . leaveshunt −> t1 . switchtob −> t2 . b . enter −> t2 . switchtoa −> timetable) . (d) timetable graph figure 4: model of the initial control system https://doi.org/10.15837/ijccc.2022.5.4444 8 stationrail = ( leave −> station) , station = ( enter −> stationrail) . (a) station b graph trainatatob = ( a . deviation −> trainatadeviationtob | a . leave −> trainatlinetob) , trainatadeviationtob = ( a . leavedeviation −> trainatatob) , trainatlinetob = (b . enter −> trainatbtob) , trainatbtob = ( switchtoa −> trainatbtoa) , trainatbtoa = (b . leave −> trainatlinetoa) , trainatlinetoa = ( a . enter −> trainatatoa) , trainatatoa = ( a . deviation −> trainatadeviationtoa | a . shunt −> trainatashunt | switchtob −> trainatatob) , trainatadeviationtoa = ( a . leavedeviation −> trainatatoa) , trainatashunt = ( a . leaveshunt −> trainatatoa ) . (b) train graph boccupied = (b . leave −> bfree) , bfree = (b . enter −> boccupied | a . leave −> bfree) . (c) correction graph timetable = ( t2 . b . leave −> t1 . a . deviation −> t2 . a . enter −> t2 . a . shunt −> t1 . a . leavedeviation −> t1 . a . leave −> t1 . b . enter −> t1 . switchtoa −> t1 . b . leave −> t1 . a . enter −> t1 . a . deviation −> t2 . a . leaveshunt −> t2 . switchtob −> t2 . a . leave −> t1 . a . leavedeviation −> t1 . switchtob −> t2 . b . enter −> t2 . switchtoa −> timetable) . (d) timetable graph figure 5: model of the updated control system whenever its leaves one of the station (i.e. transitions of the form ∗.leave are fired). that train leaves the track by entering the other stations, (i.e. the corresponding enter transition is fired). the parallel composition of the station graphs, of the train graphs and of the track graph results in a global graph with one hundred states that models all the legal behaviours of our single track line example. it enforces the safety constraints presented at beginning of the subsection and the ltsa tool analyzed it as safe from deadlocks. however, it does not model a complete control system as it does not give specific instructions on how and when the trains should travel. we compose that global graph with the graph from figure 4d that models a timetable instructing the movements of the trains. the timetable graph is cyclic to model the passage of several trains through the stations and could model a daily timetable or a weekly timetable. it is composed with the global graph while adding all transitions labels from the global graph to its dictionary. as a consequence no transition from the global graph can be fired unless it can be fired in the timetable graph. the resulting graph is the restriction of the global graph to the behaviour of the timetable graph. because the timetable does not lead to illegal situation, the resulting graph is identical to the timetable graph. if the timetable graph allowed illegal situation, the result of its composition with the global graph would be a different graph with at least one error state, corresponding to illegal states of the system. we consider the removal of the deviation track of station b. the control system requires updating to take that removal into account. the global graph needs to be updated to model all the new possible behaviour and enforce the safety rules and a new timetable must be defined. figure 5 presents the modifications of the global graph and the new timetable graph using the fsp syntax as in figure 4. figure 5a shows the updated graph for station b. it now only has transitions leave and enter. figure 5b shows the updated train graph where transitions b.deviation and b.leavedeviation where removed. the track graph is not modified in the new version. composing the updated station, train and track graph https://doi.org/10.15837/ijccc.2022.5.4444 9 results to a graph containing a possible deadlock. indeed, if a train is at station b and a train is on the track joining station a and station b, going towards station b, the global graph does not allow the train on the track to enter station b and does not allow the train at station b to leave the station. this would correspond in reality to a situation where human intervention is required to have the train on the track safely travel backwards to station a. to avoid such situation, we define the graph of figure 5c allowing trains to leave station a only when station b is free. the new global graph has fifty six states and has no potential deadlocks according to the ltsa tool. the previous timetable being obsolete, a new time table has to be defined, considering the removal of station b deviation track. figure 5d details the new timetable graph we composed with the new global graph to obtain the new control system graph. as in the previous version, that graph is the restriction of the global graph to the behaviour of the new timetable graph. its initial state is equivalent to the initial state in the first version (i.e. one train on the main tracks of each station). we chose to design the updated version with the same initial sate because we consider the new control system as managing traffic on the new line configuration as if it always were configured this way. moreover, this choice makes it easier to find updatable state according to the criteria defined by panizca la mana et al. 4.2 finding updatable states the definition of updatable states as given by panzica la mana et al. can be formally summarized in our context as : definition 1. considering a graph g = (i, s, t, l) in which i ∈ s is the initial state of g, s is the set of all its states, t is the set of all its transitions (expressed in the form of s1 l−→ s2 with s1, s2 ∈ s, l ∈ l) and l the set of all its transition labels (also called the dictionary of g), a state s ∈ s is updatable regarding an update from g to g′ = (i, s′, t ′, l′) if any sequence of transitions leading from i to s in g can be fired from i in g′ and be completed by a same sequence of transition in g′. analyzing the two versions of the control system graph, we can find updatable states according to the criteria of panzica la mana et al. we compare the graphs from figures 4d and 5d and list the states that can be reached from the initial state through the same transitions in both graph. obviously the initial state is one of these states. all states from the first3 to the sixth are reachable in both versions through transitions t2.b.leave, t1.a.deviation, t2.a.enter, t2.a.shunt, t1.a.leavedeviation and t1.a.leave. all these states are reachable in the updated graph from the initial state through a single sequence of transitions. as a consequence, all possible sequences of transitions leading to one of these states in the updated graph from the initial state are completed by the same sequence of transitions. states zero to six from the original graph are therefore updatable. the definition of weakly updatable states as given by panzica la mana et al. can be formally summarized in our context as : definition 2. a state s ∈ s is weakly updatable regarding an update from g to g′ if an updatable state si 6= i can be found in s such that all sequences of transitions that lead from i to si can be fired from si and lead to s and all sequences of transitions leading to s from si are completed by a same sequence of transitions in g′. analyzing the two versions of the control system graph, we can find no weakly updatable states. indeed no state can be reached from the sixth first states (i.e. the only updatable states of the graph) through a sequence of transitions that begins with t2.b.leave (the first transition that leads to the first updatable state from the initial state). the linear nature of the control graphs resulting from the modelling of our case study make the appearance of weakly updatable states less probable. 5 discussion and conclusion after analyzing our model of the case study, we identified six states where the control system can be safely updated at the beginning of its cycle. if the control system is at a later state, we need for 3the initial state being enumerated as state zero, the first state is the state reached by firing the first transition https://doi.org/10.15837/ijccc.2022.5.4444 10 state15 = ( t2 . a . enter −> t2 . a . shunt −> t1 . b . leave −> t1 . a . enter −> t1 . a . deviation ) . figure 6: example of transitional timetable it to finish its current cycle before updating. this is not an issue when the removal of the deviation track of station b is planned. however if an unplanned event (e.g. an accident, exceptional weather conditions) makes that track unavailable, we may not have the time to wait for the completion of the control system cycle to update it. a solution would be to guide the execution of the control system to alterability, as proposed by horiuchi et al. [6]. a specific transitional timetable, taking the special conditions into account, could be designed to lead the control system from its current non-updatable state to an updatable state. an example of such transitional timetable could be the one presented on figure 6, guiding the control system from its fifteenth state (non-updatable) to its fourth state (updatable). the transitional timetable would be composed with the newer version of the global graph, modified so that its initial state correspond to the fifteenth state of the original control system graph. the resulting transitional control system would be installed by the mean of a dynamic update. after reaching the last state of the transitional control system, another dynamic update would install the updated control system. in the case of these two updates, original and updated graphs have different initial states, disabling the usage of the results of panzica la mana et al. other methods for ensuring the safe application of these updates would have to be studied. designing generic procedures for designing transitional timetables starting from any given nonupdatable state is kept for future work as it may raise issues. for example, in our case study, if a train is stationed on the deviation track of station b, it is not possible to find a transitional timetable compliant with the new global graph. possibility for safely using the older global graph must be studied and a refinement of the model presented in this paper would probably be needed. our case study considered two timetables and found several updatable states. selecting different timetable could have lead to finding more or less updatable states. for example, switching transitions t2.a.leaveshunt with t1.b.enter fired respectively from states six and seven in the initial timetable would have made the seventh state updatable. the choice of timetables has no impact on the point of this paper. the issue of identifying optimal updated timetables maximizing the number of updatable states is kept for future work. our work showed the pertinence of using the concept of alterability to address the issue of modifications on control signaling systems. it increased the interest for the deployment of computer based systems for managing traffic on single track rail lines by increasing the degree of automation. reconfiguration of the line can be safely and automatically handled. further work on the issue of updating control systems would increase automation. as a result, operating costs on single track rail lines would decrease and human workers conditions would improve. another perspective of our work is that through the use of modelling and of formal specification, the safety of the control systems could be formally proved, increasing safety of traffic. moreover, in today’s world where updates are the cornerstone of systems safety and security, each update applied on the control system could be checked to ensure that their application guarantees the safety of passengers. references [1] j. r. abrial. the b-book: assigning programs to meanings. cambridge university press, new york and ny and usa, 1996. [2] r. ben-ayed, s. collart-dutilleul, and e. prun. formal method to tailored solution for single track low traffic french lines. international railway safety council 2016, 10 2016. [3] rahma ben ayed, simon collart-dutilleul, philippe bon, akram idani, and yves ledru. b formal validation of ertms/etcs railway operating rules. pages p124–129, june 2014. [4] clearsy. low cost high integrity platform, 2016 (accessed november 25, 2020). https://doi.org/10.15837/ijccc.2022.5.4444 11 [5] antoine ferlin, rahma ben-ayed, pengfei sun, simon collart-dutilleul, and philippe bon. implementation of ertms: a methodology based on formal methods and simulation with respect to french national rules. transportation research procedia, 14:1957 – 1966, 2016. [6] eiichi horiuchi, osamu matsumoto, and noriho koyachi. safety issues in nonstop update of running programs for mobile robots. 2005 ieee/rsj international conference on intelligent robots and systems, iros, 01 2005. [7] jeff kramer and jeff magee. the evolving philosophers problem: dynamic change management. software engineering, ieee transactions on, 16:1293–1306, 12 1990. [8] thierry lecomte, david déharbe, denis sabatier, étienne prun, patrick péronne, emmanuel chailloux, steven varoumas, adilla susungi, and sylvain conchon. low cost high integrity platform. corr, abs/2005.07191, 2020. [9] jeff magee, jeff kramer, robert chatley, sebastian uchitel, and howard foster. labelled transition system analyser, 2006 (accessed november 25, 2020). [10] sébastien martinez, dalay israel de almeida pereira, philippe bon, simon collart-dutilleul, and matthieu perin. towards safe and secure computer based railway interlocking systems. international journal of transport development and integration, 4:218–229, 07 2020. [11] sébastien martinez, fabien dagnat, and jérémy buisson. pymoult : on-line updates for python programs. in icsea 2015 : 10th international conference on software engineering advances, pages 80 – 85, barcelone, spain, november 2015. [12] v. panzica la manna, j. greenyer, c. ghezzi, and c. brenner. formalizing correctness criteria of dynamic updates derived from specification changes. in 2013 8th international symposium on software engineering for adaptive and self-managing systems (seams), pages 63–72, 2013. [13] bougacha racem, abderrahim ait wakrime, slim kallel, rahma ben ayed, and simon collartdutilleul. a model-based approach for the modeling and the verification of railway signaling system. pages 367–376, 01 2019. [14] pengfei sun, simon collart-dutilleul, and philippe bon. a formal modeling methodology of the french railway interlocking system via hcpn. june 2014. https://doi.org/10.15837/ijccc.2022.5.4444 12 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: martinez, s.; collart-dutilleul, s.; bon, p. (2022). identifying alterability states of a single track railway line control system, international journal of computers communications & control, 17(5), 4444, 2022. https://doi.org/10.15837/ijccc.2022.5.4444 introduction related works shunting lines on single track rail lines model and updatable states case study modelling finding updatable states discussion and conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 2, month: april, year: 2021 article number: 3935, https://doi.org/10.15837/ijccc.2021.2.3935 ccc publications sda-sm: an efficient secure data aggregation scheme using separate mac across wireless sensor networks m. elshrkawey, h. al-mahdi mohamed elshrkawey department of information system suez canal university, egypt ismailia 41522 e-mail: melshrkawey@ci.suez.edu.eg hassan al-mahdi* department of computer science suez canal university, egypt ismailia 41522 *corresponding author: drhassanwesf@ci.suez.edu.eg abstract securing the aggregated data of the wireless sensor networks (wsns) is a vital issue to minimize energy consumption and face potential attacks. this paper presents a novel end to end encryption scheme defined as aggregating secure data -separate mac (sda-sm). the importance of the sda-sm is twofold. first, it separates the secured aggregated data and the message authentication codes (mac) into two different packets. second, it transmits these packets in a random separate time-slot according to the scheduling of the tdma. moreover, the tdma applied in the leach protocol is modified to adequate to the proposed sda-sm scheme. the sda-sm uses macs to verify the integrity of the aggregated data and uses a sensor protected identifier to authenticate the source of data. the simulation results of the experiments assure the sda-sm objectives can be achieved with less computation of the communication overheads than earlier techniques. besides, sda-sm will be able to accomplish the integrity and confidentiality of accurate aggregated data while saving the energy to prolong the network lifetime. keywords: wsn, leach, secure data aggregation, homomorphic encryption, message authentication code. 1 introduction wireless sensor networks (wsns) are employed in various vital applications. these applications may comprise checking the human heart rates, military applications, traffic monitoring, etc. [10]. actually, the sensor nodes depend on their limited resources and their predetermined power to survive [12]. however, the functions of the wsn are sensing, aggregation and transmit secured data to the main base station [23]. these functions are faced by the problem of excess energy consumption due to https://doi.org/10.15837/ijccc.2021.2.3935 2 the transmission of repeated data using the long-range of communication among the sensor nodes [21]. on another hand, the wireless transmissions of the detected data may be prone to eavesdropping, the interception or harming the original data by injecting false data to infect it [15]. so, many scenarios were presented to offer integrity and confidentiality of data as a critical issue. accordingly, the privacy of data among sensor nodes should be preserved even from the trusted and cooperating sensor nodes [24]. in fact, these problems are managed in the framework of three paradigms: 1) wsn topology control and its routing method for transmitting the sensed data, 2) data aggregation and 3) securing aggregated data before transmission [22]. the wsn is typically self-organized network designed to transmit data in dynamic multi-hops, where vast amounts of power are expended by the sensor nodes during the data transmissions [26]. clustering topology is managed based on the leach protocol to reduce the consumed energy by the sensor nodes [18]. in the data aggregation paradigm, the aggregated sensors accept the various data from the diverse sensor nodes and remove the received data redundancy [8]. moreover, the aggregated sensors determine the valuable data and transmit it to either the upper aggregator or to the base station. accordingly, the main objectives of the aggregation process are as follows: first, increasing the wsn energy efficiency by reducing the transmitted messages among the wireless sensors and the base stations [1]. second, decreasing the exploited cost of deploying and preserving the network. in fact, a variety of data aggregation techniques were presented to shrink the amount of transmitting data over the wsn operations [9]. however, the data aggregation techniques may be suffered from a decline in the qos metrics, such as latency and accuracy of data. finally, the secured data aggregation paradigm aims to decrease the exposure of wsn to malicious intrusions and attacks via introducing the encryption process for securing data. secure data aggregation can be categorized into two groups, the hop-by-hop encryption and the end-to-end encryption [16]. in the hop-by-hop encryption, the aggregators should decrypt all the received data, combined them using a conforming function and then encrypt the aggregation result before re-sending them to the next-hop. in the end-to-end encryption schemes, each aggregator will receive the ciphertexts from its leaf nodes. the received encrypted data are assembled without decryption and forward it to the next aggregator or to the base station [4]. however, the guarantee of securing the aggregated data is still suffering from destroying or from inserting false data by the adversaries and hackers. this problem can be solved by employing the message authentication code (mac) to detect false data and achieve data integrity. in this paper, we present a novel end to end encryption scheme defined as aggregating secure data -separate mac (sda-sm). sda-sm offers confidential and integrity preserving for the aggregated data in the wsn. the main idea of the proposed sda-sm scheme is to combine the received data at the aggregators in two separate packets. the first packet contains all secured data and the second packet contains all macs data. in addition, the macs are aggregated based on the homomorphic encryption that will be explained later. two computed and related values defined as the data stamp are inrtoduced. the mac stamp is created to identify each aggregated mac to its aggregated and encrypted data packet. finally, the encryption keys and the secretly shared information are deduced from the elliptic curve cryptography. in closing, the main contributions of this paper are as follows: 1. overcome the problem of the duplicated data due to the overlap of the sensor nodes by improving the management operations of the leach protocol. 2. introduce end-to-end data confidentiality via the implementation of the homomorphic encryption. 3. increase the aggregated data integrity by forming the homomorphic mac using data encryption. 4. overcome the problem of eavesdropping by separating the computed mac and encrypted data. the remainder of this paper is organized as follows. section 2, includes related work. in section 3, the assumptions of the proposed scheme are presented. section 4 offers the detail of the proposed sda-sm scheme. the security analyses of the suggested sda-sm scheme are clarified in section 5. https://doi.org/10.15837/ijccc.2021.2.3935 3 the simulation results are represented in section 6. finally, the last section includes the conclusion of this work. 2 related work recently, different schemes have been used by researchers to transmit secure aggregated data across the wsns. this section reviews the most important previous researches in this area. in [17], eirda is implemented on a motionless clustering of a consistent spreading of sensor nodes in each cluster. the operation of eirda is initiated when the sink sends the interest packet to the all sensors in the distribution area. the cluster head elect the confidence sensors from the all interested sensors and aggregate the data received from these selected sensors within the cluster and sends them to the sink. actually, eirda objects to offer an effective aggregated data to the sink. in addition, it aims to provide an efficient energy saving scheme and accomplish higher reliability. in [25], the ipda accomplishes the integrity of data by employing the redundancy to create two separate aggregation paths to accumulate the disseminated data. the scheme is implemented by allowing each sensor node to divide its sensed data into two different pieces randomly. each set of pieces are encrypted before transmitting to one of the designated sensor nodes of the aggregation path. in the following, the two aggregated data from two different nodes are received and compared in the base station. the results from two paths are accepted only if the difference between the two sets of assembled data does not contrast with a predetermined value. the ipda based on excessive operations which increases the number of packets lost and more consumed power. in [6], the icpda scheme was applied to a cluster topology. it is executed on three sequences of actions. each sensor transmits a primary value to the remaining members in the cluster. this value is used by a node to hide its sensory data. next, the secret data is transmitted to each member in the cluster. finally, each sensor node associates its secret data to the incoming secured data and transmits the computed aggregation to the head of the cluster. this protocol suffers from severe overheads which increase periodically when the cluster size is increased. in [27] and [13], authors propose schemes based on the elliptic curve discrete logarithm. however, in [27], the presented scheme isn’t able to meet the security requirements such as confidentiality and source authentication. in [13], the authors present a hierarchical data aggregation scheme to ensure confidentiality. each cluster is assigned to its public key. the encryption is performed when the messages data from all cluster members have arrived. hence, the transmission of unprotected data may affect its integrity. in [7], the authors implemented a homomorphic hash for the encryption of the combination of data. they replace the xor process with a modular addition. however, this proposal is restricted to work only against passive attacks. finally, in the [14], the authors propose an aggregated mac (amac) that implemented based on mac to offer authentication. the implementation based on amac aimed to diminish the cost of the transmission deserved by mac. the authors offer a cluster-based scenario by applying mac to decrease the number of bits used for authentication during the communication. in this paper, the eirda, ipda and mac are elected as the most popular schemes that were investigated and compared by the researchers for testing the energy consumptions and measuring the security aspects for their proposed schemes in the wsn. however, the proposed scheme is distinguished than these schemes in a two main advantages. firstly, it separated the aggregated data packet than the aggregated mac packets and each of them transmitted in different path. second, it consumes less energy when compared by the mentioned schemes as clear in the simulation section. 3 cluster wsn description as shown in figure 1, the wsn includes a base station (bs) that gathering information to the end applications and operates as a sink node. the coverage area of the wsn is divide into clusters nc. each cluster includes ns sensor nodes. each sensor is referred to as cluster member (cm) with a unique identifier. let idji be the identifier of the cm i in the cluster j, where i = 1, 2, 3, . . . , ns and j = 1, 2, 3, . . . , nc. in each cluster, one of its cms is selected to be cluster aggregator (ca). the cm sensors periodically collect data from their monitored area into their data buffer, then transmit https://doi.org/10.15837/ijccc.2021.2.3935 4 base station cluster head cluster members cluster cell figure 1: network model of the sda-sm the data to their ca. the ca sends the collected data to the bs. the wsns are managed based on the modified leach protocol which has high significance in clustering routing protocols [5]. the operations of this protocol are implemented in successive rounds. each round includes a setup phase and a steady-state phase. in the setup process, the clusters are formed and one of its sensors is employed as ca. before the installation process of the wsn, initial keys are created in the offline state at both the cms, ca and bs. these keys are used later to generate secret shared information. the secret shared information will be deployed to create the required session encryption keys. 4 the proposed sda-sm scheme in the proposed sda-sm scheme, the initial keys consist of a private key kr and a public key ku . the value of kr is manually selected in a random manner and stored at both sensors and bs. the value of ku will be generated according to the elliptic curve cryptography (ecc) [3]. the cryptosystem of the ecc is based on the six parameters of the elliptic curve domain over hp . where hp is a finite field consists of the set of integers modulo a large prime number p. to generate ku , three out of these six parameters are used to produce it. these three parameters are denoted as p, g (gx, gy) , and n , where g (gx, gy) is a base point on the elliptic curve e (hp) and n denoting the order of base point g (gx, gy) (i.e., the number of different points over e (hp). the mentioned three parameters are combined with the value of kr to create ku using algorithm 1 as follows. at this end, each sensor node includes the tuple ( kr, id j i ) . however, the tuple (kr, ku ) is stored in the bs. after generating the initial keys, the proposed sda-sm secures the aggregated data in four successive processes as depicted in figure 2. these processes are setup process, encrypt-sign process, aggregation process, and verification process. in the setup process, the required of the session keys will be created. the encrypting process includes the implementation of the secure technique for the sensed data and generating the encrypted tag for each cm. the aggregation process is used for https://doi.org/10.15837/ijccc.2021.2.3935 5 algorithm 1 generating the initial keys 1: input a large prime number and base point order p and base point order n. 2: generate the base point g (gx, gy) using ecc. 3: kr ← random (1, n − 1) 4: ku ← kr.g (gx, gy) 5: output: the vector ku with two components. aggregating data of all sensors node to their aggregator within each cluster. in addition, a single mac based on the homomorphic property is created within each cluster [11]. setup process encryption sign aggrgation verification leach operations cluster members cluster head base station session keys generation figure 2: general processes of the sda-sm scheme finally, the arrival of the secure aggregated data to the bs should be verified in the verification process. in the following subsections, more details about the operations of each process are clarified. also, the details of the proposed contributions in the sda-sm scheme are explained and illuminated. 4.1 setup process this process is implemented at both the bs and sensors. first, the bs generates the two points sb and ab based on the value of the key ku as follows. sb = r · ku = (sx, sy) (1) and ab = d · ku = (ax, ay) . (2) where r, d = random (1, n − 1) . the points sb and ab are used later to generate the session keys at the bs. second, the bs generates the shared secret information points r and b using the ecc as follows: r = r · g (gx, gy) = (rx, ry) (3) and b = d · g (gx, gy) = (bx, by) . (4) the bs broadcasts the points r and b to the all sensors. upon the shared secret information points r and b are received by the sensors, they start to build the two points ss and as as follows: ss = kr.r = kr.r.g (gx, gy) (5) and as = kr. b = kr.d.g (gx, gy) . (6) one of the main contributions of the proposed sda-sm scheme is that the value of pairs (sb, ab) and (ss, as) are equal. this can be verified using (1), (2) and the value of ku as follows: ss = kr.r.g (gx, gy) = r. (kr.g (gx, gy)) = r.ku = (rx, ry) = sb (7) https://doi.org/10.15837/ijccc.2021.2.3935 6 and as = kr.d.g (gx, gy) = d. (kr.g (gx, gy)) = d.ku = (ax, ay) = ab (8) as a result, the generation process of the session keys at the bs using the points sb and ab gives the same results at the sensors based on the point sb and ab. finally, the generation process of the session keys kd, km and kid is started at the bs and sensors using the points (sx, sy) and (ax, ay). the kd, km and kid are used to cipher/decipher data, digital signature and identifier of each sensor respectively. for all sensors and the bs, the values of , and kid are obtained using the well-known key derivation function kf d (x, y) [2] as follows: kd = kf d ((sx, ax) , kr) , (9) km = kf d ((sy, ay) , kr) (10) and kid = kf d ((kd, km ) , kr) (11) generally, these keys can be changed from session to session by changing the base point g (gx, gy). 4.2 encrypt-sign process in this section, the plain message (pmsg) encryption procedure and cm signature tag will be explained as follows. first, in the proposed sda-sm scheme, the pmsg at each cm is divided into chunks ml, l = 1, 2, 3, · · · , n where ml has length of l bits . the encryption procedure is started by masking each chunk using ay stream of bits as ml = ml + ay. the masked chunk is encrypted through 32 stages of the unbalanced feistel network [19]. a feistel network is a general method of transforming any function into a permutation. it was proposed by horst feistel in his design of lucifer [20]. the feistel function, f takes ml and km sg as inputs and the output is cipher message (cmsg). where the value of km sg is generated at every cm based on its identifier id j i as follows: km sg = kd ⊗ p kg j i (12) where the symbol ⊗ denotes the xor logical operation and p kgji is a packet-guide used to hide the value of idji . the value of p kg j i is given as: p kg j i = id j i ⊗ kid (13) the proposed sd-sma scheme encrypts the pmsg at each sensor through 32 subsequent stages using algorithm 2. second, the sensor tag signature is generated as follows. each cm i of the cluster j computes its tag signature t ji based on the values of km , id j i and cm sg j i as follows: t j i = ( cm sg j i + id j i ) ⊗ km (14) finally, each cm i of cluster j sends the triplet ( cm sg j i , p kg j i , t j i ) to its ca as shown in figure 3. 4.3 aggregation process one of the most important contributions in this scheme is the separation among the aggregation of the encrypted messages and the message authentication code. upon the ca of cluster j receives the triplet ( cm sg j i , p kg j i , t j i ) from its ns active cm, it starts to compute the total message authentication code m acjt as follows: m ac j t = ns∑ i=1 t j i (15) https://doi.org/10.15837/ijccc.2021.2.3935 7 algorithm 2 encryption process of pmsg 1: input pmsg 2: compute km sg using 12 and 13. 3: divide pmsg into chunks ml, l = 1, 2, 3, 4. 4: set k0m sg ← km sg, w ← 1 5: while w ≤ 32 do 6: if w is odd then 7: kwm sg ← k w−1 m sg 8: else 9: kwm sg ← 2 << k w−1 m sg 10: end if 11: for j ← 1 to 4 do 12: m w+1j ← f ( m wj , k w m sg ) 13: end for 14: w ← w + 1 15: end while 16: cm sg = m w+11 + m w+1 2 + m w+1 3 + m w+1 4 cluster members ¯n �n − 1 þi l2 z1 cluster aggregator cm sgn p kgn tn cm sgn −1 p kgn −1 tn −1 cm sgi p kgi ti cm sg2 p kg2 t2 cm sg1 p kg1 t1 figure 3: cluster members send messages to their cluster head https://doi.org/10.15837/ijccc.2021.2.3935 8 once the value of m acjt is calculated, the ca of cluster j creates two stamps known as data stamp st j dat a and mac stamp st j m ac as follows: st j dat a = id j ca ⊗ kid (16) and st j m ac = id j ca ⊗ (2 << kid) (17) where idjca denoting the ca identifier of cluster j . as shown in figure 4, the ca of cluster j formulates all the received triplet ( cm sg j i , p kg j i , t j i ) , the m acjt and the stamps in two separate packets as follows: 1. for each cm i of the cluster j , the pair ( cm sg j i , p kg j i ) is filled into the aggregated data packet one by one followed by st jdat a at the end. 2. the values of m acjt and st j m ac are stored in the aggregated macs packet. (a):aggregated data packets cm sg j 1 p gk j 1 cm sg j i p gk j i cm sg j ns p gk j ns st j dat a (b):aggregated macs m ac j t st j m ac figure 4: aggregated packets for ca of cluster j 4.4 verification process for each session, the verification process is accomplished at the bs using the cluster aggregator identifier idjca and the session keys, kd, km and kid . these keys are obtained using equations (9)-(11). first, for ca of cluster j , the received aggregated data and macs packets at the bs are matched with their correspondence cluster aggregator j as follows: 1. the bs computes the data stamp st jdat a−bs and the mac stamp using (16) and (17) respectively. 2. the bs compares the values of st jdat a−bs and st j m ac−bs with the received values st j dat a and st jm ac respectively. if they are equal, this means the received aggregated data and macs packets are verified with cluster aggregator j . otherwise, the aggregated data packet and its mac are neglected. second, upon the aggregated data and mac packets are verified with cluster j , the bs starts to decrypt each packet-guide concatenated to each message in the aggregated data packet of cluster j to get the identifier of cm associated with that message and then decrypt the message itself. the process is clarified in algorithm 3. 4.5 steady-state phase of the leach protocol each round time in the leach protocol consists of a setup phase period and steady-state phase period. the setup phase period includes the wsn initialization and scheduling for the transmission process. in the steady-state phase, time is divided into slots. each set of slots makes up a frame as depicted in figure 5. the transmission process is implemented according to the time division multiple access (tdma) schedule. the scheduling process is customized by the ca of the cluster before notifying all remaining members in the cluster. according to the proposed sda-sm scheme, both of https://doi.org/10.15837/ijccc.2021.2.3935 9 algorithm 3 verification process 1: : data packet and macs packet. 2: for j ← 1 to nc do //iterate for all active ca 3: m acjt −bs ← 0 4: for i ← 1 to ns do // iterate for all active cms j 5: idji = p kg j i ⊗ kid 6: calculate the tag signature t ji using 14. 7: m acjt −bs ← m act −bs + t j i . 8: end for 9: if the received m acjt = m ac j t −bs = then 10: accept the aggregated packet. 11: for i ← 1 to ns do // iterate for all active cms j 12: decrypt the cm sagji using id j i and p kg j i 13: end for 14: else 15: reject the aggregated data packet and its mac. 16: end if 17: end for the aggregated data and mac are stored in separate packets. so, the proposed contribution for the steady-state phase is dividing each time slot within a frame into two mini-slots as shown in figure (5). the min-slot period is sufficient to transmit an aggregated data or mac packet. this division will allow each aggregator to send its two packets in scattered min-slots (i.e., random assignment of the mini-slots). when the separation process is added to the security operations performed in each cm and ca, a robust confidently and integrity will be deduced for the aggregated data. time axis round 1 round 1 setup phase time steady-state phase time frame-1 frame-2 frame-3 frame-n-1 frame-n slot-1 slot-2 slot-3 slot-k mini-slot 1 mini-slot 2 frame m(1) d(2) m(2) d(6) m(3) d(1) m(4) d(5) m(5) d(3) m(6) d(4) slot mini slot m (i): mac oacket of aggregator i d(i): data packet of aggregator i figure 5: the proposed tdma for the aggregators transmission 5 security analyses in spite of the multi-hops is an implemented approach to minimize energy consumption in wsn, it deduces a multiple of the security challenges. so, the implementation of end-to-end security services becomes obligated and required. in the following subsections, the roles of the sda-sm scheme in this context will be revealed. the sda-sm scheme is analyzed according to the security objectives such https://doi.org/10.15837/ijccc.2021.2.3935 10 as confidentiality, integrity, and authentication. additionally, the sda-sm as a robust scheme will be investigated against different types of mentioned attacks. 5.1 data confidentiality aas aforementioned, each ca sends its aggregated data and mac packets in two separate minslots within a frame with length z slots (i.e. 2z mini-slots). assume that the attacker succussed to obtain the aggregated macs packet of the cluster aggregatorwithin a certain frame. step 1: he tries to determine the ca who owned the captured aggregated macs packet by computing the value of st jm ac , (j = 1, 2, 3, . . . , nc) for each ca and compare the result with stamp mac within the captured aggregated macs packet using equation (9). to do this he needs all the possible values of the both aggregator identifier idjca and the private key kid . the length of id j ca is 32 bits (i.e., 4 bytes) while the length of kid is 34 bits. as a result, the number of trials needed for this step is given as sttrials = ns∑ j=1 j × 266 (18) the probability of success, pst−sucess, in this step is given as pst−sucess = 1 sttrials (19) step 2: assuming that, the attacker succussed in step 1. in such case, he tries to determine the correct aggregated data packet with total tag signatures equal to the m acjt . the tag signature of each message within the aggregated data packet were calculated based on the values of km , id j i and cm sg j i as illustrated in (14) and (15). all these parameters are unknown to the attacker except the message cm sgji . the number of trials needed to compute the tag signature of cm i in cluster j within the aggregated data packet is given as trials. the total number of trials for the whole aggregated data packet is given as tagg−m ac = ns∑ i=1 i264 (20) the attacker repeats this process to all the aggregated data packets with the transmitted frame. consequently, the final number of trails is given as tm ac = ns∑ i=1 i × 264 × f (21) the probability of success in this step is given as pm ac−success = 1 tm ac (22) finally, the probability of attacker failure is given using (22) and (22) as follows: pf ailure = 1 − pst−sucess × pm ac−success moreover, assume the attacker succeeded in getting some of the messages and some of macs; he will collide by both of the encryption keys used in securing the stamps. therefore, the attacker trails may approximately tend to zero. thus, the confidentiality of the sda-sm scheme is accomplished regardless of any further actions. 5.2 data integrity in the sda-sm scheme, integrity is built to accomplish the second step of security. firstly, it is based on homomorphic mac. this mac is computed based on three unknowns for the attacker. they are the encrypted message, the embedded identifier of each sensor and the mac key km . assuming https://doi.org/10.15837/ijccc.2021.2.3935 11 an adversary is succeeded in catching any encrypted message, he must know the session keys (i.e. kd, km , kid) that are used for encrypting the message components. however, these keys kd, km and kid built based on secret information that is embedded within the sensors and base station before the deploying of the wireless network topology. however, these keys can be changed every round if required by changing the two points of secret information that are ciphered and deciphered based on the values of ku and kr which are embedded within the sensors and base stations without exposing to any mutual propagations. moreover, the attacker must know the mac matched to each catches message. the existence of the related mac is obligatorily required to authenticate the catched message. on the other hand, the encrypted sensor message is required as a part of the digital signature computed for each sensor within each cluster. as a result of the two last equations, the probability of getting the required unknowns is tending to zero. again, the essential separation of the aggregated data packets and aggregated mac packets will make an adversary tries cannot effectively copy specified messages with their related macs. furthermore, if an adversary succeeded in performing any alteration in the message through transmission, the base station will be able to detect this alteration based on the strange identifier presented by an adversary through this process of the sensor authentication. because each encrypted message is assembled in the transmitted packet followed by the encryption of the identifier of its sensor (pkgi). so, the attacker will fail in performing any effect on the integrity of the message. thus, the proposed sda-sm scheme preserves robus integrity. 5.2.1 unauthorized aggregation both of idji and kid are unknowns to an adversary. in the sda-sm scheme, the aggregations of the encrypted data and the encrypted macs are performed in two separate packets based on data that already encrypted and tied by two different computed values defined as the packet stamps and mac stamp. both of aggregated data and mac are tied by two different computed values defined as the packet stamps and mac stamp. hence, these stamps should be decrypted to identify each aggregated packet to its mac. however, these stamps are the encryptions of the aggregator identifier with two different keys ( kid, id j ca ) that mentioned before. desides, each sensor data is concatenated with a packet guide that is encrypted based on the embedded identifier of its sensor idji and the encrypted key kid. so, when an attacker tries to perform the false aggregation, he must know these different session keys, kid, id j ca, and id j i for each participated sensor and the applied kid. since these secret keys and identifiers are disappeared relative to an attacker, he will not able to perform the required and right aggregations. on the other hand, these keys can be changed every round if required. so, if these keys are obtained to him, it will become useless. even though, any presented forged data to the aggregated data packet will be detected through the mac verifications that are propagated on a different channel. so, the proposed scheme is more to fight against unauthorized aggregation when compared with other schemes. ipda as an instance, the data are sliced and transmitted in different paths. but the encrypted data are decrypted within the aggregator nodes during their transmission. so, it is more exposed to false aggregation. in addition, for both of the eirda and amac, the session keys are exchanged among the base station and the sensor nodes. moreover, both the encrypted data and encrypted macs are grouped in a single packet. so, both of them may be exposed to unauthorized aggregation when these session keys or messages are obtained by the attackers. 5.2.2 malleability the malleability permits the alterations of the ciphered data regardless of inevitably know its substances or its source. the alteration may be performed simply by embedding any data similar to ciphered data. so, the sda-sm scheme offers end-to-end integrity by allowing the sink to confirm the singular data by authenticating every source node through its identifier that is defined in the base station. so, if ciphered data of a specific source is changed, the verification process will be failed and this source of aggregated data will be excluded. moreover, the sink will jump to the detection procedure about the malicious node. in the sda-sm, the detection procedure about the malicious nodes is performed in the sink through the identification process. on other hands, any alteration in transmitted ciphered data will be detected through the mac verification procedures. https://doi.org/10.15837/ijccc.2021.2.3935 12 table 1: simulation parameters parameter value number of sensor nodes 50-450 nodes range of transmission 30m dimensions of work area 450 × 450 m2 transmission power 0.650, 0. 125 mw received power 0.375, 0.75 mw preliminary energy 7.2 j noise floor -80 db simulation time 400 sec. 5.2.3 node compromise in the sda-sm, if the malicious node can capture one of the aggregators, it will not be able to access the plain contents of its members. originally, there are no plain data appear in the aggregators. the only side effect may be performed by the adversary is the editing of the encrypted message of the aggregator. however, the compromised node should also be able to get the encrypted packet of the separate mac that transmitted on a different channel. this separation presents a different guide than all the presented methods. 5.2.4 replay attack an adversary tries to acquire a portion of legitimate traffic for a period of time before resending again to impose an aggregation error to affect the entire of the aggregated results. however, an adversary tries were based on valid packets already transmitted to the base station however, an adversary will not be able to perform his try the legitimate encrypted and aggregated packet without obtaining its related mac and different session keys. because, the mac transmitted on a different channel is unknown for the attacker. even though, as very lowest probability to get both of aggregated packet and its own mac. as the modified data sent to the base station, the received cipher data will become unknown. because the applied encryption keys were changed in the next round. so, adversary operations are simply detected. 6 simulation results and performance analysis matlab 2015a is employed to simulate the sda-sm scheme and the compared schemes. the 2d elliptical gaussian distribution was applied. the wsn topology was embodied by the number of sensors between 50 and 450. the deployed work area was simulated in square region 450 × 450 m2. the preliminary energy is 7.2j for each cm. generally, all simulated parameters that are implemented to investigate the sda-sm are shown in table 1. to minimize the error of the comparisons, the experiments of the sda-sm simulation and compared schemes ipda, eirda and amac were performed regularly for 20 times. accordingly, the results of the average value of the experiments were measured for both terms of communication overhead and energy consumption. 6.1 communication overhead to verify an evident comparison among the sda-sm and the compared schemes, the communication overhead evaluated by the summation of the sending bytes from all the sensor nodes during the aggregation phase. the communication overhead was determined by the excess number of bytes that occurred as a result of the aggregation process for the ciphertexts and the mac. the excess https://doi.org/10.15837/ijccc.2021.2.3935 13 bytes are the bytes embedded by the sensor nodes to produce both of the ciphertexts and the mac before transmission. the count of the participated sensors was considered during the measure of the aggregation performance. the results are shown in figure 6. the figure reveals the amount of excess data that was generated by all schemes to perform the aggregation process for the same real sensing data. it is clear that the amount of excess data that was required by the sda-sm scheme is the minimum amount of the overhead data when compared by the other schemes for the same real sensing data. besides the fact of the excess data, when the number of sensor nodes increased, each aggregator should be able to aggregate data from more sensors. so, the waiting time will be increased for the aggregators and the sink until all aggregated data are received. so, snapshots of the sink waiting times were measured at 100 sensors for all schemes. it is noticed that the sink waited approximately for 1.2 sec to receive the aggregation of the encrypted data using ipda, 0.9 sec to receive the aggregation of the encrypted data using eirda, 0.70 sec using amac while it waited for only 0.2 sec for the sda-sm scheme. actually, the sda-sm enhancement obtained due to the reduction in the sizes of mac and the data packets. furthermore, an enhancement is deduced in bandwidth utilization due to the same reason. sda-sm amac eirda ipda 0 500 1,000 1,500 2,000 2,500 3,000 3,500 c om m un ic at io n o ve rh ea d k bp s figure 6: communication overhead 6.2 energy consumption in the following subsections, energy consumption by the applied topology and the sensor nodes through communication are introduced. in addition, a comparison among the energy consumed per packet in the proposed scheme and some of the previous schemes is illustrated 6.2.1 energy consumption by sensor nodes in the wsns, energy consumption incarnates the vital issue. most of the energy consumed by the sensors of the wireless network occurs due to the process of the transmission of the packets. when the amount of the transmitted data is increased, the energy consumption will be increased causing faster end in the life of sensor nodes. so, the measures of the average of the consumed energy by the sensor nodes of the implemented topology are revealed in figure 8 for the four compared schemes. the simulations are repeated and measured at the different number of sensors 150, 300 and 450 as exposed in figure 7. generally, the averages of the consumed energies are increased as the numbers of the sensor nodes are increased. however, the sda-sm scheme provides a great enhancement in the depletion of energy by comparing it to the other schemes. this enhancement is deduced due to the https://doi.org/10.15837/ijccc.2021.2.3935 14 reduction in the operation cost before transmitting data as compared with the operation performed in the other schemes for the same amount of the sensed data. also, the sda-sm performs its operation using the lowest of the communications overhead among all schemes. therefore, the average quantity of the energy depleted by the sensor nodes is greatly reduced in the sda-sm scheme. consequently, it will provide a momentous enhancement in the network lifetime when compared with the other schemes. sda-sm amac eirda ipda 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 a ve ra ge en er gy co ns um pt io n (j ) n = 150 n = 300 n = 450 number of sensors figure 7: average of the consumed energy by the sensor nodes 0 50 100 150 200 250 300 350 400 450 0 0.5 1 1.5 2 2.5 simulation time a ve ra ge en er gy co ns um ed /p ac ke t m w sda-sm amac eirda ipda figure 8: comparison of the average energy consumed per packet 6.2.2 average energy consumption per packet at base station these experiments were implemented to measure the average energy consumed per packet in each round over a different number of nodes. the computations performed according to the simulation https://doi.org/10.15837/ijccc.2021.2.3935 15 0 50 100 150 200 250 300 350 400 450 0 20 40 60 80 100 sensor nodes su cc es s p ac ke t r at io sda-sm amac eirda ipda figure 9: ratio of success received packet at different number of nodes parameters table. the average energy consumed per packet was computed in the following two steps. firstly, the energies consumed by all sensor nodes for transmitting the packets from their source to the sink were summed. secondly, the result of summation was divided by the number of success received packets. the total energies consumed per packet for the proposed scheme and other compared schemes are revealed in figure 8. in general, the simulation results of the compared schemes indicate the increase in energy consumption per the packets as the number of nodes is increased. truly, the increase in the number of nodes means the increase in the number of transmitted packets and consequently an increase in the depleted power through the transmitting and receiving operations. on the other hand, some of the aggregator nodes may die which leads to more increase in power consumption due to the re-configuration of the topology and change in the aggregator nodes. however, the figure shown the proposed scheme sdasm accomplishes the minimum energy consumption per the packets when compared by the other schemes. the enhancement is deduced due to two main reasons. first, the modified aggregation process clarified in subsection 4.3. second, the tdma scheduling method applied in subsection 4.5. 6.3 the throughput at the base station these experiments were implemented to compare the average ratio of success packets received at the base station for the sda-sm and the all compared schemes including eirda, ipda, and amac. the simulations of all compared schemes were run on the number of nodes 50, 100, 200, 300 and 450. moreover, each simulation was run at an average of 10 times. the results of these experiments are offered as exposed in figure 9. the marks on the different curves indicate the percentage of the success packets received at the base station for each method. it is clarified that the percentage of the success packets will be lessened as the number of nodes rises for all schemes. through the exchange of the packets among the wsns, some of the cluster heads may exhaust their energies. so, some of the packets may be lost during their transmission to their base station. therefore, the numbers of the successful packets received may be lessened if the amount of the energy of some sensor nodes especially the aggregators is exhausted. accordingly, some of the aggregated packets may be lost. however, the proposed scheme accomplishes the maximum success ratio for the received packets when compared by all other schemes. this enhancement is accomplished due to the minimum energy consumption of the proposed scheme through the minimum cost that is needed for the aggregation processes and security operations. also, https://doi.org/10.15837/ijccc.2021.2.3935 16 the reduction in the energy of the proposed scheme is deduced from using the modified leach presented in [5]. in this modified leach, the enhancement is achieved due to the method used for the cluster head selection. the selection method confirms a well-adjusted generation of aggregators among all clusters. this constancy leads to a well-adjusted choice causing the regularity in energy dissipation. 7 conclusion in this work, a novel scheme sda-sm is offered to accomplish the needed security for the exchanged aggregated data among the sensor nodes and the base stations. in addition, the sda-sm scheme aims to extend the lifetime of the wsns. the sda-sm is based on three different contexts to achieve the required objectives. firstly, it develops the tdma scheduling method used by the leach protocol to decrease the amount of the lost energy during the transmission of packets among the sensors. secondly, it is applied to an additive homomorphic on the encrypted tags to perform the required aggregation for the total mac. finally, the separation between the aggregated mac represented in a separate packet and the aggregated data represented in other packets to get robust security implementation. the sda-sm scheme is discussed and analysed to illuminate the achievement of the security objective and its fight to different attack methods to clarify its forceful in succeeding its aims. author contributions the authors 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(2018). designing prefix code to save energy for wirelessly powered wireless sensor networks, iet communications, 12(17), 2137-2144, 2018. [27] zhu, l.; yang, z.; li, m.; liu, d. (2013). an efficient data aggregation protocol concentrated on data integrity in wireless sensor networks, international journal of distributed sensor networks, 9(5), 256852, 2013. copyright ©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: elshrkawey, m.; al-mahdi, h. (2021). sda-sm: an efficient secure data aggregation scheme using separate mac across wireless sensor networks, international journal of computers communications & control, 16(2), 3935, 2021. https://doi.org/10.15837/ijccc.2021.2.3935 introduction related work cluster wsn description the proposed sda-sm scheme setup process encrypt-sign process aggregation process verification process steady-state phase of the leach protocol security analyses data confidentiality data integrity unauthorized aggregation malleability node compromise replay attack simulation results and performance analysis communication overhead energy consumption energy consumption by sensor nodes average energy consumption per packet at base station the throughput at the base station conclusion int j comput commun, issn 1841-9836 8(3):375-383, june, 2013. actuality of bankruptcy prediction models used in decision support system m. crăciun, c. raţiu, d. bucerzan, a. manolescu mihaela crăciun, dominic bucerzan "aurel vlaicu" university of arad faculty of exact sciences department of mathematics-informatics românia, 310330 arad, 2 elena drăgoi e-mail: qbt@rdslink.ro, dominic@bbcomputer.ro crina raţiu daramec srl, arad românia, sofronea fn e-mail: ratiu_anina@yahoo.com adriana manolescu agora university, oradea, romania piata tineretului nr.8, 410526 e-mail: adrianamanolescu@univagora.ro abstract: in the current conditions, the global economy is in a crisis situation. in terms of crisis management this article supports the romanian companies. this article analyses some classical bankruptcy prediction models used in decision support systems in order to validate or invalidate them in the actual romanian economical conditions. it is essential to take the right decision at the right time, to help the company overcome an eventual moment of crisis, such as insolvency or even bankruptcy. our study is based on the financial ratio of 60 romanian companies, between 2005 and 2009. the firms are classified in two categories: bankrupted companies (b) and non-bankrupted companies (n-b). keywords: bankruptcy prediction models bpm, multiple discriminant analysis mda, decision support system dss, crisis management 1 introduction the economic crisis initially appeared in 2007, in the united states of america and japan. it expanded to almost every country, becoming a global phenomenon, whose negative outcomes are also present in the romanian economy. the crisis is the consequence of an economic boom, which lasted over 25 years and it emerged when japan and the united states began to reduce interest rates to avoid recession, measure that lead to the increase of the asset price worldwide. this article analyses some classical bankruptcy prediction models used in decision support systems in order to validate or invalidate them in the actual romanian economical conditions. here are a few aspects that triggered the economic crisis of 2008: • february 1, 2007 – the law of increasing the minimum wage was passed (fair minimum wage act of 2007) fact that blocked investments and by default, loans; • february 17, 2007 – british bank northern rock is nationalized; • september 7, 2008 – the largest mortgage banks in the united states freddie mac and fannie mae are placed under federal supervision; copyright c⃝ 2006-2013 by ccc publications 376 m. crăciun, c. raţiu, d. bucerzan, a. manolescu • september 16, 2008 – u.s. central bank federal reserve and u.s. government nationalized the largest insurance group in the world, american international group (aig), threatened with bankruptcy, and brought in aid of 85 billion; • september 28th – fortis group is nationalized by the authorities of netherlands, belgium and luxemburg, to avoid closing down; • october 13th – great britain nationalized three banks: royal bank of scotland (hbs), lloyds and hbos; • december 1, 2008 – u.s. announces that it was a year in recession. the economic crisis in romania is mainly a domestic crisis caused by bad macroeconomic policies mix from recent years. the economic growth appears to be unhealthy because the domestic growth relied on consumption financed by debt. the flax tax of 16% has stimulated consumption, which was assured by imports and did not lead to the increase of production capacity of romanian companies. variants had to be found to stimulate domestic production and exports. most consumption credits went to imports [8]. vulnerabilities of an unbalanced economy, with many delayed structural reforms have now become obvious. the risk of bankruptcy is present and it grows. considering this aspect, analysis of 165 bankruptcy prediction studies published from 1965 till present reveals trends in bankruptcy prediction models development. [7]. it is important that the difficulties in discharging obligations and the economic and financial structural fragility should be determined at an early stage in order to avoid the bankruptcy by declaring insolvency. the state of insolvency can be defined as an inability of companies to deal with outstanding payments, i.e. their inability to repay the borrowed sums on time as determined by mutual agreement with third parties under an economical contract or credit [9]. even if the terms bankruptcy or failure are used to describe the different situations of companies in difficulty, it should be noted that bankruptcy is a process that begins financially and ends legally. in the following we intend to tap the first part of the statement. it is hard to say precisely when the bankruptcy occurs. current experience in bankruptcy prediction is based on several studies beginning with the anglo-saxon school (1960 1999), continuing with the continental school who has made his mark between the years 1976 1999 and last, but not the least, the romanian schools 1996 to 2000. a reference model of the anglo-saxon school is the "altman model" while the "anghel model" represents the romanian school. there were several methods to detect financial difficulties of the company. altman and anghel models are based on the starting rates method for detecting financial difficulties of the enterprise. one of the important reasons in using the rates method is linked to the theoretical and practical developments that found connections between analysis using financial ratios and statistical techniques [1]. with financial ratios, we can compare the profitability of different sized companies from different branches [2]. risk of bankruptcy can be determined by the socalled method of scoring. this is based on statistical techniques of discriminant analysis (da). the score, as final of discriminant analysis, represents a method of diagnosis that consists in measuring and interpreting the future economic risks for the company. the method uses a group of significant financial ratios, resulting mainly from the annual financial statements. 2 description of the altman model (1968) altman is a reference name cited in studies concerning the prediction of bankruptcy. the model proposed by altman considers a number of 22 potential variables (based on annual financial statements) grouped into five categories: liquidity, profitability, debt, solvency and activity. actuality of bankruptcy prediction models used in decision support system 377 in an article published in 1968 [1], altman commented upon the traditional indicators and concludes that the analysis made by the researchers were unable to establish the relevance of the indicators. altman describes how he uses statistical techniques and discriminant analysis (da) to develop a model of financial indicators that provide enterprise bankruptcy. in the model development, altman has selected a group of 33 companies with financial problems; the sample included industrial (manufacturing) companies. healthy business group companies were selected by the principle of similarity, to each of the bankrupt companies (size, industry, etc.) corresponded a healthy firm. from the initial list of 22 indicators, the author chooses the five most significant. the first model built by altman includes five variables, each having attached weights. z = 1.2x1 + 1.4x2 + 3.3x3 + 0.6x4 + 1.0x5 (1) where : • x1 = working capital/total assets, • x2 = retained earnings/total assets, • x3 = earnings before interest and taxes/total assets, • x4 = market value equity/book value of total liabilities, • x5 = sales/total assets, • z = overall index or score. to make the model operational, the two groups of companies were analyzed and classified by z score size, setting the two limits and the uncertainty (area between the two limits). altman’s model works based on the following decision rules: • z ≤ 1.8 ⇒ imminent bankruptcy situation, the company is likely headed towards bankruptcy; • 1.8 < z < 2.99 ⇒ uncertainty; • z ≥ 2.99 ⇒ good financial situation, solvent companies; the firm is most likely safe based on the financial data. of course, mismanagement, fraud, economic downturns and other factors may cause an unexpected reversal. later, altman refined the model, the aim was to reflect, through the financial information the economic reality of the companies. he modified the variable x4 substituting the market value with the book values of equity [4]. the following score function was obtained: z′ = 0.717x1 + 0.847x2 + 3.107x3 + 0.420x4 + 0.998x5 the decision rules changed as followed: • z′ ≤ 1.23 ⇒ imminent bankruptcy situation; • 1.23 < z′ < 2.90 ⇒ uncertainty; • z′ ≥ 2.90 ⇒ good financial situation. 378 m. crăciun, c. raţiu, d. bucerzan, a. manolescu finally, from its previous models, which include a variable sensitive to the type of industry (x5 = sales/total assets), altman reviews the score function, retaining only four variables: z′′ = 6.56x1 + 3.26x2 + 6.72x3 + 1.05x4 . the decision rules became: • z′′ ≤ 1.1 ⇒ imminent bankruptcy situation; • 1.1 < z′′ < 2, 6 ⇒ uncertainty; • z′′ ≥ 2, 60 ⇒ good financial situation. 3 description of the anghel model (2000) the "anghel model" is the recent model developed by the romanian school and it consist in building the function score model for the romanian economy on multiple discriminant analysis (mda). the score function consists of four variables, four parameters and one constant. to obtain the score function anghel proposed the fallowing steps. first step – choosing the sample of companies divided in two groups: n-b group–companies without financial problems and b group failed companies. his study is based on a sample of 276 companies from 12 branches of national economy. the companies were selected randomly. the information for each enterprize was taken from the annual financial statements for the period between 1994-1998. step 2 – a comparison is made over time period based on a set of significant indicators for the two groups of companies. anghel analysis 20 financial indicators to see which of them covers a particular interest for the companies and divides them in: rates of activity, rates of liquidity, rate debt, rates of return and other economic and financial information. step 3 – selecting indicators that make the best discrimination. following the selection phase he chooses the following four financial variables: revenue performance, coverage of debt to cash flow, leverage the asset and period for payment of obligations. • x1 – earning after taxes/incomes; • x2 – cash flow/total assets; • x3 – liability/total assets; • x4 – (liability/sales)·360 . step 4 – development through discriminant analysis technique of a linear combinations of relevant indicators xi. in case of the two groups of previous assumptions there is a function representing the best discrimination b versus n-b. based on economic and financial information since 1998 for a total of 276 romanian companies it was build the a score function: a = 5.676 + 6.3718x1 + 5.3932x2 − 5.1427x3 − 0.0105x4 . (2) step 5 – choosing a point (or points) of inflection which makes predictive classification of companies in the two groups. computing the a score for each enterprize from the sample and sorting ascending the information obtained for the a score results a situation for all companies. step 6 – a-priori analysis of the success rate of a score by comparing the predictive classification with the known situation of companies in the sample. the assessment is based on the following classification: actuality of bankruptcy prediction models used in decision support system 379 • a ≤ 0 ⇒ imminent bankruptcy situation; • 0 < a < 2.05 ⇒ uncertainty; • a ≥ 2.05 ⇒ good financial situation. step 7 – a-posteriori analysis of the success rate of the function a by analyzing the degree of relevance for another sample of companies. a sample of companies was considered, separated in the two groups b and n-b, and the subject of analysis was the prediction accuracy that the function a performs. the sample used to validate the proposed model includes 55 companies, 28 from the n-b and the other 27 from group b. companies in the test sample were similar in size and industry sectors with the initial sample. under these conditions the prediction success rate similar to the a-priori, the author report a prediction with the value of 97.8%. this allowed the assessment that the score a was effective and could be applied to companies in the romanian economy (retaining the limits in order to built the model). 4 the applicability of the anghel and altman models in the actual crisis conditions in this paper we test the suitability of the statistical models in key economic context changes during the period between 2006 and 2010. our hypothesis is that the functionality of the models of a previous period is questioned by the specific economic conditions in a time of crisis. to test the applicability of the models in the actual economic conditions we proceed as follows. first of all, we select groups of companies. companies were chosen from a population of 200 romanian companies, at random. the data collected is real; the romanian companies used in our study exist and operate. we worked with a sample of 60 companies classified into two groups: • group 1 – non-bankrupt companies, see table position 1-30; • group 2 – bankrupt companies, see table position 31-60. analysis retains information for 2005-2009. the second step is related to the classification of the companies presented in table. classification was done according to the indicators resulting from the annual financial statements. the next step consisted in computing the financial ratios of companies. in the anghel prediction model, we used the following indicators: • x1 – earning after taxes/incomes; • x2 – cash flow/total assets; • x3 – liability/total assets; • x4 – (liability/sales)·360 . in the altman prediction model we used as statistical indicators the following: • x1 = working capital/total assets, • x2 = retained earnings/total assets, 380 m. crăciun, c. raţiu, d. bucerzan, a. manolescu • x3 = earnings before interest and taxes/total assets, • x4 = market value equity/book value of total liabilities, • x5 = sales/total assets in this paper we test two models listed as: the altman prediction model which is a reference model in the literature and the anghel bankruptcy prediction model which is the latest romanian model. as a next step we calculated the score functions from the two prediction models, as follows: • the angel’s score function (2); • the altman’s score function (1). please note that the calculation of coefficients in the anghel model was performed according to the author notes [5]. the model coefficients in altman’s model were calculated according to his details [1]. for the table the meaning of the heading table is the following: • year represents the year for which the prediction is made; • p the current situation (related to year 2009) for the company: n-b or b; • et/i represents the ratio earning after taxes / incomes; • cf/ta represents the ratio cash flow / total assets; • l/ta represents the ratio liability / total assets; • (l/s)·360 represents the ratio (liability/sales)·360; • a represents the results of the anghel score function; • an represents the prediction of anghel’s model; • z represents the results of the altman score function; • alt represents the prediction of altman’s model. in columns an and alt the symbol "u" represents uncertainty. no. year p et/i cf/ta l/ta (l/s) · 360 a an z alt 1 2006 n-b 0.356 0.029 0.765 777.708 -4 b 2 u 2 2009 n-b 0.001 0.205 0.786 181.925 1 u 2.4 u 3 2009 n-b -0.026 -0.002 0.903 729.009 -7 b 2 b 4 2009 n-b -0.444 0.008 1.168 1078.548 -14 b 0 b 5 2008 n-b 0.053 0.255 0.987 76.220 2 u 9 n-b 6 2009 n-b -0.379 -0.006 1.342 1031.039 -15 b 0 b 7 2009 n-b -0.562 0.019 1.403 855.857 -14 b 0 b actuality of bankruptcy prediction models used in decision support system 381 8 2008 n-b 0.307 0.987 0.107 13.907 12 n-b 11 n-b 9 2008 n-b 0.038 0.462 0.738 45.367 4 n-b 5 n-b 10 2009 n-b -1.673 -0.047 0.997 2871.865 -41 b 0 b 11 2006 n-b 0.043 0.001 1.175 770.832 -8 b 2 b 12 2009 n-b 0.255 -0.002 0.142 214.982 4 n-b 4 n-b 13 2005 n-b 0.026 0.001 0.316 322.215 1 u 3 u 14 2009 n-b -0.502 -0.006 1.590 1170.032 -18 b 0 b 15 2009 n-b -1.410 -0.027 2.654 1219.977 -30 b -4 b 16 2009 n-b -0.039 0.000 1.166 692.457 -8 b 1 b 17 2009 n-b 0.013 0.011 1.474 533.522 -7 b 2 u 18 2009 n-b -2.576 0.032 1.498 2710.456 -47 b -1 b 19 2009 n-b -0.492 0.139 1.675 425.326 -10 b 0 b 20 2009 n-b -0.073 -0.005 1.550 266.427 -6 b 2 u 21 2009 n-b 0.011 -0.007 0.996 260.631 -2 b 2 u 22 2005 n-b -0.947 0.165 0.575 16249.367 -173 b 1 b 23 2008 n-b 0.550 0.994 0.049 10.577 14 n-b 16 n-b 24 2006 n-b 0.030 0.149 0.474 41.178 4 n-b 5 n-b 25 2006 n-b 0.378 -0.047 0.356 331.038 3 n-b 3 u 26 2009 n-b 0.028 0.004 1.295 156.400 -2 b 4 n-b 27 2006 n-b 0.070 0.155 0.590 124.184 3 n-b 3 u 28 2006 n-b 0.012 -0.001 0.130 141.843 4 n-b 5 n-b 29 2009 n-b -0.806 -0.113 5.977 935.200 -41 b -7 b 30 2008 n-b 0.126 1.486 0.128 6.800 14 n-b 7 n-b 31 2006 b 0.204 0.108 0.757 546.183 -2 b 1 b 32 2008 b -0.104 -0.005 1.078 497.157 -6 b 1 b 33 2008 b -111.611 -0.036 1.404 227585.775 -3103 b -1 b 34 2008 b -0.564 0.108 13.052 745.531 -72 b -6 b 35 2008 b -0.304 -0.001 0.784 260.837 -3 b 0 b 36 2009 b -0.082 -0.045 1.911 732.504 -13 b -2 b 37 2008 b -0.043 0.000 1.014 455.619 -5 b 1 b 38 2009 b -0.086 0.157 1.086 336.851 -3 b 1 b 39 2007 b -1.741 -0.001 1.294 2209.021 -35 b -1 b 40 2009 b -0.154 0.084 1.207 551.230 -7 b 1 b 41 2005 b -4.503 0.277 4.306 1892.400 -64 b -11 b 42 2008 b -1.101 0.727 10.073 6276.031 -115 b -10 b 43 2009 b 0.006 0.291 0.994 520.669 -3 b 1 b 44 2007 b 0.017 0.001 0.985 423.434 -4 b 1 b 45 2007 b -0.478 -0.212 1.051 1921.361 -24 b 0 b 46 2008 b 0.136 -0.002 2.317 347.256 -9 b 0 b 47 2009 b -3.694 -0.050 0.906 6164.473 -88 b -1 b 48 2008 b 0.066 0.019 0.502 259.779 1 u 2 b 49 2008 b -0.055 0.000 1.082 254.628 -3 b 1 b 50 2009 b -0.121 -0.016 1.216 288.245 -4 b 1 b 51 2009 b -0.359 -1.091 1.999 257.014 -15 b -0.358 b 52 2008 b -190.552 -3.129 9.518 60004.8 -1904 b -36 b 53 2008 b 0.437 0.025 0.591 955.082 -4 b 1 b 54 2008 b 0.124 0.001 0.873 327.058 -1 b 2 b 55 2009 b -1.997 0.000 1.428 3233.721 -48 b -1 b 56 2006 b -1.431 -0.022 6.249 3129.140 -69 b -5 b 382 m. crăciun, c. raţiu, d. bucerzan, a. manolescu 57 2007 b -0.122 -0.093 2.037 843.028 -15 b 1 b 58 2009 b -1.765 -0.021 1.135 770.342 -20 b -2 b 59 2006 b -4.244 0.004 2.467 2624.353 -62 b -5 b 60 2009 b -5.192 1.024 3.639 2469.939 -67 b -7 b after processing the data presented above, we obtained results about the success rates of the prediction models and types of errors that appear in the analysis. we processed the data in order to obtain the prediction both with anghel’s and altman’s model. we obtained the following success rates for the total sample: in anghel’s case we got a success rate of 63.33% and in altman’s model the success rate was 65%. both rates are unsatisfactory when compared to the success rates reported in initial studies. anghel’s model report a success rate greater than 97% and in the altman‘s model the success rates were: 93.9% for n-b companies and 97% for b companies. note that, due to the dynamic economy, the success rate obtained with anghel’s model drops from 97% to 63.33%. this leads to the idea that scoring function built in year 2000 cannot accurately predict the health of the company in the present. in case of the success rates for n-b companies the rates obtained are unsatisfying. in the anghel model the success rate is 30% while the altman’s model for n-b yields a success rate of 30%. the next result is related to the analysis of error rates for the companies in the sample, by applying the two prediction models. here we refer to "type i" errors (b companies classified as n-b) and "type ii" errors (n-b companies classified as b). this error classification is done according to [9]. "type ii" errors are less significant. after the analysis, from anghel’s model we obtained "type i" errors 0%, "type ii" errors 60% and total errors 30%. in the altman model we obtained the following results: 0% for "type i" errors, 43.33% for "type ii" errors and 21.67% for total errors. 5 conclusion and future works the changes from the economic and social life such as the inflationary phenomena, the increased competition, the technological progress, the political environment and the economic and financial crisis, more or less influence the business outcome that is always subjected to different risks: financial policy risk, operational risk, and the risk of bankruptcy. analysis of accuracy of the bankruptcy prediction models suggests that multivariate discriminant analysis and neural networks are the most promising methods for bankruptcy prediction models [7]. the altman study included a sample of 66 companies, 33 in each group. the bankrupt group (group 1) recorded failure during 1946-1965. average assets of the sample companies were $ 6.4 million, with values between $ 0.7 million and $ 25.9 million. the non-bankrupt group (group 2) includes companies with assets between $ 1-25 million, which continued to operate in year 1966, too. the period considered for analysis was 1946 1965 (20 years) [3]. the source of information was moody’s industrial manual. the anghel study of building a score for the romanian economy is based on a sample of 276 companies from 12 branches of national economy. companies were selected randomly. the information for each company was taken from the annual financial statements for the period between 1994 and 1998. in the verification sample the success rate was 97.8%. the models presented in this paper obtained a satisfying result for the economic period in which the models were developed. but taking into consideration our study on a sample of actuality of bankruptcy prediction models used in decision support system 383 companies from the present unstable economic environment we cannot recommend to use the two models mentioned above as a tool for predicting bankruptcy. a well designed decision support system is a must have tool in the success of any enterprise. this aspect is very important considering the current economic crisis. for this purpose, our article analyses some classical bankruptcy prediction models used in decision support systems in order to validate or invalidate them in the actual romanian economical conditions. taking into consideration the conclusions of this study, our future work will focus on updating the score function proposed by anghel and altman to the current economic conditions. the purpose is to obtain an external diagnostic method that predict the risk to which the investor, the creditor and the company are exposed and ensures an optimal relation between outcome and risks on the romanian economic market environment. bibliography [1] altman, e.i.: financial ratios, discriminant analysis and the prediction of corporate bankruptcy, journal of finance, september, 589-609, 1968. [2] pinches g., eubank a., mingo k. and caruthers k.: the hierarchical classification of financial ratios, journal of business research, 1975. [3] altman, e.i.: credit rating: methodologies, rationale and default risk, london risk books, 2002. [4] altman e.i., haldemon r. and narayama p.: zeta analysis, journal of banking and finance, 89-108, june 1977. [5] anghel, i.: falimentul radiografie şi predicţie, ed. economică, bucureşti, 2002. [6] szenteşi, s., lile, r., rusu, s., csorba, l. and bălan, l.: statistică economică, ediţia a iii-a, ed. universităţii aurel vlaicu, arad, 2011. [7] bellovary j., giacomino d., akers m. :a review of bankruptcy prediction studies: 1930 to present, journal of financial education, vol. 33 (winter 2007). [8] voinea, l.: criza economica interna: cauze si solutii 23 nov 2008 http://www.zf.ro/opinii/criza-economica-interna-cauze-si-solutii-3544642. [9] pisleag, a.: the importance of assessing the risk of bankruptcy under the current global crisis, bulletin of the transilvania university of brasov, vol. 3 (52) 2010 series v: economic sciences http://but.unitbv.ro/bu2010/series%20v/buletin%20v%20pdf/291%20pisleag.pdf. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 6, month: december, year: 2022 article number: 4952, https://doi.org/10.15837/ijccc.2022.6.4952 ccc publications a financial time series data mining method with different time granularity based on trend division haining yang, xuedong gao, lei han, wei cui haining yang* department of management science and engineering, school of economics and management, university of science and technology beijing, beijing 100083, china *corresponding author: yanghaining@apiins.com xuedong gao, lei han department of management science and engineering, school of economics and management, university of science and technology beijing, beijing 100083, china yanghaining@apiins.com, gaoxuedong@manage.ustb.edu.cn wei cui school of economics and management, china university of geosciences (bei-jing), beijing 100083, china cuiw@cugb.edu.cn abstract stock research is an important field of finance and time series research. stock data research is also a typical financial time series problem. in the research of financial time series, there are many methods, such as model building, data mining, heuristic algorithm, machine learning, deep learning, and so on. var, arima and other methods are widely used in practice. arima and its combination methods have good processing effect on small data sets, but there are over fitting problems, which are difficult to process large data sets and data with different time granularity. at present, this paper takes the decision table transformation method of financial time series data as the research object, and puts forward the trend division method of financial time series based on different time granularity through the trend division of financial time series. on this basis, it puts forward the trend extreme point extraction method, and constructs the stock time series decision table according to the extreme point information and combined with the stock technical indicators, the decision table is verified by support vector machine based on the decision table. the research shows that the trend division method under different time granularity can transform the extreme point information into a decision table, which will not produce over fitting problem in practical application. it is an effective time series processing method, and provides a new research method for the future time series research with different granularity. keywords: time series, trend division, time granularity, arima, support vector machine. https://doi.org/10.15837/ijccc.2022.6.4952 2 1 introduction time series analysis is an important research field of traditional statistics. after decades of development, it has laid its own theoretical foundation. the research object of traditional time series analysis is generally dynamic data with randomness, and the research method focuses on the construction of global model. at present, the research of time series mainly focuses on econometric methods and data mining methods. however, in the actual analysis of time series data, it is often necessary to analyze the local characteristics of time series, such as finding the frequent patterns, comparing different sequences or whether the same sequence has similarity in different time periods, etc. time series records the state values of observation targets at each time point to form complex highdimensional data. it can be divided into two categories. one is simple time series, which is stable and linear; the other is complex time series, which is non-stationary, nonlinear and multidimensional. the process of analyzing the current observation value of time series and inferring the value of the observed object at a future time node is called time series prediction. time characteristic is an important feature of time series data. the time characteristic of data is not only an index but also a division basis, and it has the characteristics of independent variable and dependent variable. time series data also has the characteristics of multi time granularity. taking stock data as an example, there are data with different granularity, such as 1 minute, 5 minutes, 15 minutes, 30 minutes, 1 hour, day, week, month, quarter, year and so on. there is some uncertain relationship between various granularity, which also provides a new research direction for time series prediction. stock prices, trading volume indicators and many new indicators derived from them can better reflect the internal relationship of stock prices. making full use of these indicator data often has certain guiding significance for the decision-making of individual stock trends, choices and trading time points. based on this, it is necessary to further study the multi granularity time series forecasting method based on trend division and combined with a variety of indicators, establish a reasonable and effective stock price forecasting model, enrich and develop the theory and method of stock time series research, and provide a reliable basis for investors’ decision-making. to sum up, this paper will process time series with different time granularity according to the trend division of time series, extract extreme points from general time series, build decision table, and mine stock time series according to the decision table, hoping to provide a new method for processing financial time series. 2 related work 2.1 research on univariate time series in the field of econometrics, univariate time series modeling has become a very important research field in economic management research [1, 2, 3]. time series analysis is a statistical method to reveal the dynamic structure and regular characteristics of the system according to the fluctuation of dynamic data. the core idea is: according to the limited length of time series data observed by the system, establish the corresponding data model to reflect the dynamic dependencies contained in the time series, and thus predict the future trend of the system. in the development of time series model, an important feature is the assumption of statistical equilibrium relationship, one of which is the assumption of stationarity. usually, a stationary time series can be effectively described by its mean, variance and autocorrelation function. in general, the main methods of data research are model construction [4, 5] and data analysis. in the next part, we will review different methods. financial time series is an important research field of time series, which has many data patterns. the research on the trend and price of stocks is an important research hotspot of financial time series. at present, the research of stock time series focuses more on fitting the series itself and making time series prediction based on it. under this research paradigm, the prediction is often effective in a certain time range, but the generalization ability is poor. for example, the differential integrated moving average autoregressive model (arima) can deal with non-stationary time series, but because the processing process of time series’ stationarity masks the complex and diverse characteristics of https://doi.org/10.15837/ijccc.2022.6.4952 3 fluctuation patterns in the series, it is also unable to make a more accurate prediction for complex and changeable time series. the discrete fourier transform (dft) algorithm is simple to calculate, and can concentrate most of the energy of the time series signal into a few coefficients. the time complexity is also relatively low, but the algorithm discards the high-frequency component of the signal in the process of data interception, smoothes the local maximum and minimum of the signal, and causes the omission of information, and it is not suitable for non-stationary sequences [6]. discrete wavelet transform (dwt) can consider the local characteristics of time domain and frequency domain at the same time. on the other hand, under the same data compression, dwt can better retain local details and provide more accurate approximate results than discrete fourier transform. discrete wavelet transform does not reduce the relative image error, nor does it improve the accuracy of similarity query. from the experimental results, the effect of discrete wavelet transform and discrete fourier transform is not very different [7]. in the 1970s, arima (autoregressive integrated moving average model) was proposed by g.e.p.box, an american statistician, and g.m.jenkins, a british statistician [8]. the time series prediction method using this model is also called box-jenkins method. arima (p, d, q) model includes autoregressive (ar) process, moving average (ma) process, autoregressive moving average (arma) process and arima process. where p is the autoregressive term, d is the number of differences when the time series is processed smoothly, and q is the moving average term. the stability of arma (p, q) process depends on the autoregressive part, while its reversibility depends on the moving average part. the nonstationary of time series is various, but the economic time series often has the characteristics of linear homogeneous nonstationary. if a random process contains d unit roots, the random process can be transformed into a stationary autoregressive moving average process after d times of difference, and the random process is a single integer autoregressive moving average process, which is recorded as arima (p, d, q). however, these time series analysis methods require linear models. in order to overcome this defect, nonlinear time series models have become a research hotspot. granger [9] the bilinear time series model is proposed. by using the residual information generated in the process of time series prediction, the model has the characteristics of concise structure, few parameters and strong adaptability in practical application, which makes the model have wide application value in nonlinear time series analysis. with the development of interdisciplinary research, neural network, wavelet decomposition, fourier transform and fractal theory and methods are used for data mining and prediction of time series. from the existing literature and the mathematical expression of the above time series, it is found that: 1. the premise of time series analysis is to assume that the past of variables will extend to the future, that is, the future of the system will not suddenly change by leaps and bounds, but a gradual process. 2. in time series analysis, we should first test the stability of time series, select the appropriate time series model after appropriate data processing (such as difference) and passing the test, and estimate the parameters in the model. the prediction of stock prices and trends mainly revolves around several aspects. one is technical indicators and feature engineering. hossain, e., et al. used the technical analysis of expert system based on belief rules (brbes), combined with the concept of brin belt, to predict the stock prices in the next five days, and studied the effects of different deep learning methods [10]; ji, g., et al. studied the application of feature engineering in stock prediction, proposed improved technical indicators based on wavelet denoising and a new two-stage adaptive feature selection method, and adopted random forest model as the stock prediction model [11]; li, g. z., et al. proposed a multi index feature selection method for stock price prediction based on pearson correlation coefficient (pcc) and generalized learning system (bls) [12]. the methods of stock forecasting mainly focus on deep learning and fusion models. deng, c. r., et al. developed a hybrid stock price index prediction modeling framework using long-term and short-term memory (lstm) and multivariate empirical mode decomposition (memd), which can https://doi.org/10.15837/ijccc.2022.6.4952 4 capture the intrinsic characteristics of the complex dynamics of the stock price index time series [13]; gao, r. z., et al. proposed a deep learning method combined with genetic algorithm to predict the target stock market index [14]; gao, z., et al. proposed a prediction algorithm integrating multiple support vector regression (svr) models, and used reasonable weight to combine the prediction results of multiple models to improve the accuracy of the model [15]; gupta, u., et al. in order to overcome the problem of overfitting, a new data enhancement method was proposed in the stocknet model based on gru [16]; he, q. q., et al proposed a new case-based deep transfer learning model with attention mechanism [17]; kanwal, a., et al. proposed a prediction model based on hybrid deep learning (dl), which combines deep neural network, short-term memory and one-dimensional convolutional neural network (cnn) [18]; kumar, r., et al. proposed a three-stage fusion model to process time series data and improve the accuracy of stock market prediction [19]; li, r. r., et al. proposed a multi-scale modeling strategy based on machine learning methods and econometric models [20]. there are some new methods applied to stock time series prediction. liang, m. x., et al. used sequence morphology mining to obtain candlestick morphology from multidimensional candlestick data, calculated the correlation between different morphology and corresponding future trends, and proposed a new sequence similarity, matching different candle graph sequences with existing patterns [21]; wang, c. j., et al. use the deep learning framework transformer to predict the stock market index. through the encoder decoder architecture and multi focus mechanism, research shows that transformer can better describe the basic rules of stock market dynamics [22]. 2.2 literature review and analysis through the above literature review, we find that time series has become a more and more popular research field, especially the rise of deep learning technology, which partially solves the problem of time series’ interconnection in time scale, and time series research has become an independent research direction. however, the traditional time series research attempts to use a single or multiple models to explain or characterize the time series, and make time series prediction accordingly. under this research paradigm, the prediction is often effective within a certain time range, but the generalization ability is poor (only effective for the trained data, and poor for the new data), which cannot meet the requirements of the era of big data. machine learning methods need to rely on artificial feature selection. although the deep learning algorithm has achieved good results, it is still in a black box state with weak interpretability. moreover, the deep learning algorithm is also prone to butterfly effect. a little change can have a significant impact on the results of the deep learning model, it is also easy for decision makers to make wrong conclusions. some scholars also began to explore the combination of deep learning algorithm and traditional methods to achieve better results. 3 methodology 3.1 comparative analysis of multiple time granularity prediction based on arima model as one of the traditional time series prediction models, arima model has the following general steps: 1. stability test. the adf unit root test is used to check whether the sequence is stable. if the sequence is unstable, the difference operation is performed. 2. white noise inspection. check whether the sequence is random. 3. model order determination. acf and pacf functions are used to determine the order of the model and determine the final arima model. 4. model prediction. the arima model is used to predict the sequence data. there are many evaluation indexes for the performance of the model. in this paper, the mean absolute error (mae), mean square error (mse), root mean square error (rmse) and the coefficient of determination r2 are selected to evaluate the performance of the model. https://doi.org/10.15837/ijccc.2022.6.4952 5 this study selects the growth enterprise market (gem) index (399006) 1-minute interval, 5minute interval, 15-minute interval, 30-minute interval, 60-minute interval and daily interval data, including the date from august 19, 2019 to august 19, 2022. specific data statistics are shown in table 1: table 1: basic data data introduction data sources wind database number of 1-minute interval data 176176 pieces number of 5-minute interval data 34944 pieces number of 15-minute interval data 11648 pieces number of 30-minute interval data 5816 pieces number of 60-minute interval data 2912 pieces number of daily interval data 728 pieces in this paper, the model is built for different granularity data sets, and the performance of the model is statistically analyzed. (1) arima model of daily data this paper uses python language to write arima model, and takes the last 10 data of the data set as the test set to judge the generalization ability of the model. first, the stability test of the sequence is performed. table 2 shows the results of adf unit root test: table 2: adf unit root test of daily line data differential order t p critical value1% 5% 10% 1 -26.887 0 -3.439 -2.865 -2.569 2 -16.557 2.256 -3.439 -2.865 -2.568 the white noise test for the stationary sequence after the first-order difference shows that the p value is 2.4656815e-05, which is significantly less than 0.1, proving that the sequence is non pure random sequence, which can be used for model construction. the sequence diagram after 1st order difference is shown in figure 1: figure 1: 1st order differential sequence diagram of daily interval data according to the data after the first-order difference, the acf and pacf functions are used to determine the order of the model. the acf and pacf diagrams are shown in figure 2: https://doi.org/10.15837/ijccc.2022.6.4952 6 figure 2: daily interval data acf figure 3: daily interval data pacf observing the acf chart and pacf chart in figure 3, using the bic criterion with objective criteria to determine the order of the model, it is found that when p = q = 0, the amount of bic information achieves the minimum value, and the model achieves the optimal fitting effect, that is, the fitting model of the original series is arima (0,1,0), and it can also be seen that the stock time series meets the random walk model, that is, the position of the current time is only related to the position of the previous time. the arima model obtained is used for fitting and prediction, and the fitting results are shown in figure 4: https://doi.org/10.15837/ijccc.2022.6.4952 7 figure 4: fitting results of daily line model the prediction of the model on the test set is shown in table 3: table 3: forecast value of daily interval model time true value predicted value absolute value of fitting error 2022/8/8 2675.686 2682.774 7.0876 2022/8/9 2694.8 2674.883 19.9173 2022/8/10 2658.585 2693.935 35.3507 2022/8/11 2721.493 2657.838 63.6549 2022/8/12 2690.831 2720.541 29.7095 2022/8/15 2718.593 2689.979 28.6138 2022/8/16 2731.393 2717.651 13.7426 2022/8/17 2777.91 2730.409 47.5004 2022/8/18 2775.821 2776.774 0.9534 2022/8/19 2734.218 2774.692 40.4743 model performance indicators are shown in table 4: table 4: performance indicators of daily interval model dataset index mae mse rmse mape model training set 37.8757 3270.24 57.186 0.0145 model training set 33.962 1153.419 33.962 0.0105 table 4 shows that the performance of the model on the training set is excellent and can basically meet the requirements, but the prediction effect on the test set is not particularly ideal, and the prediction error is large, reflecting the poor generalization ability of the model. since the modeling process of other granularity data is consistent with the daily data, this paper will not repeat the research process, but the performance index statistics of arima models with different time series granularity are shown in table 5: https://doi.org/10.15837/ijccc.2022.6.4952 8 table 5: performance indicators of arima model with different granularity dataset index mae mse rmse mape 1minute interval model training set 1minute interval model training set 5minute interval model training set 4.1735 79.4054 8.9109 0.0015 5minute interval model test set 3.0347 12.84771 3.5843 0.0011 15minute interval model training set 7.5064 171.1889 13.0839 0.0028 15minute interval model test set 6.956 62.6869 7.9175 0.0025 30minute interval model training set 11.0193 354.5312 18.8289 0.0041 30minute interval model test set 7.342 83.2282 9.1229 0.0026 60minute interval model training set 16.3431 749.5902 27.3786 0.0062 60minute interval model test set 9.1569 150.6016 12.2719 0.0033 note: indicates the amount of data that the computer cannot process. according to the performance index statistics of arima models with different granularity, with the decrease of granularity, the amount of data increases significantly, and the mae, mse and rmse of the training set model all show a downward trend. however, compared with the training set, the prediction effect of the model on the test set is poor, reflecting the over fitting phenomenon of the model, and with the increase of the amount of data, the over fitting phenomenon is becoming more and more obvious. this also reflects that the traditional time series research attempts to use a single or multiple models to explain or characterize the time series, and make time series prediction accordingly. under this research paradigm, the prediction is often effective in a certain time range, but the generalization ability is poor, and it is difficult to deal with multi granularity time series effectively. 3.2 establishment of multi granularity time series decision table based on trend partition in 2011, preis et al [23] a time renormalization method is proposed to analyze the switching dynamics of the german futures index (fdax). its research shows that the switch point in the german futures market index tends to have large volatility, trading volume and intensive trading time interval. first, at the price p (t) of time t, if we want to judge whether p (t) is a turning point, we must compare the prices of a window size (s) before and after p (t), that is, p (t−s) and p (t + s). if there is no price higher than any price p (t) in the range of p (t − s) to p (t + s), then any price p (t) is defined as the highest price pmax(s). in the same way, from p (t − s) to p (t + s), if no price is lower than any price p (t), then any price p (t) is defined as the lowest price pmin(s), and the turning point we mentioned earlier is the maximum price pmax(s) and the minimum price pmin(s) defined above. as shown in the figure. schematic diagram of price change over time: in the stock price trend chart, select a window size (s), take s as the range, and judge whether p (t) is the minimum or maximum value within this range from p (t − s) to p (t + s). it can be seen from the figure that in pmax(s) and pmin(s), the blue line is pmax(s) , and the red line is pmin(s). https://doi.org/10.15837/ijccc.2022.6.4952 9 figure 5: price and trend turning point next, further discuss the concept of trend from the turning point, as shown in fig 2 pmin.1(s) to pmin.1(s) the price fluctuation between them showed an upward trend, followed by a downward trend to pmin.2(s). in this section, we regard the price change of the taiwan index period as consisting of several upward and downward trends. within a certain range, each trend contains this extreme point, and this local extreme point forms the reverse trend of the time series. identifying these extreme points has important research value. 3.3 extraction of time series trend extreme points based on time series value recursion time series data is data that grows with time. the longer the data exists, the larger the amount of data. it is very important to find data points that can represent characteristics and have reference value from these data when analyzing time series data. the reason is that the data can be compressed and less data can show the trend of the overall huge amount of data. the trend division of time series should first consider the characteristics of time series. taking the stock price of financial time series as an example, the general stock information is expressed in the form of one minute, five minutes, fifteen minutes, thirty minutes, days, weeks, months and years. in different time granularity, there are often great differences in the division of trends. the original time series trend division method generally revolves around several key points, such as segmentation range, sliding window, segmentation point and threshold determination. these contents make the trend division of time series full of uncertainty. although the results can be continuously optimized through multiple experiments, a structured trend division method has not been formed, and its stability is difficult to ensure in the process of use. however, in the subsequent links such as data prediction, the over fitting phenomenon will be caused due to different parameters. dow theory points out that the main trend, medium-term trend and short-term trend are the three main manifestations of stock price changes, and the newly formed bottom and top prices are the main basis for judging the formation of the trend. the original time series trend division method generally revolves around several key points, such as segmentation range, sliding window, segmentation point and threshold determination. these contents make the trend division of time series full of uncertainty. although the results can be continuously optimized through multiple experiments, a structured trend division method has not been formed, and its stability is difficult to ensure in the process of use. however, in the subsequent links such as data prediction, the over fitting phenomenon will be caused due to different parameters. according to the characteristics of time series, this paper proposes a new time series trend division method. the main idea is that a trend must have extreme points, and a local maximum extreme point and two local minimum extreme points before and after constitute an upward (or downward) trend. therefore, in the process of trend recognition, it is particularly important to find the extreme points in the time series. this paper uses python to write programs to find the extreme points of time series, and then identify the rising and falling trends in time series. the specific algorithm is shown in table 6: https://doi.org/10.15837/ijccc.2022.6.4952 10 table 6: extraction of time series trend extreme points based on time series value recursion algorithm: input: original time series data s = {s1, s2, ..., sn}, step m output: index k of maximum point and index h of minimum point of time series s. (1) for i = {1, 2, 3, ..., n}, do (2) if s1 > s1 + m, (3) k ← 1 (4) else if s1 < s1 + m, (5) h ← 1 (6) else if si − m < si < si + m, (7) break (8) else if si − m < si > si + m, (9) k ← i (10) else si − m > si < si + m, (11) h ← i (12) organize index k, h (13) end in order to verify the feasibility of the above algorithm, a group of time series data is randomly selected to extract the extreme points. taking the time series of [1, 6, 30, 20, 15, 7, 13, 9, 2, 6, 0, -7, -15, -6, 4, 13, 8, 15, 8, 12, 3,0, 20, 7, 3, 9] as an example, using the method proposed in this paper, the step size is 1, and the extreme points are identified. the visualization results are shown in figure 6. figure 6: visualization of extreme point extraction of random time series the extreme points extracted are: maximum points: [30,13,6,13,15,12,20,9]; minimum point: [1,7,2, -15,8,8,0,3]. the results are consistent with the expected results, which shows that the proposed method is effective. the decision table, also known as the judgment table, is suitable for describing the situation where there are too many judgment conditions and the conditions are combined to form a variety of decision schemes. the decision table is composed of state variables (condition items) and decision variables (action items), and each state variable and decision variable together constitute a rule. the decision table can list complex problems according to various possible situations. using the decision table, the state variables can be understood as inputs and the decision variables as outputs. this is more similar to the idea of machine learning in data mining, so decision table can be widely used in classification, clustering, association rule mining and other fields. https://doi.org/10.15837/ijccc.2022.6.4952 11 1. determination of decision variables the above algorithm is used to process the time series data set. when the step size is set to 1 at runtime, the local maximum and local minimum of trends with different time granularity can be obtained. the maximum point is represented by "o", and the minimum point is represented by "+". the idea of two classification is adopted, and the local maximum and local minimum are used as the decision variables of the information table. at the same time, for the stock time series data set, the only intuitive information we can get is the real-time stock price and trading volume. if only the price and trading volume are included, the information in the decision table is very little, which is difficult to meet the requirements of data processing and effectively predict the decision variables. therefore, we need to further enrich the information table, select more indicators related to decision variables to form the information table. 2. determination of factors in decision table in the current stock market research, some people have studied it from the perspective of enterprise management and finance, forming a fundamental analysis method, while others have studied it from the perspective of stock price and form construction, forming a technical analysis method. stock technical analysis is based on derivative data as the research object, for example, by investigating the charts of data such as stock price and trading volume, and predicting the future trend according to the stock history and current performance. technical indicators provide data support for investors who analyze stocks from the intraday. therefore, adding the existing empirical indicators of stock technical analysis to the decision table will greatly increase the content of the decision table and enable it to use the decision table to carry out data mining. in technical analysis, the most important factors include price, quantity, time and space. • the most basic element of technical analysis is price, which is also one of the key elements of the stock market; the intrinsic value of stock is subject to change, and its fluctuation is jointly affected by the operating conditions of the stock issuer and the behavior of the stock market. when the stock issuer has a significant positive or negative effect, the stock chips will have a corresponding effect, which will be directly reflected by the rise and fall of the stock price. • another important element of technical analysis is trading volume. trading volume is an important factor affecting the formation of stock prices. the standard for confirming the price form is trading volume. in the stock market, only buying and selling can lead to price fluctuations. when the stock price is low and there are a large number of purchases, it is generally believed that the stock price will rise. when the stock price is relatively high and there are a large number of sales, the stock price will generally fall. therefore, investors generally regard the bottom volume and the top volume as one of the signs that the stock price will reverse. • time means that we need to think about the trajectory of stock price from the perspective of time, and the trend of stock price is cyclical. there are two main aspects, one is the stock price range, the other is the trend. the time here refers to the length of time that the stock price remains within a specific price range or trend and runs. the trend is difficult to change significantly in the short term, but it is not unchanged forever. as time changes, new trends will appear unconsciously. • space refers to the size and range of stock price changes, or the range of stock prices. every stock has a high point and low point that can be reached due to the influence of the company’s operation and market factors. when the stock reaches a high point or the low point reverses, the price between the high and low points is the size of the stock’s operation space. common technical indicators and classification are shown in table 7. https://doi.org/10.15837/ijccc.2022.6.4952 12 table 7: important technical indicators of stock time series technical index category name and abbreviation of technical in-dicators key parameter trend indicator ma simple moving average: ma5, ma10 5, 10 macd exponential smooth moving average: diff, dea, macd 26, 12, 9 mtm dynamic index: mtm 6, 6 trend indicator: dmi 14, 6 index average index: expma 12, 50 reverse trend indicator bias deviation rate: bias 12 kdj random index: k, d, j 9, 3, 3 roc change rate: roc 12, 6 rsi relatively strong index: rsi 6 pressure support index boll brin belt 26, 2 volume price index sobv energy tide: sobv 1wvad william variation dispersion: wvad 24, 6 volume index standard deviation of vstd trading volume: vstd 10 turnover rate: turn 1 swing index rc change rate index: rc 8 fluctuation index std standard deviation: std 26 3. based on establishment of time series information table for extreme point extraction after the above analysis, the information table of extreme points based on trend division includes the following contents: • the decision variables are given by the algorithm proposed in this paper. • the state variables include various basic data and technical indicators de-scribed above, and the time series information table shown in table 8 can be obtained. table 8: time series information table time status variable state variables decision(basic data) (technical indicators) variable minute opening maximum lowest closing volume turnoverma macdbias kdj rsi bol price price price price | | | | — — — | | | + | | | | — — — | | | the decision table with different time granularity contains 12 state variables, including 6 basic data and 6 technical index data, which are opening price, maximum price, minimum price, closing price, trading volume, trading volume, ma, macd, bias, kdj, rsi and bol. the extreme points extracted are used as decision variables. the following table shows the number of extreme points after processing at different time granularity. table 9: extreme points under different granularity data sources number of extreme points number of 5-minute interval data 16911 number of 15-minute interval data 5707 number of 30-minute interval data 2798 number of 60-minute interval data 1405 number of daily interval data 381 https://doi.org/10.15837/ijccc.2022.6.4952 13 based on the analysis of arima model, this section proposes a multi granularity time series decision table conversion method based on trend division. by using the method of time series value recursion, the extreme points of time series are extracted, and the extreme points are taken as decision variables, and the time series decision table is constructed by combining the relevant technical indicators of stock time series and other data. transforming time series data into decision table is the basis of time series classification modeling. therefore, this chapter is a key content of this paper, paving the way for the next application. 4 validation of time series decision table based on support vector machine this experiment uses python language sklearn. the svc method in the svm module is used to implement the support vector machine model. the kernel function is set to "linear" by the kernel, and the data set division method in the sklearn module is also used to realize the data set division. the data set is divided according to the ratio of 7:3. table 10 shows the accuracy of support vector machine model: table 10: accuracy of support vector machine models with different granularity data set accuracy 5-minute interval dataset 77.82% 15-minute interval dataset 79.07%it is necessary to 30-minute interval dataset 76.66% 60-minute interval dataset 76.30%it is necessary to table 11 shows the confusion matrix generated by support vector machine models with different granularity: table 11: confusion matrix of support vector machine models with different granularity predict 5-minute interval support vector machine true 0 1 0 1980 560 1 565 1969 predict 15-minute interval support vector machine true 0 1 0 685 192 1 167 669 predict 30-minute interval support vector machine true 0 1 0 320 89 1 107 324 predict 60-minute interval support vector machine true 0 1 0 164 54 1 46 158 accuracy of different granularity support vector machine models with different step sizes removed: https://doi.org/10.15837/ijccc.2022.6.4952 14 table 12: different svm models with different step removed data set remove the accuracy remove the accuracy remove the accuracyof step size 1 of steps 1 and 2 of steps 1, 2 and 3 5minute line dataset 88.58% 91.03% 93.64% 15minute line dataset 88.48% 90.25% 93.12% 30minute line dataset 89.91% 90.02% 92.31% 60minute line dataset 86.87% 88.44% 91.45% the confusion matrix generated by support vector machine models with different granularity is shown in the table below: table 13: 5-minute dataset predict remove the support vector machine with step size of 1 true 0 1 0 1489 191 1 196 1515 predict remove support vector machines with steps of 1 and 2 true 0 1 0 1160 120 1 111 1177 predict remove support vector machines with steps of 1, 2 and 3 true 0 1 0 908 63 1 69 1028 table 14: 15-minute dataset predict remove the support vector machine with step size of 1 true 0 1 0 498 66 1 65 509 predict remove the support vector machine with steps of 1 and 2 true 0 1 0 401 41 1 44 381 predict remove support vector machines with steps of 1, 2 and 3 true 0 1 0 341 28 1 21 307 https://doi.org/10.15837/ijccc.2022.6.4952 15 table 15: 30-minute dataset predict remove the support vector machine with step size of 1 true 0 1 0 254 24 1 33 254 predict remove support vector machines with steps of 1 and 2 true 0 1 0 199 26 1 17 186 predict remove the support vector machines with steps of 1, 2 and 3 true 0 1 0 138 12 1 14 172 table 16: 60-minute dataset predict remove the support vector machine with step size of 1 true 0 1 0 133 20 1 17 112 predict remove support vector machines with steps of 1 and 2 true 0 1 0 92 11 1 14 97 predict remove the support vector machines with steps of 1, 2 and 3 true 0 1 0 88 8 1 7 70 from the above data, it is known that using multi-granularity time series decision table based on trend partition and related classification methods can effectively predict the trend points of stock time series. the accuracy of prediction is high, and no fitting phenomenon will occur. better prediction results can be obtained by adjusting tolerance errors and filtering extreme points. the specific mechanism will be explored in future research. 5 conclusions this paper proposed a new time series trend division method, which uses tol-erance error (or step adjustment) to determine the trend division of time series. this method can process time series into two modes, rise and fall. based on the trend partitioning method of time series with different granularity, the series are divided into rise and fall trends, which avoid the complexity of threshold, numerical length and multiple trends. take the easiest way to identify trends. the method is simple and the time complexity is low. on the other hand, with time granularity as the main measure, the nested structure of time series with different time granularity based on trend partitioning is obtained. this structure can fully express the struc-ture characteristics of time series, which provides a good basis for further study on time series. a trend-based extreme point extraction algorithm is proposed, which can ex-tract extreme points with different time granularity. on the basis of getting the extreme points, the high (1) and low (0) extreme points are used as decision vari-ables. the other information (such as mean, volume, etc.) of the extreme points is extracted to establish time series decision tables that divide different https://doi.org/10.15837/ijccc.2022.6.4952 16 time granu-larities based on trends. on this basis, the decision table was validated by random forest method, and the results were good. the proposed multi-granularity time series decision table can accurately characterize the data characteristics of time series and can be used for complex time series mining methods. the data of decision table is symmetrical, does not produce unbalanced data phenomenon of general decision problem, has higher mining ac-curacy, and significantly improves the prediction accuracy compared with the original time series regression and measurement model. it provides a new method for the study of time series problem, and has strong theoretical significance and application value. references [1] duplakova d, teliskova m, duplak j, et al. determination of optimal production process using scheduling and simulation software[j]. international journal of simulation modelling, 2018, 17(4): 609–622. 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[23] preis, t., schneider, j. j. & stanley, h. e. switching processes in financial markets. proceedings of the national academy of sciences of the united states of america 108, 7674–8 (2011). copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: yang, h.; gao, x.; han, l.; cui, w. (2022). a financial time series data mining method with different time granularity based on trend division, international journal of computers communications & control, 17(6), 4952, 2022. https://doi.org/10.15837/ijccc.2022.6.4952 introduction related work research on univariate time series literature review and analysis methodology comparative analysis of multiple time granularity prediction based on arima model establishment of multi granularity time series decision table based on trend partition extraction of time series trend extreme points based on time series value recursion validation of time series decision table based on support vector machine conclusions int j comput commun, issn 1841-9836 8(5):689-698, october, 2013. asymptotically unbiased estimator of the informational energy with knn a. caţaron, r. andonie, y. chueh angel caţaron electronics and computers department transylvania university of braşov, romania cataron@unitbv.ro răzvan andonie* 1. computer science department central washington university, ellensburg, usa andonie@cwu.edu 2. electronics and computers department transylvania university of braşov, romania *corresponding author yvonne chueh department of mathematics central washington university, ellensburg, usa chueh@cwu.edu abstract: motivated by machine learning applications (e.g., classification, function approximation, feature extraction), in previous work, we have introduced a nonparametric estimator of onicescu’s informational energy. our method was based on the k-th nearest neighbor distances between the n sample points, where k is a fixed positive integer. in the present contribution, we discuss mathematical properties of this estimator. we show that our estimator is asymptotically unbiased and consistent. we provide further experimental results which illustrate the convergence of the estimator for standard distributions. keywords: machine learning, statistical inference, asymptotically unbiased estimator, k-th nearest neighbor, informational energy. 1 introduction inference is based on a strong assumption: using a representative training set of samples to infer a model. in this case, we select a random sample of the population, perform a statistical analysis on this sample, and use these results as an estimation to the desired statistical characteristics of the population as a whole. the more representative the sample is, the higher our confidence level reaches so that the statistical results obtained by using this sample are indeed a good estimation to the desired population parameters. we gauge the representativeness of a sample by how well its statistical characteristics reflect the probabilistic characteristics of the entire population. many standard techniques may be used to select a representative sample set [15]. however, if we do not use expert knowledge, selecting the most representative training set from a given dataset was proved to be computationally difficult (np-hard) [10]. the problem is actually more difficult, since in most applications the complete dataset is unknown, or too large to be analyzed. therefore, we have to rely on a more or less representative training set. a critical aspect of many machine learning approaches is how well an information theory measure is estimated from the available training set. this relates to a fundamental concept in statistics: probability density estimation. density estimation is the construction of an estimate of the density function from the observed data [20]. we will refer here only to nonparametric copyright c⃝ 2006-2013 by ccc publications 690 a. caţaron, r. andonie, y. chueh estimation, where less rigid assumptions will be made about the distribution of the observed data. although it will be assumed that the distribution has the probability density f, the data will be allowed to speak for themselves in determining the estimate of f more than would be the case if f were constrained to fall in a given parametric family. a common measure used in machine leaning is mutual information (mi). several methods were proposed for mi estimation [18], [22], [13]: histogram based estimators, kernel density estimators, b-spline estimators, k-th nearest neighbor (knn) estimators, and wavelet density estimators. estimating entropy and mi is known to be a non-trivial task [4]. naïve estimations (which attempt to construct a histogram where every point is the center of a sampling interval) are plagued with both systematic (bias) and statistical errors. an ideal estimator does not exist, instead the choice of the estimator depends on the structure of data to be analyzed. it is not possible to design an estimator that minimizes both the bias and the variance to arbitrarily small values. the existing studies have shown that there is always a delicate trade off between the two types of errors [4]. mi is generally based on the classical shannon type mi. however, it is computationally attractive to use one of its generalized forms: the rényi divergence measure, which uses rényi‘s quadratic entropy. the reason is that, as proved by principe et al., rényi‘s quadratic entropy (and rényi’s divergence measure) can be estimated from a set of samples using parzen’s windows approach [19]. the mi and rényi’s divergence measure are equivalent, but only in the limit α = 1, where α is the order of rényi‘s divergence measure [19]. a unilateral dependency measure can be derived from onicescu’s informational energy (ie). this measure proved to be an efficient alternative to mi, and we have estimated it from sample datasets using the parzen windows approach. we used this approach in classification and feature weighting [2], [5], [6], [3], [7]. an important drawback of this approach is the fact that parzen windows estimate cannot be applied on continuous spaces. this is also true for shannon’s type mi. therefore, this means an important machine learning domain continuous function approximation (or prediction), is left out. in previous work [8], we introduced a knn ie estimator which may be used to approximate the unilateral dependency measure both in the discrete and the continuous case. an important theoretical aspect was not discussed yet: the asymptotic behavior of this estimator in terms of unbiasedness and consistency. generally, any statistic whose mathematical expectation is equal to a parameter is called unbiased estimator of that parameter. otherwise, the statistic is said to be biased. any statistic that converges asymptotically to a parameter is called consistent estimator of that parameter [12]. consistent and unbiased are not equivalent. a simple example of a biased consistent estimator is if the mean of samples x1,x2, . . . ,xn is estimated by 1/n ∑ xi+1/n. this estimate is biased but consistent, since it approaches asymptotically the correct value. an asymptotically unbiased estimator is an estimator that is unbiased as the sample size tends to infinity. some biased estimators are asymptotically unbiased but all unbiased estimators are asymptotically unbiased. the previous estimator is biased but asymptotically unbiased. one way to prove that an estimator is consistent is to prove that it is asymptotically unbiased and the variance goes to zero. this gives the motivation for the present work. we show that our ie estimator is asymptotically unbiased and consistent. this will imply that the estimator is “good”. first, we summarize (section 2) the ie and the knn method. section 3 describes our ie approximation method, including the novel theoretical results. after the experimental results, exposed in section 4, we conclude with final remarks and a description of future work (section 5). asymptotically unbiased estimator of the informational energy with knn 691 2 background 2.1 onicescu’s informational energy generally, information measures refer to uncertainty. since shannon defined his probabilistic information measure in 1948, many other authors, with rényi, daroczy, bongard, arimoto, and guiaşu among them, have introduced new measures of information. however, information measures can also refer to certainty, and probability can be considered as a measure of certainty. more general, any monotonically growing and continuous probability function can be considered as a measure of certainty. for instance, onicescu’s ie was interpreted by several authors as a measure of expected commonness, a measure of average certainty, or as a measure of concentration. for a continuous random variable x with probability density function f(x), the ie is [11,17]: ie(x) = ∫ +∞ −∞ f2(x)dx (1) 2.2 the nearest neighbor method although classification remains the primary application of knn, we can use it to do density estimation also. since knn is non parametric, it can do estimation for arbitrary distributions. the idea is very similar to use of parzen window. instead of using hypercube and kernel functions, here we do the estimation as follows. the knn estimators represent an attempt to adapt the amount of smoothing to the “local” density of data. the degree of smoothing is controlled by an integer k, chosen to be considerably smaller than the sample size; typically k ≈ n1/2. define the distance d(x,y) between two points on the line to be |x × y| in the usual way, and for each t define d1(t) ≤ d2(t) ≤ . . . ≤ dn(t) to be the distances, arranged in ascending order, from t to the points of the sample. the knn density estimate f(t) is defined by [20]: f̂(t) = k 2ndk(t) (2) the knn was used for non-parametrical estimate of the entropy based on the k-th nearest neighbor distance between n points in a sample, where k is a fixed parameter and k ≤ n − 1. based on the first nearest neighbor distances, leonenko et al. [14] introduced an asymptotic unbiased and consistent estimator hn of the entropy h(f) in a multidimensional space. when the sample points are very close one to each other, small fluctuations in their distances produce high fluctuations of hn. in order to overcome this problem, singh et al. [21] defined an entropy estimator based on the k-th nearest neighbor distances. a knn estimate of the kullback-leibler divergence was obtained by wang et al. in [23]. a mean of several knn estimators corresponding to different values of k was used by faivishevsky et al. in [9] for developing a smooth estimator of differential entropy, mutual information, and divergence. according to [22], knn mi estimation outperforms histogram methods. knn works well if the value of k is optimally chosen. however, there is no model selection method for determining the number of neighrest neighbors k. this is a limitation of the knn estimation. 3 estimation of the informational energy we are ready now to introduce our knn method for ie approximation, using results from our previous work [8]. the described theoretical properties are however novel. mathematical proofs are omitted, since they would not fit into the page limit of this paper. 692 a. caţaron, r. andonie, y. chueh the ie can be easily computed if the data sample is extracted from known distributions. when the underlying distribution of data sample is unknown, the ie has to be estimated. more formally, our goal is to estimate (1) from a random sample x1, x2, ..., xn of n d-dimensional observations from a distribution with the unknown probability density f(x). this problem is even more difficult if the number of available points is small. the ieempirical is not a good estimate especially when the relative frequencies are far from the true probabilities. this is generally the case for small datasets and, in accordance to the central limit theorem, for an increasing number of samples, ieempirical converges probabilistically to ie. the ie is the average of f(x), therefore we have to estimate f(x). the n observations from our samples have the same probability 1 n . a convenient estimator of the ie is: ˆie (n) k (f) = 1 n n∑ i=1 f̂(xi). (3) we will determine first the probability density pik(ϵ) of the random distance ri,k,n between a fixed point xi and its k-th nearest neighbor from the remaining n − 1 points. probability pik(ϵ)dϵ of the k-th nearest neighbor to be within distance ri,k,n ∈ [ϵ,ϵ + dϵ] from xi, k − 1 points at a smaller distance and n − k − 1 at a larger distance can be expressed in terms of the trinomial formula [9]: pik(ϵ)dϵ = (n − 1)! 1!(k − 1)!(n − k − 1)! dpi(ϵ)p k−1 i (1 − pi) n−k−1, where pi(ϵ) = ∫ ∥x−xi∥<ϵ f(x)dx is the mass of the ϵ-ball centered at xi and ∫ pik(ϵ)dϵ = 1. we can express the expected value of the pi(ϵ) using the probability mass function of the trinomial distribution: epik(ϵ)(pi(ϵ)) = ∫ ∞ 0 pik(ϵ)pi(ϵ)dϵ = = k ( n − 1 k )∫ 1 0 pk−1(1 − p)n−k−1pdp = = k ( n − 1 k )∫ 1 0 p(k+1)−1(1 − p)(n−k)−1dp. this equality can be reformulated using the beta function: b(m,n) = ∫ 1 0 xm−1(1 − x)n−1 = γ(m)γ(n) γ(m + n) . we obtain: epik(ϵ)(pi(ϵ)) = k ( n − 1 k ) γ(k + 1)γ(n − k) γ(n + 1) = = k (n − 1)! (n − k − 1)!k! k!(n − k − 1)! n! , which can be rewritten as: epik(ϵ)(pi(ϵ)) = k n . (4) on the other hand, assuming that f(x) is almost constant in the entire ϵ-ball around xi [9], we have: pi(ϵ) ≈ v1rdi,k,nf(xi), asymptotically unbiased estimator of the informational energy with knn 693 where we denote the volume of the ball of radius ρr,n in a d-dimensional space by: vρr,n = v1ρ d r,n = π p 2 ρdr,n γ( p 2 + 1) . v1 is the volume of the unit ball and ri,k,n is the euclidean distance between the reference point xi and its k-th nearest neighbor. this means that v1rdi,k,n is the volume of the d-dimensional ball of radius ri,k,n. we obtain the expected value of pi(ϵ): e(pi(ϵ)) = e(v1r d i,k,nf(xi)) = v1r d i,k,nf̂(xi). (5) equations (4) and (5) both estimate e(pi(ϵ)). their results are approximatively equal: v1r d i,k,nf̂(xi) = k n , that is: f̂(xi) = k nv1r d i,k,n , i = 1...n. (6) this is the estimate of the probability density function. by substituting f̂(xi) in (3), we finally obtain the following ie approximation: ˆie (n) k (f) = 1 n n∑ i=1 k nv1r d i,k,n . (7) we have introduced this result in [8]. our main question now is to analyze its asymptotic behavior. consistency of an estimator means that as the sample size gets large, the estimate gets closer and closer to the true value of the parameter. unbiasedness is a statement about the expected value of the sampling distribution of the estimator. the ideal situation, of course, is to have an unbiased consistent estimator. this may be very difficult to achieve. yet unbiasedness is not essential, and just a little bias is permitted as long as the estimator is consistent. therefore, an asymptotically unbiased consistent estimator may be acceptable. in the following, we will use the following mathematical property (from [16]): an asymptotically unbiased estimator with asymptotic zero variance is consistent. we are ready now to state our theoretical results: 1. the informational energy estimator ˆie (n) k (f) is asymptotically unbiased. 2. limn→∞ v ar [ ĥ (n) k (f) ] = 0. therefore, we can conclude that the ˆie (n) k (f) estimator is consistent. 4 experiments when the distribution of a sample is unknown, the statistical measures cannot be calculated directly, and we have to use an estimate. the quality of an estimator can be determined by studying its asymptotic behavior. we proved that the informational energy estimator îe is 694 a. caţaron, r. andonie, y. chueh asymptotically unbiased and consistent. it provides an approximation of the informational energy regardless of the distribution where the sample was drawn from. it is interesting to compare the estimated value of the ie with its real value. for an unidimensional distribution, we can achieve this goal by generating a random sample from a known distribution f(x), with xmin and xmax being the minimum / maximum values. then, the informational energy of the distribution f(x) on the subdomain dsample = {x|x ∈ [xmin,xmax]} is iedsample = ∫ xmax xmin f2(x)dx = ∫ dsample f2(x)dx, (8) while the estimated informational energy îe is given by the formula (7). the information energy of the same distribution has a fixed value when it is computed on its entire definition domain d: ied = ∫ d f2(x)dx. (9) our experiments focus on the following distributions: exponential, unidimensional gaussian, beta, cauchy, gamma, and weibull. we use the r programming language and environment functions to generate the random samples from each distribution, with the parameters listed in tables 1–6. the first line in each table contains: the probability density function of the distribution, the support of this function (which is the domain d), the values of the parameters, and the ie computed with formula (9). sample size is the number of values from the random sample, and range is the interval limited by the minimum / maximum values from the sample used to compute the ie with formula (8). in order to study the asymptotically unbiasedness and consistency of the estimator, we determine the value of ˆie for samples with 10, 100, 1000 values, and with increasing values of k, from 1 to the squared root of the sample size [20]. table 1: exponential distribution f(x) = θe−θx, x ≥ 0, θ = 3, ied = 1.5 sample size: 10; range: [0.022, 0.777]; iedsample = 1.3 k 1 2 3 ˆie 8.133 2.464 1.023 sample size: 100; range: [0.0007, 2.599]; iedsample = 1.493 k 1 2 3 5 7 9 ˆie 4.953 2.312 2.289 2.167 1.854 1.743 sample size: 1000; range: [0.0001, 2.237]; iedsample = 1.499 k 1 2 3 10 20 30 ˆie 6.829 3.093 2.269 1.605 1.527 1.507 in general, the expected behavior was confirmed by experiments: the larger the sample size n, the more accurate estimation of the informational energy. asymptotically unbiased estimator of the informational energy with knn 695 table 2: unidimensional gaussian distribution f(x) = 1√ 2πσ e − (x−µ) 2 2σ2 , µ = 0, σ = 1, ied = 0.282 sample size: 10, range: [−1.018, 1.395], iedsample = 0.254 k 1 2 3 ˆie 2.559 0.444 0.402 sample size: 100, range: [−2.263, 2.484], iedsample = 0.281 k 1 2 3 5 7 9 ˆie 0.998 0.462 0.333 0.286 0.275 0.271 sample size: 1000, range: [−3.596, 2.781], iedsample = 0.282 k 1 2 3 10 20 30 ˆie 1.419 0.526 0.421 0.315 0.296 0.293 table 3: beta distribution f(x) = γ(α+β) γ(α)γ(β) (1 − x)β−1xα−1, 0 ≤ x ≤ 1, α = 2,β = 3, ied = 1.371 sample size: 10, range: [0.194, 0.773], iedsample = 1.170 k 1 2 3 ˆie 4.873 2.452 2.083 sample size: 100, range: [0.010, 0.842], iedsample = 1.369 k 1 2 3 5 7 9 ˆie 5.084 2.588 2.167 1.899 1.522 1.523 sample size: 1000, range: [0.010, 0.930], iedsample = 1.371 k 1 2 3 10 20 30 ˆie 101.969 2.617 2.103 1.516 1.450 1.430 table 4: cauchy distribution f(x) = b π[(x−m)2+b2] , x ∈ r, m = 0, b = 1, ied = 0.159 sample size: 10, range: [−29.068, 61.499], iedsample = 0.159 k 1 2 3 ˆie 1.342 0.253 0.193 sample size: 100, range: [−19.543, 17.052], iedsample = 0.159 k 1 2 3 5 7 9 ˆie 37.599 0.204 0.188 0.154 0.158 0.158 sample size: 1000, range: [−232.181, 165.562], iedsample = 0.159 k 1 2 3 10 20 30 ˆie 0.859 0.343 0.284 0.170 0.164 0.161 696 a. caţaron, r. andonie, y. chueh table 5: gamma distribution f(x) = x α−1e − x θ γ(α)θα , x ≥ 0, θ = 1, α = 3, ied = 0.187 sample size: 10, range: [1.381, 5.340], iedsample = 0.156 k 1 2 3 ˆie 0.204 0.232 0.240 sample size: 100, range: [0.556, 9.053], iedsample = 0.186 k 1 2 3 5 7 9 ˆie 0.624 0.318 0.285 0.264 0.233 0.232 sample size: 1000, range: [0.092, 11.866], iedsample = 0.187 k 1 2 3 10 20 30 ˆie 1.768 0.344 0.280 0.210 0.201 0.196 table 6: weibull distribution f(x) = αx α−1 βαe ( xβ ) α , x ≥ 0, α = 3, β = 4, ied = 0.213 sample size: 10, range: [2.040, 5.202], iedsample = 0.191 k 1 2 3 ˆie 1.315 0.583 0.523 sample size: 100, range: [0.899, 7.295], iedsample = 0.212 k 1 2 3 5 7 9 ˆie 0.551 0.368 0.296 0.227 0.209 0.215 sample size: 1000, range: [0.393, 7.438], iedsample = 0.213 k 1 2 3 10 20 30 ˆie 1.416 0.379 0.287 0.234 0.226 0.223 asymptotically unbiased estimator of the informational energy with knn 697 5 conclusions and future work we have introduced a novel non-parametric knn approximation method for computing the ie from data samples. in accordance to our results, the ˆie (n) k (f) estimator is consistent. in order to study the interaction between two random variables x and y , the following measure of unilateral dependency was defined by andonie et al. [1]: o(y,x) = ie(y |x) − ie(y ) this measure quantifies the unilateral dependence characterizing y with respect to x and corresponds to the amount of information detained by x about y . there is an obvious analogy between o(y,x) and the mi, since both measure the same phenomenon. however, the mi is a symmetric, not a unilateral measure. rather than approximating o(y,x) as we did in our previous studies, in our future work we will approximate directly the ie from the available dataset, using the ˆie (n) k (f) estimator. we also plan to apply our ie estimator to machine learning techniques. bibliography [1] andonie, r; petrescu, f.; interacting systems and informational energy, foundation of control engineering, 11:53-59, 1986. [2] andonie, r.; caţaron, a.; an informational energy lvq approach for feature ranking, proc. of the european symposium on artificial neural networks esann 2004, bruges, belgium, april 28-30, 2004, d-side publications, 471-476, 2004. 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(2006); a nearest-neighbor approach to estimating divergence between continuous random vectors, proc. of the ieee international symposium on information theory, isit 2006, seattle, wa, usa, july 9-14, 2006, 242-246, 2006. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 3, month: june, year: 2020 article number: 3858, https://doi.org/10.15837/ijccc.2020.3.3858 ccc publications trust based fuzzy linguistic recommender systems as reinforcement for personalized education in the field of oral surgery and implantology c. porcel, j. herce-zelaya, j. bernabé-moreno, a. tejeda-lorente, e. herrera-viedma carlos porcel* department of computer science university of jaén, spain campus las lagunillas s/n. 23071, jaén, spain *corresponding author: cporcel@ujaen.es julio herce-zelaya andalusian research institute in data science and computational intelligence, university of granada, granada 18071, spain julioherce@gmail.com juan bernabé-moreno andalusian research institute in data science and computational intelligence, university of granada, granada 18071, spain jbernabemoreno@gmail.com alvaro tejeda-lorente andalusian research institute in data science and computational intelligence, university of granada, granada 18071, spain atejeda@decsai.ugr.es enrique herrera-viedma andalusian research institute in data science and computational intelligence, university of granada, granada 18071, spain viedma@decsai.ugr.es abstract the rapid advances in web technologies are promoting the development of new pedagogic models based on virtual teaching. in this framework, personalized services are necessary. recommender systems can be used in an academic environment to assist users in their teaching-learning processes. in this paper, we present a trust based recommender system, adopting a fuzzy linguistic modeling, that provides personalized activities to students in order to reinforce their education, and applied it in the field of oral surgery and implantology. we don’t take into account users with similar ratings history but users in which each user can trust and we provide a method to aggregate the trust information. this system can be used in order to aid professors to provide students with a personalized monitoring of their studies with less effort. the results obtained in the experiments proved to be satisfactory. keywords: recommender system, e-learning, fuzzy linguistic modeling, oral surgery. https://doi.org/10.15837/ijccc.2020.3.3858 2 1 introduction the great advances in web technologies are promoting the development of new pedagogic models that complement the present education [11]. the new technologies improve the teaching-learning processes, aiding the information broadcasting in an efficient and easy manner, and providing tools for the personal and global communications that allow encouraging the collaborative learning [3, 19]. in this academic scope, personalized education [16] can be very helpful aiding students to reinforce the areas where it is necessary some help as well as maximizing those where they have potential. education must also have the ability to adapt itself to the necessities of the student dynamically. recommender systems seek to discover information items that are valuable to the users. they may be considered personalized services because they have an independent profile for each user [2, 4, 20]. therefore these systems need some information about every user, such as the ratings provided by the users about the analyzed items. this need for information introduces the requirement for the system to maintain users’ profiles containing the users’ preferences or needs. another aspect to take into consideration is which additional information is required by the system, and how this information is processed and managed to generate a list of personalized recommendations. one of the mostly used methods to generate recommendations is the collaborative approach in which the recommendations provided to a particular user are based upon the ratings provided by those users with similar profiles. in this sense, we can use the inherent connectivity from an educational community to support collaborative approach, where students rate resources and these ratings are shared with a large community [3, 4]. one key disadvantage of this approach consists of the need of many ratings to obtain a good performance. but users typically provide just a few ratings, so the systems have difficulties to compute the similarity between two users [14]. therefore, collaborative approaches tend to fail in generating recommendations since they usually fail at obtaining groups of users with similar preferences. thus, some improvements need to be introduced to overcome this situation and one promising direction is to focus on trust, which plays a crucial role in on-line social networks [5], so widespread and popular today. people tend to rely upon recommendations received from trusted users, such as friends, more than those generated by automatic systems [18]. in the literature, we can find some proposals about the incorporation of trust models in recommendation systems [10, 21]. in these systems, the recommendation engine uses the trusted network between users. the aim of this article is to present a new fuzzy linguistic recommender system that incorporates the concept of trust in the recommendations generation engine. this new system is also adapted to students of dentistry from the dentistry school of the university of granada 1 (spain). the major innovations and contributions of the system include: 1. a method to estimate the trust score between two users, because the trusted network can be huge and most users do not know each other. 2. the provision of reliable personalized information by using a recommendation approach in which users with similar ratings history or pedagogical needs are not considered, but users in which each user can trust. 3. its user-friendly nature, using a multi-granular fuzzy linguistic modeling to improve the representation of user preferences and facilitate user-system interactions [9, 12]. 4. the ability to use it in any place and at any time, providing to students the necessary freedom to organize their schedules. 5. the reliability of the information offered and the selection of exercises, endorsed by a team of experts in oral surgery from the dentistry school of the university of granada. the paper is structured as follows. in section 2, the preliminaries are presented. next in section 3, we describe our proposal. section 4 addresses the evaluation of the system and finally in section 5 we throw our conclusions. 1www.ugr.es https://doi.org/10.15837/ijccc.2020.3.3858 3 2 preliminaries 2.1 basis of recommender systems recommender systems try to guide the user in a personalized way towards suitable tasks among a wide range of possible options [2, 20]. in order to generate personalized recommendations that are tailored to the user’s preferences or needs, recommender systems must collect personal preference information. taking into account the knowledge source, different recommendations generation methods can be distinguished [1, 14]. each approach has certain advantages and disadvantages, depending on the scope settings. one solution is to use a hybrid strategy combining different approaches in order to reduce the disadvantages of each one of them and to exploit their benefits[1]. moreover, the recommendation activity is followed by a feedback phase in which the users provide the system with their satisfaction evaluations about the recommended items and the system uses these evaluations to automatically update user profiles. 2.2 fuzzy linguistic approach the fuzzy linguistic approach is a tool based on the concept of linguistic variable proposed by zadeh [22]. this theory has given very good results to model qualitative information and it has been proven to be useful in many problems. in [6] is proposed a continuous model of information representation based on 2-tuple fuzzy linguistic modelling. to define it both the 2-tuple representation model and the 2-tuple computational model to represent and aggregate the linguistic information have to be established. let s = {s0, ...,sg} be a linguistic term set with odd cardinality. we assume that the semantics of labels is given by means of triangular membership functions and consider all terms distributed on a scale on which a total order is defined. if a value β ∈ [0,g], and β /∈ {0, ...,g}, we can represent β as a 2-tuple (si,αi), where si represents the linguistic label, and αi is a numerical value expressing the value of the translation between numerical values and 2-tuple: ∆(β) = (si,α) and ∆−1(si,α) = β ∈ [0,g] [6]. in order to establish the computational model negation, comparison and aggregation operators are defined. using functions ∆ and ∆−1, any of the existing aggregation operators (such as arithmetic mean, weighted average operator or linguistic weighted average operator) can be easily be extended for dealing with linguistic 2-tuples without loss of information [6]. when different experts have different uncertainty degrees on the phenomenon or when an expert has to evaluate different concepts, several linguistic term sets with a different granularity of uncertainty are necessary. to manage this situation, in [7] a multi-granular 2-tuple fuzzy linguistic modelling based on the concept of linguistic hierarchy is proposed. a linguistic hierarchy lh, is a set of levels l(t,n(t)), where each level t is a linguistic term set with different granularity n(t). in [7] a family of transformation functions between labels from different levels was introduced. to establish the computational model we select a level that we use to make the information uniform and thereby we can use the defined operator in the 2-tuple model. this result guarantees that the transformations between levels of a linguistic hierarchy are carried out without loss of information. 2.3 trust networks trust networks are social networks in which users can explicitly assign trust scores to rate other users. but trust networks are usually very large and therefore a lot of users don’t even know the vast majority of other users. for this reason, we need to use a method to estimate the trust degree between two users. the idea is to search for a path between the two users and propagate the trust degrees found along the path. usually, we can find several paths between two users, so we may select the most relevant and aggregate the propagated trust degrees into the trust degree estimation. an upper path length limit is typically imposed what is known as horizon, h, and typical values for h are 2 or 3. to aggregate the propagated trust degrees of the paths found, we use milowa, a majority guided linguistic operator [8]. with respect to the variable inducing the reordering of the set of values to be aggregated, in a trust network we work with information about reliability, so that we use the average https://doi.org/10.15837/ijccc.2020.3.3858 4 global trust of all users of each of the founded path. to compute the global trust of a user we use pagerank [13] because is one of the most widely used global trust metric. 3 trust based recommender system to assist dentistry students in the field of oral surgery and implantology in this section we present the web system to assist students from the dentistry school of university of granada. initially it is oriented to students from these subjects: oral surgery i, oral surgery ii and implantology. the system has three main components: videos and resources, student profiles and the method for generating recommendations. 3.1 information representation in order to allow for higher flexibility in the communication processes of the system, different label sets (s1, s2, ...) are used are selected from among those that compose a lh, i.e., si ∈ lh. the different concepts assessed in the system are the following: • degree of trust of a student relative to another, which is labelled in s1. • the predicted degree of relevance of a resource for a student, which is labeled in s2. • the degree of satisfaction with a recommended resource expressed by a student, which is labeled in s3. • membership degree of a resource scope or student needs with respect to each of the defined reinforcing subgroup, which is labelled in s4. we use 5 labels to represent the degrees of trust, satisfaction and membership to reinforcing subgroup (s1 = s5, s3 = s5 and s4 = s5) and 9 labels to represent the predicted relevance degrees (s2 = s9). the linguistic terms in each level are the following ones: • s5 = {b0 = none = n, b1 = low = l, b2 = medium = m, b3 = high = h, b4 = total = t} • s9 = {c0 = none = n, c1 = v ery_low = v l, c2 = low = l, c3 = more_less_low = mll, c4 = medium = m, c5 = more_less_high = mlh, c6 = high = h, c7 = v ery_high = v h, c8 = total = t} 3.2 resource representation a multimedia database was developed and contained videos with a wide set of different oral surgeries or implants for all possible needs inside the subjects covered. also, different sets of scientific papers or class notes are introduced into the system. all videos, papers and notes can be combined among different subgroups, called activities, in the construction of a customized program for each student. videos were recorded on real surgeries produced in dentistry’s offices or in university’s labs. those activities are the items to be recommended by our system. each combination of videos, notes or papers make the different activities suitable for a student with a specific pedagogical need. once a teacher creates a new activity into the system, he/she provides the activity with an internal representation that is mainly based on its appropriateness for each reinforce subgroup. an activity i is represented as a vector v ri = (v ri1,v ri2, ...,v ri4), where each component v rij ∈ s4 is a linguistic assessment that represents how appropriate is the activity i with respect to the reinforcing subgroup j. https://doi.org/10.15837/ijccc.2020.3.3858 5 3.3 student profiles the student profiles are represented by three components: their needs, their degrees of trust in other students and the satisfaction degrees with the recommended resources. students must complete their profiles with the grades obtained in previous subjects related with this oral surgery and implantology. they have to periodically carry out different test to be able of evaluate their abilities. after obtaining the test results, the teachers assess the membership of the student need in each one of the four reinforcing subgroups. a student i is represented as a vector v si = (v si1,v si2, ...,v si4), where each component v sij ∈ s4 is a linguistic assessment that represents the degree of how appropriate i is for each reinforcing subgroup j. since student are performing test over the whole semester, their membership to the different subgroup will be changing together with their new results. besides, the students explicitly specify their degree of trust on other students using the level s1 of the lh, i.e., using one the 5 labels of s5. finally, as students receive recommendations they are asked to assess their satisfaction with the recommendations (rc ∈ s3). 3.4 recommendation approach as a large number of users have not supplied the trust degrees to many other users, to generate recommendations, we need a method to estimate the trust degree between users. then, in order to estimate the level in which a user u trust in other user v, τu,v, the mliowa operator (see section 2.3) is applied to aggregate the global trust of all users found in the several paths between u and v, according to the majority [8]. then, if we wish to estimate or upgrade the relevance of a item i for a user u, the following steps are performed: 1. identify the set of trusted users of u, γu. to do that, we estimate the trust between u and all other users taking into account the selected horizon, i.e. τu,v ∀v ∈ υ with v 6= u and υ the set of users. as s1 = s5, we consider that the user v is a trusted user of u if the trust degree is higher than the mid linguistic label. 2. to recovery the assessments provided by the trusted users of u over the item i, i.e., the linguistic satisfaction assessments sat(y,i) ∈ s3, ∀y ∈ γu. 3. the item i is recommended to u with a predicted relevance degree prel(u,i) ∈ s2 × [−0.5, 0.5] which is calculated as follows: prel(u,i) = xwl ((tf s3 s2 (sat(y1, i), 0),tf s1s2 (τu,y1 )), . . . , (tf s3 s2 (sat(yn, i), 0),tf s1s2 (τu,yn ))), (1) where y1, . . . , yn ∈ γu, xwl is the linguistic weighted average operator and tf t t′ is the transformation function between a 2-tuple that belongs to level t and another 2-tuple in level t′ 6= t. 4 system evaluation 4.1 validating the system utility in the first place, we performed a practical study where a group of 50 volunteers students tested the system during one semester in different subjects: oral surgery i, oral surgery ii and implantology i. then, we evaluated the utility of the system based on the results obtained by the students and the feedback provided. after one semester of usage, the results of the students who used the system were in average a 15.5% better than the users that only assist to lessons. the linguistic weighted average of the feedback provided by users on the improvement received by the recommendations were (b50, 7), that is between medium and high. therefore, the results demonstrate that the website is not only positively perceived by its users but also it increases their results compared to the ones who did not use it. https://doi.org/10.15837/ijccc.2020.3.3858 6 4.2 recommendation approaches evaluation we also validated the proposed approach with off-line tests, where the epinions dataset 2 [10] was used. due to performance reasons, we reduced the epinion dataset containing a sample of the first 1000 users and 2000 items. the ratings subset for this reduced subset still contained 7841 ratings and 52548 trust statement values. 4.2.1 experiments description to develop the experiments we implemented the approach considering different values for the horizon, i.e., h = 2, h = 3 and h = 4 (called trust-h2, trust-h3 and trust-h4). in order to compare the results, we also implemented some collaborative approaches. we implemented both item-based and user-based approaches [1] with different configurations. we’ve also analysed the impact of having a different number of neighbours on the similarity computation, achieved by using different values for the variable k, which represents the most similar k-users. specifically, we used following values for k: 10, 20 and 50. in addition, we combined these values with two different similarity metrics, such as cosine and pearson. we name col-itor col-usthe algorithms item and user based respectively; likewise we added to the algorithm name the suffixes cs or ps for cosine or pearson metrics; the last suffix corresponds to the number of neighbours (k10, k20 or k50). then, we carried out a 5-fold cross validation process [15] and to measure the accuracy, we adopted the mean absolute error (mae) [17]. on the other hand, we also analysed the coverage achieved with each approach [17], i.e., the proportion of ratings of the validation set the system can generate a prediction for, classifying users and items into different types [10]. the users are classified in these types: cold start users who provided from 1 to 4 ratings, heavy raters who provided more than 10 ratings, opinionated users who provided more than 4 ratings and whose standard deviation is greater than 1.5, black sheep users who provided more than 4 ratings and for which the average distance of their rating on corresponding item with respect to mean rating of the item is greater than 1. the items are classified in: niche items which received less than 5 ratings and controversial items which received ratings whose standard deviation is greater than 1.5. 4.2.2 experiments results figure 1 shows respectively the mae and coverage for cosine/pearson similarity measure for itembased and users-based collaborative approaches for the different user groups. the mae obtained is similar for all combinations (except for small differences), but the coverage is better with the userbased collaborative approach. in both cases, the higher the number of neighbours, the better the results, especially in coverage. figure 1: mae and coverage obtained with differents configurations of collaborative approach. 2http://www.trustlet.org/wiki/epinions https://doi.org/10.15837/ijccc.2020.3.3858 7 now we analyse the results obtained with the approach based on trust. figure 2 shows respectively the mae and coverage obtained with the new proposal for different horizons. these figures show that a higher horizon value does not guarantee better results in mae terms (in fact, the better mae is obtained with h = 3), but in coverage. moreover, a higher horizon value penalizes the time to results as it implies much higher execution time. figure 2: mae and coverage obtained with our suggested approach for differents horizons. figure 3 show the results of the comparison. we can see that the better mae is obtained with item-based collaborative implementation for cold users, but only for very specific situations. however, in general terms, we see that the new proposal based on trust clearly outperforms the other approaches; specifically, we have achieved an improvement of 2.71%. but, the best results of our proposal manifest in terms of coverage because it outperforms the other methods. figure 3: comparison of mae and coverage between new and the previous schemes. https://doi.org/10.15837/ijccc.2020.3.3858 8 5 concluding remarks in this paper, we present a trust based fuzzy linguistic recommender system, to provide personalized activities to students in order to reinforce their individualized education. the main idea consists of not taking into account students with similar requirements or rating history, but rather trustworthy students. to achieve this, we have proposed a method to estimate the trust score between a pair of students. this system is applied in a real environment, providing personalized activities to students in the subjects oral surgery i and ii and implantology ii of dentistry degree in the university of granada (spain). the main benefits of this system are the increase of the personalize degree of the education received by the students that also have the possibility of following the activities anywhere and anytime. we have evaluated the proposal, and the experimental results demonstrate the good results of the usage of the system as well as the perception by the students by enhancing the effectiveness of professors dealing with large group of students. as future work, we 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(2020). trust based fuzzy linguistic recommender systems as reinforcement for personalized education in the field of oral surgery and implantology, international journal of computers communications & control, 15(3), 3858, 2020. https://doi.org/10.15837/ijccc.2020.3.3858 international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 5, month: october, year: 2021 article number: 4266, https://doi.org/10.15837/ijccc.2021.5.4266 ccc publications verification of university student and graduate data using blockchain technology y. shakan, b. kumalakov, g. mutanov, zh. mamykova, y. kistaubayev yassynzhan shakan, galimkair mutanov*, zhanl mamykova, yerlan kistaubayev al-farabi kazakh national university 71 al-farabi ave., almaty, kazakhstan *corresponding author: mutanov@kaznu.kz e-mail: shakan_yassynzhan@live.kaznu.kz, zhanl.mamykova@kaznu.kz, erlan.kistaubayev@kaznu.kz bolatzhan kumalakov astana it university, block c1, expo business centre, turkestan street, nur-sultan, kazakhstan e-mail: bolatzhan.kumalakov@astanait.edu.kz abstract blockchain is a reliable and innovative technology that harnesses education and training through digital technologies. nonetheless, it has been still an issue keeping track of student/graduate academic achievement and blockchain access rights management. detailed information about academic performance within a certain period (semester) is not present in the official education documents. furthermore, academic achievement documents issued by institutions are not secured against unauthorized changes due to the involvement of intermediaries. therefore, verification of official educational documents has become a pressing issue owing to the recent development of digital technologies. however, effective tools to accelerate the verification are rare as the process takes time. this study provides a prototype of the univercert platform based on a consortium version of the decentralized, open-source ethereum blockchain technology. the proposed platform is based on a globally distributed peer-to-peer network that allows educational institutions to partner with the blockchain network, track student data, verify academic performance, and share documents with other stakeholders. the univercert platform was developed on a consortium blockchain architecture to address the problems universities face in storing and securing student data. the system provides a solution to facilitate students’ registration, verification, and authenticity of educational documents. keywords: blockchain, smart contract, academic records, verification. 1 introduction one of the most important issues in the field of education is the safe storage of student/graduate academic performance and its supporting documents (diploma, transcript etc.), as well as quick and easy access to the accuracy of this information. in most cases, the official documents (diploma, https://doi.org/10.15837/ijccc.2021.5.4266 2 certificate etc.) which confirms the student’s graduation contains the following information such as course number, course title, the letter grades, and gpa (grade point average), accordingly, detailed information on the student’s academic performance for a certain period (semester) or for throughout academic years in higher education institution will not be available. this issue not only worries the institutions but also the students/graduates, state organs, law enforcement, as well as employers. with the recent development of the internet and digital technologies in the education system, the process of verifying the authenticity of digital documents issued by higher institutions has become a pressing issue. as few tools are available to verify the authenticity of official documents. yearly, approximately 500 thousand cases of university diploma authentication are registered in the republic of kazakhstan. this process takes time, as the copies of the documents are stored in the university file. similar problems are noted in higher education systems globally. according to unesco 30% of senior managers work with fake qualifications globally [6]. in addition, the process is lengthy and inconvenient as graduates must contact the university to obtain replacement education documents in case of loss, with these shortcomings, companies face daunting tasks to verify documents. many higher education institutions (heis) develop digital versions of educational documents (university diplomas, diploma supplements, academic transcripts, certificate of non-formal education) in their learning management system (lms) systems, which employees can access. in addition, students can view or print out their academic records using a restricted, password-protected mode. however, the databases are located in a single location and vulnerable to unauthorized access [7]. according to the internal policies of the document management system, universities not authorized to transfer students’ data to third parties. thus, students who transfer to another university may obliged to confirm the authenticity of their documents. organizing external academic students is the main problem because language, writing, and administration are the major barriers. moreover, these records are stored using different standards, hindering the exchange of academic records between universities [20]. thus, creating digital tools is crucial to forming a trust base integrating educational documents, preventing stored data from falsification, and document verification and authenticity. these problems are solved using the basic principles of blockchain technology. based on the above, we will determine the main problem of guaranteeing the security and authenticity of digital documents in the education system: the lack of a description of the mechanism for verifying students’ documents and their academic history of academic performance and presentation to interested parties. the purpose of the study is to develop a software product that allows verification of documents with minimal time spent on requests and data processing, with a high level of information security and providing it depending on the level of user access. this study proposes a web platform developed by univercert using an ethereum consortium blockchain architecture to store the academic records of students/graduates, develop future digital educational documents, and provide third-party document verification services. the univercert platform is based on blockchain technology using an immutable distributed data storage system to store the academic records in a secure information system [11] and form a trusted digital register of educational documents. moreover, the applied ethereum solution helps build the logic of the transaction through smart contracts to manage data storage and retrieval. the platform allows universities to create an effective, and convenient global environment. this study provides several contributions: • first, the study provides a comparative analysis of related studies on blockchain technology in higher education; • second, we identify and justify an acceptable and open source blockchain, platform to solve the research problems; • third, we describe a mechanism for verifying the authenticity of data in the blockchain network, depending on the level of access and provision of information to interested parties; • fourth, we design and discuss the architecture that interacts with different systems and databases. https://doi.org/10.15837/ijccc.2021.5.4266 3 the rest of the paper is organized as follows. section 2 explores the related studies. section 3 provides the background of the blockchain platform. section 4 presents the results and discussion. finally, section 5 concludes the paper. 2 related work blockchain technology is a distributed network [13] to store a block data structure [15] through cryptographic processing [5], using transaction coordination algorithms [14] in the network nodes. blockchains store data using innovative software and sophisticated math rules, which are extremely difficult for attackers to fake. the development of cloud services helps higher institutions to develop digital versions of educational documents using blockchain technologies. in addition, blockchain technology offers a unique tool to protect and authenticate official university documents. at present, many higher institutions have developed different approaches to store and verify academic documents using blockchain technologies. the goal is to facilitate documents’ authenticity and reliability, thereby adhering to the principles of decentralization, transparency, and confidentiality. table 1 summarizes the parameters of blockchain approaches adopted by different institutions • reasonable choice blockchain classification to tackle assigned tasks, • covering stakeholder interests, • availability of verification process. with blockchain technology the massachusetts institute of technology (mit) [32] has implemented an effective framework to protect and confirm the validity of diplomas and certificates. mit has released several versions of the blockcerts program [19], to issue certificates since 2018, the mit university has offered students the opportunity to receive a digital version of their diplomas through the blockchain as part of a pilot program to secure academic data and facilitate their portability. the system standardizes the issuance of diplomas and stores them in a single database. thus, the university uses bitcoin platform technology to store the hash code of documents on the blockchain network [23]. the university of nicosia (unic) [35] uses a similar mit program to create and store certificates using the bitcoin platform. in addition, the university accepts bitcoins as a form of payment to study a degree program. this scenario expensive for platform users, as it involves storing big data and documents like diplomas, certificates, and transcripts. a study identifies eductx to describe a blockchain-based decentralized credit platform for higher education, using the principles and rules of the european credit transfer and accumulation system (ects) [22]. eductx offers a comprehensive and unified digital environment designed according to the following scenarios: management, assignment, and presentation of digital micro-credentials [9]. in the proposed solution, students’ academic performance is treated as ectx tokens. after the students successfully pass the exams, the professors check the results, and the administrative office transfers the corresponding credits to the student’s wallet and the central blockchain network. the proposed solution is aimed at replacing the traditional assessment system with a digital assessment system using a distributed peer-to-peer (p2p) network. while the new technology provides students the tools to manage their academic credits, the solution is devoid of the verification process. the sony corporation and sony global education is also use blockchain technology for certification training systems [34]. the technology is based on the centralized data management system that many educational organizations use to record and identify official educational data and digital transcripts. the sony corporation also uses a new service (education blockchain) manage official transcripts and grade academic records through a digital platform. since no detailed information about the technology is provided, the system appears to store and secure the official academic records. the financial university in moscow, russia has launched a blockchain-based service to verify academic records on its official website [33]. the university offers a platform where students enter their data and diploma information, then send a request to obtain a hash diploma. the service https://doi.org/10.15837/ijccc.2021.5.4266 4 is only for students at the university and not for other stakeholders in the university. moreover, the service does not take into account the interests of stakeholders attempting to verify students’ academic information. the blockfactory in switzerland developed certificationio [31], which uses the ethereum blockchain to obtain a hash result of the electronic version of the education document. with the hash result, the document is transformed using an electronic digital signature and provides the user a ready-made signature. to store data through the hash result, the general data protection regulation must be followed [18] to save resources when processing a transaction in a public blockchain network. lms systems have been integrated into the api channel of universities and services of recruiting agencies. however, certificationio cannot store the student’s progress record for the entire study period. consequently, the system cannot form a complete picture of a student’s progress and cannot regenerate digital versions of documents. smartcert is another blockchain-based digital credential verification platform developed to authenticate academic credentials for al-zaytoonah university of jordan [10]. smartcert uses cryptographic signatures for the security of educational certificates. thus, the system is useful for students and universities as it facilitates certificate verification with the prospective employer. however, a third party can attack the computer system and access protected data if in possession of a hash or digital signature. as a result, access to the legitimate user is restricted. table 1 provides a comparative analysis of related works. table 1: comparison of blockchain technologies of educational institutions institutions/ approach decentralized transparency privacy blockchain type coverage of stakeholders availability of verification process data source mit + + public institutions, graduates need improvements for the authenticity of the third party https://www.blockcerts.org/ unic + + public institutions, graduates, employer need improvements for employer verification https://block.co/ eductx + not completely fulfilled + consortium institutions, students/ graduates, employers not available https://eductx.org/ education blockchain + + + consortium institutions, students not available https://blockchain.sonyged.com/ the financial university of moscow + not completely fulfilled + private institutions, graduates not available http://www.fa.ru/ certificationio + not completely fulfilled not completely fulfilled public institutions, students/ graduates, employers verification of the authenticity of the eds within the document https://certifaction.io/ smartcert + not completely fulfilled + consortium institutions, students/ graduates, employers no specific information https://www.zuj.edu.jo/ univercert + not completely fulfilled + consortium institutions, students/ graduates, state organs, law enforcement, employers provided a clear verification algorithm for each stakeholder based on access levels under development table 1 reveals various technologies aimed at storing secured ready-made educational documents (certificates, diplomas) and compares the key principles of the technologies such as decentralization, transparency, and confidentiality. however, some of these technologies do not provide academic verhttps://doi.org/10.15837/ijccc.2021.5.4266 5 ification services, the technologies are not aimed at storing the history of academic performance and other similar data of students/graduates in digital versions. thus, novel technology is critical to store and secure education documents. the transition of education documents into digital versions using a decentralization approach will enhance a student’s progress. this study proposes a prototype of the univercert based on the decentralized, open-source ethereum blockchain platform to store and secure education documents. 3 background the proposed univercert platform is an advanced digital service information system for educational institutions. the platform is designed to modernize existing lms systems to store university data. the aims of univercert platform are: • to consider the available technological tools (for example, kaznu has lms univer 2.0, openkaznu). • to justify the choice of blockchain technology (the ethereum platform and smart contracts). • to consider the types of blockchain and digital certificates for authentication. 3.1 information system univer 2.0 the benefit of the univercert platform is its direct integration with the university’s lms system, which forms data from official education documents. the information system "univer 2.0" is an example of an automated information system used by al-farabi kazakh national university. the "univer 2.0" offers the digital transformation of the university business processes, storing data related to the applicant’s enrollment and educational documents. however, the system fails to provide a decentralized storage platform to verify the educational documents. the implementation of direct lms integration and the decentralized platform at the restapi level will optimize and protect the documents from modification. moreover, the system facilitates document approval and storage to reflect the information of student qualification and academic performance. 3.2 openkaznu rapid development in internet technology and digitalization has motivated many universities to introduce online learning at various sites in kazakhstan (e.g., moocs.kz, open.kaznu.kz, dl.kaznu.kz). kazakhstan recognizes the results of non-formal learning and includes the course credits in the student’s transcript. the mechanisms for recognizing massive open online courses (mooc) allow mooc’s students to convert into enrolling in a traditional learning [validation of non-formal moocbased learning] process. for example, through the activities of knowledge media institute (kmi) of open university, research and development have successfully been implemented through an online learning process. kmi has participated in many research initiatives using the blockchain. open university is a leader in uk distance education, and it provides kmi the opportunity to badge all open university courses and notarize them on the blockchain. 3.3 ethereum and smart contracts the ethereum solution was chosen to create the univercert platform. ethereum has many important features that distinguish it from other systems [1, 4]. table 2 shows the key aspects of different blockchain platforms. the ethereum platform allows developers to create any system based on the blockchain functions, using smart contracts to automatically perform actions. ethereum was chosen as a suitable platform, taking into account several features, such as high transaction speed per second, a large selection of programming languages, and support of smart contracts. a smart contract is a digital contract written in source code and executed by computers that integrates a blockchain security mechanism against unauthorized access [12]. smart contracts are http://moocs.kz/ https://open.kaznu.kz/ https://dl.kaznu.kz/ https://doi.org/10.15837/ijccc.2021.5.4266 6 table 2: comparison of key aspects of different blockchain-based platforms no platform average performance, tps scalability, tps consensus smart contracts development languages encryption api data source 1 bitcoin green 154 the green protocol c++, javascript, python no data https://bitcoin.org/ 2 ethereum 15 5000 proofofwork + c++, go, rust, solidity, serpent ecdsa + https://ethereum.org/ 3 ripple 1500 70000 ripple protocol consensus algorithm + solidity, serpent gost r 34.102001 + https://ripple.com/ 4 r3 corda 170-250 raft algorithm /byzantine consensus + java, kotlin ecdsa/rsa+ https://www.corda.net/ 5 hyperledger fabric 200 1350 pbft, poet1 + go, java ecdsa + https://www.hyperledger.org/ developed using the solidity programming language [17]. the code written in solidity is compiled and converted to bytecode and sent to the ethereum blockchain as a smart contract. smart contracts and blockchain applications are running on an ethereum virtual machine. smart contracts, using predefined conditions built into the algorithm, facilitate document verification without human intervention. 3.4 blockchain types before choosing a blockchain platform, it is crucial to understand the blockchain suitability for a particular use case. three main types of blockchain are as follows: 1) a public blockchain has a ledger visible to everyone on the internet, which allows the public to check and add a block of transactions to the blockchain [26]. identity verification is not required on a public blockchain, showing that privacy is not guaranteed. thus, it is unprofitable for universities, students, and third parties to connect to public blockchain networks, as they incur financial costs to store data or make transactions [25]. 2) a private blockchain is a distributed yet centralized network that permits few people in an organization to check and add blocks of transactions. however, all internet users are allowed to view the ledger [3]. 3) the consortium blockchain uses network nodes, where multiple organizations engage in transactions [27]. a consortium blockchain is a mixture of the two previous types, with only privileged participants performing operations in the consensus process. the univercert system has chosen a consortium blockchain. for decentralized structures and selfregulating consortium blockchain networks, the participating nodes must agree with the current state of the ledger content. this is a technique to pack unconfirmed data into blocks and add them to the ledger. in addition, we chose the proof-of-work (pow) algorithm as the distributed consensus algorithm to support the network state [16, 28, 29]. however, pow-based blockchain requires considerable computing power and energy consumption. at the same time, pow is one of the most common consensus algorithms, effective for preventing denial of service and distributed denial of service attacks https://doi.org/10.15837/ijccc.2021.5.4266 7 on the blockchain. 3.5 blockchain-based digital certificates a certificate is a unique, signed document issued by one party to authoritatively identify an individual’s identity. in the field of education, the certificate confirms the achievement of learning outcomes, completion of a certain stage of training, level of qualification, or total credits in nonformal education. certification is a procedure for issuing a certificate and verifying the document by a third party. educational certificates were issued to students who graduated from universities or other institutions. certificates also offer opportunities for graduates to further their studies, gain employment, and verify their data. digital certificates based on blockchain technology have many advantages: certificates cannot be forged as they accurately indicate the party that issues and the party that receives the certificate. verification can be performed by a member of the consortium who has access to the blockchain. you can obtain a certificate confirmation from an organization that no longer exists or has changed its name. in this case, the blockchains can store either cryptographic hashes of certificates or the certificates themselves. organizations provide professional training for a future employee as it is not enough for employers to have only an education diploma, which contains the university name, the full name of the recipient, the issue date, and degree awarded. this information is stored as text in the blockchain to create a degree-awarding database. as a certificate hash, the goal is to protect an issued digital certificate. with blockchain solutions for higher education, only diplomas and diploma appendices are presented as documents for validation. the proposed univercert system will store the following digital certificates: • diploma of education (a document on the completion of the bachelor’s, master’s, or doctoral degree level); • an academic transcript, which details the student’s academic performance history; • diploma supplements in three languages: kazakh, russian, and english (diploma supplement); • certificates of completion of moocs with credit transfer. 4 findings and discussion this section describes a comprehensive solution designed to track and release academic records of students/graduates by (1) choosing the appropriate type of data storage, (2) describing the system architecture, and (3) describing smart contracts. 4.1 proposed solution the proposed univercert platform was built using the ethereum blockchain platform and deployed as a p2p network consortium architecture [8]. higher education institutions are the peers of the blockchain network, and the users of the platform are organizations (e.g., governments, law enforcement agencies, and employers). the blockchain platform with a consortium architecture has the following differences: • regulatory participation and governance. many studies suggest that benefits derived from its regulations are suitable for the educational system [2, 21, 24, 30]. flexible rules of the consortium coordinate the actions of all network participants; • role-based access mechanism. the ability to assign roles according to the needs of stakeholders. access levels are adjusted according to the requirements for personal students’ security or security of educational information; • target audiences. the stakeholders are much broader than that of other types of blockchain. https://doi.org/10.15837/ijccc.2021.5.4266 8 4.2 system architecture figure 1 shows the entire process of tracking and issuing academic documents, which include processing off-chain data and processing on-chain data. given the diversity of the university lms systems in terms of the stored data structure and functionality, it is advisable to offer a restapi solution as a universal technology to integrate the university system and blockchain exchange platform. interaction via restapi restapi (representational state transfer) access to the blockchain platform is carried out through the restapi channel. the restapi uses ready-made methods to store data and search data using several filters (student id, university id). these methods are available to organizations based on their access levels. the restapi is extended with new methods to connect third-party organizations (employers, government agencies), which is one of the advantages of the proposed solution. figure 1: architecture of the proposed platform 1. application layer at the top of figure 1, user interfaces for organizing services are student/graduate registration, organization registration, assessment, and verification. below is a more detailed description of each service. https://doi.org/10.15837/ijccc.2021.5.4266 9 • student registration the university enters the student’s data in the lms system and issues a unique student id following the enrollment number. this data is obtained through the id. this data is unencrypted and stored in its original form in the blocks of the blockchain network as a genesis block. to store the above data, a single smart contact-1 is used, which is described in detail below. • membership service organizations (government, law enforcement, educational institutions) that want to connect to the platform contact the network coordinator to access information about a student/graduate. • grading system after a semester, the university uses a grading system to assign grades for all courses and transfer them to the blockchain platform. • verification in the education system, blockchain technology does not offer the verification process or a clear verification method, which is a key for confidentiality. our verification process is formulated as follows: i. any interested party (government agency, employer) must first obtain authorized access to the platform. ii. according to the request for authorized access, the platform coordinator issues an api key to connect the restapi and obtain the necessary data. iii. the entire verification process is divided into two levels, depending on the needs of the organizations. access level 1 – verifying the authenticity of academic records of students/graduates using smart contract-1. access level 2 – obtaining detailed information about the academic achievements of students/graduates using smart contract-2 and a key pair (university and student id). 2. off-chain data process after the semester examination, the student’s grades are stored in the system with the support of various smart contracts (1, 2), described in more detail in subsection 4.3. after the smart contact-2 rule is executed, the next data processing starts outside the network: • the symmetric encryption offers security for student/graduate performance data. • the decryption process is a procedure for decrypting student/graduate performance data. the second level of access enhances the confidentiality of critical academic data. 3. on-chain data process this process starts with the generation of "genesis block" or the first block for the student. then, all transactions are recorded in the blockchain. each block contains a unique header, identified by its block header hash. the block consists of three main components: a previous block hash, data (specialties, discipline, and score), and timestamp. this process is repeated each semester until the students graduate from the university. 4. hardware / network layer as illustrated in figure 1, the hardware layer consists of three components: authorized users storage, off-chain data storage, and on-chain data storage. • authorized users storage – a centralized database of an organization (government agencies, employers) registered as users. it uses a membership service that has access to retrieve data using restapi. • off-chain data storage – a centralized database of directories (specialties, disciplines). https://doi.org/10.15837/ijccc.2021.5.4266 10 • on-chain data storage – a consortium blockchain that operates under the leadership of a group of entities, enabling collaborative business transformation among organizations. in our case, the coordinator assigns unique access for each user (state organs, employer). verifier the verifier is any organization who has the right to view and verify the validity of academic records. the client-side verification process is carried out in two ways: a verifier wants to authenticate the identity of student/graduate official records (level 1) or may additionally request access to receive detailed information (level 2). only the verifier can view unverified documents to enhance confidentiality. hei (higher education institution) the proposed system optimizes the university business processes as it stores and secures student data. when a student is transferred to another university that connects to the network, the system provides the flexibility of exchanging learning outcomes among educational institutions. thus, it prevents costs associated with the processing times, manual work, postal fees, and transit. the benefits are as follows: • a collaborative environment between different institutions will be available, eliminating challenges of the education system, thereby improving the quality of education. • the system will keep the academic record unchanged and share it with all authorized organizations. coordinator our proposed platform is adapted to a consortium blockchain network. moreover, a consortium blockchain works under the governance of the coordinator. in our proposed architecture, the coordinator can become the administrator of the entire system and the main node in the network according to its associated privileges. for the coordinator, this is an indispensable tool to track the activities of higher education institutions: • the coordinator can control the quality of the services that the university offers to students, thereby preventing fraudulent activities. • the coordinator can create a unified digital register of student/graduate official academic documents stored on the blockchain network by generating electronic versions of documents such as diplomas, diploma supplements, and transcripts with the digitally signed platform. with the system, printing academic records are not required. • the coordinator can eliminate the possible costs associated with the centralized approach like processing times, manual work, postal fees, and transfer processes. to describe the system with all its entities, and how it processes as a whole, in fig.1 has been included with a brief description about obtaining information processes and about all its input, output data. • obtaining open information about the student/graduate and the university. the interested party makes a request and receives the following data: student, university, when he/she entered and graduated, specialty, qualification. these data are not encrypted in the blockchain platform, as they do not carry confidential information. this data allows the interested party to make sure that the graduate/student really studied at the specified university. • getting the student/graduate academic performance history. this is the second level of access. the administrator-the organization, according to the request of the interested party, provides this access. the interested party (state agency, university, graduate) makes a request and receives the following data: student, discipline, academic performance (credit, grade). this https://doi.org/10.15837/ijccc.2021.5.4266 11 data is encrypted with symmetric encryption based on the key (student id and university id) to maintain data confidentiality. this data is needed so that the interested party can see the true history of the student’s/graduate’s academic performance. • obtaining the rights to store data. this access is provided to universities and other educational organizations in order to they can fill the blockchain platform with data about students (students, graduates, etc.). 4.3 the usage of smart contracts in our proposal, we developed two smart contracts as basic elements of the blockchain technology, which are a set of rules that allows organizing different levels to access data (figure 2 and table 3). table 3: smart contracts smart contracts description smart contract-1 store student personal data smart contract-2 store data about the student’s performance figure 2 illustrates the smart contract sequence diagram −1. figure 2: sequence diagram of smart contract-1 smart contract-1 stores the following data: university, specialty, year of admission and graduation, level of study, and academic degree. the data is not encrypted, and it is stored in the blockchain network blocks. the student data are obtained by the student id without additional access. the interaction between the system is described below: 1. student enrollment in the lms system. the student is enrolled and provides specialty of training conditions: the study period, total number of credits, and qualifications after graduation. https://doi.org/10.15837/ijccc.2021.5.4266 12 2. access the student’s academic data. access rights are granted by the platform coordinator. access rights are the process of an api key to work with restapi. 3. when a university is registered in the platform, all reference data (specialties, qualifications, catalog of disciplines) are synchronized with the central repository of the coordinator. the offchain storage keeps all connected universities specialties (specialty names in three languages, code, requirements, and qualifications). to save memory, specialty identifiers and qualifications are stored in blocks instead of names. 4. the process of registering a student in the blockchain platform is carried out by linking the student to the university specialty. thus, the university receives a unique block address and stores it in the lms system. 5. an organization that wants to verify the accuracy of the student’s information should connect to restapi. to achieve this objective, the coordinator must provide the appropriate access (access level 1) and api key. 6. an organization that intends to validate the data makes a request to the platform using the student id. the identifier is provided by the student or graduates. in addition, the student can provide the university identifier, saved in the qr code of the diploma or transcript. 7. three methods are implemented at the smart contract 1: search by student id, search by student id and university id, as well as search by block address. all three methods provide student information and specialties in figure 3, the sequence of operations of smart contract-2 includes processes (data encryption and decryption) and an additional component (smart contract 2), in addition to the components smart contract-1. smart contract 2 handles student/graduate achievements. the interactions between the system’s components are indicated by numbers, as described below: figure 3: sequence diagram of smart contract-2 https://doi.org/10.15837/ijccc.2021.5.4266 13 1. the process of a university to gain access to the student performance data. 2. the process of synchronizing the catalog of disciplines and types of grades with the central storage (off-chain storage). all disciplines with a full description (in 3 languages, qualification obtained after completing the course) and assessment types (all types of assessment) must be synchronized with the coordinator’s database. in the future, the discipline id and the scores to be stored in the blockchain are taken from storage to save memory and optimize transaction processing. 3. the process of student learning. at the end of each semester, the student receives a final grade for each course. 4. the process of transferring the completed courses to the blockchain database. the university’s lms system connects to the restapi platform via an api key and transmits information in a specific format. at the coordinator level (inside the restapi service), symmetric data encryption takes place (the key is the student id and the university). only encrypted information is transmitted to the blockchain database, except the student and university id to facilitate its accessibility in the future. smart contract 2 returns the unique address of the block where the information is stored. the block address will be saved in the university’s lms system, and the university can use it to generate a qr code digital versions of the transcript, diploma supplement), as well as to obtain encrypted information from smart contract 2 using the block number. 5. to allow the university to have access to the student’s academic data in the platform, access rights are granted by the platform coordinator. access rights are used to obtain an api key to work with restapi. 6. the process of accessing the platform via restapi to obtain confidential information, in which an api key, student, and university ids are transmitted. 7. smart contract 2 returns encrypted information. at the restapi level, the information is decrypted using a key (student and university id). 5 conclusion and future research the problem of verifying the authenticity of documents on the education of graduates and the academic performance of students was not effectively solved. blockchain technology allows you to solve this problem. in the process of working on the problem: • we have identified and discussed blockchains and their main functions; • conducted a comparative analysis of research on blockchain technology in higher education; • presented a mechanism for verifying data in the blockchain network, depending on the level of access and provision of information to interested parties; • we have developed a consortium architecture of the univercert platform, which interacts with various systems and databases. this study designed a novel platform univercert to track academic performance, issue educational certificates, and protect data from forgery. this solution shows the entire process (off-chain and onchain) that includes a student’s registration, verification, and authenticity of educational documents. the platform facilitates credit transfer from one university to another and provides a flexible service for checking a student’s progress. for future work, we will deploy the proposed platform for many higher educational institutions. 6 acknowledgment this work was funded by the science committee of the ministry of education and science of the republic of kazakhstan [grant no. ap08857535-ot-20]. development and implementation of information system for document verification in higher education (universities) of kazakhstan which is integrated into the international platform. https://doi.org/10.15837/ijccc.2021.5.4266 14 references [1] aung, y.n. 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(2018). blockchain certificates (academic & others), https://www.unic.ac.cy/iff/blockchain-certificates/ https://doi.org/10.15837/ijccc.2021.5.4266 16 copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: shakan, y.; kumalakov b.; mutanov g.;, mamykova zh.; kistaubayev y. (2021). verification of university student and graduate data using blockchain technology, international journal of computers communications & control, 16(5), 4266, 2021. https://doi.org/10.15837/ijccc.2021.5.4266 introduction related work background information system univer 2.0 openkaznu ethereum and smart contracts blockchain types blockchain-based digital certificates findings and discussion proposed solution system architecture the usage of smart contracts conclusion and future research acknowledgment international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 2, month: april, year: 2022 article number: 4741, https://doi.org/10.15837/ijccc.2022.2.4741 ccc publications data processing by fuzzy methods in social sciences researches. example in hospitality industry o. ban, l. droj, d. tus, e, g. droj, n. bugnar olimpia ban* department of business and economy university of oradea, romania 1 university street, 410087, romania oban@uoradea.ro *corresponding author: oban@uoradea.ro laurent, iu droj university of oradea 410087 oradea, university street, 1, romania laurentiu.droj@uoradea.ro delia tus, e university of oradea 410087 oradea, university street, 1, romania dmerca@uoradea.ro gabriela droj university of oradea 410087 oradea, university street, 1, romania gdroj@uoradea.ro nicoleta bugnar university of oradea 410087 oradea, university street, 1, romania nbugnar@uoradea.ro abstract likert-type scales are a common technique used in social science. plus, the likert scale is among the most frequently used psychometric tools in social sciences and educational research. despite its frequently used, the likert scale raises up many questions mark. we can say that the use of the likert scale in its classical form is too rigid and loses valuable information. li (2013, p. 1613) calls on previous studies that "have claimed that fuzzy scales are more accurate than traditional scales due to the continuous nature of fuzzy sets". the aim of this research is to reduce the inaccuracy caused by the use of the likert scale, by proposing a method of more appropriate processing of data collected in this way. as shown in this paper, fuzzy methods can be a good alternative. https://doi.org/10.15837/ijccc.2022.2.4741 2 the research methodology consists of using the usual technique on the set of fuzzy numbers by considering the input data as linguistic variables, subsequently identified by triangular fuzzy numbers. the obtained scale is more elastic with respect to the input data, therefore it better captures the reality. the newly proposed method is applied in the concrete example of the competitors in the hotel field. the importance-performance competitor analysis is utilized. a weakness of the method is due to the use in its application of data collection with the likert scale. the results conclude on the situation of the competitors regarding each attribute considered as in the crisp version of the method, but the identification and processing of data correspond better to the aspects of subjectivity and uncertainty specific to human thinking. a novelty is also the obtaining of a hierarchy within each category of attributes from the quadrants proposed by the important-performance analysis in relation to the competition. keywords: likert scale, social research, fuzzy sets, competitor, hotels. jel classification codes: m31, z33, z32, c60, c81 1 the likert scale’s issues and the fuzzy scale likert-type scales is a commonly techniques used in the social science. "likert scales are useful in social science and attitude research projects" [18]. "likert scale is applied as one of the most fundamental and frequently used psychometric tools in educational and social sciences research" ([29], p.396). the likert scale was introduced by rensis likert in 1932 [37] as a way of measuring character and personality traits. initially it had 5 steps from 1–"strongly approve", 2–"approve", 3–"undecided", 4– "disapprove", 5–"strongly disapprove". likert used the scale on a set of questions to measure attitude, based on a composite indicator. it is likert-type items when applied for a single question and likert scales when applied for a set of questions and a composite indicator is used. likert scale is used in many fields, generally social and emotional domains, the purpose being to measure attitude. we call here fields such as: health, education, psychology, political science, economic sciences etc. the likert scale is also widely used in empirical studies in tourism and the hospitality industry and the most widely used psychometric scale in survey research ([34], p.1609). [11] showed that only in 2011, 12 articles were published in the journal of extension about likert scale with 4, 5, 6 or 7 steps. it is a widely used scale due to its advantages, including: "the numerical measurement results can be directly used for statistical inference (...) measurements based on likert scaling have demonstrated a good reliability (...) with likert scaling researchers can collect and analyze a large amount of data with less time and effort" ([34], p.1609). but the likert scale has its limits. the likert scale is considered a non-ordinary, ordinal scale. "numbers assigned to likert-type items express a "greater than" relationship; however, how much greater is not implied. because of these conditions, likert-type items fall into the ordinal measurement scale" ([11], p.3). but not everyone is of the opinion, a major debate around the likert scale being whether it is ordinal or interval type [28]. if likert were an interval scale, then the distance between two steps should be equal to the distances between the other two or two steps, an aspect which cannot be proved in this case [26] (considering the subjective aspects they record). on the one hand, we can know the order of the answers, being an ordinal scale, but we cannot know the useful number of steps. 3, 4, 5, 6, 7, 9, 10, 11 steps are used for the likert scale. some researchers choose a 3-step scale the alleged reason may be that it forces respondents to a clear position, for or against, others 4 steps but most commonly is seen as a 5-point scale [10], [19]. on the other hand, [33] considers that the 11-step likert scale is the best (next to the 6) and closer to normal. others consider the 7-step likert scale variant to be the best [29]. there are some who recommend using the scale as widely as possible, because we could always collapse points into condensed categories, but not vice versa [2]. is it true? would it be possible to adjust the scale after data collection? https://doi.org/10.15837/ijccc.2022.2.4741 3 many times, the answers are focused on a certain interval of the scale, an aspect that we discover only after data collection. bacon ([3], p.3) says regarding the direct data collection that "another problem with direct methods is that the measures may be uniformly high as some customers rate everything as very important". it seems that this concentration of responses is culturally determined [27]. concentrating most of the responses at the extremes of the scale, such as between 4 and 5, for example on a likert scale of 5, as is the case in the mediterranean area ([27], p. 298), does not help in the actual and useful differentiation between the answers. in this case, the distance between step 4 and step 5 should be greater than the distances between the previous steps and, moreover, a response of 4 has a greater significance than a response of 4. asian respondents are more reserved in their answers, so they focus on the center of the likert scale, while other respondents are not at all familiar with likert-scale questions. the idea has even emerged that respondents tend to give more extreme answers on a horizontal likert scale than on a vertical one, at least in the online survey [48]. the study done in this paper tries to solve precisely this problem of the number of steps, easy approach with fuzzy numbers. adding steps does not solve the problem but it moves the focus to other steps and even increases the confusion, as [15] points out. however, it is stated that the number of steps on the likert scale influences the answers. the same question is answered differently. "if more respondents tend to give positive responses, then a finer scale, with more response options, could result in a slightly lower mean score" ([19], p.63). we can say that the use of the likert scale in its classical form is too rigid and loses valuable information, as li ([34], p.1610) also considers. in concrete empirical studies (in different cultural areas), there may be answers between which there are differences that are too sensitive for the classic likert scale steps. [34] builds fuzzy likert scales that do not allow information to be lost, like the classic likert, but neither increases the number of steps to make the application more difficult. li ([34], p. 1613) calls on previous studies that "have claimed that fuzzy scales are more accurate than traditional scales due to the continuous nature of fuzzy sets". however, [34] increases the accuracy of the scale by adding an additional question to verify (strengthen or weaken) the answer given by each respondent. this measure increases the difficulty of applying the questionnaire and, in addition, invites the respondent to further analyzes to which he is not willing or able to participate. the creator of a likert scale data collection tool must decide how many steps to have the scale (before being applied), to take into account the concentration of responses according to cultural influences (in extremes for the mediterranean area, in the middle of the scale for asians etc.) in order to obtain useful answers that reflect the reality. vonglao [46] said that the data measured using the likert scale based on fuzzy logic scale was more suitable to be analyzed with the arithmetic mean and standard deviation from the data measured using the likert scale. in conclusion, we can say that the likert scale in its classical form is rigid and not adapted to the different cultural behaviors. 2 triangular fuzzy numbers the interest in the use of fuzzy numbers in economics increased more obviously after 2014. thus, we find multiple applications of fuzzy numbers in: methods of ranking and decision making in economics ([51]; [47]; [44]; [52]; [39]) and data collection with semantic differential scales and for representing the linquistic variables ([7]; [8]; [36]; [23]; [38]; [20]; [40]). a triangular fuzzy number is a fuzzy set on the set of real numbers given by its membership function as: https://doi.org/10.15837/ijccc.2022.2.4741 4 µ (x) =   0, if x < a x−a b−a , if a ≤ x ≤ b c−x c−b , if b ≤ x ≤ c 0, if x > c , (1) where a,b,c ∈ r,a ≤ b ≤ c. if a = b or/and b = c then the membership function µ is adapted in an obvious way. in fact, if a = b = c = k then we obtain the real number k. the real numbers a, b and c can be viewed as the smallest possible value, the most promising value and the largest possible value that describe a fuzzy quantity, respectively [35]. usually, a triangular fuzzy number as above is represented as a triple (a,b,c). in addition, difference and multiplication under a positive scalar are defined by: (a,b,c) + (a ′ ,b ′ ,c ′ ) = (a + a ′ ,b + b ′ ,c + c ′ ) (2) (a,b,c) − (a ′ ,b ′ ,c ′ ) = (a− c ′ ,b− b ′ ,c−a ′ ) (3) and λ · (a,b,c) = (λa,λb,λc) (4) between the most common approaches to convert triangular fuzzy numbers into real numbers is the expected value (ev) ([25]; [4]) defined by: ev (a,b,c) = a + 2b + c 4 (5) 3 importance performance competitor analysis we chose an example of using the likert scale, such as the construction of a diagnostic tool widely used in marketing but also in tourism: importance performance competitor analysis. importance performance competitor analysis uses likert scale data collection. importance-performance analysis is a well-known marketing technique that can assist practitioners in making marketing decisions for elaborating marketing strategies. important-performance analysis (ipa) was launched in 1977 by martilla and james [41], which analyzes the quality attributes of products / services considering the perceived importance and performance. it is an easy-to-use tool that provides a quick picture of the measures that managers need to take to improve customer satisfaction. importance-performance analysis has already been used in many areas, including in the tourism and hospitality industry ([17]; [21]; [22]; [31]; [43]; [42]; [50]). although ipa is an easy-to-use and still very popular method, there are many criticisms of it ([30]; [13]; [21]; [6]) and numerous proposals for improvement. two of the minuses are considered to be: that the ipa in its original form does not take into account the presence of competitors in the market, that is, the evaluation of the performance of a company is not done in relation to its competitors or to the most important ones ([12]; [30]) and the second, the scores (of direct data collection by likert scale on the importance of attributes) have a small inter-item variation, the values of the answers are raised uniformly ([3], p.55-71). burns [12] proposed sipa (simultaneous importance performance analysis) which "improved the ipa by expanding it to explicitly include competitors’ performance" ([9], p.4). by adding the attribute determinancy, yavas and shemwell [49] proposed a grid with 16 strategic suggestions. lee and hsieh [32] proposed a revised sipa by indirectly determining the importance of quality attributes, using multiple regression analysis. the involvement of competitors in ipa analysis is also proposed by deng et al. ([22], p.38) by "a revised ipa that integrates three-factor theory and benchmarking". they start from the idea that between the performance of the attributes of quality and the overall satisfaction there is no direct and symmetrical linear relationship. reporting on https://doi.org/10.15837/ijccc.2022.2.4741 5 competition is also found in the models proposed by taplin [45] through competitive importanceperformance analysis (cipa) and chen’s competitive zone of tolerance service quality-based ipa (czipa) [16]. albayrak proposes importance performance competitor analysis (ipca) in 2015. ipca "is basically generated by the inclusion of competitor information in the diagonal approach of ipa. in principle, the diagonal approach of ipa is a gap analysis which compares performance (p) and importance (i) scores of attributes "([1], p.137). bacon ([3], p.3) states that "some researchers refer to the examination of the difference between importance and performance as ’gap analysis’ (e.g. [24])". gap is used on the vertical axis in ipca and the pd (focal company performance minus competitor company performance, both from attribute perspective) variable is represented on the horizontal axis in ipca. gapi = pi − ii (6) pdi = pf ocal i −pcompetitor i (7) in the example given by albayrak [1], the results obtained in the ipa as compared to those obtained in the ipca differ substantially. the attributes are distributed in the ipca in 4 quadrants that suggest the position of the focal company relative to its competitor, indicating the need for action: i "solid competitive advantage", ii "head-to-head competition", iii "urgent action" and iv "null advantage" (figure 1). starting from the model of albayrak [1], the importance-performance analysis in relation to the competition (ipca) was developed for a congress destination by caber et al. [14]. figure 1: ipca (source: adapted after [14]) 4 description of the fuzzy importance-performance competitor analysis with triangular fuzzy numbers our aim is to give a fuzzy method taking into account that the input data, that is the performance and importance are triangular fuzzy numbers. let us denote by pi = (ai,bi,ci), i ∈ {1, · · · ,n} the performance of the focal entity under study, by ii = (xi,yi,zi), i ∈ {1, · · · ,n} the importance of the focal entity under study and by p compi = (acompi ,b comp i ,c comp i ), i ∈{1, · · · ,n} the performance of the competitor, each one of them with respect to an attribute ai, i ∈{1, · · · ,n}. usually, like in the application presented in section 5, the values of pi, ii, p compi , i ∈ {1, · · · ,n} are obtained by aggregating (the simplest through the mean) the answers to a questionnaire. from (3), (6) and (7) we get https://doi.org/10.15837/ijccc.2022.2.4741 6 gapi = pi − ii = (ai −zi,bi −yi,ci −xi) (8) and pdi = pi −p compi = (ai − c comp i ,bi − b comp i ,ci −a comp i ) (9) for every i ∈{1, · · · ,n}. we need to defuzzify the triangular fuzzy numbers from (8) and (9) for obtaining the four quadrants in an easily interpretable form. by applying (5) we get gapi = ev (gapi) = ai −zi + 2(bi −yi) + ci −xi 4 (10) and pdi = ev (pdi) = ai − c comp i + 2(bi − b comp i ) + ci −a comp i 4 (11) for every i ∈{1, · · · ,n}. now, we can follow the crisp interpretation, that is in table 1. table 1 gapi p di conclusion on attribute i positive positive solid competitive advantage positive negative head-to-head competition negative positive null advantage negative negative urgent action from the above description (table 1) it is easy to see that ipca [14] is acquired as a particular case of our proposed method. indeed, by taking ai = bi = ci = pi, xi = yi = zi = ii and acompi = b comp i = c comp i = p comp i , from (8) (11) we obtain gap = pi − ii and pdi = pdi = pi −p comp i , for every i ∈{1, · · · ,n}. 5 triangular fuzzy numbers application to the hotel industry 5.1 research methodology the purpose of our research is to adjust the classical interpretation of the answers to the questions of a questionnaire in order to construct the corresponding ipca matrix. to do this, we call on the subjective association of the answers of a questionnaire with triangular fuzzy numbers, thus obtaining a more elastic approach, which counteracts the effects of upper deviation arising from the use of the likert scale. the data used for the research comes from a previous study. in 2012, a survey was conducted [5] among a total of 125 customers of 4-star four hotels in oradea, romania. by profile and positioning, these 4 hotels are in direct competition. they have over 50 rooms, target the business clientele and are located on high traffic routes. the survey was conducted using a 5-step likert scale questionnaire, applied with an operator on a sample of availability. the distribution of valid questionnaires on the 4 hotels was: hotel i-39, hotel ii-21, hotel iii-29 and hotel iv-36. the distribution of responses on likert scale are presented on table 3. the data collection instrument was a questionnaire in which a scale of 21 attributes was used. evaluation of the quality of the hotel services, for measuring the importance and performance of the hotel units in these attributes (table 2). these are also the output variables but in a different hierarchy given by the fuzzification process. the validity of the questionnaire was verified with the α cronbach coefficient, the value obtained being a satisfactory one (0.827). in the present research, the collected and unused data were modeled, namely the assessment of the importance of the quality attributes from the perspective of the 39 respondents of hotel 1 (considered https://doi.org/10.15837/ijccc.2022.2.4741 7 table 2: service attributes used for hotel service quality evaluation 1. the room facilities are appropriate 2. the room is clean enough 3. the hotel has sufficient restaurant facilities 4. the staff has an appropriate and professional look 5. the location of the hotel 6. the staff provide correct information to guests 7. the staff is able to offer services in a short period of time 8. the staff is able to resolve guests’ problems 9. the staff is able to provide information in a short period of time 10. the availability of staff 11. clients complaints are resolved quickly 12. different payment facilities are available 13. the safety of the installations in the hotel 14. service professionalism 15. service customization 16. friendliness of staff 17. proper opening hours of hotel’s facilities 18. the hotel has entertaining facilities 19. big variety and proper quality of meals 20. internet connection is available 21. aesthetics of rooms and of the hotel (data processed from [5]) focal) and the evaluation of hotel 1, hotel 2 and hotel 4 from the perspective of the performance on the 21 quality attributes (table 3). the analysis is performed for hotel 1 (considered focal) compared to hotel 2/hotel 4, as its direct competitor, in the spirit of fuzzy set theory, by replacing the likert scale ratings with triangular fuzzy numbers, as shown in table 3. the data analysis from table 2 shows an increased concentration of data at the top of the 5-step likert scale, both for importance and performance for all 21 attributes. this situation prevents the clear delineation between attributes in terms of importance and performance for both hotels. 5.2 results in the moderate choice: hotel 1 versus hotel 2 as competitor step 1: assign triangular fuzzy numbers to the responses displayed on the likert scale the proposed method involves associating the responses 1,2,3,4 and 5 on the likert scale with triangular fuzzy numbers (table 4). the choice of fuzzy numbers respects to a greater extent likert scale, with equidistant values, and in the other, the good response regarding the performance is drastically penalized compared to very good. step 2: calculate the importance and performance of attributes as triangular fuzzy numbers for hotel 1 and hotel 2 under the notifications in the previous section, we obtain as the arithmetic mean of the triangular fuzzy numbers associated with answers according to table 4. the results given in table 5 and table 6 are obtained based on (2) and (4). step 3: this step consists of calculating in fuzzy numbers (moderate choice) of gap (8) and pd (9). the final results in table 5 (columns 5 and 6) are obtained by applying (10) and (11). the results are presented in table 5. depending on the combination of obtained values (positive and negative), the dial is set where the quality attribute is assigned. the number of the dial in table 4 will indicate the measure that hotel 1 must take in relation to its competitor, hotel 2. step 4: calculate gap and pd in original format and compare with gap and pd in fuzzy numbers (moderate choice) https://doi.org/10.15837/ijccc.2022.2.4741 8 table 3 hotel 1-performance on attributes (39 questionnaires) a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 vg 31 38 20 32 25 35 30 31 26 32 26 24 27 30 19 36 23 25 12 24 33 g 7 0 16 7 13 3 5 5 11 6 9 13 10 8 17 2 15 13 25 8 6 m 1 1 3 0 1 1 4 3 2 1 4 2 2 1 3 1 1 1 2 5 0 p 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 vp 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 hotel 2-performance on attributes (21 questionnaires) vg 20 19 18 15 12 19 18 17 16 18 17 18 19 18 14 17 16 18 18 19 20 g 1 1 3 6 7 2 3 3 4 2 4 2 2 3 6 3 5 3 2 2 1 m 0 1 0 0 2 0 0 1 1 1 0 1 0 0 1 1 0 0 1 0 0 p 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 vp 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 hotel 4-performance on attributes (36 questionnaires) vg 24 15 22 17 16 14 17 26 14 12 15 17 21 18 10 16 13 13 17 15 18 g 8 19 12 17 19 16 17 8 20 20 19 15 12 17 25 18 19 18 15 18 15 m 3 0 1 2 1 5 2 2 2 3 2 2 3 1 1 2 4 5 3 2 3 p 1 2 1 0 0 1 0 0 0 1 0 2 0 0 0 0 0 0 1 1 0 vp 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 hotel 1-importance of attributes (39 questionnaires) vi 22 36 14 8 22 33 32 30 29 29 32 11 17 29 24 34 15 18 29 32 21 i 15 3 22 28 14 4 3 8 7 9 7 26 21 8 12 5 23 16 8 7 17 m 1 0 2 3 2 2 4 1 3 1 0 2 1 2 3 0 1 5 2 0 1 li 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 u 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 hotel 2-importance of attributes (21 questionnaires) vi 13 19 9 5 8 16 20 11 16 16 16 14 16 4 8 14 11 8 7 12 14 i 8 2 10 12 12 5 1 9 4 5 5 7 5 13 10 6 6 8 12 8 7 m 0 0 2 4 1 0 0 1 1 0 0 0 0 4 3 1 4 5 2 1 0 li 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 u 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 legend: a-attributes, vg-very good, g-good, m-medium, p-poor, vp-very poor; vi-very important, i-important, li-less important, u-unimportant (data processed from [5]) for comparison between the original ipca and the fuzzy ipca, gap and pd were calculated in the original format (table 7). the calculation of the original and fuzzy gap and pd gives the distribution in q (quadrants) in the attributes, from which the indicated measures take. q1 represents "solid competitive advantage", q2-"head-to-head competition", q3-"urgent action" and q4-"null advantage". graphic representation of original ipca and fuzzy ipca moderate choice is found in figure 3, respectively figure 2. differences are observed only for 4 attributes (8, 9, 15 and 16) among the 21. for the attribute the staff is able to solve guests’ problems in the classic version is recommended "urgent action" and in the 1 fuzzy variant it is shown that it has "head-to-head competition". in the attribute the staff is able to provide information in a short period of time, in the classic version it is recommended "urgent action" and in variant 1 fuzzy it is shown that it has "head-to-head competition". for the service customization attribute, in the classic version it is "urgent action" while for fuzzy it is "head-to-head competition". for the friendliness of staff attribute, in the classic version it is "urgent action" while for fuzzy it is "solid advantage competition". table 4 importance likert moderate choice unimportant 1 (0, 1, 2) less important 2 (1, 1.75, 2.5) medium 3 (1.5, 2.25, 3) important 4 (2.5, 3, 3.5) very important 5 (4.5, 4.75, 5) performance likert moderate choice very poor 1 (0, 1,1.5) poor 2 (0.5, 1.5, 2.5) medium 3 (1, 2, 3) good 4 (3, 3.75, 4.5) very good 5 (4.25, 4.75, 5) https://doi.org/10.15837/ijccc.2022.2.4741 9 table 5: gap fuzzy and pd fuzzy for hotel 1 (focal) and hotel 2 competitor attributes performance hotel 1 importance hotel 1 performance hotel 2 gap fuzzy pd fuzzy q 1. 3.94, 4.5, 4.85 3.56, 3.93, 4.3 4.19, 4.70, 4.97 0.51 -0.19 2 2. 4.16, 4.67, 4.94 4.34, 4.61, 4.88 4.03, 4.57, 4.88 0.003 0.10 1 3. 3.48, 4.12, 4.64 3.12, 3.55, 3.98 4.07, 4.60, 4.92 0.53 -0.45 2 4. 4.02, 4.57, 4.91 2.83, 3.30, 3.76 3.89, 4.46, 4.85 1.21 0.09 1 5. 3.75, 4.34, 4.78 3.53, 3.91, 4.29 3.52, 4.15, 4.64 0.38 0.18 1 6. 4.07, 4.60, 4.91 4.14, 4.44, 4.74 4.13, 4.65, 4.95 0.10 -0.05 2 7. 3.75, 4.33, 4.73 4.03, 4.35, 4.67 4.07, 4.60, 4.92 -0.06 -0.26 3 8. 3.83, 4.41, 4.78 4.01, 4.32, 4.64 3.91, 4.47, 4.83 0.03 -0.06 2 9. 3.73, 4.32, 4.75 3.91, 4.24, 4.57 3.85, 4.42, 4.80 0.04 -0.09 2 10. 3.97, 4.52, 4.87 3.96, 4.28, 4.60 3.97, 4.52, 4.85 0.19 0.004 2 11. 3.62, 4.23, 4.67 4.14, 4.35, 4.73 4.01, 4.55, 4.90 -0.24 -0.31 3 12. 3.66, 4.27, 4.73 3.01, 3.45, 3.89 3.97, 4.52, 4.85 0.78 -0.23 2 13. 3.76, 4.35, 4.76 3.34, 3.74, 4.14 4.13, 4.65, 4.95 0.56 -0.28 2 14. 3.91, 4.47, 4,84 3.93, 4.26, 4.58 3.97, 4.52, 4.85 0.16 -0.04 2 15. 3.45, 4.10, 4.62 3.65, 4.01, 4.38 3.73, 4.33, 4.76 0.05 -0.21 2 16. 4.10, 4.62, 4.92 4.24, 4.52, 4.80 3.91, 4.47, 4.83 0.04 0.14 1 17. 3.68, 4.29, 4.75 3.24, 6.65, 4.06 3.95, 4.51, 4.88 0.6 -0.20 2 18. 2.66, 3.12, 3.50 3.29, 3.71, 4.12 4.07, 4.60, 4.92 -0.6 -1.44 3 19. 3.28, 3.96, 4.57 3.93, 4.26, 4.58 3.97, 4.52, 4.85 -0.31 -0.52 3 20. 3.35, 3.94, 4.38 4.14, 4.43, 4.73 4.13, 4.65, 4.95 -0.52 -0.68 3 21. 4.05, 4.59, 4.92 3.55, 3.92, 4.29 4.19, 4.70, 4.97 0.62 -0.09 2 legend: q-quadrant figure 2: ipca fuzzy (moderate choice) matrix for hotel 1 in relation to hotel 2 (source: created by the authors based on data processing from table 7.) figure 3: ipca original matrix for hotel 1 in relation to hotel 2 (source: created by the authors based on data processing from table 7.) 5.3 results in the drastic choice: hotel 1 versus hotel 2 as competitor step 5: in this step the answers different from the higher level ("very good" and "very important") were penalized more drastically and gap and pd were obtained in fuzzy numbers (drastic choice). table 6 importance likert drastic choice unimportant 1 (0, 1, 2) less important 2 (1, 2, 3) medium 3 (2, 3, 3.5) important 4 (3, 3.75, 4.5) very important 5 (4, 4.75, 5) performance likert drastic choice very poor 1 (0, 1, 2) poor 2 (1, 2, 3) medium 3 (2, 3, 3.5) good 4 (2.5, 3, 3.5) very good 5 (4, 4.75, 5) https://doi.org/10.15837/ijccc.2022.2.4741 10 table 7: gap and pd original and gap and pd fuzzy (moderate choice, drastic choice) for hotel 1 in relation to hotel 2 competitor attrib. gap original pd original q gap fuzzy moderate pd fuzzy moderate q gap fuzzy drastic pd fuzzy drastic q 1. 0.28 −0.18 2 0.51 −0.19 2 0.11 −0.25 2 2. 0.02 0.09 1 0.003 0.10 1 1.08 0.11 1 3. 0.17 −0.42 2 0.53 −0.45 2 0.16 −0.56 2 4. 0.69 0.10 1 1.21 0.09 1 0.57 0.17 1 5. 0.15 0.13 1 0.38 0.18 1 0.47 0.12 1 6. 0.07 −0.03 2 0.10 −0.05 2 0.97 −0.01 2 7. −0.05 −0.19 3 −0.06 −0.26 3 0.80 −0.15 2 8. −0.02 −0.04 3 0.03 −0.06 2 0.74 −0.02 2 9. −0.05 −0.09 3 0.04 −0.09 2 0.57 −0.15 2 10. 0.07 −0.01 2 0.19 0.004 2 0.77 −0.05 2 11. −0.25 −0.24 3 −0.24 −0.31 3 0.52 −0.24 2 12. 0.41 −0.24 2 0.78 −0.23 2 0.24 −0.39 2 13. 0.23 −0.26 2 0.56 −0.28 2 0.42 −0.35 2 14. 0.05 −0.06 2 0.16 −0.04 2 0.72 −0.14 2 15. −0.33 −0.20 3 0.05 −0.21 2 0.22 −0.29 2 16. −0.30 −0.19 3 0.04 0.14 1 0.43 −0.35 2 17. 2.17 −0.27 2 0.6 −0.20 2 0.18 −0.33 2 18. −1 −1.52 3 −0.6 −1.44 3 −0.72 −1.54 3 19. −0.43 −0.55 3 −0.31 −0.52 3 −0.03 −0.89 3 20. −0.53 −0.62 3 −0.52 −0.68 3 0.28 −0.64 2 21. 0.38 −0.10 2 0.62 −0.09 2 0.72 −0.17 2 (note: for simplification, only two decimal places were kept after the comma, q-quadrant) the assignment of fuzzy numbers for the more drastic variant is found in table 6 and the results for gap and pd in table 7. in the more drastic fuzzy variant, 7 attributes (7, 8, 9, 11, 15, 16 and 20) have different positions. in the dials compared to the classic (original) variant, respectively, they changed their position in the opposite direction of the clockwise with a dial. the changes are dramatic, respectively all 7 attributes have gone from the 3–"urgent action" dial to the 2–"head-tohead competition". it is possible that modeling with fuzzy sets may better capture reality when the responses are massively focused on certain extreme ranges of scale. figure 4: ipca fuzzy (drastic choice) matrix for hotel 1 in relation to hotel 2 (source: created by the authors based on data processing from table 7).) for ipca testing, i made all the calculations to see the situation in case hotel 2 is considered focal and hotel 1 is considered competitor. i calculated gap and pd in the original version and only the one in fuzzy numbers (drastic choice) (table 8). step 6: calculate, following the previous steps, the original and fuzzy ipca (moderate choice and drastic choice) for hotel 1 (focal) compared to hotel 4 competitor. the previous steps are resumed, respectively, are assigned triangular fuzzy numbers (moderate and drastic choice) for the recorded answers regarding the performance of the hotel 4 to the 21 quality attributes. then gap and pd are calculated for hotel 1 compared to hotel 4, taking into account the https://doi.org/10.15837/ijccc.2022.2.4741 11 table 8: gap and pd original and gap and pd fuzzy (drastic) for hotel 2 focal in relation to hotel 1 competitor attributes gap original pd original q gap fuzzy drastic pd fuzzy drastic q 1. 0.33 0.18 1 0.25 0.25 1 2. −0.04 −0.09 3 −0.07 −0.11 3 3. 0.52 0.42 1 0.35 0.56 1 4. 0.66 −0.10 2 0.36 −0.17 2 5. 0.14 −0.13 2 −0.12 −0.12 3 6. 0.14 0.03 1 0.05 0.01 1 7. −0.09 0.19 4 −0.19 0.15 4 8. 0.28 0.04 1 0.14 0.02 1 9. 0 0.09 1 −0.14 0.15 4 10. 0.04 0.01 1 −0.02 0.05 4 11. 0.04 0.24 1 −0.10 0.24 4 12. 0.14 0.24 1 0.05 0.39 1 13. 0.14 0.26 1 0.05 0.35 1 14. 0.85 0.11 1 0.64 0.14 1 15. 0.38 0.20 1 0.11 0.29 1 16. 0.14 −0.13 2 0.01 0.22 1 17. 0.42 0.19 1 0.19 0.31 1 18. 1.71 0.24 1 0.51 1.28 1 19. 0.57 0.55 1 0.42 0.89 1 20. 0.38 0.52 1 0.26 0.62 1 21. 0.28 0.10 1 0.21 0.17 1 operation of fuzzy numbers. in table 9 are the results obtained (for moderate choice), which are the basis of the calculation of fuzzy ipca (moderate choice) (figure 5). the gap and pd are calculated in the original version for hotel 1 in relation to hotel 4 and the original ipca is constructed (figure 6). table 9: gap and pd (moderate choice) for hotel 1 in relation to hotel 4 competitor attributes performance hotel 1 importance hotel 1 performance hotel 4 gap fuzzy moderate pd fuzzy moderate q 1. 3.94, 4.5, 4.85 3.56, 3.93, 4.3 3.59, 4.20, 4.65 0.51 0.28 1 2. 4.16, 4.67, 4.94 4.34, 4.61, 4.88 3.38, 4.04, 4.59 0.003 0.60 1 3. 3.48, 4.12, 4.64 3.12, 3.55, 3.98 3.63, 4.25, 4.70 0.53 −0.11 2 4. 4.02, 4.57, 4.91 2.83, 3.30, 3.76 3.47, 4.12, 4.65 1.21 0.42 1 5. 3.75, 4.34, 4.78 3.53, 3.91, 4.29 3.5, 4.14, 4.68 0.38 0.18 1 6. 4.07, 4.60, 4.91 4.14, 4.44, 4.74 3.13, 3.83, 4.43 0.10 0.73 1 7. 3.75, 4.33, 4.73 4.03, 4.35, 4.67 3.47, 4.12, 4.65 −0.06 0.19 4 8. 3.83, 4.41, 4.78 4.01, 4.32, 4.64 3.79, 4.37, 4.77 0.03 0.03 1 9. 3.73, 4.32, 4.75 3.91, 4.24, 4.57 3.37, 4.04, 4.61 0.04 0.26 1 10. 3.97, 4.52, 4.87 3.96, 4.28, 4.60 3.18, 3.87, 4.48 0.19 0.62 1 11. 3.62, 4.23, 4.67 4.14, 4.35, 4.73 3.40, 4.06, 4.62 −0.24 0.15 4 12. 3.66, 4.27, 4.73 3.01, 3.45, 3.89 3.34, 4, 4.62 0.78 0.26 1 13. 3.76, 4.35, 4.76 3.34, 3.74, 4.14 3.56, 4.18, 4.66 0.56 0.15 1 14. 3.91, 4.47, 4, 84 3.93, 4.26, 4.58 3.56, 4.20, 4.70 0.16 0.25 1 15. 3.45, 4.10, 4.62 3.65, 4.01, 4.38 3.29, 3.97, 4.59 0.05 0.11 1 16. 4.10, 4.62, 4.92 4.24, 4.52, 4.80 3.44, 4.09, 4.63 0.04 0.50 1 17. 3.68, 4.29, 4.75 3.24, 6.65, 4.06 3.22, 3.91, 4.51 0.6 0.36 1 18. 2.66, 3.12, 3.50 3.29, 3.71, 4.12 3.17, 3.86, 4.47 −0.6 −0.46 3 19. 3.28, 3.96, 4.57 3.93, 4.26, 4.58 3.35, 4.01, 4.55 −0.31 −0.03 3 20. 3.35, 3.94, 4.38 4.14, 4.43, 4.73 3.34, 4.00, 4.56 −0.52 −0.06 3 21. 4.05, 4.59, 4.92 3.55, 3.92, 4.29 3.45, 4.10, 4.62 0.62 0.47 1 https://doi.org/10.15837/ijccc.2022.2.4741 12 figure 5: ipca fuzzy (moderate choice) matrix for hotel 1 in relation to hotel 4 (source: created by the authors based on data processing from table 9.) figure 6: ipca original matrix for hotel 1 in relation to hotel 4 (source: created by the authors based on data processing from table 9.) repeat operations for building fuzzy ipca (drastic choice) for hotel 1 compared to hotel 4 (table 10 and figure 7). table 10: gap and pd original, gap and pd fuzzy (moderate choice, drastic choice) for hotel 1 (focal) and hotel 4 competitor attrib. gap original pd original q gap fuzzy moderate pd fuzzy moderate q gap fuzzy drastic pd fuzzy drastic q 1. 0.28 0.24 1 0.51 0.28 1 0.11 0.24 1 2. 0.02 0.64 1 0.003 0.60 1 1.08 0.95 1 3. 0.17 −0.09 2 0.53 −0.11 2 0.16 −0.13 2 4. 0.69 0.40 1 1.21 0.42 1 0.57 0.57 1 5. 0.15 0.19 1 0.38 0.18 1 0.47 0.31 1 6. 0.07 0.67 1 0.10 0.73 1 0.97 0.86 1 7. −0.05 0.25 4 −0.06 0.19 4 0.80 0.47 1 8. −0.02 0.05 4 0.03 0.03 1 0.74 −0.71 2 9. −0.05 0.28 4 0.04 0.26 1 0.57 0.45 1 10. 0.07 0.60 1 0.19 0.62 1 0.77 0.82 1 11. −0.25 0.20 4 −0.24 0.15 4 0.52 0.40 1 12. 0.41 0.25 1 0.78 0.26 1 0.24 0.28 1 13. 0.23 0.14 1 0.56 0.15 1 0.42 0.18 1 14. 0.05 0.27 1 0.16 0.25 1 0.72 0.43 1 15. −0.33 0.16 4 0.05 0.11 1 0.22 0.33 1 16. −0.30 0.50 4 0.04 0.50 1 0.43 0.36 1 17. 2.17 0.31 1 0.6 0.36 1 0.18 0.35 1 18. −1 0.39 4 −0.6 −0.45 3 −0.72 −0.46 3 19. −0.43 −0.07 3 −0.31 −0.03 3 −0.03 −0.23 3 20. −0.53 0.07 4 −0.52 −0.06 3 0.28 0.20 1 21. 0.38 0.42 1 0.62 0.47 1 0.72 0.57 1 (note: for simplification, only two decimal places were kept after the comma) 5.4 comparisons between original ipca and fuzzy ipca in moderate and drastic case the analysis of quadrant 1–"solid competitive advantage" in the three situations (original ipca, fuzzy ipca moderate choice and fuzzy ipca drastic choice) for focal hotel 1 compared to hotel 2 shows the positioning here in all three situations of 3 attributes (2,4,5) and in the case of ipca fuzzy moderate choice and attribute 16. in the case of ipca analysis from the perspective of hotel 1 in relation to hotel 4, in the case of the original ipca there are 9 attributes positioned here (1,2,4,5,10,12,13,14,17), in the case of ipca fuzzy moderate choice there are 13 attributes (1,2,4,5,8,9,10,12,13,14,15,16,21) and in the case of ipca fuzzy drastic choice there are 17 attributes (1,2,4,5,6,7,9,10,11,12,13,14,15,16,17,20,21). https://doi.org/10.15837/ijccc.2022.2.4741 13 figure 7: ipca fuzzy (drastic choice) matrix for hotel 1 in relation to hotel 4 (source: created by the authors based on data processing from table 10.) the analysis of quadrant 2–"head-to-head competition" in all three situations (original ipca, fuzzy ipca moderate choice and fuzzy ipca drastic choice) for focal hotel 1 compared to hotel 2, shows different quantitative situations. in the first case there are 9 attributes in this dial (1,3,6,10,12,13,14,17,21), in the second case there are 12 attributes (1,3,6,8,9,10,12,13,14,15,17,21) and in the ipca version fuzzy drastic choice are 16 attributes (1,3,6,7,8,9,10,11,12,13,14,15,16,17,20,21). these 16 attributes give the fight with the competition. "although the company goes beyond customer expectations for the attributes positioned in this quadrant, it has lower performance scores for these attributes than its competitors. in such situations, the focal company has to at least reach the competitor’s performance level. benchmarking with the best practice is recommended for performance improvement efforts." ([1], p.138) the analysis of quadrant 2–"head-to-head competition" in all three situations (original ipca, fuzzy ipca moderate choice and fuzzy ipca drastic choice) for focal hotel 1 compared to hotel 4, shows different quantitative situations. in the original ipca, in this dial there is 1 attribute (3), in the second situation there are 1 attribute (3) and in the case of ipca fuzzy drastic choice there are 2 attributes (3,8). attributes 3–the hotel has sufficient restaurant facilities and 8–the staff is able to solve guests’ problems, are attributes to which hotel 1 is in direct competition to hotel 4 but also to hotel 2. the analysis of quadrant 3–"urgent action" in all three situations (original ipca, ipca moderate choice and ipca fuzzy drastic choice) for the focal hotel 1 compared to hotel 2, shows different quantitative situations. in the first case there are 9 attributes of quality in this dial (7,8,9,11,15,16,18,19,20), in the second case there are 5 attributes (7,11,18,19,20) and in the third there are two attributes (18,19). it is noted that the two attributes (18,19) of the ipca variant fuzzy drastic choice are also found in the other two situations (fuzzy moderate and drastic choice) from hotel 1 in relation to hotel 4. the conclusion is that this hotel 1 is categorically deficient, from the perspective of its customers, at entertaining facilities (18–the hotel has entertaining facilities) both in relation to hotel 2 and in relation to hotel 4. these two attributes 18–the hotel has entertaining facilities and 19–big variety and proper quality of meals impose urgent measures. "these attributes reflect the areas of weakness of the focal company, and the quadrant is named ’urgent action’ because of the necessity of taking urgent action to improve them" ([1], p.138). in this case, fuzzy modeling has led to a higher filtering of attributes that qualify for this dial and which require effort concentration. the analysis of the quadrant 4–"null advantage" in all three situations (original ipca, ipca fuzzy moderate choice and ipca fuzzy drastic choice) for the focal hotel 1 in relation to hotel 2 shows that no attribute was positioned here. the analysis of the quadrant 4–"null advantage" in all three situations (original ipca, ipca fuzzy moderate choice and ipca fuzzy drastic choice) for focal hotel 1 compared to hotel 4 shows in the original ipca the positioning of 8 attributes (7,8,9,11,15,16,18,20), in the ipca fuzzy moderate choice of 2 attributes (7,11) and in the third situation no attributes. https://doi.org/10.15837/ijccc.2022.2.4741 14 for testing the ipca method, we also calculated the ipca in the three variants for the focal hotel 2 in relation to the competing hotel 1. the results obtained (table 9) support the conclusions obtained from the analysis of the focal hotel 1 in relation to the competing hotel 2, although they are different values of the importance of the attributes. 5.5 conclusions and future research modeling with fuzzy sets ensures greater elasticity compared to the classic likert scale. the advantage of fuzzy crowd modeling is that it can be operated after data collection. the decision regarding the number of steps of the likert scale could be made after collecting the answers, by fuzzy modeling the answers according to the general characteristics of the answers. if the collected data show a concentration of responses on certain intervals of the likert classical scale (as in the present study) which makes it difficult to distinguish between the attributes of quality, the answers can be penalized. a clearer distinction will be made between attributes. at this point, the establishment of triangular fuzzy numbers replacing classical numbers is arbitrary, with future research aimed at finding a method for assigning fuzzy numbers. although it has a clear potential, modeling with fuzzy sets still has an important limitation. the limitation is that, subjectively, the researcher must assign values to fuzzy numbers. solving this problem is one of the future directions of research. also as future research, we must take into account that there are several types of sets ("fuzzy intuitionistic", "fuzzy valued between intervals", "fuzzy intuitiveistic value-interval", "double fuzzy", etc.) and these could provide a representation finer answers, almost all possible answers (for example, fuzzy modeling of the "between important and very important" type, but also "fuzzy-value-interval" can do this). author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] albayrak, t. 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(2021). improved todim method for intuitionistic fuzzy magdm based on cumulative prospect theory and its application on stock investment selection, international journal of machine learning and cybernetics, 12(3), 891–901, 2021. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control https://doi.org/10.15837/ijccc.2022.2.4741 18 cite this paper as: ban o.; droj l.; tus, e d.; droj g.; bugnar n. (2022). data processing by fuzzy methods in social sciences researches. example in hospitality industry, international journal of computers communications & control, vol. 17, no. 2, 4741, 2022. https://doi.org/10.15837/ijccc.2022.2.4741 the likert scale's issues and the fuzzy scale triangular fuzzy numbers importance performance competitor analysis description of the fuzzy importance-performance competitor analysis with triangular fuzzy numbers triangular fuzzy numbers application to the hotel industry research methodology results in the moderate choice: hotel 1 versus hotel 2 as competitor results in the drastic choice: hotel 1 versus hotel 2 as competitor comparisons between original ipca and fuzzy ipca in moderate and drastic case conclusions and future research international journal of computers communications & control issn 1841-9836, 10(2):280-290, april, 2015. human-manipulator interface using hybrid sensors via cmac for dual robots p. zhang, g. du, b. liang, x. wang ping zhang, guanglong du* south china university of technology, china pzhang@scut.edu.cn, medgl@scut.edu.cn *corresponding author: medgl@scut.edu.cn bin liang, xueqian wang graduate school at shenzhen, tsinghua university, china bliang@tsinghua.edu.cn, wang.xq@sz.tsinghua.edu.cn abstract: this paper presents a novel method that allows a human operator to communicate his motion to dual robot manipulators by performing his double handarms movements, which would naturally carry out an object manipulation task. the proposed method uses hybrid sensors to obtain the position and orientation of the human hands. although the position and the orientation of the human hands can be obtained from the sensors, the measurement errors increase over time due to the noise of the devices and the tracking error. a cerebellar model articulation controller (cmac) is used to estimate the position and orientation of the human hands. due to the limitations of the perceptive and the motor, human operator can not accomplish the high precise manipulation without any assistants. an adaptive multi-space transformation (amt) is employed to assist the operator to improve the accuracy and reliability in determining the posture of the manipulator. with making full use of the human hand-arms motion, the operator would feel kind of immersive. using this human-robot interface, the object manipulation task done in collaboration by dual robots could be carried out flexibly through preferring the double hand-arms motion by one operator. keywords: cerebellar model articulation controller (cmac), robot teleoperation, human-robot interface. 1 introduction human intelligence is required to make a decision and control the robot especially when it is in unstructured dynamic environments. thus, robot teleoperation is necessary especially when the robot is in highly unstructured environments, where objects are unfamiliar or changing shape. there are some human-robot interfaces which have been commonly used [1], such as dials and robot replicas. however, for completing a teleoperation task, these contacting mechanical devices always require unnatural hand and arm motion. there is another way to communicate complex motions to a remote robot and it is more natural that comparing with using contacting mechanical devices. this method is to track the operator hand-arm motion which is used to complete the required task using inertial sensors, contacting electromagnetic tracking sensors, gloves instrumented with angle sensors, and exoskeletal systems [2]. however, these contacting devices may hinder natural human-limb motion. vision-based techniques are non-contacting and they are less hindering the hand-arm motion. vision-based methods often use physical markers which are placed on the anatomical body part [3]. there are a lot of applications [4] based on marker-based tracking of the human motion. however, because body markers may hinder the motion for some highly dexterous tasks and operators may be occluded, the marker-based tracking is not always practical. thus, a markerless approach seems better for many applications. copyright © 2006-2015 by ccc publications human-manipulator interface using hybrid sensors via cmac for dual robots 281 compared with image-based tracking which uses markers, markerless is not only less invasive, but also can eliminate problems of marker occlusion and identification [5]. thus, for remote robot teleoperation, markerless tracking may be a better approach. however, the existing markerless human-limb tracking techniques have many limitations so that they may be difficult to use in robot teleoperation applications. many existing markerless-tracking techniques capture images and then compute the motion later [6, 7]. the robot manipulator could be controlled with the continuous robot motion by the markerless tracking. to allow the human operator to perform hand-arm motions for a task in a natural way without any interruption, the position and orientation of the hand and arm should be provided immediately. many techniques can only provide 2d image information of the human motion [8], but the tracking methods cannot be extended for accurate 3d joint-position data. an end-effector of a remote robot would require the 3d position and orientation information of the operators limb-joint centers with respect to a fixed reference system. the problem how to identify human body parts in different orientations has always been a main challenge [9]. for robot teleoperation, some limited research towards markerless human-tracking has been done. many techniques used a human-robot interface based on hand-gesture recognition to control the robot motion. ionescu et al [10] developed markerless hand-gesture recognition methods which can be used for mobile robot control where only a few different commands are sufficient such as go, stop, left, right. however, for object manipulation in 3d space, it is not possible to achieve natural control and flexible robot motion by using gestures only. if a human operator wants to use gestures, he/she needs to think of those limited separate commands that the human-robot interface can understand like move up, down, forward. a better way of humanrobot interaction would permit the operator to focus on the complex global task as a human operator naturally does when grasping and manipulating objects in 3d space instead of thinking about which type of hand motions are required. to achieve this goal, a method that allows the operator to complete the task using the hand-arm motions naturally provides the robot with information of the hand-arm motion in real time, that is the hand and arm anatomical position and orientation [11, 12]. however, method [11] is hard to obtain the accurate orientation of the human hand since the occlusion easily happens. to achieve the initialization, the human operator must assume a simple posture with an unclothed arm in front of a dark background, and the hand should be higher than the shoulder. therefore, through the method [12], it is not possible to get precise results under a complex background. in addition, the human operator is hard to work in the chill weather with unclothed arm. and also it is limited because of lighting effect, which is difficult to work where the environment is too bright or too dark. as the manipulation taskbeing more complex, multiple robot cooperation will be a trend. vision-based methods mentioned above are hard to use in the multi-robot interface, as they can solve the occlusion problem. in addition, the contacting interfaces used in teleoperation for the multiple robot manipulators often require multiple operators [13]. this article presents a method of dual robot-manipulators interface using hybrid sensors to track the hand of the human operator (fig. 1). hand tracking based hybrid sensors is used to acquire 3d anatomical position and orientation and sent the data to the robot manipulator by a human-robot interface, which enables the robot end-effector to copy the operator hand motion in real time. fig. 2 shows the structure of our system. a position sensor provides the location of the human hand and an imu provides the orientation of the human hand. we use camshift to track the human hand. an imu consisting of gyroscopes and magnetometer can measure the orientation and the acceleration of the hand. a cmac estimation algorithm is used to estimate the positions and the accelerations of the hand. another cmac is applied to estimate the orientation and the angular velocities of the hand. in order to eliminate the influence of the noise and the 282 p. zhang, g. du, b. liang, x. wang tracking failure, speed control method is used instead of the absolute location control method. the velocity value is transmitted to the manipulator and the manipulator moves at the received rate. the velocities of the position and the orientation are processed via amt to improve the accuracy of manipulation. the natural way to communicate with the robots allows the operator to focus on the task instead of thinking the limited separate commands that the human-robot interface can understand like gesturebased approaches. there may be marker occlusion and identification when using vision-based approaches. in addition, this way let the operator feel kind of immersive in the multi-robot environment, which feels that his hands are in the robot site. in section 2, the cmac method is presented. the human hand tracking system is then detailed in section 3. section 4 describes amt method. experiments and results are presented in section 5. discussions of this paper are detailed in section 6, followed by concluding remarks in section 7. figure 1: non-invasive robot teleoperation system based on the kinect figure 2: the structure of the system 2 cerebellar model articulation controller (cmac) since the signals of the position and the orientation of human hand are time-varying and they are ill-defined when occlusion is encountered, an adaptive filter is required. in this paper, we use cmac to process the pose signal (position and orientation). the convergence of cmacoutput error can be guaranteed because the stable conditions of the adaptive learning-rates are derived based on the lyapunov function [14]. there are five components in cmac: input space, association memory space, receptive-field space, weight memory space, and output space. the flow of cmac is as follows: human-manipulator interface using hybrid sensors via cmac for dual robots 283 (1) input space c: the input vectoris c = [c1, c2, ..., cn]t ∈ r according to a given control space.ciis the input state variable and n is the input dimension. each input state variable ci must be quantized into discrete regions (element). (2) association memory space a: several elements can be accumulated as a block b and each block performs a receptivefield basis function. the number of blocks nb is greater than two and the number of components in the association memory space is na = n × nb. for given ith input cri with the mean mij and variance σij, we adopt gaussian function as the receptive-field basis function ϕij for jth block, which can be defined as: ϕij = exp[ −(ci − mij)2 σ2ij ] j = 1, 2, ..., nb (1) where ϕij(k − 1) denotes the value of ϕij(k) trough a time delay. (3) receptive-field space f: areas formed by blocks are defined as receptive-fields. the number of receptive-fields is nr, which equals nb. the jth multi-dimensional receptive-field function is represented as bj(c, mj, σj) = n∏ i=1 ϕij = exp[−( n∑ i=1 (ci − mij)2 σ2ij )] (2) where mj = [m1j, m2j, ..., mnj]t ∈ rn and σj = [σ1j, σ2j, ..., σnj]t ∈ rn. the multi-dimensional receptive-field functions can be expressed in a vector form as γ(c, m, σ) = [b1j, b2j, ..., bnj] t (3) (4) weight memory space w: each location of f to a particular adjustable value in the weight memory space can be defined as w = [w1, w2, ..., wnr] t =   w11 · · · w1p ... . . . ... wnr1 · · · wnrp   (4) where wj denotes the connecting weight value of the output associated with the jth multidimensional receptive-field.p is the number of the output value. (5) output space y: the output of rcmac is the algebraic sum of the activated weights in the weight memory, y = [y1, y2, ..., ynp] = w t γ(c, m, σ) (5) 3 hand tracking 3.1 orientation measurement the fqa, which is based on earth gravity and magnetic field measurements, is used to estimate the orientation of a rigid body [15]. but this algorithm is applied for static or slow-moving rigid body only. to make it applicable for relatively large linear accelerations, cmac fusion algorithm is used together with angular rate information to estimate orientation of dynamic body (either slow-moving or fast-moving) in the next section. for the estimation of the orientation, the quaternion states and the angular velocities (measured by imu) are used as input elements, cori = [q0, q1, q2, q3, wx, wy, wz] (6) where (wx, wy, wz) is the vector of angular velocities. the input dimension nori is seven. 284 p. zhang, g. du, b. liang, x. wang the output vector is yori = [q0, q1, q2, q3] (7) 3.2 hand position tracking since the human operator holds the imu to measure the orientation of his hand, the position of the imu is equal to the position of the human hand. moreover, the color of the imu is special, so it is easier to be identified than the human hand. in the imu position tracking system, we use a 3d camera to obtain two 2d images of the imu: color image and depth image. the imu positions are tracked through camshift algorithm, which is more and more noticed by means of its favorable performance in reality and robust. for the estimation of the position, we use the translation px, py, pz (measured by kinect) and the acceleration ax, ay, az(measured by imu) as input elements, cpos = [px, py, pz, ax, ay, az] (8) the input dimension npos is seven. the output vector is ypos = [px, py, pz] (9) 4 adaptive multispace transformation (amt) robot manipulator has inherent perceptive limitations (such as perception of distance) and motor limitations (such as physiological tremor), which prevents the operator from operating precisely and smoothly enough for certain tasks [16]. for improving the visual and motor performance of teleoperation interface, we applied the modified version of adaptive multi-space transformation (amt) [16]. in the proposed method, an interface which introduces two scaling processes link the human operator working space to the robot working space (fig. 3). the first change scales the movement produced by the human operator. another change of scale is applied between the virtual unit vector k of the central axis of the robot ee and the robot movements. such changes of scale modify the robot speed and, thus, improve performance. the scaling vector s is used to relate the actions of the human hand in master space ms and the movement of the virtual unit vector k in the visual space vs. another scaling variable u is used to relate the vs space to the robot working space ws. s and u are a function of the distance r between robot ee and the target. when s < 1, it decelerates the movement of k. instead, it accelerates the movement of k while s > 1. figure 3: representation of the human-interface-robot spaces as the movement of the virtual position in vs is affected by vector s, lets = [spos, sori], where spos is the scaling vector of the position and sori is the scaling variable of the orientation. human-manipulator interface using hybrid sensors via cmac for dual robots 285 the euler angular velocity in ms is ˙om and ȯy is the angular velocity in vs. then ˙ov = { sori ˙om ˙om ≤ δori 0 ˙om > δori (10) where δori is the threshold value. assume that ˙pm is the velocity vector of hand movement in ms and ṗv is the speed of the vector k in vs, the mapping vector of ˙pm in the vs is ˙pm . let spos = [sksk⊥] and k⊥ is a vector which is perpendicular to k. in addition,k⊥ ,k and ˙pm are coplanar. so we have pk = { sk • (| ˙pm′| • cosθ) • k = sk • k • pm′ • k sk ≤ δk 0 sk > δk pk⊥ = { sk⊥ • (| ˙pm′| • sinθ) • k⊥ = sk⊥ • k⊥ • pm′ • k⊥ sk ≤ δk 0 sk⊥ > δk⊥ (11) where δk and δk⊥ are the threshold value. then the speed ṗv of the vector k is given as ṗv = pk + pk⊥ (12) when sk = sk⊥ , the speed ˙pv results in ṗv = skpm′. while sk < sk⊥ , it requires greater precision in the direction of the central axis of ee. instead, the direction which is perpendicular to the central axis requires greater precision with sk > sk⊥ . in this paper, we use sk > sk⊥ so that the operator can easy to center the axis of the hole, as well as insert the peg into the hole quickly. since sori and are functions of distance r, the function sori(r) is defined as  sori(r) = log(r) + c1 sk⊥(r) = log(r) + c2 sk(r) = √ r + c3 (13) wherec1,c2 and c3 are constants. 5 experiments 5.1 environment of experiment a comparison experiment was designed to verify the proposed method. in the experiment, a series of screwing bolt tests were carried out to compare our method with methods [11, 12] in efficiency of the manipulation. the three methods controlled the position and orientation of robot ee directly to screw the bolt into the nut. joint angles can be achieved by the solution of robot inverse kinematics. given a posture of robot ee, the angle of each joint can be achieved by the robot inverse kinematics [17]. fig. 4 shows the environment of the experiment. two googol grb3016 robots were used in our experiments. tab. 1 lists the nominal robot link parameters of the robot. two robot manipulators were placed at the distance of 205.3 cm. the operator stood in the front of the kinect and held an imu. the leap motion sampled at 30 hz. the diameter of the nut was 75.5 mm and the diameter of the bolt was 75 mm (fig. 5). the length of the nut was 91 mm and that of bolt was 83 mm. in each task, the operator needed to control the robots and screwed the bolt into the nut. 286 p. zhang, g. du, b. liang, x. wang figure 4: environment of experiment table 1: the dh parameters for the googol grb3016 robot(a: link length.: link twist. d: link offset.: joint angle.) jointdh a(mm) α (rad) d (mm) θ (rad) 1 150 −π/2 250 0 2 570 -π 0 −π/2 3 150 π/2 0 0 4 0 −π/2 650 0 5 0 −π/2 0 −π/2 6 0 0 -200 0 figure 5: the screw nut and the screw bolt human-manipulator interface using hybrid sensors via cmac for dual robots 287 5.2 results of experiment in the proposed algorithm, cmac was used to estimate the orientation and the position of the human hand. fig. 6 shows the 3-d path of two robot end-effectors during the screwing bolt experiment. the whole time of each test was about 240s. fig. 7 shows the results of tracking the robot ee in the period of adjusting the position and orientation (about 35 s). the period of screwing blot was ignored since the position and the orientation did not change greatly. fig. 7 (a, b, c) shows the position in x, y, z and fig. 7 (d, e, f) shows the orientation in yaw, pitch and roll. the dotted lines are the measured data (ml) received from the left hand. the dash dot lines are the measured data (mr) received from the right hand. the star lines are the estimated data of the left robot ee (el). the plus lines are the estimated data of the right robot ee (er). the estimation algorithms could reduce the noise and improve the operating accuracy. since the gap between the blot and the nut was 0.5 mm, whether the operator could screw the blot into the nut was used to evaluate the accuracy of the three methods. moreover, we compared the efficiency between our method and methods [11, 12], including the operation time and times of faults in each test. figure 6: robot path the three methods were compared in terms of operation time and times of faults. the operation time includes closing the two robot end-effectors, adjusting the two robot end-effectors, screwing the bolt and separating the two robot end-effectors. the times of faults are the number of the operator tries to screw the bolt into the nut in each test. tab. 2 shows the results of the operation time and times of faults for 5 tests, respectively. for test results of our method, the operation time for 5 tests ranges from 161 s to 169 s, with the mean time of 165.0 s. for methods [11, 12], the mean time is 193.4 s and 213.4 s. the mean times of faults are 3.6 and 5.6. compared with methods [11, 12], the mean time of our method drops about 28.4 s and 48.4 s. moreover, the times of faults of our method drops about 2.19 and 4.19. table 2: the dh parameters for the googol grb3016 robot(a: link length.: link twist. d: link offset.: joint angle.) 1 2 3 4 5 mean our method time / s 165 161 166 169 164 165.0 times 2 2 1 1 1 1.41 method [11] time / s 191 188 194 198 196 193.4 times 4 3 3 5 3 3.6 method [12] time / s 211 208 214 218 216 213.4 times 6 5 5 7 5 5.6 288 p. zhang, g. du, b. liang, x. wang figure 7: peg tracking results human-manipulator interface using hybrid sensors via cmac for dual robots 289 6 discussions this paper proposes a robot control method based on hybrid sensors. the motions of robot manipulators are controlled by imitating the motions of operators. for the robot teleoperation in the remote unstructured environment, a condition is assumed that all the remote robot site components can be installed on a mobile platform and enters those unstructured environment. and the remote unstructured environments involve robotic arm, robot controller, cameras on end-effectors, and some other cameras. the method shown here was testified by screwing a bolt into a nut. this system includes the operator into the decision control loop and that is a significant advantage. firstly, it allows a robot to finish some tasks like moving without any prior knowledge like start location and even destination location. secondly, there are some similar tasks requiring decision making. when picking objects or cleaning some objects which may contain some dangerous items, decision making is always required. also it is expected that this system can be used to achieve the complex postures when the joints of the robot are limited. compared with contacting electromagnetic devices, like hand joystick and data gloves, our method would not hinder most natural human-limb motion and allows the operator concentrate on his own task instead of decomposing commands into some simple operations. compared with the non-contacting markerless method [11, 12], our method was proved more accurate and efficient. moreover, as the manipulation task to be more complex, multiple robot cooperation will become a trend. however, the contacting interfaces used in teleoperation for the multiple robot manipulators often require multiple operators [18]. in this paper, it only needs one operator to control two robot-manipulators to complete complex manipulation. in the future, gesture control will be combined with voice control. simple commands like start, end, and pause can be completed by voice. then the system will work more flexibly and advantageously. 7 conclusions this paper presents a human-robot interface system which uses a tracking technology based on hybrid sensors. to eliminate the effect of occlusion and failure of motion sensing, cmac is developed to estimate the orientation and position information of the human hand. since robot manipulator have inherent perceptive limitations and motor limitations, the high-precision tasks cannot be performed directly by the operator. thus, amt system is employed to assist the operator to complete the high-precision tasks. in the experiment raised in this paper, screwing tasks have been carried out. we compared the efficiency between our method and method [11, 12]. experimental results show that our method has better accuracy and efficiency. further investigation of finger positioning will be considered to improve the performance of the human-robot interaction method. acknowledgments project funded by national natural science foundation of china (grant no:61403145) china postdoctoral science foundation (no:2014m550436) and the fundamental research funds for 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[18] r. marin, pj. sanz, r.wirz (2005); a multimodal interface to control a robot arm via the web: a case study on remote programming, ieee trans. on industrial electronics, 52(6): 1506-1521. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 6, month: december, year: 2022 article number: 5039, https://doi.org/10.15837/ijccc.2022.6.5039 ccc publications smart agriculture: iot-based greenhouse monitoring system a. simo, s. dzitac, g. e. badea, d. meianu attila simo1*, simona dzitac2, gabriela elena badea2, dragos meianu2 1*politehnica university timişoara, timişoara 300223, romania corresponding author: attila.simo@upt.ro 2university of oradea, oradea 410087, romania abstract the term “smart agriculture” refers to a management concept that is centered on industrial agriculture. it makes use of cutting-edge technologies such as big data, cloud, and the internet of things to monitor, automate, and evaluate agricultural operations. smart agriculture is a management concept. software and sensors are used in smart agriculture, also known as precision agriculture. smart agriculture is managed by the software. this pa-per proposes the development of low-cost environment parameters and electrical quantities monitoring device. according to the findings, such internet of things devices are suited for digitizing the relevant data in order to ac-quire information that may help farmers make optimal changes. keywords: digitization, smart agriculture, wireless sensors network, internet of things. 1 introduction currently, technology is pervasive and is mostly used to decrease or optimize human work. in order to boost the quality and quantity of crops, technology is making its way into agriculture. the majority of farmers are still unfamiliar with the phrase smart agriculture [1], which refers to the use of technology such as the internet of things (iot), wireless sensors network, positioning systems, and artificial intelligence (ai) on farms [2]. examples of such technologies utilized on farms include precision irrigation [3] and exact plant nutrition [4], climate management and control in greenhouses [5][6]. all of them functioning in tandem with specialized soft-ware to oversee and regulate a farm or greenhouse in order to maximize its yield. agriculture is the cornerstone of human civilization due to the fact that it is the primary source of food and has a considerable influence on the economy. farmers still use the same methods they’ve been using for generations, which results in a lower crop yield. implementation of modern science and technology is required in order to enhance the productivity of agricultural practices. we can anticipate that iot will boost productivity in agricultural areas at a price that is reasonable if it is used to monitor the effectiveness of the soil, the temperature and humidity, the amount of precipitation, the effectiveness of the fertilizer, monitor the capacity of water tanks, and detect theft. combining time-honored agricultural methods with forward-thinking technologies like the internet of things and wireless sensor net-works is one way to bring agriculture into the modern era. the wireless sensor https://doi.org/10.15837/ijccc.2022.6.5039 2 net-work that compiles data from a wide variety of sensors and sends it to the primary server using some sort of wireless protocol. there are a number of additional factors that have a substantial influence on productivity. during the growth of the crop, it is subject to attack from a variety of wild animals and birds, as well as insects and other pests that can be managed with the application of the appropriate insecticides and pesticides. the unpredictability of monsoon rainfall is causing a decline in crop output, which, combined with a lack of available water and excessive consumption, presents a risk. as a result of the inclusion of information and communication technologies in order to get ready for the next era of agriculture, the agricultural industry is currently going through a fourth revolution that has been given the moniker of "farming 4.0." [7]. in recent years, various combinations of new technologies in smart agriculture, such as uav technology [8][9], wsn [10] [11], ai [12], big data [13], data analytics [14][15] and iot [16][17][18][19], have been the subject of intensive research and investigation. there has been a proliferation of smart agriculture-based studies, projects, and books in recent years, which has led to the spread of information. even though there have been a few publications on iotbased smart agriculture [20][21][22], the primary focus of those publications has been on the development and application of iot in agriculture, as well as the iot platform and architecture. connectivity technologies that are based on the internet of things play an important part in the agricultural sector both now and in the future. a smart greenhouse is a concept that assists in guiding the steps required to modify and reorient agricultural systems in order to successfully support their growth and assure food security in a changing climate [23][24]. a smart greenhouse is a method that assists in guiding the activities that must be made in order to alter and sustain the environment. the policy attempts to achieve three key goals: a sustainable in-crease in agricultural production; building resilience to climate change; and decreasing and eliminating greenhouse gas emissions wherever possible [25]. one of the most significant areas for the world food organization’s strategic goals of improving resource mobilization and crop output is the smart greenhouse. it is aligned with the food and agriculture organization (fao) food vision and contributes to the organization’s goal of making agriculture more productive and sustainable [26]. the internet of things is becoming increasingly popular as a means to connect various devices and collect data. iot is utilized together with its frameworks to handle and interact with data and information. within the system, users have the ability to register sensors, generate data streams, and process data. applications in agriculture are another area where the internet of things can be useful. the internet of things has a wide variety of applications, some of which are listed below: smart cities, smart environments, smart water, smart metering, security and emergency response, industrial control, smart agriculture, home automation, and e-health. one of the most important technologies that goes into intelligent agriculture is the iot. connecting two or more devices to one another through the use of the internet is the process known as "tethering." these gadgets include cellular phones, appliances for the home, as well as machinery used in industry and agriculture. they are operable and controllable due to the fact that data may be sent to them through the internet and received from them at the same time [27]. one of the most important uses of the internet of things nowadays is in the field of intelligent agriculture. iot agriculture systems are applied in a variety of spheres, including irrigation management, soil management, precision agriculture, and intelligent farming. agriculture may be made more productive and have a more intelligent cropping system through the use of precision agriculture and smart farming techniques. these techniques allow farmers to develop and optimize all possible directions. in the meantime, the monitoring of agricultural pests and diseases has been connected with iot technology. this has made it possible for farmers to enhance their profitability by safeguarding crops from pest attracts at early prediction stages. the goal is to accurately direct agriculture toward increased production at lower costs and with enhanced quality through the collection and utilization of precise information. for instance, remote sensors that are installed in fields offer farmers with accurate maps of the landscape and the resources available in a region. in addition to this, it is able to predict impending weather patterns by measuring variables such as the temperature and humidity of the soil [28]. agriculture that is supported by iot makes it easier to make better judgments on agricultural productivity. in addition, the data that are gathered and processed play https://doi.org/10.15837/ijccc.2022.6.5039 3 a significant part in the process of monitoring agricultural pests and determining the exact amount of pesticides that are necessary to prevent their improper application. procedures for collecting and analyzing data that are similarly practical and sensible can be applied to irrigation water. the monitored parameters of intelligent agricultural systems varied from one study to the next. according to the authors of [29], the majority of the efforts made use of sensors to monitor things like temperature, humidity, soil moisture, and the intensity of the light. the temperature of the soil has a significant influence on the amount of crop output. alterations in soil temperature have a direct influence on the amount of moisture in the soil as well as the uptake of nutrients. in addition, the temperature of the air that has been measured can be exploited as a tool for monitoring the conditions of crops grown in open fields and greenhouses. the relative humidity level can be detected and measured with the assistance of the air humidity sensor, the soil humidity sensor, and the relative humidity sensor. plant leaf growth, pollination, and photosynthesis are all influenced in various ways, both directly and indirectly, by humidity. concerning soil moisture, it is utilized in the process of determining the water content and quality of the soil. the resistance of the soil moisture sensor is inversely proportional to the amount of moisture that is present in the soil, which is the key factor that influences the rate at which plants grow. the amount of moisture that is present in the ground is factored into the process of maintaining the water supply and any other suitable automated procedures over the entire field. the amount of light has a direct impact on the process of photosynthesis, which in turn has an effect on the growth of crops. in addition, ph is necessary for irrigation, which is used to determine the precise nutrient content of the soil. in addition, the velocity of the wind and the amount of precipitation are frequently used as factors in the regulation of water flow and the forecasting of the possibility of precipitation. iot-based agricultural systems are still subject to challenges and limitations, despite the fact that iot devices provide beneficial information on a wide range of physical elements that can improve cultivation techniques and crop productivity in an agricultural environment. the following is a list of the key challenges associated with the implementation of smart agriculture applications, as seen by the authors of [29]. the challenges are arranged into groups according to the characteristics they present, as seen in figure 1. figure 1: categories of challenges in smart agriculture applications based on iot. concerns in this field are growing, a series of recent papers addressed the deployment of modern information and communication technologies in agriculture, for example [31][32][33]. our research collective has been dealing with this problem for some time, our concerns being more and more directed towards small farmers, basically in the western part of romania [34][35]. this paper contributes to creating smart greenhouses developing a multisensorial, low-cost, monitoring iot solution. with the help of this solution, we are able to monitor certain environmental parameters, which allows us to improve the climate within the greenhouses. in addition, we are able to monitor the electrical energy consumption of the equipment that serves the greenhouse, which allows us to reduce the amount of electrical energy used by the greenhouse. https://doi.org/10.15837/ijccc.2022.6.5039 4 2 solution architecture in this section the general architecture of the greenhouse monitoring solution is described. figure 2 shows the general scheme of the developed infrastructure. the goal is to digitize as much data as possible (environment and electrical energy consumption), to offer the possibility to be analyzed, in order to generate optimal solutions in the operation of the greenhouses. figure 2: solution architecture according to the illustration, the data of interest will be gathered through the use of the sensors, and a microcontroller will retrieve and pre-process them before pack-aging them, with a package containing the recorded data being created every 15 minutes (the data transmission interval can be set from 1 second to 1 hour via the embedded software). after the data have been packed using the wifi transmission mode, they are then transferred to a data server where they are saved in the influxdb database. the application that shows the data will subsequently collect the data from the database and display it. 2.1 network deployment a secure wifi network has been developed, including the different routers required to send the received data to a server through the network. this technology was chosen because the greenhouses in the case study where the solution was tested in real conditions are close to each other and it did not make sense to use technologies for long-distance data transmissions, such as lora, for example. wi-fi is a wireless networking technology that enables devices such as computers (laptops and desktops), mobile devices (smartphones and wearables), and other equipment to connect to the internet (printers and video cameras). it makes it possible for all of these devices and many more to communicate data, which ultimately results in the formation of a network. the connection to the internet is made through the use of a wireless router. when you join to a wi-fi network, you establish a connection to a wireless router. this establishes a connection between your wi-fi-enabled devices and the internet. 5 wifi routers provide coverage of the region of interest. these routers provides bidirectional communication between the devices connected to the network and the server, which is connected to the https://doi.org/10.15837/ijccc.2022.6.5039 5 gateway through an ethernet connection. 3 design of the monitoring devices this section presents the multisensory device for monitoring the environment in-side the greenhouse, respectively for monitoring the electricity consumption of the equipment that ensures the proper functioning of the greenhouse. it is important to mention the fact that these devices are prototypes, tested in laboratory conditions and later put into operation in a case study to test them in real conditions. the main goal is to create this solution at a lower price than commercial devices, in order to provide for small farmers access to technologies that can improve their activities. 3.1 greenhouse environment monitoring device the purpose of this device is to provide data in real time but at the same time to generate data history, which will help farmers to take the best decisions regarding the development of plants in greenhouses at the optimal time. this device monitors the following parameters: • temperature; • humidity; • co2; • light intensity; • ph. the hardware components used to build the prototype of the environmental parameters monitoring device are the following: 1. ccs811 air quality gas sensor module (fig. 3d) is a digital gas sensor that can detect a broad spectrum of total volatile organic compounds (tvocs), in addition to measuring levels of equivalent carbon dioxide (eco2) and metal oxide (mox). the sensor is useful for monitoring the quality of the air inside buildings. a microcontroller unit (mcu) that possesses both an analog-to-digital converter (adc) and an i2c interface is required in order to make use of this system. 2. sht31 humidity temperature sensor (fig. 3c) the sht31 is the next generation of temperature and humidity sensors that sensirion has developed. when compared to its predecessor, the sht31 has improved specifications in terms of its intelligence, reliability, and accuracy. it is possible to read out the temperature and humidity using i2c communications, which is part of its functionality, which also includes improved signal processing. has the following characteristics: • dual purpose temperature and humidity sensor • accuracy of ±2% relative humidity • 0-100% humidity sensing range • -40 to +125°c (-40 to +257°f) operating temperature • 8-second sensor response time 3. bh1750 ambient light sensor (fig. 3b) is a digital ambient light sensor ic that uses the i2c bus for its interface. this sensor is capable of providing precise readings of the lux value of light up to 65535. it detects light with the help of a photodiode and uses up very little electrical energy in the process. the bh1750 needs a supply voltage that ranges from 2.4v to 3.6v. in https://doi.org/10.15837/ijccc.2022.6.5039 6 order to function, the bh1750fvi, which is the core module of the sensor, needs 3.3v. one of the components of the circuit is a voltage regulator. i2c communication requires the sda and scl pins to be utilized, together with the i2c address. pullup resistors of 4.7k ohms are utilized for these pins. 4. soil ph sensor (fig. 3a) a soil ph sensor that is both watertight and dustproof, and that has the capability of measuring the ph value of the soil anywhere from 3 to 9 with an accuracy of up to 0.3 ph. the sensor includes a protective cover that has an ip68 rating and is sealed with high-density epoxy resin, both of which work together to prevent moisture from penetrating the inside of the body. the sensor may be used in a variety of settings, including agricultural cultivation and production, industrial production and monitoring of the environment, animal husbandry and treatment of sewage. figure 3: sensors – environment monitoring device the information collected by these sensors is sent to a development board which is controlled by an atmega328p microcontroller. after the data have been processed, the package containing the measurements will be transferred with the assistance of an esp8266. the components are shielded from harm by an abs housing (fig. 4), which features all of the appropriate cutouts for the sensors. this was done in order to achieve the best possible outcomes. figure 4: environment monitoring device – prototype it is the responsibility of the embedded software to collect data from sensors, package the data, and send it to the data server, from where data is retrieved and displayed for viewing and analysis. 3.2 electrical energy monitoring device the purpose of this device is to generate a database regarding the electrical energy consumption, which will allow farmers to improve the electrical energy consumption of greenhouses, of course https://doi.org/10.15837/ijccc.2022.6.5039 7 correlating these data with those monitored by the environmental device. this device monitors the following parameters: • electrical voltage; • electrical current; • electrical active power; • electrical energy. the hardware components used to build the prototype of the electrical quantities monitoring device are the following: 1. zmpt101b ac voltage sensor module – is based on a voltage transformer with a high level of accuracy called a zmpt101b, and it is used to measure the ac voltage accurately using a voltage transformer. when using arduino or esp32 to detect alternating current voltage, this is an excellent alternative. the modules are able to monitor voltage up to a maximum of 250v ac, and the analog output that corresponds to this measurement may be changed. the module is easy to use and is equipped with a trim potentiometer that has many turns, which may be used to tune and calibrate the adc output. 2. sct-013-030 non-invasive ac current sensor – is a non-invasive ac current sensor split core type clamp meter sensor that has the capacity to measure ac current of up to 100 amperes and does so without causing any damage to the circuit. current transformers, often known as cts, are a kind of sensor used to measure alternating current. they are very helpful for assessing the total power usage of a whole building. there is no need to do any high voltage electrical work in order to attach the sct-013 current sensors since they can be hooked right into either the live or neutral wire. has the following characteristics: • input current: 0-30a ac • output signal: dc 0-1 v • non-linearity: 2-3 • build-in sampling resistance (rl): 62 ohm • turn ratio: 1800:1 • resistance grade: grade b • work temperature: -25 °c +70 °c • dielectric strength (between shell and output): 1000 v ac / 1 min 5 ma similar to the environment monitoring device, this device also is based on a development board that contains an atmega328p microcontroller. the atmega328p microcontroller is responsible for taking the data from the sensors and processing them. after this, the data are sent to an esp8266, which is the module that is responsible for transmitting them to a data server so that they can be analyzed. it is the responsibility of the embedded software to collect data from sensors, package the data, and send it to the data server, from where data is retrieved and displayed for viewing and analysis. 4 results the multisensorial solution was tested and calibrated in laboratory conditions. after performing the calibrations, the solution was put into operation in real conditions, at a company in romania, timis county. figure 7 shows the greenhouses where the measurements were made, the equipment’s https://doi.org/10.15837/ijccc.2022.6.5039 8 figure 5: sensors – electrical quantities monitoring device figure 6: electrical quantities monitoring device https://doi.org/10.15837/ijccc.2022.6.5039 9 figure 7: greenhouse monitoring – case study https://doi.org/10.15837/ijccc.2022.6.5039 10 that serves these greenhouses, respectively the 3d diagram showing where the sensors were placed. our goal was the development of a multisensorial monitoring solution that would respond to the needs of small farmers, in the direction of the digitization of data from everything that means greenhouses in their farms, in order to optimize efforts, both from the point of view of production and from the point of view of electrical energy consumption. the results obtained are satisfactory, the devices work in the direction in which they were created, they provide the necessary data for further actions. the monitoring of electrical energy consumption was done in a single green-house, in march 2022. the main electricity consumers can be found in table 1, and the measurements can be seen in figures 8-11. no. components quantity 1. developing board with atmega328p 1 2. esp8266 1 3. soil ph sensor 1 4. bh1750 ambient light sensor 1 table 1: large consumers of electrical energy in case study greenhouses figure 8: electrical voltage the data collected by environmental sensors for the same period of the year (march 2022) can be seen on figures 12-15. the ph of the soil, according to the measurements of co2 concentration in air (fig. 12), is within the range 6.8-5.4, on the graph it can be seen that on march 13, there was an intervention on the ph with special solutions, in order to reduce its value. the graphs show that the temperature is at its lowest in the morning, but that it has the potential to rise throughout the course of the day. as the temperature increases, the humidity level declines, and fans are only needed in the morning and at night to circulate the air that has been heated. by spraying the flowers, the relative humidity is maintained at a level of between 60 and 70 percent. as the measurements show over a period of four days, the temperatures fits between 18-25 degrees celsius, and as for the humidity values, these are in the range of 52-75% (fig. 13). the co2 con-centration is within the range 800-1400 ppm, as the measurements show (fig. 14). the questions that need to be answered are about the price of these devices and whether or not they are worth the investment for farmers. if we focus simply on the worth of the individual parts (table 2 and table 3), we can see that the whole in-vestment is not that significant but still yields favorable outcomes. https://doi.org/10.15837/ijccc.2022.6.5039 11 figure 9: electrical current figure 10: electrical active power figure 11: electrical energy consumption https://doi.org/10.15837/ijccc.2022.6.5039 12 figure 12: soil ph measurements figure 13: humidity and temperature measurements figure 14: co2 concentration measurements https://doi.org/10.15837/ijccc.2022.6.5039 13 no. components quantity 1. developing board with atmega328p 1 2. esp8266 1 3. zmpt101b ac voltage sensor module 1 4. sct-013-030 non-invasive ac current sensor 1 5. power supply 1 6. bidirectional bus logic converter 1 7. resistor 10k 2 8. resistor 100ohm 1 9. capacitor 10uf 1 10. connecting wires 22 11. breadboard 1 total 147 € table 2: electrical quantities monitoring device components and device final price no. components quantity 1. developing board with atmega328p 1 2. esp8266 1 3. soil ph sensor 1 4. bh1750 ambient light sensor 1 5. sht31 humidity temperature sensor 1 6. ccs811 air quality gas sensor 1 7. connecting wires 16 8. breadboard 1 total 178 € table 3: environment parameters monitoring device components and device final price as the results show, the data recorded by the devices are fine for extensive analysis and to make optimizations and improvements in the processes; and regarding the price of the devices, we believe that it is an affordable price for smaller farmers as well. 5 conclusions iot is a revolutionary technical development that has energized business. it paves the way for brand new chances and challenges across a variety of economic domains. the internet of things is an exciting new technology that enables the growing of plants in greenhouses to access many new opportunities. iot makes it easier to transition from traditional greenhouses to smart solutions, such as an intelligent greenhouse that is equipped to track indoor factors and connect with the farmer. this allows the farmer to make decisions that automatically protect crops and increase production. iot for smart greenhouses consists of a wide variety of devices, each of which is outfitted with sensors and actuators. these devices are connected to one another and communicate with one another over the internet, which results in increased osmosis between the digital and physical worlds. in the internet of things, sensors and actuators are fundamental components that monitor the system’s state as well as environmental conditions. this includes obtaining data on temperature, humidity, co2, ph, water and energy consumption, movement, position, and other aspects, as well as managing the system’s dynamics. in addition, the capability to control the aforementioned indoor values within set reference ranges https://doi.org/10.15837/ijccc.2022.6.5039 14 allows for a reduction in the use of pesticides to protect crops from the most prevalent diseases. the ability to monitor and control optimal crop conditions in a protected environment helps to reduce crop damage that is caused by climate change or any other weather condition. it also enables the farmer to intervene by irrigating, heating, and fertilizing only to the extent that is necessary to achieve the pre-established objectives, which helps to effectively avoid waste and implement approaches that save water and energy. the goal of the work is to determine whether or not it is possible to create green-house environment and energy consumption monitoring devices for small farmers. these devices would provide a means by which data could be digitized and collected with the intention of increasing production while simultaneously decreasing the amount of electrical energy consumed. the results show that such devices can be developed, at a low cost and with good results regarding the digitization of the data of interest. 6 acknowledgments the research has been funded by the university of oradea, within the grants competition "scientific research of excellence related to priority areas with capitalization through technology transfer: ino transfer uo", project no. 323/2021. funding not applicable. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] g. sushanth and s. sujatha, "iot based smart agriculture system," 2018 internation-al conference on wireless communications, signal processing and networking (wispnet), 2018, pp. 1-4, doi: 10.1109/wispnet.2018.8538702. 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(eds) intelligent methods systems and applications in computing, commu-nications and control. icccc 2022. advances in intelligent systems and computing, vol 1435. springer, cham. https://doi.org/10.1007/978-3-031-16684-6_10. https://doi.org/10.15837/ijccc.2022.6.5039 17 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: a. simo, s. dzitac, g. e. badea, d. meian (2022). smart agriculture: iot-based greenhouse monitoring system, international journal of computers communications & control, 17(6), 5039, 2022. https://doi.org/10.15837/ijccc.2022.6.5039 introduction solution architecture network deployment design of the monitoring devices greenhouse environment monitoring device electrical energy monitoring device results conclusions acknowledgments international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 2, month: april, year: 2022 article number: 4541, https://doi.org/10.15837/ijccc.2022.2.4541 ccc publications pldanet: reasonable combination of pca and lda convolutional networks c. c. zhang, m. mei, z. l. mei, j. k. zhang, a. y. deng, c. l. lu caicai zhang& school of modern information technology zhejiang institute of mechanical and electrical engineering 528 binwen road, binjiang district, hangzhou, 310053, zhejiang, china zhangcaicai@zime.edu.cn mei mei& department of ultrasound, the second affiliated hospital zhejiang university school of medicine 88 jiefang road, shangcheng district, hangzhou, 310009, zhejiang, china meimeizjdx@zju.edu.cn &co-first authors: these authors contributed equally to this work. zhuolin mei school of computer and big data science jiujiang university 551 qianjin east road, jiujiang, jiangxi, 332005, china 6120153@jju.edu.cn junkang zhang school of computer and big data science jiujiang university no. 551, qianjin east road, jiujiang, jiangxi, 332005, china 6120144@jju.edu.cn anyuan deng* school of computer and big data science jiujiang university no. 551, qianjin east road, jiujiang, jiangxi, 332005, china dengay@jju.edu.cn *corresponding author: dengay@jju.edu.cn chenglang lu school of modern information technology zhejiang institute of mechanical and electrical engineering 528 binwen road, binjiang district, hangzhou, 310053, zhejiang, china luchenglang@zime.edu.cn https://doi.org/10.15837/ijccc.2022.2.4541 2 abstract integrating deep learning with traditional machine learning methods is an intriguing research direction. for example, pcanet and ldanet adopts principal component analysis (pca) and fisher linear discriminant analysis (lda) to learn convolutional kernels separately. it is not reasonable to adopt lda to learn filter kernels in each convolutional layer, local features of images from different classes may be similar, such as background areas. therefore, it is meaningful to adopt lda to learn filter kernels only when all the patches carry information from the whole image. however, to our knowledge, there are no existing works that study how to combine pca and lda to learn convolutional kernels to achieve the best performance. in this paper, we propose the convolutional coverage theory. furthermore, we propose the pldanet model which adopts pca and lda reasonably in different convolutional layers based on the coverage theory. the experimental study has shown the effectiveness of the proposed pldanet model. keywords: deep learning, principal component analysis, fisher principal component analysis, convolutional coverage; 1 introduction automatic image classification is an important task in many areas, such as face recognition [1, 15, 34], content-based image retrieval [2, 3, 29] and computer-aided medical images classification [4, 28, 30]. the performance of advanced automatic images classification models relys on good features abstracted from images. currently, deep convolutional neural networks, such as convnet [12, 13] and densenet [10] are widely used for abstracting features from images, which use the famous back-propagation algorithm to train all the parameters in the networks [20]. however, it lacks of intuitive interpretability, theoretical analysis and results instability. furthermore, the procedure of parameters training using back-propagation algorithm is time-consuming, and its accuracy highly relies on hype-parameters optimization [7, 27]. on the contrary, the traditional machine learning algorithms, such as principal components analysis (pca) [9] and linear dicriminant analysis (lda) [23] have ready-by theoretics, and can calculate perfect features of training data set directly. however, the time complexity of calculating eigen vectors in pca and lda is o(n3), where n is the dimention of input data. the high time complexity prevents them adopting in data sets with large dimention. for example, if the size of an image is 900pt∗700pt, then the dimention of the input data is 630000. therefore, some researchers consider to integrate convolutional neural networks with principal component analysis or linear discriminant analysis. chan et al. [7] propose pcanet and ldanet. pcanet leverages unsupervised pca to learn multi-stage filters, while ldanet incorporates supervised lda to learn convolutional kernels (or filters) for image classification. sun et al. [27] propose fisher pca (fpca) to learn convolutional kernels based on combination of lda and pca. however, to our knowledge, there are no existing works that study how to combine pca and lda to learn convolutional kernels to achieve the best performance. in these models, the labels of patches of images are the same as the labels of images when adopting lda in calculating each convolutional kernel. the patches of images with different categories may have similar local features. for example, the patches of backgroud areas of different images may be totally the same. adopting lda for calculating convolutional kernels may abstract unreasonable local features, and harm the classification performance. thus, although lda takes advantage of additional label information rather than pca, it is suitable for adopting lda to learn convolutional kernels only when the patches of an image carry information from the whole image. to our knowledge, there are no existing works that study how to combine pca and lda to learn convolutional kernels to achieve the best performance. to abstract better features of images, we propose an improved image classification model called pldanet, based on a reasonable combination way of pca and lda with deep convolutional neural networks. our main contributions in this article are summarized as follows. (1) the reasonable combination schema of pca and lda is studied when integrating pca and lda in calculating convolutional kernels. the combination schema is achieved based on a proposed convolutional coverage theory, which calculates the number of necessary convolutional layers to garuntee that that each local patch of the image covers information from the whole image. https://doi.org/10.15837/ijccc.2022.2.4541 3 (2) we propose a new model called pldanet model on the basis of the theory of convolutional coverage. pldanet adopts a specific number of pca convolutional layers before the subsequent lda convolutional layer. the specific number is calcuted according to the convolutional coverage theory, so that the lda convolutional layer takes advantage of labels information completely correctly. (3) we conduct an extensive experimental study to evaluate pldanet in different data sets. the experimental results show that the proposed pldanet have better performance on classification tasks. the remainder of this article is organized as follows. section 2 discusses the related work. section 3 describes our proposed convolutional coverage theory. section 4 describes our proposed pldanet model on the basis of the theory of convolutional coverage. in section 5, we report the performance evaluation of our techniques over five mnist variations. section 6 concludes this paper. 2 related work as big data containing images has been continuously generated from many areas[32, 33], automatic image features extraction and classification method is needed in many applications. pca produces the most distinctive subspace for representation by maximizing the variance ,of the extracted features and discarding noisy dimensions [9]. dpca model [16] performs a two-layer zero-phase component analysis (zca) whitening plus pca structure to learn, hierarchical features for face recognition. the whole feature representation of images is extracted by concatenating the representations from the two layers. to further improve the performance of traditional pca based image classification, researchers have proposed several versions of pca in the past decade. pereira el al. [22] proposes a modular pca algorithm that divides an image into smaller sub-images, which extracts local and global information of images aiming to reduce the effects caused by variable changes. liu et al. [17] combines the traditional pca and kernel-pca to outline the data into high dimension vector space with non-linear transformation. to achieve a better computational and accuracy rate, some studies combine pca with different techniques, such as delaunay triangulation [14], linear discriminant analysis [24], deep neural networks (dnn) [5] and convolutional neural network (cnn) [21]. machidon et al. [19] examine the integration of pca with delaunay triangulation. jing et al. [11] integrate feature extraction of color 2d-pca, and convolutional neural network into one dicision-level fusion. generalized discriminant analysis (gerda) [26] is a generalization of the classical linear discriminant analysis, which uses dnn to extract features. lu et al. [18] propose a new joint feature learning approach to automatically learn feature representations and stack a deep architecture to exploit hierarchical information. the weighted pca (wpca)[31] is used in joint feature learning to map the combined outputs of the first layer to a low-dimensional feature space for reducing the redundancy. a cnn architecture consists of multiple trainable stages stacked on top of each other, followed by a supervise classifier [25]. chan et al. [7] propose two deep model variations, pcanet and ldanet. pcanet integrates pca with cnn and can easily be employed to learn multistage filters. pcanet processes input images by cascaded pca, binary hashing [6], and block histograms. pcanet is an unsupervised deep learning baseline that mainly leverages pca to learn multi-stage filters. while ldanet is a supervised deep learning framework that mainly incorporates lda with cnn to learn filters. sun et al. [27] propose fisher pca (fpca) to learn each convolutional kernels based on mixing of pca and lda. these models adopt lda in calculating each convolutional kernel, and take the label of an image as that of all patches of the image. however, the patches of images with different categories may have similar local features. unreasonable local features may be learned by adopting lda in calculating each convolutional kernel and lead to imperfect performance. thus, it is suitable for adopting lda to learn convolutional kernels only when the patches of an image carry information from the whole image. as far as we know, we are the first to study how to combine pca and lda reasonably to learn convolutional filters. https://doi.org/10.15837/ijccc.2022.2.4541 4 3 the convolutional coverage in the lda convolutional layer, the labels of all patches of a image are the same as the label of the image. however, it is not reasonable to adopt lda to learn filter kernels for every convolutional layer, local features of images from different classes may be similar, such as background areas. it is meaningful to adopt lda to learn filter kernels only when all the patches carry information from the whole image. in this section, we propose a theory of convolutional coverage and analyze when a patch will carry information from the whole image. then a new convolutional network model based on the convolutional coverage theory will be introduced in section 4. first, the definition of the convolutional coverage is given in definition 1. the symbols in the paper are summarized in table 1. table 1: the summarization of symbols symbols description i ∈ rm×n an image i with m ∗ n pixels c[w, h] the convolutional coverage of the pixel [w, h] in i ci[w, h] the convolutional coverage of the pixel [w, h] in i in the i th convolutional layer [n] round a float point number to the next integer definition 1 (the convolutional coverage). given an image i ∈ rm×n and a pixel [w, h] in i, if k1 × k2 is the kernel size of the convolutional layer, then we say that c[w, h] = [wmin : wmax, hmin : hmax] is the convolutional coverage of the pixel [w, h] in i, where c[w, h].wmin = max{0, w − k1/2}, c[w, h].wmax = min{m − 1, w + k1/2}, c[w, h].hmin = max{0, h − k2/2}, c[w, h].hmax = min{n − 1, h + k1/2}. the convolutional coverage defines the range of the image carrying by a pixel after the convolutional operation. for example, given a image with size 28 × 28, if the kernel size is 7 × 7, then c[6, 6] = [3 : 9, 3 : 9] then the method of computing the convolutional coverage of a pixel of an image in the ith convolutional layer is given in theorem 1. theorem 1. suppose that i ∈ rm×n is an image, i i ∈ rm×n is the output in the ith convolutional layer of i, and the kernel size is k1 ×k2 in each convolutional layer. if ci[w, h] = [wmin : wmax, hmin : hmax] is the ith convolutional coverage of the pixel [w, h] in i, then wmin = max{0, w − i × k1/2}, wmax = min{m − 1, w + i × k1/2}, hmin = max{0, h − i × k2/2}, hmax = min{n − 1, h + i × k2/2}. proof. let i 1 be the output in the first convolutional layer, then we have, c1[w, h].wmin = max{0, w − k1/2}, c1[w, h].wmax = min{m − 1, w + k1/2}, c1[w, h].hmin = max{0, h − k2/2}, c1[w, h].hmax = min{n − 1, h + k1/2}. let i (i−1) ∈ rm×n be the output in the (i−1)th convolutional layer, and c(i−1)[w, h] = [wmin : wmax, hmin : https://doi.org/10.15837/ijccc.2022.2.4541 5 hmax] be the (i − 1)th convolutional coverage in i for the pixel [w, h], then we have, ci[w, h].wmin = max{0, c (i−1) [w−k1/2, h−k2/2].wmin} = max{0, c(i−j)[w−j×k1/2, h−j×k2/2].wmin} = max{0, c1[(w−(i−1)×k1/2, h−(i−1))×k2/2].wmin} = max{0, w − (i − 1) × k1/2 − k1/2} = max{0, w − i × k1/2} ci(w, h).wmax = min{m − 1, c(i−1)(w + k1/2, h + k2/2).wmax} = min{m − 1, c(i−j)(w + j × k1/2, h + j × k2/2).wmax} = min{m − 1, c1(w + (i − 1) × k1/2, h + (i − 1) × k2/2).wmax} = min{m − 1, w + (i − 1) × k1/2 + k1/2} = min{m − 1, w + i × k1/2} similarly, ci(w, h).hmin = max{0, h − i × k2/2}, ci(w, h).hmax = min{n − 1, h + i × k2/2} example 1. given an image with size 28×28, if the kernel size in each convolutional layer is 7×7, then c1[6, 6] = [3 : 9, 3 : 9] c2[6, 6] = [0 : 12, 0 : 12] next we give the theorem 2 to obtain the number of the convolutional layer that each patch of the image carries information from the whole image. theorem 2. given an image i ∈ rm×n, and the kernel size is k1×k2 in each convolutional layer, after g =[ max{(m − k1)/(k1/2), (n − k2)/(k2/2)}] convoluational layers, each patch carries information from the whole image. proof. if the left upper patch [0 : (k1−1)), 0 : (k2−1))] covers information from the whole image, then each patch does. if the pixel [(k1 − 1), (k2 − 1)] covers information from the pixel [(m − 1), (n − 1)], then the left upper patch covers information from the whole image. based on theorem 1, cg[(k1−1), (k2−1)].wmax = min{(m − 1), k1 − 1 + g ∗ k1/2} when k1 − 1 + g ∗ k1/2 = m − 1, we have g = (m − k1)/(k1/2) similarly, cg[(k1−1), (k2−1)].hmax = min{(n − 1), k2 − 1 + g ∗ k2/2} when k2 − 1 + g ∗ k2/2 = n − 1, we have g = (n − k2)/(k2/2) since g have to be an interger, we round up the value to the greater one of (m − k1)/(k1/2) and (n − k2)/(k2/2). therefore, after g =[ max{(m−k1)/(k1/2), (n−k2)/(k2/2)}] convoluational layers, each patch carries information from the whole image. example 2. given an image with size 28×28, if the kernel size in each convolutional layer is 7×7, then g =[ (28 − 7)/3] = 7. 4 the pldanet model the convolutional coverage theory calculates the necessary number of the convolutional layers, which is important for designing reasonable combination of pca and lda convolutional networks. section 4.1 gives the definitions of pca convolutional layer and lda convolutional layer. section 4.2 introduces the proposed pldanet model. https://doi.org/10.15837/ijccc.2022.2.4541 6 4.1 definitions pca extracts main features to maximum the variance of data, which is an unsupervised learning method. definition 2 defines the pca convolutional layer. definition 2 (pca convolutional layer). a convolutional layer is called pca convolutional layer if the filter kernel parameters are learned by pca. let {ii ∈ rm×n×c}ni=1 be the input images of the pca convolutional layer, where n is the number of input images, m × n × c is the size of each image ii. a patch with the filter kernel size k1 × k2 is taken around each pixel of each image. let {xi}ni=1 ∈ rk1k2×cmnn be the patch set. the filter kernel parameters in a pca convolutional layer are learned by pca on the patch set. each eigen vector calculated by pca corresponds to a filter kernel. lda extracts discriminative features to maximum the variance of data in different classes and minimum the variance of data in the same class, which is a supervised learning method. definition 2 defines the lda convolutional layer. definition 3 (lda convolutional layer). a convolutional layer is called lda convolutional layer if the filter kernel parameters are learned by lda. the filter kernel parameters in a lda convolutional layer is learned by lda on the patch set, and the labels of all patch of an image are the same as the label of the image. each eigen vector calculated by lda corresponds to a filter kernel. 4.2 the model architecture figure 1 shows the detailed diagram of the proposed pldanet model. first, we evaluate the number of pca convolutional layers in the pldanet model. g = max{(m − k1)/(k1/2), (n − k2)/(k2/2)}, where (m, n) is the size of the input image, and (k1, k2) is the size of each filter kernel. then the deep pldanet model is constructed by the number of g pca convolutional layers and a subsequent lda convolutional layer. then the hashing, histograms and the linear svm stages are the same as the pcanet. the training process of pldanet for classification is demonstrated in algorithm 1. figure 1: the detailed diagram of the proposed pldanet model example 3. given an image with size 28×28, if the kernel size in each convolutional layer is 7×7, then g =[ (28 − 7)/3] = 7. in the pldanet model, the convolutional network includes 7 pca convolutional layers and a subsequent lda convolutional layer. 5 experiments since the training time of pcanet and ldanet is much less than that of cnn, and the superior performance of pcanet and ldanet over cnn has been studied in [7, 27], the comparison of our proposed pldanet and cnn has not been conducted in our work. in this section, we compare pldanet with pcanet and ldanet. we conduct experiments on mnist and its four variations. the mnist dataset is one of the most popular datasets in image classification. it contains 60,000 training and 10,000 test images https://doi.org/10.15837/ijccc.2022.2.4541 7 algorithm 1 the training process of pldanet for image classification input: (1) the training image set {(ii, yi)}ni=1; (2) ii ∈ rm×n; (3) the size of filter kernel k1, k2; output: the results of classification. 1: (m, n) ← the size of the input image 2: g = max{(m − k1)/(k1/2), (n − k2)/(k2/2)} 3: for each l = 1 : g do 4: i li ← p ca_convolutional_layer(ii), i = 1, 2, . . . , n 5: end for 6: i (g+1) i ← lda_convolutional_layer(ii), i = 1, 2, . . . , n 7: hashing and histograms stage: take i (g+1) as input; 8: train linear svm: take histograms codes as input; 9: return the results of image classification. of 10 hand-written digits. mnist variations [8] introduce addition of rotation (rot), addition of a background composed of random pixels (bg-rand) or patches extracted from a set of images (bg-img), or combinations of these factors (rot-bg-img) to mnist digit classification. these variations make the problem become particularly challenging. the five classification datasets are summarized in table 2. the size of all the images are 28 × 28. an instance of mnist variations is shown in figure 2. table 2: details of the 5 classification tasks on mnist variations. data sets description train-valid-test basic smaller subset of mnist 10,000-2000-10,000 rot mnist with rotation 10,000-2000-50,000 bg-rand mnist with noise background 10,000-2000-50,000 bg-img mnist with image background 10,000-2000-50,000 bg-img-rot mnist with rotation and image background 10000-2000-50000 (a) rot (b) bg-rand (c) bg-img (d) bg-img-rot figure 2: the number eight in the mnist variations to evaluate the performance of pca convolutional layer and lda convolutional layer in different convolutional layers, we compare pcanet-i, pldanet-i and pldanet2-i, where pcanet-i means there are number of i pca convolutional layers, pldanet-i means there are pca convolutional layers in the first (i-1) layers and lda convolutional layer in the last layer, and pldanet2-i means there are pca convolutional layers in the first 6 layers and lda convolutional layer in the rest layers. limited to the memory size in our experimental environment, the hyperparameters of pcanet-i and pldanet-i are set as follows: (1) the number of filters are l1 = li = 8, lj = 1, 1 < j < i; (2) the patch size is k1 = k2 = 7 in all convolutional layers; (3) the block size for local histograms in the https://doi.org/10.15837/ijccc.2022.2.4541 8 output layer is set to be 7 × 7, and the block overlap ratio is 0.5. figure 3: comparison of pcanet-i and pldanet-i on basic mnist figure 4: comparison of pcanet-i and pldanet-i on rot mnist we compare pcanet-i and pldanet-i on five mnist variations when varying i from 6 to 9. figure 3 shows the accuracy rates of pcanet-i and pldanet-i on the basic mnist. figure 4 shows the accuracy rates of pcanet-i and pldanet-i on the rot mnist. figure 5 shows the accuracy rates of pcanet-i and pldanet-i on the bg-rand mnist. figure 6 shows the accuracy rates of pcanet-i and pldanet-i on the bg-img mnist. figure 7 shows the accuracy rates of pcanet-i and pldanet-i on the bg-img-rot mnist. figure 5: comparison of pcanet-i and pldanet-i on bg-rand mnist figure 6: comparison of pcanet-i and pldanet-i on bg-img mnist the experimental results show that (1) pldanet-6 have the same accuracy rates as pcanet-6 on basic mnist and bg-img mnist, and have lower accuracy rates than pcanet-6 on bg-rand, rot mnist and rot-bg-img mnist; (2) pldanet-7 have higher accuracy rates than pcanet-7 on bg-img mnist, bg-rand, rot mnist and rot-bg-img mnist, and have the same accuracy rate as pcanet-6 on the basic mnist; (3) pldanet-i (i=8, 9) have higher accuracy rates than pcanet-i (i=8, 9) on the five mnist variations. based on theorem 2, g = (m−k)/(k/2) = (28−7)/3 = 7. therefore, each patch of the image will cover information from the whole image after 7 convolutional layers. however, in the 6th convolutional layer and 7th convolutional layer, some but not all pathes carry information from the whole image, and lda convolutional layer have better or worse performance than pca convolutional layer. in the 8th convolutional layer and 9th convolutional layer, all patches carry information from the whole image, and lda convolutional layer have better performance than the pca convolutional layer. we compare pcanet-i, pldanet-i and pldanet2-i on five mnist variations when varying i from 8 to 9. figure 8 shows the accuracy rates of pcanet-i, pldanet-i and pldanet2-i on the basic mnist. figure 9 shows the accuracy rates of pcanet-i, pldanet-i and pldanet2-i on the https://doi.org/10.15837/ijccc.2022.2.4541 9 figure 7: comparison of pcanet-i and pldanet-i on bg-img-rot mnist figure 8: comparison of pcanet-i, pldanet-i and pldanet2-i on basic mnist rot mnist. figure 10 shows the accuracy rates of pcanet-i, pldanet-i and pldanet2-i on the bg-rand mnist. figure 11 shows the accuracy rates of pcanet-i, pldanet-i and pldanet2-i on the bg-img mnist. figure 12 shows the accuracy rates of pcanet-i, pldanet-i and pldanet2-i on the bg-img-rot mnist. figure 9: comparison of pcanet-i, pldanet-i and pldanet2-i on rot mnist figure 10: comparison of pcanet-i, pldanet-i and pldanet2-i on bg-rand mnist the experimental results show that pldanet2-i (i=8, 9) have higher accuracy rates than pcaneti and pldanet-i on all the five mnist variations. since pldanet-7 have higher or the same accuracy rates compared to pcanet-7 on all the five mnist variations, lda convolutional layer performs better than pca convolutional layer in the jth (j ≥ 7) convolutional layer. pldanet2-9 performs lda convolutional layer during 7th, 8th and 9th convolutional layer. the phenomenon proves our initial idea that adopting lda convolutional layer is greatly beneficial for extracting features when each patch of the image covers information from the whole image. 6 conclusion adopting pca in learning convolutional kernels takes much less time than training a cnn, and avoids the laborious fine-tuning process. the supervision of the learned kernels can be further enhanced by incorporating the labels information based on lda. however, it is meaningful to assign class labels to the patches and adopt lda only when all the patches carry information from the whole image. in this paper, we propose a convolutional coverage theory, which indicate that all patches https://doi.org/10.15837/ijccc.2022.2.4541 10 figure 11: comparison of pcanet-i, pldanet-i and pldanet2-i on bg-img mnist figure 12: comparison of pcanet-i, pldaneti and pldanet2-i on bg-img-rot mnist variations cover information from whole images after a specific number of convolutional layers. based on the convolutional coverage theory, we propose pldanet model, which includes a specific number of pca convolutional layers and a subsequent lda convolutional layer. benefiting from the reasonable combination of pca and lda, pldanet enhances the representation ability of pcanet and ldanet. finally, a variety of experiments are conducted to demonstrate the effectiveness of the proposed pldanet. funding this work was founded by the national natural science foundation of china (no. 61962029); the natural science foundation of jiangxi province (no. 20202acbl202005 and no. 20202bab212006); moe (ministry of education in china), youth fund project of humanities and social sciences research (no.21yjczh096); science and technology fundation of jiangxi educational committee (no. gjj201832). author contributions caicai zhang and mei mei contributed significantly to the conception of the study and wrote the manuscript; zhuolin mei contributed to analysis. junkang zhang performed the experiment; anyuan deng and chenglang lu helped perform the analysis with constructive discussions. conflict of interest the authors declare no conflict of interest. references [1] ahmed, s.b.; ali, s.f.; ahmad, j.; adnan, m.; and fraz, m.m. 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(2018). face recognition algorithm based on wavelet transform and local linear embedding. cluster computing, 22, 1529–1540, 2018. https://doi.org/10.15837/ijccc.2022.2.4541 13 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: zhang c.c.; mei, m.; mei, z.l.; zhang, j.k.; deng a.y.; lu, c.l. (2022). pldanet: reasonable combination of pca and lda convolutional networks, international journal of computers communications & control, 17(2), 4541, 2022. https://doi.org/10.15837/ijccc.2022.2.4541 introduction related work the convolutional coverage the pldanet model definitions the model architecture experiments conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 3, month: june, year: 2022 article number: 4697, https://doi.org/10.15837/ijccc.2022.3.4697 ccc publications covid-19 patients’ hospital occupancy prediction during the recent omicron wave via some recurrent deep learning architectures h. bouhamed, m. hamdi, r. gargouri heni bouhamed* advanced technologies for image and signal processing unit (atisp) sfax university, tunisia *corresponding author: heni.bouhamed@fsegs.usf.tn monia hamdi department of information technology, college of computer and information sciences, princess nourah bint abdulrahman university, p.o.box 84428, riyadh 11671, saudi arabia mshamdi@pnu.edu.sa rahma gargouri pneumology department, hedi chaker hospital, sfax, tunisia gargouri.bouhamed.rahma@gmail.com abstract this paper described a suggested model to predict bed occupancy for covid-19 patients by country during the rapid spread of the omicron variant. this model can be used to make decisions on the introduction or alleviation of restrictive measures and on the prediction of oxygen and health human resource requirements. to predict covid-19 hospital occupancy, we tested some recurrent deep learning architectures. to train the model, we referred to covid-19 hospital occupancy data from 15 countries whose curves started their regressions during january 2022. the studied period covers the month of december 2021 and the beginning of january 2022, which represents the period of strong contagion of the omicron variant around the world. the evolution sequences of hospital occupancy, vaccination percentages and median ages of populations were used to train our model. the results are very promising which could help to better manage the current pandemic peak. keywords: prediction, covid-19 hospital occupancy levels, deep learning, gru, vaccination. 1 introduction the coronavirus has had a huge impact on health systems and the global economy, so far and the world health organization has officially labeled covid-19 a pandemic since march 2020. by the end of december 2021, the number of people diagnosed with covid-19 has surpassed 340 million worldwide, putting hospitals and health systems in general in a bind. indeed, the pandemic is placing a burden on clinical bed capacity with oxygen sources and intensive care units that has far exceeded https://doi.org/10.15837/ijccc.2022.3.4697 2 available capabilities over the past two years. the chaotic situation in lombardy, italy in march 2020 tragically demonstrated the impact of the lack of health care capacity (rosenbaum, 2020), and the need to manage hospital bed capacity as best as possible. the authors in (phua et al., 2020, bekker et al., 2022) called on practitioners, hospital administrators, governments, and policy makers to quickly prepare for a substantial increase in oxygen bed capacity, especially with the virus variants that further complicated the situation. their recommendations include hospital capacity and staffing. with the emergence of the omicron variant in november 2021, being reported as extremely rapid contagious (le page, 2021), it has become strongly recommended to plan for increased capacity due to the rapid increase in patients in a relatively short time compared to other waves. another less studied but even more important issue is the impact on other types of care that are delayed due to covid-19 patients occupying many beds and using various forms of capacity that would normally be used for non-covid care. a 2020 study of the impact of the first wave resulted in an estimated loss of 400,000 healthy life years in the netherlands (gupta strategists, 2020). the objective of this work was to develop a predictive model that helps decision makers in any country to reserve the right number of beds, adequate human resources and quantities of oxygen needed for covid-19 patients without deteriorating the quality of service for other patients. many studies have tackled this issue over the last two years (bouhamed 2020a, bouhamed 2020b, bouhamed 2020c, findel and suarez 2021, caro et al 2021, bekker et al 2022). they have focused mainly on the prediction of the number of cases, cures, deaths and even the use of health resources (caro et al., 2021) according to the contagion rate and the old covid-19 strains severity of the old covid-19 strains. in this work we proposed to develop a model to predict the evolution of the use of hospital beds by covid-19 patients according to the high rate of contagion of the omicron variant (le page 2021) taking into consideration the massive vaccination campaign initiated in most countries and the doubts about the dangerousness of the omicron variant in comparison with the old strains. several studies invoke the supremacy of deep neural networks (dnns) that exceed the performance of classical machine learning formalisms in various applications (bouhamed and ruichek, 2018; hinton et al., 2012; mohamed et al., 2012; ciresan et al., 2010; yu et al., 2011). deep learning, considered part of the big data domain, is a set of machine learning methods for modeling high abstraction level data. it is based on articulated architectures of various transformations in nonlinear space (bouhamed and ruichek, 2018; bengio, 2009). the current interest in deep learning stems from both its conceptual advances and its technological advances allowing to run learning processes in parallel. indeed, all available solutions can exploit the huge potential of computational power, provided by current modern computers in single or multi-use (cluster of machines), by soliciting the main processor(s) (cpu) and dedicated graphics processors (gpu) (bouhamed and ruichek, 2018). a big data model can adapt to a huge volume of data and a huge amount of sequential processing beyond the capabilities of the most powerful servers (bouhamed and ruichek, 2018; zikopoulos and eaton, 2011). since prediction, in our context, depends on observations obtained at previous timings, our scope was more about temporal sequence prediction. sequence prediction poses different problems form the other types of supervised learning. the sequence imposes an order on the observations that must be preserved when training models and predictions. in general, prediction problems that involve sequence data are called sequence prediction problems, despite the existence of several other problems that differ based on input and output sequences. many artificial recurrent neural network (rnn) architectures (brownlee, 2017; hochreiter and schmidhuber, 1997) are used in the field of deep learning, including the simple recurrent neural network (srnn), long-term memory (lstm), and the grid recurrent unit (gru). unlike standard feedforward neural networks, the srnn has feedback connections. it is capable of processing not only single data points, but also entire sequences of data (such as speech or video). for example, the rnn is applicable to tasks such as non-segmented and connected handwriting recognition (graves et al., 2009), speech recognition (xiangang and xihong, 2014) and anomaly detection in network traffic or intrusion detection systems. therefore, we proposed to test these three rnn architectures (srnn, lstm, gru) for this study. the rest of this paper is organized as follows: in section 2, we introduced the rnns and their three main architectures. in section 3, we detailed the data as well as the model features and hyperhttps://doi.org/10.15837/ijccc.2022.3.4697 3 parameters. in section 4, we proceeded to the methodological and epidemiological experimentation and discussion. finally, in the last section, we drew our conclusions and suggested some perspectives. 2 rnn, srnn, lstm and gru the rnn is an artificial neural network with recurrent connections (figure 1). it consists of interconnected neuronal units that interact in a non-linear way having at least, one cycle in the structure. the neuronal units are connected by arcs with weights. the output of a neuron is a non-linear combination of its inputs. the simplest architecture is noted by srnn; it is simple and adapted to input data of different sizes. the network learning techniques are the same as for the classical neural networks, exploiting gradient backpropagation for learning. nevertheless, the srnns can be plagued by the gradient fading problem when they try to keep too many past elements in memory. other architectures deal with this problem, notably the gru and the lstm. figure 1: rnn the lstm is a widely used architecture to deal with the problem of gradient vanishing. this architecture was proposed by hochreiter and schmidhuber (hochreiter and schmidhuber, 1997). the basic idea underlying this architecture is that each computational unit is linked not only to a hidden state h, but also to a state c of the cell which acts as a memory. the transition results in the transfer of a constant gain equal to 1, so that errors propagate to previous steps, up to 1000 steps in the past, without any instance of gradient vanishing (gers et al., 2002). grus, which were introduced in 2014 by cho et al. (cho et al., 2014), have a simpler architecture than the lstm. they have a much smaller number of parameters, and therefore can train faster with a constant hidden layer size. 3 data and methods this is a multicenter study on the evolution of the number of hospitalized covid-19 patients for 15 countries (table 1) from december 01, 2021 to january 27, 2022 (supplementary material named data19012022bis.txt present the evolution of the number of hospitalized covid-19 patients during 58 day for each of the 15 countries studies depicted on our world in data1). the study focused on this period because of the prevalence of omicron in these countries from december 2021 (world health organization2). as for the countries, their choice was not arbitrary but rather justified by the fact that 1our world in data (owid) is a scientific online publication that focuses on large global problems such as poverty, disease, hunger, climate change, war, existential risks, and inequality. web site: https://ourworldindata.org/ 2the world health organization (who) is a specialized agency of the united nations responsible for international public health. web site: https://www.who.int/ https://doi.org/10.15837/ijccc.2022.3.4697 4 the curve peak of the number of hospitalized patients has already been exceeded for these 15 countries. this will allow our model to approximately predict the descent slope of this peak for the remainder of the countries in full omicron wave. owing to the information on the use of hospital beds during this period, we also introduced two other variables into the learning base, namely the vaccination rate and the national median age. the choice of the variables was inspired by data from the literature during the old virus variants waves and by the preliminary data related to the omicron variant: the median age of hospitalized patients seems to be among the parameters that are likely to vary with waves and variants. for the original alpha variant, hospitalizations involved people above 65 years (world health organization, 2021) but decreased with the delta variant. the outbreak in india proved that viral mutations cause increased pathogenicity in young, apparently even in healthy people. shocking us data showed an increasing hospitalization and death rates among the young. as time progresses, the advent of covid vaccination and its relative efficacy skews studies regarding the impact of age on the severity and morbidity of the infection. indeed, in the beginning, access to vaccines was limited to the oldest people and those with chronic diseases. and even after the generalization of vaccination, the young population was more reluctant about this issue because of doubts about the safety and efficacy of vaccines. the result of such behavior was an increase in the number of covid19 infections among unvaccinated young people, with sometimes a severe disease, especially for the delta variant. currently, with the mandatory health passport worldwide, vaccination of the young has become more common. this has coincided with the appearance of the new omicron variant. the result is currently in favor of mild forms that affect more such young people. in the same perspective, vaccination has not yet included the pediatric population, which is apparently the most vulnerable to the omicron variant. the question that needs to be addressed is on the effect of vaccination, or the extent of the virus virulence in a specific age group due to new mutations (world health organization, 2021). table 1: countries included in our study number of people median age vaccination covid-19 hospital bed (million) rate at occupancy at the the beginning beginning of 2022 per of 2022 million population croatia 4.04 42.6 52.83 417 cyprus 1.2 37.3 67.84 169 luxembourg 0.63 40.4 68 110 usa 329.5 38 61.93 292 united kingdom 67.22 40.5 69.79 210 lithuania 2.79 45.1 68.36 435 bulgaria 6.92 44.6 28 592 czech republic 10.7 42.5 62.43 300 malaysia 32.37 30.26 78.24 81 hungary 9.75 43.3 62.34 358 netherlands 17.44 43.8 71.32 68 norway 5.37 39.8 71.99 58 poland 37.95 41.7 55.97 511 slovakia 5.45 42.7 44.64 388 switzerland 8.63 43.5 67.11 184 the three most popular rnn architectures namely srnn, lstm and gru were tested. the experimental codes were written with the python language. the tensorflow and keras packages were mainly used for the deep learning (lstm, srnn, gru) and the matplotlib package for visualizing the results. all the codes, including the database itself, are available on github3. k-fold crossvalidation was used to evaluate the model. the learning process was repeated 10 times under the three architectures lstm, gru, and srnn (table 2). for the optimization of the model hyperparameters, we first tested the use of layers in a bottom-up manner (one layer then two etc.), the 3https://github.com/henibouhamed/covid-19-omicron https://doi.org/10.15837/ijccc.2022.3.4697 5 results of the cross-validation and mainly the average r2 score (pearson’s linear coefficient of determination) continuously evolved up to 4 layers before stagnating and this is the reason why we adopted four layers for the model parameterization. a dropout was used after each layer to avoid overfitting. we also tested the four most used activation functions, namely the relu, sigmoid and tangent, and the results in terms of average r2 score were largely favorable to the last. in order to validate the model, we initiated a large forecast of the occupancy of hospital beds by covid19 patients for two countries, tunisia and france between january 7 and february 23 for tunisia and between the above-mentioned dates for france during this period (period when the true occupancy values for these two countries are known), on the one hand, and detect the approximate regression date of the covid-19 hospital occupancy, on the other. we extended our investigations in order to evaluate the impact of vaccination on the overall evolution of hospital occupancy for these two countries. for tunisia, which has a vaccination rate against covid-19 of around 50%, we predicted the hospital occupancy if it had the same vaccination rate as france (around 77%). for france we reversed the case; i.e. we have made the forecast of the hospital occupancy if it had the same rate of vaccination as tunisia (around 50%). 4 results and discussion in this context, many studies have tackled this issue over the last two years (bouhamed 2020c, findel and suarez 2021, caro et al 2021, bekker et al 2022). they have focused mainly on the prediction of the number of cases, cures, deaths and even the use of health resources (caro et al., 2021) according to the contagion rate and the old covid-19 strains severity of the old covid-19 strains. however, our model predict the evolution of the use of hospital beds by covid-19 patients according to the high rate of contagion of the omicron variant (le page 2021) taking into consideration the massive vaccination campaign initiated in most countries and the doubts about the dangerousness of the omicron variant in comparison with the old strains. to our knowledge, our work is the only one to propose this conduct. the test results of our model are very promising despite the short time period that generated the data. the average r2 score of the results of the 10 cross-validation iterations is very close to 1 with the gru architecture (table 2). the predictions of the test data for each iteration and the true values are very close (figure 2). during training, the loss function that evaluates the deviation between the predictions made by the model and the true values of the observations used during training approaches 0 quickly in less than 400 epochs (figure 3). table 2: r2 average scores of our model for 10 iterations of training architecture average lstm r2 0.979 gru r2 0.996 srnn r2 0.959 the prediction curve shape is very compact with that of the true values over the study period for tunisia and france (figures 4 and 5). our model predicts the beginning of the regression of the use of hospital beds for covid-19 patients around february 18 for tunisia and february 27 for france. figure 6 shows and compares the evolution of hospital occupancy for tunisia if it had the vaccination rate of france, i.e. 77% instead of 50%. figure 7 shows and compares the evolution of hospital occupancy for france if it had the vaccination rate of tunisia, i.e. 50% instead of 77%. in general, and regardless of the country, age, type of vaccine, number of doses received, and the state of herd immunity, the impact of vaccination is evident. this observation was verified through this comparison of tunisia, which is a low-vaccination country but with considerable herd immunity due to lack of resources and adherence to barrier measures, and france, which is a well-vaccinated country. moreover, the model prediction accuracy on hospitalizations for france is the best witness: theoretically, in tunisia, vaccination, in particular by the doses of immune reinforcement targeted the old people, with multiplicity of the types of received vaccines, whereas collective immunity concerns the young people, socially active workers, https://doi.org/10.15837/ijccc.2022.3.4697 6 figure 2: test data predictions for the first two cross-validation iterations with the true values in red and respective predicted one in blue with the coexistence of the two variants delta and omicron. in france, vaccination is always more advanced among the elderly and also concerns young people and even children under 18. so this proves that finally, the vaccination rate allows predicting the fate of each country in terms of severe forms, whatever the age, viral variant, and the other interfering factors are. figure 3: evolution of the loss function (of train data in yellow and of test data in blue) with gru, lstm and srnn architectures figure 4: tunisia covid19 hospitalization prediction and real sub-values (07 to 18 january 2022) 5 conclusion and perspectives we have developed a model to predict bed occupancy of covid-19 patients by country during the rapid spread period of the omicron variant. this model can be used to make decisions, first, on the introduction or alleviation of restrictive measures and, second, the prediction of oxygen and health human resources needs without deteriorating the quality of service for other patients. we tested some https://doi.org/10.15837/ijccc.2022.3.4697 7 figure 5: france covid19 hospitalization prediction and real sub-values (07 to 18 janary 2022) figure 6: tunisia hospitalization prediction: real vaccination rate vs 77% vaccination rate (france vaccination rate) recurrent deep learning architectures. for the model training, we relied on hospitalization data from 15 countries whose curves started regressing. the studied period covers the month of december 2021 and the beginning of january 2022 which represents the period of strong contagion of the omicron variant in the world. the sequences of changes in hospital occupancy, vaccination rates and median ages of the populations were used to train our model. the results were very interesting: first and foremost, they allowed us to predict the dates of the approximate descent of bed occupancy, and second, they enabled us to feel the positive impact of vaccination on the covid-19 hospital occupancy in france and tunisia which could help better manage the pandemic peak caused by the omicron variant. in the future, we plan to develop a real-time data streaming application that will allow us to retrieve figure 7: france hospitalization prediction: real vaccination rate vs 50% vaccination rate (tunisia vaccination rate) https://doi.org/10.15837/ijccc.2022.3.4697 8 data and automatically integrate them into the learning base in order to make the model evolve according to the latest developments. acknowledgment this research was funded by princess nourah bint abdulrahman university 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(2022). covid-19 patients’ hospital occupancy prediction during the recent omicron wave via some recurrent deep learning architectures, international journal of computers communications & control, 17(3), 4697, 2022. https://doi.org/10.15837/ijccc.2022.3.4697 introduction rnn, srnn, lstm and gru data and methods results and discussion conclusion and perspectives international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 3, month: june, year: 2021 article number: 4180, https://doi.org/10.15837/ijccc.2021.3.4180 ccc publications screening key indicators for acute kidney injury prediction using machine learning jiaming wang , bing zhu, pei liu, ruiqi jia, lijing jia, wei chen, cong feng, jing li jiaming wang, bing zhu, pei liu, ruiqi jia, jing li* school of economics and management beijing jiaotong university, beijing *corresponding author: jingli@bjtu.edu.cn lijing jia, cong feng* department of emergency the first medical center to chinese people’s liberation army general hospital, beijing *corresponding author: fc194@163.com wei chen department of emergency the third medical center to chinese people’s liberation army general hospital, beijing abstract acute kidney injury is a common critical disease with a high mortality. the large number of indicators in aki patients makes it difficult for clinicians to quickly and accurately determine the patient’s condition. this study used machine learning methods to filter key indicators and use key indicator data to achieve advance prediction of aki so that a small number of indicators could be measured to reliably predict aki and provide auxiliary decision support for clinical staff. sequential forward selection based on feature importance calculated by xgboost was used to screen out 17 key indicators. three machine learning algorithms were used to make predictions, namely, logistic regression (lr), decision tree, and xgboost. to verify the validity of the method, data were extracted from the mimic iii database and the eicu-crd database for 1,009 and 1,327 aki patients, respectively. the mimic iii database was used for internal validation, and the eicu-crd database was used for external validation. for all three machine learning algorithms, the prediction performance from using only the key indicator dataset was very close to that from using the full dataset. the xgboost algorithm performed the best, and lr was the next best. the decision tree performed the worst. the key indicator screening method proposed in this study can achieve a good predictive performance while streamlining the number of indicators. keywords: acute kidney injury, key indicator screening, machine learning, sequential forward selection, xgboost. https://doi.org/10.15837/ijccc.2021.3.4180 2 1 introduction acute kidney injury (aki) is a common critical illness [1, 2, 3]. according to kdigo (kidney disease: improving global outcomes), aki is defined as the fulfillment of any of the following criteria [4]: • an increase in serum creatinine by > 0.3 mg/dl (> 26.5 µ mol/l) within 48 h, or • an increase in serum creatinine to > 1.5 times baseline within the previous 7 days, or • urine volume 6 0.5 ml/kg/h for 6 h. aki has acute morbidity and a high mortality rate [5, 6]. it occurs in at least 5% of hospitalized patients [7] and has many serious consequences, including prolonged icu and hospital stays, development of chronic kidney disease, and shortand long-term increased risk of death [8]. therefore, early prediction of aki and subsequent treatment are essential subjects of research. artificial intelligence and the establishment of diagnosis and treatment databases have combined to enable technical and data support for the development and implementation of machine learning methods based on condition prediction algorithms. scholars have also achieved some success in applying machine learning methods for early warning prediction of aki. thottakkara et al. [9] evaluated logistic regression (lr), generalized additive model (gam), naive bayes, and support vector machine techniques for predicting the performance of postoperative sepsis and aki and showed that lr, gam, and svm had better performance than naive bayes, and feature extraction using principal component analysis improved the predictive performance of all models. kate et al. [10] developed a machine learning approach based on four models—lr, svm, decision tree, and naive bayes—and their ensembles for aki prediction. their results showed that lr had the best predictive performance (auc = 0.743). in a recent study of key indicators and subsequent state prediction in emergency patients, coster et al. [11], using statistical analysis of six-month patient reports, presented key indicators associated with survival within seven days in emergency scenarios in which the patient had a serious injury. mccoy et al. [12] used machine learning algorithms to assign a risk score for sepsis patients with high mortality rates to provide decision support for early intervention against the condition and constructed a patient index system through manual screening. levin et al. [13] addressed the shortcomings of the standard of care and risk classification of patients in the u.s. emergency field, which relies heavily on subjective empirical methods, and used a random forest algorithm to predict patient outcomes based on important historical treatment data. they obtained higher accuracy than subjective methods. hohl et al. [14] adopted a prospective research method and combined it with patient indicators that they collected to establish two sets of pharmacist-led drug treatment decision rules for emergency patients. they reported high diagnostic accuracy in practical use. recent studies have shown that machine learning has provided new ideas for early prediction studies of aki. for outcome prediction and early risk warning studies related to emergency patients, machine learning has been more effective than traditional methods. however, most of the current methods for aki prediction are based on full datasets. in the field of emergency care, there are often patients with serious conditions that require urgent treatment and quick diagnostic decisions by medical personnel. at the same time, the variety of physiological and laboratory indicators for patients is very large, and it takes a lot of time to collect samples and obtain the values of the indicators. while waiting, the patient’s condition may deteriorate. the length of time patients wait for the results of the indicators impacts the timeliness of diagnosis and treatment. when the number of indicators is small, it is easy for physicians to screen key indicators by subjective methods, but when the number of physiological and laboratory indicators is large, physicians relying only on subjective methods to make decisions may make biased conclusions. as such, simultaneously meeting the requirements of speed and accuracy is difficult. this study addresses this problem by proposing a machine learning based method to screen out key indicators and generate predictions by collecting only a small number of key indicators, thus assisting clinicians in making fast and accurate judgments about patients’ conditions. this study uses extreme gradient boosting (xgboost) and sequential forward selection (sfs) to extract key indicators. the extracted key indicators are used in three algorithms: lr, decision https://doi.org/10.15837/ijccc.2021.3.4180 3 tree, and xgboost to assess their relative performance. they are also validated on the full dataset to compare and analyze the effectiveness of the key indicators’ extraction. 2 methods 2.1 data collection and processing the data sources of this study are the mimic iii database (medical information mart for intensive care iii) and eicu-crd database (eicu collaborative research database). mimic iii is a publicly available database developed by the massachusetts institute of technology computational physiology laboratory. the database contains data related to >6,000 hospitalizations between 2001 and 2012, including demographics, vital signs, laboratory tests, drugs, and other data. the eicu-crd database is a large public database created by the philips group and the massachusetts institute of technology computational physiology experiment. it contains a large amount of high-quality clinical information related to admissions to the intensive care unit in 2014 and 2015. data were extracted from the mimic iii database and the eicu-crd database according to the inclusion and exclusion criteria given by doctors. as the number of people in the non-aki group was higher than that in the aki group, we used equalization, that is, the number of individuals in the selected control group was similar to the number in the experimental group [15, 16] . from the mimic iii database, 1,009 people each in the non-aki and aki group were extracted, with a total of 47 indicators. from the eicu-crd database, 1,327 people each in the non-aki and aki group were extracted, with a total of 41 indicators. the sample set extraction process is shown in fig. 1. (a) (b) figure 1: the sample set extraction process of (a) mimic iii database and (b) eicu-crd database. owing to the differences in the time of data collection and the specificity of each patient’s condition, there were a large number of missing values in the data. the data parts wherein the proportion of missing values was too high were eliminated. finally, 39 indicators were selected from the mimic iii database, and 25 indicators were selected from the eicu-crd database. taking the data extracted from the mimic iii database as an example, the percentage of missing data is visualized in appendix fig. 1. with the development of the patient’s state and condition, various indicators also change dynamically. in this case, if the mean, median, and mode are used to fill in missing values, a large number of data entries will be the same, which will not accurately reflect the dynamic development of the patient’s physical state. in this study, we assumed that patient indicators varied linearly. therefore, we first performed linear interpolation for the same patient. for the remaining missing data, we used a clustering approach to find similar patients and then populated the missing data with values of the similar patients. 2.2 model evaluation metrics in this study, 10-fold cross validation was adopted to evaluate model performance. the dataset was randomly partitioned into 10 equal-sized subsamples, nine of which were used to train the model https://doi.org/10.15837/ijccc.2021.3.4180 4 prior to validation using the remaining subsample. to verify the generalizability of the methods and models, we used the data from the mimic iii database as internal validation data and the data from the eicu-crd database as external validation data. accuracy, recall, and precision scores were used to assess the prediction results. the f-score combines the two indicators and provides a more realistic measure of model performance using both precision and recall as follows: f = ( β2 + 1 ) ∗ precision ∗ recall β2 ∗ precision + recall the f-score takes into account both precision and recall and can be considered as a harmonic average of precision and recall. in the setting of this study, potential aki patients should be identified to the extent possible. therefore, recall is more important. this article sets β = 1.5, that is, f1.5 score. the performance evaluation of the model is mainly based on the auc (area under the receiver operating characteristic curve) value and reference to the f1.5 value. the roc (receiver operating characteristic) curve was used to evaluate algorithm performance. it is a comprehensive indicator representing the continuous variables of sensitivity and specificity. the auc represents the probability that a predicted positive sample is ranked above a negative sample, thus reflecting a classifier’s ability to sort samples. values closer to 1 indicate better model performance. 2.3 machine learning algorithms this study uses three machine learning algorithms: lr, decision tree, and xgboost. lr is a prediction tool that is often used in medical research [17] and does not require much calculation. moreover, the calculation speed is efficient. instead of fitting a straight line or hyperplane, we used the logistic function to squeeze the output of a linear equation between 0 and 1. the interpretability of this algorithm is that we can see the change of prediction when one of the features xj is changed by 1 unit, as the algorithm is an extension of the linear regression model. the essence of a decision tree is that many decision structures are combined in a tree shape, and the leaf nodes represent the final category. this algorithm is interpretable, and the logic is easy to understand. in this study, the cart decision tree algorithm was applied. xgboost is a strong classifier that integrates with decision trees, which are weak classifiers [19]. the importance of each candidate predictor is ranked by selection frequency. the sum of all importance scores is scaled to 100, which means each importance can be interpreted as the share of the overall model importance. additionally, individual predictions of xgboost can be explained by decomposing the decision path into one component per feature. therefore, a decision can be tracked through the tree and explained as a prediction by the contributions added at each decision node. the principle is as follows. let d = {(xi,yi)}(|d|= n,xi ∈ rm,yi ∈ rn) represent a database with n examples and m features. a tree boosting model output ŷi with k trees is defined as follows: ŷi = k∑ k=1 fk (xi) ,fk ∈ f where f = {f(x) = ωq(x)} ( q : rm → t,ω ∈ rt ) is the space of the regression or classification trees (i.e., cart). the tree is divided by fk into the structure part q and the leaf weights part ω. here, t indicates the number of leaves. xgboost adds second derivative information and regularization in the cost function to control the complexity of the model and prevent overfitting. for samples with missing feature values, xgboost can automatically learn the splitting direction [20]. 2.4 key indicators extraction and visualization method the key indicator screening method in this study is sfs based on xgboost. sfs is a greedy algorithm that avoids a simple exhaustive search. the set of key indicators starts with an empty set. one indicator at a time is selected to be added to the set of key indicators for iteration, and the results of each iteration of the model are recorded. during the search process, features are continuously added https://doi.org/10.15837/ijccc.2021.3.4180 5 or removed from the current key indicator set according to a certain order so as to obtain an optimized key indicator set [21]. for interpretability, this study uses shapley additive explanations (shaps). shap is an additive explanatory model inspired by cooperative game theory where all features are considered as “contributors.” for each prediction sample, the model generates a prediction value, and the shap value is the value assigned to each feature in that sample. the specific formula is as follows [22, 23]: g ( z′ ) = φ0 + m∑ j=1 φjz ′ j, where g is the explanation model, z′ ∈ {0, 1}m are the simplified features, m is the maximum features size, and φj ∈ r is the feature attribution for a feature j. the advantages of the shap value is that it can substantially reflect the characteristics in each sample and also show positive and negative effects. we used shap summary plots to reflect the impacts of all key indicators on the risk of developing aki in patients as well as partial shap dependent plots to illustrate the impact of individual indicators. 3 results 3.1 patient characteristics the demographic data, vital signs, laboratory indicators, and other data were obtained from the mimic iii database and eicu-crd database. the differences in age, gender, hospital length of stay, and mortality between the aki patients group and the non-aki patients group are shown in appendix tables 2 and 3. for continuous variables, the mann-whitney u-test was used to compare the differences between the two groups, and for the categorical variables, the chi-square test was used. the mimic iii database and eicu-crd database had significant differences in age, gender, hospital length of stay, and mortality in the hospital. the aki group had a higher mortality (mimic iii: 49.7% vs. 7.9%; eicu-crd: 40.7% vs. 6.9%). 3.2 key indicators extraction results all indicator weights for xgboost were calculated. the dataset was added in descending order of the indicator weights, and predictions were made using xgboost. a total of 39 iterations were performed, and the results of each iteration were recorded. as can be seen in table 3, the f1.5 score of the iterative process changed. as shown in fig. 2, the f1.5 score reached a high value when the number of indicators reached 17 (f1.5 = 0.8853). so, we selected the key indicator set as the top 17 indicators for input into the model. they are serum creatinine, urea nitrogen, inr, pt, ph, urine ph, leukocytes, sbp, spo2, potassium, chloride, total co2, lactate, temperature, ast, dbp, and alp. the weight of all key indicators is 0.6861. figure 2: f1.5 score for xgboost during iteration. https://doi.org/10.15837/ijccc.2021.3.4180 6 3.3 visualization of feature importance to enhance clinicians’ intuitive understanding of the importance of indicators, this study used shap to illustrate how key indicators affect aki onset (fig. 3). among the key indicators, higher urea nitrogen, ast, alt, lactate, leukocytes, inr, total co2, chloride, potassium, spo2, sbp, lower creatinine, ph, urine ph, pt, temperature, and dbp were noted. shap can also be used to illustrate the influence of individual indicators on the development of aki in patients. as shown in figs. 4, patients are at higher risk of developing aki when urea nitrogen is >40 mg/dl and when serum creatinine is <0.7 mg/dl. (a) (b) figure 3: shap summary plots of key indicators for aki patients. (a) the importance ranking of the key indicators. (b) the importance ranking of the key indicators according to the mean (|shap value|). inr: international normalized ratio; pt: prothrombin time; sbp: systolic blood pressure; ast: aspartate amino transferase; dbp: diastolic blood pressure; alp: alkaline phosphatase. (a) (b) figure 4: partial shap dependence plots. (a) serum creatinine. (b) urea nitrogen 3.4 key indicators and full dataset prediction results the key indicator data were input into the algorithm model. the roc curves and auc values of the prediction results are shown in fig. 5. from the results, it can be seen that the prediction performance of the key indicator set is comparable to that of the full dataset in internal and external validation. among the three algorithms, the xgboost algorithm had the best prediction performance (mimic iii auc: 0.9290, 95% ci: 0.8971–0.9680; eicu-crd auc: 0.9142, 95% ci: 0.8745–0.9601). lr was second best (mimic iii auc: 0.8780, 95% ci: 0.8210–0.9379; eicu-crd auc: 0.8696, 95% https://doi.org/10.15837/ijccc.2021.3.4180 7 ci: 0.8469–0.8955). decision tree had the worst performance (mimic iii auc: 0.7560, 95% ci: 0.7230–0.7890; eicu-crd auc: 0.7310, 95% ci: 0.6957–0.7664). (a) (b) figure 5: roc curves of the key indicators showing the performance of the models in predicting aki. (a) internal verification results (mimic iii database). (b) external verification results (eicu-crd database). to validate the prediction of the key indicators, we input all data into the three machine learning models. the roc curves and auc values predicted by the models are shown in fig. 6. from the internal verification and external verification results, we can see that the prediction performances of lr (mimic iii auc: 0.8743, 95% ci: 0.8375–0.9170; eicu-crd auc: 0.8699, 95% ci: 0.8459–0.8972), and xgboost (mimic iii auc: 0.9323, 95% ci: 0.9041–0.9669; eicu-crd auc: 0.9262, 95% ci: 0.9140–0.9433) were high, and the prediction performance of the decision tree was the worst (mimic iii auc: 0.7704, 95% ci: 0.7144–0.8265; eicu-crd auc: 0.7310, 95% ci: 0.6928–0.7691). (a) (b) figure 6: roc curves of the full dataset showing the performance of the models in predicting aki. (a) internal verification results (mimic iii database). (b) external verification results (eicu-crd database). the prediction results for all indicator sets and key indicator sets by the three algorithms are shown in table 1. from the results, it can be seen that the three algorithms perform consistently with respect to the key indicator dataset and all the indicators dataset. xgboost performs first best; lr was second best; and decision tree was the worst. when using the same algorithm, the prediction of the key indicator dataset is very close to that of the full indicators dataset, which verifies that using the screening key indicators method proposed in this study for prediction is effective. https://doi.org/10.15837/ijccc.2021.3.4180 8 table 1: summary of the internal and external verification results of the prediction of the full dataset and key indicator dataset of the three algorithms mimic iii eicu-crd auc f1.5 accuracy precision recall auc f1.5 accuracy precision recall the full dataset decision tree 0.7704 0.8010 0.7584 0.8060 0.8040 0.7310 0.7360 0.7363 0.7441 0.7394 lr 0.8743 0.8214 0.7851 0.8281 0.8227 0.8699 0.8132 0.8126 0.7911 0.8336 xgboost 0.9323 0.8796 0.8566 0.8962 0.8763 0.9262 0.8542 0.8538 0.8446 0.8613 the key indicator dataset decision tree 0.7793 0.8215 0.7786 0.7949 0.8413 0.7310 0.7629 0.7567 0.7221 0.7872 lr 0.8811 0.8444 0.8133 0.8621 0.8418 0.8696 0.8421 0.8311 0.8004 0.8730 xgboost 0.9302 0.8764 0.8567 0.8714 0.8819 0.9142 0.8556 0.8468 0.8267 0.8747 4 discussion aki is a common critical illness with a rapid onset and high mortality rate. early detection of the condition is important for saving the patient’s life and reducing the burden of medical costs. this research proposed a machine learning technique for screening key indicators for aki. three machine learning methods, namely, lr, decision tree, and xgboost, were compared. lr required a short training time, and it predicted well. its auc was higher than 0.85. decision tree construction is simple and interpretable, but its prediction performance was average. xgboost had the highest prediction accuracy. it also allows the existence of null values in the features and effectively prevents overfitting. for making predictions on key indicator datasets, the results showed that all three algorithms can achieve the prediction purpose. to verify the effectiveness of the key indicator index screening method, this research used the above three machine learning methods to compare the prediction effects on the full datasets. it was found that using the key indicator screening method proposed in this study, it was possible to achieve similar predictive performance using only the key indicator dataset to that from using the full dataset. to increase the interpretability of the results and give clinicians an intuitive understanding of the importance of the indicators, this study used shap to illustrate the impact of the key indicators on the risk of aki. among the existing studies on the interpretation of key indicators of aki, several authors have pointed out that serum creatinine and urea nitrogen are key indicators that affect the development of aki, but the existing studies mostly focus on aki patients with comorbidities. for example maiwall [24] studied the effect of aki on the prognosis of patients with acute chronic liver failure (aclf) for 48 hours. it was found that when the serum creatinine value is 1.14 mg/dl and the value increase is small, risk stratification of aclf patients should be considered while adopting intervention strategies. dinna [25] examined the association between increased creatinine and aki risk in the icu adult population. the study found that based on the combined effect of clinical factors and early creatinine elevation, the possibility of severe aki in critically ill patients is significantly increased. vanmassenhove [26] found an association with mortality for aki patients with septic sepsis only if the evolution of serum creatinine over the first 24 h after icu admission was taken into account. smith [27] established a latent variable mixture model for patients to evaluate the relationship between aki risk factors and changes in serum creatinine and predict the risk of aki. dewitte [28] evaluated the performance of fractional excretion urea in identifying persistent and transient aki in patients admitted to the intensive care unit. the author found that in patients in the intensive care unit, urea excretion rate of less than 40% was a sensitive and specific indicator for the identification of transient and persistent aki, especially in the case of diuretics. corte [29] assessed whether serum urea concentration or a different recommended serum urea concentration was related to hospital mortality when aki patients received rrt. this retrospective study showed that serum urea concentration and the cut-off value of serum urea concentration at the time of initiation of rrt had no predictive value for the hospital mortality of icu aki patients. however, studies have not indicated the warning range for serum creatinine and urea nitrogen in patients with aki. this study predicted the risk of aki in the adult population of icu, screened 17 key indicators, and used shap plots to find the early warning range of serum creatinine and urea nitrogen. it was found that patients with urea nitrogen >40 mg/dl and serum creatinine <0.7 mg/dl had a higher risk of aki. https://doi.org/10.15837/ijccc.2021.3.4180 9 to test the reliability of the model, this study conducted external verification. damen suggested that the future research direction of disease prediction should be in the external verification of the model [30]. this study validated the results using the mimic iii database and eicu database. from the external validation results, it can be seen that the model developed in this study performs equally on both databases, indicating its reliability. the cross-validation results also confirm that the model developed in this paper is generalizable. 5 conclusion this study provides a reference for other applications of machine learning methods and similar clinical decision support studies and improves the feasibility and timeliness of early prediction and warning of aki. in the case of urgency or limited equipment, using only the key indicator data for prediction can not only achieve similar results as using the full data but also reduce the frequency of collection of data, shorten the length of the wait for indicator results, and enable early detection and treatment. there are some limitations of this study. for example, the number of cases studied was limited. the study sample size needs to be expanded to improve prediction accuracy. the ethnicity of the data is predominantly western. more people from other ethnic groups could be included in the future. owing to the different collection times of the samples, there are long intervals between the collection of certain data, which made it difficult to fill in some data. this factor may have an impact on the prediction accuracy of the models. this study is a retrospective study based on existing data. although it has achieved an effective prediction performance, there may still be a gap in clinical use. these issues will be the focus of our future research. acknowledgment this work was partly supported by the national key research and development plan for science and technology winter olympics of the ministry of science and technology of china (2019yff0302301), the hainan provincial natural science foundation of china (818ms156) and the special fund for health care of the ministry of logistics(16bjz19). appendix appendix table. 1: mimic iii database research population baseline statistics characteristics aki (n = 1009) non-aki (n = 1009) p value age (years) 72.15 (58.58,82.19) 64.23 (50.78,77.16) < 0.01 gender, n (%) <0.01 female 533 (48.5%) 486 (44.2%) male 476 (43.3%) 614 (55.8%) hospital length of stay (days) 13.78 (6.44,25.80) 7.02 (4.31, 12.25) <0.01 mortality (%) 547 (49.7%) 87 (7.9%) <0.01 appendix table. 2: eicu-crd database research population baseline statistics characteristics aki (n = 1327) non-aki (n = 1327) p value age (years) 69.00 (57.00, 79.00) 65.00 (53.00, 76.00) <0.01 gender, n (%) <0.01 female 707 (47.1%) 667 (44.5%) male 618 (41.2%) 832 (55.5%) hospital length of stay (days) 5.70 (2.82, 10.85) 4.78 (2.71,7.95) <0.01 mortality (%) 610 (40.7%) 104 (6.9%) <0.01 https://doi.org/10.15837/ijccc.2021.3.4180 10 appendix fig. 1: the visualization of the proportion of missing data (white indicates missing data). inr: international normalized ratio; pt: prothrombin time; ck: creatine kinase; ckmb: creatine kinase-mb; ld: lactate dehydrogenase; urine rbc: urine red blood cells; alt: alanine amino transferase; alp: alkaline phosphatase; ast: aspartate amino transferase; sbp: systolic blood pressure; dbp: diastolic blood pressure; urine wbc: urine white cells. references [1] s. uchino, j. a. kellum, r. bellomo, g. 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[30] j. a. a. g. damen, l. hooft, e. schuit, t. p. a. debray, g. s. collins, i. tzoulaki, c. lassale, g. c. m. siontis, v. chiocchia, and c. roberts, "prediction models for cardiovascular disease risk in the general population: systematic review," bmj, vol. 353, 2016. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: jiaming wang , bing zhu, pei liu, ruiqi jia, lijing jia, wei chen, cong feng , jing li (2021). screening key indicators for acute kidney injury prediction using machine learning international journal of computers communications & control, 16(3), 4180, 2021. https://doi.org/10.15837/ijccc.2021.3.4180 introduction methods data collection and processing model evaluation metrics machine learning algorithms key indicators extraction and visualization method results patient characteristics key indicators extraction results visualization of feature importance key indicators and full dataset prediction results discussion conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 3, month: june, year: 2021 article number: 4221, https://doi.org/10.15837/ijccc.2021.3.4221 ccc publications job-shop scheduling over a heterogeneous platform j. a. hermosillo-gomez, h. benitez-perez jose a. hermosillo-gomez* institute of applied mathematics research and systems (iimas) national autonomous university of mexico, mexico 3000 circuito escolar, cd. universitaria, coyoacán 04510, mexico city *corresponding author: jose.hermosillo@iimas.unam.mx hector benitez-prez institute of applied mathematics research and systems (iimas) national autonomous university of mexico, mexico 3000 circuito escolar, cd. universitaria, coyoacán 04510, mexico city hector.benitez@iimas.unam.mx abstract real-time scheduling involves determining the allocation of platform resources in such a way tasks can meet their temporal restrictions. this work focuses on job-shop tasks model in which a task have a finite number of nonpreemptive different instances (jobs) that share a unique hard deadline and their time requirements are known until task arrival. non-preemptive scheduling is considered because this characteristic is widely used in industry. besides job-shop scheduling has direct impacts on the production efficiency and costs of manufacturing systems. so that the development of analysis for tasks with these characteristics is necessary. the aim of this work is to propose an online scheduling test able to guarantee the execution of a new arriving task, which is generated by human interaction with an embedded system, otherwise to discart it. an extension of the schedulability test proposed by baruah in 2006 for non-preemptive periodic tasks over an identical platform is presented in this paper. such extension is applied to non-preemptive tasks that have hard deadlines over a heterogeneous platform. to do that, some virtual changes over both the task set and the platform are effectuated. keywords: embedded systems, real-time, uniform heterogeneous platform, non-preemptive jobs. 1 introduction traditionally, the job-shop problem is an optimisation problem in which many manufacturing jobs are assigned to machines at particular times while trying to minimise the makespan. however, here it is considered a platform whose machines have the same processing capabilities but different speeds, this characteristic gives certain flexibility to the traditional job-shop problem (jsp) [1] in such a way that there is a machine sequence-dependency setup avoidance. https://doi.org/10.15837/ijccc.2021.3.4221 2 the addressed task model consists of nonpreemptive job-shops. let τi be a job-shop, this kind of task is composed by ni jobs such that ni ∈ n and ni < ∞; for the propose of this work, a job is defined as an ordered sequence of operatios that must be executed without interruption (i.e. atomic jobs). the jth job of the ith task is represented as τi,j where i = 1, . . . and j = 1, . . . ,ni. a job-shop, τi, is an ordered sequence of jobs: let τi = {τi,j: i = 1, . . . , j = 1, . . . ,ni} be a jobshop where ni ∈ n denotes its number of jobs. these jobs follow precedence constraints, that is, the job τi,j cannot start its execution if the previous job τi,j−1 has not finished yet. job-shops are also characterised by temporal restrictions such as a unique release time instant (ri), a critical unique deadline (di), and ni execution time requirements (one per job), denoted as ci,j (where j = 1, . . . ,ni). definition 1 (precedence constraint). for any two consecutive jobs τi,j−1, τi,j ∈ τi, the job τi,j−1 precedes τi,j if its finishing time fi,j−1 meets that fi,j−1 ≤ bi,j, where bi,j denotes the τi,j’s beginning instant of execution, i = 1, . . . and j = 2, . . . ,ni. scheduling research needs to focus on this type of tasks because of its importance in the inherent processes of industry (manufacturing, inventory costs, transportation schedules, etc.). under the industry 4.0, the scheduling should deal with smart and distributed manufacturing systems supporting by novel and emerging manufacturing technologies such as mass customisation, cyber-physical systems, digital twins, and smac (social, mobile, analytics, and cloud), big data, etc. [12]. embedded systems are an ideal platform to implement projects in terms of the requirements of iot and industry 4.0 regarding different resource capacities (communication, computing, memory, etc.), energy efficiency, etc. embedded systems must operate in dynamic environments where human activities occur at any moment, then some tasks, such as emergency tasks, external event tasks, human interaction tasks, etc., arrive aperiodically [11]. such aperiodic interactions might be the cause of generating new entering tasks (in this case job-shops) that should be completed within their respective deadline by the embedded systems. embedded platforms for dynamic environment domains are expected to handle several tasks at the same time, such as the task model previously described. non-preemptive scheduling is widely used in industry, and it may be preferable to preemptive scheduling for certain reasons [8]: non-preemptive scheduling algorithms are easier to implement and have lower run-time overhead than preemptive scheduling algorithms; the overhead of preemptive scheduling algorithms is more difficult to characterise and predict than of non-preemptive scheduling due to inter-task interference caused by caching and pipelining. these benefits of non-preemptive scheduling are more important on multiprocessor since the task migration overhead is higher and more difficult to predict [7]. a result that gives the possibility of thinking that for a considerable part of real-life applications on multiprocessor platforms, non-preemptive scheduling could be a better choice with respect to real time performance is specified in [7], since in it is experimentally showed that for task sets, in which the range of execution time is not very wide, the performance of edfnp (short for non-preemptive edf) is very close to edf, and dmnp (short for non-preemptive dm) performs better than dm. this work addresses the problem of scheduling sets of arriving non-preemptive job-shops that have to be scheduled over a heterogeneous platform by their temporal restrictions. by means of extending [3] it is possible to determine if a set of arriving job-shops is schedulable online. contrary to [3] that holds for static1 conditions; in this work, the attention is focused on those tasks that arrive at unknown time instants. the managed tasks, job-shops 2 also have their respective unique [6] but critical deadlines. the platform considered for scheduling these tasks is a uniform heterogeneous [4] one. let π = (s1, s2, . . . ,sm) denote such multiprocessor platform of size m with the ith processor having a speed rate si, where si ≥ si+1 for i = 1, . . . ,m− 1, as bigger the value of si the slower the ith processor is. the problem addressed in this work is about making the decision of assigning new released jobshops into a heterogeneous platform at certain instant t, then a schedulability test is defined in function of the active tasks as from time t. 1not only the number of tasks still the same but also their time computing requirements, during execution. 2there are also other names like coordinated tasks[10] in the literature to name this tasks which, at the same time, are a particular case of dag tasks https://doi.org/10.15837/ijccc.2021.3.4221 3 the rest of this paper presents a review of the analysis in which this work is based (section 2); defines the transformation operations that let apply the proposed modified bar06 analysis to schedule new entering job-shops (section 3); describes the possible transitions that the jobs may have from its arriving onto the embedded system to its execution or discard (section 4); describes the server construction (section 5); introduces an important added component to analyze entering tasks (section 6); finally enunciates a theorem that extends the usability of bar06 over job-shops in a uniform heterogeneous platform (section 7). 2 related work there is a few work dedicated to non-preemptive task schedulability analysis, one of the most known papers about this topic is [3], and [7] which is an improvement of the first. recent work focuses their efforts on developing analysis to determine schedulability of a set of fixed set of periodic tasks over a homogeneous multiprocessor platform: [9], [2]. [9] seeks to bound the worst-case response time of a set of jobs which access shared resources, and a non-work-conserving nonclairvoyant and non-preemptive scheduling framework is proposed in [2], which is able to find feasible schedules for work-conserving-infeasible task sets. although, they do not consider the constant interaction with users that produces new entry tasks in a system. the schedulability analysis of sufficiency proposed by baruah [3], henceforth called bar06, consists of validating the restriction (1) for a set of n known periodic tasks, τ, on an identical m-multiprocessor platform. vsum(τ) ≤ m− (m− 1) ·vmax(τ) (1) where vsum(τ) := ∑ τi∈τ v (τi,τ) (2) vmax(τ) := max τi∈τ {v (τi,τ)} (3) and v (τi,τ) is the utilization of the periodic non-preemptive task τi: v (τi,τ) := ci ti −emax(τ) (4) emax(τ) is the maximum of the execution requirements in τ, as appreciated at (4), it is the worst case interference time scenario caused by non-preemption: emax(τ) := max i=1,...,n {ci} (5) where ci and ti are the execution requirement and the interrelease times of the periodic task, τi ∈ τ, respectively. an obvious observation is that a task τi ∈ τ with low utilization can not pass (1) if ci > tmin, where tmin denotes the minimal period ti of the tasks in τ. 3 transformations global edfnp try to assign as many tasks with the promptest deadline as it can to available processors, then schedules the instances with the next earliest deadlines as it can and on and on, until either all processors are busy or there are no more waiting task instances. with the aim of applying the schedulability analysis proposed by baruah in [3] over job-shops scheduled by global edfnp in a heterogeneous platform, the next series of virtual transformations is realized: 1. transformation of a heterogeneous platform into a homogeneous one. https://doi.org/10.15837/ijccc.2021.3.4221 4 2. transformation of the incoming aperiodic non-preemptive job-shops into periodic tasks. lemma 1. a uniform heterogeneous platform has associated an identical (homogeneous) platform. proof. the proof is by construction. let π = (s1, s2, . . . ,sm) denote the speed rates of the uniform multiprocessor platform of size m with the kth processor having a rate of sk, where 0 < sk ≤ 1 (note that rate smax = 1 is the slowest one). to convert the current heterogeneous platform into a homogeneous one is necessary to meet that all processors speeds are the same. to do that it is necessary to find a factor for every processor in the platform, let αk be such value, k = 1, . . . ,m, such that αk·sk = smax where smax is the rate of the slowest processor smax := max k=1,...,m {sk} then αk = smax sk this new homogeneous platform is a virtual platform over which the schedulability test will be applied for the new entering tasks. note that the factor αk indicates how much the kth virtual processor must slow down to be part of the slowest homogeneous platform. also note that the bigger αk is, the slower the kth-processor will turn. by lemma 2, the execution requirements of the pseudo-periodic tasks are built under the assumption of having an underlying homogeneous platform mentioned in lemma 1. it is assumed that for every τi,j ∈ τi there exists an execution requirement ci,j (i = 1, . . . , n) that is given in terms of the slowest processor in π. it means that a job, τi,j, executed on the kth-processor of the heterogeneous platform last less than on a homogeneous one: ci,j ≥ ci,j ·sk for any k = 1, . . . ,m. lemma 2 (pseudo-periodic task). a job-shop, τi, has its equivalent associated periodic task, which is called the associated pseudo-periodic task, let denote it as τ ′i. proof. the proof is by construction. an associated pseudo-periodic task, τ ′i, can be represented as the tuple (ri,ci,ti(t)), where ri is the same release time as in τi; ci, short for c(τi), is the virtual execution requirement, which is computed as c(τi) := max τi,j∈τi {ci,j} (6) thus, for each ci,j of every non-executed job τi,j ∈ τi, it is necessary to add ci−ci,j units of unoccupied time in order to have ni instances with the same execution requirements (just like periodic task instances); ti(t), short for t(τi, t), is the imposed virtual period of the task τi that is computed as t(τi, t) := ⌊ di − t ni ⌋ (7) where di is the absolute deadline of τi and t is the time instant in which ti is calculated. it must be met that ti(t) ·ni ≤ di and ci ≤ ti(t). in this studied setting, the jobs of τi ∈ τ, for all i = 1, . . ., are finite; their execution requirements are not necessary equal; and they are consumed during run time. also, the arrival instants of new job-shops are considered during run time (online). hence the necessity to modify bar06 analysis: considering the evolution in time of the execution of the “accepted” job-shops. once τi,j ∈ τi was consumed, is discarded, therefore v(t,τi,τ) must be calculated by means of substituting definition (6) and (7) (see lemma 2) into (4) every time the server task is active at instant t. v(t,τi,τ) := ci ti(t) −emax(τ) (8) https://doi.org/10.15837/ijccc.2021.3.4221 5 where emax(τ) is the maximum execution requirement of the periodic task set τ: emax(τ) := max τi∈τ {ci} (9) once considered all this run time dynamism to the new model, innequation (1) has been modified as showed next: vsum(t,τ) ≤ m− (m− 1) ·vmax(t,τ) (10) where vmax(t,τ) := { maxτi∈τ{v(t,τi,τ)} if τ 6= φ 0 if τ = φ (11) and vsum(t,τ) := {∑ τi∈τ v(t,τi,τ) if τ 6= φ 0 if τ = φ (12) note the dependence on time for this new acceptance analysis. so far, it is known how to tailor the temporal restrictions of a fixed set of job-shops into a set of periodic tasks but not how to go through with new incoming job-shops to the system. therefore, a systematic way of converting and verifying the released tasks is described below. 4 task transitions global edfnp is the underlying algorithm for this approach and it also utilizes a task server (τs), they are adapted to a multiprocessor platform to allocate resources for released incoming tasks (here task means a job-shop). there are four possible states tasks can go through, namely released, wait, active, and dead; each state disposes of its own data structure (i.e. a heap) that helps to make reference to the treated tasks. as showed in figure 1, for instance, the state released has associated the data structure τreleased, that receives all new execution requests of arriving tasks at any time to the system. when an interruption occurs at instant t and there is enough budget for the server task (let cs denote the budget capacity of the server), tasks referenced by τreleased begin to be transformed (see lemma 2) and then they change their status to wait, it means that they were allocated to τwait by τs. formally τreleased is defined as follows τreleased := {τi : 0 ≤ ri ≤ t} when global edfnp selects τs as the highest priority task and all tasks from τreleased have been transformed, τs validates which tasks from τwait are still schedulable. definition 2 (schedulable task). at instant t, given the total utilization of the currently performing pseudo-periodic tasks, vsum(t,τactive), it is said that a task τi ∈ τwait is still schedulable at instant t if it can still be executed by its deadline, di: t + ni ·ci + vsum(t,τactive) · (di − t) m ≤ di (13) where ni is the number of jobs in τi that have not been scheduled yet and m is the number of processors. if a schedulable task in τk ∈ τwait passes bar06 analysis, then its status changes to active and is moved towards τactive that is the set of tasks currently assigned to the platform; otherwise, it is either discarded (put towards τdead) or rejected (placed back to τwait). note that, at instant t, τwait will keep schedulable tasks: τwait := {τi : satisfies (13) and vsum(t,τ) > m− (m− 1) ·vmax(t,τ)} https://doi.org/10.15837/ijccc.2021.3.4221 6 acceptance test transformation τ k re je c te d discarded interruption φ ended acceptedactive wait released (τactive) (t released) (τreleased) (τwait ) dead (τdead) t+ figure 1: states of tasks during run time where τ = τactive ∪{τi}. as it is possible to observe in figure 1, rejected tasks are still schedulable, while discarded tasks are thrown away because they cannot meet restriction (13), besides according to [3], it must be satisfied the next restriction before verifying inequality (10): the maximum of the execution requirements in any task set τ, emax(τ), must be less than or equal to the minimum of the periods of τ: emax(τ) ≤ tmin. when a task satisfies (10) is added to τactive which is a global set of pseudo-periodic tasks being scheduled by edfnp. algorithm 1 acceptance test 1: treleased ←− convert2pseudoperiodic(τreleased) 2: τwait ←− τwait ∪treleased 3: for all τ ′k ∈ τwait or c s is not used up do 4: τ ←− τactive ∪{τ ′k} 5: if t + nk · ck + vsum(t,τactive) · (dk−t) m ≤ dk and ck ≤ minτi∈τactive{ti} and tk ≥ maxτi∈τactive{ci} and vsum(t,τ) ≤ m− (m− 1) ·vmax(t,τ) then 6: τactive ←− τ 7: else if t + nk · ck + vsum(t,τactive) · (dk−t) m > dk or ck > minτi∈τactive{ti} or tk < maxτi∈τactive{ci} then 8: discard(τ ′k) 9: end if 10: end for as showed in algorithm 1, every time an interruption is triggered at instant t, every τk ∈ τreleased is stored in an auxiliary data structure, treleased, and then converted into its associated periodic task, τ ′k, before it becomes part of τwait (see line 1) and then restrictions (13) and (10) are verified for each τ ′k ∈ τwait (see lines 3-10). 5 server design generally, the server, denoted by τs, is a special periodic task whose intent is to handle new incoming aperiodic requests as soon as possible and without causing previously accepted tasks to miss their deadlines. in this case, two types of requests are considered: first, those tasks that are waiting until instant t in τreleased to be transformed; second, those tasks that are stored in τwait and need to be validated, in case to be accepted they are moved towards τactive. τs adapts itself to the current platform conditions, it means that it is able to change its budget capacity at every triggered interruption (see section 6). note that task server has not restricted execution so that it can be run on any processor. https://doi.org/10.15837/ijccc.2021.3.4221 7 the utilization for τs, us, is bounded according to the capacity left by the tasks in τactive. at instant t, when an interruption has been triggered, us is calculated as follows: us(t) = m−vsum(t,τactive) (14) since the addressed problem copes with non-preemptive tasks, us is treated as the upper bound for τs’s utilization value (us), this is 0 < us ≤ us. it is necessary to regard τs as one extra task in the set, then inequality (10) must satisfy: vsum(t,τactive) + us ≤ m− (m− 1) ·vmax(t,τactive) thus, for the non-preemptive task system, us must meet us ≤ m− (m− 1) ·vmax(t,τactive) −vsum(t,τactive) (15) it is supposed the existence of a user defined value that delimits the minimum value the τs’s utilization can take, let usmin be that value, such that 0 < usmin ≤ us. from (15) and usmin, the value for us is defined: us := max{usmin, m− (m− 1) ·vmax(t,τactive) −vsum(t,τactive)} (16) with this is pretended to assign the highest possible utilization value for us. the budget capacity for τs, cs, is upper bounded by emax(τactive), (i.e. 0 < cs ≤ emax(τactive)), this is to guarantee that there will be no need to calculate again the utilization value for all the tasks in τacepted. to guarantee predictability, it is also necessary to meet that there will be always enough budget to execute, at least, one request in the system, then cs is bounded in the next way: max{c1,c2}≤ cs ≤ emax(τactive) (17) once that cs was set, it is necessary to define the absolute deadline calculation for τs, ds. it is calculated in such a way that the interference of the task with the highest job execution requirement, emax(τactive), is considered, and all the allocated budget is used up by such deadline: ds(t) = t + ⌈ cs us + emax(τactive) ⌉ (18) although the considered task set consists of jobs with precedence constraints, the analysis is executed at job level. under the established conditions and the proposed transformations it is possible to guarantee the execution of a task through their jobs. the rules to replenish the budget are enunciated next: 1. initially, at t = 0, us is set according to (16), note that if there is no task in τactive (τactive = φ) then us = m, see definitions (11) and (12), it means that at the beginning τs could have the platform at its complete disposal. 2. every time the deadline of τs (ds) has been reached, cs is set to the lowest necessary value it is able to take: cs = max{c1,c2} (see (17)) and the next deadline ds is again calculated according to equation (18). 6 interruptions interruptions are important for two reasons. first, in every interruption occurrence, at time t, the server’s budget is reset according to the number of tasks in τactive. second, the next interruption is set at instant t+ to assist the released tasks (τreleased). it is assumed that the first interruption occurs at t = 0 and there is also a minimum utilization value, usmin, defined by the user for the server task τs, then t+ is calculated as follows https://doi.org/10.15837/ijccc.2021.3.4221 8 π1 π2 πk πm t t + di1 di2 dik dim x 1 x 2ϕ figure 2: different operation subintervals in which the interval [t,t+) may be divided: x1, x2, and φ t+ = t + ⌈ φ + x1 + x2 us ⌉ (19) where φ is the time interval that will take the platform to calculate the next interruption, t+, and the budget for the server; while x1 and x2 are the time intervals that will take the transformation and the validation operations (through condition (10)), respectively, see figure 2. to know how long it takes to calculate t+ the next suppositions are made: s0 it is considered that the server task τs always works under the worst case scenario it may have, it is us = usmin (see section 5). s1 the required time to calculate φ is constant. when implementing, it is possible to endow the receiving data structures of counters that permit to know at every time the number of total jobs or tasks in a data structure as required, let cτxjobs and c τx be the counters that store the number of jobs and tasks for the data structure τx, respectively. s2 both transformation and validation are operations that depend on the number of jobs and tasks, respectively. s3 neither transformation nor validation operations take zero time. the amount of time used by these operations is described by two constants: let c1 > 0 and c2 > 0 be such constants for the transformation and validation, respectively. from s2 and s3, it is easy to deduce that the complexity of calculating t+ only depends on the number of jobs in τreleased and the number of tasks in τwait. lemma 3 (task transformation time). the needed time to transform the tasks in τreleased depends on the number of jobs it has x1 = c1 · cτreleasedjobs that can be rewritten as x1 = c1 · ∑ τi∈τreleased ni where ni is the number of jobs in the task τi ∈ τreleased. proof. proof follows from the suppositions s2 and s3. after performing the transformation of the tasks in treleased, they are added to τwait where they wait until acceptance or removal. note also that it is possible to know in a delimited time interval if a task cannot be performed by its deadline, namely [t,t + φ + x1), this gives the possibility to the user of changing the task to other system. https://doi.org/10.15837/ijccc.2021.3.4221 9 τ’i , k π1 πl πm π1 ' πl ' πm ' π1 πl πm identical (homogeneous) uniform (heterogeneous) si , k f i ,k ri , k di , k τ i , k figure 3: correspondence between a job-shop (down) and its pseudo-periodic associated task (up) lemma 4 (task validation time). once the tasks have been transformed, the needed time for validating the tasks in τwait by bar06 is x2 = c2(2 · cτactive + cτwait ) where cτactive and cτwait represent the number of stored tasks in the data structures τactive and τwait, respectively. proof. let τwait be the data structure in which the tasks wait until either execution or removal (to discard a task). for bar06, it is needed to calculate two values from the tasks that are currently assigned to the platform, τactive; first, the maximum execution requirement, vmax(t,τactive), this operation takes proportional time to the number of tasks in τactive, cτactive; second, vsum(t,τactive) that also takes proportional time to cτactive. the tasks in τwait are validated in a sequential way, thus every other task trying to be part of τacvtive do not increase the time complexity. it is still having linear time, according to the number of tasks that need to be validated, then the time, that take the validation of all tasks in τwait, is added. as mentioned above, in supposition s1, through the use of counters in every data structure, it is possible to obtain the information of the number of tasks and jobs from τreleased, τwait, and τactive in constant time, thus the calculations of x1 and x2 can be considered as negligible and so do for φ. therefore, equation (19) can be reduced to t+ = t + ⌈ x1 + x2 us ⌉ . (20) 7 theorem after all of those transformations over both the platform and the task set; the validation of tasks, and interruptions, the next result is enunciated. theorem 1 (heterogeneous schedulability guarantee). if a set of pseudo-periodic tasks is scheduled on the associated homogeneous platform, then it is also scheduled on the underlying heterogeneous platform. proof. the job-shops are treated as periodic tasks, due to their previous transformation (see lemma 2), and their respective jobs will be chosen by global edfnp at run time. when edfnp selects a job https://doi.org/10.15837/ijccc.2021.3.4221 10 τ ′i,j ∈ τ ′ i from τactive to execute it, it actually selects its corresponding job τi,j in the job-shop τi which is assigned to the heterogeneous platform. the start si,k and release ri,j times of every job will be the same in both platforms. although the jobs assigned to the heterogeneous platform finish its processing in less time they follow the execution of the homogeneous platform (see figure 3). since τ ′i ∈ τactive its schedulability is guaranteed by bar06, as previously mentioned, the execution requirements have been given in terms of the slowest processor speed, then at run-time, when τ ′i,j is assigned to the homogeneous platform, with an execution requirement of ci, at instant si,j then the job associated ,τi,j, executed on the πl processor does not exceed the limit imposed by the homogeneous platform, this is si,j + ci · sl smax ≤ si,j + ci as 0 < sl ≤ smax ≤ 1 then slsmax ≤ 1, where sl is the speed rate of πl. as the speeds in the heterogeneous platform are faster than in the identical (homogeneous) platform (see lemma 1), the execution requirement of every job τi,j, ci,j, will meet ci,j ≤ ci, where ci is the execution requirement of the associated pseudo-periodic task τ ′i. 8 conclusions this extension to bar06 lets determine online if a new entering nonpreemptive job-shop is able to meet its deadline over a heterogeneous platform. since this schedulability analysis extension has a linear time complexity, according to the number of tasks, it is convenient to implement the presented result on embedded systems with limited computational resources where human activities occur at any moment generating new aperiodic entering tasks. it is important to emphasize that the time of estimating the stay of a task in embedded systems represent a challenge due to the availability and capabilities of resources. it should be also noted that important aspects like memory management and communication delays in task migration are not yet considered. to fulfill current system requirements most of these features should be included to provide better schedulability analysis. nevertheless this is a deterministic approximation that focuses on systems with limited resources on platforms composed of diverse components and it could be used as guidance for developing another algorithms, metrics, and strategies. the presented strategy has the potential to be implemented in decision support systems (dss). one concrete example could be dispatcherr [5] which is a family of dss that are meant to support the logistics and production control decisions to be made in the context of the continuous process industries and related fields. another example could be an air traffic management system where the scheduling conditions change frequently and the necessities of resources usage (for instance, the aircraft takeoff and landing areas) must be validated constantly. conflict of interest the authors declare no conflict of interest. references [1] d. applegate and w. cook, a computational study of the job-shop scheduling problem, informs journal on computing, vol. 3, pp. 149–156, may 1991. 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[11] s. kato and n. yamasaki, scheduling aperiodic tasks using total bandwidth server on multiprocessors, in 2008 ieee/ifip international conference on embedded and ubiquitous computing, vol. 1, pp. 82–89, dec 2008. [12] j. zhang, g. ding, y. zou, s. qin, and j. fu, review of job shop scheduling research and its new perspectives under industry 4.0, journal of intelligent manufacturing, vol. 30, no. 4, pp. 1809–1830, apr 2019. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: hermosillo-gomez, j. a.; benitez-perez, h. (2021). job-shop scheduling over a heterogeneous platform, international journal of computers communications & control, 16(3), 4221, 2021. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 6, month: december, year: 2021 article number: 4587, https://doi.org/10.15837/ijccc.2021.6.4587 ccc publications fixed point theory in fuzzy normed linear spaces: a general view s. dzitac, h.oros, d. deac, s. nădăban simona dzitac department of energy engineering, faculty of energy engineering and industrial management, university of oradea, romania universitatii 1, oradea, romania e-mail: simona.dzitac@gmail.com horea oros department of mathematics and computer science, faculty of informatics and science, university of oradea, romania universitatii 1, oradea, romania e-mail: horos@uoradea.ro dan deac, sorin nădăban department of mathematics and computer science, faculty of exact sciences, aurel vlaicu university of arad, romania elena drăgoi 2, arad, romania e-mail: dan.deac@uav.ro, snadaban@gmail.com abstract in this paper we have presented, firstly, an evolution of the concept of fuzzy normed linear spaces, different definitions, approaches as well as generalizations. a special section is dedicated to fuzzy banach spaces. in the case of fuzzy normed linear spaces, researchers have been working, until now, with a definition of completeness inspired by m. grabiec’s work in the context of fuzzy metric spaces. we propose another definition and we prove that it is much more adequate, inspired by the work of a.george and p. veeramani. finally, some important results in fuzzy fixed point theory were highlighted. keywords: fuzzy normed linear space, fuzzy banach spaces, completeness, fixed point theory. 1 fuzzy normed linear spaces nowadays, fixed point theory and functional analysis have an important role not only in mathematics but, most of all, in many and various applications in physics, computer science, economy etc. on the other hand, after l. a. zadeh had introduced in his famous paper [51] the concept of fuzzy set, many mathematicians considered the fuzzy context as a natural frame within which many notions and mathematical concepts can be generalized. in 1977, a.k. katsaras and d.b.liu [29] introduced the concept of fuzzy topological vector space which led, to what we call today, fuzzy functional analysis. in this new context fixed point theory and operator theory play and will play an important role. https://doi.org/10.15837/ijccc.2021.6.4587 2 in 1984, a.k. katsaras [28] introduced, for the first time, the concept of fuzzy normed linear space. the fuzzy norm introduced by a.k. katsaras is of minkovsky type associated to an absolutely convex and absorbing fuzzy set. in 1992, c. felbin [20] introduced a new concept of fuzzy norm, a norm which to each element of a linear space makes it correspond a fuzzy real number. this fuzzy norm induces a fuzzy metric of the kaleva and seikalla type [27]. inspired by s.c. cheng and j.n. mordeson [16], in 2003, t. bag and s.k. samanta [5] introduced a new definition of fuzzy norm that proves to be a lot more adequate and easier to work with, even if during the following years many mathematicians have tried to simplify it, to polish it, improve it or generalize it. the advantage of this fuzzy norm is that it can be decomposed in a family of semi-norms. moreover, we have to highlight that this fuzzy norm induces a fuzzy metric of kramosil and michálek type [30]. in 2005, r. saadati and s.m. vaezpour [43] introduced a new concept of fuzzy norm which induces a fuzzy metric of george and veeramani type [21]. a generalization of these spaces was done the following year by r. saadati and j.h. park [41] who, using continuous t-norms and continuous tconorms, defined the notion of intuitionistic fuzzy normed spaces. a comparative study regarding the different concepts of fuzzy norms was realized by t. bag and s.k. samanta [9] in 2008. they proved that the fuzzy norm of bag-samanta type was similar to that of katsaras type; it is only that they were defined from two different perspectives. after t. bag and s.k. samanta introduced the notion of fuzzy antinorm they showed that to one fuzzy norm of felbin type it corresponds a pair formed from a fuzzy norm of bag-samanta type and a fuzzy antinorm. in this paper we will use the definition of the fuzzy norm introduced by s. nădăban and i. dzitac [37] in 2014. definition 1. let x be a linear space over a field k (where k is r or c). a fuzzy norm is a mapping n : x × [0,∞) → [0, 1] such that: (n1) n(x, 0) = 0, (∀)x ∈ x; (n2) [n(x,τ) = 1, (∀)τ > 0] iff x = 0; (n3) n(λx,τ) = n ( x, τ|λ| ) , (∀)x ∈ x, (∀)τ ≥ 0, (∀)λ ∈ k∗; (n4) n(x + y,τ + ς) ≥ n(x,τ) ∗n(y,ς), (∀)x,y ∈ x, (∀)τ,ς ≥ 0; (n5) (∀)x ∈ x,n(x, ·) is left continuous and lim τ→∞ n(x,τ) = 1. the triple (x,n,∗) will be called fuzzy normed linear space (briefly fnls). some remarks are necessary: 1. ∗ is a continuous t-norm (see [48]), i.e. a continuous binary operation ∗ : [0, 1] × [0, 1] → [0, 1] such that (∀)s,t,r,u ∈ [0, 1] we have: (t1) s∗ t = t∗s; (t2) s∗ 1 = s; (t3) (s∗ t) ∗r = s∗ (t∗r); (t4) if s ≤ r and t ≤ u, then s∗ t ≤ r ∗u. three important examples of continuous t-norms are ∧, ·,∗l which are defined by: s ∧ t = min{s,t}, s·t = st (usual multiplication on [0, 1]) and s∗lt = max{s+t−1, 0} (the lukasiewicz t-norm). 2. i. goleţ [23] and c. alegre and s. romaguera [2] gave also this definition in the context of real vector spaces. 3. t. bag and s.k. samanta [5] gave a similar definition, but they just added two axioms: (n6) n(x,τ) > 0, (∀)τ > 0 ⇒ x = 0; https://doi.org/10.15837/ijccc.2021.6.4587 3 (n7) (∀)x 6= 0,n(x, ·) is a continuous function and strictly increasing on the subset {τ : 0 < n(x,τ) < 1} of r. the results obtained by de t.bag si s.k. samanta can be obtained in this more general context as shown in papers [37], [36]. 4. this definition is adequate because in this context, as shown in paper [37], x becomes topological vector space. more precisely, for x ∈ x,� ∈ (0, 1) and τ > 0 we define the open ball b(x,�,τ) = {y ∈ x : n(x−y,τ) > 1 − �}. then tn = {g ⊂ x : x ∈ g iff (∃)τ > 0, (∃)� ∈ (0, 1) such thatb(x,�,τ) ⊆ g} is a topology on x. moreover, in correlation with this topology, the applications x ×x 3 (x,y) → x + y ∈ x and k×x 3 (λ,x) → λx ∈ x are continuous. therefore x is a topological vector space. in the particular case in which ∗ = ∧, in the same paper it is shown that x with the topology tn becomes a locally convex space. 5. when we work with fuzzy normed linear spaces of type (x,n,∧), it is very helpful to us the family of semi-norms {||·||α}α∈(0,1) (see [37]), where ||x||α= inf{τ > 0 : n(x,τ) > α}. 2 generalized fuzzy normed linear spaces there are several generalizations for fnls-s. we will present only three that were given less attention in the literature of genre, but that deserve to be studied. first of all it is about fuzzy ψ-normed spaces, introduced by i. goleţ [23] in 2010. let ψ : r → r such that 1. ψ(x) = ψ(−x), (∀)x ∈ r; 2. ψ(1) = 1; 3. ψ is strict increasing and continuous on (0,∞); 4. lim x→0 ψ(x) = 0 ; lim x→∞ ψ(x) = ∞. there are many examples of such functions: ψ(x) = |x| ; ψ(x) = |x|α,α ∈ r+ etc. let x be a vector space over r. a fuzzy set n in x × [0,∞) is called fuzzy ψ-norm if it satisfies (n1), (n2), (n4), (n5) and (n3)* n(λx,τ) = n ( x, τ ψ(λ) ) , (∀)x ∈ x, (∀)τ ≥ 0, (∀)λ ∈ r. the triple (x,n,∗) is called fuzzy ψ-normed space. another generalization is made by s. nădăban [35]. let x be a vector space over k. a fuzzy set p in x ×r is called fuzzy pseudo-norm if it satisfies: (pn1) p(x,τ) = 0, (∀)x ∈ x, (∀)τ ≤ 0; (pn2) [p(x,τ) = 1, (∀)τ > 0] if and only if x = 0; (pn3) p(λx,τ) ≥ p(x,τ), (∀)x ∈ x, (∀)τ ∈ r, (∀)λ ∈ k, |λ|≤ 1; (pn4) p(x + y,τ + ς) ≥ min{p(x,τ),p(y,ς)}, (∀)x,y ∈ x, (∀)τ,ς ∈ r; (pn5) lim τ→∞ p(x,τ) = 1, (∀)x ∈ x; (pn6) if there exists α0 ∈ (0, 1) such that p(x,τ) > α0, (∀)τ ≥ 0, then x = 0; https://doi.org/10.15837/ijccc.2021.6.4587 4 (pn7) (∀)x ∈ x,p(x, ·) is left continuous on r. the pair (x,p) will be called fuzzy pseudo-normed linear space. finally, in paper [42], the authors worked with c*-avf normed space. in order to introduce these spaces we consider a be an order commutative c*-algebra and we denote by a+ the positive cone of a. let x 6= ∅. we consider some generalized fuzzy sets in x. these are mappings µ : x → a+, which are called c*-algebra valued fuzzy sets, briefly c*-avf sets. we denote by 0 = inf a+ and 1 = sup a+. we consider generalized t-norms on a+, namely mappings � : a+×a+ → a+ such that: 1. s� 1 = s, (∀)s ∈ a+; 2. s� t = t�s, (∀)s,t ∈ a+; 3. (s� t) �r = s� (t�r), (∀)s,t,r ∈ a+; 4. if s1,s2, t1, t2 ∈ a+ such that s1 ≤ s2 and t1 ≤ t2, then s1 � t1 ≤ s2 � t2. if in addition, for every sequences (sn), (tn) from a+ converging to s ∈ a+ and t ∈ a+, we have that lim n→∞ (sn � tn) = s� t, we will say that � is continuous. these t-norms will be called continuous triangular norms, shortly ctn. definition 2. the triple (x,n,�) is called a c*-avf normed space if x is a vector space over c, � is a ctn on a+ and n is a c*-avf set on x × [0,∞) such that: (n1) n(x, 0) = 0, (∀)x ∈ x; (n2) [n(x,τ) = 1, (∀)τ > 0] iff x = 0; (n3) n(λx,τ) = n ( x, τ|λ| ) , (∀)x ∈ x, (∀)τ ≥ 0, (∀)λ ∈ c∗; (n4) n(x + y,τ + ς) ≥ n(x,τ) �n(y,ς), (∀)x,y ∈ x, (∀)τ,ς ≥ 0; (n5) (∀)x ∈ x,n(x, ·) : x → a+ is left continuous and lim τ→∞ n(x,τ) = 1. 3 fuzzy banach spaces the definition of the convergence of a sequence (xn) in a fnls (x,n,∗) is natural and it is used by all the researchers. thus, a sequence (xn) is said to be convergent if there exists x ∈ x such that lim n→∞ n(xn −x,t) = 1, (∀)t ≥ 0. in this case, x is called the limit of (xn) and we denote lim n→∞ xn = x or xn → x. it is easy to see that xn → x iff xn → x in the topology tn. regarding the notion of cauchy sequence, the great majority of the articles use a definition inspired by the work of m. grabiec in fuzzy metric spaces (see [22]). we will call these sequences g-cauchy sequences. definition 3. a sequence (xn) is called g-cauchy sequence if lim n→∞ n(xn+p −xn, t) = 1, (∀)t ≥ 0, (∀)p ∈ n∗. let us note that (xn) is a g-cauchy sequence if and only if (∀)� ∈ (0, 1), (∀)t ≥ 0, (∀)p ∈ n∗, (∃)n0(�,t,p) ∈ n∗ : n(xn+p −xn, t) > 1 − �, (∀)n ≥ n0. we will use, in this paper, a different definition, motivated by the work of a. george and p. veeramani [21], and we will call these sequences g-v-cauchy sequences. definition 4. a sequence (xn) is called g-v-cauchy sequence if (∀)� ∈ (0, 1), (∀)t ≥ 0, (∃)n0(�,t) ∈ n∗ : n(xn+p −xn, t) > 1 − �, (∀)n ≥ n0, (∀)p ∈ n∗. https://doi.org/10.15837/ijccc.2021.6.4587 5 considering the ideas from [21], we will justify that definition 3 is the adequate one. let xn = 1 + 12 + 1 3 + · · · + 1 n in (r,n,∧), where n(x,t) = t t+|x|. thus n(xn+p −xn, t) = t t + |xn+p −xn| = t t + 1 n+1 + 1 n+2 + · · · + 1 n+p → 1, as n →∞. hence (xn) is a g-cauchy sequence. as it is natural to consider that (r,n,∧) is complete, we obtain that there exists x ∈ r such that xn → x, i.e. lim n→∞ n(xn−x,t) = 1, (∀)t ≥ 0. therefore lim n→∞ t t+|xn−x| = 1, (∀)t ≥ 0. hence |xn−x|→ 0, as n →∞. thus 1 + 12 + 1 3 + · · · + 1 n → x, which is not true. let us show that (xn) is not a g-v-cauchy sequence. we suppose that (xn) is a g-v-cauchy sequence. thus, for � = 12 ∈ (0, 1), t = 1 3 > 0, (∃)n0 ∈ n : n(xn+p −xn, t) > 1 − �, (∀)n ≥ n0, (∀)p ∈ n∗. for p = n we obtain that t t + |x2n −xn| > 1 − � ⇔ t t + 1 n+1 + 1 n+2 + · · · + 1 2n > 1 − � ⇔ 1 3 > 1 2 ( 1 3 + 1 n + 1 + 1 n + 2 + · · · + 1 2n ) ⇔ 1 3 > 1 n + 1 + 1 n + 2 + · · · + 1 2n . but 1 n+1 + 1 n+2 + · · · + 1 2n > 1 2n + 1 2n + · · · + 1 2n = 1 2 and we have reached a contradiction with the assumption that we made. therefore (xn) is not a g-v-cauchy sequence. definition 5. let (x,n,∗) be a fnls. (x,n,∗) is said to be g-v complete if any g-v-cauchy sequence in x is convergent to a point of x. a g-v complete fnls will be called fuzzy banach space. 4 fuzzy continuous mapping and fuzzy bounded linear operators let (x,n1,∗1) and (y,n2,∗2) be fnlss with continuous t-norms ∗1, ∗2. definition 6. [6] a mapping t : x → y is called fuzzy continuous at x0 ∈ x, if (∀)ε > 0, (∀)α ∈ (0, 1), (∃)δ = δ(ε,α,x0) > 0, (∃)β = β(ε,α,x0) ∈ (0, 1) s.t. (∀)x ∈ x : n1(x−x0,δ) > β we have that n2(t(x) −t(x0),ε) > α. if t is fuzzy continuous at each point of x, then t is called fuzzy continuous on x. definition 7. [34] a mapping t : x → y is said to be fuzzy lipschitzian on x if there exists l > 0 such that n2(t(x) −t(y), t) ≥ n1 ( x−y, t l ) , (∀)t > 0, (∀)x,y ∈ x. if l < 1 t is called fuzzy contraction. we note that a fuzzy lipschitzian mapping is fuzzy continuous. definition 8. [6] a linear mapping t : x → y is called strongly fuzzy bounded on x if there exists m > 0 such that n2(tx,t) ≥ n1 ( x, t m ) , (∀)t > 0, (∀)x ∈ x. in paper [6] the authors also introduce other concepts of boundedness such as weakly fuzzy bounded, uniformly bounded and they define the notion of fuzzy norm of a strongly fuzzy bounded linear operator. t. bag and s.k. samanta prove in this context the hahn-banach theorem, the open mapping theorem, the closed graph theorem and the uniform boundedness principle. fuzzy bounded linear operators have further been studied by many mathematicians (see [34], [45], [12] etc.). simultaneosly fuzzy bounded linear operators in felbin’s type fnlss have been studied (see [10], [4], [26], [49] etc.). https://doi.org/10.15837/ijccc.2021.6.4587 6 5 fuzzy fixed point theory as it was only natural, the first fixed point theorems in fuzzy context were obtained on fuzzy metric spaces. thus m. grabiec [22] introduced the notion of complete fuzzy metric space and extended banach contraction theorem in fuzzy metric space. his work was continued by j.x. fang [19] who obtained new fixed point theorems in complete fuzzy metric spaces for some contractive type mappings. after a. george and p. veeramani [21] modified the definition for a cauchy sequence, a change that led to another type of completeness for fuzzy metric spaces, many fixed point theorems have been obtained within this new frame. analyzing the many studies to fixed point theory on fuzzy metric spaces, but, at the same time, as any g-complete fuzzy metric space is g-v-complete, we conclude that the obtaining of fixed point theorems in g-v-complete fuzzy metric spaces is much more valuable. as there are many beautiful and important studies dedicated to the fixed point theorems on fuzzy metric spaces, we will make no further reference. we will refer only to a few recently published papers: [25], [14], [40]. there are significantly less studies regarding the fixed point theorems in fnlss. thus, in 2004, j. xiao and x. zhu [50] obtained the first fixed point theorems for operators on fnlss of felbin type. then, t. bag si s.k. samanta published two articles [7],[8] in 2006 and 2007. in these articles they introduced important notions as fuzzy non-expansive mapping, fuzzy normal structures, l-fuzzy compact sets, sectional fuzzy continuous mappings, but they also proved a kirk-type fixed point theorem, a schauder-type theorem and other fixed point theorems were established in fnlss. their results were processed and developed by i. sadeqi and f. solaty kia [46] with the help of the concept of fuzzy reflexive banach space. other fixed point theorems for self-mappings, coincidence point theorems, coupled fixed point theorems, approximate fixed point or common fixed point theorems for pairs of compatible mappings were obtained in the context of fuzzy normed linear spaces (see [32], [33], [47]) fuzzy n-normed linear spaces (see [18]) or in the frame of intuitionistic fuzzy normed spaces (see [13], [24]) or in generalized fuzzy normed spaces (see [23], [15]). fuzzy fixed point theory is a rapidly developing domain. however, most papers in this field do not pay much attention to its applications beyond the theory itself, narrowly understood. so, we feel obliged to refer to the applications as well. the applications of fuzzy fixed point theorems are numerous, both in mathematics as well as in engineering and economics, in problems of approximation, in game theory and many others. recently, in paper [3] there were presented new fuzzy fixed point theorems in metric spaces and their applications to fuzzy fractals. in paper [39] the authors got a version of the famous banach contraction principle in fuzzy quasi-metric spaces. using this result they proved the existence of a fixed point for a contraction on the domain of words. moreover, this approach led to the existence of solution for some recurrence equations associated to the analysis of quicksort algorithms and divide & conquer algorithms, respectively.their results were then expanded in the context of intuitionistic fuzzy quasi-metric spaces in paper [44]. in paper [31] the authors proved more fixed point theorems in the context of complete b-metric spaces. as applications, they analyzed the existence of approximate solutions for fredholm integral inclusions. in paper [1] the authors studied some common fuzzy fixed point in f-metric spaces and applied their result for existence of solution of fuzzy cauchy problems. in the following we will present some fixed point theorems in fuzzy banach spaces. even if some of them were obtained using axioms (n6) and (n7), and others using completeness in grabiec’s sense, their demonstration in this new context is similar, so we leave it to the reader. theorem 9. [17] (caccioppoli’s fixed point theorem). let (x,n,∧) be a fuzzy banach space and t : x → x satisfying: (∀)n ∈ n∗, (∃)kn > o : n(tn(x) −tn(y), t) ≥ n ( x−y, t kn ) , (∀)x,y ∈ x, (∀)t > 0 . if kn → 0, then t has a unique fixed point. theorem 10. [17] (kannan’s fixed point theorem). let (x,n,∧) be a fuzzy banach space and https://doi.org/10.15837/ijccc.2021.6.4587 7 t : x → x. if there exists k ∈ (0, 1/2) such that n(tx−ty,kt) ≥ n(x−tx,t) ∧n(y −ty,t), (∀)x,y ∈ x, (∀)t > 0 , then t has a unique fixed point. theorem 11. [47] let (x,n,∧) be a fuzzy banach space and f : [0,∞) → [0,∞) satisfying: 1. f is continuous and nondecreasing; 2. f(t) = 0 if and only if t = 0. if t : x → x satisfying: n(x−y,t) ≥ α ⇒ n(tx−ty,t−f(t)) ≥ α, (∀)x,y ∈ x, (∀)t ≥ 0, (∀)α ∈ (0, 1] , then t has a unique fixed point. theorem 12. [47] let (x,n,∧) be a fuzzy banach space, γ : (0,∞) → [0, 1] be a decreasing function and g : [0,∞) → [0,∞) satisfying: 1. g is continuous and strictly increasing; 2. g(t) = 0 if and only if t = 0. if t : x → x satisfying: n(x−y,t) ≥ α ⇒ n(tx−ty,g−1(γ(t)g(t))) ≥ α, (∀)x,y ∈ x, (∀)t ≥ 0, (∀)α ∈ (0, 1] , then t has a unique fixed point. another issue that should be studied is the problem of fuzzy approximate fixed point. this notion was introduced in papers [32], [33], motivated by the work of m. berinde [11]. let (x,n,∧) be a fuzzy normed linear space and t : x → x. let ε > 0. a point u ∈ x is called fuzzy approximate fixed point for t if there exists α ∈ (0, 1) such that ||u−tu||α< ε. we will denote by fzε (t) the set of all fuzzy approximate fixed point for t. we will say that t has the fuzzy approximate fixed point property (f.a.f.p.p.) if (∀)ε > 0,fzε (t) 6= ∅. theorem 13. [32] let (x,n,∧) be a fuzzy normed linear space, t : x → x and α ∈ (0, 1). if lim n→∞ ||tnx−tn+1x||α= 0, (∀)x ∈ x, then t has the fuzzy approximate fixed point property. another approach, with the help of the fuzzy norm would be: definition 14. let ε ∈ (0, 1). a point u ∈ x is called fuzzy approximate fixed point for t if n(u−tu,t) > 1 −ε, (∀)t > 0. theorem 15. let x ∈ x. if lim n→∞ n(tnx−tn+1x,t) = 1, (∀)t > 0, then t has the fuzzy approximate fixed point property. proof. lim n→∞ n(tnx−tn+1x,t) = 1, (∀)t > 0 ⇔ (∀)ε ∈ (0, 1), (∃)n0 ∈ n : n(tnx−tn+1x,t) > 1 −ε, (∀)n ≥ n0, (∀)t > 0. we put u = tn0x. then n(u−tu,t) > 1−ε, (∀)t > 0. thus t has the fuzzy approximate fixed point property. https://doi.org/10.15837/ijccc.2021.6.4587 8 6 applications in domain theory domain theory is the creation of d. scott who introduced some computational models for computer engineering. domain theory play an important role in denotational semantics. let (x,n,∗) be a fuzzy normed linear space. we will denote by bx the family of all closed formal balls. more precisely, for x ∈ x, α ∈ [0, 1] and t > 0 we will denote by [x,α,t] the closed ball [x,α,t] = b(x,α,t) = {y ∈ x : n(x−y,t) ≥ α} . bx will be bx := {[x,α,t] : x ∈ x,α ∈ [0, 1], t > 0}. let [x,α,t], [y,β,s] ∈ bx such that 0 < β < α < 1 and t > s. we define a partial order ⊆ on bx by: [x,α,t] ⊆ [y,β,s] ⇔ n(x−y,t−s) ≥ 1 −α + β . the pair (bx,⊆) will be called fuzzy domain normed spaces. in [38] the notion of convergence for a sequence [xn,αn, tn] of elements of bx is introduced. the authors proved that bx is a directed complete partially order set, briefly dcpo, namely every directed subset of bx has supremum if and only if each sequence of bx converge to its maximal element. finally, the authors proved banach fixed point theorem on bx: theorem 16. let (x,n,∧) be a fuzzy banach space and t : x → x be a fuzzy contraction. then g : bx → bx, defined by g[x,α,t] = [tx,α,lt], has a unique fixed point. 7 conclusion and further works in this paper we have presented an evolution of the concept of fuzzy normed linear space, different approaches and generalizations. special attention was paid to the concept of completeness. as we have shown, in the vast majority of papers, researchers are working with a definition of completeness inspired by grabiec’s work in the context of fuzzy metric spaces. we argued that a much more appropriate definition would be one that starts from the work of george and veeramani on fuzzy metric spaces. we have highlighted some important results in the field of fixed point theory in fuzzy normed linear spaces, building fertile ground for study in further papers of some fixed point theorems in fuzzy banach spaces. future results will have an impact on mathematics and numerous applications in computer science. we consider that this paper can be of interest for the researchers who work in the domain of fuzzy functional analysis, in particular in fixed point theory, but at the same time, we hope that the work will be of interest to researchers working in fields such as: 1. integrated solutions in control theory and communications, 2. computational intelligence techniques, 3. decision support systems, where fuzzy normed linear spaces will be applied in problems such as: fixed point theorems and semantics of programs, in game theory, in problems of approximation, complexity of programs and algorithms, data mining, machine learning. references [1] alansari, m.; mohammed, s.s. azam, a. 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(1965). fuzzy sets, information and control, 8, 338–353. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: dzitac s.; oros h.; deac d.; nădăban s. (2021). fixed point theory in fuzzy normed linear spaces: a general view, international journal of computers communications & control, 16(6), 4587, 2021. https://doi.org/10.15837/ijccc.2021.6.4587 fuzzy normed linear spaces generalized fuzzy normed linear spaces fuzzy banach spaces fuzzy continuous mapping and fuzzy bounded linear operators fuzzy fixed point theory applications in domain theory conclusion and further works international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 4, month: august, year: 2020 article number: 3901, https://doi.org/10.15837/ijccc.2020.4.3901 ccc publications a prediction model for ultra-short-term output power of wind farms based on deep learning y. s. wang, j. gao, z. w. xu, j. d. luo, l. x. li yongsheng wang 1. college of computer and information eng., inner mongolia agricultural university, hohhot 010018, china 2. inner mongolia autonomous region key laboratory of big data research and application for agriculture and animal husbandry, hohhot 010018, china 3. college of data science and application, inner mongolia university of technology, hohhot 010080, china 4. inner mongolia autonomous region eng. & technology research center of big data based software service, hohhot 010080, china wangys@imut.edu.cn jing gao* 1. college of computer and information eng., inner mongolia agricultural university, hohhot 010018, china 2. inner mongolia autonomous region key laboratory of big data research and application for agriculture and animal husbandry, hohhot 010018, china *corresponding author: gaojing@imau.edu.cn zhiwei xu 1. college of data science and application, inner mongolia university of technology, hohhot 010080, china 2. inner mongolia autonomous region eng. & technology research center of big data based software service, hohhot 010080, china 3. institute of computing technology, chinese academy of sciences, beijing 100080, china xuzhiwei2001@ict.ac.cn jidong luo haohan data technology co., ltd, beijing 100080, china 11034161@qq.com leixiao li 1. college of data science and application, inner mongolia university of technology, hohhot 010080, china 2. inner mongolia autonomous region eng. & technology research center of big data based software service, hohhot 010080, china llxhappy@126.com abstract the output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. however, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. this paper attempts to improve the ultra-short-term prediction accuracy of output https://doi.org/10.15837/ijccc.2020.4.3901 2 power in wind farm. for this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (tsw) and long short-term memory (lstm) network. firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the tsw algorithm. on this basis, the tsw-lstm prediction model was established to predict the output power of wind farm in ultra-short-term. next, two regression evaluation metrics were designed to evaluate the prediction accuracy. finally, the proposed tsw-lstm model was compared with four other models through experiments on the dataset from an actual wind farm. our model achieved a super-high prediction accuracy 92.7% as measured by d_mae, an evidence of its effectiveness. to sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability. keywords: wind power, output power, ultra-short-term prediction, deep learning (dl), long short-term memory (lstm) model. 1 introduction the output power of wind turbines is very unstable, due to the stochasticity and volatility of wind energy. the grid-connection of a massive amount of wind power poses a huge challenge to the operation and dispatching of the power system and the security of the grid [4, 7]. against this backdrop, it is very meaningful to predict the output power of wind farms in a future period. accurate predictions help to rationalize dispatch and maintenance plans, and improve the utilization of wind power and wind energy [15, 18]. by time scale, the output power prediction of wind farms falls into long-term prediction, mediumterm prediction, and ultra-short-term prediction. the final category refers to the rolling forecast of the output power of wind farms in the coming hours. if predicted accurately, the data on ultra-short-term output power can be used to ease the pressure on frequency adjustment, and reduce the capacity of spinning reserve, making the power system and power supply more reliable [10, 23]. for decades, both the industry and academia have probed deep into the output power prediction of wind farms. three types of prediction methods have been developed with stable performance [9]: physical modelling [3], statistical modelling [1] and intelligent computing [2]. to obtain the output power, physical modelling derives the output power curve of wind farms through hydrodynamic and thermodynamic analyses on the results of numerical weather prediction (nwp) and the surface and spatial correlation data around the wind farms. this prediction strategy, involving complex models, numerous empirical parameters, and massive data on terrain and meteorology, is faced with heavy computing loads and slow updates. therefore, physical modelling only applies to medium to long-term prediction. in this paper, the predictions of physical models are used for comparative analysis in experiments. based on time series of output power and wind speed, statistical modelling forecasts future output power by mapping the input features to the time series of output power, in the light of historical data only. the common methods of statistical modeling include the kalman filter [28], stochastic time series method [12, 14], and support vector machine [6, 8, 20]. this prediction strategy cannot always make accurate predictions, owing to the difficulty in modelling, complexity of parameters and poor ability of generalization. intelligent computing is increasingly popular in the output power prediction of wind farms, thanks to the development of computer hardware and software and artificial intelligence (ai). intelligent algorithms like wavelet analysis and genetic algorithm (ga) have all been introduced to output power prediction [11, 26]. under different principles, these algorithms extract data features with varied structural designs. the applications of intelligent computing have enriched the theories on output power prediction of wind farms. however, the prediction effects fall short of the expectations of wind power enterprises, as the parameters are too complex and randomly initialized. with the boom of deep learning (dl) [5, 17], many researchers have attempted to apply the dl in intelligent computing. the multilayered structure of deep neural networks (dnns) can fit complex nonlinear mappings, and effectively prevent vanishing gradient [13, 16]. hence, the dnns have clear advantages in handling massive samples and nonlinear data. xue et al. [24] successfully https://doi.org/10.15837/ijccc.2020.4.3901 3 combined the gated recurrent units (gru), an improved version of long short-term memory (lstm), and convolutional neural network (cnn) into a dl network. nevertheless, dl networks should not be directly adopted to predict output power of wind farms, because the output power is affected by multiple constantly-changing factors. otherwise, the dl networks will have poor prediction accuracy and generalization ability, and even fail to converge. drawing on the merits of the above prediction methods, this paper aims to develop an output power prediction strategy for wind farms, which can overcome the existing problems in the prediction task with its strong generalization ability and high forecast accuracy. for this purpose, the authors put forward a dl prediction model for output power prediction of wind farm, based on the lstm and time sliding window (tsw). the proposed model is denoted as tsw-lstm. firstly, the data from multiple sources (e.g. meteorology and historical power) were fused and cleaned. then, the tsw was introduced to set up an input dataset of wind power time series, and extract the cyclic features of output power. after that, a dl network model was constructed based on the lstm for ultra-short-term prediction of output power in wind farm. the proposed model was verified through comparative experiments on the actual dataset of a wind farm. the results show that our model achieved the accuracy of 92.7%, as measured by d_mae. this research makes two major contributions: (1) the novel concept of tsw was introduced to construct the dataset. with the aid of the tsw, the original small dataset was expanded in size, such that data features could be extracted as much as possible. thanks to the robustness of the extracted features, the proposed tsw-lstm prediction model boasts strong generalization ability and high prediction accuracy. (2) there is no unified, intuitive criterion for regression-based output power predictions of wind farms. to solve the problem, two new performance metrics were designed for statistical regression, namely, statistical distribution of maximum relative error (s_mre), and the mean distribution difference of mean absolute value (d_mae). the two metrics are suitable for communication in the wind power industry. the remainder of this paper is organized as follows: section 2 fully explains our research method from the perspectives of overall framework, data preprocessing, dataset construction, tsw-lstm model building, and evaluation of model performance; section 3 verifies the performance of our model in output power prediction of wind farms, details the sources and features of experimental data, and introduces the experimental process, including constructing dataset, setting up evaluation criteria, designing experiments, and comparative analysis of experimental results; section 4 wraps up the research by explaining the causes of good prediction effects of our tsw-lstm model. 2 methodology 2.1 overall framework the output power prediction of wind farms is essentially mapping a set of input series to a set of output series. the key issue lies in the generation of a series of predicted output powers. as shown in figure 1, this paper designs a six-step prediction approach: step 1. fusion and cleaning of multi-source data. the data on meteorology, turbine state and power were sampled from a wind farm. the sampling intervals were unified and the data sampled at the same time were stitched together. then, the missing values were imputed by multiple linear regression (mlr), and the outliers were corrected through piecewise linear interpolation (pli), creating the initial dataset. step 2. dimension reduction and standardization. the main factors affecting the output power were identified through principal component analysis (pca), aiming to reduce the dimensionality of the data, while retaining most of the effective features. next, the data were standardized through discretization, normalization and one-hot coding, producing a discrete dataset of zeros and ones that facilitates machine learning. step 3. dataset construction based on the tsw. the time cycles of historical data (i.e. meteorological data, turbine state data and power data) were identified and extracted. on this basis, the tsw was introduced to set up a training set and a test set. https://doi.org/10.15837/ijccc.2020.4.3901 4 step 4. construction of the dl model. based on the lstm, a dl model was constructed to predict the ultra-short-term output power of the wind farm. since it is capable of processing the dataset generated by the tsw, the proposed model is denoted as the tsw-lstm. the model adopts a multilayered neural network, including lstm layers, fully-connected layers, etc. step 5. model training and optimization. the dl model was trained by the training set. the series of influencing factors (e.g. meteorology and turbine state) were mapped into the series of output powers. next, the prediction effect of the trained model was evaluated and optimized based on the test set. step 6. prediction of output power series. the optimized model was applied to predict the output power series of a wind farm in a specified period in future. the prediction results were compared with the actual output power, and contrasted with the results of other prediction methods. figure 1: the workflow of output power prediction of wind farms 2.2 data fusion and cleaning during the operation, a wind farm generates a huge amount of data. the generated data fall in different categories, and differ in format and sampling frequency. there are often anomalies like missing values and outliers. the data entries are interconnected, involving key factors that affect turbine operations [25]. therefore, it is necessary to fuse and clean the original data before using them in output power prediction. in this paper, the meteorological data, turbine state data and power data collected from a wind farm are fused and cleaned to create a complete initial dataset. 2.2.1 data fusion the output power prediction of wind farm involves three types of data: meteorological data (e.g. wind speed, wind direction, humidity, temperature, atmospheric pressure and air density), turbine https://doi.org/10.15837/ijccc.2020.4.3901 5 state data (e.g. engine room temperature and generator torque), and power data (e.g. rated output power, planned output power, corrected output power and actual output power). according to the provisions of china’s national energy administration, the sampling intervals of all types of data were unified as 15min. then, the data sampled at the same time were stitched together, such that all three types of data are presented in the form of a unified 2d table at unified time points, forming the initial 2d dataset. 2.2.2 data cleaning part of the collected data may be missing or distorted under turbine failure, transmission interruption and signal interference. the missing values and outliers affect the statistical and distribution features of the collected data. in this case, the confidence interval of the collected data will widen, and the confidence coefficient will be reduced. if the data are analyzed by dl models, the ensuing errors will suppress the prediction accuracy. hence, the missing values must be imputed, and the outliers be corrected. (1) missing value imputation each missing value is usually directly removed, or simply padded with zeros, the previous value, the subsequent value or the mean value. in the data collected from the wind farm, the missing values are the time series of output powers. these values are distributed continuously or randomly in the collected data. the direct removal of these values will damage the time continuity and correlation of the time series. the simple padding will lower the variance of variables, and bring large covariance and correlation deviation, undermining the original data structure. the time series of output powers are the only missing values in the collected data, while all the other data features are complete. in other words, the data missing problem has only one variable. thus, the mlr was employed to fit and complete the missing values in the collected data. here, the output powers are regarded as a continuous time series. it is assumed that the output power at time ti is missing. let t1, t2, t3, . . ., tm be the m moments adjacent to time ti, at which the output powers are known. then, the missing output power at time ti can be imputed by the mlr: yti = β0 + β1yt1 + β2yt2 + l + βmytm (1) where, yti is the explained variable, i.e. the output power at time ti; ytk(k = 1, 2, . . .,m) are m explanatory variables, i.e. the output powers at time tk; βj (j = 1, 2, . . .,m) is the partial correlation coefficient relative to ytk, i.e. the influence of ytk over yti; µi is a random error obeying a gaussian distribution with a mean of 0 and a variance of σ2. then, the partial correlation coefficient βj was estimated by maximum likelihood, yielding β̂j. substituting the estimate to formula (1), the missing value ŷti can be estimated by: ŷti = β0 + β̂1yt1 + β̂2yt2 + · · · + β̂mytm (2) the missing values were imputed iteratively as above, producing a complete dataset without any missing value. (2) outlier detection and correction firstly, the outliers were found out through t-test. the non-suspicious values were regarded as a normally distributed population. the mean value x̄ and standard deviation s of the population were computed. meanwhile, the suspicious values were considered as a special population with a sample size of 1. if the suspicious and non-suspicious values belong to the same population, there should be no significant difference between them. the t-statistic can be defined as: k = |xd − x̄| (3) suppose the σ can be replaced with the standard deviation s. then, the t-statistic can be rewritten as k = |xd−x̄| s . if the t-statistic is greater than the threshold under the corresponding confidence, then xd must be an outlier. after that, the outliers were corrected through the pli: each two adjacent nodes were connected by a straight line, forming a polyline, i.e. the pli function in(x) satisfying in(x) = y. in each small https://doi.org/10.15837/ijccc.2020.4.3901 6 interval xi,xi+1, in(x)(i = 1, 2, . . .,n) is a linear function. in(x) and li(x) can be respectively expressed as: in(x) = ∑n i=0 yili(x) (4) li(x) =   x−xi−1 xi−xi−1 , x ∈ [xi−1,xi] x−xi−1 xi−xi+1 , x ∈ [xi+1,xi] 0, otherwise   (5) the interpolation of point x was computed by in(x), using the two nodes on the left and right of x. the computing load is independent of the number n of nodes. however, the greater the n value, the more the segments, and the smaller the interpolation error. the outliers were iteratively processed as above, until all of them had been corrected. 2.3 dimension reduction and standardization data fusion and cleaning produced the initial dataset with unified sampling frequency, complete attributes, and rational data distribution. but the dataset cannot be directly imported to the dl model. to solve the problem, the dataset was transformed into a 3d sparse matrix of zeros and ones, through pca, discretization, normalization and one-hot coding. 2.3.1 dimension reduction the initial dataset reflects the historical states of the wind farm more accurately than the collected data. the dataset contains various features, most of which have little impact on the output power. if all these features are imported, the dl model will face a heavy computing load and might not converge during the training. thus, the pca was carried out to select the key features that affect the output power, and reduce the dimensionality of the dataset, without sacrificing the effective information [21]. the pca mainly maps n-dimensional features to a k-dimensional space, that is, reconstruct kdimensional features based on the original n-dimensional features. during the pca, a set of mutually orthogonal coordinate axes were found sequentially from the original space. the first axis points to the largest variance in the original data, the second axis points to the largest variance in the plane orthogonal to the first axis, and the third axis points to the largest variance in the plane orthogonal to the first two axes. the rest can be deduced by analogy. most variances of the original data are contained in the first k axes, while the variances of the latter axes are almost zero. to reduce the dimensionality, the first k axes that contain most variances were preserved, and the other axes that contain near-zero variances were ignored. the pca was implemented through eigenvalue decomposition of the covariance matrix. to begin with, the initial dataset was rewritten as a matrix a =   a11 a12 · · · a1n a21 a22 · · · a2n ... ... ... ... am1 am2 · · · amn  , where n is the number of features and m is the number of samples. then, the dimensionality of matrix a, which contains m samples with n features, was reduced to k in the following steps: step 1. decentralization: subtract the mean value of each column from the features in that column, creating a new matrix a. step 2. calculate an n × n covariance matrix cov(a) by cov(a) = 1/m·at·a. the covariance matrix of three features can be expressed as: cov(x,y,z =)  cov(x,x) cov(x,y) cov(x,z)cov(y,x) cov(y,y) cov(y,z) cov(z,x) cov(z,y) cov(z,z)   (6) where, the diagonal element cii is the variance of the i-th feature; any other element cij is the covariance between the i-th and j-th elements. the covariance matrix is symmetric. https://doi.org/10.15837/ijccc.2020.4.3901 7 step 3. solve the eigenvalues and eigenvectors of the covariance matrix through eigenvalue decomposition. in other words, decompose matrix a into: cov(a) = q ∑ q−1 (7) where, q is the matrix of eigenvectors of matrix cov(a); σ is a diagonal matrix of eigenvalues. step 4. sort the eigenvalues in descending order and select the top k eigenvalues. then, take the k eigenvectors corresponding to the top k eigenvalues as row vectors, forming an eigenvector matrix p . step 5. map matrix a to the new space of the k eigenvectors by y = pa, marking the end of dimension reduction. 2.3.2 data standardization despite dimension reduction, the features of our data still have obvious dimensional difference. if these features are directly inputted to the prediction model, the network learning will focus on the variables with a large dimensional range. to unify the dimension, the variables were normalized by min-max scaling: x′ = (x −xmin)/(xmax −xmin) (8) where, x is the value of a feature at the current moment; xmin and xmax are the minimum and maximum of the feature, respectively; x′ is the normalized value of the feature. after normalization, the change trend and distribution law of each feature were observed carefully, and used to perform discretization and one-hot coding. take the wind direction for example. the wind direction data of the wind farm obey a continuous distribution from 0◦ to 360◦. the values of many angles are nothing but noises to dnns. in fact, the rotation plane of the blade is adjusted automatically by the yaw system of the turbine, according to the wind direction. the output power is not greatly affected by small changes in wind direction. therefore, the wind directions were discretized in segments to [0, 7], depending on their distribution law. the discretized values were then converted into a sparse matrix of zeros and ones through one-hot coding, which promotes the training effect of the prediction model. the other features were processed in a similar manner. 2.4 tsw-based dataset construction the processed data are insufficient to mine all the features of the wind farm. to expand the input data, the tsw algorithm was adopted to construct the dataset to be inputted to the prediction model, laying a good basis for accurate prediction. the actual output powers form a time series with a specific cycle. extracting the cyclic features help to improve the prediction accuracy. hence, the input dataset should cover the actual output powers. first, the cyclic features of the actual output powers were analyzed to determine the lookback of the sliding window, such that the output power curve has basically the same phase during the sliding window. next, the window was moved downward sequentially to segment the processed data: the first to the lookback-th entries were taken as the first sample, the second to the lookback+1-th entries as the second sample, and the rest can be deduced by analogy. in this way, the elements of the dataset and label set were obtained, creating the training set and test set. if there are l entries in the processed data, then the data were expanded by lookback times to (l− lookback + 1) × lookback entries. algorithm 1 shows the tsw algorithm used to construct the input dataset. the standardized 2d sparse matrix was inputted to the algorithm. the first column to the penultimate column are meteorological, turbine state and power features, while the last column are output power features. the total number of rows equals the number of entries in the collected data. the first row of the algorithm defines the width of the sliding window, i.e. lookback; the second row defines and saves the empty lists datax and datay for the dataset and label set; the sixth to tenth rows define an iteration of the algorithm. in each iteration i, a 2d matrix of lookback rows and one-fewer columns was taken from the input dataset and added to the datax list, and the element on the lookback + 1 th row in https://doi.org/10.15837/ijccc.2020.4.3901 8 the last column of the input dataset was added to the datay list. at the end of the iterative process, the two lists contain the dataset and label set required for the dl prediction model. algorithm 1 the tsw algorithm used to construct the input dataset 1: input: the processed input dataset 2: outputs: datax (list of data imported to the dl model) and datay (list of labels imported to the dl model). 3: start: 4: define the lookback of the tsw=3*96; 5: define empty lists datax and datay; 6: for all i = 1, 2. . .,n, [len(dataset) − lookback] do 7: form element a based on row i to row (i + lookback); 8: add a to datax; 9: add the last column of row (i + lookback) to datay; 10: end for 11: end. 2.5 lstm modelling 2.5.1 the lstm network both the processed input dataset and the predicted output powers are time series. to handle end-to-end series, a dl model can be established based on the recurrent neural network (rnn). the cyclic feedback structure of the rnn correlates the output state at time t with the historical signals before time t, thereby enhancing the network memory and reduces parameters. in theory, the rnn can handle time series of any length. however, if the input time series or the time series to be predicted is too long, the historical information will be replaced with the more recent information, causing vanishing or exploding gradient during model training. in our experiment, the input time series has more than 13,000 entries. the rnn cannot achieve a good prediction effect on such a long input time series. the vanishing or exploding gradient can be effectively solved by the lstm, an extension of the rnn. the lstm maintains the excellent structure of the rnn, and adds four new structures, namely, an input gate, a forget gate, an output gate and a memory cell. the additional structures can memorize and forget the entries in the input data series in a reasonable manner, allowing the long time series to propagate freely in the network being trained. the cyclic features of the input and predicted time series could be memorized well by the lstm, laying a solid basis for accurate prediction [19]. the typical structure of a memory module in the lstm network is explained in figure 2, where t(t = 1, 2, . . .,n) is time step; xt, nt, and ht are the input signal, state, and output signal of the memory module, respectively; ft, it and ot are the state signals of the forget gate, input gate and output gate, respectively. figure 2: the structure of a memory module in the lstm network let w and b be the weight and bias of each layer, respectively. the operation of the memory module can be described as follows: the forget gate determines which input information should be discarded. this gate receives the input signal xt of the current module and the output signal of the previous module ht−1, and generates https://doi.org/10.15837/ijccc.2020.4.3901 9 a signal of zero or one by sigmoid (activation function): ft = σ (wf · [ht−1,xt] + bf ) (9) the generated signal undergoes point multiplication with the state signals nt−1 of the previous module. the resulting signal ft∗nt−1 determines whether the state signal nt−1 of the previous module should be forwarded. the input gate controls the states of xt and xt flowing into the current module. based on the two signals, a signal it and a candidate signal nt are generated by sigmoid (activation function) and tanh (activation function), respectively: it = σ(wi.[ht−1,xi] + bi) (10) ñt = tanh(it (wc · [ht−1,xt] + bc ) (11) the update gate derives the state nt of the current module from the dot product nt of the above two signals. it can be seen that the state signal ft of the forget gate determines whether the state of the input time series in the previous module should be memorized. if not, the state signal of the current module nt only depends on xt and xt: nt = ft ·nt−1 + it · ñt (12) based on xt and xt, the output gate computes a signal ot by sigmoid (activation function): ot = σ (wo · [ht−1,xt] + bo) (13) meanwhile, a signal tanh(nt) is derived from nt by the tanh (activation function). the dot product between ot and tanh(nt) is the output signal of the current module ht: ht = ot · tanh (nt) (14) the above description shows that the cyclic features of the input time series can be memorized for a long time in the modules of the lstm network. the useless information can be discarded by the forget gate. through multiple trainings, the lstm network can theoretically fit the nonlinear relationship between the input time series and the output powers. 2.5.2 model construction the lstm network was integrated with the tsw-based dataset to create a prediction model for output power of wind farm. as shown in figure 3, the established tsw-lstm model consists of an input layer, two fully-connected layers, three lstm layers, a regularization layer, and a dropout layer. the structure of the model is detailed as follows: (1) the tsw-based dataset was inputted to the fully-connected layer. the dataset exists in the form of a 3d matrix x, where the first dimension is the number of elements; each element is a 2d matrix, whose row number is the size of time window and column number is the number of input features. the number of elements on the fully-connected layer equals the number of features. all the input features were transferred to the next layer. (2) the second layer of our model is the first lstm layer, which contains 32 memory modules. on this layer, the input dataset was automatically learned and encoded. the correlation between meteorological data and power data was extracted, so were the cyclic features of the two types of data. all the extracted information was transferred to the next layer. (3) the dropout layer falls between the first and second lstm layers. this layer randomly cuts off the connections between the two lstm layers, aiming to prevent overfitting. (4) the second lstm layer has 64 memory modules. on this layer, the input dataset was further learned and encoded to enhance the accuracy of nonlinear fitting. (5) the regularization layer, falling between the second and third lstm layers, helps to prevent overfitting. https://doi.org/10.15837/ijccc.2020.4.3901 10 (6) the third lstm layer involves 96 memory modules. on this layer, the signals from the previous layers were learned for the last time, making nonlinear fitting even more accurate. (7) the last layer of our model is a fully-connected layer, which outputs the series of predicted output powers. figure 3: the structure of the tsw-lstm model 2.6 performance evaluation error metrics are often adopted to evaluate the performance of regression analysis [22, 27], such as mean squared error (mse), root mean square error (rmse), mean absolute error (mae) and rsquared (r2) score. however, the prediction accuracy of output power of wind farm should be assessed by statistical metrics of accuracy. this paper designs two intuitive evaluation metrics, according to the statistical distributions of predicted values and true values. the two metrics are named as the statistical distribution of maximum relative error (s_mre), and the mean distribution difference of mean absolute value (d_mae). the s_mre value can be computed by: λi = |yi − ŷi| yi ,n = n∑ i=1 λi ≤ θ,a =   n∑ i=1 [ |yi−ŷi| yi ] ≤ θ n   % = ( n n ) % (15) where, n is the number of elements in the test set; is the ratio of the absolute difference between the i-th predicted value and the actual value to the actual value; θ is the value that can be accepted by the wind farm; n is the number of predicted values that satisfy < θ; (n/n)% is the statistical accuracy a of the prediction model. after consulting with the wind farm, the θ value was set to 0.2. the d_mae refers to the difference between the mean distribution of the mae and 1: the greater the d_mae, the higher the prediction accuracy. the d_mae value can be computed by: d_mae = 1 − 1 n ∑n i=1 |yi − ŷi| ŷi = 1 − mae y (16) 3 experimental verification the tf.keras, a high-level application programming interface (api) of tensorflow, was adopted as the dl framework of our modelling process. the data processing and visualization modules were called to process and display the collected data; the optimizers and regularizers modules were called to optimize our model; the callbacks module was called to dynamically adjust the learning rate; the lstm module was called to construct an lstm dl model based on the tsw-based dataset. the programs were compiled in python, and the experiments were conducted in a dl environment accelerated by graphics processing unit (gpu) [13, 16, 29]. https://doi.org/10.15837/ijccc.2020.4.3901 11 3.1 experimental dataset our model was trained and tested by the data from a wind farm in inner mongolia, china. the experimental data encompass two parts: the nwp data (historical meteorological data) and the power data of the wind farm. the nwp data were collected from january 1st to may 22nd, 2019 at a frequency of 15min by the anemometer tower on the wind farm, and corrected against the historical weather forecasts. there are 13,632 entries in the nwp data. each entry includes 9 fields: date and time occupy 2 fields, and meteorological features (e.g. wind speed, wind direction, air density and atmospheric pressure) occupy the other 7 fields. the power data were captured from january 1st to may 21st, 2019 at a frequency of 15min by the supervisory control and data acquisition (scada) of the wind farm. there are 13,536 entries in the power data. initial observations show that the nwp data have a high quality, without any missing values or outliers. this is because the data were collected from more than one source and cross-checked. the most important column in the power data provides the actual output powers. in this column, empty elements were found at 111 time points, which might be results from scada faults or transmission failures. further analysis reveals that the 42 of the 111 missing values are at the end of the data, and could be removed directly. the remaining 69 values are inside the data. the removal of the latter values would undermine the data integrity. through the above analysis, the nwp data were stitched with the power data. after stitching, the fields of time and date were turned into an index, which is not used for machine learning. the other features were subjected to the pca. the features (e.g. floor height and humidity) that do not greatly affect the output power were removed, leaving 6 features (e.g. wind speed, wind direction, atmospheric pressure and air density) in 13,494 effective and continuous entries. among them, the actual output power was empty in 69 entries. these empty items were completed through the mlr and the outliers were corrected. in the end, 1,3494 effectively stitched entries were obtained, and organized as the initial dataset. from an intuitive point of view, when other conditions remain the same, the output power of the wind farm should have an approximately linear relationship with the wind speed. here, the correlation between the output power and wind speed is investigated by preparing a scatterplot based on 1,920 entries collected over 20 days. as shown in figure 4, there is no linear relationship between wind speed and output power. this means the output power is influenced by various factors, making it difficult to create a physical model. the complex nonlinear relationship should be learned automatically through the dl. 2 4 6 8 10 12 14 wind speed (m/s) 0 10 20 30 40 o ut pu t p ow er (m w ) the scatter figure 4: the scatterplot between wind speed and output power 3.1.1 cyclic analysis the actual output powers in the initial dataset were subjected to cyclic analysis. the samples in three consecutive days were visualized. on each day, there are 96 entries from 00: 00 to 24: 00. as https://doi.org/10.15837/ijccc.2020.4.3901 12 shown in figure 5, the actual output powers on the three consecutive days had basically the same cyclic features. as a result, the cyclic features should be extracted from the actual output powers, in order to accurately predict the output power of the wind farm. 0:00 5:00 10:00 15:00 20:00 24:00 time of actual output power 10 15 20 25 30 35 o ut pu t p ow er (m w ) day 1 power day 2 power day 3 power figure 5: the cyclic features of actual output powers 3.1.2 tsw-based dataset construction to extract the cyclic feature of output power, the power data and nwp data were fused into the input feature, and the initial dataset was divided into a dataset and a label set. the dataset is a 2d matrix of 13,494 rows and 7 columns, while the label set is a 1d matrix of 13,494 elements. referring to the cyclic distribution of data, the time step of the lstm was set to 3 days, covering 96 sampling points; the lookback of the tsw was set to 288. firstly, the first 288 entries of the dataset were selected by the sliding window to be the first element of the input dataset, and the 289-th element in the label set was taken as the corresponding label. after that, the window was slid to the next entry in the dataset and the next element in the label set, and similar operations were performed. in the end, a 3d matrix was obtained as the input dataset. the number of elements in the input dataset equals the total number of entries in the original dataset plus one. each element is a 2d matrix defined by a sliding window. in our experiments, the final dataset and label set were (13,206, 288, 7) and (13,206, 1), respectively. figure 6 explains the procedure of the tsw-based dataset construction. for simplicity, the lookback in the figure was set to 4. figure 6: the procedure of the tsw-based dataset construction https://doi.org/10.15837/ijccc.2020.4.3901 13 3.2 error metrics 3.2.1 mse and rmse the mse refers to the mean of the squared errors between the values predicted on the test set and the actual values. for the same dataset, the mse is negatively correlated with the prediction effect. the mse can be computed by: mse = 1 n ∑n i=1 (yi − ŷi)2 (17) where, n is the number of samples; yi and ŷi are the actual values of and the values predicted on the samples, respectively; i is the serial number of samples. the rmse is the square root of the mse. the two metrics have the same meaning. the rmse is more suitable for computation and comparison, because the error dimension is reduced through the extraction of root. both the mse and rmse increase with the number of samples. hence, the magnitude of the two metrics is meaningless if the datasets are different. the same dataset was applied in all our experiments, so that the rmse could be adopted to evaluate and compare the errors of different prediction models. 3.2.2 mae the mae refers to the mean absolute error between the values predicted on the test set and the actual values. the mae is negatively correlated with the prediction effect. this metric can accurately reflect the actual prediction error. therefore, it was selected to evaluate the errors of different prediction models. the mae can be computed by: mae = 1 n |yi − ȳi| (18) 3.2.3 r2 score despite their excellence under the same dataset, rmse and mae cannot measure the prediction effect if there are different dimensions. the impact of dimensional difference can be eliminated by r2 score: r2 = 1 − ∑n i=1 (yi − ŷi)2∑n i=1 (yi − ȳi)2 (19) if r2 score < 0, the prediction error is greater than the error of using the mean value; i.e. the model is meaningless. if r2 score = 0, the numerator is equal to the denominator, and each predicted value equals the mean value, i.e. the model is still meaningless; if r2 score =1, the predicted values are equal to the actual values, i.e. the model makes error-free predictions. thus, the closer the r2 score is to 1, the better the prediction model. 3.3 experimental design table 1: the settings of hyper parameters number of nodes activation function l1 l2 dropout optimizer fully-connected layer 7 rectified linear units (relu) lstm1 32 sigmod 0.001 0.002 nadam dropout layer 0.3 lstm2 64 sigmod 0.001 0.001 nadam regularization layer 0.001 0.001 0.5 lstm3 96 sigmod 0.001 0.002 0.6 nadam fully-connected layer 1 rectified linear units (relu) nadam lr = 0.002, beta_1 = 0.9, beta_2 = 0.999, epsilon = 1e-08 reduce_lr patience = 5, factor = 0.8, mode = “auto”, verbose = 1, min_delta = 0.0001, cooldown = 0, min_lr = 0.0000001 https://doi.org/10.15837/ijccc.2020.4.3901 14 the tf.keras dl platform was deployed on the gpu. then, a dl network was constructed according to the abovementioned procedure. the established network encompasses an input layer (a fullyconnected layer), a hidden layer (two convolutional layers, a max-pooling layer, three lstm layers and three dropout layers), and an output layer. there are five types of hyper parameters in the network, namely, the number of nodes in the input layer, the number of nodes in each lstm layer, the regularization parameters l1 and l2, the dropout value, as well as the initial parameters and learning rate decay of nesterov adam (nadam), a stochastic gradient descent optimizer. the hyper parameters are configured as shown in table 1. 3.4 experimental results 3.4.1 experiments on our model the tsw-lstm model was trained for 50 iterations on the training set. the mae loss curve (figure 7) shows that the training and validation losses, relatively large at the start, exhibited a rapid decline. after 5 rounds of training, both losses started to gradually decrease. the decrease of training loss was relatively smooth, while that of validation loss fluctuated. thus, the overfitting occurred in model training. then, the model made automatic adjustments according to the preset hyper parameters, such as dropout, l1, l2 and learning rate (lr). after 30 rounds of training, the mae losses of training and validation both slowly decreased. the two loss curves were basically horizontal after 50 rounds, indicating that the model has completely converged, the losses were minimized and the prediction accuracy was maximized. the trained model was applied to predict the output power based on the test set. the predicted values were compared with the actual values of the test set. the predicted values and the actual values in 3 h are contrasted in figure 8, where the x-axis is the sampling intervals of 15 min, and the y-axis is the output power (unit: mw). it can be seen that the predicted output power (4 mw) was 0.3 mw smaller than the actual output power (4.3 mw) at the 0−th sampling point. in the 3 h ultra-short prediction period, the output power curve predicted by the tsw-lstm agrees well with the actual output power curve at the wind farm, an evidence of the good effect of our model. 0 10 20 30 40 50 epochs 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 lo ss es training and validation losses training loss validation loss figure 7: the training and validation losses of our model 0 1 2 3 4 5 6 7 8 9 10 11 12 time points 3.2 3.4 3.6 3.8 4.0 4.2 o ut pu t p ow er (m w ) actual output power curve predicted output power curve figure 8: the comparison between the output power predicted by tsw-lstm and the actual output power 3.4.2 contrastive experiments to fully demonstrate its engineering value, our model was further compared with the physical model and several dl models: decision tree (dt), random forest (rf) and svm. the four models were separately applied to the same dataset to predict the output power in the coming 24 h. the performance metrics of the models are compared in table 2 and figure 7. as shown in table 2, the three machine learning models, namely, the dt, rf and svm had similar mses, rmses and maes. the maes were all close to 20, a sign of the large gap between predicted and actual values. on https://doi.org/10.15837/ijccc.2020.4.3901 15 regression accuracy, the d_mae values of the three machine learning models were about 60% and the s_mre values were below 40%. the results show that the three models cannot realize satisfactory predictions. table 2: the comparison between the performance metrics of the models mse rmse mae r2 score d-mae s-mre physical model 220.1 149 8.53 65.7% 56.3% dt 1678.1 40.9 22.96 0.17 61.5% 37.4% rf 1162.2 34.0 19.12 0.41 61.4% 38.6% svm 1218.7 35 20.86 0.38 62.9% 39.8% tsw-lstm 11.7 3.4 1.39 0.93 92.7% 77.9% 0 24 48 72 96 time points 0 5 10 15 20 25 30 35 40 o ut pu t p ow er (m w ) actual output power predicted output power (a) physical model prediction vs. actual output 0 12 24 36 48 60 72 84 96 time points 0 20 40 60 80 100 o ut pu t p ow er (m w ) actual output power predicted output power (b) dt prediction vs. actual output 0 12 24 36 48 60 72 84 96 time points 0 20 40 60 80 o ut pu t p ow er (m w ) actual output power predicted output power (c) rf prediction vs. actual output 0 12 24 36 48 60 72 84 96 time points 0 20 40 60 80 100 o ut pu t p ow er (m w ) actual output power predicted output power (d) svm prediction vs. actual output 0 12 24 36 48 60 72 84 96 time points 0 1 2 3 4 o ut pu t p ow er (m w ) actual output power predicted output power (e) tsw-lstm prediction vs. actual output figure 9: the comparison between prediction effects the physical model of the wind farm had an mae of 8, much lower than that of any machine https://doi.org/10.15837/ijccc.2020.4.3901 16 learning model. hence, the physical model predicted the output power more accurately than the three machine learning models. the proposed tsw-lstm model achieved an mae of 1.39, a d_mae of 93% and an s_mre of 78%. this means our model far outperformed the other four models in mae and d_mae, and achieved satisfactory predictions. figure 9 compares the predicted output power (solid line) of each model and actual output power (dotted line). it can be seen that the predicted curves of physical model, dt, rf and svm for 24 h deviated far from the actual curve, i.e. none of the four models could fit the actual output power curve. by contrast, the predicted curve of the tsw-lstm agrees well with the actual curve, reflecting the high prediction accuracy of our model. 4 conclusions this paper presents a time series prediction model based on the lstm network: the tsw-lstm model. the wind power data from multiple sources were fused, and processed in multiple steps into an input dataset. under the input dataset, the output power of wind farm was predicted accurately by the proposed dl model. the main conclusions are as follows: (1) the proposed tsw-lstm model can effectively fit the output power curve of the wind farm, and clearly outperform the physical model of the wind farm and three machine learning models. (2) the wind farm data come from multiple sources, and contain many missing values and outliers. this paper fuses the multi-source data, and cleans the data through mlr and pli. the fusion and cleaning can effectively mine the data features, suppress noises, and improve prediction accuracy. (3) the tsw-lstm prediction model was trained by historical data and optimized repeated to overcome the exploding and vanishing gradients in training. the optimized model provides a desirable prediction tool. (4) considering the cyclic features of the power data of the wind farm, the historical power data were fused into the input dataset, and the tsw was introduced to construct the input dataset. in this way, the cyclic features were effectively extracted from the actual output power, pushing up the prediction accuracy. funding this work is supported by inner mongolia science and technology major special projects (2019zd016); natural science foundation of china (61462070, 61962045, 61502255, 61650205); inner mongolia agricultural university doctoral scientific research fund project (no.bj09-44); natural science foundation of inner mongolia autonomous region (2019ms03014, 2018ms-06003, 2019ms06027); inner mongolia key technological development program (2019zd015); key scientific and technological research program of inner mongolia autonomous region (2019gg273). conflict of interest authors declare no conflict of interest. references [1] alexiadis, m.c.; dokopoulos, p.s.; sahsamanoglou, h.s.; manousaridis, i.m. 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(2019). wind power prediction based on wide and deep neural network, journal of china academy of electronics and information technology, 14(3), 307–311, 2019. copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: wang, y.s.; gao, j.; xu, z. w.; luo, j. d.; li, l. x. (2020). a prediction model for ultrashort-term output power of wind farms based on deep learning, international journal of computers communications & control, 15(4), 3901, 2020. https://doi.org/10.15837/ijccc.2020.4.3901 international journal of computers communications & control issn 1841-9836, 10(3):420-427, june, 2015. engineering human stigmergy i. susnea ioan susnea dunarea de jos university of galati romania, 800008 galati, domneasca, 47 ioan.susnea@ugal.ro abstract: discovered in the context of a research about insects, stigmergy – the indirect coordination mechanism that allows ant colonies to achieve intelligent behavior – has been extensively studied with the aim to create artificial, ant-like agents. although stigmergic behavior has been also identified in human collectivities, there are relatively few reports about technological solutions that facilitate the emergence of such interactions between people. this paper proposes the concept of virtual pheromones, defined as engrams created by the agents not in the environment, but in a representation thereof – a map, and outlines several use cases, wherein pheromones embedded in maps are the key element for inducing stigmergic behavior in human multi-agent systems. without proposing a theoretic generalization, this paper aims to emphasize the broad range of possible technological applications of human stigmergy, and, maybe, to mark a new starting point for a more in-depth study of this topic. keywords: human stigmergy, virtual pheromones, pheromone maps, intelligent transportation systems, stigmergic shopping, stigmergic learning. 1 introduction stigmergy is a process of indirect coordination in multi-agent systems, by means of traces that agents create in the environment. upon sensing these traces, other agents are stimulated to perform similar actions, thus reinforcing the traces, in a self-catalytic process. as a result,simple, local and unplanned actions of the agents emerge in a complex, and apparently intelligent behavior of the system as a whole. discovered in the context of a biological research [6], the stigmergy in biological systems relies on the capacity of the agents to deploy in the environment small amounts of chemicals, called pheromones [8]. pheromones diffuse in space, so that a local source can be detected from within a certain range, and at the same time they evaporate, which makes unreinforced traces to decay and eventually disappear. a typical example of stigmergic interaction is the ant foraging behavior. when a searcher ant finds a food source, it starts leaving a pheromone trail on its way back to the nest. other ants tend to follow the path created by the searcher, reinforcing the initial trail. due to evaporation, longer trails that require more time for the ants to walk along become less attractive and, in the absence of reinforcement, they eventually disappear. in the end, the majority of ants will choose the shortest route between the food source and the nest. this processof finding the optimal route was called “ant colony optimization” (aco – [4]), and attracted a great deal of interest for the emergent intelligent behavior in swarms. numerous researchers have studied possible uses of stigmergy in robotics [19], for military applications [12], for routing data packets in computer networks [18], web mining [1], mobile sensor networks [7], or in cognitive sciences [9]. copyright © 2006-2015 by ccc publications engineering human stigmergy 421 a variety of solutions have been proposed for the implementation of the artificial pheromones, including automatic deployment of chemicals in the environment [16], storing special data structures in rfid tags [19], exchanging short range messages between agents [15], or by creating dedicated software entities [10]. all the implementations of stigmergic systems with artificial agents are based on a set of concepts and principles that have been formulated in [13]. according to parunak, a multi-agent system (mas) is a three-tuple: mas = {agents, environment, coupling} , wherein the agents are characterized by a set of states, a number of inputs and outputs, and a program that governs the transitions between internal states. the agent’s program runs autonomously (i.e. without being invoked by an external entity). the environment also has a set of states and a “dynamics” (program) that dictates the transitions between states. by means of their inputs and outputs, the agents interact with the environment by spreading pheromones, in certain conditions, according to their own program (coupling). in this approach, a mas presents the following features: agents are simple, identical entities; the environment is seen as a shared memory for all the agents; agents communicate with each other indirectly, by creating and sensing changes in the state of the environment. obviously, this model of interaction, which describes well multi-agent systems with simple, “lightweight” agents [13] is not suitable to describe interactions between so-called bdi agents (bdi stands for beliefs, desires, intentions [17]). the decision making, and the final behavior of such agents are governed by mechanisms far more complex than the simple, reactive response to environmental stimuli. however, there are many situations when human behavior is stigmergic [5], and it seems that the technological progress, which facilitates indirect communication between people, is likely to create more opportunities for human stigmergy. notable examples in this direction are: the market system, intelligent transportation systems, web page ranking (google), recommender systems (e.g. amazon ranking, imdb rating) online auctions (ebay), and many others. the present paper aims to facilitate the understanding of human stigmergy by presenting several examples of technological solutions to induce stigmergic behavior in human collectivities. without having the intention to propose theoretical generalizations, this may be the starting point for other interesting research in this direction. beyond this introduction, this work is structured as follows: section 2 discusses the concept of “virtual pheromones”, and proposes a simple model for it. section 3 presents several use cases wherein virtual pheromones are used to create stigmergic interactions between human agents, and section 4 is reserved for discussion and conclusions. 2 virtual pheromones as pheromones embedded in maps in an application of robotics [20], we proposed a solution for the implementation of artificial pheromones, wherein the agents leave traces not in the environment, but in a representation thereof – a map. in this approach, a number of agents, equipped with their own localization system and wireless communication, periodically communicate with a “pheromone server”. the “pheromone server” is a computer, running a special application software that creates and maintains a data structure called “pheromone map” (see figure 1). basically, the pheromone map is a 2d grid array, wherein each cell is associated with the following data: the coordinates of the corresponding geographic space, 422 i. susnea pheromone server pheromone server pheromone map pheromone map c o m m u n ic a ti o n communication communication c o m m u n ic a tio n gps satellites gsm infrastructure gps enabled vehicles a. mobile robots b. road vehicles figure 1: an illustration of the implementation of virtual pheromones an integer representing the resultant pheromone intensity of all the pheromone sources located in the respective cell periodically, each agent sends queries to the pheromone server, and includes in the query packet position information, as reported by its own localization system. upon reception of a query packet, the server locates the agent on the internal map, and computes for the respective position the resultant pheromone intensity, by means of a potential field model. with the notations in figure 2: s 1 s 2 s 3 s 4 s n s k vd 1 d 2 d n s figure 2: notations used to describe virtual pheromones aggregation and diffusion p(x) = { pk ( 1 − x σ ) 0 < x < σ 0 x >= σ (1) where p(x) is the intensity of the pheromones source sk, sensed at the distance dk, due to diffusion. due to the superposition of the effects (aggregation) of all n pheromone sources located within the sensitivity range σ, the resulting pheromone intensity, sensed in an arbitrary location is: pr = n∑ k=1 pk ( 1 − dk σ ) (2) engineering human stigmergy 423 and, assuming that the evaporation produces a linear decrease of the pheromone intensity, it is possible to write: pr = n∑ k=1 pk ( 1 − dk σ )( 1 − t − tk τ ) (3) where tk is the time of creation for the source sk, and τ is an evaporation constant. this simple, linear model was selected in order to reduce the computational load of the server, because the pheromone intensity (3) must be computed repeatedly, at regular time intervals, for every cell of the grid map, and this can be cumbersome in applications operating with very large maps. the value pr, computed by the server is returned to the querying agent in the response packet, and thereafter the agent acts as if it had its own pheromone sensing system. 3 applications of virtual pheromones the concept of virtual pheromones as described in section 3 of this article was initially developed in a research aiming to reduce cost of some service robots. by manipulating the pheromone maps it is possible to define easy-to-follow robot paths, by means of virtual pheromone gradients, as shown in figure 3. aa b lighter shades of gray indicate higher pheromone intensities figure 3: defining robot paths by means of pheromone gradients the idea was later used in an experiment for creating mixed robot formations with physical and virtual agents [21]. in this experiment, a real robot was programmed to follow a software simulated leader robot. though interesting, the experiments described in [20] and [21] are limited to controlling individual agents, and do not make use of the capability of pheromones to mediate stigmergic interactions. the following use cases focus on human stigmergy. 3.1 use case 1: real-time ant colony optimization for road vehicles common gps navigation systems for vehicles comprise a gps locator, which provides the information about the current position to a microcontroller that extracts from a local memory a predefined map of the geographic area, corresponding to the actual location of the vehicle. this map is displayed on a local screen, and serves for orientation. the main drawback of this solution derives from the fact that it uses static maps. no matter how often these maps are updated, they still do not include information about how fluent is the traffic at present time on a particular road segment, or whether there is a traffic congestion or a traffic jam. 424 i. susnea there are numerous attempts to overcome this limitation, (e.g. [14]) mainly based on providing an additional communication channel between the on-vehicle navigation assistant and an external device, which can be an internet server, or a roadside equipment designed to store an updated knowledge base about traffic conditions, actual practicability of the roads, etc. some web mapping services (e.g. google maps) include –for limited geographical areas color coded traffic information in maps, but these solutions usually offer just an estimate of the traffic conditions, based on previously collected data, or they rely on measuring the average speed of individual vehicles by means of gps data collected via smartphones. even so, the problem of updating the additional knowledge base still remains open, and can only be solved with substantial operation costs. a possible implementation of an improved gps navigation system for vehicles based on stigmergy would have the general structure presented in figure 1b. a fragment of the pheromone map, is transmitted by the server in response to queries sent by individual agents and superposed over the existing gps navigation maps as a transparent layer. since the pheromone map is created and maintained by the agents themselves, no additional operation costs are required for this system. 3.2 use case 2: in-store recommender systems most department stores and supermarkets use powerful inventory management software, which uses data from the pos equipment to keep a detailed database of the goods offered for sale. every item in this database has an unique barcode identifier, which is associated with substantial additional information (price, lot number, expiry date etc.) including the geographic position on the shelf where the respective item is located. having this information, it is relatively easy to set up a network like the one presented in figure 4, wherein a pheromone server maintains a map of the store and updates the pheromone distribution every time the pos reports the sale of an item. the resulting map is displayed for public on tv screens deployed throughout the store. pos equipment network hub inventory management pheromone map public display public display product database figure 4: the structure of a stigmergic in-store recommender system the shelves with frequently sold products are displayed on the map as “hot spots”, thus creating on the customers an effect similar – to a certain degree – with the amazon sales rank. the underlying idea of “value” associated with a high sales rank is likely to produce a stigmergic effect on the shopper’s behavior: some shoppers will visit the places indicated as holding highly ranked products and eventually will buy those products, contributing to a further increase of their sales (at least, this happens in the case of e-commerce see amazon.com) engineering human stigmergy 425 3.3 use case 3: self-organization of the educational objects in certain conditions, the emergence does not affect the agent’s behavior, but the environment shared by the agents. consider, for example, a database of “educational objects” [23] used by a (large) community of students, or researchers. each object is described by a set of metadata, including: an unique identifier, a set of tags (keywords) describing the educational content of the object, a means to detect that the object has been accessed by some user, a means to store virtual pheromone information every time a user accesses an object in the database, the pheromone intensity associated with that object increases. besides that, the pheromone intensity of all objects that share the same keywords with the object directly accessed is also increased, but with a lesser ratio (diffusion). on the other hand, the pheromone intensity of all objects in the database is automatically decreased by software at regular time intervals, so that, if an object is not accessed for a long time, its pheromone intensity eventually drops to zero (through evaporation). this simple scheme, automatically creates some metadata describing the “educational value” of the objects in the database, so that any user can immediately select educational objects (filtered by tags) with the highest value. though very simple, this approach, combined with a p2p environment, may be the starting point for a serious attempt to overcome the “teacher-student” learning paradigm, and replace it with a new “teach while you learn” paradigm. further research in this direction might be beneficial. 4 discussion and future work there are several aspects of the concept of virtual pheromones, as defined in this paper, worth to discuss: a. built this way, the pheromone maps can be seen as a special type of cognitive maps [24], having a very interesting feature: they create a link between the behavior implicit communication messages, generated by the agents [11] and certain geographical places. if somebody buys a product, chooses to watch an item in an exhibition, or visits a certain web page, he involuntarily sends a message carrying information about a personal decision. fusing the information from a plurality of such messages in a unique map, and making this map available for all the agents is the key element that can trigger stigmergic interactions. the idea that there is a connection between what we call swarm intelligence and the concept of cognitive maps has been suggested back in 1995 by chialvo and millonas in [2]. a closer look at the examples outlined in the above paragraphs (see also [22]), suggests that the concept of virtual pheromones as defined here offers the means to create such cognitive maps in just a few simple steps: automatic identification of behavior implicit communication (bic) signals. it is worth to note that the problem of automatic detection of certain behaviors has been extensively studied under the name activity recognition in various contexts (e.g. human robot interaction, ambient intelligence, pervasive healthcare etc.), but there are no signs of capitalization of the results of the research on activity recognition from the perspective of capturing bic messages; localization linking the bic messages to specific places within the environment. this can be done either by enabling agents with a certain degree of location awareness, as in the above examples, or by creating a number of place agents [3] (i.e. active places in the environment), capable to recognize and keep record of certain behaviors of the agents; 426 i. susnea creating a dynamic cognitive map of the environment based on the place-activity information provided by the virtual pheromones; sharing the resulting map (or parts of it) with all agents. b. the model of the virtual pheromones described by (3) does not require the absolute position of the agents to compute the pheromone intensity associated with a certain place. it only needs the relative distances between the specific place considered and the pheromone sources. this means that the proposed model can be used in principle in any metric space, whenever we can define a distance function. this leaves room for some interesting applications, where the intelligent agents operate in more abstract spaces, like databases, or social networks (see also [23]). c. designing systems that treat humans as simple “agents”, certainly may raise some ethical issues. for example, a stigmergic evacuation system designed to save lives in case of fire, involves rfid badges and readers deployed throughout the building, which can easily be used to monitor the movements of individuals. and this is not always ethical, and sometimes not even legal. to conclude, we will note that, although stigmergy was intensely studied over the past decades, the above mentioned use cases suggest that there is still plenty of room for interesting research and applications, mainly in what concerns the coordination mechanisms between complex, intelligent agents. in this context, the centralized approach on implementing virtual pheromones, presented here, 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(1948). cognitive maps in rats and men, the psychological review, 55(4): 189-208. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 2, month: april, year: 2022 article number: 4351, https://doi.org/10.15837/ijccc.2022.2.4351 ccc publications sentiment analysis using improved novel convolutional neural network (sncnn) m. kalaiarasu, c. ranjeeth kumar m. kalaiarasu associate professor department of information technology sri ramakrishna engineering college, coimbatore corresponding author:kalai.muthuswamy@srec.ac.in. c. ranjeeth kumar assistant professor (sr. gr) department of information technology sri ramakrishna engineering college, coimbatore ranjeeth.chandran@srec.ac.in abstract sentiment analysis is an important method in which many researchers are working on the automated approach for extraction and analysis of huge volumes of user achieved data, which are accessible on social networking websites. this approach helps in analyzing the direct falls under the domain of sa. sa comprises the vast field of effective classification of user-initiated text under defined polarities. the proposed work includes four major steps for solving these issues: the first step is preprocessing which holds tokenization, stop word removal, stemming, cleaning up of unwanted text information like removing of ads from web pages, text normalization for converting binary format. secondly, the feature extraction is based on the bag words, word2vec and tf-id which is a term frequency-inverse document frequency. thirdly, this feature selection includes the procedure for examining semantic gaps along with source features using teaching models and this involves target task characteristic application for improved novel convolutional neural network (incnn). the feature selection accompanies the procedure of information gain (ig) and pcc which is a pearson correlation coefficient. finally, the classification step incnn gives out sentiment posts and responses for the user-based post aspects which helps in enhancing the system performance. the experimental outcome proposes the incnn algorithm and provides higher performance rather than the existing approach. the proposed incnn classifier results in highest accuracy. keywords: sentiment analysis (sa), improved novel convolutional neural network (incnn), tf-idf is term frequency-inverse document frequency, svm is support vector machine, information gain (ig) and pearson’s correlation coefficient (pcc). https://doi.org/10.15837/ijccc.2022.2.4351 2 1 introduction community’s view and various feedbacks are constantly enhanced with most essential and valuable resources for organizations or companies. social media helps in raising new trends among each and every one; it paves way for unprecedented analysis and determination of numerous aspects in which organizations had to depend on unconventional, time consuming and error prone approaches earlier. the advancement of social media web is an important prospect to gain the valuable opinion of different people over numerous business works, political party areas, health-care causes and social media issues. these certain things have enhanced the growth of sentiment analysis in a dynamic principle of research fields [1]. social media sites like fb, twitter, blogs include specific prime platforms where users can share their important opinion on certain topics with this kind of platform diverse opportunities and challenges arise to actively use mixed techniques to extract and understand the opinion of others [2]. here, sentiment analysis holds a research area which helps in investigating the knowledge of people towards numerous matters such as various organizations, events and products. the part of sentiment analysis helps to improve the fast spread of micro blogging applications, social area networks and specific forums. presently, every single web page has its own space for responding over user’s feedback on products, services, and shares with friends on twitter, facebook or pinterest etc. it didn’t happen a few years before but for each mining the amount of opinion gives out with large information and more understanding over human actions upon precious commercial interests [3]. the sentiment analysis (sa) considers natural language processing (nlp) that consists of investigating and determining opinions with respect to products or movies. further, this is mentioned as opinions of mining. these views and analysis are posted over an online network by numerous customers of the web resources for accessing blogs and other social media related platforms. this review helps the person to obtain a sufficient study on overall people’s opinions regarding the specific tool on positive, negative and neutral [4-5]. at the same time, it is difficult to handle tasks based on time consumption. here, a single person has to undergo a task for consumption of time which basically focuses on a certain restaurant or a movie in the earlier stage of settlement for selecting and building a finite number of decisions. the important classification required for sentiment analysis is based on the three execution levels: (i) document level (ii) sentence level (iii) sub sentence level. this level helps in determining the exact sentiment analysis for documenting, processing of reviews in individual sentences and respectively obtaining a sub-expression of opinions in the required sentences [6-7]. cnn with air pollution index prediction model is studied in [24] and document classification model-based pearson correlation-based feature analysis is reviewed in [25]. single dimension cnn with feature extraction process is modeled for signal status recognition model [26] and the intrusion detection and normalization process is done with cnn [27]. the cnn lstm layer on forecasting particulate matter is reviewed in [28]. the sentiment analysis describes the perspective based on several events and situations. this repeatedly consists of infinite self-considerate feelings like happiness, sadness, kindness and regretless. this sentiment analysis label is splitted into polarity and valence of positive, negative and neutral which shows numerous feelings such as angry, sad, happy and proud. this sentiment label clarifies and influences the outcome of the analysis therefore, we must express the sentiment analysis carefully. this study defines more than three sentiment labels such as emotional feelings, score rating and acquires labels of two dimensions such as positive or negative [8-10]. together sentiment analysis performs better work for field improvement, and classifies all kinds of binary classification challenges such as performance of accuracy varied from 70 to 90 percent in characteristics with data terms. this binary classification has both positive and negative terms while the ternary classification includes all three types of terms so called positive, negative and neutral. in general, the methodology of sentiment analysis is shown in figure 1. in numerous studies, the classification of sentiments is done based on machine learning models like support vector machine (svm), naive bayes (nb), maximum entropy (me), stochastic gradient descent (sgd), and ensemble. the important features used for generating ml i.e., machine learning trained models are n-grams. the feature of unigram is based on sentiment analysis of binary classification with an accuracy of 88.94% by assisting the svm. further, it adopts the features of unigram and bigram with analysis of binary sentiment classification to obtain an accuracy of 84.4% using svm https://doi.org/10.15837/ijccc.2022.2.4351 3 figure 1: general block of sentiment analysis work process movie review data. this tackles all classification of binary data to process the features of unigram and bigram in order to attain an accuracy of 86.1% from the translated product review information of amazon. the proposed sentiment analysis of incnn framework holds four important key steps: (i) preprocessing (ii) feature extraction (iii) feature selection (iv) classification. 1. in the first step, preprocessing holds the use of tokenization, stop word removal, stemming and text cleaning features approaches. the preprocessing steps include message cleaning to get rid of hyperlink frequent posts. in the initial stage, the posts are clustered based on numerous features, tokenization is a function which segments text into words, clauses or sentences. stemming is an action of progressing the conflict word to a common system of representation. text cleanup focuses on removal of unwanted and unnecessary information like removing ads from web pages, normalizing the text converted from binary formats. 2. in step two, feature extraction is done based on the bag of words, term frequency-inverse document frequency (tf-idf) and word2vec. text feature extraction belongs to a task function which lists out text data words and transforms it to feature set classifiers. in general, it recognizes correctly the importance of features related text. word2vec for generating words as vectors. in a textual corpus, this extracts the relationship between the words and it is termed as word combinations. 3. in the third step, feature selection procedure is evaluated for semantic gaps along with source features produced using teaching model and target task characteristics before the application of improved novel convolutional neural network (incnn). this feature selection accompanies the procedure of ig which is an information gain and pcc which is a pearson’s correlation coefficient. the term ig is used for measuring the obtained number of information bits for predicting the category of presence or absence of a document. 4. finally, classification of in cnn provides sentiments towards the user post and responses to build up with a better perform ance system. the whole paper is organized as following things, 1. literature review on sentiment analysis on facebook existing schemes are briefly discussed. 2. the proposed methodology uses sentiment analysis over deep learning algorithms on the facebook system. 3. discussion and outcome of the study is presented. 4. conclusion and future enhancement of the presented work is expressed. https://doi.org/10.15837/ijccc.2022.2.4351 4 2 literature survey alnawas and arici [11] sketched a work on logistic regression (lr), naive bayes (nb), decision trees (dt) and support vector machine (svm)) in sentiment analysis word embedding from iraqi arabic. firstly, word representation helps to learn a huge corpus of work and secondly, it assists the embedded word to generate the model for corpus training in doc2vec paragraphs and constructs a architecture on dm-pv which focuses on distributed memory model of paragraph vector. at the end, four binary classifiers are used for representation of feature training such as lr, svm, dt and nb which detects sentiment analysis. here. different values of parameters are analyzed like window size, dimension and negative samples. the brighter side of the experiment concludes with a proposed approach to achieve a good performance of work based on lr and svm classifiers. salloum et al [12] presented a paper on natural language processing (nlp) which determines the classification of various comments and posts in facebook pages of arabic newspapers. here, entirely 24 numbers of arab gulf newspapers are explored and studied based on the facebook page with 6237 posts and 9372 comments. the data extraction includes various kinds of text mining methods to analyze the process of operations. in concern with arab gulf region, uae is the only country which indicates more shared posts over facebook. further, ksa and oman follow the same position. particularly, finding helped in enhancing the most attractive video post over the facebook page of arabic newspapers. nahar et al [13] designed a work on lexicon-based method, machine learning (ml) algorithms with naive bayes (nb), k-nearest neighbor (knn) and support vector machine (svm) for identifying polarity of the provided facebook comments in classification and sentiment analysis. the data samples are from local jordanian people who leave their comments on a public issue related to the services provided by the main telecommunication companies in jordan (orange, zain and umniah). the produced results regarding the evaluated arabic sentiment lexicon were notably promising. by trying the user-defined lexicon-based facebook posts on the common and comments used by jordanians, it scored (40%) negative and (60%) positive. the lexicon was used to label a set of unlabeled facebook comments to formulate a big dataset using supervised machine learning (ml) algorithms that are usually used in polarity classification. tran and shcherbakov [14] outlined an unsupervised clustering pattern on sentiment analysis in text identification and predication with positive or negative connection of facebook comments. (i) discovery of patterns with real-time sentiment text analysis. (ii) creation of forecasting algorithm choices based on data processing batch system. proposed methods are of followings: (1) batch analysis is performed based on facebook data collection for forecasting the model pattern. (2) detection of real-time patterns using text processing. (3) pattern prediction is built based on the grasping situations. (4) change in action with pattern analysis. this includes achievement of the proposed forecast with two step algorithms as follows: (i) unsupervised technique is used for pattern clustering. (ii) the nearest pattern is identified based on cluster prediction. also, this described and revealed about the three major kinds of user patterns. soliman et al [15] introduced sswil which is a slang sentimental words and idioms lexicon and enhanced svm which is a support vector machine. this is used in sentiment analysis over facebook comments in arabic slang. further, this proposes a support vector machine on the gaussian kernel to classify all the comments in arabic newspapers. the growth of the outcome has 1355 random comments and major three types of classifications. here, web users’ comments are written in the form of new brand syntax where the outcome of classification may be affected. moreover, in the initial stage of classification the extraction method fails to take out the words opinion but it gives a better performance by adding sswil. the proposed mining application applies all the comments and classifies the comments based on the first classification type known as classic lexicon which generates a lower accuracy level rate. the second classification type is expressed as sswil with classic lexicon which generates a higher accuracy level rate. finally, the testing is performed on proposed classifiers with the help of various facebook comments that are obtained on newspapers where the rate of https://doi.org/10.15837/ijccc.2022.2.4351 5 accuracy is high based on precision(p) and recall(r). meire et al [16] sketched an outline on svm support vector machine and rf random forest over facebook posts using sentiment analysis. the main motive of the review is (i) to evaluate the value attached information based on leading the existing time and to know the lagging of proposed time in creating focal posts for facebook by sentiment analysis (ii) to determine the prediction analysis (iii) to investigate the relation between sentiment and predictors. construct a sentiment prediction model that consists of information based on prime, lagging and traditional posts for the process of cross validation in rf and svm. the outcome mentions both are lagging and leading information can increase the model’s predictive performance. the key feature of a predictor consists of the integer in uppercase letters with a number of negative comments. a higher number likes increases the uppercase of likelihood positive posts. while, a higher number of comments reaches the likelihood of a negative post. the important contribution of this work phase is to context the sentiment analysis and access the added value of lagging information’s and leading information’s. ortigosa et al [17] developed a sent buk following hybrid approach that combines lexical-based svm-support vector machine and enhanced nb-naive bayes for the process of sentiment analysis over comments of the facebook. users support certain messages: (i) to clear user sentiment polarity information with positive, negative and neutral message transmission. (ii) to determine the sentiment analysis of the user model with certain emotional noted changes. the messages written by facebook users are easily retrieved from sent buk. accordingly, this classifies the polarity of the messages and results the user through gui which is a graphical user interface. here, detection of emotional changes is recorded based on the statistics and findings of messages by our friends. this is a hybrid approach where the method of classification is implemented over sent buck with lexical machine learning based techniques. this approach enhances the outcome of feasible study and helps to perform sentiment analysis with best accuracy over facebook. the users of sentiment analysis hold all the context of e-learning and also supports all the customized learning operations with various emotions. here, all the user’s activities are tackled by assisting the recommendation of suitable operators. on other side, course of the students are sentimental with feedback towards the teaching staff. in particular, during online class there will be less contact over various face connections. 3 proposed methodology sentiment analysis using proposed incnn framework contains four major steps called preprocessing, feature extraction, feature selection and classification. first things first, preprocessing step, stop word removal, tokenization, stemming. the preprocessing step includes messing up the contents to erase repeated posts and also hyperlinks. tokenization will process the segmenting of text into words, clauses or sentences. next part is feature extraction based on word2vec, term frequency-inverse document frequency (tf-idf) and bag of words. in a textual corpus, it extracts the relationship between words and it is termed as word embeddings. feature selection is the third step; its procedure for evaluating semantic gaps among source features produced using a teaching model and target task characteristics before incnn applications. feature selection follows the structure of pearson’s correlation coefficient (pcc) and information gain (ig). the term measured in ig is the number of bytes memory storage information acquired from the category of prediction by the absence or presence in a document. the figure-2 shows the overall idea of the proposed system. 3.1 preprocessing the preprocessing method includes erasing of messages to remove the replicate and hyperlinks the posts and also it is initially trying to cluster the posts formulated on different aspects. those messages are hashtags and trimmed off are gathered with them. here, crawlers collect the amount of data from facebook that has several kinds of meanings about punctuations. hence, the data is altered and punctuated to return back to text. this preprocessed text is crossed to the module for detection posture where the text features are cleaned up for removing stop words, tokenization and stemming. https://doi.org/10.15837/ijccc.2022.2.4351 6 figure 2: overall diagram of the proposed system a.tokenization the tokenization is the process of cutting down a stream of text into phrases, words, symbols or other kinds of meaningful elements. the main purpose of the tokenization is to explore the words into a particular sentence. the overall list of tokens would become an input for further processing areas such as text mining/parsing. the tokenization is applicable for both in computer science and in linguistics, where it is the form part of the lexical analysis. the textual data is the only block of characters in the initial. thus, the overall requirement of documents for that parser is tokenized. the tokenization is the operation of segmenting text into words, sentences or clauses and it is the process by the big quantity of text to be divided into smaller parts. it can be very useful in seeking such patterns to be the same as a base level step for lemmatization. usage of the function word tokenize () is to split a sentence into words and the result of word tokenize () can be transform into pandas data frame for better text performance and understanding in machine learning use cases [18]. description = nlp. word_tokenize (description) is used for tokenization. b.stop word removal stop word removal is a very important procedure of preprocessing. the procedure ensures that https://doi.org/10.15837/ijccc.2022.2.4351 7 superfluous words also with more or less not a big data content for the task under the considerations are discarded. it makes the purpose of a linked resource to perform to remove the stop word. this is part of stanford resources in nlp. according to their high-level frequency of occurrence, their presence in text mining presents a barrier in the understanding of the content in the documents. the important method of text data is to decrease the process and also to improve performance of the system where all the text document manages to operate the words which are not needed for text mining analysis. those words are already captured in this corpus named corpus. a list of stop words is made and the source document is compared with this stop list. if any word matches with this list, that word is removed from the source document. stop words don’t carry any value in matching queries to the document by removing the stop words did not affect the document semantics [18]. c.stemming the process of stemming helps in consolidating the various word forms into simpler text descriptions which is often used for text processing ir information retrieval assumptions. this poses all the queries for representing the document terms and executes all identified tokens for facebook with user generated strings. the following two features of token are: (i) main token and (ii) stemmed token where the process accepts the porter stemmer for stemming operations [19]. 3.2 feature extractions feature extraction is an important operation for extracting data in feature subsets in order to improve jobs of data classifications. the process of feature extraction text helps in identifying the text data of words list and transforms all the data into a set of feature classifiers which exactly finds out the key features of the text. the following are the feature extraction techniques used. bag of words the most common and simplest method of feature extraction is bag of words. this forms an instant feature set in the presence of words and that is described as a “bag” of words. these features help in bringing out the text from angles. further, this identifies the frequency of document words and it consists of (i) known words have frequency existence (ii) words of lexicon which is generally a word. the worst model case determines bags of words which processes the operation for scoring the famous words presence and to come out with familiar vocabulary words design. word2vec an open-source tool called word2vec developed by google for representing words as vectors. in a textual corpus, it extracts, relationship between words. it is termed as word embeddings [20]. unsupervised learning is involved in word2vec for producing word embedding representation in a meaningful manner. further, the creation of vector space consists of more dimensions such that each of the corpus words is distinctive with allocation and correspondence of the vector space. . the common word context is marked near the proximity of vector space. word2vec uses more than one architecture for processing cbow which is a continuous bag of words that holds frequent skip grams. in these current words are used for predicting context words near windows. in the architecture of cbow, prediction is not at all impacted by context word sequence and the basic model is built on bags of words. term frequency inverse document frequency (tf-idf) the particular way of scoring is known as term frequency inverse document frequency. • tf is the term frequency of new documents in word frequency • idf is the inverse document frequency of all documents to score the words the score highlights the important unique word in which a particular word represents necessary document information. thus; idf obtains high infrequent terms with low frequent terms. in order to create the glossary, the weight of all words is listed in connection with tf idf for recognizing the text mining algorithms. the frequency of word denotes repeated number of terms in the text and expands the idf as inverse document frequency. in specific, these algorithms are used for determining the https://doi.org/10.15837/ijccc.2022.2.4351 8 word text in inverse probability. following equation (1) helps in classifying tf idf equation and this equation is used for the calculation of weight as follow: wij = tfij ∗ loglogn/(dfi)(1) (1) this equation specifies,w_ij is word i weight in document j. here, n is the document number in the total set of documents. next, tf_ij is word i frequency in document j and df_i is word i holding documents number. 3.3 feature selections feature selection technique is used for evaluating semantic gaps with all sources of features produced by teaching models and target task characteristics before the application of incnn. this assists the procedure of information gain ig and pearson correlation coefficient pcc. feature selection methods can enhance the best efficiency. a.information gain (ig) the term information gain (ig) holds bit information number to obtain a categorization of predictive value which gives the presence or absence term of document forms [21]. further, m is denoted as class number and the gained information t term is expressed as in class c, feature f’s is analyzed using an information gain in sentiment analysis area. in a mutual information the value between feature f’s sentiment classes is high, then there will be an immense value of relevance between feature f’s sentiment. i(c, f ) = h(c) − h(c|f ) (2) h(c) = − ∑ cecp(c)logp(c) (3) here, the class entropy is denoted as h(c), and the class features of conditional entropy is presented as h(c|f ), h(f ) = − ∑ cecp(c|f )logp(c|f ) (4) in this dataset the classes are balanced. moreover, for negative and positive where class c’s probability is 0.5 and it generates 1 as a class entropy (c). information gain is expressed as, i(c, f ) = 1 − h(c|f ) (5) if h(c|f ) = 1, i(c, f )’s minimum value is generated and it presents there is no relation between classes c and feature f. thus, the feature f is selected in such a way that it emerges either in negative class or positive class. if p (f|c1) is same as p (f ), maximum value ofi (c|f ) can be achieved and it results in h(c_1 | f) and p(c_1 |f) value of 0.5. the p (f|c2) value results in p (c2|f ) = 0, if p (f ) = p (f|c1) and makes (c1|f )=0. the i(c, f ) value is changed between 0 to 0.5. b.pearson correlation coefficient (pcc) the pearson correlation coefficient (pcc) is the linear computation between two variables associated. the regression line slope is equal to the ratio of standard pcc deviations. the pearson correlation tackles all the variables and eliminates the single outlier correlation line to decrease the error in mainstream to follow up with normal distribution variable homoscedasticity where the same regressed variance lines are used. ppearson(x, y ) = ∑( n i = 1)(xi − (x))(yi − y))√ ( ∑i n = 1(xi(x)2) ∑i n = 1(yi(y)2) (6) this case focuses on joint normal distribution of pearson correlation coefficient (pcc) that accompanies with t-distribution n2 which is a non-dependent x and y degree of freedom. here, x and y are linearly dependent with a set pearson (x,y)=±1 . this case includes a perfect positive that denotes the linear relationship increment where pearson correlation coefficient is an important set of +1 and the case of perfect negative denotes linear relationship decrement with a set of -1. https://doi.org/10.15837/ijccc.2022.2.4351 9 if x and y are nonlinear association. however, pearson(x,y)=0 and partial linear dependency case shows -1 <p_pearson (x,y)<1 . even though, no data association scores zero correlations and it does not imply any association. pearson correlation is to analyze forms of different words to calculate all correlated event days. these words enhance the classifier feature for training and testing. an accurate prediction of a class in classification by a feature is examined using pcc while riding other features. according to the realized score of correlation p, every feature is ranked sequentially. here, the feature of x_i’s variance is defined as var(x_i) with a covariance between target class yand the feature x_i is defined as cov(x(i, ), y ) where the variable covariance is denoted as cov. simultaneously, every variable’s variance is expressed as var. 3.4 sentiment classification using incnn a specific classification of improved novel convolutional neural network (incnn) provides a process description for text classifications. here, cnn has multistage trainable neural network architecture is developed for task classifications [22]. 1.the convolutional layers are important parts of convolutional neural network model. this layer holds the amount of kernel matrix. it produces an output matrix of features and convolution on their inputs where a bias value is joined. to learn the method it aims to train the biases as shared and kernel weights in neuron connection weights. 2.the pooling layers are the specific and essential component of cnn. the purpose of the pooling layer is to behave as dimensionality depletion of the input feature. the pooling layers form subsampling output matrices of convolutional layers to connect up with nearest elements. one of the most familiar pooling functions is max-pooling. that takes in the peak value of local neighborhoods. 3.hidden layers are the exclusive part of text classification in cnn. to convert the text inputs into an applicable form of cnn is the major usage of the embedded layer. thus, convert the whole text document into a dense vector of stable size. softmax layers are the finest hidden layers which have an exceptional case of convolutional layer. it interpreted with a kernel size of 1×1. that type of layer resides with these are used in the concluding stages of cnn and a class of trainable layer weights. figure 3: architecture of incnn cnn holds the advantage of convolutional filters. those filters automatically learn the features which are apt for the specified task. following the example, cnn is used for the sentiment classification of convolutional filters that captures out semantic features of sentimental expressions and inherent syntactic where the threshold varies based on the dataset. p = cov(xiy )√ var(xi).var(y ) (7) n_w k_p denotes the expected frequency of co-occurrence, (freq(w,p)-n_w k_p indicates the difference between observed and expected frequencies. algorithm 1: incnn input: analyze a labelled dataset output: based on polarity of extracted reviews whether it is positive, negative and neutral. step 1: extract the reviews. step 2: obtain the feature vectors. step 3: joining up training dataset and feature vector list by computing the probability features using https://doi.org/10.15837/ijccc.2022.2.4351 10 (9) step 4: performance of training and testing using incnn algorithm. step 5: identify similar feature vectors using convolution layers. step 6: give out positive, negative and neutral post convolutional layer is used for extracting patterns, i.e., specific discriminative sequence words are obtained inside of the input words that would commonly follow during the instances of training. the two-word types used for encoding the elements are token spanning phrases which are used to encode all others with 0s and with 1s. here, every word of type is combined with its self-embedding. thus, when we tackle the phrase level classification of sentiment it forms a matrix in sentence s as following: every token of a comment has a corresponding alternate word embedded in the matrix of word w. it is embedded for each one of the words in two types. eventually, the whole input of the matrix in sentence is augmented with an additional set of the rows from embedded words. overall, the architecture of the proposed network remains constant. 4 results and discussion originally, the dataset was downloaded from kaggle [23]. this experimental setup indicates the online social workers in real-time environments. these datasets are gathered from a group named cheltenham facebook and major three groups of open source are selected for enhancing the experimental features. here, the dataset gives an exact set-up and senses the information of the gained matrix for sparse ratings. following are two set-ups of conducted experiments are (i) to prove effectiveness of proposed techniques in executing customized post solutions and to give out a free clutter environment group. (ii) to prove capability of the proposed work to identify the community of members in a population. these classification methods are analyzed in the terms of metric like precision (p), recall(r), f-measure(f1) and accuracy(a). in this part, svm and ann algorithms are compared for determining the performance metrics against proposed work of incnn algorithms which is further tested by a dataset of cheltenham facebook groups. dataset description. in general, the dataset is downloaded from kaggle [23]. these attributes include gid, pid, cid, timestamp, id, name, rid, msg. in the first case, for more the required models are trained on a training set (80%) and tuned using validation set with final test set (20%). evaluation metrics in order to classify each and every model various estimation of metrics are used they are listed as precision, recall, f-measure and accuracy. this needs multiple usages of metrics because they don’t specify the same values for all accounts. table 1: performance comparison metrics vs. sentiment analysis methods 1.precision(p) by considering posts as positive samples in the binary classification, precision is expressed as, in the above figure 4, this recognizes about the analysis of comparison metric in order to determine the usage of existing and approached methodology in the terms of precision. further, the methods of x-axis are grasped and the approach of y-axis helps to plot the values in precision. here, the existing method holds the svm and ann algorithms that provide a lower precision whereas the proposed incnn approach gives out the uppermost precision towards the labeled datasets. finally, the result concludes with a proposed incnn that helps improve the sentiment analysis classification method by https://doi.org/10.15837/ijccc.2022.2.4351 11 figure 4: precision of sentiment analysis methods classifying the neutral and non-neutral posts effectively for the defined dataset. 2.recall(r) the calculation of the recall(r) value is defined as: recall(r) = tp (tp + f n) (8) figure 5: recall(r) comparison of sentiment analysis methods in the shown figure 5, we observe the compatibility of the required metrics to examine the usage of previous and presented methodology in the term of recall. this approach grasps a specified process on the x-axis and recall for accurate marked y-axis values. svm and ann algorithms group the existing system method that provides a lower recall and the incnn proposed technique gives out the higher recall for a specified dataset. in general, the result concludes that the proposed incnn enhances the sentiment analysis classification process by analyzing the neutral and non-neutral posts are more efficient for the specified dataset. 3.f-measure(f1) f-measure(f1) is denoted as f 1 − score = (2xprecisionxrecall) (precision + recall) (9) the above figure 6, discovers about the comparison metric in order to estimate it using existing and proposed methodology in the terms of f-measure. further, analysis of x-axis methods are noted and simultaneously y-axis f-measure (f1) values are also plotted out. in the previous methods, svm and ann algorithms delivered bottommost f-measure in which the presented incnn technique gives out uppermost f-measure for a stated dataset. thus, the outcome concludes that the proposed incnn builds up a sentiment analysis for classification process by associating the neutral and non-neutral posts more efficiently for the given dataset. 4.accuracy(a) accuracy(a) is determined as the total accurateness of detection outcome and it is examined as the additional classification of parameters (tp+tn) divided by overall number of https://doi.org/10.15837/ijccc.2022.2.4351 12 figure 6: f-measure comparison of sentiment analysis methods detections with classification of specific parameters (tp+tn+fp+fn) accuracy = (tp + tn) (tp + tn + f p + f n) (10) in respect, the parameters tp,tn,fp and fnconsists of numbers such as true positives, true negatives, false positives and false negatives. figure 7: accuracy comparison of sentiment analysis methods in the above figure 7, the approach of the existing and proposed methodology is to assess the comparison metric in the terms of accuracy. further, in x-axis the methods are grasped and in y-axis the accuracy value is marked. here, the technique of the existing method includes svm and ann algorithms which provide a lower accuracy whereas the initiated incnn approach contains huge accuracy for a specified dataset. finally, the outcome of the proposed incnn helps to improve the sentiment analysis classification task by recognizing the neutral and non-neutral posts effectively for the given dataset. 5 conclusion and future works the four major steps included in incnn proposed work are (i) tokenization of preprocessing, terminates word removal, stemming, cleanup of text this means discarding unwanted. (ii) feature extraction based upon bags of words, word2vec, tf-id and in this an important subset feature is extracted from classification tasks for improving data. (iii) selection methods for evaluating semantic feature gaps which are generated using teaching models and characteristics of target tasks in previous incnn applications and the feature selection follows up ig/pcc procedures. (iv) incnn provides sentiment posts, user replies are based upon post aspect and the system performance is enhanced. hence, the dataset selects up a prominent keyword which is used for classifying posts more effectively. here, the incnn algorithm proposes a sentiment classification algorithm that classifies the posts as neutral or non-neutral. moreover, the proposed incnn algorithm finds the greater performance in terms of higher accuracy, precision, recall and f-measure where, the values are compared to other state-of-the art methods known as svm and ann classifiers. the future work holds (i) to https://doi.org/10.15837/ijccc.2022.2.4351 13 improve accuracy classification to tune up parameter weights and biases of incnn classifier, certain optimization algorithms are introduced for animal migration optimization (amo), harmony search optimization (hso), etc., (ii) hybrid incnn combined with deep neural network (dnn). declaration of competing interests the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. conflict of interest the authors declare no conflict of interest. references [1] r. feldman, 2013. “techniques and applications for sentiment analysis”, communications of the acm, vol.56, no.4, pp.82-89, [2] m. ghiassi, j. skinner and d. zimbra, 2013. “twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network”, expert systems with applications, vol.40, no.16, pp.6266-6282, [3] n. zainuddin, a. selamat and r. ibrahim, 2016. “improving twitter aspect-based sentiment analysis using hybrid approach”, asian conference on intelligent information and database systems, pp.151-160, [4] d. antenucci, m. cafarella, m. levenstein, c. ré and m.d. shapiro, 2014. “using social media to measure labor market flows”, national bureau of economic research, pp. 1-50, [5] n. zainuddin, a. selamat and r. ibrahim, 2018. “hybrid sentiment classification on twitter aspect-based sentiment analysis”, applied intelligence, vol.48, no.5, pp.1218-1232, [6] g. wang, j. sun, j. ma, k. xu and j. gu, 2014. “sentiment classification: the contribution of ensemble learning”, decision support systems, vol.57, pp.77-93, 2014. 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(2020). a hybrid cnn-lstm model for forecasting particulate matter (pm2.5), ieee access, 8, 26933-26940. https://doi.org/10.1109/access.2020.2971348 https://doi.org/10.15837/ijccc.2022.2.4351 15 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: kalaiarasu m.; ranjeeth kumar c. (2022). sentiment analysis using improved novel convolutional neural network (sncnn), international journal of computers communications & control, 17(2), 4351, 2022. https://doi.org/10.15837/ijccc.2022.2.4351 introduction literature survey proposed methodology preprocessing feature extractions feature selections sentiment classification using incnn results and discussion conclusion and future works international journal of computers communications & control issn 1841-9836, 9(5):623-632, october, 2014. steganalysis of ±k steganography based on noncausal linear predictor k.m. singh, y.j. chanu, t. tuithung k. manglem singh department of computer science & engineering, nit manipur, imphal, india email: manglem@gmail.com yambem jina chanu*, themrichon tuithung department of computer science & engineering, nerist, itanagar, india *corresponding author: jina.yambem@gmail.com tth@nerist.ac.in abstract: the paper proposes a novel steganalytic technique for ±k steganography based on noncausal linear predictor using prediction coefficients obtained from the autocorrelation matrix for a block of pixels in the stego-image. the image is divided into equal-size blocks, autocorrelation matrix is found for the block, and the appropriate noncausal linear prediction coefficients is selected to predict all pixels in that block. a pixel is assumed to be embedded with message bit if the absolute difference between the original pixel value and predicted pixel value exceeds the pre-defined threshold. the effectiveness of the proposed technique is verified using different images. keywords: lsb embedding, noncausal linear predictor, rs analysis, steganalysis, steganography. 1 introduction our world is becoming smaller and smaller day by day due to the rapid development in computer and network technology, empowered with unlimited expansion of communication and information technology capability globally for exchange of information with large involvement of individuals at home, private and public sectors and government organizations. both business and society now are highly dependent on the internet and multimedia technology as an integral part for communication, which on other hand serves two purposes one for the exchange of vast amount of information and knowledge for the welfare of human beings, and another for the destruction of humanity by using such medium. law enforcement personnel and criminals conceal their message, work plan, and important information in digital media. this technique is called steganography, which is the art and science of embedding secret messages inside different cover media such as text, audio, image and video without any suspicion. the internet itself provides hundreds of freeware and shareware, which can be freely used by anyone including criminals and terrorists. to detect and extract the hidden message from stego-data by the cyber security personnel and law enforcement professional is the current interest of steganalysis, which is the science of discovery of existence of hidden information. the race between steganography and steganalysis is ongoing and never-ending competition, in which the former develops newer and more robust algorithms to embed secret message into cover medium such as text, audio, image and video to form stego-data, which is not different from the original medium perceptually to the ordinary person, and does not arouse any suspicion, while the later tries to stop all steganographic techniques or detect the embedded secret message from the stego-data. two important media which appeal to hide secret message are the image and video due to the inherent presence of high redundancy in representation of such copyright © 2006-2014 by ccc publications 624 k.m. singh, y.j. chanu, t. tuithung data. the most obvious of steganography is to hide data, but its general applications include covert and invisible communication between two or more parties, secret data storing in storage devices, integrity checking of data by using hash value, access control mechanism for digital content distribution by including access key for selective extraction, and media database system by unifying the media data such as music, picture, video with other information like date and time of recording such data as metadata [1]. steganalysis finds applications in gathering and tracking criminal and cyber terrorist activities and anti-social elements over the internet, cyber forensics investigation for extraction of hidden message from compromised digital systems, cyber warfare for waging war using the internet, peaceful purposes for improving steganographic tools to identify their weakness [2]. in the recent decades, cyber security community and professionals take challenging, interesting and exciting researches in developing new and robust steganographic algorithms as well as better steganalytic algorithms to counter the steganography [3–5]. the powerful and popular lsb detection algorithms are chi-square [6], rs [7], gradient energy-flipping rate (gefr) detection [8] and histogram difference [9]. these algorithms can estimate the length of the embedded message in lsb embedding. lsb matching (lsbm) steganographic technique adds or subtract by 1 if lsb does not match with message bit [10] [11]. lsbm is a special case of k steganography with k=1 [12]. in k steganography, pixel values are either increased or decreased by k. k steganography is an important steganographic technique, and number of steganalytic algorithms for k steganography particularly based on linear prediction is very few. this motivates to develop this novel k steganalytic algorithm. this paper proposes a new steganalytic technique for k steganography using noncausal linear predictor based on prediction coefficients obtained from the autocorrelation matrix for each block of pixels in the image. most appropriate elements from that matrix are used for predicting all pixel values in that block. the difference between the predicted pixel value and the original pixel value gives the knowledge about the presence of message bit. the paper is organized as follows. section 2 deals with a short introduction on the order of prediction, which is used in the proposed algorithm. section 3 proposes the new steganalytic algorithm, followed experimental results in section 4 and conclusions in section 5. 2 order of linear prediction the order of linear prediction in images can be based on the neighbourhood set g, which is defined at a coordinate (m,n)∈ g with the points lying within a specified radius as its elements [13]. an ηth order neighbourhood set at coordinate (m,n) is defined using the euclidian distance for defining the radius as s η (m,n) = {(i,j) : 0 < (m− i)2 + (n− j)2 ≤ dη} (1) where (i,j) is the coordinates of pixels in the neighbourhood set, (i,j) ̸= (m,n),dη and is an increasing integer function of η. take for examples 1) 1st order neighbourhood set : η = 1;dη = 1; s1 (m,n) = {(m−1,n),(m + 1,n),(m,n−1),(m,n + 1)} 2) 2nd order neighbourhood set : η = 2;dη = 2; s2 (m,n) = {(m−1,n),(m + 1,n),(m,n−1),(m,n + 1),(m−1,n−1), (m + 1,n + 1),(m−1,n + 1),(m + 1,n−1)} steganalysis of ±k steganography based on noncausal linear predictor 625 similarly, the 3rd and 4th order neighbourhood sets are defined with η = 3, dη = 4 and η = 4, dη = 5 respectively. the neighbourhood sets up to order 5 for linear prediction is shown in figure. 1. the pixel to be predicted is labeled ‘0’. for the first-order prediction, the neighbourhood set consists of pixels marked ‘1’, for the second-order prediction, the neighbourhoods set involves pixels marked as ‘1’ and ‘2’ and so on. figure. 1 shows the causal and noncausal regions of supports with pixels lying to the left of dark line for the causal prediction. 3 proposed steganalytic algorithm linear prediction is used for modelling, estimating and coding of one-dimensional signals, in speech coding and understanding, geophysics and biomedical signal processing applications [14], and of two-dimensional signals in image compression, image segmentation and classification and spectrum estimation [15]. linear prediction model is used in estimating pixels at any location of the image due to strong correlation among neighbouring pixels in the image. most of the applications of linear prediction use the causal linear prediction, which predicts the current pixel value based on its surrounding past pixels only. sometime, same prediction coefficients are used to predict entire pixels for the same block in the image.asif and moura wrote that the noncausal linear prediction, which is based on both the past and future pixels in the neighbourhood of the current pixels give better results [13]. figure 1: the neighbourhood sets for the 1st-order to 5th-order prediction. the predicted pixel x̂(m,n) at the location (m,n) is given by x̂(m,n) = ∑ (i,j)∈w ∑ a(i,j)x(m− i,n− j) = xηaη (2) where w represents the prediction window that excludes (0,0) and a(i,j) is the prediction coefficient, xη is a row vector of pixels used for prediction and aη is the column vector formed by the corresponding prediction coefficients. the linear prediction error (lpe) is given by e(m,n) = x(m,n)− x̂(m,n) = x(m,n)−xηaη (3) the corresponding mean-square error e(e2(m,n)) is given by e(e2(m,n)) = e(x(m,n)−xηaη)2 = e(x2(m,n))−2áηe(x́ηx(m,n)) + áηe(áηxη)aη 626 k.m. singh, y.j. chanu, t. tuithung = rx(0,0)−2áηrxη + áηrxηaη (4) where rxη = e(x́ηxη) is the autocorrelation matrix, rxη = e(x(m,n)x́η) is its cross-correlation vector with the pixel to be predicted and rx(0,0) = e(x2(m,n)) . the optimal prediction coefficient vector is obtained by minimizing e(e2(m,n)) with respect to aη and found by the condition ∇(e(e2(m,n))) = 0 (5) where ∇ is the gradient operator with respect to the vector aη this yields in the following matrix form of normal equations or wiener equations rxηaη = rxη (6) the solution of the above matrix equation gives the prediction coefficient vector aη. using the symmetry property rx(i,j) = rx(−i,−j)of the autocorrelation function of wide-sense stationary image random field, it can be shown that [16] a(−i,−j) = a(i,j) (7) the symmetry property simplifies the computation of the prediction coefficients. the predicted pixel value x̂(m,n) should be different from the original pixel value x(m,n) by k (where k is an integer value by which the value of the pixel at (m,n) is changed to hide a binary bit) if the message bit is hidden in the pixel value x(m,n). the absolute error between the original and the predicted pixel values is given by |e(m,n)| = |x(m,n)− x̂(m,n)| (8) the total number of absolute errors, which exceeds a pre-defined threshold θ is the estimate of length of hidden message bit in the stego-image. the following sub-sections are on different causal and noncausal linear predictors for the first and second order. 3.1 first-order causal linear predictor the predicted pixel x̂(m,n) of the centre pixel x(m,n) for the first-order causal linear prediction is given by x̂(m,n) = a1(0,1)x(m,n−1) + a1(1,0)x(m−1,n) = x1a1 = [x(m,n−1)x(m−1,n)] [ a1(0,1) a1(1,0) ] (9) the pixels x(m,n−1) and x(m−1,n), marked as "1" are shown to the left side of dark line in figure.1, and used in the first-order causal linear prediction. the prediction coefficients a1(0,1) and a1(1,0) for the first-order causal predictor are related with the autocorrelation functions by the following normal equation in eq. 6: rx1a1 = rx1 where rx1 = [ rx(0,0) rx(1,−1) rx(1,−1) rx(0,0) ] and rx1 = [ rx(0,1) rx(1,0) ] the number of different elements from the autocorrelation matrix is 4 for the first-order causal predictor. steganalysis of ±k steganography based on noncausal linear predictor 627 3.2 first-order noncausal linear predictors the predicted pixelx̂(m,n) of the centre pixel x(m,n) for the first-order noncausal linear prediction is given by x̂(m,n) = a1(0,1)x(m,n−1) + a1(0,−1)x(m,n + 1) + a1(1,0)x(m−1,n) + a1(−1,0)x(m + 1,n) = a1(0,1)x(m,n−1) + x(m,n + 1) + a1(1,0)x(m−1,n) + x(m + 1,n) = x1a1 = [ x(m,n−1) + x(m,n + 1) x(m−1,n) + x(m + 1,n) ]′ [ a1(0,1) a1(1,0) ]′ (10) where a1(0,1) = a1(0,−1), and a1(1,0) = a1(−1,0) due to eq. 7. the pixels x(m,n−1),x(m,n+ 1),x(m−1,n) and x(m+1,n) marked as ”1” are used in the first-order noncausal linear prediction and shown in figure. 1. the prediction coefficients a1(0,1) and a1(1,0) the first-order noncausal predictor are related with the autocorrelation functions by the following normal equation in eq. 6: rx1a1 = rx1 where rx1 = [ rx(0,0) + rx(0,2) rx(1,−1) + rx(1,1) rx(1,−1) + rx(1,1) rx(0,0) + rx(2,0) ] and rx1 = [ rx(0,1) rx(1,0) ] the number of different elements from the autocorrelation matrix is 7 for the first-order noncausal predictor. 3.3 second-order causal linear predictor the predicted pixel x̂(m,n) of the centre pixel x(m,n) for the second-order causal linear prediction is given by x̂(m,n) = a2(0,1)x(m,n−1) + a2(0,1)x(m−1,n) + a2(1,1)x(m−1,n−1) + a2(1,−1)x(m + 1,n−1) = x2a2 =   x(m,n−1) x(m−1,n) x(m−1,n−1) x(m + 1,n−1)     a2(0,1) a2(1,0) a2(1,1) a2(1,−1)   (11) the pixels x(m,n− 1),x(m− 1,n),x(m− 1,n− 1) and x(m + 1,n−1), marked as ”1” and ”2” are shown to the left side of dark line in figure.1, and used in the second-order causal linear prediction. the prediction coefficients a2(0,1),a2(1,0),a2(1,1) and a2(1,−1) for the secondorder causal predictor are related with the autocorrelation functions by the following normal equation in eq. 6: rx2a2 = rx2 where rx2 =   rx(0,0) rx(1,−1) rx(1,0) rx(1,−2) rx(1,−1) rx(0,0) rx(0,1) rx(0,1) rx(1,0) rx(0,1) rx(0,0) rx(0,2) rx(1,−2) rx(0,1) rx(0,2) rx(0,0)  and rx2 =   rx(0,1) rx(1,0) rx(1,1) rx(1,−1)   the number of different elements from the autocorrelation matrix is 7 for the second-order causal predictor. 628 k.m. singh, y.j. chanu, t. tuithung 3.4 second-order noncausal linear predictor the predicted value x̂(m,n) of the centre pixel x(m,n) for the second-order noncausal linear prediction is given by [17] x̂(m,n) = a2(0,1)x(m,n−1) + a2(0,−1)x(m,n + 1) + a2(1,0)x(m−1,n) + a2(−1,0)x(m + 1,n)+ a2(1,1)x(m−1,n−1) + a2(−1,−1)x(m + 1,n + 1) + a2(1,−1)x(m−1,n + 1) + a2(−1,1) x(m + 1,n−1) = a2(0,1){x(m,n−1) + x(m,n + 1)}+ a2(1,0){x(m−1,n) + x(m + 1,n)}+ a2(1,1){x(m−1,n−1) + x(m + 1,n + 1)}+ a2(1,−1){x(m−1,n + 1) + x(m + 1,n−1)} = x2a2 (12) where a2(0,1) = a2(0,−1),a2(1,0) = a2(−1,0),a2(1,1) = a2(−1,−1) and a2(1,−1) = a2(−1,1) due to eq. 7. the pixels (m,n−1),x(m,n+1),x(m−1,n),x(m+1,n),x(m−1,n−1),x(m+1,n+ 1,x(m−1,n+1)) and x(m+1,n−1), marked as ”1” and ”2” are used in the second-order noncausal linear prediction and shown in figure.1. the prediction coefficients a2(0,1),a2(1,0),a2(1,1) and a2(1,−1) for the second-order noncausal predictor are related with the autocorrelation functions by the following normal equation in eq. 6: rx2a2 = rx2 where rx2 =   rx(0,0) + rx(0,2) rx(1,−1) + rx(1,1) rx(1,0) + rx(1,2) rx(1,−2) + rx(1,0) rx(1,−1) + rx(1,1) rx(0,0) + rx(2,0) rx(0,1) + rx(2,1) rx(0,1) + rx(2,−1) rx(1,0) + rx(1,2) rx(0,1) + rx(2,1) rx(0,0) + rx(2,2) rx(0,2) + rx(2,0) rx(1,−2) + rx(1,0) rx(0,1) + rx(2,−1) rx(0,2) + rx(2,0) rx(0,0) + rx(2,−2)   and rx2 = [ rx(0,1) rx(1,0) rx(1,1) rx(1,−1) ]′ the number of different elements from the autocorrelation matrix is 13 for the second-order noncausal predictor. a block of the size b × b gives an autocorrelation matrix of the size (2b−1)×(2b−1) . selective elements for the autocorrelation matrix are given below. rx(0,0) = rx2(b,b),rx(0,2) = rx2(b,b−2),rx(1,−1) = rx2(b−1,b+1),rx(1,1) = rx2(b−1,b− 1),rx(1,0) = rx2(b−1,b)rx(2,0) = rx2(b−2,b),rx(0,1) = rx2(b,b−1),rx(1,−2) = rx2(b − 1,b + 2)rx(1,2) = rx2(b − 1,b − 2),rx(2,1) = rx2(b − 2,b − 1),rx(2,−1) = rx2(b − 2,b + 1),rx(2,2) = rx2(b − 2,b − 2),rx(2,−2) = rx2(b − 2,b + 2) the first 7 elements in the above are used to predict all pixels in that blocks for the first-order noncausal prediction, and all elements in the above to predict all pixels in that block of the image for the second-order noncausal linear prediction. 4 experimental results a set of 100 high-quality rgb images was collected from the internet including satellite images, biomedical images, texture images, aerial images, photographic images, computer generated images and classic images commonly used in the color image processing literature. all images are of size 512 × 512 figure. 2 shows representative images from this set. the performance of the proposed algorithm depends on the following various parameters and results are taken based on these parameters. (i) block-size of the prediction window (ii) noncausal or causal (iii) order of prediction (iv) value of k steganalysis of ±k steganography based on noncausal linear predictor 629 figure 2: (a) lena, (b) golden gate, (c) earth, (d) oakland, (e) zelda, (f) girl, (g) building, (h) aptus, (i) hdr and (j) sail. 4.1 comparison of performance based on block-size experiments were taken to find the most appropriate block-size of the prediction using 100 different images. results were taken for both the first-order causal and noncausal for the blocksizes of 4×4, 8×8, 16×16,32×32 and 64×64 respectively. these results in terms of percentage estimated length (el) and percentage error (er) are shown in table1 and table 2. results were taken based on the averages of el and er of 100 images for k=9. the results in these tables are shown for the first-order. it was found that the performance is poor for both smaller and larger block-sizes. the most appropriate block-size is 16 × 16, which gives the least er for majority of embedding percentages for both causal and noncausal predictors. the poor performance of the smaller block-size is due to the fact that the prediction coefficients obtained from smaller blocks can give accurate prediction if the blocks contain highly similar pixel values, but a block does not contain similar pixel values usually. on the other hand, larger block-sizes also give erroneous results, because the larger blocks contain many heterogeneous pixel values. without loss of generality, block-size of 16×16 was used for all results afterward. table 1: selection of the best block-size in terms of el and er for the first-order noncausal predictor embedding percentage block-size el er 0% 10% 20% 30% 40% 50% 10% 20% 30% 40% 50% 4×4 -0.17 9.78 21.58 28.99 40.40 47.93 2.20 -15.80 10.10 -4.00 20.70 8×8 -0.64 9.77 22.83 28.88 39.56 50.58 2.30 -28.30 11.20 4.40 -5.80 16×16 -1.46 10.64 20.75 30.02 40.28 49.62 -6.40 -7.50 -0.20 -2.80 3.80 32×32 -0.94 10.13 23.50 29.69 40.28 46.60 -1.30 -35.00 3.10 -2.80 34.00 64×64 2.87 19.18 16.41 39.10 36.35 41.81 -91.80 35.90 -91.00 36.50 81.90 4.2 comparison of performance of noncausal and causal predictors table 1 and table 2 show that the noncausal predictors give less er than causal predictor. the noncausal predictor uses all past and future pixels surrounding the pixels to be predicted and the causal predictor uses only the past pixels surrounding the pixel to be predicted. this results in better performance for the noncausal predictor. 4.3 comparison of performance based on orders of prediction table 3 gives the comparison of performance based on the orders of prediction. results were based on the average results of all images for the causal and noncausal predictors with k=9. er 630 k.m. singh, y.j. chanu, t. tuithung table 2: selection of the best block-size in terms of el and er for the first-order causal predictor embedding percentage block-size el er 0% 10% 20% 30% 40% 50% 10% 20% 30% 40% 50% 4×4 -2.51 12.53 23.03 28.63 38.95 49.22 -25.30 -30.30 13.70 10.50 7.80 8×8 -1.17 10.86 22.04 28.70 39.80 49.48 -8.60 -20.40 13.00 2.00 5.20 16×16 -1.58 10.66 21.16 30.49 39.92 48.77 -6.60 -11.60 -4.90 0.80 12.30 32×32 -1.82 10.84 21.85 30.61 39.94 48.49 -8.40 -18.50 -6.10 0.60 15.10 64×64 -1.80 11.10 21.33 30.75 39.87 48.56 -11.00 -13.30 -7.50 1.30 14.40 of the first-order predictor is comparatively less than that of the second-order predictor. results of orders higher than the second are not shown, because performance deteriorates as the order increases. table 3: performance based on different images for the first-order noncausal and causal secondorder predictors in term of el and er embedding percentage type first-order noncausal linear predictor second-order noncausal linear predictor 0% 10% 20% 30% 40% 50% 0% 10% 20% 30% 40% 50% el -1.46 10.64 20.75 30.02 40.28 49.62 -3.23 12.08 23.11 31.34 39.55 47.07 er – -6.40 -7.50 -0.20 -2.80 3.80 – -20.80 -15.55 -4.46 1.12 5.86 4.4 performance for different k table4 4 gives the results of dependency of the performance of extraction on the values of k for noncausal and causal predictors on average results. the result is taken for values of k from 1 to 9 with the increment 2 for block size of 16 × 16. tables show that the performance of the proposed technique was better for higher values of k. it is because the predictor can give larger value of absolute error |e(m,n)| when the predicted pixel is different from the neighboring pixels. table 4: performance based on value of k for the first-order noncausal predictor in terms of el and er embedding percentage value of k el er 0% 10% 20% 30% 40% 50% 10% 20% 30% 40% 50% 1 18.70 23.73 17.08 29.11 25.34 28.21 -137.30 29.20 8.90 146.60 217.90 3 1.92 11.27 23.32 32.45 31.11 52.08 -12.70 -33.20 -24.50 88.90 -20.80 5 1.48 12.83 21.57 36.55 37.23 48.81 -28.30 -15.70 -65.50 27.70 11.90 7 1.78 9.10 18.62 31.88 38.16 49.45 9.00 13.80 -18.80 18.40 5.50 9 -1.46 10.64 20.75 30.02 40.28 49.62 -6.40 -7.50 -0.20 -2.80 3.80 steganalysis of ±k steganography based on noncausal linear predictor 631 5 conclusions this paper presents a novel steganalytic technique of ±k steganography based on noncausal linear predictor using prediction coefficients obtained from a block of pixels in the image. the effectiveness of the proposed technique in estimating the length of the embedded message bits in the stego-image was verified using different types of images. it was found that noncausal predictor gives better results than causal predictor, and the first-order predictor gives better performance than the second-order. it is also found that the performance is better for higher values of k. bibliography [1] kawaguchi e.; noda h.; niimi m.; eason r.o. 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(2005); maximum likehood destination of secret message length embedded using pmk steganography in spatial domain, proc. of ist/spie electronic imaging: security, steganography and watermarking of multimedia contents, vii, 5681, 595-606. 632 k.m. singh, y.j. chanu, t. tuithung [13] balram n.; moura m.f. (1996); noncausal predictive image codec, ieee trans. on image processing, 5(8): 1229-1242. [14] schroeder m.r.; atal b.s. (1985); code-excited linear prediction (celp): high quality speech at very low bit rates, ieee proc. icassp, 937-940. [15] fang w.; yagle a.e. (1994); two-dimensional linear prediction and spectral estimation in polar raster, ieee trans. on signal processing, 42(3): 628-641. [16] jain a.k. (1989); fundamental of digital image processing, prentice hall, information and system science series. [17] singh k. m. (2012); vector median filter based on noncausal linear prediction for detection of impulse noise from images, inderscience international journal of computational science and engineering, 7(4): 345-355. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 4, month: august, year: 2022 article number: 4862, https://doi.org/10.15837/ijccc.2022.4.4862 ccc publications a modified uncertainty measure of z-numbers yangxue li, francisco javier cabrerizo, enrique herrera-viedma, juan antonio morente-molinera yangxue li andalusian research institute in data science and computational intelligence, dept. of computer science and ai, university of granada, 18071 granada, spain. liyangxue@correo.ugr.es francisco javier cabrerizo andalusian research institute in data science and computational intelligence, dept. of computer science and ai, university of granada, 18071 granada, spain. cabrerizo@decsai.ugr.es enrique herrera-viedma* andalusian research institute in data science and computational intelligence, dept. of computer science and ai, university of granada, 18071 granada, spain. *corresponding author: viedma@decsai.ugr.es juan antonio morente-molinera* andalusian research institute in data science and computational intelligence, dept. of computer science and ai, university of granada, 18071 granada, spain. *corresponding author: jamoren@decsai.ugr.es abstract the z-number is a more adequate construct for describing real-life information. while considering the uncertainty of the information, it also models the partial reliability of the information. it is a combination of probabilistric restriction and possibilistric restriction. in this paper, we modified the uncertainty measurement of the discrete z-number and proposed the uncertainty measurement of the continuous z-number. some numerical examples are used to illustrate the calculation processes and advantages of the proposed method. an application of journey vehicle selection shows the effectiveness of the proposed uncertainty measurement in determining the weights of criteria. keywords: uncertainty, discrete z-numbers, continuous z-numbers, fuzzy numbers, decisionmaking. https://doi.org/10.15837/ijccc.2022.4.4862 2 1 instruction crisp numbers often cannot describe real-life information, it is insufficient. as the information is often uncertain, there are many mathematical models to handle such uncertain information, probability theory [18], fuzzy sets theory [29], dempster-shafer evidence theory [11, 22] and other soft constraints. however, the information also is partially reliable. in the above soft constraints, they default the handled information is completely reliable. unfortunately, the information in practice is not so. the z-number, as a more sufficient formal construct for describing real-life information, models the uncertainty and partial reliability of the information together. a z-number is an ordered pair of fuzzy numbers, z = (a,b), where a is a fuzzy constrict on the values of the variable, x, can take, b is also a fuzzy constrict on the reliability value of a. the reliability value is a probability of the event ’x is a’. thus, a z-number combines the possibilistic restriction and the probabilistric restriction. since the thought of z-numbers was put forward in 2011 to now, the mathematical system and application of z-numbers have become more and more complete. in applications of z-number, the most widespread application areas are decision-making, such as selection problems [2, 10], fault diagnosis [13], pattern recognition [25], risk assessment [1]. the z-number has been especially useful for describing calculations in natural language. for instance, ref. [20] analyzed features and challenges of the z-number approach to computing with words (cww). in ref. [9], z-number is used as a tool for cww and consequently subjective natural language understanding. in the mathematical system of z-number, the basic computations were summarized in ref. [8], including addition, subtraction, multiplication, division, square, square root, absolute value, ranking and distance of z-numbers. on this basis, more advanced arithmetic is proposed, including z-numbers based linear program (z-lp) [4], z-numbers function [6], approximate reasoning [7], the parametric form [21], the negation operator [17], total utility [14], z-differential equations [19], multidimensional z-numbers [23], z-number ifthen rules [5], soft likelihood function [24], z-mixture-numbers [27]. ref. [3, 26] defined the concepts of specificity and horizontal membership functions of z-numbers. besides, li et. al. proposed an uncertainty measure of discrete z-number [16]. however, we find there is an unintuitive point in this uncertainty measurement. when the second fuzzy number of the z-number becomes sharper, the reliability value of the first fuzzy number is more certain, the uncertainty of the z-number should reduce. however, in the method of li et. al. [16], the uncertainty increases slowly. in addition, there is no approach to measure the uncertainty of continuous z-number. in this paper, we modified the uncertainty measure of the discrete z-number. the proposed measurement conforms to three intuitions about the uncertainty of z-numbers: (1) when the first fuzzy number becomes shaper, the value of the variable is more accurate, the uncertainty of z-number should be smaller. (2) when the second fuzzy number becomes sharper, the reliability value of the first fuzzy number is more certain, the uncertainty of z-number should be smaller. (3) when the first fuzzy number is more reliable, the uncertainty of z-number should be smaller. we also proposed an analytical solution of the uncertainty of continuous z-number. if the analytical solution is difficult to obtain, we provided an appropriate method and set the precision to control the calculation consumption and accuracy of the result. this paper is structured as follows. section 2 lists the concepts of z-numbers and other related backgrounds. in section 3, we amend the measuring the uncertainty of discrete z-numbers. and the uncertainty measurement of continuous z-numbers is proposed. some numerical examples are used to demonstrate the advantages of the proposed methodology in section 4. in section 5, an application of journey vehicle selection shows the usefulness of the proposed uncertainty measurement in determining the weights of criteria. finally, this paper is concluded in section 5. https://doi.org/10.15837/ijccc.2022.4.4862 3 2 preliminaries in this section, some preliminaries about definitions of possibilistric restriction, probabilistric restriction, z-numbers and z-restriction are discussed. definition 1. a restriction rprob(x) on real-valued uncertain variable x is a probabilistric restriction if rprob(x) : x is p, (1) if x is a continuous variable, p is called the probability density function of x, prob(u ≤ x ≤ u + du) = ∫ p(u)du. (2) if x is a discrete variable, p is called the probability distribution of x. prob(x = u) = p(x = u). (3) definition 2. a possibilistric restriction r)poss(x) on variable x is denoted as rposs(x) : x is a, (4) where a is a fuzzy number in universe u with membership function µa : u → [0, 1]. the membership degree of a base value u ∈ u can be seen as the possibility value of ’x = u’. more explicitly, rposs(x) : poss(x = u) = µa(u) (5) definition 3. a z-number is an ordered pair of fuzzy numbers, z = (a,b). where a is a possibilistric restriction on values of variable x can take with membership function µa(u) : x → [0, 1], b is another possibilistric restriction on reliability of a with membership function µb(v) : [0, 1] → [0, 1] [28]. v is a base value of b and can be seen as the reliability value. definition 4. a z-restriction combine a possibilistric restriction and a series of probabilistric restrictions [28], rz(x) : x is z → prob(x is a) is b, (6) where prob(x is a) is the probability of the event ’x is a’ occur and can be expressed as: prob(x is a) = ∫ r µa(u)px(u)du. (7) where px is the underlying probability distribution of x and is unknown. if a and px are matching, then the centroids of µa is same with the expected value of px,∫ upx(u)du = ∫ uµa(u)du∫ µa(u)du (8) 3 uncertainty measure of z-numbers in this section, we propose a novel uncertainty measure of discrete and continuous z-numbers. we try to find the underlying probability distribution for reliability value, and then combine the underlying probability distributions with their corresponding possibilities. 3.1 uncertainty measure of discrete z-numbers for a discrete z-number z = (a,b), a =< u,µa(u) >, b =< v,µb(v) >, the underlying probability distribution pvx(u) of reliability v satisfy the following three conditions: (1) ∑n i=1 p v x(ui) = 1, https://doi.org/10.15837/ijccc.2022.4.4862 4 (2) ∑n i=1 p v x(ui)µa(ui) = v, (3) ∑n i=1 uip v x(ui) = ∑n i=1 uiµa(ui)∑n i=1 µa(ui) , where {u1,u2, · · · ,un} is the base values of fuzzy number a, v ∈ {v1,v2, · · · ,vm} is a base value of fuzzy number b. most of time, we may obtain more than one underlying probability distribution according to three conditions. the maximum entropy method [12] is used to select one with the maximum shannon entropy. then the calculation of underlying probability distribution is an optimization problem: max : h(pvx) = − n∑ k=1 pvx log(p v x), (9) subject to: n∑ i=1 pvx(ui) = 1, n∑ i=1 pvx(ui)µa(ui) = v, n∑ i=1 uip v x(ui) = ∑n i=1 uiµa(ui)∑n i=1 µa(ui) we obtained all underlying probability distributions of reliabilities {v1,v2, · · · ,vm}, the uncertainty of z is calculated by hz(z) = m∑ i=1 h(pvix )µb(vi) (10) 3.2 uncertainty measure of continuous z-numbers for a continuous z-number z = (a,b), a =< u,µa(u) >, b =< v,µb(v) >, we assume the underlying probability density function is a normal distribution: pvx(u) = n(α,β 2) = 1 √ 2πσ2 exp(− (u−α)2 2β2 ), (11) subject to: ∫ pvx(u)du = 1,∫ pvx(u)µa(u)du = v,∫ upvx(u)du = α = ∫ uµa(u)du∫ µa(u)du according to equation (11), the obtained underlying probability is a function about variable u and v. more precisely, its β is a function about variable v. then calculate the uncertainty of this normal probability density function by: h(pvx(u)) = − ∫ pvx(u)log(p v x(u))du = 1 2 (log(2πβ2) + 1) (12) where β is a function about variable v. the uncertainty of z is hz(z) = ∫ h(pvx(u))µb(v)dv = ∫ 1 2 (log(2πβ2) + 1)µb(v)dv (13) https://doi.org/10.15837/ijccc.2022.4.4862 5 4 numerical example in this section, some numerical examples are used to show the calculation process and the advantages of the proposed method. in which, example 1 and example 2 show the uncertainty calculation processes of discrete z-numbers and continuous z-numbers in detail. example 3, example 4 and example 5 verify the proposed measurement satisfies the three intuitions and compare the proposed method and the measurement in [16]. example 1. given a discrete z-number z = (a,b), the membership functions of a and b are a = µa u = 0 1 + 0.3 2 + 0.5 3 + 0.7 4 + 1 5 + 0.7 6 + 0.5 7 + 0.3 8 + 0 9 , b = µb v = 0 0.1 + 0.3 0.2 + 0.5 0.3 + 0.7 0.4 + 1 0.5 + 0.7 0.6 + 0.5 0.7 + 0.3 0.8 + 0 0.9 . probability distributions corresponding to different reliability value for z and their entropy are list as table 1. the uncertainty of z is v px(u) u1 = 1 u2 = 2 u3 = 3 u4 = 4 u5 = 5 u6 = 6 u7 = 7 u8 = 8 u9 = 9 entropy v1 = 0.1 0.3724 0.0838 0.0310 0.0115 0.0026 0.0115 0.0310 0.0838 0.3724 1.4849 v2 = 0.2 0.2759 0.1156 0.0647 0.0362 0.0152 0.0362 0.0647 0.1156 0.2759 1.8678 v3 = 0.3 0.1978 0.1240 0.0908 0.0665 0.0417 0.0665 0.0908 0.1240 0.1978 2.0875 v4 = 0.4 0.1348 0.1175 0.1072 0.0978 0.0853 0.0978 0.1072 0.1175 0.1348 2.1871 v5 = 0.5 0.0851 0.1008 0.1129 0.1264 0.1497 0.1264 0.1129 0.1008 0.0851 2.1815 v6 = 0.6 0.0478 0.0776 0.1071 0.1478 0.2395 0.1478 0.1071 0.0776 0.0478 2.0733 v7 = 0.7 0.0223 0.0514 0.0896 0.1564 0.3605 0.1564 0.0896 0.0514 0.0223 1.8552 v8 = 0.8 0.0073 0.0263 0.0617 0.1447 0.5199 0.1447 0.0617 0.0263 0.0073 1.5067 v9 = 0.9 0.0010 0.0073 0.0271 0.1011 0.7270 0.1011 0.0271 0.0073 0.0010 0.9765 table 1: probability distributions corresponding to different reliability value for z. hz(z) = 1.4849 × 0 + 1.8678 × 0.3 + 2.0875 × 0.5 + 2.1871 × 0.7 + 2.1815 × 1 + 2.0733 × 0.7 + 1.8552 × 0.5 + 1.5067 × 0.3 + 0.9765 × 0 = 8.1475 example 2. given a continuous z-number a = (a,b), where a and b are two triangular fuzzy number a = (2, 5, 8), b = (0.2, 0.5, 0.8). the membership functions of a and b are: µa(u) =   u− 2 3 2 < u ≤ 5, 8 −u 3 5 < u ≤ 8, 0, otherwise. µb(v) =   10u− 2 3 0.2 < u ≤ 0.5, 8 − 10u 3 0.5 < u ≤ 0.8, 0, otherwise. according to equation (11), the analytical solutions of underlying probability density functions is very hard to obtain in practice. thus, we use the approximate method. first set a precision pre ∈ [0.0001, 0.1], we assume the underlying probability density function is same in interval [v,v + pre]. then calculate the underlying probability density functions at points · · · ,v,v + pre,v + 2pre, · · ·. the results as shown in table 2. the precision is pre = 0.1. the uncertainty of z is https://doi.org/10.15837/ijccc.2022.4.4862 6 v1 = 0.2 v2 = 0.3 v3 = 0.4 v4 = 0.5 v5 = 0.6 v6 = 0.7 v7 = 0.8 β 3.0079 2.6319 2.2559 1.8799 1.5039 1.1279 0.7519 h(px) 2.5201 2.3866 2.2324 2.0501 1.8270 1.5392 1.1337 table 2: the value of β and entropy under different reliability. hz(z) = m−1∑ i=1 ∫ vi+pre vi h(pvix) + h(p vi+pre x ) 2 µb(v)dv = 0.0408 + 0.1154 + 0.1784 + 0.1615 + 0.0842 + 0.0223 = 0.6026 example 3. the first intuition is: when the membership function of a is sharper, the uncertainty of z should be smaller. in example 1, when the membership degrees of u2 and u8 reduce to 0, and then the membership degrees of u3 and u7 reduce to 0, and then the membership degrees of u4 and u6 reduce to 0. the graphical representations of changes are shown in figure 1. the changes of the uncertainty of z computed by the proposed approach and the measurement in [16] are shown in figure 2. 1 2 3 4 5 6 7 8 9 u 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 m e m b e rs h ip f u n c ti o n o f a figure 1: the change of membership function of fuzzy number a. as shown in figure 2, the results of the two methods are consistent with our intuition. example 4. the second intuition is: when the membership function of b is sharper, the uncertainty of z should be smaller. in example 1, when the membership degrees of v2 and v8 reduce to 0, and then the membership degrees of v3 and v7 reduce to 0, and then the membership degrees of v4 and v6 reduce to 0. the graphical representations of changes are shown in figure 3. the changes of the uncertainty of z computed by the proposed approach and the measurement in [16] are shown in figure 4. as shown in figure 4, the uncertainty calculated by method in [16] increases slowly as the second fuzzy number becomes sharper. the uncertainty calculated by the proposed measurement reduce sharply. it is consistent with our intuition. example 5. when the first fuzzy number is more reliable, the uncertainty of the z-number is smaller. given a discrete z-number z1 = (a1,b1), where a1 = a, the membership function b1 is b1 = µb v = 0 0.2 + 0.3 0.3 + 0.5 0.4 + 0.7 0.5 + 1 0.6 + 0.7 0.7 + 0.5 0.8 + 0.3 0.9 + 0 1 . hz(z1) = 7.6651 < hz(z). https://doi.org/10.15837/ijccc.2022.4.4862 7 u2,u8=0.2 u2,u8=0 u3,u7=0.3 u3,u7=0.1 u4,u6=0.6 u4,u6=0.4 u4,u6=0.2 u4,u6=0 1 2 3 4 5 6 7 8 9 u n c e rt a in ty o f z the proposed method the method of li et.al. [16] figure 2: the change of uncertainty of z as a changes. 1 2 3 4 5 6 7 8 9 v 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 m e m b e rs h ip f u n c ti o n o f b figure 3: the change of membership function of fuzzy number b. 5 application in this section, the weights of criteria in a decision-making problem are determined by the proposed uncertainty measurement. there are three different options of vehicle for journey, car (a1), taxi (a2) and train (a3). the set of alternatives is denoted as a = {a1,a2,a3}. three criteria, expense (c1), time consumption (c2) and comfort (c3) are took into account. the set of criteria is denoted as c = {c1,c2,c3}. the z-information decision matrix is shown table 3. the uncertainty of z-numbers in decision matrix are displayed in table 4 (the precision is 0.01). https://doi.org/10.15837/ijccc.2022.4.4862 8 v2,v8=0.2 v2,v8=0 v3,v7=0.3 v3,v7=0.1 v4,v6=0.6 v4,v6=0.4 v4,v6=0.2 v4,v6=0 2 3 4 5 6 7 8 9 u n c e rt a in ty o f z the proposed metho the method of li et.al. [16] figure 4: the change of uncertainty of z as b changes. c1 c2 c3 a1 ((8, 10, 12), (0.83, 1, 1)) ((70, 100, 120), (0, 0.17, 0.33)) ((3, 5, 7), (0.33, 0.5, 0.67)) a2 ((20, 24, 25), (0, 0, 0.17)) ((40, 70, 100), ((0.33, 0.5, 0.67)) ((5, 8, 11), (0.67, 0.83, 1)) a3 ((14, 15, 16), (0.83, 1, 1)) ((60, 80, 100), (0.67, 0.83, 1)) ((1, 4, 7), (0.67, 0.83, 1)) table 3: the decision matrix with z-information. the sum of uncertainty of criteria ’price’ is hsum(c1) = 0.1378 + 0.2491 − 0.1967 = −0.0854. the sum of the uncertainty of criteria ’journey time’ is hsum(c2) = 0.7719 + 0.7408 + 0.4514 = 1.9641. the sum of the uncertainty of criteria ’comfort’ is hsum(c3) = 0.2788 + 0.1389 + 0.1389 = 0.5566. as there are negative numbers, we add a constant: hnor(cj) = hsum(c1) + 0.5,j = 1, 2, 3. then the weight of criteria ’price’ is wc1 = 1/(−0.0854 + 0.5) 1/(−0.0854 + 0.5) + 1/(1.9641 + 0.5) + 1/(0.5566 + 0.5) = 0.6408. c1 c2 c3 a1 −0.1378 0.7719 0.2788 a2 0.2491 0.7408 0.1389 a3 −0.1967 0.4514 0.1389 table 4: the uncertainty of z-numbers. https://doi.org/10.15837/ijccc.2022.4.4862 9 c1 c2 c3 a1 ((0.6667, 0.8, 1), (0.83, 1, 1)) ((0.3333, 0.4, 0.5714), (0, 0.17, 0.33)) ((0.2727, 0.4545, 0.6363), (0.33, 0.5, 0.67)) a2 ((0.32, 0.3333, 0.4), (0, 0, 0.17)) ((0.4, 0.5714, 1), ((0.33, 0.5, 0.67)) ((0.4545, 0.7272, 1), (0.67, 0.83, 1)) a3 ((0.5, 0.5333, 0.5714), (0.83, 1, 1)) ((0.4, 0.5, 0.6666), (0.67, 0.83, 1)) ((0.0909, 0.3636, 0.6363), (0.67, 0.83, 1)) table 5: the normalized decision matrix. c1 c2 c3 a1 0.7985 0.1775 0.3214 a2 0.0835 0.4646 0.5311 a3 0.5195 0.4767 0.2655 table 6: the crisp numbers of z-numbers in decision matrix. the weight of criteria ’journey time’ is wc1 = 1/(1.9641 + 0.5) 1/(−0.0854 + 0.5) + 1/(1.9641 + 0.5) + 1/(0.5566 + 0.5) = 0.1078. the weight of criteria ’comfort’ is wc1 = 1/(0.5566 + 0.5) 1/(−0.0854 + 0.5) + 1/(1.9641 + 0.5) + 1/(0.5566 + 0.5) = 0.2514. the scales of criteria are different. in order to compare these z-numbers, the first fuzzy numbers must be normolized. the results are summarized in table 5. then we used the converting methods to converse the z-numbers to crisp numbers [15], the decision matrix with crisp numbers is shown in table 6. the evalution value of alternative a1 is: <(a1) = 0.7985 ∗ 0.6408 + 0.1775 ∗ 0.1078 + 0.3214 ∗ 0.2514 = 0.6116. the evalution value of alternative a2 is: <(a2) = 0.0835 ∗ 0.6408 + 0.4646 ∗ 0.1078 + 0.5311 ∗ 0.2514 = 0.2371. and the evalution value of alternative a2 is: <(a3) = 0.5195 ∗ 0.6408 + 0.4767 ∗ 0.1078 + 0.2655 ∗ 0.2514 = 0.4510. the ranking of alternatives is a1 > a3 > a2. the best option of journey is a1, car. 6 conclusion z-number is a new framework to address uncertain and partial reliable information. this paper modifies the uncertainty measure of discrete and continuous z-numbers, overcomes the shortcoming of the previous method. the proposed measurement satisfies all intuitions about the uncertainty of z-numbers. finally, some numerical examples and an application illustrate the advantages and effectiveness of the proposed method. acknowledgements this work was supported by the project b-tic-590-ugr20 co-funded by the programa operativo feder 2014-2020 and the regional ministry of economy, knowledge, enterprise and universities (ceceu) of andalusia, by the andalusian government through the project p20_00673, by the china scholarship council (csc), and by the project pid2019-103880rb-i00 funded by mcin / aei / 10.13039/501100011033. https://doi.org/10.15837/ijccc.2022.4.4862 10 references [1] abbaspour onari, mohsen, yousefi, samuel, and jahangoshai rezaee, mustafa (2021). risk assessment in discrete production processes considering uncertainty and reliability: z-number multi-stage fuzzy cognitive map with fuzzy learning algorithm. artificial intelligence review, 54(2), 1349–1383, 2021. 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[29] zimmermann, hans-jürgen (2011). fuzzy set theory—and its applications. springer science & business media, 2011. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control https://doi.org/10.15837/ijccc.2022.4.4862 12 cite this paper as: li y.; cabrerizo f.j.; herrera-viedma e.; morente-molinera j.a. (2022). a modified uncertainty measure of z-numbers, international journal of computers communications & control, 17(4), 4862, 2022. https://doi.org/10.15837/ijccc.2022.4.4862 instruction preliminaries uncertainty measure of z-numbers uncertainty measure of discrete z-numbers uncertainty measure of continuous z-numbers numerical example application conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 4, month: august, year: 2023 article number: 5392 5392, https://doi.org/10.15837/ijccc.2023.4.5392 ccc publications analysis of advertising promotion strategy based on improved collaborative filtering algorithm under digital media technology q. wu q. wu college of humanities and law shanghai business school,shanghai 200235, china corresponding author: wuqin@sbs.edu.cn abstract with the advent of the internet, individuals have access to an ever-increasing array of external information sources, and as such, advertising information has undergone a significant transformation. in this study, we present a highly accurate, high-throughput, and high-yield ad recommendation system that is capable of precisely targeting users for personalized ad recommendations. our approach involves enhancing the user interest preference model, introducing ad keywords as labels into the similarity calculation of query pages, and employing a weighted integrated similarity measure of query pages to mitigate sparsity within the similarity matrix. furthermore, we optimize the system process of ad promotion and devise an ad recommendation strategy based on multiple parallel recall and uniform sorting distribution. our model training results indicate that the proposed algorithm elevates precision, recall, and f1 by 27%, 25%, and 28%, respectively, compared to the traditional model. additionally, our system test results demonstrate enhanced scalability, with approximately four times higher concurrent performance, as well as improvements in high expansion, low latency, and strong stability. such results hold particular significance in guiding the design of actual ad recommendation system projects. keywords: collaborative filtering algorithm; advertising promotion; user preference; multiplex parallelism. 1 introduction during the era preceding the widespread adoption of the internet, advertisements were primarily disseminated through traditional news media channels, such as television, roadside billboards, newspapers, and radio. these conventional advertising display methods necessitated significant human resources for promotion, came with high investment costs and slow returns, and were challenging to manage effectively [1]. however, with the development of mobile technology, internet advertisements have become increasingly prevalent across major applications. in comparison to traditional communication media, online advertising boasts a vast coverage area, strong targeting capabilities, and an extensive audience base. the internet serves as a novel and efficient means of connecting consumers with enterprises, rendering online advertising both flexible and interactive [2]. by leveraging the internet, enterprises can instantaneously collect consumer feedback data, such as search query records, https://doi.org/10.15837/ijccc.2023.4.5392 2 ad click records, and problem feedback, among other types of information. the amassed data can be quantitatively analyzed on a collected data platform, enabling the identification of issues and the proposal of improvement plans. such plans may include refinement of placement targets, alteration of placement methods, and adjustment of budget allocation strategies. for advertisers, failure to identify the appropriate audience group for ad placement can result in information waste and increased costs. improper recommendations can even result in users developing negative opinions about the website, thereby seriously impacting advertising and website operation effects [3, 4]. users, in the age of information overload, can easily waste time when they receive excessive amounts of irrelevant information. thus, there exists practical demand for accurate ad recommendation both for ad deliverers and users. the recommendation system based on user profile information is a feasible practice that can be used to achieve accurate ad push and address market demand [5]. early research on implicit factor models of ad promotion algorithms focused on users’ explicit feedback. this refers to users expressing their personalized preferences by rating ads, such as douban movie ratings, taobao product ratings, and so on. given explicit user feedback, the recommendation problem is generally formalized as a rating prediction problem. that is, the user and ad embedding vectors in the model are iteratively optimized by minimizing the difference between predicted and true ratings. however, in practical internet application scenarios, obtaining explicit user rating feedback is often difficult. therefore, more and more research works are focusing on how to mine and use implicit feedback information from users’ browsing of products, clicking on links, and other user history behaviors to model their personalized preferences [6, 7]. the cold-start issue within ad recommendation primarily arises due to new users or ads, where the former has not visited any ads, and the latter have not been viewed by any users. this predicament poses a significant challenge in the recommendation system. for ads’ cold start issue, content attributes such as the ad topic, topic category, and keywords are predominantly explored. for instance, ads can be converted into keyword vectors, and by evaluating the vectors’ similarity, ads can be clustered and suggested to users interested in similar ads. additionally, deep learning techniques are employed to circumvent the neural network’s reliance on comparable information by incorporating interaction or rating information of some advertisements to make it robust towards new items. in addressing the system’s cold start issue, expert labeling is introduced via expert knowledge [8]. given the numerous user interest possibilities, it is vital to provide users with representative, diversified, and distinct ads to ensure high coverage, which can expedite the collection of user feedback and construct user portraits. randomly recommending ads with different topics or contents to users and subsequently evaluating their feedback can aid in achieving this goal. the foundation of information flow recommendation hinges on traffic, and the system’s rapid traffic growth poses the problem of system throughput. reduced system throughput can diminish the number of ads placed, which, in turn, can affect advertising revenue. with an increasing number of users, the disparity in experience between users can be substantial. presently, many internet companies’ information flow recommendation systems are deficient in accuracy and do not cater to personalized recommendations for users [9]. in this study, through the design of a personalized advertising recommendation system, advertisements changed from the original large-scale advertising to personalized recommendation, which mainly weakened users’ objectionable attitude towards advertisements and increased advertisers’ economic benefits so that advertising was more targeted. therefore, this paper’s main contributions are as follows: (1) enhanced the user interest preference model by incorporating advertising keywords as labels in the similarity computation of query pages, which reduced the sparsity of the similarity matrix by employing a query page weighted integrated similarity measure. (2) leveraging a multiparallel recall algorithm, this approach primarily employs multi-threading technology to fully utilize the server’s multi-core performance in parallel selection and recommendation of more material pairs under limited time consumption. 2 related works clustering algorithms bear a resemblance to user-based or item-based collaborative filtering since clustering of users or items is executed based on the distance metric values [10]. in user-based clusterhttps://doi.org/10.15837/ijccc.2023.4.5392 3 ing, users can be segregated into various target groups based on metric values, and items with higher scores in the same target group can be recommended to the target user, while item-based clustering is akin to item-based collaborative filtering [11]. currently, collaborative filtering using logistic regression is another advertising recommendation algorithm with a broad audience. the core concept of this algorithm is to transform the recommendation problem into a dichotomous problem, and classification recommendation algorithms such as logistic regression and plain bayes deliver superior performance in terms of interpretation. consequently, their usage is more feasible, and their final outcome sorts the data based on probability values by employing feature engineering, thereby achieving tuning effects [12]. the regression algorithm aligns broadly with the classification algorithm and can efficiently handle continuous rating data to predict ratings for users and items alike. with the advent of machine learning and deep learning, the use of model-based advertising recommendation algorithms has become more prevalent. in today’s industrial environment, there are a plethora of recommendation models available, including traditional machine learning models [13] such as lr, fm, ffm, gbdt+lr, and more advanced deep learning models [14] such as dnn, dssm and its variants like wide and deep, deep fm, din, etc. among these models, logistic regression, an important recommendation base model, has progressed significantly with deep learning. it can be transformed into a ctr (click through rate) problem in recommendation systems, where it is used to predict the probability of a user clicking on an item. this transforms the recommendation problem into a classification problem, which is ultimately predicted by the model to get a continuous value that ranges between 0 and 1. the continuous value is sorted according to the likelihood represented by it, or a threshold value is set to determine whether to click or not [15]. liu et al. [16] proposed a combined model of gbdt+lr based on logistic regression, which is also widely used in industrial settings. the model’s core idea is to automatically filter features and combinations, eventually generating a discrete feature vector. this vector is then fed into the lr model for ctr prediction. in the early advertising recommendation system, the recommendation technology is too simple, which mainly realizes the collaborative filtering technology of recommendation by comparing the user similarity. at the same time, there are some problems in the application of these technologies, such as cold startup, sparse user feedback matrix and high dimension. therefore, it is not enough to meet the differences between users’ needs and preferences. multi-armed bandit (mab) is a practical and widely used algorithm in reinforcement learning that seeks to maximize the cumulative reward for the whole process by finding the arm with the largest reward expectation [17, 18]. the exploration-exploitation (e&e) problem is central to mab algorithms, where exploration involves discovering new and unknown information to improve the recommendation model, and exploitation entails using known commodity information to select an optimal choice under current conditions [19]. contextual-bandit is a variant of mab that considers the user’s product characteristics and consists mainly of the linucb algorithm and its improved versions. among these, linucb is the most popular algorithm in current dynamic news recommendation. compared to the ucb algorithm, linucb accounts for the user’s characteristics and incorporates them into the mean estimation and variance calculation [20]. the linucb algorithm assumes a generalized linear relationship between the user’s feedback score and their characteristics [22? ]. however, this generalized linear model may not fit all models, and thus, the relationship between user features and predicted feedback has become a direction of improvement for the linucb algorithm. ciucanu et al. [23] used the concept of implicit features to represent additional information about users using unobserved implicit information. they combined this information with linucb’s original linear feature processing to improve user feedback rate. erginbas et al. [24] used an association matrix to represent user similarity information between users and constructed a linear relationship between users and feedback based on user similarity to further improve the prediction accuracy. in the prediction process of neural network method, the parameter scale and characteristic dimension of the model will be very large, and the training time will increase sharply. therefore, we can try to deal with this phenomenon by solving the problem of parameter miscellaneous. using attention mechanism to predict ctr can achieve good results compared with the traditional divine scripture network method. however, at present, there are few researches on the application of attention mechanism to ctr prediction, which can be carried out in this field and try to apply https://doi.org/10.15837/ijccc.2023.4.5392 4 attention mechanism to recommendation systems in various fields to improve recommendation quality. it should also be noted that in the deep learning method, data noise will affect the model’s acquisition of feature information, and the parameter learning process is subject to various restrictions. when the training data is interfered by uncertainty, the ctr prediction effect will be greatly affected, that is, the robustness is poor. therefore, ctr prediction in noisy environment still needs further study. 3 advertising promotion system based on user personalized recommendation 3.1 overall design the business request process for the ad recommendation system based on personalized user data can be roughly outlined as follows: initially, the user’s request activates the online ad traffic gateway, which then accepts and parses the request, while simultaneously acquiring the user’s corresponding profile and the experimental information associated with the traffic. utilizing this data, the recommendation system performs multiple parallel operations including recall, sorting, rule filtering, among others, to ultimately return the most relevant ad content to the user. the recall process is essential to ensure that high-quality content participates in the sorting and bidding process. as users interact with the recommended ads, their feedback is collected in the log system, which serves as a fundamental source of data to assess the system’s accuracy. the recommendation system implemented in this design is founded on parallel multiplex recall and uniform sorting distribution. specifically, multiplexed parallel recall incorporates content recall, collaborative recall, model recall, and tag recall, concurrently executed to widen the range of eligible content for the campaign. coarse ranking employs models to identify and eliminate poor quality candidates based on ad candidate recall. fine ranking, on the other hand, seeks to select the most captivating and engaging ad content with the highest probability of interaction with users. the rules applied in the system encompass those related to the advertising business, anti-fraud measures, anticheating protocols for traffic, among others. advertisers, also referred to as ad producers, submit their ad content to the platform for review and approval, after which it enters the candidate database. this database is responsible for storing, calculating, and analyzing the ad material entered by ad producers, aiding in recall by the ad engine. the recommendation effect is ultimately improved through the application of the effect evaluation system, log collection system, and model service system. the present paper introduces an ad recommendation system based on personalized user data, with figure 1 below demonstrating the specific request link and the interaction flow between the various systems. 3.2 user interest preference model user-based collaborative filtering recommendation is mainly to study the relationship between users [25], using the nearest neighbor technique, by analyzing the interest preference information of target users to recommend items that match their interest preferences, the specific process is shown in figure 2. the ctr is a reflection of a user’s input request preference (i.e., the output query page) for the search engine’s selection of displayed ads. the ad keyword factor, which not only impacts ad display, but also plays a vital role in relevance calculations affecting the number of clicks, must be taken into account [26, 27, 28, 29]. to achieve this, we propose augmenting the two-dimensional interest preference model of query pages and advertisements with the ad keyword factor, creating a comprehensive three-dimensional interest preference model that accurately describes query page preference levels. the q-k-a interest model based on ad keywords must consider three factors: the information of ad keywords matching the query page, the information of ads shown by the query page, and the relationship between the ad keywords matching the query page and the chosen ads displayed. to facilitate this, we introduce the following definition. assuming is ≡ (q,k,a,c) is an information system, where. q ≡{q1, q2, ..., qm} is the set of query pages. m is the total number of query pages. k ≡ {k1, k2, ..., kn} is the set of ad keywords. n is the total number of ad keywords. a ≡ {a1, a2, ..., ar} is the set of ads. r is the total number of ads. https://doi.org/10.15837/ijccc.2023.4.5392 5 figure 1: advertising promotion system architecture c ≡ {c(q,a)∥q ∈ q, a ∈ a}, c(q,a) denotes the number of elements in q element in q on which the a elements in a generated by the click-through rate. let t = t1, t2, ..., ts be the set of ad tags, where s is the total number of ad tags. for any t_i ∈ t.k_j ∈ k(1is, 1jr) , there is ti = kj , when and only when i=k . given an information system is=(q,k,a,c), the non-empty finite set represents the set of all advertising clicks, for x ∈ d , with x = <click, impression.a, q, k>. for any ai ∈ a . qj ∈ q(1 ≤ i ≤ r, 1 ≤ j ≤ m), x ∈ d, if x.a = ai , then q = qj . displays the click rate c(qj , ai) generated by ai on qj .c(qj , ai) is defined as follows: cqj ,ai = ∑ x∈d x.click∑ x∈d x.impression (1) where ∑ x∈d x.click represents that the qj display ai and the total number of clicks generated.∑ x∈d x.impression indicates the total number of clicks generated on qj show on ai shows the total number of impressions generated. equation 1 shows that. c(qj , ai) is determined by the number of clicks and impressions it corresponds to. the key aspect of the user-based collaborative filtering algorithm is to find users with similar preferences to the target users. in this paper, we use a modified cosine similarity measure, which not only keeps the correlation degree similar in value, but also makes all the rating curves smooth. let ixy be the combination of user and co-rated item vectors. ix and iy denote the vector of rated items of user and user respectively, then the similarity between user and user can be expressed as follows. sim(rx,i, ry,i) = ∑ i∈ixy (rx,i − rx) · (ry,i − ry)√∑ i∈ix (rx,i − rx)2 √ (ry,i − ry)2 (2) in the ad tag-based search ad recommendation system, the similarity of query pages mainly depends on three factors: the co-hit similarity between query pages, the co-label similarity and the co-contained relationship similarity. to represent user rating differences in query pages accurately, this paper utilizes the modified cosine similarity measure to calculate the three aforementioned factors, namely, the proportion of ad keywords matching between two query pages and the proportion of ad keywords matching between pages and ad-related relationships. additionally, a comprehensive weighting method is used to calculate the similarity between query pages in the recommendation algorithm to reduce sparsity in the similarity matrix calculation. furthermore, the top-n strategy is employed to decrease the size of the https://doi.org/10.15837/ijccc.2023.4.5392 6 figure 2: ad recommendation process based on user behavior preference query pages. specifically, the size of the nearest neighbor candidate set, k, is also reduced using the top-n strategy. 3.3 multiplex recall he ad recall process is responsible for selecting ad candidates with the objective of recalling as many relevant materials as possible while consuming limited time. its aim is to choose the top-level ads from a pool of hundreds of thousands of ads to present to the user, and then, after the final business strategy filtering, return 1-3 ads with the strongest relevance. ad recall content can be user-based or ad-based. user-based recall includes hashtag-based recall, user social network-based recall, and target group pack-based recall. traditional recommendation systems typically use multiple serial recalls and prioritize the recall link strategy, which can result in long recall times, large computational effort, and few candidate materials recalled by the system. to address these issues, this system is designed based on a multiple parallel recall algorithm that utilizes multi-threading technology, making full use of server multi-core performance to obtain more pairs of materials for selection and recommendation in parallel with limited time consumption. the unified sorting and distribution incorporates the mandatory intervention of product traffic policy, which enables the system to disable the distribution of ad content placed by a certain advertiser in real time and suppress certain ads during the sorting and distribution. the detailed pseudo-code of the algorithm is provided below. user traffic characteristics contain user profiles, basic data, traffic base devices, time, and other information, while ad primarily consists of unique ad identifiers. the algorithm then performs ad candidate recall using multiple parallel recall modules, such as content recall, collaborative recall, model recall, and tag recall, in order to obtain a large number of https://doi.org/10.15837/ijccc.2023.4.5392 7 algorithm 1 parallel recall input: uq:user traffic characteristics output: ad:the set of ads for this user 1: parse user traffic characteristics uq obtain user unique identifiers uid and ad space characteristics ap 2: initialize variables ad ←∅ 3: completing the user profile according to the unique user identification uid up 4: generate new features for users u f ← ap ∪ u p 5: get the number of recall modules n and timeout settings t 6: for i, n do 7: pass uf in parallel with concurrent threads to get ad candidates, and the acquired candidates will be passed to ad 8: end for 9: for t no timeout do 10: waiting 11: end for 12: for all a ∈ ad do 13: if a need to filter then 14: ad ← ad a 15: end if 16: end for 17: if ad ̸= ∅ then 18: return ad 19: end if candidate ads within the given time limit. these recall modules are designed to operate in parallel, utilizing multi-threading technology to maximize the utilization of server multi-core performance, and generate more candidate ads for recommendation. the algorithm then uses coarse ranking to eliminate poor quality candidates and selects high-quality ads based on the ad candidate recall results. finally, fine ranking is applied to select the most interesting and likely-to-interact ads for the user, based on the user’s personalized data and preferences. the algorithm also incorporates various business rules, such as anti-fraud and anti-cheating rules, during the sorting and distribution process to ensure ad content quality and user satisfaction. in summary, the algorithm 1 is a comprehensive ad recommendation system that utilizes parallel multiplex recall, uniform sorting distribution, and various user traffic characteristics to effectively select and recommend relevant and high-quality ads to users. the system also considers business rules and user feedback to continuously improve the recommendation effect. 4 experiment and analysis 4.1 experimental setup the training set comprised of 79,637 positive samples and 21,925,769 negative samples, yielding a positive to negative ratio of 1:275. the positive samples denote users who clicked on an advertisement, while the negative samples indicate those who did not. the sample set consisted of 84,327 positive samples and 22,467,358 negative samples, resulting in a positive to negative ratio of 1:266. another sample set, unlabelled, was solely utilized for actual prediction detection, comprising of 20,525,716 data pieces. the samples underwent processing through merging and de-weighting with a, whereby all positive samples were selected, and negative samples were sampled at a 1:10 ratio. subsequently, the results were mixed, shuffled, and divided into nine equal parts. of these, 8/9 constituted the new training set (about 1.6 million), while 1/9 constituted the new validation set (about 200,000). for the analysis, we set the number of interest mining to 50 and the number of iterations to 200, while the hyperparameters α and β were set to 0.5 and 0.01, respectively. by examining the identity document (id) of the keywords corresponding to different interests, we can deduce that each https://doi.org/10.15837/ijccc.2023.4.5392 8 keyword holds varying probabilities in different interests. by calculating the corresponding conditional probabilities of keywords k545 and k670, for instance, we can determine their interest preferences and generate recommendation lists based on these preferences. 4.2 analysis of results in this paper, the proposed algorithm is compared with user-based-cf, tag-based-cf and tagitem-based-cf in terms of precision, recall and f1, and the experimental results are shown in figure 3. figure 3: top10 recommendations for different models upon comparison, it was discovered that the collaborative filtering ad recommendation algorithm proposed in this paper, which incorporates labels, exhibited a 27% improvement in accuracy, a 25% improvement in recall, and a 28% increase in f-measure as compared to the traditional collaborative filtering algorithm. this improvement can be attributed to the consideration of ctr, ad keywords, and the relationship between ad keywords and ads when calculating the similarity between query pages, allowing for the integrated similarity calculation method to effectively capture the preference information of query pages for ads, the relevance of ad keywords to query pages and ads, and the characteristics of the ads themselves, thereby providing a more comprehensive description. moreover, the model training results of lightgbm, xgboost, and wide&deep were compared, as illustrated in figure 4. the experimental results indicate that lightgbm has slightly lower f1 and recall scores compared to xgboost. this is because lightgbm uses the histogram algorithm, which provides faster computing speed and lower memory usage at the expense of sacrificing some accuracy. in contrast, xgboost incorporates a regularization term in the objective function to prevent overfitting and uses both first and second-order derivatives, resulting in more accurate and customizable losses. meanwhile, the wide&deep model combines the memory capabilities of its generalized linear model with the generalization capabilities of its deep feedforward neural network model. it utilizes the generalized linear model to learn feature relevance from the underlying data of ad users and the deep feedforward neural network to uncover the interaction between ad interactions among implicit features of users, resulting in a high accuracy rate of recommendations. in this experiment, our model constructs the interest model of query pages by analyzing ad keywords, ads, and their relationship, and calculates the integrated overall similarity. this approach correctly expresses the interest model of query pages in the search ad recommendation system relative to the method that uses ctr as preference information and ad keywords as implicit preference information, ensuring the accuracy of calculating the neighborhood and improving the accuracy of the recommendation system. additionally, attention is paid to the overfitting of the two algorithms on the validation set as the number of rounds increases. we observed the change in auc values on the validation set, and the https://doi.org/10.15837/ijccc.2023.4.5392 9 figure 4: comparison of model training results results are displayed in figure 5. figure 5: training process of personalized model as depicted in figure 5, the area under the curve (auc) values of both algorithms exhibit an increasing trend on the training set as the models undergo continuous training. however, on the validation set, our proposed model exhibits superior predictive power at the 13th iteration, followed by a rapid decline in auc values, which then asymptotes after 30 iterations. similar behavior is observed in the benchmark algorithm, wherein the auc values gradually diminish as the number of training iterations increases. nonetheless, the decline rate in the benchmark algorithm is much lower than that of our model, which underscores the efficacy of incorporating feature vectors that encapsulate interaction information into the neural network layers, as opposed to relying on the original features. this feature engineering technique alleviates the overfitting problem by simplifying the mining of interaction information and lowering the training complexity. to assess the scalability performance of our proposed algorithm, we randomly select and execute 20%, 40%, 60%, and 80% of the data set size and compare their execution times against the overall data set. the experimental outcomes are illustrated in figure 6. as evident from figure 6, the algorithm’s execution time exhibits a slow and gradual increment, followed by a sharp increase, and then a gradual and smooth increase with the escalation of data size. the growth of execution time of the collaborative filtering and recommendation algorithm equipped https://doi.org/10.15837/ijccc.2023.4.5392 10 figure 6: scalability test results of multiple recall system table 1: concurrency tests of multiple recall system qps/s rt/ms cpu/ % parallel recall 2100 5.3 62.0 serial recall 520 23.4 24.6 with labels remains within an acceptable range as the data size increases, thus indicating its commendable scalability. drawing on the above experimental data, we perform a statistical analysis of the system’s performance by comparing the parallel recall sorted distribution-based approach with the conventional serial sorted distribution-based system, based on query rate per second (qps), response time (rt), and cpu usage. the evaluation results of the experimental data are presented in table 1. the experimental results indicate that the user-based personalized data advertisement recommendation system proposed in this thesis can achieve a query rate per second (qps) of 2000 on a single 8-core 16g server by leveraging multiple parallel recall and unified sorting and distribution, which is significantly higher than the serial recall on the same machine. this parallel recall approach offers a promising solution to enhance the system’s throughput and efficiency, compared to the traditional serial recall. moreover, the recall content module can be updated seamlessly by simply adjusting the configuration and adding concurrent threads to invoke the new recall module, thereby enabling efficient module scalability and promoting high service availability. this finding can serve as a valuable guideline for improving the scalability and reliability of the system. 5 conclusion the advent of the internet has revolutionized the way businesses connect with consumers, rendering online advertising more interactive and adaptable. however, it has also resulted in a surge of high-dimensional data that needs to be processed. in this context, this paper proposes a user-based personalized recommendation advertising promotion system and conducts a series of data experiments to validate its efficacy. in potential applications, users could be randomly recommended ads with different themes or content, and marketers would make a judgment based on user feedback. the basis of information flow recommendation is traffic, and the rapid growth of traffic brings about the problem of system throughput. our research solves this problem by embedding a multipath parallel algorithm, which avoids the reduction of advertising revenue due to fewer advertisements, and further improves the accuracy of recommendation to target users.this research is of practical significance for implehttps://doi.org/10.15837/ijccc.2023.4.5392 11 menting digital media technology in real-world projects. furthermore, the findings can serve as a valuable reference for advertisers in placing ads on ad delivery platforms. nonetheless, the algorithm only accounts for a few factors influencing the click-through rate of ads, and factors like location and bidding price are overlooked. consequently, future work will focus on incorporating machine learning algorithms 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(2023). analysis of advertising promotion strategy based on improved collaborative filtering algorithm under digital media technology, international journal of computers communications & control, 18(4), 5392, 2023. https://doi.org/10.15837/ijccc.2023.4.5392 introduction related works advertising promotion system based on user personalized recommendation overall design user interest preference model multiplex recall experiment and analysis experimental setup analysis of results conclusion int j comput commun, issn 1841-9836 8(5):699-707, october, 2013. feature clustering based mim for a new feature extraction method s. el ferchichi, s. zidi, k. laabidi, m. ksouri, s. maouche sabra el ferchichi* 1. university of tunis el manar national engineering school of tunis tunisia, bp 37, le belvedere 1002, tunis 2. lille1 university, science and technology france,cité scientifique, 59655 villeneuve d’ascq cedex *corresponding author: sabra.elferchichi@enit.rnu.tn salah zidi, salah maouche lille1 university, science and technology france,cité scientifique, 59655 villeneuve d’ascq cedex salah.zidi@univ-lille1.fr, salah.maouche@univ-lille1.fr kaouther laabidi, moufida ksouri university of tunis el manar, national engineering school of tunis, tunisia, bp 37, le belvedere 1002, tunis kaouther.laabidi@enit.rnu.tn, moufida.ksouri@enit.rnu.tn abstract: in this paper, a new unsupervised feature extraction appoach is presented, which is based on feature clustering algorithm. applying a divisive clustering algorithm, the method search for a compression of the information contained in the original set of features. it investigates the use of mutual information maximization (mim) to find appropriate transformation of clusterde features. experiments on uci datasets show that the proposed method often outperforms conventional unsupervised methods pca and ica from the point of view of classification accuracy. keywords: feature extraction, mutual information maximization (mim), similarity measure, clustering. 1 introduction the capabilities of a classifier are ultimately limited by the quality of the features in each input vector. using a large number of features can be wasteful of both computational and memory resources. additionally, there are irrelevant and redundant features that complicate the learning process, and can lead to inaccurate prediction. although those features may contain enough information about the output class, they can not predict the output label correctly because of the large dimension of the feature space and the reduced number of collected instances. it is important to note that for the classifier, it becomes more difficult to determine the inherent relation between the features and the class distribution [9]. this problem is commonly referred to as the curse of dimensionality [6]. a reduction of the feature space dimensionality is often necessary to alleviate this problem. to address this issue, two different approaches exist : feature selection which consists in selecting only the attributes which are relevant according to a pre-defined criterion [10]; and feature extraction which transforms the original set of feature and constructs a new one, more compact and more useful for the classification [20]. feature extraction methods like pca [15], ica [12] and feature selection methods, try either to find new statistically independent directions, or to eliminate totally the redundant features. an alternative approach is to gather the "similar" features into a much smaller number of featureclusters, and use them to re-describe the data. consequently, the potential information contained copyright c⃝ 2006-2013 by ccc publications 700 s. el ferchichi, s. zidi, k. laabidi, m. ksouri, s. maouche in these features could be preserved while the size of the feature space is reduced and good performances are maintained. the crucial step in such a procedure is the characterization of the "similarity" between features. recently, the use of clustering has been investigated for the extraction of features. the applicability of this approach was proven in the case of text classification problems [17], [1] and protein sequences analysis [4]. for each application domain, a specific functional similarity measures was determined. in this work, we develop a new unsupervised feature extraction method. it is based on the use of clustering technique combined with mutual information maximization (mim) to perform feature clustering. our main interest is to reveal the underlying structure of the feature space without any prior information about probabilities density functions or class-distribution of the data. usually, in high dimensional space there are many features that have similar tendencies along the dataset. they describe similar variations of monotonicity (increasing or decreasing trend). those features give a related discriminative information for the learning process. hence, an analysis of the variations of the monotonicity of each feature vector along the dataset can lead us to determine a form of redundancy in the data. by using trend analysis, each feature will be totally described by its signature, which is statistically distinguished from random behavior. intuitively, once the groups of similar features have been settled, feature extraction can be realized through a linear or nonlinear transformation that will determine a representative feature for each feature-cluster. in the same time, the extraction has to preserve the main characteristics of each feature-cluster and to incorporate them into the new representative feature. therefore, each feature has to be highly correlated with its corresponding group center. to satisfy this objective a reliable measure of dependency between each feature-cluster, its corresponding centroid and a search strategy are needed. within this context, mi is a suitable dependency measure [19] for our problem: it quantifies the amount of information that the center carries about the feature-cluster. it can detect either a linear or a nonlinear relationship between two random variables [18], [16]. mi measure was exploited in feature extraction and selection method but in a supervised fashion [11], [20], [13] and [2]. in section 2, feature extraction based clustering method (femc) is briefly reviewed. in section 3, we focus on the formulation of the feature-cluster transformation, based on mi maximization (mim). in section 4, the performances of the proposed feature extraction based clustering method (femc) is analyzed and discussed through multiple classification problems. in section 5, we offer some conclusions and suggestions for future work. 2 feature extraction method based clustering the feature extraction method femc was recently proposed by [7] for pattern classification problems. it aims to obtain more generalization capabilities than existing methods. it performs feature extraction without presuming any knowledge about data structure or about instances’ classes [7]. as we stated before, features that behave the same along the data may contain the same information. grouping those features and transforming them garantee there is no loss of information, better classification accuracy and reduced dimension. hence, we focus on identifying "similar" features in their tendencies along the dataset. the analysis of features monotonocity reveals a form of redundancy between these features. clustering technique was used to identify complex relationships between features and to discover the inherent data structure [8], [21]. a k-means algorithm based on a new similarity measure was permormed. analyzing the tendency of feature vectors was proposed to identify the similarity between them. this new measure was designed to overcome the limitations of euclidean distance, usually used feature clustering based mim for a new feature extraction method 701 in clustering algorithms. in fact, a trend is a semi-quantitative information, describing the evolution of the qualitative state of a variable in a time interval, by using a set of symbols such as {increasing, decreasing, steady} [5]. it was used with success for process monitoring and diagnosis [5]. the procedure of feature extrcation proposed in [7] start by computing the first order derivative of a feature vector is and fixing its sign (0, 1 or −1), at each point sample. after coding each trend, the difference of the tendency between each two vectors is computed. the distance is expressed as the squared root of the sum of the absolute difference between the occurrences of a specified value of a trend for two feature vectors. this was inspired from the value difference metric (vdm) [14]. thus, the location of a feature vector within the feature space is not defined directly by the values of its components, but by the conditional distributions of the extracted trend in each component. furthermore, the similarity measure is not affected by the ordering of samples. 3 mim for feature extraction based on clustering we consider {x1, x2, ..., xl} the d-dimensional original dataset composed of d features vj each. a clustering technique is perforrmed on the feature space to construct d < d featureclusters. we have to define an appropriate transformation for each feature-cluster in order to obtain a representative features gk, defined by the equation 1: gk = f(ck) = nk∑ h=1 l∑ i=1 wivh , (1) where,ck is the cluster composed of nk feature vectors vh. wi is the weight attributed to each component of the feature vector vh. the transform wi has to preserve any linear or nonlinear dependency between features in vh ∈ cj and their centroid gj. mi is an appropriate measure of dependency, so the optimal transform w∗ has to maximize the mi between {vj,gj}. vj is the matrix containing the nk feature vectors belonging to a cluster ck. w is the vector containing the nk weights wi. 3.1 mutual information information theory provides the possibility to measure the information with mi [9], [20]. let p(x) and p(y) be the probability density function (pdf) for random vector x and y , and p(x,y) the joint pdf. the mi between the discrete random vectors x and y is defined as: i(x,y) = ∑ x∈x ∑ y∈y p(x,y)log p(x,y) p(x)p(y) . (2) where x and y are the corresponding alphabets of x and y. if the mi between the two random vectors is large then, the two vectors are closely related. if the mi becomes zero then, the two random vectors are independent. mi for continuous random variables are defined as follows: i(x,y ) = − ∫ ∫ p(x,y)log p(x,y) p(x)p(y) dxdy . (3) the determination of the pdfs (p(x,y),p(x),p(y)) and the performance of the integrations is very complicated. consequently, the continuous input feature space is divided into several discrete 702 s. el ferchichi, s. zidi, k. laabidi, m. ksouri, s. maouche partitions. mi is then calculated using its expression for the discrete random variables. the inherent error that exists in the quantization process poses a problem. the parzen window method is then used to estimate the pdfs of continuous random variables [19]. the method places a kernel function on top of each sample and evaluate the density as a sum of the kernels. given a data of n n-dimensional training vectors {x1, ..,xn}, the pdf estimated by the parzen window method is expressed by: p̂(x) = 1 n n∑ i=1 φ(x − xi,σi) . (4) where φ(.) is the gaussian window function given by φ(x,σ) = 1 (2π) n 2 |σ| 1 2 exp(− 1 2 xt σ−1x) (5) where σ is a covariance matrix of an n-dimensional random vector z. quadratic mutual information: when the aim is not to compute an accurate value of the entropy of a particular distribution, but rather to find a distribution that maximizes or minimizes the entropy given some constraints, a large number of alternative entropy measures are produced [19]. one of these is the following continuous density: d(f,g) = ∫ x(f(x) − g(x))αdx. (6) since mi is expressed as the divergence between the joint density and the product of the marginal, we can insert this into the relation (6) and this way, the quadratic mi measure between two continuous variables x1 and x2 can be derived: i(x1,x2) = ∫ ∫ (p(x1,x2) − p(x1)p(x2))2dx1dx2 . (7) 3.2 problem formulation as we stated before, our objective is to realize an appropriate transformation on each featurecluster. each clusters’center is usually computed as the bary-center derived by the equation (1); where wj = 1 1nj . we look for a more appropriate transformation w∗, since the center is the representative feature of its cluster and it will be used as a new feature. since our objectif is to maximize the mi between each cluster cj and its corresponding center gj, we define the transformation f to apply on each feature vector vi ∈ cj, by gij = f(w,vi), which maximizes i(gj,vj) (mi between vi ∈ cj and gj) as described in figure 1. by using(7) we obtain: i(gj,vj) = ∫ ∫ (p(gj,v) − p(gj)p(v))2dgjdv . (8) we have to develop p(gj,v) to be able to compute (8). since the center gij = f(w,vi), belongs to the cluster cj: gj ∈ cj, we get the final set of features: feature clustering based mim for a new feature extraction method 703 figure 1: feature extraction procedure sfinal = vj ∪ {gj}. p(gj,v) will be expressed by: p(gj,v) = p(gj)p(gj/v) = p(gj)(p(v) − p(gj)) . (9) we insert (9) in equation (8) and we get: i(gj,vj) = ∫ ∫ p(gj) 4dgjdv . (10) we have used the parzen window estimator to determine p(gj). by using the equation (5) for the obtained set constituted of nj + 1 features sf = vj ∪ {gj}, the density p(gj,v) can be expressed by: p(gj,v) = 1 n + 1 n+1∑ i=1 φ(gj − vj,σi) . (11) we know that ∫ zcnx(µc,σc)dx = zc. henceforth, the mi becomes: i(g,v ) = ∫ g ∫ v n+1∑ i=1 n+1∑ j=1 n+1∑ k=1 n+1∑ l=1 ∏ s=1 4φg(µs,σs)dgdv (12) = ∫ g ∫ v n+1∑ i=1 n+1∑ j=1 n+1∑ k=1 n+1∑ l=1 zφg(µ,σ)dgdv (13) = ∫ v n+1∑ i=1 n+1∑ j=1 n+1∑ k=1 n+1∑ l=1 zdv . (14) where z = |2πσd| 1 2∏4 s=1 |2πσd| 1 2 ∏ a<b exp(− 1 2 (µa − µb)t bab(µa − µb)), σd = ( ∑ s=1 4σ−1s ) −1, σs = σ 2i, bab = σ −1 s σdς −1 s in figure 1, we have to maximize the mi i(g,v) to identify the optimal w∗ and to determine cluster center g. we have used the gradient descent algorithm to find the solution which involves differentiating i(g,v): di dw = di dg dg dw = di dg v . (15) 704 s. el ferchichi, s. zidi, k. laabidi, m. ksouri, s. maouche table 1: classification accuracy on sonar dataset . no. of features classification accuracy (svm) pca ica femc (without mi) femc (with mi) 1 58.6 67.2 60.60 65.94 3 54.7 69.7 71.68 75.05 6 63.0 70.2 77.39 77.89 9 70.2 68.7 82.38 82.32 12 75.1 71.7 85.02 81.40 60 82.7 table 2: classification accuracy on pima dataset . no. of features classification accuracy (svm) pca ica femc (without mi) femc(with mi) 1 66.3 73.2 68.08 67.80 2 75.1 76.7 69.72 69.85 3 75.5 76.8 75.72 75.72 5 75.5 77.2 76.80 82.32 60 78.0 4 experimental results in this section, we have conducted femc (with and without using mi) for different benchmark datasets from uci machine learning repository [3]. we have compared the femc performances with conventional unsupervised feature extraction methods pca and ica for different extracted features. we have used svm (given in matlab toolbox) for binary classification task, the used kernel function is gaussian kernel and the parameter σ is set after various tests. the k nearest neighbors (knn) classifier is used for the multi-classification problem. sonar dataset. we have used 13 fold cross-validation in experiments, as presented in [13]. for svm parameters, we have set σ = 1. the table 1 shows classification accuracy for different number of extracted features. the performances of femc are far better than pca and ica except for the case when the dimension is 1, and ica outperforms the others. since the concept of our approach is to form groups of similar features; extracting a very low number of features means gathering all features in a few numbers of clusters. this could be delicate for some datasets. we note also that in the case of dimension 9 and 12, femc can get nearly by the initial accuracy rate of 82% which is far better than ica and pca. by using mi, femc reaches much better accuracy, especially for the case of dimension 1, where it gets almost the same accuracy as ica. hence, mi increases the femc accuracy in the lower dimension like 1 and 3. pima indian diabetes dataset. we have applied pca, ica and femc for comparison. a 10-fold cross strategy was used and σ = 10. results are shown in table 2. we can note that the classification accuracy of pca and ica becomes closer as the number of extracted features becomes larger. femc performs better than pca and approaches the ica accuracy for the dimensions: 3 and 5. but ica still outperforms both pca and femc for different numbers of features especially for the lower ones (dimension 1 and 2). by using mi, the accuracy of femc has increased especially for the case of the dimension 5, where it outperforms pca, ica and surpasses the initial accuracy (78%) by getting 82.32%. feature clustering based mim for a new feature extraction method 705 table 3: classification accuracy on breast cancer dataset . no. of features classification accuracy (svm) pca ica femc (without mi) femc(with mi) 1 85.8 85.1 96.72 96.86 2 94.7 90.3 96.57 96.71 3 95.9 91.3 94.71 94.28 6 96.6 94.3 85.11 85.11 9 96.6 table 4: classification accuracy on ionosphere dataset . no. of features classification accuracy (svm) pca ica femc(without mi) femc(with mi) 1 64.07 61.28 72.09 76.09 3 85.21 81.80 75.20 83.50 6 84.79 86.05 85.21 85.49 9 84.83 86.52 87.52 87.80 12 86.31 88.04 89.20 89.20 34 91.73 breast cancer dataset. a 10-fold cross-validation was used and σ = 0.01. results of comparison are shown in table 3. with only one extracted feature, femc can get the maximum classification accuracy (96.86%). so, for a larger number of extracted features, pca outperforms both ica and femc and gets the maximum classification accuracy with 6 features. in this case, mi slightly ameliorates femc performances. ionosphere dataset. we have used a 10 fold cross-validation and σ = 0.01. the results of the comparison are shown in table 4. with only one extracted feature, femc outperforms ica and pca. for larger numbers of extracted features, femc gets either similar or better performance than pca and ica, and achieves the best classification accuracy with 12 features. in lower dimension, femc with mi reaches higher accuracy of 76.09% (better than pca and ica) especially in dimension 1. wine dataset. we have used a 10-fold cross validation strategy and the k-nearest-neighbors (knn) classifier to conduct classification task. the classification results for each feature extraction method are summarized in table 5. we must underline that the femc performances are far better than ica and pca for low dimensions. although for the dimension 2 ica outperforms femc, for larger dimension femc achieves the best classification accuracy and approaches the initial one of 80.27%. by using mi, femc can reache better accuracy especially for the dimension 3 where it gets 81.49% (better than the initial one). 5 conclusion this paper deals with the important problem of extracting relevant features for pattern classification. often, feature extraction techniques trust in some robust criterion to search for a lower dimensional representation. however, the true structure of the data is unknown, it is inherently ambiguous what constitutes a good low dimensional representation. this makes it difficult to define an appropriate criterion. we suggest a new feature extraction approach incorporating the idea of feature clustering. similar features are recognized through analyzing their tendencies along the data set and a new similarity measure is then devised. the proposed approach femc 706 s. el ferchichi, s. zidi, k. laabidi, m. ksouri, s. maouche table 5: classification accuracy on wine dataset . no. of features classification accuracy (svm) pca ica femc(without mi) femc(with mi) 1 67.93 67.42 71.02 71.88 2 71.94 73.84 72.36 76.25 3 72.48 75.22 78.19 81.49 5 75.74 91.01 79.92 80.45 13 80.27 applies clustering technique, based on the new similarity measure, into feature space to determine its underlying groups of features. an mim schema is used to find an optimal transformation of features in each obtained cluster to compute corresponding centers. the obtained set of centers represents the extracted features used to characterize the patterns. the performances of femc method have been assessed through several datasets obtained from the uci machine learning repository. the complexity of the relationships nature between features increases as the dimension gets lower. the mi can effectively identify this relationship due to its powerful background. in this work, we can clearly notice that by using mim to construct a new set of features, femc have remarkably increased the classifier accuracy especially in lower dimension case. more, this method manage to offer a better accuracy classification than ica and pca in almost cases. the main problem that would occur is time complexity in estimating and optimizing mi between features for bigger datasets. as the optimization schema of mi is done separably for each cluster, a multi-agent approach can be useful to tackle this problem. the greedy algorithm used in this work can be also replaced by a stochastic one. bibliography [1] baker, l.d. and mccallum, a.k.; distributional clustering of words for text classification, proc. 21st ann. int. acm sigir conf. on research and development in information retrieval, 1998. 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[6] cherkassky, v. and mulier, f.; learning from data: concepts, theory and methods, chapter 5, eds.john wiley & sons, 1998. feature clustering based mim for a new feature extraction method 707 [7] el ferchichi, s., zidi, s., laabidi, k., ksouri, m. and maouche, s.; a new feature extraction method based on clustering for face recognition ,” proc. 12th engineering applications of neural networks, eds. springer, ifip 363, 247-253, 2011. [8] fern, x.z. and brodley, c.e.; cluster ensembles for high dimensional clustering: an empirical study, technical report, cs06-30-02, 2004. [9] fisher, j.w., principe, j.c.; a methodology for information theoretic feature extraction, proc. 17th int’l joint conf. on neural networks, 1998. [10] guyon, i. , elisseef, a.; an introduction to variable and feature selection, journal of machine learning research, 3: 1157-1182, 2003. 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[16] schaffernicht, e., kaltenhaeuser, r.; on estimating mutual information for feature selection, proc. 17th int’l conf. on artificial neural networks, eds. springer, lncs 6352, 362-367, 2010. [17] slonim, n. and tishby, n.; the power of word clusters for text classification, proc. 23rd european colloquim on information retrieval research, 2001. [18] suzuki, t., sugiyama, m., and kanamori, t.; a least-squares approach to mutual information estimation with application in variable selection, jmlr 17th 3rd workshop on new challenges for feature selection in data mining and knowledge discovery (fsdm 2008), 2008. [19] torkkola, k. and campbell, w.m.; mutual information in learning feature transformations, proc. 17th int’l conf. on machine learning, 2000. [20] torkkola, k., feature extraction by non-parametric mutual information maximization, journal of machine learning research, 3: 1415-1438, 2003. [21] von luxburg, u., bubeck, s., jegelka, s. and kaufmann, m.; consistent minimization of clustering objective functions, neural information processing systems nips, 2007. int j comput commun, issn 1841-9836 8(6):838-844, december, 2013. active queue management of tcp flows with self-scheduled linear parameter varying controllers c. kasnakoglu cosku kasnakoglu tobb university of economics and technology department of electrical and electronics engineering ankara, turkey, 06560 kasnakoglu@etu.edu.tr abstract: control-theoretic approaches to active queue management (aqm) are typically based on linearizations of fluid flow models around design conditions. these conditions depend on the round trip time (rtt), and the aqm performance is known to degrade if rtt values during actual operation depart substantially from design values. to overcome this difficulty a self-scheduled lpv controller for aqm is considered in this paper, where the controller is modified in real-time based on rtt. simulations show that the self-scheduled lpv controller has good performance for both constant and time-varying rtts, and outperforms two other common controltheoretic approaches to aqm. keywords: aqm, congestion control, control theory, fluid model, lpv systems, self-scheduled controller 1 introduction congestion is one of the most important problems faced in communications networks. congestion occurs when a link or node is carrying so much data that its quality of service deteriorates. this results in queueing delay, packet loss or the blocking of new connections, leading to low throughput and eventually to congestion collapse. on the other hand, links carrying less data than a certain level are also not desired as this implies that the link capacities are being underutilized [1]. modern networks try to avoid these situations using congestion control techniques, among which active queue management (aqm) is of particular interest. aqm operates by dropping or ecn-marking packets before the queue is full, according to a probabilistic rule. earlier aqm disciplines such as red [2] and rem [3] required careful tuning of parameters in order to provide good performance, while modern aqm disciplines such as ared [4] and blue [5] are self-tuning. with the development of dynamical models such as the fluid flow model in [6], control theoretic approaches for aqm have gained interest, including pi/pid controllers [7, 8] and robust h∞ controllers [9–11]. these are based on the linearization of the fluid model, which produces a transfer function from probability of package mark p to queue length q. one difficulty is that the transfer function is valid only for a given round trip time (rtt), and a new transfer function must be obtained for a different rtt. in [12] switching between multiple controllers designed for different rtts is considered and it is seen that higher number of controllers results in improved performance. however, if the number of controllers is too high, the implementation becomes very difficult and complicated. in addition, since rtt takes values on an interval, designing a controller for each rtt value requires an infinite number of controllers. in this paper a self-scheduled control design for aqm is considered to overcome these difficulties. the controller is parameterized in rtt and achieves stability and small tracking error for both fixed rtt as well as time varying rtt within a prescribed range. copyright © 2006-2013 by ccc publications active queue management of tcp flows with self-scheduled linear parameter varying controllers 839 2 mathematical model of tcp/aqm a dynamical model for tcp congestion control was developed in [6] using fluid flow approximation. the dynamical model represents a bottleneck with multiple tcp flows sharing the link. the congestion avoidance is modeled as aimd (additive-increase multiplicative-decrease). the dynamical model consists of the following nonlinear time-delayed differential equations ẇ(t) = 1 θ(t) − w(t) 2 w(t − θ(t)) θ(t − θ(t)) p(t − θ(t)) (1) q̇(t) = n(t) θ(t) w(t) − c(t) for q(t) > 0 (2) where w is the tcp window size, q is the queue length, n is the number of tcp flows, c is the link capacity, p is the probability of packet mark and θ is the rtt. let c and n be constants for simplicity. the nonlinear system (1)-(2) can be linearized around an operating point to generate a transfer function from p to q. let δp := p(t) − po and δq := q(t) − qo where qo and po are the values at the operating point. then a transfer function from δp to δq can be obtained as [6] δq(s) δp(s) = (c3θ3n)e−θs 2n2θ3cs2 + (2n2cθ2 + 4n3θ)s + 4n3 (3) where θ is regarded as a parameter. one then has a different transfer function for each value of the rtt in the range [θmin,θmax], where θmin and θmax are the minimum and maximum values of rtt for the link. 3 controller design the controller must achieve stability of the closed-loop system and the tracking of a desired queue length, under the presence of constant or time-varying rtt within the specified range.1 a parameter dependent dynamic controller of the following form will be sought ζ̇ = ak(θ)ζ + bk(θ)e δp = ck(θ)ζ + dk(θ)e (4) where ζ is the controller’s internal state and e := δqd − δq is the error between the desired and the actual queue sizes. a systematic method for the selection of the controller matrices ak, bk, ck and dk in (4) was derived in [13]. for this purpose the system to be controlled, i.e. (3), must be transformed into affine-parameter dependent form. approximating the time delay e−θs in (3) with a second order pade approximation e−θs ≈ 12−6 θ s+θ 2s2 12+6 θ s+θ2s2 (5) and transforming into state space form yields ẋ =ap (θ)x + bp (θ)δp (6) δq =cp (θ)x (7) 1under ideal operation, rtt will be small for small queue size. however no such assumption will be made here, since unexpected changes in link conditions (e.g. increased propagation delay) can create longer rtts even at smaller queue lengths. thus, the controller is expected to be prepared for any rtt at any queue size (within the limits allowed). 840 c. kasnakoglu where ap (θ) =   0 1 0 0 0 0 1 0 0 0 0 1 −a3 −a2 −a1 −a0   , (8) bp (θ) = [ 0 0 0 1 ]t , (9) cp (θ) = [ b2 b1 b0 0 ] (10) and a0 =7θ −1 + 2n c θ−2, a1 = 14n c θ−3 + 18θ−2, a2 =12θ −3 + 36n c θ−4, a3 = 24n c θ−5, b0 = c2 2n , b1 = −3c2 n θ−1, b2 = 6c2 n θ−2 . letting θ := θ−1, and performing a series expansion around point θ = θo, one can write ap , bp , cp in terms of θ as ap (θ) = ap0 + ap1θ + o(θ2) (11) bp (θ) = bp0 + bp1θ + o(θ2) (12) cp (θ) = cp0 + cp1θ + o(θ2) (13) where ap0 has the same structure as in (8) but with a0 = −2nθo2 c , a1 = −28nθo3 c − 18θo2, a2 = −108nθo4 c − 24θo3, a3 = −96nθo 5 c , ap1 has the same structure as in (8) but with a0 = 4nθo c + 7, a1 = 42nθo 2 c + 36 θo, a2 = 144nθo 3 c + 36 θo 2, a3 = 120nθo 4 c , bp0 is equal to bp in (9), bp1 is zero, cp0 has the same structure as in (10) but with b0 = c2 2n , b1 = 0, b2 = −6c2θo2 n and cp1 has the same structure as in (10) but with b0 =0, b1 = −3c2 n , b2 = 12c2θo n . approximating ap , bp and cp with the constant and linear terms in (11)-(13) produces an affine parameter dependent state space system (in θ) as desired. self-scheduled controller design methods [13] can then be used to design the parameter dependent controller in (4) to meet the desired control objectives. the performance objective is to have a fast and well-damped response over the entire range of parameter values for step-like references. the control law is also required to achieve robustness by avoiding high-gain feedback at high-frequencies. this will prevent the excitation of high frequency modes and nonlinearities that were unmodeled or neglected. consider the feedback control structure depicted in figure 1. the control problem described active queue management of tcp flows with self-scheduled linear parameter varying controllers 841 figure 1: feedback control structure for aqm above can be formulated as the minimization of the l2-induced norm of the operator mapping the signal δqd to signals e and δp. the former map is the called the sensitivity function and denoted by s, and the latter map is denoted by ks. following standard h∞ design procedure, the performance objective and robustness objectives are specified through weighting filters w1(s) and w2(s), where w1(s) = 1/s for good tracking of step-like references, and w2(s) is a third order high pass butterworth filter. this yields to an h∞ optimization problem where the goal is to find a stabilizing controller k for which the inequality∥∥∥∥∥ [ w1(s)s(s) w2(s)k(s)s(s) ]∥∥∥∥∥ ∞ ≤ γ (14) can be satisfied with γ in r+ as small as possible. let acl, bcl, ccl and dcl be the state-space matrices corresponsing to the one-input two-output system defined by the transfer function matrix [w1s, w2ks]t . an lpv controller satisfying (14) can be synthesized and implemented as follows [13] 1. find a matrix xcl > 0, and controller matrices aki, bki, cki, dki so that  acl(θi) t xcl + xclacl(θi) xclbcl(θi) c t cl(θi) btcl(θi)xcl −γi d t cl(θi) ccl(θi) dcl(θi) −γi   is negative-definite for i = 1,2, where θ1 = θ −1 min and θ2 = θ −1 max. 2. for a given value of θ = θ−1, compute the matrices ak(θ), bk(θ), ck(θ) and dk(θ) defining the lpv controller as[ ak(θ) bk(θ) ck(θ) dk(θ) ] = 2∑ i=1 αi(θ) [ aki bki cki dki ] where (α1,α2) is a convex decomposition of θ such that θ = α1θ1+α2θ2 and α1+α2 = 1. 4 simulation results the performance of the closed-loop system with the lpv controller is tested using matlab/simulink and discrete event simulations.2 the number of tcp flows is taken to be n = 150, the link capacity c = 500 packets/sec, the desired queue size qo = 150 packets, and the buffer 2the results from both were consistent so only those from the former are shown for better presentation and to save space. 842 c. kasnakoglu 0 20 40 60 80 100 0 100 200 p id 0 20 40 60 80 100 0 100 200 s w itc h in g 0 20 40 60 80 100 0 100 200 l p v time (secs) figure 2: simulation results for aqm based on pid (top), switching h∞ (middle) and lpv controllers (bottom), for 15 fixed values of rtt. size qmax = 200 packets. the rtt is assumed to take values between 0.3 secs and 0.7 secs. the controller design as outlined in section 3 is carried out with the help of matlab robust control toolbox. figure 2 shows the simulation results for 15 fixed values of rtt, linearly equally spaced between 0.3 and 0.7. two other common control-theoretic approaches to aqm design are also implemented for comparison: a pid controller, and a switched h∞ controller. after subsequent trials, the best results that could be obtained with the pid controller were those based on a linearization around rtt = 0.6. the switched h∞ controller is based on two operating points rtt1 and rtt2, the best results for which were obtained when rtt1 = 0.4320 and rtt2 = 0.6080. the simulations illustrate that all controllers eventually succeed in achieving and maintaining the desired queue size of 150 packets. however, the lpv controller response is faster, with less overshoot and better damping over the entire parameter range. the controllers were also tested for the case when rtt is time varying. the pid controller and the switched controller did not produce a stable closed-loop so these responses are not shown. the result for the lpv controller is shown in figure 3. the top subfigure shows the variation in rtt versus time, which is a sinusoid with increasing frequency taking values between 0.3 and 0.7.3 the bottom subfigure shows the response of the closed-loop system. it can be observed that the lpv controller produces a stable closed-loop and is capable of maintaining the queue size very close to the desired value, even when rtt is time varying. 3different cases for the variation of rtt were also tested with successful results, including square, triangle and sawtooth waveforms, as well as the case where rtt varies randomly according to various probability densities. however, only one case is shown in the paper to save space. active queue management of tcp flows with self-scheduled linear parameter varying controllers 843 0 20 40 60 80 100 0.4 0.5 0.6 0.7 r t t ( se cs ) 0 20 40 60 80 100 0 50 100 150 200 time (secs) q u e u e le n g th ( p a ck e ts ) figure 3: simulation results for aqm based on lpv, for a time-varying rtt. 5 conclusions in this paper self-scheduled lpv control design was implemented for aqm. the controller design is based on a fluid flow approximation of tcp congestion control and utilizes rtt as the scheduling parameter. simulations "were carried out to evaluate the closed-loop system in its ability to keep the queue length at a desired level. two other control-theoretic approaches to aqm, namely pid control and switching h∞ control, were also implemented for comparison. when rtt is constant, it was seen that the lpv controller outperforms the other two, producing a faster and better damped response with lesser overshoot over the entire parameter range. when rtt is time varying, the lpv controller is still capable of maintaining the desired queue size, whereas the pid and switching controllers do not produce a stable closed-loop. bibliography [1] s. floyd and k. fall. promoting the use of end-to-end congestion control in the internet. ieee/acm transactions on networking, 7(4):458–472, 1999. 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[12] d. ustebay and h. ozbay. switching resilient pi controllers for active queue management of tcp flows. in ieee international conference on networking, sensing and control,, pages 574–578, 2007. [13] p. apkarian, p. gahinet, and g. becker. self-scheduled h-infinity control of linear parametervarying systems: a design example. automatica, 31(9):1251–1261, 1995. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 2, month: august, year: 2023 article number: 5144, https://doi.org/10.15837/ijccc.2023.2.5144 ccc publications blockchain-assisted secure routing protocol for cluster-based mobile-ad hoc networks n. ilakkiya, a. rajaram n. ilakkiya department of masters in computer application e.g.s pillay engineering college nagapattinam, tamilnadu, 611002, india *corresponding author: email: illalliyaphd@gmail.com a. rajaram department of electronics and communication engineering e.g.s pillay engineering college nagapattinam, tamilnadu, 611002, india drrajaram@egspec.org abstract manets aredecentralized network that involves mobile nodes. as the overall network is mobile and has no centralization, network management, routing, and security become very challenging. though many works have been presented, still there is a lack in organizing the network due to unauthorized access, centralized security schemes, and the dynamic nature of the nodes. this paper proposed a novel blockchain-assisted secure routing (block-sec) protocol for manets. all mobile nodes are authenticated by distributed one-time passcode (dot) based authorization scheme. all authorized nodes are segregated into multiple clusters based on weight based dynamic clustering (wdc) algorithm in which multiple metrics are considered in clustering and re-clustering processes. after cluster formation, each cluster is elected with optimal cluster head (ch) by strawberry optimization (sbo) algorithm with a new objective function. after cluster formation, the optimal route is selected by fast neural net-assisted fuzzy (fnnf) algorithm by combining multiple variables. data transmission is secured by efficient elliptic curve (e2c2) algorithm. with the combined algorithms, the proposed approach obtainedimproved efficiency in packet delivery ratio (pdr), throughput, time analysis, and security level. keywords: blockchain, distributed security, mobile nodes, dynamic clustering, manets. 1 introduction with the advancements in wireless standards such as wifi, radio networks,etc, there are many applications have been developed. however, the overall network fully depends upon centralized base stations (bss) or routers [1]. manets resolve the problem of centralization by distributing the data transmission throughout the network [2]-[4]. manets ensure ease of network deployment and data transmission. manet’s general architecture is given in figure.1. as shown in the figure, manet has no central bs or routers. it is defined as the collection of wireless movable nodes that need no infrastructure for communication. the nodes presented in the network are generally mobile. https://doi.org/10.15837/ijccc.2023.2.5144 2 figure 1: manet architecture the mobile nodes are allowed to move around the network limit [5], [6]. each mobile node is capable to act as a transmitter, receiver as well as router. due to its wide usage, manet is emerging in many growing internet of things (iot) applications such as environmental monitoring, pollution monitoring, etc [7]. due to the lack of infrastructure, routing becomes challenging in manet [8]. mainly, routing depends upon on-demand routing and distance vector-based protocols. dsr and aodv routing protocols are the two major on-demand protocols [9]. both protocols initiate route discovery only if needed for data transmission. however, the existing manet protocols fail to achieve the following efficacy, • unable to choose the best route • no evaluation is considered in route selection • high possibility for link breakages • unable to ensure prompt data delivery • increases the number of retransmission • route breakages due to mobility to resolve the above issues, routing is performed by considering network dynamics [10]. that is many algorithms use mobility as the major routing metric. however, it is proved that using combined metrics for route selection improves the efficiency of data transmission [11]. generally, routing and network organization are interrelated. routing will become simple and effective when the network is organized properly. for proper network management, manet is clustered into multiple groups [12]. cluster formation splits the network into multiple groups which makes data transmission easy. for cluster formation, k-nearest neighbor (knn), and k-means algorithms have been utilized [13]. however, these conventional algorithms only consider the distance metric which is not effective. the combined cluster-based routing approaches assist in optimal data transmission [14]. on the other hand, security is the major concern in manets [15]. because the network is probably deployed in the open and remote areas thus it increases the chances of tampering, data modification, and theft [16]. network layer attacks/issues physical •jamming •tampering •denial of service (dos) link • eavesdropping, overhearing, • manin-the-middle network • routing attacks such as blackhole, • sinkhole, • wormhole transport • unauthorized access, • ip spoofing application • malicious codes, • viruses, • application thefts in table.1, the major security issues existing in manet are summarizedconcerning the layers [17]. as the network has no infrastructure, there is no control over the network nodes. many research works have proposed security approaches such as authentication [18], encryption [19], and so on. however, security provisioning is https://doi.org/10.15837/ijccc.2023.2.5144 3 still challenging due to, • network mobility • mobile attackers • strength of attackers • central server-based authentication on the whole, data transmission and security provisioning are two major aspects of manet networks. 1.1 blockchain network blockchain is a technology that allows for the transparent, secure, and decentralized storage and transmission of information [20], [21]. it serves as a sizable archive that preserves a record of all user interactions ever since the blockchain was founded. the distributed design of blockchain, which is not hosted by a single server but rather by a small group of users, is a fantastic feature. components of the blockchain contain security measures to safeguard the system and do not need an intermediary to verify the chain’s authenticity or the accuracy of the data. blockchains are shared, decentralized, and fault-tolerant databases that are available to everyone on the network and are not under the jurisdiction of any one entity. the technology is made to function against enemies in high-stakes circumstances. blockchains are open, distributed, and fault-tolerant databases that anybody on the network may use; they are not under the jurisdiction of any one entity. the technology is built to function in dangerous scenarios against foes. in figure.2, integration of blockchain and manet is depicted [22], [23]. figure 2. blockchain and manet integration each member of the network stores an identical copy of the blockchain and is in an essentially equal position. blockchain is employed in various application scenarios and is regarded as one of the crucial strategies to speed up global growth due to its high level of security and dependability. huge research works had been carried out for multimedia wireless networks in terms of routing, congestion avoidance, security features, packet loss and reducing delay during transmission, etc. some of them are discussed here to relate the proficiency of our proposed work. 1.2 research challenges research challenges existing in manet are, dynamic network topology because of the dynamic network structure, nodes may migrate at any moment and in any direction. this leads to link breakages during routing energy consumption manet involves mainly resource-constrained devices. frequent route discovery and selection lead to high energy consumption. security breaches the involvement of the central security server increases the single node problem and is unable to provide a security level. https://doi.org/10.15837/ijccc.2023.2.5144 4 1.3 research contributions to resolve the research problems, this paper summarizes the following research contributions, • a novel blockchain-assisted secure routing (block-sec) protocol is presented for manets • all mobile nodes are authenticated by distributed one-time passcode (dot) based authentication scheme which uses blockchain for node validation • the network is formatted with weight based dynamic clustering (wdc) algorithm with strawberry optimization (sbo) algorithm-based ch selection process • for optimal route selection, fast neural net-assisted fuzzy (fnnf) algorithm is introduced with multiple metrics • data transmission security is ensured by efficient elliptic curve (e2c2) algorithm. 1.4 paper organization the remaining paper are arranged as follows: section ii provides a literature survey on existing works held on manet secure routing. in section iii, the major problem is stated which is resolved by the proposed approach. in section iv, the proposed approach is discussed. section v evaluates the overall performance through extensive simulation observations. in section, vi the contributions are concluded with future research directions. 2 related works this research uses the well-known pso (i.e., particle swarm optimization) approach to solve the problem of node mobility, and an adaptive k-nearest neighbor cluster algorithm is also developed [24]. to help with cluster formation, a multi-objective fitness function of pso is considered. general testing in a simulated networked environment shows that the recommended method works. the complexity of manet, where optimum energy is one of the key elements, was increased by mobile nodes migrating at random area of interest. it is far more difficult to maintain long battery life while simultaneously allowing for frequent changes in the architecture of mobile nodes. an effective parallel computing method that manages topological structures well is the honeycomb-based paradigm [25]. additionally, the most important objective of manet is to choose the best routes while taking energy efficiency into account. to do this, this study introduces the ieehr paradigm for manet. the model reduces the broadcasting range while performing pathfinding by combining honeycomb-based area coverage with lar. since mobile nodes have limited energy, efficient energy use is also necessary for manet in addition to good routing. a network’s performance and durability will both improve by how efficiently energy is spent in the network. here, more energy is saved when the mobile nodes are asleep, which is further used up during the efficient routing process. the trust of nodesin direct and indirect pathways are principally evaluated in this research [26], and the secure multipath is chosen while also identifying and isolating the vulnerable nodes. to protect the data packets from assaults of data transmission, the dps are then encrypted using the sh2e method. in identifying the finest way out of the multipath chosen, the lf-sso algorithm is then used. by determining a path trust-based path, residual energy of the node, and the path’s distance, this approach increases the network lifespan. then, using the found optimum path, the encrypted dps are sent from the sender to taget, and lastly, relayed to the base station.[27-31] because of the constant node migration and the limited resources available, security management is a severe challenge [32-37]. rekeying is only done for the so-called clusters of subnetworks to avoid having to repeatedly renew the group key for the entire wide network. this research proposes an integrated strategy of the hdgk management and ftbc to deal with this problem. the ftbc uses fuzzy logic principles to separate misbehaving nodes from genuine data transfer and classify trusted and untrusted nodes. no one methodis used for all application types, of simple clustering and improved weighted distributed clusteringare proposed to meet various demands. although multi-hop routing is essential in manet, it might present difficult problems during communication including a lack of data privacy. ecc is combined with the bee clustering strategy to offer a secure and resource-efficient data transport system [38]. data dependability is still questionable because of attacks like data dumping attacks and black hole attacks even if it guarantees data. the neighbour routers in these situations employ the overhearing approach, and the packet forwarding statistics are calculated using the ratio of received to forwarded packets. if the network’s packet forwarding ratio is low, an attack can be identified and a trustworthy another channel can be found for information transfer. the suggested work involves the sc-aodv integration, ecc, and a further overhearing mechanism into the bee clustering strategy, which overall assures data secrecy, data dependability, and energy efficiency. https://doi.org/10.15837/ijccc.2023.2.5144 5 to improve pdr and network lifetime, a new routing protocol for manets was proposed in hybrid optimizationbased mpr-dc-based manet. this protocol uses a hybrid optimization technique that combines aco and pso [39]. a multipath-based routing algorithm for air pollution monitoring in manets was presented in [40]. this algorithm considers the nodes’ available energy and the links’ quality to choose the best routes and enhance network performance. [41] to control the broadcasting of many packets and guarantee the stability of all mobile nodes in a manet, the proposed tcssr algorithm makes use of timer count scheduling and spectator routing. to decrease packet loss and lengthen network lifespan, clustering, and stifle limitation methods are also utilized. [42] to extend the lifespan of inexpensive and compact sensor nodes in wsns, the sicra is presented. to save energy and balance the network load, sicra calculates the ideal routing route by examining the transmission coverage, connection link, and other criteria.nmrouting is a neural controller based on perceptron and an mcdm controller that determines the optimum pathways for sending data packets in a neural-mcdm-based routing protocol that has been suggested for manets. to increase network dependability, three different queue types are also used [43]. with the use of fuzzy theory, mcdm, and an rgb model, the fsb-system is a detection system that is presented for estimating the likelihood of fire, asphyxia, and burns. fuzzy controllers are utilized to calculate probability while temperature, smoke, and light sensors are employed to make judgments under various scenarios. clusters of sensor nodes are set up, and data is sent from non-cluster heads to cluster heads [44]. the drawbacks of existing techniques including the complexity of manet, where optimum energy is one of the key elements, are increased mobile node’s random movement within a region of interest. it is more difficult to maintain long battery life while allowing for frequent changes in the architecture of mobile nodes. the ieehr paradigm reduces the broadcasting range while performing pathfinding by combining honeycomb-based area coverage with lar, which can limit the scope of the network. while the proposed dxor-rc6 with fe technique ensures data security and integrity in the e2-sr system, the security level of the other approaches is not explicitly mentioned. the use of multiple algorithms and techniques, such as the lf-sso algorithm, may increase the complexity of the network and require more resources for implementation and maintenance. to overcome these drawbacks, the proposed protocol uses a dot based authorization scheme to authenticate all mobile nodes in the network, providing a secure and reliable way of identifying legitimate nodes, the wdc algorithm is used to segregate authorized nodes into multiple clusters based on multiple metrics, which allows for better organization and management of the network. sbo algorithm is used to elect the optimal ch with a new objective function, which helps in the efficient management of the cluster and network. fnnf algorithm is used to select optimal routes by combining multiple variables, which helps in efficient routing and better performance. the proposed protocol uses the e2c2 algorithm to secure data transmission, ensuring the confidentiality and integrity of data. the combined algorithms used in the proposed protocol achieve better efficiency in pdr, throughput, time analysis, and security level, providing a more robust and reliable network 3 problem statement the issues related in secure routing for mobile networks is a critical issue because of the dynamic and decentralized network. one of the main challenges is to ensure that data is transmitted securely between nodes while minimizing energy consumption and maximizing the delivery rate. however, attackers can exploit vulnerabilities in the network to intercept or manipulate data, compromising the security and reliability of the network. to address this problem, a min-max function is formulatedfor balancingbetween energy consumption, delivery rate, and security level trade-off. the objective is to minimize energy consumption and the number of retransmissions while maximizing the delivery rate and security level. this objective is achieved by formulating the problem as a min-max function, where the energy consumption is minimized and the no. of retransmissions is to be optimized while the maximum delivery rate and the security level are to be maintained. the presence of attackers further complicates the problem, as they can exploit vulnerabilities in the network to compromise the security and reliability of the network. therefore, the problem is formulated to minimize and maximize the consecutive functions in the presence of attackers. this formulation ensures that the network is secure, reliable, and efficient, even in the presence of attackers. the problem of secure routing in the mobile network is formulated as the problem of the min-max function as follows, where ec , rt represent energy consumption and the number of retransmissions and dr,sl represent delivery rate and security level respectively. the problem is formulated as minimizing and maximizing the consecutive functions in the presence of attackers. 4 proposed work the proposed block-sec work aims at achieving high-level security with a reasonable data transmission rate. the overall work is explained in detail. https://doi.org/10.15837/ijccc.2023.2.5144 6 4.1 network model the proposed mobile network has n number of mobile nodes m1,m2,..mn with the mobility range of denote the lower and upper bound of the mobility range respectively. all mobile nodes are allowed to move around the network with varying mobility speedswithin the range. the overall network is divided into multiple clusters for ease of management. all mobile nodes within the network are authorized before cluster formation. the proposed block-sec architecture is illustrated in figure.3. with the help of the blockchain network, the nodes are validated in a distributed manner. for data, transmission is carried through the optimal route selected in the network. overall data transmission is secured by using lightweight cryptography techniques. 4.2 mobile node authentication authentication is the process of validating the authorization of the nodes presented in the network. for authentication, the dot protocol is proposed which uses the distributed blockchain network. in general, authentication credentials are stored in a single server which lacks with single node failure problem. to avoid this major issue, we presented a blockchain network for authentication. f ¯ igure.3 proposed block-sec architecture wdc is an algorithm used for the formation of clusters in manets. the algorithm considers multiple metrics such as node mobility, distance, and energy levels to create and update clusters dynamically. wdc-based cluster formation helps to improve performance of the overall network by reducing the overhead and improving the routing efficiency. a blockchain is a decentralized, distributed ledger that is used to record transactions securely and transparently. in the context of mobile networks, a blockchain network can be used to secure data transactions and ensure data integrity. the use of blockchain in mobile networks helps to reduce the risks of unauthorized access and enhances the security of the overall network. the cluster head (ch) is an important node in the cluster-based routing protocol for manets. the optimal selection of ch is critical for the performance of the network. strawberry optimization (sbo) is an optimization algorithm that can be used for selecting the optimal ch in manets. sbo-based ch selection helps to improve the network performance by reducing energy consumption and increasing the reliability of the network. dot-based authentication: distributed one-time passcode (dot) is a scheme used for node authentication in manets. in this scheme, each node is assigned a unique passcode, which is used for authentication. dot-based authentication helps to improve the security of the network by preventing unauthorized access and ensuring that only authorized nodes can participate in the network. fnnf-based optimal routing: fast neural net-assisted fuzzy (fnnf) is a routing algorithm used in manets. the algorithm uses multiple variables such as distance, mobility, energy levels, and security levels to select the optimal route for data transmission. fnnf-based optimal routing helps to improve network performance by reducing the number of retransmissions, improving the delivery rate, and increasing the security of the network. the proposed dot involves two main phases such as, 1. registration in dot – this process is initiated when a node is joining into the network. the proposed dot protocol receives three main authentication credentials such as mobile node id (m_id), password (m_pw) and certificate (mc ). the certificate is generated by the main server in the network. the server is connected with the blockchain network (i.e.) single node failure can be avoided. if i^ththe mobile node needs to register, https://doi.org/10.15837/ijccc.2023.2.5144 7 then it initiates the regini message with mi d(i),mp w (i) and mc (i) as follows, mi →reglnimi d (i)×mp w (i) .mc (i) after successful registration, the server replies regsuc messages to mi with required private and public key pairs (kpu (i),kpr (i)) with time stamp (ts ) as follows, block→regsuckpu (i),kpr (i),ts also, the server generates unique passcode (ui d (i)) by using the xor function as follows, ui d (i)=h(mi d)⊕h(mp w )|kpu h()represent the corresponding hash value. with the unique id creation, the registration phase is completed. all these credentials are stored in the blockchain network in a distributed manner. authentication phase – once the node is registered, then it will have three major credentials with public and private key pairs. whenever the node needs to participate in the network the node needs to be authenticated with the blockchain. for authentication, the node needs to initiate authini message to the blockchain network. the authini message is composed of, authini→signmi d (i),mp w (i)+ui d (i) the credentials need to be signed digitally by the mobile nodes. on receiving authini message, the server validates the credentials with the blockchain. if the credentials are correct, then it returns one-time passcode with the time stamp as follows, ot pt→r⊕mi d (i) _t the ot pt is a session passcode that is valid only for the period t. that is, the node mi can submit the passcode to the ch to join the network. it can be seen that, after the session is completed the otp will become invalid which ensures no adversary can reuse the code after sometime. in this manner, the nodes are authenticated in the network. the above procedure explains the steps involved in the proposed dot protocol. the proposed authentication protocol allows cluster formation with only valid nodes which improves the security level. https://doi.org/10.15837/ijccc.2023.2.5144 8 4.3 secure cluster formation once the nodes are authenticated, the next step is to form various clusters to make the data transmission simple and effective. we proposed a wdc method that uses weight value similarity for cluster formation. the major issue in cluster-based manet is the stability of the clusters is always questionable since the nodes move away adequately which leads to collapse in the network. thus, we presented the wdc method to improve cluster stability. first, each node mi computes weight value based on the following three metrics, mobility – it is represented asµ in terms of the moving node velocities in the network. connectivity – it is represented as ci which denotes the current node connectivity(i.e.) the no. of nodes connected with the particular node. centroid distance – it is represented as cdi. it is computed as the distance between the network’s central point and the current position of the node as euclidean distance. altogether, the weight value for the node mi (wi) is computed as follows, wi=σ µ,ci,cdi each node computes the wi, then initiates the cluster formation process. the wdc-based cluster formation involves the following steps, handshake session – first, each node transmits the clufor message as follows, clufor→mi d (i),wi,ot pt decision-making session – on receiving clufor message, each node makesa decisionbased on two conditions as follows, condition 1:validate ot pt condition 2:compute weight difference wd w d(i, j)=difference[wi,wj ] the decision is true only if both conditions are satisfied. cluster formation – if mi receives clufor from node mj , it first checks condition 1. if the condition is satisfied (i.e.) mj is valid, then it moves to the next condition 2. if the w d(i, j) is low, then the node accepts the formation message. similarly, node mj validates the mi. after decision-making, each node transmits clucon message to the optimum nodes to form clusters. this mutual session authentication improves the security level in the network. also, consideration of w d(i, j) ensures that all nodes in the cluster have the same level of mobility and distance. so that, the clusters formed by the wdc method will be stable. optimal ch selection at the end of the wdc method, the overall network is segregated into v number of clusters denoted as g1,g2,..,gv . the next step is to select the optimal ch in each cluster. in each cluster, ch is responsible to manage the nodes within the group. thus, the ch must be optimal and capable of handling the cluster management process. for optimal ch selection, we proposed an sbo algorithm with multiple objectives. the sbo algorithm is inspiredby the growth procedure of strawberry plants. the strawberry plant’s growth depends upon runners and roots. here, the runner represents the global search while a local search is represented by roots. the overall algorithm deals with the following factors, the problem domain is spread out with the runners the mother strawberry plant is developed and improved through the roots and hair. this growth is random in the problem domain. the daughter plants which are developed from the mother plants improve the growth through roots and runners. the daughter plants exist only if the growth is healthy otherwise it dies the above facts are considered in the algorithm. initialization: initialize the number of solutions as the strawberry plants. here, the solutions are the nodes presented in each group gj . problem formulation: the objective function f(x) is the combination of functions that needs to be considered in the ch selection process. in the proposed sbo, the objective is to select optimal ch and the problem ofoptimization is formulated as follows, min f(s)→ k o f if sl <s < su here, s is the solution vector and l,u represent the lower and upper bound respectively and of denotes the objective function. offormulation: the of of the proposed work is to select optimal and effective ch for each group. for that, we presented a multi-objective formulation in which multiple metrics are considered in of formulation. the proposed ofis given as follows, ofi=max (ei,sti )&min (adi) the objective function for mi (ofi) is computed in terms of energy level (ei), successful transmissions (sti) and average cluster distance (adi). by combining all three parameters, the objective is formulated to select optimal ch in the formed clusters. duplication: in the sbo algorithm, the position of the solutions is computed and updated as follows, https://doi.org/10.15837/ijccc.2023.2.5144 9 vprob (i)=[vr oot (i) vr unner (i) ] =v(i(v(i) )+[dr oot r1.dr unner r2] where vprob is the location of the ith solution and r1,r2 are the constant values.the vr oot,vr unner are computed as follows, vr oot (i)=v(1.root) (i).v(2.root) (i). . . .v(v.root) (i) vr unne (i)=v(1.runner) (i).v(2.runner) (i). . . .v(v.runner) (i) elimination: for each node presented in the cluster, update the position based on of_i and the unsuitable solutions are eliminated by the following expression, f(v(j.prob) (i) )=ofm ax,ofm in the probability is computed as follows, pj =f(vi)/(σ f (v(j.prob) (i) ) ) the solution which has a lower probability is eliminated and the solution space is updated. similarly, the node which has a higher probability is selected as the mother plant and other plants update their position based on it. in this way, the optimal node which satisfies the maximum and minimum functions is selected as ch. the above procedure explains the steps involved in cluster formation and ch selection in a combined manner. at the end of the sbo algorithm, each cluster will have optimal ch which has a higher energy level and lower cluster distance. cluster management: in dynamic manets, cluster management is one of the main issues. in the proposed approach, cluster management is performed by ch. the proposed clustering approach itself uses mobility similarity in weight value computation. thus, the nodes with similar mobility are formulated into the same cluster which means a frequent re-clustering process is not necessary. further, the cluster management is performed by ch as follows, 1. if any node increases its mobility and needs to move from the cluster, then it needs to send cluter message to the corresponding ch. 2. ch analyzes the distance and mobility to avoid unnecessary termination. if the mobility and distance differ https://doi.org/10.15837/ijccc.2023.2.5144 10 from the initial state, then ch sends a success message to that node. 3. after successful termination the particular node moves to the next cluster and initiates the same procedure for cluster join. each time the node is needed to be authenticated by the blockchain network. 4.4 optimal routing once the clusters are formed, the transmission of the data between the sender and receiver. for optimal route selection, we presented the fnnf approach which integrates a neural network with a fuzzy approach for fast route selection. the integration of neural networks and fuzzy elements is depicted in table.2. table 2. integration of fuzzy and fnn ann element equivalent fuzzy element input layers fuzzification of inputs to [0,1] hidden layers application of rules from the inference engine output layer defuzzification of output get from the system the fnnf system has three major layers as follows, input layer: the input layer of the fnnf algorithm receives inputs. in the proposed work, the available routes are the input for the fnnf approach. it can be given as (r1,r2,..,ra) where ra is the available routes between the source and target. for each rj ϵr, the necessary metrics are learned in this layer. this layer is responsible for the fuzzification process. all input values are deduced to [0,1] interval. hidden layer: in this layer, fuzzy rules are appliedto the input parameters. here, we considered three major parameters such as trust value (t((j))), energy level (e(r(j))) and hop count (hcj ). each parameter is explained as follows, tj →it is defined as each node’strust value presented in the particular route rj . it is given as, https://doi.org/10.15837/ijccc.2023.2.5144 11 tj =( ∑x i=1tij)/x where x is the no. of nodes present in the route rj . er(j) →it is defined as the total energy level of the given route as follows, er=( ∑x i=1erij)/x hcj →it gives the total number of nodes presented in the route based on the parameters, the fuzzy rules are appliedto hidden layers. the fuzzy rules are illustrated in table.3. table.3 fuzzy rules for fnnf t(ij) e(r(j)) hcj output low low low low low low high low low high low medium low high high low high low low medium high low high low high high low high high high high as given in the table, the rules are appliedto the hidden layers. based on the output value, each route is given with weight value as follows, wr= σ.fn(opr) here, σ denotes the sigmoid function and opr is the output by the fuzzy rules as [high-medium-low]? for optimal route selection, if-then rules are implemented in the hidden layers as follows, if tij=high & e(r(j))=high &hcj =low then opr=high=1 output layer: this layer is responsible for the defuzzification process. in this layer, the values that lie in the [0,1] range are defuzzified to normal values. the route which has a high weight value is further selected as the optimal route ropt for data transmission. as the route is trusted and the energy level is more than enough, data transmission is efficient and the number of retransmission is minimized. in addition, trusted route selection with authorized nodes mitigates the problem of routing attacks such as wormholes, blackholes, and sinkholes. though the data is transmitted through a trusted route, it is necessary to secure data to mitigate man-in-the-middle and eavesdropping attacks. as we discussed earlier, each node is given with kpu and kpr generated at the initial phase. in blockchain ecc algorithm is generally used. here, we improved ecc to e2c2 algorithm by improving the key generation approach. for key generation, we have proposed fourq-curve which are defined as follows, -x2+y2=1+dx2 y2 from the points x,y the keys are generated which satisfy the above equation. thus the proposed work ensures a high level of security in data transmission through trusted route selection as well as crypto technique. the proposed method is used to improve cluster stability and optimal ch selection in cluster-based manet. to form secure clusters, the weighted difference-based clustering (wdc) method is used, which utilizes weight value similarity for cluster formation. the weight value of each node is calculated based on three metrics: centroid distance, connectivity, and mobility. these three metrics are combined to calculate the weight value for each node. the cluster formation process involves three sessions: handshake session, decision-making session, and cluster formation session. the handshake session initiates with the transmission of the clufor message, which contains the mi d (i), wi, and ot pt. in the decision-making session, each node validates the ot ptand computes the weight difference between two nodes, w d(i, j). if both conditions are met, the cluster formation process begins. cluster formation involvestransmission each node clucon message to the optimum nodes to form clusters. mutual session authentication improves the security level in the network. after the wdc method, the overall network is segregated into v number of clusters denoted as g1,g2,..,gv . the next step is to select the optimal ch in each cluster. for this purpose, the strawberry-based optimization (sbo) algorithm is proposed. the sbo algorithm is inspired by the growth procedure of strawberry plants, which involves runners and roots. the sbo algorithm is initialized with the number of solutions as the strawberry plants. the optimization problem is formulated to select the optimal ch in each cluster. the objective function of the proposed work is to select optimal and effective ch for each group by combining energy level, successful transmissions, and average cluster distance. the solutions’ position is computed and updated by using the vprob (i) expression, which consists of vr oot (i)and vr unner(i). finally, unsuitable solutions are eliminated by using the ofm ax, ofm in expression. https://doi.org/10.15837/ijccc.2023.2.5144 12 5 experimental analysis the evaluation of proposed work is discussed in this section in terms of simulation observations. 5.1 simulation setup the proposed manet network is implemented in the network simulator – 3 (ns-3.25) simulation tool. the ns-3.25 is one of the event-based simulators that use c++ and tcl languages for network configuration. ns-3.25 supports major wireless network standards and allows the integration of blockchain. thus, we used ns-3 for simulations. table 4. simulation parameters parameter value no. of nodes 150 maximum clusters 15 network area 300*300m the initial energy of nodes 750j bandwidth 25 mhz no. of packets 1000 packet size 32 kb number of retransmissions 5 mobility range µl 10 m/s µu 40 m/s r1 0.01 r2 0.1 σ 0.001 number of adversaries 5 simulation time 100s in table.4 summarizes the major simulation parameters used in the proposed work. with the above setting, important performance metrics are observed. 5.2 comparative analysis for comparison, we used pdr, re) time analysis (encryption and decryption), throughput, and security level. for better analysis, two scenarios are created for analysis as follows, scenario 1: in this scenario, we vary the no. of nodes while the no. of attackers is static (i.e.) 5. in this scenario, we can observe the ability of the proposed work in varying network scales. scenario 2: we set the number of nodes as static (i.e.) 100 and change the number of attackers from 1 to 5. from this scenario, we can observe the efficacy of the proposed work in the presence of attackers. for comparison, two existing works such as pso-secure routing [30], and fuzzy clustering [31] are considered. both works have developed in the manet environment. a) pdr analysis the ratio between the total no. of packets received by the destination and the total no. of packets transmitted by the source nodeis known as pdr. https://doi.org/10.15837/ijccc.2023.2.5144 13 f ¯ igure 5. comparison on pdr in figure.5, the pdr is analyzed in both scenarios. here, it can be seen the pdr achieved by the proposed work is nearly 97% which is far better than the existing works. in scenario 1, the pdr is increased gradually with an increase in several nodes. on increase in node quantity, the opportunity for selecting the optimal route will be increased. thus, the data is transmitted through an effectual and trusted route. on the other hand, when the number of attackers is increased the pdr is decreased. this is because the involvement of increased attackers modifies the route information and leads to packet loss. however, in both scenarios, the proposed block-sec method achieves better pdr. this analysis shows that the proposed security mechanism tackles the attackers and cluster-based routing assist in achieving optimal routing. b) energy level analysis residual energy is the important performance measure that defines the current energy level of the nodes presented in the network. for i^th node, the residual energy (re) is computed as follows, rei=( ∑ _i = 1n ei(ini)-ei(cur))/n here ei (ini) is the initial energy level and ei (cur) is the current energy level of the i^th node. f ¯ igure 6. comparison ofenergy level figure.6 compares the energy level among proposed and existing works. in general, the increased energy consumption when the increase in no. of attackers in the network. the involvement of attackers attempts to drain the node’s energy level. after a particular period, the node’s energy level will be drained completely and the nodes become dead. to avoid this situation, it is necessary to protect the overall network from attackers. https://doi.org/10.15837/ijccc.2023.2.5144 14 f ¯ igure 7. number of nodes alive concerningthe time figure 7 shows the no. of alive nodes oversome time. it can be seen that the number of alive nodes decreases with the increase in the period. with that, the proposed work maintains a large no. of nodes as alive. this shows that the proposed block-sec approach protects the network from attackers as well as saves the energy level of nodes through dynamic cluster formation and optimal route selection. from the analysis, it is clear that the proposed approach is suitable for the dynamic mobile network in maintaining the proper energy levels. c) time analysis we analyze the time consumed in providing security by various methodologies. the time analysis is performed in two ways: i) by encryption time and ii) by decryption time. i) encryption time: the time taken by the cryptography technique to convert plaintext into ciphertext. itis computed at the source node. ii) decryption time: the time taken by t he cryptography technique to convert ciphertext into plaintext. it is computed at the destination. f ¯ igure 8. comparison of time analysis in figure.8, encryption and decryption time analysis are presented. when it comes to encryption and decryption the time consumption fully depends upon the packet size. when increase in the packet size then the time consumption is also increased. here, we compared the time consumed by advanced encryption standard (aes), elliptic curve encryption (ecc), and proposed e2c2 crypto techniques. in general ecc algorithm consumes lower time than the aes algorithm in both encryption and encryption. when it comes to the e2c2 algorithm it further minimizes the time consumption by generating a lightweight key by four-q curve. from the analysis, it is clear that the proposed encryption algorithm is efficient in data security. d) throughput analysis https://doi.org/10.15837/ijccc.2023.2.5144 15 f ¯ igure 9. comparison on throughput the total amount of successful data transmission to the destination at a given time is known as throughput. in figure.9, the comparison of obtained throughput with proposed and existing works.in contrast to other parameters, the throughput metric depends upon both the security level of the network andthe data transmission methodology. that is to say, throughput is affected by both security threats and non-optimal data transmission. it can be seen from scenario.2 that when the number of attackers is increased then the throughput is decreased. this is because the data transmitted at a given period is majorly attacked by the attacker nodes so that the data is not received at the destination within a given period. in both scenarios, the proposed work achieves better throughput of up to 8 mbps which is 2 times better than the existing works. the presence of an effectual security scheme and optimal network management with the routing approach in the proposed work helps in achieving the highest throughput value in the network. thus, we can summarize that the data loss is minimized drastically in the proposed work. e) security level analysis the security level is measured in terms of the total amount of packets altered or modified in the network by attackers. it shows the involvement of the attackers (i.e.) when the number of attackers presented in the network is large, then it modifiesa large number of data packets. f ¯ igure 10. comparison of security level in figure.10, the security level attained by the proposed and existing security approaches has been compared. the analysis shows that the proposed work attains 98% of the security level which is far better than the existing works. the proposed work ensures security level through the following aspects, 1. integration of blockchain ensures high-level security and the involvement of effectual authentication through the blockchain network prevents unauthorized nodes in the network limit. thus the network is free from malicious and unauthorized nodes. 2. the optimal route is selected by considering the trust value which ensures that the data will not be modified as there are no untrusted malicious nodes https://doi.org/10.15837/ijccc.2023.2.5144 16 3. data is secured by using an e2c encryption approach which will not be shared or altered by any malicious nodes with the above aspects the proposed work achieves a better security level in the network. at the same time, existing work either focus on malicious node detection or data security. further, the centralized nodes in the network may become compromised which limits the security level to 30%. 6 conclusion a new block-sec-based manet architecture is proposed to ensure high-level security in a mobile environment. the major issue of secure routing and data transmission is realized by integrating distributed blockchain network with the manet environment. first, the mobile nodes are authorized by the dot approach. this approach authorizes the nodes based on dynamic one-time passwords and digital signatures validated in the blockchain. all authorized nodes are considered the wdc approach which uses weight value-based similarity for cluster formation. in each cluster, the optimal cluster head node is selected by the sbo algorithm by considering multiple metrics. further, data transmission is performed through the optimal route selected by the fnnf algorithm based on significant trust-based parameters. data security is ensured by the e2c2 algorithm which is an effectual cryptography technique. with all security 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[44] m. s. gharajeh, a neural-mcdm-based routing protocol for packet transmission in mobile ad hoc networks, international journal of communication networks and distributed systems, vol. 21, no. 4, pp. 496–527, sept. 2018. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: ilakkiya n.; rajaram, a. (2023). blockchain-assisted secure routing protocol for cluster-based mobile-ad hoc networks, international journal of computers communications & control, 18(2), 5144, 2023. https://doi.org/10.15837/ijccc.2023.2.5144 introduction blockchain network research challenges research contributions paper organization related works problem statement proposed work network model mobile node authentication secure cluster formation optimal routing experimental analysis simulation setup comparative analysis conclusion int j comput commun, issn 1841-9836 9(2):227-242, april, 2014. dynamic behavior analysis of membrane-inspired evolutionary algorithms g. zhang, j. cheng, m. gheorghe gexiang zhang*, jixiang cheng school of electrical engineering, southwest jiaotong university chengdu, 610031, p.r. china *corresponding author: zhgxdylan@126.com marian gheorghe department of computer science, the university of sheffield, regent court, portobello street, sheffield, s1 4dp, uk abstract: a membrane-inspired evolutionary algorithm (miea) is a successful instance of a model linking membrane computing and evolutionary algorithms. this paper proposes the analysis of dynamic behaviors of mieas by introducing a set of population diversity and convergence measures. this is the first attempt to obtain additional insights into the search capabilities of mieas. the analysis is performed on the miea, qeps (a quantum-inspired evolutionary algorithm based on membrane computing), and its counterpart algorithm, qiea (a quantum-inspired evolutionary algorithm), using a comparative approach in an experimental context to better understand their characteristics and performances. also the relationship between these measures and fitness is analyzed by presenting a tendency correlation coefficient to evaluate the importance of various population and convergence measures, which is beneficial to further improvements of mieas. results show that qeps can achieve better balance between convergence and diversity than qiea, which indicates qeps has a stronger capacity of balancing exploration and exploitation than qiea in order to prevent premature convergence that might occur. experiments utilizing knapsack problems support the above made statement. keywords: membrane computing, membrane-inspired evolutionary algorithm, dynamic behavior, quantum-inspired evolutionary algorithm; knapsack problem. 1 introduction membrane computing, initiated by păun in 1998 [1], focuses on the investigation of the models, called membrane systems or p systems, abstracted from the structure and the functioning of the living cell as well as from the cooperation of cells in tissues, organs, and other populations of cells. thompson institute for scientific information, isi, listed the seminal paper as a fast breaking record and this area as an emerging research front in computer science in 2003, and thereby membrane computing becomes a branch of natural computing and has developed very fast into a vigorous scientific discipline [2–6]. aiming at investigating the interactions between membrane computing and evolutionary computation, membrane-inspired evolutionary algorithms (mieas) are considered as a class of hybrid optimization algorithms, which use the concepts and principles of meta-heuristic search methodologies and the hierarchical or network structures of p systems, and to some extent, some of the rules of p systems [7,8]. a miea is regarded as a successful paradigm extending p system models with capabilities that make them amenable to real-world applications [9]. due to a very wide range of applications of meta-heuristic search methodologies, such as genetic algorithms and tabu search, there is a very promising perspective of applying p systems to solve various complex and difficult engineering problems. copyright © 2006-2014 by ccc publications 228 g. zhang, j. cheng, m. gheorghe in recent years, many investigations referred to mieas. the first version of membrane algorithms was designed with a nested membrane structure (nms) and a local search heuristic for solving travelling salesman problems, which are well-known np-hard optimization problems [10]. an approach combining nms and genetic algorithms was presented and tested by using six benchmark functions [11]. in the preceding work, we proposed a miea, called a quantuminspired evolutionary algorithm based on p systems (qeps), which incorporates a one-level membrane structure (olms) and a quantum-inspired evolutionary algorithm (qiea) [12]. a well-known np-complete optimization problem, knapsack problem, was used to carry out extensive experiments, which show that qeps achieves better solutions than its counterpart qiea and olms has an advantage over nms. in [7, 13–15], qeps and its modified versions were presented to solve various problems, such as radar emitter signal analysis and image processing. in [16] and [17], dna sequences design was optimized by designing a miea based on crossover and mutation rules and a dynamic miea combining the fusion and division rules of p systems with active membranes and search strategies of differential evolution (de) and particle swarm optimization (pso), respectively. in [18], a memory mechanism was considered in the design of mieas. in [19], a hybrid miea was presented by combining olms with pso to solve constrained optimization problems. in [8], a miea was proposed by using the network membrane structure of a tissue p system with five cells to organize five representative de variants. however, since mieas were initiated in 2004, a question has been asked many times by researchers from the areas of membrane computing and evolutionary computation. the question refers to the role played by p systems in mieas, that is, what advantages do p systems bring to mieas? this is also a critical and tough question that has been haunting many researchers in the field of membrane computing. so the motivation of this work is to try to some extent to find an appropriate answer for this question. in this study, we propose the analysis of the dynamic characteristics of mieas by using a set of population diversity and convergence measures to comparatively investigate the evolving processes of qeps and its counterpart algorithm, qiea. due to the difficulty in theoretically reasoning about mieas, we mainly focus on the experimental analysis. also we present a tendency correlation coefficient to analyze the relationship between the population diversity and convergence measures and fitness to understand the importance of each measure and to provide suggestions on how to improve the performance of mieas with respect to population diversity and convergence. furthermore, experiments conducted on knapsack problems are presented. the rest of this study is organized as follows. section 2 gives a brief description of qiea and qeps, which is helpful to understand the dynamic behavior analysis of mieas expounded in section 3. specific examples follow in section 4 to verify the analysis presented in the preceding section. section 5 concludes this work. 2 qiea and qeps 2.1 qiea inspired by quantum computing, han and kim [20] proposed a novel evolutionary algorithm, called qiea, for a classical computer. qiea consists of three main components: quantuminspired bit (q-bit) representation, a probabilistic observation and a quantum-inspired gate (qgate) [21]. in qiea, a genotypic gene is represented by using a q-bit defined by a pair of numbers (α, β) denoted as [α β]t , where |α|2 and |β|2 are probabilities that the observation of the q-bit will render a ‘0’ or ‘1’ state. a string of q-bits is applied to represent a q-bit individual. the connection between genotypic representation (q-bit representation) and phenotypic individuals (binary solutions) is established by the probabilistic observation. the q-gate is used to produce dynamic behavior analysis of membrane-inspired evolutionary algorithms 229 offspring. generally speaking, qiea is composed of the following steps: (i) initialization: a population q(t) with n q-bit individuals is generated, q(t)={qt1, q t 2, · · · , qtn} at generation t (here t = 0), where qti (i = 1,2, · · · ,n) is an arbitrary individual in q(t), which is represented as qti = [ αti1|α t i2| · · · |α t il βti1|β t i2| · · · |β t il ] , (1) where l is the number of q-bits, i.e., the string length of the q-bit individual. (ii) observation: a probabilistic observation is used to produce binary solutions p(t), p(t)={xt1, xt2, · · · ,x t n}, by observing the states of q(t), to be specific, a binary bit 0 or 1 is obtained in terms of the probability, either |αtij| 2 or |βtij| 2 of qti, i = 1,2, · · · ,n, j = 1,2, · · · , l. thus a q-bit individual with l q-bits results in a binary solution xti (i = 1,2, · · · ,n) with l binary bits. (iii) evaluation: the binary solution xti (i = 1,2, · · · ,n) in p(t) is evaluated thus obtaining its fitness. additionally the best solution among p(t) is stored. (iv) offspring generation: q-gates are performed on q-bit individuals in q(t) to produce their corresponding individuals at the next generation. for example, the j-th q-bit in the i-th q-bit individual qti, j = 1,2, · · · , l, i = 1,2, · · · ,n is updated by applying the current q-gate gtij(θ). qiea uses a quantum rotation gate as a q-gate; this is given by gtij(θ) = [ cosθtij − sinθ t ij sinθtij cosθ t ij ] , (2) where θtij is an adjustable q-gate rotation angle. (v) termination condition: the maximal number of evolutionary generations or the maximal number of function evaluations could be utilized to stop the algorithm. if the termination condition is satisfied, the algorithm will stop and output the final results, otherwise, the generation number increases by 1, i.e., t = t + 1, and the algorithm goes back to step (ii). 2.2 qeps in the process of investigating the interactions between p systems and evolutionary algorithms, we presented a miea, qeps [12], which was designed with the hierarchical framework of a cell-like p system, the objects consisting of q-bits and classical bits, the rules made up of q-gate evolutionary rules in qiea and evolution rules in p systems. qeps uses olms, where the skin membrane contains m elementary membranes defining m regions. q-bits, organized as a q-bit individual in a proper way, are treated as multisets of objects. classical bits, obtained from their corresponding q-bits by using a probabilistic observation, are arranged as a binary string and are dealt with also as multisets of objects. in qeps, a binary string corresponds to a solution of a problem. the set of rules are responsible for evolving the system and selecting the best fit q-bit individuals. all the objects and rules are appropriately placed in the membrane structure. more precisely the p system-like framework consists of (i) a membrane structure [[ ]1, [ ]2, · · · , [ ]m]0 with m+1 regions contained in the skin membrane, denoted by 0; 230 g. zhang, j. cheng, m. gheorghe (ii) an alphabet that consists of all possible q-bits and classical bits; (iii) a set of terminal symbols, t = {0,1}; (iv) initial multisets w0 = λ, w1 = q1q2 · · · qn1 , w2 = qn1+1qn1+2 · · · qn2 , · · · wm = qn(m−1)+1qn(m−1)+2 · · · qnm , where qi, 1 ≤ i ≤ n, is a q-bit individual; nj, 1 ≤ j ≤ m, is the number of individuals in wj; ∑m j=1 nj = n, where n is the total number of individuals in this computation; (v) rules which are classified as a evolution rules in each of the compartments 1 to m which are transformation-like rules updating a q-bit individual according to the current q-gate; b observation rules which make binary solutions from q-bit individuals; c communication rules which send the best fit individual binary representation from each of the m elementary membranes into the skin membrane and then the overall best binary representation from the skin membrane to each elementary membrane. 3 dynamic behavior analysis this section analyzes the dynamic behaviors of mieas in the process of evolution from two perspectives, the population diversity and convergence. six diversity and four convergence measures are introduced to comparatively exhibit the evolutionary behaviors of qeps and qiea. we start from population diversity analysis and then turn to convergence analysis. finally, a tendency correlation coefficient is proposed to evaluate the relationship between diversity and convergence measures and the quality of solutions. 3.1 population diversity analysis population diversity is crucial for a population-based search method to prevent premature convergence toward local optima. diversity measures are used to evaluate the levels and types of varieties of individuals in a population [22]. in this subsection, six diversity measures are considered for qeps and qiea, and they are respectively (1) dqbw: q-bit distance between the best and worst q-bit individuals corresponding to the best and worst fitness values in a population, respectively. dqbw is described as dqbw = 1 m m∑ j=1 ∣∣∣|abj|2 − |awj|2∣∣∣ (3) where |abj| 2 and |awj|2 are probabilities of the j-th q-bit in the best and worst q-bit individuals, respectively; m is the number of q-bits in a q-bit individual. 0 ≤ dqbw ≤ 1. a larger value of dqbw gives a hint of larger distance between the best and worst q-bit individuals. dynamic behavior analysis of membrane-inspired evolutionary algorithms 231 (2) dqa: average q-bit distance of all q-bit individuals in a population. dqa is defined as dqa = 2 n(n − 1) n∑ i=1 n∑ j=i+1 { 1 m m∑ k=1 ∣∣∣|αik|2 − |αjk|2∣∣∣ } (4) where |aik| 2 and |ajk| 2 are probabilities of the k-th q-bit in the i-th and j-th q-bit individuals, respectively; m is the number of q-bits in a q-bit individual; n is the number of individuals in a population. dqa is the average value of the q-bit distance between n(n−1) pairs of q-bit individuals. 0 ≤ dqa ≤ 1 . a larger value of dqa suggests a larger distance between each pair of q-bit individuals in a population. the two diversity measures above are obtained in q-bit space, so they can be regarded as genotypic diversity measures for qeps and qiea. in what follows we will introduce four phenotypic diversity measures: hamming distance between the best and worst binary individuals (dhbw) in a population, mean hamming distance of all binary individuals (dhm) in a population, and two diversity measures based on dispersion statistical measures including the diversity between chromosomes (dbc) and the diversity between the alleles (dba) [22]. (3) dhbw and dhm are depicted as dhbw = 1 m m∑ i=1 (xbi ⊕ xwi) (5) dhm = 2 n(n − 1) n∑ i=1 n∑ j=i+1 { 1 m m∑ k=1 (xik ⊕ xjk) } (6) where xbi and xwi are the i-th bits in the best and worst binary solutions, respectively; m is the number of bits in a binary solution; n is the number of individuals in a population; the symbol ⊕ is exclusive or operator; xik and xjk are the k-th bits in the i-th and j-th binary solutions, respectively. dhbw and dhm vary between 0 and m. larger values of dhbw and dhm indicate more varieties between the best and worst binary individuals, and each pair of binary individuals in a population, respectively. (4) dbc and dba are defined as dbc = 1 n − 1 (∑ i s 2 i l − s2 l ∗ n ) (7) dba = 1 l − 1 (∑ j s 2 j n − s2 l ∗ n ) (8) where n is the population size; l is the length of a chromosome; s is the sum of genes ‘1’; si and sj are the sum over a row i and the sum over a column j, respectively. dbc and dba have the following properties [23]: (i) if the population is homogeneous in either 0 or 1, dbc and dba equals zero; (ii) when all the chromosomes in the population are identical, dbc is zero and dba holds a constant value that is dependent on how many genes ‘1’ are in a chromosome. in what follows we use knapsack problems, which are described in section 4.1, to show the changes of the six population diversities in the evolution. figures 1–6 illustrate comparisons of 232 g. zhang, j. cheng, m. gheorghe qeps and qiea for three knapsack problems with 400, 600 and 800 items. each subfigure in figs.1–6 provides results of 30 independent random runs (green solid lines for qeps and cyan solid lines for qiea) and the mean values over 30 runs (black bold solid lines for qeps and black bold dash-dot lines for qiea). the values of dqbw, dqa, dhbw, dhm, dbc and dba in figs.1–6 are obtained by setting population size to 20 and the maximal number 20000 of function evaluations (nofe) as the stopping condition. from the results, shown in figs.1–6, about the comparisons of population diversity between qeps and qiea, we can draw the following conclusions: (i) the three subfigures, which correspond to the respective knapsack problems with 400, 600 and 800 items, in each of figs.1–6, show respectively consistent trends for qeps and qiea, which indicates the reasonableness of the six diversity measures to a certain degree. (ii) figures 1–2 show that qeps can maintain better population diversity than qiea in q-bit space. the two algorithms have approximate values, of about 0.4, for dhbw and dhm in the initial states. as nofe mounts up, the values of dhbw and dhm of q-bit individuals in qeps decrease gradually to around 0.5 at nofe 20000, whereas the values in qiea rapidly fall below 0.5 at nofe 3000 and approximate 0 at nofe 20000. (iii) the hamming distance dhbw between the best and worst binary individuals and the mean hamming distance dhm of all binary individuals in figs.3–4 show a clear picture of qeps having a greater potential to preserve the population diversity than qiea. more specifically, qeps and qiea have almost the same initial values and decreasing values for changes of dhbw and dhm with respect to nofe, but qeps maintains a much higher level of population diversity than qiea throughout the evolutionary process. the six subfigures in figs.3–4 also demonstrate that qiea loses population diversity too fast and very quickly goes down to a small value close to 0 when nofe reaches values around 10000, which implies that the individuals in qiea become nearly identical and therefore lose exploration capability. when nofe increases to 20000, qeps still has one-fourth of initial values of dhbw and dhm. (iv) dbc and dba, two diversity measures based on dispersion statistical measures in figs.5– 6, also clearly illustrate that qeps has better population diversity than qiea through the whole process. dbc values of qiea rapidly fall down to 0 and dba values of qiea rapidly rise to and stay at a higher steady level, while qeps maintains more varieties in the population, even when the algorithm stops at values of nofe close to 20000. 100 5000 10000 15000 20000 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 nofe d q b w qiea qeps (a) 400 items 100 5000 10000 15000 20000 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 nofe d q b w qiea qeps (b) 600 items 100 5000 10000 15000 20000 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 nofe d q b w qiea qeps (c) 800 items figure 1: dqbw of qeps and qiea with items 400, 600 and 800. dynamic behavior analysis of membrane-inspired evolutionary algorithms 233 100 5000 10000 15000 20000 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 nofe d q a qiea qeps (a) 400 items 100 5000 10000 15000 20000 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 nofe d q a qiea qeps (b) 600 items 100 5000 10000 15000 20000 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 nofe d q a qiea qeps (c) 800 items figure 2: dqa of qeps and qiea with items 400, 600 and 800. 100 5000 10000 15000 20000 0 50 100 150 200 250 nofe d h b w qiea qeps (a) 400 items 100 5000 10000 15000 20000 0 50 100 150 200 250 300 350 nofe d h b w qiea qeps (b) 600 items 100 5000 10000 15000 20000 0 50 100 150 200 250 300 350 400 450 nofe d h b w qiea qeps (c) 800 items figure 3: dhbw of qeps and qiea with items 400, 600 and 800. 100 5000 10000 15000 20000 0 50 100 150 200 250 nofe d h m qiea qeps (a) 400 items 100 5000 10000 15000 20000 0 50 100 150 200 250 300 350 nofe d h m qiea qeps (b) 600 items 100 5000 10000 15000 20000 0 50 100 150 200 250 300 350 400 450 nofe d h m qiea qeps (c) 800 items figure 4: dhm of qeps and qiea with items 400, 600 and 800. the loss of population diversity means that the algorithm will fail to further explore the solution space. in the following description, we will go further to analyze the convergence performance of membrane algorithms. 3.2 convergence analysis convergence is very important for a meta-heuristic search method as it shows the speed of the method in finding a satisfactory solution to an optimization problem. in this subsection, the convergence behavior of mieas is observed by presenting four measures: best q-bit individual convergence (cqb), average q-bit individual convergence (cqa), the best fitness convergence (cfb) 234 g. zhang, j. cheng, m. gheorghe 100 5000 10000 15000 20000 0 0.1 0.2 0.3 0.4 0.5 0.6 nofe d b c qiea qeps (a) 400 items 100 5000 10000 15000 20000 0 0.1 0.2 0.3 0.4 0.5 0.6 nofe d b c qiea qeps (b) 600 items 100 5000 10000 15000 20000 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 nofe d b c qiea qeps (c) 800 items figure 5: dbc of qeps and qiea with items 400, 600 and 800. 100 5000 10000 15000 20000 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 nofe d b a qiea qeps (a) 400 items 100 5000 10000 15000 20000 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 nofe d b a qiea qeps (b) 600 items 100 5000 10000 15000 20000 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 nofe d b a qiea qeps (c) 800 items figure 6: dba of qeps and qiea with items 400, 600 and 800. and the average fitness convergence (cfa). both qeps and qiea use q-bit individuals to construct a population. thus we can apply the best q-bit individual convergence and the average q-bit individual convergence in a population to observe how much q-bits approach 0 or 1 in the searching process. their definitions are as follows. (i) the best q-bit individual convergence is described as cqb = 1 m m∑ j=1 max { |αbj| 2 , |βbj| 2 } (9) where [αbj βbj]t is the j-th q-bit in the best q-bit individual corresponding to the best fitness in a population; m is the number of q-bits in a q-bit individual. 0.5 ≤ cqb ≤ 1. (ii) the average q-bit individual convergence is depicted as cqa = 1 n n∑ i=1   1m m∑ j=1 max { |αij|2, |βij|2 }  (10) where [αij βij]t is the j-th q-bit in the i-th q-bit individual in the population with n individuals; m is the number of q-bits in a q-bit individual. 0.5 ≤ cqa ≤ 1. cqb and cqa, calculated in the q-bit space, can be regarded as genotypic convergence measures. cqb and cqa have not a direct relationship to the quality of solutions. therefore dynamic behavior analysis of membrane-inspired evolutionary algorithms 235 we employ the other two convergence measures, the best fitness and the average fitness, to observe the convergence rates of solutions. (iii) the description of cfb and cfa is given as follows cfb = n max i=1 fi(x) (11) cfa = 1 n n∑ i=1 fi(x) (12) where fi(x) is the fitness of the i-th individual. equation 11 is listed based on a maximum optimization. for a minimum problem, cfb is to find the minimal fitness among n solutions. we still apply the three knapsack problems with 400, 600 and 800 items to observe the convergence performances of qeps and qiea. the population size, nofe and independent runs are assigned as 20, 20000 and 30, respectively. the changes of cqb, cqa, cfb and cfa are shown in figs.7–10, where each subfigure provides the results of 30 independently random runs (green solid lines for qeps and cyan solid lines for qiea) and the mean values over 30 runs (black bold solid lines for qeps and black bold dash-dot lines for qiea). the comparisons between qeps and qiea, shown in figs.7–10, give us the following hints. (i) the three subfigures corresponding to the respective knapsack problems with 400, 600 and 800 items, in each of figs.7–10, show respectively similar changes for qeps and qiea. (ii) it can be seen from the results shown in figs.7–8 that cqb and cqa have similar tendencies, to be specific, qiea converges much faster in q-bit space than qeps and quickly arrives at the maximal value 1, which implies that no further improvement of solutions in qiea can be gained at the second half of evolutionary processes. the drastic convergence easily makes qiea trapped in local extrema and consequently a premature end of the evolutionary process appears. on the contrary, cqb and cqa of qeps go up much slower than those corresponding to qiea with respect to nofe and finally mount up to around 0.9 for values of nofe in the region of 20000, which suggests that the solutions can be further improved if more nofe is provided. (iii) in figs.9–10, qiea has faster increases of cfb and cfa than qeps and then stays at a relatively flat level after a certain nofe, while qeps goes through a slower start than qiea and then rapidly goes beyond qiea and keeps an ascending trend. thus qeps obtains better solutions than qiea. the observations in figs.9–10 can also be derived from the results in figs.7–8. additionally, it is worth noting, according to the six subfigures of figs.9–10, that qeps has better performance than qiea in terms of the consistency of the results obtained for 30 independent runs when mean values are considered. this suggests that qeps has better robustness properties than qiea. the convergence and population diversity are often conflicting features for population-based search methods. rapid convergence usually results in a fast loss of population diversity, whereas better varieties of individuals produce more possibilities to improve solutions. the dynamic behaviors can be observed from the changes of population diversity, shown in figs.1–6, and convergence performance in figs.7–10. the diversity and convergence analysis above indicate that qeps can achieve a better trade-off between convergence and diversity than qiea, i.e., better balance between exploration and exploitation than qiea. this better balance of these two essential features of any evolutionary approach is the principal explanation of the fact that qeps 236 g. zhang, j. cheng, m. gheorghe achieves high quality solutions, better than qiea if nofe is large enough. for example, nofe is greater than 10000 for the knapsack problems with 400, 600 and 800 items, which corresponds to 100 evolutionary generations. 100 5000 10000 15000 20000 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 nofe c q b qiea qeps (a) 400 items 100 5000 10000 15000 20000 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 nofe c q b qiea qeps (b) 600 items 100 5000 10000 15000 20000 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 nofe c q b qiea qeps (c) 800 items figure 7: cqb of qeps and qiea with items 400, 600 and 800. 100 5000 10000 15000 20000 0.7 0.75 0.8 0.85 0.9 0.95 1 nofe c q a qiea qeps (a) 400 items 100 5000 10000 15000 20000 0.7 0.75 0.8 0.85 0.9 0.95 1 nofe c q a qiea qeps (b) 600 items 100 5000 10000 15000 20000 0.7 0.75 0.8 0.85 0.9 0.95 1 nofe c q a qiea qeps (c) 800 items figure 8: cqa of qeps and qiea with items 400, 600 and 800. 100 5000 10000 15000 20000 1 1.02 1.04 1.06 1.08 1.1 1.12 1.14 1.16 1.18 1.2 x 10 4 nofe c fb qiea qeps (a) 400 items 100 5000 10000 15000 20000 1.5 1.55 1.6 1.65 1.7 1.75 1.8 x 10 4 nofe c fb qiea qeps (b) 600 items 100 5000 10000 15000 20000 2 2.05 2.1 2.15 2.2 2.25 2.3 2.35 x 10 4 nofe c fb qiea qeps (c) 800 items figure 9: cfb of qeps and qiea with items 400, 600 and 800. 3.3 correlation between measures and fitness the goal of mieas is to find the optimal solution of an optimization problem, so the relationship between the diversity and convergence measures and fitness is very important for improving dynamic behavior analysis of membrane-inspired evolutionary algorithms 237 100 5000 10000 15000 20000 0.95 1 1.05 1.1 1.15 1.2 x 10 4 nofe c fa qiea qeps (a) 400 items 100 5000 10000 15000 20000 1.45 1.5 1.55 1.6 1.65 1.7 1.75 x 10 4 nofe c fa qiea qeps (b) 600 items 100 5000 10000 15000 20000 1.9 1.95 2 2.05 2.1 2.15 2.2 2.25 2.3 2.35 x 10 4 nofe c fa qiea qeps (c) 800 items figure 10: cfa of qeps and qiea with items 400, 600 and 800. the algorithm performance with respect to diversity and convergence. in this subsection, we introduce a tendency correlation coefficient to evaluate the importance of the eight measures: dqbw, dqa, dhbw, dhm, dbc, dba, cqb and cqa. the tendency correlation coefficient of two sequences, s1 = (s11,s 1 2, · · · ,s 1 l) and s2 = (s 2 1,s 2 2, · · · ,s 2 l) , is defined as ρ = ∑ i (∆s1 · ∆s2)√∑ i ∆s1 2 · ∑ i ∆s2 2 (13) where ∆s1 = (∆s11,∆s 1 2, · · · ,∆s 1 l−1) and ∆s2 = (∆s 2 1,∆s 2 2, · · · ,∆s 2 l−1) , where ∆s 1 k and ∆s 2 k (k = 1,2, · · · ,l − 1) are respectively ∆s1k = s 1 k+1 − s 1 k (14) ∆s2k = s 2 k+1 − s 2 k (15) the tendency correlation coefficient ρ varies in the range between -1 and 1. the maximal value 1 and minimal value -1 mean that the two sequences s1 and s2 have identical and completely opposite tendencies, respectively. thus a larger absolute value of ρ indicates a stronger tendency correlation. we use the tendency correlation coefficient to analyze the relationship between each of the eight measures, dqbw, dqa, dhbw, dhm, dbc, dba, cqb and cqa, and each of two kinds of fitness, cfb and cfa. experimental results are listed in table 1, where each datum is calculated by using the statistical results of three knapsack problems with 400, 600 and 800 items in sections 3.1–3.2. it can be seen from experimental results in table 1 that the three diversity measures, dqa, dhm and dba, have stronger tendency correlation with fitness than the other five measures. so the improvement of dqa, dhm and dba could be a promising way to enhance the algorithm performance. in the column of dba, positive tendency correlation coefficients show that dba and fitness have similar trends, which implies that a larger value of dba may correspond to a better fitness. while in the columns of dqa and dhm, negative values demonstrate that the two population diversity measures have contrary trends with fitness, which might mean that smaller values of dqa and dhm could result in better solutions. 238 g. zhang, j. cheng, m. gheorghe table 1: correlation between fitness and diversity, convergence measures. measures dqbw dqa dhbw dhm dbc dba cqb cqa qiea 400 items cfb -0.7335 -0.7804 -0.7706 -0.9251 -0.4812 0.9180 0.1627 0.1707 cfa -0.6894 -0.7174ąą -0.7004 -0.8643 -0.6312 0.8500 0.1579 0.1606 600 items cfb -0.6878 -0.6976 -0.7737 -0.9064 -0.3604 0.8921 0.2663 0.2412 cfa -0.6548 -0.6818 -0.7792 -0.8940 -0.4993 0.8764 0.2591 0.2493 800 items cfb -0.6812 -0.6664 -0.8153 -0.9086 -0.4291 0.8969 0.2914 0.2865 cfa -0.6491 -0.6794 -0.8174 -0.9171 -0.4466 0.9035 0.2436 0.2749 qeps 400 items cfb -0.3260 -0.8679 -0.3008 -0.7910 -0.1042 0.7715 -0.0007 0.0061 cfa -0.2004 -0.6672 -0.2155 -0.6895 -0.4590 0.6175 0.0220 0.0246 600 items cfb -0.3004 -0.8260 -0.3192 -0.8121 -0.0638 0.7960 0.0522 0.0427 cfa -0.2300 -0.7009 -0.2148 -0.7635 -0.3809 0.7067 0.0231 0.0691 800 items cfb -0.2531 -0.8121 -0.2878 -0.8472 -0.0524 0.8356 0.1060 0.0573 cfa -0.2057 -0.7123 -0.2370 -0.7767 -0.3477 0.7336 0.0575 0.0766 4 examples in this section, a knapsack problem is described and more experiments are conduced to further verify the observations in the preceding section. 4.1 knapsack problem a knapsack problem can be described as the process of selecting from among various items those that are most profitable, given that the knapsack has limited capacity [12, 20, 21]. the knapsack problem is to select a subset from the given number of items so as to maximize the profit f(x): f(x) = k∑ i=1 pixi (16) subject to k∑ i=1 wixi ≤ ck (17) where k is the number of items; pi is the profit of the i-th item; wi is the weight of the i-th item; ck is the capacity of the given knapsack; and xi is 0 or 1. this paper uses strongly correlated sets of unsorted data: wi =uniformly random [1, 50], pi = wi + 25. the average knapsack capacity ck is applied. ck = 1 2 k∑ i=1 wi (18) 4.2 experiments and results in this subsection, we carry out the experiments on 15 knapsack problems with 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400, 2600, 2800 and 3000 items to compare the performance of qeps and qiea. in the experiments, qeps and qiea use 20 individuals and 30 independent runs are performed for each case. qeps uses the olms, where 15 elementary membranes and the maximal number 9 of iterations for each elementary membrane are considered, according to the investigation in [12]. the stopping condition for qeps and qiea is set as follows: 20000 nofe for the first four knapsack problems; 30000 nofe for the three knapsack problems with 1000, 1200 and 1400 items; 40000 nofe for the four knapsack problems dynamic behavior analysis of membrane-inspired evolutionary algorithms 239 with 1600, 1800, 2000 and 2200; 60000 nofe for the last four knapsack problems. the best, worst and average solutions over 30 independent runs are recorded and listed in table 2. we also provide the results, shown in table 3, for each problem when nofe is one-tenth of the prescribed value. several facts can be obtained from the results in tables 2 and 3. (i) the results in table 3 show that qiea achieves better results than qeps when nofe is only one-tenth of the prescribed value for each of the 15 knapsack problems. (ii) the statistical data in table 2 show that qeps is superior to qiea in terms of the quality of solutions. even the worst solution of each problem obtained by qeps is better than the best one provided by qiea. (iii) it seems that there is a conflict between the two conclusions in (i) and (ii). actually these conclusions can be explained by using the analysis of population diversity and convergence in section 3. the advantage of qiea over qeps in table 3 comes from the fact that qiea has a faster convergence speed and shows more rapid changes of population diversity than qeps. slower changes can also be regarded as a better balance, between diversity and convergence, leading to improved searching capabilities for qeps, which results in the superiority of qeps over qiea, as shown in table 2. moreover, these experimental results provide sufficient details explaining the behavior of qeps and qiea, as stated in section 3. table 2: experimental results for qeps and qiea, where nofe for each problem is prescribed. items qiea qeps best average worst best average worst 200 5885 5786 5359 5959 5945 5909 400 11650 11553 11396 11873 11837 11778 600 17403 17173 16851 17702 17647 17575 800 22940 22659 22010 23403 23257 23109 1000 28673 28333 27954 29531 29373 29198 1200 34399 33984 33424 35441 35292 35061 1400 39560 39149 38488 40886 40722 40364 1600 45277 44864 44423 47242 47018 46672 1800 50784 50163 49506 52772 52600 52395 2000 56453 55879 55129 58775 58543 58065 2200 61645 61175 59820 64513 64230 63680 2400 66683 65984 64981 70402 70015 69726 2600 72546 71992 71497 76621 76245 75296 2800 77511 76734 75924 81918 81486 80683 3000 83294 82608 82020 88207 87657 87044 5 conclusion the dynamic behavior analysis of mieas is very illustrative for a better understanding of the role played by p systems in the context of evolutionary algorithms. this paper discusses 240 g. zhang, j. cheng, m. gheorghe table 3: experimental results for qeps and qiea, where nofe for each problem is one-tenth of the prescribed value. items qiea qeps best average worst best average worst 200 5574 5476 5359 5496 5395 5306 400 10964 10836 10687 10744 10666 10548 600 16004 15928 15775 15914 15721 15549 800 21234 21096 20860 21230 20839 20686 1000 26755 26571 26309 26501 26274 26124 1200 32148 31925 31707 31849 31635 31460 1400 36990 36832 36553 36754 36482 36179 1600 42918 42588 42352 42634 42270 42038 1800 47878 47559 47353 47428 47121 46788 2000 53376 53068 52803 53001 52654 52433 2200 58646 58335 58102 58271 57957 57622 2400 63441 63195 62819 63226 62806 62328 2600 69342 69077 68676 69196 68755 68420 2800 74051 73613 73269 73524 73207 72833 3000 79630 79236 78743 79361 78886 78483 for the first time significant aspects of developmental processes occurring in mieas using a comparative approach in an experimental context, whereby a set of population diversity and convergence measures were introduced and analyzed. this work not only provides some answers to the difficult and intriguing question of what are the benefits of using p systems in evolutionary algorithms, but also suggests possible improvements for mieas. on the basis of this work, a promising research might start with a multi-objective optimization framework utilizing concepts and principles of p systems in a systematic and consistent way. acknowledgements this work was supported by the national natural science foundation of china (61170016, 61373047), the program for new century excellent talents in university (ncet-11-0715) and swjtu supported project (swjtu12cx008) bibliography [1] păun, gh. 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(2004); diversity in genetic programming: an analysis of measures and correlation with fitness, ieee t. evolut. comput., issn 1089-778x, issn 1089-778x, 8: 47-62. [23] herrera, f.; lozano, m. (1996); adaptation of genetic algorithm parameters based on fuzzy logic controllers. genetic algorithms and soft computing, 95-125. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 3, month: june, year: 2023 article number: 5005, https://doi.org/10.15837/ijccc.2023.3.5005 ccc publications invulnerability improvement of multipath protocols in different scenarios quan-hai wang, shao-feng lan, dian zhang, yi-qun wang, qunxi zhu, wen-bai chen quan-hai wang northeastern university at qinhuangdao, no. 143 taishan road, northeastern university qinhuangdao branch, qinhuangdao, china 276750743@qq.com shao-feng lan school of automation, beijing information science and technology university, no.35 north 4th ring middle road, chaoyang district, beijing, china lanshaofeng01@163.com dian zhang school of automation, beijing information science and technology university, no.35 north 4th ring middle road, chaoyang district, beijing, china iszhangdian@126.com yi-qun wang school of automation, beijing information science and technology university, no.35 north 4th ring middle road, chaoyang district, beijing, china wangyiqun@bistu.edu.cn qun-xi zhu northeastern university at qinhuangdao, no. 143 taishan road, northeastern university qinhuangdao branch, qinhuangdao, china qhddjks@qq.com wen-bai chen school of automation, beijing information science and technology university, no.35 north 4th ring middle road, chaoyang district, beijing, china *corresponding author: chenwb@bistu.edu.cn abstract developments in computer communications and networks enabled the deployment of exciting new areas. meanwhile, efficient network and communication technologies also promote the development of routing protocols. aodv(ad hoc on-demand distance vector routing, aodv) routing protocol has invulnerability to different mobile attributes of agent nodes and network damage scenarios. however, this invulnerability performance can not meet the needs of mobile multi-agent cooperative communication. therefore, this paper proposes an improved responsive https://doi.org/10.15837/ijccc.2023.3.5005 2 multipath routing protocol md_aodv(multipath destroy-resistance aodv, md_aodv). this routing protocol is based on aodv routing protocol. it is mainly improved in three aspects: multichannel concurrency mechanism, node load balancing mechanism and shortest path maintenance mechanism. firstly, in this research, an improved multipath routing protocol is modeled and implemented, which is based on opnet network simulation platform. then, network models with different degrees of impairment are built. combines the mobile attributes of mobile multi-agent task coordination to carry out invulnerability simulation experiments . these experiments study the invulnerability of the improved multipath routing protocol. finally, this paper evaluates and compares the routing invulnerability of md_aodv, aodv and aomdv(ad-hoc on-demand multipath distance vector, aomdv). the evaluation results show that md_aodv routing has better invulnerability when the network is severely damaged. keywords: aodv protocol, multipath routing, network invulnerability, opnent simulation. 1 introduction mobile multi-agent system can carry out complex task cooperation and agent mobility, which is carried out under the condition of the integrity of the communication link between the agent nodes[1]. routing control technology is the key technology of agent cooperative communication. it is also a routing protocol with high reliability and fault tolerance. the routing control technology will ensure that the mobile multi-agent can successfully complete the collaborative task when the network is impairment. therefore, it is very meaningful to study the invulnerability of mobile multi-agent routing protocols. at present, there are three routing protocols that can be applied to mobile multi intelligent systems. these three routing protocols are proactive routing protocols, reactive routing protocol and hybrid routing protocol[2]. aodv protocol is a common reactive routing protocol3. it is widely used in different scenarios, but it is a single path protocol. when the link of aodv protocol is disconnected, it is necessary to re initiate the routing request.[4] however, this will increase the route initiation frequency and end-to-end delay. in recent years, researchers have proposed many schemes to improve the performance of aodv protocol. after considering multiple routing information, zhang et al. [5]proposed a new protocol to improve aodv, called saodv(secure aodv,saodv). in saodv, improving the selected route needs to combine different routing standards. these include the length of the path and information from network status. based on these, the new route is optimized for the target application. alebed et al. [6]proposed a routing protocol called aomdv. it is an extension of the aodv protocol. this research mainly analyzes and improves aodv in multi-channel concurrency mechanism, node load balancing mechanism and shortest path maintainer. therefore, an improved responsive multipath routing protocol md_aodv is proposed. for the network environment without infrastructure support, especially the invulnerability of routing protocols in mobile multi-agent networks, the research mainly focuses on three aspects. the first is the routing invulnerability of link redundancy. the second is the research of routing invulnerability based on link repair. thirdly, routing invulnerability research based on high dynamic topology. although these three aspects are related, their respective characteristics and the application background on which the research is based are not the same. s. j.lee et al. [7]proposed aodv-br(ad hoc ondemand distance vector-backup routing, aodv-br) on-demand backup routing protocol with link redundancy. on demand backup routing stability’s disadvantage is that the backup routes enabled by the network are often not applicable to the current topology[8]. the algorithm is effective when the nodes in the network are relatively fixed. however, when the network nodes move in a large range and at a high speed, the performance is not outstanding. moreover, this algorithm does not study the invulnerability in the case of network damage. marina et al. [9]studied aomdv on-demand multipath routing protocol. it backs up multiple routing sequences from the source node to the destination node to save the cost of route discovery. the research on this protocol shows that the repair ability of this routing protocol is not as good as the traditional aodv on-demand single path routing protocol when the network topology changes dramatically and the network scale is small. yang et al. [10]considered the link failure caused by the decrease of node communication distance during data transmission. propose a manet routing strategy with maximum survival path. this strategy can reasonably https://doi.org/10.15837/ijccc.2023.3.5005 3 select the path to transmit data according to the residual energy value of the node. these studies are basically to improve the original network routing protocol, and to some extent increase the robustness of the original routing strategy. however, the above research did not study and analyze the invulnerability of network routing protocols based on network damage and task cooperative mobility attributes of mobile multi-agent. this paper proposes an improved responsive multipath routing protocol called md_aodv. in addition, this paper also studies the invulnerability of md_aodv by combining the mobile attributes of mobile multi-agent and the degree of network damage. based on md_aodv, this paper improves the multi-channel concurrency mechanism, node load balancing mechanism and shortest path maintenance mechanism. in view of the different requirements of the damage degree of the network on the invulnerability, a network model with different damage degree is constructed. this paper combines the mobile attributes of mobile multi-agent task coordination, and carries out the invulnerability simulation experiment of the improved multipath routing protocol under the scenarios of different degrees of network damage. this paper evaluates the invulnerability of the above experiments and compares them with aodv[11] and aomdv routing protocols. 2 modeling process of mobile multi-agent network 2.1 invulnerability of mobile multi-agent network from the way of network impairment, the factors affecting the invulnerability of mobile multiagent can be divided into four aspects. these four aspects are the agent’s own factors, the wireless communication mode, the agent’s mobility and the continuous change of network scale.[12] similarly, there are four strategies to improve the invulnerability of multi-agent networks. these four aspects are communication link hardware protection, topology evolution, network reconfiguration and routing control. among them, hardware protection, network reconfiguration and topology evolution of communication links are all invulnerability optimization strategies in hardware. enhancing the invulnerability of network reconfiguration and topology evolution is achieved by optimizing network heterogeneity or coverage. routing control is a necessary software optimization technology in network communication. it can improve the invulnerability of mobile multi-agent based on the original hardware and topology of the system. figure 1 shows a strategy to improve the invulnerability of mobile multi-agent systems.(figure1) figure 1: strategies for improving the invulnerability of mobile multi-agent system 2.2 mobile multi-agent invulnerability simulation platform opnet simulation platform[13] has excellent communication networks, facilities, protocols, simulation algorithms and modeling mechanisms. it has also been unanimously recognized by the professional field and research field of network communication. in order to better simulate the real network, the network model of opnet is usually composed of process model, node model and network model. among them, the process model regulates, makes decisions and implements algorithms for the behaviors of nodes in the system. the node model is used to standardize the functions of applications, https://doi.org/10.15837/ijccc.2023.3.5005 4 processes, queues and communication interfaces to nodes. the network model is used to standardize the system from high-level devices (i.e. nodes and communication links). the modeling steps of mobile multi-agent network are divided into three layers. [14]the first layer is mainly the description of network topology, network scope and subnet, that is, network domain modeling. the second layer refers to the description of various node models that make up the network topology. the interior of the network node is generally established according to the osi (open system interconnection) model[15]. each layer is similar to a process, and different threads can be run in the process according to different protocol mechanisms; between processes, that is, between layers, variables can be transferred by setting pipelines or promoting variable attributes, that is, node domain modeling. the third layer is process domain modeling. process domain modeling describes the processes that make up the node model through c/c++ language, opnet’s own api and finite state machine mechanism.[16] the process model is the main body of generating and processing events in opnet. this is the key to the invulnerability simulation of mobile multi-agent. similar to the circular data scheduling mechanism of single core processor, it enters different sub functions through flag bits and processes the data. there are four main process models involved in mobile multi-agent network modeling and simulation. the four models are network / routing layer model, mac model, communication related physical layer model and node mobility model. 3 network impairment model 3.1 impairment model design for the research on the invulnerability of mobile multi-agent network, the typical analysis method is to compare the network characteristics under different damage conditions. mobile multi-agent network can cause node failure to varying degrees due to its characteristics of node mobility, wireless communication, limited energy and network vulnerability.[17] generally, electromagnetic interference, communication distance limitation, network attack and other scenarios can be summarized as recoverable network impairment caused by node failure, that is, slight impairment.[18] generally, the node recovers its connection with the network through watchdog reset, adjusting node position and waiting for the end of the attack in about 10 100s. sometimes, the network suffers from malicious attacks, and the energy of nodes is exhausted. mechanical failure or interference of nodes due to their own and environmental reasons. all of these will cause the node to be disconnected from the network continuously, which can be summarized as the irrecoverable network damage caused by node failure, that is, severe damage. based on the typical task scenario of mobile multi-agent, 10 mobile nodes in the network are selected for different degrees of failure to simulate two different degrees of network damage. the number of mobile multi-agent nodes in the network is 50. after investigation, each mobile node and the network start to connect to the whole network and run stably, usually taking less than 10 minutes.[19] therefore, the network damage time is set after 10 minutes, and the network simulation time is set to 20 minutes. the node model created in this paper does not support the global configuration of network failure by the failure recovery module of opnet. the way to configure node invalidation under the node model established in this paper is to select break down time and break hold time parameters in the node attribute attribute to realize node invalidation and recovery. the node is only enabled outside the failure time. table1 describes the parameters of the network impairment model under the scenario of recoverable node impairment, and table 2 describes the parameters of the network impairment model under the scenario of unrecoverable node impairment. • the network is slightly damaged, that is, after the node is damaged at a certain time, it can return to normal within the set time interval (see table 1). • the network is severely impairment, that is, the node will permanently fail after being damaged https://doi.org/10.15837/ijccc.2023.3.5005 5 table 1: network impairment model parameters of node impairment recoverable scenario serial number failed node id impairment moment/s network impairment duration /s 1 2 600 100 2 7 610 90 3 10 620 80 4 14 630 70 5 26 640 60 6 29 650 50 7 31 660 40 8 35 670 30 9 44 680 20 10 47 690 10 at a certain time (see table 2). table 2: network impairment model parameters of node impairment irrecoverable scenario serial number failed node id impairment moment/s network impairment duration /s 1 2 600 600 2 7 610 590 3 10 620 580 4 14 630 570 5 26 640 560 6 29 650 550 7 31 660 540 8 35 670 530 9 44 680 520 10 47 690 510 3.2 construction of network damage model based on opnet in this paper, the degree of network damage is simulated by configuring failure parameters for the specified nodes in the same network scenario. the node failure and failure holding functions are realized in the mac process model, specifically for the idel state machine in the 802_11_csma/ca_mac processor module to import break down time and break hold time variables. the function of idel state machine is to wait for packets from mac layer and put them into wlan_interrupts_process() interrupt function for processing. the parameters related to the failure time are input from the node application layer to the mac layer. so the node failure parameters will finally take effect in the wlan_higher_layer_data_arrival(void) function. if the application layer data packet is received and there is enough space in the upper layer data buffer, the data packet is queued for mac layer processing. when the node is within the set break hold time time, the node mac layer will refuse to transmit data, so as to simulate the process of node failure. 4 improved md_ aodv multipath routing protocol 4.1 improvement of routing protocol there may be four reasons for the low invulnerability of the network.[20] the first is that the aodv routing protocol fails to transmit network information due to the damage of the communication link in the node failure scenario. second, the existing aodv multipath routing protocol node load balancing mechanism is not perfect. third, the routing update of multipath single sending routing protocol is not timely. fourth, it is difficult to adapt to the application scenario of dynamic change of mobile multi-agent network topology. in order to improve the problem of low network invulnerability , this paper designs an improved multipath routing protocol based on the following three improvement ideas. 1) multiple concurrency mechanism when the source node needs to send data to the destination node, the source node will send application layer data in parallel from multiple routes found each time. md_ aodv adopts this link https://doi.org/10.15837/ijccc.2023.3.5005 6 redundancy mechanism. when the mobile multi-agent network is disturbed and some nodes fail, the probability of data transmission failure will be inversely proportional to the number of effective paths. however, this situation is related to the transmission reliability of the same application layer data. this mechanism can effectively improve the data transmission success rate of the network.[21] 2) node load balancing mechanism when the network is establishing a route, when the node receives rreq, it will first judge whether the node is already an intermediate route of the same type of rreq. if so, it will discard the rreq data to avoid forming a route intersected by nodes. if it is not the same type of rreq, judge whether the number of local effective routing tables of the node reaches the maximum threshold. if it reaches the maximum threshold, discard the rreq to avoid the load of the node exceeding the upper limit of the node itself.[20] the final network is a network system with node load balancing and disjoint nodes in the same type of routing. 3) shortest path maintenance mechanism in the process of route repair, aodv adopts the mechanism of local repair, while md_ aodv routing adopts the mechanism of source node repair. md_ aodv always selects the route with the least hops as the available route when discovering routes. after the route is disconnected, the repair from the source node can make the effective path from the source node to the destination node update a current best route in time, that is, the route with the smallest number of hops. for the network with node failure, from the probability event analysis, the fewer nodes in the network link, that is, the smaller the number of hops, the lower the probability of link damage. the smaller the number of routing hops, the lower the network transmission delay, and the overall link quality of the network will be improved. 4.2 improved routing protocol design process the overall process of md_aodv routing protocol implementation is described as follows: step 1: initialize the status variable of the routing protocol. it mainly includes obtaining the internal relevant id and node address of the node, obtaining the data cache queue memory area of the md_aodv sub process, obtaining the number of nodes, initializing the serial number of this node, initializing rreq_id , initializing rreq retransmission times, initializing rrep sending handle, creating multi-path cache queue, initializing the rreq serial number received by the node, initializing the routing table and neighbor node table, initializing the rreq request queue set the maximum number of paths and set the maximum threshold of node load. step 2: wait and process the data from the upper and lower mac. it mainly includes determining the node corresponding to the destination address of the packet, setting the packet domain and size of the data packet, counting the packets to be transmitted, refreshing the routing table entry, packet transmission and lower layer data packet processing. step 3: data and network maintenance. it mainly includes rreq retransmission after rrep timeout, periodic hello packet transmission, rrer processing and route repair after link disconnection. the main implementation process of the improved multipath routing protocol is route discovery and route maintenance. 1) route discovery the source node needs to send data to the destination node. when the source node has multiple paths cached to the destination node, data will be sent from multiple paths in parallel. when there is no valid path to the destination node, the source node will broadcast rreq. when the node receives the rreq packet, it judges whether the number of effective paths loaded by the node reaches the set threshold, whether the rreq packet reaches the maximum number of broadcast hops, and whether it has received the repeated rreq message. if the above conditions are not met, the rreq packet will be processed in the next step. record the packet sequence number (rreq_id) and set the address of the first_hop to update the route from this node to the source node. if the node is the destination node, feed back the rrep response packet. if it is not the destination node and does not reach the maximum broadcast range, continue to forward the rreq message. the flow of updating the routing algorithm from the local node to the source node is shown in algorithm1. based on the multiple effective paths from the source node to the destination node cached during route discovery, https://doi.org/10.15837/ijccc.2023.3.5005 7 algorithm 1 update the routing algorithm from the local node to the source node in rreq input: input:the rreq data received by the node and the routing list of the source node in the rreq data output: 1: if source node serial number in rreq > the serial number of the destination node in the routing table corresponding to the source node address in rreq then 2: clear the routing sequence information corresponding to the source node address in rreq; 3: write the latest next address, the next hop address after the group, the number of hops and the lifetime to the routing sequence; 4: add the updated route sequence to the route list corresponding to the source node address in rreq; 5: else if source node serial number in rreq == the serial number of the destination node in the routing table corresponding to the source node address in rreq then 6: if number of routes corresponding to the source node address in rreq > maximum number of unicast paths (path_num) then 7: discard rreq message 8: for j=0; j < the number of paths to the destination node corresponding to the source node in rreq (path_size) do 9: if there is no node intersecting link between the local path and the original link of the routing table then 10: for j=0; j < path_size; j++ do 11: if number of routing hops in rreq (hopcount) < the number of hops in the routing sequence corresponding to the destination node address in rreq then 12: update routing sequence;add the updated route sequence to the route list corresponding to the source node address in rreq 13: end if 14: end for 15: elsediscard rreq message 16: end if 17: end for 18: end if 19: elsediscard rreq message; // expired rreq information 20: end if the destination node sends packet data immediately after receiving rrep. if multiple rreps are received, multiple packet data will be sent accordingly, md_ the aodv route discovery mechanism is shown in figure2. in the process of route discovery, the mechanism of node load balancing is selected by judging the number of effective paths loaded by nodes and the disjoint links of nodes during route update.[22] 2) route maintenance the initial stage of route maintenance is similar to the aodv mechanism. it judges whether to send rrer message by periodically broadcasting hello message. when the node receives rrer, it notifies its neighbor node and the previous hop node. delete the route with this node as the next hop, and establish the route when the data application layer sends data again.[23] the processing flow of rrer packet information is shown in algorithm2. local route repair and destination node route repair are not performed at this time. the reason is that in the scenario of node failure and frequent changes in mobile multi-agent network topology, the locally repaired route is often not the shortest route from the current destination node to the source node. based on the basic characteristics of passive routing, the source node repair is not carried out at this time, but when the source node resends rreq, in order to maintain the path from the source node to the destination node. the md_aodv route maintenance process is shown in figure3. https://doi.org/10.15837/ijccc.2023.3.5005 8 figure 2: md_ aodv route discovery process diagram 5 analysis on invulnerability of multipath routing protocol 5.1 node damage recovers invulnerability in network scenarios 5.1.1 network scenario settings the network scenario parameters of the mobile multi-agent node damage recoverable scenario are shown in table 5. the node moving speed and moving path correspond to the moving speed and moving path in different collaborative mobile task models. table 3: network impairment model parameters of node impairment irrecoverable scenario parameter set value scene range 50km×50km number of nodes 50 failed node item 10 simulation time 20 min network damage moment after 10 minutes of network operation network impairment type node damage recoverable model mobile scene random, formation maintenance, aggregation maximum communication distance of node 3000 m data transmission rate 1 mbps 5.1.2 evaluation of invulnerability 1) evaluation measure referring to the evaluation index of network topology invulnerability[24] and the definition of network robustness, this paper proposes an index suitable for the invulnerability evaluation of mobile multi-agent routing protocol, that is, network reliability. considering that the probability of node failure in the actual network scenario is basically the same, the fewer nodes required for routing and forwarding, the lower the probability of the link being hit. therefore, the average number of routing hops is used to evaluate the invulnerability of routing protocols. in this paper, the invulnerability of mobile multi-agent network routing protocol will be evaluated in combination with network throughput, reliability and average routing hops. https://doi.org/10.15837/ijccc.2023.3.5005 9 algorithm 2 rrep packet data processing algorithm input: input:rrep data received by the node, source node data and destination node data in rrep data output: if node receives rrer packet data then 2: obtain the source node address (source_addrr) and destination node address (dest_addr) in rrer;destroy the rrer packet data; if number of broadcast hops corresponding to the destination node address in rrer (broadcast_hopcount) < total number of nodes in the network +1 then 4: path_size = number of paths corresponding to the destination node address in rrer (dest_addr.path); for j = path_size-1; j ≥ 0; j– do 6: get route sequence information in the path (path_info) if next hop address in path_info (next_addr) == source_addr then 8: delete the routing information corresponding to dest_addr in path_info;// the path corresponding to the destination node address in rrer has been updated end if 10: end for if number of paths corresponding to the destination node address in rrer (dest_addr.path) == 0 && path_size > 0 then 12: while the forward address corresponding to the destination node address in rreq (dest_addr.precursor_addrlist) > 0 do clear the predecessor node (pre_node_addr) corresponding to the dest_addr. 14: end while source node address in rrer (source_addr) = local node address (my_node_addr); number of repackaging rrer; send rrer packet information to mac for processing; 16: end if end if 18: end if (1) network reliability network reliability refers to the characteristics of the system to complete the specified functions under limited conditions. after the available nodes fail, the number of effective links currently available in the network (in which the number of unicast multipath effective links ≤ 1) is consistent with the ratio of the number of links between all nodes in the network. suppose a node in the network fails. the remaining node set is gp. where n represents the total number of network nodes, lij represents the number of nodes corresponding to the effective link in the current network. if there is an effective link between nodes i and j, then lij = 1, otherwise lij =0. the network reliability is shown in formula5.1.2. mp = ∑ i∈gp ∑ j>i lij n(n − 1) (2) average routing hops in ad hoc networks, the data transmission from the source node to the destination node often needs to be forwarded by the intermediate node. the more nodes an effective path passes through in the network, the greater the possibility of the path being damaged. set the effective route in the network as l|s∼d|i , where s represents the source node, d represents the destination node. s ∼ d represents the node sequence passed by the route, and the length of the node sequence is expressed in |s ∼ d|i. i refers to the sequence number of effective routes in the network, which increases with the number of effective routes. the average number of routing hops is shown in formula 5.1.2. hp = ∑ |s ∼ d|i i 2) evaluation results https://doi.org/10.15837/ijccc.2023.3.5005 10 figure 3: md_ aodv route maintenance process diagram (1) random moving scene as shown in figure4, it is the network performance curve when nodes move randomly in the scenario of node damage recoverable. it can be seen from the figure that the throughput and reliability indexes of md_aodv are better than those of aodv and aomdv routing protocols. the average number of routing hops is close to that of aomdv. but in the end, the average number of routing hops of md_aodv drops to the optimal state. (2) formation keeping moving scene as shown in figure5, it is the network performance curve when the node formation remains moving in the scenario of node damage recoverable. from the results of throughput and average routing hops, it can be seen that the routing performance of md_aodv is better than aodv. however, in terms of reliability index, aomdv index is slightly better than md_aodv . it can be seen that when the network is damaged, the reliability index of aomdv shows a downward trend, while md_aodv shows a steady increasing trend. (3) aggregate mobile scenes figure6 is the network performance curve of node aggregation and movement in the scenario of node damage recoverable. it can be seen that the reliability and average routing hops of md_aodv are better than aodv and aomdv routing protocols. although the network throughput of md_aodv is better than aodv routing protocol, it is not significantly better than aomdv routing protocol. to sum up, when the network is slightly damaged, combined with the overall evaluation of network performance indicators in three mobile scenarios. it can be concluded that the survivability of md_aodv is better than that of aodv and aomdv routing protocols. however, the single network performance of md_aodv and aomdv in different mobile scenarios needs to be further explored. 5.2 invulnerability under the scenario of irrecoverable network with node damage 5.2.1 network scenario settings the network scenario parameters of the mobile multi-agent node damage recoverable scenario are shown in table4. the moving speed and moving path of nodes correspond to the moving speed and moving path in different cooperative mobile task models. https://doi.org/10.15837/ijccc.2023.3.5005 11 (a) throughput (b) reliability (c) average routing hops figure 4: invulnerability in random moving scenario (a) throughput (b) reliability (c) average routing hops figure 5: invulnerability performance in formation keeping moving scene https://doi.org/10.15837/ijccc.2023.3.5005 12 (a) throughput (b) reliability (c) average routing hops figure 6: invulnerability in aggregated mobile scenarios table 4: simulation scenario parameters parameter set value scene range 50km×50km number of nodes 50 number of failed nodes 10 simulation time 20 min network damage time after 10 minutes of network operation network damage model node damage irrecoverable model mobile scene random, formation maintenance, aggregation maximum communication distance of node 3000 m data transmission rate 1 mbps 5.2.2 evaluation of invulnerability in the case of unrecoverable network with node damage, the invulnerability of the routing protocol is evaluated through the indicators of network throughput, reliability and average routing hops. the evaluation results are as follows: (1) random moving scene figure 7 is the network performance curve when nodes move randomly in the scenario of irrecoverable node damage. in terms of reliability, md_aodv routing is better than aodv and aomdv routing. the average number of routing hops of md_aodv decreases continuously after the network is damaged. the throughput of md_aodv is similar to that of aomdv. (2) formation keeping moving scene figure8 is the network performance curve when the node formation keeps moving in the scenario of unrecoverable node damage. in terms of reliability and average routing hops, the performance of md_aodv is better than aodv and aomdv routing protocols before and after network damage. in terms of throughput, the performance of md_aodv is close to that of aomdv. (3) aggregate mobile scenes figure9 is the network performance curve of node aggregation and https://doi.org/10.15837/ijccc.2023.3.5005 13 (a) throughput (b) reliability (c) average routing hops figure 7: network performance in random mobile scenario (a) throughput (b) reliability (c) average routing hops figure 8: network performance in formation keeping mobile scenario https://doi.org/10.15837/ijccc.2023.3.5005 14 (a) throughput (b) reliability (c) average routing hops figure 9: network performance in aggregated mobile scenario movement in the scenario of irrecoverable node damage. it can be seen from the throughput that the performance of md_aodv and aomdv is almost the same before the network is damaged. when the network is damaged, the performance of md_aodv is better than that of aomdv routing. it can be seen from the reliability that the routing performance of md_aodv and aodv is similar before the network is damaged. after the network is damaged, the decline of aodv reliability is greater than that of md_aodv and aomdv. the average number of routing hops of md_aodv routing decreases as time goes by. to sum up, when the network is severely damaged, it can be seen that the network damage reduces the network throughput and reliability corresponding to the three routing protocols. md_aodv and aomdv have similar performance in network throughput. after the network is damaged, the average number of routing hops of md_aodv routing is less, and the reliability is better than that of aodv and aomdv routing protocols. the overall evaluation shows that the invulnerability of md_aodv is better than aomdv and aodv routing protocols. because both md_aodv and aomdv have the characteristics of multipath backup, their network throughput is high. however, in terms of the number of currently available effective links of the network after serious network damage, md_aodv routing is dominant. and the average number of routing hops of md_aodv after network damage is less, which will also help it to continue to deal with more serious network damage. 6 conclusion in order to simulate the damage of mobile multi-agent system in real environment, this paper designs two kinds of network models of damage degree, and simulates and analyzes the invulnerability performance of the proposed improved multi-path routing protocol. the results show that the improved multipath routing protocol has different invulnerability under different network damage scenarios. when the network is slightly damaged, the routing performance of md_aodv is close to that of aomdv. when the network is severely impairment, the throughput performance of the two systems is close. however, from the perspective of network stability and the potential to cope with https://doi.org/10.15837/ijccc.2023.3.5005 15 the continuous deterioration of network damage, md_aodv routing has better invulnerability. funding all authors have read and agreed to the published version of the manuscript. this work was supported by r&d program of beijing municipal education commission (km202011232023), and supported by instructional reform item of 2018gjjg423. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] dorri a, kanhere ss, jurdak r. multi-agent systems: a survey. ieee access 2018;6:28573–93. https://doi.org/10.1109/access.2018.2831228. 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"entropy weight method for evaluation of invulnerability in instant messaging network. 2009 fourth international conference on internet computing for science and engineering. ieee, 2009. https://doi.org/10.15837/ijccc.2023.3.5005 17 copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: wang, q.-h; lan, s.-f.; zhang, d.; wang, y.-q.; zhu, q.; chen, w.-b. (2023). invulnerability improvement of multipath protocols in different scenarios, international journal of computers communications & control, 18(3), 5005, 2023. https://doi.org/10.15837/ijccc.2023.3.5005 introduction modeling process of mobile multi-agent network invulnerability of mobile multi-agent network mobile multi-agent invulnerability simulation platform network impairment model impairment model design construction of network damage model based on opnet improved md_ aodv multipath routing protocol improvement of routing protocol improved routing protocol design process analysis on invulnerability of multipath routing protocol node damage recovers invulnerability in network scenarios network scenario settings evaluation of invulnerability invulnerability under the scenario of irrecoverable network with node damage network scenario settings evaluation of invulnerability conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 5, month: october, year: 2021 article number: 4268, https://doi.org/10.15837/ijccc.2021.5.4268 ccc publications ieeg based epileptic seizure detection using reconstruction independent component analysis and long short term memory network hirald dwaraka praveena, c. subhas, k. rama naidu hirald dwaraka praveena* department of electronics and communication engineering jntua, ananthapuramu-515002, india hdpraveena@gmail.com c. subhas department of electronics and communication engineering jntua college of engineering, kalikiri-517234, india schennapalli@gmail.com k. rama naidu department of electronics and communication engineering jntua college of engineering, ananthapuramu -515002, india kramanaidu@gmail.com abstract in recent decades, an epileptic seizure is a neurological disorder, which is commonly detected from intracranial electroencephalogram (ieeg) signals. however, the visual interpretation and inspection of ieeg signal is subjective variability, a time-consuming mechanism, slow and vulnerable to errors. in this research article, an automated epileptic seizure detection model is proposed to highlight the above-mentioned concerns. the proposed automated model integrates the reconstruction independent component analysis (rica) and long short term memory (lstm) for seizure detection. in the proposed model, rica is utilized to extract the features from the normalized ieeg signals, and then the obtained feature vectors are fed to the lstm network for classification, which effectively classifies inter-ictal and ictal ieeg signals. this experimental outcome showed that the proposed rica-lstm model achieved an accuracy of 98.92%, sensitivity of 99.01%, specificity of 98.68%, balanced accuracy of 99.24%, and f-score of 98.25% in epileptic seizure recognition on the swec-ethz ieeg database, which is better compared to the conventional machine learning classifiers. keywords: epileptic seizure detection, long short term memory network, normalization, reconstruction independent component analysis. https://doi.org/10.15837/ijccc.2021.5.4268 2 1 introduction an epileptic seizure is a chronic neurological disorder, which occurs due to abnormal activities in the brain that affects the movement, and sensorium of the human body [1-2]. in recent times, the world health organization reported that around 60 million people are affected with an epileptic seizure, and every year approximately 2.5 million people are diagnosed with epileptic seizures [3-4]. when compared to imaging techniques, ieeg signal is extensively employed to detect seizure activities due to the low cost [5]. the ieeg signal is pathological, and physiological data, which is effective in assessing, and evaluating the normal ieeg activities of healthy individuals, seizure-free/inter-ictal activities of epileptic patients and ictal ieeg activities of epileptic patients [6-8]. however, visual inspection of the ieeg records is time-consuming, slow, subject to inter observe variability, and vulnerable [9-10]. to highlight the aforementioned problems, a novel automated computed aided diagnostic model is proposed in this article to improve the performance of seizure classification using ieeg signals. initially, input ieeg signals are acquired from the swec-ethz database, and then signal normalization and sliding window techniques are employed to ease the representation of collected data that provides maximum spectral resolution for better epileptic seizure detection. next, the rica model is developed for the automatic extraction of feature vectors from the pre-processed ieeg signals. lastly, the learned feature vectors are given as the input to the lstm network to classify the ictal and interictal signals for the early diagnosis of seizures. in this research, the lstm network is the best choice for classification, which significantly retains the historical information about data, and realizes the learning of long-term dependence information of data. the proposed model effectively reduces the latency, where the delay that occurred between the on-set of an epileptic seizure and its detection. in this experimental phase, the proposed rica-lstm model performance is evaluated using balanced accuracy (ba), accuracy, sensitivity, f-score and specificity. this article is organized as follows. a few existing research works on the topic “epileptic seizure detection” is surveyed in section 2. the proposed rica-lstm model is briefly explained in section 3 with mathematical derivations. further, section 4 describes the experimental analysis of the proposed rica-lstm model. section 5 discussed the conclusion of this work. 2 related works prathaban and balasubramanian [11] developed a new data analytics methodology in combination with a neural-based mathematical model for epileptic seizure classification. initially, an enhanced wendling model and grey wolf optimized model-driven technique was used to track the seizure events. furthermore, a closed-loop control system was developed based on a cascade forward backpropagation network to control the seizures, especially using eeg signals. this simulation results showed that the developed model obtained significant performance in seizure classification related to the existing models in terms of prediction accuracy on a real-time database. in seizure detection, the developed model is required to train the individual patient, where it fails in cross-patient for achieving better seizure prediction. rout, and biswal, [12] utilized variational mode decomposition technique to decompose the signals into intrinsic mode functions. next, five efficacious feature vectors were calculated using hilbert transform to obtain discriminative features. finally, the error minimized random vector functional link network was applied for seizure classification. however, the developed model performance was degraded, due to temporal variations in epileptic form activity. raghu et al. [13] introduced a new eeg matrix determinant for epileptic seizure detection. initially, a filtering technique was used to remove the artifact, then the eeg data were sequentially arranged to generate a square matrix. in this study, the typical segmentation lengths were equal to the elements in the square matrix. next, the extracted feature vectors fed to different classifiers like a k-nearest neighbor, support vector machine, and multilayer perceptron (mlp) with 10 fold cross-validation for epileptic seizure classification. the presented model performance was tested on the bonn database and a real-time database in terms of f-score and classification accuracy. however, the developed model supports only binary class classification, which was inadequate in multi-class classification. https://doi.org/10.15837/ijccc.2021.5.4268 3 gao et al. [14] developed an automatic epileptic seizure detection system based on recurrence quantification analysis and approximate entropy in combination with a convolutional neural network. the presented model performance was tested on the bonn database and the simulation results confirmed that the developed model attained better performance in seizure detection in light of specificity, accuracy, and sensitivity. however, the developed deep learning model was highly expensive and often has a vanishing gradient issue. büyükçakır et al. [15] used the hilbert vibration decomposition technique to decompose the collected eeg signals into seven sub-components. the selected sub-components were fed to the mlp classifier for seizure recognition. the performance measures such as average false alarm rate, and sensitivity was confirmed that the developed model obtained effective performance in seizure classification. however, the developed model fails to analyze the multi-classification problem of eeg signals. burrello et al. [16] used local binary patterns (lbps), mean amplitudes, and line lengths of ieeg signals for seizure detection, where the signal was collected from the swec ethz database. next, the extracted feature vectors were given as the input to hyper-dimensional classifiers to classify ictal and interictal ieeg signals. additionally, burrello et al. [17] were converted the collected ieeg signals into symbolic lbp codes. next, a holographic distributed brain state representation was developed in a hyper-dimensional space, which was used to find the spatial brain regions, which detect the onset seizures, effectively. however, the classification latency was increased in the lbp feature descriptor, if the code length was higher. to highlight the aforementioned concerns, a model is proposed in this article for effective epileptic seizure detection. 3 methodology in epileptic seizure recognition, the proposed model consists of four major steps like data acquisition: swec-ethz (long-term database), data pre-processing: normalization and sliding window, dimensionality reduction: rica, and classification: lstm network. the workflow of the proposed model is graphically indicated in figure 1. figure 1: workflow of the proposed model 3.1 data acquisition and pre-processing in this research, the proposed rica-lstm model performance is tested on the swec-ethz ieeg database, which consists of eighteen patients (p1 to p18) with 116 seizure records. for every patient, https://doi.org/10.15837/ijccc.2021.5.4268 4 the seizure numbers vary between 2 to 14 and are implanted by utilizing 24 to 128 electrodes. in the swec-ethz ieeg database, the implantation technique with the number of electrodes depends on the clinical necessity. in this database, the ieeg signals are recorded by implanting grid, depth, and strip electrodes, and the ieeg recordings are sampled at 1024 hz or 512 hz, which completely depends on the implantation technique. finally, the 4th order butterworth filter is utilized to convert the ieeg signal into 0.5 hz and 120 hz after performing the 16-bit analog to digital conversion. database link: http://ieeg-swez.ethz.ch. the sample collected ieeg signals are indicated in figure 2. figure 2: sample collected ieeg signals from swec-ethz ieeg database after data collection, signal normalization is accomplished to scale the ieeg signals to an identical level, which ranges between 1 to -1. the normalization of signal amplitude is to alter the amplitude to meet a specific criterion, which means all the ieeg signals have the same power. the sample normalized ieeg signals are graphically represented in figure 3. then, the sliding window is applied to represent the ieeg signals in light of the sample value. here, the sample value of the sliding window spectrum is in the form of gabor’s signal representation, where the sliding window spectrum contains two variables such as a continuous frequency variable and a discrete-time index that indicates window position. in this research, the sampling lattice is a rectangular cell, which occupies a 2π area in the time-frequency domain, along with the window size is 512. figure 3: sample normalized ieeg signals 3.2 dimensionality reduction after data pre-processing, the rica model is developed for feature dimensionality reduction. it is an unsupervised feature learning model, which effectively captures the complete structures in the pre-processed ieeg signals by replacing the hard reconstruction which constraints with softer reconstruction cost. let us consider, total m samples as x = [ x1,x2, . . . ,xm ] , where xp ∈ <n , where, p = 1 . . . .m is stated as a linear combination of independent bases vectors ap, where, p = 1..k, which mathematically represented in the equations (1), and (2). https://doi.org/10.15837/ijccc.2021.5.4268 5 xp = sp1a 1 + sp2a 2 + · · · + spka k (1) x = as (2) where, k is represented as a number of bases, a = [ a1,a2,a3, . . .ak ] is indicated as unknown mixing bases matrix, ap ∈<n , and s = [ s1,s2,s3, ·sm ] ,sp ∈<k, and s = wx. here, w is denoted as a weight matrix that works in mapping samples x to feature vectors. in rica model, the extracted feature vectors sp are sparse like other learning models. hence, the objective function of the rica model is mathematically depicted in equation (3). q(w) = ‖wx‖ (3) by using the orthonormality constraint, the rica model is defined in equation (4). min w m∑ p=1 k∑ q=1 g (wqxp) , subject to ww t = i (4) where, g(.) = log (cosh (.)) is stated as convex functions and i is denoted as identity matrix. the weights are over complete if k > n. the orthonormality constraint ww t = i is used to ensure the learned basis orthonormal. the optimization problem of the rica model is mathematically depicted in equation (5). min w λ m m∑ p=1 ∥∥∥w t wxp −xp∥∥∥2 2 + m∑ p=1 k∑ q=1 g (wqxp) (5) where λ is denoted as a scaling constant that determines the relative importance of a reconstruction cost, with the sparsity penalty. in this scenario, the total extracted features are electrode ×512, whereas the selected features are 5 × 512. the obtained discriminative features fed to the lstm network for epileptic seizure classification. 3.3 classification after the selection of optimal feature vectors, epileptic seizure classification is carried out using the lstm network. the lstm network includes three layers like input, output, and hidden layers. the hidden layers have memory blocks that contain input and output gate, which perform the control functions at input and output activation. additionally, the forget gate is added later to the memory block where the lstm network identifies the mapping from the input sequences (extracted feature) v = (v1,v2,v3, . . .vn) to the output sequences y = (y1,y2,y3, . . .yn) using the equations (6 − 11). it = σ (zivvt + zirrt−1 + zicct−1 + bi) (6) ft = σ (zf vvt + zf rrt−1 + zf cct−1 + bf ) (7) ct = ft � ct−1 + it �g (zcvvt + zcrrt−1 + bc) (8) ot = σ (zovvt + zorrt−1 + zocct−1 + b0) (9) rt = ot �hct (10) yt = ∅(zyrrt + by) (11) https://doi.org/10.15837/ijccc.2021.5.4268 6 by inspecting the equations (6 to 11), z is indicated as weight matrix, and ziv is specified as the maximum weight of the input gate. the diagonal weights of peepholes connections is represented as zic,zf c and zoc, where the input bias vector is stated as bi. additionally, σ is represented as a sigmoid function of forgetting gate f, input gate i, output gate o, and cell activation vectors c.∅ is specified as output activation function, g is specified as cell input, and h is represented as cell output. in the multilayer lstm network, hyperbolic tangent, and sigmoid activation functions are employed to transform the selected feature vectors into input values between −1 to 1 and 0 to 1 . the hyperbolic tanh activation function is utilized as the block input and block output activation function. the sigmoid activation function is utilized as the gate input activation function. as stated earlier, the lstm classifier effectively decreases the delay that occurred between the on-set of an epileptic seizure, and its detection with better seizure classification. the parameter settings of the lstm network are given as follows; learning rate is 0.001, maximum epoch is 50 , batch size is 30 , and hidden layers is 3; layer 1: 100 units, layer 2: 125 units, and layer 3: 100 units. 4 simulation results in this work, the matlab software tool is used to simulate the proposed rica-lstm model for epileptic seizure detection. in this scenario, the proposed rica-lstm model performance is compared with a few previous models; an ensemble of hyper-dimensional classifiers [16], and hyperdimensional computing with lbp [17] to validate the effectiveness in epileptic seizure detection. the five statistical performance metrics like accuracy, specificity, sensitivity, f-score, and ba, were used to evaluate the proposed rica-lstm model performance in epileptic seizure detection. this mathematical expression of undertaken performance metrics is represented in table 1. where, tp indicates true positive, tn represents true negative, fp states false positive and fn represents false negative. in epileptic seizure detection, tn refers to correctly classified interictal signals, tp refers to correctly classified ictal signals, fp refers to incorrectly classified ictal signals, and fn refers to incorrectly classified interictal signals. table 1: the mathematical expression of undertaken performance metrics metrics mathematical expression accuracy t p +t n t n +t p +f n +f p f-score 2t pf p +2t p +f n sensitivity t p f n +t p speicficity t n t n +f p ba sensitivity + specificity2 4.1 quantitative analysis in this section, the performance analysis is carried out using dissimilar classifiers without the rica model, where the performance is evaluated by the means of accuracy, specificity, f-score, ba, and sensitivity. by inspecting table 2, the lstm classifier achieved maximum classification with an accuracy of 97.93%, the sensitivity of 96.23%, specificity of 96.98%, ba of 92.87%, and f-score value of 93.98% in epileptic seizure detection related to the comparative classifiers like naive bayes, random forest, and multi support vector machine (msvm). the lstm classifier showed a maximum of 51.44%, and a minimum of 19.45% improvement in classification accuracy which is related to the comparative classifiers in epileptic seizure detection. additionally, graphical analysis of dissimilar classifiers without the rica model is indicated in figure 4. in table 3, the performance analysis is carried out using dissimilar classifiers with the rica model, where the performance is investigated using accuracy, specificity, f-score, sensitivity, and ba. in the swec ethz ieeg database, 116 seizure records are used for experimental analysis with 80% https://doi.org/10.15837/ijccc.2021.5.4268 7 table 2: performance analysis of dissimilar classifiers without rica model without rica classifiers arcucacy (%) sensitivity (%) specificity (%) ba (%) f-score (%) random forest 78.48 81.41 81.53 80.43 78.21 msvm 46.49 54.31 58.21 51.52 29.10 neiva bayes 52.80 58.63 51.43 53.83 49.94 lstm 97.93 96.23 96.98 92.87 93.98 figure 4: graphical analysis of dissimilar classifiers without rica model training, and 20% testing of ieeg data. by investigating table 3, the combination of lstm with the rica model achieved maximum classification with the accuracy of 98.92%, the sensitivity of 99.01%, specificity of 98.68%, ba of 99.24%, and f-score value of 98.25% in epileptic seizure recognition at 50th iteration. by comparing to the existing classifiers like msvm, random forest, and naive bayes, the combination of lstm with the rica model showed a maximum of 46.80%, and a minimum of 14.67% improvement in epileptic seizure detection. this graphical analysis of dissimilar classifiers with the rica model is represented in figure 5. table 3: performance analysis of dissimilar classifiers with rica morel with rcia classifiers acyuracc (%) sensitivity (%) speciyicitf (%) ba (%) f-score (%) random ferost 84.25 84.75 83.75 84.25 84.17 msvm 52.12 54.66 62.20 55.34 33.33 naive bayes 55.89 58.96 52.81 55.89 54.49 lstm 98.92 99.01 98.68 99.24 98.25 4.2 comparative analysis the comparative analysis among the proposed and the existing models are represented in table 4. burrello et al. [16] extract lbps, line lengths, and mean amplitudes from the acquired ieeg data. further, the extracted feature vectors were fed to ensemble the hyper-dimensional classifiers to classify interictal (between seizures) and ictal (during seizures) the brain states for early diagnosis of an epileptic seizure. the extensive experiments showed that the developed model obtained a sensitivity of 96.38%, specificity of 97.31%, and accuracy of 96.85% in epileptic seizure detection. further, burrello et al. [17] transform the collected ieeg signal into symbolic lbp codes from which a holographic distributed brain state representation was developed in hyper-dimensional space. hence, the brain state representation was utilized to identify the spatial brain regions, to detect the onset seizures. in this study, an extensive experiment was conducted on the swec-ethz ieeg database where the developed model obtained 94.84% of specificity and classification accuracy of 95.42%. the proposed rica-lstm model obtained better performance in epileptic seizure detection by comparing to these existing studies. compared to the existing papers [16] and [17], the proposed rica-lstm model https://doi.org/10.15837/ijccc.2021.5.4268 8 figure 5: graphical analysis of dissimilar classifiers with rica model obtained fast algorithms with limited memory consumption for deployment on low cost embedded hardware platforms in terms of specificity, and accuracy. table 4: comparative analysis between the proposed and the existing models models specificity (%) accuracy (%) hyper-dimensional classifiers [16] 97.31 96.85 hyper-dimensional computing with lbp [17] 94.84 95.42 rica-lstm model 98.68 98.92 5 conclusion in this article, a new rica-lstm model is proposed to achieve effective performance in epileptic seizure detection. the proposed model includes two major steps as dimensionality reduction and classification. in this research, the rica model is proposed for feature extraction, and dimensionality reduction, where this process effectively reduces the curse of dimensionality issues, and system complexity. the dimensionally reduced feature vectors are fed to the lstm network for inter-ictal and ictal signal classification for early diagnosis of epileptic seizures. by comparing to the existing models the hyper-dimensional classifiers, and hyper-dimensional computing with lbp, the proposed rica-lstm model obtained a maximum accuracy of 98.92%, and specificity of 98.68% in epileptic seizure detection on the swec-ethz ieeg database. in future work, the proposed rica-lstm model is applied to multi-model data to further improve the recognition rate. funding the apc was not funded by any organization. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. https://doi.org/10.15837/ijccc.2021.5.4268 9 references [1] lahmiri, s.; shmuel, a. (2018). accurate classification of seizure and seizure-free intervals of intracranial eeg signals from epileptic patients, ieee transactions on instrumentation and measurement, 68(3), 791-796, 2018. [2] cura, o.k.; atli, s.k.; türe, h.s.; akan, a. (2020). epileptic seizure classifications using empirical mode decomposition and its derivative, biomedical engineering online, 19(1), 1-22, 2020. [3] truong, n.d.; nguyen, a.d.; kuhlmann, l.; bonyadi, m.r.; yang, j.; ippolito, s.; kavehei, o. (2018). convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram, neural networks, 105, 104-111, 2018. [4] varatharajah, y.; berry, b.; cimbalnik, j.; kremen, v.; van gompel, j.; stead, m.; brinkmann, b.; iyer, r.; worrell, g. (2018). integrating artificial intelligence with real-time intracranial eeg monitoring to automate interictal identification of seizure onset zones in focal epilepsy, journal of neural engineering, 15(4), 046035, 2018. 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(2019). hyperdimensional computing with local binary patterns: one-shot learning of seizure onset and identification of ictogenic brain regions using short-time ieeg recordings, ieee transactions on biomedical engineering, 67(2), 601-613, 2019. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: praveena hirald dwaraka; subhas c.; rama naidu k. (2021). ieeg based epileptic seizure detection using reconstruction independent component analysis and long short term memory network international journal of computers communications & control, 16(5), 4268, 2021. https://doi.org/10.15837/ijccc.2021.5.4268 introduction related works methodology data acquisition and pre-processing dimensionality reduction classification simulation results quantitative analysis comparative analysis conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 2, month: april, year: 2021 article number: 4029, https://doi.org/10.15837/ijccc.2021.2.4029 ccc publications a pervasive computational intelligence based cognitive security co-design framework for hype-connected embedded industrial iot u. tariq, t. a. ahanger , m. nusir, a. ibrahim usman tariq*, tariq ahamed ahanger , muneer nusir, atef ibrahim college of computer engineering and sciences, prince sattam bin abdulaziz university, saudi arabia u.tariq@psau.edu.sa, t.ahanger@psau.edu.sa, m.nusir@psau.edu.sa, aa.mohamed@psau.edu.sa *corresponding author: u.tariq@psau.edu.sa abstract the amplified connectivity of routine iot entities can expose various security trajectories for cybercriminals to execute malevolent attacks. these dangers are even amplified by the source limitations and heterogeneity of low-budget iot/iiot nodes, which create existing multitude-centered and fixed perimeter-oriented security tools inappropriate for vibrant iot settings. the offered emulation assessment exemplifies the remunerations of implementing context aware co-design oriented cognitive security method in assimilated iiot settings and delivers exciting understandings in the strategy execution to drive forthcoming study. the innovative features of our system is in its capability to get by with irregular system connectivity as well as node limitations in terms of scares computational ability, limited buffer (at edge node), and finite energy. based on real-time analytical data, projected scheme select the paramount probable end-to-end security system possibility that ties with an agreed set of node constraints. the paper achieves its goals by recognizing some gaps in the security explicit to node subclass that is vital to our system’s operations. keywords: cognitive security framework, industrial iot, computational intelligence methods, context awareness, co-design. 1 introduction innovation has always been a part of advances in the cyber-physical systems (cps). from inception of internet of things (iot) to enhancement in its capability of providing cognitive computing solutions, the development has only taken a decade. iot technology and its applications are creating changes in the fundamental functioning of how things work from new age transports to facilitating businesses in delivering enhanced performances. however, tariq et al. [7] in their paper suggests that possible implementation of iot is just surface level and nearly 9 percent of the data generated is still there to be utilized. the authors also suggest that iot is now entering into a cognitive era and needs to be amalgamated with artificial intelligence (ai) to ensure heightened awareness and security in workflows. to understand how iot can be used along with ai in enhancement of security aspect or working, the current study will start with relating between iot and ai. https://doi.org/10.15837/ijccc.2021.2.4029 2 iot gathers data from heterogeneous sources such as technologies used to sense, accumulate, act, process, manage and store data. large data is collected from the heterogeneous sources are known as ‘big data.’ this big data is an asset that can offer an advanced vision that makes use of machines easier and efficient by ensuring its high-level modeling and knowledge engineering [52]. however, with such a huge amount of data gathered from technological convergence environment of iot, privacy, security, and trust are some of common concerns that can be encountered in the process. an iot based system needs a method based approach that is required to prevent malicious attack an impact the security concern of technical aspects such as availability, confidentiality, and integrity [9]. ai can essentially be seen as a key to unlock this asset. network collaborated ai uses natural language processing and machine learning to develop deep-learning pattern recognition of the system. this convolution of a neural network with parallel processing of big data obtained from iotis key to enhance machine to machine learning that possesses the quality to be monitored in real time [20], [53]. ai will enable iot to be mindful of its usage in context and environment. this enables continuous learning that guides operational effectiveness with enhanced decision-making in real time. apart from the privacy concerns of usage of big data that can be prevented by adherence to legal and non-legal norms by firms, an amalgamation of ai with iot can be used for enhanced attack detection and increased resilience and recovery of the system [50]. for iot applications to function, the data scientists use data mining and machine learning tool to develop patterns and new insights from data. these applications use algorithms as a tool to handle various tasks that are learned by the system based on training data provided. however, with the amalgamation of ai with iot, machines through deep learning develop the capability to imitate intelligent human behavior. big data alone is concerned with the analysis of what will happen. whereas, with ai and networking, action derived from the analysis of the data is the primary concern [39] [31]. for enhanced security, when ai and iot together can be used in behavioral modeling for enhanced security in smart iot devices. the application usage enhanced intrusion detection system. the iot in the smart device identifies ddos/dos attacks, by classifying the movements into normal and threat patterns. with integration of ann, the devices use simulated iot network for enhanced security demonstrating over 99 percent accuracy. the behavioral modeling feature allows performance of the system to increase across varied environment [16]. this data can be used to provide insight into the environments in which the system has to perform. as in the usage of ai and iot because of the enhanced decision making based on behavioral analysis in self-drive cars. iot provides the data framework, from radar, a global positioning system (gps), computer vision, and odometer. ai enhances the security of data by sensing the environment in real time to navigating with human input [8]. additionally, ai and iot together can also be used in the mitigation of zeroday attacks. zero-day attack on an institution exposes it to previously unknown vulnerability and there is no time to prepare for a measure to counter it. it is an opportunity attack on the system. distributed diagnosis of iot devices is the first stage to mitigate the risk once the attack is found in the system. while, ai modeled to recognize the risk of new malware that has different defined signature than the system users can also help in prevention of such vulnerabilities [32], [43]. furthermore, ai and iot together can be used in prevention of advanced persistent threats (apts) that are based on adaptive learning algorithms (ala) [5]. one of the examples, highlighting the amalgamation of iot and ai for better security has been presented in a paper presented by choi and lee [12]. in this paper, authors highlight an artificial intelligence approach that can be used for financial fraud detection under iot environment. the study highlights iot uses supervised and unsupervised learning to predict customer behavior in case of detection of financial fraud such as credit card scam by usage of an algorithm such as boat [15]. the study choi and lee suggest that usage of artificial neural network (ann) can enhance the detection of fraud in the real time. there are database mining systems such as cardwatch and te module that includes graphical user interface module (guim), learning algorithms library (lal), global constants module (gcm), database interface module (dbim), and learning algorithm interface module (laim) for enhanced security of the users [45] [3][37]. ann-based ai strengthens the genetic algorithm in case https://doi.org/10.15837/ijccc.2021.2.4029 3 of noticing any anomaly in the usage pattern of a credit card by the customers in real time, ensuring minimization of loss [5]. 1.1 iot: key features iot integrates every daily object those have sensors to the cyber-physical system for providing data that can be converted into state of the art intelligence driven services. these services are driven by the following key features: heterogeneity: iot as a promising technology offers seamless and efficient interconnectivity among a large array of devices. the integration of iot is based on data arising from largenetwork systems of internet, cloud computing, industrial and social networks that require unique interdisciplinary concepts allowing the data to be discovered and accessed. such that different devices on various hardware, service, and network platforms can interact within the iot [54]. with the deep integration provided by iot, the boundaries of independent systems are disappearing and iot is opening avenues of interoperability within different data streams. other than interoperability iot offers a key design requirement for scalability of the heterogeneous data with extensibility, and modularity [41]. iot is capable of interdisciplinary territories and using the heterogeneous data arising from a different system to create a large scale usage such as in development of smart cities [22]. self-configurable: iot service delivery is based on adaptation created among the distributed and dynamic system that interact within the evolving environment. this is based on the role and capability of each device connected by iot, being aware of its capabilities and roles within the system. iot in participated approach creates resource provisioning and network organization based on information flow [1]. this information evaluation in iot is feedback based that augments machine learning created between various devices ensure that iot is capable of functioning in self-configuration [30]. the history of environmental changes is built in iot with agent approach based on the variability model for that are capable of the feedback-evaluative machine learning process. thus, if an environmental change that is previously known to the systems occur, these devices in self-reconfigure themselves to an retraining itself. self-configuration capability of iot is also dependent on both automatic and manual feedback received by any device of iot [36]. extensibility: iot applications are based on growing clients or servers in network for both collection and distribution of data. extensibility as a feature of network is the ease with which a number of peers can be securely connected within a given time. extensibility feature of the iot ensures that each application does not need to change their codes to adapt to other applications in the network. thus, it provides a method to work around complex and un-maintainable versions of network devices with compatibility issues [27]. additionally, developments such as one conducted by microsoft azure enhances the extensibility feature of iot. with azure, users can trigger server-less workflow functions that will be able to execute business logic and that too without any customized application or infrastructure. the feature allows no-code integrations that enable future scenario of developing connected field services within organizations [42]. context awareness: working of iot is ubiquitous in nature. iot is pervasiveness and deals with a different system that is linked within a working environment. the ability to deal with the linking changes between the devices that would otherwise be static points the context awareness nature of iot. the concept of iot has already proliferated to enhance the connectivity between systems, devices, and services [38]. the enormous data obtained from these sensors are raw and need context awareness from iot modeling to create recognized information. network model such as a bayesian network is capable of handling flexible data collected from the uncertain environments of devices that are configured for frequent changes. bayesian network is one such model that enable the iot to be more flexible and context-aware to design a device-oriented modeling [55]. usability: iot is representative of efforts of immense digitization and enhanced utilization of network structures that are inherent to devices connected to these networks. the usability feature of iot is enormous as it is representative of cyber-physical systems that connects cloud computing as well as an advanced version of artificial intelligence and robotics. the usability feature of iot arises https://doi.org/10.15837/ijccc.2021.2.4029 4 from the ability of iot to eliminate the gap between the physical and digital domains. iot has the ability to integrate with a large array of devices, even those that are in semi-finished. additionally, iot applications are capable of creating real-time decision eliminating the conditions of central control. iot also offers the novelty of technology by restricting the need for integrated data processing within the standard technology. the usability of iot allows ordinary objects to be evolved into intelligent devices that are equipped to make easy and rapid communication with the central control system [17]. intelligence: emergence of iot enhanced the instructiveness of a human with their surroundings. the proactive intelligence model of iotis derived on three components of monitoring, feedback, and analytics. the analytics are based on data recorded by sensors that enable the systems to make a decision in real time. feedbacks ensures that iot makes assertive decisions to suggest maximization of potential benefits for better information processing [34]. the data gathered from various devices are used by the iot to create modeled patterns that are capable of making intelligent decisions such as automatically setting thermostat temperatures depending on external weather, number of people in the room, and time of the day. it can also be used in predicting the flow of traffic in cities depending on holidays, weather conditions, and time [21]. 1.2 safety, security, privacy threats and access devices iot middleware: iot middleware as defined by mohmmad, sirajuddin, and shabana [33] is “a software layer or a set of sub-layers interposed between the technological and the application levels”. middleware architectures for iot projects follow the service oriented architecture (soa) approach. principle of soa allows decomposition of complex systems into smaller applications that consist of a simpler ecosystem that is dependent on its well-defined components. access devices of iot middleware include web-based services such as in hydra, virtual sensors and xml deployment description in gsn as fit programmable api as in google fit [35]. iot middleware has a model checking technique based on an algorithm to check properties true to the system against the model environment. the temporal logic is enhanced safety property of the system. for security enhancement, the iot middleware platforms have functionalities such as filtering and aggregation of the data received to enhance data security. tools such as tiny db and wisemid provide privacy by allowing minimum overhead on iot generated big data. middleware platforms act as autonomous links between privacy-preserving techniques. this enables iot to analyze new data entering the analytical component to examine their potential impact on the privacy of users [6]. interaction latency: iot enhances value-added services for both private and business applications that face limitation arising from interaction and communication latency. interaction latency can lead to a late response in a system that can lead to production errors or fluctuations in grid system connected through iot [14]. additionally, the new age devices are incorporated with integrity protection and encryption for prevention of system sabotage. these goals of secure communication are contradicted by low interaction latency between the system components. in case of industrial iot long-term connections and increased overhead of connection that is further enhanced with encryption and continuous process of authentication of data is a key source for latency in iot devices[19]. however, in a study hiller et. al. (2018) [18], suggest that for industrial usage, device implementation as in case of aes ccm implementation on tiny dtls on a zolertia z1 platform prevents interaction latency. this setup is capable of providing fast security processing in iot as the aes provides hardware acceleration at decrease costs. additionally, atlam, et. al. (2018) [4] are suggestive of the methods of fog computing. fog computing prevents latency as it manages and analyzes data as well as time-sensitive actions near the end user called the fog nodes. authors in the study also highlight that being close to the users, unsecured fog nodes can be used to wage a large-scale attack on the user and privacy is a concern that requires to be researched into by the firms. resource management: the core component of iot requires doing three activities that include, collection, analysis, and distribution of data over its platform. however, when it comes to pervasive iot devices they are resources constrained and a single device needs to possess storage, data processing, energy, and band width as its resources. zahoor and mir [57], suggest that pervasive applications of iot devices face limitation and for enhanced energy efficiency. this limitation can be overcome by lightweight algorithms and protocols that are implemented to complete the process of storing, https://doi.org/10.15837/ijccc.2021.2.4029 5 processing and transferring of data as required by an application to optimize the resource management. authors in this study also suggest that internet protocol version (ipv) have an impact on security of resource management and suggest that ipv6 provide more security than ipv4. resource management in iot can also be enhanced through wireless sensor networks (wsn), inclusion of mobile sink in the wsns, and cloud computing networking system of the wsn [29] [48] [46]. al-turjman et al [2] are suggestive of secure authentication and key agreement (s-saka) using elliptic-curve cryptosystems and bilinear pairing. the authors suggest that with enhanced resource management, the framework is based on mutual authentication process. this enhances safety and privacy concern of the users by data confidentiality, resilience to node-capture, user anonymity, and key impersonation among others. 1.3 authenticating communication with artificial intelligence the functioning of iot is dependent upon security of wireless communication taking place among the connected devices. with the concerns of privacy, security, and safety of big data, artificial intelligence can provide enhanced protection. multi factor authentication (mfa) based on fuzzy logic of “if–then” are being increasingly being adopted for authentication of communication. roy and dasgupta (2018), [44] in a study highlight that fuzzy logic based ai is capable of adaptive selection for authentication modalities of the users. authors further highlight that ai authentication of communication is capable of user verification over different modalities including non-biometric, cognitive behavior, and biometrics. the technique uses information infusion to stress on multi-factoring the authentication of communication process [40]. additionally, ai can be used for mutual authentication for enhanced protection over mobile communication. ai can authenticate four entities of the user including a range of frequency of greeting words, facial recognition, fingerprint match, and different salutation used for callers based on the frequency of calling to predict user pattern. the ability to create mutual authentication enhances authentication of communication over the mobile network [10] [11]. ai offers enhanced message authentication process by adoption context-adaptive signature verification strategy. the process reduces computation and communication overhead enabling the system to recognize the pivot point between authentication of data and real-time action by the ai. this system of authentication of communication by ai is also being increasingly applied to secured wireless communications of connected vehicles using vehicular ad-hoc network (vanet). such, that authentication facilitates enhanced exchange of safety messages that are an effective choice for securing wireless communications for connected devices [49]. 1.4 certification authority using secret redistribution the primary goal of big data is to preserve actionable intelligence and pattern to ensure long-term confidentiality and availability of data. the wireless mesh networks (wmns) that are developed in arrangement to form either independent networks or backbone of networks using wireless channel for internet need to be self-configuring. for self-organizing, the mesh routers (mrs) need to exchange information within the mrs without the assistance of administrators. the mrs also needs to authenticate the construct of network by authenticating themselves as well as other stations in which it comes in contact to. public key infrastructure (pki) in general controls the certification authority (ca) that is commonly shared between network’s trusted nodes [23]. however, in self-organizing networks such as wmns there is no trusted third party (ttp), hence, the ca need to be distributed over mrs. to be able to participate in active ca function, participating mrsrequire secret codes that are changed from time to time for all shareholders. fast variable share redistribution (fsrv) is the solution that allows both secret sharing and redistribution. the fsrv scheme works towards establishing threshold cryptography that will in return minimize the chances of disclosure of secret in case anyone or some shareholders get compromised. the method use multicasting that adopts ruiz tree, which works towards reducing the operational overheads and optimum utilization. the structure is capable of updating, revoking, and verifying certifications at each wmn nodes for well-organized and secured data transferring functions [24]. https://doi.org/10.15837/ijccc.2021.2.4029 6 1.5 implications for design theory of secure iot system design theory of iot system is of specific importance in industrial systems as it specifies the instruments that the organization will choose to connect to intelligent technologies. these devices, in turn, become a part of large-scale data emitting objects in the universe. configuration of iot system will determine the security and safety of data central to the firms [25]. the design theory of iot has implication of security of the system as it is dependent on a number of antecedents including communication strategy within the well-articulated design system, redefining the roles and responsibilities of employees who are responsible for security of the system, and keeping them aware of new developments to ensure network security [47]. additionally, the design theory of secure iot is dependent on its three primary goals. first is to securely connect embedded devices of edge gateway or endnote type to the cloud backend. this includes identity management of shareholders and authentication of devices trying to get on-board on the system. second is to allow collection of data from various sensors in devices to analyze and present meaningful interpretations. this process is accomplished by selecting the right data or object model that is compatible with backend infrastructure of cloud-based industrial iot.lastly, the design theory is responsible for the number of secure socket layer and transport layer security that are employed by the firms to stress on the need for encrypted data exchange between various devices connected in iot and its industrial back-end [26]. 1.6 iot derived co-design tools selection the co-design tool selection that is derived for iot is dependent on custom hardware available along with meeting the design goals of security, privacy, reliability, energy, and performance. other than this, the flexibility of the platform or chip area is also an important parameter as they may impact the mapping of computation of tools together. thereafter, the selection of the portions that should be developed to have custom compute against the parts that should be left in software. several existing iot derived systems based on hardware/software co-design use c with system c or c++ with system c module to specify the system behavior. further, co-design tool selection is based on performance of computation blocks and their usage of profiling information. other considerations include performance requirement, area coverage required, and power consumption pattern corresponding to the hardware chosen for implementation of system design goals [56]. further, in iot device design system-level design space exploration is also critical. this is because the iot design tool should be able to automate the instantiating process and quickly produce glue and control logic to implement system level design effectively. ability to eliminate manual integration of system will improve co-design productivity. also, the co-design tool selection needs to create highlevel synthesis for balancing optimization between area and performance of the overall design. the co-design hence created using the hls should offer automatic integrate encryption for the ips to secure both input-output data streams, analyze the interfaces, and allow users to enhance the security for systems [51]. hardware requirements: the industrial internet of things (iiot) uprising is by now forming data driven industrial units bursting with unconventional sensor equipment, peer-to-peer communications, and machine learning abilities by aggregation, dissemination, processing, analyzation and visualization of real-time data. contrasting its inhabitant oriented end user iot equivalent, iiot demands high-end linked nodes with applications which can be very diverse with respect to industry required provisions and necessities. as a result, iiot will restructure its routine methods, by avoiding rigidity and refining productivity. a decisive system is needed for various industrialized data exchange applications, so “control-as-aservice” needs to guarantee heuristic payload and control packet exchange from edge to the related aggregator nodes. suggested communication network package is based on terrestrial cellular radio, rf-mesh (802.1, 802.3, 802.11, 802.15.4, zigbee, lora, cellular–nb-iot) for ground data exchange, and portable nodes. other iiot hardware is based on sensors (global positioning system, gyroscopes, radars), processors (ranged (8/32/64 bit)), microprocessor and microcontroller (i.e. system-on-chip) rooted for embedded systems, power source (for energy harvesting and storage i.e. sd or hdd, dram https://doi.org/10.15837/ijccc.2021.2.4029 7 or sram (mb/gb)). by utilizing hardware capabilities, the refined processes converts sensor driven raw data into valuable knowledge. 1.6.1 co-design tools mapping process based on iot yang et al. (2015) [56] suggests in a study the co-design tool mapping process based on iot is a four-step process: setting up hardware/software (hw/sw) codesign: co-design tools mapping process based on iot starts with creating cooperative design between the hardware and software components. the unification initiates design system and functionality and movement between hw/sw. codesign between the hardware and software is to initiate a balance between the system to optimize platform design that allows interaction between the components and provides continuous feedback. evaluation of hw/sw codesign also introduces the user to trade-off of the particular allocation dynamics for further evaluation. ensuring high-level synthesis (hls): hls is required to integrate the hw/sw co-design to reduce the time in-flow for information and verify the power analysis. hls starts with compiling of functional specification with transformation of input description into functional specification. after the coding comes optimization process of eliminating dead-codes, elimination of false data dependency, folding and loop transformation. the process completes when the custom architecture automatically or semiautomatically adjust to efficiently implement the specification [13]. creating system-level ip integration: iot devices use ip components such as cpu cores, actuators, sensors, and bluetooth communication interfaces to produce hls core. in the ip integration step, the user may use pre-defined register transfer level ips as sub-function to accelerate the design process and meet system-level goals of design in the area or power, capacity, expansion, security, and privacy. ip level integration standardizes the tool assistance and ensures instantaneous connection appropriate for improvement of design process and enhance the effective design for iot design system [56]. debug, verification, and rapid prototyping of the process: the last step in co-designing tools mapping process is debugging that is critical in designing flow and analysis of verification time of the design flow. although verification effort is labor intensive backward tracing of simulations is required for identification of functional errors. designers can also use simulations for identification of functional errors, pinpointing erroneous instruction and highlighting the difference between present and optimal working of the design system. once the debugging process is over, prototyping of the design system is done to evaluate larger scale perform of the early designs. 1.6.2 operational choices to design tool mapping based on iot a systematic process is undertaken, matching among the four portions (i.e. hw/sw co-design, hls, system ip integration, and rapid prototyping) of iot system synthesis design tools/methods [56]. co-design tools and methods have been mapped based on the key features of iot system alignment with four portions. authors address the need to reflect on how key features of iot devices could be analysed from a service’s designer and developer perceptions. the cognitive security co-design framework (cscof) is built to examine and mapping the four portions with iot key features; the proposed framework-cscof is based on four relational perspectives: creating cooperative between hs/sw components, compiling hw/sw functional specification, system-level ip integration to accelerate design processes and match them with system-level goals, and finally, debugging which is responsible of design flow to identify any functional error in iot devices. when we are attempting to design a novel cscof for iot device networks, it is an essential to identify the situated co-design tools that come map out with a design process of iot systems, which the end-user and the system perform collaboratively and automatically (funk et al., 2018) [28]. in fact, the authors indicate that the proposed framework is useful for analyzing as well as designing. in addition, the cscof can motivate designers understanding how an iot devices or systems interact with its end-users. in this paper, we presented a cscof which is showing design process and activity for iot systems/devices in high-level with concentrating on specific requirements to hw/sw co-design, hls, system ip integration, and rapid prototyping. the identified framework (cscof) observes different aspects of https://doi.org/10.15837/ijccc.2021.2.4029 8 iot devices read through co-design tool mapping process based on iot industries context. cscof has been used as first step to develop the mapping process based on iot. a cscof consists of four main elements: (i) administrative policy, (ii) co-design tool mapping process based on iot, (iii) compliance functions, and (iv) operational and procedural control. 2 proposed scheme secure multicasting protocol: with the amplified dependence on the internet, and considering coagment density of legacy systems, it is gradually more sporadic for a device to toil single-handedly. such scenario necessitates cluster-driven communications which can be exhibited by a variety of techniques: compound unicast, or multicast. compound unicast requires transferring a point-to-point packet to each cluster node. multicasting was technologically advanced as a feature to transmit a packet to more than one device whereas protecting properties by broadcasting the communication only as far as it desires to go to influence each cluster node, simply one time along each route. plentiful standards were adopted to investigate protected multicast method such as cluster association administration, link resource ingestion, receiver resource requests, correspondent resource requests and reliance upon certain criteria. to ensure secure multicasting, receiver and correspondent resource necessities reflect the subsequent: a. how and by what means each node store encryption keys and exactly how large are these secrets? b. what is the handing out period involved for contributor to deliver or read messages? c. make sure if the resolution permit non-members to transmit data? d. how many contributors are tolerable? essentially does these correspondents be identified and acknowledged in before cluster formation? iiot enabled secure multicasting outbursts conditions when communications are desirable for an all-inclusive cluster as well as for rations of a crowd. due to the overhead (in polynomial-based key management) of a greater quantity of rekeying packets every time a node links or leaves that has a authorization level superior than the lowest level in the cluster, secure multicasting is functionally better when the cluster is reasonably stationary. we assume that multicast clusters are fashioned centered on core-based tree method where every node of the multicast cluster shares an undisclosed key with the central device. it is worth mentioning that key dissemination is controlled by a central node. necessitating the nodes to embrace numerous keys may raises the concern of node resources. with a assorted cluster, some nodes are imperfect in processing resources than others. due to this reason, to connect to the cluster to obtain any keys, the requestor must first indorse to a cluster regulator. the validating node could work as a mediator to permit future communications. necessities parameters and tentative statistics are highlighted in table 1. context awareness: the adversary does not requisite to interfere with the records; as an alternative, it can exploit the signature code that a mechanism is adopting to identify malicious activity/code and eradicate it from their particular signature data so the set of identification rules will be parallelized. this will encourage defense algorithm to tolerate specific vulnerabilities. moreover, the controlling adversary possibly will corrupt records by interchanging malware identification labels to demonstrate it as a legitimate code. to tolerate the dangers of adversarial malfunction, we adopted various set of processes (metadata oriented machine learning, fuzzy hash, contextual policy set, base classification, vector model probabilistic threshold) with dissimilar preparatory data collections and features. during experimental analysis, we logged ‘performance throughout simulation, such as application programming interface requests & compiled code, alert timestamp, hash signature (sha-256), identified model/malware type, malware size, process path in node/workstation/server’. we have identified that ‘logistic heap up’, where we embrace the discrete probability rates from each ‘disreputable classifier’ in the collaborative feature set, delivers amplified success of malware forecast. in accordance with figure 1, to avoid malware insertion, we adopted ‘cluster rule set’ based on code restriction policies. in policy, we outline what is reliable code, emphasize on an elastic cluster oriented strategy for marshaling executable scripts, identify which shareware can execute on iot nodes, and https://doi.org/10.15837/ijccc.2021.2.4029 9 function hypothesis projected scheme (based on encoding type: base64)(): for 1, . . . , n do allocate deepest node number at the level to be the central of cluster end for all nodes in “b* tree” do allocate deepest node number at the level to be the central of cluster if node has non-zero level then link the node to its cluster level end end for all central (curricular nodes) of clusters superior than level 1 do link central to primary of the cluster to the subsequent deepest level end for level ‘1 to n’ do establish aggregate cost to entire cost + (cost of cluster at level * level) end end function for node feature inheritance (node) do establish aggregate cost to entire cost + (cost of cluster at level * level) if level of node is non-zero and node is a sibling node then inh-node = zero level node else if the level of node is non-zero then update the linked node value end end end preserving discrete trees for every level of communication rises the sum of packets since some associations between nodes happen in various levels.during packet transmission, the message is promoted first along the subdivision from the basis to the central router, then on subdivisions from the central router to supplementary cluster participants: each router getting the message, comprising the primary router, guides it on all the edges associated to that multicast cluster distinct at the b* tree-building interval. we observed that in high mobility zones, routes are not augmented: the dissemination tree is not assembled based on the position of the basis, but all cluster associates can be bases nodes. for regulative probe (opaque genre protocol) do case: initiator state basis active control (bac): this control is first fixed when the initiation state node start conversions and is returned on the delivery of each payload from source to the specified cluster node. as soon as it terminates, the initiation state node switches to the no state.state revive control (src): this control administers when state restore packets are produced. the control is primarily agreed. it is annulled when node switches to the no state. this control is generally fixed to state-restore-intermission if no rejoinder then inh-node = zero level node else if upsurge time-to-live (ttl) value gradually then if time-to-live range(max) then prompt failure else r end outers don’t distribute to subnets with no associates end end routers which remove ttl-terminated packets may not be capable of lopping bases, which is important to avoid unnecessary bandwidth ingesting end explicit secure multicast messages types: hello; index; catalogue halt; connection/prune; bootstrap; declare state; insert; insert; acknowledgement; nominee announcement; state restore algorithm 1: evaluating projected scheme cost (based on encoding type: base64) https://doi.org/10.15837/ijccc.2021.2.4029 10 table 1: necessities parameters category metric projected scheme (from tentative statistics) simplicity of operation setup interval time quantity of regulators compound clusters one per cluster concurrent communications of intensities allow resource depletion address buffer transmitting accesses numerous multicast addresses multiple entries per connection connection rate per message sparse clusters pervasive groups lightly connected network densely connected network adequate adequate reasonable reasonable figure 1: steps for context-awareness computing illustrated autonomy of the code constraint policies. 2.1 architecture, applications and analytic packet processor: a router was interjected inside a route of data which lies between the iiot configuration and the communication network for transmitting data to and from the industrial embedded systems. it consists of a disturbance indicator, designed to practice a system workstation to execute interference discovery on layer 3 (i.e. network layer which can support connectionless-mode network service protocol and internet protocol) and layer 4 (i.e. transport layer which can support multipath tcp) of a modus operandi field, among data involved in a packet header of packets transferred to the interruption discovery system, and as soon as no imposition is sensed, categorize the packets rendering to stream and pass on the categorized packets. it is worth noting that a network port is aggregated by the route information, in which said payload is separated into a range of packets comprising an initial required dataset which are configured rendering to a desired transmission protocol. unfathomable packet assessment (upa): it allows progressive network supervision, handler provision, and security utilities as well as cluster based data mining for anomaly analyzation. upa makes it a reality to ascertain the creator or beneficiary of data signature comprising precise packets. furthermore, upa can irradiates network behavior, assist service provider to augment bandwidth and output, and can expose node performance. table 3 & 4 defines the packet filtering criteria and parameters. https://doi.org/10.15837/ijccc.2021.2.4029 11 table 2: filtering criterion feedback: packet content m, datasets, output: a boolean value demonstrating whether the packet ought to be forward to processing unit (with clock rate: 1054 mhz, number of parallel threads: 8, average packet length (bytes): 270 and standard deviation of packet length (bytes): 370) for complete configuration similarity analysis. for each m(i) do sub-pattern (xyz) m (i, i+1); if mi [xyz] = 1 then base base-table (bt) to reduce the time required to get the starting address of signature index; if signature index [bt]=1 then return true; else continue; end end return false; table 3: filtering environment parameters # of states/patterns in ruleset # of transmissions scanning deferment depth ram (mb) time (s) area slices avg. max. 379 80803 14.32 25 300 250 990 header size per packet 34 bits route time 5000 ms (device type: nodemcu lua) simulated development kit esp8266 general purpose input output (gpio) pwm (pulse width modulation) 10 latency 112 ns throughput 250 mbps non-linear packet filter 45 mhz filtering pattern ingress, egress type tcp, any action permit, deny source address any destination port 445 • precise match: this form of similarity entails precise equivalence of the filter field, i.e. there is simply one value specified in the filter for that field. • array match: a assortment match entails the assessment of a header field to be in an array quantified by the filter. • protocol impartiality: the pass through a filter system must be a sovereign protocol, so that filtering is braced for diverse protocols and at dissimilar stages. • fusion: the packet filter is capable of managing packet disintegration. • effectual regulation apprises and auditing: the scheme permits the insertion and deletion of procedures with least possible interruption in the dispensation of packets. it retains a record of all admittance endeavors, both putative and jammed, if necessary, as well as data that may be of analysis importance. • procedures ranking: in the case where a data matches more than one rubrics, the packet screener will permit subjective significances to be forced on ranked guidelines, so a unique policy will be in conclusion relevant. https://doi.org/10.15837/ijccc.2021.2.4029 12 figure 2, illustrates the persistent real time analysis based context aware cognitive iiot security co-design framework https://doi.org/10.15837/ijccc.2021.2.4029 13 figure 2: steps for context-awareness computing policy builder: it is a process for applying a range of diverse rules on a stream of packets, the routine encompassing: • acceptance a packet in a packet-exchanged system; • attaching an allowance to the packet; • decisive assembly data concerning the packet; • fill in the allowance with the assembly data; • dispatching the packet to a packet rule protocol component; • defining, at the packet rule imperative engine component, whether the packet resembles to a conjoint state for a course of action rule • given that, at the packet procedure imperative engine component, a connotation concerning the first packet and the mutual ailment where it is resolute that the packet resembles to the conjoint disorder; policy builder: it is a process for applying a range of diverse rules on a stream of packets, the routine encompassing: • acceptance a packet in a packet-exchanged system; • attaching an allowance to the packet; • decisive assembly data concerning the packet; • fill in the allowance with the assembly data; • dispatching the packet to a packet rule protocol component; • defining, at the packet rule imperative engine component, whether the packet resembles to a conjoint state for a course of action rule • given that, at the packet procedure imperative engine component, a connotation concerning the first packet and the mutual ailment where it is resolute that the packet resembles to the conjoint disorder; https://doi.org/10.15837/ijccc.2021.2.4029 14 distributed analytical and diagnostic platform: information representations were generated as an instrument to explore bulky extents of data to implement distributed information analytics. fact prototypes define the actions detected contained by an input data set, such that the information set was adhered to associate and catalogue novel data alongside the perceived performances of the input data set. suggested platform develop the procedures to practice logical workflows and disseminate diagnostic policy messages, each of the dispersed investigative policy messages allied with an observant, which has ability to update analytic model, and computer-generated behavioral data. it is worth to mention that the information flows in coordination is by means of sensors. sensors release and practice information from the industrial mechanism level and forward the information affiliate system link for supplementary handling. system taps (i.e. subpart of sensor), are implanted at key points for system prominence and reflection of network transmission to provision real-time processes. scoring engine (se): se technique consist of: (a) examining data to define an occurrence or nonexistence of each of a set of predefined features of maliciousness; (b) computing a groove centered on the existence or lack of the set of rules in the data, the merit/ratio being insightful of a hazard that the data is malevolent; and (c) additional dispensation the record based on the data ratio. proposed scheme adopted support vector machine (svm) which is a favorable technique for information taxonomy and progression and it has also been effectively practiced in malware recognition. by assembling an undeviating state line in the feature space, the svm yields nonlinear limitations in the novel dataset. process flow chart is mentioned in figure 3. furthermore, as an outcome ’malware scoring criteria’ was conceptualized and explained in table 4. figure 3: data exploration method (vulnerability assessments analysis) https://doi.org/10.15837/ijccc.2021.2.4029 15 table 4: malware scoring criteria principles for scoring malware benchmark narrative payload prospective impending of the program component to worsen or harm its board propagation prospective by what method the cypher is distributed along resentment level the set on related to the payload (i.e., whether it is intended to destroy, terminate, or simply irritate) grouping rankings for malware score riskportrayal rating description 0-25 insignificant reinforcement accessible and functional 26-50 perilous distresses bandwidth 51-75 risky disturbs storage 76-100 disastrous directed for system, network, data, and buffer space mitigate agent: performance outlines for nodes are primarily bred as the system remains functional, and the inconsistency sensor repetitively equates the discrepancy of the current profile alongside that of the standard signature log/code. mitigate agent system practices intelligent agents to accomplish tasks and is an method to a scheduling problem with difficult objectives. these agents are independent and can perform without administrative intrusion. agents are consequently instated beside the attacks at connected devices to crop the attack outlets from the risk log to evade the hazard at the core system. furthermore agents tends to implement their programmed action plans to prevent anomalies to the system. the state of retreat identification is separated into three levels: 1. if the reaction agent discovers the security of the structure in the normal threshold ratio, the matching exploit plan will persist; 2. if the reaction agent discovers the security of the structure in the medium threat threshold ratio, it will introduce supplementary defense with advanced complexity to save the coordinator from stirring into the abortive state; 3. if the reaction agent discovers the security of the structure in the high risk threshold ratio, the reply agent will lock down the structure (iiot device network) to protect it from being affected. control panel engine (data, services, end point devices): the security engine has a processor and a recollection buffer which provisions the ciphering/hashing (sha-256) and validation bounds. the system collects a packets from the portable computing node via the communication method scrambled using the cipher keys and comprising verification factors. the system deciphers the obtained packets (tcp, icmp if encrypted, payload) exhausting the secrets kept in the buffering node and validates that certification factors mined from the automated security record. dynamic analytical model (dam): suggested approach (scores, alerts) assembles records in such a way that it presents both a real-time and past view of trials. this delivers a combined outlook of risks and security gaps from a principal system and sanctions for better forecasting, quicker perseverance and improved assessment. for analyzation purpose we collected: edge and node performance data, system communication, peripheral risk intelligence sources, admittance (link) and identity administration data, evidence of amenability throughout an assessment. audit focused on node integration, data validity & distribution, data extraction (download/upload to secure/insecure device), blocking request of unauthorized communication channels. such analysis (using ml with big data, ensemble learning, and cognitive computing) helped us to understand the parameters to identify insider anomaly node. based on dam and mitigating engine’s scoring criterion, system will automatically configure itself for alert broadcast (if needed). as per figure 4, policy driven wireless network node exploration dispensation collects a range of associated data packets which are demonstrative of an imperative in which analogous packets were https://doi.org/10.15837/ijccc.2021.2.4029 16 figure 4: network analysis and routing metrics passed on to the system for link layer records of every conforming packet. it is important to timestamp the harmonized data packets which are kept in the data marts with accredited gen by entitling a first apprehension node as a dominant collection node. analysis is done based on criterion of high-end devices for packet circulation to all sort of nodes. routing metric is working out based on one or more related data principles for a connection between a spreader of a first link device and a receiver of a additional system device in a hyperconnected iiot network, in which each of the shared policy includes a conjoint data significance for the association for a dissimilar or supplementary communication modes. https://doi.org/10.15837/ijccc.2021.2.4029 17 3 performance metrics and evaluation co-design is the learning of by what method individuals relate with workstations and to what magnitude workstations are or are not industrialized for effective communication with humanoids and workers. one significant co-design feature is that diverse operators form dissimilar outsets can simulate about their collaborations and have unalike behaviors of learning and holding services awareness. whereas artificial intelligence is the replication of human cleverness progressions by technologies, specifically workstation pc’s. these methods consist of knowledge (the attainment of evidence and rubrics for exhausting the data), perception (exhausting guidelines to influence estimation or certain suppositions) and autonomic behavior. the adopted turing test is a well-established technique systemized to regulate if a workstation can essentially contemplate like a human. we implemented supervised learning methodology where record arrays are categorized so that outlines can be sensed and used to tag different record collections. it is worth mentioning that while development of our model, we consider guidelines issued by europe’s general data protection regulation (gdpr). numerous tryouts were executed to authenticate the projected iiot-enabled secure system. experimental environment outline is described in table 5. table 5: trial-and-error environment testbest experimental environment testbed regular grid (15 m x 10 m x 5 m) configurations 3 deployed iiot sensors 100 fixed nodes (one meter distance per sensor node) node capacity 60 gb storage capacity 16 tb gpio (generalpurpose input/output) 20 pins soc (system on chip) espressif esp8266, adafruit fona, intel qm87 network support 802.11bgn wifi, general packet radio services (gprs), ethernet in u-boot (the universal boot loader) transmission power (-10, -20, -30) decibels with respect to one milliwatt transmission type (if wireless) broadcast antenna model omnidirectional cc1101 low power (10mw 500m) signal propagation 450 mhz with spring antenna hop count 8 bits troubleshooting agent tracert (by applying echo packets (internet control message protocol) to visually dash the route) bandwidth 1000 mb per second max. software arduino software ide / wiring, wind river intelligent device platform xt cloud platform microsoft azure iot suite (wise platform as a service/rmm (iot device remote monitoring and management) enabled with gateway/sensor management) millions of instructions per second 100 ram double data rate (ddr3) 4 gb, up to 16 gb, built-in flash processor quad core cortex-a72 (up to 1.6ghz) number of cpus in a virtual machine 1 accelerations packet processor, security engine power advanced technology extended (atx) type power connector function: pin: 16 3.3-12 v https://doi.org/10.15837/ijccc.2021.2.4029 18 accessing console serial connection, connecting via secure shell cpu 2000 mhz cache 5 mb shared l2 exclusive invalid port number 3, 4, 5, 6, 7 (no link on this port) control ifttt ((if this, then that)), application programming interface working status indicator 3 (wakeup, reset, debug) payload 50 bits mac header size 6 bits duration 200 minutes (each day) minimum execution time 100 seconds / session event length (expressed in millions of instructions) 300 number of events 8000 packets / 10 minutes generated data size / event 300 mb minimum sampling frequency (100, 200, 400) ms retries (minimum) 4 modulation model gaussian frequency shift keying receiving sensitivity -108 dbm operating temperature -2 (min), +60 c (max) operating humidity 20 percent we accomplish our assessment exhausting two sets of research: (a) proof of recurrent security, and (b) endorsement of resource-conscious security. chart.1. illustrates that the threat recognition likelihood requires if a system can sense the anomaly accurately. methods can be considered by the metrics precision, discovery ratio and deceitful confidence level. respective metrics depends on the state of payload delivered over the security checkpoint, these classifications are indicated as the terms true/false positive/negative. figure 5: (chart.1.) association concerning false & exposure likelihood of threats during experiment, we emphasized on device tracking, encountering application, communication, utilized platform security, adopted architecture for iiot based on mentioned testbest environment. https://doi.org/10.15837/ijccc.2021.2.4029 19 data gathered for chart 1 helped us to calibrate likelihood of cyber threat prediction modeling. we observe that heterogeneity of devices may imitate in the usefulness of transmuting the policies into the preserving ratios, as dissimilar categories of resources might be desirable to challenge unrelated threats. subjective to the precise requirements of the system under observation and based on the accessible information, diverse methods/policies allow to signify the unlike particularities of the system to diminish false alerts. over the course of experiment, we constantly upgrade the system criterion based on optimization problem. min µ x∑ m=1 im(time session_time. x∑ m=1 subnet ≤ compile (1) where i_m(time) is representing random processes executed over specific time intervals. figure 6: (chart.2.) general probabilistic security threshold outcome in iiot provision the enormous number of diverse devices being coupled in iot system escalates severe challenges in terms of security for numerous causes. projected iiot test-bed / emulation is considered by imperfectexperiences in terms of both energy and data processing properties. chart 2 describes the outcome of the emulator test, which is used to develop the general criterion among security necessities for the iiot oriented anomaly defense setting. our prime focus of this experiment was to analyze edge node behavior during anomaly detection learning phase on system platform, service layer & network and systems improvement in intrusion detection overtime. by allocating a learning based control on how to assign security properties within the security components, framework helped autonomic nodes to handle varied attacks in context-aware iiot defense setting. figure 7: (chart.3.) alert processing time and process success ratio in consideration with request expiration chart 3 demonstrates the over-all handling time and the realization ratio of security necessities https://doi.org/10.15837/ijccc.2021.2.4029 20 according to the influx level of security requirements for diverse values of ending time of security call.in this segment, we extant the outcomes gained by smearing the defense method on alert streams. we programed scenario as the alerted irregularities are not essentially attacks, but can be wide-ranging technical complications. we quarry by selectively straining out alert malfunctions to process faster to use the system resources for more appropriate alerts. consequently the filtering is measured as vital objective as the irregularity recognition. proposed technique establishes an extraordinary graphical depiction of the security standing of the threatened system. the consequential chart is an easy to read representation of the security status of the iiot system. the handler can conjecture on the malevolent activity visible through the defense alert axis. if an apprehensive activity is patterned, then the context engine can go through the alerts policy to verify the rigorousness (dam modeling) of the event. figure 8: node/notification trust modeling the trust model oriented performance of our scheme has also been evaluated. the time required to deliver data from the buffer and to generate the data table have not been evaluated, as they have been reflected unrelated to the scope of the data. after thorough node specification evaluation, we fixed 260 alerts/sec/zone threshold to avoid communication & processing latency. the processing ratio of our scheme 9000 alerts per second is considerably greater than specified device set and therefore our technique is proficient of handling with extendable iiot networks with higher alert frequency rate. https://doi.org/10.15837/ijccc.2021.2.4029 21 figure 9: (chart.4.) attack nodes ratio vs. attack scalability chart 4 illustrates the evidence (gathered from 22 september 2018 to 15 december 2018) on a device’s preceding performance is one of the utmost significant characteristics of the trust modeling. considering adopted communication paradigm, there are several hypothesis causing in the payload loss and the communications between wireless devices may become unsteady. in the course of the calculation of the endorsement confidence, the endorsements from malevolent neighbor devices are first quarantined by selecting the trust confider. nevertheless, not all the endorsements from the admonishers are trustworthy. 4 conclusion and future research directions in this paper, we have recognized iiot threat representations and context aware cognitive iot security procedures, comprising verification, malware discovery, and protected depositing, which are shown to be favorable defense for the iiot. numerous experiments has been conducted to emulate the security methods in applied iot structures. while this research projected provisions for extenuating security hazards, the defense reflects the edge node side. system makers and application systems analyst ought to work in the direction of providing nodes with extra security abilities and applications with protected and easy-to-design gui. the projected mitigation methodology diminishes the security threats and probable risks. effective self-governing and autonomic defense demonstrated positive control over general system usability in balanced manner. the emphasis of this study has uniquely been on the identification of security risks, influences or threats, and appropriate countermeasures for iiot focused systems. our impending research will be to advance a context for comprehending and assessing security risks within the iiot https://doi.org/10.15837/ijccc.2021.2.4029 22 defense system. funding the authors would like to acknowledge the support of the deanship of scientific research at prince sattam bin abdulaziz 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(2018) resource management in pervasive internet of things: a survey, journal of king saud university computer and information sciences, pp. 1-15, https://doi.org/10.1016/j.jksuci.2018.08.014. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: tariq u., ahanger t. a., nusir m., ibrahim a. (2021). a pervasive computational intelligence based cognitive security co-design framework for hype-connected embedded industrial iot, international journal of computers communications & control, 16(2), 4029, 2021. https://doi.org/10.15837/ijccc.2021.2.4029 introduction iot: key features safety, security, privacy threats and access devices authenticating communication with artificial intelligence certification authority using secret redistribution implications for design theory of secure iot system iot derived co-design tools selection co-design tools mapping process based on iot operational choices to design tool mapping based on iot proposed scheme architecture, applications and analytic performance metrics and evaluation conclusion and future research directions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 15, issue: 6, month: december, year: 2020 article number: 3975, https://doi.org/10.15837/ijccc.2020.6.3975 ccc publications addressing cooperation between mobile operators in telecommunication networks via optimization: a lexicographic approach and case study in colombia f. j. macallister, l. maya, j. a. huertas, c. lozano-garzón, y. donoso francisco j. macallister, laura maya industrial engineering department systems and computing engineering department universidad de los andes, bogotá, colombia fj.mac@uniandes.edu.co,l.maya10@uniandes.edu.co jorge a. huertas center for optimization and applied probability (copa) industrial engineering department universidad de los andes, bogotá, colombia huertas.ja@uniandes.edu.co carlos lozano-garzón, yezid donoso* systems and computing engineering department universidad de los andes, bogotá, colombia calozanog@uniandes.edu.co *corresponding author: ydonoso@uniandes.edu.co abstract cooperation between telecommunications (telco) operators has been limited both by regulation and competition in previous years. however, cooperation could not only allow an overall growth in quality of service (qos) but also may benefit companies with under exploited nodes in their network infrastructure. this way, both fully deployed infrastructure by single telco companies, as well as smaller companies with increasing service demand but low infrastructure deployment could potentially benefit from cooperation agreements. this article proposes a lexicographic mixed-integer linear optimization model for telco cooperation composed by two phases: phase 1 maximizes the number of services connected to the current infrastructure assuming cooperation between operators while phase 2 minimizes the costs of connecting such services. we built a simple base scenario that allowed us to validate the intuition behind our model. furthermore, to demonstrate the applicability of our lexicographic optimization model for cooperation between mobile operators, we present a real-world case study in a rural area in colombia that allowed us to find the marginal costs of additional national roaming connections, as well as marginal profits under the cooperation schema. our results could help mobile operators to benefit from cooperation and, since the model adapts to the local necessities of the company, cooperation could be restricted to any desired level. keywords: cooperation, telecommunications, heterogeneous network, mixed-integer linear programming, lexicographic. https://doi.org/10.15837/ijccc.2020.6.3975 2 1 introduction as technology in mobile telecommunications continues to improve, demand grows to a point where connected devices outnumber the population [12]. iot and 5g technology bring new challenges to existing and future infrastructure as the number of connected devices will grow exponentially. cooperation between existing telecommunication operators (telco operators) will not only give them a competitive advantage against new entrants, but it will position them strategically for this technology revolution [3]. by cooperating, operators could increase their service level, make a profit, and provide the best quality of service (qos) leveraging on other telcos when the deployment of new infrastructure gets expensive. areas of low-density populations will start to gain more and more demand with iot and autonomous vehicles, but even today as services demand more resources with more latency requirements this topic becomes relevant. an example of cooperation occurs in colombia (south america), where the regulation requires mobile operators to cooperate if requested by another operator [12], both to improve qos provided and to allow easy customer growth while deploying new infrastructure. however, initially, this regulation included only the country’s large mobile operators who had the deployed infrastructure necessary to provide service in most of the territory: movistar, claro, and tigo. this regulation also imposes constraints on the cooperation costs and guides virtual mobile operators (vmos) to improve their competitiveness in the market by making different agreements with telecommunications operators (telcos) with deployed infrastructure [3]. it also enables new incoming operators to offer services, not only in large cities (where they do start to deploy their infrastructure) but in rural areas where infrastructure for new competitors could take a lot of time to settle: e.g., avantel in the colombian case. this regulation takes a role in decreasing possible monopolies and increasing the offer of mobile services to the entire population. moreover, as telcos coverage varies a lot because of the geography, without a cooperated-planned deployment of infrastructure, the only other way to minimize cost providing high qos is establishing agreements for cooperating with the current infrastructure. with cooperating parameters, personalization, and granular decision making, all the telcos that could participate in the cooperation could benefit in one way or another. despite all the benefits cooperation could bring, tools that enable telcos to determine specific guidelines to cooperate are still an open discussion topic. in this paper, we propose a 2-phase lexicographic optimization model that enables telcos to connect the maximum number of devices requesting services at a minimum cost under a cooperation scheme. the model acts as an intermediate that orchestrates operators so that the connections can be accomplished with all fairness. to demonstrate the applicability of our model, we use two test instances. the first instance, a simple scenario, serves to demonstrate the correctness of the model. the second instance, a real-world scenario from the colombian case, serves to measure the scalability of the model. our results indicate that we could effectively implement the model with different cooperation strategies that would benefit mobile operators and their users. with this approach, the viability of cooperation increases exponentially as ultra-personalization comes in the picture. this paper is organized as follows. section 2 presents a literature review on whatever comes here. section 3 presents the formulation of our lexicographic model. section 4 presents the computational experiments in the two test instances. finally, section 5 concludes the paper and outlines current and future work. 2 literature review heterogeneous networks have been a trending topic recently. its close relationship with innovation and its association with a contribution to country development has generated great interest among various research areas, inspiring different investigations with a wide arrange of scopes. many of these investigations focus on topics such as optimization, load balancing, quality of services, among others [7, 10, 13, 15, 23]. optimizing the resources with which a service is offered is a highly relevant issue, given the high demand volume today. for example, in colombia, where more than 65 million mobile lines [12] translates into 1.97 mobile lines per user [5], demand for mobile data has been increasing exponentially https://doi.org/10.15837/ijccc.2020.6.3975 3 over the past decade, and users demand lower rates per gb consumed every year. thence, there are many opportunities within the optimization approach, depending on the scope used in the research. for example, feng [9] used an algorithm to optimize the number of sub-channels used in the spectrum to increase the system capacity. demirtas [6] tried to optimize the efficiency in the energy used by the micro and macro cells of a heterogeneous cellular network. now, there is plenty of information about telco optimization and all the different scopes that these approaches can have, however, an even more striking issue is a cooperation between mobile operators as a strategy to improve their operating conditions. oikonomakou [17] shows how cooperation between mobile operators helps improve energy consumption in times of low demand, improving the costs of the operators involved. also, poyhonen [16] demonstrates that in a network where operators cooperate, the total use of the network is much higher and therefore represents benefits both for operators and end-users since service quantity and quality is increased. thus, in this paper we propose an optimization model that considers the resources being demanded by the users and determines how telcos should provide them under a cooperation scheme. on the other hand, most of the literature about heterogeneous networks focus on urban areas, where there is a high concentration of users and high demand for services. however, over the past decade there has been a big effort being made to try and reach the remote areas in different countries around the world. nevertheless, to the best of our knowledge, research about heterogeneous networks in rural areas is scarce. one of the few works in this domain is presented by ting [22], that seeks to provide the greatest possible coverage to satisfy the requirements of users while minimizing the number of resources required without sacrificing the qos. in this paper, we propose a lexicographic optimization model telcos cooperation in rural areas to improve the qos on areas with low antenna density and, typically, low user concentration, but with potential exponentially user growth. we also analyze this model from an economic perspective to study its viability and possible profitability. finally, we present a case study in one of colombia’s main roads, where users flow multiply significantly at specific times of the year, and then, reduce drastically. 3 mathematical model our optimization model is a lexicographic mixed-integer linear programming (milp) model which proposes a cooperation alternative between current mobile operators in the telecommunications sector. our goal is to provide users the best possible service, considering the cost of such cooperation. figure 1 presents the general outline of our lexicographic model, which comprises two phases. in the first phase, we maximize the quality of service provided by maximizing the number of connected services from those demanded. to do so, we allow cooperation between the operators. thus, any device can be connected to any antenna, even when they do not have an established contractual relationship with the operator of that antenna. the second phase minimizes the cost of the connections established in the first phase under the cooperation scheme. 3.1 phase 1 the first phase of our lexicographic model finds the maximum number of services that can be satisfied with the available infrastructure of all mobile operators. let o be the set of operators that will cooperate. let d be the set that contains all users’ devices. let s be the set of services that a device can demand. these services have different consumption requirements, as well as different latency requirements to achieve what will be defined from now on as a “good qos”. finally, let a be the set of antennas to which a user can connect to. alongside the sets presented above, it is necessary to define the following binary parameters to understand and establish all the relationships in the model. first, binary parameter αao takes the value of one if antenna a ∈ a belongs to operator o ∈ o; it takes the value of zero otherwise. from this parameter, we create a subset ao = ⋃ a∈a|αao=1{a}, which groups the devices of users with a contractual relationship to operator o ∈ o. binary parameter δdo takes the value of one if the user who owns device d ∈d has a contractual relationship with operator o ∈o. https://doi.org/10.15837/ijccc.2020.6.3975 4 figure 1: general outline of the cooperation model between telecommunications operators finally, it is important to understand that at any given time, a device may be using multiple services simultaneously. binary parameter βds takes the value of one if device d ∈ d is using service s ∈s; it takes the value of zero otherwise. from this parameter, we create a subset sd = ⋃ s∈s|βds=1{s} which groups the services being used in device d ∈d. the antennas have a peak of upload and download speed according to their infrastructure, this means that this speed must be divided among the users connected to this base, which can affect their qos [14]. depending on various other factors such as the operator owner or the geographical position, every single antenna will have different average bandwidth available. thus, we have defined a total bandwidth capacity ca on every antenna a ∈a. likewise, based on the studies presented by huidobro [14] that states that antennas cannot support infinite connections and have a maximum of connections over a specific range, we established a limit la of possible connections to an antenna a ∈a. the connection range varies among antennas as well depending on the radios installed. thus, for a device to connect to an antenna a ∈a, it must be within a maximum distance za. users above this distance will not receive enough signal strength to satisfy their demand for services with a good qos. to make a fair approach to the reality of cooperation, we also defined a percentage pa of the bandwidth of antenna a ∈ao destined to be shared with users with no contractual relationship with operator o ∈ o. thus, we allowed a different percentage of cooperation on every single antenna to limit the amount of bandwidth to share. this allows operators to avoid the antennas’ saturation in places where there is a high demand of services, which will result in prioritizing the users of the company that owns the antenna. to determine if a connection can be established between device d ∈ d and antenna a ∈ a to satisfy any of the services sd, we use theoretical formulas of received signal strength (rss) that relate to the distance nda between the device and the antenna [8]. in addition to the rss, to determine if a connection between a device and an antenna can be established to satisfy a specific service s ∈ s, we also check the requirement of bandwidth rs necessary to satisfy the service with such connection. thus, to provide good qos on a service s ∈s with a connection to an antenna, the device demanding the service must be within a fraction qs of the range of the connected antenna. this allows us to ensure the latency requirements for each service. our lexicographic model considers a binary decision variable xdas, which takes the value of one if there is a connection between a device d ∈d and an antenna a ∈a to satisfy service s ∈s required, which allows identifying all the connections established in the model. we summarize the previous notation in table 1. https://doi.org/10.15837/ijccc.2020.6.3975 5 table 1: notation table for the lexicographic model lexicographic model sets o set of mobile operators that will cooperate. a set of antennas of the mobile operators. d set of users’ devices. s set of services. binary parameters to build subsets αao 1 if a ∈a belongs to operatoro ∈o, 0 otherwise. δdo 1 if user of device d ∈d has a contract with operator o ∈o, 0 otherwise. βds 1 if service s ∈s is being used in device d ∈d. subsets ao subset of antennas of operator o ∈o. do subset of devices whose user have a contract with operator o ∈o. sd subset of services being used in device d ∈d. labels f(d) operator with whom user of device d ∈d has contract. parameters of the phase 1 of the lexicographic model ua upper bound for the bandwidth capacity of antenna a ∈a. ka maximum number of connections allowed to antenna a ∈a. za maximum distance for a device to antenna a ∈a. pa maximum percentage of the bandwidth of antenna a ∈ao available to provide services of devices whose user have no contract with operator o ∈o. rs required bandwidth to satisfy service s ∈s. qs maximum fraction within an antenna’s range to provide service s ∈s. nda distance between device d ∈d and antenna a ∈a. parameters of the phase 2 of the lexicographic model w∗ optimal value of the objective function (1). cao unitary (mbps) cost of connection from the devices of every operator o ∈o to every antenna a ∈a. decision variables xdas 1 if device d ∈d connects to antenna a ∈a to satisfy service s ∈sd. https://doi.org/10.15837/ijccc.2020.6.3975 6 the mathematical formulation of the first phase of our lexicographic model is shown in equations (1)-(7). maximize w = ∑ d∈d ∑ a∈a ∑ s∈sd xdas (1) subject to, ∑ a∈a xdas ≤ 1, ∀ d ∈d,s ∈sd; (2) ∑ d∈d ∑ s∈sd xdas ≤ ka, ∀ a ∈a; (3) ∑ d∈d ∑ s∈sd rsxdas ≤ ua, ∀ a ∈a; (4) ∑ d∈d|d/∈do ∑ s∈sd rsxdas ≤ paoua, ∀ o ∈o,a ∈ao; (5) xdas ≤ qs za nad , ∀ d ∈d,a ∈a,s ∈sd; (6) xdas ∈{0, 1}, ∀ d ∈d,a ∈a,s ∈sd. (7) objective function (1) maximizes the total amount of connections to antennas to provide the different services demanded by the devices. constraints (2) prevent a service to be fulfilled or satisfied by more than one antenna. constraints (3) limit the number of connections to each antenna. constraints (4) guarantee the available bandwidth in every antenna is not exceeded by the services connected to it. constraints (5) impose an upper bound to the available bandwidth for cooperation in the antennas of every operator. to do so, the left-hand side sums the bandwidth demanded by the services in other operators’ devices and the right-hand side specify a maximum bandwidth for cooperation with other operators. constraints (6) prohibit connections to antennas of devices outside the coverage zone of the antenna (i.e., za nad ). furthermore, (6) also guarantee latency requirements to satisfy the demanded services (i.e., qs). finally, constraints (7) describe the variables’ domain. 3.2 phase 2 optimization model (1)-(7) finds the maximum number of services that can be satisfied under the cooperation scheme. we retrieve the optimal value of objective function (1) and store it in parameter w∗. phase 2 of our lexicographic model minimizes the total cost of those connections. in many countries, regulations impose an upper monetary limit for the cooperation between telcos operators; and guides virtual mobile operators (vmos) to improve their competitiveness in the market by making different agreements with established telcos with deployed infrastructure. [3] we acknowledge that establishing the same commercial relationship with all other operators is not a feasible solution for many different reasons. for such reason, we determined a fixed unitary (mbps) cost of connection cao from the devices of every operator o ∈ o to every antenna a ∈ a. these costs are low between devices and antennas of the same operator. nevertheless, it can vary between antennas because of their geographic location and other external factors. this allows us to further increase the conditions of cooperation and possible new entrants. the mathematical formulation of the seconds phase of our lexicographic approach is presented in equations (8) and (9). minimize ∑ d∈d ∑ a∈a ∑ s∈sd ca,f(d)rsxdas (8) subject to, (2) − (7)∑ d∈d ∑ a∈a ∑ s∈sd xdas ≥ w∗. (9) https://doi.org/10.15837/ijccc.2020.6.3975 7 objective function (8) minimizes the total cost of connections. recall that label f(d) = o ∈ o retrieves the operator with whom the user of device d ∈ d has contract. thus, ca,f(d) quantifies the cost of transmitting 1 mbps from antenna a ∈ a to device d ∈ d based on these contractual relationships. constraints (2)-(7) guarantee the same operational conditions as phase 1. finally, constraints (9) ensure the same number of satisfied services determined in phase 1 of our lexicographic model. by ensuring all the above, it is possible to find the best possible service under a cooperative scenario, guaranteeing minimum-cost operation for telcos operators. 4 computational experiments to demonstrate the applicability of our lexicographic model to cooperate between telcos operators, we recreated two scenarios. the first one, the base scenario, is a small test instance that demonstrates the advantages of our model in terms of connected services and the cost of those connections under the cooperation scheme. the second scenario is a real-world rural scenario along a highway in colombia; we selected this scenario because it is a low-density area, where demand varies a lot depending on weather, traffic, date of the year, among others. also, it is the best scenario where the deployment of new infrastructure is significantly more expensive than in cities or dense areas. 4.1 base scenario description for the base scenario, we defined a study area of ten square kilometers of an open field. in this scenario, we established a highway defined by the line y = x, that traverses all the study area. thus, the highway in the base scenario is 14.1421 km long. we decided to have three mobile operators (i.e., i = 1, 2, 3), each one with three antennas each (i.e., j = 1, 2, 3), thus, we considered nine antennas in our base scenario. to locate every antenna k = 1, . . . , 9 along the highway in the base scenario, we randomly generated its horizontal and vertical position as follows: the horizontal position xk of antenna k = 1, . . . , 9 follows a continuous uniform distribution between zero to ten (i.e., xk ∼ u(0, 10)); the vertical position yk of antenna k = 1, . . . , 9 follows a triangular distribution based on its horizontal position and maximum 300 meters away from the road (i.e., yk ∼ triangular(xk − 0.3,xk,xk + 0.3)). we assigned the following parameters to each antenna: its identifier (from 1-9), its maximum number of connections, its bandwidth rate, its cooperation percentage, its theoretical range, and the maximum percentage of its bandwidth to share with other mobile operators. to assign the bandwidth available, we used an instance of a variable following a discrete uniform distribution between 800 and 1000 mb/s. to assign the total number of possible simultaneous connections we used an instance of a random variable following a discrete uniform distribution between 20 and 50 connections. later, we assigned the range of an antenna as an instance of a random variable following a discrete uniform distribution between one and three km; this was done because the equipped radios on the antennas are usually found in these ranges (i.e., 1000, 2000, or 3000), even though there are longer-range antennas, these ranges are the most used on highways based on statistics taken from [18]. the base configuration parameters for each antenna are presented in table 2. to establish the parameters to accomplish the cooperation, first, we defined the percentage of available bandwidth in each antenna by using an instance of a random variable following a continuous uniform distribution between 15% and 25%; and later we defined the costs as generic monetary units. we set up two connection costs to antennas: the first cost is related to the connections of users from the same company that owns the antenna, and the other one is related to the connection of users from another telcos. for the first case, the cost determined was one generic monetary unit; and for the second, we assigned the cost as a uniform random variable with a distribution between 5 and 25 monetary units per mb consumed. the value of these parameters for the base case can be seen in table 3. we defined three types of possible services for this scenario: service 0 is a high bandwidthdemanding service with low latency requirements, service 1 is a medium bandwidth-demanding service with a medium latency requirement, and finally, service 2 is a low bandwidth-demanding without strict https://doi.org/10.15837/ijccc.2020.6.3975 8 table 2: antennas configuration in the base scenario antenna x position y position operator bandwidth connection limit range 1 5.1 4.7225 0 864 23 2 2 0.2 0.5434 1 986 29 3 3 3.9 3.8243 2 896 36 1 4 2.4 2.6350 0 829 46 1 5 1.1 1.3085 1 819 41 1 6 6.5 6.3936 2 810 36 3 7 9.1 9.0472 0 865 40 3 8 4.2 4.2472 1 950 48 2 9 5.5 5.6837 2 853 42 2 table 3: antenna’s cooperation percentage and costs of connection in the base scenario antenna cooperation % cost operator 1 cost operator 2 cost operator 3 1 0.24 1.0000 6.9470 7.9863 2 0.19 7.0564 1.0000 10.283 3 0.24 6.9470 11.705 1.0000 4 0.18 1.0000 8.7521 6.4000 5 0.15 11.651 1.0000 8.9161 6 0.20 10.994 6.6188 1.0000 7 0.15 1.0000 6.9470 6.5094 8 0.18 7.2205 1.0000 10.228 9 0.22 8.4786 7.9863 1.0000 latency requirements. the value defined for the capacity and range requirements, of each service, can be seen in table 4. table 4: bandwidth requirements set for each service created in the base scenario service requirement mb/s percentage of range 0 0.16 0.9 1 1.50 0.4 2 2.50 0.1 finally, we generated 50 users distributed along the road. for this purpose, we used a random variable following a continuous uniform distribution between 0 and 10, to reduce the possibility of bias in to select the users’ location near of the existing antennas (i.e. k u(0,10)). also, to each user, we assigned a unique identifier and randomly defined both the mobile operator and the services used by this device. an example of the basic user’s configuration generated can be seen in table 5. 4.2 base scenario results table 6 presents the results of our case study considering cooperation and no cooperation between the three operators in our base scenario. among the three operators’ users, there were 79 services being demanded. without cooperation, only 22 of those connections were satisfied, but thanks to our lexicographic model for cooperation, this number raises to 52 under a cooperation scenario, achieving an increase on the overall service level of 136.33% (i.e., (65.82%-27.85%)/(27.85%)). however, there is a tradeoff in the cost of performing such operations. without cooperation, the overall cost of the connections is $10.22. nevertheless, when cooperating, those 79 services can be connected at a https://doi.org/10.15837/ijccc.2020.6.3975 9 table 5: users’ configuration in the base scenario user x position y position service 1 service 2 service 3 operator 1 operator 2 operator 3 0 1.056 1.056 0 1 1 1 0 0 1 8.901 8.901 1 1 0 0 0 1 2 1.41 1.41 1 1 1 1 0 0 3 8.667 8.667 1 0 0 1 0 0 4 3.225 3.225 0 1 1 0 1 0 minimum cost of $311.23, representing a cost of a cooperative policy 3,222.89% higher (i.e., (311.2310.22)/10.22). table 6: 2-phase results on the base scenario with and without cooperation without cooperation with cooperation phase phase 1 phase 2 phase 1 phase 2 assigned connections 22 22 52 52 possible connections 79 79 79 79 lost connections 57 57 27 27 service level 27.85 27.85 65.82 65.82 total cost 10.22 10.22 339.6 311.23 although it is clear that the overall cost grew, since the loss of one operator means an income increase by another operator, we conducted an analysis on the income and the net profit of performing such connections in the model. table 7 presents the roaming income, cost, and profit of every operator in both phases of our lexicographic model for cooperation. as noted, the second phase of our model is effectively reducing the roaming costs for all operators; likewise, we can observe how income is also modified in almost every case. table 7: income, cost, and profit of every operator in every phase of the lexicographic model in the base scenario phase phase 1 phase 2 operator 1 2 3 1 2 3 roaming income 107.051 155.066 69.726 115.152 124.824 61.038 roaming cost 141.087 52.873 137.883 116.473 49.196 135.345 roaming profit -34.037 102.194 -68.157 -1.321 75.628 -74.307 table 8 presents the total number of connections required by the users with a contractual relationship with every operator, as well as the number of connections accomplished with and without cooperation. for the first operator, the number of connections increased from 6 to 16 services, which represents an increase in the service level of 166.67%; for the second operator, it changed from 7 to 12 connections, increasing service level in 71.43%; and finally, for the third operator, it changed from 9 to 24, increasing its service level in 166,67%. although the main objective of our optimization model aims to reduce cost while cooperating, it reasonably enables every operator’s roaming that could yield into income. thus, it possible to establish every operator’s net profit as well. moreover, from the two previous analyses we determined the marginal cost of every additional connection made for every operator. this marginal cost can also be seen as an estimate on every telcos’ cost due to a lost connection. table 9 presents the cost and profit of an additional connection for every operator. as a result, the marginal cost would not be https://doi.org/10.15837/ijccc.2020.6.3975 10 table 8: number of connections supplied with and without cooperation in the base scenario operator total connections connections connections accomplished accomplished required without cooperating cooperating 1 27 6 16 2 16 7 12 3 36 9 24 entirely fair. although operator two has the least growth in the level of service offered (i.e., 71.43%), it has the greatest monetary benefit thanks to its cooperation with other operators. table 9: marginal cost and profit for an additional connection using roaming operator cost profit additional additional connection connection 1 11.647 -0.132 2 9.839 15.126 3 9.023 -4.954 finally, by taking the profit for every additional connection as a real connection cost after adjusting for the possible income, we could estimate the percentual growth in cost from a model without cooperation to a model with cooperation. when assuming a local operation of 1 for all operators (as the designed model does), operator 1 undergoes an 86.8% decrease in its operating costs for roaming users (i.e., (1-0.132)/1); operator 2 experiences an absolute decrease in connection cost because it receives income for every roaming user; and operator 3 experience an increase of 395.4% in the cost of every roaming user (i.e., (1-4.954)/1). from this we can observe that even though all operators benefit from increasing its service level, operators 1 and 2 also benefit from a cost reduction or even profit for the additional connections. 4.3 case study description the base scenario presented in the previous subsections served to check the correctness of our lexicographic model by proving it actually produces a cooperation strategy for telcos and minimizes the overall cost of such cooperation. nevertheless, it was a small mock scenario. to demonstrate the applicability of our approach, we implemented our model in a real-world case study on the rural section of a major artery in colombia’s road network. from this analysis we aim to observe both current and possible behaviors when implementing our model to find all the advantages and disadvantages of performing this cooperation strategy. we focused our case study on a rural area around a section of an artery in colombia’s road network between bogotá, colombia’s capital city, and girardot, a small town nearby commonly used as vacations center: bogotá-girardot artery. this artery’s section has low antenna density but high demand of services because it has a large volume of traffic with significant peaks at certain dates of the year. this could be considered an extremeor a worst-case scenario for mobile operators. in colombia, regulation requires mobile operators to cooperate if requested by another operator, both to improve qos and to allow easy customer growth while deploying new infrastructure as starting companies in the country. however, initially, this regulation included only the country’s large mobile operators, who had the deployed infrastructure necessary to provide coverage in most of the territory: claro, movistar, and tigo (telcos with deployed infrastructure). this regulation sets the upper monetary limits and guides vmos to improve their competitiveness in the market by making different agreements with established telcos with deployed infrastructure https://doi.org/10.15837/ijccc.2020.6.3975 11 [3]. table 10 presents theses monetary limits in colombia for a specific date: may 4, 2017. this regulation also helps new incoming operators, like avantel, to be able to offer services not only in large cities where they do start to deploy their infrastructure but in rural areas where infrastructure form new entrants could take a lot of time to settle. this regulation takes a role in decreasing possible monopolies and increasing the offer of mobile services to the entire population. table 10: regulatory prices in colombia for cooperation between mobile operators date may 4, 2017 cost for new competitors (mbyte) 6.40 cost for established competitors (mbyte) 11.87 we used openstreetmap’s api [18] to extract all the nodes in bogotá-girardot artery and reconstruct the highway. this information also served us to locate all the users we will consider in our case study along the road. to locate the antennas owned by the telcos with deployed infrastructure (i.e., movistar, claro, and tigo), we used the opencellid (ocid) api [19] to find all the antennas that were close to the area in our case study. furthermore, from ocid’s api we determined the range of the antenna, the technology used by the antenna (gsm, grps, umts, lte), and the operator to which it belongs. figure 2 presents the section considered of bogotá-girardot artery and the location of every operator’s antennas. some parameters of our model were not provided by ocid’s api; to determine the antennas’ capacity, we assumed on average 40 to 50 simultaneous connections. to determine the antennas’ we assumed values between 800 and 1000 mbit/s. however further sensibility analysis can be made. figure 2: bogotá-girardot artery considered in the case study and operators’ antennas the services demanded by the users in our case study can be a voip call [4, 20], a song in spotify [1, 21], and a short video on youtube [2, 11]. we determined the download bandwidth required for each service and assumed the total amount of data they will consume to fully receive the entire service: complete a phone call for voip, listen to a full song for spotify, and fully watch a video on youtube. with this information we estimated the cost of each service while roaming based on the colombian regulation, mentioned above, as a value between the minimum (i.e., $ 6.4) and the maximum cost https://doi.org/10.15837/ijccc.2020.6.3975 12 (i.e., $ 11.87) [3]. finally, we randomly allocated these services to each user, as well as the mobile operator a user had a contractual relationship with the same way we did in the base scenario. 4.4 case study results table 11 presents the results of our case study considering cooperation and no cooperation between movistar, claro, and tigo. among the three operators’ users, there were 38,441 services being demanded. without cooperation, only 31,155 of those connections were satisfied, but thanks to our lexicographic model for cooperation, this number raises to 34,940 under a cooperation scenario, achieving an increase on the overall service level of 12.14% (i.e., (34, 940 − 31, 155)/31, 155). from table 11 it is also possible to evidence there is a tradeoff between the service level and the overall cost. without cooperation, the overall cost of the connections is $43,673.82. nevertheless, when cooperating, those 34,940 services can be connected at a minimum cost of $67,149.82, representing a cost of a cooperative policy 53.75% higher. table 11: 2-phase results on the case study with and without cooperation without cooperation with cooperation phase phase 1 phase 2 phase 1 phase 2 possible connections 38441 assigned connections 31155 34940 lost connections 7286 3501 service level 81.05 90.89 total cost $46297.42 $43673.82 $316303.31 $ 67149.82 table 12 presents the roaming income, cost and profit for every operator in every phase of our lexicographic approach for cooperation. as noted, the model is effectively reducing costs from phase 1 to phase 2 for every operator, which can be interpreted in the creation of possible roaming revenues for all the operators. although movistar and tigo do not have a positive roaming profit, they significantly increased from phase 1 to phase 2 of the model as well. likewise, claro is the one operator that receives the most income for the provision of the service. considering that this operator has the most antennas in the area, it is also the one with the greatest capacity to provide roaming service to the other operators. table 12: roaming income, cost, and profit for every operator in the case study phase 1 phase 2 operator movistar claro tigo movistar claro tigo roaming income 71051.9 144830.83 83815.98 3871.47 11907.09 7224.43 roaming cost 114024.19 76404.64 109269.88 10699.23 4685.38 7618.38 roaming profit -42972.29 68426.18 -25453.89 -6827.76 7221.71 -393.95 table 13 presents the total number of connections required by the users with a contractual relationship with every operator, as well as the number of connections accomplished with and without cooperation in our case study. for the movistar, the number of connections increased from 9,505 to 11,704 services, which represents an increase in the service level of 23.135%,; for claro, it changed from 11,979 to 12,218 connections, increasing service level in 1.995%; and finally, for tigo, it changed from 9,671 to 11,018, increasing its service level in 13.928%. as we did in the base scenario, we also analyzed every operator’s marginal connection’s cost and profit in our case study. table 14 presents these values. it clearly shows that claro has the highest cost of establishing a new roaming connection, while movistar and tigo have similar marginal costs. however, even with a high marginal cost, claro obtains great profitability on cooperation agreements, https://doi.org/10.15837/ijccc.2020.6.3975 13 table 13: number of connections supplied on models with and without cooperation operator total connections connections connections accomplished accomplished required without cooperating cooperating movistar 12739 9505 11704 claro 13025 11979 12218 tigo 12677 9671 11018 obtaining average profitability of $ 30.22 for each additional connection established to non-owned infrastructure. table 14: marginal cost and profit for an additional connection using roaming operator cost profit additional additional connection connection movistar 4.87 -3.1 claro 19.6 30.22 tigo 5.66 -0.29 as noted, all three operators obtain certain benefits by cooperating with our lexicographic approach: some, like movistar and tigo, do not have utility for the provision of their antennas, however, they significantly increase their service level; other, i.e., claro, does not have a great increase in its level of service, however, allowing cooperation represents a new income that translates into a positive utility. the previous analysis added to the fact that a better service is offered to the colombian population, since coverage can be offered in more areas of the country without abusing the prices for the service, is a significant improvement of the current operation drawn from our lexicographic approach. finally, when considering the profit for an additional connection over roaming as the real connection cost of an additional user, movistar increases its original connection cost by 210% (i.e., (1 − 3.1)/1); tigo decreases its cost by 71% (i.e., (1 − 0.29)/1); and claro has a profit for those additional connections. all this considering a local antenna connection cost of $1. 5 conclusions and future work in this work, we present a lexicographic optimization model that allows the study of cooperation strategies among telecommunications operators composed of two phases: the first phase successfully maximizes the number of services attended and the second phase minimizes the overall cost of such connections. this model was conceived from a macroscopic perspective and does not consider individual interests of telcos but aims to benefit both consumers and telcos operation viewed as a whole. nevertheless, many of the optimization model parameters can be modified to analyze granular cases where decision making can be critical to a company. we demonstrated the applicability of our lexicographic model by applying it on a base senario and a real-world case study in in colombia. from those analyses, the model successfully allowed to understand and review how possible increases in qos would impact not only the cost but the profit of performing said cooperation. we successfully found a method where telcos can outperform vmo with seamless roaming connectivity for users without having a significant impact on the cost to achieve this goal. the model’s design allows for new competitors to contribute as much infrastructure capabilities as they would like while allowing all other operators to decide how much to charge certain new competitors. this makes this model available for potential growth without changes to its structure and makes its implementation significantly easy in any region where there exists an interest in cooperation. https://doi.org/10.15837/ijccc.2020.6.3975 14 finally, the model results could also help to redistribute future investment on new infrastructure, which could allow better attention to telcos, clients, lower costs on roaming for clients using other telcos infrastructure, or even to create new sources of income to the company. with this in mind, new entrants to the market could even have dedicated business lines to infrastructure only in countries where this is not currently happening. after considering all the successful results previously presented, we have identified two possible lines of research to extend this work. the first line could be to implement a dynamic approach to study its performance over time. this could potentially provide enough information to perform the least amount of connections while maintaining a certain level of qos while reducing operation cost for telcos. the line of research could be to consider the game theory behind the decisions of the telcos. author contributions francisco j. macallister and laura maya formulated the model and programmed and ran the computational experiments. jorge a. huertas and carlos lozano-garzón advised the project. yezid donoso was the investigation project director. the latter three authors contributed guiding and reviewing the investigation. all authors contributed to the paper. conflict of interest the authors declare no conflict of interests. references [1] allain, r. 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(2014). a dynamic load-balancing scheme for heterogeneous wireless networks, 2014 ieee wireless communications and networking conference (wcnc), doi: 10.1109/wcnc.2014.6952997, istanbul, 3070-3075, 2014. https://support.google.com/youtube/answer/78358?hl=en https://support.google.com/youtube/answer/78358?hl=en https://www.lafm.com.co/tecnologia/colombia-tiene-mas-lineas-de-celular-que-habitantes#:~:text=colombia%20tiene%20m%c3%a1s%20l%c3%adneas%20de%20celular%20que%20habitantes&text=el%20ministerio%20de%20las%20tecnolog%c3%adas,13%2c9%20millones%20son%20pospago https://www.lafm.com.co/tecnologia/colombia-tiene-mas-lineas-de-celular-que-habitantes#:~:text=colombia%20tiene%20m%c3%a1s%20l%c3%adneas%20de%20celular%20que%20habitantes&text=el%20ministerio%20de%20las%20tecnolog%c3%adas,13%2c9%20millones%20son%20pospago https://www.lafm.com.co/tecnologia/colombia-tiene-mas-lineas-de-celular-que-habitantes#:~:text=colombia%20tiene%20m%c3%a1s%20l%c3%adneas%20de%20celular%20que%20habitantes&text=el%20ministerio%20de%20las%20tecnolog%c3%adas,13%2c9%20millones%20son%20pospago https://www.lafm.com.co/tecnologia/colombia-tiene-mas-lineas-de-celular-que-habitantes#:~:text=colombia%20tiene%20m%c3%a1s%20l%c3%adneas%20de%20celular%20que%20habitantes&text=el%20ministerio%20de%20las%20tecnolog%c3%adas,13%2c9%20millones%20son%20pospago https://www.openstreetmap.org https://opencellid.org/downloads.php https://support.skype.com/es/faq/fa1417/que-cantidad-de-ancho-de-banda-necesita-skype https://support.skype.com/es/faq/fa1417/que-cantidad-de-ancho-de-banda-necesita-skype https://support.spotify.com/us/using_spotify/system_settings/high-quality-streaming/ https://support.spotify.com/us/using_spotify/system_settings/high-quality-streaming/ https://doi.org/10.15837/ijccc.2020.6.3975 16 copyright c©2020 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: macallister, f.j.; maya, l.; huertas, j.a.; lozano-garzón, c.; donoso, y. (2020). addressing cooperation between mobile operators in telecommunication networks via optimization: a lexicographic approach and case study in colombia, international journal of computers communications & control, 15(6), 3975, 2020. https://doi.org/10.15837/ijccc.2020.6.3975 introduction literature review mathematical model phase 1 phase 2 computational experiments base scenario description base scenario results case study description case study results conclusions and future work international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 1, month: february, year: 2022 article number: 4650, https://doi.org/10.15837/ijccc.2022.1.4650 ccc publications information bottleneck in deep learning a semiotic approach b.muşat, r. andonie bogdan muşat department of electrical engineering and computer science transilvania university of braşov str. politehnicii nr. 1, braşov, romania bogdan_musat_adrian@yahoo.com răzvan andonie department of computer science central washington university, usa 400 east university way, ellensburg, wa 98926, usa and transilvania university of braşov str. politehnicii nr. 1, braşov, romania andonie@cwu.edu abstract the information bottleneck principle was recently proposed as a theory meant to explain some of the training dynamics of deep neural architectures. via information plane analysis, patterns start to emerge in this framework, where two phases can be distinguished: fitting and compression. we take a step further and study the behaviour of the spatial entropy characterizing the layers of convolutional neural networks (cnns), in relation to the information bottleneck theory. we observe pattern formations which resemble the information bottleneck fitting and compression phases. from the perspective of semiotics, also known as the study of signs and sign-using behavior, the saliency maps of cnn’s layers exhibit aggregations: signs are aggregated into supersigns and this process is called semiotic superization. superization can be characterized by a decrease of entropy and interpreted as information concentration. we discuss the information bottleneck principle from the perspective of semiotic superization and discover very interesting analogies related to the informational adaptation of the model. in a practical application, we introduce a modification of the cnn training process: we progressively freeze the layers with small entropy variation of their saliency map representation. such layers can be stopped earlier from training without a significant impact on the performance (the accuracy) of the network, connecting the entropy evolution through time with the training dynamics of a network. keywords: deep learning, information bottleneck, semiotics https://doi.org/10.15837/ijccc.2022.1.4650 2 1 introduction modern deep neural networks (dnns) are computational machines capable of representing very complex functions which can solve a suite of extremely difficult tasks ranging from computer vision [37], [36] to natural language processing [30], [35] and robotics [38], [39]. although possessing a high expressive power, model interpretability has always been a limiting factor for use cases requiring explanations of the features involved in modelling. the field of interpretability/explainability in deep learning has witnessed an explosion of published papers in recent years. even if there is no fundamental theory that can elucidate all underlying mechanisms present in those networks, multiple works tried to deal with this issue by coming up with partial solutions, either by visual explanations [22], [18] or theoretical insights [14], [9], [19]. therefore, we can say that the black box interpretation of deep learning is not true anymore, and what we need are better techniques to interpret these models. in the following, we will refer to three methods used for the interpretation of deep learning. saliency maps. saliency maps are arguably the oldest and most frequently used explanation method for interpreting the predictions of dnns. these maps are a class of computer vision techniques used to investigate hidden layers of neural networks by generating heat maps to depict the most important or salient areas. the popularity of saliency maps comes from the fact that heatmaps can be easily visualized and visualization plays an essential role in humans’ cognitive process [43]. practically, a saliency map can be built using gradients of the output over the input, and this highlights the areas of the images which were relevant for the specific task (e.g., classification). the concept goes back to the work of kadir and brady [21]. some of the early studies on saliency maps applied to dnns was the work of zeiler and fergus [22], who used an auxiliary network (called deconvnet) to revert the activations of an intermediate layer from a dnn back to the input pixels and visualize the patches that excite most the top 9 neurons with largest activation values. since then, other methods were proposed to better understand the inner workings of neural networks [18, 23, 24, 25]. in our study, we use the popular grad-cam method [18] to generate saliency maps of neural layers in cnn architectures. semiotic superization. we proposed recently [19] an interdisciplinary method for deep learning interpretations. it is an information-theoretical approach combining concepts from semiotics. from the perspective of semiotics, also known as the study of signs and sign-using behavior, the saliency maps of cnn’s layers exhibit aggregations: signs are aggregated into supersigns and this process is called semiotic superization. superization can be characterized by a decrease of entropy and interpreted as information concentration. to measure the information concentration in cnns, we used the spatial aura matrix entropy [20] of saliency maps of neural layers. a saliency map aggregates information from the previous layer and this information is measured by the spatial entropy of the map. generally, the entropy decreases when progressing through the depth of the network, but this is not always happening. information bottleneck. another recent information-theoretical tool used for deep learning interpretations is the information bottleneck (ib) principle, introduced by tishby, pereira, and bialek [13]. in the context of dnns, the core assumption of this principle is that a good internal representation produced by a neural model should maximally compress the input data, while preserving sufficient information about the output. based on the ib theory, the authors of [9] popularized the analysis of the information plane (ip), in which estimates of the two mutual information quantities i(x; t) and i(y ; t) are the coordinate axes. two distinct phases, fitting and compression, characterize the mutual information of: a) the input x and the internal representation t; and b) the internal representation t and the output y [9]. several recent works attempted to understand dnns using the ib principle [33, 34]. the main motivation for our work is to test the ib hypothesis on a variety of new situations, considering that there are contradictory opinions and results about the ib theory (see, for instance, [8]). our thesis it that there is a significant similarity between the ib principle and semiotic superization. https://doi.org/10.15837/ijccc.2022.1.4650 3 to the extend of our knowledge, this synergetic aspect was never discussed before. we investigate the ib theory in the context of semiotic superization processes in cnn learning. in practical terms, we study the evolution of spatial entropy of cnn saliency maps to validate/invalidate the ib principle. in our experiments, we noticed a pattern similar to the fitting and compression phases appearing in the evolution of spatial entropy. we use these experimental results to train a network by freezing the redundant layers with low spatial entropy variability. this enables us to discover interesting analogies between the ib theory and semiotic superization. our contributions are twofold: we establish a link between the ib hypothesis of fitting and compression, and semiotic superization via the evolution of spatial entropy applied to saliency maps. as a practical application, we design a heuristic training strategy for layer-wise early stopping based on spatial entropy variability through time, which may be used to prevent overfitting during learning. the rest of the paper is structured as follows. section 2 reviews previous results related to applications of the ib principle in deep learning. section 3 introduces the mathematical framework used to compute the spatial entropy and mutual information in cnns. we also review here the fundamental concepts of semiotic superization. section 4 presents our thesis about the relationships between semiotic information adaptation and the ib principle. section 5 analyses experimentally the patterns present in the evolution of spatial entropy for saliency maps through time. it shows that there is a surprising connection between the ib theory of fitting-compression and the evolution of spatial entropy applied to the saliency maps of neural layers. as an application, we introduce a layer-wise early stopping criterion based on the spatial entropy and also discuss the relevance of our experiments for a semiotic interpretation of the ib principle. finally, section 6 presents conclusions and future research directions. 2 related work: the information bottleneck principle for neural networks our paper focuses on the application of the ib principle to neural networks. this section reviews published results which may be connected to our work. an overview of ib with applications in machine learning can be found, for instance, in [4]. the original formulation of the ib concept was elaborated in [13] as an information theoretical technique whose purpose is to find the best tradeoff between prediction accuracy of a variable y and compression of the input random variable x in the code t. this is realized by the minimization of the following lagrangian [13]: min pt |x i(x; t) −βi(y ; t) (1) where i(·, ·) is the mutual information of two random variables and β is a trade-off parameter. recently, the ib principle was applied to deep learning and presented by tishby and zaslavsky [14], as a theoretical concept meant to offer a possible explanation for the underlying mechanisms that govern modern deep learning architectures. the mechanism behind is similar with the one from the original formulation, optimizing for a latent representation t that represents a minimum sufficient statistic for an input x, by compressing any redundant information about it, while preserving the needed information to predict label y . this is done in the same way as in equation 1. their work also proposes theoretical bounds on the generalization capability of a neural network. it is argued that good generalization is caused by good compression of the input x in the latent variable t. the ib principle suggests that deeper layers correspond to smaller mutual information values, providing increasingly compressed statistics [4]. it is important to notice that the authors do not provide any training experiments in which they use the ib formulation. one of the first practical successes of the ib applied to a real problem was by alemi et al. [1] who used a variational approximation technique, termed as variational information bottleneck (vib), to estimate equation 1. it is known that this equation is intractable, unless x, y and t are all discrete or jointly gaussian. however, these assumptions are unrealistic in practice. by using this variational approximation, alemi et al. trained an mlp with two hidden layers on the mnist dataset [6] using https://doi.org/10.15837/ijccc.2022.1.4650 4 vib as a form of regularization. they demonstrated that vib acts as a better regularizer than other popular methods like dropout [10] or label smoothing [7]. they also tackled upon the notion of adversarial robustness and how the vib framework fits into this. adversarial robustness is considered as the resilience of a dnn in front of adversarial attacks [5, 12] small and imperceptible to human eye modifications on input pixels that can easily fool a neural network. they showed that a network trained with the vib regularizer and large enough β can resist much better than ordinary trained networks to such adversarial attacks. shwartz-ziv and tishby [9] viewed the layers of a dnn as a markov chain of successive internal representations of the input x. any latent representation t is defined through the use of an encoder p(t |x) and a decoder p(ŷ |t), where ŷ is the neural prediction. they defined the notion of information plane (ip) as the coordinate plane of the mutual information quantities ix = i(x; t) and iy = i(t ; y ) during many training epochs. for a multi-layer perceptron (mlp) with a few layers, trained on a synthetic data problem, they noticed two important phases during training: a fitting phase, where i(x; t) and i(t; y ) both increase, and a compression phase, where the mutual information i(x; t) starts decreasing, while i(t ; y ) stays mostly constant. they associated the decrease of i(x; t) with compression of input x in the latent t , which avoids overfitting, thus explaining the good generalization achieved by overparametrized dnns. saxe et al. [8] criticized the ip hypothesis of shwartz-ziv and tishby, arguing that it is not applicable to general dnns. they argued that the two phases observed in [9] are caused by the double-sided saturating nature of the tanh activation function used in their mlp, and binning of continuous activations to discrete values. the two-phase behaviour, as they empirically proved, is not present in networks which use non-saturating activation functions (like relu), employed by most modern dnns. they also tested the supposed link between compression and generalization using the network from [9], but trained it on a smaller percentage of the data, showing that even if the compression phase is noticeable in the ip, the train vs test accuracy suffers from severe overfitting. rana ali amjad et al. [2] also noticed issues with the mutual information quantity i(x; t) in the lagrangian optimization equation: for any continuous variable x ∈ rn , the term will always be infinite. this case will be true for most deterministic dnns. shwartz-ziv and tishby [9] solved this issue by quantizing the output activations, but this procedure failed to capture the true mutual information value as it encoded nontrivial information about the feature maps. another proposed solution was to use a stochastic system by injecting gaussian noise � into the intermediate representations t, and minimizing the following surrogate optimization criterion [2]: min pt |x ĩ(x; t) −βĩ(y ; t) (2) where ĩ(x; t) = i(x; t + �) and ĩ(y ; t) = i(y ; t + �). indeed, [34] uses this idea to inject intrinsic noise to the network during training. when � is relatively small (≈ 10−2), they showed that the network performs similarly to deterministic ones and that the representations learned are closely related. furthermore, the binning strategy can now accurately estimate the term ĩ(x; t), when the standard deviation σ of the injected noise is of the order of the quantization cell length. they measured the mutual information of ĩ(x; t) using a rate-optimal estimator based on monte carlo integration, and proved for the synthetic experiment from [14] that a small noisy dnn undergoes a long-term compression phase. the experiments also uncovered that compression in noisy dnns can be attributed to clustering of internal representations, with clusters comprising mostly samples from the same class. wickstrøm et al. [15] conducted the first large scale experiment using the ib principle, studying the vgg16 architecture [28] trained on cifar-10 [16]. they proposed a matrix-based rényi’s entropy coupled with tensor kernels over convolutional layers to estimate the intractable mutual information, in order to analyze the ip. using this method, there is no need anymore for binning operations. one of their observation was that compression appears mostly on the training data, and is less visible in the test dataset. going forward, they used an early stopping criterion based on a patience parameter. the training stops if the validation accuracy does not change after a predefined number of epochs. they noticed that training can be sometimes stopped even before the compression phase starts. the assumption here is that compression is linked to overfitting. https://doi.org/10.15837/ijccc.2022.1.4650 5 another practical application of the ib principle was studied by elad et al. [3]. they used the ib objective as a training criterion by maximizing the value of the lagrangian, layer by layer. they trained one layer at a time, while freezing the previously learned layers, and added a linear classification layer at the end. while this kind of training strategy is prohibitive in terms of computational time for a modern dnn, they managed to reach on par results with the end-to-end counterpart trained with the cross entropy loss on the mnist and cifar-10 datasets, using a three layered mlp. this is the first experimental illustration of the ib principle used as a training criterion instead of the well-established cross-entropy. other comprehensive reviews on the applications of the ib theory can be found in [4, 17]. some of the conclusions drawn from these reviews are that ib needs further exploration. the compression seen in ips does not necessarily represent learning a minimum sufficient statistic, nor that it produces good generalization. yet, it can provide a good geometrical explanation for some of the inherent behaviour underlying dnns, and might even open the doors for deeper theoretical understandings. from a practical point of view, the most similar work to ours is [15]. the authors used an early stopping criterion based on a patient parameter applied to the validation accuracy and noticed that training is stopped before the compression phase begins, concluding that compression is not necessarily useful for preventing overfitting, as suggested in [9]. different from [15], we embed in our training phase a layer-wise heuristic technique to progressively stop layers from training by observing the variability of the spatial entropy of the saliency maps. we employ the value of the entropy to stop the training on a local level, while in [15] the training is stopped at a global level based on a more traditional criterion. 3 background: spatial entropy and superization in cnns our approach is based on the spatial aura matrix entropy of saliency maps calculated for different neural layers. we measure the information concentration in cnns using the spatial entropy of saliency maps, and relate the decrease of entropy to semiotic superization. to make the paper self-contained, this section summarizes the basic techniques used to compute the spatial entropy and reviews the superization effect in cnn standard operations (pooling and convolution). 3.1 spatial entropy in saliency maps our work analyzes the entropy variations of 2d saliency maps. the entropy is generated by the gradient method in grad-cam. we describe in the following how we calculate the entropy. the formulas used are from [19]. let us define the joint probability of pixels at spatial locations (i,j) and (i + k, j + l) to take the value g, respectively g′ as: pgg′ (k,l) = p(xi, j = g,xi+k, j+l = g′) (3) where g and g′ are pixel intensity values (0−255). if we assume that pgg′ is independent of (i, j) (the homogeneity assumption [40]), we define for each pair (k, l) the entropy h(k, l) = − ∑ g ∑ g′ pgg′ (k, l) log pgg′ (k, l) (4) where the summations are over the number of outcome values (256 in our case). a standardized relative measure of bivariate entropy is [40]: hr(k, l) = h(k, l) −h(0) h(0) ∈ [0, 1] (5) the maximum entropy hr(k, l) = 1 corresponds to the case of two independent variables. h(0) is the univariate entropy, which assumes all pixels as being independent, and we have h(k, l) ≥ h(0). based on the relative entropy for (k, l), the spatial disorder entropy (sde) for an m×n image x was defined in [40] as: https://doi.org/10.15837/ijccc.2022.1.4650 6 hsde (x) ≈ 1 mn m∑ i=1 n∑ j=1 m∑ k=1 n∑ l=1 hr(i−k, j − l) (6) since the complexity of sde computation is high, we decided to use a simplified version the aura matrix entropy (ame, see [20]), which only considers the second order neighbors from the sde computation: ham e (x) ≈ 1 4 ( hr(−1, 0) + hr(0, −1) + hr(1, 0) + hr(0, 1) ) (7) putting it all together, starting from a map obtained by the grad-cam method, we compute the probabilities pgg′ in equation (3), and finally the ame in equation (7), which results in the spatial entropy quantity of a saliency map. the mutual information between two saliency maps m1 and m2 can be described by the following formula: i(m1,m2) = h(m1) + h(m2) −h(m1,m2) (8) where h(·) is the (spatial) entropy of a variable, and h(·, ·) is the joint (spatial) entropy between two variables. we modify the simplified aura matrix entropy to be applicable for joint entropy calculation by changing equation 3 to: pgg′g′′ g′′′ (k,l) = p(m1i, j = g,m1i+k, j+l = g ′,m2i, j = g ′′ ,m2i+k, j+l = g ′′′ ) (9) where g, g′, g′′, g′′′ are pixel intensity values. the upcoming equations from the spatial entropy computation will use pgg′g′′ g′′′ instead of pgg′. the final modification will be to equation (7), where we take into consideration four spatial positions instead of two, the first two from m1 and the last two from m2: ham e (x) ≈ 1 4 ( hr(−1, 0,−1, 0) + hr(0, −1, 0, −1) + hr(1, 0, 1, 0) + hr(0, 1, 0, 1) ) (10) we apply equation (10) to compute the joint spatial entropy between two saliency maps. we have now all the members from equation (8) computed and can calculate the mutual information. 3.2 semiotic superization in cnns semiotics is the study of signs, symbols, and signification. the fundamental semiotic building block is the triad sign-object-interpretant [44]. together, the object and interpretant make up the sign, which is the smallest unit of meaning. in a communication process, signs are agglomerated into more abstract signs called supersigns. iterating the process, we obtain from k-th level supersigns (k + 1)-th level supersigns, and this process is known as superization [11, 45, 46, 47, 48]. frank [47] identified two types of superization: type i building equivalence classes and thus reducing the number of signs; and type ii building compound supersigns from simpler component supersigns. if we consider the entropy values hk and hk+1 computed at two successive levels of supersigns, the extracted information by the interpretant can be measured by the difference hk −hk+1. we have the following property, proved in [47]: superization tends to concentrate information by decreasing entropy between successive levels: hk −hk+1 is non-negative. this property holds for both types of superization. however, this is a mathematical result which omits other interesting aspects. in communication processes, the entropy is not necessarily monotonically decreasing. from an informational psychology perspective, the entropy increases until it reaches its peak value. this phase may be associated to the informational adaptation of the perceiver [11, 45]. the subsequent entropy decrease is related to https://doi.org/10.15837/ijccc.2022.1.4650 7 the processing of structural information and the rate of decrease depends largely upon the amount of structural information. the entropy falls quickly when little structural information is available, whereas when major structural information is present, the entropy will remain high over most of its range [11, 45]. we will discuss informational adaptation later, in section 4. it is known [22] that in a cnn complex objects are composed of simpler object parts as the receptive field of the network grows and combines multiple neurons from previous layers. therefore it is interesting to observe if any form of superization is present in the training process of a cnn. in [19] we applied the above theorem to the neural layers of cnns. we computed superization with respect to the spatial entropy variations of the saliency maps. type i superization appears when we reduce the spatial resolution of a layer k + 1 by subsampling layer k. this is similar to class formation because we reduce the variation of the input values (i.e., we reduce the number of signs). in cnns, this is typically performed by a pooling (down-sampling) operator. type ii superization is produced when applying a convolutional operator to a neural layer k; as an effect, layer k + 1 will focus on more complex objects, composed of objects already detected by layer k. a multi-layered classification can be interpreted as a semiotic process [19]. at the end of a successful recognition process, the entropy of the output layer becomes 0 and no further information needs to be extracted. the last layer (the fully connected layer in a cnn network) is connected to the outer world, the world of objects. for cnn layers, it may happen that both types of superization operate concurrently. in this case, it becomes difficult to separate their effects. according to our results [19], the first type of superization is more effective for decreasing the entropy, whereas the second superization type is more responsible for building supersigns with semantic roles. 4 superization and the ib principle this section presents our thesis on the analogy between information adaptation via superization and the ib principle. beside superization, it is also interesting to study another semiotic aspect in a cnn model informational adaptation. this aspect was never discussed before. in our preliminary experiments, we observed that during training the entropy of each neural layer increases until it reaches its peak value. this phase may be associated with informational adaptation of the model. the subsequent decrease of the entropy is related to the processing of the structural information. the rate of decrease is largely dependent upon the amount of structural information in the input layer. when there is little structural information, the entropy falls quickly, whereas when there are major structural elements, the entropy of the neural layers stays high over most of its range. the manner in which the entropy changes indicate the type of information in the input layer [11]. in our opinion, this information adaptation can be related to the two distinct phases of the ib principle fitting and compression. in order to explore the presence of the ib hypothesis in saliency maps, we investigate: • the evolution of the mutual information during training, between input and intermediate saliency maps, and between intermediate and output saliency maps. • the evolution of spatial entropy for saliency maps during training. the ib plane analysis as described in [9] tracked the two mutual information quantities i(x; t) and i(y ; t) and noticed the fitting and compression patterns emerging. as such, we analyze the information planes between i(x; t) and i(y ; t) by computing the mutual information from equation 8 between the first and an intermediate saliency map, and between the last and the same intermediate saliency map. the proposed experiment is meant to uncover any resemblance to the original results from [9], but applied to a different concept like saliency maps. going a bit further, we test a possible link between the fitting and compression patterns present in ib theory with the spatial entropy of saliency maps. in [19] the spatial entropy was studied at a single point in time (after training), going along the depth of the network. we now intend to capture the dynamics of the entropy during the whole training to see if it is governed by the same patterns. https://doi.org/10.15837/ijccc.2022.1.4650 8 as we will see, while we can not draw any conclusions from the first scenario, in the second case there is a visible trend, similar to the fitting-compression phases studied in ips. regarding saliency maps, while the superization process acts in the depth of the network, the ib principle acts on a single layer. in order to connect those two concepts, we verify the dynamics of the spatial entropy of saliency maps through the whole training process in conjuction with its layer-wise behaviour. we uncover some form of continuity pattern, presented in the next section. 5 experiments we experimentally discover in this section a connection between the ib theory of fitting-compression and the evolution of spatial entropy applied to saliency maps based on similar forming patterns. we also verify the practical applicability of the spatial entropy patterns and a possible connection with the superization process. for training, we use the deep learning programming framework pytorch [26] (version 1.6.0) and the public implementation of grad-cam, modified to our needs. 5.1 evolution of mutual information we start analyzing the information planes between i(x; t) and i(y ; t) by computing the mutual information from equation 8 between the first and an intermediate saliency map, and between the last and the same intermediate saliency map. we plot the resulted values after each training epoch and we look for any visible patterns, similar to the ones observed in [9]. since the saliency maps obtained by grad-cam are discretized 8-bit arrays consisting of integer values in the range [0, 255], it is straightforward to compute the mutual information between two such maps using the equations from the previous section, unlike the quantities in [9] which are almost always continuous and require a binning operation. we train a standard vgg16 architecture on the cifar-10 dataset and then apply the gradcam method to obtain layer-wise saliency maps. we study the behaviour of the mutual information obtained from those saliency maps after each epoch. to obtain a more accurate estimate of the mutual information, we average the mutual information quantities obtained from 50 samples chosen randomly from the training set. in table 1, there are representative plots for the computed quantities at selected layers. we select only four layers for visualization (vgg16 is comprised of ≈ 30 layers, including relus and maxpooling). the other layers have a similar behaviour. it can be observed that for the first few epochs, the plots resemble the ones in [9], with an increase in i(x; t) and i(y ; t). afterwards, the mutual information does not obey any logical pattern which would indicate any form of compression. thus, we empirically conclude that the ib concept is not present in the mutual information of saliency maps. 5.2 evolution of entropy we derive a bit from the study of mutual information and analyze the evolution of spatial entropy for saliency maps through time for the same vgg16 architecture. whereas in [19] the spatial entropy was studied at a single point in time (after training), going along the depth of the network, we intend to capture now the dynamics of the entropy during the whole training, looking for any patterns. the spatial entropy was computed using the formulas from section 3 and averaged over 50 random samples from the training dataset. in table 2, there are plots for the computed spatial entropy through time for selected layers. we visualize again, only four layers. a pattern is now visible, where the spatial entropy increases during the initial phase of the training and at some point flattens out. very interesting, we noticed the same patterns on other well-known network architectures: resnet [31], densenet [42] and googlenet [29]. we notice that early layers exhibit a more abrupt increase of the entropy values during the first few epochs. this phenomenon could be attributed to the fact that the first layers of a cnn learn to detect easy concepts like edges, and the network learns to perform this task faster than latter layers which are responsible for detecting more complex concepts, like whole object parts [22]. https://doi.org/10.15837/ijccc.2022.1.4650 9 table 1: information planes for the mutual information of saliency maps mi for 4th layer mi for 8th layer mi for 12th layer mi for 16th layer in [27] it is stated that early layers are faster to learn by employing a self-supervised pretraining scheme on a single heavily augmented image. the authors prove that a single image is sufficient to learn good representations for the first few layers. in conjunction with [27], we also hypothesize that the abrupt entropy increase, observed for the first layers, is due to easier concepts being learned faster. there are however some exceptions to the patterns in table 2, present only for a few layers, like in figure 1. while we were not able yet to find a good explanation for those different patterns, we make the following supposition. as in [19], where an entire layer was pruned if a drop in spatial entropy would not happen, we assume that we could prune a layer if the spatial entropy does not follow the patterns presented in table 2 because that layer contains redundant information. figure 1: entropy for the 28th layer 5.3 freezing layers during training from the patterns observed in table 2, we test the hypothesis that there are links between the dynamics of the spatial entropy and the evolution of the training process. as such, we train the same vgg16 on the cifar-10 dataset and freeze the layers in which the spatial entropy of the saliency https://doi.org/10.15837/ijccc.2022.1.4650 10 table 2: spatial entropy through time for saliency maps entropy for 4th layer entropy for 12th layer entropy for 18th layer entropy for 27th layer map averaged over the last five epochs enters a compression phase with little variation and is below some threshold �, and observe if it achieves the same accuracy as a fully trained network in the same or less number of epochs. for a large �, the layers are frozen early in the training and learning becomes prohibitive. for a small �, layers are generally not frozen and the network is trained as usual. in practice, we found an � value of 5e− 05 to work best. in table 3, there are empirical results for a fully trained vgg16 vs a vgg16 with some layers frozen during training. the max accuracy column indicates the maximum accuracy achieved on the cifar-10 test set by the two versions until the specified epoch in the first column. table 3: vgg16 performance normal vs frozen layers. experiments performed on a tesla k80 gpu on google colaboratory [41] epoch max accuracy layers frozen running time (minutes) frozen normal frozen normal 30 85.67% 85.42% 0, 17, 28 66 36 40 86.59% 86.13% 0, 7, 17, 26, 28 88 48 50 86.89% 86.81% 0, 7, 14, 17, 26, 28 110 60 60 87.45% 87.45% 0, 7, 14, 17, 26, 28 133 72 as can be seen, the network is trained with some layers frozen, but still performs as good or better than the version with all the layers trained continuously. this training scheme can be considered as a form of early stopping applied at layer level, usually used to prevent a network from overfitting. hence, we make a connection between the patterns observed in the spatial entropy of saliency maps and the training dynamics of a dnn. we observe that layer 0, which is the first convolutional layer, is among the first ones to be frozen, which empirically proves our assumption from subsection 5.2 https://doi.org/10.15837/ijccc.2022.1.4650 11 that early layers are the fastest to be learned. the downside of this method is an overhead to the computational running time, but it was not the target of our experiment. the most similar experiment with ours is in [15], where the authors used the validation accuracy as a proxy to apply the early stopping procedure and notice that the training can be stopped before the compression phase starts. unlike their work, we use quantities observed directly in the training dynamics of the network, and apply early stopping to prove that it has effect on the validation accuracy as well. 5.4 discussion: a semiotic interpretation of the ib principle in table 4, we noticed an interesting property of the spatial entropy for saliency maps. after the superization process takes place (i.e., after a drop of the entropy value), the magnitude resulted after the compression phase is approximately the same with the magnitude of where the entropy starts before superization. we observed a tendency of continuation among layers, directed by evolution of entropy and the superization process. this might represent another inherent property of a dnn’s training dynamics: the need to increase the spatial entropy up to an upper bound determined by previous layers through superization. driven by these empirical observations, we noticed an interesting connection between ib and superization. from [19] we know that a superization process takes place inside a dnn, which concentrates information, resulting in a decrease of spatial entropy. in order to reach the starting entropy from previous layers, an increase in entropy value is required for latter layers. this increase can be of any form: linear, polynomial, exponential, but as it turns out it follows very closely the same trend of fitting and compression observed in the information bottleneck theory, described in subsection 5.2. the phenomenon visible in those plots is possible only if there is a mutual dependency between the ib theory (fitting-compression) and superization. this empirical observation might explain some of the training dynamics governing modern dnns, from an information-theoretical perspective. there are cases, when superization is not present in the form of spatial entropy decrease (see [19]), but we noticed that the fitting and compression phases are still present. while there are situations when this mutual dependency does not exist, most modern cnns include some form of subsampling through the max-pooling or strided convolution [28, 29, 31, 32], and the superization process is present nevertheless. generally, superization and fitting-compression coexist during the training process. the above semiotic superization process is in principle similar to the ib theory: fitting (entropy increase) followed by compression (entropy decrease). in our model, the spatial entropy of saliency maps measures the information content of neural layers. in our visual experiments, however, the two phases of entropy increase and decrease are separated. the spatial entropy of the neural layers increases during the fitting phase, but we did not notice any entropy decrease after the compression phase starts. the decrease in spatial entropy can be observed only after the superization process takes place, in the form of subsampling, for the subsequent layers. https://doi.org/10.15837/ijccc.2022.1.4650 12 table 4: continuation of spatial entropy for saliency maps after superization entropy for 3rd layer entropy for 5th layer entropy for 8th layer entropy for 10th layer entropy for 15th layer entropy for 17th layer entropy for 22th layer entropy for 24th layer 6 conclusions according to our experiments, there is a connection between the evolution of spatial entropy of saliency maps through time and the ib theory of fitting-compression. we noticed a mutual dependency relation between the ib theory and superization, present in dnns where there is a drop in spatial entropy magnitude and latter layers reach the same spatial entropy from which former layers start. 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(2022). information bottleneck in deep learning a semiotic approach, international journal of computers communications & control, 17(1), 4650, 2022. https://doi.org/10.15837/ijccc.2022.1.4650 introduction related work: the information bottleneck principle for neural networks background: spatial entropy and superization in cnns spatial entropy in saliency maps semiotic superization in cnns superization and the ib principle experiments evolution of mutual information evolution of entropy freezing layers during training discussion: a semiotic interpretation of the ib principle conclusions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 2, month: april, year: 2022 article number: 4356, https://doi.org/10.15837/ijccc.2022.2.4356 ccc publications cassava leaf disease identification and detection using deep learning approach j. anitha, n. saranya dr. j. anitha associate professor department of information technology sri ramakrishna engineering college, coimbatore corresponding author:anithajphd@gmail.com n. saranya assistant professor department of information technology sri ramakrishna engineering college, coimbatore abstract agriculture is the primary source of livelihood for about 60% of the world’s total population according to the food and agricultural organization (fao). the economy of the developing countries is solely dependent on agriculture commodities. as the world population is increasing at faster pace, the demand for food is also escalating tremendously. in recent days, agriculture is experiencing an automation revolution. hence the introduction of disruptive technologies like artificial intelligence plays a major role in increasing agricultural productivity. ai enabled approaches would help in overcoming the traditional challenges faced in agriculture practices, by automating various agriculture related tasks. nowadays, farmers adopt precision farming which uses ai techniques namely in crop health monitoring, weed detection, plant disease identification and detection, and forecast weather, commodity prices to increase the yield. as there is scarcity of manpower in agriculture sector, ai based equipment like bots and drones are used widely. crop diseases are a major threat to food security and the manual identification of the diseases with the help of experts will incur more cost and time, especially for larger farms. the machine-vision based techniques provide image based automatic process control, inspection, and robot guidance for pest and disease control. it provides automated process in agriculture, paving way for improved efficiency and profitability. various factors contribute for plant diseases, which includes soil health, climatic conditions, species and pests. the proposed chapter elaborates on the use of deep learning techniques in the leaf disease detection of cassava plants. the chapter initially describes the evolution of various neural network techniques used in classification and prediction. it describes the significance of using convolutional neural network (cnn) over deep neural networks. the chapter focuses on classification of leaf disease in cassava plants using images acquired real time and from kaggle dataset. in the final part of the chapter, the results of the models with original and augmented data were illustrated considering accuracy as performance metric. keywords:cassava leaf diseases, deep learning, convolutional neural network (cnn). https://doi.org/10.15837/ijccc.2022.2.4356 2 1 introduction cassava (manihot esculenta crantz) or tapioca is one of the significant staple crops grown in most of the tropical countries in africa, asia, and latin america. cassava is a perennial crop and cultivated under rain-fed or irrigated conditions for its tubers. it is one of the most drought-tolerant crops and can grow in less fertile soil. other than cassava being used as a staple food, it is processed for various products namely starch, ethanol, glucose, and cassava hey is used as animal feed and also used in the production of adhesives, textile, and cosmetics. in 2019, the global cassava market increased by 0.4in india, cassava is cultivated in 13 states with major production in the southern states, namely kerala and tamil nadu. it is grown for both food and industrial purposes. among various diseases that curb the production, indian cassava mosaic diseases, root rot, and brown leaf spot are the significant contributors [3]. it is also evident that viral diseases like cassava mosaic disease are predominantly affecting cassava production in many countries of the world. the identification of cassava disease could be done only by trained experts as many of the types of diseases show similar symptoms and crops could be infected by multiple diseases. due to this, it comes very difficult for the farmers to choose pesticides or nutrients to treat the disease as it becomes a complex task. in such tasks, deep learning techniques will provide a solution for better decision making to identify the type of diseases and to treat the diseases, and improve productivity [4]. 2 literature survey / related works ozichi emuoyibofarhe et.al [5] has proposed machine learning models for detection and classification of cassava leaf diseases as blight or mosaic disease. the cubic support vector machine (csvm) model is used to classify whether the leaf is healthy or not and the coarse gaussian support vector machine (cgsvm) algorithm is used to classify the type of the disease and the accuracy obtained with the deployment of cubic support vector machine model is 83.9% predicting the leaf is healthy or not and the coarse gaussian support vector machine with an accuracy of 61.6% in classifying the disease as either blight or mosaic. gnanasekaran, sambasivam and opiyo, geoffrey[6] proposed a predictive machine learning model for cassava disease detection and classification in imbalanced datasets using smote e (synthetic minority over-sampling technique) to avoid over fitting and provide better accuracy. in the work proposed by amanda ramcharan et.al [7], transfer learning is applied to train a deep neural network for classifying three types of leaf diseases and pest damage in cassava plants and the model is deployed in mobile devices. p. b. padol and a. a. yadav [8] proposed a machine learning model which uses a svmbased classifier to detect the leaf diseases in grape leaves and the model has achieved an accuracy of 88.89 sharada p et.al [9] proposed deep learning based mobile application for plant species identification and disease detection. it uses plant village dataset and identifies the plant species. deep learning models like googlenet and alexnet were used for identification of plant species and disease detection. through training the network from scratch, the system can able to achieve accuracy of 99.35omkar kulkarni [10] done transfer learning using mobilenet and inceptionv3 pretrained models to identify 13 types of crop species and 26 types of diseases. the system can able to achieve accuracy of 99.45%. it also makes use of plant village dataset. in all these works, the dataset used was a single leaf image with plain background. so, the system can able to achieve reliable classification accuracy. the dataset used in our work is a real time dataset captured directly through smart phone in the cassava plant field. dataset with noisy background, image quality and unbalanced dataset remains challenging part in the cassava plant disease identification. 3 description about the datase for prediction of any type of plant disease when human experts are involved, it is expensive and time consuming. this process can be automated with the help of computer vision along with deep learning techniques. computer vision-based systems are widely used in the applications which https://doi.org/10.15837/ijccc.2022.2.4356 3 involves image recognition and identification and has tremendous scope in large scale agricultural process. disease categorization that is done by human through naked eyes which can be modelled using computer vision. cassava plant is cultivated widely throughout the world and is one of the staple foods in developing countries. disease identification in these plants at the earliest helps to improve the food productivity. the major types of disease like cassava bacterial blight (cbb), cassava brown streak disease (cbsd), cassava green mite (cgm) and cassava mosaic disease (cmd) were considered for classification process. along with these disease varieties healthy leaf images were also included. in this proposed system, totally five categories have been considered. cassava dataset has been downloaded from kaggle repository. the dataset has been divided into training and validation data in the percentage split of 70-30. the distribution of original dataset has been provided in the fig. 1. sample images of five different categories of image has been provided in the fig. 2 figure 1: sample images of each category figure 2: original data distribution 4 experiment & result discussion the proposed method makes use of cnn model to identify the type of disease. the model consists of convolutional layer, maxpooling layer, batch normalization, dropout layer, flatten and dense layer https://doi.org/10.15837/ijccc.2022.2.4356 4 arranged in sequential order. fig. 3 shows the order in which layers has been arranged. number of trainable parameters can be identified using the model summary report provided in fig. 4. convolutional layer is used extract features based on the filters across the image. feature map is created as an output of convolutional layer. activation functions are is used to convert the net input into activations. most used activation function in cnn are relu(rectified linear unit) at hidden layer and softmax at fully connected layer. the dataset considered for experimentation is highly imbalanced. figure 3: cnn model representation figure 4: model summary the number of images in cmd category is high rather than other categories. the number of images in healthy category is also very low. initial experimentation was done with the actual dataset using simple cnn model. the model is able to achieve a training accuracy of 98.9% but the validation accuracy 54.35%. the results in fig. 5 shows that model is overfitting due to biased data. the confusion matrix table 1. obtained for that model shows that high misclassification in cbb, cbsd, cgm and healthy categories. table 2 shows the classification report of the model and its precision, recall, f1-score and support values. table 1: confusion matrix for actual dataset https://doi.org/10.15837/ijccc.2022.2.4356 5 figure 5: accuracy curve over original dataset table 2: classification report the dataset unbalancing and model overfitting can be reduced using data augmentation techniques. fig. 6 shows sample augmented data of a healthy image after rotating it in the r angle of 90 degrees. figure 6: accuracy curve over augmented dataset 5 conclusion deep learning-based architecture was designed with minimum number of parameters to classify cassava plant diseases. initially the model was trained using original dataset after converting the images to the size 244x244. with this the system can able to achieve the validation accuracy of only 54%. data augmentation techniques were used to increase the number of images in the dataset. after augmentation system can able to achieve validation accuracy of 90%. declaration of competing interests the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. https://doi.org/10.15837/ijccc.2022.2.4356 6 conflict of interest the authors declare no conflict of interest. references [1] https://www.globaltrademag.com/global-cassava-market-is-expected-to-successfullyresist-thecovid-pandemic/ [2] https://arxiv.org/pdf/1908.02900.pdf (kaggle dataset) [3] http://ciat-library.ciat.cgiar.org/articulos_ciat/asia/proceedings_workshop_00/61.pdf [4] https://www.isppweb.org/foodsecurity_casava_diseases.asp#:̃:text=african%20cassa va%20mosaic%20disease%20is,all%20producing%20countries%20in%20africa [5] bofarhe, ozichi & bofarhe, justice & segun, adebayo & ayandiji, adebamiji & demeji, oloyede & james, oreoluwa. (2019), detection and classification of cassava diseases using machine learning, international journal of computer science and software engineering, 8, 2409-4285. [6] gnanasekaran, sambasivam & opiyo, geoffrey. (2020). a predictive machine learning application in agriculture: cassava disease detection and classification with imbalanced dataset using convolutional neural networks. egyptian informatics journal. 22. 10.1016/j.eij.2020.02.007. [7] karthik, a., mazheriqbal, j.l. efficient speech enhancement using recurrent convolution encoder and decoder. wireless pers commun 119, 1959–1973 (2021). [8] amanda ramachandran et.al, deep learning for image-based cassava disease detection, front. plant sci., 22 september 2016 https://doi.org/10.3389/fpls.2017.01852 [9] p. b. padol and a. a. yadav, svm classifier based grape leaf disease detection, 2016 conference on advances in signal processing (casp), 2016, pp. 175-179, doi: 10.1109/casp.2016.7746160. [10] sharada p. mohanty, david p. hughes and marcel salathé (2016), using deep learning for image-based plant disease detection , front. plant sci. 7:1419. doi: 10.3389/fpls.2016.01419 [11] o. kulkarni, crop disease detection using deep learning (2018), fourth international conference on computing communication control and automation (iccubea), 2018, pp. 1-4, doi: 10.1109/iccubea.2018.8697390. https://doi.org/10.15837/ijccc.2022.2.4356 7 copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: anitha j.; saranya n. (2022). cassava leaf disease identification and detection using deep learning approach, international journal of computers communications & control, 17(2), 4356, 2022. https://doi.org/10.15837/ijccc.2022.2.4356 introduction literature survey / related works description about the datase experiment & result discussion conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 5, month: october, year: 2022 article number: 4468, https://doi.org/10.15837/ijccc.2022.5.4468 ccc publications supplier selection model based on d numbers and transformation function leihui xiong, xiaoyan su, hong qian leihui xiong school of automation engineering, shanghai university of electric power shanghai 200090, china xionglh@mail.shiep.edu.cn xiaoyan su* school of automation engineering, shanghai university of electric power shanghai 200090, china *corresponding author: suxiaoyan@shiep.edu.cn. hong qian school of automation engineering, shanghai university of electric power shanghai 200090, china qianhong.sh@163.com abstract selecting reasonable suppliers can effectively improve the efficiency of enterprise supply chain management. among them, expert evaluation is an important part of supplier selection problem, but the uncertainty, fuzziness and incompleteness of expert opinions make supplier selection problem difficult to solve. in order to systematically and effectively solve the uncertainty, ambiguity and incompleteness in supplier selection problem, this paper presents a new supplier selection method based on d numbers and transformation function. first, fuzzy preference relation is generated based on the decision matrix of pairwise comparisons given by experts. d numbers which can effectively deal with uncertain information extend fuzzy preference relation (d matrix). second, the d matrix is converted into a crisp matrix form based on the integration representation of d numbers according to different situations whether or not the information in d matrix is complete. third, the crisp matrix is converted into judgement matrix by using the transformation functions. finally, analytic hierarchy process (ahp) method is applied based on the judgment matrix to give a priority weights for decision making. three numerical examples and application of the supplier selection are used to show the feasibility and effectiveness of the proposed method. keywords: d numbers, transformation functions, analytic hierarchy process, fuzzy preference relation. https://doi.org/10.15837/ijccc.2022.5.4468 2 1 introduction multi-attribute decision making is an important part of decision theory and modern decision science [1]. it has been widely used in investment decision making [2], project evaluation[3], scheme selection[4], factory site selection [5, 6], comprehensive evaluation of economic benefits [7], etc. therefore, it is of great practical and theoretical significance to study effective and practical multi-attribute decision making methods. as the market environment becomes more and more complex and the competition among enterprises becomes more and more fierce, the supplier selection in multi-attribute decision making problem attracts more and more attention [8]. supplier selection and evaluation is an important activity for enterprises to determine their own product suppliers, and it is also the premise to optimize the supplier management system. enterprise supplier selection evaluation has a set of strict process steps. the enterprise selects suppliers mainly from four aspects: enterprise performance, business capability, quality system and enterprise environment [9]. different index elements can be obtained by refining these four aspects, and then the evaluation of each index can be established by analyzing these index elements. finally, ahp and other methods are used to make decisions [10]. traditional supplier selection process can solve this problem to a certain extent, but with the deepening of the research on supplier selection, scholars find that: due to the complexity of practical problems and the limitation of decision-makers’ knowledge and experience, there are many uncertainties and incomplete semantic information in decision makers’ determination of attribute values and expression of preference information, which leads to a large amount of uncertain information to be dealt with in supplier selection [11, 12]. in order to improve the supplier selection method, some scholars use fuzzy number theory, grey theory, d-s evidence theory to deal with uncertain information in supplier selection problem. rashidi et al.[13] applied fuzzy data envelopment analysis (dea), and the technique for order of preference by similarity to ideal solution (topsis) to the selection of logistics service providers, and compared and analyzed the two models to demonstrate the effectiveness of these two methods. chen et al. [14] combined topsis, decision making trial and evaluation laboratory (dematel) and rough fuzzy method in dealing with supplier selection problem. this method uses fuzzy number to represent internal and external uncertainties, and dematel method to reflect the interaction between attributes. xing et al. [15] proposed a new supplier selection model based on interval two-type trapezoidal fuzzy partial order and choquet integral, which can effectively deal with the interaction between criteria and consider consensus. in general, although fuzzy number theory can deal with subjective opinions effectively, fuzzy number itself is better at dealing with fuzzy information rather than other uncertain information. hekmat et al. [16] used grey principal component analysis (pca) and dea to deal with supplier selection in uncertain environment. this method can deal with uncertain information as well as insufficient data information and related situations. babak et al.[17] ranked suppliers based on topsis and grey theory, and applied this method to the selection of battery suppliers for electric vehicles. in a word, grey theory can express the incompleteness and uncertainty of information to a certain extent, but it is mainly used for fuzzy prediction and cannot express the uncertain information intuitively. sureeyatanapas et al. [18] solve the supplier selection problem based on d-s evidence theory and topsis method. in this method, d-s evidence theory is used to construct decision matrix, and then topsis method is used for further fusion. zhang et al. [19] combined d-s evidence theory and analytic network process (anp), and this model effectively solved the problem of supplier selection in an uncertain environment and considered the interaction between indicators. fei et al. [20] solved the supplier selection problem based on d-s evidence theory and an elimination and choice translating reality (electre), which can better analyze the priority relationship between suppliers while dealing with uncertain information. in general, d-s evidence theory [22] [23] can better process and express uncertain information, so it is widely used in this field. however, d-s evidence theory cannot deal with the incomplete identification framework and the correlation of basic elements. to sum up, the above methods have certain defects in dealing with uncertain information in supplier selection. therefore, the method applying d numbers to multi-attribute decision making is proposed [21]. d numbers can effectively deal with the uncertainty and fuzziness of information. at https://doi.org/10.15837/ijccc.2022.5.4468 3 table 1: comparison of methods to deal with uncertain information in supplier selection problem method feature 1 feature 2 feature 3 dea-topsis method [13] √ × × topsis, dematel and rough fuzzy method [14] √ × × interval type-2 trapezoidal fuzzy numbers method [15] √ × × grey pca-dea method [16] √ × × topsis and grey theory method [17] √ × × d-s evidence theory and topsis method [18] √ √ × d-s evidence theory and electre method [19] √ √ × d-ahp method [39] √ √ √ d-anp method [41] √ √ √ the proposed method √ √ √ the same time, d numbers extends d-s evidence theory to a certain extent , which can deal with the situation where the identification framework is incomplete and the basic elements are not independent from each other. considering the advantages of d numbers[24, 25, 26, 27, 28, 29, 30, 31] in dealing with uncertain information, d numbers is widely used in various fields [32, 33, 34, 35, 36, 37, 38]. deng et al. [39, 40] solved the supplier selection problem based on d numbers and analytic hierarchy process (ahp). on the basis of deng, fei et al. [41] selected the optimal supplier based on d numbers and network analysis method (anp). however, the applications of these methods may get counter-intuitive results under some situations. this paper presents a new supplier selection method based on d numbers and transformation function. compared with the method of deng et al. [39], the method in this paper can deal with the situations where the expert opinions are quite uncertain and the decision-making objects are similar. in addition, in the process of consistency evaluation, the method in this paper is more consistent with the actual situation and has a good theoretical basis. (table 1 shows the comparison of methods for dealing with uncertain information in supplier selection, in which feature 1 indicates that uncertain information is expressed to a certain extent, feature 2 indicates that uncertain information can be expressed intuitively, and feature 3 indicates that it can handle the situation where the identification framework is incomplete and the basic elements are not independent.) the rest of this paper is organized as follows. in section 2, the preliminaries on ahp, d numbers and transformation functions are briefly introduced. in section 3, a new method to process uncertain information in supplier selection problem is proposed and some examples which can be handled by the method in this paper but cannot be handled by deng et al.’s method [39] are given. in section 4, an application of the supplier selection is illustrated to show the rationality of this new method. in section 5, conclusions. 2 preliminaries 2.1 analytic hierarchy process (ahp) [45] the steps for the ahp are shown below: firstly, the hierarchical structure of the problem is constructed, and then the pairwise comparison matrix is constructed based on certain criteria. definition 1:when the numbers of elements is n, the pairwise comparison matrix is defined as follow. w =   w11 · · ·w1n w21 ... ... wn1 · · ·wnn   (1) where wij measures the relationship that i is more important than j. thirdly, we calculate the eigenvector corresponding to the largest eigenvalue of matrix w . finally, the matrix w ’s consistency https://doi.org/10.15837/ijccc.2022.5.4468 4 table 2: the value of ri dimension 1 2 3 4 5 6 7 8 9 10 ri 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 is verified by cr which is defined as follow: ci = λmax −n n− 1 (2) cr = ci ri (3) the value of random consistency index (ri) is shown in table 2. we can accept the consistency of the pairwise comparison matrix, when cr is less than 0.1. finally, normalizing the eigenvector to obtain the final weights. 2.2 d numbers [39] d numbers is an extension of dempster-shafer theory, which can deal with the uncertainty information. d numbers have wider application than dempster-shafer theory, because d numbers do not have constrain of integral and independent assumption. definition 2:let ω be a finite nonempty set, d number is a mapping d: 2ω → [0, 1] , which is given as follows: ∑ b⊆ω d (b) ≤ 1 and d (φ) = 0 (4) where φ is an empty set and b is a subset of ω. if ∑ b⊆ω d (b) = 1 the information is assumed to be complete. if ∑ b⊆ω d (b) < 1 , which means the information is incomplete. there is an example to show a d numbers. suppose a system’s security needs to be accessed by 10 experts. the "medium" security level is supported by 4 experts. the "high" security level is supported by 3 experts. the remaining experts do not generalize a conclusion because of their limitations in knowledge. we can express this situation using d numbers as follow. d ({high}) = 0.3,d ({medium}) = 0.4 the sum of high and medium level less than one. because the information is incomplete. fuzzy numbers and d-s theory cannot describe this well. definition 3: let d = ({b1,v1} ,{b2,v2} , · · · ,{bi,vi} , · · ·{bn,vn}) be a d number, the integration representation of d numbers can be calculated as follow: i (d) = n∑ i=1 bivi (5) 2.3 transformation functions [42] definition 4: suppose that we have a set of alternatives, x = {x1, · · · ,xn}, and associated with it a reciprocal multiplicative preference relation a = (aij ) with aij ∈ [1/9, 9]. then, the corresponding reciprocal fuzzy preference relation, p = (pij ) with pij ∈ [0, 1] , associated with a is given as follows: pij = g (aij ) = 1 2 × (1 + log9aij ) (6) with such a transformation function g we can relate the research issues obtained for both kinds of preference relations. https://doi.org/10.15837/ijccc.2022.5.4468 5 d numbers extended fuzzy preference relation (d matrix) crisp matrix judgement matrix weights of alternatives the maximum eigenvector i(rd) d numbers extended fuzzy preference relation (d matrix) crisp matrix probability matrix triangular matrix weights of alternatives triangulated crisp matrix i(rd) step1 step2 step3 step4 ranking of alternatives local information t p r t c r step1 step2 step3 p r c r d r d r c r tr r (a) deng et al.'s method [54] (b) the proposed method input input output output output figure 1: flow chart of deng et al.’s method and the proposed method in deriving priority weights of alternatives https://doi.org/10.15837/ijccc.2022.5.4468 6 3 proposed method the main flow chart of the proposed method compared with deng et al.’s method [39] are shown in figure 1. more details are illustrated as follows. 3.1 introduction of deng et al.’s method [39] after d numbers extended fuzzy preference relation (d matrix) is constructed, we can gain the crisp matrix by the integration representation of d numbers. after that the main problem is to gain the probability matrix. two different processes are given in deng et al.’s method for dealing with complete information and incomplete information to gain the probability matrix. assume that cij is the element in the i-th row and j-th column of crisp matrix. (1) d matrix with complete information when cij + cji = 1.0, we believe that cij has complete information. (i) when the cij is greater than 0.5, we replace cij with 1. (ii) when the cij is lesser than 0.5, we replace cij with 0. (2) d matrix with incomplete information when cij + cji < 1.0, we believe that cij has incomplete information. (i) if cij ≥ 0.5 or cji ≥ 0.5 , the larger one is replaced by 1 and the smaller one is replaced by 0. (ii) if cij < 0.5,cji < 0.5, we can use equation (7) and equation (8) to get elements of the probability matrix. c ′ ij = 1 − (0.5 −cij ) 1 − (cij + cji) (7) c ′ ji = 1 − (0.5 −cji) 1 − (cij + cji) (8) where cij means that the row i and column j of the crisp matrix. c ′ ij means that the row i and column j of the probability matrix. and then using triangularization method to convert the probability matrix into the triangular matrix which is used to rank the elements. this method sums up the elements in each row, records the number of rows corresponding to the largest item as i, removes the i-th row and the j-th column of the matrix, and loops until the matrix is empty. the order of deletion is sort of elements. the triangular matrix can be obtained by using this sort. finally, the interval of weights are calculated by introducing the variable λ which reflects the information of the pairwise comparison. 3.2 an improved d-ahp method 3.2.1 d matrix d matrix is an extension of fuzzy preference relation. tanino et al. [43] propose the fuzzy preference relation to deal with ambiguity in expert language. for the multiplicative preference relation, the diagonal elements are the inverse of each other, i.e. aij ×aji = 1. while the fuzzy preference relation is subjected to an additive reciprocal, i.e. rij + rji = 1. suppose a = {a1,a2, ...,an} is a set of alternatives. a fuzzy set a×a, which is calculated by a membership function (µr : a×a → [0, 1]), represent a fuzzy preference relation r as follows. r = a1 a2 ... an a1 a2 · · · an  r11 r12 · · · r1n r21 r22 · · · r2n ... ... ... ... rn1 rn2 · · · rnn   (9) where rij = µr (ai,aj ) ,∀i,j ∈ {1, 2, . . . ,n}. rij represents the degree of preference for alternative ai over alternative aj. the five scenarios for rij are as follows: (1)when rij = µr (ai,aj ) = µr (ai,aj ) = 0, it means that aj is absolutely preferred to ai; (2)when rij = µr (ai,aj ) ∈ (0, 0.5), it means that aj is preferred to ai to some degree; (3)when rij = µr (ai,aj ) = 0.5, it means that https://doi.org/10.15837/ijccc.2022.5.4468 7 indifference between ai and aj; (4)when rij = µr (ai,aj ) ∈ (0.5, 1), it means that ai is preferred to aj to some degree; (5)when rij = µr (ai,aj ) = 1, it means that ai is absolutely preferred to aj. at the same time, r must satisfy three constraints: (1) rij ≥ 0; (2) rij + rji = 1,∀i,j ∈{1, 2, . . . ,n}; (3) rii = 0.5,∀i,j ∈{1, 2, . . . ,n}. with this method, linguistic values can be a way of generating preference information. but the fuzzy preference relation still has some drawbacks. firstly, there will be inconsistencies in the fuzzy preference. how to measure the consistency of the fuzzy preference is still an open question. secondly, the fuzzy preference can’t use in some situations. for example, there are two schemes a1,a2 and 10 experts give their idea about them. situation 1: 8 experts give the conclusion that r12 = 0.7. the other 2 experts give the conclusion that r12 = 0.6. situation 2: 6 experts give the conclusion that r12 = 0.8. because of the lack of relevant knowledge, the other 4 experts did not give suggestions. in both cases, the use of the fuzzy preference alone does not adequately represent the information. to solve the above shortcomings, this paper combine the fuzzy preference relation with d number and replaces the rij in r with the d number, then the resulting new matrix is called d matrix. we can transform the fuzzy preference relation r (see equation (9)) into d matrix rd (see equation (10)). rd = a1 a2 ... an a1 a2 · · · an  d11 d12 · · · d1n d21 d22 · · · d2n ... ... ... ... dn1 dn2 · · · dnn   (10) where rd satisfies the following constraints: (1)dij = {( b ij 1 ,v ij 1 ) , ( b ij 2 ,v ij 2 ) , . . . , ( bijm,v ij m )} ,∀i,j ∈{1, 2, . . . ,n} (2)dji = {( 1 − bij1 ,v ij 1 ) , ( 1 − bij2 ,v ij 2 ) , . . . , ( 1 − bijm,vijm )} ,∀i,j ∈{1, 2, . . . ,n} (3)bijk ∈ [0, 1],∀k ∈{1, 2, . . . ,m} viewed from another perspective, the fuzzy preference is a special case of d matrix. when m = 1,vij1 = 1,∀i,j ∈{1, 2, . . . ,n}, b ij 1 is the same thing as rij. situation 1 and situation 2 can be expressed in terms of d numbers as follows. situation 1: rd1 = a1 a2 a1 a2[ {(0.5, 1.0)} {(0.7, 0.8) , (0.6, 0.2)} {(0.3, 0.8) , (0.4, 0.2)} {(0.5, 1.0)} ] (11) situation 2: rd2 = a1 a2 a1 a2[ {(0.5, 1.0)} {(0.8, 0.6)} {(0.2, 0.6)} {(0.5, 1.0)} ] (12) 3.2.2 priority weights of alternatives based on d matrix in the previous section we defined the d matrix and explained the advantages of the d matrix. the generation of priority weight is the key step of the madm and we can calculate priority weight by fuzzy preference relation. but how to get it by matrix d. the proposed method will discuss the problem in two cases. case 1: d matrix with complete information assume all the experts participate in the evaluation, and we get a d matrix with complete information on all the elements. that is to say, in equation (10) vij1 + v ij 2 + . . . + vijm = 1,∀i,j ∈ {1, 2, . . . ,n}. for example, there is a d matrix with complete https://doi.org/10.15837/ijccc.2022.5.4468 8 information. (see equation (13)). take the following steps to get the priority weights. rd = a1 a2 a3 a1 a2 a3  {(0.5, 1.0)} {(0.2, 1.0)} {(0.7, 0.8) , (0.8, 0.2)}{(0.8, 1.0)} {(0.5, 1.0)} {(0.9, 1.0)} {(0.3, 0.8) , (0.2, 0.2)} {(0.1, 1.0)} {(0.5, 1.0)}   (13) step1. use the integration representation of d numbers (see definition 3) to calculate the crisp matrix. for example, we can calculate d13 = {(0.7, 0.8), (0.8, 0.2)} which is the element in equation (13), according to equation (5). i (d13) = 0.7 × 0.8 + 0.8 × 0.2 = 0.72 and then, we can get the crisp matrix rc. rc = i (rd) = a1 a2 a3 a1 a2 a3   0.5000 0.2000 0.72000.8000 0.5000 0.9000 0.2800 0.1000 0.5000   (14) step 2. convert a crisp matrix to a judgement matrix. for instance, we can calculate the element rc(1, 3) = 0.72 of the crisp matrix equation (14), according to equation (6). rtr(1, 3)=9−1+2×0.72 = 2.6295 consequently, a judgement matrix rtr is derived as follow. rtr = a1 a2 a3 a1 a2 a3   1.0000 0.2676 2.62953.7372 1.0000 5.7995 0.3803 0.1724 1.0000   (15) step 3. calculate the maximum eigenvector and eigenvalue of the judgement matrix. the eigenvalues and eigenvectors can express the information of the matrix to the greatest extent. according to the above steps, the judgement matrix we get is similar to the paired comparison matrix in ahp, both of which are multiplicative preference matrices. the elements in the judgement matrix are on an interval of [1/9, 9] , which is same as the pairwise comparison matrix in ahp method. so we use the same treatment as ahp for the judgement matrix. according to formula rtrx = λmaxx and after normalization, we can get the final weights of alternatives a1, a2 and a3 in d matrix shown in equation (13). ω1 : ω2 : ω3 = 0.2180 : 0.6832 : 0.0988 the ranking of alternatives is a2 � a1 � a3 (the ’�’ sign means ’better than’). similarly, consistency indicators cr can be obtained. in this example, cr = 0.0155 < 0.1, so the consistency of the d matrix in equation (13) is acceptable. case 2: d matrix with incomplete information assume part of experts participate in the evaluation, and we get a d matrix with incomplete information on some of the elements. that is to say, in equation (10), vij1 + v ij 2 + . . . + vijm < 1,∀i,j ∈ {1, 2, . . . ,n}. for example, there is a d matrix with incomplete information. (see equation (16)). take the following steps to get the priority weights. rd = a1 a2 a3 a1 a2 a3  {(0.5, 1.0)} {(0.2, 1.0)} {(0.7, 0.8)}{(0.8, 1.0)} {(0.5, 1.0)} {(0.9, 1.0)} {(0.3, 0.8)} {(0.1, 1.0)} {(0.5, 1.0)}   (16) https://doi.org/10.15837/ijccc.2022.5.4468 9 step1. in this case, elements with complete information and incomplete information is processed separately. (1) dij in d matrix with complete information if m∑ k=1 v ij k = 1 , then the integration representation of d numbers is used to calculate the row i and column j element in the crisp matrix. (2) dij in d matrix with incomplete information if m∑ k=1 v ij k < 1 , a new processing method is proposed as follow. i ′ (dij ) = n∑ m=1 bmvm + 0.5 × (1 − n∑ m=1 vm) (17) where i′ (dij ) is the row i and column j element in the crisp matrix. when the pros and cons of two events are not clear, people are more likely to assume that they are similar. in fuzzy preference relation, 0.5 is used to express this situation when alternatives are assumed to be equal with respect to a certain criterion. thus, this method applies 0.5 to represent the preference assignment under the condition of incomplete information. for example, in equation (16), d13 = {(0.7, 0.8)} and d31 = {(0.3, 0.8)} are elements with incomplete information. according to equation (17) i ′ (d13) = 0.7 × 0.8 + 0.5 × (1 − 0.8) = 0.66 i ′ (d31) = 0.3 × 0.8 + 0.5 × (1 − 0.8) = 0.34 after dealing with the above two cases, then we can get the results of the crisp matrix rc. rc = i (rd) = a1 a2 a3 a1 a2 a3   0.5000 0.2000 0.66000.8000 0.5000 0.9000 0.3400 0.1000 0.5000   (18) step 2. convert a crisp matrix to a judgement matrix. just like case 1, for instance, we can calculate the element rc13 = 0.66 of the crisp matrix equation (18), according to equation (6). rtr13=9−1+2×0.66 = 2.0200 consequently, a judgement matrix rtr is derived as follow. rtr = a1 a2 a3 a1 a2 a3   1.0000 0.2676 2.02003.7372 1.0000 5.7995 0.4950 0.1724 1.0000   (19) step 3. calculate the maximum eigenvector and eigenvalue of the judgement matrix. the treatment is the same as case 1. according to formula rtrx = λmaxx and after normalization, we can get the final weights of alternatives a1, a2 and a3 in d matrix shown in equation (16). ω1 : ω2 : ω3 = 0.2015 : 0.6896 : 0.1089 the ranking of alternatives is a2 � a1 � a3 (the ’�’ sign means ’better than’). similarly, consistency indicators cr can be obtained. as cr = 0.0038 < 0.1, the consistency of the d matrix of equation (16) is acceptable. https://doi.org/10.15837/ijccc.2022.5.4468 10 3.2.3 summary of case 1 and case 2 case 1 and case 2 represent the cases of d matrix with complete information and d matrix with incomplete information, respectively. here we express the pairwise comparison opinions of experts in the form of a d matrix, that is, our input is a d matrix. it can be concluded from the above that d-matrix is an extension of fuzzy preference relation, which can deal with uncertain and incomplete information more effectively. the process can be concluded as follows (shown in figure 1). firstly, convert d matrix to a crisp matrix according to two different situations: when the information is complete, the integration representation of d numbers is directly used; when the information is incomplete, a new method is proposed. secondly, convert the crisp matrix to the judgement matrix based on the transformation functions to get a good preparation for using ahp. finally, ahp is used to deal with the judgement matrix. the eigenvector corresponding to the largest eigenvalue of the judgement matrix is calculated to get the weights. the cr is also used to measure the consistency. 4 numerical examples in this part, three examples are illustrated to show effectiveness and superiority of the proposed method compared with the deng et al.’s method [39]. assume that the following three examples are expert opinions in different situations. here we directly use the d matrix to express this information. deng et.al’s method and the proposed method are applied to get the priority weights of alternatives, respectively. 4.1 example 1 rd = a1 a2 a3 a1 a2 a3  {(0.5000, 1)} {(0.4990, 1)} {(0.5010, 1)}{(0.5010, 1)} {(0.5000, 1)} {(0.4990, 1)} {(0.4990, 1)} {(0.5010, 1)} {(0.5000, 1)}   (20) in the first example, the elements of the d matrix are complete information. the pairwise comparison values are all close to 0.5, indicating that a1,a2, and a3 are approximately equally important. 4.1.1 deng et al.’s method firstly, dealing with the d matrix by the integration representation of d numbers. then, the crisp matrix rc is obtained in equation (21). rc = i (rd) = a1 a2 a3 a1 a2 a3   0.5000 0.4990 0.50100.5010 0.5000 0.4990 0.4990 0.5010 0.5000   (21) secondly, construct the probability matrix. the elements in the d numbers are all information complete. so there are only two choices of 0 or 1 of the elements in the probability matrix rp . the probability matrix can be obtained as follow in equation (22). rp = a1 a2 a3 a1 a2 a3   0.0000 0.0000 1.00001.0000 0.0000 0.0000 0.0000 1.0000 0.0000   (22) finally, the triangularization method is used to rank the alternatives. however, based on the probability matrix the sum of each row is equal, the subsequent steps cannot be carried out. https://doi.org/10.15837/ijccc.2022.5.4468 11 4.1.2 the proposed method step 1 the information of the d matrix is all complete. so the process just like the deng’s method. the crisp matrix is same as the equation (21). step 2 according to equation (6), the judgement matrix can be obtained. for instance, rtr13=9−1+2×i(d13) = 0.9956. the judgement matrix is as follow in equation (23). rtr = a1 a2 a3 a1 a2 a3   1.0000 0.9956 1.00441.0044 1.0000 0.9956 0.9956 1.0044 1.0000   (23) step 3 according to the rtrx = λx, the largest eigenvalue of the judgement matrix is calculated. normalized the eigenvector of the largest eigenvalue and then we can get the final weights. the final weights of the three alternatives are 0.3333 0.3333 0.3333 and cr < 0.1. 4.1.3 discussion of example 1 in this example, the alternatives are quite similar, and it can be seen that the d number matrix has good consistency. the correct result can be obtained by the proposed method, and cr is less than 0.1. however, deng et al.’s method cannot be applied since the sum of each row in the probability matrix rp is equal, so that the following interval cannot be processed, and the consistency index cannot be obtained. 4.2 example 2 rd = a1 a2 a3 a1 a2 a3  {(0.5, 1.0)} {(0.1, 0.5)} {(0.0, 0.4)}{(0.9, 0.5)} {(0.5, 1.0)} {(0.4, 0.2)} {(1.0, 0.4)} {(0.6, 0.2)} {(0.5, 1.0)}   (24) in the second example, from the first column of the d-matrix, the pairwise comparisons of a2 and a1, a3 and a1, are both greater than 0.5. and the pairwise comparison of a3 and a1 is larger than the pairwise comparison of a2 and a1. this shows that a1 is the worst, a3 and a2 are both superior to a1, and a3 is superior to a1 to a greater extent than a2. meanwhile, the value of the pairwise comparison between a3 and a2 is greater than 0.5, and the value of the pairwise comparison between a2 and a3 is less than 0.5, which further indicates that a3 is superior to a2. so given the d matrix, the order from best to worst should be a3, a2, a1. 4.2.1 deng et al.’s method firstly, same as the first step of example 1. the equation (5) is used to obtain the crisp matrix rc as follows. rc = i (rd) = a1 a2 a3 a1 a2 a3   0.5000 0.0050 1.00000.4500 0.5000 0.0800 0.4000 0.1200 0.5000   (25) secondly, in this d matrix, some elements are information complete and other elements are information incomplete. the probability matrix(rp) can be obtained in equation (26). rp = a1 a2 a3 a1 a2 a3   0.0000 0.1000 0.17000.9000 0.0000 0.4750 0.8300 0.5250 0.0000   (26) finally, the sum of the first row of the rp matrix is 0.27, the sum of the second row is 1.375, and the sum of the third row is 1.355, so a2 is ranked first, a3 is second, and a1 is third. https://doi.org/10.15837/ijccc.2022.5.4468 12 4.2.2 the proposed method step 1 because of incomplete information, processing is divided into two categories. if the information is complete, the operation shown as equation (5) is implemented. if the information is incomplete, the equation (17) is used. the crisp matrix is derived. rc = i (rd) = a1 a2 a3 a1 a2 a3   0.5000 0.3000 0.30000.7000 0.5000 0.4800 0.7000 0.5200 0.5000   (27) step 2 the operator of the crisp matrix is shown in equation (6). we can get the judgement matrix as is shown in equation (28). rtr = a1 a2 a3 a1 a2 a3   1.0000 0.4152 0.41522.4082 1.0000 0.9159 2.4082 1.0918 1.0000   (28) step 3 same as the third step of the method proposed in this article in example 1. the weights can be obtained. the cr can be calculate by equation (3). finally, the weights of three alternatives a1, a2, a3 are: 0.1718 0.3989 0.4292, and cr = 0.0013 < 0.1. sort the weight of three alternatives from the largest to the smallest, and the ranking from the best to the worst can be a3, a2, a1. 4.2.3 discussion of example 2 in this example, as discussed in the first paragraph in section 4.2, the reasonable ranking of the alternatives should be a3 � a2 � a1. the weights of the alternatives by the proposed method are 0.1718 0.3989 0.4292, which is consistent with the conclusion made in section 4.2. however, using deng et al.’s method, the ranking of the alternatives is a2 � a3 � a1, which is inconsistent with the actual situation. the reason for this result is that deng et al’s method directly uses the distance ratio to divide the reliability of the unknown part into the information we know from the beginning, which lacks solid physical foundation. 4.3 example 3 rd = a1 a2 a3 a4 a1 a2 a3 a4  {(0.5, 1.0)} {(0.1, 1.0)} {(0.6, 0.8)} {(0.3, 0.6)} {(0.9, 1.0)} {(0.5, 1.0)} {(0.8, 1.0)} {(0.6, 1.0)} {(0.4, 0.8)} {(0.2, 1.0)} {(0.5, 1.0)} {(0.9, 1.0)} {(0.7, 0.6)} {(0.4, 1.0)} {(0.1, 1.0)} {(0.5, 1.0)}   (29) in the third example, the value of the pairwise comparison of a4 and a1 is greater than the value of the pairwise comparison of a3 and a1 and both are greater than 0.5, indicating that a4 is superior to a3. however, the pairwise comparison value of a3 and a4 is greater than 0.5, indicating that a3 is superior to a4. that is to say, d matrix has obvious inconsistency. 4.3.1 deng et al.’s method the processing method is the same as the deng et al.’s method in example 2. we can get the crisp matrix and the probability matrix as are shown in equation (30) and equation (31). further i.d. can be obtained, according to the following calculation. i.d. = 0.1 + 0.8 4 (4 − 1) /2 = 0.15 https://doi.org/10.15837/ijccc.2022.5.4468 13 rc = a1 a2 a3 a4 a1 a2 a3 a4  0.5000 0.1000 0.4800 0.1800 0.9000 0.5000 0.8000 0.6000 0.3200 0.2000 0.5000 0.9000 0.4200 0.4000 0.1000 0.5000   (30) rc = a1 a2 a3 a4 a1 a2 a3 a4  0.0000 0.0000 0.9000 0.2000 1.0000 0.0000 1.0000 1.0000 0.1000 0.0000 0.0000 1.0000 0.8000 0.0000 0.0000 0.0000   (31) 4.3.2 the proposed method the processing method is the same as the method proposed in this article in example 2. we can get the crisp matrix and the judgement matrix in equation (32) and equation (33). we can get cr according to the equation (3). rc = a1 a2 a3 a4 a1 a2 a3 a4  0.5000 0.1000 0.5800 0.6000 0.9000 0.5000 0.8000 0.6000 0.4200 0.2000 0.5000 0.9000 0.4000 0.4000 0.1000 0.5000   (32) rtr = a1 a2 a3 a4 a1 a2 a3 a4  1.0000 0.1724 1.4228 0.5902 5.7995 1.0000 3.7372 1.5519 0.7036 0.2676 1.0000 5.7995 1.6944 0.6444 0.1724 1.0000   (33) finally, the weights of alternatives a1, a2, a3, a4 are: 0.1300 0.4746 0.2549 0.1406 and cr = 0.3952 > 0.1. 4.3.3 discussion of example 3 from the first column of the d number matrix given in example 3, it can be seen that a3 is worse than a1 with a greater probability, and a4 is better than a1 with a greater probability. however, a3 can be obtained a4 is less than a3 with a particularly large probability, which is obviously in conflict. in other words, the elements (evaluations given by experts) of this d number matrix is inconsistent. using the proposed method, cr is close to 0.4 and far greater than 0.1, which correctly illustrates this point. however, using deng et al.’s method, the consistency index is 0.15 which shows that the d number matrix is consistent. this is obviously inappropriate. 4.4 discussion and comparison deng et al.’s method proposed an interesting method, i.e., the ahp method extended by d numbers preference relation, which can deal with the complex decision problem under uncertain environment [39]. however, some situations cannot be well handled in deng et al.’s method. three examples in this section shows the certain situations. from these examples, some conclusions can be made. (1) the proposed method can handle situations that cannot be handled by deng et al.’s method, as is shown in example 1. (2) for the processing of incomplete information, this article considers that the incomplete information is because some of the information is unknown, and the unknown part should be expressed as the same importance of the two alternatives to be pairwise compared, that is, they cannot be distinguished. in the proposed method, 0.5 is multiplied by the degree of uncertainty. this method of expression is more in line with thinking habits and has a clearer physical meaning. deng et https://doi.org/10.15837/ijccc.2022.5.4468 14 global supplier selection cost(c1) qualitiy(c2) service performance(c3) supplier s profile(c4) risk factor(c5) supplier s1 supplier s2 supplier s3 a1 level 1: overall objective (o) level 2: criteria (c) level 3: attributes (a) level 4: decision alternatives (s) a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 a18 a19 figure 2: the hierarchical structure for the supplier selection al.’s method redistributes information according to distance which may not work out well under certain situations. meanwhile, information is artificially added during the redistribution process. example 2 shows this point. (3) in the evaluation of consistency of d matrix, the consistency ration cr in ahp can be directly adopted in the proposed method since a bridge (the transformation functions) is built between the crisp matrix of d matrix and the judgement matrix, which has a relatively sufficient theoretical basis. the consistency evaluation process in deng et al.’s method may draw intuitive results. in example 3, the consistency indicator obtained is unreasonable and shows this point. (4)as can be seen in figure 1, the proposed method is more simple and convenient to use. in summary, the proposed method has the merit of dealing with uncertain information, and can also improve the flexibility, efficiency and performance compared with deng et al.’s method. 5 case study in this section, the problem of selecting the best supplier [44] is adopted to illustrate the use and effectiveness of the proposed method. the hierarchical structure of the problem is shown in figure 2 [44]. as shown in the figure, there are four levels. the overall objective is placed at the first level. the second level is the criteria. the third level is the attributes. the final level is the decision alternatives. the second level is consisted of five components. it contains the product’s cost(c1), product’s quality(c2), service performance (c3), supplier’s profile(c4) and factor of the risk(c5). each item of the third level corresponds to each item of the second level. the define of attribute is as follow. a1: product’s total price; a2: prices of delivery; a3: tariff and custom duties; a4: rejection rate of the product; a5: increased lead time; a6: quality assessment; a7: remedy for quality problems; a8: delivery schedule; a9: technological and support; a10: response to changes; a11: ease of communication; a12: financial status; a13: customer base; a14: performance history; a15: production facility and capacity; a16: geographical location; a17: political stability; a18: political stability; a19: terrorism. more details are given in [44]. for this problem, we can construct the d matrix at each level based on expert evaluation. for example, we can construct d matrix between the overall objective and criteria in table.3 [39]. according to the method proposed in the paper, we can get the priority weights of criteria. wdeng and wp roposed on the last two columns of table 3 respectively represent the relative weights obtained by deng et al.’s method and the relative weights obtained by the proposed method. it can be seen that the weights derived by deng et al’s method and the proposed method are consistent. in a similar way, we can get the d matrix of the attributes corresponding to the criteria, and then calculate the priority weights of the attributes corresponding to the criteria as shown from column 2 to column 6 of table 4. by integrating the weights of the criteria level and attributes level, we can get the contribution ratio and ranking of attributes relative to the overall objective. the results are shown on column 7 of table 4, and you can see that the most important attribute is a1. it can be https://doi.org/10.15837/ijccc.2022.5.4468 15 0 0.05 0.1 0.15 0.2 0.25 0.3 a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 a18 a19 w e ig h ts attributes deng et al.'s method proposed method figure 3: compare deng et al.’s method with the proposed method to produce the weights of attributes with respect to the overall objective table 3: d numbers preference relation of criteria with respect to the overall objective. o c1 c2 c3 c4 c5 wdeng wp roposed c1 {(0.5,1.0)} {(0.6,1.0)} {(0.65,0.6)} {(0.85,1.0)} {(0.9,1.0)} 0.3650 0.3786 c2 {(0.4,1.0)} {(0.5,1.0)} {(0.8,1.0)} {(0.7,1.0)} {(0.7,1.0)} 0.3150 0.2929 c3 {(0.35,0.6)} {(0.2,1.0)} {(0.5,1.0)} {(0.65,1.0)} {(0.6,1.0)} 0.1650 0.1520 c4 {(0.15,1.0)} {(0.3,1.0)} {(0.35,1.0)} {(0.5,1.0)} {(0.55,1.0)} 0.0900 0.0917 c5 {(0.1,1.0)} {(0.3,1.0)} {(0.4,1.0)} {(0.45,1.0)} {(0.5,1.0)} 0.0650 0.0849 seen from figure 3 that the weights of attributes with respect to the overall objective obtained by deng et al.’s method and proposed method is not significantly different. in the same way, we can construct the d matrix of suppliers corresponding to attributes. from column 3 to column 5 of table 5 shows the priority weight of suppliers with respect to each attribute. the final weight of suppliers with respect to the overall objective is obtained at the bottom of table 5. figure 4 shows the final result of using deng et al.’s method and the proposed method to deal with the problem, it can be seen that the suppliers generated by the two methods have the same ranking: s1 � s3 � s2. the result is consistent with the expert’s final assessment. furthermore, the results obtained by the method in this paper are more accurate, and the difference of priority weights between two suppliers is larger, which indicates that the division of different suppliers is more obvious. the reason for this may be concluded that the proposed method deal with the uncertain information more reasonably, especially with the incomplete information cases. 6 conclusion in this study, a new supplier selection method based on d numbers and transformation function is proposed. firstly, d numbers theory is used to express the uncertain information in preference relation and form a d matrix; secondly, the d matrix is converted into a crisp matrix form according to different situations whether or not the information in d matrix is complete. thirdly, the crisp matrix are converted into judgement matrix by using the transformation functions. finally, ahp is used to select the best supplier. the proposed method has the following advantages: (1) due to the characteristics of d numbers itself, the method in this paper is more advantageous in the expression of uncertain information. (2) be able to deal with a variety of situations, including situations with large uncertainties in expert opinions or situations with similar decision-making objects. (3) the consistency evaluation index is consistent with the actual situation and has a good theoretical basis. in the future research, the integration rules of d numbers should be further improved to make full use of incomplete information rather than simple discount processing. in addition, the method in this https://doi.org/10.15837/ijccc.2022.5.4468 16 table 4: the weights and ranking of attributes with respect to the overall objective based on proposed method. criteria c1 c2 c3 c4 c5 weight ranking0.3786 0.2929 0.1520 0.0917 0.0849 attributes a1 0.6330 0.2397 1 a2 0.2330 0.0882 3 a3 0.1340 0.0507 8 a4 0.4620 0.1353 2 a5 0.1870 0.0548 7 a6 0.2630 0.0770 4 a7 0.0880 0.0258 13 a8 0.4610 0.0701 5 a9 0.2010 0.0306 11 a10 0.2360 0.0359 10 a11 0.1020 0.0155 14 a12 0.6180 0.0567 6 a13 0.0970 0.0089 18 a14 0.1670 0.0153 15 a15 0.1180 0.0108 17 a16 0.1700 0.0144 16 a17 0.4400 0.0374 9 a18 0.3200 0.0272 12 a19 0.0700 0.0059 19 table 5: the priority weights and ranking of suppliers with respect to the overall objective based on the proposed method. attributes suppliers ai s1 s2 s3 a1 0.2397 0.4670 0.1660 0.3670 a2 0.1353 0.4270 0.1960 0.3770 a3 0.0882 0.6170 0.1670 0.2160 a4 0.0770 0.5340 0.2330 0.2330 a5 0.0701 0.5410 0.1180 0.3410 a6 0.0567 0.2830 0.5330 0.1840 a7 0.0548 0.5930 0.1640 0.2430 a8 0.0507 0.4170 0.1160 0.4670 a9 0.0374 0.6170 0.0660 0.3170 a10 0.0359 0.4330 0.4330 0.1340 a11 0.0306 0.6730 0.0140 0.3130 a12 0.0272 0.3330 0.3330 0.3340 a13 0.0258 0.6000 0.2000 0.2000 a14 0.0155 0.3170 0.5170 0.1660 a15 0.0153 0.3240 0.0230 0.6530 a16 0.0144 0.6000 0.3000 0.1000 a17 0.0108 0.4330 0.2340 0.3330 a18 0.0089 0.2930 0.5130 0.1940 a19 0.0059 0.5870 0.1060 0.3070 proposed method 0.4858 0.2054 0.3089 supplier’s ranking 1 3 2 https://doi.org/10.15837/ijccc.2022.5.4468 17 0.0 0.1 0.2 0.3 0.4 0.5 s1 s2 s3 p ri o ri ty w e ig h t supplier proposed method deng et al.'s method figure 4: compare deng et al.’s method with the proposed method to produce the final priority weights paper did not consider the interaction between indicators, so anp and other methods were considered for improvement. acknowledgments the work is partially sponsored by shanghai natural science foundation (grant no.19zr1420700), shanghai rising-star program (grant no. 21qa1403400), shanghai key laboratory of power station automation technology (grant no. 13dz2273800). author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] pelissari, r.; khan, s.; ben-amor, s.(2021). application of multi-criteria decision-making methods in sustainable manufacturing management: a systematic literature review and analysis of the prospects, international journal of information technology & decision making, 2(21),493– 515, 2021. 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(2022). supplier selection model based on d numbers and transformation function, international journal of computers communications & control, 17(5), 4468, 2022. https://doi.org/10.15837/ijccc.2022.5.4468 introduction preliminaries analytic hierarchy process (ahp) saaty1982analytic d numbers deng2014 transformation functions herrera2004some proposed method introduction of deng et al.'s method deng2014 an improved d-ahp method d matrix priority weights of alternatives based on d matrix summary of case 1 and case 2 numerical examples example 1 deng et al.'s method the proposed method discussion of example 1 example 2 deng et al.'s method the proposed method discussion of example 2 example 3 deng et al.'s method the proposed method discussion of example 3 discussion and comparison case study conclusion int j comput commun, issn 1841-9836 8(5):754-759, october, 2013. a quick location method for high dynamic gnss receiver based on time assistance p. wu, s. jing, w. liu, f. wang peng wu*, shourang jing, wenxiang liu, feixue wang college of electronic science and engineering, national university of defense technology, changsha 410073, china wp4nnc@gmail.com, hanchongjsr@163.com, liuwenxiang8888@163.com, wangfeixue365@sina.com *corresponding author: wp4nnc@gmail.com abstract: traditional a-gps positioning method when quickly calculate a position, need a condition that the approximate position must not exceed 150km, otherwise the calculation will be very complex. this paper proposes a time-assisted fast positioning method for high dynamic gnss receiver, effectively solving the problem of large search calculation in traditional method, even if exact position is unknown after the signal is recaptured. according to the known auxiliary time information and implied elevation information, this paper put forwards a custom coordinate system for building twodimensional search space, which could reduce the number of search-dimensions. it proposes a search method based on receiver clock calculated by analyzing the influence of time auxiliary accuracy. by using gps ephemeris data provided by the igs, it builds a simulation environment and analyzes the influence of different preferred satellites based on the custom coordinate system on the calculation, and thus puts forward a principle for choosing the preferred satellites. simulation examples show that through the rational combination of satellites to create a custom coordinate system, and when time auxiliary accuracy is less than 60us, the calculation can 100% guarantee to restore a complete satellite signal emission time and obtain an accurate position. keywords: global navigation satellite system(gnss), signal transmission time recovery, high dynamic, time to first fix(ttff). 1 introduction since the low speed of the message affects the speed of frame synchronization [1], the traditional non-auxiliary gps receiver can not obtained the complete time of satellite signal emission instantly, which will affect ttff (time to first fix). take the gps l1 as an example, from catching to accomplishing code phase synchronization of several satellites, ordinary receivers take one or two seconds to receive the time of satellite signal emission. but it takes about fifteen seconds to accomplish bit and frame synchronization by message, and receivers can not locate before frame synchronization completed. through auxiliary methods [2, 3], before frame synchronization, receivers can recover the milliseconds of the signal emission to accelerate ttff. however, there are some conditions when using auxiliary gps, that is only when the error of location estimation is not beyond 150km, can the receiver recover instantly the milliseconds of the satellite and calculate the exact position of the receiver [4–6]. when the receiver is in high dynamics, such that when the design speed reaches 900m/s, and when it lost the signal for a while, such that when the off power and loss of lock time is beyond 167s, the scope of the general location will be beyond 150km, which will increase the work of calculation. literature [7, 8] proposes to reduce the calculation in searching through narrowing the searching area by visibility of satellite and through removing unreasonable values by calculation. but this method is still complex, for it is based on the calculation of the receiver’s dimensional position and clock error, copyright c⃝ 2006-2013 by ccc publications a quick location method for high dynamic gnss receiver based on time assistance 755 in which the dimensional search does not make use of information hidden on the surface of the earth. literature [9–11] puts forward a kind of location by atomic clock assisted. this paper puts forward a new idea that when calculating the general location, the four unknown numbers (the three dimensions and clock error) can be reduced to two (the two dimensions of the receiver on the earth), which is a combination of a custom coordinate system and the hidden information on the earth of the receiver. when calculating the two dimensions, there is new two-dimensional searching space and a searching method based on receiver clock calculated. 2 building the searching space with auxiliary time information and elevation information, search of the user’s location is a search of the two dimensions in the horizontal level. to be convenient, the two dimensions in the horizontal level is redefined by the custom polar coordinate. a b o x y z γ α c d t τ figure 1: custom coordinate system as the graph shows, o represents the earth, with a and b two satellites. satellite a and satellite b who build the custom coordinate system are called the preferred satellites for the convenience, while other satellites are called validate satellites. in the custom right-angle coordinate system, o is origin, −→ oa is axis z, axis y is vertical toward plat −−−→ oab, and axis x is vertical toward plat −−−→ oy z and on the left hand of yz. the unit vector e⃗x, e⃗y, e⃗z of x, y, z can be stated as e⃗x = e⃗y×e⃗z |e⃗y×e⃗z| , e⃗y = −→ oa× −−→ ob∣∣∣−→oa×−−→ob∣∣∣, e⃗z = −→ oa∣∣∣−→oa∣∣∣ . “×” represents outer calculation of the vector. therefore, the arbitrary point c can be represented as (−−→ oc · e⃗x, −−→ oc · e⃗y, −−→ oc · e⃗z ) in the system. suppose satellite a arrives at the receiver signal ac, whose intersection with plat xy is d. then the physical meaning of the circle ctd they form is a collection of the same transmission time delay of the satellite signal when it arrives at the earth. bd represents the shortest distance when satellite b arrives at the receiver, on the condition that time delay of satellite a is in certainty. then the problem of satellite b’s obscure time delay is to calculate the length of bd. r is the distance between the satellite and the earth center, such as oa. τ is the intersection angle of two satellites against the earth center. r is the distance between the receiver and the earth center, such as od, oc. the result is a combination of the radius of the earth semi-major and the elevation the receiver estimates. 756 p. wu, s. jing, w. liu, f. wang we can see that when r is known by the estimated elevation, the receiver in the horizontal level is decided by angle α and angle γ. it will be clearer when we cite the polar coordinate in threedimension. the referential value is the length between the point and o. the first referential angle is the intersection angle α of aod, and the second referential angle is the intersection angle γ of ctd against the surface of circular cross-section. then the coordinates of a, b, c in the space can be presented as a : (r,0,0), b : (r cosτ,r sinτ,0), c : (r cosα,r sinαcosγ,r sinαsinγ). then: ac = √ r2 + r2 − 2rr cosα, bc = √ r2 + r2 − 2rr cosαcosτ − 2rr sinαsinτ cosγ 3 search method on the basis of clock error c c′ α α γ γ r r a o c′ c e β a b c figure 2: influence on receiver position by time bias as the graph shows, c is the real location of the receiver, with coordinate (r, α1, γ1). c′ is the deviated location caused by clock error, with coordinate (r, α2, γ2). according to the custom coordinate system, coordinate values of c and c′ in the space are: c : (r cosα2,r sinα2 cosγ2,r sinα2 sinγ2), c′ : (r cosα1,r sinα1 cosγ1,r sinα1 sinγ1). suppose ∆α = α2 −α1, ∆γ = γ2 −γ1, local time error is ∆b, error of the direction vector −−→ c′c is  x⃗ = cosγ1 |ac| r tanα1 ∆b − |ac| r sinτ ( 1 − |ac| |bc| cosτ + |ac| |bc| cotα1 sinτ cosγ1 ) ∆b y⃗ = sinγ1 |ac| r tanα1 ∆b + |ac| r sinτ tanγ1 ( 1 − |ac| |bc| cosτ + |ac| |bc| cotα1 sinτ cosγ1 ) ∆b z⃗ = − |ac| r ∆b namely, −−→ c′c is the only value that corresponds ∆b, so it can be presented as −−→ c′c = f (∆b). a quick location method for high dynamic gnss receiver based on time assistance 757 from the validate satellite e, we can search for packs in the space according to the geometric relations and determine the vectors −−→ c′c and −−→ ec′ as well as the included angle β, thus working out −−→ ec. assuming that these vectors are a, b, c, then we have the following expression:  a = |cc′| = f (∆b) b = |ec′| = c + ∆b c = √ a2 + b2 − 2abcosβ the only unknown in the equation set ∆b can be solved by simultaneous equation. after working out the ∆b, we can compute the variance of clock error packs and make the minimum variance as the truth value due to the uniformity of ∆b of different validate satellites. 4 experiment and analysis for the experiment, gps constellation of igs ephemeris is selected, the time being 25 december 2011. when the receiver is at a position of n28.2◦e112.9◦, a total of ten satellites are visible and their distribution and position are as follows: 30 210 60 240 90270 120 300 150 330 180 0 15 30 45 60 75 90 1 4 7 8 11 17 19 20 27 31 figure 3: sky charts of visual satellites the chosen sphere of movement of the receiver is 10◦n∼30◦n, 120◦e∼140◦e. simulation test imitates the movement of the receiver within this sphere at a pace of 1◦ per movement. height precision error is set at no more than 10km, time auxiliary error at 0∼200us. in order to illustrate the impact on the calculation by the two satellites, we select two pacts: the first pact is of no. 1 and no. 4 satellites, and the second pact no. 8 and no. 20. in order to illustrate the impact of different pacts of satellites on algorithms, we compare two pacts to show the success rate distribution of both ways of calculation when there are 2, 4, 6 and 8 satellites involved. the results are as follows: the above result shows that: 1. using auxiliary time information, the two pacts can both lead to the launching time of the satellite correctly. but the success rate, as it is subject to the precision of the time auxiliary, is more or less reduced as the error increases. 2. normally, the more the validate satellites the higher success rate. in reality, however, the success rate of pacts of two satellites is remarkably lower than those of pacts of more than 758 p. wu, s. jing, w. liu, f. wang 0 2 4 6 8 10 12 14 16 18 20 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 clock error / 10us s u c c e s s r a te / % verified by 2 satellites verified by 3 satellites verified by 4 satellites verified by 8 satellites 0 2 4 6 8 10 12 14 16 18 20 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 clock error / 10us s u c c e s s r a te / % verified by 2 satellites verified by 3 satellites verified by 4 satellites verified by 8 satellites figure 4: success rate of algorithm in different conditions two satellites. it means that when the number of validate satellites is more than two, an increase in the number of validate satellites does not lead to an obvious increase in success rate. 3. the algorithm of the first pact of first choice satellites does not guarantee a success rate of 100%. even if the clock error is 0, the success rate is only as high as 98%. as the clock error increases, the success rate drops quickly enough to render the result virtually useless. 4. the second pact of first choice satellites can yield a success rate of 100% when the clock error is below 60us. when the error increases, the success rate drops at a lower rate than with the first pact. therefore, it can be applied to cases of a wider range of time errors. 5 conclusion to sum up, to safeguard the success rate of algorithm, we can determine the principle of first choice satellite selection. in other words, when selecting preferred satellites, we must work out the area where the receiver can capture the signal of all the satellites according to the distribution of visible satellites. the selection principle is: 1. select pacts on the same side on the verge of the area. make sure that the line connecting the first choice satellites is nowhere near the top of the receiver. 2. under the above principle, select pacts of satellites with greater distances. the first principle can overcome the situation when pdop value is infinite. the second principle is conducive to further reducing pdop value and increasing the application scope of local time auxiliary precision. it is also good for reducing scope of searching space and reducing calculation load. a quick location method for high dynamic gnss receiver based on time assistance 759 bibliography [1] kaplan, e. d., hegarty, c. j.; understanding gps: principles and applications, 2nd ed. norwood, ma, artech house, 2006. 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[13] wu peng, wang feixue. the method and analysis of dop calculation in three-satellite positioning in bd system, the second sector of the chinese satellite navigation conference, (csnc). shanghai, 2011. international journal of computers communications & control issn 1841-9836, 10(3):370-381, june, 2015. applications of ubiquitous sensor network: micro-scale air quality monitoring s. lee, s. lim sangdong lee korea institute of science technology information (kisti) 245 daehangno, yuseong, daejeon 305-806, korea sdlee@kisti.re.kr sang boem lim department of advanced technology fusion and division of interdisciplinary studies konkuk university, seoul, korea 1 hwayang-dong, kwangjin-gu seoul 143-701, korea *corresponding author: sblim@konkuk.ac.kr abstract: the rise of densely populated societies in metropolitan areas has led to several forms of pollution, thereby leading to increasing health concerns. among the various types of pollution, air pollution has become a crucial factor in the quality of life. currently, the meteorological administration in korea provides a regional scale of air quality index. however, this broad-based information is not relevant to local urban areas. we believe that air quality information about local environments such as homes, workplaces, and parks would be more relevant to people. we have developed a micro-scale air quality monitoring system called airscope that covers local environments using a ubiquitous air quality sensor network. in this study, we focus on the applications of airscope. we have developed airscope as a general-purpose desktop pc application as well as a mobile application that can increase user mobility and provide real-time air quality information about the local user environment. keywords:air pollution monitoring, usn, airscope, mobile application 1 introduction the rise of densely populated societies in metropolitan areas has led to several forms of pollution, thereby leading to increasing health concerns. among the various types of pollution, air pollution has become a crucial factor in the quality of life. however, currently, there is no public access to information about air quality. efforts that provide air quality information and hence air pollution warnings are restricted to very few areas. a majority of such information is provided as an air quality index for entire cities. such an index cannot provide information that adequately represents the air quality in local urban areas in a city. currently, the meteorological administration in korea provides a regional scale of air quality index. however, this broad-based information is not relevant to local urban areas. we believe that air quality information about local environments such as home, workplaces, and parks would be more relevant to people. to this end, we have developed a micro-scale air quality monitoring system called airscope that covers local environments using a ubiquitous air quality sensor network. our previous studies have already provided a general overview of the airscope system [1] [2]. a primary feature of airscope is that it can be run as a smartphone application. smartphones provide the advantages of mobility and access to information from anywhere at any time. further, applications for smartphones directly target the end-user, and thus, the smartphone is particularly suited for providing air quality information. in this study, we focus on the applications of airscope. we have developed airscope as a general-purpose desktop pc application as well as a mobile application that can increase user copyright © 2006-2015 by ccc publications applications of ubiquitous sensor network: micro-scale air quality monitoring 371 mobility and provide real-time air quality information about the local user environment. these applications are examples of airscope usage which can be adopted as a service of company that is interested in real-time air quality service. in order to provide our services to the other research group or companies, we offer open api of airscope. this paper is mainly demonstration of usage of airscope using its open api. the desktop and smartphone user applications are discussed in section 2, while section 3 provides a brief introduction to airscope. two of the most important sub-systems of airscope local airscope and global airscope are explained in sections 4 and 5. 2 user applications our desktop application is designed to provide a wide variety of air quality information services. on the other hand, our mobile application is designed to increase user mobility and provide real-time location-based air quality information. common services for both the desktop and mobile applications include maps, charts, and 3d services. the desktop application provides a rich set of graphics, useful environment information, and high 3d performance, while the strength of the smartphone application is its location-based service. in the study, we have selected iphone, which provides gps services, as our choice of smartphone. the application uses gps information to provide location-based airscope services. this means that when a user launches the airscope application using his/her smartphone, the map service automatically detects his/her current location and displays it on the map. 2.1 desktop application the desktop application provides real-time data services using maps, one-day data services using charts, data search and bookmark services, and local environmental information. fig. 1 shows the airscope desktop portal interface. figure 1: desktop portal interface of airscope 372 s. lee, s. lim in the study, we have used google maps [3] for real-time data services. a marker that is positioned at the top of the map indicates the location of the air quality sensors. the portal requests data regarding the sensor location from global airscope when the real-time data service is launched. global airscope replies to the portal with the name of the location, longitude and latitude of sensor, and current sensor data. upon receiving this information, the airscope portal displays a marker at the top of the map. when the user clicks on this marker, the current air quality information is displayed by smartwindow (see fig. 2). smartwindow shows an image of the building in which the sensor is located and the current value of temperature, humidity, co and co2 concentrations, and pm-10 pollution levels. further, smartwindow provides the local environment data for the previous 24 hours when the user clicks on the average button. fig. 3 shows the temperature data for the past 24 hours. figure 2: display of real-time sensor data in smartwindow the periodic air quality data service (fig. 4) provides air quality information over certain time periods. the goal of this service is to provide a general idea of the amount of pollutants for specific areas; thus, the user can compare the amount of pollutants between different areas. in order to use this service, the user begins by choosing the relevant start and end data. the user can select one or more sensor locations and the pollutant whose concentration he/she wants to check. the selection provides the user with a bar graph of the concentrations of the selected pollutant for comparison. further, the application provides a line graph of the pollutant for online access. this graph allows the user to study changes in the amount of pollutants. the iphone application also provides the same information. the difficulty in identifying a single location on a map is addressed by the airscope systems bookmarker service; the bookmarker service allows faster search and easier map navigation. this service works similar to the bookmark feature in any internet browser. a user can bookmark his/her choices of locations in the airscope portal by create a location folder. subsequently, he/she can click on a location in the map and enter the location name. the user can revisit a location by simply clicking on the saved bookmark. applications of ubiquitous sensor network: micro-scale air quality monitoring 373 figure 3: one-hour average data service for local sensor environment figure 4: . real-time monitoring service 374 s. lee, s. lim 2.2 mobile airscope until 2009, iphone sdk 2.0 [4] did not support map api development. at that time, we developed the airscope map service in the server by using google maps. in addition, we developed the web-based application with the iphone browser. in 2010, iphone sdk 3.0 [5] included map development api libraries called the mapkit framework. our test results with sdk 3.0 showed a significant increase in process speeds and reliability of our application. therefore, we redesigned and rebuilt the mobile airscope application for iphone sdk 3.0. this has resulted in maximized use of sdk 3.0s enhanced touch features and cursor movement speed when compared with our previous version. the loading speed of text and pictures is also enhanced because the browser component does not require loading. the application interface has been retained with increased reliability of the mobile application. the smartphone application essentially provides the same functionalities that are available with the desktop application. the application includes real-time data services, sensor data chart services, and computational fluid dynamic (cfd) modeling (fig. 5). a characteristic mobile service is a location-based user service. the gps data from the smartphone is used for the map and notification services. the map service automatically displays the users current position and notifies him when the air quality in the environment changes. figure 5: services of mobile airscope 2.3 airscope open api applications recent emergence of web 2.0 [5], most of services are provided by the form of open api [6]. currently, various projects are using ad providing open api to fulfill openness and scalability of system. project areas are including but not limited to mobile network api [7], enterprise application integration [8], and even in area of biology research [9] and geographic information system (gis) research [10]. particularly micro-scale environment services are used in variety of applications. to support these services and an api, we are providing open api of our airscope system. a company, called green ecos, developed a mobile application using our open api. in this application, they provide real-time sensor data, historical chart, air quality augmented reality (ar) and virtual reality (vr) application of current air quality, social network service connecapplications of ubiquitous sensor network: micro-scale air quality monitoring 375 tion, and tracking of expose to harmful meterals. other than social network service connection, they are using airscope open api to provide services. once they get sensor data using open api, they will process data for their services. fig. fig:gre shows ui of green ecos application. figure 6: green ecos application 3 airscope system in this section, we provide an overview of the airscope architecture, including the specifications of sensors, communication between the systems involved, and the need and use of cfd modeling data. there are various ubiquitous applications using wireless sensor network researches [11] [12]. each project has different goals. former project targeted integrate development of an application between domain experts and network experts without specific knowledge of each others area. later research uses bicycle to increase mobility. our project targeted general audience to use our system. local airscope, global airscope, and their user applications are discussed in the following section. 3.1 general architecture fig. 7 shows the general architecture of airscope. our system comprises three important components local airscope, global airscope, and their end-user applications. a portion of our study includes the installation of several sensors (approximately 1,000 to 10,000 sensors) in one district in seoul (which comprises 25 districts). the impossibility of handling all the data by one server led to the plan of having one or more servers in a district (based on its size and population). we call this server as local airscope. it is expected to handle one area comprising the installed sensors and collect and process the sensor data in its operational area, store the data in a local database (db), and manage the sensors. local airscope can solve the problem of server overloading. however, we require a mechanism to manage and gather information from local airscope to provide end-user services. global airscope is used to address this issue. this server manages local airscopes, distributes queries to obtain air quality information, processes the received data, and sends information to the user application. in this study, we have created the desktop portal and smartphone applications; however, there is no limit on the number and 376 s. lee, s. lim types of user applications. our architecture has been designed to accept any user applications regardless of the platforms and programming languages used. figure 7: architecture of airscope system 4 local airscope local airscope is a system that manages the collected data from the sensors and cfd model and provides requested data to the user to provide micro-scale air quality monitoring services. in our study, the purpose of this system was to parse and store data from both the sensors and the cfd model; further, it was required to manage sensor information such as name, type, and location. we assumed the presence of more than one local airscope system in the study because the actual implementation of the system will involve the installation of several thousand sensors in one of the districts in seoul. the large quantities of data generated will require many servers. for the purposes of the study, we assume one local airscope system per district but the actual coverage of this system is expected to depend on the number of sensors in the area. in the following section, we describe the architecture, data packet protocol, data processing, event generation, and db schema of local airscope in detail. 4.1 architecture of local airscope local airscope system (fig. 8) comprises three layers communication layer, data processing layer, and data storage layer. the communication layer manages communication between local airscope and global airscope and between local airscope and the sensor gateway. by means of this layer, one can send data requests and responses and obtain data from the gateway. the data processing layer is responsible for managing sensor data, cfd data, and sensor information. applications of ubiquitous sensor network: micro-scale air quality monitoring 377 all the actual sensor data, cfd modeling data, and sensor information are stored in the data storage layer. figure 8: local airscope architecture the communication layer uses web service and socket as components of the communication module. we have explained the web service communication module in a previous section. the web service (restful) is used for communication with global airscope. among the current web service technologies, we used a light weight communication package with a light weight data format called rest and json, respectively. the socket communication module is used between local airscope and the sensor gateway to collect sensor data. all the sensors send data to the sensor gateway which in turn sends data to local airscope. the reason for the use of socket communication is due to the sensor gateway that uses embedded linux as its operating system; embedded linux does not provide any communication method other than socket communication. the data processing layer contains management modules for sensor data, cfd modeling data, sensor information, and event processing. the main task of the sensor data management module is to obtain sensor data from the communication module, store data in the server db, and respond to data requests from global airscope. the sensor data from the communication module is in the form of 39-byte hexadecimal raw data. this data cannot be understood by humans unless it is processed according to the sensor manufacturers protocol. the sensor data management module processes the incoming raw sensor data (fig. 9) that contains temperature, humidity, co, co2, and pm-10 information into meaningful data and stores this processed information in the sensor db. this raw data also contains a sensor id to pinpoint the data source. this id acts as a prime key to the db. the cfd modeling data management module manages data from the cfd modeling that can be considered as data from virtual sensors in order to overcome the shortage of sensors in our test-bed. this allows us to provide micro-scale air quality information without the installation of many actual sensors. the virtual sensor data is obtained in double number formats (e.g., 0.495508e-10, 0647687e-11) that need to be processed in order to be understood. the cfd modeling data management module processes this data and stores it in the cfd modeling db. in addition, this module responds to data requests from global airscope for locations that do not have actual sensors. when a user registers with our system through the internet, his/her user id, name, age, 378 s. lee, s. lim figure 9: data parsing favorite location information, and his/her related medical history are collected. this information is managed by the sensor information management module. the favorite location is used to display a map of the environment when a user logs into the system. consequently, users do not have to recall their list of most frequented locations or their locations of interest every time. the users age, air-quality related ailments, and his/her favorite location information are used to alert the user about air quality via the smartphone application. our definition of an event is the occurrence of any unexpected circumstances such as those outside the upper and lower bounds of sensor data or the complete lack of any data from the sensor(s). such events require to be reported to the system administrator in order to correct the problem. when such an event occurs, the event generator module generates a proper message that contains the event information and sends it to the system administrator. further, this event generator can be activated when crucial events like fire occur. the data storage layer comprises three dbs that hold the actual data. each db is described in detail in the following section. 5 global airscope the main task of global airscope is to provide integrated query and management of the distributed local airscope servers and manage sensor and user information. in the early stage of our project, we did not design or accommodate a global airscope because our target area (the test-bed) was limited to a small number of sensors (fig. 10). therefore, all the system functionalities have been integrated into one sensor data management system. once our idea is implemented in the seoul metro area, we plan to divide our system functionalities into two systems local airscope to manage sensors and sensor data for small areas and global airscope to manage the various local airscopes and user information. in this section, we focus on the architecture and the various modules of global airscope. applications of ubiquitous sensor network: micro-scale air quality monitoring 379 figure 10: system architecture for existing sensor network 5.1 architecture of global airscope the main functionalities of global airscope include user authentication and authorization, integrated search capability, sensor management, and management of local airscopes (fig. 11). further, a service creator is added for user notification services. global airscope is queried for a service by users. in order to respond to the query, it collects data from local airscopes. global airscope integrates and processes the collected data and sends it back to the users device. figure 11: global airscope architecture one of the advantages of the system is the fact that users can use it without registration. however, users are provided with richer personalized services such as user notification and bookmark services upon registration; the registration procedure is simple and easy to use. in addition to general registration, we are collecting any disease information to provide personalized health service. this information can provide health alerts based on the air pollutant rates in his/her current location. further, users can bookmark their favorite or most frequented locations. when a user logs in to our system, the map automatically displays his/her residence and the surrounding 380 s. lee, s. lim area. because users may request for data from multiple local airscopes, we designed global airscope to perform and respond to distributed queries. this design provides better user accessibility. when data from multiple local airscopes are requested, global airscope queries the multiple local airscopes and obtains and integrates the relevant data. the integrated data is sent back to the user. global airscope manages local airscopes because it receives all the connecting information between local airscopes. the main purpose of local airscope management service is sensor management [13] [11] (such as automatic sensor failure detection and notification to the system administrator). the manual handling of data from large numbers of sensors is almost impossible, and it may cause critical problems. under abnormal circumstances such as sensor system failure or environmental disasters, a sensor generates erroneous data. the event generator module of local airscope has been mentioned in a previous section of the paper. if data outside the upper and lower bounds of the sensor are detected, local airscope generates an event and sends to it to global airscope for user notification. based on the event, global airscope generates a personalized message by utilizing the user information collected during registration; the age, sex, and the relevant medical history of the user are utilized to generate a personal message. this service is designed to send the message to the web portal on a desktop pc and to mobile devices including smartphones, feature phones, and ipads. the user can also receive the message via a windows widget and pop-up windows on the desktop pc without logging into the portal. the message format is generated to suit the capabilities of the users mobile device. for example, we can send a text message to a feature phone, while a combination of text and pictures can be sent to a smartphone. 6 conclusions we believe that air quality information about local environments such as homes, workplaces, and parks would be more relevant to people. to this end, we have developed a micro-scale air quality monitoring system called airscope that covers local environments using a ubiquitous air quality sensor network. a primary feature of airscope is that it can be run as a smartphone application. smartphones provide the advantages of mobility and access to information from anywhere at any time. further, applications for smartphones directly target the end-user, and thus, the smartphone is particularly suited for providing air quality information. our desktop application is designed to provide a wide variety of air quality information services. on the other hand, our mobile application is designed to increase user mobility and provide real-time location-based air quality information. common services for both the desktop and mobile applications include maps, charts, and 3d services. the desktop application provides a rich set of graphics, useful environment information, and high 3d performance, while the strength of the smartphone application is its location-based service. we provide an overview of the airscope architecture, including the specifications of sensors, communication between the systems involved, and the need and use of cfd modeling data. local airscope is a system that manages the collected data from the sensors and cfd model and provides requested data to the user to provide micro-scale air quality monitoring services. in our study, the purpose of this system was to parse and store data from both the sensors and the cfd mode. the main task of global airscope is to provide integrated query and management of the distributed local airscope servers and manage sensor and user information. applications of ubiquitous sensor network: micro-scale air quality monitoring 381 bibliography [1] woo, j. et al (2011); constructing u-city of seoul by future foresight analysis, concurrency and computation: practice and experience, wiley, issn 1532-0626, 23(10):1114-1126. 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[14] jiang, n. et al (2014); distributed compressed sensing algorithm for cluster architectures of wsns, international journal of computers communications & control (ijccc), issn 1841-9844, 9(4):430-438. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 6, month: december, year: 2022 article number: 4588, https://doi.org/10.15837/ijccc.2022.6.4588 ccc publications design of moving coverage algorithm of ecological monitoring network for curved surface song liu, runlan zhang, yongheng shi song liu key laboratory of information, computing science, guizhou normal university guiyang 550001, china runlan zhang information technology college, guizhou vocational technology institute guiyang 550023, china corresponding author: rlzhang@gztvu.cn yongheng shi information technology college, guizhou vocational technology institute guiyang 550023, china abstract micro-structured sensors that can perceive and communicate at the same time have emerged as a result of the quick growth of microelectronics technology, wireless communication technology, and sensor technology. this sensor has the ability to sense many types of environmental data, gather it at the sink node, and then send it to the data centre. in the civic, industrial, agricultural, military, and other domains, wireless sensor networks are frequently employed. according to the needs of curved surface ecological monitoring such as grasslands, wetlands, deserts, coastal beaches, a virtual force model of moving coverage of curved surface ecological monitoring network is presented, and a moving coverage algorithm of curved surface ecological monitoring network is given. the moving coverage algorithm of curved surface ecology monitoring network, by the virtual force between sensor nodes in the ecological monitoring network, push the sensor nodes to the uncovered area on the monitored surface, and repairs the monitoring blind zone on the monitored surface. to confirm the effectiveness of moving coverage algorithm of curved surface ecological monitoring network, the moving coverage process of the moving coverage algorithm of the ecological monitoring network is simulated. the simulation results show that the moving coverage algorithm proposed in this paper can accurately locate the monitoring blind zone of the ecological monitoring network and push the sensor nodes to the monitoring blind zone for coverage, and effectively improve the coverage of the ecological monitoring network on the monitored surface. the coverage ratio of node deployment phase can reach 85%∼90%, and the final coverage ratio is more than 95%. keywords: mobile sensor network; curved surface; coverage; algorithm; simulation. https://doi.org/10.15837/ijccc.2022.6.4588 2 1 introduction the ecological monitoring of curved surfaces, such as grasslands, wetlands, deserts, coastal beaches, can be achieved by a mobile sensor network composed of self-moving vehicles (ships) or unmanned aerial vehicles loaded with ecological monitoring sensors. the curved surface ecological monitoring network consists of several mobile sensor nodes, each of which can monitor the ecological environment around the sensor nodes, collect the ecological environment data around the sensor nodes, and transmit them to the convergence node of the monitoring network. in addition, the ecological monitoring network will also monitor specific targets in the monitored surface through the movement of sensor nodes, so as to continuously and comprehensively monitor the monitored surface [1, 2]. in recent years, the research on coverage control in mobile sensor networks mainly focuses on two-dimensional planar coverage algorithm and three-dimensional spatial coverage algorithm, and many research results have been achieved. among them, the representative planar coverage algorithms include: decentralized algorithm for maximizing coverage and lifetime [3], sweep coverage optimization algorithm [4], mobile-aware path selection algorithm [5], improved virtual force relocation coverage enhancement algorithm [6], hole-healing improved algorithm for mobile sensor networks [7], redundant hole identification and healing algorithm for homogeneous distributed wireless sensor network [8], and so on. recent researches on three-dimensional coverage include: sensor deployment for target coverage in under water mobile sensor network [9], 3d coverage algorithm with adjustable radius [10], three-dimensional coverage algorithm with multiple moving nodes [11], three-dimensional spatial target autonomous coverage algorithm [12], mountainous 3d ring fence coverage algorithm for wireless sensor network [13], underwater 3d sensor network coverage algorithm based on vertical sampling [14], 3d wireless sensor network coverage enhancement method based on particle swarm optimization algorithm [15], and so on. for mobile sensor networks deployed in curved surface, the two-dimensional planar coverage algorithm cannot guarantee that the network can cover the entire monitoring surface. three-dimensional spatial coverage algorithms are based on full-space mobile coverage, which is effective for sensor networks deployed in air or water, and not fully applicable for curved surface full coverage. for mobile sensor networks of curved surface ecological monitoring, the sensor nodes are not spatially distributed, and the monitoring surface of the network is closer to the three-dimensional surface. unlike two-dimensional or three-dimensional coverage, moving coverage on curved surface considers not only the horizontal position of sensor nodes, but also the spatial height of sensor nodes. since the sensor node moves on a curved surface, the trajectory of the sensor node changes with the change of the curved surface terrain. 2 related work in recent research on node deployment and mobile control of mobile sensor networks, under certain conditions, some coverage algorithms can also be used for curved surface coverage of mobile sensor networks, such as sparrow search algorithm [16], flower pollination algorithms [17], fusion coverage algorithm [18], enhanced sparrow search algorithm [19], improved particle swarm algorithm [20], and so on. unlike the design ideas of these coverage algorithms, this paper uses an improved virtual force method to construct a curved surface coverage algorithms for mobile sensor networks in order to make curved surface ecological monitoring more convenient and effective. coverage by robots path planning, or the process of having one or more robots completely traverse a specified area, is a well-known issue in the field of robotics and motion planning. such planning aims to give almost complete coverage while also reducing areas that are visited twice. in this study, we concentrate on the path planning scenario on general surface, which includes general surface in 3d space, complex terrain, and planar domains with complex topology [26]. micro-structured sensors that can perceive and communicate at the same time has emerged as a result of the quick growth of micro electronic technology, wireless communication technology, and sensor technology. this sensor has the ability to sense many types of environmental data, gather it at the sensor nodes, and then send this to the data centre. in the civic, industrial, agricultural, military, and other domains, wireless sensor networks are frequently employed [27]. considering that most of the existing coverage algorithms cannot be directly applied to moving coverage of curved surfaces, in this paper, according to the needs of curved surface ecological monitoring such as grassland, wetland, desert, coastal beach, the virtual force model of curved surface ecological monitoring network for moving coverage will be built, and the moving coverage algorithm of curved surface ecological monitoring network will be designed. to confirm the effectiveness of the moving coverage algorithm for curved surface ecological monitoring network, the moving coverage process of the moving coverage algorithm of the ecological monitoring network will be simulated. https://doi.org/10.15837/ijccc.2022.6.4588 3 figure 1: schematic diagram of moving coverage of ecological monitoring network figure 2: force model of sensor nodes on curved surface 3 virtual force model for moving coverage a mobile sensor network for ecological monitoring needs to be deployed in the monitored surface in order to fully monitor the ecology of curved surfaces such as grasslands, wetlands, deserts and coastal beaches. a curved surface ecological monitoring network contains several mobile sensor nodes. each sensor node can monitor the ecological environment around the sensor node, collect the ecological environment data around the sensor node, and transmit them to convergence node of the ecological monitoring network. in addition, the curved surface ecological monitoring network will also monitor specific targets in the monitored surface through the movement of sensor nodes, so as to continuously and comprehensively monitor the monitored surface. it is assumed that there is some virtual repulsion or suction between the sensor nodes of the curved surface ecological monitoring network. the resultant force of this virtual force acts against the sensor nodes on the network monitoring surface, driving them to move along the monitored surface to cover the monitoring blind zone. accordingly, the virtual force model of moving coverage of curved surface ecological monitoring network will be established [21]. it is assumed that the curved surface ecological monitoring network has several sensor nodes deployed on the monitored surface shown in figure 1. under the continuous action of virtual repulsion or suction, the sensor nodes are moved and positioned to reach their virtual force balance positions, forming the coverage status shown in figure 1. in this state, the area that the ecological monitoring network can monitor is relatively large and the monitoring blind zones are relatively small. set a ecological monitoring network with n nodes is distributed over s-surface, as shown in figure 2. any two nodes i,j ∈{1,2,···,n},i 6= j on an s-surface intersect a plane with an s-surface through i,j to obtain a curve sij. set the curve distance from j to i to dij and the normal vector of s-surface at node i to −→n i, the tangent vector of the curve sij at node i is −→e ij, the direction is from the j node to the i node. the force −−→ vf ij exerted by the j https://doi.org/10.15837/ijccc.2022.6.4588 4 node on the i node can be calculated as equation (1). −−→ vf ij =  dij a− (√ 3r )a dij a · −→e ij, dij ≤ √ 3r e √ 3r−dij ·sin 3r−dij( 3− √ 3 ) r π · −→e ij, √ 3r < dij ≤ 3r e √ 3r−dij ·sin dij−3r( 2 √ 3−3 ) r π · −→e ij, 3r < dij ≤ 2 √ 3r e √ 3r−dij · −→e ij, dij > 2 √ 3r (1) in equation (1), α (α ≥ 1) is the coefficient of gravitation; r is perception radius of sensor nodes. for a ecological monitoring network with n nodes, the sum of the forces exerted by other nodes on node i is∑n j=1,i6=j ⇀ vf ij. set bj as the j-th border or impediment the monitored surface of the ecological monitoring network, dbj is the shortest surface distance from sensor node i to bj, and −−→ vf ibj is the repulsion of the j-th border or impediment in the monitored surface to sensor node i. by equation (2), −−→ vf ibj can be calculated [22]. −−→ vf ibj =   √ 3r 2  ff −dibjα dibjα · ⇀ e ibj, dibj ≤ √ 3r 2 0, dibj > √ 3r 2 (2) in equation (2), bj is the j-th border or impediment on the monitored surface, and ⇀ e ibj is the unit repulsion vector of bj acting on sensor node i. ~ni is the normal vector of sensor node i on the s-surface, and ⇀ e ibj is perpendicular to ~ni. if the number of borders or impediments in the monitored surface is m, for the sensor node i, the sum of the repulsion forces of borders or impediments in the monitored surface is ∑m j=1 −−→ vf ibj. to sum up, for an ecological monitoring network with n nodes deployed, the total force suffered by the sensor node i in the network can be calculated as equation (3). ⇀ vf i = ∑n j=1,i6=j ⇀ vf ij + ∑m j=1 ⇀ vf ibj,i ∈{1,2,···,n} (3) 4 moving coverage algorithm according to the virtual force model of moving coverage of curved surface ecological monitoring network, we propose the following moving coverage algorithm of curved surface ecological monitoring network [21, 23]. assume that in the initial state, ⇀ vf i0 is the force exerted on sensor node i in the network monitoring surface, and ⇀ l i0 is the position vector of sensor node i relative to the coordinate origin. under the action of ⇀ vf i0, sensor node i moves for the first time, and its position vector is ⇀ l i1= ⇀ l i0 +v ·∆t · ⇀ vf i0 ⇀ vf i0 . then, under the action of ⇀ vf i(r−1), sensor node i moves r(r > 1) times, and its position vector is ⇀ l ir: ⇀ l ir= ⇀ l i(r−1) +∆ ⇀ l i(r−1)= ⇀ l i0 + ∑r−1 j=0 ∆ ⇀ l ij,i ∈{1,2,···,n} (4) after several times of movement, when the force on each sensor node in the curved surface ecological monitoring network has been small enough, it can be considered that each sensor node has moved to a suitable position on the monitored surface. at this time, the movement of sensor nodes of the ecological monitoring network can be stopped and the moving coverage of the ecological monitoring network is completed [24]. the moving coverage process of the curved surface ecological monitoring network consists of the following 7 steps. (1) set r = 0, given the initial position vectors of sensor nodes: ⇀ l10, ⇀ l20, ···, ⇀ ln0; (2) based on the sensor node position vector, the distances between sensor nodes are calculated respectively: ~d(i,j)r = ~lir −~ljr,i,j ∈{1,2,···,n},i 6= j; https://doi.org/10.15837/ijccc.2022.6.4588 5 figure 3: computational flow diagram of moving coverage algorithm (3) based on the sensor node position vector, the distance of sensor nodes to border or impediment in the monitored surface are calculated: d (r) ibj,i ∈{1,2,···,n},j ∈{1,2,···,m}; (4) according to equation (3), the resultant forces of sensor nodes are calculated: −−→ vf 1r, −−→ vf 2r ,···, −−→ vf nr; (5) if −−→ vf i r > ø, i ∈{1,2,···,n}, turn (6); otherwise, the nodes stop moving, and the moving coverage process end. (6) calculate the moving distance vector of sensor nodes:[−−→ vf 0r−−→ vf 0r −−→ vf 1r−−→ vf 1r ... −−→ vf nr−−→ vf nr ] ·v ·∆t; (7) set r = r+1, according to equation (4), calculates the new position vector of sensor nodes: ~l1r,~l2r,···,~lnr. turn (2). the computational flow diagram of moving coverage algorithm of curved surface ecological monitoring network is shown in figure 3. 5 simulation experiment to confirm the effectiveness of moving coverage algorithm of curved surface ecological monitoring network, the moving coverage process of the moving coverage algorithm of ecological monitoring network is simulated by using matlab simulation tool [21, 25]. 5.1 impediment-free moving coverage simulation conditions: the curved surface ecological monitoring network consists of 30 sensor nodes. at the beginning of the simulation, all nodes are randomly deployed on the saddle surface with the spatial surface equation z = (x2 + y2)/182, and the monitored surface is limited to 0-25m, that is, 0 ≤ x ≤ 25, 0 ≤ y ≤ 25. sensor node perception radius is 3m. figures 4 and 5 show the moving trajectory of sensor nodes of the curved surface ecological monitoring network. here, figure 4 shows the moving trajectory of sensor nodes on the network monitoring surface, while https://doi.org/10.15837/ijccc.2022.6.4588 6 figure 4: trajectory of moving coverage of sensor nodes figure 5: horizontal projection of moving coverage trajectory of sensor nodes figure 5 shows the projection of the moving trajectory of sensor nodes on the network monitoring surface on horizontal plane. as shown in figures 4 and 5, at the beginning of the simulation, the sensor nodes of the ecological monitoring network are randomly distributed in the center of the monitored surface. during the node deployment phase, the sensor nodes begin to move, and driven by repulsion, the sensor nodes quickly spread around the monitored surface. during the node positioning phase, the sensor nodes approach the balanced position, and there will be position fluctuations of different magnitudes to adjust each sensor node to reach the balanced position with the largest coverage area. figure 8 shows the change of coverage ratio of the curved surface ecological monitoring network during moving coverage. as shown in figure 8, during the node deployment phase, coverage increases rapidly, after 16 moves, the coverage ratio reaches 90%. during the node positioning phase, the network nodes adjust and correct near the balance position on the monitoring surface, coverage increases slowly, after 28 moves, the coverage ratio reaches 98.5%, and the moving coverage process of the curved surface ecological monitoring network can be ended. 5.2 moving coverage with impediment the moving coverage algorithm of curved surface ecological monitoring network proposed in this paper can be applied to the situation that there are impediments in the network monitoring surface. the simulation conditions are the same as section 4.1, the curved surface ecological monitoring network contains 30 sensor nodes, sensor node perception radius is 3m, and the monitored surface is limited to 0 ∼ 25m. as shown in figures 6 and 7, 7.5m×5.5m rectangular impediment is set at the lower left corner of the network monitoring surface. figure 6 is the moving track of sensor nodes on the monitored surface with impediment, while figure 7 shows the projection of the moving trajectory of sensor nodes on the monitored surface with impediment on horizontal plane. as shown in figure 6 and 7, during the node deployment phase, driven by the virtual repulsion between the sensor nodes of the ecological monitoring network, the sensor nodes move quickly to https://doi.org/10.15837/ijccc.2022.6.4588 7 figure 6: trajectory of moving coverage of sensor nodes (with impediment) figure 7: horizontal projection of moving coverage trajectory of sensor nodes (with impediment) the balance position nearby. during the node positioning phase, the network nodes adjust and correct near the balance position on the monitored surface. when the sensor node approaches the border or impediment of the monitored surface, driven by the virtual repulsion of the border or impediment, the sensor node will turn back to the nearby balanced position. as shown in figure 8, the moving coverage process of the monitored surface with impediments is different from that of the monitored surface without impediments. due to the influence of impediments, the number of mobile location adjustment of sensor nodes near impediments will be increased, the moving coverage efficiency will be reduced, and the network coverage will be reduced. as shown in figure 8, after 16 moves, the coverage ratio is 85%, after 28 moves, the coverage ratio is 95%, and after 30 moves, the coverage ratio is only 96%. the simulation shows that whether there are impediments on the monitored surface or not, the movement of sensor nodes in the surface ecological monitoring network is "fast in front and slow in back". during the node deployment phase, the sensor nodes begin to move, and driven by repulsion, the sensor nodes quickly spread around the monitored surface. during the node positioning phase, the sensor nodes approach the balanced position, and there will be position fluctuations of different magnitudes to adjust each sensor node to reach the balanced position with the largest coverage area. in addition, the simulation results also show that the moving coverage algorithm of the curved surface ecological monitoring network can accurately locate the monitoring blind zone of the ecological monitoring network on the monitored surface, and push the sensor node to the monitoring blind zone for coverage, which effectively improve the coverage of the ecological monitoring network. during the node deployment phase, after about 15 moves, the coverage ratio reaches 85%∼90%. at the end of the mobile coverage (about 30 moves), the coverage ratio reaches 95% or more. https://doi.org/10.15837/ijccc.2022.6.4588 8 figure 8: change of the coverage ratio in network monitoring surface 6 conclusion the ecological monitoring of curved surfaces, such as grasslands, wetlands, deserts, coastal beaches, can be achieved by a ecological monitoring network composed of self-moving vehicles (ships) or unmanned aerial vehicles loaded with ecological monitoring sensors. for ecological monitoring networks deployed in curved surface, the two-dimensional planar coverage algorithm cannot guarantee that the network can effectively cover the entire monitored surface. three-dimensional spatial coverage algorithms are based on moving coverage in full space and are not suitable for ecological monitoring of full coverage on curved surface. for the curved surface ecological monitoring network, the sensor nodes are not spatially distributed, and the monitored surface of the ecological monitoring network is closer to the three-dimensional surface. moving coverage on a three-dimensional surface considers not only the horizontal position of the sensor nodes, but also the spatial height of the sensor nodes. according to the needs of curved surface ecological monitoring such as grasslands, wetlands, deserts, coastal beaches, a virtual force model of curved surface ecological monitoring network for moving coverage is constructed, and a moving coverage algorithm for curved surface ecological monitoring network is presented. the moving coverage algorithm of curved surface ecological monitoring network, by the virtual force between sensor nodes in the monitoring network, push the sensor nodes to the uncovered area on the monitored surface, and repairs the monitoring blind zone on the monitored surface, so as to improve the coverage of ecological monitoring network on the monitored surface. to confirm confirm the effectiveness of the moving coverage algorithm of curved surface ecological monitoring network, the moving coverage process of the coverage algorithm of curved surface ecological monitoring network is simulated and analyzed. the simulation results show that the moving coverage algorithm of the curved surface ecological monitoring network proposed in this paper can accurately locate the monitoring blind zone of the ecological monitoring network on the monitored surface, and push the sensor nodes to the monitoring blind zone for coverage, which effectively improve the coverage of the ecological monitoring network on the monitored surface. the coverage ratio of node deployment phase can reach 85%∼90%, and the final coverage ratio is more than 95%. declaration of competing interests the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. conflict of interest the authors declare no conflict of interest. references [1] boubrima a, bechkit w, hervé r. on the deployment of wireless sensor networks for air quality 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(2022). design of moving coverage algorithm of ecological monitoring network for curved surface, international journal of computers communications & control, 17(6), 4588, 2022. https://doi.org/10.15837/ijccc.2022.6.4588 introduction related work virtual force model for moving coverage moving coverage algorithm simulation experiment impediment-free moving coverage moving coverage with impediment conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 4, month: august, year: 2022 article number: 4863, https://doi.org/10.15837/ijccc.2022.4.4863 ccc publications decision support model for raw water availability for purification in a region in chile c. j. macuada, a. m. oddershede, l. e. quezada, p. i. palominos claudio j. macuada, astrid m. oddershede, luis e. quezada, pedro i. palominos industrial engineering department university of santiago of chile, santiago, chile claudio.macuada@usach.cl astrid.oddershede@usach.cl luis.quezada@usach.cl pedro.palominos@usach.cl abstract this article proposes a decision model to identify the most sustainable solution(s) to ensure the availability of raw water to be subsequently treated to be converted into drinking water as a consequence of the climate change scenario, particularly the drought currently experienced by the metropolitan region in chile, derived from the technical and regulatory requirements associated with the availability of water resources from its capture to its drink ability to meet the future demand of the region. from the perspective of drought, the solution must provide security levels that guarantee the availability of raw water is one of the main concerns of the stakeholders. in turn, the need to adapt current regulations regarding raw water sources, as well as community acceptance of some proposals for converting raw water into potable water and climate dependency, involve qualitative as well as technical aspects that may affect the investment and operating costs of the different solutions required to ensure raw water availability. therefore, through a multi-criteria approach, it is possible to incorporate quantifiable and intangible aspects and to address conflicting objectives. through a case study, we present a decision model based on the analytic hierarchy process to define and evaluate the most sustainable solution(s) to secure raw water for drinking. this study proposes to integrate technical and qualitative attributes to identify the challenging criteria and the associated linkage to the problem of selecting proposals for the most sustainable solution(s) to secure raw water, being a guide to decide the implementation of the most appropriate solution. keywords: decision support model, raw water, potable, climate change, multi-criteria approach. 1 introduction in face of one of the worst droughts in history [1], it is not enough to increase water availability regardless of the cost. in chile we have realized that there are some weaknesses in the use of the https://doi.org/10.15837/ijccc.2022.4.4863 2 resource [2], in particular, because current irrigation technologies should be changed for more efficient ones (e.g., drip irrigation as in israel); improve the efficiency of its use at the industrial level; reduce losses in drinking water distribution networks and reduce intra-household consumption. with respect to the sanitary sector, the percentage of this resource that has not been allocated at the national level amounts to 32.9% of the volume produced [3]. there-fore it is essential to develop actions to reduce this indicator. it should be noted that the main items that constitute non-revenue water (nrw) are mainly physical losses in the network (due to breaks, leaks, etc.), but, also, include other items such as metering errors, clandestine connections, fraud and also non-revenue uses: washing of networks, fire fighting, etc. in recent years, as a result of climate change and increased demand, water scarcity has worsened [4] and competition for the reuse of treated wastewater and desalination has intensified [5]. however, in the report on water resources availability [6], chile appears as a rich country. indeed, the average per capita level is well above the world average. however, this index does not take into account the disparity of resources between the desert north and the rainy south, which is manifested in the fact that water scarcity has been a recurrent problem in the past, both in the north and in the center of our country [7]. this has meant that, historically, the available water has been reused, especially through the surface water distribution system, which divides each river into sections that receive the element in dry years, respectively. on the other hand, at present, the urban sewerage coverage is 97.2% and, of this, practically 100% receives sewage treatment [3]. in other words, there is a relevant potential source consisting of about 300 operational treatment systems, which treat an average flow of about 40 m3/s. the most commonly used treatment technologies are: activated sludge (63%); aerated lagoons (18%) and submarine outfalls (11%). once treated, most of the resource is discharged into different bodies of surface water, where it is mixed. strictly speaking, however, the additional flow available is much less, because not all treatment systems are located in areas of water scarcity. in the northern part of the country, a significant part is already being reused by mining. in addition, in the central part of the country, which concentrates most of the population and demand, a large amount is being reused directly or indirectly for irrigation. it is also important to note that, due to water scarcity, the mining industry has adopted the use of treated wastewater for industrial processes and road irrigation. likewise, the country has a great potential for desalination [8], however, it is noted that its costs are high, particularly if it is desired to supply areas far from the coast. however, in 2012, the ministry of public works, in its national water resources strategy 2012-2025 [6], identified desalination as: "a safe source that guarantees stability of supply, given the variability of natural sources and the scarcity of the resource in the basins of the north of the country". in view of this, it is important to identify which of the alternatives for obtaining drinking water is more feasible to use in the medium term. the research methodology is based on a case study, integrating planning processes, data analysis, scoring method interacting with the multi-criteria approach and using the analytic hierarchy process (ahp) [9, 10, 11]. therefore, this paper focuses on developing a decision model by identifying the levels and processes involved in the treatment of raw water to make it potable, collecting, managing and interpreting the available information. a case study based on the current situation in the metropolitan region of chile has been carried out to determine the most feasible process to be implemented in the medium term to ensure the availability of drinking water to the population. these results lead to the development of an investment plan that can be converted into action plans for the sanitary company in charge of drinking water supply. the following section provides a brief literature, review, section 3 a description of the system, the following sections present the development of the model, its results and conclusions. 2 literature review there have been some studies related to the water treatment system, in the literature is that of [16] ramirez, kraslawski, & cisternas, who present an evaluation approach concerning the influence https://doi.org/10.15837/ijccc.2022.4.4863 3 of energy consumption in the water system and its impact on the environment presenting a decision support framework. on the other hand, herrera sebastian et al [17], propose an optimization approach for the design of water supply systems in non-coastal areas with water scarcity. they determine the location and size of desalination plants and the design of the water conveyance net-work, including pipelines of specified length and diameter and pumping stations that minimize the capital and operating costs of the entire system. the document reported in [18], examines the water challenges facing the latin america and caribbean region in meeting the sustainable development goal (sdg) of ensuring water availability, sustainable water management and sanitation for all. it uses the case of china as a context for analysis due to its particular performance and experiences in this area. this document specifically compiles the results of the technical discussion "challenges and opportunities for improving water management in chile", from which the main messages and lessons learned are extracted for the formulation of recommendations, both for chile and for other countries in the region. 3 system description the study refers to the development of a decision model. since the people in charge of defining the medium-term planning of investments in water infrastructure want to know which infrastructure alternative would be the most feasible to develop in order to increase the resilience of the drinking water supply system, the criteria are not clear and there are different opinions that are expected to be agreed upon in order to generate the most viable alternative(s). however, the criteria are not clear and there are several opinions that are expected to be agreed upon in order to generate the most viable alternative(s).the system consisted of two stages. the first stage deals with the identification of the decisive factors and attributes to determine which criterion is most relevant at the time of making an investment in each of the solutions for obtaining raw water for potabilization. empirical data were collected through inter-views, observation and a questionnaire containing open and semi-structured questions on the attributes most valued when defining the most appropriate infrastructure to meet the population’s future demand for drinking water. as a result, numerous factors were raised and the critical effects were determined according to the valuation of the different benefits to be obtained. this practice made it possible to specify the criteria and structure the problem situation. once the criteria and elements involved have been identified, the next step is to prioritize the different attributes by implementing a multi-criteria approach using ahp [12]. subsequently, a comparison process is carried out. the team of experts was made up of representatives of a sanitation company from the metropolitan region and representatives of the supervising agency, i.e., the superintendence of sanitation services (siss). the total number of participants from the sanitation company was 20 people, while from the supervising agency there were 10 people, ranging in age from 35 to 60 years old. once the criteria, factors and main representatives were established, a hierarchy structure was built. this incorporates the quantitative and qualitative variables and their relationships. the critical categories at each level were identified through the opinions issued by the expert panel. the multicriteria approach allows conflicting objectives to be addressed through the use of ahp [13, 14, 15], which helps to establish criteria and rank user preferences. 4 case study application methodology the system under study is carried out in two parts. initially, the concerns are to identify the decisive factors and attributes to define the hydraulic infrastructure required to ensure the supply of drinking water to the population in the medium term. information is gathered from representatives of the metropolitan region’s sanitation company and the regulatory body to define the main factors to be analyzed. thus, many factors emerge and, therefore, it is necessary to identify the critical impacts. this practice makes it possible to state the criteria and structure the problem situation to generate a hierarchical structure to represent the problem situation and make an assessment. the next step is to prioritize the different attributes through a peer comparison process. one team of experts is formed by representatives of the health company and another by the regulatory agency. the ahp method is https://doi.org/10.15837/ijccc.2022.4.4863 4 used to rank and evaluate the different factors and elements considered in the hierarchy. the experts’ judgment is based on their own experience and knowledge. the last step is to record the weighting of the measured attributes of each activity to obtain a rating on the water infrastructure that will increase the safety of drinking water supply. 4.1 structuring the ahp hierarchy model the hierarchy structure model is shown in figure 1. the general objective is indicated at the first level, in reference to the water infrastructure required to support the entire current drinking water supply system in the medium term. the next level refers to the relevant criteria that will allow defining, according to the second level, the technology to meet the general objective. figure 1: primary hierarchy model. 4.2 pairwise evaluation a first results for a overall relative prioritization for criteria, level 1 and technology are shown in figure 2. figure 2: primary hierarchy model. the team of experts made up of the two types of representatives went through an evaluation process, along the hierarchy based on their judgment of the allocation made for the current situation. from this overall result, the wastewater reuse swap and treated wastewater potabilization are more important for all participants, with a priority of 20.5% and 18.9% respectively. now, if we consider the importance between the wastewater reuse swap and the treated wastewater purification swap, as shown in figure 3, the group of experts considers that the wastewater reuse swap is more relevant because it has lower costs and the execution times are shorter, which allows having water security in a shorter period of time. figure 4 shows the comparison between the wastewater reuse swap and increased underground exploitation (wells), with the wastewater reuse swap being more relevant because it provides greater coverage of raw water treatment, thus increasing the exploitation of wells, given the sustained decline in groundwater https://doi.org/10.15837/ijccc.2022.4.4863 5 figure 3: swap of treated wastewater vs. potabilization of treated wastewater. figure 4: swap of treated wastewater vs. largest underground exploitation. 5 result analysis by using the ahp methodology [9, 10] and data processing through the expert choicetm (ec) software[11], a relative order was acquired. the priority results showed that the costs (investment and operation) is a determining factor when deciding which hydraulic infrastructure to consider to ensure drinking water supply in the metropolitan region. this result is consistent with the preliminary vision of the sanitation company and the regulatory body to propose future action plans to adapt to climate change, ensuring the continuity of drinking water service in a timely manner and thus avoid affecting customers and impacting the city. these results are consistent with the policy of studying alternatives for reusing wastewater and thus generating a virtuous circle, known as the circular economy, given that much of the water consumed can be recovered. however, a preliminary issue to generate these investments is to educate the population about this new alternative to obtain drinking water, since there is no knowledge and there is disbelief regarding the use of wastewater as an initial source for the generation of drinking water. the first column of table 1 shows the overall results of the technology where it is recommended to invest in projects associated with the swap of treated wastewater. table 1: local and global priority results https://doi.org/10.15837/ijccc.2022.4.4863 6 based on the results, it should be noted that the relevance assigned to operating costs (investment and operation) would depend on the country and/or region in which it is evaluated, depending on the availability of water and future projections of its impact, due to the change in clients. current investments in water infrastructure are generally highly complex, and the industry has historically generated infrastructure to use available water from rivers and groundwater, since there was no need to look for alternative sources, which is no longer feasible, due to water scarcity in chile. the ahp allows to perform sensitivity analysis, allowing to identify possible changes in the ordering processes, modifying the importance of the criteria. the relevance of the analysis lies in the current water situation in the country, the regulations in force and the knowledge of the available technologies for the generation of raw water for drinking water. figure 5 shows a sensitivity analysis that allows visualizing, depending on the assessment made by the group of experts at a future opportunity, the feasible infrastructure may be different depending on the relative weight resulting from these criteria, considering the previous analysis. the results of the model show alignment with the sanitary policies currently being envisioned by the industry to increase the availability of raw water. figure 5: sensitivity analysis for factor “performance” 6 conclusions this paper proposes a methodology using a multi-criteria approach using ahp to develop a decision model based on a real case study. the use of the ahp model helps to improve the understanding of technology needs and the selection process in a conflicting environment. the ranking obtained not only helps to initiate implementation actions but also to generate an investment plan. it allows recognizing the critical factors that influence the decision to choose the hydraulic infrastructure that will help improve the quality of service and adapt to changes in sanitation needs. the challenges facing the sanitation system in developing countries include technology, infrastructure, cost efficiency and water resource sustainability. regarding the solution obtained through ahp, there is consistency considering that in the north of the country, industries such as mining and specific irrigation programs are promoting the reuse of treated wastewater. also, the siss is currently studying specific regulations on the reuse of treated wastewater. in addition, it has become an essential part of integrated water resource planning and management, both in developed and developing countries, since the problem of water scarcity is not only in chile, but there are several countries in the same situation, where they are also facing an increase in population and demand for food and concern about environmental pollution. however, as shown by the results for the case of swap of wastewater reuse and potabilization of treated wastewater, it entails changes in the traditional structures of water resources allocation, https://doi.org/10.15837/ijccc.2022.4.4863 7 financing of structures, consideration of water quality standards that should be approved by the siss and encourage the use of these alter-natives by subsidizing their investment so that they are adopted as soon as possible. acknowledgment special thanks to the department of industrial engineering from the university of santiago de chile for the support during the development of the study. references [1] bbc news mundo (2020), “megasequía in chile: satellite images showing the consequences of the country’s rain shortage, the worst since 1915", [online] https://www.bbc.com/mundo/noticias52288489. 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(2000). fundamentals of decision making and priority theory with the analytic hierarchy process (vol. 6). new york: rws publications. 478p. [13] macuada c. j., oddershede a. m., quezada l. e., palominos p. i. (2021). “methodological proposal to define the degree of automation in the sanitary industry in chile to adapt to cli-mate change”. in: dzitac i., dzitac s., filip f., kacprzyk j., manolescu mj., oros h. https://doi.org/10.15837/ijccc.2022.4.4863 8 (eds) intelligent methods in computing, communications and control. icccc 2020. advances in intelligent systems and computing, vol 1243. springer, cham. https://doi.org/10.1007/9783-030-53651-0_24 [14] macuada, c. j., oddershede, a. m, quezada, l. e. (2018). “dm methodology for automating technology system in water treatment plants”, in i. dzitac, f.g. filip, m.j. manolescu et al. 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(2018), “integrated water resources management in chile: advances and challenges”, water policy in chile, guillermo donoso (ed.), springer. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: macuada, c.j; oddershede, a.m.; quezada, l.e.; palominos, p.i. (2022). decision support model for raw water availability for purification in a region in chile, international journal of computers communications & control, 17(4), 4863, 2022. https://doi.org/10.15837/ijccc.2022.4.4863 introduction literature review system description case study application methodology structuring the ahp hierarchy model pairwise evaluation result analysis conclusions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 16, issue: 3, month: june, year: 2021 article number: 4186, https://doi.org/10.15837/ijccc.2021.3.4186 ccc publications secure real-time computational intelligence system against malicious qr code links h. wahsheh, m. al-zahrani heider a. m. wahsheh* department of information systems, college of computer science and information technology king faisal university, p.o box: 400, al-ahsa, 31982, saudi arabia. *corresponding author:hwahsheh@kfu.edu.sa mohammed s. al-zahrani department of computer networks and communications, college of computer science and information technology king faisal university, p.o box: 400, al-ahsa, 31982, saudi arabia. malzahrani@kfu.edu.sa abstract web attackers aim to propagate malicious links using various techniques to deceive users. they attempt to control victims’ devices or obtain their passwords remotely, thereby acquiring access to bank accounts, financial transactions, or private and sensitive information they trade via the internet. qr codes are accessible, free, easy to use, and can be scanned through several free apps on smartphones. as there is no standard structure or authentication phase in qr code generation, such codes are vulnerable to suspicious online content embedding, i.e., phishing, cross-site scripting (xss), and malware. many studies have highlighted the attacks that may be perpetrated using barcodes, and there are some security countermeasures. several of these solutions are limited to malicious link detection methods or require knowledge of cryptographic techniques. this study’s main objective is to detect malicious urls embedded in qr codes. a dataset of 90 000 benign and malicious urls was collected from various resources, and their lexical properties were extracted. two computational intelligence models, fuzzy logic and multilayer perceptron artificial neural network (mlp-ann), were applied and compared. an mlp-ann was identified as the best classifier for detecting malicious urls, and a proactive, secure, real-time computational intelligence barcode scanner implementation (barci) against malicious qr code links was proposed based on this classifier. the results demonstrate that this approach enables efficient real-time attack detection with 82.9% accuracy. keywords: qr codes, barcode scanners, malicious links, real-time, computational intelligence. 1 introduction a qr code is a two-dimensional (2d) matrix barcode symbol (iso/iec 18004) created by denso wave in 1994 that can be easily be used by several types of equipment, such as fixed and handy scanners https://doi.org/10.15837/ijccc.2021.3.4186 2 and terminals [1]. nowadays, smartphone cameras contain three main components: a barcode lighting system, a sensor that uses qr code characteristics to read qr codes, and a decoder. the content of a qr code can be decoded at ultra-high speeds by verifying the validity of the code received from the sensor. thus, retrieval of the information encoded in a qr code occurs within a few seconds [2]. qr codes have been used extensively due to the limited technological characteristics of linear (onedimensional, 1d) barcodes. qr codes have become widespread in several fields and can be attached to any screen, poster, or product surface. this ability enables marketers to establish bridges among advertising products. qr codes can encode advertiser urls to facilitate interaction with users or allow users to retrieve additional product information without typing web addresses or searching for company names on the internet [2, 3]. besides, qr codes can be employed to link physical objects to electronic resources, which can be effectively used in education, transportation, product tracking, ticketing, book returning methods in libraries, payment transfer systems, and tourism promotion [2, 3]. qr code may store different data types, such as numeric (0–9), alphanumeric, and binary data, as well as kanji characters [4]. furthermore, qr codes can help healthcare services in effective patient identity management. sensitive information can be linked with the qr codes on wristbands of patients. healthcare services can use a qr code scanner application on their smartphone to access patient information, medication, and medical reports [5]. qr codes are practical tools that are easy to use, free, highly rated, and used in various applications [2], but there are several security risks associated with them. the main problem is that barcodes are not readable by humans but can only be read using specific scanning devices (sensors) or smartphone reader applications. as no standard structure or authentication phase is adopted in qr generation, these codes are vulnerable to the embedding of suspicious online content, i.e., phishing, cross-site scripting (xss), and malware [6]. many studies have highlighted the attacks that could be implemented using barcodes, and there are several security countermeasures. however, several of these solutions are limited in their malicious link detection methods or require knowledge of cryptographic techniques [3, 6, 7]. the main objective of this study is to detect malicious urls embedded in barcode qr codes; hence, we collected 90 000 benign and malicious urls from various resources and extracted their lexical properties. we compared two computational intelligence (ci) classifiers: fuzzy logic and multilayer perceptron artificial neural network (mlp-ann). we adopted the mlp-ann classifier as the best one for detecting malicious urls. we developed a proof-of-concept proactive, secure real-time ci barcode scanner implementation (barci) based on this classifier against malicious qr code links. barci exhibited efficient and effective real-time attack. the remainder of this paper is organized as follows. section 2 presents the related work on qr code attacks and the existing countermeasures. section 3 explores our proposed methodology. section 4 presents the experiments and evaluation results. section 5 proposes barci and discusses the comparison results. finally, section 6 presents the conclusions and topics for future work. 2 related work this section presents an overview of various 2d barcode attacks and then illustrates the available countermeasures and solutions to protect 2d barcodes. attack scenarios: phishing. a qrishing attack is a particular type of phishing attack in which the attacker uses a qr code as a medium to encode a malicious url [8, 9]. when a potential victim scans such a 2d barcode using his or her smartphone, he or she will be redirected to a fake web page that attempts to obtain sensitive information such as login details and credit card numbers. the authors performed two main experiments: a survey of user-qr code interaction and a qrishing experiment. the survey results indicated that most users (75%) scanned the distributed qr codes due to their curiosity to know the embedded content or for fun. the results of the qrishing experiment showed that 85% of the users who read the qr codes also visited the phishing web pages. https://doi.org/10.15837/ijccc.2021.3.4186 3 barcodes fraud and counterfeiting. reference [10] presented an attack based on tampering with 2d barcode standards by embedding one 2d barcode inside another, called a "barcode-in-barcode" attack. the attackers aim to mislead the users who try to read the 2d barcode by providing different content without arousing any suspicion of malicious behavior. malware attacks. reference [11] described the usage of qr codes to perform drive-by download attacks, in which the attackers encode urls inside barcodes to redirect users to malicious web pages. these web pages aim to download files (malware and viruses) directly without user permission. in the experiments, 94 770 qr code images of products and services were extracted from 14 million unique web pages using a particular content image search crawler. the researchers checked the shortened extracted qr code urls against possible threats using a blacklist and phishtank. the results showed that 145 of the crawled qr codes were malicious intent, mainly phishing and malware propagation. barcodes security solutions references [3, 12, 13] highlighted the gaps in and limitations of the available 2d barcode protection mechanisms. the researchers compared and evaluated 2d barcode security systems using cryptographic characteristics and their security levels. they explored how various usability features affect qr code scanning and assessed several cryptographic techniques concerning qr code usability. although the results showed that some asymmetric solutions lead to break qr code usability, other solutions such as the elliptic curve digital signature algorithm (ecdsa) were recommended. the results also showed that symmetric methods were appropriate solutions. reference [14] proposed a qr code tamper detection system that depends on a digital signature. the approach is to hide the digital signature using a special stereographic algorithm. the experiments showed that the tamper detection system works with small sizes. the researchers did not evaluate the proposed method with the actual signature, which was mentioned as a topic for future work. however, all the previous countermeasures require knowledge of cryptographic techniques and can be used only during the generation of qr codes with a particular structure. reference [15] proposed a qr link security detection method for android devices. the authors’ employed link characteristics with several permissions and achieved flexible and scalable results. however, the researchers used a minimal dataset (i.e., 2000 data) of malicious and benign links. the results can be considered promising but are not reliable enough to build a model to detect malicious links for qr codes. 3 research methodology as the creation of qr codes does not follow a formal structure and does not require an authentication phase, qr codes may include suspicious online content. our investigation included the following main phases: 1. dataset collection: we collected a dataset of 90 000 benign and malicious urls that could be embedded in qr and bia codes from several resources. 2. url feature extraction: we extracted the url lexical properties without fetching the web page content or dns or whois information to decrease the network delay. 3. application of computational intelligence ci methods: we applied two ci methods: fuzzy logic and mlp-ann classifiers. we evaluated them and compared their prediction quality. 4. adoption of the best model to detect malicious urls: we developed barci based on the best classifier against qr code malicious links. 5. discussion of the comparison results: we analyzed the advantages of barci over the existing practical solutions for malicious qr codes. https://doi.org/10.15837/ijccc.2021.3.4186 4 3.1 data collection we collected a dataset of 90 000 benign and malicious urls that could be embedded in qr codes from several resources. the dataset contained 45 000 malicious urls collected from the most recent phishing [16] and malware domains blacklists [17, 18]. moreover, we collected 45 000 benign urls, mainly of well-known (non-spam) sites such as university, hospital, banking, education, news, entertainment, and other government web pages [19, 20, 21]. 3.2 features extraction to detect the malicious urls with reduced network delay, we analyzed the lexical properties of urls. we did not depend on any external service such as domain name system (dns) or whois or analyzing the web page content. figure 1 presents the url taxonomy [22]. figure 1: url taxonomy. as shown in figure 1, attackers generally use the second-level domain, first-level domain (fld), and path directory to perform their attacks. the domain could be a popular website such as facebook, google, or twitter. the fld could be .edu, .net, .org, etc. when the browser finds a url as text, it asks the dns to retrieve the ip address. attackers attempt to hide their malicious links and bypass blacklists using url shortening services such as url shortener [23]. thus, there is a potential need to extract the full lexical properties of urls [24], i.e., white lists, blacklists, and popular malicious tokens [16, 17]. the dataset can be downloaded from https://tinyurl.com/y3tqcn34. table 1 [24] shows our adopted url lexical properties. 3.3 computational intelligence (ci) this section will describe the computational intelligence (ci) methods that we used in this study. 1. fuzzy logic (fl): in 1965, l.a. zadeh first proposed the concept of fuzzy sets. fuzzy logic is a method for managing imprecise and uncertain information. it is a rule-based system that consists of a set of if-then rules. in the fuzzy system, values are indicated by a number from range 0 to 1, where 0 denotes absolute falseness, and 1 represents absolute truthfulness. this paper has illustrated fuzzy logic to help users decide whether the given url in the scanned qr code is malicious or benign. it classifies the urls according to a set of predefined rules [25, 26]. 2. multilayer perceptron artificial neural network (mlp-ann): the mlp classifier is a feedforward artificial neural network (ann) that is used for data classification. it consists of three or more layers ( i.e., input and an output layer with one or more hidden layers) of nonlinearlyactivating nodes. this algorithm employs the backpropagation supervised learning method and works through determining the most proper synaptic weight in classifying patterns in the training dataset [27]. https://doi.org/10.15837/ijccc.2021.3.4186 5 table 1: url lexical properties. property description length of url number of characters in url # of tokens number of tokens split by (.), (/), (?), (;), (=), (-), (@) free hosting domain search for the second level domain (sld) in the free hosting list domains black list domain search for the sld in the black list domains lexicon of the most popular domains check if the most popular domains used in the domain or path directory most popular domains check if the sld in the white list of top domains lexicons of the phishing token search for phishing tokens in the path and sub-domain lexicons of the malware token search for malware tokens in the path and sub-domain # of dots number of (.) in the url text # of at symbol number of (@) in the url text # of semicolon number of (;) in the url text # of slash number of (/) in the url text # of dash number of (-) in the url text 4 experiments and evaluation results this section presents the results of the experiments in which we applied the two ci methods: mlp and fuzzy logic. we evaluated them using the approach of used 66% as training and 34% as testing instances of the url dataset. we compared the results using the following prediction quality measures: true positive (tp), true negative (tn), false positive (fp), false negative (fn), precision (p), recall (r), and f-measure (f-m), as expressed in eqs. 1–4 [28]. accuracy = t p + t n t p + f p + t n + f n (1) p recision = tp tp + fp (2) recall = tp tp + fn (3) f − m easure = 2 × p recision × recall p recision + recall (4) the fuzzy logic experiments yielded an accuracy of 82.37% with an error rate of 17.62%. table 2 shows the detailed accuracy of the fuzzy logic classifier. table 2: detailed accuracy results of the fuzzy logic classifier. class tp rate fp rate precision recall f-measure benign 0.834 0.187 0.819 0.834 0.827 malicious 0.813 0.166 0.829 0.813 0.821 weighted avg. 0.824 0.176 0.824 0.824 0.824 the fuzzy logic classifier was able to classify 25 208 urls correctly. it predicted 12 360 malicious urls correctly. https://doi.org/10.15837/ijccc.2021.3.4186 6 table 3: detailed accuracy results of the mlp-ann classifier. class tp rate fp rate precision recall f-measure benign 0.810 0.151 0.845 0.810 0.827 malicious 0.849 0.190 0.815 0.849 0.832 weighted avg. 0.829 0.170 0.830 0.829 0.829 the overall accuracy achieved using the mlp-ann classifier was 82.9% with slightly enhanced results comparing with fuzzy logic results. highly accurate detection was performed for both the benign and malicious classes. the detailed results are shown in table 3. the mlp-ann classifier was able to classify 25 382 urls correctly. it predicted 12 909 malicious urls correctly. it correctly classified over 550 malicious urls, more than the fuzzy logic classifier did. however, the main target of this study was to find the best model for identifying malicious urls. mlp-ann classifier is better than the fuzzy logic model for detecting malicious urls. 5 proactive secure real-time ai qr code scanner implementation and discussion according to the results in section 4, we adopted the mlp-ann model as it yielded the most accurate prediction results for malicious links. we subsequently implemented barci with the recent recommendations for secure and usable barcode reader applications [7] and utilized the zxing library [29]. the following features characterize our implementation: • barcode type: our approach supports the reading of 1d and 2d barcodes and detecting suspicious online content, enabling the detection of malicious usage of qr codes and bias. • barcode format: the format of the scanned barcode is displayed, so the user will recognize which barcode content was retrieved; • independent implementation: this approach uses an mlp-ann proactive model with highly accurate malicious links detection and does not require any external tool or web service; • compatible: this method can deal with and detect any barcode regardless of the barcode generation method; • simple interface: this approach provides default basic functionalities without requiring awareness of the particular structure or technique; • save barcode space: our implementation does not require the use of any byte of barcode size (there is no size overhead), which will increase barcode usability and decrease the barcode scanning time [13]; • warnings: this model displays a notification regarding the url detection results. it requires user confirmation before visiting the url whether there are suspicious or safe links; • least-privilege permissions (lpp): this approach limits permissions to camera access (to scan the barcode image) and the internet (to get the complete expanded urls); • prevent command execution: this method prevents the execution of any encoded commands in user devices; • free barcode scanner; our implementation is will be freely available via google play store. in this section, we discuss and compare some practical barcode scanner apps from google play store [7, 30] that claim to provide security services with regards to our implementation. similar to our proactive, secure real-time ai qr code scanner implementation, g data qr code reader, g-scan, g-tos scan sensor, kaspersky qr scanner, and norton snap qr code scanner are displaying fully expanded urls and notify users of malicious urls. kaspersky qr scanner and https://doi.org/10.15837/ijccc.2021.3.4186 7 norton snap qr code scanner visit urls without user confirmation if the urls are considered safe. g data qr code reader, g-scan, and g-tos scan sensor do not provide details regarding their url checking techniques [7, 30]. other url security applications such as qr code scanner & barcode reader for cm browser, teacapps barcode scanner, trend micro, fansec, dennings, avira, itechso, kidcontrol, itechsol, and x&c hi-tech check encoded urls. however, they do not obtain fully expanded urls, and users cannot find the final url destinations. there is no publicly available description of trend micro, fansec, dennings, avira, itechso, kidcontrol, itechsol, or x&c hi-tech regarding their benign or malicious url validation methods [7, 30]. our implementation of computational intelligence barci provides a comprehensive solution for all other url security app limitations. table 4 compares barci and various barcode scanners that validates qr code urls. table 4: summary of the security features of barcode scanners that check urls. app name independent get full url direct open url validating method lpp barci 3 3 7 mlp-ann model 3 kaspersky 7a 7 3b kaspersky virusdesk 7 gdata 3 3 7 n/a 7 norton 7a 3 3b norton safe web 7 trend 7a 7 7 n/a 7 dennings 7a 7 7 google safe browsing 7 kidcontrol 7a 7 7 n/a 7 avira 7a 7 7 n/a 7 itechsol 3 7 7 n/a 3 teacapps 3 7 7 google safe browsing 3 gfects 7a 3 7 n/a 7 a depends on particular web service. b directly opens the url if it is safe. n/a means not available. 6 conclusions this paper highlights the qr code online attacks of phishing and malware propagation. it built a dataset containing 90 000 urls classified as benign and malicious and extracted their features. moreover, two computational intelligence methods were applied, fuzzy logic and mlp-ann. this paper compared them and assessed their accuracies. the results indicated that the mlp-ann classifier is the best model for detecting qr code malicious links. we further developed barci and highlighted its advantages as a comprehensive solution among the existing practical apps. the implementation results demonstrate that this approach enables efficient, real-time attack detection with an accuracy of 82.9%. we plan to extend our analysis by exploring the web page contents beside the url lexical features in future work. also, we plan to employ computational intelligence methods to detect sql and command injection. acknowledgement the authors 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(2019). evaluating security, privacy and usability features of qr code readers, proceedings of the 5th international conference on information systems security and privacy (icissp 2019), scitepress, 266–273. 2019. copyright ©2021 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: wahsheh, h., al-zahrani, m. (2021). secure real-time computational intelligence system against malicious qr code links international journal of computers communications & control, 16(4), 4186, 2021. https://doi.org/10.15837/ijccc.2021.3.4186 introduction related work research methodology data collection features extraction computational intelligence (ci) experiments and evaluation results proactive secure real-time ai qr code scanner implementation and discussion conclusions international journal of computers communications & control issn 1841-9836, 9(5):610-622, october, 2014. flexible service oriented network architecture for wireless sensor networks a.p. singh, o.p. vyas, s. varma akhilendra pratap singh, o.p. vyas, shirshu varma department of information technology indian institute of infomation technology allahabad,211012, u.p., india *corresponding author: akhil121282@gmail.com opvyas@iiita.ac.in, shirshu@iiita.ac.in abstract: wireless sensor network (wsn) is a combination of homogeneous and heterogeneous sensor nodes which are physically deployed at different places. heterogeneous wsm can be characterized with different parameters such as hardware resources, application, services, software platform and network. wsn are powerful technology, which is used to build up many applications in the era of technological development. these developments demand homogeneous or heterogeneous services as per the user requirements at applications level. the requirement results in increasing the number of services over the network and hence the number of complexities. complexities of wireless sensor networks are increasing due to the lack of adoption of new services, new protocols and interoperability between heterogeneous services with common communication architecture. this paper deals with these issues of wireless sensor networks, elaborating the need of generalized communication architecture for applications, developers and users, proposing generalized flexible service oriented network architecture (fsona) to solve above issues along with the detailed functionality of the proposed architecture. keywords: wireless sensor networks (wsn), flexible service oriented network architecture (fsona). middleware architecture, interoperability, localization. 1 introduction in the modern epoch wireless sensor networks are increasing rapidly in current environment for different application such as health monitoring, target tracking, mobility of object, petroleum industries, pressure measurement at various levels, environmental change etc. sensors are small devices which are used to compute the environmental changes according to application. these devices have been developed from organizations and vendors, due to this reason hardware platform, software platform and specification can be dissimilar. heterogeneous sensor network introduces a major issue to sensor node with its various different specification, failure prone sensors and operation in the current dynamic environment. failure prone sensors may affect the performance of the network. therefore multi objective method enhances the performance of task and network life time after detecting the failure prone sensor nodes [6]. so there is requirement to identify the failure prone nodes. to provide a common interface between the sensor node and its requirement for sensor network, middleware architecture has been evolved which is capable to tackle the heterogeneous environment without changing the specification and its platform. sensor middleware can be defined as a standard interface which is useful for communication and requirements of sensor, users in terms of services without affecting the platform [16]. middleware architecture is able to manage the requirements of node in the form of request, storage of data and constraints like when, how the data and service are needed for the node operation. middleware architecture supports a range of machine which have different configuration and provides compatibility for communication among the network and client. wireless sensor network has the various challenging issues copyright © 2006-2014 by ccc publications flexible service oriented network architecture for wireless sensor networks 611 which can be handled with service oriented computing model [5]. service oriented computing model is based on the approach of service oriented architecture. service oriented computing model intends to provide better service availability, communication protocols and accessibility of services in efficient way without knowing low level detail of hardware implementation and infrastructures. service oriented architecture has various advantages over traditional approach such as reusability, flexibility, interoperability and loose coupling which is beneficial to develop middleware architecture. service oriented computing method covers the functional and non functional requirements of the application in the current dynamic environment. service oriented middleware [20] provides a connection between various components and assist for communication over a multiple channel. middleware layer is an approach to satisfy the need of wireless sensor network at the level of design and implementation. various approaches have been proposed as a middleware in wireless sensor network [8]. these middleware architecture deals with dissimilar applications like installation of software in efficient manner and aggregation of data. sometimes application needs more advanced functions to reduce the complicated design and implementation with the middleware architecture. in the current dynamic scenario service oriented architecture is an approach which may attempt these challenges. this approach provides a flexibility which is very much useful to add a component or replace the old component to fulfil the demands of application. various services [9] can be managed at network level with wireless sensor network by way of service oriented model i.e. flexible service oriented model. very few researchers have done the survey on the middleware architecture for wireless sensor as well as network architecture [8]. integration of heterogeneous platforms with a single standard interface is the demand of current era to support technological development [12]. som is a possible way to do such kind of things because of its property. to perform the integration among various components flexibility should be there. so in this paper we have proposed a flexible service oriented network architecture which is based on the approach of basic service oriented architecture [19] to provide flexibility, loose coupling and reusability. flexible service oriented network architecture consists of following components: 1. building blocks, 2. building block repository, 3. workflow engine. the proposed flexible service oriented network architecture is able to couple or decouple the components according to the requirement of applications. in this paper we discuss the service oriented architecture, issues of wireless sensor network with middleware architecture, related works, proposed flexible service oriented network architecture for wsn as middleware, comparison and conclusion. 1.1 service oriented model in this model service provider play a role to develop new service module, service broker registers the service with its description in uddi (universal description discovery and integration), service requester requests for service to service broker as per its need. service oriented architecture is valuable to make a thing possible in heterogeneous environment. these advantages are: it can provide a common interface to publish or subscribe the service in the heterogeneous environment; many applications can run on different platforms and interact easily with each other, service user can be coupled for time duration according to need of application, it can reduce the dependency among the components due to loose coupling, service user can consume an exact service without knowing its location. from the above features of service oriented model we can use those models which can work as middleware architecture. this type of middleware is capable to integrate the components 612 a.p. singh, o.p. vyas, s. varma figure 1: service oriented model and components reusability on demand in heterogeneous computing environment. it can handle the functional (service creation, communication, invocation, group management, and real-time operation) and non functional (service discovery, interoperability, reusability) requirements of application. numerous middleware architectures have been proposed to add a new functionality like enterprise system. this approach is used to provide reusability of components like protocol, software etc. service oriented model based middleware is a demand of todays environment. 2 issues of wireless sensor networks with middleware architecture middleware architecture is software infrastructure which will provide connectivity between components like hardware, operating system, communication protocol, network corresponding to application. middleware architecture should be capable to provide runtime support, connectivity and execution of applications in dynamic environment. middleware architecture provides isolation among various components at abstraction layer. various challenges are present to develop service oriented model based middleware architecture for wireless sensor network. challenges are consist of two things: one is development of middleware and another is wireless sensor network. development of flexible service oriented network architecture is a challenging issue which will support the applications of wireless sensor network. some issues are discussed below. service heterogeneity: heterogeneity is a major problem in wireless sensor network at application level. this heterogeneity can be defined in terms of variation of services, platforms and networks. services heterogeneity is increasing to fulfill the need of users requirements. various services are present to perform the same operation in wireless sensor network at different levels of application. it is very much difficult to provide standard service in the service oriented environment to achieve the quality of service. therefore service oriented environment needs a common architectural platform for wireless sensor networks, which can provide a method to access the services form different platforms. adoption of protocol: requirement of users are increasing according to application, consequently number of protocols are also increasing to full fill the requirements. in current environment users or programmers are developing a protocol to support the application requirements. number of protocol will present on the web in future but protocols may not be common to satisfy the requirements of application. maintenance of protocol is also an issue. to address these issues, flexible middleware architecture is needed. flexible middleware architecture should provide loose coupling. loose coupling will provide flexibility to couple or decouple the protocols with application requirement. so flexible middleware architecture will also helpful for maintenance of protocol and users will have flexibility to choose the protocol from the web according to need. flexible service oriented network architecture for wireless sensor networks 613 integration with networks: integration mechanism will provide interaction to share the services or information with other network according to requirements of application. so middleware architecture should have capability to integrate the wsn application with external environment. middleware architecture can provide interface for communication. interoperability: in service oriented environment service vendors, service providers and platforms are increasing to perform the task in wireless sensor network. these services are developed on different platforms such as c, java etc. interoperability between different platforms should be present to provide a service composition. therefore wireless sensor network needs interoperability mechanism to support different type of services and platforms. adoption of new service: innovation of new technique and improvement of previous technology provides new services for various application domains such as wireless sensor networks, adhoc networks, cloud computing etc. these developments are the result of requirements and feedback of users after using the applications. all platforms may not be support the newly developed services. therefore adoption of new service is one of the crucial issues in wireless sensor network. security: sensor network is use to capture the data or information from the environment. information or data can be sensitive according to application like military services, health services etc. this type of application requires security modules to secure the data from attacker. so security is also an issue to provide secure communication and data between wsn and system. service oriented model can provide security modules as a service as per requirement. very few works have been done on the security with service oriented model [2]. therefore flexible service oriented network architecture will suit to resolve the security issue. 3 related works various methods have been proposed to address the issues of wireless sensor network. this section provides overview of various solutions in the context of wireless sensor network issues and its solutions. these solutions are serving the way to tackle the problems. wireless sensor network are suffering from the problem of data aggregation, resource management, energy efficiency, heterogeneity, hardware, routing etc. to solve these issues authors have been proposed various approach in terms of middleware technology. middleware approach provides abstraction at various levels such as interoperability, language, hardware, complexity etc. we are discussing the some work in comprehensive mode. comis: this is component based middleware architecture [10] which is used to satisfy the constraints like power and memory. components of this middleware can be varying as per the behaviour of sensor nodes and loaded memory based application. components of this middleware are listener, register, discovery, send and update. each component is responsible to perform its task according to functionality. it provides a various additional functionality such as discovery of k components according to distance, component management, and registration and component updation. it is more suitable for collaborative applications. soma: an soma [1] tries to fulfill the gap of interoperability among homogeneous platform. this middleware (service oriented middleware architecture) approach provides a communication model for wireless sensor network. to reduce the power consumption this solution is feasible at high traffic. standards of middleware components are inspired with the web services, which will helpful to accommodate the component interoperability and reusability. this solution provides a way to manage the homogeneous platform with interoperable manner. it has some advantages like complexity reduction, service specific interface etc. in this approach major drawback is gateway dependency because failure of single point will affect the performance of whole network. 614 a.p. singh, o.p. vyas, s. varma misense: it is component based middleware layer [13] which is use to support distributed sensor application. misense reduces the complexity of system by imposing structure on top of component model. this middleware provides a resource management, network abstraction and communication without knowing the detail of low level complexities. it is a feasible to handle the issues like data aggregation, topology management and event detection. som: service oriented model [3] is much efficient than traditional middleware. service oriented computing environment provides better availability, easy accessibility with some standard models and protocols. this approach provides a facility of interoperability and loose coupling between services in distributed scenario. som is capable to handle the communication, service discovery and publication among various services. oasis: oasis is a programming framework [14] which provides abstraction layer to hide the complexity at low level. this is planned for resource constraint application devices. oasis framework is combination of various service modules such as composer, node manager, object manager, service discovery protocol. these modules are responsible to perform the specific task. node manager is responsible for routing, service discovery is responsible for search the service domain, object manger is use identify the object and composer is responsible for initiating a service graph, parsing of graph and binding of service. this framework is feasible to support the single object at particular instance but it can be improve. smc: this framework is based on service oriented architecture [4]. in this middleware various components are defined as a service. these components are use full to support various application domains. service based midtier component (smc) is capable to deal with different functions. this model consists of layers and each layer is responsible to perform particular task. layers are independent to each other. service bus layer provides a facility to establish communication between protocols. binding is responsible to bind the service according to application. interface repository service layer is used for repository and classification of domain. data exchange service provides facility to exchange the data among similar nodes. encryption is used for security of data. synthesizer service layer is use to facilitate the processing of data and its composition. routing layer is responsible to handle various queries at same time. useme: useme is programming framework [7] which is based on service oriented approach. this framework provides facility to developer to deploy the service on sensor node and actuator. these services are developed to satisfy the real time constraint and specification. it is platform independent. this framework support to wireless sensor network as a middleware. useme framework is combination of various components. constraint management is use full to fulfill the demand of real time environment, configuration management is responsible to maintain the frequency of service discovery, invocation and communication is used to deliver the packet among various nodes, publication and service discovery is used to store the record of service and group management is responsible to form a group to achieve the efficiency and scalability. gem: gem is a generic event service middleware framework [11]. this framework is designed to support the new event based service. gem is generic middleware, which is use to facilitate the wireless sensor application along with service package. generic middleware architecture provides multilevel event detection, event language description and mote module to encode the code. language of event defines the format of data and storage in mote. information of every mote is required to identify the event at group level. a range of solutions are existing to address above issues.these solutions are capable to resolve the above issues of wireless sensor network.some issues of wireless sensor networks still exist such as adoption of new services, new protocols, and interoperability between heterogeneous services.therefore we are providing a flexible service oriented network architecture as a solution which is capable to tackle these issues. flexible service oriented network architecture for wireless sensor networks 615 4 proposed work this is generic architecture of the system with proposed flexible service oriented network architecture. generic architecture consists with four layers (figure 2). every layer is responsible to perform specific task as per the need of application. proposed flexible service oriented network architecture exists on top of every layer. other layers are application software, xoperating system and system software. application s/w is repository of softwares like web browsers, database spread sheet etc, which provides a communication between proposed architecture and xoperating system. xoperating system is component based operating system. system s/w provides a communication between h/w and xoperating system. figure 2: generic system architecture in proposed flexible service oriented network architecture, a new solution for the wireless sensor network which is constraint based and constraint free. constraint based and constraint free network can have various issues such as adoption of new service, adoption of new protocol, interoperability, service selection and composition availability of service, security, heterogeneity of platforms. detail functionality of flexible service oriented network architecture in figure 3. service user will be able to find out the service as per the requirements of application. service user can be classified in terms of domain expert and general user with its knowledge. users can give input to api with requirement such as application requirements, user requirement, and network requirements. service broker will get the request through the api. broker will search the service in its local repository as per the requirement. if service will available then broker will return optimized service for application otherwise send the request to another broker to get best service. service provider will provide the service to user through broker. service provider will compose the service as per the user requirements and reply back to broker. broker will send the service to user without knowing the low level detail of service. completion of above steps user will get the best service as per the requirements. then user will be able to deploy the service for wireless application like sensor network through the api. service user: in this architecture level of users are classified in two categories such as non parametric user and parametric user based on expertise. the execution of services in flexible 616 a.p. singh, o.p. vyas, s. varma figure 3: flexible service oriented network architecture service oriented network architecture begins with requirements of application as a user input. non parametric user: non parametric users are passive recipient of service without knowing the technical or low level details. the service request of non parametric user is forwarded to broker through the application programming interface without any technical requirements; the requested services are fetched from service provider with default setting and delivered to the user through the broker. in this case non parametric user may not get filtered services due to lack of technical requirements as an input. parametric user: parametric users are service users, who have detailed technical knowledge of application requirements. these requirements are application requirements, user requirements and network requirements. a parametric user sends request in the form of requirements parameters of application to the service broker. service broker will fetch the service from the service provider after composition as per the requirements and reply back to the service users. composed service will be filtered and most suitable for the application. service users will deploy the service in application after getting the reply from broker. service broker: there three types of functionalities, which are performed by a broker to complete the success full communication in flexible service oriented environment. protocol graph generator: a specific pattern of combination or sequence of building block known as protocol graph. complex services can be made with the combination of various simple service s. a graph sketch specifies the specific pattern, which is needed for getting a service of user. every node has capacity to generate the protocol graph. requirements like user, application, and network are playing important role for dynamic generation of protocol graph. message translator: in this module some messages are composed with the combination of more than one message, which is performed by the message list. a building block can add message, create message read message and removes the message from message list as per the compatibility. application building block is used for message transformation. this building block also plays a role of bridge between workflow and application. message transmission: this module is responsible for the transmission between application and network building blocks. message formats for communication following pattern is flexible service oriented network architecture for wireless sensor networks 617 used for communication in flexible service oriented network environment. msg 1 and msg2: a xml file name in string format. msg4 and msg5: this is msg1,built in message list format. msg3 and msg6: tagged message with filename and fragmented data to be transferred. figure 4: communication process between brokers after receiving the request from user end, broker generates a list of unavailable building blocks (service module) in xml format. building blocks information sent to application building block in the form of msg1 (figure 4) [15]. up port of transmission building block receives a msg2 from application building block after translation. transmission building block forwards msg3 to the data port of network building block. then received msg3 sent to the other network building block with same msg format. at the receiver end, network building block sends msg to the down port of transmission building block. the data pull out from transmission building block, will be copied in xml format. above process will be repeated for reply msg in appropriate format. service providers: the service providers are entity of our proposed architecture, which provides service to user as per the requirements. these services are classified according to its nature in various categories such as compound, composition, conventional, template and services in future. a service provider plays a role of developer and provider for consumer. service provider provides a many flavour of service along with same or different providers. service providers have facility to develop a new service based on predefine, precompiled and precomposed methods as per the requirement parameters of users. these above methods are helpful at the time of service composition. the service provider can use static or dynamic methods to develop a new service. when parameters of service need not change frequently at run time then developer follows the static method otherwise dynamic method to support the frequent change at runtime. services: service is functional module, which is used to perform single or multiple tasks. functional module of service may be a single or combination of other modules. these modules can be implemented by any technology on any platform with scalability, flexibility and fault tolerance. the goal of concerning things are interoperability, heterogeneous system services should be capability to connect, loose coupling, to minimize interdependencies between modules and heterogeneous data type, interaction pattern at the time of platform binding, logical deployment. these are performed with loose coupling. our flexible service oriented network architecture supports for wide range of wireless sensor network services.conventional service represents those type of services which developed in previous days and we are using without any required modification. these services have some flaws like network protocol adaption, localization algorithms and its complexity, tight coupling etc. 618 a.p. singh, o.p. vyas, s. varma template: template is a service, which is defined as set of reusable service related data that will minimize the required time to create service. this is applied for designer, developer and user to create or use the service. the template is used by various applications in order to save time needed to develop network interface, network load balancer and one or more virtual machines. this architecture provides a facility for developer, architect to develop or design new template as a service and register it for use of public environment. compound service: the compound service is a result from combination of two or more modules, which may be different or same according to configuration. development of compound service is not easy task because before the development of this service user should know the compatibility of application programming interface, protocol and integration requirements for application. prior knowledge of low level detail of different services and platform is not easy. so there is need of architecture to support in the development and deployment of compound services for application. our architecture resolves above issues and provides support to developer and user without knowing the low level details of other services. service composition: service composition is a way to build a new service from a set of service modules at run time. service location and selection plays an important role in the process of composition. in this architecture we are considering two types of service compositions. static service composition all requirements are fixed at the time of service designing. the requirements cannot be changed frequently at run time because lack of flexibility, agile for run time. therefore we are also considering dynamic service composition to accomplish the requirements of user as a service. in dynamic service composition various complications comes like operating service, service selection criteria. it is not necessary that composition process will always give correct result as per the user requirements. so through the application programming interface provides a facility to change in the requirements to get an available service. dynamic service composition reduces the human effort in service selection. we are considering both environments for service selection in our proposed architecture. service in future services in future are vision to develop a new service, protocol, tools, platform, language etc. in the current era of development researchers and developers are providing a new solution for the specific problem as a service to fulfill the demand of users with fast speed. so services are increasing with rapid rate on the internet. users may not know all solutions of specific problem. therefore we are providing architecture which will give the best service which will available on internet in future. technical requirements: many solutions are available in the form of service for specific task on the web. these services are required to execute the application. user will have flexibility to give an input in terms of requirement for service selection and composition [18]. therefore user has option to select a service with specification of requirements. being a user, requirements can be categorized such as user requirement, application requirement and network requirement which are given in figure 5. user requirements can be defined as a goal of application.this requirement is useful to find out the type of service for application [17]. there are various type of applications are used in sensor network so requirements of application will also be differ as per the performance. application requirements may be defined as accuracy, computation time, bandwidth, delay etc. network requirements is defined as, need of network related constraints. these constraints may be the selection of protocol, level of fault tolerance, transmission speed, life time of network etc. these requirements play an important role to find out the best service for the application. let s be the set of services which is represented as: s= ∑l i=0 ui + ∑n j=0 aj + ∑m k=0 km. user requirement may be single or multiple, which is mandatory. a is application requireflexible service oriented network architecture for wireless sensor networks 619 figure 5: technical requirements ments, n is network requirements.these requirements are optional, which can be represented as: a={a1,a2,a3, .......................an} n= {n1,n2,n3, .....................nk}. when n, k=0 then interaction pattern of application is similar to current pattern, where user will get minimal facility of service selection. when j=1...n and k= 1...m, then this condition will increase the better tune of service. the application uses different protocols for communication and also there are many applications which can work with diverse of protocols. let p be the total number of protocols available which can be represented as: p={p1,p2,p3................pn} , af ={pf|pf ⊂ p}. convergence of service request interpretation: e(x −s)2= 0 then x=’s’ with probability 1 or x converges to s with probability 1 let σ2x is variability of measurement according to chebyshev inequality: p{|x −a| ≤ ϵ} ≥|σ2x ÷ ϵ2x. if σx is very much smaller than ϵ then observed variable x is between (s-ϵ) and (s+ϵ) which is almost certain and one measurement of x is sufficient. however if σx is not sufficiently smaller compared to ϵ, then the result will not be of sufficient accuracy. 5 comparison of various service oriented approaches with flexible service oriented architecture wireless sensor network is distributed service over the network with well-defined interface. these services are easily accessible and deployable by wsn application through service oriented model based interfaces. service oriented model covers functional and non functional requirements of users to fulfill the gap of applications. these applications can be use the services to get the better result as per the requirements. to achieve result communication should be perform in between two parties with indirect manner i.e. with service brokers. services are accessible through multiple paths; best path will increase the response time of service with udp, soap transport. in the comparison table 1. we have tried cover all the requirements of user and applications with flexible service oriented architecture for wsn. few of them are covered in individual model but no one covers all the requirements and flexibility of module implementation 620 a.p. singh, o.p. vyas, s. varma as per the new requirements, which may come in future. therefore we have shown comparative analysis in table 1. table 1: comparison with main features si. no. soa based approach environment features covered requierments 1 flexible service oriented network architecture for wsn wireless/wires sensor networks, supports tcp/ip based networks flexibility to innovation, loose coupling, automatic service discovery, adaption of new service, support real time constrains of users run time support,service transparancy, interoperability among service,qos,integration with other sytem,service discovery, service abstraction. 2 useme wireless/wires sensor networks handles publicationand discovery,management of real time constraint runtime support service discovery service abstraction 3 oasis wireless sensor networks environment based sepration concerns,dynamic service discovery and deployment runtime support system, service discovery,abstraction. 4 misense wireless sensor networks content based on sepration of concers and subscription model, flexible data management,programming api service transparancy,efiicient large amount of data. 5 stream ware sensor network (wired/wireless) query based access. hetroginity management,scalability service discovery,management,efficient handling of large amount of data. 6 (si) 2 smart items network platform independent service, description, deployment run time support, service discovery and deployment. abstraction, integration with other system. 7 dyssco wireless sensor network dynamic self configuration service coverage service deployment, transparency, abstraction, configurable service. 8 b-vis distributed rfid and sensor networks real time tracking and monitoring trough sensors, programming for real time data. abstraction ,interoperability, efficient for handling large amount of data. integration with other system. 9 somdm wireless sensor network component architecture to reduce data processing service transparency, abstraction to heterogeneous nodes, interoperability between other nodes. 6 conclusions and future works our proposed flexible service oriented network architecture is developed with java platform. xml format is used to provide interoperability between different services. flexible service oriented network architecture is providing a facility to user for service selection and composition, adoption of new service, protocol. through this architecture users can get best service from the broker as per the requirements of application. also, the architecture provides a facility to couple the module (service) in the real time service domain and feasible to connect the heterogeneous and homogeneous services on common flexible service oriented network architecture for wireless sensor networks 621 platform. this architecture can be used by novice users having no or little knowledge regarding the low level technical detail of services. in future we will convert the xml format to rdf format, rating of service on constraints. bibliography [1] abangar h. et al. 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(2011); functional composition in future networks, computer networks, special issue on architectures and protocols for the future internet, 55(4): 987-998. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 1, month: february, year: 2022 article number: 4542, https://doi.org/10.15837/ijccc.2022.1.4542 ccc publications random permutation set y. deng yong deng* 1. institute of fundamental and frontier science university of electronic science and technology of china, chengdu, 610054, china 2. school of education shaanxi normal university, xi’an, 710062, china 3. school of knowledge science japan advanced institute of science and technology, nomi, ishikawa 923-1211, japan 4. department of management, technology, and economics eth zurich, zurich, 8093, switzerland *corresponding author: dengentropy@uestc.edu.cn; prof.deng@hotmail.com abstract for exploring the meaning of the power set in evidence theory, a possible explanation of power set is proposed from the view of pascal’s triangle and combinatorial number. here comes the question: what would happen if the combinatorial number is replaced by permutation number? to address this issue, a new kind of set, named as random permutation set (rps), is proposed in this paper, which consists of permutation event space (pes) and permutation mass function (pmf). the pes of a certain set considers all the permutation of that set. the elements of pes are called the permutation events. pmf describes the chance of a certain permutation event that would happen. based on pes and pmf, rps can be viewed as a permutation-based generalization of random finite set. besides, the right intersection (ri) and left intersection (li) of permutation events are presented. based on ri and li, the right orthogonal sum (ros) and left orthogonal sum (los) of pmfs are proposed. in addition, numerical examples are shown to illustrate the proposed conceptions. the comparisons of probability theory, evidence theory, and rps are discussed and summarized. moreover, an rps-based data fusion algorithm is proposed and applied in threat assessment. the experimental results show that the proposed rps-based algorithm can reasonably and efficiently deal with uncertainty in threat assessment with respect to threat ranking and reliability ranking. keywords: dempster-shafer evidence theory, permutation number, random permutation set, permutation mass function, orthogonal sum, random finite set, threat assessment 1 introduction since uncertainty exists in everywhere of our life, many theories have been developed to deal with uncertainty, such as probability theory [9], fuzzy set theory [22], dempster-shafer evidence theory (evidence theory) [2, 11], rough sets [10], and z-numbers [18, 23]. https://doi.org/10.15837/ijccc.2022.1.4542 2 most existing theories are based on set theory. set theory provides a tool for describing the collection of objects or elements, which is important and fundamental in math science [8]. for instance, the sample space of an experiment in probability theory is the set of all possible outcomes of that experiment [9]. in evidence theory, the power set is the set considering all possible subsets of frame of discernment, which is a basis for basic probability assignment [2, 11]. recently, a possible physical explanation of power set is proposed [12]. given a set s with n hypotheses, its corresponding power set, denoted as 2s, can be seen as all possible k-combination, where k ranges from 0 to n [12]. in addition, the relationship between combinatorial number, pascal’s triangle, and power set is quantitatively revealed by deng entropy [5] from the view of information measure [12]. here comes the straightforward question: if we consider all possible k-permutation of a set s with n hypotheses instead of the k-combination, namely, if the combinatorial number in the pascal’s triangle is replaced by permutation number, what would happen? to address this issue, a new kind of set, named as random permutation set (rps), is proposed in this paper, which consists of permutation event space (pes) and permutation mass function (pmf). the pes of a certain set θ considers all the permutation of θ. the elements of pes are named as the permutation events. pmf describes the chance of a certain permutation event that would happen. based on pes and pmf, rps can be viewed as a permutation-based generalization of random finite set. in addition, the right intersection (ri) and left intersection (li) of permutation events are presented. based on ri and li, the right orthogonal sum (ros) and left orthogonal sum (los) of pmfs are proposed. next, numerical examples are shown to illustrate the proposed conceptions about rps. the comparisons of probability theory, evidence theory, and rps are discussed and summarized. moreover, an rps-based data fusion algorithm is proposed for threat assessment. the experimental results show that, with respect to threat ranking and reliability ranking, the proposed rps-based algorithm can handle uncertainty of threat assessment reasonably and efficiently. the rest of this article is as follows. section 2 introduces the preliminaries. section 3 proposes the conceptions of rps. section 4 compares probability theory, evidence theory, and rps. section 5 proposes an rps-based data fusion algorithm for threat assessment. section 6 makes a brief conclusion. 2 preliminaries in this section, we briefly review some preliminaries of this paper, including dempster-shafer evidence theory, pascal’s triangle, and entropic explanation of power set. 2.1 dempster-shafer evidence theory dempster-shafer evidence theory [2, 11] is an efficient tool for dealing with uncertainty [7] and has broad applications, such as decision making [15], engineering budget [1], and fault diagnosis [17]. researchers have developed evidence theory theoretically and practically. in 2019, yager proposed the generalized dempster–shafer (d-s) structures, providing a view of the d-s structure based on a two-step process [21]. this generalized d-s structure is further applied in fuzzy rule bases for fuzzy system modeling [19]. xiao presented complex evidence theory in 2020, which is a complexvalued generalization of evidence theory [16]. in order to handle the uncertainty in open world, deng present generalized evidence theory in 2015 [4]. for measuring the uncertainty of evidence theory, yager presented an interval valued entropy [20]. in 2016, deng proposed a novel entropy for bpa, called deng entropy [5]. deng entropy is further developed into information volume [6], which has been applied in many areas [3, 14]. some basic conceptions about evidence theory are summarized as follows. definition 2.1 (frame of discernment). let θ, called the frame of discernment (fod), denotes a fixed set of n mutually exclusive and exhaustive elements, indicated by θ = {θ1, θ2, . . . , θn}. the power set of θ, denoted as 2θ, contains all possible subsets of θ, which is indicated by 2θ = {a1, a2, · · · , a2n} = {∅,{θ1},{θ2}, · · · ,{θn},{θ1, θ2},{θ1, θ3}, · · · ,{θ1, θn}, · · · , θ}. https://doi.org/10.15837/ijccc.2022.1.4542 3 definition 2.2 (mass function). given a fod of θ, a mass function, also called a basic probability assignment (bpa), is a mapping from 2θ to [0, 1], formally defined by: m : 2θ → [0, 1] (1) satisfying m(∅) = 0 and ∑a∈2θ m(a) = 1, where m(a) is the belief value with the proposition a. if m(a) > 0, then a is called a focal element. definition 2.3 (dempster’s combination rule). given two mass functions indicated by m1 and m2, their combination m1 ⊕ m2 is mathematically defined as: m(a) =   1 1−k ∑ b∩c=a m1(b)m2(c), a 6= ∅ 0, a = ∅ (2) where a, b, c ∈ 2θ and k is calculated by k = ∑b∩c=∅ m1(b)m2(c). 2.2 pascal’s triangle in mathematics, pascal’s triangle is a triangular array of the binomial coefficients that arises in probability theory [13]. as is illustrated in figure 1 (a), pascal’s triangle is a geometric arrangement of binomial coefficients in a triangle, which satisfies: • the x-th number in the n-th row can be expressed as c(n−1, x−1), meaning the combinatorial number of drawing x − 1 elements from n − 1 elements. • in the triangle, the sum of the x-th and the x + 1-th number in the n-th row equals to the x + 1-th number in the n + 1-th row, namely, c(n, x) = c(n − 1, x − 1) + c(n − 1, x). • the sum of all the numbers in the n-th row is 2n. figure 1: (a) the illustration of pascal’s triangle; (b) the illustration of pascal’s triangle where combinatorial number is replaced by permutation number 2.3 entropic explanation of power set a power set of a fod θ is defined as the set of all subsets of θ, including θ itself and empty set , denoted as 2θ. dempster-shafer evidence theory extends the fod to power set. however, the meaning of power set in evidence theory needs exploration. hence, an entropic explanation of power set in evidence theory is proposed [12]. in evidence theory, a power set of fod whose cardinality is n can be seen as all the possible k-combination of fod, where k ranges from 0 to n. with respect to different n, all the k-combination can form pascal’s triangle as is shown in figure 1 (a). in addition, the relationship between combinatorial https://doi.org/10.15837/ijccc.2022.1.4542 4 number, pascal’s triangle, and power set is quantitatively revealed by deng entropy [5] from the view of information measure [12]. here comes the question: if we consider all possible k-permutation of a set s with n hypotheses instead of the k-combination, namely, if the combinatorial number in the pascal’s triangle is replaced by permutation number as is shown in figure 1 (b), what would happen? this will be detailed in section 3. 3 random permutation set in this section, some definitions about random permutation set (rps) are proposed. definition 3.1 (permutation event space). given a fixed set of n mutually exclusive and exhaustive elements θ = {θ1, θ2, . . . , θn}, its permutation event space (pes) is a set of all possible permutations of θ defined as follows: pes (θ) ={ aij | i = 0, ..., n; j = 1, ..., p(n, i)} (3) ={ ∅, (θ1) , (θ2) , ..., (θn) , (θ1, θ2) , (θ2, θ1) , ..., (θn−1, θn) , (θn , θn−1) , ..., (θ1, θ2, ..., θn) , ..., (θn , θn−1, ..., θ1) } (4) in which p(n, i) is the i-permutation of n defined as p(n, i) = n! (n−i)! . the element aij in pes is called the permutation event, which is a tuple representing a possible permutation of θ, where i indicates the index for the cardinality of aij and j denotes the index for the possible permutation. for better understanding of pes, let us consider the following example. example 3.1. assume there are three balls in a box, whose colors are red, blue, and green, respectively. with respect to different colors, these balls in the box can be denoted by θ = {r, b, g}. then, we randomly take out a number of balls from the box in sequence without replacement. for example, if we firstly take out the red ball and secondly take out the blue ball, this result will be represented by (r, b). by contrast, if we firstly take out the blue ball and secondly take out the red ball, this result will be represented by (b, r). hence, (r, b) and (b, r) are two different events. all the possible results are illustrated in figure 2. in fact, the result are all the permutations of θ, which can be represented by the pes of θ, detailed as follows: pes ({r, b, g}) = { a01, a11, a12, a13, (5) a21, a22, a23, a24, a25, a26, a31, a32, a33, a34, a35, a36 } = { ∅, (r) , (b) , (g) , (6) (r, b) , (r, g) , (b, g) , (b, r) , (g, r) , (g, b) , (r, b, g) , (r, g, b) , (b, r, g) , (b, g, r) , (g, b, r) , (g, r, b) } example 3.1 shows that the pes of θ is a set containing all the possible permutations of θ. besides, permutation event (r, b) is different from (b, r), because they have different orders of elements in θ. definition 3.2 (random permutation set). given a fixed set of n mutually exclusive and exhaustive elements θ = {θ1, θ2, . . . , θn}, its random permutation set (rps) is a set of pairs defined as follows: rps (θ) = {〈a, m (a)〉 | a ∈ pes (θ)} (7) https://doi.org/10.15837/ijccc.2022.1.4542 5 figure 2: the illustration for example 3.1: all the possible results of randomly taking out a number of balls from the box in sequence without replacement where m is called the permutation mass function (pmf), which is defined as: m : pes(θ) → [0, 1] (8) constrained by m (∅) = 0 and ∑a∈pes(θ) m (a) = 1. for better understanding of rps, let us consider the following example. example 3.2. following example 3.1, given θ = {r, b, g}, its corresponding pes is shown in eq. (5). then, one possible rps can be given as: rps(θ) = {〈(r), 0.4〉 ,〈(r, b), 0.3〉 ,〈(b, r), 0.3〉} (9) where 〈(r, b), 0.3〉 means that the pmf of the permutation event (r, b), i.e., one takes out the red ball for the first time, then takes out the blue ball for the second time, is 0.3. by contrast, 〈(b, r), 0.3〉 means that the pmf of the permutation event (b, r), i.e., one takes out the blue ball for the first time, then takes out the red ball for the second time, is also 0.3. besides, comparing 〈(r), 0.4〉 and 〈(r, b), 0.3〉, we can get the information that the permutation event (r), i.e., one only take out the red ball, has a higher chance to happen compared with the permutation event (r, b), i.e., one takes out the red ball for the first time, then takes out the blue ball for the second time. example 3.2 shows that, 〈(r, b), 0.3〉 and 〈(b, r), 0.3〉 are two different pmfs, since permutation events (r, b) and (b, r) and are distinguished from the different orders of elements in θ. in addition, pmf in rps describes the chance of a certain permutation event that would happen, which is similar to the probability distribution in probability theory and the bpa in evidence theory. definition 3.3 (intersection of permutation events). given two permutation events a, b ∈ pes(θ), the right intersection (ri) and the left intersection (li) of a and b are defined as follows: a −→u b = b\\ ⋂ θ∈b,θ /∈a {θ} (ri) (10) a ←−u b = a\\ ⋂ θ∈a,θ /∈b {θ} (li) (11) where x\\y denotes removing y from x. for better understanding of ri and li, let us consider the following example. example 3.3. let two rpss defined on θ = {r, b, g} be as follows: rps1(θ) = {〈(r), 0.4〉 ,〈(r, b), 0.3〉 ,〈(b, r), 0.3〉} (12) rps2(θ) = {〈(g), 0.4〉 ,〈(r, b), 0.1〉 ,〈(r, g, b), 0.15〉 ,〈(g, b, r), 0.35〉}. https://doi.org/10.15837/ijccc.2022.1.4542 6 take two permutation events as an instance, i.e., (r, b) of rps1(θ) and (g, b, r) of rps2(θ). the ri and li of these two permutation events can be respectively obtained as: (r, b) −→u (g, b, r) = (g, b, r)\\{g} = (b, r) (13) (r, b) ←−u (g, b, r) = (r, b)\\∅ = (r, b) (14) next, all intersection results (including ri and li) of permutation events of rps1(θ) and rps2(θ) are shown in table 1. table 1: ri and li of permutation events of rps1(θ) and rps2(θ) rps1(θ) rps2(θ) li ri rps1(θ) rps2(θ) li ri rps1(θ) rps2(θ) li ri (r) (g) ∅ ∅ (r,b) (g) ∅ ∅ (b,r) (g) ∅ ∅ (r,b) (r) (r) (r,b) (r,b) (r,b) (r,b) (b,r) (r,b) (r,g,b) (r) (r) (r,g,b) (r,b) (r,b) (r,g,b) (b,r) (r,b) (g,b,r) (r) (r) (g,b,r) (r,b) (b,r) (g,b,r) (b,r) (b,r) example 3.3 shows that, because permutation event considers the order of the elements in θ, the result of ri for two permutation events is different from that of li. definition 3.4 (orthogonal sum of permutation mass functions). given two pmfs m1 and m2, the right orthogonal sum (ros) of these two pmfs is denoted as m1 −→⊕m2 and defined by: m r(a) =   1 1− −→ k ∑ b −→u c=a m1(b)m2(c), a 6= ∅ 0, a = ∅ (15) where a, b, c ∈ pes(θ), −→u denotes the right intersection, and −→ k is defined as −→ k = ∑ b −→u c=∅ m1(b)m2(c). (16) the left orthogonal sum (los) of these two pmfs is denoted as m1 ←−⊕m2 and defined by: m l(a) =   1 1− ←− k ∑ b ←−u c=a m1(b)m2(c), a 6= ∅ 0, a = ∅ (17) where a, b, c ∈ pes(θ), ←−u denotes the left intersection, and ←− k is defined as ←− k = ∑ b ←−u c=∅ m1(b)m2(c). (18) for better understanding of ros and los, let us consider the following example. example 3.4. following example 3.3, given two rpss defined on θ = {r, b, g} be as follows: rps1(θ) = {〈(r), 0.4〉 ,〈(r, b), 0.3〉 ,〈(b, r), 0.3〉} (19) rps2(θ) = {〈(g), 0.4〉 ,〈(r, b), 0.1〉 ,〈(r, g, b), 0.15〉 ,〈(g, b, r), 0.35〉} whose pmfs are respectively denoted by m1 and m2, then we will show the calculating procedure of ros m1 −→⊕m2 and los m1 ←−⊕m2. take m r(r, b) of m1 −→⊕m2 and m l(r, b) of m1 ←−⊕m2 as examples. they are respectively calculated as: −→ k =m1(r)m2(g) + m1(r, b)m2(g) + m1(b, r)m2(g) (20) =0.4 ∗ 0.4 + 0.3 ∗ 0.4 + 0.3 ∗ 0.4 = 0.4 https://doi.org/10.15837/ijccc.2022.1.4542 7 m r(r, b) = 1 1 − −→ k [m1(r, b)m2(r, b) + m1(r, b)m2(r, g, b) (21) + m1(b, r)m2(r, b) + m1(b, r)m2(r, g, b)] = 1 1 − 0.4 (0.3 ∗ 0.1 + 0.3 ∗ 0.15 + 0.3 ∗ 0.1 + 0.3 ∗ 0.15) =0.25 ←− k =m1(r)m2(g) + m1(r, b)m2(g) + m1(b, r)m2(g) (22) =0.4 ∗ 0.4 + 0.3 ∗ 0.4 + 0.3 ∗ 0.4 = 0.4 m l(r, b) = 1 1 − ←− k [m1(r, b)m2(r, b) + m1(r, b)m2(r, g, b) (23) + m1(r, b)m2(g, b, r)] = 1 1 − 0.4 (0.3 ∗ 0.1 + 0.3 ∗ 0.15 + 0.3 ∗ 0.35) =0.3 next, the results for the orthogonal sum (including ros and los) of m1 and m2 are shown in table 2. table 2: the results for ros and los of m1 and m2 permutation event ros m1 −→⊕m2 los m1 ←−⊕m2 (r) m r(r) = 0.4 m l(r) = 0.4 (r, b) m r(r, b) = 0.25 m l(r, b) = 0.3 (b, r) m r(b, r) = 0.35 m l(b, r) = 0.3 example 3.4 shows that ros is different from los, since they take different intersection of permutation events into account, namely, ros considers ri while los uses li. 4 numerical examples and discussions in this section, some numerical examples are shown to compare rps, evidence theory, and probability theory. example 4.1. given a fixed set of n mutually exclusive and exhaustive elements θ = {x, y}, its corresponding pes is that pes(θ) = {∅, (x), (y), (x, y), (y, x)} (24) consider the following two scenarios: • if the order of the element in permutation event is ignored, then permutation events (x, y) and (y, x) will be the same. to be specific, both of (x, y) and (y, x) will degenerate into the set {x, y}, since the order of x and y is regardless. as a result, if the order of the element in pes(θ) is ignored, pes(θ) will degenerate into the power set in evidence theory [2, 11]: 2θ = {∅, {x}, {y}, {x, y}}. • if each permutation event is limited to containing just one element, then permutation events (x, y) and (y, x) will disappear, so that pes(θ) will degenerate into the sample space in probability theory [9]: ω = {{x}, {y}}. example 4.1 shows that pes is compatible with the power set in evidence theory and the sample space in probability theory. https://doi.org/10.15837/ijccc.2022.1.4542 8 example 4.2. let two rpss defined on θ = {x, y, z} be as follows: rps1 (θ) = {〈 (x) , 1 7 〉 , 〈 (y) , 1 7 〉 , 〈 (z) , 1 7 〉 , 〈 (x, y) , 1 7 〉 , (25)〈 (y, z) , 1 7 〉 , 〈 (x, z) , 1 7 〉 , 〈 (x, y, z) , 1 7 〉} rps2 (θ) = {〈 (x) , 1 3 〉 , 〈 (y) , 1 3 〉 , 〈 (z) , 1 3 〉} (26) whose pmfs are respectively denoted by m1 and m2, which are discussed as follows: • since the pes of rps1 (θ) degenerates into the power set in evidence theory: 2θ = {∅,{x},{y}, {z},{x, y},{y, z},{x, z},{x, y, z}}, the pmf m1 of rps1 (θ) is actually a bpa in evidence theory [2, 11]: m ({x}) = m ({y}) = m ({z}) = m ({x, y}) = (27) m ({y, z}) = m ({x, z}) = m ({x, y, z}) = 1 7 . • because the pes of rps2 (θ) degenerates into the sample space in evidence theory: ω = {{x},{y},{z}}, m2 of rps2 (θ) is actually a probability distribution in probability theory [9]: p ({x}) = p ({y}) = p ({z}) = 1 3 . (28) example 4.2 shows that pmf of rps is compatible with the bpa in evidence theory and the probability distribution in probability theory. example 4.3. following example 3.4, given two rpss defined on θ = {r, b, g} be as follows: rps1(θ) = {〈(r), 0.4〉 ,〈(r, b), 0.3〉 ,〈(b, r), 0.3〉} (29) rps2(θ) = {〈(g), 0.4〉 ,〈(r, b), 0.1〉 ,〈(r, g, b), 0.15〉 ,〈(g, b, r), 0.35〉} whose pmfs are respectively denoted by m1 and m2. consider the following two scenarios: • if the order of the element in permutation event is ignored, then m1 and m2 will degenerate into bpas: m1 : m1 ({r}) = 0.4, m1 ({r, b}) = 0.6 (30) m2 : m2 ({g}) = 0.4, m2 ({r, b}) = 0.1, m2 ({r, g, b}) = 0.5 (31) use dempster’s rule [2, 11] to combine m1 and m2. the result is that: m : m ({r}) = 0.4, m ({r, b}) = 0.6 (32) • the associated ros and los of m1 and m2 are calculated in example 3.4, which are shown as follows: m r : m r (r) = 0.4, m r (r, b) = 0.25, m r (b, r) = 0.35 (33) m l : m l (r) = 0.4, m l (b, r) = 0.3, m l (r, b) = 0.3 (34) if the order of the element in permutation event is ignored, then m r and m l will degenerate into bpas: mr : mr ({r}) = 0.4, mr ({r, b}) = 0.6 (35) ml : ml ({r}) = 0.4, ml ({r, b}) = 0.6 (36) comparing eq. (32), eq. (35), and eq. (36), we can find that all of them are the same, which shows that, if pmf becomes bpa, both ros and los will degenerate into dempster’s combination rule. example 4.3 shows that ros and los are compatible with dempster’s combination rule in evidence theory. for clearance, the comparisons of probability theory, evidence theory, and rps discussed from example 4.1 to 4.3 are summarized in table 3. https://doi.org/10.15837/ijccc.2022.1.4542 9 table 3: comparisons of probability theory, evidence theory, and rps theory probability theory evidence theory random permutation set (rps) space sample space power set permutation event space (pes) mapping function probability distribution bpa permutation mass function (pmf) fusion rule bayes fusion rule dempster’s rule right orthogonal sum (ros) left orthogonal sum (los) 5 application in sensor data fusion and threat assessment in this section, to illustrate the performance of rps, an rps-based data fusion algorithm is proposed and applied in threat assessment. 5.1 problem statement threat assessment is a real practice of determining the seriousness of a potential threat, and evaluating the probability that a threat will become a reality. threat assessment has several major procedures, including identification of potential threatening target, initial assessment for the seriousness of the targets, and follow-up assessment, i.e., data fusion and decision making. threat assessment can be applied in many fields, such as business, education, risk management, and so on. in the scenario of enemy aircraft detection, assume there are three types of aircraft (potential threatening target) represented by {a, b, c}. in order to detect the aircraft, assume there are three radars remarked from 1 to 3. for threat assessment of the aircraft, the report provided by each radar should includes the following three aspects: (i) representation of the threat event there three types of aircraft, and each type of aircraft may pose a threat, which is regarded as a one-type threat event. however, in a real world scenario, the enemy may not just send one type of aircraft, but allocate an aircraft fleet with several different types of aircraft, which is referred as a multi-types threat event. for example, if the enemy sent an aircraft fleet with two types of aircraft (a and c), this threat is called the two-types threat event, which is denoted as {a, c}. (ii) threat ranking of the threat event in aircraft fleet, different aircraft have different levels of threat. hence, there exists threat ranking in the threat event. for example, the threat event {a, c} mentioned in aspect (i) has two kinds of threat ranking, which can be represented by a � c and c � a. based on permutation event of rps, these two rankings can be denoted as (a, c) and (c, a). in this sense, according to the seriousness of the aircraft, all the possible threat ranking of threat event can be represented as pes: pes ({a, b, c}) = { ∅, (a) , (b) , (c) , (37) (a, b) , (a, c) , (b, c) , (b, a) , (c, a) , (c, b) , (a, b, c) , (a, c, b) , (b, a, c) , (b, c, a) , (c, b, a) , (c, a, b) } to better understand, let’s consider the following example. (a, c, b) indicate that the threat ranking of threat event {a, b, c} is a � c � b, which means that aircraft a is very threatening, while the threat of b is the lowest (but can not be ignored) compared with a and c. besides, (a, b) means that the threat of c is trivial which can be ignored, while the threat of a and b should be taken into consideration: their ranking of threat is a � b. for clearance and concise, in the rest of the part, we use tuple (·) to represent threat event with threat ranking, inside of using {·}. (iii) threat degree of the threat event threat degree of a certain threat event is regarded as the chance that the threat event will become a reality, which can be represented by pmf m : pes({a, b, c}) → [0, 1]. for instance, this is a pmf https://doi.org/10.15837/ijccc.2022.1.4542 10 obtained by radar: m : m (c) = 0.1 m (c, a) = 0.3 m (c, a, b) = 0.6 (38) where m (c) = 0.1 indicates that aircraft c may cause a potential threat, but the threat degree is relatively low. besides, m (c, a) = 0.3 means that aircraft c and a are likely to threaten the security, and the threat of c is more serious than that of a, while the threat of b can be ignored. m (c, a, b) = 0.6 denotes that an aircraft fleet with three types of aircraft may constitute a big threat, the threat ranking of them is c � a � b. in order to representing the three aspects mentioned above, the threat assessment reports by radars should be based on rps, i.e., pes of rps for indicating aspect (i) and (ii), and pmf of rps for representing aspect (iii). besides, in real practice, due to difference of resolution and mechanical structure, the reliability of the radars would be different. hence, the reliability ranking of the radar should be taken into account. it should be noted that, the order in threat ranking of threat event is for aircraft (targets), while the order in reliability ranking is for radars (information sources). to sum up, to make full use of the threat assessment reports and the reliability ranking, as well as to recognize the most threatening aircraft of the three, the data should be suitably treated and fused. in the next subsection, an rps-based data fusion algorithm is proposed for threat assessment. 5.2 rps-based data fusion algorithm for threat assessment in this subsection, an rps-based data fusion algorithm is proposed, where rps and its orthogonal sum are used to process the data. given a sample space θ = {θ1, ..., θn} and m rpss based on pes(θ), the proposed algorithm is as follows. step 1: input threat assessment reports, and obtain n rpss by different radars which are denoted as rpsi, i = 1, 2, ... , m. (39) step 2: based on the reliability ranking rpsx � rpsy � ... � rpsz, combine the pmf associated with the m rps by using the left orthogonal sum (los) for m − 1 times, and then get the left-combined pmf: m l = mx ←−⊕ my ←−⊕ ...←−⊕ mz (40) step 3: based on m l, calculate the relative threat degree (rtd) for the j-th threat event aij with i types of aircraft: rtd(aij) = m l ( aij ) ∑ p(n,i) j=1 m l ( aij ) aij ∈ pes(θ) (41) where i = 1, ..., n is the number of types for aircraft, and j = 1, ..., p(n, i) is the index for the i-types threat event (the threat event with i types of aircraft). p(n, i) = n! (n−i)! is the permutation number. step 4: based on rtd, output the final threat assessment (fta) for different i-types threat events: ftai = arg max 1≤j≤p(n,i) [ rtd ( aij )] 1 ≤ i ≤ n, aij ∈ pes(θ) (42) https://doi.org/10.15837/ijccc.2022.1.4542 11 table 4: the rps-based threat assessment reports by three radars m (·) (a) (b) (a, b) (b, a) (a, c) (a, b, c) (a, c, b) (b, a, c) (c, a, b) rps1 0.12 0.10 0.15 0.15 0.03 0.10 0.25 0.08 0.02 rps2 0.10 0.15 0.07 0.23 0.10 0.10 0.05 0.12 0.08 rps3 0.18 0.05 0.15 0.05 0.00 0.12 0.30 0.10 0.05 table 5: the left-combined pmf based on los (a) (b) (a, b) (b, a) (a, c) (c, a) (a, b, c) (a, c, b) (b, a, c) (c, a, b) m l ′ (·) 0.267 0.123 0.260 0.069 0.016 0.002 0.055 0.139 0.046 0.023 m l(·) 0.350 0.201 0.251 0.063 0.033 0.003 0.021 0.052 0.017 0.009 5.3 experiment and result the threat assessment reports for the aircraft by three radars are collected in table 4, and the reliability ranking of the three radars can be obtained by experts and is shown as follows: rps3 � rps1 � rps2. to fuse the information in threat assessment reports and the reliability ranking, as well as to obtain the final threat assessment, the data is processed based on the proposed rps-based data fusion algorithm. the procedure is as follows. step 1: input threat assessment reports, and obtain three rpss: rps1, rps2, rps3. step 2: based on the reliability ranking rps3 � rps1 � rps2, combine their associated pmf by using los for two times, and then get the left-combined pmf: m l = m3 ←−⊕ m1 ←−⊕ m2 (43) which can be calculated by: m l ′ = m3 ←−⊕ m1 (44) m l = m l ′ ←−⊕ m2 (45) whose results are summarized in table 5. step 3: based on m l, calculate the relative threat degree (rtd). for one-type threat events (a) and (b), their corresponding rtds are that: rtd (a) = m l (a) m l (a) + m l (b) = 0.350 0.350 + 0.201 = 0.635 (46) rtd (b) = m l (b) m l (a) + m l (b) = 0.201 0.350 + 0.201 = 0.365 (47) for two-types threat events (a, b), (b, a), (a, c), and (c, a), their rtds are that: rtd(a, b) = 0.717 rtd(b, a) = 0.180 (48) rtd(a, c) = 0.094 rtd(c, a) = 0.009 for three-types threat events (a, b, c), (a, c, b), (b, a, c), and (c, a, b), their rtds are that: rtd(a, b, c) = 0.212 rtd(a, c, b) = 0.525 (49) rtd(b, a, c) = 0.172 rtd(c, a, b) = 0.091 https://doi.org/10.15837/ijccc.2022.1.4542 12 step 4: based on rtd, output the final threat assessment (fta) for one-type threat events, two-type threat events, and three-type threat events, which are respectively shown as follows: fta1 = arg max [rtd (a) , rtd (b)] = (a) (50) fta2 = arg max [rtd (a, b) , ... , rtd (c, a)] = (a, b) (51) fta3 = arg max [rtd (a, b, c) , ... , rtd (c, a, b)] = (a, c, b) (52) from these ftas, it can be concluded that: • if the enemy just sends one type of aircraft, namely one-type threat event, then aircraft a is the most threatening aircraft to us. • if the enemy sends an aircraft fleet with two types of aircraft, namely two-types threat event, then aircraft fleet with a and b is the most threatening fleet to us. the threat ranking is a � b, which means that, in this fleet, aircraft a is more threatening than b. • if the enemy sends an aircraft fleet with three types of aircraft, namely three-types threat event, then aircraft fleet with a, c, and b is the most threatening fleet to us. the threat ranking is a � c � b, which indicates that, in this fleet, aircraft a is the most threatening aircraft, c also poses a potential threat to us, while the threat of b is the lowest. 5.4 analysis and discussion in this subsection, to show the efficiency of the proposed rps-based algorithm in threat assessment, we compare it with the method based on bpa and dempster’s rule of combination [2]. as is illustrated in example 4.1, if the order of the element in pes(θ) is ignored, pes(θ) will degenerate into the power set in evidence theory, so that rps in table 4 will boil down to body of evidence: e1, e2, and e3, which are listed in table 6. table 6: the bpa-based threat assessment reports by three radars m(·) {a} {b} {a, b} {a, c} {a, b, c} e1 0.12 0.10 0.30 0.03 0.45 e2 0.10 0.15 0.30 0.10 0.35 e3 0.18 0.05 0.20 0.00 0.57 table 7: data fusion results based on two methods bpa+d’s rulea {a} {b} {a, b} {a, c} {a, b, c} md(·) 0.350 0.201 0.314 0.036 0.099 rtdbpa(·) 0.635 0.365 0.897 0.103 1.000 ftabpai {a} {a, b} {a, b, c} rps+losb (a) (b) (a, b) (b, a) (a, c) (c, a) (a, b, c) (a, c, b) (b, a, c) (c, a, b) m l(·) 0.350 0.201 0.251 0.063 0.033 0.003 0.021 0.052 0.017 0.009 rtdrps(·) 0.635 0.365 0.717 0.180 0.094 0.009 0.212 0.525 0.172 0.091 ftarpsi (a) (a, b) (a, c, b) a: “bpa+d’s rule” denotes bpa and dempster’s rule of combination. b: “rps+los” indicates rps and left orthogonal sum (los). then, e1, e2, and e3 are combined based on dempster’s rule of combination, and the data fusion result md is summarized in table 7. for comparison, the data fusion result m l in table 5 is listed again in table 7. besides, the results of rtd and fta based on the two methods are also summarized in table 7. it can be seen from table 7 that: https://doi.org/10.15837/ijccc.2022.1.4542 13 • los (as well as ros) considers the order of combination, which is a generalization of dempster’s rule. if the order of the element in pes(θ) is ignored, rps will degenerate into body of evidence, so that m l based on los will further degenerate into md based on dempster’s rule, such as md({a, b}) = m l(a, b) + m l(b, a). besides, los can combine rps based on the reliability ranking. for example, if the reliability ranking is rps3 � rps1 � rps2, then the combination of rpss based on los is that m3 ←−⊕ m1 ←−⊕ m2. however, dempster’s rule does not have this sense of reliability ranking. • rps takes the order of the element into consideration, so that it can distinguish from (a, b, c) and (a, c, b), and assign different pmf to them. however, bpa of evidence theory does not have the sense of order. hence, power set just has the element {a, b, c}, which is a set not a tuple, so that element {a, b, c} just has a single bpa. • as for rtd and fta, since rps assigns different pmf for (a, c, b) and (a, b, c), which can be further used to represent relative threat degree (rtd), such as rtdrps(a, c, b) = 0.525, so that ftarps3 = (a, c, b), from which we can obtain the threat ranking a � c � b. nevertheless, the element {a, b, c} in power set just has a single bpa, which is not suitable for representing rtd. we can see from table 7, rtdbpa({a, b, c}) = 1.000, so that ftabpa3 is always {a, b, c}. hence, bpa is not capable to represent rtd and is not suitable to be used for generating fta. to summarize, since rps takes the ranking and order of element into consideration, the proposed rps-based data fusion algorithm is able to reasonably and efficiently handle uncertainty in threat assessment with respect to threat ranking and reliability ranking. the comparisons of evidence theory and rps with respect to threat assessment are summarized in table 8. table 8: comparisons of evidence theory and rps with respect to threat assessment evidence theory random permutation set (rps) reliability ranking × x (based on los or ros) threat ranking × x (based on pes) threat degree x (based on bpa) x (based on pmf) rtd × x (based on rps and los) fta × x (based on rps and los) 6 conclusion for exploring the meaning of the power set in evidence theory, a possible explanation of power set is proposed from the view of pascal’s triangle and combinatorial number. here comes the question: what would happen if the combinatorial number is replaced by permutation number? to address this issue, a new kind of set, named as random permutation set, is proposed in this paper. the main contributions and some remarks of this paper are summarized as follows: • random permutation set (rps) is proposed, which consists of permutation event space (pes) and permutation mass function (pmf). the pes of a certain set considers all the permutation of that set. the elements of pes are called the permutation events. pmf describes the chance of a certain permutation event that would happen. • the right intersection (ri) and left intersection (li) of permutation events are presented. based on ri and li, the right orthogonal sum (ros) and left orthogonal sum (los) of pmfs are proposed. • the comparisons of probability theory, evidence theory, and rps are discussed and summarized. the pes of rps is compatible with the power set in evidence theory and sample space https://doi.org/10.15837/ijccc.2022.1.4542 14 in probability theory. the pmf of rps are compatible with the bpa and the probability distribution. the ros and los of rps is compatible with dempster’s rule. also, since rps extends power set (a collection for combination of elements) into pes (a collection for permutation of elements), rps is actually a permutation-based generalization of random finite set. • a rps-based data fusion algorithm is proposed for threat assessment. the results show that, because rps considers the ranking and order of element, the proposed rps-based algorithm can reasonably and efficiently process uncertain information in threat assessment with threat ranking and reliability ranking. acknowledgment the author greatly appreciates china academician of the academy of engineering, professor shan zhong and professor you he, for their encouragement to do this research. the author greatly appreciates professor yugeng xi to support this work. research assistant jixiang deng and ph.d student luyuan chen discussed many issues of this work. this work is continuously funded for the past nearly twenty years including national natural science foundation of china, grant nos. 30400067, 60874105, 61174022, 61573290 and 61973332, program for new century excellent talents in university, grant no. ncet-08-0345, shanghai rising-star program grant no.09qa1402900, chongqing natural science foundation for distinguished scientist, grant no. csct, 2010ba2003, jsps invitational fellowships 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[23] zadeh, l.a., 2011. a note on z-numbers. information sciences 181, 2923–2932. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-andcontrol cite this paper as: deng y. (2022). random permutation set, international journal of computers communications & control, 17(1), 4542, 2022. https://doi.org/10.15837/ijccc.2022.1.4542 introduction preliminaries dempster-shafer evidence theory pascal's triangle entropic explanation of power set random permutation set numerical examples and discussions application in sensor data fusion and threat assessment problem statement rps-based data fusion algorithm for threat assessment experiment and result analysis and discussion conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 6, month: december, year: 2022 article number: 4957, https://doi.org/10.15837/ijccc.2022.6.4957 ccc publications observer-based feedback control of interval-valued fuzzy singular system with time-varying delay and stochastic faults h. jerbi, m. kchaou, o. alshammari, r. abassi, d. popescu houssem jerbi* college of engineering university of hail po.box 2440, hail, kingdom of saudi arabia. *corresponding author: h.jerbi@uoh.edu.sa mourad kchaou college of engineering university of hail po.box 2440, hail, kingdom of saudi arabia. mouradkchaou@gmail.com obaid alshammari college of engineering university of hail po.box 2440, hail, kingdom of saudi arabia. os.alshammari@uoh.edu.sa rabeh abassi college of engineering university of hail po.box 2440, hail, kingdom of saudi arabia. r.abbassi@uoh.edu.sa dumitru.popescu faculty of automatics and computers, university politehnica of bucharest, romania dumitru.popescu@acse.pub.ro abstract there are countless applications of non-linear systems that incorporate delay and algebraic equations. despite current improvements in control theory, stochastic actuator defects still pose challenges when it comes to these systems. furthermore, when it is not possible to measure the states of the system, and when uncertainties affect the system under investigation, the problem becomes even more complex. this paper is concerned with fault-tolerant observer-based controller synthesis for non-linear delayed singular systems with uncertainties and stochastic actuator failures. on the basis of interval valued models, a new lyapunov-krasovskii functional is built to develop a less conservative criterion to ensure that the closed-loop system is admissible in the mean-square sense. in addition, as these matrices are coupled with multiple variables, finding the parametric matrices of the observer and controller in terms of the obtained condition is more complex and challenging. the proposed method employs the matrix inequality decoupling technique to resolve this issue. eventually, simulations are carried out to demonstrate the applicability of the proposed method. keywords: singular system, (ivf) model , time varying delay, probabilistic faults, reliable observer-based control. https://doi.org/10.15837/ijccc.2022.6.4957 2 1 introduction this section includes the literature review, notations, and acronyms used in the document, as well as an outline of the publication and its goals. 1.1 literature review singular systems which are described by a couple of algebraic and differential equations, are characterized by their different modes namely finite dynamic modes, infinite non-dynamic modes, and infinite dynamic modes, respectively. the infinite dynamic modes has the feature to destroy the stability and the performances of the system. thus, the admissibility, that includes stability, regularity, and non-impulsiveness/causality should be verified when dealing with this class of systems. as a consequence, the investigation of singular systems is both theoretically and practically important [1, 4, 39]. it is worth noting that time delays are common in many physical plants, and they can have a substantial negative impact on performance and even the stability of practical systems [7, 27, 32, 42, 43, 44]. singular models and time-delay phenomena are general enough to enable some fundamental results from the theory of state-space systems to be extended to this class of systems (see for instance [3, 5, 10, 15, 17, 45]). on the other hand, the research on nonlinear systems [9] is an extremely hard issue due to their inherent complexity. due to its rigorous mathematical structure, the t-s fuzzy model [14] has recently been applied to handle nonlinear complex systems, since this model has been known for its powerful approximation of smoothly nonlinear systems. in many cases, uncertainty arises such as partially unknown parameters, unpredictable disturbances in the system, varying interpretations of linguistic variables of the fuzzy models, etc. it is worth noting, however, that the membership functions of type-1 fuzzy sets are well-known, and the control problem can not be handled directly for nonlinear plants with parameter uncertainties. research on (ivf) fuzzy systems has attracted attention due to the advantages of (ivf) sets over type 1 fuzzy sets in dealing with parameter uncertainties, and many control design results have been developed [24, 29]. among others, in [33], the fuzzy decentralized output feedback control is investigated using the event-triggered approach for interval type-2 fuzzy systems against input saturation. the study in [18] discussed the issue of filter design for ivf systems with d-stability constraint. the authors in [13] investigated the discrete-time interval-valued fuzzy systems with actuator faults to study the reliable non-fragile control problem with h∞ performance. very recently, the ivf approach has been extended to non-linear singular systems with uncertainties. to mention a few, the admissibilization for ivf singular systems is studied in [6]. based on the lmi approach, the dynamic output-feedback control design issue is investigated in [30] for singular interval-valued fuzzy systems. we emphasize that, the evolution of industrial engineering has led to numerous practical plants becoming quite complex with failures. control communities become concerned about this issue as actuator/sensor failures can adversely affect system performance. the goal of this issue is to introduce the concept of fault-tolerant control (ftc) and fault diagnosis as critical approaches for designing reliable controllers that are capable of maintaining the critical functionality of systems subject to problems and failures [47, 50]. kavarian et al develop a method for designing fault-tolerant controllers for power systems subject to random changes and actuator failures in [12]. the ftc method for wind-diesel hybrid systems with time-varying bounded sensor faults has been proposed in [11]. in [37], the reliable observer-based control problem for discrete-time takagi-sugeno fuzzy systems with time-varying delay and stochastic actuator faults is formulated from the input-output approach. we also report on some results relating to ftc for singular systems. for systems with actuator and/or sensor faults, sliding mode control was used [25]. the reliable control problem for nonlinear singularly perturbed systems with random actuator failures is discussed in [38]. it is worth pointing out that, actuator faults sometimes manifest themselves in a stochastic manner since the faults may occur occasionally with a random phenomenon, and a deterministic model cannot adequately describe actuator faults. as such, it is more appropriate and significant to consider a fault factor that obeys a certain distribution of probabilities. up till now, the ftc based on a stochastic scenario has been the subject of many published results. among others, we cite, the authors in [21] proposed a resilient and reliable controller for markovian jump systems subject to stochastic intermittent actuator faults and stochastic https://doi.org/10.15837/ijccc.2022.6.4957 3 controller gain fluctuations. for a class of networked control systems with random occurred actuator failures, an ftc control scheme was designed in [36]. the authors in [31] utilized an observer to develop a reliable consensus protocol for a multiagent system against stochastic actuator failures. in [35], a reliable fuzzy h∞ control law was synthesized and applied for permanent magnet synchronous motor suffered from random actuator faults we know that, to the best of our knowledge, the problem ftc for ivf systems employing a reliable controller with stochastic faults has not been completely addressed in the literature, which motivates us to carry out this study. in addition, it is also emphasized that, there are numerous complex plants with non-linearities where the state variables are not accessible due to a variety of factors, such as the lack of sensors that are able to detect specific states, or the increasing number of sensors making the system more complicated. various approaches have been used in output feedback design, including static output feedback [26], dynamic output feedback [16, 30], observer adaptive control [22, 23], and fuzzy observerbased approaches [20, 28, 41]. designing observer-based controllers by the ivf t-s fuzzy models is a crucial issue that needs to be extensively explored so that many result have been recently published [8, 48, 49]. to the extent of our knowledge, a few research efforts have been made on ivf singular systems with unmeasurable premise variables, time-varying delay and randomly occurring actuator failures. this establishes the second motivation for the present work. 1.2 objective and outline the discussion above has inspired us to study, in this study, the observer-based ftc problem for a class of uncertain non-linear singular systems against the random event of actuator failures. this article is noteworthy for the following features. (i) as an alternative to existing control schemes developed for type-1 fuzzy singular systems with delay and actuator failure [38, 40, 46], this study addresses a novel reliable controller design for interval-valued fuzzy systems which may exhibit actuator faults represented by stochastic variables with bernoulli distribution (ii) contrary to the work in [33], which considers a fuzzy decentralized observer-based event-triggered control for interval type-2 fuzzy systems assuming that the premise variables are measurable, this work supposes that the premise variables are unknown. (iii) unlike the existing studies where the finsler lemma is used in [2] to design the controller and observer gains, this work formulate a feasible control strategy for the considered control problem using the decoupling matrix procedure. after setting out the introduction and aims of the study, the paper is organized as follows: in section 2, the model and assumptions as well as the problem characterization are provided. our main findings are presented and discussed in section 3. the focus of this section is on the development of a new delay-dependent stochastic admissibility criterion for the system under steady using a new lyapunov-krasovskii functional. in addition, the design of the controller and observer gains is carried out by employing the matrix inequality decoupling technique to resolve the bilinear matrix inequality problem. section 4 presents numerical simulations as a mean of exhibiting the potential applications of the suggested control strategy and validating its significance. in section 5, we present some conclusions regarding the obtained results and some suggestions for future research. 1.3 notations table 1 lists the notations and acronyms that should be used in this study. 2 preliminaries and problem statement the aim of this section is to introduce some preliminaries that facilitate understanding of our proposal and state the problem we are investigating. https://doi.org/10.15837/ijccc.2022.6.4957 4 table 1: list of notations and acronyms used in the paper. symbol acronym/notation r set of the real numbers x ∈ rn n-dimensional euclidean space x ∈ rn×m n×m real matrix x > 0 real symmetric positive definite matrix x ‖x‖ norm of the matrix x x> transpose of the matrix x sym(x) x + x> λ() eigenvalue of a matrix e mathematical expectation ∗ term that is induced by symmetry r number of if-then rules lmi linear matrix inequality bmi bi-linear matrix inequality ivf interval-valued fuzzy t-s takagi-sugeno 2.1 ivf model consider a class of non-linear singular system which can be described by the following ivf model: ri : if θ1(x(t)) is m1i and if θ2(x(t)) is m 2 i · · ·if θs(x(t)) is m s i , then  eẋ(t) = aix(t) + adix(t−d(t)) + biuf (t) y(t) = cix(t) x(t) = φ(t),∀t ∈ [−d2 0], (1) where mki is an ivf set of rule i corresponding to the premise variable θi(x(t)), k = 1, 2, · · · ,s ; k is the number of premise variables, and i ∈ s , {1, 2, . . . ,r} is the number of rules. x(t) ∈ rn, y(t) ∈ rny , and uf (t) ∈ rm, define, respectively, the state, the output and input vectors. matrices ai, adi, and bi in model (1) are known with appropriate dimensions. d(t) stands for the time varying delay, and φ(t) defines the initial state for all t ∈ [−d2 0]. 2.2 assumptions and resulting model a1 d(t) is a continuous function such that 0 < d1 ≤ d(t) ≤ d2, 0 ≤ ḋ(t) ≤ dr (2) where d1 represents the lower delay bound, d2 stands for upper delay bounds, and dr is the delay variation rate. a2 singular matrix e satisfies rank(e) = q < n. a3 the actuator fault has the following form: uf (t) = γu(t) = m∑ s=1 γsσsu(t) (3) where γ = diag{γ1,γ2, · · · ,γm} is a diagonal matrix with m uncorrelated random variables, and σs = diag{0, · · · , 0︸ ︷︷ ︸ s−1 , 1, 0, · · · , 0︸ ︷︷ ︸ s−m }. https://doi.org/10.15837/ijccc.2022.6.4957 5 assume that γs ∈{0, 1} is a bernoulli distributed stochastic variable with a probabilistic density function ps(γs), where the expectation and variance of γs are respectively, defined as γ̄s and πs. define γ̄ = e{γ} = diag{γ̄1, γ̄2, · · · , γ̄m}. it follows that for any matrix z > 0 e { (γ − γ̄) } = ∑m s=1(γs − γ̄s)σs = 0 e { (γ − γ̄)>z(γ − γ̄) } = ∑m s=1 π 2 sς>s zσs (4) based on the ivf approach, the following interval defines the firing strength of the ith rule: mi = [∏s k=1 ωmki (θ(x(t))) ∏s k=1 ω̄mki (θ(x(t))) ] = [ µ i (x(t)) µ̄i(x(t)) ] where µ i (x(t)) ≥ 0, and µ̄i(x(t)) ≥ 0 represent, respectively, the the lower and upper membership functions. accordingly, ωmki (θ(x(t))) ≥ 0, and ω̄mki (θ(x(t))) ≥ 0 stands, respectively, for the lower and upper grades of membership. therewith, the non-linear singular system can be described as  eẋ(t) = r∑ i=1 µi(x(t)) ( aix(t) + adix(t−d(t)) + biuf (t) ) y(t) = r∑ i=1 µi(x(t))cix(t), (5) µi(x(t)) denotes the grade of membership of the ith local system defined as µi(x(t)) = αi(x(t))µi(x(t)) + ᾱi(x(t))µ̄i(x(t)), r∑ i=1 µi(x(t)) = 1 where αi(x(t)) and ᾱi(x(t) are two weighting coefficient functions satisfying 0 ≤ αi(x(t)), ᾱi(x(t)) ≤ 1, αi(x(t)) + ᾱi(x(t)) = 1 (6) note that, by introducing weighting coefficient functions, we can represent any time-variant or timeinvariant unmeasured parameters of the general non-linear system. moreover, these functions are not necessarily known but exist and satisfy (6). 2.3 fuzzy observer-based controller generally, the system’s states cannot be fully measured in practice. to estimate the state variables of system (5), an observer is required. we will proceed to address this issue by considering the following ivf observer for system (5): ri : if θ1(x̂(t)) is m1i and if θ2(x̂(t)) is m 2 i · · ·if θs(x̂(t)) is m s i , then{ e ˙̂x(t) = alx̂(t) + bluf (t) + ll(y(t) − ŷ(t)) ŷ(t) = clx̂(t), where x̂(t) denotes the state of the observer and ŷ(t) is the observer output. ll, (l ∈ s) is the observer gain to be evaluated. the whole fuzzy observer can be inferred as follows:  e ˙̂x(t) = r∑ l=1 µl(x̂(t)) ( alx̂(t) + bl uf (t) + ll(y(t) − ŷ(t)) ) ŷ(t) = r∑ l=1 µl(x̂(t))(clx̂(t)) (7) to increase the flexibility of fuzzy controller design, we propose a controller that uses exclusive membership functions that would not be shared with (5). the state-feedback ivf controller has the following structure: ri : if ϑ1(x̂(t)) is n 1i and if ϑ2(x(t)) is n 2 i · · ·if ϑs(x̂(t)) is n s i , then u(t) = kjx̂(t) https://doi.org/10.15837/ijccc.2022.6.4957 6 where ϑ(t) = [ϑ1(x̂(t)),ϑ2(x̂(t)), . . . ,ϑs(x̂(t))] is the premise variable vector, n kcj , kc = 1, 2, · · · ,s stands for the ivf sets, and kj is the state feedback gain matrix of rule j. following is the firing interval for the jth rule: n i = [ νj(x̂(t)) ν̄j(x̂(t)) ] , i ∈ s where νj(x̂(t)) = s∏ kc=1 ωn ki (ϑ(t)) ≥ 0, ν̄j(x̂(t)) = s∏ kc=1 ω̄n kc i (ϑ(t)) ≥ 0, (8) νj(x̂(t)) and ν̄j(x̂(t)) represent,respectively, the lower and upper membership functions. ωn kc i (ϑ(t)) ≥ 0 and ω̄n kc i (ϑ(t)) ≥ 0 are the lower and upper grades of membership of θ(t) in n j i, respectively. the global fuzzy model will be defined as follows: u(t) = r∑ j=1 νj(x̂(t))(kjx̂(t)) (9) νj(x̂(t)) = β j (x̂(t))νj(x̂(t)) + β̄j(x̂(t))ν̄j(x̂(t)) r∑ l=1 (β l (x̂(t))νl(x̂(t)) + β̄l(x̂(t))ν̄l(x̂(t))) , νj(x̂(t)) ≥ 0, r∑ i=1 νj(x̂(t)) = 1 (10) with the error e(t) = x(t) − x̂(t), together with (5), (7), and (9) we obtain the following closed-loop system: ẽ ˙̃x(t) = r∑ i=1 r∑ j=1 r∑ l=1 µiν̂jµ̂l ( ãijlx̃(t) + ãdix̃(t−d(t)) + b̃ilγ̃k̃jx̃(t) ) (11) where x̃(t) = [x>(t), e>(t)]>, γ̃ = γ − γ̄, and ãijl = [ ai + biγ̄kj −biγ̄kj (ai −al) + (bi −bl)γ̄kj −ll(ci −cl) ai − (bi −bl)γ̄kj −llcl ] , ãdi = [ adi 0 adi 0 ] b̃il = [ bi (bi −bl) ] , k̃j = [ kj −kj ] ẽ = [ e 0 0 e ] remark 1. generally, the fuzzy observers and fuzzy controllers based on type-1 fuzzy share the same membership functions of the model. however, the membership functions used to describe the ivf systems are unknown except for their bounds. thus, in this study, the membership functions of fuzzy observers are calculated based on the estimated state variable x̂(t) rather than x(t). 2.4 problem statement it is our primary objective in this paper to design an ivf observer-based controller that maintains the closed-loop system admissible in the event of random actuator failure in non-linear singular systems expressed by the ivf model as specified in (5). let us begin by stating the following lemma that will be used in our main results. lemma 2. [34] let p be a symmetric matrix such that e>lpel > 0. then, matrix pe + u >xv > is non singular so that (pe + u>xv >)−1 = p̄e> + v x̄u where p̄ ∈ rn×n is a symmetric matrix verifying e>rp̄er = (e > lpel) −1, x̄ ∈ r(n−q)×(n−q) is a non singular matrix satisfying x̄ = (v >v )−1v −1(uu>)−1, u be a full row rank matrix so that ue = 0 , and v be a full column rank matrix such that ev = 0. el and er are full column rank matrices with e = ele>r. https://doi.org/10.15837/ijccc.2022.6.4957 7 3 main results 3.1 admissibility analysis as we will see below, we are able to obtain sufficient delay-dependent conditions for the closed-loop system stated in (11) to be stochastically admissible. theorem 3. for given constants d1, d2 and 0 ≤ dr < 1, assume (a1) holds. system (11) is admissible, if there exist positive matrices pk ∈ rn×n, q̃ ∈ r2n×2n, r̃1 ∈ r2n×2n, r̃2 ∈ r2n×2n, r̃3 > 0, q̃a = [ q̃11 q̃12 q̃>12 q̃22 ] ∈ r4n×4n, s̃a = [ s̃11 s̃12 s̃>12 s̃22 ] ∈ r4n×4n, non-singular matrices xk ∈ rn×n, m̃k, ñk, t̃k, and ṽk, (k = 1, 2) such that the following inequality holds : ψ̃ijl(ẽ,ãijl,ãdi) =   ψ̃11ijl(ẽ,ãijl,ãdi) √ d1 2 ψ̃ijlr̃1 √ d12 2 ψ̃ijlr̃2 √ d12ψ̃ijlr̃3 ψ̃15i iπ ⊗ ψ̃16ijl ∗ −r̃1 0 0 0 0 ∗ ∗ −r̃2 0 0 0 ∗ ∗ ∗ −r̃3 0 0 ∗ ∗ ∗ ∗ −ψ̃55 0 ∗ ∗ ∗ ∗ ∗ −ψ̃66   < 0 (12) where d12 = d2 −d1, r̃ = d1 2 r̃1 + d12 2 r̃2 + d12r̃3, π1 = p1e + u>x1v >, π2 = p2e + u>x2v >, ψ̃11ijl(ẽ,ãijl,ãdi) =   ψ̃11i ψ̃12i ψ̃13i 0 s̃12 0 ∗ ψ̃22i 0 ψ̃24i 0 ψ̃26i ∗ ∗ ψ̃33i −q̃12 ψ̃35i 0 ∗ ∗ ∗ ψ̃44i 0 0 ∗ ∗ ∗ ∗ ψ̃55i −s̃12 ∗ ∗ ∗ ∗ ∗ ψ̃66i   ψ̃ijl = col { ã>ijl ã > di 0 0 0 0 } ψ̃15 = [ ψ̃151 ψ̃152 ψ̃153 ψ̃154 ] ψ̃16ijl = [ π1(b̃ilς1k̃j)>, . . . ,πm(b̃ilσmk̃j)> ] ψ̃55 = diag { r̃1, r̃2, r̃3, r̃3 } ψ̃66 = diag { r̃−1, · · · , r̃−1 } , ψ̃151 = √ d1 2 col { ẽm̃1 0 ẽm̃2 0 0 0 } ψ̃152 = √ d12 2 col { 0 0 ẽñ1 0 ẽñ2 0 } , ψ̃153 = √ d12 col { 0 ẽt̃1 0 0 0 ẽt̃2 } ψ̃154 = √ d12 col { 0 ẽṽ2 0 ẽṽ1 0 0 } iπ = col { i 0 0 0 0 0 } ψ̃11i = q̃11 + s̃11 + q̃1 + sym(m̃1ẽ) + sym(π̃>ãijl) ψ̃12i = (π̃>ãdi) ψ̃13i = q̃12 −m̃1ẽ + (m̃2ẽ)> ψ̃15i = −ñ1ẽ + (ñ2ẽ)> + s̃12 ψ̃22i = −(1 −hd)q̃1 + sym(t̃1ẽ) − sym(ṽ2ẽ) ψ̃24i = −ṽ2ẽ + (ṽ1ẽ)> ψ̃26i = −t̃1ẽ + (t̃2ẽ)> ψ̃33i = q̃22 − q̃11 − sym(m̃2ẽ) + sym(ñ1ẽ) ψ̃35i = −ñ1ẽ + ñ2ẽ ψ̃44i = −q̃22 + sym(ṽ1ẽ) ψ̃55i = s̃22 − s̃11 − sym(ñ2ẽ) ψ̃66i = −s̃22 − sym(t̃2ẽ) π̃ = diag{π1, π2} matrices u ∈ r(n−q)×(n) and v ∈ r(n)×(n−q) are of full rank such that ue = 0 and ev = 0. proof. first, we will prove the stability of system (11). for this purpose, we choose the following https://doi.org/10.15837/ijccc.2022.6.4957 8 lyapunov-krasovsky functional: v (x̃(t)) = v1(x̃(t)) + v2(x̃(t)) + v3(x̃(t)) v1(x̃(t)) = x̃t (t)ẽ>p̃ẽx̃(t) v2(x̃(t)) = ∫ t t−d12 ζ̃>1 (s)q̃aζ̃1(s)ds + ∫ t t−d22 ζ̃>2 (s)s̃aζ̃ > 2 (s)ds + ∫ t t−d(t) x̃>(s)qx̃(s)ds v3(x̃(t)) = ∫ 0 −d12 ∫ t t+θ ˙̃x>(s)ẽ>r̃1ẽ ˙̃x(s)dsdθ + ∫ −d12 −d22 ∫ t t+θ ˙̃x>(s)ẽ>r̃2ẽ ˙̃x(s)dsdθ + ∫ −d1 −d2 ∫ t t+θ ˙̃x>(s)ẽ>r̃3ẽ ˙̃x(s)dsdθ (13) where ζ̃1(t) = [ x̃t (t) x̃>(t− d12 ) ]> , ζ̃2(t) = [ x̃t (t) x̃>(t− d22 ) ]> , define the infinitesimal operator l of v (x̃(t)) as follows: l(v (x̃(t))) = lim ∆−→0+ 1 ∆ {e{v (x̃(t + ∆))|x̃(t)}−v (x̃(t))} (14) evaluating the derivative of v (x̃(t)) along the solutions of system (11), and noting ue = 0, it results in e{lv1(x̃(t))} = e{2x̃t (t)ẽ>p̃ẽ ˙̃x(t)} = 2x̃t (t)π̃>(ãijlx̃(t) + ãdix̃(t−d(t)) + b̃ilγ̃k̃jx̃(t)) = 2x̃t (t)π̃>(ãijlx̃(t) + ãdix̃(t−d(t))) e{lv2(x̃(t))}≤ ζ̃>1 (t)q̃aζ̃1(t) − ζ̃ > 1 (t− d1 2 )q̃aζ̃1(t− d1 2 ) + ζ̃>2 (t)s̃aζ̃2(t) − ζ̃ > 2 (t− d2 2 )s̃aζ̃2(t− d2 2 ) + x̃t (t)q̃1x̃(t) − (1 −hd)x̃>(t−d(t))q̃1x̃(t−d(t)) e{lv3(x̃(t))} = d1 2 (ãijlx̃(t) + ãdix̃(t−d(t)))>r̃1(ãijlx̃(t) + ãdix̃(t−d(t))) + d12 2 (ãijlx̃(t) + ãdix̃(t−d(t)))>r̃2(ãijlx̃(t) + ãdix̃(t−d(t))) + d12(ãijlx̃(t) + ãdix̃(t−d(t)))>r̃3(ãijlx̃(t) + ãdix̃(t−d(t))) + e { (b̃ilγ̃k̃jx̃(t))>r̃(b̃ilγ̃k̃jx̃(t)) } − ∫ t t−d12 ˙̃x>(s)ẽ>r̃1ẽ ˙̃x(s)ds− ∫ t−d12 t−d22 ˙̃x>(s)ẽ>r̃2ẽ ˙̃x(s)ds− ∫ t−d1 t−d2 ˙̃x>(s)ẽ>r̃3ẽ ˙̃x(s)ds (15) let ζ̃3(t) = [ x̃>(t− d12 ) x̃ >(t− d22 ) ]> , ζ̃4(t) = [ x̃>(t−d(t)) x̃>(t−d2) ]> ζ̃5(t) = [ x̃>(t−d1) x̃>(t−d(t)) ]> , m̃ = [ m̃>1 m̃ > 2 ]> , ñ = [ ñ>1 ñ > 2 ]> , t̃ = [ t̃>1 t̃ > 2 ]> , and ṽ = [ ṽ >1 ṽ > 2 ]> . by defining the following expression: ∫ t t−d12 [ ẽẋ(s) ξ1(t) ]>[ r̃1 m̃ ∗ m̃ẽr̃−11 ẽ >m̃> ][ ẽẋ(s) ξ1(t) ] ds ≥ 0, (16) ∫ t−d12 t−d22 [ ẽẋ(s) ξ2(t) ]>[ r̃2 ñ ∗ ñẽr̃−12 ẽ >ñ> ][ ẽẋ(s) ξ2(t) ] ds ≥ 0 (17) https://doi.org/10.15837/ijccc.2022.6.4957 9 we know that − ∫ t t−d12 ẋ(s)>ẽ>r̃1ẽẋ(s)ds ≤ 2ξ>1 (t)m̃ [ ẽ − ẽ ] ξ1(t) + d1 2 ξ>1 (t)m̃ẽr̃ −1 1 ẽ >m̃>ξ1(t) (18) − ∫ t−d12 t−d22 ẋ(s)>ẽ>r̃2ẽẋ(s)ds ≤ 2ξ>3 (t)ñ [ ẽ − ẽ ] ξ3(t) + d1 2 ξ>3 (t)ñẽr̃ −1 2 ẽ >ñ>ξ3(t) (19) moreover, we have − ∫ t−d1 t−d2 ˙̃x>(s)ẽ>r̃3ẽ ˙̃x(s)ds = − ∫ t−d(t) t−d2 ˙̃x>(s)ẽ>r̃3ẽ ˙̃x(s)ds− ∫ t−d1 t−d(t) ˙̃x>(s)ẽ>r̃3ẽ ˙̃x(s)ds (20) and − ∫ t−d(t) t−d2 ẋ(s)>ẽ>r̃3ẽẋ(s)ds ≤ 2ξ>4 (t)t̃ [ ẽ − ẽ ] ξ4(t) + (d2 −d1)ξ>4 (t)t̃ẽr̃ −1 3 ẽ >t̃>ξ4(t) − ∫ t−d1 t−d(t) ẋ(s)>ẽ>r̃3ẽẋ(s)ds ≤ 2ξ>5 (t)ṽ [ ẽ − ẽ ] ξ5(t) + (d2 −d1)ξ>5 (t)ṽ ẽr̃ −1 3 ẽ >ṽ >ξ5(t) (21) then, from (4), we get e { (b̃ilγ̃k̃jx̃(t))>r̃(b̃ilγ̃k̃jx̃(t)) } = x̃>(t) { m∑ s=1 π2s(b̃ilσsk̃j) >r̃b̃ilσsk̃j } x̃(t) (22) let ξ(t) = [ x̃t (t) xt (t−d(t)) xt (t− d1 2 ) xt (t−d1) xt (t− d2 2 ) xt (t−d2) ]> . combining (15)-(22), yields e{lv (x̃(t))}≤ r∑ i=1 r∑ j=1 r∑ l=1 µiν̂jµ̂lξ t (t) ( ψ̃11ijl(ẽ,ãijl,ãdi) + ψ̃ijlr̃ψ̃>ijl + m∑ s=1 π2siπ ⊗ (b̃ilσsk̃j) >r̃(i>π ⊗ (b̃ilσsk̃j) ) ξ(t) (23) hence, by performing the complement of schur to (12), yields ψ̃11ijl(ẽ,ãijl,ãdi) + ψ̃ijlr̃ψ̃>ijl + m∑ s=1 π2siπ ⊗ (b̃ilσsk̃j) >r̃(i>π ⊗ (b̃ilσsk̃j) < 0 thus, it is obvious that e{lv (x̃(t))} < 0, and system (11) is stochastically stable. next, we prove the regularity and impulse-free properties of system (11). from (12), we know ψ̃11ijl < 0 which implies that sym(π̃>ãijl) + sym(m̃1ẽ) < 0 (24) for matrix ẽ, there exist m and n, two non-singular matrices such that e = mẽn = [ i2q 0 0 0 ] , âijl = mãijln = [ âijl11 âijl12 âijl21 âijl22 ] , π̂ = m−t π̃n = [ π̂11 π̂12 π̂21 π̂22 ] m̂1 = m−tm1m−1 = [ m̂111 m̂112 m̂121 m̂122 ] (25) based on lemma 2, we know that ẽ>π̃ = π̃>ẽ which implies that π̂12 = 0, using (25). preand post-multiplying (24) by n> and n, respectively, the inequality shown below holds sym (π̂>22âijl22) < 0 which means that âijl22 is non-singular and system (11) is regular and impulse free, according to the definitions stated in [4]. https://doi.org/10.15837/ijccc.2022.6.4957 10 3.2 observer design as a result of the bilinear terms involved in condition (12), it is difficult to parameterize the controller and observer gains. in the sequel, we provide the procedure to synthesize the gains k̄i, and l̄i. theorem 4. for given constants d1, d2 and 0 ≤ dr < 1, assume ( a1) holds. system (11) is stochastically admissible, if there exist scalars α, β, λc, c = 1, 2, 3, 4 and matrices p̄k > 0, jk > 0, m̄k, n̄k, t̄k v̄k, x̄k, k = 1, 2, q̄ > 0, r̄1 > 0, r̄2 > 0, r̄3 > 0, q̄a = [ q̄11 q̄12 q̄>12 q̄22 ] > 0, s̄a = [ s̄11 s̄12 s̄>12 s̄22 ] > 0, λ, w̄l, yj ∈ rm×n, and fl ∈ rn×ny , the following lmis hold under the condition µ̂l −σlν̂l ≥ 0 where σl is positive scalar for i,j, l ∈ s:  ξijl − λ < 0 σjξijj −σjλ + λ < 0 σl(ξijl − λ) + σj(ξilj − λ) + 2λ < 0, l > j (26) where π̄1 = p̄1e> + u>x̄1v >, π̄2 = p̄2e> + u>x̄2v >, π̄ = diag{π̄1, π̄2}, π̆1 = diag{π̄1, π̄1} ξijl =   ξ̄ijl αυ1ij υ2 βυ3l υ4l ∗ −α sym(π̄1) + j1 0 0 0 ∗ ∗ −j1 0 0 ∗ ∗ ∗ −β sym(w̄l) + j2 0 ∗ ∗ ∗ ∗ −j2   (27) ξ̄ijl =   ξ̄11ijl √ d1 2 ψ̄ √ d12 2 ψ̄ √ d12ψ̄ ψ̄15 iπ ⊗ ψ̄16ij ∗ ψ̄22 0 0 0 0 ∗ ∗ ψ̄33 0 0 0 ∗ ∗ ∗ ψ̄44 0 0 ∗ ∗ ∗ ∗ −ψ̄55 0 ∗ ∗ ∗ ∗ ∗ −ψ̄66   υ1ij = col { b̄γij 0 0 0 0 0 √ d1 2 b̄γij √ d12 2 b̄γij √ d12b̄γij 0 b̄>σij } υ3l = col { f̄l 0 0 0 0 0 √ d1 2 f̄l √ d12 2 f̄l √ d12f̄l 0 0 } ψ̄ = col { ā>ijl ā > di 0 0 0 0 } ψ̄151 = √ d1 2 col { ē>m̄1 0 ē>m̄2 0 0 0 } ψ̄152 = √ d12 2 col { 0 0 ē>n̄1 0 ē>n̄2 0 } , ψ̄153 = √ d12 col { 0 ē>t̄1 0 ē>t̄2 0 0 } ψ̄154 = √ d12 col { 0 ē>v̄1 ē>v̄2 0 0 0 } b̄σij = [ π1(b̄1σij) >, . . . ,πm(b̄mσij) > ] ψ̄16ij = [ π1(b̄1σij × [1 − 1]) >, . . . ,πm(b̄mσij × [1 − 1]) > ] υ2 = col { π̄>12 0 0 0 0 0 } υ4l = col { c̄>il 0 0 0 0 0 } ψ̄22 = λ21r̃1 −λ1 sym(π̄) ψ̄33 = λ22r̃2 −λ2 sym(π̄) ψ̄44 = λ23r̃3 −λ3 sym(π̄) ψ̄15 = [ ψ̄151 ψ̄152 ψ̄153 ψ̄154 ] ψ̄55 = diag { r̄1, r̄2, r̄3, r̄3 } ψ̄66 = λ24r̃ −λ4 sym(π̆1), https://doi.org/10.15837/ijccc.2022.6.4957 11 ξ̄11ijl =   ξ̂11ijl ξ̂12i ξ̂13i 0 s̄12 0 ∗ ξ̂22i 0 ξ̂24i 0 ξ̂26i ∗ ∗ ξ̂33i −q̄12 ξ̂35i 0 ∗ ∗ ∗ ξ̂44i 0 0 ∗ ∗ ∗ ∗ ξ̂55i −s̄12 ∗ ∗ ∗ ∗ ∗ ξ̂66i   ξ̄11ijl = q̄11 + s̄11 + q̄1 + sym(m̄1ē>) + sym(āijl) ξ̄12i = ādi ξ̄13i = q̄12 −m̄1ē> + (m̄2ē>)> ξ̄15i = −n̄1ē> + (n̄2ē>)> ξ̄22i = −(1 −hd)q̄1 + sym(t̄1ē>) − sym(v̄2ē>) ξ̄24i = v̄2ē> − (v̄1ē>)> ξ̄26i = −t̄1ē> + (t̄2ē>)> ξ̄33i = q̄22 − q̄11 − sym(m̄2ē>) + sym(n̄1ē>) ξ̄35i = −n̄1ē> + n̄2ē> ξ̄44i = −q̄22 + sym(v̄1ē>) ξ̄55i = s̄22 − s̄11 − sym(n̄2ē>) ξ̄66i = −s̄22 − sym(t̄2ē>) āijl = [ aiπ̄1 + biγ̄yj −biγ̄yj (ai −al)π̄1 + (bi −bl)γ̄yj −fl(ci −cl) aiπ̄2 − (bi −bl)γ̄yj −flcl ] , ādi = [ adiπ̄1 0 adiπ̄1 0 ] b̄γij = [ biγ̄yj (bi −bl)γ̄yj ] , b̄sσij = [ biσsyj (bi −bl)σsyj ] , π̄12 = [ 0 π̄1 − π̄2 ] , f̄l = [ 0 fl ] c̄il = [ w̄l(ci −cl) − (ci −cl)π̄1 w̄lci −ciπ̄1 ] moreover, parameters kj and ll are computed by kj = yjπ̄−11 and ll = flw̄ −1 l , respectively. proof. in order to obtain less conservative results, the slack matrix provided below is used. r∑ i=1 r∑ j=1 r∑ l=1 µiν̂j ( ν̂l − µ̂l ) λ = 0 (28) where λ is an arbitrary matrix with appropriate dimensions. then, it can be known that r∑ i=1 r∑ j=1 r∑ l=1 µiν̂jµ̂lξijl = r∑ i=1 r∑ j=1 r∑ l=1 { µiν̂jµ̂lξijl + µiν̂j ( ν̂l − µ̂l ) λ } = r∑ i=1 r∑ j=1 r∑ l=1 { µiν̂j(µ̂l + σlν̂l −σlν̂l)ξijl + µiν̂j ( ν̂l − µ̂l + σlν̂l −σlν̂l ) λ } = r∑ i=1 r∑ j=1 r∑ l=1 µiν̂j { ν̂l ( σlξijl −σlλ + λ ) + (µ̂l −σlν̂l) ( ξijl − λ )} = r∑ i=1 µi ( r∑ j=1 ν̂2j (σjξijj −σjλ + λ) + r−1∑ j=1 r∑ l=j+1 ν̂jν̂l(σjξijl −σjλ + λ + σlξilj −σlλ + λ) + r∑ i=1 r∑ j=1 ν̂i(µ̂l −σlν̂l)(ξijl − λ) ) (29) in light of the conditions in (26), the expression shown below holds. r∑ i=1 r∑ j=1 r∑ l=1 µiν̂jµ̂lξijl < 0 (30) from ξijl < 0, we know that −λ1 sym(π̄) + λ21r̄1 < 0, and thus π̄ is non-singular. https://doi.org/10.15837/ijccc.2022.6.4957 12 moreover, for p̄1 > 0 , it is easy to verify that e>lp̄1el > 0. so according to lemma 2 we have π̄−1k = (pke + u >xkv >) = πk, k = 1, 2 π̄−1 = diag{π1, π2} using the fact that for any λp > 0, p = 1, 2, 3 the following condition holds: 0 ≤ (λpπ̄ − r̃−1p ) >r̃p(λpπ̄ − r̃−1p ) = r̃ −1 p −λp sym(π̄) + λ 2 pπ̄ >r̃pπ̄ then, −r̃−1p ≤−λp sym(π̄) + λ 2 pπ̄ >r̃pπ̄ define z =  i 0 0 0 00 i i 0 0 0 0 0 i i   > , and γijl = [ i υ1ijπ̄−11 υ3lw̄ −1 l ] . multiplying z> and its transposition in left and right sides to (30), respectively, to get r∑ i=1 r∑ j=1 r∑ l=1 µiν̂jµ̂l  ξ̄ijl αυ1ij + υ2 βυ3l + υ4il∗ −α sym(π̄1) 0 ∗ ∗ −β sym(w̄l)   < 0 (31) then, by congruence transformation by γijl, we know that r∑ i=1 r∑ j=1 r∑ l=1 µiν̂jµ̂l(ξ̄ijl + sym (υ1ijπ̄−11 υ > 2 + υ3lw̄ −1 l υ > 4il)) < 0 (32) let yj = kjπ̄1 and f̄l = llw̄l. from the terms υ1ijπ̄−11 υ>2 , and υ3lw̄ −1 l υ > 4il one can calculate f̄lw̄ −1 l c̄il = [ 0 0 f̄l(ci −cl) −ll(ci −cl)π̄1 f̄lci −llciπ̄2 ] , b̄γijπ̄−11 π̄12 = [ 0 biγ̄yj −biγ̄kjπ2 0 (bi −bl)γ̄yj − (bi −bl)γ̄kjπ2 ] b̄sσijπ̄ −1 1 π̄12 = [ 0 biσsyj −biσskjπ2 0 (bi −bl)σsyj − (bi −bl)σskjπ2 ] ,s = 1, 2, · · ·m thus, it can be concluded that āijl + b̄γijπ̄−11 π̄12 + f̄lw̄ −1 l c̄il = ãijlπ̄, b̄ s σij × [1 − 1] + b̄ s σijπ̄ −1 1 π̄12 = b̃ilσsk̃jπ̄ (33) using (33), and performing the congruence transformation to (32) by diag(π, π, π, π, π, π,i,i,i,i) and its transpose r∑ i=1 r∑ j=1 r∑ l=1 µiν̂jµ̂lψ̃ijl(ẽ,ãijl,ãdi) < 0 holds, using the following expressions: q̃uv = π̄−tq̄uvπ̄−1, s̃uv = π̄−ts̄uvπ̄−1, w̃k = π̄−tw̄kπ̄−1, m̃k = π̄−tm̄kπ̄−1, ṽk = π̄−tv̄kπ̄−1, t̃k = π̄−tt̄kπ̄−1, r̃c = π̄−1r̄cπ̄−t , (u,v,k = 1, 2, c = 1, 2, 3), hence, according to theorem 3, closed-loop system (11) is stochastically admissible. remark 5. here, we address the issue of mismatched property between µi(x(t)) and νi(x̂(t)). by introducing a slack matrix λ to express (28) which is combined with the condition µ̂l − σlν̂l ≥ 0 to get relaxed conditions as stated in (26). this technique incorporates membership function information into stability conditions, leading to less conservative conditions. remark 6. our approach for designing the controller and observer gains relies on decoupling the bmi terms by using slack matrices as defined in (33). comparatively, this approach differs from that presented in [2], in which the finsler lemma is investigated and auxiliary variables ti are employed to validate citi = [1 0] and reach the desired gains. https://doi.org/10.15837/ijccc.2022.6.4957 13 4 numerical examples in this section, we provide a concise description of the computational framework and present two examples to illustrate the effectiveness and advantages of the proposed control scheme. 4.1 computational framework and algorithm computing experiments were performed using the matlab programming language and a computer with the following characteristics:(i) [os] windows 10 enterprise for 64 bits; (ii) [ram] 8 gigabytes; and (iii) [processor] intel(r) core(tm) i7-4790t cpu @ 2.70 gigahertz. a detailed explanation of the design procedure is given in algorithm 1 and the flowchart displayed in figure 1. using yalmip software and the optimization toolbox mosek, the algorithm 1 was executed. algorithm 1 procedure design 1: describe the non-linear system by the ivf system. 2: choose the parameters of the actuator fault model described in (3). 3: determine the gains kj, ll of the lmi established in theorem 4. 4: design the observer indicated in (7) 5: apply the designed control law expressed in (9) to the model. eẋ(t) = g(x(t) + ∆x(t), x(t − d(t)), uf (t))    eẋ(t) = r∑ i=1 µi(x(t)) { aix(t) + adix(t − d(t)) + biuf (t) } y(t) = r∑ i=1 µi(x(t))cix(t) µ̄i(x(t)), µi(x(t))    e ˙̂x(t) = r∑ i=1 µi(x̂(t)) { aix̂(t) + li(y(t) − ŷ(t)) + biuf (t) } ŷ(t) = r∑ i=1 µi(x̂(t))cix(t) u(t) = r∑ j=1 νj(x̂(t))kjx̂(t) ν̄i(x̂(t)), νi(x̂(t)) theorem 3.2 u(t) = r∑ j=1 νj(x̂(t))kjx̂(t)γ ki li uf (t) figure 1: flowchart of the control procedure. the flowchart displayed in figure 1 provides a clear description of the proposed design procedure. 4.2 example 1 the efficiency and correctness of the suggested control scheme is illustrated with the example provided from [30] with the following matrices: e =  1 0 00 1 0 0 0 0   , a1 =   0 1 09.8 0 1 0 −1 −0.6   a2 =  0 1 00 0 1 0 −1 0.6   ad1 = ad2 =   0 0 00 0 0 −0.13 0 0   , b1 =  0 1 11 0 0 1 0 1   , b2 =  0 1 11 0 0 0 1 1   c21 = [ 1 1 0 1 0 1 ] , c22 = [ 1 1 0 1 0 0 ] https://doi.org/10.15837/ijccc.2022.6.4957 14 the lower and upper bounds of membership functions of the corresponding interval-valued ts fuzzy model are listed in table 5. lower membership functions upper membership functions µ1(x2) = 0.95 − 0.95 1 + exp ( x2 + 4.5 8 ) µ2(x2) = 1 −µ1(x2) µ̄1(x2) = 0.95 − 0.925 1 + ( x2 + 3.5 8 ) µ̄2(x2) = 1 − µ̄2(x2) table 2: lower and upper membership functions of the plant. the weighting functions are chosen as αi = sin2(x2(t)) and ᾱi = 1 − αi for i = 1, 2. for the aforementioned interval type-2 fuzzy singular system, we are going to design an observer-based ivf controller that makes a closed-loop system stochastically admissible. accordingly, we have defined the lower and upper bounds of membership functions for interval-valued fuzzy controllers in table 6. to determine what constitutes membership functions, non-linear weight functions are selected as β j (x2(t)) = cos2(x2(t)) and β̄j(x1(t)) = 1 −βj(x2(t)). lower membership functions upper membership functions ν1(x2) = 1 − 1 1 + exp ( x2 + 5 2 ) ν2(x2) = 1 −ν1(x2) ν̄1(x2) = 1 − 1 1 + exp ( x2 + 4 2 ) ν̄2(x2) = 1 − ν̄1(x2) table 3: lower and upper membership functions of the controller. in this example, it is assumed that d(t) = 0.2 + 0.1 sin(t), which provides that d1 = 0.1, d2 = 0.3, dr = 0.1. define el = er =  1 00 1 0 0   , u = v t = [0 0 1] let σ1 = 0.1, σ2 = 0.1, α = 0.01, β = 0.002, λ1 = 0.1, λ2 = 5, λ3 = 12, and λ4 = 0.001. to demonstrate the efficacy of the proposed control strategy, two cases are considered using the previous values. case 1 the actuator faults are assumed to occur randomly with a bernoulli-distribution satisfying with γ̄ = diag{0.6, 0.55, 0.65}. case 2 for this case, the controller is designed for a system without actuator faults, i.e. we take γ̄ = diag{1, 1, 1}. according to the algorithm above, the lmis (26) in theorem 4, are solved for both cases with parameters displayed in table 4. https://doi.org/10.15837/ijccc.2022.6.4957 15 case parameters case 1 k1 =  −31.437 −12.916 21.486−30.49 −12.922 20.022 24.519 12.621 −25.486   , k2 =  −31.594 −12.971 21.314−30.656 −12.977 19.833 24.7 12.686 −25.253   , l1 =   5.3154 2.08635.1943 0.2834 −0.92299 1.5574   , l2 =   4.1972 2.19515.1622 0.033923 −0.44853 0.36828   case 2 k1 =  −11.896 −6.8293 −0.20661−11.698 −7.0008 −1.3811 11.704 7.7511 −2.5207   , k2 =  −11.932 −6.8332 −0.20605−11.727 −7.0052 −1.3798 11.741 7.755 −2.5222   , l1 =   4.5107 1.58446.7738 −3.0472 −1.3806 1.9677   , l2 =   3.5379 2.30548.0944 −3.6425 −0.94405 0.57315   table 4: controller and observer gains for both cases three scenarios are considered in the numerical simulations: 1. the reliable observer-based controller obtained in the first case is applied to a healthy system; 2. the same observer-based controller designed in the first case is performed to a system suffering from actuator failure; 3. a system with an actuator failure is controlled by the unreliable observer-based controller developed in the second case. for all cases, the simulations are undergone with initial condition φ(t) = [−0.73140, 0.5140, −0.5237]>, t ∈ [−0.3 0], and the results are depicted in figures 2-4, from where the inputs as well as system and its related observer state variables are displayed. we observe that, when the reliable control law is applied the system dynamics are stabilized despite uncertainties and stochastic actuator failures, however, when the unreliable control law is applied to the system with failures, we see that the performance of the system is degraded. thus, the simulations validate that the proposed control scheme is effective in accommodating actuator faults in the system and shows its robustness in spite of uncertainty. https://doi.org/10.15837/ijccc.2022.6.4957 16 0 1 2 3 4 5 6 7 8 t -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 x1 x̂1 (a) the curves of x1(t), and x̂1(t) 0 1 2 3 4 5 6 7 8 t -0.4 -0.2 0 0.2 0.4 0.6 0.8 x2 x̂2 (b) the curves of x2(t), and x̂2(t) 0 1 2 3 4 5 6 7 8 t -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 x3 x̂3 (c) trajectories of x3(t), and x̂3(t) 0 0.5 1 1.5 2 2.5 3 -1 0 1 2 0 0.5 1 1.5 2 2.5 3 -0.5 0 0.5 1 0 0.5 1 1.5 2 2.5 3 0 0.5 1 (d) input trajectories figure 2: simulation plots for failure mode 0 1 2 3 4 5 6 7 8 t -0.6 -0.4 -0.2 0 0.2 0.4 0.6 x1 x̂1 (a) the curves of x1(t), and x̂1(t) 0 1 2 3 4 5 6 7 8 t -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 x2 x̂2 (b) the curves of x2(t), and x̂2(t) 0 1 2 3 4 5 6 7 8 t -4 -3 -2 -1 0 1 2 3 x3 x̂3 (c) trajectories of x3(t), and x̂3(t) 0 0.5 1 1.5 2 2.5 3 -2 0 2 0 0.5 1 1.5 2 2.5 3 -0.5 0 0.5 1 0 0.5 1 1.5 2 2.5 3 -0.5 0 0.5 1 (d) input trajectories figure 3: simulation plots for normal mode https://doi.org/10.15837/ijccc.2022.6.4957 17 0 1 2 3 4 5 6 7 8 t -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 x1 x̂1 (a) the curves of x1(t), and x̂1(t) 0 1 2 3 4 5 6 7 8 t 0 0.1 0.2 0.3 0.4 0.5 0.6 x2 x̂2 (b) the curves of x2(t), and x̂2(t) 0 1 2 3 4 5 6 7 8 t -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 x3 x̂3 (c) trajectories of x3(t), and x̂3(t) exam 0 0.5 1 1.5 2 2.5 3 3.5 4 0 1 2 0 0.5 1 1.5 2 2.5 3 3.5 4 0 1 2 0 0.5 1 1.5 2 2.5 3 3.5 4 -2 -1 0 1 (d) input trajectories figure 4: simulation plots for non-reliable controller gains in case 2 4.3 bio-economic system using a bio-economic system adapted from [19], the proposed control scheme can be shown to be both efficient and correct. the bio-economic system is stated as  ż1(t) = −0.5z1(t) + 0.15z2(t) − 0.01z21 (t) −e(t)z1(t) + u1(t), ż2(t) = 0.5z1(t) − 0.1z2(t), 0 = e(t)(z1(t) − 50) + u2(t), (34) with z1(t) and z2(t) being the population density of, respectively, immature and mature species at time t. e(t) corresponds to the harvest effort on the immature population, u1(t) and u2(t) state, respectively, the capture of an immature population and the government regulation, by means of a tax or subsidy, of a biological resource. based on model (34), the following model is obtained by translating the positive equilibriums to zero [19] :  ẋ1(t) = −1.25x1(t) + 0.15x2(t) − 50x3(t) − 0.01x21(t) −x1(t)x3(t) + u1(t), ẋ2(t) = 0.5x1(t) − 0.1x2(t), 0 = −0.75x1(t) + x1(t)x3(t) + u2(t), (35) assume that x1(t) ∈ [−10 10], and 0 ≤ ∆x1(t) ≤ 2. given the uncertainty associated with the parameter ∆x1(t), it is evident that the it-2 t-s fuzzy system should be adopted to model nonlinear system (35). the lower and upper bounds of membership functions of the corresponding it-2 ts fuzzy model are listed in table 5. lower membership functions upper membership functions µ1 = 12 −x1(t) 22 µ̄1 = 12 − (x1(t) + 2) 22 µ2 = 10 + x1(t) 22 µ̄2 = 10 + (x1(t) + 2) 22 table 5: lower and upper membership functions of the plant. https://doi.org/10.15837/ijccc.2022.6.4957 18 the weighting functions are chosen as αi = sin2(x2(t)) and ᾱi = 1 −αi for i = 1, 2. the interval-valued fuzzy system (1) is defined by ai = cāi, adi = (1 − c)āi and the following matrices: e =  1 0 00 1 0 0 0 0   , ā1 =  −1.15 0.15 −400.5 −0.1 0 −0.75 0 −10   , ā2 =  −1.37 0.15 −620.5 −0.1 0 −0.75 0 12   , b1 = b2 =  1 00 0 0 1   c21 = [ 1 0 0 0 1 1 ] , c22 = [ 1 0 0 0 1 −1 ] this example aims to design a fuzzy controller (9) that guarantees the admissibility of closed-loop systems. to accomplish this goal, table 6 lists the lower and upper bounds of membership functions to characterize the interval-valued fuzzy controller. lower membership functions upper membership functions ν1(x1) = 0.3e−(x 2 1/0.35) ν̄1(x1) = ν1(x1) ν2(x1) = 1 − 0.3e−(x 2 1/0.35) ν̄2(x1) = ν2(x1) table 6: lower and upper membership functions of the controller. to well determine the membership functions, the non-linear weight functions are selected as β j (x2(t)) = cos2(x2(t)) and β̄j(x2(t)) = 1 −βj(x2(t)). when dealing with the reliable observer-based feedback control problem for the bio-economic system under a stochastic fault, we can solve the lmis (26) in theorem 4, with c = 0.98, d1 = 0.2, d2 = 0.5, dr = 0.3, λ1 = 3, λ2 = 11, λ3 = 3, λ4 = 11, and γ̄ = diag{0.5, 0.6}, to obtain a feasible solution that involves the following corresponding observer and controller gains: k1 = [ −8.9716 −0.058112 −224 0.24388 −0.0037578 −973.93 ] , k2 = [ −9.0421 −0.05894 −434.76 0.23901 −0.0035811 −982.23 ] , l1 =   1.9782 −0.315570.48519 0.84128 −0.42174 1.4254   , l2 =   3.1661 0.250490.44715 1.317 −0.85805 −2.3441   (36) for initial condition φ(t) = [10, 0.5, 12]>, t ∈ [−0.5 0], and d(t) = 0.2 + 0.3|sin(t)|, figure 5 shows the simulation results of the resulting closed-loop system, which was achieved by applying the developed fault-tolerant controller (36) to an uncertain system (34). this figure records the output responses of the system, and the control input uf (t). it can be concluded that, under actuator faults, uncertainties, and unmeasured states, the suggested controller would retain the closed-loop system dynamically stable. https://doi.org/10.15837/ijccc.2022.6.4957 19 0 5 10 15 20 25 30 -2 0 2 4 6 8 10 12 (a) the curves of y1(t), and ŷ1(t) 0 5 10 15 20 25 30 35 40 45 50 -15 -10 -5 0 5 10 15 (b) the curves of y2(t), and ŷ2(t) 0 2 4 6 8 10 12 14 -25 -20 -15 -10 -5 0 5 10 15 20 (c) trajectories of uf1 (t), and u f 2 (t) figure 5: simulation plots for bio-economic system 5 comparative explanations for non-linear singular systems exhibiting uncertain states with time varying delay as well as stochastic actuator failures, this paper explores the fault-tolerant observer-based control issue. the advantages over existing methods are listed below. 1. in comparison with existing findings [14, 20, 28, 37, 41], the outcomes developed in this paper are more realistic and general since the fuzzy model incorporates the intrinsic uncertainty of the system. moreover, fuzzy systems and fuzzy controllers have different premise membership functions. 2. despite having studied the observer-based control problem for interval-valued fuzzy systems [8, 48, 51], the present investigation differed with the following features: • our approach is more general since the system under consideration is a singular one, in which e 6= i, • typically, stochastic actuator failure is considered to cope with the reliable control problem for this class of systems. 3. compared to the fuzzy static and dynamic controllers proposed in [6, 30], the observer-based control strategy developed in this study assumes that the premise variables are unmeasurable as suggested in [51] 6 conclusion an attempt is made in this study to deliver solutions to the main challenges that come up when dealing with non-linear singular systems, such as uncertainty, time-varying delay, and stochastic actuator failures. based on an ivf model that exploits both the lower and upper membership functions to adequately characterize the uncertainties, an ivf observer is designed to estimate the unmeasured states, and then an ivf controller is synthesized to stabilize the system under consideration. the analysis of the existing of such observer-based controller is carried out by involving an appropriate https://doi.org/10.15837/ijccc.2022.6.4957 20 lyapunov-krasovskii functional, and the key point of the conceived control scheme lies in the use of decoupling matrix technique to establish a set of feasible lmi-based constraints so that the closed-loop system is stochastically admissible. the proposed control scheme has been validated by two numerical simulations. as part of prospective research topics, we will extend the suggested developments to non-linear systems with event-triggered output feedback control problems. acknowledgment this research has been funded by research deanship in university of hail-saudi arabia through project number rg-21 119 author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] fault tolerant control for singular systems with actuator saturation and nonlinear perturbation. automatica, 46(3):569–576, 2010. 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[51] tao zhao, zhenbo wei, songyi dian, and jian xiao. observer-based h∞ controller design for interval -2 t–s fuzzy systems. neurocomputing, 177:9–25, 2016. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: jerbi, h.; kchaou, m.; alshammari, o.; abassi, r.; popescu, d. (2022). observer-based feedback control of interval-valued fuzzy singular system with time-varying delay and stochastic faults, international journal of computers communications & control, 17(6), 4957, 2022. https://doi.org/10.15837/ijccc.2022.6.4957 introduction literature review objective and outline notations preliminaries and problem statement ivf model assumptions and resulting model fuzzy observer-based controller problem statement main results admissibility analysis observer design numerical examples computational framework and algorithm example 1 bio-economic system comparative explanations conclusion int j comput commun, issn 1841-9836 8(3):477-485, june, 2013. improved aco algorithm with pheromone correction strategy for the traveling salesman problem m. tuba, r. jovanovic milan tuba faculty of computer science megatrend university, belgrade, serbia bulevar umetnosti 29 11070 n. belgrade, serbia e-mail: tuba@ieee.org raka jovanovic texas am university at qatar po box 23874, doha, qatar e-mail: rakabog@yahoo.com abstract: a new, improved ant colony optimization algorithm with novel pheromone correction strategy is introduced. it is implemented and tested on the traveling salesman problem. algorithm modification is based on undesirability of some elements of the current best found solution. the pheromone values for highly undesirable links are significantly lowered by this a posteriori heuristic. this new hybridized algorithm with the strategy for avoiding stagnation by leaving local optima was tested on standard benchmark problems from the tsplib library and superiority of our method to the basic ant colony optimization and also to the particle swarm optimization is shown. the best found solutions are improved, as well as the mean values for multiple runs. the computation cost increase for our modification is negligible. keywords: ant colony optimization (aco), traveling salesman problem (tsp), nature inspired algorithms, metaheuristics, swarm intelligence. 1 introduction most real-life problems can be represented as some kind of optimization. nowadays only hard optimization problems are of research interest. many discrete (combinatorial) as well as continuous optimization problems are of great practical interest but intractable i.e. cannot be solved within reasonable time by standard, mathematical, deterministic methods. traveling salesman problem (tsp) is a classic combinatorial example that was researched for the longest period of time and because of that is often used as a benchmark. there are also a number of engineering design problems with mixed continuous and discrete variable and nonlinear objective function and nonlinear constraints that are used as benchmarks for optimization algorithms. for such intractable problems acceptable suboptimal solutions can usually be found by some metaheuristics, recently very successfully by nature inspired algorithms and as a subclass, swarm intelligence algorithms which include artificial bee colony [1], [2], particle swarm optimization [3], [4], cuckoo search algorithm [5], [6], seeker optimization algorithm [7], [8] etc. a large number of different algorithms have been developed to find suboptimal solutions for the traveling salesman problem (tsp) in polynomial time. examples are nearest neighbor, greedy, insertion heuristics, christofides, 2-opt and 3-opt. the non-deterministic metaheuristics like simulated annealing (sa) [9], [10], tabu search [11], genetic algorithms (ga) [12] and particle swarm optimization (pso) [13] have also been used, giving better results than previously mentioned deterministic algorithms. there are efficient algorithms for the tsp [14] this research was supported by ministry of science of republic of serbia, grant iii-44006. copyright c⃝ 2006-2013 by ccc publications 478 m. tuba, r. jovanovic (http://www.tsp.gatech.edu/concorde.html) but the research continues on different metaheuristic algorithms [15]. ant colony optimization (aco) is another metaheuristic that was successfully used for the tsp [16]. the effectiveness of the aco has been improved by use of different types of hybridization, like addition of local searchers [17] or combining aco with ga [18], [19], [20], differential evolution (de) [21] or kangaroo algorithm [22]. these types of hybridization methods usually have a drawback of complexity of implementation and computation time penalty. it has also been shown that, although adding a local searcher is a good approach in the majority of cases, it may prevent aco from finding the optimal solution [23]. a very interesting hybridization of aco is given in article [24] were scout ants, which search the solution space in a more systematic way, are added to the algorithm. multi-colony systems [25] have been developed, as well as variations of the basic aco like elitist ant colony, rank based ant colony system and min-max ant system (mmas) to improve the performance on the tsp [26] in a more natural way, with different pheromone strategies. all these variations have the problem of becoming trapped in local optima due to the fact that they increase the efficiency by making their search more greedy by intensifying the search near the best found solution. min-max ant system (mmas) [27] tries to solve this problem by bounding the pheromone values and resetting the pheromone trail if better solutions have not been found in a large number of iterations. minimum pheromone threshold strategy (mpts) is another approach to avoid early stagnation applied to the quadratic assignment problems [28]. mpts adds an additional minimum threshold value and if a pheromone value falls below, it is set to the maximal allowed value of the pheromone trail. thus the mpts avoids reinitialization of the pheromone trail like in the mmas and explores the solution search space more systematically. another interesting approach is in introducing variable pheromone sensitivity within population of ants [29]. in this paper we propose a new type of hybridization for the aco and implement it for the tsp. we improve the aco algorithm with a strategy for avoiding stagnation i.e. leaving local optima if trapped in search for optimal solution. this method is based on the pheromone trail correction. the correction calculation is based on the properties of the best-found solution so far. we have previously used a similar type of improvement on the minimum weight vertex cover problem with great success [30]. we have adopted this approach to tsp and further improved it. the basic idea of this correction is to lower the possibility of edges with high level of undesirability to belong to the optimal solution. we show that our strategy is simple to implement, computationally inexpensive and improves results compared to the pure aco or pso [13]. other relevant research includes [31], [32], [33], [34], [35]. in section 2 we give an outline of the aco algorithm. in section 3 our hybridization used for escaping search stagnation is explained. in section 4 we analyze and compare experimental results of our algorithm against pure aco and pso algorithms for tsp tested on benchmark problems from the tsplib [36]. 2 ant colony optimization the aco algorithm is based on mimicking the behavior of ants colony while gathering food. each ant starts from the nest and walks towards food until it reaches an intersection, where it has to decide which path to select. in the beginning this choice is random, but after some time the majority of ants will be moving along the optimal path. this happens because of the colony’s collective intelligence. each ant, as he moves, deposits chemical called pheromone thus marking the route taken. pheromone trail evaporates as time passes. accordingly, a shorter path will have more pheromone because it will have less time to evaporate before it is deposited again. each ant chooses paths that have more pheromone so shorter routes will be selected with higher improved aco algorithm with pheromone correction strategy for the traveling salesman problem 479 and higher probabilities until practically all ants go along shortest path. but, it case of dynamic change, some new obstacle or new passage, ants will quickly adopt to new situation. the described behavior can be converted into a computational system as presented by marco dorigo and luca maria gambardella [16], with small modifications: s = { arg max u/∈mk { τrs αηrs β } , q ≤ q0 s , q > q0 (1) pkrs =   τrs αηrs β∑ u/∈mk τruαηruβ , s /∈ mk 0 , s ∈ mk (2) equations (1) and (2) define the probabilistic decision method that is used by ants during the iterative construction of the path. an artificial ant at step k, currently at node r, after visiting nodes in mk, uses these probabilities for choosing the next node s. in these expressions q is a random variable chosen uniformly from [0,1] and q0 is a predefined parameter that gives a balance between exploitation (use of known good paths, q ≤ q0) and exploration (search for new paths, q > q0). in the case of exploitation, the next node is selected by the highest value of s, which gives the value of desirability of an edge depending on the amount of pheromone and its length. in equations (1) and (2), τrs is the value corresponding to the amount of pheromone deposited on edge connecting r and s, and ηrs is the length of rs which is used as a heuristic. α and β are predefined parameters that specify the influence of the pheromone and the heuristic, respectively. in the case of exploration the next node is chosen at random with a probability distribution given by equation (2), where prs is the probability of choosing edge rs. the pheromone trail is created using two types of updates. global update is used to reward good paths by depositing more pheromone on better paths. this is achieved by using the following formula: τij = (1 − γ)τij + γ∆τk , ∀(ij) ∈ bk (3) where bk is the set of all edges in the path ant k used, ∆τk is the quality of that solution, and γ is a predefined constant from the interval [0, 1]. in practical application of aco, not all the ants deposit pheromone but only the one with the best path. in some cases, only the best path found so far is used to deposit pheromone, to make the search more greedy. the local updating is used to avoid creation of a very strong edge used by all ants, and it emulates pheromone evaporation. every time an edge is chosen by an ant it loses some pheromone according to the following formula: τij = (1 − δ)τij + δτ0 (4) where δ is a predefined constant from the interval [0, 1], and τ0 is the quality of the solution created by the greedy algorithm for solving tsp when the heuristic function is the length of an edge. 480 m. tuba, r. jovanovic 3 suspicious elements exclusion pheromone correction strategy (see) the analysis of the aco algorithm for the tsp when it gets trapped in local optima indicates which corrections should be made, or more precisely what should not appear in the shortest path. there are two simple criteria that can be used on the edges belonging to the best found tour: very long edges and intersecting edges are very unlikely to be a part of the optimal path. the next step was to find a way to, without major corrections to the aco algorithm, remove suspicious elements from the ants search path. the solution was to significantly lower the amount of pheromone on randomly selected highly suspicious edges belonging to the best path and letting the colony resume its search. we have divided the correction into two parts: one considering the edge lengths, and the other one that is related to intersecting edges. all intersecting edges are considered highly suspicious and the pheromone trail is corrected on them. edges for which pheromone trail correction will be applied due to their length are defined in the following way. first we define a heuristic for suspicion sus(rs) = length(rs). using this heuristic, we define the probability of edge rs being selected for pheromone correction pselected(rs) = rk − ranksusp(rs) 2 ∗ rk (5) in equation (5) instead of using the value of sus for edges, we used ranksusp which represents their rank by suspicion. rk is the maximal number of edges that are considered for correction. the final step is to lower the pheromone trail for the selected edges: (∀(rs) ∈ selected)(τrs = δτrs) (6) where τrs is the amount of pheromone deposited on edge rs and δ is a fixed small constant that is used to significantly reduce the value of pheromone on edge rs. the use of suspicion defined as length is not fully effective because the same group of edges would be repetitively selected until a better tour was found. because of this we introduce an improved suspicion criteria: corsusp(rs) = susp(rs) ∗ exsusep(rs) (7) the improvement consists of tracking which edges have already been selected and preferring the selection of new edges. to do this, a new array exsusp is introduced with elements initially set to 1. if edge rs is selected, the following correction is done exsusp(rs) = exsusp(rs) ∗ λ ∗ len(rs) len(maxedge) (8) in equation (8) λ ∈ (0, 1) is a fixed parameter and maxedge is the longest edge in the best solution. the fraction in equation (8) makes the probability that the edge will be removed proportional to the length of the inspected edge, which makes longer edges selected more often that short ones, even after a large number of corrections. this is an improvement to our previous implementation of see to the minimum weight vertex cover problem were the suspicion was always equally reduced [30]. if a new best set is found, the values of exsusp are reset to 1. a more complex suspicion criteria could have been used that would better analyze the properties of the best found solution, but we wished to show that even with a relatively simple heuristic, improvements can be archived. improved aco algorithm with pheromone correction strategy for the traveling salesman problem 481 for stagnation criterion we used the fact that there was no change to the global best solution for at least n iterations. if this criterion is satisfied, we apply previously defined correction algorithm to the pheromone trail. the pseudo-code for an iteration step for the improved aco is: reset solution for allants while(! allantsfinished) do for allants do if(antnotfinished) do begin add new edge rs to the solution based on probability local update rule for rs end for; end while; compute global update if (stagnation) and (usingsuspisionimprovement) use suspisioncorrectionmethod the computation cost for this modification is negligible: fraction of one percent of the total computation time. it can be estimated as follows: the time required to generate one ant solution is of the order n2 since it includes n steps to create a solution (adding nodes) and for each of these steps on average n/2 steps to recalculate probabilities and pheromone corrections. computation time for one correction is also of the order n2. it consists of three steps where the first one is dominant. the first step is to check all pairs of nodes for intersections and it requires (n − 1)n/2 ∼ n2 operations. this step is not even performed if the solution has not been improved since last correction. the second step is to sort links, which requires order of n ∗ log(n) operations and finally, to select suspicious elements in n operations. the total time is (n − 1) ∗ n/2 + n ∗ log(n) + n ∼ n2, or even less when the first term is missing. one corrections is computationally equivalent (or less) to one ant solution generation and since the correction is done rarely, for our tests we used 30 iterations as a criterion, and each iteration includes 10 ants, the total correction time is 1/300 of the total time or 0.33%. the most important difference between our proposed algorithm and all other algorithms used so far is in the way how trapping in local minima is avoided. all other algorithms include fresh elements in the search by rather nondiscriminatory increasing pheromone level for large number of elements currently not in the best solution. out method, however, lowers the pheromone levels for few undesirable elements in the current best solution letting the ant colony replace them with the most appropriate elements. this proved to be a significant new approach applicable to many situations [30], [37]. 4 tests and results in this section we present the results of applying our improved version of the aco on 11 standard benchmark problems from tsplib [36], [38] (acceptable by concorde software). all of the test have been done on the symmetric version of the tsp. we compared our hybridized aco algorithm with the pso algorithm [13] and simple aco in mmas variation. for each of these three methods we compared the best found solution and the average error over all runs. the results for aco are calculated using our software system [39]. results are presented in table 1. columns 3, 5 and 7 (best) show best results of all runs, columns 4, 6 and 8 (avgerr) show quality of average solution of all runs: avgerr = (avg − opt)/opt ∗ 100%. for the first five problems we used 100 runs to make it comparable to [13]. we also included 6 larger cases and for these cases we used 10 runs since there was no significant difference. for 482 m. tuba, r. jovanovic table 1: comparison of pso, simple aco and aco combined with see problem opt pso aco see best avgerr% best avgerr% best avgerr% eil51 426 427 2.58 427 0.52 427 0.23 berlin52 7542 7542 3.85 7542 0.28 7542 0.13 st70 675 675 3.34 676 1.50 675 1.36 eil76 538 546 4.17 538 1.21 538 1.19 pr76 108159 108280 3.82 108359 2.94 108358 2.62 kroa100 21282 21282 0.77 21282 0.72 lin105 14379 14379 0.38 14379 0.38 pr124 59030 59385 0.84 59030 0.72 pr136 96772 96785 1.09 96781 0.70 u159 42080 42080 1.27 42080 0.45 kroa200 29368 29532 1.21 29490 0.93 each of the runs we created 200,000 tours. all the colonies had the following parameters: α = 4 and β = 1 which control the influence of pheromone and edge length in the transition rule, q0 = 0.9 specifies the exploitation/exploration rate, γ = 0.1 and δ = 0.1 specify the global and local update rules, the initial value of the pheromone is given by τ0 = 1/n ∗ lnn where lnn is the length of the path calculated by the nearest neighbor heuristic. for see we used the following parameters rk = problemsize/10, λ = 0.9, δ = 0.1 and n = 30. in our test we used 10 colonies for both, aco and the improved see version. in both cases we used random seeds with values from 0 to 9. this make possible comparison of the effect of see. see used a separate random number generator for the corrections which provides ants having the same behavior. the first five problems are the same as used in [13] and the results of our method are better compared to the pso algorithm. best solutions were equal in three cases, better in one and worse in one case, but average solutions were significantly better in all five cases. we also compared our hybridized algorithm to simple aco in mmas variation. here we have best solutions equal 6 times and improved 5 times by our algorithm, but again average solutions are improved in 10 cases and equal in one. 5 conclusion we have introduced a new type of hybridization for the aco applied to the tsp. it adds a pheromone trail correction method to aco metaheuristic which is activated in the situations when the search algorithm has started to stagnate. it is based on the analysis of properties of the best-found tour. see adds a new heuristic for determining the undesirability of edges belonging to the tour and significantly decreases their pheromone values. the calculation time for this heuristic is negligible compared to the other parts of the algorithm. we compared our hybridization to the use of the simple aco and pso and the positive effect of this hybridization was evident on standard benchmark problems. this type of improvement has the potential of 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[38] http://www.iwr.uni heidelberg.de/groups/comopt/software/tsplib95/tsp/ . [39] jovanovic, r., tuba, m., and simian, d., an object-oriented framework with corresponding graphical user interface for developing ant colony optimization based algorithms, wseas transactions on computers, 7(12):1948–1957, 2008. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 3, month: june, year: 2023 article number: 5045, https://doi.org/10.15837/ijccc.2023.3.5045 ccc publications womdi-apriori data mining algorithm for clustered indicators analysis of specialty groups in higher vocational colleges fei gao, jing yang*, yang yang, xiaojing yuan fei gao non-governmental higher education institute of china, zhejiang shuren college hangzhou 310015, china gaofei@zjsru.edu.cn jing yang* hebei women and children activity center shijiazhuang 050081, china *corresponding author: pljinger@126.com *orcid: 0000-0003-3216-9444. yang yang school of transportation science and engineering, beihang university beijing 100191, china yangphd@buaa.edu.cn xiaojing yuan school of traffic and transportation, beijing jiaotong university beijing 100044, china xjyuan@bjtu.edu.cn abstract the cluster effect of specialty groups plays an important role in the development of higher vocational colleges. the purpose of this research is to scientifically explore the interaction mechanism of specialty groups clustering indexes in higher vocational colleges, quantitatively analyze the correlation of these indexes, and explore reasonable measures to promote the specialty groups clustering effect in higher vocational colleges. firstly, data denoising and field screening were carried out on the original data, and then the variables were clustered and divided into lhs (left hand side) and rhs (right hand side). then, an improved multi-dimensional interactive apriori association rule mining algorithm considering index weights and orientation constraints was proposed. the improved apriori algorithm and the traditional apriori algorithm were applied to mine the structured data sets. the results show that the improved womdi-apriori algorithm in this study improves the accuracy by 79.96% compared with the traditional apriori algorithm. the results indicate that, when the indicators of brand, key and characteristic majors at or above the provincial level, proportion of full-time teachers with double qualifications, and the number of internship students accepted by cooperative enterprises are at a low level, the number of projects and satisfaction proportion of employers with graduates would be negatively affected; the major https://doi.org/10.15837/ijccc.2023.3.5045 2 category of equipment manufacturing is subjected to various factors coupling, which may lead to different graduates’ counterpart employment rate; for association rules where the successor of the mining results is dominated by negative results, measures should be taken to avoid or reduce the possibility of their occurrence as much as possible. for association rules in which the successors of the mining results are dominated by positive results, measures should be taken to facilitate the occurrence of these frequent item sets whenever possible. the framework proposed in this research can provide theoretical guidance for analyzing operating characteristics and promoting the positive effects of specialty groups in higher vocational colleges. keywords: womdi-apriori data mining algorithm, clustered indicators analysis, specialty groups, higher vocational colleges. 1 introduction "double high plan" is another major project after the demonstration, backbone, high-quality higher vocational college construction plan in china, in which high-level college and high-level specialty (specialty group) is essentially a symbiotic relationship. in the limited resources of the fierce competition environment, higher vocational colleges, will inevitably focus on strengthening the construction of advantageous specialty groups, choose the cluster development mode, and the growth of specialty groups is indeed conducive to breaking through bottlenecks such as scattered resources, indistinctive features and insufficient linkage to improve the adaptability of talents, carry out technological innovation and focus on regional needs. specialty groups are a collection of majors or directions with common foundations, complementary advantages and resource sharing, and are committed to changing the division between majors and promoting cross-border cooperation in knowledge flow. although not all countries have the terminology of specialty group, the research on interdisciplinary education can still provide important reference. camilleri et al. pointed out that european professional higher education, as an applied education, requires to break the curriculum boundary and run through professional experience to promote knowledge integration [1]. norton et al. proposed that american community colleges entrust multidisciplinary courses to achieve education content integration[2]. scholars have carried out relevant research on the connotation definition, existing problems and development paths of specialty groups. around the definition of specialty groups, the different interpretations contain the similarity theory, the joint force theory and the common theory, which are the most representative. in recent years, various views have shown a trend of convergence, such as gu yong’an [3]. specialty groups also face a series of obstacles in the process of formation, operation and exertion of influence. zeng xianwen and zhang shu analyzed the role of specialty groups with the help of human capital value measurement models [4]. zeng xianwen and yan meng introduced concepts such as class density, group intensity, and concentration of specialty groups to quantitatively analyze specialty groups [5]. around the existing problems, scholars have elaborated on the optimization path from different angles, such as zong cheng, he has pointed out that recombine talent training models, innovate the formation of teacher teams, and jointly build a sharing training base [6]. zhao mengcheng believes that it is necessary to establish specialty group faculty, inter-specialty team, curriculum sharing mechanism, and information sharing platform to achieve micro-organizational changes [7]. with the development of computer technology, the acquisition of big data has become possible [8]. the research and solution of problems in the social science field also increasingly rely on the use of big data methods. for example, the genetic algorithm is used to optimize production scheduling problem[9].the intelligent mechanism is established to realize the coordination and subsequent response of emergency resources [10]. text data mining is used to analyze the relationship between innovation and development in economic field[11]. machine learning and data mining tools are often able to solve some problems that cannot be efficiently discovered by traditional means. for example, the economic lot-size problem is explained through machine learning to optimize resource allocation[12]. with the help of machine learning and educational data mining, students’ performance can be predicted based on video learning systems[13]. data mining technology is a data analysis method that extracts implicit and potentially valuable laws for decision making from a large amount of data, and its process is user-oriented and knowledge discovery-oriented data analysis process, while association rule https://doi.org/10.15837/ijccc.2023.3.5045 3 analysis is an important branch of data mining technology. in the work of traffic accident causation analysis and risk identification, association rule analysis is a common research method for data mining of this problem, and the apriori association rule mining algorithm is one of the main methods of association rule analysis [14]; the association rule mining algorithm was first proposed by agrawal et al. when they analyzed the problem of market shopping baskets, and the algorithm can accurately and effectively mining the correlation between two or more factors, but it cannot quantify the importance of a single factor in the association rule, and the computational effort will increase exponentially when there are more data items, the computational efficiency decreases, and it is easy to ignore rare data [15]. in the work of association rule mining for professional clustering effect, the factors are essentially treated with equal weights, which cannot reflect the degree of influence of different indicators on professional clustering effect and easily ignore the factors that need to be focused on, and at the same time, it is impossible to filter out the factors with weak influence on the results, which causes a large number of useless operations and affects the efficiency of model calculation [16]. as a result, many researchers began to improve the traditional apriori algorithm. an intelligent method is used for improving the apriori algorithm in order to extract frequent itemsets[17]. the improved apriori algorithm can reduce the time complexity of association rule mining[18]. at present, the academic community has carried out useful exploration in the field of specialty groups, but it has yet to be enriched. on the one hand, the research theme still lacks in-depth analysis of the elements interrelationship and action mechanism within the specialty group, and the internal law of the development of the specialty group needs to be further excavated. on the one hand, the research method is mainly qualitative research, and in general it lacks the strong support of first-hand data and systematic empirical analysis. this study will use womdi-apriori data mining algorithms to analyze cluster indicators for 232 specialty groups of higher vocational colleges, establish a corresponding analysis framework, and explore basic problems such as the essential attributes and operating mechanisms of specialty groups, so as to provide theoretical support for related research, and provide new ideas and perspectives for creatively understanding and solving problems, so as to contribute to the deepening of related research and the development of practice. 2 data pre-processing the original sample data set contains a total of 242 base data, each covering 41 field variables, constituting a 242*41 matrix. before model construction and data mining analysis, the sample structure design needs to be implemented, and the first task is data pre-processing, including data cleaning (denoising), field screening, variable coding and numeralization, and the final available matrix output. the sample structure design process is shown in figure 1. figure 1: sample structure design flow https://doi.org/10.15837/ijccc.2023.3.5045 4 2.1 data denoising data mining refers to the process of extracting hidden, deep and potentially valuable information from large, fuzzy and noisy big data through purification and denoising, algorithm design and other means. data mining contains many meanings: (1) the data must be real and valid; (2) it contains the information resources required for data mining; (3) the information mined has the value of utilization; (4) it is not necessary to mine the general knowledge in the massive data, but more to mine specific laws. data pre-processing is the first step in the process of data mining work, and it is also the most crucial one. data pre-processing usually accounts for nearly 60% of the entire data mining workload, which is extremely time-consuming. it follows that when data mining is carried out, it is necessary to ensure the reliability and validity of the data at the very beginning, and effectively doing the work of data pre-processing can further improve the quality of the data in the database, effectively compensate for the incompleteness of the research data set, and provide more reliable data information for data mining work. the most obvious problems of the original data in the record information of specialty groups are incompleteness, inconsistency and noisiness, which can directly cause the waste of computational resources and even lead to the bias of computational results. the main problematic characteristics of the data set are as follows: (1) worthless field variables, (2) incomplete missing data, (3) noisy data, (4) inconsistency and compatible transformation problems, and (5) redundant data and similar mergeable data. the general process of dealing with such data problems in this study includes: purification and cleaning, compatible conversion, and integration and subsumption. specific examples are as follows: (1) merging of redundant data: the value of the "category of backbone major" field of the data of serial number code 226 in the original data is "manufacturing", the "category of backbone major" field of code 155 in the original data takes the value of "equipment", considering that they are actually "equipment manufacturing", therefore, both are modified. the data such as these are processed in a unified manner. (2) abnormal data deletion: the 10 data items indicating employment rate of "9625%" and "0%" are deleted. (3) data correction: the indicator value of "graduates’ counterpart employment rate" is changed from "823" to "82.3"; the field " the value of "number of full-time teachers (sum)" in a data entry is "150.0018", which is changed to "150"; the field "number of hours taught by part-time faculty as a percentage of total professional hours in one academic year (%)" was changed to "84.2";. the value of "%" in a data entry is "447", which is replaced by "44.7"; the value of "funding for horizontal projects (million yuan)" in a data entry is "(+)". the value of "(+)17" in one data is changed to "17"; the value of 3 data in the field "brand, key and characteristic majors at or above provincial level" is empty and is filled with the value of "0" is filled in. (4) no value field variable rejection: all values of the "rank" field are empty, which is a worthless field, so it is rejected here. 2.2 field filtering according to the characteristics of the association rule mining algorithm, the fields with too much dispersion (biased data fields) should be deleted in the design of this data sample structure, and the facing industries of the specialty groups should be the main object of this analysis. the fields that are not considered include "serial number", "school name", "name of specialty group", "city where the school is located", "name of included majors", "name of involved colleges", "name of included major categories", "ranking ". 2.3 variable coding (1) clustering analysis of variable values in the original data of this study, all fields take values in discrete variable form. the association rule mining algorithm for continuous variables generates memory overflow errors in the computation process, so the input data set needs to be in discrete variable form. however, too discrete variables (e.g., all integers with field values from 1 to 100) can cause the results to have too little support, https://doi.org/10.15837/ijccc.2023.3.5045 5 and valuable association rules can be easily ignored and missed, so the discrete variables need to be clustered to obtain more focused and reliable association rule mining results (2) division of lhs (left hand side) and rhs (right hand side) according to the characteristics of association rule mining, in the data mining process, there are causative and resultant terms, i.e., it is necessary to define the precedence term (lhs) and the successor term (rhs). here, the filtered and de-noised data were divided into causal dimensions, and the fields related to "status quo" and "input" were used as lhs, and the causal dimension fields were analyzed by professionalism: industry oriented, including major quantity, number of colleges involved, including major categories, number of majors sharing cooperative enterprises, number of majors sharing employers, number of majors sharing courses, number of majors sharing on campus and off campus training bases, number of majors sharing full-time teachers, brand key and characteristic majors at or above the provincial level, number of majors sharing off-campus part-time teachers, number of full-time students in specialty group , number of full-time teachers, proportion of full-time teachers with double qualifications number of hours taught by part-time faculty as a percentage of total professional hours for one academic year, number of specialty group training bases , average equipment value of students on campus training bases, frequency of use of on-campus training bases for one academic year, total number of cooperative enterprises , total number of courses jointly developed by cooperative enterprises , number of cooperative enterprises support part-time teachers , number of internship students accepted by cooperative enterprises , total value of equipment donated by the cooperative enterprise. the fields of "consequence" and "output" related factors are used as rhs, and the fields of the result dimension through professional analysis include: the initial employment rate of graduates of the specialty group, graduates’ counterpart employment rate, satisfaction proportion of employers with graduates , the number of graduates accepted by off-campus internship training bases , number of students accepted by the cooperative enterprise, total number of employees trained for the enterprise, number of provincial or above teaching achievement awards, number of provincial or above scientific research achievement awards, horizontal project funds, number of invention patent , number of industry standard, number of provincial or above awards for college student, number of provincial or above scientific research projects. 2.4 sample structure design based on the above analysis, the clustering calculation is performed by the look up function embedded in the database, based on the "if" loop and copying the values of the clustered variables and returning them to the source file, processing all the fields according to the above process work and constructing the final input value matrix. the results of dimensional division and variable value clustering are shown in table 1 below. table 1: sample structured design data set dimension variable value clustering value quantity lhs category of backbone major finance and trade, electronics and information, equipment manufacturing, medicine and health, civil engineering, culture and art, education and sports, tourism, public management and services, agriculture, forestry, fisheries and animal husbandry, light industry and textiles, food, drugs and grains, transportation, energy, power and materials, and other categories 16 including major quantity 3,4,5,6 4 https://doi.org/10.15837/ijccc.2023.3.5045 6 including major categories 1,2,3,4 4 brand, key and characteristic majors at or above the provincial level 0,1,2,3,4 5 number of majors sharing courses 0,1,2,3,4,5,6,7,8,10,11 11 number of majors sharing on campus and off campus training bases 1,2,3,4,5,6,7,8,9,10,11,14,17,18,33 15 number of majors sharing full-time teachers < 10, 11 ∼ 50, > 50 3 number of full-time students in specialty group < 1000, 1000 ∼ 2000, 2000 ∼ 3000, > 3000 4 proportion of full-time teachers with double qualifications < 50, 50 ∼ 70, 70 ∼ 90, 90 ∼ 100 4 average equipment value of students on campus training bases < 1, 1 ∼ 10, 10 ∼ 100, > 100 4 number of cooperative enterprises support part-time teachers < 10, 10 ∼ 50, 50 ∼ 100, > 100 4 number of internship students accepted by cooperative enterprises < 100, 100 ∼ 500, > 500 3 total value of equipment donated by the cooperative enterprise < 50, 50 ∼ 100, 100 ∼ 500, 500 ∼ 1000, > 1000 5 rhs graduates’ counterpart employment rate < 50, 50 ∼ 70, 70 ∼ 90, 90 ∼ 100 4 satisfaction proportion of employers with graduates 50 ∼ 90, 90 ∼ 100 3 number of students accepted by the cooperative enterprise < 100, 100 ∼ 300, > 300 3 number of provincial or above teaching achievement awards 0,1,2,3,4,5 6 number of provincial or above scientific research achievement awards 0,1,2 3 horizontal project funds < 10, 10 ∼ 100, > 100 3 number of invention patent 0 ∼ 5, 5 ∼ 10, > 10 3 number of industry standard 0,1,2,3,4,5,7,10 8 number of provincial or above awards for college students < 50, 50 ∼ 100, > 100 3 https://doi.org/10.15837/ijccc.2023.3.5045 7 number of provincial or above scientific research projects < 5, 5 ∼ 10, > 10 3 total number of employees trained for the enterprise < 100, 100 ∼ 1000, 1000 ∼ 10000, > 10000 4 3 algorithm design and modeling 3.1 association rules mining association rule mining is one of the main technologies of data mining, and it is also the most common form of mining patterns in unsupervised learning systems. association rule mining is to mine valuable knowledge from a large amount of data to describe the relationship between data items [19]. a complete association rule can be expressed as the implication form of "x => y". x is the leading term, also known as the cause layer, y is the subsequent term, also known as the result layer. the association rule "x => y" is the basic condition that can be seen and established in the study, which meets the requirements of the preset support, confidence and lift. support, confidence and lift are three important parameters that characterize the association rule, among which: (1) support: the number of transactions of itemset x contained in dataset d is called the support number of itemset x, expressed as σx. the support rate (also known as support) of item set x is recorded as: support (x), that is, probability p (x): support(x) = σx {d} × 100% (1) where {d} is the number of transactions in dataset d. if support (x) is not less than the preset minimum support threshold (min_support), x is called a frequent itemset, otherwise x is an infrequent itemset. the support of item set (d) is the support rate of association rule x => y, which is essentially the proportion of transactions in d containing (x ∪ y ), that is, the frequency of probability p(x ∪ y ), as support (x => y): support(x => y ) = |x ⋃ y | |d| = support(x ⋃ y ) = p(x ⋃ y ) (2) (2) confidence: two association rules defined on i and d, such as x => y, whose confidence means that "the transaction meeting condition x also meets condition y ". the confidence of association rules with x => y is the conditional probability p(y | x) of itemset y on the premise of including itemset x, which is recorded as: confidence (x => y). confidence(x => y) = p (y | x) = p (x, y ) p (x) = sup(x => y ) sup(x) = sup(x ∩ y ) sup(x) (3) where x ⊆ i, y ⊆ i, x ∩ y = ∅. (3) lift: the lifting degree is used to characterize the correlation degree of the leading item and the subsequent item. in order to avoid the interference of pseudo strong association rules and prevent invalid association rules from appearing in the final result, the lifting degree index is hereby introduced, and this index is also used as the judgment condition of effective association rules: lift(x => y ) = p (y | x) p (y ) = conf (x => y ) sup(y ) = sup(x => y ) sup(x)sup(y ) (4) the greater the degree of promotion, the higher the degree of correlation between itemset x and itemset y. generally, we believe that only association rules with lift greater than 1 are effective strong association rules, and x and y are positively correlated at this time; if the lifting degree lift < 1, it https://doi.org/10.15837/ijccc.2023.3.5045 8 means that x and y have no correlation degree or are mutually exclusive item sets. such non effective association rules will not be considered in the results. (4) minimum support, minimum confidence threshold and strong association rules in the modeling process, users can specify the minimum support (recorded as min_support) and the minimum confidence (recorded as min_confidence). the former describes the minimum importance that association rules must meet, and the latter specifies the minimum reliability that association rules must meet, and min_support ∈ (0, 1], min_confidence ∈ (0, 1]. data set d meets the minimum support threshold and the minimum trust threshold on item set i, and the association rules with the lift greater than 1 are called valuable strong association rules. 3.2 weighting model in order to eliminate the weight deviation of subjective weighting method and objective weighting method in the process of weight assignment as much as possible, a combined weighting method based on the sum of deviation squares is adopted [20], and the subjective weight obtained by iahp method and the objective weight obtained by rough set model are integrated and optimized to calculate the actual consideration weight of the corresponding index: combine the weight vector ω′ obtained by the subjective weighting method and the weight vector ω∗ obtained by the objective weighting method to obtain the final reasonable weight vector ω̄. assuming that w is the optimal weight vector, the deviation between the subjective weight vector w and the objective weight vector w should be minimized [21]. establish optimization model:  min θ n∑ j=1 ( ω′j − ω̄j )2 + (1 − θ) n∑ j=1 ( ω∗j − ω̄j )2 s.t   ω̄ ≥ 0 n∑ j=1 ω̄j = 1 (5) where θ denotes the trust degree in the result of subjective weighting; 1 − θ denotes the trust degree in the result of objective weighting; ω̄j represents the weight of the j th index attribute. there is an optimal solution ω̄ = [ ωl, ωu ] in formula (5) of the optimization model, where ωl = ( ωl1 , ω l 2 , ω l 3 . . . , ω l n ) is the lower bound of the interval number of model solutions, ωu = ( ωu1 , ω u 2 , ω u 3 . . . , ω u n ) is the upper bound of the interval number of model solutions, and ω̄ represents the interval weight vector of the final combination weighting. the steps of the established subjective and objective joint weighting model are as follows: step 1: determine the set of objects to be evaluated and the corresponding index set. let x be a collection of objects denoted as x = {x1, x2 . . . , xn} , a = {a1, a2, . . . , an} as the index set, a(x) is the value of object x on attribute a, meanwhile, the index value can be discrete value or continuous value. step 2: interval number feature vector method is used to determine the subjective weight. according to the index comparison scale, interval number judgment matrix b = [ bl, bu ] is determined, and the weight vector ω′ = [ ω ′l, ω ′u ] of each index aj (j ≤ m) is calculated based on iahp method. step 3: apply rough set theory to get objective attribute weight. attribute set c = {c1, c2, . . . cn} is the set of evaluation indicators determined in step1, and the domain u = {u1, u2, u3 . . . , un} is a set of events with various possible causes at the corresponding time. the value of each traffic crash recorded on the sub-index is regarded as a piece of information of ut, ut = {c1t, c2t . . . cnt}, and discretize it to establish a discretized twodimensional information table. then, the weight vector is calculated according to formula (5). step 4: obtain the optimal combination of weights. calculate the final weight vector ω̄ = [ ωl, ωu ] according to formula (5). the weight value is assigned to the field variables of traffic crash analysis, and the relative support, relative confidence, and relative lift of each association rule are determined. https://doi.org/10.15837/ijccc.2023.3.5045 9 3.3 construction of the womdi-apriori algorithm the traditional accident tree analysis and fp-tree algorithm and other methods are convenient for operators to grasp the overall characteristics of the problem, but they can’t realize the relevance thinking between the multi-attributes of indicators in each dimension, nor can they effectively quantify and analyze the causes of the negative results. the association rule mining algorithm was first proposed by agrawal et al. when analyzing the market basket problem. the algorithm can accurately and effectively mine the correlation between two or more factors, but it can’t quantify the importance of a single factor in the association rule. in addition, when there are many data items, the amount of calculation will multiply, the computational efficiency will be reduced, and it is easy to ignore rare data [19]. at the same time, the traditional algorithm can’t filter out the factors that have a weak influence on the results, leading to a large number of useless operations, affecting the calculation efficiency of the model. most importantly, because the association rule mining algorithm was first developed for market basket analysis, some scholars did not improve and optimize the algorithm in an orderly way when applying the algorithm to analyze the association rules of other problems. if the traditional apriori association rule mining algorithm is directly applied to study the problems in the fields of economics or education, a large number of invalid or even incorrect disordered association rules are output in the result [20]. this research improves and optimizes the algorithm from three perspectives: 1) the algorithm is constrained by the form of orderly and directional rule association, so that the traditional apriori association rule mining algorithm for the application scope of the shopping basket field can be compatible with the problem of clustered indicators analysis of special groups in higher vocational colleges; 2) through the subjective and objective weighting model, the index weights of all field variables are calculated, and based on the weight optimization results, the concepts of "relative support", "relative confidence" and "relative improvement" are proposed; 3) breaking the traditional mining output method of "cause leading item => consequence subsequent item", introducing the idea of multi-dimensional interactive association, not only considering the association relationship of "cause leading item => consequence subsequent item", but also exploring the association rules between dimensions from the perspective of the autocorrelation (leading item) of the dimension part of the cause layer and the internal autocorrelation (subsequent item) of the result dimension. figure 2 shows the steps of the multi-dimensional interactive improved apriori algorithm considering directional constraints proposed in this paper. figure 2: professional clustering effect association rule mining process https://doi.org/10.15837/ijccc.2023.3.5045 10 4 association rule mining analysis 4.1 parameter calibration first, the input of data of each dimension is carried out; then, the initial threshold is set, and the minimum support threshold min_sup=0.30 and the minimum confidence threshold min_conf=0.35 are set after continuous manual debugging considering the main features of the specialty group clustering effect in higher vocational colleges; meanwhile, the min_lift threshold of the lift is set to 1, so that the frequent itemsets with no positive association between the preceding and following items can be eliminated. 4.2 improving the accuracy improvement calibration of the algorithm the original apriori association rule mining algorithm and the improved womdi-apriori association rule mining algorithm are applied to the input dataset by calling the "arules" function package in r language [22], and the mining results are summarized in figure 3. the above calculation results can be identified: the original apriori association rule mining algorithm without loaded orientation constraints mines a total of 7379 items, while applying the womdi-apriori model proposed in this paper, under the same threshold setting (min_sup=0.30, min_conf=0.35), only the mining results output only 1472 eligible association rules. in other words, if the algorithm is not optimized and improved, at least 5907 invalid association rules will be generated, and the improved womdi-apriori model improves the computational accuracy by 79.96% over the traditional aprori model under the conditions of the base data in this paper. it can be seen that the direct application of the traditional apriori algorithm to such problems will cause some confusion to the results, so the optimization and improvement of the apriori algorithm in this paper is necessary for such research problems of association rule analysis of professional clusters in higher education schools. figure 3: comparison of original apriori algorithm and improved apriori algorithm figure 3 can show the percentage of the total frequent item set in the sparse matrix of the mining results. through the above mining summary information can also be found, after the algorithm improvement and optimization, the rules that contain more items in the mining results are: 204 association rules for those containing 2 items, 498 association rules for those containing 3 items, 504 association rules for those containing 4 items, 230 association rules for those containing 5 items, and 36 association rules for those containing 6 items the median is 4, which means that 50% of the frequent itemsets contain no more than 4 items, and the mean value of 3.59 means that the average number of items contained in all frequent itemsets is 3.59. 5 results and discussion 5.1 valuable association rule extraction the parameter scatter plot of the mining results is plotted by the "plot function" in arulesviz, a visual analysis toolkit for association rules in r language, as shown in figure 4. the horizontal https://doi.org/10.15837/ijccc.2023.3.5045 11 coordinate in the figure represents the relative support (r-sup), the vertical coordinate represents the relative confidence (r-conf), and the color shade is the relative lift (r-lift) size. figure 4: scatter plot of association rule mining results the scatter plot of association rules of the mining results in figure 4 shows that the relative support of some association rules ranges from 0.30 to 0.65, and another part ranges from 0.75 to 0.95, indicating that high-frequency rules and low-frequency rules coexist among all threshold-eligible rules; from the confidence index, the relative confidence of most association rules is between 0.3 and 1, but the region below 0.9 relative confidence has a lighter color, which means that the relative lift of this part of association rules is not enough, and it is possible that the association rules with insufficient degree of association between item sets or even invalid ones; meanwhile, for the lift index, by observing the colors in the scatter plot, about half of the association rules have a relative lift less than 1, indicating that a large proportion of the rules fail to satisfy the constraint of a lift greater than 1, i.e., they are invalid association rules. on the basis of filtering out the valid association rules (r-lift>1) and focus locking on the regions with high confidence and high support (blue circles in figure 4), the valuable strong association rules are further extracted. 5.2 analysis of high support association rule extraction results in order to extract the valuable strong association rules more precisely, the "inspect" and "sort" functions in r studio are called, and the 1472 valid association rules obtained by applying womdiapriori association rule mining algorithm are conditionally sorted according to the relative support (r-sup) from the largest to the smallest, and the rules with relative lift (r-lift) less than 1 are eliminated by extension, and the extracted output results are shown in table 2. the high support association rule characterized by the relative support ranking corresponds to the higher frequency of frequent item sets, and by analyzing the results in table 2, the following pattern is summarized: (1) the relative confidence of the three association rules with the highest ranking of high support is also at a high level, and their relative confidence is greater than 9; indicating that these association rules are all strongly correlated rules. the relative lift is greater than 1 and less than 1.1, which means that these association rules are positively correlated, but the correlation is at a low level. (2) the lower level indicators including brand, key and characteristic majors at or above the provincial level, proportion of full-time teachers with double qualifications, and number of internship students accepted by cooperative enterprises may basically cause the loss of horizontal and vertical projects, and satisfaction proportion of employers with graduates may be affected negatively as well; on the contrary, the specialty group of medicine and the health in the higher vocational colleges with three brand, key and characteristic majors at or above the provincial level , often have a high number https://doi.org/10.15837/ijccc.2023.3.5045 12 table 2: extraction of high relative support results rules r-support r-confidence r-lift {brand, key and characteristic majors at or above the provincial level= 0, proportion of full-time teachers with double qualifications=<50, number of internship students accepted by cooperative enterprises =<100}=> { satisfaction proportion of employers with graduates = 50∼90, horizontal project funds =<10, number of provincial or above scientific research projects =<5} 0.941 0.998 1.01 { category of backbone major=medicine and health, brand, key and characteristic majors at or above the provincial level= 3, number of full-time students in specialty group =>3000, cooperative enterprises support part-time teachers total =>100, cooperative enterprises accept internship studentsnumber =>500}=> { satisfaction proportion of employers with graduates = 90∼100, number of scientific research projects at or above the provincial level= 5∼10} 0.933 0.905 1.01 {including major categories= 2, total value of equipment donated by the cooperative enterprise = 100∼500}=> {scientific research achievement award at or above the provincial level=1} 0.892 0.973 1.02 of students, cooperative enterprises support part-time teachers, and cooperative enterprises accept internship students. higher vocational colleges with such conditions are at a high level of satisfaction proportion of employers with graduates , and there are also a considerable number of scientific research projects. (3) in most situations, if the specialty groups with two major categories, and total value of equipment donated by the cooperative enterprise is between 1 ∼ 5 million rmb, they can often get 1 provincial or above scientific research achievement award. 5.3 analysis of high-confidence association rule extraction results in order to extract the valuable strong association rules more precisely, the "inspect" and "sort" functions in r studio are called, and the 1472 valid association rules obtained by applying womdiapriori association rule mining algorithm are conditionally sorted according to the relative confidence (r-conf) from the largest to the smallest, while those rules with relative lift (r-lift) less than 1 are eliminated by extension, and the extracted output results are shown in table 3. the conditional probability of occurrence of high confidence association rules characterizing frequent item sets according to the relative confidence ranking is higher, and the following pattern is summarized by analyzing the results in table 3. (1) by observing table 3, we can find that the association rules with the highest three relative confidence levels do not have high relative support, indicating that the item sets with high conditional probabilities are not necessarily frequent item sets. however, their r-lifts are all greater than 2, suggesting that these high-confidence association rules are strongly correlated frequent item sets. (2) also in major category of equipment manufacturing, when the number of full-time students in specialty group is greater than 3000, counterpart employment rate of graduates is instead at a lower level of 50%-70%; on the contrary, when in the case of number of full-time students in specialty group is less than 1000, graduates’ counterpart employment rate is higher. possible explanations are that other conditions have changed, affecting the prior distribution of conditional probabilities, such as brand, key and characteristic majors at or above the provincial level, proportion of full-time teachers https://doi.org/10.15837/ijccc.2023.3.5045 13 table 3: extraction of high relative confidence results rules r-support r-confidence r-lift { category of backbone major=equipment manufacturing, brand, key and characteristic majors at or above the provincial level=3, number of full-time students in specialty group =>3000, proportion of full-time teachers with double qualifications=<50}=>{ satisfaction proportion of employers with graduates =50∼90, graduates’ counterpart employmentrate =50∼70} 0.351 0.998 2.49 {category of backbone major=equipment manufacturing, including major quantity=5, number of full-time students in specialty group =<1000, total value of equipment donated by the cooperative enterprise =>1000}=>{ graduates’ counterpart employmentrate=90∼100, number of provincial or above awards for college student =>100} 0.359 0.996 2.5 {number of majors sharing on campus and off campus training bases =9, average equipment value of students on campus training base =10∼100, total value of equipment donated by the cooperative enterprise =100∼500}=>{teaching achievement award at or above provincial level=4, invention patent=5∼10 } 0.361 0.996 2.49 with double qualifications and differences in conditions such as including major quantity. (3) medium level input of major sharing on campus and off campus training bases, equipment in the campus training base and equipment donated by the cooperative enterprise can create a medium level of output of teaching achievement awards at or above provincial level and invention patents. 5.4 recommendations based on association rule mining results for association rules in tables 2 and 3 where the successor of the mining results is dominated by negative results, measures should be taken to avoid or reduce the possibility of their occurrence as much as possible. for association rules in which the successors of the mining results in tables 2 and 3 are dominated by positive results, measures should be taken to facilitate the occurrence of these frequent item sets whenever possible. based on the above results, the specific measures are as follows: (1) the high support association rules indicates that such frequent item sets have a high frequency of occurrence, and if the successor item is a negative outcome, the occurrence of the preceding item should be avoided as much as possible to reduce the frequency of negative outcomes; for example, the first and third association rules in table 2 should take measures to prevent the occurrence of the preceding item to avoid higher education institutions from obtaining lower employers satisfaction , and fewer project and research awards. (2) the high support association rules indicates that such frequent item sets have a high frequency of occurrence, and if the successor item is a positive outcome, then the frequency of the preceding item should be increased as much as possible to enhance the frequency of the positive outcome; for example, the second association rule in table 2 should take measures to promote the occurrence of the preceding item so that higher education institutions can obtain higher employers satisfaction and more research projects. (3) the high confidence association rules indicates that such frequent item sets have a high frequency of occurrence. if the successor is a negative result, once the preceding term occurs, extra attention should be paid at this time; for example, the first association rule in table 3, once the combination of the frequent set of items of the preceding term matches the situation in the table, extra https://doi.org/10.15837/ijccc.2023.3.5045 14 attention should be paid to prevent the lower cunterpart employment rate of graduates and employers’ satisfaction proportion with graduates. (4) the high confidence association rules characterize such frequent item sets with high probability of occurrence. if the successor is a positive outcome, it should contribute to the frequent item set coupling condition of the prior as much as possible, e.g., the second association rule in table 3 should set the coupling condition of the prior as much as possible in order to facilitate higher graduates’ counterpart employment rate and more provincial or above awards for college student. 6 conclusions the main findings of this research are obtained as follows: (1) the improved womdi-apriori algorithm proposed in this study has 79.96% higher accuracy than the traditional apriori algorithm, which will cause some confusion to the results if the traditional association rule mining algorithm is applied directly. therefore, it is necessary to improve the apriori algorithm for the problem of association rule analysis of clustering effect of specialty groups in higher vocational colleges. (2) when brand, key and characteristic majors at or above the provincial level, proportion of full-time teachers with double qualifications, and cooperative enterprises accept internship students’ number are at low levels, the number of projects obtained by higher vocational colleges and the satisfaction of employers with graduates are negatively affected. for the major category of equipment manufacturing, the number of full-time students in specialty group of higher vocational colleges does not fully determine the graduates’ counterpart employment rate, and the level of this indicator is also related to the coupling effect of other factors. (3) high support association rules characterize the high frequency of such frequent item sets. if the successor items in the specialty group cluster effect mining results are negative results, the occurrence of the leading items should be avoided as much as possible to reduce the frequency of negative results; if the successor items in the specialty group cluster effect mining results are positive results, the occurrence frequency of the leading items should be increased as much as possible to improve the frequency of positive results. high confidence association rules characterize such frequent item sets with a high probability of occurrence. if the successor in the specialty group cluster effect mining result is a negative result, once the preceding term occurs, extra attention should be paid at this time; if the successor in the specialty group cluster effect mining result is a positive result, the frequent term set coupling condition of the preceding term should be contributed as much as possible. further research directions: (1) in analyzing the clustering effect of specialty groups in higher vocational colleges, the lhs and rhs divisions for each field variable in the original data may be unreasonable and need to be further optimized and adjusted in the future. (2) in the future, the fields need to be clustered and divided into multiple dimensional field clusters, so that each field may appear in the mining results as a causative factor or a result. for the analysis of the cluster taking values of each field index, further refinement is needed in the future based on the position of statistical quartiles, medians, and means. acknowledgments this work was supported by humanities and social science planning fund project of chinese ministry of education “research on the generation mechanism and measurement model of specialty groups agglomeration effect in higher vocational colleges under the background of ‘double high plan’" (21yja880013). author contributions the authors contributed equally to this work. https://doi.org/10.15837/ijccc.2023.3.5045 15 conflict of interest the authors declare no conflict of interest. references [1] camilleri, a., delplace, s., frankowicz, m. et al.(2014) . professional higher education in europe characteristics, practice examples and national differences, brussels: european association of institutions in higher education. 2014. 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(2022). multiple criteria optimization (mco): a gene selection deterministic tool in rstudio, plos one, 17. 2022. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: gao, f.; yang, j.; yang, y.; yuan x.j. (2023). womdi-apriori data mining algorithm for clustered indicators analysis of specialty groups in higher vocational colleges, international journal of computers communications & control, 18(3), 5045, 2023. https://doi.org/10.15837/ijccc.2023.3.5045 introduction data pre-processing data denoising field filtering variable coding sample structure design algorithm design and modeling association rules mining weighting model construction of the womdi-apriori algorithm association rule mining analysis parameter calibration improving the accuracy improvement calibration of the algorithm results and discussion valuable association rule extraction analysis of high support association rule extraction results analysis of high-confidence association rule extraction results recommendations based on association rule mining results conclusions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 4, month: august, year: 2022 article number: 4749, https://doi.org/10.15837/ijccc.2022.4.4749 ccc publications a multi-objective location decision making model for emergency shelters giving priority to subjective evaluation of residents yiying wang, zeshui xu yiying wang business school sichuan university chengdu, sichuan, 610064, china wanggyiying@163.com zeshui xu∗ business school sichuan university chengdu, sichuan, 610064, china xuzeshui@263.net ∗corresponding author abstract earthquake is regarded as the most destructive and terrible disaster among all-natural disasters [1]. experts agree that immediate emergency evacuation is the safest and most effective response to the earthquake disaster [2]. in the research of emergency evacuation planning, the influence of human subjectivity has gradually attracted researchers’ attention. in this paper, we take the human subjectivity as one of the most important factors for emergency evacuation planning. based on the preferences of the residents at each demand point for the attributes of every candidate emergency shelter, the subjective score of each candidate emergency shelter is obtained. the preferences of residents will change with the refuge time, so do the weights of residents’ subjective scores of all attributes of candidate emergency shelters. therefore, we use the subjective score function to describe the change of residents’ evaluations for the emergency shelter over time, and take the average value of subjective scores at all refuge times as the primary basis for location decision making. on these bases, we build a multi-objective location decision making model for emergency shelters giving priority to subjective evaluation of residents. in the model, we consider transfer distance, the efficiency of construction funds and the distribution of people among emergency shelters. considering fairness, we minimize the standard deviation of the scores and the standard deviation of the transfer distances in the model. this model is applied to a case, which verifies its feasibility and shows that human subjectivity plays an important role in emergency evacuation planning. keywords: emergency shelter location; multi-objective optimization; multi-attribute; subjective preference; total refuge time. https://doi.org/10.15837/ijccc.2022.4.4749 2 1 introduction the frequent occurrence of natural disasters such as earthquakes has caused a large number of casualties and property losses [3], [4]. therefore, emergency evacuation planning has attracted more and more attention. emergency planning includes evacuation of disaster victims, site selection and construction of emergency shelters, etc. next, we review the related literature. the location problem originated from weber’s study, which selected a position for the warehouse by minimizing the distance between the warehouse and all customers [5]. from then on, scholars have proposed models such as p-median problem [6], [7], set covering location problem [8] and maximal covering location problem [9]. however, the location of emergency shelters and evacuation of disaster victims are complex issues, involving many factors, while the single-objective model ignores some important objectives [10]. therefore, the multi-objective model was put forward. some scholars regarded minimizing the total evacuation distance and the total cost as the targets of emergency evacuation site selection [10], [11]. coutinho-rodrigues et al. presented a mixed integer linear programming model with such objectives as risks associated with paths and shelter locations, evacuation path lengths, and the final evacuation time [12]. wang et al. proposed a model with three objectives to minimize construction cost, minimize traveling distance and maximize final number of survivors [13]. wang et al. proposed a multi-objective optimization model, in which the first objective is to minimize the total service distance, the second objective is to maximize the network reliability level [14]. a central challenge in developing an evacuation plan is in determining the distribution of evacuees into the safe areas, that is, deciding where and from which road each evacuee should go [15]. therefore, scholars have developed bilevel models with the multi-objective optimization in the upper-level problem or in the lower-level problem or both in the upper-level and lower-level problems. xu et al. proposed scenario-based hybrid bilevel model that considers the dynamic number of evacuees and its implementation for earthquake emergency shelter location and allocation [10]. kulshrestha et al. developed a bilevel model, in which the planning authority determines the shelter locations along with their capacities, whereas the evacuees choose the shelters and routes to evacuate [16]. from the previous literature review, we can see that scholars have begun to pay attention to residents’ willingness to transfer, such as the choice of transfer route. in fact, the decision-making theory based on subjective preference has been widely studied and applied in many aspects, such as commodity selection [17], investment decision [18]. however, subjectivity associated with factors for shelter location decisions are not given much research attention [19]. recent research considered self-interest (non-cooperative behavior) of evacuees during large evacuations [10], [20]. in this paper, the subjective willingness of the affected residents to move in an emergency is not only reflected in the choice of the transfer route, but also in their satisfaction with the emergency shelters, including their satisfaction with the types of emergency shelters, supporting facilities, accessibility, environment and so on. only when we take into account the affected residents’ willingness to transfer to emergency shelters, can they cooperate with the emergency management department to complete the implementation of various emergency management measures to the greatest extent. in addition to paying attention to the satisfaction of residents with the emergency shelters, equity is another concern in emergency planning. however, there is not any common definition for equity [21]. ng and park proposed a model that yielded shelter assignments, which accounts for fairness in the sense that the algorithm assigns evacuees to shelters that are as close as possible to their points of origin [22]. tsai and yeh thought that the regional allocation of disaster evacuation/refuge shelters must consider timeliness and fairness. the indicator for fairness is the number of victims each single shelter can accommodate; the more the victims, the higher the service [23]. sabouhi et al. developed a model, in which equity in relief provided for various affected areas is achieved by defining objective functions that minimize the maximum time for the transportation of evacuees to shelters, the transportation of injured people to hospitals, and the distribution of relief commodities [21]. in this paper, the residents of each demand point evaluate the attributes of emergency shelters according to their own preferences, and get the score of each emergency shelter. for the fairness, we assume that the residents in a demand point should be transferred only to their higher-scored emergency shelters that are within the service distance of this demand point. in addition, we take the standard deviation of the subjective scores and the standard deviation of the transfer distance as the factors to https://doi.org/10.15837/ijccc.2022.4.4749 3 be considered, and minimize these two standard deviations as two goals in model, which is another embodiment of fairness. therefore, in this paper the location planning and construction of emergency shelters proposed is based on the subjectivity of regional residents, and the construction of emergency shelters in our study is carried out in advance before the disaster, rather than the temporary planning and construction during and after the disaster [24]. in recent years, scholars have paid more and more attention to the standards of site selection and construction of emergency shelters. nappi and souza raised 10 possible criteria and their relevant aspects, including: location, optimal distribution, urban infrastructure, safety, physical adequacy, cultural adequacy, privacy, environmental comfort, universal accessibility and economic aspects [25]. aman and aytac defined 15 criteria to evaluate the suitability in terms of spatial, natural structure and accessibility of each potential open space for emergency gathering [26]. wu et al. established an indicator system comprising five aspects: scale and location; risk of disaster; rescue facilities; feasibility; resident aspect [27]. song et al. summarized the evaluation criteria for shelter-site selection, including location & logistic efficiency, costs, environmental conservation and social aspects [28]. in this paper, we combine the above-mentioned site selection and construction standards of emergency shelters, and refer to china earthquake administration’s standard which is called site and supporting facilities for earthquake emergency shelter [29], and put forward six site selection and construction indexes of emergency shelters, including distance, accessibility, scale, supporting facilities, environment and type. the contributions of this paper are as follows: we comprehensively use the methods of normal distribution, ordered weighted aggregation (owa) operator [30], [31], [32] and analytic hierarchy process (ahp) [33] to form the subjective score and the initial weight of each attribute score; on the basis of reasonable assumptions, the dynamic weight of each attribute score is obtained; the concept of total refuge time is formed, and the subjective score function of each demand point for each candidate site is obtained with the total refuge time as the independent variable; and the average value of subjective scores at all refuge times is taken as the primary basis for location decision making. this paper expands the meaning of fairness in emergency shelter location, and brings the standard deviation of residents’ subjective scores and the standard deviation of residents’ transfer distances together into the scope of fairness in emergency shelter location. as human subjectivity is paid more and more attention, the model in this paper could be used as an important part of the government decision support system for emergency shelter location [34]. the rest of the paper is arranged as follows: section 2 presents a methodology to determine the value and the weight of each attribute. section 3 describes the formulation of the model. then, in section 4, the previous multi-objective model is applied to a case. meanwhile, we conduct some analyses about the case. finally, we make conclusions about our study in section 5. 2 methodology 2.1 description of the problem and determining the value of each attribute the candidate emergency shelters in this paper are the sites built for other purposes, such as the park, the green space, the square, the stadium and the indoor public place. if a site needs to be used as an emergency shelter, it needs to be constructed to improve the function, so as to meet the requirements of being an emergency shelter. this paper focuses on people’s subjective preferences for the attributes of the emergency shelters. emergency shelter has many attributes. we select six attributes to analyze people’s subjective preferences. these attributes include the distance from the demand point to the emergency shelter, the accessibility, scale, supporting facilities, environment and type of the emergency shelter. the first two are external attributes, and the last four are internal attributes. according to different evaluation methods, these attributes are divided into three categories: 1) the distance from the demand point to the emergency shelter; 2) the accessibility, the scale, the supporting facilities and the environment of the emergency shelters; 3) the types of the emergency shelters. next, we discuss the subjective evaluation of these attributes by the affected residents in turn. https://doi.org/10.15837/ijccc.2022.4.4749 4 figure 1: graph of function y = 1/x. 1. evaluation of the distance attribute: some researchers studied people’s behaviors under the condition that the travel cost is proportional to distance [35]. usually, people’s willingness to move to a place decreases with the increase of the distance, that is, the farther away the place is, the more reluctant people are to move, and people prefer to move to a place closer to themselves. this paper assumes that the score of the distance between the demand point i and the shelter j is inversely proportional to the distance between them, as shown in fig 1. let the distance between the demand point i and the shelter j be rij, and the distance between the demand point i and its nearest shelter be ri0, then the distance score of the demand point i to the shelter j could be expressed as xij,d = 100 · ri0rij . 2. evaluation of the accessibility, the scale, the supporting facilities and the environment: emergency shelters are distributed in various areas of the city. due to different actual construction conditions, their attributes are at different levels. according to the construction standards, we suppose that the levels of the accessibility, the scale, the supporting facilities and the environment are divided into five grades. the scores from grade 1 to grade 5 are set sequentially to be: 100, 90, 80, 70 and 60. then, the scores of the accessibility, the scale, the supporting facilities and the environment of the shelter j could be obtained, which are expressed as xj,c, xj,sc, xj,su and xj,en respectively. when a candidate emergency site is selected as the emergency site, we can take measures to improve the level of the attributes of this emergency site to obtain a higher score. 3. evaluation of the type: different residents have different preferences for different types of sites. we use the combination of normal distribution and the owa operator to evaluate different types of sites [30], [31], [32]. the evaluation process is as follows: residents evaluate the type of each shelter according to their preferences, and get the subjective score of type ranging from 1 to 100. the scores obtained are arranged in descending order. we use k to indicate the type of the emergency shelter, i.e., k represents the park (pa), the green space (gr), the square (sq), the stadium (st) and the indoor public place (id). let u be the number of residents participating in the evaluation, and the following evaluation matrix is obtained: https://doi.org/10.15837/ijccc.2022.4.4749 5   hpa1 h gr 1 h sq 1 h st 1 h id 1 hpa2 h gr 2 h sq 2 h st 2 h id 2 ... ... ... ... ... hpaq h gr q h sq q h st q h id q ... ... ... ... ... hpau−1 h gr u−1 h sq u−1 h st u−1 h id u−1 hpau h gr u h sq u h st u h id u   in which, hpaq , hgrq , hsqq , hstq , hidq respectively represent the score of the ith resident for the park (pa), the green space (gr), the square (sq), the stadium (st), the indoor public place (id), which could be uniformly expressed as hkq. the method of determining the owa weight based on normal distribution refers to weighting those too large or too small data by small values to eliminate or reduce the impact of unfair evaluation on decision-making results [32]. the owa weights of residents’ scores are solved by using the method of normal distribution, so as to obtain residents’ scores for different types of sites. calculating the weight coefficient ωk,q, which is the evaluation of the resident q for the site type k (q = 1, 2, 3, . . . ,u): ωk,q = e − [ (hkq−µk) 2 /2σk 2 ] ∑u r=1 e − [ (hkr−µk) 2 /2σk 2 ] ,µk = ∑u q=1 h k q u ,σk = √√√√1 u u∑ q=1 ( hkq −µk )2 , in which, µk represents the average value of scores of all residents participating in the evaluation on the site type k; σk represents the standard deviation of scores of all residents participating in the evaluation on the site type k. we calculate the type score hk of the type k by the following formula: hk = ∑u q=1 ωk,qh k q. therefore, the final evaluation matrix of each site type is: [ hpa hgr hsq hst hid ] , in which, hpa, hgr, hsq, hst , hid represent the final score of the park, the green space, the square, the stadium, the indoor public place respectively. through the above analysis, we have obtained all the attribute scores of the shelter j. the distance score, the accessibility score, the scale score, the supporting facilities score, the environment score and the type score are expressed as xij,d, xj,c, xj,sc, xj,su, xj,en, xj,k (here, xj,k is the above mentioned hk) respectively. 2.2 dynamic weights of the attributes ahp can quantify people’s experience judgment, and it is more practical when there are many and complicated factors [36]. through ahp, the relative importance of pairwise attributes is judged, the weight judgment matrix is constructed, the consistency is checked, and the weights are normalized, so that the weight of each attribute can be obtained. because this paper needs to deal with many attributes of the demand points, we use the ahp [33] to get the attribute weights of each demand point. in this paper, the attribute weight reflects the degree of resident’s concern on the attribute of the candidate emergency shelter: ωi(0) = (ωi,d(0),ωi,c(0),ωi,sc(0),ωi,su(0),ωi,en(0),ωi,k(0)), in which, ωi,d(0), ωi,c(0), ωi,sc(0), ωi,su(0), ωi,en(0), ωi,k(0) represent respectively the weight of the distance score, the accessibility score, the scale score, the supporting facilities score, the environment score and the type score that are evaluated by the demand point i, and meet: https://doi.org/10.15837/ijccc.2022.4.4749 6 figure 2: the graph of function f1(t). figure 3: the graph of function f2(t). ωi,d(0) + ωi,c(0) + ωi,sc(0) + ωi,su(0) + ωi,en(0) + ωi,k(0) = 1. in fact, for different total refuge time (i.e., the total time that the residents stayed in an emergency shelter at one time of refuge), the resident’s preference for each attribute of the emergency shelter is also different. that is, the weight of each attribute score of the emergency shelter will change with the total refuge time. therefore, the weight obtained by the above ahp method is only taken as the initial weight. we use the dynamic weight to express the change of people’s preference for the attributes of the emergency shelters for different total refuge times. furthermore, people’s preferences for external attributes and internal attributes change in the opposite direction with the change of the total refuge time. the longer the total refuge time, the more attention they pay to internal attributes, and the less attention they pay to external attributes. this paper assumes that the change of people’s preferences for external attributes and internal attributes meets f1(t) = 11+αt2 and f2(t) = 2 − 1 1+αt2 respectively, where t (t ≥ 0) represents the total refuge time, α (α > 0) is a constant. the following two figures are the graphs of f1(t) and f2(t) respectively. from fig 2 and fig 3, we could see that f1(t) is a decreasing function and f2(t) is an increasing function, which satisfy the change of resident’s external attribute preference and internal attribute preference respectively. the first and second derivatives of f1(t) = 11+αt2 are: f ′1(t) = − 2αt (1 + αt2)2 ,f ′′1 (t) = 2α(3αt2 − 1) (1 + αt2)3 . when f ′′1 (t) = 0, i.e., 2α(3αt2−1) (1+αt2)3 = 0, we have t = 1√ 3α . when t < 1√ 3α , we have f ′′1 (t) < 0; when t > 1√ 3α , we have f ′′1 (t) > 0. therefore, when t = 1√ 3α , f ′1(t) get the minimum value, and when t = 1√ 3α , f1(t) decreases the fastest. in order to get the weight function of each attribute, it is necessary to determine the value of α. this paper assumes that f1(t) decreases the fastest when t = 3, then α = 127 . therefore, the weight functions of the distance score, the accessibility score, the scale score, the https://doi.org/10.15837/ijccc.2022.4.4749 7 supporting facilities score, the environment score and the type score are as follows: ωi,d(t) = ωi,d(0)f1(t) = 27ωi,d(0) 27 + t2 , ωi,c(t) = ωi,c(0)f1(t) = 27ωi,c(0) 27 + t2 , ωi,sc(t) = ωi,sc(0)f2(t) = ωi,sc(0) ( 2 − 27 27 + t2 ) , ωi,su(t) = ωi,su(0)f2(t) = ωi,su(0) ( 2 − 27 27 + t2 ) , ωi,en(t) = ωi,en(0)f2(t) = ωi,en(0) ( 2 − 27 27 + t2 ) , ωi,k(t) = ωi,k(0)f2(t) = ωi,k(0) ( 2 − 27 27 + t2 ) . when t = 0, we have f1(0) = f2(0) = 1, the weight of each attribute score is the initial weight under this condition. when t > 0, the sum of the attribute weights of the demand point i is: ωi(t) = ωi,d(t) + ωi,c(t) + ωi,sc(t) + ωi,su(t) + ωi,en(t) + ωi,k(t) = f1(t)(ωi,d(0) + ωi,c(0)) + f2(t)(ωi,sc(0) + ωi,su(0) + ωi,en(0) + ωi,k(0)). assume that f1(t) = f1(0) − ∆(t). since f1(t) + f2(t) = 2, we have f2(t) = f2(0) + ∆(t). then ωi(t) = (f1(0) − ∆(t))(ωi,d(0) + ωi,c(0)) + (f2(0) + ∆(t))(ωi,sc(0) + ωi,su(0) + ωi,en(0) + ωi,k(0)) = f1(0)(ωi,d(0) + ωi,c(0)) − ∆(t)(ωi,d(0) + ωi,c(0)) + f2(0)(ωi,sc(0) + ωi,su(0) + ωi,en(0) + ωi,k(0)) +∆(t)(ωi,sc(0) + ωi,su(0) + ωi,en(0) + ωi,k(0)) = 1 + ∆(t)[(ωi,sc(0) + ωi,su(0) + ωi,en(0) + ωi,k(0)) − (ωi,d(0) + ωi,c(0))]. when (ωi,sc(0) + ωi,su(0) + ωi,en(0) + ωi,k(0))−(ωi,d(0) + ωi,c(0)) 6= 0, we get ωi(t) 6= 1. therefore, we need to normalize ωi(t): ωi,d(t) = ωi,d(t)/ωi(t),ωi,c(t) = ωi,c(t)/ωi(t),ωi,sc(t) = ωi,sc(t)/ωi(t), ωi,su(t) = ωi,su(t)/ωi(t),ωi,en(t) = ωi,en(t)/ωi(t),ωi,k(t) = ωi,k(t)/ωi(t), in which, ωi,d(t), ωi,c(t), ωi,sc(t), ωi,su(t), ωi,en(t), ωi,k(t) are the normalized weights of the distance score, the accessibility score, the scale score, the supporting facilities score, the environment score and the type score respectively. here, ωi,d(t) + ωi,c(t) + ωi,sc(t) + ωi,su(t) + ωi,en(t) + ωi,k(t) = 1. 3 the formulation of the model 3.1 the formulation of the score function and the standard deviation function when the total refuge time is t, the distance score, the accessibility score, the scale score, the supporting facilities score, the environment score and the type score of the emergency shelter j that are evaluated by the demand point i could be expressed as follows: yij,d(t) = ωi,d(t) ·xij,d,yij,c(t) = ωi,c(t) ·xj,c, yij,sc(t) = ωi,sc(t) ·xj,sc,yij,su(t) = ωi,su(t) ·xj,su, https://doi.org/10.15837/ijccc.2022.4.4749 8 yij,en(t) = ωi,en(t) ·xj,en,yij,k(t) = ωi,k(t) ·xj,k. therefore, when the total refuge time is t, the subjective score of the demand point i for the candidate shelter j is: yij(t) = yij,d(t) + yij,c(t) + yij,sc(t) + yij,su(t) + yij,en(t) + yij,k(t) = ωi,d(t) ·xij,d + ωi,c(t) ·xj,c + ωi,sc(t) ·xj,sc + ωi,su(t) ·xj,su + ωi,en(t) ·xj,en + ωi,k(t) ·xj,k. assume that ωi(t) = (ωi,d(t),ωi,c(t),ωi,sc(t),ωi,su(t),ωi,en(t),ωi,k(t)), xj = (xij,d,xj,c,xj,sc,xj,su,xj,en,xj,k)t , then we get: yij(t) = ωi(t) · xj. assume that there are m (i = 1, 2, . . . ,m) demand points, and n (j = 1, 2, . . . ,n) candidate shelters. let x = (x1, x2, . . . , xn−1, xn), ω(t) =   ω1(t) ω2(t) ... ωm−1(t) ωm(t)  ,then the score matrix of all demand points for all candidate shelters is: y(t) = ω(t) · x =   ω1(t) ω2(t) ... ωm−1(t) ωm(t)   (x1, x2, . . . , xn−1, xn) notice that, xij,d in xj should change with ωi(t). the total subjective score of the demand point i for all candidate emergency shelters is: y0i (t) = ∑n j=1 yij(t). it could be seen from the above analysis that when the total refuge time t changes, people’s subjective score for the emergency shelters also changes. obviously, it is not feasible to select emergency shelters once for each t. assume that the maximum value of t is t, it is a feasible method to derive the average value of subjective scores for all t within the interval [0,t], and use this average value to make location decision. we divide the interval [0,t] into n equal parts, and each equal part is ∆t. then, we take a value of t for every interval t/n from 0, and let ts = ts/n (s = 1, 2, . . . ,n). if the probability of each t in the interval [0,t] is the same, then, from 0 to t, the average value of the subjective scores for the emergency shelter j evaluated by the demand point i is: yij(t) = ∑n s=1 yij(ts) n = ∆t · ∑n s=1 yij(ts) ∆t ·n = ∑n s=1 yij(ts) · ∆t ∆t ·n . when ∆t → 0, the above formula becomes: yij(t) = ∫ t 0 yij (t)dt t . the total subjective score for all candidate emergency shelters evaluated by the demand point i is: y0i (t) = n∑ j=1 yij(t) = n∑ j=1 1 t ∫ t 0 yij(t)dt = 1 t ∫ t 0 ( n∑ j=1 yij(t))dt = 1 t ∫ t 0 y0i (t)dt. we define a binary variable yij. when j provides service for the demand point i, yij = 1; otherwise, yij = 0. then, the total subjective score of the demand point i for the selected shelters that provides services for i is: y1i (t) = n∑ j=1 yij(t)yij = n∑ j=1 1 t ∫ t 0 yij(t)yijdt = 1 t ∫ t 0 ( n∑ j=1 yij(t)yij)dt. let y1i (t) = ∑n j=1 yij(t)yij, then y1i (t) = 1 t ∫t 0 y 1 i (t)dt. https://doi.org/10.15837/ijccc.2022.4.4749 9 let pij = yij(t)yij/y1i (t),y1ij(t) = yij(t)yij, then pij = 1 t ∫t 0 yij(t)yijdt 1 t ∫t 0 y 1 i (t)dt = ∫t 0 yij(t)yijdt∫t 0 y 1 i (t)dt = ∫t 0 y 1 ij(t)dt∫t 0 y 1 i (t)dt . the larger the pij is, the more people transfer from the demand point i to the selected shelter j. we suppose that the number of people transferred from the demand point i to the selected shelter j is directly proportional to pij, so the number of people transferred from the demand point i to the selected shelter j is: aij = pij ·ai, where, ai represents the number of people at the demand point i. here, ai = ∑n j=1 aij, which could be proven as follows: n∑ j=1 aij = n∑ j=1 pijai = ai n∑ j=1 pij = ai n∑ j=1 yij(t)yij/y1i (t) = (ai/y 1 i (t)) n∑ j=1 yij(t)yij = ai, then ai = ∑n j=1 aij holds. let z1i,s be the number of the emergency shelters that provide services for the demand point i, then: z1i,s = n∑ j=1 yij. the average subjective score of the emergency shelters that provide services for the demand point i is: y1i (t) = y1i (t) z1i,s . let zs be the number of the selected sites, y(t)zs be the total subjective score of all selected emergency shelters evaluated by all demand points, then y(t)zs = m∑ i=1 aiy 1 i (t). let a be the total number of residents at all demand points, then per capita score for all selected shelters is: y(t) zs = ( m∑ i=1 aiy 1 i (t) ) /a = ( m∑ i=1 aiy 1 i (t) ) / m∑ i=1 ai. the variance of the subjective scores of residents at all demand points is: fy = 1 a m∑ i=1 ai ( y1i (t) −y(t) zs )2 . the standard deviation of the subjective scores of residents at all demand points is: f ′y = √√√√1 a m∑ i=1 ai ( y1i (t) −y(t) zs )2 . 3.2 the formulation of the transfer distance function and the standard deviation function let dij be the shortest distance from the demand point i to the emergency shelter j, then the transfer distance of residents at the demand point i to the selected shelters that provide services for i is ∑n j=1 aijdij, and the total transfer distance of residents at all demand points is ∑m i=1 ∑n j=1 aijdij. so, the per capita transfer distance of residents at all demand points is: d = 1 a m∑ i=1 n∑ j=1 aijdij. https://doi.org/10.15837/ijccc.2022.4.4749 10 therefore, the variance of the transfer distance of residents at all demand points is: fd = 1 a m∑ i=1 n∑ j=1 aij(dij −d)2. the standard deviation of the transfer distance of residents at all demand points is: f ′d = √√√√1 a m∑ i=1 n∑ j=1 aij(dij −d)2. 3.3 cost and the formulation of the standard deviation function of the numbers of residents transferred to every emergency shelter let bj,1 be the supporting construction cost of the emergency shelter j, bj,2 be the upgrading construction cost of the emergency shelter j, b1 be the total cost, then b1 = n∑ j=1 (bj,1 + bj,2)yj, in which, yj is a binary variable. when j is selected, yj = 1; otherwise, yj = 0. the variance of the numbers of residents that transfer to every selected shelter is: fp = 1 zs n∑ j=1 ( m∑ i=1 aij − a zs )2 . the standard deviation of the numbers of residents that transfer to every selected shelter is: f ′p = √√√√√ 1 zs n∑ j=1 ( m∑ i=1 aij − a zs )2 . 3.4 goals and constraints the research shows that in the process of the large-scale emergency evacuation, the affected residents do not transfer completely according to the government planning, and they follow self-interests [10], [20]. this paper comprehensively considers the subjective and objective factors affecting the location of emergency shelters. the subjective evaluation of residents at the demand point for the emergency shelter reflects their satisfaction with the emergency shelters. therefore, the subjective evaluation of the residents will be reflected in their emergency transfer behavior. so, this paper takes the residents’ subjective evaluation for the emergency shelter as an important basis for location decision making, this is an aspect of humanistic consideration. on the other hand, in order to ensure fairness, we take the standard deviation of the scores of the residents and the standard deviation of the transfer distance as the factors for emergency shelter planning. to further ensure fairness, in the following model, we assume that a demand point can only be served by the emergency shelters that are within the service distance rd of this demand point. among the selected emergency shelters, only those emergency shelters that meet this requirement can provide services for the corresponding demand points. in order to improve the efficiency of the emergency evacuation and save the transfer cost, we take the minimization of the transfer distance of the residents as a goal. in fact, minimizing the transfer distance is also the overall desire of the residents at all demand points in the region. in order to avoid residents’ excessive gathering in the emergency shelters, we also take the minimization of the standard deviation of the numbers of the residents that transfer to every selected emergency shelter as a goal in the model, which is a consideration of safety. in terms of the construction cost, previous studies mainly focused on planning new shelters [20]. this paper considers providing the supporting facilities and upgrading the site level on the basis of https://doi.org/10.15837/ijccc.2022.4.4749 11 the existing sites, so as to make full use of the existing resources and save the construction funds. therefore, the cost in the model includes the cost of the supporting construction and the cost for upgrading the site level as analysis in section 3.3. • assumptions in addition to some assumptions put forward in the previous analysis, the model also includes the following assumptions: 1. the candidate sites and the demand points are two different finite point sets in spatial network; 2. the spatial distribution and the number of the candidate sites are known; 3. based on the road network, the shortest distance between any candidate site and demand point is known; 4. a candidate site can provide services for multiple demand points; 5. the candidate site has no capacity constraint. • sets m: the set of the demand points in the network {i|i = 1, 2, 3, . . . ,m}. n: the set of the candidate sites {j|j = 1, 2, 3, . . . ,n}. • parameters y(t) zs: the per capita score of all residents for all selected shelters. y1i (t): the average subjective score of the sites that provide services for the demand point i. a: the total number of the residents at all demand points. ai: the number of residents at the demand point i, ∀i ∈ m. aij: the number of people transferred from the demand point i to the shelter j. dij: the shortest distance between the demand point i and the candidate site j, ∀i ∈ m, ∀j ∈ n. d: the per capita transfer distance of residents at all demand points. f ′d: the standard deviation of the transfer distance of residents at all demand points. f ′y: the standard deviation of the subjective scores of residents at all demand points. f ′p: the standard deviation of the numbers of residents that transfer to every selected shelter. rd: the service distance. b: the total cost. bj,1: the supporting construction cost of the site j. bj,2: the upgrading construction cost of the site j. zs: the total number of the selected sites. z0i,s: the maximal number of the selected sites that provide service for the demand point i. z1i,s: the actual number of the selected sites that provide service for the demand point i. • decision variables yj = { 1, if the site is located at j,∀j ∈ n. 0, otherwise. yij = { 1, if the site j provide service for the demand point i,∀i ∈ m,∀j ∈ n. 0, otherwise. https://doi.org/10.15837/ijccc.2022.4.4749 12 objectives: max y(t) zs = ∑m i=1 aiy 1 i (t) a = ∑m i=1 aiy 1 i (t)∑m i=1 ai (1) min f ′y = √√√√1 a m∑ i=1 ai ( y1i (t) −y(t) zs )2 (2) min d = 1 a m∑ i=1 n∑ j=1 aijdij (3) min f ′d = √√√√1 a m∑ i=1 n∑ j=1 aij(dij −d)2 (4) min b1 = n∑ j=1 (bj,1 + bj,2)yj (5) min f ′p = √√√√√ 1 zs n∑ j=1 ( m∑ i=1 aij − a zs )2 (6) subject to: dij ≤ rdyij,∀i ∈ m,∀j ∈ n (7) zs = n∑ j=1 yj,∀j ∈ n (8) 1 ≤ n∑ j=1 yij ≤ z0i,s,∀i ∈ m (9) yij ≤ yj,∀i ∈ m,∀j ∈ m (10) yi,yij ∈{0, 1}, for ∀i ∈ m,∀j ∈ m (11) objectives (1)-(6) have already been analyzed above. constraint (7) means that a demand point can only be served by the emergency shelters that are within the service distance rd of this demand point. constraint (8) requires that the total number of selected emergency sites is zs. constraint (9) requires that the number of the emergency shelters serving a demand point is at least 1 and at most z0i,s. constraint (10) means that a site could provide service for a demand point only when this site is selected. constraint (11) means that both yj and yij are binary variables. 4 case study 4.1 basic data some scholars have used the sioux falls network (or a simplified sioux falls network) to conduct research [37], [38]. the simplified sioux falls network is shown in fig 4. in our case, we also use a simplified sioux falls network (undirected) to test our model, and the number marked next to each side is the length of the side. table 1 is the initial value of the attribute weight of each demand point for each attribute of the shelter. table 2 shows the number of the residents at each demand point. table 3 shows the supporting construction cost (scc) and the upgrading construction cost (ucc) of each candidate site. table 4 shows the attribute grade and score of each candidate site after upgrading. table 5 shows the distance between each demand point and each candidate site and the corresponding distance score. https://doi.org/10.15837/ijccc.2022.4.4749 13 figure 4: the network. demand point external attributes internal attributesdistance accessibility scale supporting facilities environment type a 0.50 0.02 0.07 0.20 0.09 0.12 b 0.51 0.05 0.06 0.15 0.08 0.15 c 0.49 0.04 0.08 0.17 0.11 0.11 d 0.50 0.07 0.06 0.18 0.06 0.13 e 0.44 0.04 0.10 0.14 0.08 0.20 f 0.48 0.03 0.09 0.16 0.12 0.12 g 0.53 0.06 0.07 0.13 0.10 0.11 i 0.42 0.10 0.09 0.15 0.07 0.17 k 0.52 0.08 0.07 0.14 0.11 0.08 m 0.43 0.03 0.11 0.16 0.13 0.14 n 0.47 0.05 0.12 0.20 0.07 0.09 p 0.39 0.11 0.09 0.22 0.11 0.08 r 0.55 0.07 0.05 0.15 0.11 0.07 u 0.44 0.08 0.10 0.12 0.13 0.13 table 1: the initial value of the attribute weight of each demand point for each attribute of the shelter. https://doi.org/10.15837/ijccc.2022.4.4749 14 demand point the number of the residents demand point the number of the residents a 1500 i 900 b 2000 k 1800 c 800 m 2000 d 900 n 1700 e 2500 p 2300 f 500 r 1800 g 1000 u 1100 table 2: the number of the residents at each demand point. candidate site scc ucc candidate site scc ucc h 110 180 s 95 70 j 120 60 t 60 20 l 90 40 v 70 24 o 130 30 w 125 160 q 105 100 x 85 30 table 3: the supporting construction cost (scc) and the upgrading construction cost (ucc) of each candidate site. candidate accessibility scale supporting facilities environment type site grade score grade score grade score grade score name score h 2 90 2 90 3 80 2 90 gr 68 j 1 100 2 90 3 80 2 90 sq 72 l 2 90 4 70 2 90 2 90 st 83 o 1 100 1 100 2 90 2 90 sq 72 q 2 90 3 80 3 80 2 90 gr 68 s 1 100 1 100 3 80 2 90 pa 78 t 2 90 2 90 1 100 1 100 id 92 v 1 100 2 90 2 90 1 100 id 92 w 4 70 3 80 3 80 2 90 gr 68 x 2 90 3 80 3 80 2 90 st 83 table 4: the attribute grade and score of each candidate site after upgrading. https://doi.org/10.15837/ijccc.2022.4.4749 15 i\ j h j l o q s t v w a 24, 96 23, 100 78, 29 86, 27 143, 16 103, 22 151, 15 152, 15 130, 18 b 96, 27 49, 53 46, 57 26, 100 74, 35 99, 26 91, 29 92, 28 169, 15 c 150, 13 103, 19 54, 37 77, 26 20, 100 150, 13 100, 20 143, 14 172, 12 d 64, 83 60, 88 115, 46 53, 100 110, 48 70, 76 118, 45 119, 45 90, 59 e 155, 14 108, 19 117, 18 45, 47 102, 21 31, 68 28, 75 21, 100 99, 21 f 214, 9 167, 12 148, 14 104, 19 85, 24 90, 22 46, 43 38, 53 67, 30 g 176, 13 129, 18 184, 12 120, 19 128, 18 49, 47 89, 26 54, 43 24, 96 i 70, 43 85, 35 30, 100 102, 29 104, 29 165, 18 132, 23 168, 18 224, 13 k 76, 34 29, 90 26, 100 46, 57 94, 28 109, 24 111, 23 112, 23 179, 15 m 120, 20 73, 33 24, 100 50, 48 50, 48 123, 20 78, 31 116, 21 191, 13 n 82, 34 35, 80 90, 31 28, 100 85, 33 45, 62 93, 30 94, 30 115, 24 p 140, 19 93, 29 49, 55 30, 90 27, 100 103, 26 53, 51 96, 28 166, 16 r 150, 17 103, 24 77, 32 40, 63 55, 45 84, 30 25, 100 74, 34 138, 18 u 150, 15 103, 22 158, 15 96, 24 153, 15 23, 100 81, 28 32, 72 50, 46 table 5: the distance between each demand point and each candidate site and the corresponding distance score. (note: the first number in the table represents the distance and the last number represents the distance score.) 4.2 analysis and discussions 4.2.1 analysis in this case, we set rd = 120, z0i,s = 2. we set min zs = 2, and solve the model respectively under the condition of zs = 2, 3, . . . , 10. since the emergency evacuation time may be short-term or long-term [39], we set the maximum value of t be 20, and solve the model respectively under the condition of t = 1, 2, 3, . . . , 20. we set the priority of the objectives in the order of y(t) zs, f ′y, d, f ′d, b and f ′ p to solve the model. table 6 to table 9 are the solutions of the model. fig 5 is the three-dimensional figure of the y(t) zs, in which t and zs are both independent variables. fig 6 is a figure which reflects the y(t) zs changes with zs, when t = 4, 8, 12, 16, 20. from table 6, we could see that y(t) zs shows an upward trend with the increase of t , when zs remains unchanged; y(t) zs shows an upward trend with the increase of zs, when t remains unchanged. from table 7, we could see that d shows an upward trend with the increase of t, when zs remains unchanged; d shows a downward trend with the increase of zs, when t remains unchanged. from table 6, f ′y shows an overall downward trend with the increase of zs, when t remains unchanged. from table 7, f ′d shows an overall downward trend with the increase of zs, when t remains unchanged. from table 6, fig 5 and fig 6, we notice that when t is constant, the increment of y(t) zs is fast first and then slow with the increase of zs, on the whole. this indicate that, when t is constant, the utilization efficiency of costs is different with the change of zs. next, we analyze the utilization efficiency of the costs under the condition of constant t, based on the cost and the per capita score when zs is 2. let bzs1 (t) be the total cost, y(t) zs be the per capita score, and βzs (t) be the increment of y(t) zs generated by the unit increment of the cost when the biggest total refuge time is t and the number of selected sites is zs. then βzs (t) = ( y(t) zs −y(t) 2) / ( bzs1 (t) − b21(t) ) , in which, zs = 3, 4, . . . , 10. for example, when t = 4, from table 8, we have b21(4) = 240, b31(4) = 370, b61(4) = 934, b91(4) = 1419. https://doi.org/10.15837/ijccc.2022.4.4749 16 t \ zs 2 3 4 5 6 7 8 9 10 y f ′y y f ′ y y f ′ y y f ′ y y f ′ y y f ′ y y f ′ y y f ′ y y f ′ y 1 72.77 9.96 76.56 9.86 79.39 9.4 82.20 8.33 83.37 7.56 84.51 7.23 85.62 5.06 86.29 4.60 86.78 4.43 2 73.35 9.68 76.95 9.52 79.71 9.10 82.41 8.06 83.50 7.32 84.60 6.98 85.65 4.92 86.30 4.50 86.76 4.32 3 74.19 9.28 77.51 9.04 80.18 8.67 82.72 7.67 83.74 7.49 84.72 6.06 85.69 4.73 86.31 4.34 86.74 4.18 4 75.15 8.81 78.17 8.62 80.73 8.32 83.08 7.22 84.02 7.01 84.89 5.72 85.74 4.52 86.32 4.18 86.72 4.02 5 76.16 8.32 78.88 8.10 81.32 7.86 83.44 6.76 84.30 6.52 85.09 5.37 85.79 4.29 86.34 4.02 86.69 3.87 6 77.13 7.85 79.57 7.60 81.9 7.41 83.81 6.32 84.59 6.05 85.29 5.03 85.88 4.03 86.37 3.83 86.68 3.69 7 78.04 7.41 80.28 7.92 82.47 7.00 84.17 5.92 84.88 5.62 85.52 4.70 86.00 3.77 86.44 3.63 86.71 3.50 8 78.88 7.00 81.02 7.49 83.00 6.63 84.50 5.55 85.15 5.23 85.72 4.42 86.11 4.39 86.50 3.47 86.73 3.35 9 79.65 6.63 81.70 7.11 83.47 6.30 84.81 5.23 85.40 4.89 85.90 4.19 86.26 4.17 86.55 3.33 86.75 3.22 10 80.34 6.29 82.32 6.76 83.9 6.01 85.08 4.96 85.62 4.60 86.07 3.99 86.39 3.99 86.66 3.25 86.82 3.15 11 80.96 5.99 82.87 6.45 84.29 5.75 85.33 4.72 85.82 4.35 86.22 3.82 86.50 3.84 86.75 3.19 86.89 3.10 12 81.53 5.72 83.37 6.17 84.65 5.51 85.56 4.51 86.02 4.13 86.36 3.68 86.62 3.71 86.85 3.13 86.96 3.05 13 82.04 5.47 83.83 5.92 85.00 5.30 85.79 4.32 86.21 3.94 86.52 3.55 86.74 3.59 86.96 3.07 87.05 3.00 14 82.50 5.25 84.24 5.69 85.31 5.12 86.02 4.17 86.40 3.78 86.67 3.45 86.86 2.99 87.07 3.02 87.14 2.97 15 82.93 5.05 84.62 5.48 85.59 4.95 86.22 4.03 86.58 3.65 86.81 3.36 86.99 2.95 87.17 2.99 87.22 2.95 16 83.31 4.87 84.96 5.30 85.85 4.80 86.44 3.92 86.76 3.54 86.96 3.30 87.13 2.92 87.29 2.96 87.33 2.94 17 83.66 4.70 85.28 5.12 86.09 4.66 86.63 3.82 86.93 3.45 87.11 3.24 87.26 2.91 87.40 2.95 87.42 2.93 18 83.99 4.55 85.58 4.96 86.31 4.54 86.81 3.73 87.09 3.36 87.23 3.09 87.38 2.90 87.50 2.93 87.50 2.93 19 84.28 4.41 85.86 4.82 86.51 4.42 86.98 3.65 87.23 3.30 87.36 3.05 87.49 2.89 87.60 2.93 87.60 2.93 20 84.56 4.28 86.11 4.69 86.7 4.32 87.13 3.58 87.37 3.24 87.48 3.03 87.59 2.89 87.68 2.93 87.68 2.93 table 6: the per capita score and the standard deviation of the subjective scores of the residents. (note: here, y represents y(t) zs.) figure 5: the three-dimensional figure of the per capita score. figure 6: the figure of the per capita score. https://doi.org/10.15837/ijccc.2022.4.4749 17 t \ zs 2 3 4 5 6 7 8 9 10 d f ′d d f ′ d d f ′ d d f ′ d d f ′ d d f ′ d d f ′ d d f ′ d d f ′ d 1 62.42 22.77 51.45 22.62 45.96 18.65 39.88 13.62 36.89 11.86 35.13 11.83 32.42 7.51 31.77 7.39 31.16 7.53 2 62.51 22.76 51.50 22.61 46.01 18.65 40.47 14.10 37.48 12.54 35.17 11.85 32.45 7.53 31.80 7.41 31.19 7.55 3 62.65 22.74 51.57 22.60 46.09 18.66 40.56 14.16 38.21 14.03 35.49 11.35 32.49 7.54 31.84 7.43 31.22 7.57 4 62.80 22.73 52.27 22.33 46.80 18.56 40.66 14.23 38.27 14.07 35.54 11.38 32.54 7.57 31.89 7.46 31.27 7.59 5 62.96 22.71 52.39 22.35 46.93 18.61 40.77 14.29 38.33 14.11 36.37 12.12 33.36 8.90 31.94 7.48 31.31 7.61 6 63.11 22.70 53.74 21.76 48.29 18.31 42.11 14.43 40.22 15.02 38.27 13.44 36.04 11.75 34.60 11.05 33.96 11.29 7 63.24 22.69 57.80 21.91 48.42 18.36 42.22 14.50 40.31 15.08 38.35 13.51 36.14 11.87 34.70 11.17 34.05 11.41 8 63.37 22.68 57.94 21.93 48.54 18.41 42.32 14.56 40.38 15.14 38.43 13.57 36.99 13.21 34.78 11.28 34.12 11.52 9 63.48 22.67 58.06 21.95 48.65 18.45 42.41 14.62 40.45 15.19 38.51 13.63 37.05 13.26 36.26 11.30 35.60 11.62 10 63.58 22.67 58.16 21.97 48.75 18.49 42.50 14.67 40.51 15.23 38.57 13.68 37.11 13.31 36.33 11.38 35.66 11.70 11 63.67 22.66 58.26 21.98 48.84 18.53 42.57 14.72 40.56 15.27 38.63 13.72 37.16 13.35 36.40 11.45 35.72 11.77 12 63.75 22.66 58.34 22.00 50.93 18.10 44.65 15.04 42.62 15.84 40.69 14.62 39.22 14.48 38.47 12.89 37.79 13.28 13 63.82 22.65 58.42 22.01 51.02 18.13 45.93 16.97 43.89 17.82 42.06 16.82 40.58 16.81 39.84 15.54 39.15 15.93 14 63.88 22.65 58.49 22.02 51.10 18.16 46.01 17.02 43.95 17.87 42.12 16.88 41.39 15.69 39.91 15.61 39.22 16.00 15 63.94 22.65 58.55 22.04 51.17 18.19 48.32 16.78 46.25 17.90 44.43 17.16 43.70 16.10 42.22 16.23 41.53 16.70 16 63.99 22.65 58.60 22.05 51.24 18.21 48.40 16.82 46.31 17.95 44.49 17.21 43.78 16.16 42.29 16.30 41.60 16.77 17 64.04 22.64 61.75 24.74 51.72 18.96 48.89 17.71 46.80 18.84 44.98 18.18 43.85 16.21 42.36 16.35 41.66 16.83 18 64.09 22.64 61.80 24.74 51.78 18.98 48.95 17.74 46.85 18.87 45.72 17.12 43.91 16.26 42.42 16.41 41.71 16.88 19 64.13 22.64 61.84 24.74 51.82 18.99 49.01 17.77 46.90 18.90 45.78 17.16 43.96 16.31 42.47 16.45 42.47 16.45 20 64.16 22.64 61.88 24.74 51.87 19.01 49.06 17.80 46.94 18.93 45.83 17.20 44.23 16.27 42.74 16.43 42.74 16.43 table 7: the per capita transfer distance and the standard deviation of the transfer distances of the residents. t \ zs 2 3 4 5 6 7 8 9 10 b1 f ′ p b1 f ′ p b1 f ′ p b1 f ′ p b1 f ′ p b1 f ′ p b1 f ′ p b1 f ′ p b1 f ′ p 1 240 1483 420 2392 514 1872 644 977 849 1173 1139 1296 1254 1313 1419 1438 1704 1471 2 240 1455 420 2395 514 1877 644 1332 849 1434 1139 1297 1254 1314 1419 1440 1704 1472 3 240 1416 420 2399 514 1883 644 1342 934 1434 1049 1384 1254 1317 1419 1442 1704 1474 4 240 1371 370 2133 464 1752 644 1353 934 1436 1049 1383 1254 1319 1419 1445 1704 1476 5 240 1326 370 2136 464 1761 644 1364 934 1438 1049 1523 1254 1385 1419 1448 1704 1478 6 240 1283 370 1756 464 1715 644 1300 934 1645 1049 1636 1254 1383 1419 1397 1704 1432 7 240 1244 334 3031 464 1729 644 1313 934 1651 1049 1640 1254 1388 1419 1400 1704 1434 8 240 1208 334 3037 464 1743 644 1324 934 1657 1049 1644 1214 1646 1419 1403 1704 1436 9 240 1176 334 3042 464 1755 644 1334 934 1662 1049 1647 1214 1647 1419 1658 1704 1662 10 240 1147 334 3047 464 1766 644 1343 934 1666 1049 1650 1214 1649 1419 1661 1704 1664 11 240 1122 334 3051 464 1775 644 1352 934 1670 1049 1653 1214 1651 1419 1663 1704 1666 12 240 1099 334 3056 464 2204 644 1738 934 1944 1049 1839 1214 1781 1419 1800 1704 1789 13 240 1078 334 3060 464 2216 644 1955 934 2110 1049 2041 1214 1940 1419 1941 1704 1923 14 240 1060 334 3064 464 2227 644 1964 934 2117 1049 2046 1254 2087 1419 1946 1704 1927 15 240 1044 334 3067 464 2237 644 2395 934 2460 1049 2328 1254 2331 1419 2156 1704 2118 16 240 1029 334 3070 464 2246 644 2403 934 2467 1049 2334 1254 2337 1419 2160 1704 2122 17 240 1015 334 2427 464 2389 644 2507 934 2534 1049 2394 1254 2342 1419 2164 1704 2126 18 240 1003 334 2431 464 2396 644 2514 934 2539 1139 2536 1254 2347 1419 2168 1704 2130 19 240 992 334 2435 464 2402 644 2520 934 2543 1139 2540 1254 2351 1419 2172 1704 2174 20 240 981 334 2439 464 2408 644 2526 934 2547 1139 2545 1254 2379 1419 2198 1704 2197 table 8: the total cost and the standard deviation of the numbers of residents that transfer to each selected shelter. https://doi.org/10.15837/ijccc.2022.4.4749 18 t \ zs 2 3 4 5 6 7 8 9 10 1 ot jot jotv jlotv jloqtv hjloqtv hjloqtvx hjloqstvx hjloqstvwx 2 ot jot jotv jlotv jloqtv hjloqtv hjloqtvx hjloqstvx hjloqstvwx 3 ot jot jotv jlotv hjlotv hjlotvx hjloqtvx hjloqstvx hjloqstvwx 4 ot lot lotv jlotv hjlotv hjlotvx hjloqtvx hjloqstvx hjloqstvwx 5 ot lot lotv jlotv hjlotv hjlotvx hjloqtvx hjloqstvx hjloqstvwx 6 ot lot lotv jlotv hjlotv hjlotvx hjloqtvx hjloqstvx hjloqstvwx 7 ot otv lotv jlotv hjlotv hjlotvx hjloqtvx hjloqstvx hjloqstvwx 8 ot otv lotv jlotv hjlotv hjlotvx hjlostvx hjloqstvx hjloqstvwx 9 ot otv lotv jlotv hjlotv hjlotvx hjlostvx hjloqstvx hjloqstvwx 10 ot otv lotv jlotv hjlotv hjlotvx hjlostvx hjloqstvx hjloqstvwx 11 ot otv lotv jlotv hjlotv hjlotvx hjlostvx hjloqstvx hjloqstvwx 12 ot otv lotv jlotv hjlotv hjlotvx hjlostvx hjloqstvx hjloqstvwx 13 ot otv lotv jlotv hjlotv hjlotvx hjlostvx hjloqstvx hjloqstvwx 14 ot otv lotv jlotv hjlotv hjlotvx hjloqtvx hjloqstvx hjloqstvwx 15 ot otv lotv jlotv hjlotv hjlotvx hjloqtvx hjloqstvx hjloqstvwx 16 ot otv lotv jlotv hjlotv hjlotvx hjloqtvx hjloqstvx hjloqstvwx 17 ot otv lotv jlotv hjlotv hjlotvx hjloqtvx hjloqstvx hjloqstvwx 18 ot otv lotv jlotv hjlotv hjloqtv hjloqtvx hjloqstvx hjloqstvwx 19 ot otv lotv jlotv hjlotv hjloqtv hjloqtvx hjloqstvx hjloqstvwx 20 ot otv lotv jlotv hjlotv hjloqtv hjloqtvx hjloqstvx hjloqstvwx table 9: the combination of the selected sites. from table 6, we have y(4) 2 = 75.15, y(4) 3 = 78.17, y(4) 6 = 84.02, y(4) 9 = 86.32. so β3(4) = (78.17 − 75.15)/(370 − 240) = 0.023231, β6(4) = (84.02 − 75.15)/(934 − 240) = 0.012781, β9(4) = (86.32 − 75.15)/(1419 − 240) = 0.009474. the value of βzs (t) is shown in table 10. we draw the figure of βzs (t) under t = 4, 8, 12, 16, 20, as shown in fig 7. from table 10 and fig 7, we could see that the value of βzs (t) is relatively large when zs is small; and the value of βzs (t) is relatively small when zs is large. this indicates that, the increment of b has relatively large influence on y(t) zs when zs is small. however, the increment of b has relatively small influence on y(t) zs when zs is large. so, we could see that, the effect of b on y(t) zs is obvious when zs is small, and the effect of b on y(t) zs is not obvious when zs is large. we also notice that, when t is constant, the reduction of d is fast first and then slow with the increase of zs, on the whole. similar to the previous analysis about y(t) zs, let dzs (t) be the per capita transfer distance, γzs (t) be the reduction of d generated by the unit increment of the cost when the biggest total refuge time is t and the number of selected sites is zs. then γzs (t) = (d2(t) −dzs (t))/(bzs1 (t) − b21(t)), in which, zs = 3, 4, . . . , 10. for example, when t = 4, from table 8, we have b21(4) = 240, b31(4) = 370, b61(4) = 934, b91(4) = 1419. from table 7, we have d2(4) = 62.8, d3(4) = 52.27, d6(4) = 38.27, d9(4) = 31.89. then γ3(4) = (62.8 − 52.27)/(370 − 240) = 0.081, γ6(4) = (62.8 − 38.27)/(934 − 240) = 0.035346, γ9(4) = (62.8 − 31.89)/(1419 − 240) = 0.026217. the value of γzs (t) is shown in table 11. we draw the figure of γzs (t) under t = 4, 8, 12, 16, 20, as shown in fig 8. from table 11 and fig 8, we could see that, on the whole, the smaller the zs, the larger the γzs (t), and the larger the zs, the smaller the γzs (t). then, we draw the conclusion that the increment of b has relatively large influence on d when zs is small. while the increment of b has relatively small influence on d when zs is large. therefore, the effect of b on d is obvious when zs is small, and the effect of b on d is not obvious when zs is large. https://doi.org/10.15837/ijccc.2022.4.4749 19 figure 7: the graph of βzs (t). figure 8: the graph of γzs (t). t \zs 3 4 5 6 7 8 9 10 1 0.021056 0.024161 0.023342 0.017406 0.013059 0.012673 0.011467 0.009570 2 0.020000 0.023212 0.022426 0.016667 0.012514 0.012130 0.010984 0.009160 3 0.018444 0.021861 0.021114 0.013761 0.013016 0.011341 0.010280 0.008572 4 0.023231 0.024911 0.019629 0.012781 0.012040 0.010444 0.009474 0.007903 5 0.020923 0.023036 0.018020 0.011729 0.011038 0.009497 0.008634 0.007193 6 0.018769 0.021295 0.016535 0.010749 0.010087 0.008629 0.007837 0.006523 7 0.023830 0.019777 0.015173 0.009856 0.009246 0.007850 0.007125 0.005922 8 0.022766 0.018393 0.013911 0.009035 0.008455 0.007423 0.006463 0.005362 9 0.021809 0.017054 0.012772 0.008285 0.007726 0.006786 0.005852 0.004850 10 0.021064 0.015893 0.011733 0.007608 0.007083 0.006211 0.005360 0.004426 11 0.020319 0.014866 0.010817 0.007003 0.006502 0.005688 0.004911 0.004051 12 0.019574 0.013929 0.009975 0.006470 0.005970 0.005226 0.004512 0.003709 13 0.019043 0.013214 0.009282 0.006009 0.005538 0.004825 0.004173 0.003422 14 0.018511 0.012545 0.008713 0.005620 0.005155 0.004300 0.003876 0.003169 15 0.017979 0.011875 0.008144 0.005259 0.004796 0.004004 0.003596 0.002930 16 0.017553 0.011339 0.007748 0.004971 0.004512 0.003767 0.003376 0.002746 17 0.017234 0.010848 0.007351 0.004712 0.004265 0.003550 0.003172 0.002568 18 0.016915 0.010357 0.006980 0.004467 0.003604 0.003343 0.002977 0.002398 19 0.016809 0.009955 0.006683 0.004251 0.003426 0.003166 0.002816 0.002268 20 0.016489 0.009554 0.006361 0.004049 0.003248 0.002988 0.002646 0.002131 table 10: the increment of per capita score generated by the unit increment of cost. https://doi.org/10.15837/ijccc.2022.4.4749 20 t \zs 3 4 5 6 7 8 9 10 1 0.060944 0.060073 0.055792 0.041921 0.030356 0.029586 0.025997 0.021352 2 0.061167 0.060219 0.054554 0.041100 0.030412 0.029645 0.026047 0.021393 3 0.061556 0.060438 0.054678 0.035216 0.033572 0.029744 0.026132 0.021469 4 0.081000 0.071429 0.054802 0.035346 0.033696 0.029842 0.026217 0.021537 5 0.081308 0.071563 0.054926 0.035490 0.032868 0.029191 0.026310 0.021619 6 0.072077 0.066161 0.051980 0.032983 0.030705 0.026696 0.024182 0.019911 7 0.057872 0.066161 0.052030 0.033040 0.030766 0.026726 0.024207 0.019939 8 0.057766 0.066205 0.052104 0.033127 0.030828 0.027084 0.024249 0.019980 9 0.057660 0.066205 0.052153 0.033184 0.030865 0.027136 0.023087 0.019044 10 0.057660 0.066205 0.052178 0.033242 0.030915 0.027177 0.023113 0.019071 11 0.057553 0.066205 0.052228 0.033300 0.030952 0.027218 0.023130 0.019092 12 0.057553 0.057232 0.047277 0.030447 0.028504 0.025185 0.021442 0.017732 13 0.057447 0.057143 0.044282 0.028718 0.026897 0.023860 0.020339 0.016851 14 0.057340 0.057054 0.044233 0.028718 0.026897 0.022179 0.020331 0.016844 15 0.057340 0.057009 0.038663 0.025490 0.024116 0.019961 0.018422 0.015307 16 0.057340 0.056920 0.038589 0.025476 0.024104 0.019931 0.018405 0.015294 17 0.024362 0.055000 0.037500 0.024841 0.023560 0.019911 0.018388 0.015287 18 0.024362 0.054955 0.037475 0.024841 0.020434 0.019901 0.018380 0.015287 19 0.024362 0.054955 0.037426 0.024827 0.020412 0.019892 0.018372 0.014795 20 0.024255 0.054866 0.037376 0.024813 0.020389 0.019655 0.018168 0.014631 table 11: the reduction of per capita distance generated by the unit increment of cost. 4.2.2 comparison we have set y(t) zs prior to d and solved the model above. next, we set the priority of the objectives in the order of d, f ′d, y(t) zs, f ′y, b and f ′p (which is noted as the second order) to solve the model, i.e., d has priority over y(t) zs. the combinations of the selected sites under the second order are shown in table 12. we could derive the differences of y(t) zs (noted as ∆y) and the differences of d (noted as ∆d) respectively under the two orders for each t and each zs. the results are shown in table 13. we could see from table 13 that, the solutions of y(t) zs and d under the first order are both not lower than that under the second order, for the same t and zs. from table 9 and table 12, we know that the combinations of the selected sites are the same under the two orders, when the difference is 0 in table 13. otherwise, the combinations of the selected sites are not the same under the two orders, when the difference is not 0 in table 13. 4.2.3 discussions in this case, we set the different orders of priority for the six objectives to solve the model. in the first order, we take the y(t) zs as the highest priority, from the solutions, we know that, when t remains constant, the y(t) zs and the d shows upward and downward trend respectively with the increase of zs. it could be seen that the satisfaction of the residents and the transfer efficiency are both constantly rising with the increase of zs. so, in order to improve the satisfaction of the residents and the transfer efficiency, we could consider selecting more emergency shelters. meanwhile, we could see that, on the whole, f ′y and f ′d both show downward trend with the increase of zs when t remains constant. this reveals that more emergency sites mean greater fairness. so, in order to improve the fairness in location decision making, we could consider selecting more emergency sites. from the previous analysis, we also notice that, when t remains constant, the same increase of fund could produce larger increment of βzs (t) and larger reduction of γzs (t) when zs is relatively small, then the efficiency of funds is obvious. otherwise, the efficiency of funds is not obvious when zs is relatively big. so, in order to make full use of funds, we should avoid selecting excessive emergency shelters. in addition, from the analysis in 4.2.2, we know that, if we take y(t) zs as the highest priority of the objectives in the model, the satisfaction of the residents for the emergency sites will be improved. thus, https://doi.org/10.15837/ijccc.2022.4.4749 21 t \zs 2 3 4 5 6 7 8 9 10 1 ow los jlov jlovx jloqvx jloqtvx hjloqtvx hjloqstvx hjloqstvwx 2 ow los losv jlovx jloqvx hjloqvx hjloqtvx hjloqstvx hjloqstvwx 3 ow los losv jlovx jloqtv jloqtvx hjloqtvx hjloqstvx hjloqstvwx 4 ow los losv jlovx jloqtv jloqtvx hjloqtvx hjloqstvx hjloqstvwx 5 ow los losv jlovx jloqtv hjloqtv hjloqstv hjloqstvx hjloqstvwx 6 ow los losv jlovx jloqvx hjloqvx hjloqsvx hjloqsvwx hjloqstvwx 7 ow los losv jlovx jloqvx hjloqvx hjloqsvx hjloqsvwx hjloqstvwx 8 ow los losv jlovx jloqvx hjloqvx hjloqsvx hjloqsvwx hjloqstvwx 9 ow los losv jlovx jloqvx hjloqvx hjloqsvx hjloqsvwx hjloqstvwx 10 ow los losv jlovx jloqvx hjloqvx hjloqsvx hjloqsvwx hjloqstvwx 11 ow los losx jlosx jloqvx hjloqvx hjloqsvx hjloqsvwx hjloqstvwx 12 ow los losx jlosx jloqvx hjloqvx hjloqsvx hjloqsvwx hjloqstvwx 13 ow los losx jlosx jloqvx hjloqvx hjloqsvx hjloqsvwx hjloqstvwx 14 ow los losx jlosx jloqvx hjloqvx hjloqsvx hjloqsvwx hjloqstvwx 15 ow los losx jlosx jloqvx hjloqvx hjloqsvx hjloqsvwx hjloqstvwx 16 ow los losx jlosx jloqvx hjloqvx hjloqsvx hjloqsvwx hjloqstvwx 17 ow los losx jlosx hjlosx hjloqsx hjloqswx hjloqsvwx hjloqstvwx 18 ow los losx jlosx hjlosx hjloqsx hjloqswx hjloqsvwx hjloqstvwx 19 ow los losx jlosx hjlosx hjloqsx hjloqswx hjloqstvx hjloqstvwx 20 ow los losx jlosx hjlosx hjloqsx hjloqswx hjloqstvx hjloqstvwx table 12: the combinations of the selected sites under the second order. t \zs 2 3 4 5 6 7 8 9 10 ∆y ∆d ∆y ∆d ∆y ∆d ∆y ∆d ∆y ∆d ∆y ∆d ∆y ∆d ∆y ∆d ∆y ∆d 1 0.19 6.00 1.23 2.62 0.55 2.42 1.76 0.51 1.48 0.58 0.03 0.95 0.00 0.00 0.00 0.00 0.00 0.00 2 0.35 6.02 1.28 2.60 1.07 2.20 1.78 0.51 1.50 0.58 1.51 0.58 0.00 0.00 0.00 0.00 0.00 0.00 3 0.59 6.06 1.35 2.58 1.15 2.22 1.82 0.51 0.04 0.64 0.05 0.65 0.00 0.00 0.00 0.00 0.00 0.00 4 0.85 6.09 1.45 3.17 1.24 2.86 1.86 0.50 0.10 0.60 0.09 0.60 0.00 0.00 0.00 0.00 0.00 0.00 5 1.14 6.13 1.57 3.19 1.35 2.92 1.89 0.50 0.15 0.56 0.08 1.05 0.14 0.38 0.00 0.00 0.00 0.00 6 1.42 6.17 1.70 4.43 1.47 4.06 1.94 1.73 1.75 2.14 1.67 2.07 1.34 1.20 1.52 0.40 0.00 0.00 7 1.68 6.19 1.87 8.40 1.60 4.12 2.00 1.74 1.83 2.13 1.76 2.08 1.38 1.24 1.55 0.45 0.00 0.00 8 1.92 6.22 2.12 8.45 1.73 4.18 2.05 1.75 1.91 2.11 1.83 2.10 1.41 2.03 1.57 0.48 0.00 0.00 9 2.15 6.25 2.35 8.49 1.83 4.24 2.11 1.76 1.98 2.10 1.89 2.12 1.49 2.04 1.58 1.92 0.00 0.00 10 2.35 6.27 2.56 8.52 1.92 4.29 2.15 1.78 2.04 2.08 1.95 2.13 1.56 2.06 1.66 1.95 0.00 0.00 11 2.53 6.29 2.75 8.56 3.36 3.49 3.43 0.70 2.10 2.06 2.00 2.14 1.61 2.06 1.72 1.98 0.00 0.00 12 2.7 6.31 2.91 8.58 3.45 5.53 3.47 2.72 2.17 4.07 2.05 4.16 1.68 4.09 1.79 4.02 0.00 0.00 13 2.84 6.33 3.07 8.61 3.55 5.57 3.53 3.94 2.24 5.28 2.13 5.50 1.75 5.41 1.88 5.36 0.00 0.00 14 2.98 6.34 3.21 8.64 3.63 5.61 3.60 3.97 2.32 5.29 2.21 5.52 1.82 6.19 1.96 5.40 0.00 0.00 15 3.11 6.35 3.34 8.66 3.70 5.64 3.66 6.23 2.41 7.55 2.28 7.80 1.91 8.48 2.04 7.69 0.00 0.00 16 3.22 6.36 3.45 8.67 3.77 5.68 3.75 6.27 2.50 7.57 2.37 7.83 2.01 8.53 2.14 7.74 0.00 0.00 17 3.32 6.38 3.57 11.79 3.84 6.13 3.82 6.72 3.82 6.73 3.69 6.77 3.71 6.21 2.22 0.98 0.00 0.00 18 3.42 6.39 3.67 11.81 3.90 6.16 3.89 6.54 3.89 6.54 3.75 7.28 3.78 6.03 2.26 0.99 0.00 0.00 19 3.50 6.40 3.78 11.82 3.96 6.17 3.96 6.57 3.95 6.57 3.83 7.31 3.85 6.06 0.00 0.00 0.00 0.00 20 3.59 6.41 3.87 11.83 4.01 6.20 4.01 6.20 4.02 6.20 3.90 6.95 3.91 5.92 0.00 0.00 0.00 0.00 table 13: the ∆y and the ∆d under the two orders. https://doi.org/10.15837/ijccc.2022.4.4749 22 the behavior of residents will better follow the planning of the government in emergency planning. in the case, we only take f ′p as the lowest priority among the six objectives of the model. if we upgrade the priority level of f ′p, the numbers of the residents among all emergency shelters will be more balanced. thus, the residents will not be excessively concentrated in some emergency shelters. therefore, emergency planner should consider the priority order of the six objectives, according to the actual situation. 5 conclusion this paper study the emergency planning problem considering the subjective preference. we select six attributes to analyze the subjective preference of the residents. according to different evaluation methods, the six attributes are divided into three categories, then initial subjective score of every demand point for every candidate emergency site is obtained. base on this, we assume that the subjective preferences of the residents for the attributes of the emergency shelters change with the total refuge time, i.e., the preference weights of the residents for the attributes of the emergency shelters are dynamic. therefore, the subjective scores of the residents for the emergency shelters also change with the total refuge time. in the emergency planning, it is impossible for us to make a location decision for each total refuge time. under the condition that the probability of each total refuge time is the same, we take the average value of the scores at all total refuge times as the primary basis for location decision making. for improving the transfer efficiency, we minimize the per capita transfer distance. considering fairness, we minimize respectively the standard deviations of the scores and the distances of the residents at all demand points. considering safety, the standard deviation of the numbers of the residents among the selected shelters is taken as a factor in emergency planning, so as to avoid the excessive gathering of the residents. in addition, we propose the objective of minimizing the total cost. the model is applied to a case, we find that, the more the emergency shelters, the higher the satisfaction degree of the residents, and the shorter the per capita distance, the more fairness is satisfied. however, the utilization effect of funds decreases with the increase of the number of the emergency shelters. therefore, in order to improve residents’ satisfaction and transfer efficiency, and avoid the inefficient utilization of funds, we should consider selecting an appropriate number of emergency shelters. in this paper, we assume the emergency shelter has no capacity constraint. in the future, we will consider making the location decision under the condition of capacity constraint, and taking more attributes of emergency shelters as the research object. furthermore, we could conduct more specific research on the emergency planning in the actual disaster area. declaration of interests no potential conflict of interest was reported by the authors. references [1] ipong, l. g., ongy, e. e., bales, m. c. impact of magnitude 6.5 earthquake on the lives and livelihoods of affected communities: the case of barangay lake danao, ormoc city, leyte, philippines. international journal of disaster risk reduction, 2020, 46: 101520. 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4749, 2022. https://doi.org/10.15837/ijccc.2022.4.4749 introduction methodology description of the problem and determining the value of each attribute dynamic weights of the attributes the formulation of the model the formulation of the score function and the standard deviation function the formulation of the transfer distance function and the standard deviation function cost and the formulation of the standard deviation function of the numbers of residents transferred to every emergency shelter goals and constraints case study basic data analysis and discussions analysis comparison discussions conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 3, month: june, year: 2023 article number: 5433, https://doi.org/10.15837/ijccc.2023.3.5433 ccc publications categorical feature encoding techniques for improved classifier performance when dealing with imbalanced data of fraudulent transactions d. breskuvienė, g. dzemyda dalia breskuvienė* data science and digital technologies institute vilnius university, lithuania akademijos g. 4, vilnius 08412, lithuania dalia.breskuviene@mif.vu.lt *corresponding author: dalia.breskuviene@mif.vu.lt gintautas dzemyda data science and digital technologies institute vilnius university, lithuania akademijos g. 4, vilnius 08412, lithuania gintautas.dzemyda@mif.vu.lt abstract fraudulent transaction data tend to have several categorical features with high cardinality. it makes data preprocessing complicated if categories in such features do not have an order or meaningful mapping to numerical values. even though many encoding techniques exist, their impact on highly imbalanced massive data sets is not thoroughly evaluated. two transaction datasets with an imbalance lower than 1% of frauds have been used in our study. six encoding methods were employed, which belong to either target-agnostic or target-based groups. the experimental procedure has involved the use of several machine-learning techniques, such as ensemble learning, along with both linear and non-linear learning approaches. our study emphasizes the significance of carefully selecting an appropriate encoding approach for imbalanced datasets and machine learning algorithms. using target-based encoding techniques can enhance model performance significantly. among the various encoding methods assessed, the james-stein and weight of evidence (woe) encoders were the most effective, whereas the catboost encoder may not be optimal for imbalanced datasets. moreover, it is crucial to bear in mind the curse of dimensionality when employing encoding techniques like hashing and one-hot encoding. keywords: imbalanced data, classifier, feature encoding, high-cardinality, fraud detection. 1 introduction financial fraud is a significant issue for businesses and individuals alike, with losses amounting to billions of euros each year. the negative effect of fraud is felt across all sectors of every country and https://doi.org/10.15837/ijccc.2023.3.5433 2 can significantly lower the overall quality of life. fraud has clear economic impacts, including reduced financial stability for private enterprises, decreased quality of public services, diminished disposable income for individuals, and lessened resources for philanthropic organizations. the leading organizations fighting fraud are financial institutions such as banks. they are using multiple approaches to detect and prevent financial crime. machine learning techniques are often employed in fraud detection since they have shown promising results in detecting such transactions [1], [22], [33]. in this case, however, we face a significant challenge due to the imbalanced nature of the data. most transactions are legitimate, while fraudulent transactions are rare, resulting in imbalanced datasets that negatively impact classifier performance. in the real world, imbalanced data problems are found not only in transactional data but also in cyber-security [9], churn prediction [8], and even protein classification [34], etc. another challenge when using machine learning in financial crime detection is categorical features, such as city, merchant category code (mcc), or credit card brand. the majority of machine learning algorithms are built for numerical features. when feature categories have an order, converting them to numerical values is straightforward. the challenges come when working with non-ordered categorical features, especially if they have high cardinality. high-cardinality refers to a case where a dataset contains a large number of distinct values or categories in a particular feature or column. one potential solution to this problem is to use categorical feature encoding techniques to transform categorical data into numerical representations that machine learning classifiers can process more effectively. however, the effectiveness of different encoding techniques when dealing with imbalanced data has not been thoroughly evaluated. in this paper, we compare and evaluate several popular categorical feature encoding techniques for improving the performance of classifiers when dealing with imbalanced data of fraudulent transactions. we explore the impact of these techniques on a range of classifiers, including decision trees, random forests, and gradient boosting. we investigate encoding implications for machine learning performance using two encoding technique groups target agnostic, which does not rely on any target information, and target based, which transfers statistical information of the target to the variable. both groups have their strengths and weaknesses. target-agnostic techniques ignore the relationship between the values of categorical features and the target value, while target-based methods can suffer from prediction shift. prediction shift is a phenomenon that occurs when the underlying distribution of a dataset changes over time which leads to a shift in the relationships between the input features and the target value. in other words, the patterns and correlations in the training data may no longer hold for new data, which can result in inaccurate predictions and reduced model performance [31]. prediction shifts can occur for various reasons, including changes in the population being studied, changes in the data collection process, or changes in external factors that affect the relationship between the input features and the target. prediction shift is also known as concept drift, which can happen in many fields. it can be quantified through the kullback-leibler divergence to determine the change in posterior probability distributions for different moments of incoming data streams [24]. the rest of the paper is organized as follows. section 2 provides an overview of related work in feature encoding techniques, specifically in imbalanced data. section 3 describes the encoding techniques and classifiers used in this study. section 4 contains information on the datasets used in the experiment, and section 5 explains our experimental setup. in section 5, we also present and analyze the results of our experiments. finally, in section 6, we discuss our findings and provide recommendations for practitioners in selecting appropriate categorical feature encoding techniques when dealing with imbalanced data of fraudulent transactions. 2 related work in this section, firstly, we review valuable and impactful papers in the scope of high-cardinality categorical features encoding with different sizes of datasets and a variety of applications. in the second part, we review the high-cardinality feature encoding impact when dealing with highly imbalanced datasets, where the minority class contains less than 1% samples from the whole dataset. https://doi.org/10.15837/ijccc.2023.3.5433 3 2.1 high-cardinality categorical features encoding comprehensive research on high-cardinality feature encoding for classification and regression problems using balanced datasets is presented in the paper [23]. the authors compare seven encoding techniques using five machine-learning algorithms on 24 datasets. datasets used in the research are binary or multi-class and relatively balanced compared to fraudulent transaction datasets. chosen datasets differ in size; the smallest is less than a thousand entries, and the biggest is more than a million. the datasets consist of 1 to 20 categorical features, each with over 10 levels (distinct values of a particular feature). the highest number of levels for a feature varies from 14 to 30114. the article suggests that target-based encoders outperform target-agnostic encoding techniques. uyar et al. [32] compared automatically calculated techniques against expert judgment. feature encoding techniques were investigated in ivf (in-vitro fertilization) implantation prediction. the suggested frequency-based encoding technique outperforms expert judgment. a special case was presented in [29], where the bayesian encoding technique was developed for wework’s lead scoring engine. the company faces a high-cardinality feature problem as they have categorical features with more than 300k categories. the authors state that the auc metric improved from 0.87 to 0.97. however, when researchers compared performance on the publicly available dataset, the developed solution was not so impressive. due to high-cardinality, these types of features are sometimes excluded from the modeling scope. however, [11], [21] showed that the model’s performance increases statistically significantly when they are included. 2.2 features encoding for imbalanced data [7] investigates the impact of feature encoding techniques on highly imbalanced fraudulent transaction dataset. the data used for the research is from a major french bank, and data protection law does not allow sharing it. in this case, replicating the experiment is not possible. however, the results and conclusions inspire more profound research. another study on real fraudulent dataset [27] proposes a way to encode categorical features by applying word2vec embedding, which is usually used for sentence encoding. the outcome of the research was a 50% reduction in memory usage and slightly improved performance. j. m. johnson and t. m. khoshgoftaar published several papers regarding high-cardinality categorical features encoding on medicare fraud prediction [16], [17]. the dataset used in the research is highly imbalanced as in 56 million rows, only 0.06% are fraudulent. with [16], researchers showed that semantic embedding performs significantly better than the traditional one-hot encoder, and the sg embedding performs best overall. one-hot encoding is a technique used to transform categorical data into numerical data, and it defines categorical data as binary vectors. in this method, each category is represented as a binary vector with a length equal to the total number of categories. the vector contains 1 in the position corresponding to the category and 0 elsewhere. sg (skip-gram) embedding is a neural network trained to predict the surrounding words given a target word. the experiments in [17] showed that one-hot encoding is unsuitable for high-cardinality features when using ensemble learners. 3 methodology this paper aims to find which categorical feature encoder impacts the classification model performance the most. consider the multidimensional dataset as an array x = {xi = (xi1, . . . , xim), i = 1, . . . , n} of m-dimensional data points (in general, samples) xi ∈ rm. data point xi = (xi1, . . . , xim) is the result of the observation of some object or phenomenon dependent on m features x1, ..., xm. some of the features are numerical, while others are categorical. in addition, each data point belongs to some class yi, where the value of yi is the class label of the sample xi. in our case, features describe particular characteristics of customers’ financial behavior, where we have two classes labeled by 0 or 1 regular/legitimate and fraudulent transactions, i.e., the target variable y gets values yi ∈ {0; 1}, i = 1, ..., n. https://doi.org/10.15837/ijccc.2023.3.5433 4 3.1 selection of machine learning algorithms to evaluate the impact of encoding techniques on the classification algorithms, we select different models in terms of framework, used loss function, regularization, complexity, and speed. we compare ensemble learning models with non-linear and linear models. ensemble learning can be visually explained as a judgment of the crowd when the decision is taken by voting. a real-life example of a crowd decision can be a famous tv show named "who wants to be a millionaire". the idea of the show was to answer fifteen questions in a row correctly and win one million dollars. the participant had a chance to ask for help for an intelligent friend or audience. the intelligent friend was right almost 65% of the time. unexpectedly, the audience of random people was correctly answering 91% of the time [30]. ensemble learning can improve the accuracy of a model compared to a single model by reducing the risk of overfitting and underfitting when combining multiple models. it is also less sensitive to outliers, and noise [28]. ensemble learning has a subgroup called gradient-boosting, with examples like xgboost, lightgbm, and catboost. ensemble learning is usually built on decision trees. 3.1.1 decision tree the abbreviation cart is used for "classification and regression trees" which was introduced by l. breiman [4]. cart is a decision tree algorithm that recursively partitions data into smaller subsets, represented by nodes, with the final subsets being represented by leaf nodes. for each partition, the best splitting feature is selected. this algorithm typically employs entropy or gini index to identify the best feature to split data. generally, entropy quantifies the degree of uncertainty in the decision tree algorithm. in the context of classification, a partition with low entropy is considered relatively pure, where the majority of the points have the same label. in contrast, a partition with high entropy indicates that the class labels are mixed, and there is no clear majority class. entropy(d) = − c∑ i=1 p(i) log2 p(i), where entropy(d) is the entropy of some dataset d, where c is the number of classes and p(i) is the probability of the sample from d to belong to class i. if dataset d is fully pure, i.e., it only has the same class label, then the entropy is equal to zero. inf ormation gain(ig) is used to determine whether a given split leads to a decrease in overall entropy. ig = entropy(d) − k∑ j=1 nj n entropy(dj ), where k is a number of unique values in the splitting feature, nj is the number of samples in subset dj , and n is the total number of samples d. entropy(dj ) is the entropy of subset dj , which is calculated in the same way as the entropy of the d. a greater reduction in entropy indicates higher information gain, leading to better split points. the gini index is a measure used for evaluating the purity of a split point as well, and it is defined as follows: gini(d) = 1 − c∑ i=1 p2(i), when a partition is pure, the gini index is 0 because there is only one class, where the probability is 1, and all other classes have a probability of 0. a split may be better if it has a lower weighted gini index value, where the weighted gini index is defined as follows: wgini = k∑ j=1 nj n gini(dj ), trees can grow very large when working with large data sets, and that leads to overfitting. to mitigate this, we can specify a minimum number of samples in the leaf or decide on the maximum https://doi.org/10.15837/ijccc.2023.3.5433 5 depth of the tree. another technique to mitigate overfitting is called pruning. this procedure prevents the decision tree from growing to its full depth. pruning involves removing nodes or branches from the decision tree that does not significantly improve the model’s performance. the result is a smaller and simpler decision tree that is less likely to overfit and more likely to generalize well to new, unseen data. the most significant advantages of a decision tree are its simplicity and good performance. 3.1.2 random forest l. breiman in 2001 [5] introduced a random forest algorithm. it is one of the most used ensemble learning algorithms, primarily for its simplicity and prediction power. [15] showed that random forest can beat other classifiers from 17 families under different kinds of problems by using 121 databases from uci. on the other hand, this research does not provide insights and experiments on highly imbalanced data sets. a random forest is a machine-learning algorithm that combines multiple decision trees to make a final prediction. the number of decision trees in a random forest is a hyperparameter that can be set before training the model. each decision tree in the random forest is trained on a random subset of the training data and a random subset of the features. this process is repeated multiple times to create a diverse set of decision trees. the final result is obtained during prediction by aggregating the predictions of all the individual trees in the forest. the aggregated prediction is either by taking the majority of individual predictions (for classification) or the mean of the predicted values (for regression). 3.1.3 xgboost extreme gradient boosting xgboost stands for extreme gradient boosting [13]. the xgboost classifier algorithm starts by initializing the model with a single decision tree called the base learner. on the other hand, xgboost also supports other types of base learners, such as linear models. the base learner is typically a shallow decision tree with few nodes, which serves as a weak learner. the model then calculates the gradient of the loss function with respect to the predictions made by the base learner. this gradient represents the direction in which the model needs to update the predictions to reduce the loss. the xgboost classifier constructs a new decision tree to correct the errors of the base learner. the construction of this tree is done greedily by iteratively adding nodes that minimize the loss function. the tree is built by selecting the best-split point at each node based on the gradient of the loss function. once the new tree is constructed, the xgboost classifier updates its predictions by adding the new tree’s predictions to the previous trees’ predictions. this process is repeated for a fixed number of iterations or until the model converges to acceptable performance. xgboost includes several regularization techniques to prevent overfitting, such as l1 and l2 regularization and tree pruning. the xgboost predicted value is as given below [13]: ŷi = k∑ k=1 fk(xi), fk ∈ f, where k is the number of decision trees, fk(xi) is the function of input in the k-th decision tree, and f is the set of all possible classification and regression trees (cart). the loss function of the xgboost consists of training error and regularization: l = n∑ i=1 l(yi, ŷi) + k∑ k=1 ω(fk), ω(fk) = γt + 1 2 λ ∥w∥2 , where l is the loss function, t is the number of the leaf nodes, w is the score of the leaf nodes, γ is the leaf penalty coefficient, and λ controls the scale of w. https://doi.org/10.15837/ijccc.2023.3.5433 6 as the model is trained in an additive way, we can rewrite the loss function as £(k) = n∑ i=1 l(yi, ŷik−1 + fk(xi)) + ω(fk). using second-order approximation (an estimate of the second derivative of the loss function with respect to each parameter), we can optimize the loss function: £̃(k) = n∑ i=1 [l(yi, ŷik−1) + gifk(xi) + 1 2 hif 2 i (xi)] + ω(fk), where gi = ∂ŷ(k−1) l(yi, ŷi (k−1)) and hi = ∂2ŷ(k−1) l(yi, ŷi (k−1)). the solutions for the optimal values of w and loss function are [13] wj = gj hj + λ , l = − 1 2 t∑ j=1 g2j hj + λ + λt, where gj = ∑ i∈ij gi, hj = ∑ i∈ij hi, and ij is the instance set of leaf j. this greedy optimization makes xbgoost a fast algorithm but does not necessarily lead to the optimal solution. 3.1.4 lightgbm light gradient boosting machine gradient boosting decision trees (gbdt) is a machine learning algorithm combining decision trees and gradient boosting to create an ensemble model. gradient boosting iteratively improves a model’s predictions by adding new models to the ensemble, each focusing on previously misclassified examples. gbdt face challenges when dealing with large data samples, and they can require a large amount of memory, especially when the number of features or trees is high. for each feature, gbdt requires scanning through all data instances to calculate the information gain (ig) for every potential split point. reducing the number of data instances or features seems like a simple solution to address this issue. however, it is not a trivial task. no weight is assigned to the data instance in the gbdt, and the gradient of the loss function is used to update the model in each iteration instead. data instances with more significant gradients have a more considerable impact on constructing the decision tree. it means that they also have a more significant influence on the computation of information gain, even though no exact weight is assigned to each data instance. this conclusion is one of the prominent uniqueness of the lightgbm [19]. thus, when undersampling the data instances, we should better keep those instances with large gradients to maintain the accuracy of information gain estimation and only randomly drop those instances with slight gradients. the paper [19] proves that the mentioned strategy can increase information gain estimation accuracy better than uniformly random sampling. this approach is called gradient based one side sampling (goss). additionally, light gbm implemented exclusive feature bundling (efb) algorithm. the authors design an efficient algorithm to solve the optimal bundling problem by reducing it to a graph coloring problem and solving it using a greedy algorithm with a constant approximation ratio which means that the solution it produces is always within a constant factor of the optimal solution. lightgbm can encode categorical features inside the algorithm. however, in this paper, we are not using this option and feed already encoded data to achieve the goal of the research. 3.1.5 catboost category boosting catboost is another boosting algorithm released in 2017 [14] after xgboost and lightgbm, [25]. catboost can automatically handle categorical features by combining one-hot and integer encoding if https://doi.org/10.15837/ijccc.2023.3.5433 7 needed. it also uses target encoding to deal with high-cardinality categorical features. however, the novelty of this method is that it addresses and suggests solutions for solving the prediction-shifting problem. the solution is called ordered boosting. catboost addresses prediction shifts by creating new datasets at each boosting step, which are independent of the previous datasets, to obtain unshifted residuals. this is accomplished by applying the current model to new training examples. no instances may be used for training the previous models to ensure unbiased residuals for all training examples. in this case, catboost maintains a set of models that differ in the examples used for training. when calculating the residual for a particular example, catboost uses a model that was trained without that example. the random permutation of the training examples is used to achieve this. 3.1.6 logistic regression logistic regression is one of the most widespread classical machine learning models, and it is used in many applications and domains. the reason for its popularity, first of all, is its simplicity and explainability. besides that, logistic regression does not require much computational power. on the other hand, it performs better when the data is linearly separable. it is a machine learning algorithm based on a statistical model with the binary dependent variable. logistic regression describes data and the relationship between one dependent variable and independent variables. the logistic regression model can be written as follows: log( p 1 − p ) = m∑ j=0 βj xj , p = e ∑m j=0 βj xj 1 + e ∑m j=0 βj xj , where p is the probability that the event will happen. xj are the individual variables, j = 1, .., m and x0 = 1. logistic regression aims to estimate βj , where j = 0, ..., m. 3.2 selection of encoding techniques most machine learning algorithms are built for numerical data. hence researchers and developers must decide how to encode categorical variables. various encoding techniques exist for this purpose. they can be grouped based on their relation to the target. namely, target-based and target-agnostic. another way to group encoding techniques is based on their impact on dataset dimensionality. encoders like one-hot or hashing encoders are the ones that increase the dimensionality of the data set. our paper analyzes four target-based and two target-agnostic techniques presented in fig.1. figure 1: encoders. https://doi.org/10.15837/ijccc.2023.3.5433 8 3.2.1 m-estimate encoder m-estimate encoder is a target-based encoder. it has one hyperparameter — m, representing the power of regularization where a higher value of m results in stronger shrinking. recommended values for the m are in the range of 1 to 100. the formula to compute estimated values for a category is [20]: si = niy + ppriorm ni + m , where si is the encoded value for category i, ni is the number of times the category i appears in the dataset, niy is the number of times the binary target has value 1 (y = 1) when the category is i, pprior is a prior probability of y = 1 without considering categories. 3.2.2 james-stein encoder james-stein encoder is a target-based encoder as well. initially, the james-stein estimator was not meant to be used for binary classification and was defined only for normal distributions. in our case, we want to apply it for binary classification, so firstly, we convert the mean target value to the log-odds ratio. the james-stein encoder is a method for shrinking mean estimates only when the variances of those means are assumed to be equal. however, this assumption is often only valid when the sample sizes of each group are equal. in most real-world scenarios, sample sizes and variances of the means are not equal, which makes it difficult to determine the appropriate course of action. for the execution of the james-stain encoder, we use the scikit-learn library category encoders, which has implemented a binary version of the james-stein encoder proposed in the paper [35] 3.2.3 catboost encoder catboost encoder uses the same formula as the m-estimate encoder. however, it was noticed [25] that the usage of the whole sample to compute si leads to a target shift. permutations of the training set were suggested in [25]. for the execution, we use the scikit-learn library category encoders, where the implementation is time-aware (it does not use random permutation). 3.2.4 weight of evidence encoder the weight of evidence (woe) is a statistical measure that quantifies the strength of the relationship between a categorical variable and a binary target variable. the woe for a particular category is calculated by taking the natural logarithm of the ratio of the percentage of observations in that category that belongs to the class y = 0 to the percentage of observations in that category that belongs to the class y = 1. w oei = ln pi(y =0) pi(y =1) , where pi(y =0) is percentage of y = 0 when the category is i; pi(y =1) is percentage of y = 1 when the category is i. 3.2.5 label/ordinal encoder label/ordinal encoder is a target-agnostic encoder, and it does not use the statistical information of the target variable. label encoder depends on the ordering of the encoded data because it assigns integer numbers from 0 to k − 1 despite the meaning of the data, where k is the number of different values of a particular categorical feature. the advantage of this method is its simplicity. however, it gives an unwanted order and weight for the categories. https://doi.org/10.15837/ijccc.2023.3.5433 9 3.2.6 hashing encoder feature hashing is a technique for converting categorical features into numerical features for machine learning models. it works by mapping each category of a categorical feature to an integer within a pre-determined range. the output range is usually smaller than the input range, meaning multiple categories may be mapped to the same integer. such conditions are called collisions. however, collisions are often rare in practice and do not significantly affect performance. feature hashing is similar to one-hot encoding but with a few key differences. one of the main advantages of feature hashing is that it allows for control over the output dimensions. additionally, feature hashing can be faster and more memory-efficient than one-hot encoding, especially when dealing with large datasets. this encoder applies the hashing trick to a categorical feature and then encodes the resulting integers as numerical features. the basic idea behind the hashing trick is to use a hash function to map the input data to a fixed-size output space. the hash function takes the input data (e.g., a word or categorical feature) as input and produces a hash value that is an integer between 0 and a predefined maximum value. 4 data used for experiment the availability of necessary data sets with high volume, velocity, and variety is needed to accelerate scientific research. research in the area of fraudulent transactions is limited by data availability. many law regulations regarding private data usage exist in the real world, such as gdpr (general data protection regulation), ccpa (california consumer privacy act), "act on payment services and electronic money", etc. synthetic data is a promising technology that helps to solve privacy, fairness, data augmentation, and many other issues. the definition of synthetic data proposed in [18] is "synthetic data is data that has been generated using a purpose-built mathematical model or algorithm, intending to solve a (set of) data science task(s)." synthetic data plays a vital role in research and developments where data availability is limited by laws and its nature to be rare. a great application of synthetic data set is presented in [10]. this synthetic data set is used to generate realistic cyber data for machine learning classifiers for network intrusion detection systems [10]. the paper [10] concludes that their chosen generative methods ctgan and tvae generate synthetic cyber data reasonably well. nevertheless, ml models trained with only synthetic data resulted in low classification recall. further, the authors suggest having at least 15% of actual data when training the model. financial fraud is one of those areas where access to data is very limited. that was the main reason to work with synthetic data generated by erik altman [2]. this data set aims to allow researchers and developers to work on the data that represents buying habits of u.s. citizens. the dataset is like a virtual world with customers, merchants, and fraudsters. the data was created so that features kept their main statistics like mean and standard deviation that would be the same as in the actual population. but it’s not just averages and standard deviations that are needed. erik altman [2] selects characteristic values for individuals by stochastic sampling, generally from a gaussian distribution. the advantage as compared to other synthetic data sets [3] is that the individuals’ activities are related. for instance, if an individual is in travel mode, he/she will have different spending behavior. similarly, the same logic applies if the purchase happens on weekdays or weekends, and much more evidence that this data set reflects the actual population can be found in the [2]. another essential thing to be mentioned is that this data set’s virtual credit card world includes actual banking events like creating the chip in the card. chips were introduced on a large scale in the u.s. in 2014 before that magnetic stripe technology was used. after that, it became harder to perform "card-present" fraud (a transaction in which the fraudster physically presented the stolen credit card to the merchant). this data set will be called dataset1 in the rest of the paper. the study conducted in the paper [6] involves analyzing this dataset and recommending strategies for achieving balance through clustering. https://doi.org/10.15837/ijccc.2023.3.5433 10 by creating smaller and more homogeneous clusters, undersampling methods can be employed without sacrificing crucial information needed for machine learning algorithms. the second data source (dataset2) used for the experiment is also synthetic. the dataset was generated using the sparkov data generation tool. this data set is smaller than the previous one, with 1.3 million transactions, of which 0,57% are fraudulent. the generated data set has five categorical features from eleven in total. 5 experimental results data preparation is one of the essential parts of successful data science research. in the scope of this research, we are targeting massive datasets with less than a 1% imbalanced ratio. additionally, we are interested in the datasets containing categorical features with high cardinality. our goal is to find the best-fitting encoder for highly imbalanced massive data, so we are not hyper-tuning selected machine learning models or changing thresholds. we calculate results using cross-validation with a stratified split of five-fold. we have performed the cross-validation four times with different seeds. we believe that by using the grid search algorithm, we could achieve better results in general. for the encoding algorithms, we use default parameters as well. our focus is on univariate encoding, where features are always encoded separately. the experiment aims to analyze and show which encoding methods are best suited for imbalanced data. even though lightgbm and catboost have their own feature encoding methods inside the algorithms, we are not comparing these encoding with others as they use optimization strategies. the results would not be comparable. both datasets have categorical features with different cardinality. the cardinality of each categorical feature is presented in table1. training set size 5 969 329 categorical feature cardinality card brand 4 card type 3 has chip 2 use chip 3 merchant city 11 391 mcc 109 error1_cat 8 error2_cat 5 gender 2 city 1 074 state 51 (a) dataset1 training set size 907 672 feature cardinality category of mcc 14 gender 2 city 894 state 51 job 494 (b) dataset2 table 1: feature cardinality. below, we present an example of differences in encoding technique performance by plotting histograms of encoded values of the categorical feature "state" from dataset1 (fig.2 fig.6). the x-axis represents encoded values, and the y-axis shows the number of cases of the appearance of the particular encoded value. the maximum histogram of the label encoder is much lower than that of the carboost encoder. this means that the catboost encoder shrinks categorical values, resulting in a much higher number of cases for a particular encoded value. upon analyzing the target-based techniques, it is evident that the encoded values display notable variability in terms of their size and shape, as represented in the histograms. more specifically, the james-stein encoder demonstrates a compact range of values, whereas the woe encoding method yields predominantly negative values. additionally, the catboost encoder results in a distribution of encoded values that are asymmetrically skewed. https://doi.org/10.15837/ijccc.2023.3.5433 11 target-agnostic techniques are not comparable in our case, as the ordinal encoder does not expand dataset dimensionality while hashing encoder does. the histogram of the encoded variable "state" with an ordinal encoder represents the frequency of the values encoded with no logical order. figure 2: state encoded using m-estimate encoder. figure 3: state encoded using james-stein encoder. visual comparison of the feature "state" encoded with different encoders can be challenging, owing to their differing scales. in an attempt to mitigate this challenge, we opt to standardize the encoded values by scaling them within the range of zero and one. following, we plotted the density function, as depicted in figure 7. this visualization allowed us to easily discern that the james-stein, mestimate, and woe encoders demonstrate similar shapes and density amplitude. however, their primary variation lies in their position along the x-axis. conversely, values encoded with the catboost encoder are characterized by a significant level of skewness, as previously noted. moreover, we observed that the encoded values with catboost appear to be compressed, as evident from the density function plot. we can draw several conclusions based on the presented results in fig.8 fig.9. firstly, we can observe that target-based encoding methods outperform target-agnostic ones on dataset1 and dataset2. this indicates that incorporating the target in the encoding process can result in better performance of the machine learning model. target-based encoding methods allow the model to capture the relationship between the input features and the target variable more effectively, thus improving the model’s predictive power. secondly, the results indicate that logistic regression without hyper-tuning is unsuitable for highly imbalanced datasets. the performance of the logistic regression model was very poor for both datasets, indicating that this algorithm is not robust enough to handle imbalanced data. therefore, other machine learning algorithms that can handle imbalanced data, such as gradient boosting machines, should be used in such cases. finally, we can see from the box plot on the bars that lightgbm is highly sensitive to encoding techniques. this suggests that choosing the appropriate encoding method is crucial for achieving optimal performance when using lightgbm. therefore, it is essential to experiment with different encoding techniques to identify the best one for a given dataset and machine learning algorithm. the results presented in fig.9 are presented by the encoders used in the machine learning models. the figure shows that the james-stein and woe encoders are consistently chosen as the bestperforming ones across the different machine-learning models. furthermore, it is worth noting that the https://doi.org/10.15837/ijccc.2023.3.5433 12 figure 4: state encoded using catboost encoder. figure 5: state encoded using woe encoder. figure 6: state encoded using label encoder. https://doi.org/10.15837/ijccc.2023.3.5433 13 figure 7: density functions of the encoded feature "state" using different encoders. figure 8: experimental results grouped by models. https://doi.org/10.15837/ijccc.2023.3.5433 14 catboost encoder performs poorly among the target-based encoding techniques. this suggests that the catboost encoder may not be suitable for imbalanced datasets, as it fails to effectively capture the relationship between the input features and the target. the performance of the hashing encoder is consistent with the findings reported in related research [17]. it is important to note that we did not include the one-hot encoder in our evaluation. however, it is well known that one-hot encoding can lead to growing dimensionality of the dataset, known as the curse of dimensionality. similarly, hashing encoding can also increase the dimensionality of the dataset. therefore, both encoding techniques may not be optimal choices for datasets with a large number of categorical features and instances. figure 9: experimental results grouped by encoders. 6 discussion and conclusions our research aims to determine the most appropriate encoding technique for handling highly imbalanced datasets. we conducted an experiment to test six encoding methods, both from the target-agnostic and target-based groups. to conduct the experiment, we utilized various machinelearning methods, including ensemble learning, as well as linear and non-linear learning. specifically, we focused on transaction datasets, where the target variable indicates whether the transaction is regular or fraudulent. these datasets are complex, with several high-cardinality features. several papers have been written regarding this subject matter, showcasing experiments conducted on publicly accessible balanced datasets. nevertheless, minimal investigation has been conducted until now when dealing with highly imbalanced datasets. https://doi.org/10.15837/ijccc.2023.3.5433 15 the findings presented in fig.8 fig.9 highlight the importance of selecting the appropriate encoding method when working with imbalanced datasets and machine learning algorithms, as well as the benefits of using target-based encoding methods to improve model performance. the results presented in fig.9 suggest that the choice of encoding technique can significantly impact the performance of the machine learning models. the james-stein and woe encoders appear to be the most effective among the encoding techniques evaluated. additionally, the catboost encoder may not be suitable for imbalanced datasets. finally, it is vital to consider the potential curse of dimensionality of the dataset when using encoding techniques such as hashing and one-hot encoding. the discovered properties will lead to the development of more efficient new classification methods and the improvement of existing ones, e.g., [6] for highly unbalanced financial data. funding the research was funded by vilnius university. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] alarfaj, f. k.; malik, i.; khan, h. u.; almusallam, n.; ramzan, m.; ahmed, m. 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(2015). shrinkage estimation of log-odds ratios for comparing mobility tables, sociol methodology, 45(1), 320–356, 2015. doi: 10.1177/0081175015570097. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: breskuvienė, d.; dzemyda, g. (2023). categorical feature encoding techniques for improved classifier performance when dealing with imbalanced data of fraudulent transactions, international journal of computers communications & control, 18(3), 5433, 2023. https://doi.org/10.15837/ijccc.2023.3.5433 introduction related work high-cardinality categorical features encoding features encoding for imbalanced data methodology selection of machine learning algorithms decision tree random forest xgboost extreme gradient boosting lightgbm light gradient boosting machine catboost category boosting logistic regression selection of encoding techniques m-estimate encoder james-stein encoder catboost encoder weight of evidence encoder label/ordinal encoder hashing encoder data used for experiment experimental results discussion and conclusions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 3, month: june, year: 2023 article number: 5006, https://doi.org/10.15837/ijccc.2023.3.5006 ccc publications improved model for traffic accident management system using kdd and big data: case study jordan faisal alzyoud faisal alzyoud department of computer science isra univerity, jordan faisal.alzyoud@iu.edu.jo abstract this paper addresses the challenge of the increasing number of traffic accidents resulting from population growth and increased vehicle usage, leading to significant economic and environmental impacts. to address this issue, this paper proposes an improved model for traffic accident management that employs knowledge discovery in databases (kdd) and extensive data analysis techniques to extract the main factors contributing to car accidents. a case study of traffic accidents in jordan is conducted, utilizing actual data from the department of statistics and the public security directorate records between 2016 to 2020 to identify the primary factors contributing to road accidents and their effects on various types of injuries. the study identifies 11 factors that contribute to car accidents, with driver error being the primary factor for increasing the number of accidents and injuries. additionally, minor car accidents are found to cause more injuries based on the analysis of the proposed approach. the findings provide a solid foundation for developing an accurate and precise scheme to reduce or eliminate the number of road accidents, which is essential for transitioning to smart cities. using big data and kdd techniques could significantly improve current traffic accident management practices, informing the development of new policies and regulations to improve road safety and reduce economic and environmental impacts. this paper presents a promising solution for addressing traffic accident management, contributing to safer and sustainable urban environments. keywords: artificial intelligence, machine learning, big data, jordan, kdd, road accidents, traffic management 1 introduction the widespread adoption of iot devices, sensors, smart technologies, and rfid technology in smart cities has led to the generation of large and complex datasets known as big data. these datasets can be leveraged to extract valuable information that can help address many problems, such as traffic congestion [1]. traffic management is a significant challenge many countries face, as it poses problems related to human factors and economic implications. one of the critical concerns of traffic engineers is identifying the peak hours of congestion on urban and city roads. numerous policies have been proposed to manage traffic congestion during peak hours [2]. extensive studies have been https://doi.org/10.15837/ijccc.2023.3.5006 2 conducted to determine the sources and causes of congestion, such as traffic volume, time of day, weather conditions, and others. various mitigating measures and solutions have been proposed to reduce traffic congestion during peak hours, including enhancing the roadway capacity, promoting carpooling and public transportation, encouraging flexible working hours, and increasing parking fees in city centres [3]. road accidents are a significant challenge worldwide, especially in developing countries. jordan is no exception, with frequent car accidents on rural and suburban roads. as such, previous studies have focused on analyzing accidents, identifying their causes, and analyzing driver faults. distracted driving is the second-most common driver fault leading to accidents in jordan, with the number of accidents increasing due to rapid population growth and the number of vehicles, as the number of inhabitants reached 10806000 with a number of recorded vehicles 1502420 [4]. these accidents severely impact society and the economy, with the number of deaths in jordan reaching 461 in 2020. there is a growing need to develop improved traffic accident management systems in response to this issue. numerous endeavours have been undertaken to tackle the problem of road accidents, which are projected to rank as the fifth leading cause of mortality by 2030, as per the world health organization’s report [5]. the emergence of digital devices and technological advancements have led to the proliferation of the digital economy, which can provide a means of buying and selling goods and services without requiring physical infrastructure. consequently, it can potentially alleviate traffic congestion [6]. this paper proposes an improved model for traffic accident management using knowledge discovery in databases (kdd) and big data analysis techniques to extract the primary factors contributing to road accidents. a case study is presented using data from the department of statistics and the public security directorate records between 2016 and 2020 to identify the main factors contributing to road accidents and their effects on various types of injuries. the study identifies 11 factors that contribute to car accidents, with driver error being the primary factor for increasing the number of accidents and injuries. the findings provide a solid foundation for developing an accurate and precise scheme to reduce or eliminate the number of road accidents, which is essential for transitioning to smart cities. using big data and kdd techniques could significantly improve current traffic accident management practices, informing the development of new policies and regulations to improve road safety and reduce economic and environmental impacts. 2 research background and related work the process of extracting valuable knowledge from a dataset is known as knowledge discovery in databases (kdd). kdd uses data mining techniques to identify patterns and extract specific information from the data. the importance of kdd has increased since the late 1990s, as companies in the commercial, production, and service sectors have become interested in extracting valuable information from their databases to improve their understanding of consumer preferences and behaviour [7]. kdd is an engineering process that involves several steps, as shown in figure 1. first, data is entered and aggregated according to association rules. then, the data discovery process uses statistical or query tools to identify the desired data. the data is reprocessed by developing a model that considers redundant and null attributes to reduce data dimensionality. the data is then analyzed using appropriate methodologies based on the discovery goals. data science extracts valuable knowledge using reports, actions, and real-time monitoring. a mathematical model can induce reports, actions, and real-time monitoring to extract the desired knowledge. the target and goals of kdd are based on the intended use of the system for data discovery [8]. however, kdd suffers from a high number of redundant records, as it was found that about 78% and 75% of the records are duplicated in kdd train and test sets, respectively [9]. additionally, kdd has difficulty dealing with unstructured data. the trend towards developing smart cities that incorporate devices, sensors, and new technology platforms is generating an ever-increasing amount of data. this large and complex data collection, with its diverse types and forms, is commonly called "big data". big data is characterized by its size and complexity to the extent that it becomes difficult to manage using conventional database management tools or data processing applications [11, 12]. consequently, big data presents several https://doi.org/10.15837/ijccc.2023.3.5006 3 figure 1: kdd process steps challenges related to data capture, creation, storage, search, participation, transportation, analysis, and visualization [13]. the first level of big data involves the raw data source, which can be captured by collecting data from different devices such as machine sensors or by generating data from trends in additional information derived from the analysis of a single large set of related data, as illustrated in figure 2. there are three types of data: structured, semi-structured, and unstructured. structured data can be easily managed, and any data source problems can be identified during the pre-processing phase, with processing policies varying based on the data category. semi-structured data can be transformed into a predetermined relational model, such as converting xml documents to free text, although language processing is required to process textual data [14]. unstructured data, such as images, video, audio, and emails, are more challenging and often processed as text files. these types of data lack correlation and the relation between them. semi-structured data can be managed using xml files, which are treated as a data set and schema and can be prepared for the next level of information. information is pooled together and transformed into meta information level using various techniques, with data cleaning used to eliminate duplication, null fields, compensate empty sets, and boundaries information. data auditing tools find contradictions by analyzing the data and detecting relationships among data and rules. ontology matching techniques, such as the automated and robust matching algorithm system ’agreementmakerlight’, can be used for this purpose. it is designed to process huge ontologies while preserving maximum flexibility and expandability of the purebred agreementmaker structure [15]. the combination of big data and ontology-based services can assist in solving complex problems by extracting useful knowledge, with the web of data providing an excellent opportunity for ontology-based services [10]. big data analysis can be approached in two ways: through data science and mathematical modelling. data science uses rules to analyze and present knowledge derived from information analysis. on the other hand, mathematical models are used to analyze information mathematically using different mathematical techniques. the ultimate level of big data analysis is knowledge representation, achieved after the presented information has been analyzed and transformed into meaningful information or prediction tools that can be used to predict new knowledge. another way of using knowledge representation is using measuring tools to check the accuracy of the inferred knowledge. finally, the transformed data is stored to allow for rapid access to the data. this approach allows for data integration for knowledge sharing and exploration aided by mapping and reduced programming, such as the rdf method. applying the described technique aims to improve data processing without prior integration [9, 16]. however, big data faces several challenges and limitations, such as data acquisition, storage, search algorithms, information sharing, analysis, and visualization. moreover, computer architecture limitations, such as low-level cpu and i/o, pose a significant challenge. addressing these challenges can lead to a bright future for big data, especially as information continues to exceed our capacity to harness [17, 18]. table 1 outlines the benefits and challenges of kdd and big data. the proposed approach suggests a hybrid approach between kdd and big data to enable system discovery from big data and overcome the challenges kdd faces. big data analysis can be approached in two ways: through data science and mathematical modhttps://doi.org/10.15837/ijccc.2023.3.5006 4 figure 2: kdd process steps elling. data science uses rules to analyze and present knowledge derived from information analysis. on the other hand, mathematical models are used to analyze information mathematically using different mathematical techniques. the ultimate level of big data analysis is knowledge representation, achieved after the presented information has been analyzed and transformed into meaningful information or prediction tools that can be used to predict new knowledge. another way of using knowledge representation is using measuring tools to check the accuracy of the inferred knowledge. finally, the transformed data is stored to allow for rapid access to the data. this approach allows for the integration of data for knowledge sharing and exploration aided by the use of mapping and reduced programming, such as through the use of the rdf method. applying the described technique aims to improve data processing without prior integration [9, 16]. however, big data faces several challenges and limitations, such as data acquisition, storage, search algorithms, information sharing, analysis, and visualization. moreover, computer architecture limitations, such as low-level cpu and i/o, pose a significant challenge. addressing these challenges can lead to a bright future for big data, especially as information continues to exceed our capacity to harness [17, 18]. table 1 outlines the benefits and challenges of kdd and big data. the proposed approach suggests a hybrid approach between kdd and big data to enable system discovery from big data and overcome the challenges kdd faces. amos is a software program used for structural equation modelling (sem) analysis, which allows users to test and fit various models. with amos, users can view the contents of data files and group variables within a database to facilitate testing models involving multiple groups of subjects [27]. amos can also handle missing data and empty records, allowing for the estimation of means and intercepts even when the dataset is incomplete. the software is compatible with various data file formats, including access, microsoft excel, foxpro, lotus, spss, and comma-delimited text files. sem is a statistical method used to test theoretical models, which hypothesizes how sets of variables define constructs and how these constructs are related to each other. researchers typically use sets of indicator variables to define latent variables, independent or dependent variables, and other measurement instruments to obtain observed or latent variables. the chi-square (x2) statistic is an essential indicator used in sem to evaluate the model’s goodness of fit. it indicates the difference between the observed and expected variance-covariance matrices [28]. the chi-square (x2) statistic is an essential tool used in structural equation modelling (sem) to https://doi.org/10.15837/ijccc.2023.3.5006 5 challenges advantages big data security and privacy usefulness and reliability data heterogeneity [19] easy to deal with very large databases [20] ethical issues automated update analysis time better accuracy support multiple formats kdd not easy to deal with huge databases and unstructured data [21] facilitate data mining and implementation of machine intelligence [22] data integrity and missing values support decision making [23] data scarcity helping the system developer [24] data dimensionality reduction provide valuable solutions mathematical model table 1: a comparison between kdd and big data determine the critical n statistic at which the null hypothesis can be rejected. it is used to fit and derive statistics, and the correct chi-square value represents the model’s goodness of fit. chi-square is a reliable indicator of the robustness of the model-fit measure and is the only statistical test used to test the theoretical model’s significance. the chi-square value ranges from zero for a saturated model with all paths to a maximum value for the independent model with no paths. in this research, the observed results are almost identical and compatible with the expected results. therefore, the chi-square values obtained in this study are almost zero, indicating no significant difference between the observed and expected values. the application of big data analysis in the study of traffic accidents has facilitated the identification of significant factors that contribute to their occurrence[32]. one research study utilized the uw-drivenet transportation big data platform to collect data on over 30,000 traffic crashes in washington state in 2016. this study employed a joint weighting model based on the interval analytic hierarchy process (iahp) and the grey relational degree to evaluate the subjective and objective significance of data fields in six dimensions: people, vehicle, road, environment, crash, and time. the model effectively quantified the weight of each dimension, thus providing deeper insights into the factors that contribute to traffic accidents [29]. additionally, a separate study was conducted to examine the freeway traffic crash data obtained from wdot (washington department of transportation). to accomplish this, a multi-dimensional multilevel system was developed. to ensure load balancing, the fp-growth (frequent patterngrowth) algorithm was parallelly optimized on the hadoop platform, which facilitated efficient and accurate association rule mining calculations on large volumes of traffic crash data [30]. a thorough analysis was conducted using historical crash data to study the impact of environmental and road factors on traffic accidents. the study examined 11 factors that could influence the severity of accidents from both the road and environmental perspectives. these factors were discretized to establish an xgboost (extreme gradient boosting) model. to better understand the model’s predictions, the shap (shapley additive explanation) value was introduced and used to interpret the xgboost model. this study aimed to establish a prediction model specifically geared towards determining the severity of traffic crashes on freeways[31]. this study aims to enhance the management of traffic accidents by introducing an improved model that incorporates kdd (knowledge discovery in databases) and big data analysis. to achieve this, a thorough analysis of traffic accidents in jordan was conducted, which considered several factors contributing to accidents, such as road conditions, weather, and driver behaviour. the proposed model utilizes data mining techniques to scrutinize vast amounts of data and extract patterns that can be utilized to forecast and forestall traffic accidents. https://doi.org/10.15837/ijccc.2023.3.5006 6 3 data set the raw data used in this study was obtained from traffic accident records in jordan between 2016 and 2020. the data was generated using several methods and includes information on a total of 1,729,343 vehicles, as well as 117,743 transit vehicles and 122,970 accidents by the year 2020. the data was collected from 12 states across jordan, with three main data centres located in the north, middle, and south regions. the data encompasses a wide range of variables, including vehicle accidents, injuries (age, gender), accident locations, driver information (age, gender), vehicle types, weather conditions, accident images, and road types and conditions [4]. 4 proposed approach the rate of information growth in the current century has surpassed moore’s law due to the diverse aspects of data generation. this study introduces a novel hybrid system integrating knowledge discovery in databases (kdd) and big data analytics to extract valuable insights, as depicted in figure 3. the proposed approach operates at three distinct levels. at the initial stage of the proposed hybrid system, the kdd and bd approaches concentrate on the data set, comprising two factors: data capture and data collection. the bd approach is responsible for data capture and collects data in diverse forms, such as structured, semi-structured, and unstructured data, including real-time, streaming, and acd data. semi-structured data does not conform to the formal structure of data models used in relational databases and occurs when two data sources use different languages or schema structures, such as xml and owl, and can include hosted data, mirage data, or data lake. in contrast, structured data is straightforward to handle since it comprises entities and clear relationships between them. the kdd approach is concerned with data collection, and the output is a dataset stored in a structured or semi-structured format. the transformation process identifies the source of data belonging to either the kdd or bd approach and converts unstructured data to structured data, which includes several steps such as acquiring data conditions dependent on the domain of study, determining the path or direction of data, restructuring the data using a tall array or map-reduce techniques, and producing a structured dataset. the choice of the technique (tall array or map-reduce) depends on factors such as accuracy, performance, and scale. this approach has effectively extracted valuable insights from the data, which is crucial for decision-making in various fields [25]. the second tier is responsible for managing information and making decisions and acts as a mediator between the pre-process and organized levels. to ensure consistency, the transformation process involves comparing the attributes (items) being instantiated with the dataset’s content. the tall array confirms the location and content of data, while map-reduce confirms the consistency of the item with its content by generating random pairs. pre-processing uses a set of rules to monitor the entire dataset and clean the data by detecting duplicates, missing values, nulls, empties, and other inconsistencies. the third layer of the data analysis process pertains to the analysis stage, which involves two main types of analysis: analysis by target and unsupervised analysis. analysis by target entails generating rules that provide a clear pathway for making accurate decisions based on the domain instructions. to achieve this, a formatting transformation instruction and filter method are required to minimize the number of rules related to the target and/or sub-target and to specify the relationship among decisions. this ensures that the right decision is made for the right target. on the other hand, unsupervised analysis involves making a cross between properties (items) and target (decision) as presented in the formula below: ∝= { (sub rules), number of rules > 1 target a target a, number of rules = 1 (1) paths = { (sub rules), number of rules > 1 target a rule, number of rules = 1 target a (2) https://doi.org/10.15837/ijccc.2023.3.5006 7 figure 3: hybrid approach between kdd and big data to extract knowledge this involves analyzing the relationships among the sub-rules, number of rules, and targets to identify paths to making accurate decisions without needing prior guidance or instruction. the goal of the analysis stage is to decide based on the target (action) with a short response time and high precision. this requires a trade-off between the number of rules and sub-rules, both of which serve the same purpose of achieving the target. to strike this balance, measures such as time complexity, performance, overhead, capacity, and quality value are adjusted based on the domain studies. this ensures that the analysis process is optimized for efficiency and accuracy while fulfilling the requirements of velocity and veracity. 5 results and discussion the results of this study show that knowledge discovery (kdd) offers more effective and valuable detection solutions than traditional big data analytics methods. this is because kdd relies heavily on unique and advanced analytical techniques. we combined the two methodologies and utilized tall array techniques to capitalize on the strengths of kdd and big data analytics. to test the effectiveness of this approach, we conducted a case study focused on road accidents in jordan between the years 2016-2020. the results demonstrate that the proposed approach can effectively identify and analyze complex patterns within the data, leading to more accurate and informed decision-making. the data utilized in this study was obtained from traffic accident records in jordan between 20162020. the data includes information on 1,729,343 vehicles, with an additional 117,743 transit vehicles entering jordan. the analysis reveals that the main factors contributing to accidents in jordan are driver behaviour, road state, vehicle type and condition, weather condition, and peak hours. figure 4 illustrates the number of accidents in jordan between 2016-2020. it is observed that the population and number of vehicles have increased exponentially during the interval, increasing the number of road accidents, injuries, and fatalities. however, in 2020, there was a significant decrease in road accidents due to the country-wide lockdown imposed in response to covid-19. the traffic accident data set obtained from the statistical department in jordan was very large, with different formats and many redundancy and anomalies. to eliminate these defects and extract valuable knowledge, we applied the proposed approach to the dataset using a tall array of big data. the result revealed eight main factors responsible for road accidents, as shown in table 2. these https://doi.org/10.15837/ijccc.2023.3.5006 8 figure 4: road accidents information during 2016-2020 in jordan factors can be used to build a mathematical and simulation model to allocate the main contributing factors in road accidents and test the optimal parameters with the least property accidents’ occurrence using amos “sps”, as described above. the generated data aligned with the expected results generated by the amos statistical model. the factors are responsible for road accidents were inferred and presented in table 2. the analysis revealed that driver mistakes are the main factor responsible for road accidents and cause over 50% of human injuries. one potential solution to this issue is to provide more education and training to drivers using various methods. additionally, smart cars may have restrictions that reduce driver’s mistakes, but some drivers may turn these options off. road accidents causes and reasons slight injuries medium injuries severe injuries fatal injuries traffic light violation 2% 1.30% 0.90% 0% road lane changes crashes 23.50% 26.40% 30.50% 34.50% speed 1.30% 1.30% 1.60% 3.40% drivers’ mistakes 43.60% 51.50% 53.70% 50.70% roads rules’ violations 11.50% 7.80% 4.70% 2.30% violation of road traffic signs and regulations 13.50% 8.80% 5.60% 2.80% road close follower driving 3.40% 1.20% 1.40% 1.60% parking in prohibited areas 0.80% 1.30% 0.70% 3.40% others 0.40% 0.40% 0.90% 1.30% table 2: road accidents causes and reasons with corresponding injury levels we have applied feature selection techniques on the data set to remove non-optimal features and selected only 11 features responsible for causing injuries in traffic accidents. these 11 factors include https://doi.org/10.15837/ijccc.2023.3.5006 9 the type of vehicles, license driving type, road surface, weather conditions, region, day, time, month, and speed. these factors are summarized in figure 5. based on our analysis, we have found that small cars have the highest number of injuries in road accidents. this could be because small car drivers are often unqualified and not properly trained, which increases their likelihood of making mistakes and being unable to avoid injuries during accidents. other factors, such as weather conditions, daytime running lights, and road surface, significantly reduce the number of injuries in case of road accidents. this suggests that drivers pay more attention to these factors and take appropriate measures to reduce the likelihood of injuries. based on our analysis, collisions result in more injuries than run-over and deterioration incidents, as shown in figure 5 -c. we recommend eliminating these incident types in future analyses. we attribute this to the advanced technology used in cars and increased awareness of people in crowded cities. weekdays and weekends have a similar percentage of injuries, as shown in figure 5-i, although fewer injuries occur on weekends. however, the difference is not significant. additionally, july has a higher number of injuries than other months, as shown in figure 5-h. this may be due to increased traffic from workers returning from neighbouring countries such as saudi arabia, oman, and the uae. interestingly, higher speeds over 100 km/h are not a significant factor in increasing the number of injuries. conversely, accidents at lower speeds result in more injuries, and this is because drivers pay more attention at higher speeds. most accidents occur between 4 pm and midnight, as shown in figure 5-g. according to the road department, people get tired after 4 pm and need to rest after a long day, or some start a second job. identifying 11 factors that contribute to car accidents, with driver error being the most significant factor leading to an increase in the number of accidents and injuries, is a significant contribution of this study. the reliable and valid interpretation of the results through the application of big data and kdd techniques offers the potential for informing the development of new policies and regulations to improve road safety and reduce economic and environmental impacts. 6 conclusion and future work the management of road traffic accidents is a critical aspect of traffic management in most countries, given its significant impact on economies and lives. despite extensive efforts to study the factors that contribute to road accidents through daily data recording, the increase in data size in volume, velocity, variety, and veracity necessitates the exploration of new trends in big data analysis. to extract knowledge that assists decision-makers in minimizing the number of road accidents, this research proposes an approach that merges big data techniques with knowledge discovery in databases (kdd) methods. real data collected from the department of statistics and public security directorate were used to apply feature selection methods, resulting in the selection of 11 factors. the analysis revealed that driver mistakes are the primary factors causing accidents and injuries, with small car accidents causing more injuries. however, some detailed factors, such as road surface and weather conditions, do not affect the increase of injuries and could be eliminated in the future. meanwhile, other factors have a small percentage and could affect the accuracy of the results. to address these challenges, future work will include generating a simulation model that incorporates these extracted data to determine the best conditions for traffic management through merging big data analysis and knowledge data discovery. additionally, we propose research that we utilizes artificial intelligence algorithms such as machine learning and deep neural network to simulate real-world scenarios and explore expected optimal solutions for traffic accident management. furthermore, new telecommunication technologies and virtual reality approaches will also be incorporated to enhance the accuracy and effectiveness of the simulation model. this research provides valuable insights that will help policy and decision-makers with strategies to reduce the number of road accidents, injuries, and fatalities. https://doi.org/10.15837/ijccc.2023.3.5006 10 figure 5: number of injuries in road accidents based on factor type references [1] alzyoud, faisal and sharman, nesreen al and al-roosan, thamer and alsalah, yahya (2019). smart accident management in jordan using cup carbon simulation. european journal of scientific research, 152(2), 128–135, 2019. 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[32] tarawneh, monther and alzyoud, fiasal and sharab, yousef (2023). artificial intelligence traffic analysis framework for smart cities, computing conference, ieee, 2023. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open-access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of and subscribes to the principles of the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control https://doi.org/10.15837/ijccc.2023.3.5006 13 cite this paper as: faisal.alzyoud (2023). improved model for traffic accident management system using kdd and big data: case study jordan, international journal of computers communications & control, 18(3), 5006, 2023. https://doi.org/10.15837/ijccc.2023.3.5006 introduction research background and related work data set proposed approach results and discussion conclusion and future work int j comput commun, issn 1841-9836 8(3):384-394, june, 2013. an agent-based solution for the berth allocation problem c. cubillos, r. díaz, e. urra, d. cabrera-paniagua, g. cabrera, g. lefranc claudio cubillos, rené díaz, enrique urra, guillermo cabrera, gastón lefranc pontificia universidad católica de valparaíso av. brasil 2241, valparaíso, chile claudio.cubillos@ucv.cl, imrede@gmail.com, enrique.urra@gmail.com, guillermo.cabrera@ucv.cl, glefranc@ucv.cl daniel cabrera-paniagua escuela de ingeniería comercial universidad de valparaíso pasaje la paz 1301, viña del mar, chile daniel.cabrera@uv.cl abstract: this work presents the development of mabap, a decision support system based on the agent technology that helps in solving the problem of berth allocation for ships within a port. the berth allocation problem (bap) regards the logistics involved in planning and controlling the berthing of vessels. a software architecture in terms of agents is presented; berths and ships representing the actors in the system, berthrequest and berthplanner as representatives of ships and berths in the planning process, and finally the dock and central agents representing the dock or pier. the architecture modeling was done using passi methodology for the design of agent-oriented systems, and the implementation was done in jade, a javabased development environment for multiagent systems. to validate the resulting support system, tests were carried out in which the user can choose different portpolicy scenarios, ranging from maximizing vessels throughput to maximize berths use. keywords: artificial intelligence, decision support system, multiagent architecture, ports planning. 1 introduction maritime traffic is of significant importance to our country and the world, as reported by the united nations conference on trade and development (unctad) [1], which places chile in the top 20 of container traffic. however, while container traffic has increased, the percentage of change from year to year has been declining and it is expected that this trend will continue. the latter, is largely due to the world economical state. under such settings, an enormous potential can be developed through the optimization of related processes, obtaining competitive advantage in the market. the operations involved in maritime traffic and more precisely the activities carried out in ports are one of the most complex ones within the transportation industry. such complexity is due to three reasons: 1) the wide variety of involved actors, 2) the interaction under a highly dynamic environment and 3) the distributed nature of the problem. of the vast number of problems present in port logistics, there is one referred as the berth allocation problem (bap), which can be defined as a tactical planning problem, where the objective is to assign, ideally optimally, the position of ships inside the port for loading and unloading, so as to minimize the costs of container movements within the port. the problem, in the most complete form, involves two stages: a first stage of assigning the ships to each section of the port, and then a second stage of sequencing each vessel in the temporary space of the different sections. this work focuses on the second stage, considering the presence of only one big section in the copyright c⃝ 2006-2013 by ccc publications an agent-based solution for the berth allocation problem 385 port and its further division into berths. given the complex, distributed and dynamic nature of the problem, the use of multi-agent technology reveals useful to build a distributed software architecture and get a flexible, adaptable and robust decision support system solution capable to deliver a better quality of service together with attempting to obtain competitive results close to the pareto-optimal frontier. the main contributions of the present work are 1) to provide the design of a decision support system based on agent technology for the berth allocation planning, 2) provide a distributed planning process based on a greedy insertion heuristic solver [15] and the contract net protocol [4], and 3) provide an implementation of such and its validation through tests that show how diverse port policies can be chosen. 2 multiagent technology in literature there is no exact definition of what an agent is. however, one of the most cited is wooldridge’s definition [2]: "an agent is a computer system located in an environment that is capable of performing actions independently to achieve its design goals". while this definition identifies some characteristics of an agent, it is not clearly distinguishable from a conventional distributed system. however, the main mentioned feature in which there is a consensus is autonomy. in the rest, there is often little agreement as many of the features have different relevance depending on the domains. for example, the characteristic of learning can be desirable for a certain application, while for another one it would not only be unimportant, but even undesirable. despite the differences, one can forget about a precise definition in order to identify certain common properties, such as [2]: − autonomy: agents are capable of task selection, prioritization, goal-oriented behavior, and decision making without human intervention. − social ability: they can "contract" other components through some communication and coordination, and cooperation in solving any given task. − reactivity: they can perceive the context in which they operate and react appropriately. multiagent systems multiagent architectures arise from the need to develop advanced complex applications consisting of a number of subsystems that interact to achieve distribute intelligence among various actors, giving rise to the creation of multi-agent systems (mas). the use of a multiagent architecture appears as an appropriate solution when dealing with physically distributed problems, or where experience is required for integrating heterogeneous technologies or where the problem in question is defined on a computer network. the use of a multiagent architecture features in these cases as the most suitable alternative to leverage a distributed solution, adaptable to changes in structure and environment. in addition, an associated approach will build a total system from different autonomous units. in a mas, subsystems are defined with absolute local decision-making, and therefore a definition of policies to cooperate, negotiate and coordinate their actions is necessary. therefore we can find different types of agents according to their capabilities [8]. the passi methodology passi (a process for agent societies specification and implementation) [3] is a step-by-step methodology that allows to go from the requirements to the system code. aimed at designing 386 c. cubillos, r. díaz, e. urra, d. cabrera-paniagua, g. cabrera, g. lefranc and developing multi-agent societies, it integrates design models and concepts from software engineering and object-oriented multi-agent systems using uml notation. thus allows providing the engineering formalism required in such systems’ design. the passi methodology consists of five phases at various design levels, and twelve steps in the process of building a multi-agent system. examples of its use can be found in literature devoted to the design of a bookstore system [3], a virtual enterprise for transportation [16] and an open agent system [17], among others. 3 the berth allocation problem in container shipping, the hub and spoke model is widely adopted. the ocean-going ships, also called mother ships (mother vessels) operate between a limited number of transfer terminals (hubs). smaller vessels (feeders) link the hubs to the other ports (spokes). in recent years, the mother vessels have increased markedly in size making transport up to 8000 teu (twenty foot equivalent units) and larger sizes are planned. transshipment ports are large intermodal platforms and only a limited number of them handle a significant part of world traffic. however, when a ship arrives at a port, it must wait for a space to tie up to the dock. for this reason it has different sections, mooring points or berths. in addition, there are gantry cranes available for loading/unloading and smaller vehicles for the rest of the transportation work, such as forklifts. once docked at the berth, containers destined to the port must be unloaded and new containers directed to other ports must be loaded before the ship can continue its course. the demand on those ports for the load/unload procedure to run with maximum efficiency will become greater, as the shipping companies will continue to increase their fleet size and the capacity of new ships. the logistics involved in planning and controlling the docking of ships is called the berth allocation problem (bap). in the bap, the port management should try that: − the ships dock as soon as possible to ensure a rapid turnover. − the forklifts load/unload the required containers in the shortest time possible. − the cost of transshipment of containers is minimal. a frequent problem associated with the latter has to do with the allocation of forklifts to each of the arriving ships, which is directly associated with the handling time and has a strong impact on the bap, known as the crane scheduling problem [5]. the problem can be represented in a two-dimensional space as shown in figure 1, where the rectangles represent the ship whose dimensions are its length, including a safety margin and the handling time. these rectangles are positioned in the decision space without overlapping and meet certain restrictions. in the spatial dimension, there are restrictions on water depth (draft allowance) and the maximum distance in relation to the most favorable location along the pier, calculated considering the outbound-containers location and the space reserved for the inbound containers. in the temporal dimension, restrictions are expressed as time windows. some of them are soft and can be relaxed by an appropriate penalty cost. related work in literature, many studies on the problem can be found. first, the bap can be modeled in a discrete case if the dock is seen as a finite set of berths. in this case, the berths are described as fixed length segments or if the spatial dimension is ignored, as dots. when considering that the length of vessels is very variable, one could divide the dock in sections to make the assignment, an agent-based solution for the berth allocation problem 387 figure 1: bap two-dimensional (space vs. time). although it would be difficult because the requirements vary dynamically. if large segments are used, space sub-use will happen in some cases, while smaller segments will make it difficult to find feasible solutions. to overcome this, a dynamic model is considered which defines that the ships can tie up anywhere in the dock. for the discrete case, the bap can be modeled as an unrelated parallel machine scheduling problem [10], where each boat is seen as a work and each docking point as a machine. the time of arrival of each vessel is the release time of the work. in the continuous case, the bap is a two-dimensional problem of the cutting stock problem with additional constraints. in either case, bap complexity turns out to be np-hard [11]. in [12] has been proposed a dynamic formulation of the bap or dynamic berth allocation problem (dbap), which is represented as a finite set of docking berths and the arrival of the ships is considered random. in other words, the spatial dimension of the ships and berths is not considered. this formulation is called "dynamic" compared to the previous one called static berth allocation problem (sbap) [13] which considers that all the ships are already in port when the berths are available. the sbap can be solved in polynomial time with the hungarian method proposed in [14], since it can be reduced to an assignment problem. for the dbap, the authors take advantage of this feature and propose a lagrange relaxation for reaching a desirable allocation sub problem. the computational results show that dbap is relatively easy to solve while the cases are "close" to the static case, in the sense that most of the ships are already in port when the berths are becoming available. the objective function is the sum of service times of the ships and do not consider the existence of certain vessels priority over others. on the other hand, in literature there is no much research covering the bap from a software architecture perspective, nor using agent technology as modeling paradigm. in the following are mentioned the two most relevant works in the area for the present research. the first is madarp [6], an agent software architecture for the dial-a-ride problem (darp), because bap can be seen as a passenger transportation problem, in which ships correspond to passengers and the berths to the vehicles. madarp architecture identifies different layers: interface, planning, service and ontology and agents associated with each of the actors involved: customers and vehicles. in [9] the work was further advanced and a novel solver for darp was leveraged based on genetic algorithms. a second alternative found tackling partially bap is [7], which proposes a multiagent architecture as solution to a port terminal operations automation problem. the system architecture divides the problem into sub-problems, which are determined by specific agents. however, the above solutions do not provide enough details on the solvers used and on how the resulting planning & control support systems aid port decision-makers to implement diverse planning policies according to their needs. 388 c. cubillos, r. díaz, e. urra, d. cabrera-paniagua, g. cabrera, g. lefranc 4 the mabap planning system this chapter presents the functional specification of the multiagent decision support system and its technical design, in terms of software engineering artifacts. regarding the bap, some of the specifications for the requirements and restrictions considered for the problem are described in the following: − it is considered the discrete case of the problem, in which the docking points are considered fixed in size and number. − each ship must have its eta (estimated time of arrival), the estimated processing time and allowed time windows for its processing. − the berthing points have time windows that limit their availability. − the implementation must consider the existence of dynamism, due to events that may occur, such as a ship’s cancellation of arrival, delays and/or closure of any berth. − the length of the ship must not exceed the length of the largest section in the dock. − a section can only process the ships one by one, and as we know they should not overlap in the time-space diagram. proposed multiagent architecture figure 2 shows the proposed conceptual architecture, based on the madarp architecture [6]. therefore, the paper considers making an adaptation of this architecture to the particular problem of bap maintaining its structure, leading agents to have similar functions, i.e. the vessels are considered as "clients" that request a service, not of transportation in this case but of a docking point assignment. on the other hand, berthing points or its subsections are considered as "vehicles" that offer a service, not of transportation but of vessels stowage. the following figure 2 depicts the main agents of the mabap architecture. in the first layer, berth agents and ship agents leverage as interface agents, plus the berthplanner and berthrequest agents tackling the planning & scheduling tasks. figure 2: multiagent architecture for bap. there is also another agent called dock agent, representing the management of the port company to act as intermediary between berths and ships. the central agent is a matchmaker, responsible for delivering the list of berth agents that meet the requirements of a given ship agent. the agents participating in the system and their main responsibilities are described in the following: an agent-based solution for the berth allocation problem 389 • ship agent: it is the one who communicates with the operator and takes the tasks of ship registering and the reporting of events that take place with the ship. it also initiates the docking process with a request, being entitled to cancel and change such request. the created requests are sent to the berthrequest agent. • berthrequest agent: this agent represents each ship in the negotiation process when requesting a new berthing point. it is responsible for receiving orders to process a new request and terminate its execution. together with the ship agent, they embody each of the vessels requiring stowage. • berth agent: represents each docking point or berth in the system, creating its corresponding berthplanner agent. it also manages the berth registration with the central agent. • berthplanner agent: it corresponds to the planner agent for each docking point or berth. it keeps track of the ships stowage sequence and it is involved in the process of allocation of new docking points, evaluating the diverse berth requests from ships as they arrive. it also provides its actual schedule to other agents in the society upon request. • central agent: together with the dock agent, this agent embodies the third main actor, the dock or pier. it keeps up a registry of the berths available at the dock and is responsible for providing the list of berths that happen to be candidates for a particular request coming from a ship. • dock agent: this agent is devoted to manage the process of allocation of berthing points, handling incoming requests and initiating negotiations with the various berths of the dock. the main interaction scenario (figure 3) is when a ship calls for the allocation of a new docking point. the sequence starts with the ship which sends a docking request through the berthrequest agent to the dock agent. such request has a profile, wherein the information includes the ship’s name, its approximate time of processing and estimated dates (time) of arrival and departure. the dock agent sends the request to the central agent, requesting a list of candidate sections that meet the profile of the request. the latter generates the list and sends it back to the dock agent. the dock initiates a contract-net [4] with the berthplanner agents; therefore, it sends a request for berthing to all the berthplanner agents from each of the sections in the matching list. these latter agents perform a greedy algorithm for evaluating the insertion of the new ship in its space-time diagram and send back a proposal profile. from the arrived proposals, the dock agent selects the best alternative according to its objective function (e.g. minimum total time of stay of the ship in port). then, the agent communicates to the selected berthplanner agent to insert the ship in its sequence and informs the other berthplanner agents the rejection of its proposals. finally reports the ship agent on the result of its request. 5 insertion algorithm within the mabap system, the evaluation of the inclusion of a new ship into a berth sequence and to choose within the proposals from each berth which is most suitable, are the fundamental objectives of the problem and, therefore, a key aspect of the application implementation. the algorithm used in the application is based on the insertion heuristic proposed by jaw et al. [15] for the darp problem, and used on a past research [9] which has been adapted to fit the bap 390 c. cubillos, r. díaz, e. urra, d. cabrera-paniagua, g. cabrera, g. lefranc figure 3: role identification diagram for berth request scenario. an agent-based solution for the berth allocation problem 391 problem. these changes consider using a single event that is associated with the ship service at the dock, which involved working with a single time window. in the case of darp are considered two events; one for the pickup of the passenger and another for his delivery. the creation of the initial time window for each ship is related to the processing time associated with it, as shown in figure 4a. for the ship a, with estimated arrival and departure times of et and lt respectively, and processing time n, its maximum time window corresponds to [et, lt] and the feasible initial time to [et, lt-n]. the idle time or "slack" in darp is associated with the time in which the vehicle is idle; either stopped or traveling without having to go to pickup or deliver a passenger. in the case of bap, it is determined by the difference of the time the berth is ready to receive a new ship and the time of the ship actual arrival to the berth. (a) time windows. (b) input file example with distribution of ships arrival. figure 4: initial parameters of mabap. therefore, unless the port has a very high ship arrival rate, there will always be idle times at the berthing points of the port. hence, it was decided to remove the restriction of the original heuristic as to allow slack times between ship arrivals, providing a more realistic scenario. furthermore, slack times will also allow discriminating better solutions among the proposals provided by each of the berths. 6 tests and results this section presents the results obtained from the tests. to gather data a typical scenario that considers a dock with 10 berthing points has been considered together with the arrival of 50 ships within a time horizon of 60 time units. each docking point takes 1 time unit to be ready to serve the arrival of a new ship (getreadytime). no space restrictions are considered as any docking place can attend any ship. the generation of instances was performed by an automated process, generating ships data with random time windows of 5 time units maximum. figure 4b shows a graphical representation of the ships arrival within the time horizon for a sample input file. each box represents a ship and its time windows width. the objective function considered the weighted sum of two factors: on one hand, the number of processed ships and on the other hand, the idle time (slack) of berths between ships services. 392 c. cubillos, r. díaz, e. urra, d. cabrera-paniagua, g. cabrera, g. lefranc results the tests were carried out under two scenarios for planning the distribution of ships on berths, which considered different values for weighting the two factors of the objective function; the shipsnumberfactor and the slackfactor. a first scenario had weights of 80% 20% and a second scenario had weights of 20% 80% respectively. to obtain the results on each scenario, 10 input files were used and 20 runs were made under each scenario for a total of 200 executions. the graphics show the total estimated cost of the ten sequences under the two scenarios. by starting with the scene of strong restriction on the number of ships (figure 5), the total cost is in principle almost a straight line and keeps controlled equally for each docking point. (a) graphic of total costs for scene 80%-20%. (b) graphic of total costs for scene 20%-80%. figure 5: graphical results. discussion the following graphics show how the multiagent system can be tuned according to the policies of the port decision-maker. in a first case, the user can prefer solutions in which a higher number of ships are served per berth (80%-20%) while in another case the user may prefer to minimize the idle time of berths (20%-80%). of course the proposed solution can consider other performance measures either for the operator (e.g maximizing throughput) or for the ships it serves (e.g. minimizing ships’ stay). other aspect to analyze refers to the distributed nature of the planning. in this case the planning process involves two steps: 1) the assignment of ships to available berths and 2) the scheduling of the assigned ships inside each berth. our actual prototype of support system provides a distributed solution by using the contact-net protocol [4] as coordination/connection mechanism. other protocols can be used, such as dutch or english auctions, however in a general case no significant improvement should be obtained as in such cases there is no bids selection process. as early described, for each berth request coming for a ship the dock agent makes a callfor-bids to the available berthplanner agents in charge of managing the ships’ schedule of each berth. the underlying optimization problem is known to be np-hard, hence the actual prototype uses a greedy insertion heuristic which ensures less than a second per request. such solver used inside the berthplanners can be changed according to the given needs (by incorporating soft computing techniques) for best solutions alternatives at a cost of more processing time. finally, from a more political perspective, the ship arrival into a port and its berth allocation triggers a transversal process that involves diverse entities and decision-makers (port operator, port authority, berth operators, etc.). therefore, a centralized system is unrealistic. the multiagent system for berth allocation enables such integration of the different actors involved in the berth planning process. in addition, the resulting software architecture is flexible enough to permit an agent-based solution for the berth allocation problem 393 the inclusion of other actors (e.g. customs authority, customs brokers, crane & floor/yard operators, etc.) in a transparent way. in this sense, an interesting approach would be to make it an open agent system by incorporating what developed in [17]. the integration of the diverse port enterprises through a multiagent system capable of wrapping the diverse enterprises’ systems, leverages as a feasible alternative in port operations in general and in the berth planning process in particular. 7 conclusion this paper has tackled one of the many problems that can be found in the daily operation of a port, the berth allocation problem (bap). the complexity of the problem, its mathematical modeling and its variants have been reviewed. a multiagent system has been presented which solves the underlying optimization problem by making use of a greedy insertion heuristic. good results were obtained, allowing the decision-maker to choose solutions according to the port authority policies. further research considers refining the events to be managed by the system (e.g. delays, cancels, breakdowns), introducing multi-objective optimization, thus providing a curve of solutions, and the incorporation of a 3d graphical interface to show the planned solution. acknowledgments this work has been partially funded the pontifical catholic university of valparaíso (www.pucv.cl), through regular project no. 037.425 bibliography [1] united nations conference on trade and development (unctad), review maritime transport, cap v, pp. 73-74, cap vii. 2005. 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[14] papadimitriou, c. h., steiglitz, k., combinatorial optimization; algorithms and complexity, prentice-hall, englewood cliff, nj. 1982. [15] jaw, j. odoni, a. r. psaraftis, h. n. wilson, n.m.h. a heuristic algorithm for the multivehicle advance-request dial-a-ride problem with time windows, transportation research b, 20b(2): 243 257, 1986. [16] cabrera-paniagua, d., herrera, g., cubillos, c., donoso, m. towards a model for dynamic formation and operation of virtual organizations for transportation, studies in informatics and control, issn 1220-1766, 20 (3): 255-264, 2011. [17] cubillos, c., donoso m., rodríguez n., guidi-polanco f., cabrera-paniagua d., towards open agent systems through dynamic incorporation, int j comput commun, issn 1841-9836, 5(5):675-683, 2010. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 4, month: august, year: 2023 article number: 5162, https://doi.org/10.15837/ijccc.2023.4.5162 ccc publications an effect of user experience on a data-driven fuzzy inference of web service quality d. kalibatienė, j. miliauskaitė diana kalibatienė vilnius gediminas technical university saulėtekio al. 11, lt-10223 vilnius, lithuania diana.kalibatiene@vilniustech.lt jolanta miliauskaitė* vilnius gediminas technical university saulėtekio al. 11, lt-10223 vilnius, lithuania *corresponding author: jolanta.miliauskaite@vilniustech.lt abstract today, various stakeholders provide a large number of functionally similar web services (ws) to meet increasingly complex business needs. therefore, to distinguish similar ws, some researchers have proposed using the non-functional characteristics, named quality of service (qos), and user’s needs, expressed through quality of experience (qoe). thus, all those qos and qoe attributes should be taken into account in predicting ws quality jointly, called ws qose (quality of service and experience). however, these attributes are different in nature, i.e., qos is data-driven and numerical, while qoe is expert-based and linguistic. consequently, to predict ws qose, in this paper, we propose a hybrid fuzzy inference approach, composing both quantitative and qualitative data inputs into ws qose output by applying the adaptive neuro-fuzzy inference system (anfis). the developed prototype allows us to implement the proposed approach, investigate its performance, and study the effect of qoe attributes on ws qose. the results of the two experiments show good performance and suitability of the proposed hybrid fuzzy inference approach for predicting ws qose based on combining qos and qoe attributes. we expect that those results inspire researchers and practitioners to understand the ws qose better and develop user needs matching ws. keywords: web service, fuzzy prediction, quality of service, quality of experience, anfis. 1 introduction recently, service-oriented architecture (soa) has been employed by stakeholders since it offers a standard way of achieving interoperability between heterogeneous systems, regardless of the technology and platform of their implementation [5]. however, an atomic web service (ws) has limited functionality and, therefore, cannot always satisfy complex and vaguely defined users’ needs and appropriately reflect sophisticated business processes https://doi.org/10.15837/ijccc.2023.4.5162 2 [5]. for this reason, stakeholders provide a plethora of wss with similar functionality. therefore, to distinguish similar wss, some researchers have proposed using the non-functional characteristics, like availability, reliability, response time, etc., named quality of service (qos) [16],[11], source code metrics [39], assessing cloud qos using owa in neural network [18], qos (response time and throughput) prediction based on the deep fusion of features [12], etc. these characteristics depend on ws implementation and development quality (i.e., taken from the technological level and data-driven) and can be evaluated automatically. other attributes, like a service design, ease of use, cost, reputation, satisfaction, etc., depend on the user satisfaction with ws. they are subjective and significantly depend on the user’s preferences, mood, morale, interests, and other subjective factors and unpredictable reasons [27],[33]. these informal and human-dependent characteristics are known as the quality of experience (qoe) for ws [21]. they are qualitative and can be evaluated through surveys. summing up, both qos and qoe attributes [47] should be taken into account in predicting ws quality jointly, called quality of service and experience (ws qose). nevertheless, some authors propose ws qos/qoe determining approaches, they are insufficiently personalized and accurate [4],[31],[1]. it is unclear what quality attributes should be considered and how to measure them [16]. in addition, qos values from ws providers do not reflect the real ws qoe value, i.e., they differ for various users, depend on a context, and dynamically change over time according to several parameters [38]. moreover, for incorporating qoe into ws quality assessment, we need real data on the user’s experience or/and satisfaction. however, qoe data is missing in many problem domains. in [41], the authors used a real-world qws dataset [2] for qos attribute values and the amazon customer review feedback dataset [3] for qoe values. however, qoe remains subjective, and its evaluation is expensive and tedious, requiring a high human involvement [6]. so, considering that users cannot invoke all ws to obtain qoe due to increased time cost and huge resource overhead [49],[53], the involvement and use of domain experts to obtain ws qose values makes sense and is applicable in this paper. authors of [29] use interviews, questionnaires and webbased surveys to get subjective information from users about products or services. authors of [21] created a synthetic dataset for ws recommendation that contained actual user reviews of services published on service portals, and mapped these reviews to the services in ws-dream dataset. in [7], the ws-dream dataset is used for ws quality prediction in cloud computing and the internet of things (iot) using graph convolutional network. authors of [17] use the same the ws-dream dataset with the collaborative filtering (cf) method for qos prediction. authors of [26] showed that three analysed qos metrics (i.e., packet loss, latency, and jitter) have a very significant relationship with the qoe of iot services. the authors [34] have stated that the qos and qoe concepts are often studied separately, and making the comprehensive quality analysis is problematic and not trivial. therefore, they have studied qos and qoe in mobile applications and found that qos attributes directly impact qoe perceived by the user. summing up, the question under consideration is how to combine qos and qoe into qose? and how does the inclusion of qoe attributes in the prediction affect the final qose? the bayesian approach is popular to combine subjective judgment or prior experience with test samples data [40], to personalise manufacturing service recommendations [52], to evaluate multivariate qos attributes at run-time [51]. however, the authors proposed weighted bayesian runtime monitor [50] approach sometimes produces false positives and false negatives errors that decrease the capacity of making decision. the main advantage of using the bayesian approach is augmenting the quality of data, thereby reducing the uncertainty of decision-making [40]. since quality is a vague and subjective concept, it is recommended to model and assess it by applying a fuzzy inference [36],[45]. fuzzy controllers, such as mamdani, sugeno, etc., are the most widely used in practice since they provide satisfactory results with quantitative numerical data input [48]. also, the adaptive neuro-fuzzy inference system (anfis) it allows us to get more successful and accurate results in prediction [24]. therefore, in this research, we apply anfis for data-driven ws qos prediction. the main aim of this paper is to extend the concept of ws qose by investigating how to combine qos and qoe into qose employing fuzzy inference approach, and investigating how the inclusion of https://doi.org/10.15837/ijccc.2023.4.5162 3 qoe attributes in the prediction affects the final qose. consequently, this paper proposes a hybrid fuzzy inference approach for predicting ws qose with anfis. the advantage of the research is that we have supplemented and combined objective measures of real ws from the wsdream dataset by collected real-world subjective measures (expert evaluations) of qoe on the same ws dataset. consequently, a complete picture of service quality is obtained during the ws planning process. the main advantages and scientific contribution of this paper are as follows: 1. a proposed new hybrid fuzzy inference approach composes both quantitative and qualitative data inputs and formalize the vague concept of qose. 2. the proposed approach assesses the ws quality from the user’s perspective, not just from a numerical perspective. 3. the proposed approach employees the content separation principle of the subjective and objective quality attributes, which are combined into ws qose through their combination by fuzzification and normalization. 4. the successful implementation and demonstration of the hybrid fuzzy inference ws qose planning system demonstrates its design contribution. 5. the proposed approach and its implementation into the hybrid fuzzy inference ws qose planning system allows us to investigate the impact of subjective attributes on ws qose by statistical, correlation and similarity analysis. 6. the obtained experiments of hybrid fuzzy inference ws qose planning showed a significant influence of qos attributes on whole ws qose performance value. the novelty of the current study is that we combined quantitative and qualitative ws characteristics into the hybrid ws qose concept, which encompasses both objective and subjective attributes. this ws qose concept is implemented into hybrid fuzzy inference ws qose planning system, used to predict, plan and investigate the impact of subjective and objective attributes on ws qose. the rest of this research paper is formed as follows. section 2 overviews related works. section 3 describes a hybrid fuzzy inference approach for predicting ws qose performance. section 4 presents the two experiments, and section 5 discusses the obtained results. finally, section 6 concludes the paper. 2 a fuzzy inference approach for ws qose planning this section describes a new hybrid fuzzy inference approach for ws qose planning (figure 1), which consists of the following main parts: 1) fuzzy inference design of domain experts’ knowledge on ws qoe; 2) fuzzy inference controller (fuzzic); 3) data-driven optimization with anfis, for which an external domain dataset for optimization of fuzzy rules and membership function (mf) parameters is used; and 4) linguistic approximation, which transforms crisp results into linguistic form. figure 1: the proposed approach reference schema the main advantage of this proposed approach is the synergy between expert knowledge and an external domain dataset for the planning of ws qose. 2.1 fuzzy inference design this component transforms user experience expressed in a linguistic form into crisp data. it corresponds to the qose modelling, when experts express their opinion on ws qose attributes. ws https://doi.org/10.15837/ijccc.2023.4.5162 4 qos is a tuple qs (q1,q2,. . . ,qn), where qi denotes the objective attributes of ws qos, like response time, execution cost, availability, etc. applying a fuzzy set theory, a linguistic variable of ws qose q consists of linguistic attributes qs , whose value set t, in turn, can be divided into meaningful crisp intervals. linguistic meaning of these intervals denotes the end-user’s satisfaction level as “good” (g), “moderate” (m), “low” (l). in general, the ws qose q is expressed by eq. 1: q = {qs , t, u, m (t )}, (1) where u is the universe of discourse with elements u in interval (0, +∞). t is a set of linguistic terms l1, l2, . . . , ln of qs . m (t ) is a set of semantic rules used to link each term with its meaning, where m (t ) is a sub-set of u and defined by different types of membership functions (mf). in this research, a triangular mf (eq. 2) is used as an initial shape, and a trapezoidal mf – the revised one (eq. 3) [36],[10]. µ t riang ai (x) =   0, if x < c1, c3 ≤ x, x−c1 c2−c1 , if c1 ≤ x < c2, c3−x c3−c2 , if c2 ≤ x < c3, (2) µ t rap ai (x) =   0, if x < c1, c4 ≤ x, x−c1 c2−c1 , if c1 ≤ x < c2, 1, if c2 ≤ x < c3, c4−x c4−c3 , if c3 ≤ x < c4, (3) where: c1, c2, c3, c4 are the parameters of mf. since user’s satisfaction is vague by its nature, a semantic rule m(t) determines the grade of membership µ(li)(u) of u to a particular linguistic term li. so, the closer the value µ(li)(u) is to 1, the more u belongs to li. experts e express their opinion on partition intervals of linguistic terms li in u of ws qos attributes (eq. 4): e = {(exj ) | j ∈ [1; m], m ∈ n+}, inter li i = {[u li 1 ; u li 2 ] | u li 1 ∈ r>0, u li 2 ∈ r>0, u li 1 < u li 2 }, (4) ∀exj : t (li) → interlii , where each expert (exj ) assigns a partition interval (interlii ) to each linguistic term t (li). all these assignments form a set of expert rules rexij and used as the output of the fuzzy inference design component. 2.2 fuzzy inference controller (fuzzic) fuzzic is a classical fuzzy inference controller [23],[43], consisting of the following parts: fuzzification, fuzzy inference, and deffuzification. fuzzification, employing a particular fuzzification method to convert a crisp dataset input into a fuzzy dataset. since measured quality attributes can have different measurement units, they should be normalized applying different techniques. here, a linear max-min normalization [46] (eq. 5), which map fuzzy numbers to the interval from 0 to 1, is used. benefit criteria: ũi = ui − min(ui) max(ui) − min(ui) , (5) cost criteria: ũi = max(ui) − ui max(ui) − min(ui) . https://doi.org/10.15837/ijccc.2023.4.5162 5 in particular, weights can be assigned to experts according to their experience in the application domain, etc. [25]. moreover, the fuzzified data is presented for balancing, since consensus should be found between different expert opinions. for balancing of intervals, the labelled equilibrium fuzzy relation ρt (li) ′ eqlb of normalized terms of linguistic variable t (li) ′ is suggested in [22]. after balancing, the mfs shapes are redefined using trapezoidal (eq. 3), l-shaped [13] and γ-shaped [15] mfs. fuzzy inference applies a set of fuzzy rules and a particular fuzzy inference approach, like mamdani or takagi-sugeno (t–s), to fuzzy inputs for generating a fuzzy output. defuzzification translates the obtained fuzzy output into a crisp user understandable output, using a particular defuzzification method, like centroids, max-membership, weighted average, etc. [8]. 2.3 data-driven optimization (anfis) in order to improve the performance of the proposed system, mf parameters and fuzzy rules are optimized, using a particular optimization method such as genetic algorithm, particle swarm optimization, pattern search, adaptive neuro-fuzzy inference, etc. [22]. for better performance and feasibility, anfis, which is a combination of a fuzzy inference system (fis) and an adaptive neural network [20], is used for optimization. it is based on a set of fuzzy if-then rules (eq. 6) to generate the predefined input-output pairs. ri : if (x1isa (i) 1 ) . . . and . . . (xnisa (i) n ) then fi = a t i · x + bi, (6) where (x ∈ rn) is an inputs vector in the premise part characterized by a particular mf, and (ai, bi) is a pair of the coefficients of linear takagi–sugeno consequents. anfis training is the process of determining the premise and consequence parameters using a particular optimization algorithm. it can be presented in five layers as follows: layer 1: fuzzification: inputs ((x1, x2), n = 2) are fuzzified using trapezoidal mfs (eq. 3) whose parameters c1, c2, c3, c4 are adapted by the learning process. in the result, as the parameter values change, mfs of the linguistic terms a(i)n change too. layer 2: evaluation of the rule strength: each node has the rule strength wi (eq. 7): wi = ∏ µi(x). (7) layer 3: normalization: all rule strengths wi are normalized (w̄i) (eq. 8): w̄i = wi∑ i wi . (8) layer 4: application: the rule (ri) is obtained to get the output fi (eq. 6). layer 5: computation: the global model response (f) is determined (eq. 9): f = ∑ i w̄ifi. (9) after training, the anfis performance is determined by various statistical tests. here, we applied the following statistical tests. the coefficient of determination (r2) [9] measures how differences in one variable can be explained by differences in another variable (eq. 10). the higher r2, the higher the percentage of points through which the drawn line passes when the data points are plotted. r2 = 1 − ssres sstot (10) sstot = ∑ i (ftraini − f̄train) 2 and ssres = ∑ i (ftraini − fi) 2, https://doi.org/10.15837/ijccc.2023.4.5162 6 where ssres denotes the sum of the squares of residuals, and sstot – the total sum of squares. the mean squared error (mse) [9] (eq. 11) measure the average of the squares of errors between the output f and the training ftrain. m se = 1/n n∑ i=1 (fi − ftrain)2, (11) where fi and ftraini show the anfis output and the measured value from the i-th element. the closer mse is to zero, the more accurate the predicted results are. the root mean squared error (rmse) is the square root of the average squared distance between the actual and the predicted scores [9] (eq. 12). rm se = √ ∑ i(fi − ftraini )2 n . (12) rmse is always positive, and a value of 0 indicates the perfect fit of the data. generally, a lower rmse would be better than a higher one. since rmse can vary in the interval [0, +∞), some authors, like [19], propose using normalized rmse (eq. 13). n rm se = rm se (maxfi − minfi ) . (13) then, the normalized rmse (nrmse) ranges in the interval [0; 1], where values closer to 0 represent better fitting predictions. the sum of squares due to error (sse) is the total deviation of the response values from the fit to the response values [28] (eq.14). sse = n∑ i=1 (fi − ftraini ) 2. (14) an sse value closer to 0 indicates a smaller random error component in the prediction model, i.e., the fit is more useful for prediction. 2.4 linguistic approximation after fuzzic gives us the result in the form of a mathematical expression, it should be translated into a linguistic expression, which is better comprehensible and interpretable by the user [14]. for example, the end-user understands a linguistic expression, like “high quality” or “low quality”, better than the crisp number, like, “the quality equals to 0.24”. linguistic approximation is a process of assigning linguistic expressions to various mathematical objects that are obtained as outputs of fuzzy models [42]. in the literature, we can find various approaches to linguistic approximation, like the “best-fit” methods, linguistic hedges and connectives, etc. in this research, we employ the most frequently used the “best-fit” approach for linguistic approximation [42]. in this paper, the “best-fit” linguistic approximation of the crisp output out, obtained from fuzzic, is a process of finding the most suitable linguistic term louti , such that the fuzzy-number meaning of the approximating linguistic term louti is the closest to the approximated fuzzy set (eq. 15). louti = arg mini d(li, out). (15) for the aim of linguistic approximation, the euclidean distance (the “best-fit” technique), is applied (eq. 16 [35]). https://doi.org/10.15837/ijccc.2023.4.5162 7 d(li, out) = √√√√ 1 d d∑ i=1 (µli (ui) − µout(ui))2, (16) where: li and out are fuzzy sets in u, and d is the fuzzy number (i.e., d=3 for triangular mf, d=4 for trapezoidal mf). for n, the euclidean distance matrix [n×n] is developed and the minimum distance to the nearest linguistic term is found. it is treated as a linguistic term for the approximated fuzzy number out. if the approximated fuzzy set out corresponds to the meaning of some of the linguistic values of li and, the correct linguistic label that fits 100 % is found, the distance will be minimal. however, this rarely happens, and in reality 0 < d(li, out) < 1, even with the “best fitting” linguistic label. this is due to the transformation of fuzzy sets into natural language descriptions. in addition, ambiguous situations occur, where the minimal distance from several meanings of linguistic labels is the identical or similar. in this case, a more suitable label can be chosen from the provided. summing up, the result of the “best-fit” linguistic approximation depends on the choice of distance/similarity measure and the underlying linguistic variable. finally, the output of the proposed fuzzy inference ws qose planning system in the form of a set of linguistic terms is obtained. 3 results this section describes a new hybrid fuzzy inference approach for ws qose planning (figure 1), which consists of the following main parts: 1) fuzzy inference design of domain experts’ knowledge on ws qoe; 2) fuzzy inference controller (fuzzic); 3) data-driven optimization with anfis, for which an external domain dataset for optimization of fuzzy rules and membership function (mf) parameters is used; and 4) linguistic approximation, which transforms crisp results into linguistic form. two experiments are conducted to validate the proposed approach as follows: 1. experiment 1, using objective and subjective attributes for predicting ws qose by the proposed approach; and 2. experiment 2, using only objective attributes for predicting ws qos applying only anfis. 3.1 obtaining ws qose performance (experiment 1) in this section, we describe experiment 1 for ws qose performance prediction with the proposed ws qose planning system (see figure 1) using objective and subjective attributes of ws. the flowchart of this experiment is presented in figure 2. figure 2: the flowchart of obtaining ws qose performance with the proposed system. 3.2 processing subjective attributes according to figure 2, linguistic knowledge about response time and throughput is collected from experts (i.e., “collect linguistic knowledge from experts”) and an input of the fuzzy inference ws qose planning system is formed in table 1. this table presents the experts’ (exn) opinions on https://doi.org/10.15837/ijccc.2023.4.5162 8 partition intervals of linguistic terms li in uod of ws qos attributes.the experts were from various role groups as follows: four domain users (ex1 − ex4) and four application developers (ex5 − ex8). table 1: throughput and response time ws qose data collected from experts response time (sec) throughput (mbps) high moderate low very high high moderate low ex1 0.80 2.9 2.9 6.4 6.4 24.0 760 947 458 779 219 531 107 205 ex2 0.10 2.3 2.3 4.6 4.6 21.0 607 972 604 608 204 464 118 199 ex3 0.70 4.8 4.8 8.7 8.7 19.0 698 912 626 784 198 421 83 190 ex4 0.80 3.0 3.0 6.6 6.6 18.0 812 986 404 747 208 292 68 208 ex5 0.50 4.5 4.5 8.9 8.9 18.5 650 976 426 666 208 284 97 200 ex6 0.01 6.6 6.6 9.3 9.3 18.0 651 917 596 806 203 497 83 250 ex7 0.10 5.5 5.5 10.3 10.3 19.0 741 922 446 749 204 430 112 180 ex8 0.50 7.2 7.2 11.2 11.2 21.5 831 978 437 653 218 346 103 218 this data is transferred to fuzzic (figure 1) (i.e., “perform fuzzification of response time and throughput” in figure 3a). when the response time is “high”, the user feels an uninterrupted flow of thoughts, even if he/she notices the delay [44],[37] (figure 3a). the “moderate” response time is about the limit for keeping the user’s attention on the dialogue. for a longer delay (the response time is “low”), the user would like to perform other tasks while awaiting the computer to finish. figure 3: input mfs from experts: a) response time (sec); b) throughput (mbps). similarly, for throughput, presented in figure 3b, the values that fall under the “very high” and “high” ranges shall exhibit no interruptions of user’s work. moreover, under the “very high” throughput the user feels that the system is reacting instantaneously. meanwhile, under the “moderate” throughput user notices the delay, but it is acceptable for keeping his/her attention on the work. finally, the “low” throughput disturbs with the user’s focus on the work. after processing all of the ws qose data collected from the experts, response time and throughput datasets are combined into combinatorial variants of the qose characteristic performance see table 2. the fuzzified pair of inputs then goes through the sugeno-type fuzzy inference component (see section 2.2) consisting of fuzzy if-then rules for determining the defuzzified crisp values of ws qose performance. in fuzzic, we have a 12 (31 × 41) verbose fuzzy rule set (see table 2). it should be noted that increasing the partitions of the input space exponentially increases the number of rules and significantly slows down the learning and application speed of the system. based on the defined fuzzy if-then rules by eq. 6 and applying values from table 2, the ws qose performance surface (figure 4) is pictorized by fis, which is configured in matlab fuzzy https://doi.org/10.15837/ijccc.2023.4.5162 9 table 2: the 12 fuzzy if-then rules according to the experts’ opinions in the fuzzic (c – constant, vh – very high, h – high, m – moderate, l – low, vl – very low). no if response time is and throughput is then qose performance t (l1) inter (l1) i t (l2) inter (l2) i output inter out i /c 1. h (0; 7.2) h (404; 804) m–h (0.333; 0.667) 2. m (2.3; 11.2) m (195; 531) m 0.333 3. l (8.25; 20) l (0; 250) l–m (0; 0.333) 4. h (0; 7.2) vh (607; 987) h 0.667 5. h (0; 7.2) l (0; 250) m 0.333 6. h (0; 7.2) m (195; 531) m–h (0.333; 0.667) 7. m (2.3; 11.2) l (0; 250) l–m (0; 0.333) 8. m (2.3; 11.2) h (404; 804) m 0.333 9. m (2.3; 11.2) vh (607; 987) m–h (0.333; 0.667) 10. l (8.25; 20) m (195; 531) m 0.333 11. l (8.25; 20) h (404; 804) m 0.333 12. l (8.25; 20) vh (607; 987) m–h (0.333; 0.667) logic toolbox for analyzing, designing, and simulating fuzzy logic systems. figure 4: the ws qose performance surface using fis. during the implication process of response time and throughput, we have got two cases of qose performance: 1) interval (like high–moderate (0.333; 0.667) in figure 4), where the surface varies (see figure 4); and 2) constant (like moderate (0.333) in figure 4), where the surface remains constant (see figure 4). consequently, constant qose performance values belong to one linguistic term. meanwhile, interval qose performance values cannot be assigned to a single linguistic term, rather to the interval of two linguistic terms with different membership degrees. therefore, qose performance can be categorized into revised seven linguistic terms as follows: low, low–moderate, moderate, moderate–high, high, high–very high, and very high. all these obtained information form fim, which is transferred to anfis (i.e., “transfer obtained fuzzy inference model (fim) to anfis” in figure 2). 3.3 employing anfis for optimization with crisp data here, the following steps are performed “load a ws dataset to anfis” and “perform fim optimization with anfis” (figure 2). https://doi.org/10.15837/ijccc.2023.4.5162 10 figure 5: the mfs of the output of ws qose performance obtained using fis. for the data-driven optimization (see section 2.3), we have used a ws dataset collected from wsdream dataset [54],[55], which presents response time and throughput values from 339 users on real-world 5 825 wss. anfis hyper-parameters for optimization with crisp data are shown in table 3. table 3: anfis hyper-parameters hyper-parameters description/value fuzzy structure fis training data sugeno/genfis1 generation of fis object grid partition on the data mf type (input / output) trapmf (trapezoidal) / constant number of variables (inputs/outputs) 2 / 1 number of terms (input1 / input2) 3 / 4 optimization method hybrid training algorithm to model the training data least-squares and backpropagation gradient descent methods maximum number of training epochs 100 initial step size 0.001 step size decrease rate / increase rate 0.9/2 andmethod / impmethod / aggmethod / defuzzmethod prod / prod / sum / wtaver data for training / data for testing 80/20 the principal schema of the fuzzy inference with anfis is presented in figure 6. in layer 1, the input data (response time (x1) and throughput (x2) is fuzzified based on eq. 3 and mfs are obtained, where each mf is presented as an adaptive node (see figure 6). the anfis network’s layer 2 is formed when the fuzzy rules are established by obtaining all possible combinations of mfs pairs. consequently, we have got 12 pairs of (x1) (response time) and (x2) (throughput). using eq. 7, a firing strength (weight for each fuzzy rule is 1) are computed. in figure 5, each obtained rule is presented as non-adaptive node. in layer 3, the rule strengths are normalized (eq. 8) and presented as non-adaptive node. consequently, in layer 4, the defuzzification is performed, and fuzzy rules are applied (see eq. 6) by adopting each node. layer 5 is the summation layer, which has a single fixed non-adaptive output of the overall anfis network obtained using eq. 9. the 12 fuzzy rules and mf parameters are taken from fuzzic (shown in table 1) and loaded to anfis. thus, in anfis undergoes optimization of those mf parameters and fuzzy rules from fis based on objective crisp data. after this optimization, those optimized parameters and fuzzy rules are translated back to fuzzic (i.e., “load an optimized fim to fuzzic” in figure 2). further, the fuzzic makes inferencing https://doi.org/10.15837/ijccc.2023.4.5162 11 figure 6: the proposed anfis network for ws qose performance prediction. based on the optimized mf parameters and fuzzy rules. based on this scenario, the ws qose performance value may change after optimization and it may fall into another predefined linguistic term interval. for example, before the optimization, we have “moderate” performance, and after, it becomes “high”. the resulting surface of the optimised ws qose performance is presented in figure 7. as can be seen, it becomes smoother. figure 7: the resulting surface of the optimised ws qose performance. the obtained fuzzy if-then rules after optimization are presented in table 4. note that qose performance is characterized by a linguistic term, its interval, and a crisp value of the qose performance in this interval. the ws qose performance after optimization employing anfis looks like presented in figure 8. the ws qose performance linguistic terms have been changed after optimization applying anfis. as a result, we have got five linguistic terms: low, low–moderate, moderate, moderate–high, high (i.e., “perform linguistic approximation” in figure 2). thus, the first experiment (experiment 1) is completed. 3.4 obtaining ws qos performance with anfis (experiment 2) in this section, we describe experiment 2 for ws qos performance prediction with anfis using only objective attributes of the same wsdream dataset (see figure 9). https://doi.org/10.15837/ijccc.2023.4.5162 12 table 4: the optimized 12 fuzzy if-then rules (c – constant, vh – very high, h – high, m – moderate, l – low, vl – very low). no if response time is and throughput is then qose performance t(l_1) inter_i^(l_1 ) t(l_2) inter_i^(l_2 ) output inter_i^out/c 1. h (0.552; 1) h (0.334; 1) h (0.65; 1) / 0.835 2. m (0.103; 1) m (0; 0.667) m (0.052; 0.835) / 0.444 3. l (0; 0.552) l (0; 0.338) l (0; 0.386) / 0.052 4. h (0.552; 1) vh (0.667; 1) h (0.823; 1) / 1.000 5. h (0.552; 1) l (0; 0.338) m (0.277; 0.67) / 0.501 6. h (0.552; 1) m (0; 0.667) m–h (0.666; 0.835) / 0.668 7. m (0.103; 1) l (0; 0.338) l–m (0.052; 0.386) / 0.277 8. m (0.103; 1) h (0.334; 1) m–h (0.66; 0.835) / 0.610 9. m (0.103; 1) vh (0.667; 1) m–h (0.777; 1) / 0.777 10. l (0; 0.552) m (0; 0.667) l–m (0.052; 0.38) / 0.219 11. l (0; 0.552) h (0.334; 1) m (0.05; 0.777) / 0.386 12. l (0; 0.552) vh (0.667; 1) m (0.33; 0.67) / 0.552 figure 8: the mfs of the output of ws qose performance obtained after anfis. note that in experiment 1, in anfis we have used subjective attributes and fuzzy rules loaded from fuzzic. in experiment 2, we put to anfis only the response time and throughput values of the ws dream dataset, which is classified using the grid partition method, the triangular mfs and the number of input partitions (i.e., “load a ws dataset to anfis” in figure 9). based on the described initial settings and using anfis intelligence, we have obtained the prediction surface (figure 10) (i.e., “perform fuzzy inference with anfis” in figure 9). it represents the dependency between inputs (response time and throughput) and output (performance). note that the anfis structure is the same as in experiment 1. however, the values of fuzzy rules and mf parameters differ because of the optimisation process in experiment 1. a complete set of 12 (31 × 41) fuzzy rules of experiment 2 is presented in table 5, where ws qos performance is characterized by a linguistic term, its interval, and a crisp value in this interval. the defuzzified output of ws qos performance from anfis ranges into five intervals as follows (figure 11): low, low–moderate, moderate, moderate–high, high (i.e., “perform linguistic approximation” in figure 9). thus, the second experiment (experiment 2) is completed. https://doi.org/10.15837/ijccc.2023.4.5162 13 figure 9: the flowchart of obtaining ws qos performance with anfis. figure 10: the ws qos performance surface obtained using anfis from objective data. 3.5 comparison of experiments the obtained results of two experiments are compared as follows: 1) statistical tests (r2, mse, rmse, and sse) (table 6); 2) correlation analysis (the pearson’s correlation coefficient); and 3) the euclidian distances to measure the differences (i.e., similarity [32]) between the main linguistic terms high, moderate and low (table 7). as can be seen, both implemented ws quality prediction systems perform accurately enough and can be used for ws quality prediction. the results of the correlation analysis show very strong linear relationship between qos performance and qose performance (the pearson’s correlation coefficient equals 0.995). the euclidean distances, obtained using eq. 16, are presented in table 7. they range in the interval from 0 to 1, where 0 means no distance between linguistic terms, i.e., they are the same, and 1 – the highest possible distance between linguistic terms, i.e., none similar. consequently, we have characterized the obtained distances using five-point similarity scale as follows: (0.3; 0] – very strong similarity, (0.5; 0.3] – strong similarity, (0.7; 0.5] – moderate similarity, (1; 0.7] – weak similarity, and 1 – none. as can be seen, the low, moderate and high linguistic terms obtained in experiment 1 and experiment 2 have very strong similarity. 4 discussion in this research, we have analysed an impact of users’ subjective opinion on the data-driven ws quality planning and have proposed the fuzzy inference approach for ws qose planning and its implementing system that can deal with numerical objective data and linguistic subjective data inputs, i.e., enabling the expression of user experience through vague concepts. the main knowledge contribution of the proposed approach is deeper understanding of the ws quality planning problem. the obtained knowledge allows us to understand that quality planning is a subjective problem that cannot be solved by only objective and numerical data. in real-world problems, however, restrictions may arise based on modelling assumptions [30]. moreover, the ws quality planning system is developed for users. therefore, their subjective needs cannot be ignored. consequently, we have proposed an approach to combine objective (measurable and expressed in a numerical form) and subjective (experienced and https://doi.org/10.15837/ijccc.2023.4.5162 14 table 5: the 12 fuzzy if-then rules obtained from anfis using the grid partition method (c – constant, vh – very high, h – high, m – moderate, l – low, vl – very low). no if response time is and throughput is then qos performance t (l1) inter (l1) i t (l2) inter (l2) i output inter out i /c 1. h (0.599; 1) h (0.398; 0.899) m–h (0.446; 0.975) / 0.823 2. m (0.236; 0.955) m (0.002; 0.577) m (0.127; 0.823) / 0.446 3. l (0; 0.448) l (0; 0.288) l (0; 0.446) / 0 4. h (0.599; 1) vh (0.75; 1) h (0.625; 0.975) / 0.975 5. h (0.599; 1) l (0; 0.253) m (0.29; 0.637) / 0.489 6. h (0.599; 1) m (0.002; 0.577) m–h (0.29; 0.823) / 0.666 7. m (0.236; 0.955) l (0; 0.288) l–m (0.125; 0.665) / 0.290 8. m (0.236; 0.955) h (0.398; 0.899) m–h (0.56; 0.975) / 0.650 9. m (0.236; 0.955) vh (0.75; 1) m–h (0.65; 0.975) / 0.758 10. l (0; 0.448) m (0.002; 0.577) l–m (0.122; 0.65) / 0.392 11. l (0; 0.448) h (0.398; 0.899) l–m (0.244; 0.535) / 0.359 12. l (0; 0.448) vh (0.75; 1) m–h (0.384; 0.758) / 0 figure 11: output mfs for ws qos performance from anfis using only objective attributes. expressed in a linguistic form) ws quality attributes into qose. another knowledge contribution is that in our proposed hybrid fuzzy inference ws qose planning approach, we have applied the content separation principle by distinguishing between the subjective and objective viewpoints to the quality attributes. those qos and qoe attributes are combined into qose through their combination by fuzzification and normalization (answer to the research question how to combine qos and qoe into qose ). the design contribution of our research lies in the successful implementation and demonstration of the proposed hybrid fuzzy inference ws qose planning system. in order to demonstrate its suitability for ws qose performance prediction and feasibility, we developed a system prototype and conducted two experiments. in experiment 1, objective and subjective attributes of ws were combined using the approach proposed in this research, and the ws qose performance prediction was carried out. in experiment 2, only objective attributes were used for the ws qose performance prediction. finally, the results of both experiments were compared using statistical tests (r2, mse, nrmse, and sse), the pearson’s correlation coefficient and the euclidean distances among linguistic terms. the statistical analysis shows a very good fitting prediction of both experiments. nevertheless, the second experiment (experiment 2) obtained more accurate results than the first one (experiment 1). this small deviation shows the influence of subjective attributes on the ws qose prediction (answer to the research question how does the inclusion of qoe attributes in the prediction affect the https://doi.org/10.15837/ijccc.2023.4.5162 15 table 6: the 12 fuzzy if-then rules obtained from anfis using the grid partition method. statistical tests experiment 1 (fis+anfis) experiment 2 (anfis) r2 0.9839 0.999 mse 0.0002 8.99743e-07 nrmse 0.0143 0.0009 sse 0.0797 0.0034 table 7: the euclidian distances between linguistic terms in experiment 1 and experiment 2. experiment 2 experiment 1 low moderate high low 0.059 (verystrong similarity) 0.464 (strong similarity) 0.955 (weak similarity) moderate 0.557 (moderatesimilarity) 0.071 (very strong similarity) 0.486 (strong similarity) high 1 (nonesimilarity) 0.516 (moderate similarity) 0 (very strong similarity) final qose ). the correlation analysis (0.999) shows a very strong linear relationship between ws qos performance and ws qose performance. consequently, the obtained results confirm that the similar prediction results can be obtained using both approaches, i.e., ws qose prediction and ws qos prediction. nevertheless, the user experience is related to the use and enjoyment of the user by the service, depending on the personality and current state of the user. so, the results of the ws quality prediction differ. moreover, depending on the experts this difference can vary from a small to a significant deviation. also, the small deviation obtained in our study can be explained by the fact that domain experts are well acquainted with the needs of users. deeper research is also needed in this area with different users, experts and ws data to thoroughly investigate the variation in quality prediction and its dependence on user’s needs. the analysis of the comparison of linguistic terms showed that they do not contradict each other and the same terms in experiment 1 and experiment 2 have very strong similarity. summing up, the proposed fuzzy inference ws qose planning approach is feasible and suitable to be used for ws quality planning according to objective and subjective attributes by increasing the user satisfaction level. the performed research shows the impact of user experience on the ws qose prediction that helps us to plan ws quality matching user’s needs. nevertheless, this paper has several limitations, they at once provide future research opportunities. first, the experiments show that they should be extended for bigger number of linguistic terms, which will allow us to see a greater difference and influence of subjective attributes on the whole ws qose performance. second, we need a dataset for subjective and objective ws quality attributes from the same application domain. now, the most researches use well-known several datasets, like ws dream dataset, qws dataset, etc. therefore, the proposed approaches are limited to their realisation and experimentation. moreover, the datasets become obsolete and do not correspond to the real-world problems as the data were collected with old technologies. 5 conclusions the analysis of the related work on ws qos / qoe shows that most of the compared studies are based on quantitative data and well-known datasets (especially the ws-dream dataset is popular). qualitative feedbacks are found in a few studies, because of the high time cost and huge resource overhead. therefore, qoe data is missing in many problem domains. consequently, ws planning based on qos and qoe remains a challenging issue, since there is a knowledge gap in how to combine qos and qoe into qose and how it affects the final qose. the newly proposed hybrid fuzzy inference https://doi.org/10.15837/ijccc.2023.4.5162 16 approach for predicting ws qose allows us to assess the ws quality from the user’s (subjective) and numerical (objective) perspectives combining them through their combination by their fuzzifying and normalizing. experiments have shown that the proposed approach is appropriate for the ws qose prediction, when input data is not only quantitative numerical but also qualitative. both experiments produce sufficiently accurate results. however, the incorporation of subjective attributes into ws qose predictions gives a slight deviation that shows an effect of subjectivity. the statistical analysis shows a very good fitting prediction of both experiments. the correlation analysis shows a very strong linear relationship between the ws qos performance and the ws qose performance. finally, the analysis of the comparison of linguistic terms showed that they did not contradict each other and the same terms in both experiments had a very strong similarity. summing up, the proposed fuzzy inference ws qose planning approach is feasible and suitable for the ws qose planning according to objective and subjective attributes. the future research areas based on the obtained results are planned as follows: • extending related works review to systematic review in order to grasp the main trends and evoliution of ws qose application and new methods for ws qose prediction. • expanding the proposed approach with deep learning algorithms. • experimentation with bigger number of linguistic terms for ws qose performance prediction applying the proposed approach. • finding new datasets for wider experimentation. thus, the research topic under consideration remains relevant and with the emergence of new technologies and approaches, like deep learning, iot, etc., new opportunities for its development open up. funding no funding. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] adeli s.; moradi p. 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(2010). distributed qos evaluation for real-world web services, 2010 ieee international conference on web services, july 05-10th 2010, ieee. doi: https://doi.org/10.1109/icws.2010.10, 83–90, 2010. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: kalibatienė, d.; miliauskaitė, j. (2023). an effect of user experience on a data-driven fuzzy inference of web service quality, international journal of computers communications & control, 18(4), 5162, 2023. https://doi.org/10.15837/ijccc.2023.4.5162 introduction a fuzzy inference approach for ws qose planning fuzzy inference design fuzzy inference controller (fuzzic) data-driven optimization (anfis) linguistic approximation results obtaining ws qose performance (experiment 1) processing subjective attributes employing anfis for optimization with crisp data obtaining ws qos performance with anfis (experiment 2) comparison of experiments discussion conclusions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 2, month: april, year: 2022 article number: 4476, https://doi.org/10.15837/ijccc.2022.2.4476 ccc publications fuzzy logic-based system for the estimation of the usability level in user tests g.e. chanchí-golondrino, l.m. sierra-martínez, w.y. campo-muñoz gabriel elías chanchí-golondrino* faculty of engineering university of cartagena, colombia ave. del consulado, cll. 30 # 39 b-192, cartagena, colombia *corresponding author: gchanchig@unicartagena.edu.co luz marina sierra-martínez faculty of electronic engineering and telecommunications university of cauca, colombia cll. 5 # 4-70, popayán, colombia lsierra@unicauca.edu.co wilmar yesid campo-muñoz faculty of engineering university of quindío, colombia cra. 15 cll. 12 norte, armenia, colombia wycampo@uniquindio.edu.co abstract starting from the challenge of obtaining the usability level in numerical and linguistic terms within a user test based on the three attributes that define usability, the development of a system based on fuzzy logic is proposed for estimating the level of output usability in a test with users based on the iso 9241-11 standard. the attributes are effectiveness, efficiency, and satisfaction, which have different metrics and numerical scales. for the development of the system, five methodological stages were defined: characterization of the structure of a usability test, definition of membership functions for the inputs and outputs of the system, design of the inference rules that relate the inputs and outputs, design and implementation of the fuzzy system, and development of the case study. the proposed system was implemented using the fcl (fuzzy control language) and the jfuzzylogic api that takes as inputs the values calculated for the attributes of effectiveness, efficiency, and satisfaction, and obtains the usability level as output considering the membership functions of the inputs and outputs, as well as a set of inference rules defined by a set of experts. as a case study, the proposed fuzzy system was validated from the results obtained in a usability test with 5 users which was developed on the sigma electrónica website. from the results obtained in the case study, it could be concluded that the implemented system is adequate in terms of obtaining a level of usability in numerical and linguistic terms in conventional usability tests developed in a usability laboratory considering the attributes of iso 9241-11. keywords: iso 9241-11, fuzzy logic, usability test, usability. https://doi.org/10.15837/ijccc.2022.2.4476 2 1 introduction from the increase in the number of applications available in the cloud and in software stores, user-centered design and usability has become key aspects in order to improve the quality of the software and promote competitiveness of companies in the market [2, 31, 33]. according to the iso 9241-11 standard, usability can be defined as the extent to which a software product can be used by specific users to achieve specific objectives with effectiveness, efficiency, and satisfaction in a specific context of use [15, 16, 17, 30]. in this sense, to consider a software usable, it must comply with the attributes of effectiveness, efficiency, and satisfaction. in the context of usability, effectiveness refers to the fulfilment of tasks by the user in an adequate manner within the software; efficiency refers to the optimization of resources by the user in the interaction, where speeding the times for the development of tasks within the software is one of the key aspects; finally, satisfaction according to this standard refers to the existence of positive attitudes towards the use of the software [1, 6, 9]. on the other hand, according to the iso 9126-1 standard, usability can be defined in terms of the ability of a software product to be understood, learned, operated, or used and that is attractive to the user under specific conditions [12, 19, 25, 28]. there is a relationship between the attributes defined in the iso 9241-11 and iso 9126-1 standards, in such a way that the ability to be understood and learned can be related to the efficiency attribute, while the ability to be operated or used is related to the effectiveness attribute. likewise, the possibility that the software is attractive to the user can be associated with the satisfaction attribute. there are different methods to evaluate the usability of a software product. among them, the ones that stand out are the usability tests where a group of users develop a set of tasks within a given software; they are developed in a usability laboratory and supervised by a group of observers or coordinators of the test. they are in charge of remotely monitoring the tasks carried out by the user in order to apply the metrics that allow determining the effectiveness, efficiency, and satisfaction for the iso 9241-11 standard [1, 5, 20, 22]. in this way, the user effectiveness percentage can be determined in terms of the number of sub-tasks developed by each task designed to be executed within the software that has to be evaluated. the percentage of efficiency can be determined by relating the time estimated by the coordinators for the development of tasks regarding the time employed by users to develop those tasks. finally, the satisfaction calculation can be carried out from the processing of the survey done after the test [13]. one of the challenges from the calculation of the attributes of effectiveness, efficiency, and satisfaction of the different users who participate in a user test is the precise determination of an output usability level, taking into account the different characteristics and scales obtained in the 3 attributes, as well as the need to determine not only a result in numerical terms but also linguistic. in this sense, given the particularities of the problem, a system based on fuzzy logic can be designed and adapted to the context of the tests with users in order to estimate the level of output usability, taking as input the values calculated by the coordinators of the tests for effectiveness, efficiency, and satisfaction, as well as a set of inference rules that relate the inputs and the output level [10, 18]. fuzzy logic can be understood as a mathematical instrument that facilitates the process of reaching a specific conclusion from input information that can be considered undefined, inaccurate, or vague. all of this considering the knowledge of experts as a basis [7, 14, 26, 32]. this way, the importance of fuzzy logic focuses on the fact of enabling the generation of intelligible results that relate numerical data with linguistic terms which are closer to natural language [3, 21, 29]. different contributions have been developed in the field of usability in which fuzzy logic has been used. thus in [7], the authors proposed a tool for the development of usability inspections based on fuzzy logic; it allows to obtain the level of usability from the qualification of usability criteria by expert users. in [4], the authors proposed a tool based on fuzzy logic to obtain the level of usability from the ratings made by users from the usability attributes of the iso 9126-1 standard. in [23], the authors proposed a model based on fuzzy logic for evaluating software quality, taking as input the ratings made on the metrics proposed in the iso 9126-1 standard. in [24], the authors developed a fuzzy system to obtain the level of usability from the qualification of software quality parameters for the evaluation of websites. finally in [11], the authors proposed a fuzzy system for determining usability in virtual learning environments based on the qualification of criteria of the iso 9126-1 standard adapted to that https://doi.org/10.15837/ijccc.2022.2.4476 3 context. previous works have been focused on the application of fuzzy logic in usability evaluations based on the weighting of criteria by expert users, without focusing on the determination of usability from evaluation approaches such as tests with users developed in a usability lab. in this article we propose the development of a software system based on fuzzy logic for estimating the level of usability output in tests with users developed in a usability laboratory, considering the attributes of effectiveness, efficiency, and satisfaction as defined in the iso 9241-11 standard, as well as the metrics associated with these attributes. thus, the proposed system takes as input the calculated values of effectiveness, efficiency, and satisfaction from the interaction of the users who participated in the test, in order to obtain a percentage level of output usability in linguistic and numerical terms, taking into account a set of inference rules defined by a group of experts in the development of the usability tests. the proposed system aims to guide the coordinators of a usability test to obtain clearer indicators regarding the level of usability of a software application evaluated in the context of a user test. the rest of the article is organized as follows: section 2 presents the different stages of the methodology considered for the development of this research. section 3 describes the results obtained from this research, which includes the design of the membership functions for the inputs and outputs of the fuzzy system, as well as the inference rules that allow to obtain the level of output usability from the system inputs. in the same way, the functionality of the fuzzy system developed to estimate the level of output usability in user tests is described, and a case study is presented where the functionality of the proposed system is verified with the results obtained from a test of usability developed with 5 users over the electronic accessories store website sigma electrónica [27]. finally, section 4 shows the conclusions and future work derived from this research. 2 material and methods for the development of this research, the following five methodological stages were defined: characterization of tests with users, definition of membership functions, design of inference rules, construction of the fuzzy system; and finally, development of the case study. the process was adapted from what is proposed in [8] (see figure 1) figure 1: methodology considered for this research (own elaboration) in phase 1, the characterization of the structure of a test with conventional users developed in a usability laboratory was carried out. the test was made up of 4 stages: confidentiality agreement, pre-test questionnaire, list of tasks, and post-test questionnaire. the confidentiality agreement is a document that informs the user about the scope of the test and guarantees that the test information will be used for academic purposes only. the pre-test questionnaire is an instrument that seeks to obtain the user profile, as well as the previous experience in using applications similar to those that the user will evaluate in the test. the list of tasks corresponds to the tasks defined by the test coordinators and that the user will do within the application to be evaluated. finally, the post-test questionnaire is a quantitative and qualitative instrument that seeks to inquire about the user’s experience with the evaluated application, which includes the perception of satisfaction in the use of the software. based on the supervision done by the observers about the tasks performed by the user, the attributes of effectiveness, efficiency, and satisfaction are calculated according to the iso 9241-11 standard. the percentage of ef f ectiveness pertask can be determined by the following equation, where each task in the test must have a set of sub-tasks. %ef f ectiveness = (number of subtasks perf ormed x 100)/(number of def inedsubtasks) https://doi.org/10.15837/ijccc.2022.2.4476 4 on the other hand, the percentage of ef f iciency per task can be determined from what is presented in the following equation, where the test coordinators have estimated a duration for each task. %ef f iciency = (estimated time per task x 100)/(time employed per task) finally, regarding satisf action, the evaluation given by the end users to the questions of the posttest questionnaire that involve the user’s perception of satisfaction is averaged, as it is presented in the following equation. satisf action = (sum of the questions ratings)/(number of questions) in stage 2, the design of the membership functions for both the input (% effectiveness, % efficiency, and satisfaction) and output (level of output usability) variables of the system takes place. these functions were designed from the scale of the three attributes, and they were specified through the fcl (fuzzy control language). in stage 3, a total of 27 inference rules were defined, which relate the inputs of the system to the output and were also specified in the fcl language. starting from the membership functions and the inference rules, the development of the fuzzy system for determining the level of usability in user tests takes place in stage 4, making use of the java language and the jfuzzylogic library. finally, in stage 5 of the methodology, a case study was developed in which the proposed fuzzy system was used to obtain the level of output usability from the data of a user test developed by five users over the electronics store website: sigma electrónica [27]. 3 results this section presents the description of the membership functions of the inputs and output of the system, as well as the ranges defined for the system. in the same way, the different views of the implemented system and a case study that demonstrates the functionality of the system are presented. 3.1 membership functions in figure 2, the 3 membership functions defined are presented. on each one of them, three fuzzy sets (low, medium, and high) have been considered under the experts’ criteria, considering the equations associated with effectiveness, efficiency, and satisfaction presented in section 2, as well as the ranges belonging to these attributes. similarly, figure 3 presents the membership function designed for the output usability level that according to the usability experts, is made up of four fuzzy sets: deficient, acceptable, good, and excellent. in this way, the system will not only obtain a level of usability as a result, but also the linguistic term associated with that value. 3.2 inference rules taking into account the membership functions defined for the input and output variables of the system, a total of 27 rules were defined that relate the fuzzy sets considered for effectiveness, efficiency, and satisfaction (low, medium and high), with the fuzzy sets considered for the level of output usability (deficient, acceptable, good, and excellent). thus, table 1 presents a fragment of the 27 inference rules defined, which are expressed in the fcl language. 3.3 fuzzy system proposed and case study from the membership functions of the input variables and the output variable, as well as the defined inference rules that relate the inputs to the output, a fuzzy system was developed in the java programming language using the jfuzzylogic library and the fcl fuzzy logic description language. the system allows to obtain the output usability level from the effectiveness, efficiency, and satisfaction attributes of the iso 9241-11 standard, which are calculated from the metrics of each attribute. to https://doi.org/10.15837/ijccc.2022.2.4476 5 figure 2: input variables of the membership functions (own elaboration) figure 3: membership function: level of output usability (own elaboration) describe the operation and the different views or interfaces of the proposed fuzzy system, the results obtained in a usability test were used. the test was developed with 5 users in a virtual laboratory over the sigma electrónica website (see figure 4). in this way, at the time the functionality of the implemented system is shown, the results obtained for the case study are shown. in the first place, the proposed system allows to load the data captured by the coordinators of a user test from a plain text file for configuration. this way, it is possible to calculate the value of the usability attributes per task and at the test level. thus, figure 4 presents a plain text file that describes the results obtained in a test with users developed over the sigma electrónica website. the file in figure 5 shows how the test developed over the sigma electrónica website was carried out by 5 users who were assigned a total of 3 tasks (t1: register on the sigma electrónica website using a test email provided by the coordinators, t2: interact with the site’s shopping cart by searching for three products available in the store, t3: enter the blog section and comment on any of the articles in this section). in the same way, at the level of the effectiveness attribute, task 1 has 2 subtasks while task 2 has 3 subtasks associated, and task 3 includes 2 tasks. in this sense, it can be observed how user 1 (u1) developed the 2 subtasks corresponding to tasks 1 and 3. on task 2, u1 performed 2 of the 3 subtasks. at the efficiency level, it is possible to see how the coordinators of the user test established 1 minute for task 1, 3 minutes for task 2, and 2 minutes for task 3. it is also observed how user 4 (u4) developed task 1 in 2 minutes, task 2 in 2 minutes, and task 3 in 3 minutes. finally, at the level of satisfaction, the 5 users rated 6 questions in the range from 1 to 5 related to the perception of the website after the test. thus, in the case of user 5, the scores on the 6 perception questions were https://doi.org/10.15837/ijccc.2022.2.4476 6 table 1: fuzzy system inference rules id inference rules 1 if effectiveness is low and efficiency is low and satisfaction is low then usability_level is deficient 4 if effectiveness is low and efficiency is low and satisfaction is low then usability_level is deficient 8 if effectiveness is low and efficiency is high and satisfaction is medium then usability_level is acceptable 12 if effectiveness is medium and efficiency is low and satisfaction is high then usability_level is acceptable 15 if effectiveness is medium and efficiency is medium and satisfaction is high then usability_level is good 17 if effectiveness is medium and efficiency is high and satisfaction is medium then usability_level is good 21 if effectiveness is high and efficiency is low and satisfaction is high then usability_level is good 24 if effectiveness is high and efficiency is medium and satisfaction is high then usability_level is excellent 26 if effectiveness is high and efficiency is high and satisfaction is medium then usability_level is excellent 27 if effectiveness is high and efficiency is high and satisfaction is high then usability_level is excellent figure 4: usability test developed (own elaboration) 3, 4, 4, 4, 2, and 4. figure 5: test configuration file (own elaboration) from a configuration file as the one presented in figure 5, the proposed fuzzy system loads the test data and performs the calculation of the effectiveness and efficiency per task, as well as the satisfaction obtained from the evaluations made by the users about the 6 questions of the case study. thus, figure 6 shows the interface of the proposed fuzzy system, which has been loaded with the data from the configuration file shown in figure 5. the graphical interface of the fuzzy system is composed of 5 tabs: effectiveness, efficiency, satisfaction, fuzzy analysis, and graphic analysis. in the "effectiveness" tab, the user effectiveness per task is calculated from the configuration file of the test performed in the sigma electrónica website. in this way, it is observed in figure 6 how the effectiveness of users 2, 3, and 5 is 100% in all tasks. likewise, for the case of user 1 (u1), the https://doi.org/10.15837/ijccc.2022.2.4476 7 figure 6: graphical interface of the proposed fuzzy system (own elaboration) effectiveness for each task is 100%, 66.67%, and 100% respectively while for the case of user 4 (u4), the effectiveness for each task is 100%, 100%, and 50% respectively. similarly, figure 7 shows the "efficiency" tab, where the user efficiency per task is calculated from the configuration file of the case study test. figure 7: tab for the “efficiency” of the system (own elaboration) it can be seen how user 1 (u1) performed all the tasks of the user test in a time greater than that defined by the coordinators (the efficiency is less than 100%) while in the case of user 2 (u2), the time used to perform each of the tasks is less than it was defined by the test coordinators (the efficiency is greater than 100%). regarding users 4 (u4) and 5 (u5), the time used is greater than what it was defined by the coordinators (the efficiency is less than 100%) in 2 of the 3 tasks while in the remaining task the time is less than what it was defined by the coordinators (the efficiency is greater than 100%). concerning user 3, the time used is less than what it was defined by the test coordinators (the efficiency is greater than 100%) in 2 of the three tasks while in the remaining task, the time is greater than what it was defined by the coordinators (efficiency less than 100%). on the other hand, figure 8 shows the "satisfaction" tab where the ratings assigned by the users of the case study test are loaded from the configuration file to each of the 6 questions related to the perception, which are done by the users after the development of the tasks. in the "fuzzy analysis" tab, the system calculates and presents the consolidated effectiveness, https://doi.org/10.15837/ijccc.2022.2.4476 8 figure 8: tab for the “satisfaction” of the system (own elaboration) efficiency, and satisfaction for the test of the case study taking into account the data loaded and presented in the first three tabs. likewise, the system calculates and presents the level of output usability based on the degrees of belonging to the membership functions of the input variables (effectiveness, efficiency, and satisfaction) and the inference rules defined for each user and for the entire test. similarly, the system graphically displays the membership role of the output usability level along with the resulting estimated level for each user and for the entire test. in addition, the system presents the membership levels of the three input variables, as well as the inference rules that are activated from the input values and the weighting provided to each of the rules that are activated in the lower text area. thus, from the data of the case study shown in figure 5, figure 9 shows how the level of output usability for 4 of the 5 users (u2, u3, u4, and u5) is "excellent" while for the case of user 1 (u1) the output usability level is 61.92 and has been classified according to the membership functions as "acceptable". likewise, it can be seen how for user 1 (u1), 2 inference rules are activated (rule 22 and 25), which correspond to the “acceptable” and “good” levels with a membership degree of 0.268 and 0.038 respectively. therefore, the defined usability level of user 1 was classified as "acceptable". finally, it can be seen how from the membership functions, the usability level obtained for the entire test of the case study is 93.04, which corresponds to the "excellent" classification with a degree of belonging of 0.6517. figure 9: tab for the “fuzzy analysis” of the system (own elaboration) https://doi.org/10.15837/ijccc.2022.2.4476 9 lasty, the "graphic analysis" tab presents the membership functions of the inputs (effectiveness, efficiency, and satisfaction) and the output usability level, as well as the degree of membership estimated in each function (black vertical line on each membership function) from the values of these variables for each user of the usability test of the case study and for the whole test. as an example, based on the test data presented in figure 5, figure 10 shows the membership functions for the inputs and outputs of the fuzzy system corresponding to user 1. thus, the level obtained for effectiveness is "high", the level obtained for efficiency is "medium", and the level obtained for satisfaction is "medium". likewise, an "acceptable" output usability level is obtained through the defined inference rules; although, the rule associated with the "good" level is also activated as previously presented, but with a lower degree of membership. in the same way, in the "graphical analysis" tab, it is possible to obtain the level of output usability for the entire test in a numerical and graphical way from the average input values of effectiveness, efficiency, and satisfaction. figure 10: tab for the “graphic analysis” of the system (own elaboration) 4 conclusions and future work in this study, the design and construction of a fuzzy system for estimating the output level of usability in tests with users was proposed based on the input values associated with the attributes of the iso 9241-11 standard (effectiveness, efficiency, and satisfaction) and considering a set of inference rules defined by experts in the development of usability tests. the proposed fuzzy system aims to contribute to the problem of estimating the level of usability comprehending the different scales obtained in the three attributes, as well as the need to obtain not only a result in numerical but also linguistic terms. the system proposed in this article allows, prior to the application of fuzzy logic, the calculation of effectiveness and efficiency per task, as well as the satisfaction evaluated by users in the post-test questionnaire. for the above, the data of a usability test is loaded into the system from a configuration file, in which the number of users, the number of tasks, the number of subtasks for each task, and the estimated time for the development of each task are specified. likewise, the file contains the number of subtasks performed by the user regarding each task and the times used by each user to perform the tasks in the test. finally, the file also includes the evaluations made by the users to the questions of the post-test questionnaire related to the perception of the users regarding the software evaluated in the usability test. the fcl language and java’s jfuzzylogic api proved to be suitable for the definition of the membership functions of the inputs and outputs. it also proved right for the design of the inference rules and the implementation of the functional modules of the fuzzy system. in this sense, the data from the hypothetical test uploaded to the fuzzy system allowed to demonstrate its usefulness and https://doi.org/10.15837/ijccc.2022.2.4476 10 relevance in order to obtain the level of usability per user, as well as the level of usability throughout the test. the application of the fuzzy system proposed to the data and/or that were obtained in the user test developed on the sigma electrónica website allowed to demonstrate the usefulness of the tool in terms of estimating the level of output usability for the five participating users and for the consolidation of the test. in this sense, it was obtained that for 4 of the 5 users the estimated output usability level is "excellent" while the estimated output usability level is "acceptable" for the remaining user. based on the above, the usability level for the entire test of the case study is 93.04, which corresponds to the "excellent" classification with a degree of membership of 0.6517 at that level. finally, the adaptation of the proposed system to tests with users supported by other quality standards, such as the iso 250010 standard, is intended as future work derived from this research. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] albornoz, d. a.; moncayo, s. a.; ruano-hoyos, s.; chanchí-golondrino, g.e.; márceles-villalba, k. 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(2009). the utilization of metrics usability to evaluate the software quality, icctd 2009 2009 international conference on computer technology and development, doi: 10.1109/icctd.2009.58, 1, 243–246, 2009. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: chanchí-golondrino g.e.; sierra-martínez l.m.; campo-muñoz w.y. (2022). fuzzy logic-based system for the estimation of the usability level in user tests, international journal of computers communications & control, 17(2), 4476, 2022. https://doi.org/10.15837/ijccc.2022.2.4476 introduction material and methods results membership functions inference rules fuzzy system proposed and case study conclusions and future work international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 17, issue: 4, month: august, year: 2022 article number: 4606, https://doi.org/10.15837/ijccc.2022.4.4606 ccc publications a novel self-organizing fuzzy cerebellar model articulation controller based overlapping gaussian membership function for controlling robotic system thanh-quyen ngo, dinh-khoi hoang, trong-toan tran, thanh-thuan nguyen van-tho nguyen, long-ho le thanh-quyen ngo* faculty of electrical engineering technology industrial of university ho chi minh city, vietnam *corresponding author: ngothanhquyen@iuh.edu.vn dinh-khoi hoang faculty of electrical engineering technology industrial of university ho chi minh city, vietnam hoangdinhkhoi@iuh.edu.vn trong-toan tran faculty of electrical engineering technology industrial of university ho chi minh city, vietnam trantrongtoan@iuh.edu.vn thanh-thuan nguyen faculty of electrical engineering technology industrial of university ho chi minh city, vietnam nguyenthanhthuan@iuh.edu.vn van-tho nguyen faculty of electrical engineering technology industrial of university ho chi minh city, vietnam nguyenvantho@iuh.edu.vn long-ho le faculty of electrical engineering technology industrial of university ho chi minh city, vietnam lelongho@iuh.edu.vn abstract this paper introduces an effective intelligent controller for robotic systems with uncertainties. the proposed method is a novel self-organizing fuzzy cerebellar model articulation controller (nsofc) which is a combination of a cerebellar model articulation controller (cmac) and sliding https://doi.org/10.15837/ijccc.2022.4.4606 2 mode control (smc). we also present a new gaussian membership function (gmf) that is designed by the combination of the prior and current gmf for each layer of cmac. in addition, the relevant data of the prior gmf is used to check tracking errors more accurately. the inputs of the proposed controller can be mixed simultaneously between the prior and current states according to the corresponding errors. moreover, the controller uses a self-organizing approach which can increase or decrease the number of layers, therefore the structures of nsofc can be adjusted automatically. the proposed method consists of a nsofc controller and a compensation controller. the nsofc controller is used to estimate the ideal controller, and the compensation controller is used to eliminate the approximated error. the online parameters tuning law of nsofc is designed based on lyapunov’s theory to ensure stability of the system. finally, the experimental results of a 2 dof robot arm are used to demonstrate the efficiency of the proposed controller. keywords: self-organizing technique, cerebellar model articulation controller, manipulator system, cerebellar model articulation controller, gaussian membership function. 1 introduction robots has been used widely in many different applications such as automated manufacture, transportation or in daily services. technology in controlling robotic systems has been heavily investigated in recent decades [1][2][3][4][5][6]. one of the challenging problems when designing controller for robotic systems is uncertainties which come from several aspects such as unknown parameters, unknown frictions, and external disturbances, etc. although the researches mentioned above also consider uncertainties and noises in their works, the results presented are still mainly relied on the nominal parameters of the systems. however, in many practical applications, the parameters of the robots usually change from time to time due to the change of the momentum of the systems or different loads [7]. for example, a robot continuously moves an object between two stations will make the centrifugal of it changes between two different values [8]. there are also cases where the robot has to carry different types of objects due to customer’s demands [9][10]. in general, we may know the nominal parameters of the system and the load themselves but we do not know how they will change when the system is operated. not to mention, there are also different noises that affects the system when it is running, which then add more uncertainties into the dynamic model. in general, it is difficult to calculate accurately the mathematical model of the robotic system due to the reasons mentioned above and therefore designing the controller based on dynamic system model is not a trivial task. one of the main challenges here is how to learn the unknown parameters of the system and how to adapt when these parameters change. there are several methods have been researched to tackle this problem such as evolutionary algorithms, sliding mode control, robust control, neuron network, etc. evolutionary algorithms [28] (eas) are one of the learning methods that mimicking biological evolution in nature. it is a well-known class of optimization algorithms to deal with black-box optimization problems. one of the main advantages is that eas do not require any prior knowledge about the problem, but rather start with a randomly distributed population and update the population by reproducing offspring with unique operators. there are several methods have been studied such as genetic algorithm (ga), particle swam optimization (pso), differential evolution (de), oppositionbased learning, etc. many studies in this area focus on different searching methods to increase the convergence speed of the learning procedure. in [29], the author presents a modified opposition-based learning method that uses a beta distribution with a selection switching scheme and a partial dimension changing scheme to accelerate the convergence speed. in [30], m. elsisi developed a learning method called future search algorithm (fsa), which is a heuristic algorithm that mimic the person’s life to search for unknown parameters. the idea of fsa is based on the behavior of a person trying to search for his best life by imitating the successful persons. one of the advantages of fsa is that it can update the random initial and utilizes the local search between people and the global search between the histories optimal persons to achieve the best solutions. it is shown in the paper that fsa has low computational effort and high convergence speed compared to other heuristic algorithms such as ga, pso. however, these methods are currently applied successfully only in theory and mainly focus on the learning procedure. taking a step further, m. elsisi utilized lightning search algorithm (lsa) [31] to find the optimal parameters for a variable structure controller (vsc) that is used to control a nuclear reactor power https://doi.org/10.15837/ijccc.2022.4.4606 3 system [32]. in general, the main problem when designing a vsc controller is the choice of the switching vector and the feedback gains. this can usually be done by using a searching algorithm which is lsa in this work. the author also incorporated lyapunov function when designing the controller, such that ensures the stability of the system. the controller is then verified in simulation of controlling a nuclear reactor. the author showed that vsc based on lsa has better performance compared to vsc based on other ga which is the main purpose of this work. however, the convergence speed of lsa is still approximately 1.5 minutes on a rather high-end computer, which make it incapable of controlling fast systems such as motors or robotic systems. with the same approach as vsc, the author in [33] takes advantage of a searching algorithm named multi-tracker optimization algorithm (mtoa) to properly select the parameters of a model predictive controller (mpc). mpc is a powerful control method that utilizes the past and current values of the output to predict the behavior of the system in the future. however, mpc needs to be properly configured by selecting the weight matrices of the objective function, the prediction horizon, control horizon and the sample time. an inappropriate selection may lead to less effective in controlling or less efficient in computation time. the proposed modified mtoa in this work is carried out based on opposition-based learning (obl) and quasi obl approaches. the proposed approach improves the exploration behavior of mtoa to prevent it from becoming trapped in a local minimum. the author then applied the mpc controller with the parameters are tuned by the proposed searching algorithm on a robotic manipulator to track different linear and nonlinear trajectories. the results show the improvement in term of tracking error compared to other approaches such as ga-based pid, original mtoa-based mpc, etc. however, the searching part in this work is still performed offline and the obtained parameters are then used to control the system after that. also using mpc-based controller to control autonomous vehicle under vision dynamic, the author in [34] comes up with a different searching approach based on ai-techniques to tune the parameters of the controller. the ai-based searching method is called social ski driver algorithm (ssda) which imitates the behavior of the sky drivers’ tracking during the downhill. ssda combines the advantages of different ai techniques in literatures to overcome the trapping in local optima. like previous approach, ssda is used in this work to tune the parameters of a hybrid discrete-time laguerre function mpc (dtlf-mpc) to improve the performance of the vehicle system and reduce the computational effort. the main results in the paper show that the convergence speed of the algorithm is fast enough to be calculated online, which is the main advantage of this work. however, as the searching method is based on ai-technique, there is no stability proof of the controller is provided. recently, many researches focus on intelligent control technologies such as fuzzy neural network (fnn), neuron network (nn), cerebellar model articulation controller (cmac), etc. the general requirement of these intelligent controllers is to reduce the impacts of unknown parameters and unstructured disturbances by utilizing the learning abilities of controlled networks without the need of knowing details about the system during the design phase. this is usually done by approximating a part of the nonlinear model of the system or the whole of it using the learning ability of the controllers [11]. intelligent controllers has been used and applied successfully in many applications, especially in adaptive control [12][13][14][15]. in general, there are two commonly approaches that have been used the most in this area: first, the cerebellar model articulation controller is designed by combining the advantages of model-based controllers such as sliding mode, linear feedback controllers, etc and the advantages of fnn, nn and cmac to approximate the unknown nonlinear equation in the dynamic model of the system [11], second, an intelligent-based approach is constructed to identify the complete mathematical model of the system. the advantage of the second method is that the dynamic model can be completely unknown but it requires a large amount of time for the algorithm to converge. nevertheless, both approaches have been researched and applied successfully in many real-life application. generally, fnn, nn are able to approximate any nonlinear function with their learning ability. however, the convergence speed is rather slow. to overcome this problem, cmac is proposed by albus in [16] for identifying and controlling complex dynamic systems. cmac has several advantages compare to the normal nn such as its fast learning speed, more general and can be implemented easily in different hardwares [17][18][19]. chiang and lin [20] and chih-min lin; hsin-yi li [21] continued to https://doi.org/10.15837/ijccc.2022.4.4606 4 develop cmac network with gaussian and wavelet basic functions. in their work, they analyze the convergence capability of the network and present different results to prove the efficiency of the method [17][18][19][20][21]. however, in most of the aforementioned researches about cmac, the structure of it is established to be unchanged, hence it is difficult to calculate the optimal memory space for the learning speed of the network. if the memory space is too large, the learning speed will be decreased as it requires more computational effort from the hardware. therefore, it is difficult to implement this method in online applications. therefore, several researches [22][23][24] are proposed and successfully implemented cmac network with self-organizing structure to reduce the size of the memory using different techniques such as group data, multilayer hierarchical cmac model, shannon’s entropy measure and golden-section search method to increase or decrease number of layers of the network. to increase the adaptive capability of cmac network for online learning and reduce the computational effort when the system converges, this study proposes a novel self-organizing fcmac called nsofc for robotic systems with uncertainties. this proposed control scheme allows rcmac in [22][25] have better local respond to determine the input values for rfbfs unit and the weight of the hypercubes. the approach uses the integrated sliding surface and the overlapping of prior and current gmfs at each layer to create a synthesis of two states for predicting the estimated errors. then the self-organizing method is used to determine whether it is necessary to increase or decrease the number of current layers. the proposed control scheme is more general than the local feedback control scheme. utilizing the recursive unit, it is possible to determine a new membership function, the input values for rfbfs and the weight values of hypercubes for cmac. the adaptive pi is also employed for adjusting the adaptive parameters. moreover, the lyapunov function is used to ensure the stability of the system. finally, the experimental results of the 2 dof robot arm are presented to prove the efficiency of the proposed control scheme. 2 system description in general, a dynamic equation of an n joint robotic system can be written as follow: m(q)q̈ + c(q, q̇)q̇ + g(q) = τ (1) where q, q̇, q̈ ∈ rn are the joint position, velocity, and acceleration vectors respectively, m(q) ∈ rn×n is the inertia matrix, c(q, q̇) represents the centrifugal and coriolis forces, g(q) ∈ rn×1 represents the gravity vector, τ ∈ rn×1 is the applied control torque. equation (1) can be reformulated as: q̈ = −m−1(q) (c(q, q̇)q̇ + g(q)) + m−1(q)τ = f(x) + g(x)τ (2) f(x) =   f1(x) f2(2) ... fn(x)   = −m−1(q) (c(q, q̇)q̇ + g(q)) ∈ rn×1, g(x) =   g11(x) · · · g1n(x) ... ... ... gn1(x) · · · gnn(x)   = m−1(q) ∈ rn×n. where f(x), g(x) are nonlinear dynamic functions. these functions are usually difficult to obtain due to measurement errors, different loads, frictions, and external disturbances. to solve this problem, we assume that f0(x) ∈ rn×1, g0(x) ∈ rn×n are the nominal component of the real value of f(x), g(x) respectively. we also assume that l(x) is the sum of the bounded and uncertain components. suppose that the system (1) is controllable and is inversible for all q. then equation (2) can be rewritten as: q̈(t) = f0(x) + g0(x)τ + l(x) (3) https://doi.org/10.15837/ijccc.2022.4.4606 5 where x = [ qt q̇t ]t is the joint position and velocity vectors respectively. in addition, tracking error can be defined as: e = qd −q (4) where qd = [qd1 qd2 · · ·qdn]t ∈ rn is the desired joint position. then, the tracking error vector can be written as: ē = [ et ėt · · ·e(n−1)t ]t ∈ rm×n (5) next, we define the sliding surface: s = en−1 + k1en−2 + · · · + kn ∫ t 0 e(τ)dτ (6) where s(t) = [s1(t) s2(t) · · ·si(t) · · ·sni(t)] ∈ rni, and ni = m, ki = diag(ki1,ki2, . . . ,kim) ∈ rm×m is a positive constant matrix. if f0(x), g0(x) and l(x) are known accurately, then the ideal controller can be obtained as below: uid = g−10 (x) [ q̈d −f0(x) − l(x) + ktet ] (7) substituting (7) into (2), we get the dynamic error equation: ṡ = e(n) + kt ē = 0 (8) in (8), tracking error ē → 0 when t →∞ if k is chosen appropriately according to hurwitz stability criterion. however, l(x) usually cannot be verified in practical applications. thus, uid in (7) cannot be identified. figure 1: block diagram of nsofc control system it can be seen from eq. (8) that the error will reach zero if the matrix k is selected such that all roots of the characteristic equation stay in the left half of the “s” plane. however, uid in eq. (7) cannot be determined in the real scenario since l(x) is unknown. therefore, we propose a new controller which consists of a nsofc controller, and an adaptive compensation described as follow: u = unsofc + ucc (9) where unsofc is the main controller which approximates the ideal controller in (7) and the compensation controller is used to estimate the estimated error. https://doi.org/10.15837/ijccc.2022.4.4606 6 3 design the nsofc controller 3.1 fuzzy cmac controller with overlapping gaussian membership function both aforementioned cmac [27] and rcmac [26] controllers are only designed with one gmf activated at each layer. our proposed controller uses two gmf for each layer instead. table 1 shows the comparison between the local feedback rcmac and the proposed controller. the proposed control scheme has two adaptive coefficients λk and βk, therefore it is more flexible for the control system to identify the input values of rfbfs and hypercubes. when a trajectory shifts from the current activating state to the next state, the prior gmf still being activated as well as the current gmf. the self-organizing technique is used to adjust the number of layers in fcmac. if the error exceeds the threshold, a new layer will be created. the nsofc structure uses overlapping gmf, presented in fig. 2, which includes three continuous mappings and one output, can be written as follow:  i → a (input → associated memory) a → r (associated memory → receptive field) r → w (receptive field → network weight memory) (10) outputo : o(i) = whsynthesis, (11) figure 2: structure of the proposed nsofc with overlapping gmf the following fuzzy control laws are used for the proposed controller: rjk : if i1 is ω1k,i2 is ω2k, . . . , and ini is ωnik then ŵjk = vp (ŵjkp ) + vi (ŵjki) for j = 1, 2, . . . ,m and k = 1, 2, . . . ,nk. (12) input state variables i = [i1 i2 · · · ini]t are connected to association memory a only contains one block for each layer. each variable is arranged in one block of each layer. at each block, there are two gmfs define one synthesis rfbf. these two gmfs, ωpresentik và ω prior ik are presented in fig. 3. the value of ωpriorik is stored in memory, and the values of ω present ik , ω prior ik are recursively calculated https://doi.org/10.15837/ijccc.2022.4.4606 7 figure 3: two overlapping gmfs for calculation of the synthesis rfbf table 1: comparison between the proposed controller and the local feedback rcmac input value of rfbf λk βk local feedback rcmac [27] ii(t) = λkω prior ik + βkii = λkω prior ik + ii online tuning 1 proposed controller ii(t) = λkω prior ik + βkω present ik online tuning online tuning (a) value of hypercube block local feedback rcmac [27] hsynthesis = ∏n i=1 ω present ik = ∏n i=1 exp [ − ( ii −m present ik )2( σ present ik )2 ] proposed controller hsynthesis = λkh prior k + βk ∏n i=1 exp [ − ( ii −m present ik )2( σ present ik )2 ] (b) as follow: ωpriorik (ii) = exp [ − (ii −mpriorik ) 2 (σpriorik )2 ] , i = 1, 2, . . . ,ni, k = 1, 2, . . . ,nk (13) ωpresentik (ii) = exp [ − (ii −mpresentik ) 2 (σpresentik )2 ] , i = 1, 2, . . . ,ni, k = 1, 2, . . . ,nk (14) the input values of rfbf are defined as: ii(t) = λkω prior ik + βkω present ik (15) where ωpresentik is the current rfbf, m present ik is the mean value and σ present ik is the current state variance of the kth layer corresponding to the ith input variable. similarly, ωpriorik is the prior rfbf, m prior ik is the prior mean value and σpriorik is the prior state variance. λk and βk are the adaptive estimation gains. the prior state is stored in the memory as an estimation for the next state. therefore, when the current state is activated, it will combine with the prior state to estimate the next state. the example in fig. 4 illustrates the 2d space, the output from the nsofc is the sum values of the present (6, 5) and the prior (5,4) hypercubes. one synthesis hypercube is defined as below: hsynthesis ≡ h = λhprior + βhpresent = λkh prior k + βk ni∏ i=1 ωpresentik (16) where λ = [λ1, λ2, · · · , λk, · · · , λnk] and β = [β1, β2, · · · , βk, · · · , βnk] are the adaptive estimated weights of the prior and current hypercubes. https://doi.org/10.15837/ijccc.2022.4.4606 8 hprior = ∏ni i=1 ω prior ik = [h prior 1 h prior 2 · · · h prior k · · · h prior nk ] t are the values of the prior hypercubes. hpresent = ∏ni i=1 ω present ik = [h present 1 h present 2 · · · h present k · · · h present nk ] t are the values of the current hypercubes. h = λhprior + βhpresent = [ h1, h1, h2, ..., hk, ..., hnk] t ∈rn k are the synthesis values of the prior and current hypercubes. for nsofc, the weight gain w and hypercube h are defined as vjk = wjkhk, where wjk is the weight of the kth hypercube respectively with the jth output. the jth output is defined as: u = o = wth = [w1 w2 · · · wj · · · wm]t   h1 h2 ... hnk   (17) u = oj = wt [ λhprior + βhpresent ] , j = 1, 2, · · · , m (18) where w = [w1 w2 · · · wj · · · wm], wj= [w1j w2j · · · wnkj] t . design an optimal controller u∗: uid = u∗ + ∆ = w ∗th∗ + ∆ (19) where ∆ is the approximated error and w ∗, h∗, m∗, σ∗ represent the optimal parameter matrices and vectors of w, h, m, σ respectively. in practice, these optimal matrices and vectors are difficult to verify the values which approximate to ideal controller. therefore,û is estimated online to define u∗. the estimated function û is defined as: û = ŵtĥ (20) where ŵand ĥ are the estimated parameter matrix and vector of w ∗, h∗ respectively, and m̂, σ̂, λ̂, β̂ are the estimated parameter vectors of m∗, σ∗, λ∗ và β∗ respectively. the estimation error is defined as: ũ = uid − ûnsofc = w ∗th∗ −ŵtĥ + ∆ = w̃th̃ + ŵth̃ + w̃tĥ + ∆ (21) where w̃ = w ∗ − ŵ and h̃ = h∗ − ĥ. here taylor expansion is used to convert nonlinear functions into linear functions [12]: h̃ = hmm̃ + hσσ̃ + hλλ̃ + hββ̃ + θ (22) where m̃ = m∗ − m̂, σ̃ = σ∗ − σ̂, λ̃ = λ∗ − λ̂, β̃ = β∗ − β̂, θ is a high-order vector. hm = [ ∂h1 ∂m ∂h2 ∂m · · · ∂hnk ∂m ]t ∣∣.m=m̂ , hσ = [ ∂h1 ∂σ ∂h2 ∂σ ... ∂hnk ∂σ ]t ∣∣.σ=σ̂ , hλ = [ ∂h1 ∂λ ∂h2 ∂λ · · · ∂hnk ∂λ ]t ∣∣∣. λ=λ̂ , hβ = [ ∂h1 ∂β ∂h2 ∂β ... ∂hnk ∂β ]t ∣∣∣. β=β̂ , substitute (22) into (21), we get: ũ = w̃th̃ + ŵt ( hmm̃ + hσσ̃ + hλλ̃ + hββ̃ + θ ) + w̃tĥ + ∆ = w̃tĥ + m̃thtmŵ + σ̃ thtσ ŵ + λ̃ thtλ ŵ + β̃ thtβ ŵ + ŵ t φ + w̃th̃ + ∆ (23) to increase the convergence of the output weight, w ∗ is divided into two parts [12]: w ∗ = vpw∗p + viw ∗ i (24) https://doi.org/10.15837/ijccc.2022.4.4606 9 figure 4: 2d structure of nsofc with the overlapping gmfs where w∗p and w∗i represents the proportional element and integral element of w ∗ and w∗i =∫ t 0 w ∗ pdτ respectively, vp and vi are positive constants. similarly, ŵ is also divided into two parts ŵ = vp ŵp + viŵi (25) where ŵp and ŵi are the proportional and integral elements of ŵ , ŵi = ∫ t 0 ŵp dτ. therefore, w̃ is defined as follow: w̃ = viw̃i −vp ŵp + vp w∗p (26) where w̃i = wi∗ŵi. substitute (26) into (23), we get: ũ = (viw̃i −vp ŵp + vp w∗p ) t ĥ + m̃thtmŵ + σ̃ thtσ ŵ + λ̃ thtλ ŵ + β̃ thtβ ŵ + ŵ t θ + w̃th̃ + ∆ = viw̃ti ĥ −vp ŵ t pĥ + m̃ thtmŵ + σ̃ thtσ ŵ + λ̃ thtλ ŵ + β̃ thtβ ŵ + ε (27) where the uncertain coefficient ε = vpv∗tp ĥ + ŵt θ + w̃th̃ + ∆ is the approximation of total errors, bounded by 0 ≤‖ε‖≤ el , where el is a positive constant. 3.2 self-organizing structure the online self-learning for structure and parameters of nsofc is proposed as in fig. 1. the first task of structured learning is to verify whether it is necessary to add a new layer into the associated memory a and create hypercubes with its weight memory. if the current inputs are in the range of the defined data, then nsofc does not create a new layer but updates the parameters for the current laws. mean distance, dmk, is defined as: dmk = ‖i −mk‖2 (28) where mk = [ m1k, ..., mik, ..., mni k ] t to check whether a new layer need to be added [20]: k̂ = arg min 1≤k≤nk dmk (29) if maxi dmk̂ > βg then a new layer is created, where βg is the pre-defined threshold. if the distance between input data and the mean values are bigger than the threshold, it means the current values of the current gmf is too small, then a new layer is added. the layer number is increased as follow: nk(t + 1) = nk(t) + 1 (30) https://doi.org/10.15837/ijccc.2022.4.4606 10 where nk(t) is the current layer number at t. the output weight values are randomly generated for a new layer, the mean value, and the variance of gmfs are defined as: mink = ii (31) σink = σint (32) wink = δr (33) where ii is the new input, σint is the pre-defined constant, and δr is a random constant. to learn the structure, any unnecessary layer must be deleted. to decrease the layer number [20], the ratio of the kth element of jth output is defined as: sejk = vjk/oj (34) then the maximum ratio of the jth output respective to the minimum element is computed as: k̃ = arg min 1≤k≤nk max 1≤j≤m sejk (35) if sejk ≤ βd then the kth layer must be deleted, where βd is the pre-defined threshold. (36) the function (36) shows that if the element of the current layer is smaller than the threshold then it will be deleted. 3.3 adaptive nsofc control system the nsofc system uses adaptive pi algorithm which is showed in fig. 1. the control law (9) is substituted into (3). using (6) and (7), then the sum errors function become: ṡ = e(n) + kte = g0(uid − ûnsofc −ucc) (37) using (27), then (37) is rewritten as: ṡ = g0(viw̃ti ĥ −vp ŵ t pĥ + m̃ thtmŵ + σ̃ thtσ ŵ + λ̃ thtλ ŵ + β̃ thtβ ŵ + ε−ucc) (38) to ensure the stability of the control system, a lyapunov function is chosen as: v = 1 2 stg−10 s + vi 2 tr ( w̃ti w̃i ) + 1 2vm m̃tm̃ + 1 2vσ σ̃t σ̃ + 1 2vλ λ̃t λ̃ + 1 2vβ β̃t β̃ + 1 2vde ẽ2l (39) where vi, vp, vm, vσ, vλ, vβ and el are the positive constants with the role of learning rate. the uncertain bound of el cannot be measured in practice, therefore the estimated bound êl is used. the error bound is computed as: ẽl = el − êl. differentiate equation (39), and using (38), we get: v̇ = stg−10 ṡ−vitr ( w̃ti ˙̂wi ) − 1 vm m̃t ˙̂m− 1 vσ σ̃t ˙̂σ − 1 vλ λ̃t ˙̂ λ− 1 vβ β̃t ˙̂ β − 1 vde ẽl ˙̂ el = vi m∑ j=1 w̃tji (sjĥ − ˙̂wji) + m̃ t ( sthtmŵ − ˙̂m vm ) + σ̃t ( sthtσ ŵ − ˙̂σ vσ ) + λ̃t  sthtλ ŵ − ˙̂λvλ   + β̃t  sthtβ ŵ − ˙̂βvβ  −vp m∑ j=1 ŵtjpsjĥ + s t (ε−ucc) − 1 vel ẽl ˙̂ el (40) the functions vitr ( w̃ti ˙̂wi ) = vi ∑m j=1 w̃ t jiw̃ji and st w̃ t i ĥ = ∑m j=1 sjw̃ t jiĥ are used. https://doi.org/10.15837/ijccc.2022.4.4606 11 then the adaptive laws are written as follow: ŵjp = sjĥ −ηi(ŵji −w0) (41) ˙̂wji = sjĥ −ηi(ŵji −w0) (42) ˙̂m = vm[sthtmŵ −ηm(m̂−m0)] (43) ˙̂σ = vσ[sthtσ ŵ −ησ(σ̂ −σ0)] (44) ˙̂ λ = vλ[sthtλ ŵ −ηλ(λ̂−λ0)] (45) ˙̂ β = vβ[sthtβ ŵ −ηβ(β̂ −β0)] (46) where ηi, ηm, ησ, ηλ and ηβ are small positive constants; w0, m0, σ0, λ0, and β0 are the initial estimation vectors of w ∗, m∗, σ∗, λ∗, and β∗ respectively, then the compensation controller is written as: ucc = êltanh ( s ξ ) (47) the estimation law for the bound: ˙̂ el = vel [ s tanh ( s ξ ) −ηel(êl − êl0) ] (48) where tanh(.) is a hyperpol tengent function, el0 is the initial value of el , ξ and ηel are the small positive constants. using (41) – (48), (40) becomes: v̇ = m∑ j=1 w̃tjiviηi (ŵji −w0) + m̃ tηm (m̂−m0) + σ̃tησ (σ̂ −σ0) + λ̃tηλ ( λ̂−λ0 ) + β̃tηβ ( β̂ −β0 ) −vp m∑ j=1 ŵtjp [ŵjp + ηi (ŵji −w0)] + sε−sêl tanh ( s ξ ) − 1 vel ẽlvel [ s tanh ( s ξ ) −ηel ( êl − êl0 )] ≤ m∑ j=1 w̃tjiviηi (ŵji −w0) + m̃ tηm (m̂−m0) + σ̃tησ (σ̂ −σ0) + λ̃tηλ ( λ̂−λ0 ) + β̃tηβ ( β̂ −β0 ) − m∑ j=1 ŵtjpvpηi (ŵji −w0) + ∣∣∣st ∣∣∣el −stêl tanh (s ξ ) − ẽl [ s tanh ( s ξ ) −ηel ( êl − êl0 )] (49) with any ξ > 0: 0 ≤ ∣∣∣st ∣∣∣−st tanh (s ξ ) ≤ κξ (50) where k is a positive constant which satisfies k = exp(-(k + 1)) [13]. using (50), (49) becomes: v̇ = m∑ j=1 w̃tjiviηi (ŵji −w0) + m̃ tηm (m̂−m0) + σ̃tησ (σ̂ −σ0) + λ̃tηλ ( λ̂−λ0 ) + β̃tηβ ( β̂ −β0 ) − m∑ j=1 ŵtjpvpηi (ŵji −w0) + elκξ + ẽl [ ηel ( êl − êl0 )] ≤− 1 2 viηiξwi − 1 2 ηmξm − 1 2 ησξσ − 1 2 ηλξλ − 1 2 ηβξβ − 1 2 vpηiξwp − 1 2 ηelξel + elκξ (51) https://doi.org/10.15837/ijccc.2022.4.4606 12 where ξwi = m∑ j=1 [∥∥∥w∗ji − ŵji∥∥∥2 + ∥∥∥w∗ji −w0∥∥∥2 −‖ŵji −w0‖2 ] (52) ξm = [ ‖m∗ − m̂‖2 + ‖m∗ −m0‖2 −‖m̂−m0‖2 ] (53) ξσ = [ ‖σ∗ − σ̂‖2 + ‖σ∗ −σ0‖2 −‖σ̂ −σ0‖2 ] (54) ξλ = [∥∥∥λ∗ − λ̂∥∥∥2 + ‖λ∗ −λ0‖2 −∥∥∥λ̂−λ0∥∥∥2] (55) ξβ = [∥∥∥β∗ − β̂∥∥∥2 + ‖β∗ −β0‖2 −∥∥∥β̂ −β0∥∥∥2] (56) ξwp = m∑ j=1 [∥∥∥w∗jp − ŵji∥∥∥2 + ∥∥∥w∗jp −w0∥∥∥2 −‖ŵjp − ŵji‖2 −‖ŵjp −w0‖2 ] (57) ξel = [∣∣∣el − êl∣∣∣2 + |el −el0|2 − ∣∣∣êl −el0∣∣∣2] (58) then (51) is rewritten: v̇ ≤− 1 2  viηi m∑ j=1 ‖w̃ji‖2 + ηm‖m̃‖2 + ησ ‖σ̃‖2 + ηλ ∥∥∥λ̃∥∥∥2 + ηβ ∥∥∥β̃∥∥∥2 + ηel ∣∣∣ẽl∣∣∣2   + 1 2  viηi m∑ j=1 ‖ŵji −w0‖2 + ηm‖m̂−m0‖2 + ησ ‖σ̂ −σ0‖2 + ηλ ∥∥∥λ̂−λ0∥∥∥2 + ηβ ∥∥∥β̂ −β0∥∥∥2   + vpηi m∑ j=1 ( ‖w̃jp − ŵji‖2 + ‖w̃jp −w0‖2 ) + ηel |el −el0| 2 + elκξ (59) where ‖ .‖ is eulicdean norm. using (39), (59) becomes: v̇ ≤−ρ1v + ρ2 (60) where ρ1 and ρ2 are the constants defined as: ρ1 = min(ηi, ηmvm,ησvσ,ηλvλ,ηβvβ,ηelvel) (61) ρ2 = 1 2  viηi ∑mj=1 ‖ŵji −w0‖2 + ηm‖m̂−m0‖2 + ησ ‖σ̂ −σ0‖2 + ηλ ∥∥∥λ̂−λ0∥∥∥2 + ηβ ∥∥∥β̂ −β0∥∥∥2 +vpηi ∑m j=1 ( ‖w̃jp − ŵji‖2 + ‖w̃jp −w0‖2 ) + ηel |el −el0| 2 + ρ1s2   + elκξ (62) if (62) satisfies: 0 ≤ v (t) ≤ ψ + (v (0) − ψ) exp(−ρ1t) (63) where ψ = ρ2 ρ1 > 0, then e, wi, σ, λ, βand el steadily bounded. using (39) and (60) and given any ξ > 2ψ, if t exists with any t ≥ t , then error satisfied: |e(t)| ≤ ξ (64) thus, the proposed nsofc is stable. 4 experimental results the real model of the robot is presented in fig. 5 while it is simplified as a 2d model in fig. 6 to obtain the dynamic equation of the system. the two servos are located at joint a and b with the respective control angles as θa and θb. the four arms of the robot have the same length lb and are linked to each other. the end-effector is located at joint e. the positions of joint a and b in the https://doi.org/10.15837/ijccc.2022.4.4606 13 figure 5: quanser 2-dof robot model figure 6: quanser robot model in xy plane current x-y coordinate system are (0,0) and (2lb,0) respectively. in this experiment, the end-effector of the robot is controlled to follow a circular trajectory defined as{ exd = rcos(ω) eyd = rsin(ω) with r = 0.75, ω = t (rad/s). the starting position of the robot is e0 = [ lb lb ]t with θ0 = [ 0 0 ]t . the wfcmac controller has the form: τ = τwfcmac + τasifc and the parameters of the two controllers are chosen as in table 2. the system uses an ni pcie-6351 card to collect sensor data and control the robot. the experimental results of the two controllers are presented in fig. 7, 8, 9. the results of wfcmac (a) are on the left side and the results of nsosc (b) are on the right side. position and tracking errors of the two joints are illustrated in fig. 7, the end-effector’s position, tracking errors are illustrated in fig. 8. the control efforts are illustrated in fig. 9. moreover, the tracking errors of the end-effector of the two controllers are defined base on the error standards of iae, ise, itae, itse and iste, are also presented in table 3. the proposed controller presents the high accurate tracking ability, tracking speed, and quickly eliminate error which approaching to 0 even the system is an uncertain one. here we use the iae, ise, itae, itse and iste error standards to compare the two controllers. the result is showed in table 3. it is showed that the proposed nsofc has the data much smaller than the data of the wfcmac controller. it proves that the proposed controller has higher efficiency than the conventional cmac controller. it also proves that the modification of the number of layer help flexibly change for well adaptive ability of the control system even the external disturbances existing in the control plant. 5 conclusion and future work this paper proposes a novel nsofc controller for controlling uncertain robot arm systems. in the proposed nsofc, the dynamic of the control system is not needed. the online adjusting law of adaptive parameters of the nsofc and the compensation controller is defined based on lyapunov stability theorem to ensure the stability of the system. this paper not only develops successfully the nsofc control system for an uncertain non-linear system, but also derives the online self-organization algorithm for control the layer number of the nsofc controller to reduce the memory space as much as possible. finally, the experimental results prove that the proposed controller is possible to track the given trajectories. this controller can also be applied for the other uncertain n dof robots, even uncertain non-linear systems. for future work, the control scheme can be extended to more complex robotic systems such as articulated robots with higher degree of freedom. in addition, the learning rules of the parameters of nsofc still need to be further improved to reduce the “chattering” effect https://doi.org/10.15837/ijccc.2022.4.4606 14 table 2: the parameters comparison between two controllers: wfcmac and nsofc wfcmac controller nsofc controller main controller layer number: nk = 5 (fixed) block number: nj = 11 sliding surface: s(t) = ė(t) + ke(t), k = 100i2 initial mean and variance of gmf: mi = [−1 − 0.8 − 0.6 · · ·0.6 0.8 1] σi = 0.15 initial output weight: w0 = 0.05 learning rate: η = 0.001,v = 0.01 layer number: nk = 5 ( can be changed) sliding surface: s(t) = ė(t) + ke(t), k = 100i2 initial mean and variance of gmf: m prior i = [−2 − 1 0 1 2] m present i = [−2 − 1 0 1 2] σ prior i = 0.15 σ present i = 0.15 initial output weight: w0 = 10 learning rate: η = 0.001,v = 0.01 estimated coefficients: λ0 = 2 and β0 = 10 thresholds: βg = 0.35 and βd = 0.01 compensation controller fuzzy set’s parameters: ai = 0.2 bio = [−1 − 0.75 − 0.5 0 0.5 0.75 1] bound’s estimation function: cio = [−3.5 − 2.5 − 1.5 0 1.5 2.5 3.5] learning rate: β = 0.01 bound’s estimation function: el = 0.1 learning rate: ηe = 0.001,ve = 0.01 table 3: the comparison between two controllers:wfcmac and nsofc comparison efficiency controller wfcmac controller nsofc iae 0.3062 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(2021). optimal design of low computational burden model predictive control based on ssda towards autonomous vehicle under vision dynamics, international journal of intelligent systems, 2021, 36(11), 6968–6987. portico. https://doi.org/10.1002/int.22576. copyright ©2022 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: ngo t-q.; hoang d.-k.; tran t.-t.; nguyen t.-t.; nguyen v.-t.; le l.-h.(2022). a novel selforganizing fuzzy cerebellar model articulation controller based overlapping gaussian membership function for controlling robotic system, international journal of computers communications & control, 17(4), 4606, 2022. https://doi.org/10.15837/ijccc.2022.4.4606 introduction system description design the nsofc controller fuzzy cmac controller with overlapping gaussian membership function self-organizing structure adaptive nsofc control system experimental results conclusion and future work international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 1, month: february, year: 2023 article number: 4973, https://doi.org/10.15837/ijccc.2023.1.4973 ccc publications healthcare monitoring using machine learning based data analytics s.r.janani, r. subramanian, s. karthik, c. vimalarani s.r.janani department of computer science and engineering, sns college of technology, coimbatore, 641035, india corresponding author mail id:jananiselvaraj.mit@gmail.com r. subramanian department of electrical and electronics engineering, sns college of technology, coimbatore, 641035, india drrsubramanian@hotmail.com s. karthik department of computer science and engineering, sns college of technology, coimbatore, 641035, india profskarthik@gmail.com c. vimalarani department of information technology, karpagam institute of technology, coimbatore, 641105, india drvimalaranic@gmail.com abstract in this paper, we develop a machine learning based healthcare monitoring and analytics from various internet of medical things (iomt) devices for possible prediction of cardiovascular risk in patients. the study uses random forest for feature selection and then the fuzzy logic classifier is used for prediction of cardio vascular disease (cvd). the simulation is conducted to test the efficacy of the proposed machine learning based data analytics model over various other methods. the results show than the proposed method has higher rate of classification accuracy in classifying the cvd with higher recall and f1-score than other methods. keywords: machine learning, healthcare monitoring, iomt devices, fuzzy logic. 1 introduction the technology that supports the internet of things (iot) is continually undergoing progress. connecting computers, mechanical and digital devices, objects, animals, and humans with sensors and actuators is essential to achieving the aims of the iot, such as data collection and enhancing health, productivity, and efficiency [1]. the employment of the iot based remote patient monitoring is one of the most promising new technical solutions developing to narrow the global health equity gap. inequitable access to medical treatment is to blame for this disparity. these interoperable medical devices are also known as the internet of medical things (iomt). the https://doi.org/10.15837/ijccc.2023.1.4973 2 iomt has the potential to make an impact in a wide variety of fields, not just healthcare and security. by linking our bodies to the web, we can gain insight into our routines, physical and mental states, and the contexts in which we operate. thanks to this, doctors can keep a constant eye on patients from afar [2]. using the collected information to assist detect, diagnose, and treat medical problems early on would be a terrific approach to use the data potential. this is a great approach to put the information to good use. the human body, on the other hand, is not easily capable of being connected to the internet in the same way that digital or mechanical technology is. making a digital device that can connect to the internet without much hassle requires integrating a sensing and networking system. the problem is that even if we were able to properly implant a sensor system in a human being, we still wouldn’t be able to link it to the internet. taking measurements typically necessitates the employment of sizable sensing or measuring equipment. large measurement equipment, on the other hand, has a restriction in that it can only be exploited in carefully supervised conditions for a finite length of time at a time. because of this, it is not possible to connect a human body to the internet with the extensive range of sensing and measuring equipment that is currently available [3]. this severely limits the iot utility in critical infrastructure protection and medical care. as the global population ages, chronic diseases like cardiovascular disease (cvd) become a greater burden on healthcare systems. as a result, there is a growing interest in the development of remote patient monitoring (rpm) systems, which are intended to support medical professionals in the treatment of chronic diseases through the analysis of enormous amounts of data obtained from wearable sensors and health record data [4,5]. this not only ensures regular operation even if the network connection is weak or nonexistent, but also reduces transmission costs [6]. on the other hand, data analysis from a server in the cloud to a mobile device has its own obstacles and problems. our goal was to create a system that would be affordable for everyone while still providing accurate results. recent developments in mobile technology have made local deployment of mlas possible on mobile devices. in this paper, the study uses a novel contribution using machine learning algorithms (mla) that combines random forest with a fuzzy logic classifier, where the data is stored on remote computers, which is capable of performing intensive computing tasks. the main contribution of the paper involves the following: 1. the authors develop a machine learning based healthcare monitoring and analytics from various iomt devices for possible prediction of cardiovascular risk in patients. 2. the authors use random forest for feature selection and then the fuzzy logic classifier is used for prediction of cvd. 2 related works a lot of research effort has gone into developing smart healthcare iot-based technologies for health prediction. anuar et al. [6] describe a wearable core body temperature (cbt) sensor device that operates on the principle of a single heat flux. while testing the sensor in several locations, it was determined that the forehead provided the most accurate estimation of cbt. cbt sensor readings were on average only 0.05 degrees celsius off from those taken with a clinical thermometer, indicating a negligible discrepancy. extending the distance range from 50 to 100 centimetres was demonstrated by huang et al. [7] using a technique based on neural network regression. since the automatic face tracking function depends on a well-focused human face, this is an important consideration whenever facial measurements are being taken. the study utilises an app or the web to get to the data and results. the wearable temperature monitors that huang et al. [8] created has found medical applications. rahaman et al. [9] reviewed the various types of intelligent health monitoring systems and zeroed in on the benefits and drawbacks of the technologies now in use in health care systems. several iot-based technologies that could be employed in telemedicine and healthcare services for the treatment and prevention of a wide range of illnesses were studied in depth by albahri et al. [10]. in order to ensure the safety of patients, researchers looked at the viability of a wireless iomt system that could immediately notify parents or guardians of any health issues. certain researchers developed a remote patient monitoring system that featured the detection of important body data like photoplethysmography (ppg), ecg, and temperature with the goal of detecting the patient present state of health. this investigation followed a similar pattern. privacy and security issues related to iot-based smart healthcare systems are also discussed. paganelli et al. [11] compared the effectiveness of several topologies for keeping tabs on patients. it possible that any of these designs will lead to the discovery of the virus. the proposed architecture consists primarily of three layers: the application layer, the data collection layer, and the data transmission layer. the iotbased healthcare solutions, however, have some restrictions, including communication delays, latency, and other problems. https://doi.org/10.15837/ijccc.2023.1.4973 3 bassam et al. [12] demonstrated the use of a wearable health monitoring system in patients. the system has an in-built gps that allowed for real-time tracking. the system is connected to an android interface via an api in order to track the health of a discharged patient. all these issues can be addressed and a solution found with the help of fog computing and data mining methods. any iomt smart health system must ensure the confidentiality of patient information. a block-based encryption method could be utilised to secure cloud-based data storage. body language, facial emotions, and gestures may be picked up by non-invasive remote monitoring to help diagnose epilepsy seizures. this would supplement the standard practise of examining vital signs to determine a patient health condition. disease management, patient experience, effective treatment, and the importance of 5g in communication are all discussed in [13] that foresees the usage of iot in health monitoring of humans. iot (iomt) is important to all of these discussions. traditional approaches to disease diagnosis rely on measuring key bodily functions. iomt-based home systems are utilised to forecast the onset of urinary tract virus infections, as shown by bhatia et al. [14]. there are many different kinds of diabetes, cystine, hepatitis, liver disease, and other similar illnesses. in order to solve concerns such as cloud security, storage allocation, communication delay, data retrieval, etc., li et al. [15] made a detailed assessment on the efficacy of machine learning utilising big data analytics. predicting an individual behaviour in the future may be possible by using machine learning algorithms to information gathered through the iomt about that person activities, facial expressions, and health, among other things. kondaka [16] emphasises the necessity of machine learning in effectively assessing and managing the data kept on the cloud in order to create accurate forecasts regarding diseases. utilizing deep learning technology can assist reduce the number of errors that arise in iomt smart health systems. the cloud system presented by onasanya et al. [17] uses the iomt and wireless sensor networks to identify and treat cancer at an early stage. to better pinpoint potential covid-19, researchers have created a second iomt system. in order to distinguish between the symptoms, this system employs a total of eight distinct learning algorithms. in order to shorten the amount of time it takes for an ambulance or other sort of medical help to reach a patient throughout a smart city, researchers are examining how an iomt system could be used to track patients in the city. uslu et al. [18] describe these factors in detail. the improper use of patient information, cybercrime, data aggregation, and so on are among the most significant problems that can arise during the development of an iot-based health monitoring system. it is of the utmost need to have a case monitoring system that is dependable and accurate in order to maintain track, as this will help government officials to keep track of patients and, maybe, limit the spread of the disease. iot, or the iomt, might form the backbone of this infrastructure. using massive volumes of data acquired on biological signals to carry out real-time monitoring of individual patients should significantly reduce the frequency. wan et al. [19] designed a wearable iomt health monitoring system with its own network. this network is referred to as a body area network, and it is on this network that numerous sensors continuously measure and store characteristics. each of these locations creates a distinct set of difficulties for the system, yet all iomt technologies have found their way into healthcare, the home, or wearables. there are a number of considerations that must be made during the design and deployment of the automated iot health monitoring system. 3 proposed method the conventional ml systems were little more than signal gathering platforms, sending real-time physiological data from a single sensor to a server that is placed in distance location. the proposed model automatically analyse the data and improve forecast accuracy with fewer assumptions than traditional statistical approaches, they are finding increased usage in monitoring systems. the proposed monitoring system is seen in figure 1. the study begins by manually gathering and classifying the training data. it is necessary to perform preprocessing on the data, collect features, and maybe combine the data with additional dataset before using them as inputs to a classifier. the machine learning algorithm is put through its paces in a number of settings after it has been trained. https://doi.org/10.15837/ijccc.2023.1.4973 4 figure 1: proposed model in this investigation, we explore the feasibility of integrating a mobile platform into the structure of a remote monitoring system. ultimately, we aim to streamline the process so that it more convenient for users. during the process of creating a mobile ml system, there are four major decision milestones that have a domino impact on the whole system efficiency. there are four major checkpoints to consider:(1) the method used to label training data,(2) the data split, (3) the classifier selection, and (4) the modification of classifier requirements to account for the available computational resources. the study initiates automatic labelling with training data. the study proposes testing classifiers namely fuzzy logic on mobile devices to provide accuracy-computational profiles of each potential model. the system will choose a model dynamically based on the available computing power. data acquisition/collection layer the proposed healthcare system incorporates insights from two substantial databases. during normal health monitoring, a variety of physiological measurements are taken, including but not limited to heart rate, blood pressure, glucose level, blood sugar, blood oxygen, respiratory rate, activity level, cholesterol, electromyogram, electrocardiogram, and electroencephalogram. these records are then transmitted wirelessly via bluetooth to a remote gateway device, where they undergo preliminary processing before being employed in a health risk assessment. the patient whole medical record is kept in the cloud, from their smoking and diabetes histories through their observation reports and complete clinical reports. pre-processing layer university of california, irvine researchers are examining patient data from cleveland and hungary stored in the institution machine learning repository for indications of cardiac disease. due to the inherent inconsistency, incompleteness, and noise of real-world data, data pre-processing has become a precondition for the use of machine learning techniques. correct heart disease prediction utilising the heart disease dataset requires processing of missing data, standardisation of the data, and feature selection. problems with missing values and noise in data from wearable sensors might make it difficult to anticipate heart disease and increase the risk of an inaccurate diagnosis or incorrect classification. in this specific case, the widely-used data-cleaning method of kalman filtering is put to use. using this method, we may efficiently clean up the data by getting rid of extraneous information like noise, duplicates, and outliers. because to its straightforward layout, it necessitates comparatively little processing time. this unsupervised filtering technique is optimised to return values that are free of noise and closer to what the sensor is actually telling you, making it useful for processing large amounts of real-time sensor data. also, the figures it returns are closer to the sensor true measurements. we additionally employ two additional unsupervised filters during this stage of processing to remove superfluous values and complete gaps in the data, respectively. the initial https://doi.org/10.15837/ijccc.2023.1.4973 5 filter retains as much as 90% of the maximal variance while excluding unnecessary features. the second filter fills in missing values in the structured dataset by using its mean and median. random forest based feature selection feature selection is the first step in machine learning, occurring before any actual learning takes place. a more precise machine learning system can be achieved through the use of feature selection. by selecting features using a random forest, the chances of underand over-fitting are minimised. when a number of decision trees representing several classifiers are utilised, the likelihood that any single tree would fail to correctly forecast the target value is eliminated (an ensemble of classifiers). the rf draws its conclusion by averaging the data from all of the trees. the rf margin function is described by equation (1), the generalisation error by equation (2), and forecast confidence by equation (3). mg(x, y) = av ik(hk(x) = y) − maxj (hk(x) = j) (1) where i indicator function. the error in generalisation, can be expressed as follows in equation (2): pe∗ = p_x,y(mg(x, y)>0) (2) when a coordinate system with x and y axes is used to plot the probabilities. with an increasing number of classifiers (decision trees) in random forests, hk(x) = h(x,θk) holds true for all repetition sequence (tr) sequences. probability of convergence (pe) to equation (3) is given below, as predicted by the strong law of large numbers and the tree topology. p_x,y(pθ(h(x, θ) = y) − max_j(y_pθ(h(x, θ) = j)<0) (3) where θ – random vector x – input vector y – random vector distribution here, we send training data comprised of x and y vectors to a set of classifiers called decision trees, denoted h1(x), h2(x), etc. equation (1) provides an expression for the margin function. the rf bagging algorithm is also part of the bootstrapping procedure with the base classifiers. several trees, rather than simply one, are generated using this strategy, which is typical of ensemble learning. as the degree of similarity across trees increases, the robust classifier favours a lower rate of error. the attributes extraction and the number of decision trees are crucial factors in rf classifier training. this approach prevents overfitting and takes noise and outliers into account without sacrificing accuracy. 3.3. data prediction layer sequence prediction issues have been present for some time, and their difficulty is widely acknowledged as being among the highest. the data prediction layer consists of the following machine learning algorithm that is discussed below: the fuzzifier, inference engine, knowledge base, and defuzzifier are the four main parts of a typical fuzzy logic (fl). typically, input to a fuzzy system might be either numerical data or textual descriptions (fuzzy sets). 1. the term fuzzification is used to describe the process of feeding a clear input into the appropriate fuzzy set. 2. the inference engine uses expert knowledge, represented as a set of fuzzy conditional rules in the knowledge base, to translate the values of the input variables into the language values of the output variables. 3. knowledge bases include domain-specific expertise and formalise it in a database and set of rules. the language control rules are stored in a database, and domain expert knowledge is incorporated into the rule set. 4. defuzzification adds discrete data to the fuzzy set that is created, providing numerical data along with the linguistic values. the first method is a fis (fuzzy inference system) algorithm, and its goal is to classify patient medical histories in accordance with the risk that they would develop cardiovascular disease. algorithm 1: classification using fis 1. determine fuzzy using input and membership function µ1 https://doi.org/10.15837/ijccc.2023.1.4973 6 2. find the risk state of cvd using µ1 (maxheartrate1, lowheartrate1, bp1) as low or normal or high 3. if the risk = high (a) alert the patient and doctor (b) save the health record of the patient 4. else save the health record of the patient 5. end the process the inputs for maximum heart rate, ecg, and blood pressure, are created and member function are fed, which are fuzzified into fuzzy sets using a fuzzy value range. the variable risk of high is determined based on exceeding the value of maxheartrate1. an individual highest and lowest recorded heart rate and blood pressure are all sent into a member function that enables the decision making. the inputs are converted into fuzzy sets with a corresponding value range. the fis receives the produced fuzzy sets as input and uses them to categorise patients based on their medical records. in the db, these procedures are codified as fuzzy conditional rules. the combination of the rule base member functions and the fuzzy rules determines the final ranking of the outcomes. in the prediction process, information on people who have previously been identified as being at high risk for cardiovascular disease is analysed in greater depth. membership function this sigmoid function is used as a activation function and it is defined in the following equation: sig (x;a,c) = 11+exp[−a(x−c)] whereas : c degree of membership. a slope. 4 results and discussions the rf-fl is trained and tested on a pc running windows 7 with matlab 2014a, with a 2.2 ghz intel i7 processor and 12 gb of ram. software developers now have access to a novel training method for gauging the efficacy of their classifier creations. in this way, our approach may be used to train classifiers for any mobile app, expanding the utility of the rf-fl training method. our approach would require more time to train the model than would be necessary using 5-fold training. the methods we have proposed require the development of an automated system that can transfer the classifier to the mobile device and assess its computational efficacy. the purpose of this research was to use a machine learning model on a dataset related to heart disease to evaluate the performance of sequential prediction models. both the cleveland and the hungarian heart disease datasets are employed in the system, and both may be found in the online ml and data mining repository at uci. there are a total of 303 and 294 records, respectively, in the cleveland (https://archive.ics.uci.edu/ml/datasets/heart+disease) and hungarian heart disease databases (https://www.kaggle.com/sid321axn/heart-statlog-cleveland-hungaryfinal), and each record has 14 unique properties. as a way to test the robustness of the proposed rf-fl model, we used the dataset generator mockaroo to raise the record count to 100,000. an example of this is dividing a dataset of 100,000 records into a training set of 70,000 and a testing set of 30,000. the training of the model is carried out from the cleveland and hungary and then the testing is made from the iomt devices. depending on the precision requirements, the optimal number of iomt nodes is chosen automatically. the learning rate is 0.16 times slower than the decay rate of 0.96. we have a momentum parameter of 0.82, an epoch size of arbitrary precision, and a batch size of 128 transactions. wireless body sensor networks (wbsns) are responsible for transmitting data collected from iot devices to the cloud, where it will be analysed and organised. using apache spark and cassandra as the server and storage architecture for the tensorflow ml package, the experiment was conducted on the i2k2 cloud platform. the major limitations involve the use of limited iomt devices as increasing data may lead to poor pre-processing and thereby the precision would reduce. evaluation indices accuracy, precision, sensitivity specificity, and f1-score are the few indicators used when determining a model value. accuracy evaluates how well the proposed rf-fl model performs by comparing the actual outcomes with the expected ones. we measure the classifier model performance in determining if a patient has heart illness by comparing the true positive (tp) and true negative (tn) rates. how well a model predicts is measured by the proportion https://doi.org/10.15837/ijccc.2023.1.4973 7 of false positives (fp) and false negatives (fn) it produces. false positives (fp) and false negatives (fn) are abbreviations for these types of errors. examining the proportion of true positive observations to total positive cases reveals the accuracy. accuracy defines the overall correctness of the proposed model as in following expression: accuracy=(tn+tp)/(tp+fp+fn+tn) sensitivity defines the correctness of classifying the specific class as in following expression: sensitivity = (tp)/(fp+tp) specificity defines the how many times, a specific class is classified correctly as in following expression: specificity=(tn)/(tn+fp) f1-score is the harmonic mean of the precision and recall and it is expressed below f1 measure=(2tp)/(2tp+fp+fn) figure 2: sensitivity https://doi.org/10.15837/ijccc.2023.1.4973 8 figure 3: specificity figure 4: f1 score https://doi.org/10.15837/ijccc.2023.1.4973 9 figure 5: accuracy figure 2-5 shows the results of various metrics of the proposed classifier with existing methods. the simulation shows that the proposed method has higher classification accuracy than the existing methods. 5 conclusions in this study, we assessed the classifiers based on accuracy and computational performance is universal, in contrast to the majority of our recommendations, which are tailored to particular stages. here, rf features are ranked according to how much of an impact they have on model correctness on available system resources. these results pave the way for the next generation of personalised predictive monitoring systems. the classification using fuzzy logic enables proper classification of instances than other methods and the selection of correct features improves the process of obtaining proper classified results. from the results of simulation, it is seen that the proposed method achieves a higher rate of accuracy in classifying the dataset than the other existing methods. the model achieves a higher degree of f1-score than other methods that shows a better efficacy of the model in classifying the dataset. in future, real-time applicability of the model is tested when the proposed rf-fl is applied over various types of dataset. 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(2020). analysis of factors affecting iot-based smart hospital design. journal of cloud computing, 9, 1-23. [21] wan, j., aah al-awlaqi, m., li, m., o’grady, m., gu, x., wang, j., & cao, n. (2018). wearable iot enabled real-time health monitoring system. eurasip journal on wireless communications and networking, 2018, 1-10. https://doi.org/10.15837/ijccc.2023.1.4973 11 copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: janani, s.r.; subramanian r.; karthik, s.; vimalarani, c. (2023). healthcare monitoring using machine learning based data analytics, international journal of computers communications & control, 18(1), 4973, 2023. https://doi.org/10.15837/ijccc.2023.1.4973 introduction related works proposed method results and discussions conclusions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 3, month: june, year: 2023 article number: 5465, https://doi.org/10.15837/ijccc.2023.3.5465 ccc publications status, challenges and trends in videoconferencing platforms a.m. suduc, m. bizoi, f.g. filip ana-maria suduc* valahia university of targoviste aleea sinalia 13, 130004, targoviste, romania *corresponding author: ana.suduc@valahia.ro mihai bizoi valahia university of targoviste aleea sinalia 13, 130004, targoviste, romania bizoi@valahia.ro florin gheorghe filip romanian academy calea victoriei 125, sector 1, 010071 bucharest, romania ffilip@acad.ro abstract although videoconferencing platforms were a growing market even before the covid-19 pandemic, they became very popular and widely used within weeks of the beginning of the pandemic. the compulsion of social distancing, lockdown, the need to work from home have led to an exponential demand for videoconferencing applications and an adaptation of the way we carry out our current activities such as work, decision-making, communicating with family and colleagues, or education. with the diversification of the categories of users of videoconferencing systems and the contexts in which they were used, many other needs have emerged and the demand to adapt these platforms, both in terms of ease of use and functionality, as well as security. videoconferencing systems are no longer meant only for decision-makers or trainers, but also for general public. this paper reviews various data regarding the use of videoconferencing systems in recent years, a number of problems encountered by users, and expected trends in the future. keywords: collaboration technologies, decision support platforms, pandemic, virtual meetings. 1 introduction a collaborative group consists of a number of members who work together to achieve a common set of objectives by carrying out activities and using specific procedures and techniques [10]. communication has always been one of the main enablers of an effective collaboration. it can be synchronous or asynchronous. synchronous communication has traditionally involved face-to-face or online meetings of group members who are placed in the same or different locations, respectively. although, before the pandemic, there were quite a few companies that regularly held online or hybrid meetings for decision https://doi.org/10.15837/ijccc.2023.3.5465 2 making and problem solving (some using dedicated group decision support systems or computer supported cooperative work systems [8] [4], most of them mainly, if not always, held face-to-face, same-place meetings. in the pandemic period, companies were forced to go online. in order to continue operating effectively, businesses have had to develop new procedures for decision-making and communication. at the same time, the digital platform technologies (such as big data, internet of things (iot) and cloud computing) and working style are spreading as wildfire and contribute to the progress towards a platform society and economy [5]. as statistical data and analyses witness, the ever-growing market for videoconferencing platforms, it is useful to evaluate the current state and identity the trends in this area so that we can adapt ourselves to the new situation and see how we can integrate the appropriate tools with the view to increase work efficiency. this paper reviews various statistical data on the use of the most popular videoconferencing platforms in recent years and present the results of comparisons of these systems made by various authors in the literature. a number of challenges encountered by users of videoconferencing systems and the main promising trends in the development of such systems and corresponding enabling tools, that are expected to have a significant impact on the way we communicate and make-decisions in the future, are also presented. 2 current status on the most popular videoconferencing platforms covid-19 experience has shown that online meetings are a viable and rather acceptable alternative to face-to-face and same-place meetings. prior to the pandemic outbreak in 2020, working from home has not been too widespread. videoconferencing platforms have regularly been mainly used by a range of users such as policy makers and trainers. with the new needs that have emerged in recent years due to the pandemic context, companies, public administration offices, education institutions, and various people had to imagine new solutions. unified communication and collaboration tools such as zoom, microsoft teams, cisco webex, gotomeeting or google hangouts/meet saw an increase in demand when remote working intensified following the covid-19 outbreak in 2020 [38]. such tools have been largely adopted, in all fields, for both professional and personal purposes since the early months of the pandemic. although pandemic conditions no longer force using virtual meetings, due to the many advantages identified and experienced, many businesses still prefer to use videoconferencing instead of face-to-face, same-place meetings. in 2021, companies’ top it investment priorities to support working from home have been based on cloud-based technologies and digital collaboration tools [29]. today, videoconferencing platforms represent the increasingly popular means of communicating with business partners, friends, colleagues, and family members and a convenient and broadly used way to provide education. videoconferencing platforms that include tools tailored for different categories of meetings can make possible a higher productivity in teams or efficiency in learning by facilitating collaboration, problem solving or decision making. 2.1 strengths over the years, various authors have analysed the different advantages and disadvantages of videoconferencing platforms and remote working in geographically dispersed work teams. as one may expect, the main advantages are related to costs (e.g. meeting materials and office expenses, travel costs, and so on) and time savings. according to [33], communication and decision-making performed by using virtual meetings can be achieved in a shorter time and more efficient way. that explains the early trend of companies to use videoconferencing solutions in order to facilitate decision-making and reduce the costs associated with holding physical meetings. sometimes the option of a face-to-face and some-place meeting may not be possible or desirable. in such situations, online meetings could ensure that the necessary tasks and processes are accomplished. in addition, agenda-driven online meetings tend to be more productive and efficient, partly due to the sense of urgency given by the shorter online attention spans and also because there is less table talk and more productive conversation. according to [28], a significant percentage of 57% of business professionals felt that in working remotely during the pandemic, in 2020, their productivity was higher than before. only 17% of them considered themselves less productive and 24% did not notice a difference. virtual meetings have become universally accessible, and the convenience of having members from the same or different companies in the same https://doi.org/10.15837/ijccc.2023.3.5465 3 meeting from any location is also a great benefit of online meetings [19]. according to [24], the best videoconferencing apps offer high picture and sound quality and include essential features for initiating and managing online meetings (e.g. easy meeting scheduling, collaboration features, meeting recording). following testing of 30 videoconferencing apps, pot [24] identified five best videoconferencing platforms in 2022, each of which, according to the author, is recommended for certain usage settings of use or the quality of certain attributes. they are as follows: a) zoom (for reliable and large video calls), b) google meet (for google workspace users), c) microsoft teams (best combination of team chat and videoconferencing app), d) whereby web (best videoconferencing app for an easy option), and e) webex meeting (for video quality). there are numerous authors who have made various comparisons between different videoconferencing systems, taking into account attributes such as the functions offered, the devices on which they operate, the size of meetings or their prices [34]. in the analysis carried out by [31], using different parameters as a basis for comparison, the three most popular videoconferencing platforms are in most cases placed on an equal footing with each other. in terms of video quality alone, microsoft team stands out, and zoom stands out in terms of meeting recording and local recording storage. suduc & bizoi [34] developed a list of strengths of the three most popular videoconferencing systems, as highlighted in the literature, considering seven criteria such as: (1) performance and reliability, (2) meeting size (number of participants), (3) integration, (4) security, (5) platforms (supported devices), (6) best features, and (7) what they are best suited for (“best for. . . ”). table 1 shows the features identified by suduc & bizoi according to the classification made in [34], to which other features from other comparisons in the literature have been added [3][31]. analysing the results, it is difficult to conclude that one platform stands out from the others in terms of any of the criteria analysed. the situations/contexts in which platforms are commonly viewed as the most suitable may tip the balance in choosing one product over another. according to the results obtained by [34], zoom is the most suitable platform for education and for contexts where users only need audio/video communication and less for other tools. many authors consider google meet to be the natural and best choice for google workspace users. there are currently over three billion google workspace users [12], of which six million are paying business users [1]. google meet therefore targets a very wide audience. however, google workspace is the second most popular project management tool after microsoft teams [1]. [34] place microsoft teams as the best videoconferencing platform for microsoft 365 users, education and corporate settings. microsoft teams also has an impressive number of users, reaching more than 280 million monthly active users in the second quarter of 2023 [14]. such systems offer similar functions, which makes choosing one platform over another a difficult task. as shown below, the moves made by major videoconferencing platform providers to embrace interoperability will apparently make life easier for users. 2.2 statistics on the use and market of videoconferencing systems the global market for videoconferencing platforms is expected to grow significantly in the coming period, from usd 7.65 billion in 2022 to around usd 18.56 billion in 2030. this growth is driven, according to [25], by the trend of remote working, geographically dispersed operations and due to globalization. in january 2022, up to 78% of us corporations used videoconferencing software routinely, and a little over 58% of companies did so daily to keep operations running. according to recent surveys and studies 89% of employees, especially those who work remotely, said they feel that video conferencing helps them feel more connected with their colleagues and at home in their company [32]. the period that marked a boom in the use of videoconferencing platforms was the beginning of the pandemic. according to sydow [35], during the week of march 15-21, 2020, there were 62 million downloads, 45% more than the previous week, of business apps from the app store and google play. compared to the weekly average for the first four months of 2019, video conferencing apps were downloaded dozens of times more the week of march 15-21, 2020. for example, google hangouts meet was downloaded 24 times more in the uk, 30 times more in the us and 140 times more in italy. zoom and teams also saw significant increases, with varying figures from country to country. this growth continued into the second half of 2020 and beyond. according to smart systems [32], google meet had over 100 million daily video conference partichttps://doi.org/10.15837/ijccc.2023.3.5465 4 table 1: strengths of the most known videoconferencing systems as reflected in the literature (after [34]) zoom google meet microsoft teams performance and reliability -reliable calls -clear, consistent, and reliable video experience -great video quality large video calls -up to 1000 interactive participants via zoom large meetings add-on -up to 250 participants interactive participants, and up to 100,000 viewers via live streaming in the domain -up to 1000 interactive participants integrations -integrate with learning management systems -dozens of integrations and add-ons -integrate with google apps -integrate with various add-ons and apps -integrate with microsoft apps -integrate with learning management systems security -two-factor authentication -end-to-end encryption -waiting-room -two-factor authentication -ediscovery -two-factor authentication -microsoft advanced threat protection -data loss protection -ediscovery platforms -macos, windows, ios,android, web -ios, android, web -web, macos, windows, ios, android, linux best features -screen sharing (multiple options) -breakout rooms -participant reporting feature -live captioning for audio and video chats -screen sharing options and controls -dial-in for free with any of paid plans -clear and straightforward interface -video quality -breakout rooms -together mode -best whiteboard -best track record of security and the most transparent privacy policy -all-in-one collaboration hub -automatic captioning best for. . . -education -users who need only video conferencing -google workspace users -microsoft 365 users -education -corporate settings https://doi.org/10.15837/ijccc.2023.3.5465 5 ipants in 2020, and, by 2021, there were approximately 250 million daily active users on microsoft’s virtual meeting platform, microsoft teams. cisco webex is one of the current top videoconferencing platforms this year (2022), with about 325 million daily users citesmartsystems2022. from the data it had concerning the perceived buyer interest, trustradius has made a list of top leaders in the video conferencing market in 2021 [28]. in 2021, zoom has 50% of the video conferencing market, followed by microsoft teams (23%), webex meetings (11%) and google meet (4%). a study on the market share of videoconferencing applications in 2022, worldwide [37], conducted in 2022 with 4495 respondents, presents zoom as the leading videoconferencing software (55.44%). second place was taken by microsoft teams (20.93%), followed by gotomeeting (12.88%), webex (9.43%), ringcentral (5.67%) and google meet (5.32%). another report on popularity of video conferencing platforms in 118 countries can be found in [2]. according to the report, zoom is the leading videoconferencing platform in 80 countries (66% of countries surveyed), followed by google meet (28 countries) and microsoft teams (7 countries). according to the above author, microsoft teams has lost a lot of popularity from 2021, when it was the most popular app in 41 countries, to just 7 in 2022. from all these data, it can be concluded that the main competitors in videoconferencing systems are zoom, microsoft teams, google meet and webex. 3 videoconferencing challenges and issues between 17 march and 5 april 2021 [30], a survey was conducted, worldwide, to find out what challenges respondents encountered in video meetings when working remotely in 2021. the main challenges of online meetings indicated by the 2181 respondents were: a) encountered technical problems (for 58% of respondents), and b) fatigue associated to a large number of online meetings (28%). other challenges that were indicated by significant percentages of respondents to the above survey are: a) difficulties associated with non-verbal communication (28%), background distractions (26%), awkwardness of small talk (22%), difficulty intervening politely in the discussion (19%), difficulty meeting start times (13%), difficulty of collaborating/interacting (12%), screen sharing issues (11%), and inefficient and unproductive meetings (11%). according to the remote work report 2020 of owl labs [23], the biggest challenges related to working from home for 62% of the total of 2025 respondents (full-time us workers) were interruptions and talking over others. the second biggest challenge for respondents (59%) was the background distractions from other participants. for example, 57% of respondents had difficulty staying focused. a significant percentage of respondents also indicated technical issues as challenges: audio quality (57%), video quality (56%), internet speed or connectivity (52%) and meeting setup (50%). a detailed analysis of the various challenges that users typically experienced in virtual meetings during the pandemic can be found in [15]. according to [17], in working from home, the boundaries between daily life and work have become quite blurred which has led to many difficulties, especially distractions. lee et al [17], in their study, analysed different situations in which videoconference participants were distracted by various factors. the distracting experiences identified were generated by both the technical equipment/videoconferencing system and by people/pets/work space in the meeting or the user’s physical environment. the participants to the study indicated also several possible videoconferencing improvements to help them to deal with the distractions: (1) higher quality virtual backgrounds in which the user’s face is detected and everything but that face is processed (hidden), (2) a status selection function (e.g. away for a period of time) that can easily be used when other more important situations outside the meeting require the user’s attention, (3) a wider range of integration between applications and devices allowing them to deal with different situations in the environment without having to leave their seat during the videoconference, (4) video conferencing transmission of only the user’s voice and cancellation of all other ambient noise, including other people’s voices. as it can be noticed in the following section, major videoconferencing system providers are already working on smart features that will most likely significantly reduce distractions. for example, cisco’s webex claims that its audio intelligence feature has, since its launch after the bubblelab acquisition in september 2020 and https://doi.org/10.15837/ijccc.2023.3.5465 6 by june 2021, eliminated background noise from over 16 billion minutes of videoconferencing sound [16]. 4 future of video conferencing and artificial intelligence in video conferencing in recent years, artificial intelligence (ai) based technologies have become almost ubiquitous even though one can notice various concerns and fears expressed in the very recent newspapers and magazines [36][11]. after the initial wave of ai applications focused on internal efficiency, digital platform companies have been exploring the possibilities of using these innovations for new business opportunities. the role of ai has grown significantly over time, becoming the central part of digital platforms and even a platform in its own right [20]. and for videoconferencing systems, the use of ai technologies has grown in recent years and continues to grow. according to precedence research [25], in the near future, the growth of the videoconferencing systems market will be influenced on the one hand by the integration of cloud, internet of things (iot) and artificial intelligence technologies and, on the other hand, by factors such as the growing demand for video communication, e-learning and e-government. concerns in using ai to improve video conferencing systems have been around since before the pandemic. for example, webex first introduced ai in 2017, when it added machine learning-based noise detection and speaker tracking, as well as camera framing of participants. over the next few years, webex continued to add new ai-based features that proved very useful. in recent years, ai based methods have proven that they can contribute significantly to improving the user experience. various implementations of ai in videoconferencing systems are reported in the literature. suduc & bizoi [34] have listed a number of such ai integrations within zoom, meet, and teams. in all three platforms, ai technologies are mainly aimed at video and audio enhancement (e.g. static or dynamic virtual background, video compression, autozoom, background noise suppression, de-reverberation, etc.), but also live transcription (e.g. live captions, auto translation, speech-to-text, summarisation, etc.) or integration with other applications useful in such virtual meetings. the cisco webex platform also includes several ai-based features. according to [6], cisco webex has focused its efforts on using ai for audio and voice, bots and assistants, relational intelligence and computer vision for facilitating collaboration. in june 2021, webex announced the latest additions to its artificial intelligence features targeting hybrid work namely: a) the webex assistant for devices, which includes multiple voice commands in multiple languages, b) the "optimize for my voice" feature, which distinguishes between foreground and background speakers, and c) the "people insights" feature, which provides private data for each individual person to support better-informed decisions about meetings. cisco webex is also working on improving virtual background replacement and gesture recognition [27]. webex videoconferencing software together with webex devices offers a number of ai-based features that significantly enhance hybrid meetings. dhingra [6] presents these features, in chronological order of their development and implementation. some of these ai-based features are: automatic framing of talking participants, close-up images, people counting, presenter tracking, facial recognition with name labels, proactive collaboration assistant (proactively start a conversation), a personal in-meeting assistant, speaker labelling in transcripts and region of interest encoding [6]. a paper published by mendes et al [18] presents a list of eleven situations in which ai could be used to address the needs that are commonly encountered in the usage of videoconferencing systems such as: a) verifying user identity, b) counting and possibly anonymizing participants, c) detecting user attention/engagement, or d) summarizing the meeting. some of these use cases have already been analysed by providers of videoconferencing systems and partially or fully covered by specific features. according to [13], ai could transform videoconferencing in many ways. for example, it can be used for: a) making predictions or suggesting useful applications or documents based on statistical data collected in meetings, b) video and audio enhancement, c) automated transcription/translation, d) use of bots, e) use of augmented reality and more. experts expect that ai technologies will also be adopted in the future to improve and diversify the functions of videoconferencing systems. an important aspect of the videoconferencing market in recent times is the openness of large https://doi.org/10.15837/ijccc.2023.3.5465 7 companies in the field to interoperability. video conferencing interoperability refers to the ability of online meeting platforms to connect and communicate with each other to create the easiest possible user experience. to improve the overall experience of online and hybrid meetings, many videoconferencing platform manufacturers have embraced interoperability. for example, in october 2022, cisco announced that company’s hardware will support microsoft teams in the future. the users of cisco meeting devices and rooms can now choose to natively run either cisco webex or microsoft teams [7]. this interoperability also allows microsoft teams users directly within the application to schedule, access or cancel webex meetings. cisco webex allows to schedule and join virtual meetings not only in microsoft teams, but also in google meet and zoom from a webex device with just a few clicks [39]. poly solutions are interoperable with microsoft teams, zoom, cisco webex, gotoroom, bluejeans, dialpad, starleaf and ringcentral, and neat hardware solutions with zoom and microsoft teams. another important partnership, announced in september 2015, is between cisco and apple. as a result of this partnership, iphone and ipad users have the ability to share either camera in webex meetings and annotate [21]. webex meetings for apple carplay allows viewing upcoming meetings, joining, audio only, a meeting and playing a recording. interoperability of videoconferencing systems enables creating an improved user experience and has the potential to ensure better collaboration and increased productivity [26]. 5 conclusions the way business works has undergone a notable change in the last two years. videoconferencing has become one very visible enabler of the new normal, and this is having an impact on the productivity of companies, how they save resources, and how intra and inter communications are carried out. they provide an ever more effective and timely dimension of the contribution of information technology to improving human wellbeing and resilience [9]. many institutions have chosen to use more than one videoconferencing platform for different purposes and situations within the same organisation [22]. considering that 85% of organizations use more than one meeting platform and 62% of videoconferencing companies use more than three different videoconferencing software options [32], adoption of interoperability by many videoconferencing system manufacturers will facilitate and completely redefine the virtual and hybrid meeting experience. although companies, people and governments were taken by surprise at the start of the pandemic, they have had to adapt to the new conditions by rapidly adopting solutions that allow them to continue to operate. the providers of videoconferencing systems have had to meet a huge demand in a very short time. manufacturers have therefore focused their efforts on responding promptly to the different needs of users, enhancing existing functions and integrating new features and technologies. ai technologies have been among the most widely used technologies for developing videoconferencing systems in recent years. and in the future, artificial intelligence, along with iot and cloud technologies, are expected to drive the growth of the videoconferencing systems market by improving user experience, productivity and efficiency of virtual meetings. an early and partial version of this paper was presented at itqm 2022 conference and published by elsevier in procedia computer science, vol. 214, pp. 288-294, under the title 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(2022). join any meeting on webex devices, [online]. available: https://www.webex.com/content/dam/wbx/us/ebook/join-any-meeting-on-webex-devices_cm4462.pdf, accesed on feb 2023. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: suduc, a.m; bizoi, m.; filip, f.g (2023). status, challenges and trends in videoconferencing platforms, international journal of computers communications & control, 18(3), 5465, 2023. https://doi.org/10.15837/ijccc.2023.3.5465 introduction current status on the most popular videoconferencing platforms strengths statistics on the use and market of videoconferencing systems videoconferencing challenges and issues future of video conferencing and artificial intelligence in video conferencing conclusions international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 2, month: april, year: 2023 article number: 5049, https://doi.org/10.15837/ijccc.2023.2.5049 ccc publications directed search based on improved whale optimization algorithm for test case prioritization bin yang, huilai li, ying xing, fuping zeng∗, chengdong qian, youzhi shen, jiongbo wang bin yang china unicom research institute no. 9 shouti south road, haidian district, beijing 100048, china, researcher_yang@outlook.com ying xing, huilai li school of artificial intelligence beijing university of posts and telecommunications, china no. 10, xitucheng road, haidian district, beijing 100876, china xingying@bupt.edu.cn, lihuilai@bupt.edu.cn fuping zeng* school of reliability and systems engineering beijing university of aeronautics and astronautics, china no. 37, xueyuan road, haidian district, beijing 100191, china *corresponding author: zfp@buaa.edu.cn chengdong qian, youzhi shen, jiongbo wang phytium technology co., ltd., china 7th floor, quantum core block, zhichun road, haidian district, beijing 100086, china qianchengdong@phytium.com.cn, shenyouzhi@phytium.com.cn, wangjiongbo@phytium.com.cn abstract with the advent of the information age, the iterative speed of software update is gradually accelerating which makes software development severely limited by software testing. test case prioritization is an effective way to accelerate software testing progress. with the introduction of heuristic algorithm to this task, the processing efficiency of test cases has been greatly improved. however, to overcome the shortcomings of slow convergence speed and easy fall into local optimum, the improved whale optimization algorithm is proposed for test case prioritization. firstly, a model called n-dimensional directed search space is established for the swarm intelligence algorithm. secondly, the enhanced whale optimization algorithm is applied to test case prioritization while the backtracking behavior is conducted for individuals when hitting the wall. in addition, a separate storage space for pareto second optimization is also designed to filter the optimal solutions of the multi-objective tasks. finally, both single-objective and multi-objective optimization experiments are carried out for open source projects and real-world projects, respectively. the results show that the improved whale optimization algorithm using n-dimensional directed search space is more conducive to the decisions of test case prioritization with fast convergence speed. keywords: software testing, test case prioritization, swarm intelligence algorithm, whale optimization algorithm, directed search space. https://doi.org/10.15837/ijccc.2023.2.5049 2 1 introduction under the diversified user requirements, the software system is updated iteratively more frequently[1]. to discover new defects after software upgrading, necessary tests must be carried out[2]. however, every time a software is upgraded, new test cases need to be added to the original test case set, which leads to a gradual increase in test costs due to its large scale[3, 4]. especially for large-scale software regression testing, the process may take a long time. enterprises often need to set up pretreatment modules to complete production more efficiently[5, 6]. wong et al.[7] first proposed research on test case prioritization (tcp). by handling the test cases and determining the test order according to the priority, the test cases that can cover more nodes can be executed early so that the internal defects can be found faster, and the time consumed for software testing can be significantly reduced[8, 9]. test case prioritization is a pre-decision process for software testing. the test cases that can achieve faster full coverage of the system will be prioritized first. tcp technology plays a vital role in reducing the cost of software testing and speeding up the progress of digital testing[10]. most of the early studies focused on prioritizing itself. for example, fan hui et al.[11] gave methods of total prioritization algorithm and additional prioritization algorithm according to the definition of test case prioritization. however, it has been proved that the prioritization of test cases belongs to a np-hard problem[12], so the search without information or search with information can hardly meet this requirement. as we know, the overall prioritization scale of test cases is much larger than the scale of the test cases themselves, which is the main problem to be faced in tcp. with the advance of the research on test case prioritization, the solution to this problem has gradually transited to intelligent algorithms. for example, dharmveer et al.[13] proposed a fuzzy inference system, which uses the fault detection rate and program execution time as indicators to verify its effectiveness[14]. zhang et al.[15] proposed an intelligent test case prioritization method based on the genetic algorithm. they designed corresponding coding strategies, crossover operators, mutation operators, and fitness functions for program coverage, which improved the automation level of software testing. xu et al.[16] designed an intelligent test case prioritization algorithm based on the artificial immune algorithm (ia), which has great global search performance. among these intelligent algorithms, the performance of swarm intelligence algorithm is more obvious. the swarm intelligence algorithm with the advantages of good convergence and comprehensive optimization can effectively avoid falling into the problem of local optimization. the so-called swarm intelligence algorithm is an optimal search that simulates the intelligent activities of natural creatures. after setting the population and specific rules, it can completes the search task in the search space [17–20]. it can give consideration to both the purpose and randomness of the search. at present, more and more researchers the use swarm intelligence algorithms to solve the tcp problem and have achieved good results. for example, andreea et al.[21] proposed a test case prioritization strategy based on the ant colony algorithm (aco). they tried to find the most significant fault with the highest severity first according to the number and severity of defects. xing et al.[22] used the artificial fish school algorithm (afsa) to optimize the test case prioritization by using the swarm behavior, foraging behavior, and tail-chasing behavior and verified its effectiveness through experiments. gouda et al.[23] combined the crow search algorithm and chaotic drosophila optimization algorithm to enhance the optimization results of test case prioritization. anu and sangwan[24] use the bat algorithm to research the tcp problem, and their results are greatly improved compared to traditional methods. manar et al.[25] designed an improved harris hawk optimization algorithm to improve prioritizing efficiency in many ways. bajaj et al.[26] used the discrete cuckoo search algorithm and introduced the adaptive strategy of asexual reproduction to reasonably improve the solutions. through reasonable search principles, the swarm intelligence algorithm can complete the global search[27]. at the same time, it has improved in solving the problems of falling into the local optimum (lo) and converging too fast. however, in the past, when the swarm intelligence algorithm was used to solve the tcp problem, it often only improved the solution of the optimal agent without establishing a clear search space. this paper applies a whale optimization algorithm (woa) further improve the solution to the tcp problem through reasonable modeling. the whale optimization algorithm is a new method after the grey wolf algorithm which has the advantages of few input parameters, strong independence of the https://doi.org/10.15837/ijccc.2023.2.5049 3 search mechanism and fast solving speed[28, 29]. however, the initial whale optimization algorithm converges quickly and sometimes falls into local optimization[30]. therefore, a whale optimization algorithm with an enhanced search mechanism is proposed subsequently[31]. in addition, the usual tcp process based on an intelligent algorithm only partially updates the solution, which is challenging to solve the problem with enormous complexity. therefore, this paper establishes an n-dimensional directed search space and applies reinforced exploration mechanism whale optimization algorithm (rem-woa) to optimize test case prioritization decisions. finally, our experiments show that remwoa can achieve better results in less iterations when dealing with tcp problems. the main contributions of this paper are as follows: •for the test case prioritization problem based on swarm intelligence algorithm, a general modeling method of n-dimensional directed search space is proposed. •this paper introduces different variants of the whale optimization algorithm. we select the best one through experiments, and apply it to the solution of the tcp problem. •the backtracking problem of the swarm intelligence algorithm in directed search space is proposed to avoid the search limitation caused by individuals hitting the wall. •the prioritization effects of different heuristic algorithms are compared through experiments, and the performance is analyzed for single-objective and multi-objective optimization. •different heuristic algorithms are applied to the tcp problem of the real-world gateway project. their decision effects and convergence rates are compared and analyzed. the rest of this paper is arranged as follows. the second section introduces the basic technical background. the third section gives the specific modeling process and algorithm principle. the fourth section introduces the specific settings of the experiment. in the fifth section, the effectiveness experiment, single-objective experiment and multi-objective experiment are shown, respectively. section 6 prioritizes the test cases of the real-world project. section 7 summarizes the full text and points out the future research direction. 2 technical background this section reviews the basic definition of test case prioritization. the whale optimization algorithm, the woa of population updating, convergence-weighted woa and rem-woa are also introduced. 2.1 test case prioritization to better illustrate the problem of test case prioritization, this paper introduces a specific example. in table 1, there are 5 test cases and 9 elements to be covered. assuming that t 1-t 3 is selected, all elements can be covered as quickly as possible. obviously, the execution sequence of t 1-t 3-t 2-t 5-t 4 is better than the original sequence of t 1-t 2-t 3-t 4-t 5. the tester can handle the problems in the program more quickly. table 1: element coverage of test case set test case element f 1 f 2 f 3 f 4 f 5 f 6 f 7 f 8 f 9 t 1 1 1 1 1 1 1 t 2 1 1 1 1 1 t 3 1 1 1 t 4 1 1 1 1 1 t 5 1 1 1 1 test case prioritization is frequently used in software testing, which can significantly improve the efficiency of regression testing. the test case prioritization problem is defined as[32]: the given test case set is t, all possible prioritizing sets of test cases in t are pt, and the mapping from pt to the real number set is f. the prioritization problem of test cases is to find t ′ϵp t, so that for any t ′′ϵp t and t ′ ̸= t ′′, there is |f (t ′) ⩾ f (t ′′)|. https://doi.org/10.15837/ijccc.2023.2.5049 4 where, f is a quantitative description of the objectives to measure the effectiveness of the prioritization. here, we define that the larger the f, the better the effect. in practical applications, testers set different test objectives, like the coverage speed of test points and the detection rate of faults[33, 34]. 2.2 selection of test objectives the swarm intelligence algorithm requires determining the fitness function, that is, the f value in the above tcp definition. it can be divided into single-objective and multi-objective optimization according to requirements. the effective execution time (eet) of the test case sequence represents the time consumed by the test case when the test case sequence reaches the maximum statement coverage for the first time. in addition, during single-objective optimization, many optimization objectives for code coverage have been developed[35]. for the black box testing, if an element is covered for enough time, it will nearly have no mistakes[36]. therefore, this paper selects the average percentage of test point coverage (aptc) as one of the optimization objectives to indicate the coverage speed to test points in the program. the specific definitions of eet and aptc are as follows: eet = n ′∑ i=1 eti (1) ap t c = 1 − t t1 + t t2 + . . . + t tm m · n + 1 2 · n (2) where n represents the number of test cases, m represents the number of statements of the program, t i represents the position of the test case in the execution sequence where the test point is detected for the first time, n’ represents the number of test cases executed when the maximum statement coverage is reached for the first time, and et i represents the time consumed by the execution of the ith test case. 2.3 whale optimization algorithm and its variants to solve the problem of test case prioritization, we introduced the algorithms of whale optimization, including the original woa, pr-woa(population redistribution based whale optimization algorithm), caw-woa(convergence adaptive weighting based whale optimization algorithm) and rem-woa. these algorithms concern both randomness and certainty, and can obtain better optimization results. 2.3.1 whale optimization algorithm woa is a swarm intelligence algorithm inspired by whales preying. according to the random agents and best agents in the population, the method simulates the spiral bubble attack when whales surround prey. it is mainly divided into three search mechanisms: (1) surround prey: the information of individuals in the population is shared, which enables each individual to move towards the position of the prey and continuously reduce the enclosure. (2) random search: to improve the global search ability of whales, each agent has a random search behavior, which slows down the search speed and prevents the results from local optimization. (3) spiral search: when a certain probability is reached, the whale will update the spiral position and spit out bubbles to spiral upward to surround the prey. the above three strategies are executed according to the probability parameters. the fitness function of the algorithm is f (xi), and the individual position is determined by the fitness. 2.3.2 woa of population renewal strategy although the woa strategy has good optimization ability, it often converges searching quickly and easily falls into local optimization. when updating the individual position of the population in each iteration, the following three position redistribution strategies need to be combined. https://doi.org/10.15837/ijccc.2023.2.5049 5 (1) random substitution method: this population redistribution strategy aims to protect the best solution while enhancing other stochastic strategies. the strategy points out that the position of the current agent will be replaced by the position of the best agent. (2) small-scale redistribution method: this method aims to enhance the algorithm’s global optimization ability and significantly avoid omitting the best value in the process of the whale moving toward the prey. (3) population inverse solution: the main idea is to generate an inverse solution for each individual in the process of optimization, so as to update the population information and obtain the obest individuals as the new population position[37]. the essence of this method is to set up a new individual at a position symmetrical to the current individual’s position. 2.3.3 convergent adaptive weighted woa when the search space is large, the adaptive weighting strategy can refine the search process, accelerate the convergence speed of the algorithm, and find the best solution faster. when the search space is large, this strategy can enhance the global search ability and avoid solving the lo problem. the essence of this method is to adjust the speed at which other individuals approach a certain point. so it can balance the local search and the global search. 2.3.4 reinforced exploration mechanism whale optimization algorithm the rem-woa introduces the population redistribution strategy and the adaptive weighting strategy into the whale optimization algorithm, which enhances the global optimization ability and convergence ability, and it also avoids falling into the local optimization. by changing the individual moving speed and the updating process of the fitness function, the optimal population of each iteration is determined, which ensures the quality of the final solutions. 3 test case prioritization based on woa since the data processed by the whale optimization algorithm are numerical values, the simple test case information cannot be directly input into the model. therefore, the corresponding search space should be designed according to the prioritization requirements. this chapter introduces the preprocessing process of search space, the whale optimization algorithm, the edge problem, and the secondary selection of the optimal solution. 3.1 establishment of search space after the upper and lower limits of the search space are set, the population can move for searching tasks. however, if you want to combine the tcp problem with it, you need to manually bind a certain sequence to a specific location in the search space. since there may be hundreds of test cases, and the number of them in the whole set is even more challenging to count, it is necessary to select them randomly. test case 5 test case 3 test case 16 test case 1 test case 27 test case 23 test case 45 test case 32 …… test case i test case 5 test case 3 test case 16 test case 1 test case 27 test case 23 test case 45 test case 32 …… test case i each position represents a random arrangement search space n nn n …… …… …… test case 5 test case 3 test case 16 test case 1 test case 27 test case 23 test case 45 test case 32 …… test case i each position represents a random arrangement search space n n …… …… …… figure 1: search space establishment. https://doi.org/10.15837/ijccc.2023.2.5049 6 assuming that there are two optimization objectives, take the two-dimensional search space as an example. first, we should extract an appropriate number of test cases and randomly generate n2 possible orders. as shown in figure 1, each sort is taken as an element, and these elements are arranged in a square to constitute a global search. all whale individuals can only move in the square array, so each position can only be taken as an integer. the comprehensiveness of prioritization depends on the number of test cases extracted and the size of the square array. 3.2 search space preprocessing after the search space is determined, the swarm intelligent algorithm can be used to search. but, no matter what kind of swarm intelligence algorithm is used, its essence is random search. this is because there is no association between each element in the search space, and there is no mathematical relationship between the adjacent elements. therefore, we must preprocess the search globally for a certain trend relationship between each element. we mainly designed two methods, and the specific principle is shown in figure 2. the first method is based on the maximum value. for the optimization objective 1, the square array is rearranged according to the maximum value of each row. for optimization objective 2, the square array is rearranged according to the maximum value or minimum value of each column. if the local search ability of the algorithm is strong, the method is more likely to obtain the real optimal solution. but the execution result may be unstable, so multiple experiments are required. the second method is based on the average value. for the optimization objective 1, the square array is rearranged according to the average value of each row. for the optimization objective 2, the square array is rearranged according to the average value of each column. this method may not find the real best, but the execution result is more stable, and the trend relationship between various elements is more obvious. a1 b4 …a1 b4 … a2… a2… a3 b2 b5 …a3 b2 b5 … a4 b3 bn…a4 b3 bn… b1 a5 …b1 a5 … an …an … … … … … …… …… … … … …… … a1 b4 … a2… a3 b2 b5 … a4 b3 bn… b1 a5 … an … … … … … …… … a p t c eet a1 b4 … a2… a3 b2 b5 … a4 b3 bn… b1 a5 … an … … … … … …… … a p t c eet sorted by max or min a1 b4 … a2… a3 b2 b5 … a4 b3 bn… b1 a5 … an … … … … … …… … a p t c eet sorted by max or min …… …… …… …… …… …… … … … … …… …… … … … …… … a p t c eet d1 d2 d3 d4 d5 dn…d1 d2 d3 d4 d5 dn… c1 c2 c3 c4 cn c5 … c1 c2 c3 c4 cn c5 … sorted by mean … … … … … … … … … … …… … a p t c eet d1 d2 d3 d4 d5 dn… c1 c2 c3 c4 cn c5 … sorted by mean a1 b4 … a2… a3 b2 b5 … a4 b3 bn… b1 a5 … an … … … … … …… … a p t c eet sorted by max or min … … … … … … … … … … …… … a p t c eet d1 d2 d3 d4 d5 dn… c1 c2 c3 c4 cn c5 … sorted by mean figure 2: search space preprocessing. 3.3 whale optimization model after completing the establishment of the global search and data processing, the population is redistributed to the search space, so that the swarm intelligence algorithm can be used in it. we take the rem-woa as an example to give a complete solution process. 3.3.1 search strategy rem-woa has three optimized search mechanisms: surrounding prey, random search and spiral search[38]. each iteration executes one search strategy according to the random variables. (1) surrounding prey: when the random variable p is greater than 0.5, and the absolute value of the random iteration variable a is less than 1, the strategy of surrounding prey is implemented, and the search agent approaches the best individual. the position update equation is: b = ∣∣∣c · x⃗best(t) − x⃗local(t)∣∣∣ (3) https://doi.org/10.15837/ijccc.2023.2.5049 7 x⃗local(t + 1) = x⃗best(t) − a · b (4) where x⃗best(t) represents the position of the best individual of the tth iteration in the population and x⃗local(t) represents the position of one individual in the tth iteration. to adjust its convergence speed, after adding the convergence weighting strategy, eq.4 becomes: x⃗local(t + 1) = v · x⃗best(t) − a · b (5) v = 2(rand − 0.5)/exp(tan(π · t/tmax)) (6) where, the size of the weight v changes with the number of iterations, and a and b are coefficient vectors. their expression methods are: h = 2 − 2 · t/tmax (7) a = 2h · rand − h (8) c = 2 · rand (9) where rand represents a random number between 0 and 1. the size of h is related to the current number of iterations, and it decreases linearly from 2 to 0. and tmax is the maximum number of iterations. (2) random search: when the random variable p is greater than 0.5 and the absolute value of the random iteration variable a is greater than 1, the random search strategy is executed to make the agent approach the random individual. the location update equation is: b = c · x⃗rand(t) − x⃗local(t) (10) x⃗local(t + 1) = x⃗rand(t) − a · b (11) where x⃗local(t) is the position of a random agent in the population at the tth iteration. similar to the surrounding prey strategy, after the convergence weighting mechanism is added, eq.11 becomes: x⃗local(t + 1) = v · x⃗rand(t) − a · b (12) (3) spiral search: when the random variable p is less than 0.5, the spiral search strategy is executed. here, the position information of the current individual and the prey needs to be determined. then a spiral formula to simulate the spiral movement of the whale can be established. the expression is: x⃗local(t + 1) = x⃗best(t) + bp · eblcos(2πl) (13) bp = ∣∣∣x⃗best(t) − x⃗local(t)∣∣∣ (14) where b is a constant used to determine the shape of the helix, and l is a random number between -1 and 1. 3.3.2 fitness update strategy the fitness updating process of rem-woa mainly includes three parts: random substitution, population inverse solution and small-scale redistribution. then three times the number of individuals is generated for further search. (1) random substitution: when the following probabilities are met, the solution of the current individual is replaced by the best individual to accelerate the convergence speed of the algorithm[39]. tan(π · (rand − 0.5)) < (1 − t/tmax) (15) (2) population inverse solution: in order to improve the global search ability of woa, this strategy can be applied. assuming that our search space is multidimensional, we know that our existing https://doi.org/10.15837/ijccc.2023.2.5049 8 population is (x1, x2, . . . , xn), and the upper bound and lower bound of the search space are lb(i) and ub(i). then the inverse solution is: x⃗iop = lb(i) + ub(i) − x⃗i (16) (3) small-range redistribution: to improve the global search ability of the model, certain randomness should be added to it, so that all individuals can move freely in the maximum range without crossing. the expression is: x⃗local(t + 1) = x⃗local(t) + r · rand · sign(rand − 0.5) (17) r = |ub − lb| 2 · s (18) where r is the moving range and s is the number of individuals. 3.4 backtracking of whale individuals since the newly set search space is not a continuous space, the real-world range of a single dimension of the search space is small. the individuals in the population are very likely to encounter an edge, resulting in some individuals being unable to move. to solve this problem, it is necessary to trace back the whale individuals who hit the edge. the specific principle is shown in figure 3. assuming that the side length of the square matrix of the search space is n, the number of backtracking is set to a random integer between 0 and n/3. after this operation step, the algorithm’s random search ability is further improved, and the problem of population concentration toward the boundary is avoided. backtracking from corner to random position backtracking from edge to random position n (0, / 3)rand n (0, / 3)rand n backtracking from corner to random position backtracking from edge to random position n (0, / 3)rand n (0, / 3)rand n (0 , / 3 ) ra n d nbacktracking from corner to random position backtracking from edge to random position n (0, / 3)rand n (0, / 3)rand n (0 , / 3 ) ra n d n figure 3: individual whale back-tracing. 3.5 selection of optimal solution although there are certain relations between the adjacent elements, they are not prioritized strictly according to the objective value. so the best solution obtained in the next iteration may not be better than the best solution obtained in the previous iteration. to speed up the solution speed and avoid the waste of the best solution obtained in each iteration, a new set should be created to store all the best solutions in the iteration process. finally, the pareto optimal solution set should be obtained. the specific principle is shown in figure 4. run algorithm store the optimal solution of each iteration pareto optimality final solutionrun algorithm store the optimal solution of each iteration pareto optimality final solution figure 4: quadratic selection of optimal solution set. https://doi.org/10.15837/ijccc.2023.2.5049 9 4 experimental settings after modeling, specific experimental configurations are required to solve the tcp problem. this chapter introduces the benchmark functions, tested programs, compared algorithms and experimental parameters. 4.1 benchmark functions before using rem-woa to prioritize test cases, we first verify its effectiveness and compare the performance of different variants of the whale optimization algorithm. before the experiment, it is necessary to select a suitable reference function. because the whale optimization algorithm and its variants converge so fast, selecting a single peak benchmark function will make the contrast effect inapparent. therefore, four multi-peak reference functions are chosen in this paper[40]. more details are in table 2. table 2: relevant information of benchmark function benchmark function formula range f1 rastrigin’s function f1(x) = ∑d−1 i=1 (x 2 i − 10cos(2πxi) + 10) [-100,100] f2 high conditioned elliptic function f2(x) = ∑d i=1(106) i−1 d−1 x2i [-800,800] f3 ackley’s function f3(x) = −20exp(−0.2 √ 1 d ∑d i=1 x 2 i ) − exp( 1 d ∑d i=1 cos(2πxi)) [-800,800] f4 griewank’s function f4(x) = ∑d i=1 x2 i 4000 − ∏d i=1 cos( xi i ) + 1 [-800,800] 4.2 tested programs in this paper, the data set of siemens open-source test cases are selected for comparative experiments[41]. to ensure the reliability of the test, this paper selects several test suites with the highest utilization rate. we conduct static software measurements on them respectively to meet the diversity of the tested programs as much as possible. we find that in the alternative programs, the number of lines of code is positively related to such indicators as files, functions, and code complexity, but not closely related to percent branch statement and percent lines with comments. therefore, under the principle of meeting the diversity of the two indicators of percent branch statement and percent lines with comments, we selected two groups of tested programs with different scales to avoid the accidental impact of the tested programs on the experimental results as much as possible. among them, the number of code lines for grep and flex is large, while the number of code lines for printtokens and schedule is small. specific software metrics are shown in table 3. table 3: static metrics of the tested program grep flex printtokens schedule files 46 51 3 1 lines 50784 79200 725 412 statements 13991 23185 366 207 percent branch statement 24.1% 11.9% 26.0% 17.9% percent lines with comments 27.7% 6.8% 28.8% 20.6% functions 192 137 0 7 average statements per function 28.6 36.4 0.0 4.7 complexity of most complex function 158 220 0 3 maximum block depth 9+ 8 5 5 average block depth 2.30 0.94 1.72 1.48 average complexity 12.16 11.44 0.00 1.86 4.3 compared algorithms to test the effect of rem-woa in solving tcp problems clearly, this paper selects two heuristic algorithms as comparative experiments: the artificial fish school algorithm and immune algorithm. https://doi.org/10.15837/ijccc.2023.2.5049 10 the specific reasons are as follows: first of all, the ant colony algorithm does show excellent performance in the test case prioritization problem, but with more and more intelligent algorithms being applied in software engineering, the effectiveness of aco is challenged. after testing, we found that the artificial fish school algorithm has better convergence characteristics and global search ability[22]. however, the artificial fish school algorithm has a strong random search ability and is more suitable for the search method based on n-dimensional directed space. these problems make afsa effectively avoid falling into local optimal problems. secondly, we found that the performance of the immune algorithm in this field is significantly better than that of the genetic algorithm[16]. in theory, according to the comparative analysis of the characteristics of the two, genetic algorithm is dominated by cross operation, and its solving behavior is unitary and exclusive, while ai is dominated by mutation operation whose global search ability is stronger[42]. as shown in figure 5, for the prioritization problem, small range of cross can not significantly improve the output results, but rearranging all test cases can effectively avoid falling into the local optimal problem. original state cross and mutation state 1 rearrange all state 2.1 state 2.2 state 2.3 figure 5: cross mutation and rearrangement. in addition, there will be different test requirements according to different test scenarios. for single-objective optimization, the objective can be directly used as the fitness function, which is more convenient to solve problems. for multi-objective optimization, pareto optimality should be adopted when selecting solutions. during data preprocessing, elements in each row are rearranged in ascending order according to the maximum value of aptc, when elements in each column are rearranged in descending order according to the maximum value of eet. 4.4 experimental parameters settings the parameters used in this paper are the default values because previous studies have shown that using default parameters is also a reasonable choice[43, 44]. in addition, the parameter adjustment process of the algorithm is time-consuming and costly, and the results obtained in the tcp problem may not be better[36]. however, the side length of the square array is particular. its value is set to maximize the use of computing memory. the backtracking size of the population was obtained through trial and error experiments. the specific parameter settings are shown in table 4. the number of test cases randomly selected during prioritizing is 50. the experiments are conducted 30 times respectively, and the experimental data are recorded. 5 analysis of experimental results 5.1 effectiveness of rem-woa before the formal tcp task study, we first researched the effectiveness of different woa variants. the woa with weighted search strategy is caw-woa, the woa with three search strategies is https://doi.org/10.15837/ijccc.2023.2.5049 11 table 4: experimental parameter settings rem-woa afsa ia algorithm parameters b=1 visual range=0.8 a=0.6 visual attenuation=0.98 b=0.4 congestion threshold=0.66 variation rate=0.6 population size 50 number of iteration 100 side length of square array 300 population backtracking 80 pr-woa, and rem-woa is a combination of the two. take the single-objective optimization algorithm as an example and use the four benchmark functions as the fitness. compare the optimization effects of woa, pr-woa, caw-woa and remwoa. draw the changes in the fitness of the four algorithms with the number of iterations. 0 100 200 300 -15 -10 -5 0 5 x 10 8 iteration fi tn e ss f4 0 100 200 300 -25 -20 -15 -10 -5 0 iteration fi tn e ss f3 woa caw-woa pr-woa rem-woa 0 10 20 30 0 2 4 6 8 x 10 11 iteration fi tn e ss f2 0 10 20 30 0 2 4 6 8 10 x 10 4 iteration fi tn e ss f1 figure 6: convergence curves of different algorithms. figure 6 shows the convergence of four kinds of algorithms for different benchmark functions. it can be seen from the figure that the convergence speed of the conventional woa is the slowest, and the fitness obtained by using the benchmark function f3 falls into the local optimization, which is far from reaching the optimization effect. pr-woa is better than woa and has been able to get rid of lo problems. however, it has little impact on improving the convergence speed because the essence of the algorithm is to increase the population number and redistribute in space without improving the algorithm itself. the effect of caw-woa is obviously better than that of woa and pr-woa. it has not only fast convergence speed but also strong optimization ability. rem-woa combines the advantages of pr-woa and caw-woa. compared with the other three algorithms, rem-woa has the fastest convergence speed and will not fall into lo. therefore, rem-woa is selected in the subsequent test case prioritization. https://doi.org/10.15837/ijccc.2023.2.5049 12 5.2 test case prioritization based on single objective we take aptc and eet as the objectives, summarize the results of 30 experiments, and draw violin figures as shown in figure 7 and figure 8. it can be seen that when the improved whale optimization algorithm is applied to the single-objective test case prioritization, its output results are more concentrated, and has no abnormal values appear. first, except the median eet of the immune algorithm is better when executing flex, the median aptc output by rem-woa is slightly higher than the other two algorithms. and the median eet output is slightly lower than the other two algorithms. second, there are two abnormal data in the aptc value output by the afsa after executing printtokens. in addition, in most cases, rem-woa is more concentrated in the box graph, and there is little difference in the outputs of multiple experiments. this shows that rem-woa runs more stably after reasonable modeling, and the final solution is closer to the real optimal solution. rem-woa afsa ia 0.960 0.965 0.970 0.975 0.980 0.985 0.990 a pt c grep rem-woa afsa ia 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 a pt c flex rem-woa afsa ia 0.88 0.90 0.92 0.94 0.96 0.98 a pt c printtokens rem-woa afsa ia 0.88 0.90 0.92 0.94 0.96 a pt c schedule figure 7: single-objective comparison diagram of aptc. we know that rem-woa has a high efficiency when dealing with a single-objective task. however, we still need to research the stability of the model and the efficiency of the algorithm. as shown in table 5, the performance of grep is the best within 5 output results in 30 experiments. in the other 3 programs, different results are output. when these programs reaches the best solution, the average number of iterations varies from 23 to 65. the average value of aptc is similar to the median. in addition, when seeking the optimal value of eet alone, the output of the grep program is also the most concentrated, but the advantage is not obvious compared with other experiments. when the test case prioritization of these programs reaches the best solution, the average number of iterations is about 40. to sum up, rem-woa sacrifices the algorithm’s complexity, performs well in the single-objective tcp, and its output is closer to the real optimal solution. in addition, the convergence process of the model is fast, and the output can reach its steady state without too many iterations. however, there are some differences in the number of iterations when reaching the best solution for. table 6 shows the relationship between the number of iterations of rem-woa and eet. the average value is about 40, which also shows excellent algorithm stability. https://doi.org/10.15837/ijccc.2023.2.5049 13 table 5: optimal aptc and iteration times num grep flex printtokens schedule aptc iteration aptc iteration aptc iteration aptc iteration 1 0.9833 45 0.9628 64 0.9357 27 0.9300 57 2 0.9833 96 0.9542 82 0.9500 28 0.9388 76 3 0.9833 28 0.9585 22 0.9242 10 0.9344 16 4 0.9833 29 0.9585 32 0.9471 54 0.9433 51 5 0.9833 79 0.9542 1 0.9585 83 0.9300 32 6 0.9833 2 0.9557 35 0.9442 94 0.9100 1 7 0.9788 23 0.9414 7 0.9157 7 0.9388 72 8 0.9833 4 0.9457 7 0.9442 23 0.9300 45 9 0.9766 1 0.9557 62 0.9242 32 0.9388 65 10 0.9833 20 0.9442 4 0.9414 59 0.9433 91 11 0.9788 1 0.9628 6 0.9185 69 0.9411 67 12 0.9811 3 0.9600 10 0.9585 1 0.9211 1 13 0.9833 14 0.9442 23 0.9157 14 0.9188 50 14 0.9811 44 0.9600 32 0.9328 1 0.9433 1 15 0.9766 68 09428 79 0.9500 6 0.9144 67 16 0.9833 18 0.9514 63 0.9185 34 0.9522 73 17 0.9744 11 0.9471 24 0.9385 92 0.9388 29 18 0.9788 2 0.9685 23 0.9414 27 0.9522 3 19 0.9811 1 0.9614 88 0.9185 16 0.9122 30 20 0.9766 45 0.9385 14 0.9442 89 0.9166 16 21 0.9833 1 0.9571 13 0.9385 14 0.9388 41 22 0.9744 7 0.9614 14 0.9642 1 0.9300 94 23 0.9811 21 0.9628 94 0.9185 84 0.9100 7 24 0.9833 6 0.9481 19 0.9500 88 0.9277 23 25 0.9766 1 0.9557 54 0.9414 95 0.9100 20 26 0.9833 2 0.9657 1 0.9442 49 0.9344 86 27 0.9766 11 0.9628 63 0.9414 90 0.9388 26 28 0.9833 93 0.9457 12 0.9442 15 0.9144 65 29 0.9833 3 0.9600 90 0.9357 10 0.9322 10 30 0.9811 19 0.9414 33 0.9157 1 0.9233 33 mean 0.9807 23 0.9542 35 0.9371 65 0.9302 41 5.3 test case prioritization based on multi-objective the artificial fish school algorithm and immune algorithm are also selected for comparison in multi-objective prioritization. the parameter selection of the three algorithms is the same as that of rem-woa afsa ia 1.0 1.5 2.0 2.5 3.0 e e t grep rem-woa afsa ia 1 2 3 4 5 e e t flex rem-woa afsa ia 5 10 15 20 25 e e t printtokens rem-woa afsa ia 4 6 8 10 12 14 16 18 20 e e t schedule figure 8: single-objective comparison diagram of eet. https://doi.org/10.15837/ijccc.2023.2.5049 14 table 6: optimal eet and iteration times num grep flex printtokens schedule eet iteration eet iteration eet iteration eet iteration 1 1.5609 12 2.4682 31 14.2876 18 10.4308 64 2 1.8371 8 2.4243 15 11.7309 9 10.6100 37 3 1.8741 51 3.0433 28 7.9485 3 11.4762 25 4 1.4187 94 2.0968 89 15.0273 73 14.1090 1 5 1.6032 68 2.5865 51 13.2166 87 8.5437 87 6 1.7547 64 3.3033 94 12.9345 72 10.3571 31 7 1.7766 44 2.8082 64 7.2060 38 9.4584 44 8 1.8866 36 3.4880 66 13.6794 21 8.3123 25 9 1.8755 50 2.4089 11 11.8613 56 10.4858 41 10 1.3640 20 2,6650 14 8.6725 21 10.4916 85 11 1.2555 1 2.6724 4 9.6084 21 9.2608 82 12 1.6374 38 2.5339 98 8.6374 47 10.9394 52 13 1.7547 9 2.8920 21 12.0604 3 9.9642 46 14 1.9146 94 2.3389 25 11.6528 24 11.4702 40 15 1.7470 73 1.3953 78 5.7971 11 11.9914 76 16 1.5117 7 2.4198 93 13.5992 13 10.4778 41 17 1.6069 82 2.9156 69 7.8503 78 7.0093 19 18 2.0111 12 2.9297 39 12.8290 85 11.9372 61 19 1.6512 62 2.0528 10 14.2332 79 13.2129 33 20 1.6849 37 3.2483 1 12.7407 43 7.2387 7 21 1.8704 89 3.6133 23 12.9655 36 12.5916 72 22 1.5029 30 3.4393 35 13.7013 11 10.2217 29 23 1.7510 11 2.7667 25 16.2932 34 11.0732 12 24 1.3763 57 1.7216 1 12.3183 21 11.8723 69 25 1.9199 47 3.3919 41 11.6285 73 11.2386 25 26 1.8181 42 2.5625 1 6.9967 94 11.9184 60 27 1.8407 42 2.7219 49 11.8222 16 9.3439 48 28 1.4280 14 3.3496 63 11.3882 3 9.3513 6 29 1.7600 21 2.1809 77 11.5110 84 10.9822 11 30 1.8704 48 3.4254 89 7.6206 70 12.5483 82 mean 1.6954 42 2.7287 43 11.3939 41 10.6269 43 single-objective optimization. however, because it is a multi-objective solution, the pareto optimal solution should be taken when comparing the fitness of individuals. the final result is not necessarily a single solution but a series of solution sets. taking aptc as the abscissa and eet as the ordinate, the above three algorithms were applied to four programs for some experiments. record the experimental data and draw the corresponding scatter chart. the closer the point is inclined to the lower right corner, the better the effect is. it can be seen from figure 9 that rem-woa performs better than other algorithms in prioritizing test cases of flex and printtokens. while in grep and schedule, rem-woa has obvious advantages over ia. however, compared with afsa, rem-woa has no apparent benefits. the latter is slightly better than the former in convergence and optimization objective value. finally, to further compare rem-woa and afsa, we counted the number of iterations when they reached their best position for the first time, and drew the corresponding three-dimensional scatter diagram. it can be seen from figure 10 that the iteration number distribution of rem-woa when finding the best solution is more uniform, which shows its advantages of convergence. however, the number of iterations of afsa is concentrated in the final stage of the iteration, which indicates that the stability process is slow. 6 case study of real-world application to better evaluate the n-dimensional space search and rem-woa, we selected the real-world project for further testing. the project is the code developed by phytium for the gateway protocol, which uses c++ to exchange routing information between gateways. in this paper, the immune algorithm and artificial fish school algorithm are still used to test and analyze single-objective and multiple-objective tasks, respectively. https://doi.org/10.15837/ijccc.2023.2.5049 15 0.86 0.88 0.9 0.92 0.94 0.96 0 5 10 15 20 25 30 aptc e e t schedule 0.8 0.85 0.9 0.95 1 5 10 15 20 25 30 aptc e e t printtokens 0.85 0.9 0.95 1 1 2 3 4 5 6 7 aptc e e t grep 0.85 0.9 0.95 1 0 2 4 6 8 10 aptc e e t flex rem-woa afsa ia figure 9: two-dimensional scatter diagram of multi-objective optimization. 0.9 0.95 1 0 5 10 0 50 100 aptc grep eet it er at io n 0.8 0.9 1 0 5 10 0 50 100 aptc flex eet it er at io n 0.8 0.9 1 0 20 40 0 50 100 aptc printtokens eet it er at io n 0.8 0.9 1 0 20 40 0 50 100 aptc schedule eet it er at io n rem-woa afsa figure 10: three-dimensional scatter plot of multi-objective optimization. 6.1 test case prioritization based on single objective because there are many coverage points in the gateway protocol program, the side length of square array needs to be reduced appropriately due to the limitation of computing devices. considering the memory of the computer, the side length is finally set to 220. the backtracking size of individuals in the population is set to 60. the other parameters are set unchanged, the experiment are repeated 30 times, and the statistical data are collated to draw a violin chart. we take aptc and eet as the tested objectives respectively for single-objective optimization. more details are showed in figure 11 and figure 12. first, it can be seen from the figure that when aptc is used as the fitness function, the median obtained by using rem-woa is relatively maximum, and its quartile is also the highest among the three algorithms. however, in terms of data concentration, afsa performs best, and the algorithm effect is between rem-woa and ia. according to our previous experiments, this is because its iteration takes a long time, and it will not search for a better solution in time. next, when compared with the swarm intelligence algorithm, https://doi.org/10.15837/ijccc.2023.2.5049 16 the immune algorithm has the worst performance, and its performance is far inferior to the former in terms of data concentration and fitness function. rem-woa afsa ia 0.94 0.95 0.96 0.97 0.98 a pt c figure 11: comparison of aptc of real-world project. rem-woa afsa ia 2 4 6 8 10 e e t figure 12: comparison of eet of real-world project. when eet is used as the fitness function, the median and quartile obtained by rem-woa are the smallest. when comparing the concentration of the data distribution, although afsa is still the best, rem-woa gets some excellent results. next, the improved whale optimization algorithm has the best data concentration. in addition, the worst effect is still the immune algorithm, which has the highest median distribution and the most dispersed data, but this also makes its quartile effect not the worst. to sum up, rem-woa based on n-dimensional directed space search has the best convergence speed and global search ability when dealing with tcp. afsa has a weak effectiveness caused by the complex search mechanism. as for the immune algorithm, its effectiveness is far less than that of the swarm intelligence algorithm using n-dimensional directed space search. because ia has no population and cannot conduct thorough searches. the method based on partial exchange mutation is difficult to solve the problem of high complexity, and the initial state has a significant impact on its final results. in addition, it is also true that the data obtained by ia are seriously scattered. 6.2 test case prioritization based on multi-objective we prioritize the multi-objective test cases for phytium’s gateway protocol, and the objectives functions are still aptc and eet. however, through the analysis of algorithm complexity, it can be seen that there is a specific correlation between complexity and performance effect. we repeated the experiment 30 times, and drew a three-dimensional scatter plot as shown in figure 13. from the horizontal plane, the rem-woa distribution is more inclined to on the right, which means that the prioritization effect is better. second, the data of afsa are almost on the right in the figure, but they are distributed almost on the top. similarly, when dealing with multi-objective problems, the immune algorithm has the worst performance and the most decentralized data distribution. in terms of the number of iterations, the improved whale optimization algorithm ranges from 0 to 100, and the immune algorithm has a small number of iterations. however, the final number of https://doi.org/10.15837/ijccc.2023.2.5049 17 0.9 0.95 1 0 10 20 0 50 100 aptceet it e ra ti o n afsa rem-woa ia figure 13: comparison of multi-objective prioritization of real-world project. iterations of the artificial fish school algorithm is mostly between 90 and 100. in general, the swarm intelligence algorithm has a high complexity and a relatively high number of iterations, but the effect is better. for the algorithm without population, the algorithm’s performance is slightly poor, and the data distribution is not centralized. 7 conclusion and future work to reduce the cost of software testing, this paper takes software testing as an example to improve the decision-making method of prioritization for digital systems. we introduce the improved whale optimization algorithm and apply it to test case prioritization. before using this algorithm, we established an n-dimensional spatial search model and improved the optimization method by processing the global data. the experiment shows that the whale algorithm with an enhanced exploration mechanism has more advantages than other variants. to achieve good results in tcp tasks, the optimization algorithm used should have good global search ability, and it can effectively avoid falling into global optimization. in addition, the performance of the algorithm has different effects on its efficiency. if the search mechanism of the algorithm is added to improve the prioritization effect, it may reduce the efficiency of the model and increase the number of iterations. in the next stage, we will try other optimization algorithms, integrate the improved whale optimization algorithm with the deterministic algorithm, and avoid the potential problems implied by the fast convergence of the whale optimization algorithm. in addition, in the future, we will further deal with the search space for test case prioritization, making the optimization process of the swarm intelligent algorithm more accurate, and further reducing the operating cost of the digital system. author contributions bin yang: investigation, methodology, project administration, data pre-processing, writingreviewing and editing. huilai li: conceptualization, methodology, experiment, software, visualization, writingoriginal draft preparation. ying xing: investigation, supervision, methodology, validation, resources, writingreviewing and editing. fuping zeng: supervision, resources, writingreviewing. chengdong qian, youzhi shen, jiongbo wang: investigation, supervision, dataset provision. conflict of interest the authors declare no conflict of interest. https://doi.org/10.15837/ijccc.2023.2.5049 18 references [1] minimol, a.j. 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(2020). a test case prioritization genetic algorithm guided by the hypervolume indicator, in transactions on software engineering, 46(6), 674–696, 2020. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: bin y., huilai l., ying x., fuping z., chengdong q., youzhi s., jiongbo w.. (2023). directed search based on improved whale optimization algorithm for test case prioritization, international journal of computers communications & control, 18(2), 5049, 2023. https://doi.org/10.15837/ijccc.2023.2.5049 introduction technical background test case prioritization selection of test objectives whale optimization algorithm and its variants whale optimization algorithm woa of population renewal strategy convergent adaptive weighted woa reinforced exploration mechanism whale optimization algorithm test case prioritization based on woa establishment of search space search space preprocessing whale optimization model search strategy fitness update strategy backtracking of whale individuals selection of optimal solution experimental settings benchmark functions tested programs compared algorithms experimental parameters settings analysis of experimental results effectiveness of rem-woa test case prioritization based on single objective test case prioritization based on multi-objective case study of real-world application test case prioritization based on single objective test case prioritization based on multi-objective conclusion and future work international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 1, month: february, year: 2023 article number: 4756, https://doi.org/10.15837/ijccc.2023.1.4756 ccc publications a deep learning approach for efficient anomaly detection in wsns arul jothi s, venkatesan r arul jothi s* department of computer science and engineering psg college of technology, coimbatore, 641004, india *corresponding author:saj.cse@psgtech.ac.in venkatesan r department of computer science and engineering psg college of technology, coimbatore, india rve.cse@psgtech.ac.in abstract data reliability in wireless sensor networks (wsns) has a substantial influence on their smooth functioning and resource limitations. in a wsn, the data aggregated from clustered sensor nodes are forwarded to the base station for analysis. anomaly detection (ad) focuses on detecting outlier data to ensure consistency during data aggregation. as wsns have critical resource limitations concerning energy consumption and sensor node lifetime, ad is supposed to provide data integrity with minimum energy consumption, which has been an active research problem. hence, researchers are striving for methods to improve the accuracy of data handled with a concern on the constraints of wsns. this paper introduces a feed-forward autoencoder neural network (fann) model to detect abnormal instances with improved accuracy and reduced energy consumption. the proposed model also acts as a false positive reducer intending to reduce false alarms. it has been compared with the other dominant unsupervised algorithms over robustness and other significant metrics with real-time datasets. relatively, our proposed model yields an improved accuracy with fewer false alarms thereby supporting a sustainable wsn. keywords: anomaly detection, autoencoder neural network, data aggregation, false positive, unsupervised algorithms, wireless sensor networks. 1 introduction sensor networks make use of devices that detect or measure a physical property and can record, indicate, or respond to it. wsn assists observations and creates supremacy over physical environments from inaccessible locations with higher correctness. they are suitable to use in the fields such as natural observation, and armed forces to obtain sensing information. sensor nodes used in a wsn have acute energy and cost limitations as they have to constantly gather and send data from the environment. this has restricted wsns to follow critical resource constraints enabling sustainability in network deployment. in a wsn, sensor nodes are employed in a distributed manner in such a way that data gathering would continue even if some of them are dead due to power depletion or other https://doi.org/10.15837/ijccc.2023.1.4756 2 figure 1: distributed deployment scenario of wsn with anomaly detection in chs events. for continuous observance in the environment, data aggregation could be employed by forming clusters of sensor nodes with neighborhood nodes. figure 1 depicts the implementation setup of a typical wsn that performs data aggregation in cluster heads (chs). clusters play a major role in increasing the lifetime of a wsn by the way of energy conservation, as all the data gathered by each sensor node are not blindly transmitted. the ch in each cluster is responsible for data aggregation and forwarding. the data aggregation process must ensure integrity by detecting and removing anomalous instances received from sensor nodes in a cluster [1]. these aggregated data after anomaly detection are forwarded to the base station suitable for further investigations and decisions. 1.1 data aggregation in wsns data aggregation is the method of scrutinizing the gathered data from several sensors, estimating the quantified response about the sensed environment, and providing fused information to the base station [2]. data aggregation desires a scheme for converting the sensed data into high-quality information [3]. the major functions of data aggregation are: avoiding data redundancy, reducing data transmission, and improving data accuracy [4, 5]. this can be achieved through ad during data aggregation. during data aggregation, the collection of data occurrences that are detected as erratic during aggregation amongst the residual of the dataset is defined as an anomaly or outlier [6]. moreover, anomalies in sensor data are quantified as those important deviations in sensed data from the normal values [7]. paying attention to anomalies and detecting abnormalities in data will increase data accuracy in wsns [8]. the energy utilized for ad is a challenging task in a wsn due to its limitations on node lifetime. deep learning provides solutions to overcome these issues with minimized resource utilization and extending the life expectancy of the network [9]. our proposed work aims to perform ad during data aggregation in ch and reduce communication costs with reduced energy consumption. 1.2 anomaly detection in wsn sensing data are collected in the form of data streams in huge volumes of real observations from the environment and are stored in a database. modern wsns are aimed to collect multivariate types of data from the field instantaneously. nowadays, ad models should be capable of dealing with data without any prior knowledge. this can be approached with unsupervised techniques that try to extract useful features from the dataset. figure 1.1 depicts the implementation of the ad model in a single cluster which can be considered for all chs in the network. 2 literature background researchers have focused on classical methods of unsupervised anomaly detection, which can be coarsely classified as clustering-based algorithms, classification algorithms, and deep learning methods. https://doi.org/10.15837/ijccc.2023.1.4756 3 figure 2: implementation of ad model in a single cluster authors in [10] have implemented a prototype-based agglomerative hierarchical clustering method to identify features of uncertain data based on the probability distribution to be clustered and achieve average accuracy on all datasets. ensemble clustering along with gaussian mixture model and oneclass support vector machine (ocsvm) [11] extracts features based on appropriate choice rather than manually by clustering anomalies into a specific type. also, in [12] a hybrid combination of subspace clustering with ocsvm distinguishes between abnormal and normal instances based on mapping the features in the space as clusters. the classification-based approach used in [13] identifies outliers based on spatiotemporal correlation with support vector data description (svdd). moreover, in [14], fixing the neighborhood makes the algorithm converge faster and provides robustness with prior assumptions on several anomalies. the authors of [15] introduced ocsvm to be suitable for both univariate and multivariate datasets by training the model using histogram-based labeling. the subspace method, robust principal component analysis (rpca) used in [16] performed low-rank and sparse regularization for spatio-temporal data distribution and detection of the anomalies. rpca has not considered the detailed information of spatio-temporal data distribution. many authors have used deep learning approaches [17] as it acts more suitable for unsupervised anomaly detection. in [18], a deep variational autoencoder with a feed-forward neural network model reduces inefficient learning in a semi-supervised context (for training) restraining the learning model in taking widespread values from the weight space. authors in [19] discuss the various deep learning techniques like convolution neural network, long short-term memory, and autoencoder which learn commonalities within the data to facilitate outlier detection. hybrid models using deep learning techniques extract robust features within hidden layers. distributed anomaly detection using an autoencoder neural network [20] model adapts the unforeseeable and new changes in a non-stationary environment. this model introduced a priority scheme to lower the false-positive rate. in [21], fast outlier detection was introduced by using deep belief networks (dbn) to extract invariant features for complex and dimensional datasets. anomaly detection using one-class neural network in [22] discerns anomalies in complex datasets when the decision boundary between normal and anomalous is highly non-linear. unsupervised anomaly detection algorithms in [23] aid a comparative evaluation of nearest neighbor-based algorithms, clustering-based algorithms, and statistical approaches dealing with both local and global anomalies. in recent years, q-learning algorithms [24, 25] were implemented for handling reward noise and inverting gradient procedures for reducing loss. in addition, it is identified that deep reinforcement learning reduces the false-positive rates involving humans and learns meta-policy to optimize anomalies. a combination of the gaussian process and graph-based detection [26] with hampel identifier overcomes the limitation of datasets having dense and sparse dimensions. this model identifies outliers in data across time. the fuzzy logical model in [27] uses neighborhood information with a fuzzy decision process to detect anomalies enabling a high detection rate and low false positive alarm rate. in [28], the authors have suggested recurrent neural networks with long short-term memory units (rnn with lstm) to efficiently identify anomalies in health log data that was compiled from various https://doi.org/10.15837/ijccc.2023.1.4756 4 devices. the trials only used a tiny amount of data. in both instances, the data also needed to go through a quick preparation step before analysis. the data were organized to match each method’s input. the model suggested in [29] operates in two phases and is effective in detecting network anomalies. improved grey wolf optimization (imgwo) is used for feature selection in the first phase to achieve the best possible trade-off between two goals, namely decreased error rate and feature-set minimization. in the second stage for network anomaly categorization, the model uses an improved convolution neural network (imcnn). the model suffers from high computation because of intricate design and parameter optimization during feature selection. to recognize outliers with a resourceful focus on the production of false alarms and incorrect conclusions, [30] created a method in which each cluster head was applied a denoising auto-encoder with a gaussian kernel for outlier detection after performing a basic cluster algorithm based on the residual energies of the sensor nodes. the literature review of related works indicates that the following research gaps (rg) exist in anomaly detection, which is worthwhile to ponder upon: rg (i). the works in [14, 18, 23] indicate the need for tuning learning parameters to improve the model’s detection. additional activation functions and alternative structures are required to gauge the method’s success [28]. rg (ii). the experiments in [19, 21, 22] express less detection accuracy with a low area under curve (auc) score. rg (iii). in [11, 13, 14, 20, 22, 24, 29, 30] the models require high computational time that consumes energy when dealing with large datasets. rg (iv). subsequently, the methods in [12, 15, 19] suffer from a false positive rate (fpr). rg (v). the methods in [23, 25, 27] perform poorly for high-dimensional data. the proposed feed-forward autoencoder neural network (fann) model is built to detect abnormal instances by employing multiple layers in the encoder and decoder to address a few of the aforementioned research issues. in particular, the focus is given to lowering the false positives, increasing the ad accuracy, and conserving the energy of the wsn without affecting the computational time. 3 proposed feed-forward autoencoder neural network for anomaly detection 3.1 overview autoencoders are typical neural networks that take input data, learn its significant characteristics, and produce output that is analogous to the input. furthermore, the autoencoder learns to estimate a characteristics function, to construct an output value. the block diagram of the proposed model is shown in figure 3, where initially normal instances are considered for training, which is archived in the encoding phase as regular data patterns through a series of hidden layers. the decoder in the network tries to reconstruct the data which is the same as the input normal pattern. when abnormal instances are fed, the model gathers the relevant regular instances in memory for reconstruction and generates an output. for abstract explanation, the archive is implemented as a single memory entry [31]. an additional component in the autoencoder model is included to compute anomaly score (as) to reduce false alarms. to ensure the durability of the model, the hyper-parameters like the number of layers for the encoder, the number of layers for the decoder, activation functions, and loss functions are tuned manually to get the appropriate threshold value for classification. in the proposed work, the autoencoder model tries to detect anomalies with fewer false positives, which is explained in the following section. 3.2 schema of fann model the proposed feed-forward autoencoder neural network (fann) is composed of multiple hidden layers for encoding and decoding. figure 3.1 shows the schema of it with an input layer and 6 dense layers encompassing 3 layers for encoding and 3 layers for decoding. https://doi.org/10.15837/ijccc.2023.1.4756 5 figure 3: block diagram of proposed feed-forward autoencoder neural network 3.3 model functionality as portrayed in figure 3.1 there are six dense layers indicated as f1, f2, f3, f4, f5, and f6, respectively. the fusion of all these tasks is represented as f(x) = f6(f5(f4(f3(f2(f1(x)))))) generally, this process describes the structure of deep learning neural network. the procedure for unsupervised anomaly detection is discussed below: 1. in the preparation phase, the encoder in the proposed fann model learns the given unlabeled data xa by compressing data representation with the function f’ and determines the attribute vector ha from xa: ha = f ′(xa) 2. the attribute vector ha is considered for archiving the normal behavior of data instances. this memory archive data is used during the testing phase for evaluating the reconstruction error of the model. 3. the decoder tries to reproduce xa: from the attribute vector ha by a mapping function g’: r = g′(ha) = g′(f ′(xa)) 4. typically, training data instances cannot be used to estimate the characteristics of hidden layers but it encompasses a hidden correlation with them. the learning phase in the model evolves a procedure to assess the characteristics of hidden layers to obtain the best possible outcomes at the output layer. moreover, this learning feature of the model must trace the correlation in the hidden layers with training data instances. 5. in the testing phase, the learning algorithm tries to interpret the knowledge gained during training with the same procedure of encoding and decoding to reproduce data instances. the reconstruction error of the model is generated using the loss function (discussed in section 3.4) to analyze the model’s detection accuracy. 6. each dense layer’s weights are initialized randomly using xavier initialization [32] such that the variance across each layer is the same. equation 1 shows the random process initialization applied to all weights and equation 2 is used for estimating the variance of weights. xavier initialization is considered efficient for activation functions since it prevents gradients from diminishing or setting off. for each layer weight is calculated as w [l] (m,n) = n ( 0, 1 n[l−1] ) (1) and variance v is calculated as v ar(w) = 1 n[l−1] (2) 7. moreover, the model needs to be employed with a suitable activation function and loss function for improving detection accuracy and reducing false positives. a comparative analysis of various activation functions and loss functions is presented in the proceeding sections. https://doi.org/10.15837/ijccc.2023.1.4756 6 3.4 activation function analysis for the model the activation function is considered one of the hyper-parameters that plays a significant role in the deep learning model. choosing the best activation function in hidden layers and the output layer would enhance the model’s capability to do the desired task (in the proposed work context, the objective is to detect anomalous data). in [33, 34] authors have discussed various activation functions suitable for deep learning techniques. based on the study from existing works, the following activation functions with their respective equation mentioned in table 1 are considered for performance evaluation. the performance analysis is presented in section 4.3. table 1: existing activation functions with its estimation formula reference activation function equation [34] hyperbolic tangent f(x) = tanh(x) = ex−e−x ex+e−x rectified linear unit f(x) = max(0,x) = { xi, ifxi ≥ 0 0, ifxi < 0 exponential linear unit f(x) = { α(exp(x) − 1) if x ≤ 0 x for x > 0 } leaky relu f(x) = (ax + x) = { x for x < 0 ax for x ≥ 0 softmax f(xi) = exp(xi)∑ j exp(xj ) swish f(x) = x.sigmoid(x) = 11+e−x [35] gaussian error linear unit f(x) = f(x) = xp(x ≤ x) = xφ = x12 [1 + er ∗f( x√ 2 ] 3.5 loss function analysis for the model for training the proposed fann with improved accuracy, reconstruction error must be reduced. thus, two different loss functions are considered and their effectiveness is analyzed for model suitability. this section discusses the significance of arctan mean square error (amse) [6] and huber loss (hl) [36] functions which are considered for analysis. 3.5.1 arctan mean square error (amse) the mean square error function (mse) is an essential function that provides robust behavior of the model during the testing and training phase to identify data overfit when validation error goes beyond the training error. mse of a model relating to test data can be estimated as the mean of the squared prediction errors overall data in the test set. thus, the loss function of mse represented in equation 3 calculates the dissimilarity between the true value and the predicted value for the data instance. mse = ∑n (i=1)(yi −λ(xi)) 2 n (3) where, yi is actual output value for test data instance xi , λ(xi) is the reconstructed output value for test data instance xi and n is the number of test data instances. mse is sensitive toward outliers, but minimizing mse will respond better in identifying outliers effectively. moreover, decreasing squared error will improve the accuracy of a model when the number of training data instances is known. thus, a modified mean squared error value for training the model called arctan mse (amse) in equation 4 is considered. amse as discussed in [6] will gradually drop https://doi.org/10.15837/ijccc.2023.1.4756 7 the rate of loss for independent training data. amse = 1 n ∑ ( p = 1)p ∑ ( i = 1)n (tan−1((xpi − (g(y))pi))) 2 (4) where, xpi is the predicted value for data point i, g(y)pi is the actual value for the data point i and n is total number of data points 3.5.2 huber loss (hl) huber loss is more robust to outliers in data than the mse loss which is suitable for differentiation by 0. there is a direct impact by tuning the hyper parameter,δ (delta) to reduce the error. thus huber loss considers δ 0 and δ ∞ as like mse, mae and is given as: lδ(y,f(x)) = { 1 2 (y −f(x)) 2, |y −f(x)|≤ δ δ ∗ (|y −f(x)|−12δ), otherwise (5) where, δ(y −f(x))2 is the difference between observed value and predicted value, δ is the hyper-parameter delta, tuned to obtain minimum error (delta value chosen to converge to best solution is 0.1). particularly, the loss reaches its minima by decreasing the gradient for mse. hence to avoid this, the hl function in equation 5 bends the minima in turn by decreasing the gradient by merging the optimum features from both mse and mae. for the proposed model, loss estimation is carried out by using hl and amse for 100 epochs. the analysis is presented in section 4.4.2. 3.6 false positive rate reduction generally, anomaly detection may produce false positives which could affect the model’s efficiency. to mitigate this drawback, the proposed fann model is trained to perform precise prediction of actual anomalies by eliminating false positives with high recall and high precision. during the training phase, an acceptable threshold level is maintained for detecting abnormal data and false-positive rates. this will be considered over the test set also. an additional component (thresholding filter) is included in the model that removes false positives similar to [37]. to filter a false abnormal detection in the model, the results of the following were considered: • receiver operator characteristic (roc) – roc is capable of analyzing the model’s performance based on various thresholds. the reason for choosing the roc approach for false-positive reduction can be observed in section 4.2. • loss function – the outcome of reconstruction error from the loss functions viz, amse, hl is considered as the threshold to be fixed for filtering false positives in the model. • conformance threshold as proposed in [38], the conformance threshold is evaluated based on the mean distribution of input instances. in this research, an assumption is made that anomalies detected in the model have more false positives if it reaches a threshold of more than 1%. for estimating the threshold value, the central limit theorem (clt) in probability theory is used. clt performs sampling of the given dataset to a normal distribution. the threshold is calculated as: threshold(t) = (µ(# ofanomalies) −σ(# ofanomalies)) (6) where, µ is the mean or average of anomalies and σ is the standard deviation of the anomalies https://doi.org/10.15837/ijccc.2023.1.4756 8 figure 5: sensors for weather observations in aws the conformance threshold stated in equation 6 helps in maintaining a stable f1 score. this conformance threshold factor also impacts the performance of the model in terms of increasing the detection accuracy. the major intention of this research is to come up with a consistent model capable of categorizing abnormal instances. 4 experimental results 4.1 dataset exploration the performance of the proposed model is evaluated considering climate data [39]. prediction of weather is considered to be the most important task in real-time. many sensor stations are built around the world by various research labs to monitor climatic changes like temperature, humidity, pressure, precipitation, etc., these data need to ensure integrity for analysis like weather prediction, global warming rate, and disaster alerts related to the environment. weather underground is a service provider for real-time weather information retrieved with the help of automatic weather stations (aws) available to over 250,000 around the world. an aws is a set of weather measuring sensors that can be installed either in-home or in business buildings. figure 3.6 shows the sensor setup of a weather station (ws) utilized for weather observations. indira gandhi international airport (igia) station acts as ws and provides real-time weather observation for delhi weather forecasts. airport wss routinely issue reports used by pilots, air traffic controllers, meteorologists, climatologists, and other researchers. in the delhi region, 12 automatic weather stations around a 50 km radius have been set up that measure 24x7 hours of weather in igi airport. here, ‘g’ aggregator nodes are considered in the ws network. a total of 20 features related to temperature, humidity, pressure, dew point, and wind speed with statistical readings are observed along with precipitation at each time interval. nearly 20 years of climatic information are collected for ad investigation. these weather data are prone to errors, outages, and other defects. the following section discusses the implementation setup of ad using the fann model. 4.2 data processing and visualization the dataset is considered as unlabeled pre-processed data with temporary labels assigned for each column. the normal instances and abnormal instances are categorized for generating a training set. moreover, the test set data is generated with both normal instances and abnormal instances. the distribution of data is visualized in figure 4.1. box plot representation provides a great benefit for understanding the outlier rate in the dataset. it is also observed that the dataset has a highly imbalanced dataset. https://doi.org/10.15837/ijccc.2023.1.4756 9 figure 6: data distribution analysis using box plot figure 7: accuracy of activation functions for 100 epochs 4.3 analysis of activation functions with a loss function the processed data is fed into the proposed fann for training. as discussed in section 3.4, various activation functions have been experimented with for the given dataset. the result shown in figure 4.2 depicts the line plot of the accuracy of the activation functions trained for 100 epochs. from the graph figure 4.2 of activation function accuracy, hyperbolic tangent activation maintains a stable accuracy of data when the model is executed for 100 epochs. in many research works, leakyrelu is considered a suitable activation function but it will affect the computation and may overfit the model losing linearity. based on the proposed fann model’s focus, hyperbolic tangent (tanh) activation function is identified as appropriate for the model to detect anomalies with high accuracy and minimum error. 4.4 methods for mitigating false alarm rate 4.4.1 auc roc curve to rate the performance of the model, the classified outcomes are measured using receiver operating characteristics curve repetitively. the area under the curve (auc), the integral of the roc, is the probability that the classifier ranks based on positive examples for differentiating between positive and negative classes. this perceptive ensures that the auc assessment method is more suitable for false-positive reduction and detection accuracy. for empirical illustration in unsupervised ad, auc-based assessment is rationally considered to be the actual standard for estimation. in figure 4.4.1 auc-roc curve of the proposed fann model provides the relationship between the true positive rate and the false positive rate for assessing detection accuracy. it is observed that https://doi.org/10.15837/ijccc.2023.1.4756 10 figure 8: auc-roc curve of proposed fann model figure 9: (a) reconstruction error using arctan mse (b) reconstruction error using huber loss.reconstruction error comparison of proposed model the auc score obtained for the fann model using hl 0.98 is compared to amse 0.96. 4.4.2 loss functions: amse and hl the proposed fann model experiments with two loss functions: amse and hl during the training and test phase. the model’s training loss and test loss are accumulated for examining loss functions’ average reconstruction error. from the result of test loss, the threshold is varied to obtain outliers accurately and estimated with a range between 0.01 – 0.02. figure 4.4.2(a) and 4.4.2(b) display the reconstruction error obtained by the model after varying threshold for 100 epochs. from the above graph, it is evident that the fann model with the hl function reacts more robustly to outliers for training and test data than the amse loss function. this resulted in a false alarm rate of 32% using amse and 16% using hl respectively. 4.4.3 conformance threshold the conformance threshold value is calculated from table 2 which represents the mean and standard deviation of the model’s reconstruction error. 5 results comparison and discussions the proposed fann model’s performance is evaluated in terms of success and competence. it has been compared with existing unsupervised algorithms as discussed in section 2. figure 4.4.3 shows the https://doi.org/10.15837/ijccc.2023.1.4756 11 table 2: thresholding comparisons using mean and standard deviation of the anomalies detected reconstruction error amse huber loss mean (µ) 0.10211 0.09656 standard deviation (σ) 0.09061 0.08506 min 0.000021 0.000003 25% 0.000521 0.000116 50% 0.000822 0.000291 75% 0.001372 0.000699 max 0.296708 0.240890 # of false positives 504 220 figure 10: categorization of unsupervised algorithms for comparative analysis categorization of various algorithms such as clustering techniques u-ahc [10], dep-ssec [11], sscocsvm [12], classification techniques n-stasvdd [13], ocsvm [6, 15], subspace method rpca [16], and deep learning models ae-(dada-s) [20], ocnn [22], lstm [28], hybrid learning model [29], denoising ae [30]. figure 5 shows the auc score that is standardized for the 10 <= k <= 100 range. ‘k’ indicates the batch size of data instances. in our evaluation, many different ‘k’ values are considered and averaged by computing the auc mentioned in table 3. environment: experiments were run on a 4-core processor in windows os with intel core i7-8th gen@1.8 ghz and 8gb ram. from figure 5 it is observed that the proposed fann achieves better reconstruction performance table 3: analyses of auc score of proposed fann with unsupervised algorithms techniques method auc score clustering u-ahc 0.84 ssc-ocsvm 0.88 dep-ssec 0.77 classification n-stasvdd 0.92 ocsvm 0.94 subspace method rpca 0.95 deep learning ae – (dada-s) 0.92 ocnn 0.89 proposed fann model (i) arctan loss 0.96 (ii) huber loss 0.98 https://doi.org/10.15837/ijccc.2023.1.4756 12 figure 11: generated auc curves with different k values 10<=k<=100 with a higher auc score for ad, which attempts to reduce rg (ii). 5.1 anomaly detection performance the performance metrics selected for evaluation are accuracy, precision, recall (or sensitivity), specificity, f1 score, false alarm rate (far), and matthews correlation coefficient (mcc) based on a confusion matrix representing the number of true positive rates (t.p rates), true negative rates (t.n rates), false-positive rates (f.p rates) and false-negative rates (f.n rates). the following equations are utilized for calculating the metrics, accuracy = #(t.prates+t.nrates)#(t.prates+t.nrates+f.prates+f.nrates) precision = #(t.prates)#(t.prates+f.prates) recall(or)sensitivity = #(t.prates)#(t.prates+f.nrates) specificity = #(t.nrates)(#(t.nrates+f.prates) f1score = 2∗#(t.prates)#(2∗t.prates+f.prates+f.nrates) falsealarmrate(far) = #(f.prates)#(f.prates+t.nrates) mathhewscorrelationcoefficient = (#(t.prates∗t.nrates)−#(f.prates∗f.nrates))√ (#(t.prates+f.prates)∗#(t.prates+f.nrates)∗#(t.nrates+f.prates)∗#(t.nrates+f.nrates)) for comparing the accuracy of our proposed model, experiments with different test sets are been carried out. the training data and test data are in the ratio of 3:2. to measure the accuracy of the models, in the 40% of test data various proportions of abnormality ranging from 1% to 15% are used. table 4 shows the average accuracy calculated for each technique with various test window sizes. based on experimentation, the accuracy of the proposed fann model produces an improved accuracy by >98% making it suitable for ad in cluster head(ch) comparative to other existing techniques. table 5 shows the evaluation results related to the precision, recall, specificity, f1 score, and false alarm rate (far) of the proposed fann with other unsupervised algorithms discussed in the related work. figure 5.1 depicts the overall evaluation results of the models for ad. the results show that our proposed fann yields higher detection performance. this addresses rg (i). the motive of our proposed model is to design a false positive reducer model capable of reducing unnecessary false positives and avoiding data loss. this can be noticed in figure 5.1(a), where the proposed fann using hl produces lower false positives when compared with existing algorithms discussed in section 2, thereby addressing rg (iv). finally, the metric matthews correlation coefficient (mcc) indicates that the model produces good results only if it classifies both positive and negative elements. in figure 5.1(b), the algorithms that produce lower mcc convey a random guess model whereas the proposed fann has produced an mcc of 0.8 showing upright performance in unsupervised anomaly detection. this is a positive move towards reducing the rg (v). for an imbalanced https://doi.org/10.15837/ijccc.2023.1.4756 13 table 4: average accuracy evaluation for ad with different episodes (training set and test set) techniques method average accuracy (%) clustering u-ahc 0.84 ssc-ocsvm 0.88 dep-ssec 0.77 classification n-stasvdd 0.92 ocsvm 0.94 subspace method rpca 0.95 deep learning lstm 91.0 hybrid learning 92.4 denoising autoencoder 95.4 ae – (dada-s) 0.92 ocnn 0.89 proposed fann model (i) arctan loss 0.96 proposed fann model(ii) huber loss 0.98 table 5: result of proposed models’ efficiency with sensitivity, specificity, f1 score, far and mathews correlation coefficient (mcc) methods precision sensitivity specificity f1 score far mcc u-ahc 92.6 90.8 11.5 91.7 88.5 0.02 ssc-ocsvm 90.7 92 9 91.3 90.7 0.01 dep-ssec 95.5 91.5 17 93.5 83.3 0.06 n-stasvdd 94.8 94.4 20 94.6 80 0.14 ocsvm 96.9 96.4 30 96.7 70 0.25 rpca 98.7 98.7 40 98.7 60 0.38 lstm 95.8 96.2 64 92.7 51 0.54 hybrid learning 96.9 89.7 72 97.4 33 0.62 denoising autoencoder 97.5 95.4 80 96 39.2 0.49 ae – (dada-s) 99 98.9 55.8 98.9 44 0.58 ocnn 98.1 98.5 30.7 98.3 69.2 0.32 fann model (i) arctan loss 99.5 67.1 99.5 99.5 32.8 0.67 fann model (ii) huber loss 99.7 83.6 99.8 99.8 16.3 0.8 dataset, analyzing the detection accuracy alone will not be a consistent measure. this exposes an over-optimistic estimation of the model in terms of efficiency. to get rid of the problem of uneven classification, the matthews correlation coefficient (mcc) is estimated to identify the proposed model’s capability concerning effectiveness. ad model must motivate on accurate detection of anomalies but all the compared algorithms such as, • clustering-based algorithms fail to enhance their accuracy due to their sensitivity to outliers, • classification and subspace methods lag in identifying true negatives and false positives for a large high-dimensional dataset with increased processing time, • deep learning algorithms fall back with lower mcc but produce higher false alarms, and require high computation time, therefore, the proposed fann model can be identified as a robust model achieving expected detection performance for ad. https://doi.org/10.15837/ijccc.2023.1.4756 14 (a) f1 score and far analysis (b) mcc score analysis figure 12: comparison plot of significant performance metrics (f1 score, far, and mcc) 5.2 energy performance in this section, to determine the utilization of energy during anomaly detection, complexity analysis is performed. table 6 provides the complexity analysis used in similar approaches [40]. the computational complexity of fann is o(rng)based on the evaluation of ‘r’ -number of inputs at the time, ‘n’ total dimension of the instances, and ‘g’ number of aggregators and the communication overhead obtained is o((r-a)n)where ‘a’ number of abnormal instances that are removed as it uses deep learning model. the energy performance of sensor nodes for our work is evaluated based on the table 6: complexity analysis of anomaly detection techniques computational complexity communication complexity classification o(rn2) o(rvn2) clustering o(rn6g) o(rn3) subspace method o(r3g) o(r3n) deep learning o(rng) o((r-a)n) following factors [41]: • computing energy – the energy consumed by ch for data processing and analysis • communicating energy – the energy consumed by ch for receiving, and transmitting data with ad and without ad a decentralized algorithm was discussed in [42] that computes energy in the dynamic network by developing a network coding subgraph that satisfies user-defined quality and security constraints. the efficient data collection aware of spatio-temporal correlation (east) algorithm for energyaware data forwarding in wsns [43] takes full advantage of both spatial and temporal correlation mechanisms to save energy while maintaining real-time, accurate data reports toward the sink node. to minimize the overall energy consumption, a dynamic optimization model [44] developed multi-hop topologies to mitigate data transmission costs. for ad energy consumption, the following are the premises considered: let etot be the initial energy unit of each aggregator node,etrn be the energy unit of data packet communication,erec be the energy unit for receiving a data packet, and ead be the energy utilized by aggregator nodes ‘g’ for anomaly detection [45]. then the residual energy erd of sensor node after reception can be expressed as, erd = etot −erec (7) and the energy consumed by ead can be calculated as, ead = con.tpc (8) where, con – power consumed to start the processor https://doi.org/10.15837/ijccc.2023.1.4756 15 figure 13: energy model for sensor nodes tpcthe time utilized by the number of instructions and the microcontroller operating frequency, (#ins) fq . now, the remaining energy etxrd available for transmission is expressed as, etxrd = erd −ead (9) here the equation 9 states that there will be energy consumption due to ead before transmitting the aggregated data. to infer minimum energy consumption by ead, the following expectations are considered: the total energy consumed by an aggregator node (sensor node) etot is measured with erec = (n∗x)j/s, etrn = (m∗y )j/s and ead = (a)j/s as the assumed energy consumption unit. the value of ‘n’ indicates number of data instances sent to aggregator node and ‘m’ indicates number of data instances transmitted from aggregator node. the value of ‘m’ may vary or remains the same as ‘n’ depending on number of anomalies detected. a comparison of energy consumption of aggregator node is expressed in the following manner as: • considering the energy consumption without ad for aggregator node, e1tot = psttst + erec + etrn = psttst + (n∗x + m∗y )j/s (10) here, etrn = erec since the number data transmitted will be the same as received. • considering the energy consumption with ad for aggregator node, e1tot = psttst + erec + etrn + ead = psttst + (n∗x + (m−o) ∗y + a)j/s (11) here, etrn = (m − o) ∗ y means energy consumed will be based on number data instances removed after ad. psttst is the startup power and startup time for aggregator node o indicates the number anomalous data instances. an energy model is built for equation (11) and simulated using matlab shown in figure 5.2. this energy model predicted the lifetime of node energy with and without ad. it can be observed from the x axis that the lifetime of sensor nodes increases with ad. illustration: let 0.004 j/s, 0.01 j/s and 0.01 j/s are the assumed energy consumption units for processing, data receiving and data transmission. suppose the number of data instances received ‘n’ by aggregator node a1 is 100 then energy consumed by erecandead will be, erec = 100 ∗ 0.01 = 1j/s ead = 100 ∗ 0.004 = 0.4j/s let the number of anomalies detected ‘o’ for aggregator node a1 is 10 then energy consumed by etrn will be, etrn = (100 − 10) ∗ 0.01 = 0.9j/s then e2tot with 0.1 j/s as psttst will be, e2tot = psttst + erec + etrn + ead https://doi.org/10.15837/ijccc.2023.1.4756 16 figure 14: energy consumption of aggregator node with ad using energy model figure 15: energy consumption of transmission after ad using energy model figure 16: comparison of average energy consumption of proposed ad model https://doi.org/10.15837/ijccc.2023.1.4756 17 figure 17: energy consumption analysis of various deep learning model = 0.01 + 1 + 0.4 + 0.9j/s = 2.31j/s the above illustration expresses the reduction in transmission energy due to the removal of anomalous instances as depicted in figure 5.2. based on the premises discussed in the energy model shown in figure 5.2, it can be inferred that fann consumes proportionately less energy for ad than transmission and reception. the comparative analysis of energy consumed by ad for various models is depicted in figure 5.2. from the comparison, it is identified that the proposed deep learning model consumes reduced energy than existing deep learning models and energy models for ad. the computation of average energy consumption at a ch is analyzed. following are the parameters used in our experiment: (i) number of data packets is kept in a range of 150 to 750 (ii) data corruption rate is kept in a range of 1.5% to 15% also, the initial energy was set to 500 j/s and gradually decreased concerning processing various data packets using ad. a comparison of residual energy with ad against the residual energy without ad yields the inference. the energy consumption of various state-of-art techniques versus the proposed model is depicted in 5.2. the figure shows that the residual energy after ad using the proposed model appears to be marginally greater than the residual energy without ad. other existing models, on the other hand, produce less residual energy after ad than residual energy without ad. this supports the proposed model for sustainable development in wsn. consequently, the proposed fann also ensures less computation time for ad compared to the other models as shown in 5.2, which is relatable to lower energy consumption, addressing rg (iii). 6 conclusion ad using deep learning is a significant research area that has attracted the attention of researchers. specific techniques can be adopted depending upon the application domain. the detection process https://doi.org/10.15837/ijccc.2023.1.4756 18 figure 18: computation time analysis of different unsupervised algorithms must have high accuracy consuming less energy when it is applied to wsns. the implemented fann model presented in this paper is promising in ad handling unlabeled data gathered in a wsn environment. the selection of hyper parameters for the model provides a unique perspective for improving the accuracy of the model in eliminating outliers. moreover, the proposed model is capable of handling high-volume multidimensional datasets with less computation time and reduced energy utilization. the performance of the proposed model is evaluated using the auc-roc score and suitable metrics for anomaly detection such as accuracy, precision, recall, specificity, f1 score, and mcc. from the experimental results, it is inferred that our model outperforms the other algorithms discussed in the literature concerning detection accuracy. additionally, the proposed fann model proves to be a false positive reducer achieving a lower far, which is a major concern for ensuring less data loss. the energy consumption of the proposed ad model is also observed as low in the aggregator node. the above discussions show that applying ad on the aggregator node will ensure sustainable improvement in wsns. furthermore, promising directions leading to extended research can focus on ensemble learning and auto-tuning the hyper parameters to further improve ad accuracy. researchers can also work towards recovering lost data after an attack detection to empower data consistency in wsns. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] miao xie; song han; biming tian; sazia parvin (2011). anomaly detection in wireless sensor networks: a survey, journal of network and computer 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[45] mohamed elshrkawey; samiha m. elsherif; m. elsayedwahed (2018). an enhancement approach for reducing the energy consumption in wireless sensor networks, journal of king saud university – computer and information sciences, vol. 30, 259–267, 2018. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: arul jothi, s.; venkatesan, r. (2023). a deep learning approach for efficient anomaly detection in wsns, international journal of computers communications & control, 18(1), 4756, 2023. https://doi.org/10.15837/ijccc.2023.1.4756 introduction data aggregation in wsns anomaly detection in wsn literature background proposed feed-forward autoencoder neural network for anomaly detection overview schema of fann model model functionality activation function analysis for the model loss function analysis for the model arctan mean square error (amse) huber loss (hl) false positive rate reduction experimental results dataset exploration data processing and visualization analysis of activation functions with a loss function methods for mitigating false alarm rate auc roc curve loss functions: amse and hl conformance threshold results comparison and discussions anomaly detection performance energy performance conclusion international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 4, month: august, year: 2023 article number: 5173, https://doi.org/10.15837/ijccc.2023.4.5173 ccc publications a project recommender based on customized graph neural networks in online labor markets y. ma, z. ma, y. li, h. gao, y. xue yixuan ma* school of software engineering beijing jiaotong university, china *corresponding author: mayx@bjtu.edu zeyao ma school of computer science beijing university of posts and telecommunications, china mazeyao@bupt.edu.cn yankai li school of information engineering china university of geosciences (beijing), china 1006192217@cugb.edu.cn haoyu gao school of software engineering beijing jiaotong university, china 19301113@cugb.edu.cn yukai xue school of software engineering beijing jiaotong university, china 19301104@bjtu.edu.cn abstract project recommendation is a crucial task in the online labor markets, where freelancers seek projects that match their skills and preferences. the challenge in project recommendation is to effectively capture the complex relationships between projects, skills, and freelancers in a highdimensional space. traditional recommender systems, such as matrix factorization, often suffer from sparsity, making it difficult to accurately recommend projects to freelancers. to address the challenges, this research proposes a customized graph neural network approach, called gcnrec, specifically using light graph convolution network (lightgcn) to effectively capture both explicit and implicit relationships between projects, skills, and freelancers, and make personalized project recommendations. the experiment results on real-world dataset demonstrate the effectiveness of the proposed method in solving the challenges in project recommendation in the online labor market. keywords: project recommendation, online labor markets, graph neural networks, lightgcn. https://doi.org/10.15837/ijccc.2023.4.5173 2 1 introduction the online labor market is a thriving platform that links clients seeking services with freelancers possessing the necessary skills and knowledge. this industry has experienced substantial growth in recent years and is favored for its convenience and flexibility, enabling individuals to monetize their abilities. the largest freelancing platforms, including upwork and freelancer, have attracted millions of clients and freelancers from across the globe. as of 2022, the global online freelance market had surpassed $1.5 trillion and is projected to continue growing in the future. nevertheless, freelancers face the challenge of identifying suitable projects within a diverse and expansive market. to address this problem, project recommendation systems have been designed to furnish personalized and effective suggestions based on freelancers’ abilities and preferences. recommender systems have been developed to assist individuals in making personalized and effective decisions in various fields, including e-commerce[13], entertainment[4], and news[18]. the objective of a recommender system is to predict the preferences of a user based on their past behaviors and preferences, as well as the behaviors and preferences of similar users. in the case of project recommendation in the online labor market, the challenge lies in accurately capturing the complex relationships between projects, skills, and freelancers in a high-dimensional space. the uniqueness of project recommendations compared to other recommendation systems lies in the need to consider multiple factors such as the skill required for a project, the preferences of the freelancer, and the project budget and deadline. these factors can interact in complex ways, making it difficult to make accurate project recommendations using traditional recommendation methods, such as matrix factorization. to address these challenges, recent research has explored the use of graph neural networks for project recommendation. graph neural networks are a powerful tool for learning from graph-structured data[23], and have been successfully applied to a wide range of tasks, including node classification[21], link prediction[27], and graph generation[26]. by treating the project recommendation problem as a graph-based learning task, graph neural networks can effectively capture the relationships between projects, skills, and freelancers, and make personalized project recommendations. light graph convolution network (lightgcn) [8], a type of graph convolutional network is designed to handle large-scale graphs with a lightweight architecture, making it well-suited for the online labor market, where the number of projects, skills, and freelancers can be very large. the model constructs the graph structure for the project recommendation task, where the nodes in the graph represent projects, skills, and freelancers, and the edges represent the relationships between these entities. however, traditional lightgcn only focuses on explicit connections of users, such as defined skills required for the project, which can limit the performance of recommendations. this is because explicit connections may not capture all of the relationships between entities, such as indirect or latent relationships. for example, two freelancers who lack direct connections may still have comparable project preferences. furthermore, the traditional lightgcn technique is not adept at capturing long-distant dependencies in the graph structure, which are crucial for comprehending the intricate relationships between workers and projects. in this paper, we propose a novel project recommendation framework based on customized graph neural networks, called gcnrec, in the online labor market. specifically, we customized lightgcn by incorporating both explicit and implicit interaction between workers and projects and capturing long-distance dependencies between entities. we evaluate the proposed method on a real-world dataset and show that it outperforms existing recommendation methods in terms of accuracy and efficiency. the rest of the paper is organized as follows. section 2 provides a background on project recommendation and graph neural networks. section 3 describes the proposed method in detail. section 4 presents the experimental results and evaluation. section 5 concludes the paper and discusses future work. 2 related work project recommendation in the online labor markets has been a long-standing challenge in the field of recommender systems. the traditional approach to project recommendation has been collaborative https://doi.org/10.15837/ijccc.2023.4.5173 3 filtering (cf), which was introduced by goldberg in 1992 through the creation of tapestry [7]. this approach relied on detecting similarities in user behaviors and item preferences. however, this type of recommendation system has too many requirements for users and is inconvenient in certain scenarios such as inputting query sentences. resnick proposed the first automatic recommender system, grouplens, in 1994 which utilized rating methods (from 1 to 5) to measure user preferences [12]. this traditional approach of cf suffers from the "cold start" problem, where the algorithm is limited by the user-item data and thus results in lower-quality recommendations [5]. recent advances in graph neural networks (gnns) have provided solutions to overcome the limitations of traditional cf. graph convolutional networks (gcns) have been proposed to model the interaction between users and projects as a binary graph, with the edges representing the interactions and the weights of the relationships being expressed through edge properties [15]. gcns can extract feature vectors of different dimensions for different modeling purposes and has higher data access efficiency than adjacency matrices. several variations of gcn have been proposed, such as lightgcn[8], which is a simplified version that reduces training difficulty, and knowledge graph attention networks (kgat)[16], which links items with their attributes. graph heterogeneous collaborative filtering (ghcf)[2] adds both node and relation representations for multi-relational prediction, and performs well in dealing with the cold-start problem. wu et al.[22] proposed a model that considers the impact of the user on user iteration to address the data sparsity and cold start issues in traditional algorithms. sheu and li et al. proposed the context-aware graph embedding (cage) [14] framework to enrich the semantics of news articles and refine their embeddings through gcn. khatua et al.[9] used gcn-based graph convolution network (gun) to match recruiters and job seekers through twitter data. in the field of project recommendation, our work is inspired by the lightgcn approach. in the online labor market, the relationship between users, items, and skills is not reciprocal. we introduce an attention mechanism to focus on projects that match user skills to effectively improve the accuracy of recommendations. this is based on the idea that a user may pay more attention to projects that match their skills, not just because they are interested, but because it increases their completion rate. this work differs from previous efforts in the field as it considers the influence of skills on project recommendation, while previous methods have focused on social recommendation and the influence of users on users. 3 methodology in this section, we provide an overview of lightgcn architecture, including the embedding layer, light graph convolution, and layer combination. then, we describe our proposed recommendation framework gcnrec based on lightgcn and present the construction of graphs that include both explicit and implicit interactions. thereafter, we explain the model training process and the regularization approach included in our framework. 3.1 lightgcn architecture light graph convolution network (lightgcn) is a graph convolutional network architecture for graph-based recommendation systems[8]. it is designed to handle large-scale graph data, such as the data found in online labor markets, by using a scalable and efficient graph convolution operation. the lightgcn consists of multiple graph convolutional layers that aggregate information from the graph’s neighbors to update the representations of the nodes (e.g., workers and projects). in each layer, the representation of each node is updated by taking a weighted sum of the representations of its neighbors, where the weights are learned by the model. this process is repeated multiple times to aggregate information from multiple hops in the graph, allowing lightgcn to capture complex relationships between nodes. finally, the node representations learned by lightgcn can be used for recommendation by computing a score for each user-project pair, indicating the likelihood of the user being interested in the project. these scores can be used to rank the projects for each user and make recommendations. https://doi.org/10.15837/ijccc.2023.4.5173 4 the architecture of lightgcn consists of the embedding layer, the design of light graph convolution, and layer combination. 3.1.1 embedding layer the vast majority of recommendation systems learn user and item representations to estimate user’s preference on any item[20]. in online labor markets, workers and projects are described with an embedding vector hw ∈ rd, hp ∈ rd, respectively. the embedding matrix can be built as follows: h =   hw1 ... hwn hp1 ... hpm   =   hw11 . . . hw1d ... . . . ... hwn 1 . . . hwn d hp11 . . . hp1d ... . . . ... hpm 1 . . . hpm d   (1) where n and m are the numbers of users and items. hw and hp are id embeddings that will be fed into worker-project interaction graphs. 3.1.2 light graph convolution lightgcn implements the concept of graph convolutional network and acquires node representations through the smoothing of features across nodes that are connected and at a maximum of k steps away from the central node. this method has been presented in previous research such as [10, 19]. lightgcn simplifies the traditional graph convolutional layer by eliminating feature transformation and nonlinear activation. the only operation maintained in this model is a straightforward weighted sum aggregation, which is defined as: h(k+1)w = ∑ p∈nw 1√ |nw| √ |np| hkp (2) h(k+1)p = ∑ w∈ni 1√ |np| √ |nw| hkw (3) where hkw,hkp is the embedding of worker and project in k lgc layer. nw,np denote the direct neighbor of the target worker and project. |nw| and |np| denote the neighbor amount. the symmetric normalization term 1√ |np| √ |nw | follows the design of standard gcn[10], which helps prevent the growth of the scale of embeddings during graph convolution operations. other normalization options, such as the l1 norm, can also be considered, but through empirical testing, the use of symmetric normalization has been found to yield favorable results [8]. 3.1.3 layer combination in lightgcn, the final representation of the node is computed by combining all embeddings obtained in each lgc layer. it’s worth noticing that the origin embedding at 0-th layer, i.e., h(0)w for the worker and h(0)p for the project, is as well needed to be combined. thus, the final representation of a worker or a project is form by combining the embeddings obtained at each layer: hw = k∑ k=0 αkh (k) w (4) hp = k∑ k=0 αkh (k) p (5) https://doi.org/10.15837/ijccc.2023.4.5173 5 where αk ≥ 0 is the significance of the k-th layer embedding in forming the final embedding represented by a weighting factor, which can either be manually tuned as a hyperparameter or learned as a model parameter. in this paper, we closely follow the value of αk in lightgcn work [8], which sets αk uniformly as 1/(k + 1) to maintain the simplicity of lightgcn and avoid overcomplicating the model. 3.2 proposed framework 3.2.1 overview of the proposed framework conventional methods focus on the user’s behavior data and construct a user-item graph for recommendation purposes. however, this approach is limited as it ignores the attributes of users and items and their impact on the recommendation. behavioral data is important but not the only consideration in the recommendation system, especially in the case of the labor market. conventional graph neural network methods such as lightgcn only take into account explicit connections and extract information from a user’s point of view. however, there are also crucial implicit connections between users and items in the labor market recommendation. therefore, this paper argues that the recommendation problems in the labor market are mutual selection problems that require considering both the user’s preferences and their abilities. however, most current recommendation strategies are user-oriented, neglecting the important factor of item matching, leading to suboptimal recommendation results. the proposed recommendation framework incorporates attribute features derived from regular worker behavior and fuses them with explicit features to build a comprehensive worker-project network structure. skill is chosen as the bridge for implicit feature construction, and the model establishes a graph structure of worker-skill-item. the framework also includes a matching degree matrix to extract skill information between workers and projects, providing a bidirectional recommendation that takes into account both worker preferences and abilities. the overview of our proposed gcnrec framework is shown in figure 1. figure 1: an overview of our proposed gcnrec framework. in this model, the number of lgc layers on explicit interaction and implicit interaction is set to 3 and 2, respectively, which leads to good performance in general. 3.2.2 graph construction and lgc transformation our initial goal is to create a graph that incorporates both explicit and implicit interactions in the online labor market field, as mentioned earlier. we extract interactions from dataset and form two symmetrically normalized adjacency matrix ã1, ã2 same as lightgcn[8],ã1 includes the workerproject explicit interaction and ã2 includes the worker-project implicit interaction. based on different kinds of interaction, we can employ different numbers of lgc layers as figure 1 uses 3 lgc layers and 2 lgc layers, respectively. the result of lgc transformation can be computed as follows: h1 = 1 k1 + 1 (h (0)1 + ã1h (0) 1 + · · · + ã1 k1 h (0) 1 ) (6) https://doi.org/10.15837/ijccc.2023.4.5173 6 h2 = 1 k2 + 1 (h (0)2 + ã2h (0) 2 + · · · + ã2 k1 h (0) 2 ) (7) where k1 and k2 denotes the number of lgc layer of two interactions, h (0) 1 ∈ r (n +m )×d denotes the original embedding of user and project in which d is the size of the embedding dimension. 3.2.3 embedding fusion given that explicit and implicit features may have varying levels of influence on the recommendation, simply combining their feature matrices is not appropriate. to incorporate the impact of implicit features without compromising the influence of dominant features, we train the weights of both dominant and implicit features through backpropagation through a linear layer. this linear layer adjusts the relative importance of each feature, leading to a more effective final recommendation result. after the lgc transformation, we need to split the result h1 to obtain the worker embedding w1 and project embedding p1. we do the same operation on p2. then, we concatenate w1 and w2 as well as p1 and p2, which is the most straightforward way to combine embeddings[24]: w = w1 || w2 (8) p = p1 || p2 (9) where w ∈ r(n ×2d),p ∈ r(m ×2d). after embedding concatenation, we do linear transformation to adjust the size of embedding: ew = w mw (10) ep = p mp (11) where wu, wp ∈ r(2d×d) is a trainable matrix whose value can be adjusted automatically through backpropagation. according to the nature of matrix multiplication, the linear transformation can act as a distributor to adaptively distribute the amount of influence of explicit interaction and implicit interaction in the final representation, which guarantees the expressive power of our framework. 3.2.4 project ranking finally, to obtain the rank of each project to target user, we conduct dot product: ŷ(w, p) = etwep (12) where ŷ is the prediction score of project p to user u. eu ∈ eu is the final embedding of a single user and ep ∈ ep is the final embedding of a target project. the dot product is a widely used approach to ranking project [20]. 3.3 model training and regularization we train our recommendation framework by optimizing bayesian personalized ranking (bpr) loss[11], which are widely used in various kinds of recommendations[3, 17]. bpr loss encourages the model to compare the relative order between observed worker-project interactions and their counterparts: lbp r = − m∑ w=1 ∑ i∈nw ∑ j /∈nw ln σ(ŷwi − ŷwj ) + λ || e(0) ||2 (13) where λ denotes l2 coefficient used for regularization. we employ adam optimizer[6] and divide the dataset into mini-batch. https://doi.org/10.15837/ijccc.2023.4.5173 7 4 experiments 4.1 dataset data used in this research was collected from freelancer.com, a major online labor marketplace, through the website’s open api. the data covers the period between january 2000 and march 2017 and contains details about users, projects, bids, and project reviews. to guarantee the dataset’s quality, we selected only those workers who had interacted with at least 20 projects. to tackle the cold-start issue, we partitioned each worker’s projects into training, validation, and test sets. we randomly selected 80% of the projects for training and kept 10% as a validation set to fine-tune the parameters. this approach ensured that all workers appeared in both the training and test sets. the data after preprocessed is available in table 1. the workers’ explicit interactions with projects refer to their bidding operation in the original dataset, while implicit interactions are those where a worker’s skill tags match the project’s category tags. we obtained the implicit interactions by extracting the skills tags of each worker and matching them with the project’s category tags. table 1: dataset after preprocessing attributes amount number of workers 10,692 number of projects 46,534 number of total categories 989 number of worker-project explicit interaction 1,683,062 number of worker-project implicit interaction 3,170,345 4.2 experimental settings 4.2.1 baseline models to illustrate the improvements of our framework in recommendation in the online labor market. we compare our framework with the following models: • mf[11]: matrix factorization(mf) is a basic recommendation model in collaborative filtering. by utilizing bayesian personalized ranking (bpr) loss, we streamline the model optimization process to solely consider the user-project interaction. • ngcf[17]: neural graph collaborative filtering (ngcf) is a model that leverages a graph convolutional network and surpasses other gcn-based models such as gc-mc [1] and pinsage [25]. ngcf’s approach entails utilizing graph convolutional layer propagation to investigate high-order connectivity in the user-item graph. 4.2.2 evaluation metrics in order to assess the effectiveness of the recommendation model, we utilize four popular evaluation methods: precision@k, recall@k, f1@k, and ndcg@k. for our study, we set k to a default value of 20. the precision, recall, and f1 metrics are utilized to determine the accuracy of the recommendation. precision measures the ratio of user bids for a project within the recommended k items. recall measures the fraction of user bids for a project among the complete project set. the aforementioned metrics can be calculated using the following equations: p recision@k(u) = | rk(u) ∩ t (u) | k (14) recall@k(u) = | rk(u) ∩ t (u) | | t (u) | (15) https://doi.org/10.15837/ijccc.2023.4.5173 8 f 1@k(u) = 2 × p recision@k(u) × recall@k(u) p recision@k(u) + recall@k(u) (16) where t (u) denotes the ground truth project set, k(u) denotes the top-k recommended project set. ndcg takes the project ranking position into account, which is not considered in the previous three metrics. n dcg@k = 1 | u | ∑ u∈u ∑k k=1 i(rk k (u)∈t (u)) log(k+1)∑k k=1 1 log(k+1) (17) where rk(u) is the kth project in top-k recommended project set rk(u). 4.3 experiment results 4.3.1 model training process in all models, the embedding size for both the worker and project are fixed to 64, and parameters are initialized utilizing the glorot & bengio method [6]. the learning rate is set to 0.001, and the mini-batch size is set to 2048. we search for the l2 regularization coefficient λ in the range of 1e−6, 1e−5, . . . , 1e−2. the number of layers for the two graphs is explored within the ranges of 1, 2, 3, 4 and 1, 2, . . . , 10, respectively. the models are trained for 1000 epochs with an early stopping mechanism of 100. the optimal performance is achieved with λ=0.001, k1=2, and k2=3. 4.3.2 model performance comparison table 2 shows a comparison of the overall performance of three different models: matrix factorization (mf), ngcf (neural graph collaborative filtering), and gcnrec (graph convolutional networks for recommendation). the performance measures are precision, recall, f1, and ndcg (normalized discounted cumulative gain). table 2: overall performance comparison models precision recall f1 ndcg mf 0.0363 0.0373 0.0368 0.0469 ngcf 0.0434 0.0476 0.0454 0.0572 gcnrec 0.0458 0.0516 0.0485 0.0610 %improv. 26.17% 12.83% 31.79% 30.06% the gcnrec model showed the highest improvement over the mf model with a 26.17% increase in precision, a 12.83% increase in recall, a 31.79% increase in f1, and a 30.06% increase in ndcg. it demonstrates the inability of the mf model to aggregate high-hop information and the strong capabilities of gnn-based models. this result highlights the importance of indirect interactions between a project/worker and the target worker in the online labor market. specifically, gcnrec achieves 5.53% higher precision, 8.4% higher recall, 6.83% higher f1, and 6.64% higher ndcg compared to ngcf, emphasizing the crucial role of project and worker properties in project recommendation in the online labor market. 4.4 the influence of lgc layer in table 3, a comparison is presented among various combinations of explicit and implicit layers employed in a recommendation system, evaluated based on four performance metrics, namely, precision, recall, f1, and ndcg. the explicit interaction is modeled using 1 to 4 lgc layers, while the implicit interaction is modeled using 1 to 4 lgc layers as well. for every combination of explicit and implicit layers, the table displays the corresponding metric values. https://doi.org/10.15837/ijccc.2023.4.5173 9 table 3: performance comparison of different layers explicit layer implicit layer precision recall f1 ndcg 1 1 0.0449 0.0501 0.0474 0.0594 2 0.0445 0.0497 0.0469 0.0591 3 0.0438 0.0487 0.0461 0.0584 4 0.0453 0.0506 0.0478 0.0608 2 1 0.0451 0.0503 0.0476 0.0601 2 0.0449 0.0502 0.0474 0.0601 3 0.0448 0.0502 0.0473 0.0599 4 0.0455 0.0514 0.0483 0.0607 3 1 0.0457 0.0510 0.0483 0.0608 2 *0.0458 *0.0516 *0.0485 *0.0610 3 0.0453 0.0510 0.0480 0.0607 4 0.0453 0.0513 0.0482 0.0602 4 1 0.0449 0.0500 0.0473 0.0599 2 0.0443 0.0508 0.0479 0.0608 3 0.0458 0.0505 0.0480 0.0609 4 0.0455 0.0515 0.0483 0.0608 the results show that the best overall performance is achieved with 3 lgc layers for explicit interaction and 2 lgc layers for implicit interaction, as indicated by the asterisks (*), as the precision, f1 and ndcg values are the highest. when the number of layers is greater than 3, the performance drops quickly. the reason is that too many layers can lead to an over-fitting problem, which makes the node embedding indistinguishable. the table also shows that the values of precision, recall, f1, and ndcg become more stable as the number of layers increases, and the average value of each metric is higher than the two baselines, indicating the ability of the model to reduce the over-fitting problem and the stability of the framework. 4.5 user case analysis history skills and profile skills are important factors that could be used to identify a suitable project for a worker. the profile skills represent the skills that the worker has in their profile, and these skills are usually used to describe their expertise and areas of interest. the history skills, on the other hand, are the skills that the worker has utilized in the past and could indicate the types of projects that they have worked on and the areas where they have experience. by considering both the profile skills and the history skills of a worker, it is possible to determine the types of projects that would be best suited to their expertise and experience, increasing the chances of project success. in this section, we choose a specific worker with id 6611. table 4 provides detailed skill information about this worker. table 4: detailed skill information of user 6611 skill type skill name profile skill mobile phone iphone objective c ipad swift apple watch history skill iphone mobile phone objective c ipad swift https://doi.org/10.15837/ijccc.2023.4.5173 10 we made a comparison of our proposed model gcnrec with bpr and ngcf in recommending projects to worker 6611. each model recommends 20 projects, which are listed in table 5 along with a ranking (number in parentheses) and a star (*) indicating if the project is deemed as the most suitable for the worker. table 5: top 20 recommended projects of user 6611 model top 20 projects bpr 24169(2) 23660(1) 23732(2) 23656(2) 23729(2) 24106(1) 23670(1) 24019(2) 23673(1) 24117(1) 23645(1) 24128(1) 24167(1) 23657(1) *24114(1) 24094(1) 23304(1) 23727(1) 23983(1) 24298(1) ngcf 24221(1) 23732(2) *23731(2) 23645(1) 24117(1) 23983(1) 24120(1) 24169(2) 23977(2) 23660(1) 23725(1) *23985(1) 23676(1) 23304(1) 23331(1) 23648(1) 23729(2) 24271(2) 24109(2) 24158(1) gcnrec 23670(1) 23657(1) 23729(2) 23983(1) 24280(2) 23981(2) 44379(2) *23730(2) 23645(1) 23732(2) *23731(2) 24094(1) 23977(2) *24114(1) *23329(1) 23996(1) 24271(2) 23649(2) 23993(2) 24217(2) in order to demonstrate the importance of incorporating domain knowledge in the online labor market, we have calculated the overlap between the skill tags associated with a worker and the recommended projects. the worker’s tags are skills that we have previously mentioned, while the tags for projects are required skills provided by the employer. both the worker and projects can have multiple skill tags. table 5 shows that the average number of overlapping tags between the worker and projects recommended by bpr, ngcf, and gcnrec is 1.25, 1.35, and 1.5, respectively. the highest value of average overlap indicates that gcnrec is able to effectively uncover the relationship between the worker’s skills and project categories. this demonstrates the usefulness of this capability, as the number of accurately recommended projects by bpr, ngcf, and gcnrec is 1, 2, and 4, respectively. 5 conclusion in this paper, we present a novel project recommendation system for the online labor market, called gcnrec, that leverages the power of graph convolutional networks and unique features of the online labor market. our framework focuses on matching the skills of freelancers with the requirements of projects by considering both explicit interactions, such as the match between a worker’s skills and a project’s requirements, and implicit interactions, such as the worker’s bidding history. the results of our experiments on a large real-world dataset demonstrate the outstanding performance of gcnrec compared to other recommendation systems. our model has the potential to improve the work efficiency and income of freelancers by assisting them in finding the right projects for their skills. in future work, we plan to incorporate additional relevant information from the online labor market into our recommendation framework. funding the work is supported by the young scientists fund of the national natural science foundation of china (grant no.72101023) and the beijing social science foundation (grant no. 21jcc092), and in part by beijing logistics informatics research base, china. https://doi.org/10.15837/ijccc.2023.4.5173 11 references [1] berg, r., kipf, t. & welling, m. 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(2023). a project recommender based on customized graph neural networks in online labor market, international journal of computers communications & control, 18(4), 5173, 2023. https://doi.org/10.15837/ijccc.2023.4.5173 introduction related work methodology lightgcn architecture embedding layer light graph convolution layer combination proposed framework overview of the proposed framework graph construction and lgc transformation embedding fusion project ranking model training and regularization experiments dataset experimental settings baseline models evaluation metrics experiment results model training process model performance comparison the influence of lgc layer user case analysis conclusion international journal of computers communications & control issn 1841-9836, 11(3):348-357, june 2016. dynamic secure interconnection for security enhancement in cloud computing l. he, f. huang, j. zhang, b. liu, c. chen, z. zhang, y. yang, w. lu liwen he*, feiyi huang, jie zhang, bin liu, chunling chen, weifeng lu nanjing university of posts and telecommunications 66 new mofan road (p. code:210003), nanjing, china helw@njupt.edu.cn, feiyi.huang@gmail.com, zhangjie@njupt.edu.cn, clchen@njupt.edu.cn, luwf@njupt.edu.cn *corresponding author: helw@njupt.edu.cn zonghua zhang institut mines-télécom of france rue guglielmo marconi, 59650, villeneuve-d’ascq, france zonghua.zhang@lifl.fr yang yang shanghaitech university, chinese academy of sciences information building no 1, 280 linhong road, 200335, shanghai, china yang.yang@shrcwc.org abstract: cloud computing brings efficiency improvement on resource utilization and other benefits such as on-demand service provisioning, location independence and ubiquitous access, elastic resource pooling, pay as usage pricing mode, etc. however, it also introduces new security issues because the data management and ownership are separated, and the management is operated on a virtualized platform. in this paper, a novel dynamic secure interconnection (dsi) mechanism is proposed to isolate the cloud computing system into a couple of dynamic virtual trust zones with different security policies implemented for different customers so as to enhance security. experimental results are presented to demonstrate the feasibility and effectiveness of the dsi mechanism. keywords: cloud computing, virtualization management, security, dynamic secure interconnection 1 introduction in recent years, cloud computing is drawing more and more attention with its capabilities of efficient resource utilization, virtual machine live migration and multi-tenancy operational mode. virtualization is the fundamental technology for both public and private cloud, virtual machine is expected to be dynamically allocated according to the requirements of customers, to be seamlessly migrated from one physical machine to another, and to be managed appropriately to prevent illegal access. however, cloud computing brings unprecedented challenges on security issues. as long as customers upload their sensitive data into a cloud computing system, the cloud computing service provider (csp) is responsible for managing the data. customers will lose the control of the data, who is using them, and when it is deleted. and in a cloud computing environment, virtualization cannot be protected by conventional network security solution, such as ą°security zoneąą separation, firewall, vpn, intrusion detection/prevention system, anti-ddos solution, deep packet inspection technology [1]. in this paper, we propose a security enhanced virtual machine management mechanism named dynamic secure interconnection (dsi) for cloud computing system. a ą°dynamic virtual trust copyright © 2006-2016 by ccc publications dynamic secure interconnection for security enhancement in cloud computing 349 zonesąą is established to enhance information and virtualization security. in section 2, backgrounds about the cloud computing and virtualization security issues are reviewed. section 3 provides a typical cloud computing model and states the typical security problems and requirements, and section 4 proposes the dsi mechanism and operational procedure in details. a testbed and some experimental results are presented in section 5. the paper is concluded in section 6. 2 related work the security issues are the major concerns for enterprise to adopt cloud computing [2] [3] [4]. seven cloud computing security risks are identified by gartner [5], i.e. privileged use access, regulatory compliance, data location, data segregation, recovery, investigation support, long-term viability. the root cause of these security risks is data storage, management and computation are performed on a shared and virtualized environment. since vms work over hypervisor, malicious vms cannot gain access to other vms or launch cross-vm attacks when security countermeasures are implemented on hypervisors. however, this security boundary can be broken and malicious vms can get full access to the physical host so as to get access to other vms located on the same host illegally [6] [7]. virtualization security has been studied from many aspects. in [8], an out-of-vm monitoring mechanism is proposed by using a trust vm to monitor the statues of guest vms which deliver services to customers. the solution assumes the trust vm can prevent a variety of security threats. however, there is a large performance overhead associated with this solution when traffic is switched between the guest vms and the trust vm. in order to provide a trusted vm on untrusted computing os, a secure virtualization architecture is proposed to provide a secure execution environment [9]. the architecture includes a secure run-time environment, secure network interface and a secure secondary storage. apart from the secure architecture, trust platform module has be used to establish the root of trust for vms [10]. in [11], a virtual layer management framework is presented to ensure that cloud providers properly isolate vms that run in the same physical platform, a cloud computing system is divided into a number of domains on the virtualization layer. corresponding protocols are also proposed to manage the domain creation, interaction and termination. the interaction among the domains is based on secure channels [12] to establish trustworthy self-management foundation. data protection is another critical issue. service providers such as foursquare which provides a location based service and reddit which supplies social news voting services use amazon ec2 (elastic cloud computing platform). in 2011, the crash of amazon ec2 service takes down the service of foursquare, reddit, cydia, discovr and scvngr [13]. also, application (software) service provider can only rely on the infrastructure service provider to ensure the business continuity under the umbrella of sla (service level agreement). they can implement their own security policies to achieve data security, preventing data loss or leakage. in [14], hwang and li propose a data coloring and software watermarking technique to establish trust among cloud service providers. in particular, if data objects and software modules are shared over multiple data centres, the trust-overlay network can establish a reputation system to protect data security and integrity. cloud privacy is to ensure the personal or sensitive information only be accessed by intended and authorized person or applications. the privacy issue originates from the lack of user access control and information transparency. that is when adopting a cloud storage service, e.g. the dropbox [15], the customers are difficult to implement mechanisms to protect their information from unauthorized access or misuses. promising privacy preservation solutions include minimizing personal information stored in the cloud, maximizing use control, allowing user to 350 l. he, f. huang, j. zhang, b. liu, c. chen, z. zhang, y. yang, w. lu choose, specify and limit the data usage [16]. in addition, data encryption is always a popular way, despite the extra overhead and complexity resulting from encryption algorithms and key management issues. in [17], a data secure sharing mechanism is proposed to enforce data access control, strengthening data encryption and improving the key sharing process when cloud customers store their data in a public cloud platform. the solution can protect the cloud storage providers from unauthorized access, ensuring data confidentiality and privacy. in [18], a privacypreserving public auditing supported secure cloud storage system is proposed, which enables that data privacy of cloud storage to be publicly audited by a third party auditor. in particular, the homomorphic liner authenticator and random masking techniques are utilized to guarantee that the third party auditor would not learn any knowledge about the data content stored on the cloud server during the auditing process. 3 problem description a number of open source cloud computing platform is based on the policies configured such as user authentication, authorization and accounting, vm allocation, drifting and state management, host machine management, service provision management. in the model, the security issues become much more complicated. first, the conventional network security solutions become less effective since they are usually deployed at the edge of a physical network to control and protect the incoming or outgoing traffic of a lan. second, new virtualization security countermeasures should be implemented on the virtualized perimeters where the physical network perimeter does not exist. third, in the multi-tenancy environment, customers who share the same local network should have logically or physically separated computing, storage and networking resources, especially when customers come from different enterprises. that means cloud service provider should allocate each customer and their resources within a same virtualized trust group, permitting the interconnection within the same group and control the communication among different groups. finally, when customers are on travel, the vms related to them will be drifted and migrated from one physical machine to another, the security policies that implemented by the customer and on related vms are expected to move along with migration. 4 dynamic secure interconnection mechanism in this section, a novel mechanism, dsi-vm management mechanism is proposed to enhance security in a cloud computing system. a new concept of ą°virtual trust zoneąą is also introduced. 4.1 definitions and assumptions virtual trust zone: vms are the basic operation unit to implement management and security policies. when customers login and get service from a cloud computing system, they are allocated with virtualized resources in terms of vms according to their requirements. vms that assigned to the same customer should be aggregated in a same group and implemented with the unified management and security policies. thus, the vms that stay in the same group have basic trust among each other, and this group is defined as a ą°virtual trust zoneąą. virtual bridge:vms that operate over a physical machine share the same physical mac and ip addresses when the physical machine have only one nic card. each vm has its own virtual mac and virtual ip addresses. a virtual bridge is a function module implemented on the hypervisor. it forwards packets with virtual mac and ip address to their destination. a virtual bridge can serve all vms on a hypervisor as well as a single vm. dynamic secure interconnection for security enhancement in cloud computing 351 4.2 the dsi components the dsi components include a dsi server, several virtual bridges and dsi clients. the dsi server works at a centralized mode while virtual bridge and dsi clients works at a peer-to-peer mode. figure 1: overview of dynamic secure interconnection mechanism dsi server the dsi server is the central controller for handling the management and security policies. when a vm is initialized, it is connected with the dsi server to register and start to operate in the system. when the vm state changes, e.g. suspend, restart, drift or phase out, it will inform dsi server to update the vm state. thus, the dsi server maintains all vm properties and states, such as the virtual mac (vmac) and virtual ip (vip) addresses of vms, the vm owner, the corresponding virtual bridge, the real-time vm state, etc. in addition, dsi server maintains the vm communication protocols, policies and activities. if vms stay within a same local network, they can talk with each other using vmac and vip. if vms stay in different local network, especially behind nat devices, vip based tunnels will be established to connect vms. meanwhile, appropriate traffic control policies will be implemented during the connection bootstrapping stage, such as encryption algorithms, key management protocol and traffic redirection. dsi client and virtual bridge the dsi clients are a large number of vms. the properties of each dsi client includes vmac and vip addresses, vm state, vm owner, corresponding virtual bridge, host and its own virtual trust zone id. virtual bridges are in charge of performing and implementing the communication protocols and policies. the communication between two dsi clients is performed at a peer-topeer mode. as shown in the fig. 1, virtual bridge 1 and 5 can establish a direct connection between vm 1 and vm 9 based on virtual mac addresses since they belong to the same local 352 l. he, f. huang, j. zhang, b. liu, c. chen, z. zhang, y. yang, w. lu network and can communicate with each other via vmac and vip. however, virtual bridge 1 and 9 have to establish vpn tunnels to transit through the nat device based on the vip to connect vm 1 and vm 17. 4.3 dsi operation the dsi operation refers to the interactions among dsi server, several dsi clients and virtual bridges. more specifically, the system administrator specifies the management policy on the dsi server, which then allocates the corresponding communication control policies to individual virtual bridges. virtual bridges control the communication among dsi clients by relaying, blocking or rate-limiting packets to establish virtual trust zones. policy configuration the system management and security policies are configured on the dsi server according to the administrative requirements. that includes the dsi client initialization procedures, dsi client state change procedures, virtual bridge switching protocols and some other traffic management and security protection policies such as client registration, vm state management, access control, network isolation, transmission encryption, traffic redirection, etc. client initialization and state maintenance a new user registration or additional resource request from existing users will incur the creation of vms (dsi clients). this process is managed by the cloud computing platform based on policies such as load balancing, energy efficiency. after that, the newly generated vms (dsi clients) will be registered on the dsi server, and provide the dsi server with information such as vmac and vip addresses, virtual bridge, vm owner and host machine name. then the dsi server instructs the dsi clients and corresponding virtual bridges to perform bootstrapping process. that includes the notification of virtual trust zone id, other clients within the same virtual trust zone, communication protocols and policies. when the vms (dsi clients) start to change their states, e.g. suspended, drifted or terminated, the dsi server will be notified with the change. the related communication protocols and policies will then be updated by the dsi server and reconfigured on each virtual bridge. for example, the vm 1 and vm 2 are suspended when their owners travel to other cities. the vms are drifted and migrated into another data centre and will be allocated on virtual bridge 11 and 12 respectively, with their previous virtual mac and ip addresses inherited. previous and existing virtual bridges (virtual bridge 1, 11 and 12) will then report dsi server about the update and new tenant. dsi server will then update the related information in all virtual bridges to make sure the drifted vm 1 and drifted vm 2 can be connected seamlessly. virtual bridge communication management virtual bridge is responsible for managing vm interconnection, traffic flow and virtual network topology. in fig. 1, the vm 1 and vm 2 reside on the same virtual bridge 1 and serve the same customer. thus, by allowing the interconnection between the vm 1 and vm 2, these two dsi clients are allocated into a same virtual trust zone. on the other hand, if vm 1 and vm 3 are serving customers from different enterprises, the interconnection between them will be blocked by virtual bridge 1 and 2. thus, the vm 1 and vm 3 are regarded as staying in different virtual trust zone. virtual bridges configure and maintain acl (access control list) to authenticate vmac addresses to start interconnection between dsi clients. therefore, vmac dynamic secure interconnection for security enhancement in cloud computing 353 based communication management is more suitable between vms within the same local network where the virtual mac addresses can be recognized. if the two clients stay in different networks or behind nat devices, the vip address of a dsi client registered on the dsi server may be meaningless for another dsi client. the virtual ip addresses based tunnelling among vms is performed by establishing peer-to-peer tunnels between virtual bridges, e.g. vm 1 and vm 17 in fig. 1. the dsi server configures the virtual bridges to create tunnels with proper parameters such as the vip address of the destination, the tunnelling protocols and encapsulation options. by doing that, access control and virtual network isolation can be further extended between vms that stay in different local network. 4.4 security the dynamic security interconnection mechanism enhances cloud computing security by implementing access control mechanisms among vms. in particular, virtual trust zones can be established by building the tunnels among virtual bridges. virtual trust zone establishment a virtual trust zone is a group of dsi clients (vms) that interconnected by virtual bridges with some interconnection policies. a dsi client (e.g. vm 1) will be generated when a customer first login the system and request for computing and storage resources. when the customer requests for additional resources, a virtual trust zone is established to include the newly generated dsi client and the original one. the clients are trusted with each other and thus the interconnection between them is permitted. when the newly generated clients share the same physical host (e.g. vm 2) or local network (e.g. vm 9) with the original client, the virtual mac address based access control mechanism and corresponding policies are implemented. if a new client resides in a remote data centre (e.g. vm 17), the ip tunnels based interconnection will be implemented. the ip tunnels based interconnection is also operational between vms within a data centre (e.g. between vm 1 and vm 9). the virtual bridges will select the light-weighted vmac based protocol in order to reduce the operation overhead and the management complexity of ip tunnelling protocol. encrypted tunnel establishment when the customer travels from one city to another, the drifted dsi clients (e.g. vm 1 and vm 2) will migrate into a different network. the communication within a virtual trust zone, e.g. between the drifted vm 1 and vm 9, may go through an insecure public network. the encrypted tunnel will be established to protect the information exchange against various attacks such as eavesdropping. the dsi server may provide additional information to facilitate tunnel setup authentication, e.g. the certificate fingerprint [19]. in that case, dsi server presents an ike/ipsec tunnel for nat traversal, e.g. the udp encapsulation of ipsec tunnelling [20]. traffic redirection the virtual bridges can redirect the outgoing traffic of vms to a dedicated traffic analysis and cleaning device before relaying them to their destination when the customers require them or when the system is under attacks. the dedicated device may be a secure vm or conventional security system such as anti-ddos solution [1]. as an example in fig. 1, the traffic from vm 11 and vm 12 are redirected to a traffic cleaning centre before it is forwarded to their destination, vm 13 and vm 14. the cost of this kind of security solution is performance degradation and operation overhead. 354 l. he, f. huang, j. zhang, b. liu, c. chen, z. zhang, y. yang, w. lu security policy consistency since the network is separated into several virtual trust zones, security countermeasures can be implemented on a per-trust-zone basis. when the vms in a virtual trust zone migrated from one host to another, e.g. vm 1 and 2, virtual bridge 1, 11 and 12 will then update dsi server about this information. and the dsi server will then reconfigure the tunnels among the virtual bridges accordingly. as a result, dsi server maintains the information about the dynamic trust zone no matter where the vms migrate. the security policies can also be shifted along with the vm movement. comparison and discussion with the dsi mechanism, the traffic among vms in the same trust zone is permitted while the traffic among vms in different trust zones is controlled. thus the trust zones are separated by simply managing the interconnection among vms. this mechanism has several advantages. • first, compared with the virtual layer management framework proposed in [11], our solution is relatively simple. in [11], several domains and complicated management mechanisms are introduced to manage the virtual layer. the dsi maintains virtual trust zones based only on the interconnection control mechanism. • second, dsi is very practical to make full use of all existing protocols, hypervisors and platforms to ensure the compatibility with most of existing cloud computing system. 5 testbed and experiment results a proof-of-concept testbed is constructed for demonstration of the dsi mechanism,and a simple cloud computing platform named ą°vm management platformąą is implemented to perform the virtualized resource management, as shown in fig.2. configurations. libvirt toolkit and its virtualization apis are utilized to construct the platform based on hypervisors of kvm, xen server or virtual box. several vm management functionalities and policies are established written by c programs. vm initialization policies include vm instant created on host whose cpu/ram is most idle; vm instant created on hosts already power on as far as possible; vm instant created on all hosts in average. apart from that, vm management policies also include the vm suspend, migration, error control and disaster recovery policies. the vm management platform manages all vms on host 2, 3 and 4. hardware settings. the testbed is composed of a cisco catalyst 2960pd-8tt-l switch and four pcs, host 1 is used for management, host 2, 3 and 4 are used for resource provision. each pcs has a intel core i5 four core 3.3ghz cpu, 4g ram and 320g hard disk, and is able to accommodate 4 vms. on host 1, 2, 3 and 4, hypervisor is installed on linux redhat enterprise 5.6. in the experiment, three typical open source hypervisors of kvm, xen server and virtual box are selected to operate on the os. the kvm and virtual box are type 2 hypervisors, while the xen server belongs to the type 1. virtual bridge functionality is implemented on host 2, 3, and 4. it is enabled based on the tun/tap device of linux. apart from switching, protocols of traffic filtering, traffic redirection, tunnel establishment are achieved by a set of c programs. the dsi server functionality is enabled by running a set of c program on the os of host 1. the dsi functionalities include a management user interface (ui) and maintenance on dsi client information database. in the experiment, the dsi mechanism operates normally on each of the three hypervisors, no matter whether it is type 1 or type 2. first, four vms are configured on host 2, four on host dynamic secure interconnection for security enhancement in cloud computing 355 figure 2: testbed and experiment 3 and ip addresses are assigned from 10.0.0.1 to 10.0.0.8. vm 1, 2, 5, 6 are configured in the same virtual trust zone and vm 3, 4, 7, 8 in the same zone. that can be achieved by permitting the interconnection between virtual bridge 1 and 3, and between virtual bridge 2 and 4. ping command is used to check the interconnection control within and among virtual trust zones. the ą°pingąą between vm 1 and 5 is successful and between vm 1 to 3 is failed. in the second test case, vm 1 drifts from host 2 to 4, the virtual bridges on hosts inform the dsi server about this change, and dsi server updates the dsi client information database and informs related virtual bridge to update their interconnection configuration accordingly. in the test, the database is updated as expected. the ą°pingąą command from drifted vm 1 to vm 5 and 3 get the same result with the first test case. it is shown that the drifted vms still stay within the same virtual trust zone and security policies keep the same after the migration. 6 conclusion and future work in this paper, dynamic secure interconnection (dsi) mechanism is proposed, analyzed and tested. by managing the vm interconnection and traffic direction of a cloud computing system, the virtualized network can be isolated into a couple of virtual trust zones. direct connection within the same zone is established regardless the vm location while the traffic among different virtual trust zones will be carefully controlled. coped with corresponding security service level agreement, security can be enhanced for customers to adopt cloud computing platform. our proposed mechanism can protect sensitive data and information against various attacks such as eavesdropping to enhance cloud computing security. as stated in section 4.4, traffic redirection is an important security feature of the dsi mechanism. it can release the working load of traffic scanning and monitoring on vms and potentially facilitate the deployment of conventional security mechanisms such as anti-ddos, virus, malware systems. however, this solution may consume extra amount of bandwidth when the traffic is redirected to a monitoring centre. more studies on this issue will be conducted in the future. in addition, the testbed with the vm management functionality is currently implemented only for concept proof, so a real-life cloud computing platform will be established by using open source tools such as openstack or eucalyptus to create more practical scenarios. furthermore, our current experiments only 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[20] a. huttunen, b. swander, v. volpe, l. diburro and m. stenberg (2005); rfc 3948 udp encapsulation of ipsec esp packets, january 2005. international journal of computers communications & control online issn 1841-9844, issn-l 1841-9836, volume: 18, issue: 4, month: august, year: 2023 article number: 5475, https://doi.org/10.15837/ijccc.2023.4.5475 ccc publications gestalt principles governed fitness function for genetic pythagorean neutrosophic waspas game scene generation a. petrovas, r. bausys, e. k. zavadskas aurimas petrovas department of graphical systems vilnius gediminas technical university, lithuania saulėtekio al. 11, vilnius, 10223, lithuania aurimas.petrovas@vilniustech.lt romualdas bausys department of graphical systems vilnius gediminas technical university, lithuania saulėtekio al. 11, vilnius, 10223, lithuania romualdas.bausys@vilniustech.lt edmundas kazimieras zavadskas* institute of sustainable construction vilnius gediminas technical university, lithuania saulėtekio al. 11, vilnius, 10223, lithuania *corresponding author: edmundas.zavadskas@vilniustech.lt abstract the maintenance of visual appeal and coherence in the procedural game scene generation is still a difficult problem. traditional procedural game scene generation algorithms produce samples that show a noticeable resemblance to each other. the proposed algorithm allows us to add diverse game object compositions and increase creativity value in that way. result diversity is formed by the proposed genetic algorithm modification and mcdm method based on the fitness function. video game immersion is reached by aesthetic game element pattern composition, and one of the solutions for this issue is to apply automated aesthetic modelling of the generated game levels. in this research, the construction of fitness function was extended by the modelling of aesthetic principles, which were reverse-engineered from gestalt principles. all rules were implemented by construction of a focal function with a square zone for each matrix cell of the single game scene. five types of gestalt rules were modelled and combined into a pythagorean neutrosophic waspas method and the final score calculation algorithm was proposed. the proposed approach to generating game scenes strikes a balance between functionality and aesthetics to provide players with an engaging and immersive gaming experience. keywords: gestalt principles, mcdm,genetic algorithm, procedural generation, video game, pythagorean neutrosophic set. https://doi.org/10.15837/ijccc.2023.4.5475 2 1 introduction novadays, creativity modelling popularity is increasing and is being used in different fields of applications. even if creativity modelling is a popular direction, there is no common definition of creativity among different fields of applications. creativity structure is also abstract and open for interpretation. creativity, in general, includes a set of different creative intelligence abilities, which is why simulation presents a difficult technical challenge [9, 37]. it means that the abilities that humans usually use to create are not that easy to understand and model. creativity modelling perspective explains how creativity traits can be understood or extracted from the existing knowledge, works, or its environment. there is a lot of information about creativity indirectly encoded in the surrounding world. it is important to understand how creativity is defined for modelling effective generation systems. one of the most popular creativity modelling perspective groups categorizes them into four categories. these groups are: person (creativity agent characteristics and model), process (actions undertaken during the creative process), product (generated creative artefacts), press (meta information, which is indirectly related to the work and surrounding culture of the result) [10, 34]. science community usually works with reverse engineering systems to create a basis for creativity simulations, which are aligned with the product approach [31]. one of the current computational creativity problems is the generation of visually appealing and functional artefact results. in the proposed research, the focus is on the analysis and modelling of creative process. the definition of creativity usually involves new concept division and it is usually hard to learn new concepts only by data that are already created. computational creativity is a field of artificial intelligence that studies the building of computational systems that have creative behaviours. part of the creativity process involves methods to combine goals of different nature. modern applications of computational creativity mostly include generative art, and generative art is an algorithmically created artefact that resembles artistic motivations [3]. it is used mainly to generate different forms of media. generation examples include assets, such as sound, including music [6], image generation from text [25], specialised pixel art sheets [27], image inpainting [19] and text generation [7, 17]. closely relevant context for generative art is the digital world or game-level generation, and for that, procedural generations are used quite often. the procedural generation is a data creation method that uses algorithms and automation to create results [28] and, as a practical concept in games, has existed for more than three decades with one of the first notable games called ‘elite’ [4]. the idea of generating distinct and not repetitive visual results is still a novel concept today. the procedural generation techniques in games vary, some of the more common approaches include [18] search (usually genetic or fitness-based algorithms), solver, rule with a stricter control [21] or grammar-based (originating in linguistics) algorithms. some examples include game-level generation using generative adversarial networks (gan) [13, 33] to train and then use neural nets for the generation of new levels. another example uses cellular automata [11], evaluation agents iterate over the element batches of game level. some methods analyse gameplay videos to train their models [14]. there is also a more direct approach to game design pattern application for a search-based algorithm [1]. those systems usually analyse hand-crafted or indexed creative artefacts to learn and generalise pattern generation systems. applications and methods for procedural generation vary, but most of them try to create indistinguishable results or assist the creation process compared to manually created artefacts. this process automates part of cognitive work and evolves the style in which media can be created [12]. search-based procedural generation of assets or their composition in video games usually requires evaluation, which evaluates generated artefacts and rates them based on their fitness function performance. construction of fitness function is usually a key aspect of the successful generator, because it decides the direction of the result generation. the advantage of the search algorithm is that existing solutions (based on a fitness function) are always found. there are two types of fitness criteria: aesthetic and functional. the aesthetic criteria define the visual aspect (pleasing appearance of things) of the generated level, and the functional criteria define the application of the rules such as existence of key object elements or ability to use the artefact according to his purpose. one of the challenges is the seamless integration of both types of criteria into the final result [15], as one or the other quite often can contradict each other. functional criteria are also easier to integrate than aesthetic criteria, https://doi.org/10.15837/ijccc.2023.4.5475 3 because aesthetic criteria follow more abstract rules and are more difficult to integrate seamlessly. there are different ways or rules to follow for aesthetic criteria result [10, 16] which can include relations to such examples as apollonian order, dionysian chaos; gestalt, individual and repetition shape; style, multiplicity or cohesion variations. these methods can be used to derive aesthetic criteria for procedural generation. one of the challenges which comes with this choice is to model their execution as, more often than not, they are defined in abstract terms, but for mathematical algorithm, we need exact definitions for algorithm elements, and this is the exact aim of this research. at the same time, for artefacts to have high creative value, we need to device methods, which could apply high-level aesthetic concepts. multicriteria decision making (mcdm) is one of the solutions to effectively combine fitness function criteria. it models strategies on how to select alternatives in a finite pool of possible solutions [41]. these methods can be applied to the fitness function of the genetic algorithm to optimise the genetic operators. some mcdm method variations can also increase non determinism, by using fuzzy sets instead of crisp numbers. the concept of fuzzy sets was first introduced by lotfi a. zadeh in 1965 [39]. zadeh proposed a new kind of set that allowed for degrees of membership, where an element can have a degree of membership to a set that ranges from 0 to 1, depending on how closely it matches the set’s criteria. this concept is called a "fuzzy set". in 1973, he published a seminal paper [40], which outlined the potential applications of fuzzy sets to a wide range of fields, including control theory, decision making, and pattern recognition. neutrosophic sets are a type of mathematical framework that allows for the representation of uncertain or indeterminate information and they were introduced by florentin smarandache as an extension of fuzzy logic in 1999 [29]. one of the neutrosophic set extension is called pythagorean neutrosophic sets, which are a more flexible tool for representing uncertainty than regular neutrosophic sets [35]. the novelty point of this research is automatic application of abstract gestalt principles in a procedural video game scene object layout generation. the following structure of the paper is as follows: gestalt principles are explained in the second chapter and the modeling of these principles in the third chapter. full model is explained in the fourth chapter and results in the fifth chapter. 2 gestalt principles the aesthetic aspects of created products are usually realised by applying so-called visual principles. to utilise aesthetic criteria, we need to choose a set of aesthetic rules, and one of the high-level sets of rules for this purpose is called gestalt principles [36]. gestalt principles are rules of element organisation and perception, which explains how elements should be placed in relation with each other to achieve a specific aesthetic visual effect. in our context, the elements are game objects. their core is based on optical perception. looking at the world, the brains can see complex compositions of objects, which in turn are composed of smaller objects, and the hierarchy continues [32]. it subconsciously perceives the world in layers of abstraction, which means that we have a hierarchy of visual elements defined by their scope size. layers of abstraction is usually also learnt by deep neural networks [26] and it is also common to see this behaviour with convolutional neural nets [30, 38]. the gestalt principles aim to formulate regularities for visual input grouping. there is a set of abstract rules on which these principles are formed. there are usually between 5 and 10 gestalt principles based on how they are grouped. also, some sets of principles were not introduced with the initial definition of gestalt principles. these rules use a high level of abstraction, and their application varies based on the selected objective. originally, they were not devised for automated tasks, but they have the advantage of low-level building strategy for high value aesthetic results. some examples of an application include visual data representation. [20] explores how network data can be visually grouped and animated for clarity purpose. another common example is computer interfaces [8] this is the most popular practical application of gestalt principles, because visual grouping can make processing of information for humans more efficient. it can also be used for edge detection in images [5]. visual data might not be clear and have some continuation issues, so by its core, more abstract and error forgiving methods like gestalt principles can be used as base of the algorithm. the gestalt principles are (table 1): aesthetic criteria can be categorized by an abstraction level. high-level definition is abstract and https://doi.org/10.15837/ijccc.2023.4.5475 4 table 1: gestalt principles name of the principle explanation similarity similar looking objects are visually grouped regardless of their proximity to each other (figure 1) proximity when objects are close to each other, they are perceived as groups (figure 2) continuity aligned and smoothest paths are integrated into perceptual wholes (figure 3) focal point an object that is different compared to a whole will stand out (figure 4) common region when objects are within a closed region, they are perceived as a group (figure 5) closure incomplete object patterns are perceived as complete (figure 6) figure ground separation of objects between foreground and background based on their shape and associations (figure 7) common fate objects that point to the same direction are grouped together (figure 8) figure 1: similarity figure 2: proximity figure 3: continuity https://doi.org/10.15837/ijccc.2023.4.5475 5 figure 4: focal point figure 5: common region figure 6: closure https://doi.org/10.15837/ijccc.2023.4.5475 6 low-level definition is concrete and can usually be expressed with a direct algorithm. the presented gestalt principles forms high-level aesthetic criteria in game scene generation. despite the fact that gestalt principles are quite widely applied in areas of interface design, it is quite novel application in the procedural game level generation. high-level aesthetic criteria can expose advantages for modern mcdm technologies, which handle the vague initial information, namely by the application of neutrosophic sets. neutrosophic sets are used to define and process linguistic or abstract value indeterminism. in the next step, the basic ideas of an extension of the genetic algorithm by mcdm, namely pythagorean neutrosophic waspas, are presented. 3 modelling of gestalt principles for the incorporation into the fitness function computational creativity framework consists of two steps: developed mathematical model and applied numerical algorithm. the main ideas for the developing of mathematical model of gestalt principles is outlined below. the goal of this model is to find a way to algorithmically implement abstract visual aesthetic evaluations for the automatic evolution of the functional game level. a set of gestalt principles is usually chosen in the context of the task. too many rules applied for a small area can usually create a result, with numbed down individual rule exposition, so we can choose the ones that would have highest visibility with our tools environment context. five gestalt principles were integrated into the model as fitness criteria functions: similarity, proximity, continuity, focal points and common region. all of the selected principles can be applied in the rectangular matrix of symmetric game objects using focal functions. some gestalt principles were skipped because they require more complex object systems and assignable traits for individual objects. the closure and figure ground criteria were skipped, because it requires generation of identifiable structures, which would be broken by other criteria intervention. the closure criteria uses an unfinished identifiable structure, which cannot be identified if these structures do not exist. figure ground might not be figure 7: figure ground figure 8: common fate https://doi.org/10.15837/ijccc.2023.4.5475 7 identifiable visually as separate objects for foreground and background separation or a larger whole because the algorithm generated abstract patterns are not identifiable real-world multicell objects. another skipped criterion is common fate, where objects must have direction vectors, and objects considered in this type of research are symmetrical. in total, 5 principles were implemented, which are compatible with the core generation algorithm and object structure. the mathematical model for each criterion consists of iteration over the whole grid, except for the bounding rows and columns. each criterion is formulated as a global function in terms of raster algebra. for all five applied gestalt principles, we apply this procedure: for every cell, we construct a focal function with a square zone (figure 9). matrix cell values will represent such situations: 0 – player (starting spot of the player agent), 1 – exit (spot, which should be reached for the player to finish the level), 2 – empty space, 3 – wall (space, which blocks the movement of the player), 4 – enemy (hazardous zone, which player wants to avoid), 5 – collectible (zone, which is favourable for the player to step in). letter a marks the centre cell in the neighbourhood, and letter b marks the surrounding cells. indexes i and j indicate the location of the cell in the scene matrix of the game. the general criteria normalisation formula is the same for all modelled gestalt criteria and can be expressed as a formula (equation 1). we seek that final value for each criterion would stay between 0 and 1 without getting close to the range edges. we multiply the value by 0.9 to stabilise the neutrosophic algebra. sm = 0.9 tm r ∗ nx ∗ ny (1) where the members are as follows: s single-criteria fitness score, r – relevant neighbours (r=8 on all criteria, except continuity criteria, where it is r=1 as it uses a special condition for the score to increase (exactly two identical neighboars)) nx and ny define the size of the matrix grid, t defines the total amount of relevance of the criteria before normalisation and m defines the index of criteria from the gestalt criteria list. the mathematical models for each considered gestalt principle are presented below: if the object types are identical in the neighbourhood of the cell, the similarity score for each individual cell is increased. after the iteration over each cell is completed, the amount of similar object pairs is divided by the total amount of possible pairs (amount of total matrix cells multiplied by eight). this criterion searches for chunks of similar object areas (figure 10, equation 2). t2 = nx−1∑ i=2 ny −1∑ j=2 8∑ k=1 { 1, bijk = aij ∨ 2 0, otherwise (2) the proximity criterion has an algorithmic basis similar to the similarity criterion, but one of the pair members can also be identified as an empty space for the score value to increase (figure 11, equation 3). this criterion searches islands for similar objects in the area. figure 9: cell vision grid https://doi.org/10.15837/ijccc.2023.4.5475 8 figure 10: similarity evaluation for a single cell figure 11: proximity evaluation for a single cell (number 2 identifies empty space) https://doi.org/10.15837/ijccc.2023.4.5475 9 t2 = nx−1∑ i=2 ny −1∑ j=2 8∑ k=1 { 1, bijk = aij ∨ 2 0, otherwise (3) the continuity criterion has two score values for each level of the cycle. the total continuity score calculation works the same as the total score value in other criteria. focal continuity increases only if the cell with its surrounding satisfies the continuity requirement. for each cell, it is compared to the horizontal and vertical touching cells. we avoid the corner cells because in the smaller grid matrix consisting of squares, it is more difficult to see diagonal continuity as they touch only by a single point, which might break the illusion of continuity (figure 12). each cell checks for exactly two touching similar object types. if there are more or less similar touching objects, the cell is not considered continuous (figure 13, equation 4). t3 = nx−1∑ i=2 ny −1∑ j=2 { 1, ( 4∑ k=1 { 1, bij2k = aij 0, otherwise ) = 2 (4) the focal point criterion calculates the total amount of focal point effect. it is achieved by checking how much each cell is surrounded by the continuity of a different object (b is not equal to a). it checks each object and then calculates the amount of its repetitions in the surrounding 8 cells. the highest number of repetitions is used as a cell score. this algorithm searches for identifiable points in the game-level pattern (figure 14, equation 5). figure 12: continuity evaluation for a single cell. exactly two out of four neighbouring cells must satisfy the b=a criteria figure 13: possible continuity connections. the lines in the centre cell show which b=a patterns increase continuity https://doi.org/10.15837/ijccc.2023.4.5475 10 t4 = nx−1∑ i=2 ny −1∑ j=2 max l∈[c] 8∑ k=1 { 1, (bijlk ̸= aij ) ∧ (bijlk = bijl) 0, otherwise (5) c defines the number of possible object types. the final implemented criterion is common region, which works in the same manner as proximity algorithmically, but the searched boundaries are walls instead of empty space (figure 15, equation 6) it searches for bounded groups of similar objects in the area. t5 = nx−1∑ i=2 ny −1∑ j=2 8∑ k=1 { 1, bijk = aij ∨ 3 0, otherwise (6) applying the proposed mathematical model of gestalt principles, we can provide aesthetically appealing game scene design. in the computational realization of creative genetic algorithm we combine these aesthetic criteria of gestalt principles into fitness function by applying pyhagorian neutrosophic waspas extension application on game scene. then the calculated scores can be used to evolve the game scenes using a genetic algorithm. 4 game scene generation game scene generation is performed by the proposed genetic pythagorean neutrosophic waspas approach. the proposed mathematical model employs a multitude of different criteria. there are three types of criteria in our model. the first batch of criteria are aesthetic criteria, which include figure 14: focal point evaluation for a single cell. b is not equal to a figure 15: common region evaluation for a single cell (number 3 identifies walls) https://doi.org/10.15837/ijccc.2023.4.5475 11 table 2: full criteria list criteria type value range similarity (s1), max aesthetic value (focal function) 0-1 proximity (s2), max aesthetic value (focal function) 0-1 continuity (s3), max aesthetic value (focal function) 0-1 focal point (s4), max aesthetic value (focal function) 0-1 common region (s5), max aesthetic value (focal function) 0-1 symmetry (v1), max aesthetic value (global function) 0-1 empty space balance (v2), max aesthetic value (global function) 0-1 player-exit distance (f1), max more area of game scene is explored by the player 0-1 safe space (f2), max key areas do not have hazardous objects nearby 0-1 player exists (c1), boolean the level is playable 0 or 1 exit exists (c2), boolean the level is playable 0 or 1 player-exit path exists (c3), boolean the level is playable 0 or 1 the high level criteria, governed by gestalt rule derivatives s and low-level criteria v (symmetry and empty space balance). the second batch is the functional criteria f, which optimise the evaluations of the game design, such as the distance between key objects and the safe zones around them. the last batch is constraint criteria c, which always must be true to have non-zero fitness (existence and possible path between player and exit). having more than one criterion, it is needed to have a strategy to combine multiple criteria into a single fitness value. some key considerations are how we weight and what algebra we use while combining different criteria – this is a part of fitness function modelling. most of the examples in literature do not focus on the impact of criteria combination but are centered around how to choose and define individual criterion. fitness modelling has unexplored room for algorithm effectiveness improvements. the proposed modelling approach of gestalt principles is incorporated into the genetic algorithm, where the fitness function incorporates all the criteria of mathematical model applying mcdm methods. these methods allow to effectively use many criteria for fitness evaluation. for the evaluation of the generated game level: the criteria and alternative matrix were assembled (equation 7). x =   s11 s12 . . . s1n s21 s22 s2n ... . . . ... sm1 sm2 · · · smn   (7) each alternative column sj is generated with a genetic algorithm and corresponds to a chromosome of the genetically generated population. the complete set of si for each alternative includes 12 criteria (table 2). most evaluated criteria are normalised into the range 0 to 1 and constraint evaluations are binary, if it does not satisfy the required criteria the final result for the chromosome is multiplied by 0. each criteria value is converted to a pythagorean neutrosophic set, which has improved correlation compared to a single-valued neutrosophic set [24] a pythagorean neutrosophic set is a mathematical concept that combines the ideas of pythagorean fuzzy sets and neutrosophic sets. a pythagorean fuzzy set is a generalisation of fuzzy sets, which assigns three values to each element: membership degree, nonmembership degree, and hesitancy degree. on the other hand, a neutrosophic set is a generalisation of fuzzy sets that deals with uncertain, indeterminate, and inconsistent information. https://doi.org/10.15837/ijccc.2023.4.5475 12 in a pythagorean neutrosophic set, each element is assigned three values: truth-membership degree, indeterminacy degree, and falsity-membership degree. these values indicate the degree of truth, indeterminacy, and falsity of the element, respectively. the truth-membership degree and falsitymembership degree add up to one, while the indeterminacy degree can take any value between 0 and 1. pythagorean neutrosophic sets have been used in decision making problems that involve uncertain and inconsistent information, and they have shown promising results in handling such situations [2]. neutrosophic sets were added to the algorithm, as they increase nondeterminism of the criteria evaluation. they allow us to express information of neutrality and generalise fuzzy and intuitionistic fuzzy sets [29]. an object of the form a, which is a neutrosophic pythagorean set on the universe r, consists of dependent neutrosophic pythagorean components t and f, as well as an independent component u (equation 8, equation 9, equation 10). a = {< x, ta, ua, fa >: r ∈ r} (8) (ta)2 + (fa)2 ≤ 1 (9) (ta)2 + (ua)2 + (fa)2 ≤ 2 (10) here, ta(x) is the truth membership, ua(x) is indeterminancy membership and fa(x) is the false membership. subsequently, the neutrosophic sets are combined with a multi-criteria weighted aggregated sum product assessment method (waspas) [22]. a joint generalised criterion is calculated (equation 11). qi =  0.5 · n∑ j=1 τij · wj   ⊕  0.5 · n∏ j=1 τij wj⊙ (11) q is a combined neutrosophic number for one genetically generated alternative. n is the number of criteria; x is a single-criterion fitness score expressed in a neutrosophic number and w is a singlecriterion weight. the pythagorean neutrosophic number is converted to a crisp number by score function(equation 12): s (qi) = 3 + 3ξ2 − 2ϑ2 − η2 6 (12) the final waspas result is then used as an evaluation score. the algebraic operations are as follows (equation 13, equation 14, equation 15, equation 16): τ1 ⊕ τ2 = 〈( 1 − ( 1 − ξ21 )( 1 − ξ22 ))1 2 , ϑ1ϑ2, η1η2 〉 (13) τ1 ⊗ τ2 = 〈 ξ1ξ2, ( 1 − ( 1 − ϑ21 )( 1 − ϑ22 ))1 2 , ( 1 − ( 1 − η21 )( 1 − η22 ))1 2 〉 (14) λ · τ1 = 〈( 1 − ( 1 − ξ21 )λ)12 , ϑλ1 , η λ 1 〉 (15) λ ⊙ τ1 = 〈 ξλ1 , ( 1 − ( 1 − ϑ21 )λ)12 , ( 1 − ( 1 − η21 )λ)12 〉 (16) the triplet is called pythagorean neutrosophic number and lamda is a real number. each execution of this algorithm usually generates a differing pattern of game object composition as there are many possible fitness points to converge to and that in turn creates creativity through variety. the convergence points are also dependent on the initial values, as the answer is in many cases based on the relative object positions due to focal functions. the initial layouts of the game scene are initialised with random values and then evolved with a genetic algorithm (figure 16). the initial https://doi.org/10.15837/ijccc.2023.4.5475 13 population for the genetic algorithm consists of a group of randomly generated levels of the game scene. genetic operators, such as selection or mutation, are used to remove low scoring levels and create new ones. the scoring for each population member is governed by the mcdm algorithm with game design criteria. in this research, gestalt principle-based evaluation criteria for aesthetic pattern evolution were proposed, which, in turn, can create novel game object layout patterns. genetical algorithm loop is repeated with a fixed number of iterations (500 is still safe to have visible results and it usually does not go past 2000 with 10x10 game scene resolution). the functional criteria and restrictions were reused from previous research. figure 16: game scene generation 5 result the gestalt principle based fitness function was integrated into the genetic waspas game scene generator [22] the experiments include a single gestalt principle addition to the base fitness function and then a combination of all gestalt principles. for reference and comparison, the generated level without gestalt principles was provided (figure 17). figure 17: base result without integration of the gestalt principle common region criteria enclose the game objects in walls and we can see a generated narrow rooms or corridors. increasing the presence of this criterion can remove most of the empty space (figure 18). focal-point criteria grab a player’s attention to a point that stands out in the environment. we can see that the exit (blue flag) and a few flame assets stand out in relation to the environment (figure 19). the continuity criterion attempts to generate continuous lines of objects. we can see generated patterns, where building blocks are mostly a combination of lines across the level (figure 20). the proximity criteria generate isolated islands of objects. we can see two islands of walls and coins generated (figure 21). similarity criteria group similar objects together. the generated example splits the room into the 4 regions of different types of objects (figure 22). https://doi.org/10.15837/ijccc.2023.4.5475 14 figure 18: common region result figure 19: focal point result figure 20: continuity result https://doi.org/10.15837/ijccc.2023.4.5475 15 figure 21: proximity criteria figure 22: similarity result https://doi.org/10.15837/ijccc.2023.4.5475 16 after inspecting how each criteria is combined into the full level generator, all criteria were combined into the fitness function. we can see the patterns of individual gestalt rules in the result and the difference between three iterations of the same algorithm. the first example shows an area with balanced empty space not many walls and no hazardous objects (figure 23). the second one has much more walls combined into an “8” pattern (figure 24). and then the third one creates enclosed space with a coin cluster in the middle with a hidden exit (figure 25) figure 23: the result of the combined gestalt principles 1 figure 24: the result of the combined gestalt principles 2 it was noticed that gestalt principles can form distinguishable patterns in the default 10x10 matrix. a higher matrix with a resolution of 20x20 was tested, and we can still see the pattern formation, but it has a lower high-scale presence, than when resolution is lower and application is based on a small 3x3 matrix focal function (figure 26). initially, fitness moves up very quickly and then slows down until it fine-tunes itself. the initial jump takes around 0.5 seconds. the complete evolution takes around 10 seconds to become stagnant, and the fitness value with the proposed criteria goes up to around 0.7 (figure 27) (it cannot reach 1, as the criteria conflict with each other). due to more conflicting criteria, the final fitness is lower compared to previous research (0.85 > 0.7) ([22], [23]), but the gestalt used result has a higher visual value due to the higher presence of visual patterns. the engine was tested with an intel i9-9980hk 2.4 ghz cpu. 6 conclusion the proposed gestalt principle-based fitness function was applied and integrated into the genetic pythagorean neutrosophic waspas game scene generator. when inspecting the results, it can be seen that coherent and visually appealing patterns were integrated into the final generated game https://doi.org/10.15837/ijccc.2023.4.5475 17 figure 25: the result of the combined gestalt principles 3 figure 26: the combined gestalt principles result with a 20x20 matrix figure 27: example evolution curves with separate iterations of the algorithm https://doi.org/10.15837/ijccc.2023.4.5475 18 scenes. compared to the originally generated levels, aesthetic evaluation improves in the final scene generations. usually, modelling abstract criterion to a mathematical model presents a challenge if we want to keep all the information from the rules intact. the mathematical models of gestalt principles are utilised by automated cell neighbourhood algorithms. both functional and aesthetic criteria were combined into the final algorithm to keep the original game design rules intact and improve the original game scene generation idea. following this example, other aesthetic modelling methods could be used to improve the results, but the ratio between rules and amount of generated matrix size can impact the pressesnce of each integrated rule. a large number of rules in a small scene matrix can create chaotic results. funding this research received no funding. author contributions the authors contributed equally to this work. conflict of interest the authors declare no conflict of interest. references [1] baldwin a.; dahlskog s.; font j.m.; holmberg j. 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(2014). state of art surveys of overviews on mcdm/madm methods, technological and economic development of economy, 20(1), 165– 179, 2014. copyright ©2023 by the authors. licensee agora university, oradea, romania. this is an open access article distributed under the terms and conditions of the creative commons attribution-noncommercial 4.0 international license. journal’s webpage: http://univagora.ro/jour/index.php/ijccc/ this journal is a member of, and subscribes to the principles of, the committee on publication ethics (cope). https://publicationethics.org/members/international-journal-computers-communications-and-control cite this paper as: petrovas, a.; bausys, r.; zavadskas, e. k. (2021). gestalt principles governed fitness function for genetic pythagorean neutrosophic waspas game scene generation, international journal of computers communications & control, 18(4), 5475, 2023. https://doi.org/10.15837/ijccc.2023.4.5475 introduction gestalt principles modelling of gestalt principles for the incorporation into the fitness function game scene generation result conclusion